[
  {
    "path": ".coveragerc",
    "content": "[run]\nomit = */tests/*,bluepyopt/_version.py\n[report]\nomit=bluepyopt/_version.py\n"
  },
  {
    "path": ".gitattributes",
    "content": "bluepyopt/_version.py export-subst\n"
  },
  {
    "path": ".github/workflows/build.yml",
    "content": "name: Build\n\non:\n  push:\n    branches:\n      - master\n    tags:\n      - '[0-9]+.[0-9]+.[0-9]+'\n\njobs:\n  call-test-workflow:\n    uses: BlueBrain/BluePyOpt/.github/workflows/test.yml@master\n  \n  build-tag-n-publish:\n    name: Build, tag and publish on PyPI\n    runs-on: ubuntu-latest\n    needs: call-test-workflow\n    permissions:\n      contents: write\n    steps:\n      - uses: actions/checkout@v3\n\n      - name: Set up Python 3.10\n        uses: actions/setup-python@v4\n        with:\n          python-version: \"3.10\"\n\n      - name: Bump version and push tag\n        uses: anothrNick/github-tag-action@1.64.0\n        if: ${{ !startsWith(github.ref, 'refs/tags/') }}\n        id: tag\n        env:\n          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}\n          WITH_V: false\n          DEFAULT_BUMP: patch\n\n      - name: Build a source tarball and wheel\n        run: |\n            pip install build\n            python -m build\n\n      - name: Get and store tag from 'Bump version and push tag' step\n        if: ${{ !startsWith(github.ref, 'refs/tags/') }}\n        run: echo \"TAG_NAME=${{ steps.tag.outputs.new_tag }}\" >> $GITHUB_ENV\n      - name: Get and store tag from triggered tag push\n        if: ${{ startsWith(github.ref, 'refs/tags/') }}\n        run: echo \"TAG_NAME=${{ github.ref_name }}\" >> $GITHUB_ENV\n\n      - name: Release\n        uses: softprops/action-gh-release@v1\n        with:\n          tag_name: ${{ env.TAG_NAME }}\n          name: ${{ env.TAG_NAME }}\n          generate_release_notes: true\n\n      - name: Publish package to PyPI\n        uses: pypa/gh-action-pypi-publish@release/v1\n        with:\n          user: __token__\n          password: ${{ secrets.PYPI_PASSWORD }}\n"
  },
  {
    "path": ".github/workflows/keep-alive.yml",
    "content": "name: Keep-alive\n\non:\n  schedule:\n    # Runs every sunday at 3 a.m.\n    - cron: '0 3 * * SUN'\n\njobs:\n  call-test-workflow:\n    uses: BlueBrain/BluePyOpt/.github/workflows/test.yml@master\n  \n  keep-workflow-alive:\n    name: Keep workflow alive\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v2\n        with:\n          ref: master\n\n      - name: Get date from 50 days ago\n        run: |\n          datethen=`date -d \"-50 days\" --utc +%FT%TZ`\n          echo \"datelimit=$datethen\" >> $GITHUB_ENV\n\n      - name: setup git config\n        if: github.event.repository.pushed_at <= env.datelimit\n        run: |\n          # setup the username and email.\n          git config user.name \"Github Actions Keepalive Bot\"\n          git config user.email \"<>\"\n\n      - name: commit IF last commit is older than 50 days\n        if: github.event.repository.pushed_at <= env.datelimit\n        run: |\n          git commit -m \"Empty commit to keep the gihub workflows alive\" --allow-empty\n          git push origin master"
  },
  {
    "path": ".github/workflows/mirror-ebrains.yml",
    "content": "name: Mirror to Ebrains\n\non:\n  push:\n    branches: [ master ]\n\njobs:\n  to_ebrains:\n    runs-on: ubuntu-latest\n    steps:\n      - name: syncmaster\n        uses: wei/git-sync@v3\n        with:\n          source_repo: \"BlueBrain/BluePyOpt\"\n          source_branch: \"master\"\n          destination_repo: \"https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/BlueBrain/bluepyopt.git\"\n          destination_branch: \"master\"\n      - name: synctags\n        uses: wei/git-sync@v3\n        with:\n          source_repo: \"BlueBrain/BluePyOpt\"\n          source_branch: \"refs/tags/*\"\n          destination_repo: \"https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/BlueBrain/bluepyopt.git\"\n          destination_branch: \"refs/tags/*\""
  },
  {
    "path": ".github/workflows/test.yml",
    "content": "name: Test\n\non:\n  pull_request:\n  # allows this workflow to be reusable (e.g. by the build workflow)\n  workflow_call:\n\njobs:\n  test:\n    name: Test for python ${{ matrix.python-version }} on ${{ matrix.os }}\n    runs-on: ${{ matrix.os }}\n    strategy:\n      matrix:\n        os: [ubuntu-latest]\n        python-version: [\"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n        include:\n          - os: macos-12\n            python-version: \"3.10\"\n\n    steps:\n    - uses: actions/checkout@v2\n\n    - name: Set up Python ${{ matrix.python-version }}\n      uses: actions/setup-python@v2\n      with:\n        python-version: ${{ matrix.python-version }}\n\n    - name: Install dependencies\n      run: |\n        python -m pip install --upgrade pip setuptools\n        pip install tox tox-gh-actions\n\n    - name: Run tox\n      run: tox\n\n    - name: \"Upload coverage to Codecov\"\n      uses: codecov/codecov-action@v4\n      with:\n        token: ${{ secrets.CODECOV_TOKEN }}\n        fail_ci_if_error: false\n"
  },
  {
    "path": ".gitignore",
    "content": "*.pyc\n*.swp\nx86_64\n/bluepyopt.egg-info/\n/build/\n/dist/\n.DS_Store\n/.tox\n.ipynb_checkpoints\n/.python-version\n/cov_reports\n.coverage\ncoverage.xml\n.idea/"
  },
  {
    "path": ".readthedocs.yaml",
    "content": "# .readthedocs.yml\n# Read the Docs configuration file\n# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details\n\n# Required\nversion: 2\n\nsphinx:\n  configuration: docs/source/conf.py\n  fail_on_warning: true\n\npython:\n  install:\n    - method: pip\n      path: .\n    - requirements: requirements_docs.txt\n\nbuild:\n  os: ubuntu-22.04\n  tools:\n    python: \"3.10\"\n"
  },
  {
    "path": ".zenodo.json",
    "content": "{\n  \"title\" : \"BluePyOpt\",\n  \"license\": \"LGPL-3.0\",\n  \"upload_type\": \"software\",\n  \"description\": \"The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools. It simplifies the task of creating and sharing these optimisations, and the associated techniques and knowledge. This is achieved by abstracting the optimisation and evaluation tasks into various reusable and flexible discrete elements according to established best-practices. Further, BluePyOpt provides methods for setting up both small- and large-scale optimisations on a variety of platforms, ranging from laptops to Linux clusters and cloud-based compute infrastructures.\",\n  \"creators\": [\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Van Geit, Werner\",\n      \"orcid\": \"0000-0002-2915-720X\"\n    },\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Gevaert, Michael\",\n      \"orcid\": \"0000-0002-7547-3297\"\n    },\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Damart, Tanguy\",\n      \"orcid\": \"0000-0003-2175-7304\"\n    },\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Rössert, Christian\",\n      \"orcid\": \"0000-0002-4839-2424\"\n    },\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Courcol, Jean-Denis\",\n      \"orcid\": \"0000-0002-9351-1461\"\n    },\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Chindemi, Guiseppe\",\n      \"orcid\": \"0000-0001-6872-2366\"\n    },\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Jaquier, Aurélien\",\n      \"orcid\": \"0000-0001-6202-6175\"\n    },\n    {\n      \"affiliation\": \"Blue Brain Project, EPFL\",\n      \"name\": \"Muller, Eilif\",\n      \"orcid\": \"0000-0003-4309-8266\"\n    }\n  ]\n}\n"
  },
  {
    "path": "AUTHORS.txt",
    "content": "Werner Van Geit @ BBP\nChristian Roessert @ BBP\nMike Gevaert @ BBP\nJean-Denis Courcol @ BBP\nGiuseppe Chindemi @ BBP\nTanguy Damart @ BBP\nElisabetta Iavarone @ BBP\nAnil Tuncel @ BBP\nAurelien Jaquier @ BBP\n"
  },
  {
    "path": "COPYING",
    "content": "                    GNU GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n                            Preamble\n\n  The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n  The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works.  By contrast,\nthe GNU General Public License is intended to guarantee your freedom to\nshare and change all versions of a program--to make sure it remains free\nsoftware for all its users.  We, the Free Software Foundation, use the\nGNU General Public License for most of our software; it applies also to\nany other work released this way by its authors.  You can apply it to\nyour programs, too.\n\n  When we speak of free software, we are referring to freedom, not\nprice.  Our General Public Licenses are designed to make sure that you\nhave the freedom to distribute copies of free software (and charge for\nthem if you wish), that you receive source code or can get it if you\nwant it, that you can change the software or use pieces of it in new\nfree programs, and that you know you can do these things.\n\n  To protect your rights, we need to prevent others from denying you\nthese rights or asking you to surrender the rights.  Therefore, you have\ncertain responsibilities if you distribute copies of the software, or if\nyou modify it: responsibilities to respect the freedom of others.\n\n  For example, if you distribute copies of such a program, whether\ngratis or for a fee, you must pass on to the recipients the same\nfreedoms that you received.  You must make sure that they, too, receive\nor can get the source code.  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If your rights have been terminated and not permanently\nreinstated, you do not qualify to receive new licenses for the same\nmaterial under section 10.\n\n  9. Acceptance Not Required for Having Copies.\n\n  You are not required to accept this License in order to receive or\nrun a copy of the Program.  Ancillary propagation of a covered work\noccurring solely as a consequence of using peer-to-peer transmission\nto receive a copy likewise does not require acceptance.  However,\nnothing other than this License grants you permission to propagate or\nmodify any covered work.  These actions infringe copyright if you do\nnot accept this License.  Therefore, by modifying or propagating a\ncovered work, you indicate your acceptance of this License to do so.\n\n  10. Automatic Licensing of Downstream Recipients.\n\n  Each time you convey a covered work, the recipient automatically\nreceives a license from the original licensors, to run, modify and\npropagate that work, subject to this License.  You are not responsible\nfor enforcing compliance by third parties with this License.\n\n  An \"entity transaction\" is a transaction transferring control of an\norganization, or substantially all assets of one, or subdividing an\norganization, or merging organizations.  If propagation of a covered\nwork results from an entity transaction, each party to that\ntransaction who receives a copy of the work also receives whatever\nlicenses to the work the party's predecessor in interest had or could\ngive under the previous paragraph, plus a right to possession of the\nCorresponding Source of the work from the predecessor in interest, if\nthe predecessor has it or can get it with reasonable efforts.\n\n  You may not impose any further restrictions on the exercise of the\nrights granted or affirmed under this License.  For example, you may\nnot impose a license fee, royalty, or other charge for exercise of\nrights granted under this License, and you may not initiate litigation\n(including a cross-claim or counterclaim in a lawsuit) alleging that\nany patent claim is infringed by making, using, selling, offering for\nsale, or importing the Program or any portion of it.\n\n  11. Patents.\n\n  A \"contributor\" is a copyright holder who authorizes use under this\nLicense of the Program or a work on which the Program is based.  The\nwork thus licensed is called the contributor's \"contributor version\".\n\n  A contributor's \"essential patent claims\" are all patent claims\nowned or controlled by the contributor, whether already acquired or\nhereafter acquired, that would be infringed by some manner, permitted\nby this License, of making, using, or selling its contributor version,\nbut do not include claims that would be infringed only as a\nconsequence of further modification of the contributor version.  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To \"grant\" such a patent license to a\nparty means to make such an agreement or commitment not to enforce a\npatent against the party.\n\n  If you convey a covered work, knowingly relying on a patent license,\nand the Corresponding Source of the work is not available for anyone\nto copy, free of charge and under the terms of this License, through a\npublicly available network server or other readily accessible means,\nthen you must either (1) cause the Corresponding Source to be so\navailable, or (2) arrange to deprive yourself of the benefit of the\npatent license for this particular work, or (3) arrange, in a manner\nconsistent with the requirements of this License, to extend the patent\nlicense to downstream recipients.  \"Knowingly relying\" means you have\nactual knowledge that, but for the patent license, your conveying the\ncovered work in a country, or your recipient's use of the covered work\nin a country, would infringe one or more identifiable patents in that\ncountry that you have reason to believe are valid.\n\n  If, pursuant to or in connection with a single transaction or\narrangement, you convey, or propagate by procuring conveyance of, a\ncovered work, and grant a patent license to some of the parties\nreceiving the covered work authorizing them to use, propagate, modify\nor convey a specific copy of the covered work, then the patent license\nyou grant is automatically extended to all recipients of the covered\nwork and works based on it.\n\n  A patent license is \"discriminatory\" if it does not include within\nthe scope of its coverage, prohibits the exercise of, or is\nconditioned on the non-exercise of one or more of the rights that are\nspecifically granted under this License.  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No Surrender of Others' Freedom.\n\n  If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License.  If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all.  For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n  13. Use with the GNU Affero General Public License.\n\n  Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU Affero General Public License into a single\ncombined work, and to convey the resulting work.  The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the special requirements of the GNU Affero General Public License,\nsection 13, concerning interaction through a network will apply to the\ncombination as such.\n\n  14. Revised Versions of this License.\n\n  The Free Software Foundation may publish revised and/or new versions of\nthe GNU General Public License from time to time.  Such new versions will\nbe similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n  Each version is given a distinguishing version number.  If the\nProgram specifies that a certain numbered version of the GNU General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation.  If the Program does not specify a version number of the\nGNU General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n  If the Program specifies that a proxy can decide which future\nversions of the GNU General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n  Later license versions may give you additional or different\npermissions.  However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n  15. 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Interpretation of Sections 15 and 16.\n\n  If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n                     END OF TERMS AND CONDITIONS\n\n            How to Apply These Terms to Your New Programs\n\n  If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n  To do so, attach the following notices to the program.  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Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n  You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n<https://www.gnu.org/licenses/>.\n\n  The GNU General Public License does not permit incorporating your program\ninto proprietary programs.  If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library.  If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License.  But first, please read\n<https://www.gnu.org/licenses/why-not-lgpl.html>.\n\n\n"
  },
  {
    "path": "COPYING.lesser",
    "content": "                   GNU LESSER GENERAL PUBLIC LICENSE\n                       Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>\n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n\n  This version of the GNU Lesser General Public License incorporates\nthe terms and conditions of version 3 of the GNU General Public\nLicense, supplemented by the additional permissions listed below.\n\n  0. Additional Definitions.\n\n  As used herein, \"this License\" refers to version 3 of the GNU Lesser\nGeneral Public License, and the \"GNU GPL\" refers to version 3 of the GNU\nGeneral Public License.\n\n  \"The Library\" refers to a covered work governed by this License,\nother than an Application or a Combined Work as defined below.\n\n  An \"Application\" is any work that makes use of an interface provided\nby the Library, but which is not otherwise based on the Library.\nDefining a subclass of a class defined by the Library is deemed a mode\nof using an interface provided by the Library.\n\n  A \"Combined Work\" is a work produced by combining or linking an\nApplication with the Library.  The particular version of the Library\nwith which the Combined Work was made is also called the \"Linked\nVersion\".\n\n  The \"Minimal Corresponding Source\" for a Combined Work means the\nCorresponding Source for the Combined Work, excluding any source code\nfor portions of the Combined Work that, considered in isolation, are\nbased on the Application, and not on the Linked Version.\n\n  The \"Corresponding Application Code\" for a Combined Work means the\nobject code and/or source code for the Application, including any data\nand utility programs needed for reproducing the Combined Work from the\nApplication, but excluding the System Libraries of the Combined Work.\n\n  1. Exception to Section 3 of the GNU GPL.\n\n  You may convey a covered work under sections 3 and 4 of this License\nwithout being bound by section 3 of the GNU GPL.\n\n  2. Conveying Modified Versions.\n\n  If you modify a copy of the Library, and, in your modifications, a\nfacility refers to a function or data to be supplied by an Application\nthat uses the facility (other than as an argument passed when the\nfacility is invoked), then you may convey a copy of the modified\nversion:\n\n   a) under this License, provided that you make a good faith effort to\n   ensure that, in the event an Application does not supply the\n   function or data, the facility still operates, and performs\n   whatever part of its purpose remains meaningful, or\n\n   b) under the GNU GPL, with none of the additional permissions of\n   this License applicable to that copy.\n\n  3. Object Code Incorporating Material from Library Header Files.\n\n  The object code form of an Application may incorporate material from\na header file that is part of the Library.  You may convey such object\ncode under terms of your choice, provided that, if the incorporated\nmaterial is not limited to numerical parameters, data structure\nlayouts and accessors, or small macros, inline functions and templates\n(ten or fewer lines in length), you do both of the following:\n\n   a) Give prominent notice with each copy of the object code that the\n   Library is used in it and that the Library and its use are\n   covered by this License.\n\n   b) Accompany the object code with a copy of the GNU GPL and this license\n   document.\n\n  4. Combined Works.\n\n  You may convey a Combined Work under terms of your choice that,\ntaken together, effectively do not restrict modification of the\nportions of the Library contained in the Combined Work and reverse\nengineering for debugging such modifications, if you also do each of\nthe following:\n\n   a) Give prominent notice with each copy of the Combined Work that\n   the Library is used in it and that the Library and its use are\n   covered by this License.\n\n   b) Accompany the Combined Work with a copy of the GNU GPL and this license\n   document.\n\n   c) For a Combined Work that displays copyright notices during\n   execution, include the copyright notice for the Library among\n   these notices, as well as a reference directing the user to the\n   copies of the GNU GPL and this license document.\n\n   d) Do one of the following:\n\n       0) Convey the Minimal Corresponding Source under the terms of this\n       License, and the Corresponding Application Code in a form\n       suitable for, and under terms that permit, the user to\n       recombine or relink the Application with a modified version of\n       the Linked Version to produce a modified Combined Work, in the\n       manner specified by section 6 of the GNU GPL for conveying\n       Corresponding Source.\n\n       1) Use a suitable shared library mechanism for linking with the\n       Library.  A suitable mechanism is one that (a) uses at run time\n       a copy of the Library already present on the user's computer\n       system, and (b) will operate properly with a modified version\n       of the Library that is interface-compatible with the Linked\n       Version.\n\n   e) Provide Installation Information, but only if you would otherwise\n   be required to provide such information under section 6 of the\n   GNU GPL, and only to the extent that such information is\n   necessary to install and execute a modified version of the\n   Combined Work produced by recombining or relinking the\n   Application with a modified version of the Linked Version. (If\n   you use option 4d0, the Installation Information must accompany\n   the Minimal Corresponding Source and Corresponding Application\n   Code. If you use option 4d1, you must provide the Installation\n   Information in the manner specified by section 6 of the GNU GPL\n   for conveying Corresponding Source.)\n\n  5. Combined Libraries.\n\n  You may place library facilities that are a work based on the\nLibrary side by side in a single library together with other library\nfacilities that are not Applications and are not covered by this\nLicense, and convey such a combined library under terms of your\nchoice, if you do both of the following:\n\n   a) Accompany the combined library with a copy of the same work based\n   on the Library, uncombined with any other library facilities,\n   conveyed under the terms of this License.\n\n   b) Give prominent notice with the combined library that part of it\n   is a work based on the Library, and explaining where to find the\n   accompanying uncombined form of the same work.\n\n  6. Revised Versions of the GNU Lesser General Public License.\n\n  The Free Software Foundation may publish revised and/or new versions\nof the GNU Lesser General Public License from time to time. Such new\nversions will be similar in spirit to the present version, but may\ndiffer in detail to address new problems or concerns.\n\n  Each version is given a distinguishing version number. If the\nLibrary as you received it specifies that a certain numbered version\nof the GNU Lesser General Public License \"or any later version\"\napplies to it, you have the option of following the terms and\nconditions either of that published version or of any later version\npublished by the Free Software Foundation. If the Library as you\nreceived it does not specify a version number of the GNU Lesser\nGeneral Public License, you may choose any version of the GNU Lesser\nGeneral Public License ever published by the Free Software Foundation.\n\n  If the Library as you received it specifies that a proxy can decide\nwhether future versions of the GNU Lesser General Public License shall\napply, that proxy's public statement of acceptance of any version is\npermanent authorization for you to choose that version for the\nLibrary.\n"
  },
  {
    "path": "Dockerfile",
    "content": "# Copyright (c) 2016-2022, EPFL/Blue Brain Project\n#\n# This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n#\n# This library is free software; you can redistribute it and/or modify it under\n# the terms of the GNU Lesser General Public License version 3.0 as published\n# by the Free Software Foundation.\n#\n# This library is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n# details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with this library; if not, write to the Free Software Foundation, Inc.,\n# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\nFROM  andrewosh/binder-base\nMAINTAINER Werner Van Geit\n\nUSER root\n\nRUN apt-get update\nRUN apt-get install -y wget libx11-6 python-dev git build-essential libncurses-dev\nRUN wget https://bootstrap.pypa.io/get-pip.py\nRUN python get-pip.py\nRUN wget http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.x86_64.deb\nRUN dpkg -i nrn-7.4.x86_64.deb\nRUN rm nrn-7.4.x86_64.deb\n\nRUN pip install bluepyopt\n\nENV PYTHONPATH /usr/local/nrn/lib/python:$PYTHONPATH\n"
  },
  {
    "path": "LICENSE.txt",
    "content": "BluePyOpt - Bluebrain Python Optimisation Library\n\nBluePyOpt is licensed under the LGPL, unless noted otherwise, e.g., for external \ndependencies. See files COPYING and COPYING.lesser for the full license. \nExamples and test are BSD-licensed.\nExternal dependencies are either LGPL or BSD-licensed. \nSee file ACKNOWLEDGEMENTS.txt and AUTHORS.txt for further details.\n\nCopyright (c) Blue Brain Project/EPFL 2016-2022.\n\nThis program is free software: you can redistribute it and/or modify it under \nthe terms of the GNU Lesser General Public License as published by the \nFree Software Foundation, either version 3 of the License, or (at your option) \nany later version.\n\nThis program is distributed in the hope that it will be useful, \nbut WITHOUT ANY WARRANTY; \nwithout even the implied warranty of \nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  \n\nSee the GNU Lesser General Public License for more details.\n\nYou should have received a copy of the GNU Lesser General Public License \nalong with this program.  If not, see <http://www.gnu.org/licenses/>.\n"
  },
  {
    "path": "MANIFEST.in",
    "content": "include versioneer.py\ninclude bluepyopt/_version.py\ninclude bluepyopt/ephys/static/arbor_mechanisms.json\ninclude bluepyopt/ephys/templates/cell_template.jinja2\ninclude bluepyopt/ephys/templates/acc/_json_template.jinja2\ninclude bluepyopt/ephys/templates/acc/decor_acc_template.jinja2\ninclude bluepyopt/ephys/templates/acc/label_dict_acc_template.jinja2\n\ninclude.txt\ninclude AUTHORS.txt\ninclude COPYING\ninclude COPYING.lesser\nrecursive-include bluepyopt/tests *\n"
  },
  {
    "path": "Makefile",
    "content": "TEST_REQUIREMENTS=nose coverage mock\n\nall: install\ninstall:\n\tpip install -q . --upgrade\ndoc: install\n\tpip install -q sphinx sphinx-autobuild sphinx_rtd_theme\n\tcd docs; $(MAKE) clean; $(MAKE) html\ndocopen: doc\n\topen docs/build/html/index.html\ndocpdf: install\n\tpip install sphinx sphinx-autobuild\n\tcd docs; $(MAKE) clean; $(MAKE) latexpdf\nl5pc_nbconvert: jupyter\n\tcd examples/l5pc && \\\n\t\tjupyter nbconvert --to python L5PC.ipynb && \\\n\t\tsed '/get_ipython/d;/plt\\./d;/plot_responses/d;/import matplotlib/d;/neurom/d;/axes/d;/fig/d;/for index/d' L5PC.py >L5PC.tmp && \\\n\t\tmv L5PC.tmp L5PC.py && \\\n\t\tpython l5pc_validate_neuron_arbor_pm.py --prepare-only --regions somatic --param-values ../../bluepyopt/tests/testdata/l5pc_validate_neuron_arbor/param_values.json && \\\n\t\tjupyter nbconvert --to python l5pc_validate_neuron_arbor_somatic.ipynb && \\\n\t\tsed '/get_ipython/d;/plt\\./d;/import matplotlib/d;/from IPython.display/d;/multiprocessing/d;s/pool.map/map/g;s/# test_l5pc: insert //g;/# test_l5pc: skip/d' l5pc_validate_neuron_arbor_somatic.py >l5pc_validate_neuron_arbor_somatic.tmp && \\\n\t\tmv l5pc_validate_neuron_arbor_somatic.tmp l5pc_validate_neuron_arbor_somatic.py\nl5pc_nrnivmodl:\n\tcd examples/l5pc && nrnivmodl mechanisms\nl5pc_zip:\n\tcd examples/l5pc && \\\n\t\tzip -qr l5_config.zip config/ morphology/ mechanisms/ l5pc_model.py l5pc_evaluator.py checkpoints/checkpoint.pkl\t\nl5pc_prepare: l5pc_nbconvert l5pc_nrnivmodl\nstochkv_prepare: \n\tcd examples/stochkv && ls mechanisms && nrnivmodl mechanisms\nsc_prepare: jupyter\n\tcd examples/simplecell && \\\n\t\tjupyter nbconvert --to python simplecell.ipynb && \\\n\t\tsed '/get_ipython/d;/plt\\./d;/plot_responses/d;/import matplotlib/d' simplecell.py >simplecell.tmp && \\\n\t\tmv simplecell.tmp simplecell.py && \\\n\t\tjupyter nbconvert --to python simplecell_arbor.ipynb && \\\n\t\tsed '/get_ipython/d;/plt\\./d;/plot_responses/d;/import matplotlib/d' simplecell_arbor.py >simplecell_arbor.tmp && \\\n\t\tmv simplecell_arbor.tmp simplecell_arbor.py\n\nmeta_prepare: jupyter\n\tcd examples/metaparameters && \\\n\t\tjupyter nbconvert --to python metaparameters.ipynb && \\\n\t\tsed '/get_ipython/d;/plt\\./d;/plot_responses/d;/import matplotlib/d' metaparameters.py >metaparameters.tmp && \\\n\t\tmv metaparameters.tmp metaparameters.py\ncoverage_unit: unit\n\tcd bluepyopt/tests; coverage html -d coverage_html; open coverage_html/index.html \ncoverage_test: test\n\tcd bluepyopt/tests; coverage html -d coverage_html; open coverage_html/index.html \njupyter:\n\tpip install jupyter\n\tpip install ipython --upgrade\n\tpip install papermill\n\tpip install scipy\ninstall_test_requirements:\n\tpip install -q $(TEST_REQUIREMENTS) --upgrade\ntest: clean unit functional\nunit: install install_test_requirements\n\tcd bluepyopt/tests; nosetests -a 'unit' -s -v -x --with-coverage --cover-xml \\\n\t\t--cover-package bluepyopt;\nfunctional: install install_test_requirements stochkv_prepare l5pc_prepare sc_prepare\n\tcd bluepyopt/tests; nosetests -a '!unit' -s -v -x --with-coverage --cover-xml \\\n\t\t--cover-package bluepyopt;\npypi: test\n\tpip install twine --upgrade\n\trm -rf dist\n\tpython setup.py sdist bdist\n\ttwine upload dist/*\nexample: install\n\tcd examples/simplecell && \\\n\tpython ./opt_simplecell.py\nclean:\n\trm -rf build\n\trm -rf docs/build\n\trm -rf bluepyopt/tests/.coverage\n\trm -rf bluepyopt/tests/coverage.xml\n\trm -rf bluepyopt/tests/coverage_html\n\trm -rf examples/l5pc/L5PC.py\n\trm -rf examples/l5pc/l5pc_validate_neuron_arbor_somatic.ipynb\n\trm -rf examples/l5pc/l5pc_validate_neuron_arbor_somatic.py\n\trm -rf examples/l5pc/x86_64\n\trm -rf examples/stochkv/x86_64\n\trm -rf x86_64\n\trm -rf .coverage\n\trm -rf coverage.xml\n\trm -rf channels\n\trm -rf LEMS_l5pc.xml\n\trm -rf LEMS_l5pc_nrn.py\n\trm -rf l5pc.Pop_l5pc_0_0.v.dat\n\trm -rf time.dat\n\trm -rf l5pc.hoc\n\trm -rf l5pc.net.nml\n\trm -rf l5pc_0_0.cell.nml\n\trm -rf l5pc_0_0.hoc\n\trm -rf loadcell.hoc\n\trm -rf *.mod\n\tfind . -name \"*.pyc\" -exec rm -rf {} \\;\nl5pc_start: install\n\tcd examples/l5pc && \\\n\t@nrnivmodl mechanisms && \\\n\tpython ./opt_l5pc.py --start\nl5pc_cont: install\n\tcd examples/l5pc && \\\n\t@nrnivmodl mechanisms && \\\n\tpython ./opt_l5pc.py --continue_cp\nl5pc_analyse: install\n\tcd examples/l5pc && \\\n\t@nrnivmodl mechanisms && \\\n\tpython ./opt_l5pc.py --analyse\npush: clean test\n\tgit push\n\tgit push --tags\ncheck_codecov:\n\tcat codecov.yml | curl --data-binary @- https://codecov.io/validate\ntoxbinlinks:\n\tcd ${TOX_ENVBINDIR}; find $(TOX_NRNBINDIR) -type f -exec ln -sf \\{\\} . \\;\n"
  },
  {
    "path": "README.rst",
    "content": ".. warning::\n   The Blue Brain Project concluded in December 2024, so development has ceased under the BlueBrain GitHub organization.\n   Future development will take place at: https://github.com/openbraininstitute/BluePyOpt\n\n|banner|\n\nBluePyOpt\n=========\n\n\n+----------------+------------+\n| Latest Release | |pypi|     |\n+----------------+------------+\n| Documentation  | |docs|     |\n+----------------+------------+\n| License        | |license|  |\n+----------------+------------+\n| Build Status \t | |build|    |\n+----------------+------------+\n| Coverage       | |coverage| |\n+----------------+------------+\n| Gitter         | |gitter|   |\n+----------------+------------+\n| Zenodo         | |zenodo|   |\n+----------------+------------+\n\n\nIntroduction\n============\n\nThe Blue Brain Python Optimisation Library (BluePyOpt) is an extensible\nframework for data-driven model parameter optimisation that wraps and\nstandardises several existing open-source tools.\n\nIt simplifies the task of creating and sharing these optimisations,\nand the associated techniques and knowledge.\nThis is achieved by abstracting the optimisation and evaluation tasks\ninto various reusable and flexible discrete elements according to established\nbest-practices.\n\nFurther, BluePyOpt provides methods for setting up both small- and large-scale\noptimisations on a variety of platforms,\nranging from laptops to Linux clusters and cloud-based compute infrastructures.\n\nCitation\n========\n\nWhen you use the BluePyOpt software or method for your research, we ask you to cite the following publication (**this includes poster presentations**):\n\n`Van Geit W, Gevaert M, Chindemi G, Rössert C, Courcol J, Muller EB, Schürmann F, Segev I and Markram H (2016). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. Front. Neuroinform. 10:17. doi: 10.3389/fninf.2016.00017 <http://journal.frontiersin.org/article/10.3389/fninf.2016.00017>`_.\n\n.. code-block:: \n\n\t@ARTICLE{bluepyopt,\n\tAUTHOR={Van Geit, Werner  and  Gevaert, Michael  and  Chindemi, Giuseppe  and  Rössert, Christian  and  Courcol, Jean-Denis  and  Muller, Eilif Benjamin  and  Schürmann, Felix  and  Segev, Idan  and  Markram, Henry},   \n\tTITLE={BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience},\n\tJOURNAL={Frontiers in Neuroinformatics},\n\tVOLUME={10},\n\tYEAR={2016},\n\tNUMBER={17},\n\tURL={http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2016.00017/abstract},\n\tDOI={10.3389/fninf.2016.00017},\n\tISSN={1662-5196}\n\t}\n\n\nPublications that use or mention BluePyOpt\n==========================================\nThe list of publications that use or mention BluePyOpt can be found on `the github wiki page <https://github.com/BlueBrain/BluePyOpt/wiki/Publications-that-use-or-mention-BluePyOpt>`_.\n\nSupport\n=======\nWe are providing support using a chat channel on `Gitter <https://gitter.im/BlueBrain/BluePyOpt>`_, or the `Github discussion page <https://github.com/BlueBrain/BluePyOpt/discussions>`_.\n\nNews\n====\n- 2023/01: BluePyOpt now supports the Arbor simulator.\n- 2022/12: Support for LFPy models merged into master. Examples and preprint: https://github.com/alejoe91/multimodalfitting, https://www.biorxiv.org/content/10.1101/2022.08.03.502468v1.full\n- 2022/12: BluePyOpt now has the ability to write out NeuroML files: https://github.com/BlueBrain/BluePyOpt/tree/master/bluepyopt/neuroml\n- 2021/08/30: BluePyOpt dropped Python 2.7 support.\n- 2017/01/04: BluePyOpt is now considered compatible with Python 3.6+.\n- 2016/11/10: BluePyOpt now supports NEURON point processes. This means we can fit parameters of Adex/GIF/Izhikevich models, and also synapse models.\n- 2016/06/14: Started a wiki: https://github.com/BlueBrain/BluePyOpt/wiki\n- 2016/06/07: The BluePyOpt paper was published in Frontiers in Neuroinformatics (for link, see above)\n- 2016/05/03: The API documentation was moved to `ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>`_\n- 2016/04/20: BluePyOpt now contains the code of the IBEA selector, no need to install a BBP-specific version of DEAP anymore\n- 2016/03/24: Released version 1.0\n\nRequirements\n============\n\n* `Python 3.9+ <https://www.python.org/downloads/release/python-390/>`_\n* `Pip <https://pip.pypa.io>`_ (installed by default in newer versions of Python)\n* `Neuron 7.4+ <http://neuron.yale.edu/>`_ (compiled with Python support)\n* `eFEL eFeature Extraction Library <https://github.com/BlueBrain/eFEL>`_ (automatically installed by pip)\n* `Numpy <http://www.numpy.org>`_ (automatically installed by pip)\n* `Pandas <http://pandas.pydata.org/>`_ (automatically installed by pip)\n* The instruction below are written assuming you have access to a command shell on Linux / UNIX / MacOSX / Cygwin\n\nInstallation\n============\n\nIf you want to use the ephys module of BluePyOpt, you first need to install NEURON with Python support on your machine.\n\nAnd then bluepyopt itself:\n\n\n.. code-block:: bash\n\n    pip install bluepyopt\n\nSupport for simulators other than NEURON is optional and not installed by default. If you want to use [Arbor](https://arbor-sim.org/) to run your models, use the following line instead to install bluepyopt.\n\n.. code-block:: bash\n\n    pip install bluepyopt[arbor]\n\nCloud infrastructure\n====================\n\nWe provide instructions on how to set up an optimisation environment on cloud\ninfrastructure or cluster computers\n`here <https://github.com/BlueBrain/BluePyOpt/tree/master/cloud-config>`_\n\nQuick Start\n===========\n\nSingle compartmental model\n--------------------------\n\nAn iPython notebook with an introductory optimisation of a one compartmental\nmodel with 2 HH channels can be found at\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb (NEURON)\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell_arbor.ipynb (Arbor)\n\n\n|landscape_example|\n\n\n**Figure**: The solution space of a single compartmental model with two parameters: the maximal conductance of Na and K ion channels. The color represents how well the model fits two objectives: when injected with two different currents, the model has to fire 1 and 4 action potential respectively during the stimuli. Dark blue is the best fitness. The blue circles represent solutions with a perfect score.\n\nNeocortical Layer 5 Pyramidal Cell\n----------------------------------\nScripts for a more complex neocortical L5PC are in\n`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc>`__\n\nWith a notebook:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb (NEURON)\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC_arbor.ipynb (Arbor)\n\nThalamocortical Cells\n---------------------\nScripts for 2 thalamocortical cell types are in\n`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/thalamocortical-cell>`__\n\nWith a notebook:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/thalamocortical-cell/thalamocortical-cell_opt.ipynb\n\n\nTsodyks-Markram Model of Short-Term Plasticity\n----------------------------------------------\nScripts for 2 version of fitting the Tsodyks-Markram model to synaptic traces are in\n`this directory <https://github.com/BlueBrain/BluePyOpt/tree/master/examples/tsodyksmarkramstp>`__\n\nWith 2 notebooks:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp_multiplefreqs.ipynb\n\nExporting cell in neuroml format\n--------------------------------\nAn iPython notebook showing how to export a BluePyOpt cell in the neuroml format, how to create a LEMS simulation,\nand how to run the LEMS simulation with the neuroml cell can be found at:\n\nhttps://github.com/BlueBrain/BluePyOpt/blob/master/examples/neuroml/neuroml.ipynb\n\n\nAPI documentation\n=================\nThe API documentation can be found on `ReadTheDocs <http://bluepyopt.readthedocs.io/en/latest/>`_.\n\nFunding\n=======\nThis work has been partially funded by the European Union Seventh Framework Program (FP7/2007­2013) under grant agreement no. 604102 (HBP), the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270, 785907 (Human Brain Project SGA1/SGA2) and by the EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).\nThis project/research was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.\n\nCopyright (c) 2016-2024 Blue Brain Project/EPFL\n\n..\n    The following image is also defined in the index.rst file, as the relative path is \n    different, depending from where it is sourced.\n    The following location is used for the github README\n    The index.rst location is used for the docs README; index.rst also defined an end-marker, \n    to skip content after the marker 'substitutions'.\n\n.. |pypi| image:: https://img.shields.io/pypi/v/bluepyopt.svg\n               :target: https://pypi.org/project/bluepyopt/\n               :alt: latest release\n\n.. |docs| image:: https://readthedocs.org/projects/bluepyopt/badge/?version=latest\n               :target: https://bluepyopt.readthedocs.io/\n               :alt: latest documentation\n\n.. |license| image:: https://img.shields.io/pypi/l/bluepyopt.svg\n                  :target: https://github.com/BlueBrain/bluepyopt/blob/master/LICENSE.txt\n                  :alt: license\n\n.. |build| image:: https://github.com/BlueBrain/BluePyOpt/workflows/Build/badge.svg?branch=master\n                :target: https://github.com/BlueBrain/BluePyOpt/actions\n                :alt: actions build status\n\n.. |coverage| image:: https://codecov.io/github/BlueBrain/BluePyOpt/coverage.svg?branch=master\n                   :target: https://codecov.io/gh/BlueBrain/bluepyopt\n                   :alt: coverage\n\n.. |gitter| image:: https://badges.gitter.im/Join%20Chat.svg\n                 :target: https://gitter.im/BlueBrain/blueptopt\n                 :alt: Join the chat at https://gitter.im/BlueBrain/BluePyOpt\n\n.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8135890.svg\n                :target: https://doi.org/10.5281/zenodo.8135890\n\n.. substitutions\n.. |banner| image:: docs/source/logo/BluePyOptBanner.png\n.. |landscape_example| image:: examples/simplecell/figures/landscape_example.png\n"
  },
  {
    "path": "bluepyopt/__init__.py",
    "content": "\"\"\"Init script\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0611\nfrom importlib.metadata import version\n\n__version__ = version(\"bluepyopt\")\n\nfrom . import tools  # NOQA\n\nfrom .api import *  # NOQA\nimport bluepyopt.optimisations\nimport bluepyopt.deapext.algorithms\nimport bluepyopt.stoppingCriteria\nimport bluepyopt.deapext.optimisations\nimport bluepyopt.deapext.optimisationsCMA\n\n# Add some backward compatibility for the time when DEAPoptimisation not in\n# deapext yet\n# TODO deprecate this\nbluepyopt.optimisations.DEAPOptimisation = \\\n    bluepyopt.deapext.optimisations.DEAPOptimisation\n\nimport bluepyopt.evaluators\nimport bluepyopt.objectives\nimport bluepyopt.parameters  # NOQA\n\n# TODO let objects read / write themselves using json\n# TODO create 'Variables' class\n# TODO use 'locations' instead of 'location'\n# TODO add island functionality to optimiser\n# TODO add plotting functionality\n# TODO show progress bar during optimisation\n"
  },
  {
    "path": "bluepyopt/api.py",
    "content": "\"\"\"Common API functionality\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\n'''\nimport logging\nlogger = logging.getLogger(__name__)\n\n\ndef set_verboselevel(level):\n    \"\"\"Set verbose level\"\"\"\n    logger.setLevel(level)\n'''\n"
  },
  {
    "path": "bluepyopt/deapext/CMA_MO.py",
    "content": "\"\"\"Multi Objective CMA-es class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=R0912, R0914\n\nimport logging\nimport numpy\nimport copy\nfrom math import log\n\nimport deap\nfrom deap import base\nfrom deap import cma\n\nfrom .stoppingCriteria import MaxNGen, Stagnationv2\nfrom . import utils\nfrom . import hype\n\nlogger = logging.getLogger(\"__main__\")\n\n\ndef get_hyped(pop, ubound_score=250., threshold_improvement=240.):\n    \"\"\"Compute the hypervolume contribution of each individual.\n    The fitness space is first bounded and all dimension who do not show\n    improvement are ignored.\n    \"\"\"\n\n    # Cap the obj at 250\n    points = numpy.array([ind.fitness.values for ind in pop])\n    points[points > ubound_score] = ubound_score\n    lbounds = numpy.min(points, axis=0)\n    ubounds = numpy.max(points, axis=0)\n\n    # Remove the dimensions that do not show any improvement\n    to_remove = []\n    for i, lb in enumerate(lbounds):\n        if lb >= threshold_improvement:\n            to_remove.append(i)\n    points = numpy.delete(points, to_remove, axis=1)\n    lbounds = numpy.delete(lbounds, to_remove)\n    ubounds = numpy.delete(ubounds, to_remove)\n\n    if not len(lbounds):\n        logger.warning(\"No dimension along which to compute the hypervolume.\")\n        return [0.] * len(pop)\n\n    # Rescale the objective space\n    # Note: 2 here is a magic number used to make the hypercube larger than it\n    # really is. It makes sure that the individual always have a non-zero\n    # hyper-volume contribution and improves the results while avoiding an\n    # edge case.\n    points = (points - lbounds) / numpy.max(ubounds.flatten())\n    ubounds = numpy.max(points, axis=0) + 2.0\n\n    hv = hype.hypeIndicatorSampled(\n        points=points, bounds=ubounds, k=5, nrOfSamples=1000000\n    )\n    return hv\n\n\nclass CMA_MO(cma.StrategyMultiObjective):\n    \"\"\"Multiple objective covariance matrix adaption\"\"\"\n\n    def __init__(\n        self,\n        centroids,\n        offspring_size,\n        sigma,\n        max_ngen,\n        IndCreator,\n        RandIndCreator,\n        weight_hv=0.5,\n        map_function=None,\n        use_scoop=False,\n        use_stagnation_criterion=True,\n    ):\n        \"\"\"Constructor\n\n        Args:\n            centroid (list): initial guess used as the starting point of\n            the CMA-ES\n            offspring_size (int): number of offspring individuals in each\n                 generation\n            sigma (float): initial standard deviation of the distribution\n            max_ngen (int): total number of generation to run\n            IndCreator (fcn): function returning an individual of the pop\n            RandIndCreator (fcn): function creating a random individual.\n            weight_hv (float): between 0 and 1. Weight given to the\n                hypervolume contribution when computing the score of an\n                individual in MO-CMA. The weight of the fitness contribution\n                is computed as 1 - weight_hv.\n            map_function (map): function used to map (parallelize) the\n                 evaluation function calls\n            use_scoop (bool): use scoop map for parallel computation\n            use_stagnation_criterion (bool): whether to use the stagnation\n                stopping criterion on top of the maximum generation criterion\n        \"\"\"\n\n        if offspring_size is None:\n            lambda_ = int(4 + 3 * log(len(RandIndCreator())))\n        else:\n            lambda_ = offspring_size\n\n        if centroids is None:\n            starters = [RandIndCreator() for i in range(lambda_)]\n        else:\n            if len(centroids) != lambda_:\n                from itertools import cycle\n\n                generator = cycle(centroids)\n                starters = [\n                    copy.deepcopy(next(generator)) for i in range(lambda_)\n                ]\n            else:\n                starters = centroids\n\n        cma.StrategyMultiObjective.__init__(\n            self, starters, sigma, mu=int(lambda_ * 0.5), lambda_=lambda_\n        )\n\n        self.population = []\n        self.problem_size = len(starters[0])\n\n        self.weight_hv = weight_hv\n\n        self.map_function = map_function\n        self.use_scoop = use_scoop\n\n        # Toolbox specific to this CMA-ES\n        self.toolbox = base.Toolbox()\n        self.toolbox.register(\"generate\", self.generate, IndCreator)\n        self.toolbox.register(\"update\", self.update)\n\n        if self.use_scoop:\n            if self.map_function:\n                raise Exception(\n                    \"Impossible to use scoop and provide self defined map \"\n                    \"function: %s\" % self.map_function\n                )\n            from scoop import futures\n\n            self.map_function = futures.map\n\n        # Set termination conditions\n        self.active = True\n        if max_ngen <= 0:\n            max_ngen = 100 + 50 * (self.problem_size + 3) ** 2 / numpy.sqrt(\n                lambda_\n            )\n\n        self.stopping_conditions = [\n            MaxNGen(max_ngen),\n        ]\n        if use_stagnation_criterion:\n            self.stopping_conditions.append(\n                Stagnationv2(lambda_, self.problem_size)\n            )\n\n    def _select(self, candidates):\n        \"\"\"Select the best candidates of the population\n\n         Fill the next population (chosen) with the Pareto fronts until there\n         is not enough space. When an entire front does not fit in the space\n         left we rely on a mixture of hypervolume and fitness. The respective\n         weights of hypervolume and fitness are \"hv\" and \"1-hv\". The remaining\n         fronts are explicitly not chosen\"\"\"\n\n        if self.weight_hv == 0.0:\n            fit = [numpy.sum(ind.fitness.values) for ind in candidates]\n            idx_scores = list(numpy.argsort(fit))\n\n        elif self.weight_hv == 1.0:\n            hv = get_hyped(candidates)\n            idx_scores = list(numpy.argsort(hv))[::-1]\n\n        else:\n            hv = get_hyped(candidates)\n            idx_hv = list(numpy.argsort(hv))[::-1]\n            fit = [numpy.sum(ind.fitness.values) for ind in candidates]\n            idx_fit = list(numpy.argsort(fit))\n            scores = []\n            for i in range(len(candidates)):\n                score = (self.weight_hv * idx_hv.index(i)) + (\n                    (1.0 - self.weight_hv) * idx_fit.index(i)\n                )\n                scores.append(score)\n            idx_scores = list(numpy.argsort(scores))\n\n        chosen = [candidates[i] for i in idx_scores[: self.mu]]\n        not_chosen = [candidates[i] for i in idx_scores[self.mu:]]\n        return chosen, not_chosen\n\n    def get_population(self, to_space):\n        \"\"\"Returns the population in the original parameter space\"\"\"\n        pop = copy.deepcopy(self.population)\n        for i, ind in enumerate(pop):\n            for j, v in enumerate(ind):\n                pop[i][j] = to_space[j](v)\n        return pop\n\n    def get_parents(self, to_space):\n        \"\"\"Returns the population in the original parameter space\"\"\"\n        pop = copy.deepcopy(self.parents)\n        for i, ind in enumerate(pop):\n            for j, v in enumerate(ind):\n                pop[i][j] = to_space[j](v)\n        return pop\n\n    def generate_new_pop(self, lbounds, ubounds):\n        \"\"\"Generate a new population bounded in the normalized space\"\"\"\n        self.population = self.toolbox.generate()\n        return utils.bound(self.population, lbounds, ubounds)\n\n    def update_strategy(self):\n        self.toolbox.update(self.population)\n\n    def set_fitness(self, fitnesses):\n        for f, ind in zip(fitnesses, self.population):\n            ind.fitness.values = f\n\n    def set_fitness_parents(self, fitnesses):\n        for f, ind in zip(fitnesses, self.parents):\n            ind.fitness.values = f\n\n    def check_termination(self, gen):\n        stopping_params = {\n            \"gen\": gen,\n            \"population\": self.population,\n        }\n\n        [c.check(stopping_params) for c in self.stopping_conditions]\n        for c in self.stopping_conditions:\n            if c.criteria_met:\n                logger.info(\n                    \"CMA stopped because of termination criteria: \" +\n                    \"\" + \" \".join(c.name)\n                )\n                self.active = False\n"
  },
  {
    "path": "bluepyopt/deapext/CMA_SO.py",
    "content": "\"\"\"Single Objective CMA-es class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=R0912, R0914\n\nimport logging\nimport numpy\nfrom math import sqrt, log\nimport copy\n\nfrom deap import base\nfrom deap import cma\n\nfrom .stoppingCriteria import (\n    MaxNGen,\n    Stagnationv2,\n    TolHistFun,\n    EqualFunVals,\n    NoEffectAxis,\n    TolUpSigma,\n    TolX,\n    ConditionCov,\n    NoEffectCoor,\n)\n\nfrom . import utils\n\nlogger = logging.getLogger(\"__main__\")\n\n\nclass CMA_SO(cma.Strategy):\n    \"\"\"Single objective covariance matrix adaption\"\"\"\n\n    def __init__(\n        self,\n        centroids,\n        offspring_size,\n        sigma,\n        max_ngen,\n        IndCreator,\n        RandIndCreator,\n        map_function=None,\n        use_scoop=False,\n        use_stagnation_criterion=True,\n    ):\n        \"\"\"Constructor\n\n        Args:\n            centroid (list): initial guess used as the starting point of\n            the CMA-ES\n            offspring_size (int): number of offspring individuals in each\n                generation\n            sigma (float): initial standard deviation of the distribution\n            max_ngen (int): total number of generation to run\n            IndCreator (fcn): function returning an individual of the pop\n            RandIndCreator (fcn): function creating a random individual.\n            map_function (map): function used to map (parallelize) the\n                evaluation function calls\n            use_scoop (bool): use scoop map for parallel computation\n            use_stagnation_criterion (bool): whether to use the stagnation\n                stopping criterion on top of the maximum generation criterion\n        \"\"\"\n\n        if offspring_size is None:\n            lambda_ = int(4 + 3 * log(len(RandIndCreator())))\n        else:\n            lambda_ = offspring_size\n\n        if centroids is None:\n            starter = RandIndCreator()\n        else:\n            starter = centroids[0]\n\n        cma.Strategy.__init__(self, starter, sigma, lambda_=lambda_)\n\n        self.population = []\n        self.problem_size = len(starter)\n\n        self.map_function = map_function\n        self.use_scoop = use_scoop\n\n        # Toolbox specific to this CMA-ES\n        self.toolbox = base.Toolbox()\n        self.toolbox.register(\"generate\", self.generate, IndCreator)\n        self.toolbox.register(\"update\", self.update)\n\n        # Set termination conditions\n        self.active = True\n        if max_ngen <= 0:\n            max_ngen = 100 + 50 * (self.problem_size + 3) ** 2 / numpy.sqrt(\n                lambda_\n            )\n\n        self.stopping_conditions = [\n            MaxNGen(max_ngen),\n            TolHistFun(lambda_, self.problem_size),\n            EqualFunVals(lambda_, self.problem_size),\n            NoEffectAxis(self.problem_size),\n            TolUpSigma(float(self.sigma)),\n            TolX(),\n            ConditionCov(),\n            NoEffectCoor(),\n        ]\n        if use_stagnation_criterion:\n            self.stopping_conditions.append(\n                Stagnationv2(lambda_, self.problem_size)\n            )\n\n    def update(self, population):\n        \"\"\"Update the current covariance matrix strategy from the\n        population\"\"\"\n\n        population.sort(key=lambda ind: ind.fitness.weighted_reduce,\n                        reverse=True)\n\n        old_centroid = self.centroid\n        self.centroid = numpy.dot(self.weights, population[0:self.mu])\n\n        c_diff = self.centroid - old_centroid\n\n        # Cumulation : update evolution path\n        self.ps = (1 - self.cs) * self.ps + sqrt(\n            self.cs * (2 - self.cs) * self.mueff\n        ) / self.sigma * numpy.dot(\n            self.B, (1.0 / self.diagD) * numpy.dot(self.B.T, c_diff)\n        )  # noqa\n\n        hsig = float(\n            (\n                numpy.linalg.norm(self.ps)\n                / sqrt(1.0 - (1.0 - self.cs) **\n                (2.0 * (self.update_count + 1.0)))\n                / self.chiN\n                < (1.4 + 2.0 / (self.dim + 1.0))\n            )\n        )  # noqa\n\n        self.update_count += 1\n\n        self.pc = (1 - self.cc) * self.pc + hsig * sqrt(\n            self.cc * (2 - self.cc) * self.mueff\n        ) / self.sigma * c_diff\n\n        # Update covariance matrix\n        artmp = population[0:self.mu] - old_centroid\n        self.C = (\n            (\n                1\n                - self.ccov1\n                - self.ccovmu\n                + (1 - hsig) * self.ccov1 * self.cc * (2 - self.cc)\n            )\n            * self.C\n            + self.ccov1 * numpy.outer(self.pc, self.pc)\n            + self.ccovmu * numpy.dot((self.weights * artmp.T), artmp)\n            / self.sigma ** 2\n        )\n\n        self.sigma *= numpy.exp(\n            (numpy.linalg.norm(self.ps) / self.chiN - 1.0) * self.cs\n            / self.damps\n        )\n\n        self.diagD, self.B = numpy.linalg.eigh(self.C)\n        indx = numpy.argsort(self.diagD)\n\n        self.cond = self.diagD[indx[-1]] / self.diagD[indx[0]]\n\n        self.diagD = self.diagD[indx] ** 0.5\n        self.B = self.B[:, indx]\n        self.BD = self.B * self.diagD\n\n    def get_population(self, to_space):\n        \"\"\"Returns the population in the original parameter space\"\"\"\n        pop = copy.deepcopy(self.population)\n        for i, ind in enumerate(pop):\n            for j, v in enumerate(ind):\n                pop[i][j] = to_space[j](v)\n        return pop\n\n    def generate_new_pop(self, lbounds, ubounds):\n        \"\"\"Generate a new population bounded in the normalized space\"\"\"\n        self.population = self.toolbox.generate()\n        return utils.bound(self.population, lbounds, ubounds)\n\n    def update_strategy(self):\n        self.toolbox.update(self.population)\n\n    def set_fitness(self, fitnesses):\n        for f, ind in zip(fitnesses, self.population):\n            ind.fitness.values = f\n\n    def check_termination(self, gen):\n        stopping_params = {\n            \"gen\": gen,\n            \"population\": self.population,\n            \"centroid\": self.centroid,\n            \"pc\": self.pc,\n            \"C\": self.C,\n            \"B\": self.B,\n            \"sigma\": self.sigma,\n            \"diagD\": self.diagD,\n            \"cond\": self.cond,\n        }\n\n        [c.check(stopping_params) for c in self.stopping_conditions]\n        for c in self.stopping_conditions:\n            if c.criteria_met:\n                logger.info(\n                    \"CMA stopped because of termination criteria: \" +\n                    \"\" + \" \".join(c.name)\n                )\n                self.active = False\n"
  },
  {
    "path": "bluepyopt/deapext/__init__.py",
    "content": "\"\"\"Init script\"\"\"\n"
  },
  {
    "path": "bluepyopt/deapext/algorithms.py",
    "content": "\"\"\"Optimisation class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=R0914, R0912\n\n\nimport random\nimport logging\nimport shutil\nimport os\nimport time\n\nimport deap.algorithms\nimport deap.tools\nimport pickle\n\nfrom .stoppingCriteria import MaxNGen\nfrom . import utils\n\nlogger = logging.getLogger('__main__')\n\n\ndef _define_fitness(pop, obj_size):\n    ''' Re-instanciate the fitness of the individuals for it to matches the\n    evaluation function.\n    '''\n    from .optimisations import WSListIndividual\n\n    new_pop = []\n    if pop:\n        for ind in pop:\n            new_pop.append(WSListIndividual(list(ind), obj_size=obj_size))\n\n    return new_pop\n\n\ndef _evaluate_invalid_fitness(toolbox, population):\n    '''Evaluate the individuals with an invalid fitness\n\n    Returns the count of individuals with invalid fitness\n    '''\n    invalid_ind = [ind for ind in population if not ind.fitness.valid]\n    fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n    for ind, fit in zip(invalid_ind, fitnesses):\n        ind.fitness.values = fit\n\n    return len(invalid_ind)\n\n\ndef _get_offspring(parents, toolbox, cxpb, mutpb):\n    '''return the offspring, use toolbox.variate if possible'''\n    if hasattr(toolbox, 'variate'):\n        return toolbox.variate(parents, toolbox, cxpb, mutpb)\n    return deap.algorithms.varAnd(parents, toolbox, cxpb, mutpb)\n\n\ndef _check_stopping_criteria(criteria, params):\n    for c in criteria:\n        c.check(params)\n        if c.criteria_met:\n            logger.info('Run stopped because of stopping criteria: ' +\n                        c.name)\n            return True\n    else:\n        return False\n\n\ndef eaAlphaMuPlusLambdaCheckpoint(\n        population,\n        toolbox,\n        mu,\n        cxpb,\n        mutpb,\n        ngen,\n        stats=None,\n        halloffame=None,\n        cp_frequency=1,\n        cp_period=None,\n        cp_filename=None,\n        continue_cp=False,\n        terminator=None,\n        param_names=None):\n    r\"\"\"This is the :math:`(~\\alpha,\\mu~,~\\lambda)` evolutionary algorithm\n\n    Args:\n        population(list of deap Individuals)\n        toolbox(deap Toolbox)\n        mu(int): Total parent population size of EA\n        cxpb(float): Crossover probability\n        mutpb(float): Mutation probability\n        ngen(int): Total number of generation to run\n        stats(deap.tools.Statistics): generation of statistics\n        halloffame(deap.tools.HallOfFame): hall of fame\n        cp_frequency(int): generations between checkpoints\n        cp_period(float): minimum time (in s) between checkpoint.\n            None to save checkpoint independently of the time between them\n        cp_filename(string): path to checkpoint filename\n        continue_cp(bool): whether to continue\n        terminator (multiprocessing.Event): exit loop when is set.\n            Not taken into account if None.\n        param_names(list): names of the parameters optimized by the evaluator\n    \"\"\"\n\n    if param_names is None:\n        param_names = []\n\n    if cp_filename:\n        cp_filename_tmp = cp_filename + '.tmp'\n\n    if continue_cp:\n        # A file name has been given, then load the data from the file\n        cp = pickle.load(open(cp_filename, \"rb\"))\n        population = cp[\"population\"]\n        parents = cp[\"parents\"]\n        start_gen = cp[\"generation\"]\n        halloffame = cp[\"halloffame\"]\n        logbook = cp[\"logbook\"]\n        history = cp[\"history\"]\n        random.setstate(cp[\"rndstate\"])\n\n        # Assert that the fitness of the individuals match the evaluator\n        obj_size = len(population[0].fitness.wvalues)\n        population = _define_fitness(population, obj_size)\n        parents = _define_fitness(parents, obj_size)\n        _evaluate_invalid_fitness(toolbox, parents)\n        _evaluate_invalid_fitness(toolbox, population)\n\n    else:\n        # Start a new evolution\n        start_gen = 1\n        parents = population[:]\n        logbook = deap.tools.Logbook()\n        logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])\n        history = deap.tools.History()\n\n        invalid_count = _evaluate_invalid_fitness(toolbox, population)\n\n        utils.update_history_and_hof(halloffame, history, population)\n        utils.record_stats(\n            stats, logbook, start_gen, population, invalid_count\n        )\n\n    stopping_criteria = [MaxNGen(ngen)]\n\n    # Begin the generational process\n    gen = start_gen + 1\n    stopping_params = {\"gen\": gen}\n    time_last_save = time.time()\n    while utils.run_next_gen(\n            not (_check_stopping_criteria(stopping_criteria, stopping_params)),\n            terminator):\n        offspring = _get_offspring(parents, toolbox, cxpb, mutpb)\n\n        population = parents + offspring\n\n        invalid_count = _evaluate_invalid_fitness(toolbox, offspring)\n        utils.update_history_and_hof(halloffame, history, population)\n        utils.record_stats(stats, logbook, gen, population, invalid_count)\n\n        # Select the next generation parents\n        parents = toolbox.select(population, mu)\n\n        logger.info(logbook.stream)\n\n        if (cp_filename and cp_frequency and\n           gen % cp_frequency == 0 and\n           (cp_period is None or time.time() - time_last_save > cp_period)):\n            cp = dict(population=population,\n                      generation=gen,\n                      parents=parents,\n                      halloffame=halloffame,\n                      history=history,\n                      logbook=logbook,\n                      rndstate=random.getstate(),\n                      param_names=param_names)\n            pickle.dump(cp, open(cp_filename_tmp, \"wb\"))\n            if os.path.isfile(cp_filename_tmp):\n                shutil.copy(cp_filename_tmp, cp_filename)\n                logger.debug('Wrote checkpoint to %s', cp_filename)\n\n            time_last_save = time.time()\n\n        gen += 1\n        stopping_params[\"gen\"] = gen\n\n    return population, halloffame, logbook, history\n"
  },
  {
    "path": "bluepyopt/deapext/hype.py",
    "content": "\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\nimport numpy\n\n\ndef hypesub(la, A, actDim, bounds, pvec, alpha, k):\n    \"\"\"HypE algorithm sub function\"\"\"\n\n    h = numpy.zeros(la)\n    i = numpy.argsort(A[:, actDim - 1])\n    S = A[i]\n    pvec = pvec[i]\n\n    for i in range(1, S.shape[0] + 1):\n\n        if i < S.shape[0]:\n            extrusion = S[i, actDim - 1] - S[i - 1, actDim - 1]\n        else:\n            extrusion = bounds[actDim - 1] - S[i - 1, actDim - 1]\n\n        if actDim == 1:\n            if i > k:\n                break\n            if alpha[i - 1] >= 0:\n                h[pvec[0:i]] += extrusion * alpha[i - 1]\n        elif extrusion > 0.0:\n            h += extrusion * hypesub(\n                la, S[0:i, :], actDim - 1, bounds, pvec[0:i], alpha, k\n            )\n\n    return h\n\n\ndef hypeIndicatorExact(points, bounds, k):\n    \"\"\"HypE algorithm. Python implementation of the Matlab code available at\n    https://sop.tik.ee.ethz.ch/download/supplementary/hype/\n\n    Args:\n        points(array): 2D array containing the objective values of the\n        population\n        bounds(array): 1D array containing the reference point from which to\n        compute the hyper-volume\n        k(int): HypE parameter\n    \"\"\"\n\n    Ps = points.shape[0]\n    if k < 0:\n        k = Ps\n    actDim = points.shape[1]\n    pvec = numpy.arange(points.shape[0])\n\n    alpha = []\n    for i in range(1, k + 1):\n        j = numpy.arange(1, i)\n        alpha.append(numpy.prod((k - j) / (Ps - j) / i))\n    alpha = numpy.asarray(alpha)\n\n    return hypesub(points.shape[0], points, actDim, bounds, pvec, alpha, k)\n\n\ndef hypeIndicatorSampled(points, bounds, k, nrOfSamples):\n    \"\"\"Monte-Carlo approximation of the HypE algorithm. Python implementation\n    of the Matlab code available at\n    https://sop.tik.ee.ethz.ch/download/supplementary/hype/\n\n    Args:\n        points(array): 2D array containing the objective values of the\n        population\n        bounds(array): 1D array containing the reference point from which to\n        compute the hyper-volume\n        k(int): HypE parameter\n        nrOfSamples(int): number of random samples to use for the\n        Monte-Carlo approximation\n    \"\"\"\n\n    nrP = points.shape[0]\n    dim = points.shape[1]\n    F = numpy.zeros(nrP)\n\n    BoxL = numpy.min(points, axis=0)\n\n    alpha = []\n    for i in range(1, k + 1):\n        j = numpy.arange(1, i)\n        alpha.append(numpy.prod((k - j) / (nrP - j) / i))\n    alpha = numpy.asarray(alpha + [0.0] * nrP)\n\n    S = numpy.random.uniform(low=BoxL, high=bounds, size=(nrOfSamples, dim))\n\n    dominated = numpy.zeros(nrOfSamples, dtype=\"uint\")\n    for j in range(1, nrP + 1):\n        B = S - points[j - 1]\n        ind = numpy.sum(B >= 0, axis=1) == dim\n        dominated[ind] += 1\n\n    for j in range(1, nrP + 1):\n        B = S - points[j - 1]\n        ind = numpy.sum(B >= 0, axis=1) == dim\n        x = dominated[ind]\n        F[j - 1] = numpy.sum(alpha[x - 1])\n\n    F = F * numpy.prod(bounds - BoxL) / nrOfSamples\n\n    return F\n"
  },
  {
    "path": "bluepyopt/deapext/optimisations.py",
    "content": "\"\"\"Optimisation class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=R0912, R0914\n\n\nimport random\nimport logging\nimport functools\n\nimport deap\nimport deap.base\nimport deap.algorithms\nimport deap.tools\n\nfrom . import algorithms\nfrom . import tools\nfrom . import utils\n\nimport bluepyopt.optimisations\n\nlogger = logging.getLogger('__main__')\n\n# TODO decide which variables go in constructor,which ones go in 'run' function\n# TODO abstract the algorithm by creating a class for every algorithm, that way\n# settings of the algorithm can be stored in objects of these classes\n\n\nclass WeightedSumFitness(deap.base.Fitness):\n\n    \"\"\"Fitness that compares by weighted sum\"\"\"\n\n    def __init__(self, values=(), obj_size=None):\n        self.weights = [-1.0] * obj_size if obj_size is not None else [-1]\n\n        super(WeightedSumFitness, self).__init__(values)\n\n    @property\n    def weighted_sum(self):\n        \"\"\"Weighted sum of wvalues\"\"\"\n        return sum(self.wvalues)\n\n    @property\n    def sum(self):\n        \"\"\"Weighted sum of values\"\"\"\n        return sum(self.values)\n\n    def __le__(self, other):\n        return self.weighted_sum <= other.weighted_sum\n\n    def __lt__(self, other):\n        return self.weighted_sum < other.weighted_sum\n\n    def __deepcopy__(self, _):\n        \"\"\"Override deepcopy\"\"\"\n\n        cls = self.__class__\n        result = cls.__new__(cls)\n        result.__dict__.update(self.__dict__)\n        return result\n\n\nclass WSListIndividual(list):\n\n    \"\"\"Individual consisting of list with weighted sum field\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Constructor\"\"\"\n        self.fitness = WeightedSumFitness(obj_size=kwargs['obj_size'])\n        del kwargs['obj_size']\n        super(WSListIndividual, self).__init__(*args, **kwargs)\n\n\nclass DEAPOptimisation(bluepyopt.optimisations.Optimisation):\n\n    \"\"\"DEAP Optimisation class\"\"\"\n\n    def __init__(self, evaluator=None,\n                 use_scoop=False,\n                 seed=1,\n                 offspring_size=10,\n                 eta=10,\n                 mutpb=1.0,\n                 cxpb=1.0,\n                 map_function=None,\n                 hof=None,\n                 selector_name=None):\n        \"\"\"Constructor\n\n        Args:\n            evaluator (Evaluator): Evaluator object\n            use_scoop (bool): use scoop map for parallel computation\n            seed (float): Random number generator seed\n            offspring_size (int): Number of offspring individuals in each\n                generation\n            eta (float): Parameter that controls how far the crossover and\n                mutation operator disturbe the original individuals\n            mutpb (float): Mutation probability\n            cxpb (float): Crossover probability\n            map_function (function): Function used to map (parallelise) the\n                evaluation function calls\n            hof (hof): Hall of Fame object\n            selector_name (str): The selector used in the evolutionary\n                algorithm, possible values are 'IBEA' or 'NSGA2'\n        \"\"\"\n\n        super(DEAPOptimisation, self).__init__(evaluator=evaluator)\n\n        self.use_scoop = use_scoop\n        self.seed = seed\n        self.offspring_size = offspring_size\n        self.eta = eta\n        self.cxpb = cxpb\n        self.mutpb = mutpb\n        self.map_function = map_function\n\n        self.selector_name = selector_name\n        if self.selector_name is None:\n            self.selector_name = 'IBEA'\n\n        self.hof = hof\n        if self.hof is None:\n            self.hof = deap.tools.HallOfFame(10)\n\n        # Create a DEAP toolbox\n        self.toolbox = deap.base.Toolbox()\n\n        self.setup_deap()\n\n    def setup_deap(self):\n        \"\"\"Set up optimisation\"\"\"\n\n        # Number of objectives\n        OBJ_SIZE = len(self.evaluator.objectives)\n\n        # Set random seed\n        random.seed(self.seed)\n\n        # Eta parameter of crossover / mutation parameters\n        # Basically defines how much they 'spread' solution around\n        # The lower this value, the more spread\n        ETA = self.eta\n\n        # Number of parameters\n        IND_SIZE = len(self.evaluator.params)\n        if IND_SIZE == 0:\n            raise ValueError(\n                \"Length of evaluator.params is zero. At least one \"\n                \"non-fix parameter is needed to run an optimization.\"\n            )\n\n        # Bounds for the parameters\n\n        LOWER = []\n        UPPER = []\n\n        for parameter in self.evaluator.params:\n            LOWER.append(parameter.lower_bound)\n            UPPER.append(parameter.upper_bound)\n\n        # Register the 'uniform' function\n        self.toolbox.register(\"uniformparams\", utils.uniform, LOWER, UPPER,\n                              IND_SIZE)\n\n        # Register the individual format\n        # An indiviual is create by WSListIndividual and parameters\n        # are initially\n        # picked by 'uniform'\n        self.toolbox.register(\n            \"Individual\",\n            deap.tools.initIterate,\n            functools.partial(WSListIndividual, obj_size=OBJ_SIZE),\n            self.toolbox.uniformparams)\n\n        # Register the population format. It is a list of individuals\n        self.toolbox.register(\n            \"population\",\n            deap.tools.initRepeat,\n            list,\n            self.toolbox.Individual)\n\n        # Register the evaluation function for the individuals\n        # import deap_efel_eval1\n        self.toolbox.register(\n            \"evaluate\",\n            self.evaluator.init_simulator_and_evaluate_with_lists\n        )\n\n        # Register the mate operator\n        self.toolbox.register(\n            \"mate\",\n            deap.tools.cxSimulatedBinaryBounded,\n            eta=ETA,\n            low=LOWER,\n            up=UPPER)\n\n        # Register the mutation operator\n        self.toolbox.register(\n            \"mutate\",\n            deap.tools.mutPolynomialBounded,\n            eta=ETA,\n            low=LOWER,\n            up=UPPER,\n            indpb=0.5)\n\n        # Register the variate operator\n        self.toolbox.register(\"variate\", deap.algorithms.varAnd)\n\n        # Register the selector (picks parents from population)\n        if self.selector_name == 'IBEA':\n            self.toolbox.register(\"select\", tools.selIBEA)\n        elif self.selector_name == 'NSGA2':\n            self.toolbox.register(\"select\", deap.tools.emo.selNSGA2)\n        else:\n            raise ValueError('DEAPOptimisation: Constructor selector_name '\n                             'argument only accepts \"IBEA\" or \"NSGA2\"')\n\n        import copyreg\n        import types\n        copyreg.pickle(types.MethodType, utils.reduce_method)\n\n        if self.use_scoop:\n            if self.map_function:\n                raise Exception(\n                    'Impossible to use scoop is providing self '\n                    'defined map function: %s' %\n                    self.map_function)\n\n            from scoop import futures\n            self.toolbox.register(\"map\", futures.map)\n\n        elif self.map_function:\n            self.toolbox.register(\"map\", self.map_function)\n\n    def run(self,\n            max_ngen=10,\n            offspring_size=None,\n            continue_cp=False,\n            cp_filename=None,\n            cp_frequency=1,\n            cp_period=None,\n            parent_population=None,\n            terminator=None):\n        \"\"\"Run optimisation\"\"\"\n        # Allow run function to override offspring_size\n        # TODO probably in the future this should not be an object field\n        # anymore\n        # keeping for backward compatibility\n        if offspring_size is None:\n            offspring_size = self.offspring_size\n\n        # Generate the population object\n        if parent_population is not None:\n\n            if len(parent_population) != offspring_size:\n                offspring_size = len(parent_population)\n                self.offspring_size = len(parent_population)\n                logger.warning(\n                    'The length of the provided population is different from '\n                    'the offspring_size. The offspring_size will be '\n                    'overwritten.'\n                )\n\n            OBJ_SIZE = len(self.evaluator.objectives)\n            IND_SIZE = len(self.evaluator.params)\n\n            pop = []\n            for ind in parent_population:\n\n                if len(ind) != IND_SIZE:\n                    raise Exception(\n                        'The length of the provided individual is not equal '\n                        'to the number of parameter in the evaluator ')\n\n                pop.append(WSListIndividual(ind, obj_size=OBJ_SIZE))\n\n        else:\n            pop = self.toolbox.population(n=offspring_size)\n\n        stats = deap.tools.Statistics(key=lambda ind: ind.fitness.sum)\n        import numpy\n        stats.register(\"avg\", numpy.mean)\n        stats.register(\"std\", numpy.std)\n        stats.register(\"min\", numpy.min)\n        stats.register(\"max\", numpy.max)\n\n        param_names = []\n        if hasattr(self.evaluator, \"param_names\"):\n            param_names = self.evaluator.param_names\n\n        pop, hof, log, history = algorithms.eaAlphaMuPlusLambdaCheckpoint(\n            pop,\n            self.toolbox,\n            offspring_size,\n            self.cxpb,\n            self.mutpb,\n            max_ngen,\n            stats=stats,\n            halloffame=self.hof,\n            cp_frequency=cp_frequency,\n            cp_period=None,\n            continue_cp=continue_cp,\n            cp_filename=cp_filename,\n            terminator=terminator,\n            param_names=param_names)\n\n        # Update hall of fame\n        self.hof = hof\n\n        return pop, self.hof, log, history\n\n\nclass IBEADEAPOptimisation(DEAPOptimisation):\n\n    \"\"\"IBEA DEAP class\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Constructor\"\"\"\n\n        super(IBEADEAPOptimisation, self).__init__(*args, **kwargs)\n"
  },
  {
    "path": "bluepyopt/deapext/optimisationsCMA.py",
    "content": "\"\"\"CMA Optimisation class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport logging\nimport numpy\nimport pickle\nimport random\nimport functools\nimport shutil\nimport os\nimport time\n\nimport deap.tools\n\nfrom .CMA_SO import CMA_SO\nfrom .CMA_MO import CMA_MO\nfrom . import utils\n\nimport bluepyopt.optimisations\n\nlogger = logging.getLogger(\"__main__\")\n\n\ndef _ind_convert_space(ind, convert_fcn):\n    \"\"\"util function to pass the individual from normalized to real space and\n    inversely\"\"\"\n    return [f(x) for f, x in zip(convert_fcn, ind)]\n\n\nclass DEAPOptimisationCMA(bluepyopt.optimisations.Optimisation):\n\n    \"\"\"Optimisation class for CMA-based evolution strategies\"\"\"\n\n    def __init__(\n        self,\n        evaluator=None,\n        use_scoop=False,\n        seed=1,\n        offspring_size=None,\n        centroids=None,\n        sigma=0.4,\n        map_function=None,\n        hof=None,\n        selector_name=\"single_objective\",\n        weight_hv=0.5,\n        fitness_reduce=numpy.sum,\n        use_stagnation_criterion=True,\n    ):\n        \"\"\"Constructor\n\n        Args:\n            evaluator (Evaluator): Evaluator object\n            use_scoop (bool): use scoop map for parallel computation\n            seed (float): Random number generator seed\n            offspring_size (int): Number of offspring individuals in each\n                generation\n            centroids (list): list of initial guesses used as the starting\n                points of the CMA-ES\n            sigma (float): initial standard deviation of the distribution\n            map_function (function): Function used to map (parallelize) the\n                evaluation function calls\n            hof (hof): Hall of Fame object\n            selector_name (str): The selector used in the evolutionary\n                algorithm, possible values are 'single_objective' or\n                'multi_objective'\n            weight_hv (float): between 0 and 1. Weight given to the\n                hyper-volume contribution when computing the score of an\n                individual in MO-CMA. The weight of the fitness contribution\n                is computed as 1 - weight_hv.\n            fitness_reduce (fcn): function used to reduce the objective values\n                to a single fitness score\n            use_stagnation_criterion (bool): whether to use the stagnation\n                stopping criterion on top of the maximum generation criterion\n        \"\"\"\n\n        super(DEAPOptimisationCMA, self).__init__(evaluator=evaluator)\n\n        self.use_scoop = use_scoop\n        self.seed = seed\n        self.map_function = map_function\n\n        self.hof = hof\n        if self.hof is None:\n            self.hof = deap.tools.HallOfFame(10)\n\n        self.offspring_size = offspring_size\n\n        self.fitness_reduce = fitness_reduce\n        self.centroids = centroids\n        self.sigma = sigma\n\n        if weight_hv > 1.0 or weight_hv < 0.0:\n            raise Exception(\"weight_hv has to be between 0 and 1.\")\n        self.weight_hv = weight_hv\n\n        self.selector_name = selector_name\n        if self.selector_name == \"single_objective\":\n            self.cma_creator = CMA_SO\n        elif self.selector_name == \"multi_objective\":\n            self.cma_creator = CMA_MO\n        else:\n            raise Exception(\n                \"The selector_name has to be 'single_objective' \"\n                \"or 'multi_objective'. Not \"\n                \"{}\".format(self.selector_name)\n            )\n\n        self.use_stagnation_criterion = use_stagnation_criterion\n\n        # Number of objective values\n        self.problem_size = len(self.evaluator.params)\n\n        # Number of parameters\n        self.ind_size = len(self.evaluator.objectives)\n\n        # Create a DEAP toolbox\n        self.toolbox = deap.base.Toolbox()\n\n        # Bounds for the parameters\n        self.lbounds = [p.lower_bound for p in self.evaluator.params]\n        self.ubounds = [p.upper_bound for p in self.evaluator.params]\n\n        # Instantiate functions converting individuals from the original\n        # parameter space to (and from) a normalized space bounded to [-1.;1]\n        self.ubounds = numpy.asarray(self.ubounds)\n        self.lbounds = numpy.asarray(self.lbounds)\n        bounds_radius = (self.ubounds - self.lbounds) / 2.0\n        bounds_mean = (self.ubounds + self.lbounds) / 2.0\n\n        self.to_norm = []\n        self.to_space = []\n        for r, m in zip(bounds_radius, bounds_mean):\n            self.to_norm.append(\n                functools.partial(\n                    lambda param, bm, br: (param - bm) / br,\n                    bm=m,\n                    br=r)\n            )\n            self.to_space.append(\n                functools.partial(\n                    lambda param, bm, br: (param * br) + bm,\n                    bm=m,\n                    br=r\n                )\n            )\n\n        # Overwrite the bounds with -1. and 1.\n        self.lbounds = numpy.full(self.problem_size, -1.0)\n        self.ubounds = numpy.full(self.problem_size, 1.0)\n\n        self.setup_deap()\n\n        # In case initial guesses were provided, rescale them to the norm space\n        if self.centroids is not None:\n            self.centroids = [\n                self.toolbox.Individual(_ind_convert_space(ind, self.to_norm))\n                for ind in centroids\n            ]\n\n    def setup_deap(self):\n        \"\"\"Set up optimisation\"\"\"\n\n        # Set random seed\n        random.seed(self.seed)\n        numpy.random.seed(self.seed)\n\n        # Register the 'uniform' function\n        self.toolbox.register(\n            \"uniformparams\",\n            utils.uniform,\n            self.lbounds,\n            self.ubounds,\n            self.ind_size\n        )\n\n        # Register the individual format\n        self.toolbox.register(\n            \"Individual\",\n            functools.partial(\n                utils.WSListIndividual,\n                obj_size=self.ind_size,\n                reduce_fcn=self.fitness_reduce,\n            ),\n        )\n\n        # A Random Individual is created by ListIndividual and parameters are\n        # initially picked by 'uniform'\n        self.toolbox.register(\n            \"RandomInd\",\n            deap.tools.initIterate,\n            self.toolbox.Individual,\n            self.toolbox.uniformparams,\n        )\n\n        # Register the population format. It is a list of individuals\n        self.toolbox.register(\n            \"population\", deap.tools.initRepeat, list, self.toolbox.RandomInd\n        )\n\n        # Register the evaluation function for the individuals\n        self.toolbox.register(\n            \"evaluate\",\n            self.evaluator.init_simulator_and_evaluate_with_lists\n        )\n\n        import copyreg\n        import types\n\n        copyreg.pickle(types.MethodType, utils.reduce_method)\n\n        if self.use_scoop:\n            if self.map_function:\n                raise Exception(\n                    \"Impossible to use scoop is providing self defined map \"\n                    \"function: %s\" % self.map_function\n                )\n\n            from scoop import futures\n\n            self.toolbox.register(\"map\", futures.map)\n\n        elif self.map_function:\n            self.toolbox.register(\"map\", self.map_function)\n\n    def run(\n        self,\n        max_ngen=0,\n        cp_frequency=1,\n        cp_period=None,\n        continue_cp=False,\n        cp_filename=None,\n        terminator=None,\n    ):\n        \"\"\" Run the optimizer until a stopping criteria is met.\n\n        Args:\n            max_ngen(int): Total number of generation to run\n            cp_frequency(int): generations between checkpoints\n            cp_period(float): minimum time (in s) between checkpoint.\n                None to save checkpoint independently of the time between them\n            continue_cp(bool): whether to continue\n            cp_filename(string): path to checkpoint filename\n            terminator (multiprocessing.Event): exit loop when is set.\n                Not taken into account if None.\n        \"\"\"\n        if cp_filename:\n            cp_filename_tmp = cp_filename + '.tmp'\n\n        stats = self.get_stats()\n\n        if continue_cp:\n\n            # A file name has been given, then load the data from the file\n            cp = pickle.load(open(cp_filename, \"rb\"))\n            gen = cp[\"generation\"]\n            self.hof = cp[\"halloffame\"]\n            logbook = cp[\"logbook\"]\n            history = cp[\"history\"]\n            random.setstate(cp[\"rndstate\"])\n            numpy.random.set_state(cp[\"np_rndstate\"])\n            CMA_es = cp[\"CMA_es\"]\n            CMA_es.map_function = self.map_function\n\n        else:\n\n            history = deap.tools.History()\n            logbook = deap.tools.Logbook()\n            logbook.header = [\"gen\", \"nevals\"] + stats.fields\n\n            # Instantiate the CMA strategy centered on the centroids\n            CMA_es = self.cma_creator(\n                centroids=self.centroids,\n                offspring_size=self.offspring_size,\n                sigma=self.sigma,\n                max_ngen=max_ngen,\n                IndCreator=self.toolbox.Individual,\n                RandIndCreator=self.toolbox.RandomInd,\n                map_function=self.map_function,\n                use_scoop=self.use_scoop,\n                use_stagnation_criterion=self.use_stagnation_criterion,\n            )\n\n            if self.selector_name == \"multi_objective\":\n                CMA_es.weight_hv = self.weight_hv\n                to_evaluate = CMA_es.get_parents(self.to_space)\n                fitness = self.toolbox.map(self.toolbox.evaluate, to_evaluate)\n                fitness = list(map(list, fitness))\n                CMA_es.set_fitness_parents(fitness)\n\n            gen = 1\n\n        pop = CMA_es.get_population(self.to_space)\n\n        param_names = []\n        if hasattr(self.evaluator, \"param_names\"):\n            param_names = self.evaluator.param_names\n\n        time_last_save = time.time()\n        # Run until a termination criteria is met\n        while utils.run_next_gen(CMA_es.active, terminator):\n            logger.info(\"Generation {}\".format(gen))\n\n            # Generate the new populations\n            n_out = CMA_es.generate_new_pop(\n                lbounds=self.lbounds, ubounds=self.ubounds\n            )\n            logger.debug(\n                \"Number of individuals outside of bounds: {} ({:.2f}%)\".format(\n                    n_out,\n                    100.0 * n_out / len(CMA_es.population)\n                )\n            )\n\n            # Get all the individuals in the original space for evaluation\n            to_evaluate = CMA_es.get_population(self.to_space)\n\n            # Compute the fitness\n            fitness = self.toolbox.map(self.toolbox.evaluate, to_evaluate)\n            fitness = list(map(list, fitness))\n            nevals = len(to_evaluate)\n            CMA_es.set_fitness(fitness)\n\n            # Update the hall of fame, history and logbook\n            pop = CMA_es.get_population(self.to_space)\n            utils.update_history_and_hof(self.hof, history, pop)\n            record = utils.record_stats(stats, logbook, gen, pop, nevals)\n            logger.info(logbook.stream)\n\n            # Update the CMA strategy using the new fitness and check if\n            # termination conditions were reached\n            CMA_es.update_strategy()\n            CMA_es.check_termination(gen)\n            if (\n                cp_filename and\n                cp_frequency and\n                gen % cp_frequency == 0 and\n                (cp_period is None or time.time() - time_last_save > cp_period)\n            ):\n\n                # Map function shouldn't be pickled\n                temp_mf = CMA_es.map_function\n                CMA_es.map_function = None\n\n                cp = dict(\n                    population=pop,\n                    generation=gen,\n                    halloffame=self.hof,\n                    history=history,\n                    logbook=logbook,\n                    rndstate=random.getstate(),\n                    np_rndstate=numpy.random.get_state(),\n                    CMA_es=CMA_es,\n                    param_names=param_names,\n                )\n                pickle.dump(cp, open(cp_filename_tmp, \"wb\"))\n                if os.path.isfile(cp_filename_tmp):\n                    shutil.copy(cp_filename_tmp, cp_filename)\n                    logger.debug(\"Wrote checkpoint to %s\", cp_filename)\n\n                CMA_es.map_function = temp_mf\n\n                time_last_save = time.time()\n\n            gen += 1\n\n        return pop, self.hof, logbook, history\n\n    def get_stats(self):\n        \"\"\"Get the stats that will be saved during optimisation\"\"\"\n        stats = deap.tools.Statistics(key=lambda ind: ind.fitness.reduce)\n        stats.register(\"avg\", numpy.mean)\n        stats.register(\"std\", numpy.std)\n        stats.register(\"min\", numpy.min)\n        stats.register(\"max\", numpy.max)\n        return stats\n"
  },
  {
    "path": "bluepyopt/deapext/stoppingCriteria.py",
    "content": "\"\"\"StoppingCriteria class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=R0912, R0914\n\nimport logging\nimport numpy\n\nfrom collections import deque\n\nimport bluepyopt.stoppingCriteria\n\nlogger = logging.getLogger(\"__main__\")\n\n\ndef isclose(a, b, rel_tol=1e-09, abs_tol=0.0):\n    return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)\n\n\nclass MaxNGen(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"Max ngen stopping criteria class\"\"\"\n\n    name = \"Max ngen\"\n\n    def __init__(self, max_ngen):\n        \"\"\"Constructor\"\"\"\n        super(MaxNGen, self).__init__()\n        self.max_ngen = max_ngen\n\n    def check(self, kwargs):\n        \"\"\"Check if the maximum number of iteration is reached\"\"\"\n        gen = kwargs.get(\"gen\")\n        if gen > self.max_ngen:\n            self.criteria_met = True\n\n\nclass Stagnation(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"Stagnation stopping criteria class\"\"\"\n\n    name = \"Stagnation\"\n\n    def __init__(self, lambda_, problem_size):\n        \"\"\"Constructor\"\"\"\n        super(Stagnation, self).__init__()\n\n        self.lambda_ = lambda_\n        self.problem_size = problem_size\n        self.stagnation_iter = None\n\n        self.best = []\n        self.median = []\n\n    def check(self, kwargs):\n        \"\"\"Check if the population stopped improving\"\"\"\n        ngen = kwargs.get(\"gen\")\n        population = kwargs.get(\"population\")\n        fitness = [ind.fitness.reduce for ind in population]\n        fitness.sort()\n\n        # condition to avoid duplicates when re-starting\n        if len(self.best) < ngen:\n            self.best.append(fitness[0])\n            self.median.append(fitness[int(round(len(fitness) / 2.0))])\n        self.stagnation_iter = int(\n            numpy.ceil(\n                0.2 * ngen + 120 + 30.0 * self.problem_size / self.lambda_\n            )\n        )\n\n        cbest = len(self.best) > self.stagnation_iter\n        cmed = len(self.median) > self.stagnation_iter\n        cbest2 = numpy.median(self.best[-20:]) >= numpy.median(\n            self.best[-self.stagnation_iter:-self.stagnation_iter + 20]\n        )\n        cmed2 = numpy.median(self.median[-20:]) >= numpy.median(\n            self.median[-self.stagnation_iter:-self.stagnation_iter + 20]\n        )\n        if cbest and cmed and cbest2 and cmed2:\n            self.criteria_met = True\n\n\nclass Stagnationv2(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"Stagnation stopping criteria class\"\"\"\n\n    name = \"Stagnationv2\"\n\n    def __init__(\n        self, lambda_, problem_size, threshold=0.01, std_threshold=0.02\n    ):\n        \"\"\"Constructor\n\n        Args:\n            lambda_ (int): offspring size\n            problem_size (int): problem size\n            threshold (float): 1st criterion is triggered if best fitness\n                improves less than this threshold for 100 generations\n            std_threshold (float): 2nd criterion is triggered if\n                standard deviation of the best fitness over\n                the last 20 generations is below the best fitness multiplied\n                by this threshold\n        \"\"\"\n        super(Stagnationv2, self).__init__()\n\n        self.lambda_ = lambda_\n        self.problem_size = problem_size\n        self.stagnation_iter = None\n        self.threshold = threshold\n        self.std_threshold = std_threshold\n\n        self.best = []\n\n    def check(self, kwargs):\n        \"\"\"Check if best model fitness does not improve over 1% over 100 gens\n            and is not noisy in the last 20 generations\n        \"\"\"\n        ngen = kwargs.get(\"gen\")\n        population = kwargs.get(\"population\")\n        fitness = [ind.fitness.reduce for ind in population]\n        fitness.sort()\n\n        # condition to avoid duplicates when re-starting\n        if len(self.best) < ngen:\n            self.best.append(fitness[0])\n\n        self.stagnation_iter = int(\n            numpy.ceil(\n                0.2 * ngen + 120 + 30.0 * self.problem_size / self.lambda_\n            )\n        )\n\n        crit1 = len(self.best) > self.stagnation_iter\n        crit2 = numpy.median(self.best[-20:]) * (1 + self.threshold) \\\n            > numpy.median(self.best[-120:-100])\n        crit3 = numpy.std(self.best[-20:]) < (\n            self.std_threshold * self.best[-1]\n        )\n\n        if crit1 and crit2 and crit3:\n            self.criteria_met = True\n\n\nclass TolHistFun(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"TolHistFun stopping criteria class\"\"\"\n\n    name = \"TolHistFun\"\n\n    def __init__(self, lambda_, problem_size):\n        \"\"\"Constructor\"\"\"\n        super(TolHistFun, self).__init__()\n        self.tolhistfun = 10 ** -12\n        self.mins = deque(\n            maxlen=10 + int(numpy.ceil(30.0 * problem_size / lambda_)))\n\n    def check(self, kwargs):\n        \"\"\"Check if the range of the best values is smaller than\n        the threshold\"\"\"\n        population = kwargs.get(\"population\")\n        self.mins.append(numpy.min([ind.fitness.reduce for ind in population]))\n\n        if (\n            len(self.mins) == self.mins.maxlen\n            and max(self.mins) - min(self.mins) < self.tolhistfun\n        ):\n            self.criteria_met = True\n\n\nclass EqualFunVals(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"EqualFunVals stopping criteria class\"\"\"\n\n    name = \"EqualFunVals\"\n\n    def __init__(self, lambda_, problem_size):\n        \"\"\"Constructor\"\"\"\n        super(EqualFunVals, self).__init__()\n        self.problem_size = problem_size\n        self.equalvals = float(problem_size) / 3.0\n        self.equalvals_k = int(numpy.ceil(0.1 + lambda_ / 4.0))\n        self.equalvalues = []\n\n    def check(self, kwargs):\n        \"\"\"Check if in 1/3rd of the last problem_size iterations the best and\n        k'th best solutions are equal\"\"\"\n        ngen = kwargs.get(\"gen\")\n        population = kwargs.get(\"population\")\n\n        fitness = [ind.fitness.reduce for ind in population]\n        fitness.sort()\n\n        if isclose(fitness[0], fitness[-self.equalvals_k], rel_tol=1e-6):\n            self.equalvalues.append(1)\n        else:\n            self.equalvalues.append(0)\n\n        if (\n            ngen > self.problem_size\n            and sum(self.equalvalues[-self.problem_size:]) > self.equalvals\n        ):\n            self.criteria_met = True\n\n\nclass TolX(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"TolX stopping criteria class\"\"\"\n\n    name = \"TolX\"\n\n    def __init__(self):\n        \"\"\"Constructor\"\"\"\n        super(TolX, self).__init__()\n        self.tolx = 10 ** -12\n\n    def check(self, kwargs):\n        \"\"\"Check if all components of pc and sqrt(diag(C)) are smaller than\n        a threshold\"\"\"\n        pc = kwargs.get(\"pc\")\n        C = kwargs.get(\"C\")\n\n        if all(pc < self.tolx) and all(numpy.sqrt(numpy.diag(C)) < self.tolx):\n            self.criteria_met = True\n\n\nclass TolUpSigma(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"TolUpSigma stopping criteria class\"\"\"\n\n    name = \"TolUpSigma\"\n\n    def __init__(self, sigma0):\n        \"\"\"Constructor\"\"\"\n        super(TolUpSigma, self).__init__()\n        self.sigma0 = sigma0\n        self.tolupsigma = 10 ** 20\n\n    def check(self, kwargs):\n        \"\"\"Check if the sigma/sigma0 ratio is bigger than a threshold\"\"\"\n        sigma = kwargs.get(\"sigma\")\n        diagD = kwargs.get(\"diagD\")\n\n        if sigma / self.sigma0 > float(diagD[-1] ** 2) * self.tolupsigma:\n            self.criteria_met = True\n\n\nclass ConditionCov(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"ConditionCov stopping criteria class\"\"\"\n\n    name = \"ConditionCov\"\n\n    def __init__(self):\n        \"\"\"Constructor\"\"\"\n        super(ConditionCov, self).__init__()\n        self.conditioncov = 10 ** 14\n\n    def check(self, kwargs):\n        \"\"\"Check if the condition number of the covariance matrix is\n        too large\"\"\"\n        cond = kwargs.get(\"cond\")\n\n        if cond > self.conditioncov:\n            self.criteria_met = True\n\n\nclass NoEffectAxis(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"NoEffectAxis stopping criteria class\"\"\"\n\n    name = \"NoEffectAxis\"\n\n    def __init__(self, problem_size):\n        \"\"\"Constructor\"\"\"\n        super(NoEffectAxis, self).__init__()\n        self.conditioncov = 10 ** 14\n        self.problem_size = problem_size\n\n    def check(self, kwargs):\n        \"\"\"Check if the coordinate axis std is too low\"\"\"\n        ngen = kwargs.get(\"gen\")\n        centroid = kwargs.get(\"centroid\")\n        sigma = kwargs.get(\"sigma\")\n        diagD = kwargs.get(\"diagD\")\n        B = kwargs.get(\"B\")\n\n        noeffectaxis_index = ngen % self.problem_size\n\n        if all(\n            centroid\n            == centroid\n            + 0.1 * sigma * diagD[-noeffectaxis_index] * B[-noeffectaxis_index]\n        ):\n            self.criteria_met = True\n\n\nclass NoEffectCoor(bluepyopt.stoppingCriteria.StoppingCriteria):\n    \"\"\"NoEffectCoor stopping criteria class\"\"\"\n\n    name = \"NoEffectCoor\"\n\n    def __init__(self):\n        \"\"\"Constructor\"\"\"\n        super(NoEffectCoor, self).__init__()\n\n    def check(self, kwargs):\n        \"\"\"Check if main axis std has no effect\"\"\"\n        centroid = kwargs.get(\"centroid\")\n        sigma = kwargs.get(\"sigma\")\n        C = kwargs.get(\"C\")\n\n        if any(centroid == centroid + 0.2 * sigma * numpy.diag(C)):\n            self.criteria_met = True\n"
  },
  {
    "path": "bluepyopt/deapext/tools/__init__.py",
    "content": "\"\"\"Init\"\"\"\n\nfrom .selIBEA import *  # NOQA\n"
  },
  {
    "path": "bluepyopt/deapext/tools/selIBEA.py",
    "content": "\"\"\"IBEA selector\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\nThe code in this file was original written in 2015 at the\nBlueBrain Project, EPFL, Lausanne\nThe authors were Werner Van Geit, Michael Gevaert and Jean-Denis Courcol\nIt is based on a C implementation of the IBEA algorithm in the PISA\noptimization framework developed at the ETH, Zurich\nhttp://www.tik.ee.ethz.ch/pisa/selectors/ibea/?page=ibea.php\n\"\"\"\n\n\nimport numpy\nimport random\n\n\ndef selIBEA(population, mu, alpha=None, kappa=.05, tournament_n=4):\n    \"\"\"IBEA Selector\"\"\"\n\n    if alpha is None:\n        alpha = len(population)\n\n    # Calculate a matrix with the fitness components of every individual\n    components = _calc_fitness_components(population, kappa=kappa)\n\n    # Calculate the fitness values\n    _calc_fitnesses(population, components)\n\n    # Do the environmental selection\n    population[:] = _environmental_selection(population, alpha)\n\n    # Select the parents in a tournament\n    parents = _mating_selection(population, mu, tournament_n)\n\n    return parents\n\n\ndef _calc_fitness_components(population, kappa):\n    \"\"\"returns an N * N numpy array of doubles, which is their IBEA fitness \"\"\"\n    # DEAP selector are supposed to maximise the objective values\n    # We take the negative objectives because this algorithm will minimise\n    population_matrix = numpy.fromiter(\n        iter(-x for individual in population\n             for x in individual.fitness.wvalues),\n        dtype=numpy.float64)\n    pop_len = len(population)\n    feat_len = len(population[0].fitness.wvalues)\n    population_matrix = population_matrix.reshape((pop_len, feat_len))\n\n    # Calculate minimal square bounding box of the objectives\n    box_ranges = (numpy.max(population_matrix, axis=0) -\n                  numpy.min(population_matrix, axis=0))\n\n    # Replace all possible zeros to avoid division by zero\n    # Basically 0/0 is replaced by 0/1\n    box_ranges[box_ranges == 0] = 1.0\n\n    components_matrix = numpy.zeros((pop_len, pop_len))\n    for i in range(0, pop_len):\n        diff = population_matrix - population_matrix[i, :]\n        components_matrix[i, :] = numpy.max(\n            numpy.divide(diff, box_ranges),\n            axis=1)\n\n    # Calculate max of absolute value of all elements in matrix\n    max_absolute_indicator = numpy.max(numpy.abs(components_matrix))\n\n    # Normalisation\n    if max_absolute_indicator != 0:\n        components_matrix = numpy.exp(\n            (-1.0 / (kappa * max_absolute_indicator)) * components_matrix.T)\n\n    return components_matrix\n\n\ndef _calc_fitnesses(population, components):\n    \"\"\"Calculate the IBEA fitness of every individual\"\"\"\n\n    # Calculate sum of every column in the matrix, ignore diagonal elements\n    column_sums = numpy.sum(components, axis=0) - numpy.diagonal(components)\n\n    # Fill the 'ibea_fitness' field on the individuals with the fitness value\n    for individual, ibea_fitness in zip(population, column_sums):\n        individual.ibea_fitness = ibea_fitness\n\n\ndef _choice(seq):\n    \"\"\"Python 2 implementation of choice\"\"\"\n\n    return seq[int(random.random() * len(seq))]\n\n\ndef _mating_selection(population, mu, tournament_n):\n    \"\"\"Returns the n_of_parents individuals with the best fitness\"\"\"\n\n    parents = []\n    for _ in range(mu):\n        winner = _choice(population)\n        for _ in range(tournament_n - 1):\n            individual = _choice(population)\n            # Save winner is element with smallest fitness\n            if individual.ibea_fitness < winner.ibea_fitness:\n                winner = individual\n        parents.append(winner)\n\n    return parents\n\n\ndef _environmental_selection(population, selection_size):\n    \"\"\"Returns the selection_size individuals with the best fitness\"\"\"\n\n    # Sort the individuals based on their fitness\n    population.sort(key=lambda ind: ind.ibea_fitness)\n\n    # Return the first 'selection_size' elements\n    return population[:selection_size]\n\n\n__all__ = ['selIBEA']\n"
  },
  {
    "path": "bluepyopt/deapext/utils.py",
    "content": "\"\"\"Utils function\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport numpy\nimport random\n\nimport deap.base\n\n# pylint: disable=R0914, R0912\n\n\nclass WeightedReducedFitness(deap.base.Fitness):\n\n    \"\"\"Fitness that compares by weighted objective values\"\"\"\n\n    def __init__(self, values=(), obj_size=None, reduce_fcn=numpy.sum):\n        self.weights = [-1.0] * obj_size if obj_size is not None else [-1]\n        self.reduce_fcn = reduce_fcn\n\n        super(WeightedReducedFitness, self).__init__(values)\n\n    @property\n    def reduce(self):\n        \"\"\"Reduce of values\"\"\"\n        return self.reduce_fcn(self.values)\n\n    @property\n    def weighted_reduce(self):\n        \"\"\"Reduce of weighted values\"\"\"\n        return self.reduce_fcn(self.wvalues)\n\n    def __le__(self, other):\n        return self.weighted_reduce <= other.weighted_reduce\n\n    def __lt__(self, other):\n        return self.weighted_reduce < other.weighted_reduce\n\n    def __deepcopy__(self, _):\n        \"\"\"Override deepcopy\"\"\"\n\n        cls = self.__class__\n        result = cls.__new__(cls)\n        result.__dict__.update(self.__dict__)\n        return result\n\n\nclass WSListIndividual(list):\n\n    \"\"\"Individual consisting of a list with weighted fitness\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Constructor\"\"\"\n\n        reduce_fcn = kwargs.get(\"reduce_fcn\", numpy.sum)\n        self.fitness = WeightedReducedFitness(\n            obj_size=kwargs[\"obj_size\"], reduce_fcn=reduce_fcn\n        )\n\n        # Index of the parent, used by MO-CMA\n        self._ps = \"p\", 0\n\n        del kwargs[\"obj_size\"]\n        if \"reduce_fcn\" in kwargs:\n            del kwargs[\"reduce_fcn\"]\n        super(WSListIndividual, self).__init__(*args, **kwargs)\n\n\ndef update_history_and_hof(halloffame, history, population):\n    \"\"\"Update the hall of fame with the generated individuals\n    Note: History and Hall-of-Fame behave like dictionaries\n    \"\"\"\n    if halloffame is not None:\n        halloffame.update(population)\n\n    history.update(population)\n\n\ndef record_stats(stats, logbook, gen, population, invalid_count):\n    \"\"\"Update the statistics with the new population\"\"\"\n    record = stats.compile(population) if stats is not None else {}\n    logbook.record(gen=gen, nevals=invalid_count, **record)\n\n\ndef closest_feasible(individual, lbounds, ubounds):\n    \"\"\"Returns the closest individual in the parameter bounds\"\"\"\n    # TODO: Fix 1e-9 hack\n    for i, (u, l, el) in enumerate(zip(ubounds, lbounds, individual)):\n        if el >= u:\n            individual[i] = u - 1e-9\n        elif el <= l:\n            individual[i] = l + 1e-9\n    return individual\n\n\ndef bound(population, lbounds, ubounds):\n    \"\"\"Bounds the population based on lower and upper parameter bounds.\"\"\"\n    n_out = 0\n    for i, ind in enumerate(population):\n        if numpy.any(numpy.less(ind, lbounds)) or numpy.any(\n            numpy.greater(ind, ubounds)\n        ):\n            population[i] = closest_feasible(ind, lbounds, ubounds)\n            n_out += 1\n    return n_out\n\n\ndef uniform(lower_list, upper_list, dimensions):\n    \"\"\"Uniformly pick an individual\"\"\"\n    if hasattr(lower_list, \"__iter__\"):\n        return [\n            random.uniform(lower, upper) for lower, upper in\n            zip(lower_list, upper_list)\n        ]\n    else:\n        return [random.uniform(lower_list, upper_list) for _ in\n                range(dimensions)]\n\n\ndef reduce_method(meth):\n    \"\"\"Overwrite reduce\"\"\"\n    return (getattr, (meth.__self__, meth.__func__.__name__))\n\n\ndef run_next_gen(criteria, terminator):\n    \"\"\"Condition to stay inside the loop.\"\"\"\n    if terminator is None:\n        return criteria\n    return criteria and not terminator.is_set()\n"
