[ { "path": ".github/dependabot.yml", "content": "version: 2\nupdates:\n # - package-ecosystem: pip\n # directory: \"/\"\n # schedule:\n # interval: daily\n - package-ecosystem: \"github-actions\"\n directory: \"/\"\n schedule:\n # Check for updates once a week\n interval: \"weekly\"\n" }, { "path": ".github/workflows/ci.yaml", "content": "name: CI\n\non:\n push:\n branches:\n - master\n pull_request:\n branches:\n - \"*\"\n schedule:\n - cron: \"0 0 * * *\" # Daily “At 00:00”\n workflow_dispatch: # allows you to trigger manually\n\njobs:\n build:\n runs-on: ubuntu-latest\n defaults:\n run:\n shell: bash -l {0}\n strategy:\n fail-fast: false\n matrix:\n include:\n # Warning: Unless in quotations, numbers below are read as floats. 3.10 < 3.2\n - python-version: \"3.11\"\n esmf-version: 8.4\n - python-version: \"3.12\"\n esmf-version: 8.6\n - python-version: \"3.13\"\n esmf-version: 8.8\n - python-version: \"3.14\"\n esmf-version: 8.9\n steps:\n - name: Cancel previous runs\n uses: styfle/cancel-workflow-action@0.13.1\n with:\n access_token: ${{ github.token }}\n - name: Checkout source\n uses: actions/checkout@v6\n - name: Create conda environment\n uses: mamba-org/setup-micromamba@v3\n with:\n cache-downloads: true\n micromamba-version: \"latest\"\n environment-file: ci/environment.yml\n create-args: >-\n python=${{ matrix.python-version }}\n esmpy=${{ matrix.esmf-version }}\n - name: Fix env for esmpy 8.4\n run: |\n if [ \"${{ matrix.esmf-version }}\" == \"8.4\" ]; then\n conda install \"importlib-metadata<8.0.0\"\n fi\n - name: Install Xesmf (editable)\n run: |\n python -m pip install --no-deps -e .\n - name: Conda list information\n run: |\n conda env list\n conda list\n - name: Run tests\n run: |\n python -m pytest --cov=./ --cov-report=xml --verbose\n - name: Upload coverage to Codecov\n uses: codecov/codecov-action@v6.0.0\n with:\n files: ./coverage.xml\n fail_ci_if_error: false\n\n upstream-dev:\n name: upstream-dev\n runs-on: ubuntu-latest\n defaults:\n run:\n shell: bash -l {0}\n steps:\n - name: Cancel previous runs\n uses: styfle/cancel-workflow-action@0.13.1\n with:\n access_token: ${{ github.token }}\n - uses: actions/checkout@v6\n - name: Create conda environment\n uses: mamba-org/setup-micromamba@v3\n with:\n cache-downloads: true\n micromamba-version: \"latest\"\n environment-file: ci/environment-upstream-dev.yml\n - name: Install Xesmf (editable)\n run: |\n python -m pip install -e .\n - name: Conda list information\n run: |\n conda env list\n conda list\n - name: Run tests\n run: |\n python -m pytest --cov=./ --cov-report=xml --verbose\n" }, { "path": ".github/workflows/linting.yaml", "content": "name: linting\n\non:\n push:\n branches:\n - master\n pull_request:\n branches: \"*\"\n\njobs:\n linting:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v6\n - uses: actions/setup-python@v6\n with:\n python-version: \"3.x\"\n - uses: pre-commit/action@v3.0.1\n" }, { "path": ".github/workflows/pypi.yaml", "content": "name: Publish to PyPI\n\non:\n pull_request:\n push:\n branches:\n - master\n release:\n types:\n - published\n\ndefaults:\n run:\n shell: bash\n\njobs:\n packages:\n runs-on: ubuntu-latest\n steps:\n - uses: actions/checkout@v6\n\n - name: Set up Python\n uses: actions/setup-python@v6\n with:\n python-version: \"3.x\"\n\n - name: Get tags\n run: git fetch --depth=1 origin +refs/tags/*:refs/tags/*\n\n - name: Install build tools\n run: |\n python -m pip install --upgrade build\n\n - name: Build binary wheel\n run: python -m build --sdist --wheel . --outdir dist\n\n - name: CheckFiles\n run: |\n ls dist\n python -m pip install --upgrade check-manifest\n check-manifest --verbose\n\n - name: Test wheels\n run: |\n # We cannot run this step b/c esmpy is not available on PyPI\n # cd dist && python -m pip install *.whl && cd ..\n python -m pip install --upgrade build twine\n python -m twine check dist/*\n\n - name: Publish a Python distribution to PyPI\n if: success() && github.event_name == 'release'\n uses: pypa/gh-action-pypi-publish@release/v1\n with:\n user: __token__\n password: ${{ secrets.PYPI_TOKEN }}\n" }, { "path": ".gitignore", "content": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n_version.py\n\n# Sphinx documentation\ndocs/_build/\ndoc/_build/\n\n# notebook\n.ipynb_checkpoints\n\n# OS-generated\n.DS_Store\n\n# NetCDF files\n*.nc\n*.nc4\n\n# ESMPy log files\nPET0.ESMF_LogFile\n\n# Unit test / coverage reports\n.cache\n.coverage\ncoverage.xml\ndask-worker-space/\n" }, { "path": ".pre-commit-config.yaml", "content": "default_language_version:\n python: python3\n\nrepos:\n - repo: https://github.com/pre-commit/pre-commit-hooks\n rev: v6.0.0\n hooks:\n - id: trailing-whitespace\n - id: end-of-file-fixer\n - id: check-docstring-first\n - id: check-json\n - id: check-yaml\n - id: double-quote-string-fixer\n\n - repo: https://github.com/psf/black-pre-commit-mirror\n rev: 26.3.1\n hooks:\n - id: black\n\n - repo: https://github.com/keewis/blackdoc\n rev: v0.4.6\n hooks:\n - id: blackdoc\n\n - repo: https://github.com/PyCQA/flake8\n rev: 7.3.0\n hooks:\n - id: flake8\n\n - repo: https://github.com/PyCQA/isort\n rev: 8.0.1\n hooks:\n - id: isort\n\n - repo: https://github.com/pre-commit/mirrors-prettier\n rev: v4.0.0-alpha.8\n hooks:\n - id: prettier\n\n - repo: https://github.com/deathbeds/prenotebook\n rev: f5bdb72a400f1a56fe88109936c83aa12cc349fa\n hooks:\n - id: prenotebook\n args:\n [\n \"--keep-output\",\n \"--keep-metadata\",\n \"--keep-execution-count\",\n \"--keep-empty\",\n ]\n\n - repo: https://github.com/tox-dev/pyproject-fmt\n rev: v2.21.0\n hooks:\n - id: pyproject-fmt\n\nci:\n autofix_commit_msg: |\n [pre-commit.ci] auto fixes from pre-commit.com hooks\n\n for more information, see https://pre-commit.ci\n autofix_prs: true\n autoupdate_commit_msg: \"[pre-commit.ci] pre-commit autoupdate\"\n autoupdate_schedule: monthly\n skip: []\n submodules: false\n" }, { "path": ".prettierrc.toml", "content": "tabWidth = 2\nsemi = false\nsingleQuote = true\n" }, { "path": "CHANGES.rst", "content": "What's new\n==========\n\n0.9.2 (2025-11-27)\n------------------\nThis release drops support for Python < 3.11. xESMF aims to preserve support for older python and ESMF version as long as possible with its reduced maintaining team. The most recent windows release of ESMF is currently 8.4.2 and new versions of xESMF will support it as long as it is not updated. All fixes in :pull:`463`, by `Pascal Bourgault `_.\n\n* Rewrote ``xe.smm.gen_mask_from_weights`` to remove scipy-dependent code.\n* Fix the CI reenable testing with previous python versions.\n* Avoid a ``SpatialAverager`` bug that happens when polygon segments have a length of exactly 1 on ESMF 8.4.2. The bug is not actually fixed in xESMF, but \"segmentizing\" the polygons with 0.99 seems to fix the issue.\n\n0.9.1 (2025-11-25)\n------------------\n* Rewrote ``xe.smm.add_nans_to_weight`` (called when ``unmapped_to_nan`` is True)to remove scipy-dependent code, which also resulted in a significant (>=4x) speedup of that step (:pull:`461`). By `Pascal Bourgault `_.\n* Fix some name collision issues in the parallel regridder initialisation (:pull:`461`). By `Pascal Bourgault `_.\n\n0.9.0 (2025-11-21)\n------------------\n* Added ``Regridder`` option ``post_mask_source`` to mask contributions of specified source grid cells, with a special setting for masking domain edge cells to avoid extrapolation with ``nearest_s2d`` when remapping to a larger domain (``post_mask_source = 'domain_edge'``, :pull:`444`). By `Martin Schupfner `_.\n* Added support for target masks when regridding ``LocStream`` to ``Grid`` with ``nearest_s2d`` (:pull:`445`). By `Martin Schupfner `_.\n* ``xesmf.util.cf_grid_2d`` returns bounds as coordinates, as ``grid_2d`` does and as usually expected. (:pull:`453`). By `Pascal Bourgault `_.\n* Accept masks in \"X, Y\" order (:issue:`447`, :pull:`456`). By `Pascal Bourgault `_.\n* Correctly use arguments ``input_dims`` and ``output_dims`` in ``Regridder`` when grids are defined as dictionaries of numpy arrays (:issue:`362`, :pull:`455`). By `Aaron G Meyer `_.\n\n0.8.10 (2025-04-29)\n-------------------\n* Fix issue introduced by :pull:`418` for passing grids as dictionaries. (:issue:`428`, :pull:`429`). By `Pascal Bourgault `_.\n\n0.8.9 (2025-04-15)\n------------------\n* Destroy grids explicitly once weights are computed. Do not store them in `grid_in` and `grid_out` attributes. This fixes segmentation faults introduced by the memory fix of last version. By `Pascal Bourgault `_.\n* Do not add scalar coordinates of the target grid to the regridded output (:issue:`417`, :pull:`418`). `xe.Regridder.out_coords` is now a dataset instead of a dictionary. By `Pascal Bourgault `_.\n\n0.8.8 (2024-11-01)\n------------------\n* Fix ESMpy memory issues by explictly freeing the Grid memory upon garbage collection of ``Regridder`` objects. By `Pascal Bourgault `_.\n* Address deprecation for xarray 2024.10 in the parallel weight generation. By `Pascal Bourgault `_.\n* Address an upcoming change in sparse 0.16 where COO fill values will distinguish between 0.0 and -0.0. This issue would affect spatial averaging over polygons with holes. By `Pascal Bourgault `_.\n\n0.8.7 (2024-07-16)\n------------------\n* Cast grid sizes to python's int (another Numpy 2.0 fix). (:pull:`377`) By `Pascal Bourgault `_.\n\n0.8.6 (2024-06-26)\n------------------\n* New ``xe.util.cell_area`` utility to compute the cell area using ESMF's internal mechanism. (:pull:`372`, :issue:`369`) By `Jiawei Zhuang `_ and `Pascal Bourgault `_.\n* Compatibility with Numpy 2.0 (NaN vs nan) (:pull:`373`) By `Pascal Bourgault `_.\n\n0.8.5 (2024-04-11)\n------------------\n* Reverted to the chunking behaviour of xESMF 0.7 for cases where the spatial dimensions are not chunked on the source data. (:pull:`348`) By `Pascal Bourgault `_.\n\n0.8.4 (2024-02-26)\n------------------\n* Fix regression from :pull:`332` that made ``Regridder`` fail with rectilinear datasets and ``parallel=True``. (:issue:`343`, :pull:`344`).\n* Allow Python 3.12 (and higher) again. (:pull:`345`).\n\n0.8.3 (2024-02-20)\n------------------\n* Remove usage of private method of xarray that was removed in its 2024.02.0 version (:issue:`338`, :issue:`340`) By `Pascal Bourgault `_.\n\nInternal changes\n~~~~~~~~~~~~~~~~\n* Test against ESMF 8.6\n\n\n0.8.2 (2023-09-18)\n------------------\n\nBug fixes\n~~~~~~~~~\n* Raise a meaningful error messages when the output grid has no chunks with `parallel=True` (:issue:`299`, :pull:`304`). By `Pascal Bourgault `_.\n* Correct guess of output chunks for ``SpatialAverager``.\n\n0.8.1 (2023-09-05)\n------------------\n\nBug fixes\n~~~~~~~~~\n* Change import to support shapely 1 and 2.\n\n0.8.0 (2023-09-01)\n------------------\n\nThis release of xESMF improves support for parallelization with dask: weights can now be computed in parallel, and those weights can be applied over chunks spanning the horizontal grid dimensions. Previously, computing weights in parallel was only possible using MPI, and datasets could only be chunked over non-spatial dimensions.\n\nThese new features are the outcome of `Charles Gauthier `_'s internship at `Ouranos `_ during the summer of 2023. Thanks to Charles for his hard work and sharp analysis, which led to a permanent position at Ouranos!\n\n\nNew features\n~~~~~~~~~~~~\n* Added a check in SpatialAverager that warns user if they are using polygons with long segments that could cause errors (:pull:`293`). By `Charles Gauthier `_\n* Add an option (``parallel``) to generate regridding weights in parallel using dask (:pull:`290`). By `Charles Gauthier `_\n* Add the ability to apply weights using dask on chunked horizontal/core dimensions. The ``output_chunks`` argument to the `Regridder` class\n allows setting the chunk sizes of the output data (:pull:`280`). By `Charles Gauthier `_\n* Added a `w` property to the `Regridder` and `SpatialAverager` classes, returning the weights reshaped according to\n the input and output grid dimensions. This is mostly intended for debugging and visualisation purposes (:pull:`276`). By `David Huard `_\n\nDocumentation\n~~~~~~~~~~~~~\n* Move URLs from earthsystemcog.org to earthsystemmodeling.org (:pull:`292`).\n\nInternal changes\n~~~~~~~~~~~~~~~~\n* Remove Python 3.7 from the project classifiers\n* Build docs using Python 3.9\n\n\n0.7.1 (2023-04-03)\n------------------\n\nBug fixes\n~~~~~~~~~\n* Fix ``Mesh.from_polygons`` and unpin Shapely to add support for Shapely 2.0 (:pull:`219`). By `Pascal Bourgault `_\n* Implement workaround for setup conda problem (:pull:`229`). By `Raphael Dussin `_\n* Update CI and doc - fix for DataArrays (:pull:`230`). By `Pascal Bourgault `_\n* Fix ci/cd badge for build status (:pull:`231`). By `Pierre Manchon `_\n* Update CI for Micromamba environments (:pull:`233`). By `Trevor James Smith `_\n* Fix error in test with Shapely 2.0 (:pull:`251`). By `David Huard `_\n\nNew features\n~~~~~~~~~~~~\n* Add util to build tripolar grid (:pull:`228`). By `Raphael Dussin `_\n\nDocumentation\n~~~~~~~~~~~~~\n* Document installation options for ESMpy (:pull:`241`). By `Matthew Plough `_\n\nInternal changes\n~~~~~~~~~~~~~~~~\n* Modernize the package configuration / publish to PyPI (:pull:`248`). By `Filipe Fernandes `_\n\n\n0.7.0 (2022-12-16)\n------------------\n\nBug fixes\n~~~~~~~~~\n- Fix bug in `util.grid_global` where grid centers could go beyond 180 degrees (:issue:`181`). By `David Huard `_\n\nNew features\n~~~~~~~~~~~~\n- Support both [-180, 180] and [0, 360] conventions in `grid_global` (:issue:`149`). By `David Huard `_\n\n\nDocumentation\n~~~~~~~~~~~~~\n- Fix API doc build (:pull:`194`). By `David Huard `_\n- Include `conservative_normed` into the notebook comparing regridding algorithms. By `David Huard `_\n- Fix typos (:pull:`191`). By `Jemma Stachelek `_\n- Copy-editing (:pull:`178`, :pull:`179`). By `RichardScottOZ `_\n\nInternal changes\n~~~~~~~~~~~~~~~~\n- Constrain `numba>=0.55.2`. See (:issue:`185`).\n- Constrain `shapely<2.0`. See (:issue:`216`).\n- Add support for esmpy name change in import. See (:pull:`214`,:issue:`212`)\n\n\n0.6.3 (29-06-2022)\n------------------\n\nBug fixes\n~~~~~~~~~\n- Spatial coordinates of `ds_out` are kept within the regridder and transferred to the regridded DataArray or Dataset (:pull:`175`). By `Pascal Bourgault `_\n- Added `numba` as an explicit dependency to fix installation with conda (:pull:`168`). By `Pascal Bourgault `_\n\nInternal changes\n~~~~~~~~~~~~~~~~\n- Use `cf-xarray` to guess missing CF coordinates before extracting bounds (:pull:`147`). By `Pascal Bourgault `_\n\n\n0.6.2 (23-11-2021)\n------------------\n\nBug fixes\n~~~~~~~~~\n- The introduction of `sparse`, with `numba` under the hood, restricted input data to little-endian dtypes. For big-endian dtypes, xESMF will convert to little-endian, regrid and convert back (:pull:`135`). By `Pascal Bourgault `_\n- ``SpatialAverager`` did not compute the same weights as ``Regridder`` when source cell areas were not uniform (:pull:`128`). By `David Huard `_\n- Refactor of how the regridding is called internally, to fix a bug with dask and sparse (:pull:`135`). By `Pascal Bourgault `_\n\nInternal changes\n~~~~~~~~~~~~~~~~\n- Deprecation of ``regrid_numpy`` and ``regrid_dask`` is scheduled for 0.7.0. All checks on shape, array layout and numba support are now done at call time, rather then at computation time (:pull:`135`).\n\n0.6.1 (23-09-2021)\n------------------\nNote that this version creates very large dask task graphs that can affect performance for large grids.\n\nInternal changes\n~~~~~~~~~~~~~~~~\n- Weights are now stored in a ``xr.DataArray`` backed by ``sparse.COO``, which allows to pass them as an argument to the ``xr.apply_ufunc`` and decrease memory usage when using dask. By `Pascal Bourgault `_\n- New dependency `sparse `_ replacing ``scipy``.\n\n\n0.6.0 (07-08-2021)\n------------------\n\nNew features\n~~~~~~~~~~~~\n- Add the ``skipna`` and ``na_threshold`` options to deal with masks over non-spatial dimensions (:pull:`29`). This is useful when, for example, masks vary over time. By `Stéphane Raynaud `_\n- Add ``unmapped_to_nan`` argument to regridder frontend. When True, this sets target cells outside the source domain to NaN instead of zero for all regridding methods except nearest neighbour (:pull:`94`). By `Martin Schupfner `_\n\nBug fixes\n~~~~~~~~~\n- Drop the PyPi badge and replace by a Conda badge (:pull:`97`). By `Ray Bell `_\n\n\n0.5.3 (04-12-2021)\n------------------\n\nBug fixes\n~~~~~~~~~\n- Fix regression regarding support for non-CF-compliant coordinate names (:pull:`73`). By `Sam Levang `_\n- Infer `bounds` dimension name using cf-xarray (:pull:`78`). By `Pascal Bourgault `_\n- Do not regrid variables that are not defined over horizontal dimensions (:pull:`79`). By `Pascal Bourgault `_\n- Ensure locstream dimension name is consistent with `ds_out` (:pull:`81`). By `Mattia Almansi `_\n\nDocumentation\n~~~~~~~~~~~~~\n- Add release instructions (:pull:`75`). By `David Huard `_\n- Update Zenodo DOI badge\n\n\n0.5.2 (01-20-2021)\n------------------\n\nBug fixes\n~~~~~~~~~\n\n* Restore original behavior for lon/lat discovery, uses cf-xarray if lon/lat not found in dataset (:pull:`64`)\n* Solve issue of dimension order in dataset (#53) with (:pull:`66`)\n\n0.5.1 (01-11-2021)\n------------------\n\nDocumentation\n~~~~~~~~~~~~~\n* Update installation instructions to mention that PyPi only holds xesmf up to version 0.3.0.\n\nNew features\n~~~~~~~~~~~~\n* Regridded xarray.Dataset now preserves the name and attributes of target coordinates (:pull:`60`)\n\nBug fixes\n~~~~~~~~~\n* Fix doc build for API/Regridder (:pull:`61`)\n\n\n0.5.0 (11-11-2020)\n------------------\n\nBreaking changes\n~~~~~~~~~~~~~~~~\n* Deprecate `esmf_grid` in favor of `Grid.from_xarray`\n* Deprecate `esmf_locstream` in favor of `LocStream.from_xarray`\n* Installation requires numpy>=1.16 and cf-xarray>=0.3.1\n\nNew features\n~~~~~~~~~~~~\n* Create `ESMF.Mesh` objects from `shapely.polygons` (:pull:`24`). By `Pascal Bourgault `_\n* New class `SpatialAverager` offers user-friendly mechanism to average a 2-D field over a polygon. Includes support to handle interior holes and multi-part geometries. (:pull:`24`) By `Pascal Bourgault `_\n* Automatic detection of coordinates and computation of vertices based on cf-xarray. (:pull:`49`) By `Pascal Bourgault `_\n\nBug fixes\n~~~~~~~~~\n* Fix serialization bug when using dask's distributed scheduler (:pull:`39`).\n By `Pascal Bourgault `_.\n\nInternal changes\n~~~~~~~~~~~~~~~~\n* Subclass `ESMF.Mesh` and create `from_polygon` method\n* Subclass `ESMF.Grid` and `ESMF.LocStream` and create `from_xarray` methods.\n* New `BaseRegridder` class, with support for `Grid`, `LocStream` and `Mesh` objects. Not all regridding methods are supported for `Mesh` objects.\n* Refactor `Regridder` to subclass `BaseRegridder`.\n\n\n0.4.0 (01-10-2020)\n------------------\nThe git repo is now hosted by pangeo-data (https://github.com/pangeo-data/xESMF)\n\nBreaking changes\n~~~~~~~~~~~~~~~~\n* By default, weights are not written to disk, but instead kept in memory.\n* Installation requires ESMPy 8.0.0 and up.\n\nNew features\n~~~~~~~~~~~~\n* The `Regridder` object now takes a `weights` argument accepting a scipy.sparse COO matrix,\n a dictionary, an xarray.Dataset, or a path to a netCDF file created by ESMF. If None, weights\n are computed and can be written to disk using the `to_netcdf` method. This `weights` parameter\n replaces the `filename` and `reuse_weights` arguments, which are preserved for backward compatibility (:pull:`3`).\n By `David Huard `_ and `Raphael Dussin `_\n* Added documentation discussion how to compute weights from a shell using MPI, and reuse from xESMF (:pull:`12`).\n By `Raphael Dussin `_\n* Add support for masks in :py:func`esmf_grid`. This avoid NaNs to bleed into the interpolated values.\n When using a mask and the `conservative` regridding method, use a new method called\n `conservative_normed` to properly handle normalization (:pull:`1`).\n By `Raphael Dussin `_\n\n\n0.3.0 (06-03-2020)\n------------------\n\nNew features\n~~~~~~~~~~~~\n* Add support for `ESMF.LocStream` `(#81) `_\n By `Raphael Dussin `_\n\n\n0.2.2 (07-10-2019)\n------------------\n\nNew features\n~~~~~~~~~~~~\n* Add option to allow degenerated grid cells `(#61) `_\n By `Jiawei Zhuang `_\n\n\n0.2.0 (04-08-2019)\n------------------\n\nBreaking changes\n~~~~~~~~~~~~~~~~\nAll user-facing APIs in v0.1.x should still work exactly the same. That said, because some internal codes have changed a lot, there might be unexpected edge cases that break current user code. If that happens, you can revert to the previous version by `pip install xesmf==0.1.2` and follow `old docs `_.\n\nNew features\n~~~~~~~~~~~~\n* Lazy evaluation on dask arrays (uses :py:func:`xarray.apply_ufunc` and :py:func:`dask.array.map_blocks`)\n* Automatic looping over variables in an xarray Dataset\n* Add tutorial notebooks on those new features\n\nBy `Jiawei Zhuang `_\n\n\n0.1.2 (03-08-2019)\n------------------\nThis release mostly contains internal clean-ups to facilitate future development.\n\nNew features\n~~~~~~~~~~~~\n* Deprecates `regridder.A` in favor of `regridder.weights`\n* Speed-up test suites by using coarser grids\n* Use parameterized tests when appropriate\n* Fix small memory leaks from `ESMF.Grid`\n* Properly assert ESMF enums\n\nBy `Jiawei Zhuang `_\n\n\n0.1.1 (31-12-2017)\n------------------\nInitial release.\nBy `Jiawei Zhuang `_\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2017 Jiawei Zhuang\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "MANIFEST.in", "content": "include LICENSE\ninclude *.rst\n\ngraft xesmf\n\nprune .github\nprune *.egg-info\nprune binder\nprune ci\nprune doc\n\nexclude *.yaml\nexclude *.yml\nexclude .*.txt\nexclude .gitignore\nexclude .pre-commit-config.yaml\nexclude .prettierrc.toml\nexclude xesmf/_version.py\n" }, { "path": "README.rst", "content": "xESMF: Universal Regridder for Geospatial Data\n==============================================\n\n|Binder| |conda| |Build Status| |codecov| |docs| |license| |DOI|\n\nxESMF is a Python package for\n`regridding `_.\nIt is\n\n- **Powerful**: It uses ESMF_/ESMPy_ as backend and can regrid between **general curvilinear grids**\n with all `ESMF regridding algorithms `_,\n such as **bilinear**, **conservative** and **nearest neighbour**.\n- **Easy-to-use**: It abstracts away ESMF's complicated infrastructure\n and provides a simple, high-level API, compatible with xarray_ as well as basic numpy arrays.\n- **Fast**: It is faster than ESMPy's original Fortran regridding engine in serial case, and also supports dask_ for `out-of-core, parallel computation `_.\n\nPlease see `online documentation `_, or `play with example notebooks on Binder `_.\n\nFor new users, I also recommend reading `How to ask for help `_ and `How to support xESMF `_.\n\n.. _ESMF: https://earthsystemmodeling.org/\n.. _ESMPy: http://earthsystemmodeling.org/esmpy/\n.. _xarray: http://xarray.pydata.org\n.. _dask: https://dask.org/\n\n.. |conda| image:: https://anaconda.org/conda-forge/xesmf/badges/version.svg\n :target: https://anaconda.org/conda-forge/xesmf/\n\n.. |Build Status| image:: https://img.shields.io/github/actions/workflow/status/pangeo-data/xESMF/ci.yaml?branch=master\n :target: https://github.com/pangeo-data/xESMF/actions\n :alt: github-ci build status\n\n.. |codecov| image:: https://codecov.io/gh/pangeo-data/xESMF/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/pangeo-data/xESMF\n :alt: code coverage\n\n.. |docs| image:: https://readthedocs.org/projects/pangeo-xesmf/badge/?version=latest\n :target: http://xesmf.readthedocs.io/en/latest/?badge=latest\n :alt: documentation status\n\n.. |license| image:: https://img.shields.io/badge/License-MIT-blue.svg\n :target: https://github.com/pangeo-data/xESMF/blob/master/LICENSE\n :alt: license\n\n.. |DOI| image:: https://zenodo.org/badge/281126933.svg\n :target: https://zenodo.org/badge/latestdoi/281126933\n :alt: DOI\n\n.. |Binder| image:: https://mybinder.org/badge_logo.svg\n :target: https://mybinder.org/v2/gh/pangeo-data/xESMF/master?filepath=doc%2Fnotebooks\n :alt: binder\n" }, { "path": "binder/environment.yml", "content": "channels:\n - conda-forge\ndependencies:\n - python=3.7\n - esmpy==7.1.0r\n - xarray\n - dask\n - numpy\n - scipy\n - shapely\n - matplotlib\n - cartopy\n - cf_xarray>=0.3.1\n - pip:\n - xesmf==0.2.2\n" }, { "path": "ci/doc.yml", "content": "name: xesmf\nchannels:\n - conda-forge\ndependencies:\n # Python pin only to accelerate solving\n - python>=3.12\n - cf_xarray>=0.5.1\n - esmpy >=8.0.0\n - numba >=0.55.2\n - numpy >=1.16\n - shapely\n - sparse>=0.8.0\n - xarray>=0.17.0\n # Doc\n - numpydoc\n - geopandas\n - descartes\n - ipython\n - Pygments>=2.6\n - nbsphinx\n - sphinx\n - sphinx_rtd_theme\n - docutils!=0.18\n" }, { "path": "ci/environment-upstream-dev.yml", "content": "name: xesmf\nchannels:\n - conda-forge\ndependencies:\n - cftime\n - codecov\n - dask\n - esmpy\n - netcdf4\n - numba\n - numpy\n - pip\n - pre-commit\n - pytest\n - pytest-cov\n - shapely\n - sparse>=0.8.0\n - pip:\n - git+https://github.com/pydata/xarray.git\n - git+https://github.com/xarray-contrib/cf-xarray.git\n" }, { "path": "ci/environment.yml", "content": "name: xesmf\nchannels:\n - conda-forge\ndependencies:\n - python>=3.11\n - cf_xarray>=0.5.1\n - dask\n - esmpy\n - numba >=0.55.2\n - numpy >=1.16\n - shapely\n - sparse>=0.8.0\n - xarray>=0.17.0\n # Testing and extras\n - cftime\n - codecov\n - netcdf4\n - pip\n - pre-commit\n - pytest\n - pytest-cov\n" }, { "path": "codecov.yml", "content": "codecov:\n require_ci_to_pass: no\n max_report_age: off\n\ncomment: false\n\nignore:\n - \"xesmf/tests/*\"\n - \"setup.py\"\n\ncoverage:\n precision: 2\n round: down\n status:\n project:\n default:\n target: 95\n informational: true\n patch: off\n changes: off\n" }, { "path": "doc/Makefile", "content": "# Minimal makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS =\nSPHINXBUILD = python -msphinx\nSPHINXPROJ = xESMF\nSOURCEDIR = .\nBUILDDIR = _build\n\n# Put it first so that \"make\" without argument is like \"make help\".\nhelp:\n\t@$(SPHINXBUILD) -M help \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n\n.PHONY: help Makefile\n\n# Catch-all target: route all unknown targets to Sphinx using the new\n# \"make mode\" option. $(O) is meant as a shortcut for $(SPHINXOPTS).\n%: Makefile\n\t@$(SPHINXBUILD) -M $@ \"$(SOURCEDIR)\" \"$(BUILDDIR)\" $(SPHINXOPTS) $(O)\n" }, { "path": "doc/changes.rst", "content": ".. include:: ../CHANGES.rst\n" }, { "path": "doc/conf.py", "content": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# xESMF documentation build configuration file, created by\n# sphinx-quickstart on Sun Dec 17 19:49:04 2017.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport datetime\nimport xesmf as xe\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n 'sphinx.ext.autodoc',\n 'numpydoc',\n 'sphinx.ext.extlinks',\n 'sphinx.ext.autosummary',\n 'sphinx.ext.mathjax',\n 'nbsphinx',\n 'IPython.sphinxext.ipython_console_highlighting',\n]\n\nextlinks = {\n 'issue': ('https://github.com/pangeo-data/xESMF/issues/%s', 'GH/%s'),\n 'pull': ('https://github.com/pangeo-data/xESMF/pull/%s', 'PR/%s'),\n}\n\n# IPython.sphinxext.ipython_console_highlighting is required for anaconda.\n# See the issue: https://github.com/spatialaudio/nbsphinx/issues/24\n\n# https://stackoverflow.com/questions/12206334/sphinx-autosummary-toctree-contains-reference-to-nonexisting-document-warnings\nnumpydoc_show_class_members = False\n\n# NOT to sort autodoc functions in alphabetical order\nautodoc_member_order = 'bysource'\n\n# avoid automatic execution for notebooks\nnbsphinx_execute = 'never'\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\ncurrent_year = datetime.datetime.now().year\nproject = 'xESMF'\ncopyright = f'{current_year}, Jiawei Zhuang and the xESMF development team'\nauthor = 'Jiawei Zhuang'\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = xe.__version__\n# The full version, including alpha/beta/rc tags.\nrelease = xe.__version__\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\nlanguage = 'en'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages. See the documentation for\n# a list of builtin themes.\n#\nhtml_theme = 'sphinx_rtd_theme'\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further. For a list of options available for each theme, see the\n# documentation.\n#\n# html_theme_options = {}\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\n# html_static_path = ['_static']\n\n# Custom sidebar templates, must be a dictionary that maps document names\n# to template names.\n#\n# This is required for the alabaster theme\n# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars\nhtml_sidebars = {\n '**': [\n 'about.html',\n 'navigation.html',\n 'relations.html', # needs 'show_related': True theme option to display\n 'searchbox.html',\n 'donate.html',\n ]\n}\n\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'xESMFdoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n # The paper size ('letterpaper' or 'a4paper').\n #\n # 'papersize': 'letterpaper',\n # The font size ('10pt', '11pt' or '12pt').\n #\n # 'pointsize': '10pt',\n # Additional stuff for the LaTeX preamble.\n #\n # 'preamble': '',\n # Latex figure (float) alignment\n #\n # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n# author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n (master_doc, 'xESMF.tex', 'xESMF Documentation', 'Jiawei Zhuang', 'manual'),\n]\n\n\n# -- Options for manual page output ---------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [(master_doc, 'xesmf', 'xESMF Documentation', [author], 1)]\n\n\n# -- Options for Texinfo output -------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n# dir menu entry, description, category)\ntexinfo_documents = [\n (\n master_doc,\n 'xESMF',\n 'xESMF Documentation',\n author,\n 'xESMF',\n 'One line description of project.',\n 'Miscellaneous',\n ),\n]\n" }, { "path": "doc/index.rst", "content": "xESMF: Universal Regridder for Geospatial Data\n==============================================\n\nxESMF is a Python package for\n`regridding `_.\nIt is\n\n- **Powerful**: It uses ESMF_/ESMPy_ as backend and can regrid between **general curvilinear grids**\n with all `ESMF regridding algorithms `_,\n such as **bilinear**, **conservative** and **nearest neighbour**.\n- **Easy-to-use**: It abstracts away ESMF's complicated infrastructure\n and provides a simple, high-level API, compatible with xarray_ as well as basic numpy arrays.\n- **Fast**: It is :doc:`faster than <./notebooks/Backend>` ESMPy's original Fortran regridding engine in the serial case, and also supports dask_ for `out-of-core, parallel computation `_ .\n\n\n.. _ESMF: https://earthsystemmodeling.org/\n.. _ESMPy: http://earthsystemmodeling.org/esmpy/\n.. _xarray: http://xarray.pydata.org\n.. _dask: https://dask.org/\n\n\nContents\n--------\n\n.. toctree::\n :maxdepth: 1\n :caption: Overview\n\n why\n other_tools\n limitations\n\n.. toctree::\n :maxdepth: 1\n :caption: Beginner tutorials\n\n installation\n notebooks/Rectilinear_grid\n notebooks/Curvilinear_grid\n notebooks/Pure_numpy\n notebooks/Dataset\n\n.. toctree::\n :maxdepth: 1\n :caption: Intermediate tutorials\n\n notebooks/Dask\n notebooks/Compare_algorithms\n notebooks/Reuse_regridder\n notebooks/Using_LocStream\n notebooks/Masking\n large_problems_on_HPC\n notebooks/Spatial_Averaging\n\n.. toctree::\n :maxdepth: 1\n :caption: Technical notes\n\n changes\n notebooks/Backend\n releases\n\n.. toctree::\n :maxdepth: 1\n :caption: API\n\n user_api\n internal_api\n\n\nHow to ask for help\n-------------------\n\nThe `GitHub issue tracker `_ is the primary place for bug reports. If you hit any issues, I recommend the following steps:\n\n- First, `search for existing issues `_. Other people are likely to hit the same problem and probably have already found the solution. You might also want to search issues in the original repository ``_.\n\n- For a new bug, please `craft a minimal bug report `_ with reproducible code. Use synthetic data or `upload `_ a small sample of input data (~1 MB) so I can quickly reproducible your error.\n\n- For platform-dependent problems (such as kernel dying and installation errors), please also show how to reproduce your system environment, otherwise we have no way to diagnose the issue. The best approach is probably finding an `official Docker image `_ that is closest to your OS (such as `Ubuntu `_ or `CentOS `_), and build your Python environment starting with such an image, to see whether the error still exists. Alternatively you can select from public cloud images, such as `Amazon Machine Images `_ or `Google Cloud Images `_. If the error only happens on your institution's HPC cluster, please contact the system administrator for help.\n\nFor general \"how-to\" questions that are not bugs, you can also post on `StackOverflow `_ (ref: `xarray questions `_).\n\n\nHow to support xESMF\n--------------------\nYour support in any form will be appreciated. The easy ways (takes several seconds):\n\n- `Give a star `_ to its `GitHub repository `_.\n- Share it via social media like Twitter; introduce it to your friends/advisors/students.\n\nMore advanced ways:\n\n- Cite xESMF in your scientific publications. Currently the best way is to cite the DOI: https://doi.org/10.5281/zenodo.4294774.\n- If you'd like to contribute code, see this `preliminary contributor guide `_. Also see `Contributing to xarray `_ for more backgrounds.\n" }, { "path": "doc/installation.rst", "content": ".. _installation-label:\n\nInstallation\n============\n\nTry on Binder without local installation\n----------------------------------------\n\nThe `Binder project `_ provides pre-configured environment in the cloud. You just need a web browser to access it. Please follow the Binder link on `xESMF's GitHub page `_.\n\nInstall on local machine with Conda\n-----------------------------------\n\nxESMF requires Python>=3.8. The major dependencies are xarray and ESMPy, and the best way to install them is using Conda_.\nNote that the latest xarray releases require Python 3.9 or later.\n\nFirst, `install miniconda `_. Then, we recommend creating a new, clean environment:\n\n.. code-block:: bash\n\n $ conda create -n xesmf_env\n $ conda activate xesmf_env\n\nGetting xESMF is as simple as:\n\n.. code-block:: bash\n\n $ conda install -c conda-forge xesmf\n\nWe also highly recommend those extra packages for full functionality:\n\n.. code-block:: bash\n\n # to support all features in xESMF\n $ conda install -c conda-forge dask netCDF4\n\n # optional dependencies for executing all notebook examples\n $ conda install -c conda-forge matplotlib cartopy jupyterlab\n\n\nAlternatively, you can first install dependencies, and then use ``pip`` to install xESMF:\n\n.. code-block:: bash\n\n $ conda install -c conda-forge esmpy xarray numpy shapely cf_xarray sparse numba\n $ pip install git+https://github.com/pangeo-data/xesmf.git\n\nThis will install the latest version from the github repo. To install a specific release, append the version tag to the url (e.g. `@v0.5.0`).\n\nNotes about ESMpy\n-----------------\n\n* ESMpy 8.4 is only compatible with xESMF >= 0.7.\n* ESMpy must be installed through Conda or compiled manually; it is not available through PyPI. When installing xESMF with pip, the ESMpy package must be manually installed first.\n\nTesting your installation\n-------------------------\n\nxESMF itself is a lightweight package, but its dependency ESMPy is a quite heavy and sometimes might be installed incorrectly. To validate & debug your installation, you can use pytest to run the test suites:\n\n.. code-block:: bash\n\n $ conda install pytest\n $ pytest -v --pyargs xesmf # should all pass\n\nA common cause of error (especially for HPC cluster users) is that pre-installed modules like NetCDF, MPI, and ESMF are incompatible with the conda-installed equivalents. Make sure you have a clean environment when running ``conda install`` (do not ``module load`` other libraries). See `this issue `_ for more discussions.\n\nNotes for Windows users\n-----------------------\n\nThe ESMPy conda package is usually only available for Linux and Mac OSX.\nBuilds for windows have been made for some versions (8.4.2).\nWindows users can try the\n`Linux subsystem `_\nor `docker-miniconda `_ .\n\nInstalling scientific software on Windows can often be a pain, and\n`Docker `_ is a pretty good workaround.\nIt takes some time to learn but worths the effort.\nCheck out this `tutorial on using Docker with Anaconda `_.\n\nThis problem is being investigated. See `this other issue `_.\n\nInstall development version from GitHub repo\n--------------------------------------------\n\nTo get the latest version that is not uploaded to PyPI_ yet::\n\n $ pip install --upgrade git+https://github.com/pangeo-data/xESMF.git\n\nDevelopers can track source code change::\n\n $ git clone https://github.com/pangeo-data/xESMF.git\n $ cd xESMF\n $ pip install -e .\n\n.. _xarray: http://xarray.pydata.org\n.. _ESMPy: http://earthsystemmodeling.org/esmpy/\n.. _Conda: https://docs.conda.io/\n.. _PyPI: https://pypi.python.org/pypi\n.. _NESII: https://www.esrl.noaa.gov/gsd/nesii/\n" }, { "path": "doc/internal_api.rst", "content": "Internal API\n############\n\nfrontend\n========\n\n.. automodule:: xesmf.frontend\n :members:\n\nbackend\n=======\n\n.. automodule:: xesmf.backend\n :members:\n\nsmm\n===\n\n.. automodule:: xesmf.smm\n :members:\n" }, { "path": "doc/large_problems_on_HPC.rst", "content": ".. _largeproblems-label:\n\n.. |polarstereo| image:: images/elevation_southpolarstero.png\n :width: 600\n :alt: elevation in polar stereographic\n\n.. |regular| image:: images/elevation_regulargrid.png\n :width: 600\n :alt: elevation on regular lat/lon grid\n\nSolving large problems using HPC\n================================\n\nIn some cases, the sizes of the source and target grids lead to weights that either take\ntoo long to compute or can't fit into memory on a regular desktop/laptop machine. But fear not,\nthere are solutions to solve large regridding problems, provided you have access to a High\nPerformance Computing machine. Your ESMF installation (from conda or equivalent) comes with\ncommand line tools (`ESMF_RegridWeightGen and ESMF_Regrid `_) that can be executed in parallel with\nMPI. This allows very large regridding to be performed in minutes on hundred of compute cores.\nUsing these tools, we are able to regrid data at 500 meters resolution (13300x13300 pts) from\na South Polar Stereographic projection to a 15' regular longitude/latitude grid (6720x86400 pts).\nThe original:\n\n|polarstereo|\n\nand after regridding:\n\n|regular|\n\nAlthough these tools are very performant, they lack critical documentation which makes them\nhard to understand and operate. We're going to try to bridge those gaps with some real-life\nexamples.\nThe roadblocks that you are most likely to find on your way are related to netcdf attributes\nrequired by the ESMF tools. Error messages are not very informative and one may need to read the\nsource code to figure out what the problem is. The first **trick** you need to know is that\ngeographical coordinates both need **units** netcdf attributes and these units can only be\nfrom the lists:\n\n* *degrees_east, degree_east, degrees_E, degree_E, degreesE, degreeE* for longitude\n* *degrees_north, degree_north, degrees_N, degree_N, degreesN, degreeN* for latitude\n\nOtherwise, ESMF will fail with an error along the lines of *file type not recognized*.\nFor conservative regridding, the units of the longitude and latitude arrays at the cell\ncorners have to be *grid_corner_lon* and *grid_corner_lat*.\n\n\n.. compound::\n\n output from **ncdump -h** on your netcdf file should look like::\n\n variables:\n double lat(lat) ;\n lat:_FillValue = 1.e+20 ;\n lat:units = \"degrees_north\" ;\n double lon(lon) ;\n lon:_FillValue = 1.e+20 ;\n lon:units = \"degrees_east\" ;\n\n dimensions and coordinates names do not need to be the same and coordinates can be 2d.\n\n\nCreating weights on HPC and using them in xESMF\n-----------------------------------------------\n\nWith your source and destination grids ready, you can now generate weights on your HPC system that\nyou can later use in xESMF by providing the **filename** and **reuse_weights=True** when creating\na regridder. The invocation to the weights generation on a MPI parallel system using 252 cores\nwill look like:\n\n.. code-block:: bash\n\n $ mpirun -np 252 ESMF_RegridWeightGen -s source.nc -d destination.nc -w weights.nc -m bilinear\n\nIn this example, we use bilinear regridding but all the methods available in xESMF are here too.\nYou can then import your weights generated on your HPC system in xESMF with:\n\n.. code-block:: python\n\n import xarray as xr\n import xesmf as xe\n\n ds_in = xr.open_dataset(\"source.nc\")\n ds_out = xr.open_dataset(\"destination.nc\")\n regridder = xe.Regridder(ds_in, ds_out, \"bilinear\", filename=\"weights.nc\", reuse_weights=True)\n\nThere is a lot of options you can provide to **ESMF_RegridWeightGen** and you can have a list using:\n\n.. code-block:: bash\n\n $ ESMF_RegridWeightGen --help\n\nSome of particular interest are:\n\n* **--netcdf4**: netcdf3 cannot handle very large files that can be produced here\n* **--src_regional / --dst_regional**: if one of your grid is not periodic in longitude\n\n\nRegrid variable(s) on HPC system\n--------------------------------\n\nIf the weights you have generated don't fit into memory when using xESMF (e.g. you have an error of the\ntype *buffer size too small*), you still have the option to do the regridding of your variable on\nthe HPC using **ESMF_Regrid**. Here again, there is a **second trick** that you need to know:\n\n.. compound::\n\n all the variables you want to regrid need to have a netcdf attribute named **coordinates**\n that gives the list of its geographical coordinates, e.g.::\n\n variables:\n double lat(lat) ;\n lat:_FillValue = 1.e+20 ;\n lat:units = \"degrees_north\" ;\n double lon(lon) ;\n lon:_FillValue = 1.e+20 ;\n lon:units = \"degrees_east\" ;\n short elevation(lat, lon) ;\n elevation:_FillValue = 32767s ;\n elevation:units = \"m\" ;\n elevation:standard_name = \"height_above_reference_ellipsoid\" ;\n elevation:long_name = \"Elevation relative to sea level\" ;\n elevation:coordinates = \"lon lat\" ;\n\n Also specifying a _FillValue explicitly instead of a NaN is also a good idea ;)\n\n**ESMF_Regrid** will overwrite the destination.nc file and add the regridded variables so you\nmay want to make a copy in case (say output.nc). We can now invoke the regridding for the\nvariable *elevation* on the HPC using:\n\n.. code-block:: bash\n\n $ mpirun -np 720 ESMF_Regrid -s source.nc -d output.nc -m bilinear --src_var elevation --dst_var elevation --netcdf4\n\nAnd this gets the job done! If for some reason, **ESMF_Regrid** dies with a MPI error, try increasing the\nnumber of compute cores. Similarly, you can get the numerous available options with:\n\n.. code-block:: bash\n\n $ ESMF_Regrid --help\n\nThere is a lot to unpack when it comes to the options so this might be a good time to start\nexploring by yourself. Hopefully this gave you enough information to work it out.\n\n\nTechnical point: mpi4py considerations\n--------------------------------------\n\nIf your HPC system does not provide a satisfying ESMF module, you may need to install it yourself\nthrough conda. This is fine and should mostly work smoothly except that you may have some MPI issues\nor conflicts. To use ESMF_Regrid, you will need to activate your conda env but it is likely that the\nmpirun in it will not work on your HPC system because it hasn't been set up properly.\n\nThe solution is to install mpi4py from scratch and customize its mpi.cfg file to your MPI libraries\nspecifications. The block to add to mpi.cfg should look like this:\n\n.. code-block:: bash\n\n [gaea-gnu]\n mpi_dir = /opt/cray/pe/mpt/7.7.11/gni/mpich-gnu/8.2\n include_dirs = %(mpi_dir)s/include\n libraries = mpich\n library_dirs = %(mpi_dir)s/lib\n runtime_library_dirs = %(mpi_dir)s/lib\n mpicc = /opt/gcc/8.2.0/bin/gcc\n mpicxx = /opt/gcc/8.2.0/bin/g++\n\nAnd then recompile mpi4py from scratch:\n\n.. code-block:: bash\n\n wget https://bitbucket.org/mpi4py/mpi4py/downloads/mpi4py-3.0.3.tar.gz\n tar -zxf mpi4py-3.0.3.tar.gz\n conda activate myenv\n cat gaea_mpi.cfg >> mpi4py-3.0.3/mpi.cfg\n pushd mpi4py-3.0.3\n python setup.py build --mpi=gaea-gnu\n python setup.py install\n" }, { "path": "doc/limitations.rst", "content": "Current limitations\n===================\n\n.. _irregular_meshes-label:\n\nIrregular meshes\n----------------\n\nESMPy is actually able to deal with general irregular meshes\n(`example `_),\nbut designing an elegant front-end for that is very challenging.\nPlain 2D arrays cannot describe irregular meshes.\nThere needs to be additional information for connectivity, as suggested by\n`UGRID Conventions `_.\n\nxESMF supports quadrilateral grids and has limited support of\ntriangular or hexagonal meshes. xESMF also supports complex polygonal\nmeshes, but only in the context of regional averaging.\n\nxarray's data model, although powerful, can only describe quadrilateral grids\n(including multi-tile quadrilateral grids like the cubed-sphere).\nIf there is an elegant data model in Python for irregular meshes, interfacing\nwith ESMPy should not be very difficult. Pull requests along these lines are welcome.\n\n\nVector regridding\n-----------------\n\nLike almost all regridding packages, xESMF assumes scalar fields.\nThe most common way to remap winds is to rotate/re-decompose the\nwind components (U and V) to the new direction,\nand then regrid each component individually using a scalar regridding function.\n\nExact conservation of vector properities (like divergence and vorticity)\nis beyond the scope of almost all regridding packages.\nUsing bilinear algorithm on each component should lead to OK results in most cases.\n" }, { "path": "doc/make.bat", "content": "@ECHO OFF\r\n\r\npushd %~dp0\r\n\r\nREM Command file for Sphinx documentation\r\n\r\nif \"%SPHINXBUILD%\" == \"\" (\r\n\tset SPHINXBUILD=python -msphinx\r\n)\r\nset SOURCEDIR=.\r\nset BUILDDIR=_build\r\nset SPHINXPROJ=xESMF\r\n\r\nif \"%1\" == \"\" goto help\r\n\r\n%SPHINXBUILD% >NUL 2>NUL\r\nif errorlevel 9009 (\r\n\techo.\r\n\techo.The Sphinx module was not found. Make sure you have Sphinx installed,\r\n\techo.then set the SPHINXBUILD environment variable to point to the full\r\n\techo.path of the 'sphinx-build' executable. Alternatively you may add the\r\n\techo.Sphinx directory to PATH.\r\n\techo.\r\n\techo.If you don't have Sphinx installed, grab it from\r\n\techo.http://sphinx-doc.org/\r\n\texit /b 1\r\n)\r\n\r\n%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\r\ngoto end\r\n\r\n:help\r\n%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS%\r\n\r\n:end\r\npopd\r\n" }, { "path": "doc/notebooks/Backend.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# xESMF backend usage and benchmark\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"xESMF isn't just a wrapper of ESMPy. It only uses ESMPy to generate regridding\\n\",\n \"weights, but has its own Scipy-based method for applying weights (see\\n\",\n \"[more about regridding weights](./Reuse_regridder.ipynb#Why-applying-regridding-is-so-fast?)).\\n\",\n \"\\n\",\n \"We switch to the Scipy method because its serial performance is much higher than\\n\",\n \"ESMPy's own engine and can also reuse weights\\n\",\n \"([issue#2](https://github.com/JiaweiZhuang/xESMF/issues/2)). ESMPy's native\\n\",\n \"method is available in the backend, mainly for benchmarking Scipy results in\\n\",\n \"unit tests.\\n\",\n \"\\n\",\n \"Here we show how to use xESMF backend and compare the performance of two\\n\",\n \"methods. Note that the backend is still pretty easy to use compared to the\\n\",\n \"original ESMPy -- it just doesn't have a fancy API and cannot deal with xarray\\n\",\n \"metadata.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\\n\",\n \"import numpy as np\\n\",\n \"import xesmf as xe\\n\",\n \"\\n\",\n \"# backend functions\\n\",\n \"from xesmf.backend import (\\n\",\n \" Grid,\\n\",\n \" esmf_regrid_build,\\n\",\n \" esmf_regrid_apply,\\n\",\n \")\\n\",\n \"from xesmf.smm import read_weights, apply_weights\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Prepare data\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We use the same data as in the\\n\",\n \"[reusing regridder example](./Reuse_regridder.ipynb), but convert xarray DataSet\\n\",\n \"to pure numpy arrays to work with the backend.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_in = xe.util.grid_2d(\\n\",\n \" -120, 120, 0.4, -60, 60, 0.3 # longitude range and resolution\\n\",\n \") # latitude range and resolution\\n\",\n \"ds_out = xe.util.grid_2d(-120, 120, 0.6, -60, 60, 0.4)\\n\",\n \"ds_in.coords[\\\"time\\\"] = np.arange(1, 11)\\n\",\n \"ds_in.coords[\\\"lev\\\"] = np.arange(1, 51)\\n\",\n \"ds_in[\\\"data2D\\\"] = xe.data.wave_smooth(ds_in[\\\"lon\\\"], ds_in[\\\"lat\\\"])\\n\",\n \"ds_in[\\\"data4D\\\"] = ds_in[\\\"time\\\"] * ds_in[\\\"lev\\\"] * ds_in[\\\"data2D\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10, 50, 400, 600)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# backend only accepts pure numpy array\\n\",\n \"lon_in = ds_in[\\\"lon\\\"].values\\n\",\n \"lat_in = ds_in[\\\"lat\\\"].values\\n\",\n \"\\n\",\n \"lon_out = ds_out[\\\"lon\\\"].values\\n\",\n \"lat_out = ds_out[\\\"lat\\\"].values\\n\",\n \"\\n\",\n \"data_in = ds_in[\\\"data4D\\\"].values\\n\",\n \"data_in.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Make ESMF Grid objects\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\\n\",\n \"grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This is a native ESMPy Grid object:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xesmf.backend.Grid\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"type(grid_in)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We pass the transpose (`lon.T`) because ESMPy prefer Fortran-ordering to\\n\",\n \"C-ordering (see this\\n\",\n \"[issue](https://github.com/nawendt/esmpy-tutorial/issues/4)).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" C_CONTIGUOUS : True\\n\",\n \" F_CONTIGUOUS : False\\n\",\n \" OWNDATA : True\\n\",\n \" WRITEABLE : True\\n\",\n \" ALIGNED : True\\n\",\n \" WRITEBACKIFCOPY : False\\n\",\n \" UPDATEIFCOPY : False\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lon_in.flags # numpy arrays are mostly C-ordered\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" C_CONTIGUOUS : False\\n\",\n \" F_CONTIGUOUS : True\\n\",\n \" OWNDATA : False\\n\",\n \" WRITEABLE : True\\n\",\n \" ALIGNED : True\\n\",\n \" WRITEBACKIFCOPY : False\\n\",\n \" UPDATEIFCOPY : False\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lon_in.T.flags # a memory view on its tranpose would be Fortran-ordered\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Compute weights\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"filename = \\\"test_weights.nc\\\" # weight filename\\n\",\n \"if os.path.exists(filename):\\n\",\n \" os.remove(filename) # ESMPy will complain if the file exists\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Computing weights takes ~7s, as in the\\n\",\n \"[reusing regridder example](./Reuse_regridder.ipynb#Build-Regridder).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 4.08 s, sys: 207 ms, total: 4.29 s\\n\",\n \"Wall time: 4.35 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"regrid = esmf_regrid_build(grid_in, grid_out, 'bilinear',\\n\",\n \" extra_dims=[50, 10], # reversed to Fortran-ordering\\n\",\n \" filename=filename)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It returns a native ESMPy Regrid object:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"ESMF.api.regrid.Regrid\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"type(regrid)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It also writes weights to disk so we can then read them back for Scipy.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"netcdf test_weights {\\n\",\n \"dimensions:\\n\",\n \"\\tn_s = 480000 ;\\n\",\n \"variables:\\n\",\n \"\\tdouble S(n_s) ;\\n\",\n \"\\tint col(n_s) ;\\n\",\n \"\\tint row(n_s) ;\\n\",\n \"}\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%bash\\n\",\n \"ncdump -h test_weights.nc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Apply weights using ESMPy backend\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It takes ~3s with ESMPy's native method.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 1.59 s, sys: 2.51 s, total: 4.1 s\\n\",\n \"Wall time: 12.3 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"data_out_esmpy = esmf_regrid_apply(regrid, data_in.T).T\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The first `.T` converts C-ordering to F-ordering for ESMPy, and the second `.T`\\n\",\n \"converts the result back to C-ordering. It just gets a memory view and thus\\n\",\n \"incurs almost no overhead.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \" C_CONTIGUOUS : True\\n\",\n \" F_CONTIGUOUS : False\\n\",\n \" OWNDATA : False\\n\",\n \" WRITEABLE : True\\n\",\n \" ALIGNED : True\\n\",\n \" WRITEBACKIFCOPY : False\\n\",\n \" UPDATEIFCOPY : False\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_out_esmpy.flags\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10, 50, 300, 400)\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_out_esmpy.shape # broadcasted over extra dimensions\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Apply weights using Scipy backend\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Read weights back for Scipy. `read_weights` needs to know the shape of the\\n\",\n \"sparse matrix, i.e. how many points in input and output grids.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"<120000x240000 sparse matrix of type ''\\n\",\n \"\\twith 480000 stored elements in COOrdinate format>\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"weights = read_weights(filename, lon_in.size, lon_out.size)\\n\",\n \"weights\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`apply_weights` needs to know shape of the output grid.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(300, 400)\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lon_out.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 842 ms, sys: 776 ms, total: 1.62 s\\n\",\n \"Wall time: 6.07 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"data_out_scipy = apply_weights(weights, data_in, lon_in.shape, lon_out.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is several times faster than ESMPy's native method. The conclusion seems to\\n\",\n \"be pretty robust across different platforms (feel free to verify on your own),\\n\",\n \"so we choose Scipy as the default backend.\\n\",\n \"\\n\",\n \"A likely explanation for this performance discrepancy is, the original ESMF is\\n\",\n \"optimized for large processor counts (~1000 CPUs) at the expense of serial\\n\",\n \"performance (ESMF team, personal communication).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(10, 50, 300, 400)\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_out_scipy.shape # broadcasted over extra dimensions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"np.testing.assert_equal(data_out_scipy, data_out_esmpy) # exactly the same\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.remove(filename) # clean-up\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "doc/notebooks/Compare_algorithms.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Comparison of six regridding algorithms\\n\",\n \"\\n\",\n \"xESMF exposes five different regridding algorithms from the ESMF library:\\n\",\n \"\\n\",\n \"- `bilinear`: `ESMF.RegridMethod.BILINEAR`\\n\",\n \"- `conservative`: `ESMF.RegridMethod.CONSERVE`\\n\",\n \"- `conservative_normed`: `ESMF.RegridMethod.CONSERVE`\\n\",\n \"- `patch`: `ESMF.RegridMethod.PATCH`\\n\",\n \"- `nearest_s2d`: `ESMF.RegridMethod.NEAREST_STOD`\\n\",\n \"- `nearest_d2s`: `ESMF.RegridMethod.NEAREST_DTOS`\\n\",\n \"\\n\",\n \"where `conservative_normed` is just the `conservative` method with the\\n\",\n \"normalization set to `ESMF.NormType.FRACAREA` instead of the default\\n\",\n \"`norm_type=ESMF.NormType.DSTAREA`.\\n\",\n \"\\n\",\n \"This notebook demonstrates how these algorithms behave in different situations.\\n\",\n \"\\n\",\n \"## Notes\\n\",\n \"\\n\",\n \"- `bilinear` and `conservative` should be the most commonly used methods. They\\n\",\n \" are both monotonic (i.e. will not create new maximum/minimum).\\n\",\n \"- Nearest neighbour methods, either source to destination (s2d) or destination\\n\",\n \" to source (d2s), could be useful in special cases. Keep in mind that d2s is\\n\",\n \" highly non-monotonic.\\n\",\n \"- Patch is ESMF's unique method, producing highly smooth results but quite slow.\\n\",\n \"- From the ESMF documentation:\\n\",\n \"\\n\",\n \" > The weight $w_{ij}$ for a particular source cell $i$ and destination cell\\n\",\n \" > $j$ are calculated as $w_{ij}=f_{ij} * A_{si}/A_{dj}$. In this equation\\n\",\n \" > $f_{ij}$ is the fraction of the source cell $i$ contributing to destination\\n\",\n \" > cell $j$, and $A_{si}$ and $A_{dj}$ are the areas of the source and\\n\",\n \" > destination cells.\\n\",\n \"\\n\",\n \" For `conservative_normed`,\\n\",\n \"\\n\",\n \" > ... then the weights are further divided by the destination fraction. In\\n\",\n \" > other words, in that case $w_{ij}=f_{ij} * A_{si}/(A_{dj}*D_j)$ where $D_j$\\n\",\n \" > is fraction of the destination cell that intersects the unmasked source\\n\",\n \" > grid.\\n\",\n \"\\n\",\n \"Detailed explanations are available on\\n\",\n \"[ESMPy documentation](http://www.earthsystemmodeling.org/esmf_releases/last_built/esmpy_doc/html/api.html#regridding).\\n\",\n \"\\n\",\n \"## Preparation\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cartopy.crs as ccrs\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import xesmf as xe\\n\",\n \"\\n\",\n \"method_list = [\\n\",\n \" \\\"bilinear\\\",\\n\",\n \" \\\"conservative\\\",\\n\",\n \" \\\"conservative_normed\\\",\\n\",\n \" \\\"nearest_s2d\\\",\\n\",\n \" \\\"nearest_d2s\\\",\\n\",\n \" \\\"patch\\\",\\n\",\n \"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_in = xe.util.grid_global(20, 15) # input grid\\n\",\n \"ds_fine = xe.util.grid_global(4, 4) # high-resolution target grid\\n\",\n \"ds_coarse = xe.util.grid_global(30, 20) # low-resolution target grid\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Make a wave field that is widely used in regridding benchmarks.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset>\\n\",\n       \"Dimensions:  (y: 12, x: 18, y_b: 13, x_b: 19)\\n\",\n       \"Coordinates:\\n\",\n       \"    lon      (y, x) float64 -170.0 -150.0 -130.0 -110.0 ... 130.0 150.0 170.0\\n\",\n       \"    lat      (y, x) float64 -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\\n\",\n       \"    lon_b    (y_b, x_b) int64 -180 -160 -140 -120 -100 ... 100 120 140 160 180\\n\",\n       \"    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\\n\",\n       \"Dimensions without coordinates: y, x, y_b, x_b\\n\",\n       \"Data variables:\\n\",\n       \"    data     (y, x) float64 2.016 2.009 1.997 1.987 ... 1.987 1.997 2.009 2.016
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (y: 12, x: 18, y_b: 13, x_b: 19)\\n\",\n \"Coordinates:\\n\",\n \" lon (y, x) float64 -170.0 -150.0 -130.0 -110.0 ... 130.0 150.0 170.0\\n\",\n \" lat (y, x) float64 -82.5 -82.5 -82.5 -82.5 ... 82.5 82.5 82.5 82.5\\n\",\n \" lon_b (y_b, x_b) int64 -180 -160 -140 -120 -100 ... 100 120 140 160 180\\n\",\n \" lat_b (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\\n\",\n \"Dimensions without coordinates: y, x, y_b, x_b\\n\",\n \"Data variables:\\n\",\n \" data (y, x) float64 2.016 2.009 1.997 1.987 ... 1.987 1.997 2.009 2.016\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_in[\\\"data\\\"] = xe.data.wave_smooth(ds_in[\\\"lon\\\"], ds_in[\\\"lat\\\"])\\n\",\n \"ds_in\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ds_in[\\\"data\\\"].plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def regrid(ds_in, ds_out, dr_in, method):\\n\",\n \" \\\"\\\"\\\"Convenience function for one-time regridding\\\"\\\"\\\"\\n\",\n \" regridder = xe.Regridder(ds_in, ds_out, method, periodic=True)\\n\",\n \" dr_out = regridder(dr_in)\\n\",\n \" return dr_out\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When dealing with global grids, we need to set `periodic=True`, otherwise data\\n\",\n \"along the meridian line will be missing.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Increasing resolution\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"bilinear\\n\",\n \"CPU times: user 386 ms, sys: 24.9 ms, total: 410 ms\\n\",\n \"Wall time: 408 ms\\n\",\n \"\\n\",\n \"conservative\\n\",\n \"CPU times: user 66.8 ms, sys: 3.79 ms, total: 70.6 ms\\n\",\n \"Wall time: 70.4 ms\\n\",\n \"\\n\",\n \"conservative_normed\\n\",\n \"CPU times: user 84.6 ms, sys: 266 µs, total: 84.9 ms\\n\",\n \"Wall time: 84.6 ms\\n\",\n \"\\n\",\n \"nearest_s2d\\n\",\n \"CPU times: user 29.6 ms, sys: 0 ns, total: 29.6 ms\\n\",\n \"Wall time: 29.4 ms\\n\",\n \"\\n\",\n \"nearest_d2s\\n\",\n \"CPU times: user 8.44 ms, sys: 0 ns, total: 8.44 ms\\n\",\n \"Wall time: 8.22 ms\\n\",\n \"\\n\",\n \"patch\\n\",\n \"CPU times: user 420 ms, sys: 12.3 ms, total: 432 ms\\n\",\n \"Wall time: 431 ms\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"for method in method_list:\\n\",\n \" print(method)\\n\",\n \" %time ds_fine[method] = regrid(ds_in, ds_fine, ds_in['data'], method)\\n\",\n \" print('')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Nearest neighbour algorithms are very fast while the patch method is quite slow.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(3, 2, figsize=[8, 8])\\n\",\n \"\\n\",\n \"for i, method in enumerate(method_list):\\n\",\n \" ax = axes.flatten()[i]\\n\",\n \" ds_fine[method].plot.pcolormesh(ax=ax)\\n\",\n \" ax.set_title(method, fontsize=15)\\n\",\n \"\\n\",\n \"plt.tight_layout()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When regridding from low-resolution to high-resolution, `bilinear` and `patch`\\n\",\n \"will produce smooth results, while `conservative` and `nearest_s2d` will\\n\",\n \"preserve the original coarse grid structure (although the data is now defined on\\n\",\n \"a finer grid.).\\n\",\n \"\\n\",\n \"`nearest_d2s` is quite different from others: One source point can be mapped to\\n\",\n \"**only one destination point**. Because we have far less source points (on a\\n\",\n \"low-resolution grid) than destination points (on a high-resolution grid), most\\n\",\n \"destination points cannot receive any data so they just have zero values. Only\\n\",\n \"the destination points that are closest to source points can receive data.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Decreasing resolution\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"for method in method_list:\\n\",\n \" ds_coarse[method] = regrid(ds_in, ds_coarse, ds_in[\\\"data\\\"], method)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, axes = plt.subplots(3, 2, figsize=[8, 8])\\n\",\n \"\\n\",\n \"for i, method in enumerate(method_list):\\n\",\n \" ax = axes.flatten()[i]\\n\",\n \" ds_coarse[method].plot.pcolormesh(ax=ax)\\n\",\n \" ax.set_title(method, fontsize=15)\\n\",\n \"plt.tight_layout()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When regridding from high-resolution to low-resolution, all methods except\\n\",\n \"`nearest_d2s` produce similar results here. But that's largely because the input\\n\",\n \"data is smooth. For real-world data, it is generally recommended to use\\n\",\n \"`conservative` for decreasing the resolution, because it takes average over\\n\",\n \"small source grid boxes, while `bilinear` and `nearest_s2d` effectively throw\\n\",\n \"away most of source grid boxes.\\n\",\n \"\\n\",\n \"`nearest_d2s` is again different: **Every** source point **has to be** mapped to\\n\",\n \"a destination point. Because we have far more source points (on a\\n\",\n \"high-resolution grid) than destination points (on a low-resolution grid), a\\n\",\n \"single destination point will receive data from multiple source points, which\\n\",\n \"can accumulate to a large value (notice the colorbar range).\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.13\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "doc/notebooks/Curvilinear_grid.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Regrid between curvilinear grids\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cartopy.crs as ccrs\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import xesmf as xe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Prepare data\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Input data\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Here we regrid the built-in \\\"rasm\\\" demo data. This data is used by another\\n\",\n \"[xarray tutorial](http://xarray.pydata.org/en/stable/examples/multidimensional-coords.html#examples-multidim).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset> Size: 17MB\\n\",\n       \"Dimensions:  (time: 36, y: 205, x: 275)\\n\",\n       \"Coordinates:\\n\",\n       \"  * time     (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n       \"    xc       (y, x) float64 451kB ...\\n\",\n       \"    yc       (y, x) float64 451kB ...\\n\",\n       \"Dimensions without coordinates: y, x\\n\",\n       \"Data variables:\\n\",\n       \"    Tair     (time, y, x) float64 16MB ...\\n\",\n       \"Attributes:\\n\",\n       \"    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\\n\",\n       \"    institution:               U.W.\\n\",\n       \"    source:                    RACM R1002RBRxaaa01a\\n\",\n       \"    output_frequency:          daily\\n\",\n       \"    output_mode:               averaged\\n\",\n       \"    convention:                CF-1.4\\n\",\n       \"    references:                Based on the initial model of Liang et al., 19...\\n\",\n       \"    comment:                   Output from the Variable Infiltration Capacity...\\n\",\n       \"    nco_openmp_thread_number:  1\\n\",\n       \"    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\\n\",\n       \"    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...
\"\n ],\n \"text/plain\": [\n \" Size: 17MB\\n\",\n \"Dimensions: (time: 36, y: 205, x: 275)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n \" xc (y, x) float64 451kB ...\\n\",\n \" yc (y, x) float64 451kB ...\\n\",\n \"Dimensions without coordinates: y, x\\n\",\n \"Data variables:\\n\",\n \" Tair (time, y, x) float64 16MB ...\\n\",\n \"Attributes:\\n\",\n \" title: /workspace/jhamman/processed/R1002RBRxaaa01a/l...\\n\",\n \" institution: U.W.\\n\",\n \" source: RACM R1002RBRxaaa01a\\n\",\n \" output_frequency: daily\\n\",\n \" output_mode: averaged\\n\",\n \" convention: CF-1.4\\n\",\n \" references: Based on the initial model of Liang et al., 19...\\n\",\n \" comment: Output from the Variable Infiltration Capacity...\\n\",\n \" nco_openmp_thread_number: 1\\n\",\n \" NCO: netCDF Operators version 4.7.9 (Homepage = htt...\\n\",\n \" history: Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d...\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = xr.tutorial.open_dataset(\\n\",\n \" \\\"rasm\\\"\\n\",\n \") # use xr.tutorial.load_dataset() for xarray\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.DataArray 'Tair' (time: 36, y: 205, x: 275)> Size: 16MB\\n\",\n       \"[2029500 values with dtype=float64]\\n\",\n       \"Coordinates:\\n\",\n       \"  * time     (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n       \"    xc       (y, x) float64 451kB ...\\n\",\n       \"    yc       (y, x) float64 451kB ...\\n\",\n       \"Dimensions without coordinates: y, x\\n\",\n       \"Attributes:\\n\",\n       \"    units:           C\\n\",\n       \"    long_name:       Surface air temperature\\n\",\n       \"    type_preferred:  double\\n\",\n       \"    time_rep:        instantaneous
\"\n ],\n \"text/plain\": [\n \" Size: 16MB\\n\",\n \"[2029500 values with dtype=float64]\\n\",\n \"Coordinates:\\n\",\n \" * time (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n \" xc (y, x) float64 451kB ...\\n\",\n \" yc (y, x) float64 451kB ...\\n\",\n \"Dimensions without coordinates: y, x\\n\",\n \"Attributes:\\n\",\n \" units: C\\n\",\n \" long_name: Surface air temperature\\n\",\n \" type_preferred: double\\n\",\n \" time_rep: instantaneous\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dr = ds[\\\"Tair\\\"]\\n\",\n \"dr\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12, 2))\\n\",\n \"ax = plt.axes(projection=ccrs.PlateCarree())\\n\",\n \"dr[0].plot.pcolormesh(ax=ax, x=\\\"xc\\\", y=\\\"yc\\\")\\n\",\n \"ax.coastlines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Input grid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"`xc` and `yc` are longitude and latitude values. They are both 2D arrays,\\n\",\n \"describing a curvilinear grid over high-latitudes. Note that it is totally fine\\n\",\n \"for a grid to span over the south or north pole. ESMF performs regridding in the\\n\",\n \"Cartesian space (x, y, z) so there will be no polar singularities.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'lat')\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(ds[\\\"xc\\\"], ds[\\\"yc\\\"], s=0.01) # plot grid locations\\n\",\n \"plt.ylim([-90, 90])\\n\",\n \"plt.xlabel(\\\"lon\\\")\\n\",\n \"plt.ylabel(\\\"lat\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We need to rename the coordinate names to `lon` and `lat` because xESMF has no\\n\",\n \"way to guess variable meaning.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset> Size: 17MB\\n\",\n       \"Dimensions:  (time: 36, y: 205, x: 275)\\n\",\n       \"Coordinates:\\n\",\n       \"  * time     (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n       \"    lon      (y, x) float64 451kB 189.2 189.4 189.6 189.7 ... 17.4 17.15 16.91\\n\",\n       \"    lat      (y, x) float64 451kB 16.53 16.78 17.02 17.27 ... 28.01 27.76 27.51\\n\",\n       \"Dimensions without coordinates: y, x\\n\",\n       \"Data variables:\\n\",\n       \"    Tair     (time, y, x) float64 16MB ...\\n\",\n       \"Attributes:\\n\",\n       \"    title:                     /workspace/jhamman/processed/R1002RBRxaaa01a/l...\\n\",\n       \"    institution:               U.W.\\n\",\n       \"    source:                    RACM R1002RBRxaaa01a\\n\",\n       \"    output_frequency:          daily\\n\",\n       \"    output_mode:               averaged\\n\",\n       \"    convention:                CF-1.4\\n\",\n       \"    references:                Based on the initial model of Liang et al., 19...\\n\",\n       \"    comment:                   Output from the Variable Infiltration Capacity...\\n\",\n       \"    nco_openmp_thread_number:  1\\n\",\n       \"    NCO:                       netCDF Operators version 4.7.9 (Homepage = htt...\\n\",\n       \"    history:                   Fri Aug  7 17:57:38 2020: ncatted -a bounds,,d...