  },
  {
    "path": "bluepyopt/ephys/__init__.py",
    "content": "\"\"\"Init script\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nfrom . import base  # NOQA\nfrom . import simulators  # NOQA\nfrom . import models  # NOQA\nfrom . import evaluators  # NOQA\nfrom . import mechanisms  # NOQA\nfrom . import locations  # NOQA\nfrom . import parameterscalers  # NOQA\nfrom . import parameters  # NOQA\nfrom . import morphologies  # NOQA\nfrom . import efeatures  # NOQA\nfrom . import objectives  # NOQA\nfrom . import protocols  # NOQA\nfrom . import responses  # NOQA\nfrom . import recordings  # NOQA\nfrom . import objectivescalculators  # NOQA\nfrom . import stimuli  # NOQA\n\n# TODO create all the necessary abstract methods\n# TODO check inheritance structure\n# TODO instantiate using 'simulation env' as parameter, instead of cell\n"
  },
  {
    "path": "bluepyopt/ephys/acc.py",
    "content": "'''Dependencies of Arbor simulator backend'''\n\ntry:\n    import arbor\nexcept ImportError as e:\n    class arbor:\n        def __getattribute__(self, _):\n            raise ImportError(\"Exporting cell models to ACC/JSON, loading\"\n                              \" them or optimizing them with the Arbor\"\n                              \" simulator requires missing dependency arbor.\"\n                              \" To install BluePyOpt with arbor,\"\n                              \" run 'pip install bluepyopt[arbor]'.\")\n\n\nclass ArbLabel:\n    \"\"\"Arbor label\"\"\"\n\n    def __init__(self, type, name, s_expr):\n        if type not in ['locset', 'region', 'iexpr']:\n            raise ValueError('Invalid Arbor label type %s' % type)\n        self._type = type\n        self._name = name\n        self._s_expr = s_expr\n\n    @property\n    def defn(self):\n        \"\"\"Label definition for label-dict\"\"\"\n        return '(%s-def \"%s\" %s)' % (self._type, self._name, self._s_expr)\n\n    @property\n    def ref(self):\n        \"\"\"Reference to label defined in label-dict\"\"\"\n        return '(%s \"%s\")' % (self._type, self._name)\n\n    @property\n    def name(self):\n        \"\"\"Name of the label\"\"\"\n        return self._name\n\n    @property\n    def loc(self):\n        \"\"\"S-expression defining the location of the label\"\"\"\n        return self._s_expr\n\n    def __eq__(self, other):\n        if other is None:\n            return False\n        elif not isinstance(other, ArbLabel):\n            raise TypeError('%s is not an ArbLabel' % str(other))\n        else:\n            return self._s_expr == other._s_expr\n\n    def __hash__(self):\n        return hash(self._s_expr)\n\n    def __repr__(self):\n        return self.defn\n"
  },
  {
    "path": "bluepyopt/ephys/base.py",
    "content": "'''Base class for ephys classes'''\n\n\nclass BaseEPhys(object):\n    '''Base class for ephys classes'''\n\n    def __init__(self, name='', comment=''):\n        self.name = name\n        self.comment = comment\n\n    def __str__(self):\n        return '%s: %s (%s)' % (self.__class__.__name__,\n                                self.name, self.comment)\n"
  },
  {
    "path": "bluepyopt/ephys/create_acc.py",
    "content": "\"\"\"create JSON/ACC files for Arbor from a set of BluePyOpt.ephys parameters\"\"\"\n\n# pylint: disable=R0914\n\nimport io\nimport logging\nimport pathlib\nfrom collections import ChainMap, namedtuple, OrderedDict\nimport re\n\nimport jinja2\nimport json\nimport shutil\n\nfrom bluepyopt.ephys.acc import arbor\nfrom bluepyopt.ephys.morphologies import ArbFileMorphology\nfrom bluepyopt.ephys.create_hoc import (\n    Location,\n    RangeExpr,\n    PointExpr,\n    _get_template_params,\n    format_float,\n)\n\nlogger = logging.getLogger(__name__)\n\n# Inhomogeneous expression for scaled parameter in Arbor\nRangeIExpr = namedtuple(\"RangeIExpr\", \"name, value, scale\")\n\n\nclass ArbVar:\n    \"\"\"Definition of a Neuron to Arbor parameter conversion\"\"\"\n\n    def __init__(self, name, conv=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): Arbor parameter name\n            conv (): Conversion of parameter value from Neuron units\n            to Arbor (defaults to identity)\n        \"\"\"\n        self.name = name\n        self.conv = conv\n\n    def __repr__(self):\n        return \"ArbVar(%s, %s)\" % (self.name, self.conv)\n\n\nclass Nrn2ArbParamAdapter:\n    \"\"\"Converts a Neuron parameter to Arbor format (name and value)\"\"\"\n\n    _mapping = dict(\n        v_init=ArbVar(name=\"membrane-potential\"),\n        celsius=ArbVar(\n            name=\"temperature-kelvin\", conv=lambda celsius: celsius + 273.15\n        ),\n        Ra=ArbVar(name=\"axial-resistivity\"),\n        cm=ArbVar(\n            name=\"membrane-capacitance\", conv=lambda cm: cm / 100.0\n        ),  # NEURON: uF/cm^2, Arbor: F/m^2\n        **{\n            species + loc[0]: ArbVar(\n                name='ion-%sternal-concentration \"%s\"' % (loc, species)\n            )\n            for species in [\"na\", \"k\", \"ca\"]\n            for loc in [\"in\", \"ex\"]\n        },\n        **{\n            \"e\" + species: ArbVar(name='ion-reversal-potential \"%s\"' % species)\n            for species in [\"na\", \"k\", \"ca\"]\n        },\n    )\n\n    @classmethod\n    def _param_name(cls, name):\n        \"\"\"Neuron to Arbor parameter renaming\n\n        Args:\n            name (str): Neuron parameter name\n        \"\"\"\n        return cls._mapping[name].name if name in cls._mapping else name\n\n    @classmethod\n    def _param_value(cls, param):\n        \"\"\"Neuron to Arbor units conversion for parameter values\n\n        Args:\n            param (): A Neuron parameter with a value in Neuron units\n        \"\"\"\n\n        if (\n            param.name in cls._mapping\n            and cls._mapping[param.name].conv is not None\n        ):\n            return format_float(\n                cls._mapping[param.name].conv(float(param.value))\n            )\n        else:\n            return (\n                param.value\n                if isinstance(param.value, str)\n                else format_float(param.value)\n            )\n\n    @classmethod\n    def _conv_param(cls, param, name):\n        \"\"\"Convert a Neuron parameter to Arbor format (name and units)\n\n        Args:\n            param (): A Neuron parameter\n            name (): Parameter name without mech prefix/suffix\n        \"\"\"\n\n        if isinstance(param, Location):\n            return Location(\n                name=cls._param_name(name), value=cls._param_value(param)\n            )\n        elif isinstance(param, RangeExpr):\n            return RangeExpr(\n                location=param.location,\n                name=cls._param_name(name),\n                value=cls._param_value(param),\n                value_scaler=param.value_scaler,\n            )\n        elif isinstance(param, PointExpr):\n            return PointExpr(\n                name=cls._param_name(name),\n                point_loc=param.point_loc,\n                value=cls._param_value(param),\n            )\n        else:\n            raise CreateAccException(\n                \"Unsupported parameter expression type %s.\" % type(param)\n            )\n\n    @classmethod\n    def format(cls, param, mechs):\n        \"\"\"Find a parameter's mechanism and convert name to Arbor format\n\n        Args:\n            param (): A parameter in Neuron format\n            mechs (): List of co-located NMODL mechanisms\n\n        Returns:\n            A tuple of mechanism name (None for a non-mechanism parameter) and\n            parameter in Arbor format\n        \"\"\"\n        if not isinstance(param, PointExpr):\n            mech_matches = [\n                i\n                for i, mech in enumerate(mechs)\n                if param.name.endswith(\"_\" + mech)\n            ]\n        else:\n            param_pprocesses = [loc.pprocess_mech for loc in param.point_loc]\n            mech_matches = [\n                i for i, mech in enumerate(mechs) if mech in param_pprocesses\n            ]\n\n        if len(mech_matches) == 0:\n            return None, cls._conv_param(param, name=param.name)\n\n        elif len(mech_matches) == 1:\n            mech = mechs[mech_matches[0]]\n            if not isinstance(param, PointExpr):\n                name = param.name[: -(len(mech) + 1)]\n            else:\n                name = param.name\n            return mech, cls._conv_param(param, name=name)\n\n        else:\n            raise CreateAccException(\n                \"Parameter name %s matches\" % param.name\n                + \" multiple mechanisms %s\"\n                % [repr(mechs[i]) for i in mech_matches]\n            )\n\n\nclass Nrn2ArbMechGrouper:\n    \"\"\"Group parameters by mechanism and convert them to Arbor format\"\"\"\n\n    @staticmethod\n    def _is_global_property(loc, param):\n        \"\"\"Returns if a label-specific variable is a global property in Arbor\n\n        Args:\n            loc (): An Arbor label describing the location\n            param (): A parameter in Arbor format (name and units)\n        \"\"\"\n\n        return loc == ArbFileMorphology.region_labels[\"all\"] and (\n            param.name\n            in [\n                \"membrane-potential\",\n                \"temperature-kelvin\",\n                \"axial-resistivity\",\n                \"membrane-capacitance\",\n            ]\n            or param.name.split(\" \")[0]\n            in [\n                \"ion-internal-concentration\",\n                \"ion-external-concentration\",\n                \"ion-reversal-potential\",\n            ]\n        )\n\n    @classmethod\n    def _separate_global_properties(cls, loc, mechs):\n        \"\"\"Separates global properties from a label-specific dict of mechanisms\n\n        Args:\n            loc (): An Arbor label describing the location\n            mechs (): A mapping of mechanism name to list of parameters in\n            Arbor format (None for non-mechanism parameters).\n\n        Returns:\n            A split of mechs into mechanisms without Arbor global properties\n            (first component) and a dict with Arbor global properties\n            (second component)\n        \"\"\"\n\n        local_mechs = dict()\n        global_properties = []\n\n        for mech, params in mechs.items():\n            if mech is None:\n                local_properties = []\n                for param in params:\n                    if cls._is_global_property(loc, param):\n                        global_properties.append(param)\n                    else:\n                        local_properties.append(param)\n                local_mechs[mech] = local_properties\n            else:\n                local_mechs[mech] = params\n\n        return local_mechs, {None: global_properties}\n\n    @staticmethod\n    def _format_params_and_group_by_mech(params, channels):\n        \"\"\"Group list of parameters by mechanism and turn them to Arbor format\n\n        Args:\n            params (): List of parameters in Neuron format\n            channels (): List of co-located NMODL mechanisms\n\n        Returns:\n            Mapping of Arbor mechanism name to list of parameters in Arbor\n            format\n        \"\"\"\n        mech_params = [\n            Nrn2ArbParamAdapter.format(param, channels) for param in params\n        ]\n        mechs = {mech: [] for mech, _ in mech_params}\n        for mech in channels:\n            if mech not in mechs:\n                mechs[mech] = []\n        for mech, param in mech_params:\n            mechs[mech].append(param)\n        return mechs\n\n    @classmethod\n    def process_global(cls, params):\n        \"\"\"Group global BluePyOpt params by mech, convert them to Arbor format\n\n        Args:\n            params (): List of global parameters in Neuron format\n\n        Returns:\n            A mapping of mechanism to parameters representing Arbor global\n            properties. The mechanism parameters are in Arbor format\n            (mechanism name is None for non-mechanism parameters).\n        \"\"\"\n        return cls._format_params_and_group_by_mech(\n            [\n                Location(name=name, value=value)\n                for name, value in params.items()\n            ],\n            [],  # no default mechanisms\n        )\n\n    @classmethod\n    def process_local(cls, params, channels):\n        \"\"\"Group local BluePyOpt params by mech, convert them to Arbor format\n\n        Args:\n            params (): List of Arbor label/local parameters pairs in Neuron\n            format\n            channels (): Mapping of Arbor label to co-located NMODL mechanisms\n\n        Returns:\n            The return value is a tuple. In the first component, a two-level\n            mapping of Arbor label to mechanism to parameters. The mechanism\n            parameters are in Arbor format (mechanism name is None for\n            non-mechanism parameters). In the second component, the\n            Arbor global properties found are returned.\n        \"\"\"\n        local_mechs = dict()\n        global_properties = dict()\n        for loc, loc_params in params:\n            mechs = cls._format_params_and_group_by_mech(\n                loc_params, channels[loc]\n            )\n\n            # move Arbor global properties to global_params\n            mechs, global_props = cls._separate_global_properties(loc, mechs)\n            if global_props.keys() != {None}:\n                raise CreateAccException(\n                    \"Support for Arbor default mechanisms not implemented.\"\n                )\n            # iterate over global_props items if above exception triggers\n            global_properties[None] = (\n                global_properties.get(None, []) + global_props[None]\n            )\n            local_mechs[loc] = mechs\n        return local_mechs, global_properties\n\n\ndef _arb_filter_point_proc_locs(pprocess_mechs):\n    \"\"\"Filter locations from point process parameters\n\n    Args:\n        pprocess_mechs (): Point process mechanisms with parameters in\n        Arbor format\n    \"\"\"\n\n    result = {loc: dict() for loc in pprocess_mechs}\n\n    for loc, mechs in pprocess_mechs.items():\n        for mech, point_exprs in mechs.items():\n            result[loc][mech.name] = dict(\n                mech=mech.suffix,\n                params=[\n                    Location(point_expr.name, point_expr.value)\n                    for point_expr in point_exprs\n                ],\n            )\n\n    return result\n\n\ndef _arb_append_scaled_mechs(mechs, scaled_mechs):\n    \"\"\"Append scaled mechanism parameters to constant ones\"\"\"\n    for mech, scaled_params in scaled_mechs.items():\n        if mech is None and len(scaled_params) > 0:\n            raise CreateAccException(\n                \"Non-mechanism parameters cannot have inhomogeneous\"\n                \" expressions in Arbor %s\" % scaled_params\n            )\n        mechs[mech] = mechs.get(mech, []) + [\n            RangeIExpr(\n                name=p.name,\n                value=p.value,\n                scale=p.value_scaler.acc_scale_iexpr(p.value),\n            )\n            for p in scaled_params\n        ]\n\n\n# An mechanism's NMODL GLOBAL and RANGE variables in Arbor\nMechMetaData = namedtuple(\"MechMetaData\", \"globals, ranges\")\n\n\nclass ArbNmodlMechFormatter:\n    \"\"\"Loads catalogue metadata and reformats mechanism name for ACC\"\"\"\n\n    def __init__(self, ext_catalogues):\n        \"\"\"Load metadata of external and Arbor's built-in mechanism catalogues\n\n        Args:\n            ext_catalogues (): Mapping of catalogue name to directory\n            with NMODL files defining the mechanisms.\n        \"\"\"\n        self.cats = self._load_mech_catalogue_meta(ext_catalogues)\n\n    @staticmethod\n    def _load_catalogue_meta(cat_dir):\n        \"\"\"Load mechanism catalogue metadata from NMODL files\n\n        Args:\n            cat_dir (): Path to directory with NMODL files of catalogue\n\n        Returns:\n            Mapping of name to meta data for each mechanism in the directory\n        \"\"\"\n        # used to generate arbor_mechanisms.json on NMODL from arbor/mechanisms\n\n        nmodl_pattern = r\"^\\s*%s\\s+((?:\\w+\\,\\s*)*?\\w+)\\s*?$\"  # NOQA\n        suffix_pattern = nmodl_pattern % \"SUFFIX\"\n        globals_pattern = nmodl_pattern % \"GLOBAL\"\n        ranges_pattern = nmodl_pattern % \"RANGE\"\n\n        def process_nmodl(nmodl_str):\n            \"\"\"Extract global and range params from Arbor-conforming NMODL\"\"\"\n            try:\n                nrn = re.search(\n                    r\"NEURON\\s+{([^}]+)}\", nmodl_str, flags=re.MULTILINE\n                ).group(1)\n                suffix_ = re.search(suffix_pattern, nrn, flags=re.MULTILINE)\n                suffix_ = suffix_ if suffix_ is None else suffix_.group(1)\n                globals_ = re.search(globals_pattern, nrn, flags=re.MULTILINE)\n                globals_ = (\n                    globals_\n                    if globals_ is None\n                    else re.findall(r\"\\w+\", globals_.group(1))\n                )\n                ranges_ = re.search(ranges_pattern, nrn, flags=re.MULTILINE)\n                ranges_ = (\n                    ranges_\n                    if ranges_ is None\n                    else re.findall(r\"\\w+\", ranges_.group(1))\n                )\n            except Exception as e:\n                raise CreateAccException(\n                    \"NMODL-inspection for %s failed.\" % nmodl_file\n                ) from e\n\n            # skipping suffix_\n            return MechMetaData(globals=globals_, ranges=ranges_)\n\n        mechs = dict()\n        cat_dir = pathlib.Path(cat_dir)\n        for nmodl_file in cat_dir.glob(\"*.mod\"):\n            with open(cat_dir.joinpath(nmodl_file)) as f:\n                mechs[nmodl_file.stem] = process_nmodl(f.read())\n\n        return mechs\n\n    @classmethod\n    def _load_mech_catalogue_meta(cls, ext_catalogues):\n        \"\"\"Load metadata of external and Arbor's built-in mechanism catalogues\n\n        Args:\n            ext_catalogues (): Mapping of catalogue name to directory\n            with NMODL files defining the mechanisms\n\n        Returns:\n            Ordered mapping of catalogue name -> mechanism name -> meta data\n            for external and built-in catalogues (external ones taking\n            precedence)\n        \"\"\"\n\n        arb_cats = OrderedDict()\n\n        if ext_catalogues is not None:\n            for cat, cat_nmodl in ext_catalogues.items():\n                arb_cats[cat] = cls._load_catalogue_meta(\n                    pathlib.Path(cat_nmodl).resolve()\n                )\n\n        builtin_catalogues = (\n            pathlib.Path(__file__)\n            .parent.joinpath(\"static/arbor_mechanisms.json\")\n            .resolve()\n        )\n        with open(builtin_catalogues) as f:\n            builtin_arb_cats = json.load(f)\n\n        for cat in [\"BBP\", \"default\", \"allen\"]:\n            if cat not in arb_cats:\n                arb_cats[cat] = {\n                    mech: MechMetaData(**meta)\n                    for mech, meta in builtin_arb_cats[cat].items()\n                }\n\n        return arb_cats\n\n    @staticmethod\n    def _mech_name(name):\n        \"\"\"Neuron to Arbor mechanism name conversion\n\n        Args:\n            name (): A Neuron mechanism name\n        \"\"\"\n        if name in [\"Exp2Syn\", \"ExpSyn\"]:\n            return name.lower()\n        else:\n            return name\n\n    @classmethod\n    def _translate_mech(cls, mech_name, mech_params, arb_cats):\n        \"\"\"Translate NMODL mechanism to Arbor ACC format\n\n        Args:\n            mech_name (): NMODL mechanism name (suffix)\n            mech_params (): Mechanism parameters in Arbor format\n            arb_cats (): Mapping of catalogue names to mechanisms\n            with theirmeta data\n\n        Returns:\n            Tuple of mechanism name with NMODL GLOBAL parameters integrated and\n            catalogue prefix added as well as the remaining RANGE parameters\n        \"\"\"\n\n        arb_mech = None\n        arb_mech_name = cls._mech_name(mech_name)\n\n        for cat in arb_cats:  # in order of precedence\n            if arb_mech_name in arb_cats[cat]:\n                arb_mech = arb_cats[cat][arb_mech_name]\n                mech_name = cat + \"::\" + arb_mech_name\n                break\n\n        if arb_mech is None:  # not Arbor built-in mech, no qualifier added\n            if mech_name is not None:\n                logger.warn(\n                    \"create_acc: Could not find Arbor mech for %s (%s).\"\n                    % (mech_name, mech_params)\n                )\n            return (mech_name, mech_params)\n        else:\n            if arb_mech.globals is None:  # only Arbor range params\n                for param in mech_params:\n                    if param.name not in arb_mech.ranges:\n                        raise CreateAccException(\n                            \"%s not a GLOBAL or RANGE parameter of %s\"\n                            % (param.name, mech_name)\n                        )\n                return (mech_name, mech_params)\n            else:\n                for param in mech_params:\n                    if (\n                        param.name not in arb_mech.globals\n                        and param.name not in arb_mech.ranges\n                    ):\n                        raise CreateAccException(\n                            \"%s not a GLOBAL or RANGE parameter of %s\"\n                            % (param.name, mech_name)\n                        )\n                mech_name_suffix = []\n                remaining_mech_params = []\n                for mech_param in mech_params:\n                    if mech_param.name in arb_mech.globals:\n                        mech_name_suffix.append(\n                            mech_param.name + \"=\" + mech_param.value\n                        )\n                        if isinstance(mech_param, RangeIExpr):\n                            remaining_mech_params.append(\n                                RangeIExpr(\n                                    name=mech_param.name,\n                                    value=None,\n                                    scale=mech_param.scale,\n                                )\n                            )\n                    else:\n                        remaining_mech_params.append(mech_param)\n                if len(mech_name_suffix) > 0:\n                    mech_name += \"/\" + \",\".join(mech_name_suffix)\n                return (mech_name, remaining_mech_params)\n\n    def translate_density(self, mechs):\n        \"\"\"Translate all density mechanisms in a specific region\"\"\"\n        return dict(\n            [\n                self._translate_mech(mech, params, self.cats)\n                for mech, params in mechs.items()\n            ]\n        )\n\n    def translate_points(self, mechs):\n        \"\"\"Translate all point mechanisms for a specific locset\"\"\"\n        result = dict()\n\n        for synapse_name, mech_desc in mechs.items():\n            mech, params = self._translate_mech(\n                mech_desc[\"mech\"], mech_desc[\"params\"], self.cats\n            )\n            result[synapse_name] = dict(mech=mech, params=params)\n\n        return result\n\n\ndef _arb_project_scaled_mechs(mechs):\n    \"\"\"Returns all (iexpr) parameters of scaled mechanisms in Arbor\"\"\"\n    scaled_mechs = dict()\n    for mech, params in mechs.items():\n        range_iexprs = [p for p in params if isinstance(p, RangeIExpr)]\n        if len(range_iexprs) > 0:\n            scaled_mechs[mech] = range_iexprs\n    return scaled_mechs\n\n\ndef _arb_populate_label_dict(local_mechs, local_scaled_mechs, pprocess_mechs):\n    \"\"\"Creates a dict of labels from label-specific parameters/mechanisms\n\n    Args:\n        local_mechs (): label-specific parameters/density mechanisms\n        local_scaled_mechs (): label-specific iexpr parameters/density mechs\n        pprocess_mechs (): label-specific point processes\n\n    Returns:\n        A dict mapping label name to ArbLabel for each label in the input\n    \"\"\"\n\n    label_dict = dict()\n\n    acc_labels = ChainMap(local_mechs, local_scaled_mechs, pprocess_mechs)\n\n    for acc_label in acc_labels:\n        if (\n            acc_label.name in label_dict\n            and acc_label != label_dict[acc_label.name]\n        ):\n            raise CreateAccException(\n                \"Label %s already exists in\"\n                % acc_label.name\n                + \" label_dict with different s-expression: \"\n                \" %s != %s.\" % (label_dict[acc_label.name].loc, acc_label.loc)\n            )\n        elif acc_label.name not in label_dict:\n            label_dict[acc_label.name] = acc_label\n\n    return label_dict\n\n\ndef _read_templates(template_dir, template_filename):\n    \"\"\"Expand Jinja2 template filepath with glob and\n    return dict of target filename -> parsed template\"\"\"\n    if template_dir is None:\n        template_dir = (\n            pathlib.Path(__file__).parent.joinpath(\"templates\").resolve()\n        )\n\n    template_paths = pathlib.Path(template_dir).glob(template_filename)\n\n    templates = dict()\n    for template_path in template_paths:\n        with open(template_path) as template_file:\n            template = template_file.read()\n            name = template_path.name\n            if name.endswith(\".jinja2\"):\n                name = name[:-7]\n            if name.endswith(\"_template\"):\n                name = name[:-9]\n            if \"_\" in name:\n                name = \".\".join(name.rsplit(\"_\", 1))\n            templates[name] = jinja2.Template(template)\n\n    if templates == {}:\n        raise FileNotFoundError(\n            f\"No templates found for JSON/ACC-export in {template_dir}\"\n        )\n\n    return templates\n\n\ndef _arb_loc_desc(location, param_or_mech):\n    \"\"\"Generate Arbor location description for label dict and decor\"\"\"\n    return location.acc_label()\n\n\ndef create_acc(\n    mechs,\n    parameters,\n    morphology=None,\n    morphology_dir=None,\n    ext_catalogues=None,\n    ignored_globals=(),\n    replace_axon=None,\n    create_mod_morph=False,\n    template_name=\"CCell\",\n    template_filename=\"acc/*_template.jinja2\",\n    disable_banner=None,\n    template_dir=None,\n    custom_jinja_params=None,\n):\n    \"\"\"return a dict with strings containing the rendered JSON/ACC templates\n\n    Args:\n        mechs (): All the mechs for the decor template\n        parameters (): All the parameters in the decor/label-dict template\n        morphology (str): Name of morphology\n        morphology_dir (str): Directory of morphology\n        ext_catalogues (): Name to path mapping of non-Arbor built-in\n        NMODL mechanism catalogues compiled with modcc\n        ignored_globals (iterable str): Skipped NrnGlobalParameter in decor\n        replace_axon (): Axon replacement morphology\n        create_mod_morph (): Create ACC morphology with axon replacement\n        template_filename (str): file path of the cell.json , decor.acc and\n        label_dict.acc jinja2 templates (with wildcards expanded by glob)\n        template_dir (str): dir name of the jinja2 templates\n        custom_jinja_params (dict): dict of additional jinja2 params in case\n        of a custom template\n    \"\"\"\n\n    if custom_jinja_params is None:\n        custom_jinja_params = {}\n\n    if pathlib.Path(morphology).suffix.lower() not in [\".swc\", \".asc\"]:\n        raise CreateAccException(\n            \"Morphology file %s not supported in Arbor \"\n            \" (only supported types are .swc and .asc).\" % morphology\n        )\n\n    if replace_axon is not None:\n        if not hasattr(arbor.segment_tree, \"tag_roots\"):\n            raise NotImplementedError(\n                \"Need a newer version of Arbor\" \" for axon replacement.\"\n            )\n        logger.debug(\n            \"Obtain axon replacement by applying \"\n            \"ArbFileMorphology.replace_axon after loading \"\n            \"morphology in Arbor.\"\n        )\n        replace_axon_path = (\n            pathlib.Path(morphology).stem + \"_axon_replacement.acc\"\n        )\n        replace_axon_acc = io.StringIO()\n        arbor.write_component(replace_axon, replace_axon_acc)\n        replace_axon_acc.seek(0)\n\n        if create_mod_morph:\n            modified_morphology_path = (\n                pathlib.Path(morphology).stem + \"_modified.acc\"\n            )\n            modified_morpho = ArbFileMorphology.load(\n                pathlib.Path(morphology_dir).joinpath(morphology),\n                replace_axon_acc,\n            )\n            replace_axon_acc.seek(0)\n            modified_morphology_acc = io.StringIO()\n            arbor.write_component(modified_morpho, modified_morphology_acc)\n            modified_morphology_acc.seek(0)\n            modified_morphology_acc = modified_morphology_acc.read()\n        else:\n            modified_morphology_path = None\n            modified_morphology_acc = None\n\n        replace_axon_acc = replace_axon_acc.read()\n    else:\n        replace_axon_path = None\n        modified_morphology_path = None\n\n    templates = _read_templates(template_dir, template_filename)\n\n    default_location_order = list(ArbFileMorphology.region_labels.values())\n\n    template_params = _get_template_params(\n        mechs,\n        parameters,\n        ignored_globals,\n        disable_banner,\n        default_location_order,\n        _arb_loc_desc,\n    )\n\n    filenames = {\n        name: template_name + (name if name.startswith(\".\") else \"_\" + name)\n        for name in templates.keys()\n    }\n\n    # postprocess template parameters for Arbor\n    channels = template_params[\"channels\"]\n    point_channels = template_params[\"point_channels\"]\n    banner = template_params[\"banner\"]\n\n    # global_mechs refer to default density mechs/params in Arbor\n    # [mech -> param] (params under mech == None)\n    global_mechs = Nrn2ArbMechGrouper.process_global(\n        template_params[\"global_params\"]\n    )\n\n    # local_mechs refer to locally painted density mechs/params in Arbor\n    # [label -> mech -> param.name/.value] (params under mech == None)\n    local_mechs, additional_global_mechs = Nrn2ArbMechGrouper.process_local(\n        template_params[\"section_params\"], channels\n    )\n    for mech, params in additional_global_mechs.items():\n        global_mechs[mech] = global_mechs.get(mech, []) + params\n\n    # scaled_mechs refer to iexpr params of scaled density mechs in Arbor\n    # [label -> mech -> param.location/.name/.value/.value_scaler]\n    range_params = {loc: [] for loc in default_location_order}\n    for param in template_params[\"range_params\"]:\n        range_params[param.location].append(param)\n    range_params = list(range_params.items())\n\n    local_scaled_mechs, global_scaled_mechs = Nrn2ArbMechGrouper.process_local(\n        range_params, channels\n    )\n\n    # join each mech's constant params with inhomogeneous ones on mechanisms\n    _arb_append_scaled_mechs(global_mechs, global_scaled_mechs)\n    for loc in local_scaled_mechs:\n        _arb_append_scaled_mechs(local_mechs[loc], local_scaled_mechs[loc])\n\n    # pprocess_mechs refer to locally placed mechs/params in Arbor\n    # [label -> mech -> param.name/.value]\n    pprocess_mechs, global_pprocess_mechs = Nrn2ArbMechGrouper.process_local(\n        template_params[\"pprocess_params\"], point_channels\n    )\n    if any(len(params) > 0 for params in global_pprocess_mechs.values()):\n        raise CreateAccException(\n            \"Point process mechanisms cannot be\" \" placed globally in Arbor.\"\n        )\n\n    # Evaluate synapse locations\n    # (no new labels introduced, but locations explicitly defined)\n    pprocess_mechs = _arb_filter_point_proc_locs(pprocess_mechs)\n\n    # NMODL formatter loads metadata of external and Arbor's built-in\n    # mech catalogues\n    nmodl_formatter = ArbNmodlMechFormatter(ext_catalogues)\n\n    # translate mechs to Arbor's nomenclature\n    global_mechs = nmodl_formatter.translate_density(global_mechs)\n    local_mechs = {\n        loc: nmodl_formatter.translate_density(mechs)\n        for loc, mechs in local_mechs.items()\n    }\n    pprocess_mechs = {\n        loc: nmodl_formatter.translate_points(mechs)\n        for loc, mechs in pprocess_mechs.items()\n    }\n\n    # get iexpr parameters of scaled density mechs\n    global_scaled_mechs = _arb_project_scaled_mechs(global_mechs)\n    local_scaled_mechs = {\n        loc: _arb_project_scaled_mechs(mechs)\n        for loc, mechs in local_mechs.items()\n    }\n\n    # populate label dict\n    label_dict = _arb_populate_label_dict(\n        local_mechs, local_scaled_mechs, pprocess_mechs\n    )\n\n    ret = {\n        filenames[name]: template.render(\n            template_name=template_name,\n            banner=banner,\n            morphology=morphology,\n            replace_axon=replace_axon_path,\n            modified_morphology=modified_morphology_path,\n            filenames=filenames,\n            label_dict=label_dict,\n            global_mechs=global_mechs,\n            global_scaled_mechs=global_scaled_mechs,\n            local_mechs=local_mechs,\n            local_scaled_mechs=local_scaled_mechs,\n            pprocess_mechs=pprocess_mechs,\n            **custom_jinja_params,\n        )\n        for name, template in templates.items()\n    }\n\n    if replace_axon is not None:\n        ret[replace_axon_path] = replace_axon_acc\n        if modified_morphology_path is not None:\n            ret[modified_morphology_path] = modified_morphology_acc\n\n    return ret\n\n\ndef write_acc(\n    output_dir,\n    cell,\n    parameters,\n    template_filename=\"acc/*_template.jinja2\",\n    ext_catalogues=None,\n    create_mod_morph=False,\n    sim=None,\n):\n    \"\"\"Output mixed JSON/ACC format for Arbor cable cell to files\n\n    Args:\n        output_dir (str): Output directory. If not exists, will be created\n        cell (): Cell model to output\n        parameters (): Values for mechanism parameters, etc.\n        template_filename (str): file path of the cell.json , decor.acc and\n        label_dict.acc jinja2 templates (with wildcards expanded by glob)\n        ext_catalogues (): Name to path mapping of non-Arbor built-in\n        NMODL mechanism catalogues compiled with modcc\n        create_mod_morph (str): Output ACC with axon replacement\n        sim (): Neuron simulator instance (only used used with axon\n        replacement if morphology has not yet been instantiated)\n    \"\"\"\n    output = cell.create_acc(\n        parameters,\n        template=template_filename,\n        ext_catalogues=ext_catalogues,\n        create_mod_morph=create_mod_morph,\n        sim=sim,\n    )\n\n    cell_json = [\n        comp_rendered\n        for comp, comp_rendered in output.items()\n        if pathlib.Path(comp).suffix == \".json\"\n    ]\n    if len(cell_json) != 1:\n        raise CreateAccException(\n            \"JSON file from create_acc is non-unique: %s\" % cell_json\n        )\n\n    cell_json = json.loads(cell_json[0])\n\n    output_dir = pathlib.Path(output_dir)\n    if not output_dir.exists():\n        output_dir.mkdir()\n    for comp, comp_rendered in output.items():\n        comp_filename = output_dir.joinpath(comp)\n        if comp_filename.exists():\n            raise CreateAccException(\"%s already exists!\" % comp_filename)\n        with open(output_dir.joinpath(comp), \"w\") as f:\n            f.write(comp_rendered)\n\n    morpho_filename = output_dir.joinpath(cell_json[\"morphology\"][\"original\"])\n    if morpho_filename.exists():\n        raise CreateAccException(\"%s already exists!\" % morpho_filename)\n    shutil.copy2(cell.morphology.morphology_path, morpho_filename)\n\n\n# Read the mixed JSON/ACC-output, to be moved to Arbor in future release\ndef read_acc(cell_json_filename):\n    \"\"\"Return constituents to build an Arbor cable cell from create_acc-export\n\n    Args:\n        cell_json_filename (str): The path to the JSON file containing\n        meta-information on morphology, label-dict and decor of exported cell\n    \"\"\"\n\n    with open(cell_json_filename) as cell_json_file:\n        cell_json = json.load(cell_json_file)\n\n    cell_json_dir = pathlib.Path(cell_json_filename).parent\n\n    morpho_filename = cell_json_dir.joinpath(\n        cell_json[\"morphology\"][\"original\"]\n    )\n    replace_axon = cell_json[\"morphology\"].get(\"replace_axon\", None)\n    if replace_axon is not None:\n        replace_axon = cell_json_dir.joinpath(replace_axon)\n    morpho = ArbFileMorphology.load(morpho_filename, replace_axon)\n\n    decor = arbor.load_component(\n        cell_json_dir.joinpath(cell_json[\"decor\"])\n    ).component\n    labels = arbor.load_component(\n        cell_json_dir.joinpath(cell_json[\"label_dict\"])\n    ).component\n\n    return cell_json, morpho, decor, labels\n\n\nclass CreateAccException(Exception):\n    \"\"\"Exceptions generated by create_acc module\"\"\"\n\n    def __init__(self, message):\n        \"\"\"Constructor\"\"\"\n\n        super(CreateAccException, self).__init__(message)\n"
  },
  {
    "path": "bluepyopt/ephys/create_hoc.py",
    "content": "'''create a hoc file from a set of BluePyOpt.ephys parameters'''\n\n# pylint: disable=R0914\n\nimport os\nimport re\n\nfrom collections import defaultdict, namedtuple, OrderedDict\nfrom datetime import datetime\n\nimport jinja2\nimport bluepyopt\nfrom bluepyopt.ephys.locations import (NrnSeclistCompLocation,\n                                       NrnSeclistLocation,\n                                       NrnSectionCompLocation,\n                                       NrnSomaDistanceCompLocation,\n                                       NrnSecSomaDistanceCompLocation,\n                                       NrnTrunkSomaDistanceCompLocation,\n                                       ArbLocation)\n\nfrom bluepyopt.ephys.mechanisms import (Mechanism,\n                                        NrnMODMechanism,\n                                        NrnMODPointProcessMechanism)\n\nfrom bluepyopt.ephys.parameters import (NrnGlobalParameter,\n                                        NrnSectionParameter,\n                                        NrnRangeParameter,\n                                        NrnPointProcessParameter,\n                                        MetaParameter)\n\nfrom bluepyopt.ephys.parameterscalers import (NrnSegmentSomaDistanceScaler,\n                                              NrnSegmentLinearScaler,\n                                              FLOAT_FORMAT,\n                                              format_float)\n\nPointExpr = namedtuple('PointExpr', 'name, point_loc, value')\nRangeExpr = namedtuple('RangeExpr', 'location, name, value, value_scaler')\n\n# Consider renaming Location as name already used in locations module\nLocation = namedtuple('Location', 'name, value')\nRange = namedtuple('Range', 'location, param_name, value')\n\nDEFAULT_LOCATION_ORDER = [\n    'all',\n    'apical',\n    'axonal',\n    'basal',\n    'somatic',\n    'myelinated']\n\n\ndef generate_channels_by_location(mechs, location_order):\n    \"\"\"Create a OrderedDictionary of all channel mechs for hoc template.\n\n    Args:\n        mechs (list of bluepyopt.ephys.mechanisms.Mechanism): mechanisms\n        location_order (list of str): order of locations\n\n    Returns: tuple of channels, point_channels and location order\n    \"\"\"\n    loc_desc = _loc_desc\n    return _generate_channels_by_location(mechs, location_order, loc_desc)\n\n\ndef _generate_channels_by_location(mechs, location_order, loc_desc):\n    \"\"\"Create a OrderedDictionary of all channel mechs for hoc template.\"\"\"\n    channels = OrderedDict((location, []) for location in location_order)\n    point_channels = OrderedDict((location, []) for location in location_order)\n    for mech in mechs:\n        name = mech.suffix\n        for location in mech.locations:\n            if isinstance(mech, NrnMODPointProcessMechanism):\n                point_channels[loc_desc(location, mech)].append(mech)\n            else:\n                channels[loc_desc(location, mech)].append(name)\n    return channels, point_channels\n\n\ndef generate_reinitrng(mechs) -> str:\n    \"\"\"Create re_init_rng function\"\"\"\n\n    for mech in mechs:\n        if isinstance(mech, NrnMODPointProcessMechanism):\n            raise NotImplementedError(\n                'HOC generation for models with point process mechanisms'\n                ' is not yet supported.')\n\n    reinitrng_hoc_blocks = ''\n\n    for mech in mechs:\n        reinitrng_hoc_blocks += mech.generate_reinitrng_hoc_block()\n\n    reinitrng_content = NrnMODMechanism.hash_hoc_string\n\n    reinitrng_content += NrnMODMechanism.reinitrng_hoc_string % {\n        'reinitrng_hoc_blocks': reinitrng_hoc_blocks}\n\n    return reinitrng_content\n\n\ndef range_exprs_to_hoc(range_params):\n    \"\"\"Process raw range parameters to hoc strings\"\"\"\n\n    ret = []\n    for param in range_params:\n        value = param.value_scaler.inst_distribution\n        value = re.sub(r'math\\.', '', value)\n        value = re.sub(r'\\&', '&&', value)\n        value = re.sub('{distance}', FLOAT_FORMAT, value)\n        value = re.sub('{value}', format_float(param.value), value)\n        if hasattr(param.value_scaler, \"step_begin\"):\n            value = re.sub(\n                '{step_begin}',\n                format_float(param.value_scaler.step_begin),\n                value\n            )\n            value = re.sub(\n                '{step_end}', format_float(param.value_scaler.step_end), value\n            )\n        ret.append(Range(param.location, param.name, value))\n    return ret\n\n\ndef _loc_desc(location, param_or_mech):\n    \"\"\"Generate Neuron location description for HOC template\"\"\"\n\n    if isinstance(param_or_mech, Mechanism):\n        if isinstance(param_or_mech, NrnMODMechanism):\n            if isinstance(location, NrnSeclistLocation):\n                return location.seclist_name\n            else:\n                raise CreateHocException(\n                    \"%s is currently not supported for mechs.\" %\n                    type(location).__name__)\n        elif isinstance(param_or_mech, NrnMODPointProcessMechanism):\n            raise CreateHocException(\"%s is currently not supported.\" %\n                                     type(param_or_mech).__name__)\n    elif not isinstance(location, (NrnSeclistCompLocation,\n                                   NrnSectionCompLocation,\n                                   NrnSomaDistanceCompLocation,\n                                   NrnSecSomaDistanceCompLocation,\n                                   NrnTrunkSomaDistanceCompLocation,\n                                   ArbLocation)) and \\\n            not isinstance(param_or_mech, NrnPointProcessParameter):\n        return location.seclist_name\n    else:\n        raise CreateHocException(\"%s is currently not supported.\" %\n                                 type(param_or_mech).__name__)\n\n\ndef generate_parameters(parameters):\n    \"\"\"Create a list of parameters that need to be added to the hoc template\n\n    Args:\n        parameters (list of bluepyopt.Parameters): parameters in hoc template\n\n    Returns: tuple of global, section, range, pprocess and location order\n    \"\"\"\n    location_order = DEFAULT_LOCATION_ORDER\n    loc_desc = _loc_desc\n    return _generate_parameters(parameters, location_order, loc_desc)\n\n\ndef _generate_parameters(parameters, location_order, loc_desc):\n    \"\"\"Create a list of parameters that need to be added to the hoc template\"\"\"\n    param_locations = defaultdict(list)\n    global_params = {}\n    for param in parameters:\n        if isinstance(param, NrnGlobalParameter):\n            global_params[param.param_name] = param.value\n        elif isinstance(param, MetaParameter):\n            pass\n        else:\n            assert isinstance(\n                param.locations, (tuple, list)), 'Must have locations list'\n            for location in param.locations:\n                locs = loc_desc(location, param)\n                if not isinstance(locs, list):\n                    param_locations[locs].append(param)\n                else:\n                    for loc in locs:\n                        param_locations[loc].append(param)\n\n    section_params = defaultdict(list)\n    pprocess_params = defaultdict(list)\n    range_params = []\n\n    for loc in param_locations:\n        if loc not in location_order:\n            location_order.append(loc)\n\n    for loc in location_order:\n        if loc not in param_locations:\n            continue\n        for param in param_locations[loc]:\n            if not isinstance(param.param_dependencies, list) or \\\n                    len(param.param_dependencies) > 0:\n                raise CreateHocException(  # also an ACC exception\n                    'Exporting models with parameters that have'\n                    ' param_dependencies is not yet supported.')\n            if isinstance(param, NrnRangeParameter):\n                if isinstance(\n                        param.value_scaler,\n                        NrnSegmentSomaDistanceScaler):\n                    range_params.append(\n                        RangeExpr(loc,\n                                  param.param_name,\n                                  param.value,\n                                  param.value_scaler))\n                elif isinstance(param.value_scaler, NrnSegmentLinearScaler):\n                    value = param.value_scale_func(param.value)\n                    section_params[loc].append(\n                        Location(param.param_name, format_float(value)))\n            elif isinstance(param, NrnSectionParameter):\n                value = param.value_scale_func(param.value)\n                section_params[loc].append(\n                    Location(param.param_name, format_float(value)))\n            elif isinstance(param, NrnPointProcessParameter):\n                value = param.value\n                pprocess_params[loc].append(\n                    PointExpr(param.param_name, param.locations,\n                              format_float(value)))\n\n    ordered_section_params = [(loc, section_params[loc])\n                              for loc in location_order]\n\n    ordered_pprocess_params = [(loc, pprocess_params[loc])\n                               for loc in location_order]\n\n    return global_params, ordered_section_params, range_params, \\\n        ordered_pprocess_params, location_order\n\n\ndef _read_template(template_dir, template_filename):\n    \"\"\"Read Jinja2 hoc template to render\"\"\"\n    if template_dir is None:\n        template_dir = os.path.abspath(\n            os.path.join(\n                os.path.dirname(__file__),\n                'templates'))\n\n    template_path = os.path.join(template_dir, template_filename)\n    with open(template_path) as template_file:\n        template = template_file.read()\n        template = jinja2.Template(template)\n    return template\n\n\ndef _get_template_params(\n        mechs,\n        parameters,\n        ignored_globals,\n        disable_banner,\n        default_location_order,\n        loc_desc):\n    '''return parameters to render Jinja2 templates with simulator descriptions\n\n    Args:\n        mechs (): All the mechs for the hoc template\n        parameters (): All the parameters in the hoc template\n        ignored_globals (iterable str): HOC coded is added for each\n        NrnGlobalParameter\n        that exists, to test that it matches the values set in the parameters.\n        This iterable contains parameter names that aren't checked\n        default_location_order (): list of ordered simulator-specific locations\n        to use by default\n        loc_desc (): method that extracts simulator-specific location\n        description from pair of locations and mechanisms/parameters\n    '''\n\n    global_params, section_params, range_params, \\\n        pprocess_params, location_order = \\\n        _generate_parameters(parameters, default_location_order, loc_desc)\n\n    channels, point_channels = _generate_channels_by_location(\n        mechs, location_order, loc_desc)\n\n    ignored_global_params = {}\n    for ignored_global in ignored_globals:\n        if ignored_global in global_params:\n            ignored_global_params[\n                ignored_global] = global_params[ignored_global]\n            del global_params[ignored_global]\n\n    if not disable_banner:\n        banner = 'Created by BluePyOpt(%s) at %s' % (\n            bluepyopt.__version__, datetime.now())\n    else:\n        banner = None\n\n    return dict(global_params=global_params,\n                ignored_global_params=ignored_global_params,\n                section_params=section_params,\n                range_params=range_params,\n                pprocess_params=pprocess_params,\n                location_order=location_order,\n                channels=channels,\n                point_channels=point_channels,\n                banner=banner)\n\n\ndef create_hoc(mechs,\n               parameters,\n               morphology=None,\n               ignored_globals=(),\n               replace_axon=None,\n               template_name='CCell',\n               template_filename='cell_template.jinja2',\n               disable_banner=None,\n               template_dir=None,\n               custom_jinja_params=None):\n    '''return a string containing the hoc template\n\n    Args:\n        mechs (): All the mechs for the hoc template\n        parameters (): All the parameters in the hoc template\n        morpholgy (str): Name of morphology\n        ignored_globals (iterable str): HOC coded is added for each\n        NrnGlobalParameter\n        that exists, to test that it matches the values set in the parameters.\n        This iterable contains parameter names that aren't checked\n        replace_axon (str): String replacement for the 'replace_axon' command.\n        Must include 'proc replace_axon(){ ... }\n        template_filename (str): file name of the jinja2 template\n        template_dir (str): dir name of the jinja2 template\n        custom_jinja_params (dict): dict of additional jinja2 params in case\n        of a custom template\n    '''\n\n    template = _read_template(template_dir, template_filename)\n\n    template_params = _get_template_params(mechs,\n                                           parameters,\n                                           ignored_globals,\n                                           disable_banner,\n                                           DEFAULT_LOCATION_ORDER,\n                                           _loc_desc)\n\n    # delete empty dicts to avoid conflict with custom_jinja_params\n    del template_params['pprocess_params']\n    del template_params['point_channels']\n\n    template_params['range_params'] = range_exprs_to_hoc(\n        template_params['range_params']\n    )\n    re_init_rng = generate_reinitrng(mechs)\n\n    if custom_jinja_params is None:\n        custom_jinja_params = {}\n\n    return template.render(template_name=template_name,\n                           morphology=morphology,\n                           replace_axon=replace_axon,\n                           re_init_rng=re_init_rng,\n                           **template_params,\n                           **custom_jinja_params)\n\n\nclass CreateHocException(Exception):\n\n    \"\"\"All exceptions generated by create_hoc module\"\"\"\n\n    def __init__(self, message):\n        \"\"\"Constructor\"\"\"\n\n        super(CreateHocException, self).__init__(message)\n"
  },
  {
    "path": "bluepyopt/ephys/efeatures.py",
    "content": "\"\"\"eFeature classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=R0914\n\nimport logging\nimport numpy as np\n\nfrom bluepyopt.ephys.base import BaseEPhys\nfrom bluepyopt.ephys.serializer import DictMixin\nfrom .extra_features_utils import *\n\nlogger = logging.getLogger(__name__)\n\n\ndef masked_cosine_distance(exp, model):\n    from scipy.spatial import distance\n\n    exp_mask = np.isfinite(exp)\n    model_mask = np.isfinite(model)\n    valid_mask = exp_mask & model_mask\n\n    score = distance.cosine(\n        exp[valid_mask], model[valid_mask]\n    )\n\n    score *= sum(exp_mask) / len(valid_mask)\n\n    return score\n\n\nclass EFeature(BaseEPhys):\n\n    \"\"\"EPhys feature\"\"\"\n    pass\n\n\nclass eFELFeature(EFeature, DictMixin):\n\n    \"\"\"eFEL feature\"\"\"\n\n    SERIALIZED_FIELDS = ('name', 'efel_feature_name', 'recording_names',\n                         'stim_start', 'stim_end', 'exp_mean',\n                         'exp_std', 'threshold', 'comment')\n\n    def __init__(\n            self,\n            name,\n            efel_feature_name=None,\n            recording_names=None,\n            stim_start=None,\n            stim_end=None,\n            exp_mean=None,\n            exp_std=None,\n            threshold=None,\n            stimulus_current=None,\n            comment='',\n            interp_step=None,\n            double_settings=None,\n            int_settings=None,\n            string_settings=None,\n            force_max_score=False,\n            max_score=250\n    ):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the eFELFeature object\n            efel_feature_name (str): name of the eFeature in the eFEL library\n                (ex: 'AP1_peak')\n            recording_names (dict): eFEL features can accept several recordings\n                as input\n            stim_start (float): stimulation start time (ms)\n            stim_end (float): stimulation end time (ms)\n            exp_mean (float): experimental mean of this eFeature\n            exp_std(float): experimental standard deviation of this eFeature\n            threshold(float): spike detection threshold (mV)\n            comment (str): comment\n            interp_step(float): interpolation step (ms)\n            double_settings(dict): dictionary with efel double settings that\n                should be set before extracting the features\n            int_settings(dict): dictionary with efel int settings that\n                should be set before extracting the features\n            string_settings(dict): dictionary with efel string settings that\n                should be set before extracting the features\n        \"\"\"\n\n        super(eFELFeature, self).__init__(name, comment)\n\n        self.recording_names = recording_names\n        self.efel_feature_name = efel_feature_name\n        self.exp_mean = exp_mean\n        self.exp_std = exp_std\n        self.stim_start = stim_start\n        self.stim_end = stim_end\n        self.threshold = threshold\n        self.interp_step = interp_step\n        self.stimulus_current = stimulus_current\n        self.double_settings = double_settings\n        self.int_settings = int_settings\n        self.string_settings = string_settings\n        self.force_max_score = force_max_score\n        self.max_score = max_score\n\n    def _construct_efel_trace(self, responses):\n        \"\"\"Construct trace that can be passed to eFEL\"\"\"\n\n        trace = {}\n        if '' not in self.recording_names:\n            raise Exception(\n                'eFELFeature: \\'\\' needs to be in recording_names')\n        for location_name, recording_name in self.recording_names.items():\n            if location_name == '':\n                postfix = ''\n            else:\n                postfix = ';%s' % location_name\n\n            if recording_name not in responses:\n                logger.debug(\n                    \"Recording named %s not found in responses %s\",\n                    recording_name,\n                    str(responses))\n                return None\n\n            if responses[self.recording_names['']] is None or \\\n                    responses[recording_name] is None:\n                return None\n            trace['T%s' % postfix] = \\\n                responses[self.recording_names['']]['time']\n            trace['V%s' % postfix] = responses[recording_name]['voltage']\n            trace['stim_start%s' % postfix] = [self.stim_start]\n            trace['stim_end%s' % postfix] = [self.stim_end]\n\n        return trace\n\n    def _setup_efel(self):\n        \"\"\"Set up efel before extracting the feature\"\"\"\n\n        import efel\n        efel.reset()\n\n        if self.threshold is not None:\n            efel.setThreshold(self.threshold)\n\n        if self.stimulus_current is not None:\n            efel.setDoubleSetting('stimulus_current', self.stimulus_current)\n\n        if self.interp_step is not None:\n            efel.setDoubleSetting('interp_step', self.interp_step)\n\n        if self.double_settings is not None:\n            for setting_name, setting_value in self.double_settings.items():\n                efel.setDoubleSetting(setting_name, setting_value)\n\n        if self.int_settings is not None:\n            for setting_name, setting_value in self.int_settings.items():\n                efel.setIntSetting(setting_name, setting_value)\n\n        if self.string_settings is not None:\n            for setting_name, setting_value in self.string_settings.items():\n                efel.setStrSetting(setting_name, setting_value)\n\n    def calculate_feature(self, responses, raise_warnings=False):\n        \"\"\"Calculate feature value\"\"\"\n\n        efel_trace = self._construct_efel_trace(responses)\n\n        if efel_trace is None:\n            feature_value = None\n        else:\n            self._setup_efel()\n\n            import efel\n            values = efel.getMeanFeatureValues(\n                [efel_trace],\n                [self.efel_feature_name],\n                raise_warnings=raise_warnings)\n            feature_value = values[0][self.efel_feature_name]\n\n            efel.reset()\n\n        logger.debug(\n            'Calculated value for %s: %s',\n            self.name,\n            str(feature_value))\n\n        return feature_value\n\n    def calculate_score(self, responses, trace_check=False):\n        \"\"\"Calculate the score\"\"\"\n\n        efel_trace = self._construct_efel_trace(responses)\n\n        if efel_trace is None:\n            score = self.max_score\n        else:\n            self._setup_efel()\n\n            import efel\n            score = efel.getDistance(\n                efel_trace,\n                self.efel_feature_name,\n                self.exp_mean,\n                self.exp_std,\n                trace_check=trace_check,\n                error_dist=self.max_score\n            )\n            if self.force_max_score:\n                score = min(score, self.max_score)\n\n            efel.reset()\n\n        logger.debug('Calculated score for %s: %f', self.name, score)\n\n        return score\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"%s for %s with stim start %s and end %s, \" \\\n            \"exp mean %s and std %s and AP threshold override %s\" % \\\n            (self.efel_feature_name,\n             self.recording_names,\n             self.stim_start,\n             self.stim_end,\n             self.exp_mean,\n             self.exp_std,\n             self.threshold)\n\n\nclass extraFELFeature(EFeature, DictMixin):\n    \"\"\"extraFEL feature\"\"\"\n\n    SERIALIZED_FIELDS = ('name', 'extrafel_feature_name', 'recording_names',\n                         'somatic_recording_name', 'fcut', 'fs',\n                         'channel_ids', 'stim_start', 'stim_end',\n                         'exp_mean', 'exp_std', 'threshold', 'comment')\n\n    def __init__(\n            self,\n            name,\n            extrafel_feature_name=None,\n            recording_names=None,\n            somatic_recording_name=None,\n            fcut=None,\n            fs=None,\n            filt_type=None,\n            ms_cut=None,\n            upsample=None,\n            skip_first_spike=True,\n            skip_last_spike=True,\n            channel_ids=None,\n            stim_start=None,\n            stim_end=None,\n            exp_mean=None,\n            exp_std=None,\n            threshold=None,\n            comment='',\n            interp_step=None,\n            double_settings=None,\n            int_settings=None,\n            force_max_score=False,\n            max_score=250,\n    ):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the extraFELFeature object\n            extrafel_feature_name (str): name of the eFeature in the\n                spikefeatures library (ex: 'halfwidth')\n            recording_names (dict): eFEL features can accept several\n                recordings as input\n            somatic_recording_name (str): intracellualar recording from soma,\n                used to detect spikes. If None, spikes are detected from\n                extracellular trace\n            fcut (float, array, or None): cutoff frequency(ies) for filter. If\n                float, a high-pass filter is used. If array-like a bandpass\n                filter is used. If None, traces are note filtered\n            fs (float): sampling frequency to resample extracellular traces\n                (in kHz)\n            filt_type (str): type of the bandpass filter used\n                (default 'filtfilt')\n            ms_cut (float, list, or None): cut in ms before and after the\n                intra peak. If scalar, the cut is symmetrical\n            upsample (int, or None): upsample factor for average waveform\n                before computing features\n            skip_first_spike (bool): if True, the first spike is skipped\n                before computing the average waveform\n                (to avoid artifacts)\n            skip_last_spike (bool): if True, the last spike is skipped\n                before computing the average waveform\n                (to avoid artifacts)\n            channel_ids (int, np.array, or None): if None, all channels are\n                used to compute the feature and calculate the score\n                (using the cosine_dist). If int, a single channel is used and\n                the score is the normalised deviation form the exp value.\n                If list/array, the cosine distance is computed over a subset\n                of channels\n            stim_start (float): stimulation start time (ms)\n            stim_end (float): stimulation end time (ms)\n            exp_mean (list of floats): experimental mean of this eFeature\n            exp_std (list of floats): experimental standard deviation\n                of this eFeature\n            threshold (float): spike detection threshold (mV)\n            comment (str): comment\n            interp_step (float): interpolation step (ms)\n            double_settings (dict): dictionary with efel double settings that\n                should be set before extracting the features\n            int_settings (dict): dictionary with efel int settings that\n                should be set before extracting the features\n        \"\"\"\n\n        super(extraFELFeature, self).__init__(name, comment)\n\n        self.recording_names = recording_names\n        self.somatic_recording_name = somatic_recording_name\n        self.extrafel_feature_name = extrafel_feature_name\n        self.fcut = fcut\n        self.fs = fs\n        self.filt_type = filt_type\n        self.ms_cut = ms_cut\n        self.upsample = upsample\n        self.skip_first_spike = skip_first_spike\n        self.skip_last_spike = skip_last_spike\n        self.channel_ids = channel_ids\n        self.exp_mean = exp_mean\n        self.exp_std = exp_std\n        self.stim_start = stim_start\n        self.stim_end = stim_end\n        self.threshold = threshold\n        self.interp_step = interp_step\n        self.double_settings = double_settings\n        self.int_settings = int_settings\n        self.force_max_score = force_max_score\n        self.max_score = max_score\n\n    def _construct_somatic_efel_trace(self, responses):\n        \"\"\"Construct trace that can be passed to eFEL\"\"\"\n\n        trace = {}\n        if self.somatic_recording_name not in responses:\n            logger.debug(\n                \"Recording named %s not found in responses %s\",\n                self.somatic_recording_name,\n                str(responses),\n            )\n            return None\n\n        if responses[self.somatic_recording_name] is None:\n            return None\n\n        response = responses[self.somatic_recording_name]\n\n        trace[\"T\"] = response[\"time\"]\n        trace[\"V\"] = response[\"voltage\"]\n        trace[\"stim_start\"] = [self.stim_start]\n        trace[\"stim_end\"] = [self.stim_end]\n\n        return trace\n\n    def _setup_efel(self):\n        \"\"\"Set up efel before extracting the feature\"\"\"\n\n        import efel\n\n        efel.reset()\n\n        if self.threshold is not None:\n            efel.setThreshold(self.threshold)\n\n        if self.interp_step is not None:\n            efel.setDoubleSetting(\"interp_step\", self.interp_step)\n\n        if self.double_settings is not None:\n            for setting_name, setting_value in self.double_settings.items():\n                efel.setDoubleSetting(setting_name, setting_value)\n\n        if self.int_settings is not None:\n            for setting_name, setting_value in self.int_settings.items():\n                efel.setIntSetting(setting_name, setting_value)\n\n    def _get_peak_times(self, responses, raise_warnings=False):\n\n        efel_trace = self._construct_somatic_efel_trace(responses)\n\n        if efel_trace is None:\n            peak_times = None\n        else:\n            self._setup_efel()\n\n            import efel\n\n            peaks = efel.getFeatureValues(\n                [efel_trace], [\"peak_time\"], raise_warnings=raise_warnings\n            )\n            peak_times = peaks[0][\"peak_time\"]\n\n            efel.reset()\n\n        return peak_times\n\n    def calculate_feature(\n            self,\n            responses,\n            raise_warnings=False,\n            return_waveforms=False,\n    ):\n        from .extra_features_utils import calculate_features\n\n        \"\"\"Calculate feature value\"\"\"\n        peak_times = self._get_peak_times(\n            responses, raise_warnings=raise_warnings\n        )\n        if peak_times is None:\n            if return_waveforms:\n                return None, None\n            else:\n                return None\n\n        if len(peak_times) > 1 and self.skip_first_spike:\n            peak_times = peak_times[1:]\n\n        if len(peak_times) > 1 and self.skip_last_spike:\n            peak_times = peak_times[:-1]\n\n        if responses[self.recording_names[\"\"]] is not None:\n            response = responses[self.recording_names[\"\"]]\n        else:\n            if return_waveforms:\n                return None, None\n            else:\n                return None\n\n        if np.std(np.diff(response[\"time\"])) > 0.001 * np.mean(\n                np.diff(response[\"time\"])\n        ):\n            assert self.fs is not None\n            logger.info(\"extraFELFeature.calculate_feature: interpolate\")\n            response_interp = _interpolate_response(response, fs=self.fs)\n        else:\n            response_interp = response\n\n        if self.fcut is not None:\n            logger.info(\"extraFELFeature.calculate_feature: enabled\")\n            response_filter = _filter_response(response_interp,\n                                               fcut=self.fcut,\n                                               filt_type=self.filt_type)\n        else:\n            logger.info(\"extraFELFeature.calculate_feature: filter disabled\")\n            response_filter = response_interp\n\n        ewf = _get_waveforms(response_filter, peak_times, self.ms_cut)\n        mean_wf = np.mean(ewf, axis=0)\n\n        values = calculate_features(\n            mean_wf,\n            self.fs * 1000,\n            upsample=self.upsample,\n            feature_names=[self.extrafel_feature_name]\n        )\n\n        feature_value = values[self.extrafel_feature_name]\n\n        if self.channel_ids is not None:\n            feature_value = feature_value[self.channel_ids]\n\n        logger.debug(\n            \"Calculated value for %s: %s\", self.name, str(feature_value)\n        )\n\n        if return_waveforms:\n            return feature_value, mean_wf\n        else:\n            return feature_value\n\n    def calculate_score(self, responses, trace_check=False):\n        \"\"\"Calculate the score\"\"\"\n\n        if (\n                responses[self.recording_names[\"\"].replace(\"soma.v\",\n                                                           \"MEA.LFP\")]\n                is None\n                or responses[self.recording_names[\"\"]] is None\n        ):\n            return self.max_score\n\n        feature_value = self.calculate_feature(responses)\n\n        if np.isscalar(feature_value):\n            # scalar feature\n            if np.isfinite(feature_value):\n                score = np.abs((feature_value - self.exp_mean)) / self.exp_std\n            else:\n                score = self.max_score\n            if not np.isfinite(score):\n                logger.debug(\n                    f\"Found score nan value {self.extrafel_feature_name} \"\n                    f\"- std: {self.exp_std} - channel: {self.channel_ids}\"\n                )\n                score = self.max_score\n        else:\n            score = masked_cosine_distance(\n                np.asarray(self.exp_mean),\n                np.asarray(feature_value)\n            )\n\n        if np.isnan(score):\n            score = self.max_score\n\n        if self.force_max_score:\n            score = min(score, self.max_score)\n\n        logger.debug(\"Calculated score for %s: %f\", self.name, score)\n\n        return score\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return (\"%s for %s with stim start %s and end %s, \"\n                \"exp mean %s and std %s and AP threshold override %s\"\n                % (self.extrafel_feature_name,\n                   self.recording_names,\n                   self.stim_start,\n                   self.stim_end,\n                   self.exp_mean,\n                   self.exp_std,\n                   self.threshold)\n                )\n\n\ndef _interpolate_response(response, fs=20.0):\n    from scipy.interpolate import interp1d\n\n    x = response[\"time\"]\n    y = response[\"voltage\"]\n    f = interp1d(x, y, axis=1)\n    xnew = np.arange(np.min(x), np.max(x), 1.0 / fs)\n    ynew = f(xnew)  # use interpolation function returned by `interp1d`\n\n    response_new = {}\n    response_new[\"time\"] = xnew\n    response_new[\"voltage\"] = ynew\n\n    return response_new\n\n\ndef _filter_response(response, fcut=[0.5, 6000], order=2, filt_type=\"lfilter\"):\n    import scipy.signal as ss\n\n    fs = 1 / np.mean(np.diff(response[\"time\"])) * 1000\n    fn = fs / 2.0\n\n    trace = response[\"voltage\"]\n\n    if isinstance(fcut, (float, int, np.floating, np.integer)):\n        btype = \"highpass\"\n        band = fcut / fn\n    else:\n        assert isinstance(fcut, (list, np.ndarray)) and len(fcut) == 2\n        btype = \"bandpass\"\n        band = np.array(fcut) / fn\n\n    b, a = ss.butter(order, band, btype=btype)\n\n    if len(trace.shape) == 2:\n        if filt_type == \"filtfilt\":\n            filtered = ss.filtfilt(b, a, trace, axis=1)\n        else:\n            filtered = ss.lfilter(b, a, trace, axis=1)\n    else:\n        if filt_type == \"filtfilt\":\n            filtered = ss.filtfilt(b, a, trace)\n        else:\n            filtered = ss.lfilter(b, a, trace)\n\n    response_new = {}\n    response_new[\"time\"] = response[\"time\"]\n    response_new[\"voltage\"] = filtered\n\n    return response_new\n\n\ndef _get_waveforms(response, peak_times, snippet_len_ms):\n    times = response[\"time\"]\n    traces = response[\"voltage\"]\n\n    assert np.std(np.diff(times)) < 0.001 * np.mean(\n        np.diff(times)\n    ), \"Sampling frequency must be constant\"\n\n    fs = 1.0 / np.mean(np.diff(times))  # kHz\n\n    reference_frames = (peak_times * fs).astype(int)\n\n    if isinstance(snippet_len_ms, (tuple, list, np.ndarray)):\n        snippet_len_before = int(snippet_len_ms[0] * fs)\n        snippet_len_after = int(snippet_len_ms[1] * fs)\n    else:\n        snippet_len_before = int((snippet_len_ms + 1) / 2 * fs)\n        snippet_len_after = int((snippet_len_ms - snippet_len_before) * fs)\n\n    num_snippets = len(peak_times)\n    if len(traces.shape) == 2:\n        num_channels = traces.shape[0]\n    else:\n        num_channels = 1\n        traces = traces[np.newaxis, :]\n    num_frames = len(times)\n    snippet_len_total = int(snippet_len_before + snippet_len_after)\n    waveforms = np.zeros(\n        (num_snippets, num_channels, snippet_len_total), dtype=traces.dtype\n    )\n\n    for i in range(num_snippets):\n        snippet_chunk = np.zeros(\n            (num_channels, snippet_len_total), dtype=traces.dtype\n        )\n        if 0 <= reference_frames[i] < num_frames:\n            snippet_range = np.array(\n                [\n                    int(reference_frames[i]) - snippet_len_before,\n                    int(reference_frames[i]) + snippet_len_after,\n                ]\n            )\n            snippet_buffer = np.array([0, snippet_len_total], dtype=\"int\")\n            # The following handles the out-of-bounds cases\n            if snippet_range[0] < 0:\n                snippet_buffer[0] -= snippet_range[0]\n                snippet_range[0] -= snippet_range[0]\n            if snippet_range[1] >= num_frames:\n                snippet_buffer[1] -= snippet_range[1] - num_frames\n                snippet_range[1] -= snippet_range[1] - num_frames\n            snippet_chunk[:, snippet_buffer[0]:snippet_buffer[1]] = \\\n                traces[:, snippet_range[0]:snippet_range[1]]\n        waveforms[i] = snippet_chunk\n\n    return waveforms\n"
  },
  {
    "path": "bluepyopt/ephys/evaluators.py",
    "content": "\"\"\"Cell evaluator class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\n# pylint: disable=W0511\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nimport bluepyopt as bpopt\nimport bluepyopt.tools\n\n\nclass CellEvaluator(bpopt.evaluators.Evaluator):\n\n    \"\"\"Simple cell class\"\"\"\n\n    def __init__(\n            self,\n            cell_model=None,\n            param_names=None,\n            fitness_protocols=None,\n            fitness_calculator=None,\n            isolate_protocols=None,\n            sim=None,\n            use_params_for_seed=False,\n            timeout=None):\n        \"\"\"Constructor\n\n        Args:\n            cell_model (ephys.models.CellModel): CellModel object to evaluate\n            param_names (list of str): names of the parameters\n                (parameters will be initialised in this order)\n            fitness_protocols (dict of str -> ephys.protocols.Protocol):\n                protocols used during the fitness evaluation\n            fitness_calculator (ObjectivesCalculator):\n                ObjectivesCalculator object used for the transformation of\n                Responses into Objective objects\n            isolate_protocols (bool): whether to use multiprocessing to\n                isolate the simulations\n                (disabling this could lead to unexpected behavior, and might\n                hinder the reproducability of the simulations)\n            sim (ephys.simulators.NrnSimulator): simulator to use for the cell\n                evaluation\n            use_params_for_seed (bool): use a hashed version of the parameter\n                dictionary as a seed for the simulator\n            timeout (int): duration in second after which a Process will\n                be interrupted when using multiprocessing\n        \"\"\"\n\n        super(CellEvaluator, self).__init__(\n            fitness_calculator.objectives,\n            cell_model.params_by_names(param_names))\n\n        if sim is None:\n            raise ValueError(\"CellEvaluator: you have to provide a Simulator \"\n                             \"object to the 'sim' argument of the \"\n                             \"CellEvaluator constructor\")\n        self.sim = sim\n\n        self.cell_model = cell_model\n        self.param_names = param_names\n        # Stimuli used for fitness calculation\n        self.fitness_protocols = fitness_protocols\n        # Fitness value calculator\n        self.fitness_calculator = fitness_calculator\n\n        self.isolate_protocols = isolate_protocols\n        self.timeout = timeout\n        self.use_params_for_seed = use_params_for_seed\n\n    def param_dict(self, param_array):\n        \"\"\"Convert param_array in param_dict\"\"\"\n        param_dict = {}\n        for param_name, param_value in \\\n                zip(self.param_names, param_array):\n            param_dict[param_name] = param_value\n\n        return param_dict\n\n    def objective_dict(self, objective_array):\n        \"\"\"Convert objective_array in objective_dict\"\"\"\n        objective_dict = {}\n        objective_names = [objective.name\n                           for objective in self.fitness_calculator.objectives]\n\n        if len(objective_names) != len(objective_array):\n            raise Exception(\n                'CellEvaluator: list given to objective_dict() '\n                'has wrong number of objectives')\n\n        for objective_name, objective_value in \\\n                zip(objective_names, objective_array):\n            objective_dict[objective_name] = objective_value\n\n        return objective_dict\n\n    def objective_list(self, objective_dict):\n        \"\"\"Convert objective_dict in objective_list\"\"\"\n        objective_list = []\n        objective_names = [objective.name\n                           for objective in self.fitness_calculator.objectives]\n        for objective_name in objective_names:\n            objective_list.append(objective_dict[objective_name])\n\n        return objective_list\n\n    @staticmethod\n    def seed_from_param_dict(param_dict):\n        \"\"\"Return a seed value based on a param_dict\"\"\"\n\n        sorted_keys = sorted(param_dict.keys())\n\n        string = ''\n        for key in sorted_keys:\n            string += '%s%s' % (key, str(param_dict[key]))\n\n        return bluepyopt.tools.uint32_seed(string)\n\n    def run_protocol(\n            self,\n            protocol,\n            param_values,\n            isolate=None,\n            cell_model=None,\n            sim=None,\n            timeout=None):\n        \"\"\"Run protocol\"\"\"\n\n        sim = self.sim if sim is None else sim\n\n        if self.use_params_for_seed:\n            sim.random123_globalindex = self.seed_from_param_dict(param_values)\n\n        # Try/except added for backward compatibility\n        try:\n            return protocol.run(\n                self.cell_model if cell_model is None else cell_model,\n                param_values,\n                sim=sim,\n                isolate=isolate,\n                timeout=timeout)\n        except TypeError as e:\n            if \"unexpected keyword\" in str(e):\n                return protocol.run(\n                    self.cell_model if cell_model is None else cell_model,\n                    param_values,\n                    sim=sim,\n                    isolate=isolate)\n            else:\n                raise\n\n    def run_protocols(self, protocols, param_values):\n        \"\"\"Run a set of protocols\"\"\"\n\n        responses = {}\n\n        for protocol in protocols:\n            responses.update(self.run_protocol(\n                protocol,\n                param_values=param_values,\n                isolate=self.isolate_protocols,\n                timeout=self.timeout))\n\n        return responses\n\n    def evaluate_with_dicts(self, param_dict=None, target='scores'):\n        \"\"\"Run evaluation with dict as input and output\"\"\"\n\n        if target not in ['scores', 'values']:\n            raise Exception(\n                'CellEvaluator: target has to be \"scores\" or \"values\".')\n\n        if self.fitness_calculator is None:\n            raise Exception(\n                'CellEvaluator: need fitness_calculator to evaluate')\n\n        logger.debug('Evaluating %s', self.cell_model.name)\n\n        responses = self.run_protocols(\n            self.fitness_protocols.values(),\n            param_dict)\n\n        if target == 'scores':\n            return self.fitness_calculator.calculate_scores(responses)\n\n        elif target == 'values':\n            return self.fitness_calculator.calculate_values(responses)\n\n    def evaluate_with_lists(self, param_list=None, target='scores'):\n        \"\"\"Run evaluation with lists as input and outputs\"\"\"\n\n        param_dict = self.param_dict(param_list)\n\n        obj_dict = self.evaluate_with_dicts(\n            param_dict=param_dict, target=target\n        )\n\n        return self.objective_list(obj_dict)\n\n    def init_simulator_and_evaluate_with_lists(\n        self, param_list=None, target='scores'\n    ):\n        \"\"\"Set NEURON variables and run evaluation with lists.\n\n        Setting the NEURON variables is necessary when using ipyparallel,\n        since the new subprocesses have pristine NEURON.\n        \"\"\"\n        self.sim.initialize()\n        return self.evaluate_with_lists(param_list=param_list, target=target)\n\n    def evaluate(self, param_list=None, target='scores'):\n        \"\"\"Run evaluation with lists as input and outputs\"\"\"\n\n        return self.evaluate_with_lists(param_list, target=target)\n\n    def __str__(self):\n\n        content = 'cell evaluator:\\n'\n\n        content += '  cell model:\\n'\n        if self.cell_model is not None:\n            content += '    %s\\n' % str(self.cell_model)\n\n        content += '  fitness protocols:\\n'\n        if self.fitness_protocols is not None:\n            for fitness_protocol in self.fitness_protocols.values():\n                content += '    %s\\n' % str(fitness_protocol)\n\n        content += '  fitness calculator:\\n'\n        if self.fitness_calculator is not None:\n            content += '    %s\\n' % str(self.fitness_calculator)\n\n        return content\n"
  },
  {
    "path": "bluepyopt/ephys/examples/__init__.py",
    "content": "\"\"\"Init\"\"\"\n\nfrom . import simplecell  # NOQA\n"
  },
  {
    "path": "bluepyopt/ephys/examples/simplecell/__init__.py",
    "content": "\"\"\"Init\"\"\"\n\nfrom .simplecell import *  # NOQA\n"
  },
  {
    "path": "bluepyopt/ephys/examples/simplecell/simple.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n"
  },
  {
    "path": "bluepyopt/ephys/examples/simplecell/simplecell.py",
    "content": "\"\"\"Simple cell test model\"\"\"\n\nimport os\n\nimport bluepyopt.ephys as ephys\n\n\nclass SimpleCell:\n    def __init__(self):\n        self.nrn_sim = ephys.simulators.NrnSimulator()\n\n        self.morph = ephys.morphologies.NrnFileMorphology(\n            os.path.join(\n                os.path.dirname(os.path.abspath(__file__)),\n                'simple.swc'))\n        self.somatic_loc = ephys.locations.NrnSeclistLocation(\n            'somatic',\n            seclist_name='somatic')\n        self.somacenter_loc = ephys.locations.NrnSeclistCompLocation(\n            name='somacenter',\n            seclist_name='somatic',\n            sec_index=0,\n            comp_x=0.5)\n        self.hh_mech = ephys.mechanisms.NrnMODMechanism(\n            name='hh',\n            suffix='hh',\n            locations=[self.somatic_loc])\n\n        self.cm_param = ephys.parameters.NrnSectionParameter(\n            name='cm',\n            param_name='cm',\n            value=1.0,\n            locations=[self.somatic_loc],\n            frozen=True)\n\n        self.gnabar_param = ephys.parameters.NrnSectionParameter(\n            name='gnabar_hh',\n            param_name='gnabar_hh',\n            locations=[self.somatic_loc],\n            bounds=[0.05, 0.125],\n            frozen=False)\n        self.gkbar_param = ephys.parameters.NrnSectionParameter(\n            name='gkbar_hh',\n            param_name='gkbar_hh',\n            bounds=[0.01, 0.075],\n            locations=[self.somatic_loc],\n            frozen=False)\n\n        self.cell_model = ephys.models.CellModel(\n            name='simple_cell',\n            morph=self.morph,\n            mechs=[self.hh_mech],\n            params=[self.cm_param, self.gnabar_param, self.gkbar_param])\n\n        self.default_param_values = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\n\n        self.efel_feature_means = {\n            'step1': {\n                'Spikecount': 1}, 'step2': {\n                'Spikecount': 5}}\n\n        self.objectives = []\n\n        self.soma_loc = ephys.locations.NrnSeclistCompLocation(\n            name='soma',\n            seclist_name='somatic',\n            sec_index=0,\n            comp_x=0.5)\n\n        self.stim = ephys.stimuli.NrnSquarePulse(\n            step_amplitude=0.01,\n            step_delay=100,\n            step_duration=50,\n            location=self.soma_loc,\n            total_duration=200)\n        self.rec = ephys.recordings.CompRecording(\n            name='Step1.soma.v',\n            location=self.soma_loc,\n            variable='v')\n        self.protocol = ephys.protocols.SweepProtocol(\n            'Step1', [self.stim], [self.rec])\n\n        self.stim_start = 100\n        self.stim_end = 150\n\n        self.feature_name = 'Step1.Spikecount'\n        self.feature = ephys.efeatures.eFELFeature(\n            self.feature_name,\n            efel_feature_name='Spikecount',\n            recording_names={'': '%s.soma.v' % self.protocol.name},\n            stim_start=self.stim_start,\n            stim_end=self.stim_end,\n            exp_mean=1.0,\n            exp_std=0.05)\n        self.objective = ephys.objectives.SingletonObjective(\n            self.feature_name,\n            self.feature)\n\n        self.score_calc = \\\n            ephys.objectivescalculators.ObjectivesCalculator([self.objective])\n\n        self.nrn = ephys.simulators.NrnSimulator()\n\n        self.cell_evaluator = ephys.evaluators.CellEvaluator(\n            cell_model=self.cell_model,\n            param_names=['gnabar_hh', 'gkbar_hh'],\n            fitness_protocols={'Step1': self.protocol},\n            fitness_calculator=self.score_calc,\n            sim=self.nrn)\n"
  },
  {
    "path": "bluepyopt/ephys/extra_features_utils.py",
    "content": "\"\"\"Extra features functions\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport numpy as np\n\nall_1D_features = [\n    \"peak_to_valley\",\n    \"halfwidth\",\n    \"peak_trough_ratio\",\n    \"repolarization_slope\",\n    \"recovery_slope\",\n    \"neg_peak_relative\",\n    \"pos_peak_relative\",\n    \"neg_peak_diff\",\n    \"pos_peak_diff\",\n    \"neg_image\",\n    \"pos_image\",\n]\n\n\ndef calculate_features(\n    waveforms,\n    sampling_frequency,\n    upsample=None,\n    feature_names=None,\n    recovery_slope_window=0.7\n):\n    \"\"\"Calculate features for all waveforms\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute features for\n    sampling_frequency  : float\n        rate at which the waveforms are sampled (Hz)\n    feature_names : list or None (if None, compute all)\n        features to compute\n    recovery_slope_window : float\n        windowlength in ms after peak wherein recovery slope is computed\n\n    Returns\n    -------\n    metrics : dict  (num_waveforms x num_metrics)\n        Dictionary with computed metrics. Keys are the metric names, values\n            are the computed features\n\n    \"\"\"\n    metrics = dict()\n\n    if feature_names is None:\n        feature_names = all_1D_features\n    else:\n        for name in feature_names:\n            assert name in all_1D_features, f\"{name} not in {all_1D_features}\"\n\n    if upsample is not None:\n        assert upsample > 0\n        waveforms = _upsample_wf(waveforms, int(upsample))\n        sampling_frequency = upsample * sampling_frequency\n\n    if \"peak_to_valley\" in feature_names:\n        metrics[\"peak_to_valley\"] = peak_to_valley(\n            waveforms=waveforms, sampling_frequency=sampling_frequency\n        )\n    if \"peak_trough_ratio\" in feature_names:\n        metrics[\"peak_trough_ratio\"] = peak_trough_ratio(waveforms=waveforms)\n\n    if \"halfwidth\" in feature_names:\n        metrics[\"halfwidth\"] = halfwidth(\n            waveforms=waveforms, sampling_frequency=sampling_frequency\n        )\n\n    if \"repolarization_slope\" in feature_names:\n        metrics[\"repolarization_slope\"] = repolarization_slope(\n            waveforms=waveforms,\n            sampling_frequency=sampling_frequency,\n        )\n\n    if \"recovery_slope\" in feature_names:\n        metrics[\"recovery_slope\"] = recovery_slope(\n            waveforms=waveforms,\n            sampling_frequency=sampling_frequency,\n            window=recovery_slope_window,\n        )\n\n    if \"neg_peak_diff\" in feature_names:\n        metrics[\"neg_peak_diff\"] = peak_time_diff(\n            waveforms=waveforms, fs=sampling_frequency, sign=\"negative\"\n        )\n\n    if \"pos_peak_diff\" in feature_names:\n        metrics[\"pos_peak_diff\"] = peak_time_diff(\n            waveforms=waveforms, fs=sampling_frequency, sign=\"positive\"\n        )\n\n    if \"neg_peak_relative\" in feature_names:\n        metrics[\"neg_peak_relative\"] = relative_amplitude(\n            waveforms=waveforms, sign=\"negative\"\n        )\n\n    if \"pos_peak_relative\" in feature_names:\n        metrics[\"pos_peak_relative\"] = relative_amplitude(\n            waveforms=waveforms, sign=\"positive\"\n        )\n\n    if \"neg_image\" in feature_names:\n        metrics[\"neg_image\"] = peak_image(waveforms=waveforms, sign=\"negative\")\n\n    if \"pos_image\" in feature_names:\n        metrics[\"pos_image\"] = peak_image(waveforms=waveforms, sign=\"positive\")\n\n    return metrics\n\n\ndef peak_to_valley(waveforms, sampling_frequency):\n    \"\"\"\n    Time between trough and peak. If the peak precedes the trough,\n    peak_to_valley is negative.\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute feature for\n    sampling_frequency  : float\n        rate at which the waveforms are sampled (Hz)\n\n    Returns\n    -------\n    np.ndarray (num_waveforms)\n        peak_to_valley in seconds\n\n    \"\"\"\n    trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms)\n    ptv = (peak_idx - trough_idx) * (1 / sampling_frequency)\n    ptv[ptv == 0] = np.nan\n    return ptv\n\n\ndef peak_trough_ratio(waveforms):\n    \"\"\"\n    Normalized ratio peak height and trough depth\n\n    Assumes baseline is 0\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute feature for\n\n    Returns\n    -------\n\n    np.ndarray (num_waveforms)\n        Peak to trough ratio\n\n    \"\"\"\n    trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms)\n    ptratio = np.empty(trough_idx.shape[0])\n    ptratio[:] = np.nan\n    for i in range(waveforms.shape[0]):\n        if peak_idx[i] == 0 and trough_idx[i] == 0:\n            continue\n        ptratio[i] = np.abs(waveforms[i, peak_idx[i]] /\n                            waveforms[i, trough_idx[i]])\n\n    return ptratio\n\n\ndef halfwidth(waveforms, sampling_frequency, return_idx=False):\n    \"\"\"\n    Width of waveform at its half of amplitude.\n    If the peak precedes the trough, halfwidth is negative.\n\n    Computes the width of the waveform peak at half it's height\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute features for\n    sampling_frequency  : float\n        rate at which the waveforms are sampled (Hz)\n    return_idx : bool\n        if true, also returns index of threshold crossing before and\n        index of threshold crossing after peak\n\n    Returns\n    -------\n\n    np.ndarray or (np.ndarray, np.ndarray, np.ndarray)\n        Halfwidth of the waveforms or (Halfwidth of the waveforms,\n        index_cross_pre_peak, index_cross_post_peak)\n\n    \"\"\"\n    trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms)\n    hw = np.empty(waveforms.shape[0])\n    hw[:] = np.nan\n    cross_pre_pk = np.empty(waveforms.shape[0], dtype=int)\n    cross_post_pk = np.empty(waveforms.shape[0], dtype=int)\n\n    for i in range(waveforms.shape[0]):\n        if peak_idx[i] >= trough_idx[i]:\n            trough_val = waveforms[i, trough_idx[i]]\n            threshold = (\n                0.5 * trough_val\n            )  # threshold is half of peak heigth (assuming baseline is 0)\n\n            cpre_idx = np.where(waveforms[i, :trough_idx[i]] < threshold)[0]\n            cpost_idx = np.where(waveforms[i, trough_idx[i]:] < threshold)[0]\n\n            if len(cpre_idx) == 0 or len(cpost_idx) == 0:\n                continue\n\n            cross_pre_pk[i] = (\n                cpre_idx[0] - 1\n            )  # last occurence of waveform lower than thr, before peak\n            cross_post_pk[i] = (\n                cpost_idx[-1] + 1 + trough_idx[i]\n            )  # first occurence of waveform lower than peak, after peak\n\n            hw[i] = (cross_post_pk[i] - cross_pre_pk[i]) * (\n                1 / sampling_frequency\n            )  # + peak_idx[i]\n        else:\n            peak_val = waveforms[i, peak_idx[i]]\n            threshold = (\n                0.5 * peak_val\n            )  # threshold is half of peak heigth (assuming baseline is 0)\n\n            cpre_idx = np.where(waveforms[i, :peak_idx[i]] > threshold)[0]\n            cpost_idx = np.where(waveforms[i, peak_idx[i]:] > threshold)[0]\n\n            if len(cpre_idx) == 0 or len(cpost_idx) == 0:\n                continue\n\n            cross_pre_pk[i] = (\n                cpre_idx[0] - 1\n            )  # last occurence of waveform lower than thr, before peak\n            cross_post_pk[i] = (\n                cpost_idx[-1] + 1 + trough_idx[i]\n            )  # first occurence of waveform lower than peak, after peak\n\n            hw[i] = -(cross_post_pk[i] - cross_pre_pk[i]) * (\n                1 / sampling_frequency\n            )  # + peak_idx[i]\n\n    if not return_idx:\n        return hw\n    else:\n        return hw, cross_pre_pk, cross_post_pk\n\n\ndef repolarization_slope(waveforms, sampling_frequency, return_idx=False):\n    \"\"\"\n    Return slope of repolarization period between trough and baseline\n\n    After reaching its maxumum polarization, the neuron potential will\n    recover. The repolarization slope is defined as the dV/dT of the action\n    potential between trough and baseline.\n\n    Optionally the function returns also the indices per waveform where the\n    potential crosses baseline.\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute features for\n    sampling_frequency  : float\n        rate at which the waveforms are sampled (Hz)\n    return_idx : bool\n        if true, also returns index of threshold crossing before and\n        index of threshold crossing after peak\n\n    Returns\n    -------\n\n    np.ndarray or (np.ndarray, np.ndarray)\n        Repolarization slope of the waveforms or (Repolarization slope of the\n        waveforms, return to base index)\n    \"\"\"\n    trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms)\n\n    rslope = np.empty(waveforms.shape[0])\n    rslope[:] = np.nan\n    return_to_base_idx = np.empty(waveforms.shape[0], dtype=np.int_)\n    return_to_base_idx[:] = 0\n\n    time = np.arange(0, waveforms.shape[1]) * (1 / sampling_frequency)  # in s\n    for i in range(waveforms.shape[0]):\n        if trough_idx[i] == 0:\n            continue\n\n        rtrn_idx = np.where(waveforms[i, trough_idx[i]:] >= 0)[0]\n        if len(rtrn_idx) == 0:\n            continue\n\n        return_to_base_idx[i] = (\n            rtrn_idx[0] + trough_idx[i]\n        )  # first time after  trough, where waveform is at baseline\n\n        if return_to_base_idx[i] - trough_idx[i] < 3:\n            continue\n        slope = _get_slope(\n            time[trough_idx[i]:return_to_base_idx[i]],\n            waveforms[i, trough_idx[i]:return_to_base_idx[i]]\n        )\n        rslope[i] = slope[0]\n\n    if not return_idx:\n        return rslope\n    else:\n        return rslope, return_to_base_idx\n\n\ndef recovery_slope(waveforms, sampling_frequency, window):\n    \"\"\"\n    Return the recovery slope of input waveforms. After repolarization,\n    the neuron hyperpolarizes until it peaks. The recovery slope is the\n    slope of the action potential after the peak, returning to the baseline\n    in dV/dT. The slope is computed within a user-defined window after\n    the peak.\n\n    Takes a numpy array of waveforms and returns an array with\n    recovery slopes per waveform.\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute features for\n    sampling_frequency  : float\n        rate at which the waveforms are sampled (Hz)\n    window : float\n        length after peak wherein to compute recovery slope (ms)\n\n\n    Returns\n    -------\n    np.ndarray\n        Recovery slope of the waveforms\n    \"\"\"\n    _, peak_idx = _get_trough_and_peak_idx(waveforms)\n    rslope = np.empty(waveforms.shape[0])\n    rslope[:] = np.nan\n\n    time = np.arange(0, waveforms.shape[1]) * (1 / sampling_frequency)  # in s\n\n    for i in range(waveforms.shape[0]):\n        if peak_idx[i] in [0, waveforms.shape[1]]:\n            continue\n        max_idx = int(peak_idx[i] + ((window / 1000) * sampling_frequency))\n        max_idx = np.min([max_idx, waveforms.shape[1]])\n\n        if len(time[peak_idx[i]:max_idx]) < 3:\n            continue\n        slope = _get_slope(\n            time[peak_idx[i]:max_idx], waveforms[i, peak_idx[i]:max_idx]\n        )\n        rslope[i] = slope[0]\n\n    return rslope\n\n\ndef peak_image(waveforms, sign=\"negative\"):\n    \"\"\"\n    Normalized amplitude at the time of minimum or maximum peak.\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute features for\n    sign : str\n        \"pos\" | \"neg\"\n\n    Returns\n    -------\n    np.ndarray\n        Peak images for the waveforms\n\n    \"\"\"\n    assert len(waveforms) > 1\n\n    if sign == \"negative\":\n        funarg = np.argmin\n        fun = np.min\n    else:\n        funarg = np.argmax\n        fun = np.max\n\n    peak_channel, peak_time = np.unravel_index(\n        funarg(waveforms), waveforms.shape\n    )\n    relative_peaks = waveforms[:, peak_time] / fun(waveforms[peak_channel])\n\n    return relative_peaks\n\n\ndef relative_amplitude(waveforms, sign=\"negative\"):\n    \"\"\"\n    Normalized amplitude with respect to channel with largest amplitude.\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute features for\n    fs : float\n        Sampling rate in Hz\n    sign : str\n        \"positive\" | \"negative\"\n\n    Returns\n    -------\n    np.ndarray\n        Relative amplitudes for the waveforms\n\n    \"\"\"\n    assert len(waveforms) > 1\n\n    if sign == \"negative\":\n        fun = np.min\n    else:\n        fun = np.max\n\n    peak_amp = np.abs(fun(waveforms))\n    relative_peaks = np.abs(fun(waveforms, 1)) / peak_amp\n\n    return relative_peaks\n\n\ndef peak_time_diff(waveforms, fs, sign=\"negative\"):\n    \"\"\"\n    Peak time differences with respect to channel with largest amplitude.\n\n    Parameters\n    ----------\n    waveforms  : numpy.ndarray (num_waveforms x num_samples)\n        waveforms to compute features for\n    fs : float\n        Sampling rate in Hz\n    sign : str\n        \"positive\" | \"negative\"\n\n    Returns\n    -------\n    np.ndarray\n        Peak time differences for the waveforms\n\n    \"\"\"\n    assert len(waveforms) > 1\n\n    if sign == \"negative\":\n        argfun = np.argmin\n    else:\n        argfun = np.argmax\n\n    peak_chan = np.unravel_index(argfun(waveforms), waveforms.shape)[0]\n    peak_time = argfun(waveforms[peak_chan])\n    relative_peak_times = (argfun(waveforms, 1) - peak_time) / fs\n\n    return relative_peak_times\n\n\ndef _get_slope(x, y):\n    \"\"\"\n    Retrun the slope of x and y data, using scipy.signal.linregress\n    \"\"\"\n    from scipy.stats import linregress\n\n    slope = linregress(x, y)\n    return slope\n\n\ndef _get_trough_and_peak_idx(waveform, after_max_trough=False):\n    \"\"\"\n    Return the indices into the input waveforms of the detected troughs\n    (minimum of waveform) and peaks (maximum of waveform, after trough).\n\n    Assumes negative troughs and positive peaks\n\n    Returns 0 if not detected\n    \"\"\"\n    if after_max_trough:\n        max_through_idx = np.unravel_index(\n            np.argmin(waveform),\n            waveform.shape)[1]\n        trough_idx = (\n            np.argmin(waveform[:, max_through_idx:], axis=1) + max_through_idx\n        )\n        peak_idx = (\n            np.argmax(waveform[:, max_through_idx:], axis=1) + max_through_idx\n        )\n    else:\n        trough_idx = np.argmin(waveform, axis=1)\n        peak_idx = np.argmax(waveform, axis=1)\n\n    return trough_idx, peak_idx\n\n\ndef _upsample_wf(waveforms, upsample):\n    from scipy.signal import resample_poly\n\n    ndim = len(waveforms.shape)\n    waveforms_up = resample_poly(waveforms, up=upsample, down=1, axis=ndim - 1)\n\n    return waveforms_up\n"
  },
  {
    "path": "bluepyopt/ephys/locations.py",
    "content": "\"\"\"Location classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nimport itertools\n\nfrom bluepyopt.ephys.base import BaseEPhys\nfrom bluepyopt.ephys.serializer import DictMixin\nfrom bluepyopt.ephys.parameterscalers import format_float\nfrom bluepyopt.ephys.acc import ArbLabel\nfrom bluepyopt.ephys.morphologies import ArbFileMorphology\nimport numpy as np\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n\nclass Location(BaseEPhys):\n\n    \"\"\"Location\"\"\"\n    pass\n\n# TODO make all these locations accept a cell name\n# TODO instantiate should get the entire simulation environment\n# TODO find better/more general name for this\n# TODO specify in document abrevation comp=compartment, sec=section, ...\n\n\ndef _nth_isectionlist(isectionlist, index):\n    \"\"\"Get nth element of isectionlist\n\n    Sectionlists don't support direct indexing\n    \"\"\"\n    isection = next(\n        itertools.islice(\n            isectionlist,\n            index,\n            index + 1))\n    return isection\n\n\nclass NrnSeclistCompLocation(Location, DictMixin):\n\n    \"\"\"Compartment in a sectionlist\"\"\"\n\n    SERIALIZED_FIELDS = (\n        'name',\n        'comment',\n        'seclist_name',\n        'sec_index',\n        'comp_x',\n    )\n\n    def __init__(\n            self,\n            name,\n            seclist_name=None,\n            sec_index=None,\n            comp_x=None,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the object\n            seclist_name (str): name of Neuron section list (ex: 'somatic')\n            sec_index (int): index of the section in the section list\n            comp_x (float): segx (0..1) of segment inside section\n        \"\"\"\n\n        super(NrnSeclistCompLocation, self).__init__(name, comment)\n        self.seclist_name = seclist_name\n        self.sec_index = sec_index\n        self.comp_x = comp_x\n\n    def instantiate(self, sim=None, icell=None):  # pylint: disable=W0613\n        \"\"\"Find the instantiate compartment\"\"\"\n        iseclist = getattr(icell, self.seclist_name)\n\n        iseclist_size = len([x for x in iseclist])\n        if self.sec_index >= iseclist_size:\n            raise Exception(\n                'NrnSeclistCompLocation: section index %d falls out of '\n                'SectionList size of %d' %\n                (self.sec_index, iseclist_size))\n        isection = _nth_isectionlist(iseclist, self.sec_index)\n        icomp = isection(self.comp_x)\n\n        # The code above seems to add put a section on the stack\n        # TODO remove line below once we figure out where the section is pushed\n        sim.neuron.h.pop_section()\n\n        return icomp\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        raise EPhysLocAccException(\n            '%s not supported in Arbor' % type(self).__name__ +\n            ' (uses branches instead of NEURON sections).'\n            ' Use ArbBranchRelLocation/ArbSegmentRelLocation/'\n            'ArbLocsetLocation instead (consider using the'\n            ' Arbor GUI to identify the precise branch/segment index'\n            ' and relative position).')\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%s[%s](%s)' % (self.seclist_name, self.sec_index, self.comp_x)\n\n\nclass NrnSectionCompLocation(Location, DictMixin):\n\n    \"\"\"Compartment in a section\"\"\"\n\n    SERIALIZED_FIELDS = (\n        'name',\n        'comment',\n        'seclist_name',\n        'sec_index',\n        'comp_x',\n    )\n\n    def __init__(\n            self,\n            name,\n            sec_name=None,\n            comp_x=None,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the object\n            sec_name (str): name of Neuron section (ex: 'soma[0]')\n            comp_x (float): segx (0..1) of segment inside section\n        \"\"\"\n\n        super(NrnSectionCompLocation, self).__init__(name, comment)\n        self.sec_name = sec_name\n        self.comp_x = comp_x\n\n    def instantiate(self, sim=None, icell=None):  # pylint: disable=W0613\n        \"\"\"Find the instantiate compartment\"\"\"\n\n        # Dont see any other way but to use eval, apart from parsing the\n        # sec_name string which can be complicated\n        isection = eval('icell.%s' % self.sec_name)  # pylint: disable=W0123\n\n        icomp = isection(self.comp_x)\n        return icomp\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        raise EPhysLocAccException(\n            '%s not supported in Arbor' % type(self).__name__ +\n            ' (uses branches instead of NEURON sections).'\n            ' Use ArbBranchRelLocation/ArbSegmentRelLocation/'\n            'ArbLocsetLocation instead (consider using the'\n            ' Arbor GUI to identify the precise branch/segment index'\n            ' and relative position).')\n\n    def __str__(self):\n        return '%s(%s)' % (self.sec_name, self.comp_x)\n\n\nclass NrnPointProcessLocation(Location):\n\n    \"\"\"Point process location\"\"\"\n\n    def __init__(\n            self,\n            name,\n            pprocess_mech,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the object\n            pprocess_mech (str): point process mechanism\n        \"\"\"\n\n        super(NrnPointProcessLocation, self).__init__(name, comment)\n        self.pprocess_mech = pprocess_mech\n\n    def instantiate(self, sim=None, icell=None):  # pylint: disable=W0613\n        \"\"\"Find the instantiated point processes\"\"\"\n\n        return self.pprocess_mech.pprocesses\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return [loc.acc_label() for loc in self.pprocess_mech.locations]\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%s' % (self.pprocess_mech.name)\n\n\nclass NrnSeclistLocation(Location, DictMixin):\n\n    \"\"\"Section in a sectionlist\"\"\"\n\n    SERIALIZED_FIELDS = ('name', 'comment', 'seclist_name', )\n\n    def __init__(\n            self,\n            name,\n            seclist_name=None,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the object\n            seclist_name (str): name of NEURON section list (ex: 'somatic')\n        \"\"\"\n\n        super(NrnSeclistLocation, self).__init__(name, comment)\n        self.seclist_name = seclist_name\n\n    def instantiate(self, sim=None, icell=None):  # pylint: disable=W0613\n        \"\"\"Find the instantiate compartment\"\"\"\n\n        isectionlist = getattr(icell, self.seclist_name)\n\n        return (isection for isection in isectionlist)\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return ArbFileMorphology.region_labels[self.seclist_name]\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%s' % (self.seclist_name)\n\n\nclass NrnSeclistSecLocation(Location, DictMixin):\n\n    \"\"\"Section in a sectionlist\"\"\"\n\n    SERIALIZED_FIELDS = ('name', 'comment', 'seclist_name', 'sec_index', )\n\n    def __init__(\n            self,\n            name,\n            seclist_name=None,\n            sec_index=None,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            seclist_name (str): name of Neuron section list (ex: 'somatic')\n            sec_index (int): index of the section\n        \"\"\"\n\n        super(NrnSeclistSecLocation, self).__init__(name, comment)\n        self.seclist_name = seclist_name\n        self.sec_index = sec_index\n\n    def instantiate(self, sim=None, icell=None):  # pylint: disable=W0613\n        \"\"\"Find the instantiate compartment\"\"\"\n\n        isectionlist = getattr(icell, self.seclist_name)\n        isection = _nth_isectionlist(isectionlist, self.sec_index)\n        return isection\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        raise EPhysLocAccException(\n            '%s not supported in Arbor' % type(self).__name__ +\n            ' (uses branches instead of NEURON sections).'\n            ' Use ArbBranchLocation/ArbSegmentLocation/ArbRegionLocation'\n            ' instead (consider using the Arbor GUI to identify the'\n            ' precise branch/segment index).')\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%s[%s]' % (self.seclist_name, self.sec_index)\n\n\nclass NrnSomaDistanceCompLocation(Location, DictMixin):\n\n    \"\"\"Compartment at distance from soma\"\"\"\n\n    SERIALIZED_FIELDS = ('name', 'comment', 'soma_distance', 'seclist_name', )\n\n    def __init__(\n            self,\n            name,\n            soma_distance=None,\n            seclist_name=None,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            soma_distance (float): distance from soma to this compartment\n            seclist_name (str): name of Neuron section list (ex: 'apical')\n        \"\"\"\n\n        super(NrnSomaDistanceCompLocation, self).__init__(name, comment)\n        self.soma_distance = soma_distance\n        self.seclist_name = seclist_name\n\n    # TODO this definitely has to be unit-tested\n    # TODO add ability to specify origin\n    def find_icomp(self, sim, iseclist):\n        \"\"\"Find the index of the compartment based on a list of isec\n           and a distance\"\"\"\n        icomp = None\n\n        for isec in iseclist:\n            start_distance = sim.neuron.h.distance(1, 0.0, sec=isec)\n            end_distance = sim.neuron.h.distance(1, 1.0, sec=isec)\n\n            min_distance = min(start_distance, end_distance)\n            max_distance = max(start_distance, end_distance)\n\n            if min_distance <= self.soma_distance <= max_distance:\n                comp_x = float(self.soma_distance - min_distance) / \\\n                    (max_distance - min_distance)\n\n                comp_diam = isec(comp_x).diam\n\n                if comp_diam > 0.0:\n                    icomp = isec(comp_x)\n\n        if icomp is None:\n            raise EPhysLocInstantiateException(\n                'No comp found at %s distance from soma' %\n                self.soma_distance)\n\n        return icomp\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Find the instantiate compartment\"\"\"\n\n        soma = icell.soma[0]\n\n        sim.neuron.h.distance(0, 0.5, sec=soma)\n\n        iseclist = getattr(icell, self.seclist_name)\n\n        return self.find_icomp(sim, iseclist)\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        # Potentially non-unique location - in that case to be refined in the\n        # Arbor GUI (create ArbLocsetLocation directly).\n        # Alternatives to (on-components 0.5 (region \"soma\")) are\n        #  - '(segment <id>)'\n        #  - '(proximal (region %s)))' % self.seclist_name\n        # If outer restrict results in non-unique location (cf. GUI) use\n        # specific branch or similar instead of seclist_name, e.g.\n        #  - (proximal-interval (distal (branch <id>)))\n        # for a branch distally from the desired location\n        acc_label = ArbLabel(\n            'locset', self.name,\n            '(restrict (distal-translate (on-components 0.5 %s) %s) %s)' %\n            (ArbFileMorphology.region_labels['somatic'].ref,\n             format_float(self.soma_distance),\n             ArbFileMorphology.region_labels[self.seclist_name].ref))\n        logger.warning(\n            'Make sure that ACC label %s' % acc_label.loc +\n            ' for NrnSomaDistanceCompLocation (%s) ' % str(self) +\n            ' instantiates to a unique location on the morphology.'\n            ' Use the Arbor GUI to validate/refine the location expression.')\n        return acc_label\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%f micron from soma in %s' % (\n            self.soma_distance, self.seclist_name)\n\n\nclass NrnSecSomaDistanceCompLocation(NrnSomaDistanceCompLocation):\n\n    \"\"\"Compartment on a section defined both by a section index and distance\n       from the soma \"\"\"\n\n    SERIALIZED_FIELDS = ('name', 'comment', 'soma_distance', 'seclist_name',\n                         'sec_index', )\n\n    def __init__(\n        self,\n        name,\n        soma_distance=None,\n        sec_index=None,\n        seclist_name=None,\n        comment=\"\"\n    ):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            soma_distance (float): distance from soma to this compartment\n            sec_index (int): index of the section  to consider\n            seclist_name (str): name of Neuron sections (ex: 'apic')\n        \"\"\"\n        super(NrnSecSomaDistanceCompLocation, self).__init__(\n            name,\n            soma_distance=soma_distance,\n            seclist_name=seclist_name,\n            comment=comment,\n        )\n        self.sec_index = sec_index\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Find the instantiate compartment\"\"\"\n\n        if self.sec_index is None:\n            raise EPhysLocInstantiateException(\n                \"No apical point was given\")\n\n        sections = getattr(icell, self.seclist_name)\n        section = _nth_isectionlist(sections, self.sec_index)\n\n        branches = []\n        while True:\n\n            name = str(section.name()).split(\".\")[-1]\n            if name == \"soma[0]\":\n                break\n\n            branches.append(section)\n\n            if sim.neuron.h.SectionRef(sec=section).has_parent():\n                section = sim.neuron.h.SectionRef(sec=section).parent\n            else:\n                raise EPhysLocInstantiateException(\n                    \"soma[0] was not reached from isec point \"\n                    \"%f\" % self.sec_index\n                )\n\n        soma = icell.soma[0]\n\n        sim.neuron.h.distance(0, 0.5, sec=soma)\n\n        return self.find_icomp(sim, branches)\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        raise EPhysLocAccException('%s not supported in Arbor.' %\n                                   type(self).__name__)\n\n\nclass NrnTrunkSomaDistanceCompLocation(NrnSecSomaDistanceCompLocation):\n    \"\"\"Location at a distance from soma along a main direction.\n\n    We search for the section that is the furthest away from some along\n    a direction, and pick a location at a given distance from soma along\n    the path to that section.\n\n    If direction == 'radial', the largest radial direction is used.\n\n    This is most useful to follow the trunk of an apical dendrite\n    without knowing the apical point, but only that apical trunk goes along y.\n    \"\"\"\n\n    def __init__(\n        self,\n        name,\n        soma_distance=None,\n        sec_index=None,\n        seclist_name=None,\n        direction=None,\n        comment=\"\"\n    ):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            soma_distance (float): distance from soma to this compartment\n            sec_index (int): index of the section  to consider\n            sec_name (str): name of Neuron sections (ex: 'apic')\n            direction (list of 3 elements): 3d vector representing direction,\n                if None, default is [0, 1, 0]\n        \"\"\"\n        super(NrnTrunkSomaDistanceCompLocation, self).__init__(\n            name,\n            soma_distance=soma_distance,\n            sec_index=sec_index,\n            seclist_name=seclist_name,\n            comment=comment\n        )\n\n        if direction is None:\n            direction = [0.0, 1.0, 0.0]\n        self.direction = direction\n\n    def set_sec_index(self, icell=None):\n        \"\"\"Search for the point furthest away along given direction.\"\"\"\n        points = np.array(\n            [\n                [\n                    section.x3d(section.n3d() - 1),\n                    section.y3d(section.n3d() - 1),\n                    section.z3d(section.n3d() - 1),\n                ]\n                for section in getattr(icell, self.seclist_name)\n            ]\n        )\n        if len(points) > 0:\n            if self.direction == 'radial':\n                self.sec_index = int(np.argmax(np.linalg.norm(points, axis=1)))\n            else:\n                self.sec_index = int(np.argmax(points.dot(self.direction)))\n        else:\n            raise EPhysLocInstantiateException(\n                \"Empty seclist: %s\" % self.seclist_name\n            )\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\" \"\"\"\n        if self.sec_index is None:\n            self.set_sec_index(icell=icell)\n        return super().instantiate(sim=sim, icell=icell)\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        raise EPhysLocAccException('%s not supported in Arbor.' %\n                                   type(self).__name__)\n\n\nclass ArbLocation(Location):\n    \"\"\"Arbor Location\"\"\"\n\n    def instantiate(self, sim=None, icell=None):  # pylint: disable=W0613\n        \"\"\"Find the instantiate compartment (default implementation)\"\"\"\n        raise EPhysLocInstantiateException(\n            '%s not supported in NEURON.' % type(self).__name__)\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n        return '%s \\'%s\\'' % (type(self).__name__, self.acc_label().defn)\n\n\nclass ArbSegmentLocation(ArbLocation):\n    \"\"\"Segment in an Arbor morphology.\n    \"\"\"\n\n    def __init__(self, name, segment, comment=''):\n        super().__init__(name, comment)\n        self.segment = segment\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return ArbLabel('region', self.name, '(segment %s)' % (self.segment))\n\n\nclass ArbBranchLocation(ArbLocation):\n    \"\"\"Branch in an Arbor morphology.\n\n    Arbor's counterpart of NrnSeclistSecLocation.\n    \"\"\"\n\n    def __init__(self, name, branch, comment=''):\n        super().__init__(name, comment)\n        self.branch = branch\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return ArbLabel('region', self.name, '(branch %s)' % (self.branch))\n\n\nclass ArbSegmentRelLocation(ArbLocation):\n    \"\"\"Relative position on a segment in an Arbor morphology.\n    \"\"\"\n\n    def __init__(self, name, segment, pos, comment=''):\n        super().__init__(name, comment)\n        self.segment = segment\n        self.pos = pos\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return ArbLabel('locset', self.name,\n                        '(on-components %s (segment %s))' %\n                        (format_float(self.pos), self.segment))\n\n\nclass ArbBranchRelLocation(ArbLocation):\n    \"\"\"Relative position on a branch in an Arbor morphology.\n\n    Arbor's counterpart of NrnSeclistCompLocation.\n    \"\"\"\n\n    def __init__(self, name, branch, pos, comment=''):\n        super().__init__(name, comment)\n        self.branch = branch\n        self.pos = pos\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return ArbLabel('locset', self.name,\n                        '(location %s %s)' %\n                        (self.branch, format_float(self.pos)))\n\n\nclass ArbLocsetLocation(ArbLocation):\n    \"\"\"Arbor location set defined by a user-supplied string (S-expression).\n    \"\"\"\n\n    def __init__(self, name, locset, comment=''):\n        super().__init__(name, comment)\n        self.locset = locset\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return ArbLabel('locset', self.name, self.locset)\n\n\nclass ArbRegionLocation(ArbLocation):\n    \"\"\"Arbor region defined by a user-supplied string (S-expression).\n    \"\"\"\n\n    def __init__(self, name, region, comment=''):\n        super().__init__(name, comment)\n        self.region = region\n\n    def acc_label(self):\n        \"\"\"Arbor label\"\"\"\n        return ArbLabel('region', self.name, self.region)\n\n\nclass EPhysLocInstantiateException(Exception):\n\n    \"\"\"All exceptions generated by location instantiation\"\"\"\n\n    def __init__(self, message):\n        \"\"\"Constructor\"\"\"\n\n        super(EPhysLocInstantiateException, self).__init__(message)\n\n\nclass EPhysLocAccException(Exception):\n\n    \"\"\"All exceptions generated by ACC label creation\"\"\"\n\n    def __init__(self, message):\n        \"\"\"Constructor\"\"\"\n\n        super(EPhysLocAccException, self).__init__(message)\n"
  },
  {
    "path": "bluepyopt/ephys/mechanisms.py",
    "content": "\"\"\"\nMechanism classes\n\nTheses classes represent mechanisms in the model\n\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nimport logging\n\nfrom . import base\nfrom . import serializer\n\nlogger = logging.getLogger(__name__)\n\n# TODO: use Location class to specify location\n\n\nclass Mechanism(base.BaseEPhys):\n\n    \"\"\"Base parameter class\"\"\"\n    pass\n\n\nclass NrnMODMechanism(Mechanism, serializer.DictMixin):\n\n    \"\"\"Neuron mechanism\"\"\"\n\n    SERIALIZED_FIELDS = (\n        'name',\n        'comment',\n        'mod_path',\n        'suffix',\n        'locations',\n        'preloaded',\n    )\n\n    def __init__(\n            self,\n            name,\n            mod_path=None,\n            suffix=None,\n            locations=None,\n            preloaded=True,\n            deterministic=True,\n            prefix=None,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            mod_path (str): path to the MOD file (not used for the moment)\n            suffix (str): suffix of this mechanism in the MOD file\n            locations (list of Locations): a list of Location objects pointing\n                to where this mechanism should be added to.\n            preloaded (bool): should this mechanism be side-loaded by\n                BluePyOpt, or was it already loaded and compiled by the user ?\n                (not used for the moment)\n            prefix (str): Deprecated. Use suffix instead.\n        \"\"\"\n\n        super(NrnMODMechanism, self).__init__(name, comment)\n        self.mod_path = mod_path\n        self.suffix = suffix\n        self.locations = locations\n        self.preloaded = preloaded\n        self.cell_model = None\n        self.deterministic = deterministic\n\n        if prefix is not None and suffix is not None:\n            raise TypeError('NrnMODMechanism: it is not allowed to set both '\n                            'prefix and suffix in constructor: %s %s' %\n                            (self.prefix, self.suffix))\n        elif prefix is not None:\n            self.suffix = prefix\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Instantiate\"\"\"\n\n        for location in self.locations:\n            isec_list = location.instantiate(sim=sim, icell=icell)\n            for isec in isec_list:\n                try:\n                    isec.insert(self.suffix)\n                except ValueError as e:\n                    raise ValueError(str(e) + ': ' + self.suffix)\n                self.instantiate_determinism(\n                    self.deterministic,\n                    icell,\n                    isec,\n                    sim)\n\n        logger.debug(\n            'Inserted %s in %s', self.suffix, [\n                str(location) for location in self.locations])\n\n    def instantiate_determinism(self, deterministic, icell, isec, sim):\n        \"\"\"Instantiate enable/disable determinism\"\"\"\n\n        if 'Stoch' in self.suffix:\n            setattr(\n                isec,\n                'deterministic_%s' %\n                (self.suffix),\n                1 if deterministic else 0)\n\n            # Set the seeds even when deterministic,\n            # that way neuron's psection does not crash\n            # when encountering a stoch mech var that is not set (e.g. rng)\n            short_secname = sim.neuron.h.secname(sec=isec).split('.')[-1]\n            for iseg in isec:\n                seg_name = '%s.%.19g' % (short_secname, iseg.x)\n                getattr(sim.neuron.h,\n                        \"setdata_%s\" % self.suffix)(iseg.x, sec=isec)\n                seed_id1 = icell.gid\n                seed_id2 = self.hash_py(seg_name)\n                getattr(\n                    sim.neuron.h,\n                    \"setRNG_%s\" % self.suffix)(seed_id1, seed_id2)\n        else:\n            if not deterministic:\n                # can't do this for non-Stoch channels\n                raise TypeError(\n                    'Deterministic can only be set to False for '\n                    'Stoch channel, not %s' %\n                    self.suffix)\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy mechanism instantiation\"\"\"\n\n        pass\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"%s: %s at %s\" % (\n            self.name, self.suffix,\n            [str(location) for location in self.locations])\n\n    @staticmethod\n    def hash_hoc(string, sim):\n        \"\"\"Calculate hash value of string in Python\"\"\"\n\n        # Load hash function in hoc, only do this once\n        if not hasattr(sim.neuron.h, 'hash_str'):\n            sim.neuron.h(NrnMODMechanism.hash_hoc_string)\n\n        return sim.neuron.h.hash_str(string)\n\n    @staticmethod\n    def hash_py(string):\n        \"\"\"Calculate hash value of string in Python\"\"\"\n\n        hash_value = 0.0\n        for char in string:\n            # Multiplicative hash function using Mersenne prime close to 2^32\n            hash_value = (hash_value * 31.0 + ord(char)) % (2.0 ** 31.0 - 1.0)\n\n        return hash_value\n\n    def generate_reinitrng_hoc_block(self):\n        \"\"\"\"Create re_init_rng code blocks for this channel\"\"\"\n\n        reinitrng_hoc_block = ''\n\n        if 'Stoch' in self.suffix:\n            # TODO this is dangerous, implicitely assumes type of location\n            for location in self.locations:\n                if self.deterministic:\n                    reinitrng_hoc_block += \\\n                        '    forsec %(seclist_name)s { ' \\\n                        'deterministic_%(suffix)s = 1 }\\n' % {\n                            'seclist_name': location.seclist_name,\n                            'suffix': self.suffix}\n                else:\n                    reinitrng_hoc_block += \\\n                        '    forsec %(seclist_name)s {%(mech_reinitrng)s' \\\n                        '    }\\n' % {\n                            'seclist_name': location.seclist_name,\n                            'mech_reinitrng':\n                            self.mech_reinitrng_block_template % {\n                                'suffix': self.suffix}}\n\n        return reinitrng_hoc_block\n\n    @property\n    def prefix(self):\n        \"\"\"Deprecated, prefix is now replaced by suffix\"\"\"\n\n        return self.suffix\n\n    @prefix.setter\n    def prefix(self, value):\n        \"\"\"Deprecated, prefix is now replaced by suffix\"\"\"\n\n        self.suffix = value\n\n    hash_hoc_string = \\\n        \"\"\"\nfunc hash_str() {localobj sf strdef right\n  sf = new StringFunctions()\n\n  right = $s1\n\n  n_of_c = sf.len(right)\n\n  hash = 0\n  char_int = 0\n  for i = 0, n_of_c - 1 {\n     sscanf(right, \"%c\", & char_int)\n     hash = (hash * 31 + char_int) % (2 ^ 31 - 1)\n     sf.right(right, 1)\n  }\n\n  return hash\n}\n\"\"\"\n\n    reinitrng_hoc_string = \"\"\"\nproc re_init_rng() {localobj sf\n    strdef full_str, name\n\n    sf = new StringFunctions()\n\n    if(numarg() == 1) {\n        // We received a third seed\n        channel_seed = $1\n        channel_seed_set = 1\n    } else {\n        channel_seed_set = 0\n    }\n\n%(reinitrng_hoc_blocks)s\n}\n\"\"\"\n\n    mech_reinitrng_block_template = \"\"\"\n        for (x, 0) {\n            setdata_%(suffix)s(x)\n            sf.tail(secname(), \"\\\\\\\\.\", name)\n            sprint(full_str, \"%%s.%%.19g\", name, x)\n            if (channel_seed_set) {\n                setRNG_%(suffix)s(gid, hash_str(full_str), channel_seed)\n            } else {\n                setRNG_%(suffix)s(gid, hash_str(full_str))\n            }\n        }\n\"\"\"\n\n\nclass NrnMODPointProcessMechanism(Mechanism):\n\n    \"\"\"Neuron mechanism\"\"\"\n\n    def __init__(\n            self,\n            name,\n            mod_path=None,\n            suffix=None,\n            locations=None,\n            preloaded=True,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            mod_path (str): path to the MOD file (not used for the moment)\n            suffix (str): suffix of this mechanism in the MOD file\n            locations (list of Locations): a list of Location objects pointing\n                to compartments where this mechanism should be added to.\n            preloaded (bool): should this mechanism be side-loaded by\n                BluePyOpt, or was it already loaded and compiled by the user ?\n                (not used for the moment)\n        \"\"\"\n\n        super(NrnMODPointProcessMechanism, self).__init__(name, comment)\n        self.mod_path = mod_path\n        self.suffix = suffix\n        self.locations = locations\n        self.preloaded = preloaded\n        self.cell_model = None\n        self.pprocesses = None\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Instantiate\"\"\"\n\n        self.pprocesses = []\n        for location in self.locations:\n            icomp = location.instantiate(sim=sim, icell=icell)\n            try:\n                iclass = getattr(sim.neuron.h, self.suffix)\n                self.pprocesses.append(iclass(icomp.x, sec=icomp.sec))\n            except AttributeError as e:\n                raise AttributeError(str(e) + ': ' + self.suffix)\n\n        logger.debug(\n            'Inserted %s at %s ', self.suffix, [\n                str(location) for location in self.locations])\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy mechanism instantiation\"\"\"\n\n        self.pprocesses = None\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"%s: %s at %s\" % (\n            self.name, self.suffix,\n            [str(location) for location in self.locations])\n"
  },
  {
    "path": "bluepyopt/ephys/models.py",
    "content": "\"\"\"Cell template class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\n# pylint: disable=W0511\n\n# TODO take into account that one might want to run protocols on different\n# machines\n# TODO rename this to 'CellModel' -> definitely\n\nimport sys\nimport os\nimport collections\nimport string\n\nfrom . import create_hoc, create_acc\nfrom . import morphologies\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n\nclass Model(object):\n\n    \"\"\"Model\"\"\"\n\n    def __init__(self, name):\n        \"\"\"Constructor\n        Args:\n            name (str): name of the model\n        \"\"\"\n        self.name = name\n\n    def instantiate(self, sim=None):\n        \"\"\"Instantiate model in simulator\"\"\"\n        pass\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy instantiated model in simulator\"\"\"\n        pass\n\n\nclass CellModel(Model):\n\n    \"\"\"Cell model class\"\"\"\n\n    def __init__(\n        self,\n        name,\n        morph=None,\n        mechs=None,\n        params=None,\n        gid=0,\n        seclist_names=None,\n        secarray_names=None\n    ):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n                        should be alphanumeric string, underscores are allowed,\n                        first char should be a letter\n            morph (Morphology):\n                underlying Morphology of the cell\n            mechs (list of Mechanisms):\n                Mechanisms associated with the cell\n            params (list of Parameters):\n                Parameters of the cell model\n            seclist_names (list of strings):\n                Names of the lists of sections\n            secarray_names (list of strings):\n                Names of the sections\n        \"\"\"\n\n        super(CellModel, self).__init__(name)\n\n        self.check_name()\n        self.morphology = morph\n        self.mechanisms = mechs\n        self.params = collections.OrderedDict()\n        if params is not None:\n            for param in params:\n                self.params[param.name] = param\n\n        # Cell instantiation in simulator\n        self.icell = None\n        self.icell_existing_secs = None\n\n        self.param_values = None\n        self.gid = gid\n\n        if seclist_names is None:\n            self.seclist_names = [\n                'all', 'somatic', 'basal', 'apical', 'axonal', 'myelinated'\n            ]\n        else:\n            self.seclist_names = seclist_names\n\n        if secarray_names is None:\n            self.secarray_names = [\n                'soma', 'dend', 'apic', 'axon', 'myelin'\n            ]\n        else:\n            self.secarray_names = secarray_names\n\n    def check_name(self):\n        \"\"\"Check if name complies with requirements\"\"\"\n\n        allowed_chars = string.ascii_letters + string.digits + '_'\n\n        if sys.version_info[0] < 3:\n            translate_args = [None, allowed_chars]\n        else:\n            translate_args = [str.maketrans('', '', allowed_chars)]\n\n        if self.name == '' \\\n                or self.name[0] not in string.ascii_letters \\\n                or not str(self.name).translate(*translate_args) == '':\n            raise TypeError(\n                'CellModel: name \"%s\" provided to constructor does not comply '\n                'with the rules for Neuron template name: name should be '\n                'alphanumeric '\n                'non-empty string, underscores are allowed, '\n                'first char should be letter' % self.name)\n\n    def params_by_names(self, param_names):\n        \"\"\"Get parameter objects by name\"\"\"\n\n        return [self.params[param_name] for param_name in param_names]\n\n    def freeze(self, param_dict):\n        \"\"\"Set params\"\"\"\n\n        for param_name, param_value in param_dict.items():\n            self.params[param_name].freeze(param_value)\n\n    def unfreeze(self, param_names):\n        \"\"\"Unset params\"\"\"\n\n        for param_name in param_names:\n            self.params[param_name].unfreeze()\n\n    @staticmethod\n    def create_empty_template(\n            template_name,\n            seclist_names=None,\n            secarray_names=None):\n        '''create an hoc template named template_name for an empty cell'''\n\n        objref_str = 'objref this, CellRef'\n        newseclist_str = ''\n\n        if seclist_names:\n            for seclist_name in seclist_names:\n                objref_str += ', %s' % seclist_name\n                newseclist_str += \\\n                    '             %s = new SectionList()\\n' % seclist_name\n\n        create_str = ''\n        if secarray_names:\n            create_str = 'create '\n            create_str += ', '.join(\n                '%s[1]' % secarray_name\n                for secarray_name in secarray_names)\n            create_str += '\\n'\n\n        template = '''\\\n        begintemplate %(template_name)s\n          %(objref_str)s\n          proc init() {\\n%(newseclist_str)s\n            forall delete_section()\n            CellRef = this\n          }\n\n          gid = 0\n\n          proc destroy() {localobj nil\n            CellRef = nil\n          }\n\n          %(create_str)s\n        endtemplate %(template_name)s\n               ''' % dict(template_name=template_name, objref_str=objref_str,\n                          newseclist_str=newseclist_str,\n                          create_str=create_str)\n        return template\n\n    @staticmethod\n    def create_empty_cell(\n            name,\n            sim,\n            seclist_names=None,\n            secarray_names=None):\n        \"\"\"Create an empty cell in Neuron\"\"\"\n\n        # TODO minize hardcoded definition\n        # E.g. sectionlist can be procedurally generated\n        hoc_template = CellModel.create_empty_template(\n            name,\n            seclist_names,\n            secarray_names)\n        sim.neuron.h(hoc_template)\n\n        template_function = getattr(sim.neuron.h, name)\n\n        return template_function()\n\n    def instantiate_morphology(self, sim=None):\n        \"\"\"Instantiate morphology in simulator\"\"\"\n\n        # TODO replace this with the real template name\n        if not hasattr(sim.neuron.h, self.name):\n            self.icell = self.create_empty_cell(\n                self.name,\n                sim=sim,\n                seclist_names=self.seclist_names,\n                secarray_names=self.secarray_names)\n        else:\n            self.icell = getattr(sim.neuron.h, self.name)()\n\n        self.icell.gid = self.gid\n\n        self.morphology.instantiate(sim=sim, icell=self.icell)\n\n        self.icell_existing_secs = [\n            sec for sec in self.secarray_names\n            if sim.neuron.h.section_exists(sec, self.icell)]\n\n    def instantiate_morphology_3d(self, sim=None):\n        \"\"\"Instantiate morphology and fill in 3d pts for stylized geometry\"\"\"\n\n        self.instantiate_morphology(sim=sim)\n        sim.neuron.h.define_shape()\n\n    def instantiate(self, sim=None):\n        \"\"\"Instantiate model in simulator\"\"\"\n\n        self.instantiate_morphology(sim)\n\n        if self.mechanisms is not None:\n            for mechanism in self.mechanisms:\n                mechanism.instantiate(sim=sim, icell=self.icell)\n\n        if self.params is not None:\n            for param in self.params.values():\n                param.instantiate(sim=sim, icell=self.icell,\n                                  params=self.params)\n\n    def destroy(self, sim=None):  # pylint: disable=W0613\n        \"\"\"Destroy instantiated model in simulator\"\"\"\n\n        # Make sure the icell's destroy() method is called\n        # without it a circular reference exists between CellRef and the object\n        # this prevents the icells from being garbage collected, and\n        # cell objects pile up in the simulator\n        self.icell.destroy()\n\n        # The line below is some M. Hines magic\n        # DON'T remove it, because it will make sure garbage collection\n        # is called on the icell object\n        sim.neuron.h.Vector().size()\n\n        self.icell = None\n        self.icell_existing_secs = None\n\n        self.morphology.destroy(sim=sim)\n        for mechanism in self.mechanisms:\n            mechanism.destroy(sim=sim)\n        for param in self.params.values():\n            param.destroy(sim=sim)\n\n    def check_nonfrozen_params(self, param_names):  # pylint: disable=W0613\n        \"\"\"Check if all nonfrozen params are set\"\"\"\n\n        for param_name, param in self.params.items():\n            if not param.frozen:\n                raise Exception(\n                    'CellModel: Nonfrozen param %s needs to be '\n                    'set before simulation' %\n                    param_name)\n\n    def _create_sim_desc(self, param_values,\n                         ignored_globals=(), template=None,\n                         disable_banner=False,\n                         template_dir=None,\n                         extra_params=None,\n                         sim_desc_creator=None):\n        \"\"\"Create simulator description for this model\"\"\"\n\n        to_unfreeze = []\n        for param in self.params.values():\n            if not param.frozen:\n                param.freeze(param_values[param.name])\n                to_unfreeze.append(param.name)\n\n        template_name = self.name\n        morphology = os.path.basename(self.morphology.morphology_path)\n\n        if sim_desc_creator is create_hoc.create_hoc:\n            if self.morphology.do_replace_axon:\n                replace_axon = self.morphology.replace_axon_hoc\n            else:\n                replace_axon = None\n\n            if (\n                self.morphology.morph_modifiers is not None\n                and self.morphology.morph_modifiers_hoc is None\n            ):\n                logger.warning('You have provided custom morphology'\n                               ' modifiers, but no corresponding hoc files.')\n            elif (\n                self.morphology.morph_modifiers is not None\n                and self.morphology.morph_modifiers_hoc is not None\n            ):\n                if replace_axon is None:\n                    replace_axon = ''\n                for morph_modifier_hoc in self.morphology.morph_modifiers_hoc:\n                    replace_axon += '\\n'\n                    replace_axon += morph_modifier_hoc\n        elif sim_desc_creator is create_acc.create_acc:\n            if self.morphology.do_replace_axon:\n\n                replace_axon = morphologies.\\\n                    ArbFileMorphology.extract_nrn_seclists(\n                        self.icell, [sl for sl in ['axon', 'myelin']\n                                     if sl in self.icell_existing_secs])\n\n            else:\n                replace_axon = None\n        else:\n            raise ValueError('Unsupported sim_desc_creator %s '\n                             '(choose either create_hoc.create_hoc or '\n                             'create_acc.create_acc)', str(sim_desc_creator))\n\n        if extra_params is None:\n            extra_params = dict()\n\n        ret = sim_desc_creator(mechs=self.mechanisms,\n                               parameters=self.params.values(),\n                               morphology=morphology,\n                               ignored_globals=ignored_globals,\n                               replace_axon=replace_axon,\n                               template_name=template_name,\n                               template_filename=template,\n                               template_dir=template_dir,\n                               disable_banner=disable_banner,\n                               **extra_params)\n\n        self.unfreeze(to_unfreeze)\n\n        return ret\n\n    def create_hoc(self, param_values,\n                   ignored_globals=(), template='cell_template.jinja2',\n                   disable_banner=False,\n                   template_dir=None):\n        \"\"\"Create hoc code for this model\"\"\"\n        return self._create_sim_desc(param_values,\n                                     ignored_globals, template,\n                                     disable_banner,\n                                     template_dir,\n                                     sim_desc_creator=create_hoc.create_hoc)\n\n    def create_acc(self, param_values,\n                   ignored_globals=(), template='acc/*_template.jinja2',\n                   disable_banner=False,\n                   template_dir=None,\n                   ext_catalogues=None,\n                   create_mod_morph=False,\n                   sim=None):\n        \"\"\"Create JSON/ACC-description for this model\"\"\"\n        destroy_cell = False\n        if self.morphology.do_replace_axon:\n            if self.icell is None:\n                if sim is None:\n                    raise ValueError('Need an instance of NrnSimulator in sim'\n                                     ' to instantiate morphology in order to'\n                                     ' create JSON/ACC-description with'\n                                     ' axon replacement.')\n                self.instantiate_morphology_3d(sim=sim)\n                destroy_cell = True\n\n        extra_params = dict(\n            morphology_dir=os.path.dirname(self.morphology.morphology_path),\n            create_mod_morph=create_mod_morph,\n            ext_catalogues=ext_catalogues\n        )\n\n        ret = self._create_sim_desc(param_values,\n                                    ignored_globals, template,\n                                    disable_banner,\n                                    template_dir,\n                                    extra_params=extra_params,\n                                    sim_desc_creator=create_acc.create_acc)\n\n        if destroy_cell:\n            self.destroy(sim=sim)\n        return ret\n\n    def write_acc(self, output_dir, param_values,\n                  template_filename='acc/*_template.jinja2',\n                  ext_catalogues=None,\n                  create_mod_morph=False,\n                  sim=None):\n        \"\"\"Write JSON/ACC-description for this model to output directory\"\"\"\n        create_acc.write_acc(output_dir, self, param_values,\n                             template_filename=template_filename,\n                             ext_catalogues=ext_catalogues,\n                             create_mod_morph=create_mod_morph,\n                             sim=sim)\n\n    def __str__(self):\n        \"\"\"Return string representation\"\"\"\n\n        content = '%s:\\n' % self.name\n\n        content += '  morphology:\\n'\n\n        if self.morphology is not None:\n            content += '    %s\\n' % str(self.morphology)\n\n        content += '  mechanisms:\\n'\n        if self.mechanisms is not None:\n            for mechanism in self.mechanisms:\n                content += '    %s\\n' % mechanism\n\n        content += '  params:\\n'\n        if self.params is not None:\n            for param in self.params.values():\n                content += '    %s\\n' % param\n\n        return content\n\n\nclass HocMorphology(morphologies.Morphology):\n\n    '''wrapper for Morphology so that it has a morphology_path'''\n\n    def __init__(self, morphology_path):\n        super(HocMorphology, self).__init__()\n        if not os.path.exists(morphology_path):\n            raise Exception('HocCellModel: Morphology not found at: %s'\n                            % morphology_path)\n        self.morphology_path = morphology_path\n\n\nclass HocCellModel(CellModel):\n\n    '''Wrapper class for a hoc template so it can be used by BluePyOpt'''\n\n    def __init__(self, name, morphology_path, hoc_path=None, hoc_string=None):\n        \"\"\"Constructor\n\n        Args:\n            name(str): name of this object\n            sim(NrnSimulator): simulator in which to instatiate hoc_string\n            hoc_path(str): Path to a hoc file\n                (hoc_path and hoc_string can't be used simultaneously,\n                but one of them has to specified)\n            hoc_string(str): String that of hoc code that defines a template\n                (hoc_path and hoc_string can't be used simultaneously,\n                but one of them has to specified))\n            morphology_path(str path): path to morphology that can be loaded by\n                                       Neuron\n        \"\"\"\n        super(HocCellModel, self).__init__(name,\n                                           morph=None,\n                                           mechs=[],\n                                           params=[])\n\n        if hoc_path is not None and hoc_string is not None:\n            raise TypeError('HocCellModel: cant specify both hoc_string '\n                            'and hoc_path argument')\n        if hoc_path is not None:\n            with open(hoc_path) as hoc_file:\n                self.hoc_string = hoc_file.read()\n        else:\n            self.hoc_string = hoc_string\n\n        self.morphology = HocMorphology(morphology_path)\n        self.cell = None\n        self.icell = None\n\n    def params_by_names(self, param_names):\n        pass\n\n    def freeze(self, param_dict):\n        pass\n\n    def unfreeze(self, param_names):\n        pass\n\n    def instantiate(self, sim=None):\n        sim.neuron.h.load_file('stdrun.hoc')\n        template_name = self.load_hoc_template(sim, self.hoc_string)\n\n        morph_path = self.morphology.morphology_path\n        assert os.path.exists(morph_path), \\\n            'Morphology path does not exist: %s' % morph_path\n        if os.path.isdir(morph_path):\n            # will use the built in morphology name, if the init() only\n            # gets one parameter\n            self.cell = getattr(sim.neuron.h, template_name)(morph_path)\n        else:\n            morph_dir = os.path.dirname(morph_path)\n            morph_name = os.path.basename(morph_path)\n            self.cell = getattr(sim.neuron.h, template_name)(morph_dir,\n                                                             morph_name)\n        self.icell = self.cell.CellRef\n\n    def destroy(self, sim=None):\n        self.cell = None\n        self.icell = None\n\n    def check_nonfrozen_params(self, param_names):\n        pass\n\n    def __str__(self):\n        \"\"\"Return string representation\"\"\"\n        return (\n            '%s: %s of %s(%s)' %\n            (self.__class__,\n             self.name,\n             self.get_template_name(self.hoc_string),\n             self.morphology.morphology_path,))\n\n    @staticmethod\n    def get_template_name(hoc_string):\n        \"\"\"Find the template name from hoc_string\n\n        Note: this will fail if there is a begintemplate in a `/* */` style\n        comment before the real begintemplate\n        \"\"\"\n        for i, line in enumerate(hoc_string.split('\\n')):\n            if 'begintemplate' in line:\n                line = line.strip().split()\n                assert line[0] == 'begintemplate', \\\n                    'begintemplate must come first, line %d' % i\n                template_name = line[1]\n                logger.info('Found template %s on line %d', template_name, i)\n                return template_name\n        else:  # pylint: disable=W0120\n            raise Exception('Could not find begintemplate in hoc file')\n\n    @staticmethod\n    def load_hoc_template(sim, hoc_string):\n        \"\"\"Have neuron hoc template, and detect what the name template name is\n\n        The template must have an init that takes two parameters, the second of\n        which is the path to a morphology.\n\n        It must also have a CellRef member that is the result of\n            `Import3d_GUI(...).instantiate()`\n        \"\"\"\n        template_name = HocCellModel.get_template_name(hoc_string)\n        if not hasattr(sim.neuron.h, template_name):\n            sim.neuron.h(hoc_string)\n            assert hasattr(sim.neuron.h, template_name), \\\n                'NEURON does not have template: ' + template_name\n\n        return template_name\n\n\nclass LFPyCellModel(Model):\n\n    \"\"\"LFPy.Cell model class\"\"\"\n\n    def __init__(\n        self,\n        name,\n        electrode=None,\n        morph=None,\n        mechs=None,\n        params=None,\n        dt=0.025,\n        v_init=-65.0,\n        gid=0,\n        seclist_names=None,\n        secarray_names=None,\n    ):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n                        should be alphanumeric string, underscores are allowed,\n                        first char should be a letter\n            morph (Morphology):\n                underlying Morphology of the cell\n            mechs (list of Mechanisms):\n                Mechanisms associated with the cell\n            params (list of Parameters):\n                Parameters of the cell model\n            seclist_names (list of strings):\n                Names of the lists of sections\n            secarray_names (list of strings):\n                Names of the sections\n        \"\"\"\n        super(LFPyCellModel, self).__init__(name)\n        self.check_name()\n        self.morphology = morph\n        self.mechanisms = mechs\n        self.params = collections.OrderedDict()\n        if params is not None:\n            for param in params:\n                self.params[param.name] = param\n\n        # Cell instantiation in simulator\n        self.icell = None\n        self.lfpy_cell = None\n        self.electrode = electrode\n        self.lfpy_electrode = None\n\n        self.dt = dt\n        self.v_init = v_init\n\n        self.param_values = None\n        self.gid = gid\n\n        if seclist_names is None:\n            self.seclist_names = [\n                \"all\",\n                \"somatic\",\n                \"basal\",\n                \"apical\",\n                \"axonal\",\n                \"myelinated\",\n            ]\n        else:\n            self.seclist_names = seclist_names\n\n        if secarray_names is None:\n            self.secarray_names = [\"soma\", \"dend\", \"apic\", \"axon\", \"myelin\"]\n        else:\n            self.secarray_names = secarray_names\n\n    def check_name(self):\n        \"\"\"Check if name complies with requirements\"\"\"\n\n        allowed_chars = string.ascii_letters + string.digits + \"_\"\n\n        if sys.version_info[0] < 3:\n            translate_args = [None, allowed_chars]\n        else:\n            translate_args = [str.maketrans(\"\", \"\", allowed_chars)]\n\n        if (\n            self.name == \"\"\n            or self.name[0] not in string.ascii_letters\n            or not str(self.name).translate(*translate_args) == \"\"\n        ):\n            raise TypeError(\n                'CellModel: name \"%s\" provided to constructor does not comply '\n                \"with the rules for Neuron template name: name should be \"\n                \"alphanumeric \"\n                \"non-empty string, underscores are allowed, \"\n                \"first char should be letter\" % self.name\n            )\n\n    def params_by_names(self, param_names):\n        \"\"\"Get parameter objects by name\"\"\"\n\n        return [self.params[param_name] for param_name in param_names]\n\n    def freeze(self, param_dict):\n        \"\"\"Set params\"\"\"\n\n        for param_name, param_value in param_dict.items():\n            self.params[param_name].freeze(param_dict[param_name])\n\n    def unfreeze(self, param_names):\n        \"\"\"Unset params\"\"\"\n\n        for param_name in param_names:\n            self.params[param_name].unfreeze()\n\n    @staticmethod\n    def create_empty_template(\n        template_name, seclist_names=None, secarray_names=None\n    ):\n        \"\"\"create an hoc template named template_name for an empty cell\"\"\"\n\n        objref_str = \"objref this, CellRef\"\n        newseclist_str = \"\"\n\n        if seclist_names:\n            for seclist_name in seclist_names:\n                objref_str += \", %s\" % seclist_name\n                newseclist_str += (\n                    \"             %s = new SectionList()\\n\" % seclist_name\n                )\n\n        create_str = \"\"\n        if secarray_names:\n            create_str = \"create \"\n            create_str += \", \".join(\n                \"%s[1]\" % secarray_name for secarray_name in secarray_names\n            )\n            create_str += \"\\n\"\n\n        template = \"\"\"\\\n        begintemplate %(template_name)s\n          %(objref_str)s\n          proc init() {\\n%(newseclist_str)s\n            forall delete_section()\n            CellRef = this\n          }\n\n          gid = 0\n\n          proc destroy() {localobj nil\n            CellRef = nil\n          }\n\n          %(create_str)s\n        endtemplate %(template_name)s\n               \"\"\" % dict(\n            template_name=template_name,\n            objref_str=objref_str,\n            newseclist_str=newseclist_str,\n            create_str=create_str,\n        )\n\n        return template\n\n    @staticmethod\n    def create_empty_cell(name, sim, seclist_names=None, secarray_names=None):\n        \"\"\"Create an empty cell in Neuron\"\"\"\n\n        # TODO minize hardcoded definition\n        # E.g. sectionlist can be procedurally generated\n        hoc_template = CellModel.create_empty_template(\n            name, seclist_names, secarray_names\n        )\n        sim.neuron.h(hoc_template)\n\n        template_function = getattr(sim.neuron.h, name)\n\n        return template_function()\n\n    def instantiate(self, sim=None):\n        \"\"\"Instantiate model in simulator\"\"\"\n        from LFPy import Cell\n        from lfpykit import RecExtElectrode\n\n        # TODO replace this with the real template name\n        if not hasattr(sim.neuron.h, self.name):\n            self.icell = self.create_empty_cell(\n                self.name,\n                sim=sim,\n                seclist_names=self.seclist_names,\n                secarray_names=self.secarray_names,\n            )\n        else:\n            self.icell = getattr(sim.neuron.h, self.name)()\n\n        self.icell.gid = self.gid\n\n        self.morphology.instantiate(sim=sim, icell=self.icell)\n\n        self.lfpy_cell = Cell(\n            morphology=sim.neuron.h.allsec(),\n            dt=self.dt,\n            v_init=self.v_init,\n            pt3d=True,\n            delete_sections=False,\n            nsegs_method=None,\n        )\n\n        self.lfpy_electrode = RecExtElectrode(\n            self.lfpy_cell, probe=self.electrode\n        )\n\n        if self.mechanisms is not None:\n            for mechanism in self.mechanisms:\n                mechanism.instantiate(sim=sim, icell=self.icell)\n\n        if self.params is not None:\n            for param in self.params.values():\n                param.instantiate(sim=sim, icell=self.icell,\n                                  params=self.params)\n\n    def destroy(self, sim=None):  # pylint: disable=W0613\n        \"\"\"Destroy instantiated model in simulator\"\"\"\n\n        # Make sure the icell's destroy() method is called\n        # without it a circular reference exists between CellRef and the object\n        # this prevents the icells from being garbage collected, and\n        # cell objects pile up in the simulator\n        self.icell.destroy()\n\n        # The line below is some M. Hines magic\n        # DON'T remove it, because it will make sure garbage collection\n        # is called on the icell object\n        sim.neuron.h.Vector().size()\n\n        self.icell = None\n\n        self.morphology.destroy(sim=sim)\n        for mechanism in self.mechanisms:\n            mechanism.destroy(sim=sim)\n        for param in self.params.values():\n            param.destroy(sim=sim)\n\n    def check_nonfrozen_params(self, param_names):  # pylint: disable=W0613\n        \"\"\"Check if all nonfrozen params are set\"\"\"\n\n        for param_name, param in self.params.items():\n            if not param.frozen:\n                raise Exception(\n                    \"CellModel: Nonfrozen param %s needs to be \"\n                    \"set before simulation\" % param_name\n                )\n\n    def create_hoc(\n        self,\n        param_values,\n        ignored_globals=(),\n        template=\"cell_template.jinja2\",\n        disable_banner=False,\n        template_dir=None,\n    ):\n        \"\"\"Create hoc code for this model\"\"\"\n\n        to_unfreeze = []\n        for param in self.params.values():\n            if not param.frozen:\n                param.freeze(param_values[param.name])\n                to_unfreeze.append(param.name)\n\n        template_name = self.name\n        morphology = os.path.basename(self.morphology.morphology_path)\n        if self.morphology.do_replace_axon:\n            replace_axon = self.morphology.replace_axon_hoc\n        else:\n            replace_axon = None\n\n        ret = create_hoc.create_hoc(\n            mechs=self.mechanisms,\n            parameters=self.params.values(),\n            morphology=morphology,\n            ignored_globals=ignored_globals,\n            replace_axon=replace_axon,\n            template_name=template_name,\n            template_filename=template,\n            template_dir=template_dir,\n            disable_banner=disable_banner,\n        )\n\n        self.unfreeze(to_unfreeze)\n\n        return ret\n\n    def __str__(self):\n        \"\"\"Return string representation\"\"\"\n\n        content = \"%s:\\n\" % self.name\n\n        content += \"  morphology:\\n\"\n\n        if self.morphology is not None:\n            content += \"    %s\\n\" % str(self.morphology)\n\n        content += \"  mechanisms:\\n\"\n        if self.mechanisms is not None:\n            for mechanism in self.mechanisms:\n                content += \"    %s\\n\" % mechanism\n\n        content += \"  params:\\n\"\n        if self.params is not None:\n            for param in self.params.values():\n                content += \"    %s\\n\" % param\n\n        return content\n"
  },
  {
    "path": "bluepyopt/ephys/morphologies.py",
    "content": "\"\"\"Morphology classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nimport os\nimport platform\nimport logging\nimport pathlib\nimport bisect\nimport numpy\nfrom bluepyopt.ephys.base import BaseEPhys\nfrom bluepyopt.ephys.serializer import DictMixin\nfrom bluepyopt.ephys.acc import arbor, ArbLabel\n\nlogger = logging.getLogger(__name__)\n\n# TODO define an addressing scheme\n\n\nclass Morphology(BaseEPhys):\n\n    \"\"\"Morphology class\"\"\"\n    pass\n\n\nclass NrnFileMorphology(Morphology, DictMixin):\n\n    \"\"\"Morphology loaded from a file\"\"\"\n    SERIALIZED_FIELDS = ('morphology_path', 'do_replace_axon', 'do_set_nseg',\n                         'replace_axon_hoc', 'nseg_frequency',\n                         'morph_modifiers', 'morph_modifiers_hoc',\n                         'morph_modifiers_kwargs')\n\n    def __init__(\n            self,\n            morphology_path,\n            do_replace_axon=False,\n            do_set_nseg=True,\n            comment='',\n            replace_axon_hoc=None,\n            axon_stub_length=60,\n            axon_nseg_frequency=40,\n            nseg_frequency=40,\n            morph_modifiers=None,\n            morph_modifiers_hoc=None,\n            morph_modifiers_kwargs=None):\n        \"\"\"Constructor\n\n        Args:\n            morphology_path (str or Path): location of the file describing the\n                morphology\n            do_replace_axon (bool): Does the axon need to be replaced by an AIS\n                stub with default function ?\n            replace_axon_hoc (str): Translation in HOC language for the\n                'replace_axon' method. This code will 'only' be used when\n                calling create_hoc on a cell model. While the model is run in\n                python, replace_axon is used instead. Must include\n                'proc replace_axon(){ ... }\n                If None,the default replace_axon is used\n            axon_stub_length (float): Length of replacement axon\n            axon_nseg_frequency (int): frequency of nseg, for axon\n            nseg_frequency (float): frequency of nseg\n            do_set_nseg (bool): if True, it will use nseg_frequency\n            morph_modifiers (list): list of functions to modify the icell\n                with (sim, icell) as arguments\n            morph_modifiers_hoc (list): list of hoc strings corresponding\n                to morph_modifiers\n            morph_modifiers_kwargs (dict): kwargs for morph_modifiers functions\n        \"\"\"\n        name = os.path.basename(morphology_path)\n        super(NrnFileMorphology, self).__init__(name=name, comment=comment)\n        # TODO speed up loading of morphologies from files\n        # Path to morphology\n        if isinstance(morphology_path, pathlib.Path):\n            morphology_path = str(morphology_path)\n        self.morphology_path = morphology_path\n        self.do_replace_axon = do_replace_axon\n        self.axon_stub_length = axon_stub_length\n        self.axon_nseg_frequency = axon_nseg_frequency\n        self.do_set_nseg = do_set_nseg\n        self.nseg_frequency = nseg_frequency\n        self.morph_modifiers = morph_modifiers\n        self.morph_modifiers_hoc = morph_modifiers_hoc\n        self.morph_modifiers_kwargs = morph_modifiers_kwargs\n        if self.morph_modifiers_kwargs is None:\n            self.morph_modifiers_kwargs = {}\n\n        if replace_axon_hoc is None:\n            self.replace_axon_hoc = self.default_replace_axon_hoc\n        else:\n            self.replace_axon_hoc = replace_axon_hoc\n\n    def __str__(self):\n        \"\"\"Return string representation\"\"\"\n\n        return self.morphology_path\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Load morphology\"\"\"\n\n        logger.debug('Loading morphology %s', self.morphology_path)\n\n        if not os.path.exists(self.morphology_path):\n            raise IOError(\n                'Morphology not found at \\'%s\\'' %\n                self.morphology_path)\n\n        sim.neuron.h.load_file('stdrun.hoc')\n        sim.neuron.h.load_file('import3d.hoc')\n\n        extension = self.morphology_path.split('.')[-1]\n\n        if extension.lower() == 'swc':\n            imorphology = sim.neuron.h.Import3d_SWC_read()\n        elif extension.lower() == 'asc':\n            imorphology = sim.neuron.h.Import3d_Neurolucida3()\n        else:\n            raise ValueError(\"Unknown filetype: %s\" % extension)\n\n        # TODO this is to get rid of stdout print of neuron\n        # probably should be more intelligent here, and filter out the\n        # lines we don't want\n\n        imorphology.quiet = 1\n\n        if platform.system() == 'Windows':\n            sim.neuron.h.hoc_stdout('NUL')\n        else:\n            sim.neuron.h.hoc_stdout('/dev/null')\n\n        imorphology.input(str(self.morphology_path))\n        sim.neuron.h.hoc_stdout()\n\n        morphology_importer = sim.neuron.h.Import3d_GUI(imorphology, 0)\n\n        morphology_importer.instantiate(icell)\n\n        # TODO Set nseg should be called after all the parameters have been\n        # set\n        # (in case e.g. Ra was changed)\n        if self.do_set_nseg:\n            self.set_nseg(icell)\n\n        if self.do_replace_axon:\n            self.replace_axon(sim=sim, icell=icell,\n                              axon_stub_length=self.axon_stub_length,\n                              axon_nseg_frequency=self.axon_nseg_frequency)\n\n        if self.morph_modifiers is not None:\n            for morph_modifier in self.morph_modifiers:\n                morph_modifier(sim=sim, icell=icell,\n                               **self.morph_modifiers_kwargs)\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy morphology instantiation\"\"\"\n        pass\n\n    def set_nseg(self, icell):\n        \"\"\"Set the nseg of every section\"\"\"\n        for section in icell.all:\n            section.nseg = 1 + 2 * int(section.L / self.nseg_frequency)\n\n    @staticmethod\n    def replace_axon(sim=None, icell=None,\n                     axon_stub_length=60, axon_nseg_frequency=40):\n        \"\"\"Replace axon\"\"\"\n\n        nsec = len([sec for sec in icell.axonal])\n\n        if nsec == 0:\n            ais_diams = [1, 1]\n        elif nsec == 1:\n            ais_diams = [icell.axon[0].diam, icell.axon[0].diam]\n        else:\n            ais_diams = [icell.axon[0].diam, icell.axon[0].diam]\n            # Define origin of distance function\n            sim.neuron.h.distance(0, 0.5, sec=icell.soma[0])\n\n            for section in icell.axonal:\n                # If distance to soma is larger than\n                # axon_stub_length, store diameter\n                if sim.neuron.h.distance(1, 0.5, sec=section) \\\n                        > axon_stub_length:\n                    ais_diams[1] = section.diam\n                    break\n\n        for section in icell.axonal:\n            sim.neuron.h.delete_section(sec=section)\n\n        # Create new axon array\n        sim.neuron.h.execute('create axon[2]', icell)\n\n        for index, section in enumerate(icell.axon):\n            section.L = axon_stub_length / 2\n            section.nseg = 1 + 2 * int(section.L / axon_nseg_frequency)\n            section.diam = ais_diams[index]\n            icell.axonal.append(sec=section)\n            icell.all.append(sec=section)\n\n        icell.axon[0].connect(icell.soma[0], 1.0, 0.0)\n        icell.axon[1].connect(icell.axon[0], 1.0, 0.0)\n\n        logger.debug(f\"Replace axon with AIS, {axon_stub_length=}\")\n\n    default_replace_axon_hoc = \\\n        '''\nproc replace_axon(){ local nSec, D1, D2\n  // preserve the number of original axonal sections\n  nSec = sec_count(axonal)\n\n  // Try to grab info from original axon\n  if(nSec == 0) { //No axon section present\n    D1 = D2 = 1\n  } else if(nSec == 1) {\n    axon[0] D1 = D2 = diam\n  } else {\n    axon[0] D1 = D2 = diam\n    soma distance() //to calculate distance from soma\n    forsec axonal{\n      //if section is longer than 60um then store diam and exit from loop\n      if(distance(0.5) > 60){\n        D2 = diam\n        break\n      }\n    }\n  }\n\n  // get rid of the old axon\n  forsec axonal{\n    delete_section()\n  }\n\n  create axon[2]\n\n  axon[0] {\n    L = 30\n    diam = D1\n    nseg = 1 + 2*int(L/40)\n    all.append()\n    axonal.append()\n  }\n  axon[1] {\n    L = 30\n    diam = D2\n    nseg = 1 + 2*int(L/40)\n    all.append()\n    axonal.append()\n  }\n  nSecAxonal = 2\n  soma[0] connect axon[0](0), 1\n  axon[0] connect axon[1](0), 1\n}\n        '''\n\n\nclass ArbFileMorphology(Morphology, DictMixin):\n    \"\"\"Arbor morphology utilities\"\"\"\n\n    # Arbor morphology tags\n    tags = dict(\n        soma=1,\n        axon=2,\n        dend=3,\n        apic=4,\n        myelin=5\n    )\n\n    # Correspondence of BluePyOpt seclists to Arbor region labels\n    # (renaming locations according to SWC convention: using\n    # 'dend' for basal dendrite, 'apic' for apical dendrite)\n    region_labels = dict(\n        all=ArbLabel(\n            type='region', name='all', s_expr='(all)'),\n        somatic=ArbLabel(\n            type='region', name='soma', s_expr='(tag %i)' % tags['soma']),\n        axonal=ArbLabel(\n            type='region', name='axon', s_expr='(tag %i)' % tags['axon']),\n        basal=ArbLabel(\n            type='region', name='dend', s_expr='(tag %i)' % tags['dend']),\n        apical=ArbLabel(\n            type='region', name='apic', s_expr='(tag %i)' % tags['apic']),\n        myelinated=ArbLabel(\n            type='region', name='myelin', s_expr='(tag %i)' % tags['myelin']),\n    )\n\n    @staticmethod\n    def load(morpho_filename, replace_axon):\n        '''Load morphology and optionally perform axon replacement\n\n        Args:\n            morpho_filename (str): Path to file with original morphology.\n            replace_axon (): Path to/ACC string for morphology to replace\n            axon with (if not None).\n        '''\n\n        morpho_suffix = pathlib.Path(morpho_filename).suffix\n\n        if morpho_suffix == '.acc':\n            morpho = arbor.load_component(morpho_filename).component\n        elif morpho_suffix == '.swc':\n            morpho = arbor.load_swc_arbor(morpho_filename)\n            # turn loaded_morphology into morphology type\n            morpho = morpho.morphology\n        elif morpho_suffix == '.asc':\n            morpho = arbor.load_asc(morpho_filename).morphology\n        else:\n            raise RuntimeError(\n                'Unsupported morphology %s' % morpho_filename +\n                ' (only .swc and .asc supported)')\n\n        if replace_axon is not None:\n            replacement = arbor.load_component(replace_axon).component\n            morpho = ArbFileMorphology.replace_axon(morpho, replacement)\n\n        return morpho\n\n    @staticmethod\n    def extract_nrn_seclists(icell, seclists):\n        '''Extract section lists from an instantiated cell (axon replacement)\n\n        Args:\n            icell (): Instantiated cell model in the NEURON simulator.\n            seclists (): List of section lists to extract\n            (typically ['axon'] or ['axon', 'myelin']).\n        '''\n        replace_axon = arbor.segment_tree()\n        nrn_seg_to_dist = dict()\n        nrn_seg_to_arb_seg = dict()\n        for sec in seclists:\n            for section in getattr(icell, sec):\n\n                if replace_axon.size == 0:  # root\n                    arb_parent_seg = arbor.mnpos\n                else:\n                    parent_seg = section.parentseg()\n                    parent_sec = parent_seg.sec.name()\n                    parent_x = parent_seg.x\n                    parent_seg_id = bisect.bisect_left(\n                        nrn_seg_to_dist[parent_sec],\n                        parent_x)\n                    arb_parent_seg = \\\n                        nrn_seg_to_arb_seg[parent_sec][parent_seg_id]\n\n                pts3d = section.psection()['morphology']['pts3d']\n                if len(pts3d) == 0:\n                    # stylized geometry, must use sim.neuron.h.define_shape()\n                    raise ValueError('Before exporting to ACC, embed'\n                                     ' stylized geometry in 3d'\n                                     ' by instantiating morphology with'\n                                     ' cell_model.instantiate_morphology_3d.')\n\n                pts3d = numpy.array(pts3d)\n                dist_x = numpy.cumsum(\n                    numpy.linalg.norm(\n                        pts3d[1:, :3] - pts3d[:-1, :3], axis=1)) /\\\n                    section.psection()['morphology']['L']\n                relative_length_err = abs(1. - dist_x[-1])\n                if relative_length_err > 1e-4:\n                    logger.warn('pts3d length does not add up to'\n                                ' section length, relative error = %s' %\n                                relative_length_err)\n                    if relative_length_err > 1e-2:\n                        raise ValueError('pts3d length inconsistent'\n                                         ' with section length, relative'\n                                         ' error = %s' %\n                                         relative_length_err)\n\n                dist_x[-1] = 1.\n                nrn_seg_to_dist[section.name()] = dist_x\n\n                arb_seg_ids = []\n                for i in range(1, len(pts3d)):\n                    prox = pts3d[i - 1]\n                    dist = pts3d[i]\n                    arb_parent_seg = replace_axon.append(\n                        arb_parent_seg,\n                        arbor.mpoint(*prox[:3], 0.5 * prox[3]),\n                        arbor.mpoint(*dist[:3], 0.5 * dist[3]),\n                        ArbFileMorphology.tags[sec])\n                    arb_seg_ids.append(arb_parent_seg)\n                # dist, arb_seg_id pairs\n                nrn_seg_to_arb_seg[section.name()] = arb_seg_ids\n\n        replace_axon = arbor.morphology(replace_axon)\n\n        return replace_axon\n\n    @staticmethod\n    def replace_axon(morphology, replacement=None):\n        '''return a morphology with the axon replaced by another morphology\n\n        Args:\n            morphology (arbor.morphology): The original Arbor morphology\n            replacement (): An Arbor morphology to replace the axon with\n        '''\n\n        # Check if tag_roots is available\n        if not hasattr(arbor.segment_tree, 'tag_roots'):\n            raise NotImplementedError(\n                \"Need a newer version of Arbor for axon replacement.\")\n\n        # Arbor tags\n        axon_tag = ArbFileMorphology.tags['axon']\n\n        # prune morphology at axon root\n        st = morphology.to_segment_tree()\n        axon_roots = st.tag_roots(axon_tag)\n        if len(axon_roots) > 1:\n            raise ValueError(\"Axon replacement is only supported for \"\n                             \"morphologies with a single axon root.\")\n        elif len(axon_roots) == 1:\n            axon_root = axon_roots[0]\n            logger.debug('Axon replacement: splitting segment tree'\n                         ' at segment %d.', axon_root)\n            pruned_st, axon_st = st.split_at(axon_root)\n            axon_parent = st.parents[axon_root]\n        else:\n            pruned_st = st\n            axon_parent = arbor.mnpos\n\n        # join pruned segment tree and replacement at axon parent\n        axon_replacement_st = replacement.to_segment_tree()\n\n        logger.debug('Axon replacement: joining replacement onto'\n                     'pruned tree at parent segment %d.', axon_parent)\n        joined_st = pruned_st.join_at(\n            axon_parent, axon_replacement_st)\n\n        return arbor.morphology(joined_st)\n"
  },
  {
    "path": "bluepyopt/ephys/objectives.py",
    "content": "\"\"\"Objective classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport bluepyopt\n\n\nclass EFeatureObjective(bluepyopt.objectives.Objective):\n\n    \"\"\"EPhys feature objective\"\"\"\n\n    def __init__(self, name, features=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            features (list of eFeatures): features used in the Objective\n        \"\"\"\n\n        super(EFeatureObjective, self).__init__(name)\n        self.name = name\n        self.features = features\n\n    def calculate_feature_scores(self, responses):\n        \"\"\"Calculate the scores for the individual features\"\"\"\n\n        scores = []\n        for feature in self.features:\n            scores.append(feature.calculate_score(responses))\n\n        return scores\n\n    def calculate_feature_values(self, responses):\n        \"\"\"Calculate the value of an individual features\"\"\"\n\n        values = []\n        for feature in self.features:\n            values.append(feature.calculate_feature(responses))\n\n        return values\n\n\nclass SingletonObjective(EFeatureObjective):\n\n    \"\"\"Single EPhys feature\"\"\"\n\n    def __init__(self, name, feature):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            features (EFeature): single eFeature inside this objective\n        \"\"\"\n\n        super(SingletonObjective, self).__init__(name, [feature])\n\n    def calculate_score(self, responses):\n        \"\"\"Objective score\"\"\"\n\n        return self.calculate_feature_scores(responses)[0]\n\n    def calculate_value(self, responses):\n        \"\"\"Objective value\"\"\"\n\n        return self.calculate_feature_values(responses)[0]\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '( %s )' % self.features[0]\n\n\nclass SingletonWeightObjective(SingletonObjective):\n\n    \"\"\"Single EPhys feature\"\"\"\n\n    def __init__(self, name, feature, weight):\n        \"\"\"Constructor\n        Args:\n            name (str): name of this object\n            features (EFeature): single eFeature inside this objective\n            weight (float): weight to scale to the efeature with\n        \"\"\"\n\n        super(SingletonWeightObjective, self).__init__(name, feature)\n        self.weight = weight\n\n    def calculate_score(self, responses):\n        \"\"\"Objective score\"\"\"\n\n        return self.calculate_feature_scores(responses)[0] * self.weight\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '( %s ), weight:%f' % (self.features[0], self.weight)\n\n\nclass MaxObjective(EFeatureObjective):\n\n    \"\"\"Max of list of EPhys feature\"\"\"\n\n    def calculate_score(self, responses):\n        \"\"\"Objective score\"\"\"\n\n        return max(self.calculate_feature_scores(responses))\n\n\nclass WeightedSumObjective(EFeatureObjective):\n\n    \"\"\"Weighted sum of list of eFeatures\"\"\"\n\n    def __init__(self, name, features, weights):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            features (list of EFeatures): eFeatures in the objective\n            weights (list of float): weights of the eFeatures\n        \"\"\"\n\n        super(WeightedSumObjective, self).__init__(name, features)\n        if len(weights) != len(features):\n            raise Exception(\n                'WeightedSumObjective: number of weights must be equal to '\n                'number of features')\n        self.weights = weights\n\n    def calculate_score(self, responses):\n        \"\"\"Objective score\"\"\"\n\n        score = 0.0\n        feature_scores = self.calculate_feature_scores(responses)\n\n        for feature_score, weight in zip(feature_scores, self.weights):\n            score += weight * feature_score\n\n        return score\n"
  },
  {
    "path": "bluepyopt/ephys/objectivescalculators.py",
    "content": "\"\"\"Score calculator classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nclass ObjectivesCalculator(object):\n\n    \"\"\"Score calculator\"\"\"\n\n    def __init__(\n            self,\n            objectives=None):\n        \"\"\"Constructor\n\n        Args:\n            objectives (list of Objective): objectives over which to calculate\n        \"\"\"\n\n        self.objectives = objectives\n\n    def calculate_scores(self, responses):\n        \"\"\"Calculator the score for every objective\"\"\"\n\n        return {objective.name: objective.calculate_score(responses)\n                for objective in self.objectives}\n\n    def calculate_values(self, responses):\n        \"\"\"Calculator the value of each objective\"\"\"\n\n        return {objective.name: objective.calculate_value(responses)\n                for objective in self.objectives}\n\n    def __str__(self):\n        return 'objectives:\\n  %s' % '\\n  '.join(\n            [str(obj) for obj in self.objectives]) \\\n            if self.objectives is not None else 'objectives:\\n'\n"
  },
  {
    "path": "bluepyopt/ephys/parameters.py",
    "content": "\"\"\"Parameter classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nimport logging\n\nimport bluepyopt\nfrom bluepyopt.ephys.serializer import DictMixin\nfrom . import parameterscalers\n\nlogger = logging.getLogger(__name__)\n\n# TODO location and stimulus parameters should also be optimisable\n\n\nclass NrnParameter(bluepyopt.parameters.Parameter):\n\n    \"\"\"Abstract Parameter class for Neuron object parameters\"\"\"\n\n    def __init__(\n            self,\n            name,\n            value=None,\n            frozen=False,\n            bounds=None,\n            param_dependencies=None):\n        \"\"\"Constructor\"\"\"\n\n        super(NrnParameter, self).__init__(\n            name,\n            value=value,\n            frozen=frozen,\n            bounds=bounds,\n            param_dependencies=param_dependencies)\n\n    def instantiate(self, sim=None, icell=None, params=None):\n        \"\"\"Instantiate the parameter in the simulator\"\"\"\n        pass\n\n    def destroy(self, sim=None):\n        \"\"\"Remove parameter from the simulator\"\"\"\n        pass\n\n\nclass MetaParameter(NrnParameter):\n\n    \"\"\"Parameter class that controls attributes of other objects\"\"\"\n\n    def __init__(\n            self,\n            name,\n            obj=None,\n            attr_name=None,\n            value=None,\n            frozen=False,\n            bounds=None,\n            param_dependencies=None):\n        \"\"\"Constructor\"\"\"\n\n        super(MetaParameter, self).__init__(\n            name,\n            value=value,\n            frozen=frozen,\n            bounds=bounds,\n            param_dependencies=param_dependencies)\n\n        self.obj = obj\n        self.attr_name = attr_name\n        setattr(self.obj, self.attr_name, value)\n\n    @bluepyopt.parameters.Parameter.value.setter\n    def value(self, _value):\n        \"\"\"Setter for value\"\"\"\n        # Call setter of superclass\n        super(MetaParameter, self.__class__).value.fset(self, _value)\n        setattr(self.obj, self.attr_name, _value)\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n        return '%s: %s.%s = %s' % (self.name,\n                                   self.obj.name,\n                                   self.attr_name,\n                                   self.value)\n\n\nclass NrnMetaListEqualParameter(bluepyopt.parameters.MetaListEqualParameter):\n    \"\"\"Nrn version of MetaListEqualParameter, implements instantiate\"\"\"\n\n    def instantiate(self, sim=None, icell=None, params=None):\n        \"\"\"Instantiate\"\"\"\n\n        for sub_parameter in self.sub_parameters:\n            sub_parameter.instantiate(sim=sim, icell=icell)\n\n        logger.debug('Set %s to %s', self.name, str(self.value))\n\n    def destroy(self, sim=None):\n        \"\"\"Remove parameter from the simulator\"\"\"\n        for sub_parameter in self.sub_parameters:\n            sub_parameter.destroy(sim=sim)\n\n\nclass NrnGlobalParameter(NrnParameter, DictMixin):\n\n    \"\"\"Parameter set in the global namespace of neuron\"\"\"\n    SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name',)\n\n    def __init__(\n            self,\n            name,\n            value=None,\n            frozen=False,\n            bounds=None,\n            param_name=None,\n            param_dependencies=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            value (float): Value for the parameter, required if Frozen=True\n            frozen (bool): Whether the parameter can be varied, or its values\n            is permently set\n            bounds (indexable): two elements;\n                the lower and upper bounds (Optional)\n            param_name (str): name used within NEURON\n        \"\"\"\n\n        super(NrnGlobalParameter, self).__init__(\n            name,\n            value=value,\n            frozen=frozen,\n            bounds=bounds,\n            param_dependencies=param_dependencies)\n\n        self.param_name = param_name\n\n    def instantiate(self, sim=None, icell=None, params=None):\n        \"\"\"Instantiate\"\"\"\n\n        setattr(sim.neuron.h, self.param_name, self.value)\n\n        logger.debug('Set %s to %s', self.param_name, str(self.value))\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n        return '%s: %s = %s' % (self.name,\n                                self.param_name,\n                                self.value if self.frozen else self.bounds)\n\n\nclass NrnSectionParameter(NrnParameter, DictMixin):\n\n    \"\"\"Parameter of a section\"\"\"\n    SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name',\n                         'value_scaler', 'locations', 'param_dependencies')\n\n    def __init__(\n            self,\n            name,\n            value=None,\n            frozen=False,\n            bounds=None,\n            param_name=None,\n            value_scaler=None,\n            locations=None,\n            param_dependencies=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the Parameter\n            value (float): Value for the parameter, required if Frozen=True\n            frozen (bool): Whether the parameter can be varied, or its values\n            is permently set\n            bounds (indexable): two elements; the lower and upper bounds\n                (Optional)\n            param_name (str): name used within NEURON\n            value_scaler (float): value used to scale the parameter value\n            locations (list of ephys.locations.Location): locations on which\n                to instantiate the parameter\n            param_dependencies (list): dependencies needed to instantiate\n                the parameter\n        \"\"\"\n\n        super(NrnSectionParameter, self).__init__(\n            name,\n            value=value,\n            frozen=frozen,\n            bounds=bounds,\n            param_dependencies=param_dependencies)\n\n        self.locations = locations\n        self.param_name = param_name\n        # TODO value_scaler has to be made more general\n        self.value_scaler = value_scaler\n        # TODO add a default value for a scaler that is picklable\n        if self.value_scaler is None:\n            self.value_scaler = parameterscalers.NrnSegmentLinearScaler()\n        self.value_scale_func = self.value_scaler.scale\n\n    def instantiate(self, sim=None, icell=None, params=None):\n        \"\"\"Instantiate\"\"\"\n        if self.value is None:\n            raise Exception(\n                'NrnSectionParameter: impossible to instantiate parameter \"%s\"'\n                ' without value' %\n                self.name)\n\n        _values = {\"value\": self.value}\n        for param in self.param_dependencies:\n            _values[param] = params[param].value\n\n        for location in self.locations:\n            iseclist = location.instantiate(sim=sim, icell=icell)\n            for section in iseclist:\n                setattr(section, self.param_name,\n                        self.value_scale_func(_values, section, sim=sim))\n            logger.debug(\n                'Set %s in %s to %s',\n                self.param_name,\n                location,\n                self.value)\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n        return '%s: %s %s = %s' % (self.name,\n                                   [str(location)\n                                    for location in self.locations],\n                                   self.param_name,\n                                   self.value if self.frozen else self.bounds)\n\n\nclass NrnPointProcessParameter(NrnParameter, DictMixin):\n\n    \"\"\"Parameter of a section\"\"\"\n    SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name',\n                         'value_scaler', 'locations', 'param_name',\n                         'param_dependencies')\n\n    def __init__(\n            self,\n            name,\n            value=None,\n            frozen=False,\n            bounds=None,\n            locations=None,\n            param_name=None,\n            param_dependencies=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the Parameter\n            value (float): Value for the parameter, required if Frozen=True\n            frozen (bool): Whether the parameter can be varied, or its values\n            is permently set\n            bounds (indexable): two elements; the lower and upper bounds\n                (Optional)\n            locations: an iterator of the point process locations you want to\n                       set the parameters of\n            param_name (str): name of parameter used within the point process\n            param_dependencies (list): dependencies needed to intantiate\n                the parameter\n        \"\"\"\n\n        super(NrnPointProcessParameter, self).__init__(\n            name,\n            value=value,\n            frozen=frozen,\n            bounds=bounds,\n            param_dependencies=param_dependencies)\n\n        self.locations = locations\n        self.param_name = param_name\n\n    def instantiate(self, sim=None, icell=None, params=None):\n        \"\"\"Instantiate\"\"\"\n        if self.value is None:\n            raise Exception(\n                'NrnSectionParameter: impossible to instantiate parameter \"%s\"'\n                ' without value' %\n                self.name)\n\n        for location in self.locations:\n            for pprocess in location.instantiate(sim=sim, icell=icell):\n                setattr(pprocess, self.param_name, self.value)\n                logger.debug(\n                    'Set %s to %s for point process',\n                    self.param_name,\n                    self.value)\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n        return '%s: %s = %s' % (self.name,\n                                self.param_name,\n                                self.value if self.frozen else self.bounds)\n\n# TODO change mech_suffix and mech_param to param_name, and maybe add\n# NrnRangeMechParameter\n\n\nclass NrnRangeParameter(NrnParameter, DictMixin):\n\n    \"\"\"Parameter that has a range over a section\"\"\"\n    SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name',\n                         'value_scaler', 'locations', 'param_dependencies')\n\n    def __init__(\n            self,\n            name,\n            value=None,\n            frozen=False,\n            bounds=None,\n            param_name=None,\n            value_scaler=None,\n            locations=None,\n            param_dependencies=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the Parameter\n            value (float): Value for the parameter, required if Frozen=True\n            frozen (bool): Whether the parameter can be varied, or its values\n            is permently set\n            bounds (indexable): two elements; the lower and upper bounds\n                (Optional)\n            param_name (str): name used within NEURON\n            value_scaler (float): value used to scale the parameter value\n            locations (list of ephys.locations.Location): locations on which\n                to instantiate the parameter\n            param_dependencies (list): dependencies needed to intantiate\n                the parameter\n        \"\"\"\n\n        super(NrnRangeParameter, self).__init__(\n            name,\n            value=value,\n            frozen=frozen,\n            bounds=bounds,\n            param_dependencies=param_dependencies)\n\n        self.locations = locations\n        self.param_name = param_name\n        # TODO value_scaler has to be made more general\n        self.value_scaler = value_scaler\n        if self.value_scaler is None:\n            self.value_scaler = parameterscalers.NrnSegmentLinearScaler()\n        self.value_scale_func = self.value_scaler.scale\n\n    def instantiate(self, sim=None, icell=None, params=None):\n        \"\"\"Instantiate\"\"\"\n        if self.value is None:\n            raise Exception(\n                'NrnRangeParameter: impossible to instantiate parameter \"%s\" '\n                'without value' % self.name)\n\n        _values = {\"value\": self.value}\n        for param in self.param_dependencies:\n            _values[param] = params[param].value\n\n        for location in self.locations:\n            for isection in location.instantiate(sim=sim, icell=icell):\n                for seg in isection:\n                    setattr(seg, '%s' % self.param_name,\n                            self.value_scale_func(_values, seg, sim=sim))\n        logger.debug(\n            'Set %s in %s to %s with scaler %s', self.param_name,\n            [str(location)\n             for location in self.locations],\n            self.value,\n            self.value_scaler)\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n        return '%s: %s %s = %s' % (self.name,\n                                   [str(location)\n                                    for location in self.locations],\n                                   self.param_name,\n                                   self.value if self.frozen else self.bounds)\n"
  },
  {
    "path": "bluepyopt/ephys/parameterscalers/__init__.py",
    "content": "from .parameterscalers import *\n"
  },
  {
    "path": "bluepyopt/ephys/parameterscalers/acc_iexpr.py",
    "content": "\"\"\"Translate spatially varying parameter-scaler expressions to Arbor iexprs\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport ast\n\n\n# Utilities to generate Arbor S-expressions for morphologically\n# inhomogeneous parameter scalers\nclass ArbIExprValueEliminator(ast.NodeTransformer):\n    \"\"\"Divide expression (symbolically) by named variable and replace\n    non-linear occurrences by numeric value\"\"\"\n    def __init__(self, variable_name, value):\n        self._stack = []\n        self._nodes_to_remove = []\n        self._remove_count = 0\n        self._variable_name = variable_name\n        self._value = value\n\n    def generic_visit(self, node):\n        self._stack.append(node)  # keep track of visitor stack\n\n        node = super(ArbIExprValueEliminator, self).generic_visit(node)\n\n        nodes_removed = []\n        for node_to_remove in self._nodes_to_remove:\n            if node_to_remove in ast.iter_child_nodes(node):\n                # replace this node and remove child\n                node = node.left if node.right == node_to_remove \\\n                    else node.right\n                nodes_removed.append(node_to_remove)\n                self._remove_count += 1\n                if self._remove_count > 1:\n                    raise ValueError(\n                        'Unsupported inhomogeneous expression in Arbor'\n                        ' - must be linear in the parameter value.')\n        self._nodes_to_remove = [n for n in self._nodes_to_remove\n                                 if n not in nodes_removed]\n\n        self._stack.pop()\n\n        # top-level expression node that is non-linear in the value\n        if len(self._stack) == 2 and self._remove_count == 0:\n            return ast.BinOp(left=node, op=ast.Div(),\n                             right=ast.Constant(value=self._value))\n        else:\n            return node\n\n    def _is_linear(self, node):\n        \"\"\"Check if expression is linear in this node\"\"\"\n        prev_frame = node\n        for next_frame in reversed(self._stack[2:]):\n            if not isinstance(next_frame, ast.BinOp) or \\\n                not (isinstance(next_frame.op, ast.Mult) or\n                     isinstance(next_frame.op, ast.Div) and\n                     next_frame.left == prev_frame):\n                return False\n            prev_frame = next_frame\n        return True\n\n    def visit_Name(self, node):\n        if node.id == self._variable_name:\n            # remove if expression is linear in value, else replace by constant\n            if self._is_linear(node) and \\\n                    self._remove_count + len(self._nodes_to_remove) == 0:\n                self._nodes_to_remove.append(node)\n                return node\n            else:\n                return ast.Constant(value=self._value)\n        else:\n            return node\n\n\nclass ArbIExprEmitter(ast.NodeVisitor):\n    \"\"\"Emit Arbor S-expression from parse tree\n    replacing named variables by specified S-expression\"\"\"\n\n    _iexpr_symbols = {\n        ast.Constant: 'scalar',\n        ast.Num: 'scalar',\n        ast.Add: 'add',\n        ast.Sub: 'sub',\n        ast.Mult: 'mul',\n        ast.Div: 'div',\n        'math.pi': 'pi',\n        'math.exp': 'exp',\n        'math.log': 'log',\n    }\n\n    def __init__(self, var_name_to_sexpr, constant_formatter):\n        self._base_stack = []\n        self._emitted = []\n        self._var_name_to_sexpr = var_name_to_sexpr\n        self._constant_formatter = constant_formatter\n\n    def emit(self):\n        return ' '.join(self._emitted)\n\n    def _emit(self, expr):\n        return self._emitted.append(expr)\n\n    def generic_visit(self, node):\n        self._base_stack.append(node)\n\n        # fail if more than base stack\n        if len(self._base_stack) > 2:\n            raise ValueError('Arbor inhomogeneous expression generation'\n                             ' failed: Unsupported node %s' % repr(node))\n\n        ret = super(ArbIExprEmitter, self).generic_visit(node)\n        self._base_stack.pop()\n        return ret\n\n    def visit_Constant(self, node):\n        self._emit(\n            '(%s %s)' % (self._iexpr_symbols[type(node)],\n                         self._constant_formatter(node.value))\n        )\n\n    def visit_Num(self, node):\n        self._emit(\n            '(%s %s)' % (self._iexpr_symbols[type(node)],\n                         self._constant_formatter(node.n))\n        )\n\n    def visit_Attribute(self, node):\n        if node.value.id == 'math' and node.attr == 'pi':\n            self._emit(\n                '(%s)' % self._iexpr_symbols['math.pi']\n            )\n        else:\n            raise ValueError('Unsupported attribute %s in Arbor'\n                             % node)\n\n    def visit_UnaryOp(self, node):\n        if isinstance(node.op, ast.UAdd):\n            self.visit(node.value)\n        elif isinstance(node.op, ast.USub):\n            if isinstance(node.operand, ast.Constant):\n                self.visit(ast.Constant(-node.operand.value))\n            else:\n                self.visit(ast.BinOp(left=ast.Constant(-1),\n                                     op=ast.Mult(),\n                                     right=node.operand))\n        else:\n            raise ValueError('Unsupported unary operation %s in Arbor'\n                             % node.op)\n\n    def visit_BinOp(self, node):\n        op_type = type(node.op)\n        if op_type not in self._iexpr_symbols:\n            raise ValueError('Unsupported binary operation %s in Arbor'\n                             % op_type)\n        self._emit(\n            '(' + self._iexpr_symbols[type(node.op)]\n        )\n        self.visit(node.left),\n        self.visit(node.right)\n        self._emit(\n            ')'\n        )\n\n    def visit_Call(self, node):\n        func = node.func\n        if isinstance(func, ast.Attribute):\n            if isinstance(func.value, ast.Name):\n                if func.value.id == 'math':\n                    if len(node.args) > 1:\n                        raise ValueError('Arbor iexpr generation failed -'\n                                         ' math functions can only have a'\n                                         ' single argument.')\n                    func_symbol = func.value.id + '.' + func.attr\n                    if func_symbol not in self._iexpr_symbols:\n                        raise ValueError('Arbor iexpr generation failed -'\n                                         ' unknown symbol %s.' % func_symbol)\n                    self._emit(\n                        '(' + self._iexpr_symbols[func_symbol]\n                    )\n                    self.visit(node.args[0])\n                    self._emit(\n                        ')'\n                    )\n                else:\n                    raise ValueError('Arbor iexpr generation failed -'\n                                     ' unsupported module %s.' % func.value.id)\n            else:\n                raise ValueError('Arbor iexpr generation failed -'\n                                 ' unsupported attribute %s.' %\n                                 func.value.attr)\n        else:\n            raise ValueError('Arbor iexpr generation failed -'\n                             ' unsupported function %s.' % func.id)\n\n    def visit_Name(self, node):\n        if node.id in self._var_name_to_sexpr:\n            self._emit(\n                self._var_name_to_sexpr[node.id]\n            )\n        else:\n            raise ValueError('Arb iexpr generation failed:'\n                             ' No valid substitution for %s.' % node.id)\n\n\ndef generate_acc_scale_iexpr(iexpr, variables, constant_formatter):\n    \"\"\"Translate parameter-scaler python arithmetic expression to Arbor iexpr\n\n    Args:\n        iexpr (str): Python arithmetic expression (instantiated distribution)\n        variables (): Mapping of variable name (referenced in the iexpr\n        argument) to Arbor iexpr representation\n\n    Returns:\n        The Arbor iexpr corresponding to the python arithmetic expression\n        with the variables substituted by their value.\n    \"\"\"\n\n    if 'value' not in variables:\n        raise ValueError('Arbor iexpr generation failed for %s:' % iexpr +\n                         ' \\'value\\' not in variables dict: %s' % variables)\n\n    emit_dict = {'_arb_parse_iexpr_' + k: v\n                 for k, v in variables.items()}\n\n    scaler_expr = iexpr.format(\n        **{k: '_arb_parse_iexpr_' + k for k in variables})\n\n    # Parse expression\n    scaler_ast = ast.parse(scaler_expr)\n    # Turn into scaling expression, replacing non-linear occurrences of value\n    value_eliminator = ArbIExprValueEliminator(\n        variable_name='_arb_parse_iexpr_value',\n        value=variables['value'])\n    scaler_ast = value_eliminator.visit(scaler_ast)\n\n    # Generate S-expression\n    iexpr_emitter = ArbIExprEmitter(\n        var_name_to_sexpr=emit_dict,\n        constant_formatter=constant_formatter)\n\n    iexpr_emitter.visit(scaler_ast)\n    return iexpr_emitter.emit()\n"
  },
  {
    "path": "bluepyopt/ephys/parameterscalers/parameterscalers.py",
    "content": "\"\"\"Parameter scaler classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nimport string\n\nfrom bluepyopt.ephys.base import BaseEPhys\nfrom bluepyopt.ephys.parameterscalers.acc_iexpr import generate_acc_scale_iexpr\nfrom bluepyopt.ephys.serializer import DictMixin\nfrom bluepyopt.ephys.morphologies import ArbFileMorphology\n\nFLOAT_FORMAT = '%.17g'\n\n\ndef format_float(value):\n    \"\"\"Return formatted float string\"\"\"\n    return FLOAT_FORMAT % value\n\n\nclass MissingFormatDict(dict):\n\n    \"\"\"Extend dict for string formatting with missing values\"\"\"\n\n    def __missing__(self, key):  # pylint: disable=R0201\n        \"\"\"Return string with format key for missing keys\"\"\"\n        return '{' + key + '}'\n\n\nclass ParameterScaler(BaseEPhys):\n\n    \"\"\"Parameter scalers\"\"\"\n    pass\n\n# TODO get rid of the 'segment' here\n\n\nclass NrnSegmentLinearScaler(ParameterScaler, DictMixin):\n\n    \"\"\"Linear scaler\"\"\"\n    SERIALIZED_FIELDS = ('name', 'comment', 'multiplier', 'offset', )\n\n    def __init__(\n            self,\n            name=None,\n            multiplier=1.0,\n            offset=0.0,\n            comment=''):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            multiplier (float): slope of the linear scaler\n            offset (float): intercept of the linear scaler\n        \"\"\"\n\n        super(NrnSegmentLinearScaler, self).__init__(name, comment)\n        self.multiplier = multiplier\n        self.offset = offset\n\n    def scale(self, values, segment=None, sim=None):  # pylint: disable=W0613\n        \"\"\"Scale a value based on a segment\"\"\"\n        if isinstance(values, dict):\n            value = values[\"value\"]\n        else:\n            value = values\n        return self.multiplier * value + self.offset\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%s * value + %s' % (self.multiplier, self.offset)\n\n\nclass NrnSegmentSectionDistanceScaler(ParameterScaler, DictMixin):\n\n    \"\"\"Scaler based on distance from soma\"\"\"\n    SERIALIZED_FIELDS = ('name', 'comment', 'distribution',\n                         \"distribution\", \"dist_param_names\",\n                         \"ref_sec\", \"ref_location\",)\n\n    def __init__(\n            self,\n            name=None,\n            distribution=None,\n            comment='',\n            dist_param_names=None,\n            ref_section='soma[0]',\n            ref_location=0,):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            distribution (str): distribution of parameter dependent on distance\n                from soma. string can contain `distance` and/or `value` as\n                placeholders for the distance to the soma and parameter value\n                respectivily\n            dist_param_names (list): list of names of parameters that\n                parametrise the distribution. These names will become\n                attributes of this object.\n                The distribution string should contain these names, and they\n                will be replaced by values of the corresponding attributes\n            ref_section (str): string with name of reference section to\n                compute distance (e.g. \"soma[0]\", \"dend[2]\")\n            ref_location (float): location along the soma used as origin\n                from which to compute the distances. Expressed as a fraction\n                (between 0.0 and 1.0).\n        \"\"\"\n\n        super(NrnSegmentSectionDistanceScaler, self).__init__(name, comment)\n        self.distribution = distribution\n\n        self.dist_param_names = dist_param_names\n        self.ref_location = ref_location\n        self.ref_section = ref_section\n\n        if not (0.0 <= self.ref_location <= 1.0):\n            raise ValueError(\"ref_location must be between 0 and 1.\")\n\n        if self.dist_param_names is not None:\n            for dist_param_name in self.dist_param_names:\n                if dist_param_name not in self.distribution:\n                    raise ValueError(\n                        'NrnSegmentSectionDistanceScaler: \"{%s}\" '\n                        'missing from distribution string \"%s\"' %\n                        (dist_param_name, distribution))\n                setattr(self, dist_param_name, None)\n\n    @property\n    def inst_distribution(self):\n        \"\"\"The instantiated distribution\"\"\"\n\n        dist_dict = MissingFormatDict()\n\n        if self.dist_param_names is not None:\n            for dist_param_name in self.dist_param_names:\n                dist_param_value = getattr(self, dist_param_name)\n                if dist_param_value is None:\n                    raise ValueError(\"NrnSegmentSomaDistanceScaler: %s \"\n                                     \"was uninitialised\" % dist_param_name)\n                dist_dict[dist_param_name] = dist_param_value\n\n        # Use this special formatting to bypass missing keys\n        return string.Formatter().vformat(self.distribution, (), dist_dict)\n\n    def scale_dict(self, values, distance):\n        \"\"\"Create scale dictionary\"\"\"\n        scale_dict = {}\n        if isinstance(values, dict):\n            for k, v in values.items():\n                scale_dict[k] = format_float(v)\n        else:\n            scale_dict[\"value\"] = format_float(values)\n        scale_dict[\"distance\"] = format_float(distance)\n\n        return scale_dict\n\n    def eval_dist(self, values, distance):\n        \"\"\"Create the final dist string\"\"\"\n        scale_dict = self.scale_dict(values, distance)\n\n        return self.inst_distribution.format(**scale_dict)\n\n    def scale(self, values, segment, sim=None):\n        \"\"\"Scale a value based on a segment\"\"\"\n\n        # find section\n        target_sec = None\n        for sec in segment.sec.wholetree():\n            if \".\" in sec.name():  # deal with templates\n                sec_name = sec.name().split(\".\")[1]\n            else:\n                sec_name = sec.name()\n            if self.ref_section in sec_name:\n                target_sec = sec\n                break\n\n        if target_sec is None:\n            raise Exception(f\"Could not find section {self.ref_section} \"\n                            f\"in section list\")\n\n        # Initialise origin\n        sim.neuron.h.distance(0, self.ref_location, sec=target_sec)\n\n        distance = sim.neuron.h.distance(1, segment.x, sec=segment.sec)\n\n        # Find something to generalise this\n        import math  # pylint:disable=W0611 #NOQA\n\n        # This eval is unsafe (but is it ever dangerous ?)\n        # pylint: disable=W0123\n        return eval(self.eval_dist(values, distance))\n\n    def acc_scale_iexpr(self, value, constant_formatter=format_float):\n        \"\"\"Generate Arbor scale iexpr for a given value\"\"\"\n        raise ValueError(\n            \"Parameter scaling based on general Neuron segment/section\"\n            \" distances not supported in Arbor.\")\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return self.distribution\n\n\nclass NrnSegmentSomaDistanceScaler(NrnSegmentSectionDistanceScaler,\n                                   ParameterScaler, DictMixin):\n\n    \"\"\"Scaler based on distance from soma\"\"\"\n    SERIALIZED_FIELDS = ('name', 'comment', 'distribution', )\n\n    def __init__(\n            self,\n            name=None,\n            distribution=None,\n            comment='',\n            dist_param_names=None,\n            soma_ref_location=0.5):\n        \"\"\"Constructor\n        Args:\n            name (str): name of this object\n            distribution (str): distribution of parameter dependent on distance\n                from soma. string can contain `distance` and/or `value` as\n                placeholders for the distance to the soma and parameter value\n                respectivily\n            dist_param_names (list): list of names of parameters that\n                parametrise the distribution. These names will become\n                attributes of this object.\n                The distribution string should contain these names, and they\n                will be replaced by values of the corresponding attributes\n            soma_ref_location (float): location along the soma used as origin\n                from which to compute the distances. Expressed as a fraction\n                (between 0.0 and 1.0).\n        \"\"\"\n\n        super(NrnSegmentSomaDistanceScaler, self).__init__(\n            name, distribution, comment, dist_param_names,\n            ref_section='soma[0]', ref_location=soma_ref_location)\n\n    def acc_scale_iexpr(self, value, constant_formatter=format_float):\n        \"\"\"Generate Arbor scale iexpr for a given value\"\"\"\n\n        iexpr = self.inst_distribution\n\n        variables = dict(\n            value=value,\n            distance='(distance %s)' %  # could be a ctor param if required\n            ArbFileMorphology.region_labels['somatic'].ref\n        )\n        return generate_acc_scale_iexpr(iexpr, variables, constant_formatter)\n\n\nclass NrnSegmentSomaDistanceStepScaler(NrnSegmentSomaDistanceScaler,\n                                       ParameterScaler, DictMixin):\n\n    \"\"\"Scaler based on distance from soma with a step function\"\"\"\n    SERIALIZED_FIELDS = ('name', 'comment', 'distribution', )\n\n    def __init__(\n            self,\n            name=None,\n            distribution=None,\n            comment='',\n            dist_param_names=None,\n            soma_ref_location=0.5,\n            step_begin=None,\n            step_end=None):\n        \"\"\"Constructor\n        Args:\n            name (str): name of this object\n            distribution (str): distribution of parameter dependent on distance\n                from soma. string can contain `distance` and/or `value` as\n                placeholders for the distance to the soma and parameter value\n                respectively. It can also contain step_begin and step_end.\n            dist_param_names (list): list of names of parameters that\n                parametrise the distribution. These names will become\n                attributes of this object.\n                The distribution string should contain these names, and they\n                will be replaced by values of the corresponding attributes\n            soma_ref_location (float): location along the soma used as origin\n                from which to compute the distances. Expressed as a fraction\n                (between 0.0 and 1.0).\n            step_begin (float): distance at which the step begins\n            step_end (float): distance at which the step ends\n        \"\"\"\n\n        super(NrnSegmentSomaDistanceStepScaler, self).__init__(\n            name, distribution, comment, dist_param_names,\n            soma_ref_location=soma_ref_location)\n        self.step_begin = step_begin\n        self.step_end = step_end\n\n    def scale_dict(self, values, distance):\n        scale_dict = super().scale_dict(values, distance)\n        scale_dict[\"step_begin\"] = self.step_begin\n        scale_dict[\"step_end\"] = self.step_end\n\n        return scale_dict\n"