\"\n ],\n \"text/plain\": [\n \" Size: 17MB\\n\",\n \"Dimensions: (time: 36, y: 205, x: 275)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n \" lon (y, x) float64 451kB 189.2 189.4 189.6 189.7 ... 17.4 17.15 16.91\\n\",\n \" lat (y, x) float64 451kB 16.53 16.78 17.02 17.27 ... 28.01 27.76 27.51\\n\",\n \"Dimensions without coordinates: y, x\\n\",\n \"Data variables:\\n\",\n \" Tair (time, y, x) float64 16MB ...\\n\",\n \"Attributes:\\n\",\n \" title: /workspace/jhamman/processed/R1002RBRxaaa01a/l...\\n\",\n \" institution: U.W.\\n\",\n \" source: RACM R1002RBRxaaa01a\\n\",\n \" output_frequency: daily\\n\",\n \" output_mode: averaged\\n\",\n \" convention: CF-1.4\\n\",\n \" references: Based on the initial model of Liang et al., 19...\\n\",\n \" comment: Output from the Variable Infiltration Capacity...\\n\",\n \" nco_openmp_thread_number: 1\\n\",\n \" NCO: netCDF Operators version 4.7.9 (Homepage = htt...\\n\",\n \" history: Fri Aug 7 17:57:38 2020: ncatted -a bounds,,d...\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = ds.rename({\\\"xc\\\": \\\"lon\\\", \\\"yc\\\": \\\"lat\\\"})\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Output grid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Say we want to regrid it to a global $4^\\\\circ \\\\times 5^\\\\circ$ grid. xESMF\\n\",\n \"provides a shortcut to make this output grid.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset> Size: 106kB\\n\",\n       \"Dimensions:  (y: 45, x: 72, y_b: 46, x_b: 73)\\n\",\n       \"Coordinates:\\n\",\n       \"    lon      (y, x) float64 26kB -177.5 -172.5 -167.5 ... 167.5 172.5 177.5\\n\",\n       \"    lat      (y, x) float64 26kB -88.0 -88.0 -88.0 -88.0 ... 88.0 88.0 88.0 88.0\\n\",\n       \"    lon_b    (y_b, x_b) float64 27kB -180.0 -175.0 -170.0 ... 170.0 175.0 180.0\\n\",\n       \"    lat_b    (y_b, x_b) float64 27kB -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\\n\",\n       \"Dimensions without coordinates: y, x, y_b, x_b\\n\",\n       \"Data variables:\\n\",\n       \"    *empty*
\"\n ],\n \"text/plain\": [\n \" Size: 106kB\\n\",\n \"Dimensions: (y: 45, x: 72, y_b: 46, x_b: 73)\\n\",\n \"Coordinates:\\n\",\n \" lon (y, x) float64 26kB -177.5 -172.5 -167.5 ... 167.5 172.5 177.5\\n\",\n \" lat (y, x) float64 26kB -88.0 -88.0 -88.0 -88.0 ... 88.0 88.0 88.0 88.0\\n\",\n \" lon_b (y_b, x_b) float64 27kB -180.0 -175.0 -170.0 ... 170.0 175.0 180.0\\n\",\n \" lat_b (y_b, x_b) float64 27kB -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\\n\",\n \"Dimensions without coordinates: y, x, y_b, x_b\\n\",\n \"Data variables:\\n\",\n \" *empty*\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out = xe.util.grid_global(5, 4)\\n\",\n \"ds_out # contains lat/lon values of cell centers and boundaries.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The output coordinates are all 2D arrays. They happen to be a rectilinear grid\\n\",\n \"in this case (`lat` is constant over `x` axis, and `lon` is constant over `y`\\n\",\n \"axis), but you can use 2D arrays to specify any curvilinear grids.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Perform regridding\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Regridding is straightforward, just like the previous example.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regridder = xe.Regridder(ds, ds_out, \\\"bilinear\\\")\\n\",\n \"dr_out = regridder(dr)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Check results\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Extra dimensions and coordinate values are all correct, like in the previous\\n\",\n \"example.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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<xarray.DataArray (time: 36, y: 45, x: 72)> Size: 933kB\\n\",\n       \"array([[[ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        ...,\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan]],\\n\",\n       \"\\n\",\n       \"       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        ...,\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan]],\\n\",\n       \"\\n\",\n       \"       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        ...,\\n\",\n       \"...\\n\",\n       \"        ...,\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan]],\\n\",\n       \"\\n\",\n       \"       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        ...,\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan]],\\n\",\n       \"\\n\",\n       \"       [[ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        [ 0.,  0.,  0., ...,  0.,  0.,  0.],\\n\",\n       \"        ...,\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan],\\n\",\n       \"        [nan, nan, nan, ..., nan, nan, nan]]], shape=(36, 45, 72))\\n\",\n       \"Coordinates:\\n\",\n       \"  * time     (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n       \"    lon      (y, x) float64 26kB -177.5 -172.5 -167.5 ... 167.5 172.5 177.5\\n\",\n       \"    lat      (y, x) float64 26kB -88.0 -88.0 -88.0 -88.0 ... 88.0 88.0 88.0 88.0\\n\",\n       \"Dimensions without coordinates: y, x\\n\",\n       \"Attributes:\\n\",\n       \"    regrid_method:  bilinear
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0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" ...,\\n\",\n \" [nan, nan, nan, ..., nan, nan, nan],\\n\",\n \" [nan, nan, nan, ..., nan, nan, nan],\\n\",\n \" [nan, nan, nan, ..., nan, nan, nan]],\\n\",\n \"\\n\",\n \" [[ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" ...,\\n\",\n \" [nan, nan, nan, ..., nan, nan, nan],\\n\",\n \" [nan, nan, nan, ..., nan, nan, nan],\\n\",\n \" [nan, nan, nan, ..., nan, nan, nan]]], shape=(36, 45, 72))\\n\",\n \"Coordinates:\\n\",\n \" * time (time) object 288B 1980-09-16 12:00:00 ... 1983-08-17 00:00:00\\n\",\n \" lon (y, x) float64 26kB -177.5 -172.5 -167.5 ... 167.5 172.5 177.5\\n\",\n \" lat (y, x) float64 26kB -88.0 -88.0 -88.0 -88.0 ... 88.0 88.0 88.0 88.0\\n\",\n \"Dimensions without coordinates: y, x\\n\",\n \"Attributes:\\n\",\n \" regrid_method: bilinear\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dr_out\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The regridding result is consistent with the original data, but now on a\\n\",\n \"rectilinear grid with a coarser resolution. `nan` is mapped to `nan`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12, 4))\\n\",\n \"ax = plt.axes(projection=ccrs.PlateCarree())\\n\",\n \"dr_out[0].plot.pcolormesh(ax=ax, x=\\\"lon\\\", y=\\\"lat\\\")\\n\",\n \"ax.coastlines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Undesired extrapolation\\n\",\n \"\\n\",\n \"### when using the `nearest_s2d` method\\n\",\n \"\\n\",\n \"This section is a short excursion into the masking topic specifically for\\n\",\n \"regional (often curvilinear) source grids. General guidance regarding masking\\n\",\n \"can be found in the general [Masking](Masking.ipynb) section.\\n\",\n \"\\n\",\n \"When remapping to a larger domain with the `nearest_s2d` method, target grid\\n\",\n \"cells outside the original source domain will get the value of the closest\\n\",\n \"source grid cell at the domain edge, owed to the logic behind the `nearest_s2d`\\n\",\n \"algorithm.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Compute remapping weights with method `nearest_s2d`\\n\",\n \"regridder_nearest = xe.Regridder(ds, ds_out, \\\"nearest_s2d\\\")\\n\",\n \"\\n\",\n \"# Remap the data\\n\",\n \"dr_out = regridder_nearest(dr)\\n\",\n \"\\n\",\n \"# Plot the result\\n\",\n \"plt.figure(figsize=(12, 4))\\n\",\n \"ax = plt.axes(projection=ccrs.PlateCarree())\\n\",\n \"dr_out[0].plot.pcolormesh(ax=ax, x=\\\"lon\\\", y=\\\"lat\\\")\\n\",\n \"ax.coastlines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If this is undesired, it can be avoided by including the arguments\\n\",\n \"`post_mask_source=\\\"domain_edge\\\"` (and optionally `unmapped_to_nan=True` if all\\n\",\n \"unmapped cells should be set to NaN rather than zero) when generating the\\n\",\n \"remapping weights. This will alter the remapping weights **after** their\\n\",\n \"generation to remove the contribution of the outermost source grid cells of the\\n\",\n \"original domain (i.e. the source grid cells at the domain edge).\\n\",\n \"\\n\",\n \"**Note:** While this prevents the often undesired extrapolation artifacts\\n\",\n \"outside the original domain, this means that the information of the source grid\\n\",\n \"cells at the domain edge will be lost in the remapped data.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Compute remapping weights with method `nearest_s2d`\\n\",\n \"regridder_nearest = xe.Regridder(\\n\",\n \" ds, ds_out, \\\"nearest_s2d\\\", post_mask_source=\\\"domain_edge\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Remap the data\\n\",\n \"dr_out = regridder_nearest(dr)\\n\",\n \"\\n\",\n \"# Plot the result\\n\",\n \"plt.figure(figsize=(12, 4))\\n\",\n \"ax = plt.axes(projection=ccrs.PlateCarree())\\n\",\n \"dr_out[0].plot.pcolormesh(ax=ax, x=\\\"lon\\\", y=\\\"lat\\\")\\n\",\n \"ax.coastlines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Alternatively, one can specify a tailored mask for the target grid. If there is\\n\",\n \"no tailored mask at hand, one could generate it from the bilinear weights:\\n\",\n \"\\n\",\n \"- Generate mask from the bilinear regridding weights (created with active\\n\",\n \" `unmapped_to_nan` option!)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Generate weights\\n\",\n \"regridder = xe.Regridder(ds, ds_out, \\\"bilinear\\\", unmapped_to_nan=True)\\n\",\n \"\\n\",\n \"# Generate mask from this weights\\n\",\n \"mask = xe.smm.gen_mask_from_weights(regridder.weights, nlat=45, nlon=72)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- Add the mask to the output `xarray.Dataset`\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_out[\\\"mask\\\"] = xr.DataArray(dims=(\\\"y\\\", \\\"x\\\"), data=mask)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- Conduct the remapping as usual\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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yZRSaBSU5OFkaPHi34+fkJBgYGgo2NjdCgQYMik6fk5uYK48ePF1xcXAQdHR3Bx8dHmD9//muNITo6WujVq5dgYWEh6OnpCTVq1BAOHTpUqNzEiRMFT09PQVdXVzAzMxNatGghnDx58rXaKun5EgRBSEhIEPr37y/Y2toK2trago+PjzBjxowCSXZeRf73rKiXq6urulz+96ukfQKKTB708OFDoUOHDoKJiYlgYGAgNG7cWLhy5UqRfduwYYNQvnx5QUdHR7CzsxOGDBkipKWlFSr3PtsSERER+ViRCMJbCgosIiIiIiIiIiIiIqIRMRGPiIiIiIiIiIiIyHtEFOAiIiIiIiIiIiIi7xFRgIuIiIiIiIiIiIi8R0QBLiIiIiIiIiIiIvIeEQW4iIiIiIiIiIiIyHtEFOAiIiIiIiIiIiIi75ESJ+LJzs4mNzf3XfZFRERERERERETkNdDR0UFPT+9Dd+ON+DfasjSPG0oowLOzs3F3dyc6Ovpd90dERERERERERKSE2NnZERYWVurEaHZ2Npb6RmSifKPjS+u48ymRAM/NzSU6OponT55gYmLyrvskIiIiIiIiIiKigdTUVJydncnNzS11QjQ3N5dMlPTCEZ3X9IjOJY810U9L5bjzKbELCoCJiYkowEVERERERERERN4K+hIZOpLXE+AyQQKlPI+7uAhTRERERERERERE5D3yWhZwERERERERERERkbeFVAIyyWseA6XeAi4KcBERERERERERkQ+CTCJBJnk9BS7jNRX7R4gowEVERERERERERD4IsjewgMveTVfeK6IAFxERERERERER+SCIFnARERERERERERGR98h/1QIuRkEREREREREREREReY+IFnCR/wSCICAIAlLp82dOhUJBamoqmZmZ6OjooKuri56eHjo6OkheczrsYyA5OZns7GxkMlmBlyAInD17lgMHDvDkyRNycnIKvQC0tLRe66WtrY2DgwNubm64u7vj5uaGk5MTWlriZUVEREREpGSILigiIh85CQkJyOVyjIyMMDQ0RCKRcPDgQf755x8ePXpEZGQkycnJpKSkkJaWhkKhQKlUolAoyMvLQyqV4urqSk5ODikpKWRkZLyyLV1dXbUgf/mvvr4+JiYmmJqaYmZmhp2dHT4+Pvj4+ODn5/fOs3IplUpu3brFrVu3uHnzpvpvVFRUsce5uLjg5+eHrq4u5ubm6jHq6uoikUhQKBQFXvnnLv+Vk5NT6P2+ffsKtCuTyXB2dlYLcnd3d/T19WnYsCGBgYHv9LyIiIiIiJQ+JLy+O0bpl9+iABf5SFAqlWoBnZOTQ1JSEnFxccTFxXH//n2OHTtGUFCQuryzszO+vr4cPnwYNzc3PD098fb2xsLCAlNTU4yNjdWWWplMhpaWFnK5nLCwMPT19TEzM8PU1BRTU1MMDAyQy+VkZ2eTk5Oj8W9mZiYpKSlERERw/fp17t69q+5X3bp1OXnyJFlZWSQlJZGWllbglZ2drbbGg8oyb2BgQN26dbGwsCAqKopLly5x6dIlrl27hpGREWXKlMHPz48yZcpgb2/PH3/8wc8//wyAm5sb5cqVo2/fvpQtWxYjIyO1cFYqlSiVSvLy8qhYsSL+/v4ltuwrFApWrFhBRkYGubm5pKenq8eQnp6OXC7HxsaGxo0bk5WVRVhYGCEhIYSEhBAeHk54eHihOvPHLCIiIiIiks9/1QIuEUpwV0xNTcXU1JSUlBQxFb3IW8fKyoqEhIQi9+np6eHm5kb9+vWpX78+RkZGpKWlcfnyZS5fvkyPHj34+uuv36nLiCAIrF+/Xm1pDw8Px97enqSkJMLDw5HL5eqydnZ2yOXyV47nVUilUqytrYmJiQHAxsaGypUrk5mZyf3794mNjX3lsUZGRoSGhmJtbf1mAyyC4OBgfHx8/nU9BgYGzJ49GxcXF3R0dIiPjycuLq7Iv3l5eZiZmWFubq7+GxgYSK9evUqlS5CIiIjIu6Y067P8vo/X90DvNVPRZwt5TMwKLZXjzkcU4CJvjbPhrxadGWlp/DblV45t+oOydRqTp1SSp1Bg7ezGqe3r1OWaduxKu259MLeyxszCEj0Dw0LiKzYjt9h+PIhN19jXhp5Wxe6v62Gp/n/ZsmV88803hcpUrN2A3OxsFAo5D25cAaBGk1a4lymLpa09cj1j9AyM0DUwRM/QEF0DQ7R1dEEiQYIEiUTC4buxyDNSiLt7kcz4SExdfDFz80PP3BaJREJ6pkrcyzNSyYx7jDwtmdz0JBLvnSf+1ml1X9w7j8bcr47Gcb8KN3fzQtvSYyNICr2DTEcPmY7K/UaqrYNMWwdBEMhKiiUzIYbMxFgyE2PQlqdjbueIkZklR9cuUtcjkUgKWL+1tLUxs7DE1Nzy2V8LzCyskEilaOekcuHCBcLCwtTlhw4dyty5c994bO+KqORXuzDlk6ss/vKaIc/TWIeRTvE3pte1HBWFo7mhxjJh8Wmv3KdUKomOfIqttTUGhq+uK1epebzuVsYay5QWbkWlFNoWHfmUuzeu8Tg8jLiYKOKjo5DKZFStVZsaderj6u5R4JrnayPec0VeTWnWZ/l9n2TggZ7k9eKaZAtKxmWWbgEuuqCIvFNSk5NYu2Qe29b8QU52FgChN6/gVbEaEl1dLh/aDYClgzNKuZzDO//i6O7t2Dm54OkXgKdfAAq5nDxlHkqlAqVSQVpmDhKpFH1DQ5QKJcnxsXQZ8D1W9o5vpc852VksXryY3bt3c/v2bZ4+farep6enp/aBvn7mOA5unji4elCjaWvsnN34dOD3KpENRKXnaGxLS1cfLV19XOq0K7actqEJpoZl1e8darZV/5+akPm6QywRRjZOGNk4qd/rar0kBF19C7wt72wGQOzjEI6uXYREKqXep19i5eSOlZMrJpY2NCrriYGREdlZmWSkp5GVkc7tK5e4eOoooUH3eBL2EIVCAaBe0FmtWrV3Mr73TUpyEmGhISTExREfF0t8XCyR0THo6unh6VMGK2sbLp09TXjoQyytbbC2scPa1hZXZ0e8fcvg6OT8wWYCtmzZwqefflpgm76BAfr6+mRkZJCTnV1g36DhIxg5dsJ77OHHS1ZmJnu2buLBvTtcOHWCR6EPATA0Msba1g47e3uysrL4e/cOlEolDk7OdPysB10/74Otnf0H7r2IiMi7QhTgIu+MjLQ0Pm9Rl4TYGPoO/QGzgBoYmVti5eCsLpOTlcmVQ3sIuXmZpOinaEslxMfF8PRRKE8fhXLy7z0lasvO1YMO/Qa9UT9zcrJJTkggJSmBy6ePs+XPpSQnxtOsWTM+//xz5syZo44Ukp2dTbNmzfjyyy/JciyHoXHpfPJ+l9i4ePLb0QeFto9rVYEZzx7CXiagUlUqVK/FD8OHUK5cOQICAjA3L2yV/5gQBIHEhHgeP3rEo/Awnjx6RGJSIj169cHN3YOkxATmz5lNYkICubm5/L1vN8pnDxf5GJuYYmxiSmTEYwAsLK3wDSjLk0fhxMdEk5gQX8h3Pjw+9b0L8eTk5ELbsjIzycos+uHP1s7uHfeo9LBw+q+sXb5Y/X74mEm069oNSyuVy5jOswDI6WmpXD5/jqP/7Gfl4gUsmzuTpq3aMnbkD9SsWfOD9F1E5H3wX40DLrqgiGjk5s2bBAcHY2FhgYWFBY6OjlhaWhYSAS+7oCiVSsYO6seJv/fi4uGFe2AddPUNiAi+g46uPt1HT0PfsOB0s7+1ahFhYmwMd65dYtaoYWRmPp/qd/fxo3bbT0hJiCM7MxOvcpXwrVgFGydXdX9edkGR5+awf+EvRD1ULZZ0r1gdO30ZKUkJXDl7iuTEeHVZLW1tWnb6jNm/jsPLywtBEOjVqxfr1q0rUGfZsmWZsOGfYs9bSSzg/9yKLnZ/vgtKcbwNC3hRLigvU8gC/hL5FvBXcWTtIg6vnFdg24T5KwioVAVbB5Wl/UXXn4+dz3r2YvP6ter3ZmbmyLRkJMQ//z5ZWVvj4emNto4OwQ/uExtd+PMuE1AOXX19sjIz8S9XAQNDQ3Kys8nOyiI9LZUnYSGEhjxUlw96Glcg0s77dEERBIFtmzZw8ughYmNisHd0xNvXjyYtWmFja4t1EdeFF/kvuqBkpKdxeN9uUlOS2bRqBTnZ2fQZOIRKVWvgG1AOA13tQselpaawY/NG1v6+hIjHj3j69CkXL14kPDwcfX19AgICqFChAkZGRh9gVCIfE6VZn+X3faqR5xu5oIxMDymV485HFOD/QR4+fMjevXsJCgrC2dkZS0tL6tSpQ0BAQKGycXFx2NjYFNpuYmKCp6cnHh4e+Pn5MWjQIEKytAh9cJ91S+ahraODrp4+Lh5e5OUpeXjvDhfPnSEu4pG6jnFbjmHt5FqgXn9r1Q0lKSGO/VvW8+fsKQX2y7S0WHMhBKns1T/WlwV4wtNHzP+ieYFtHr7+6BsYUrF6LZzdPJFIJOTm5qCQy0lJSkCZHENYWBhhYWEFXFAkEgm6urrs2rWLNPuyFIcowAuSkZLE1nH9CX1wn/4/jKVTry+RvfQ5liYB/sfaDfT/ohdl/AOYNnselatUJTMzk7/37UUuz0VfX58GTVuip6+vPiY8NISTx47w997dnD11Qr1dV1cXdy8fdHR1yc7KQldPDz19lYuHsZEh5haWtGzbnpp16hY6Zx+DD3g+OrLivyP/RQH+InEx0YwdNpBL504jz81FV08POwdHbOzssbWzx8bODhMTUzLS00lLS+XGlUukJCZgbGxMUFAQenp6ZD9z95HJZJQvX54aNWrQsWNHmjZt+iGGKPKBKc36LL/v04090X9NAZ4lKPkxTRTgIqWIEydO0KBBgyL3FfVVUCqVxSZW0dHRITdXtShy6vK1pCQl8ttPQwFw8/YlPFgVOtDazp4f1hwg+OoFfh/ZH6lMC69K1TA0MUPP0Bg9QyPyFAoMJAqSE+K5ePJIoen6Zh0/pcPn/TD38C92jC8K8Ly8PBIjHxEdcp+/Jg8HoFr7HsycrbLE/tjvM84fP1zgeEsbW3y9PHFzc8PDwwMPDw/c3d3x8PDA0dFRLYC23Yosth+iAC/IpX1b2D5rDL/vPIxvuYpFlilNAjwqOYO9u3bw8w/fER8XR4PGTej71TfUqlNXvRCxqEWYp44f5YvPOqNQKNDV00PfwJDM9HRc3D1IT0vDycWV78dOwtPHF0Mj41KxCDMfUYAXpKhFmAC5OTncvXmd2zeuEhf1lNjoaGKio4iLjiI1NQUjI2MMjY3JSE8n8sljjI2NGT9+PI6OjsTHx3P27Fn27dtHUlISAPb29kRGFn89Evn/pDTrs/y+zzLxeiMB/n3qw1I57nxEH/D/Ax4+fMiNGzeQSCRIpVISExOJiIjA39+fLl26qMv17NmT9evXF1lHhw4ditwuk8k4deoUbdq0oWzZsvj4+JCbm0twcDA3btxQ+0YDjPz6c3aev0X5KjW4efk8kY/C1fvioqP4sVlFAFz8yhFQqyFRIQ/ITEshIfIJ2ZnpyLS0MTM2Rt/QgIBKVbl56VyBvhzcsZnIx2GMWv5XsRZwgJysDMKvX2DvgomkxqtC+5lY2RFQvwXV2/cEIDsrs4D4tnNyoUb9xljZ2lPRwwEzMzOMjY3R09NDJpORlpZWyPooUnIkUtW5s3V01lCy9NCmfUeatmjF7h3bWLF0Mb0+e/57+3XaTHr2669+f+v6NVYsWcjRQ/9Qs259psyah0zfiFp+bgA8DLpH+cpVuHz+DN1aNwag19ff8stvU9/rmETePTq6ulSsWp2KVaurfcBfZtPqP5jw43eAygd/+HCVAUEikWBoaIiOjg5WVlaYm5vzxRdfEBERgZOTU5F1iYh8zIg+4MVQmp+wSjs3I4u2oATducWhfbs4+vc+QoLuFdovk8nw9i/Hmr1HSclRWZIXTx7DjtXLC5TT1tHFwtqGKUvW4FHG/5UCsyjDlkIu53FYCEkJcSQlxGOsr0+D5q3Iy8vj1rXLnDz0N4f37eZJeGihY1fuPoyTqzsAenr66D7zaQ27corrV6+yf+9ubly/Rtv2HXH38ODo4UPcvnUTgDOXruLt41uoToD09HQ6tGzKzZs3n7e1ajX169fH+gVXGiMDlVvA48ePuXjxIjdu3GD69Olqa/6r2LxlC23bqiKQZGkIIfcgMbvY/QBj9twtdn98ZKrGOhKevDpGeD5pUQ+L3S/T0S92P4BXjeKjkXzfobALE6hmVmIiHnFm/w62LJ4JwJAhQ5g3b16R5UsrgiBw9uxZ6tR5Hg5y7969VKtWjezsbOrXr49UKqV9+/b8/PPPWFpakpCQgJVV0SExJRIJO3fupF274iPk/L+hKbyjvZkhCoWCR48ekZiYSGJiIpmZmTRp0gRj47dnPU9MK35mycLY4F/XAZDzilCVCfFxnDl1Eh1DE+RyOQf37uLg/j2kpaquCcYmpqSlFrw/fD5gKC07dcXdu0yB7baGhf3MX+ZjmXmITtEc3lOTarE30zyj8zCu+BkdL+uP43xoojTrs/y+zzV9Mwv4sJTSbQEXBfhHzosCXKlUcuzvvaz9fQnXL53H2NSU+k1aUL1+E3T19Lh19RIR4aFkZqRz6cxJajVogpdfWXSMjLFxcMbWwQlbRycEQSA+JhpHV3fCgu7y5+wp3Lp8HpmWFgEVqzD9z00YGBZc3POiABcEgavnTpGTk0NOVhZ3rl/hzrXLZKalkp2dRcSjcIb+PIFTRw5y9cJZjWPU1dWjbtMWDB83hdbVCrqX9OzVh9r16mFuboFcLictLZX0tHTS09PIyMjAzMwMe3sH7BwccHBwJD09jXo1qhaoY+my5fTo2bPAtnwB/iKffPIJW7duBVTip27dutSpWxelUkl2VhamZmZ899136Oio4mBn5DzPNqmjo1Po4UUU4LB16Rz+WjKzyPJyubxY96bSyq5du9i1axfHjh0rkBFUW1ubmzdvUqZMmSKPS01NJTo6GldXV3R1dd9Tbz8+niam8Sg8jMSEBNJSU0lLS1VlYU1NJSY6mrs3r3H58mUyi4jAcvXqVSpVqvRW+vGhBHhUZCQXzp3hYfADJBIJCkHCxtV/kpuTzSc9+lCtVm18yvhj5+DI1UsX6Na2mfpY3WfGDFNzC6IiHiPJzzkgVeUdsLSypmLlKlQIrEKlwKqUq1gJHR0dQBTgLyMK8HdPft8XmL2ZAB+cLApwkXfIiwJ8+dwZLJoxGQBjU1MCylciKSGekOAgFHI5+gaGuLh7gkRC1JNHpKYkY2FtQ2ZGOtkv3KwMjU2o0bAZ9Vu158LxQ+zbtAZHF3fadevFqgUzUSjk1GzYjLTkJPQMDOj21SACa9ZWH/849CHdGtdQvzcwMqJmgybY2NiSnZVFdlYmf+/axhffDic9LYUr588QExVJRlpaAT/zjt17U6N+Yw7t2c7hvTsxMTXDz9+fpxERKJUKcnNzyUhPVy86ykcqlaKnr4+Otg4ZGekFMlHmU8bPj2rVqlG9eg26fPIJBgYFb5ZFCXBBELh48SLr1q1j06ZNxMfHY2pqipmZmeph49krNzdXHav6RbS1tdHT18fayprKVargElCJgIqBePuVRfvZTe5l/t8F+PlDe5kz4rkbxqBBg1iwYIHGtv4fEASBBw8ecOfOHfT09PD29sbb2/tDd+ujQqFQcPfuXa5evcq1a9dUf69fJyNdczKtFxck5nP+/HmqV6/+Vvr2bwW4IAjcuveAlJRk0tPTSU9LIy4ujpV/rCAhPp6s7Cyys7LJzsnG2tqGgHLluHPrFk8eqxaq29jaIpVKUSiUWFpZs3z9X9g7Fu1ikpClugZGP41g4vABePj44u5TRt0PY20peXl5REdFcu3yJW7fvE52VhbWNrYM+WEkPfr0+ygF+L07t5k+5VdCQx4SFxNLVnYW2traaMm0kGnJMDUzw93DC08vL9w9PPHw8sLdw4vK/t5IpcWvRxAF+Icnv++Lzb3fSIAPTAoulePORxTgHzkvCvDwh8GMHzEYXV1dMjLSsbaxxdLaFlcvXypUqY6XXwArF87ij3kzsHdy4fsJU3Hx8CQxPZPEuBiehofy9FEYF08cJiIsBACpTEaeUgmAu3cZvv15Enu3rCM1OQljUzNOHdxHldoNmLNmi7of8bHRtK/+PAKIVCrlwLWH2FiqFvJFR0bQvm4VBv00Fi9fP1YtnkfQnVukJCdh7+RCvaYtuX3tMsmJCWw7cZn0tFSWzfqNB3ducu/mtQJuIPr6+hgZG6tCE5YgvbuTszNjx46le4+exZYrSoC/SFp6OlevXOHY8eNkZmaiq6uLnq4uurq6aOvooKWlRZ5EipZMC4lEglyeS1ZWNtnZWTyNiODK5cvcuHEdhVyORCJh8uI/adiysCvB/7sAz+faqSOsm/0ridERJCYm/qctvP9llEolx48f5/Tp05w6dYrz58+TkaESXD4+PlSuXBl37zJkZWUS/CCI6KgoYqKjiY2JVl8XdHR08PT0xMvLS/3Xy8uL+vXrFwjP+G95HQGelpamXhMTEhLCsWPHOHbsGDExMYWOC6xSlVp16qCvp4+evh5auno8Cgvj3p3bBJQrT7UaNalWoybWNrYApOdqXriaL8BfxcsuKJkZGaxbuYKpE8fi6OzCqau3PzoBnpqSQuM6NVEqFbRs3RYbOzsMDQ1RKJQoFAqUCgXx8XGEhYQQFhrCo/AwlM/uZYGBgSxevJg6derg6alaUG9jY4OdnR0ODg44OjqSo2VAparV1TMALyMK8HdPft+XmXujL31NAZ6npL8owEXeJS/7gMvlcrIyMtA3MFBbVXMUqgt0TnY27WqVJykhvlA9RVG5dn0eBQeREPs8EoedozNZmRlkZWSQm5uDTEuL7yfNoH23zwsce+rQATIz0rl5+QI7169CR1cPC0tLlWtIWhqWNrZMX7qSvh1bAio3k7KVqnDlvCp9+uyVm/jui8+oXrcB7j5lsHd0xs7RGQ9bc4IfBKGrq4dUKiUtLZXk5GSEvDysrK2xsLREX99AfaG1sbXF2NiYpxER7N65nc0b1qOnp0dUTGyx7g2aBHhWtmb3EU0+4Lcikwm+e4uvOrUAoG6TFnz25UAq13g+m/BfEOB5eXl0q/R84WVycjKmpqYa2xP5/2Pu3LkMHz4ciURCmzZtqFOnDjVq1MDb25uzZ8+ye/du9uzdS1JiIuYWFvj5B+Dl7YOXjw9e3r54ensTGOD7XhZDl0SAK5VK/vjjD8aOHUtsrOp3KJVKqVKlCo0aNaJ8pSpYWllhZGyEsZExRkZGmJiaFoiV/iof8HzehgDPjH7E9SuXuHH1CjeuXeHe7Vvk5uZiYWnJ+N9m0LZjl49OgIc8DKZFw3qkp6XhH1CWVm3boaWtTVhICE8eP0KhUCCVStUvhUJBWGgIMdHRLFy4kKioKCZPnlxsW0N+GMXgH0YVuU8U4O+e/L7/buGDwWsK8Mw8JV8lPiiV487n/88B8/+Is2fPUru2SqyZmpmTmZmBPN8KpKuLhaU1Tq6u6Ojqo6Wjg7a2NtXqNMDO0Ql3Lx8sbWzR1tYhRyLjwJb1/L21YASUq2dOYGRiStnA6ri4e2JobIyeviH6hobo6xugb2iIb9kKeJYpLKzqNlUJ6yZtO1GvWWvCgu+TlZqMkbEJxiamVK1dD0traz7p1ZeH9+8SfO8uVy+cAcDM0oqr589Qq0ETIiMeExb8gKSEeOTy4hdAlgRrGxsGDvz2o4hWoqunR9nKVdl26ipH9+/m8J4dDOnZiVFT59K6S7cP3b33wpUTh5g+pI/6vVQq5ciRI3Tq1OnDdUrkvaJQKDh8+DBr1qxh48aNAFy4cIGqVauSlJTEjz/+yJo1a8jNzSUgIIDP+/SlWcvWVKwcWKQbwYf4bWdlZalDsmprayOTycjJyWHu3LmMHDkSIyMjrl+/jqmpKZaWlurFoCXxAX+XPLx3h89b1iuwrWXbDtSsU58yAQG06dBZo6vGh8LTy5sbQSEcP3KYvbt3snTRArRkWrh7euLi6oauri65crnaITwvLw9rGxtatW3PN317o1Qq6dq1KxcuXODixYsEBQWRmppKSkoKZmZmVKldn74DBn/gUYrAG0ZBeb/JgN8JogX8IyY0NBRPT08A/ALK0bVnLwwMjdDV0yU9LY2njx/z5HE4uTmqBDJyuZyc7Gxu37pB5rNp3RE/j6PrF18jlcr4e88OEASkUhmW1tb4+AVgbWuHRCJB+dLXIDQ4iBULZpObk0NuTg4B3h7079+fcuXKqcsoFArWrl3LkiVLuHLlCs5uHjg6u5CenkZyYgJZmZms3vU3l8+eZtJPw1HI5ZiZmWFpaYlCoUAulxMbG6v2p27WrBl9v/uZsOAHnDj8DzFRT0lLSSE5KZH42BgqVqlOty++IvJpBAlxMTx98phHIcFoaWnT/ctvOLxvNw/u3kZbRxd9A0MMjIwwNTOnev1G1GvWCmtbe3XffSyKn6ouyQKr10WhUDBgwABWrFjBpEmTGDNmDLeji7dw/7TrjsZ6Lx04o7FMyuPCkXJexK95l2L3A3zV3q/Y/V39VVFmUpKTSUxMQCKRsHXzJmZMfZ5M6dtvv+Wbb76hbNnikxiJlE5y0gtHbbKydyL9mU/31KlT6dGjB05OTuzevZtvvvmGjIwMfv75Z7p06aK+3v1b5HGP/3Udoyb9xuHjJ4mIjCIhManAPolEUihvwu1LZ/D29CiwTdDWPLOEpHgBnCfTHMEk8xWzcefPnaVN81cn6KlZqzbmFhbExMQQGxODTCbDyMgIQyMjjE1MMDIyonKVqnw14FuKCuH+6/gxHD9yCEsrK6ysrDEzt0AqlSIIAiampjRp3pKKlQPVFn9rXY1yA+EV50MQBCQSCQsXLWLu/IU8jXgCgJ6+PtVr1KJx8xb07vc1MpkM7RI8U9iYal6oWRoozfosv+9/Wr6ZBbxvgmgBF3lHaGlp4V3Gj4jHjxg18Vdq129YZDmZRIJcLufyhfPs+GsToSHBagE+c8okZk6ZBMAvsxeSl5dHVORT7J2csLKxJSsrk33b/0KmrU3tBo0ZM2wgt65dJjsrS21tBzh+EIKCgjh06BCgsmBt3ryZOXPmAPDzzz8THpNATORT0tPTeBymCj149cI5fhn5HU1atWPl0oXY2dkBcOXKFa5fv86hQ4fYvHkzgYGBTJkyhVmLlrF59R/o6OrhVcYPVw9PHh5QiUdLGxsm/DCUrMwMjE1M8PAuQ4XAaty5eY0J3z+3ZJhZWGJgaMSd61dQKhScOvw3M8f+iJObB+v/OY3OB/I/1tLSYvny5bi4uDBu3DhCQ0Pp//MvGBqVjqnOkiAIAj5uRS8U275nPx3btHzPPRL5UNy9d495Cxapxfek8eP46aefePLkCZ06dWLHjh20adOGJUuWvJX41XK5nHXr1nHixAm08+TY2drgYGeLvZ0tlSuUxd5W5VN94/Zdwh49pmWThsWuRdi8fRc21lYMG/AVjvb26OhoI5fLnxkPFOgaGGBqaoqpiTE21taFxPfHQI2atYhPzUCZJ5CelkZQ0H2C7t3DzNycPbt2sP2vLdSt3wBvbx9q1qmLkJdHenras0hT6UQ8ecLObVvZtmUz+gYG6Onpo6+vj56+Pnp6+hzYuxsHR0csLCyJi4sl+EEQeXl5SCQSoqMimT9rOo5OzgwaPoKeffr+q7Hki/gffvypwPbsrCxOHDvCiWNHcHF1o0lz8RpT2pBJJK+dUOxtJCD70IgW8I+UqKgo6tWrR3aunBUb/sLbt+jQZXExMcybPoVdW7eQmfls9bhEgqOjE1Wq16R+46Z8N/ArdXmJRIKxiQmpKSnYOzqTkZFGemoqgiCgpa2NPDcXYxMTeg8YwolDf3P3xjWUSiV+fn4cOXIEe3t7srKyCkUV2bhxIwH1VRe+y+fP0LdTawCVxV5XF2//AM6dOEZKSgoDBw5k06ZNSCQSvLy8GDFiBP369ePXX39lwoQJDB8zia69vlAL033b/2Ls8IEo5HIaNGvJ6N9mYWNnr74gKxQKNq9awZzJE9DW1iYrK1O9sPRlho2bTIuOXanq6VDs+X8XFvAXWbt2Lf369cPB2YVdJy+9chq4tFjABUHg0u+/snbVygLbv+w/gCHDv8fK2hqZTPZ/Y3USKZp8C3h6ejq+AeXR1dVlQP+v+aJ3L6ysLFm76S+GDRuGiYkJc+bMoWvXrgX8of8Nw4YNY/78+VSuXBlBkUtUdAwxcfHk5eVha23Nw6tn0NXVxbNSTZ48jaRVs8ZsW/07CYlJXLhyjaTkZHJz5eTk5mCgr8+EqbMIKOPLnk2ri3R7EbSKXrxXoMwHtoDno8wrOstxTHQ0Do6OqnaKajsvj2WLFvDwwQMys7LIzo/c8uxvbm4OP40ZT+NmqnUuL8oJhULBxXNnWbf6T/bu3M72/QdpWVdzhJpXWcDPX7jATyNHcfHixSL3S6VSzMzNsbCwxMLSEjMzc8zNzTG3sHi2XfXX3MKCwCrVcHewKbKe0kZp1mf5fV9n7ftGFvCecUGlctz5vJYFPDw8HD8/P7S1NV8QRN4chUKBi4sLCoWCGQuX4uziWmS5HVs2Mv7H75FIVBdTB0cnxvzyG/UaNsbohYQU9m6exEZF4ulTBkdnF7R1dLh26QL7dvyFvr4+3b/4GkEqZdSgr7hy/ixpqal4ePvw9dARZGdlMXLgl1y9cEYtEvX09OjRo0eBrJr//PMPEnN7/MtXpEqN2lyPSOTEwQMM7duDDp/1YMMfy9DSUkUM0dHRYdGiRXzxxRfo66tuUIIgMGPGDD7p1ZcvBg4pMM7WnT7Bxd2D65cv0L1v/0I3Qy0tLXp8+Q1b169WJyWSSqUEVKpKs3Yd0dLW5uaVi9y5doW5k0azYMp4GjVqzI+jRlM5MJDMzEyys7KwsHw/adAFQeDGjRvI5XJq1mtEclIi5haWb02IfAjkuTmFxDeApaUlts9mPUT+G8yZt4BRY8YCsGDubL7q99zyuXz5csqUKcORI0fe2kLclJQU5syZw7x585g7dy5Dhw5Vu6Dk5OTw+TdD2Lnvb/YdPMLl6zdJeJa+ff/BI1h4+JOV9XzBtUQiQVdXh9xcuWq2MCYWc3c/Uh4FlerfZ1HIZDK1+H4VUqmUAYOHAhTpglIcWlpa1KpbjzV//o4gCEQ+fVpg//37QazftIlqVarQoH49jIyMiIuLJyg4GKlMRhlfXywsLFAoFFy9do2dO3dx8eJFevfqxdBR48nJySYpMZHkpCSSkxJJSsr/P4mUpESSkpIICXlI8mXV/8lJSerF+65u7qxa+Sf169cv9Z9rCWyoHz2iBbwY8p9S8pHJZCxevJivv/76nXZOE7m5uRw8eJBNmzYRGRmJp6cn3t7e9OzZEweH4i2cHzNZWVl06NCBgwcPAqr40hUDq/LT+EkoFUru3bnFvTu32bx2Ff5lyxMbE0V8XBy6enoIeXnqcF31Gzdl9ebtZGiwjgAoBYGkhAQ+b9cUKxtb5v25AVNzVVjBpIQEOtSrQq9evZg7d676GLlczqlTp9izZw87d+4kPDycCoHVMLe05GHQPSIehSOVSrGysSUxIR4fb29SUlKIj1dFaRk0aBC//fab+oGucePGhD2OwMnVDYlEwpT5yzAxMyvxebt78zpxMdHYOLvj4OyKVhEPiolxsRw9sJt1i2YTExODpaUVCQnxSKVS+n75NT+PGYupmdk7tYBHR0fj4uKCXC5HR1eX3JwcvMr40+HT7rTu1BVL6+eWmdJgAQ+9eZk533Rl9PiJtGzdhuAHD1i76k+OHj5EtRo12X3goPomJ1rAS05sbCwPHz5EV1cXHR0dsrOzefLkCRERESiVSmrWrEnlypVfGUbtQ5CTnsLUGTOZMOlXANq1aU21qlUZPnQwMpmMMRN/ZcGCBfTq1YvBgwcXWFPyuqSmpjJ79mzmzp1LdnY2gwcPZvr06aqwoM8E+Iixk5i/7A/MzUxJSk7ByNCQzz/tzL6DR3gcoRKEsydPoF3LZjjY2SKTyZBIJCSnpHDg8DG++e4nlXi8ew0jo4Lf3dJuAS/Ujob9JRHgRcmJTetW88PQQTg4OjFyxHc0btQQJBIaN21OSmoqWVmq2N5GRoYkJSUXONbG2hqA2Li4Atuu3A8tVjgX5QOel5dHeloaj8LDGfnDd1y+eAFHR0datWpF69atady4MUZGRuTl5ZGVlaWK3f7slZmZibGxMdbW1lhaWn4UCcQuXbrE0qVLOXbsGGFhYaXSEpyvLTfb+r2RBfzTmHulctz5vJEABxgwYACLFy9+Zx3LR6lUEhkZSWhoKOfPn+f06dOcPXuW7OxsJBIJGRkZ+Pv7ExAQQGhoKPfv30cmkyGVSsnKykIuV1kyJkyYQI0aNdDW1iYmJgYzMzN8fHxwc3P7KCJm3Lx5E4VCQdmyZdU3VIVCwYGT57l66QIb16wk6K5KjGlra+PlUwbfgAD69v8WfT09/ly2mI1rClsgx02eRmp6BoIgYGvviIOTMzb29ri6F1zslL8IMz874csXt7+WzOKXX37B39+f1q1b06pVK2rWrKn2oVQoFEyZv5RLZ0+RnJSEi7sHlavVZN/2LTwMusfidVtpU7syAAkJCSxatIhffvmFevXq8ddff2FhYcGuXbvo0KGDus1Nfx/Hr1yF17ZQZOQW7X7yIlmP77Fm1Urs7R1wcnYmNjaGmdOnkZ2VhYenF+XKBuDn50eVKlVo06bNW7/ghoeHs3//fgYPHkxeXuE72/LNu6hRt36pEOBXj+xj5ViVD765uQVfD/wWJydndm7fypFDB9m6ay916zcARAFeElJSUpg+fTpz5swhKyur0H5dXV0kEgnZ2dno6elRvXp1ypUrR7169fjkk08+QI+fk5OeQkpKCus3buJB8EMeBAdz9NhxKleqiJWVFUikasMCqGZIIiIi3ih+96hRo5g6dSrDhg3jhx9+KGB0yRfgR0+epnOvr8jJzcXPx5s794P4adi3ONjasnDFSoKCQzj7z26qVKrw2u2LArwwr5ITjx+FM/7nHzl26KA6moybmytHDx4kLS2VQ4ePkJKaiq+PD94+PiiVeQQ9CCIoKIisrGzatGmNVColLi4OQwMDytVsUGw/NC3CVCqV3Ll6kX379rFv3z6Cg4PR1tZGR0eHzMzMYq3KEomEihUrMnbsWDp27KjplLwzatWqxblz59TvS6MQzdeWWxz830iAd428WyrHnc9rCfDp06fj6upKzZo1cXZ21nTYvyI7O5slS5YwZ84cnjxRrXY2MjKiRo0a1K5dW52dsGXLlpQrV04t0hITE/ntt9+QyWQ4OTnx4MGDYjPv6ejo4OrqipmZGcbGxhgbG+Ph4YG+vj6PHj0iLS2N8uXLU7lyZfz9/XF1dX3jZA9KpZLz589z/vx5goKCCA4OpnLlyjRu3JiOHTuSm5uLi4sLEydO5PPPPyc5OZlxk6diYWnFpz17c/if/SAIeJfxx9HJCRNTM0A1FZOdnU3Pzm25fOF8ifqycf9RylcKfN43DV8Dfxsjdu3apb5gxcTEoK+vT506dejduzc9evTgVlQKaakp/LlwLqkpyVSv24BNK5eTlZnJjGUraVm94E3uyJEjtGjRAi8vL27duoWWlhajfp3O1LHPF9lo6+hga++Ai5sHxqammFtaYWVji7WNLQ7OLpiZW2BsaoqRsQkKhYKUpCQio2P4Z+dW9PT1adHxE6xs7LCwtikg5IuKghIVFcXf+/fxIOg+oQ+DuXv3LpGRkbi5ubF06VKaN29eonNbUg4fPkzTpk35tPeXmFlY8PD+XY4c2AtAxSrVmfX7amae15x86EMK8M0zxnJ6x/oiSqvQ0tJi99+HCKxSFRAFeD6xsbGcO3eOK1eucPnyZW7fvg2o3Lvi4uLIyclh+PDhfPbZZyiVSnJyctDR0cHJyQkrKysUCgXXrl1jxYoV/P777+p6d+3ahbe3N35+xUeseVcUFQVl5eo1nDx9mqzMLLJz5WRmZpKSksKNGzeoU6cOR44cKWDFDw4ORktLC3t7+2Kvtbt376Z9+/asX7+e7t27F9j3YhSUQ8dO0vrTzzm0YxNdv+iPg70d104cfLm610YU4IXRJCd0c1I4c/YsN2/dpnu3z3AuYvHtq3zAXyQhp/j9rxsFJTg4mEOHDpGTk4ORkZH6ZWysit2ur69PamoqcXFxxMTEsGbNGk6fPk1YWBhubm6aG3sFWVlZTJo0iYyMDGxsbLC1tVX/NTQ0LJB92dnZGXd3d7XB8Pr166xZswaZTMbMmTNLpRAVBfhrCPC48AeYmLw6YoOO+b/39cxNiiYrK4vBI0ay8a/tfNalI507tMPN2Rkfb09k+pojRqQoC1src3NziY6KQqlU4GBnq/IPexhMcHAwj8PDSU9PJy09jdTUVEJDQsjNycHVxRl9fX1u3bpNzLMkCxKJBAd7e1xcnHFzdaVK5cq0atkCd7fnftp3791j7/4DnDx+jISkZGwsLdHV0+X0+YskJiVjoK+Pj5cHbi5OHDp2iozMTPx8vZk7eQLLVq9j+54DeHm4ERMbT1p6OmZmZnRo355/Dh4kKioKUPnmBQYG0qRxYypXroSvjw/u7u7s3rOHHj1VSXO8vLywsrLCxcEOW1sbMp6lo/cv48u3X39ZYOFfppZRoXOmVCrJyMjA2NgYhfBcvN67e4f6NZ8nZSnj58fW3fvo16snF86dxcDQEHt7B0IeBqOjo4NSqUSpVNKgSTN+njQFT28fAIb178ue7VsBaNqqDW07dqFd+w48jXhCVORT4mJjiY2OJjIygpDgYA7/8zdQMn83GxtblEoFCc+yZ9atW5cZM2epp7zjswqnkn+RtBzV3eb+nVvM+mUsl8+fYc7va2nQtIW6TID9v/vR51v8Bw4cSN06dYiJieH8hQscPHiQxMREBg4cSJMBYzTWM3138cl8AFLji49HfHJiY411WBbhlnPgwAHGjRtHuXLl2Lt3L3EvTBWDambn37gZfMwobx/RWEbuUaPA+9u3bzN33nw2b9mCQqHA1tqKyuXLUs6/DFpaWmTn5KCro8PXvbrhaK/5ehoVE0un3l9z5cbtAtsrlQug5ycdqFapAjZWFthYmmNoYPDK2SQtB1+NbeWFFL347UUELQ0RhjTEnB42bioLV21Qv7cwM8Xe1gZHO1sqli1DjcCK1KlRHXMzUxQKBfou/gCsWjCDT9q2VAt5aebzsIG7Dp2g04Af+bRNU/YePc35bX/i7+WmcSyahLHG/YDCRPNnmGdY/NoTaW5GsfsB8nSKf7DNQ/MsYp4GKSAtwUykpuuqvpbmc5aYXXwdABZ6xc9IliRGtExafKHi6ti0cSNffdmPunVqU65sAI8fPyEkNIyMzAyUCtX9ztzcHGt7B+ztHdDS1iLjmTuLqakZA4cMw79sWb4bNJCN69bg4eFJamoKCQkJxT7E6Orp4enlg3eZMnj7+lGnQUPcPDyp6OlcKoVovrbc6hzwRgK8y5M7pXLc+byWAB87cgTfDRpQKAJGPm8iwDMzM5k5cyYxMTEoFAqS4mL45/BRUtPSmD31V779ul+B8iWxKBQlwAv0swS/Tn2eZxYTBIHIqCiCHz4kPPwR4Y8eqf6Gh3P1+g1yc3Px9yuDj7c3aWnpHDl2DCMjI+pWr4KDvR3xiYmkp2dQLbASrZo0pErF8uqn2Oi4eGLj4innX0Z9c7xy/SazFi3HxdmRKrUb8P3332Nvb4+/vz/9+vUjLy+PB0FBHD5yhGPHjpGYmAg8c03x8sRA34D7QUHqFM8A5mZmtGrelHkzpiKVSkhNSyclNVWVmCA1jbiMXOLj47lx/To3rl8jLDRUHT5MX98AH19fvHx8sLd34OHDYP7ep7LUduzchTbtOmBgaEC3LqrkKg5OTpibm5OclKyO1Wpja4uOrh7RkU/5Y+NW6jRoxJWLF9i8dhUymYyTx46gra2NsbExd2/fomWbdvw4ehxePiphEBcbS2U/T36ZOpOAcmWJjIjgyZNHhD58yIljR4mNiaFD5y706vslzrbWuLu7A3DjxnXu37vPt98OBGDFH3/SuHFjpMYWxX7+aTl5yHNzObB7O2eOH2Hf9i3oGxhy7v5jtTvKvxXgycnJDB0yhMNHjhAZGYmWlha+vr5IJBJu377Nit9/x6JGa431fAgBLggCd+/eZd++fezfv58zZ86gUCioWrUqnTt3pnPnznh5eWmsszRTUgEuCALHjx9n7rz5HDx0CCcnJwZ9+y2dO3fCxUj6rxeBCUoF67ft4vL1W7Rq0oDs7BzWbd3JvsPHkcufX8f09fRwtLOlQe3qtGnakIZ1amDwbBH0qwR4fuxleH0BrlAouHHvAWcuXeNBaDgdWjSmSb1aBcpnZWdz8sIVLl2/TevG9Zj7+1rW71BdWxZNHU9ScgqR0bE8iYzi8o3bRMXEIZPJqFezKk3r1WHWkhUkPPMbbly3Fgt+m4C3h1sBAT5t6WrGzllGs7rVOXr2MoYGenzTrRO/fNe/+MGIArxgP0QBriYlJYU///iDC2dPE/wwBDc3V7w8PTA2NkFLS+UCm5CQwKPIGKKjolAoFM+s6sbcv3eXR+FhtO3QEaVSyf49u2narDmbtm5HqVSSkBBPfFwcGRmZ6OrqIJfqIJPJiHjymOD793j4IIjg+/cIuneX9PQ0bO3siYmOKpVCVC3A3cph+JoCPCNPSZfwW6Vy3Pm8kQ+4q4szw779hn69ehSIo/omArx69erqsEIBAQGYGhnQsF5dPvukEz5ehZMyfAgBXiTPTltaWhqHjx7j4OEjPHkSQa48l149etC1Syf0FJovnGi4qCmNbTV0QyAqKpKg+/cJevCAoAcPeBT+iNDQUBKTkjA0NCA8/BFaWlrIpFJycl+dbVIqlRbpjwzg6OSEubkFSUmJJCUmkpmZyfARPzJq7Hh1mZi4WNavXsWyRQtITUlBKpMhlUjIyclhxKjRGJmaM2HkCLb/c5QKlasUqH/Et/3ZsWUjFStXoX3nLixftICkpETuhEago6PDjCm/sGD2DA6eOk/ZsgUzc546cYw+3T+jddv2zF+6HBOdgj/kuLg43FxdCmxz8/Ckees2tGzTnkrP3CNeJC0nj2P/7GfwF88zVjZr055vfxiNi6s72jo6/1qAgyqGbb5AmzFzJhcvXiQjI4MGDRqwY/t2jj3N1ljHuxDgeXl5XL96lbS0VORyOVFRUSTGRvP48WMeP37MgwcPePr0Kfr6+jRu3Fi9kMnFxeUVLfz/oUmAy+UKNl57yvz587l+4wbly5Vj2LBhdOncSb3wWJYa/a/7IRGK/s2mpqXzKOIpcfEJxMbHExOXQNjjJ/x99CQh4Y/R19OjUZ0atG7akHY9+uH4UkSMS5cuUa2aaqaratWqOFsY4WxvS8OaVWjbuG6h9rKys7lwO5jTl65x+uJVzl29QXpGwe9dp5ZNCHn0BIVCgUKp5FFEFNk5Oejr6ZGVnU2tKpU4e/kaZcv4cHr3BgwNCj70PYqI5O/jZ9j992GOnTmPQqGgSsVyZGRkci84BCsLcyJunEU753mCq8u37tFj2BgePopQb+vQtD7bFk/VcGJFAV6gH6IAL4R2XvHZm5OK0CJyuZy/Nm5gzoxpRDx5TBk/Pz7r3oPBQ4cXWUdKTtFrmlS5P85x+sQxlsydVSqFaL623OZRHsPXXIuXoVTSOfRmqRx3Pq8lwJfNn8W6TVu5dPUq2dk5WFqY81mXTvTu8RkVypV9bQEuCAJ9+/Zl1apVAFhYWPBVn558N3ggZq8IUfWxCfDieNEK80r+pQBX9eXVznlu7h5q9xkdHR3q1KpBnjKPPCFP5WKSnklwaAh+/mVJTUnhwYMgAHbs3kNA2bLoaOtwYP8+pk2dSviz5DoARsbG1KpdhwqVKpOSnERUZBTpGelcunCe9LQ0Bg4Zhr6+PmdOneT82ec+ytVq1mbj7gOF+nn/zm3CQh7Stl17pFIpX3T/hDu3bnH07CWWLZrP3BlT+Xn8JAYMGY7WS59fo1rVuXf3Dtv27KdW3XqFBPiqlSv59tuBGBkZkZ6ejkwmo03HTpw+foyE+Hi+HjSE0RMnFzgmLSdPZeW9dZ0zx49w9vhRrl++gEKhQCqV4uDkTJtWLZkyZQrmz6LFvAnZWVnExMRQr359dHV16d27N3Xr1CEwMBCZTMaBkGSNdbxNAa5UKlm0YB5/LF/G04jngkUikWBvb4+rqysuLi64ubnRsGFD6tev/8ZrIko7xQnwnUdOM+y3RUTExNGkcWOGDR1Ko0YNC1m736UAL8ALZQRBICgkjP2Hj7H30DHOXlLF+m/VqhULFizAw0OVUCYrKwtDQ0MEQaB27drEPn1McLhqRmvJryP56tMOHDp9gePnr3D68nUu3bxHrlyOibERtatUpE7Vyvh7e9Dxq2Hqtn08XGlcpwbaWlrItGQ42NrQrF4tfDxcWbN1N2u27qZzmxZ80+uzV4e7laqu78kpqRw4coJdfx/i76MnUSgVLJn+C59/0rHQtffkxWs07P4N/bq2Z/O+Q5T19uDU5mXFp2EXBXjBfogCvBBvIsDzkcvlKgu2hvC3rxLg+aSlpVLBw6lUCtF8bbnDu8IbCfCOwTdK5bjzeWMf8Hv3g1i9YTMbtmwlJjYOVxdnOnXuQtmyZTExMcHExARTU1P8/Pw0npy8vDzu3LnDypUr+f335ZiZqNxd2rRshtVLX05RgBfVl1ffgJcuW8a8efNwd3MlMioaQRAwMjTE0MAAAwMDzExNKOPrw1+79nL3zh20tLT4pOunzJg9B0PD5xf1jOxczp05rQ51eP7sWU6fOsG9O7exsLTCzt4eQyMj5HI5BgYGTJ09F3NzC+7fvUPDWtVxdXNn9tIVuHl4Ymb+avcPnWcXxU3r1zDmh++QSKXIc3MZ9uMohn7/IxKJpJAAP3LoIH26daV7r95Mmz2vkABv3KghIaGhdOncmQ0bNpCSksK5m/ewtbNn5PDBbFm/lu1/Hyaw6vMkEfk+4C+SnpbKnRvXeBweSnjIQ3ZsXIO+vj4zZ86kQoUKGBoa4uDg8FqCNDsri8ZNmnDr1i2uXLlSaFHS+xTgMTHR9O/3BWdPn6b75734tFt3HBwc0dLSwtrGBntLM43t/JcoSoDn5eUxfuEqfvt9A+0a1mLs1NmULVv2lXV8CAH+MolJyRy4fJ9x48YhCAKPHz9fwFi9enXu3btHeHg43Tq05uAp1SLvwb27UtbHi/6jp2BnbUmdKhWpW7UitatXpVwZ7wKRpZRKJQlJKSQmp+Dl4aoxopAg1RBxqIj9WVnZZOfkYG6mMtzkX3vz8vKY/cd6Rs9aQmBAGSoG+LJsw3ZqVirL0XWL0dEpZoGjKMAL9kMU4IX4NwI8Hx0N/RAFeNH8pwV4PnK5nM3bdtBv4NAijzU1NWXQoEEMHToU62cxPV9GqVRy584dzp8/z85tf3Hg4GFA5RLxWZeOzJ8xFWNj1SJBUYAX1Zfib8AluYCnSfQ5euQwZfz8i4xwU4JQ4kVGUlEoFPyxbCndP++FTL/wQs+XefFidP/eXZYtmEvvL/tjaGjIru1bqV2vAa4uzjx9GsHd27fYsfUvrly6iIGhIWs3/VWkBXzf3r2MHPkTevr6uLm6MWrUKFz8K7B9yyZ+/n4otnb2bN61H7sXwpi9KMAz0tOIehpBQlwsvv7lMLNQPUCY5aUxaNAgdu7cqS7bpEkTDh06pPlkPSMrM5MuXbqw/8ABfl++nJ49exbY/64FuCAI5MSF0dEhirWrVWEsl/25irr16hcqW9QizKJITk7m8ePHREVFYW5uTtWqVdWWX0EQSE9Px/hZoqicnBwOHTrEhQsX0NXVxdDQkJ49e77yWvEx8bIAT0lL5/ORv3Hg1EUmD+3HD30/ReFZs9g63pUAHz99LlPmLsbKwpyaVSoT4OuFv68XAb4++Hq6o6v7PIKHIAjcjc/hhx9+4NChQ+zevZvHjx9jaWnJkydPGD16NI0aNcJKT8L6XX/j7eZMdFwCaRmZtGpYm93LZz3/fP/lIkx4MwFeqEhmEtFxCfQeMYHDZ1QujlXK+XH51j1+/W4AP/XvWbz1G0QB/nI/RAFeCFGA/zvyteVO30pvJMA7BF0rlePO57WCG9dq3IIa1aqwYtE89TZtbW3at27FNO/5PAgO4cCBA9SoUYOUlBQSExPZsGEDc+fOZfbs2fz4449MmDABUIX92bt3L/v37+fcuXNkZGQgk8nw8/VR152Xl8eGLdvYvG0n6TGPNV8wRd4YmUxG02ZvN8weqMLQ9f92EKA5VNbLlPHzZ87i5aSmptClTQvu373DvJnTCtTdoHETlvyxkmYtW79ycXDrNm1o3aZNgW0r1m5g+ICv6PxpN36dMQcDw6JvYCnJSTSpEkBWpuomaGpuzvCfJ9CpWy8cHR3ZsWMHDx48YO3atUyePFntM1tSJBIJHTt2ZP+BA+88eZSQpyQ3JYacuDCyEx6hzEgm/fF15CnRrDAzo3Wbtvw8bjx2dvZv3MbatWvp27cvCsXzm2hgYCCDBg0iOjqadevWcefOHdzc3PDz8+PMmTOkpqZiZ2dHXl4esbGxXLlyhXXr1r2NIb837oU+ovPQ8cQmJLN70WRa1n297wFAWno6V27c4t6Dh9wPDsHBzpYu7Vrh+UKEpZKS73sdn5jEnoNHuHjtBjFxqgRYMpkMb3dXfDzdiY1P4F5wCCmpaepj27Vrh5aWFgqFQv23Tp06/PhJI9o2rsfJi1dxdbTD09WZJrWqfrSZBH+euUgtvvV0dfFwceSrzzryRZc2SDWIHhERkfeHVCZBWpInnhePKcFD5cfOGy3C7Ny5M0+fPsXKyoqqVavy008/oaurS3x8PJaWqpTaU6ZM4dSpUyQnJxMaGkpsbCzt2rVj165dnDlzhjp16gDg7+9P7969qVGjBoGBgRgaGjJ79mxu3bqFjo4Ojx8/plGjRvzwww8AKMOulmBUxQt1iULzwrY8/eLTJJcovmsJrOSa2tGOLj5+c0n6kmeg2UdZ0Cs+vGNeCcYrp/jzLldqPh8vZ6B88uQJrVq1IigoSB3RoWPnLnT5pCt16zco4CaTj94zK4sgCOzatZOwsDDy8vLIU+Y9i0GczLJlywB4HJNQYCFxPvnZQy+dP8enbZsTUK4CP42fxK6/NrNt8wYqVanKiOHDqFu3LqNHj2bt2rW0bNmS9evXv5ZPuDw6hGu37tC5T39SUtOYMeFn+vb4VL1/bpDmc2ak++rn6NzsLPb+Poe4Oxe5f/8+OTmqALq2trbY2tpSvXp1OnfuTMOGDf91RsUr+zdTpfVnABga6FPRvww1Aytw894DDp48i76eHm3atqVRw4bcvXePe/fuUbNGDTp27KiOW129Rg0iIiKYMWMGffr0+agfupVPbgGwcec+vhk5CVdHB7atmIu3+wuCWVm89UoRrkq0dOjiTb6cuoyYpBS0tWR4OdrxOCaejOwcWjaoxZ7fZ79S6L7KYhwVG8f8lRuZsWw1Oxb8Sp3A8twNCef4xetExcbz8EkktpbmXLv7gLshj5BIJHRr35IBn39KjcrliUtIYsvef0jLyGDkwH5ISmCh0hTDWSLXEMCZErjUKDTXIeRmk5SSxrkbd3kUGU3tSuUo7+vxfL+Gz0XVTvGzoEKe5jpklpofqvN0NFy/NVjIAY33PI2zCoAkt3g3NYlSw6wwIGgYiyDTfI2Jlmsuk6vhXpKQpbmvbmbFz9aU5MqjyYpuJNX8HZFo+D5LM4rPBZGalo5lmSql0hKcry33lAt8Iwt421tXSuW483ktAT5kyBCePn1KSkoKDg4OxMXFcfDgQRo2bEjLli1p0qQJ5cuXB1QZzpycnKhSpQqWlpZ06dJFbR2Uy+XMmjWLefPmERsby+TJk2nZsiU+Pirrd1xcHHp6ehgYGHDmzBnOnDnDsWPHWLhwIWVNSnDhFAV4wTZKqQCPi4ujefPmxMXFERkZyVdffcXq1atVERQUCgLKlqVlqzY0atKEyoFV1Iu28gX4vHlz+XnUKExNTdXZUfX19UlMTCQjI4N2HTuxcOnvxQpwpVLJyuVLWDR7OpkZGdRp0AhbO3vu3bnFjatXADAzM2Pu3Ln06tXrta2B8ugQAFJS0xgxfjKrN23l5J4t1Kiiyhr6bwR4Rkoys775hISoCPr0+pyyZcvi7+9P+fLlsbGxKfKYf0Pq3XM0/LQfV28//862bVKfHb/P5UlkNGYmxuh5Vi62jicREdSoUYPExEQOHz5M48aawyN+KDKCL/P9pOksW/cXPTq2YfFvYwpE7QBKJMCPXLlN6xFTaVq1HFMHdMfXxYHsXDkLt/3DhD/+AiDqwgFsLIteO1GcuBIEAT2f6swfPYSvPmlDv9HTWLv7IMaGBtSrUoGG1SsRk5DEjD82MqJ/H6aOGvbqzpbEfeQjEuDF7hcFeOEqRAFegI9VgN+8G0RObi5GhgacOHuRQ8dPs/vgkVIpRPO15d6KVTCUvV626QylgjbXL5fKcefzWgK8qIGuXLmSlStXcvnyZbKysqhVqxZOTk5s2bKFRYsWMXDgwFfWm5Oj8jssLlPliyxatIj+LWtoLigK8IJtlBIBnpSYSPC926SnpxMcHMy0adOQSCSMHj2aH374gadPn2Jubk5KZg6HDv7DiGFDiH0W4UVLS4sNm/+iStVq2FpZkJubi52tDTKZjPYdOpCelkZqWhppqalcvaqaRXFwdKRNuw506NyFyoFVCojnjJfcZZKTEtm1dQv7du3g8oVzBFarwfo1qzh9+jQtW7Z8Y/eRfAEOqgdTU49yTB8/ikFf9gb+nQC/deYoS0b048cVO5jWr8Mb9e91UIZfZ8+h43T8umA4raObVlCvuirrqty2YLxpQRAIDQ1VP2j//c8/pKWlMWrUKEaOHPnqSBgfkLy8PLZu3crYUT/y6GkUcyeM5MvunYt++NIg9FLuXWHs71tYvOMgF36fjLONJUt2HGLR9n9Izciic4Pq9O7xKc3rvfq6p0lc2VVpjKWpMY2qV2bJpl0AfNu9I/dCH3H22m2yc1R+rL27tOOPmZNeXZEowAvWIQrwwu2IArwAb1uAZ2VlY+JV8Xn7Mhn1a1bl6OnzpVKI5mvL/YFV30iAt7pyqVSOO59/LcDzkcvl7Ny5k40bN5KWloa3tzeDBg3i559/VkXeMDKiU6dOVK5cGWdn5wIr4ZOSkrh37x5BQUFoa2tjZWVFYmIiR44cwdTUlL///pt79+4xdepURnRtWoJRiQK8QBulQICvXb2KsaNHkZaqit8rk8n48ssvcXZ2ZswYVTbI3bt306JFC7Zs38mcmTO4cvkSlpZWmJmZERLyUF3XmrVr6dy5C1cuX2bZ8mWEhoRgYmKKsYkxJsYmGBgaYGFtS8STJ+zZtYO4ZyJ+1fpNtGyt8hXPkOfx4P492jWuS8eu3Rg84iccHFURSi6eO0vPTm0YM2YM48eP59/wogAH6PLFAM5cuMyRHRvw9/V+YwGukOfyx9ghPLh6jt92X2BogzL/qp8lQRl+Xf3/d5NmMH+lKqOhvp4eVcr742xvh4O3P06OjmRmZXHp0iXOnT1LdEwMEomEcmXL0qBhQ77//vuPMp64XC7nwIEDjB8/nuvXr9OyUV2mjBxKuTI+rz7oFUIvJTWN3zduZ9bSP0lOz6B3y/p4ONgwZc1OFEolX7RuwLCurXG1s0Lb2bvYfmkSV9sPHOGvPfs5feUWUXGqm3nooU24ONiSnZPL1bsPcPXywdFOw4JvUYAXrEMU4IXbEQV4Ad6FBfzvoydp+/nXAJzZu4UyXh6l3gXlfQjw3377je3bt3P//n309fWpVasW06ZNw9f3uVFIEAQmTpzI8uXLSUpKonr16ixatIiAgIBian5z3poAL4oBAwawdOnSQtvd3NwICwsrtP3BgwcMGjSIx48f4+TkxKVLl0h9JsgqVKjAkiVLqGanYZU9iAL85TY+YgEe8vAhDx4E0f/LvtSr34D5c+dgZWWFoaEh2traVKtWjUuXLgGqOOYmJibEx8dTqXIgDRo24sTxY4Q8DCYlJUVd546du2jWrFmxfUnPVV0YlUola1b+wcgR3wEwc94CmjVviZGlDZFPI6hT0V99jKe3Dx27dqN6rTqsX7WC44f+ISmpBJFuijsnLwnwxKRkGnXsRtijJ8ycOJr0ap9qdGt5UYDLc3M4t/cvDq5dQlJsNP2nLqV83aYMqOH2r/pZEl4U4CmpaRw4fprE5FQeR0YTERXNk8gYIuISiYyMRFtbm0qVKlGrZk1q1apFjRo1MDMzQ0+/BL+r90hmZiYHDx5kx44d7Nmzh6SkJOrWrcuUKVOo6Vr8bxcoJMDDnjxlwaqN/Ll5Bzm5uXzerC7fd2vDyCUb2HPmCv3bN2F0747YmD+v+98KcACyUhEEgSt3HhAVG0/bRrUL7jcswboFUYAXrEMU4IXbEQV4Ad6VD3hqWjpVmnVAR1ub32dPoV77bqVagB+oUg1DDeFJXyZDoaDl5YslHneLFi347LPPqFq1KgqFgtGjR3Pr1i3u3r2rXks2bdo0Jk+ezKpVq/Dx8eHXX3/l5MmTBAUFqSN3vU3eqQBPTU3lyJEj6OjocOzYMZYuXUpGRgaTJk1i7NixBcreu3ePLl26kJmZSfv27QkNDcXNzY2xY8cikUjUizvFRZiv35ePUYBnZGTw68Tx/L5sKYIgoKury6at2+nQplWRx2ZmZtK9e3e8vb1p3b4j3T/9hKjISPX+Ad8Oou+XXyOTyTh++CD1GzTAw8ODlX/+yfHjx7Czs8fH1wdvbx8cHByITUrh9MkTlK9QkXoNGnL44D+sWfkHRw8fQhAEfP0DqFO/IZUCq7J7+18c3L+3UJ+6d+/O+vXrNZ6XYs/JSwJcNdYsfpz4G8vXbCCgTlM+/Xk6egavDuH4ogCf2rc9T4JuU6VJW1r0+RZ7d5V4e98C/FXIbX3Jy1MlOZIVsejmYxHgWVlZjBkzhqVLl5KZmUlAQAAdO3akY8eOVKpUSXUterYIs1heEHo37gZRs+PnGBsa0r9HFwZ83hXrzFh+W7uTX1dtZ/OkobSpHVioirclwItFFOBFtCMK8AL7RQFeiJcFeOTTp8yfM5u4uFj09fX5Y8nCIq9zL1ISAS4IAncfPOTwybNcuXkbS3Mz0jMyWbVpG03r1+bQiTOlWoD/U73GGwnw5hfe3PUmLi4OGxsbTpw4Qb169RAEAQcHB4YNG8ZPP/0EqNykbW1tmTZtGv3793/tNjTxeiN+TUxMTOjYsSMArVu3ZubMmUWWO3nyJM2bN8fV1ZW9e/e+M3O/yMfBo/BwunRoR1RUJJMmT6HzJ12xtrYpNuKFgYGBOt52yKOIAuK7VZu2DBo6DC0tLRrXr0vEkyeYmZnx+4o/+O674VStWpWHISGsWrWS3NzncVv19PTIzs7Gzt6ebwYOZvmfq8nKzuLk8eMcOXKEfTu388eShYX68v3P47Cysqb3Z53f3kkpMFZ9Fk6bRNMGdenx7Qh2z/+FriOnaT4QsLJ3Ii0xnt7jZiF9zVXl74uPObIJqFxNatasyf379xk5ciTdu3dXLxD/N4yfvRgXBzuu7t+MwbMHDUV4LBbGhuQJAm72b39hrIiIyNtBLpfz9GkEjx+Fk5aSStMWLdHX0y2wv2+vnoSGhJCenoalldUbt5WVlcWVa9d5GBLKqRPHOHr6HJHRsejq6lAxwI8bt+/xKEJ1D6wRWJFDJ85oqPHjRiKTIHnNMIQSQVU+30siH11d3SIDK7xM/qy5xbO8HmFhYURHRxeYPdfV1aV+/fqcPXu29AnwkjJkyBB0dHTYv38/Tk8vknuqGMuSluZFWcqYx4W25eXlcfDafe4/iWFQy1rINIgAbbfifWYl9p4a+yHRYEEBkGalFLs/L0tzEgbSkovdLUQXdvd5GS1rx+L7UQIrukyDlcXYzpOIiAg6tWuNTCbjxo0beHsXb90rCk9XJy5fvsyjR48IDAzE1VUV9i0zMxPdZ6H0unRoSwVvVUKhkcMH0aZFMxQKBY8jnhITE4tC15jAwEBu3rzB8mXL+HXiOBYvmMucefNwd3Fi1IjhBKxcQWJiIsHBwTx48IDg4GCuX79Oz086UrFixdfud1Fo2736e9TlC0/S0KVv3750rBvI999/r/HCctXDgfDr5+lfXXPGwbeNzK2i5jLvvhv/mqSkJG7cuMGyBXPp83kPAHJT4guVk8Y/1VhXXnoyAFfuh7D3yElWjhmIbvwj8m2nyrin9Kruw2hdbbYfOIJfEWtc8lKLD0Om7aj5WiQ1Ld6KWhJrriZLHZTAel2C5DVCVlqx+/Myit8PlMharwmJTvEZbSVovheVyPKs4ZzJUmM0t6NTfJKsPN3iE/WAZgu3JE9zghxpelyx+zXNkADYmWte/6EpyZ1bCXKG5WnQErqGxuTk5DB58mTWrVvH48ePUb4wc/Ldd98xfVgfklNS+XPzDhav3siTyBjGDR/A+JkLmfbTYPRi72sei9fzBdaJiYksXryY+fPnExenOpcVKlSge89eNG3alLp166L/7OFdEASysrJQKBT8MnuR5gF/xPwbAf5y4sDx48er8828CkEQ+O6776hTp446U3F0tCopmq1twbUwtra2PHr06LX6VlI+uAAXBIFhw4bx3XffMWzYMLb+0P2tt7H/0h26TPlD/d7HzpKWgX4kZ2QxY8cxroY8ITUzh/bVy9KlVgU87Eow5Sfy2sjlcpo3b44gCBw9evRfLbQLDAwkMLDgdL2BgQF3797l5oVT+JfxRSaTYWtjzYVLl2nTohlaWlp4uLni4eZKrq7K9ady5UCWLlvO6DFjad+uLT26dVPXN2zYMObMmYODgwP16xfODPk+6NOnD/fu3WPcuHHMnz8fq2dWFalUStOmTRk9erT6Cf7+/fv8+eefzJw5872L7/8nrK2tMTIyIjE5+a3VqSWToaejzYaDp2lZsxLmxs9dihLTs0jPzkWRl0euQoFOEZ9ddq6cuJQ0MrJzycjOxcnaDFuz0jXdLCJSmrh69Sq9e/fm/v37fPXVV1SoUAEPDw/c3d3Ztm0bo0aNIibsATv/OUKuXI6dtRUOttZMmLWIWlUq8Vn7liDPKlSvUqnk8q37GBnqE+Ctik1/8+ZNFi1axLp168jLy6Nv37589dVX+Pj4vDLBnEQiwcDAoJAFuDQilUmRyl7voVkqqMo/efKkgAtKSazfgwYN4ubNm5w+fbrQvpfXXAmC8M6SjX2wu3RSUhJly5YlMjISHR0dtLW12bNnD02fPGTCl12xMTflYUQ0vq4OeDiUIB17ESiVeaw6coHBS1SxdPV0tFg0oCvNKvmSq1Cw7ewN5u4+QR0/d7zsLZmx4ygTN/2Do6UptQPLU6OcL1oyGclpGRgZ6BHo50UFHw8M9EqwEFSkEOvXr+fu3btcv379nUW50NHRoXzZ5y5MLZo0Zv2mrYz5sXjrsbOzM+cvXCQuLpaMjEy6f/apOmnNywiCwIoVK5g1axZ//vkntWrVeuvjyEcikTB9+nT69OnD6tWryc3NVVs+li9fzp9//snAgQMxMDBg//79ODo6MmDAgHfWn/8CEokEDw8PQsPC31qdFbzd2DHtR7qOnk1A9+Es+K4fnRuqLF/aMin+znZM336U7edusmzgJ6Rm5RAek8itR5FcCY3kzqNIFMrnllJtLRmf1gtkQs82uBY/eSUiIvIa5ObmMnX6DKbNmEm5cuW4fPkyFSpUKFBmxIgR7N27l0OnztKwVjXuPggh9HEEAT6ezBw7gi5tmiGRSHgQ9pj1u/6mVmB5MjKz2Xv0NPuPnyUuUWXBb92wNpbOnqxZswYHBwd++uknBgwYgLW19YcYeqnExMTktXzABw8ezO7duzl58iROTk7q7XZ2doDKEm5v/zwbdGxsbCGr+NvigwlwqVRKYmIioPrC5+bm8sMPP3Bw5xYaDZpYoOyC7/vydfsShB98gb9OXWPK5n8IeqoKMSeTSjkwcSDXw57SeOxirodGIn82nTSsXX1aBvqRnp3D8VsPORf0iPNh0ew4eg5BEDAzNiQ1I5NcuQKZTIqfuzOBlSvRp2tH6lQvvGhKpGiOHj1K9erVC13M3iUVK5Rj9YZNHD91huZNGhVbVkdHB8dnoQY9PT158OBBkeXGjh3L5MmTcXR0pF27dpw7d+6NXGleB39/f6ZNK+gHPmnSJCZOnMjChQvVEWIWLlyInl7xU+cimvH09ORhSOgr98vlcrYfOEb9KuVxsC7ZjFmjwLIcnDeGWl+PYdPhM2oBbmlsyMWZw7geFkmvOetpPHZJkcdX8nTGw84KAYFd526y7uhF6pfzpk/54pMbiYj8V5DL5cQlJBITFweCgLGRMSbGRpiaGKOrq0v44yes3LCZcxcvY2Vjj52dLd0++5TAys9/Q998O4gNGzcxfPhwpk6dWmSGYJlMxrZt2xgz/FtWbNxGgI8ne9cspkGNqgQ0as/3k2ago6ONXK5AW0uL3GdZnP293PmiSxvaNKpNWEQUvy1exb5jZ5g1axaDBw/+KPMevBfewAUF4fXKC4LA4MGD2bFjB8ePH8fd3b3Afnd3d+zs7Dh06BCVKlUCVNr0xIkThe69b4t3GgWlJAQFBXHy5El8fX2pW7cuOSc3cvDiTXS1tXCzt6HMZ0P5onVDapT1Zvepy7g62NK/Uwu0tKRIJVJOXL2Nvq4OnzWrq67zyb1bePWbiFQqYdfYr3kSn8TARVsAlRBvXsmXphV9CXCxw8veCluzwhFAtN3KoFQqkUqlSCQScuVy7oQ85vLdYK7eD+H0rWCCQsJoVKcG474bSO2qhW+CJfEB14SQEqu5kIZ28jREBIC34wOuyd+x64AR3LlzhxEjRhAQEEDt2rWLLf+m5CapfLmW/rGKYT/+TJcO7fhj8bwCFvB8F5RX8fuypYwaNYo5c+bQsmVL3NzcEASBZcuWMWDAAKZNm0a/fv2oXbs26enpHD169K0s1BP597w4ZZiXl/dGiz5nzpzJ2LFjefrwHkZGBSPQHDl+gu9++pn7QQ9wtrPm7yVT8HF1KrKefB/w6IRkTly7w+yNe4lPTuXM8snYWZqhiC64XiU5I4vGYxZz/5nhwNbMGGdrCwx0tXkYFUdiWia62lrU8vfgp0+aUc3X7e34gJcgOkVJfID5D/mAl6gO0xIsrNW0WDpPwznlLfmAa4hQU5LPX1OEsZL4gCtL6AMuCAJbdu5h5bpNRMXGEhsXT0Liq33DdXR0kMvlGBka0qhebVIysjl/4QJVAgPp3u0zEhMTiYiIYPmKP8jLy8PR0ZGIiIgi6zp48CB9+vQhPS2VCd8NZGDvzxAEGDJ2Cn9s2s7SqePIys7BzECHTs0acPzCVfw83fBwKXifzcvLI0xi/a8MOO9Sn71r8vt+rEk9jLRfzx6cLlfQ8PDJEo974MCBbNiwgV27dhWI/W1qaqr2q582bRq//fYbK1euxNvbmylTpnD8+PHSGYbwTcg9tanA+4Duw3kYEY1EIkEmlaJ4RfionLNb1f/Hh9ynxdhF3AqP4p9fBlLZy5l1xy7jaW9FJQ8nLPQ132w0LcIUbN3Z9c8RfpmzhFv3HlCzSkWa1K1JBf8y6OrqoK2lhYGOFg62Nrg5vVmWRPj/EuDz1+9k7ty5REZGkpeXx65du2jXrp3Gel+X3KRoBEHA1a8CSckpbFz1O21aFIwLrkmAx8fG0L59e+7evYu+vj4DBgxgz5493Lt3j2+++YbFixcjkUiIioqiUaNGpKSkcOTIEfz8/N76eERKzurVq9XZd3V0dEhJSWHx4sV88803r1VPeHg47u7uNGpQn6++6IWvtzd3799ny7ad7N63nzo1azCqRxuGz1iCpZkpJ/6cWaSfYF56MoIg4NVlEBFxifi6OLBq7LdU9lX5fr4swAH6zt/EjvM36VijPKO6NMbH3blQmRcRBXgRiAK8YBX/ZwL8/rULDBs1juOnz9G4Xh38fL2xsbbC1toKG2trbK2tkMmkpKalk5KaRmpaOqlpqZiamNChVQsMDQ3IMzCnQeMmnL9wEQAzMzOcHB1xdnZCJpPxSddP6dWrV4F2s7OzGTlyJPPmzaNJkyb8MeUnHO1sSUxOodOXQ7lw7SYLfx1Nv26qCFmSHM1BFF5chPkm/D8I8OPNG7yRAG/wz/ESj/tVftwrV66kT58+wPNEPMuWLSuQiCd/oebb5qMX4A+eRPHgcSRlPVzQ09Hm5M0gzt9+gJmxIdeCQtl/5gq3Ns3H1c6aE1fv8NfhM/x1+DR5goCPow2CINC8sh/je7RE69mFTpBrtkyXNApKXl4eu/45wrqtezh14TJJKYUXRGxcMI1P2hSfGOZV/D8J8PyIHwqFgsaNG5OZmcmFCxfeeli6fAv4w9AwhowYyZHjJ/ll7Ch+HD7keRkNAtzIQPVEnJaWxhdffMG+ffvo0KEDffv2pUmTJgV+zDExMTRp0oSEhATOnDlTaGpL5P2we/duOnbsSNeuXXFyclKHPf3pp5/w8/Pjk08+eeWCpqJY/ftSFi37nUtXnuce8HB3Y8LoUbRv04ojG1dw/X4Ik5atY/uc8bStX/hGmpeeTMjTGPy7DWPL5O9oX7dqgf35AlwQBG4+iuLig0dU8XLh57V7OXknlDn9OtC/XcNi+ykK8CIQBXjBKv5PBHh6ejpTpk5j/sJFuDk7Mee3iTRv1EBjnUWRZ2BOdnY2mZmZmJqaForXrWuosniGhITwww8/EBQUREiIKm/DtGnTGDx4MMLTOwAsWbOJwWOm0KllE8Z9N4CyviqLtijAiye/7ydbNXwjAV5v/7FSOe58PjoBXhIUCgWzZs1i6tSpeHl5sX79emrVqkVCQgIODg5ERkYilUoxNzcnIUEVwuvMmTPvdLEcqMR4XFwcCoUCuVxOwu4ljF27l5thkZycPgx7i8LnTiewSbF1CiUIdaYoIuzii0hKELpRYlj85yo10Dz9ItEu/iYu0X1+kzhx4SqNegzgq886IJPKiElIoEIZb2pWq0L1iuUwMnz1DUXmWnIfckEQGDt2LFOmTOHvv/9Wx/jMTdbwYPPSzUapVBZOqPDCjTEmNo6GbTohCALH9+3A1sYaHaui3RJE3g1Vq1bF1NSUnV81YeSafSzar1rhLpVIyBMEDoz/mnoBKrFq8MmPJa73/v37xMTEULZsWQ4dOkRwcDBLliwhKioKP3tLrI0NeZqcxpERPTHULfhbsxoyizVr1tC7d28SExMxNy/4IJv513QUSiXdZq5l/xVV0q1yrvZ81awGQ37fwbFfB1I9wOvfnBbgLYnJt1CHpgcB0CywhUzNUR8EDUaJklwTpebFi2eJVHNQTamh5uumpqRAUmMLjXXkaUikpjTVPAurSRxrJRftjlGgDg0PcpoS9QBoxRVOTiYIAlv/Psb3vy0gMSWV0WPGMmLEiBJFvHhT5DFhKBQKBv4wmlUb/2Jg3154ebjRtEE9fL1UM1jCQ1WW5lsPHzFo5gquPwjDysyEkO2LAdCp3fWd9S+fj02fvQ75fT/VttEbCfC6e46WynHn83FnxHiJ5ORkpk6diru7OyNHjkQqlSIIAuXKlcPY2LhAWBmZTEZCQgIeHh5s2rSJmjVrvvP+SaVSbG1tcXR0xM3NDX8XO2b264hcoaDy4Kn8/vdZ8kpgzfgvUL96Zfp1bc+G3f9w7tpNUtMymLdqM8179MeyfF3a9xvCnkPHKcHzYbFIJBImTZpEuXLlWLt27RvXoymbma2NNdvXryQ0/BGzFha9iE7k3REZGcnly5fp3LkzUqmUsZ8249rcEcSv+5Vu9SpjoKtNVe83i7xTpkwZ6tevz+LFi+nWrRvz589Xz3IkZGQzuVMDopLT+Hnb0ULHCoLAmjVrqFixYiHxnc/Cfaf551oQfw75jLPThxKXms6Q33dgZWxIJQ/xIU7kv03I46e0+vJ7Phs2jsCyZbi9bx2jR49+p+I7n46ff8WazduYOm4Uc6dMYNCXfdTi+0XKeblyYukvfNWhKUYG4iJ4kZLx0QcLfvToEUePHiUmJobly5cTGRlJq1at8Pb25tixY/j5+TF9+nQ+++wzdaiYy5cvs2XLFho3bkzjxo01iqd3ibudJVcX/MToNXsZumwrUqmEfs3e/cNAaWD5lJ9ZNnlUgQVz9x5FcvTMRdbt2EvHr4bRokFt+nzSnuqVyuPsYPdG7Tx69IiIiAiqV6/+NrtfiNt3VBbMu/cfcPnadWo1FcXT+8La2ppq1aoxfvx4Wv82ACsTQ4z1rfnuj52sP3GFz+pWQu81LSwvs3HjRnr16sXq1avVaYxjUzPYdvk+gW72bLhwh86BftTzdWHp8ausP3cL43WnuHTpEvv3739lvTfDo6jh68qndVQr72/O+5HYlDQsjQ3R1ioNqYtERN4+Obm5zPh9A1OWrsHO2oKdS6bStlGd99b+7XtB/HPsBKsXz6Fbp/bFln0SE8/381Zx/vYDyohxQV8biUyK5DXjgEvySpX9uEg+SgEuCAI3b95k2bJlLF26tIAV1MDAgH379qFUKpkyZQqjRo0qdHxRSVo+JBbGhiz59lOeJiSzdN8p/J3tqFHG7Z0Fdy9NvHgOpFIp/t6e+Ht7MqhPN/YcPsGPk2fx2bcqd4Eq5f3p/Ul73CtFoK+vj5GREdbW1jg6OhYZKiovL4+VK1fy448/YmZmxtSpU9/pWNq0aMpvE0az9M811G7Wlu7du/Pbb7/h7OwsftbvCEEQSE1NRRAERo0aRceOHXH9chIbR3xO26oBXHjwGF1tLTadukagpzMDW7155B0tLS309fXJysqiadOmyKQSpBIJcw9fVJcZu/M4zQI8mHf4Is0CPLDy8aFLly60bNnylfUa6+sSn5qufm+op4O7npgMTOS/y9FzV/h2wkxCIyL5vm83Rg/ojaGBZveVt8nmnXswNzOlc5tX/3bzGbd8E2du3qdlzcp0avjvfLr/i0hlIH3NMITS/wNngo9KgCclJfHHH38wa9YsdVrQfFxcXGjVqhXa2tp4eHjQrVu3dxYc/V3xQ6fG9Ju3gcY/L6BZ5TKsG9Ebzd59/13aNqlP2yb1iY6N5/Slq6zeupsh46YWcksJDAzk8uXL6vf79+9n9+7d7N+/nydPntCrVy9mzJihzhj5rtDT0+O7b79h6DdfseGv7fw0YTIbNmzAzMwMf39/OnfuzIABA9Qhj0T+HRcvXmTAgAFcvapaJNmxY0f1vqV/n8XZypyTvw1CJpXy46o9/Lx2H5U9Hanh6/ZG7TVo0IAVK1bwzTffEBwcTN86FQmLT+bw3TDcLE2JTE4jPD6Z9edu06GSL0s+b4ntsDmAaiHX4cOHiYqKwt/fn3bt2nHmzBmm//Yn5++Hk5ad+8osmCIi/xWSU1IYPmISG/YcxM3Rnkvb/qB8mX+/BuJNeBgaThlvryKNOy+Skp7Jhn9OUcbNkRWjB76n3v1/IZFKkEhfMxX9a5b/GPmorvbNmzfn0qVL6vfGxsb069eP7t27U6VKlVJrRXyakMyVh0+wNjHij6E9iE1J5fOZa9l44grf1mn9obv30WNnY0WX1s3o0roZ6RmZZJi5kpWVRXp6OkOHDkWheL5g8sGDB7RurTqnAwYM4PPPP38v/v8vIpPJ+PyzT+jYvTcnT57k7t27XLt2jZ9++olr1679K190EZW/988//8zq1aupVKkS69atIy4ujrFjx+Ls7IyOPJMTt0OoM3I+jSt4s/H7XvzasyUXgx/ReOwSavu5c7T9MI031pcZN24cCxYs4M6dOzRv3pzfd+zA28acdhW9OR38BGtjQ1qV9+LTav4EOFhzJjiCU8OHs3fvXh4+fIhMJsPCwoK4uDhANZvnaW3KsHb1aVLBRxTfIv95Dh05zoY9BwEIfxrFjzMWs2/5DFLTM1i6cScAzva29PiuBjKZjLS0NH788Ue8vLz4/vvv32pfalatzOjJ08nOzkGviOzXMXFx9Bk+mRNXVZFQTAz0USiUaIluY6+NVPoGqeiVogvKW2XixIns2LGDOnXq4O/vT7ly5d7LQot3zaQNf7P26PNp6otzRlA3wJOhy7ZyI0XJrHE/YmykOUyUCBgZGmDq8HxFv52dHaGhzzMWent7s2DBAoYOHUpgYOB7F98vYmFhQYcOHejQoQMAixYtYsiQIZiamtKnTx+qVKnywfpWWgkLC6N8+fLo6uqyZMkSvvzyS7S0tMjMzGT48OH069ePMVWsOBcUzvXQSCZt/oep247wS4+W7BnzFbsu3GbAkr9YtmwZgwcPLrattLQ0rl+/zpMnTzhx4gTh4eGAKtzh4sWLqa2dxogthwmOTcJEX5dqHg7suv6A309ew9rYgLi0TJycnGjdujUzZ86kYcOGmJiYsHfvXo4dO4auri79PbWwNjEqth8iIv8VPunUnhblXYhLTOby7fv0/vFXnOt1RCaVkpSahoGeHgnJKaz++wwbNmzg5s2bLF26FIA+ffpgafn2XLfq1apBTk4uF69ep16twuuH1mzaxrlbQcwa2psWNSvhZl+CkJMiRSJ5g0yYr5058yPktQR4zqnN5BQTHk6iIY2qppA8LVu2LNZXsrSh32E4AP73c+CZANfX16f8gF85Nngqy5Yt4/vhw3DVUfLjpy2KrENioDm8jlbZ4v1a0/ev11iHPCOr2P0GdpovbJrClGm7aM4UKdHSbJVUxD8Ph9UywIk+mzdzf+fveDmrhPmABn5sDSzHlpVL6VPdrcg6ZMZWxTdSkjjCGsKH5SU+KvC+T10/rnVuzd6d2/jzjxVc27UK32afaW5HRI25uTmyPDldKvvTwyCarA2/AnDnSQwADbQTmLDxijoEIYC1qerh1lhfl54NAjlzL5RJkyZRp04dAgICClnCT58+zeTJkzl66CC5SpWjob2pIWnZuZR3tGa4mx7Sdb/RM7AM1Z1tkUkluFmYIpVKUOblsfHSPe5ExfNJYBkqOdmoZu6CDpMVdJgsoCpQ1RYgFxIgLeHV2fsA5BnFx1aWlmBxqY6JhjjRuZpjPCuyczWW0bSQyrAE1xFN16KSkCcvfjya9gPomj0pdr+BX3mNdUisNS/EzjPUcE4yEjTWIU2P11hGYx3ZGsI/yjSHblTev1Dsfm0nzTHrjTISMdIF90AfnOePZf+5a0QlJPFzr46429tw/Npdek9aSLky3mTlPP9OrhkzkAFtGwCg12qAxnY0UcFMgrmJESf+3kt9NzMAMrKy2Xn0LHdDH7HxwDGatWjJkFl//Ou2iiP7YPH1Z7+F34vIh+GjsoD/vzJ8+HBiY2OZM2cOWVlZHD58mNatWzNw4EB2/bmIxbuPk5GdQ4/GNfBxKl1+7R+a9g1q4GJnzaDfFnNg0S9qN6XOTevx3fTFJKWkYW769lPIvgn6eros//Un4hOTsa3Zhuv3gvF9s/xM/1nMzMyo7+/B5jM3md6jBRKJBEEQWHzwPDpaMpwsTbgWGoGNqRG/D/oUZV6eOvZ3PhO6teDQ/T+oXLkyMpmMpUuX8uWXXwKQkpJCly5dsLW1ZWzrOtTxckJLKsXT2gxlnoCWVIr0Bd9Db5uCoQVlUik9qwe8+xMhIvIfoEaANzUCCqZpb1DJn/MLRlF5wC+kZ+Vw+/cJ/Pj7VrafvqoW4G8DqVRKw6oVOHjuCuO+6QnAnLXbmLh0HS52Nvh7uDJ+/Pi31t5/mTeKgvKa5T9GSv8ISgG6urrMmjWL4cNVFvE2bdrw5InKujKrf1daVA1g6d4TdJyw+EN2s1RiZKDP4p8HceTidb79bRFpGZkARMUloP0R+tTK5Qp2HjkFQGp65gfuTelj586d7L58jzaBZbgSGsmELYep8MMC1p68zsK+7bj3NI6z98OJTUknJSOL5pXKoK9T0HJna2bM/fv3mTRpEkqlktTU54ldxo8fT3p6Ovv27ePruhXxt7fCx9YCmVSKjpasgPgWERH5MBjp66oX4ztbW+BsbUFapuaMz69LyzrVuHg7iLikZDKystl2+DRdm9UjZP9q9i78hYoVK771Nv+LSKTSN3qVdj4+hfJ/ikQiYfbs2bRu3Zrw8HDs7e0B8HK0Yemwz8lVKLkYFIZSmYfs/+DJ7n3SvFYg8378hlHzV3LgzGU+bdWIxRt3MbhHx4/C+h0ZE8/5G3e4eOMOa3f+TXR8IgA7D5/kq9EfuHOljEOHDgGw7tR11p26joWRPu0C/fi0Vntql3Flxu6T6rK95m4gIiGFoW3rFaon3xcbVA/E+/fvZ9++fSxdupQpU6bg5OSEhlypIiIiHwBBEPhm3jpyni2+/+fyHbS0ZCSkZZCXl4f0LQqzFrVVwR/GLFzN8UvXiYxLZPaI/m+tfhEVUtkbLML8P9BJogB/zzRu3LjQttjkNLRkUkIj4/j012V81rAqNf29cLQye/8dLKUM7NqGVnWqMnjqYpZs2k2P1o0Z9VX3D9qnlLR0fpy+iBVb9gBgbmJMUupzP8talcp+qK6VWhYsWMD3fgY8TUwlR64g0MMRrRcuxL3qVeaXbcfU7w/feEA5V3t0tGQEPY3lYVQ8uQolkmPhBAcHo62tTZkyZRAEAXt7eyZOnPjWoymIiIi8PR7HJLDt1FWWDOnBzrPXGfXndn4f3otFu45x5Np9mgb6v7W27Kws6Nm6MX/u+Jt6geXYu/BXvF3ERDtvnTdwQUEU4CJvg/0XbrL28HkA9l28xb6LtwDwsLdi1ZhBVPP/MHFQSxtuDrbsmT8RdHTfScjKO0EPmb74T2ITEsnMyiJPqUSZl4e9jTUONtbY21pjZmJMXGIS0bHx7D96ktT0DOaNGYavhwttvv6B+tUqcuLidQC+7dn5rffx/x2pVIq1iSHWJoWjBt1+HEOtsUvV7+sFeHL0ZjBHbwYDKv9sNxtz9HV1EJ6kEBgYyOHDh5kwYQLdu3fHy8ur1IY6FRH5rxCfonIZq+TlQnp2DqduBVPDz4PK3i78sn4vjSuVeavtzR7Rny5N69C8VpW3al0XeY5E+gY+4P8Hn4VEeDmrSRGkpqZiampKSkoKJiaao3KIvB7JyclUrFiRR49UUTNcXFwwNzfnxo0bADR2tWdwoB9lLE2LPN7E3b7Y+ksyVZMaFlXsfpme5ugk2obFJ5ixCHDXWIemSCoAMvN3H+5JyM5Q/58rV7Dz7HUGL9yApYkR5T2cMNTT4fDtcGJjVY4KhoaGSKVSsrKysLGxwd7eHl9TGRN6tcPewpR6w6eTq1ASGh1HVo4cADsLUyZ0aYSpoR6NynpgoFv4HBv1GPfOx/r/wpYtW/j0008B6FTBm5SsHJr4ulLR0YYJB85SxtaCJr6uHH3wmL23HxKXkY2/tTk7erREu4jfiJ5l8dc6bUPN39XkB8VH0shOyih2P4CBTdG/+3w0RUkBzVE/clJzNNZRkhukoCw+PZ2W3r+3+Wg6H6D5mliSc6bUEPXFxK34NgC0jDSHmNRq3LP4AndOFr8fkHhXLXa/EHZdYx3KuKfFt6GlOQqKkFv8ec2JjdNYh0G54rNYbz1ylh5jZ/NgznDWn77Bgn/OETrve07eD6fdjHX8/lUHvly+Q2M7/y+UZn2W3/dr/TthrKv5+/UiaTlyKi3bXirHnY9oAf8IMDMz4+bNm1y8eJGpU6dy5MgRJBIJNWrU4Pz58xx/HM2RR1E0drXnu6r+eJqXzi9baSNXrmDMqp2sO3yepGcLJm3NTbgZGkF6Vjb+FQPVAjwjQyWkKleuzJUrVwDI3r8EhVLJqD92cCMsguMzfyAzO4esXDmdJy0hOjGFb57dKMwM9Lg6c0iRll2RktG1a1eaNGlCzKwR5AkCD2KTiEnLIE8QeJqSxoVHUay+eAf7/7F31mFRbW0bv2eGbgkJBQElJFQQC7FFwRbFDsCOYx7s7j52d2CLioldhJigggjSDSpdwzzfH3zMObyiMjAwDOzfda1L2Xuvte41wObZaz+hJI8+JvrobdIILbTVwWZ2vRkYxIZ7/u9gqqMOTWUFBMclo4GqEkbtvohHn76CzWJhxaWHGLkjl6k4LEZUJKiyNuyAMwZ4DUFJSQndu3dHt27dEBAQADc3N/j5FbulFP3/S4qHUQkoIsL+nqIrLlNXyMrNw9A1B/HwXQj/mKaKEmzNGkNdWRGxqd9x/vETdOzYEcuWLcOzZ8/w4MED9OzZk389EaH34p14GvQFG8cPQqv/lEDPur4bgV9jIcfNwdpLj3DeJxAjt5+D97Jx1bnMWoeqqioOBIVhkdezn87p1VPE0REOMKmvisKMP+88MzAw1Czy8gvg7fcOTtbFNSXeRMRDX6Mebrz9jGHtLJGVX4CI5O/IyMhgDHAxojgNoWAVRFmc39fhEAcYA7yGwWKx0Lp1a/j7+8Pb2xvOgwbxDXAC8Dg6Ed9y86EqK/4VQmsqqemZsJ+zGcHRxW45PVqawc3BDr1aW0JSgoPsvHwYuyyBnp4e1q1bh/bt26Nbt25YsWJFqXGiktLwNKjY/7hP22aIS/2BgkIuMnPzYKCljuaNdREZFgZleRkoy8nANzQaEcnfYFBftbqXXKvILSgEiwX0t2iCgc2NoCong6hvGVCVl0HTchSCYWBgqHnk5OXDecEmfMvIwpB2xQHsaopyuP8hHGoKctjh0gcy/1+USkmTqachTtTVPOCMAV5DkZeXx8CBAzHIpBEuhERicxcbuD96BQD4lPYDdkzBnirhfVgU2kz61+/ac8VUOLQqna1ERlISbBYLw4cPh62tbZnjZGdn48d/KpSZj/99wYbTM4diwr4rOPP0HZYM7lqJFTA4mBnAJyIe90OjcDUoDCb162Hn4G6w0P5D9dM6Qk4hF+HpWUjIykFQ6g9009NCUxnhuD4REWKzc6ElJwPJWvCKmKFmkJmdi4Hu6/Hm81dc27oIzeWLdz9L4mbSc/Pw5FMEejY3+t0wDDUUNpstcIBrbQiIZQzwGs4o88a4EBIJBUkJzGjZFEcDv8BQRfS5rWsjHyNi+ca3tKQE/Hctgomu1k/XcThs9GxljoMHD2Ljxo3Yu3cv7O3tUVRUBENDQ6xZswarVq1C+Ml1CDy4HAlp6SgsKkIhtwgyUpK46vMOB248KR6LzUYRj4cjDwKQW1CINka61brm2oi+qjJOju4FbhEPr2OSsPjGcww4dBWnx/RCW30dUcsTOkSEuxHx+Jj6HdoKchhhZvjT+TdJ3/A0Lgnf8wpw42sccrn/vr5NyyvA8mZNKzw/l8dD8PcMBKR8g1dkHCIysyEnwUFDeTkUFBVhjqUJOmhrVHh8Bobpmw/g/ZdI3Ny+BO0sTZEXUpygIOF7Bpo30sL7qEQM2XEOb9ZPRWNN5i2XuMHsgDPUSIxVlaAsLYnP3zIw1doU45sbQ6oW/ODVRNSVFTHC3hbtzI3hbGsJ5d9kdRlr3w5nHvgDAKZOnco/7uTkhCtXrgAAtl7yxqbxg2HUoPTbClNdLagpysOskQ6stBQx79RtvA6Pg3M7S3RvxqScFBYSHDba6GvDa9JAjD19G6NP3cJ5l76w1q1db488v0Rj0ZM3/K8vf45CYxVF9DDQQXd9Hcx5GIDbX+OgLisNdVlpdGhQH95Rxe5Vq9s3R89G2kDe77OX/IpCHg+Tnwbgdcp3yHDY6KRdH9PMmiAyMxtJuXnwTU7D9eh4xgBnqDA3nr/C+XsvsPPv8WhnWTrFYE5+IULiU/lfR6WmMwY4g9jApCEUA1q3bg1LS0scOXJE1FJ+i2/XTr89r9n8zwUMJGT+7NsuqST32/PlSbuo2LjRb8/LDfr7p2M8Hg+pqan48eMHCgoKMG/ePDx58gQ5OaVLyuvr6yMyMhIA4OfnhzZt2vxRD0PVkpOTA3Nzc3Tr1g2HDx/GlymD/9gnKyHjt+dzv/+bci05Px8noqPxICUVmVwutlqYo62qKsxG2v12jJhH7/6oIz/91ykC7yckY+mHEIwePRr//PMPjhw5UqqQUGZmJuzs7JCXlwc3NzcUFBSgUaNGmDp1Klq3bo379++DxWIhZsmfg3911/x7/4mKisLYsWMREBCAgoICXLlyBT179oSUlBQyMjIQEBCApKQkbN26FU2aNMH58+f/OL648af7HQBYLRjzx2vS37377Xn5Bn9+eCnMyPnteSmVP6dD5Kj9Pq1igvfjP46h0vj39/jypLNVcl0FoPjNzc6dOzF79mwMGDAA586dg5RUcf+Uf2YDAKacvo3Hn6Nxwq0vTvt9wKr+HaEiJwON2f/8cZ7agjjbZyXaP80fA8Uy0vD+jsz8AphtPCmW6y6B2QEXA6SkpJCf/+c8vQxVx86dOzFz5syfjp86dQq5ubk4f/48AgMDkZKSgsjISFhYWGDdunVo3bq1CNQy/C9ycnJgs9lQU6ua3bGzsbG4mpCIRrKyyORyMffDRxjIycG7nzXUFX//wFgRfhQU4GxkDI6GR8HBwQEHDx6EhIQEJk6cyDfAFRQUICUlBUtLS5w+fRrz58/n92/SpAmuX79eocJDcXFx6Nq1K4gIq1atQpcuXWBtbQ2g2Ghq3749Pnz4wL9++PDhlVwtQ12Dy+Vi9uzZ2L17N/7++29s3LjxJ5/f7PxC3P4QjpwCLnzCY7FjmD1TSEtMYbEqkIaQJf6eAIwBLgZERESgS5cuopZR5yAivHz5EqdOncKePXsAALKyssjNLQ6u7NixIy5evIibN2+iqKgIzZo1w5gxY9CjRw9069YNHAHTKjFUHUSExMREaGn97NNfXvKKiuAZGYfvBQXorauN+vj3+5vNLYKKpAQOWrXAtPeBkONwEJiRgQXn7+Hw+P7CWEIpvGITcTQ8ChbKStizZw8kJIpv5QoKCggLC4OZmRmysrLQpEkTxMTEwMrKClZWVpCXl8euXbsQHh4OPT09DB06FLt37xZo7lu3buHr168ICwtD48aN+cfXrVuHxYsXAwCWLFmCuXPnIiIiAmZmwisNzlD7yczMxLBhw3D37l3s378fkyZNKvO6x5+jkFPAhYttM6y75YPXUYmY1NEKjdSUsXXBAsyYMQM6OrUv5qM2wviAM9RIcnNzER8fX+oPHUPV8jY8FscnTMCdO3cQGxsLTU1NLF++HFOnToWNjQ0aN24Mc3Nz7N27F6ampti+fTucnZ2hyaS+qlZevnwJLy8v6OnpwcDAgP/A07BhQxgaGoLL5aKwsBCSkpIICgpCTk4OGjT4sxvU/8Ll8XAjJgHnI2IQnpEFWQkODodGwFhBHjssLaEkKQlbNVXcTErC0uBgLDUxQRMFeQz2f4nLAZ/g1MoMvYScneHNt++wUVXBvjbWMDQsHXTZuHFjvHr1CuvXr4esrCymTp2Kli3/rS64du1aTJkyBTdv3sS+fftw/vx5HOrTDq3K6Rtf8hmuXr0ax48f5x//r7EzadIkqKiowMrKqhKrZKhrJKVnYUjHjggPD8fNmzdL1VX4X95EJ0JbWQGbBneFbeMG2HzXD077LvPPq6mpwd3dvTpkM1QSxgBnqJE8ePAAANCiRQvRCqkjBEbGw3HFfmhoN8DgwYPRr18/dOzYkW/cNW7cGI8fP4a/vz82b96MmTNn8ncfGaqHkJAQ/P3337h58ybq1auH9PR08HilgwjZbPZPx2xsbNCnT58yx8zML8S5T1/xJikNQcnfIcVho4GMDBrKy+LD9wyEpmfCsp4yTndqA0UJCTxPTsXGwBA8SU1DX20tdFZXx3qzptgXEYnpgYE4a9MSp21a4p/sVIzcewlNdTTg2tEKbp2swKlk+qwDX77ieUoa5pkZ//IaS0tLeHh4lHlOUVERp0+fBgCsWbMGK1aswPAzt7Gwayv0MtWH9h+qsfbq1QubNm3CvHnzsG3bNqiqFuetd3FxgaqqKvr374/Lly9j7NixUFFRqdgiGWo13CIeTj17hyfBEVjp3A1KstI4+fQt9t17Ccgq4MWLF7C0tCyz7+7du7F5zVEkZWSjW1N9AMAAKxP0a26MfU/ewD8iDs8ikvDmzZsy+zPUPNgcdrlit/63j7jDWA41nBMnTqBFixaMAV5NTN9/CXoa9eD3/j0UFH4OXPL29kZ8fDzk5eWhrs7klRYFq1evxs2bN7Fjxw5MmzYNXC4XcXFxICIQEaKiohAWFgZZWVlISkqisLAQRIS+fftCTq5sf+xLIZHY+vIDWutowMmkEYqI8DXpBz58z4C8BAenOraGWT1l+KekweXZSxgrKaK5sjJOREejmbISGsnJoaO6OiyVlDD81Wus+RyK5aYmOD5pIO4EhuHyy0+Yf94b+x8GYHT75hhj1xyqCoL7hifm5uF4eBTcGuvDuVHDyn6UWLJkCVxcXDC5Rwesuf8Smx+/xvZ+HeFgqv/bfg0bFs/t5+eHXr168Y/37dsXgwcPxqxZszB37lzs2rULU6ZMqbROhtqD35cY/HXcC6EJaVBVkEXLhXtAANgsFpzbWGCdx3Xo6emV2ffjx4+YPXs2ejbVh00jDiJSf+Dx5yh0MtbDzocBWHfLB3JSEmhm/euHbQaGmgJjgNdwGjZsiMePHyM/Px/S0kz1y6omPTsXYQmpmDt3Lvbv3/9TUI+kpCQaNfp9BhWGqmXRokXw8PBAkyZNwOFwwOFwSrlhNGnSBN26dRNozPisHOgrK+Jk3478Y/+bBaWICLP93yGviIfg9AycbtkS8z9+wri37zC3SWM41K+PelJSWG5qgpUhn+Hy5i1c1SSgp66MIxP6Y3pUaxx4+AobbzzHTm8/nJo8CGWbGb8mo7AQXCI0ki/beCcifPr0CU+fPkVgYCAKCgpQVFQELS0tuLi4wNTU9Kc+DRs2xD6nrsjIK8DkKw+x8fFr9DRp9NuAtsGDB+PcuXMYPHgw7t27h/bt2wMoruR78eJFxMTEoGnTppg6dSoeP36MXr16YezYsQKulqE2kZ6Thwl7L+H+h3BY6mriyfLx0FNTwUW/IHA4bPSzNkV9ZQUo/cL4BoDnz5+Dy+Viz8ieGHvUC0FxKRhywBP6asqITEvH3z3aYG6PNtCau6MaV8ZQWVhsluBBmGzxD7hl0hDWcJ4+fYpOnTrhw4cPMDc3F7WcGkGwa7/fnpetX++PY+hvPAGgOD1dQEAAYmNj8eHDB2zYsIF/TUZGBhQVmaJHNY1rnW0x4IkvtrVshg71f34Lodzoz99/o32XAABpaWm4efMmZs6cCUtLSzx9+vSXfYgIZmZmCAkJgYmJCYKDg5GZmYkZM2bgxIkTcHR0xJw5c9C0aVMUFRVhwoQJ8Pb2BgD0k1PFaAVNcFgs/OBxMSM1DLnEg5dNayj8wYWJeKVv0XNDPiEkOwtOmlqwVakHw6aaUJaWxNf0LGx7FYyApDRIsFhorKIIaQ4bHDYL0UVspKWloVOnTnB1dUXPnj35Aak8Hg9sNhvh4eGYPXs2Hj58iIyMjD9WmsvLy4OjoyPevHkDb2/vn9Jtlhjw6urq4HK5ePHiBROQWQEi/h79x2uk/5BmkCP15722BP/Q356Xkv9zmjjZ+iq/PPeX1zP4pGRg7ty5cHFxqVA8xt27d+Hg4AAdHR3Ex8djy5YtsLGxwe7du9GoUSNs3ry5zmVCEWf7rER7+LopUCxHCuL/kpmXj8aL9onluktgdsBrOJKSkgCAgoICESupXeTm5kJRURFFRUWljvfs2RNpaWn466+/GOO7hqIlK4MGsjJ4mJhcpgFeHt69e4fevXsjPj4eADBkyBDs27fvt32OHTuG8PBwAICysjKICEpKSjh+/DgGDhyI2bNnw97eHhISEti4cSNOnTqFc2a2mP8tAtdzvsEnLwP95NXgKKuKLrIq8MvLhPwvMuW8/PEdL9N/IConFyoSklCXksJwbR14pSThVUY6AOBkfBxOxscBn/5N+WekooidXWxgq6MBOcl/b++G+y/iypUr2L9/P1xcXAAAJiYmyM7ORlxcHBQVFZGZmQlVVVXs2LGjXGWeZWRkcP36dTg4OKB///4ICAiAru6/lVxdXV1x7NgxDB06FEePHkXbtm3x9u1bJqBcjIlIz8KFL1Hg8gjzbMwgWc5dy5TsXAQmpcFp8FB+ppyKYGdnh0aNGiEqKgoA4Onpiblz56JTpz/nZGeouTBBmAw1En9/f3A4HJiYmIhaSq2iZcuWPxnfDx8+ZNI9igEcFgtSHA5YqNhOV3J2LiaNGoXk5GR4eHigWbNmMDMz++POWVBQEAoLC7FgwQJs2LABp0+fxpgxxYVW+vfvj169eiEyMhKbNm2Cu7s7Nm/ejL9JFqc1TDA1NQzJvEIczUzC7ZzvMJCQgRSLhXweDzL/McK3fg1HdF4u3mYUu79YKioiITsP0Wl5OJMQ95OmhYZN0LiJOjIKCiHNYaNjA01wyng1Ky0tjeHDh2P48OFISkrCw4cP8eLFC6ioqEBXVxcZGRlQV1fHsGHDICv76wqw/4uioiIuX74MbW1t6OnpYdasWfjnn+IiKAMGDMCxY8ewZ88esNlsdOjQocy4CoaaT2ZhIQ68DMXZ0EjISUggq5ALCTYL822K38oSUZm/Pz9y8/E0Mh4bnr5FVkFhpe+vvr6+iIqKwqVLl1BQUIARI0YgODgYTZs2rdS4DKKFyQPOUCMxNTUFj8fD+vXrsXr1alHLEXuif2RCXU4GOjo6CA4ORmBgIJo2bcpkMhEjPmdkIiIrG5ONDCrUf+2L90jLKcL79+8Fcomws7PD9u3bMWfOHGzatAl5eXmlzktKSsLIyAiHDh3C0qVL0b59e8xLjEBP2XpI5hWikYQ0orj5SCgqQFdZFfjlZ2DShyAcbdYcnP83XrxTU5D7n+wto3Uaoo2yCiZ9DERIdjYAYIxOA0ix2dCVkUVnVTVo6Aj2FkBTU5NvjAsDDY1/KzVmZPzrNx8WFgagOB1cSEgIE7QspsRm52DC01dILyzEXGszPI5NQkBSGuQlJbDKPwhBqT/wNT0TXXW10K+ZEXoYFb8FeZ+QirGXHyIjvwCtG9bH9l52aDdyZKW0lLgaGBgYwNzcHEpKSrhy5UqldtUZRA+LwwFbwLoZrFpQZ4OxOmo4Dg4OmDhxIi5cuMAY4JUg5kcmTr79jMMBn/jH5s6d+8tUVww1l9VBwdCTk0VrNVWB+2bkF+DO1zis37BBYH9kGRkZAIC5uTl4PB4/E0hZ6OnpISwsDCYKyrid+x0AEMfNx1ZVQ4QU5sBBth40OZLYlh6H60mJGKhVXAZ8TIOGOBATjbYqKpjWSB+NZOVAPAIRwAJw0NwSxvI1axf57t27AIBHjx6hc+fO/OMzZszA0qVLkZubi5CQENjZ2YlIIYOgFBFh78cw+CSlIi4nF6rSUrju2BlyEhxsfl18Dz0Q9AU68nKw0VRFp4b1sT/wC25FxuOuSx8Yqang0sevyMgvwIVhPWDToL5QdJX8DiYmJsLCwqLUAx8Dg7gh/nv4dYAuXbogNDQUx44dE7UUsWXMxfs4HPAJJuoqAIqNqI0bN4pWFEOFiMrOQRt1VUhVIJ82m8WCopQkrl+/LnDfdu3awdTUFDIyMpg4cSI/88ev4HA40OP8G1hUnyMFA0kZOMqpgsViwU5GGX3q18fOyAgE/PgBADD//7iD0OxsqEr+G/Tm2lAXBPBdU2oK2dnZGDJkCMzNzdGhQ4dS5yQkJHDx4kVoampi/PjxIlLIUBE8wqJwNDQCBkryGGKoi4MdbKAjL4vrX2P518yxaoob/TtjrW0LTG9ugsbKiuCwWNBXKd6l7mygA0k2GxOvPsan5G9C0bVp0yY0aNAAnTt3xpgxYyAnJ4cJEyYIZWwG0VHiAy5oE3eYHXAxYMiQIXj48CHGjx+P/Px8TJ48WdSSxIr7YTGI/J4JAPic+gMAcPHiRaZUvJjiqKOFi9FxkOZwMNO0iUB997/9jAIeD7NmzRJ4XlVVVQQFBYHL5fJ34kqIiYlBhw4dICEhASUlJSQnJyMu7l+f7eZS8nBX/nnHfI5BYyTm52NrRDjOWbWEhYIidKSlEZ+fj8WfQ7DT3AIA8Cr9BwDAXKFmBQaz2Wzk5+djzJgxZf4+9erVCy1btsS3b8IxwBiqh1YaxQ+4kZnZaKQgD3UZaRTyeFCWksL+rq1hp6NRyuc77EcmwtKL77EHAz5iWDMjdGvcEP6TB2H0pfuYfuMZro/q9avp/khhYSHWrFkDDw8P7NmzB2fOnMHFixdx6dIl1K8vnN11BtFRV4MwmTSEYkJRURH69euHW7duITU1FWpqaqKWJDYUFBSgRYsWCA4OBgAsWLAA69evF7EqhopCRGjTpg20tLRw5coVxMXFoWHDhr99oIqKisLo0aPx4sULTJkyBS5fg/84j7SyNGKzcpBeUAhJNgtGyorI5RbhXmwiPn/PRPdFK9C/f3/Iycnh4sWLGDVqFABAV1cXzs7O2LZtG4BiI/TQoUOlSrX/Fy8vL/Tr1w/v379HbGwsevfuDaC4cmdAQAAAIDw8HE5OToiMjERwcPAvxxIFw4cPx7Vr12Bra4sZM2agX7/SaUKdnZ2RkJCA58+fi0hh7Sdu5aTfns//kfnHMRLfxJb62vfbN+z+GoHInBwM1tFBqLo