  },
  {
    "path": "bluepyopt/ephys/protocols.py",
    "content": "\"\"\"Protocol classes\"\"\"\nfrom .recordings import LFPRecording\nfrom .simulators import LFPySimulator\nfrom .stimuli import LFPStimulus\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nimport os\nimport sys\nimport collections\nimport tempfile\n\n# TODO: maybe find a better name ? -> sweep ?\nimport logging\nlogger = logging.getLogger(__name__)\n\nfrom . import models\nfrom . import locations\nfrom . import simulators\nfrom . import stimuli\nfrom .responses import TimeVoltageResponse\nfrom .acc import arbor\nfrom . import create_acc\n\n\nclass Protocol(object):\n\n    \"\"\"Class representing a protocol (stimulus and recording).\"\"\"\n\n    def __init__(self, name=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the feature\n        \"\"\"\n\n        self.name = name\n\n\nclass SequenceProtocol(Protocol):\n\n    \"\"\"A protocol consisting of a sequence of other protocols\"\"\"\n\n    def __init__(self, name=None, protocols=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            protocols (list of Protocols): subprotocols this protocol\n                consists of\n        \"\"\"\n        super(SequenceProtocol, self).__init__(name)\n        self.protocols = protocols\n\n    def run(\n            self,\n            cell_model,\n            param_values,\n            sim=None,\n            isolate=None,\n            timeout=None):\n        \"\"\"Instantiate protocol\"\"\"\n\n        responses = collections.OrderedDict({})\n\n        for protocol in self.protocols:\n\n            # Try/except added for backward compatibility\n            try:\n                response = protocol.run(\n                    cell_model=cell_model,\n                    param_values=param_values,\n                    sim=sim,\n                    isolate=isolate,\n                    timeout=timeout)\n            except TypeError as e:\n                if \"unexpected keyword\" in str(e):\n                    response = protocol.run(\n                        cell_model=cell_model,\n                        param_values=param_values,\n                        sim=sim,\n                        isolate=isolate)\n                else:\n                    raise\n\n            key_intersect = set(\n                response.keys()).intersection(set(responses.keys()))\n            if len(key_intersect) != 0:\n                raise Exception(\n                    'SequenceProtocol: one of the protocols (%s) is trying to '\n                    'add already existing keys to the response: %s' %\n                    (protocol.name, key_intersect))\n\n            responses.update(response)\n\n        return responses\n\n    def subprotocols(self):\n        \"\"\"Return subprotocols\"\"\"\n\n        subprotocols = collections.OrderedDict({self.name: self})\n\n        for protocol in self.protocols:\n            subprotocols.update(protocol.subprotocols())\n\n        return subprotocols\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        content = 'Sequence protocol %s:\\n' % self.name\n\n        content += '%d subprotocols:\\n' % len(self.protocols)\n        for protocol in self.protocols:\n            content += '%s\\n' % str(protocol)\n\n        return content\n\n\nclass SweepProtocol(Protocol):\n\n    \"\"\"Sweep protocol\"\"\"\n\n    def __init__(\n            self,\n            name=None,\n            stimuli=None,\n            recordings=None,\n            cvode_active=None,\n            deterministic=False):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            stimuli (list of Stimuli): Stimulus objects used in the protocol\n            recordings (list of Recordings): Recording objects used in the\n                protocol\n            cvode_active (bool): whether to use variable time step\n            deterministic (bool): whether to force all mechanism\n                to be deterministic\n        \"\"\"\n\n        super(SweepProtocol, self).__init__(name)\n        self.stimuli = stimuli\n        self.recordings = recordings\n        self.cvode_active = cvode_active\n        self.deterministic = deterministic\n\n    @property\n    def total_duration(self):\n        \"\"\"Total duration\"\"\"\n\n        return max([stimulus.total_duration for stimulus in self.stimuli])\n\n    def subprotocols(self):\n        \"\"\"Return subprotocols\"\"\"\n\n        return collections.OrderedDict({self.name: self})\n\n    def adjust_stochasticity(func):\n        \"\"\"Decorator method to adjust the stochasticity of the mechanisms\"\"\"\n        def inner(self, cell_model, param_values, **kwargs):\n            \"\"\"Inner function\"\"\"\n            previous_stoch_state = []\n            if self.deterministic:\n                for mech in cell_model.mechanisms:\n                    previous_stoch_state.append(mech.deterministic)\n                    mech.deterministic = True\n\n            responses = func(self, cell_model, param_values, **kwargs)\n\n            if self.deterministic:\n                for i, mech in enumerate(cell_model.mechanisms):\n                    mech.deterministic = previous_stoch_state[i]\n\n            return responses\n\n        return inner\n\n    def _run_func(self, cell_model, param_values, sim=None):\n        \"\"\"Run protocols\"\"\"\n\n        try:\n            cell_model.freeze(param_values)\n            cell_model.instantiate(sim=sim)\n\n            self.instantiate(sim=sim, cell_model=cell_model)\n\n            try:\n                if isinstance(sim, LFPySimulator):\n                    sim.run(\n                        lfpy_cell=cell_model.lfpy_cell,\n                        lfpy_electrode=cell_model.lfpy_electrode,\n                        tstop=self.total_duration,\n                        cvode_active=self.cvode_active)\n                else:\n                    sim.run(\n                        self.total_duration, cvode_active=self.cvode_active\n                    )\n            except (RuntimeError, simulators.NrnSimulatorException):\n                logger.debug(\n                    'SweepProtocol: Running of parameter set {%s} generated '\n                    'an exception, returning None in responses',\n                    str(param_values))\n                responses = {recording.name:\n                             None for recording in self.recordings}\n            else:\n                responses = {\n                    recording.name: recording.response\n                    for recording in self.recordings}\n\n            self.destroy(sim=sim)\n\n            cell_model.destroy(sim=sim)\n\n            cell_model.unfreeze(param_values.keys())\n\n            return responses\n        except BaseException as e:\n            raise SweepProtocolException(\n                'Failed to run Neuron Sweep Protocol') from e\n\n    @adjust_stochasticity\n    def run(\n            self,\n            cell_model,\n            param_values,\n            sim=None,\n            isolate=None,\n            timeout=None):\n        \"\"\"Instantiate protocol\"\"\"\n\n        if isolate is None:\n            isolate = True\n\n        if isolate:\n            def _reduce_method(meth):\n                \"\"\"Overwrite reduce\"\"\"\n                return (getattr, (meth.__self__, meth.__func__.__name__))\n\n            import copyreg\n            import types\n            copyreg.pickle(types.MethodType, _reduce_method)\n            import pebble\n            from concurrent.futures import TimeoutError\n            import multiprocessing\n\n            # Default context for python>=3.8 on macos is spawn.\n            # Spwan context would reset NEURON properties, such as dt.\n            if sys.platform == 'win32':\n                multiprocessing_context = multiprocessing.get_context('spawn')\n                if (\n                    sim is not None and not sim.cvode_active and\n                    sim.dt != 0.025\n                ):\n                    logger.warning(\"On Windows, evaluation might break when\"\n                                   \"using non-default fixed time steps.\")\n            else:\n                multiprocessing_context = multiprocessing.get_context('fork')\n\n            if timeout is not None:\n                if timeout < 0:\n                    raise ValueError(\"timeout should be > 0\")\n            with pebble.ProcessPool(\n                max_workers=1,\n                max_tasks=1,\n                context=multiprocessing_context\n            ) as pool:\n                tasks = pool.schedule(self._run_func, kwargs={\n                    'cell_model': cell_model,\n                    'param_values': param_values,\n                    'sim': sim},\n                    timeout=timeout)\n                try:\n                    responses = tasks.result()\n                except TimeoutError:\n                    logger.debug('SweepProtocol: task took longer than '\n                                 'timeout, will return empty response '\n                                 'for this recording')\n                    responses = {recording.name:\n                                 None for recording in self.recordings}\n        else:\n            responses = self._run_func(cell_model=cell_model,\n                                       param_values=param_values,\n                                       sim=sim)\n        return responses\n\n    def instantiate(self, sim=None, cell_model=None):\n        \"\"\"Instantiate\"\"\"\n\n        for stimulus in self.stimuli:\n            if isinstance(stimulus, LFPStimulus):\n                stimulus.instantiate(sim=sim, lfpy_cell=cell_model.lfpy_cell)\n            else:\n                stimulus.instantiate(sim=sim, icell=cell_model.icell)\n\n        for recording in self.recordings:\n            try:\n                if isinstance(recording, LFPRecording):\n                    recording.instantiate(sim=sim,\n                                          lfpy_cell=cell_model.lfpy_cell,\n                                          electrode=cell_model.lfpy_electrode)\n                else:\n                    recording.instantiate(sim=sim, icell=cell_model.icell)\n            except locations.EPhysLocInstantiateException:\n                logger.debug(\n                    'SweepProtocol: Instantiating recording generated '\n                    'location exception, will return empty response for '\n                    'this recording')\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy protocol\"\"\"\n\n        for stimulus in self.stimuli:\n            stimulus.destroy(sim=sim)\n\n        for recording in self.recordings:\n            recording.destroy(sim=sim)\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        content = '%s:\\n' % self.name\n\n        content += '  stimuli:\\n'\n        for stimulus in self.stimuli:\n            content += '    %s\\n' % str(stimulus)\n\n        content += '  recordings:\\n'\n        for recording in self.recordings:\n            content += '    %s\\n' % str(recording)\n\n        return content\n\n\nclass StepProtocol(SweepProtocol):\n\n    \"\"\"Protocol consisting of step and holding current\"\"\"\n\n    def __init__(\n            self,\n            name=None,\n            step_stimulus=None,\n            holding_stimulus=None,\n            recordings=None,\n            cvode_active=None,\n            deterministic=False):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            step_stimulus (list of Stimuli): Stimulus objects used in protocol\n            recordings (list of Recordings): Recording objects used in the\n                protocol\n            cvode_active (bool): whether to use variable time step\n            deterministic (bool): whether to force all mechanism\n                to be deterministic\n        \"\"\"\n\n        super(StepProtocol, self).__init__(\n            name,\n            stimuli=[\n                step_stimulus,\n                holding_stimulus]\n            if holding_stimulus is not None else [step_stimulus],\n            recordings=recordings,\n            cvode_active=cvode_active)\n\n        self.step_stimulus = step_stimulus\n        self.holding_stimulus = holding_stimulus\n\n    @property\n    def step_delay(self):\n        \"\"\"Time stimulus starts\"\"\"\n        return self.step_stimulus.step_delay\n\n    @property\n    def step_duration(self):\n        \"\"\"Time stimulus starts\"\"\"\n        return self.step_stimulus.step_duration\n\n\nclass ArbSweepProtocol(Protocol):\n\n    \"\"\"Arbor Sweep protocol\"\"\"\n\n    def __init__(\n            self,\n            name=None,\n            stimuli=None,\n            recordings=None,\n            use_labels=False):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            stimuli (list of Stimuli): Stimulus objects used in the protocol\n            recordings (list of Recordings): Recording objects used in the\n                protocol\n            use_labels (bool): Add stimuli/recording locations to label dict\n        \"\"\"\n\n        super(ArbSweepProtocol, self).__init__(name)\n        self.stimuli = stimuli\n        self.recordings = recordings\n        self.use_labels = use_labels\n\n    @property\n    def total_duration(self):\n        \"\"\"Total duration\"\"\"\n\n        return max([stimulus.total_duration for stimulus in self.stimuli])\n\n    def subprotocols(self):\n        \"\"\"Return subprotocols\"\"\"\n\n        return collections.OrderedDict({self.name: self})\n\n    def _run_func(self, cell_json, param_values, sim=None):\n        \"\"\"Run protocols\"\"\"\n\n        try:\n            # Loading cell constituents from ACC\n            cell_json, morph, decor, labels = \\\n                create_acc.read_acc(cell_json)\n\n            # Locations of stimuli and recordings can be instantiated\n            # as labels (useful for visualization in Arbor GUI)\n            if self.use_labels:\n                labels = self.instantiate_locations(labels)\n\n            # Adding stimuli to decor (could also be written/loaded from ACC)\n            decor = self.instantiate_iclamp_stimuli(\n                decor,\n                use_labels=self.use_labels)\n\n            arb_cell_model = sim.instantiate(morph, decor, labels)\n\n            # Adding synaptic stimuli to cell model (no representation in ACC)\n            arb_cell_model = self.instantiate_synaptic_stimuli(\n                arb_cell_model,\n                use_labels=self.use_labels)\n\n            # Adding recordings to cell model (no representation in ACC)\n            arb_cell_model = self.instantiate_recordings(\n                arb_cell_model,\n                use_labels=self.use_labels)\n\n            try:\n                sim.run(arb_cell_model, tstop=self.total_duration)\n            except (RuntimeError, simulators.ArbSimulatorException):\n                logger.debug(\n                    'ArbSweepProtocol: Running of parameter set {%s} '\n                    'generated an exception, returning None in responses',\n                    str(param_values))\n                responses = {recording.name:\n                             None for recording in self.recordings}\n            else:\n                if len(self.recordings) != len(arb_cell_model.traces):\n                    raise ValueError('Number of Arbor voltage traces '\n                                     '(%d) != number of recordings (%d)' %\n                                     (len(self.recordings),\n                                      len(arb_cell_model.traces)))\n                responses = {\n                    recording.name: TimeVoltageResponse(\n                        recording.name, trace.time, trace.value)\n                    for recording, trace in zip(self.recordings,\n                                                arb_cell_model.traces)}\n\n            return responses\n        except BaseException as e:\n            raise ArbSweepProtocolException(\n                'Failed to run Arbor Sweep Protocol') from e\n\n    def run(\n            self,\n            cell_model,\n            param_values,\n            sim=None,\n            isolate=None,\n            timeout=None):\n        \"\"\"Instantiate protocol\"\"\"\n\n        # Export cell model to mixed JSON/ACC-format\n        with tempfile.TemporaryDirectory() as acc_dir:\n            cell_model.write_acc(acc_dir, param_values,\n                                 ext_catalogues=sim.ext_catalogues)\n\n            cell_json = os.path.join(acc_dir, cell_model.name + '.json')\n\n            # protocols are directly instantiated on Arbor cell\n            # (serialization would require representation for probes, events)\n\n            if isolate is None:\n                isolate = True\n\n            if isolate:\n                def _reduce_method(meth):\n                    \"\"\"Overwrite reduce\"\"\"\n                    return (getattr, (meth.__self__, meth.__func__.__name__))\n\n                import copyreg\n                import types\n                copyreg.pickle(types.MethodType, _reduce_method)\n                import pebble\n                from concurrent.futures import TimeoutError\n\n                if timeout is not None:\n                    if timeout < 0:\n                        raise ValueError(\"timeout should be > 0\")\n\n                with pebble.ProcessPool(max_workers=1, max_tasks=1) as pool:\n                    tasks = pool.schedule(self._run_func, kwargs={\n                        'cell_json': cell_json,\n                        'param_values': param_values,\n                        'sim': sim},\n                        timeout=timeout)\n                    try:\n                        responses = tasks.result()\n                    except TimeoutError:\n                        logger.debug('SweepProtocol: task took longer than '\n                                     'timeout, will return empty response '\n                                     'for this recording')\n                        responses = {recording.name:\n                                     None for recording in self.recordings}\n            else:\n                responses = self._run_func(cell_json=cell_json,\n                                           param_values=param_values,\n                                           sim=sim)\n        return responses\n\n    def instantiate_locations(self, label_dict):\n        \"\"\"Instantiate protocol (stimuli/recordings) locations on label_dict\"\"\"\n\n        stim_rec_labels = []\n\n        for stim in self.stimuli:\n            if hasattr(stim, 'location'):\n                arb_loc = stim.location.acc_label()\n            else:\n                arb_loc = [label for loc in stim.locations\n                           for label in loc.acc_label()]\n            for loc in (arb_loc if isinstance(arb_loc, list)\n                        else [arb_loc]):\n                stim_rec_labels.append((loc.name, loc.loc, stim))\n\n        for rec in self.recordings:\n            arb_loc = rec.location.acc_label()\n            if isinstance(arb_loc, list) and len(arb_loc) != 1:\n                raise ValueError('ArbSweepProtocol: ACC label %s' % arb_loc +\n                                 ' of recording with length != 1.')\n            stim_rec_labels.append((arb_loc.name, arb_loc.loc, rec))\n\n        stim_rec_label_dict = dict()\n\n        for label_name, label_loc, stim_rec in stim_rec_labels:\n            if label_name in label_dict and \\\n                    label_loc != label_dict[label_name]:\n                raise ValueError(\n                    'Label %s already exists in' % label_name +\n                    ' label_dict with different value: '\n                    ' %s != %s.' % (label_dict[label_name], label_loc) +\n                    ' Choose different location name for %s.' % stim_rec)\n            elif label_name in stim_rec_label_dict and \\\n                    label_loc != stim_rec_label_dict[label_name]:\n                raise ValueError(\n                    'Label %s defined multiple times' % label_name +\n                    '  with different values: '\n                    ' %s != %s.' % (stim_rec_label_dict[label_name],\n                                    label_loc) +\n                    ' Choose different location name for %s.' % stim_rec)\n            elif label_name not in label_dict and \\\n                    label_name not in stim_rec_label_dict:\n                stim_rec_label_dict[label_name] = label_loc\n\n        label_dict.append(arbor.label_dict(stim_rec_label_dict))\n\n        return label_dict\n\n    def instantiate_iclamp_stimuli(self, decor, use_labels=False):\n        \"\"\"Instantiate iclamp stimuli\"\"\"\n\n        for i, stim in enumerate(self.stimuli):\n            if not isinstance(stim, stimuli.SynapticStimulus):\n                if hasattr(stim, 'envelope'):\n                    envelope = stim.envelope()\n                    envelope = [\n                        (t * arbor.units.ms, curr * arbor.units.nA)\n                        for (t, curr) in envelope\n                    ]\n                    arb_iclamp = arbor.iclamp(envelope)\n                else:\n                    raise ValueError('Stimulus must provide envelope method '\n                                     ' or be of type NrnNetStimStimulus to be'\n                                     ' supported in Arbor.')\n\n                arb_loc = stim.location.acc_label()\n                for loc in (arb_loc if isinstance(arb_loc, list)\n                            else [arb_loc]):\n                    decor.place(loc.ref if use_labels else loc.loc,\n                                arb_iclamp,\n                                '%s.iclamp.%d.%s' % (self.name, i, loc.name))\n\n        return decor\n\n    def instantiate_synaptic_stimuli(self, cell_model, use_labels=False):\n        \"\"\"Instantiate synaptic stimuli\"\"\"\n\n        for i, stim in enumerate(self.stimuli):\n            if isinstance(stim, stimuli.SynapticStimulus):\n                for acc_events in stim.acc_events():\n                    cell_model.event_generator(acc_events)\n\n        return cell_model\n\n    def instantiate_recordings(self, cell_model, use_labels=False):\n        \"\"\"Instantiate recordings\"\"\"\n\n        # Attach voltage probe sampling at 10 kHz (every 0.1 ms)\n        for i, rec in enumerate(self.recordings):\n            # alternatively arbor.cable_probe_membrane_voltage\n            arb_loc = rec.location.acc_label()\n            if isinstance(arb_loc, list) and len(arb_loc) != 1:\n                raise ValueError('ArbSweepProtocol: ACC label %s' % arb_loc +\n                                 ' of recording with length != 1.')\n\n            if hasattr(cell_model, 'cable_cell'):\n                rec_locations = cell_model.cable_cell.locations(arb_loc.loc)\n                if len(rec_locations) != 1:\n                    raise ValueError(\n                        'Recording %s\\'s' % rec.name +\n                        ' location \"%s\"' % arb_loc.loc +\n                        ' is non-unique in Arbor: %s.' % rec_locations)\n\n            cell_model.probe('voltage',\n                             arb_loc.ref if use_labels else arb_loc.loc,\n                             f\"probe-{i}\",\n                             # frequency could be a parameter\n                             frequency=10 * arbor.units.kHz)\n\n        return cell_model\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        content = '%s:\\n' % self.name\n\n        content += '  stimuli:\\n'\n        for stimulus in self.stimuli:\n            content += '    %s\\n' % str(stimulus)\n\n        content += '  recordings:\\n'\n        for recording in self.recordings:\n            content += '    %s\\n' % str(recording)\n\n        return content\n\n\nclass SweepProtocolException(Exception):\n\n    \"\"\"All exceptions generated by SweepProtocol\"\"\"\n\n    def __init__(self, message):\n        \"\"\"Constructor\"\"\"\n\n        super(SweepProtocolException, self).__init__(message)\n\n\nclass ArbSweepProtocolException(Exception):\n\n    \"\"\"All exceptions generated by ArbSweepProtocol\"\"\"\n\n    def __init__(self, message):\n        \"\"\"Constructor\"\"\"\n\n        super(ArbSweepProtocolException, self).__init__(message)\n"
  },
  {
    "path": "bluepyopt/ephys/recordings.py",
    "content": "\"\"\"Recording classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nimport logging\n\nfrom . import responses\n\nlogger = logging.getLogger(__name__)\n\n\nclass Recording(object):\n\n    \"\"\"Class to represent object that record variables during simulations\"\"\"\n\n    def __init__(self, name=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n        \"\"\"\n\n        self.name = name\n\n\nclass CompRecording(Recording):\n\n    \"\"\"Response to stimulus\"\"\"\n\n    def __init__(\n            self,\n            name=None,\n            location=None,\n            variable='v'):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            location (Location): location in the model of the recording\n            variable (str): which variable to record from (e.g. 'v')\n        \"\"\"\n\n        super(CompRecording, self).__init__(\n            name=name)\n        self.location = location\n        self.variable = variable\n\n        self.varvector = None\n        self.tvector = None\n\n        self.time = None\n        self.voltage = None\n\n        self.instantiated = False\n\n    @property\n    def response(self):\n        \"\"\"Return recording response\"\"\"\n\n        if not self.instantiated:\n            return None\n\n        return responses.TimeVoltageResponse(self.name,\n                                             self.tvector.to_python(),\n                                             self.varvector.to_python())\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Instantiate recording\"\"\"\n\n        logger.debug('Adding compartment recording of %s at %s',\n                     self.variable, self.location)\n\n        self.varvector = sim.neuron.h.Vector()\n        seg = self.location.instantiate(sim=sim, icell=icell)\n        self.varvector.record(getattr(seg, '_ref_%s' % self.variable))\n\n        self.tvector = sim.neuron.h.Vector()\n        self.tvector.record(sim.neuron.h._ref_t)  # pylint: disable=W0212\n\n        self.instantiated = True\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy recording\"\"\"\n\n        self.varvector = None\n        self.tvector = None\n        self.instantiated = False\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%s: %s at %s' % (self.name, self.variable, self.location)\n\n\nclass LFPRecording(Recording):\n\n    \"\"\"Extracellular electrode response to stimulus\"\"\"\n\n    location = \"extracellular\"\n    variable = \"v\"\n\n    def __init__(self, name=None):\n        \"\"\"Constructor\n        Args:\n            name (str): name of this object\n        \"\"\"\n\n        super(LFPRecording, self).__init__(name=name)\n\n        self.electrode = None\n        self.cell = None\n        self.tvector = None\n        self.time = None\n        self.sim = None\n\n        self.instantiated = False\n\n    @property\n    def response(self):\n        \"\"\"Return recording response\"\"\"\n\n        if not self.instantiated:\n            return None\n        self.tvector = self.cell.tvec\n        return responses.TimeLFPResponse(\n            self.name, self.tvector, self.electrode.data\n        )\n\n    def instantiate(self, sim=None, lfpy_cell=None, electrode=None):\n        import LFPy\n\n        \"\"\"Instantiate recording\"\"\"\n\n        logger.debug(\n            \"Adding recording of %s at %s\", self.variable, self.location\n        )\n\n        assert isinstance(\n            lfpy_cell, LFPy.Cell\n        ), \"LFPRecording is only available for LFPCellModel\"\n        self.cell = lfpy_cell\n        self.tvector = None\n        self.electrode = electrode\n        self.sim = sim\n\n        self.instantiated = True\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy recording\"\"\"\n\n        self.electrode = None\n        self.LFP = None\n        self.tvector = None\n        self.electrode = None\n        self.cell = None\n        self.instantiated = False\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"%s: %s at %s\" % (self.name, self.variable, self.location)\n"
  },
  {
    "path": "bluepyopt/ephys/responses.py",
    "content": "\"\"\"Responses classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nimport pandas\n\n\nclass Response(object):\n\n    \"\"\"Response to stimulus\"\"\"\n\n    def __init__(self, name):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n        \"\"\"\n\n        self.response = None\n        self.name = name\n\n    def __str__(self):\n        return '%s: %s' % (self.__class__.__name__, self.name)\n\n\nclass TimeVoltageResponse(Response):\n\n    \"\"\"Response to stimulus\"\"\"\n\n    def __init__(self, name, time=None, voltage=None):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            time (list of floats): time series\n            voltage (list of floats): voltage series\n        \"\"\"\n\n        super(TimeVoltageResponse, self).__init__(name)\n\n        self.response = pandas.DataFrame()\n        self.response['time'] = pandas.Series(time)\n        self.response['voltage'] = pandas.Series(voltage)\n\n    def read_csv(self, filename):\n        \"\"\"Load response from csv file\"\"\"\n\n        self.response = pandas.read_csv(filename)\n\n    def to_csv(self, filename):\n        \"\"\"Write response to csv file\"\"\"\n\n        self.response.to_csv(filename)\n\n    def __getitem__(self, index):\n        \"\"\"Return item at index\"\"\"\n\n        return self.response.__getitem__(index)\n\n    # This plot has to be generalised to several subplots\n    def plot(self, axes):\n        \"\"\"Plot the response\"\"\"\n\n        axes.plot(\n            self.response['time'],\n            self.response['voltage'],\n            label='%s' %\n            self.name)\n\n\nclass TimeLFPResponse(TimeVoltageResponse):\n\n    \"\"\"Response to stimulus\"\"\"\n\n    def __init__(self, name, time=None, lfp=None):\n        \"\"\"Constructor\n        Args:\n            name (str): name of this object\n            time (list of floats): time series\n            lfp (list of floats): voltage series\n        \"\"\"\n\n        super(TimeLFPResponse, self).__init__(name, time=time, voltage=None)\n        self.response = {}\n        self.response[\"time\"] = time\n        self.response[\"voltage\"] = lfp\n\n    def plot(self, axes):\n        raise NotImplementedError\n"
  },
  {
    "path": "bluepyopt/ephys/serializer.py",
    "content": "'''Mixin class to make dictionaries'''\n\n# Disabling lines below, generate error when loading ephys.examples\n# from future import standard_library\n# standard_library.install_aliases()\n\n\nSENTINAL = 'class'\n\n\nclass DictMixin(object):\n\n    '''Mixin class to create dictionaries of selected elements'''\n    SERIALIZED_FIELDS = ()\n\n    @staticmethod\n    def _serializer(value):\n        \"\"\"_serializer\"\"\"\n        if hasattr(value, 'to_dict'):\n            return value.to_dict()\n        elif isinstance(value, (list, tuple)) and \\\n                value and hasattr(value[0], 'to_dict'):\n            return [v.to_dict() for v in value]\n        elif (isinstance(value, dict) and value and\n                hasattr(\n                    next(iter(list(value.values()))), 'to_dict')):\n            return {k: v.to_dict() for k, v in list(value.items())}\n        return value\n\n    @staticmethod\n    def _deserializer(value):\n        \"\"\"_deserializer\"\"\"\n        if (isinstance(value, list) and value and\n           isinstance(value[0], dict) and SENTINAL in value[0]):\n            return [instantiator(v) for v in value]\n        elif isinstance(value, dict) and value:\n            if SENTINAL in value:\n                return instantiator(value)\n            model_value = next(iter(list(value.values())))\n            if isinstance(model_value, dict) and SENTINAL in model_value:\n                return {k: instantiator(v) for k, v in list(value.items())}\n        return value\n\n    def to_dict(self):\n        '''create dictionary'''\n        ret = {}\n        for field in self.SERIALIZED_FIELDS:\n            ret[field] = DictMixin._serializer(getattr(self, field))\n        ret['class'] = repr(self.__class__)\n        return ret\n\n    @classmethod\n    def from_dict(cls, fields):\n        '''create class from serialized values'''\n        klass = fields[SENTINAL]\n        assert klass == repr(cls), 'Class names much match %s != %s' % (\n            klass, repr(cls))\n        del fields['class']\n        for name in list(fields.keys()):\n            fields[name] = DictMixin._deserializer(fields[name])\n        return cls(**fields)\n\n\ndef instantiator(fields):\n    \"\"\"instantiator\"\"\"\n    klass = fields[SENTINAL]\n    for subclass in DictMixin.__subclasses__():\n        if repr(subclass) == klass:\n            return subclass.from_dict(fields)\n    raise Exception('Could not find class \"%s\" to instantiate' % klass)\n"
  },
  {
    "path": "bluepyopt/ephys/simulators.py",
    "content": "\"\"\"Simulator classes\"\"\"\n\n# pylint: disable=W0511\n\nimport ctypes\nimport importlib.util\nimport logging\nimport os\nimport pathlib\nimport platform\nimport warnings\n\nfrom bluepyopt.ephys.acc import arbor\n\nlogger = logging.getLogger(__name__)\n\n\nclass NrnSimulator(object):\n    \"\"\"Neuron simulator\"\"\"\n\n    def __init__(\n        self,\n        dt=None,\n        cvode_active=True,\n        cvode_minstep=None,\n        random123_globalindex=None,\n        mechanisms_directory=None,\n    ):\n        \"\"\"Constructor\n\n        Args:\n            dt (float): the integration time step used by Neuron.\n            cvode_active (bool): should Neuron use the variable time step\n                integration method\n            cvode_minstep (float): the minimum time step allowed for a cvode\n                step. Default is 0.0.\n            random123_globalindex (int): used to set the global index used by\n                all instances of the Random123 instances of Random\n            mechanisms_directory (str): path to the parent directory of the\n                directory containing the mod files. If the mod files are in\n                \"./data/mechanisms\", then mechanisms_directory should be\n                \"./data/\".\n        \"\"\"\n\n        # hoc.so does not exist on NEURON Windows or MacOS\n        # although \\\\hoc.pyd can work here, it gives an error for\n        # nrn_nobanner_ line\n        self.disable_banner = platform.system() not in [\"Windows\", \"Darwin\"]\n        self.banner_disabled = False\n        self.mechanisms_directory = mechanisms_directory\n\n        self.dt = dt if dt is not None else self.neuron.h.dt\n\n        self.cvode_minstep_value = cvode_minstep\n\n        self.cvode_active = cvode_active\n\n        self.initialize()\n\n        self.random123_globalindex = random123_globalindex\n\n    @property\n    def cvode(self):\n        \"\"\"Return cvode instance\"\"\"\n\n        return self.neuron.h.CVode()\n\n    @property\n    def cvode_minstep(self):\n        \"\"\"Return cvode minstep value\"\"\"\n\n        return self.cvode.minstep()\n\n    @cvode_minstep.setter\n    def cvode_minstep(self, value):\n        \"\"\"Set cvode minstep value\"\"\"\n\n        self.cvode.minstep(value)\n\n    @staticmethod\n    def _nrn_disable_banner():\n        \"\"\"Disable Neuron banner\"\"\"\n\n        nrnpy_path = pathlib.Path(\n            importlib.util.find_spec(\"neuron\").origin\n        ).parent\n\n        hoc_so_list = list(nrnpy_path.glob(\"hoc*.so\"))\n\n        if len(hoc_so_list) != 1:\n            warnings.warn(\n                \"Unable to find Neuron hoc shared library in %s, \"\n                \"not disabling banner\" % nrnpy_path\n            )\n        else:\n            hoc_so = hoc_so_list[0]\n            nrndll = ctypes.cdll[str(hoc_so)]\n            ctypes.c_int.in_dll(nrndll, \"nrn_nobanner_\").value = 1\n\n    # pylint: disable=R0201\n    @property\n    def neuron(self):\n        \"\"\"Return Neuron module\"\"\"\n\n        if self.disable_banner and not self.banner_disabled:\n            NrnSimulator._nrn_disable_banner()\n            self.banner_disabled = True\n\n        import neuron  # NOQA\n\n        if self.mechanisms_directory is not None:\n            neuron.load_mechanisms(\n                self.mechanisms_directory, warn_if_already_loaded=False\n            )\n\n        return neuron\n\n    def initialize(self):\n        \"\"\"Initialize simulator: Set Neuron variables\"\"\"\n        self.neuron.h.load_file(\"stdrun.hoc\")\n        self.neuron.h.dt = self.dt\n        self.neuron.h.cvode_active(1 if self.cvode_active else 0)\n\n    def run(\n        self,\n        tstop=None,\n        dt=None,\n        cvode_active=None,\n        random123_globalindex=None,\n    ):\n        \"\"\"Run protocol\"\"\"\n\n        self.neuron.h.tstop = tstop\n\n        if cvode_active and dt is not None:\n            raise ValueError(\n                \"NrnSimulator: Impossible to combine the dt argument when \"\n                \"cvode_active is True in the NrnSimulator run method\"\n            )\n\n        if cvode_active is None:\n            cvode_active = self.cvode_active\n\n        if not cvode_active and dt is None:  # use dt of simulator\n            if self.neuron.h.dt != self.dt:\n                raise Exception(\n                    \"NrnSimulator: Some process has changed the \"\n                    \"time step dt of Neuron since the creation of this \"\n                    \"NrnSimulator object. Not sure this is intended:\\n\"\n                    \"current dt: %.6g\\n\"\n                    \"init dt: %.6g\" % (self.neuron.h.dt, self.dt)\n                )\n            dt = self.dt\n\n        self.neuron.h.cvode_active(1 if cvode_active else 0)\n        if self.cvode_minstep_value is not None:\n            save_minstep = self.cvode_minstep\n            self.cvode_minstep = self.cvode_minstep_value\n\n        if cvode_active:\n            logger.debug(\"Running Neuron simulator %.6g ms, with cvode\", tstop)\n        else:\n            self.neuron.h.dt = dt\n            self.neuron.h.steps_per_ms = 1.0 / dt\n            logger.debug(\n                \"Running Neuron simulator %.6g ms, with dt=%r\", tstop, dt\n            )\n\n        if random123_globalindex is None:\n            random123_globalindex = self.random123_globalindex\n\n        if random123_globalindex is not None:\n            rng = self.neuron.h.Random()\n            rng.Random123_globalindex(random123_globalindex)\n\n        try:\n            self.neuron.h.run()\n        except Exception as e:\n            raise NrnSimulatorException(\"Neuron simulator error\", e)\n\n        if self.cvode_minstep_value is not None:\n            self.cvode_minstep = save_minstep\n\n        logger.debug(\"Neuron simulation finished\")\n\n\nclass NrnSimulatorException(Exception):\n    \"\"\"All exception generated by Neuron simulator\"\"\"\n\n    def __init__(self, message, original):\n        \"\"\"Constructor\"\"\"\n\n        super(NrnSimulatorException, self).__init__(message)\n        self.original = original\n\n\nclass LFPySimulator(NrnSimulator):\n    \"\"\"LFPy simulator\"\"\"\n\n    def __init__(\n        self,\n        dt=None,\n        cvode_active=True,\n        cvode_minstep=None,\n        random123_globalindex=None,\n        mechanisms_directory=None,\n    ):\n        \"\"\"Constructor\n\n        Args:\n            dt (float): the integration time step used by neuron.\n            cvode_active (bool): should neuron use the variable time step\n                integration method\n            cvode_minstep (float): the minimum time step allowed for a cvode\n                step. Default is 0.0.\n            random123_globalindex (int): used to set the global index used by\n                all instances of the Random123 instances of Random\n            mechanisms_directory (str): path to the parent directory of the\n                directory containing the mod files. If the mod files are in\n                \"./data/mechanisms\", then mechanisms_directory should be\n                \"./data/\".\n        \"\"\"\n\n        super(LFPySimulator, self).__init__(\n            dt=dt,\n            cvode_active=cvode_active,\n            cvode_minstep=cvode_minstep,\n            random123_globalindex=random123_globalindex,\n            mechanisms_directory=mechanisms_directory,\n        )\n\n    def run(\n        self,\n        lfpy_cell,\n        lfpy_electrode,\n        tstop=None,\n        dt=None,\n        cvode_active=None,\n        random123_globalindex=None,\n    ):\n        \"\"\"Run protocol\"\"\"\n        _ = self.neuron\n\n        lfpy_cell.tstart = 0.0\n        lfpy_cell.tstop = tstop\n\n        if dt is not None:\n            lfpy_cell.dt = dt\n\n        if cvode_active and dt is not None:\n            raise ValueError(\n                \"NrnSimulator: Impossible to combine the dt argument when \"\n                \"cvode_active is True in the NrnSimulator run method\"\n            )\n\n        if cvode_active is None:\n            cvode_active = self.cvode_active\n\n        if cvode_active is not None:\n            self.cvode_active = cvode_active\n\n        if random123_globalindex is None:\n            random123_globalindex = self.random123_globalindex\n\n        if random123_globalindex is not None:\n            rng = self.neuron.h.Random()\n            rng.Random123_globalindex(random123_globalindex)\n\n        probes = [lfpy_electrode] if lfpy_electrode is not None else None\n\n        sim_params = {\n            \"probes\": probes,\n            \"rec_vmem\": False,\n            \"rec_imem\": False,\n            \"rec_ipas\": False,\n            \"rec_icap\": False,\n            \"rec_variables\": [],\n            \"variable_dt\": self.cvode_active,\n            \"atol\": 0.001,\n            \"to_memory\": True,\n            \"to_file\": False,\n            \"file_name\": None,\n        }\n\n        try:\n            lfpy_cell.simulate(**sim_params)\n        except Exception as e:\n            raise LFPySimulatorException(\"LFPy simulator error\", e)\n\n        logger.debug(\"LFPy simulation finished\")\n\n\nclass LFPySimulatorException(Exception):\n    \"\"\"All exception generated by LFPy simulator\"\"\"\n\n    def __init__(self, message, original):\n        \"\"\"Constructor\"\"\"\n\n        super(LFPySimulatorException, self).__init__(message)\n        self.original = original\n\n\nclass ArbSimulator(object):\n    \"\"\"Arbor simulator\"\"\"\n\n    def __init__(self, dt=None, ext_catalogues=None):\n        \"\"\"Constructor\n\n        Args:\n            dt (float): the integration time step used by Arbor.\n            ext_catalogues (): Name to path mapping of non-Arbor built-in\n            NMODL mechanism catalogues compiled with modcc\n        \"\"\"\n\n        self.dt = dt\n        self.ext_catalogues = ext_catalogues\n        if ext_catalogues is not None:\n            for cat, cat_path in ext_catalogues.items():\n                cat_lib = \"%s-catalogue.so\" % cat\n                cat_path = pathlib.Path(cat_path).resolve()\n                if not os.path.exists(cat_path / cat_lib):\n                    raise ArbSimulatorException(\n                        \"Cannot find %s at %s - first build\"\n                        % (cat_lib, cat_path)\n                        + \" mechanism catalogue with modcc:\"\n                        + \" arbor-build-catalogue %s %s\" % (cat, cat_path)\n                    )\n        # TODO: add parameters for discretization\n\n    def initialize(self):\n        \"\"\"Initialize simulator\"\"\"\n        pass\n\n    def instantiate(self, morph, decor, labels):\n        cable_cell = arbor.cable_cell(\n            morphology=morph, decor=decor, labels=labels\n        )\n\n        arb_cell_model = arbor.single_cell_model(cable_cell)\n\n        # Add catalogues with explicit qualifiers\n        arb_cell_model.properties.catalogue = arbor.catalogue()\n\n        # User-supplied catalogues take precedence\n        if self.ext_catalogues is not None:\n            for cat, cat_path in self.ext_catalogues.items():\n                cat_lib = \"%s-catalogue.so\" % cat\n                cat_path = pathlib.Path(cat_path).resolve()\n                arb_cell_model.properties.catalogue.extend(\n                    arbor.load_catalogue(cat_path / cat_lib), cat + \"::\"\n                )\n\n        # Built-in catalogues are always added (could be made optional)\n        if self.ext_catalogues is None or \"default\" not in self.ext_catalogues:\n            arb_cell_model.properties.catalogue.extend(\n                arbor.default_catalogue(), \"default::\"\n            )\n\n        if self.ext_catalogues is None or \"BBP\" not in self.ext_catalogues:\n            arb_cell_model.properties.catalogue.extend(\n                arbor.bbp_catalogue(), \"BBP::\"\n            )\n\n        if self.ext_catalogues is None or \"allen\" not in self.ext_catalogues:\n            arb_cell_model.properties.catalogue.extend(\n                arbor.allen_catalogue(), \"allen::\"\n            )\n\n        return arb_cell_model\n\n    def run(self, arb_cell_model, tstop=None, dt=None):\n        dt = dt if dt is not None else self.dt\n\n        if dt is not None:\n            return arb_cell_model.run(\n                tfinal=tstop * arbor.units.ms, dt=dt * arbor.units.ms\n            )\n        else:\n            return arb_cell_model.run(tfinal=tstop * arbor.units.ms)\n\n\nclass ArbSimulatorException(Exception):\n    \"\"\"All exception generated by Arbor simulator\"\"\"\n\n    def __init__(self, message):\n        \"\"\"Constructor\"\"\"\n\n        super(ArbSimulatorException, self).__init__(message)\n"
  },
  {
    "path": "bluepyopt/ephys/static/arbor_mechanisms.json",
    "content": "{\n    \"allen\": {\n        \"CaDynamics\": {\n            \"globals\": [\n                \"F\"\n            ],\n            \"ranges\": [\n                \"decay\",\n                \"gamma\",\n                \"minCai\",\n                \"depth\"\n            ]\n        },\n        \"Ca_HVA\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\"\n            ]\n        },\n        \"Ca_LVA\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\"\n            ]\n        },\n        \"Ih\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\"\n            ]\n        },\n        \"Im\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"g\",\n                \"ik\"\n            ]\n        },\n        \"Im_v2\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"ik\"\n            ]\n        },\n        \"K_P\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"g\",\n                \"ik\"\n            ]\n        },\n        \"K_T\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\"\n            ]\n        },\n        \"Kd\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"ik\"\n            ]\n        },\n        \"Kv2like\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\"\n            ]\n        },\n        \"Kv3_1\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"ik\"\n            ]\n        },\n        \"NaTa\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"g\",\n                \"ina\"\n            ]\n        },\n        \"NaTs\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"g\",\n                \"ina\"\n            ]\n        },\n        \"NaV\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\"\n            ]\n        },\n        \"Nap\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"g\",\n                \"ina\"\n            ]\n        },\n        \"SK\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"ik\"\n            ]\n        }\n    },\n    \"BBP\": {\n        \"CaDynamics_E2\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"decay\",\n                \"gamma\",\n                \"minCai\",\n                \"depth\",\n                \"initCai\"\n            ]\n        },\n        \"Ca_HVA\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gCa_HVAbar\"\n            ]\n        },\n        \"Ca_LVAst\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gCa_LVAstbar\"\n            ]\n        },\n        \"Ih\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gIhbar\"\n            ]\n        },\n        \"Im\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gImbar\"\n            ]\n        },\n        \"K_Pst\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gK_Pstbar\"\n            ]\n        },\n        \"K_Tst\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gK_Tstbar\"\n            ]\n        },\n        \"NaTa_t\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gNaTa_tbar\"\n            ]\n        },\n        \"NaTs2_t\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gNaTs2_tbar\"\n            ]\n        },\n        \"Nap_Et2\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gNap_Et2bar\"\n            ]\n        },\n        \"SK_E2\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gSK_E2bar\"\n            ]\n        },\n        \"SKv3_1\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gSKv3_1bar\"\n            ]\n        }\n    },\n    \"default\": {\n        \"exp2syn\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"tau1\",\n                \"tau2\",\n                \"e\"\n            ]\n        },\n        \"expsyn\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"tau\",\n                \"e\"\n            ]\n        },\n        \"expsyn_stdp\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"tau\",\n                \"taupre\",\n                \"taupost\",\n                \"e\",\n                \"Apost\",\n                \"Apre\",\n                \"max_weight\"\n            ]\n        },\n        \"gj\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"g\"\n            ]\n        },\n        \"hh\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gnabar\",\n                \"gkbar\",\n                \"gl\",\n                \"el\",\n                \"q10\"\n            ]\n        },\n        \"kamt\": {\n            \"globals\": [\n                \"minf\",\n                \"mtau\",\n                \"hinf\",\n                \"htau\"\n            ],\n            \"ranges\": [\n                \"gbar\",\n                \"q10\"\n            ]\n        },\n        \"kdrmt\": {\n            \"globals\": [\n                \"minf\",\n                \"mtau\"\n            ],\n            \"ranges\": [\n                \"gbar\",\n                \"q10\",\n                \"vhalfm\"\n            ]\n        },\n        \"nax\": {\n            \"globals\": null,\n            \"ranges\": [\n                \"gbar\",\n                \"sh\"\n            ]\n        },\n        \"nernst\": {\n            \"globals\": [\n                \"R\",\n                \"F\"\n            ],\n            \"ranges\": [\n                \"coeff\"\n            ]\n        },\n        \"pas\": {\n            \"globals\": [\n                \"e\"\n            ],\n            \"ranges\": [\n                \"g\"\n            ]\n        }\n    }\n}"
  },
  {
    "path": "bluepyopt/ephys/stimuli.py",
    "content": "\"\"\"Stimuli classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=W0511\n\nimport numpy as np\nimport logging\n\nfrom bluepyopt.ephys.acc import arbor\n\nlogger = logging.getLogger(__name__)\n\n\nclass Stimulus(object):\n\n    \"\"\"Stimulus protocol\"\"\"\n    pass\n\n\nclass SynapticStimulus(Stimulus):\n\n    \"\"\"Synaptic stimulus protocol\"\"\"\n    pass\n\n\nclass LFPStimulus(Stimulus):\n\n    \"\"\"Base class for stimulus supporting LFPy cells.\"\"\"\n\n    def instantiate(self, sim=None, lfpy_cell=None):\n        \"\"\"Run stimulus\"\"\"\n        raise NotImplementedError\n\n\nclass NrnCurrentPlayStimulus(Stimulus):\n\n    \"\"\"Current stimulus based on current amplitude and time series\"\"\"\n\n    def __init__(self,\n                 time_points=None,\n                 current_points=None,\n                 location=None):\n        \"\"\"Constructor\n\n        Args:\n            time_points(): time series (ms)\n            current_points(): current series of injected current amplitudes(nA)\n            location(Location): location of stimulus\n        \"\"\"\n\n        super(NrnCurrentPlayStimulus, self).__init__()\n        self.time_points = time_points\n        self.current_points = current_points\n        self.location = location\n        self.total_duration = max(time_points)\n        self.iclamp = None\n        self.current_vec = None\n        self.time_vec = None\n\n    def envelope(self):\n        \"\"\"Stimulus envelope\"\"\"\n\n        envelope = list(zip(self.time_points, self.current_points))\n\n        return envelope\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Run stimulus\"\"\"\n\n        icomp = self.location.instantiate(sim=sim, icell=icell)\n        logger.debug(\n            'Adding current play stimulus to %s', str(self.location))\n\n        self.iclamp = sim.neuron.h.IClamp(\n            icomp.x,\n            sec=icomp.sec)\n        self.current_vec = sim.neuron.h.Vector(self.current_points)\n        self.time_vec = sim.neuron.h.Vector(self.time_points)\n        self.iclamp.dur = self.total_duration\n        self.iclamp.delay = 0\n        self.current_vec.play(\n            self.iclamp._ref_amp,  # pylint:disable=W0212\n            self.time_vec,\n            1,\n            sec=icomp.sec)\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy stimulus\"\"\"\n\n        self.iclamp = None\n        self.time_vec = None\n        self.current_vec = None\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"Current play at %s\" % (self.location)\n\n\nclass NrnNetStimStimulus(SynapticStimulus):\n\n    \"\"\"Current stimulus based on current amplitude and time series\"\"\"\n\n    def __init__(self,\n                 locations=None,\n                 total_duration=None,\n                 interval=None,\n                 number=None,\n                 start=None,\n                 noise=0,\n                 weight=1):\n        \"\"\"Constructor\n\n        Args:\n            location: synapse point process location to connect to\n            interval: time between spikes (ms)\n            number: average number of spikes\n            start: most likely start time of first spike (ms)\n            noise: fractional randomness (0 deterministic,\n                   1 negexp interval distribution)\n        \"\"\"\n\n        super(NrnNetStimStimulus, self).__init__()\n        if total_duration is None:\n            raise ValueError(\n                'NrnNetStimStimulus: Need to specify a total duration')\n        else:\n            self.total_duration = total_duration\n\n        self.locations = locations\n        self.interval = interval\n        self.number = number\n        self.start = start\n        self.noise = noise\n        self.weight = weight\n        self.connections = {}\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Run stimulus\"\"\"\n\n        for location in self.locations:\n            self.connections[location.name] = []\n            for synapse in location.instantiate(sim=sim, icell=icell):\n                netstim = sim.neuron.h.NetStim()\n                netstim.interval = self.interval\n                netstim.number = self.number\n                netstim.start = self.start\n                netstim.noise = self.noise\n                netcon = sim.neuron.h.NetCon(netstim, synapse)\n                netcon.weight[0] = self.weight\n\n                self.connections[location.name].append((netcon, netstim))\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy stimulus\"\"\"\n\n        self.connections = {}\n\n    def acc_events(self):\n        event_generators = []\n\n        for loc in self.locations:\n            if self.noise == 0.:\n                schedule = arbor.explicit_schedule(\n                    [self.start + i * self.interval\n                     for i in range(self.number)])\n            elif self.noise == 1.:\n                schedule = arbor.poisson_schedule(\n                    tstart=self.start,\n                    freq=1. / self.interval,\n                    seed=0,\n                    tstop=self.start + self.number * self.interval)\n            else:\n                raise ValueError(\n                    'Only noise = 0 or 1 for NrnNetStimStimulus'\n                    ' supported in Arbor.')\n\n            event_generators.append(\n                arbor.event_generator(target=loc.pprocess_mech.name,\n                                      weight=self.weight,\n                                      sched=schedule))\n\n        return event_generators\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"Netstim at %s\" % ','.join(\n            location\n            for location in self.locations) \\\n            if self.locations is not None else \"Netstim\"\n\n# TODO Add 'current' to the name\n\n\nclass NrnSquarePulse(Stimulus):\n\n    \"\"\"Square pulse current clamp injection\"\"\"\n\n    def __init__(self,\n                 step_amplitude=None,\n                 step_delay=None,\n                 step_duration=None,\n                 total_duration=None,\n                 location=None):\n        \"\"\"Constructor\n\n        Args:\n            step_amplitude (float): amplitude (nA)\n            step_delay (float): delay (ms)\n            step_duration (float): duration (ms)\n            total_duration (float): total duration (ms)\n            location (Location): stimulus Location\n        \"\"\"\n\n        super(NrnSquarePulse, self).__init__()\n        self.step_amplitude = step_amplitude\n        self.step_delay = step_delay\n        self.step_duration = step_duration\n        self.location = location\n        self.total_duration = total_duration\n        self.iclamp = None\n\n    def envelope(self):\n        \"\"\"Stimulus envelope\"\"\"\n\n        envelope = [(0., 0.),\n                    (self.step_delay, 0.),\n                    (self.step_delay, self.step_amplitude),\n                    (self.step_delay + self.step_duration,\n                        self.step_amplitude),\n                    (self.step_delay + self.step_duration, 0.),\n                    (self.total_duration, 0.)]\n\n        return envelope\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Run stimulus\"\"\"\n\n        icomp = self.location.instantiate(sim=sim, icell=icell)\n        logger.debug(\n            'Adding square step stimulus to %s with delay %f, '\n            'duration %f, and amplitude %f',\n            str(self.location),\n            self.step_delay,\n            self.step_duration,\n            self.step_amplitude)\n\n        self.iclamp = sim.neuron.h.IClamp(\n            icomp.x,\n            sec=icomp.sec)\n        self.iclamp.dur = self.step_duration\n        self.iclamp.amp = self.step_amplitude\n        self.iclamp.delay = self.step_delay\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy stimulus\"\"\"\n\n        self.iclamp = None\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"Square pulse amp %f delay %f duration %f totdur %f at %s\" % (\n            self.step_amplitude,\n            self.step_delay,\n            self.step_duration,\n            self.total_duration,\n            self.location)\n\n\nclass NrnRampPulse(Stimulus):\n\n    \"\"\"Ramp current clamp injection\"\"\"\n\n    def __init__(self,\n                 ramp_amplitude_start=None,\n                 ramp_amplitude_end=None,\n                 ramp_delay=None,\n                 ramp_duration=None,\n                 total_duration=None,\n                 location=None):\n        \"\"\"Constructor\n\n        Args:\n            ramp_amplitude_start (float): amplitude at start of ramp (nA)\n            ramp_amplitude_start (float): amplitude at end of ramp (nA)\n            ramp_delay (float): delay of ramp (ms)\n            ramp_duration (float): duration oframp (ms)\n            total_duration (float): total duration (ms)\n            location (Location): stimulus Location\n        \"\"\"\n\n        super(NrnRampPulse, self).__init__()\n        self.ramp_amplitude_start = ramp_amplitude_start\n        self.ramp_amplitude_end = ramp_amplitude_end\n        self.ramp_delay = ramp_delay\n        self.ramp_duration = ramp_duration\n        self.location = location\n        self.total_duration = total_duration\n        self.iclamp = None\n        self.persistent = []  # TODO move this into higher abstract classes\n\n    def envelope(self):\n        \"\"\"Stimulus envelope\"\"\"\n\n        envelope = [\n            # at time 0.0, current is 0.0\n            (0.0, 0.0),\n\n            # until time ramp_delay, current is 0.0\n            (self.ramp_delay, 0.0),\n\n            # at time ramp_delay, current is ramp_amplitude_start\n            (self.ramp_delay, self.ramp_amplitude_start),\n\n            # at time ramp_delay+ramp_duration, current is ramp_amplitude_end\n            (self.ramp_delay + self.ramp_duration,\n             self.ramp_amplitude_end),\n\n            # after ramp, current is set 0.0\n            (self.ramp_delay + self.ramp_duration, 0.0),\n\n            (self.total_duration, 0.0)\n        ]\n\n        return envelope\n\n    def instantiate(self, sim=None, icell=None):\n        \"\"\"Run stimulus\"\"\"\n\n        icomp = self.location.instantiate(sim=sim, icell=icell)\n        logger.debug(\n            'Adding ramp stimulus to %s with delay %f, '\n            'duration %f, amplitude at start %f and end %f',\n            str(self.location),\n            self.ramp_delay,\n            self.ramp_duration,\n            self.ramp_amplitude_start,\n            self.ramp_amplitude_end\n        )\n\n        times, amps = zip(*self.envelope())\n\n        # create vector to store the times at which stim amp changes\n        times = sim.neuron.h.Vector(times)\n        # create vector to store to which stim amps over time\n        amps = sim.neuron.h.Vector(amps)\n\n        # create a current clamp\n        self.iclamp = sim.neuron.h.IClamp(\n            icomp.x,\n            sec=icomp.sec)\n        self.iclamp.dur = self.total_duration\n\n        # play the above current amplitudes into the current clamp\n        amps.play(self.iclamp._ref_amp, times, 1)  # pylint: disable=W0212\n\n        # Make sure the following objects survive after instantiation\n        self.persistent.append(times)\n        self.persistent.append(amps)\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy stimulus\"\"\"\n\n        # Destroy all persistent objects\n        self.persistent = []\n        self.iclamp = None\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"Ramp pulse amp_start %f amp_end %f delay %f duration %f \" \\\n            \"totdur %f at %s\" % (\n                self.ramp_amplitude_start,\n                self.ramp_amplitude_end,\n                self.ramp_delay,\n                self.ramp_duration,\n                self.total_duration,\n                self.location)\n\n\nclass LFPySquarePulse(LFPStimulus):\n\n    \"\"\"Square pulse current clamp injection\"\"\"\n\n    def __init__(self,\n                 step_amplitude=None,\n                 step_delay=None,\n                 step_duration=None,\n                 total_duration=None,\n                 location=None):\n        \"\"\"Constructor\n        Args:\n            step_amplitude (float): amplitude (nA)\n            step_delay (float): delay (ms)\n            step_duration (float): duration (ms)\n            total_duration (float): total duration (ms)\n            location (Location): stimulus Location\n        \"\"\"\n\n        super(LFPySquarePulse, self).__init__()\n        self.step_amplitude = step_amplitude\n        self.step_delay = step_delay\n        self.step_duration = step_duration\n        self.location = location\n        self.total_duration = total_duration\n        self.iclamp = None\n\n    def instantiate(self, sim=None, lfpy_cell=None):\n        \"\"\"Run stimulus\"\"\"\n        import LFPy\n        from .locations import NrnSomaDistanceCompLocation\n\n        if hasattr(self.location, \"sec_index\"):\n            sec_index = self.location.sec_index\n        elif isinstance(self.location, NrnSomaDistanceCompLocation):\n            # compute sec_index closest to soma_distance\n            cell_seg_locs = np.array([lfpy_cell.x, lfpy_cell.y, lfpy_cell.z]).T\n            soma_loc = lfpy_cell.somapos\n            dist_from_soma = np.array(\n                [np.linalg.norm(loc - soma_loc) for loc in cell_seg_locs]\n            )\n            sec_index = np.argmin(\n                np.abs(dist_from_soma - self.location.soma_distance)\n            )\n        else:\n            raise NotImplementedError(\n                f\"{type(self.location)} is currently not implemented \"\n                \"with the LFPy backend\"\n            )\n\n        self.iclamp = LFPy.StimIntElectrode(\n            cell=lfpy_cell,\n            idx=sec_index,\n            pptype=\"IClamp\",\n            amp=self.step_amplitude,\n            delay=self.step_delay,\n            dur=self.step_duration,\n            record_current=True\n        )\n        logger.debug(\n            \"Adding square step stimulus to %s with delay %f, \"\n            \"duration %f, and amplitude %f\",\n            str(self.location),\n            self.step_delay,\n            self.step_duration,\n            self.step_amplitude,\n        )\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy stimulus\"\"\"\n\n        self.iclamp = None\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return \"Square pulse amp %f delay %f duration %f totdur %f at %s\" % (\n            self.step_amplitude,\n            self.step_delay,\n            self.step_duration,\n            self.total_duration,\n            self.location,\n        )\n"
  },
  {
    "path": "bluepyopt/ephys/templates/acc/_json_template.jinja2",
    "content": "{ \n  \"cell_model_name\": \"{{template_name}}\",\n  {%- if banner %}\n  \"produced_by\": \"{{banner}}\",\n  {%- endif %}\n  {%- if morphology %} {# feed morphology separately as a SWC/ASC file #}\n  {%- if replace_axon is not none %}\n  \"morphology\": {\n     \"original\": \"{{morphology}}\",\n     \"replace_axon\": \"{{replace_axon}}\"{%- if modified_morphology is not none %},\n     \"modified\": \"{{modified_morphology}}\"{%- endif %}\n  },\n  {%- else %}\n  \"morphology\": {\n     \"original\": \"{{morphology}}\"\n  },\n  {%- endif %}\n  {%- else %}\n    execerror(\"Template {{template_name}} requires morphology name to instantiate\")\n  {%- endif %}\n  \"label_dict\": \"{{filenames['label_dict.acc']}}\",\n  \"decor\": \"{{filenames['decor.acc']}}\"\n}"
  },
  {
    "path": "bluepyopt/ephys/templates/acc/decor_acc_template.jinja2",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (decor\n    {%- for mech, params in global_mechs.items() %}\n    {%- if mech is not none %}\n    {%- if mech in global_scaled_mechs %}\n    (default (scaled-mechanism (density (mechanism \"{{ mech }}\" {%- for param in params if param.value is not none %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})){%- for param in global_scaled_mechs[mech] %} (\"{{ param.name }}\" {{ param.scale }}){%- endfor %}))\n    {%- else %}\n    (default (density (mechanism \"{{ mech }}\" {%- for param in params %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})))\n    {%- endif %}\n    {%- else %}\n    {%- for param in params %}\n    (default ({{ param.name }} {{ param.value }} (scalar 1.0)))\n    {%- endfor %}\n    {%- endif %}\n    {%- endfor %}\n\n\n    {%- for loc, mech_parameters in local_mechs.items() %}{# paint-to-region instead of default #}\n\n    {%- for mech, params in mech_parameters.items() %}\n    {%- if mech is not none %}\n    {%- if mech in local_scaled_mechs[loc] %}\n    (paint {{loc.ref}} (scaled-mechanism (density (mechanism \"{{ mech }}\" {%- for param in params if param.value is not none %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})){%- for param in local_scaled_mechs[loc][mech] %} (\"{{ param.name }}\" {{ param.scale }}){%- endfor %}))\n    {%- else %}\n    (paint {{loc.ref}} (density (mechanism \"{{ mech }}\" {%- for param in params %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})))\n    {%- endif %}\n    {%- else %}\n    {%- for param in params %}\n    (paint {{loc.ref}} ({{ param.name }} {{ param.value }} (scalar 1.0)))\n    {%- endfor %}\n    {%- endif %}\n    {%- endfor %}\n\n    {%- for synapse_name, mech_params in pprocess_mechs[loc].items() %}\n    (place {{loc.ref}} (synapse (mechanism \"{{ mech_params.mech }}\" {%- for param in mech_params.params %} (\"{{ param.name }}\" {{ param.value }} (scalar 1.0)){%- endfor %})) \"{{ synapse_name }}\")\n    {%- endfor %}\n\n    {%- endfor %}))\n"
  },
  {
    "path": "bluepyopt/ephys/templates/acc/label_dict_acc_template.jinja2",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (label-dict\n  {%- for loc, label in label_dict.items() %}\n    {{ label.defn }}\n  {%- endfor %}))\n"
  },
  {
    "path": "bluepyopt/ephys/templates/cell_template.jinja2",
    "content": "/* \n{%- if banner %}\n{{banner}} \n{%- endif %}\n*/\n{load_file(\"stdrun.hoc\")}\n{load_file(\"import3d.hoc\")}\n\n{%- if global_params %}\n/*\n * Check that global parameters are the same as with the optimization\n */\nproc check_parameter(/* name, expected_value, value */){\n  strdef error\n  if($2 != $3){\n    sprint(error, \"Parameter %s has different value %f != %f\", $s1, $2, $3)\n    execerror(error)\n  }\n}\nproc check_simulator() {\n  {%- for param, value in global_params.items() %}\n  check_parameter(\"{{param}}\", {{value}}, {{param}})\n  {%- endfor %}\n}\n{%- endif %}\n{%- if ignored_global_params %}\n/* The following global parameters were set in BluePyOpt\n{%- for param, value in ignored_global_params.items() %}\n * {{param}} = {{value}}\n{%- endfor %}\n */\n{%- endif %}\n\nbegintemplate {{template_name}}\n  public init, morphology, geom_nseg_fixed, geom_nsec, gid\n  public channel_seed, channel_seed_set\n  public soma, dend, apic, axon, myelin\n  create soma[1], dend[1], apic[1], axon[1], myelin[1]\n\n  objref this, CellRef, segCounts\n\n  public all, somatic, apical, axonal, basal, myelinated, APC\n  objref all, somatic, apical, axonal, basal, myelinated, APC\n\nobfunc getCell(){\n        return this\n}\n\nproc init(/* args: morphology_dir, morphology_name */) {\n  all = new SectionList()\n  apical = new SectionList()\n  axonal = new SectionList()\n  basal = new SectionList()\n  somatic = new SectionList()\n  myelinated = new SectionList()\n\n  //gid in this case is only used for rng seeding\n  gid = 0\n\n  //For compatibility with BBP CCells\n  CellRef = this\n\n  forall delete_section()\n\n  if(numarg() >= 2) {\n    load_morphology($s1, $s2)\n  } else {\n  {%- if morphology %}\n    load_morphology($s1, \"{{morphology}}\")\n  {%- else %}\n    execerror(\"Template {{template_name}} requires morphology name to instantiate\")\n  {%- endif %}\n  }\n\n  geom_nseg()\n  {%- if replace_axon %}\n    replace_axon()\n  {%- endif %}\n  insertChannel()\n  biophys()\n\n  // Initialize channel_seed_set to avoid accidents\n  channel_seed_set = 0\n  // Initialize random number generators\n  re_init_rng()\n}\n\nproc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension\n  strdef morph_path\n  sprint(morph_path, \"%s/%s\", $s1, $s2)\n\n  sf = new StringFunctions()\n  extension = new String()\n\n  sscanf(morph_path, \"%s\", extension.s)\n  sf.right(extension.s, sf.len(extension.s)-4)\n\n  if( strcmp(extension.s, \".asc\") == 0 ) {\n    morph = new Import3d_Neurolucida3()\n  } else if( strcmp(extension.s, \".swc\" ) == 0) {\n    morph = new Import3d_SWC_read()\n  } else {\n    printf(\"Unsupported file format: Morphology file has to end with .asc or .swc\" )\n    quit()\n  }\n\n  morph.quiet = 1\n  morph.input(morph_path)\n\n  import = new Import3d_GUI(morph, 0)\n  import.instantiate(this)\n}\n\n/*\n * Assignment of mechanism values based on distance from the soma\n * Matches the BluePyOpt method\n */\nproc distribute_distance(){local x localobj sl\n  strdef stmp, distfunc, mech\n\n  sl = $o1\n  mech = $s2\n  distfunc = $s3\n  this.soma[0] distance(0, 0.5)\n  sprint(distfunc, \"%%s %s(%%f) = %s\", mech, distfunc)\n  forsec sl for(x, 0) {\n    // use distance(x) twice for the step distribution case, e.g. for calcium hotspot\n    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))\n    execute(stmp)\n  }\n}\n\nproc geom_nseg() {\n  this.geom_nsec() //To count all sections\n  //TODO: geom_nseg_fixed depends on segCounts which is calculated by\n  //  geom_nsec.  Can this be collapsed?\n  this.geom_nseg_fixed(40)\n  this.geom_nsec() //To count all sections\n}\n\nproc insertChannel() {\n  {%- for location, names in channels.items() %}\n  forsec this.{{location}} {\n  {%- for channel in names %}\n    insert {{channel}}\n  {%- endfor %}\n  }\n  {%- endfor %}\n}\n\nproc biophys() {\n  {% for loc, parameters in section_params %}\n  forsec CellRef.{{ loc }} {\n  {%- for param in parameters %}\n    {{ param.name }} = {{ param.value }}\n  {%- endfor %}\n  }\n  {% endfor %}\n  {%- for location, param_name, value in range_params %}\n  distribute_distance(CellRef.{{location}}, \"{{param_name}}\", \"{{value}}\")\n  {%- endfor %}\n}\n\nfunc sec_count(/* SectionList */) { local nSec\n  nSec = 0\n  forsec $o1 {\n      nSec += 1\n  }\n  return nSec\n}\n\n/*\n * Iterate over the section and compute how many segments should be allocate to\n * each.\n */\nproc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize\n  chunkSize = $1\n  soma area(.5) // make sure diam reflects 3d points\n  secIndex = 0\n  forsec all {\n    nseg = 1 + 2*int(L/chunkSize)\n    segCounts.x[secIndex] = nseg\n    secIndex += 1\n  }\n}\n\n/*\n * Count up the number of sections\n */\nproc geom_nsec() { local nSec\n  nSecAll = sec_count(all)\n  nSecSoma = sec_count(somatic)\n  nSecApical = sec_count(apical)\n  nSecBasal = sec_count(basal)\n  nSecMyelinated = sec_count(myelinated)\n  nSecAxonalOrig = nSecAxonal = sec_count(axonal)\n\n  segCounts = new Vector()\n  segCounts.resize(nSecAll)\n  nSec = 0\n  forsec all {\n    segCounts.x[nSec] = nseg\n    nSec += 1\n  }\n}\n\n/*\n * Replace the axon built from the original morphology file with a stub axon\n */\n{%- if replace_axon %}\n    {{replace_axon}}\n{%- endif %}\n\n\n{{re_init_rng}}\n\nendtemplate {{template_name}}\n"
  },
  {
    "path": "bluepyopt/evaluators.py",
    "content": "\"\"\"Cell evaluator class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nfrom abc import abstractmethod\n\n\nclass Evaluator(object):\n\n    \"\"\"Evaluator class\n\n    An Evaluator maps a set of parameter values to objective values\n        Args:\n            objectives (Objectives):\n                The objectives that will be the output of the evaluator.\n            params (Parameters):\n                The parameters that will be evaluated.\n\n        Attributes:\n            objectives (Objectives):\n                Objective objects.\n            params (Objectives):\n                Parameter objects.\n    \"\"\"\n\n    def __init__(self, objectives=None, params=None):\n        self.objectives = objectives\n        self.params = params\n\n    # TODO add evaluate_with_dicts\n    @abstractmethod\n    def evaluate_with_dicts(self, param_dict):\n        \"\"\"Evaluate parameter a parameter set (abstract).\n\n        Args:\n            params (dict with values Parameters, and keys parameter names):\n                The parameter values to be evaluated.\n\n        Returns:\n            objectives (dict with values Parameters, and keys objective names):\n                Dict of Objective with values calculated by the Evaluator.\n\n        \"\"\"\n\n    @abstractmethod\n    def evaluate_with_lists(self, params):\n        \"\"\"Evaluate parameter a parameter set (abstract).\n\n        Args:\n            params (list of Parameters):\n                The parameter values to be evaluated.\n\n        Returns:\n            objectives (list of Objectives):\n                List of Objectives with values calculated by the Evaluator.\n\n        \"\"\"\n"
  },
  {
    "path": "bluepyopt/ipyp/__init__.py",
    "content": ""
  },
  {
    "path": "bluepyopt/ipyp/bpopt_tasksdb.py",
    "content": "\"\"\"Get stats out of ipyparallel's tasks.db\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport sys\nimport os\nimport argparse\nimport sqlite3\nimport collections\nimport datetime\nimport dateutil.parser\nimport itertools\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef get_engine_data(tasksdb_filename):\n    \"\"\"Main\"\"\"\n    conn = sqlite3.connect(tasksdb_filename)\n\n    cursor = conn.cursor()\n\n    tasks = collections.defaultdict(list)\n\n    SQL = 'SELECT started, completed, engine_uuid FROM \"ipython-tasks\";'\n\n    for started, completed, engine_uuid in cursor.execute(SQL).fetchall():\n        if started and completed:\n            started = dateutil.parser.parse(started)\n            completed = dateutil.parser.parse(completed)\n\n            duration = (\n                completed -\n                started).total_seconds() if completed else None\n            task = {'started': started,\n                    'completed': completed,\n                    'duration': duration,\n                    'engine_uuid': engine_uuid}\n\n            tasks[engine_uuid].append(task)\n\n    if len(tasks) == 0:\n        raise Exception(\"No completed tasks found in the db\")\n\n    engine_number_map = dict(zip(tasks.keys(), range(len(tasks.keys()))))\n    return tasks, engine_number_map\n\n\ndef plot_usage(tasks, engine_number_map):\n    \"\"\"Plot usage stats\"\"\"\n\n    plt.figure(figsize=(8, 8))\n    for engine_uuid, task_list in tasks.items():\n        engine_number = engine_number_map[engine_uuid]\n        number_list = [engine_number] * len(task_list)\n        start_list = [task['started'] for task in task_list]\n        completed_list = [task['completed'] for task in task_list]\n        plt.plot(\n            [number_list, number_list],\n            [start_list, completed_list], linewidth=10,\n            solid_capstyle=\"butt\")\n\n    plt.xlim(min(engine_number_map.values()) - 1,\n             max(engine_number_map.values()) + 1)\n    plt.xlabel('Compute engine number')\n    plt.ylabel('Compute time')\n\n    idle_time, idle_perc = calculate_unused_compute(tasks)\n    plt.title(\n        'Cumulative idle time: %s, perc: %.2f %%' %\n        (idle_time, idle_perc))\n\n\ndef plot_duration_histogram(tasks):\n    \"\"\"Plot duration histogram\"\"\"\n    plt.figure(figsize=(8, 8))\n\n    durations = np.fromiter((t['duration']\n                             for task_list in tasks.values()\n                             for t in task_list),\n                            dtype=np.float64)\n    plt.hist(durations, 100)\n\n    plt.xlabel('Duration (s)')\n    plt.ylabel('Count')\n    plt.title('Histogram of task execution')\n    plt.grid(True)\n\n\ndef calculate_unused_compute(tasks):\n    \"\"\"Calculate unused compute time\"\"\"\n\n    all_tasks = list(itertools.chain.from_iterable(tasks.values()))\n    start_time = min(task['started'] for task in all_tasks)\n    end_time = max(task['completed'] for task in all_tasks)\n    total_compute_time = sum(\n        (datetime.timedelta(seconds=task['duration'])\n         for task in all_tasks), datetime.timedelta())\n\n    n_of_engines = len(tasks.keys())\n\n    idle_time = n_of_engines * (end_time - start_time) - total_compute_time\n    idle_perc = 100 * \\\n        (idle_time.total_seconds() / total_compute_time.total_seconds())\n    return idle_time, idle_perc\n\n\ndef run(arg_list):\n    \"\"\"Main run\"\"\"\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('tasksdb_filename')\n    args = parser.parse_args(arg_list)\n\n    if not os.path.isfile(args.tasksdb_filename):\n        raise IOError('Tasks db file not found at: %s' % args.tasksdb_filename)\n\n    tasks, engine_number_map = get_engine_data(args.tasksdb_filename)\n    plot_usage(tasks, engine_number_map)\n    plot_duration_histogram(tasks)\n\n    plt.show()\n\n\ndef main():\n    \"\"\"Main\"\"\"\n\n    run(sys.argv[1:])\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/Ca.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta3.xsd\" id=\"Ca\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"Ca\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"ca\">\n\n        <notes>NeuroML file containing a single Channel description\n        \n            Note: was called Ca_HVA in Hay et al 2011: http://www.opensourcebrain.org/projects/l5bpyrcellhayetal2011\n            \n        </notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"Ca\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Calcium channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table1\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHrates\" instances=\"2\">\n            <forwardRate type=\"HHExpLinearRate\" rate=\"0.209per_ms\" scale=\"3.8mV\" midpoint=\"-27mV\"/>\n            <reverseRate type=\"HHExpRate\" rate=\"0.94per_ms\" scale=\"-17mV\" midpoint=\"-75mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHrates\" instances=\"1\">\n            <forwardRate type=\"HHExpRate\" rate=\"0.000457per_ms\" scale=\"-50mV\" midpoint=\"-13mV\"/>\n            <reverseRate type=\"HHSigmoidRate\" rate=\"0.0065per_ms\" scale=\"28mV\" midpoint=\"-15mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/Ca_HVA.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"Ca_HVA\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"Ca_HVA\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"ca\">\n\n        <notes>High voltage activated Ca2+ current. \n            \nComment from original mod file: \nReuveni, Friedman, Amitai, and Gutnick, J.Neurosci. 1993</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"Ca_HVA\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Calcium channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table1\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHrates\" instances=\"2\">\n            <forwardRate type=\"HHExpLinearRate\" rate=\"0.209per_ms\" scale=\"3.8mV\" midpoint=\"-27mV\"/>\n            <reverseRate type=\"HHExpRate\" rate=\"0.94per_ms\" scale=\"-17mV\" midpoint=\"-75mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHrates\" instances=\"1\">\n            <forwardRate type=\"HHExpRate\" rate=\"0.000457per_ms\" scale=\"-50mV\" midpoint=\"-13mV\"/>\n            <reverseRate type=\"HHSigmoidRate\" rate=\"0.0065per_ms\" scale=\"28mV\" midpoint=\"-15mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/Ca_LVAst.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"Ca_LVAst\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"Ca_LVAst\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"ca\">\n\n        <notes>Low voltage activated Ca2+ current\n            \nComment from original mod file: \nNote: mtau is an approximation from the plots\n:Reference : :\t\tAvery and Johnston 1996, tau from Randall 1997\n:Comment: shifted by -10 mv to correct for junction potential\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"Ca_LVAst\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Ca channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table1\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHtauInf\" instances=\"2\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <timeCourse type=\"Ca_LVAst_m_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"6mV\" midpoint=\"-40mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHtauInf\" instances=\"1\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <timeCourse type=\"Ca_LVAst_h_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"-6.4mV\" midpoint=\"-90mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n    <ComponentType name=\"Ca_LVAst_m_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\" value=\"(5 + 20 / (1 + (exp ((V+35)/5) ))) * TIME_SCALE\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n    <ComponentType name=\"Ca_LVAst_h_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\" value=\"(20 + 50 / (1 + (exp ((V+50)/7) ))) * TIME_SCALE\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/Ih.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"Ih\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"Ih\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"hcn\">\n\n        <notes>Non-specific cation current\n            \nComment from original mod file: \nReference : :\t\tKole,Hallermann,and Stuart, J. Neurosci. 2006</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"Ih\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHrates\" instances=\"1\">\n            <forwardRate type=\"HHExpLinearRate\" rate=\"0.076517per_ms\" scale=\"-11.9mV\" midpoint=\"-154.9mV\"/>\n            <reverseRate type=\"HHExpRate\" rate=\"0.193per_ms\" scale=\"33.1mV\" midpoint=\"0mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/Im.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"Im\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"Im\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"k\">\n\n        <notes>Muscarinic K+ current\n            \nComment from original mod file: \n:Reference : :\t\tAdams et al. 1982 - M-currents and other potassium currents in bullfrog sympathetic neurones\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"Im\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>K channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table3\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHrates\" instances=\"1\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <forwardRate type=\"HHExpRate\" rate=\"0.0033per_ms\" scale=\"10mV\" midpoint=\"-35mV\"/>\n            <reverseRate type=\"HHExpRate\" rate=\"0.0033per_ms\" scale=\"-10mV\" midpoint=\"-35mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/K_Pst.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"K_Pst\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"K_Pst\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"k\">\n\n        <notes>Slow inactivating K+ current\n            \nComment from original mod file: \n:Comment : The persistent component of the K current\n:Reference : :\t\tVoltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000\n:Comment : shifted -10 mv to correct for junction potential\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"K_Pst\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>K channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table3\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHtauInf\" instances=\"2\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <timeCourse type=\"K_Pst_m_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"12mV\" midpoint=\"-11mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHtauInf\" instances=\"1\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <timeCourse type=\"K_Pst_h_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"-11mV\" midpoint=\"-64mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n    <ComponentType name=\"K_Pst_m_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <ConditionalDerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\">\n                <Case condition=\"V  .lt. ( -60 )\" value=\"( (1.25 + 175.03 * (exp ((V+10) * 0.026))) ) * TIME_SCALE\"/>\n                <Case value=\"( (1.25 + 13 * (exp ((V+10) * -0.026)))) * TIME_SCALE\"/>\n            </ConditionalDerivedVariable>\n        </Dynamics>\n\n    </ComponentType>\n\n    <ComponentType name=\"K_Pst_h_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\" value=\"(360 + (1010 + 24*(V+65)) * (exp (-1 *((V+85)/48)*((V+85)/48)))) * TIME_SCALE\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/K_Tst.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"K_Tst\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"K_Tst\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"k\">\n\n        <notes>Fast inactivating K+ current\n            \nComment from original mod file: \n:Comment : The transient component of the K current\n:Reference : :\t\tVoltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000\n:Comment : shifted -10 mv to correct for junction potential\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"K_Tst\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>K channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table3\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHtauInf\" instances=\"4\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <timeCourse type=\"K_Tst_m_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"19mV\" midpoint=\"-10mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHtauInf\" instances=\"1\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <timeCourse type=\"K_Tst_h_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"-10mV\" midpoint=\"-76mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n    <ComponentType name=\"K_Tst_m_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\" value=\"(0.34 + 0.92 * (exp (-1 *((V+81)/59)^2))) * TIME_SCALE\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n    <ComponentType name=\"K_Tst_h_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\" value=\"(8 + 49 * (exp (-1 * ((V+83)/23)^2))) * TIME_SCALE\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/KdShu2007.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" \n         xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" \n         xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" \n         id=\"KdShu2007\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"KdShu2007\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"kdshu\">\n\n        <notes>K-D current for prefrontal cortical neuron - Yuguo Yu 2007</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"KdShu2007\">\n                    \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>K channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table3\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHtauInf\" instances=\"1\">\n            <!-- qt=q10^((celsius-22)/10) is in mod file but never used!\n            <q10Settings type=\"q10ExpTemp\" q10Factor=\"2.3\" experimentalTemp=\"22 degC\"/>-->\n            <timeCourse type=\"fixedTimeCourse\" tau=\"0.6ms\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"8mV\" midpoint=\"-43mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHtauInf\" instances=\"1\">\n            <!-- qt=q10^((celsius-22)/10) is in mod file but never used!\n            <q10Settings type=\"q10ExpTemp\" q10Factor=\"2.3\" experimentalTemp=\"22 degC\"/>-->\n            <timeCourse type=\"fixedTimeCourse\" tau=\"1500ms\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"-7.3mV\" midpoint=\"-67mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/NaTa_t.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"NaTa_t\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"NaTa_t\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"na\">\n\n        <notes>Fast inactivating Na+ current\n            \nComment from original mod file: \n:Reference :Colbert and Pan 2002</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"NaTa_t\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Na channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table2\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHrates\" instances=\"3\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <forwardRate type=\"HHExpLinearRate\" rate=\"1.092per_ms\" scale=\"6mV\" midpoint=\"-38mV\"/>\n            <reverseRate type=\"HHExpLinearRate\" rate=\"0.744per_ms\" scale=\"-6mV\" midpoint=\"-38mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHrates\" instances=\"1\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <forwardRate type=\"HHExpLinearRate\" rate=\"0.09per_ms\" scale=\"-6mV\" midpoint=\"-66mV\"/>\n            <reverseRate type=\"HHExpLinearRate\" rate=\"0.09per_ms\" scale=\"6mV\" midpoint=\"-66mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/NaTs2_t.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"NaTs2_t\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"NaTs2_t\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"na\">\n\n        <notes>Fast inactivating Na+ current. Comment from mod file (NaTs2_t.mod): took the NaTa and shifted both activation/inactivation by 6 mv</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"NaTs2_t\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Na channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table2\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHrates\" instances=\"3\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <forwardRate type=\"HHExpLinearRate\" rate=\"1.092per_ms\" scale=\"6mV\" midpoint=\"-32mV\"/>\n            <reverseRate type=\"HHExpLinearRate\" rate=\"0.744per_ms\" scale=\"-6mV\" midpoint=\"-32mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHrates\" instances=\"1\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <forwardRate type=\"HHExpLinearRate\" rate=\"0.09per_ms\" scale=\"-6mV\" midpoint=\"-60mV\"/>\n            <reverseRate type=\"HHExpLinearRate\" rate=\"0.09per_ms\" scale=\"6mV\" midpoint=\"-60mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/Nap_Et2.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"Nap_Et2\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"Nap_Et2\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"na\">\n\n        <notes>Persistent Na+ current\n            \nComment from original mod file: \n:Comment : mtau deduced from text (said to be 6 times faster than for NaTa)\n:Comment : so I used the equations from NaT and multiplied by 6\n:Reference : Modeled according to kinetics derived from Magistretti and Alonso 1999\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"Nap_Et2\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Na channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table2\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHratesTauInf\" instances=\"3\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <forwardRate type=\"HHExpLinearRate\" rate=\"1.092per_ms\" scale=\"6mV\" midpoint=\"-38mV\"/>\n            <reverseRate type=\"HHExpLinearRate\" rate=\"0.744per_ms\" scale=\"-6mV\" midpoint=\"-38mV\"/>\n            <timeCourse type=\"Nap_Et2_m_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"4.6mV\" midpoint=\"-52.6mV\"/>\n        </gate>\n\n        <gate id=\"h\" type=\"gateHHratesInf\" instances=\"1\">\n            <q10Settings type=\"q10Fixed\" fixedQ10=\"2.95288264\"/>\n            <forwardRate type=\"HHExpLinearRate\" rate=\"1.33344e-05per_ms\" scale=\"-4.63mV\" midpoint=\"-17mV\"/>\n            <reverseRate type=\"HHExpLinearRate\" rate=\"1.82522e-05per_ms\" scale=\"2.63mV\" midpoint=\"-64.4mV\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"-10mV\" midpoint=\"-48.8mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n    <ComponentType name=\"Nap_Et2_m_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n        <Requirement name=\"alpha\" dimension=\"per_time\"/>\n        <Requirement name=\"beta\" dimension=\"per_time\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"ALPHA\" dimension=\"none\" value=\"alpha * TIME_SCALE\"/>\n            <DerivedVariable name=\"BETA\" dimension=\"none\" value=\"beta * TIME_SCALE\"/>\n            <ConditionalDerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\">\n                <Case condition=\"(ALPHA + BETA) .eq. 0\" value=\"( 0 ) * TIME_SCALE\"/>\n                <Case condition=\"(ALPHA + BETA)  .gt. ( 0 )\" value=\"( 6/( (ALPHA + BETA) ) ) * TIME_SCALE\"/>\n                <Case value=\"( 0) * TIME_SCALE\"/>\n            </ConditionalDerivedVariable>\n        </Dynamics>\n\n    </ComponentType>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/SK_E2.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"SK_E2\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"SK_E2\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"k\">\n\n        <notes>Small-conductance, Ca2+ activated K+ current\n            \nComment from original mod file: \n: SK-type calcium-activated potassium current\n: Reference : Kohler et al. 1996</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"SK_E2\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>K channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table3\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"z\" type=\"gateHHtauInf\" instances=\"1\">\n            <timeCourse type=\"SK_E2_z_tau_tau\"/>\n            <steadyState type=\"SK_E2_z_inf_inf\"/>\n        </gate>\n                            \n    </ionChannel>\n\n    <ComponentType name=\"SK_E2_z_tau_tau\" extends=\"baseVoltageConcDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n        <Constant name=\"CONC_SCALE\" dimension=\"concentration\" value=\"1 mol_per_cm3\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"ca_conc\" dimension=\"none\" value=\"caConc / CONC_SCALE\"/>\n            <DerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\" value=\"(1) * TIME_SCALE\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n    <ComponentType name=\"SK_E2_z_inf_inf\" extends=\"baseVoltageConcDepVariable\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n        <Constant name=\"CONC_SCALE\" dimension=\"concentration\" value=\"1 mol_per_cm3\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"ca_conc\" dimension=\"none\" value=\"caConc / CONC_SCALE\"/>\n            <DerivedVariable name=\"x\" exposure=\"x\" dimension=\"none\" value=\"1/(1+(4.3e-10/ca_conc)^4.8)\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/SKv3_1.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"SKv3_1\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"SKv3_1\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"k\">\n\n        <notes>Fast, non inactivating K+ current\n            \nComment from original mod file: \n:Reference : :\t\tCharacterization of a Shaw-related potassium channel family in rat brain, The EMBO Journal, vol.11, no.7,2473-2486 (1992)</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"SKv3_1\">\n                    \n                    <bqmodel:isDescribedBy xmlns:bqmodel=\"http://biomodels.net/model-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties,\n            Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011</rdf:li>\n                            <rdf:li rdf:resource=\"http://www.ncbi.nlm.nih.gov/pubmed/21829333\"/>\n                        </rdf:Bag>\n                    </bqmodel:isDescribedBy>\n\n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>K channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table3\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n\n        <gate id=\"m\" type=\"gateHHtauInf\" instances=\"1\">\n            <timeCourse type=\"SKv3_1_m_tau_tau\"/>\n            <steadyState type=\"HHSigmoidVariable\" rate=\"1\" scale=\"9.7mV\" midpoint=\"18.7mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n    <ComponentType name=\"SKv3_1_m_tau_tau\" extends=\"baseVoltageDepTime\">\n        <Constant name=\"TIME_SCALE\" dimension=\"time\" value=\"1 ms\"/>\n        <Constant name=\"VOLT_SCALE\" dimension=\"voltage\" value=\"1 mV\"/>\n\n        <Dynamics>\n            <DerivedVariable name=\"V\" dimension=\"none\" value=\"v / VOLT_SCALE\"/>\n            <DerivedVariable name=\"t\" exposure=\"t\" dimension=\"time\" value=\"(4/(1 + exp( (V+46.56) / (-44.140) ))) * TIME_SCALE\"/>\n        </Dynamics>\n\n    </ComponentType>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/StochKv_deterministic.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"StochKv_deterministic\">\n\n    <notes>NeuroML2 file containing a single Channel description</notes>\n\n    <ionChannel id=\"StochKv_deterministic\" conductance=\"10pS\" type=\"ionChannelHH\" species=\"k\">\n\n        <notes>Deterministic version of StochKv channel. See https://github.com/OpenSourceBrain/BlueBrainProjectShowcase/tree/master/NMC/NEURON/test</notes>\n                \n        <annotation>\n            <rdf:RDF xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n                <rdf:Description rdf:about=\"StochKv_deterministic\">\n                    \n                \n                    <bqbiol:isVersionOf xmlns:bqbiol=\"http://biomodels.net/biology-qualifiers/\">\n                        <rdf:Bag>\n                            <rdf:li>K channels</rdf:li>\n                            <rdf:li rdf:resource=\"http://senselab.med.yale.edu/neurondb/channelGene2.aspx#table3\"/>\n                        </rdf:Bag>\n                    </bqbiol:isVersionOf>\n\n                </rdf:Description>\n            </rdf:RDF>\n        </annotation>\n        \n        <q10ConductanceScaling q10Factor=\"2.3\" experimentalTemp=\"23 degC\"/>\n\n        <gate id=\"n\" type=\"gateHHrates\" instances=\"1\">\n            <q10Settings type=\"q10ExpTemp\" q10Factor=\"2.3\" experimentalTemp=\"23 degC\"/>\n            <forwardRate type=\"HHExpLinearRate\" rate=\"0.18 per_ms\" scale=\"9mV\" midpoint=\"-40mV\"/>\n            <reverseRate type=\"HHExpLinearRate\" rate=\"0.018 per_ms\" scale=\"-9mV\" midpoint=\"-40mV\"/>\n        </gate>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/baseCaDynamics_E2_NML2.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" \n         xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" \n         xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta3.xsd\" \n         id=\"CaDynamics_E2_NML2\">\n\n    <notes>NeuroML 2 implementation of the Ca Pool mechanism</notes>\n\n    <!--<decayingPoolConcentrationModel id=\"CaDynamics_E2_NML2\" restingConc=\"1e-10mol_per_cm3\" decayConstant=\"80ms\" ion=\"ca\" shellThickness=\"2.787e-4cm\"/>-->\n    \n    <concentrationModel id=\"CaDynamics_E2_NML2\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" decay=\"80 ms\" depth=\"0.1 um\" gamma=\"0.05\" ion=\"ca\"/>\n    \n    <concentrationModel id=\"CaDynamics_E2_NML2__cSTUT_7_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.010353\" decay=\"64.277990 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cSTUT_7-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cSTUT_7_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000511\" decay=\"731.707637 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cSTUT_7-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__dNAC_1_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.010353\" decay=\"64.277990 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in dNAC_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__dNAC_1_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000511\" decay=\"731.707637 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in dNAC_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bNAC_1_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.001739\" decay=\"468.069681 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in bNAC_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bNAC_1_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000500\" decay=\"645.079741 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in bNAC_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bSTUT_1_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.001739\" decay=\"468.069681 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in bSTUT_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bSTUT_1_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000500\" decay=\"645.079741 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in bSTUT_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_229_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.016713\" decay=\"384.114655 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cADpyr_229-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_229_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000533\" decay=\"342.544232 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cADpyr_229-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_230_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000502\" decay=\"179.044149 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cADpyr_230-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_230_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.002253\" decay=\"739.416497 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cADpyr_230-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_231_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.001734\" decay=\"103.091390 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cADpyr_231-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_231_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000996\" decay=\"873.498863 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cADpyr_231-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cNAC_149_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.010353\" decay=\"64.277990 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cNAC_149-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cNAC_149_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000511\" decay=\"731.707637 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cNAC_149-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cIR_1_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000503\" decay=\"573.007045 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cIR_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cIR_1_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000814\" decay=\"967.678789 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cIR_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bIR_1_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.003923\" decay=\"20.715642 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in bIR_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bIR_1_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000893\" decay=\"605.033222 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in bIR_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bAC_1_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.003923\" decay=\"20.715642 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in bAC_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__bAC_1_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000893\" decay=\"605.033222 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in bAC_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cACint_237_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000503\" decay=\"573.007045 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cACint_237-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cACint_237_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000814\" decay=\"967.678789 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cACint_237-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__dSTUT_1_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.010353\" decay=\"64.277990 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in dSTUT_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__dSTUT_1_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000511\" decay=\"731.707637 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in dSTUT_1-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_232_axonal\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.002910\" decay=\"287.198731 ms\" depth=\"0.1 um\"/>  <!-- For group axonal in cADpyr_232-->\n\n    <concentrationModel id=\"CaDynamics_E2_NML2__cADpyr_232_somatic\" ion=\"ca\" type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" gamma=\"0.000609\" decay=\"210.485284 ms\" depth=\"0.1 um\"/>  <!-- For group somatic in cADpyr_232-->\n\n\n    <!--\n    This is a new, custom ComponentType to handle the calcium mechanism with parameters\n\n    PARAMETER\t{\n        gamma = 0.05 : percent of free calcium (not buffered)\n        decay = 80 (ms) : rate of removal of calcium\n        depth = 0.1 (um) : depth of shell\n        minCai = 1e-4 (mM)\n    }\n\n    and derivative mechanism:\n\n    DERIVATIVE states\t{\n        cai' = -(10000)*(ica*gamma/(2*FARADAY*depth)) - (cai - minCai)/decay\n    }\n\n    See https://github.com/OpenSourceBrain/L5bPyrCellHayEtAl2011/blob/master/neuroConstruct/cellMechanisms/CaDynamics_E2_init_mod/CaDynamics_E2.mod\n\n    -->\n    \n\n    <ComponentType name=\"concentrationModelHayEtAl\" extends=\"concentrationModel\" description=\"Model of buffering of concentration of specific to Hay Et Al 2011\">\n\n        <Parameter name=\"gamma\" dimension=\"none\"/>\n        <Parameter name=\"minCai\" dimension=\"concentration\"/>\n        <Parameter name=\"decay\" dimension=\"time\"/>\n        <Parameter name=\"depth\" dimension=\"length\"/>\n       \n\n        <Constant name=\"Faraday\" dimension=\"charge_per_mole\" value=\"96485.3C_per_mol\"/>\n        <Constant name=\"AREA_SCALE\" dimension=\"area\" value=\"1m2\"/>\n        <Constant name=\"LENGTH_SCALE\" dimension=\"length\" value=\"1m\"/>\n\n        <Requirement name=\"iCa\" dimension=\"current\"/>\n\n        <Text name=\"ion\"/>\n\n        <Dynamics>\n\n            <StateVariable name=\"concentration\" exposure=\"concentration\" dimension=\"concentration\"/>\n            <StateVariable name=\"extConcentration\" exposure=\"extConcentration\" dimension=\"concentration\"/>\n\n            <DerivedVariable name=\"currDensCa\" dimension=\"currentDensity\" value=\"iCa / surfaceArea\"/>\n            \n\n            <TimeDerivative variable=\"concentration\" value=\"(currDensCa*gamma/(2*Faraday*depth)) - ((concentration - minCai) / decay)\"/>\n\n            <OnStart>\n                <StateAssignment variable=\"concentration\" value=\"initialConcentration\"/>\n                <StateAssignment variable=\"extConcentration\" value=\"initialExtConcentration\"/>\n            </OnStart>\n\n        </Dynamics>\n\n    </ComponentType>\n\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/NeuroML2_mechanisms/pas.channel.nml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<neuroml xmlns=\"http://www.neuroml.org/schema/neuroml2\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.neuroml.org/schema/neuroml2 https://raw.github.com/NeuroML/NeuroML2/development/Schemas/NeuroML2/NeuroML_v2beta4.xsd\" id=\"pas\">\n\n    <notes>NeuroML file containing a single Channel description</notes>\n\n    <ionChannel id=\"pas\" conductance=\"10pS\" type=\"ionChannelPassive\">\n\n        <notes>Simple example of a leak/passive conductance. Note: for GENESIS cells with a single leak conductance,\n        it is better to use the Rm and Em variables for a passive current.</notes>\n                            \n    </ionChannel>\n\n</neuroml>\n"
  },
  {
    "path": "bluepyopt/neuroml/__init__.py",
    "content": ""
  },
  {
    "path": "bluepyopt/neuroml/biophys.py",
    "content": "\"\"\"Functions to create neuroml biophysiology\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport neuroml\n\nfrom bluepyopt import ephys\n\nignore_params = [\"cm\", \"Ra\", \"ena\", \"ek\"]\n\n# Due to the use of 1e-4 in BREAKPOINT in StochKv.mod:\n# ik = 1e-4 * gk * (v - ek)\ndensity_scales = {\"StochKv\": 1e-4}\n\nchannel_substitutes = {\"StochKv\": \"StochKv_deterministic\"}\n\nchannel_ions = {\n    \"Ih\": \"hcn\",\n    \"NaTa_t\": \"na\",\n    \"NaTs2_t\": \"na\",\n    \"Nap_Et2\": \"na\",\n    \"K_Tst\": \"k\",\n    \"K_Pst\": \"k\",\n    \"SKv3_1\": \"k\",\n    \"SK_E2\": \"k\",\n    \"StochKv\": \"k\",\n    \"KdShu2007\": \"k\",\n    \"Im\": \"k\",\n    \"Ca\": \"ca\",\n    \"Ca_HVA\": \"ca\",\n    \"Ca_LVAst\": \"ca\",\n    \"pas\": \"pas\",\n    \"CaDynamics_E2\": \"ca\",\n}\n\nion_erevs = {\n    \"na\": \"50.0 mV\",\n    \"k\": \"-85.0 mV\",\n    \"hcn\": \"-45.0 mV\",\n    \"ca\": \"nernst\",\n    \"pas\": \"pas\",\n}\n\ndefault_capacitances = {\n    \"axonal\": \"1.0 uF_per_cm2\",\n    \"somatic\": \"1.0 uF_per_cm2\",\n    \"basal\": \"1.0 uF_per_cm2\",\n    \"apical\": \"1.0 uF_per_cm2\",\n}\n\n\ndef get_nml_mech_dir():\n    \"\"\"Returns path to repo containing neuroml mechanisms.\"\"\"\n    return os.path.abspath(\n        os.path.join(os.path.dirname(__file__), \"NeuroML2_mechanisms\")\n    )\n\n\ndef adapt_CaDynamics_nml(\n    new_concentrations,\n    channel_dir=\"channels\",\n):\n    \"\"\"Write concentration model if not present in CaDynamics nml file.\n\n    We have to use this non-neuroml2 approved trick until\n    https://github.com/NeuroML/NeuroML2/issues/153 is solved.\n\n    Arguments:\n        new_concentrations (dict): dict of the form\n            \"model_name\": {\"gamma\": value \"decay\": value}\n            describing the concentrations to append to\n            ./{channel_dir}/CaDynamics_E2_NML2.nml\n        channel_dir (str): repo in which to copy the channel files\n    \"\"\"\n    place_after = (\n        '    <concentrationModel id=\"CaDynamics_E2_NML2\" '\n        'type=\"concentrationModelHayEtAl\" '\n        'minCai=\"1e-4 mM\" decay=\"80 ms\" depth=\"0.1 um\" '\n        'gamma=\"0.05\" ion=\"ca\"/>\\n'\n    )\n    cadyn_filename = \"baseCaDynamics_E2_NML2.nml\"\n    new_cadyn_filename = \"CaDynamics_E2_NML2.nml\"\n    nml_mech_dir = get_nml_mech_dir()\n\n    with open(Path(nml_mech_dir) / cadyn_filename, \"r\") as f:\n        lines = f.readlines()\n\n    for model, value_dict in new_concentrations.items():\n        new_concentration = (\n            f'    <concentrationModel id=\"{model}\" ion=\"ca\" '\n            'type=\"concentrationModelHayEtAl\" minCai=\"1e-4 mM\" '\n            f'gamma=\"{value_dict[\"gamma\"]}\" '\n            f'decay=\"{value_dict[\"decay\"]} ms\" depth=\"0.1 um\"/>\\n'\n        )\n        if new_concentration not in lines:\n            idx = lines.index(place_after) + 2  # 2 to account for blank line\n            lines.insert(idx, f\"{new_concentration}\\n\")\n\n    Path(channel_dir).mkdir(exist_ok=True)\n    with open(Path(channel_dir) / new_cadyn_filename, \"w\") as f:\n        contents = \"\".join(lines)\n        f.write(contents)\n\n\ndef get_channel_from_param_name(param_name):\n    \"\"\"Return channel name, given parameter name.\n\n    Arguments:\n        param_name (str): parameter name used within NEURON\n    \"\"\"\n    split_name = param_name.split(\"_\")\n    if len(split_name) == 4:\n        channel = \"_\".join(split_name[2:4])\n    elif len(split_name) == 3:\n        channel = \"_\".join(split_name[1:3])\n    elif len(split_name) == 2:\n        channel = split_name[1]\n    else:\n        raise Exception(\n            f\"Could not extract channel from parameter name {param_name}\"\n        )\n\n    return channel\n\n\ndef format_dist_fun(raw_expr, value, dist_param_names):\n    \"\"\"Format and return the distribution expression.\n\n    Arguments:\n        raw_expr (str): the function expression to be formatted\n        value (float): the value to be put in the expression\n        dist_param_names (list): list of names of parameters that parametrise\n            the distribution\n    \"\"\"\n    if dist_param_names is not None:\n        raise NotImplementedError(\n            \"Functions that depend on other parameters, \"\n            \"like decay function, are not implemented yet.\"\n        )\n    new_expr = raw_expr.format(distance=\"p\", value=value)\n    if \"math\" in new_expr:\n        new_expr = new_expr.replace(\"math.\", \"\")\n    if \"(p)\" in new_expr:\n        new_expr = new_expr.replace(\"(p)\", \"p\")\n\n    return new_expr\n\n\ndef add_nml_channel_to_nml_cell_file(\n    cell_doc,\n    included_channels,\n    channel_name=None,\n    channel_nml2_file=None,\n    channel_dir=\"channels\",\n    skip_channels_copy=False,\n):\n    \"\"\"Add NeuroML channel file to NeuroML cell file.\n\n    And copy channel in the current directory.\n\n    Arguments:\n        cell_doc (NeuroMLDocument): nml document of the cell model\n        included_channels (list): list of already included channels\n        channel_name (str): name of the channel\n        channel_nml2_file (str): name of the neuroml channel file\n        channel_dir (str): repo in which to copy the channel files\n        skip_channels_copy (bool): True to skip the copy pasting\n            of the neuroml channel files\n    \"\"\"\n    if channel_nml2_file is None:\n        if channel_name is None:\n            raise ValueError(\n                \"Plaise provide either a channel name or a channel nml2 file.\"\n            )\n        channel_nml2_file = f\"{channel_name}.channel.nml\"\n\n    if channel_nml2_file not in included_channels:\n        channel_new_path = Path(channel_dir) / channel_nml2_file\n        cell_doc.includes.append(neuroml.IncludeType(href=channel_new_path))\n\n        # for some reason, pynml cannot accept absolute paths in IncludeType,\n        # so copy paste files in current directory for the simulation to work\n        if not skip_channels_copy:\n            Path(channel_dir).mkdir(exist_ok=True)\n            nml_mech_dir = Path(get_nml_mech_dir())\n            channel_path = nml_mech_dir / channel_nml2_file\n            if channel_path.is_file():\n                shutil.copy(channel_path, channel_new_path)\n\n        included_channels.append(channel_nml2_file)\n\n\ndef get_channel_ion(channel, custom_channel_ion=None):\n    \"\"\"Get ion name given channel name.\n\n    Arguments:\n        channel (str): ion channel (e.g. StochKv)\n        custom_channel_ion (dict): dict mapping channel to ion\n    \"\"\"\n    ion = channel_ions.get(channel, None)\n    if ion is None and custom_channel_ion is not None:\n        ion = custom_channel_ion.get(channel, None)\n    if ion is None:\n        raise KeyError(\n            f\"Ion not found for channel {channel}.\"\n            \" Please set channel-ion mapping using custom_channel_ion.\"\n        )\n    return ion\n\n\ndef get_erev(ion, custom_ion_erevs=None):\n    \"\"\"Get reversal potential as str given ion name.\n\n    Arguments:\n        ion (str): ion name (e.g. na)\n        custom_ion_erevs (dict): dict mapping ion to erev (reversal potential)\n    \"\"\"\n    erev = ion_erevs.get(ion, None)\n    if erev is None and custom_ion_erevs is not None:\n        erev = custom_ion_erevs.get(ion, None)\n    if erev is None:\n        raise KeyError(\n            f\"Reversal potential not found for ion {ion}.\"\n            \" Please set ion-erev mapping using custom_ion_erevs.\"\n        )\n    return erev\n\n\ndef get_arguments(\n    params,\n    parameter_name,\n    section_list,\n    channel,\n    channel_name,\n    variable_parameters,\n    cond_density,\n    release_params,\n    custom_channel_ion=None,\n    custom_ion_erevs=None,\n):\n    \"\"\"Get arguments for channel density function.\n\n    Arguments:\n        params (dict): contains the cell's parameters\n        parameter_name (str): name of the parameter (e.g. e_pas)\n        section_list (str): name of the location of the parameter (e.g. axonal)\n        channel (str): ion channel (e.g. StochKv)\n        channel_name (str): ion channel name used in the neuroML channel file\n            (e.g. StochKv_deterministic)\n        variable_parameters (list of neuroml.VariableParameter):\n            parameters for non-uniform distributions\n        cond_density (str): conductance density\n        release_params (dict): optimized parameters\n        custom_channel_ion (dict): dict mapping channel to ion\n        custom_ion_erevs (dict): dict mapping ion to erev (reversal potential)\n    \"\"\"\n    arguments = {}\n\n    arguments[\"ion\"] = get_channel_ion(channel, custom_channel_ion)\n    erev = get_erev(arguments[\"ion\"], custom_ion_erevs)\n\n    channel_class = \"ChannelDensity\"\n\n    if erev == \"nernst\":\n        erev = None\n        channel_class = \"ChannelDensityNernst\"\n    elif erev == \"pas\":\n        erev = params[f\"e_pas.{section_list}\"].value\n        if erev is None:\n            # non frozen parameter\n            erev = release_params[f\"e_pas.{section_list}\"]\n        erev = f\"{erev} mV\"\n        arguments[\"ion\"] = \"non_specific\"\n\n    if variable_parameters is not None:\n        channel_class += \"NonUniform\"\n    else:\n        arguments[\"segment_groups\"] = section_list\n\n    if erev is not None:\n        arguments[\"erev\"] = erev\n    arguments[\"id\"] = f\"{section_list}_{parameter_name}\"\n    if cond_density is not None:\n        arguments[\"cond_density\"] = cond_density\n    arguments[\"ion_channel\"] = channel_name\n    if variable_parameters is not None:\n        arguments[\"variable_parameters\"] = variable_parameters\n\n    return arguments, channel_class\n\n\ndef extract_parameter_value(\n    parameter, section_list, channel, skip_non_uniform, release_params\n):\n    \"\"\"Returns conductance density and variable parameters.\n\n    Arguments:\n        parameter (ephys.parameters)\n        section_list (str): location\n        channel (str): ion channel\n        skip_non_uniform (bool): True to skip non uniform distributions\n        release_params (dict): optimized parameters\n    \"\"\"\n    cond_density = None\n    variable_parameters = None\n\n    # uniform\n    if isinstance(\n        parameter.value_scaler, ephys.parameterscalers.NrnSegmentLinearScaler\n    ):\n        value = parameter.value\n        if value is None:\n            # non frozen parameter\n            value = release_params[parameter.name]\n        if channel in density_scales:\n            value = value * density_scales[channel]\n        cond_density = f\"{value} S_per_cm2\"\n    # non uniform\n    elif not skip_non_uniform:\n        value = parameter.value\n        if value is None:\n            # non frozen parameter\n            value = release_params[parameter.name]\n        # did not mulyiply by 1e4. Is that ok?\n        new_expr = format_dist_fun(\n            raw_expr=parameter.value_scaler.distribution,\n            value=value,\n            dist_param_names=parameter.value_scaler.dist_param_names,\n        )\n\n        iv = neuroml.InhomogeneousValue(\n            inhomogeneous_parameters=f\"PathLengthOver_{section_list}\",\n            value=new_expr,\n        )\n        variable_parameters = [\n            neuroml.VariableParameter(\n                segment_groups=section_list,\n                parameter=\"condDensity\",\n                inhomogeneous_value=iv,\n            )\n        ]\n    else:\n        return None, None\n\n    return cond_density, variable_parameters\n\n\ndef get_density(\n    cell_doc,\n    cell,\n    parameter,\n    section_list,\n    included_channels,\n    skip_non_uniform,\n    release_params,\n    skip_channels_copy,\n    custom_channel_ion=None,\n    custom_ion_erevs=None,\n):\n    \"\"\"Return density.\n\n    Arguments:\n        cell_doc (NeuroMLDocument): nml document of the cell model\n        cell (ephys.CellModel): bluepyopt cell\n        parameter (ephys.parameters)\n        section_list (str): location\n        included_channels (list): list of channels already included\n            in the nml file\n        skip_non_uniform (bool): True to skip non uniform distributions\n        release_params (dict): optimized parameters\n        skip_channels_copy (bool): True to skip the copy pasting\n            of the neuroml channel files\n        custom_channel_ion (dict): dict mapping channel to ion\n        custom_ion_erevs (dict): dict mapping ion to erev (reversal potential)\n    \"\"\"\n    channel = get_channel_from_param_name(parameter.param_name)\n\n    cond_density, variable_parameters = extract_parameter_value(\n        parameter, section_list, channel, skip_non_uniform, release_params\n    )\n    if cond_density is None and variable_parameters is None:\n        return None, None\n\n    channel_name = channel\n    if channel in channel_substitutes:\n        channel_name = channel_substitutes[channel]\n\n    # add nml channel to nml cell file\n    add_nml_channel_to_nml_cell_file(\n        cell_doc,\n        included_channels,\n        channel_name=channel_name,\n        skip_channels_copy=skip_channels_copy,\n    )\n\n    arguments, channel_class = get_arguments(\n        params=cell.params,\n        parameter_name=parameter.param_name,\n        section_list=section_list,\n        channel=channel,\n        channel_name=channel_name,\n        variable_parameters=variable_parameters,\n        cond_density=cond_density,\n        release_params=release_params,\n        custom_channel_ion=custom_channel_ion,\n        custom_ion_erevs=custom_ion_erevs,\n    )\n\n    density = getattr(neuroml, channel_class)(**arguments)\n\n    return density, channel_class\n\n\ndef get_specific_capacitance(capacitance_overwrites):\n    \"\"\"Returns the specific capacitance.\n\n    Arguments:\n        capacitance_overwrites (dict): capacitance values from parameters\n            to overwrites default ones.\n    \"\"\"\n    specific_capacitances = []\n    for section_list in default_capacitances:\n        if section_list in capacitance_overwrites:\n            capacitance = capacitance_overwrites[section_list]\n        elif \"all\" in capacitance_overwrites:\n            capacitance = capacitance_overwrites[\"all\"]\n        else:\n            capacitance = default_capacitances[section_list]\n\n        specific_capacitances.append(\n            neuroml.SpecificCapacitance(\n                value=capacitance, segment_groups=section_list\n            )\n        )\n\n    return specific_capacitances\n\n\ndef get_biophys(\n    cell,\n    cell_doc,\n    release_params,\n    skip_non_uniform=False,\n    skip_channels_copy=False,\n    custom_channel_ion=None,\n    custom_ion_erevs=None,\n):\n    \"\"\"Get biophys in neuroml format.\n\n    Arguments:\n        cell (ephys.CellModel): bluepyopt cell\n        cell_doc (NeuroMLDocument): nml document of the cell model\n        release_params (dict): optimized parameters\n        skip_non_uniform (bool): True to skip non uniform distributions\n        skip_channels_copy (bool): True to skip the copy pasting\n            of the neuroml channel files\n        custom_channel_ion (dict): dict mapping channel to ion\n        custom_ion_erevs (dict): dict mapping ion to erev (reversal potential)\n    \"\"\"\n    concentrationModels = {}\n\n    # Membrane properties\n    included_channels = []\n    channel_densities = []\n    channel_density_nernsts = []\n    channel_density_non_unif_nernsts = []\n    channel_density_non_uniforms = []\n    species = []\n\n    capacitance_overwrites = {}\n\n    for parameter in cell.params.values():\n        if not (\n            isinstance(parameter, ephys.parameters.NrnGlobalParameter)\n            or isinstance(parameter, ephys.parameters.MetaParameter)\n        ):\n            for location in parameter.locations:\n                section_list = location.seclist_name\n\n                if (\n                    parameter.param_name != \"e_pas\"\n                    and \"CaDynamics_E2\" not in parameter.param_name\n                    and parameter.param_name not in ignore_params\n                ):\n                    density, channel_class = get_density(\n                        cell_doc,\n                        cell,\n                        parameter,\n                        section_list,\n                        included_channels,\n                        skip_non_uniform,\n                        release_params,\n                        skip_channels_copy,\n                        custom_channel_ion,\n                        custom_ion_erevs,\n                    )\n\n                    if density is not None:\n                        # add density to list of densities\n                        if channel_class == \"ChannelDensityNernst\":\n                            channel_density_nernsts.append(density)\n                        elif (\n                            channel_class == \"ChannelDensityNernstNonUniform\"\n                        ):\n                            channel_density_non_unif_nernsts.append(\n                                density\n                            )\n                        elif channel_class == \"ChannelDensityNonUniform\":\n                            channel_density_non_uniforms.append(density)\n                        else:\n                            channel_densities.append(density)\n\n                elif \"gamma_CaDynamics_E2\" in parameter.param_name:\n                    model = f\"CaDynamics_E2_NML2__{cell.name}_{section_list}\"\n                    value = parameter.value\n                    if value is None:\n                        # non frozen parameter\n                        value = release_params[parameter.name]\n\n                    if model not in concentrationModels:\n                        concentrationModels[model] = {}\n                    concentrationModels[model][\"gamma\"] = value\n\n                elif \"decay_CaDynamics_E2\" in parameter.param_name:\n                    model = f\"CaDynamics_E2_NML2__{cell.name}_{section_list}\"\n                    species.append(\n                        neuroml.Species(\n                            id=\"ca\",\n                            ion=\"ca\",\n                            initial_concentration=\"5.0E-11 mol_per_cm3\",\n                            initial_ext_concentration=\"2.0E-6 mol_per_cm3\",\n                            concentration_model=model,\n                            segment_groups=section_list,\n                        )\n                    )\n\n                    channel_nml2_file = \"CaDynamics_E2_NML2.nml\"\n                    add_nml_channel_to_nml_cell_file(\n                        cell_doc,\n                        included_channels,\n                        channel_nml2_file=channel_nml2_file,\n                    )\n\n                    value = parameter.value\n                    if value is None:\n                        # non frozen parameter\n                        value = release_params[parameter.name]\n\n                    if model not in concentrationModels:\n                        concentrationModels[model] = {}\n                    concentrationModels[model][\"decay\"] = value\n\n                elif parameter.param_name == \"cm\":\n                    capacitance_overwrites[\n                        section_list\n                    ] = f\"{parameter.value} uF_per_cm2\"\n\n    # append new concentrations to the CaDynamics_E2_NML2.nml file\n    if concentrationModels and not skip_channels_copy:\n        adapt_CaDynamics_nml(concentrationModels)\n\n    specific_capacitances = get_specific_capacitance(capacitance_overwrites)\n\n    v_init = cell.params[\"v_init\"].value\n    init_memb_potentials = [\n        neuroml.InitMembPotential(value=f\"{v_init} mV\", segment_groups=\"all\")\n    ]\n\n    membrane_properties = neuroml.MembraneProperties(\n        channel_densities=channel_densities,\n        channel_density_nernsts=channel_density_nernsts,\n        channel_density_non_uniform_nernsts=channel_density_non_unif_nernsts,\n        channel_density_non_uniforms=channel_density_non_uniforms,\n        specific_capacitances=specific_capacitances,\n        init_memb_potentials=init_memb_potentials,\n    )\n\n    # Intracellular Properties\n    Ra = cell.params[\"Ra.all\"].value\n    resistivities = [\n        neuroml.Resistivity(value=f\"{Ra} ohm_cm\", segment_groups=\"all\")\n    ]\n\n    intracellular_properties = neuroml.IntracellularProperties(\n        resistivities=resistivities,\n        species=species,\n    )\n\n    # Biophysical Properties\n    biophysical_properties = neuroml.BiophysicalProperties(\n        id=\"biophys\",\n        intracellular_properties=intracellular_properties,\n        membrane_properties=membrane_properties,\n    )\n\n    return biophysical_properties\n"
  },
  {
    "path": "bluepyopt/neuroml/cell.py",
    "content": "\"\"\"Functions to create neuroml cell\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport logging\n\nfrom neuroml import NeuroMLDocument\nfrom pyneuroml import pynml\n\nfrom .biophys import get_biophys\nfrom .morphology import add_segment_groups\nfrom .morphology import create_morph_nml\n\nlogger = logging.getLogger(__name__)\n\n\ndef create_neuroml_cell(\n    bpo_cell,\n    release_params,\n    skip_channels_copy=False,\n    custom_channel_ion=None,\n    custom_ion_erevs=None,\n):\n    \"\"\"Create the cell.\n\n    Arguments:\n        bpo_cell (ephys.CellModel): bluepyopt cell\n        release_params (dict): the optimized parameters\n        skip_channels_copy (bool): True to skip the copy pasting\n            of the neuroml channel files\n        custom_channel_ion (dict): dict mapping channel to ion\n        custom_ion_erevs (dict): dict mapping ion to erev (reversal potential)\n\n    :returns: name of the cell nml file\n    \"\"\"\n    # Create the nml file and add the ion channels\n    cell_doc = NeuroMLDocument(id=bpo_cell.name)\n    # the network name\n    network_filename = f\"{bpo_cell.name}.net.nml\"\n\n    # Morphology\n    logger.info(\n        \"This will create a cell hoc file in order to create a cell nml file\"\n    )\n    create_morph_nml(bpo_cell, network_filename, release_params)\n\n    # change the network temperature.\n    # because the pyneurom.export_to_neuroml2 sets it automatically to 6C.\n    network_doc = pynml.read_neuroml2_file(network_filename)\n    network = network_doc.networks[0]\n    network.temperature = f\"{bpo_cell.params['celsius'].value} degC\"\n    pynml.write_neuroml2_file(\n        nml2_doc=network_doc, nml2_file_name=network_filename, validate=True\n    )\n\n    # get the cell\n    nml_cell_loc = f\"{bpo_cell.name}_0_0.cell.nml\"\n    nml_doc = pynml.read_neuroml2_file(nml_cell_loc)\n\n    cell = nml_doc.cells[0]\n\n    # add segment groups (cell)\n    add_segment_groups(cell)\n\n    # get biophys\n    bio_prop = get_biophys(\n        bpo_cell,\n        cell_doc,\n        release_params,\n        skip_non_uniform=True,\n        skip_channels_copy=skip_channels_copy,\n        custom_channel_ion=custom_channel_ion,\n        custom_ion_erevs=custom_ion_erevs,\n    )\n\n    # Append biophys to cell\n    cell.biophysical_properties = bio_prop\n\n    # Adding notes\n    notes = \"This cell was exported from BluePyOpt.\"\n    cell.notes = notes\n\n    # write neuroml cell doc\n    cell_doc.cells.append(cell)\n    pynml.write_neuroml2_file(\n        nml2_doc=cell_doc, nml2_file_name=nml_cell_loc, validate=True\n    )\n\n    return nml_cell_loc\n"
  },
  {
    "path": "bluepyopt/neuroml/morphology.py",
    "content": "\"\"\"Functions to create neuroml morphology\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport logging\nimport os\nfrom pathlib import Path\n\nimport neuroml\nfrom pyneuroml.neuron import export_to_neuroml2\n\nlogger = logging.getLogger(__name__)\n\n\ndef create_loadcell_hoc(\n    loadcell_hoc_filename, hoc_filename, morphology_path, v_init, cell_name\n):\n    \"\"\"Create a hoc file able to load the cell.\n\n    Arguments:\n        loadcell_hoc_filename (str): path to the loadcell hoc file to output\n        hoc_filename (str): file name of the cell hoc file\n        morphology_path (str): path to the morphology file\n        v_init (float): inital voltage in mV\n        cell_name (str): cell name\n    \"\"\"\n    morph_path = Path(morphology_path)\n    morph_dir = morph_path.parent\n    morph_file = morph_path.name\n    cell_cmd = '\"cell = new %s(\\\\\"%s\\\\\", \\\\\"%s\\\\\")\"'\n    cell = f'{cell_cmd}, \"{cell_name}\", \"{morph_dir}\", \"{morph_file}\"'\n    loadcell_hoc = f\"\"\"\n        load_file(\"nrngui.hoc\")\n        load_file(\"import3d.hoc\")\n        load_file(\"{hoc_filename}\")\n\n        // ================== constants ==================\n        v_init={v_init}\n        // ================== creating cell object ==================\n        objref cell\n\n        proc create_cell() {{ localobj cellstring\n            cellstring = new String()\n            sprint(cellstring.s, {cell})\n            execute(cellstring.s)\n        }}\n        create_cell(0)\n    \"\"\"\n    with open(loadcell_hoc_filename, \"w\", encoding=\"utf-8\") as hoc_file:\n        hoc_file.write(loadcell_hoc)\n\n\ndef create_morph_nml(bpo_cell, network_filename, release_params):\n    \"\"\"Create cell hoc file, then cell nml file.\n\n    Arguments:\n        bpo_cell (ephys.CellModel): bluepyopt cell\n        network_filename (str): name of the neuroml network file\n        release_params (dict): name and values of optimised parameters\n    \"\"\"\n    import pebble\n\n    hoc_filename = f\"{bpo_cell.name}.hoc\"\n    loadcell_hoc_filename = \"loadcell.hoc\"\n\n    # write the cell in a hoc file\n    cell_hoc = bpo_cell.create_hoc(release_params)\n    with open(hoc_filename, \"w\", encoding=\"utf-8\") as hoc_file:\n        hoc_file.write(cell_hoc)\n\n    # write a hoc file able to load the cell\n    create_loadcell_hoc(\n        loadcell_hoc_filename,\n        hoc_filename,\n        bpo_cell.morphology.morphology_path,\n        bpo_cell.params[\"v_init\"].value,\n        bpo_cell.name,\n    )\n\n    if not os.path.isdir(\"x86_64\"):\n        logger.warning(\n            \"It seems you have not compiled the mechanisms. \"\n            \"This program will likely fail.\"\n        )\n\n    # isolate the export_to_neuroml to a subprocess\n    # so that the cell remain non-instantiated in the main process\n    # that way, using this function will not prevent us to run the cell\n    # with bluepyopt\n    with pebble.ProcessPool(max_workers=1, max_tasks=1) as pool:\n        tasks = pool.schedule(\n            export_to_neuroml2,\n            kwargs={\n                \"hoc_or_python_file\": loadcell_hoc_filename,\n                \"nml2_file_name\": network_filename,\n                \"separateCellFiles\": True,\n                \"includeBiophysicalProperties\": False,\n            },\n        )\n        tasks.result()\n\n\ndef add_segment_groups(cell):\n    \"\"\"Add the segment groups to be consistent with naming in biophys.\n\n    Arguments:\n        cell (neuroml.Cell): neuroml cell\n    \"\"\"\n    groups = {\"somatic\": [], \"axonal\": [], \"basal\": [], \"apical\": []}\n    for seg in cell.morphology.segment_groups:\n        if \"soma\" in seg.id:\n            groups[\"somatic\"].append(seg)\n        elif \"axon\" in seg.id:\n            groups[\"axonal\"].append(seg)\n        elif \"dend\" in seg.id:\n            groups[\"basal\"].append(seg)\n        elif \"apic\" in seg.id:\n            groups[\"apical\"].append(seg)\n\n    for g, segments in groups.items():\n        new_seg_group = neuroml.SegmentGroup(id=g)\n        cell.morphology.segment_groups.append(new_seg_group)\n        for sg in segments:\n            new_seg_group.includes.append(neuroml.Include(sg.id))\n        if g in [\"basal\", \"apical\"]:\n            new_seg_group.inhomogeneous_parameters.append(\n                neuroml.InhomogeneousParameter(\n                    id=f\"PathLengthOver_{g}\",\n                    variable=\"p\",\n                    metric=\"Path Length from root\",\n                    proximal=neuroml.ProximalDetails(translation_start=\"0\"),\n                )\n            )\n\n    cell.morphology.segment_groups.append(\n        neuroml.SegmentGroup(\n            id=\"soma_group\", includes=[neuroml.Include(\"somatic\")]\n        )\n    )\n    cell.morphology.segment_groups.append(\n        neuroml.SegmentGroup(\n            id=\"axon_group\", includes=[neuroml.Include(\"axonal\")]\n        )\n    )\n    cell.morphology.segment_groups.append(\n        neuroml.SegmentGroup(\n            id=\"dendrite_group\",\n            includes=[neuroml.Include(\"basal\"), neuroml.Include(\"apical\")],\n        )\n    )\n"
  },
  {
    "path": "bluepyopt/neuroml/simulation.py",
    "content": "\"\"\"Functions to create neuroml  simulation\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport logging\n\nimport neuroml\nfrom neuroml import ExplicitInput\n\nfrom pyneuroml import pynml\nfrom pyneuroml.lems import generate_lems_file_for_neuroml\n\nlogger = logging.getLogger(__name__)\n\n\ndef create_neuroml_simulation(\n    network_filename, protocols, dt, cell_name, lems_filename\n):\n    \"\"\"Append simulation data to a neuroml network.\n\n    Arguments:\n        network_filename (str): name of the neuroml network file\n        protocols (ephys.protocols.Protocol): protocols\n        dt (float): timestep\n        cell_name (str): name of the cell\n        lems_filename (str): file name under which to register the lems file\n    \"\"\"\n    # Get neuroml netowrk file\n    new_net_doc = pynml.read_neuroml2_file(network_filename)\n    new_net = new_net_doc.networks[0]\n\n    pop_id = new_net.populations[0].id\n\n    stim_sim_duration = 0\n\n    # Create neuroml stimuli\n    # Expects ephys.stimuli.NrnSquarePulse objects\n    for i, stim in enumerate(protocols.stimuli):\n        stim_sim_duration = stim.total_duration\n        stim_ref = f\"stimulus_{i}\"\n\n        # create Pulse\n        new_nml_stim = neuroml.PulseGenerator(\n            id=stim_ref,\n            delay=f\"{stim.step_delay}ms\",\n            duration=f\"{stim.step_duration}ms\",\n            amplitude=f\"{stim.step_amplitude}nA\",\n        )\n        new_net_doc.pulse_generators.append(new_nml_stim)\n\n        exp_input = ExplicitInput(target=f\"{pop_id}[0]\", input=stim_ref)\n        new_net.explicit_inputs.append(exp_input)\n\n    # write updated netowrk file\n    pynml.write_neuroml2_file(new_net_doc, network_filename)\n\n    local_nml2_cell_dir = \".\"  # target dir\n    generate_lems_file_for_neuroml(\n        cell_name,\n        network_filename,\n        \"network\",\n        stim_sim_duration,\n        dt,\n        lems_filename,\n        local_nml2_cell_dir,\n        copy_neuroml=False,\n        simulation_seed=1234,\n    )\n"
  },
  {
    "path": "bluepyopt/objectives.py",
    "content": "\"\"\"Objective classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nclass Objective(object):\n\n    \"\"\"Objective of the optimisation algorithm\"\"\"\n\n    def __init__(self, name, value=None):\n        \"\"\"Constructor\"\"\"\n\n        self.name = name\n        self.value = value\n"
  },
  {
    "path": "bluepyopt/optimisations.py",
    "content": "\"\"\"Optimisation class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nclass Optimisation(object):\n\n    \"\"\"Optimisation class\"\"\"\n\n    _instance_counter = 0\n\n    def __init__(self, evaluator=None):\n        \"\"\"Constructor\"\"\"\n\n        self.evaluator = evaluator\n"
  },
  {
    "path": "bluepyopt/parameters.py",
    "content": "\"\"\"Parameter classes\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nclass Parameter(object):\n\n    \"\"\"Base parameter class\"\"\"\n\n    def __init__(self, name, value=None, frozen=False, bounds=None,\n                 param_dependencies=None):\n        \"\"\"Constructor\"\"\"\n\n        self.name = name\n        self.prefix = None\n        self.bounds = bounds\n        self._value = value\n        self.check_bounds()\n        self.frozen = frozen\n        self.param_dependencies = param_dependencies\n        if param_dependencies is None:\n            self.param_dependencies = []\n\n    @property\n    def lower_bound(self):\n        \"\"\"Lower bound\"\"\"\n        if self.bounds is not None:\n            return self.bounds[0]\n        else:\n            return None\n\n    @property\n    def upper_bound(self):\n        \"\"\"Lower bound\"\"\"\n        if self.bounds is not None:\n            return self.bounds[1]\n        else:\n            return None\n\n    @property\n    def value(self):\n        \"\"\"Parameter value\"\"\"\n        return self._value\n\n    def freeze(self, value):\n        \"\"\"Freeze parameter to certain value\"\"\"\n        self.value = value\n        self.frozen = True\n\n    def unfreeze(self):\n        \"\"\"Unfreeze parameter\"\"\"\n        self._value = None\n        self.frozen = False\n\n    @value.setter\n    def value(self, value):\n        \"\"\"Set parameter value\"\"\"\n        if self.frozen:\n            raise Exception(\n                'Parameter: parameter %s is frozen, unable to change value' %\n                self.name)\n        else:\n            self._value = value\n            self.check_bounds()\n\n    def check_bounds(self):\n        \"\"\"Check if parameter is within bounds\"\"\"\n        if self.bounds and self._value is not None:\n            if not self.lower_bound <= self._value <= self.upper_bound:\n                raise ValueError(\n                    'Parameter %s has value %s outside of bounds [%s, %s]' %\n                    (self.name, self._value, str(self.lower_bound),\n                     str(self.upper_bound)))\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n        return '%s: value = %s' % (\n            self.name, self.value if self.frozen else self.bounds)\n\n\nclass MetaListEqualParameter(Parameter):\n\n    \"\"\"Metaparameter that makes sure list of parameter values are all equal\"\"\"\n\n    def __init__(self, name, value=None, frozen=False,\n                 bounds=None, sub_parameters=None):\n        \"\"\"Constructor\"\"\"\n\n        if sub_parameters is None:\n            raise ValueError(\n                'MetaListEqualParameter: impossible to have None as '\n                'sub_parameters attribute')\n        else:\n            self.sub_parameters = sub_parameters\n\n        super(\n            MetaListEqualParameter,\n            self).__init__(\n            name,\n            value=value,\n            frozen=frozen,\n            bounds=bounds)\n\n        if value is not None:\n            for sub_parameter in self.sub_parameters:\n                sub_parameter.value = value\n\n    @Parameter.value.setter\n    def value(self, value):\n        \"\"\"Set parameter value\"\"\"\n\n        for sub_parameter in self.sub_parameters:\n            sub_parameter.value = value\n\n        Parameter.value.fset(self, value)\n\n    def freeze(self, value):\n        \"\"\"Freeze parameter to certain value\"\"\"\n\n        super(MetaListEqualParameter, self).freeze(value)\n\n        for sub_parameter in self.sub_parameters:\n            sub_parameter.frozen = True\n\n    def unfreeze(self):\n        \"\"\"Unfreeze parameter\"\"\"\n        for sub_parameter in self.sub_parameters:\n            sub_parameter.unfreeze()\n\n        super(MetaListEqualParameter, self).unfreeze()\n\n    def check_bounds(self):\n        \"\"\"Check if parameter is within bounds\"\"\"\n\n        for sub_parameter in self.sub_parameters:\n            sub_parameter.check_bounds()\n\n        super(MetaListEqualParameter, self).check_bounds()\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '%s (sub_params: %s): value = %s' % (self.name, \",\".join(\n            str(sub_param)\n            for sub_param in\n            self.sub_parameters),\n            self.value\n            if self.frozen else\n            self.bounds)\n"
  },
  {
    "path": "bluepyopt/stoppingCriteria.py",
    "content": "\"\"\"Stopping Criteria class\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nclass StoppingCriteria(object):\n    \"\"\"Stopping Criteria class\"\"\"\n\n    def __init__(self):\n        \"\"\"Constructor\"\"\"\n        self.criteria_met = False\n        pass\n\n    def check(self, kwargs):\n        \"\"\"Check if the stopping criteria is met\"\"\"\n        pass\n\n    def reset(self):\n        self.criteria_met = False\n"
  },
  {
    "path": "bluepyopt/tests/.gitignore",
    "content": "/.coverage\n/coverage.xml\n/coverage_html/\n"
  },
  {
    "path": "bluepyopt/tests/__init__.py",
    "content": ""
  },
  {
    "path": "bluepyopt/tests/disable_simplecell_scoop.py",
    "content": "'''\nNote: this is a bizarre test due to the fact that scoop can't be started from\nwithin python:\nhttps://github.com/soravux/scoop/issues/29\n\nTherefore, the way this works is for the test_ that's detected by nt then\ncreates a subprocess that runs scoop using the normal command line arguments\n\nIt then captures the output, and looks for the BEST: magic string which\nshould match the precomputed output\n'''\n\nimport os\n\nimport subprocess\n\nimport bluepyopt as nrp\nimport bluepyopt.ephys as nrpel\n\nSIMPLE_SWC = os.path.join(os.path.abspath(os.path.dirname(__file__)),\n                          '../../examples/simplecell/simple.swc')\n\n\n# Disabled this test. Doesn't work on a mac for some reason\n# Error message: can't import scoop\n# TODO Renable once this is fixed\ndef disabled_scoop():\n    \"\"\"Simplecell: test scoop\"\"\"\n    cmd = ['python', '-m', 'scoop', '-n', '2', __file__]\n    output = subprocess.check_output(cmd)\n    for line in output.split('\\n'):\n        if line.startswith('BEST'):\n            break\n    assert line == 'BEST: [0.11268238279399023, 0.038129859413828474]'\n\n\n# The rest defines the optimization we run with scoop\n\nmorph = nrpel.morphologies.NrnFileMorphology(SIMPLE_SWC)\nsomatic_loc = nrpel.locations.NrnSeclistLocation('somatic',\n                                                 seclist_name='somatic')\n\nhh_mech = nrpel.mechanisms.NrnMODMechanism(name='hh',\n                                           suffix='hh',\n                                           locations=[somatic_loc])\n\ncm_param = nrpel.parameters.NrnSectionParameter(name='cm',\n                                                param_name='cm',\n                                                value=1.0,\n                                                locations=[somatic_loc],\n                                                frozen=True)\n\n\ngnabar_param = nrpel.parameters.NrnSectionParameter(name='gnabar_hh',\n                                                    param_name='gnabar_hh',\n                                                    locations=[somatic_loc],\n                                                    bounds=[0.05, 0.125],\n                                                    frozen=False)\n\ngkbar_param = nrpel.parameters.NrnSectionParameter(name='gkbar_hh',\n                                                   param_name='gkbar_hh',\n                                                   bounds=[0.01, 0.075],\n                                                   locations=[somatic_loc],\n                                                   frozen=False)\n\nsimple_cell = nrpel.celltemplate.CellTemplate(name='simple_cell',\n                                              morph=morph,\n                                              mechs=[hh_mech],\n                                              params=[cm_param,\n                                                      gnabar_param,\n                                                      gkbar_param])\n\nsoma_loc = nrpel.locations.NrnSeclistCompLocation(name='soma',\n                                                  seclist_name='somatic',\n                                                  sec_index=0,\n                                                  comp_x=0.5)\n\nprotocols = {}\nfor protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\n    stim = nrpel.stimuli.NrnSquarePulse(step_amplitude=amplitude,\n                                        step_delay=100,\n                                        step_duration=50,\n                                        location=soma_loc,\n                                        total_duration=200)\n    rec = nrpel.recordings.CompRecording(name='%s.soma.v' % protocol_name,\n                                         location=soma_loc,\n                                         variable='v')\n    protocol = nrpel.protocols.Protocol(protocol_name, [stim], [rec])\n    protocols[protocol.name] = protocol\n\n\ndefault_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\nresponses = simple_cell.run_protocols(protocols,\n                                      param_values=default_params)\n\nefel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\n\nobjectives = []\n\nfor protocol_name, protocol in protocols.items():\n    stim_start = protocol.stimuli[0].step_delay\n    stim_end = stim_start + protocol.stimuli[0].step_duration\n    for efel_feature_name, mean in \\\n            efel_feature_means[protocol_name].items():\n        feature_name = '%s.%s' % (protocol_name, efel_feature_name)\n        feature = nrpel.efeatures.eFELFeature(\n            feature_name,\n            efel_feature_name=efel_feature_name,\n            recording_names={'': '%s.soma.v' % protocol_name},\n            stim_start=stim_start,\n            stim_end=stim_end,\n            exp_mean=mean,\n            exp_std=0.05 * mean)\n        objective = objective = nrpel.objectives.SingletonObjective(\n            feature_name,\n            feature)\n        objectives.append(objective)\n\n\nscore_calc = nrpel.scorecalculators.ObjectivesScoreCalculator(objectives)\ncell_evaluator = nrpel.cellevaluator.CellEvaluator(\n    cell_template=simple_cell,\n    param_names=[\n        'gnabar_hh',\n        'gkbar_hh'],\n    fitness_protocols=protocols,\n    fitness_calculator=score_calc)\n\noptimisation = nrp.Optimisation(\n    evaluator=cell_evaluator,\n    eval_function=cell_evaluator.evaluate_with_lists,\n    offspring_size=10,\n    use_scoop=True)\n\nif __name__ == '__main__':\n    final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=2)\n    print('BEST:', hall_of_fame[0])\n"
  },
  {
    "path": "bluepyopt/tests/expected_results.json",
    "content": "{\n    \"TestL5PCEvaluator.test_eval\": {\n        \"bAP.soma.AP_width\": 1.9999999999995453,\n        \"bAP.soma.AP_height\": 2.50384474984601,\n        \"bAP.soma.Spikecount\": 0.0,\n        \"bAP.dend1.AP_amplitude_from_voltagebase\": 0.8267263765251129,\n        \"bAP.dend2.AP_amplitude_from_voltagebase\": 0.5795372919702172,\n        \"Step3.soma.AP_height\": 0.9933359179333321,\n        \"Step3.soma.AHP_slow_time\": 1.7605122073692951,\n        \"Step3.soma.ISI_CV\": 0.8718173723988226,\n        \"Step3.soma.doublet_ISI\": 0.39855072463652236,\n        \"Step3.soma.adaptation_index2\": 1.1379787461057103,\n        \"Step3.soma.mean_frequency\": 1.7684352065813025,\n        \"Step3.soma.AHP_depth_abs_slow\": 2.24354209144176,\n        \"Step3.soma.AP_width\": 3.044293354575099,\n        \"Step3.soma.time_to_first_spike\": 0.07352941183310188,\n        \"Step3.soma.AHP_depth_abs\": 0.8314513271562025,\n        \"Step2.soma.AP_height\": 0.40331820266634766,\n        \"Step2.soma.AHP_slow_time\": 0.5635329569575162,\n        \"Step2.soma.ISI_CV\": 0.4167583130087039,\n        \"Step2.soma.doublet_ISI\": 0.08411961809835558,\n        \"Step2.soma.adaptation_index2\": 0.9608331502738571,\n        \"Step2.soma.mean_frequency\": 0.3526065669686068,\n        \"Step2.soma.AHP_depth_abs_slow\": 0.3002832227214548,\n        \"Step2.soma.AP_width\": 2.5539797463199387,\n        \"Step2.soma.time_to_first_spike\": 1.1294134000889067,\n        \"Step2.soma.AHP_depth_abs\": 0.47825588583451795,\n        \"Step1.soma.AP_height\": 1.193221084786481,\n        \"Step1.soma.AHP_slow_time\": 0.4051335332920494,\n        \"Step1.soma.ISI_CV\": 0.6241490491551318,\n        \"Step1.soma.doublet_ISI\": 0.4396536563682781,\n        \"Step1.soma.adaptation_index2\": 0.1682382448448352,\n        \"Step1.soma.mean_frequency\": 0.9054156314648848,\n        \"Step1.soma.AHP_depth_abs_slow\": 0.48627438571478304,\n        \"Step1.soma.AP_width\": 3.062383612663639,\n        \"Step1.soma.time_to_first_spike\": 1.0572856593789417,\n        \"Step1.soma.AHP_depth_abs\": 0.770012863966287\n    }\n}"
  },
  {
    "path": "bluepyopt/tests/test_bluepyopt.py",
    "content": "\"\"\"Tests of the main bluepyopt module\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\nimport pytest\nimport numpy\n\n\n@pytest.mark.unit\ndef test_import():\n    \"\"\"bluepyopt: test importing bluepyopt\"\"\"\n    import bluepyopt  # NOQA\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/__init__.py",
    "content": ""
  },
  {
    "path": "bluepyopt/tests/test_deapext/deapext_test_utils.py",
    "content": "import random\nimport numpy as np\n\nfrom deap import base\nfrom deap import creator\n\n\ndef make_mock_population(features_count=5, population_count=5):\n    \"\"\"Create pop of inds that we have full control over,creating a DEAP one\"\"\"\n    # TODO: Use mock instead\n    class Individual(object):\n        class Fitness(object):\n            def __init__(self, wvalues, valid):\n                self.wvalues = wvalues\n                self.valid = valid\n\n        def __init__(self, wvalues, valid, ibea_fitness):\n            self.fitness = Individual.Fitness(wvalues, valid)\n            self.ibea_fitness = ibea_fitness\n\n    MU, SIGMA = 0, 1\n    np.random.seed(0)\n    random.seed(0)\n\n    # create individuals w/ a random weight values, and an ibea_fitness\n    # according to their position\n    return [Individual(np.random.normal(MU, SIGMA, features_count), bool(\n        i % 2), i) for i in range(population_count)]\n\n\ndef make_population(features_count=5, population_count=5):\n    '''create population w/ DEAP Individuals\n    '''\n\n    creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0, -1.0))\n    creator.create(\"Individual\", list, fitness=creator.FitnessMin)\n\n    random.seed(0)\n\n    population = [creator.Individual(range(i * features_count,\n                                           (i + 1) * features_count))\n                  for i in range(population_count)]\n    return population\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/test_algorithms.py",
    "content": "\"\"\"bluepyopt.optimisations tests\"\"\"\n\nimport numpy\nfrom unittest import mock\n\nimport deap.creator\nimport deap.benchmarks\n\n\nimport bluepyopt.deapext.algorithms\n\nimport pytest\n\n\n@pytest.mark.unit\ndef test_eaAlphaMuPlusLambdaCheckpoint():\n    \"\"\"deapext.algorithms: Testing eaAlphaMuPlusLambdaCheckpoint\"\"\"\n\n    deap.creator.create('fit', deap.base.Fitness, weights=(-1.0,))\n    deap.creator.create(\n        'ind',\n        numpy.ndarray,\n        fitness=deap.creator.__dict__['fit'])\n\n    population = [deap.creator.__dict__['ind'](x)\n                  for x in numpy.random.uniform(0, 1,\n                                                (10, 2))]\n\n    toolbox = deap.base.Toolbox()\n    toolbox.register(\"evaluate\", deap.benchmarks.sphere)\n    toolbox.register(\"mate\", lambda x, y: (x, y))\n    toolbox.register(\"mutate\", lambda x: (x,))\n    toolbox.register(\"select\", lambda pop, mu: pop)\n    toolbox.register(\"variate\", lambda par, toolb, cxpb, mutpb: par)\n\n    population, hof, logbook, history = \\\n        bluepyopt.deapext.algorithms.eaAlphaMuPlusLambdaCheckpoint(\n            population=population,\n            toolbox=toolbox,\n            mu=1.0,\n            cxpb=1.0,\n            mutpb=1.0,\n            ngen=2,\n            stats=None,\n            halloffame=None,\n            cp_frequency=1,\n            cp_filename=None,\n            continue_cp=False)\n\n    assert isinstance(population, list)\n    assert len(population) == 20\n    assert isinstance(logbook, deap.tools.support.Logbook)\n    assert isinstance(history, deap.tools.support.History)\n\n\n@pytest.mark.unit\ndef test_eaAlphaMuPlusLambdaCheckpoint_with_checkpoint():\n    \"\"\"deapext.algorithms: Testing eaAlphaMuPlusLambdaCheckpoint\"\"\"\n\n    deap.creator.create('fit', deap.base.Fitness, weights=(-1.0,))\n    deap.creator.create(\n        'ind',\n        numpy.ndarray,\n        fitness=deap.creator.__dict__['fit'])\n\n    population = [deap.creator.__dict__['ind'](x)\n                  for x in numpy.random.uniform(0, 1,\n                                                (10, 2))]\n\n    toolbox = deap.base.Toolbox()\n    toolbox.register(\"evaluate\", deap.benchmarks.sphere)\n    toolbox.register(\"mate\", lambda x, y: (x, y))\n    toolbox.register(\"mutate\", lambda x: (x,))\n    toolbox.register(\"select\", lambda pop, mu: pop)\n\n    with mock.patch('pickle.dump'):\n        with mock.patch('bluepyopt.deapext.algorithms.open',\n                        mock.mock_open()):\n            population, hof, logbook, history = \\\n                bluepyopt.deapext.algorithms.eaAlphaMuPlusLambdaCheckpoint(\n                    population=population,\n                    toolbox=toolbox,\n                    mu=1.0,\n                    cxpb=1.0,\n                    mutpb=1.0,\n                    ngen=2,\n                    stats=None,\n                    halloffame=None,\n                    cp_frequency=1,\n                    cp_filename='cp_test',\n                    continue_cp=False)\n\n    import random\n    with mock.patch('pickle.load', return_value={'population': population,\n                                                 'logbook': logbook,\n                                                 'history': history,\n                                                 'parents': None,\n                                                 'halloffame': None,\n                                                 'rndstate': random.getstate(),\n                                                 'generation': 1}):\n        with mock.patch('bluepyopt.deapext.algorithms.open',\n                        mock.mock_open()):\n            new_population, hof, logbook, history = \\\n                bluepyopt.deapext.algorithms.eaAlphaMuPlusLambdaCheckpoint(\n                    population=population,\n                    toolbox=toolbox,\n                    mu=1.0,\n                    cxpb=1.0,\n                    mutpb=1.0,\n                    ngen=0,\n                    stats=None,\n                    halloffame=None,\n                    cp_frequency=1,\n                    cp_filename='cp_test',\n                    continue_cp=True)\n    for ind1, ind2 in zip(new_population, population):\n        assert list(ind1) == list(ind2)\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/test_hype.py",
    "content": "\"\"\"bluepyopt.deapext.hype tests\"\"\"\n\nimport numpy\n\nimport bluepyopt.deapext.hype\n\nimport pytest\n\n\n@pytest.mark.unit\ndef test_hypeIndicatorExact():\n    \"\"\"deapext.hype: Testing hypeIndicatorExact\"\"\"\n\n    points = numpy.asarray([[250., 250.], [0., 0.], [240., 240.]])\n    bounds = numpy.asarray([250., 250.])\n\n    hv = bluepyopt.deapext.hype.hypeIndicatorExact(points, bounds, k=5)\n\n    assert hv[0] == 0\n    assert hv[1] == 62500\n    assert hv[2] == 100\n\n\n@pytest.mark.unit\ndef test_hypeIndicatorSampled():\n    \"\"\"deapext.hype: Testing hypeIndicatorSampled\"\"\"\n\n    points = numpy.asarray([[250., 250.], [0., 0.], [240., 240.]])\n    bounds = numpy.asarray([250., 250.])\n\n    numpy.random.seed(42)\n    hv = bluepyopt.deapext.hype.hypeIndicatorSampled(\n        points, bounds, nrOfSamples=1000000, k=5\n    )\n\n    assert hv[0] == 0\n    assert hv[1] == 62500\n    assert numpy.abs((hv[2] / 100) - 1) < 0.05\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/test_optimisations.py",
    "content": "\"\"\"bluepyopt.optimisations tests\"\"\"\n\n\nimport bluepyopt.optimisations\nimport bluepyopt.ephys.examples.simplecell\n\nimport pytest\nimport numpy\n\nimport deap.tools\n\n\n@pytest.mark.unit\ndef test_DEAPOptimisation_constructor():\n    \"deapext.optimisation: Testing constructor DEAPOptimisation\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    optimisation = bluepyopt.deapext.optimisations.DEAPOptimisation(\n        simplecell.cell_evaluator, map_function=map)\n\n    assert isinstance(optimisation,\n                      bluepyopt.deapext.optimisations.DEAPOptimisation)\n    assert isinstance(optimisation.evaluator,\n                      bluepyopt.evaluators.Evaluator)\n\n    pytest.raises(\n        ValueError,\n        bluepyopt.deapext.optimisations.DEAPOptimisation,\n        simplecell.cell_evaluator,\n        selector_name='wrong')\n\n\n@pytest.mark.unit\ndef test_IBEADEAPOptimisation_constructor():\n    \"deapext.optimisation: Testing constructor IBEADEAPOptimisation\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    optimisation = bluepyopt.deapext.optimisations.IBEADEAPOptimisation(\n        simplecell.cell_evaluator, map_function=map)\n\n    assert isinstance(optimisation,\n                      bluepyopt.deapext.optimisations.IBEADEAPOptimisation)\n\n\n@pytest.mark.unit\ndef test_DEAPOptimisation_run():\n    \"deapext.optimisation: Testing DEAPOptimisation run\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    optimisation = bluepyopt.optimisations.DEAPOptimisation(\n        simplecell.cell_evaluator, offspring_size=1)\n\n    pop, hof, log, hist = optimisation.run(max_ngen=1)\n\n    ind = [0.06007731830843009, 0.06508319290092013]\n    assert len(pop) == 1\n    numpy.testing.assert_almost_equal(pop[0], ind)\n    numpy.testing.assert_almost_equal(hof[0], ind)\n    assert log[0]['nevals'] == 1\n    numpy.testing.assert_almost_equal(hist.genealogy_history[1], ind)\n\n\n@pytest.mark.unit\ndef test_DEAPOptimisation_run_from_parents():\n    \"deapext.optimisation: Testing DEAPOptimisation run using prior parents\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    optimisation = bluepyopt.optimisations.DEAPOptimisation(\n        simplecell.cell_evaluator, offspring_size=1)\n\n    parent_population = [[0.060, 0.065]]\n    pop, hof, log, hist = optimisation.run(max_ngen=0,\n                                           parent_population=parent_population)\n\n    assert len(pop) == 1\n    numpy.testing.assert_almost_equal(pop[0], parent_population[0])\n\n\n@pytest.mark.unit\ndef test_selectorname():\n    \"deapext.optimisation: Testing selector_name argument\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n\n    # Test default value\n    ibea_optimisation = bluepyopt.optimisations.DEAPOptimisation(\n        simplecell.cell_evaluator)\n    assert ibea_optimisation.selector_name == 'IBEA'\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n\n    # Test NSGA2 selector\n    nsga2_optimisation = bluepyopt.optimisations.DEAPOptimisation(\n        simplecell.cell_evaluator, selector_name='NSGA2')\n\n    assert (\n        nsga2_optimisation.toolbox.select.func\n        == deap.tools.emo.selNSGA2)\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n\n    # Test IBEA selector\n    ibea_optimisation = bluepyopt.optimisations.DEAPOptimisation(\n        simplecell.cell_evaluator, selector_name='IBEA')\n\n    assert (\n        ibea_optimisation.toolbox.select.func\n        == bluepyopt.deapext.tools.selIBEA)\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/test_optimisationsCMA.py",
    "content": "\"\"\"bluepyopt.optimisationsCMA tests\"\"\"\n\nimport pytest\nimport bluepyopt\nimport bluepyopt.ephys.examples.simplecell\n\n\n@pytest.mark.unit\ndef test_optimisationsCMA_normspace():\n    \"\"\"deapext.optimisationsCMA: Testing optimisationsCMA normspace\"\"\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    evaluator = simplecell.cell_evaluator\n    optimisation = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA(\n        evaluator=evaluator)\n\n    x = [n * 0.1 for n in range(len(evaluator.params))]\n    y = [f2(f1(p)) for p, f1, f2 in zip(x, optimisation.to_norm,\n                                        optimisation.to_space)]\n\n    for a, b in zip(x, y):\n        assert b == pytest.approx(a, abs=1e-5)\n\n\n@pytest.mark.unit\ndef test_optimisationsCMA_SO_run():\n    \"\"\"deapext.optimisationsCMA: Testing optimisationsCMA run from centroid\"\"\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    evaluator = simplecell.cell_evaluator\n    x = [n * 0.1 for n in range(len(evaluator.params))]\n\n    optimiser = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA\n    optimisation = optimiser(evaluator=evaluator, centroids=[x])\n    pop, hof, log, hist = optimisation.run(max_ngen=2)\n\n    assert log.select(\"avg\")[-1] == pytest.approx(53.3333, abs=1e-4)\n    assert log.select(\"std\")[-1] == pytest.approx(83.7987, abs=1e-4)\n    assert pop[0][0] == pytest.approx(0.10525059698894745, abs=1e-6)\n    assert pop[0][1] == pytest.approx(0.01000000003249999, abs=1e-6)\n\n\n@pytest.mark.unit\ndef test_optimisationsCMA_MO_run():\n    \"\"\"deapext.optimisationsCMA: Testing optimisationsCMA run from centroid\"\"\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    evaluator = simplecell.cell_evaluator\n\n    optimiser = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA\n    optimisation = optimiser(\n        selector_name=\"multi_objective\",\n        offspring_size=3,\n        evaluator=evaluator,\n        seed=42\n    )\n    pop, hof, log, hist = optimisation.run(max_ngen=2)\n\n    assert log.select(\"avg\")[-1] == pytest.approx(40., abs=1e-4)\n    assert log.select(\"std\")[-1] == pytest.approx(16.32993, abs=1e-4)\n    assert pop[0][0] == pytest.approx(0.09601241274168831, abs=1e-6)\n    assert pop[0][1] == pytest.approx(0.024646650865379722, abs=1e-6)\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/test_selIBEA.py",
    "content": "\"\"\"selIBEA tests\"\"\"\n\n\nimport deap\nimport numpy\n\nimport bluepyopt.deapext\nfrom bluepyopt.deapext.tools.selIBEA \\\n    import (_calc_fitness_components, _mating_selection,)\nfrom .deapext_test_utils import make_mock_population\n\nimport pytest\n\n\n@pytest.mark.unit\ndef test_calc_fitness_components():\n    \"\"\"deapext.selIBEA: test calc_fitness_components\"\"\"\n    KAPPA = 0.05\n    population = make_mock_population()\n\n    components = _calc_fitness_components(population, kappa=KAPPA)\n\n    expected = numpy.array(\n        [\n            [1.00000000e+00, 4.30002298e-05, 4.26748513e-09, 2.06115362e-09,\n             9.71587289e-03],\n            [5.11484499e-09, 1.00000000e+00, 2.02317572e-07, 4.79335491e-05,\n             3.52720088e-08],\n            [6.75130710e-07, 1.23735078e+00, 1.00000000e+00, 2.77149617e-01,\n             8.37712763e-06],\n            [2.06115362e-09, 3.04444453e-04, 8.15288827e-08, 1.00000000e+00,\n             2.06115362e-09],\n            [2.06115362e-09, 6.75565231e-04, 4.39228177e-07, 2.12142918e-07,\n             1.00000000e+00]\n        ])\n\n    assert numpy.allclose(expected, components)\n\n\n@pytest.mark.unit\ndef test_mating_selection():\n    \"\"\"deapext.selIBEA: test mating selection\"\"\"\n\n    PARENT_COUNT = 10\n    population = make_mock_population()\n    parents = _mating_selection(population, PARENT_COUNT, 5)\n    assert len(parents) == PARENT_COUNT\n    expected = [1, 1, 1, 1, 1, 0, 1, 0, 0, 0]\n    assert expected == [ind.ibea_fitness for ind in parents]\n\n\n@pytest.mark.unit\ndef test_selibea_init():\n    \"\"\"deapext.selIBEA: test selIBEA init\"\"\"\n\n    deap.creator.create('fit', deap.base.Fitness, weights=(-1.0,))\n    deap.creator.create(\n        'ind',\n        numpy.ndarray,\n        fitness=deap.creator.__dict__['fit'])\n\n    numpy.random.seed(1)\n\n    population = [deap.creator.__dict__['ind'](x)\n                  for x in numpy.random.uniform(0, 1,\n                                                (10, 2))]\n\n    for ind in population:\n        ind.fitness.values = (numpy.random.uniform(0, 1), )\n\n    mu = 5\n    parents = bluepyopt.deapext.tools.selIBEA(population, mu)\n\n    assert len(parents) == mu\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/test_stoppingCriteria.py",
    "content": "\"\"\"bluepyopt.stoppingCriteria tests\"\"\"\n\n\nimport bluepyopt.stoppingCriteria\n\nimport pytest\n\n\n@pytest.mark.unit\ndef test_MaxNGen():\n    \"\"\"deapext.stoppingCriteria: Testing MaxNGen\"\"\"\n\n    max_gen = 3\n    criteria = bluepyopt.deapext.stoppingCriteria.MaxNGen(max_gen)\n\n    assert criteria.criteria_met is False\n    criteria.check({\"gen\": max_gen + 1})\n    assert criteria.criteria_met is True\n    criteria.reset()\n    criteria.check({\"gen\": max_gen})\n    assert criteria.criteria_met is False\n"
  },
  {
    "path": "bluepyopt/tests/test_deapext/test_utils.py",
    "content": "\"\"\"bluepyopt.utils tests\"\"\"\n\nimport multiprocessing\nimport time\n\nimport bluepyopt.deapext.utils as utils\nimport pytest\n\n\ndef flag(event):\n    \"\"\"Send a multiprocessing event.\"\"\"\n    time.sleep(1)\n    event.set()\n\n\ndef catch_event(event):\n    \"\"\"Verify that run_next_gen changes when event is caught.\"\"\"\n    # None case\n    assert utils.run_next_gen(True, None)\n\n    # event is not set case\n    assert utils.run_next_gen(True, event)\n\n    # event is set by another process case\n    time.sleep(2)\n    assert not (utils.run_next_gen(True, event))\n\n\n@pytest.mark.unit\ndef test_run_next_gen_condition():\n    \"\"\"deapext.utils: Testing run_next_gen.\"\"\"\n    event = multiprocessing.Event()\n    p1 = multiprocessing.Process(target=catch_event, args=(event,))\n    p2 = multiprocessing.Process(target=flag, args=(event,))\n\n    p1.start()\n    p2.start()\n\n    p1.join()\n    p2.join()\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/__init__.py",
    "content": ""
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_acc.py",
    "content": "\"\"\"Unit tests for acc.\"\"\"\n\nfrom bluepyopt.ephys.acc import arbor, ArbLabel\n\n\nimport pytest\n\n\n@pytest.mark.unit\ndef test_arbor_labels():\n    \"\"\"Test Arbor labels.\"\"\"\n\n    region_label = ArbLabel(type='region',\n                            name='first_branch',\n                            s_expr='(branch 0)')\n\n    assert region_label.defn == '(region-def \"first_branch\" (branch 0))'\n    assert region_label.ref == '(region \"first_branch\")'\n    assert region_label.name == 'first_branch'\n    assert region_label.loc == '(branch 0)'\n    assert region_label == region_label\n    assert region_label is not None\n\n    locset_label = ArbLabel(type='locset',\n                            name='first_branch_center',\n                            s_expr='(location 0 0.5)')\n\n    assert locset_label.defn == \\\n        '(locset-def \"first_branch_center\" (location 0 0.5))'\n    assert locset_label.ref == '(locset \"first_branch_center\")'\n    assert locset_label.name == 'first_branch_center'\n    assert locset_label.loc == '(location 0 0.5)'\n    assert locset_label == locset_label\n    assert locset_label is not None\n\n    assert locset_label != region_label\n\n    arbor.label_dict({region_label.name: region_label.loc,\n                      locset_label.name: locset_label.loc})\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_create_acc.py",
    "content": "\"\"\"Tests for create_acc.py\"\"\"\n\n# pylint: disable=W0212\n\nimport json\nimport os\nimport pathlib\nimport re\nimport sys\nimport tempfile\n\nimport pytest\n\nfrom bluepyopt import ephys\nfrom bluepyopt.ephys import create_acc\nfrom bluepyopt.ephys.acc import ArbLabel, arbor\nfrom bluepyopt.ephys.create_acc import (\n    ArbNmodlMechFormatter,\n    Nrn2ArbMechGrouper,\n    Nrn2ArbParamAdapter,\n)\nfrom bluepyopt.ephys.morphologies import ArbFileMorphology\nfrom bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceScaler\n\nfrom . import utils\n\nDEFAULT_ARBOR_REGION_ORDER = [\n    (\"soma\", 1),\n    (\"axon\", 2),\n    (\"dend\", 3),\n    (\"apic\", 4),\n    (\"myelin\", 5),\n]\n\n\ntestdata_dir = pathlib.Path(__file__).parent.joinpath(\"testdata\")\n\n\n@pytest.mark.unit\ndef test_read_templates():\n    \"\"\"Unit test for _read_templates function.\"\"\"\n    template_dir = testdata_dir / \"acc/templates\"\n    template_filename = \"*_template.jinja2\"\n    templates = create_acc._read_templates(template_dir, template_filename)\n    assert templates.keys() == {\"label_dict.acc\", \"cell.json\", \"decor.acc\"}\n\n    with pytest.raises(FileNotFoundError):\n        create_acc._read_templates(\"DOES_NOT_EXIST\", template_filename)\n\n\n@pytest.mark.unit\ndef test_Nrn2ArbParamAdapter_param_name():\n    \"\"\"Test Neuron to Arbor parameter mapping.\"\"\"\n    # Identity\n    mech_param_name = \"gSKv3_1bar_SKv3_1\"\n    assert Nrn2ArbParamAdapter._param_name(mech_param_name) == mech_param_name\n\n    # Non-trivial transformation\n    global_property_name = \"v_init\"\n    assert (\n        Nrn2ArbParamAdapter._param_name(global_property_name)\n        == \"membrane-potential\"\n    )\n\n\n@pytest.mark.unit\ndef test_Nrn2ArbParamAdapter_param_value():\n    \"\"\"Test Neuron to Arbor parameter units conversion.\"\"\"\n    # Identity for region parameter\n    mech_param = create_acc.Location(name=\"gSKv3_1bar_SKv3_1\", value=\"1.025\")\n    assert Nrn2ArbParamAdapter._param_value(mech_param) == \"1.025\"\n\n    # Non-trivial name transformation, but identical value/units\n    global_property = create_acc.Location(name=\"v_init\", value=-65)\n    assert Nrn2ArbParamAdapter._param_value(global_property) == \"-65\"\n\n    # Non-trivial name and value/units transformation\n    global_property = create_acc.Location(name=\"celsius\", value=34)\n    assert Nrn2ArbParamAdapter._param_value(global_property) == (\n        \"307.14999999999998\"\n    )\n\n\n@pytest.mark.unit\ndef test_Nrn2ArbParamAdapter_format():\n    \"\"\"Test Neuron to Arbor parameter reformatting.\"\"\"\n    # Constant mechanism parameter\n    mech_param = create_acc.Location(name=\"gSKv3_1bar_SKv3_1\", value=\"1.025\")\n    mech = \"SKv3_1\"\n    arb_mech_param = create_acc.Location(name=\"gSKv3_1bar\", value=\"1.025\")\n    assert Nrn2ArbParamAdapter.format(mech_param, mechs=[mech]) == (\n        mech,\n        arb_mech_param,\n    )\n\n    # Non-unique mapping to mechanisms\n    with pytest.raises(create_acc.CreateAccException):\n        Nrn2ArbParamAdapter.format(mech_param, mechs=[\"SKv3_1\", \"1\"])\n\n    # Global property with non-trivial transformation\n    global_property = create_acc.Location(name=\"celsius\", value=\"0\")\n    mech = None\n    arb_global_property = create_acc.Location(\n        name=\"temperature-kelvin\", value=\"273.14999999999998\"\n    )\n    # Non-trivial name and value/units transformation\n    assert Nrn2ArbParamAdapter.format(global_property, []) == (\n        mech,\n        arb_global_property,\n    )\n\n    # Inhomogeneuos mechanism parameter\n    apical_region = ArbLabel(\"region\", \"apic\", \"(tag 4)\")\n    param_scaler = NrnSegmentSomaDistanceScaler(\n        name=\"soma-distance-scaler\",\n        distribution=\"(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}\",\n    )\n\n    iexpr_param = create_acc.RangeExpr(\n        location=apical_region,\n        name=\"gkbar_hh\",\n        value=\"0.025\",\n        value_scaler=param_scaler,\n    )\n    mech = \"hh\"\n    arb_iexpr_param = create_acc.RangeExpr(\n        location=apical_region,\n        name=\"gkbar\",\n        value=\"0.025\",\n        value_scaler=param_scaler,\n    )\n    assert Nrn2ArbParamAdapter.format(iexpr_param, mechs=[mech]) == (\n        mech,\n        arb_iexpr_param,\n    )\n\n    # Point process mechanism parameter\n    loc = ephys.locations.ArbLocsetLocation(\n        name=\"somacenter\", locset=\"(location 0 0.5)\"\n    )\n\n    mech = ephys.mechanisms.NrnMODPointProcessMechanism(\n        name=\"expsyn\", suffix=\"ExpSyn\", locations=[loc]\n    )\n\n    mech_loc = ephys.locations.NrnPointProcessLocation(\n        \"expsyn_loc\", pprocess_mech=mech\n    )\n\n    point_expr_param = create_acc.PointExpr(\n        name=\"tau\", value=\"10\", point_loc=[mech_loc]\n    )\n\n    arb_point_expr_param = create_acc.PointExpr(\n        name=\"tau\", value=\"10\", point_loc=[mech_loc]\n    )\n    assert Nrn2ArbParamAdapter.format(point_expr_param, mechs=[mech]) == (\n        mech,\n        arb_point_expr_param,\n    )\n\n\n@pytest.mark.unit\ndef test_Nrn2ArbMechGrouper_format_params_and_group_by_mech():\n    \"\"\"Test grouping of parameters by mechanism.\"\"\"\n    params = [\n        create_acc.Location(name=\"gSKv3_1bar_SKv3_1\", value=\"1.025\"),\n        create_acc.Location(name=\"ena\", value=\"-30\"),\n    ]\n    mechs = [\"SKv3_1\"]\n    local_mechs = Nrn2ArbMechGrouper._format_params_and_group_by_mech(\n        params, mechs\n    )\n    assert local_mechs == {\n        None: [\n            create_acc.Location(\n                name='ion-reversal-potential \"na\"', value=\"-30\"\n            )\n        ],\n        \"SKv3_1\": [create_acc.Location(name=\"gSKv3_1bar\", value=\"1.025\")],\n    }\n\n\n@pytest.mark.unit\ndef test_Nrn2ArbMechGrouper_process_global():\n    \"\"\"Test adapting global parameters from Neuron to Arbor.\"\"\"\n    params = {\"ki\": 3, \"v_init\": -65}\n    global_mechs = Nrn2ArbMechGrouper.process_global(params)\n    assert global_mechs == {\n        None: [\n            create_acc.Location(\n                name='ion-internal-concentration \"k\"', value=\"3\"\n            ),\n            create_acc.Location(name=\"membrane-potential\", value=\"-65\"),\n        ]\n    }\n\n\n@pytest.mark.unit\ndef test_Nrn2ArbMechGrouper_is_global_property():\n    \"\"\"Test adapting local parameters from Neuron to Arbor.\"\"\"\n    all_regions = ArbLabel(\"region\", \"all_regions\", \"(all)\")\n    param = create_acc.Location(name=\"axial-resistivity\", value=\"1\")\n    assert Nrn2ArbMechGrouper._is_global_property(all_regions, param) is True\n\n    soma_region = ArbLabel(\"region\", \"soma\", \"(tag 1)\")\n    assert Nrn2ArbMechGrouper._is_global_property(soma_region, param) is False\n\n\n@pytest.mark.unit\ndef test_separate_global_properties():\n    \"\"\"Test separating global properties from label-specific mechs.\"\"\"\n    all_regions = ArbLabel(\"region\", \"all_regions\", \"(all)\")\n    mechs = {\n        None: [create_acc.Location(name=\"axial-resistivity\", value=\"1\")],\n        \"SKv3_1\": [create_acc.Location(name=\"gSKv3_1bar\", value=\"1.025\")],\n    }\n    local_mechs, global_properties = (\n        Nrn2ArbMechGrouper._separate_global_properties(all_regions, mechs)\n    )\n    assert local_mechs == {None: [], \"SKv3_1\": mechs[\"SKv3_1\"]}\n    assert global_properties == {None: mechs[None]}\n\n\n@pytest.mark.unit\ndef test_Nrn2ArbMechGrouper_process_local():\n    \"\"\"Test adapting local parameters from Neuron to Arbor.\"\"\"\n    all_regions = ArbLabel(\"region\", \"all_regions\", \"(all)\")\n    soma_region = ArbLabel(\"region\", \"soma\", \"(tag 1)\")\n    params = [\n        (all_regions, [create_acc.Location(name=\"cm\", value=\"100\")]),\n        (\n            soma_region,\n            [\n                create_acc.Location(name=\"v_init\", value=\"-65\"),\n                create_acc.Location(name=\"gSKv3_1bar_SKv3_1\", value=\"1.025\"),\n            ],\n        ),\n    ]\n    channels = {all_regions: [], soma_region: [\"SKv3_1\"]}\n    local_mechs, global_properties = Nrn2ArbMechGrouper.process_local(\n        params, channels\n    )\n    assert local_mechs.keys() == {all_regions, soma_region}\n    assert local_mechs[all_regions] == {None: []}\n    assert local_mechs[soma_region] == {\n        None: [create_acc.Location(name=\"membrane-potential\", value=\"-65\")],\n        \"SKv3_1\": [create_acc.Location(name=\"gSKv3_1bar\", value=\"1.025\")],\n    }\n    assert global_properties == {\n        None: [create_acc.Location(name=\"membrane-capacitance\", value=\"1\")]\n    }\n\n\n@pytest.mark.unit\ndef test_ArbNmodlMechFormatter_load_mech_catalogue_meta():\n    \"\"\"Test loading Arbor built-in mech catalogue metadata.\"\"\"\n    nmodl_formatter = ArbNmodlMechFormatter(None)\n\n    assert isinstance(nmodl_formatter.cats, dict)\n    assert nmodl_formatter.cats.keys() == {\"BBP\", \"default\", \"allen\"}\n    assert \"Ca_HVA\" in nmodl_formatter.cats[\"BBP\"]\n\n\n@pytest.mark.unit\ndef test_ArbNmodlMechFormatter_mech_name():\n    \"\"\"Test mechanism name translation.\"\"\"\n    assert ArbNmodlMechFormatter._mech_name(\"Ca_HVA\") == \"Ca_HVA\"\n    assert ArbNmodlMechFormatter._mech_name(\"ExpSyn\") == \"expsyn\"\n\n\n@pytest.mark.unit\ndef test_ArbNmodlMechFormatter_translate_density():\n    \"\"\"Test NMODL GLOBAL parameter handling in mechanism translation.\"\"\"\n    mechs = {\n        \"hh\": [\n            create_acc.Location(name=\"gnabar\", value=\"0.10000000000000001\"),\n            create_acc.RangeIExpr(\n                name=\"gkbar\",\n                value=\"0.029999999999999999\",\n                scale=(\n                    \"(add (scalar -0.62109375) (mul (scalar 0.546875) \"\n                    '(log (add (mul (distance (region \"soma\"))'\n                    \" (scalar 0.421875) ) (scalar 1.25) ) ) ) )\"\n                ),\n            ),\n        ],\n        \"pas\": [\n            create_acc.Location(name=\"e\", value=\"0.25\"),\n            create_acc.RangeIExpr(\n                name=\"g\",\n                value=\"0.029999999999999999\",\n                scale=(\n                    \"(add (scalar -0.62109375) (mul (scalar 0.546875) \"\n                    '(log (add (mul (distance (region \"soma\"))'\n                    \" (scalar 0.421875) ) (scalar 1.25) ) ) ) )\"\n                ),\n            ),\n        ],\n    }\n    nmodl_formatter = ArbNmodlMechFormatter(None)\n    translated_mechs = nmodl_formatter.translate_density(mechs)\n    assert translated_mechs.keys() == {\"default::hh\", \"default::pas/e=0.25\"}\n    assert translated_mechs[\"default::hh\"] == mechs[\"hh\"]\n    assert translated_mechs[\"default::pas/e=0.25\"] == mechs[\"pas\"][1:]\n\n\n@pytest.mark.unit\ndef test_arb_populate_label_dict():\n    \"\"\"Unit test for _populate_label_dict.\"\"\"\n    local_mechs = {ArbLabel(\"region\", \"all\", \"(all)\"): {}}\n    local_scaled_mechs = {ArbLabel(\"region\", \"first_branch\", \"(branch 0)\"): {}}\n    pprocess_mechs = {}\n\n    label_dict = create_acc._arb_populate_label_dict(\n        local_mechs, local_scaled_mechs, pprocess_mechs\n    )\n    assert label_dict.keys() == {\"all\", \"first_branch\"}\n\n    with pytest.raises(create_acc.CreateAccException):\n        other_pprocess_mechs = {\n            ArbLabel(\"region\", \"first_branch\", \"(branch 1)\"): {}\n        }\n        create_acc._arb_populate_label_dict(\n            local_mechs, local_scaled_mechs, other_pprocess_mechs\n        )\n\n\n@pytest.mark.unit\ndef test_create_acc():\n    \"\"\"ephys.create_acc: Test create_acc\"\"\"\n    mech = utils.make_mech()\n    parameters = utils.make_parameters()\n\n    acc = create_acc.create_acc(\n        [\n            mech,\n        ],\n        parameters,\n        morphology=\"CCell.swc\",\n        template_name=\"CCell\",\n    )\n\n    ref_dir = testdata_dir / \"acc/CCell\"\n    cell_json = \"CCell.json\"\n    decor_acc = \"CCell_decor.acc\"\n    label_dict_acc = \"CCell_label_dict.acc\"\n\n    # Testing keys\n    assert cell_json in acc\n    cell_json_dict = json.loads(acc[cell_json])\n    assert \"cell_model_name\" in cell_json_dict\n    assert \"produced_by\" in cell_json_dict\n    assert \"morphology\" in cell_json_dict\n    assert \"label_dict\" in cell_json_dict\n    assert \"decor\" in cell_json_dict\n    # Testing values\n    with open(ref_dir / cell_json) as f:\n        ref_cell_json = json.load(f)\n    for k in ref_cell_json:\n        if k != \"produced_by\":\n            assert ref_cell_json[k] == cell_json_dict[k]\n\n    # Testing building blocks\n    assert decor_acc in acc\n    assert acc[decor_acc].startswith(\"(arbor-component\")\n    assert \"(decor\" in acc[decor_acc]\n    # Testing values\n    with open(ref_dir / decor_acc) as f:\n        ref_decor = f.read()\n    assert ref_decor == acc[decor_acc]  # decor data not exposed in Python\n\n    # Testing building blocks\n    assert label_dict_acc in acc\n    assert acc[label_dict_acc].startswith(\"(arbor-component\")\n    assert \"(label-dict\" in acc[label_dict_acc]\n    matches = re.findall(\n        r'\\(region-def \"(?P<loc>\\w+)\" \\(tag (?P<tag>\\d+)\\)\\)',\n        acc[label_dict_acc],\n    )\n    for pos, loc_tag in enumerate(DEFAULT_ARBOR_REGION_ORDER):\n        assert matches[pos][0] == loc_tag[0]\n        assert matches[pos][1] == str(loc_tag[1])\n    # Testing values\n    ref_labels = arbor.load_component(ref_dir / label_dict_acc).component\n    with tempfile.TemporaryDirectory() as test_dir:\n        test_labels_filename = pathlib.Path(test_dir).joinpath(label_dict_acc)\n        with open(test_labels_filename, \"w\") as f:\n            f.write(acc[label_dict_acc])\n        test_labels = arbor.load_component(test_labels_filename).component\n    assert dict(ref_labels.items()) == dict(test_labels.items())\n\n\n@pytest.mark.unit\ndef test_create_acc_filename():\n    \"\"\"ephys.create_acc: Test create_acc template_filename\"\"\"\n    mech = utils.make_mech()\n    parameters = utils.make_parameters()\n    custom_param_val = str(__file__)\n\n    acc = create_acc.create_acc(\n        [\n            mech,\n        ],\n        parameters,\n        morphology=\"CCell.asc\",\n        template_name=\"CCell\",\n        template_filename=\"acc/templates/*_template.jinja2\",\n        template_dir=testdata_dir,\n        custom_jinja_params={\"custom_param\": custom_param_val},\n    )\n    cell_json = \"CCell_cell.json\"\n    decor_acc = \"CCell_decor.acc\"\n    label_dict_acc = \"CCell_label_dict.acc\"\n\n    assert cell_json in acc\n    cell_json_dict = json.loads(acc[cell_json])\n    assert \"cell_model_name\" in cell_json_dict\n    assert \"produced_by\" in cell_json_dict\n    assert \"morphology\" in cell_json_dict\n    assert \"label_dict\" in cell_json_dict\n    assert \"decor\" in cell_json_dict\n\n    assert decor_acc in acc\n    assert acc[decor_acc].startswith(\"(arbor-component\")\n    assert \"(decor\" in acc[decor_acc]\n\n    assert label_dict_acc in acc\n    assert acc[label_dict_acc].startswith(\"(arbor-component\")\n    assert \"(label-dict\" in acc[label_dict_acc]\n    matches = re.findall(\n        r'\\(region-def \"(?P<loc>\\w+)\" \\(tag (?P<tag>\\d+)\\)\\)',\n        acc[label_dict_acc],\n    )\n    for pos, loc_tag in enumerate(DEFAULT_ARBOR_REGION_ORDER):\n        assert matches[pos][0] == loc_tag[0]\n        assert matches[pos][1] == str(loc_tag[1])\n\n    assert '(meta-data (info \"test-decor\"))' in acc[decor_acc]\n    assert '(meta-data (info \"test-label-dict\"))' in acc[label_dict_acc]\n    assert custom_param_val in cell_json_dict[\"produced_by\"]\n\n\n@pytest.mark.unit\ndef test_create_acc_replace_axon():\n    \"\"\"ephys.create_acc: Test create_acc with axon replacement\"\"\"\n    mech = utils.make_mech()\n    parameters = utils.make_parameters()\n\n    replace_axon_st = arbor.segment_tree()\n    latest_seg = arbor.mnpos\n\n    for prox_x, dist_x in [(5, 35), (35, 65)]:\n        latest_seg = replace_axon_st.append(\n            latest_seg,\n            arbor.mpoint(prox_x, 0, 0, 0.5),\n            arbor.mpoint(dist_x, 0, 0, 0.5),\n            ArbFileMorphology.tags[\"axon\"],\n        )\n\n    replace_axon = arbor.morphology(replace_axon_st)\n\n    try:\n        acc = create_acc.create_acc(\n            [\n                mech,\n            ],\n            parameters,\n            morphology_dir=testdata_dir,\n            morphology=\"simple.swc\",\n            template_name=\"CCell\",\n            replace_axon=replace_axon,\n        )\n    except Exception as e:  # fail with an older Arbor version\n        assert isinstance(e, NotImplementedError)\n        assert (\n            len(e.args) == 1\n            and e.args[0]\n            == \"Need a newer version of Arbor for axon replacement.\"\n        )\n        return\n\n    cell_json = \"CCell.json\"\n    cell_json_dict = json.loads(acc[cell_json])\n    assert \"replace_axon\" in cell_json_dict[\"morphology\"]\n\n    with open(testdata_dir / \"acc/CCell/simple_axon_replacement.acc\") as f:\n        replace_axon_ref = f.read()\n\n    assert (\n        acc[cell_json_dict[\"morphology\"][\"replace_axon\"]] == replace_axon_ref\n    )\n\n\ndef make_cell(replace_axon):\n    morph_filename = testdata_dir / \"simple_ax2.swc\"\n    morph = ephys.morphologies.NrnFileMorphology(\n        morph_filename, do_replace_axon=replace_axon\n    )\n    somatic_loc = ephys.locations.NrnSeclistLocation(\n        \"somatic\", seclist_name=\"somatic\"\n    )\n    mechs = [\n        ephys.mechanisms.NrnMODMechanism(\n            name=\"hh\", suffix=\"hh\", locations=[somatic_loc]\n        )\n    ]\n    gkbar_hh_scaler = (\n        \"(-0.62109375 + 0.546875*math.log(\"\n        \"({distance})*0.421875 + 1.25))*{value}\"\n    )\n    params = [\n        ephys.parameters.NrnSectionParameter(\n            name=\"gnabar_hh\", param_name=\"gnabar_hh\", locations=[somatic_loc]\n        ),\n        ephys.parameters.NrnRangeParameter(\n            name=\"gkbar_hh\",\n            param_name=\"gkbar_hh\",\n            value_scaler=ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n                distribution=gkbar_hh_scaler\n            ),\n            locations=[somatic_loc],\n        ),\n    ]\n    return ephys.models.CellModel(\n        \"simple_ax2\", morph=morph, mechs=mechs, params=params\n    )\n\n\ndef run_short_sim(cable_cell):\n    # Create cell model\n    arb_cell_model = arbor.single_cell_model(cable_cell)\n    arb_cell_model.properties.catalogue = arbor.catalogue()\n    arb_cell_model.properties.catalogue.extend(\n        arbor.default_catalogue(), \"default::\"\n    )\n    arb_cell_model.properties.catalogue.extend(arbor.bbp_catalogue(), \"BBP::\")\n\n    # Run a very short simulation to test mechanism instantiation\n    arb_cell_model.run(tfinal=0.1 * arbor.units.ms)\n\n\n@pytest.mark.unit\ndef test_cell_model_write_and_read_acc():\n    \"\"\"ephys.create_acc: Test write_acc and read_acc w/o axon replacement\"\"\"\n    cell = make_cell(replace_axon=False)\n    param_values = {\"gnabar_hh\": 0.1, \"gkbar_hh\": 0.03}\n\n    with tempfile.TemporaryDirectory() as acc_dir:\n        cell.write_acc(acc_dir, param_values)\n        cell_json, arb_morph, arb_decor, arb_labels = create_acc.read_acc(\n            pathlib.Path(acc_dir).joinpath(cell.name + \".json\")\n        )\n    assert \"replace_axon\" not in cell_json[\"morphology\"]\n\n    cable_cell = arbor.cable_cell(\n        morphology=arb_morph, decor=arb_decor, labels=arb_labels\n    )\n    assert isinstance(cable_cell, arbor.cable_cell)\n    assert len(cable_cell.cables('\"soma\"')) == 1\n    assert len(cable_cell.cables('\"axon\"')) == 1\n    assert (\n        len(arb_morph.branch_segments(cable_cell.cables('\"soma\"')[0].branch))\n        == 5\n    )\n    assert (\n        len(arb_morph.branch_segments(cable_cell.cables('\"axon\"')[0].branch))\n        == 5\n    )\n\n    run_short_sim(cable_cell)\n\n\n@pytest.mark.unit\ndef test_cell_model_write_and_read_acc_replace_axon():\n    \"\"\"ephys.create_acc: Test write_acc and read_acc w/ axon replacement\"\"\"\n    cell = make_cell(replace_axon=True)\n    param_values = {\"gnabar_hh\": 0.1, \"gkbar_hh\": 0.03}\n\n    with tempfile.TemporaryDirectory() as acc_dir:\n        try:\n            nrn_sim = ephys.simulators.NrnSimulator()\n            cell.write_acc(acc_dir, param_values, sim=nrn_sim)\n        except Exception as e:  # fail with an older Arbor version\n            assert isinstance(e, NotImplementedError)\n            assert (\n                len(e.args) == 1\n                and e.args[0]\n                == \"Need a newer version of Arbor for axon replacement.\"\n            )\n            return\n\n        # Axon replacement implemented in installed Arbor version\n        cell_json, arb_morph, arb_decor, arb_labels = create_acc.read_acc(\n            pathlib.Path(acc_dir).joinpath(cell.name + \".json\")\n        )\n\n    assert \"replace_axon\" in cell_json[\"morphology\"]\n    cable_cell = arbor.cable_cell(\n        morphology=arb_morph, decor=arb_decor, labels=arb_labels\n    )\n    assert isinstance(cable_cell, arbor.cable_cell)\n    assert len(cable_cell.cables('\"soma\"')) == 1\n    assert len(cable_cell.cables('\"axon\"')) == 1\n    assert (\n        len(arb_morph.branch_segments(cable_cell.cables('\"soma\"')[0].branch))\n        == 6\n    )\n    assert (\n        len(arb_morph.branch_segments(cable_cell.cables('\"axon\"')[0].branch))\n        == 6\n    )\n    assert cable_cell.cables('\"soma\"')[0].prox == 0.0\n    assert (\n        abs(\n            cable_cell.cables('\"soma\"')[0].dist\n            - cable_cell.cables('\"axon\"')[0].prox\n        )\n        < 1e-6\n    )\n    assert cable_cell.cables('\"axon\"')[0].dist == 1.0\n\n    run_short_sim(cable_cell)\n\n\n@pytest.mark.unit\ndef test_cell_model_create_acc_replace_axon_without_instantiate():\n    \"\"\"ephys.create_acc: Test write_acc and read_acc w/ axon replacement\"\"\"\n    cell = make_cell(replace_axon=True)\n    param_values = {\"gnabar_hh\": 0.1, \"gkbar_hh\": 0.03}\n\n    with pytest.raises(\n        ValueError,\n        match=\"Need an instance of NrnSimulator in sim\"\n        \" to instantiate morphology in order to\"\n        \" create JSON/ACC-description with\"\n        \" axon replacement.\",\n    ):\n        cell.create_acc(param_values)\n\n\ndef check_acc_dir(test_dir, ref_dir):\n    assert sorted(os.listdir(ref_dir)) == sorted(os.listdir(test_dir))\n\n    ref_dir_ver_suffix = \"_py\" + \"\".join(sys.version.split(\".\")[:2])\n    ref_dir_ver = ref_dir.parent / (ref_dir.name + ref_dir_ver_suffix)\n\n    for file in os.listdir(ref_dir):\n        if (ref_dir_ver / file).exists():\n            ref_dir_file = ref_dir_ver\n        else:\n            ref_dir_file = ref_dir\n\n        if file.endswith(\".json\"):\n            with open(os.path.join(test_dir, file)) as f:\n                cell_json_dict = json.load(f)\n            with open(ref_dir_file / file) as f:\n                ref_cell_json = json.load(f)\n            for k in ref_cell_json:\n                if k != \"produced_by\":\n                    assert ref_cell_json[k] == cell_json_dict[k]\n        else:\n            with open(os.path.join(test_dir, file)) as f:\n                test_file = f.read()\n            with open(ref_dir_file / file) as f:\n                ref_file = f.read()\n            assert ref_file == test_file\n            # check that load_component is not raising any error here\n            fpath = pathlib.Path(test_dir) / file\n            if fpath.suffix == \"acc\":\n                arbor.load_component(fpath).component\n\n\n@pytest.mark.unit\ndef test_write_acc_simple():\n    SIMPLECELL_PATH = str(\n        (\n            pathlib.Path(__file__).parent / \"../../../examples/simplecell\"\n        ).resolve()\n    )\n    sys.path.insert(0, SIMPLECELL_PATH)\n    ref_dir = (testdata_dir / \"acc/simplecell\").resolve()\n    old_cwd = os.getcwd()\n    try:\n        os.chdir(SIMPLECELL_PATH)\n        import simplecell_model\n\n        param_values = {\n            \"gnabar_hh\": 0.10299326453483033,\n            \"gkbar_hh\": 0.027124836082684685,\n        }\n\n        cell = simplecell_model.create(do_replace_axon=True)\n        nrn_sim = ephys.simulators.NrnSimulator()\n        cell.instantiate_morphology_3d(nrn_sim)\n\n        with tempfile.TemporaryDirectory() as test_dir:\n            cell.write_acc(\n                test_dir,\n                param_values,\n                # ext_catalogues=ext_catalogues,\n                create_mod_morph=True,\n            )\n\n            check_acc_dir(test_dir, ref_dir)\n    except NotImplementedError as e:  # fail with an older Arbor version\n        assert (\n            len(e.args) == 1\n            and e.args[0]\n            == \"Need a newer version of Arbor for axon replacement.\"\n        )\n    finally:\n        cell.destroy(nrn_sim)\n        os.chdir(old_cwd)\n        sys.path.pop(0)\n\n\n@pytest.mark.unit\ndef test_write_acc_l5pc():\n    L5PC_PATH = str(\n        (pathlib.Path(__file__).parent / \"../../../examples/l5pc\").resolve()\n    )\n    sys.path.insert(0, L5PC_PATH)\n    ref_dir = (testdata_dir / \"acc/l5pc\").resolve()\n    old_cwd = os.getcwd()\n    try:\n        import l5pc_model\n\n        param_values = {\n            \"gNaTs2_tbar_NaTs2_t.apical\": 0.026145,\n            \"gSKv3_1bar_SKv3_1.apical\": 0.004226,\n            \"gImbar_Im.apical\": 0.000143,\n            \"gNaTa_tbar_NaTa_t.axonal\": 3.137968,\n            \"gK_Tstbar_K_Tst.axonal\": 0.089259,\n            \"gamma_CaDynamics_E2.axonal\": 0.002910,\n            \"gNap_Et2bar_Nap_Et2.axonal\": 0.006827,\n            \"gSK_E2bar_SK_E2.axonal\": 0.007104,\n            \"gCa_HVAbar_Ca_HVA.axonal\": 0.000990,\n            \"gK_Pstbar_K_Pst.axonal\": 0.973538,\n            \"gSKv3_1bar_SKv3_1.axonal\": 1.021945,\n            \"decay_CaDynamics_E2.axonal\": 287.198731,\n            \"gCa_LVAstbar_Ca_LVAst.axonal\": 0.008752,\n            \"gamma_CaDynamics_E2.somatic\": 0.000609,\n            \"gSKv3_1bar_SKv3_1.somatic\": 0.303472,\n            \"gSK_E2bar_SK_E2.somatic\": 0.008407,\n            \"gCa_HVAbar_Ca_HVA.somatic\": 0.000994,\n            \"gNaTs2_tbar_NaTs2_t.somatic\": 0.983955,\n            \"decay_CaDynamics_E2.somatic\": 210.485284,\n            \"gCa_LVAstbar_Ca_LVAst.somatic\": 0.000333,\n        }\n\n        cell = l5pc_model.create(do_replace_axon=True)\n        nrn_sim = ephys.simulators.NrnSimulator()\n        cell.instantiate_morphology_3d(nrn_sim)\n\n        with tempfile.TemporaryDirectory() as test_dir:\n            cell.write_acc(\n                test_dir,\n                param_values,\n                # ext_catalogues=ext_catalogues,\n                create_mod_morph=True,\n            )\n\n            check_acc_dir(test_dir, ref_dir)\n    except Exception as e:  # fail with an older Arbor version\n        assert isinstance(e, NotImplementedError)\n        assert (\n            len(e.args) == 1\n            and e.args[0]\n            == \"Need a newer version of Arbor for axon replacement.\"\n        )\n    finally:\n        cell.destroy(nrn_sim)\n        os.chdir(old_cwd)\n        sys.path.pop(0)\n\n\n@pytest.mark.unit\ndef test_write_acc_expsyn():\n    EXPSYN_PATH = str(\n        (pathlib.Path(__file__).parent / \"../../../examples/expsyn\").resolve()\n    )\n    sys.path.insert(0, EXPSYN_PATH)\n    ref_dir = (testdata_dir / \"acc/expsyn\").resolve()\n    old_cwd = os.getcwd()\n    try:\n        import expsyn\n\n        param_values = {\"expsyn_tau\": 10.0}\n\n        cell = expsyn.create_model(sim=\"arb\", do_replace_axon=False)\n\n        with tempfile.TemporaryDirectory() as test_dir:\n            cell.write_acc(\n                test_dir,\n                param_values,\n                # ext_catalogues=ext_catalogues,\n                create_mod_morph=True,\n            )\n\n            check_acc_dir(test_dir, ref_dir)\n    finally:\n        os.chdir(old_cwd)\n        sys.path.pop(0)\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_create_hoc.py",
    "content": "\"\"\"Tests for create_hoc.py\"\"\"\n\n# pylint: disable=W0212\n\nimport os\n\nfrom bluepyopt.ephys.acc import ArbLabel\nfrom bluepyopt.ephys.locations import NrnSomaDistanceCompLocation\nfrom bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceScaler\nfrom bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceStepScaler\n\nfrom . import utils\nfrom bluepyopt.ephys import create_acc, create_hoc\n\n\nimport pytest\n\nDEFAULT_LOCATION_ORDER = [\n    'all',\n    'apical',\n    'axonal',\n    'basal',\n    'somatic',\n    'myelinated']\n\n\n@pytest.mark.unit\ndef test_generate_channels_by_location():\n    \"\"\"ephys.create_hoc: Test generate_channels_by_location\"\"\"\n    mech = utils.make_mech()\n    public_res = create_hoc.generate_channels_by_location(\n        [mech], DEFAULT_LOCATION_ORDER,\n    )\n    private_res = create_hoc._generate_channels_by_location(\n        [mech], DEFAULT_LOCATION_ORDER, create_hoc._loc_desc\n    )\n    assert public_res == private_res\n\n\n@pytest.mark.unit\ndef test__generate_channels_by_location():\n    \"\"\"ephys.create_hoc: Test _generate_channels_by_location\"\"\"\n    mech = utils.make_mech()\n    channels, point_channels = create_hoc._generate_channels_by_location(\n        [mech, ], DEFAULT_LOCATION_ORDER, create_hoc._loc_desc)\n\n    assert len(channels['apical']) == 1\n    assert len(channels['basal']) == 1\n\n    assert channels['apical'] == ['Ih']\n    assert channels['basal'] == ['Ih']\n\n    for loc in point_channels:\n        assert len(point_channels[loc]) == 0\n\n\n@pytest.mark.unit\ndef test_generate_parameters():\n    \"\"\"ephys.create_hoc: Test generate_parameters\"\"\"\n    parameters = utils.make_parameters()\n\n    assert create_hoc.generate_parameters(parameters) == \\\n        create_hoc._generate_parameters(parameters,\n                                        DEFAULT_LOCATION_ORDER,\n                                        create_hoc._loc_desc)\n\n\n@pytest.mark.unit\ndef test__generate_parameters():\n    \"\"\"ephys.create_hoc: Test _generate_parameters\"\"\"\n    parameters = utils.make_parameters()\n\n    global_params, section_params, range_params, \\\n        pprocess_params, location_order = \\\n        create_hoc._generate_parameters(parameters,\n                                        DEFAULT_LOCATION_ORDER,\n                                        create_hoc._loc_desc)\n\n    assert global_params == {'gSKv3_1bar_SKv3_1': 65}\n    assert len(section_params[1]) == 2\n    assert len(section_params[4]) == 2\n    assert section_params[4][0] == 'somatic'\n    assert len(section_params[4][1]) == 2\n    assert range_params == []\n    for loc, pparams in pprocess_params:\n        assert loc in DEFAULT_LOCATION_ORDER\n        assert len(pparams) == 0\n    assert location_order == DEFAULT_LOCATION_ORDER\n\n\n@pytest.mark.unit\ndef test_create_hoc():\n    \"\"\"ephys.create_hoc: Test create_hoc\"\"\"\n    mech = utils.make_mech()\n    parameters = utils.make_parameters()\n\n    hoc = create_hoc.create_hoc([mech, ], parameters, template_name='CCell')\n    assert 'load_file' in hoc\n    assert 'CCell' in hoc\n    assert 'begintemplate' in hoc\n    assert 'endtemplate' in hoc\n\n\n@pytest.mark.unit\ndef test_create_hoc_filename():\n    \"\"\"ephys.create_hoc: Test create_hoc template_filename\"\"\"\n    mech = utils.make_mech()\n    parameters = utils.make_parameters()\n    custom_param_val = 'printf(\"Hello world!\")'\n\n    hoc = create_hoc.create_hoc([mech, ],\n                                parameters, template_name='CCell',\n                                template_filename='test.jinja2',\n                                template_dir=os.path.join(\n                                    os.path.dirname(__file__),\n                                    'testdata'),\n                                custom_jinja_params={\n                                    'custom_param': custom_param_val})\n    assert 'load_file' in hoc\n    assert 'CCell' in hoc\n    assert 'begintemplate' in hoc\n    assert 'endtemplate' in hoc\n    assert 'Test template' in hoc\n    assert custom_param_val in hoc\n\n\n@pytest.mark.unit\ndef test_generate_reinitrng():\n    \"\"\"ephys.create_hoc: Test generate_reinitrng\"\"\"\n    mech = utils.make_mech()\n    re_init_rng = create_hoc.generate_reinitrng([mech])\n    assert 'func hash_str() {localobj sf strdef right' in re_init_rng\n    assert ' hash = (hash * 31 + char_int) % (2 ^ 31 - 1)' in re_init_rng\n\n\n@pytest.mark.unit\ndef test_range_exprs_to_hoc():\n    \"\"\"ephys.create_hoc: Test range_exprs_to_hoc\"\"\"\n    apical_region = ArbLabel(\"region\", \"apic\", \"(tag 4)\")\n    param_scaler = NrnSegmentSomaDistanceScaler(\n        name='soma-distance-scaler',\n        distribution='(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}'\n    )\n\n    range_expr = create_acc.RangeExpr(\n        location=apical_region,\n        name=\"gkbar_hh\",\n        value=0.025,\n        value_scaler=param_scaler\n    )\n\n    hoc = create_hoc.range_exprs_to_hoc([range_expr])\n    assert hoc[0].param_name == 'gkbar_hh'\n    val_gt = '(-0.8696 + 2.087*exp((%.17g)*0.0031))*0.025000000000000001'\n    assert hoc[0].value == val_gt\n\n\n@pytest.mark.unit\ndef test_range_exprs_to_hoc_step_scaler():\n    \"\"\"ephys.create_hoc: Test range_exprs_to_hoc with step scaler\"\"\"\n    # apical_region = ArbLabel(\"region\", \"apic\", \"(tag 4)\")\n    apical_location = NrnSomaDistanceCompLocation(\n        name='apic100',\n        soma_distance=100,\n        seclist_name='apical',\n    )\n    param_scaler = NrnSegmentSomaDistanceStepScaler(\n        name='soma-distance-step-scaler',\n        distribution='{value} * (0.1 + 0.9 * int('\n                     '({distance} > {step_begin}) & ('\n                     '{distance} < {step_end})))',\n        step_begin=300,\n        step_end=500)\n\n    range_expr = create_hoc.RangeExpr(\n        location=apical_location,\n        name=\"gCa_LVAstbar_Ca_LVAst\",\n        value=1,\n        value_scaler=param_scaler\n    )\n\n    hoc = create_hoc.range_exprs_to_hoc([range_expr])\n    assert hoc[0].param_name == 'gCa_LVAstbar_Ca_LVAst'\n    val_gt = '1 * (0.1 + 0.9 * int((%.17g > 300) && (%.17g < 500)))'\n    assert hoc[0].value == val_gt\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_evaluators.py",
    "content": "\"\"\"Test ephys model objects\"\"\"\n\n# pylint: disable=R0914\n\nimport os\n\n\nimport pytest\nimport numpy\n\nfrom bluepyopt import ephys\n\nTESTDATA_DIR = os.path.join(\n    os.path.dirname(\n        os.path.abspath(__file__)),\n    'testdata')\n\nsimple_morphology_path = os.path.join(TESTDATA_DIR, 'simple.swc')\n\n\n@pytest.mark.unit\ndef test_CellEvaluator_init():\n    \"\"\"ephys.evaluators: Test CellEvaluator init\"\"\"\n    sim = ephys.simulators.NrnSimulator()\n\n    model = ephys.models.CellModel('test_model', params=[])\n\n    fitness_calc = ephys.objectivescalculators.ObjectivesCalculator()\n\n    evaluator = ephys.evaluators.CellEvaluator(\n        cell_model=model,\n        param_names=[],\n        fitness_calculator=fitness_calc,\n        sim=sim)\n\n    assert isinstance(evaluator, ephys.evaluators.CellEvaluator)\n    assert (\n        str(evaluator) ==\n        'cell evaluator:\\n  cell model:\\n    test_model:\\n  morphology:\\n  '\n        'mechanisms:\\n  params:\\n\\n  fitness protocols:\\n  '\n        'fitness calculator:\\n    objectives:\\n\\n')\n\n\n@pytest.mark.unit\ndef test_CellEvaluator_evaluate():\n    \"\"\"ephys.evaluators: Test CellEvaluator evaluate\"\"\"\n    sim = ephys.simulators.NrnSimulator()\n\n    simple_morph = ephys.morphologies.NrnFileMorphology(\n        simple_morphology_path,\n        do_replace_axon=True)\n\n    all_loc = ephys.locations.NrnSeclistLocation('all', 'all')\n\n    cm = ephys.parameters.NrnRangeParameter(\n        name='cm',\n        param_name='cm',\n        bounds=[.5, 1.5],\n        locations=[all_loc])\n\n    cell_model = ephys.models.CellModel('CellModel',\n                                        morph=simple_morph,\n                                        mechs=[],\n                                        params=[cm])\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.1,\n        step_delay=100.0,\n        step_duration=100,\n        total_duration=200,\n        location=soma_loc)\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[rec_soma])\n\n    mean = -65\n\n    efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature',\n                                           efel_feature_name='voltage_base',\n                                           recording_names={'': 'soma.v'},\n                                           stim_start=100.0,\n                                           stim_end=200.0,\n                                           exp_mean=-65,\n                                           exp_std=1)\n\n    s_obj = ephys.objectives.SingletonObjective(\n        'singleton',\n        feature=efeature)\n\n    fitness_calc = ephys.objectivescalculators.ObjectivesCalculator(\n        objectives=[s_obj])\n\n    evaluator = ephys.evaluators.CellEvaluator(\n        cell_model=cell_model,\n        param_names=['cm'],\n        fitness_calculator=fitness_calc,\n        fitness_protocols={'sweep': protocol},\n        sim=sim)\n\n    responses = protocol.run(cell_model, {'cm': 1.0}, sim=sim)\n\n    feature_value = efeature.calculate_feature(responses)\n    feature_value_eva = evaluator.evaluate_with_dicts(\n        {'cm': 1.0}, target='values'\n    )\n\n    score = evaluator.evaluate([1.0])\n    expected_score = abs(mean - feature_value)\n\n    numpy.testing.assert_almost_equal(score, expected_score)\n\n    score_dict = evaluator.objective_dict(score)\n\n    numpy.testing.assert_almost_equal(score_dict['singleton'], expected_score)\n    numpy.testing.assert_almost_equal(\n        feature_value, feature_value_eva['singleton']\n    )\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_extra_features_utils.py",
    "content": "\"\"\"Tests for ephys.extra_features_utils\"\"\"\n\nimport os\n\nimport numpy\nimport pytest\n\nfrom bluepyopt import ephys\n\n\ntestdata_dir = os.path.join(\n    os.path.dirname(os.path.abspath(__file__)), 'testdata'\n)\nwaveforms_fpath = os.path.join(testdata_dir, 'mean_waveforms.dat')\nwaveforms = numpy.loadtxt(waveforms_fpath)\nwaveform = numpy.array([waveforms[0]])\nsampling_freq = 10000\n\n\n@pytest.mark.unit\ndef test_peak_to_valley():\n    \"\"\"ephys.extra_features_utils: Test peak_to_valley\"\"\"\n    ptv = ephys.extra_features_utils.peak_to_valley(waveform, sampling_freq)\n    assert len(ptv) == 1\n    assert ptv[0] == pytest.approx(0.0013)\n\n\n@pytest.mark.unit\ndef test_peak_trough_ratio():\n    \"\"\"ephys.extra_features_utils: Test peak_trough_ratio\"\"\"\n    ptratio = ephys.extra_features_utils.peak_trough_ratio(waveform)\n    assert len(ptratio) == 1\n    print(ptratio)\n    assert ptratio[0] == pytest.approx(0.53804035)\n\n\n@pytest.mark.unit\ndef test_halfwidth():\n    \"\"\"ephys.extra_features_utils: Test halfwidth\"\"\"\n    ret = ephys.extra_features_utils.halfwidth(waveform, sampling_freq, True)\n    assert len(ret) == 3\n\n    hw = ephys.extra_features_utils.halfwidth(waveform, sampling_freq)\n    assert len(hw) == 1\n    assert hw[0] == pytest.approx(0.0015)\n\n\n@pytest.mark.unit\ndef test_repolarization_slope():\n    \"\"\"ephys.extra_features_utils: Test repolarization_slope\"\"\"\n    ret = ephys.extra_features_utils.repolarization_slope(\n        waveform, sampling_freq, True\n    )\n    assert len(ret) == 2\n\n    rslope = ephys.extra_features_utils.repolarization_slope(\n        waveform, sampling_freq\n    )\n    assert len(rslope) == 1\n    assert rslope[0] == pytest.approx(73.12572131)\n\n\n@pytest.mark.unit\ndef test_recovery_slope():\n    \"\"\"ephys.extra_features_utils: Test recovery_slope\"\"\"\n    window = 0.7\n    rslope = ephys.extra_features_utils.recovery_slope(\n        waveform, sampling_freq, window=window\n    )\n    assert len(rslope) == 1\n    assert rslope[0] == pytest.approx(-3.63355521)\n\n\n@pytest.mark.unit\ndef test_peak_image():\n    \"\"\"ephys.extra_features_utils: Test peak_image\"\"\"\n    rel_peaks = ephys.extra_features_utils.peak_image(\n        waveforms, sign=\"negative\"\n    )\n    assert len(rel_peaks) == 209\n    assert rel_peaks[0] == pytest.approx(0.06084468)\n\n    rel_peaks = ephys.extra_features_utils.peak_image(\n        waveforms, sign=\"positive\"\n    )\n    assert len(rel_peaks) == 209\n    assert rel_peaks[0] == pytest.approx(0.10850117)\n\n\n@pytest.mark.unit\ndef test_relative_amplitude():\n    \"\"\"ephys.extra_features_utils: Test relative_amplitude\"\"\"\n    rel_amp = ephys.extra_features_utils.relative_amplitude(\n        waveforms, sign=\"negative\"\n    )\n    assert len(rel_amp) == 209\n    assert rel_amp[0] == pytest.approx(0.09513392)\n\n    rel_amp = ephys.extra_features_utils.relative_amplitude(\n        waveforms, sign=\"positive\"\n    )\n    assert len(rel_amp) == 209\n    assert rel_amp[0] == pytest.approx(0.2135929)\n\n\n@pytest.mark.unit\ndef test_peak_time_diff():\n    \"\"\"ephys.extra_features_utils: Test peak_time_diff\"\"\"\n    peak_t = ephys.extra_features_utils.peak_time_diff(\n        waveforms, sampling_freq, sign=\"negative\"\n    )\n    assert len(peak_t) == 209\n    assert peak_t[0] == pytest.approx(0.0009)\n\n    peak_t = ephys.extra_features_utils.peak_time_diff(\n        waveforms, sampling_freq, sign=\"positive\"\n    )\n    assert len(peak_t) == 209\n    assert peak_t[0] == pytest.approx(0.0007)\n\n\n@pytest.mark.unit\ndef test__get_trough_and_peak_idx():\n    \"\"\"ephys.extra_features_utils: Test _get_trough_and_peak_idx\"\"\"\n    t_idx, p_idx = ephys.extra_features_utils._get_trough_and_peak_idx(\n        waveform\n    )\n    assert t_idx == 102\n    assert p_idx == 115\n\n\n@pytest.mark.unit\ndef test_calculate_features():\n    \"\"\"ephys.extra_features_utils: Test calculate_features\"\"\"\n    feats = ephys.extra_features_utils.calculate_features(\n        waveforms, sampling_freq\n    )\n    for feature_name in ephys.extra_features_utils.all_1D_features:\n        assert feature_name in feats\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_features.py",