6AgMDARRXBG7atCkuX74MKSkpZGdno2/fvnj06BEAoImiAo61bwVZCQ6isrIx6tlLODTQwpWo2J/m/dVanoUXX1tfUQ6zPB/jU2IaZnS0wqxO1jghoYe9e/ciISHhj+PVdsTZPivRHrNnHpRkBUtDmJGbD91pm8Ry3SUwBrgYER8fDxMTE7i6umLnzp2iliNWvHv3DlZWVgCKd1OYoEvxRlVVFd+/f4e2tjYSEhIgIyODXbt2Yfz48fjx4wfOnj2L/Px8sNlsFBUVYd++fSgsLMSiRYswcuRIfBrcFxmFhXiV9h2JuXlIzs9HWn4BcrhFyOFyIcXhoACEVyn/7txqysogvaAAeUWlS9zPnj0bq1evxu7du3Hq1Cl8/PiRX4HTzMwMHz9+/O1aCgoK0KRJExgZGUFaWhq3b9/GmjVr4OrqWsrQ/vHjB4yNjdGiRQs4ODhAT08PTk5OyMvLw6lTp1BYWAhDQ0M4Ojrydyc/fPiAgwcPokWLFiAi9OzZ87e+6xUhLS0NBw8exN27d/HkyRN07NgRTZo0gaqqKr58+YJr166hYcOGiImJEeq8DP9SFQY4AGRzudgQ+gXP0tJg0aIF1qxZg3Xr1uHFixcAwP9+A8X55C93bY9bsYk4Hh6JYfq6+NuiOHvXqfAo7AkJQ2JKyh83j+JWTkJCRjY67TqPnEIuJNlsGKgpY/vAzmimUxzwuzqBhdu3b/PTEdZlxNk+K9Eeu39BhQzwhpM3iOW6S2CsEDFCR0cHy5cvx/z58zFmzBjY2NiIWpLYYGlpia5du+LLly+M8V0L2LhxI+Li4pCcnIwuXbrAw8MD8+bNA5fLxdKlS/Hjxw9IS0ujqKgIHA4Hurq6uH37Nho3bozIyEjMexOEp8mpKCKCDIeN+tLSUJOWhrwEB6rSUkgvKASbw8aGds1hqKSAxJxcvEr+jvqy0ujWsDjVX4/rjwEAtra2kJeXx/z58zFv3jx8/PgRN27cwPbt2/m74r9DSkoKe/fuhZubG1JSUgAA9vb2P+1yq6ioYM+ePZgwYQL8/PyQmZmJ5s2bQ15eHj4+PpCWlkZ+fj4cHBxgZmaGtLQ0XLlyBdLS0ti1axcAYMuWLZg7d65QvxdqampYuHAhFixYgOPHj+PWrVsICgpCamoqjIyMMHv2bAwZMkSoczJUD/ISElht1hT5PB70T55C3759ERUVhS5duqB169Zo1qwZ/1o2mw1DRQW8+fYdLABtNf41tNtqqGJHMOHLly/lenu79p4/ZCUlML9ba/zIzcM0uxaQ+f+89rmFXJw8eRZ///230NfLIBpYbJbgLii1oBImY4mIGTNnzsS5c+fQu3dvPHnypMzS0gw/w+FwMHr0aLi6uiI3N1egXMcMNY//Dbzq3Lkz6tevjylTpgAALl++DDU1Nejq6sLAwACRkZFYsmQJ7t+/j+TkZGhIS2FOUyN0qq+O+jLSZeYwllb+d0fGtJ4SOjfQLHV++fLlWLlyZakc/SwWCxYWFrCwsMCCBQvKvZ4+ffogPDwc165dQ35+PtasWYPBgwdDXV0dsrKysLCwgIaGBpydneHs7AygOCfyypUrUVhYiIsXL2Lw4MHw8vLCggULEBISAllZWeTn5+Pvv/9Gu3bt0KNHjyqtEshiseDq6gpXV9cqm4Oh+iEiHIqMxCUTE6ipqeHly5e/zbvdT1cHQd/T0UTp32w9JTn7//eFe0hICO7fv4/Q0FB8/vwZaWlp0M79jhufvmJLv44YZv3z3zcej1BYWAhpacF2TBkYahqMAS5mSEpK4s6dO+jcuTO6d++O0NBQyMnJiVpWjef79+84evQo1NXV61yp4rqAv78/AMDR0RGBgYEYNGgQ/1y9evWQnZ0NdXV1TJo0Cebm5tA8vBcKkpW7/bm6umLlypUICQkRSjpLRUVFjBo1CqdOnYKXlxe8vLxKnQ8MDCw1T7t27XDnzp1S16irqyMuLg7p6en8Y6GhoXBwcAAANG/evNI6GeoWVxIScDY2DqtWrcKcOXMgLy//2+tVpaVAALg8HlLz8nEnPhEPE5IhL8FBkyb/Bk3fuXMHTk5OKCoqQpMmTWBsbIwGDRrgzd0bsDfWw5AWpYvP5XOL8CA0Gkf9P0BSUhIDBgyogtUyiAIWuwKFeCqQBaumwRjgYoi6ujpOnz4NKysrDBs2DG3btsW4ceOgqan55851jK9fv2L8+PF4+vQpJCUlcf/+/Z8yWDCIN5mZmZg2bRrs7e1x8+ZNZGVlITg4GMrKyggLC8O7d++gqKiIcePG8Y2HVycOVmrO7EIu5k2eDDabDWtra2Esg8+gQYP4xbc+f/7MP56QkPBHQz8nJ6eU8T1w4ECcPn2aHwDJZP5hEJQP/5/68tWrV8jMzPyjAZ6alw8WgD0h4XiaVOxS1VRFCeusLaGhoYGvX79i6dKlOHv2LBwdHTFmzBh4eXkhKCgI+fn5sNFWw8a+HRGRlo6Zno/wJeU78gqLUPj/Baqa62jg4sWLMDc3r9J1M1QfLDYHLLaAhXgEvL4mwgRhiilEhBUrVuDGjRsIDQ1Fbm4ubG1tceLECRgYVKxCYG0iPj4eq1atwpUrV6CsrAx3d3f07t0bDRo0ELU0BiFBRHj8+DHmzZuHT58+4ePHj9DX1y9X31e9uv3xmv+6oPzUPzkNbg9fom/fvjh//nyNcmny9vbGq1ev0L9/f5ibm4OIYGtri5SUFLx7944pB18LqaogTKC4KM/95BTsTknB0KFDsXfv3l+O8bqvPWKzc7DgTRDkOBxYq9XDSEM9KEpKAgBUdx6EoaEh//oSu4LFYvHL2deTkca6PnaY7/UUqnKyGNHSFDISHMhISsC6YX2Y1FdFg+UH/rieuoI422cl2hOOr4SSnGAbYxk5edB2WS6W6y6BMcBrAUlJSfjnn3+wY8cOsFgsnDt37qdUYHWJ1NRUtGjRAlwuF3379sW6detKlctmEH9evnyJ6dOnIyAgAC1btsTOnTtha2tbbfMXFhbCUl4ZXwpz0UZaEc7yGmgk8bMv+cDE32dAqQ5u376NXr164f79++jW7c8PHgy1jxd2Hf54TWZC1u/HGNEHO3bsQGRkJFRVBatC++3bN4waNQre3t4oKiriH+dwOD99LSUlhdzcXPTq1QtnzpyBioqKQHMBwG5lkz9eMz398x+vEQfE2T7jG+AnV1fMAB+zVCzXXYL4O9EwQFNTExs2bEBKSgocHBwwZMgQfP/+XdSyqh0iwvHjx2FmZoaMjAw8ffoUhw4dYozvWsa7d+9gb2+PoqIi3Lp1CwEBAdVqfAPFsRjrVQ0wQVEb7wuyMefbV7ilhsIl+TOWfovEpawUxHPz/zhOQkICJk+eDAsLC75L2cmTJ4WqdfXq1bCxsUHXrl2FOi5D3WLq1KngcDgVKmJVUFCAhw8fwsLCAnFxcfyYjRLju1OnTmjbti2uXLmC8PBwnD9/HtevX6+Q8c0gfrA4nAo1QXj69Cn69u0LHR0dsFgsXL16tdT5Eq8CHR0dyMrKonPnzn9MIVtZGAO8FqGgoIDdu3eDy+XizJkz/ON5eXkiVFU98Hg8TJgwAa6urrC3t0dwcDCMjY1FLYuhClixYgUaNmyIx48fl8p5LQp6ytXDMQ1jLFPRQzcZFTjI1YM8m4MrOamYnhaO0aNH/7JvUFAQzM3NceXKFXTs2BGtWrVCVFQUxo4di44dO2Lnzp24fft2qR1CQUlISICvry9mzZrFBB8zVAptbW3s2LEDp06dwoEDgrmAaGlpYd26dQgMDISzszM/tmH//v0YPnw4jh07Bl9fX/Tr1w/a2toYMmQIE6/AIFSys7PRvHlz7N69u8zzmzZtwrZt27B7924EBARAS0sL9vb2yMz8s/tWRWFcUGohw4cPh7+/P0JCQpCQkABTU1MYGxujQ4cOWLBggdALcdQEVqxYgVWrVuHYsWMYO3asqOUwVBEpKSnQ1dXF6tWr4e7uLlItnlq/DgLLJx7u5HzHiawkHD9+HP379/9pN2/QoEEICgqCr68vPzcyEWHr1q3w9PREQEAACgsL0bhxY+jr60NOTg6dOnXi5/lmsVhITEzEjh07EBUVBRkZGXTv3h3du3fnl+ceP348jhw5wlTPreMIwwXFIfwtiAgzZ87Enj17EBgYKHAgZIkLVE5ODuTk5PDgwQOB+pcXxgVFPCjRnnh2Y4VcULSGz6/QulksFjw9PfmZdIgIOjo6mDVrFubPnw8AyM/Ph6amJjZu3IhJk34fY1FRmB3wWsjixYsRFRWF3bt3Izg4GHl5ecjIyMCRI0dgZWVV63bEi4qKsG3bNsyZM4cxvms5Fy5cAJfLhZubm6il/BZpFhv95FTRuXNnuLi4QE1NDXZ2dti4cSOio6MBFGczys/PL2UYs1gs/P3333jx4gXy8vLg4eGBFi1aQF1dHXl5eViyZAksLCzQuHFjDB06FEZGRjhw4ADi4+MREBCAkSNHQlNTE82aNUOXLl1w5MgRTJ48mTG+GYQCi8VC9+7dwePxEBv757Ly/0thYSEePnyIV69eifwBmqEGwWYDbI6Ardh8zcjIKNXy8//s+ve/REREIDExET169OAfk5aWRqdOneDj4yO0Zf4vTBrCWoiFhQUmTZqEVatW4d27dwCAVatWQVlZGf3798ebN2+q3We2vDx//hznz59HUlIS6tWrByUlJcTHx0NWVhYTJ05E69atkZ2djfz8fH4gUGpqKjIzM9GqVSsRq2eoajQ0NFBUVFQpt4zqgsVi4cGDBwgNDcWzZ89w8+ZNrFq1CosXL4ajoyPu37/Pz89dFmw2G8OHD8fw4cP5x3Jzc/HkyRNcu3YN/v7+mDNnDqZPnw4NDQ0UFhZi2rRpUFJSQkZGBtLS0rBz505+caKqgMfjYdeuXTAzM0O9evVgbm5eozLCMAiX2NhY9O/fH02aNKlQ7nsvLy9s27YNTk5OTE56Bj6VyQOuq6tb6vjy5cuxYsUKgcZKTEwEgJ9SOWtqaiIqKkqgsQSBMcBrKStXroSHhwfWrl0LQ0NDHDx4ELdu3YKxsTGWLVsGb29vsKs5kX12djbu3buH58+f49u3b+jQoQOICPLy8nB2dgabzYazszMSExPRrVs3hIWFISMjAw0aNEBcXByOHz+OwYMH486dO8jKysKmTZswZ84c1K9fH7q6unj69CmGDh1arWtiqF5KAmo/fvzId7OoybDZbJiamsLU1BQTJkxAVlYWjhw5ghMnTmDatGlYs2aNQOPJysrCwcGhTMNdUlIS8+bNw6JFi7Bnzx7+Z/Xt2zcoKysL3ad2//79Pxn3kyZNwv79+4U6D0PNoUGDBrC2tkZwcDBevHjBr8paXpSVlbFy5coqUscgtrD+f1db0D4AYmJiSrmgVKZC6v/GyZSkxqwqGAO8lqKhoYG1a9di+vTpAIoL0jg6OmLdunUYPHgwDh48iMmTJ1ebnvfv36N///6IioqCvr4+VFRUcOzYMX7+1/3798Pc3ByJiYnQ1NTEvXv3Sv3gFxUVYfr06fD09MSECROQnZ2NuXPnQkVFBW5ubhg+fDgOHTqEzZs3M5VBayl+fn4YNmwYLC0t0bJlS1HLqVCKQQUFBcycORMzZ86sAkVAWFgYLl68iKSkJDx58gQREREwNDTEli1bMHfuXKHOVfIW4syZM9DW1saNGzewY8cOzJgxA2ZmZkKdi6FytH/+7I/X3Nb/8450yVsdXV1dvH//XmADvLqoLf7ddQZ2BQzw/79eSUmp0r7vWlpaAIp3wrW1tfnHk5OTq7TAIeMDXouZOnUq7t69y39F8+LFCyxcuBAdO3bEmjVrEBMTU+UaoqOjMX/+fNja2kJNTQ3BwcGIiIjA27dv8e3bN+Tk5ODOnTvIycmBt7c3zMzM4Orq+tNTJ4fDwb59+5CYmIjNmzdjz549cHNzw8yZM5GWlobJkycjJydH4B1FhpoPj8fD4sWL0a5dO+jq6uLhw4diF2xUXdjb2wMoTrmVnJzMfxUraN7m8mBnZwcAkJKSQpcuXbBu3Tro6elhxowZ4P1/1UKG2se3b9+QlZUFGxsbUUthYBAKBgYG0NLSwr179/jHCgoK8OTJkyp112UM8FoMi8VC586dMW/ePADAiBEj8OXLFzRt2hQSEhKws7PD4cOHUVBQUCXzX7t2Daampjhw4ACmT5+OZ8+ewdTUlH++Xr16kJGRQc+ePeHv74/Q0FB8/PgR69evL9fanJ2dkZWVhYSEBBgYGGDixIk4depUlayFofp5+/YtBg0aBAsLC6xbtw4bNmyAv78/1NXVRS2txsLhcLBq1SoAxf6LJTnFAwIChD5X8+bN0aFDB/zzzz8Ail/97tu3Dw8ePMDq1auRnZ0t9DkZRMuNGzdgZ2cHWVlZdO/eXdRyGGoJJT7ggjZByMrKwrt37/hxcREREXj37h2io6PBYrEwa9YsrFu3Dp6envjw4QNcXFwgJyeHESNGVMGKi2EM8FrO9u3b8ddffwEAdu3aBScnJ5w9exaHDx+Grq4uJk6ciC1btgh93oSEBAwbNgwODg6IjY3Fxo0bhe4aEhcXBwD8DA+2traIjY1FSEiIUOdhqH7evn2Ldu3aITg4GB07dsStW7cwf/58JjdwOVi0aFGpr3V0dLBv374qeTh1d3eHj48Phg4dCi6Xi549e8Ld3R0rVqyAmpoaFixYgMLCwlJ9iAjJycnMLrmYkZiYiLFjx6Jp06Z48OABFBQURC2JobYgcAYUwV1WXr16BSsrK1hZWQEA5syZAysrKyxbtgwAMG/ePMyaNQtTp06FjY0N4uLi4O3tDUVFRaEvtwQmD3gtJzk5GYaGhsjOzoaJiQnfIC8qKoKXlxc2bdoELy8vnD17Fj169BBawMHVq1cxcOBAxMbGokGDBkIZ83/59u0bjIyM0KdPH5w4cQL5+flo2LAhxowZgxkzZmD+/Pn4+vUrgoOD4e/vz/iliglcLhe2trbIzc1FQEAAZGQEyw/LANy9excODg7YtGkT3N3d4eLigosXLyIgIECovwdEhAMHDmDKlCno3bs3jh07Bg0NDYSGhuLMmTNYu3Yt2rZti7/++gvW1tY4ffo0jh49itjYWBgbG+PZs2diEUxbF/iTD/jhlk3w/PlzfPz4kXkLVYMQZ/usRHvyjYNQkhdsgy4jOwf1+0wUy3WXwOyA13Lq16+PuLg4bNiwAZ8/f4ajoyPMzc3B4/Hg6uqK7du3o2XLlnBwcMDSpUuFNm9JhpWqzLSiqqqKjRs34uTJk3jy5AmkpaXRqlUrhIeH46+//sL58+cREBCArKwsgQtGMIiGkJAQWFlZ4fXr1zhw4ABjfFeQHj16wMbGBl5eXiAi7NmzB/r6+nBxcUE59lzKDYvFwuTJk3Hp0iXcvn0bmzdvxq1bt6CqqoqVK1fiyZMnyMjIwLBhw2BsbIzNmzfzs7OUGOkMNR//nAxcuXIFO3fuZIxvBqFTHaXoayKMAV4HUFZWxvz585GVlYUtW7bg7t27YLFYCAgIQFxcHO7evQstLS0EBQUJ7bVwicH78OFDoYz3K9zc3NCuXTuMGjUKb9++RXJyMurVq4dFixZh9+7d8PLyAgCYmPy5MhqD6Jk0aRJyc3Ph6+tbY3PViwMsFgsrVqzAs2fP4OHhAXl5eX6J5a1btwp9vkGDBkFHRwebN29G79690atXLwBA+/btERgYiJiYGFy+fBmXLl3C27dvARQ/QG/ZsgWTJk3C5s2b+cb6nygqKkJCQgLCwsKE+jDBUDbZvCLs+xaHnj17YsiQIaKWw1AbYbMr1sQcxgWlDjJq1CgEBwcjPT0dOjo6ePLkCWbOnIldu3ahadOmuHnzJgwMDCo1h6enJ5ycnBAQEFDl0fJRUVHo2bMnfvz4gaSkJJw6dQqjRo1CSEgIVq1ahV69eqF9+/aVXhND1ZKdnQ0FBQUcOXKkxle6FBeGDh2KJ0+e4PPnz1BWVsbMmTP5FXKNjY2FOtfkyZNx4MABAMVvvv63WNLNmzcxdOhQfnDm3bt3sW/fPkRHR+Pz58/84zo6OmjatCnq16+PpKQkJCUlIS8vD0VFRcjLyyvlPz5z5kz8888/VZqrty5DRBg0aBAePHiAt2/fQkFBAf3794eamhpOnjxZJdl1GARDnO2zEu0pd49VyAVFo6erWK67BPF/hGAQGAkJCURGRqJz58549uwZDh48iJ07d8LPzw/5+fkYMGAAvzJURTl37hxatWpVLamqGjVqhDNnzqBdu3aYNm0af5dm2rRpOHv2LM6cOcMY3zUcIsKmTZsAgElvJkS2bNmCvLw8jB49GjweD0uXLgWPx8PFixeFPldJNhSgOHVkYWEhMjMzweVy4eHhgX79+qF79+78HfDg4GB4enri9evXSE9PR2BgIM6cOQM3NzcoKysjLi4O6urq6NatGwYNGoThw4dj0qRJ2Lt3L65fv45NmzZhx44d2Lhxo9DXwlBcNn7evHnw9PTEqVOnUK9ePXTs2BF+fn64efMm/Pz8RC2RobZQDUGYNRIqB+np6QSA0tPTy3M5Qw0nNDSU+vXrRwD4bceOHUREFBgYSFpaWqStrU0vXryo8BwGBgb0999/C0tyhViyZAl/fYWFhSLVwvB71qxZQwDor7/+ErWUWsetW7cIAK1Zs4aIiFxcXAgA9e7dm75//y7UucLDw2nEiBGl7i0lzcXFhbhcLhERTZ8+nSQkJGj69Ol04cIFSktLq9B8U6dOJXV1dbp48SLzOy5EgoKCyNramthsNm3evJmIij9rRUVFcnNzI3l5ecrIyBCxSgYi8bbPSrSnPjhDBX6eArXUB2fEdt0lMC4odZjz589j2LBh/K+fP3+O9u3bIyEhAUOGDMHbt2/x9OlTWFtbCzx2iR+2u7u7MCWXIi8vD1++fEFKSgq4XC5kZGRgYGDALzxERDAxMUFaWhqSk5OZFHY1lKCgIDRr1gzLly/nF45hEC4sFgtjx47F8ePHUVhYiEuXLmHatGmwt7fH+fPnhTpXYWEhZs6cCRMTE2hqaiI3NxdycnJwdnbmB2UXFBRg/fr1OHjwIOLj4yEjI4Nbt26hS5cuAs0VHR0NZ2dnvHz5Eu3atYOHhwf09fWFup66RHZ2NtasWYOtW7fCyMgIx48fR6tWrZCQkIAGDRpg06ZNOHbsGJo1a4azZ8+KWi4DxNs+K9Ge+vAslBQEdEHJyoF61+FiuW4+5bHSxfkJi+H3eHt783eoPDw8+MezsrKoVatWpKmpSTExMQKNyeVyic1m0/79+4Utl4iI3r9/T05OTmXusgGgTp06ka+vL3348IESExMpJyenSnQwCIcZM2aQuro6FRQUiFpKrcXIyIjMzMzo8+fP/GOHDx8mAPTmzRsRKiOKjY2lrl27krq6Oj1//rxCY7x48YL09fVJWVm5wmNUBVu2bKFevXrR9OnTKSsrS9RyfiI4OJi+fv1Kq1atovHjx5Ouri5JS0vTihUrKDc3l3/dt2/fCABNnz6dANDdu3dFqJrhv4izfVaiPe3xBSp8dUOglvb4gtiuuwSx2gEnIkyfPh12dnYYNmwYE3gjBIgIy5YtQ4MGDTBp0qRSn2lycjKsrKzQunVreHp6lnvMqKgo6Ovr4+rVq+jfv79Q9T558gSdO3cu89zw4cNBRPj48SOCgoIAFKdhPHbsGD8rA0PNIiUlBXZ2dtDT0ytVBphBuHz48AGDBg1CQkICrly5gu7du4PL5UJLSwtTpkzB6tWrRaovNTUV9evXB5vNRl5eHiQkJAQeo+S+065dO/j4+FSByvKRmZmJy5cvo6CgAJMnTwYRQVZWFmPHjsW+fftEpuu/ZGRkYN++fViwYAEAQElJCcbGxmjatCmWL1+Oxo0b/9SnQ4cOeP78OZo0aYKQkBDmjWINoabYZxWhRHva4wsV2gFX6zxELNddguB3ORGSm5uL48ePY+/evTAwMMC2bdswZswY9OnTR9TSxBYWi/XLP77169fH/PnzMXfuXAQHB6Np06blGrNhw4bQ0tLCxYsXBTbA09PT4e/vj8DAQEhISCAzMxMWFhYYOHAgCgoKShnfEyZMwPr167FixQr4+vriwYMHSE5OhpaWFpSUlNCkSRNoaWmhd+/emD17NtavXw9paWmB9DBULRMnTkRqamqVBAUy/IuFhQUCAgLQv39/TJ48GV++fIGEhARkZGRqRCo/WVlZ6Ovrw9DQsELGNwB8//4dABAbG4v8/Hz+73pOTg7S0tL4rmlVwY0bN3D9+nV8+vQJPj4+pT7Tc+fOISYmBgsXLsTq1aurPI82l8vFnj17cPbsWWhra6NDhw4YPnw4wsLC4O/vj5cvX8LLywv5+fno2rUrLCwssGTJEn5+9l+xb98+dOjQARs3bmSMbwbhwmYLHlRZC9IQip0Lyvfv3ykqKorOnj1LAEhRUZEA0JQpUyg2NlbU8modmZmZxOFwaM6cOQL127t3LwEot/tKRkYGDR06lCQlJQkAycvLk5ycHN+tJDExkYiI3rx5Q2fOnKH4+Pgyx7l37x5NmjSJrKysCACdPn2aVq5cyR/XysqKXrx4QUVFRQKth6FqMDU1peHDh4taRp3h/PnzBIDevXtHRETdunWj1q1b84MjRUFBQQG1bt2aZGRkKDg4uFJjXb16laSlpal79+7k5eVFM2fOJGVlZVJUVKT8/HwhKf4ZU1NT0tbWppEjR9KMGTPo2LFj9OjRI3r27BnxeDz69OkTAaBbt25VmQYiotevX5OVlRWx2WwaNGgQdejQgQBQ27Zt+ffV1q1b06ZNmwR2LSQixk2sBlKT7DNB4bugPL9Che/uCtTSnl8R23WXIHYG+H8JCAig9evXk6ysLAGg+fPnl/JbY6g8R44cIQC0bds2gfqlpKQQi8Wi5cuXl+v67du3E5vNpm3bttHnz5+Jx+MRl8ulfv36VSjinsfjkYODA9+AnzlzJs2fP5//tZWVFfn5+Qk0JoPwcXFxIRaLRV5eXqKWUic4dOgQAaDQ0FAiInr69CmxWCyaM2cOZWZmVrue58+fU5MmTQgA+fj4CGXMhw8fkpqaGgEgdXV1GjlyJAGggIAAoYz/v0RGRpKqqirNmjXrl9cUFRWRlZUVWVpaVln2kLt375KEhAQ1a9aMXr58SURE+fn5/L+P6urqTKaYWkhNtc/KQ4n2b77XiBt0X6D2zfea2K67BLE2wEtITU2lzp07EwBq2LAhBQYGilpSrWHbtm0EoEKBTUuXLiUA5Onp+cdr//rrLzIzMyt17MKF4iCLGzduCDw3EVFhYSENGzaMb3Q3a9aMdHR0+F8PGjSI+vbtS23btqUhQ4aQkZER2dvb04oVK8jf379CczIIBpfLpU6dOlH79u1FLaVOcPv2bQJAERER/GNr1qwhNptNVlZWFU4HWFEsLCxITU2N1q9fL9RxuVwuRUREUF5eHiUlJREAunbtmlDniI2Npblz55KCggLp6+tTQkLCb68PCgoiJSUlatu2LV26dEmoWgIDA0lBQYF69erF36XOz8+nBQsWUL169QgAsVgsJniyFlLT7bPfwTfA/W8Q9+Mjgdo3/xtiu+4SaoETDaCmpgZNTU0Axf5/S5cuFbGi2sOECRNgYmICNzc3PH/+XKC+K1euRI8ePbBs2TIUFhb+8rqoqChcv379pwIst27dAgB0795dcOEoLjjUvXt3qKiowNXVFXZ2drC1tYWLiwvWr1+PadOmwcvLC35+fnj48CEsLS0hJyeHFStWoE2bNrC2tsaHDx8qNDdD+eBwOFBTU4OkpKSopdQJmjdvDgB48eIF/9jixYvx5s0bxMTEYMyYMdWmJTc3Fx8+fMDChQv5wYDCgsPhQF9fH9LS0nx/bGEG7d+5cweNGzfG4cOH8ddff+Hly5fQ0tL6bR8LCwtcv34dfn5+GDt2rNC0AMC6deugra2N8+fP83+XJk2ahG3btmHw4MH8YNDHjx8LdV4GBqFQRwvx1AoDHADatGkDAGjRogXmz58vYjW1BwUFBVy/fh1ycnLo0KEDhg4dioKCgnL1ZbFYWLVqFYKDg9GqVSvMnz8f8fHxiI6OxvXr17F27Vr06NEDJiYmKCoqKvVH+OPHj7hx4wZMTU0hJSVVIe05OTlQVFTE0aNHMX/+fOzZswcXL17EsWPHwGKxcPfuXQwePBgAMGDAAFy+fBlHjx5FcHAw1NTU8PbtW1haWuLMmTNITEzEsWPHcOHCBbx586ZCehjKxsbGhjHAqwltbW3Y2NjAw8OjVKn45s2bY926dbh9+zbi4+OrXMfVq1fRqFEjAMWGeFWipKQEDoeDuLg4oYz35MkTDBw4EPb29oiOjsa6dev+GMBYQqdOnXD+/HlkZ2cLNfA4LCwMGhoakJP7N5PE+/fvMWTIEKxevRpv3rxBy5YtsXz58grPQUTw8vLC+fPnERERgfDwcGFIZ2Cou5Rnm1wcXnEUFRXR06dPmSCRKqKoqIhOnjxJUlJSZGRkRO7u7uTh4UF5eXl/7Hvnzh0aMWIEqaqqkry8PN8FRElJiRwdHWnbtm0//WyZmpoSAHJ3d6+w5nHjxpXKD66np0e5ubmUnZ1dZv7wYcOGEZvN/un4uHHjaPDgwaWOMT7LwuPw4cPEYrGqNEiO4V+OHz9OLBaL2rZtWypw/fv376SmpkYjRoyosrnT09Ppxo0bpKSkRN26daP79+9Xy/fdxsaGRo0aVelxbt68SQoKCtStW7cKxxvxeDwaNGgQqampCS03+P3794nFYtGGDRv4xwYNGsS/X9WrV48ePHhQqTmePn1a6h6ooaFRWdkMQkAc7LNfUaL9++u7VPT5uUDt++u7YrvuEmqNAc5QPfj4+JCbmxtpa2sTALKzs6P379+Xq+/Xr1/p77//pnPnzlFcXNxvM5G0a9eOAFQqYOn27dukoKBQ6o9GUFAQERF5eHhQx44dydXVlXr37k1t2rQhAMThcGjq1Kl0+vRpevToEW3cuJGys7Np0aJFxOFwSEZGhho0aEBfv36tsC6G0hw8eJDYbDbz8FyNvHjxgjQ1NX/KQHPixAkCQBMmTCAejyfUOXNycqhRo0b8bBzVWQBo5syZZGhoWOH+eXl5tGjRImKz2dSvX79KG85BQUEEoNJG8X+ZP38+SUhI8INNCwsL6caNG3Tnzh2h+PZPnjyZGjVqRDExMfz7qbB/RhgER5ztM74B/uY+FX3xFah9f3NfbNddAmOAM1SYx48fk4mJCbFYLHJ0dKQrV67w0wVWFjs7O7K3t6/0OBkZGXTt2jV6+fIlffv27ZfXcblc+ueff+jt27eVnpNBMMaPH08GBgaillHn2LdvH7FYLPr48SP/GI/Ho1WrVhEAevz4caXniI+Pp0uXLlGXLl2ocePGBIDOnz9f7RlXPDw8CAAlJycL3JfL5ZKNjQ1JSEjQqlWrhJKyMScnhwCUqhZ87949Wr16dYWN2vz8fLKxsSEjI6Mq+Xy7du1KQ4cOJaJ/Kyi/ePFC6PMwCIY422d8A/ztAyoK8xeofX/7QGzXXUKt8QFnqH46deqEwMBAHD58GCkpKXBycoKWlhZ69uyJhw8flgq8jIiIwKlTp3Dnzh3k5+f/dtyIiAh8+PABJiYm/GNcLpffBEFRURH9+vVDq1atUK9evV9ex+FwMGvWLLRo0UKg8RkqBxHBw8MDw4cPF7WUOoebmxs0NDRw4sQJ/jEWiwV3d3e0bNkSS5YsqfDYBQUFcHJyQoMGDTB48GDk5+fD1tYWJ0+exJAhQ6CgoCCMJZSbkhihly9fCtTv8+fPmD59Ol69eoVdu3Zh6dKlQilCU1Kps23btsjOzsa8efPQo0cPLF26FHfu3KnQmFJSUvDw8EBCQgJatWol8Fp/x6NHj/Dw4UPIy8sDACIjIwEUxxQwMFQaFrtiTcwR/xUwiBQpKSm4ubkhICAA4eHhOH78OBISEtCtWzfUq1cPo0aNgpOTE5o0aYIxY8bA0dERDRo0KPVH/38ZM2YMfvz4gd27d8PY2BgDBgxAgwYNoKioCBkZGZibm+PkyZPVuEqGqiIzMxM8Hg/KysqillLnkJKSQu/eveHh4YHo6Gj+cRkZGTg6OlYqA9DRo0fh6emJPXv24OvXr3j+/DlOnjyJ0aNHC0O6wBgYGEBDQwPXr18v1/WfP3+GoaEhTE1NceHCBSxZsgTjx48Xmp6SQG53d3dYWFhg586dWL9+Pdq0aVOpcvVGRkbw9/eHgoICf4NEGHh4eAAA9PT0cOzYMUycOBF9+/aFgYGBUMZnqNsQi12hJu6I/woYagyGhoYYO3Ys3r17B39/f8yfPx/v379HQkICdu/ejfT0dAQGBsLR0REuLi5YtWpVmWWwJ0+eDA0NDXTv3h22trbIz8/HgAEDsGzZMqxduxYmJiYYO3YsOnTowC8/zSB+EBEWLFgAFouFESNGiFpOnWTJkiWQkJCAjY0Nrl69yj9++fJl/PjxQ6CxEhIScPToUfTp0wdTpkxB8+bNMWXKFBgYGAg1BWBFYLFYWLhwIQ4dOoRnz5798fpdu3YhLi4OFy5cQFxcHFavXg0JCQmh6Zk6dSp27twJKSkp2NjY4MOHD5g/fz5sbW3x+fPnSo1tZmaGp0+fwsTEBEOGDAGPx6u03oYNGwIofmDw8vKCnZ1dqZ8XBgaGClAePxVx9jFiqJmsXbuWANDs2bMr5FO5f/9+vj8pg3ji4uJCAGjr1q2illKnSUpKon79+hEAcnV1pdzcXHJ3dycAlJqa+sf+sbGx1K1bN36xl3bt2tHhw4cpOzu7GtSXHy6XS7a2ttSkSZM/Bvz27t2bHB0dq0nZv+zdu5ckJCSEUtH53r17BEAohel8fHxKBbN37NiRvn79Wq6fD4aqRZztM34hnsBnxI14K1D7FvhMbNddArMDziASFi1ahF27dmHHjh2wtbXF0qVL4eHhUe6cwCX+4YqKilUpk6EK6dGjBwAgOTlZxErqNvXr18fVq1dx5MgRnD17Fs7OzpgyZQoAwNvbu8w+PB4Pe/fuxbhx49CyZUuEhITgxIkTSE5Oho+PD8aNG1cqJ3VNgMPhYNu2bQgLC+P7YP8KExMThIaGVpOyf+nQoQO4XK7ARc/KwtbWFpKSknjy5Emlx2rXrh2/ZgIAPH36FIaGhmjQoAHGjx+PsLCwSs/BUIdhsSrWxBzGAGcQGdOnT8ejR4+goKCA48ePY+TIkWjSpAmGDx+OW7duYenSpThz5kyZfW/cuAEdHR1069atmlUzCIvhw4djwYIF2Llz5x8DcxmqFhaLBTc3N1y9ehXe3t5YsGABmjVrhitXrvCv+fjxIxYtWoQJEybAwsIC06ZNg4+PD7p164bXr19jzJgxUFdXF+Eq/kyrVq2gpKT0RwPcyMgIERERQnHfEARzc3Po6Ojg8OHDICJ8+fIFLi4uGDNmjMA++XJycmjdurVQDHCg+DP5X/Lz83HkyBGMGzdOKHMw1FHY7Io1MUd4Tm0MDBWgY8eOePDgAQAgKCgIBw8exL1793Du3Dn+Nc2aNYOlpWWpfklJSdDX169wlUyGmoGKigpYLBbzfawh9OzZE+fOncPgwYPB4/GQlpYGT09P3L59GydPnoScnBwMDQ3RqlUr7N+/Hx07dhS1ZIFgs9mwtrbG69evf3udrKwseDwecnJyqjVjC4vFwurVqzFu3DjMnz8fDx484AesS0hIwN3dHU2bNi33eLq6ukhMTBSKtqysLP7/R44ciVu3bvFjcJ4+fYpnz57BwsLit9mmGBjKoiJBlUwQJgODELG0tMSuXbsQHByMsLAwzJw5EwAwc+bMUjd/AEhJSUFGRoZQ5iUiLFq0CPXr18eWLVsQExPDj/pnqFqMjY2Rk5ODjx8/iloKw/8zcOBA/s53XFwcnJyccOrUKQwYMABfv37Fq1evcOLEiTKN79DQUKGmv6sKunbtilu3bv22NL2Hhwesra35afeqEwsLCwDFDwupqalQUFCAlJQUjh07BnNzc4wYMeKn++GvSEtLE9pbibVr10JXVxdAsXtLfHw8fHx8+GkZO3bsiEaNGiE+Pl4o8zHUIZg0hAwMNQMWi4XGjRtj+/btuHnzJvz9/eHq6lrKTcHf3x+dOnWq8BxEBC6Xi9DQUHTr1g3r169HSkoK9u7dC3Nzc4wcOZKfQo2h6ujSpQuUlJQwZMgQvH//XtRyGP6fEteLJk2aICIiAjk5OTh37hxUVFRKXZecnIyAgACcOXMGrq6uMDExQZs2bWr0782MGTMgLy+PefPmlXk+Ly8P3t7eGD9+vEizt8TGxuLu3bvo0aMH0tLSkJqaiv379+PKlSvYuXPnH/sXFBQgKipKKAZ4eno6FBUVcffuXfTv3x/Tpk1D27Zt4e3tjejoaHTv3h1AcVrRvXv3Vno+BoY6QXkiNcU5ypZB/Ll8+TIBoLZt2xKPx6PY2FgCQOvXrxd4rPz8fFq6dClpaWnxS883btyYZGRkSkX5lzQZGRm6dOnSb8fk8XhUWFhIeXl59PXrV7px40aNywBRk3n06BEpKioSm82mFStWUGFhoagl1XmePXvG/x1ISEggouKf82/fvtGbN29o1apV1L59e5KQkOBfZ2FhQTY2NgSAYmNjRbyC33Py5EkCQHfu3PnpXGZmJgGgs2fPikBZ8eeso6NDrVu3JgB07dq1UuenTZtGqqqqlJKS8ssxvnz5QnZ2diQpKUk3b96slJ7r168TAHr//j0RERUVFZW6R1pbW5f6WlVVtVLzMQiGONtnJdrTQl5RYVyIQC0t5JXYrrsExgBnEAuUlZUJAGVkZFB+fj4ZGRlR9+7dBS7bPHDgQJKSkiIHBwdydXWlffv2UXZ2Nl24cIEAkJSUFO3Zs4dSU1MpJiaGHB0dCQB17tyZWrRoQY0aNaKNGzdSdnY2jRkzhhQUFIjD4fD/+LBYLAJAmzdvrqJPonZSUFBAy5YtIxaLRRs3bhS1HAYqTi+ooqJCLVu2pJEjR/IfWgGQgoICOTs70/bt2+nt27eUlJRERMVp9AAIJY1eVdO8eXNycnL66fiTJ08IAJ07d04EqohCQ0MJABkYGJC6ujrl5+eXOh8XF0fq6uqkp6dHI0eOpLCwMP653NxcmjNnDklKSpKenh75+PhUSktRURHp6+sTANq3bx//eFmbFSVt3rx5lZqTQTDE2T7jG+Cf31BhfKhALe3zG7FddwmMAc4gFixdupR/g1+2bBl5enoSAJozZ85PRnhWVlaZ+WlzcnJIQkKC/vnnn5/O9e7dmwCQqalpqfG+fv1Krq6uNGLECJoyZQq5urqW+mPTpk0bvtHx37Zz506hfwZ1gaFDh5KKispPu34MouHly5fUtm1batOmDc2bN4/Onz9PL168+OUbng4dOlCnTp2qV2QF+eeff0hSUpK+ffvGPxYcHEwsFovYbDZt2LBB4Ad8YRAeHs6/j/z1119lXvPmzRuaOnUq6evrk7KyMvn6+lJeXh45OjqSjIwMrV27lnJyciqtZcKECXwtJTvgRERubm6ko6PDP2dkZMT/f1paWqXnZSg/4myflWhPDX1LBQlhArXU0Ldiu+4SGAOcQSyIjIwkc3Nz/k1+zJgxtGbNGjI2NqbJkydTRkYGff/+nXbt2kWqqqqldmNycnKooKCAhg0bRgDo+fPnP41vYGDAd0nJysr6rZb379/TkSNH6OnTp/xjL1++pE6dOvFdKQ4dOiT0z6AukJSURL179yYOh0P37t0TtRwGAcjKyiIJCQnas2ePqKWUixJXthMnThBRsetHixYtCAANGDCAANCECROq3Qh//Pgx//41aNAgioyM/OW1L1++JAC0fft26ty5M8nIyND9+/crrYHL5ZKbmxtfx4cPH0qdf/v2Lf+chIQEhYWFkZeXF/9YWa49DFWDONtnfAP8y3sqSPwqUEv98l5s110CY4AziA15eXn06NEj2r59O/+P48qVK/k3/Xr16pGEhASpq6sTANLT0yNZWVlq1aoVdevWjSQkJGj27Nllvh7fuXMnAaDevXtXWmPHjh1JSkqKHj9+XKmx6ipcLpfs7e1JS0vrjxULGWoOp06dIgClXCJqOu3bt6dWrVpRdnY2DRo0iACQtLQ0KSkpUfv27QkAHTx4sFo1zZs3j7S0tKh///4kKytLtra2v7z26dOnfJcgbW1tevLkiVA09OzZk39f/e9Gw3+ZP38+/575+vVrkpKSKlUpk6F6EGf7jG+AhwVSQVKEQC01LFBs110CY4AziCW2trb8m72ZmRm1atWKJkyYQCoqKiQhIUHz5s3ju4jMmjWLmjRpQrdv3/7leEVFRRQRESGUAMD/uqT07duXIiIiKj1mXePhw4cEgD5+/ChqKQzlgMfjkY2NDfXs2VPUUgTi+fPnJC8vT5KSkgSAdu3axfcB9/X1JRcXF1JTU6vWoOrmzZsTANq9ezddvXqVANDr16/LvJbH45GTkxP16dOH74dfWZKSkvj3r5CQkDKvSU1NpXHjxvGDcFksFsnJydGlS5do4cKFpKamJhL3nbqIONtnjAFeDsT5G8xQO/n+/TstXryYdHR0+H+wStqoUaOIqDhQyNjYWCR/CHg8Hp07d450dXWpYcOGjBEuIBkZGaSoqEiDBw8mLpcrajkMf6DkgenWrVuiliIwz549IxUVFXJxcaGioiLy9vbm7+R//fqVOBwOHT58uNr0lOzEA6DDhw+TrKwsbd26tdrm5/F4dP/+fcrLy/vlNRMnTuS/LQBAcnJylJSURDwej44ePUoAhPZAwPB7xNk+4xvg4R+oIDlKoJYa/kFs110CkwecQSxRUVHBmjVrEBcXh5cvX2LkyJH8c4aGhgCAEydOgMViYdu2bdWuj8ViYejQofDz80NRURHs7e2Rl5dX7TrEFUVFRRw/fhyenp5o27Ytbty4ASIStSyGX7BmzRpYW1vDwcFB1FIExs7ODqmpqTh27BjYbDYaNWoEAIiKioKBgQEkJCR+W7RH2Jw4cQL+/v5wc3PD1KlT0bx5c2zYsAEXL15EZmZmlc5NRDhy5Aj8/Pz4ueDLuubgwYMAAC6XCwDIyclBbGwsnJ2d4ebmBgBISEioUq0MtYeSSpiCNnFH/FfAUOeRkpLC6dOn8fDhQ7x58wYrV64EAIwZMwZDhw6Fu7s7YmJiRKJNR0cH/fv3R1hYGHr06MEv3czwZ5ycnPD48WOw2Wz07dsX7u7uQjfCf/z4gYcPH+Lo0aM4cOAA9u7dCw8Pj18aHww/4+3tjYcPH2LJkiUiLVxTGUqqOQKAnp4eZGRkcPv2bfj6+oLNZkNOTq7atMjLy6N169ZYt24dpKSkkJubi0aNGmHIkCGwtbVFVFRUlc19+fJlTJgwAUuWLMGgQYPKvIbFYsHNzQ2TJ09GRkYGlixZAnl5edSrVw+XL1/mX2diYlJlOhlqGSw2wBaw1QIDnHFBYajVfP36lQCQp6enyDTweDx6/vw5qaqqkoaGBtna2pKOjg5JSUmRoaEhubm50ZUrV5jiPb9hx44dBIC6du1a6SC/79+/04EDB6h9+/bEZrP5r/s5HA7fp3X48OFMAGg5KCgoIFNTU+rUqVOt8vldvnw5/+fCxMSEoqKiqmQeHo9HmZmZv/zsbt26RQDo0aNH9O7dO9LW1iYVFZVf+oRXhkePHpGSkhINGDCADh06RABo7ty5v/QDLyEoKIg4HA6ZmJjwP7NFixYJXR9D2YizfVaiPSUylPK/JQjUUiJDxXbdJdSCRwgGhl8TEhICANDQ0BCZBhaLhfbt2/NfK6uqqsLU1BRbtmxB79694efnBycnJ2hqauLUqVMi01mTmTFjBry9vREREYGWLVvC29v7p2sSEhJw5coVbNiwAXPnzsW6devw8OFDfPnyBUBx2fSZM2dCW1sbU6ZMgaKiIvbv34/g4GDk5+eDy+WisLAQly5dwoULF3DgwIHqXqbYsXXrVoSGhmLnzp1iu/tdFgsWLMDevXvx9OlTBAUFQU9Pr0rmWbJkCRQVFSEnJwdTU1M4ODjgwYMH/PPa2toAinfomzdvjg8fPsDY2Bi9e/fGjx8/hKKhsLAQ69atQ8+ePdGqVSucPHkSY8aMwezZs7F161aYmppi48aNv+xvYWEBPz8/GBkZAQDOnj2LtWvXCkUbQx2Bxa5YE3fKY6WL8xMWQ92mZAf8+vXropbyWz5//kyjR4/m7zoxlE16ejo5OjoSh8MhV1dX8vHxIU9PTxo4cCC/IqmysjKZmJiQoqIif0dOT0+P2Gw2ycvL0+rVqykuLu638wwbNox0dHQoPj6eoqOjydPTk2bOnEmtW7cWqzR7Vcnz58+Jw+EwlQ8rwbp16wgADRs2jGbNmkU2NjYkKytLvr6+RFT8tkZdXZ3Gjh3L7xMbG0uKioq0ZMmSSs+fk5ND3bp1438f/zfwsuT3p7zVab9//15pTQyCIc72GX8HPCqM8r8nCdRSosLEdt0lMAY4Q63m48ePBEAsioPweDzauHEjAaBx48bVqlf6wqSwsJC2bt1aqhJfixYtaNeuXZSQkMD/3AoKCigkJISuXr1Kf/31Fx06dIhSUlLKNUdsbCzp6OgQh8Phu6mU/Nu5c+c67y4UFxdHOjo6ZGdnx7jqVILs7Gyys7MjFRUVevv2LeXk5JCdnR3Vq1ePXzBs3rx5pKenV6pfly5daMiQIZWam8fj0dChQ0lSUvKnXN/5+fnk6OhIAGjw4MHMvagGI872Gd8Aj/5K+T9SBGop0V/Fdt0lMAY4Q60mJCSEANDo0aNFLaVccLlcmjx5MgEQWlGN2kp+fj69ePGiynKFR0RE0J49e+jQoUP05csXKiwspHPnzhEAsra2FkrOeHEkMzOTrKysqGHDhn98k8DwZ378+EE2NjYkLy9Pp0+fpu/fv1OnTp1IWlqaTpw4QTdv3iQAtGPHDn4fMzMzGj9+fKXmXbJkCQGgCxcu/HRuzZo1xOFw6Ny5c0wa0BqOONtnJdqTYyIoLz1NoJYcEyG26y6BMcAZai2JiYk0evRokpeXp9DQUFHLKTc8Ho/09PRo6tSpopbCUAbPnz8nAOTh4SFqKdVOTk4O9erVixQVFen9+/eillNryMrKolGjRhGLxaKAgADKy8sjV1dXAkCbN2+mv/76iyQlJenHjx9ERNS3b1+yt7cXeJ7Lly/TgAEDyNDQ8LeuJY0aNSJbW1tm51sMEGf7jG+Ax0ZRXsZ3gVpybJTYrruEWuDFzsDwM8nJyTAzM8OpU6ewfft2foCQOMBiseDk5ARPT08mHV4NpEmTJgAASUlJESupXmJiYtChQwc8evQIly5dQrNmzUQtqdYgLy+PY8eOoVmzZhg9ejS+fv2Ko0ePYuHChXB3d4eSkhLYbDasra3h4+ODLl264N69ezh79uwfx87IyAAR4ePHjxg0aBASExMxcOBAeHp6wt3dvcw+GzZsgI+PD44dO1apdeXk5FSqP0MdgcWqWBNzGAOcoVayaNEifPv2DY8ePcL48eNFLUdgBg0ahISEBAwYMABZWVmilsPwHx49egQAaNOmjYiVVA9EhJMnT6JZs2ZISUnBixcv0KNHD1HLqnVISEjAw8MDAGBtbY2HDx9i7dq1cHFxwdq1a5Gfn4+YmBjY2dkhJycHvXr1wrp1636bG//Ro0eoX78+bGxsMH78eEhISODBgwfYsmULBgwY8MvMNcOGDUP9+vUFMsDv3buHZ8+e8b9+8+YN5OXl0bZtW4SFhZV7HIY6SDVlQdm7dy8MDAwgIyODli1blvp5FQnl2SYX51ccDHWLoqIiunHjBj84LycnR9SSKgSPx6PVq1cTAAoICBC1HIb/MGLECNLQ0BC1jGohKSmJ+vTpQwBo5MiRlJqaKmpJtZ7s7Gxq06YNtWnThng8HvF4PIqPj6dPnz5R+/bt+dl+SlpGRsYvx1FRUaFOnTpR7969qUePHnT27Nly6xg3bhwBoL///puKioooNjaWunfvTt26daPdu3fTzZs36cWLF/T06VOaPn06ASBJSUl+//nz5/M1tm3bttKfC0PZiLN9xndBiY+lvKwMgVpyfKxA6z537hxJSkrSoUOH6NOnTzRz5kySl5evshz/5UFCBDY/A0OVMWHCBBw9ehTt27fHvn37ICsrK2pJFYLH4+H9+/eQlpaGpqamqOUw/IeEhATIyMiIWkaV8+7dO/Tv3x95eXm4du0a+vXrJ2pJdQI5OTksWrQI/fv3x4cPH2BpaQltbW1oa2vjwYMHaN26NX78+IFu3bqhffv2UFRULHOcr1+/4sePH1i7di3at28vkIaIiAh4enoCALZs2YJOnTph2LBhyM7OBoBSucr/y3/zhcfExMDCwgIKCgpVWr2TQfypSGl5Qa/ftm0bxo0bx38jvn37dty9exf79u3D+vXrBRpLWDAuKAy1Bh6PBx8fH1hbW+PZs2ewtLQUtaQKc+nSJVy6dAkmJibw8vLClStXkJmZKWpZDABSUlLQtWtXUcuoUt69e4dOnTpBQ0MDr169YozvasbBwQGqqqpYvnw5CgsL+celpaXh4eEBHo8HDw8PtGjR4pdjZGRkAMAvDfTfsWPHDnA4HHh7e+Pz58/w8PDgG9+/YtKkSZg1axb/69atW+PDhw/w8/NDo0aNyuxTVFQksDaGWkglXFAyMjJKtfz8/J+GLygowOvXr39ynevRowd8fHyqZYllwRjgDLWGmzdvIiQkBDt27BD7qnwODg7o168fUlNTMXPmTAwaNAhOTk6YOHEizp49i7S0NFFLrJO8evUKHz58wMCBA0Utpcr4+vUrHB0dYWxsjMePH0NXV1fUkuocUlJSOHz4MG7cuIEhQ4aUCsY2NzfH3LlzUVhYiHr16v1yjJLA83fv3gk8f3JyMiwsLGBvbw99fX1+sOeZM2eQnp6O169f4+jRoxgyZAhmzZqF+Ph47N+/v9R9t3Pnzvz/nzhxgv//yMhIXL16FdOnT4eMjAzMzMwwffp00fvjMogMYrEq1ABAV1cXysrK/FbWbnZqaiqKiop+epusqamJxMTEalljmZTHT0WcfYwY6gYpKSmkrKxM+vr6opYidHg8HjVu3JgAUKNGjQgAcTgc6tOnD128eJFyc3NFLbHOMGnSJKpfv36tLT7D4/GoTZs21KRJE0pKShK1nDrPtWvXCACdPn261PFFixYRAHJ2dv7t98ne3p6MjY0FqlDp4eFBSkpKNHDgQCIifh7ykvtPyT2opPXt2/eXY9nY2BAAfmVPf39/fj9VVVVasGABTZw4kerXr08cDofJK18BxNk+K9GekJhE2Tm5ArWExCQCQDExMZSens5v/1vNlai4cBgA8vHxKXV8zZo1ZGJiUl3L/QlmB5yhVsBms5Geno5JkyaJWorQYbFYGDRoEDZu3IiIiAjExcVhx44dSElJgbOzM5o1a4bU1FRRy6wT2NjYIDk5Ga9fvxa1FKHz48cPzJw5E/7+/ti5cyfq168vakl1nn79+qF///5YsmRJqVfra9aswYULF3Dx4kUcPXr0l/23bt2K+Ph4tGjRAgUFBX+cLzAwECNGjICjoyN/XG9vbxgYGCAmJgbS0tKIiori73QPGDAAq1at+uV4z549w9evX9G2bVsAgKWlJRo0aACg2GWwV69eOHDgAIKCglBUVFTKHeDHjx9YsmQJ2rZtC1tbWyxduhQxMTF/XAOD+MEjqlADACUlpVJNWlr6p/HV1dXB4XB+2u1OTk4WbYxVeax0cX7CYqgbxMTE8HeGx44dW2cKSLx//55YLBbp6OjQrFmzaMeOHRQcHFxn1l/dcLlcsra2JktLy1pVjj40NJSaNm1K8vLytG3bNlHLYfgPnz59Ig6HQ6tXr/7p3MiRI0lNTY1mzJhBERERZfYv2cEODw//41x3794lAPzMEEVFRdS8eXMaPHgwERW/aezXrx8BoGPHjlVoPXl5efTw4UNq165dqZ10ALR8+XIiIvLy8qJ69eqRrKwsjRw5koYPH06KiookISFBo0ePpoMHD9KnT58qdZ9LTEyky5cvi/3bLHG2z0q0xyUkUmZ2jkAtLiFRoHW3bt2apkyZUupY06ZNacGCBVWxtHLBGOAMtYKcnBzq1q0b/0aemJgoaknVhoyMDP/1sJSUFAGghg0bUvfu3aljx47UsWNHOnbsGBUVFYlaaq0gMDCQ5OTkaMiQIbWmTLeFhQUBIH9/f1FLYSiDhQsXkqSkJL148aLU8cTERBo8eDBpaGiQubl5mWlXg4OD+WkkFy5cSOfOnaPs7Gy6d+8eJSQklLr2yJEjxGKx+KkNL1++TADo8ePHtG7dOgJA9vb2pKqqSv369SvTAH7//j1du3aN/Pz86M6dO2RmZkaNGjUiGxsbGjt2LN29e5eIiJ/e8r/N2NiYPDw8SEFBgXr37k3x8fH8cTMzM2nbtm1kZmZGbDabAFDz5s1pz549/AqhgtC5c2f+vA8ePBC4f01BnO2zEu2xCYmUkZ0jUIsV0AAvSUN45MgR+vTpE82aNYvk5eUpMjKyilf5axgDnKHWUFIifODAgXVqBzg4OJi/Y5WVlUW3bt2i2bNn0+DBg2nkyJHk4OBAAMjKyoqePXsmYrW1g0uXLhGbzabRo0eLvRH+9u1bYrFYdPjwYVFLYfgFeXl51LFjR1JRUaGgoKCfzt++fZsA0Js3b346V1BQQMOHDydjY2PS1tYmACQrK0sASElJiTp37kw3btyg6OhokpCQoObNm/P7+vr6EgBq164dGRkZEQBycnLi+6AHBweXmis/P7+UQW1tbf2TkQ2AQkND6cSJE/wc4StXrqSNGzdS8+bNCQCZm5tTZmbmLz+PnJwcunbtGg0YMIA4HA5JSUmRo6Mj7d+/v5TR/jseP37M/xwA0Jo1a8rVr6YhzvZZifbo+ET6kZUjUIuOF8wAJyLas2cPf6PK2tqanjx5UoWr+zOMAc5QK7h9+zZJSkpS+/btKS0tTdRyahwvXrygNm3aEIfDYQwtIXH27FnicDg0btw4sX7gGzhwIJmZmYlt0aq6Qnp6OjVr1oxMTU1/Koi0f/9+4nA4f/we8ng8WrFiBW3evJlevXpFixcvptatW5O0tDTfEHV0dCzVx9XVlQAQi8UiACQhIUFjxowhALRx48af5nBxcSEApK2tTQcOHCB3d3dasmQJLVu2jDp27EgAaNWqVUREtGTJklKGuZOTE23bto2io6PL/bnExcXRP//8Q507dyYOh0OSkpK0bdu2cv1OxsbGUteuXfnzV9StRpSIs31Woj0qLoG+Z2YL1KLiEsR23SUwBjhDraBVq1bUsWNHys/PF7WUGkthYSFNnjyZANC8efOYqoZC4Pjx4wSADhw4IGopFeLcuXPE4XBoy5YtopbCUA4+ffpE9erVIw0NDTpz5gzfyJw1axYZGxtXaMy0tDS+X/X27dt/8iXPzc3lG6j29vakrq5eymguyXDyXzIyMn5pAAcHB/OzsqSlpdHQoUNp9+7ddPr0aapfvz4BIGVlZRo7dmyZO/q/IzU1lebMmUMAyNXV9Zdvp9LT06mwsJBOnTpVai39+/cnIhIrdz1xts9KtEfEJlBaRrZALSKWMcAZGGoEpqam/LRZDL+Gx+PRhg0bSEJCgjgcDmloaNDAgQN/Wc6a4c9MmTKFpKSkyv3qu6bA4/HIyMiI+vbtS4WFhaKWw1BOEhISaMiQIQSAJk+eTEVFRTRp0iSytraukvnu379PAEhfX5+6du1K+/btK2W0CuvhLTk5mZ4+fUpnzpyh5cuXk56eHgGgjh07/uT7/idKHowvX77MPxYYGEhXr14lPz8/kpCQoAYNGpCcnBx/HS1btiQfHx/68OEDsVgssXFJEWf77L8GeGpGtkCNMcAZGGoI27ZtK84n+j9BRQxlk5SURAcPHqSVK1eSjIxMma+SGcpHWloaSUpK0tatW0UtRSB8fHzEPgCtLnP48GFisVjUqVMn0tPT42dCWrVqFYWFhQltntu3b/ODHnv37k1ExZlVOBwOycrK/pRbuSLweDwyNDQs9gf+f/eTwsJCunjxIrVo0YI0NDQECrTk8XhkaWlJAKhDhw505MgRMjc3L9MfHQB16dKFv2MfHh7OP+7n51fptVU14myf1XUDnMkDzlArCAoKAgBcuHAB9P/5QRl+Tf369TFhwgQsW7YMXbt2hbe3t6gliS2qqqpo1qwZDh48KGopAvHlyxcA+GWZcIaazbhx4+Dh4QEJCQnk5eUhLS0N3t7e2Lp1K4yNjfHXX38hLCwMhYWFyMzMxJw5c3DgwAF8+fJFoHukg4MDXr9+jevXr8PDwwMA0KtXL7x69QphYWFo165dpdcSHByMr1+/AgCSkpIAABISEhg8eDC8vLyQlZWF3bt3l3s8FosFPz8/nD59GhERERg3bhzMzMx+uk5bWxuPHz+Gt7c3P7e5oaEhWrZsCQDo0KEDjhw5wtfGUDXwqGJN7CmPlS7OT1gMdYPk5GQaP378H33/GH5m27ZtJC0tzQThVYKjR48SALpw4YKopZSbqKgo0tDQIHl5ebFzn2H4lxIXESUlJTp//jzl5OTQnj17frnbC4D09PTo+fPnopbOJzGxOKOFoqJimffuAQMGUOfOnQUeNyIigr/mKVOmUF5eHl29epWWLVtGJ06cKLNqItG/qRsB8ANUd+7cSXl5eTXuPinO9lmJ9rDoeEr6kSVQC4uOF9t1l8Ai+vOjcEZGBpSVlZGeng4lJSVhPwMwMAiNU6dOYcyYMTh16hRGjRolajligb+/P9q2bQtfX19+xToGwenQoQOUlJRw8+ZNUUspN9u3b8fs2bMRGRnJ7ISLKVlZWThx4gT27NmDkJAQmJubo127dkhPT4efnx/09PTQqVMndOzYEW3btsWzZ8+wbNkypKamYsCAAdDX14e9vT0sLCxEug4iQnZ2NhQUFEodf/bsGYYOHQoiQkJCwi/7c7lc3L9/H7dv34aZmRmcnJygqqqKBg0aoEGDBrh+/Tq/Cmd5CAkJwb1796CgoAA3NzcAgLGxMUJDQ7Fx40bMnDmzzKqL1Y0422cl2kOj46EooPbMjAwY6+mI5bpLYAxwhlpHjx498PXrV/j6+kJDQ0PUcmo8J06cgIuLC9LS0qCqqipqOWLLtm3bsGjRIqSkpEBRUVHUcsrFtGnTcO/ePYSGhopaCkMlyc7OxsWLF+Hr6wsfHx8kJiZCVVUVYWFhAIC2bdvCxsYGjRo1QlJSEjZt2gQAYLPZ4PF46N+/PzZt2gQjIyP4+vpi7dq1kJCQQKdOnTBhwgR4eXnh3bt36NatG5SVlbFjxw5ISUnhxIkTfA2ZmZn4+PEjwsPDERERgalTp1bqnlJUVARLS0vExcXBxsYGBQUFaNGiBaZOnYqmTZvyr3v37h2GDRuGz58/Q1dXF3FxcWCxWFizZg3at2+PYcOGgcvl4t27d9DW1hZYR2xsLIKDg/Hu3TvMmzcPACAlJYXz588jIyMDo0eP5ruwVDfibJ+VaP8cVTED3KSReBvgjAsKQ60jPDyc1NTUaMKECaKWIhaMGzeOLC0tRS1D7Hnw4AEBoIcPH4paSrmZPHky6enpMVlQajHJycl06NAhGjJkCBkbG5OCgkIpdxR5eXl+jm93d3davHjxTy4rQ4cO/aU7C1Fx2sHp06eThIREqXM7duyolPa4uDh+dV8Oh0Nt2rShBg0akIqKCj18+JAyMzOpqKiINm/eTCwWi/z8/IjH41FycjJNnTqVJCUlKTg4mBITE6l+/frk7OxMMTExldLk7+/PX5+SkhIBEKk7jzjbZyXagyPjKfZblkAtOJJxQWFgqJFs2LABy5cvR0REBHR0dEQtp8YSFhaGli1bYty4cdi2bZuo5Yg1kyZNwqVLlxAdHQ15eXlRyykXAQEBaN26NeOyVcfgcrlgs9kIDQ3F6dOnUVBQgEGDBqF169Zo3rw5P6jdzMwMsrKyUFBQwKdPn8Bms/lBkqNHj4aLiwuio6Mxffp0ZGdn88eXk5PDihUrMHv2bEhISFRKa1ZWFoqKiiAtLQ0ZGRmkp6ejX79+ePr0KQBATU0NBQUF4PF4SElJgaysLAAgNzcXzZs3R2FhIe7fvw9fX1+MHj0aAODl5YXevXtXeNf6wYMHmDJlCpSVlfHq1SsAQF5enkhcUsTZPivRHhxZsR3wpvrivQPOGOAMtZL09HQ0atQI48ePx5YtW0Qtp0YSGxuLLl26AABevXoFZWVlESsSbw4fPoyJEyciLCwMhoaGopZTbnr06IHU1FT4+/tDUlJS1HIYREx6ejoSEhLQqFEj8Hi8Uj7ZkpKSiIqK4rtxhIeHo0mTJvzzDg4OmDJlCnr16lVpw/t35OXl4eHDh0hPT8fHjx8hISGBCRMm/OTjHR0dje7duyMtLQ07duxAcHAw1q1bBwAYMmQITp48WSGjuSR2AgA4HA6KioqQmZn5k/96dSDO9lmJ9k8RcRUywM0MGojluktg0hAy1EqUlZUxbdo07N+/HykpKaKWU+OIiYlB586dUVhYCG9vb8b4FgIjRoyAiooKVqxYIWopAjFv3jy8e/cO48ePF7UUhhqAsrIyTE1NISsrC3l5eYSHh/N3jgsLC/npKwHg5cuX/P/3798fXl5e6NevX5Ua3wAgIyODXr16Yfjw4bC3t4ebm1uZAZZ6enrw8/ODjY0NRo8ezTe+geKUtZMnT67Q/BMnTsQ///wDJycnDBo0CADg7OyM1NTUii2ojsOrYBN3mB1whlpLamoqGjduDDc3N/zzzz+illNjSE9Ph7W1NXg8Hh49egR9fX1RS6o17NmzB9OnT0d0dDR0dXVFLafcLF68GHv37sX3799FLYWhhvLt2zdERUWhRYsWfNeNvLw83LhxAyYmJrCwsCi3SwcRYffu3Xj06BFGjRqFAQMGgM0WfD/w7t27cHBwQOPGjfHly5dfzk9ESEpKwufPnxEaGooDBw4gJiYGR44cQZ8+fQSe97+8ffsW1tbWAIA1a9Zg8eLFlRpPUMTZPivRHhQRB0VFAXfAMzNgKeY74FX7mMrAIELU1dXh7u6O1atXY9asWUyatf9n/fr1SEhIwMePHxnjW8i0aNECQPFDjjgZ4HJychV2PyEicLlcxn2llqOqqvpTRhMZGRkMHjxY4LFycnIwY8YMAICnpyd69OiBOXPmQFNTE5aWlvj48SNCQ0PRp08fyMjI/HKcS5cuASh2hQkJCSmVGeW/sFgsaGlpQUtLi5/VRVhYWVkhNjYWcXFxIk/lKK7wiMATsICeoNfXRBgXFIZazezZs1GvXr1q35WoqQQFBeGff/7BvHnzYGBgIGo5tY4HDx6AzWaL3YONlJQUcnNzK1RFdurUqTAyMuIH5zEw/AoigoeHB7p3745WrVoBAFq2bInPnz/DwcEBVlZWUFdXR/PmzeHs7IxRo0b98mcyIyMDFy9ehKurKwAIVClT2DRo0ACtW7eGnJycyDSIM7+tGvWbJu4wBjhDrUZeXh7r1q3DmTNncO/ePVHLESk+Pj5wdHRE06ZN4e7uLmo5tY7bt29j1apVcHd3F0kwVmUwNDREVlYW0tLSBOqXlpaG/fv3IyoqCtOmTasidQy1hf3792PkyJFQUlKCpqYmFi1ahCtXriAiIgLv3r3DxYsXMWbMGFy8eBGmpqbw8fH55VjBwcFIT09Ht27dMGvWLBw/fhzR0dHVuBoGhsrBuKAw1HpcXV1x8uRJTJw4EcHBwb99pVlbycnJgaOjI4gIfn5+YpMmT1xISUnB8OHD0atXL6xdu1bUcgSmxIcyJyen3H1iYmKwfft2AMW74Hv37kV4eDgaN25cFRIZxAwiKuWTnZqaiqlTp8LS0hJ379796frmzZujefPmGDRoEKytrRESEoLjx4//0q/7w4cPAICOHTuid+/e2L59O54/f44RI0ZUzYIYqgweFTdB+4g7zA44Q62HxWKhf//+iIyMxLdv30QtRyQ8ePAAGRkZCAgIQMOGDUUtp9Zx5MgRFBQU4OjRo+BwOKKWIzBSUlIAgIKCgnL3mTBhArZt24bJkydj8+bNYLFYePjwYVVJZBADEhISMG7cODRr1gxycnJYtmwZ8vPzARTnyVdQUMD69et/O0Z6ejrevXsHOzs7jB079pfXxcTEQENDA7q6ulBQUACLxcLbt29x4cKFCrlSMYgQAkjAVht8UBgDnKFO0LdvX3A4HBw5ckTUUkQCl8sFUByYyiB8Lly4gH79+ont56uhoQEApVLM/Y7IyEgEBQVBV1cXu3btgpycHHR1dREREVGVMhlqMG/fvoWpqSmOHj2KwsJCTJkyBRs2bICzszMOHz6MK1euYMeOHejdu/dvx5k9ezYUFRVx/vz5316noaGBlJQUcLlcSEhIYMCAAdiyZQuGDh2KlStX1igj3NfXF2vWrClVrIjhX3igCjVxhzHAGeoETZo0gbu7O5YtW4YpU6YgMzNT1JKqlZLqcIK4GDCUj4yMDHz8+BF2dnaillJhSnbA09PT/3jt/fv3YWVlBSkpKTx69AgSEhIoLCyErKwsPD09a5Thw1A9REdHY9CgQdDW1sbOnTvx5s0bbNu2DVevXsXdu3f5WUd4vD9nb378+DHq1av3x2vr1asHAPy4hS1btmD9+vWYO3cuVq5ciWvXrlVyVcLj2LFjWLp0KebMmSNqKTUSQXe/+bvgYg5jgDPUGdatW4f9+/fjxIkTaNq0KS5dulRnjIUSv/fc3FwRK6l9XLhwAVwuFwMHDhS1lAqzfv16qKuro3///mWeLyoqwu3btzFy5Ej07dsXbdu2xdu3b/n+3jNnzsTnz58REhKCoqKi6pTOIGJycnLQr18/EBFu3bqFv/76i//A36tXL8THxyM4OBiPHj0ql3/2sWPHwOVyYWxsjPHjxyM+Pr7M6/z9/aGpqclPjWhoaIgFCxZg0aJFAFCjdptLsiIdPnxY6GMXFhYKfczqpsQHXNAm7jAGOEOdgcViYdKkSfj06ROsra3h7OwMBwcHvH79WtTSqpyS7ABaWloiVlL7eP78OVq2bFlmJT5x4NSpUzh27BiWL1/ON5z+y8qVKyElJYVevXohMDAQkyZNwtWrV6GiosK/pqSIyoQJE6q8CiJDzSA1NRXz58+Hnp4e3r9/j8WLF8PQ0PCn69TU1GBqaorOnTuXK01f586d8eHDByxevBheXl4wNzf/KWiTiHDz5k0MHjz4p/zzqqqqMDAwKFWlU9T8/fffsLe3B4/Hw4YNG4Qypq+vLyZNmiS2bm8MjAHOUAfR19fH9evXce3aNURGRsLGxgYDBgzAu3fvRC2tyggMDISBgYHYVgyryURGRopdTvWioiJcu3YNPXr0wJgxY+Dm5oYpU6aUeZ2HhwcMDAwQEBCAwMBAbN++HdLS0j9dJycnhwMHDlTXEhhESEFBAQYPHoxNmzYhLS0Nurq6GDBggNDGL6ndEBwcDFtbWzg4OKB58+Z48OABgOKUquHh4b+sYtmjRw8cOnSo3DENVY2UlBTf8C7J3lIZ8vLyYGtri4MHD1Z6rJoA44LCwFDH6NevHz5+/IgTJ07gw4cPsLKygqenp6hlVQlBQUGwtLQUtYxaiY6ODhISEkQto9ycPXsWRkZGGDBgANLT03Hy5EkcPHiwzOwtFy5cQGhoKE6cOAEbG5syU8IVFBTg4MGDGDduXLlLkTOIN+fPn8eTJ0/QpUsXAMC8efOqZCdWVVUV169fx/HjxyElJQUHBwdMnjwZU6ZMgb6+Pnr06FFmvxUrVkBCQgKHDh0SuqaKYm1tjZycHJw+fbrCY+Tm5sLZ2RmysrJQUFDAgAEDcPXqVeGJFBFMECYDQx1EQkICY8aMQUhICCwsLDBy5Ehs2bKlVvmGx8XFwd/fH82aNRO1lFqJtrY2oqKi+JlmajI7duzAiBEj0KJFC/j7+8Pf3x+jR4/mu5D8Lz9+/ICEhARsbW1/OWZiYiJ4PN4vjSGG2kdoaChUVFRw69YtfP36FdOnT6+yuTgcDsaOHYt79+5hzpw5uHv3LuTl5XH+/Plf/txqaWmhe/fuCAgIqDJdFaEsFy9B8PHxwaVLl8BisZCVlYUePXrwH4LEGWYHnIGhDiMhIYHHjx/DxcUF7u7uZRaKEFdOnz4NLpeLGTNmiFpKrcTJyQlxcXE1OsNBZmYmpkyZglmzZmHevHm4fPkyWrdu/cd+urq64HK5v00vePPmTXA4HLRr106YkhlqKHfv3sWmTZvQvXt3yMjIVJv7lYqKCjZu3IiIiAj4+vr+8eeXzWbj27dv5cq8Ii5YW1ujffv2UFBQQNeuXeHs7CxqSUKBR1ShJu4wBjgDw/+jpqaGPXv2QE5ODh8/fhS1HKERHh4OExMTfq5nBuHSvn177Nq1C7t27eL7qNYkMjMz4ejoiNOnT2P79u3YsGFDuV1FOnXqBBkZmd++Nj9//jy6d+8ONTU1YUlmqKEkJSVh+vTpaN++faVcKaqDv/76C4GBgbCwsICHh0eteKtZr149PH/+HBkZGXjw4EGtCcAs4lWsiTuMAc7A8B9YLBaMjY1rTPBOZcnMzMSDBw9gbGwsaim1msmTJ8PY2BjHjh0TtZRSfP/+HRYWFggMDMT9+/cxc+ZMgfy0FRUV0b17dzx79qzM89HR0Xjy5AmGDx8uLMkMNQgiwokTJzB//nw4OTnByMgIWVlZ2L1790+BuDWNTp06wdfXF40bN8bIkSPh4ODAzwZV1QQFBcHf379WGP0MVQdjgDMw/A8aGhq1omQ9EcHNzQ0pKSlYuXKlqOXUakpSXF64cKFGBWS+ffsW0dHR8Pb2Rps2bQTun5aWBl9fX5ibm5d5/uzZs5CRkRHrHOgM/0JEePnyJY4dOwZ3d3c0aNAALi4uOH/+PFJSUuDu7o43b97AzMxM1FLLRdu2beHl5YVbt27h3bt3aNSoEV69elXl81pZWaFt27ZwdHREYmJilc8n7jAuKAwMDAAABQUFZGVliVpGpfHw8MClS5dw/PhxZge8Ghg3bhykpaWxdetWUUvh06BBA7DZbDx58kTgvkSECRMmID8/HwsXLizzmgsXLqBPnz5MestaQEhICOzs7NCmTRu4ublh//79GDx4MG7fvo3IyEg8e/YMS5cuhba2tqilCoyjoyPu3bsHAFi6dGmV70yXxNvcvXsXVlZWNS4YtKbBI0KRgI0xwBkYaiHS0tK1omLk06dP0axZMzg5OYlaSp1AWVkZ8+fPx44dO/D27VtRywEAmJiYYPr06Vi3bh3y8/MF6nvmzBl4enrC2dkZmpqaP52PiIjAmzdvak0gWF3k27dvOHjwIObPn482bdrg+/fvuHHjBpKTkxEfH4+dO3fCwcFB1DKFQrNmzXDgwAHcuXMHPj4+VTrXli1bMH78eADFWYJat27N5Mj/DcWVLQXdARe16srDGOAMDP9DbGys2FY1BIp3Ljds2AAPDw+0bNlS1HLqFPPmzYOlpSX69OlTY0phjxkzBhkZGVi3bp1A/e7fvw9ra2scPXq0zPOXL1+GjIwMevXqJQyZDCKgVatWmDRpErZs2YJx48bBx8cHvXv3hoaGBhQVFUUtT+iMHz8empqasLOzw4wZM6rMPYTNZuPgwYPYuHEj/9jkyZOxf//+KplP3GGCMBkYGAAAYWFhaNKkiahlCERBQQF8fX1x+PBhLF26FAsXLoSrq2uNcoeoC0hJSeHEiROIj4+Hv7+/qOUAAFq0aAGg+GdEEGRkZH6b2/z69evo0aMHFBQUKiOPQUTk5+fj69evAIrd1bZt2wYVFRXRiqpi2Gw2Fi1aBDabjV27dlVpalYWi4V58+bh48eP/IdUfX39KpuPQfyQELUABoaaRFZWFhITE9G4cWNRSykX7969w8KFC/HkyZNSbjPt27fHjh07mMqEIqDk7cn3799FrKQYHo8HBQUFSEpKCtSvY8eOOHDgAD59+vRT0F1GRgZ8fX2xa9cuYUplqEYkJCQgLS2NyZMnY+jQoaKWU23MmDEDf/31F6ytrfHx40fweLxfFvQRBmZmZrh582aVjV8bqEhQJeMDzsBQy1u+TTwAAEU1SURBVAgLCwOAGr0D/v37d6xcuRLt27dHy5YtERMTg1WrVuHly5coLCwEEeH58+eM8S0ipKSkAEBgn+uq4sePH8jKykJsbKxA/QYPHgwlJSVcu3btp3Pe3t7gcrlM9Usx5vjx48jPz8fo0aNFLaXaYbFYWLFiBT59+oRRo0YhLy9P1JLqNIIGYJY0cYfZAWdg+A+fP38GUBy8VhO5ffs2xo8fj4yMDDg4OODAgQMYO3aswLubDFWHvLw86tevj+DgYJHqICLcv38f27ZtA1BsUAuClJQUdHR0yjTcjx8/jlatWsHQ0FAoWhmqH39/f9SvXx9WVlailiIS+vfvj4sXL2L06NGIiIjAnTt3oKysLGpZdRIeIHBQZS1wAWd2wBkY/svnz5+hoaEBVVVVUUv5iaNHj6JXr16wtLREcHAwLl68iPHjxzPGdw2DxWKhTZs28PPzE8n8PB4Px48fh6WlJXr06IHY2FhcuXKlQsGSDg4OOHHiBFatWoXExEScOXMG9vb2uHXrFsaNG1cF6hmqi/79+yM5ORn3798XtRSRMXjwYDx9+hQhISHo3bt3rch+JY4U8ahCTdxhDHAGhv8QEhICU1NTUcv4icePH2PSpEmYOHEibt++jYYNG4paEsNvsLW1ha+vr0j+oE+YMAGurq4wNDTE48ePERgYWOFCOStXrsSYMWOwYcMGaGtrY9SoUeDxeNizZw8/zRqDeFISo+Do6PjbYNvaTqtWrXD79m34+Pjg3LlzopZTJ6EKFOGpDVVGWVSOVWRkZEBZWRnp6elMwQWGWo2dnR0MDQ1x8uRJUUspRffu3ZGVlYVnz54xO95iwOfPn2Fqaopr166hX79+1Tp3w4YNERcXBx6PJ7Q4gLS0NFy6dAmtWrWCtbW1UMZkEC3//dkIDQ2FkZGRCNWInk6dOqFevXq4evWqqKUIhDjbZyXaLwV8gZyCYGkvc7IyMbiVkViuuwRmB5yB4T8kJibWyEpvX758gZ2dHWN8iwkmJibQ1dXFs2fPqn1uGxsbKCsrCzUIV01NDZMmTWKM71pEyfeSxWJBT09PxGpEj56eXo3JXFTXKKKKNXGHMcAZGP5DYmIitLS0RC3jJ0xMTBASEiJqGQwC0KVLF1y6dKnas6FERkbC0dGxWudkED8eP36Mhg0bomvXrpCWlha1HJGTnp5eK9waxBHBq2AypegZGGoVr1+/RnZ2do1MQRgTEwMDAwNRy2AQgPnz5yM6Ohpz586ttj/subm5eP/+Pezt7atlPgbxRVFREXPmzMGLFy+Y4EMAzs7OePbsWZWXqWf4GSYIk4GhjrNu3To0adKkxpXW9vX1RUhICGxtbUUthUEAzMzMsHfvXuzZswerV6+uljm9vLwAABYWFtUyH4N406NHD7BYLLi4uIhaisgZOXIkpKWla0wF27oEswPOwFCHISJcu3YNEydOBIfDEbUcPjdu3EDXrl3RunVrDBo0SNRyGARk0qRJWLx4MdauXYvIyMgqm4eIsG7dOgwfPhz9+/dHq1atqmwuhtqDubk5Fi9eDE9PT4SHh4tajkjJyMhAfn5+jUxBW9upaT7ga9euha2tLeTk5KCiolLmNdHR0ejbty/k5eWhrq6OGTNmoKCgQKB5GAOcgQHFgUgqKioC/wJVJWlpaXBzc0O3bt3w5MkTfoVFBvFi4cKFUFNTw6JFi6psji1btmDx4sVYtGgRLl26xFRBZSg3s2fPhpycXI3L/FTdXL16FSwWi3HfYkBBQQGcnZ0xZcqUMs8XFRWhd+/eyM7OxvPnz3Hu3DlcvnwZc+fOFWgephImAwOKdxClpKSQkZEhail83N3dUVBQgMOHD0NGRkbUchgqiLy8PNasWYNx48Zh6tSpsLOzE+r44eHhWLJkCdzd3avN1YWh9iAnJwdlZWVkZ2eLWopIuXHjBtq2bQsdHR1RS6lzVMSlpCpdUFauXAmguOJvWXh7e+PTp0+IiYnh/7xs3boVLi4uWLt2bbnTIjI74AwMAAICApCQkAAHBwdRSwFQ/Mfg2LFj2LRpU43MysIgGC4uLmjVqhUmT56MvLw8oY2bnZ0NZ2dnNGjQAMuWLRPauAx1i+7du+PChQvVnrGnJpGYmIjGjRuLWkadhMejCjWg2HXov606foZ9fX1hYWFR6mGtZ8+eyM/Px+vXr8s9DmOAMzAAyMnJAYAaYex6eHjAyckJffv2ZaoN1hLYbDaOHDmCL1++YMmSJUIZ89u3b+jevTtCQ0Ph6ekJBQUFoYzLUPdwd3dHXFwcDhw4IPSxU1JS4OHhgYkTJ6J///7o2rUr2rZti9WrV9eYN45FRUUIDg6Gvr6+qKXUSXgV8P8uSYKiq6sLZWVlflu/fn2V601MTISmpmapY/Xq1YOUlBQSExPLPQ5jgDMwAPzy858/fxaZhqKiIkyYMAEjR47EiBEjcPnyZbDZzK9obcHS0hKTJk3ChQsXKj1WdHQ0OnTogC9fvuDRo0do3ry5EBQy1FVMTU3Rp0+fSleBTE9Px8WLF7Fw4UIsXrwYbdq0gaamJkaOHAlfX19wuVxoampCX18fa9euhZqaGrS0tDBnzhzExcUJZzEV4PLly/j27RsGDBggMg11mcpkQYmJiUF6ejq/LVy4sMw5VqxYARaL9dv26tWrcmsuK86GiASKv2F8wBkYAKiqqoLNZiM+Pl5kGi5cuIDDhw9j//79mDhxIhNIVwu5cOFCpV1Q7t+/j1GjRkFWVhbPnz/nPzwyMFQGFRUVJCUlVajv/fv34enpiTNnziA9PR06OjogIrRv3x5TpkxBz549f6owHB8fj6tXryI8PBy7d+/GP//8Ay8vL/Tp06fUdc+ePYOxsfFPO47C4smTJ5g7dy7s7e3RsmXLKpmD4fcUEaFIQJ/ukuuVlJTK5XM9ffp0DBs27LfXlPcNiJaW1k/pKr9//47CwkKBfk4ZA5yBAcCLFy/A4/HQrl07kcwfFxeHZcuWoWfPnpg0aZJINDBUPUZGRoiKihK4X2JiIp4+fYpjx47hzp076Ny5M86fP4/69etXgUqGukZaWhr8/PzQrFkzgfv6+fnB3t4eBgYGmDBhAmbPnl2uQEYdHR1MnToVAODq6gpLS0s4OTkhJiaGb8ScPn0ao0ePxtSpU7Fnzx6Btf2JT58+oVu3bmjXrh0OHTok9PEZag7q6upQV1cXyljt2rXD2rVrkZCQwH+w9Pb2hrS0tEAPcYwBzsAA4NatW9DS0kKLFi2qZOyYmJhf7mqnpqbC1tYWPB4P27dvF/r8DDUHe3t7LF++HBkZGaV2bbKysnD58mVcuXIFeXl5iI2NhbGxMbhcLp4/f44fP34AANq0aYMzZ85g+PDhzBsSBqHRq1cvfPnyBfPnzy/zvK+vL06fPo3ExETExMTAyMgIKioqSExMxOvXr2FkZISQkJAKu8xZWFggODgYXbp0wYABA/DgwQOsXLkSmzZtAlDsnlcVrFq1Ctra2njw4AGT5lWE/DeoUpA+VUX0/7V353FRluvjxz8My7Dv+6IIKIoK7opKCpqaS3o8Sp1yaTn2MzM937KyLE1LOy1mm5VZaWZ7RllmmmguiaYYsoMsyiYIsu/MzPP7w8N8v5w2QBFGrvfrNS+Qmed5rhtFrrmf677unBxKS0vJyclBq9USFxcHQEBAANbW1kyaNImgoCDmz5/Piy++SGlpKStWrGDRokWt7oACkoALQUNDAx9++CF///vfOySpWbZsmf42q7+/P0uXLsXV1ZUePXqQmprKQw89RHV1NXFxcfj4+Fzz64uuo3l2JCAggOeeew5jY2NeeuklUlJS0Ol0jBkzBmdnZ8aPH096ejr19fVMmTIFKysrHn/8cenSIDpETU0NFhYWZGZmUltbi6WlJQBnzpzh2WefZffu3fTo0QN/f3/69+9PamoqNTU1eHp6MmrUKNatW3fV61X69u3LCy+8wIIFC1i4cCG7d+/G2tqa6upqoqOjKSoquqZlKEeOHOGzzz5j+/btXSb5Li0t5cEHH6Rv3748+eST3eZNtpa2b6zTMW/Jrli9ejUffPCB/s+DBw8G4NChQ4wfPx5jY2P27NnDkiVLGDNmDBYWFtxxxx289NJLbbqOkaL8deFNZWUldnZ2VFRUtCm7F8IQNN/mTE5Opl+/ftf8/B988IF+q2cjIyOaf+RUKhU6nY5+/frx5ptvMn78+Gt+bdH1/Pzzz6xdu5Yff/wRgDlz5jBp0iQmTpxIr169Ojk60R3l5OTw1ltvsWnTJpycnPDz88PExIQjR47Qs2dPZs+ezYYNGzo8Uc3OzmbKlCmkp6dz6623snv3bv1zr7zyCsuXL79m17rnnns4evQo6enpXSbRzc7Oxs/PD4AZM2bwr3/9i4iIiD89xpDzs+bYNx6Ix8LKpk3H1tVU8fDEYIMcdzNJwEW3N3LkSOzt7dm3b1+HXSM+Pp5p06YRFBTE6tWr0Wq1nDt3DkdHR2bOnCndTroZRVE4ffo0VlZWBAUFdXY4QgCQkZHBK6+8QlVVFbW1tYwaNYrly5djYnL9bpZrNBr27dtHUlLSb0pivvnmG2699darvsbPP//MTTfdxIYNG/6w7Kaz/M///I++FNHY2JiCgoI/XethyPlZc+wv/Hi2XQn4ozeHGOS4m0kCLrq1xMREBg4cSFRUVIe3oBo+fDhlZWXs379fP8shhBDi9ymKQmFhIUuWLOHcuXMkJSWxbds2/R3F9oqMjCQlJYW4uDiMjY2vTbDXSGNjI9OnT9ffIXv++ee5//77sbH5/QTVkPOz5tj/vS8O8zYm4PU1VaycPMggx91Mpt1Et6XVann++edxcXFh2rRpHX69rVu3UlJSwhtvvNHh1xJCCENnZGSEh4cHUVFRJCQkEBkZybp162jFvOEf0ul07Nu3j8jIyC6XfAOYmZmxa9cufUeaxx57DFtbW6Kjozs5MnGtSQIuuq1Vq1axc+dOFixYgKmpaYdfLzs7m4qKig7ptCKEEDcyIyMj5s+fT3Z2NmfPnm3z8Yqi0NDQwMmTJ6msrOzSpV82Njbs3Lmzxddee+01mpqa0Gg0fPPNNyQkJHRSdNeeVqe062HoJAEX3VZTUxMAzz77bIdfKy4ujnvuuYfZs2czf/78Dr+eEELcaMLDw+nbty9Tpkxh8+bNnDt3Do1Go38+ISGB8PDwFrshlpWVsWbNGtzc3DA3N2f06NF4eHgwYMCAzhhCqw0cOJDw8HBGjBjB4sWL2b17N56envTp04dZs2YRHBxMcXFxZ4d5TXTXBFzaEIpuy83NDWtr62uywEin0xEbG4uNjQ0WFhYUFBRQUFBAZmYmn3/+ub5X7nvvvddlVtwLIYQhsbKy4vDhw9xzzz3861//QqPRYGFhoU9UX3zxRWpqavjpp59wdnZm7Nix3HnnnRQWFnL33XczfPhwrKysuPnmm7t83bCiKJSVlTFo0CDeeust7r//fr744guKiopITEwkKSkJZ2dnqqqqOjvUq6bV0eaEWqvroGCuI0nARbc1fvx4HnvsMY4fP85NN910Vec6ePAgN99882++bmFhweTJk3niiSeYNm0aarX6qq4jhBDdmaurK9999x0VFRWcPHmSs2fPsmfPHtatW8ff//53Fi1axPbt21m5ciUajYaQkBASEhIMqsVnVVUVN998M3FxcfouLcHBwVy6dInXX3+dpKQkFixYcMNM5rRnRltmwIUwYFlZWcC12WUtMDAQgKVLlzJjxgw8PDzw8vLCwcHhhvlPUgghugo7OzsmTZrEpEmTeOSRR2hsbNT3Kb/55ptZu3YtJ06cYO7cuQY38ZGQkMDJkycBWsS+ePFiioqKmDVrFk899VRnhXfNSQIuRDfz4osvMn36dMLDw6/6XElJSQAsWbKkQzbzEUII8ceak2+tVotKpSIgIICAgIBOjqptdDode/fuJSwsjB07drB161Zmz57NiRMnsLKyIisri3Xr1vHkk092dqjiGpAEXHRLTU1NJCYmcvfdd7fr+HXr1rFv3z5mz56Np6enfhOfCxcuSAIuhBCdQKPR4OPjQ2FhIbNmzWLSpEnEx8eTnp5ObW0tr732GsOHD+/sMH9XVVUV06ZN4+jRo9xyyy0EBASwePFijh49yokTJ3jvvfcICgpixYoVnR3qNadrxwy4TmbAhTBMqampNDY2EhIS0uZjy8vL9avqn3jiCRobG3F2dmb16tVMmDChA6IVQgjxVzQaDYWFhQDk5eWxZMkSAgMD6devHzExMURHR3fZBPydd97h6NGj3HzzzezduxdA34rw2WefpaqqilOnTmFubt6ZYXYIrdKOEpSr6AXfVUgCLrqluLg4AD788EN0Oh3jxo1r9bF2dnZYWloSHh6OiYkJY8aMYfHixR0UqRBCiNYwNzdnzZo1rF+/nh07duDr64uFhQVarRZvb28qKio6O8TfdfDgQVasWMHChQt55513iIqK4tlnn0Wr1eLp6YmNjQ0vvPACAwcO7OxQO0R3rQGXPuCiWzpx4gRwZXfK8ePH6xPy1jAyMuKpp57i008/ZefOnTzwwAM0NjZ2UKRCCCFa64knnsDU1JRvv/0WCwsLFEVh3bp1FBUVERER0dnh/UZWVhaLFi1izJgxbNu2DVNTU/bt20dqaipvv/02CQkJxMTEEBYW1tmhdpju2gdcEnDRLf3yyy/6z1UqVZtaVCmKou+c0lx6ci16iQshhLg6ZmZmzJw5k+eff57Y2FgeeeQR/fb1v9cqtrNotVq2bNnCsGHDUKlU7NixA0VRmDdvHtu2bWPbtm3cdNNN3aKLlkantOth6CQBF93S2LFj6dmzJwBDhw7Fzs6u1cfGxMTw5JNPsmrVKgoLC1mzZg0qlfwoCSFEV/D6669jYWHBsGHD2LhxI6GhoXz++eedHRYApaWlbNu2jcGDB7N48WJmzpzJiRMnMDIyYsmSJXz88cfccsstzJs3r7NDFR1Mpu1Et6PT6fjoo48oLy9HrVa3uQ1hUlISKpWK1atXY2xs3EFRCiGEaA9nZ2fi4+NJSEhArVYzatSozg4JgCNHjhAZGcmlS5eIiIjgxIkTjBw5kkOHDjFx4kTs7Ozw9fXtdov5u2sNuCTgotvJyMiguLiYxYsXU1hYyPz581t13O7du/nqq684efIkgwYN0vedFUII0bU4Ojq2aXF9R3v77bd58MEHGTNmDKdPn8bb21v/3L///W+Cg4P5+eefsbS07MQoO4e0IRSimzh16hQA69evx9HRsVXHPPjgg7zxxhsEBwfj6urK448/3pEhCiGEuEFER0ezZMkSlixZwiuvvNJizdCBAwfYv38/n3/+ebdMvuE/bQjb2FbwRmhDKIWroltJTU1l5cqVDBkypNXJd25uLps3b2b9+vXExcVx+PBhpkyZ0sGRCiGEuBG8+eabKIqClZUVUVFRaLVampqaeP3117ntttsYPXo0c+bM6ewwO410QRHiBtfY2Mi4cePIy8sjODiYkydPtuq4kpISjI2N+fbbb0lJSUG5Ad55CyGEuD4eeOABhg4dyueff05kZCROTk64urqyfPlyZs+ezddff90tup38ke6agEsJiug2TExMePTRR3nppZfYvn07BQUF+i3k/8zgwYM5evQot912G/3798fFxYX9+/czaNCgjg9aCCGEQYuIiOD06dPAlRa40dHRaDQaZsyYIb9HujFJwEW30dzvu7CwkMjISLZs2dLqY0eNGkVSUhKrV69m06ZNsgBTCCFEm40YMYIRI0Z0dhhdSnftgiIlKKLbUBSFf//730yYMIFPP/0Ue3v7Vh+bnZ3Nfffdx6uvvkp4eDhBQUEdF6gQos0URSEmJkZKxIQwMFpFh1bXxoei6+ywr5rMgItuo7a2llOnTvHee++1qd5Oq9Uya9YsLl++zOuvv84999zTgVEKIdpKp9NhbW1NXV0dLi4u9OnThy1bttC/f//ODk0I8RekDaEQNzhLS0tMTEyor69v9TGKovD2228THx9PTExMl9nQQQjxv4qLi6mrq9N/XlxcTGpqqiTgQhgArU5B1Q1LUCQBF92GkZERTk5OlJSUtOr1aWlpzJs3j9OnT7NgwQJJvoXoohwdHRk7diwlJSXY2toyePBgZs2a1dlhCSFaQaMDozYm1BrDr0CRBFx0L4MGDeL999/nwQcfxMHB4U9fO3fuXBobG/n++++ZPHlyu6+Zn5+PjY0Ntra27T6HEOKPmZqacvTo0c4OQwghWk0WYYpu5bHHHuPChQut7gE+fPhwbrnlFlSq1v+oJCYmEhkZiaOjIxEREfTo0QMvLy8+/fTT9oYthBBC3JC6ax9wScBFt3LhwgUAPDw8/vR1VVVVeHl5sXPnTr744otWn19RFAYOHMgXX3xBWVkZhw4dQqfTUV1dzfbt268mdCGEEOKGIwm4EN3A7NmziYyMZPTo0Tz88MMUFRW1eD4nJ4dly5bh6urKDz/8AEBkZCQajaZV56+vr8fe3h6VSsXUqVOxtbVl1KhRPPTQQ5KACyGEEP+luybgRkormqZWVlZiZ2dHRUWF1LEKg5eVlYW/vz9wpXZ05cqVrF69GmNjY3r27EleXh4mJiaEh4dz+vRpvLy8OHPmDCYmrVsy0dDQQFVVFc7Ozh05DCGEEN2cIednzbFHvLQPEwurNh2rqavh4IrJBjnuZjIDLrodX19fpk2bBkBTUxPPPPMMlpaW9O3bl9zcXFQqFenp6ezbt4+9e/eSnJzMihUrqKmpadX51Wq1JN9CCCFEK3TXGXBJwEW3o1Kp+O677/j888/1iyubmppIT08H4Pnnn8fX1xe4sm3whg0beOONN+jRowePPvoou3btIjMzs7PCF0IIIYSBkwRcdFtz584lNzeXJUuWYGpqipGREbNmzcLV1ZW4uDj9ltaPPvoou3fvprS0lBdffJE5c+YQEBDAzz//3MkjEEIIIQyboigoujY+/rp6usuTPuCiW/P09GTz5s2sWrWKzZs388EHH/D1118DEBISwrBhw6ivr+ebb75pcZyXlxeDBw/uhIiFEEKIG4dOp7R5a/kbYSt6mQEXgiuJ+Pr168nLy6Oqqoo9e/bQv39/4uPj9TXgeXl5/PLLLyQkJJCdnY2lpWVnhy2EEEIYNEVR2vUwdDIDLsR/sba2ZurUqUydOvU3z3l5eXVCREIIIcSNqbmspK3HGDpJwIUQQgghRKeQEhQhhBBCCCFEh5MZcCGEEEII0SkU3ZVHW48xdJKACyGEEEKITtGeRZWyCFMIIYQQQoh2khpwIYQQQgghrqM2b8LTjq4prXX+/HnuvfdeevXqhYWFBf7+/qxZs4bGxsYWr8vJyWHGjBlYWVnh7OzMsmXLfvOavyIz4EIIIYQQonO0J6HuoAQ8NTUVnU7Hli1bCAgIIDExkUWLFlFTU8NLL70EgFarZdq0abi4uHDs2DEuX77MwoULURSF119/vdXXkgRcCCGEEEJ0e1OmTGHKlCn6P/v5+ZGWlsZbb72lT8D3799PcnIyubm5eHp6ArBx40buuusu1q9fj62tbauuJSUoQgghhBCiU+gUpV0PgMrKyhaPhoaGax5fRUUFjo6O+j/HxMQwYMAAffINMHnyZBoaGoiNjW31eSUBF0IIIYQQnUJR2lED/p8E3MfHBzs7O/3jueeeu6axZWZm8vrrr7N48WL91woLC3Fzc2vxOgcHB8zMzCgsLGz1uaUERYgOlpqaypkzZ7Czs2PKlCkYGxt3dkhCCCFEl3A1W9Hn5ua2KPlQq9W/+/qnn36atWvX/uk5T506xbBhw/R/LigoYMqUKcydO5d//vOfLV5rZGT025gU5Xe//kckARfiGtPpdOTm5hIXF8eHH37Irl279M8tWrSId955pxOjE0IIIboOnQ6M2tyG8MpHW1vbVtVcL126lNtvv/1PX+Pr66v/vKCggPDwcEJDQ3/zO9vd3Z2TJ0+2+FpZWRlNTU2/mRn/M5KAC3GVFEWhoKCAoqIinn32WaKiov7wtbfddtt1jEwIIYTo2q7HRjzOzs44Ozu36rX5+fmEh4czdOhQtm3bhkrVslo7NDSU9evXc/HiRTw8PIArCzPVajVDhw5tdUySgAvRTg0NDbz77ru8+uqrnDt37jfPjx8/HgBvb28WLlxIWFjYH94eE0IIIUTnKigoYPz48fTo0YOXXnqJ4uJi/XPu7u4ATJo0iaCgIObPn8+LL75IaWkpK1asYNGiRa3ugAKSgAvRLufPn+e+++4jOjqayMhIfQK+adMmFixY0GLFtBDdnUajoaamBhsbm9/MJonWUxSFqKgoioqKGD9+PP369evskK6pLVu28Nprr/H0008zd+7czg5HXCeK7sqjrcd0hP3795ORkUFGRgbe3t4tr/mfWXdjY2P27NnDkiVLGDNmDBYWFtxxxx36NoWtZaS0Yh6/srISOzs7Kioq2pTdC3GjOXbsGOvXr+eHH37AxsaGqKgoJkyYgIeHB2VlZRQVFWFnZ9fZYQrRaaqrq9m/fz+HDx8mLi6OtLQ0ioqKAFCpVLi4uODr68uMGTNYtmwZNjY2nRzxtaHT6fj+++8JDw/Hysrqmp//119/Zfny5Rw9ehRjY2O0Wi3Dhg1j9erVTJ8+/S8XfzXH99NPP1FbW0tKSgoJCQkMGzaM5cuXc8stt1zzmH+PRqOhurqauro6FEWhqamJixcvUlhYyOrVq0lISMDMzIysrCy8vLyuS0yGzJDzs+bY+y37HGO1ZZuO1TbUkvJapEGOu5nMgAvRChkZGTz++ON8+eWXhISEsH37dnr37s3q1auZOXMmNTU1AKSnpzN8+PBOjlaI60+n0/Hmm2/yyCOPUF9fj5+fH8OHDyc8PBwfHx+sra2pqKigsLCQpKQknnzySY4ePcoHH3zQpoVLXZFOp2PdunWsXbuWAQMG8OOPP+pvV1+ts2fPsmzZMo4cOULfvn3Zv38/Y8eOZd++fbz66qvceuutDBkyhJUrVxISEoKJyZVf6+Xl5eTl5XH+/HlSU1PZvXs3+fn5+Pr6Ym9vj4+PD/fffz/R0dFMnTqV0aNHM3DgQJqamggLC2P69Om/WzNbUlLC5s2b+eqrrygqKsLe3p7evXvj6emJoiikpqZSW1uLqakpXl5eeHt7Y21tTUJCAsnJyWRnZ6PVan93rFZWVkRFRfHAAw8wdepU1q9fT0BAADU1NdTV1aHRaGhoaKC8vFy/7bdWq0Wj0WBsbIy5uTnW1tY4OjrSv39/7O3tr8nfgehYV9MFxZDJDLgQf+H06dNMmDABOzs71q9fz6233sojjzzCu+++S58+fVi0aBFeXl5MnDix1Ys8hDBUza22qqur+eSTT9i6dSuFhYXU1tZy+fJlZs2axcaNG/Hz8/vT87z88ss8/vjj6HQ65syZw6BBg/D29mbq1Kk4ODi0eG1jYyMHDhygR48eBAUFXdcyloSEBPbu3cuvv/6KpaUltra2XLx4kZycHAoKClCr1dTW1pKXl8ecOXM4duwYxcXF+o06mrsiqdVqHB0dUavVlJeXU1pairOzs/4W9oULF9DpdFRXV5ORkYFarcbW1pbo6GiCgoJYs2YNs2bNwtTUVB+boigcOnSIDRs2EB0d/bvxq9VqfH19mTRpEnfeeScjR45s8byiKHz77bds3ryZkpISFEUhLi4ORVHw8/Ojd+/eODs7o1KpKCoq4qeffsLY2JjIyEj8/PwoLS0lIyODwsJCtFot/fr1w87Ojvr6evLz88nPz6eiooL+/fszYMAA/fnMzc1RqVQYGxvj7u6Oh4cHTk5OGBsbk5iYyN///nfS09Pb/fdmZGTECy+8wIoVK9p9DkNgyPlZc+yBSz5t1wx42pu3G+S4m0kCLsRfWLNmDevWraO0tBQHBwfCwsKIjY3lxRdf5L777mvxC1GIG41OpyM+Pp69e/fyww8/EBMTg4ODAzU1NdTW1jJt2jQGDx6MsbExN910EzfddFOre92XlpbywQcf8MEHH5CTk0NZWRnm5ubceuuthIeHY2trS0JCAjt37iQvLw8ACwsLevToga+vLz179sTV1RVbW1uMjIz429/+hr+//1WPuaCggKNHj/LJJ5/wzTffYGVlxZAhQ2hoaKCyshIPDw98fHzw9PSkqakJRVGYOXMmYWFhFBYW8t1333H69GkuXboEXNkspKmpidLSUhobG7G2tsbZ2ZmCggIOHz6MTqejV69emJqaYm5uTkBAAI2NjeTn5zNr1izuvfdezMzM/jTm1NRULl26RFNTE3ClPZu3tzdubm5tfsOSl5fHsWPHOHnyJBcuXKC4uBhFUXB2diYsLIyFCxd2+GSDoiicP3+enJwcbGxssLS0xNTUFFNTU+zs7DA3NweulDWZmJig1WppaGigqqqKkpISBg4cCMDmzZsJDQ1l4MCB+rsDNxJDzs8kAZcEXIg/9cMPP3DLLbdw9OhRxo4di6enJ1ZWVvzwww/X5Je9ENeaoih8//335ObmEhgYyNixY1v1RlGj0XD8+HESEhJIT08nOTmZs2fPUlxcjLW1NREREYwfP57q6mpMTU2588478fHxuWZxFxUVsX37dj777DP9LKybmxtTp07lwQcfpLy8nLi4OC5cuKB/FBcXU1VVRUNDA87OzsTGxuLq6trma2s0Gt59911ee+01UlJSAAgMDOSpp55i7ty5f5kAi67l2LFjPP300xw+fBiNRoOVlRUjRowgKCgIf39/fH199W/knJyc9Mf9+uuvVFRUMGbMGIOYXDHk/Kw59t73f9KuBPzcW/8wyHE3kwRciP84deoUb7zxBunp6UyfPp3HHnuMsrIyZs6cSVpaGllZWdjZ2XH06FFuv/12CgoKGDhwIAsXLmxz+yEhOkpjYyN33HEHu3bt0i/WCwsLY+nSpTg7O1NVVcW4ceP09bENDQ28/fbbxMTEEB0dTUlJCaampvj5+dGvXz8GDBhAREQEo0ePvq5tNHU6HTU1NVhbW7dqd7m8vDyGDh3K4MGD+eGHH9p0LY1Gw/z58/nss8+IjIzkb3/7G+PHjzf42nQBdXV1xMbGEhMTQ0xMDOnp6WRlZVFXV6d/jZOTk/5NW/ObL0dHRxYsWMDtt9/O0KFDu+zsuSHnZ82xB/y/j9uVgGdsucMgx91MEnAhgJiYGMLDw/H19aV///58/fXXPPLII/z888+kpKSwd+/eFosrq6ur2bNnD1FRUXz11VcEBgZy+PBhaT94g1IUhcbGxnYnoBqNhrKyMurr69FoNJiammJvb4+1tfVVx1ZaWsrZs2fJysoiJyeHL7/8kszMTD766CNuvfVWDh48yMMPP0xSUpL+GDs7O+6++2569+7Nrl27OHr0KKNGjWLMmDH87W9/Y+jQoa0uI+kqCgsLue2220hLS6OwsPB3X5Oenk56ejo1NTVUVVWRm5tLZmYmsbGxnDt3jo8//pjIyMjrHLm43hRFoaSkhJycHLKyskhLS6O0tJSmpiZGjx5Nnz59+Pzzz3n//fcpKSlBrVbj6uqKq6srnp6e9O3bl1WrVnWJjleGnJ81x+5/30cYm7UxAW+sJfOdOw1y3M0kAReCK/27n3jiCcrLy1Gr1Tz//POsXLkSCwsLHn74YZ555pk/PDYlJYVRo0Yxe/Zs3n///VbN1omuT1EU8vPz+fnnn3n11Vc5deoUo0ePprq6Wr/AKyIiAoC0tDRefvllDhw4oC/XCA4OJiAggG+++YaCggJ0ut82rrW1tcXJyQl7e3vuv/9+7r33XlQqFY2NjdTW1lJfX6+Ppbl9W1ZWFhcuXCAnJ4eDBw9y6tQp/flcXFyIiIhg+fLlhIaGtrhWQUEBtbW1qFQq3nrrLXbu3ElJSQmDBw9m1apVzJw5swO/mx0nNTWVxx57jN27dwOwcuVKnnvuuRavURSFhx56iFdeeUX/NSMjI9zd3fH19aVv377cfffdhIWFXc/QRRen0Wg4deoUv/zyC8XFxVy6dImLFy9y5MgR1Go1Y8aMISIigrvuuqvT2mkacn7WHLvfP3eiamMCrmusJevdeQY57maSgAsBPPPMM2zcuJHy8nLgyi/sxYsX8+6772Jpacnp06cJDAz8w+O3bdvGPffcwx133MGOHTsMbvbQkFVUVHD8+HF967G2zirX19eTk5Ojryk+e/Ysx48f17dTAxg6dChz587l5MmTODs7k5KSwrFjx5gzZw6KovDjjz9iZ2fHzJkz8fX1pbq6mjfffBOdTsfChQvp06cPLi4uWFpaYmxsjEajobS0lPz8fEpLS0lPT+err76iV69eaLVacnJy/jRmExMTvLy8GD58ONOnT2fEiBEEBAS0qWZVq9XS1NSkX8xmqJYuXcrmzZsBWLJkif7zZiUlJTz99NNs3ryZl19+mcjISGxtbbGwsOiyZQWia8vMzGTr1q2cOnWKI0eOYG5uTr9+/fDx8dFvq25jY4OFhQUajQYjIyMcHBxwdnamR48e9OnTh+Dg4GtSY27I+Vlz7L3u/bBdCXj2e/MNctzNJAEX3c6lS5f47LPP2L9/PydPntS3/po9ezYff/yx/nX19fX07t2b6upqHB0d9cnXH/nkk0+48847ef/997nrrruuw0i6j9LSUt555x2io6MxNzfHw8ODhx9+GGNjYyZOnMiFCxcA8PDwYOnSpVhYWJCbm0tWVhb5+fkUFRWh1WqxtbVl3rx52Nvbs2/fPuLj4/XHwpVZUX9/f8aMGUNISAj+/v4MHz4cDw+PFvHodDree+89Nm3ahIeHB0OHDv3NLemKigrUanWrE9yYmBi2b9+Og4MDvXv3xtbWFrVare9gYWpqioWFBb169cLLy0t2lPyPhIQEZs+eTUZGBhMmTODAgQP65z788EOWLl2KRqNh3bp1PPzww50YqbgR5ebm8vHHH5Oenk5eXh7GxsYoiqLfbMjU1BStVkt5eTnFxcX6SR4LCwuGDBnCoEGDmDVrFhMmTGjX3VNDzs8kAZcEXLRDeno6gYGBODo64uXlhZubG3Z2dvrWVx4eHpSUlKBSqXjooYewsLDo7JCBK7N+jo6OVFZWEh4ezpAhQygsLCQjI4N33nmH4OBg4Mqtx/z8fCIiIggKCuLEiRPMmjWLrVu3/un5+/fvT3h4OG+88cb1GE63kJSUxOzZs8nJyWHixImoVCpiY2PJz8/Xv+bUqVPU1NSwfPly8vLyqKurw8vLCz8/P3x8fHBzc8PExISsrCw+/PBDTExMCAsLY9iwYfTr10/f0s7b21u6XRiYs2fPMmjQIAAOHjzIwIEDycnJYffu3TzzzDPccccdvPzyy7i4uHRuoEJwJZ9KSkri+PHjxMbGcvLkSbKysli1ahXPPvtsu85nqPlZc+y+d+9oVwJ+ftsCgxx3M7n/JtokKioKtVpNRkYGcGVmsrS0lISEhD88ZuPGjVy+fLlV7+6zsrL4+uuvueeeezpkFzOVSoW3tzfJycmcOXOGQ4cO6Z8LCQlh9uzZJCUlkZGRgVarRa1Ws2nTJlJTU/V9iP/M2LFj+fDDD1m9enW7WqGJK+U/WVlZHDp0iKioKPbu3Yu/vz+JiYn6to+VlZUcOnSI2tpa3NzcGDZsGABxcXF/ef7HH38cT0/PLrGASlw9GxsbXF1duXTpkr4mH678rD/xxBOsXbtW7haILsPW1pbQ0FBCQ0Npampi0aJFZGVl0atXr84OrdPodAq0cWdLneyEKa61nJwczpw5w8CBA/Hz8/tN0pqSkoK9vT3u7u7XfbGfVqv9y5pJtVqNk5MTPj4++Pj4cPDgQYYPH853330HXBlfRkaGvizA09OTESNGAFfq6u6++26Ki4txcnJi4sSJ9O7dm8bGRkpLSwkNDWXevHl/OENZW1vLli1bGDBggP52Xm1tLZaWli2+V7W1tURFRZGbm0uPHj2IjY3l5ZdfJiAgAJVKhZ2dHaampjQ0NFBQUMDFixfx8vIiKirqL7eZz8/Px8fHh6effprVq1e35dvbrZ0/f553332XU6dOcebMGf3dkzFjxrBgwQIWLFggM9PiTzWXAJSVleHj40NgYKC8yRJdVlNTE3feeSdRUVG8++67LFiwoNuWoPjMf79dM+C5H95jkONuJgl4F/N7P4Dz589n69atpKWlERISAoCZmRl9+vTB2dmZn376CYAdO3Ywb96835wnPz+fqKgozM3NmTdv3lUtulq8eDFbtmzhyy+/xMHBAa1Wq1/01ryAbc+ePSQkJDBo0CCsra3RarXk5+dTUFCARqPRn8ve3l5fD9ds1KhRbNmyhe3bt3PmzBkyMjIwNzfH2tqas2fPolarsbKywsLCAjc3Nzw9PQkKCsLd3Z133nmHtLQ0FEXB2tqauro6tFot7u7ujBkzhsWLF+Po6EhJSQlJSUmcPHmSw4cPt2hZZmZmhouLC56ennh5edG7d29CQ0OZPHkylpZ//R+ETqdj3LhxGBkZceTIkXZ/n7uTXbt2MWfOHOzt7bnpppsYPHgwI0aMYOTIkS02yBBCCENWWVnJZ599RlZWFj/99BOxsbF88cUXV9WByJDzs+bYve/c2q4EPO+jRQY57maSgF9DjY2NJCcnEx8fT1paGmq1Gjc3N6ysrCgqKtLP7Pr5+WFvb49Go6G2tpaLFy9y6dIlrKys+OabbygqKtKXeDRbu3Yt48ePZ9y4cX94fX9/f/Lz8zE1NaV///7Y2tpy7tw5srOz9QtB+vTpw7fffou9vT0nTpwgODiYHj16tHqMBQUFjBgxgtLSUnx9fXFwcOC+++5r8e5dURS2bt3Kr7/+Sk1NDSqVCk9PT3x8fAgICMDf319fa1taWsqvv/6KmZkZdnZ2BAUF/eEse3JyMj/++CP19fXU1NRw6dIlcnNzSUpK4sKFC0ydOpUNGzZQW1vL4cOHsbOzw9ramrS0NHbv3t2iTMbCwoLBgwcTFhbG2LFj6dWrF97e3votrduiurqa7OxscnJy2LRpE9HR0WzdupV//vOfbTpPdzVlyhT27dsHXGnn16dPn06OSAghro3c3Fx27NhBUlIS0dHRXL58GR8fH3r16sXq1asZP378VZ3fkPOz5ti9/rEFlVnb1onpGuvI/+T/GeS4m0kC/jsURflNEtbU1ERKSgpVVVVotVpqa2u5fPkyhYWFJCYmkpCQQEJCAo2NjQB4e3uj0Wi4dOkSOp0OGxsbnJ2dKS4uprq6usW5TU1NcXFxoaamhoqKijbHa2RkpJ8x9PPzo7GxkcTERCorK+nduzchISFMmzaN/Px8pk+fTm5uLjqdjua/+kGDBtGvXz9cXV3121Z7enpSW1tLWVmZfoMPIyMjzMzM0Ol0fPvttxgbG9PQ0MCPP/6Iv78/x44dw93dvZ3f9avT1NT0py2ddDodp0+fxsTEBEdHR7y9va+6BZlOp2PHjh0sXbqUmpoaAAICAnjjjTeYPHnyVZ27OygvLycqKoq3336bX375BYDLly/LZkZCCIOn0+n44osvWLZsGfX19QQHBzNkyBBWrFiBj4/PNbuOIedn3T0Bl0WY/6HT6XjiiSfYt28fmZmZ+Pj4MGvWLH3NclxcnH5TjP/L0tKSoKAggoODWbhwIUOGDCE4OFjflF+j0dDY2KgvX1AUhcuXL1NVVYWJiQnm5uY4OTnpFwlpNBrKy8spKyujqqoKMzMzzMzMMDIyQqvVotFo9B9NTEz0O3S1Jmmxt7cnOTmZHTt2YGpqSlhYGLGxsezdu5ecnBzi4uLYvHlzizKRZmq1GiMjIxoaGvi/79ma36hkZmZy6dKlTkvA/6qfqkql0teat4eiKJSVlZGTk0NKSgqHDx/mwIEDZGZmMm/ePO6//368vLzw9vaWHuC/Q6PRkJqayi+//MKJEyeIiYnR78wYERHBli1bmD17tiTfQgiD9/XXX7Ny5UrS0tKYMWMG7733nnTh+ROKVoui1bb5GEMnM+D/ceDAAW6++Wb9jmjR0dEkJibSq1cvevXqxZAhQxg+fDiOjo6YmJhgYWGBk5MTFhYWN9TOh7W1tZw8eZKysjIsLS2v7FLl54erqytGRkb6/qYXL14kLy+P7Oxs8vLymDlzpr4VmKHT6XQcPXqUs2fPkpKSQlJSEomJiZSVlelf07dvX8aNG8e8efMYO3ZsJ0bbuRRFobS0lMLCQurr62lsbKSiooLCwkIKCgpIS0sjJSWFhIQE6uvrMTIyon///oSGhjJ69GgmTJhwTWeDhBCiM+3Zs4dbb72VyZMn8+STTzJ69OgOvZ4h52fNsXvMfR2VaRtnwJvquPjFgwY57mYGm4DrdDpqa2vbvOtdM0VRyMzM5Pvvv+fYsWPk5+dz/Phx/vGPf7TYjEUYvubFLqampiiKQm1tLVVVVdTW1mJqaoqZmRkmJiY0NTXR2NjIwYMH+fXXX1Gr1fTp04f+/fszYMAA+vTpQ8+ePfHz8/vTDXluVLm5uezcuZO0tDTOnz9Pbm4uBQUFv3tnCMDR0ZHevXvTr18/QkJCGDx4MEOGDOm0LZuFEKKjDRgwAG9vb77//vvr0v6yK+ZnrdUcu/vfX2lXAl64618GOe5mBluC8tBDD/Hqq68ycuRIpk+fjk6n05duXLp0iUuXLhESEsLy5ctRqVRUVFToywfi4+M5ceIEhYWFmJqaEhoaio+PD4sXL+a2227r7KGJa+ypp57i2LFjLb5mampK79690Wg0NDU10dTUpC/36dmzJ4cOHSIsLKxbl5M0NDTot1o+fPgw0dHRWFhY0L9/f3r16sWIESPw8vLSd4yxsLDAzMwMGxsb3NzcUKvVnT0EIYS4rrKysujfvz+tmNsU/6HotCi6NpagtPH1XZHBJuDe3t4A2NnZ8eKLL+rLJRwcHHBzc2PgwIF8++23vPfeey2Oc3Nzo3///tx9992EhoYyfvx4mZG7we3YsYM1a9aQmZlJbm4uubm5NDU1kZ+fz4kTJ+jbt29nh/iXampquHDhAufPnycjI4Pa2lr8/f0JCAhg4MCBV7WgtL6+nrS0NJKTk0lPTycjI4Nz584RFxdHQ0MDNjY2jB07lldffZUFCxbIz4sQQvyBVatW8dRTT9GrVy+ee+65G6pEtaNIAt6FnD9/niVLllBUVISrqysuLi4EBgYyc+ZM+vfvj5GREdOnT+exxx6joKCAVatWMW7cOIYMGdJiMV5NTQ3Hjx/HysrqSp2Rh4cs8upGFEVh3bp1HD58mPj4eEpLS7GxsUGtVtPQ0EBFRQUfffQRzzzzTGeH+ofq6+t5/vnn2bhxI1VVVcCVBbEWFhb6Huqurq5ERkbyyCOPtKmlJMC6devYsGEDDQ0NwJU3qAEBAQQGBnL77bdz0003ERwcfNUdY4QQojtYtWoVRkZGrFq1io8//hgXFxccHR255ZZbeOihhzo7PNGFdMka8O+++44ZM2YAMHXqVMrLy4mPj6e6upphw4bx3HPPMXHiRI4dO8bGjRvZv38/tbW1eHt789BDD+Hu7o6NjQ22trY4Ojri7OyMk5PTX3bKEDeWixcv4unpSe/evZk/fz5ubm5UVlZSWVmJo6MjkydPpm/fvl12huLXX39lzpw55OTkcN999xEZGanvV65SqSgtLSU5OZmoqCh27tyJq6srp06davVGS/X19VhbW7NgwQIWLVpEv379sLe31z+vKEqLEp3mEhMhhBD/Kysri/Pnz+v/rywuLmbTpk0t9p4A2LdvH5MmTbqm174RasBdZvwblWnbNgjUNdVT/O1Kgxx3sy6ZgBcXF7NhwwbeeOMNbGxsiIiIYMCAAezatYvExEQA/ew4XOkBffr0aV566SW+/vprdDrd7563uW2fWq3GxcWFIUOGMHToUEaMGMGQIUOwsrLq8LGJ6+fcuXP6TV3uuOMO3nzzzeuyNXVubi4xMTFMnjy5XdcrLS1ly5YtrF27lgEDBvDRRx8RGBj4p8fEx8cTEhLCRx99xB133NGq65SVleHq6qrvAQ9XfpZKS0spKSmhpKSkRUvKoKAgfetAIYTo7jQajf4uova/2uL5+Pjg7++Pp6cnbm5uODg48OCDD7aY5LgWboQE3Gna+nYl4Jf3rDLIcTfrkveVXVxc2LRpE4sXL+aTTz7hxx9/5MCBA9jb2xMQEICfn1+L1cXNCyl37dql745SVVVFZWWlPpm4fPkydXV1NDQ0UF9fT35+PrGxsXz99dfU1dUB8Mgjj/DCCy901rDFNda7d29iY2PZvXs3GzZs4MiRI8ydO5eRI0fq74o4Ozvj4eHR6sWWp0+f5vTp0/p2lGZmZlhZWWFlZYW1tTV79+5l7dq1+nKRV155heXLl//h+RRF4csvv+TEiROcP3+e7OxskpOT0Wg0LFu2jPXr12Nh8furw3U6HTk5OSQkJHD69GkAUlJSWv39cXBw4JNPPmHXrl36rxkbGzNo0CD998fKygpTU1O++uordu3axciRI/n444/x9/dv9XWEEOJGtGXLFtavX8+aNWu48847MTMzw9TUFDs7uz/8f1v8VnetAe+SM+DXklar1cf/3y2BCgsLOXr0KO+88w4HDhwAroxVFpndeE6cOMF7773H999/T0FBQYvn1Gq1vu55yJAhhIaGMmzYsN/9t25paal/w/ZHRo4cyQMPPMCCBQt49NFHef755/80rtDQUOBKQjxs2DAGDx7M/Pnz8fLy0vfYLiwsJC8vT78QMysri/T0dH0sLi4uDB06lKeffpqRI0e2+vvS3Mc7NzeXixcvUlZWpu8m1DwLXlJSQmFhIXFxcQBMnDiRH3/8sdXXEEKIG4miKGzcuJHHH3+c2267jZ07d3ZaLIacnzXH7jB5TbtmwMv2rTXIcTcz+ARcUZTfJNZ9+vShsbGR0tJSKisrgSvJTWhoKD179iQ1NZXk5GSKiooA6NGjBxMnTuSBBx5gyJAh130M4vpRFIWKigouX77M5cuXKS4uJjMzU79hTGxsrP7fTM+ePRkwYACBgYE4Ojpib29PXV0de/bsITY2Vj/L/Uc8PT1JSkr601uO58+fZ+HChZw8eVK/EPKPqNVqfH199Y++ffvSt29fgoKC8PHx0deyK4pCdnY2v/zyCxkZGWi1WnQ6HTqdjqamJn0yn5eXR25uLrW1tS2uY2Jigr29Pc7Ozri4uODs7IyrqyuBgYEEBgYSGhqKg4NDK77bQghx42ku+QO45ZZbeOaZZxg6dGinxNKV87O/ok/AJz6JURsTcKWpnrIDzxrkuJvdEAn4ihUrePnll3/znIODA9OmTWPGjBmkpKRw/PhxCgoK9EnLgAEDCA0N1bc0FEKn05Gamsrp06f1O2BmZGRQVlZGWVlZi5poAHNzczw9PQkICCAoKIgePXrg6emJvb09ISEhuLu7t+q6dXV1HDx4kKysLPLy8sjJySE3N5dLly7R0NCgf5OpKAomJibY2trqH46Ojnh7e9OjRw+sra155plnSE1NBa7MjJuamqJSqVCpVJiYmODm5oa3t7f+0aNHD3x8fPRdgqysrLrswlQhhOgKjh07xsGDB/niiy84d+4cixYt4p///CfBwcHX9f/Prpyf/RVJwA08Af9vZ8+eZcuWLZw9e5YzZ85QX1/PwYMHCQ8P7+zQhIFTFIW6ujpKS0vJz88nNzdXnyynpqaSmJhIbm4ucGVdQt++fZk1axbLli3T75ypKAoXL14kPj6+xSMtLY3Gxkb9tf7vjLOjo6M+gVapVDQ1NVFZWUlFRQWVlZVcvnxZvyulTqcjNDSUVatW6WvdhRBCdIz6+nr+/e9/8+abb1JcXIy7uzsRERGMHj2a0NBQBg4c2KEd2AwpP/tvzbHbT3gcI5M2JuCaesqjnzPIcTe74RLwZoqisHjxYn1994QJEzo7JNENlJeXk5iYSHx8PLGxsXz66acYGRnx8MMPk5aWRnR0NCUlJQBYW1szcOBAgoODCQoKolevXvj6+tKzZ892/Zw1t79yd3e/LlsgCyGEuKKhoYFjx46xb98+jhw5wpkzZ2hqasLc3JzBgwczaNAg/P399Y++fftek8TcEPOzZs2x24U/hpFJ23ZOVjQNVBx63iDH3azDE3CdTqefrVOr1a2+JX+19u/fz+TJkxk1ahSPPfaYvo7V39+/W28vLq6vkpISVq5cybZt2xg6dCiTJ09m2LBhBAcH07NnT0mUhRDiBlRXV0dsbCynTp3il19+ISkpiczMTP2am5CQEDZv3oybmxtOTk6/2yiiNW6EBNx23Ip2JeCVh18yyHE3a1MCHhkZibOzMwMHDtS3eNu/fz92dnZMmTKFsLAwYmJi2LVrFwkJCZSXl1NVVcX/vYS/vz/jxo1j7NixjBo1isDAwA5JQvbu3cvUqVMxMjJqcf2AgADuvfdevLy8cHR0xMnJSf9RrVZjbGyMSqXSf5QESVwLTU1NshGUEEJ0Y4qiUFRURHx8PHfddRcXL17UP6dSqXBycmLcuHFERkbS0NCg74BVVFSETqfDz89PP4MeEBCAu7s7VVVVBp+A24Q91K4EvOroywY57mZtSsB79uyJlZUVaWlpaLVaLC0tiYiIoKysjJiYGHQ6HUZGRowdO5awsDAcHBywt7e/covBzo7KykoOHz7M4cOHSUxMRFEUzM3NcXNza/Hw9vYmICBA/4/MycmpVYsamlsOmpubY25ujpGREVqtVt8LPDc3l61bt/L999//pvPDn7GwsCA3NxcnJ6dWHyOEEEII8Xuqq6vJysrSd+S6fPkyFy9e5IsvviA5ORkAGxsb3N3d9ZUDmZmZLdroWlpa4uvrS3JyskEmos25pfWY5e1KwKt/ftUgx92sTQl4YWEhbm5uNDQ0kJ2dTc+ePfXN5svKyjhx4gSDBg3Cw8PjLy9cWVnJqVOnSExMpKioqMUjNzdX3yIQ/rctmp2dXYuPVlZW+h7FFy9e5NKlS/pdMFUqFdbW1vpH80YpXl5e+Pn54enpia2tLYWFhZw9e5azZ8+SmZn5hz2eGxsbZQZTCCGEEB2meaG+vb09lpaWv3m+traW7OxsMjMzycjIICsri82bNxtkIioJeBddhFlVVUVWVhYZGRkUFRVRUVFBeXl5i4/V1dU4OTnp3yE2t1FraGigpqaG6upqqqur9Z9XVlaSl5dHdnY2ubm5+tIUlUqFh4cHPj4+eHl54enpiYeHBx4eHnh6ejJ06FCZ/RZCCCFEl3Ij1IBbhS5tVwJeE/OGQY67WZfcih6u3HoJCQnRN7u/1hobG8nJycHc3Bx3d3dMTLrst0IIIYQQ4oak02kx6oZb0XfbrNPMzIyAgIDODkMIIYQQottStDowamMCrtV1UDTXT7dNwIUQQgghROdSFC20dQZckRlwIYQQQggh2kXRads+A34DlKBIk2shhBBCCCGuI5kBF0IIIYQQnaK7zoC3KQGvrKzsqDiEEEIIIUQb3Ah5mdJU3/aEWtvUMcFcR61KwM3MzHB3d8fHx6ej4xFCCCGEEK3k7u6OmZlZZ4fRZs25ZWHy5+063lDH3axVG/EA1NfX09jY2NHxCCGEEEKIVjIzM8Pc3Lyzw2iXq8ktDXnc0IYEXAghhBBCCHH1pAuKEEIIIYQQ15Ek4EIIIYQQQlxHkoALIYQQQghxHUkCLoQQQgghxHUkCbgQQgghhBDXkSTgQgghhBBCXEeSgAshhBBCCHEd/X8omQkzogKbvAAAAABJRU5ErkJggg==\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Compute remapping weights with method `nearest_s2d`\\n\",\n \"regridder_nearest = xe.Regridder(ds, ds_out, \\\"nearest_s2d\\\")\\n\",\n \"\\n\",\n \"# Remap the data\\n\",\n \"dr_out = regridder_nearest(dr)\\n\",\n \"\\n\",\n \"# Plot the result\\n\",\n \"plt.figure(figsize=(12, 4))\\n\",\n \"ax = plt.axes(projection=ccrs.PlateCarree())\\n\",\n \"dr_out[0].plot.pcolormesh(ax=ax, x=\\\"lon\\\", y=\\\"lat\\\")\\n\",\n \"ax.coastlines()\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.12.11\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "doc/notebooks/Dask.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Lazy evaluation on Dask arrays\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If you are unfamiliar with Dask, read\\n\",\n \"[Parallel computing with Dask](https://docs.xarray.dev/en/stable/user-guide/dask.html)\\n\",\n \"in Xarray documentation first.\\n\",\n \"\\n\",\n \"Recall that the regridding process is divided in two steps : computing the\\n\",\n \"weights and applying the weights. Dask support is much more advanced for the\\n\",\n \"latter, and this what the first part of this notebook is about.\\n\",\n \"\\n\",\n \"Dask allows [lazy evaluation](https://en.wikipedia.org/wiki/Lazy_evaluation) and\\n\",\n \"[out-of-core computing](https://en.wikipedia.org/wiki/External_memory_algorithm),\\n\",\n \"to allow processing large volumes of data with limited memory. You may also get\\n\",\n \"a speed-up by parallelizing the process in some cases, but a general rule of\\n\",\n \"thumb is that if the data fits in memory, regridding will be faster without\\n\",\n \"dask.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import dask.array as da # need to have dask.array installed, although not directly using it here.\\n\",\n \"import xarray as xr\\n\",\n \"import xesmf as xe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## A simple example\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prepare input data\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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<xarray.Dataset>\\n\",\n       \"Dimensions:  (lat: 25, time: 2920, lon: 53)\\n\",\n       \"Coordinates:\\n\",\n       \"  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n       \"  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n       \"  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n       \"Data variables:\\n\",\n       \"    air      (time, lat, lon) float32 dask.array<chunksize=(500, 25, 53), meta=np.ndarray>\\n\",\n       \"Attributes:\\n\",\n       \"    Conventions:  COARDS\\n\",\n       \"    title:        4x daily NMC reanalysis (1948)\\n\",\n       \"    description:  Data is from NMC initialized reanalysis\\\\n(4x/day).  These a...\\n\",\n       \"    platform:     Model\\n\",\n       \"    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 25, time: 2920, lon: 53)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n \" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \"Data variables:\\n\",\n \" air (time, lat, lon) float32 dask.array\\n\",\n \"Attributes:\\n\",\n \" Conventions: COARDS\\n\",\n \" title: 4x daily NMC reanalysis (1948)\\n\",\n \" description: Data is from NMC initialized reanalysis\\\\n(4x/day). These a...\\n\",\n \" platform: Model\\n\",\n \" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = xr.tutorial.open_dataset(\\\"air_temperature\\\", chunks={\\\"time\\\": 500})\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Frozen({'time': (500, 500, 500, 500, 500, 420), 'lat': (25,), 'lon': (53,)})\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds.chunks\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \"dask.array\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds[\\\"air\\\"].data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Build regridder\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF Regridder \\n\",\n \"Regridding algorithm: bilinear \\n\",\n \"Weight filename: bilinear_25x53_59x87.nc \\n\",\n \"Reuse pre-computed weights? False \\n\",\n \"Input grid shape: (25, 53) \\n\",\n \"Output grid shape: (59, 87) \\n\",\n \"Periodic in longitude? False\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out = xr.Dataset(\\n\",\n \" {\\n\",\n \" \\\"lat\\\": ([\\\"lat\\\"], np.arange(16, 75, 1.0)),\\n\",\n \" \\\"lon\\\": ([\\\"lon\\\"], np.arange(200, 330, 1.5)),\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"regridder = xe.Regridder(ds, ds_out, \\\"bilinear\\\")\\n\",\n \"regridder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Apply to xarray Dataset/DataArray\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 8.17 ms, sys: 2.32 ms, total: 10.5 ms\\n\",\n \"Wall time: 10.2 ms\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