    "content": "\"\"\"Tests for ephys.efeatures\"\"\"\n\nimport os\nfrom os.path import join as joinp\n\nimport pytest\nimport numpy\n\nfrom bluepyopt.ephys import efeatures\nfrom bluepyopt.ephys.responses import TimeVoltageResponse, TimeLFPResponse\nfrom bluepyopt.ephys.serializer import instantiator\n\n\n@pytest.mark.unit\ndef test_EFeature():\n    \"\"\"ephys.efeatures: Testing EFeature creation\"\"\"\n    efeature = efeatures.EFeature('name')\n    assert efeature.name == 'name'\n\n\n@pytest.mark.unit\ndef test_eFELFeature():\n    \"\"\"ephys.efeatures: Testing eFELFeature creation\"\"\"\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n    efeature = efeatures.eFELFeature(name='test_eFELFeature',\n                                     efel_feature_name='voltage_base',\n                                     recording_names=recording_names,\n                                     stim_start=700,\n                                     stim_end=2700,\n                                     exp_mean=1,\n                                     exp_std=1)\n\n    response = TimeVoltageResponse('mock_response')\n    testdata_dir = joinp(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    ret = efeature.calculate_feature(responses, raise_warnings=True)\n    numpy.testing.assert_almost_equal(ret, -72.05761247316858)\n\n    score = efeature.calculate_score(responses)\n    numpy.testing.assert_almost_equal(score, 73.05761247316858)\n\n    assert efeature.name == 'test_eFELFeature'\n    assert 'voltage_base' in str(efeature)\n\n\n@pytest.mark.unit\ndef test_eFELFeature_max_score():\n    \"\"\"ephys.efeatures: Testing eFELFeature max_score option\"\"\"\n\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n\n    response = TimeVoltageResponse('mock_response')\n    testdata_dir = joinp(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    efeature_normal = efeatures.eFELFeature(name='test_eFELFeature',\n                                            efel_feature_name='AP_amplitude',\n                                            recording_names=recording_names,\n                                            stim_start=600,\n                                            stim_end=700,\n                                            exp_mean=1,\n                                            exp_std=1)\n    score_normal = efeature_normal.calculate_score(responses)\n    numpy.testing.assert_almost_equal(score_normal, 250)\n\n    efeature_150 = efeatures.eFELFeature(name='test_eFELFeature',\n                                         efel_feature_name='AP_amplitude',\n                                         recording_names=recording_names,\n                                         stim_start=600,\n                                         stim_end=700,\n                                         exp_mean=1,\n                                         exp_std=1,\n                                         max_score=150)\n\n    score_150 = efeature_150.calculate_score(responses)\n    numpy.testing.assert_almost_equal(score_150, 150)\n\n\n@pytest.mark.unit\ndef test_eFELFeature_force_max_score():\n    \"\"\"ephys.efeatures: Testing eFELFeature force_max_score option\"\"\"\n\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n\n    response = TimeVoltageResponse('mock_response')\n    testdata_dir = joinp(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    efeature_normal = efeatures.eFELFeature(name='test_eFELFeature',\n                                            efel_feature_name='voltage_base',\n                                            recording_names=recording_names,\n                                            stim_start=700,\n                                            stim_end=2700,\n                                            exp_mean=1,\n                                            exp_std=.001)\n    score_normal = efeature_normal.calculate_score(responses)\n    assert score_normal > 250\n\n    efeature_force = efeatures.eFELFeature(name='test_eFELFeature',\n                                           efel_feature_name='voltage_base',\n                                           recording_names=recording_names,\n                                           stim_start=700,\n                                           stim_end=2700,\n                                           exp_mean=1,\n                                           exp_std=.001,\n                                           force_max_score=True)\n\n    score_force = efeature_force.calculate_score(responses)\n    numpy.testing.assert_almost_equal(score_force, 250)\n\n\n@pytest.mark.unit\ndef test_eFELFeature_double_settings():\n    \"\"\"ephys.efeatures: Testing eFELFeature double_settings\"\"\"\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n    efeature = efeatures.eFELFeature(name='test_eFELFeature',\n                                     efel_feature_name='voltage_base',\n                                     recording_names=recording_names,\n                                     stim_start=700,\n                                     stim_end=2700,\n                                     exp_mean=1,\n                                     exp_std=1)\n    efeature_ds = efeatures.eFELFeature(\n        name='test_eFELFeature_other_perc',\n        efel_feature_name='voltage_base',\n        recording_names=recording_names,\n        stim_start=700,\n        stim_end=2700,\n        exp_mean=1,\n        exp_std=1,\n        double_settings={\n            'voltage_base_start_perc': 0.01})\n\n    response = TimeVoltageResponse('mock_response')\n    testdata_dir = joinp(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    vb_other_perc = efeature_ds.calculate_feature(\n        responses,\n        raise_warnings=True)\n    vb = efeature.calculate_feature(responses, raise_warnings=True)\n\n    assert vb_other_perc != vb\n\n\n@pytest.mark.unit\ndef test_eFELFeature_int_settings():\n    \"\"\"ephys.efeatures: Testing eFELFeature int_settings\"\"\"\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n    efeature = efeatures.eFELFeature(name='test_eFELFeature',\n                                     efel_feature_name='Spikecount',\n                                     recording_names=recording_names,\n                                     stim_start=1200,\n                                     stim_end=2000,\n                                     exp_mean=1,\n                                     exp_std=1)\n    efeature_strict = efeatures.eFELFeature(\n        name='test_eFELFeature_strict',\n        efel_feature_name='Spikecount',\n        recording_names=recording_names,\n        stim_start=1200,\n        stim_end=2000,\n        exp_mean=1,\n        exp_std=1,\n        int_settings={\n            'strict_stiminterval': True})\n\n    response = TimeVoltageResponse('mock_response')\n    testdata_dir = joinp(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    spikecount = efeature.calculate_feature(responses)\n    spikecount_strict = efeature_strict.calculate_feature(responses)\n\n    assert spikecount_strict != spikecount\n\n\n@pytest.mark.unit\ndef test_eFELFeature_string_settings():\n    \"\"\"ephys.efeatures: Testing eFELFeature string_settings\"\"\"\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n    efeature = efeatures.eFELFeature(name='test_eFELFeature_vb_default',\n                                     efel_feature_name='voltage_base',\n                                     recording_names=recording_names,\n                                     stim_start=700,\n                                     stim_end=2700)\n    efeature_median = efeatures.eFELFeature(\n        name='test_eFELFeature_vb_median',\n        efel_feature_name='voltage_base',\n        recording_names=recording_names,\n        stim_start=700,\n        stim_end=2700,\n        string_settings={\n            'voltage_base_mode': \"median\"})\n\n    response = TimeVoltageResponse('mock_response')\n    testdata_dir = joinp(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    vb_median = efeature_median.calculate_feature(\n        responses,\n        raise_warnings=True)\n    vb_default = efeature.calculate_feature(responses, raise_warnings=True)\n\n    assert vb_median != vb_default\n\n\n@pytest.mark.unit\ndef test_eFELFeature_serialize():\n    \"\"\"ephys.efeatures: Testing eFELFeature serialization\"\"\"\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n    efeature = efeatures.eFELFeature(name='test_eFELFeature',\n                                     efel_feature_name='voltage_base',\n                                     recording_names=recording_names,\n                                     stim_start=700,\n                                     stim_end=2700,\n                                     exp_mean=1,\n                                     exp_std=1)\n    serialized = efeature.to_dict()\n    deserialized = instantiator(serialized)\n    assert isinstance(deserialized, efeatures.eFELFeature)\n    assert deserialized.stim_start == 700\n    assert deserialized.recording_names == recording_names\n\n\n@pytest.mark.unit\ndef test_extraFELFeature():\n    \"\"\"ephys.efeatures: Testing extraFELFeature calculation\"\"\"\n    import pandas as pd\n\n    somatic_recording_name = 'soma_response'\n    recording_names = {'': 'lfp_response'}\n    channel_ids = 0\n    extrafel_feature_name = 'halfwidth'\n    name = 'test_extraFELFeature'\n    stim_start = 400\n    stim_end = 1750\n    fs = 10\n    ms_cut = [10, 25]\n\n    # load responses from file\n    testdata_dir = os.path.join(\n        os.path.dirname(os.path.abspath(__file__)), 'testdata'\n    )\n    soma_time = numpy.load(os.path.join(testdata_dir, 'lfpy_soma_time.npy'))\n    soma_voltage = numpy.load(\n        os.path.join(testdata_dir, 'lfpy_soma_voltage.npy')\n    )\n    lfpy_time = numpy.load(os.path.join(testdata_dir, 'lfpy_time.npy'))\n    lfpy_voltage = numpy.load(os.path.join(testdata_dir, 'lfpy_voltage.npy'))\n\n    soma_response = TimeVoltageResponse(\n        name='soma_response', time=soma_time, voltage=soma_voltage\n    )\n    lfpy_response = TimeLFPResponse(\n        name=\"lfpy_response\", time=lfpy_time, lfp=lfpy_voltage\n    )\n    responses = {\n        somatic_recording_name: soma_response,\n        recording_names['']: lfpy_response,\n    }\n\n    # compute for all electrodes\n    efeature = efeatures.extraFELFeature(\n        name=name,\n        extrafel_feature_name=extrafel_feature_name,\n        somatic_recording_name=somatic_recording_name,\n        recording_names=recording_names,\n        channel_ids=None,\n        exp_mean=0.001,\n        exp_std=0.001,\n        stim_start=stim_start,\n        stim_end=stim_end,\n        fs=fs,\n        ms_cut=ms_cut\n    )\n\n    ret = efeature.calculate_feature(responses, raise_warnings=True)\n    assert len(ret) == 209\n\n    # compute for 1 electrode\n    efeature = efeatures.extraFELFeature(\n        name=name,\n        extrafel_feature_name=extrafel_feature_name,\n        somatic_recording_name=somatic_recording_name,\n        recording_names=recording_names,\n        channel_ids=channel_ids,\n        exp_mean=0.001,\n        exp_std=0.001,\n        stim_start=stim_start,\n        stim_end=stim_end,\n        fs=fs,\n        ms_cut=ms_cut\n    )\n\n    ret = efeature.calculate_feature(responses, raise_warnings=True)\n    numpy.testing.assert_almost_equal(ret, 0.0015)\n\n    score = efeature.calculate_score(responses)\n    numpy.testing.assert_almost_equal(score, 0.5)\n\n    assert efeature.name == name\n    assert extrafel_feature_name in str(efeature)\n\n\n@pytest.mark.unit\ndef test_masked_cosine_distance():\n    \"\"\"ephys.efeatures: Testing masked_cosine_distance\"\"\"\n    from scipy.spatial import distance\n\n    exp = numpy.array([0.5, 0.5, 0.5])\n    model = numpy.array([0.7, 0.8, 0.4])\n\n    score = efeatures.masked_cosine_distance(exp, model)\n    assert score == distance.cosine(exp, model)\n\n    # test nan in model feature values\n    model = numpy.array([0.7, numpy.nan, 0.4])\n    score = efeatures.masked_cosine_distance(exp, model)\n    assert score == distance.cosine([0.5, 0.5], [0.7, 0.4])\n\n    # test nan in experimental feature values\n    exp = numpy.array([0.5, 0.5, numpy.nan])\n    model = numpy.array([0.7, 0.8, 0.4])\n\n    score = efeatures.masked_cosine_distance(exp, model)\n    assert score == distance.cosine([0.5, 0.5], [0.7, 0.8]) * 2. / 3.\n\n    # test na in both exp and model feature values\n    exp = numpy.array([0.5, 0.5, numpy.nan])\n    model = numpy.array([0.7, numpy.nan, 0.4])\n\n    score = efeatures.masked_cosine_distance(exp, model)\n    assert score == distance.cosine([0.5], [0.7]) * 2. / 3.\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_init.py",
    "content": "\"\"\"bluepy.ephys test\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\n\nimport pytest\nimport numpy\n\n\n@pytest.mark.unit\ndef test_import():\n    \"\"\"ephys: test importing bluepyopt.ephys\"\"\"\n    import bluepyopt.ephys  # NOQA\n\n\n@pytest.mark.unit\ndef test_ephys_base():\n    \"\"\"ephys: test ephys base class\"\"\"\n    import bluepyopt.ephys as ephys\n    base = ephys.base.BaseEPhys(name='test', comment='comm')\n\n    assert str(base) == 'BaseEPhys: test (comm)'\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_locations.py",
    "content": "\"\"\"bluepyopt.ephys.simulators tests\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612, W0201\nimport json\n\nimport numpy as np\nimport pytest\n\nfrom bluepyopt import ephys\nfrom bluepyopt.ephys.serializer import instantiator\n\n\n@pytest.mark.unit\ndef test_location_init():\n    \"\"\"ephys.locations: test if Location works\"\"\"\n\n    loc = ephys.locations.Location(\"test\")\n    assert isinstance(loc, ephys.locations.Location)\n    assert loc.name == \"test\"\n\n\n@pytest.mark.unit\nclass TestNrnSectionCompLocation(object):\n    \"\"\"Test class for NrnSectionCompLocation\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.loc = ephys.locations.NrnSectionCompLocation(\n            name=\"test\", sec_name=\"soma[0]\", comp_x=0.5\n        )\n        self.loc_dend = ephys.locations.NrnSectionCompLocation(\n            name=\"test\", sec_name=\"dend[1]\", comp_x=0.5\n        )\n        assert self.loc.name == \"test\"\n        self.sim = ephys.simulators.NrnSimulator()\n\n    def test_instantiate(self):\n        \"\"\"ephys.locations.NrnSomaDistanceCompLocation: test instantiate\"\"\"\n\n        # Create a little test class with a soma and two dendritic sections\n        class Cell(object):\n            \"\"\"Cell class\"\"\"\n\n            pass\n\n        cell = Cell()\n        soma = self.sim.neuron.h.Section()\n        dend1 = self.sim.neuron.h.Section(name=\"dend1\")\n        dend2 = self.sim.neuron.h.Section(name=\"dend2\")\n\n        cell.soma = [soma]\n        cell.dend = [dend1, dend2]\n\n        soma_comp = self.loc.instantiate(sim=self.sim, icell=cell)\n        assert soma_comp == soma(0.5)\n\n        dend_comp = self.loc_dend.instantiate(sim=self.sim, icell=cell)\n        assert dend_comp == dend2(0.5)\n\n\n@pytest.mark.unit\nclass TestNrnSeclistCompLocation(object):\n    \"\"\"Test class for NrnSectionCompLocation\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.loc = ephys.locations.NrnSeclistCompLocation(\n            name=\"test\", seclist_name=\"somatic\", sec_index=0, comp_x=0.5\n        )\n        self.loc_dend = ephys.locations.NrnSeclistCompLocation(\n            name=\"test\", seclist_name=\"basal\", sec_index=1, comp_x=0.5\n        )\n        assert self.loc.name == \"test\"\n        self.sim = ephys.simulators.NrnSimulator()\n\n    def test_instantiate(self):\n        \"\"\"ephys.locations.NrnSeclistCompLocation: test instantiate\"\"\"\n\n        # Create a little test class with a soma and two dendritic sections\n        class Cell(object):\n            \"\"\"Cell class\"\"\"\n\n            pass\n\n        cell = Cell()\n        soma = self.sim.neuron.h.Section()\n        dend1 = self.sim.neuron.h.Section(name=\"dend1\")\n        dend2 = self.sim.neuron.h.Section(name=\"dend2\")\n\n        cell.somatic = self.sim.neuron.h.SectionList()\n        cell.somatic.append(soma)\n        cell.basal = self.sim.neuron.h.SectionList()\n        cell.basal.append(dend1)\n        cell.basal.append(dend2)\n\n        soma_comp = self.loc.instantiate(sim=self.sim, icell=cell)\n        assert soma_comp == soma(0.5)\n\n        dend_comp = self.loc_dend.instantiate(sim=self.sim, icell=cell)\n        assert dend_comp == dend2(0.5)\n\n        for _ in range(10000):\n            soma_comp = self.loc.instantiate(sim=self.sim, icell=cell)\n\n\n@pytest.mark.unit\nclass TestNrnSeclistSecLocation(object):\n    \"\"\"Test class for NrnSeclistSecLocation\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.loc = ephys.locations.NrnSeclistSecLocation(\n            name=\"test\", seclist_name=\"somatic\", sec_index=0\n        )\n        self.loc_dend = ephys.locations.NrnSeclistSecLocation(\n            name=\"test\", seclist_name=\"basal\", sec_index=1\n        )\n        assert self.loc.name == \"test\"\n        self.sim = ephys.simulators.NrnSimulator()\n\n    def test_instantiate(self):\n        \"\"\"ephys.locations.NrnSeclistSecLocation: test instantiate\"\"\"\n\n        # Create a little test class with a soma and two dendritic sections\n        class Cell(object):\n            \"\"\"Cell class\"\"\"\n\n            pass\n\n        cell = Cell()\n        soma = self.sim.neuron.h.Section()\n        dend1 = self.sim.neuron.h.Section(name=\"dend1\")\n        dend2 = self.sim.neuron.h.Section(name=\"dend2\")\n\n        cell.somatic = self.sim.neuron.h.SectionList()\n        cell.somatic.append(soma)\n        cell.basal = self.sim.neuron.h.SectionList()\n        cell.basal.append(dend1)\n        cell.basal.append(dend2)\n\n        soma_comp = self.loc.instantiate(sim=self.sim, icell=cell)\n        assert soma_comp == soma\n\n        dend_comp = self.loc_dend.instantiate(sim=self.sim, icell=cell)\n        assert dend_comp == dend2\n\n\n@pytest.mark.unit\nclass TestNrnSomaDistanceCompLocation(object):\n    \"\"\"Test class for NrnSomaDistanceCompLocation\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.loc = ephys.locations.NrnSomaDistanceCompLocation(\n            \"test\", 125, \"testdend\"\n        )\n        assert self.loc.name == \"test\"\n        self.sim = ephys.simulators.NrnSimulator()\n\n    def test_instantiate(self):\n        \"\"\"ephys.locations.NrnSomaDistanceCompLocation: test instantiate\"\"\"\n\n        # Create a little test class with a soma and two dendritic sections\n        class Cell(object):\n            \"\"\"Cell class\"\"\"\n\n            pass\n\n        cell = Cell()\n        soma = self.sim.neuron.h.Section()\n        cell.soma = [soma]\n        cell.testdend = self.sim.neuron.h.SectionList()\n        dend1 = self.sim.neuron.h.Section(name=\"dend1\")\n        dend2 = self.sim.neuron.h.Section(name=\"dend2\")\n\n        cell.testdend.append(sec=dend1)\n        cell.testdend.append(sec=dend2)\n\n        pytest.raises(\n            ephys.locations.EPhysLocInstantiateException,\n            self.loc.instantiate,\n            sim=self.sim,\n            icell=cell,\n        )\n\n        dend1.connect(soma(0.5), 0.0)\n        dend2.connect(dend1(1.0), 0.0)\n\n        comp = self.loc.instantiate(sim=self.sim, icell=cell)\n        assert comp == dend2(0.5)\n\n\n@pytest.mark.unit\nclass TestNrnSecSomaDistanceCompLocation(object):\n    \"\"\"Test class for NrnSecSomaDistanceCompLocation\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.loc = ephys.locations.NrnSecSomaDistanceCompLocation(\n            \"test\", 125, 1, \"testdend\"\n        )\n        self.loc_other = ephys.locations.NrnSecSomaDistanceCompLocation(\n            \"test\", 250, 4, \"testdend\"\n        )\n        assert self.loc.name == \"test\"\n        self.sim = ephys.simulators.NrnSimulator()\n\n    def test_instantiate(self):\n        \"\"\"ephys.locations.NrnSomaDistanceCompLocation: test instantiate\"\"\"\n\n        # Create a little test class with a soma and two dendritic sections\n        class Cell(object):\n            \"\"\"Cell class\"\"\"\n\n            pass\n\n        cell = Cell()\n        soma = self.sim.neuron.h.Section(name=\"soma[0]\")\n        cell.soma = [soma]\n        cell.testdend = self.sim.neuron.h.SectionList()\n        dend1 = self.sim.neuron.h.Section(name=\"dend[0]\")\n        dend2 = self.sim.neuron.h.Section(name=\"dend[1]\")\n        dend3 = self.sim.neuron.h.Section(name=\"dend[2]\")\n        dend4 = self.sim.neuron.h.Section(name=\"dend[3]\")\n        dend5 = self.sim.neuron.h.Section(name=\"dend[4]\")\n\n        cell.testdend.append(sec=dend1)\n        cell.testdend.append(sec=dend2)\n        cell.testdend.append(sec=dend3)\n        cell.testdend.append(sec=dend4)\n        cell.testdend.append(sec=dend5)\n\n        dend1.connect(soma(0.5), 0.0)\n        dend2.connect(dend1(1.0), 0.0)\n        dend3.connect(dend1(1.0), 0.0)\n        dend4.connect(dend3(1.0), 0.0)\n        dend5.connect(dend4(1.0), 0.0)\n\n        comp = self.loc.instantiate(sim=self.sim, icell=cell)\n        assert comp == dend2(0.5)\n        comp = self.loc_other.instantiate(sim=self.sim, icell=cell)\n        assert comp == dend4(0.5)\n\n\n@pytest.mark.unit\nclass TestNrnTrunkSomaDistanceCompLocation(object):\n    \"\"\"Test class for NrnTrunkSomaDistanceCompLocation\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.loc = ephys.locations.NrnTrunkSomaDistanceCompLocation(\n            \"test\", soma_distance=150, seclist_name=\"testdend\"\n        )\n        self.loc_other = ephys.locations.NrnTrunkSomaDistanceCompLocation(\n            \"test\", soma_distance=350, seclist_name=\"testdend\"\n        )\n\n        assert self.loc.name == \"test\"\n        self.sim = ephys.simulators.NrnSimulator()\n\n    def test_instantiate(self):\n        \"\"\"ephys.locations.NrnSomaDistanceCompLocation: test instantiate\"\"\"\n\n        # Create a little test class with a soma and two dendritic sections\n        class Cell(object):\n            \"\"\"Cell class\"\"\"\n\n            pass\n\n        cell = Cell()\n        soma = self.sim.neuron.h.Section(name=\"soma[0]\")\n        cell.soma = [soma]\n        cell.testdend = self.sim.neuron.h.SectionList()\n        dend1 = self.sim.neuron.h.Section(name=\"dend[0]\")\n        dend2 = self.sim.neuron.h.Section(name=\"dend[1]\")\n        dend3 = self.sim.neuron.h.Section(name=\"dend[2]\")\n        dend4 = self.sim.neuron.h.Section(name=\"dend[3]\")\n        dend5 = self.sim.neuron.h.Section(name=\"dend[4]\")\n\n        cell.testdend.append(sec=dend1)\n        cell.testdend.append(sec=dend2)\n        cell.testdend.append(sec=dend3)\n        cell.testdend.append(sec=dend4)\n        cell.testdend.append(sec=dend5)\n\n        x0 = self.sim.neuron.h.Vector([0] * 10)\n        d = self.sim.neuron.h.Vector([1] * 10)\n\n        x1 = self.sim.neuron.h.Vector(np.linspace(0, 100, 10))\n        self.sim.neuron.h.pt3dadd(x0, x1, x0, d, sec=dend1)\n        x2 = self.sim.neuron.h.Vector(np.linspace(100, 200, 10))\n        self.sim.neuron.h.pt3dadd(x0, x2, x0, d, sec=dend2)\n        x3 = self.sim.neuron.h.Vector(np.linspace(200, 300, 10))\n        self.sim.neuron.h.pt3dadd(x0, x3, x0, d, sec=dend3)\n        x4 = self.sim.neuron.h.Vector(np.linspace(300, 400, 10))\n        self.sim.neuron.h.pt3dadd(x0, x4, x0, d, sec=dend4)\n        x5 = self.sim.neuron.h.Vector(np.linspace(400, 500, 10))\n        self.sim.neuron.h.pt3dadd(x0, x5, x0, d, sec=dend5)\n\n        dend1.connect(soma(0.5), 0.0)\n        dend2.connect(dend1(1.0), 0.0)\n        dend3.connect(dend1(1.0), 0.0)\n        dend4.connect(dend3(1.0), 0.0)\n        dend5.connect(dend4(1.0), 0.0)\n\n        comp = self.loc.instantiate(sim=self.sim, icell=cell)\n        assert comp == dend3(0.5)\n        comp = self.loc_other.instantiate(sim=self.sim, icell=cell)\n        assert comp == dend5(0.5)\n\n\n@pytest.mark.unit\ndef test_serialize():\n    \"\"\"ephys.locations: Test serialize functionality\"\"\"\n    from bluepyopt.ephys.locations import (\n        NrnSeclistCompLocation,\n        NrnSeclistLocation,\n        NrnSeclistSecLocation,\n        NrnSomaDistanceCompLocation,\n    )\n\n    seclist_name, sec_index, comp_x, soma_distance = \"somatic\", 0, 0.5, 800\n    locations = (\n        NrnSeclistCompLocation(\n            \"NrnSeclistCompLocation\", seclist_name, sec_index, comp_x\n        ),\n        NrnSeclistLocation(\"NrnSeclistLocation\", seclist_name),\n        NrnSeclistSecLocation(\n            \"NrnSeclistSecLocation\", seclist_name, sec_index\n        ),\n        NrnSomaDistanceCompLocation(\n            \"NrnSomaDistanceCompLocation\", soma_distance, seclist_name\n        ),\n    )\n\n    for loc in locations:\n        serialized = loc.to_dict()\n        assert isinstance(json.dumps(serialized), str)\n        deserialized = instantiator(serialized)\n        assert isinstance(deserialized, loc.__class__)\n        assert deserialized.seclist_name == seclist_name\n        assert deserialized.name == loc.__class__.__name__\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_mechanisms.py",
    "content": "\"\"\"Tests for ephys.mechanisms\"\"\"\n\nimport string\nimport random\nimport json\nimport difflib\n\n\nimport pytest\n\nfrom . import utils\nfrom bluepyopt import ephys\nimport bluepyopt.ephys.examples.simplecell\nsimplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n\nfrom bluepyopt.ephys.serializer import instantiator\n\nsimple_cell = simplecell.cell_model\nsimple_cell.freeze(simplecell.default_param_values)\nsim = simplecell.nrn_sim\n\n\n@pytest.mark.unit\ndef test_mechanism_serialize():\n    \"\"\"ephys.mechanisms: Testing serialize\"\"\"\n    mech = utils.make_mech()\n    serialized = mech.to_dict()\n    assert isinstance(json.dumps(serialized), str)\n    deserialized = instantiator(serialized)\n    assert isinstance(deserialized, ephys.mechanisms.NrnMODMechanism)\n\n\n@pytest.mark.unit\ndef test_nrnmod_instantiate():\n    \"\"\"ephys.mechanisms: Testing insert mechanism\"\"\"\n\n    test_mech = ephys.mechanisms.NrnMODMechanism(\n        'test.pas',\n        suffix='pas',\n        locations=[simplecell.somatic_loc])\n\n    assert str(test_mech) == \"test.pas: pas at ['somatic']\"\n\n    simple_cell.instantiate(sim=sim)\n\n    test_mech.instantiate(sim=sim, icell=simple_cell.icell)\n    test_mech.destroy(sim=sim)\n\n    simple_cell.destroy(sim=sim)\n\n    pytest.raises(TypeError, ephys.mechanisms.NrnMODMechanism,\n                  'test.pas',\n                  suffix='pas',\n                  prefix='pas',\n                  locations=[simplecell.somatic_loc])\n\n    test_mech = ephys.mechanisms.NrnMODMechanism(\n        'test.pas',\n        prefix='pas',\n        locations=[simplecell.somatic_loc])\n\n    assert test_mech.suffix == 'pas'\n\n    test_mech.prefix = 'pas2'\n    assert test_mech.suffix == 'pas2'\n\n    test_mech = ephys.mechanisms.NrnMODMechanism(\n        'unknown',\n        suffix='unknown',\n        locations=[simplecell.somatic_loc])\n\n    simple_cell.instantiate(sim=sim)\n\n    pytest.raises(\n        ValueError,\n        test_mech.instantiate,\n        sim=sim,\n        icell=simple_cell.icell)\n\n    test_mech.destroy(sim=sim)\n    simple_cell.destroy(sim=sim)\n\n\ndef compare_strings(s1, s2):\n    \"\"\"Compare two strings\"\"\"\n\n    diff = list(difflib.unified_diff(s1.splitlines(1), s2.splitlines(1)))\n\n    if len(diff) > 0:\n        print(''.join(diff))\n        return False\n    else:\n        return True\n\n\n@pytest.mark.unit\ndef test_nrnmod_reinitrng_block():\n    \"\"\"ephys.mechanisms: Testing reinitrng_block\"\"\"\n\n    test_mech = ephys.mechanisms.NrnMODMechanism(\n        'stoch',\n        suffix='Stoch',\n        locations=[simplecell.somatic_loc])\n\n    block = test_mech.generate_reinitrng_hoc_block()\n    expected_block = '    forsec somatic { deterministic_Stoch = 1 }\\n'\n\n    assert compare_strings(block, expected_block)\n\n    test_mech = ephys.mechanisms.NrnMODMechanism(\n        'stoch',\n        suffix='Stoch',\n        deterministic=False,\n        locations=[simplecell.somatic_loc])\n\n    block = test_mech.generate_reinitrng_hoc_block()\n\n    expected_block = \\\n        \"\"\"    forsec somatic {\n        for (x, 0) {\n            setdata_Stoch(x)\n            sf.tail(secname(), \"\\\\\\\\.\", name)\n            sprint(full_str, \"%s.%.19g\", name, x)\n            if (channel_seed_set) {\n                setRNG_Stoch(gid, hash_str(full_str), channel_seed)\n            } else {\n                setRNG_Stoch(gid, hash_str(full_str))\n            }\n        }\n    }\n\"\"\"\n\n    assert compare_strings(block, expected_block)\n\n\n@pytest.mark.unit\ndef test_nrnmod_determinism():\n    \"\"\"ephys.mechanisms: Testing determinism\"\"\"\n\n    test_mech = ephys.mechanisms.NrnMODMechanism(\n        'pas',\n        suffix='pas',\n        deterministic=False,\n        locations=[simplecell.somatic_loc])\n\n    simple_cell.instantiate(sim=sim)\n\n    pytest.raises(\n        TypeError,\n        test_mech.instantiate,\n        sim=sim,\n        icell=simple_cell.icell)\n    test_mech.destroy(sim=sim)\n\n    simple_cell.destroy(sim=sim)\n\n\n@pytest.mark.unit\ndef test_pprocess_instantiate():\n    \"\"\"ephys.mechanisms: Testing insert point process\"\"\"\n\n    test_pprocess = ephys.mechanisms.NrnMODPointProcessMechanism(\n        name='expsyn',\n        suffix='ExpSyn',\n        locations=[simplecell.somacenter_loc])\n\n    assert (\n        str(test_pprocess) ==\n        \"expsyn: ExpSyn at ['somatic[0](0.5)']\")\n\n    simple_cell.instantiate(sim=sim)\n\n    assert test_pprocess.pprocesses is None\n\n    test_pprocess.instantiate(sim=sim, icell=simple_cell.icell)\n    assert len(test_pprocess.pprocesses) == 1\n    pprocess = test_pprocess.pprocesses[0]\n\n    assert hasattr(pprocess, 'tau')\n    test_pprocess.destroy(sim=sim)\n\n    assert test_pprocess.pprocesses is None\n\n    simple_cell.destroy(sim=sim)\n\n    test_pprocess = ephys.mechanisms.NrnMODPointProcessMechanism(\n        name='expsyn',\n        suffix='Exp',\n        locations=[simplecell.somacenter_loc])\n\n    simple_cell.instantiate(sim=sim)\n\n    pytest.raises(\n        AttributeError,\n        test_pprocess.instantiate,\n        sim=sim,\n        icell=simple_cell.icell)\n\n    test_pprocess.destroy(sim=sim)\n    simple_cell.destroy(sim=sim)\n\n\n@pytest.mark.unit\ndef test_string_hash_functions():\n    \"\"\"ephys.mechanisms: Testing string hash function\"\"\"\n\n    n_of_strings = 100\n    max_size = 50\n\n    random.seed(1)\n\n    test_strings = ['', 'a']\n    test_strings += [''.join\n                     (random.choice\n                      (string.printable)\n                      for _ in range(random.choice(range(max_size))))\n                     for _ in range(n_of_strings)]\n    hashes_py = [\n        ephys.mechanisms.NrnMODMechanism.hash_py\n        (test_string) for test_string in test_strings]\n    hashes_hoc = [\n        ephys.mechanisms.NrnMODMechanism.hash_hoc\n        (test_string, simplecell.nrn_sim) for test_string in test_strings]\n\n    assert hashes_py == hashes_hoc\n    assert hashes_py[:2] == [0.0, 97.0]\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_models.py",
    "content": "\"\"\"Test ephys model objects\"\"\"\n\nimport os\nimport tempfile\nimport contextlib\n\n\nimport pytest\nimport numpy\n\nfrom bluepyopt import ephys\n\nsim = ephys.simulators.NrnSimulator()\nTESTDATA_DIR = os.path.join(\n    os.path.dirname(\n        os.path.abspath(__file__)),\n    'testdata')\nsimple_morphology_path = os.path.join(TESTDATA_DIR, 'simple.swc')\napic_morphology_path = os.path.join(TESTDATA_DIR, 'apic.swc')\n\n\n@contextlib.contextmanager\ndef yield_blank_hoc(template_name):\n    \"\"\"Create blank hoc template\"\"\"\n    hoc_template = ephys.models.CellModel.create_empty_template(template_name)\n    temp_file = tempfile.NamedTemporaryFile(suffix='test_models')\n    with temp_file as fd:\n        fd.write(hoc_template.encode('utf-8'))\n        fd.flush()\n        yield temp_file.name\n\n\ntest_morph = ephys.morphologies.NrnFileMorphology(simple_morphology_path)\n\n\n@pytest.mark.unit\ndef test_create_empty_template():\n    \"\"\"ephys.models: Test creation of empty template\"\"\"\n    template_name = 'FakeTemplate'\n    hoc_template = ephys.models.CellModel.create_empty_template(template_name)\n    sim.neuron.h(hoc_template)\n    assert hasattr(sim.neuron.h, template_name)\n\n\n@pytest.mark.unit\ndef test_model():\n    \"\"\"ephys.models: Test Model class\"\"\"\n    model = ephys.models.Model('test_model')\n    model.instantiate(sim=None)\n    model.destroy(sim=None)\n    assert isinstance(model, ephys.models.Model)\n\n\n@pytest.mark.unit\ndef test_cellmodel():\n    \"\"\"ephys.models: Test CellModel class\"\"\"\n    model = ephys.models.CellModel('test_model', morph=test_morph, mechs=[])\n\n    assert (\n        str(model)\n        == 'test_model:\\n  morphology:\\n    %s\\n  mechanisms:\\n  params:\\n' %\n        simple_morphology_path)\n\n    model.instantiate(sim=sim)\n    model.destroy(sim=sim)\n    assert isinstance(model, ephys.models.CellModel)\n\n\n@pytest.mark.unit\ndef test_cellmodel_namecheck():\n    \"\"\"ephys.models: Test CellModel class name checking\"\"\"\n\n    # Test valid name\n    for name in ['test3', 'test_3']:\n        ephys.models.CellModel(name, morph=test_morph, mechs=[])\n\n    # Test invalid names\n    for name in ['3test', '', 'test$', 'test 3']:\n        pytest.raises(\n            TypeError,\n            ephys.models.CellModel,\n            name,\n            morph=test_morph,\n            mechs=[])\n\n\n@pytest.mark.unit\ndef test_load_hoc_template():\n    \"\"\"ephys.models: Test loading of hoc template\"\"\"\n\n    template_name = 'test_load_hoc'\n    hoc_string = ephys.models.CellModel.create_empty_template(template_name)\n    ephys.models.HocCellModel.load_hoc_template(sim, hoc_string)\n    assert hasattr(sim.neuron.h, template_name)\n\n\n@pytest.mark.unit\ndef test_HocCellModel():\n    \"\"\"ephys.models: Test HOCCellModel class\"\"\"\n    template_name = 'test_HocCellModel'\n    hoc_string = ephys.models.CellModel.create_empty_template(template_name)\n    hoc_cell = ephys.models.HocCellModel(\n        'test_hoc_model', simple_morphology_path, hoc_string=hoc_string)\n    hoc_cell.instantiate(sim)\n    assert hoc_cell.icell is not None\n    assert hoc_cell.cell is not None\n\n    assert 'simple.swc' in str(hoc_cell)\n\n    # these should be callable, but don't do anything\n    hoc_cell.freeze(None)\n    hoc_cell.unfreeze(None)\n    hoc_cell.check_nonfrozen_params(None)\n    hoc_cell.params_by_names(None)\n\n    hoc_cell.destroy(sim=sim)\n\n\n@pytest.mark.unit\ndef test_CellModel_create_empty_cell():\n    \"\"\"ephys.models: Test create_empty_cell\"\"\"\n    template_name = 'create_empty_cell'\n    cell = ephys.models.CellModel.create_empty_cell(template_name, sim)\n    assert callable(cell)\n    assert hasattr(sim.neuron.h, template_name)\n\n\n@pytest.mark.unit\ndef test_CellModel_create_hoc():\n    \"\"\"ephys.models: Test create_hoc\"\"\"\n\n    morph0 = ephys.morphologies.NrnFileMorphology(\n        simple_morphology_path,\n        do_replace_axon=True)\n\n    cell_model = ephys.models.CellModel('CellModel',\n                                        morph=morph0,\n                                        mechs=[],\n                                        params=[])\n\n    hoc_string = cell_model.create_hoc({})\n    assert 'begintemplate CellModel' in hoc_string\n    assert 'proc replace_axon()' in hoc_string\n    cell_model_hoc = ephys.models.HocCellModel(\n        'CellModelHOC',\n        simple_morphology_path,\n        hoc_string=hoc_string)\n\n    assert isinstance(cell_model_hoc, ephys.models.HocCellModel)\n\n\n@pytest.mark.unit\ndef test_CellModel_destroy():\n    \"\"\"ephys.models: Test CellModel destroy\"\"\"\n    morph0 = ephys.morphologies.NrnFileMorphology(simple_morphology_path)\n    cell_model0 = ephys.models.CellModel('CellModel_destroy',\n                                         morph=morph0,\n                                         mechs=[],\n                                         params=[])\n    morph1 = ephys.morphologies.NrnFileMorphology(simple_morphology_path)\n    cell_model1 = ephys.models.CellModel('CellModel_destroy',\n                                         morph=morph1,\n                                         mechs=[],\n                                         params=[])\n\n    assert not hasattr(sim.neuron.h, 'CellModel_destroy')\n\n    cell_model0.instantiate(sim=sim)\n    assert hasattr(sim.neuron.h, 'CellModel_destroy')\n    assert 1 == len(sim.neuron.h.CellModel_destroy)\n\n    cell_model1.instantiate(sim=sim)\n    assert 2 == len(sim.neuron.h.CellModel_destroy)\n\n    # make sure cleanup works\n    cell_model0.destroy(sim=sim)\n    assert 1 == len(sim.neuron.h.CellModel_destroy)\n\n    cell_model1.destroy(sim=sim)\n    assert 0 == len(sim.neuron.h.CellModel_destroy)\n\n\n@pytest.mark.unit\ndef test_lfpy_create_empty_template():\n    \"\"\"ephys.models: Test creation of lfpy empty template\"\"\"\n    template_name = 'FakeTemplate'\n    hoc_template = ephys.models.LFPyCellModel.create_empty_template(\n        template_name\n    )\n    sim.neuron.h(hoc_template)\n    assert hasattr(sim.neuron.h, template_name)\n\n\n@pytest.mark.unit\ndef test_lfpycellmodel():\n    \"\"\"ephys.models: Test LFPyCellModel class\"\"\"\n    model = ephys.models.LFPyCellModel('test_lfpy_model', morph=test_morph,\n                                       mechs=[], v_init=-80)\n\n    assert (\n        str(model)\n        == 'test_lfpy_model:\\n  morphology:\\n    %s\\n  '\n           'mechanisms:\\n  params:\\n' %\n        simple_morphology_path)\n\n    model.instantiate(sim=sim)\n    model.destroy(sim=sim)\n    assert isinstance(model, ephys.models.LFPyCellModel)\n\n\n@pytest.mark.unit\ndef test_lfpycellmodel_namecheck():\n    \"\"\"ephys.models: Test LFPyCellModel class name checking\"\"\"\n\n    # Test valid name\n    for name in ['test3', 'test_3']:\n        ephys.models.LFPyCellModel(name, morph=test_morph, mechs=[])\n\n    # Test invalid names\n    for name in ['3test', '', 'test$', 'test 3']:\n        with pytest.raises(TypeError):\n            ephys.models.LFPyCellModel(name, morph=test_morph, mechs=[])\n\n\n@pytest.mark.unit\ndef test_load_lfpy_hoc_template():\n    \"\"\"ephys.models: Test loading of hoc template with lfpy cell\"\"\"\n\n    template_name = 'test_load_hoc'\n    hoc_string = ephys.models.LFPyCellModel.create_empty_template(\n        template_name\n    )\n    ephys.models.HocCellModel.load_hoc_template(sim, hoc_string)\n    assert hasattr(sim.neuron.h, template_name)\n\n\n@pytest.mark.unit\ndef test_LFPyCellModel_create_empty_cell():\n    \"\"\"ephys.models: Test create_empty_cell with lfpy cell\"\"\"\n    template_name = 'create_empty_cell'\n    cell = ephys.models.LFPyCellModel.create_empty_cell(template_name, sim)\n    assert callable(cell)\n    assert hasattr(sim.neuron.h, template_name)\n\n\n@pytest.mark.unit\ndef test_LFPyCellModel_create_hoc():\n    \"\"\"ephys.models: Test create_hoc with lfpy cell\"\"\"\n\n    morph0 = ephys.morphologies.NrnFileMorphology(\n        simple_morphology_path,\n        do_replace_axon=True\n    )\n\n    cell_model = ephys.models.LFPyCellModel(\n        'LFPyCellModel',\n        morph=morph0,\n        mechs=[],\n        params=[]\n    )\n\n    hoc_string = cell_model.create_hoc({})\n    assert 'begintemplate LFPyCellModel' in hoc_string\n    assert 'proc replace_axon()' in hoc_string\n    cell_model_hoc = ephys.models.HocCellModel(\n        'CellModelHOC',\n        simple_morphology_path,\n        hoc_string=hoc_string)\n\n    assert isinstance(cell_model_hoc, ephys.models.HocCellModel)\n\n\n@pytest.mark.unit\ndef test_LFPyCellModel_destroy():\n    \"\"\"ephys.models: Test LFPyCellModel destroy\"\"\"\n    morph0 = ephys.morphologies.NrnFileMorphology(simple_morphology_path)\n    cell_model0 = ephys.models.LFPyCellModel(\n        'LFPyCellModel_destroy', morph=morph0, mechs=[], params=[]\n    )\n    morph1 = ephys.morphologies.NrnFileMorphology(simple_morphology_path)\n    cell_model1 = ephys.models.LFPyCellModel(\n        'LFPyCellModel_destroy', morph=morph1, mechs=[], params=[]\n    )\n\n    assert not hasattr(sim.neuron.h, 'LFPyCellModel_destroy')\n\n    cell_model0.instantiate(sim=sim)\n    assert hasattr(sim.neuron.h, 'LFPyCellModel_destroy')\n    assert 1 == len(sim.neuron.h.LFPyCellModel_destroy)\n\n    cell_model1.instantiate(sim=sim)\n    assert 2 == len(sim.neuron.h.LFPyCellModel_destroy)\n\n    # make sure cleanup works\n    cell_model0.destroy(sim=sim)\n    assert 1 == len(sim.neuron.h.LFPyCellModel_destroy)\n\n    cell_model1.destroy(sim=sim)\n    assert 0 == len(sim.neuron.h.LFPyCellModel_destroy)\n\n\n@pytest.mark.unit\ndef test_metaparameter():\n    \"\"\"ephys.models: Test model with MetaParameter\"\"\"\n\n    morph = ephys.morphologies.NrnFileMorphology(apic_morphology_path)\n\n    dist = \"({A} + {B} * math.exp({distance} * {C})) * {value}\"\n\n    scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n        distribution=dist, dist_param_names=['A', 'B', 'C'])\n\n    all_loc = ephys.locations.NrnSeclistLocation('all', 'all')\n\n    paramA = ephys.parameters.MetaParameter('ParamA', scaler, 'A', -1)\n    paramB = ephys.parameters.MetaParameter(\n        'ParamB',\n        scaler,\n        'B',\n        bounds=[1.0, 3.0])\n    paramC = ephys.parameters.MetaParameter(\n        'ParamC',\n        obj=scaler,\n        attr_name='C',\n        value=0.003,\n        frozen=True)\n\n    cm = ephys.parameters.NrnRangeParameter(\n        name='cm',\n        param_name='cm',\n        bounds=[.5, 1.5],\n        value_scaler=scaler,\n        locations=[all_loc])\n\n    test_params = {'ParamA': -1, 'ParamB': 2.0, 'cm': 1.0}\n\n    cell_model = ephys.models.CellModel('CellModel',\n                                        morph=morph,\n                                        mechs=[],\n                                        params=[cm, paramA, paramB, paramC])\n\n    pytest.raises(Exception,\n                  cell_model.freeze,\n                  {'ParamC': 2.0})\n\n    cell_model.freeze(test_params)\n\n    cell_model.instantiate(sim=sim)\n\n    assert (scaler.eval_dist(1.0, 1.0)\n            == '(-1 + 2.0 * math.exp(1 * 0.003)) * 1')\n\n    numpy.testing.assert_almost_equal(\n        scaler.scale(\n            1.0,\n            cell_model.icell.apic[0](.5),\n            sim=sim),\n        1.0764239941636502)\n\n    cell_model.unfreeze(param_names=['ParamA', 'ParamB'])\n\n    hoc_code = cell_model.create_hoc(param_values=test_params)\n\n    assert ('distribute_distance(CellRef.all, \"cm\", \"(-1 + 2.0 * '\n            'exp(%.17g * 0.003)) * 1\")' in hoc_code)\n\n    cell_model.destroy(sim=sim)\n\n    hoc_model = ephys.models.HocCellModel(\n        'hoc_model', '.', hoc_string=hoc_code)\n    hoc_model.instantiate(sim=sim)\n    hoc_model.destroy()\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_morphologies.py",
    "content": "\"\"\"ephys/morphologies.py unit tests\"\"\"\n\nimport json\nimport os\n\n\nimport pytest\n\nimport bluepyopt.ephys as ephys\nfrom bluepyopt.ephys.serializer import instantiator\n\ntestdata_dir = os.path.join(\n    os.path.dirname(\n        os.path.abspath(__file__)),\n    'testdata')\n\nsimpleswc_morphpath = os.path.join(testdata_dir, 'simple.swc')\nsimpleswc_ax1_morphpath = os.path.join(testdata_dir, 'simple_ax1.swc')\nsimpleswc_ax2_morphpath = os.path.join(testdata_dir, 'simple_ax2.asc')\nsimplewrong_morphpath = os.path.join(testdata_dir, 'simple.wrong')\n\n\n@pytest.mark.unit\ndef test_morphology_init():\n    \"\"\"ephys.morphologies: testing Morphology constructor\"\"\"\n\n    morph = ephys.morphologies.Morphology()\n    assert isinstance(morph, ephys.morphologies.Morphology)\n\n\n@pytest.mark.unit\ndef test_nrnfilemorphology_init():\n    \"\"\"ephys.morphologies: testing NrnFileMorphology constructor\"\"\"\n    sim = ephys.simulators.NrnSimulator()\n\n    morph = ephys.morphologies.NrnFileMorphology('wrong.swc')\n    pytest.raises(IOError, morph.instantiate, sim=sim)\n\n    morph = ephys.morphologies.NrnFileMorphology(simplewrong_morphpath)\n\n    assert str(morph) == simplewrong_morphpath\n\n    pytest.raises(ValueError, morph.instantiate, sim=sim)\n\n    morph = ephys.morphologies.NrnFileMorphology(\n        simpleswc_morphpath,\n        do_set_nseg=False)\n\n    morph.instantiate(sim=sim)\n    morph.destroy(sim=sim)\n\n\n@pytest.mark.unit\ndef test_nrnfilemorphology_replace_axon():\n    \"\"\"ephys.morphologies: testing NrnFileMorphology replace_axon\"\"\"\n    sim = ephys.simulators.NrnSimulator()\n\n    morph = ephys.morphologies.NrnFileMorphology(\n        simpleswc_morphpath,\n        do_replace_axon=True)\n\n    cell = ephys.models.CellModel(name='cell_replace')\n    icell = cell.create_empty_cell(\n        cell.name,\n        sim=sim,\n        seclist_names=cell.seclist_names,\n        secarray_names=cell.secarray_names)\n\n    morph.instantiate(sim=sim, icell=icell)\n\n    assert len([sec for sec in icell.axon]) == 2\n\n    morph.destroy(sim=sim)\n    icell.destroy()\n\n\n@pytest.mark.unit\ndef test_nrnfilemorphology_replace_axon_ax1():\n    \"\"\"ephys.morphologies: testing NrnFileMorphology replace_axon with ax1\"\"\"\n    sim = ephys.simulators.NrnSimulator()\n\n    morph = ephys.morphologies.NrnFileMorphology(\n        simpleswc_ax1_morphpath,\n        do_replace_axon=True)\n\n    cell = ephys.models.CellModel(name='cell_ax1')\n    icell = cell.create_empty_cell(\n        cell.name,\n        sim=sim,\n        seclist_names=cell.seclist_names,\n        secarray_names=cell.secarray_names)\n\n    morph.instantiate(sim=sim, icell=icell)\n\n    assert len([sec for sec in icell.axon]) == 2\n\n    morph.destroy(sim=sim)\n    icell.destroy()\n\n\n@pytest.mark.unit\ndef test_nrnfilemorphology_replace_axon_ax2():\n    \"\"\"ephys.morphologies: testing NrnFileMorphology replace_axon with ax2\"\"\"\n    sim = ephys.simulators.NrnSimulator()\n\n    morph = ephys.morphologies.NrnFileMorphology(\n        simpleswc_ax2_morphpath,\n        do_replace_axon=True)\n\n    cell = ephys.models.CellModel(name='cell_ax2')\n    icell = cell.create_empty_cell(\n        cell.name,\n        sim=sim,\n        seclist_names=cell.seclist_names,\n        secarray_names=cell.secarray_names)\n\n    morph.instantiate(sim=sim, icell=icell)\n\n    assert len([sec for sec in icell.axon]) == 2\n\n    morph.destroy(sim=sim)\n    icell.destroy()\n\n\n@pytest.mark.unit\ndef test_serialize():\n    \"\"\"ephys.morphology: testing serialization\"\"\"\n\n    morph = ephys.morphologies.NrnFileMorphology(simpleswc_morphpath)\n    serialized = morph.to_dict()\n    assert isinstance(json.dumps(serialized), str)\n    deserialized = instantiator(serialized)\n    assert isinstance(deserialized, ephys.morphologies.NrnFileMorphology)\n    assert deserialized.morphology_path == simpleswc_morphpath\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_objectives.py",
    "content": "\"\"\"Tests for ephys.efeatures\"\"\"\n\nimport os\n\n\nimport pytest\nimport numpy\n\nimport bluepyopt.ephys as ephys\n\n\n@pytest.mark.unit\ndef test_EFeatureObjective():\n    \"\"\"ephys.objectives: Testing EFeatureObjective\"\"\"\n\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n\n    mean = 1\n\n    efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature',\n                                           efel_feature_name='voltage_base',\n                                           recording_names=recording_names,\n                                           stim_start=700,\n                                           stim_end=2700,\n                                           exp_mean=mean,\n                                           exp_std=1)\n\n    e_obj = ephys.objectives.EFeatureObjective(\n        'singleton',\n        features=[efeature])\n\n    assert e_obj.name == 'singleton'\n    assert e_obj.features == [efeature]\n\n    response = ephys.responses.TimeVoltageResponse('mock_response')\n    testdata_dir = os.path.join(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    efeature_value = efeature.calculate_feature(responses)\n\n    numpy.testing.assert_almost_equal(\n        e_obj.calculate_feature_scores(responses),\n        [abs(efeature_value - mean)])\n\n\n@pytest.mark.unit\ndef test_SingletonObjective():\n    \"\"\"ephys.objectives: Testing SingletonObjective\"\"\"\n\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n\n    mean = 1\n\n    efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature',\n                                           efel_feature_name='voltage_base',\n                                           recording_names=recording_names,\n                                           stim_start=700,\n                                           stim_end=2700,\n                                           exp_mean=mean,\n                                           exp_std=1)\n\n    s_obj = ephys.objectives.SingletonObjective(\n        'singleton',\n        feature=efeature)\n\n    assert s_obj.name == 'singleton'\n    assert s_obj.features == [efeature]\n    assert str(s_obj) == '( %s )' % str(efeature)\n\n    response = ephys.responses.TimeVoltageResponse('mock_response')\n    testdata_dir = os.path.join(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    efeature_value = efeature.calculate_feature(responses)\n    efeature_value_obj = s_obj.calculate_value(responses)\n\n    numpy.testing.assert_almost_equal(\n        s_obj.calculate_score(responses),\n        abs(efeature_value - mean))\n    numpy.testing.assert_almost_equal(efeature_value_obj, efeature_value)\n\n\n@pytest.mark.unit\ndef test_SingletonWeightObjective():\n    \"\"\"ephys.objectives: Testing SingletonWeightObjective\"\"\"\n\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n\n    mean = 1\n    weight = 0.5\n\n    efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature',\n                                           efel_feature_name='voltage_base',\n                                           recording_names=recording_names,\n                                           stim_start=700,\n                                           stim_end=2700,\n                                           exp_mean=mean,\n                                           exp_std=1)\n\n    s_obj = ephys.objectives.SingletonWeightObjective(\n        'singleton',\n        feature=efeature,\n        weight=weight)\n\n    assert s_obj.name == 'singleton'\n    assert s_obj.features == [efeature]\n    assert s_obj.weight == weight\n    assert str(s_obj) == '( %s ), weight:%f' % (str(efeature), weight)\n\n    response = ephys.responses.TimeVoltageResponse('mock_response')\n    testdata_dir = os.path.join(\n        os.path.dirname(os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    efeature_value = efeature.calculate_feature(responses)\n\n    numpy.testing.assert_almost_equal(\n        s_obj.calculate_score(responses),\n        abs(efeature_value - mean) * weight)\n\n\n@pytest.mark.unit\ndef test_MaxObjective():\n    \"\"\"ephys.objectives: Testing MaxObjective\"\"\"\n\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n\n    mean = 1\n\n    efeature1 = ephys.efeatures.eFELFeature(name='test_eFELFeature',\n                                            efel_feature_name='voltage_base',\n                                            recording_names=recording_names,\n                                            stim_start=700,\n                                            stim_end=2700,\n                                            exp_mean=mean,\n                                            exp_std=1)\n    efeature2 = ephys.efeatures.eFELFeature(\n        name='test_eFELFeature',\n        efel_feature_name='steady_state_voltage',\n        recording_names=recording_names,\n        stim_start=700,\n        stim_end=2700,\n        exp_mean=mean,\n        exp_std=1)\n\n    max_obj = ephys.objectives.MaxObjective(\n        'max',\n        features=[efeature1, efeature2])\n\n    assert max_obj.name == 'max'\n    assert max_obj.features == [efeature1, efeature2]\n\n    response = ephys.responses.TimeVoltageResponse('mock_response')\n    testdata_dir = os.path.join(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    efeature_value1 = efeature1.calculate_feature(responses)\n    efeature_value2 = efeature2.calculate_feature(responses)\n\n    numpy.testing.assert_almost_equal(\n        max_obj.calculate_score(responses),\n        max(abs(efeature_value1 - mean), abs(efeature_value2 - mean)))\n\n\n@pytest.mark.unit\ndef test_WeightedSumObjective():\n    \"\"\"ephys.objectives: Testing WeightedSumObjective\"\"\"\n\n    recording_names = {'': 'square_pulse_step1.soma.v'}\n\n    mean = 1\n    std = 1\n\n    efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature',\n                                           efel_feature_name='voltage_base',\n                                           recording_names=recording_names,\n                                           stim_start=700,\n                                           stim_end=2700,\n                                           exp_mean=mean,\n                                           exp_std=std)\n\n    weight = .5\n\n    w_obj = ephys.objectives.WeightedSumObjective(\n        'weighted',\n        features=[efeature],\n        weights=[weight])\n\n    assert w_obj.name == 'weighted'\n    assert w_obj.features == [efeature]\n    assert w_obj.weights == [weight]\n\n    response = ephys.responses.TimeVoltageResponse('mock_response')\n    testdata_dir = os.path.join(\n        os.path.dirname(\n            os.path.abspath(__file__)),\n        'testdata')\n    response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv'))\n    responses = {'square_pulse_step1.soma.v': response, }\n\n    efeature_value = efeature.calculate_feature(responses)\n\n    numpy.testing.assert_almost_equal(\n        w_obj.calculate_score(responses),\n        abs(efeature_value - mean) * weight)\n\n    pytest.raises(Exception, ephys.objectives.WeightedSumObjective,\n                  'weighted',\n                  features=[efeature],\n                  weights=[1, 2])\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_parameters.py",
    "content": "\"\"\"ephys.parameters tests\"\"\"\n\nimport json\n\n\nimport pytest\nimport numpy\n\nfrom . import utils\nfrom bluepyopt import ephys\nfrom bluepyopt.ephys.serializer import instantiator\n\nimport bluepyopt.ephys.examples.simplecell\n\n\n@pytest.mark.unit\ndef test_pprocessparam_instantiate():\n    \"\"\"ephys.parameters: Testing point process parameter\"\"\"\n\n    simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n    simple_cell = simplecell.cell_model\n    simple_cell.freeze(simplecell.default_param_values)\n    sim = simplecell.nrn_sim\n\n    expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism(\n        name='expsyn',\n        suffix='ExpSyn',\n        locations=[simplecell.somacenter_loc])\n\n    expsyn_loc = ephys.locations.NrnPointProcessLocation(\n        'expsyn_loc',\n        pprocess_mech=expsyn_mech)\n\n    expsyn_tau_param = ephys.parameters.NrnPointProcessParameter(\n        name='expsyn_tau',\n        param_name='tau',\n        value=2,\n        locations=[expsyn_loc])\n\n    simple_cell.mechanisms.append(expsyn_mech)\n    simple_cell.params[expsyn_tau_param.name] = expsyn_tau_param\n    simple_cell.instantiate(sim=sim)\n\n    assert expsyn_mech.pprocesses[0].tau == 2\n\n    simple_cell.destroy(sim=sim)\n\n\n@pytest.mark.unit\ndef test_serialize():\n    \"\"\"ephys.parameters: Test serialize\"\"\"\n    parameters = utils.make_parameters()\n\n    for param in parameters:\n        serialized = param.to_dict()\n        assert isinstance(json.dumps(serialized), str)\n        deserialized = instantiator(serialized)\n        assert isinstance(deserialized, param.__class__)\n        assert deserialized.name == param.__class__.__name__\n\n\n@pytest.mark.unit\ndef test_metaparameter():\n    \"\"\"ephys.parameters: Test MetaParameter\"\"\"\n\n    dist = \"({A} + {B} * math.exp({distance} * {C}) * {value}\"\n\n    scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n        distribution=dist, dist_param_names=['A', 'B', 'C'])\n\n    scaler.A = -0.9\n    scaler.B = 2\n    scaler.C = 0.003\n\n    meta_param = ephys.parameters.MetaParameter('Param A', scaler, 'A', -1)\n\n    assert meta_param.attr_name == 'A'\n    assert meta_param.value == -1\n    assert scaler.A == -1\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_parameterscalers.py",
    "content": "\"\"\"Test ephys.parameterscalers\"\"\"\n\nimport json\nimport pathlib\nimport tempfile\nimport arbor\n\nimport pytest\n\n\nfrom bluepyopt.ephys.parameterscalers import (NrnSegmentLinearScaler,\n                                              NrnSegmentSomaDistanceScaler, )\nfrom bluepyopt.ephys.serializer import instantiator\n\nimport bluepyopt.ephys as ephys\n\n\n@pytest.mark.unit\ndef test_NrnSegmentSomaDistanceScaler_dist_params():\n    \"\"\"ephys.parameterscalers: dist_params of NrnSegmentSomaDistanceScaler\"\"\"\n\n    dist = \"({A} + {B} * math.exp({distance} * {C}) * {value}\"\n\n    scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n        distribution=dist, dist_param_names=['A', 'B', 'C'])\n\n    assert hasattr(scaler, 'A')\n    assert hasattr(scaler, 'B')\n    assert hasattr(scaler, 'C')\n    scaler.A = -0.9\n    assert scaler.A == -0.9\n    scaler.B = 2\n    assert scaler.B == 2\n    scaler.C = 0.003\n    assert scaler.C == 0.003\n\n    assert (scaler.eval_dist(1.0, 1.0)\n            == '(-0.9 + 2 * math.exp(1 * 0.003) * 1')\n\n\n@pytest.mark.unit\ndef test_NrnSegmentSectionDistanceScaler_eval_dist_with_dict():\n    \"\"\"ephys.parameterscalers: eval_dist of NrnSegmentSectionDistanceScaler\"\"\"\n\n    dist = '{param1_somatic} + (1 - (abs({distance} - 8) / 4)) * {value}'\n\n    scaler = ephys.parameterscalers.NrnSegmentSectionDistanceScaler(\n        distribution=dist)\n\n    _values = {'value': 1, 'param1_somatic': 0.5}\n\n    assert (scaler.eval_dist(values=_values, distance=10)\n            == '0.5 + (1 - (abs(10 - 8) / 4)) * 1')\n\n\n@pytest.mark.unit\ndef test_NrnSegmentSomaDistanceStepScaler_eval_dist_with_dict():\n    \"\"\"ephys.parameterscalers: eval_dist of NrnSegmentSomaDistanceStepScaler\"\"\"\n\n    dist = '{value} * (0.1 + 0.9 * int(' \\\n           '({distance} > {step_begin}) & ({distance} < {step_end})))'\n\n    scaler = ephys.parameterscalers.NrnSegmentSomaDistanceStepScaler(\n        distribution=dist, step_begin=300, step_end=500)\n\n    _values = {'value': 1}\n\n    assert (scaler.eval_dist(values=_values, distance=10)\n            == '1 * (0.1 + 0.9 * int((10 > 300) & (10 < 500)))')\n\n\n@pytest.mark.unit\ndef test_serialize():\n    \"\"\"ephys.parameterscalers: test serialization\"\"\"\n\n    multiplier, offset, distribution = 12.12, 3.58, '1 + {distance}'\n    paramscalers = (\n        NrnSegmentLinearScaler(\n            'NrnSegmentLinearScaler',\n            multiplier,\n            offset),\n        NrnSegmentSomaDistanceScaler(\n            'NrnSegmentSomaDistanceScaler',\n            distribution),\n    )\n\n    for ps in paramscalers:\n        serialized = ps.to_dict()\n        assert isinstance(json.dumps(serialized), str)\n        deserialized = instantiator(serialized)\n        assert isinstance(deserialized, ps.__class__)\n        assert deserialized.name == ps.__class__.__name__\n\n\n@pytest.mark.unit\ndef test_parameterscalers_iexpr_generator():\n    \"\"\"ephys.parameterscalers: Test iexpr generation from python expression\"\"\"\n\n    value = 2.125\n    value_scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n        name='soma_distance_scaler',\n        distribution='(0.62109375 - math.log( math.pi ) * math.exp('\n                     '({distance}) / 0.421875)) * {value}')\n\n    iexpr = value_scaler.acc_scale_iexpr(\n        value=value, constant_formatter=lambda v: '%.9g' % v)\n\n    assert iexpr == '(sub (scalar 0.62109375) ' \\\n                    '(mul (log (pi) ) ' \\\n                    '(exp (div (distance (region \"soma\")) ' \\\n                    '(scalar 0.421875) ) ) ) )'\n\n\n@pytest.mark.unit\ndef test_parameterscalers_iexpr_generator_non_existent_op():\n    \"\"\"ephys.parameterscalers: Test iexpr generation from python expression\n    with invalid node\"\"\"\n\n    value = 2.125\n    value_scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n        name='soma_distance_scaler',\n        distribution='(0.62109375 - math.log( math.pi ) * non_existent_func('\n                     '({distance}) / 0.421875)) * {value}')\n\n    with pytest.raises(ValueError,\n                       match='Arbor iexpr generation failed - '\n                             'unsupported function non_existent_func.'):\n        iexpr = value_scaler.acc_scale_iexpr(\n            value=value, constant_formatter=lambda v: '%.9g' % v)\n\n\n@pytest.mark.unit\ndef test_parameterscalers_iexpr_generator_unsupported_attr():\n    \"\"\"ephys.parameterscalers: Test iexpr generation from python expression\n    with invalid node\"\"\"\n\n    value = 2.125\n    value_scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n        name='soma_distance_scaler',\n        distribution='(0.62109375 - math.log( math.pi )* math.tau.hex('\n                     '({distance}) / 0.421875)) * {value}')\n\n    with pytest.raises(ValueError,\n                       match='Arbor iexpr generation failed - '\n                             'unsupported attribute tau.'):\n        iexpr = value_scaler.acc_scale_iexpr(\n            value=value, constant_formatter=lambda v: '%.9g' % v)\n\n\n@pytest.mark.unit\ndef test_parameterscalers_iexpr():\n    \"\"\"ephys.parameterscalers: Test iexpr\"\"\"\n    # iexpr from bluepyopt/tests/test_ephys/test_parameterscalers.py\n    iexpr = '(sub (scalar 0.62109375) ' \\\n            '(mul (log (pi) ) ' \\\n            '(exp (div (distance (region \"soma\")) ' \\\n            '(scalar 0.421875) ) ) ) )'\n\n    # modified decor as in\n    # bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple_cell_decor.acc\n    simple_cell_decor_with_iexpr = \\\n        '(arbor-component\\n' \\\n        '  (meta-data (version \"0.9-dev\"))\\n' \\\n        '  (decor\\n' \\\n        '    (paint (region \"soma\") ' \\\n        '(membrane-capacitance 0.01 (scalar 1.0)))\\n' \\\n        '    (paint (region \"soma\") ' \\\n        '(scaled-mechanism (density (mechanism \"default::hh\" ' \\\n        '(\"gnabar\" 0.10299326453483033) (\"gkbar\" 0.027124836082684685))) ' \\\n        f'(\"gkbar\" {iexpr})))))'\n\n    with tempfile.TemporaryDirectory() as test_dir:\n        decor_filename = pathlib.Path(test_dir).joinpath(\"decor.acc\")\n        with open(decor_filename, \"w\") as f:\n            f.write(simple_cell_decor_with_iexpr)\n        # check that load_component is not raising any error here\n        arbor.load_component(decor_filename).component\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_protocols.py",
    "content": "\"\"\"bluepyopt.ephys.simulators tests\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\n\nimport pytest\n\nimport bluepyopt.ephys as ephys\nfrom .testmodels import dummycells\n\n\n@pytest.mark.unit\ndef test_distloc_exception():\n    \"\"\"ephys.protocols: test if protocol raise dist loc exception\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    # icell = dummy_cell.instantiate(sim=nrn_sim)\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n    dend_loc = ephys.locations.NrnSomaDistanceCompLocation(\n        name='dend_loc',\n        soma_distance=800,\n        seclist_name='apical')\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n    rec_dend = ephys.recordings.CompRecording(\n        name='dend.v',\n        location=dend_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[\n            rec_soma,\n            rec_dend])\n\n    responses = protocol.run(\n        cell_model=dummy_cell,\n        param_values={},\n        sim=nrn_sim)\n\n    assert responses['soma.v'] is not None\n    assert responses['dend.v'] is None\n\n    protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\ndef run_RuntimeError(\n        self,\n        tstop=None,\n        dt=None,\n        cvode_active=None,\n        random123_globalindex=None):\n    \"\"\"Mock version of run that throws runtimeerror\"\"\"\n    raise RuntimeError()\n\n\ndef run_NrnSimulatorException(\n        self,\n        tstop=None,\n        dt=None,\n        cvode_active=None,\n        random123_globalindex=None):\n    \"\"\"Mock version of run that throws runtimeerror\"\"\"\n    raise ephys.simulators.NrnSimulatorException('mock', None)\n\n\n@pytest.mark.unit\ndef test_sweepprotocol_init():\n    \"\"\"ephys.protocols: Test SweepProtocol init\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    # icell = dummy_cell.instantiate(sim=nrn_sim)\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[rec_soma])\n\n    assert isinstance(protocol, ephys.protocols.SweepProtocol)\n    assert protocol.total_duration == 50\n    assert (\n        protocol.subprotocols() == {'prot': protocol})\n\n    assert 'somatic[0](0.5)' in str(protocol)\n\n    protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_sequenceprotocol_init():\n    \"\"\"ephys.protocols: Test SequenceProtocol init\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    # icell = dummy_cell.instantiate(sim=nrn_sim)\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    sweep_protocol = ephys.protocols.SweepProtocol(\n        name='sweep_prot',\n        stimuli=[stim],\n        recordings=[rec_soma])\n\n    seq_protocol = ephys.protocols.SequenceProtocol(\n        name='seq_prot',\n        protocols=[sweep_protocol])\n\n    assert isinstance(seq_protocol, ephys.protocols.SequenceProtocol)\n    assert (\n        seq_protocol.subprotocols() == {\n            'seq_prot': seq_protocol, 'sweep_prot': sweep_protocol})\n\n    sweep_protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_sequenceprotocol_run():\n    \"\"\"ephys.protocols: Test SequenceProtocol run\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    # icell = dummy_cell.instantiate(sim=nrn_sim)\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    sweep_protocol = ephys.protocols.SweepProtocol(\n        name='sweep_prot',\n        stimuli=[stim],\n        recordings=[rec_soma])\n\n    seq_protocol = ephys.protocols.SequenceProtocol(\n        name='seq_prot',\n        protocols=[sweep_protocol])\n\n    responses = seq_protocol.run(\n        cell_model=dummy_cell,\n        param_values={},\n        sim=nrn_sim)\n\n    assert responses is not None\n\n    sweep_protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_sequenceprotocol_overwrite():\n    \"\"\"ephys.protocols: Test SequenceProtocol overwriting keys\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n\n    sweep_protocols = []\n    for x in [.2, .5]:\n        soma_loc = ephys.locations.NrnSeclistCompLocation(\n            name='soma_loc',\n            seclist_name='somatic',\n            sec_index=0,\n            comp_x=x)\n\n        rec_soma = ephys.recordings.CompRecording(\n            name='soma.v',\n            location=soma_loc,\n            variable='v')\n\n        stim = ephys.stimuli.NrnSquarePulse(\n            step_amplitude=0.0,\n            step_delay=0.0,\n            step_duration=50,\n            total_duration=50,\n            location=soma_loc)\n\n        sweep_protocols.append(ephys.protocols.SweepProtocol(\n            name='sweep_prot',\n            stimuli=[stim],\n            recordings=[rec_soma]))\n\n    seq_protocol = ephys.protocols.SequenceProtocol(\n        name='seq_prot',\n        protocols=sweep_protocols)\n\n    pytest.raises(Exception, seq_protocol.run,\n                  cell_model=dummy_cell,\n                  param_values={},\n                  sim=nrn_sim)\n\n    for sweep_protocol in sweep_protocols:\n        sweep_protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_stepprotocol_init():\n    \"\"\"ephys.protocols: Test StepProtocol init\"\"\"\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=5.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n    hold_stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    step_protocol = ephys.protocols.StepProtocol(\n        name='step_prot',\n        step_stimulus=stim,\n        holding_stimulus=hold_stim,\n        recordings=[rec_soma])\n\n    assert step_protocol.step_delay == 5.0\n    assert step_protocol.step_duration == 50\n\n\n@pytest.mark.unit\ndef test_sweepprotocol_run_unisolated():\n    \"\"\"ephys.protocols: Test SweepProtocol unisolated run\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    # icell = dummy_cell.instantiate(sim=nrn_sim)\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n    unknown_loc = ephys.locations.NrnSomaDistanceCompLocation(\n        name='unknown_loc',\n        seclist_name='somatic',\n        soma_distance=100)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n    rec_unknown = ephys.recordings.CompRecording(\n        name='unknown.v',\n        location=unknown_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[rec_soma, rec_unknown])\n\n    responses = protocol.run(\n        cell_model=dummy_cell,\n        param_values={},\n        sim=nrn_sim,\n        isolate=False)\n\n    assert 'soma.v' in responses\n    assert 'unknown.v' in responses\n    assert responses['unknown.v'] is None\n\n    protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_sweepprotocol_run_isolated():\n    \"\"\"ephys.protocols: Test SweepProtocol isolated run\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator(dt=0.1)\n    dummy_cell = dummycells.DummyCellModel1()\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n    unknown_loc = ephys.locations.NrnSomaDistanceCompLocation(\n        name='unknown_loc',\n        seclist_name='somatic',\n        soma_distance=100)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n    rec_unknown = ephys.recordings.CompRecording(\n        name='unknown.v',\n        location=unknown_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[rec_soma, rec_unknown])\n\n    responses = protocol.run(\n        cell_model=dummy_cell,\n        param_values={},\n        sim=nrn_sim,\n        isolate=True)\n\n    assert 'soma.v' in responses\n    assert 'unknown.v' in responses\n    assert responses['unknown.v'] is None\n\n    protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_nrnsimulator_exception():\n    \"\"\"ephys.protocols: test if protocol raise nrn sim exception\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[rec_soma])\n\n    nrn_sim.run = run_RuntimeError\n\n    responses = protocol.run(\n        cell_model=dummy_cell,\n        param_values={},\n        sim=nrn_sim,\n        isolate=False)\n\n    assert responses['soma.v'] is None\n\n    nrn_sim.run = run_NrnSimulatorException\n\n    responses = protocol.run(\n        cell_model=dummy_cell,\n        param_values={},\n        sim=nrn_sim,\n        isolate=False)\n\n    assert responses['soma.v'] is None\n\n    protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_sweepprotocol_instantiate_with_LFPyCellModel():\n    \"\"\"ephys.protocols: Test SweepProtocol instantiate with LFPyCellModel\"\"\"\n    import LFPy\n\n    dummy_cell = dummycells.DummyLFPyCellModel1()\n    nrn_sim = ephys.simulators.LFPySimulator()\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5\n    )\n\n    rec = ephys.recordings.LFPRecording(\n        name='lfpy_rec'\n    )\n\n    stim = ephys.stimuli.LFPySquarePulse(\n        step_amplitude=0.1,\n        step_delay=70,\n        step_duration=100,\n        location=soma_loc,\n        total_duration=300\n    )\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[rec])\n\n    dummy_cell.instantiate(sim=nrn_sim)\n    protocol.instantiate(sim=nrn_sim, cell_model=dummy_cell)\n\n    # check that recording and stimuli have been instantiated with LFPy\n    assert rec.instantiated\n    assert isinstance(rec.cell, LFPy.Cell)\n    assert isinstance(stim.iclamp, LFPy.StimIntElectrode)\n\n    protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_recordings.py",
    "content": "\"\"\"bluepyopt.ephys.simulators tests\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\n\nimport os\n\nimport pytest\n\nimport bluepyopt.ephys as ephys\n\n\n@pytest.mark.unit\ndef test_comprecording_init():\n    \"\"\"ephys.recordings: Test CompRecording init\"\"\"\n\n    recording = ephys.recordings.CompRecording()\n\n    assert isinstance(recording, ephys.recordings.CompRecording)\n\n    assert recording.response is None\n\n    '''\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    # icell = dummy_cell.instantiate(sim=nrn_sim)\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma_loc',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    rec_soma = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=soma_loc,\n        variable='v')\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.0,\n        step_delay=0.0,\n        step_duration=50,\n        total_duration=50,\n        location=soma_loc)\n\n    protocol = ephys.protocols.SweepProtocol(\n        name='prot',\n        stimuli=[stim],\n        recordings=[rec_soma])\n\n    assert_true(isinstance(protocol, ephys.protocols.SweepProtocol))\n    assert_equal(protocol.total_duration, 50)\n    assert_equal(\n        protocol.subprotocols(), {'prot': protocol})\n\n    assert_true('somatic[0](0.5)' in str(protocol))\n\n    protocol.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n    '''\n\n\n@pytest.mark.unit\ndef test_lfprecording_init():\n    \"\"\"ephys.recordings: Test LFPRecording init\"\"\"\n\n    recording = ephys.recordings.LFPRecording(name=\"rec\")\n\n    assert isinstance(recording, ephys.recordings.LFPRecording)\n\n    assert recording.response is None\n    assert str(recording) == \"rec: v at extracellular\"\n\n\n@pytest.mark.unit\ndef test_lfprecording_instantiate():\n    \"\"\"ephys.recordings: Test LFPRecording instantiate\"\"\"\n    TESTDATA_DIR = os.path.join(\n        os.path.dirname(os.path.abspath(__file__)), 'testdata'\n    )\n    simple_morphology_path = os.path.join(TESTDATA_DIR, 'simple.swc')\n    test_morph = ephys.morphologies.NrnFileMorphology(simple_morphology_path)\n\n    recording = ephys.recordings.LFPRecording()\n    lfpy_cell = ephys.models.LFPyCellModel(\n        name=\"lfpy_cell\", morph=test_morph, mechs=[]\n    )\n    neuron_sim = ephys.simulators.LFPySimulator()\n    lfpy_cell.instantiate(sim=neuron_sim)\n\n    recording.instantiate(\n        sim=neuron_sim, lfpy_cell=lfpy_cell.lfpy_cell\n    )\n\n    assert recording.instantiated\n\n    lfpy_cell.destroy(sim=neuron_sim)\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_serializer.py",
    "content": "\"\"\"Test for ephys.serializer\"\"\"\n\nimport json\n\nimport pytest\nimport numpy\n\nimport bluepyopt.ephys as ephys\n\n\nclass ClassforTesting(ephys.serializer.DictMixin):\n\n    \"\"\"Test class for serializer\"\"\"\n    SERIALIZED_FIELDS = ('string', 'boolean', 'float_', 'list_', 'dict_')\n\n    def __init__(self, string, boolean, float_, list_, dict_):\n        self.string = string\n        self.boolean = boolean\n        self.float_ = float_\n        self.list_ = list_\n        self.dict_ = dict_\n\n\nclass NestedClassforTesting(ephys.serializer.DictMixin):\n\n    \"\"\"Nested test class for serializer\"\"\"\n\n    SERIALIZED_FIELDS = ('test', 'tuples', 'lists', 'dicts', )\n\n    def __init__(self, test, tuples, lists, dicts):\n        self.test = test\n        self.tuples = tuples\n        self.lists = lists\n        self.dicts = dicts\n\n\n@pytest.mark.unit\ndef test_serializer():\n    \"\"\"ephys.serializer: test serialization of test class\"\"\"\n    tc = ClassforTesting('some string', False, 1.0, [1, 2, 3], {'0': 0})\n    serialized = tc.to_dict()\n    assert isinstance(serialized, dict)\n    json.dumps(serialized)\n\n\n@pytest.mark.unit\ndef test_roundtrip_serializer():\n    \"\"\"ephys.serializer: test round trip of serialization of test class\"\"\"\n\n    tc = ClassforTesting('some string', False, 1.0, [1, 2, 3], {'0': 0})\n    serialized = tc.to_dict()\n    instantiated = ephys.serializer.instantiator(serialized)\n    assert isinstance(instantiated, ClassforTesting)\n\n\n@pytest.mark.unit\ndef test_nested_serializer():\n    \"\"\"ephys.serializer: test a nested serialization of test class\"\"\"\n\n    tc = ClassforTesting('some string', False, 1.0, [1, 2, 3], {'0': 0})\n    ntc = NestedClassforTesting(\n        test=tc, tuples=(tc,),\n        lists=[tc] * 3, dicts={0: tc})\n    serialized = ntc.to_dict()\n    json.dumps(serialized, indent=2)\n\n    instantiated = ephys.serializer.instantiator(serialized)\n    assert isinstance(instantiated, NestedClassforTesting)\n    assert isinstance(instantiated.lists[0], ClassforTesting)\n    assert isinstance(instantiated.dicts[0], ClassforTesting)\n\n\n@pytest.mark.unit\ndef test_non_instantiable():\n    \"\"\"ephys.serializer: test non instantiable class\"\"\"\n    with pytest.raises(Exception):\n        ephys.serializer.instantiator(\n            {'some': 'fake', 'class': ephys.serializer.SENTINAL, })\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_simulators.py",
    "content": "\"\"\"bluepyopt.ephys.simulators tests\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\nimport os\nimport pathlib\nimport types\nfrom unittest import mock\n\nimport numpy\nimport pytest\n\nimport bluepyopt.ephys as ephys\nimport bluepyopt.ephys.examples as examples\n\n\n@pytest.mark.unit\ndef test_nrnsimulator_init():\n    \"\"\"ephys.simulators: test if NrnSimulator constructor works\"\"\"\n\n    neuron_sim = ephys.simulators.NrnSimulator()\n    assert isinstance(neuron_sim, ephys.simulators.NrnSimulator)\n\n\n@pytest.mark.unit\ndef test_nrnsimulator_init_windows():\n    \"\"\"ephys.simulators: test if NrnSimulator constructor works on Windows\"\"\"\n\n    with mock.patch(\"platform.system\", mock.MagicMock(return_value=\"Windows\")):\n        neuron_sim = ephys.simulators.NrnSimulator()\n        assert isinstance(neuron_sim, ephys.simulators.NrnSimulator)\n        assert not neuron_sim.disable_banner\n        assert not neuron_sim.banner_disabled\n\n        neuron_sim.neuron.h.celsius = 34\n\n        assert not neuron_sim.disable_banner\n        assert not neuron_sim.banner_disabled\n\n\n@pytest.mark.unit\ndef test_nrnsimulator_cvode_minstep():\n    \"\"\"ephys.simulators: test if NrnSimulator constructor works\"\"\"\n\n    # Check with minstep specified\n    neuron_sim = ephys.simulators.NrnSimulator()\n    assert neuron_sim.cvode.minstep() == 0.0\n    assert neuron_sim.cvode_minstep == 0.0\n\n    # Check default minstep before and after run\n    neuron_sim = ephys.simulators.NrnSimulator(cvode_minstep=0.01)\n    assert neuron_sim.cvode.minstep() == 0.0\n    neuron_sim.run(tstop=10)\n    assert neuron_sim.cvode.minstep() == 0.0\n\n    # Check with that minstep is set back to the original value after run\n    neuron_sim = ephys.simulators.NrnSimulator(cvode_minstep=0.0)\n    neuron_sim.cvode_minstep = 0.05\n    assert neuron_sim.cvode.minstep() == 0.05\n    neuron_sim.run(tstop=10)\n    assert neuron_sim.cvode.minstep() == 0.05\n\n    # Check that the minstep is effective\n    cvode_minstep = 0.012\n    params = {\n        \"gnabar_hh\": 0.10299326453483033,\n        \"gkbar_hh\": 0.027124836082684685,\n    }\n    simplecell = examples.simplecell.SimpleCell()\n    evaluator = simplecell.cell_evaluator\n    evaluator.cell_model.unfreeze(params.keys())\n    evaluator.sim = ephys.simulators.NrnSimulator(cvode_minstep=cvode_minstep)\n    responses = evaluator.run_protocols(\n        protocols=evaluator.fitness_protocols.values(), param_values=params\n    )\n    ton = list(evaluator.fitness_protocols.values())[0].stimuli[0].step_delay\n    toff = (\n        ton\n        + list(evaluator.fitness_protocols.values())[0]\n        .stimuli[0]\n        .step_duration\n    )\n    t_series = numpy.array(responses[\"Step1.soma.v\"][\"time\"])\n    t_series = t_series[((ton + 1.0) < t_series) & (t_series < (toff - 1.0))]\n    min_dt = numpy.min(numpy.ediff1d(t_series))\n    assert (min_dt >= cvode_minstep) == 1\n    evaluator.cell_model.freeze(params)\n\n\n@pytest.mark.unit\ndef test_neuron_import():\n    \"\"\"ephys.simulators: test if bluepyopt.neuron import was successful\"\"\"\n    from bluepyopt import ephys  # NOQA\n\n    neuron_sim = ephys.simulators.NrnSimulator()\n    assert isinstance(neuron_sim.neuron, types.ModuleType)\n\n\n@pytest.mark.unit\ndef test_nrnsim_run_dt_exception():\n    \"\"\"ephys.simulators: test if run return exception when dt was changed\"\"\"\n\n    from bluepyopt import ephys  # NOQA\n\n    neuron_sim = ephys.simulators.NrnSimulator()\n    neuron_sim.neuron.h.dt = 1.0\n    pytest.raises(Exception, neuron_sim.run, 10, cvode_active=False)\n\n\n@pytest.mark.unit\ndef test_nrnsim_run_cvodeactive_dt_exception():\n    \"\"\"ephys.simulators: test if run return exception cvode and dt both used\"\"\"\n\n    from bluepyopt import ephys  # NOQA\n\n    neuron_sim = ephys.simulators.NrnSimulator()\n    neuron_sim.neuron.h.dt = 1.0\n    pytest.raises(ValueError, neuron_sim.run, 10, dt=0.1, cvode_active=True)\n\n\n@pytest.mark.unit\n@mock.patch.object(pathlib.Path, \"glob\")\ndef test_disable_banner_exception(mock_glob):\n    \"\"\"ephys.simulators: test if disable_banner raises exception\"\"\"\n    mock_glob.return_value = []\n\n    import warnings\n\n    with warnings.catch_warnings(record=True) as warnings_record:\n        ephys.simulators.NrnSimulator._nrn_disable_banner()\n        assert len(warnings_record) == 1\n\n\n@pytest.mark.unit\ndef test_lfpysimulator_init():\n    \"\"\"ephys.simulators: test if LFPySimulator constructor works\"\"\"\n\n    empty_cell = ephys.models.LFPyCellModel(name=\"empty_cell\")\n    neuron_sim = ephys.simulators.LFPySimulator()\n    assert isinstance(neuron_sim, ephys.simulators.LFPySimulator)\n\n\n@pytest.mark.unit\ndef test_lfpyimulator_init_windows():\n    \"\"\"ephys.simulators: test if LFPySimulator constructor works on Windows\"\"\"\n\n    with mock.patch(\"platform.system\", mock.MagicMock(return_value=\"Windows\")):\n        empty_cell = ephys.models.LFPyCellModel(name=\"empty_cell\")\n        neuron_sim = ephys.simulators.LFPySimulator()\n        assert isinstance(neuron_sim, ephys.simulators.LFPySimulator)\n        assert not neuron_sim.disable_banner\n        assert not neuron_sim.banner_disabled\n\n        neuron_sim.neuron.h.celsius = 34\n\n        assert not neuron_sim.disable_banner\n        assert not neuron_sim.banner_disabled\n\n\n@pytest.mark.unit\ndef test__lfpysimulator_neuron_import():\n    \"\"\"ephys.simulators: test neuron import from LFPySimulator\"\"\"\n    from bluepyopt import ephys  # NOQA\n\n    empty_cell = ephys.models.LFPyCellModel(name=\"empty_cell\")\n    neuron_sim = ephys.simulators.LFPySimulator()\n    assert isinstance(neuron_sim.neuron, types.ModuleType)\n\n\n@pytest.mark.unit\ndef test_lfpysim_run_cvodeactive_dt_exception():\n    \"\"\"ephys.simulators: test if LFPySimulator run returns exception\"\"\"\n\n    from bluepyopt import ephys  # NOQA\n\n    TESTDATA_DIR = os.path.join(\n        os.path.dirname(os.path.abspath(__file__)), \"testdata\"\n    )\n    simple_morphology_path = os.path.join(TESTDATA_DIR, \"simple.swc\")\n    test_morph = ephys.morphologies.NrnFileMorphology(simple_morphology_path)\n\n    lfpy_cell = ephys.models.LFPyCellModel(\n        name=\"lfpy_cell\", morph=test_morph, mechs=[]\n    )\n    neuron_sim = ephys.simulators.LFPySimulator()\n    lfpy_cell.instantiate(sim=neuron_sim)\n\n    with pytest.raises(\n        ValueError,\n        match=(\n            \"NrnSimulator: \"\n            \"Impossible to combine the dt argument when \"\n            \"cvode_active is True in the NrnSimulator run method\"\n        ),\n    ):\n        neuron_sim.run(\n            tstop=10,\n            dt=0.1,\n            cvode_active=True,\n            lfpy_cell=lfpy_cell.lfpy_cell,\n            lfpy_electrode=lfpy_cell.lfpy_electrode,\n        )\n\n    lfpy_cell.destroy(sim=neuron_sim)\n\n\n@pytest.mark.unit\n@mock.patch.object(pathlib.Path, \"glob\")\ndef test_lfpysimulator_disable_banner_exception(mock_glob):\n    \"\"\"ephys.simulators: test LFPySimulator disable_banner raises exception\"\"\"\n    mock_glob.return_value = []\n\n    import warnings\n\n    with warnings.catch_warnings(record=True) as warnings_record:\n        ephys.simulators.LFPySimulator._nrn_disable_banner()\n        assert len(warnings_record) == 1\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/test_stimuli.py",
    "content": "\"\"\"bluepyopt.ephys.simulators tests\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\nimport numpy\n\n\nimport pytest\nimport numpy\n\nimport bluepyopt.ephys as ephys\nfrom .testmodels import dummycells\n\n\n@pytest.mark.unit\ndef test_stimulus_init():\n    \"\"\"ephys.stimuli: test if Stimulus constructor works\"\"\"\n\n    stim = ephys.stimuli.Stimulus()\n    assert isinstance(stim, ephys.stimuli.Stimulus)\n\n\n@pytest.mark.unit\ndef test_NrnNetStimStimulus_init():\n    \"\"\"ephys.stimuli: test if NrnNetStimStimulus constructor works\"\"\"\n\n    pytest.raises(ValueError, ephys.stimuli.NrnNetStimStimulus)\n\n    stim = ephys.stimuli.NrnNetStimStimulus(total_duration=100)\n    assert isinstance(stim, ephys.stimuli.NrnNetStimStimulus)\n\n    assert str(stim) == 'Netstim'\n\n\n@pytest.mark.unit\ndef test_NrnNetStimStimulus_instantiate():\n    \"\"\"ephys.stimuli: test if NrnNetStimStimulus instantiate works\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    icell = dummy_cell.instantiate(sim=nrn_sim)\n\n    somacenter_loc = ephys.locations.NrnSeclistCompLocation(\n        name=None,\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism(\n        name='expsyn',\n        suffix='ExpSyn',\n        locations=[somacenter_loc])\n\n    expsyn_mech.instantiate(sim=nrn_sim, icell=icell)\n\n    expsyn_loc = ephys.locations.NrnPointProcessLocation(\n        'expsyn_loc',\n        pprocess_mech=expsyn_mech)\n\n    netstim = ephys.stimuli.NrnNetStimStimulus(\n        total_duration=200,\n        number=5,\n        interval=5,\n        start=20,\n        weight=5e-4,\n        locations=[expsyn_loc])\n\n    netstim.instantiate(sim=nrn_sim, icell=icell)\n\n    nrn_sim.run(netstim.total_duration)\n\n    expsyn_mech.destroy(sim=nrn_sim)\n    netstim.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_NrnCurrentPlayStimulus_instantiate():\n    \"\"\"ephys.stimuli: test if NrnNetStimStimulus instantiate works\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    icell = dummy_cell.instantiate(sim=nrn_sim)\n\n    somacenter_loc = ephys.locations.NrnSeclistCompLocation(\n        name=None,\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    time_points = [10, 50]\n    current_points = [0.1, 0.2]\n    current_stim = ephys.stimuli.NrnCurrentPlayStimulus(\n        time_points=time_points,\n        current_points=current_points,\n        location=somacenter_loc)\n\n    assert current_stim.time_points == time_points\n    assert current_stim.current_points == current_points\n    assert str(current_stim) == 'Current play at somatic[0](0.5)'\n    current_stim.instantiate(sim=nrn_sim, icell=icell)\n\n    nrn_sim.run(100)\n\n    current_stim.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_NrnRampPulse_init():\n    \"\"\"ephys.stimuli: test if NrnRampPulse constructor works\"\"\"\n    stim = ephys.stimuli.NrnRampPulse()\n    assert isinstance(stim, ephys.stimuli.NrnRampPulse)\n\n\n@pytest.mark.unit\ndef test_NrnRampPulse_instantiate():\n    \"\"\"ephys.stimuli: test if NrnRampPulse injects correct current\"\"\"\n\n    nrn_sim = ephys.simulators.NrnSimulator()\n    dummy_cell = dummycells.DummyCellModel1()\n    icell = dummy_cell.instantiate(sim=nrn_sim)\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name=None,\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n    recording = ephys.recordings.CompRecording(\n        location=soma_loc,\n        variable='v')\n\n    ramp_amplitude_start = 0.1\n    ramp_amplitude_end = 1.0\n    ramp_delay = 20.0\n    ramp_duration = 20.0\n    total_duration = 50.0\n\n    stim = ephys.stimuli.NrnRampPulse(\n        ramp_amplitude_start=ramp_amplitude_start,\n        ramp_amplitude_end=ramp_amplitude_end,\n        ramp_delay=ramp_delay,\n        ramp_duration=ramp_duration,\n        total_duration=total_duration,\n        location=soma_loc)\n\n    assert (\n        str(stim) ==\n        'Ramp pulse amp_start 0.100000 amp_end 1.000000 '\n        'delay 20.000000 duration 20.000000 totdur 50.000000'\n        ' at somatic[0](0.5)')\n    stim.instantiate(sim=nrn_sim, icell=icell)\n\n    recording.instantiate(sim=nrn_sim, icell=icell)\n\n    stim_i_vec = nrn_sim.neuron.h.Vector()\n    stim_i_vec.record(stim.iclamp._ref_i)  # pylint: disable=W0212\n    nrn_sim.run(stim.total_duration)\n\n    current = numpy.array(stim_i_vec.to_python())\n    time = numpy.array(recording.response['time'])\n    voltage = numpy.array(recording.response['voltage'])\n\n    # make sure current is 0 before stimulus\n    assert numpy.max(\n        current[numpy.where((0 <= time) & (time < ramp_delay))]) == 0\n\n    # make sure voltage stays at v_init before stimulus\n    assert numpy.max(\n        voltage[\n            numpy.where((0 <= time)\n                        & (time < ramp_delay))]) == nrn_sim.neuron.h.v_init\n\n    # make sure current is at right amp at end of stimulus\n    assert (\n        current[numpy.where(time == ramp_delay)][-1] ==\n        ramp_amplitude_start)\n    # make sure current is at right amp at end of stimulus\n    assert (\n        current[numpy.where(time == (ramp_delay + ramp_duration))][0] ==\n        ramp_amplitude_end)\n\n    # make sure current is 0 after stimulus\n    assert numpy.max(\n        current[\n            numpy.where(\n                (ramp_delay + ramp_duration < time)\n                & (time <= total_duration))]) == 0\n\n    # make sure voltage is correct after stimulus\n    numpy.testing.assert_almost_equal(numpy.mean(\n        voltage[\n            numpy.where(\n                (ramp_delay + ramp_duration < time)\n                & (time <= total_duration))]), -57.994437612124869)\n    recording.destroy(sim=nrn_sim)\n    stim.destroy(sim=nrn_sim)\n    dummy_cell.destroy(sim=nrn_sim)\n\n\n@pytest.mark.unit\ndef test_LFPySquarePulse_init():\n    \"\"\"ephys.stimuli: test if LFPySquarePulse constructor works\"\"\"\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name=None,\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n    stim = ephys.stimuli.LFPySquarePulse(\n        step_amplitude=0.1,\n        step_delay=70,\n        step_duration=100,\n        location=soma_loc,\n        total_duration=300\n    )\n    assert isinstance(stim, ephys.stimuli.LFPySquarePulse)\n    assert stim.step_amplitude == 0.1\n    assert stim.step_delay == 70\n    assert stim.step_duration == 100\n    assert isinstance(stim.location, ephys.locations.NrnSeclistCompLocation)\n    assert stim.total_duration == 300\n\n    assert str(stim) == (\n        \"Square pulse amp 0.100000 delay 70.000000 duration 100.000000 \"\n        \"totdur 300.000000 at somatic[0](0.5)\"\n    )\n\n\n@pytest.mark.unit\ndef test_LFPySquarePulse_instantiate():\n    \"\"\"ephys.stimuli: test if LFPySquarePulse instantiate works\"\"\"\n\n    lfpy_sim = ephys.simulators.LFPySimulator()\n    dummy_cell = dummycells.DummyLFPyCellModel1()\n    _, lfpy_cell = dummy_cell.instantiate(sim=lfpy_sim)\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name=None,\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=.5)\n\n    stim = ephys.stimuli.LFPySquarePulse(\n        step_amplitude=0.1,\n        step_delay=70,\n        step_duration=100,\n        location=soma_loc,\n        total_duration=300\n    )\n\n    stim.instantiate(sim=lfpy_sim, lfpy_cell=lfpy_cell)\n    lfpy_sim.run(\n        lfpy_cell=lfpy_cell,\n        lfpy_electrode=dummy_cell.lfpy_electrode,\n        tstop=stim.total_duration\n    )\n\n    stim.destroy(sim=lfpy_sim)\n    dummy_cell.destroy(sim=lfpy_sim)\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/TimeVoltageResponse.csv",
    "content": 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39\n1903,1231.5955626,-60.035281016\n1904,1231.8231126,-60.0262303986\n1905,1232.0506626,-60.0195032902\n1906,1232.27821259,-60.0124359301\n1907,1232.50576259,-60.0037181778\n1908,1232.73331259,-59.9953178173\n1909,1232.96086258,-59.9880570789\n1910,1233.18841258,-59.9803655198\n1911,1233.41596258,-59.9726308047\n1912,1233.64351258,-59.9656569734\n1913,1233.87106257,-59.9589391748\n1914,1234.09861257,-59.9515736397\n1915,1234.32616257,-59.9433845567\n1916,1234.55371256,-59.9351953308\n1917,1234.78126256,-59.9269500139\n1918,1235.00881256,-59.9191489396\n1919,1235.23636256,-59.9117713353\n1920,1235.46391255,-59.9052279676\n1921,1235.69146255,-59.8987225901\n1922,1235.91901255,-59.8914315866\n1923,1236.14656254,-59.8815129897\n1924,1236.37411254,-59.8702697942\n1925,1236.60166254,-59.8613661126\n1926,1236.82921254,-59.8539166188\n1927,1237.05676253,-59.8453750424\n1928,1237.28431253,-59.8355990546\n1929,1237.51186253,-59.8266420498\n1930,1237.73941252,-59.8197382422\n1931,1237.96696252,-59.8135840206\n1932,1238.19451252,-59.8059747128\n1933,1238.42206252,-59.7967372118\n1934,1238.64961251,-59.7880485878\n1935,1238.87716251,-59.7813469811\n1936,1239.10471251,-59.7750141339\n1937,1239.3322625,-59.7665641097\n1938,1239.5598125,-59.7560151184\n1939,1239.7873625,-59.7473972426\n1940,1240.0149125,-59.7420678615\n1941,1240.24246249,-59.7363474636\n1942,1240.47001249,-59.7252399677\n1943,1240.69756249,-59.7130091429\n1944,1240.83779628,-59.7082259816\n1945,1240.97803007,-59.7041333247\n1946,1241.11826387,-59.6992244593\n1947,1241.25849766,-59.6940281782\n1948,1241.39873146,-59.6896609578\n1949,1241.53896525,-59.6866107501\n1950,1241.67919904,-59.6830908107\n1951,1241.81943284,-59.6780860984\n1952,1241.95966663,-59.6722029286\n1953,1242.09990042,-59.6673313647\n1954,1242.24013422,-59.6630708748\n1955,1242.38036801,-59.6594295742\n1956,1242.5206018,-59.6560206022\n1957,1242.6608356,-59.6515347457\n1958,1242.80106939,-59.6464734831\n1959,1242.94130318,-59.6419038426\n1960,1243.08153698,-59.6376637462\n1961,1243.22177077,-59.6330719901\n1962,1243.36200457,-59.6278299674\n1963,1243.57616793,-59.6210572076\n1964,1243.7903313,-59.6152187206\n1965,1244.00449467,-59.608907508\n1966,1244.21865804,-59.601012187\n1967,1244.43282141,-59.5925753432\n1968,1244.64698478,-59.58415436\n1969,1244.86114815,-59.57745708\n1970,1245.07531151,-59.5715269032\n1971,1245.28947488,-59.5649719241\n1972,1245.50363825,-59.5570494392\n1973,1245.82796449,-59.5443529928\n1974,1246.15229072,-59.5330713958\n1975,1246.47661696,-59.5231407972\n1976,1246.8009432,-59.5121803749\n1977,1247.12526943,-59.4998710437\n1978,1247.44959567,-59.4857173758\n1979,1247.7739219,-59.4751857834\n1980,1247.96749791,-59.4702154349\n1981,1248.16107392,-59.463903384\n1982,1248.35464993,-59.45716481\n1983,1248.54822594,-59.4506460374\n1984,1248.74180195,-59.4441636199\n1985,1248.93537796,-59.4378271231\n1986,1249.12895397,-59.4310673479\n1987,1249.32252998,-59.4245025569\n1988,1249.51610599,-59.4180923639\n1989,1249.709682,-59.4116369134\n1990,1249.90325801,-59.4049393494\n1991,1250.45272553,-59.3863073688\n1992,1251.00219306,-59.3666954412\n1993,1251.55166059,-59.3482568877\n1994,1252.10112811,-59.328540231\n1995,1252.65059564,-59.3092331433\n1996,1253.20006316,-59.2901851721\n1997,1253.74953069,-59.2703362041\n1998,1254.29899822,-59.2502099766\n1999,1254.84846574,-59.230745224\n2000,1255.39793327,-59.2115474084\n2001,1255.9474008,-59.1916661893\n2002,1256.49686832,-59.1714998359\n2003,1257.04633585,-59.1519361238\n2004,1257.59580338,-59.13158981\n2005,1258.1452709,-59.1111313723\n2006,1258.69473843,-59.0913556098\n2007,1259.61533449,-59.0572548891\n2008,1260.53593056,-59.0243006011\n2009,1261.45652662,-58.990372251\n2010,1262.37712268,-58.9575315138\n2011,1263.29771875,-58.9233701285\n2012,1264.21831481,-58.8903741259\n2013,1264.44846383,-58.8818065332\n2014,1264.67861284,-58.8731106973\n2015,1264.90876186,-58.8646096085\n2016,1265.45633045,-58.844014024\n2017,1266.00389904,-58.8240938542\n2018,1266.55146763,-58.80435105\n2019,1267.09903622,-58.7836674686\n2020,1267.6466048,-58.7625032949\n2021,1268.19417339,-58.7423501667\n2022,1268.74174198,-58.7214341749\n2023,1269.28931057,-58.7010100601\n2024,1269.83687916,-58.6798278495\n2025,1270.38444775,-58.6581611842\n2026,1270.60563388,-58.6496327864\n2027,1270.82682,-58.6411373357\n2028,1271.26316447,-58.6241290674\n2029,1271.69950893,-58.6072826308\n2030,1272.49236674,-58.5764956083\n2031,1273.86817793,-58.5222644841\n2032,1275.24398912,-58.4670576213\n2033,1277.84871546,-58.3606923144\n2034,1280.4534418,-58.2506350612\n2035,1283.05816814,-58.135975463\n2036,1285.66289448,-58.0106624745\n2037,1286.31407607,-57.976254347\n2038,1286.96525765,-57.9387850455\n2039,1287.61643924,-57.898100141\n2040,1288.62589395,-57.8274378848\n2041,1289.63534865,-57.7504488852\n2042,1289.88771233,-57.7297643143\n2043,1290.140076,-57.7080512085\n2044,1290.39243968,-57.6851949902\n2045,1290.77327111,-57.6471957943\n2046,1291.15410254,-57.605196092\n2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  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/CCell/CCell.json",
    "content": "{ \n  \"cell_model_name\": \"CCell\",\n  \"produced_by\": \"Created by BluePyOpt(1.12.62) at 2022-07-28 17:15:28.166082\", \n  \"morphology\": {\n     \"original\": \"CCell.swc\"\n  },\n  \"label_dict\": \"CCell_label_dict.acc\",\n  \"decor\": \"CCell_decor.acc\"\n}"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/CCell/CCell_decor.acc",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (decor\n    (default (gSKv3_1bar_SKv3_1 65 (scalar 1.0)))\n    (paint (region \"soma\") (gSKv3_1bar_SKv3_1 65 (scalar 1.0)))\n    (paint (region \"soma\") (gSKv3_1bar_SKv3_1 65 (scalar 1.0)))\n    (paint (region \"dend\") (density (mechanism \"BBP::Ih\")))\n    (paint (region \"apic\") (gSKv3_1bar_SKv3_1 65 (scalar 1.0)))\n    (paint (region \"apic\") (gSKv3_1bar_SKv3_1 65 (scalar 1.0)))\n    (paint (region \"apic\") (density (mechanism \"BBP::Ih\")))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/CCell/CCell_label_dict.acc",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (label-dict \n    (region-def \"all\" (all)) \n    (region-def \"apic\" (tag 4)) \n    (region-def \"axon\" (tag 2)) \n    (region-def \"dend\" (tag 3)) \n    (region-def \"soma\" (tag 1)) \n    (region-def \"myelin\" (tag 5))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/CCell/simple_axon_replacement.acc",
    "content": "(arbor-component \n  (meta-data \n    (version \"0.9-dev\"))\n  (morphology \n    (branch 0 -1 \n      (segment 0 \n        (point 5.000000 0.000000 0.000000 0.500000)\n        (point 35.000000 0.000000 0.000000 0.500000)\n        2)\n      (segment 1 \n        (point 35.000000 0.000000 0.000000 0.500000)\n        (point 65.000000 0.000000 0.000000 0.500000)\n        2))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/expsyn/simple.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/expsyn/simple_cell.json",
    "content": "{ \n  \"cell_model_name\": \"simple_cell\",\n  \"produced_by\": \"Created by BluePyOpt(1.12.113) at 2022-11-07 01:06:09.370611\", \n  \"morphology\": {\n     \"original\": \"simple.swc\"\n  },\n  \"label_dict\": \"simple_cell_label_dict.acc\",\n  \"decor\": \"simple_cell_decor.acc\"\n}\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/expsyn/simple_cell_decor.acc",