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<xarray.Dataset>\\n\",\n       \"Dimensions:  (time: 2920, lat: 59, lon: 87)\\n\",\n       \"Coordinates:\\n\",\n       \"  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n       \"  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n       \"  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n       \"Data variables:\\n\",\n       \"    air      (time, lat, lon) float32 dask.array<chunksize=(500, 59, 87), meta=np.ndarray>\\n\",\n       \"Attributes:\\n\",\n       \"    regrid_method:  bilinear
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (time: 2920, lat: 59, lon: 87)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \" * lat (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n \" * lon (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n \"Data variables:\\n\",\n \" air (time, lat, lon) float32 dask.array\\n\",\n \"Attributes:\\n\",\n \" regrid_method: bilinear\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# only build the dask graph; actual computation happens later when calling compute()\\n\",\n \"%time ds_out = regridder(ds)\\n\",\n \"ds_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \"dask.array\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out[\\\"air\\\"].data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 756 ms, sys: 155 ms, total: 911 ms\\n\",\n \"Wall time: 600 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%time result = ds_out['air'].compute() # actually applies regridding\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(numpy.ndarray, (2920, 59, 87))\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"type(result.data), result.data.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Chunking behaviour\\n\",\n \"\\n\",\n \"xESMF will adjust its default behaviour according to the input data. On spatial\\n\",\n \"dimensions where the data has only one chunk, the output of a `Regridder` call\\n\",\n \"will also have only one chunk, no matter the new dimension size. This like the\\n\",\n \"previous example.\\n\",\n \"\\n\",\n \"However, if the input has more than one chunk along a spatial dimension, then\\n\",\n \"the regridder will try to preserve the chunk size. When upscaling data, this\\n\",\n \"means the number of chunks increases and with it the number of dask tasks added\\n\",\n \"to the graph. This can actually decrease performance if the graph becomes too\\n\",\n \"large, filled up with many small tasks.\\n\",\n \"\\n\",\n \"One can always override xESMF's default behaviour by passing `output_chunks` to\\n\",\n \"the `Regridder` call.\\n\",\n \"\\n\",\n \"In the example below, the input has three chunks along `lon`:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \"dask.array\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_3lon = ds.chunk({\\\"lat\\\": 25, \\\"lon\\\": 25, \\\"time\\\": -1})\\n\",\n \"ds_3lon.air.data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In this case, the output DataArray will have the same chunk size on longitude,\\n\",\n \"but still only one chunk along latitude.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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Dask graph 54 chunks in 10 graph layers
Data type float32 numpy.ndarray
\\n\",\n \" 		\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" 87\\n\",\n \" 59\\n\",\n \" 2920\\n\",\n \"\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \"dask.array\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_spatial_out = regridder(ds_spatial, output_chunks={\\\"lat\\\": 10, \\\"lon\\\": 10})\\n\",\n \"ds_spatial_out[\\\"air\\\"].data\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Parallel weight generation with Dask\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Dask can also be used to build the regridder and compute its weights in\\n\",\n \"parallel. To do so, xESMF uses the chunks on the destination grid and computes\\n\",\n \"subsets of weights on each chunk in parallel.\\n\",\n \"\\n\",\n \"This feature is currently in an experimental state and it will force dask to use\\n\",\n \"processes to parallelize the computation. Moreover, it is slower than then\\n\",\n \"normal method abd thus is it _only_ useful if the **destination** grid does not\\n\",\n \"fit in memory. Recall that the parallization is done over chunks of the\\n\",\n \"destination grid and each iteration will need to load the source grid in memory.\\n\",\n \"\\n\",\n \"For a more performant way to generate weights in parallel, it might be better to\\n\",\n \"use `ESMF` directly instead, assuming you have an MPI-enabled version. See the\\n\",\n \"\\\"Solving large problems using HPC\\\" notebook.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Parallel weight generation example\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prepare input data\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset>\\n\",\n       \"Dimensions:  (lat: 25, time: 2920, lon: 53)\\n\",\n       \"Coordinates:\\n\",\n       \"  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n       \"  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n       \"  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n       \"Data variables:\\n\",\n       \"    air      (time, lat, lon) float32 dask.array<chunksize=(500, 25, 53), meta=np.ndarray>\\n\",\n       \"Attributes:\\n\",\n       \"    Conventions:  COARDS\\n\",\n       \"    title:        4x daily NMC reanalysis (1948)\\n\",\n       \"    description:  Data is from NMC initialized reanalysis\\\\n(4x/day).  These a...\\n\",\n       \"    platform:     Model\\n\",\n       \"    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 25, time: 2920, lon: 53)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n \" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \"Data variables:\\n\",\n \" air (time, lat, lon) float32 dask.array\\n\",\n \"Attributes:\\n\",\n \" Conventions: COARDS\\n\",\n \" title: 4x daily NMC reanalysis (1948)\\n\",\n \" description: Data is from NMC initialized reanalysis\\\\n(4x/day). These a...\\n\",\n \" platform: Model\\n\",\n \" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = xr.tutorial.open_dataset(\\\"air_temperature\\\", chunks={\\\"time\\\": 500})\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prepare output dataset and chunk it\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Frozen({'time': (36,), 'y': (50, 50, 50, 50, 5), 'x': (50, 50, 50, 50, 50, 25)})\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out = xr.tutorial.open_dataset(\\\"rasm\\\")\\n\",\n \"ds_out = ds_out.chunk({\\\"y\\\": 50, \\\"x\\\": 50})\\n\",\n \"ds_out.chunks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Create regridder, generating the weights in parallel\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[WARNING] yaksa: 10 leaked handle pool objects\\n\",\n \"[WARNING] yaksa: 10 leaked handle pool objects\\n\",\n \"[WARNING] yaksa: 10 leaked handle pool objects\\n\",\n \"[WARNING] yaksa: 10 leaked handle pool objects\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF Regridder \\n\",\n \"Regridding algorithm: bilinear \\n\",\n \"Weight filename: bilinear_25x53_205x275.nc \\n\",\n \"Reuse pre-computed weights? False \\n\",\n \"Input grid shape: (25, 53) \\n\",\n \"Output grid shape: (205, 275) \\n\",\n \"Periodic in longitude? False\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"para_regridder = xe.Regridder(ds, ds_out, \\\"bilinear\\\", parallel=True)\\n\",\n \"para_regridder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Attempting to build the Regridder using the option `parallel=True` with either\\n\",\n \"`reuse_weights=True` or with `weights != None` will produce a warning. In both\\n\",\n \"cases, since the weights are already generated, the regridder will be built\\n\",\n \"skipping the parallel part.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Using a mask to chunk an empty Dataset\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"If the destination grid has no variables and contains 1D lat/lon coordinates,\\n\",\n \"using xarray's `.chunk()` method will not work\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset>\\n\",\n       \"Dimensions:  (lat: 59, lon: 87)\\n\",\n       \"Coordinates:\\n\",\n       \"  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n       \"  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n       \"Data variables:\\n\",\n       \"    *empty*
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 59, lon: 87)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n \" * lon (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n \"Data variables:\\n\",\n \" *empty*\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out = xr.Dataset(\\n\",\n \" {\\n\",\n \" \\\"lat\\\": ([\\\"lat\\\"], np.arange(16, 75, 1.0), {\\\"units\\\": \\\"degrees_north\\\"}),\\n\",\n \" \\\"lon\\\": ([\\\"lon\\\"], np.arange(200, 330, 1.5), {\\\"units\\\": \\\"degrees_east\\\"}),\\n\",\n \" }\\n\",\n \")\\n\",\n \"ds_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Frozen({})\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out.chunk({\\\"lat\\\": 25, \\\"lon\\\": 25})\\n\",\n \"ds_out.chunks\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To deal with this issue, we can create a `mask` and add it to `ds_out`. Using a\\n\",\n \"boolean mask ensures `ds_out` is not bloated by data and setting the mask to be\\n\",\n \"`True` everywhere will not affect regridding.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Frozen({'lat': (25, 25, 9), 'lon': (25, 25, 25, 12)})\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"mask = da.ones((ds_out.lat.size, ds_out.lon.size), dtype=bool, chunks=(25, 25))\\n\",\n \"ds_out[\\\"mask\\\"] = (ds_out.dims, mask)\\n\",\n \"\\n\",\n \"# Now we check the chunks of ds_out\\n\",\n \"ds_out.chunks\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.7\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "doc/notebooks/Dataset.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Regrid xarray Dataset with multiple variables\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import xesmf as xe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Starting v0.2.0, xESMF is able to take `xarray.Dataset` as input data, and\\n\",\n \"automatically loop over all variables.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## A simple example\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Prepare input data\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
xarray.Dataset
    • lat: 25
    • lon: 53
    • time: 2920
    • lat
      (lat)
      float32
      75.0 72.5 70.0 ... 20.0 17.5 15.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\\n\",\n       \"       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\\n\",\n       \"       15. ], dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\\n\",\n       \"       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\\n\",\n       \"       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\\n\",\n       \"       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\\n\",\n       \"       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\\n\",\n       \"       325. , 327.5, 330. ], dtype=float32)
    • time
      (time)
      datetime64[ns]
      2013-01-01 ... 2014-12-31T18:00:00
      standard_name :
      time
      long_name :
      Time
      array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n       \"       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n       \"       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n       \"      dtype='datetime64[ns]')
    • air
      (time, lat, lon)
      float32
      ...
      long_name :
      4xDaily Air temperature at sigma level 995
      units :
      degK
      precision :
      2
      GRIB_id :
      11
      GRIB_name :
      TMP
      var_desc :
      Air temperature
      dataset :
      NMC Reanalysis
      level_desc :
      Surface
      statistic :
      Individual Obs
      parent_stat :
      Other
      actual_range :
      [185.16 322.1 ]
      [3869000 values with dtype=float32]
  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis\\n\",\n \"(4x/day). These are the 0.9950 sigma level values.
    platform :
    Model
    references :
    http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 25, lon: 53, time: 2920)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n \" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \"Data variables:\\n\",\n \" air (time, lat, lon) float32 ...\\n\",\n \"Attributes:\\n\",\n \" Conventions: COARDS\\n\",\n \" title: 4x daily NMC reanalysis (1948)\\n\",\n \" description: Data is from NMC initialized reanalysis\\\\n(4x/day). These a...\\n\",\n \" platform: Model\\n\",\n \" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = xr.tutorial.open_dataset(\\\"air_temperature\\\")\\n\",\n \"ds # air temperature in Kelvin\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
xarray.Dataset
    • lat: 25
    • lon: 53
    • time: 2920
    • lat
      (lat)
      float32
      75.0 72.5 70.0 ... 20.0 17.5 15.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\\n\",\n       \"       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\\n\",\n       \"       15. ], dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\\n\",\n       \"       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\\n\",\n       \"       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\\n\",\n       \"       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\\n\",\n       \"       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\\n\",\n       \"       325. , 327.5, 330. ], dtype=float32)
    • time
      (time)
      datetime64[ns]
      2013-01-01 ... 2014-12-31T18:00:00
      standard_name :
      time
      long_name :
      Time
      array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n       \"       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n       \"       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n       \"      dtype='datetime64[ns]')
    • air
      (time, lat, lon)
      float32
      241.2 242.5 243.5 ... 296.19 295.69
      long_name :
      4xDaily Air temperature at sigma level 995
      units :
      degK
      precision :
      2
      GRIB_id :
      11
      GRIB_name :
      TMP
      var_desc :
      Air temperature
      dataset :
      NMC Reanalysis
      level_desc :
      Surface
      statistic :
      Individual Obs
      parent_stat :
      Other
      actual_range :
      [185.16 322.1 ]
      array([[[241.2    , 242.5    , ..., 235.5    , 238.59999],\\n\",\n       \"        [243.79999, 244.5    , ..., 235.29999, 239.29999],\\n\",\n       \"        ...,\\n\",\n       \"        [295.9    , 296.19998, ..., 295.9    , 295.19998],\\n\",\n       \"        [296.29   , 296.79   , ..., 296.79   , 296.6    ]],\\n\",\n       \"\\n\",\n       \"       [[242.09999, 242.7    , ..., 233.59999, 235.79999],\\n\",\n       \"        [243.59999, 244.09999, ..., 232.5    , 235.7    ],\\n\",\n       \"        ...,\\n\",\n       \"        [296.19998, 296.69998, ..., 295.5    , 295.1    ],\\n\",\n       \"        [296.29   , 297.19998, ..., 296.4    , 296.6    ]],\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"       [[245.79   , 244.79   , ..., 243.98999, 244.79   ],\\n\",\n       \"        [249.89   , 249.29   , ..., 242.48999, 244.29   ],\\n\",\n       \"        ...,\\n\",\n       \"        [296.29   , 297.19   , ..., 295.09   , 294.38998],\\n\",\n       \"        [297.79   , 298.38998, ..., 295.49   , 295.19   ]],\\n\",\n       \"\\n\",\n       \"       [[245.09   , 244.29   , ..., 241.48999, 241.79   ],\\n\",\n       \"        [249.89   , 249.29   , ..., 240.29   , 241.68999],\\n\",\n       \"        ...,\\n\",\n       \"        [296.09   , 296.88998, ..., 295.69   , 295.19   ],\\n\",\n       \"        [297.69   , 298.09   , ..., 296.19   , 295.69   ]]], dtype=float32)
    • celsius
      (time, lat, lon)
      float32
      -31.949997 -30.649994 ... 22.540009
      array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\\n\",\n       \"         -37.649994, -34.550003],\\n\",\n       \"        [-29.350006, -28.649994, -28.449997, ..., -40.350006,\\n\",\n       \"         -37.850006, -33.850006],\\n\",\n       \"        [-23.149994, -23.350006, -24.259995, ..., -39.949997,\\n\",\n       \"         -36.759995, -31.449997],\\n\",\n       \"        ...,\\n\",\n       \"        [ 23.450012,  23.049988,  23.25    , ...,  22.25    ,\\n\",\n       \"          21.950012,  21.549988],\\n\",\n       \"        [ 22.75    ,  23.049988,  23.640015, ...,  22.75    ,\\n\",\n       \"          22.75    ,  22.049988],\\n\",\n       \"        [ 23.140015,  23.640015,  23.950012, ...,  23.75    ,\\n\",\n       \"          23.640015,  23.450012]],\\n\",\n       \"\\n\",\n       \"       [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\\n\",\n       \"         -39.550003, -37.350006],\\n\",\n       \"        [-29.550003, -29.050003, -28.949997, ..., -42.149994,\\n\",\n       \"         -40.649994, -37.449997],\\n\",\n       \"        [-19.949997, -20.259995, -21.050003, ..., -42.350006,\\n\",\n       \"         -39.759995, -34.649994],\\n\",\n       \"        ...,\\n\",\n       \"        [ 23.25    ,  22.75    ,  23.049988, ...,  22.25    ,\\n\",\n       \"          21.950012,  21.640015],\\n\",\n       \"        [ 23.049988,  23.549988,  23.640015, ...,  22.450012,\\n\",\n       \"          22.350006,  21.950012],\\n\",\n       \"        [ 23.140015,  24.049988,  24.25    , ...,  23.25    ,\\n\",\n       \"          23.25    ,  23.450012]],\\n\",\n       \"\\n\",\n       \"       [[-30.850006, -30.949997, -30.850006, ..., -38.850006,\\n\",\n       \"         -37.050003, -34.449997],\\n\",\n       \"        [-28.550003, -28.759995, -29.149994, ..., -42.850006,\\n\",\n       \"         -41.149994, -37.449997],\\n\",\n       \"        [-16.950012, -17.649994, -18.949997, ..., -41.949997,\\n\",\n       \"         -39.949997, -34.949997],\\n\",\n       \"        ...,\\n\",\n       \"        [ 22.450012,  22.25    ,  22.25    , ...,  23.140015,\\n\",\n       \"          22.140015,  21.850006],\\n\",\n       \"        [ 23.049988,  23.350006,  23.140015, ...,  23.25    ,\\n\",\n       \"          22.850006,  22.450012],\\n\",\n       \"        [ 23.25    ,  23.140015,  23.25    , ...,  23.850006,\\n\",\n       \"          23.850006,  23.640015]],\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"       [[-29.660004, -30.160004, -31.059998, ..., -28.960007,\\n\",\n       \"         -28.660004, -28.259995],\\n\",\n       \"        [-24.059998, -24.160004, -24.559998, ..., -32.559998,\\n\",\n       \"         -31.86    , -30.460007],\\n\",\n       \"        [-10.459991, -10.959991, -11.459991, ..., -33.759995,\\n\",\n       \"         -31.460007, -27.960007],\\n\",\n       \"        ...,\\n\",\n       \"        [ 21.640015,  22.140015,  24.339996, ...,  22.339996,\\n\",\n       \"          22.23999 ,  21.540009],\\n\",\n       \"        [ 23.640015,  24.73999 ,  25.140015, ...,  22.339996,\\n\",\n       \"          22.339996,  21.640015],\\n\",\n       \"        [ 25.040009,  26.040009,  25.640015, ...,  22.940002,\\n\",\n       \"          22.640015,  22.640015]],\\n\",\n       \"\\n\",\n       \"       [[-27.36    , -28.36    , -29.660004, ..., -29.86    ,\\n\",\n       \"         -29.160004, -28.36    ],\\n\",\n       \"        [-23.259995, -23.86    , -24.660004, ..., -31.86    ,\\n\",\n       \"         -30.660004, -28.86    ],\\n\",\n       \"        [-10.76001 , -11.359985, -11.859985, ..., -32.660004,\\n\",\n       \"         -30.059998, -26.259995],\\n\",\n       \"        ...,\\n\",\n       \"        [ 20.540009,  20.73999 ,  22.23999 , ...,  21.940002,\\n\",\n       \"          21.540009,  21.140015],\\n\",\n       \"        [ 23.140015,  24.040009,  24.440002, ...,  22.140015,\\n\",\n       \"          21.940002,  21.23999 ],\\n\",\n       \"        [ 24.640015,  25.23999 ,  25.339996, ...,  22.540009,\\n\",\n       \"          22.339996,  22.040009]],\\n\",\n       \"\\n\",\n       \"       [[-28.059998, -28.86    , -29.86    , ..., -31.460007,\\n\",\n       \"         -31.660004, -31.36    ],\\n\",\n       \"        [-23.259995, -23.86    , -24.759995, ..., -33.559998,\\n\",\n       \"         -32.86    , -31.460007],\\n\",\n       \"        [-10.160004, -10.959991, -11.76001 , ..., -33.259995,\\n\",\n       \"         -30.559998, -26.86    ],\\n\",\n       \"        ...,\\n\",\n       \"        [ 20.640015,  20.540009,  21.940002, ...,  22.140015,\\n\",\n       \"          21.940002,  21.540009],\\n\",\n       \"        [ 22.940002,  23.73999 ,  24.040009, ...,  22.540009,\\n\",\n       \"          22.540009,  22.040009],\\n\",\n       \"        [ 24.540009,  24.940002,  24.940002, ...,  23.339996,\\n\",\n       \"          23.040009,  22.540009]]], dtype=float32)
    • slice
      (lat, lon)
      float32
      241.2 242.5 243.5 ... 296.79 296.6
      long_name :
      4xDaily Air temperature at sigma level 995
      units :
      degK
      precision :
      2
      GRIB_id :
      11
      GRIB_name :
      TMP
      var_desc :
      Air temperature
      dataset :
      NMC Reanalysis
      level_desc :
      Surface
      statistic :
      Individual Obs
      parent_stat :
      Other
      actual_range :
      [185.16 322.1 ]
      array([[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    , 238.59999],\\n\",\n       \"       [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999, 239.29999],\\n\",\n       \"       [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   , 241.7    ],\\n\",\n       \"       ...,\\n\",\n       \"       [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    , 294.69998],\\n\",\n       \"       [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    , 295.19998],\\n\",\n       \"       [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   , 296.6    ]],\\n\",\n       \"      dtype=float32)
  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis\\n\",\n \"(4x/day). These are the 0.9950 sigma level values.
    platform :
    Model
    references :
    http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 25, lon: 53, time: 2920)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n \" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \"Data variables:\\n\",\n \" air (time, lat, lon) float32 241.2 242.5 243.5 ... 296.49 296.19 295.69\\n\",\n \" celsius (time, lat, lon) float32 -31.949997 -30.649994 ... 22.540009\\n\",\n \" slice (lat, lon) float32 241.2 242.5 243.5 244.0 ... 296.9 296.79 296.6\\n\",\n \"Attributes:\\n\",\n \" Conventions: COARDS\\n\",\n \" title: 4x daily NMC reanalysis (1948)\\n\",\n \" description: Data is from NMC initialized reanalysis\\\\n(4x/day). These a...\\n\",\n \" platform: Model\\n\",\n \" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# input dataset can contain variables of different shapes (e.g. 2D, 3D, 4D), as long as horizontal shapes are the same.\\n\",\n \"ds[\\\"celsius\\\"] = ds[\\\"air\\\"] - 273.15 # Kelvin -> celsius\\n\",\n \"ds[\\\"slice\\\"] = ds[\\\"air\\\"].isel(time=0)\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Build regridder\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF Regridder \\n\",\n \"Regridding algorithm: bilinear \\n\",\n \"Input grid shape: (25, 53) \\n\",\n \"Output grid shape: (59, 87) \\n\",\n \"Output grid dimension name: ('lat', 'lon') \\n\",\n \"Periodic in longitude? False\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out = xr.Dataset(\\n\",\n \" {\\n\",\n \" \\\"lat\\\": ([\\\"lat\\\"], np.arange(16, 75, 1.0)),\\n\",\n \" \\\"lon\\\": ([\\\"lon\\\"], np.arange(200, 330, 1.5)),\\n\",\n \" }\\n\",\n \")\\n\",\n \"\\n\",\n \"regridder = xe.Regridder(ds, ds_out, \\\"bilinear\\\")\\n\",\n \"regridder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Apply to data\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"using dimensions ('lat', 'lon') from data variable air as the horizontal dimensions for this dataset.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
xarray.Dataset
    • lat: 59
    • lon: 87
    • time: 2920
    • time
      (time)
      datetime64[ns]
      2013-01-01 ... 2014-12-31T18:00:00
      standard_name :
      time
      long_name :
      Time
      array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n       \"       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n       \"       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n       \"      dtype='datetime64[ns]')
    • lon
      (lon)
      float64
      200.0 201.5 203.0 ... 327.5 329.0
      array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\\n\",\n       \"       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\\n\",\n       \"       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\\n\",\n       \"       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\\n\",\n       \"       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\\n\",\n       \"       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\\n\",\n       \"       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\\n\",\n       \"       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\\n\",\n       \"       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])
    • lat
      (lat)
      float64
      16.0 17.0 18.0 ... 72.0 73.0 74.0
      array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\\n\",\n       \"       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\\n\",\n       \"       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\\n\",\n       \"       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\\n\",\n       \"       72., 73., 74.])
    • air
      (time, lat, lon)
      float64
      296.1 296.4 296.6 ... 241.0 241.5
      array([[[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\\n\",\n       \"         296.43398913, 296.19924566],\\n\",\n       \"        [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\\n\",\n       \"         296.07802394, 295.72098714],\\n\",\n       \"        [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\\n\",\n       \"         295.73997197, 295.35693248],\\n\",\n       \"        ...,\\n\",\n       \"        [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\\n\",\n       \"         235.51802332, 238.0780694 ],\\n\",\n       \"        [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\\n\",\n       \"         235.33999633, 237.63241841],\\n\",\n       \"        [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\\n\",\n       \"         235.41999207, 237.49641598]],\\n\",\n       \"\\n\",\n       \"       [[296.25399643, 296.70203773, 297.03166485, ..., 296.06514956,\\n\",\n       \"         296.03998263, 296.01773136],\\n\",\n       \"        [296.2179898 , 296.56767711, 296.82291528, ..., 295.7292558 ,\\n\",\n       \"         295.6800262 , 295.5138904 ],\\n\",\n       \"        [296.23999022, 296.42058286, 296.56714652, ..., 295.50442291,\\n\",\n       \"         295.41998903, 295.19133215],\\n\",\n       \"        ...,\\n\",\n       \"        [245.52028453, 245.73709231, 245.85148963, ..., 231.64759509,\\n\",\n       \"         232.67802699, 234.83033953],\\n\",\n       \"        [243.29994515, 243.61404829, 243.85326489, ..., 231.80653129,\\n\",\n       \"         232.72003168, 234.51923375],\\n\",\n       \"        [242.70001369, 243.03800427, 243.31726258, ..., 232.22256285,\\n\",\n       \"         233.15997775, 234.71925176]],\\n\",\n       \"\\n\",\n       \"       [[296.31998597, 296.35233477, 296.37027072, ..., 296.69703874,\\n\",\n       \"         296.59998477, 296.42993717],\\n\",\n       \"        [296.23999022, 296.37072264, 296.42865111, ..., 296.39312798,\\n\",\n       \"         296.20003046, 295.98447426],\\n\",\n       \"        [296.07996829, 296.20134835, 296.24744824, ..., 296.17051878,\\n\",\n       \"         295.85798008, 295.63218076],\\n\",\n       \"        ...,\\n\",\n       \"        [246.92034241, 246.75294557, 246.50912779, ..., 231.18562131,\\n\",\n       \"         232.24003595, 234.61904532],\\n\",\n       \"        [244.13992017, 244.03040136, 243.89556811, ..., 231.78222568,\\n\",\n       \"         232.82012307, 234.90301222],\\n\",\n       \"        [243.22002405, 243.13672999, 243.05876072, ..., 233.39835074,\\n\",\n       \"         234.45993206, 236.27912467]],\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"       [[297.62998356, 298.25582152, 298.65503226, ..., 295.7786526 ,\\n\",\n       \"         295.66999665, 295.50299763],\\n\",\n       \"        [297.07004997, 297.7199817 , 298.19914665, ..., 295.58670885,\\n\",\n       \"         295.55000304, 295.2150872 ],\\n\",\n       \"        [296.38994762, 296.98154159, 297.52424514, ..., 295.48204978,\\n\",\n       \"         295.46998597, 295.05016151],\\n\",\n       \"        ...,\\n\",\n       \"        [251.81041188, 251.72296686, 251.55990593, ..., 240.75713161,\\n\",\n       \"         241.37000425, 242.46456685],\\n\",\n       \"        [247.96982451, 247.87036819, 247.69574495, ..., 241.55307104,\\n\",\n       \"         241.93009016, 242.64624471],\\n\",\n       \"        [245.73007797, 245.53418764, 245.25570503, ..., 242.92917313,\\n\",\n       \"         243.20994271, 243.68633428]],\\n\",\n       \"\\n\",\n       \"       [[297.1899857 , 297.6237982 , 297.95503528, ..., 295.4505457 ,\\n\",\n       \"         295.32998658, 295.05487915],\\n\",\n       \"        [296.59005424, 297.09596253, 297.49915579, ..., 295.29059716,\\n\",\n       \"         295.1700073 , 294.7989566 ],\\n\",\n       \"        [295.76992812, 296.23054587, 296.65708274, ..., 295.15442138,\\n\",\n       \"         295.00998537, 294.626334  ],\\n\",\n       \"        ...,\\n\",\n       \"        [252.39037715, 252.04962511, 251.65524335, ..., 241.72163807,\\n\",\n       \"         242.61000973, 243.93278497],\\n\",\n       \"        [249.06987326, 248.66822819, 248.21431545, ..., 242.12742803,\\n\",\n       \"         242.79003593, 243.74867727],\\n\",\n       \"        [247.43005818, 246.93209439, 246.37428385, ..., 242.84748108,\\n\",\n       \"         243.38996739, 244.10871142]],\\n\",\n       \"\\n\",\n       \"       [[297.04997563, 297.38785212, 297.63503348, ..., 296.09892817,\\n\",\n       \"         295.98999483, 295.690696  ],\\n\",\n       \"        [296.4100463 , 296.84401245, 297.17914422, ..., 295.82701626,\\n\",\n       \"         295.79001767, 295.49075727],\\n\",\n       \"        [295.62992871, 296.00626644, 296.35697279, ..., 295.59472609,\\n\",\n       \"         295.56998294, 295.28282378],\\n\",\n       \"        ...,\\n\",\n       \"        [252.51039665, 252.14637887, 251.70761893, ..., 240.09173906,\\n\",\n       \"         240.75006397, 241.87173824],\\n\",\n       \"        [248.92985255, 248.55305942, 248.11071361, ..., 240.21550695,\\n\",\n       \"         240.53002923, 241.23719787],\\n\",\n       \"        [247.01007067, 246.58490681, 246.10268406, ..., 240.91155301,\\n\",\n       \"         241.00997318, 241.45322238]]])
    • celsius
      (time, lat, lon)
      float64
      22.98 23.24 23.49 ... -32.14 -31.7
      array([[[ 22.98400285,  23.23669915,  23.48890434, ...,  23.32491403,\\n\",\n       \"          23.28399523,  23.04925177],\\n\",\n       \"        [ 22.82801481,  23.03275408,  23.27535111, ...,  22.94262952,\\n\",\n       \"          22.92803004,  22.57099324],\\n\",\n       \"        [ 22.89002377,  22.98556885,  23.15248584, ...,  22.62693524,\\n\",\n       \"          22.58997807,  22.20693858],\\n\",\n       \"        ...,\\n\",\n       \"        [-28.10981478, -27.7891234 , -27.58903202, ..., -39.21370283,\\n\",\n       \"         -37.63197058, -35.0719245 ],\\n\",\n       \"        [-29.87008348, -29.37479887, -28.97624337, ..., -39.33408115,\\n\",\n       \"         -37.80999757, -35.51757549],\\n\",\n       \"        [-30.90996101, -30.27087087, -29.71224358, ..., -39.30207549,\\n\",\n       \"         -37.73000183, -35.65357792]],\\n\",\n       \"\\n\",\n       \"       [[ 23.10400254,  23.55204383,  23.88167095, ...,  22.91515567,\\n\",\n       \"          22.88998873,  22.86773746],\\n\",\n       \"        [ 23.06799591,  23.41768321,  23.67292138, ...,  22.5792619 ,\\n\",\n       \"          22.5300323 ,  22.3638965 ],\\n\",\n       \"        [ 23.08999633,  23.27058897,  23.41715262, ...,  22.35442901,\\n\",\n       \"          22.26999514,  22.04133825],\\n\",\n       \"        ...,\\n\",\n       \"        [-27.62970937, -27.41290159, -27.29850427, ..., -41.50239881,\\n\",\n       \"         -40.4719669 , -38.31965437],\\n\",\n       \"        [-29.85004875, -29.5359456 , -29.29672901, ..., -41.3434626 ,\\n\",\n       \"         -40.42996222, -38.63076015],\\n\",\n       \"        [-30.4499802 , -30.11198963, -29.83273132, ..., -40.92743105,\\n\",\n       \"         -39.99001614, -38.43074214]],\\n\",\n       \"\\n\",\n       \"       [[ 23.16999207,  23.20234088,  23.22027682, ...,  23.54704484,\\n\",\n       \"          23.44999087,  23.27994327],\\n\",\n       \"        [ 23.08999633,  23.22072874,  23.27865721, ...,  23.24313408,\\n\",\n       \"          23.05003657,  22.83448036],\\n\",\n       \"        [ 22.9299744 ,  23.05135446,  23.09745435, ...,  23.02052488,\\n\",\n       \"          22.70798618,  22.48218686],\\n\",\n       \"        ...,\\n\",\n       \"        [-26.22965149, -26.39704832, -26.64086611, ..., -41.96437258,\\n\",\n       \"         -40.90995795, -38.53094857],\\n\",\n       \"        [-29.01007373, -29.11959253, -29.25442578, ..., -41.36776822,\\n\",\n       \"         -40.32987082, -38.24698168],\\n\",\n       \"        [-29.92996985, -30.0132639 , -30.09123317, ..., -39.75164316,\\n\",\n       \"         -38.69006184, -36.87086923]],\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"       [[ 24.47998966,  25.10582762,  25.50503836, ...,  22.6286587 ,\\n\",\n       \"          22.52000275,  22.35300373],\\n\",\n       \"        [ 23.92005608,  24.56998781,  25.04915275, ...,  22.43671496,\\n\",\n       \"          22.40000914,  22.0650933 ],\\n\",\n       \"        [ 23.23995372,  23.83154769,  24.37425124, ...,  22.33205589,\\n\",\n       \"          22.31999207,  21.90016761],\\n\",\n       \"        ...,\\n\",\n       \"        [-21.33958202, -21.42702704, -21.59008797, ..., -32.39286229,\\n\",\n       \"         -31.77998965, -30.68542705],\\n\",\n       \"        [-25.18016938, -25.27962571, -25.45424895, ..., -31.59692286,\\n\",\n       \"         -31.21990373, -30.50374918],\\n\",\n       \"        [-27.41991592, -27.61580626, -27.89428887, ..., -30.22082077,\\n\",\n       \"         -29.94005118, -29.46365962]],\\n\",\n       \"\\n\",\n       \"       [[ 24.0399918 ,  24.4738043 ,  24.80504138, ...,  22.3005518 ,\\n\",\n       \"          22.17999269,  21.90488526],\\n\",\n       \"        [ 23.44006035,  23.94596863,  24.34916189, ...,  22.14060326,\\n\",\n       \"          22.02001341,  21.64896271],\\n\",\n       \"        [ 22.61993422,  23.08055197,  23.50708884, ...,  22.00442749,\\n\",\n       \"          21.85999148,  21.4763401 ],\\n\",\n       \"        ...,\\n\",\n       \"        [-20.75961675, -21.10036879, -21.49475055, ..., -31.42835583,\\n\",\n       \"         -30.53998416, -29.21720893],\\n\",\n       \"        [-24.08012063, -24.48176571, -24.93567845, ..., -31.02256587,\\n\",\n       \"         -30.35995796, -29.40131662],\\n\",\n       \"        [-25.71993572, -26.21789951, -26.77571005, ..., -30.30251281,\\n\",\n       \"         -29.76002651, -29.04128248]],\\n\",\n       \"\\n\",\n       \"       [[ 23.89998173,  24.23785823,  24.48503958, ...,  22.94893427,\\n\",\n       \"          22.84000093,  22.5407021 ],\\n\",\n       \"        [ 23.26005241,  23.69401855,  24.02915032, ...,  22.67702236,\\n\",\n       \"          22.64002378,  22.34076337],\\n\",\n       \"        [ 22.47993481,  22.85627254,  23.20697889, ...,  22.44473219,\\n\",\n       \"          22.41998905,  22.13282989],\\n\",\n       \"        ...,\\n\",\n       \"        [-20.63959725, -21.00361502, -21.44237497, ..., -33.05825483,\\n\",\n       \"         -32.39992993, -31.27825565],\\n\",\n       \"        [-24.22014135, -24.59693448, -25.03928029, ..., -32.93448695,\\n\",\n       \"         -32.61996466, -31.91279602],\\n\",\n       \"        [-26.13992323, -26.56508709, -27.04730983, ..., -32.23844089,\\n\",\n       \"         -32.14002072, -31.69677152]]])
    • slice
      (lat, lon)
      float64
      296.1 296.4 296.6 ... 235.4 237.5
      array([[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\\n\",\n       \"        296.43398913, 296.19924566],\\n\",\n       \"       [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\\n\",\n       \"        296.07802394, 295.72098714],\\n\",\n       \"       [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\\n\",\n       \"        295.73997197, 295.35693248],\\n\",\n       \"       ...,\\n\",\n       \"       [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\\n\",\n       \"        235.51802332, 238.0780694 ],\\n\",\n       \"       [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\\n\",\n       \"        235.33999633, 237.63241841],\\n\",\n       \"       [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\\n\",\n       \"        235.41999207, 237.49641598]])
  • regrid_method :
    bilinear
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 59, lon: 87, time: 2920)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \" * lon (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n \" * lat (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n \"Data variables:\\n\",\n \" air (time, lat, lon) float64 296.1 296.4 296.6 ... 240.9 241.0 241.5\\n\",\n \" celsius (time, lat, lon) float64 22.98 23.24 23.49 ... -32.24 -32.14 -31.7\\n\",\n \" slice (lat, lon) float64 296.1 296.4 296.6 296.9 ... 233.8 235.4 237.5\\n\",\n \"Attributes:\\n\",\n \" regrid_method: bilinear\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# the entire dataset can be processed at once\\n\",\n \"ds_out = regridder(ds)\\n\",\n \"ds_out\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"air True\\n\",\n \"celsius True\\n\",\n \"slice True\\n\"\n ]\n }\n ],\n \"source\": [\n \"# verify that the result is the same as regridding each variable one-by-one\\n\",\n \"for k in ds.data_vars:\\n\",\n \" print(k, ds_out[k].equals(regridder(ds[k])))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Invalid dimension orderings to avoid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"xESMF assumes the horizontal dimensions are the last/rightmost dimensions, which\\n\",\n \"matches the convention of most NetCDF data.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
xarray.Dataset
    • lat: 25
    • lon: 53
    • time: 2920
    • lat
      (lat)
      float32
      75.0 72.5 70.0 ... 20.0 17.5 15.0
      standard_name :
      latitude
      long_name :
      Latitude
      units :
      degrees_north
      axis :
      Y
      array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\\n\",\n       \"       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\\n\",\n       \"       15. ], dtype=float32)
    • lon
      (lon)
      float32
      200.0 202.5 205.0 ... 327.5 330.0
      standard_name :
      longitude
      long_name :
      Longitude
      units :
      degrees_east
      axis :
      X
      array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\\n\",\n       \"       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\\n\",\n       \"       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\\n\",\n       \"       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\\n\",\n       \"       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\\n\",\n       \"       325. , 327.5, 330. ], dtype=float32)
    • time
      (time)
      datetime64[ns]
      2013-01-01 ... 2014-12-31T18:00:00
      standard_name :
      time
      long_name :
      Time
      array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n       \"       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n       \"       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n       \"      dtype='datetime64[ns]')
    • air
      (lon, lat, time)
      float32
      241.2 242.09999 ... 295.19 295.69
      long_name :
      4xDaily Air temperature at sigma level 995
      units :
      degK
      precision :
      2
      GRIB_id :
      11
      GRIB_name :
      TMP
      var_desc :
      Air temperature
      dataset :
      NMC Reanalysis
      level_desc :
      Surface
      statistic :
      Individual Obs
      parent_stat :
      Other
      actual_range :
      [185.16 322.1 ]
      array([[[241.2    , 242.09999, 242.29999, ..., 243.48999, 245.79   ,\\n\",\n       \"         245.09   ],\\n\",\n       \"        [243.79999, 243.59999, 244.59999, ..., 249.09   , 249.89   ,\\n\",\n       \"         249.89   ],\\n\",\n       \"        [250.     , 253.2    , 256.19998, ..., 262.69   , 262.38998,\\n\",\n       \"         262.99   ],\\n\",\n       \"        ...,\\n\",\n       \"        [296.6    , 296.4    , 295.6    , ..., 294.79   , 293.69   ,\\n\",\n       \"         293.79   ],\\n\",\n       \"        [295.9    , 296.19998, 296.19998, ..., 296.79   , 296.29   ,\\n\",\n       \"         296.09   ],\\n\",\n       \"        [296.29   , 296.29   , 296.4    , ..., 298.19   , 297.79   ,\\n\",\n       \"         297.69   ]],\\n\",\n       \"\\n\",\n       \"       [[242.5    , 242.7    , 242.2    , ..., 242.98999, 244.79   ,\\n\",\n       \"         244.29   ],\\n\",\n       \"        [244.5    , 244.09999, 244.39   , ..., 248.98999, 249.29   ,\\n\",\n       \"         249.29   ],\\n\",\n       \"        [249.79999, 252.89   , 255.5    , ..., 262.19   , 261.79   ,\\n\",\n       \"         262.19   ],\\n\",\n       \"        ...,\\n\",\n       \"        [296.19998, 295.9    , 295.4    , ..., 295.29   , 293.88998,\\n\",\n       \"         293.69   ],\\n\",\n       \"        [296.19998, 296.69998, 296.5    , ..., 297.88998, 297.19   ,\\n\",\n       \"         296.88998],\\n\",\n       \"        [296.79   , 297.19998, 296.29   , ..., 299.19   , 298.38998,\\n\",\n       \"         298.09   ]],\\n\",\n       \"\\n\",\n       \"       [[243.5    , 243.09999, 242.29999, ..., 242.09   , 243.48999,\\n\",\n       \"         243.29   ],\\n\",\n       \"        [244.7    , 244.2    , 244.     , ..., 248.59   , 248.48999,\\n\",\n       \"         248.39   ],\\n\",\n       \"        [248.89   , 252.09999, 254.2    , ..., 261.69   , 261.29   ,\\n\",\n       \"         261.38998],\\n\",\n       \"        ...,\\n\",\n       \"        [296.4    , 296.19998, 295.4    , ..., 297.49   , 295.38998,\\n\",\n       \"         295.09   ],\\n\",\n       \"        [296.79   , 296.79   , 296.29   , ..., 298.29   , 297.59   ,\\n\",\n       \"         297.19   ],\\n\",\n       \"        [297.1    , 297.4    , 296.4    , ..., 298.79   , 298.49   ,\\n\",\n       \"         298.09   ]],\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"       [[232.79999, 232.     , 234.29999, ..., 244.18999, 243.29   ,\\n\",\n       \"         241.68999],\\n\",\n       \"        [232.79999, 231.     , 230.29999, ..., 240.59   , 241.29   ,\\n\",\n       \"         239.59   ],\\n\",\n       \"        [233.2    , 230.79999, 231.2    , ..., 239.39   , 240.48999,\\n\",\n       \"         239.89   ],\\n\",\n       \"        ...,\\n\",\n       \"        [295.4    , 295.4    , 296.29   , ..., 295.49   , 295.09   ,\\n\",\n       \"         295.29   ],\\n\",\n       \"        [295.9    , 295.6    , 296.4    , ..., 295.49   , 295.29   ,\\n\",\n       \"         295.69   ],\\n\",\n       \"        [296.9    , 296.4    , 297.     , ..., 296.09   , 295.69   ,\\n\",\n       \"         296.49   ]],\\n\",\n       \"\\n\",\n       \"       [[235.5    , 233.59999, 236.09999, ..., 244.48999, 243.98999,\\n\",\n       \"         241.48999],\\n\",\n       \"        [235.29999, 232.5    , 232.     , ..., 241.29   , 242.48999,\\n\",\n       \"         240.29   ],\\n\",\n       \"        [236.39   , 233.39   , 233.2    , ..., 241.68999, 243.09   ,\\n\",\n       \"         242.59   ],\\n\",\n       \"        ...,\\n\",\n       \"        [295.1    , 295.1    , 295.29   , ..., 295.38998, 294.69   ,\\n\",\n       \"         295.09   ],\\n\",\n       \"        [295.9    , 295.5    , 296.     , ..., 295.49   , 295.09   ,\\n\",\n       \"         295.69   ],\\n\",\n       \"        [296.79   , 296.4    , 297.     , ..., 295.79   , 295.49   ,\\n\",\n       \"         296.19   ]],\\n\",\n       \"\\n\",\n       \"       [[238.59999, 235.79999, 238.7    , ..., 244.89   , 244.79   ,\\n\",\n       \"         241.79   ],\\n\",\n       \"        [239.29999, 235.7    , 235.7    , ..., 242.68999, 244.29   ,\\n\",\n       \"         241.68999],\\n\",\n       \"        [241.7    , 238.5    , 238.2    , ..., 245.18999, 246.89   ,\\n\",\n       \"         246.29   ],\\n\",\n       \"        ...,\\n\",\n       \"        [294.69998, 294.79   , 295.     , ..., 294.69   , 294.29   ,\\n\",\n       \"         294.69   ],\\n\",\n       \"        [295.19998, 295.1    , 295.6    , ..., 294.79   , 294.38998,\\n\",\n       \"         295.19   ],\\n\",\n       \"        [296.6    , 296.6    , 296.79   , ..., 295.79   , 295.19   ,\\n\",\n       \"         295.69   ]]], dtype=float32)
    • celsius
      (time, lat, lon)
      float32
      -31.949997 -30.649994 ... 22.540009
      array([[[-31.949997, -30.649994, -29.649994, ..., -40.350006,\\n\",\n       \"         -37.649994, -34.550003],\\n\",\n       \"        [-29.350006, -28.649994, -28.449997, ..., -40.350006,\\n\",\n       \"         -37.850006, -33.850006],\\n\",\n       \"        [-23.149994, -23.350006, -24.259995, ..., -39.949997,\\n\",\n       \"         -36.759995, -31.449997],\\n\",\n       \"        ...,\\n\",\n       \"        [ 23.450012,  23.049988,  23.25    , ...,  22.25    ,\\n\",\n       \"          21.950012,  21.549988],\\n\",\n       \"        [ 22.75    ,  23.049988,  23.640015, ...,  22.75    ,\\n\",\n       \"          22.75    ,  22.049988],\\n\",\n       \"        [ 23.140015,  23.640015,  23.950012, ...,  23.75    ,\\n\",\n       \"          23.640015,  23.450012]],\\n\",\n       \"\\n\",\n       \"       [[-31.050003, -30.449997, -30.050003, ..., -41.149994,\\n\",\n       \"         -39.550003, -37.350006],\\n\",\n       \"        [-29.550003, -29.050003, -28.949997, ..., -42.149994,\\n\",\n       \"         -40.649994, -37.449997],\\n\",\n       \"        [-19.949997, -20.259995, -21.050003, ..., -42.350006,\\n\",\n       \"         -39.759995, -34.649994],\\n\",\n       \"        ...,\\n\",\n       \"        [ 23.25    ,  22.75    ,  23.049988, ...,  22.25    ,\\n\",\n       \"          21.950012,  21.640015],\\n\",\n       \"        [ 23.049988,  23.549988,  23.640015, ...,  22.450012,\\n\",\n       \"          22.350006,  21.950012],\\n\",\n       \"        [ 23.140015,  24.049988,  24.25    , ...,  23.25    ,\\n\",\n       \"          23.25    ,  23.450012]],\\n\",\n       \"\\n\",\n       \"       [[-30.850006, -30.949997, -30.850006, ..., -38.850006,\\n\",\n       \"         -37.050003, -34.449997],\\n\",\n       \"        [-28.550003, -28.759995, -29.149994, ..., -42.850006,\\n\",\n       \"         -41.149994, -37.449997],\\n\",\n       \"        [-16.950012, -17.649994, -18.949997, ..., -41.949997,\\n\",\n       \"         -39.949997, -34.949997],\\n\",\n       \"        ...,\\n\",\n       \"        [ 22.450012,  22.25    ,  22.25    , ...,  23.140015,\\n\",\n       \"          22.140015,  21.850006],\\n\",\n       \"        [ 23.049988,  23.350006,  23.140015, ...,  23.25    ,\\n\",\n       \"          22.850006,  22.450012],\\n\",\n       \"        [ 23.25    ,  23.140015,  23.25    , ...,  23.850006,\\n\",\n       \"          23.850006,  23.640015]],\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"       [[-29.660004, -30.160004, -31.059998, ..., -28.960007,\\n\",\n       \"         -28.660004, -28.259995],\\n\",\n       \"        [-24.059998, -24.160004, -24.559998, ..., -32.559998,\\n\",\n       \"         -31.86    , -30.460007],\\n\",\n       \"        [-10.459991, -10.959991, -11.459991, ..., -33.759995,\\n\",\n       \"         -31.460007, -27.960007],\\n\",\n       \"        ...,\\n\",\n       \"        [ 21.640015,  22.140015,  24.339996, ...,  22.339996,\\n\",\n       \"          22.23999 ,  21.540009],\\n\",\n       \"        [ 23.640015,  24.73999 ,  25.140015, ...,  22.339996,\\n\",\n       \"          22.339996,  21.640015],\\n\",\n       \"        [ 25.040009,  26.040009,  25.640015, ...,  22.940002,\\n\",\n       \"          22.640015,  22.640015]],\\n\",\n       \"\\n\",\n       \"       [[-27.36    , -28.36    , -29.660004, ..., -29.86    ,\\n\",\n       \"         -29.160004, -28.36    ],\\n\",\n       \"        [-23.259995, -23.86    , -24.660004, ..., -31.86    ,\\n\",\n       \"         -30.660004, -28.86    ],\\n\",\n       \"        [-10.76001 , -11.359985, -11.859985, ..., -32.660004,\\n\",\n       \"         -30.059998, -26.259995],\\n\",\n       \"        ...,\\n\",\n       \"        [ 20.540009,  20.73999 ,  22.23999 , ...,  21.940002,\\n\",\n       \"          21.540009,  21.140015],\\n\",\n       \"        [ 23.140015,  24.040009,  24.440002, ...,  22.140015,\\n\",\n       \"          21.940002,  21.23999 ],\\n\",\n       \"        [ 24.640015,  25.23999 ,  25.339996, ...,  22.540009,\\n\",\n       \"          22.339996,  22.040009]],\\n\",\n       \"\\n\",\n       \"       [[-28.059998, -28.86    , -29.86    , ..., -31.460007,\\n\",\n       \"         -31.660004, -31.36    ],\\n\",\n       \"        [-23.259995, -23.86    , -24.759995, ..., -33.559998,\\n\",\n       \"         -32.86    , -31.460007],\\n\",\n       \"        [-10.160004, -10.959991, -11.76001 , ..., -33.259995,\\n\",\n       \"         -30.559998, -26.86    ],\\n\",\n       \"        ...,\\n\",\n       \"        [ 20.640015,  20.540009,  21.940002, ...,  22.140015,\\n\",\n       \"          21.940002,  21.540009],\\n\",\n       \"        [ 22.940002,  23.73999 ,  24.040009, ...,  22.540009,\\n\",\n       \"          22.540009,  22.040009],\\n\",\n       \"        [ 24.540009,  24.940002,  24.940002, ...,  23.339996,\\n\",\n       \"          23.040009,  22.540009]]], dtype=float32)
    • slice
      (lat, lon)
      float32
      241.2 242.5 243.5 ... 296.79 296.6
      long_name :
      4xDaily Air temperature at sigma level 995
      units :
      degK
      precision :
      2
      GRIB_id :
      11
      GRIB_name :
      TMP
      var_desc :
      Air temperature
      dataset :
      NMC Reanalysis
      level_desc :
      Surface
      statistic :
      Individual Obs
      parent_stat :
      Other
      actual_range :
      [185.16 322.1 ]
      array([[241.2    , 242.5    , 243.5    , ..., 232.79999, 235.5    , 238.59999],\\n\",\n       \"       [243.79999, 244.5    , 244.7    , ..., 232.79999, 235.29999, 239.29999],\\n\",\n       \"       [250.     , 249.79999, 248.89   , ..., 233.2    , 236.39   , 241.7    ],\\n\",\n       \"       ...,\\n\",\n       \"       [296.6    , 296.19998, 296.4    , ..., 295.4    , 295.1    , 294.69998],\\n\",\n       \"       [295.9    , 296.19998, 296.79   , ..., 295.9    , 295.9    , 295.19998],\\n\",\n       \"       [296.29   , 296.79   , 297.1    , ..., 296.9    , 296.79   , 296.6    ]],\\n\",\n       \"      dtype=float32)
  • Conventions :
    COARDS
    title :
    4x daily NMC reanalysis (1948)
    description :
    Data is from NMC initialized reanalysis\\n\",\n \"(4x/day). These are the 0.9950 sigma level values.
    platform :
    Model
    references :
    http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 25, lon: 53, time: 2920)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n \" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \"Data variables:\\n\",\n \" air (lon, lat, time) float32 241.2 242.09999 ... 295.19 295.69\\n\",\n \" celsius (time, lat, lon) float32 -31.949997 -30.649994 ... 22.540009\\n\",\n \" slice (lat, lon) float32 241.2 242.5 243.5 244.0 ... 296.9 296.79 296.6\\n\",\n \"Attributes:\\n\",\n \" Conventions: COARDS\\n\",\n \" title: 4x daily NMC reanalysis (1948)\\n\",\n \" description: Data is from NMC initialized reanalysis\\\\n(4x/day). These a...\\n\",\n \" platform: Model\\n\",\n \" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# xESMF doesn't like horizontal dimensions to be the first/leftmost dimensions\\n\",\n \"ds_bad = ds.copy()\\n\",\n \"ds_bad[\\\"air\\\"] = ds_bad[\\\"air\\\"].transpose()\\n\",\n \"ds_bad\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# regridder(ds_bad) # comment this line to see the error message\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"using dimensions ('lat', 'lon') from data variable celsius as the horizontal dimensions for this dataset.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
xarray.Dataset
    • lat: 59
    • lon: 87
    • time: 2920
    • time
      (time)
      datetime64[ns]
      2013-01-01 ... 2014-12-31T18:00:00
      standard_name :
      time
      long_name :
      Time
      array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n       \"       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n       \"       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n       \"      dtype='datetime64[ns]')
    • lon
      (lon)
      float64
      200.0 201.5 203.0 ... 327.5 329.0
      array([200. , 201.5, 203. , 204.5, 206. , 207.5, 209. , 210.5, 212. , 213.5,\\n\",\n       \"       215. , 216.5, 218. , 219.5, 221. , 222.5, 224. , 225.5, 227. , 228.5,\\n\",\n       \"       230. , 231.5, 233. , 234.5, 236. , 237.5, 239. , 240.5, 242. , 243.5,\\n\",\n       \"       245. , 246.5, 248. , 249.5, 251. , 252.5, 254. , 255.5, 257. , 258.5,\\n\",\n       \"       260. , 261.5, 263. , 264.5, 266. , 267.5, 269. , 270.5, 272. , 273.5,\\n\",\n       \"       275. , 276.5, 278. , 279.5, 281. , 282.5, 284. , 285.5, 287. , 288.5,\\n\",\n       \"       290. , 291.5, 293. , 294.5, 296. , 297.5, 299. , 300.5, 302. , 303.5,\\n\",\n       \"       305. , 306.5, 308. , 309.5, 311. , 312.5, 314. , 315.5, 317. , 318.5,\\n\",\n       \"       320. , 321.5, 323. , 324.5, 326. , 327.5, 329. ])
    • lat
      (lat)
      float64
      16.0 17.0 18.0 ... 72.0 73.0 74.0
      array([16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27., 28., 29.,\\n\",\n       \"       30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\\n\",\n       \"       44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55., 56., 57.,\\n\",\n       \"       58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69., 70., 71.,\\n\",\n       \"       72., 73., 74.])
    • celsius
      (time, lat, lon)
      float64
      22.98 23.24 23.49 ... -32.14 -31.7
      array([[[ 22.98400285,  23.23669915,  23.48890434, ...,  23.32491403,\\n\",\n       \"          23.28399523,  23.04925177],\\n\",\n       \"        [ 22.82801481,  23.03275408,  23.27535111, ...,  22.94262952,\\n\",\n       \"          22.92803004,  22.57099324],\\n\",\n       \"        [ 22.89002377,  22.98556885,  23.15248584, ...,  22.62693524,\\n\",\n       \"          22.58997807,  22.20693858],\\n\",\n       \"        ...,\\n\",\n       \"        [-28.10981478, -27.7891234 , -27.58903202, ..., -39.21370283,\\n\",\n       \"         -37.63197058, -35.0719245 ],\\n\",\n       \"        [-29.87008348, -29.37479887, -28.97624337, ..., -39.33408115,\\n\",\n       \"         -37.80999757, -35.51757549],\\n\",\n       \"        [-30.90996101, -30.27087087, -29.71224358, ..., -39.30207549,\\n\",\n       \"         -37.73000183, -35.65357792]],\\n\",\n       \"\\n\",\n       \"       [[ 23.10400254,  23.55204383,  23.88167095, ...,  22.91515567,\\n\",\n       \"          22.88998873,  22.86773746],\\n\",\n       \"        [ 23.06799591,  23.41768321,  23.67292138, ...,  22.5792619 ,\\n\",\n       \"          22.5300323 ,  22.3638965 ],\\n\",\n       \"        [ 23.08999633,  23.27058897,  23.41715262, ...,  22.35442901,\\n\",\n       \"          22.26999514,  22.04133825],\\n\",\n       \"        ...,\\n\",\n       \"        [-27.62970937, -27.41290159, -27.29850427, ..., -41.50239881,\\n\",\n       \"         -40.4719669 , -38.31965437],\\n\",\n       \"        [-29.85004875, -29.5359456 , -29.29672901, ..., -41.3434626 ,\\n\",\n       \"         -40.42996222, -38.63076015],\\n\",\n       \"        [-30.4499802 , -30.11198963, -29.83273132, ..., -40.92743105,\\n\",\n       \"         -39.99001614, -38.43074214]],\\n\",\n       \"\\n\",\n       \"       [[ 23.16999207,  23.20234088,  23.22027682, ...,  23.54704484,\\n\",\n       \"          23.44999087,  23.27994327],\\n\",\n       \"        [ 23.08999633,  23.22072874,  23.27865721, ...,  23.24313408,\\n\",\n       \"          23.05003657,  22.83448036],\\n\",\n       \"        [ 22.9299744 ,  23.05135446,  23.09745435, ...,  23.02052488,\\n\",\n       \"          22.70798618,  22.48218686],\\n\",\n       \"        ...,\\n\",\n       \"        [-26.22965149, -26.39704832, -26.64086611, ..., -41.96437258,\\n\",\n       \"         -40.90995795, -38.53094857],\\n\",\n       \"        [-29.01007373, -29.11959253, -29.25442578, ..., -41.36776822,\\n\",\n       \"         -40.32987082, -38.24698168],\\n\",\n       \"        [-29.92996985, -30.0132639 , -30.09123317, ..., -39.75164316,\\n\",\n       \"         -38.69006184, -36.87086923]],\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"       [[ 24.47998966,  25.10582762,  25.50503836, ...,  22.6286587 ,\\n\",\n       \"          22.52000275,  22.35300373],\\n\",\n       \"        [ 23.92005608,  24.56998781,  25.04915275, ...,  22.43671496,\\n\",\n       \"          22.40000914,  22.0650933 ],\\n\",\n       \"        [ 23.23995372,  23.83154769,  24.37425124, ...,  22.33205589,\\n\",\n       \"          22.31999207,  21.90016761],\\n\",\n       \"        ...,\\n\",\n       \"        [-21.33958202, -21.42702704, -21.59008797, ..., -32.39286229,\\n\",\n       \"         -31.77998965, -30.68542705],\\n\",\n       \"        [-25.18016938, -25.27962571, -25.45424895, ..., -31.59692286,\\n\",\n       \"         -31.21990373, -30.50374918],\\n\",\n       \"        [-27.41991592, -27.61580626, -27.89428887, ..., -30.22082077,\\n\",\n       \"         -29.94005118, -29.46365962]],\\n\",\n       \"\\n\",\n       \"       [[ 24.0399918 ,  24.4738043 ,  24.80504138, ...,  22.3005518 ,\\n\",\n       \"          22.17999269,  21.90488526],\\n\",\n       \"        [ 23.44006035,  23.94596863,  24.34916189, ...,  22.14060326,\\n\",\n       \"          22.02001341,  21.64896271],\\n\",\n       \"        [ 22.61993422,  23.08055197,  23.50708884, ...,  22.00442749,\\n\",\n       \"          21.85999148,  21.4763401 ],\\n\",\n       \"        ...,\\n\",\n       \"        [-20.75961675, -21.10036879, -21.49475055, ..., -31.42835583,\\n\",\n       \"         -30.53998416, -29.21720893],\\n\",\n       \"        [-24.08012063, -24.48176571, -24.93567845, ..., -31.02256587,\\n\",\n       \"         -30.35995796, -29.40131662],\\n\",\n       \"        [-25.71993572, -26.21789951, -26.77571005, ..., -30.30251281,\\n\",\n       \"         -29.76002651, -29.04128248]],\\n\",\n       \"\\n\",\n       \"       [[ 23.89998173,  24.23785823,  24.48503958, ...,  22.94893427,\\n\",\n       \"          22.84000093,  22.5407021 ],\\n\",\n       \"        [ 23.26005241,  23.69401855,  24.02915032, ...,  22.67702236,\\n\",\n       \"          22.64002378,  22.34076337],\\n\",\n       \"        [ 22.47993481,  22.85627254,  23.20697889, ...,  22.44473219,\\n\",\n       \"          22.41998905,  22.13282989],\\n\",\n       \"        ...,\\n\",\n       \"        [-20.63959725, -21.00361502, -21.44237497, ..., -33.05825483,\\n\",\n       \"         -32.39992993, -31.27825565],\\n\",\n       \"        [-24.22014135, -24.59693448, -25.03928029, ..., -32.93448695,\\n\",\n       \"         -32.61996466, -31.91279602],\\n\",\n       \"        [-26.13992323, -26.56508709, -27.04730983, ..., -32.23844089,\\n\",\n       \"         -32.14002072, -31.69677152]]])
    • slice
      (lat, lon)
      float64
      296.1 296.4 296.6 ... 235.4 237.5
      array([[296.13399675, 296.38669304, 296.63889823, ..., 296.47490793,\\n\",\n       \"        296.43398913, 296.19924566],\\n\",\n       \"       [295.97800871, 296.18274797, 296.42534501, ..., 296.09262341,\\n\",\n       \"        296.07802394, 295.72098714],\\n\",\n       \"       [296.04001766, 296.13556275, 296.30247974, ..., 295.77692914,\\n\",\n       \"        295.73997197, 295.35693248],\\n\",\n       \"       ...,\\n\",\n       \"       [245.04017912, 245.36087049, 245.56096188, ..., 233.93629106,\\n\",\n       \"        235.51802332, 238.0780694 ],\\n\",\n       \"       [243.27991042, 243.77519503, 244.17375053, ..., 233.81591274,\\n\",\n       \"        235.33999633, 237.63241841],\\n\",\n       \"       [242.24003289, 242.87912303, 243.43775032, ..., 233.84791841,\\n\",\n       \"        235.41999207, 237.49641598]])
  • regrid_method :
    bilinear
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 59, lon: 87, time: 2920)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \" * lon (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n \" * lat (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n \"Data variables:\\n\",\n \" celsius (time, lat, lon) float64 22.98 23.24 23.49 ... -32.24 -32.14 -31.7\\n\",\n \" slice (lat, lon) float64 296.1 296.4 296.6 296.9 ... 233.8 235.4 237.5\\n\",\n \"Attributes:\\n\",\n \" regrid_method: bilinear\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# besides ordering dimensions properly, another simple fix is to drop bad variables\\n\",\n \"regridder(ds_bad.drop(\\\"air\\\"))\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "doc/notebooks/Masking.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Masking and extrapolation in xESMF\\n\",\n \"\\n\",\n \"(contributed by [Raphael Dussin](https://github.com/raphaeldussin), based on\\n\",\n \"previous work from [jhamman](https://github.com/jhamman),\\n\",\n \"[RondeauG](https://github.com/RondeauG), [trondkr](https://github.com/trondkr)\\n\",\n \"and others)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"By default, xESMF treats NaNs like regular values hence potentially resulting in\\n\",\n \"missing values bleeding into the regridded field and creating insconsistencies\\n\",\n \"in the resulting masked array. To overcome this issue, we can use explicit\\n\",\n \"masking of the source and target grids.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import xarray as xr\\n\",\n \"import xesmf\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import warnings\\n\",\n \"\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Preparing the grids\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For this tutorial, we're using a dataset from the ROMS ocean model from this\\n\",\n \"[xarray tutorial](http://xarray.pydata.org/en/stable/examples/ROMS_ocean_model.html)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds = xr.tutorial.open_dataset(\\\"ROMS_example.nc\\\", chunks={\\\"ocean_time\\\": 1})\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To use conservative regidding, we need the cells corners. Since they are not\\n\",\n \"provided, we are creating some using a crude approximation. **Please don't try\\n\",\n \"this at home!**\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"lon_centers = ds[\\\"lon_rho\\\"].values\\n\",\n \"lat_centers = ds[\\\"lat_rho\\\"].values\\n\",\n \"\\n\",\n \"lon_corners = 0.25 * (\\n\",\n \" lon_centers[:-1, :-1]\\n\",\n \" + lon_centers[1:, :-1]\\n\",\n \" + lon_centers[:-1, 1:]\\n\",\n \" + lon_centers[1:, 1:]\\n\",\n \")\\n\",\n \"\\n\",\n \"lat_corners = 0.25 * (\\n\",\n \" lat_centers[:-1, :-1]\\n\",\n \" + lat_centers[1:, :-1]\\n\",\n \" + lat_centers[:-1, 1:]\\n\",\n \" + lat_centers[1:, 1:]\\n\",\n \")\\n\",\n \"\\n\",\n \"ds[\\\"lon_psi\\\"] = xr.DataArray(data=lon_corners, dims=(\\\"eta_psi\\\", \\\"xi_psi\\\"))\\n\",\n \"ds[\\\"lat_psi\\\"] = xr.DataArray(data=lat_corners, dims=(\\\"eta_psi\\\", \\\"xi_psi\\\"))\\n\",\n \"\\n\",\n \"ds = ds.assign_coords({\\\"lon_psi\\\": ds[\\\"lon_psi\\\"], \\\"lat_psi\\\": ds[\\\"lat_psi\\\"]})\\n\",\n \"\\n\",\n \"# remove exterior rho points and cut 9 extra points to make\\n\",\n \"# zeta divisible by 10 for coarsening\\n\",\n \"ds = ds.isel(\\n\",\n \" eta_rho=slice(1, -10),\\n\",\n \" xi_rho=slice(1, -10),\\n\",\n \" eta_psi=slice(0, -9),\\n\",\n \" xi_psi=slice(0, -9),\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We also need a coarse resolution grid. We're going to build one by coarsening\\n\",\n \"the ROMS dataset. **coarsen.mean()** typically works as a nan-mean on the 10x10\\n\",\n \"blocks of the grid so the resulting land mask looks like a flooded version of\\n\",\n \"the original.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_coarse = xr.Dataset()\\n\",\n \"\\n\",\n \"ds_coarse[\\\"zeta\\\"] = xr.DataArray(\\n\",\n \" ds[\\\"zeta\\\"].coarsen(xi_rho=10, eta_rho=10).mean().values,\\n\",\n \" dims=(\\\"ocean_time\\\", \\\"eta_rho\\\", \\\"xi_rho\\\"),\\n\",\n \")\\n\",\n \"# we want to subsample coordinates instead of coarsening them\\n\",\n \"ds_coarse[\\\"lon_rho\\\"] = xr.DataArray(\\n\",\n \" ds[\\\"lon_rho\\\"].values[::10, ::10], dims=(\\\"eta_rho\\\", \\\"xi_rho\\\")\\n\",\n \")\\n\",\n \"ds_coarse[\\\"lon_psi\\\"] = xr.DataArray(\\n\",\n \" ds[\\\"lon_psi\\\"].values[::10, ::10], dims=(\\\"eta_psi\\\", \\\"xi_psi\\\")\\n\",\n \")\\n\",\n \"ds_coarse[\\\"lat_rho\\\"] = xr.DataArray(\\n\",\n \" ds[\\\"lat_rho\\\"].values[::10, ::10], dims=(\\\"eta_rho\\\", \\\"xi_rho\\\")\\n\",\n \")\\n\",\n \"ds_coarse[\\\"lat_psi\\\"] = xr.DataArray(\\n\",\n \" ds[\\\"lat_psi\\\"].values[::10, ::10], dims=(\\\"eta_psi\\\", \\\"xi_psi\\\")\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We now have our 2 grids to test the masking in xESMF. Now let's say we want to\\n\",\n \"conservatively remap the fine ocean model output onto the coarse resolution\\n\",\n \"grid.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ds[\\\"zeta\\\"].isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ds_coarse[\\\"zeta\\\"].isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Regridding without a mask\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As usual, xESMF expects fixed variable names for longitude/latitude in cell\\n\",\n \"centers and corners:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds[\\\"lon\\\"] = ds[\\\"lon_rho\\\"]\\n\",\n \"ds[\\\"lat\\\"] = ds[\\\"lat_rho\\\"]\\n\",\n \"ds[\\\"lon_b\\\"] = ds[\\\"lon_psi\\\"]\\n\",\n \"ds[\\\"lat_b\\\"] = ds[\\\"lat_psi\\\"]\\n\",\n \"\\n\",\n \"ds_coarse[\\\"lon\\\"] = ds_coarse[\\\"lon_rho\\\"]\\n\",\n \"ds_coarse[\\\"lat\\\"] = ds_coarse[\\\"lat_rho\\\"]\\n\",\n \"ds_coarse[\\\"lon_b\\\"] = ds_coarse[\\\"lon_psi\\\"]\\n\",\n \"ds_coarse[\\\"lat_b\\\"] = ds_coarse[\\\"lat_psi\\\"]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In our first test, there is no masking involved and we define the regridder the\\n\",\n \"typical way:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regrid_nomask = xesmf.Regridder(ds, ds_coarse, method=\\\"conservative\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"zeta_remapped = regrid_nomask(ds[\\\"zeta\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"zeta_remapped.isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Because of the missing values (NaNs) bleeding into the regridding, we end up\\n\",\n \"with a land mask that is much bigger than the one of the coarse grid. That's\\n\",\n \"where masking is gonna help us getting it right.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Regridding with a mask\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To use masking, we need to add a dataarray named **mask** to our datasets. Let's\\n\",\n \"define our masks on the high and coarse resolution grids from the missing values\\n\",\n \"in the zeta array:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds[\\\"mask\\\"] = xr.where(~np.isnan(ds[\\\"zeta\\\"].isel(ocean_time=0)), 1, 0)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ds[\\\"mask\\\"].plot(cmap=\\\"binary_r\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_coarse[\\\"mask\\\"] = xr.where(\\n\",\n \" ~np.isnan(ds_coarse[\\\"zeta\\\"].isel(ocean_time=0)), 1, 0\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ds_coarse[\\\"mask\\\"].plot(cmap=\\\"binary_r\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's try to regrid again:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regrid_mask = xesmf.Regridder(ds, ds_coarse, method=\\\"conservative_normed\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"zeta_remapped = regrid_mask(ds[\\\"zeta\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"zeta_remapped.isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now we have our conservative remapping consistent with the coarse grid, yay!!\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Limitations and warnings\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"- mask can only be 2D (ESMF design) so regridding a 3D field requires to\\n\",\n \" generate regridding weights for each vertical level.\\n\",\n \"\\n\",\n \"- conservative method will give you a normalization by the total area of the\\n\",\n \" target cell. Except for some specific cases, you probably want to use\\n\",\n \" conservative_normed.\\n\",\n \"\\n\",\n \"- results with other methods (e.g. bilinear) may not give masks consistent with\\n\",\n \" the coarse grid.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 1. Conservative (un-normed) example\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"regrid_masked2 = xesmf.Regridder(ds, ds_coarse, method=\\\"conservative\\\")\\n\",\n \"zeta_remapped2 = regrid_masked2(ds[\\\"zeta\\\"])\\n\",\n \"zeta_remapped2.isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### 2. Bilinear example\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"regrid_masked3 = xesmf.Regridder(ds, ds_coarse, method=\\\"bilinear\\\")\\n\",\n \"zeta_remapped3 = regrid_masked3(ds[\\\"zeta\\\"])\\n\",\n \"zeta_remapped3.isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Adaptive masking\\n\",\n \"\\n\",\n \"The adaptive masking technique allows the reuse of weights for equal 2D fields\\n\",\n \"that are only masked differently (eg. 3D fields with different land-sea masks /\\n\",\n \"orography masks for each model layer or fields with masks varying over time). It\\n\",\n \"is applicable for the **conservative**, **patch** and **bilinear** remapping\\n\",\n \"methods and will either mask target cells or renormalize their resulting value,\\n\",\n \"depending on how big of a fraction of the overlapping source grid cells is\\n\",\n \"masked.\\n\",\n \"\\n\",\n \"To use adaptive masking, the parameter **skipna** (and optionally also\\n\",\n \"**na_thres**) has to be specified when applying the remapping weights, eg.:\\n\",\n \"\\n\",\n \"```python\\n\",\n \"ds_remapped = regridder(ds, [...] , skipna=True, na_thres=.25)\\n\",\n \"```\\n\",\n \"\\n\",\n \"In case **skipna** is active, a given output point is set to NaN only if the\\n\",\n \"ratio of missing values exceeds the threshold level set by **na_thres**, and\\n\",\n \"else, a renormalization is conducted. For instance, when the center of a cell is\\n\",\n \"computed linearly from its four corners, one of which is missing, the output\\n\",\n \"value is set to NaN if na_thres is smaller than 0.25. Else, a renormalization is\\n\",\n \"conducted.\\n\",\n \"\\n\",\n \"**na_thres** can be any value in the interval [0., 1.] (the **default\\n\",\n \"being 1.**), with `na_thres = 0.` meaning that adaptive masking will not have\\n\",\n \"any effect. With the setting `na_thres = 1.`, applying adaptive masking together\\n\",\n \"with **conservative** weights is indistinguishable from applying\\n\",\n \"**conservative_normed** weights (including a defined mask, as it has been shown\\n\",\n \"in an example above).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Applying the 'conservative' weights without defined mask from an example above\\n\",\n \"# with active adaptive masking and the default na_thres=1.\\n\",\n \"zeta_remapped = regrid_nomask(ds[\\\"zeta\\\"], skipna=True)\\n\",\n \"zeta_remapped.isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Extrapolation\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As we saw in the previous example, the bilinear interpolation was not providing\\n\",\n \"a value at all the destination points. This is where the extrapolation becomes\\n\",\n \"useful. xESMF allows to use the ESMF algorithms described in this\\n\",\n \"[section of the ESMF documentation](http://earthsystemmodeling.org/docs/release/ESMF_8_0_1/ESMF_refdoc/node9.html#SECTION090117000000000000000).\\n\",\n \"xESMF provides several extrapolation options to fill missing values outside the\\n\",\n \"source domain:\\n\",\n \"\\n\",\n \"- `nearest_s2d`: assigns values from the nearest valid source cell.\\n\",\n \"- `inverse_dist`: computes a weighted average of nearby source cells using\\n\",\n \" inverse-distance weighting.\\n\",\n \"- `creep_fill`: iteratively propagates values from valid cells into neighboring\\n\",\n \" missing regions.\\n\",\n \"\\n\",\n \"Please refer to the aforementioned documentation for more details.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 1. Extrapolation with `nearest_s2d`\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regrid_extrap_nearest_s2d = xesmf.Regridder(\\n\",\n \" ds, ds_coarse, method=\\\"bilinear\\\", extrap_method=\\\"nearest_s2d\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"zeta_remapped_extrap_nearest_s2d = regrid_extrap_nearest_s2d(ds[\\\"zeta\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"zeta_remapped_extrap_nearest_s2d.isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"#### 2. Extrapolation with `creep_fill`\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Unlike nearest-neighbor approaches, `creep_fill` produces smoother and more\\n\",\n \"physically consistent fields because it propagates information gradually across\\n\",\n \"neighboring cells instead of introducing sharp, pointwise jumps. When using\\n\",\n \"`creep_fill`, a few constraints must be respected:\\n\",\n \"\\n\",\n \"- `extrap_num_levels` must be explicitly provided\\n\",\n \"- `creep_fill` cannot be used together with conservative or conservative_normed\\n\",\n \" regridding.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regrid_extrap_creep_fill = xesmf.Regridder(\\n\",\n \" ds,\\n\",\n \" ds_coarse,\\n\",\n \" method=\\\"bilinear\\\",\\n\",\n \" extrap_method=\\\"creep_fill\\\",\\n\",\n \" extrap_num_levels=10,\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"zeta_remapped_extrap_creep_fill = regrid_extrap_creep_fill(ds[\\\"zeta\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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T62pUMTPS1C79u+r5URfu3lBQuq31ENIAti87zofRc1A+lxx57rNjje/fulUTg5rUoj15bbDJL+h7R61NldFv6muu1T9+3ZQWi2sdL+57p9bfoF8jw9S18PdJrnl7byrvu6ePah8kNDZZLZinDr5FtvTRo1HP5qquuKrV9/SKkmWgNkrSfzxNPPFHsfAsfs1hek5HAwcvAgQNNClwvatpJ0stmI/1GqBcFbRrRC0RZ9BuM/l3tzDd58mTTSVRt2LDBfGso2Rm5JE1Ja4peL+b6DS387XvRokUmraydYKOh30JuueUWsw/67Vl/LymSb7HhD/+S3/j1glAV9JuRngv6rUo7xn7zzTflBi9atmfPnuYYaJpeL5xKMzf6uupFOtLOrFWhKpuN9NunBi66T9qBVQO3aISbA/V10ot70TprBuTOO+8sDIQ1SNKmHm3SKyv40w8azQRpJ9dwJ/Dy6PNLnjsrV640nWi1OSMWqitb4/a1qI5mI70eaeCiXww1cCnvvaQTceq1Qr8IanNrmGYk9H11xhlnFK4799xzTQdqzbaGj5kGA/o+1O4Bem10Q7NXOvlkNPXS94N+WdTrZ0l6bumXnPCX4vKCx1hfk5GgwYs2RWjzgTYVaBSt37r1oqPfNrXpQdN+FX2LLJlejtRf//pX+eMf/2jeFNreXvLNUHQGW83O6IVf66ojqDR1rM/VSF5T40XpG1ozFZqyDdMRN9oEoG202qdA+4/odjTCL5lJCs9MaTsb7qWXXmoyRn/+858LZ/4tKfxNQvsfaPClb379IK3otdQPM71QaNpfAzvNLmifIL1YR2rUqFHmQqR9XfQCo9+yNJDVD82iH6xl0XL6GmpKXC84eiF89NFHTT8fHXnkZTt1eRdkt/TCqYGL0iZMHe2lS9Fv6uEMidJ91nOtrNE7YXoB//3vf2/6cumFXQNGPbe0+UY/kMLBt/Yl0veEjs7ROuix0n5U2rdDj7n2VQoHr7qNAQMGmCySfhBoql0DIe2j889//tOU0/eKfjnQc0frqPumX1b0vIt0NGBlwllSPU81I6n7pB/8bvuM2LJ9Lcqj2YaqoNcTfV9oRkJHjOnvRWeK1UA4nIXRjIQ2N+tx0Q9tfd9pdk+vDXpNLtpkou8zbZLW66MeO71OT5o0yVz/SmaCtF+TLkrf1wcOHDCj/MLNY7pUxLZeWge9hmqzpF77dGSXlteRizrSUrdh0+zj5pqsTU/hZlUNDjXDFd43PbeLvicRASdOZ9gNj/7QUQVh2gv/oYceMiNFtGe39hrXeTfC8ytUh/DIE9vRSjpCRUdK6JwPOirjnHPOcdauXVuqnD5Xt12SzuKoI2Z05JXOIKujYcqaKE/nJdFybuiIgrJGlBSls+22bt3ajKwpa56XsoTnQ9F91nk6dAbMZcuWlRrREZ7npbIRHjpfic71oRMB6nHX2Tt1squVK1dWOtqo6Dwv+rd0dIG+TiXPmfD5VtacHOXta3i+j5J0OzpBXHWpbDRd0ddcRwfpOp1TyXaeF53DQicd03NMZ0Ita54XnbFUXyd9jfW460ReJScM1JEsetx09JduT+cB0uPy+OOPF5bJzc11br75ZjNrqZ7zxx57rBkhoudK0ZEoFR3vSEba6OgkHQWk9bKd58XmuJdXT5vXItbKm+OmvNFKOhGirmvRooV5H+p5Ud6oOr3G6bVOr3l6Pug1cOnSpaXKlTVHk9vRUrb10vN5+vTpTvfu3c3oRa2bjvqaNm1amZM8lsf2mlzRZ4UfZi2PdwH9XyRBD+KHfnPRbyA6UqHoPCxASdqJV7MbmhWpqm/wAFDd4maSOkRO+9DojcIIXGBzrmjfFAIXAH5G5gVAQtKkcmV31S46XwgA/yDzAiAhaYdJ7TBe0aKjVAD4D5kXAAlJh+fqaKWKVDZ6DkB8IngBAAC+QrMRAADwFd9MUhcpnU1VpzPXSdbomAcAqKyjtzY56iSFbu4P5ZZO2qczhUcrIyOjwlvNJKqED140cInVtOIAgMSktzeo6Iaj0QYurVvWlq3bKx4NZ6NJkyaybt26pAtgEj54Cd9TqNeJt0taWuUHNyXP/mTKq2t3Yz3lpNoPx3RcjNyc/cJY8ZO+o3+dLr6qhdJiM9w1ZH+IJWR/82fXFk6+NnYbR0I787yHvK6C3pPCvqyLeVPfe6Pie8RFYs+ePeYLb/izIxY046KBy7qlLSW7TuTZnT17Q9K6249mewQvCSbcVKSBi1XwErIPXkKWdwWOZfCSnZ0tfpKaEZtvB4EYBS8BF8FLIIbBi9+OM+KHzXXPr8FLLN8X1dHNQAOXaIKXZJbwmRcAAOJR0AlJ0Inu+cmK4AUAAA+ExDFLNM9PVuSrAACAr5B5AQDAAyHzX3TPT1YELwAAeCCoNw910UG5rOcnK5qNAACAr5B5AQDAA3TYjRzBCwAAHgUvQUYbRYRmIwAA4CtkXgAA8ADNRpEjeAEAwAOMNoocwQsAAB7QWVqim+cledHnBQAA+AqZFwAAPBCMcrRRMInvbUTwAgCAB/SO0tHdVVqSFs1GAADAV8i8AADgATrsRo7gBQAAD4QkIEEJRPX8ZEXwEoWM3QXWZZ0U+5MslGHfmnfqKROty+bVTbcu+8nMmyUWam3Oty7rpNu/DgVZ9mUdF42leXVcHLea7i4kqfYvBRCxQKzG07o53d3c/dhF0VNOn2Rd9uPZt9tvGHGP4AUAAA+EnF+XaJ6frAheAADwQDDKZqNgEjcbMdoIAAD4CsELAAAeZl6iWWJl586dMmLECKlbt65Z9Oddu3ZV+BzHcWTChAnSrFkzqVGjhvTt21e++eabYmV0XSAQKLYMGzbMdf0IXgAA8EDICUS9xMrFF18sK1askA8++MAs+rMGMBWZPHmyTJkyRaZNmyZffPGFNGnSRE4//XTZu3dvsXKjRo2SLVu2FC5PPPGEv4KXBQsWyFlnnWWiNI2+3nzzzVJlVq1aJUOGDDGRX506daRXr16yYcMGT+oLAECiZ15WrVplApannnpKevfubZbp06fLO++8I6tXry436zJ16lS588475bzzzpOOHTvKc889JwcOHJCXXnqpWNmaNWuawCa86Oe7r4KX/fv3S5cuXUyUVpbvv/9eTjjhBGnbtq3MmzdPvvzyS7nrrrskKyur2usKAEA82rNnT7ElNzc3qu199tlnJqDo2bNn4TpNHOi6hQsXlvmcdevWydatW6V///6F6zIzM+Xkk08u9ZwZM2ZIw4YNpUOHDnLzzTeXyszE/WijgQMHmqU8GsGdeeaZJhUVdsQRR1RT7QAAiJ2gpJgl8uf/qnnz5lLU3XffbfqeREqDkEaNGpVar+v0sfKeoxo3blxsvf7+448/Fv4+fPhwad26tcm4fP311zJ+/HiTmJg9e3ZiDJUOhULy7rvvyq233ioDBgyQ5cuXmx3WHT3nnHPKfZ5GnEWjTo1CAQCIN06U/Vac/z1348aNkp2dXSzjURYNaO65554Kt6l9VZR25Sj995wy1xdV8vGSz9H+LmHatHTUUUdJ9+7dZdmyZXLssceK7zvsbt++Xfbt2yeTJk2SM844Qz788EM599xzTVva/Pnzy33exIkTC3tH61IyIgUAIJFkZ2cXW8oLXsaMGWP6s1S0aEChWZFt27aVev6OHTtKZVbC9DmqZGZGP8vLe47SgCU9PV3WrFmTOJkXdfbZZ8uNN95ofu7atatpO3v88cdNO1pZNDMzbty4YpkXAhgAQLJPUtewYUOzVEY76O7evVs+//xz6dGjh1m3ePFis+74448v8znhpiBt/jnmmGPMury8PJNsePDBB8v9WzqUOj8/X5o2bepqX+I286IvcFpamrRv377Y+nbt2lU42kgjzpJRKAAA8SbopES9xIJ+zmqLhzbxLFq0yCz68+DBg6VNmzaF5XQwzcyZM83P2jR0ww03yAMPPGDWaX+Wyy67zIws0mHX4UE49957ryxZskTWr18v7733nlxwwQUm2OnTp09iZF4yMjLkuOOOKzUs67vvvpOWLVt6Vi8AABLdjBkzZOzYsYWjh3TKkpIjg/XzWbMxYdpH9eDBgzJ69GgzyZ2OVtIuHzrNSfhzfc6cOfLwww+bbiHaKjJo0CDTwTg1NdU/wYtWfu3atcWGWulEOPXr15cWLVrILbfcIkOHDpWTTjpJ+vXrZ8adv/3222bYNAAAfhaSgISiaAAJubkFt0v6Ofziiy9WWEY74xal2RftFFzeSCcNVirqs+qb4EVTRxqUhIX7qowcOVKeffZZ00FX+7doJ1yNADVd9frrr5u5XwAA8DNuzOjT4EXvcVAycivpiiuuMAsAAEBc93kBACCRRdvpNljJl/9ERvACAIBnfV4iHyodiuFdpeMdwQsAAB4IRXl7gFAMO+zGu6QJXgJBnaK48gNtUeT/l80P31mick6qfYQcyrA/mQtq2R/CUJp9HfqeUf6kQtHsWyJ/T0h1ey+0uJ1lKfn0629/vs/98LaY1iXZBdw0hfw6l6mVU/s+YFWuoCDHfqPwTNIELwAAxBP6vESO4AUAAI+ajeJ1npd4R+IaAAD4CpkXAAA8EHQCZonm+cmK4AUAAA8EoxxtFKTZCAAAwB/IvAAA4IGQk2KWyJ/vSLIieAEAwAM0G0WO0UYAAMBXyLwAAOCBUJQjhkKSvAheAADw5SR1KZKsCF4AAPDl7QFSJFkl754DAABfIvMCAIAHQhIwSzTPT1YELwAAeIBmo8jRbAQAAHyFzAsAAL6cpC5FkhXBCwAAHgg5AbNE8/xklbxhGwAA8CUyLwAAeEAnmYum6SeUxPmHpAleQhkpEkqr/EAHgvYTLjvpLk6cFPv0XkqBfR3yatofwtTc2OxbMN1+30Jp9mUDLm6YmpoXikl9a/xkv92cQ1IlVnqMnGJd9vPnxsWsHhDp1/9B65dh7oe3ef6SuXnPpRTYv+mcGF3TAkEXb/yQk+R3lU6RZJW8ew4AAHwpaTIvAADEk6AEzBLN85MVwQsAAB6g2ShyBC8AAHggGGX2JCjJiz4vAADAVzwNXhYsWCBnnXWWNGvWTAKBgLz55pvllr3qqqtMmalTp1ZrHQEAiGWzUTRLsvJ0z/fv3y9dunSRadOmVVhOg5rFixebIAcAgES6MWM0S7LytM/LwIEDzVKR//73vzJmzBj597//LYMGDaq2ugEAgPgU1x12Q6GQjBgxQm655Rbp0KGD1XNyc3PNErZnz54Y1hAAgMg4EpBQFB12nSQeKh3XOacHH3xQ0tLSZOzYsdbPmThxotStW7dwad68eUzrCABAJGg2SsDgZenSpfLwww/Ls88+azrq2ho/frzs3r27cNm4cWNM6wkAAKpX3AYvn3zyiWzfvl1atGhhsi+6/Pjjj3LTTTdJq1atyn1eZmamZGdnF1sAAIg3IScQ9ZKs4rbPi/Z1Oe2004qtGzBggFl/+eWXe1YvAACqQjDKu0oH4zf/kNjBy759+2Tt2rWFv69bt05WrFgh9evXNxmXBg0aFCufnp4uTZo0kTZt2nhQWwAAIMkevCxZskT69etX+Pu4cePMvyNHjjR9XQAASFTRNv2EaDbyRt++fcVxHOvy69evj2l9AACoLiFJMUs0z09WcdvnBQCARBZ0AmaJ5vnJKnnDNgAA4EtkXgAA8AB9XiKXNMGLkxoQJ63yFFswK9V6mwH77jquOCn2qcDUnJB12dx69vuWmmu/c2kH7euQmm+/b24youl7C+zrkO4i4eiiDln2Rf+3bfuN52aTJI2lgIu+d4ls7oe3WZftO3BybCoRis2xCFhu17ZcVXCivDO0k8Q3ZkzePQcAAL6UNJkXAADiSVACZonm+cmK4AUAAA9oC1V087xI0qLZCAAA+AqZFwAAPBCKssNuKIk77BK8AADggZAEzBLN85MVwQsAAB5ght3IJW/OCQAA+BKZFwAAPECfl8gRvAAA4FWfl2iGSkvy9nmh2QgAAPgKmRcAADzgRDnayEnizAvBCwAAHuCu0pGj2QgAAPgKmRcAADzAaKPIEbwAAOABmo0iR7MRAADwFTIvAAB4gHsbRY7gBQAAD9BsFDmCFwAAPEDwErmkCV4CBb8m6KpSMNO+y1DAsd9uQab9xEPBLPs65Na1327GHvsKZ+wNWpdNPZhvXTYlJxiT3lup+yUm0va660IWyrAvn/mz/fHo3+te67IfLvqjxMKAbnfHZLsSsD+H/71kgnjt5EGTrcvOf/dW++2eab9dN9xs19X0aC4uvSkF9ud6oCBY5edOSkHVfk4gNpImeAEAIJ6QeYkco40AAPAweIlmiZWdO3fKiBEjpG7dumbRn3ft2lXhc9544w0ZMGCANGzYUAKBgKxYsaJUmdzcXLnuuutMmVq1asmQIUNk06ZNrutH8AIAAIq5+OKLTfDxwQcfmEV/1gCmIvv375c+ffrIpEmTyi1zww03yMyZM+WVV16RTz/9VPbt2yeDBw+WYNBFEyDNRgAAeEN790R3Y8bYWLVqlQlYFi1aJD179jTrpk+fLr1795bVq1dLmzZtynxeOLhZv359mY/v3r1bnn76aXnhhRfktNNOM+tefPFFad68uXz00Ucma2OLzAsAAD5uNtqzZ0+xRZtmovHZZ5+ZpqJw4KJ69epl1i1cuDDi7S5dulTy8/Olf//+heuaNWsmHTt2dL1dT4OXBQsWyFlnnWUqr+1jb775ZuFjuoO33XabdOrUybSLaZlLL71UNm/e7GWVAQCIK82bNy/sm6LLxIkTo9re1q1bpVGjRqXW6zp9LJrtZmRkyCGHHFJsfePGjV1v19PgRdvHunTpItOmTSv12IEDB2TZsmVy1113mX+1I9B3331nOvcAAOB3VZV52bhxo2mSCS/jx48v8+9NmDDBJAoqWpYsWWLK6s8lOY5T5vpoRbJdT4dKDxw40Cxl0ehx9uzZxdY98sgj0qNHD9mwYYO0aNGimmoJAED8DpXOzs42S2XGjBkjw4YNq7BMq1atZOXKlbJt27ZSj+3YscNkSSLVpEkTycvLMyOZimZftm/fLscff3zizvOiEaVGZ/Xq1Su3jLb1FW3v0/Y/AACSXcOGDc1SGe2Yq5+3n3/+uUkYqMWLF5t1boOMorp16ybp6ekmMXHhhReadVu2bJGvv/5aJk92N/Gibzrs5uTkyO23326Gb1UUYWpbX9G2P20LBAAg3sTrPC/t2rWTM844Q0aNGmVGHOmiP+uQ5qIjjdq2bWuGPYf98ssvZkj1t99+a37XkUn6e7g/i34mX3nllXLTTTfJnDlzZPny5XLJJZeYvq3h0UcJFbxo511NdYVCIXn00UcrLKttfUXb/rQtEACAeOM4gaiXWJkxY4YJKnRkkC6dO3c2Q5yL0uBEP2fDZs2aJcccc4wMGjTI/K6f2/r7448/XljmoYceknPOOcdkXnROmJo1a8rbb78tqampidVspIGL7uS6devk448/rrRdLzMz0ywAAMSzX++4F0WfF4ld8FK/fn0zB0tlHW2Luuyyy8xSkaysLNN/VZdopPkhcFmzZo3MnTtXGjRo4HWVAACAxzwNXnRa4LVr1xb+rtkVbR/TiE/ndTn//PPNMOl33nnHTB0cbjfTx3WsOAAAfsWNGX0avOh48n79+hX+Pm7cOPPvyJEjzXh0bT9TXbt2LfY8zcL07du3mmsLAEDVibbfihPDPi/xztPgRQOQkm1mRVX0GAAASE5x3ecFAIBERbNR5AheAADwAM1GkfPFPC8AAABJl3lJzQ1JajBUabmARZmwg3Wy7Cvgov+Ok2rfCSunnn3ZghrWRSU1x75sMMs+Bk7JcxEvpwTty9ofNknJzZdYcIJuvwuk2xdNiU3HvAHd7rYu+++l91iXDRS4OCAubsjmpMXmdfh49u3WZU85fZL4ScBF18G46P8ZclFhN6dZyO564uYzoCoyL9HMkuvExQHzRtIELwAAxBMN06IZl+JI8qLZCAAA+AqZFwAAPKDT++t/0Tw/WRG8AADgAUYbRY7gBQAAD2hn3UAUnW5DSdxhlz4vAADAV8i8AADgAR1pFNVoI0eSFsELAAAeoM9L5Gg2AgAAvkLmBQAAD5B5iRzBCwAAHmC0UeRoNgIAAL5C5gUAAA8w2ihyBC8AAHgWvERzV2lJWjQbAQAAXyHzAgCABxhtFDmCFwAAPKCtPtG0/DiSvAheAADwAJmXyNHnBQAA+AqZFwAAvEC7UcSSJnhxUgJmqUxKnn0r4sEG9omrlAL77Wbuti+bvaHAuuzu1vaHO5RmP3yvINP+dXDqpVuXTXdRh9SDQfs6hELWZQO5+fZlg+6GPAYK7OvhpHmfJD2j8x/sCwfsXwsn1b5sIN/+OLtxyumTYrLduOiU4GI8bcBFfQP2p68EQi7q4Ob96WascNCp2nJVwQlENVRaonmuz3l/RQQAAHAhaTIvAADEE2bYjRzBCwAAHmC0UeRoNgIAAL5C5gUAAC9oh1s67Pov87JgwQI566yzpFmzZhIIBOTNN98s9rjjODJhwgTzeI0aNaRv377yzTffeFZfAACqus9LNEuy8jR42b9/v3Tp0kWmTZtW5uOTJ0+WKVOmmMe/+OILadKkiZx++umyd+/eaq8rAACID542Gw0cONAsZdGsy9SpU+XOO++U8847z6x77rnnpHHjxvLSSy/JVVddVc21BQCgCjFJXeJ12F23bp1s3bpV+vfvX7guMzNTTj75ZFm4cGG5z8vNzZU9e/YUWwAAiNfRRtEsySpugxcNXJRmWorS38OPlWXixIlSt27dwqV58+YxrysAAFFlXyJZkljcBi9h2pG3ZHNSyXVFjR8/Xnbv3l24bNy4sRpqCQAAJNmHSmvnXKVZlqZNmxau3759e6lsTFHatKQLAADxjEnqEjDz0rp1axPAzJ49u3BdXl6ezJ8/X44//nhP6wYAgKdNRk5yNx15mnnZt2+frF27tlgn3RUrVkj9+vWlRYsWcsMNN8gDDzwgRx11lFn055o1a8rFF1/sZbUBAECyBi9LliyRfv36Ff4+btw48+/IkSPl2WeflVtvvVUOHjwoo0ePlp07d0rPnj3lww8/lDp16nhYawAAqoL234xmxFAgaQ+Dp8GLzpirHXDLox1zdYZdXQAASCjM81L9wUswGDTT+a9atcoEGe3atZOzzz5bUlNTI68NAABALIIX7acyaNAg2bRpk7Rp08ZkT7777jszp8q7774rv/nNbyLZLAAAyYPMS/WONho7dqwcccQRZg6VZcuWyfLly2XDhg1mhJA+BgAALO8qHc2SpCLKvOhw5UWLFplRQWENGjSQSZMmSZ8+fSQeOWkBs1TpNl2Efnl17P92jZ+C1mXT9+Rbl83eYF+HPc3tm/9y6tu/EGk59mP7nBT70zM13b4O6an2r0Mg074OgWBI3EjJKbAu6+QHYvOVJOBmu/Ybdtxs1wXHRR3cSN+Va1025OKciJWUYIzGyLrYbEqe/XUqJc/Fe8PFuRPIzbMvm29X35Sg/TbhnYjehToJXFl3dtahzxkZGVVRLwAAEpqOV6lgzIrV85NVRF9jBg8eLL///e9l8eLFpr+LLpqJufrqq2XIkCFVX0sAABINk9RVb/Dyt7/9zXTK7d27t2RlZZlFm4uOPPJIefjhhyOvDQAAyYI+L9XbbFSvXj156623ZM2aNfKf//zHZF7at29vghcAAIBYiqrnWXjafgAA4E7A+XWJVCCJ+7ykRTpBnU7fP2fOHHOX51CoeE/yjz/+uKrqBwBAYmKel+oNXq6//noTvOhEdR07djQz7AIAAMRt8PLKK6/Ia6+9JmeeeWbV1wgAgGQQ7URzTvImDiIKXnQuFzrnAgAQBZqNqneo9E033WSGRFd0R2gAAABPMy/nnXdeqU6577//vnTo0EHS09OLPfbGG29UXQ0BAEhEZF5iH7zUrVu32O/nnntu5H8VAIBkR/AS++DlmWeeMf8WFBTIjBkzZMCAAdKkSZPI/zIAAEB19HlJS0uTa665RnJz7e/CCgAASuD2ANXbYbdnz56yfPnyyP8qAABJLjzDbjRLsopoqPTo0aPNiKNNmzZJt27dpFatWsUe79y5c1XVDwCAxESfl+oNXoYOHWr+HTt2bOE6nWVXh07rv3r7AAAAgLgJXtatW1f1NQEAAIhV8NKyZUurcnrvo6eeekqaNm0ayZ8BACBh6eT+Ud1VWpJXRB12bS1YsEAOHjwYyz8BAACSTESZFz8KZqRIIL3yWC1tv30sm5ZjHzLn17LfbsiinmGx6m2etdN+w/ub2u9bMMtF2fRU67KZu128vpn2r2/agdj138rItd92Sm6+dVnHxV3enSz7S4Dj4ng4qS7qkGp/PFJyC6zLDuh2t3VZSXHxniuwP26p++3fRyefOdm6rLh4fVOC9nVIzbF/fVPyQ9ZlxUUdAiEX23UjGKracgl+Y8adO3eafq2zZs0yvw8ZMkQeeeQRqVevXrnP0dn1n3jiCVm6dKn8/PPPZlRy165di5Xp27evzJ8/v1Q/Wr3hc9xkXgAAQCWjjaJZYuTiiy+WFStWyAcffGAW/XnEiBEVPmf//v3Sp08fmTRpUoXlRo0aJVu2bClcNOBxK2kyLwAAoHKrVq0yAcuiRYvMvG5q+vTp0rt3b1m9erW0adOmzOeFg5v169dXuP2aNWtGPUM/mRcAAHycedmzZ0+xJdoZ8D/77DNzP8Nw4KJ69epl1i1cuDDavTa3GGrYsKG5sfPNN98se/fudb0NMi8AAHgg2llyA/97bvPmzYutv/vuu2XChAkRb3fr1q3SqFGjUut1nT4WjeHDh0vr1q1N5uXrr7+W8ePHy5dffimzZ8+On+DljjvukPr168fyTwAAkNQ2btwo2dnZhb9nZmaWWU4DmnvuuafCbX3xxRfmX51wtqTwRLTR0P4uYR07dpSjjjpKunfvLsuWLZNjjz22eoKXb7/9VjZs2CB5eXnF1muvZKURVbT0Ltb6gmuaSSM+nTPmsssukz/84Q+S4mKEAAAAiXh7gOzs7GLBS3nGjBkjw4YNq7BMq1atZOXKlbJt27ZSj+3YsUMaN24sVUkDlvT0dFmzZk3sg5cffvhBzj33XPnqq68KbwugwhFZVd4e4MEHH5THH39cnnvuOdM+tmTJErn88stN29v1119fZX8HAIBEvrdRw4YNzVIZ7Zi7e/du+fzzz6VHjx5m3eLFi826448/XqrSN998I/n5+a4ns40odaFBg7ZZaWSmvYb1j+uEdJr6mTdvnlQl7Th09tlnm9l6NSI8//zzpX///iaIAQDAr+L1rtLt2rWTM844wzTx6IgjXfTnwYMHFxtp1LZtW5k5c2bh77/88osZUq2tMkpHJunv4X4y33//vdx7773m81tHJL333ntywQUXyDHHHGOGWMc8eNGAQitw6KGHmqYbXU444QSZOHFisZs1VgXd7pw5c+S7774zv2vHnk8//VTOPPPMMstrL+uSPa8BAIA97arRqVMnkyzQpXPnzvLCCy8UK6PBiWZjwnRCOw1ENNmgtIlKf9fWE5WRkWE+zwcMGGCCII0XdNsfffSRpKbaT4IZcbORNgvVrl3b/KwpqM2bN5uK6D2PdGeq0m233WZeHI3wdOf0b//pT3+Siy66qMzyGkBV1iEJAADPxfEMu/Xr15cXX3yx4j//vy4jYdofVZfy6KiokrPrRiqizIv2ENYOPUrHgU+ePFn+7//+z2RjjjjiCKlKr776qnkBX3rpJdMbWfu+/OUvfzH/lkU7CWuwE160FzYAAHEnjmfYjXcRZV50pI9OA6zuv/9+0w524oknSoMGDVzfn6Ayt9xyi9x+++2FPaQ1jfXjjz+aDMvIkSNLldchYuUNEwMAAEkavGh7VZhmWrRzjnbUOeSQQ6IeA17SgQMHSg2J1uajUKxu3gUAgI8mqUtGETUbXXHFFaWm89X2MQ009LGqdNZZZ5k+Lu+++67pnaw9m6dMmWKGagMA4Fs0G1Vv8KL9TQ4ePFhqva57/vnnpSrpLbh1ePTo0aPN8C29D8JVV10l9913X5X+HQAAkIDNRjrsWHsX66KZl6ysrMLHdBSQjtku634I0ahTp45MnTrVLAAAJIxo52pxJGm5Cl7q1atn+rTocvTRR5d6XNczTBkAgPibYTdpg5e5c+earMspp5wir7/+erGbLurkMzrPS7NmzWJRTwAAAPfBy8knn2z+Xbdunbkh4xNPPGGm+/3Xv/4lhx12mJl9T28boLPiAgCACpB5qd6h0npfghEjRsjw4cNl+fLlZkp+pf1gHnjgAdP3Jd6E0gJmqUx+bfuXJO2gfc4uELIfQh5ycVRyD0l3UQf7+mb9XGBddm9L+zrkNLAuKpm7XJT9/zNUV+pAQ/tpqAOOfZ/22v+1f82Uk+5iOux9pTvIlyeQbn8ChTKiurF8Bdu137dQWkpMzmE3AgX2Uy8E8u1vPJvfMNNXw15T8uz3LSXH3fluy6ni6TYK2U6v4VTfNBwMla7m0UY6MZ3eq2D69OnmVtZherdJnQUXAAAgroIXvX/RSSedVGp9dna27Nrl4usyAABAdQQvTZs2lbVr15Zar3d7rup7GwEAkJCYpK56gxedJO7666+XxYsXm+HReldpvX22TiCnk8kBAAC7Pi/RLMkqot56t956q7ljc79+/SQnJ8c0IenNEDV4GTNmTNXXEgAA4H8iHmqg9xu68847zU0Z9SaJ7du3l9q1a0e6OQAAkk8SZ0+iEdU4yZo1a0r37t2jqgAAAEmJeV6qt88LAACAV2IzQxUAAKgQk9RFjuAFAAAv0GwUMZqNAACAr5B5AQDAAzQbRY7gBQAAL9BsFDGCFwAAvEDwEjH6vAAAAF8h8wIAgAfo8xI5ghcAALxAs1HEaDYCAAC+QuYFAAAvkHmJGMELAAAeoM9L5JImeMlpkCqpGamVlsvaGbDeZm49+1a3lHzrohLMtK9Dap592fQ9BfZ1yLDft9x69vd0D9YOWZfN3JUak9esoJZ1UUnfJzGpgylfK926bMp++7IBx/54pBy0PzGddPvjEUi1P38Cqfb1dVxsN2V/nn0dQvbnZahmhnXZgpr2r5njohH/09dvti574jl/ti5bUKOGddka23Jicp6l7DtoXVbS7F9fJJakCV4AAIgrNBtFjOAFAAAP0GwUOUYbAQAAXyHzAgCAF2g2ihjBCwAAXiB4Sexmo//+979yySWXSIMGDaRmzZrStWtXWbp0qdfVAgAgYoEqWJJV3Gdedu7cKX369JF+/frJ+++/L40aNZLvv/9e6tWr53XVAACAB+I+eHnwwQelefPm8swzzxSua9Wqlad1AgAgajQbJW6z0axZs6R79+5ywQUXmKzLMcccI9OnTy+3fG5uruzZs6fYAgBAvA6VjmZJVnEfvPzwww/y2GOPyVFHHSX//ve/5eqrr5axY8fK888/X2b5iRMnSt26dQsXzdoAAIDEEffBSygUkmOPPVYeeOABk3W56qqrZNSoUSagKcv48eNl9+7dhcvGjRurvc4AAFg3G0WzJKm47/PStGlTad++fbF17dq1k9dff73M8pmZmWYBACDuJXEAktCZFx1ptHr16mLrvvvuO2nZsqVndQIAAN6J++DlxhtvlEWLFplmo7Vr18pLL70kTz75pFx77bVeVw0AgIjRYTeBg5fjjjtOZs6cKS+//LJ07NhR7rvvPpk6daoMHz7c66oBABA5+rwkbp8XNXjwYLMAAAD4IngBACDRRDtXSyCJO/sSvAAA4AVm2I0YwQsAAB4g8xK5pAle9jUTSc2qvFxBDfs+zAU17P9++n77ssF0+3uFph4MWZctqJlqXTavtv3rEGyYb13WzW1Qgxn29S2oYb/hAovzICyUar/dUHpazFK+qQft5y5KOZBnXdZJsz/OjovXIpAXtC6blltgXTZlf651WScQiMnQBTd1CKXaXyRS7F8yOfHcv9gXdvE6pOTZX09SDrp434fst+vUyLAuGyiw367kWr4vQvbvH3gnaYIXAADiCs1GESN4AQDACwQviTvPCwAAQFFkXgAA8AAddiNH8AIAgBdoNooYzUYAAMBXyLwAAOCBgOOYJZrnJyuCFwAAvECzUcRoNgIAAL5C5gUAAA8w2ihyBC8AAHiBZqOIEbwAAOABMi+Ro88LAADwFTIvAAB4gWajiBG8AADgAZqNIkezEQAA8BUyLwAAeIFmo4gRvAAA4GHTEdyj2QgAABSzc+dOGTFihNStW9cs+vOuXbukPPn5+XLbbbdJp06dpFatWtKsWTO59NJLZfPmzcXK5ebmynXXXScNGzY05YYMGSKbNm0StwheAADwgt5YMdolRi6++GJZsWKFfPDBB2bRnzWAKc+BAwdk2bJlctddd5l/33jjDfnuu+9McFLUDTfcIDNnzpRXXnlFPv30U9m3b58MHjxYgsGgq/olTbNRQb2QhLJClZZzUuzjufT99n8/p6F92fS99mV/yU6XWEhxcR45BfavWSDDfsMFtezrkFfHvqzj4qwvcPHyBio/vYrJq+3idQvVsC6b8YuL7ebbVzp1W/nfukoKHlrPuqwTCNhvt3aWddmAi4thKMv+QDup9vV1o8aWA/Z1+Pwr67KpHdvYbzfD/s0R2GNfX8nKsK9DempMzp1ATq5dwVCeJPtoo1WrVpmAZdGiRdKzZ0+zbvr06dK7d29ZvXq1tGlT+pzS7Mzs2bOLrXvkkUekR48esmHDBmnRooXs3r1bnn76aXnhhRfktNNOM2VefPFFad68uXz00UcyYMAA6zqSeQEAwMf27NlTbNGmmWh89tlnJhgJBy6qV69eZt3ChQutt6PBSiAQkHr1fv0ys3TpUtO81L9//8Iy2rzUsWNHV9tVBC8AAHg52iiaRcRkLsJ9U3SZOHFiVNXaunWrNGrUqNR6XaeP2cjJyZHbb7/dND9lZ2cXbjcjI0MOOeSQYmUbN25svd2kazYCACCeaFOz2+bmks9XGzduLAwQVGZmppRlwoQJcs8990hFvvjiC/OvZkxKchynzPUlaXZl2LBhEgqF5NFHH620vO12iyJ4AQDAx/O8ZGdnFwteyjNmzBgTVFSkVatWsnLlStm2bVupx3bs2GGyJJUFLhdeeKGsW7dOPv7442L1atKkieTl5ZmRTEWzL9u3b5fjjz9e3CB4AQAgCTRs2NAsldGOudpf5fPPPzcdbtXixYvNuoqCjHDgsmbNGpk7d640aNCg2OPdunWT9PR007FXy6ktW7bI119/LZMnT07cPi/ajqepJR1qBQCAn4VHG0WzxEK7du3kjDPOkFGjRpkRR7rozzqkuehIo7Zt25phz6qgoEDOP/98WbJkicyYMcMMfdZ+LLpotkVpf5wrr7xSbrrpJpkzZ44sX75cLrnkEjM3THj0UcJlXrQd7sknn5TOnTt7XRUAAKIX7VwtTuzmedEAZOzYsYUjg3S+lmnTphUro8OmNRujdKK5WbNmmZ+7du1arJxmYfr27Wt+fuihhyQtLc1kXg4ePCinnnqqPPvss5Kaaj9E3jfBi05iM3z4cDPO/P777/e6OgAAJLT69eubOVgq62hbtK9M0d/Lk5WVZeZ/0SUavmg2uvbaa2XQoEFWaSUd315yzDsAAPEmXpuN/CDuMy86hbBONRwevmXTL6ayoWAAAHiOu0onZuZFx65ff/31JnWlqSYb48ePN21w4UW3AQAAEkdcZ150KmEd/63Dq8K0B/OCBQtMxyFtIirZyUcn5ylvgh4AAOJFvN7byA/iOnjRXshffVX8BmSXX365GZ6lt9522zsZAIC4EcejjeJdXAcvderUMTdsKqpWrVpm4puS6wEAQHKI6+AFAIBERbNREgUv8+bN87oKAABEj9FGyRO8AACQCMi8JOhQaQAAgJLIvAAA4IWQ8+sSzfOTVNIEL05AxLHIMxXUtj8ZgjXs/37agYB12ZCLEeBu6pDvYt8ydtvXt/Ya+9Nof2sXr0OGfX1zGtpvN6XAuqikHXBRNkfcCcTmnAhl2hdOzQ9Zlw02OcS+bA37cyK/jn3ZUKr9ayYuihZkuSjsQijdfrs5jezfzDUyMiQWUrbttC9cYP9GcmrbTTKq8uvXtC6bsXm3fR3+d2fjSss5duWqBH1eIkazEQAA8JWkybwAABBPNC8X1Qy7krwIXgAA8AIz7EaMZiMAAOArZF4AAPAA87xEjuAFAAAvMNooYjQbAQAAXyHzAgCABwKOY5Zonp+sCF4AAPCCzhEZivL5SYrgBQAAD5B5iRx9XgAAgK+QeQEAwAuMNooYwQsAAF5ght2I0WwEAAB8hcwLAAAeYIbdyBG8AADgBZqNIkazEQAA8BUyLwAAeCAQ+nWJ5vnJiuAFAAAv0GwUMZqNAACAr5B5KSFUw0UeLsX+pljB2gHrsrmNXHZXty2aax+r5jSy325Kvv2+Zfycal02lG5dVNIO2pfNrW+/b6F0+30LZogr+TXtty1N7N+qoVT7spm7s6zLFtSITTo7fb+Lm8u5KJpfy/71Lahpv10JxOi8zLY/bnt+1926bGqefR3Scupbl03fZ3+QUwpcXKdclE1pUNu6bNq+/XZ/Xy88e6V6MEldxAheAADwAPc2ihzBCwAAXqDPS8To8wIAAHyFzAsAAF7QPi/RDHd2JGkRvAAA4AH6vCRws9HEiRPluOOOkzp16kijRo3knHPOkdWrV3tdLQAA4JG4D17mz58v1157rSxatEhmz54tBQUF0r9/f9m/327YGwAAcckMlXaiWCRpxX2z0QcffFDs92eeecZkYJYuXSonnXSSZ/UCACAqjDZK3OClpN27d5t/69cvezKl3Nxcs4Tt2bOn2uoGAABiL+6bjYpyHEfGjRsnJ5xwgnTs2LHcPjJ169YtXJo3b17t9QQAoFKhKliSlK+ClzFjxsjKlSvl5ZdfLrfM+PHjTXYmvGzcuLFa6wgAgJvRRtEsyco3zUbXXXedzJo1SxYsWCCHH354ueUyMzPNAgBAXKPPS+IGL9pUpIHLzJkzZd68edK6dWuvqwQAADwU98GLDpN+6aWX5K233jJzvWzdutWs1/4sNWq4uM0tAADxhMxL4vZ5eeyxx0zflb59+0rTpk0Ll1dffdXrqgEAELmo5nhxfl2SlC+ajQAAAHwTvAAAkJB0qHMgyucnKYIXAAA8wI0ZE7jPCwAAQFFkXgAA8AKjjSKWNMFL6oEUSQlVnmgK1nTRiBiwb6x0Ul10PHZT1kV7qZNdYL/ZNPvXIRi0T+AF3fS/DtnvXLBmqnXZlLzYHLdgDXeN17kN7V9jJy02HdeD2+xft6CLuR/z6waty6YUuDgebnLFbjr7H5JnXTQ9y/59tP9AhnXZlF/Srctm7HLzxndRNM3Fey7T/typsd2+DmkH7cvW2WRf37QdllNrWHxOVJmQo21H0T0/SdFsBAAAfCVpMi8AAMQVmo0iRvACAIAnop1ozpFkRfACAIAXyLxEjD4vAADAV8i8AADgBTNaiNFGkSB4AQDAC07o1yWa5ycpmo0AAICvkHkBAMALdNiNGMELAABeoM9LxGg2AgAAvkLmBQAAL9BsFDGCFwAAvGBGSkcxVNqRpEWzEQAA8BWCFwAAvGw2imaJkZ07d8qIESOkbt26ZtGfd+3aVW75/Px8ue2226RTp05Sq1YtadasmVx66aWyefPmYuX69u0rgUCg2DJs2DDX9SN4AQDAC6FQ9EuMXHzxxbJixQr54IMPzKI/awBTngMHDsiyZcvkrrvuMv++8cYb8t1338mQIUNKlR01apRs2bKlcHniiSdc148+LwAAeCFOO+yuWrXKBCyLFi2Snj17mnXTp0+X3r17y+rVq6VNmzalnqPZmdmzZxdb98gjj0iPHj1kw4YN0qJFi8L1NWvWlCZNmkRVRzIvAACg0GeffWaCkXDgonr16mXWLVy4UGzt3r3bNAvVq1ev2PoZM2ZIw4YNpUOHDnLzzTfL3r17xS0yLwAA+DjzsmfPnmKrMzMzzRKprVu3SqNGjUqt13X6mI2cnBy5/fbbTfNTdnZ24frhw4dL69atTebl66+/lvHjx8uXX35ZKmtTGYKXEhwXuSinRtC6bCAtNm2TGTXzrcvWyMqLSR3yC1KtywZD9i+w4wSsyxZkuahDvpuEo30dAqnujnEgYH/RCthXQ0IF9vuXk+WizgUuKpFnX4dQvQLrsl2O2Ghd9rzGy6zLnlnrR+uyDVJqWZf9vmCfddmP9x9lXXbeztJp+/J8vb2pddm9u2pYl5W96dZFc+u5eN+7OM3ya7l4L6dbftwF7a/r8TLDbvPmzYutvvvuu2XChAmliuu6e+65p8JNfvHFF+ZfzZiU5DhOmevL6ryrnXBDoZA8+uijpfq7hHXs2FGOOuoo6d69u+knc+yxx4otghcAAHxs48aNxbIb5WVdxowZU+nInlatWsnKlStl27ZtpR7bsWOHNG7cuNLA5cILL5R169bJxx9/XKxeZdGAJT09XdasWUPwAgBAvHOckFmieb7SAKGyIEFpPxNdKqMdc7W/yueff2463KrFixebdccff3ylgYsGInPnzpUGDRpU+re++eYb87ymTe0zhIoOuwAAeEH7rISiWJzYjDZq166dnHHGGaaJR0cc6aI/Dx48uNhIo7Zt28rMmTPNzwUFBXL++efLkiVLTIfcYDBo+sfokpf3a5eF77//Xu69915TZv369fLee+/JBRdcIMccc4z06dPHVR0JXgAAQDEagOiEc/379zdL586d5YUXXihWRodNazZGbdq0SWbNmmX+7dq1q8mkhJfwCKWMjAyZM2eODBgwwARBY8eONdv+6KOPJDXVvt+ios8LAABeMJmT+JvnRdWvX19efPFFqYh24C3aV6bo72XRjsXz58+XquCLzIv2VtahVVlZWdKtWzf55JNPvK4SAAAJO8NuvIv74OXVV1+VG264Qe68805Zvny5nHjiiTJw4EAzYx8AAEg+cR+8TJkyRa688kr53e9+ZzoRTZ061aSeHnvsMa+rBgBAQt6YMd7FdZ8X7aG8dOlSM0tfUdrBp7wpinNzc80SVnLmQQAA4oETCokTiH6odDKK68zLTz/9ZIZblZwUR38vb4riiRMnFt7CW5eSMw8CABAXyLwkZvASVnI64oqmKNb7JOjQrfCiMw8CAIDEEdfNRjoToI79Lpll2b59e7lTFEd7QyoAAKqFTjTn4h5npTjJ2+clrjMvOqGNDo0uebdJ/b2iKYoBAPBHs1EoisWRZBXXmRc1btw4GTFihLnrpN5v4cknnzTDpK+++mqvqwYAADwQ98HL0KFD5eeffzb3Q9iyZYu5hbbeD6Fly5ZeVw0AgIg5IUecKJqNHDIv8W306NFmAQAgYZihzlEMd3YYKg0AAOALcd9sBABAIqLZKHIELwAAeIFmo4glfPAS7tAUysmxKh9KtW9DdCRoXTaQFpu2yaDk25cN5cemDgWpLupQ9uSCZXEc+7KhPPs6hArczBBgX4dAirtjHHDTUc++GhIKutg/N69FgYtK5LnYblqBddH8/XnWZQ/us9/uXhd3501PsX/f7yuw3+7BA7F5HYIH/v/tUioTOujiGB+0fx2COfbnQ9C+ulKQb//6FlhuOFyuOjrDFuj124ny+Ukq4CR4d+VNmzZxiwAAgCs6O/vhhx8ek1ctJydHWrduXe5tbtxo0qSJrFu3TrKysiSZJHzwEgqFZPPmzVKnTp1itxTQGzbqfY/0BM3OzpZEwr75E8fNnzhuiXXc9CNx79690qxZM0lJid08rhrA6M2Hq2Iy16wkC1ySotlIT76Komc9aRMteAlj3/yJ4+ZPHLfEOW56U99Y04AjGYOOpLg9AAAAQEkELwAAwFeSNnjRO0/ffffdCXkHavbNnzhu/sRx86dEPm7JIOE77AIAgMSStJkXAADgTwQvAADAVwheAACArxC8AAAAX0nK4OXRRx81UzPrBEHdunWTTz75RPxuwoQJZgbhootOG+1XCxYskLPOOsvMcqn78uabbxZ7XPuZ6z7r4zVq1JC+ffvKN998I4mwb5dddlmpY9mrVy+JdxMnTpTjjjvOzGbdqFEjOeecc2T16tUJcdxs9s2vx0099thj0rlz58IJ23r37i3vv/++74+bzb75+bgls6QLXl599VW54YYb5M4775Tly5fLiSeeKAMHDpQNGzaI33Xo0EG2bNlSuHz11VfiV/v375cuXbrItGnTynx88uTJMmXKFPP4F198YQK1008/3Uzr7fd9U2eccUaxY/nee+9JvJs/f75ce+21smjRIpk9e7YUFBRI//79zf76/bjZ7Jtfj5vSWcgnTZokS5YsMcspp5wiZ599dmGA4tfjZrNvfj5uSc1JMj169HCuvvrqYuvatm3r3H777Y6f3X333U6XLl2cRKSn6cyZMwt/D4VCTpMmTZxJkyYVrsvJyXHq1q3rPP74446f902NHDnSOfvssx2/2759u9m/+fPnJ9xxK7lviXTcwg455BDnqaeeSqjjVnLfEvG4JYukyrzoTbCWLl1qvjEVpb8vXLhQ/G7NmjUmratNYsOGDZMffvhBEpHeQVXvxlr0OOpEUyeffHJCHEc1b9480zxx9NFHy6hRo2T79u3iN7t37zb/1q9fP+GOW8l9S6TjFgwG5ZVXXjFZJW1iSaTjVnLfEum4JZuEvzFjUT/99JM5eRs3blxsvf5eFbcm91LPnj3l+eefN2++bdu2yf333y/HH3+8SY02aNBAEkn4WJV1HH/88UfxO23GvOCCC6Rly5bmg+Ouu+4yqW4NvP0yG6gmlcaNGycnnHCCdOzYMaGOW1n7lgjHTZuZ9QNd73Zcu3ZtmTlzprRv374wQPHzcStv3xLhuCWrpApewrRDVsmLUcl1fqNvwLBOnTqZN+pvfvMbee6558yFNhEl4nFUQ4cOLfxZPxy7d+9uLqzvvvuunHfeeeIHY8aMkZUrV8qnn36acMetvH3z+3Fr06aNrFixQnbt2iWvv/66jBw50vT1SYTjVt6+aQDj9+OWrJKq2ahhw4aSmppaKsuiKcKS3yr8rlatWiaI0aakRBMeRZUMx1E1bdrUXEz9ciyvu+46mTVrlsydO9d0lkyk41beviXCccvIyJAjjzzSfHjr6CrtVP7www8nxHErb98S4bglq6QKXvQE1qHROlqgKP1dm1gSSW5urqxatcq8ERON9unRC2rR46j9mfSbVKIdR/Xzzz/Lxo0b4/5Y6jdxzUq88cYb8vHHH5vjlCjHrbJ98/Nxq2if9Tri5+NW2b4l4nFLGk6SeeWVV5z09HTn6aefdr799lvnhhtucGrVquWsX7/e8bObbrrJmTdvnvPDDz84ixYtcgYPHuzUqVPHt/u1d+9eZ/ny5WbR03TKlCnm5x9//NE8riMfdLTDG2+84Xz11VfORRdd5DRt2tTZs2eP4+d908f0WC5cuNBZt26dM3fuXKd3797OYYcdFvf7ds0115hjoufhli1bCpcDBw4UlvHrcats3/x83NT48eOdBQsWmLqvXLnSueOOO5yUlBTnww8/9PVxq2zf/H7cklnSBS/q73//u9OyZUsnIyPDOfbYY4sNd/SroUOHmouJBmbNmjVzzjvvPOebb75x/EovIvrBXnLRYY1Kh2/q8HAdwpmZmemcdNJJ5qLq933TD8P+/fs7hx56qDmWLVq0MOs3bNjgxLuy9kmXZ555prCMX49bZfvm5+OmrrjiisJrou7DqaeeWhi4+Pm4VbZvfj9uySyg//M6+wMAAGArqfq8AAAA/yN4AQAAvkLwAgAAfIXgBQAA+ArBCwAA8BWCFwAA4CsELwAAwFcIXoAEMWHCBOnatWvEz1+/fr250Z7ewA4A4hnBC5Agbr75ZpkzZ47X1QCAmEuL/Z8AUB1q165tlkjojfYAwC/IvAA+sWPHDnN33wceeKBw3eLFi83d0j/88ENXzUaXXXaZnHPOOTJx4kRp1qyZHH300YWP/fDDD9KvXz+pWbOmdOnSRT777LNiz3399delQ4cOkpmZKa1atZK//vWvVbiXAFA5ghfAJw499FD5xz/+YYKUJUuWyL59++SSSy6R0aNHS//+/V1vT5uYVq1aJbNnz5Z33nmncP2dd95pmqC074sGNRdddJEUFBSYx5YuXSoXXnihDBs2TL766itTl7vuukueffbZKt1XAKgIzUaAj5x55pkyatQoGT58uBx33HGSlZUlkyZNimhbtWrVkqeeespkbsIddpUGLoMGDTI/33PPPSbLsnbtWmnbtq1MmTJFTj31VBOwKA1uvv32W/nzn/9ssjkAUB3IvAA+85e//MVkQl577TWZMWOGCWAi0alTp8LApajOnTsX/ty0aVPz7/bt282/mqnp06dPsfL6+5o1ayQYDEZUDwBwi+AF8Bntk7J582YJhULy448/RrwdzbyUJT09vfBnHTqt9G8px3EK14XpOgCoTjQbAT6io4K0yWjo0KGmGefKK680fU8aN25cLX+/ffv28umnnxZbt3DhQtN8lJqaWi11AACCF8BHtDPt7t275W9/+5sZFv3++++bAKZoh9tYuummm0xfm/vuu88EUDoSadq0afLoo49Wy98HAEXwAvjEvHnzZOrUqTJ37lzJzs4261544QXTR+Wxxx6rljoce+yxpq/NH//4RxPAaJ+Ye++9l866AKpVwKHBGgAA+AgddgEAgK8QvAAJfKuAspZPPvnE6+oBQFRoNgISkE4qV57DDjtMatSoUa31AYCqRPACAAB8hWYjAADgKwQvAADAVwheAACArxC8AAAAXyF4AQAAvkLwAgAAfIXgBQAA+ArBCwAAED/5fzCT4fvl6NPnAAAAAElFTkSuQmCC\",\n 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\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"zeta_remapped_extrap_creep_fill.isel(ocean_time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Avoid extrapolation when remapping to a larger domain with the `nearest_s2d` method\\n\",\n \"\\n\",\n \"When remapping to a larger domain with the `nearest_s2d` method, target grid\\n\",\n \"cells outside the original source domain will get the value of the closest\\n\",\n \"source grid cell at the domain edge, independent of the selected\\n\",\n \"`extrap_method`. This can be an undesired behaviour. Please see\\n\",\n \"[Curvilinear Grid - Undesired Extrapolation](Curvilinear_grid.ipynb#Undesired-extrapolation)\\n\",\n \"for more details.\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.14.4\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "doc/notebooks/Pure_numpy.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Use pure numpy array\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Despite the \\\"x\\\" in its name (indicating xarray-compatible), xESMF can also work\\n\",\n \"with basic numpy arrays. You don't have to use xarray data structure if you\\n\",\n \"don't need to track metadata. As long as you have numpy arrays describing the\\n\",\n \"input data and input/output coordinate values, you can perform regridding.\\n\",\n \"\\n\",\n \"Code in this section is adapted from\\n\",\n \"[an xarray example](http://xarray.pydata.org/en/stable/plotting.html#multidimensional-coordinates).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# not importing xarray here!\\n\",\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import xesmf as xe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Rectilinear grid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Input data\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Just make some fake data.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"data = np.arange(20).reshape(4, 5)\\n\",\n \"plt.pcolormesh(data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Define grids\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"In previous examples we use xarray `DataSet` as input/output grids. But you can\\n\",\n \"also use a simple dictionary:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grid_in = {\\\"lon\\\": np.linspace(0, 40, 5), \\\"lat\\\": np.linspace(0, 20, 4)}\\n\",\n \"\\n\",\n \"# output grid has a larger coverage and finer resolution\\n\",\n \"grid_out = {\\\"lon\\\": np.linspace(-20, 60, 51), \\\"lat\\\": np.linspace(-10, 30, 41)}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Perform regridding\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF Regridder \\n\",\n \"Regridding algorithm: bilinear \\n\",\n \"Input grid shape: (4, 5) \\n\",\n \"Output grid shape: (41, 51) \\n\",\n \"Output grid dimension name: ('lat', 'lon') \\n\",\n \"Periodic in longitude? False\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"regridder = xe.Regridder(grid_in, grid_out, \\\"bilinear\\\")\\n\",\n \"regridder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The `regridder` here has no difference from the ones made from xarray `DataSet`.\\n\",\n \"You can use it to regrid `DataArray` or just a basic `numpy.ndarray`:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(41, 51)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_out = regridder(data) # regrid a basic numpy array\\n\",\n \"data_out.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Check results\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.pcolormesh(data_out)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Curvilinear grid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Grids\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We use the previous input data, but now assume it is on a curvilinear grid\\n\",\n \"described by 2D arrays. We also computed the cell corners, for two purposes:\\n\",\n \"\\n\",\n \"- Visualization with `plt.pcolormesh` (using cell centers will miss one\\n\",\n \" row&column)\\n\",\n \"- Conservative regridding with xESMF (corner information is required for\\n\",\n \" conservative method)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# cell centers\\n\",\n \"lon, lat = np.meshgrid(np.linspace(-20, 20, 5), np.linspace(0, 30, 4))\\n\",\n \"lon += lat / 3\\n\",\n \"lat += lon / 3\\n\",\n \"\\n\",\n \"# cell corners\\n\",\n \"lon_b, lat_b = np.meshgrid(np.linspace(-25, 25, 6), np.linspace(-5, 35, 5))\\n\",\n \"lon_b += lat_b / 3\\n\",\n \"lat_b += lon_b / 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'lat')\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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ZfdPdPtxuT4kriTW92ybFCbz7Xg+BC4wsTnkgoJlp8e2s1LtNcGKutvLfJ2PujnEjRqTWkJ6utoXfr4mn/SJd6yt2NyzQXjVAz+w/AfwSuMLMfT3lqAHgyzolJOHSrjNmb6Tf/0FtDnMdXbd7B6XsOAExg8w5G+jp5XFKC9stKfwk8DPwvqjfAqxt192LfZzhCag3pSas1qAnErzR3lAokdlopby4aDu5+HDgOfBSgdp+jucBCM1vo7q/EP0XJMrWGzigMGiXxprfS3NHIN5+nymtrgM43pP8l8GVgJdXbWVwO7ASujm9qxRBbazi5CI4NQqUPSuNwyUFYeCKe15JpkgqC0JeUJNs63bb/n8CNwKPufq2Z/SK1NiEZdHIRHBk6/56ASn/1MSggpohqSUmtoHu6VUb2dRoO4+5+2Mx6zKzH3R83sy/GOjOZuWODze9DdGww0nAo4pJSVoJArUHi1mk4HKvdWnsr8HUzG6H6k9xkFmJbUqq0uN9Qq/EC6rQ1ZCUM8kStIQydhsNG4CzwX4B/AywGfieuSckslcarS0nNxiOSx9YQShDE1RrKlQHKlWU4vRhlekuH6C3pZE9RdRQO7j71do/3xTSXQon1+OolB6fvOUD1PkSXRHu+O3ShhEESypUBxisrqL8nwOmrPUYBUVDt3gQ3CjTrgAa4uy+KZVYyO/V9hZhOK4XYGi59Ys7kx2OLjOqJ7PDE1xqW0ew+ROXKskjDQUtK4Wj3PoeBpCZSJIm86W3hiUKfTJoaBlNVg0Eu5C2+FbQal/zT77x0JYutoVUQSOeMMk7jgQWL8NyJWkNYFA4J060yZq+oYRDn8dXe0qFpew5VE/SWDsX3opJpCgfJvKjCQEtKrdX3FeI6rRRia1jz6PSW/Ph3ivUTChQO0rEklpSK2graSeJNb72l0UKfTLowDIpO4ZAgLSk1SioM1BrSk8XW0G0QFK01gMJBOhRFa1ArmBndKmP21Aq6p3BISBFbQ1bCQK0hPWm0BgVBNBQO0lYnrSErQZA3ag3txR0GRVxSAoVDIvLYGkIJg7haw1jPAOdK50/2zKkcon+iuJu5SVErSI7CQdoKJQiSMtYzwNnSCrDz9yE6W6reh0gBcV4US0pph0FRWwMoHGIXYmtY+fj5v21rvb7RudKyyWCYZD2cKy2LNByKtqSUdhDIdAqHgpsaBHkTV7DpPkTtddIash4GRW4NkHI4mNnXgA8CI+7+1trYUuB+YB3wMnCnux9Na455000YqDU0l8h9iHLYGrIeBjJd2n/V+TPgD4E/nzJ2N/CYu99jZnfXHgcZ4VlYUspzM7iYOINtTuXQtD0HAHyCORXdh6gu9CAoemuAlMPB3bea2boLhjcCt9Q+vg94gkDDIWlRBoFaQ2v1fYW4TiuF2BrWfPf8tY8v1gGGPEi7OTSzwt33A7j7fjNb3uyTzGwTsAlg7dq1CU6vM0m0hqK2gnaSCLb+idHCnkyaGgQXUjDkRxbDoSPuvhnYDDA8PJy9m7dELMkgUGtITxZbw8XCII+0pFSVxXA4YGZDtdYwBIykPaFuRdEa1ApmRsE2O0ULAmkti+HwEHAXcE/t1wfTnU78shQE+uZaLFGGQR6WlNQazkv7KOs3qG4+LzOzvcDnqYbCA2b2MeAV4I70Zti9TlpDlsJAsiPuJSW1AulG2qeVPtriqfckOpEYKQjgbP8ApxcMMtHTS89EmfmnDjJ3LPpvVGo90yUZBmoN+ZPFZaWghRwGcXxzPds/wMmByybfEzBR6qs+HiWWgAjVbFuDWoFETeEQoV+480tpTyFzTi8YbHofotMLBiMNh6K1hiyFgVpDPikcBIjvm+tET/M/Yq3Gi6iT1pClMJBi0P+hEVFraK5nosxEqfE+RD0T0d2HKG+tIaQgiKs1jC5ZzNGVK6j09VEaH2fJvgMMHD0ey2upNTSncJBYv7nOP3Vw2p4DAD7B/FMHY3vN0IQUBkkYXbKYw2tW4aXqn5lKfz+H16wCiC0gpJHCIQJqDa3NHRuFUWI7rRRaa1j1N69Pe1xZuiClmWTX0ZUrJoOhzks9HF25QuGQIIVDwSXxzXXu2GhhTyZdGAZ5EteSUqWvcRnyYuOzoSWl1hQOs6TWIHXdBoFaQ3Ol8XEq/f1NxyU5CgcJVtpLSnluBe3EeXx1yb4D0/YcAKwywZJ9ByJ9HbWGi1M4zELorSHtb64hiToI1Bpaq+8rJHVaSZpTOEiQ4g62IreCdpJ409vA0eOxhoFaQ3sKh4w5vWCA0aXLqfT2UiqXGTgywvxTug9RnJIOArUGCYHCYYbiWFI6vWCA44NDeE/tfHdfH8cHhwBiCYhQzTbY1ApmTrfKKA6FQ4aMLl0+GQx13tPD6NLlkYZDkVpD1oJArUFCoXCYgbg2oiu9zX87Wo0XUbtgy1oYiIRK33UypFQuN32jT6ms+xC1ojCAE8uXcOiKVZTn9NN7boxlu19j0cjRyF9HS0rFonDoUpzHVweOjEzbcwCwiQkGjgT3Y7RjMfjgS7BkcdrTmLE4lpROLF/Cgasux0slAMpz53DgqssBYgkIKQ6FQ4bU9xWSOK0UgsEHX5o+EHAwxOXQFasmg6HOSyUOXbEq0nBQaygehUMXknjT2/xTo7GFQZaXlBqCIGfi2oguz2m8zcTFxkU6pXCQVHQdBmoNTfWeG6M8t/Fv9b3nxiJ7DbWGYlI4FESarSHvraCdOI+vLtv92rQ9BwCrVFi2+7XYXjMkCoWZUzh0KPT7KCUp8jBQa2ipvq8Q12mlkFqDgiBaCocCiLM1FL0VtJPEm94WjRwt5MkkhUG8FA4dUGs4L/EwUGuQGoVBshQOOTeb1qBWUGxpLikpCNKncGijSK1BYRAt3UepcwqD7MlsOJjZbcDvAyXgq+5+T8pTCk671pD5MIhhSen46kFGrl5Pef4cek+fY/lze1i892DkrxO6OFuDgiAMmQwHMysBXwHeC+wFnjazh9z9+STnkafWkPkgSMDx1YPsf8eb8N7arSYWzGX/O94EEHlAqDWcpzAIUybDAbge2OXuuwHM7JvARiDRcAjZgm9tY/Gll6Y9jZmLoTWMXL1+MhjqvLfEyNXr1R6mmE1rUBDkR1bDYRXw6pTHe4Ebpn6CmW0CNgGsXbs2uZll0IJvbWsY6w05GGJSnt/8m16r8ZkqUmtQGORXVsOh2WK5T3vgvhnYDDA8POxNPn9Wsryk1CwMciWm46u9p89RXjC36bhUXaw1KAiKJavhsBdYM+XxamBfSnNJ1UyCQK2hueXP7Zm25wBg5QrLn9sT2WvkqTUoDIotq+HwNHClma0HXgM+AvzrpF48zdaQ+1bQToxveqvvK+i0UqPvbvtvaU9BMiaT4eDuZTP7T8B3qB5l/Zq7P5fytCIXRxCoNVzc4r0HFQYoDKS9TIYDgLv/LfC3Sb9unK2h8K2gncBvlZHVJSUFgcxEZsMhdGkEgVqDgMJAoqFwmGI2rUGtoNjSbA0KA4mDwmGGFAYRC3xJKSkKAkmKwqEDIQRBHEtKx69YycjwBsoL59F78gzLt+9k8e5Cnii+qDhbg8JA0qJwqJm6pBRCGMTt+BUr2f/Oa/C+6h+R8sB89r/zGoDoA0KtAVAQSLYoHGpCDoQ4WsPI8IbJYKjzvl5GhjeoPUwxm9agMJAsUzgA7+25I+0pZE554byuxmesIK1BQSChUTgELq7jq70nz1AemN90XNpTGEjoCh8Oag3NLd++c9qeA4CNl1m+fWd0LxJ4a6gvKSkIJI8KHw4hi/NNb/V9BZ1WavTwS19MewoisSt0OKg1XNzi3fsKHwYKAimqQoeDpCijS0oKA5EqhUOgdB+laCgMRJorbDhoSSlFKbUGBYFI5wobDiFTa+iMwkBk5goZDnG1hhMb1nP45usoL1pA74lTXLp1B4t2RvcjKHMhptagIBCJViHDIQ4nNqxn5Labzt+LaPFCRm67CSDSgFBrqFIYiMSrcOEQV2s4fPN1Te9FdPjm69Qe6mbYGhQEIskrXDjEpbyo+Q3YWo3PRFFag8JAJH2FCoc4Tyj1njhFefHCpuPSmoJAJJsKFQ5xunTrjml7DlC9F9GlW3ekOKvsURiIhEHhEJH6vkJcp5VCXFJ6+OCfpD0FEZmhwoRDEm96W7RzT6E3nxUGIvlRmHAIWVZbg8JAJL8KEQ66VcbsKQhEiqUQ4RCytFqDwkCk2HIfDmoN7SkIRORCqYSDmd0B/HdgA3C9u2+f8txngI8BFeA/u/t30phjFsTVGhQGItJOWs3hJ8CHgP89ddDM3gJ8BLgaWAk8amZvcvfKTF5ErUFBICIzk0o4uPtOADO78KmNwDfd/Rywx8x2AdcD/5jsDMOlMBCRKGRtz2EVsG3K4721sQZmtgnYBLB27dr4Z5awTpaUFAQiEpfYwsHMHgUua/LU59z9wVZf1mTMm32iu28GNgMMDw83fE4el5QUBiKSlNjCwd1vncGX7QXWTHm8GtgXzYzCoiAQkTRlbVnpIeAvzezLVDekrwSe6vZfEmJreGTi/6Y9BRGRSWkdZb0d+ANgEPi2mf3I3d/n7s+Z2QPA80AZ+I2ZnlTKMgWBiGRdWqeVtgBbWjz3BeALM/13Z7E1KAxEJDRZW1bKBYWBiIQuV+GQRmtQEIhIHuUqHJKgMBCRIlA4XISCQESKKjfhEMWSksJARKQqN+HQLQWBiEhruQiHl3bs5gZbf9HPURiIiHQuF+FwIQWBiMjsmHvT+9oFxcwOAj9Lex5TLAMOpT2JCOXtekDXFApdU7wud/fBZk/kIhyyxsy2u/tw2vOISt6uB3RNodA1pacn7QmIiEj2KBxERKSBwiEem9OeQMTydj2gawqFrikl2nMQEZEGag4iItJA4SAiIg0UDhExs3vN7AUz+7GZbTGzS6Y89xkz22VmL5rZ+9KcZzfM7A4ze87MJsxs+ILngrwmADO7rTbvXWZ2d9rzmQkz+5qZjZjZT6aMLTWzR8zsp7Vfl6Q5x26Y2Roze9zMdtb+zH2iNh7yNc01s6fM7JnaNf2P2ngQ16RwiM4jwFvd/eeAl4DPAJjZW4CPAFcDtwF/ZGal1GbZnZ8AHwK2Th0M+Zpq8/wK8H7gLcBHa9cTmj+j+t9+qruBx9z9SuCx2uNQlIHfdvcNwI3Ab9R+X0K+pnPAu939GuDtwG1mdiOBXJPCISLu/l13L9cebgNW1z7eCHzT3c+5+x5gF3B9GnPslrvvdPcXmzwV7DVRnecud9/t7mPAN6leT1DcfStw5ILhjcB9tY/vA/5VopOaBXff7+4/rH08CuwEVhH2Nbm7n6w97Kv94wRyTQqHePw74OHax6uAV6c8t7c2FrKQrynkubezwt33Q/WbLbA85fnMiJmtA64FfkDg12RmJTP7ETACPOLuwVxTLm+8FxczexS4rMlTn3P3B2uf8zmqFfnr9S9r8vmZOT/cyTU1+7ImY5m5pjZCnnvumdlC4K+A33T3E2bNfrvC4e4V4O21PcgtZvbWtOfUKYVDF9z91os9b2Z3AR8E3uPn30CyF1gz5dNWA/vimWH32l1TC5m+pjZCnns7B8xsyN33m9kQ1b+tBsPM+qgGw9fd/a9rw0FfU527HzOzJ6juEwVxTVpWioiZ3QZ8GvgVdz895amHgI+Y2RwzWw9cCTyVxhwjFPI1PQ1caWbrzayf6sb6QynPKSoPAXfVPr4LaNX8MseqFeFPgZ3u/uUpT4V8TYP1U4tmNg+4FXiBQK5J75COiJntAuYAh2tD29z939ee+xzVfYgy1br8cPN/S7aY2e3AHwCDwDHgR+7+vtpzQV4TgJn9MvB7QAn4mrt/IeUpdc3MvgHcQvX2zweAzwP/D3gAWAu8Atzh7hduWmeSmb0T+B7wLDBRG/4s1X2HUK/p56huOJeo/kX8AXf/HTO7lACuSeEgIiINtKwkIiINFA4iItJA4SAiIg0UDiIi0kDhICIiDRQOIhEws5PtP0skHAoHERFpoHAQiZBV3WtmPzGzZ83sw7XxW8zsCTP7Vu3nfnzdQr9xkOSa7q0kEq0PUb13/zVU3738tJnVfx7GtVR/BsY+4EngJuD7aUxSpB01B5FovRP4hrtX3P0A8PfAv6g995S77z/RT00AAAB+SURBVHX3CeBHwLqU5ijSlsJBJFoXWyo6N+XjCmrukmEKB5FobQU+XPshL4PAzYRzx1qRSfqbi0i0tgA/DzxD9YcIfcrdXzezN6c7LZHu6K6sIiLSQMtKIiLSQOEgIiINFA4iItJA4SAiIg0UDiIi0kDhICIiDRQOIiLS4P8DPaFg4fA6kzoAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.pcolormesh(lon_b, lat_b, data)\\n\",\n \"plt.scatter(lon, lat) # show cell center\\n\",\n \"plt.xlabel(\\\"lon\\\")\\n\",\n \"plt.ylabel(\\\"lat\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For the output grid, just use a simple rectilinear one:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"lon_out_b = np.linspace(-30, 40, 36) # bounds\\n\",\n \"lon_out = 0.5 * (lon_out_b[1:] + lon_out_b[:-1]) # centers\\n\",\n \"\\n\",\n \"lat_out_b = np.linspace(-20, 50, 36)\\n\",\n \"lat_out = 0.5 * (lat_out_b[1:] + lat_out_b[:-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To use conservative algorithm, both input and output grids should contain 4\\n\",\n \"variables: `lon`, `lat`, `lon_b`, `lon_b`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"grid_in = {\\\"lon\\\": lon, \\\"lat\\\": lat, \\\"lon_b\\\": lon_b, \\\"lat_b\\\": lat_b}\\n\",\n \"\\n\",\n \"grid_out = {\\n\",\n \" \\\"lon\\\": lon_out,\\n\",\n \" \\\"lat\\\": lat_out,\\n\",\n \" \\\"lon_b\\\": lon_out_b,\\n\",\n \" \\\"lat_b\\\": lat_out_b,\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Regridding\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF Regridder \\n\",\n \"Regridding algorithm: conservative \\n\",\n \"Input grid shape: (4, 5) \\n\",\n \"Output grid shape: (35, 35) \\n\",\n \"Output grid dimension name: ('lat', 'lon') \\n\",\n \"Periodic in longitude? False\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"regridder = xe.Regridder(grid_in, grid_out, \\\"conservative\\\")\\n\",\n \"regridder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(35, 35)\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data_out = regridder(data)\\n\",\n \"data_out.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Results\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'lat')\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.pcolormesh(lon_out_b, lat_out_b, data_out)\\n\",\n \"plt.xlabel(\\\"lon\\\")\\n\",\n \"plt.ylabel(\\\"lat\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## All possible combinations\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"All $2 \\\\times 2\\\\times 2 = 8$ combinations would work:\\n\",\n \"\\n\",\n \"- Input grid: `xarray.DataSet` or `dict`\\n\",\n \"- Output grid: `xarray.DataSet` or `dict`\\n\",\n \"- Input data: `xarray.DataArray` or `numpy.ndarray`\\n\",\n \"\\n\",\n \"The output data type will be the same as input data.\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": false,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {\n \"height\": \"200px\",\n \"left\": \"2px\",\n \"right\": \"20px\",\n \"top\": \"101px\",\n \"width\": \"212px\"\n },\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "doc/notebooks/Rectilinear_grid.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Regrid between rectilinear grids\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cartopy.crs as ccrs\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import xesmf as xe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Prepare data\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Input data\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We regrid xarray's built-in demo data. This data is also used by\\n\",\n \"[xarray plotting tutorial](http://xarray.pydata.org/en/stable/plotting.html).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset>\\n\",\n       \"Dimensions:  (lat: 25, time: 2920, lon: 53)\\n\",\n       \"Coordinates:\\n\",\n       \"  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n       \"  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n       \"  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n       \"Data variables:\\n\",\n       \"    air      (time, lat, lon) float32 ...\\n\",\n       \"Attributes:\\n\",\n       \"    Conventions:  COARDS\\n\",\n       \"    title:        4x daily NMC reanalysis (1948)\\n\",\n       \"    description:  Data is from NMC initialized reanalysis\\\\n(4x/day).  These a...\\n\",\n       \"    platform:     Model\\n\",\n       \"    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 25, time: 2920, lon: 53)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\\n\",\n \" * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \"Data variables:\\n\",\n \" air (time, lat, lon) float32 ...\\n\",\n \"Attributes:\\n\",\n \" Conventions: COARDS\\n\",\n \" title: 4x daily NMC reanalysis (1948)\\n\",\n \" description: Data is from NMC initialized reanalysis\\\\n(4x/day). These a...\\n\",\n \" platform: Model\\n\",\n \" references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds = xr.tutorial.open_dataset(\\n\",\n \" \\\"air_temperature\\\"\\n\",\n \") # use xr.tutorial.load_dataset() for xarray\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes(projection=ccrs.PlateCarree())\\n\",\n \"dr.isel(time=0).plot.pcolormesh(ax=ax, vmin=230, vmax=300)\\n\",\n \"ax.coastlines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Input grid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Its grid resolution is $2.5^\\\\circ \\\\times 2.5^\\\\circ$:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. ,\\n\",\n \" 47.5, 45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5,\\n\",\n \" 20. , 17.5, 15. ], dtype=float32),\\n\",\n \" array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. ,\\n\",\n \" 222.5, 225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5,\\n\",\n \" 245. , 247.5, 250. , 252.5, 255. , 257.5, 260. , 262.5, 265. ,\\n\",\n \" 267.5, 270. , 272.5, 275. , 277.5, 280. , 282.5, 285. , 287.5,\\n\",\n \" 290. , 292.5, 295. , 297.5, 300. , 302.5, 305. , 307.5, 310. ,\\n\",\n \" 312.5, 315. , 317.5, 320. , 322.5, 325. , 327.5, 330. ],\\n\",\n \" dtype=float32))\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds[\\\"lat\\\"].values, ds[\\\"lon\\\"].values\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Output grid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Say we want to downsample it to $1.0^\\\\circ \\\\times 1.5^\\\\circ$. Just define the\\n\",\n \"output grid as an xarray `Dataset`. Notice here that we take care of passing\\n\",\n \"some attributes to the coordinate variables. This ensures xESMF and it's\\n\",\n \"underlying helper, cf-xarray, understand which is which.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.Dataset>\\n\",\n       \"Dimensions:  (lat: 59, lon: 87)\\n\",\n       \"Coordinates:\\n\",\n       \"  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n       \"  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n       \"Data variables:\\n\",\n       \"    *empty*
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lat: 59, lon: 87)\\n\",\n \"Coordinates:\\n\",\n \" * lat (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n \" * lon (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n \"Data variables:\\n\",\n \" *empty*\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out = xr.Dataset(\\n\",\n \" {\\n\",\n \" \\\"lat\\\": ([\\\"lat\\\"], np.arange(16, 75, 1.0), {\\\"units\\\": \\\"degrees_north\\\"}),\\n\",\n \" \\\"lon\\\": ([\\\"lon\\\"], np.arange(200, 330, 1.5), {\\\"units\\\": \\\"degrees_east\\\"}),\\n\",\n \" }\\n\",\n \")\\n\",\n \"ds_out\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Perform regridding\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Make a regridder by `xe.Regridder(grid_in, grid_out, method)`. `grid` is just an\\n\",\n \"xarray `Dataset` containing `lat` and `lon` values. In most cases, `'bilinear'`\\n\",\n \"should be good enough. For other methods see\\n\",\n \"[Comparison of 5 regridding algorithms](./Compare_algorithms.ipynb).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF Regridder \\n\",\n \"Regridding algorithm: conservative \\n\",\n \"Weight filename: conservative_25x53_59x87.nc \\n\",\n \"Reuse pre-computed weights? False \\n\",\n \"Input grid shape: (25, 53) \\n\",\n \"Output grid shape: (59, 87) \\n\",\n \"Periodic in longitude? False\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"regridder = xe.Regridder(ds, ds_out, \\\"conservative\\\")\\n\",\n \"regridder # print basic regridder information.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The regridder says it can transform data from shape `(25, 53)` to shape\\n\",\n \"`(59, 87)`.\\n\",\n \"\\n\",\n \"Regrid the `DataArray` is straightforward:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.DataArray 'air' (time: 2920, lat: 59, lon: 87)>\\n\",\n       \"array([[[296.1936 , 296.4933 , 296.64383, ..., 296.6239 , 296.57   ,\\n\",\n       \"         296.35767],\\n\",\n       \"        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\\n\",\n       \"         295.43332],\\n\",\n       \"        [295.9    , 296.09998, 296.19998, ..., 295.9    , 295.9    ,\\n\",\n       \"         295.43332],\\n\",\n       \"        ...,\\n\",\n       \"        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\\n\",\n       \"         237.96663],\\n\",\n       \"        [243.79999, 244.26666, 244.5    , ..., 233.63335, 235.29999,\\n\",\n       \"         237.96663],\\n\",\n       \"        [241.87102, 242.6313 , 243.01498, ..., 233.68292, 235.44838,\\n\",\n       \"         237.66943]],\\n\",\n       \"\\n\",\n       \"       [[296.26776, 296.8064 , 297.0761 , ..., 296.19287, 296.17752,\\n\",\n       \"         296.2103 ],\\n\",\n       \"        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\\n\",\n       \"         295.23334],\\n\",\n       \"        [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5    ,\\n\",\n       \"         295.23334],\\n\",\n       \"...\\n\",\n       \"        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\\n\",\n       \"         243.68997],\\n\",\n       \"        [249.89   , 249.49   , 249.29   , ..., 241.69   , 242.48999,\\n\",\n       \"         243.68997],\\n\",\n       \"        [246.84814, 246.2443 , 245.9487 , ..., 243.05266, 243.60286,\\n\",\n       \"         244.30954]],\\n\",\n       \"\\n\",\n       \"       [[297.2945 , 297.6252 , 297.79266, ..., 296.21606, 296.0664 ,\\n\",\n       \"         295.7324 ],\\n\",\n       \"        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\\n\",\n       \"         295.35666],\\n\",\n       \"        [296.09   , 296.62332, 296.88998, ..., 295.69   , 295.69   ,\\n\",\n       \"         295.35666],\\n\",\n       \"        ...,\\n\",\n       \"        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\\n\",\n       \"         241.22331],\\n\",\n       \"        [249.89   , 249.49   , 249.29   , ..., 239.82333, 240.29   ,\\n\",\n       \"         241.22331],\\n\",\n       \"        [246.3288 , 245.82306, 245.57744, ..., 241.16115, 241.18028,\\n\",\n       \"         241.57034]]], dtype=float32)\\n\",\n       \"Coordinates:\\n\",\n       \"  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n       \"  * lon      (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n       \"  * lat      (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n       \"Attributes:\\n\",\n       \"    long_name:      4xDaily Air temperature at sigma level 995\\n\",\n       \"    units:          degK\\n\",\n       \"    precision:      2\\n\",\n       \"    GRIB_id:        11\\n\",\n       \"    GRIB_name:      TMP\\n\",\n       \"    var_desc:       Air temperature\\n\",\n       \"    dataset:        NMC Reanalysis\\n\",\n       \"    level_desc:     Surface\\n\",\n       \"    statistic:      Individual Obs\\n\",\n       \"    parent_stat:    Other\\n\",\n       \"    actual_range:   [185.16 322.1 ]\\n\",\n       \"    regrid_method:  conservative
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"array([[[296.1936 , 296.4933 , 296.64383, ..., 296.6239 , 296.57 ,\\n\",\n \" 296.35767],\\n\",\n \" [295.9 , 296.09998, 296.19998, ..., 295.9 , 295.9 ,\\n\",\n \" 295.43332],\\n\",\n \" [295.9 , 296.09998, 296.19998, ..., 295.9 , 295.9 ,\\n\",\n \" 295.43332],\\n\",\n \" ...,\\n\",\n \" [243.79999, 244.26666, 244.5 , ..., 233.63335, 235.29999,\\n\",\n \" 237.96663],\\n\",\n \" [243.79999, 244.26666, 244.5 , ..., 233.63335, 235.29999,\\n\",\n \" 237.96663],\\n\",\n \" [241.87102, 242.6313 , 243.01498, ..., 233.68292, 235.44838,\\n\",\n \" 237.66943]],\\n\",\n \"\\n\",\n \" [[296.26776, 296.8064 , 297.0761 , ..., 296.19287, 296.17752,\\n\",\n \" 296.2103 ],\\n\",\n \" [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5 ,\\n\",\n \" 295.23334],\\n\",\n \" [296.19998, 296.53333, 296.69998, ..., 295.56668, 295.5 ,\\n\",\n \" 295.23334],\\n\",\n \"...\\n\",\n \" [249.89 , 249.49 , 249.29 , ..., 241.69 , 242.48999,\\n\",\n \" 243.68997],\\n\",\n \" [249.89 , 249.49 , 249.29 , ..., 241.69 , 242.48999,\\n\",\n \" 243.68997],\\n\",\n \" [246.84814, 246.2443 , 245.9487 , ..., 243.05266, 243.60286,\\n\",\n \" 244.30954]],\\n\",\n \"\\n\",\n \" [[297.2945 , 297.6252 , 297.79266, ..., 296.21606, 296.0664 ,\\n\",\n \" 295.7324 ],\\n\",\n \" [296.09 , 296.62332, 296.88998, ..., 295.69 , 295.69 ,\\n\",\n \" 295.35666],\\n\",\n \" [296.09 , 296.62332, 296.88998, ..., 295.69 , 295.69 ,\\n\",\n \" 295.35666],\\n\",\n \" ...,\\n\",\n \" [249.89 , 249.49 , 249.29 , ..., 239.82333, 240.29 ,\\n\",\n \" 241.22331],\\n\",\n \" [249.89 , 249.49 , 249.29 , ..., 239.82333, 240.29 ,\\n\",\n \" 241.22331],\\n\",\n \" [246.3288 , 245.82306, 245.57744, ..., 241.16115, 241.18028,\\n\",\n \" 241.57034]]], dtype=float32)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \" * lon (lon) float64 200.0 201.5 203.0 204.5 ... 324.5 326.0 327.5 329.0\\n\",\n \" * lat (lat) float64 16.0 17.0 18.0 19.0 20.0 ... 70.0 71.0 72.0 73.0 74.0\\n\",\n \"Attributes:\\n\",\n \" long_name: 4xDaily Air temperature at sigma level 995\\n\",\n \" units: degK\\n\",\n \" precision: 2\\n\",\n \" GRIB_id: 11\\n\",\n \" GRIB_name: TMP\\n\",\n \" var_desc: Air temperature\\n\",\n \" dataset: NMC Reanalysis\\n\",\n \" level_desc: Surface\\n\",\n \" statistic: Individual Obs\\n\",\n \" parent_stat: Other\\n\",\n \" actual_range: [185.16 322.1 ]\\n\",\n \" regrid_method: conservative\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dr_out = regridder(dr, keep_attrs=True)\\n\",\n \"dr_out\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The horizontal shape is now `(59, 87)`, as expected. The regridding operation\\n\",\n \"broadcasts over extra dimensions (`time` here), so there are still 2920 time\\n\",\n \"frames. `lon` and `lat` coordinate values are updated accordingly, and the value\\n\",\n \"of the extra dimension `time` is kept the same as input.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"**Important note:** Extra dimensions must be on the left, i.e.\\n\",\n \"`(time, lev, lat, lon)` is correct but `(lat, lon, time, lev)` would not work.\\n\",\n \"Most data sets should have `(lat, lon)` on the right (being the fastest changing\\n\",\n \"dimension in the memory). If not, use `DataArray.transpose` or `numpy.transpose`\\n\",\n \"to preprocess the data.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Check results on 2D map\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The regridding result is consistent with the original data, with a much finer\\n\",\n \"resolution:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ax = plt.axes(projection=ccrs.PlateCarree())\\n\",\n \"dr_out.isel(time=0).plot.pcolormesh(ax=ax, vmin=230, vmax=300)\\n\",\n \"ax.coastlines()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Check broadcasting over extra dimensions\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"xESMF tracks coordinate values over extra dimensions, since horizontal\\n\",\n \"regridding should not affect them.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
<xarray.DataArray 'time' (time: 2920)>\\n\",\n       \"array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n       \"       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n       \"       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n       \"      dtype='datetime64[ns]')\\n\",\n       \"Coordinates:\\n\",\n       \"  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n       \"Attributes:\\n\",\n       \"    standard_name:  time\\n\",\n       \"    long_name:      Time
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n \" '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n \" '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n \" dtype='datetime64[ns]')\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \"Attributes:\\n\",\n \" standard_name: time\\n\",\n \" long_name: Time\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dr_out[\\\"time\\\"]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# exactly the same as input\\n\",\n \"xr.testing.assert_identical(dr_out[\\\"time\\\"], ds[\\\"time\\\"])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"We can plot the time series at a specific location, to make sure the\\n\",\n \"broadcasting is correct:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.subplot(2, 1, 1)\\n\",\n \"dr.sel(lon=260, lat=40).plot() # input data\\n\",\n \"plt.subplot(2, 1, 2)\\n\",\n \"dr_out.sel(lon=260, lat=40).plot() # output data\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.9\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": false\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "doc/notebooks/Reuse_regridder.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Save time by reusing regridder\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"There is an important reason why the regridding is broken into two steps (making\\n\",\n \"the regridder and perform regridding). For high-resolution grids, making the\\n\",\n \"regridder (i.e. \\\"computing regridding weights\\\", explained later) is quite\\n\",\n \"computationally expensive, but performing regridding on data (\\\"applying\\n\",\n \"regridding weights\\\") is still pretty fast.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import xesmf as xe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Prepare data\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The grids in previous examples were all quite small and the regridding was\\n\",\n \"almost instantaneous. Let's try a large-ish grid here.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
xarray.Dataset
    • x: 600
    • x_b: 601
    • y: 400
    • y_b: 401
    • lon
      (y, x)
      float64
      -119.8 -119.4 ... 119.4 119.8
      array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       ...,\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])
    • lat
      (y, x)
      float64
      -59.85 -59.85 ... 59.85 59.85
      array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\\n\",\n       \"       [-59.55, -59.55, -59.55, ..., -59.55, -59.55, -59.55],\\n\",\n       \"       [-59.25, -59.25, -59.25, ..., -59.25, -59.25, -59.25],\\n\",\n       \"       ...,\\n\",\n       \"       [ 59.25,  59.25,  59.25, ...,  59.25,  59.25,  59.25],\\n\",\n       \"       [ 59.55,  59.55,  59.55, ...,  59.55,  59.55,  59.55],\\n\",\n       \"       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])
    • lon_b
      (y_b, x_b)
      float64
      -120.0 -119.6 ... 119.6 120.0
      array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       ...,\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])
    • lat_b
      (y_b, x_b)
      float64
      -60.0 -60.0 -60.0 ... 60.0 60.0
      array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\\n\",\n       \"       [-59.7, -59.7, -59.7, ..., -59.7, -59.7, -59.7],\\n\",\n       \"       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\\n\",\n       \"       ...,\\n\",\n       \"       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\\n\",\n       \"       [ 59.7,  59.7,  59.7, ...,  59.7,  59.7,  59.7],\\n\",\n       \"       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])
    \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (x: 600, x_b: 601, y: 400, y_b: 401)\\n\",\n \"Coordinates:\\n\",\n \" lon (y, x) float64 -119.8 -119.4 -119.0 -118.6 ... 119.0 119.4 119.8\\n\",\n \" lat (y, x) float64 -59.85 -59.85 -59.85 -59.85 ... 59.85 59.85 59.85\\n\",\n \" lon_b (y_b, x_b) float64 -120.0 -119.6 -119.2 ... 119.2 119.6 120.0\\n\",\n \" lat_b (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 ... 60.0 60.0 60.0 60.0\\n\",\n \"Dimensions without coordinates: x, x_b, y, y_b\\n\",\n \"Data variables:\\n\",\n \" *empty*\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_in = xe.util.grid_2d(\\n\",\n \" -120, 120, 0.4, -60, 60, 0.3 # longitude range and resolution\\n\",\n \") # latitude range and resolution\\n\",\n \"ds_in\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
    xarray.Dataset
      • x: 400
      • x_b: 401
      • y: 300
      • y_b: 301
      • lon
        (y, x)
        float64
        -119.7 -119.1 ... 119.1 119.7
        array([[-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\\n\",\n       \"       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\\n\",\n       \"       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\\n\",\n       \"       ...,\\n\",\n       \"       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\\n\",\n       \"       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7],\\n\",\n       \"       [-119.7, -119.1, -118.5, ...,  118.5,  119.1,  119.7]])
      • lat
        (y, x)
        float64
        -59.8 -59.8 -59.8 ... 59.8 59.8
        array([[-59.8, -59.8, -59.8, ..., -59.8, -59.8, -59.8],\\n\",\n       \"       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\\n\",\n       \"       [-59. , -59. , -59. , ..., -59. , -59. , -59. ],\\n\",\n       \"       ...,\\n\",\n       \"       [ 59. ,  59. ,  59. , ...,  59. ,  59. ,  59. ],\\n\",\n       \"       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\\n\",\n       \"       [ 59.8,  59.8,  59.8, ...,  59.8,  59.8,  59.8]])
      • lon_b
        (y_b, x_b)
        float64
        -120.0 -119.4 ... 119.4 120.0
        array([[-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\\n\",\n       \"       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\\n\",\n       \"       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\\n\",\n       \"       ...,\\n\",\n       \"       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\\n\",\n       \"       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ],\\n\",\n       \"       [-120. , -119.4, -118.8, ...,  118.8,  119.4,  120. ]])
      • lat_b
        (y_b, x_b)
        float64
        -60.0 -60.0 -60.0 ... 60.0 60.0
        array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\\n\",\n       \"       [-59.6, -59.6, -59.6, ..., -59.6, -59.6, -59.6],\\n\",\n       \"       [-59.2, -59.2, -59.2, ..., -59.2, -59.2, -59.2],\\n\",\n       \"       ...,\\n\",\n       \"       [ 59.2,  59.2,  59.2, ...,  59.2,  59.2,  59.2],\\n\",\n       \"       [ 59.6,  59.6,  59.6, ...,  59.6,  59.6,  59.6],\\n\",\n       \"       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])
      \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (x: 400, x_b: 401, y: 300, y_b: 301)\\n\",\n \"Coordinates:\\n\",\n \" lon (y, x) float64 -119.7 -119.1 -118.5 -117.9 ... 118.5 119.1 119.7\\n\",\n \" lat (y, x) float64 -59.8 -59.8 -59.8 -59.8 ... 59.8 59.8 59.8 59.8\\n\",\n \" lon_b (y_b, x_b) float64 -120.0 -119.4 -118.8 ... 118.8 119.4 120.0\\n\",\n \" lat_b (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 ... 60.0 60.0 60.0 60.0\\n\",\n \"Dimensions without coordinates: x, x_b, y, y_b\\n\",\n \"Data variables:\\n\",\n \" *empty*\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_out = xe.util.grid_2d(-120, 120, 0.6, -60, 60, 0.4)\\n\",\n \"ds_out\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Also make a large-ish 4D data, with multiple time frames and vertical levels.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
      \\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
      xarray.Dataset
        • lev: 50
        • time: 10
        • x: 600
        • x_b: 601
        • y: 400
        • y_b: 401
        • lon
          (y, x)
          float64
          -119.8 -119.4 ... 119.4 119.8
          array([[-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       ...,\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8],\\n\",\n       \"       [-119.8, -119.4, -119. , ...,  119. ,  119.4,  119.8]])
        • lat
          (y, x)
          float64
          -59.85 -59.85 ... 59.85 59.85
          array([[-59.85, -59.85, -59.85, ..., -59.85, -59.85, -59.85],\\n\",\n       \"       [-59.55, -59.55, -59.55, ..., -59.55, -59.55, -59.55],\\n\",\n       \"       [-59.25, -59.25, -59.25, ..., -59.25, -59.25, -59.25],\\n\",\n       \"       ...,\\n\",\n       \"       [ 59.25,  59.25,  59.25, ...,  59.25,  59.25,  59.25],\\n\",\n       \"       [ 59.55,  59.55,  59.55, ...,  59.55,  59.55,  59.55],\\n\",\n       \"       [ 59.85,  59.85,  59.85, ...,  59.85,  59.85,  59.85]])
        • lon_b
          (y_b, x_b)
          float64
          -120.0 -119.6 ... 119.6 120.0
          array([[-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       ...,\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ],\\n\",\n       \"       [-120. , -119.6, -119.2, ...,  119.2,  119.6,  120. ]])
        • lat_b
          (y_b, x_b)
          float64
          -60.0 -60.0 -60.0 ... 60.0 60.0
          array([[-60. , -60. , -60. , ..., -60. , -60. , -60. ],\\n\",\n       \"       [-59.7, -59.7, -59.7, ..., -59.7, -59.7, -59.7],\\n\",\n       \"       [-59.4, -59.4, -59.4, ..., -59.4, -59.4, -59.4],\\n\",\n       \"       ...,\\n\",\n       \"       [ 59.4,  59.4,  59.4, ...,  59.4,  59.4,  59.4],\\n\",\n       \"       [ 59.7,  59.7,  59.7, ...,  59.7,  59.7,  59.7],\\n\",\n       \"       [ 60. ,  60. ,  60. , ...,  60. ,  60. ,  60. ]])
        • time
          (time)
          int64
          1 2 3 4 5 6 7 8 9 10
          array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])
        • lev
          (lev)
          int64
          1 2 3 4 5 6 7 ... 45 46 47 48 49 50
          array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\\n\",\n       \"       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,\\n\",\n       \"       37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50])
        • data2D
          (y, x)
          float64
          1.872 1.869 1.866 ... 1.869 1.872
          array([[1.87234253, 1.86931698, 1.86631691, ..., 1.86631691, 1.86931698,\\n\",\n       \"        1.87234253],\\n\",\n       \"       [1.87003418, 1.86695393, 1.86389961, ..., 1.86389961, 1.86695393,\\n\",\n       \"        1.87003418],\\n\",\n       \"       [1.86771234, 1.86457706, 1.86146818, ..., 1.86146818, 1.86457706,\\n\",\n       \"        1.86771234],\\n\",\n       \"       ...,\\n\",\n       \"       [1.86771234, 1.86457706, 1.86146818, ..., 1.86146818, 1.86457706,\\n\",\n       \"        1.86771234],\\n\",\n       \"       [1.87003418, 1.86695393, 1.86389961, ..., 1.86389961, 1.86695393,\\n\",\n       \"        1.87003418],\\n\",\n       \"       [1.87234253, 1.86931698, 1.86631691, ..., 1.86631691, 1.86931698,\\n\",\n       \"        1.87234253]])
        • data4D
          (time, lev, y, x)
          float64
          1.872 1.869 1.866 ... 934.7 936.2
          array([[[[  1.87234253,   1.86931698,   1.86631691, ...,   1.86631691,\\n\",\n       \"            1.86931698,   1.87234253],\\n\",\n       \"         [  1.87003418,   1.86695393,   1.86389961, ...,   1.86389961,\\n\",\n       \"            1.86695393,   1.87003418],\\n\",\n       \"         [  1.86771234,   1.86457706,   1.86146818, ...,   1.86146818,\\n\",\n       \"            1.86457706,   1.86771234],\\n\",\n       \"         ...,\\n\",\n       \"         [  1.86771234,   1.86457706,   1.86146818, ...,   1.86146818,\\n\",\n       \"            1.86457706,   1.86771234],\\n\",\n       \"         [  1.87003418,   1.86695393,   1.86389961, ...,   1.86389961,\\n\",\n       \"            1.86695393,   1.87003418],\\n\",\n       \"         [  1.87234253,   1.86931698,   1.86631691, ...,   1.86631691,\\n\",\n       \"            1.86931698,   1.87234253]],\\n\",\n       \"\\n\",\n       \"        [[  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\\n\",\n       \"            3.73863396,   3.74468505],\\n\",\n       \"         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\\n\",\n       \"            3.73390785,   3.74006836],\\n\",\n       \"         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\\n\",\n       \"            3.72915412,   3.73542468],\\n\",\n       \"         ...,\\n\",\n       \"         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\\n\",\n       \"            3.72915412,   3.73542468],\\n\",\n       \"         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\\n\",\n       \"            3.73390785,   3.74006836],\\n\",\n       \"         [  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\\n\",\n       \"            3.73863396,   3.74468505]],\\n\",\n       \"\\n\",\n       \"        [[  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\\n\",\n       \"            5.60795094,   5.61702758],\\n\",\n       \"         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\\n\",\n       \"            5.60086178,   5.61010254],\\n\",\n       \"         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\\n\",\n       \"            5.59373117,   5.60313702],\\n\",\n       \"         ...,\\n\",\n       \"         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\\n\",\n       \"            5.59373117,   5.60313702],\\n\",\n       \"         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\\n\",\n       \"            5.60086178,   5.61010254],\\n\",\n       \"         [  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\\n\",\n       \"            5.60795094,   5.61702758]],\\n\",\n       \"\\n\",\n       \"        ...,\\n\",\n       \"\\n\",\n       \"        [[ 89.87244122,  89.72721511,  89.58321189, ...,  89.58321189,\\n\",\n       \"           89.72721511,  89.87244122],\\n\",\n       \"         [ 89.76164062,  89.61378848,  89.46718135, ...,  89.46718135,\\n\",\n       \"           89.61378848,  89.76164062],\\n\",\n       \"         [ 89.65019231,  89.49969879,  89.35047252, ...,  89.35047252,\\n\",\n       \"           89.49969879,  89.65019231],\\n\",\n       \"         ...,\\n\",\n       \"         [ 89.65019231,  89.49969879,  89.35047252, ...,  89.35047252,\\n\",\n       \"           89.49969879,  89.65019231],\\n\",\n       \"         [ 89.76164062,  89.61378848,  89.46718135, ...,  89.46718135,\\n\",\n       \"           89.61378848,  89.76164062],\\n\",\n       \"         [ 89.87244122,  89.72721511,  89.58321189, ...,  89.58321189,\\n\",\n       \"           89.72721511,  89.87244122]],\\n\",\n       \"\\n\",\n       \"        [[ 91.74478375,  91.59653209,  91.44952881, ...,  91.44952881,\\n\",\n       \"           91.59653209,  91.74478375],\\n\",\n       \"         [ 91.6316748 ,  91.48074241,  91.33108096, ...,  91.33108096,\\n\",\n       \"           91.48074241,  91.6316748 ],\\n\",\n       \"         [ 91.51790465,  91.36427585,  91.2119407 , ...,  91.2119407 ,\\n\",\n       \"           91.36427585,  91.51790465],\\n\",\n       \"         ...,\\n\",\n       \"         [ 91.51790465,  91.36427585,  91.2119407 , ...,  91.2119407 ,\\n\",\n       \"           91.36427585,  91.51790465],\\n\",\n       \"         [ 91.6316748 ,  91.48074241,  91.33108096, ...,  91.33108096,\\n\",\n       \"           91.48074241,  91.6316748 ],\\n\",\n       \"         [ 91.74478375,  91.59653209,  91.44952881, ...,  91.44952881,\\n\",\n       \"           91.59653209,  91.74478375]],\\n\",\n       \"\\n\",\n       \"        [[ 93.61712627,  93.46584907,  93.31584572, ...,  93.31584572,\\n\",\n       \"           93.46584907,  93.61712627],\\n\",\n       \"         [ 93.50170898,  93.34769633,  93.19498057, ...,  93.19498057,\\n\",\n       \"           93.34769633,  93.50170898],\\n\",\n       \"         [ 93.38561699,  93.22885291,  93.07340887, ...,  93.07340887,\\n\",\n       \"           93.22885291,  93.38561699],\\n\",\n       \"         ...,\\n\",\n       \"         [ 93.38561699,  93.22885291,  93.07340887, ...,  93.07340887,\\n\",\n       \"           93.22885291,  93.38561699],\\n\",\n       \"         [ 93.50170898,  93.34769633,  93.19498057, ...,  93.19498057,\\n\",\n       \"           93.34769633,  93.50170898],\\n\",\n       \"         [ 93.61712627,  93.46584907,  93.31584572, ...,  93.31584572,\\n\",\n       \"           93.46584907,  93.61712627]]],\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"       [[[  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\\n\",\n       \"            3.73863396,   3.74468505],\\n\",\n       \"         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\\n\",\n       \"            3.73390785,   3.74006836],\\n\",\n       \"         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\\n\",\n       \"            3.72915412,   3.73542468],\\n\",\n       \"         ...,\\n\",\n       \"         [  3.73542468,   3.72915412,   3.72293635, ...,   3.72293635,\\n\",\n       \"            3.72915412,   3.73542468],\\n\",\n       \"         [  3.74006836,   3.73390785,   3.72779922, ...,   3.72779922,\\n\",\n       \"            3.73390785,   3.74006836],\\n\",\n       \"         [  3.74468505,   3.73863396,   3.73263383, ...,   3.73263383,\\n\",\n       \"            3.73863396,   3.74468505]],\\n\",\n       \"\\n\",\n       \"        [[  7.4893701 ,   7.47726793,   7.46526766, ...,   7.46526766,\\n\",\n       \"            7.47726793,   7.4893701 ],\\n\",\n       \"         [  7.48013672,   7.46781571,   7.45559845, ...,   7.45559845,\\n\",\n       \"            7.46781571,   7.48013672],\\n\",\n       \"         [  7.47084936,   7.45830823,   7.44587271, ...,   7.44587271,\\n\",\n       \"            7.45830823,   7.47084936],\\n\",\n       \"         ...,\\n\",\n       \"         [  7.47084936,   7.45830823,   7.44587271, ...,   7.44587271,\\n\",\n       \"            7.45830823,   7.47084936],\\n\",\n       \"         [  7.48013672,   7.46781571,   7.45559845, ...,   7.45559845,\\n\",\n       \"            7.46781571,   7.48013672],\\n\",\n       \"         [  7.4893701 ,   7.47726793,   7.46526766, ...,   7.46526766,\\n\",\n       \"            7.47726793,   7.4893701 ]],\\n\",\n       \"\\n\",\n       \"        [[ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\\n\",\n       \"           11.21590189,  11.23405515],\\n\",\n       \"         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\\n\",\n       \"           11.20172356,  11.22020508],\\n\",\n       \"         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\\n\",\n       \"           11.18746235,  11.20627404],\\n\",\n       \"         ...,\\n\",\n       \"         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\\n\",\n       \"           11.18746235,  11.20627404],\\n\",\n       \"         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\\n\",\n       \"           11.20172356,  11.22020508],\\n\",\n       \"         [ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\\n\",\n       \"           11.21590189,  11.23405515]],\\n\",\n       \"\\n\",\n       \"        ...,\\n\",\n       \"\\n\",\n       \"        [[179.74488244, 179.45443022, 179.16642378, ..., 179.16642378,\\n\",\n       \"          179.45443022, 179.74488244],\\n\",\n       \"         [179.52328123, 179.22757696, 178.93436269, ..., 178.93436269,\\n\",\n       \"          179.22757696, 179.52328123],\\n\",\n       \"         [179.30038461, 178.99939758, 178.70094504, ..., 178.70094504,\\n\",\n       \"          178.99939758, 179.30038461],\\n\",\n       \"         ...,\\n\",\n       \"         [179.30038461, 178.99939758, 178.70094504, ..., 178.70094504,\\n\",\n       \"          178.99939758, 179.30038461],\\n\",\n       \"         [179.52328123, 179.22757696, 178.93436269, ..., 178.93436269,\\n\",\n       \"          179.22757696, 179.52328123],\\n\",\n       \"         [179.74488244, 179.45443022, 179.16642378, ..., 179.16642378,\\n\",\n       \"          179.45443022, 179.74488244]],\\n\",\n       \"\\n\",\n       \"        [[183.48956749, 183.19306418, 182.89905761, ..., 182.89905761,\\n\",\n       \"          183.19306418, 183.48956749],\\n\",\n       \"         [183.26334959, 182.96148481, 182.66216191, ..., 182.66216191,\\n\",\n       \"          182.96148481, 183.26334959],\\n\",\n       \"         [183.03580929, 182.72855169, 182.42388139, ..., 182.42388139,\\n\",\n       \"          182.72855169, 183.03580929],\\n\",\n       \"         ...,\\n\",\n       \"         [183.03580929, 182.72855169, 182.42388139, ..., 182.42388139,\\n\",\n       \"          182.72855169, 183.03580929],\\n\",\n       \"         [183.26334959, 182.96148481, 182.66216191, ..., 182.66216191,\\n\",\n       \"          182.96148481, 183.26334959],\\n\",\n       \"         [183.48956749, 183.19306418, 182.89905761, ..., 182.89905761,\\n\",\n       \"          183.19306418, 183.48956749]],\\n\",\n       \"\\n\",\n       \"        [[187.23425254, 186.93169815, 186.63169144, ..., 186.63169144,\\n\",\n       \"          186.93169815, 187.23425254],\\n\",\n       \"         [187.00341795, 186.69539266, 186.38996114, ..., 186.38996114,\\n\",\n       \"          186.69539267, 187.00341795],\\n\",\n       \"         [186.77123397, 186.45770581, 186.14681775, ..., 186.14681775,\\n\",\n       \"          186.45770581, 186.77123397],\\n\",\n       \"         ...,\\n\",\n       \"         [186.77123397, 186.45770581, 186.14681775, ..., 186.14681775,\\n\",\n       \"          186.45770581, 186.77123397],\\n\",\n       \"         [187.00341795, 186.69539266, 186.38996114, ..., 186.38996114,\\n\",\n       \"          186.69539267, 187.00341795],\\n\",\n       \"         [187.23425254, 186.93169815, 186.63169144, ..., 186.63169144,\\n\",\n       \"          186.93169815, 187.23425254]]],\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"       [[[  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\\n\",\n       \"            5.60795094,   5.61702758],\\n\",\n       \"         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\\n\",\n       \"            5.60086178,   5.61010254],\\n\",\n       \"         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\\n\",\n       \"            5.59373117,   5.60313702],\\n\",\n       \"         ...,\\n\",\n       \"         [  5.60313702,   5.59373117,   5.58440453, ...,   5.58440453,\\n\",\n       \"            5.59373117,   5.60313702],\\n\",\n       \"         [  5.61010254,   5.60086178,   5.59169883, ...,   5.59169883,\\n\",\n       \"            5.60086178,   5.61010254],\\n\",\n       \"         [  5.61702758,   5.60795094,   5.59895074, ...,   5.59895074,\\n\",\n       \"            5.60795094,   5.61702758]],\\n\",\n       \"\\n\",\n       \"        [[ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\\n\",\n       \"           11.21590189,  11.23405515],\\n\",\n       \"         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\\n\",\n       \"           11.20172356,  11.22020508],\\n\",\n       \"         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\\n\",\n       \"           11.18746235,  11.20627404],\\n\",\n       \"         ...,\\n\",\n       \"         [ 11.20627404,  11.18746235,  11.16880906, ...,  11.16880906,\\n\",\n       \"           11.18746235,  11.20627404],\\n\",\n       \"         [ 11.22020508,  11.20172356,  11.18339767, ...,  11.18339767,\\n\",\n       \"           11.20172356,  11.22020508],\\n\",\n       \"         [ 11.23405515,  11.21590189,  11.19790149, ...,  11.19790149,\\n\",\n       \"           11.21590189,  11.23405515]],\\n\",\n       \"\\n\",\n       \"        [[ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\\n\",\n       \"           16.82385283,  16.85108273],\\n\",\n       \"         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\\n\",\n       \"           16.80258534,  16.83030762],\\n\",\n       \"         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\\n\",\n       \"           16.78119352,  16.80941106],\\n\",\n       \"         ...,\\n\",\n       \"         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\\n\",\n       \"           16.78119352,  16.80941106],\\n\",\n       \"         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\\n\",\n       \"           16.80258534,  16.83030762],\\n\",\n       \"         [ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\\n\",\n       \"           16.82385283,  16.85108273]],\\n\",\n       \"\\n\",\n       \"        ...,\\n\",\n       \"\\n\",\n       \"        [[269.61732366, 269.18164533, 268.74963567, ..., 268.74963567,\\n\",\n       \"          269.18164533, 269.61732366],\\n\",\n       \"         [269.28492185, 268.84136544, 268.40154404, ..., 268.40154404,\\n\",\n       \"          268.84136544, 269.28492185],\\n\",\n       \"         [268.95057692, 268.49909637, 268.05141755, ..., 268.05141755,\\n\",\n       \"          268.49909637, 268.95057692],\\n\",\n       \"         ...,\\n\",\n       \"         [268.95057692, 268.49909637, 268.05141755, ..., 268.05141755,\\n\",\n       \"          268.49909637, 268.95057692],\\n\",\n       \"         [269.28492185, 268.84136544, 268.40154404, ..., 268.40154404,\\n\",\n       \"          268.84136544, 269.28492185],\\n\",\n       \"         [269.61732366, 269.18164533, 268.74963567, ..., 268.74963567,\\n\",\n       \"          269.18164533, 269.61732366]],\\n\",\n       \"\\n\",\n       \"        [[275.23435124, 274.78959627, 274.34858642, ..., 274.34858642,\\n\",\n       \"          274.78959627, 275.23435124],\\n\",\n       \"         [274.89502439, 274.44222722, 273.99324287, ..., 273.99324287,\\n\",\n       \"          274.44222722, 274.89502439],\\n\",\n       \"         [274.55371394, 274.09282754, 273.63582209, ..., 273.63582209,\\n\",\n       \"          274.09282754, 274.55371394],\\n\",\n       \"         ...,\\n\",\n       \"         [274.55371394, 274.09282754, 273.63582209, ..., 273.63582209,\\n\",\n       \"          274.09282754, 274.55371394],\\n\",\n       \"         [274.89502439, 274.44222722, 273.99324287, ..., 273.99324287,\\n\",\n       \"          274.44222722, 274.89502439],\\n\",\n       \"         [275.23435124, 274.78959627, 274.34858642, ..., 274.34858642,\\n\",\n       \"          274.78959627, 275.23435124]],\\n\",\n       \"\\n\",\n       \"        [[280.85137881, 280.39754722, 279.94753716, ..., 279.94753716,\\n\",\n       \"          280.39754722, 280.85137881],\\n\",\n       \"         [280.50512693, 280.043089  , 279.5849417 , ..., 279.5849417 ,\\n\",\n       \"          280.043089  , 280.50512693],\\n\",\n       \"         [280.15685096, 279.68655872, 279.22022662, ..., 279.22022662,\\n\",\n       \"          279.68655872, 280.15685096],\\n\",\n       \"         ...,\\n\",\n       \"         [280.15685096, 279.68655872, 279.22022662, ..., 279.22022662,\\n\",\n       \"          279.68655872, 280.15685096],\\n\",\n       \"         [280.50512693, 280.043089  , 279.5849417 , ..., 279.5849417 ,\\n\",\n       \"          280.043089  , 280.50512693],\\n\",\n       \"         [280.85137881, 280.39754722, 279.94753716, ..., 279.94753716,\\n\",\n       \"          280.39754722, 280.85137881]]],\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"       ...,\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"       [[[ 14.9787402 ,  14.95453585,  14.93053532, ...,  14.93053532,\\n\",\n       \"           14.95453585,  14.9787402 ],\\n\",\n       \"         [ 14.96027344,  14.93563141,  14.91119689, ...,  14.91119689,\\n\",\n       \"           14.93563141,  14.96027344],\\n\",\n       \"         [ 14.94169872,  14.91661646,  14.89174542, ...,  14.89174542,\\n\",\n       \"           14.91661646,  14.94169872],\\n\",\n       \"         ...,\\n\",\n       \"         [ 14.94169872,  14.91661646,  14.89174542, ...,  14.89174542,\\n\",\n       \"           14.91661646,  14.94169872],\\n\",\n       \"         [ 14.96027344,  14.93563141,  14.91119689, ...,  14.91119689,\\n\",\n       \"           14.93563141,  14.96027344],\\n\",\n       \"         [ 14.9787402 ,  14.95453585,  14.93053532, ...,  14.93053532,\\n\",\n       \"           14.95453585,  14.9787402 ]],\\n\",\n       \"\\n\",\n       \"        [[ 29.95748041,  29.9090717 ,  29.86107063, ...,  29.86107063,\\n\",\n       \"           29.9090717 ,  29.95748041],\\n\",\n       \"         [ 29.92054687,  29.87126283,  29.82239378, ...,  29.82239378,\\n\",\n       \"           29.87126283,  29.92054687],\\n\",\n       \"         [ 29.88339744,  29.83323293,  29.78349084, ...,  29.78349084,\\n\",\n       \"           29.83323293,  29.88339744],\\n\",\n       \"         ...,\\n\",\n       \"         [ 29.88339744,  29.83323293,  29.78349084, ...,  29.78349084,\\n\",\n       \"           29.83323293,  29.88339744],\\n\",\n       \"         [ 29.92054687,  29.87126283,  29.82239378, ...,  29.82239378,\\n\",\n       \"           29.87126283,  29.92054687],\\n\",\n       \"         [ 29.95748041,  29.9090717 ,  29.86107063, ...,  29.86107063,\\n\",\n       \"           29.9090717 ,  29.95748041]],\\n\",\n       \"\\n\",\n       \"        [[ 44.93622061,  44.86360755,  44.79160595, ...,  44.79160595,\\n\",\n       \"           44.86360755,  44.93622061],\\n\",\n       \"         [ 44.88082031,  44.80689424,  44.73359067, ...,  44.73359067,\\n\",\n       \"           44.80689424,  44.88082031],\\n\",\n       \"         [ 44.82509615,  44.74984939,  44.67523626, ...,  44.67523626,\\n\",\n       \"           44.74984939,  44.82509615],\\n\",\n       \"         ...,\\n\",\n       \"         [ 44.82509615,  44.74984939,  44.67523626, ...,  44.67523626,\\n\",\n       \"           44.74984939,  44.82509615],\\n\",\n       \"         [ 44.88082031,  44.80689424,  44.73359067, ...,  44.73359067,\\n\",\n       \"           44.80689424,  44.88082031],\\n\",\n       \"         [ 44.93622061,  44.86360755,  44.79160595, ...,  44.79160595,\\n\",\n       \"           44.86360755,  44.93622061]],\\n\",\n       \"\\n\",\n       \"        ...,\\n\",\n       \"\\n\",\n       \"        [[718.97952976, 717.81772088, 716.66569513, ..., 716.66569513,\\n\",\n       \"          717.81772088, 718.97952976],\\n\",\n       \"         [718.09312494, 716.91030783, 715.73745076, ..., 715.73745076,\\n\",\n       \"          716.91030783, 718.09312494],\\n\",\n       \"         [717.20153845, 715.99759031, 714.80378015, ..., 714.80378015,\\n\",\n       \"          715.99759031, 717.20153845],\\n\",\n       \"         ...,\\n\",\n       \"         [717.20153845, 715.99759031, 714.80378015, ..., 714.80378015,\\n\",\n       \"          715.99759031, 717.20153845],\\n\",\n       \"         [718.09312494, 716.91030783, 715.73745076, ..., 715.73745076,\\n\",\n       \"          716.91030783, 718.09312494],\\n\",\n       \"         [718.97952976, 717.81772088, 716.66569513, ..., 716.66569513,\\n\",\n       \"          717.81772088, 718.97952976]],\\n\",\n       \"\\n\",\n       \"        [[733.95826996, 732.77225673, 731.59623044, ..., 731.59623044,\\n\",\n       \"          732.77225673, 733.95826996],\\n\",\n       \"         [733.05339838, 731.84593925, 730.64864766, ..., 730.64864766,\\n\",\n       \"          731.84593925, 733.05339838],\\n\",\n       \"         [732.14323717, 730.91420678, 729.69552557, ..., 729.69552557,\\n\",\n       \"          730.91420678, 732.14323717],\\n\",\n       \"         ...,\\n\",\n       \"         [732.14323717, 730.91420678, 729.69552557, ..., 729.69552557,\\n\",\n       \"          730.91420678, 732.14323717],\\n\",\n       \"         [733.05339838, 731.84593925, 730.64864766, ..., 730.64864766,\\n\",\n       \"          731.84593925, 733.05339838],\\n\",\n       \"         [733.95826996, 732.77225673, 731.59623044, ..., 731.59623044,\\n\",\n       \"          732.77225673, 733.95826996]],\\n\",\n       \"\\n\",\n       \"        [[748.93701017, 747.72679258, 746.52676576, ..., 746.52676576,\\n\",\n       \"          747.72679258, 748.93701017],\\n\",\n       \"         [748.01367181, 746.78157066, 745.55984455, ..., 745.55984455,\\n\",\n       \"          746.78157066, 748.01367181],\\n\",\n       \"         [747.08493588, 745.83082324, 744.58727099, ..., 744.58727099,\\n\",\n       \"          745.83082324, 747.08493588],\\n\",\n       \"         ...,\\n\",\n       \"         [747.08493588, 745.83082324, 744.58727099, ..., 744.58727099,\\n\",\n       \"          745.83082324, 747.08493588],\\n\",\n       \"         [748.01367181, 746.78157066, 745.55984455, ..., 745.55984455,\\n\",\n       \"          746.78157066, 748.01367181],\\n\",\n       \"         [748.93701017, 747.72679258, 746.52676576, ..., 746.52676576,\\n\",\n       \"          747.72679258, 748.93701017]]],\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"       [[[ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\\n\",\n       \"           16.82385283,  16.85108273],\\n\",\n       \"         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\\n\",\n       \"           16.80258534,  16.83030762],\\n\",\n       \"         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\\n\",\n       \"           16.78119352,  16.80941106],\\n\",\n       \"         ...,\\n\",\n       \"         [ 16.80941106,  16.78119352,  16.7532136 , ...,  16.7532136 ,\\n\",\n       \"           16.78119352,  16.80941106],\\n\",\n       \"         [ 16.83030762,  16.80258534,  16.7750965 , ...,  16.7750965 ,\\n\",\n       \"           16.80258534,  16.83030762],\\n\",\n       \"         [ 16.85108273,  16.82385283,  16.79685223, ...,  16.79685223,\\n\",\n       \"           16.82385283,  16.85108273]],\\n\",\n       \"\\n\",\n       \"        [[ 33.70216546,  33.64770567,  33.59370446, ...,  33.59370446,\\n\",\n       \"           33.64770567,  33.70216546],\\n\",\n       \"         [ 33.66061523,  33.60517068,  33.550193  , ...,  33.550193  ,\\n\",\n       \"           33.60517068,  33.66061523],\\n\",\n       \"         [ 33.61882211,  33.56238705,  33.50642719, ...,  33.50642719,\\n\",\n       \"           33.56238705,  33.61882211],\\n\",\n       \"         ...,\\n\",\n       \"         [ 33.61882211,  33.56238705,  33.50642719, ...,  33.50642719,\\n\",\n       \"           33.56238705,  33.61882211],\\n\",\n       \"         [ 33.66061523,  33.60517068,  33.550193  , ...,  33.550193  ,\\n\",\n       \"           33.60517068,  33.66061523],\\n\",\n       \"         [ 33.70216546,  33.64770567,  33.59370446, ...,  33.59370446,\\n\",\n       \"           33.64770567,  33.70216546]],\\n\",\n       \"\\n\",\n       \"        [[ 50.55324819,  50.4715585 ,  50.39055669, ...,  50.39055669,\\n\",\n       \"           50.4715585 ,  50.55324819],\\n\",\n       \"         [ 50.49092285,  50.40775602,  50.32528951, ...,  50.32528951,\\n\",\n       \"           50.40775602,  50.49092285],\\n\",\n       \"         [ 50.42823317,  50.34358057,  50.25964079, ...,  50.25964079,\\n\",\n       \"           50.34358057,  50.42823317],\\n\",\n       \"         ...,\\n\",\n       \"         [ 50.42823317,  50.34358057,  50.25964079, ...,  50.25964079,\\n\",\n       \"           50.34358057,  50.42823317],\\n\",\n       \"         [ 50.49092285,  50.40775602,  50.32528951, ...,  50.32528951,\\n\",\n       \"           50.40775602,  50.49092285],\\n\",\n       \"         [ 50.55324819,  50.4715585 ,  50.39055669, ...,  50.39055669,\\n\",\n       \"           50.4715585 ,  50.55324819]],\\n\",\n       \"\\n\",\n       \"        ...,\\n\",\n       \"\\n\",\n       \"        [[808.85197098, 807.54493599, 806.24890702, ..., 806.24890702,\\n\",\n       \"          807.54493599, 808.85197098],\\n\",\n       \"         [807.85476556, 806.52409631, 805.20463211, ..., 805.20463211,\\n\",\n       \"          806.52409631, 807.85476556],\\n\",\n       \"         [806.85173075, 805.4972891 , 804.15425266, ..., 804.15425266,\\n\",\n       \"          805.4972891 , 806.85173075],\\n\",\n       \"         ...,\\n\",\n       \"         [806.85173075, 805.4972891 , 804.15425266, ..., 804.15425266,\\n\",\n       \"          805.4972891 , 806.85173075],\\n\",\n       \"         [807.85476556, 806.52409631, 805.20463211, ..., 805.20463211,\\n\",\n       \"          806.52409631, 807.85476556],\\n\",\n       \"         [808.85197098, 807.54493599, 806.24890702, ..., 806.24890702,\\n\",\n       \"          807.54493599, 808.85197098]],\\n\",\n       \"\\n\",\n       \"        [[825.70305371, 824.36878882, 823.04575925, ..., 823.04575925,\\n\",\n       \"          824.36878882, 825.70305371],\\n\",\n       \"         [824.68507317, 823.32668165, 821.97972861, ..., 821.97972861,\\n\",\n       \"          823.32668165, 824.68507317],\\n\",\n       \"         [823.66114181, 822.27848262, 820.90746626, ..., 820.90746626,\\n\",\n       \"          822.27848262, 823.66114181],\\n\",\n       \"         ...,\\n\",\n       \"         [823.66114181, 822.27848262, 820.90746626, ..., 820.90746626,\\n\",\n       \"          822.27848262, 823.66114181],\\n\",\n       \"         [824.68507317, 823.32668165, 821.97972861, ..., 821.97972861,\\n\",\n       \"          823.32668165, 824.68507317],\\n\",\n       \"         [825.70305371, 824.36878882, 823.04575925, ..., 823.04575925,\\n\",\n       \"          824.36878882, 825.70305371]],\\n\",\n       \"\\n\",\n       \"        [[842.55413644, 841.19264165, 839.84261148, ..., 839.84261148,\\n\",\n       \"          841.19264165, 842.55413644],\\n\",\n       \"         [841.51538079, 840.12926699, 838.75482511, ..., 838.75482511,\\n\",\n       \"          840.12926699, 841.51538079],\\n\",\n       \"         [840.47055287, 839.05967615, 837.66067986, ..., 837.66067986,\\n\",\n       \"          839.05967615, 840.47055287],\\n\",\n       \"         ...,\\n\",\n       \"         [840.47055287, 839.05967615, 837.66067986, ..., 837.66067986,\\n\",\n       \"          839.05967615, 840.47055287],\\n\",\n       \"         [841.51538079, 840.12926699, 838.75482511, ..., 838.75482511,\\n\",\n       \"          840.12926699, 841.51538079],\\n\",\n       \"         [842.55413644, 841.19264165, 839.84261148, ..., 839.84261148,\\n\",\n       \"          841.19264165, 842.55413644]]],\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"       [[[ 18.72342525,  18.69316981,  18.66316914, ...,  18.66316914,\\n\",\n       \"           18.69316981,  18.72342525],\\n\",\n       \"         [ 18.7003418 ,  18.66953927,  18.63899611, ...,  18.63899611,\\n\",\n       \"           18.66953927,  18.7003418 ],\\n\",\n       \"         [ 18.6771234 ,  18.64577058,  18.61468177, ...,  18.61468177,\\n\",\n       \"           18.64577058,  18.6771234 ],\\n\",\n       \"         ...,\\n\",\n       \"         [ 18.6771234 ,  18.64577058,  18.61468177, ...,  18.61468177,\\n\",\n       \"           18.64577058,  18.6771234 ],\\n\",\n       \"         [ 18.7003418 ,  18.66953927,  18.63899611, ...,  18.63899611,\\n\",\n       \"           18.66953927,  18.7003418 ],\\n\",\n       \"         [ 18.72342525,  18.69316981,  18.66316914, ...,  18.66316914,\\n\",\n       \"           18.69316981,  18.72342525]],\\n\",\n       \"\\n\",\n       \"        [[ 37.44685051,  37.38633963,  37.32633829, ...,  37.32633829,\\n\",\n       \"           37.38633963,  37.44685051],\\n\",\n       \"         [ 37.40068359,  37.33907853,  37.27799223, ...,  37.27799223,\\n\",\n       \"           37.33907853,  37.40068359],\\n\",\n       \"         [ 37.35424679,  37.29154116,  37.22936355, ...,  37.22936355,\\n\",\n       \"           37.29154116,  37.35424679],\\n\",\n       \"         ...,\\n\",\n       \"         [ 37.35424679,  37.29154116,  37.22936355, ...,  37.22936355,\\n\",\n       \"           37.29154116,  37.35424679],\\n\",\n       \"         [ 37.40068359,  37.33907853,  37.27799223, ...,  37.27799223,\\n\",\n       \"           37.33907853,  37.40068359],\\n\",\n       \"         [ 37.44685051,  37.38633963,  37.32633829, ...,  37.32633829,\\n\",\n       \"           37.38633963,  37.44685051]],\\n\",\n       \"\\n\",\n       \"        [[ 56.17027576,  56.07950944,  55.98950743, ...,  55.98950743,\\n\",\n       \"           56.07950944,  56.17027576],\\n\",\n       \"         [ 56.10102539,  56.0086178 ,  55.91698834, ...,  55.91698834,\\n\",\n       \"           56.0086178 ,  56.10102539],\\n\",\n       \"         [ 56.03137019,  55.93731174,  55.84404532, ...,  55.84404532,\\n\",\n       \"           55.93731174,  56.03137019],\\n\",\n       \"         ...,\\n\",\n       \"         [ 56.03137019,  55.93731174,  55.84404532, ...,  55.84404532,\\n\",\n       \"           55.93731174,  56.03137019],\\n\",\n       \"         [ 56.10102539,  56.0086178 ,  55.91698834, ...,  55.91698834,\\n\",\n       \"           56.0086178 ,  56.10102539],\\n\",\n       \"         [ 56.17027576,  56.07950944,  55.98950743, ...,  55.98950743,\\n\",\n       \"           56.07950944,  56.17027576]],\\n\",\n       \"\\n\",\n       \"        ...,\\n\",\n       \"\\n\",\n       \"        [[898.7244122 , 897.2721511 , 895.83211891, ..., 895.83211891,\\n\",\n       \"          897.2721511 , 898.7244122 ],\\n\",\n       \"         [897.61640617, 896.13788479, 894.67181346, ..., 894.67181346,\\n\",\n       \"          896.13788479, 897.61640617],\\n\",\n       \"         [896.50192306, 894.99698789, 893.50472518, ..., 893.50472518,\\n\",\n       \"          894.99698789, 896.50192306],\\n\",\n       \"         ...,\\n\",\n       \"         [896.50192306, 894.99698789, 893.50472518, ..., 893.50472518,\\n\",\n       \"          894.99698789, 896.50192306],\\n\",\n       \"         [897.61640617, 896.13788479, 894.67181346, ..., 894.67181346,\\n\",\n       \"          896.13788479, 897.61640617],\\n\",\n       \"         [898.7244122 , 897.2721511 , 895.83211891, ..., 895.83211891,\\n\",\n       \"          897.2721511 , 898.7244122 ]],\\n\",\n       \"\\n\",\n       \"        [[917.44783745, 915.96532091, 914.49528806, ..., 914.49528806,\\n\",\n       \"          915.96532091, 917.44783745],\\n\",\n       \"         [916.31674797, 914.80742406, 913.31080957, ..., 913.31080957,\\n\",\n       \"          914.80742406, 916.31674797],\\n\",\n       \"         [915.17904646, 913.64275847, 912.11940696, ..., 912.11940696,\\n\",\n       \"          913.64275847, 915.17904646],\\n\",\n       \"         ...,\\n\",\n       \"         [915.17904646, 913.64275847, 912.11940696, ..., 912.11940696,\\n\",\n       \"          913.64275847, 915.17904646],\\n\",\n       \"         [916.31674797, 914.80742406, 913.31080957, ..., 913.31080957,\\n\",\n       \"          914.80742406, 916.31674797],\\n\",\n       \"         [917.44783745, 915.96532091, 914.49528806, ..., 914.49528806,\\n\",\n       \"          915.96532091, 917.44783745]],\\n\",\n       \"\\n\",\n       \"        [[936.17126271, 934.65849073, 933.1584572 , ..., 933.1584572 ,\\n\",\n       \"          934.65849073, 936.17126271],\\n\",\n       \"         [935.01708976, 933.47696332, 931.94980568, ..., 931.94980568,\\n\",\n       \"          933.47696333, 935.01708976],\\n\",\n       \"         [933.85616985, 932.28852905, 930.73408873, ..., 930.73408873,\\n\",\n       \"          932.28852905, 933.85616985],\\n\",\n       \"         ...,\\n\",\n       \"         [933.85616985, 932.28852905, 930.73408873, ..., 930.73408873,\\n\",\n       \"          932.28852905, 933.85616985],\\n\",\n       \"         [935.01708976, 933.47696332, 931.94980568, ..., 931.94980568,\\n\",\n       \"          933.47696333, 935.01708976],\\n\",\n       \"         [936.17126271, 934.65849073, 933.1584572 , ..., 933.1584572 ,\\n\",\n       \"          934.65849073, 936.17126271]]]])
      \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (lev: 50, time: 10, x: 600, x_b: 601, y: 400, y_b: 401)\\n\",\n \"Coordinates:\\n\",\n \" lon (y, x) float64 -119.8 -119.4 -119.0 -118.6 ... 119.0 119.4 119.8\\n\",\n \" lat (y, x) float64 -59.85 -59.85 -59.85 -59.85 ... 59.85 59.85 59.85\\n\",\n \" lon_b (y_b, x_b) float64 -120.0 -119.6 -119.2 ... 119.2 119.6 120.0\\n\",\n \" lat_b (y_b, x_b) float64 -60.0 -60.0 -60.0 -60.0 ... 60.0 60.0 60.0 60.0\\n\",\n \" * time (time) int64 1 2 3 4 5 6 7 8 9 10\\n\",\n \" * lev (lev) int64 1 2 3 4 5 6 7 8 9 10 ... 41 42 43 44 45 46 47 48 49 50\\n\",\n \"Dimensions without coordinates: x, x_b, y, y_b\\n\",\n \"Data variables:\\n\",\n \" data2D (y, x) float64 1.872 1.869 1.866 1.863 ... 1.863 1.866 1.869 1.872\\n\",\n \" data4D (time, lev, y, x) float64 1.872 1.869 1.866 ... 933.2 934.7 936.2\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_in.coords[\\\"time\\\"] = np.arange(1, 11)\\n\",\n \"ds_in.coords[\\\"lev\\\"] = np.arange(1, 51)\\n\",\n \"ds_in[\\\"data2D\\\"] = xe.data.wave_smooth(ds_in[\\\"lon\\\"], ds_in[\\\"lat\\\"])\\n\",\n \"ds_in[\\\"data4D\\\"] = ds_in[\\\"time\\\"] * ds_in[\\\"lev\\\"] * ds_in[\\\"data2D\\\"]\\n\",\n \"ds_in\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is almost 1GB!\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.96\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ds_in[\\\"data4D\\\"].nbytes / 1e9 # Byte -> GB\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The data itself is not too interesting... We only focus on performance here.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'extra dimensions to test broadcasting')\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=[12, 3])\\n\",\n \"\\n\",\n \"plt.subplot(121)\\n\",\n \"ds_in[\\\"data4D\\\"].isel(time=0, lev=0).plot()\\n\",\n \"plt.title(\\\"2D field\\\")\\n\",\n \"\\n\",\n \"plt.subplot(122)\\n\",\n \"ds_in[\\\"data4D\\\"].mean(dim=[\\\"x\\\", \\\"y\\\"]).plot()\\n\",\n \"plt.title(\\\"extra dimensions to test broadcasting\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Build Regridder\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Making a bilinear regridder takes ~7s on my Mac! (`'conservative'` would take\\n\",\n \"even longer. Try it yourself.)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 4.19 s, sys: 228 ms, total: 4.41 s\\n\",\n \"Wall time: 4.43 s\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"regridder = xe.Regridder(ds_in, ds_out, 'bilinear')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF Regridder \\n\",\n \"Regridding algorithm: bilinear \\n\",\n \"Input grid shape: (400, 600) \\n\",\n \"Output grid shape: (300, 400) \\n\",\n \"Output grid dimension name: ('y', 'x') \\n\",\n \"Periodic in longitude? False\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"regridder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Apply regridding\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"However, applying the regridder to 1GB of data only takes ~0.5s\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 408 ms, sys: 205 ms, total: 613 ms\\n\",\n \"Wall time: 612 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"dr_out = regridder(ds_in['data4D'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Why applying regridding is so fast?\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Most regridding algorithms (including all 5 algorithms in ESMF) are linear, i.e.\\n\",\n \"the output data field is linearly dependent on the input data field. Any linear\\n\",\n \"transform can be viewed as a matrix-vector multiplication $y = Ax$, where $A$ is\\n\",\n \"a matrix containing **regridding weights**, and $x$, $y$ are input and output\\n\",\n \"data fields flatten to 1D.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Computing the **weight matrix** $A$ is expensive, but $A$ **only depends on\\n\",\n \"input and output grids**, not on input data. That means we can use the same $A$\\n\",\n \"on different input fields $x$, as long as the grid structure is not changed.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"An xESMF regridder has an attribute `weights`, i.e. the weight matrix.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"<120000x240000 sparse matrix of type ''\\n\",\n \"\\twith 480000 stored elements in COOrdinate format>\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"regridder.weights\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is typically very sparse, because a single destination point will only\\n\",\n \"receive contribution from a small number of source points.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'output grid indices')\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.spy(regridder.weights)\\n\",\n \"plt.xlabel(\\\"input grid indices\\\")\\n\",\n \"plt.ylabel(\\\"output grid indices\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Retrieve regridder\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"To avoid recomputing the same weights later, you can write the weights to disk\\n\",\n \"and reload them later. First write the weights using the `to_netcdf` method. A\\n\",\n \"default file name will be chosen if none is given.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"bilinear_400x600_300x400.nc\\n\"\n ]\n }\n ],\n \"source\": [\n \"fn = regridder.to_netcdf()\\n\",\n \"print(fn)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"7.4M\\tbilinear_400x600_300x400.nc\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%bash\\n\",\n \"du -sh bilinear_400x600_300x400.nc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When you open the notebook next time, simply set the `weights` argument to the\\n\",\n \"path to the netCDF file. The weight file is typically pretty small (due to\\n\",\n \"sparsity), so reading it is almost instantaneous.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 33.7 ms, sys: 2.3 ms, total: 36 ms\\n\",\n \"Wall time: 33.1 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"regridder2 = xe.Regridder(ds_in, ds_out, 'bilinear', weights=fn)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The second-step, applying those weights to data, is just a matrix multiplication\\n\",\n \"$y=Ax$. With highly-optimized sparse matrix multiplication library, it is\\n\",\n \"blazingly fast.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"CPU times: user 739 ms, sys: 191 ms, total: 929 ms\\n\",\n \"Wall time: 926 ms\\n\"\n ]\n }\n ],\n \"source\": [\n \"%%time\\n\",\n \"dr_out2 = regridder2(ds_in['data4D'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The retrieved regridder gives the same result as the first regridder.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xr.testing.assert_identical(dr_out, dr_out2) # they are equal\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"For even larger grids, you might spend several minutes computing the weights.\\n\",\n \"But once they are computed, you don't have to do it again.\\n\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n },\n \"toc\": {\n \"nav_menu\": {},\n \"number_sections\": true,\n \"sideBar\": true,\n \"skip_h1_title\": false,\n \"toc_cell\": false,\n \"toc_position\": {},\n \"toc_section_display\": \"block\",\n \"toc_window_display\": true\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "doc/notebooks/Spatial_Averaging.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Averaging over a region\\n\",\n \"\\n\",\n \"Although this may not sound like _real_ regridding, averaging a gridded field\\n\",\n \"over a region is supported by `ESMF`. This works because the `conservative`\\n\",\n \"regridding method preserves the areal average of the input field. That is, _the\\n\",\n \"value at each output grid cell is the average input value over the output grid\\n\",\n \"area_. Instead of mapping the input field unto rectangular outputs cells, it's\\n\",\n \"mapped unto an irregular mesh defined by an outer polygon. In other words,\\n\",\n \"applying the regridding weights computes the exact areal-average of the input\\n\",\n \"grid over each polygon.\\n\",\n \"\\n\",\n \"This process relies on converting `shapely.Polygon` and `shapely.MultiPolygon`\\n\",\n \"objects into `ESMF.Mesh` objects. However, ESMF meshes do not support all\\n\",\n \"features that come with shapely's (Multi)Polyons. Indeed, mesh elements do not\\n\",\n \"support interior holes, or multiple non-touching parts, as do `shapely` objects.\\n\",\n \"The `xesmf.SpatialAverager` class works around these issues by computing\\n\",\n \"independent weights for interior holes and multi-part geometries, before\\n\",\n \"combining the weights.\\n\",\n \"\\n\",\n \"Transforming polygons into a `ESMF.Mesh` is a slow process. Users looking for\\n\",\n \"faster (but approximate) methods may want to explore\\n\",\n \"[regionmask](https://regionmask.readthedocs.io/) or\\n\",\n \"[clisops](https://clisops.readthedocs.io).\\n\",\n \"\\n\",\n \"Also, note that low resolution polygons such as large boxes might get distorted\\n\",\n \"when mapped on a sphere. Make sure polygon segments are at sufficiently high\\n\",\n \"resolution, on the order of 1°. The `shapely` package (v2) has a\\n\",\n \"[`segmentize` function](https://shapely.readthedocs.io/en/latest/reference/shapely.segmentize.html)\\n\",\n \"to add vertices to polygon segments.\\n\",\n \"\\n\",\n \"The following example shows just how simple it is to compute the average over\\n\",\n \"different countries. The notebook used `geopandas`, a simple and efficient\\n\",\n \"container for geometries, and `descartes` for plotting maps. Make sure both\\n\",\n \"packages are installed, as they are not `xesmf` dependencies.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 1,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import geopandas as gpd\\n\",\n \"import pandas as pd\\n\",\n \"from shapely.geometry import Polygon, MultiPolygon\\n\",\n \"import numpy as np\\n\",\n \"import xarray as xr\\n\",\n \"import xesmf as xe\\n\",\n \"import warnings\\n\",\n \"\\n\",\n \"warnings.filterwarnings(\\\"ignore\\\")\\n\",\n \"xr.set_options(display_style='text')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Simple example\\n\",\n \"\\n\",\n \"In this example we'll create a synthetic global field, then compute its average\\n\",\n \"over six countries.\\n\",\n \"\\n\",\n \"### Download country outlines\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
      \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
      idnamegeometry
      5032ArgentinaMULTIPOLYGON (((-67.19287 -22.82225, -67.02727...
      9156ChinaMULTIPOLYGON (((77.79858 35.49614, 77.66178 35...
      37710South AfricaMULTIPOLYGON (((19.98200 -24.75230, 20.10800 -...
      67724SpainMULTIPOLYGON (((-5.34065 35.84736, -5.37665 35...
      98466MaliPOLYGON ((-12.26352 14.77561, -12.13752 14.784...
      155484MexicoMULTIPOLYGON (((-97.13797 25.96581, -97.16677 ...
      \\n\",\n \"
      \"\n ],\n \"text/plain\": [\n \" id name geometry\\n\",\n \"5 032 Argentina MULTIPOLYGON (((-67.19287 -22.82225, -67.02727...\\n\",\n \"9 156 China MULTIPOLYGON (((77.79858 35.49614, 77.66178 35...\\n\",\n \"37 710 South Africa MULTIPOLYGON (((19.98200 -24.75230, 20.10800 -...\\n\",\n \"67 724 Spain MULTIPOLYGON (((-5.34065 35.84736, -5.37665 35...\\n\",\n \"98 466 Mali POLYGON ((-12.26352 14.77561, -12.13752 14.784...\\n\",\n \"155 484 Mexico MULTIPOLYGON (((-97.13797 25.96581, -97.16677 ...\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Load some polygons from the internet\\n\",\n \"regs = gpd.read_file(\\n\",\n \" \\\"https://cdn.jsdelivr.net/npm/world-atlas@2/countries-10m.json\\\"\\n\",\n \")\\n\",\n \"\\n\",\n \"# Select a few countries for the sake of the example\\n\",\n \"regs = regs.iloc[[5, 9, 37, 67, 98, 155]]\\n\",\n \"\\n\",\n \"# Simplify the geometries to a 0.02 deg tolerance, which is 1/100 of our grid.\\n\",\n \"# The simpler the polygons, the faster the averaging, but we lose some precision.\\n\",\n \"regs[\\\"geometry\\\"] = regs.simplify(tolerance=0.02, preserve_topology=True)\\n\",\n \"regs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
      <xarray.Dataset>\\n\",\n       \"Dimensions:  (x: 180, x_b: 181, y: 90, y_b: 91)\\n\",\n       \"Coordinates:\\n\",\n       \"    lon      (y, x) float64 -179.0 -177.0 -175.0 -173.0 ... 175.0 177.0 179.0\\n\",\n       \"    lat      (y, x) float64 -89.0 -89.0 -89.0 -89.0 ... 89.0 89.0 89.0 89.0\\n\",\n       \"    lon_b    (y_b, x_b) int64 -180 -178 -176 -174 -172 ... 172 174 176 178 180\\n\",\n       \"    lat_b    (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\\n\",\n       \"Dimensions without coordinates: x, x_b, y, y_b\\n\",\n       \"Data variables:\\n\",\n       \"    field    (y, x) float64 2.0 2.0 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0 2.0 2.0
      \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (x: 180, x_b: 181, y: 90, y_b: 91)\\n\",\n \"Coordinates:\\n\",\n \" lon (y, x) float64 -179.0 -177.0 -175.0 -173.0 ... 175.0 177.0 179.0\\n\",\n \" lat (y, x) float64 -89.0 -89.0 -89.0 -89.0 ... 89.0 89.0 89.0 89.0\\n\",\n \" lon_b (y_b, x_b) int64 -180 -178 -176 -174 -172 ... 172 174 176 178 180\\n\",\n \" lat_b (y_b, x_b) int64 -90 -90 -90 -90 -90 -90 -90 ... 90 90 90 90 90 90\\n\",\n \"Dimensions without coordinates: x, x_b, y, y_b\\n\",\n \"Data variables:\\n\",\n \" field (y, x) float64 2.0 2.0 2.0 2.0 2.0 2.0 ... 2.0 2.0 2.0 2.0 2.0 2.0\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Create synthetic global data\\n\",\n \"ds = xe.util.grid_global(2, 2)\\n\",\n \"ds = ds.assign(field=xe.data.wave_smooth(ds.lon, ds.lat))\\n\",\n \"ds\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Display the global field and countries' outline.\\n\",\n \"fig, ax = plt.subplots()\\n\",\n \"ds.field.plot(ax=ax, x=\\\"lon\\\", y=\\\"lat\\\")\\n\",\n \"regs.plot(ax=ax, edgecolor=\\\"k\\\", facecolor=\\\"none\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Compute the field average over each country\\n\",\n \"\\n\",\n \"`xesmf.SpatialAverager` is a class designed to average an `xarray.DataArray`\\n\",\n \"over a list of polygons. It behaves similarly to `xesmf.Regridder`, but has\\n\",\n \"options to deal specifically with polygon outputs. It uses the `conservative`\\n\",\n \"regridding, and can store and reuse weights.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"xESMF SpatialAverager \\n\",\n \"Weight filename: spatialavg_90x180_6.nc \\n\",\n \"Reuse pre-computed weights? False \\n\",\n \"Input grid shape: (90, 180) \\n\",\n \"Output list length: 6 \"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"savg = xe.SpatialAverager(ds, regs.geometry, geom_dim_name=\\\"country\\\")\\n\",\n \"savg\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"When called, the `SpatialAverager` instance returns a `DataArray` of averages\\n\",\n \"over the `geom` dimension, here countries. `lon` and `lat` coordinates are the\\n\",\n \"centroids each polygon.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
      <xarray.DataArray 'field' (country: 6)>\\n\",\n       \"array([1.58448717, 1.49195654, 2.48197742, 2.5740491 , 2.89305772,\\n\",\n       \"       1.26271837])\\n\",\n       \"Coordinates:\\n\",\n       \"    lon      (country) float64 -65.18 103.8 25.09 -3.646 -3.541 -102.5\\n\",\n       \"    lat      (country) float64 -35.39 36.56 -29.0 40.23 17.35 23.95\\n\",\n       \"  * country  (country) object 'Argentina' 'China' ... 'Mali' 'Mexico'\\n\",\n       \"Attributes:\\n\",\n       \"    regrid_method:  conservative
      \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"array([1.58448717, 1.49195654, 2.48197742, 2.5740491 , 2.89305772,\\n\",\n \" 1.26271837])\\n\",\n \"Coordinates:\\n\",\n \" lon (country) float64 -65.18 103.8 25.09 -3.646 -3.541 -102.5\\n\",\n \" lat (country) float64 -35.39 36.56 -29.0 40.23 17.35 23.95\\n\",\n \" * country (country) object 'Argentina' 'China' ... 'Mali' 'Mexico'\\n\",\n \"Attributes:\\n\",\n \" regrid_method: conservative\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"out = savg(ds.field)\\n\",\n \"out = out.assign_coords(country=xr.DataArray(regs[\\\"name\\\"], dims=(\\\"country\\\",)))\\n\",\n \"out\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"As the order of the polygons is conserved in the output, we can easily include\\n\",\n \"the results back into our `geopandas` dataframe.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
      \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
      idnamegeometryfield_avg
      5032ArgentinaMULTIPOLYGON (((-67.19287 -22.82225, -67.02727...1.584487
      9156ChinaMULTIPOLYGON (((77.79858 35.49614, 77.66178 35...1.491957
      37710South AfricaMULTIPOLYGON (((19.98200 -24.75230, 20.10800 -...2.481977
      67724SpainMULTIPOLYGON (((-5.34065 35.84736, -5.37665 35...2.574049
      98466MaliPOLYGON ((-12.26352 14.77561, -12.13752 14.784...2.893058
      155484MexicoMULTIPOLYGON (((-97.13797 25.96581, -97.16677 ...1.262718
      \\n\",\n \"
      \"\n ],\n \"text/plain\": [\n \" id name geometry \\\\\\n\",\n \"5 032 Argentina MULTIPOLYGON (((-67.19287 -22.82225, -67.02727... \\n\",\n \"9 156 China MULTIPOLYGON (((77.79858 35.49614, 77.66178 35... \\n\",\n \"37 710 South Africa MULTIPOLYGON (((19.98200 -24.75230, 20.10800 -... \\n\",\n \"67 724 Spain MULTIPOLYGON (((-5.34065 35.84736, -5.37665 35... \\n\",\n \"98 466 Mali POLYGON ((-12.26352 14.77561, -12.13752 14.784... \\n\",\n \"155 484 Mexico MULTIPOLYGON (((-97.13797 25.96581, -97.16677 ... \\n\",\n \"\\n\",\n \" field_avg \\n\",\n \"5 1.584487 \\n\",\n \"9 1.491957 \\n\",\n \"37 2.481977 \\n\",\n \"67 2.574049 \\n\",\n \"98 2.893058 \\n\",\n \"155 1.262718 \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"regs[\\\"field_avg\\\"] = out.values\\n\",\n \"regs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(figsize=(12, 5))\\n\",\n \"ds.field.plot(ax=ax, x=\\\"lon\\\", y=\\\"lat\\\", cmap=\\\"Greys_r\\\")\\n\",\n \"handles = regs.plot(\\n\",\n \" column=\\\"field_avg\\\", ax=ax, edgecolor=\\\"k\\\", vmin=1, vmax=3, cmap=\\\"viridis\\\"\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"### Extract the weight mask from the averager\\n\",\n \"\\n\",\n \"The weights are stored in a sparse matrix structure in\\n\",\n \"`SpatialAverager.weights`. The sparse matrix can be converted to a full\\n\",\n \"DataArray, but note that this will increase memory usage proportional to the\\n\",\n \"number of polygons.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Convert sparse matrix to numpy array, it has size : (n_in, n_out)\\n\",\n \"# So reshape to the same shape as ds + polygons\\n\",\n \"w = xr.DataArray(\\n\",\n \" savg.weights.toarray().reshape(regs.geometry.size, *ds.lon.shape),\\n\",\n \" dims=(\\\"country\\\", *ds.lon.dims),\\n\",\n \" coords=dict(country=out.country, **ds.lon.coords),\\n\",\n \")\\n\",\n \"plt.subplots_adjust(top=0.9)\\n\",\n \"facets = w.plot(col=\\\"country\\\", col_wrap=2, aspect=2, vmin=0, vmax=0.05)\\n\",\n \"facets.cbar.set_label(\\\"Averaging weights\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"This also allows to quickly check that the weights are indeed normalized, that\\n\",\n \"the sum of each mask is 1.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"pycharm\": {\n \"name\": \"#%%\\n\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1., 1., 1., 1., 1., 1.])\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"w.sum(dim=[\\\"y\\\", \\\"x\\\"]).values\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.5\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "doc/notebooks/Using_LocStream.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"# Using ESMF LocStream objects\\n\",\n \"\\n\",\n \"(contributed by [Raphael Dussin](https://github.com/raphaeldussin))\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"ESMF\\n\",\n \"[LocStream](http://www.earthsystemmodeling.org/esmf_releases/last_built/esmpy_doc/html/locstream.html?highlight=locstream)\\n\",\n \"objects describe a list of geographical points, represented by 1-dimensional\\n\",\n \"arrays of lat/lon coordinates. It is useful for remapping gridded data (e.g.\\n\",\n \"from model output) to/from observation locations, or creating model boundary\\n\",\n \"conditions.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import xarray as xr\\n\",\n \"import xesmf as xe\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Remapping from a grid to a LocStream\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Let's open a gridded dataset (for example the xarray air temperature dataset):\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"airtemps = xr.tutorial.open_dataset(\\\"air_temperature\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"airtemps[\\\"air\\\"].isel(time=0).plot(vmin=230, vmax=300)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Now let's define a list of geographical points (1-D arrays). The name of the\\n\",\n \"dimension used for the LocStream is not important.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_locs = xr.Dataset()\\n\",\n \"ds_locs[\\\"lon\\\"] = xr.DataArray(\\n\",\n \" data=[220, 230, 240, 250, 260, 270], dims=(\\\"locations\\\")\\n\",\n \")\\n\",\n \"ds_locs[\\\"lat\\\"] = xr.DataArray(data=[20, 30, 40, 50, 60, 70], dims=(\\\"locations\\\"))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Creating a `Regridder` for LocStream object can be done by setting\\n\",\n \"`locstream_out` or `locstream_in` (or both) to `True`. Some algorithms like\\n\",\n \"`conservative` are not allowed with locstream input/output. See\\n\",\n \"[this comment](https://github.com/JiaweiZhuang/xESMF/pull/81#issuecomment-588462933)\\n\",\n \"for more discussions.\\n\",\n \"\\n\",\n \"With `locstream_out=True`, the regridder behaves like\\n\",\n \"[Xarray's advanced indexing](http://xarray.pydata.org/en/stable/interpolation.html#advanced-interpolation).\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regridder = xe.Regridder(airtemps, ds_locs, \\\"bilinear\\\", locstream_out=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"using dimensions ('lat', 'lon') from data variable air as the horizontal dimensions for this dataset.\\n\"\n ]\n }\n ],\n \"source\": [\n \"airtemps_locs = regridder(airtemps)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"airtemps_locs[\\\"air\\\"].plot(x=\\\"time\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## Remapping from LocStream to grid\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"The opposite transformation is also possible, but only available methods are\\n\",\n \"`nearest_s2d` and `nearest_d2s`.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regridder_back_s2d = xe.Regridder(\\n\",\n \" airtemps_locs, airtemps, \\\"nearest_s2d\\\", locstream_in=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
      \\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide data repr\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"Show/Hide attributes\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"
      xarray.Dataset
        • locations: 6
        • time: 2920
        • time
          (time)
          datetime64[ns]
          2013-01-01 ... 2014-12-31T18:00:00
          standard_name :
          time
          long_name :
          Time
          array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',\\n\",\n       \"       '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',\\n\",\n       \"       '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],\\n\",\n       \"      dtype='datetime64[ns]')
        • lon
          (locations)
          int64
          220 230 240 250 260 270
          array([220, 230, 240, 250, 260, 270])
        • lat
          (locations)
          int64
          20 30 40 50 60 70
          array([20, 30, 40, 50, 60, 70])
        • air
          (time, locations)
          float64
          292.8 288.9 268.1 ... 255.5 236.8
          array([[292.79000854, 288.8999939 , 268.1000061 , 269.79000854,\\n\",\n       \"        247.69999695, 247.88999939],\\n\",\n       \"       [293.        , 289.79000854, 262.3999939 , 267.69998169,\\n\",\n       \"        246.        , 246.29998779],\\n\",\n       \"       [292.29000854, 289.5       , 256.69998169, 269.8999939 ,\\n\",\n       \"        244.5       , 243.88999939],\\n\",\n       \"       ...,\\n\",\n       \"       [296.29000854, 290.58999634, 263.19000244, 266.19000244,\\n\",\n       \"        259.79000854, 234.78999329],\\n\",\n       \"       [296.48999023, 289.48999023, 261.08999634, 270.88998413,\\n\",\n       \"        259.98999023, 237.98999023],\\n\",\n       \"       [297.19000244, 289.58999634, 260.79000854, 268.38998413,\\n\",\n       \"        255.48999023, 236.78999329]])
      • regrid_method :
        bilinear
      \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (locations: 6, time: 2920)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\\n\",\n \" lon (locations) int64 220 230 240 250 260 270\\n\",\n \" lat (locations) int64 20 30 40 50 60 70\\n\",\n \"Dimensions without coordinates: locations\\n\",\n \"Data variables:\\n\",\n \" air (time, locations) float64 292.8 288.9 268.1 ... 268.4 255.5 236.8\\n\",\n \"Attributes:\\n\",\n \" regrid_method: bilinear\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"airtemps_locs\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"using dimensions ('locations',) from data variable air as the horizontal dimensions for this dataset.\\n\"\n ]\n }\n ],\n \"source\": [\n \"airtemps_gridded2 = regridder_back_s2d(airtemps_locs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"airtemps_gridded2[\\\"air\\\"].isel(time=0).plot(vmin=230, vmax=300)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"Since we drastically undersampled the original dataset, the reconstruction is\\n\",\n \"very different from the original. The other nearest-neighbor option (d2s) will\\n\",\n \"only map one destination grid point per LocStream point:\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regrid_back_d2s = xe.Regridder(\\n\",\n \" airtemps_locs, airtemps, \\\"nearest_d2s\\\", locstream_in=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"using dimensions ('locations',) from data variable air as the horizontal dimensions for this dataset.\\n\"\n ]\n }\n ],\n \"source\": [\n \"airtemps_gridded3 = regrid_back_d2s(airtemps_locs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"airtemps_gridded3[\\\"air\\\"].isel(time=0).plot()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"## LocStream to LocStream\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \"It is also possible to remap from one LocStream to another, again only nearest\\n\",\n \"neighbor methods are available.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ds_locs2 = xr.Dataset()\\n\",\n \"ds_locs2[\\\"lon\\\"] = xr.DataArray(\\n\",\n \" data=[225, 235, 245, 255, 265, 275], dims=(\\\"locations\\\")\\n\",\n \")\\n\",\n \"ds_locs2[\\\"lat\\\"] = xr.DataArray(\\n\",\n \" data=[20, 30, 40, 50, 60, 70], dims=(\\\"locations\\\")\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"regrid_l2l = xe.Regridder(\\n\",\n \" ds_locs, ds_locs2, \\\"nearest_s2d\\\", locstream_in=True, locstream_out=True\\n\",\n \")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"using dimensions ('locations',) from data variable air as the horizontal dimensions for this dataset.\\n\"\n ]\n }\n ],\n \"source\": [\n \"airtemps_locs2 = regrid_l2l(airtemps_locs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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      \"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"airtemps_locs2[\\\"air\\\"].plot(x=\\\"time\\\")\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.8.2\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "doc/other_tools.rst", "content": ".. _other_tools-label:\n\nOther geospatial regridding tools\n=================================\n\nHere is a brief overview of other regridding tools that the authors are aware of\n(for geospatial data on the sphere, excluding traditional image resizing functions).\nThey are all great tools and have helped the author a lot in both scientific research\nand xESMF development. Check them out if xESMF cannot suit your needs.\n\n- `ESMF `_ (*Fortran package*)\n\nAlthough its name \"Earth System Modeling Framework\" doesn't indicate a regridding\nfunctionality, it actually contains a very powerful regridding engine.\nIt is widely used in Earth System Models (ESMs), serving as both the software infrastructure\nand the regridder for transforming data between the atmosphere, ocean, and land components.\nIt can deal with general irregular meshes, in either 2D or 3D.\n\nESMF is a huge beast, containing one million lines of source code.\nEven just compiling it requires some effort.\nIt is more for building ESMs than for data analysis.\n\n- `ESMPy `_ (*Python interface to ESMF*)\n\nESMPy provides a much simpler way to use ESMF's regridding functionality.\nThe greatest thing is, it is pre-compiled as a\n`conda package `_,\nso you can install it with one-click and don't have to go through\nthe daunting compiling process on your own.\n\nHowever, ESMPy is a complicated Python API that controls a huge Fortran beast\nhidden underneath. It is not as intuitive as native Python packages, and even\na simple regridding task requires more than 10 lines of arcane code. The\npurpose of xESMF is to provide a friendlier interface to the xarray community.\nCheck out this nice `tutorial `_\nbefore going to the\n`official doc `_.\n\n- `TempestRemap `_\n (*C++ package*)\n\nA pretty modern and powerful package,\nsupporting arbitrary-order conservative remapping.\nIt can also generate cubed-sphere grids on the fly\nand can be modified to support many cubed-sphere grid variations\n(`example `_, only if you can read C++).\n\n- `SCRIP `_ (*Fortran package*)\n\nAn old package, once popular but **no longer maintained** (long live SCRIP).\nYou should not use it now, but should know that it exists.\nNewer regridding packages often follow its standards --\nyou will see \"SCRIP format\" here and there, for example in ESMF or TempestRemap.\n\n- `Regridder in NCL `_\n (*NCAR Command Language*)\n\nHas bilinear and conservative algorithms for rectilinear grids,\nand also supports some specialized curvilinear grids.\nThere is also an `ESMF wrapper `_\nthat works for more grid types.\n\n- `Regridder in NCO `_\n (*command line tool*)\n\n- `Regridder in Iris `_\n (*Python package*)\n\n- `Regridder in xCDAT `_\n (*Python package*)\n\n Offers regridding algorithms from xESMF and `regrid2`, originally from the CDAT `cdutil` package, it also includes code for vertical regridding.\n" }, { "path": "doc/releases.rst", "content": "Release how-to\n==============\n\nHere are the step by step instructions to release a new version of xESMF.\n\n#. Make sure :file:`CHANGES.rst` is up to date and includes a section on the version to be released, usually this is done through a pull request.\n#. On GitHub, go the Releases_ page and click on :guilabel:`Draft a new release`;\n#. Enter new version in :guilabel:`Tag version` (e.g. v..);\n#. Enter the :guilabel:`Release title` (e.g. the same tag);\n#. Copy the relevant section of :file:`CHANGES.rst` in the description;\n#. Click :guilabel:`Publish release`;\n\n.. _Releases: https://github.com/pangeo-data/xESMF/releases\n.. _Actions: https://github.com/pangeo-data/xESMF/actions\n" }, { "path": "doc/requirements.txt", "content": "numpydoc\ngeopandas\ndescartes\nipython\nPygments>=2.6\nnbsphinx\nsphinx<7\nsphinx_rtd_theme\ncf_xarray\ndocutils!=0.18\n" }, { "path": "doc/user_api.rst", "content": "User API\n########\n\nRegridder\n=========\n\n.. autoclass:: xesmf.frontend.Regridder\n :inherited-members:\n :special-members: __init__, __call__\n\n.. autoclass:: xesmf.frontend.SpatialAverager\n :members:\n :special-members: __init__\n\n\nutil\n====\n\n.. automodule:: xesmf.util\n :members:\n\ndata\n====\n\n.. automodule:: xesmf.data\n :members:\n" }, { "path": "doc/why.rst", "content": "Why invent a new regridding package?\n====================================\n\nFor scientific correctness\n--------------------------\n\nTraditional interpolation routines, such as\n`interp2d in Scipy `_\nand\n`interp2 in MATLAB `_,\nassume flat 2D planes and do not consider the spherical geometry of the earth.\nThey are great for image processing, but will produce incorrect/distorted results for geospatial data.\n\nAlso, traditional interpolation algorithms are typically based on piecewise polynomials (\"splines\").\nWhile being highly accurate in terms of error convergence, they often lack desired physical properties such as\nconservation (total mass should be conserved) and monotonicity (air density cannot go negative).\n\nFor emerging new grid types\n---------------------------\n\nNon-orthogonal grids are popular in numerical models\n(`Staniforth and Thuburn 2012 `_),\nbut traditional tools often assume standard lat-lon grids.\n\nxESMF can regrid between general curvilinear (i.e. quadrilateral or \"logically rectilinear\") grids,\nas long as the geographic latitude and longitude can be given as 2D or 1D arrays. For example :\n\n- The `Cubed-Sphere `_ grid\n in `GFDL-FV3 `_\n- The `Latitude-Longitude-Cap grid `_\n in `MITgcm `_\n- The `Lambert Conformal grid `_\n in WRF\n\nHowever, xESMF does not yet support non-quadrilateral grids,\nlike the hexagonal grid in `MPAS `_.\nSee :ref:`irregular_meshes-label` for more information.\n\nFor usability and simplicity\n----------------------------\n\n:ref:`Current geospatial regridding tools ` tend to have non-trivial learning curves.\nxESMF tries to be simple and intuitive.\nInstead of inventing a new data structure, it relies on well-established standards\n(numpy and xarray), so users don't need to learn a bunch of new syntax or even a new software stack.\n\nxESMF can track metadata in ``xarray.DataArray`` / ``xarray.Dataset``, and\nalso work with basic ``numpy.ndarray``.\nThis means any Python users can use it easily, even if unfamiliar with xarray.\n\nThe choice of Python and Anaconda also makes xESMF :ref:`extremely easy to install `.\n" }, { "path": "pyproject.toml", "content": "[build-system]\nbuild-backend = \"setuptools.build_meta\"\nrequires = [\n \"setuptools>=41.2\",\n \"setuptools-scm\",\n \"wheel\",\n]\n\n[project]\nname = \"xesmf\"\ndescription = \"Universal Regridder for Geospatial Data\"\nlicense = { text = \"MIT\" }\nauthors = [\n { name = \"Jiawei Zhuang\", email = \"jiaweizhuang@g.harvard.edu\" },\n]\nrequires-python = \">=3.11\"\nclassifiers = [\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Science/Research\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3 :: Only\",\n \"Programming Language :: Python :: 3.11\",\n \"Programming Language :: Python :: 3.12\",\n \"Programming Language :: Python :: 3.13\",\n \"Programming Language :: Python :: 3.14\",\n \"Topic :: Scientific/Engineering\",\n]\ndynamic = [\n \"dependencies\",\n \"readme\",\n \"version\",\n]\nurls.documentation = \"https://xesmf.readthedocs.io/en/latest/\"\nurls.homepage = \"https://github.com/pangeo-data/xESMF\"\nurls.repository = \"https://github.com/pangeo-data/xESMF\"\n\n[tool.setuptools]\npackages = [\n \"xesmf\",\n \"xesmf.tests\",\n]\nlicense-files = [\n \"LICENSE\",\n]\ndynamic.dependencies = { file = [\n \"requirements.txt\",\n] }\ndynamic.readme = { file = \"README.rst\", content-type = \"text/x-rst\" }\n\n[tool.setuptools_scm]\nwrite_to = \"xesmf/_version.py\"\nwrite_to_template = \"__version__ = '{version}'\"\ntag_regex = \"^(?Pv)?(?P[^\\\\+]+)(?P.*)?$\"\n\n[tool.black]\nline-length = 100\ntarget-version = [\n \"py311\",\n]\nskip-string-normalization = true\n\n[tool.isort]\nknown_first_party = \"xesmf\"\nknown_third_party = [\n \"cf_xarray\",\n \"cftime\",\n \"dask\",\n \"numba\",\n \"numpy\",\n \"pytest\",\n \"shapely\",\n \"sparse\",\n \"xarray\",\n]\nmulti_line_output = 3\ninclude_trailing_comma = true\nforce_grid_wrap = 0\ncombine_as_imports = true\nline_length = 100\nskip = [\n \"setup.py\",\n \"doc/conf.py\",\n]\n\n[tool.pyproject-fmt]\n# maximum Python version to use when generating version specifiers\nmax_supported_python = \"3.14\"\n" }, { "path": "readthedocs.yml", "content": "version: 2\n\nbuild:\n os: \"ubuntu-24.04\"\n tools:\n python: \"mambaforge-23.11\"\n jobs:\n pre_install:\n - conda list\n\nconda:\n environment: ci/doc.yml\n\npython:\n install:\n - method: pip\n path: .\n\nsphinx:\n configuration: doc/conf.py\n # fail_on_warning might generate hard to fix error, in this case it can be\n # disabled but this also means those errors will fail silently, choose wisely.\n fail_on_warning: false\n" }, { "path": "requirements.txt", "content": "cf-xarray>=0.5.1\n# We cannot list this here b/c it is not on PyPI.\n#esmpy>=8.0.0\nnumba >=0.55.2\nnumpy>=1.16\nshapely\nsparse>=0.8.0\nxarray>=0.17.0\n" }, { "path": "setup.cfg", "content": "[bumpversion]\ncurrent_version = 0.6.2\n\n[flake8]\nexclude = doc\nignore =\nmax-line-length = 100\nmax-complexity = 18\nselect = B,C,E,F,W,T4,B9\nextend-ignore = E203,E501,E402,W503,W605,C901\n" }, { "path": "xesmf/__init__.py", "content": "# flake8: noqa\n\nfrom . import data, util\nfrom .frontend import Regridder, SpatialAverager\n\ntry:\n from ._version import __version__\nexcept ImportError:\n __version__ = 'unknown'\n" }, { "path": "xesmf/backend.py", "content": "\"\"\"\nBackend for xESMF. This module wraps ESMPy's complicated API and can create\nESMF Grid and Regrid objects only using basic numpy arrays.\n\nGeneral idea:\n\n1) Only use pure numpy array in this low-level backend. xarray should only be\nused in higher-level APIs which interface with this low-level backend.\n\n2) Use simple, procedural programming here. Because ESMPy Classes are\ncomplicated enough, building new Classes will make debugging very difficult.\n\n3) Add some basic error checking in this wrapper level.\nESMPy is hard to debug because the program often dies in the Fortran level.\nSo it would be helpful to catch some common mistakes in Python level.\n\"\"\"\n\nimport os\nimport warnings\nfrom collections.abc import Sequence\n\ntry:\n import esmpy as ESMF\nexcept ImportError:\n import ESMF\nimport numpy as np\nimport numpy.lib.recfunctions as nprec\n\n\ndef warn_f_contiguous(a):\n \"\"\"\n Give a warning if input array if not Fortran-ordered.\n\n ESMPy expects Fortran-ordered array. Passing C-ordered array will slow down\n performance due to memory rearrangement.\n\n Parameters\n ----------\n a : numpy array\n \"\"\"\n if not a.flags['F_CONTIGUOUS']:\n warnings.warn('Input array is not F_CONTIGUOUS. ' 'Will affect performance.')\n\n\ndef warn_lat_range(lat):\n \"\"\"\n Give a warning if latitude is outside of [-90, 90]\n\n Longitute, on the other hand, can be in any range,\n since the it the transform is done in (x, y, z) space.\n\n Parameters\n ----------\n lat : numpy array\n \"\"\"\n if (lat.max() > 90.0) or (lat.min() < -90.0):\n warnings.warn('Latitude is outside of [-90, 90]')\n\n\nclass Grid(ESMF.Grid):\n @classmethod\n def from_xarray(cls, lon, lat, periodic=False, mask=None):\n \"\"\"\n Create an ESMF.Grid object, for constructing ESMF.Field and ESMF.Regrid.\n\n Parameters\n ----------\n lon, lat : 2D numpy array\n Longitute/Latitude of cell centers.\n\n Recommend Fortran-ordering to match ESMPy internal.\n\n Shape should be ``(Nlon, Nlat)`` for rectilinear grid,\n or ``(Nx, Ny)`` for general quadrilateral grid.\n\n periodic : bool, optional\n Periodic in longitude? Default to False.\n Only useful for source grid.\n\n mask : 2D numpy array, optional\n Grid mask. According to the ESMF convention, masked cells\n are set to 0 and unmasked cells to 1.\n\n Shape should be ``(Nlon, Nlat)`` for rectilinear grid,\n or ``(Nx, Ny)`` for general quadrilateral grid.\n\n Returns\n -------\n grid : ESMF.Grid object\n \"\"\"\n\n # ESMPy expects Fortran-ordered array.\n # Passing C-ordered array will slow down performance.\n for a in [lon, lat]:\n warn_f_contiguous(a)\n\n warn_lat_range(lat)\n\n # ESMF.Grid can actually take 3D array (lon, lat, radius),\n # but regridding only works for 2D array\n assert lon.ndim == 2, 'Input grid must be 2D array'\n assert lon.shape == lat.shape, 'lon and lat must have same shape'\n\n staggerloc = ESMF.StaggerLoc.CENTER # actually just integer 0\n\n if periodic:\n num_peri_dims = 1\n else:\n num_peri_dims = None\n\n # ESMPy documentation claims that if staggerloc and coord_sys are None,\n # they will be set to default values (CENTER and SPH_DEG).\n # However, they actually need to be set explicitly,\n # otherwise grid._coord_sys and grid._staggerloc will still be None.\n grid = cls(\n np.array(lon.shape),\n staggerloc=staggerloc,\n coord_sys=ESMF.CoordSys.SPH_DEG,\n num_peri_dims=num_peri_dims,\n )\n\n # The grid object points to the underlying Fortran arrays in ESMF.\n # To modify lat/lon coordinates, need to get pointers to them\n lon_pointer = grid.get_coords(coord_dim=0, staggerloc=staggerloc)\n lat_pointer = grid.get_coords(coord_dim=1, staggerloc=staggerloc)\n\n # Use [...] to avoid overwritting the object. Only change array values.\n lon_pointer[...] = lon\n lat_pointer[...] = lat\n\n # Follows SCRIP convention where 1 is unmasked and 0 is masked.\n # See https://github.com/NCPP/ocgis/blob/61d88c60e9070215f28c1317221c2e074f8fb145/src/ocgis/regrid/base.py#L391-L404\n if mask is not None:\n # remove fractional values\n mask = np.where(mask == 0, 0, 1)\n # convert array type to integer (ESMF compat)\n grid_mask = mask.astype(np.int32)\n if not (grid_mask.shape == lon.shape):\n raise ValueError(\n 'mask must have the same shape as the latitude/longitude '\n 'coordinates, got: mask.shape = %s, lon.shape = %s' % (mask.shape, lon.shape)\n )\n grid.add_item(ESMF.GridItem.MASK, staggerloc=ESMF.StaggerLoc.CENTER, from_file=False)\n grid.mask[0][:] = grid_mask\n\n return grid\n\n def get_shape(self, loc=ESMF.StaggerLoc.CENTER):\n \"\"\"Return shape of grid for specified StaggerLoc\"\"\"\n # We cast explicitly to python's int (numpy >=2)\n return tuple(map(int, self.size[loc]))\n\n\nclass LocStream(ESMF.LocStream):\n @classmethod\n def from_xarray(cls, lon, lat):\n \"\"\"\n Create an ESMF.LocStream object, for contrusting ESMF.Field and ESMF.Regrid\n\n Parameters\n ----------\n lon, lat : 1D numpy array\n Longitute/Latitude of cell centers.\n\n Returns\n -------\n locstream : ESMF.LocStream object\n \"\"\"\n\n if len(lon.shape) > 1:\n raise ValueError('lon can only be 1d')\n if len(lat.shape) > 1:\n raise ValueError('lat can only be 1d')\n\n assert lon.shape == lat.shape\n\n location_count = len(lon)\n\n locstream = cls(location_count, coord_sys=ESMF.CoordSys.SPH_DEG)\n\n locstream['ESMF:Lon'] = lon.astype(np.dtype('f8'))\n locstream['ESMF:Lat'] = lat.astype(np.dtype('f8'))\n\n return locstream\n\n def get_shape(self):\n \"\"\"Return LocStream shape.\"\"\"\n return (self.size, 1)\n\n\ndef add_corner(grid, lon_b, lat_b):\n \"\"\"\n Add corner information to ESMF.Grid for conservative regridding.\n\n Not needed for other methods like bilinear or nearest neighbour.\n\n Parameters\n ----------\n grid : ESMF.Grid object\n Generated by ``Grid.from_xarray()``. Will be modified in-place.\n\n lon_b, lat_b : 2D numpy array\n Longitute/Latitude of cell corner\n Recommend Fortran-ordering to match ESMPy internal.\n Shape should be ``(Nlon+1, Nlat+1)``, or ``(Nx+1, Ny+1)``\n \"\"\"\n\n # codes here are almost the same as Grid.from_xarray(),\n # except for the \"staggerloc\" keyword\n staggerloc = ESMF.StaggerLoc.CORNER # actually just integer 3\n\n for a in [lon_b, lat_b]:\n warn_f_contiguous(a)\n\n warn_lat_range(lat_b)\n\n assert lon_b.ndim == 2, 'Input grid must be 2D array'\n assert lon_b.shape == lat_b.shape, 'lon_b and lat_b must have same shape'\n assert np.array_equal(lon_b.shape, grid.max_index + 1), 'lon_b should be size (Nx+1, Ny+1)'\n assert (grid.num_peri_dims == 0) and (\n grid.periodic_dim is None\n ), 'Cannot add corner for periodic grid'\n\n grid.add_coords(staggerloc=staggerloc)\n\n lon_b_pointer = grid.get_coords(coord_dim=0, staggerloc=staggerloc)\n lat_b_pointer = grid.get_coords(coord_dim=1, staggerloc=staggerloc)\n\n lon_b_pointer[...] = lon_b\n lat_b_pointer[...] = lat_b\n\n\nclass Mesh(ESMF.Mesh):\n @classmethod\n def from_polygons(cls, polys, element_coords='centroid'):\n \"\"\"\n Create an ESMF.Mesh object from a list of polygons.\n\n All exterior ring points are added to the mesh as nodes and each polygon\n is added as an element, with the polygon centroid as the element's coordinates.\n\n Parameters\n ----------\n polys : sequence of shapely Polygon\n Holes are not represented by the Mesh.\n element_coords : array or \"centroid\", optional\n If \"centroid\", the polygon centroids will be used (default)\n If an array of shape (len(polys), 2) : the element coordinates of the mesh.\n If None, the Mesh's elements will not have coordinates.\n\n Returns\n -------\n mesh : ESMF.Mesh\n A mesh where each polygon is represented as an Element.\n \"\"\"\n node_num = sum(len(e.exterior.coords) - 1 for e in polys)\n elem_num = len(polys)\n # Pre alloc arrays. Special structure for coords makes the code faster.\n crd_dt = np.dtype([('x', np.float32), ('y', np.float32)])\n node_coords = np.empty(node_num, dtype=crd_dt)\n node_coords[:] = (np.nan, np.nan) # Fill with impossible values\n element_types = np.empty(elem_num, dtype=np.uint32)\n element_conn = np.empty(node_num, dtype=np.uint32)\n # Flag for centroid calculation\n calc_centroid = isinstance(element_coords, str) and element_coords == 'centroid'\n if calc_centroid:\n element_coords = np.empty(elem_num, dtype=crd_dt)\n inode = 0\n iconn = 0\n for ipoly, poly in enumerate(polys):\n ring = poly.exterior\n if calc_centroid:\n element_coords[ipoly] = poly.centroid.coords[0]\n element_types[ipoly] = len(ring.coords) - 1\n for coord in ring.coords[:-1] if ring.is_ccw else ring.coords[:0:-1]:\n crd = np.asarray(coord, dtype=crd_dt) # Cast so we can compare\n node_index = np.where(node_coords == crd)[0]\n if node_index.size == 0: # New node\n node_coords[inode] = crd\n element_conn[iconn] = inode\n inode += 1\n else: # Node already exists\n element_conn[iconn] = node_index[0]\n iconn += 1\n node_num = inode # With duplicate nodes, inode < node_num\n\n mesh = cls(2, 2, coord_sys=ESMF.CoordSys.SPH_DEG)\n mesh.add_nodes(\n node_num,\n np.arange(node_num) + 1,\n nprec.structured_to_unstructured(node_coords[:node_num]).ravel(),\n np.zeros(node_num),\n )\n if calc_centroid:\n element_coords = nprec.structured_to_unstructured(element_coords)\n if element_coords is not None:\n element_coords = element_coords.ravel()\n try:\n mesh.add_elements(\n elem_num,\n np.arange(elem_num) + 1,\n element_types,\n element_conn,\n element_coords=element_coords,\n )\n except ValueError as err:\n raise ValueError(\n 'ESMF failed to create the Mesh, this usually happen when some polygons are invalid (test with `poly.is_valid`)'\n ) from err\n return mesh\n\n def get_shape(self, loc=ESMF.MeshLoc.ELEMENT):\n \"\"\"Return the shape of the Mesh at specified MeshLoc location.\"\"\"\n return (self.size[loc], 1)\n\n\ndef esmf_regrid_build( # noqa: C901\n sourcegrid,\n destgrid,\n method,\n filename=None,\n extra_dims=None,\n extrap_method=None,\n extrap_dist_exponent=None,\n extrap_num_src_pnts=None,\n extrap_num_levels=None,\n ignore_degenerate=None,\n vector_regrid=None,\n):\n \"\"\"\n Create an ESMF.Regrid object, containing regridding weights.\n\n Parameters\n ----------\n sourcegrid, destgrid : ESMF.Grid or ESMF.Mesh object\n Source and destination grids.\n\n Should create them by ``Grid.from_xarray()``\n (with optionally ``add_corner()``),\n instead of ESMPy's original API.\n\n method : str\n Regridding method. Options are\n\n - 'bilinear'\n - 'conservative', **need grid corner information**\n - 'conservative_normed', **need grid corner information**\n - 'patch'\n - 'nearest_s2d'\n - 'nearest_d2s'\n\n filename : str, optional\n Offline weight file. **Require ESMPy 7.1.0.dev38 or newer.**\n With the weights available, we can use Scipy's sparse matrix\n multiplication to apply weights, which is faster and more Pythonic\n than ESMPy's online regridding. If None, weights are stored in\n memory only.\n\n extra_dims : a list of integers, optional\n Extra dimensions (e.g. time or levels) in the data field\n\n This does NOT affect offline weight file, only affects online regrid.\n\n Extra dimensions will be stacked to the fastest-changing dimensions,\n i.e. following Fortran-like instead of C-like conventions.\n For example, if extra_dims=[Nlev, Ntime], then the data field dimension\n will be [Nlon, Nlat, Nlev, Ntime]\n\n extrap_method : str, optional\n Extrapolation method. Options are\n\n - 'inverse_dist'\n - 'nearest_s2d'\n - 'creep_fill'\n\n extrap_dist_exponent : float, optional\n The exponent to raise the distance to when calculating weights for the\n extrapolation method. If none are specified, defaults to 2.0\n\n extrap_num_src_pnts : int, optional\n The number of source points to use for the extrapolation methods\n that use more than one source point. If none are specified, defaults to 8\n\n extrap_num_levels : int, optional\n Number of extrapolation levels to apply for the 'creep_fill' method.\n\n The creep fill algorithm iteratively fills unmapped target points by\n propagating values from neighboring mapped cells. Each level corresponds\n to one iteration of this filling process. Larger values allow extrapolation\n to reach farther into unmapped regions, but may increase computational cost\n and smoothness of the result.\n\n Required when ``extrap_method='creep_fill'``.\n\n ignore_degenerate : bool, optional\n If False (default), raise error if grids contain degenerated cells\n (i.e. triangles or lines, instead of quadrilaterals)\n\n vector_regrid : bool, optional\n If True, treat a single extra (non-spatial) dimension in the source and\n destination data fields as the components of a vector. (If True and\n there is more than one extra dimension in either the source or\n destination data fields, an error will be raised.) If not specified,\n defaults to False.\n\n Only vector dimensions of size 2 are supported. The first entry is\n interpreted as the east component and the second as the north component.\n i.e., ``extra_dims`` must be ``[2]``.\n\n Requires ESMPy 8.9.0 or newer.\n\n Returns\n -------\n regrid : ESMF.Regrid object\n\n \"\"\"\n\n # use shorter, clearer names for options in ESMF.RegridMethod\n method_dict = {\n 'bilinear': ESMF.RegridMethod.BILINEAR,\n 'conservative': ESMF.RegridMethod.CONSERVE,\n 'conservative_normed': ESMF.RegridMethod.CONSERVE,\n 'patch': ESMF.RegridMethod.PATCH,\n 'nearest_s2d': ESMF.RegridMethod.NEAREST_STOD,\n 'nearest_d2s': ESMF.RegridMethod.NEAREST_DTOS,\n }\n try:\n esmf_regrid_method = method_dict[method]\n except Exception:\n raise ValueError('method should be chosen from ' '{}'.format(list(method_dict.keys())))\n\n # use shorter, clearer names for options in ESMF.ExtrapMethod\n extrap_dict = {\n 'inverse_dist': ESMF.ExtrapMethod.NEAREST_IDAVG,\n 'nearest_s2d': ESMF.ExtrapMethod.NEAREST_STOD,\n 'creep_fill': ESMF.ExtrapMethod.CREEP_FILL,\n None: None,\n }\n try:\n esmf_extrap_method = extrap_dict[extrap_method]\n except KeyError:\n raise KeyError(\n '`extrap_method` should be chosen from ' '{}'.format(list(extrap_dict.keys()))\n )\n # CREEP_FILL requires a finite number of fill levels and is unsupported\n # for conservative regridding methods.\n if extrap_method == 'creep_fill':\n if extrap_num_levels is None:\n raise ValueError(\n '`extrap_num_levels` must be provided when `extrap_method=\"creep_fill\"`.'\n )\n if method in ['conservative', 'conservative_normed']:\n raise ValueError(\n '`extrap_method=\"creep_fill\"` is not supported with conservative regridding methods.'\n )\n # until ESMPy updates ESMP_FieldRegridStoreFile, extrapolation is not possible\n # if files are written on disk\n if (extrap_method is not None) & (filename is not None):\n raise ValueError('`extrap_method` cannot be used along with `filename`.')\n\n # conservative regridding needs cell corner information\n if method in ['conservative', 'conservative_normed']:\n if not isinstance(sourcegrid, ESMF.Mesh) and not sourcegrid.has_corners:\n raise ValueError(\n 'source grid has no corner information. ' 'cannot use conservative regridding.'\n )\n if not isinstance(destgrid, ESMF.Mesh) and not destgrid.has_corners:\n raise ValueError(\n 'destination grid has no corner information. ' 'cannot use conservative regridding.'\n )\n\n if vector_regrid:\n # Check this ESMPy requirement in order to give a more helpful error message if it\n # isn't met\n if not (isinstance(extra_dims, Sequence) and len(extra_dims) == 1 and extra_dims[0] == 2):\n raise ValueError('`vector_regrid` currently requires `extra_dims` to be `[2]`')\n\n # ESMF.Regrid requires Field (Grid+data) as input, not just Grid.\n # Extra dimensions are specified when constructing the Field objects,\n # not when constructing the Regrid object later on.\n if isinstance(sourcegrid, ESMF.Mesh):\n sourcefield = ESMF.Field(sourcegrid, meshloc=ESMF.MeshLoc.ELEMENT, ndbounds=extra_dims)\n else:\n sourcefield = ESMF.Field(sourcegrid, ndbounds=extra_dims)\n if isinstance(destgrid, ESMF.Mesh):\n destfield = ESMF.Field(destgrid, meshloc=ESMF.MeshLoc.ELEMENT, ndbounds=extra_dims)\n else:\n destfield = ESMF.Field(destgrid, ndbounds=extra_dims)\n\n # ESMF bug? when using locstream objects, options src_mask_values\n # and dst_mask_values produce runtime errors\n allow_masked_values = True\n if isinstance(sourcefield.grid, ESMF.LocStream):\n allow_masked_values = False\n if isinstance(destfield.grid, ESMF.LocStream):\n allow_masked_values = False\n\n # ESMPy will throw an incomprehensive error if the weight file\n # already exists. Better to catch it here!\n if filename is not None:\n assert not os.path.exists(\n filename\n ), 'Weight file already exists! Please remove it or use a new name.'\n\n # re-normalize conservative regridding results\n # https://github.com/JiaweiZhuang/xESMF/issues/17\n if method == 'conservative_normed':\n norm_type = ESMF.NormType.FRACAREA\n else:\n norm_type = ESMF.NormType.DSTAREA\n\n # Calculate regridding weights.\n # Must set unmapped_action to IGNORE, otherwise the function will fail,\n # if the destination grid is larger than the source grid.\n kwargs = dict(\n filename=filename,\n regrid_method=esmf_regrid_method,\n unmapped_action=ESMF.UnmappedAction.IGNORE,\n ignore_degenerate=ignore_degenerate,\n norm_type=norm_type,\n extrap_method=esmf_extrap_method,\n extrap_dist_exponent=extrap_dist_exponent,\n extrap_num_src_pnts=extrap_num_src_pnts,\n extrap_num_levels=extrap_num_levels,\n factors=filename is None,\n )\n if allow_masked_values:\n kwargs.update(dict(src_mask_values=[0], dst_mask_values=[0]))\n # Only add the vector_regrid argument if it is given and true; this supports backwards\n # compatibility with versions of ESMPy prior to 8.9.0 that do not have this option.\n # (If the user explicitly sets the vector_regrid argument, however, that will still be\n # passed through; this will lead to an exception with older ESMPy versions.)\n if vector_regrid:\n kwargs['vector_regrid'] = vector_regrid\n\n regrid = ESMF.Regrid(sourcefield, destfield, **kwargs)\n\n return regrid\n\n\ndef esmf_regrid_apply(regrid, indata):\n \"\"\"\n Apply existing regridding weights to the data field,\n using ESMPy's built-in functionality.\n\n xESMF use Scipy to apply weights instead of this.\n This is only for benchmarking Scipy's result and performance.\n\n Parameters\n ----------\n regrid : ESMF.Regrid object\n Contains the mapping from the source grid to the destination grid.\n\n Users should create them by esmf_regrid_build(),\n instead of ESMPy's original API.\n\n indata : numpy array of shape ``(Nlon, Nlat, N1, N2, ...)``\n Extra dimensions ``(N1, N2, ...)`` are specified in\n ``esmf_regrid_build()``.\n\n Recommend Fortran-ordering to match ESMPy internal.\n\n Returns\n -------\n outdata : numpy array of shape ``(Nlon_out, Nlat_out, N1, N2, ...)``\n\n \"\"\"\n\n # Passing C-ordered input data will be terribly slow,\n # since indata is often quite large and re-ordering memory is expensive.\n warn_f_contiguous(indata)\n\n # Get the pointers to source and destination fields.\n # Because the regrid object points to its underlying field&grid,\n # we can just pass regrid from ESMF_regrid_build() to ESMF_regrid_apply(),\n # without having to pass all the field&grid objects.\n sourcefield = regrid.srcfield\n destfield = regrid.dstfield\n\n # pass numpy array to the underlying Fortran array\n sourcefield.data[...] = indata\n\n # apply regridding weights\n destfield = regrid(sourcefield, destfield)\n\n return destfield.data\n\n\ndef esmf_regrid_finalize(regrid):\n \"\"\"\n Free the underlying Fortran array to avoid memory leak.\n\n After calling ``destroy()`` on regrid or its fields, we cannot use the\n regrid method anymore, but the input and output data still exist.\n\n Parameters\n ----------\n regrid : ESMF.Regrid object\n\n \"\"\"\n # We do not destroy the Grids here, as they might be reused between multiple regrids\n regrid.destroy()\n regrid.srcfield.destroy()\n regrid.dstfield.destroy()\n\n # double check\n assert regrid.finalized\n assert regrid.srcfield.finalized\n assert regrid.dstfield.finalized\n\n\n# Deprecated as of version 0.5.0\n\n\ndef esmf_locstream(lon, lat):\n warnings.warn(\n '`esmf_locstream` is being deprecated in favor of `LocStream.from_xarray`',\n DeprecationWarning,\n )\n return LocStream.from_xarray(lon, lat)\n\n\ndef esmf_grid(lon, lat, periodic=False, mask=None):\n warnings.warn(\n '`esmf_grid` is being deprecated in favor of `Grid.from_xarray`', DeprecationWarning\n )\n return Grid.from_xarray(lon, lat)\n" }, { "path": "xesmf/data.py", "content": "\"\"\"\nStandard test data for regridding benchmark.\n\"\"\"\n\nimport numpy as np\n\n\ndef wave_smooth(lon, lat):\n r\"\"\"\n Spherical harmonic with low frequency.\n\n Parameters\n ----------\n lon, lat : 2D numpy array or xarray DataArray\n Longitute/Latitude of cell centers\n\n Returns\n -------\n f : 2D numpy array or xarray DataArray depending on input\n 2D wave field\n\n Notes\n -----\n Equation from [1]_ [2]_:\n\n .. math:: Y_2^2 = 2 + \\cos^2(\\\\theta) \\cos(2 \\phi)\n\n References\n ----------\n .. [1] Jones, P. W. (1999). First-and second-order conservative remapping\n schemes for grids in spherical coordinates. Monthly Weather Review,\n 127(9), 2204-2210.\n\n .. [2] Ullrich, P. A., Lauritzen, P. H., & Jablonowski, C. (2009).\n Geometrically exact conservative remapping (GECoRe): regular\n latitude–longitude and cubed-sphere grids. Monthly Weather Review,\n 137(6), 1721-1741.\n \"\"\"\n # degree to radius, make a copy\n lat = lat / 180.0 * np.pi\n lon = lon / 180.0 * np.pi\n\n f = 2 + np.cos(lat) ** 2 * np.cos(2 * lon)\n return f\n" }, { "path": "xesmf/frontend.py", "content": "\"\"\"\nFrontend for xESMF, exposed to users.\n\"\"\"\n\nimport warnings\n\nimport cf_xarray as cfxr\nimport numpy as np\nimport sparse as sps\nimport xarray as xr\nfrom shapely.geometry import LineString\nfrom xarray import DataArray, Dataset\n\nfrom .backend import Grid, LocStream, Mesh, add_corner, esmf_regrid_build, esmf_regrid_finalize\nfrom .smm import (\n _combine_weight_multipoly,\n _parse_coords_and_values,\n add_nans_to_weights,\n apply_weights,\n check_shapes,\n mask_source_indices,\n post_apply_target_mask_to_weights,\n read_weights,\n)\nfrom .util import (\n LAT_CF_ATTRS,\n LON_CF_ATTRS,\n _get_edge_indices_2d,\n _rename_dataset,\n _unname_dataset,\n split_polygons_and_holes,\n)\n\ntry:\n import dask.array as da\n\n dask_array_type = (da.Array,) # for isinstance checks\nexcept ImportError:\n dask_array_type = ()\n\n\ndef subset_regridder(\n ds_out, ds_in, method, in_dims, out_dims, locstream_in, locstream_out, periodic, **kwargs\n):\n \"\"\"Compute subset of weights\"\"\"\n kwargs.pop('filename', None) # Don't save subset of weights\n kwargs.pop('reuse_weights', None)\n\n ds_in = _rename_dataset(ds_in, locstream_in, in_dims, '_in')\n ds_out = _rename_dataset(ds_out, locstream_out, out_dims, '_out')\n\n regridder = Regridder(\n ds_in, ds_out, method, locstream_in, locstream_out, periodic, parallel=False, **kwargs\n )\n return regridder.w\n\n\ndef as_2d_mesh(lon, lat):\n if (lon.ndim, lat.ndim) == (2, 2):\n assert lon.shape == lat.shape, 'lon and lat should have same shape'\n elif (lon.ndim, lat.ndim) == (1, 1):\n lon, lat = np.meshgrid(lon, lat)\n else:\n raise ValueError('lon and lat should be both 1D or 2D')\n\n return lon, lat\n\n\ndef _get_lon_lat(ds):\n \"\"\"Return lon and lat extracted from ds.\"\"\"\n if ('lat' in ds and 'lon' in ds) or ('lat' in ds.coords and 'lon' in ds.coords):\n # Old way.\n return ds['lon'], ds['lat']\n # else : cf-xarray way\n try:\n lon = ds.cf['longitude']\n lat = ds.cf['latitude']\n except (KeyError, AttributeError, ValueError):\n # KeyError if cfxr doesn't detect the coords\n # AttributeError if ds is a dict\n raise ValueError('dataset must include lon/lat or be CF-compliant')\n\n return lon, lat\n\n\ndef _get_lon_lat_bounds(ds):\n \"\"\"Return bounds of lon and lat extracted from ds.\"\"\"\n if 'lat_b' in ds and 'lon_b' in ds:\n # Old way.\n return ds['lon_b'], ds['lat_b']\n # else : cf-xarray way\n if 'longitude' not in ds.cf.coordinates:\n # If we are here, _get_lon_lat() didn't fail, thus we should be able to guess the coords.\n ds = ds.cf.guess_coord_axis()\n try:\n lon_bnds = ds.cf.get_bounds('longitude')\n lat_bnds = ds.cf.get_bounds('latitude')\n except KeyError: # bounds are not already present\n if ds.cf['longitude'].ndim > 1:\n # We cannot infer 2D bounds, raise KeyError as custom \"lon_b\" is missing.\n raise KeyError('lon_b')\n lon_name = ds.cf['longitude'].name\n lat_name = ds.cf['latitude'].name\n ds = ds.cf.add_bounds([lon_name, lat_name])\n lon_bnds = ds.cf.get_bounds('longitude')\n lat_bnds = ds.cf.get_bounds('latitude')\n\n # Convert from CF bounds to xESMF bounds.\n # order=None is because we don't want to assume the dimension order for 2D bounds.\n lon_b = cfxr.bounds_to_vertices(lon_bnds, ds.cf.get_bounds_dim_name('longitude'), order=None)\n lat_b = cfxr.bounds_to_vertices(lat_bnds, ds.cf.get_bounds_dim_name('latitude'), order=None)\n return lon_b, lat_b\n\n\ndef ds_to_ESMFgrid(ds, need_bounds=False, periodic=None, append=None):\n \"\"\"\n Convert xarray DataSet or dictionary to ESMF.Grid object.\n\n Parameters\n ----------\n ds : xarray DataSet or dictionary\n Contains variables ``lon``, ``lat``,\n and optionally ``lon_b``, ``lat_b`` if need_bounds=True.\n\n Shape should be ``(n_lat, n_lon)`` or ``(n_y, n_x)``,\n as normal C or Python ordering. Will be then tranposed to F-ordered.\n\n need_bounds : bool, optional\n Need cell boundary values?\n\n periodic : bool, optional\n Periodic in longitude?\n\n Returns\n -------\n grid\n ESMF.Grid object\n shape\n Shape of the grid\n dim_names\n Dimension names of the grid\n\n \"\"\"\n # use np.asarray(dr) instead of dr.values, so it also works for dictionary\n\n lon, lat = _get_lon_lat(ds)\n if hasattr(lon, 'dims'):\n if lon.ndim == 1:\n dim_names = lat.dims + lon.dims\n else:\n dim_names = lon.dims\n else:\n dim_names = None\n lon, lat = as_2d_mesh(np.asarray(lon), np.asarray(lat))\n\n if 'mask' in ds:\n # Ensure mask has same dim order as coordinates, and then tranpose for F-order, as below\n mask = np.asarray(ds['mask'].transpose(*reversed(dim_names)))\n else:\n mask = None\n\n # tranpose the arrays so they become Fortran-ordered\n grid = Grid.from_xarray(lon.T, lat.T, periodic=periodic, mask=mask)\n\n if need_bounds:\n lon_b, lat_b = _get_lon_lat_bounds(ds)\n lon_b, lat_b = as_2d_mesh(np.asarray(lon_b), np.asarray(lat_b))\n add_corner(grid, lon_b.T, lat_b.T)\n\n return grid, lon.shape, dim_names\n\n\ndef ds_to_ESMFlocstream(ds):\n \"\"\"\n Convert xarray DataSet or dictionary to ESMF.LocStream object.\n\n Parameters\n ----------\n ds : xarray DataSet or dictionary\n Contains variables ``lon``, ``lat``.\n\n Returns\n -------\n locstream : ESMF.LocStream object\n\n \"\"\"\n\n lon, lat = _get_lon_lat(ds)\n if hasattr(lon, 'dims'):\n dim_names = lon.dims\n else:\n dim_names = None\n lon, lat = np.asarray(lon), np.asarray(lat)\n\n if len(lon.shape) > 1:\n raise ValueError('lon can only be 1d')\n if len(lat.shape) > 1:\n raise ValueError('lat can only be 1d')\n\n assert lon.shape == lat.shape\n\n locstream = LocStream.from_xarray(lon, lat)\n\n return locstream, (1,) + lon.shape, dim_names\n\n\ndef polys_to_ESMFmesh(polys):\n \"\"\"\n Convert a sequence of shapely Polygons to a ESMF.Mesh object.\n\n MultiPolygons are split in their polygon parts and holes are ignored.\n\n Parameters\n ----------\n polys : sequence of shapely Polygon or MultiPolygon\n\n Returns\n -------\n exterior : ESMF.Mesh\n A mesh where elements are the exterior rings of the polygons\n tuple\n The shape of the mesh : (1, N_elements)\n\n \"\"\"\n ext, holes, _, _ = split_polygons_and_holes(polys)\n if len(holes) > 0:\n warnings.warn(\n 'Some passed polygons have holes, those are not represented in the returned Mesh.'\n )\n return Mesh.from_polygons(ext), (1, len(ext))\n\n\nclass BaseRegridder(object):\n def __init__(\n self,\n grid_in,\n grid_out,\n method,\n filename=None,\n reuse_weights=False,\n extrap_method=None,\n extrap_dist_exponent=None,\n extrap_num_src_pnts=None,\n extrap_num_levels=None,\n weights=None,\n ignore_degenerate=None,\n input_dims=None,\n output_dims=None,\n unmapped_to_nan=False,\n parallel=False,\n post_mask_source=None,\n ):\n \"\"\"\n Base xESMF regridding class supporting ESMF objects: `Grid`, `Mesh` and `LocStream`.\n\n Create or use existing subclasses to support other types of input objects. See for example `Regridder`\n to regrid `xarray.DataArray` objects, or `SpatialAverager`\n to average grids over regions defined by polygons.\n\n Parameters\n ----------\n grid_in, grid_out : ESMF Grid or Locstream or Mesh\n Input and output grid structures as ESMFpy objects.\n\n method : str\n Regridding method. Options are\n\n - 'bilinear'\n - 'conservative', **need grid corner information**\n - 'conservative_normed', **need grid corner information**\n - 'patch'\n - 'nearest_s2d'\n - 'nearest_d2s'\n\n filename : str, optional\n Name for the weight file. The default naming scheme is::\n\n {method}_{Ny_in}x{Nx_in}_{Ny_out}x{Nx_out}.nc\n\n e.g. bilinear_400x600_300x400.nc\n\n reuse_weights : bool, optional\n Whether to read existing weight file to save computing time.\n False by default (i.e. re-compute, not reuse).\n\n extrap_method : str, optional\n Extrapolation method. Options are\n\n - 'inverse_dist'\n - 'nearest_s2d'\n - 'creep_fill'\n\n extrap_dist_exponent : float, optional\n The exponent to raise the distance to when calculating weights for the\n extrapolation method. If none are specified, defaults to 2.0\n\n extrap_num_src_pnts : int, optional\n The number of source points to use for the extrapolation methods\n that use more than one source point. If none are specified, defaults to 8\n\n extrap_num_levels : int, optional\n Number of extrapolation levels to apply for the 'creep_fill' method.\n\n The creep fill algorithm iteratively fills unmapped target points by\n propagating values from neighboring mapped cells. Each level corresponds\n to one iteration of this filling process. Larger values allow extrapolation\n to reach farther into unmapped regions, but may increase computational cost\n and smoothness of the result.\n\n Required when ``extrap_method='creep_fill'``.\n\n weights : None, coo_matrix, dict, str, Dataset, Path,\n Regridding weights, stored as\n - a scipy.sparse COO matrix,\n - a dictionary with keys `row_dst`, `col_src` and `weights`,\n - an xarray Dataset with data variables `col`, `row` and `S`,\n - or a path to a netCDF file created by ESMF.\n If None, compute the weights.\n\n ignore_degenerate : bool, optional\n If False (default), raise error if grids contain degenerated cells\n (i.e. triangles or lines, instead of quadrilaterals)\n\n input_dims : tuple of str, optional\n A tuple of dimension names to look for when regridding DataArrays or Datasets.\n If not given or if those are not found on the regridded object, regridding\n uses the two last dimensions of the object (or the last one for input LocStreams and Meshes).\n\n output_dims : tuple of str, optional\n A tuple of dimension names to look for when regridding DataArrays or Datasets.\n If not given or if those are not found on the regridded object, regridding\n uses the two last dimensions of the object (or the last one for output LocStreams and Meshes)\n\n unmapped_to_nan: boolean, optional\n Set values of unmapped points to `np.nan` instead of zero (ESMF default). This is useful for\n target cells lying outside of the source domain when no output mask is defined.\n If an output mask is defined, or regridding method is `nearest_s2d` or `nearest_d2s`,\n this option has no effect.\n\n parallel: bool, optional\n Are the weights generated in parallel with Dask. Default is False. When True, the weight\n generation in the BaseRegridder is skipped and weights are generated in paralell in the\n subsest_regridder instead.\n\n post_mask_source : str or array-like, optional\n Optionally applies a post-processing step to remove selected source grid cells from\n contributing to the regridding weight matrix.\n\n Note: This differs from the typical masking approach, which prevents source cells from\n being used during weight generation. Here, the regridding weights are modified *after*\n creation to remove the contribution of specified source grid cells.\n\n Options:\n\n - If set to ``\"domain_edge\"``, the outermost edge cells of the source grid are\n automatically detected and their contribution to the regridding weights is removed.\n This is useful to avoid extrapolation beyond the domain boundary when using the\n nearest-neighbor method ``'nearest_s2d'``, particularly when remapping from a smaller\n to a larger domain (as is common with regional source grids like CORDEX).\n Only supported for ``Grid`` type ESMF objects as source grid.\n - If an array-like of integers is provided, it is interpreted as flat indices\n (i.e., 1D indices of the flattened source grid) identifying source cells\n whose contribution to the regridding weights should be removed.\n\n Default is ``None``, meaning no post-weight-generation source grid cell masking is applied.\n\n Returns\n -------\n baseregridder : xESMF BaseRegridder object\n\n \"\"\"\n self.method = method\n self.reuse_weights = reuse_weights\n self.extrap_method = extrap_method\n self.extrap_dist_exponent = extrap_dist_exponent\n self.extrap_num_src_pnts = extrap_num_src_pnts\n self.extrap_num_levels = extrap_num_levels\n self.ignore_degenerate = ignore_degenerate\n self.periodic = getattr(grid_in, 'periodic_dim', None) is not None\n self.sequence_in = isinstance(grid_in, (LocStream, Mesh))\n self.sequence_out = isinstance(grid_out, (LocStream, Mesh))\n\n if input_dims is not None and len(input_dims) != int(not self.sequence_in) + 1:\n raise ValueError(f'Wrong number of dimension names in `input_dims` ({len(input_dims)}.')\n self.in_horiz_dims = input_dims\n\n if output_dims is not None and len(output_dims) != int(not self.sequence_out) + 1:\n raise ValueError(\n f'Wrong number of dimension names in `output dims` ({len(output_dims)}.'\n )\n self.out_horiz_dims = output_dims\n\n # record grid shape information\n # We need to invert Grid shapes to respect xESMF's convention (y, x).\n self.shape_in = grid_in.get_shape()[::-1]\n self.shape_out = grid_out.get_shape()[::-1]\n self.n_in = self.shape_in[0] * self.shape_in[1]\n self.n_out = self.shape_out[0] * self.shape_out[1]\n\n # Validate post_mask_source\n self.post_mask_source = None\n if isinstance(post_mask_source, str) and post_mask_source == 'domain_edge':\n if self.sequence_in:\n raise ValueError(\n \"post_mask_source='domain_edge' is only supported for 'Grid' type ESMF objects \"\n 'as source grid (i.e. structured - rectilinear or curvilinear - grids. '\n f\"Grid type detected: {type(grid_in)}\"\n )\n self.post_mask_source = _get_edge_indices_2d(self.shape_in[1], self.shape_in[0])\n elif post_mask_source is not None:\n try:\n self.post_mask_source = np.asarray(post_mask_source)\n except Exception as e:\n raise TypeError(\n f\"`post_mask_source` must be array-like of integers. Got: {type(post_mask_source)}\"\n ) from e\n if self.post_mask_source is not None and not np.issubdtype(\n self.post_mask_source.dtype, np.integer\n ):\n raise TypeError(\n f\"`post_mask_source` must be of integer type. Got dtype: {self.post_mask_source.dtype}\"\n )\n\n # some logic about reusing weights with either filename or weights args\n if reuse_weights and (filename is None) and (weights is None):\n raise ValueError('To reuse weights, you need to provide either filename or weights.')\n\n # decide whether unmapped cells should be mapped to NaN\n self.unmapped_to_nan = False\n if (\n (grid_out.mask is not None) and (grid_out.mask[0] is not None)\n ) or unmapped_to_nan is True:\n self.unmapped_to_nan = True\n\n if not parallel:\n if not reuse_weights and weights is None:\n weights = self._compute_weights(grid_in, grid_out) # Dictionary of weights\n else:\n weights = filename if filename is not None else weights\n\n assert weights is not None\n\n # Convert weights, whatever their format, to a sparse coo matrix\n self.weights = read_weights(weights, self.n_in, self.n_out)\n\n # Optionally post-apply output mask for LocStream input and Grid output\n # as xesmf.backend.esmf_regrid_build filters the output mask in that case\n # (ESMF does not support output masks for LocStream input and Grid output)\n # Only method supported is `nearest_s2d`:\n # For other methods the masking approach may lead to unexpected results\n # as the weights are applied post weight generation and other methods may have\n # the source-target mapping depending on the location of masked cells.\n if (\n isinstance(grid_in, LocStream)\n and isinstance(grid_out, Grid)\n and grid_out.mask is not None\n and grid_out.mask[0] is not None\n and method == 'nearest_s2d'\n ):\n self.weights = post_apply_target_mask_to_weights(self.weights, grid_out.mask[0])\n\n # Optionally apply post_mask_source to manipulate the weights and removing\n # the contribution of the specified source cells\n if self.post_mask_source is not None:\n self.weights = mask_source_indices(self.weights, self.post_mask_source)\n\n # replace zeros by NaN for weight matrix entries of unmapped target cells if specified or a mask is present\n if self.unmapped_to_nan:\n self.weights = add_nans_to_weights(self.weights)\n\n # follows legacy logic of writing weights if filename is provided\n if filename is not None and not reuse_weights:\n self.to_netcdf(filename=filename)\n\n # set default weights filename if none given\n self.filename = self._get_default_filename() if filename is None else filename\n\n @property\n def A(self):\n message = (\n 'regridder.A is deprecated and will be removed in future versions. '\n 'Use regridder.weights instead.'\n )\n\n warnings.warn(message, DeprecationWarning)\n # DeprecationWarning seems to be ignored by certain Python environments\n # Also print to make sure users notice this.\n print(message)\n return self.weights\n\n @property\n def w(self) -> xr.DataArray:\n \"\"\"Return weights as a 4D DataArray with dimensions (y_out, x_out, y_in, x_in).\n\n ESMF stores the weights in a 2D array with dimensions (out_dim, in_dim), the size of the output and input\n grids respectively (ny x nx). This property returns the weights reshaped as a 4D array to simplify\n comparisons with the original grids.\n \"\"\"\n # TODO: Add coords ?\n s = self.shape_out + self.shape_in\n data = self.weights.data.reshape(s)\n dims = 'y_out', 'x_out', 'y_in', 'x_in'\n return xr.DataArray(data, dims=dims)\n\n def _get_default_filename(self):\n # e.g. bilinear_400x600_300x400.nc\n filename = '{0}_{1}x{2}_{3}x{4}'.format(\n self.method,\n self.shape_in[0],\n self.shape_in[1],\n self.shape_out[0],\n self.shape_out[1],\n )\n\n if self.periodic:\n filename += '_peri.nc'\n else:\n filename += '.nc'\n\n return filename\n\n def _compute_weights(self, grid_in, grid_out):\n regrid = esmf_regrid_build(\n grid_in,\n grid_out,\n self.method,\n extrap_method=self.extrap_method,\n extrap_dist_exponent=self.extrap_dist_exponent,\n extrap_num_src_pnts=self.extrap_num_src_pnts,\n extrap_num_levels=self.extrap_num_levels,\n ignore_degenerate=self.ignore_degenerate,\n )\n\n w = regrid.get_weights_dict(deep_copy=True)\n esmf_regrid_finalize(regrid) # only need weights, not regrid object\n return w\n\n def __call__(self, indata, keep_attrs=False, skipna=False, na_thres=1.0, output_chunks=None):\n \"\"\"\n Apply regridding to input data.\n\n Parameters\n ----------\n indata : numpy array, dask array, xarray DataArray or Dataset.\n If not an xarray object or if `input_dìms` was not given in the init,\n the rightmost two dimensions must be the same as ``ds_in``.\n Can have arbitrary additional dimensions.\n\n Examples of valid shapes\n\n - (n_lat, n_lon), if ``ds_in`` has shape (n_lat, n_lon)\n - (n_time, n_lev, n_y, n_x), if ``ds_in`` has shape (Ny, n_x)\n\n Either give `input_dims` or transpose your input data\n if the horizontal dimensions are not the rightmost two dimensions\n\n Variables without the regridded dimensions are silently skipped when passing a Dataset.\n\n keep_attrs : bool, optional\n Keep attributes for xarray DataArrays or Datasets.\n Defaults to False.\n\n skipna: bool, optional\n Whether to skip missing values when regridding.\n When set to False, an output value is masked when a single\n input value is missing and no grid mask is provided.\n When set to True, missing values do not contaminate the regridding\n since only valid values are taken into account.\n In this case, a given output point is set to NaN only if the ratio\n of missing values exceeds the level set by `na_thres`:\n for instance, when the center of a cell is computed linearly\n from its four corners, one of which is missing, the output value\n is set to NaN if `na_thres` is smaller than 0.25.\n\n na_thres: float, optional\n A value within the [0., 1.] interval that defines the maximum\n ratio of missing grid points involved in the regrdding over which\n the output value is set to NaN. For instance, if `na_thres` is set\n to 0, the output value is NaN if a single NaN is found in the input\n values that are used to compute the output value; similarly,\n if `na_thres` is set to 1, all input values must be missing to\n mask the output value.\n\n output_chunks: dict or tuple, optional\n The desired chunks to have on the output along the spatial axes, if indata is a dask array.\n Other non-spatial axes inherit the same chunks as indata.\n Default behavior depends on the chunking of indata. If it is not chunked along\n the spatial dimension, the output will also not be chunked,\n equivalent to passing ``output_chunks=(-1, -1)``.\n If it is chunked, the output will preserve the chunk sizes,\n equivalent to passing ``output_chunks=ìndata.chunks``.\n Chunks have to be specified for all spatial dimensions\n of the output data otherwise regridding will fail. output_chunks can\n either be a tuple the same size as the spatial axes of outdata or it\n can be a dict with defined dims. If output_chunks is a dict, the\n keys must match the dims of the output grid passed when initializing this Regridder.\n\n Returns\n -------\n outdata : Data type is the same as input data type, except for datasets.\n On the same horizontal grid as ``ds_out``,\n with extra dims in ``dr_in``.\n\n Assuming ``ds_out`` has the shape of (n_y_out, n_x_out),\n examples of returning shapes are\n\n - (n_y_out, n_x_out), if ``dr_in`` is 2D\n - (n_time, n_lev, n_y_out, n_x_out), if ``dr_in`` has shape\n (n_time, n_lev, n_y, n_x)\n\n Datasets with dask-backed variables will have modified dtypes.\n If all input variables are 'float32', all output will be 'float32',\n for any other case, all outputs will be 'float64'.\n\n \"\"\"\n if isinstance(indata, dask_array_type + (np.ndarray,)):\n return self.regrid_array(\n indata,\n self.weights.data,\n skipna=skipna,\n na_thres=na_thres,\n output_chunks=output_chunks,\n )\n elif isinstance(indata, xr.DataArray):\n return self.regrid_dataarray(\n indata,\n keep_attrs=keep_attrs,\n skipna=skipna,\n na_thres=na_thres,\n output_chunks=output_chunks,\n )\n elif isinstance(indata, xr.Dataset):\n return self.regrid_dataset(\n indata,\n keep_attrs=keep_attrs,\n skipna=skipna,\n na_thres=na_thres,\n output_chunks=output_chunks,\n )\n else:\n raise TypeError('input must be numpy array, dask array, xarray DataArray or Dataset!')\n\n @staticmethod\n def _regrid(indata, weights, *, shape_in, shape_out, skipna, na_thres):\n # skipna: set missing values to zero\n if skipna:\n missing = np.isnan(indata)\n indata = np.where(missing, 0.0, indata)\n\n # apply weights\n outdata = apply_weights(weights, indata, shape_in, shape_out)\n\n # skipna: Compute the influence of missing data at each interpolation point and filter those not meeting acceptable threshold.\n if skipna:\n fraction_valid = apply_weights(weights, (~missing).astype('d'), shape_in, shape_out)\n tol = 1e-6\n bad = fraction_valid < np.clip(1 - na_thres, tol, 1 - tol)\n fraction_valid[bad] = 1\n outdata = np.where(bad, np.nan, outdata / fraction_valid)\n\n return outdata\n\n def regrid_array(self, indata, weights, skipna=False, na_thres=1.0, output_chunks=None):\n \"\"\"See __call__().\"\"\"\n if self.sequence_in:\n indata = np.reshape(indata, (*indata.shape[:-1], 1, indata.shape[-1]))\n\n # If output_chunk is dict, order output chunks to match order of out_horiz_dims and convert to tuple\n if isinstance(output_chunks, dict):\n output_chunks = tuple([output_chunks.get(key) for key in self.out_horiz_dims])\n\n kwargs = {\n 'shape_in': self.shape_in,\n 'shape_out': self.shape_out,\n }\n\n check_shapes(indata, weights, **kwargs)\n\n kwargs.update(skipna=skipna, na_thres=na_thres)\n\n if isinstance(indata, dask_array_type): # dask\n # Dask path: keep weights reshaped to 4D so the chunk layout\n # (ny_out, nx_out, ny_in, nx_in) matches the spatial dims on\n # both input and output, one chunk per output block × input block.\n weights = self.weights.data.reshape(self.shape_out + self.shape_in)\n if output_chunks is None:\n # Default : same chunk size as the input to preserve chunksize\n # Unless the input is not chunked along the dimension (shape_in == in_chunk_size), in which case we do not chunk along the dimension\n # This preserves the pre-0.8 behaviour.\n output_chunks = tuple(\n min(chnkin, shpout) if shpin != chnkin else shpout\n for shpout, shpin, chnkin in zip(\n self.shape_out, self.shape_in, indata.chunksize[-2:]\n )\n )\n fac = np.prod(\n [np.ceil(shp / chnk) for shp, chnk in zip(self.shape_out, output_chunks)]\n )\n if fac > 4: # Dask's built-in threshold is 10\n warnings.warn(\n (\n f'Regridding is increasing the number of chunks by a factor of {fac}, '\n 'you might want to specify sizes in `output_chunks` in the regridder call. '\n f'Default behaviour is to preserve the chunk sizes from the input {indata.chunksize[-2:]}.'\n ),\n da.core.PerformanceWarning,\n stacklevel=3,\n )\n if len(output_chunks) != len(self.shape_out):\n if len(output_chunks) == 1 and self.sequence_out:\n output_chunks = (1, output_chunks[0])\n else:\n raise ValueError(\n f'output_chunks must have same dimension as ds_out,'\n f' output_chunks dimension ({len(output_chunks)}) does not '\n f'match ds_out dimension ({len(self.shape_out)})'\n )\n weights = da.from_array(weights, chunks=(output_chunks + indata.chunksize[-2:]))\n outdata = self._regrid(indata, weights, **kwargs)\n else: # numpy — keep weights 2D; apply_weights does flat contraction\n outdata = self._regrid(indata, self.weights.data, **kwargs)\n return outdata\n\n def regrid_numpy(self, indata, **kwargs):\n warnings.warn(\n '`regrid_numpy()` will be removed in xESMF 0.7, please use `regrid_array` instead.',\n category=FutureWarning,\n )\n return self.regrid_array(indata, self.weights.data, **kwargs)\n\n def regrid_dask(self, indata, **kwargs):\n warnings.warn(\n '`regrid_dask()` will be removed in xESMF 0.7, please use `regrid_array` instead.',\n category=FutureWarning,\n )\n return self.regrid_array(indata, self.weights.data, **kwargs)\n\n def regrid_dataarray(\n self, dr_in, keep_attrs=False, skipna=False, na_thres=1.0, output_chunks=None\n ):\n \"\"\"See __call__().\"\"\"\n\n input_horiz_dims, temp_horiz_dims = self._parse_xrinput(dr_in)\n kwargs = dict(skipna=skipna, na_thres=na_thres, output_chunks=output_chunks)\n dr_out = xr.apply_ufunc(\n self.regrid_array,\n dr_in,\n self.weights,\n kwargs=kwargs,\n input_core_dims=[input_horiz_dims, ('out_dim', 'in_dim')],\n output_core_dims=[temp_horiz_dims],\n dask='allowed',\n keep_attrs=keep_attrs,\n )\n\n return self._format_xroutput(dr_out, temp_horiz_dims)\n\n def regrid_dataset(\n self, ds_in, keep_attrs=False, skipna=False, na_thres=1.0, output_chunks=None\n ):\n \"\"\"See __call__().\"\"\"\n\n # get the first data variable to infer input_core_dims\n input_horiz_dims, temp_horiz_dims = self._parse_xrinput(ds_in)\n\n kwargs = dict(skipna=skipna, na_thres=na_thres, output_chunks=output_chunks)\n\n non_regriddable = [\n name\n for name, data in ds_in.data_vars.items()\n if not set(input_horiz_dims).issubset(data.dims)\n ]\n ds_in = ds_in.drop_vars(non_regriddable)\n\n ds_out = xr.apply_ufunc(\n self.regrid_array,\n ds_in,\n self.weights,\n kwargs=kwargs,\n input_core_dims=[input_horiz_dims, ('out_dim', 'in_dim')],\n output_core_dims=[temp_horiz_dims],\n dask='allowed',\n keep_attrs=keep_attrs,\n )\n\n return self._format_xroutput(ds_out, temp_horiz_dims)\n\n def _parse_xrinput(self, dr_in):\n # dr could be a DataArray or a Dataset\n # Get input horiz dim names and set output horiz dim names\n if self.in_horiz_dims is not None and all(dim in dr_in.dims for dim in self.in_horiz_dims):\n input_horiz_dims = self.in_horiz_dims\n else:\n if isinstance(dr_in, Dataset):\n name, dr_in = next(iter(dr_in.items()))\n else:\n # For warning purposes\n name = dr_in.name\n\n if self.sequence_in:\n input_horiz_dims = dr_in.dims[-1:]\n else:\n input_horiz_dims = dr_in.dims[-2:]\n\n # help user debugging invalid horizontal dimensions\n warnings.warn(\n (\n f'Using dimensions {input_horiz_dims} from data variable {name} '\n 'as the horizontal dimensions for the regridding.'\n ),\n UserWarning,\n )\n\n if self.sequence_out:\n temp_horiz_dims = ['dummy', 'locations']\n else:\n temp_horiz_dims = [s + '_new' for s in input_horiz_dims]\n\n if self.sequence_in and not self.sequence_out:\n temp_horiz_dims = ['dummy_new'] + temp_horiz_dims\n return input_horiz_dims, temp_horiz_dims\n\n def _format_xroutput(self, out, new_dims=None):\n out.attrs['regrid_method'] = self.method\n return out\n\n def __repr__(self):\n info = (\n 'xESMF Regridder \\n'\n 'Regridding algorithm: {} \\n'\n 'Weight filename: {} \\n'\n 'Reuse pre-computed weights? {} \\n'\n 'Input grid shape: {} \\n'\n 'Output grid shape: {} \\n'\n 'Periodic in longitude? {}'.format(\n self.method,\n self.filename,\n self.reuse_weights,\n self.shape_in,\n self.shape_out,\n self.periodic,\n )\n )\n\n return info\n\n def to_netcdf(self, filename=None):\n \"\"\"Save weights to disk as a netCDF file.\"\"\"\n from . import __version__\n\n if filename is None:\n filename = self.filename\n w = self.weights.data\n dim = 'n_s'\n ds = xr.Dataset(\n {'S': (dim, w.data), 'col': (dim, w.coords[1, :] + 1), 'row': (dim, w.coords[0, :] + 1)}\n )\n method_labels = {\n 'bilinear': 'Bilinear',\n 'conservative': 'Conservative',\n 'conservative_normed': 'Conservative',\n 'patch': 'Patch',\n 'nearest_s2d': 'Nearest source to destination',\n 'nearest_d2s': 'Nearest destination to source',\n }\n method_label = method_labels[self.method]\n ds = ds.assign_attrs(\n title='ESMPy Regrid Class Weight File - generated by xESMF',\n ESMF_regrid_method=method_label,\n map_method=f'{method_label} remapping',\n xesmf_version=__version__,\n )\n ds.to_netcdf(filename)\n return filename\n\n\nclass Regridder(BaseRegridder):\n def __init__(\n self,\n ds_in,\n ds_out,\n method,\n locstream_in=False,\n locstream_out=False,\n periodic=False,\n parallel=False,\n **kwargs,\n ):\n \"\"\"\n Make xESMF regridder\n\n Parameters\n ----------\n ds_in, ds_out : xarray Dataset, DataArray, or dictionary\n Contain input and output grid coordinates.\n All variables that the cf-xarray accessor understand are accepted.\n Otherwise, look for ``lon``, ``lat``,\n optionally ``lon_b``, ``lat_b`` for conservative methods,\n and ``mask``. Note that for `mask`, the ESMF convention is used,\n where masked values are identified by 0, and non-masked values by 1.\n\n For conservative methods, if bounds are not present, they will be\n computed using `cf-xarray` (only 1D coordinates are currently supported).\n\n Shape can be 1D (n_lon,) and (n_lat,) for rectilinear grids,\n or 2D (n_y, n_x) for general curvilinear grids.\n Shape of bounds should be (n+1,) or (n_y+1, n_x+1).\n CF-bounds (shape (n, 2) or (n, m, 4)) are also accepted if they are\n accessible through the cf-xarray accessor.\n\n If either dataset includes a 2d ``mask`` variable, that will also be\n used to inform the regridding.\n\n If DataArrays are passed, the are simply converted to Datasets.\n\n If dictionaries of numpy arrays are passed, one should pass\n ``input_dims`` and/or ``output_dims`` to the call so that regridding\n from and to xarray objects is possible.\n\n method : str\n Regridding method. Options are\n\n - 'bilinear'\n - 'conservative', **need grid corner information**\n - 'conservative_normed', **need grid corner information**\n - 'patch'\n - 'nearest_s2d'\n - 'nearest_d2s'\n\n periodic : bool, optional\n Periodic in longitude? Default to False.\n Only useful for global grids with non-conservative regridding.\n Will be forced to False for conservative regridding.\n\n parallel : bool, optional\n Compute the weights in parallel with Dask. Default to False.\n If True, weights are computed in parallel with Dask on subsets of the output grid using\n chunks of the output grid.\n\n filename : str, optional\n Name for the weight file. The default naming scheme is::\n\n {method}_{Ny_in}x{Nx_in}_{Ny_out}x{Nx_out}.nc\n\n e.g. bilinear_400x600_300x400.nc\n\n reuse_weights : bool, optional\n Whether to read existing weight file to save computing time.\n False by default (i.e. re-compute, not reuse).\n\n extrap_method : str, optional\n Extrapolation method. Options are\n\n - 'inverse_dist'\n - 'nearest_s2d'\n - 'creep_fill'\n\n extrap_dist_exponent : float, optional\n The exponent to raise the distance to when calculating weights for the\n extrapolation method. If none are specified, defaults to 2.0\n\n extrap_num_src_pnts : int, optional\n The number of source points to use for the extrapolation methods\n that use more than one source point. If none are specified, defaults to 8\n\n extrap_num_levels : int, optional\n Number of extrapolation levels to apply for the 'creep_fill' method.\n\n The creep fill algorithm iteratively fills unmapped target points by\n propagating values from neighboring mapped cells. Each level corresponds\n to one iteration of this filling process. Larger values allow extrapolation\n to reach farther into unmapped regions, but may increase computational cost\n and smoothness of the result.\n\n Required when ``extrap_method='creep_fill'``.\n\n weights : None, coo_matrix, dict, str, Dataset, Path,\n Regridding weights, stored as\n - a scipy.sparse COO matrix,\n - a dictionary with keys `row_dst`, `col_src` and `weights`,\n - an xarray Dataset with data variables `col`, `row` and `S`,\n - or a path to a netCDF file created by ESMF.\n\n If None, compute the weights.\n\n ignore_degenerate : bool, optional\n If False (default), raise error if grids contain degenerated cells\n (i.e. triangles or lines, instead of quadrilaterals)\n\n unmapped_to_nan: boolean, optional\n Set values of unmapped points to `np.nan` instead of zero (ESMF default). This is useful for\n target cells lying outside of the source domain when no output mask is defined.\n If an output mask is defined, or regridding method is `nearest_s2d` or `nearest_d2s`,\n this option has no effect.\n\n post_mask_source : str or array-like, optional\n Optionally applies a post-processing step to remove selected source grid cells from\n contributing to the regridding weight matrix.\n\n Note: This differs from the typical masking approach, which prevents source cells from\n being used during weight generation. Here, the regridding weights are modified *after*\n creation to remove the contribution of specified source grid cells.\n\n Options:\n\n - If set to ``\"domain_edge\"``, the outermost edge cells of the source grid are\n automatically detected and their contribution to the regridding weights is removed.\n This is useful to avoid extrapolation beyond the domain boundary when using the\n nearest-neighbor method ``'nearest_s2d'``, particularly when remapping from a smaller\n to a larger domain (as is common with regional source grids like CORDEX).\n Only supported for ``Grid`` type ESMF objects as source grid.\n\n - If an array-like of integers is provided, it is interpreted as flat indices\n (i.e., 1D indices of the flattened source grid) identifying source cells\n whose contribution to the regridding weights should be removed.\n\n Default is ``None``, meaning no post-weight-generation source grid cell masking is applied.\n\n Returns\n -------\n regridder : xESMF regridder object\n \"\"\"\n methods_avail_ls_in = ['nearest_s2d', 'nearest_d2s']\n methods_avail_ls_out = ['bilinear', 'patch'] + methods_avail_ls_in\n\n if locstream_in and method not in methods_avail_ls_in:\n raise ValueError(\n f'locstream input is only available for method in {methods_avail_ls_in}'\n )\n if locstream_out and method not in methods_avail_ls_out:\n raise ValueError(\n f'locstream output is only available for method in {methods_avail_ls_out}'\n )\n\n reuse_weights = kwargs.get('reuse_weights', False)\n\n weights = kwargs.get('weights', None)\n\n if parallel and (reuse_weights or weights is not None):\n parallel = False\n warnings.warn(\n 'Cannot use parallel=True when reuse_weights=True or when weights is not None. Building Regridder normally.'\n )\n\n # Record basic switches\n if method in ['conservative', 'conservative_normed']:\n need_bounds = True\n periodic = False # bound shape will not be N+1 for periodic grid\n else:\n need_bounds = False\n\n # Ensure we have Datasets and not DataArrays.\n if isinstance(ds_in, xr.DataArray):\n ds_in = ds_in._to_temp_dataset()\n\n if isinstance(ds_out, xr.DataArray):\n ds_out = ds_out._to_temp_dataset()\n\n # Construct ESMF grid, with some shape checking\n if locstream_in:\n grid_in, shape_in, input_dims = ds_to_ESMFlocstream(ds_in)\n else:\n grid_in, shape_in, input_dims = ds_to_ESMFgrid(\n ds_in, need_bounds=need_bounds, periodic=periodic\n )\n if locstream_out:\n grid_out, shape_out, output_dims = ds_to_ESMFlocstream(ds_out)\n else:\n grid_out, shape_out, output_dims = ds_to_ESMFgrid(ds_out, need_bounds=need_bounds)\n\n # Add input/output dims if included in kwargs\n if 'input_dims' in kwargs:\n input_dims = kwargs.pop('input_dims')\n if 'output_dims' in kwargs:\n output_dims = kwargs.pop('output_dims')\n\n # Create the BaseRegridder\n super().__init__(\n grid_in,\n grid_out,\n method,\n input_dims=input_dims,\n output_dims=output_dims,\n parallel=parallel,\n **kwargs,\n )\n\n # Weights are computed, we do not need the grids anymore\n grid_in.destroy()\n grid_out.destroy()\n\n # Record output grid and metadata\n lon_out, lat_out = _get_lon_lat(ds_out)\n if not isinstance(lon_out, DataArray):\n if output_dims is not None:\n dims = [output_dims, output_dims]\n elif lon_out.ndim == 2:\n dims = [('y', 'x'), ('y', 'x')]\n elif self.sequence_out:\n dims = [('locations',), ('locations',)]\n else:\n dims = [('lon',), ('lat',)]\n lon_out = xr.DataArray(lon_out, dims=dims[0], name='lon', attrs=LON_CF_ATTRS)\n lat_out = xr.DataArray(lat_out, dims=dims[1], name='lat', attrs=LAT_CF_ATTRS)\n\n if lat_out.ndim == 2:\n self.out_horiz_dims = lat_out.dims\n elif self.sequence_out:\n if lat_out.dims != lon_out.dims:\n raise ValueError(\n 'Regridder expects a locstream output, but the passed longitude '\n 'and latitude are not specified along the same dimension. '\n f'(lon: {lon_out.dims}, lat: {lat_out.dims})'\n )\n self.out_horiz_dims = ('dummy',) + lat_out.dims\n else:\n self.out_horiz_dims = (lat_out.dims[0], lon_out.dims[0])\n\n if isinstance(ds_out, Dataset):\n out_coords = ds_out.coords.to_dataset()\n grid_mapping = {\n var.attrs['grid_mapping']\n for var in ds_out.data_vars.values()\n if 'grid_mapping' in var.attrs\n }\n # to keep : grid_mappings and non-scalar coords that have the spatial dims\n self.out_coords = out_coords.drop_vars(\n [\n name\n for name, crd in out_coords.coords.items()\n if not (\n (name in grid_mapping)\n or (len(crd.dims) > 0 and set(self.out_horiz_dims).issuperset(crd.dims))\n )\n ]\n )\n else:\n self.out_coords = xr.Dataset(coords={lat_out.name: lat_out, lon_out.name: lon_out})\n\n if parallel:\n # Generate the weights in parallel\n self._init_para_regrid(ds_in, ds_out, kwargs)\n\n def _init_para_regrid(self, ds_in, ds_out, kwargs):\n # Check if we have bounds as variable and not coords, and add them to coords in both datasets\n if 'lon_b' in ds_out.data_vars:\n ds_out = ds_out.set_coords(['lon_b', 'lat_b'])\n if 'lon_b' in ds_in.data_vars:\n ds_in = ds_in.set_coords(['lon_b', 'lat_b'])\n if not (set(self.out_horiz_dims) - {'dummy'}).issubset(ds_out.chunksizes.keys()):\n raise ValueError(\n 'Using `parallel=True` requires the output grid to have chunks along all spatial dimensions. '\n 'If the dataset has no variables, consider adding an all-True spatial mask with appropriate chunks.'\n )\n # Drop everything in ds_out except mask or create mask if None. This is to prevent map_blocks loading unnecessary large data\n if self.sequence_out:\n ds_out_dims_drop = set(ds_out.variables).difference(ds_out.data_vars)\n ds_out = ds_out.drop_dims(ds_out_dims_drop)\n else:\n if 'mask' in ds_out:\n mask = ds_out.mask\n ds_out = ds_out.coords.to_dataset()\n ds_out['mask'] = mask\n else:\n ds_out_chunks = tuple([ds_out.chunksizes[i] for i in self.out_horiz_dims])\n ds_out = ds_out.coords.to_dataset()\n mask = da.ones(self.shape_out, dtype=bool, chunks=ds_out_chunks)\n ds_out['mask'] = (self.out_horiz_dims, mask)\n\n ds_out_dims_drop = set(ds_out.cf.coordinates.keys()).difference(\n ['longitude', 'latitude']\n )\n ds_out = ds_out.cf.drop_dims(ds_out_dims_drop)\n\n # Drop unnecessary variables in ds_in to save memory\n if not self.sequence_in:\n # Drop unnecessary dims\n ds_in_dims_drop = set(ds_in.cf.coordinates.keys()).difference(['longitude', 'latitude'])\n ds_in = ds_in.cf.drop_dims(ds_in_dims_drop)\n\n # Drop unnecessary vars\n ds_in = ds_in.coords.to_dataset()\n\n # Ensure ds_in is not dask-backed\n ds_in = ds_in.load()\n\n # if bounds in ds_out, we switch to cf bounds for map_blocks\n if 'lon_b' in ds_out and (ds_out.lon_b.ndim == ds_out.cf['longitude'].ndim):\n ds_out = ds_out.assign_coords(\n lon_bounds=cfxr.vertices_to_bounds(\n ds_out.lon_b, ('bounds', *ds_out.cf['longitude'].dims)\n ),\n lat_bounds=cfxr.vertices_to_bounds(\n ds_out.lat_b, ('bounds', *ds_out.cf['latitude'].dims)\n ),\n )\n # Make cf-xarray aware of the new bounds\n ds_out[ds_out.cf['longitude'].name].attrs['bounds'] = 'lon_bounds'\n ds_out[ds_out.cf['latitude'].name].attrs['bounds'] = 'lat_bounds'\n ds_out = ds_out.drop_dims(ds_out.lon_b.dims + ds_out.lat_b.dims)\n # rename dims to avoid map_blocks confusing ds_in and ds_out dims.\n ds_in = _unname_dataset(ds_in, self.sequence_in, self.in_horiz_dims, '_in')\n ds_out = _unname_dataset(ds_out, self.sequence_out, self.out_horiz_dims, '_out')\n\n out_chunks = {k: ds_out.chunks.get(k) for k in ['y_out', 'x_out']}\n in_chunks = {k: ds_in.chunks.get(k) for k in ['y_in', 'x_in']}\n chunks = out_chunks | in_chunks\n\n weights_dims = ('y_out', 'x_out', 'y_in', 'x_in')\n templ = sps.zeros((self.shape_out + self.shape_in))\n w_templ = xr.DataArray(templ, dims=weights_dims).chunk(\n chunks\n ) # template has same chunks as ds_out\n\n # If post_mask_source is specified, set it to None, as it needs to be dealt with\n # for the final weights only\n if 'post_mask_source' in kwargs:\n kwargs['post_mask_source'] = None\n\n # Compute weights in parallel\n w = xr.map_blocks(\n subset_regridder,\n ds_out,\n args=[\n ds_in,\n self.method,\n self.in_horiz_dims,\n self.out_horiz_dims,\n self.sequence_in,\n self.sequence_out,\n self.periodic,\n ],\n kwargs=kwargs,\n template=w_templ,\n )\n w = w.compute(scheduler='processes')\n weights = w.stack(out_dim=weights_dims[:2], in_dim=weights_dims[2:])\n weights.name = 'weights'\n self.weights = weights\n\n # Optionally apply post_mask_source to manipulate the weights and removing\n # the contribution of the specified source cells\n if self.post_mask_source is not None:\n self.weights = mask_source_indices(self.weights, self.post_mask_source)\n # replace zeros by NaN for weight matrix entries of unmapped target cells\n # if specified or a mask is present\n if self.unmapped_to_nan:\n self.weights = add_nans_to_weights(self.weights)\n\n # follows legacy logic of writing weights if filename is provided\n if 'filename' in kwargs:\n filename = kwargs['filename']\n else:\n filename = None\n if filename is not None and not self.reuse_weights:\n self.to_netcdf(filename=filename)\n\n # set default weights filename if none given\n self.filename = self._get_default_filename() if filename is None else filename\n\n def _format_xroutput(self, out, new_dims=None):\n if new_dims is not None:\n # rename dimension name to match output grid\n out = out.rename({nd: od for nd, od in zip(new_dims, self.out_horiz_dims)})\n\n out = out.assign_coords(self.out_coords.coords)\n out.attrs['regrid_method'] = self.method\n\n if self.sequence_out:\n out = out.squeeze(dim='dummy')\n\n return out\n\n\nclass SpatialAverager(BaseRegridder):\n def __init__(\n self,\n ds_in,\n polys,\n ignore_holes=False,\n periodic=False,\n filename=None,\n reuse_weights=False,\n weights=None,\n ignore_degenerate=False,\n geom_dim_name='geom',\n ):\n \"\"\"Compute the exact average of a gridded array over a geometry.\n\n This uses the ESMF `conservative` regridding method to compute and apply weights\n mapping a 2D field unto geometries defined by polygons. The `conservative` method\n preserves the areal average of the input field. That is, *the value at each output\n grid cell is the average input value over the output grid area*. Here, the output\n grid cells are not rectangles defined by four corners, but polygons defined by\n multiple vertices (`ESMF.Mesh` objects). The regridding weights thus compute the\n areal-average of the input grid over each polygon.\n\n For multi-parts geometries (shapely.MultiPolygon), weights are computed for each\n geometry, then added, to compute the average over all geometries.\n\n When polygons include holes, the weights over the holes can either be substracted,\n or ignored.\n\n Parameters\n ----------\n ds_in : xr.DataArray or xr.Dataset or dictionary\n Contain input and output grid coordinates. Look for variables\n ``lon``, ``lat``, ``lon_b`` and ``lat_b``.\n\n Optionally looks for ``mask``, in which case the ESMF convention is used,\n where masked values are identified by 0, and non-masked values by 1.\n\n Shape can be 1D (n_lon,) and (n_lat,) for rectilinear grids,\n or 2D (n_y, n_x) for general curvilinear grids.\n Shape of bounds should be (n+1,) or (n_y+1, n_x+1).\n DataArrays are converted to Datasets.\n\n polys : sequence of shapely Polygons and MultiPolygons\n Sequence of polygons (lon, lat) over which to average `ds_in`.\n\n ignore_holes : bool\n Whether to ignore holes in polygons.\n Default (True) is to subtract the weight of holes from the weight of the polygon.\n\n filename : str, optional\n Name for the weight file. The default naming scheme is::\n\n spatialavg_{Ny_in}x{Nx_in}_{Npoly_out}.nc\n\n e.g. spatialavg_400x600_30.nc\n\n reuse_weights : bool, optional\n Whether to read existing weight file to save computing time.\n False by default (i.e. re-compute, not reuse).\n\n weights : None, coo_matrix, dict, str, Dataset, Path,\n Regridding weights, stored as\n - a scipy.sparse COO matrix,\n - a dictionary with keys `row_dst`, `col_src` and `weights`,\n - an xarray Dataset with data variables `col`, `row` and `S`,\n - or a path to a netCDF file created by ESMF.\n\n If None, compute the weights.\n\n ignore_degenerate : bool, optional\n If False (default), raise error if grids contain degenerated cells\n (i.e. triangles or lines, instead of quadrilaterals)\n\n self.geom_dim_name : str\n Name of dimension along which averages for each polygon are stored.\n\n Returns\n -------\n xarray.DataArray\n Average over polygons along `geom_dim_name` dimension. The `lon` and\n `lat` coordinates are the polygon centroid coordinates.\n\n References\n ----------\n This approach is inspired by `OCGIS `_.\n \"\"\"\n # Note, I suggest we refactor polys -> geoms\n self.ignore_holes = ignore_holes\n self.polys = polys\n self.geom_dim_name = geom_dim_name\n\n # Ensure we have a Dataset\n if isinstance(ds_in, xr.DataArray):\n ds_in = ds_in._to_temp_dataset()\n\n grid_in, shape_in, input_dims = ds_to_ESMFgrid(ds_in, need_bounds=True, periodic=periodic)\n\n # Create an output locstream so that the regridder knows the output shape and coords.\n # Latitude and longitude coordinates are the polygon centroid.\n lon_out, lat_out = _get_lon_lat(ds_in)\n if hasattr(lon_out, 'name'):\n self._lon_out_name = lon_out.name\n self._lat_out_name = lat_out.name\n else:\n self._lon_out_name = 'lon'\n self._lat_out_name = 'lat'\n\n # Check length of polys segments\n self._check_polys_length(polys)\n\n poly_centers = [poly.centroid.xy for poly in polys]\n self._lon_out = np.asarray([c[0][0] for c in poly_centers])\n self._lat_out = np.asarray([c[1][0] for c in poly_centers])\n\n # We put names 'lon' and 'lat' so ds_to_ESMFlocstream finds them easily.\n # _lon_out_name and _lat_out_name are used on the output anyway.\n ds_out = {'lon': self._lon_out, 'lat': self._lat_out}\n locstream_out, shape_out, _ = ds_to_ESMFlocstream(ds_out)\n\n # BaseRegridder with custom-computed weights and dummy out grid\n super().__init__(\n grid_in,\n locstream_out,\n 'conservative',\n input_dims=input_dims,\n weights=weights,\n filename=filename,\n reuse_weights=reuse_weights,\n ignore_degenerate=ignore_degenerate,\n unmapped_to_nan=False,\n )\n # Weights are computed, we do not need the grids anymore\n grid_in.destroy()\n locstream_out.destroy()\n\n @staticmethod\n def _check_polys_length(polys, threshold=1):\n # Check length of polys segments, issue warning if too long\n check_polys, check_holes, _, _ = split_polygons_and_holes(polys)\n check_polys.extend(check_holes)\n poly_segments = []\n for check_poly in check_polys:\n b = check_poly.boundary.coords\n # Length of each segment\n poly_segments.extend([LineString(b[k : k + 2]).length for k in range(len(b) - 1)])\n if np.any(np.array(poly_segments) > threshold):\n warnings.warn(\n f'`polys` contains large (> {threshold}°) segments. This could lead to errors over large regions. For a more accurate average, segmentize (densify) your shapes with `shapely.segmentize(polys, {threshold})`',\n UserWarning,\n stacklevel=2,\n )\n\n def _compute_weights_and_area(self, grid_in, mesh_out):\n \"\"\"Return the weights and the area of the destination mesh cells.\"\"\"\n\n # Build the regrid object\n regrid = esmf_regrid_build(\n grid_in,\n mesh_out,\n method='conservative',\n ignore_degenerate=self.ignore_degenerate,\n )\n\n # Get the weights and convert to a DataArray\n weights = regrid.get_weights_dict(deep_copy=True)\n w = _parse_coords_and_values(weights, self.n_in, mesh_out.element_count)\n\n # Get destination area - important for renormalizing the subgeometries.\n regrid.dstfield.get_area()\n dstarea = regrid.dstfield.data.copy()\n\n return w, dstarea\n\n def _compute_weights(self, grid_in, grid_out):\n \"\"\"Return weight sparse matrix.\n\n This function first explodes the geometries into a flat list of Polygon exterior objects:\n - Polygon -> polygon.exterior\n - MultiPolygon -> list of polygon.exterior\n\n and a list of Polygon.interiors (holes).\n\n Individual meshes are created for the exteriors and the interiors, and their regridding weights computed.\n We cannot compute the exterior and interior weights at the same time, because the meshes overlap.\n\n Weights for the subgeometries are then aggregated back to the original geometries. Because exteriors and\n interiors are computed independently, we need to normalize the weights according to their area.\n \"\"\"\n\n # Explode geometries into a flat list of polygon exteriors and interiors.\n # Keep track of original geometry index.\n # The convention used here is to list the exteriors first and then the interiors.\n exteriors, interiors, i_ext, i_int = split_polygons_and_holes(self.polys)\n geom_indices = np.array(i_ext + i_int)\n\n # Create mesh from external polygons (positive contribution)\n mesh_ext = Mesh.from_polygons(exteriors)\n\n # Get weights for external polygons\n w, area = self._compute_weights_and_area(grid_in, mesh_ext)\n mesh_ext.destroy() # release mesh memory\n\n # Get weights for interiors and append them to weights from exteriors as a negative contribution.\n if len(interiors) > 0 and not self.ignore_holes:\n mesh_int = Mesh.from_polygons(interiors)\n\n # Get weights for interiors\n w_int, area_int = self._compute_weights_and_area(grid_in, mesh_int)\n mesh_int.destroy() # release mesh memory\n\n # Append weights from holes as negative weights\n # In sparse >= 0.16, a fill_value of -0.0 is different from 0.0 and the concat would fail\n inv_w_int = -w_int\n inv_w_int.data.fill_value = 0.0\n w = xr.concat((w, inv_w_int), 'out_dim')\n\n # Append areas\n area = np.concatenate([area, area_int])\n\n # Combine weights for all the subgeometries belonging to the same geometry\n return _combine_weight_multipoly(w, area, geom_indices).T\n\n @property\n def w(self) -> xr.DataArray:\n \"\"\"Return weights as a 3D DataArray with dimensions (geom, y_in, x_in).\n\n ESMF stores the weights in a 2D array with dimensions (out_dim, in_dim), the size of the output and input\n grids respectively (ny x nx). This property returns the weights reshaped as a 3D array to simplify\n comparisons with the original grids.\n \"\"\"\n s = self.shape_out[1:2] + self.shape_in\n data = self.weights.data.reshape(s)\n dims = self.geom_dim_name, 'y_in', 'x_in'\n return xr.DataArray(data, dims=dims)\n\n def _get_default_filename(self):\n # e.g. bilinear_400x600_300x400.nc\n filename = 'spatialavg_{0}x{1}_{2}.nc'.format(\n self.shape_in[0], self.shape_in[1], self.n_out\n )\n\n return filename\n\n def __repr__(self):\n info = (\n 'xESMF SpatialAverager \\n'\n 'Weight filename: {} \\n'\n 'Reuse pre-computed weights? {} \\n'\n 'Input grid shape: {} \\n'\n 'Output list length: {} \\n'.format(\n self.filename, self.reuse_weights, self.shape_in, self.n_out\n )\n )\n\n return info\n\n def _format_xroutput(self, out, new_dims=None):\n out = out.squeeze(dim='dummy')\n\n # rename dimension name to match output grid\n out = out.rename(locations=self.geom_dim_name)\n\n # append output horizontal coordinate values\n # extra coordinates are automatically tracked by apply_ufunc\n out.coords[self._lon_out_name] = xr.DataArray(self._lon_out, dims=(self.geom_dim_name,))\n out.coords[self._lat_out_name] = xr.DataArray(self._lat_out, dims=(self.geom_dim_name,))\n out.attrs['regrid_method'] = self.method\n return out\n" }, { "path": "xesmf/smm.py", "content": "\"\"\"\nSparse matrix multiplication (SMM) using scipy.sparse library.\n\"\"\"\n\nimport warnings\nfrom pathlib import Path\n\nimport numba as nb\nimport numpy as np\nimport sparse as sps\nimport xarray as xr\n\n\ndef read_weights(weights, n_in, n_out):\n \"\"\"\n Read regridding weights into a DataArray (sparse COO matrix).\n\n Parameters\n ----------\n weights : str, Path, xr.Dataset, xr.DataArray, sparse.COO\n Weights generated by ESMF. Can be a path to a netCDF file generated by ESMF, an xarray.Dataset,\n a dictionary created by `ESMPy.api.Regrid.get_weights_dict` or directly the sparse\n array as returned by this function.\n\n n_in, n_out : integers\n ``(N_out, N_in)`` will be the shape of the returning sparse matrix.\n They are the total number of grid boxes in input and output grids::\n\n N_in = Nx_in * Ny_in\n N_out = Nx_out * Ny_out\n\n We need them because the shape cannot always be inferred from the\n largest column and row indices, due to unmapped grid boxes.\n\n Returns\n -------\n xr.DataArray\n A DataArray backed by a sparse.COO array, with dims ('out_dim', 'in_dim')\n and size (n_out, n_in).\n \"\"\"\n if isinstance(weights, (str, Path, xr.Dataset, dict)):\n weights = _parse_coords_and_values(weights, n_in, n_out)\n\n elif isinstance(weights, sps.COO):\n weights = xr.DataArray(weights, dims=('out_dim', 'in_dim'), name='weights')\n\n elif not isinstance(weights, xr.DataArray):\n raise ValueError(f'Weights of type {type(weights)} not understood.')\n\n return weights\n\n\ndef _parse_coords_and_values(indata, n_in, n_out):\n \"\"\"Creates a sparse.COO array from weights stored in a dict-like fashion.\n\n Parameters\n ----------\n indata: str, Path, xr.Dataset or dict\n A dictionary as returned by ESMF.Regrid.get_weights_dict\n or an xarray Dataset (or its path) as saved by xESMF.\n n_in : int\n The number of points in the input grid.\n n_out : int\n The number of points in the output grid.\n\n Returns\n -------\n sparse.COO\n Sparse array in the COO format.\n \"\"\"\n\n if isinstance(indata, (str, Path, xr.Dataset)):\n if not isinstance(indata, xr.Dataset):\n if not Path(indata).exists():\n raise IOError(f'Weights file not found on disk.\\n{indata}')\n ds_w = xr.open_dataset(indata)\n else:\n ds_w = indata\n\n if not {'col', 'row', 'S'}.issubset(ds_w.variables):\n raise ValueError(\n 'Weights dataset should have variables `col`, `row` and `S` storing the indices and '\n 'values of weights.'\n )\n\n col = ds_w['col'].values - 1 # Python starts with 0\n row = ds_w['row'].values - 1\n s = ds_w['S'].values\n\n elif isinstance(indata, dict):\n if not {'col_src', 'row_dst', 'weights'}.issubset(indata.keys()):\n raise ValueError(\n 'Weights dictionary should have keys `col_src`, `row_dst` and `weights` storing the '\n 'indices and values of weights.'\n )\n col = indata['col_src'] - 1\n row = indata['row_dst'] - 1\n s = indata['weights']\n\n crds = np.stack([row, col])\n return xr.DataArray(sps.COO(crds, s, (n_out, n_in)), dims=('out_dim', 'in_dim'), name='weights')\n\n\ndef check_shapes(indata, weights, shape_in, shape_out):\n \"\"\"Compare the shapes of the input array, the weights and the regridder and raises\n potential errors.\n\n Parameters\n ----------\n indata : array\n Input array with the two spatial dimensions at the end,\n which should fit shape_in.\n weights : array\n Weights 2D array of shape (out_dim, in_dim).\n First element should be the product of shape_out.\n Second element should be the product of shape_in.\n shape_in : 2-tuple of int\n Shape of the input of the Regridder.\n shape_out : 2-tuple of int\n Shape of the output of the Regridder.\n\n Raises\n ------\n ValueError\n If any of the conditions is not respected.\n \"\"\"\n # COO matrix is fast with F-ordered array but slow with C-array, so we\n # take in a C-ordered and then transpose)\n # (CSR or CRS matrix is fast with C-ordered array but slow with F-array)\n if hasattr(indata, 'flags') and not indata.flags['C_CONTIGUOUS']:\n warnings.warn('Input array is not C_CONTIGUOUS. ' 'Will affect performance.')\n\n # Limitation from numba : some big-endian dtypes are not supported.\n try:\n nb.from_dtype(indata.dtype)\n nb.from_dtype(weights.dtype)\n except (NotImplementedError, nb.core.errors.NumbaError):\n warnings.warn(\n 'Input array has a dtype not supported by sparse and numba.'\n 'Computation will fall back to scipy.'\n )\n\n # get input shape information\n shape_horiz = indata.shape[-2:]\n\n if shape_horiz != shape_in:\n raise ValueError(\n f'The horizontal shape of input data is {shape_horiz}, different from that '\n f'of the regridder {shape_in}!'\n )\n\n if shape_in[0] * shape_in[1] != weights.shape[1]:\n raise ValueError('ny_in * nx_in should equal to weights.shape[1]')\n\n if shape_out[0] * shape_out[1] != weights.shape[0]:\n raise ValueError('ny_out * nx_out should equal to weights.shape[0]')\n\n\ndef apply_weights(weights, indata, shape_in, shape_out):\n \"\"\"\n Apply regridding weights to data.\n\n Parameters\n ----------\n weights : sparse COO matrix of shape ``(n_out, n_in)`` (2D) OR\n ``(ny_out, nx_out, ny_in, nx_in)`` (4D, legacy). 2D is preferred —\n passing 4D triggers a flatten-to-2D step here.\n indata : numpy array of shape ``(..., n_lat, n_lon)`` or ``(..., n_y, n_x)``.\n Should be C-ordered. Will be then tranposed to F-ordered.\n shape_in, shape_out : tuple of two integers\n Input/output data shape.\n For rectilinear grid, it is just ``(n_lat, n_lon)``.\n\n Returns\n -------\n outdata : numpy array of shape ``(..., shape_out[0], shape_out[1])``.\n Extra dimensions are the same as `indata`.\n If input data is C-ordered, output will also be C-ordered.\n \"\"\"\n extra_shape = indata.shape[0:-2]\n\n # Limitation from numba : some big-endian dtypes are not supported.\n indata_dtype = indata.dtype\n try:\n nb.from_dtype(indata.dtype)\n nb.from_dtype(weights.dtype)\n except (NotImplementedError, nb.core.errors.NumbaError):\n indata = indata.astype('= n_source\n if np.any(invalid) or np.any(np.asarray(source_indices_to_mask) < 0):\n raise ValueError(\n f\"Some of the provided source indices are out of valid range [0, {n_source}). \"\n f\"Invalid indices: {np.asarray(source_indices_to_mask)[invalid]}\"\n )\n\n # Boolean mask for the source_idx - False for masked source indices\n mask = ~np.isin(src_idx, source_indices_to_mask)\n\n # Create new sparse matrix with only non-masked entries\n data_masked = data[mask]\n # Create new coordinates array by vertical (row-wise) stacking of the new target and source indices\n coords_masked = np.vstack([tgt_idx[mask], src_idx[mask]])\n\n # Create new sparse weight matrix and assign it to the weights DataArray\n weights = xr.DataArray(\n sps.COO(coords_masked, data_masked, shape=W.shape), dims=('out_dim', 'in_dim')\n )\n\n return weights\n\n\ndef gen_mask_from_weights(weights, nlat, nlon):\n \"\"\"Generate a 2D mask from the regridding weights sparse matrix.\n\n This function will generate a 2D binary mask out of a regridding weights sparse matrix.\n The mask shows which pixels of the output grid are mapped in the input grid.\n This is only feasible when the regridder weights have been created with ``unmapped_to_nan=True``.\n\n\n Parameters\n ----------\n weights : DataArray backed by a sparse.COO array\n Sparse weights matrix, as computed by the `Regridder`.\n nlat, nlon: int\n Shape of the final matrix.\n nlat * nlon must be equal to the size of the `out_dim` dimension of the weights.\n\n Returns\n -------\n numpy.ndarray of type numpy.int32 and of shape (nlat, nlon)\n Binary mask.\n\n Examples\n --------\n This function acts on lower level inputs, but the regridder has all information needed to construct a DataArray.\n\n >>> reg = xe.Regridder(ds_in, ds_out, 'bilinear', unmapped_to_nan=True)\n >>> mask = xe.smm.gen_mask_from_weights(reg.weights, *reg.shape_out)\n >>> mask_da = xr.DataArray(mask, dims=reg.out_horiz_dims, coords=reg.out_coords.coords)\n \"\"\"\n return 1 * (~weights.isnull().any('in_dim').data.todense().reshape(nlat, nlon))\n\n\ndef _combine_weight_multipoly(weights, areas, indexes):\n \"\"\"Reduce a weight sparse matrix (csc format) by combining (adding) columns.\n\n This is used to sum individual weight matrices from multi-part geometries.\n\n Parameters\n ----------\n weights : DataArray\n Usually backed by a sparse.COO array, with dims ('out_dim', 'in_dim')\n areas : np.array\n Array of destination areas, following same order as weights.\n indexes : array of integers\n Columns with the same \"index\" will be summed into a single column at this\n index in the output matrix.\n\n Returns\n -------\n sparse matrix (CSC)\n Sum of weights from individual geometries.\n \"\"\"\n\n sub_weights = weights.rename(out_dim='subgeometries')\n\n # Create a sparse DataArray with the mesh areas\n # This ties the `out_dim` (the dimension for the original geometries) to the\n # subgeometries dimension (the exploded polygon exteriors and interiors).\n crds = np.stack([indexes, np.arange(len(indexes))])\n a = xr.DataArray(\n sps.COO(crds, areas, (indexes.max() + 1, len(indexes)), fill_value=0),\n dims=('out_dim', 'subgeometries'),\n name='area',\n )\n\n # Weight the regridding weights by the area of the destination polygon and sum over sub-geometries\n out = (sub_weights * a).sum(dim='subgeometries')\n\n # Renormalize weights along in_dim\n wsum = out.sum('in_dim')\n\n # Change the fill_value to 1\n wsum = wsum.copy(\n data=sps.COO(wsum.data.coords, wsum.data.data, shape=wsum.data.shape, fill_value=1)\n )\n\n return out / wsum\n" }, { "path": "xesmf/tests/__init__.py", "content": "" }, { "path": "xesmf/tests/conftest.py", "content": "import dask\nimport pytest\n\n\n@pytest.fixture(scope='function')\ndef threaded_scheduler():\n with dask.config.set(scheduler='threads'):\n yield\n\n\n@pytest.fixture(scope='function')\ndef processes_scheduler():\n with dask.config.set(scheduler='processes'):\n yield\n\n\n@pytest.fixture(scope='function')\ndef distributed_scheduler():\n from dask.distributed import Client, LocalCluster\n\n cluster = LocalCluster(threads_per_worker=1, n_workers=2, processes=True)\n client = Client(cluster)\n yield\n client.close()\n del client\n cluster.close()\n del cluster\n\n\ndef pytest_addoption(parser):\n parser.addoption('--runtestcases', action='store_true', default=False, help='run test cases')\n\n\ndef pytest_configure(config):\n config.addinivalue_line('markers', 'testcases: mark test cases')\n\n\ndef pytest_collection_modifyitems(config, items):\n if config.getoption('--runtestcases'):\n # --runtestcases given in cli: do not skip test cases\n return\n skip_testcases = pytest.mark.skip(reason='need --runtestcases option to run')\n for item in items:\n if 'testcases' in item.keywords:\n item.add_marker(skip_testcases)\n" }, { "path": "xesmf/tests/test_backend.py", "content": "import os\n\ntry:\n import esmpy as ESMF\nexcept ImportError:\n import ESMF\n\nimport numpy as np\nimport pytest\nimport xarray as xr\nfrom numpy.testing import assert_almost_equal, assert_equal\n\nimport xesmf as xe\nfrom xesmf.backend import (\n Grid,\n LocStream,\n add_corner,\n esmf_regrid_apply,\n esmf_regrid_build,\n esmf_regrid_finalize,\n warn_f_contiguous,\n warn_lat_range,\n)\nfrom xesmf.smm import apply_weights, read_weights\n\n# We use pure numpy arrays to test backend\n# xarray DataSet is only used at the very beginning as a quick way to make data\ncoord_names = ['lon', 'lat', 'lon_b', 'lat_b']\n\nds_in = xe.util.grid_global(20, 12)\nlon_in, lat_in, lon_b_in, lat_b_in = [ds_in[name].values for name in coord_names]\n\nds_out = xe.util.grid_global(15, 9)\nlon_out, lat_out, lon_b_out, lat_b_out = [ds_out[name].values for name in coord_names]\n\n# shortcut to test a single grid\nlon, lat, lon_b, lat_b = [lon_in, lat_in, lon_b_in, lat_b_in]\n\n# input test data\nds_in['data'] = xe.data.wave_smooth(ds_in['lon'], ds_in['lat'])\ndata_in = ds_in['data'].values\n\n# reference output data, calculated analytically\nds_out['data_ref'] = xe.data.wave_smooth(ds_out['lon'], ds_out['lat'])\ndata_ref = ds_out['data_ref'].values\n\n# 4D data to test broadcasting, increasing linearly with time and lev\nds_in.coords['time'] = np.arange(1, 11)\nds_in.coords['lev'] = np.arange(1, 51)\nds_in['data4D'] = ds_in['time'] * ds_in['lev'] * ds_in['data']\ndata4D_in = ds_in['data4D'].values\n\n\ndef test_warn_f_on_array():\n a = np.zeros([2, 2], order='C')\n with pytest.warns(UserWarning):\n warn_f_contiguous(a)\n\n\ndef test_warn_f_on_grid():\n # should throw a warning if not passing transpose\n with pytest.warns(UserWarning):\n Grid.from_xarray(lon, lat)\n\n\ndef test_warn_lat_range():\n # latitude goes to -100 (invalid value)\n ds_temp = xe.util.grid_2d(-180, 180, 10, -100, 90, 5)\n with pytest.warns(UserWarning):\n warn_lat_range(ds_temp['lat'].values)\n with pytest.warns(UserWarning):\n warn_lat_range(ds_temp['lat_b'].values)\n\n\ndef test_esmf_grid_with_corner():\n # only center coordinate, no corners\n # remember to pass transpose (F-ordered) to backend\n grid = Grid.from_xarray(lon.T, lat.T)\n\n # make sure coordinate values agree\n assert_equal(grid.coords[0][0], lon.T)\n assert_equal(grid.coords[0][1], lat.T)\n\n # make sure meta data agree\n assert not grid.has_corners # no corner yet!\n assert grid.staggerloc == [True, False, False, False]\n assert grid.coord_sys is ESMF.CoordSys.SPH_DEG\n assert grid.rank == 2\n assert_equal(grid.size[0], lon.T.shape)\n assert_equal(grid.upper_bounds[0], lon.T.shape)\n assert_equal(grid.lower_bounds[0], np.array([0, 0]))\n\n # now add corner information\n add_corner(grid, lon_b.T, lat_b.T)\n\n # coordinate values\n assert_equal(grid.coords[3][0], lon_b.T)\n assert_equal(grid.coords[3][1], lat_b.T)\n\n # metadata\n assert grid.has_corners # should have corner now\n assert grid.staggerloc == [True, False, False, True]\n assert_equal(grid.size[3], lon_b.T.shape)\n assert_equal(grid.upper_bounds[3], lon_b.T.shape)\n assert_equal(grid.lower_bounds[3], np.array([0, 0]))\n\n\ndef test_esmf_build_bilinear():\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n\n regrid = esmf_regrid_build(grid_in, grid_out, 'bilinear')\n assert regrid.unmapped_action is ESMF.UnmappedAction.IGNORE\n assert regrid.regrid_method is ESMF.RegridMethod.BILINEAR\n\n # they should share the same memory\n regrid.srcfield.grid is grid_in\n regrid.dstfield.grid is grid_out\n\n esmf_regrid_finalize(regrid)\n\n\ndef test_esmf_extrapolation():\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n\n regrid = esmf_regrid_build(grid_in, grid_out, 'bilinear')\n data_out_esmpy = esmf_regrid_apply(regrid, data_in.T).T\n # without extrapolation, the first and last lines/columns = 0\n assert data_out_esmpy[0, 0] == 0\n\n regrid = esmf_regrid_build(\n grid_in,\n grid_out,\n 'bilinear',\n extrap_method='inverse_dist',\n extrap_num_src_pnts=3,\n extrap_dist_exponent=1,\n )\n data_out_esmpy = esmf_regrid_apply(regrid, data_in.T).T\n # the 3 closest points in data_in are 2.010, 2.005, and 1.992. The result should be roughly equal to 2.0\n assert np.round(data_out_esmpy[0, 0], 1) == 2.0\n\n\ndef test_esmf_extrapolation_creep_fill():\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n\n regrid_no_extrap = esmf_regrid_build(grid_in, grid_out, 'bilinear')\n data_out_no_extrap = esmf_regrid_apply(regrid_no_extrap, data_in.T).T\n\n # without extrapolation\n assert data_out_no_extrap[0, 0] == 0\n\n regrid_creep_fill = esmf_regrid_build(\n grid_in,\n grid_out,\n 'bilinear',\n extrap_method='creep_fill',\n extrap_num_levels=4,\n )\n data_out_creep_fill = esmf_regrid_apply(regrid_creep_fill, data_in.T).T\n\n # Same point should now be filled.\n assert data_out_creep_fill[0, 0] != 0\n assert np.isfinite(data_out_creep_fill[0, 0])\n\n esmf_regrid_finalize(regrid_no_extrap)\n esmf_regrid_finalize(regrid_creep_fill)\n\n\ndef test_regrid():\n # use conservative regridding as an example,\n # since it is the most well-tested studied one in papers\n\n # TODO: possible to break this long test into smaller tests?\n # not easy due to strong dependencies.\n\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n\n # no corner info yet, should not be able to use conservative\n with pytest.raises(ValueError):\n esmf_regrid_build(grid_in, grid_out, 'conservative')\n\n # now add corners\n add_corner(grid_in, lon_b_in.T, lat_b_in.T)\n add_corner(grid_out, lon_b_out.T, lat_b_out.T)\n\n # also write to file for scipy regridding\n filename = 'test_weights.nc'\n if os.path.exists(filename):\n os.remove(filename)\n regrid = esmf_regrid_build(grid_in, grid_out, 'conservative', filename=filename)\n assert regrid.regrid_method is ESMF.RegridMethod.CONSERVE\n\n # apply regridding using ESMPy's native method\n data_out_esmpy = esmf_regrid_apply(regrid, data_in.T).T\n\n rel_err = (data_out_esmpy - data_ref) / data_ref # relative error\n assert np.max(np.abs(rel_err)) < 0.05\n\n # apply regridding using scipy\n weights = read_weights(filename, lon_in.size, lon_out.size).data\n shape_in = lon_in.shape\n shape_out = lon_out.shape\n w = weights.reshape(shape_out + shape_in) # 4D weights\n data_out_scipy = apply_weights(w, data_in, shape_in, shape_out)\n\n # must be almost exactly the same as esmpy's result!\n assert_almost_equal(data_out_scipy, data_out_esmpy)\n\n # finally, test broadcasting with scipy\n # TODO: need to test broadcasting with ESMPy backend?\n # We only use Scipy in frontend, and ESMPy is just for backend benchmark\n # However, it is useful to compare performance and show scipy is 3x faster\n data4D_out = apply_weights(w, data4D_in, shape_in, shape_out)\n\n # data over broadcasting dimensions should agree\n assert_almost_equal(data4D_in.mean(axis=(2, 3)), data4D_out.mean(axis=(2, 3)), decimal=10)\n\n # clean-up\n esmf_regrid_finalize(regrid)\n os.remove(filename)\n\n\ndef test_regrid_periodic_wrong():\n # not using periodic grid\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n\n assert grid_in.num_peri_dims == 0\n assert grid_in.periodic_dim is None\n\n regrid = esmf_regrid_build(grid_in, grid_out, 'bilinear')\n data_out_esmpy = esmf_regrid_apply(regrid, data_in.T).T\n\n rel_err = (data_out_esmpy - data_ref) / data_ref # relative error\n assert np.max(np.abs(rel_err)) == 1.0 # some data will be missing\n\n # clean-up\n esmf_regrid_finalize(regrid)\n\n\ndef test_regrid_periodic_correct():\n # only need to specific periodic for input grid\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T, periodic=True)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n\n assert grid_in.num_peri_dims == 1\n assert grid_in.periodic_dim == 0 # the first axis, longitude\n\n regrid = esmf_regrid_build(grid_in, grid_out, 'bilinear')\n data_out_esmpy = esmf_regrid_apply(regrid, data_in.T).T\n\n rel_err = (data_out_esmpy - data_ref) / data_ref # relative error\n assert np.max(np.abs(rel_err)) < 0.065\n # clean-up\n esmf_regrid_finalize(regrid)\n\n\ndef test_esmf_locstream():\n lon = np.arange(5)\n lat = np.arange(5)\n\n ls = LocStream.from_xarray(lon, lat)\n assert isinstance(ls, ESMF.LocStream)\n\n lon2d, lat2d = np.meshgrid(lon, lat)\n with pytest.raises(ValueError):\n ls = LocStream.from_xarray(lon2d, lat2d)\n with pytest.raises(ValueError):\n ls = LocStream.from_xarray(lon, lat2d)\n with pytest.raises(ValueError):\n ls = LocStream.from_xarray(lon2d, lat)\n\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T, periodic=True)\n esmf_regrid_build(grid_in, ls, 'bilinear')\n esmf_regrid_build(ls, grid_in, 'nearest_s2d')\n\n\ndef test_read_weights(tmp_path):\n fn = tmp_path / 'weights.nc'\n\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n\n regrid_memory = esmf_regrid_build(grid_in, grid_out, method='bilinear')\n esmf_regrid_build(grid_in, grid_out, method='bilinear', filename=str(fn))\n\n w = regrid_memory.get_weights_dict(deep_copy=True)\n sm = read_weights(w, lon_in.size, lon_out.size)\n\n # Test Path and string to netCDF file against weights dictionary\n np.testing.assert_array_equal(\n read_weights(fn, lon_in.size, lon_out.size).data.todense(), sm.data.todense()\n )\n np.testing.assert_array_equal(\n read_weights(str(fn), lon_in.size, lon_out.size).data.todense(), sm.data.todense()\n )\n\n # Test xr.Dataset\n np.testing.assert_array_equal(\n read_weights(xr.open_dataset(fn), lon_in.size, lon_out.size).data.todense(),\n sm.data.todense(),\n )\n\n # Test COO matrix\n np.testing.assert_array_equal(\n read_weights(sm, lon_in.size, lon_out.size).data.todense(), sm.data.todense()\n )\n\n # Test failures\n with pytest.raises(IOError):\n read_weights(tmp_path / 'wrong_file.nc', lon_in.size, lon_out.size)\n\n with pytest.raises(ValueError):\n read_weights({}, lon_in.size, lon_out.size)\n\n with pytest.raises(ValueError):\n ds = xr.open_dataset(fn)\n read_weights(ds.drop_vars('col'), lon_in.size, lon_out.size)\n\n\ndef test_vector_regrid():\n grid_in = Grid.from_xarray(lon_in.T, lat_in.T)\n grid_out = Grid.from_xarray(lon_out.T, lat_out.T)\n data_in_vec = np.stack(\n [np.cos(data_in) * np.sign(lat_in), np.sin(data_in) * np.sign(lon_in)], axis=0\n )\n\n try:\n regrid_vec = esmf_regrid_build(\n grid_in, grid_out, 'bilinear', extra_dims=[2], vector_regrid=True\n )\n except TypeError:\n pytest.skip('vector_regrid argument not supported by ESMPy versions < 8.9')\n assert regrid_vec.vector_regrid is True\n data_out_vec = esmf_regrid_apply(regrid_vec, data_in_vec.T).T\n\n regrid_nonvec = esmf_regrid_build(grid_in, grid_out, 'bilinear', extra_dims=[2])\n assert regrid_nonvec.vector_regrid in (None, False)\n data_out_nonvec = esmf_regrid_apply(regrid_nonvec, data_in_vec.T).T\n\n # The result of vector regridding should differ from the result of non-vector\n # regridding, but not by much. (This threshold of 0.1 was determined empirically and\n # may need to be adjusted if details of the test inputs change.)\n assert np.array_equal(data_out_vec, data_out_nonvec) is False\n assert np.max(np.abs(data_out_vec - data_out_nonvec)) < 0.1\n\n esmf_regrid_finalize(regrid_vec)\n esmf_regrid_finalize(regrid_nonvec)\n\n\ndef test_deprecated():\n from xesmf.backend import esmf_grid, esmf_locstream\n\n lon = np.arange(5)\n lat = np.arange(5)\n gr = Grid.from_xarray(lon_in.T, lat_in.T)\n ls = LocStream.from_xarray(lon, lat)\n\n with pytest.warns(DeprecationWarning):\n np.testing.assert_allclose(gr.coords[0], esmf_grid(lon_in.T, lat_in.T).coords[0])\n\n with pytest.warns(DeprecationWarning):\n out = dict(esmf_locstream(lon, lat).items())\n for key, val in ls.items():\n np.testing.assert_array_equal(val, out[key])\n" }, { "path": "xesmf/tests/test_frontend.py", "content": "import os\nimport warnings\n\nimport cf_xarray as cfxr\nimport dask\nimport numpy as np\nimport pytest\nimport xarray as xr\nfrom dask.array.core import PerformanceWarning\nfrom esmpy import __version__ as esmf_version\nfrom numpy.testing import assert_allclose, assert_almost_equal, assert_equal\nfrom packaging.version import Version\nfrom shapely import segmentize\nfrom shapely.geometry import MultiPolygon, Polygon\n\nimport xesmf as xe\nfrom xesmf.frontend import as_2d_mesh\n\ndask_schedulers = ['threaded_scheduler', 'processes_scheduler', 'distributed_scheduler']\npytestmark = pytest.mark.filterwarnings('ignore:Input array is not C_CONTIGUOUS')\n\n# same test data as test_backend.py, but here we can use xarray DataSet\nds_in = xe.util.grid_global(20, 12)\nds_in.lat.attrs['standard_name'] = 'latitude'\nds_in.lon.attrs['standard_name'] = 'longitude'\nds_out = xe.util.grid_global(15, 9)\n\nhoriz_shape_in = ds_in['lon'].shape\nhoriz_shape_out = ds_out['lon'].shape\n\nds_in['data'] = xe.data.wave_smooth(ds_in['lon'], ds_in['lat'])\nds_out['data_ref'] = xe.data.wave_smooth(ds_out['lon'], ds_out['lat'])\n\n# 4D data to test broadcasting, increasing linearly with time and lev\nds_in.coords['time'] = np.arange(7) + 1\nds_in.coords['lev'] = np.arange(11) + 1\nds_in['data4D'] = ds_in['time'] * ds_in['lev'] * ds_in['data']\nds_in['data4D_f4'] = ds_in['data4D'].astype('f4')\nds_out['data4D_ref'] = ds_in['time'] * ds_in['lev'] * ds_out['data_ref']\n\n# use non-divisible chunk size to catch edge cases\nds_in_chunked = ds_in.chunk({'time': 3, 'lev': 2})\nds_spatial_chunked = ds_in.chunk({'time': 3, 'lev': 2, 'y': 5, 'x': 9})\nds_out_chunked = ds_out.chunk({'y': 5, 'x': 5})\n\nds_locs = xr.Dataset()\nds_locs['lat'] = xr.DataArray(data=[-20, -10, 0, 10], dims=('locations',))\nds_locs['lon'] = xr.DataArray(data=[0, 5, 10, 15], dims=('locations',))\nds_locs_chunked = ds_locs.chunk(2)\n\n\n# For polygon handling and spatial average\nds_savg = xr.Dataset(\n coords={\n 'lat': (('lat',), [0.5, 1.5]),\n 'lon': (('lon',), [0.5, 1.5, 2.5]),\n 'lat_b': (('lat_b',), [0, 1, 2]),\n 'lon_b': (('lon_b',), [0, 1, 2, 3]),\n },\n data_vars={'abc': (('lon', 'lat'), [[1.0, 2.0], [3.0, 4.0], [2.0, 4.0]])},\n)\npolys_raw = [\n Polygon([[0.5, 0.5], [0.5, 1.5], [1.5, 0.5]]), # Simple triangle polygon\n MultiPolygon(\n [\n Polygon([[0.25, 1.25], [0.25, 1.75], [0.75, 1.75], [0.75, 1.25]]),\n Polygon([[1.25, 1.25], [1.25, 1.75], [1.75, 1.75], [1.75, 1.25]]),\n ]\n ), # Multipolygon on 2 and 4\n Polygon(\n [[0, 0], [0, 1], [2, 1], [2, 0]],\n holes=[[[0.5, 0.25], [0.5, 0.75], [1.0, 0.75], [1.0, 0.25]]],\n ), # Simple polygon covering 1 and 3 with hole over 1\n Polygon([[1, 1], [1, 3], [3, 3], [3, 1]]), # Polygon partially outside, covering a part of 4\n Polygon([[3, 3], [3, 4], [4, 4], [4, 3]]), # Polygon totally outside\n Polygon(\n [\n [0, 0],\n [0.5, 0.5],\n [0, 1],\n [0.5, 1.5],\n [0, 2],\n [2, 2],\n [1.5, 1.5],\n [2, 1],\n [1.5, 0.5],\n [2, 0],\n ]\n ), # Long multifaceted polygon\n [\n Polygon([[0.5, 0.5], [0.5, 1.5], [1.5, 0.5]]),\n MultiPolygon(\n [\n Polygon([[0.25, 1.25], [0.25, 1.75], [0.75, 1.75], [0.75, 1.25]]),\n Polygon([[1, 1], [1, 2], [2, 2], [2, 1]]),\n ]\n ),\n ], # Combination of Polygon and MultiPolygon with two different areas\n]\n\n\ndef _segmentize(p):\n if isinstance(p, list):\n return list(map(_segmentize, p))\n return segmentize(p, 0.99)\n\n\npolys = list(map(_segmentize, polys_raw))\nexps_polys = [1.75, 3, 2.1429, 4, 0, 2.5, [1.75, 3.6]]\n\n\ndef test_as_2d_mesh():\n # 2D grid should not change\n lon2d = ds_in['lon'].values\n lat2d = ds_in['lat'].values\n assert_equal((lon2d, lat2d), as_2d_mesh(lon2d, lat2d))\n\n # 1D grid should become 2D\n lon1d = lon2d[0, :]\n lat1d = lat2d[:, 0]\n assert_equal((lon2d, lat2d), as_2d_mesh(lon1d, lat1d))\n\n # mix of 1D and 2D should fail\n with pytest.raises(ValueError):\n as_2d_mesh(lon1d, lat2d)\n\n\n# 'patch' is too slow to test\nmethods_list = ['bilinear', 'conservative', 'nearest_s2d', 'nearest_d2s']\n\n\n@pytest.mark.parametrize(\n 'locstream_in,locstream_out,method,unmapped_to_nan',\n [\n (False, False, 'conservative', False),\n (False, False, 'bilinear', False),\n (False, True, 'bilinear', False),\n (False, False, 'nearest_s2d', False),\n (False, True, 'nearest_s2d', False),\n (True, False, 'nearest_s2d', False),\n (True, True, 'nearest_s2d', False),\n (False, False, 'nearest_d2s', False),\n (False, True, 'nearest_d2s', False),\n (True, False, 'nearest_d2s', False),\n (True, True, 'nearest_d2s', False),\n (False, False, 'conservative', True),\n (False, False, 'bilinear', True),\n (False, True, 'bilinear', True),\n (False, False, 'nearest_s2d', True),\n (False, True, 'nearest_s2d', True),\n (True, False, 'nearest_s2d', True),\n (True, True, 'nearest_s2d', True),\n (False, False, 'nearest_d2s', True),\n (False, True, 'nearest_d2s', True),\n (True, False, 'nearest_d2s', True),\n (True, True, 'nearest_d2s', True),\n ],\n)\ndef test_build_regridder(method, locstream_in, locstream_out, unmapped_to_nan):\n din = ds_locs if locstream_in else ds_in\n dout = ds_locs if locstream_out else ds_out\n\n regridder = xe.Regridder(\n din, dout, method, locstream_in=locstream_in, locstream_out=locstream_out\n )\n\n # check screen output\n assert repr(regridder) == str(regridder)\n assert 'xESMF Regridder' in str(regridder)\n assert method in str(regridder)\n\n\ndef test_regridder_creep_fill_validation():\n with pytest.raises(ValueError, match='extrap_num_levels'):\n xe.Regridder(\n ds_in,\n ds_out,\n 'bilinear',\n extrap_method='creep_fill',\n )\n\n with pytest.raises(ValueError, match='not supported with conservative'):\n xe.Regridder(\n ds_in,\n ds_out,\n 'conservative',\n extrap_method='creep_fill',\n extrap_num_levels=4,\n )\n\n\ndef test_existing_weights():\n # the first run\n method = 'bilinear'\n regridder = xe.Regridder(ds_in, ds_out, method)\n fn = regridder.to_netcdf()\n\n # make sure we can reuse weights\n assert os.path.exists(fn)\n regridder_reuse = xe.Regridder(ds_in, ds_out, method, weights=fn)\n assert regridder_reuse.weights.shape == regridder.weights.shape\n\n # this should also work with reuse_weights=True\n regridder_reuse = xe.Regridder(ds_in, ds_out, method, reuse_weights=True, weights=fn)\n assert regridder_reuse.weights.shape == regridder.weights.shape\n\n # or can also overwrite it\n xe.Regridder(ds_in, ds_out, method)\n\n # check legacy args still work\n regridder = xe.Regridder(ds_in, ds_out, method, filename='wgts.nc')\n regridder_reuse = xe.Regridder(ds_in, ds_out, method, reuse_weights=True, filename='wgts.nc')\n assert regridder_reuse.weights.shape == regridder.weights.shape\n\n # check fails on non-existent file\n with pytest.raises(OSError):\n xe.Regridder(ds_in, ds_out, method, reuse_weights=True, filename='fakewgts.nc')\n\n # check fails if no weights are provided\n with pytest.raises(ValueError):\n xe.Regridder(ds_in, ds_out, method, reuse_weights=True)\n\n\ndef test_regridder_w():\n \"\"\"Check that the `w` property for dimensioned weights works.\"\"\"\n regridder = xe.Regridder(ds_in, ds_out, method='bilinear')\n w = regridder.w\n assert w.shape == ds_out.lon.shape + ds_in.lon.shape\n\n p = segmentize(Polygon([(-10, -10), (10, -10), (10, 10), (-10, 10)]), 1)\n\n averager = xe.SpatialAverager(ds_in, [p])\n assert averager.w.shape == (1,) + ds_in.lon.shape\n\n ds_in_cf = xe.util.grid_global(15, 15, cf=True)\n ds_out_cf = xe.util.grid_global(30, 30, cf=True)\n\n regridder_cf = xe.Regridder(ds_in_cf, ds_out_cf, method='bilinear')\n w_cf = regridder_cf.w\n assert w_cf.shape == (\n ds_out_cf.lat.shape + ds_out_cf.lon.shape + ds_in_cf.lat.shape + ds_in_cf.lon.shape\n )\n\n averager = xe.SpatialAverager(ds_in_cf, [p, p])\n assert averager.w.shape == (2,) + ds_in_cf.lat.shape + ds_in_cf.lon.shape\n\n\n@pytest.mark.parametrize('unmapped_to_nan', [True, False])\ndef test_to_netcdf(tmp_path, unmapped_to_nan):\n from xesmf.backend import Grid, esmf_regrid_build\n\n # Let the frontend write the weights to disk\n xfn = tmp_path / 'ESMF_weights.nc'\n method = 'bilinear'\n regridder = xe.Regridder(ds_in, ds_out, method, unmapped_to_nan=unmapped_to_nan)\n regridder.to_netcdf(filename=xfn)\n\n grid_in = Grid.from_xarray(ds_in['lon'].values.T, ds_in['lat'].values.T)\n grid_out = Grid.from_xarray(ds_out['lon'].values.T, ds_out['lat'].values.T)\n\n # Let the ESMPy backend write the weights to disk\n efn = tmp_path / 'weights.nc'\n esmf_regrid_build(grid_in, grid_out, method=method, filename=str(efn))\n\n x = xr.open_dataset(xfn)\n # Our title has \" - Generated by xESMF\" appended, so not comparable\n # This also minimally tests the presence of the attributes\n del x.attrs['title']\n del x.attrs['xesmf_version']\n e = xr.open_dataset(efn)\n e.attrs.pop('title', None)\n\n # ESMF added attributes in 8.9.1\n if Version(esmf_version) < Version('8.9.1'):\n x = x.drop_attrs()\n e = e.drop_attrs()\n\n if unmapped_to_nan:\n # Reformat to sparse COO matrix\n smat = xe.smm.read_weights(e, np.prod(ds_in['lon'].shape), np.prod(ds_out['lon'].shape))\n # Add NaNs to weights\n smat = xe.smm.add_nans_to_weights(smat)\n # Updating the dataset\n e = xr.Dataset(\n {\n 'S': (['n_s'], smat.data.data),\n 'row': (['n_s'], smat.data.coords[0, :] + 1),\n 'col': (['n_s'], smat.data.coords[1, :] + 1),\n },\n attrs=e.attrs,\n )\n\n xr.testing.assert_identical(x, e)\n\n\ndef test_conservative_without_bounds():\n with pytest.raises(KeyError):\n xe.Regridder(ds_in.drop_vars('lon_b'), ds_out, 'conservative')\n\n\ndef test_regridder_from_dict():\n lon_in = ds_in['lon'].values\n lat_in = ds_in['lat'].values\n lon_out = ds_out['lon'].values\n lat_out = ds_out['lat'].values\n reg = xe.Regridder({'lon': lon_in, 'lat': lat_in}, {'lon': lon_out, 'lat': lat_out}, 'bilinear')\n with pytest.warns(UserWarning, match=r\"Using dimensions \\('y', 'x'\\) from data variable\"):\n reg(ds_in['data'])\n\n\ndef test_regrid_periodic_wrong():\n # not using periodic option\n regridder = xe.Regridder(ds_in, ds_out, 'bilinear', unmapped_to_nan=False)\n\n dr_out = regridder(ds_in['data']) # xarray DataArray\n\n # compare with analytical solution\n rel_err = (ds_out['data_ref'] - dr_out) / ds_out['data_ref']\n assert np.max(np.abs(rel_err)) == 1.0 # some data will be missing\n\n\ndef test_regrid_periodic_correct():\n regridder = xe.Regridder(ds_in, ds_out, 'bilinear', periodic=True)\n\n dr_out = regridder(ds_in['data'])\n\n # compare with analytical solution\n rel_err = (ds_out['data_ref'] - dr_out) / ds_out['data_ref']\n assert np.max(np.abs(rel_err)) < 0.065\n\n\ndef ds_2d_to_1d(ds):\n ds_temp = ds.reset_coords()\n ds_1d = xr.merge([ds_temp['lon'][0, :], ds_temp['lat'][:, 0]])\n ds_1d.coords['lon'] = ds_1d['lon']\n ds_1d.coords['lat'] = ds_1d['lat']\n return ds_1d\n\n\n@pytest.mark.parametrize('dtype', ['float32', 'float64'])\n@pytest.mark.parametrize(\n 'data_in',\n [\n pytest.param(np.array(ds_in['data']), id='np.ndarray'),\n pytest.param(ds_in['data'].copy(), id='xr.DataArray input'),\n pytest.param(ds_in[['data']].copy(), id='xr.Dataset input'),\n pytest.param(ds_in['data'].chunk(), id='da.Array input'),\n ],\n)\ndef test_regridded_respects_input_dtype(dtype, data_in):\n \"\"\"Tests regridded output has same dtype as input\"\"\"\n data_in = data_in.astype(dtype)\n regridder = xe.Regridder(ds_in, ds_out, 'bilinear') # Make this a fixture?\n out = regridder(data_in)\n\n if isinstance(data_in, xr.Dataset):\n # When data_in is xr.Dataset, a mapping...\n assert out['data'].dtype == data_in['data'].dtype\n else:\n assert out.dtype == data_in.dtype\n\n\ndef test_regrid_with_1d_grid():\n ds_in_1d = ds_2d_to_1d(ds_in)\n ds_out_1d = ds_2d_to_1d(ds_out)\n\n regridder = xe.Regridder(ds_in_1d, ds_out_1d, 'bilinear', periodic=True)\n\n dr_out = regridder(ds_in['data'])\n\n # compare with analytical solution\n rel_err = (ds_out['data_ref'] - dr_out) / ds_out['data_ref']\n assert np.max(np.abs(rel_err)) < 0.065\n\n # metadata should be 1D\n assert_equal(dr_out['lon'].values, ds_out_1d['lon'].values)\n assert_equal(dr_out['lat'].values, ds_out_1d['lat'].values)\n\n\ndef test_regrid_with_1d_grid_infer_bounds():\n ds_in_1d = ds_2d_to_1d(ds_in).swap_dims(x='lon', y='lat')\n ds_out_1d = ds_2d_to_1d(ds_out).swap_dims(x='lon', y='lat')\n\n regridder = xe.Regridder(ds_in_1d, ds_out_1d, 'conservative', periodic=True)\n with pytest.warns(UserWarning, match=r\"Using dimensions \\('y', 'x'\\) from data variable\"):\n dr_out = regridder(ds_in['data'])\n\n # compare with provided-bounds solution\n dr_exp = xe.Regridder(ds_in, ds_out, 'conservative', periodic=True)(ds_in['data'])\n\n assert_allclose(dr_out, dr_exp)\n\n\ndef test_regrid_cfbounds():\n # Test regridding when bounds are given in cf format with a custom \"bounds\" name.\n ds = ds_in.copy().drop_vars(['lat_b', 'lon_b'])\n ds['lon_bounds'] = cfxr.vertices_to_bounds(ds_in.lon_b, ('bnds', 'y', 'x'))\n ds['lat_bounds'] = cfxr.vertices_to_bounds(ds_in.lat_b, ('bnds', 'y', 'x'))\n ds.lat.attrs['bounds'] = 'lat_bounds'\n ds.lon.attrs['bounds'] = 'lon_bounds'\n\n regridder = xe.Regridder(ds, ds_out, 'conservative', periodic=True)\n dr_out = regridder(ds['data'])\n # compare with provided-bounds solution\n dr_exp = xe.Regridder(ds_in, ds_out, 'conservative', periodic=True)(ds_in['data'])\n assert_allclose(dr_out, dr_exp)\n\n\n# TODO: consolidate (regrid method, input data types) combination\n# using pytest fixtures and parameterization\n\n\n@pytest.mark.parametrize('use_cfxr', [True, False])\ndef test_regrid_dataarray(use_cfxr):\n # xarray.DataArray containing in-memory numpy array\n if use_cfxr:\n ds_in2 = ds_in.rename(lat='latitude', lon='longitude')\n ds_out2 = ds_out.rename(lat='latitude', lon='longitude')\n else:\n ds_in2 = ds_in\n ds_out2 = ds_out\n\n regridder = xe.Regridder(ds_in2, ds_out2, 'conservative')\n\n outdata = regridder(ds_in2['data'].values) # pure numpy array\n dr_out = regridder(ds_in2['data']) # xarray DataArray\n\n # DataArray and numpy array should lead to the same result\n assert_equal(outdata, dr_out.values)\n\n # compare with analytical solution\n rel_err = (ds_out2['data_ref'] - dr_out) / ds_out2['data_ref']\n assert np.max(np.abs(rel_err)) < 0.05\n\n # check metadata\n lat_name = 'latitude' if use_cfxr else 'lat'\n lon_name = 'longitude' if use_cfxr else 'lon'\n xr.testing.assert_identical(dr_out[lat_name], ds_out2[lat_name])\n xr.testing.assert_identical(dr_out[lon_name], ds_out2[lon_name])\n\n # test broadcasting\n dr_out_4D = regridder(ds_in2['data4D'])\n\n # data over broadcasting dimensions should agree\n assert_almost_equal(\n ds_in2['data4D'].values.mean(axis=(2, 3)),\n dr_out_4D.values.mean(axis=(2, 3)),\n decimal=10,\n )\n\n # check metadata\n xr.testing.assert_identical(dr_out_4D['time'], ds_in2['time'])\n xr.testing.assert_identical(dr_out_4D['lev'], ds_in2['lev'])\n\n # test transposed\n dr_out_4D_t = regridder(ds_in2['data4D'].transpose(..., 'time', 'lev'))\n xr.testing.assert_identical(dr_out_4D, dr_out_4D_t)\n\n # test renamed dim\n if not use_cfxr:\n with pytest.warns(UserWarning, match=r\"Using dimensions \\('why', 'x'\\)\"):\n dr_out_rn = regridder(ds_in2.rename(y='why')['data'])\n xr.testing.assert_identical(dr_out, dr_out_rn)\n\n\n@pytest.mark.parametrize('use_dask', [True, False])\ndef test_regrid_dataarray_endianess(use_dask):\n # xarray.DataArray containing in-memory numpy array\n regridder = xe.Regridder(ds_in, ds_out, 'conservative')\n\n exp = regridder(ds_in['data']) # Normal (little-endian)\n\n if use_dask:\n indata = ds_in.data.astype('>f8').chunk()\n else:\n indata = ds_in.data.astype('>f8')\n\n with pytest.warns(UserWarning, match='Input array has a dtype not supported'):\n out = regridder(indata) # big endian\n\n # Results should be the same\n assert_equal(exp.values, out.values)\n assert out.dtype == '>f8'\n\n\ndef test_regrid_from_dataarray():\n # Check that creating Regridder from DataArrays doesn't fail\n xe.Regridder(ds_in['data'], ds_out['data_ref'], 'bilinear')\n\n\ndef test_regrid_dataarray_to_locstream():\n # xarray.DataArray containing in-memory numpy array\n\n regridder = xe.Regridder(ds_in, ds_locs, 'bilinear', locstream_out=True)\n\n outdata = regridder(ds_in['data'].values) # pure numpy array\n dr_out = regridder(ds_in['data']) # xarray DataArray\n dr_out_t = regridder(ds_in['data'].transpose()) # Transpose to test name matching\n\n # DataArray and numpy array should lead to the same result\n assert_equal(outdata.squeeze(), dr_out.values)\n assert_equal(outdata.squeeze(), dr_out_t.values)\n\n with pytest.raises(ValueError):\n regridder = xe.Regridder(ds_in, ds_locs, 'conservative', locstream_out=True)\n\n\ndef test_regrid_dataarray_from_locstream():\n # xarray.DataArray containing in-memory numpy array\n\n regridder = xe.Regridder(ds_locs, ds_in, 'nearest_s2d', locstream_in=True)\n\n outdata = regridder(ds_locs['lat'].values) # pure numpy array\n dr_out = regridder(ds_locs['lat']) # xarray DataArray\n # New dim and transpose to test name-matching\n dr_out_2D = regridder(ds_locs['lat'].expand_dims(other=[1]).transpose('locations', 'other'))\n\n # DataArray and numpy array should lead to the same result\n assert_equal(outdata, dr_out.values)\n assert_equal(outdata, dr_out_2D.sel(other=1).values)\n\n with pytest.raises(ValueError):\n regridder = xe.Regridder(ds_locs, ds_in, 'bilinear', locstream_in=True)\n with pytest.raises(ValueError):\n regridder = xe.Regridder(ds_locs, ds_in, 'patch', locstream_in=True)\n with pytest.raises(ValueError):\n regridder = xe.Regridder(ds_locs, ds_in, 'conservative', locstream_in=True)\n\n\n@pytest.mark.parametrize('scheduler', dask_schedulers)\ndef test_regrid_dask(request, scheduler):\n # chunked dask array (no xarray metadata)\n scheduler = request.getfixturevalue(scheduler)\n regridder = xe.Regridder(ds_in, ds_out, 'conservative')\n\n indata = ds_in_chunked['data'].data\n outdata = regridder(indata)\n\n assert dask.is_dask_collection(outdata)\n\n # lazy dask arrays have incorrect shape attribute due to last chunk\n assert outdata.shape == indata.shape[:-2] + horiz_shape_out\n\n # Check that the number of tasks is as predicted\n # ds_in has 1 chunk\n # thus output also has 1 chunk (output is not chunked if input isn't)\n # regridding adds 3 tasks, wrapping the weights adds 2\n n_task_out = len(outdata.__dask_graph__().keys())\n n_task_in = len(indata.__dask_graph__().keys())\n assert n_task_out == n_task_in + 5\n\n # Use very small chunks\n indata_chunked = indata.rechunk((5, 6)) # Now has 9 chunks (5, 6)\n with pytest.warns(\n PerformanceWarning,\n match=r'Regridding is increasing the number of chunks by a factor of 16.0',\n ):\n outdata = regridder(indata_chunked)\n # This is the case where we preserve chunk size\n assert outdata.chunksize == indata_chunked.chunksize\n n_task_out = len(outdata.__dask_graph__().keys())\n n_task_in = len(indata_chunked.__dask_graph__().keys())\n # input has 9 chunks\n # output has 16\n # Regridding adds 2 * 9 * 16 + 16 + 64 (I'm not sure I fully understand how dasks sums at the end)\n # Wrapping the weights adds 9 * 16 + 1\n assert n_task_out == n_task_in + 513\n\n # Prescribe chunks\n outdata = regridder(indata, output_chunks=(-1, 12))\n n_task_out = len(outdata.__dask_graph__().keys())\n n_task_in = len(indata.__dask_graph__().keys())\n # input has 1 chunks\n # output has 2\n # Regridding adds 2 * 1 * 2 + 2\n # Wrapping the weights adds 1 * 2 + 1\n assert n_task_out == n_task_in + 9\n\n outdata_ref = ds_out['data_ref'].values\n rel_err = (outdata.compute() - outdata_ref) / outdata_ref\n assert np.max(np.abs(rel_err)) < 0.05\n\n\n@pytest.mark.parametrize('scheduler', dask_schedulers)\ndef test_regrid_dask_to_locstream(request, scheduler):\n # chunked dask array (no xarray metadata)\n\n scheduler = request.getfixturevalue(scheduler)\n regridder = xe.Regridder(ds_in, ds_locs, 'bilinear', locstream_out=True)\n\n indata = ds_in_chunked['data4D'].data\n outdata = regridder(indata)\n assert dask.is_dask_collection(outdata)\n\n\n@pytest.mark.parametrize('scheduler', dask_schedulers)\ndef test_regrid_dask_from_locstream(request, scheduler):\n # chunked dask array (no xarray metadata)\n\n scheduler = request.getfixturevalue(scheduler)\n regridder = xe.Regridder(ds_locs, ds_in, 'nearest_s2d', locstream_in=True)\n\n outdata = regridder(ds_locs.chunk()['lat'].data)\n assert dask.is_dask_collection(outdata)\n\n\n@pytest.mark.parametrize('scheduler', dask_schedulers)\ndef test_regrid_dataarray_dask(request, scheduler):\n # xarray.DataArray containing chunked dask array\n scheduler = request.getfixturevalue(scheduler)\n regridder = xe.Regridder(ds_in, ds_out, 'conservative')\n\n dr_in = ds_in_chunked['data4D']\n dr_out = regridder(dr_in)\n assert dask.is_dask_collection(dr_out)\n\n assert dr_out.data.shape == dr_in.data.shape[:-2] + horiz_shape_out\n\n # data over broadcasting dimensions should agree\n assert_almost_equal(dr_in.values.mean(axis=(2, 3)), dr_out.values.mean(axis=(2, 3)), decimal=10)\n\n # check metadata\n xr.testing.assert_identical(dr_out['time'], dr_in['time'])\n xr.testing.assert_identical(dr_out['lev'], dr_in['lev'])\n assert_equal(dr_out['lat'].values, ds_out['lat'].values)\n assert_equal(dr_out['lon'].values, ds_out['lon'].values)\n\n\n@pytest.mark.parametrize('scheduler', dask_schedulers)\ndef test_regrid_dataarray_dask_to_locstream(request, scheduler):\n # xarray.DataArray containing chunked dask array\n scheduler = request.getfixturevalue(scheduler)\n regridder = xe.Regridder(ds_in, ds_locs, 'bilinear', locstream_out=True)\n\n dr_in = ds_in_chunked['data4D']\n dr_out = regridder(dr_in)\n assert dask.is_dask_collection(dr_out)\n\n\n@pytest.mark.parametrize('scheduler', dask_schedulers)\ndef test_regrid_dataarray_dask_from_locstream(request, scheduler):\n # xarray.DataArray containing chunked dask array\n\n scheduler = request.getfixturevalue(scheduler)\n regridder = xe.Regridder(ds_locs, ds_in, 'nearest_s2d', locstream_in=True)\n\n outdata = regridder(ds_locs.chunk()['lat'])\n assert dask.is_dask_collection(outdata)\n\n\ndef test_dask_output_chunks():\n regridder = xe.Regridder(ds_in, ds_out, 'conservative')\n\n test_output_chunks_tuple = (10, 12)\n test_output_chunks_dict = {'y': 10, 'x': 12}\n indata = ds_spatial_chunked['data4D'].data # Data chunked along spatial dims\n # Use ridiculous small chunk size value to be sure it _isn't_ impacting computation.\n with dask.config.set({'array.chunk-size': '1MiB'}), pytest.warns(PerformanceWarning):\n outdata = regridder(indata)\n outdata_spec_tuple = regridder(indata, output_chunks=test_output_chunks_tuple)\n outdata_spec_dict = regridder(indata, output_chunks=test_output_chunks_dict)\n\n assert dask.is_dask_collection(outdata)\n assert dask.is_dask_collection(outdata_spec_tuple)\n assert dask.is_dask_collection(outdata_spec_dict)\n\n # Verify that the default chunking is correct\n assert outdata.shape == indata.shape[:-2] + horiz_shape_out\n assert outdata.chunksize == indata.chunksize\n\n # Verify that we get specified outputchunks when the argument is provided\n assert outdata_spec_tuple.shape == indata.shape[:-2] + horiz_shape_out\n assert outdata_spec_tuple.chunksize == indata.chunksize[:-2] + test_output_chunks_tuple\n\n assert outdata_spec_dict.shape == indata.shape[:-2] + horiz_shape_out\n assert (\n outdata_spec_dict.chunksize == indata.chunksize[:-2] + test_output_chunks_tuple\n ) # dict should've been converted to tuple\n\n\ndef test_para_weight_gen():\n # Generating weights in serial and parallel\n regridder = xe.Regridder(ds_in, ds_out, 'conservative')\n para_regridder = xe.Regridder(ds_in, ds_out_chunked, 'conservative', parallel=True)\n\n # weights should be identical between serial and parallel\n assert all(regridder.w.data.data == para_regridder.w.data.data)\n\n # Should work with a rectilinear version too (where dims == coords)\n ds_in_cf = xe.util.cf_grid_2d(-90, 90, 20, -45, 45, 12)\n ds_out_cf = xe.util.cf_grid_2d(-90, 90, 15, -45, 45, 9)\n ds_in_cf['data'] = xe.data.wave_smooth(ds_in_cf['lon'], ds_in_cf['lat'])\n ds_out_cf['data_ref'] = xe.data.wave_smooth(ds_out_cf['lon'], ds_out_cf['lat']).chunk(\n {'lat': 5, 'lon': 5}\n )\n\n # Generating weights in serial and parallel\n regridder = xe.Regridder(ds_in_cf, ds_out_cf, 'conservative')\n para_regridder = xe.Regridder(ds_in_cf, ds_out_cf, 'conservative', parallel=True)\n\n # weights should be identical between serial and parallel\n assert all(regridder.w.data.data == para_regridder.w.data.data)\n\n # Ensure para weight gen works with locstream_in as well\n reggrider_locs = xe.Regridder(ds_locs, ds_out_chunked, 'nearest_s2d', locstream_in=True)\n para_regridder_locs = xe.Regridder(\n ds_locs, ds_out_chunked, 'nearest_s2d', parallel=True, locstream_in=True\n )\n assert all(reggrider_locs.w.data.data == para_regridder_locs.w.data.data)\n\n # Same as above with locstream_out\n regridder_locs = xe.Regridder(ds_in, ds_locs, 'nearest_s2d', locstream_out=True)\n para_regridder_locs = xe.Regridder(\n ds_in, ds_locs_chunked, 'nearest_s2d', parallel=True, locstream_out=True\n )\n assert all(regridder_locs.w.data.data == para_regridder_locs.w.data.data)\n\n\ndef test_para_weight_gen_errors():\n with pytest.raises(ValueError, match='requires the output grid to have chunks'):\n xe.Regridder(ds_in, ds_out, 'conservative', parallel=True)\n\n\ndef test_regrid_dataset():\n # xarray.Dataset containing in-memory numpy array\n regridder = xe.Regridder(ds_in, ds_out, 'conservative')\n\n # `ds_out` already refers to output grid object\n # TODO: use more consistent variable namings across tests\n ds_result = regridder(ds_in)\n\n # output should contain all data variables\n assert set(ds_result.data_vars.keys()) == set(ds_in.data_vars.keys())\n\n # compare with analytical solution\n rel_err = (ds_out['data_ref'] - ds_result['data']) / ds_out['data_ref']\n assert np.max(np.abs(rel_err)) < 0.05\n\n # data over broadcasting dimensions should agree\n assert_almost_equal(\n ds_in['data4D'].values.mean(axis=(2, 3)),\n ds_result['data4D'].values.mean(axis=(2, 3)),\n decimal=10,\n )\n\n assert ds_result['data4D'].dtype == np.dtype('f8')\n assert ds_result['data4D_f4'].dtype == np.dtype('f4')\n\n # check metadata\n xr.testing.assert_identical(ds_result['time'], ds_in['time'])\n xr.testing.assert_identical(ds_result['lev'], ds_in['lev'])\n assert_equal(ds_result['lat'].values, ds_out['lat'].values)\n assert_equal(ds_result['lon'].values, ds_out['lon'].values)\n\n # Allow (but skip) other non spatial variables\n ds_result2 = regridder(ds_in.assign(nonspatial=ds_in.x * ds_in.time))\n xr.testing.assert_identical(ds_result2, ds_result)\n\n\ndef test_regrid_dataset_extracoords():\n ds_out2 = ds_out.copy().assign_coords(\n x=np.arange(24),\n y=np.arange(20), # coords to be transfered\n latitude_longitude=xr.DataArray(), # grid_mapping\n bogus=ds_out.lev * ds_out.lon, # coords not to be transfered\n scalar1=1, #\n scalar2=1, #\n )\n ds_out2['data_ref'].attrs['grid_mapping'] = 'latitude_longitude'\n ds_out2['data4D_ref'].attrs['grid_mapping'] = 'latitude_longitude'\n\n ds_in2 = ds_in.assign_coords(scalar2=5)\n regridder = xe.Regridder(ds_in, ds_out2, 'conservative')\n ds_result = regridder(ds_in2)\n\n assert 'x' in ds_result.coords\n assert 'y' in ds_result.coords\n assert 'bogus' not in ds_result.coords\n assert 'scalar1' not in ds_result.coords\n assert ds_result.scalar2 == 5\n assert 'latitude_longitude' in ds_result.coords\n\n\n@pytest.mark.parametrize('scheduler', dask_schedulers)\ndef test_regrid_dataset_dask(request, scheduler):\n scheduler = request.getfixturevalue(scheduler)\n # xarray.Dataset containing dask array\n regridder = xe.Regridder(ds_in, ds_out, 'conservative')\n\n # `ds_out` already refers to output grid object\n ds_result = regridder(ds_in.chunk())\n\n # output should contain all data variables\n assert set(ds_result.data_vars.keys()) == set(ds_in.data_vars.keys())\n assert dask.is_dask_collection(ds_result)\n assert ds_result.data.dtype == ds_in.data.dtype\n\n ds_in_f4 = ds_in.copy()\n ds_in_f4['data'] = ds_in_f4.data.astype('float32')\n ds_in_f4['data4D'] = ds_in_f4.data4D.astype('float32')\n ds_result = regridder(ds_in_f4.chunk())\n assert ds_result.data.dtype == 'float32'\n\n\ndef test_regrid_dataset_to_locstream():\n # xarray.Dataset containing in-memory numpy array\n\n regridder = xe.Regridder(ds_in, ds_locs, 'bilinear', locstream_out=True)\n regridder(ds_in)\n\n\n@pytest.mark.parametrize('transpose', [True, False])\ndef test_build_regridder_with_masks(transpose):\n dsi = ds_in.copy()\n mask = xr.DataArray(np.random.randint(2, size=ds_in['data'].shape), dims=('y', 'x'))\n if transpose:\n mask = mask.T\n dsi['mask'] = mask\n # 'patch' is too slow to test\n for method in [\n 'bilinear',\n 'conservative',\n 'conservative_normed',\n 'nearest_s2d',\n 'nearest_d2s',\n ]:\n regridder = xe.Regridder(dsi, ds_out, method)\n\n # check screen output\n assert repr(regridder) == str(regridder)\n assert 'xESMF Regridder' in str(regridder)\n assert method in str(regridder)\n\n\ndef test_regrid_dataset_from_locstream():\n # xarray.Dataset containing in-memory numpy array\n\n regridder = xe.Regridder(ds_locs, ds_in, 'nearest_s2d', locstream_in=True)\n regridder(ds_locs)\n\n\ndef test_ds_to_ESMFlocstream():\n try:\n import esmpy as ESMF\n except ImportError:\n import ESMF\n\n from xesmf.frontend import ds_to_ESMFlocstream\n\n locstream, shape, names = ds_to_ESMFlocstream(ds_locs)\n assert isinstance(locstream, ESMF.LocStream)\n assert shape == (\n 1,\n 4,\n )\n assert names == ('locations',)\n with pytest.raises(ValueError):\n locstream, shape, names = ds_to_ESMFlocstream(ds_in)\n ds_bogus = ds_in.copy()\n ds_bogus['lon'] = ds_locs['lon']\n with pytest.raises(ValueError):\n locstream, shape, names = ds_to_ESMFlocstream(ds_bogus)\n\n\n@pytest.mark.parametrize('use_dask', [True, False])\n@pytest.mark.parametrize('poly,exp', list(zip(polys, exps_polys)))\ndef test_spatial_averager(poly, exp, use_dask):\n if isinstance(poly, (Polygon, MultiPolygon)):\n poly = [poly]\n if use_dask:\n ds_in = ds_savg.chunk(lat=10)\n else:\n ds_in = ds_savg\n savg = xe.SpatialAverager(ds_in, poly, geom_dim_name='my_geom')\n out = savg(ds_in.abc)\n assert_allclose(out, exp, rtol=1e-3)\n\n assert 'my_geom' in out.dims\n\n\ndef test_spatial_averager_warns():\n with pytest.warns(UserWarning, match=r'contains large \\(> 1°\\) segments.'):\n xe.SpatialAverager(ds_savg, [polys_raw[0]], geom_dim_name='my_geom')\n\n\ndef test_spatial_averager_with_zonal_region():\n # We expect the spatial average for all regions to be one\n zonal_south = Polygon([(0, -90), (10, 0), (0, 0)])\n zonal_north = Polygon([(0, 90), (10, 0), (0, 0)])\n zonal_short = Polygon([(0, -10), (10, -10), (10, 10), (0, 10)])\n zonal_full = Polygon([(0, -90), (10, 0), (0, 90), (0, 0)]) # This yields 0... why?\n\n polys = [zonal_south, zonal_north, zonal_short, zonal_full]\n polys = segmentize(polys, 0.9)\n\n # Create field of ones on a global grid\n ds = xe.util.grid_global(20, 12, cf=True)\n ds['a'] = xr.DataArray(\n np.ones((ds.lon.size, ds.lat.size)),\n coords={'lat': ds.lat, 'lon': ds.lon},\n dims=('lon', 'lat'),\n )\n out = xe.SpatialAverager(ds, polys)(ds.a)\n assert_allclose(out, 1, rtol=1e-3)\n\n\n@pytest.mark.filterwarnings('ignore:`polys` contains large')\ndef test_compare_weights_from_poly_and_grid():\n \"\"\"Confirm that the weights are identical when they are computed from a grid->grid and grid->poly.\"\"\"\n pytest.importorskip(\n 'cf_xarray', minversion='0.10.9', reason='cf-xarray 0.10.8 broken for singleton coordinates'\n )\n # Global grid\n ds = xe.util.grid_global(20, 12, cf=True)\n\n # A single destination tile\n tile = xe.util.cf_grid_2d(-40, -80, -40, 0, -80, -80)\n ds['a'] = xr.DataArray(\n np.ones((ds.lon.size, ds.lat.size)),\n coords={'lat': ds.lat, 'lon': ds.lon},\n dims=('lon', 'lat'),\n )\n\n # Create polygon from tile corners\n x1, x2 = tile.lon_bounds.isel(lon=0)\n y1, y2 = tile.lat_bounds.isel(lat=0)\n poly = Polygon([(x1, y1), (x2, y1), (x2, y2), (x1, y2)])\n\n # Regrid using two identical destination grids (in theory)\n rgrid = xe.Regridder(ds, tile, method='conservative')\n rpoly = xe.SpatialAverager(ds, [poly])\n\n # Visualize the weights\n wg = np.reshape(rgrid.weights.data.todense(), ds.a.T.shape)\n wp = np.reshape(rpoly.weights.data.todense(), ds.a.T.shape)\n\n # Normally, weights should be identical, but this fails\n np.testing.assert_array_almost_equal(wg, wp)\n\n # Check that source area affects weights\n ds['wg'] = (('lat', 'lon'), wg)\n ds['wp'] = (('lat', 'lon'), wp)\n # i.e. weights are larger for a cell closer to the equator, considering two completely covered cells\n assert ds.wg.sel(lon=-70, lat=-60) > ds.wg.sel(lon=-70, lat=-72)\n\n\ndef test_polys_to_ESMFmesh():\n try:\n import esmpy as ESMF\n except ImportError:\n import ESMF\n\n from xesmf.frontend import polys_to_ESMFmesh\n\n # No overlap but multi + holes\n with warnings.catch_warnings(record=True) as rec:\n mesh, shape = polys_to_ESMFmesh([polys[1], polys[2], polys[4]])\n\n assert isinstance(mesh, ESMF.Mesh)\n assert shape == (1, 4)\n assert len(rec) == 1\n assert 'Some passed polygons have holes' in rec[0].message.args[0]\n\n\n@pytest.mark.parametrize(\n 'method, skipna, na_thres, nvalid',\n [\n ('bilinear', False, 1.0, 380),\n ('bilinear', True, 1.0, 395),\n ('bilinear', True, 0.0, 380),\n ('bilinear', True, 0.5, 388),\n ('bilinear', True, 1.0, 395),\n ('conservative', False, 1.0, 385),\n ('conservative', True, 1.0, 394),\n ('conservative', True, 0.0, 385),\n ('conservative', True, 0.5, 388),\n ('conservative', True, 1.0, 394),\n ],\n)\ndef test_skipna(method, skipna, na_thres, nvalid):\n dai = ds_in['data4D'].copy()\n dai[0, 0, 4:6, 4:6] = np.nan\n rg = xe.Regridder(ds_in, ds_out, method)\n dao = rg(dai, skipna=skipna, na_thres=na_thres)\n assert int(dao[0, 0, 1:-1, 1:-1].notnull().sum()) == nvalid\n\n\ndef test_non_cf_latlon():\n ds_in_noncf = ds_in.copy()\n ds_in_noncf.lon.attrs = {}\n ds_in_noncf.lat.attrs = {}\n # Test non-CF lat/lon extraction for both DataArray and Dataset\n xe.Regridder(ds_in_noncf['data'], ds_out, 'bilinear')\n xe.Regridder(ds_in_noncf, ds_out, 'bilinear')\n\n\n@pytest.mark.parametrize(\n 'var_renamer,dim_out',\n [\n ({}, 'locations'),\n ({'lon': {'locations': 'foo'}, 'lat': {'locations': 'foo'}}, 'foo'),\n ({'lon': {'locations': 'foo'}, 'lat': {'locations': 'bar'}}, None),\n ],\n)\ndef test_locstream_dim_name(var_renamer, dim_out):\n ds_locs_renamed = ds_locs.copy()\n for var, renamer in var_renamer.items():\n ds_locs_renamed[var] = ds_locs_renamed[var].rename(renamer)\n\n if dim_out is None:\n with pytest.raises(ValueError, match='not specified along the same dimension'):\n regridder = xe.Regridder(ds_in, ds_locs_renamed, 'bilinear', locstream_out=True)\n else:\n regridder = xe.Regridder(ds_in, ds_locs_renamed, 'bilinear', locstream_out=True)\n expected = {'lev', 'time', 'x_b', 'y_b', dim_out}\n actual = set(regridder(ds_in).dims)\n assert expected == actual\n\n\ndef test_spatial_averager_mask():\n poly = Polygon([[0.5, 0.5], [1.5, 0.5], [1.5, 1.5], [0.5, 1.5]])\n ds = ds_savg.copy(deep=True)\n ds.abc[1, 1] = np.nan\n savg = xe.SpatialAverager(ds, [poly], geom_dim_name='my_geom')\n\n # Without mask, we expect NaNs to propagate\n out = savg(ds.abc)\n assert out.isnull()\n\n # With masking, the NaN should be ignored.\n mask = ds.abc.notnull()\n dsm = ds.assign(\n mask=mask.T\n ) # TODO: open an issue about the fact that this fails without a transpose.\n savg = xe.SpatialAverager(dsm, [poly], geom_dim_name='my_geom')\n out = savg(dsm.abc)\n assert_allclose(out, 2, rtol=1e-3)\n\n\ndef test_post_mask_source():\n # Define source and target grids\n ds_in = xr.Dataset(\n {'lat': (['lat'], np.linspace(1, 10, 10)), 'lon': (['lon'], np.linspace(1, 10, 10))}\n )\n ds_out = xr.Dataset(\n {'lat': (['lat'], np.linspace(0, 15, 20)), 'lon': (['lon'], np.linspace(0, 15, 20))}\n )\n\n # Build regridder with post_source_mask='domain_edge'\n regridder = xe.Regridder(ds_in, ds_out, 'nearest_s2d', post_mask_source='domain_edge')\n\n # Regridding\n da_in = xr.DataArray(np.ones((10, 10)), dims=['lat', 'lon'])\n da_in[0, :] = 10 # top edge\n da_in[-1, :] = 10 # bottom edge\n da_out = regridder(da_in)\n\n # Check that edge values (10) did not make it into the result\n assert not np.any(da_out.values > 1), 'Edge contributions were not removed'\n\n # Check that cells outside the original domain are 0\n lat_mask = (ds_out.lat >= 10) | (ds_out.lat <= 1)\n lon_mask = (ds_out.lon >= 10) | (ds_out.lon <= 1)\n assert np.all(da_out.sel(lat=lat_mask).values == 0)\n assert np.all(da_out.sel(lon=lon_mask).values == 0)\n\n\ndef test_post_mask_source_exceptions():\n # LocStream source should give an exception\n ds_src = xr.Dataset(\n {\n 'lat': (['location'], np.linspace(-90, 90, 5)),\n 'lon': (['location'], np.linspace(-180, 180, 5)),\n }\n )\n ds_dst = xe.util.grid_global(5, 5)\n with pytest.raises(ValueError, match=\"post_mask_source='domain_edge' is only supported.*\"):\n xe.Regridder(\n ds_src, ds_dst, 'nearest_s2d', post_mask_source='domain_edge', locstream_in=True\n )\n\n # Not using an array-like should give an exception\n ds_src = xe.util.grid_global(10, 10)\n\n class NotArrayLike:\n def __array__(self, dtype=None):\n raise ValueError\n\n with pytest.raises(TypeError, match='must be array-like of integers'):\n xe.Regridder(ds_src, ds_dst, 'bilinear', post_mask_source=NotArrayLike())\n\n # Not using integer indices should give an exception\n bad_index_mask = np.array([0.1, 2.5, 3.7])\n with pytest.raises(TypeError, match='must be of integer type'):\n xe.Regridder(ds_src, ds_dst, 'bilinear', post_mask_source=bad_index_mask)\n\n # Using out of bounds indices should give an exception\n bad_index_mask = np.array([100, 200, 300, 400, 500, 600, 700])\n with pytest.raises(ValueError, match='indices are out of valid range \\\\[0, 648\\\\)'):\n xe.Regridder(ds_src, ds_dst, 'bilinear', post_mask_source=bad_index_mask)\n\n\ndef test_post_mask_source_parallel_mode():\n # Create source and destination grids\n ds_src = xe.util.grid_global(10, 10)\n ds_dst = xe.util.grid_global(15, 15)\n\n # Chunk destination data to enable parallel mode\n mask = np.ones((12, 24))\n ds_dst['mask'] = xr.DataArray(data=mask, dims=('y', 'x'))\n ds_dst = ds_dst.chunk({'y': 12, 'x': 12})\n\n # Define source data with known values\n data = xr.DataArray(\n np.ones((18, 36)),\n dims=['y', 'x'],\n coords={'lat': ds_src['lat'], 'lon': ds_src['lon']},\n )\n\n # Choose some source indices to mask (e.g., corners)\n mask_indices = np.array([0, 1, 2, 3, 4])\n\n # Create regridder with post_mask_source and parallel=True\n regridder = xe.Regridder(\n ds_src,\n ds_dst,\n 'nearest_s2d',\n post_mask_source=mask_indices,\n parallel=True,\n unmapped_to_nan=True,\n )\n\n # Regrid and compute the result\n result = regridder(data).compute()\n\n # Assert that the result contains NaNs\n assert result.shape == (12, 24)\n assert isinstance(result, xr.DataArray)\n assert result.isnull().any()\n\n\ndef test_locstream_input_grid_output_with_target_mask_applied():\n # Create locstream input (6 coordinate points)\n locstream_in = xr.Dataset(\n {'var': xr.DataArray(np.ones((6)), dims=['location'])},\n coords={\n 'lat': ('location', np.linspace(0, 5, 6)),\n 'lon': ('location', np.linspace(0, 10, 6)),\n },\n )\n\n # Create Grid output with target mask (3x3 grid)\n ds_out = xe.util.cf_grid_2d(0, 10, 10.0 / 3.0, 0, 5, 5.0 / 3.0)\n target_mask_2d = np.ones((3, 3), dtype=bool)\n target_mask_2d[-1, :] = False\n ds_out['target_mask'] = xr.DataArray(target_mask_2d, dims=['lat', 'lon'])\n\n # Create Grid output with target mask (3x3 grid)\n ds_out = xe.util.cf_grid_2d(0, 10, 10.0 / 3.0, 0, 5, 5.0 / 3.0)\n target_mask_2d = np.ones((3, 3), dtype=bool)\n target_mask_2d[-1, :] = False\n ds_out['mask'] = xr.DataArray(target_mask_2d, dims=['lat', 'lon'])\n\n # Generate weights\n regridder = xe.Regridder(\n ds_in=locstream_in, ds_out=ds_out, method='nearest_s2d', locstream_in=True\n )\n\n # Apply weights and check results - the northmost cells should be masked\n da_out = regridder(locstream_in)['var']\n assert np.all(np.isnan(da_out[-1, :]))\n assert np.all(da_out[:-1, :] == 1)\n\n\ndef test_input_output_dims():\n # The only way that inputs_dims is necessary is when numpy arrays are passed through dictionaries\n grid_in = {'lon': ds_in.lon.values, 'lat': ds_in.lat.values}\n grid_out = {'lon': ds_out.lon.values, 'lat': ds_out.lat.values}\n\n regridder = xe.Regridder(\n grid_in, grid_out, 'bilinear', input_dims=('y', 'x'), output_dims=('y2', 'x2')\n )\n ds_result = regridder(ds_in.data.T)\n\n # Check the dimensions of the output\n assert 'y2' in ds_result.dims\n assert 'x2' in ds_result.dims\n\n # Check that the shapes match the output grid\n assert ds_result['y2'].shape == ds_out['y'].shape\n assert ds_result['x2'].shape == ds_out['x'].shape\n" }, { "path": "xesmf/tests/test_oceanmodels.py", "content": "import cftime\nimport numpy as np\nimport pytest\nimport xarray as xr\n\nimport xesmf\n\nmom6like = xr.Dataset(\n data_vars=dict(\n tos=(['time', 'yh', 'xh'], np.random.rand(2, 180, 360)),\n ),\n coords=dict(\n xq=xr.DataArray(\n np.arange(-300, 60 + 1),\n dims=['xq'],\n attrs={\n 'long_name': 'q point nominal longitude',\n 'units': 'degrees_east',\n 'cartesian_axis': 'X',\n },\n ),\n yq=xr.DataArray(\n np.arange(-90, 90 + 1),\n dims=['yq'],\n attrs={\n 'long_name': 'q point nominal latitude',\n 'units': 'degrees_north',\n 'cartesian_axis': 'Y',\n },\n ),\n xh=xr.DataArray(\n 0.5 + np.arange(-300, 60),\n dims=['xh'],\n attrs={\n 'long_name': 'h point nominal longitude',\n 'units': 'degrees_east',\n 'cartesian_axis': 'X',\n },\n ),\n yh=xr.DataArray(\n 0.5 + np.arange(-90, 90),\n dims=['yh'],\n attrs={\n 'long_name': 'h point nominal latitude',\n 'units': 'degrees_north',\n 'cartesian_axis': 'Y',\n },\n ),\n time=xr.DataArray(\n [\n cftime.DatetimeNoLeap(2007, 1, 16, 12, 0, 0, 0),\n cftime.DatetimeNoLeap(2007, 2, 15, 0, 0, 0, 0),\n ],\n dims=['time'],\n ),\n reference_time=cftime.DatetimeNoLeap(1901, 1, 1, 0, 0, 0, 0),\n ),\n attrs=dict(description='Synthetic MOM6 data'),\n)\n\n\ndef test_mom6like_to_5x5():\n \"\"\"regression test for MOM6 grid\"\"\"\n\n grid_5x5 = xr.Dataset()\n grid_5x5['lon'] = xr.DataArray(data=0.5 + np.arange(0, 360, 5), dims=('x'))\n grid_5x5['lat'] = xr.DataArray(data=0.5 - 90 + np.arange(0, 180, 5), dims=('y'))\n\n # multiple definition for lon/lat results in failure to determine\n # which coordinate set to use.\n with pytest.raises(ValueError):\n regrid_to_5x5 = xesmf.Regridder(mom6like, grid_5x5, 'bilinear', periodic=True)\n\n regrid_to_5x5 = xesmf.Regridder(\n mom6like.rename({'xh': 'lon', 'yh': 'lat'}), grid_5x5, 'bilinear', periodic=True\n )\n\n with pytest.warns(UserWarning, match=r\"Using dimensions \\('yh', 'xh'\\)\"):\n tos_regridded = regrid_to_5x5(mom6like['tos'])\n assert tos_regridded.shape == ((2, 36, 72))\n" }, { "path": "xesmf/tests/test_smm.py", "content": "import numpy as np\nimport pytest\nimport sparse as sps\nimport xarray as xr\n\nimport xesmf as xe\n\n\ndef test_add_nans_to_weights():\n \"\"\"testing adding Nans to empty rows in sparse matrix\"\"\"\n # create input sparse matrix with one empty row (j=2)\n coords = np.array([[0, 3, 1, 0], [0, 3, 1, 2]])\n data = np.array([4.0, 5.0, 7.0, 9.0])\n Matin = sps.COO(coords, data, shape=(4, 4))\n\n # this is what is expected to come out (Nan added at i=0, j=2)\n coords = np.array([[0, 3, 1, 0, 2], [0, 3, 1, 2, 0]])\n data = np.array([4.0, 5.0, 7.0, 9.0, np.nan])\n expected = sps.COO(coords, data, shape=(4, 4))\n\n Matout = xe.smm.add_nans_to_weights(xr.DataArray(Matin, dims=('in', 'out')))\n assert np.allclose(expected.todense(), Matout.data.todense(), equal_nan=True)\n\n # Matrix without empty rows should return the same\n coords = np.array([[0, 3, 1, 0, 2], [0, 3, 1, 2, 1]])\n data = np.array([4.0, 5.0, 7.0, 9.0, 10.0])\n Matin = sps.COO(coords, data, shape=(4, 4))\n\n Matout = xe.smm.add_nans_to_weights(xr.DataArray(Matin, dims=('in', 'out')))\n assert np.allclose(Matin.todense(), Matout.data.todense())\n\n\ndef test_mask_source_indices():\n # Create a small sparse matrix\n coords = np.array([[0, 1, 2, 3, 4, 8], [0, 2, 4, 10, 11, 19]])\n data = np.array([0.3, 0.5, 0.2, 0.26, 0.7, 0.25])\n shape = (10, 20)\n W = sps.COO(coords, data, shape=shape)\n weights = xr.DataArray(W, dims=('out_dim', 'in_dim'))\n\n masked = xe.smm.mask_source_indices(weights, source_indices_to_mask=[4, 11])\n\n # Only selected entries should remain\n expected_coords = np.array([[0, 1, 3, 8], [0, 2, 10, 19]])\n expected_data = np.array([0.3, 0.5, 0.26, 0.25])\n\n assert np.array_equal(masked.data.coords, expected_coords)\n assert np.allclose(masked.data.data, expected_data)\n\n\ndef test_gen_mask_from_weights():\n \"\"\"testing creating mask out of weight matrix Nans\"\"\"\n # Create input and output Dataset\n ds_in = xe.util.grid_2d(20, 40, 1, 20, 30, 1)\n ds_out = xe.util.grid_2d(20, 40, 2, 20, 30, 2)\n\n # Create random mask for ds_out\n mask = np.random.randint(low=0, high=2, size=(5, 10), dtype=np.int32)\n ds_out['mask'] = xr.DataArray(data=mask, dims=['y', 'x'])\n\n # Create remapping weights\n Weights = xe.Regridder(ds_in, ds_out, method='bilinear').weights\n\n # Generate mask from weights\n maskwgts = xe.smm.gen_mask_from_weights(Weights, 5, 10)\n\n # Assert equality between both masks\n assert np.array_equal(mask, maskwgts, equal_nan=False)\n\n\ndef test_post_apply_target_mask_to_weights():\n # Create a small sparse weights matrix with shape (9 target, 4 source)\n # coords = [[target_indices], [source_indices]]\n coords = np.array([[0, 1, 1, 2, 3, 3, 4, 5], [0, 0, 1, 1, 2, 3, 2, 3]])\n data = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.45, 0.7, 0.8])\n shape = (6, 4)\n W_sparse = sps.COO(coords, data, shape=shape)\n weights = xr.DataArray(W_sparse, dims=('out_dim', 'in_dim'))\n\n # Define a 3x3 mask for target (flattened size = 9):\n # If all goes to plan, weights of cells 3 and 4 (i.e. index 2 and 3)\n # will be set to 0.\n target_mask_2d = np.array([[True, False], [True, True], [False, True]])\n\n # Apply mask\n masked_weights = xe.smm.post_apply_target_mask_to_weights(weights, target_mask_2d)\n\n # Check results\n np.testing.assert_array_equal(masked_weights.data.data, np.array([0.1, 0.2, 0.3, 0.7, 0.8]))\n np.testing.assert_array_equal(\n masked_weights.data.coords, np.array([[0, 1, 1, 4, 5], [0, 0, 1, 2, 3]])\n )\n\n\ndef test_post_apply_target_mask_to_weights_exceptions():\n # Create a weights DataArray & mask\n coords = np.array([[0, 1], [0, 1]])\n data = np.array([0.5, 0.5])\n shape = (2, 2)\n W_sparse = sps.COO(coords, data, shape=shape)\n weights = xr.DataArray(W_sparse, dims=('out_dim', 'in_dim'))\n valid_mask = np.array([[True, False]])\n\n # Mask not array-like\n with pytest.raises(\n TypeError,\n match=\"Argument 'target_mask_2d' must be array-like and convertible to a numeric/boolean array\",\n ):\n xe.smm.post_apply_target_mask_to_weights(weights, 'not_array_like')\n\n # Shape mismatch\n wrong_shape_mask = np.array([[True, False, True]])\n with pytest.raises(\n ValueError, match='Mismatch: weight matrix has 2 target cells, but mask has 3 elements'\n ):\n xe.smm.post_apply_target_mask_to_weights(weights, wrong_shape_mask)\n\n # Mask not 2D\n wrong_shape_mask = np.array([[[True]], [[True]]])\n with pytest.raises(\n ValueError, match=\"Argument 'target_mask_2d' must be 2D, got shape \\\\(2, 1, 1\\\\)\"\n ):\n xe.smm.post_apply_target_mask_to_weights(weights, wrong_shape_mask)\n\n # That should work\n xe.smm.post_apply_target_mask_to_weights(weights, valid_mask)\n" }, { "path": "xesmf/tests/test_util.py", "content": "import numpy as np\nimport pytest\nfrom numpy.testing import assert_almost_equal\n\nimport xesmf as xe\n\n\ndef test_grid_global():\n ds = xe.util.grid_global(1.5, 1.5)\n refshape = (120, 240)\n refshape_b = (121, 241)\n\n assert ds['lon'].values.shape == refshape\n assert ds['lat'].values.shape == refshape\n assert ds['lon_b'].values.shape == refshape_b\n assert ds['lat_b'].values.shape == refshape_b\n\n # Issue #181 (https://github.com/pangeo-data/xESMF/issues/181)\n d_lon = 360 / 4320\n d_lat = 180 / 2160\n ds = xe.util.grid_global(d_lon, d_lat)\n assert ds.lon.max() <= 180\n\n ds = xe.util.grid_global(1.5, 1.5, lon1=180)\n assert ds['lon_b'].isel(x_b=-1)[-1] == 180\n\n ds = xe.util.grid_global(1.5, 1.5, lon1=360)\n assert ds['lon_b'].isel(x_b=-1)[-1] == 360\n\n\ndef test_grid_global_bad_resolution():\n with pytest.warns(UserWarning):\n xe.util.grid_global(1.5, 1.23)\n\n with pytest.warns(UserWarning):\n xe.util.grid_global(1.23, 1.5)\n\n\ndef test_cell_area():\n ds = xe.util.grid_global(2.5, 2)\n area = xe.util.cell_area(ds)\n\n # total area of a unit sphere\n assert_almost_equal(area.sum(), np.pi * 4)\n\n\ndef test_get_edge_indices_2d():\n ny, nx = 3, 5\n edge_inds = xe.util._get_edge_indices_2d(nx, ny)\n\n # Flat indices: [0, 1, 2, 3, 4,\n # 5, 6, 7, 8, 9,\n # 10, 11, 12, 13, 14]\n # Expected: all indices but 6, 7, 8\n expected = np.array([0, 1, 2, 3, 4, 5, 9, 10, 11, 12, 13, 14])\n\n assert np.array_equal(np.sort(edge_inds), expected)\n\n\ndef test_simple_tripolar_grid():\n lon, lat = xe.util.simple_tripolar_grid(360, 180, lat_cap=60, lon_cut=-300.0)\n\n assert lon.min() >= -300.0\n assert lon.max() <= 360.0 - 300.0\n assert lat.min() >= -90\n assert lat.max() <= 90\n\n lon, lat = xe.util.simple_tripolar_grid(180, 90, lat_cap=60, lon_cut=-300.0)\n\n assert lon.min() >= -300.0\n assert lon.max() <= 360.0 - 300.0\n assert lat.min() >= -90\n assert lat.max() <= 90\n" }, { "path": "xesmf/util.py", "content": "import warnings\n\nimport numpy as np\nimport xarray as xr\nfrom shapely.geometry import MultiPolygon, Polygon\n\ntry:\n import esmpy as ESMF\nexcept ImportError:\n import ESMF\n\nLON_CF_ATTRS = {'standard_name': 'longitude', 'units': 'degrees_east'}\nLAT_CF_ATTRS = {'standard_name': 'latitude', 'units': 'degrees_north'}\n\n\ndef _grid_1d(start_b, end_b, step):\n \"\"\"\n 1D grid centers and bounds\n\n Parameters\n ----------\n start_b, end_b : float\n start/end position. Bounds, not centers.\n\n step: float\n step size, i.e. grid resolution\n\n Returns\n -------\n centers : 1D numpy array\n\n bounds : 1D numpy array, with one more element than centers\n \"\"\"\n\n bounds = np.arange(start_b, end_b + step / 2, step)\n centers = (bounds[:-1] + bounds[1:]) / 2\n\n return centers, bounds\n\n\ndef grid_2d(lon0_b, lon1_b, d_lon, lat0_b, lat1_b, d_lat):\n \"\"\"\n 2D rectilinear grid centers and bounds\n\n Parameters\n ----------\n lon0_b, lon1_b : float\n Longitude bounds\n\n d_lon : float\n Longitude step size, i.e. grid resolution\n\n lat0_b, lat1_b : float\n Latitude bounds\n\n d_lat : float\n Latitude step size, i.e. grid resolution\n\n Returns\n -------\n ds : xarray DataSet with coordinate values\n\n \"\"\"\n\n lon_1d, lon_b_1d = _grid_1d(lon0_b, lon1_b, d_lon)\n lat_1d, lat_b_1d = _grid_1d(lat0_b, lat1_b, d_lat)\n\n lon, lat = np.meshgrid(lon_1d, lat_1d)\n lon_b, lat_b = np.meshgrid(lon_b_1d, lat_b_1d)\n\n ds = xr.Dataset(\n coords={\n 'lon': (['y', 'x'], lon, {'standard_name': 'longitude'}),\n 'lat': (['y', 'x'], lat, {'standard_name': 'latitude'}),\n 'lon_b': (['y_b', 'x_b'], lon_b),\n 'lat_b': (['y_b', 'x_b'], lat_b),\n }\n )\n\n return ds\n\n\ndef cf_grid_2d(lon0_b, lon1_b, d_lon, lat0_b, lat1_b, d_lat):\n \"\"\"\n CF compliant 2D rectilinear grid centers and bounds.\n\n Parameters\n ----------\n lon0_b, lon1_b : float\n Longitude bounds\n\n d_lon : float\n Longitude step size, i.e. grid resolution\n\n lat0_b, lat1_b : float\n Latitude bounds\n\n d_lat : float\n Latitude step size, i.e. grid resolution\n\n Returns\n -------\n ds : xarray.DataSet with coordinate values\n\n \"\"\"\n from cf_xarray import vertices_to_bounds\n\n lon_1d, lon_b_1d = _grid_1d(lon0_b, lon1_b, d_lon)\n lat_1d, lat_b_1d = _grid_1d(lat0_b, lat1_b, d_lat)\n\n ds = xr.Dataset(\n coords={\n 'lon': (\n 'lon',\n lon_1d,\n {'bounds': 'lon_bounds', **LON_CF_ATTRS},\n ),\n 'lat': (\n 'lat',\n lat_1d,\n {'bounds': 'lat_bounds', **LAT_CF_ATTRS},\n ),\n 'latitude_longitude': xr.DataArray(),\n 'lon_bounds': vertices_to_bounds(lon_b_1d, ('bound', 'lon')),\n 'lat_bounds': vertices_to_bounds(lat_b_1d, ('bound', 'lat')),\n },\n )\n\n return ds\n\n\ndef grid_global(d_lon, d_lat, cf=False, lon1=180):\n \"\"\"\n Global 2D rectilinear grid centers and bounds\n\n Parameters\n ----------\n d_lon : float\n Longitude step size, i.e. grid resolution\n d_lat : float\n Latitude step size, i.e. grid resolution\n cf : bool\n Return a CF compliant grid.\n lon1 : {180, 360}\n Right longitude bound. According to which convention is used longitudes will\n vary from -180 to 180 or from 0 to 360.\n\n Returns\n -------\n ds : xarray DataSet with coordinate values\n\n \"\"\"\n\n if not np.isclose(360 / d_lon, 360 // d_lon):\n warnings.warn(\n '360 cannot be divided by d_lon = {}, '\n 'might not cover the globe uniformly'.format(d_lon)\n )\n\n if not np.isclose(180 / d_lat, 180 // d_lat):\n warnings.warn(\n '180 cannot be divided by d_lat = {}, '\n 'might not cover the globe uniformly'.format(d_lat)\n )\n lon0 = lon1 - 360\n\n if cf:\n return cf_grid_2d(lon0, lon1, d_lon, -90, 90, d_lat)\n\n return grid_2d(lon0, lon1, d_lon, -90, 90, d_lat)\n\n\ndef _flatten_poly_list(polys):\n \"\"\"Iterator flattening MultiPolygons.\"\"\"\n for i, poly in enumerate(polys):\n if isinstance(poly, MultiPolygon):\n for sub_poly in poly.geoms:\n yield (i, sub_poly)\n else:\n yield (i, poly)\n\n\ndef split_polygons_and_holes(polys):\n \"\"\"Split the exterior boundaries and the holes for a list of polygons.\n\n If MultiPolygons are encountered in the list, they are flattened out\n in their constituents.\n\n Parameters\n ----------\n polys : Sequence of shapely Polygons or MultiPolygons\n\n Returns\n -------\n exteriors : list of Polygons\n The polygons without any holes\n holes : list of Polygons\n Holes of the polygons as polygons\n i_ext : list of integers\n The index in `polys` of each polygon in `exteriors`.\n i_hol : list of integers\n The index in `polys` of the owner of each hole in `holes`.\n \"\"\"\n exteriors = []\n holes = []\n i_ext = []\n i_hol = []\n for i, poly in _flatten_poly_list(polys):\n exteriors.append(Polygon(poly.exterior))\n i_ext.append(i)\n holes.extend(map(Polygon, poly.interiors))\n i_hol.extend([i] * len(poly.interiors))\n\n return exteriors, holes, i_ext, i_hol\n\n\n# Constants\nPI_180 = np.pi / 180.0\n_default_Re = 6371.0e3 # MIDAS\nHUGE = 1.0e30\n\n\ndef simple_tripolar_grid(nlons, nlats, lat_cap=60, lon_cut=-300):\n \"\"\"Generate a simple tripolar grid, regular under `lat_cap`.\n\n Parameters\n ----------\n nlons: int\n Number of longitude points.\n nlats: int\n Number of latitude points.\n lat_cap: float\n Latitude of the northern cap.\n lon_cut: float\n Longitude of the periodic boundary.\n\n \"\"\"\n\n # first generate the bipolar cap for north poles\n nj_cap = np.rint(nlats * lat_cap / 180.0).astype('int')\n\n lams, phis, _, _ = _generate_bipolar_cap_mesh(\n nlons, nj_cap, lat_cap, lon_cut, ensure_nj_even=True\n )\n\n # then extend south\n lams_south_1d = lams[0, :]\n phis_south_1d = np.linspace(-90, lat_cap, nlats - nj_cap + 1)[:-1]\n\n lams_south, phis_south = np.meshgrid(lams_south_1d, phis_south_1d)\n\n # concatenate the 2 parts\n lon = np.concatenate([lams_south, lams], axis=0)\n lat = np.concatenate([phis_south, phis], axis=0)\n\n return lon, lat\n\n\n# these functions are copied from https://github.com/NOAA-GFDL/ocean_model_grid_generator\n# rather than using the package as a dependency\n\n\ndef _bipolar_projection(lamg, phig, lon_bp, rp, metrics_only=False):\n \"\"\"Makes a stereographic bipolar projection of the input coordinate mesh (lamg,phig)\n Returns the projected coordinate mesh and their metric coefficients (h^-1).\n The input mesh must be a regular spherical grid capping the pole with:\n latitudes between 2*arctan(rp) and 90 degrees\n longitude between lon_bp and lonp+360\n \"\"\"\n # symmetry meridian resolution fix\n phig = 90 - 2 * np.arctan(np.tan(0.5 * (90 - phig) * PI_180) / rp) / PI_180\n tmp = _mdist(lamg, lon_bp) * PI_180\n sinla = np.sin(tmp) # This makes phis symmetric\n sphig = np.sin(phig * PI_180)\n alpha2 = (np.cos(tmp)) ** 2 # This makes dy symmetric\n beta2_inv = (np.tan(phig * PI_180)) ** 2\n rden = 1.0 / (1.0 + alpha2 * beta2_inv)\n\n if not metrics_only:\n B = sinla * np.sqrt(rden) # Actually two equations +- |B|\n # Deal with beta=0\n B = np.where(np.abs(beta2_inv) > HUGE, 0.0, B)\n lamc = np.arcsin(B) / PI_180\n # But this equation accepts 4 solutions for a given B, {l, 180-l, l+180, 360-l }\n # We have to pickup the \"correct\" root.\n # One way is simply to demand lamc to be continuous with lam on the equator phi=0\n # I am sure there is a more mathematically concrete way to do this.\n lamc = np.where((lamg - lon_bp > 90) & (lamg - lon_bp <= 180), 180 - lamc, lamc)\n lamc = np.where((lamg - lon_bp > 180) & (lamg - lon_bp <= 270), 180 + lamc, lamc)\n lamc = np.where((lamg - lon_bp > 270), 360 - lamc, lamc)\n # Along symmetry meridian choose lamc\n lamc = np.where(\n (lamg - lon_bp == 90), 90, lamc\n ) # Along symmetry meridian choose lamc=90-lon_bp\n lamc = np.where(\n (lamg - lon_bp == 270), 270, lamc\n ) # Along symmetry meridian choose lamc=270-lon_bp\n lams = lamc + lon_bp\n\n # Project back onto the larger (true) sphere so that the projected equator shrinks to latitude \\phi_P=lat0_tp\n # then we have tan(\\phi_s'/2)=tan(\\phi_p'/2)tan(\\phi_c'/2)\n A = sinla * sphig\n chic = np.arccos(A)\n phis = 90 - 2 * np.arctan(rp * np.tan(chic / 2)) / PI_180\n # Calculate the Metrics\n rden2 = 1.0 / (1 + (rp * np.tan(chic / 2)) ** 2)\n M_inv = rp * (1 + (np.tan(chic / 2)) ** 2) * rden2\n chig = (90 - phig) * PI_180\n rden2 = 1.0 / (1 + (rp * np.tan(chig / 2)) ** 2)\n N = rp * (1 + (np.tan(chig / 2)) ** 2) * rden2\n N_inv = 1 / N\n cos2phis = (np.cos(phis * PI_180)) ** 2\n\n h_j_inv_t1 = cos2phis * alpha2 * (1 - alpha2) * beta2_inv * (1 + beta2_inv) * (rden**2)\n h_j_inv_t2 = M_inv * M_inv * (1 - alpha2) * rden\n h_j_inv = h_j_inv_t1 + h_j_inv_t2\n\n # Deal with beta=0. Prove that cos2phis/alpha2 ---> 0 when alpha, beta ---> 0\n h_j_inv = np.where(np.abs(beta2_inv) > HUGE, M_inv * M_inv, h_j_inv)\n h_j_inv = np.sqrt(h_j_inv) * N_inv\n\n h_i_inv = cos2phis * (1 + beta2_inv) * (rden**2) + M_inv * M_inv * alpha2 * beta2_inv * rden\n # Deal with beta=0\n h_i_inv = np.where(np.abs(beta2_inv) > HUGE, M_inv * M_inv, h_i_inv)\n h_i_inv = np.sqrt(h_i_inv)\n\n if not metrics_only:\n return lams, phis, h_i_inv, h_j_inv\n else:\n return h_i_inv, h_j_inv\n\n\ndef _generate_bipolar_cap_mesh(Ni, Nj_ncap, lat0_bp, lon_bp, ensure_nj_even=True):\n # Define a (lon,lat) coordinate mesh on the Northern hemisphere of the globe sphere\n # such that the resolution of latg matches the desired resolution of the final grid along the symmetry meridian\n print('Generating bipolar grid bounded at latitude ', lat0_bp)\n if Nj_ncap % 2 != 0 and ensure_nj_even:\n print(' Supergrid has an odd number of area cells!')\n if ensure_nj_even:\n print(\" The number of j's is not even. Fixing this by cutting one row.\")\n Nj_ncap = Nj_ncap - 1\n\n lon_g = lon_bp + np.arange(Ni + 1) * 360.0 / float(Ni)\n lamg = np.tile(lon_g, (Nj_ncap + 1, 1))\n latg0_cap = lat0_bp + np.arange(Nj_ncap + 1) * (90 - lat0_bp) / float(Nj_ncap)\n phig = np.tile(latg0_cap.reshape((Nj_ncap + 1, 1)), (1, Ni + 1))\n rp = np.tan(0.5 * (90 - lat0_bp) * PI_180)\n lams, phis, h_i_inv, h_j_inv = _bipolar_projection(lamg, phig, lon_bp, rp)\n h_i_inv = h_i_inv[:, :-1] * 2 * np.pi / float(Ni)\n h_j_inv = h_j_inv[:-1, :] * PI_180 * (90 - lat0_bp) / float(Nj_ncap)\n print(' number of js=', phis.shape[0])\n return lams, phis, h_i_inv, h_j_inv\n\n\ndef _mdist(x1, x2):\n \"\"\"Returns positive distance modulo 360.\"\"\"\n return np.minimum(np.mod(x1 - x2, 360.0), np.mod(x2 - x1, 360.0))\n\n\n# end code from https://github.com/NOAA-GFDL/ocean_model_grid_generator\n\n\ndef cell_area(ds, earth_radius=None):\n \"\"\"\n Get cell area of a grid, assuming a sphere.\n\n Parameters\n ----------\n ds : xarray Dataset\n Input grid, longitude and latitude required.\n Curvilinear coordinate system also require cell bounds to be present.\n earth_radius : float, optional\n Earth radius, assuming a sphere, in km.\n\n Returns\n -------\n area : xarray DataArray\n Cell area. If the earth radius is given, units are km^2, otherwise they are steradian (sr).\n \"\"\"\n from .frontend import _get_lon_lat, ds_to_ESMFgrid # noqa\n\n grid, _, names = ds_to_ESMFgrid(ds, need_bounds=True)\n field = ESMF.Field(grid)\n field.get_area() # compute area\n\n # F-ordering to C-ordering\n # copy the array to make sure it persists after ESMF object is freed\n area = field.data.T.copy()\n field.destroy()\n\n # Wrap in xarray\n area = xr.DataArray(\n area,\n dims=names,\n attrs={\n 'units': 'sr',\n 'standard_name': 'cell_area',\n 'long_name': 'Cell area, assuming a sphere.',\n },\n )\n # Fancy trick to get all related coordinates without needing to list them explicitly.\n # We add all and let xarray choose which one to keep when selecting the variable\n area = ds.coords.to_dataset().assign(area=area).area\n\n if earth_radius is not None:\n area = (area * earth_radius**2).assign_attrs(units='km2')\n return area\n\n\ndef _get_edge_indices_2d(nlons, nlats):\n \"\"\"Get edge indices for a 2D nlats x nlons grid.\"\"\"\n edge_mask = np.zeros((nlats, nlons), dtype=bool)\n edge_mask[:1, :] = True\n edge_mask[-1:, :] = True\n edge_mask[:, :1] = True\n edge_mask[:, -1:] = True\n return np.where(edge_mask.ravel())[0]\n\n\ndef _unname_dataset(ds, sequence, dims, suffix):\n \"\"\"Rename everything in a dataset so that it can be aligned without modification with another.\"\"\"\n if sequence:\n dim = list(set(dims) - {'dummy'})[0]\n ds = ds.rename({dim: f'x{suffix}'})\n else:\n ds = ds.rename({dims[0]: f'y{suffix}', dims[1]: f'x{suffix}'})\n if ds[f'x{suffix}'].attrs.get('bounds'):\n ds = ds.rename({ds[f'x{suffix}'].attrs['bounds']: f'x{suffix}_bounds'})\n ds[f'x{suffix}'].attrs['bounds'] = f'x{suffix}_bounds'\n if not sequence and ds[f'y{suffix}'].attrs.get('bounds'):\n ds = ds.rename({ds[f'y{suffix}'].attrs['bounds']: f'y{suffix}_bounds'})\n ds[f'y{suffix}'].attrs['bounds'] = f'y{suffix}_bounds'\n\n # If coords and dims are the same, renaming has already been done.\n ds = ds.rename(\n {\n coord: coord + suffix\n for coord in ds.coords.keys()\n if coord not in (f'y{suffix}', f'x{suffix}')\n }\n )\n return ds\n\n\ndef _rename_dataset(ds, sequence, dims, suffix):\n \"\"\"Restore coordinate names from an \"unnamed\" dataset\"\"\"\n ds = ds.rename(\n {\n coord: coord.rstrip(suffix)\n for coord in ds.coords.keys()\n if coord not in dims\n and coord.endswith(suffix)\n and coord not in (f'y{suffix}', f'x{suffix}')\n }\n )\n if sequence:\n ds = ds.rename({f'x{suffix}': dims[0]})\n else:\n ds = ds.rename({f'y{suffix}': dims[0], f'x{suffix}': dims[1]})\n return ds\n" } ]