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36\r\n          (  -55.31  -142.38   -44.25     0.46)  ; 37\r\n          (  -58.83  -141.42   -46.03     0.46)  ; 38\r\n          (  -60.04  -142.30   -48.92     0.46)  ; 39\r\n          (  -62.22  -141.02   -54.32     0.46)  ; 40\r\n          (  -62.22  -141.02   -54.38     0.46)  ; 41\r\n          (  -64.53  -139.17   -57.42     0.46)  ; 42\r\n          (  -64.53  -139.17   -57.45     0.46)  ; 43\r\n          (  -69.49  -136.15   -59.02     0.46)  ; 44\r\n          (  -69.49  -136.15   -59.05     0.46)  ; 45\r\n          (  -70.96  -135.90   -60.75     0.46)  ; 46\r\n          (  -72.38  -133.85   -63.38     0.46)  ; 47\r\n          (  -77.06  -131.97   -65.38     0.46)  ; 48\r\n          (  -76.61  -131.86   -65.38     0.46)  ; 49\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  272.87  -214.64     6.52     0.46)  ; 1\r\n            (  269.62  -214.80     6.05     0.46)  ; 2\r\n            (  261.18  -214.99     5.70     0.46)  ; 3\r\n            (  255.74  -213.88     4.60     0.46)  ; 4\r\n            (  233.48  -211.33     1.75     0.46)  ; 5\r\n            (  219.54  -212.22     0.82     0.46)  ; 6\r\n            (  211.42  -211.72    -2.55     0.46)  ; 7\r\n            (  171.11  -214.61    -0.47     0.46)  ; 8\r\n            (  149.78  -204.08    -0.35     0.46)  ; 9\r\n            (  145.36  -203.31    -0.95     0.46)  ; 10\r\n            (  115.35  -191.84    -1.45     0.46)  ; 11\r\n            (   59.04  -169.93   -21.98     0.46)  ; 12\r\n            (   42.98  -167.72   -26.30     0.46)  ; 13\r\n            (   17.85  -167.04   -15.63     0.46)  ; 14\r\n            (   11.38  -165.57   -12.17     0.46)  ; 15\r\n            (  -10.60  -152.22   -18.27     0.46)  ; 16\r\n            (  -19.34  -147.09   -26.63     0.46)  ; 17\r\n            (  -45.81  -146.73   -39.70     0.46)  ; 18\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  249.58  -211.73     2.85     0.46)  ; 1\r\n            (  186.55  -212.18    -5.60     0.46)  ; 2\r\n            (  160.23  -206.40    -1.00     0.46)  ; 3\r\n            (  126.50  -195.20     0.80     0.46)  ; 4\r\n            (  102.64  -185.86    -9.35     0.46)  ; 5\r\n            (  104.74  -184.77    -9.35     0.46)  ; 6\r\n            (   99.56  -184.79    -8.13     0.46)  ; 7\r\n            (   81.76  -172.37   -14.35     0.46)  ; 8\r\n            (   71.72  -171.73   -19.02     0.46)  ; 9\r\n            (   54.93  -168.50   -23.10     0.46)  ; 10\r\n            (   37.53  -166.61   -21.30     0.46)  ; 11\r\n            (   32.99  -165.28   -18.97     0.46)  ; 12\r\n            (    6.43  -162.55    -8.68     0.46)  ; 13\r\n            (    3.62  -162.61    -8.95     0.46)  ; 14\r\n            (  -31.66  -148.79   -33.67     0.46)  ; 15\r\n            (  -77.19  -131.39   -65.38     0.46)  ; 16\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (    1.85  -163.78    -8.52     0.46)  ; 1, R-1-1-1-2\r\n          (   -0.07  -163.64    -6.22     0.46)  ; 2\r\n          (   -0.07  -163.64    -6.20     0.46)  ; 3\r\n          (   -0.82  -164.42    -1.47     0.46)  ; 4\r\n          (   -2.30  -164.16     0.60     0.46)  ; 5\r\n          (   -4.53  -164.69     2.78     0.46)  ; 6\r\n          (   -7.49  -164.19     2.85     0.46)  ; 7\r\n          (   -7.22  -165.31     3.97     0.46)  ; 8\r\n          (   -7.22  -165.31     3.95     0.46)  ; 9\r\n          (   -7.39  -166.55     6.07     0.46)  ; 10\r\n          (   -8.60  -167.44     7.90     0.46)  ; 11\r\n          (   -8.60  -167.44     7.87     0.46)  ; 12\r\n          (   -9.89  -165.94     8.75     0.46)  ; 13\r\n          (  -10.52  -167.28    10.00     0.46)  ; 14\r\n          (  -11.99  -167.03    12.07     0.46)  ; 15\r\n          (  -11.99  -167.03    12.05     0.46)  ; 16\r\n          (  -13.34  -167.34    13.80     0.46)  ; 17\r\n          (  -14.67  -167.66    14.72     0.46)  ; 18\r\n          (  -16.51  -169.89    15.17     0.46)  ; 19\r\n          (  -16.43  -172.25    15.17     0.46)  ; 20\r\n          (  -19.42  -173.55    15.17     0.46)  ; 21\r\n          (  -20.31  -173.76    17.10     0.46)  ; 22\r\n\r\n          (Dot\r\n            (Color Cyan)\r\n            (Name \"Marker 1\")\r\n            (  -13.81  -163.28    12.20     0.46)  ; 1\r\n            (  -17.94  -173.80    15.17     0.46)  ; 2\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color RGB (255, 128, 255))\r\n            (Name \"Marker 2\")\r\n            (   -0.38  -164.31    -6.20     0.46)  ; 1\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  164.12  -212.93    -1.70     0.46)  ; 1, R-1-1-2\r\n        (  161.76  -212.88    -1.70     0.46)  ; 2\r\n        (  158.82  -212.38    -2.73     0.46)  ; 3\r\n        (  156.00  -212.44    -3.80     0.46)  ; 4\r\n        (  152.48  -211.47    -3.80     0.46)  ; 5\r\n        (  149.36  -212.21    -2.90     0.46)  ; 6\r\n        (  146.66  -212.83    -2.03     0.46)  ; 7\r\n        (  143.41  -213.00    -2.03     0.46)  ; 8\r\n        (  140.15  -213.17    -1.27     0.46)  ; 9\r\n        (  137.03  -213.90    -1.27     0.46)  ; 10\r\n        (  133.77  -214.07    -2.03     0.46)  ; 11\r\n        (  131.22  -215.26    -2.03     0.46)  ; 12\r\n        (  130.77  -215.37    -2.03     0.46)  ; 13\r\n        (  128.85  -215.22    -2.90     0.46)  ; 14\r\n        (  125.15  -215.49    -2.90     0.46)  ; 15\r\n        (  118.95  -215.16    -4.00     0.46)  ; 16\r\n        (  114.21  -215.07    -4.55     0.46)  ; 17\r\n        (  109.17  -215.65    -4.55     0.46)  ; 18\r\n        (  105.78  -215.26    -4.55     0.46)  ; 19\r\n        (  103.41  -215.22    -4.00     0.46)  ; 20\r\n\r\n        (Dot\r\n          (Color Cyan)\r\n          (Name \"Marker 1\")\r\n          (  129.12  -216.36    -2.90     0.46)  ; 1\r\n          (  125.86  -216.53    -2.90     0.46)  ; 2\r\n          (  108.99  -216.89    -4.55     0.46)  ; 3\r\n        )  ;  End of markers\r\n\r\n        (OpenCircle\r\n          (Color RGB (255, 128, 255))\r\n          (Name \"Marker 2\")\r\n          (  152.61  -212.05    -3.80     0.46)  ; 1\r\n          (  143.86  -212.90    -2.03     0.46)  ; 2\r\n          (  137.47  -213.80    -1.27     0.46)  ; 3\r\n          (  118.95  -215.16    -4.00     0.46)  ; 4\r\n        )  ;  End of markers\r\n        (\r\n          (  101.93  -216.16    -1.75     0.46)  ; 1, R-1-1-2-1\r\n          (  102.06  -216.73     0.40     0.46)  ; 2\r\n          (  101.30  -217.51     2.47     0.46)  ; 3\r\n          (  100.24  -218.96     4.30     0.46)  ; 4\r\n          (  100.24  -218.96     5.90     0.46)  ; 5\r\n          (   97.87  -218.91     8.10     0.46)  ; 6\r\n          (   98.41  -215.20     7.72     0.46)  ; 7\r\n          (   99.54  -211.95     7.02     0.46)  ; 8\r\n          (  100.02  -210.05     6.78     0.46)  ; 9\r\n          (  100.12  -206.44     7.50     0.46)  ; 10\r\n          (   99.92  -203.50     8.40     0.46)  ; 11\r\n          (   99.78  -202.93     9.38     0.46)  ; 12\r\n          (   99.12  -200.10    10.40     0.46)  ; 13\r\n          (   98.32  -196.70    12.17     0.46)  ; 14\r\n          (   99.67  -196.39    13.85     0.46)  ; 15\r\n          (   99.67  -196.39    13.82     0.46)  ; 16\r\n          (   99.00  -193.57    15.57     0.46)  ; 17\r\n\r\n          (Dot\r\n            (Color Cyan)\r\n            (Name \"Marker 1\")\r\n            (   99.05  -207.88     7.50     0.46)  ; 1\r\n            (   97.11  -197.59    12.17     0.46)  ; 2\r\n            (   98.56  -193.67    15.57     0.46)  ; 3\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color RGB (255, 128, 255))\r\n            (Name \"Marker 2\")\r\n            (  101.75  -217.40     2.47     0.46)  ; 1\r\n            (   99.12  -200.10    10.40     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (   97.70  -214.17    -4.63     0.46)  ; 1, R-1-1-2-2\r\n          (   94.71  -215.47    -4.63     0.46)  ; 2\r\n          (   91.89  -215.53    -4.63     0.46)  ; 3\r\n          (   88.64  -215.70    -4.63     0.46)  ; 4\r\n          (   84.48  -216.07    -5.90     0.46)  ; 5\r\n          (   82.69  -216.49    -5.90     0.46)  ; 6\r\n          (   79.17  -215.53    -5.97     0.46)  ; 7\r\n          (   77.38  -215.95    -6.05     0.46)  ; 8\r\n          (   77.38  -215.95    -6.07     0.46)  ; 9\r\n          (   74.31  -214.87    -5.32     0.46)  ; 10\r\n          (   71.63  -215.50    -5.32     0.46)  ; 11\r\n          (   68.81  -215.56    -5.03     0.46)  ; 12\r\n          (   66.58  -216.09    -5.03     0.46)  ; 13\r\n          (   63.68  -213.77    -8.02     0.46)  ; 14\r\n          (   61.76  -213.63    -8.27     0.46)  ; 15\r\n          (   59.17  -216.63    -9.00     0.46)  ; 16\r\n          (   57.25  -216.49    -9.00     0.46)  ; 17\r\n          (   54.13  -217.22    -7.25     0.46)  ; 18\r\n          (   50.60  -216.25    -5.40     0.46)  ; 19\r\n          (   50.60  -216.25    -5.42     0.46)  ; 20\r\n          (   48.55  -215.54    -3.95     0.46)  ; 21\r\n          (   48.55  -215.54    -3.97     0.46)  ; 22\r\n          (   45.04  -214.57    -3.22     0.46)  ; 23\r\n          (   41.63  -214.17    -3.78     0.46)  ; 24\r\n          (   37.80  -213.88    -4.75     0.46)  ; 25\r\n          (   34.27  -212.91    -5.72     0.46)  ; 26\r\n          (   31.77  -212.30    -6.67     0.46)  ; 27\r\n          (   28.83  -211.80    -6.52     0.46)  ; 28\r\n          (   25.26  -212.64    -6.52     0.46)  ; 29\r\n          (   21.60  -211.11    -6.52     0.46)  ; 30\r\n          (   17.89  -211.37    -5.80     0.46)  ; 31\r\n          (   15.08  -211.44    -5.80     0.46)  ; 32\r\n          (   10.99  -210.02    -5.80     0.46)  ; 33\r\n          (    8.47  -209.40    -4.47     0.46)  ; 34\r\n          (    6.30  -208.12    -4.47     0.46)  ; 35\r\n          (    4.37  -207.98    -3.70     0.46)  ; 36\r\n          (    2.28  -209.06    -2.05     0.46)  ; 37\r\n          (   -1.56  -208.78    -2.05     0.46)  ; 38\r\n          (   -5.35  -206.67    -0.97     0.46)  ; 39\r\n          (   -8.88  -205.71    -0.02     0.46)  ; 40\r\n          (  -10.53  -206.69     0.93     0.46)  ; 41\r\n          (  -13.87  -204.48     1.42     0.46)  ; 42\r\n          (  -17.40  -203.53     1.65     0.46)  ; 43\r\n          (  -21.55  -203.90     1.65     0.46)  ; 44\r\n          (  -27.53  -200.52     0.57     0.46)  ; 45\r\n          (  -29.76  -201.05     0.57     0.46)  ; 46\r\n          (  -34.05  -200.86    -0.35     0.46)  ; 47\r\n          (  -36.41  -200.81     1.00     0.46)  ; 48\r\n          (  -39.03  -199.64     2.17     0.46)  ; 49\r\n          (  -41.14  -200.73     3.35     0.46)  ; 50\r\n          (  -43.90  -198.98     4.17     0.46)  ; 51\r\n          (  -47.87  -198.13     4.68     0.46)  ; 52\r\n          (  -51.00  -198.86     5.40     0.46)  ; 53\r\n          (  -54.97  -198.00     5.92     0.46)  ; 54\r\n          (  -57.34  -197.95     6.67     0.46)  ; 55\r\n          (  -58.62  -196.47     7.38     0.46)  ; 56\r\n          (  -61.27  -195.29     8.42     0.46)  ; 57\r\n          (  -63.89  -194.11     9.57     0.46)  ; 58\r\n          (  -68.90  -192.89     9.57     0.46)  ; 59\r\n          (  -72.28  -192.50     8.60     0.46)  ; 60\r\n          (  -75.35  -191.43     7.35     0.46)  ; 61\r\n          (  -79.83  -192.48     7.35     0.46)  ; 62\r\n          (  -84.24  -191.72     8.25     0.46)  ; 63\r\n          (  -83.79  -191.61     8.23     0.46)  ; 64\r\n          (  -87.90  -190.19     9.15     0.46)  ; 65\r\n          (  -91.42  -189.22    10.15     0.46)  ; 66\r\n          (  -94.18  -187.49    11.45     0.46)  ; 67\r\n          (  -98.16  -186.62    12.62     0.46)  ; 68\r\n          ( -101.99  -186.33    13.92     0.46)  ; 69\r\n          ( -108.34  -185.42    14.07     0.46)  ; 70\r\n          ( -113.33  -184.20    14.50     0.46)  ; 71\r\n          ( -116.98  -182.67    13.95     0.46)  ; 72\r\n          ( -119.04  -181.96    14.55     0.46)  ; 73\r\n          ( -121.66  -180.78    13.85     0.46)  ; 74\r\n          ( -124.17  -180.18    12.57     0.46)  ; 75\r\n          ( -127.43  -180.34    13.07     0.46)  ; 76\r\n          ( -130.23  -180.41    13.07     0.46)  ; 77\r\n          ( -132.42  -179.13    14.70     0.46)  ; 78\r\n          ( -137.42  -177.90    15.57     0.46)  ; 79\r\n          ( -140.94  -176.94    16.27     0.46)  ; 80\r\n          ( -145.18  -174.95    17.47     0.46)  ; 81\r\n          ( -147.10  -174.81    18.48     0.46)  ; 82\r\n          ( -150.18  -173.73    19.58     0.46)  ; 83\r\n          ( -152.94  -171.99    19.23     0.46)  ; 84\r\n          ( -157.17  -169.99    19.23     0.46)  ; 85\r\n          ( -161.46  -169.81    19.23     0.46)  ; 86\r\n          ( -164.22  -168.07    19.73     0.46)  ; 87\r\n          ( -166.14  -167.92    19.73     0.46)  ; 88\r\n          ( -170.17  -168.86    20.25     0.46)  ; 89\r\n          ( -174.13  -168.00    21.00     0.46)  ; 90\r\n          ( -177.22  -166.93    22.38     0.46)  ; 91\r\n          ( -180.16  -166.43    22.92     0.46)  ; 92\r\n          ( -183.31  -164.18    23.33     0.46)  ; 93\r\n          ( -186.70  -163.79    23.33     0.46)  ; 94\r\n          ( -190.35  -162.26    22.83     0.46)  ; 95\r\n          ( -190.81  -162.36    22.83     0.46)  ; 96\r\n          ( -194.01  -160.73    24.15     0.46)  ; 97\r\n          ( -195.80  -161.14    25.57     0.46)  ; 98\r\n          ( -198.74  -160.65    27.20     0.46)  ; 99\r\n          ( -201.69  -160.13    28.33     0.46)  ; 100\r\n          ( -204.63  -159.63    29.25     0.46)  ; 101\r\n          ( -207.76  -160.36    29.52     0.46)  ; 102\r\n          ( -211.99  -158.37    28.88     0.46)  ; 103\r\n          ( -213.60  -157.56    29.92     0.46)  ; 104\r\n          ( -213.60  -157.56    29.90     0.46)  ; 105\r\n          ( -216.29  -158.18    31.13     0.46)  ; 106\r\n          ( -218.52  -158.70    31.95     0.46)  ; 107\r\n          ( -221.59  -157.64    31.95     0.46)  ; 108\r\n          ( -224.58  -158.93    32.02     0.46)  ; 109\r\n          ( -227.63  -162.03    31.20     0.46)  ; 110\r\n          ( -228.31  -165.19    31.20     0.46)  ; 111\r\n          ( -228.48  -166.42    32.22     0.46)  ; 112\r\n          ( -227.65  -168.02    33.85     0.46)  ; 113\r\n\r\n          (Dot\r\n            (Color Cyan)\r\n            (Name \"Marker 1\")\r\n            (   89.35  -216.73    -4.63     0.46)  ; 1\r\n            (   69.31  -213.66    -5.03     0.46)  ; 2\r\n            (   42.39  -213.39    -3.78     0.46)  ; 3\r\n            (   18.47  -211.84    -5.80     0.46)  ; 4\r\n            (    4.24  -207.41    -3.70     0.46)  ; 5\r\n            (    0.22  -208.36    -2.05     0.46)  ; 6\r\n            (  -20.97  -204.37     1.65     0.46)  ; 7\r\n            (  -32.89  -201.77    -0.35     0.46)  ; 8\r\n            (  -44.09  -200.22     4.17     0.46)  ; 9\r\n            (  -50.41  -199.32     5.40     0.46)  ; 10\r\n            (  -63.89  -194.11     9.57     0.46)  ; 11\r\n            (  -90.35  -187.78    10.15     0.46)  ; 12\r\n            ( -113.06  -185.34    14.50     0.46)  ; 13\r\n            ( -123.91  -181.31    12.57     0.46)  ; 14\r\n            ( -129.53  -181.43    13.07     0.46)  ; 15\r\n            ( -144.78  -176.64    17.47     0.46)  ; 16\r\n            ( -169.01  -169.78    20.25     0.46)  ; 17\r\n            ( -177.08  -167.49    22.38     0.46)  ; 18\r\n            ( -189.95  -163.96    22.83     0.46)  ; 19\r\n            ( -197.45  -162.13    27.20     0.46)  ; 20\r\n            ( -211.73  -159.51    28.88     0.46)  ; 21\r\n            ( -217.36  -159.63    31.95     0.46)  ; 22\r\n            ( -226.70  -166.00    33.85     0.46)  ; 23\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color RGB (255, 128, 255))\r\n            (Name \"Marker 2\")\r\n            (   32.22  -212.20    -6.67     0.46)  ; 1\r\n            (   11.12  -210.57    -5.80     0.46)  ; 2\r\n            (    6.74  -208.02    -4.47     0.46)  ; 3\r\n            (  -58.62  -196.47     7.38     0.46)  ; 4\r\n            (  -72.41  -191.94     8.60     0.46)  ; 5\r\n            (  -77.59  -191.96     7.35     0.46)  ; 6\r\n            (  -94.18  -187.49    11.45     0.46)  ; 7\r\n            ( -118.59  -181.86    14.55     0.46)  ; 8\r\n            ( -152.49  -171.88    19.23     0.46)  ; 9\r\n            ( -166.14  -167.92    19.73     0.46)  ; 10\r\n            ( -180.16  -166.43    22.92     0.46)  ; 11\r\n          )  ;  End of markers\r\n           High\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  292.80  -219.06     8.60     0.92)  ; 1, R-1-2\r\n      (  295.55  -226.77     8.60     0.92)  ; 2\r\n      (  296.22  -229.60     7.25     0.92)  ; 3\r\n      (\r\n        (  294.71  -230.43     4.75     0.46)  ; 1, R-1-2-1\r\n        (  294.71  -230.43     4.72     0.46)  ; 2\r\n        (  294.21  -232.34     2.33     0.46)  ; 3\r\n        (  291.98  -232.86     0.82     0.46)  ; 4\r\n        (  291.98  -232.86     0.80     0.46)  ; 5\r\n        (  290.50  -232.61    -0.95     0.46)  ; 6\r\n        (  290.50  -232.61    -0.97     0.46)  ; 7\r\n        (  290.18  -233.27    -3.27     0.46)  ; 8\r\n        (  289.03  -232.36    -5.37     0.46)  ; 9\r\n        (  289.03  -232.36    -5.40     0.46)  ; 10\r\n        (  287.82  -233.23    -6.93     0.46)  ; 11\r\n        (  286.17  -234.22    -9.27     0.46)  ; 12\r\n        (  285.02  -233.29   -11.37     0.46)  ; 13\r\n        (  284.39  -234.64   -13.57     0.46)  ; 14\r\n        (  282.32  -233.93   -16.10     0.46)  ; 15\r\n        (  282.32  -233.93   -16.13     0.46)  ; 16\r\n        (  282.46  -234.49   -16.67     0.46)  ; 17\r\n        (  280.68  -234.91   -19.77     0.46)  ; 18\r\n        (  279.78  -235.12   -22.55     0.46)  ; 19\r\n        (  279.78  -235.12   -22.57     0.46)  ; 20\r\n        (  279.15  -236.47   -23.47     0.46)  ; 21\r\n        (  277.95  -237.35   -25.87     0.46)  ; 22\r\n        (  277.95  -237.35   -25.90     0.46)  ; 23\r\n        (  275.71  -237.87   -26.75     0.46)  ; 24\r\n        (  275.09  -239.21   -29.08     0.46)  ; 25\r\n        (  274.14  -241.22   -31.10     0.46)  ; 26\r\n        (  274.14  -241.22   -31.13     0.46)  ; 27\r\n        (  272.50  -242.21   -33.25     0.46)  ; 28\r\n        (  269.38  -242.94   -34.25     0.46)  ; 29\r\n\r\n        (OpenCircle\r\n          (Color Yellow)\r\n          (Name \"Marker 2\")\r\n          (  286.04  -233.65   -11.37     0.46)  ; 1\r\n          (  282.02  -234.60   -16.67     0.46)  ; 2\r\n          (  279.34  -235.22   -23.47     0.46)  ; 3\r\n          (  275.71  -237.87   -26.75     0.46)  ; 4\r\n        )  ;  End of markers\r\n        (\r\n          (  271.07  -243.13   -36.58     0.46)  ; 1, R-1-2-1-1\r\n          (  271.07  -243.13   -36.63     0.46)  ; 2\r\n          (  271.65  -243.59   -38.83     0.46)  ; 3\r\n          (  271.65  -243.59   -38.85     0.46)  ; 4\r\n          (  270.00  -244.57   -40.88     0.46)  ; 5\r\n          (  270.00  -244.57   -40.90     0.46)  ; 6\r\n          (  269.29  -243.55   -43.45     0.46)  ; 7\r\n          (  269.29  -243.55   -43.47     0.46)  ; 8\r\n          (  267.77  -245.09   -44.90     0.46)  ; 9\r\n          (  267.14  -246.44   -46.70     0.46)  ; 10\r\n          (  266.51  -247.79   -48.88     0.46)  ; 11\r\n          (  266.51  -247.79   -48.90     0.46)  ; 12\r\n          (  265.88  -249.12   -51.35     0.46)  ; 13\r\n          (  265.88  -249.12   -51.38     0.46)  ; 14\r\n          (  265.38  -251.03   -53.53     0.46)  ; 15\r\n          (  265.38  -251.03   -53.55     0.46)  ; 16\r\n          (  264.05  -251.33   -56.67     0.46)  ; 17\r\n          (  264.05  -251.33   -56.70     0.46)  ; 18\r\n          (  266.28  -250.82   -60.03     0.46)  ; 19\r\n          (  267.62  -250.50   -63.33     0.46)  ; 20\r\n          (  267.62  -250.50   -63.35     0.46)  ; 21\r\n          (  268.78  -251.43   -65.75     0.46)  ; 22\r\n          (  268.78  -251.43   -65.80     0.46)  ; 23\r\n          (  269.36  -251.89   -68.35     0.46)  ; 24\r\n          (  270.52  -252.81   -70.45     0.46)  ; 25\r\n          (  270.52  -252.81   -70.50     0.46)  ; 26\r\n          (  270.34  -254.05   -72.95     0.46)  ; 27\r\n          (  271.76  -256.10   -87.90     0.46)  ; 28\r\n          (  271.76  -256.10   -87.95     0.46)  ; 29\r\n          (  272.03  -257.24   -96.57     0.46)  ; 30\r\n          (  272.52  -255.33  -102.65     0.46)  ; 31\r\n          (  272.97  -255.23  -106.43     0.46)  ; 32\r\n          (  271.63  -255.54  -112.20     0.46)  ; 33\r\n          (  271.63  -255.54  -116.75     0.46)  ; 34\r\n          (  272.79  -256.46  -120.82     0.46)  ; 35\r\n          (  271.89  -256.67  -125.55     0.46)  ; 36\r\n          (  270.65  -259.36  -128.70     0.46)  ; 37\r\n          (  270.65  -259.36  -128.72     0.46)  ; 38\r\n          (  273.01  -259.39  -133.75     0.46)  ; 39\r\n          (  274.47  -259.65  -136.17     0.46)  ; 40\r\n          (  274.47  -259.65  -136.25     0.46)  ; 41\r\n          (  275.18  -260.68  -139.90     0.46)  ; 42\r\n          (  275.18  -260.68  -139.95     0.46)  ; 43\r\n          (  274.87  -261.34  -143.65     0.46)  ; 44\r\n          (  275.94  -259.90  -147.10     0.46)  ; 45\r\n          (  275.94  -259.90  -147.20     0.46)  ; 46\r\n          (  276.84  -259.69  -149.32     0.46)  ; 47\r\n          (  277.55  -260.72  -152.00     0.46)  ; 48\r\n          (  278.57  -261.08  -154.75     0.46)  ; 49\r\n          (  278.57  -261.08  -154.82     0.46)  ; 50\r\n          (  279.92  -260.77  -157.73     0.46)  ; 51\r\n          (  279.92  -260.77  -157.95     0.46)  ; 52\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  268.38  -249.72   -65.82     0.46)  ; 1\r\n            (  272.71  -254.10  -106.43     0.46)  ; 2\r\n            (  271.53  -259.15  -128.72     0.46)  ; 3\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  276.71  -259.13  -149.32     0.46)  ; 1\r\n            (  275.02  -255.94  -151.15     0.46)  ; 2\r\n          )  ;  End of markers\r\n          (\r\n            (  282.11  -262.04  -160.50     0.46)  ; 1, R-1-2-1-1-1\r\n            (  282.11  -262.04  -160.52     0.46)  ; 2\r\n            (  282.81  -263.07  -163.70     0.46)  ; 3\r\n            (  282.81  -263.07  -163.75     0.46)  ; 4\r\n            (  284.20  -260.95  -166.10     0.46)  ; 5\r\n            (  288.10  -259.44  -167.68     0.46)  ; 6\r\n            (  289.92  -257.22  -169.40     0.46)  ; 7\r\n            (  292.28  -257.26  -171.65     0.46)  ; 8\r\n            (  292.28  -257.26  -171.70     0.46)  ; 9\r\n            (  292.60  -256.59  -174.58     0.46)  ; 10\r\n            (  293.86  -253.91  -176.50     0.46)  ; 11\r\n            (  293.86  -253.91  -176.52     0.46)  ; 12\r\n            (  295.96  -252.83  -178.52     0.46)  ; 13\r\n            (  295.51  -252.93  -178.55     0.46)  ; 14\r\n            (  297.30  -252.51  -181.43     0.46)  ; 15\r\n            (  299.84  -251.31  -184.60     0.46)  ; 16\r\n            (  301.05  -250.43  -187.50     0.46)  ; 17\r\n            (  301.05  -250.43  -187.57     0.46)  ; 18\r\n            (  300.08  -248.28  -189.70     0.46)  ; 19\r\n            (  300.08  -248.28  -189.80     0.46)  ; 20\r\n            (  300.26  -247.03  -192.55     0.46)  ; 21\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  297.17  -251.93  -181.43     0.46)  ; 1\r\n            )  ;  End of markers\r\n             Low\r\n          |\r\n            (  281.03  -263.49  -160.15     0.46)  ; 1, R-1-2-1-1-2\r\n            (  283.84  -263.42  -163.48     0.46)  ; 2\r\n            (  283.84  -263.42  -163.50     0.46)  ; 3\r\n            (  285.63  -263.00  -167.32     0.46)  ; 4\r\n            (  285.98  -266.50  -169.50     0.46)  ; 5\r\n            (  285.98  -266.50  -169.52     0.46)  ; 6\r\n            (  287.57  -267.34  -172.45     0.46)  ; 7\r\n            (  288.15  -267.79  -176.02     0.46)  ; 8\r\n            (  288.15  -267.79  -176.05     0.46)  ; 9\r\n            (  289.63  -268.04  -179.73     0.46)  ; 10\r\n            (  292.00  -268.08  -182.45     0.46)  ; 11\r\n            (  292.00  -268.08  -182.50     0.46)  ; 12\r\n            (  293.16  -269.00  -186.22     0.46)  ; 13\r\n            (  293.16  -269.00  -186.25     0.46)  ; 14\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  292.84  -269.67  -183.50     0.46)  ; 1\r\n            )  ;  End of markers\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (  266.68  -243.58   -33.27     0.46)  ; 1, R-1-2-1-2\r\n          (  266.68  -243.58   -33.35     0.46)  ; 2\r\n          (  261.01  -245.50   -32.05     0.46)  ; 3\r\n          (  259.10  -245.35   -30.45     0.46)  ; 4\r\n          (  256.99  -246.44   -30.45     0.46)  ; 5\r\n          (  254.64  -246.39   -28.85     0.46)  ; 6\r\n          (  254.64  -246.39   -28.88     0.46)  ; 7\r\n          (  254.00  -247.73   -29.88     0.46)  ; 8\r\n          (  251.90  -248.83   -28.42     0.46)  ; 9\r\n          (  248.91  -250.12   -26.52     0.46)  ; 10\r\n          (  245.90  -251.42   -26.52     0.46)  ; 11\r\n          (  244.39  -252.97   -24.80     0.46)  ; 12\r\n          (  244.39  -252.97   -24.82     0.46)  ; 13\r\n          (  241.85  -254.17   -23.63     0.46)  ; 14\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  254.87  -243.35   -27.45     0.46)  ; 1\r\n            (  255.98  -240.10   -26.52     0.46)  ; 2\r\n            (  252.48  -249.28   -28.42     0.46)  ; 3\r\n            (  246.93  -251.77   -26.50     0.46)  ; 4\r\n            (  242.12  -255.30   -23.63     0.46)  ; 5\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  261.46  -245.39   -32.05     0.46)  ; 1\r\n          )  ;  End of markers\r\n          (\r\n            (  240.87  -254.99   -27.35     0.46)  ; 1, R-1-2-1-2-1\r\n            (  240.87  -254.99   -27.38     0.46)  ; 2\r\n            (  238.73  -257.88   -28.95     0.46)  ; 3\r\n            (  237.78  -259.89   -30.27     0.46)  ; 4\r\n            (  236.39  -262.00   -31.45     0.46)  ; 5\r\n            (  236.39  -262.00   -31.48     0.46)  ; 6\r\n            (  235.31  -263.46   -32.83     0.46)  ; 7\r\n            (  235.31  -263.46   -32.85     0.46)  ; 8\r\n            (  233.48  -265.67   -34.28     0.46)  ; 9\r\n            (  233.12  -268.15   -35.00     0.46)  ; 10\r\n            (  231.86  -270.82   -35.00     0.46)  ; 11\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  232.77  -264.65   -34.28     0.46)  ; 1\r\n              (  230.83  -270.47   -35.00     0.46)  ; 2\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  238.28  -257.98   -28.98     0.46)  ; 1\r\n            )  ;  End of markers\r\n             Normal\r\n          |\r\n            (  239.88  -255.82   -23.50     0.46)  ; 1, R-1-2-1-2-2\r\n            (  239.88  -255.82   -23.52     0.46)  ; 2\r\n            (  237.91  -257.48   -23.52     0.46)  ; 3\r\n            (  235.23  -258.11   -23.52     0.46)  ; 4\r\n            (  232.68  -259.30   -23.52     0.46)  ; 5\r\n            (  231.29  -261.42   -23.52     0.46)  ; 6\r\n            (  227.85  -262.82   -22.25     0.46)  ; 7\r\n            (  225.12  -265.25   -23.23     0.46)  ; 8\r\n            (  223.47  -266.23   -21.18     0.46)  ; 9\r\n            (  219.81  -264.71   -19.97     0.46)  ; 10\r\n            (  219.81  -264.71   -20.03     0.46)  ; 11\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  234.65  -257.64   -23.52     0.46)  ; 1\r\n              (  225.12  -265.25   -23.23     0.46)  ; 2\r\n            )  ;  End of markers\r\n            (\r\n              (  217.19  -263.54   -20.03     0.46)  ; 1, R-1-2-1-2-2-1\r\n              (  214.37  -263.59   -18.92     0.46)  ; 2\r\n              (  214.37  -263.59   -18.95     0.46)  ; 3\r\n              (  212.18  -262.32   -17.63     0.46)  ; 4\r\n              (  209.38  -262.38   -16.40     0.46)  ; 5\r\n              (  207.90  -262.13   -14.43     0.46)  ; 6\r\n              (  205.98  -261.98   -13.02     0.46)  ; 7\r\n              (  203.47  -261.36   -12.30     0.46)  ; 8\r\n              (  200.20  -261.52   -11.75     0.46)  ; 9\r\n              (  197.88  -259.69   -11.75     0.46)  ; 10\r\n              (  195.08  -259.75   -11.50     0.46)  ; 11\r\n              (  192.63  -257.33   -10.95     0.46)  ; 12\r\n              (  189.11  -256.37   -10.12     0.46)  ; 13\r\n              (  185.72  -255.96    -8.88     0.46)  ; 14\r\n              (  182.19  -255.01    -8.07     0.46)  ; 15\r\n              (  179.82  -254.97    -7.22     0.46)  ; 16\r\n              (  178.21  -254.14    -6.00     0.46)  ; 17\r\n              (  178.08  -253.58    -6.00     0.46)  ; 18\r\n              (  177.06  -253.22    -4.60     0.46)  ; 19\r\n              (  177.06  -253.22    -4.63     0.46)  ; 20\r\n              (  176.35  -252.19    -3.05     0.46)  ; 21\r\n              (  176.35  -252.19    -3.08     0.46)  ; 22\r\n              (  171.98  -249.63    -1.88     0.46)  ; 23\r\n              (  169.43  -250.82    -1.32     0.46)  ; 24\r\n              (  167.86  -254.18    -1.13     0.46)  ; 25\r\n              (  166.61  -256.86    -1.00     0.46)  ; 26\r\n              (  165.66  -258.88    -1.00     0.46)  ; 27\r\n              (  164.54  -262.13    -1.32     0.46)  ; 28\r\n              (  162.27  -264.45     0.12     0.46)  ; 29\r\n              (  160.79  -264.20     0.95     0.46)  ; 30\r\n              (  158.37  -265.95     1.55     0.46)  ; 31\r\n              (  155.52  -267.81     1.50     0.46)  ; 32\r\n              (  151.76  -269.90     1.50     0.46)  ; 33\r\n              (  148.06  -270.17     2.20     0.46)  ; 34\r\n              (  145.82  -270.70     2.20     0.46)  ; 35\r\n              (  144.43  -272.81     2.20     0.46)  ; 36\r\n              (  142.64  -273.23     2.67     0.46)  ; 37\r\n              (  137.68  -276.18     4.20     0.46)  ; 38\r\n              (  135.72  -277.84     4.68     0.46)  ; 39\r\n              (  133.48  -278.37     5.07     0.46)  ; 40\r\n              (  131.33  -281.25     5.55     0.46)  ; 41\r\n              (  129.81  -282.80     6.20     0.46)  ; 42\r\n              (  128.34  -282.55     6.20     0.46)  ; 43\r\n              (  126.38  -284.21     6.47     0.46)  ; 44\r\n              (  122.35  -285.15     6.47     0.46)  ; 45\r\n              (  119.93  -286.91     6.50     0.46)  ; 46\r\n              (  118.60  -287.23     4.63     0.46)  ; 47\r\n              (  118.60  -287.23     4.60     0.46)  ; 48\r\n              (  116.94  -288.21     3.22     0.46)  ; 49\r\n              (  112.30  -290.49     2.73     0.46)  ; 50\r\n              (  109.61  -291.12     2.73     0.46)  ; 51\r\n              (  109.16  -291.23     2.73     0.46)  ; 52\r\n              (  105.08  -293.98     1.90     0.46)  ; 53\r\n              (  103.25  -296.20     1.02     0.46)  ; 54\r\n              (  101.99  -298.88     0.77     0.46)  ; 55\r\n              (   98.25  -300.95     0.35     0.46)  ; 56\r\n              (   97.30  -302.96     0.35     0.46)  ; 57\r\n              (   95.33  -304.62     0.35     0.46)  ; 58\r\n              (   93.81  -306.17    -0.32     0.46)  ; 59\r\n              (   92.47  -306.49    -0.32     0.46)  ; 60\r\n              (   90.64  -308.71    -0.32     0.46)  ; 61\r\n              (   87.32  -310.67    -0.32     0.46)  ; 62\r\n              (   85.68  -311.66    -0.32     0.46)  ; 63\r\n              (   83.26  -313.42    -0.32     0.46)  ; 64\r\n              (   80.13  -314.16    -0.37     0.46)  ; 65\r\n              (   77.45  -314.78    -0.93     0.46)  ; 66\r\n              (   74.91  -315.98    -0.93     0.46)  ; 67\r\n              (   73.38  -317.53    -2.63     0.46)  ; 68\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  192.45  -258.56   -10.95     0.46)  ; 1\r\n                (  159.39  -266.31     1.55     0.46)  ; 2\r\n                (  152.77  -270.26     1.50     0.46)  ; 3\r\n                (  118.78  -285.99     4.60     0.46)  ; 4\r\n                (  112.57  -291.63     2.73     0.46)  ; 5\r\n                (  100.70  -297.38     0.77     0.46)  ; 6\r\n                (   94.93  -302.92     0.35     0.46)  ; 7\r\n                (   81.29  -315.07    -0.37     0.46)  ; 8\r\n                (   75.18  -317.11    -0.93     0.46)  ; 9\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  206.56  -262.44   -13.02     0.46)  ; 1\r\n                (  176.48  -252.76    -3.08     0.46)  ; 2\r\n                (  174.74  -251.38    -1.88     0.46)  ; 3\r\n                (  167.41  -254.28    -1.13     0.46)  ; 4\r\n                (  142.20  -273.33     2.67     0.46)  ; 5\r\n                (   98.25  -300.95     0.35     0.46)  ; 6\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  216.76  -267.80   -19.07     0.46)  ; 1, R-1-2-1-2-2-2\r\n              (  216.15  -269.14   -17.90     0.46)  ; 2\r\n              (  216.15  -269.14   -17.92     0.46)  ; 3\r\n              (  215.78  -271.62   -17.08     0.46)  ; 4\r\n              (  215.65  -271.05   -17.08     0.46)  ; 5\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  214.75  -271.26   -17.08     0.46)  ; 1\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (  296.30  -231.98     7.38     0.92)  ; 1, R-1-2-2\r\n        (\r\n          (  294.90  -233.85     5.40     0.46)  ; 1, R-1-2-2-1\r\n          (  294.28  -235.20     4.07     0.46)  ; 2\r\n          (  294.28  -235.20     4.05     0.46)  ; 3\r\n          (  294.41  -235.77     1.05     0.46)  ; 4\r\n          (  294.41  -235.77     1.02     0.46)  ; 5\r\n          (  293.07  -236.08    -0.95     0.46)  ; 6\r\n          (  293.07  -236.08    -0.97     0.46)  ; 7\r\n          (  293.33  -237.21    -4.05     0.46)  ; 8\r\n          (  293.33  -237.21    -4.07     0.46)  ; 9\r\n          (  291.99  -237.53    -6.70     0.46)  ; 10\r\n          (  291.99  -237.53    -6.73     0.46)  ; 11\r\n          (  290.65  -237.84    -9.38     0.46)  ; 12\r\n          (  290.91  -238.97   -11.50     0.46)  ; 13\r\n          (  290.15  -239.75   -13.15     0.46)  ; 14\r\n          (  289.09  -241.20   -15.15     0.46)  ; 15\r\n          (  288.77  -241.87   -17.25     0.46)  ; 16\r\n          (  288.19  -241.41   -20.00     0.46)  ; 17\r\n          (  288.46  -242.53   -22.60     0.46)  ; 18\r\n          (  287.69  -243.31   -25.60     0.46)  ; 19\r\n          (  288.27  -243.78   -27.65     0.46)  ; 20\r\n          (  288.27  -243.78   -27.67     0.46)  ; 21\r\n          (  287.25  -243.42   -29.92     0.46)  ; 22\r\n          (  285.99  -246.10   -32.60     0.46)  ; 23\r\n          (  285.67  -246.77   -35.22     0.46)  ; 24\r\n          (  285.23  -246.88   -38.20     0.46)  ; 25\r\n          (  285.23  -246.88   -38.22     0.46)  ; 26\r\n          (  284.60  -248.22   -41.22     0.46)  ; 27\r\n          (  283.84  -248.99   -43.20     0.46)  ; 28\r\n          (  283.84  -248.99   -43.22     0.46)  ; 29\r\n          (  283.98  -249.55   -45.70     0.46)  ; 30\r\n          (  283.80  -250.79   -47.90     0.46)  ; 31\r\n          (  283.35  -250.90   -51.08     0.46)  ; 32\r\n          (  284.06  -251.93   -54.52     0.46)  ; 33\r\n          (  284.06  -251.93   -54.58     0.46)  ; 34\r\n          (  282.01  -251.21   -58.13     0.46)  ; 35\r\n          (  282.22  -254.15   -60.95     0.46)  ; 36\r\n          (  280.83  -256.27   -64.57     0.46)  ; 37\r\n          (  280.83  -256.27   -64.60     0.46)  ; 38\r\n          (  279.94  -256.48   -67.13     0.46)  ; 39\r\n          (  279.94  -256.48   -67.15     0.46)  ; 40\r\n          (  279.89  -258.27   -69.92     0.46)  ; 41\r\n          (  279.89  -258.27   -69.95     0.46)  ; 42\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  285.14  -244.50   -32.60     0.46)  ; 1\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  290.47  -239.07   -11.50     0.46)  ; 1\r\n            (  288.01  -242.64   -22.60     0.46)  ; 2\r\n            (  283.21  -250.33   -47.90     0.46)  ; 3\r\n            (  280.70  -255.70   -64.60     0.46)  ; 4\r\n          )  ;  End of markers\r\n          (\r\n            (  276.48  -257.99   -71.35     0.46)  ; 1, R-1-2-2-1-1\r\n            (  273.81  -258.61   -72.03     0.46)  ; 2\r\n            (  271.44  -258.57   -74.90     0.46)  ; 3\r\n            (  269.20  -259.08   -76.97     0.46)  ; 4\r\n            (  265.63  -259.93   -79.60     0.46)  ; 5\r\n            (  263.26  -259.88   -79.65     0.46)  ; 6\r\n            (  260.59  -260.51   -81.00     0.46)  ; 7\r\n            (  257.65  -260.01   -83.20     0.46)  ; 8\r\n            (  254.96  -260.64   -85.28     0.46)  ; 9\r\n            (  253.89  -262.08   -88.25     0.46)  ; 10\r\n            (  252.73  -261.16   -91.58     0.46)  ; 11\r\n            (  250.99  -259.78   -93.65     0.46)  ; 12\r\n            (  250.99  -259.78   -93.75     0.46)  ; 13\r\n            (  248.36  -258.60   -96.47     0.46)  ; 14\r\n            (  248.36  -258.60   -96.50     0.46)  ; 15\r\n            (  246.00  -258.56   -99.20     0.46)  ; 16\r\n            (  246.00  -258.56   -99.25     0.46)  ; 17\r\n            (  243.94  -257.84  -102.02     0.46)  ; 18\r\n            (  243.94  -257.84  -102.07     0.46)  ; 19\r\n            (  241.58  -257.81  -104.85     0.46)  ; 20\r\n            (  238.05  -256.83  -107.60     0.46)  ; 21\r\n            (  233.37  -254.94  -108.57     0.46)  ; 22\r\n            (  233.37  -254.94  -108.60     0.46)  ; 23\r\n            (  231.32  -254.23  -110.35     0.46)  ; 24\r\n            (  231.32  -254.23  -110.37     0.46)  ; 25\r\n            (  230.12  -255.11  -113.75     0.46)  ; 26\r\n            (  226.46  -253.58  -115.90     0.46)  ; 27\r\n            (  222.36  -252.15  -117.85     0.46)  ; 28\r\n            (  218.02  -253.76  -119.25     0.46)  ; 29\r\n            (  215.08  -253.27  -121.75     0.46)  ; 30\r\n            (  212.44  -252.10  -125.80     0.46)  ; 31\r\n            (  210.47  -253.75  -126.60     0.46)  ; 32\r\n            (  210.47  -253.75  -126.62     0.46)  ; 33\r\n            (  208.11  -253.71  -127.90     0.46)  ; 34\r\n            (  205.69  -255.47  -128.75     0.46)  ; 35\r\n            (  205.69  -255.47  -128.77     0.46)  ; 36\r\n            (  204.54  -254.54  -130.77     0.46)  ; 37\r\n            (  204.54  -254.54  -130.80     0.46)  ; 38\r\n            (  201.36  -257.08  -131.48     0.46)  ; 39\r\n            (  198.41  -256.57  -132.18     0.46)  ; 40\r\n            (  198.41  -256.57  -132.20     0.46)  ; 41\r\n            (  195.28  -257.31  -134.05     0.46)  ; 42\r\n            (  195.28  -257.31  -134.13     0.46)  ; 43\r\n            (  193.06  -257.82  -135.65     0.46)  ; 44\r\n            (  191.83  -259.91  -137.27     0.46)  ; 45\r\n            (  191.83  -259.91  -137.30     0.46)  ; 46\r\n            (  189.29  -261.11  -139.32     0.46)  ; 47\r\n            (  186.74  -262.31  -141.07     0.46)  ; 48\r\n            (  186.74  -262.31  -141.10     0.46)  ; 49\r\n            (  184.52  -262.83  -143.32     0.46)  ; 50\r\n            (  184.33  -264.06  -146.30     0.46)  ; 51\r\n            (  183.25  -265.51  -149.20     0.46)  ; 52\r\n            (  181.28  -267.16  -150.55     0.46)  ; 53\r\n            (  178.88  -268.92  -152.85     0.46)  ; 54\r\n            (  178.88  -268.92  -152.88     0.46)  ; 55\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  273.81  -258.61   -72.03     0.46)  ; 1\r\n              (  241.58  -257.81  -104.85     0.46)  ; 2\r\n              (  213.55  -254.81  -125.82     0.46)  ; 3\r\n              (  212.44  -252.10  -125.82     0.46)  ; 4\r\n              (  206.46  -254.69  -128.50     0.46)  ; 5\r\n              (  192.61  -257.93  -135.65     0.46)  ; 6\r\n              (  181.15  -266.60  -150.55     0.46)  ; 7\r\n            )  ;  End of markers\r\n             Normal\r\n          |\r\n            (  278.82  -259.72   -72.38     0.46)  ; 1, R-1-2-2-1-2\r\n            (  279.09  -260.86   -74.82     0.46)  ; 2\r\n            (  279.04  -262.66   -76.45     0.46)  ; 3\r\n            (  279.17  -263.22   -82.63     0.46)  ; 4\r\n            (  278.85  -263.89   -85.97     0.46)  ; 5\r\n            (  278.85  -263.89   -86.00     0.46)  ; 6\r\n            (  279.12  -265.03   -89.72     0.46)  ; 7\r\n            (  279.12  -265.03   -89.75     0.46)  ; 8\r\n            (  277.20  -264.87   -93.05     0.46)  ; 9\r\n            (  276.45  -265.66   -96.25     0.46)  ; 10\r\n            (  276.45  -265.66   -96.27     0.46)  ; 11\r\n            (  275.67  -266.43  -100.68     0.46)  ; 12\r\n            (  273.13  -267.63  -103.15     0.46)  ; 13\r\n            (  272.95  -268.86  -107.22     0.46)  ; 14\r\n            (  273.35  -270.56  -110.47     0.46)  ; 15\r\n            (  273.35  -270.56  -110.50     0.46)  ; 16\r\n            (  272.32  -270.20  -113.57     0.46)  ; 17\r\n            (  272.32  -270.20  -113.60     0.46)  ; 18\r\n            (  271.56  -270.99  -116.15     0.46)  ; 19\r\n            (  271.56  -270.99  -116.17     0.46)  ; 20\r\n            (  271.38  -272.22  -119.27     0.46)  ; 21\r\n            (  271.38  -272.22  -119.30     0.46)  ; 22\r\n            (  270.63  -272.99  -121.77     0.46)  ; 23\r\n            (  270.63  -272.99  -121.80     0.46)  ; 24\r\n            (  271.60  -275.15  -124.20     0.46)  ; 25\r\n            (  271.60  -275.15  -124.30     0.46)  ; 26\r\n            (  272.75  -276.08  -128.15     0.46)  ; 27\r\n            (  273.02  -277.20  -131.10     0.46)  ; 28\r\n            (  272.97  -279.01  -133.60     0.46)  ; 29\r\n            (  273.68  -280.03  -135.98     0.46)  ; 30\r\n            (  271.82  -278.08  -138.67     0.46)  ; 31\r\n            (  271.90  -280.45  -141.35     0.46)  ; 32\r\n            (  271.90  -280.45  -141.38     0.46)  ; 33\r\n            (  273.06  -281.37  -143.63     0.46)  ; 34\r\n            (  273.06  -281.37  -143.65     0.46)  ; 35\r\n            (  275.16  -280.29  -146.17     0.46)  ; 36\r\n            (  275.16  -280.29  -146.22     0.46)  ; 37\r\n            (  276.18  -280.64  -149.00     0.46)  ; 38\r\n            (  276.13  -282.44  -151.32     0.46)  ; 39\r\n            (  276.45  -281.77  -153.63     0.46)  ; 40\r\n            (  276.40  -283.58  -157.23     0.46)  ; 41\r\n            (  276.53  -284.15  -160.57     0.46)  ; 42\r\n            (  276.53  -284.15  -160.63     0.46)  ; 43\r\n            (  277.11  -284.60  -163.15     0.46)  ; 44\r\n            (  275.72  -286.72  -164.50     0.46)  ; 45\r\n            (  276.70  -288.88  -167.25     0.46)  ; 46\r\n            (  277.91  -288.00  -169.07     0.46)  ; 47\r\n            (  278.62  -289.03  -171.92     0.46)  ; 48\r\n            (  278.62  -289.03  -172.55     0.46)  ; 49\r\n            (  278.17  -289.13  -174.85     0.46)  ; 50\r\n            (  279.02  -290.73  -177.62     0.46)  ; 51\r\n            (  279.02  -290.73  -177.68     0.46)  ; 52\r\n            (  279.02  -290.73  -181.15     0.46)  ; 53\r\n            (  279.42  -292.43  -185.30     0.46)  ; 54\r\n            (  280.70  -293.91  -187.85     0.46)  ; 55\r\n            (  280.97  -295.05  -190.42     0.46)  ; 56\r\n            (  280.97  -295.05  -190.45     0.46)  ; 57\r\n            (  282.26  -296.53  -193.60     0.46)  ; 58\r\n            (  283.37  -299.27  -198.27     0.46)  ; 59\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  278.14  -262.86   -76.45     0.46)  ; 1\r\n              (  271.29  -269.84  -110.55     0.46)  ; 2\r\n              (  271.55  -276.95  -131.10     0.46)  ; 3\r\n              (  274.43  -285.23  -164.50     0.46)  ; 4\r\n              (  277.54  -290.47  -181.15     0.46)  ; 5\r\n              (  279.10  -293.09  -185.27     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  273.68  -280.03  -135.98     0.46)  ; 1\r\n              (  275.74  -280.75  -149.00     0.46)  ; 2\r\n            )  ;  End of markers\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (  297.10  -235.38     8.38     0.92)  ; 1, R-1-2-2-2\r\n          (  298.21  -238.09     9.48     0.92)  ; 2\r\n          (  298.87  -240.93    10.70     0.92)  ; 3\r\n          (  300.30  -242.97    11.63     0.92)  ; 4\r\n          (  300.30  -242.97    11.60     0.92)  ; 5\r\n          (  301.14  -244.57    12.67     0.92)  ; 6\r\n          (\r\n            (  303.39  -244.87    14.15     0.46)  ; 1, R-1-2-2-2-1\r\n            (  306.78  -245.28    15.38     0.46)  ; 2\r\n            (  309.59  -245.22    15.38     0.46)  ; 3\r\n            (  313.30  -244.94    15.38     0.46)  ; 4\r\n            (  317.14  -245.24    15.38     0.46)  ; 5\r\n            (  321.87  -245.31    15.30     0.46)  ; 6\r\n            (  324.55  -244.69    14.57     0.46)  ; 7\r\n            (  328.26  -244.43    14.57     0.46)  ; 8\r\n            (  331.38  -243.69    14.57     0.46)  ; 9\r\n            (  334.19  -243.63    15.65     0.46)  ; 10\r\n            (  338.35  -243.25    16.70     0.46)  ; 11\r\n            (  342.49  -242.87    16.70     0.46)  ; 12\r\n            (  345.76  -242.70    16.70     0.46)  ; 13\r\n            (  350.35  -242.23    17.35     0.46)  ; 14\r\n            (  352.58  -241.70    18.13     0.46)  ; 15\r\n            (  356.87  -241.90    18.77     0.46)  ; 16\r\n            (  360.57  -241.63    19.40     0.46)  ; 17\r\n            (  364.28  -241.35    19.35     0.46)  ; 18\r\n            (  367.09  -241.29    20.47     0.46)  ; 19\r\n            (  369.46  -241.33    21.38     0.46)  ; 20\r\n            (  371.88  -239.58    22.27     0.46)  ; 21\r\n            (  375.40  -240.53    22.27     0.46)  ; 22\r\n            (  380.14  -240.63    22.32     0.46)  ; 23\r\n            (  383.21  -241.69    21.18     0.46)  ; 24\r\n            (  387.05  -241.98    21.18     0.46)  ; 25\r\n            (  388.96  -242.13    21.18     0.46)  ; 26\r\n            (  390.31  -241.82    21.18     0.46)  ; 27\r\n            (  394.48  -241.47    21.18     0.46)  ; 28\r\n            (\r\n              (  395.42  -239.46    18.63     0.46)  ; 1, R-1-2-2-2-1-1\r\n              (  396.90  -239.71    17.47     0.46)  ; 2\r\n              (  396.45  -239.81    17.42     0.46)  ; 3\r\n              (  398.68  -239.29    16.42     0.46)  ; 4\r\n              (  400.91  -238.77    15.52     0.46)  ; 5\r\n              (  402.84  -238.91    14.95     0.46)  ; 6\r\n              (  404.93  -237.82    14.13     0.46)  ; 7\r\n              (  406.98  -238.54    13.02     0.46)  ; 8\r\n              (  406.98  -238.54    13.00     0.46)  ; 9\r\n              (  408.28  -240.03    12.98     0.46)  ; 10\r\n              (  410.78  -240.63    12.42     0.46)  ; 11\r\n              (  410.78  -240.63    12.40     0.46)  ; 12\r\n              (  413.14  -240.67    12.38     0.46)  ; 13\r\n              (  416.39  -240.51    12.60     0.46)  ; 14\r\n              (  419.52  -239.78    12.60     0.46)  ; 15\r\n              (  422.60  -240.84    11.68     0.46)  ; 16\r\n              (  425.69  -241.92    10.35     0.46)  ; 17\r\n              (  425.69  -241.92    10.33     0.46)  ; 18\r\n              (  429.07  -242.32     9.20     0.46)  ; 19\r\n              (  429.07  -242.32     9.17     0.46)  ; 20\r\n              (  432.33  -242.15     8.55     0.46)  ; 21\r\n              (  432.33  -242.15     8.52     0.46)  ; 22\r\n              (  434.53  -243.43     7.02     0.46)  ; 23\r\n              (  439.87  -242.18     6.73     0.46)  ; 24\r\n              (  442.56  -241.55     5.80     0.46)  ; 25\r\n              (  445.50  -242.05     5.30     0.46)  ; 26\r\n              (  445.50  -242.05     5.27     0.46)  ; 27\r\n              (  447.10  -242.86     4.88     0.46)  ; 28\r\n              (  447.10  -242.86     4.82     0.46)  ; 29\r\n              (  449.78  -242.24     3.65     0.46)  ; 30\r\n              (  449.64  -241.67     3.65     0.46)  ; 31\r\n              (  452.59  -242.18     3.65     0.46)  ; 32\r\n              (  455.15  -240.98     3.65     0.46)  ; 33\r\n              (  459.44  -241.18     2.70     0.46)  ; 34\r\n              (  459.44  -241.18     2.67     0.46)  ; 35\r\n              (  463.27  -241.47     1.88     0.46)  ; 36\r\n              (  466.08  -241.41     1.00     0.46)  ; 37\r\n              (  469.61  -242.37    -0.90     0.46)  ; 38\r\n              (  471.39  -241.95    -2.05     0.46)  ; 39\r\n              (  473.71  -243.80    -3.10     0.46)  ; 40\r\n              (  473.71  -243.80    -3.13     0.46)  ; 41\r\n              (  477.42  -243.52    -2.83     0.46)  ; 42\r\n              (  477.42  -243.52    -3.17     0.46)  ; 43\r\n              (  480.49  -244.61    -4.13     0.46)  ; 44\r\n              (  483.30  -244.54    -5.50     0.46)  ; 45\r\n              (  487.14  -244.83    -6.65     0.46)  ; 46\r\n              (  490.97  -245.12    -8.05     0.46)  ; 47\r\n              (  493.35  -245.16    -9.25     0.46)  ; 48\r\n              (  493.35  -245.16    -9.27     0.46)  ; 49\r\n              (  496.29  -245.68   -10.80     0.46)  ; 50\r\n              (  500.39  -247.10   -11.80     0.46)  ; 51\r\n              (  503.87  -249.87   -13.05     0.46)  ; 52\r\n              (  503.87  -249.87   -13.07     0.46)  ; 53\r\n              (  508.42  -251.19   -15.05     0.46)  ; 54\r\n              (  511.37  -251.70   -15.40     0.46)  ; 55\r\n              (  514.57  -253.33   -15.43     0.46)  ; 56\r\n              (  517.39  -253.27   -16.17     0.46)  ; 57\r\n              (  517.39  -253.27   -16.20     0.46)  ; 58\r\n              (  521.80  -254.02   -16.70     0.46)  ; 59\r\n              (  524.75  -254.53   -16.70     0.46)  ; 60\r\n              (  529.17  -255.28   -16.73     0.46)  ; 61\r\n              (  531.50  -255.92   -15.52     0.46)  ; 62\r\n              (  535.46  -256.78   -14.75     0.46)  ; 63\r\n              (  537.65  -258.06   -14.75     0.46)  ; 64\r\n              (  542.83  -258.04   -14.75     0.46)  ; 65\r\n              (  545.91  -259.11   -14.05     0.46)  ; 66\r\n              (  548.14  -258.59   -13.52     0.46)  ; 67\r\n              (  551.52  -258.99   -13.52     0.46)  ; 68\r\n              (  553.46  -259.14   -13.52     0.46)  ; 69\r\n              (  556.44  -257.83   -13.20     0.46)  ; 70\r\n              (  559.56  -257.10   -13.20     0.46)  ; 71\r\n              (  563.99  -257.86   -13.20     0.46)  ; 72\r\n              (  568.40  -258.62   -13.20     0.46)  ; 73\r\n              (  574.03  -258.50   -12.55     0.46)  ; 74\r\n              (  574.03  -258.50   -12.57     0.46)  ; 75\r\n              (  579.16  -260.27   -12.57     0.46)  ; 76\r\n              (  584.16  -261.50   -12.57     0.46)  ; 77\r\n              (  587.82  -263.02   -12.57     0.46)  ; 78\r\n              (  593.12  -263.57   -12.57     0.46)  ; 79\r\n              (  595.75  -264.74   -12.13     0.46)  ; 80\r\n              (  597.99  -264.22   -12.13     0.46)  ; 81\r\n              (  605.04  -266.15   -11.75     0.46)  ; 82\r\n              (  609.01  -267.02   -11.75     0.46)  ; 83\r\n              (  612.52  -267.98   -13.65     0.46)  ; 84\r\n              (  612.52  -267.98   -13.67     0.46)  ; 85\r\n              (  616.63  -269.40   -15.55     0.46)  ; 86\r\n              (  621.49  -270.06   -17.17     0.46)  ; 87\r\n              (  626.50  -271.27   -18.45     0.46)  ; 88\r\n              (  631.81  -271.83   -18.45     0.46)  ; 89\r\n              (  636.67  -272.47   -18.45     0.46)  ; 90\r\n              (  639.48  -272.41   -20.47     0.46)  ; 91\r\n              (  643.90  -273.17   -21.63     0.46)  ; 92\r\n              (  643.90  -273.17   -21.65     0.46)  ; 93\r\n              (  647.15  -273.01   -22.80     0.46)  ; 94\r\n              (  647.15  -273.01   -22.83     0.46)  ; 95\r\n              (  654.12  -272.56   -23.33     0.46)  ; 96\r\n              (  658.72  -272.08   -23.33     0.46)  ; 97\r\n              (  662.16  -270.68   -22.22     0.46)  ; 98\r\n              (  662.16  -270.68   -22.25     0.46)  ; 99\r\n              (  664.26  -269.59   -19.85     0.46)  ; 100\r\n              (  666.19  -269.73   -17.85     0.46)  ; 101\r\n              (  669.57  -270.14   -17.15     0.46)  ; 102\r\n              (  671.94  -270.19   -18.00     0.46)  ; 103\r\n              (  676.67  -270.27   -18.02     0.46)  ; 104\r\n              (  682.29  -270.14   -18.90     0.46)  ; 105\r\n              (  685.69  -270.54   -19.73     0.46)  ; 106\r\n              (  690.60  -269.40   -20.52     0.46)  ; 107\r\n              (  694.30  -269.12   -21.28     0.46)  ; 108\r\n              (  697.43  -268.39   -21.28     0.46)  ; 109\r\n              (  701.72  -268.57   -21.95     0.46)  ; 110\r\n              (  706.01  -268.76   -23.52     0.46)  ; 111\r\n              (  706.01  -268.76   -23.57     0.46)  ; 112\r\n              (  710.60  -268.28   -25.03     0.46)  ; 113\r\n              (  715.02  -269.05   -26.13     0.46)  ; 114\r\n              (  721.67  -268.08   -26.13     0.46)  ; 115\r\n              (  723.91  -267.55   -27.15     0.46)  ; 116\r\n              (  726.72  -267.49   -27.15     0.46)  ; 117\r\n              (  728.19  -267.74   -29.50     0.46)  ; 118\r\n              (  730.42  -267.22   -30.92     0.46)  ; 119\r\n              (  733.55  -266.49   -31.32     0.46)  ; 120\r\n              (  733.55  -266.49   -31.35     0.46)  ; 121\r\n              (  736.50  -266.98   -33.05     0.46)  ; 122\r\n              (  740.02  -267.96   -34.32     0.46)  ; 123\r\n              (  740.02  -267.96   -34.35     0.46)  ; 124\r\n              (  745.33  -268.50   -35.65     0.46)  ; 125\r\n              (  751.98  -268.74   -35.97     0.46)  ; 126\r\n              (  755.69  -268.47   -37.52     0.46)  ; 127\r\n              (  755.69  -268.47   -37.55     0.46)  ; 128\r\n              (  760.42  -268.55   -37.55     0.46)  ; 129\r\n              (  765.72  -269.11   -37.55     0.46)  ; 130\r\n              (  768.99  -268.94   -38.45     0.46)  ; 131\r\n              (  774.03  -268.35   -39.63     0.46)  ; 132\r\n              (  778.19  -267.97   -41.93     0.46)  ; 133\r\n              (  782.77  -267.49   -41.93     0.46)  ; 134\r\n              (  789.44  -267.73   -41.93     0.46)  ; 135\r\n              (  794.03  -267.24   -42.92     0.46)  ; 136\r\n              (  796.40  -267.29   -43.30     0.46)  ; 137\r\n              (  796.40  -267.29   -43.33     0.46)  ; 138\r\n              (  799.47  -268.35   -42.65     0.46)  ; 139\r\n              (  799.47  -268.35   -42.67     0.46)  ; 140\r\n              (  802.24  -270.10   -43.00     0.46)  ; 141\r\n              (  805.49  -269.93   -44.30     0.46)  ; 142\r\n              (  808.63  -269.20   -45.83     0.46)  ; 143\r\n              (  812.64  -268.25   -47.33     0.46)  ; 144\r\n              (  816.03  -268.65   -47.42     0.46)  ; 145\r\n              (  816.03  -268.65   -47.45     0.46)  ; 146\r\n              (  818.41  -268.69   -49.02     0.46)  ; 147\r\n              (  821.21  -268.63   -50.38     0.46)  ; 148\r\n              (  821.21  -268.63   -50.40     0.46)  ; 149\r\n              (  824.33  -267.90   -51.57     0.46)  ; 150\r\n              (  829.07  -267.99   -52.88     0.46)  ; 151\r\n              (  829.07  -267.99   -52.90     0.46)  ; 152\r\n              (  834.50  -269.10   -53.97     0.46)  ; 153\r\n              (  837.33  -269.03   -54.27     0.46)  ; 154\r\n              (  837.33  -269.03   -54.30     0.46)  ; 155\r\n              (  841.17  -269.33   -54.30     0.46)  ; 156\r\n              (  844.88  -269.06   -55.42     0.46)  ; 157\r\n              (  844.88  -269.06   -55.45     0.46)  ; 158\r\n              (  849.46  -268.58   -57.13     0.46)  ; 159\r\n              (  854.60  -270.37   -55.97     0.46)  ; 160\r\n              (  856.79  -270.45   -55.00     0.46)  ; 161\r\n              (  860.18  -270.85   -57.28     0.46)  ; 162\r\n              (  863.12  -271.35   -59.22     0.46)  ; 163\r\n              (  868.12  -272.57   -58.05     0.46)  ; 164\r\n              (  871.54  -277.14   -57.17     0.46)  ; 165\r\n              (  873.55  -279.66   -56.28     0.46)  ; 166\r\n              (  876.89  -281.87   -55.57     0.46)  ; 167\r\n              (  880.24  -284.06   -55.57     0.46)  ; 168\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  409.62  -239.72    12.40     0.46)  ; 1\r\n                (  449.06  -241.21     3.65     0.46)  ; 2\r\n                (  487.27  -245.40    -6.65     0.46)  ; 3\r\n                (  625.74  -272.06   -18.45     0.46)  ; 4\r\n                (  653.50  -273.91   -23.33     0.46)  ; 5\r\n                (  714.40  -270.38   -26.13     0.46)  ; 6\r\n                (  815.41  -269.99   -47.45     0.46)  ; 7\r\n                (  875.56  -282.18   -55.57     0.46)  ; 8\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  400.02  -238.97    15.52     0.46)  ; 1\r\n                (  413.14  -240.67    12.38     0.46)  ; 2\r\n                (  434.96  -243.33     7.02     0.46)  ; 3\r\n                (  466.08  -241.41     1.00     0.46)  ; 4\r\n                (  468.13  -242.11    -0.90     0.46)  ; 5\r\n                (  473.71  -243.80    -3.13     0.46)  ; 6\r\n                (  496.29  -245.68   -10.80     0.46)  ; 7\r\n                (  498.78  -246.28   -11.77     0.46)  ; 8\r\n                (  595.75  -264.74   -12.13     0.46)  ; 9\r\n                (  598.70  -265.25   -12.13     0.46)  ; 10\r\n                (  602.09  -265.65   -11.75     0.46)  ; 11\r\n                (  608.56  -267.13   -11.75     0.46)  ; 12\r\n                (  669.57  -270.14   -17.15     0.46)  ; 13\r\n                (  768.54  -269.04   -38.45     0.46)  ; 14\r\n                (  778.77  -268.43   -41.93     0.46)  ; 15\r\n                (  796.40  -267.29   -43.33     0.46)  ; 16\r\n                (  800.72  -271.65   -41.35     0.46)  ; 17\r\n                (  855.05  -270.26   -56.00     0.46)  ; 18\r\n                (  854.68  -272.73   -55.00     0.46)  ; 19\r\n                (  863.56  -271.25   -59.22     0.46)  ; 20\r\n                (  880.24  -284.06   -55.57     0.46)  ; 21\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  394.60  -242.01    21.18     0.46)  ; 1, R-1-2-2-2-1-2\r\n              (  396.83  -241.49    21.18     0.46)  ; 2\r\n              (  399.65  -241.43    22.15     0.46)  ; 3\r\n              (  401.18  -239.88    22.15     0.46)  ; 4\r\n              (  404.13  -240.39    22.73     0.46)  ; 5\r\n              (  407.25  -239.65    23.27     0.46)  ; 6\r\n              (  410.51  -239.49    22.55     0.46)  ; 7\r\n              (  413.72  -241.12    23.33     0.46)  ; 8\r\n              (  415.06  -240.80    23.50     0.46)  ; 9\r\n              (  419.40  -239.19    24.45     0.46)  ; 10\r\n              (  422.83  -237.79    24.45     0.46)  ; 11\r\n              (  425.19  -237.83    24.48     0.46)  ; 12\r\n              (  428.59  -238.23    24.88     0.46)  ; 13\r\n              (  432.29  -237.96    26.13     0.46)  ; 14\r\n              (  435.24  -238.47    26.55     0.46)  ; 15\r\n              (  437.48  -237.94    26.55     0.46)  ; 16\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  321.11  -246.10    15.30     0.46)  ; 1\r\n                (  330.76  -245.03    14.57     0.46)  ; 2\r\n                (  355.98  -242.11    18.77     0.46)  ; 3\r\n                (  394.78  -240.77    21.18     0.46)  ; 4\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  306.78  -245.28    15.38     0.46)  ; 1\r\n                (  337.90  -243.35    16.70     0.46)  ; 2\r\n                (  345.76  -242.70    16.70     0.46)  ; 3\r\n                (  360.71  -242.19    19.40     0.46)  ; 4\r\n                (  363.39  -241.56    19.35     0.46)  ; 5\r\n                (  369.33  -240.76    21.38     0.46)  ; 6\r\n                (  372.59  -240.60    22.27     0.46)  ; 7\r\n                (  375.40  -240.53    22.27     0.46)  ; 8\r\n                (  380.14  -240.63    22.32     0.46)  ; 9\r\n                (  406.80  -239.76    23.27     0.46)  ; 10\r\n                (  415.06  -240.80    23.50     0.46)  ; 11\r\n                (  428.59  -238.23    24.95     0.46)  ; 12\r\n              )  ;  End of markers\r\n              (\r\n                (  439.04  -234.60    24.80     0.46)  ; 1, R-1-2-2-2-1-2-1\r\n                (  442.67  -231.97    23.75     0.46)  ; 2\r\n                (  442.67  -231.97    23.72     0.46)  ; 3\r\n                (  445.13  -228.40    23.05     0.46)  ; 4\r\n                (  448.44  -226.44    22.05     0.46)  ; 5\r\n                (  450.72  -224.10    22.05     0.46)  ; 6\r\n                (  451.53  -221.53    21.60     0.46)  ; 7\r\n                (  453.05  -219.98    21.60     0.46)  ; 8\r\n                (  456.04  -218.68    21.60     0.46)  ; 9\r\n                (  458.76  -216.25    22.27     0.46)  ; 10\r\n                (  461.23  -212.69    23.25     0.46)  ; 11\r\n                (  464.27  -209.58    24.15     0.46)  ; 12\r\n                (  467.90  -206.94    24.42     0.46)  ; 13\r\n                (  471.07  -204.41    24.45     0.46)  ; 14\r\n                (  475.14  -201.66    23.42     0.46)  ; 15\r\n                (  476.09  -199.65    22.60     0.46)  ; 16\r\n                (  478.76  -199.03    21.90     0.46)  ; 17\r\n                (  478.81  -197.22    20.95     0.46)  ; 18\r\n                (  480.91  -196.12    20.95     0.46)  ; 19\r\n                (  483.06  -193.24    19.75     0.46)  ; 20\r\n                (  484.58  -191.68    19.02     0.46)  ; 21\r\n                (  486.55  -190.03    19.02     0.46)  ; 22\r\n                (  488.38  -187.81    18.72     0.46)  ; 23\r\n                (  490.35  -186.15    20.15     0.46)  ; 24\r\n                (  492.18  -183.93    21.35     0.46)  ; 25\r\n                (  495.05  -182.08    22.38     0.46)  ; 26\r\n                (  496.31  -179.38    24.30     0.46)  ; 27\r\n                (  497.95  -178.40    25.55     0.46)  ; 28\r\n                (  498.00  -176.60    25.45     0.46)  ; 29\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  445.39  -229.53    23.05     0.46)  ; 1\r\n                  (  449.02  -226.90    22.05     0.46)  ; 2\r\n                  (  458.18  -215.79    22.27     0.46)  ; 3\r\n                  (  463.12  -208.66    24.15     0.46)  ; 4\r\n                  (  473.45  -204.46    23.42     0.46)  ; 5\r\n                  (  478.58  -200.26    21.90     0.46)  ; 6\r\n                  (  496.79  -177.48    25.45     0.46)  ; 7\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  452.10  -222.00    21.60     0.46)  ; 1\r\n                  (  466.57  -207.25    24.42     0.46)  ; 2\r\n                  (  470.75  -205.08    24.45     0.46)  ; 3\r\n                  (  483.06  -193.24    19.75     0.46)  ; 4\r\n                  (  486.11  -190.14    19.02     0.46)  ; 5\r\n                  (  488.51  -188.38    18.72     0.46)  ; 6\r\n                  (  490.49  -186.73    20.17     0.46)  ; 7\r\n                  (  494.78  -180.93    22.38     0.46)  ; 8\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  441.44  -238.80    26.55     0.46)  ; 1, R-1-2-2-2-1-2-2\r\n                (  445.46  -237.86    25.25     0.46)  ; 2\r\n                (  447.70  -237.34    25.25     0.46)  ; 3\r\n                (  450.07  -237.39    26.15     0.46)  ; 4\r\n                (  451.41  -237.07    26.15     0.46)  ; 5\r\n                (  455.69  -237.25    26.70     0.46)  ; 6\r\n                (  458.94  -237.09    26.70     0.46)  ; 7\r\n                (  464.25  -237.63    27.35     0.46)  ; 8\r\n                (  468.15  -236.13    28.17     0.46)  ; 9\r\n                (  470.02  -236.87    28.82     0.46)  ; 10\r\n                (  474.00  -237.75    29.05     0.46)  ; 11\r\n                (  476.67  -237.12    29.05     0.46)  ; 12\r\n                (  479.16  -237.73    29.72     0.46)  ; 13\r\n                (  482.29  -236.99    30.23     0.46)  ; 14\r\n                (  485.69  -237.39    30.57     0.46)  ; 15\r\n                (  489.71  -236.44    30.57     0.46)  ; 16\r\n                (  495.33  -236.33    31.00     0.46)  ; 17\r\n                (  498.99  -237.85    31.52     0.46)  ; 18\r\n                (  501.80  -237.79    30.83     0.46)  ; 19\r\n                (  506.09  -237.99    30.83     0.46)  ; 20\r\n                (  512.55  -239.45    29.88     0.46)  ; 21\r\n                (  516.26  -239.19    29.58     0.46)  ; 22\r\n                (  519.78  -240.15    29.58     0.46)  ; 23\r\n                (  522.02  -239.63    29.58     0.46)  ; 24\r\n                (  525.73  -239.35    29.58     0.46)  ; 25\r\n                (  528.53  -239.29    30.33     0.46)  ; 26\r\n                (  528.53  -239.29    30.30     0.46)  ; 27\r\n                (  530.76  -238.77    31.30     0.46)  ; 28\r\n                (  536.97  -239.11    32.50     0.46)  ; 29\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  512.60  -237.65    29.88     0.46)  ; 1\r\n                  (  533.32  -237.58    32.50     0.46)  ; 2\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  447.70  -237.34    25.25     0.46)  ; 1\r\n                  (  468.15  -236.13    28.17     0.46)  ; 2\r\n                  (  499.44  -237.75    31.52     0.46)  ; 3\r\n                  (  505.64  -238.09    30.83     0.46)  ; 4\r\n                  (  522.46  -239.52    29.58     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (  537.38  -234.83    30.07     0.46)  ; 1, R-1-2-2-2-1-2-2-1\r\n                  (  537.38  -234.83    30.05     0.46)  ; 2\r\n                  (  539.18  -228.43    29.32     0.46)  ; 3\r\n                  (  539.28  -224.82    29.70     0.46)  ; 4\r\n                  (  539.28  -224.82    29.65     0.46)  ; 5\r\n                  (  538.22  -220.30    28.45     0.46)  ; 6\r\n                  (  540.54  -216.18    27.97     0.46)  ; 7\r\n                  (  540.54  -216.18    27.95     0.46)  ; 8\r\n                  (  542.83  -213.85    28.45     0.46)  ; 9\r\n                  (  542.83  -213.85    28.52     0.46)  ; 10\r\n                  (  546.63  -209.97    28.95     0.46)  ; 11\r\n                  (  549.10  -206.41    28.70     0.46)  ; 12\r\n                  (  549.10  -206.41    28.67     0.46)  ; 13\r\n                  (  549.38  -201.57    27.97     0.46)  ; 14\r\n                  (  550.76  -199.45    26.95     0.46)  ; 15\r\n                  (  550.76  -199.45    26.92     0.46)  ; 16\r\n                  (  550.86  -195.84    26.25     0.46)  ; 17\r\n                  (  551.68  -193.27    26.25     0.46)  ; 18\r\n                  (  555.12  -191.87    25.67     0.46)  ; 19\r\n                  (  555.12  -191.87    25.65     0.46)  ; 20\r\n                  (  556.99  -187.84    25.13     0.46)  ; 21\r\n                  (  560.75  -185.77    24.60     0.46)  ; 22\r\n                  (  560.75  -185.77    24.58     0.46)  ; 23\r\n                  (  563.16  -184.00    25.87     0.46)  ; 24\r\n                  (  563.16  -184.00    25.85     0.46)  ; 25\r\n                  (  564.99  -181.78    26.32     0.46)  ; 26\r\n                  (  569.46  -180.74    25.63     0.46)  ; 27\r\n                  (  569.46  -180.74    25.60     0.46)  ; 28\r\n                  (  570.01  -177.02    24.52     0.46)  ; 29\r\n                  (  570.01  -177.02    24.50     0.46)  ; 30\r\n                  (  572.74  -174.59    23.13     0.46)  ; 31\r\n                  (  574.08  -174.29    21.50     0.46)  ; 32\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  545.83  -212.54    28.95     0.46)  ; 1\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  537.26  -234.27    30.05     0.46)  ; 1\r\n                    (  538.43  -229.21    29.32     0.46)  ; 2\r\n                    (  549.64  -202.70    27.95     0.46)  ; 3\r\n                    (  552.33  -196.09    27.92     0.46)  ; 4\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  575.64  -170.95    22.92     0.46)  ; 1, R-1-2-2-2-1-2-2-1-1\r\n                    (  575.56  -168.57    21.55     0.46)  ; 2\r\n                    (  578.60  -165.47    20.30     0.46)  ; 3\r\n                    (  580.96  -165.52    19.50     0.46)  ; 4\r\n                    (  582.67  -162.72    19.50     0.46)  ; 5\r\n                    (  585.72  -159.62    19.50     0.46)  ; 6\r\n                    (  587.68  -157.97    19.50     0.46)  ; 7\r\n                    (  588.44  -157.20    20.35     0.46)  ; 8\r\n                    (  588.44  -157.20    20.33     0.46)  ; 9\r\n                    (  590.28  -154.98    19.17     0.46)  ; 10\r\n                    (  591.98  -152.19    18.23     0.46)  ; 11\r\n                    (  593.81  -149.97    17.13     0.46)  ; 12\r\n                    (  595.64  -147.74    15.95     0.46)  ; 13\r\n                    (  595.64  -147.74    15.92     0.46)  ; 14\r\n                    (  597.76  -146.65    15.15     0.46)  ; 15\r\n                    (  600.16  -144.90    14.75     0.46)  ; 16\r\n                    (  603.30  -144.16    14.65     0.46)  ; 17\r\n                    (  608.46  -144.14    14.07     0.46)  ; 18\r\n                    (  610.96  -144.75    13.72     0.46)  ; 19\r\n                    (  613.73  -146.50    13.80     0.46)  ; 20\r\n                    (  613.73  -146.50    13.72     0.46)  ; 21\r\n                    (  616.94  -148.13    13.47     0.46)  ; 22\r\n                    (  618.86  -148.27    10.95     0.46)  ; 23\r\n                    (  618.86  -148.27    10.77     0.46)  ; 24\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  583.80  -159.47    19.50     0.46)  ; 1\r\n                      (  596.05  -149.44    15.92     0.46)  ; 2\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  579.18  -165.94    20.30     0.46)  ; 1\r\n                      (  581.15  -164.28    19.50     0.46)  ; 2\r\n                      (  590.28  -154.98    19.17     0.46)  ; 3\r\n                      (  599.85  -145.56    14.75     0.46)  ; 4\r\n                      (  608.33  -143.57    14.07     0.46)  ; 5\r\n                      (  610.83  -144.18    13.72     0.46)  ; 6\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  575.68  -175.10    20.45     0.46)  ; 1, R-1-2-2-2-1-2-2-1-2\r\n                    (  576.16  -179.17    19.63     0.46)  ; 2\r\n                    (  577.59  -181.21    18.85     0.46)  ; 3\r\n                    (  577.59  -181.21    18.80     0.46)  ; 4\r\n                    (  577.72  -181.79    18.50     0.46)  ; 5\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  576.48  -178.50    19.63     0.46)  ; 1\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  575.62  -182.89    18.15     0.46)  ; 1, R-1-2-2-2-1-2-2-1-2-1\r\n                      (  575.62  -182.89    18.13     0.46)  ; 2\r\n                      (  576.33  -183.92    15.90     0.46)  ; 3\r\n                      (  577.48  -184.84    13.13     0.46)  ; 4\r\n                      (  577.05  -184.94    13.10     0.46)  ; 5\r\n                      (  580.36  -182.98    11.50     0.46)  ; 6\r\n                      (  580.36  -182.98    11.42     0.46)  ; 7\r\n\r\n                      (Dot\r\n                        (Color Yellow)\r\n                        (Name \"Marker 1\")\r\n                        (  580.30  -184.78     9.85     0.46)  ; 1\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  577.49  -184.82    18.50     0.46)  ; 1, R-1-2-2-2-1-2-2-1-2-2\r\n                      (  577.26  -187.87    16.65     0.46)  ; 2\r\n                      (  578.06  -191.27    14.97     0.46)  ; 3\r\n                      (  578.45  -192.96    12.90     0.46)  ; 4\r\n                      (  580.64  -194.25    11.55     0.46)  ; 5\r\n                      (  581.04  -195.94     9.65     0.46)  ; 6\r\n                      (  581.04  -195.94     9.60     0.46)  ; 7\r\n                      (  583.08  -196.65     7.02     0.46)  ; 8\r\n                      (  584.82  -198.03     6.25     0.46)  ; 9\r\n                      (  585.09  -199.17     5.20     0.46)  ; 10\r\n                      (  586.38  -200.65     3.90     0.46)  ; 11\r\n                      (  586.38  -200.65     3.88     0.46)  ; 12\r\n                      (  587.76  -204.50     2.55     0.46)  ; 13\r\n                      (  589.05  -206.01     0.50     0.46)  ; 14\r\n                      (  589.05  -206.01     0.47     0.46)  ; 15\r\n                      (  590.66  -206.82    -1.25     0.46)  ; 16\r\n                      (  592.35  -210.01    -2.50     0.46)  ; 17\r\n                      (  591.22  -213.26    -4.25     0.46)  ; 18\r\n                      (  590.86  -215.73    -6.02     0.46)  ; 19\r\n                      (  590.86  -215.73    -6.05     0.46)  ; 20\r\n                      (  592.60  -217.12    -7.60     0.46)  ; 21\r\n                      (  592.60  -217.12    -7.63     0.46)  ; 22\r\n                      (  593.13  -219.37    -8.75     0.46)  ; 23\r\n                      (  596.73  -222.71    -9.40     0.46)  ; 24\r\n                      (  597.98  -226.00    -9.43     0.46)  ; 25\r\n                      (  599.40  -228.06   -10.60     0.46)  ; 26\r\n                      (  601.84  -230.47   -11.88     0.46)  ; 27\r\n                      (  603.73  -232.43   -13.42     0.46)  ; 28\r\n                      (  603.73  -232.43   -13.45     0.46)  ; 29\r\n                      (  606.22  -233.03   -15.15     0.46)  ; 30\r\n                      (  606.22  -233.03   -15.17     0.46)  ; 31\r\n                      (  609.88  -234.56   -16.05     0.46)  ; 32\r\n                      (  612.06  -235.84   -15.07     0.46)  ; 33\r\n                      (  615.09  -238.72   -16.05     0.46)  ; 34\r\n                      (  615.09  -238.72   -16.07     0.46)  ; 35\r\n                      (  616.25  -239.63   -17.63     0.46)  ; 36\r\n                      (  616.25  -239.63   -17.65     0.46)  ; 37\r\n                      (  617.15  -239.42   -19.73     0.46)  ; 38\r\n                      (  617.15  -239.42   -19.75     0.46)  ; 39\r\n                      (  619.27  -242.51   -21.50     0.46)  ; 40\r\n                      (  619.27  -242.51   -21.55     0.46)  ; 41\r\n                      (  621.78  -243.12   -23.25     0.46)  ; 42\r\n                      (  625.26  -245.88   -24.65     0.46)  ; 43\r\n                      (  625.26  -245.88   -24.67     0.46)  ; 44\r\n                      (  627.18  -246.03   -26.15     0.46)  ; 45\r\n                      (  629.04  -247.99   -27.27     0.46)  ; 46\r\n                      (  631.10  -248.69   -29.22     0.46)  ; 47\r\n                      (  634.31  -250.33   -30.10     0.46)  ; 48\r\n                      (  635.60  -251.82   -31.88     0.46)  ; 49\r\n                      (  635.60  -251.82   -31.90     0.46)  ; 50\r\n                      (  637.20  -252.64   -33.33     0.46)  ; 51\r\n                      (  640.10  -254.94   -34.63     0.46)  ; 52\r\n                      (  640.10  -254.94   -34.65     0.46)  ; 53\r\n                      (  643.90  -257.04   -36.55     0.46)  ; 54\r\n                      (  647.23  -259.25   -37.72     0.46)  ; 55\r\n                      (  647.23  -259.25   -37.75     0.46)  ; 56\r\n                      (  649.86  -260.42   -40.45     0.46)  ; 57\r\n                      (  649.86  -260.42   -41.35     0.46)  ; 58\r\n                      (  653.06  -262.05   -41.90     0.46)  ; 59\r\n                      (  655.31  -261.53   -42.85     0.46)  ; 60\r\n                      (  658.38  -262.61   -42.85     0.46)  ; 61\r\n                      (  662.80  -263.36   -44.23     0.46)  ; 62\r\n                      (  662.80  -263.36   -44.25     0.46)  ; 63\r\n                      (  666.27  -266.13   -44.25     0.46)  ; 64\r\n                      (  671.09  -268.59   -44.60     0.46)  ; 65\r\n                      (  674.62  -269.54   -44.60     0.46)  ; 66\r\n                      (  677.56  -270.06   -46.00     0.46)  ; 67\r\n                      (  677.56  -270.06   -46.08     0.46)  ; 68\r\n                      (  679.88  -271.90   -47.65     0.46)  ; 69\r\n                      (  679.88  -271.90   -47.67     0.46)  ; 70\r\n                      (  684.56  -273.78   -48.67     0.46)  ; 71\r\n                      (  687.95  -274.18   -50.25     0.46)  ; 72\r\n                      (  690.72  -275.93   -52.15     0.46)  ; 73\r\n                      (  690.72  -275.93   -52.17     0.46)  ; 74\r\n                      (  694.64  -278.59   -54.70     0.46)  ; 75\r\n                      (  694.64  -278.59   -54.72     0.46)  ; 76\r\n                      (  700.03  -281.51   -56.28     0.46)  ; 77\r\n                      (  702.66  -282.68   -58.50     0.46)  ; 78\r\n                      (  702.66  -282.68   -58.60     0.46)  ; 79\r\n                      (  705.74  -283.75   -59.80     0.46)  ; 80\r\n                      (  707.91  -283.74   -60.60     0.46)  ; 81\r\n                      (  710.98  -284.81   -62.60     0.46)  ; 82\r\n                      (  710.98  -284.81   -62.65     0.46)  ; 83\r\n                      (  715.39  -285.56   -64.05     0.46)  ; 84\r\n                      (  715.39  -285.56   -64.07     0.46)  ; 85\r\n                      (  718.74  -287.77   -65.00     0.46)  ; 86\r\n                      (  718.74  -287.77   -65.03     0.46)  ; 87\r\n                      (  720.74  -290.29   -66.40     0.46)  ; 88\r\n                      (  723.55  -290.22   -68.07     0.46)  ; 89\r\n                      (  727.03  -292.99   -68.88     0.46)  ; 90\r\n                      (  727.03  -292.99   -68.90     0.46)  ; 91\r\n                      (  731.18  -292.61   -70.20     0.46)  ; 92\r\n                      (  733.10  -292.76   -70.07     0.46)  ; 93\r\n                      (  733.37  -293.89   -71.43     0.46)  ; 94\r\n                      (  736.76  -294.30   -72.03     0.46)  ; 95\r\n                      (  739.65  -296.60   -72.03     0.46)  ; 96\r\n                      (  742.59  -297.12   -73.85     0.46)  ; 97\r\n                      (  745.28  -296.48   -76.80     0.46)  ; 98\r\n                      (  748.09  -296.42   -79.32     0.46)  ; 99\r\n                      (  748.09  -296.42   -79.35     0.46)  ; 100\r\n                      (  751.74  -297.95   -80.92     0.46)  ; 101\r\n                      (  755.27  -298.91   -82.72     0.46)  ; 102\r\n                      (  755.27  -298.91   -82.95     0.46)  ; 103\r\n\r\n                      (Dot\r\n                        (Color Yellow)\r\n                        (Name \"Marker 1\")\r\n                        (  583.61  -198.92     5.20     0.46)  ; 1\r\n                        (  595.98  -223.48    -9.43     0.46)  ; 2\r\n                        (  638.63  -254.70   -34.65     0.46)  ; 3\r\n                      )  ;  End of markers\r\n\r\n                      (OpenCircle\r\n                        (Color Yellow)\r\n                        (Name \"Marker 2\")\r\n                        (  577.57  -187.20    16.65     0.46)  ; 1\r\n                        (  578.45  -192.96    12.90     0.46)  ; 2\r\n                        (  582.95  -196.09     7.02     0.46)  ; 3\r\n                        (  591.80  -213.72    -4.27     0.46)  ; 4\r\n                        (  609.88  -234.56   -16.05     0.46)  ; 5\r\n                        (  628.91  -247.42   -27.27     0.46)  ; 6\r\n                        (  655.44  -262.11   -42.85     0.46)  ; 7\r\n                        (  659.14  -261.83   -42.85     0.46)  ; 8\r\n                        (  684.56  -273.78   -48.67     0.46)  ; 9\r\n                        (  718.74  -287.77   -65.03     0.46)  ; 10\r\n                        (  744.84  -296.59   -76.80     0.46)  ; 11\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  )  ;  End of split\r\n                |\r\n                  (  540.82  -239.41    30.23     0.46)  ; 1, R-1-2-2-2-1-2-2-2\r\n                  (  544.78  -240.26    30.63     0.46)  ; 2\r\n                  (  548.35  -239.42    32.05     0.46)  ; 3\r\n                  (  551.75  -239.83    33.25     0.46)  ; 4\r\n                  (  554.11  -239.87    32.95     0.46)  ; 5\r\n                  (  556.30  -241.14    33.10     0.46)  ; 6\r\n                  (  559.43  -240.41    33.47     0.46)  ; 7\r\n                  (  564.74  -240.96    33.88     0.46)  ; 8\r\n                  (  567.54  -240.90    33.88     0.46)  ; 9\r\n                  (  568.89  -240.58    33.88     0.46)  ; 10\r\n                  (  571.83  -241.09    34.72     0.46)  ; 11\r\n                  (  576.12  -241.28    34.72     0.46)  ; 12\r\n                  (  577.72  -242.10    35.50     0.46)  ; 13\r\n                  (  577.28  -242.20    35.50     0.46)  ; 14\r\n                  (  579.02  -243.58    37.70     0.46)  ; 15\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  565.00  -242.10    33.88     0.46)  ; 1\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  540.82  -239.41    30.23     0.46)  ; 1\r\n                    (  551.17  -239.36    33.25     0.46)  ; 2\r\n                  )  ;  End of markers\r\n                   High\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  301.49  -248.08    12.65     0.92)  ; 1, R-1-2-2-2-2\r\n            (  301.34  -253.49    12.65     0.92)  ; 2\r\n            (  302.27  -257.45    12.65     0.92)  ; 3\r\n            (  304.10  -261.20    11.50     0.92)  ; 4\r\n            (  305.34  -264.49    10.02     0.92)  ; 5\r\n            (  306.18  -266.08     9.20     0.92)  ; 6\r\n            (  306.18  -266.08     9.17     0.92)  ; 7\r\n            (  304.61  -269.44     8.10     0.92)  ; 8\r\n            (  306.75  -272.53     7.38     0.92)  ; 9\r\n            (  307.63  -278.28     7.38     0.92)  ; 10\r\n            (  307.27  -280.77     8.93     0.92)  ; 11\r\n            (  307.17  -284.36    10.28     0.92)  ; 12\r\n            (  307.38  -287.30    11.68     0.92)  ; 13\r\n            (  309.52  -290.38    12.57     0.92)  ; 14\r\n            (  312.42  -292.69    12.57     0.92)  ; 15\r\n            (  312.32  -296.30    12.90     0.92)  ; 16\r\n            (  310.70  -301.45    12.90     0.92)  ; 17\r\n            (  310.68  -307.43    11.98     0.92)  ; 18\r\n            (  311.43  -312.63    10.45     0.92)  ; 19\r\n            (  311.43  -312.63    10.43     0.92)  ; 20\r\n            (  312.23  -316.02    10.43     0.92)  ; 21\r\n            (\r\n              (  309.50  -317.45    10.43     0.46)  ; 1, R-1-2-2-2-2-1\r\n              (  307.71  -317.87     9.00     0.46)  ; 2\r\n              (  305.62  -318.97     7.77     0.46)  ; 3\r\n              (  304.27  -319.28     6.18     0.46)  ; 4\r\n              (  303.52  -320.06     4.63     0.46)  ; 5\r\n              (  303.02  -321.96     3.32     0.46)  ; 6\r\n              (  303.02  -321.96     3.30     0.46)  ; 7\r\n              (  302.70  -322.62     1.67     0.46)  ; 8\r\n              (  301.36  -322.94     0.20     0.46)  ; 9\r\n              (  302.20  -324.54     0.20     0.46)  ; 10\r\n              (  300.42  -324.96    -0.37     0.46)  ; 11\r\n              (  300.37  -326.76    -1.75     0.46)  ; 12\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  303.02  -321.96     3.30     0.46)  ; 1\r\n              )  ;  End of markers\r\n              (\r\n                (  300.33  -328.57    -3.45     0.46)  ; 1, R-1-2-2-2-2-1-1\r\n                (  299.43  -328.78    -5.07     0.46)  ; 2\r\n                (  297.91  -330.33    -6.45     0.46)  ; 3\r\n                (  297.16  -331.09    -8.00     0.46)  ; 4\r\n                (  295.80  -331.41   -10.30     0.46)  ; 5\r\n                (  296.33  -333.68   -12.22     0.46)  ; 6\r\n                (  294.69  -334.66   -14.25     0.46)  ; 7\r\n                (  293.79  -334.87   -16.55     0.46)  ; 8\r\n                (  293.79  -334.87   -16.57     0.46)  ; 9\r\n                (  292.90  -335.08   -18.63     0.46)  ; 10\r\n                (  292.00  -335.29   -21.72     0.46)  ; 11\r\n                (  290.67  -335.61   -23.45     0.46)  ; 12\r\n                (  290.67  -335.61   -23.47     0.46)  ; 13\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  297.51  -328.63    -6.45     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  300.33  -328.57    -3.45     0.46)  ; 1\r\n                  (  294.99  -334.00   -14.25     0.46)  ; 2\r\n                  (  291.12  -335.50   -23.50     0.46)  ; 3\r\n                )  ;  End of markers\r\n                (\r\n                  (  288.47  -340.30   -25.47     0.46)  ; 1, R-1-2-2-2-2-1-1-1\r\n                  (  286.95  -341.85   -28.05     0.46)  ; 2\r\n                  (  285.61  -342.16   -29.77     0.46)  ; 3\r\n                  (  284.54  -343.61   -31.80     0.46)  ; 4\r\n                  (  284.54  -343.61   -31.82     0.46)  ; 5\r\n                  (  282.43  -344.71   -33.33     0.46)  ; 6\r\n                  (  280.64  -345.12   -35.70     0.46)  ; 7\r\n                  (  280.73  -347.49   -36.82     0.46)  ; 8\r\n                  (  279.21  -349.04   -39.00     0.46)  ; 9\r\n                  (  279.21  -349.04   -39.02     0.46)  ; 10\r\n                  (  279.48  -350.17   -40.85     0.46)  ; 11\r\n                  (  279.48  -350.17   -40.88     0.46)  ; 12\r\n                  (  279.30  -351.41   -42.75     0.46)  ; 13\r\n                  (  278.67  -352.75   -45.03     0.46)  ; 14\r\n                  (  278.67  -352.75   -45.05     0.46)  ; 15\r\n                  (  277.91  -353.54   -48.02     0.46)  ; 16\r\n                  (  276.56  -353.84   -49.83     0.46)  ; 17\r\n                  (  276.12  -353.95   -49.90     0.46)  ; 18\r\n                  (  275.62  -355.85   -52.88     0.46)  ; 19\r\n                  (  274.72  -356.06   -55.25     0.46)  ; 20\r\n                  (  273.53  -356.95   -56.72     0.46)  ; 21\r\n                  (  273.53  -356.95   -56.75     0.46)  ; 22\r\n                  (  271.87  -357.93   -59.35     0.46)  ; 23\r\n                  (  271.87  -357.93   -59.40     0.46)  ; 24\r\n                  (  269.58  -360.26   -60.55     0.46)  ; 25\r\n                  (  269.58  -360.26   -60.63     0.46)  ; 26\r\n                  (  268.07  -361.81   -63.97     0.46)  ; 27\r\n                  (  265.97  -362.89   -66.55     0.46)  ; 28\r\n                  (  265.97  -362.89   -66.63     0.46)  ; 29\r\n                  (  263.87  -363.99   -69.18     0.46)  ; 30\r\n                  (  262.83  -363.63   -72.07     0.46)  ; 31\r\n                  (  261.19  -364.61   -74.40     0.46)  ; 32\r\n                  (  261.19  -364.61   -74.42     0.46)  ; 33\r\n                  (  260.56  -365.94   -77.47     0.46)  ; 34\r\n                  (  257.70  -367.82   -79.53     0.46)  ; 35\r\n                  (  256.04  -368.81   -80.88     0.46)  ; 36\r\n                  (  253.63  -370.56   -83.18     0.46)  ; 37\r\n                  (  252.60  -370.21   -86.00     0.46)  ; 38\r\n                  (  252.60  -370.21   -86.03     0.46)  ; 39\r\n                  (  250.51  -371.29   -89.22     0.46)  ; 40\r\n                  (  248.27  -371.81   -91.30     0.46)  ; 41\r\n                  (  248.27  -371.81   -91.32     0.46)  ; 42\r\n                  (  247.91  -374.30   -94.10     0.46)  ; 43\r\n                  (  247.91  -374.30   -94.12     0.46)  ; 44\r\n                  (  246.83  -375.74   -96.32     0.46)  ; 45\r\n                  (  246.83  -375.74   -96.35     0.46)  ; 46\r\n                  (  245.18  -376.72   -98.77     0.46)  ; 47\r\n                  (  243.34  -378.95  -100.30     0.46)  ; 48\r\n                  (  240.79  -380.14  -101.57     0.46)  ; 49\r\n                  (  240.79  -380.14  -101.60     0.46)  ; 50\r\n                  (  236.73  -382.88  -104.12     0.46)  ; 51\r\n                  (  235.38  -383.20  -106.43     0.46)  ; 52\r\n                  (  232.21  -385.74  -108.77     0.46)  ; 53\r\n                  (  229.27  -385.23  -110.90     0.46)  ; 54\r\n                  (  228.91  -387.70  -113.72     0.46)  ; 55\r\n                  (  229.94  -388.06  -116.38     0.46)  ; 56\r\n                  (  230.20  -389.19  -119.95     0.46)  ; 57\r\n                  (  229.58  -390.53  -122.72     0.46)  ; 58\r\n                  (  229.58  -390.53  -122.75     0.46)  ; 59\r\n                  (  228.36  -391.42  -125.52     0.46)  ; 60\r\n                  (  228.36  -391.42  -125.55     0.46)  ; 61\r\n                  (  225.50  -393.28  -127.82     0.46)  ; 62\r\n                  (  221.80  -393.55  -129.22     0.46)  ; 63\r\n                  (  218.40  -393.15  -130.50     0.46)  ; 64\r\n                  (  218.40  -393.15  -130.52     0.46)  ; 65\r\n                  (  215.59  -393.21  -132.52     0.46)  ; 66\r\n                  (  213.36  -393.73  -134.63     0.46)  ; 67\r\n                  (  213.36  -393.73  -134.68     0.46)  ; 68\r\n                  (  213.36  -393.73  -138.07     0.46)  ; 69\r\n                  (  211.70  -394.72  -140.07     0.46)  ; 70\r\n                  (  209.79  -394.56  -141.63     0.46)  ; 71\r\n                  (  209.79  -394.56  -141.65     0.46)  ; 72\r\n                  (  207.69  -395.67  -143.77     0.46)  ; 73\r\n                  (  207.69  -395.67  -143.80     0.46)  ; 74\r\n                  (  206.21  -395.41  -146.52     0.46)  ; 75\r\n                  (  206.17  -397.22  -149.70     0.46)  ; 76\r\n                  (  206.17  -397.22  -149.73     0.46)  ; 77\r\n                  (  204.06  -398.30  -151.27     0.46)  ; 78\r\n                  (  202.28  -398.72  -154.40     0.46)  ; 79\r\n                  (  202.28  -398.72  -154.43     0.46)  ; 80\r\n                  (  199.68  -401.71  -156.07     0.46)  ; 81\r\n                  (  199.55  -401.15  -156.07     0.46)  ; 82\r\n                  (  197.89  -402.13  -158.35     0.46)  ; 83\r\n                  (  196.74  -401.21  -160.63     0.46)  ; 84\r\n                  (  196.29  -401.32  -160.63     0.46)  ; 85\r\n                  (  195.21  -402.76  -162.15     0.46)  ; 86\r\n                  (  195.21  -402.76  -162.23     0.46)  ; 87\r\n                  (  191.37  -402.47  -164.45     0.46)  ; 88\r\n                  (  189.37  -399.96  -164.45     0.46)  ; 89\r\n                  (  187.82  -397.33  -164.45     0.46)  ; 90\r\n                  (  184.48  -395.12  -164.45     0.46)  ; 91\r\n                  (  180.73  -391.23  -165.10     0.46)  ; 92\r\n                  (  180.73  -391.23  -165.12     0.46)  ; 93\r\n                  (  176.36  -388.67  -165.95     0.46)  ; 94\r\n                  (  171.86  -385.54  -164.77     0.46)  ; 95\r\n                  (  168.33  -384.58  -162.68     0.46)  ; 96\r\n                  (  165.08  -384.75  -159.90     0.46)  ; 97\r\n                  (  162.44  -383.58  -158.48     0.46)  ; 98\r\n                  (  158.19  -381.60  -158.48     0.46)  ; 99\r\n                  (  154.99  -379.95  -158.48     0.46)  ; 100\r\n                  (  150.57  -379.21  -158.07     0.46)  ; 101\r\n                  (  145.84  -379.11  -157.68     0.46)  ; 102\r\n                  (  140.58  -376.76  -157.68     0.46)  ; 103\r\n                  (  137.63  -376.26  -156.95     0.46)  ; 104\r\n                  (  134.16  -373.49  -156.32     0.46)  ; 105\r\n                  (  132.60  -370.87  -155.38     0.46)  ; 106\r\n                  (  130.86  -369.49  -153.98     0.46)  ; 107\r\n                  (  130.86  -369.49  -154.00     0.46)  ; 108\r\n                  (  129.12  -368.11  -153.07     0.46)  ; 109\r\n                  (  128.14  -365.94  -151.85     0.46)  ; 110\r\n                  (  124.22  -363.28  -151.65     0.46)  ; 111\r\n                  (  118.95  -360.93  -151.65     0.46)  ; 112\r\n                  (  115.75  -359.29  -150.55     0.46)  ; 113\r\n                  (  111.52  -357.30  -150.55     0.46)  ; 114\r\n                  (  110.79  -356.27  -149.35     0.46)  ; 115\r\n                  (  109.51  -354.79  -148.18     0.46)  ; 116\r\n                  (  107.77  -353.40  -146.43     0.46)  ; 117\r\n                  (  105.46  -351.56  -144.82     0.46)  ; 118\r\n                  (  105.46  -351.56  -144.85     0.46)  ; 119\r\n                  (  103.14  -349.71  -143.22     0.46)  ; 120\r\n                  (  103.14  -349.71  -143.25     0.46)  ; 121\r\n                  (   99.48  -348.18  -142.10     0.46)  ; 122\r\n                  (   95.96  -347.21  -140.88     0.46)  ; 123\r\n                  (   93.46  -346.60  -139.60     0.46)  ; 124\r\n                  (   90.56  -344.30  -138.50     0.46)  ; 125\r\n                  (   87.48  -343.23  -137.77     0.46)  ; 126\r\n                  (   84.72  -341.50  -137.77     0.46)  ; 127\r\n                  (   80.34  -338.93  -137.77     0.46)  ; 128\r\n                  (   74.64  -336.69  -137.38     0.46)  ; 129\r\n                  (   71.11  -335.72  -139.13     0.46)  ; 130\r\n                  (   65.23  -334.72  -140.15     0.46)  ; 131\r\n                  (   65.23  -334.72  -140.18     0.46)  ; 132\r\n                  (   61.43  -332.61  -141.07     0.46)  ; 133\r\n                  (   56.18  -330.27  -142.70     0.46)  ; 134\r\n                  (   56.18  -330.27  -142.73     0.46)  ; 135\r\n                  (   50.33  -327.46  -143.72     0.46)  ; 136\r\n                  (   44.68  -323.41  -143.72     0.46)  ; 137\r\n                  (   39.46  -319.26  -142.30     0.46)  ; 138\r\n                  (   39.59  -319.82  -142.32     0.46)  ; 139\r\n                  (   37.27  -317.99  -140.75     0.46)  ; 140\r\n                  (   35.27  -315.47  -139.43     0.46)  ; 141\r\n                  (   31.03  -313.47  -138.67     0.46)  ; 142\r\n                  (   26.53  -310.35  -138.67     0.46)  ; 143\r\n                  (   23.32  -308.72  -140.07     0.46)  ; 144\r\n                  (   23.32  -308.72  -140.10     0.46)  ; 145\r\n                  (   20.29  -305.85  -141.38     0.46)  ; 146\r\n                  (   20.29  -305.85  -141.40     0.46)  ; 147\r\n                  (   16.24  -302.62  -142.00     0.46)  ; 148\r\n                  (   16.24  -302.62  -142.02     0.46)  ; 149\r\n                  (   12.64  -299.28  -141.67     0.46)  ; 150\r\n                  (    9.01  -295.95  -140.55     0.46)  ; 151\r\n                  (    4.38  -292.25  -140.20     0.46)  ; 152\r\n                  (    1.18  -290.61  -140.20     0.46)  ; 153\r\n                  (   -2.03  -288.98  -140.20     0.46)  ; 154\r\n                  (   -3.15  -286.26  -139.30     0.46)  ; 155\r\n                  (   -8.09  -283.24  -139.02     0.46)  ; 156\r\n                  (  -12.47  -280.68  -139.02     0.46)  ; 157\r\n                  (  -14.64  -279.40  -138.72     0.46)  ; 158\r\n                  (  -17.01  -279.36  -138.15     0.46)  ; 159\r\n                  (  -21.19  -275.57  -138.15     0.46)  ; 160\r\n                  (  -23.46  -271.92  -138.88     0.46)  ; 161\r\n                  (  -25.92  -269.50  -137.82     0.46)  ; 162\r\n                  (  -27.91  -266.98  -136.85     0.46)  ; 163\r\n                  (  -31.00  -265.92  -135.50     0.46)  ; 164\r\n                  (  -34.26  -266.09  -133.77     0.46)  ; 165\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  230.12  -386.83  -110.90     0.46)  ; 1\r\n                    (  201.60  -401.87  -156.07     0.46)  ; 2\r\n                    (  129.35  -365.07  -151.85     0.46)  ; 3\r\n                    (   12.59  -301.09  -141.67     0.46)  ; 4\r\n                    (    3.32  -293.69  -140.20     0.46)  ; 5\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  288.34  -339.73   -25.47     0.46)  ; 1\r\n                    (  269.45  -359.68   -60.63     0.46)  ; 2\r\n                    (  225.50  -393.28  -127.82     0.46)  ; 3\r\n                    (  213.36  -393.73  -138.07     0.46)  ; 4\r\n                    (  206.35  -395.98  -149.73     0.46)  ; 5\r\n                    (  191.51  -403.04  -164.45     0.46)  ; 6\r\n                    (  176.36  -388.67  -165.95     0.46)  ; 7\r\n                    (  165.08  -384.75  -159.90     0.46)  ; 8\r\n                    (  150.57  -379.21  -158.07     0.46)  ; 9\r\n                    (  146.15  -378.44  -157.68     0.46)  ; 10\r\n                    (  119.40  -360.83  -151.65     0.46)  ; 11\r\n                    (  111.52  -357.30  -150.55     0.46)  ; 12\r\n                    (   95.83  -346.65  -140.88     0.46)  ; 13\r\n                    (   85.17  -341.40  -137.77     0.46)  ; 14\r\n                    (   61.75  -331.95  -141.07     0.46)  ; 15\r\n                    (   50.33  -327.46  -143.72     0.46)  ; 16\r\n                    (   39.46  -319.26  -142.32     0.46)  ; 17\r\n                    (  -14.51  -279.96  -138.72     0.46)  ; 18\r\n                    (  -21.19  -275.57  -138.15     0.46)  ; 19\r\n                    (  -22.75  -272.94  -138.15     0.46)  ; 20\r\n                    (  -24.76  -270.42  -137.07     0.46)  ; 21\r\n                    (  -27.34  -267.45  -136.32     0.46)  ; 22\r\n                    (  -33.81  -265.98  -133.77     0.46)  ; 23\r\n                    (  -37.33  -265.00  -133.02     0.46)  ; 24\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  288.34  -333.76   -25.15     0.46)  ; 1, R-1-2-2-2-2-1-1-2\r\n                  (  287.81  -331.50   -26.85     0.46)  ; 2\r\n                  (  286.08  -330.12   -28.15     0.46)  ; 3\r\n                  (  285.63  -330.22   -28.17     0.46)  ; 4\r\n                  (  283.63  -327.71   -29.58     0.46)  ; 5\r\n                  (  282.34  -326.21   -31.15     0.46)  ; 6\r\n                  (  281.19  -325.29   -33.00     0.46)  ; 7\r\n                  (  278.42  -323.55   -34.17     0.46)  ; 8\r\n                  (  276.10  -321.70   -36.07     0.46)  ; 9\r\n                  (  273.78  -319.86   -37.30     0.46)  ; 10\r\n                  (  272.36  -317.81   -38.92     0.46)  ; 11\r\n                  (  271.78  -317.34   -41.00     0.46)  ; 12\r\n                  (  271.78  -317.34   -41.03     0.46)  ; 13\r\n                  (  270.94  -315.74   -43.10     0.46)  ; 14\r\n                  (  268.75  -314.47   -46.15     0.46)  ; 15\r\n                  (  267.90  -312.87   -48.15     0.46)  ; 16\r\n                  (  265.40  -312.26   -50.40     0.46)  ; 17\r\n                  (  265.40  -312.26   -50.42     0.46)  ; 18\r\n                  (  263.41  -309.76   -52.57     0.46)  ; 19\r\n                  (  261.35  -309.03   -54.97     0.46)  ; 20\r\n                  (  259.71  -310.02   -57.92     0.46)  ; 21\r\n                  (  257.19  -309.41   -60.75     0.46)  ; 22\r\n                  (  256.13  -310.85   -62.42     0.46)  ; 23\r\n                  (  253.81  -309.01   -63.85     0.46)  ; 24\r\n                  (  251.71  -310.11   -65.80     0.46)  ; 25\r\n                  (  249.08  -308.94   -68.82     0.46)  ; 26\r\n                  (  249.08  -308.94   -68.85     0.46)  ; 27\r\n                  (  249.08  -308.94   -71.35     0.46)  ; 28\r\n                  (  249.08  -308.94   -71.38     0.46)  ; 29\r\n                  (  247.60  -308.68   -73.05     0.46)  ; 30\r\n                  (  245.42  -307.40   -76.50     0.46)  ; 31\r\n                  (  243.95  -307.15   -79.65     0.46)  ; 32\r\n                  (  243.95  -307.15   -79.70     0.46)  ; 33\r\n                  (  242.34  -306.33   -83.70     0.46)  ; 34\r\n                  (  241.90  -306.43   -83.72     0.46)  ; 35\r\n                  (  237.47  -305.68   -86.00     0.46)  ; 36\r\n                  (  234.53  -305.17   -88.42     0.46)  ; 37\r\n                  (  233.06  -304.92   -91.13     0.46)  ; 38\r\n                  (  233.06  -304.92   -91.15     0.46)  ; 39\r\n                  (  231.33  -303.53   -95.82     0.46)  ; 40\r\n                  (  231.33  -303.53   -95.85     0.46)  ; 41\r\n                  (  228.96  -303.49   -98.42     0.46)  ; 42\r\n                  (  225.26  -303.76  -102.50     0.46)  ; 43\r\n                  (  221.85  -303.36  -104.48     0.46)  ; 44\r\n                  (  219.18  -303.99  -105.63     0.46)  ; 45\r\n                  (  219.18  -303.99  -105.65     0.46)  ; 46\r\n                  (  215.79  -303.59  -107.90     0.46)  ; 47\r\n                  (  215.79  -303.59  -107.92     0.46)  ; 48\r\n                  (  214.32  -303.34  -110.77     0.46)  ; 49\r\n                  (  214.32  -303.34  -110.85     0.46)  ; 50\r\n                  (  212.26  -302.63  -113.70     0.46)  ; 51\r\n                  (  212.26  -302.63  -113.75     0.46)  ; 52\r\n                  (  208.74  -301.67  -116.15     0.46)  ; 53\r\n                  (  205.67  -300.59  -118.97     0.46)  ; 54\r\n                  (  203.16  -299.99  -121.10     0.46)  ; 55\r\n                  (  202.01  -299.06  -123.65     0.46)  ; 56\r\n                  (  200.98  -298.71  -125.95     0.46)  ; 57\r\n                  (  198.61  -298.66  -128.70     0.46)  ; 58\r\n                  (  198.61  -298.66  -128.72     0.46)  ; 59\r\n                  (  196.12  -298.05  -132.32     0.46)  ; 60\r\n                  (  194.95  -297.14  -133.90     0.46)  ; 61\r\n                  (  191.74  -295.50  -136.10     0.46)  ; 62\r\n                  (  190.19  -292.87  -137.45     0.46)  ; 63\r\n                  (  188.77  -290.81  -139.00     0.46)  ; 64\r\n                  (  187.74  -290.47  -140.88     0.46)  ; 65\r\n                  (  186.00  -289.08  -144.05     0.46)  ; 66\r\n                  (  184.46  -286.46  -145.42     0.46)  ; 67\r\n                  (  181.41  -283.59  -147.80     0.46)  ; 68\r\n                  (  180.88  -281.31  -149.75     0.46)  ; 69\r\n                  (  178.97  -281.16  -152.05     0.46)  ; 70\r\n                  (  178.34  -282.51  -154.70     0.46)  ; 71\r\n                  (  178.34  -282.51  -154.73     0.46)  ; 72\r\n                  (  177.18  -281.59  -157.63     0.46)  ; 73\r\n                  (  177.18  -281.59  -157.65     0.46)  ; 74\r\n                  (  174.36  -281.65  -159.52     0.46)  ; 75\r\n                  (  174.36  -281.65  -159.55     0.46)  ; 76\r\n                  (  173.20  -280.73  -162.27     0.46)  ; 77\r\n                  (  170.98  -281.25  -162.20     0.46)  ; 78\r\n                  (  169.19  -281.67  -164.82     0.46)  ; 79\r\n                  (  169.19  -281.67  -164.85     0.46)  ; 80\r\n                  (  167.59  -280.86  -167.57     0.46)  ; 81\r\n                  (  167.59  -280.86  -168.10     0.46)  ; 82\r\n                  (  165.21  -280.81  -169.67     0.46)  ; 83\r\n                  (  163.61  -279.99  -171.47     0.46)  ; 84\r\n                  (  162.09  -281.53  -171.47     0.46)  ; 85\r\n                  (  159.47  -280.37  -172.90     0.46)  ; 86\r\n                  (  159.47  -280.37  -172.92     0.46)  ; 87\r\n                  (  157.86  -279.56  -174.75     0.46)  ; 88\r\n                  (  157.86  -279.56  -174.77     0.46)  ; 89\r\n                  (  156.78  -281.00  -177.35     0.46)  ; 90\r\n                  (  156.78  -281.00  -177.45     0.46)  ; 91\r\n                  (  153.98  -281.06  -180.63     0.46)  ; 92\r\n                  (  151.79  -279.78  -182.85     0.46)  ; 93\r\n                  (  149.69  -280.87  -184.88     0.46)  ; 94\r\n                  (  148.03  -281.86  -186.45     0.46)  ; 95\r\n                  (  147.27  -282.63  -188.85     0.46)  ; 96\r\n                  (  143.75  -281.66  -191.13     0.46)  ; 97\r\n                  (  140.66  -280.59  -192.02     0.46)  ; 98\r\n                  (  140.58  -278.22  -193.77     0.46)  ; 99\r\n                  (  140.58  -278.22  -193.80     0.46)  ; 100\r\n                  (  136.93  -276.69  -195.83     0.46)  ; 101\r\n                  (  130.59  -275.78  -196.22     0.46)  ; 102\r\n                  (  128.09  -275.18  -198.00     0.46)  ; 103\r\n                  (  125.47  -274.01  -200.20     0.46)  ; 104\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  155.62  -280.07  -177.45     0.46)  ; 1\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  283.63  -327.71   -29.58     0.46)  ; 1\r\n                    (  281.63  -325.18   -33.00     0.46)  ; 2\r\n                    (  276.10  -321.70   -36.07     0.46)  ; 3\r\n                    (  221.73  -302.81  -104.48     0.46)  ; 4\r\n                    (  205.35  -301.26  -118.97     0.46)  ; 5\r\n                    (  201.43  -298.61  -125.95     0.46)  ; 6\r\n                    (  186.00  -289.08  -144.05     0.46)  ; 7\r\n                    (  171.43  -281.15  -162.20     0.46)  ; 8\r\n                  )  ;  End of markers\r\n                   Low\r\n                )  ;  End of split\r\n              |\r\n                (  301.62  -330.05    -1.75     0.46)  ; 1, R-1-2-2-2-2-1-2\r\n                (  301.70  -332.42    -1.75     0.46)  ; 2\r\n                (  301.60  -336.02    -0.70     0.46)  ; 3\r\n                (  301.60  -336.02    -0.72     0.46)  ; 4\r\n                (  300.97  -337.38    -0.72     0.46)  ; 5\r\n                (  300.57  -341.65     0.15     0.46)  ; 6\r\n                (  300.46  -345.25    -0.02     0.46)  ; 7\r\n                (  299.39  -346.70    -0.02     0.46)  ; 8\r\n                (  300.50  -349.42    -0.02     0.46)  ; 9\r\n                (  300.14  -351.89    -0.02     0.46)  ; 10\r\n                (  299.46  -355.04    -0.97     0.46)  ; 11\r\n                (  300.39  -359.01    -0.97     0.46)  ; 12\r\n                (  300.34  -360.81    -1.45     0.46)  ; 13\r\n                (  300.11  -363.85    -1.45     0.46)  ; 14\r\n                (  299.98  -363.28    -1.45     0.46)  ; 15\r\n                (  301.85  -365.23    -2.42     0.46)  ; 16\r\n                (  303.73  -367.18    -4.32     0.46)  ; 17\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  299.78  -332.27    -1.75     0.46)  ; 1\r\n                  (  301.68  -344.36    -0.02     0.46)  ; 2\r\n                  (  301.67  -350.35    -0.02     0.46)  ; 3\r\n                  (  301.15  -358.22    -0.97     0.46)  ; 4\r\n                )  ;  End of markers\r\n                (\r\n                  (  306.39  -366.55    -5.63     0.46)  ; 1, R-1-2-2-2-2-1-2-1\r\n                  (  308.31  -366.70    -5.63     0.46)  ; 2\r\n                  (  312.01  -366.43    -5.63     0.46)  ; 3\r\n                  (  314.37  -366.47    -5.63     0.46)  ; 4\r\n                  (  318.66  -366.66    -6.15     0.46)  ; 5\r\n                  (  323.09  -367.42    -6.18     0.46)  ; 6\r\n                  (  325.84  -369.15    -6.25     0.46)  ; 7\r\n                  (  328.35  -369.77    -6.40     0.46)  ; 8\r\n                  (  332.72  -372.33    -6.85     0.46)  ; 9\r\n                  (  333.42  -373.36    -7.97     0.46)  ; 10\r\n                  (  336.06  -374.53    -9.35     0.46)  ; 11\r\n                  (  338.95  -376.84   -10.55     0.46)  ; 12\r\n                  (  342.42  -379.61   -11.27     0.46)  ; 13\r\n                  (  345.91  -382.37   -12.35     0.46)  ; 14\r\n                  (  345.91  -382.37   -12.38     0.46)  ; 15\r\n                  (  347.77  -384.32   -13.35     0.46)  ; 16\r\n                  (  349.64  -386.28   -14.32     0.46)  ; 17\r\n                  (  351.82  -387.55   -13.82     0.46)  ; 18\r\n                  (  354.47  -388.73   -12.57     0.46)  ; 19\r\n                  (  355.75  -390.21   -11.58     0.46)  ; 20\r\n                  (  358.38  -391.39   -11.05     0.46)  ; 21\r\n                  (  362.17  -393.49   -10.75     0.46)  ; 22\r\n                  (  363.46  -394.97   -10.57     0.46)  ; 23\r\n                  (  365.52  -395.69   -10.57     0.46)  ; 24\r\n                  (  367.31  -395.27   -10.05     0.46)  ; 25\r\n                  (  368.14  -396.86   -10.05     0.46)  ; 26\r\n                  (  367.70  -396.97   -10.05     0.46)  ; 27\r\n                  (  370.46  -398.71   -10.77     0.46)  ; 28\r\n                  (  372.77  -400.56   -10.77     0.46)  ; 29\r\n                  (  376.00  -402.20   -11.55     0.46)  ; 30\r\n                  (  379.33  -404.41   -11.82     0.46)  ; 31\r\n                  (  381.25  -404.54   -12.02     0.46)  ; 32\r\n                  (  383.31  -405.26   -12.15     0.46)  ; 33\r\n                  (  390.53  -405.95   -12.65     0.46)  ; 34\r\n                  (  392.72  -407.24   -13.52     0.46)  ; 35\r\n                  (  392.72  -407.24   -13.55     0.46)  ; 36\r\n                  (  394.72  -409.75   -14.18     0.46)  ; 37\r\n                  (  396.91  -411.02   -14.18     0.46)  ; 38\r\n                  (  399.27  -411.06   -14.18     0.46)  ; 39\r\n                  (  401.90  -412.24   -14.18     0.46)  ; 40\r\n                  (  407.21  -412.79   -14.60     0.46)  ; 41\r\n                  (  409.44  -412.26   -14.82     0.46)  ; 42\r\n                  (  411.37  -412.41   -14.82     0.46)  ; 43\r\n                  (  415.21  -412.70   -14.82     0.46)  ; 44\r\n                  (  415.07  -412.14   -14.82     0.46)  ; 45\r\n                  (  418.28  -413.78   -14.82     0.46)  ; 46\r\n                  (  422.12  -414.07   -15.43     0.46)  ; 47\r\n                  (  424.17  -414.79   -15.45     0.46)  ; 48\r\n                  (  426.09  -414.94   -16.17     0.46)  ; 49\r\n                  (  429.93  -415.23   -17.20     0.46)  ; 50\r\n                  (  434.40  -414.19   -17.80     0.46)  ; 51\r\n                  (  438.81  -414.94   -18.05     0.46)  ; 52\r\n                  (  443.23  -415.70   -19.35     0.46)  ; 53\r\n                  (  444.71  -414.75   -19.65     0.46)  ; 54\r\n                  (  444.71  -414.75   -19.70     0.46)  ; 55\r\n                  (  447.97  -414.59   -20.97     0.46)  ; 56\r\n                  (  447.97  -414.59   -21.00     0.46)  ; 57\r\n                  (  451.81  -414.88   -21.15     0.46)  ; 58\r\n                  (  456.10  -415.08   -22.22     0.46)  ; 59\r\n                  (  458.79  -414.45   -23.30     0.46)  ; 60\r\n                  (  462.04  -414.28   -24.00     0.46)  ; 61\r\n                  (  466.45  -415.04   -24.55     0.46)  ; 62\r\n                  (  470.47  -414.09   -25.45     0.46)  ; 63\r\n                  (  473.60  -413.35   -26.60     0.46)  ; 64\r\n                  (  476.86  -413.19   -27.55     0.46)  ; 65\r\n                  (  481.19  -411.58   -28.47     0.46)  ; 66\r\n                  (  483.87  -410.95   -28.47     0.46)  ; 67\r\n                  (  487.13  -410.79   -29.20     0.46)  ; 68\r\n                  (  489.37  -410.26   -30.07     0.46)  ; 69\r\n                  (  492.04  -409.63   -31.10     0.46)  ; 70\r\n                  (  495.48  -408.23   -31.10     0.46)  ; 71\r\n                  (  500.22  -408.31   -32.02     0.46)  ; 72\r\n                  (  501.11  -408.10   -32.38     0.46)  ; 73\r\n                  (  503.79  -407.47   -33.70     0.46)  ; 74\r\n                  (  503.79  -407.47   -33.72     0.46)  ; 75\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  330.79  -372.18    -6.85     0.46)  ; 1\r\n                    (  350.04  -387.97   -13.82     0.46)  ; 2\r\n                    (  406.00  -413.67   -14.60     0.46)  ; 3\r\n                    (  471.18  -415.11   -25.45     0.46)  ; 4\r\n                    (  487.75  -409.44   -30.07     0.46)  ; 5\r\n                    (  500.93  -409.34   -31.88     0.46)  ; 6\r\n                    (  503.60  -408.72   -33.67     0.46)  ; 7\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  319.11  -366.55    -6.15     0.46)  ; 1\r\n                    (  358.25  -390.82   -11.05     0.46)  ; 2\r\n                    (  365.38  -395.13   -10.57     0.46)  ; 3\r\n                    (  372.77  -400.56   -10.77     0.46)  ; 4\r\n                    (  395.17  -409.64   -14.18     0.46)  ; 5\r\n                    (  448.56  -415.05   -21.00     0.46)  ; 6\r\n                    (  459.35  -414.91   -23.30     0.46)  ; 7\r\n                    (  475.84  -412.84   -27.55     0.46)  ; 8\r\n                    (  495.61  -408.80   -31.10     0.46)  ; 9\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  305.27  -369.79    -2.55     0.46)  ; 1, R-1-2-2-2-2-1-2-2\r\n                  (  307.14  -371.75    -2.55     0.46)  ; 2\r\n                  (  306.77  -374.22    -3.08     0.46)  ; 3\r\n                  (  306.55  -377.26    -3.08     0.46)  ; 4\r\n                  (  306.27  -382.10    -3.75     0.46)  ; 5\r\n                  (  306.17  -385.71    -4.57     0.46)  ; 6\r\n                  (  307.42  -389.00    -4.57     0.46)  ; 7\r\n                  (  308.53  -391.72    -5.00     0.46)  ; 8\r\n                  (  308.29  -394.77    -5.00     0.46)  ; 9\r\n                  (  309.09  -398.16    -5.00     0.46)  ; 10\r\n                  (  309.49  -399.85    -5.53     0.46)  ; 11\r\n                  (  309.97  -403.93    -6.22     0.46)  ; 12\r\n                  (  310.33  -407.43    -6.65     0.46)  ; 13\r\n                  (  311.13  -410.83    -7.02     0.46)  ; 14\r\n                  (  311.92  -414.22    -7.02     0.46)  ; 15\r\n                  (  311.99  -416.58    -7.02     0.46)  ; 16\r\n                  (  313.12  -419.32    -7.02     0.46)  ; 17\r\n                  (  312.43  -422.46    -7.97     0.46)  ; 18\r\n                  (  312.64  -425.40    -9.02     0.46)  ; 19\r\n                  (  312.86  -428.32    -9.72     0.46)  ; 20\r\n                  (  313.71  -429.92    -9.72     0.46)  ; 21\r\n                  (  313.79  -432.29    -8.77     0.46)  ; 22\r\n                  (  313.11  -435.44    -8.77     0.46)  ; 23\r\n                  (  313.46  -438.94    -8.88     0.46)  ; 24\r\n                  (  312.96  -440.85    -9.05     0.46)  ; 25\r\n                  (  312.03  -442.86    -9.80     0.46)  ; 26\r\n                  (  312.03  -442.86    -9.82     0.46)  ; 27\r\n                  (  312.43  -444.56   -10.95     0.46)  ; 28\r\n                  (  310.14  -446.88   -12.35     0.46)  ; 29\r\n                  (  309.69  -446.99   -12.35     0.46)  ; 30\r\n                  (  308.88  -449.57   -13.18     0.46)  ; 31\r\n                  (  308.57  -450.24   -15.27     0.46)  ; 32\r\n                  (  308.57  -450.24   -15.30     0.46)  ; 33\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  310.58  -392.44    -5.00     0.46)  ; 1\r\n                    (  312.13  -433.29    -8.77     0.46)  ; 2\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  306.56  -371.30    -2.55     0.46)  ; 1\r\n                    (  306.55  -377.26    -3.08     0.46)  ; 2\r\n                    (  306.27  -382.10    -3.75     0.46)  ; 3\r\n                    (  307.28  -388.44    -4.57     0.46)  ; 4\r\n                    (  309.62  -400.43    -5.53     0.46)  ; 5\r\n                    (  310.77  -407.32    -6.65     0.46)  ; 6\r\n                    (  311.92  -414.22    -7.02     0.46)  ; 7\r\n                    (  312.29  -421.90    -7.97     0.46)  ; 8\r\n                    (  312.25  -423.69    -9.02     0.46)  ; 9\r\n                    (  312.73  -427.77    -9.72     0.46)  ; 10\r\n                    (  313.71  -429.92    -9.72     0.46)  ; 11\r\n                    (  309.69  -446.99   -12.35     0.46)  ; 12\r\n                    (  308.88  -449.57   -13.18     0.46)  ; 13\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            |\r\n              (  313.90  -319.09    11.85     0.92)  ; 1, R-1-2-2-2-2-2\r\n              (  315.59  -322.28    13.05     0.92)  ; 2\r\n              (  315.49  -325.89    14.07     0.92)  ; 3\r\n              (  316.15  -328.72    15.15     0.92)  ; 4\r\n              (  317.08  -332.69    16.25     0.92)  ; 5\r\n              (  318.32  -335.98    17.42     0.92)  ; 6\r\n              (  319.70  -339.83    18.02     0.46)  ; 7\r\n              (  321.12  -341.88    19.10     0.46)  ; 8\r\n              (  319.82  -346.37    19.07     0.46)  ; 9\r\n              (  318.29  -347.92    19.07     0.46)  ; 10\r\n              (  319.26  -350.08    19.07     0.46)  ; 11\r\n              (  319.49  -353.01    19.35     0.46)  ; 12\r\n              (  319.61  -359.56    19.35     0.46)  ; 13\r\n              (  319.82  -362.50    19.35     0.46)  ; 14\r\n              (  319.79  -368.36    15.85     0.46)  ; 15\r\n              (  320.43  -377.17    17.20     0.46)  ; 16\r\n              (  321.39  -391.28    18.08     0.46)  ; 17\r\n              (  324.69  -411.41    18.08     0.46)  ; 18\r\n              (  328.03  -435.70    18.92     0.46)  ; 19\r\n              (  328.03  -435.70    19.10     0.46)  ; 20\r\n              (  331.25  -453.46    19.90     0.46)  ; 21\r\n              (  331.25  -453.46    19.95     0.46)  ; 22\r\n              (  332.28  -475.91    21.45     0.46)  ; 23\r\n              (  330.87  -483.99    22.50     0.46)  ; 24\r\n              (  332.33  -490.23    22.95     0.46)  ; 25\r\n              (  332.00  -496.87    23.25     0.46)  ; 26\r\n              (  331.01  -500.69    23.57     0.46)  ; 27\r\n              (  331.49  -504.75    23.25     0.46)  ; 28\r\n              (  330.81  -507.90    23.25     0.46)  ; 29\r\n              (  329.20  -513.05    23.25     0.46)  ; 30\r\n              (  328.88  -513.73    20.95     0.46)  ; 31\r\n              (  327.77  -516.98    19.02     0.46)  ; 32\r\n              (  327.77  -516.98    19.00     0.46)  ; 33\r\n              (  326.23  -518.53    17.05     0.46)  ; 34\r\n              (  326.23  -518.53    17.00     0.46)  ; 35\r\n              (  326.58  -522.03    16.67     0.46)  ; 36\r\n              (  326.89  -527.33    16.67     0.46)  ; 37\r\n              (  324.96  -533.16    15.75     0.46)  ; 38\r\n              (  324.96  -533.16    15.60     0.46)  ; 39\r\n              (  323.63  -542.80    16.50     0.46)  ; 40\r\n              (  322.77  -553.14    14.15     0.46)  ; 41\r\n              (  322.77  -553.14    14.07     0.46)  ; 42\r\n              (  322.27  -571.17    13.75     0.46)  ; 43\r\n              (  322.27  -571.17    13.47     0.46)  ; 44\r\n              (  326.57  -603.60    12.75     0.46)  ; 45\r\n              (  326.73  -614.32    14.55     0.46)  ; 46\r\n              (  326.70  -626.27    14.55     0.46)  ; 47\r\n              (  326.27  -626.38    14.55     0.46)  ; 48\r\n              (  325.74  -640.23    14.55     0.46)  ; 49\r\n              (  323.99  -644.82    13.32     0.46)  ; 50\r\n              (  323.99  -644.82    13.30     0.46)  ; 51\r\n              (  323.90  -648.43    11.35     0.46)  ; 52\r\n              (  322.95  -650.44     9.80     0.46)  ; 53\r\n              (  322.18  -651.21     7.55     0.46)  ; 54\r\n              (  322.58  -652.91     5.42     0.46)  ; 55\r\n              (  321.20  -655.03     3.47     0.46)  ; 56\r\n              (  320.34  -659.41     1.95     0.46)  ; 57\r\n              (  320.34  -659.41     1.90     0.46)  ; 58\r\n              (  319.30  -665.02     0.05     0.46)  ; 59\r\n              (  319.91  -669.65    -2.85     0.46)  ; 60\r\n              (  319.91  -669.65    -2.92     0.46)  ; 61\r\n              (  319.86  -671.46    -4.72     0.46)  ; 62\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  327.47  -511.68    23.25     0.46)  ; 1\r\n                (  322.11  -648.85     9.80     0.46)  ; 2\r\n              )  ;  End of markers\r\n               Incomplete\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (  280.96  -100.13     5.05     0.46)  ; 1, R-2\r\n    (  282.62   -99.15     6.13     0.46)  ; 2\r\n    (  284.58   -97.49     6.62     0.46)  ; 3\r\n    (  287.32   -95.05     6.90     0.46)  ; 4\r\n    (\r\n      (  290.57   -94.90     7.80     0.46)  ; 1, R-2-1\r\n      (  292.09   -93.34     7.80     0.46)  ; 2\r\n      (  293.62   -91.79     8.38     0.46)  ; 3\r\n      (  293.67   -89.99     8.82     0.46)  ; 4\r\n      (  293.53   -89.42     8.82     0.46)  ; 5\r\n      (  295.37   -87.20     9.00     0.46)  ; 6\r\n      (  298.23   -85.34     8.55     0.46)  ; 7\r\n      (  300.91   -84.71    10.48     0.46)  ; 8\r\n      (  302.43   -83.15    10.63     0.46)  ; 9\r\n      (  304.08   -82.17     9.65     0.46)  ; 10\r\n      (  306.31   -81.65     9.65     0.46)  ; 11\r\n      (  308.11   -81.23     9.32     0.46)  ; 12\r\n      (  311.99   -79.72    10.10     0.46)  ; 13\r\n      (  313.32   -79.41    11.00     0.46)  ; 14\r\n      (  316.32   -78.11    11.93     0.46)  ; 15\r\n      (  317.85   -76.56    11.93     0.46)  ; 16\r\n      (  320.12   -74.23    12.30     0.46)  ; 17\r\n      (  323.75   -71.60    12.75     0.46)  ; 18\r\n\r\n      (Dot\r\n        (Color Yellow)\r\n        (Name \"Marker 1\")\r\n        (  299.75   -83.78    10.63     0.46)  ; 1\r\n      )  ;  End of markers\r\n\r\n      (OpenCircle\r\n        (Color Yellow)\r\n        (Name \"Marker 2\")\r\n        (  292.09   -93.34     7.80     0.46)  ; 1\r\n        (  295.19   -88.43     9.00     0.46)  ; 2\r\n        (  297.02   -86.21     8.55     0.46)  ; 3\r\n        (  308.11   -81.23     9.32     0.46)  ; 4\r\n      )  ;  End of markers\r\n      (\r\n        (  327.47   -71.33    11.95     0.46)  ; 1, R-2-1-1\r\n        (  329.98   -71.95    11.47     0.46)  ; 2\r\n        (  331.77   -71.53    11.13     0.46)  ; 3\r\n        (  332.83   -70.08    11.13     0.46)  ; 4\r\n        (\r\n          (  337.57   -70.17     9.95     0.46)  ; 1, R-2-1-1-1\r\n          (  341.22   -71.70     9.27     0.46)  ; 2\r\n          (  341.22   -71.70     9.25     0.46)  ; 3\r\n          (  344.62   -72.10     8.40     0.46)  ; 4\r\n          (  347.43   -72.05     8.60     0.46)  ; 5\r\n          (  347.43   -72.05     8.55     0.46)  ; 6\r\n          (  350.38   -72.54     9.48     0.46)  ; 7\r\n          (  353.32   -73.05    10.60     0.46)  ; 8\r\n          (  356.58   -72.88    11.70     0.46)  ; 9\r\n          (  358.50   -73.02    10.15     0.46)  ; 10\r\n          (  360.23   -74.41     8.35     0.46)  ; 11\r\n          (  363.31   -75.48     7.38     0.46)  ; 12\r\n          (  367.91   -74.99     6.65     0.46)  ; 13\r\n          (  372.20   -75.19     6.65     0.46)  ; 14\r\n          (  375.72   -76.16     6.38     0.46)  ; 15\r\n          (  378.66   -76.65     5.60     0.46)  ; 16\r\n          (  380.62   -77.05     5.04     0.46)  ; 17\r\n          (  382.50   -76.96     4.60     0.46)  ; 18\r\n          (  382.50   -76.96     4.57     0.46)  ; 19\r\n          (  385.14   -78.13     3.80     0.46)  ; 20\r\n          (  389.73   -77.65     3.45     0.46)  ; 21\r\n          (  391.03   -79.15     3.45     0.46)  ; 22\r\n          (  395.57   -80.46     3.15     0.46)  ; 23\r\n          (  397.05   -80.72     6.13     0.46)  ; 24\r\n          (  399.99   -81.22     6.95     0.46)  ; 25\r\n          (  405.43   -82.34     6.73     0.46)  ; 26\r\n          (  407.93   -82.94     5.70     0.46)  ; 27\r\n          (  407.93   -82.94     5.68     0.46)  ; 28\r\n          (  410.82   -85.26     4.47     0.46)  ; 29\r\n          (  411.67   -86.85     2.70     0.46)  ; 30\r\n          (  411.67   -86.85     2.67     0.46)  ; 31\r\n          (  413.22   -89.47     1.38     0.46)  ; 32\r\n          (  416.57   -91.68     0.37     0.46)  ; 33\r\n          (  419.96   -92.07    -0.12     0.46)  ; 34\r\n          (  424.33   -94.63    -0.12     0.46)  ; 35\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  336.81   -70.95     9.95     0.46)  ; 1\r\n            (  354.48   -73.96    11.70     0.46)  ; 2\r\n            (  373.85   -74.21     6.38     0.46)  ; 3\r\n            (  394.59   -78.30     3.15     0.46)  ; 4\r\n            (  409.84   -83.09     4.47     0.46)  ; 5\r\n            (  417.20   -90.33     0.37     0.46)  ; 6\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  347.43   -72.05     8.55     0.46)  ; 1\r\n            (  360.10   -73.84     8.35     0.46)  ; 2\r\n            (  363.31   -75.48     7.38     0.46)  ; 3\r\n            (  385.57   -78.02     3.80     0.46)  ; 4\r\n          )  ;  End of markers\r\n          (\r\n            (  427.09   -96.37    -0.28     0.46)  ; 1, R-2-1-1-1-1\r\n            (  427.09   -96.37    -0.32     0.46)  ; 2\r\n            (  429.99   -98.67    -1.17     0.46)  ; 3\r\n            (  431.77   -98.26    -1.17     0.46)  ; 4\r\n            (  434.15   -98.29    -2.10     0.46)  ; 5\r\n            (  435.56  -100.36    -2.65     0.46)  ; 6\r\n            (  438.51  -100.86    -1.80     0.46)  ; 7\r\n            (  441.90  -101.26    -1.80     0.46)  ; 8\r\n            (  446.76  -101.91    -2.88     0.46)  ; 9\r\n            (  448.82  -102.63    -3.67     0.46)  ; 10\r\n            (  451.05  -102.10    -4.47     0.46)  ; 11\r\n            (  453.29  -101.58    -5.30     0.46)  ; 12\r\n            (  456.18  -103.89    -6.50     0.46)  ; 13\r\n            (  459.52  -106.08    -7.05     0.46)  ; 14\r\n            (  462.78  -105.92    -6.93     0.46)  ; 15\r\n            (  464.84  -106.64    -6.10     0.46)  ; 16\r\n            (  466.75  -106.78    -5.55     0.46)  ; 17\r\n            (  470.01  -106.62    -5.37     0.46)  ; 18\r\n            (  473.08  -107.68    -5.63     0.46)  ; 19\r\n            (  476.29  -109.32    -6.52     0.46)  ; 20\r\n            (  476.29  -109.32    -6.55     0.46)  ; 21\r\n            (  477.90  -110.14    -6.78     0.46)  ; 22\r\n            (  479.45  -112.76    -6.88     0.46)  ; 23\r\n            (  481.82  -112.80    -6.75     0.46)  ; 24\r\n            (  483.24  -114.86    -6.75     0.46)  ; 25\r\n            (  486.06  -114.80    -7.77     0.46)  ; 26\r\n            (  488.24  -116.07    -7.77     0.46)  ; 27\r\n            (  490.74  -116.68    -8.25     0.46)  ; 28\r\n            (  494.57  -116.98    -8.95     0.46)  ; 29\r\n            (  497.21  -118.16    -9.60     0.46)  ; 30\r\n            (  498.81  -118.97   -10.88     0.46)  ; 31\r\n            (  498.81  -118.97   -10.92     0.46)  ; 32\r\n            (  500.55  -120.36   -11.53     0.46)  ; 33\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  446.14  -103.25    -2.88     0.46)  ; 1\r\n              (  467.01  -107.91    -5.55     0.46)  ; 2\r\n              (  473.93  -109.28    -6.55     0.46)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  502.25  -117.57   -12.38     0.46)  ; 1, R-2-1-1-1-1-1\r\n              (  505.69  -116.17   -13.20     0.46)  ; 2\r\n              (  506.27  -116.62   -14.43     0.46)  ; 3\r\n              (  507.80  -115.07   -14.43     0.46)  ; 4\r\n              (  510.02  -114.56   -15.02     0.46)  ; 5\r\n              (  511.68  -113.57   -16.77     0.46)  ; 6\r\n              (  513.02  -113.25   -17.75     0.46)  ; 7\r\n              (  514.36  -112.94   -19.52     0.46)  ; 8\r\n              (  516.09  -114.33   -21.63     0.46)  ; 9\r\n              (  518.33  -113.80   -23.52     0.46)  ; 10\r\n              (  520.24  -113.95   -25.20     0.46)  ; 11\r\n              (  522.80  -112.75   -26.40     0.46)  ; 12\r\n              (  525.92  -112.02   -28.15     0.46)  ; 13\r\n              (  525.92  -112.02   -28.17     0.46)  ; 14\r\n              (  531.10  -112.01   -29.25     0.46)  ; 15\r\n              (  534.54  -110.59   -30.10     0.46)  ; 16\r\n              (  537.98  -109.19   -30.85     0.46)  ; 17\r\n              (  541.68  -108.92   -31.38     0.46)  ; 18\r\n              (  544.78  -110.01   -32.13     0.46)  ; 19\r\n              (  547.45  -109.38   -32.92     0.46)  ; 20\r\n              (  550.09  -110.55   -33.42     0.46)  ; 21\r\n              (  552.14  -111.27   -34.80     0.46)  ; 22\r\n              (  554.06  -111.41   -35.02     0.46)  ; 23\r\n              (  555.53  -111.66   -37.77     0.46)  ; 24\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  518.60  -114.93   -23.52     0.46)  ; 1\r\n                (  530.70  -110.31   -29.25     0.46)  ; 2\r\n                (  550.92  -112.15   -34.80     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  541.68  -108.92   -31.38     0.46)  ; 1\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  502.47  -120.51   -11.75     0.46)  ; 1, R-2-1-1-1-1-2\r\n              (  504.35  -122.47   -11.75     0.46)  ; 2\r\n              (  506.58  -121.94   -11.32     0.46)  ; 3\r\n              (  509.66  -123.00   -11.37     0.46)  ; 4\r\n              (  512.47  -122.95   -11.77     0.46)  ; 5\r\n              (  514.21  -124.34   -11.77     0.46)  ; 6\r\n              (  516.32  -123.25   -11.77     0.46)  ; 7\r\n              (  519.70  -123.65   -12.72     0.46)  ; 8\r\n              (  521.43  -125.04   -13.57     0.46)  ; 9\r\n              (  524.96  -125.99   -13.87     0.46)  ; 10\r\n              (  528.93  -126.85   -14.23     0.46)  ; 11\r\n              (  531.44  -127.46   -15.50     0.46)  ; 12\r\n              (  533.35  -127.61   -16.88     0.46)  ; 13\r\n              (  535.41  -128.33   -17.65     0.46)  ; 14\r\n              (  537.63  -127.80   -18.90     0.46)  ; 15\r\n              (  540.90  -127.63   -19.58     0.46)  ; 16\r\n              (  544.87  -128.50   -18.20     0.46)  ; 17\r\n              (  544.87  -128.50   -18.23     0.46)  ; 18\r\n              (  547.37  -129.10   -16.73     0.46)  ; 19\r\n              (  549.82  -131.52   -16.27     0.46)  ; 20\r\n              (  552.58  -133.26   -17.30     0.46)  ; 21\r\n              (  555.08  -133.86   -17.98     0.46)  ; 22\r\n              (  557.97  -136.18   -18.77     0.46)  ; 23\r\n              (  560.87  -138.48   -18.35     0.46)  ; 24\r\n              (  565.23  -141.05   -18.55     0.46)  ; 25\r\n              (  569.08  -141.33   -18.55     0.46)  ; 26\r\n              (  572.29  -142.97   -19.13     0.46)  ; 27\r\n              (  572.29  -142.97   -19.15     0.46)  ; 28\r\n              (  576.52  -144.96   -19.92     0.46)  ; 29\r\n              (  579.34  -144.90   -20.57     0.46)  ; 30\r\n              (  583.76  -145.66   -20.17     0.46)  ; 31\r\n              (  583.76  -145.66   -20.23     0.46)  ; 32\r\n              (  586.57  -145.60   -18.48     0.46)  ; 33\r\n              (  589.33  -147.34   -19.50     0.46)  ; 34\r\n              (  589.33  -147.34   -19.52     0.46)  ; 35\r\n              (  591.25  -147.48   -21.52     0.46)  ; 36\r\n              (  591.25  -147.48   -21.55     0.46)  ; 37\r\n              (  592.98  -148.87   -21.77     0.46)  ; 38\r\n              (  596.98  -149.71   -22.15     0.46)  ; 39\r\n              (  600.94  -150.57   -22.15     0.46)  ; 40\r\n              (  603.25  -152.42   -21.20     0.46)  ; 41\r\n              (  603.25  -152.42   -20.70     0.46)  ; 42\r\n              (  606.32  -153.48   -20.57     0.46)  ; 43\r\n              (  610.57  -155.48   -19.95     0.46)  ; 44\r\n              (  613.96  -155.88   -21.28     0.46)  ; 45\r\n              (  617.05  -156.94   -22.27     0.46)  ; 46\r\n              (  616.91  -156.37   -22.30     0.46)  ; 47\r\n              (  619.36  -158.79   -22.85     0.46)  ; 48\r\n              (  623.45  -160.21   -23.60     0.46)  ; 49\r\n              (  626.54  -161.29   -23.82     0.46)  ; 50\r\n              (  629.17  -162.46   -23.70     0.46)  ; 51\r\n              (  631.08  -162.61   -23.75     0.46)  ; 52\r\n              (  634.43  -164.81   -23.65     0.46)  ; 53\r\n              (  637.07  -165.99   -24.90     0.46)  ; 54\r\n              (  640.14  -167.06   -26.38     0.46)  ; 55\r\n              (  643.66  -168.02   -27.08     0.46)  ; 56\r\n              (  646.17  -168.64   -26.38     0.46)  ; 57\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  509.04  -124.36   -11.37     0.46)  ; 1\r\n                (  519.97  -124.77   -12.72     0.46)  ; 2\r\n                (  565.50  -142.17   -18.55     0.46)  ; 3\r\n                (  569.34  -142.47   -18.55     0.46)  ; 4\r\n                (  591.65  -149.18   -21.77     0.46)  ; 5\r\n                (  622.84  -161.56   -23.60     0.46)  ; 6\r\n                (  626.23  -161.96   -23.82     0.46)  ; 7\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  531.44  -127.46   -15.50     0.46)  ; 1\r\n                (  549.37  -131.63   -16.27     0.46)  ; 2\r\n                (  552.44  -132.69   -17.30     0.46)  ; 3\r\n                (  557.84  -135.61   -18.77     0.46)  ; 4\r\n                (  611.15  -155.94   -19.95     0.46)  ; 5\r\n                (  630.77  -163.29   -23.75     0.46)  ; 6\r\n              )  ;  End of markers\r\n              (\r\n                (  646.79  -167.28   -25.17     0.46)  ; 1, R-2-1-1-1-1-2-1\r\n                (  648.40  -168.11   -22.65     0.46)  ; 2\r\n                (  648.08  -168.79   -20.50     0.46)  ; 3\r\n                (  649.69  -169.60   -17.20     0.46)  ; 4\r\n                (  648.35  -169.91   -14.88     0.46)  ; 5\r\n                (  648.79  -169.81   -11.95     0.46)  ; 6\r\n                (  648.21  -169.35   -11.95     0.46)  ; 7\r\n                (  650.40  -170.62    -9.93     0.46)  ; 8\r\n                (  650.80  -172.32    -7.97     0.46)  ; 9\r\n                (  651.95  -173.25    -6.42     0.46)  ; 10\r\n                (  652.93  -175.40    -5.15     0.46)  ; 11\r\n                (  654.68  -176.79    -3.75     0.46)  ; 12\r\n                (  655.52  -178.38    -2.53     0.46)  ; 13\r\n                (  658.41  -180.69    -1.38     0.46)  ; 14\r\n                (  662.07  -182.22    -0.77     0.46)  ; 15\r\n                (  668.23  -184.37    -1.05     0.46)  ; 16\r\n                (  673.03  -186.81    -1.05     0.46)  ; 17\r\n                (  675.41  -186.86    -0.15     0.46)  ; 18\r\n                (  676.97  -189.48     1.27     0.46)  ; 19\r\n                (  678.88  -189.63     2.33     0.46)  ; 20\r\n                (  680.36  -189.88     3.85     0.46)  ; 21\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  649.11  -169.13   -20.50     0.46)  ; 1\r\n                  (  650.17  -173.67    -6.42     0.46)  ; 2\r\n                  (  653.33  -177.10    -3.75     0.46)  ; 3\r\n                  (  680.17  -191.12     3.85     0.46)  ; 4\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  649.24  -169.70   -27.95     0.46)  ; 1, R-2-1-1-1-1-2-2\r\n                (  652.31  -170.78   -29.17     0.46)  ; 2\r\n                (  654.51  -172.05   -29.92     0.46)  ; 3\r\n                (  654.51  -172.05   -30.92     0.46)  ; 4\r\n                (  655.98  -172.30   -31.35     0.46)  ; 5\r\n                (  655.98  -172.30   -31.38     0.46)  ; 6\r\n                (  659.50  -173.28   -32.00     0.46)  ; 7\r\n                (  659.50  -173.28   -32.08     0.46)  ; 8\r\n                (  661.68  -174.55   -33.10     0.46)  ; 9\r\n                (  664.18  -175.15   -32.30     0.46)  ; 10\r\n                (  668.11  -176.60   -33.35     0.46)  ; 11\r\n                (  670.93  -176.54   -33.35     0.46)  ; 12\r\n                (  674.00  -177.62   -33.77     0.46)  ; 13\r\n                (  676.64  -178.79   -34.65     0.46)  ; 14\r\n                (  679.72  -179.86   -35.17     0.46)  ; 15\r\n                (  682.08  -179.91   -35.57     0.46)  ; 16\r\n                (  685.73  -181.43   -36.88     0.46)  ; 17\r\n                (  685.28  -181.54   -36.90     0.46)  ; 18\r\n                (  688.81  -182.52   -37.55     0.46)  ; 19\r\n                (  688.81  -182.52   -37.58     0.46)  ; 20\r\n                (  691.05  -181.99   -38.47     0.46)  ; 21\r\n                (  692.65  -182.80   -38.47     0.46)  ; 22\r\n                (  695.86  -184.44   -39.67     0.46)  ; 23\r\n                (  698.05  -185.71   -40.55     0.46)  ; 24\r\n                (  700.85  -185.65   -41.47     0.46)  ; 25\r\n                (  704.83  -186.52   -43.00     0.46)  ; 26\r\n                (  704.83  -186.52   -43.02     0.46)  ; 27\r\n                (  709.06  -188.51   -44.02     0.46)  ; 28\r\n                (  713.48  -189.27   -44.87     0.46)  ; 29\r\n                (  713.48  -189.27   -44.90     0.46)  ; 30\r\n                (  715.09  -190.08   -45.60     0.46)  ; 31\r\n                (  719.91  -192.54   -45.92     0.46)  ; 32\r\n                (  722.08  -193.81   -45.92     0.46)  ; 33\r\n                (  723.51  -195.88   -45.92     0.46)  ; 34\r\n                (  725.88  -195.92   -44.80     0.46)  ; 35\r\n                (  730.42  -197.23   -44.00     0.46)  ; 36\r\n                (  730.42  -197.23   -44.05     0.46)  ; 37\r\n                (  734.72  -197.43   -43.47     0.46)  ; 38\r\n                (  734.72  -197.43   -43.50     0.46)  ; 39\r\n                (  740.91  -197.76   -41.97     0.46)  ; 40\r\n                (  740.91  -197.76   -42.00     0.46)  ; 41\r\n                (  744.49  -196.93   -41.82     0.46)  ; 42\r\n                (  744.49  -196.93   -41.85     0.46)  ; 43\r\n                (  747.74  -196.76   -43.30     0.46)  ; 44\r\n                (  747.74  -196.76   -43.33     0.46)  ; 45\r\n                (  749.80  -197.47   -44.58     0.46)  ; 46\r\n                (  752.12  -199.32   -44.78     0.46)  ; 47\r\n                (  758.33  -199.66   -45.10     0.46)  ; 48\r\n                (  763.94  -199.54   -45.10     0.46)  ; 49\r\n                (  768.37  -200.28   -45.10     0.46)  ; 50\r\n                (  772.33  -201.16   -45.88     0.46)  ; 51\r\n                (  774.56  -200.63   -43.67     0.46)  ; 52\r\n                (  777.34  -202.36   -43.05     0.46)  ; 53\r\n                (  780.99  -203.90   -41.88     0.46)  ; 54\r\n                (  785.60  -203.42   -41.88     0.46)  ; 55\r\n                (  790.45  -204.07   -41.88     0.46)  ; 56\r\n                (  795.14  -205.96   -43.07     0.46)  ; 57\r\n                (  798.92  -208.06   -43.22     0.46)  ; 58\r\n                (  803.15  -210.05   -43.22     0.46)  ; 59\r\n                (  808.03  -210.70   -43.75     0.46)  ; 60\r\n                (  808.03  -210.70   -43.78     0.46)  ; 61\r\n                (  810.16  -213.79   -44.30     0.46)  ; 62\r\n                (  815.60  -214.90   -44.30     0.46)  ; 63\r\n                (  821.05  -216.01   -44.52     0.46)  ; 64\r\n                (  826.04  -217.22   -43.22     0.46)  ; 65\r\n                (  831.49  -218.34   -42.17     0.46)  ; 66\r\n                (  831.49  -218.34   -42.20     0.46)  ; 67\r\n                (  833.90  -216.59   -41.32     0.46)  ; 68\r\n                (  837.02  -215.85   -40.45     0.46)  ; 69\r\n                (  839.85  -215.79   -39.63     0.46)  ; 70\r\n                (  844.70  -216.43   -39.63     0.46)  ; 71\r\n                (  848.49  -218.53   -39.15     0.46)  ; 72\r\n                (  852.64  -218.16   -41.57     0.46)  ; 73\r\n                (  858.94  -220.86   -41.42     0.46)  ; 74\r\n                (  863.49  -222.19   -41.42     0.46)  ; 75\r\n                (  866.88  -222.58   -40.55     0.46)  ; 76\r\n                (  871.34  -221.54   -39.30     0.46)  ; 77\r\n                (  874.15  -221.50   -37.00     0.46)  ; 78\r\n                (  878.62  -220.45   -35.97     0.46)  ; 79\r\n                (  878.17  -220.55   -35.97     0.46)  ; 80\r\n                (  881.12  -221.06   -34.95     0.46)  ; 81\r\n                (  883.93  -220.99   -33.85     0.46)  ; 82\r\n                (  888.08  -220.62   -34.47     0.46)  ; 83\r\n                (  891.02  -221.13   -34.47     0.46)  ; 84\r\n                (  895.18  -220.75   -35.92     0.46)  ; 85\r\n                (  899.91  -220.83   -35.92     0.46)  ; 86\r\n                (  904.34  -221.59   -37.02     0.46)  ; 87\r\n                (  904.34  -221.59   -37.05     0.46)  ; 88\r\n                (  907.14  -221.53   -37.05     0.46)  ; 89\r\n                (  909.63  -222.14   -37.05     0.46)  ; 90\r\n                (  911.74  -221.04   -37.05     0.46)  ; 91\r\n                (  916.60  -221.70   -37.05     0.46)  ; 92\r\n                (  918.39  -221.28   -35.97     0.46)  ; 93\r\n                (  918.39  -221.28   -36.10     0.46)  ; 94\r\n                (  921.92  -222.25   -36.17     0.46)  ; 95\r\n                (  929.01  -222.37   -36.17     0.46)  ; 96\r\n                (  931.24  -221.85   -36.17     0.46)  ; 97\r\n                (  936.42  -221.83   -36.17     0.46)  ; 98\r\n                (  941.47  -221.25   -37.28     0.46)  ; 99\r\n                (  945.50  -220.31   -38.25     0.46)  ; 100\r\n                (  945.50  -220.31   -38.27     0.46)  ; 101\r\n                (  949.77  -220.50   -39.13     0.46)  ; 102\r\n                (  952.09  -222.34   -40.32     0.46)  ; 103\r\n                (  952.09  -222.34   -40.35     0.46)  ; 104\r\n                (  952.93  -223.94   -40.97     0.46)  ; 105\r\n                (  952.93  -223.94   -41.00     0.46)  ; 106\r\n                (  955.35  -222.18   -41.52     0.46)  ; 107\r\n                (  955.35  -222.18   -41.55     0.46)  ; 108\r\n                (  954.69  -219.35   -40.90     0.46)  ; 109\r\n                (  953.89  -215.95   -39.65     0.46)  ; 110\r\n                (  953.36  -213.68   -39.65     0.46)  ; 111\r\n                (  952.65  -212.66   -38.38     0.46)  ; 112\r\n                (  950.86  -213.07   -36.25     0.46)  ; 113\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  678.96  -180.64   -35.17     0.46)  ; 1\r\n                  (  883.31  -222.34   -33.85     0.46)  ; 2\r\n                  (  890.40  -222.47   -34.47     0.46)  ; 3\r\n                  (  899.73  -222.07   -35.92     0.46)  ; 4\r\n                  (  955.30  -223.98   -41.57     0.46)  ; 5\r\n                  (  954.26  -213.47   -41.70     0.46)  ; 6\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  664.18  -175.15   -32.30     0.46)  ; 1\r\n                  (  692.65  -182.80   -38.47     0.46)  ; 2\r\n                  (  715.09  -190.08   -45.60     0.46)  ; 3\r\n                  (  772.91  -201.62   -45.88     0.46)  ; 4\r\n                  (  821.50  -215.90   -44.52     0.46)  ; 5\r\n                  (  839.85  -215.79   -39.63     0.46)  ; 6\r\n                  (  858.62  -221.53   -41.42     0.46)  ; 7\r\n                  (  863.49  -222.19   -41.42     0.46)  ; 8\r\n                  (  870.89  -221.65   -39.30     0.46)  ; 9\r\n                  (  951.18  -212.40   -36.25     0.46)  ; 10\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  427.40   -91.87    -0.88     0.46)  ; 1, R-2-1-1-1-2\r\n            (  428.66   -89.19    -1.40     0.46)  ; 2\r\n            (  429.29   -87.85    -1.65     0.46)  ; 3\r\n            (  431.69   -86.08    -1.65     0.46)  ; 4\r\n            (  434.42   -83.66    -2.45     0.46)  ; 5\r\n            (  434.47   -81.85    -3.88     0.46)  ; 6\r\n            (  437.96   -78.65    -4.57     0.46)  ; 7\r\n            (  437.96   -78.65    -4.60     0.46)  ; 8\r\n            (  438.72   -77.87    -5.27     0.46)  ; 9\r\n            (  441.45   -75.44    -6.07     0.46)  ; 10\r\n            (  444.18   -73.01    -6.80     0.46)  ; 11\r\n            (  447.04   -71.15    -7.35     0.46)  ; 12\r\n            (  449.64   -68.15    -7.35     0.46)  ; 13\r\n            (  451.91   -65.83    -8.18     0.46)  ; 14\r\n            (  452.91   -62.01    -8.18     0.46)  ; 15\r\n            (  453.84   -60.00    -8.52     0.46)  ; 16\r\n            (  455.56   -57.21    -8.52     0.46)  ; 17\r\n            (  455.11   -57.31    -8.52     0.46)  ; 18\r\n            (  456.76   -56.33    -8.52     0.46)  ; 19\r\n            (  456.67   -53.96    -8.52     0.46)  ; 20\r\n            (  458.06   -51.84    -8.88     0.46)  ; 21\r\n            (  460.66   -48.84    -9.17     0.46)  ; 22\r\n            (  460.66   -48.84    -9.20     0.46)  ; 23\r\n            (  460.27   -47.15    -9.80     0.46)  ; 24\r\n            (  462.99   -44.71   -10.57     0.46)  ; 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42\r\n            (  488.23     2.40   -15.90     0.46)  ; 43\r\n            (  490.15     8.24   -16.25     0.46)  ; 44\r\n            (  491.28    11.48   -16.27     0.46)  ; 45\r\n            (  491.77    13.39   -17.15     0.46)  ; 46\r\n            (  493.35    16.74   -17.80     0.46)  ; 47\r\n            (  495.49    19.63   -18.63     0.46)  ; 48\r\n            (  497.95    23.20   -19.33     0.46)  ; 49\r\n            (  500.47    28.56   -20.20     0.46)  ; 50\r\n            (  500.47    28.56   -20.25     0.46)  ; 51\r\n            (  504.59    33.11   -20.85     0.46)  ; 52\r\n            (  504.59    33.11   -20.87     0.46)  ; 53\r\n            (  508.56    38.22   -21.32     0.46)  ; 54\r\n            (  509.42    42.61   -21.32     0.46)  ; 55\r\n            (  512.39    48.07   -21.42     0.46)  ; 56\r\n            (  513.40    51.91   -19.58     0.46)  ; 57\r\n            (  515.86    55.47   -20.13     0.46)  ; 58\r\n            (  518.64    59.71   -20.42     0.46)  ; 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127\r\n            (  519.24   224.07   -54.02     0.46)  ; 128\r\n            (  519.59   226.55   -54.78     0.46)  ; 129\r\n            (  514.94   228.34   -54.32     0.46)  ; 130\r\n            (  514.94   228.34   -54.35     0.46)  ; 131\r\n            (  511.20   232.24   -54.35     0.46)  ; 132\r\n            (  508.80   236.46   -55.07     0.46)  ; 133\r\n            (  507.03   242.00   -55.83     0.46)  ; 134\r\n            (  505.70   247.66   -56.92     0.46)  ; 135\r\n            (  505.70   247.66   -56.95     0.46)  ; 136\r\n            (  503.82   249.62   -58.05     0.46)  ; 137\r\n            (  502.98   251.21   -59.22     0.46)  ; 138\r\n            (  499.51   253.98   -59.95     0.46)  ; 139\r\n            (  497.68   257.73   -60.95     0.46)  ; 140\r\n            (  497.68   257.73   -60.97     0.46)  ; 141\r\n            (  495.82   259.69   -62.28     0.46)  ; 142\r\n            (  493.32   260.29   -63.42     0.46)  ; 143\r\n            (  491.31   262.81   -64.80     0.46)  ; 144\r\n            (  491.31   262.81   -64.82     0.46)  ; 145\r\n            (  489.12   264.09   -66.70     0.46)  ; 146\r\n            (  487.44   267.28   -68.10     0.46)  ; 147\r\n            (  487.48   269.07   -69.88     0.46)  ; 148\r\n            (  487.53   270.88   -71.92     0.46)  ; 149\r\n            (  486.25   272.37   -73.22     0.46)  ; 150\r\n            (  485.89   275.87   -75.18     0.46)  ; 151\r\n            (  485.89   275.87   -75.20     0.46)  ; 152\r\n            (  486.57   279.02   -76.90     0.46)  ; 153\r\n            (  486.57   279.02   -76.92     0.46)  ; 154\r\n            (  486.80   282.06   -78.20     0.46)  ; 155\r\n            (  486.80   282.06   -78.22     0.46)  ; 156\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  427.22   -93.11    -0.88     0.46)  ; 1\r\n              (  437.83   -78.08    -5.27     0.46)  ; 2\r\n              (  459.18   -48.59    -9.20     0.46)  ; 3\r\n              (  483.78    -8.79   -17.80     0.46)  ; 4\r\n              (  487.60     1.06   -14.85     0.46)  ; 5\r\n              (  491.18     7.87   -16.25     0.46)  ; 6\r\n              (  511.23    49.00   -21.42     0.46)  ; 7\r\n              (  519.35    58.68   -20.42     0.46)  ; 8\r\n              (  529.04    77.67   -19.75     0.46)  ; 9\r\n              (  537.51   117.88   -27.47     0.46)  ; 10\r\n              (  540.55   131.13   -28.10     0.46)  ; 11\r\n              (  506.60   247.88   -56.97     0.46)  ; 12\r\n              (  499.02   258.05   -60.97     0.46)  ; 13\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  426.82   -85.44    -1.65     0.46)  ; 1\r\n              (  431.25   -86.19    -1.65     0.46)  ; 2\r\n              (  443.60   -72.55    -6.80     0.46)  ; 3\r\n              (  449.32   -68.82    -7.35     0.46)  ; 4\r\n              (  455.69   -57.78    -8.52     0.46)  ; 5\r\n              (  463.53   -41.01   -11.42     0.46)  ; 6\r\n              (  468.64   -32.64   -14.30     0.46)  ; 7\r\n              (  474.37   -22.94   -15.05     0.46)  ; 8\r\n              (  484.77    -4.98   -17.13     0.46)  ; 9\r\n              (  538.25   106.70   -25.60     0.46)  ; 10\r\n              (  539.76   140.50   -30.45     0.46)  ; 11\r\n              (  536.81   157.12   -30.13     0.46)  ; 12\r\n              (  535.99   170.67   -28.02     0.46)  ; 13\r\n              (  536.69   185.76   -27.00     0.46)  ; 14\r\n              (  525.99   189.23   -28.27     0.46)  ; 15\r\n              (  518.44   205.37   -44.97     0.46)  ; 16\r\n              (  520.02   214.70   -49.45     0.46)  ; 17\r\n              (  519.59   226.55   -54.78     0.46)  ; 18\r\n              (  502.98   251.21   -59.25     0.46)  ; 19\r\n              (  489.44   264.76   -66.72     0.46)  ; 20\r\n              (  487.48   269.07   -71.92     0.46)  ; 21\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (  334.92   -67.41    12.88     0.46)  ; 1, R-2-1-1-2\r\n          (  334.92   -67.41    12.85     0.46)  ; 2\r\n          (  336.39   -67.66    15.02     0.46)  ; 3\r\n          (  337.86   -67.91    16.92     0.46)  ; 4\r\n          (  338.81   -65.90    17.38     0.46)  ; 5\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  334.73   -68.64    12.85     0.46)  ; 1\r\n          )  ;  End of markers\r\n          (\r\n            (  340.85   -66.61    17.38     0.46)  ; 1, R-2-1-1-2-1\r\n            (  343.80   -67.11    18.72     0.46)  ; 2\r\n            (  345.58   -66.69    20.00     0.46)  ; 3\r\n            (  347.25   -65.71    21.50     0.46)  ; 4\r\n            (  348.98   -67.10    22.27     0.46)  ; 5\r\n            (  350.45   -67.35    22.92     0.46)  ; 6\r\n            (  352.50   -68.06    23.17     0.46)  ; 7\r\n            (  354.16   -67.08    23.17     0.46)  ; 8\r\n            (  356.21   -67.78    23.90     0.46)  ; 9\r\n            (  358.18   -66.13    24.52     0.46)  ; 10\r\n            (  359.20   -66.49    25.80     0.46)  ; 11\r\n            (  359.20   -66.49    25.77     0.46)  ; 12\r\n            (  360.67   -66.74    27.92     0.46)  ; 13\r\n            (  363.04   -66.78    29.20     0.46)  ; 14\r\n            (  366.39   -68.98    30.00     0.46)  ; 15\r\n            (  370.79   -69.74    30.87     0.46)  ; 16\r\n            (  373.12   -71.59    30.87     0.46)  ; 17\r\n            (  376.20   -72.65    31.65     0.46)  ; 18\r\n            (  378.25   -73.37    32.55     0.46)  ; 19\r\n            (  379.98   -74.76    34.50     0.46)  ; 20\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  347.38   -66.27    21.50     0.46)  ; 1\r\n              (  360.36   -67.41    27.92     0.46)  ; 2\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  343.80   -67.11    18.72     0.46)  ; 1\r\n              (  351.03   -67.81    22.92     0.46)  ; 2\r\n              (  365.36   -68.64    30.00     0.46)  ; 3\r\n            )  ;  End of markers\r\n             High\r\n          |\r\n            (  339.89   -64.45    15.80     0.46)  ; 1, R-2-1-1-2-2\r\n            (  343.49   -61.81    15.07     0.46)  ; 2\r\n            (  345.66   -58.92    16.30     0.46)  ; 3\r\n            (  347.80   -56.02    16.30     0.46)  ; 4\r\n            (  352.31   -53.18    15.00     0.46)  ; 5\r\n            (  355.05   -50.74    15.00     0.46)  ; 6\r\n            (  358.93   -49.23    15.77     0.46)  ; 7\r\n            (  358.93   -49.23    15.73     0.46)  ; 8\r\n            (  360.14   -48.36    17.08     0.46)  ; 9\r\n            (  362.55   -46.60    17.60     0.46)  ; 10\r\n            (  364.65   -45.50    18.60     0.46)  ; 11\r\n            (  365.86   -44.63    19.60     0.46)  ; 12\r\n            (  365.86   -44.63    19.58     0.46)  ; 13\r\n            (  368.27   -42.87    20.35     0.46)  ; 14\r\n            (  370.06   -42.45    20.27     0.46)  ; 15\r\n            (  372.92   -40.59    20.27     0.46)  ; 16\r\n            (  374.40   -40.84    20.77     0.46)  ; 17\r\n            (  378.14   -38.76    20.35     0.46)  ; 18\r\n            (  379.66   -37.21    20.85     0.46)  ; 19\r\n            (  382.35   -36.58    20.85     0.46)  ; 20\r\n            (  384.58   -36.06    20.85     0.46)  ; 21\r\n            (  386.55   -34.41    21.25     0.46)  ; 22\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  343.32   -63.04    15.07     0.46)  ; 1\r\n              (  351.29   -52.81    15.00     0.46)  ; 2\r\n              (  361.52   -46.24    17.60     0.46)  ; 3\r\n              (  368.53   -44.00    20.35     0.46)  ; 4\r\n              (  372.42   -42.49    20.27     0.46)  ; 5\r\n              (  380.83   -38.13    20.85     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  339.89   -64.45    15.80     0.46)  ; 1\r\n              (  345.66   -58.92    16.30     0.46)  ; 2\r\n              (  353.84   -51.62    15.00     0.46)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  385.88   -31.57    20.25     0.46)  ; 1, R-2-1-1-2-2-1\r\n              (  385.88   -31.57    20.20     0.46)  ; 2\r\n              (  382.99   -29.27    19.13     0.46)  ; 3\r\n              (  382.99   -29.27    19.10     0.46)  ; 4\r\n              (  380.85   -26.19    18.27     0.46)  ; 5\r\n              (  378.98   -24.23    17.60     0.46)  ; 6\r\n              (  378.98   -24.23    17.57     0.46)  ; 7\r\n              (  377.38   -23.41    16.38     0.46)  ; 8\r\n              (  376.67   -22.39    14.80     0.46)  ; 9\r\n              (  376.67   -22.39    14.75     0.46)  ; 10\r\n              (  375.24   -20.33    13.30     0.46)  ; 11\r\n              (  375.24   -20.33    13.27     0.46)  ; 12\r\n              (  373.19   -19.63    11.50     0.46)  ; 13\r\n              (  370.61   -16.64    10.80     0.46)  ; 14\r\n              (  370.61   -16.64    10.77     0.46)  ; 15\r\n              (  368.21   -12.43     9.95     0.46)  ; 16\r\n              (  368.21   -12.43     9.93     0.46)  ; 17\r\n              (  365.89   -10.58     9.05     0.46)  ; 18\r\n              (  365.89   -10.58     9.02     0.46)  ; 19\r\n              (  363.32    -7.61    10.10     0.46)  ; 20\r\n              (  361.30    -5.09    10.97     0.46)  ; 21\r\n              (  358.85    -2.68    11.85     0.46)  ; 22\r\n              (  356.72     0.41    12.70     0.46)  ; 23\r\n              (  355.74     2.56    13.40     0.46)  ; 24\r\n              (  352.01     6.47    14.40     0.46)  ; 25\r\n              (  349.10     8.77    14.90     0.46)  ; 26\r\n              (  347.23    10.73    14.40     0.46)  ; 27\r\n              (  345.54    13.90    15.15     0.46)  ; 28\r\n              (  343.23    15.76    14.67     0.46)  ; 29\r\n              (  341.54    18.94    15.32     0.46)  ; 30\r\n              (  339.85    22.13    15.63     0.46)  ; 31\r\n              (  339.40    22.03    15.63     0.46)  ; 32\r\n              (  338.29    24.74    15.12     0.46)  ; 33\r\n              (  337.22    29.29    16.05     0.46)  ; 34\r\n              (  337.20    33.44    15.07     0.46)  ; 35\r\n              (  335.77    35.50    13.95     0.46)  ; 36\r\n              (  335.77    35.50    13.92     0.46)  ; 37\r\n              (  333.19    38.48    13.25     0.46)  ; 38\r\n              (  332.66    40.75    12.77     0.46)  ; 39\r\n              (  331.36    42.24    12.77     0.46)  ; 40\r\n              (  330.65    43.26    12.22     0.46)  ; 41\r\n              (  330.65    43.26    11.95     0.46)  ; 42\r\n              (  328.74    43.41    12.13     0.46)  ; 43\r\n              (  326.33    45.71    12.13     0.46)  ; 44\r\n              (  325.09    49.00    11.30     0.46)  ; 45\r\n              (  322.50    51.98    11.30     0.46)  ; 46\r\n              (  320.29    57.43    11.10     0.46)  ; 47\r\n              (  318.91    61.29    10.52     0.46)  ; 48\r\n              (  317.09    65.04    10.28     0.46)  ; 49\r\n              (  315.36    66.43     9.35     0.46)  ; 50\r\n              (  313.79    69.05     8.68     0.46)  ; 51\r\n              (  313.26    71.31     8.05     0.46)  ; 52\r\n              (  312.41    72.90     7.50     0.46)  ; 53\r\n              (  312.33    75.28     6.55     0.46)  ; 54\r\n              (  312.33    75.28     6.52     0.46)  ; 55\r\n              (  310.64    78.46     5.27     0.46)  ; 56\r\n              (  308.28    78.51     4.27     0.46)  ; 57\r\n              (  308.28    78.51     4.25     0.46)  ; 58\r\n              (  307.93    82.01     3.57     0.46)  ; 59\r\n              (  307.48    81.90     3.55     0.46)  ; 60\r\n              (  306.82    84.73     3.02     0.46)  ; 61\r\n              (  306.82    84.73     3.00     0.46)  ; 62\r\n              (  305.08    86.11     2.47     0.46)  ; 63\r\n              (  304.68    87.81     1.80     0.46)  ; 64\r\n              (  303.57    90.54     1.38     0.46)  ; 65\r\n              (  302.28    92.02     0.70     0.46)  ; 66\r\n              (  301.88    93.73    -0.30     0.46)  ; 67\r\n              (  301.05    95.32    -1.63     0.46)  ; 68\r\n              (  301.05    95.32    -1.65     0.46)  ; 69\r\n              (  299.04    97.83    -2.38     0.46)  ; 70\r\n              (  299.04    97.83    -2.40     0.46)  ; 71\r\n              (  297.75    99.32    -3.80     0.46)  ; 72\r\n              (  298.24   101.23    -5.32     0.46)  ; 73\r\n              (  296.76   101.48    -6.55     0.46)  ; 74\r\n              (  296.95   102.71    -6.88     0.46)  ; 75\r\n              (  296.95   102.71    -6.90     0.46)  ; 76\r\n              (  296.23   103.76    -8.00     0.46)  ; 77\r\n              (  296.23   103.76    -8.02     0.46)  ; 78\r\n              (  294.50   105.13    -8.70     0.46)  ; 79\r\n              (  294.10   106.83    -9.90     0.46)  ; 80\r\n              (  294.10   106.83    -9.93     0.46)  ; 81\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  358.10    -3.44    11.85     0.46)  ; 1\r\n                (  351.95     4.67    14.40     0.46)  ; 2\r\n                (  338.63    21.25    15.63     0.46)  ; 3\r\n                (  336.47    28.50    16.00     0.46)  ; 4\r\n                (  333.14    36.67    13.25     0.46)  ; 5\r\n                (  319.39    57.22    11.10     0.46)  ; 6\r\n                (  313.03    68.27     8.68     0.46)  ; 7\r\n                (  308.55    83.34     3.00     0.46)  ; 8\r\n                (  303.08    88.64     1.38     0.46)  ; 9\r\n                (  295.57   106.58    -9.93     0.46)  ; 10\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  376.41   -21.26    13.27     0.46)  ; 1\r\n                (  365.89   -10.58     9.02     0.46)  ; 2\r\n                (  363.32    -7.61    10.10     0.46)  ; 3\r\n                (  347.68    10.83    14.40     0.46)  ; 4\r\n                (  343.67    15.86    14.67     0.46)  ; 5\r\n                (  328.74    43.41    12.13     0.46)  ; 6\r\n                (  315.22    67.00     9.35     0.46)  ; 7\r\n                (  298.33    98.86    -3.80     0.46)  ; 8\r\n              )  ;  End of markers\r\n              (\r\n                (  292.12   109.25   -10.60     0.46)  ; 1, R-2-1-1-2-2-1-1\r\n                (  292.12   109.25   -10.63     0.46)  ; 2\r\n                (  290.69   111.29   -12.22     0.46)  ; 3\r\n                (  290.69   111.29   -12.25     0.46)  ; 4\r\n                (  289.28   113.35   -13.72     0.46)  ; 5\r\n                (  289.28   113.35   -13.75     0.46)  ; 6\r\n                (  288.30   115.52   -15.45     0.46)  ; 7\r\n                (  287.14   116.43   -17.35     0.46)  ; 8\r\n                (  287.06   118.80   -18.82     0.46)  ; 9\r\n                (  285.50   121.42   -20.23     0.46)  ; 10\r\n                (  285.73   124.46   -21.45     0.46)  ; 11\r\n                (  283.14   127.44   -21.92     0.46)  ; 12\r\n                (  280.97   128.72   -22.85     0.46)  ; 13\r\n                (  280.97   128.72   -22.88     0.46)  ; 14\r\n                (  278.69   132.36   -24.20     0.46)  ; 15\r\n                (  278.69   132.36   -24.22     0.46)  ; 16\r\n                (  276.42   136.01   -24.70     0.46)  ; 17\r\n                (  276.42   136.01   -24.72     0.46)  ; 18\r\n                (  275.58   137.60   -25.95     0.46)  ; 19\r\n                (  273.57   140.12   -27.32     0.46)  ; 20\r\n                (  273.57   140.12   -27.35     0.46)  ; 21\r\n                (  271.84   141.50   -28.55     0.46)  ; 22\r\n                (  271.71   142.08   -28.55     0.46)  ; 23\r\n                (  270.29   144.12   -29.70     0.46)  ; 24\r\n                (  270.29   144.12   -29.72     0.46)  ; 25\r\n                (  266.10   147.92   -30.38     0.46)  ; 26\r\n                (  264.80   149.42   -31.88     0.46)  ; 27\r\n                (  264.80   149.42   -31.90     0.46)  ; 28\r\n                (  263.70   152.13   -33.57     0.46)  ; 29\r\n                (  261.68   154.65   -34.28     0.46)  ; 30\r\n                (  259.95   156.04   -35.13     0.46)  ; 31\r\n                (  259.51   155.94   -35.15     0.46)  ; 32\r\n                (  256.35   159.37   -36.20     0.46)  ; 33\r\n                (  256.27   161.75   -37.32     0.46)  ; 34\r\n                (  256.27   161.75   -37.35     0.46)  ; 35\r\n                (  255.02   165.04   -38.27     0.46)  ; 36\r\n                (  255.02   165.04   -38.30     0.46)  ; 37\r\n                (  251.15   169.50   -39.25     0.46)  ; 38\r\n                (  249.01   172.58   -38.20     0.46)  ; 39\r\n                (  245.71   176.59   -36.65     0.46)  ; 40\r\n                (  242.37   178.79   -35.27     0.46)  ; 41\r\n                (  237.82   180.11   -35.13     0.46)  ; 42\r\n                (  235.82   182.63   -36.63     0.46)  ; 43\r\n                (  234.39   184.69   -35.80     0.46)  ; 44\r\n                (  234.39   184.69   -35.88     0.46)  ; 45\r\n                (  231.19   186.32   -37.17     0.46)  ; 46\r\n                (  230.74   186.22   -37.17     0.46)  ; 47\r\n                (  227.39   188.42   -38.92     0.46)  ; 48\r\n                (  227.39   188.42   -38.95     0.46)  ; 49\r\n                (  225.52   190.36   -40.88     0.46)  ; 50\r\n                (  225.52   190.36   -40.90     0.46)  ; 51\r\n                (  223.03   190.98   -42.45     0.46)  ; 52\r\n                (  219.68   193.17   -43.65     0.46)  ; 53\r\n                (  218.04   198.17   -44.65     0.46)  ; 54\r\n                (  214.43   201.50   -44.70     0.46)  ; 55\r\n                (  212.25   202.78   -44.70     0.46)  ; 56\r\n                (  209.36   205.09   -45.50     0.46)  ; 57\r\n                (  207.66   208.28   -46.20     0.46)  ; 58\r\n                (  204.94   211.83   -46.32     0.46)  ; 59\r\n                (  201.16   213.92   -47.88     0.46)  ; 60\r\n                (  201.16   213.92   -47.90     0.46)  ; 61\r\n                (  198.85   215.76   -48.95     0.46)  ; 62\r\n                (  195.23   219.10   -49.65     0.46)  ; 63\r\n                (  191.04   222.89   -50.27     0.46)  ; 64\r\n                (  187.84   224.53   -51.25     0.46)  ; 65\r\n                (  185.52   226.38   -52.22     0.46)  ; 66\r\n                (  182.62   228.68   -52.22     0.46)  ; 67\r\n                (  179.86   230.42   -53.42     0.46)  ; 68\r\n                (  177.14   233.96   -53.92     0.46)  ; 69\r\n                (  175.59   236.59   -54.32     0.46)  ; 70\r\n                (  174.08   241.01   -55.85     0.46)  ; 71\r\n                (  171.94   244.09   -57.42     0.46)  ; 72\r\n                (  169.81   247.18   -58.40     0.46)  ; 73\r\n                (  168.75   251.71   -59.45     0.46)  ; 74\r\n                (  168.75   251.71   -59.47     0.46)  ; 75\r\n                (  168.53   254.65   -61.40     0.46)  ; 76\r\n                (  167.15   258.51   -62.55     0.46)  ; 77\r\n                (  167.15   258.51   -62.57     0.46)  ; 78\r\n                (  165.96   263.59   -63.02     0.46)  ; 79\r\n                (  163.47   270.17   -63.02     0.46)  ; 80\r\n                (  160.62   274.29   -63.13     0.46)  ; 81\r\n                (  157.52   279.53   -63.13     0.46)  ; 82\r\n                (  156.54   281.69   -61.65     0.46)  ; 83\r\n                (  156.09   281.59   -61.65     0.46)  ; 84\r\n                (  154.10   284.10   -58.07     0.46)  ; 85\r\n                (  151.63   286.52   -56.60     0.46)  ; 86\r\n                (  149.32   288.35   -55.32     0.46)  ; 87\r\n                (  149.32   288.35   -55.35     0.46)  ; 88\r\n                (  146.92   292.58   -54.40     0.46)  ; 89\r\n                (  146.92   292.58   -54.05     0.46)  ; 90\r\n                (  144.08   296.68   -53.62     0.46)  ; 91\r\n                (  141.86   302.13   -53.05     0.46)  ; 92\r\n                (  139.13   305.67   -52.50     0.46)  ; 93\r\n                (  136.64   306.18   -51.32     0.46)  ; 94\r\n                (  133.08   311.32   -50.92     0.46)  ; 95\r\n                (  131.53   313.95   -49.72     0.46)  ; 96\r\n                (  129.92   314.76   -48.15     0.46)  ; 97\r\n                (  128.37   317.39   -47.17     0.46)  ; 98\r\n                (  125.97   321.60   -46.25     0.46)  ; 99\r\n                (  122.93   324.47   -45.45     0.46)  ; 100\r\n                (  122.93   324.47   -45.50     0.46)  ; 101\r\n                (  121.06   326.42   -44.30     0.46)  ; 102\r\n                (  118.74   328.27   -43.88     0.46)  ; 103\r\n                (  118.74   328.27   -43.90     0.46)  ; 104\r\n                (  116.88   330.22   -42.72     0.46)  ; 105\r\n                (  115.01   332.16   -41.50     0.46)  ; 106\r\n                (  111.66   334.36   -40.32     0.46)  ; 107\r\n                (  109.79   336.32   -39.17     0.46)  ; 108\r\n                (  108.50   337.80   -37.15     0.46)  ; 109\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  288.38   113.14   -13.75     0.46)  ; 1\r\n                  (  198.09   214.99   -48.95     0.46)  ; 2\r\n                  (  183.02   226.98   -52.22     0.46)  ; 3\r\n                  (  119.01   327.13   -43.90     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  286.48   119.25   -18.82     0.46)  ; 1\r\n                  (  246.29   176.13   -36.65     0.46)  ; 2\r\n                  (  219.68   193.17   -43.65     0.46)  ; 3\r\n                  (  176.04   236.70   -54.32     0.46)  ; 4\r\n                  (  157.78   278.39   -63.13     0.46)  ; 5\r\n                  (  139.13   305.67   -52.50     0.46)  ; 6\r\n                  (  131.66   313.37   -49.72     0.46)  ; 7\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  292.93   108.96   -12.15     0.46)  ; 1, R-2-1-1-2-2-1-2\r\n                (  291.82   111.69   -12.67     0.46)  ; 2\r\n                (  292.20   114.16   -13.72     0.46)  ; 3\r\n                (  291.92   115.30   -15.63     0.46)  ; 4\r\n                (  292.47   119.00   -17.82     0.46)  ; 5\r\n                (  294.04   122.36   -20.07     0.46)  ; 6\r\n                (  294.04   122.36   -20.13     0.46)  ; 7\r\n                (  295.11   123.80   -22.55     0.46)  ; 8\r\n                (  295.11   123.80   -22.57     0.46)  ; 9\r\n                (  295.29   125.04   -24.22     0.46)  ; 10\r\n                (  295.29   125.04   -24.25     0.46)  ; 11\r\n                (  295.47   126.27   -25.67     0.46)  ; 12\r\n                (  293.43   126.99   -27.12     0.46)  ; 13\r\n                (  291.42   129.50   -31.72     0.46)  ; 14\r\n                (  291.42   129.50   -31.75     0.46)  ; 15\r\n                (  289.86   132.13   -33.38     0.46)  ; 16\r\n                (  289.07   135.53   -36.72     0.46)  ; 17\r\n                (  286.62   137.93   -37.60     0.46)  ; 18\r\n                (  284.29   139.78   -39.75     0.46)  ; 19\r\n                (  284.29   139.78   -39.80     0.46)  ; 20\r\n                (  282.56   141.16   -41.97     0.46)  ; 21\r\n                (  282.56   141.16   -42.05     0.46)  ; 22\r\n                (  282.35   144.10   -43.55     0.46)  ; 23\r\n                (  282.35   144.10   -43.58     0.46)  ; 24\r\n                (  280.47   146.04   -45.72     0.46)  ; 25\r\n                (  279.68   149.44   -46.82     0.46)  ; 26\r\n                (  279.68   149.44   -46.85     0.46)  ; 27\r\n                (  278.57   152.17   -48.25     0.46)  ; 28\r\n                (  278.57   152.17   -48.30     0.46)  ; 29\r\n                (  277.72   153.76   -50.38     0.46)  ; 30\r\n                (  276.74   155.92   -52.30     0.46)  ; 31\r\n                (  276.74   155.92   -52.35     0.46)  ; 32\r\n                (  276.08   158.76   -54.52     0.46)  ; 33\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  292.40   111.22   -12.67     0.46)  ; 1\r\n                  (  295.29   125.04   -24.25     0.46)  ; 2\r\n                  (  289.20   134.95   -36.72     0.46)  ; 3\r\n                  (  275.63   158.65   -54.52     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  276.37   158.19    -7.22     0.46)  ; 1\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  390.27   -32.93    21.42     0.46)  ; 1, R-2-1-1-2-2-2\r\n              (  390.27   -32.93    21.40     0.46)  ; 2\r\n              (  392.87   -29.93    21.40     0.46)  ; 3\r\n              (  395.99   -29.20    22.70     0.46)  ; 4\r\n              (  398.67   -28.58    23.88     0.46)  ; 5\r\n              (  400.32   -27.59    25.15     0.46)  ; 6\r\n              (  402.74   -25.83    25.83     0.46)  ; 7\r\n              (  406.62   -24.32    25.83     0.46)  ; 8\r\n              (  407.39   -23.54    26.70     0.46)  ; 9\r\n              (  409.62   -23.02    27.45     0.46)  ; 10\r\n              (  410.95   -22.71    29.15     0.46)  ; 11\r\n              (  410.95   -22.71    29.10     0.46)  ; 12\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  409.89   -24.16    29.10     0.46)  ; 1\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  396.44   -29.09    22.70     0.46)  ; 1\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (  321.59   -67.56    11.70     0.46)  ; 1, R-2-1-2\r\n        (  320.79   -64.16    10.60     0.46)  ; 2\r\n        (  320.79   -64.16    10.57     0.46)  ; 3\r\n        (  320.71   -61.79     9.82     0.46)  ; 4\r\n        (  320.71   -61.79     9.80     0.46)  ; 5\r\n        (  321.39   -58.64     9.32     0.46)  ; 6\r\n        (  321.44   -56.84     8.05     0.46)  ; 7\r\n        (  321.04   -55.15     6.78     0.46)  ; 8\r\n        (  320.19   -53.55     5.97     0.46)  ; 9\r\n        (  320.19   -53.55     5.95     0.46)  ; 10\r\n        (  320.24   -51.75     5.15     0.46)  ; 11\r\n        (  320.24   -51.75     5.13     0.46)  ; 12\r\n        (  318.95   -50.25     4.60     0.46)  ; 13\r\n        (  318.95   -50.25     4.57     0.46)  ; 14\r\n        (  318.16   -46.87     4.72     0.46)  ; 15\r\n        (  316.60   -44.25     4.92     0.46)  ; 16\r\n        (  316.33   -43.11     4.92     0.46)  ; 17\r\n        (  317.14   -40.53     4.47     0.46)  ; 18\r\n\r\n        (Dot\r\n          (Color Yellow)\r\n          (Name \"Marker 1\")\r\n          (  319.43   -60.30     9.32     0.46)  ; 1\r\n          (  317.66   -48.77     4.72     0.46)  ; 2\r\n        )  ;  End of markers\r\n\r\n        (OpenCircle\r\n          (Color Yellow)\r\n          (Name \"Marker 2\")\r\n          (  320.48   -48.71     4.00     0.46)  ; 1\r\n        )  ;  End of markers\r\n        (\r\n          (  316.35   -37.13     3.92     0.46)  ; 1, R-2-1-2-1\r\n          (  316.45   -33.52     3.02     0.46)  ; 2\r\n          (  314.89   -30.91     1.70     0.46)  ; 3\r\n          (  314.89   -30.91     1.67     0.46)  ; 4\r\n          (  316.41   -29.36     0.32     0.46)  ; 5\r\n          (  316.41   -29.36     0.28     0.46)  ; 6\r\n          (  316.33   -26.99    -0.52     0.46)  ; 7\r\n          (  314.46   -25.03    -1.55     0.46)  ; 8\r\n          (  315.53   -23.59    -3.10     0.46)  ; 9\r\n          (  315.45   -21.21    -4.05     0.46)  ; 10\r\n          (  315.68   -18.18    -4.68     0.46)  ; 11\r\n          (  315.33   -14.68    -5.22     0.46)  ; 12\r\n          (  316.26   -12.67    -6.38     0.46)  ; 13\r\n          (  316.13   -12.10    -7.70     0.46)  ; 14\r\n          (  314.98   -11.18    -8.95     0.46)  ; 15\r\n          (  314.76    -8.24   -10.22     0.46)  ; 16\r\n          (  314.81    -6.44    -9.77     0.46)  ; 17\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  317.37   -37.49     3.92     0.46)  ; 1\r\n            (  315.58   -27.77    -0.52     0.46)  ; 2\r\n            (  316.74   -22.72    -4.05     0.46)  ; 3\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  321.45   -28.77    -0.52     0.46)  ; 1\r\n            (  316.94    -9.53   -10.22     0.46)  ; 2\r\n          )  ;  End of markers\r\n          (\r\n            (  315.62    -3.86   -11.70     0.46)  ; 1, R-2-1-2-1-1\r\n            (  316.97    -3.55   -12.98     0.46)  ; 2\r\n            (  315.86    -0.83   -13.85     0.46)  ; 3\r\n            (  315.86    -0.83   -13.87     0.46)  ; 4\r\n            (  316.21     1.65   -14.40     0.46)  ; 5\r\n            (  316.49     6.49   -14.82     0.46)  ; 6\r\n            (  314.93     9.11   -14.65     0.46)  ; 7\r\n            (  314.14    12.51   -15.40     0.46)  ; 8\r\n            (  314.14    12.51   -15.43     0.46)  ; 9\r\n            (  314.50    14.98   -16.90     0.46)  ; 10\r\n            (  314.73    18.02   -18.48     0.46)  ; 11\r\n            (  314.73    18.02   -18.50     0.46)  ; 12\r\n            (  316.69    19.67   -20.03     0.46)  ; 13\r\n            (  318.14    23.60   -20.97     0.46)  ; 14\r\n            (  318.95    26.17   -21.77     0.46)  ; 15\r\n            (  320.52    29.53   -22.65     0.46)  ; 16\r\n            (  321.10    35.03   -23.30     0.46)  ; 17\r\n            (  321.10    35.03   -23.33     0.46)  ; 18\r\n            (  320.76    38.54   -23.67     0.46)  ; 19\r\n            (  321.48    43.48   -24.45     0.46)  ; 20\r\n            (  321.13    46.99   -25.00     0.46)  ; 21\r\n            (  321.86    51.93   -25.67     0.46)  ; 22\r\n            (  322.27    56.21   -26.42     0.46)  ; 23\r\n            (  321.69    56.67   -27.97     0.46)  ; 24\r\n            (  321.42    57.81   -28.82     0.46)  ; 25\r\n            (  321.42    57.81   -28.85     0.46)  ; 26\r\n            (  322.10    60.95   -30.30     0.46)  ; 27\r\n            (  323.59    66.67   -31.40     0.46)  ; 28\r\n            (  324.71    69.92   -31.40     0.46)  ; 29\r\n            (  325.52    72.50   -31.40     0.46)  ; 30\r\n            (  326.91    74.62   -31.88     0.46)  ; 31\r\n            (  326.74    79.35   -32.50     0.46)  ; 32\r\n            (  327.02    84.20   -33.03     0.46)  ; 33\r\n            (  327.12    87.80   -34.20     0.46)  ; 34\r\n            (  328.06    89.82   -35.13     0.46)  ; 35\r\n            (  327.40    92.65   -35.20     0.46)  ; 36\r\n            (  327.94    96.35   -35.65     0.46)  ; 37\r\n            (  328.39   100.54   -35.42     0.46)  ; 38\r\n            (  328.45   102.34   -36.85     0.46)  ; 39\r\n            (  328.22   105.27   -37.60     0.46)  ; 40\r\n            (  328.22   105.27   -37.63     0.46)  ; 41\r\n            (  329.80   108.63   -38.67     0.46)  ; 42\r\n            (  329.27   110.88   -38.15     0.46)  ; 43\r\n            (  329.54   115.73   -38.15     0.46)  ; 44\r\n            (  329.63   119.33   -38.15     0.46)  ; 45\r\n            (  332.10   122.90   -37.17     0.46)  ; 46\r\n            (  332.51   127.18   -37.10     0.46)  ; 47\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  314.69     0.10   -14.40     0.46)  ; 1\r\n              (  322.74    46.18   -25.00     0.46)  ; 2\r\n              (  333.13   122.55   -37.17     0.46)  ; 3\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  315.38     9.21   -14.65     0.46)  ; 1\r\n              (  313.65    10.60   -17.05     0.46)  ; 2\r\n              (  314.27    11.94   -15.27     0.46)  ; 3\r\n              (  318.28    23.03   -20.97     0.46)  ; 4\r\n              (  322.10    60.95   -30.30     0.46)  ; 5\r\n              (  326.91    74.62   -31.88     0.46)  ; 6\r\n              (  328.07    95.79   -35.67     0.46)  ; 7\r\n            )  ;  End of markers\r\n            (\r\n              (  334.92   128.94   -35.83     0.46)  ; 1, R-2-1-2-1-1-1\r\n              (  334.92   128.94   -35.88     0.46)  ; 2\r\n              (  338.82   130.44   -34.78     0.46)  ; 3\r\n              (  338.82   130.44   -34.80     0.46)  ; 4\r\n              (  342.20   130.04   -33.92     0.46)  ; 5\r\n              (  344.49   132.37   -33.10     0.46)  ; 6\r\n              (  348.06   133.21   -32.42     0.46)  ; 7\r\n              (  350.33   135.54   -31.00     0.46)  ; 8\r\n              (  352.27   135.39   -30.65     0.46)  ; 9\r\n              (  354.36   136.48   -29.97     0.46)  ; 10\r\n              (  357.66   138.45   -28.77     0.46)  ; 11\r\n              (  360.35   139.07   -28.77     0.46)  ; 12\r\n              (  363.21   140.93   -28.77     0.46)  ; 13\r\n              (  364.86   141.92   -28.77     0.46)  ; 14\r\n              (  367.54   142.54   -28.77     0.46)  ; 15\r\n              (  370.09   143.75   -28.05     0.46)  ; 16\r\n              (  373.65   144.58   -27.22     0.46)  ; 17\r\n              (  377.28   147.23   -26.88     0.46)  ; 18\r\n              (  379.97   147.85   -26.07     0.46)  ; 19\r\n              (  381.49   149.41   -25.05     0.46)  ; 20\r\n              (  383.01   150.96   -24.20     0.46)  ; 21\r\n              (  385.42   152.72   -22.67     0.46)  ; 22\r\n              (  385.42   152.72   -22.70     0.46)  ; 23\r\n              (  388.29   154.58   -22.47     0.46)  ; 24\r\n              (  390.38   155.67   -22.47     0.46)  ; 25\r\n              (  391.14   156.44   -22.47     0.46)  ; 26\r\n              (  395.75   156.92   -22.47     0.46)  ; 27\r\n              (  398.60   158.79   -23.57     0.46)  ; 28\r\n              (  402.44   158.50   -23.67     0.46)  ; 29\r\n              (  406.02   159.33   -23.72     0.46)  ; 30\r\n              (  408.69   159.96   -22.12     0.46)  ; 31\r\n              (  411.25   161.15   -20.60     0.46)  ; 32\r\n              (  414.77   160.19   -19.92     0.46)  ; 33\r\n              (  418.92   160.57   -20.27     0.46)  ; 34\r\n              (  422.63   160.84   -20.27     0.46)  ; 35\r\n              (  425.75   161.57   -20.27     0.46)  ; 36\r\n              (  428.44   162.20   -19.52     0.46)  ; 37\r\n              (  432.90   163.24   -19.52     0.46)  ; 38\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  341.49   131.07   -33.92     0.46)  ; 1\r\n                (  351.05   134.51   -31.00     0.46)  ; 2\r\n                (  405.70   158.66   -23.72     0.46)  ; 3\r\n                (  417.40   159.01   -20.27     0.46)  ; 4\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  354.23   137.04   -29.97     0.46)  ; 1\r\n                (  385.29   153.28   -22.70     0.46)  ; 2\r\n                (  383.01   150.96   -22.70     0.46)  ; 3\r\n                (  398.47   159.36   -23.57     0.46)  ; 4\r\n                (  402.89   158.60   -23.67     0.46)  ; 5\r\n                (  411.12   161.72   -20.60     0.46)  ; 6\r\n                (  428.44   162.20   -19.52     0.46)  ; 7\r\n              )  ;  End of markers\r\n              (\r\n                (  433.75   161.65   -19.67     0.46)  ; 1, R-2-1-2-1-1-1-1\r\n                (  435.29   159.02   -20.10     0.46)  ; 2\r\n                (  435.29   159.02   -20.13     0.46)  ; 3\r\n                (  436.27   156.87   -20.55     0.46)  ; 4\r\n                (  436.94   154.04   -21.50     0.46)  ; 5\r\n                (  434.84   152.95   -22.92     0.46)  ; 6\r\n                (  431.59   152.79   -23.35     0.46)  ; 7\r\n                (  428.01   151.94   -24.22     0.46)  ; 8\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  436.46   158.10   -20.55     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  436.22   155.07   -21.50     0.46)  ; 1\r\n                  (  428.46   152.05   -24.22     0.46)  ; 2\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  434.87   164.90   -19.52     0.46)  ; 1, R-2-1-2-1-1-1-2\r\n                (  435.50   166.24   -19.52     0.46)  ; 2\r\n                (  437.55   165.53   -18.67     0.46)  ; 3\r\n                (  439.65   166.61   -18.05     0.46)  ; 4\r\n                (  441.69   165.91   -16.95     0.46)  ; 5\r\n                (  443.93   166.43   -15.88     0.46)  ; 6\r\n                (  447.63   166.70   -15.88     0.46)  ; 7\r\n                (  451.04   166.30   -16.98     0.46)  ; 8\r\n                (  452.95   166.15   -18.08     0.46)  ; 9\r\n                (  454.47   167.70   -18.50     0.46)  ; 10\r\n                (  457.73   167.87   -19.70     0.46)  ; 11\r\n                (  460.41   168.50   -20.42     0.46)  ; 12\r\n                (  462.77   168.45   -21.75     0.46)  ; 13\r\n                (  462.77   168.45   -22.22     0.46)  ; 14\r\n                (  466.16   168.05   -22.92     0.46)  ; 15\r\n                (  469.29   168.79   -23.60     0.46)  ; 16\r\n                (  469.29   168.79   -23.63     0.46)  ; 17\r\n                (  472.86   169.62   -24.27     0.46)  ; 18\r\n                (  477.60   169.54   -24.52     0.46)  ; 19\r\n                (  478.88   169.24   -23.23     0.46)  ; 20\r\n                (  481.13   169.76   -21.82     0.46)  ; 21\r\n                (  481.13   169.76   -21.80     0.46)  ; 22\r\n                (  482.33   170.65   -20.55     0.46)  ; 23\r\n                (  481.88   170.55   -20.55     0.46)  ; 24\r\n                (  484.96   169.48   -20.10     0.46)  ; 25\r\n                (  487.51   170.67   -19.50     0.46)  ; 26\r\n                (  489.29   171.08   -18.77     0.46)  ; 27\r\n                (  491.80   170.48   -18.33     0.46)  ; 28\r\n                (  496.26   171.53   -17.70     0.46)  ; 29\r\n                (  499.83   172.36   -17.70     0.46)  ; 30\r\n                (  503.40   173.20   -17.70     0.46)  ; 31\r\n                (  506.49   172.13   -17.72     0.46)  ; 32\r\n                (  509.43   171.62   -18.82     0.46)  ; 33\r\n                (  510.59   170.70   -19.05     0.46)  ; 34\r\n                (  510.59   170.70   -19.15     0.46)  ; 35\r\n                (  512.24   171.69   -20.20     0.46)  ; 36\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  508.67   170.84   -18.82     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  439.20   166.52   -18.05     0.46)  ; 1\r\n                  (  443.93   166.43   -15.88     0.46)  ; 2\r\n                  (  457.28   167.76   -19.70     0.46)  ; 3\r\n                  (  477.15   169.43   -24.60     0.46)  ; 4\r\n                  (  479.79   169.44   -21.80     0.46)  ; 5\r\n                  (  491.80   170.48   -18.33     0.46)  ; 6\r\n                  (  499.52   171.70   -17.70     0.46)  ; 7\r\n                  (  512.24   171.69   -20.20     0.46)  ; 8\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  332.11   128.87   -38.33     0.46)  ; 1, R-2-1-2-1-1-2\r\n              (  331.58   131.14   -39.52     0.46)  ; 2\r\n              (  332.12   134.85   -40.40     0.46)  ; 3\r\n              (  332.92   137.43   -40.90     0.46)  ; 4\r\n              (  333.56   138.76   -41.97     0.46)  ; 5\r\n              (  333.79   141.81   -42.75     0.46)  ; 6\r\n              (  334.02   144.85   -43.38     0.46)  ; 7\r\n              (  334.02   144.85   -43.40     0.46)  ; 8\r\n              (  334.11   148.45   -44.07     0.46)  ; 9\r\n              (  334.03   150.82   -44.75     0.46)  ; 10\r\n              (  334.21   152.05   -45.52     0.46)  ; 11\r\n              (  332.97   155.35   -45.90     0.46)  ; 12\r\n              (  333.83   159.72   -46.08     0.46)  ; 13\r\n              (  333.48   163.23   -46.77     0.46)  ; 14\r\n              (  332.82   166.07   -47.22     0.46)  ; 15\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  334.44   155.09   -45.90     0.46)  ; 1\r\n                (  334.72   159.93   -46.08     0.46)  ; 2\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  329.03   129.95   -39.52     0.46)  ; 1\r\n                (  331.84   130.00   -39.52     0.46)  ; 2\r\n              )  ;  End of markers\r\n              (\r\n                (  330.50   167.91   -47.22     0.46)  ; 1, R-2-1-2-1-1-2-1\r\n                (  327.56   168.41   -46.65     0.46)  ; 2\r\n                (  324.48   169.49   -46.10     0.46)  ; 3\r\n                (  322.16   171.33   -45.25     0.46)  ; 4\r\n                (  320.43   172.72   -44.23     0.46)  ; 5\r\n                (  320.43   172.72   -44.25     0.46)  ; 6\r\n                (  318.69   174.10   -43.50     0.46)  ; 7\r\n                (  316.68   176.61   -42.37     0.46)  ; 8\r\n                (  314.94   178.00   -41.10     0.46)  ; 9\r\n                (  310.89   181.23   -41.10     0.46)  ; 10\r\n                (  306.26   184.92   -40.75     0.46)  ; 11\r\n                (  304.65   185.73   -42.88     0.46)  ; 12\r\n                (  301.62   188.61   -43.58     0.46)  ; 13\r\n                (  300.91   189.64   -44.70     0.46)  ; 14\r\n                (  300.91   189.64   -44.72     0.46)  ; 15\r\n                (  298.72   190.92   -46.05     0.46)  ; 16\r\n                (  298.72   190.92   -46.08     0.46)  ; 17\r\n                (  297.04   194.10   -47.00     0.46)  ; 18\r\n                (  293.15   198.58   -47.55     0.46)  ; 19\r\n                (  293.15   198.58   -47.57     0.46)  ; 20\r\n                (  289.41   202.47   -47.35     0.46)  ; 21\r\n                (  285.00   203.23   -46.42     0.46)  ; 22\r\n                (  281.47   204.19   -47.37     0.46)  ; 23\r\n                (  281.47   204.19   -47.40     0.46)  ; 24\r\n                (  277.92   209.33   -46.88     0.46)  ; 25\r\n                (  273.29   213.02   -46.28     0.46)  ; 26\r\n                (  273.29   213.02   -46.30     0.46)  ; 27\r\n                (  270.72   215.86   -45.92     0.46)  ; 28\r\n                (  270.50   218.80   -44.45     0.46)  ; 29\r\n                (  270.05   218.69   -44.20     0.46)  ; 30\r\n                (  267.88   219.98   -43.35     0.46)  ; 31\r\n                (  267.43   219.87   -43.35     0.46)  ; 32\r\n                (  265.82   220.69   -41.97     0.46)  ; 33\r\n                (  263.50   222.53   -40.57     0.46)  ; 34\r\n                (  263.50   222.53   -40.60     0.46)  ; 35\r\n                (  262.97   224.79   -39.70     0.46)  ; 36\r\n                (  262.90   227.16   -38.55     0.46)  ; 37\r\n                (  261.79   229.89   -37.17     0.46)  ; 38\r\n                (  261.31   233.96   -36.22     0.46)  ; 39\r\n                (  259.80   238.38   -35.42     0.46)  ; 40\r\n                (  259.80   238.38   -35.45     0.46)  ; 41\r\n                (  259.97   239.63   -34.22     0.46)  ; 42\r\n                (  259.63   243.11   -33.25     0.46)  ; 43\r\n                (  258.20   245.17   -32.42     0.46)  ; 44\r\n                (  256.50   248.36   -31.85     0.46)  ; 45\r\n                (  252.64   252.83   -31.85     0.46)  ; 46\r\n                (  251.39   256.13   -31.60     0.46)  ; 47\r\n                (  250.15   259.42   -31.60     0.46)  ; 48\r\n                (  247.97   260.69   -31.05     0.46)  ; 49\r\n                (  246.28   263.88   -30.05     0.46)  ; 50\r\n                (  243.68   266.86   -30.05     0.46)  ; 51\r\n                (  240.98   270.40   -30.05     0.46)  ; 52\r\n                (  238.71   274.05   -30.05     0.46)  ; 53\r\n                (  235.73   278.73   -30.05     0.46)  ; 54\r\n                (  231.86   283.19   -30.85     0.46)  ; 55\r\n                (  229.14   286.73   -30.85     0.46)  ; 56\r\n                (  229.00   287.30   -30.85     0.46)  ; 57\r\n                (  227.31   290.49   -30.77     0.46)  ; 58\r\n                (  224.56   292.24   -30.77     0.46)  ; 59\r\n                (  224.02   294.49   -30.77     0.46)  ; 60\r\n                (  220.47   299.63   -30.85     0.46)  ; 61\r\n                (  218.02   302.04   -30.02     0.46)  ; 62\r\n                (  214.99   304.91   -30.02     0.46)  ; 63\r\n                (  212.14   309.02   -29.80     0.46)  ; 64\r\n                (  210.06   313.91   -29.85     0.46)  ; 65\r\n                (  208.23   317.66   -29.25     0.46)  ; 66\r\n                (  205.65   320.64   -27.95     0.46)  ; 67\r\n                (  203.19   323.06   -26.75     0.46)  ; 68\r\n                (  202.13   327.59   -25.63     0.46)  ; 69\r\n                (  201.20   331.55   -25.63     0.46)  ; 70\r\n                (  198.35   335.65   -25.63     0.46)  ; 71\r\n                (  196.22   338.74   -25.63     0.46)  ; 72\r\n                (  194.27   343.06   -25.63     0.46)  ; 73\r\n                (  194.05   345.99   -25.63     0.46)  ; 74\r\n                (  190.71   348.20   -25.63     0.46)  ; 75\r\n                (  189.34   352.05   -25.63     0.46)  ; 76\r\n                (  189.30   356.22   -26.32     0.46)  ; 77\r\n                (  187.57   357.60   -27.05     0.46)  ; 78\r\n                (  185.36   358.25   -27.05     0.46)  ; 79\r\n                (\r\n                  (  185.87   360.79   -27.63     0.46)  ; 1, R-2-1-2-1-1-2-1-1\r\n                  (  185.08   364.19   -27.20     0.46)  ; 2\r\n                  (  184.28   367.59   -26.75     0.46)  ; 3\r\n                  (  183.09   372.68   -26.75     0.46)  ; 4\r\n                  (  183.00   375.06   -26.75     0.46)  ; 5\r\n                  (  182.92   377.42   -26.75     0.46)  ; 6\r\n                  (  181.99   381.38   -27.32     0.46)  ; 7\r\n                  (  180.43   384.00   -25.92     0.46)  ; 8\r\n                  (  180.80   386.48   -24.75     0.46)  ; 9\r\n                  (  179.69   389.20   -24.15     0.46)  ; 10\r\n                  (  180.21   391.00   -22.57     0.46)  ; 11\r\n                  (  180.26   392.80   -21.00     0.46)  ; 12\r\n                  (  180.30   394.60   -19.58     0.46)  ; 13\r\n                  (  180.53   397.65   -18.38     0.46)  ; 14\r\n                  (  178.98   400.27   -17.17     0.46)  ; 15\r\n                  (  177.87   402.99   -16.20     0.46)  ; 16\r\n                  (  177.52   406.49   -15.35     0.46)  ; 17\r\n                  (  176.85   409.32   -14.82     0.46)  ; 18\r\n                  (  176.90   411.12   -13.57     0.46)  ; 19\r\n                  (  177.58   414.27   -13.07     0.46)  ; 20\r\n                  (  177.23   417.76   -12.10     0.46)  ; 21\r\n                  (  176.52   418.79   -10.73     0.46)  ; 22\r\n                  (  176.31   421.73    -9.43     0.46)  ; 23\r\n                  (  175.64   424.56    -8.82     0.46)  ; 24\r\n                  (  175.64   424.56    -8.80     0.46)  ; 25\r\n                  (  175.11   426.83    -8.40     0.46)  ; 26\r\n                  (  173.69   428.87    -7.35     0.46)  ; 27\r\n                  (  173.69   428.87    -7.38     0.46)  ; 28\r\n                  (  172.26   430.93    -6.73     0.46)  ; 29\r\n                  (  169.81   433.36    -5.75     0.46)  ; 30\r\n                  (  168.38   435.40    -4.63     0.46)  ; 31\r\n                  (  166.25   438.49    -3.57     0.46)  ; 32\r\n                  (  166.25   438.49    -3.00     0.46)  ; 33\r\n                  (  164.56   441.68    -2.27     0.46)  ; 34\r\n                  (  163.72   443.27    -1.02     0.46)  ; 35\r\n                  (  163.00   444.29     0.57     0.46)  ; 36\r\n                  (  163.05   446.10     1.50     0.46)  ; 37\r\n                  (  162.66   447.79     2.60     0.46)  ; 38\r\n                  (  163.15   449.71     2.60     0.46)  ; 39\r\n                  (  162.62   451.96     3.72     0.46)  ; 40\r\n                  (  161.78   453.56     5.22     0.46)  ; 41\r\n                  (  161.25   455.82     6.02     0.46)  ; 42\r\n                  (  160.00   459.11     5.65     0.46)  ; 43\r\n                  (  159.65   462.61     7.35     0.46)  ; 44\r\n                  (  159.65   462.61     7.32     0.46)  ; 45\r\n                  (  158.41   465.91     8.77     0.46)  ; 46\r\n                  (  157.43   468.06    10.80     0.46)  ; 47\r\n                  (  157.35   470.43    11.93     0.46)  ; 48\r\n                  (  155.92   472.48    11.93     0.46)  ; 49\r\n                  (  154.36   475.10    12.80     0.46)  ; 50\r\n                  (  152.50   477.06    13.72     0.46)  ; 51\r\n                  (  152.47   481.24    13.82     0.46)  ; 52\r\n                  (  152.47   481.24    13.80     0.46)  ; 53\r\n                  (  150.60   483.18    15.07     0.46)  ; 54\r\n                  (  150.60   483.18    15.05     0.46)  ; 55\r\n                  (  150.20   484.89    16.32     0.46)  ; 56\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  306.66   183.22   -40.75     0.46)  ; 1\r\n                    (  263.12   230.20   -37.17     0.46)  ; 2\r\n                    (  258.28   242.81   -33.25     0.46)  ; 3\r\n                    (  191.65   350.20   -25.63     0.46)  ; 4\r\n                    (  186.02   366.21   -26.75     0.46)  ; 5\r\n                    (  163.80   440.90    -2.27     0.46)  ; 6\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  220.47   299.63   -30.85     0.46)  ; 1\r\n                    (  215.57   304.45   -30.02     0.46)  ; 2\r\n                    (  210.63   313.45   -29.85     0.46)  ; 3\r\n                    (  208.37   317.09   -29.25     0.46)  ; 4\r\n                    (  202.13   327.59   -25.63     0.46)  ; 5\r\n                    (  182.34   377.88   -26.75     0.46)  ; 6\r\n                    (  181.99   381.38   -27.32     0.46)  ; 7\r\n                    (  179.69   389.20   -24.15     0.46)  ; 8\r\n                    (  179.55   399.80   -17.15     0.46)  ; 9\r\n                    (  176.90   411.12   -13.57     0.46)  ; 10\r\n                    (  172.71   431.04    -6.73     0.46)  ; 11\r\n                    (  170.25   433.46    -5.07     0.46)  ; 12\r\n                    (  168.96   434.94    -4.10     0.46)  ; 13\r\n                    (  163.50   446.20     1.50     0.46)  ; 14\r\n                    (  157.88   468.17    10.80     0.46)  ; 15\r\n                    (  156.06   471.92    11.93     0.46)  ; 16\r\n                    (  152.95   477.17    13.72     0.46)  ; 17\r\n                    (  150.33   484.31    16.32     0.46)  ; 18\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  183.19   359.54   -24.92     0.46)  ; 1, R-2-1-2-1-1-2-1-2\r\n                  (  180.12   360.60   -22.97     0.46)  ; 2\r\n                  (  177.55   359.41   -21.20     0.46)  ; 3\r\n                  (  174.61   359.91   -19.40     0.46)  ; 4\r\n                  (  172.57   360.62   -17.47     0.46)  ; 5\r\n                  (  170.70   362.58   -15.27     0.46)  ; 6\r\n                  (  168.38   364.43   -15.02     0.46)  ; 7\r\n                  (  165.51   366.63   -13.47     0.46)  ; 8\r\n                  (  163.77   368.02   -12.27     0.46)  ; 9\r\n                  (  161.91   369.97   -10.95     0.46)  ; 10\r\n                  (  160.61   371.46    -9.60     0.46)  ; 11\r\n                  (  159.63   373.61    -7.87     0.46)  ; 12\r\n                  (  158.21   375.67    -6.10     0.46)  ; 13\r\n                  (  156.73   375.92    -4.85     0.46)  ; 14\r\n                  (  155.26   376.18    -3.62     0.46)  ; 15\r\n                  (  153.52   377.55    -2.55     0.46)  ; 16\r\n                  (  152.19   377.24    -1.10     0.46)  ; 17\r\n                  (  151.47   378.27     0.93     0.46)  ; 18\r\n                  (  150.18   379.76     2.40     0.46)  ; 19\r\n                  (  149.16   380.11     4.00     0.46)  ; 20\r\n                  (  147.10   380.84     5.10     0.46)  ; 21\r\n                  (  146.25   382.42     6.35     0.46)  ; 22\r\n                  (  146.76   384.33     7.57     0.46)  ; 23\r\n                  (  146.81   386.14     8.80     0.46)  ; 24\r\n                  (  145.97   387.73    10.55     0.46)  ; 25\r\n                  (  144.17   387.31    12.50     0.46)  ; 26\r\n                  (  144.17   387.31    12.47     0.46)  ; 27\r\n                  (  146.59   389.06    14.38     0.46)  ; 28\r\n                  (  146.33   390.20    16.15     0.46)  ; 29\r\n                  (  146.33   390.20    16.13     0.46)  ; 30\r\n                  (  144.90   392.25    17.42     0.46)  ; 31\r\n                  (  143.93   394.42    18.52     0.46)  ; 32\r\n                  (  142.10   398.16    19.45     0.46)  ; 33\r\n                  (  142.10   398.16    19.42     0.46)  ; 34\r\n                  (  140.23   400.11    20.17     0.46)  ; 35\r\n                  (  138.94   401.61    21.88     0.46)  ; 36\r\n\r\n                  (Dot\r\n                    (Color Cyan)\r\n                    (Name \"Marker 1\")\r\n                    (  171.46   363.35   -15.27     0.46)  ; 1\r\n                    (  154.94   375.50    -3.62     0.46)  ; 2\r\n                    (  148.98   378.88     4.00     0.46)  ; 3\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color RGB (255, 128, 255))\r\n                    (Name \"Marker 2\")\r\n                    (  168.38   364.43   -15.02     0.46)  ; 1\r\n                    (  161.91   369.97   -10.77     0.46)  ; 2\r\n                    (  159.63   373.61    -7.87     0.46)  ; 3\r\n                    (  146.45   383.66     7.57     0.46)  ; 4\r\n                    (  144.24   395.08    18.52     0.46)  ; 5\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (  333.75   168.06   -48.58     0.46)  ; 1, R-2-1-2-1-1-2-2\r\n                (  335.33   171.40   -49.83     0.46)  ; 2\r\n                (  334.53   174.80   -51.63     0.46)  ; 3\r\n                (  334.67   180.22   -51.63     0.46)  ; 4\r\n                (  335.84   185.27   -51.90     0.46)  ; 5\r\n                (  335.81   189.45   -52.00     0.46)  ; 6\r\n                (  335.64   194.18   -52.35     0.46)  ; 7\r\n                (  335.16   198.24   -52.95     0.46)  ; 8\r\n                (  335.16   198.24   -52.97     0.46)  ; 9\r\n                (  335.96   200.82   -53.42     0.46)  ; 10\r\n                (  335.48   204.88   -53.42     0.46)  ; 11\r\n                (  335.58   208.49   -53.33     0.46)  ; 12\r\n                (  336.39   211.08   -54.15     0.46)  ; 13\r\n                (  337.07   214.21   -54.15     0.46)  ; 14\r\n                (  337.75   217.36   -54.95     0.46)  ; 15\r\n                (  337.66   219.74   -56.20     0.46)  ; 16\r\n                (  337.89   222.77   -57.60     0.46)  ; 17\r\n                (  339.11   229.63   -57.92     0.46)  ; 18\r\n                (  339.07   233.80   -58.42     0.46)  ; 19\r\n                (  339.07   233.80   -58.45     0.46)  ; 20\r\n                (  338.99   236.16   -58.67     0.46)  ; 21\r\n                (  341.06   241.43   -59.17     0.46)  ; 22\r\n                (  342.18   244.68   -58.75     0.46)  ; 23\r\n                (  343.04   249.05   -58.60     0.46)  ; 24\r\n                (  343.14   252.66   -58.60     0.46)  ; 25\r\n                (  344.44   257.15   -59.80     0.46)  ; 26\r\n                (  344.80   259.63   -60.63     0.46)  ; 27\r\n                (  345.61   262.20   -61.63     0.46)  ; 28\r\n                (  345.61   262.20   -61.67     0.46)  ; 29\r\n                (  346.59   266.01   -62.20     0.46)  ; 30\r\n                (  347.59   269.83   -63.10     0.46)  ; 31\r\n                (  348.01   274.11   -63.95     0.46)  ; 32\r\n                (  349.12   277.35   -64.65     0.46)  ; 33\r\n                (  349.12   277.35   -64.67     0.46)  ; 34\r\n                (  349.84   282.31   -63.95     0.46)  ; 35\r\n                (  351.47   287.47   -64.53     0.46)  ; 36\r\n                (  350.73   292.66   -64.80     0.46)  ; 37\r\n                (  351.89   297.71   -62.80     0.46)  ; 38\r\n                (  351.89   297.71   -62.82     0.46)  ; 39\r\n                (  352.36   301.85   -59.70     0.46)  ; 40\r\n                (  351.88   305.92   -58.25     0.46)  ; 41\r\n                (  351.88   305.92   -58.27     0.46)  ; 42\r\n                (  352.28   310.19   -60.22     0.46)  ; 43\r\n                (  353.09   312.78   -62.37     0.46)  ; 44\r\n                (  353.09   312.78   -62.40     0.46)  ; 45\r\n                (  352.79   318.09   -63.70     0.46)  ; 46\r\n                (  352.79   318.09   -63.75     0.46)  ; 47\r\n                (  353.07   322.93   -65.32     0.46)  ; 48\r\n                (  353.07   322.93   -65.35     0.46)  ; 49\r\n                (  352.64   328.80   -65.45     0.46)  ; 50\r\n                (  353.49   333.18   -65.55     0.46)  ; 51\r\n                (  354.48   337.00   -65.55     0.46)  ; 52\r\n                (  353.64   338.59   -65.55     0.46)  ; 53\r\n                (  354.00   341.06   -66.50     0.46)  ; 54\r\n                (  355.00   344.87   -67.57     0.46)  ; 55\r\n                (  356.17   349.92   -68.42     0.46)  ; 56\r\n                (  356.39   352.96   -67.85     0.46)  ; 57\r\n                (  356.39   352.96   -67.88     0.46)  ; 58\r\n                (  357.07   356.11   -68.82     0.46)  ; 59\r\n                (  357.04   360.29   -69.30     0.46)  ; 60\r\n                (  356.24   363.69   -69.53     0.46)  ; 61\r\n                (  356.21   367.85   -69.53     0.46)  ; 62\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  337.96   214.42   -54.15     0.46)  ; 1\r\n                  (  355.73   355.79   -68.82     0.46)  ; 2\r\n                  (  355.00   366.98   -69.53     0.46)  ; 3\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  336.26   189.55   -52.00     0.46)  ; 1\r\n                  (  335.89   209.16   -53.33     0.46)  ; 2\r\n                  (  339.56   229.73   -57.92     0.46)  ; 3\r\n                  (  342.18   244.68   -58.75     0.46)  ; 4\r\n                  (  343.14   252.66   -58.62     0.46)  ; 5\r\n                  (  352.77   328.24   -65.45     0.46)  ; 6\r\n                  (  354.45   341.17   -66.50     0.46)  ; 7\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  314.04    -3.06    -9.05     0.46)  ; 1, R-2-1-2-1-2\r\n            (  311.85    -1.79    -7.07     0.46)  ; 2\r\n            (  308.34    -0.82    -6.02     0.46)  ; 3\r\n            (  306.29    -0.10    -4.47     0.46)  ; 4\r\n            (  303.91    -0.06    -3.60     0.46)  ; 5\r\n            (  300.84     1.00    -2.35     0.46)  ; 6\r\n            (  298.92     1.15    -1.82     0.46)  ; 7\r\n            (  298.92     1.15    -1.88     0.46)  ; 8\r\n            (  294.68     3.15    -0.72     0.46)  ; 9\r\n            (  289.82     3.79    -0.72     0.46)  ; 10\r\n            (  286.16     5.33     0.22     0.46)  ; 11\r\n            (  281.75     6.08     0.80     0.46)  ; 12\r\n            (  278.68     7.16    -0.28     0.46)  ; 13\r\n            (  278.68     7.16    -0.30     0.46)  ; 14\r\n            (  274.43     9.15    -2.50     0.46)  ; 15\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  301.87     0.65    -2.35     0.46)  ; 1\r\n              (  286.61     5.43     0.22     0.46)  ; 2\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (  319.61   -38.80     6.40     0.46)  ; 1, R-2-1-2-2\r\n          (  319.61   -38.80     6.42     0.46)  ; 2\r\n          (  321.70   -37.71     7.22     0.46)  ; 3\r\n          (  322.65   -35.70     8.42     0.46)  ; 4\r\n          (  325.64   -34.40    10.07     0.46)  ; 5\r\n          (  328.05   -32.63    10.07     0.46)  ; 6\r\n          (  331.41   -28.87    10.07     0.46)  ; 7\r\n          (  334.14   -26.44    10.07     0.46)  ; 8\r\n          (  337.94   -22.56     8.77     0.46)  ; 9\r\n          (  340.17   -22.04     8.35     0.46)  ; 10\r\n          (  343.54   -18.26     7.55     0.46)  ; 11\r\n          (  346.97   -16.86     7.05     0.46)  ; 12\r\n          (  346.97   -16.86     7.02     0.46)  ; 13\r\n          (  348.85   -12.83     7.95     0.46)  ; 14\r\n          (  351.45    -9.83     7.80     0.46)  ; 15\r\n          (  354.93    -6.63     7.80     0.46)  ; 16\r\n          (  357.09    -3.74     8.68     0.46)  ; 17\r\n          (  360.97    -2.24     9.10     0.46)  ; 18\r\n          (  363.33    -2.27     9.10     0.46)  ; 19\r\n          (  365.44    -1.18    10.25     0.46)  ; 20\r\n          (  365.31    -0.61    10.25     0.46)  ; 21\r\n          (  367.99     0.01    11.45     0.46)  ; 22\r\n          (  370.21     0.53    13.25     0.46)  ; 23\r\n          (  372.06     2.75    14.97     0.46)  ; 24\r\n          (  373.71     3.74    14.97     0.46)  ; 25\r\n          (  375.23     5.29    15.65     0.46)  ; 26\r\n          (  378.41     7.82    16.80     0.46)  ; 27\r\n          (  378.63    10.86    17.42     0.46)  ; 28\r\n          (  380.60    12.52    17.75     0.46)  ; 29\r\n          (  382.57    14.18    19.15     0.46)  ; 30\r\n          (  382.57    14.18    19.13     0.46)  ; 31\r\n          (  384.80    14.70    21.52     0.46)  ; 32\r\n          (  385.56    15.47    23.42     0.46)  ; 33\r\n          (  385.12    15.37    23.40     0.46)  ; 34\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  337.45   -24.47     8.77     0.46)  ; 1\r\n            (  346.76   -13.92     7.95     0.46)  ; 2\r\n            (  350.29    -8.92     7.80     0.46)  ; 3\r\n            (  360.66    -2.90     9.10     0.46)  ; 4\r\n            (  370.03    -0.70    13.25     0.46)  ; 5\r\n            (  372.63     2.29    14.97     0.46)  ; 6\r\n            (  377.04    11.69    19.05     0.46)  ; 7\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  319.61   -38.80     6.42     0.46)  ; 1\r\n            (  322.65   -35.70     8.42     0.46)  ; 2\r\n            (  328.81   -31.86    10.07     0.46)  ; 3\r\n            (  321.63   -35.35    10.07     0.46)  ; 4\r\n            (  356.64    -3.85     8.68     0.46)  ; 5\r\n            (  375.23     5.29    15.65     0.46)  ; 6\r\n            (  378.41     7.82    16.80     0.46)  ; 7\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  286.67   -92.36     6.90     0.46)  ; 1, R-2-2\r\n      (  285.12   -89.75     6.90     0.46)  ; 2\r\n      (  283.69   -87.69     6.15     0.46)  ; 3\r\n      (\r\n        (  282.14   -85.06     7.22     0.46)  ; 1, R-2-2-1\r\n        (  281.69   -85.17     7.22     0.46)  ; 2\r\n        (  279.83   -83.23     7.87     0.46)  ; 3\r\n        (  276.60   -81.59     7.53     0.46)  ; 4\r\n        (\r\n          (  274.47   -78.50     7.53     0.46)  ; 1, R-2-2-1-1\r\n          (  272.10   -78.46     7.53     0.46)  ; 2\r\n          (  270.16   -74.14     7.53     0.46)  ; 3\r\n          (  268.52   -69.15     7.92     0.46)  ; 4\r\n          (\r\n            (  269.63   -65.90     8.75     0.46)  ; 1, R-2-2-1-1-1\r\n            (  269.41   -62.97     9.50     0.46)  ; 2\r\n            (  269.51   -59.37     9.88     0.46)  ; 3\r\n            (  268.90   -54.73    11.45     0.46)  ; 4\r\n            (  268.37   -52.46    12.47     0.46)  ; 5\r\n            (  268.42   -50.65    13.80     0.46)  ; 6\r\n            (  268.28   -50.09    15.17     0.46)  ; 7\r\n            (  265.88   -45.88    15.90     0.46)  ; 8\r\n            (  262.91   -41.21    15.90     0.46)  ; 9\r\n            (  261.66   -37.91    14.85     0.46)  ; 10\r\n            (  260.02   -32.93    14.30     0.46)  ; 11\r\n            (  258.65   -29.07    14.88     0.46)  ; 12\r\n            (  257.54   -26.34    14.70     0.46)  ; 13\r\n            (  255.85   -23.15    14.40     0.46)  ; 14\r\n            (  254.43   -21.10    14.40     0.46)  ; 15\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  268.14   -55.51    11.45     0.46)  ; 1\r\n              (  265.12   -46.66    15.90     0.46)  ; 2\r\n              (  259.26   -33.70    14.30     0.46)  ; 3\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  267.97   -50.76    15.17     0.46)  ; 1\r\n              (  263.61   -42.23    15.90     0.46)  ; 2\r\n              (  257.57   -30.51    16.70     0.46)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  254.39   -16.92    14.40     0.46)  ; 1, R-2-2-1-1-1-1\r\n              (  253.46   -12.97    14.40     0.46)  ; 2\r\n              (  252.66    -9.57    15.35     0.46)  ; 3\r\n              (  251.68    -7.40    16.00     0.46)  ; 4\r\n              (  251.68    -7.40    15.97     0.46)  ; 5\r\n              (  250.73    -5.37    17.20     0.46)  ; 6\r\n              (  250.08    -2.55    18.02     0.46)  ; 7\r\n              (  249.09    -0.39    17.52     0.46)  ; 8\r\n              (  249.02     1.97    19.10     0.46)  ; 9\r\n              (  247.58     4.03    19.70     0.46)  ; 10\r\n              (  247.18     5.73    20.20     0.46)  ; 11\r\n              (  247.56     8.20    21.15     0.46)  ; 12\r\n              (  245.85    11.39    22.50     0.46)  ; 13\r\n              (  244.89    13.55    22.75     0.46)  ; 14\r\n              (  242.03    17.67    22.27     0.46)  ; 15\r\n              (  241.24    21.06    23.27     0.46)  ; 16\r\n              (  239.11    24.14    24.10     0.46)  ; 17\r\n              (  237.11    26.65    24.67     0.46)  ; 18\r\n              (  233.01    28.08    24.67     0.46)  ; 19\r\n              (  232.07    32.04    23.72     0.46)  ; 20\r\n              (  230.38    35.23    22.75     0.46)  ; 21\r\n              (  230.38    35.23    22.73     0.46)  ; 22\r\n              (  227.17    36.87    21.55     0.46)  ; 23\r\n              (  227.17    36.87    21.52     0.46)  ; 24\r\n              (  225.12    37.59    22.63     0.46)  ; 25\r\n              (  222.93    38.87    21.25     0.46)  ; 26\r\n              (  222.93    38.87    21.02     0.46)  ; 27\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  250.93    -8.19    15.97     0.46)  ; 1\r\n                (  237.87    27.43    24.67     0.46)  ; 2\r\n                (  224.72    39.29    20.92     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  250.26    -1.31    17.52     0.46)  ; 1\r\n                (  245.47    13.08    22.75     0.46)  ; 2\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  250.43   -22.17    13.32     0.46)  ; 1, R-2-2-1-1-1-2\r\n              (  245.97   -23.22    11.88     0.46)  ; 2\r\n              (  241.50   -24.26    11.98     0.46)  ; 3\r\n              (  238.50   -25.56    11.17     0.46)  ; 4\r\n              (  236.27   -26.08     9.90     0.46)  ; 5\r\n              (  232.52   -28.15     9.17     0.46)  ; 6\r\n              (  227.79   -28.07     8.57     0.46)  ; 7\r\n              (  225.56   -28.59     6.97     0.46)  ; 8\r\n              (  221.98   -29.44     6.28     0.46)  ; 9\r\n              (  221.98   -29.44     6.25     0.46)  ; 10\r\n              (  219.43   -30.63     5.55     0.46)  ; 11\r\n              (  216.30   -31.36     4.90     0.46)  ; 12\r\n              (  213.63   -31.98     4.15     0.46)  ; 13\r\n              (  213.63   -31.98     4.13     0.46)  ; 14\r\n              (  211.27   -31.94     2.78     0.46)  ; 15\r\n              (  209.79   -31.70     2.10     0.46)  ; 16\r\n              (  207.42   -31.66     1.20     0.46)  ; 17\r\n              (  203.27   -32.03     0.57     0.46)  ; 18\r\n              (  199.43   -31.73    -0.43     0.46)  ; 19\r\n              (  199.43   -31.73    -0.45     0.46)  ; 20\r\n              (  196.31   -32.46     0.52     0.46)  ; 21\r\n              (  192.68   -35.11    -0.22     0.46)  ; 22\r\n              (  187.64   -35.70    -1.13     0.46)  ; 23\r\n              (  185.68   -37.35    -1.82     0.46)  ; 24\r\n              (  182.05   -39.98    -2.63     0.46)  ; 25\r\n              (  178.93   -40.72    -2.42     0.46)  ; 26\r\n              (  174.59   -42.33    -2.50     0.46)  ; 27\r\n              (  170.97   -44.97    -2.83     0.46)  ; 28\r\n              (  167.71   -45.14    -4.32     0.46)  ; 29\r\n              (  167.71   -45.14    -4.35     0.46)  ; 30\r\n              (  165.61   -46.23    -5.75     0.46)  ; 31\r\n              (  163.70   -46.08    -4.90     0.46)  ; 32\r\n              (  161.01   -46.72    -6.55     0.46)  ; 33\r\n              (  158.78   -47.23    -7.82     0.46)  ; 34\r\n              (  158.15   -48.57    -8.60     0.46)  ; 35\r\n              (  154.57   -49.41    -9.25     0.46)  ; 36\r\n              (  150.72   -49.10    -9.25     0.46)  ; 37\r\n              (  148.57   -52.00    -9.25     0.46)  ; 38\r\n              (  144.77   -55.87    -9.48     0.46)  ; 39\r\n              (  139.86   -57.02   -10.38     0.46)  ; 40\r\n              (  137.19   -57.65   -11.40     0.46)  ; 41\r\n              (  133.74   -59.06   -12.72     0.46)  ; 42\r\n              (  133.74   -59.06   -12.75     0.46)  ; 43\r\n              (  132.40   -59.38   -15.00     0.46)  ; 44\r\n              (  129.54   -61.24   -15.70     0.46)  ; 45\r\n              (  128.47   -62.68   -15.70     0.46)  ; 46\r\n              (  124.89   -63.51   -16.57     0.46)  ; 47\r\n              (  121.45   -64.92   -16.90     0.46)  ; 48\r\n              (  118.01   -66.32   -16.90     0.46)  ; 49\r\n              (  112.39   -66.45   -18.35     0.46)  ; 50\r\n              (  107.79   -66.93   -18.35     0.46)  ; 51\r\n              (  105.43   -66.89   -20.23     0.46)  ; 52\r\n              (  105.43   -66.89   -20.25     0.46)  ; 53\r\n              (  101.85   -67.72   -22.77     0.46)  ; 54\r\n              (   98.15   -68.00   -22.77     0.46)  ; 55\r\n              (   95.65   -68.56   -24.25     0.46)  ; 56\r\n              (   93.87   -68.97   -25.70     0.46)  ; 57\r\n              (   93.87   -68.97   -25.73     0.46)  ; 58\r\n              (   91.50   -68.93   -26.98     0.46)  ; 59\r\n              (   90.11   -71.05   -28.70     0.46)  ; 60\r\n              (   86.72   -70.66   -30.02     0.46)  ; 61\r\n              (   86.59   -70.09   -30.02     0.46)  ; 62\r\n              (   83.64   -69.59   -31.45     0.46)  ; 63\r\n              (   81.85   -70.01   -33.10     0.46)  ; 64\r\n              (   80.21   -70.99   -35.02     0.46)  ; 65\r\n              (   79.58   -72.33   -37.20     0.46)  ; 66\r\n              (   76.89   -72.95   -39.58     0.46)  ; 67\r\n              (   76.89   -72.95   -39.60     0.46)  ; 68\r\n              (   74.22   -73.58   -41.47     0.46)  ; 69\r\n              (   70.52   -73.86   -41.28     0.46)  ; 70\r\n              (   67.24   -74.03   -43.33     0.46)  ; 71\r\n              (   63.82   -75.42   -44.35     0.46)  ; 72\r\n              (   59.39   -74.68   -46.00     0.46)  ; 73\r\n              (   56.98   -76.44   -47.47     0.46)  ; 74\r\n              (   49.74   -75.73   -47.47     0.46)  ; 75\r\n              (   48.46   -74.25   -49.70     0.46)  ; 76\r\n              (   47.20   -76.93   -50.52     0.46)  ; 77\r\n              (   44.21   -78.23   -51.70     0.46)  ; 78\r\n              (   38.59   -78.35   -52.85     0.46)  ; 79\r\n              (   35.50   -77.29   -53.70     0.46)  ; 80\r\n              (   30.52   -76.06   -54.45     0.46)  ; 81\r\n              (   30.52   -76.06   -54.47     0.46)  ; 82\r\n              (   25.51   -74.85   -53.67     0.46)  ; 83\r\n              (   22.26   -75.02   -53.65     0.46)  ; 84\r\n              (   19.18   -73.94   -53.47     0.46)  ; 85\r\n              (   15.61   -74.78   -52.15     0.46)  ; 86\r\n              (   12.79   -74.85   -51.50     0.46)  ; 87\r\n              (    8.77   -75.79   -51.50     0.46)  ; 88\r\n              (    4.63   -76.16   -50.40     0.46)  ; 89\r\n              (   -0.69   -75.62   -49.67     0.46)  ; 90\r\n              (   -3.81   -76.35   -49.27     0.46)  ; 91\r\n              (   -8.11   -76.16   -49.72     0.46)  ; 92\r\n              (  -12.65   -74.84   -49.72     0.46)  ; 93\r\n              (  -18.77   -76.87   -49.45     0.46)  ; 94\r\n              (  -22.30   -77.10   -48.42     0.46)  ; 95\r\n              (  -26.19   -78.61   -48.42     0.46)  ; 96\r\n              (  -31.50   -78.05   -48.13     0.46)  ; 97\r\n              (  -35.21   -78.32   -47.75     0.46)  ; 98\r\n              (  -39.04   -78.04   -47.75     0.46)  ; 99\r\n              (  -41.58   -79.23   -46.52     0.46)  ; 100\r\n              (  -44.53   -78.72   -45.60     0.46)  ; 101\r\n              (  -47.03   -78.12   -45.60     0.46)  ; 102\r\n              (  -50.47   -79.52   -45.60     0.46)  ; 103\r\n              (  -55.38   -80.67   -45.20     0.46)  ; 104\r\n              (  -59.67   -80.47   -42.80     0.46)  ; 105\r\n              (  -63.64   -79.62   -42.22     0.46)  ; 106\r\n              (  -68.11   -80.67   -41.32     0.46)  ; 107\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  246.68   -24.24    11.88     0.46)  ; 1\r\n                (  205.32   -32.74     0.57     0.46)  ; 2\r\n                (  192.81   -35.67    -0.22     0.46)  ; 3\r\n                (  182.10   -38.18    -2.63     0.46)  ; 4\r\n                (  175.56   -44.50    -2.50     0.46)  ; 5\r\n                (   83.91   -70.72   -31.45     0.46)  ; 6\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  219.88   -30.52     5.55     0.46)  ; 1\r\n                (  216.43   -31.92     4.90     0.46)  ; 2\r\n                (  209.34   -31.80     2.10     0.46)  ; 3\r\n                (  200.01   -32.18    -0.45     0.46)  ; 4\r\n                (  163.83   -46.64    -4.90     0.46)  ; 5\r\n                (  155.02   -49.30    -9.25     0.46)  ; 6\r\n                (  144.32   -55.97    -9.48     0.46)  ; 7\r\n                (  118.45   -66.22   -16.90     0.46)  ; 8\r\n                (  121.45   -64.92   -16.90     0.46)  ; 9\r\n                (  108.23   -66.83   -18.33     0.46)  ; 10\r\n                (   98.73   -68.46   -22.77     0.46)  ; 11\r\n                (  102.30   -67.62   -22.77     0.46)  ; 12\r\n                (   91.64   -69.50   -26.98     0.46)  ; 13\r\n                (   70.96   -73.75   -41.28     0.46)  ; 14\r\n                (   64.12   -74.76   -44.35     0.46)  ; 15\r\n                (   23.37   -77.74   -53.65     0.46)  ; 16\r\n                (   22.71   -74.91   -53.65     0.46)  ; 17\r\n                (   15.61   -74.78   -52.15     0.46)  ; 18\r\n                (    9.66   -75.58   -51.50     0.46)  ; 19\r\n                (  -18.46   -76.20   -49.45     0.46)  ; 20\r\n                (  -54.93   -80.56   -45.20     0.46)  ; 21\r\n                (  -60.12   -80.58   -42.80     0.46)  ; 22\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          |\r\n            (  265.02   -68.28     7.43     0.46)  ; 1, R-2-2-1-1-2\r\n            (  262.56   -65.87     8.47     0.46)  ; 2\r\n            (  261.13   -63.82     9.75     0.46)  ; 3\r\n            (  259.98   -62.89    10.33     0.46)  ; 4\r\n            (  257.92   -62.19    10.95     0.46)  ; 5\r\n            (  257.92   -62.19    10.92     0.46)  ; 6\r\n            (  256.50   -60.13    11.55     0.46)  ; 7\r\n            (  252.90   -56.79    12.60     0.46)  ; 8\r\n            (  250.58   -54.94    12.60     0.46)  ; 9\r\n            (  248.57   -52.43    11.00     0.46)  ; 10\r\n            (  245.81   -50.69     9.95     0.46)  ; 11\r\n            (  242.03   -48.60     9.67     0.46)  ; 12\r\n            (  241.41   -48.12     9.67     0.46)  ; 13\r\n            (\r\n              (  240.44   -45.97     7.47     0.46)  ; 1, R-2-2-1-1-2-1\r\n              (  239.01   -43.91     6.15     0.46)  ; 2\r\n              (  236.39   -42.74     5.30     0.46)  ; 3\r\n              (  233.75   -41.55     6.32     0.46)  ; 4\r\n              (  231.31   -39.15     6.90     0.46)  ; 5\r\n              (  228.85   -36.73     6.90     0.46)  ; 6\r\n              (  225.64   -35.09     7.20     0.46)  ; 7\r\n              (  223.95   -31.91     7.77     0.46)  ; 8\r\n              (  219.26   -30.03     8.42     0.46)  ; 9\r\n              (  217.14   -26.94     9.55     0.46)  ; 10\r\n              (  214.10   -24.07    10.45     0.46)  ; 11\r\n              (  212.19   -23.92    10.75     0.46)  ; 12\r\n              (  211.53   -21.09    10.75     0.46)  ; 13\r\n              (  208.58   -20.58    11.15     0.46)  ; 14\r\n              (  206.71   -18.64    11.15     0.46)  ; 15\r\n              (  203.63   -17.56    10.77     0.46)  ; 16\r\n              (  201.05   -14.59    12.40     0.46)  ; 17\r\n              (  201.05   -14.59    12.42     0.46)  ; 18\r\n              (  196.99   -11.36    12.42     0.46)  ; 19\r\n              (  192.62    -8.80    12.42     0.46)  ; 20\r\n              (  189.60    -5.93    11.93     0.46)  ; 21\r\n              (  186.12    -3.16    12.30     0.46)  ; 22\r\n              (  183.22    -0.86    13.18     0.46)  ; 23\r\n              (  180.06     2.58    13.27     0.46)  ; 24\r\n              (  175.74     6.95    14.77     0.46)  ; 25\r\n              (  171.98     8.93    14.52     0.46)  ; 26\r\n              (  168.76    10.56    14.52     0.46)  ; 27\r\n              (  165.16    13.89    15.38     0.46)  ; 28\r\n              (  159.59    15.58    15.38     0.46)  ; 29\r\n              (  157.00    18.55    16.10     0.46)  ; 30\r\n              (  154.51    19.16    16.10     0.46)  ; 31\r\n              (  151.47    22.03    16.77     0.46)  ; 32\r\n              (  146.80    23.93    16.77     0.46)  ; 33\r\n              (  144.03    25.66    17.27     0.46)  ; 34\r\n              (  142.92    28.39    17.45     0.46)  ; 35\r\n              (  141.00    28.54    17.63     0.46)  ; 36\r\n              (  138.41    31.51    17.63     0.46)  ; 37\r\n              (  135.84    34.50    17.63     0.46)  ; 38\r\n              (  133.12    38.04    18.02     0.46)  ; 39\r\n              (  130.93    39.32    18.02     0.46)  ; 40\r\n              (  129.19    40.71    16.98     0.46)  ; 41\r\n              (  125.09    42.13    16.92     0.46)  ; 42\r\n              (  121.48    45.46    16.92     0.46)  ; 43\r\n              (  119.79    48.65    17.20     0.46)  ; 44\r\n              (  118.68    51.38    16.45     0.46)  ; 45\r\n              (  116.73    55.70    16.07     0.46)  ; 46\r\n              (  114.99    57.09    15.50     0.46)  ; 47\r\n              (  113.43    59.70    15.50     0.46)  ; 48\r\n              (  110.77    65.05    14.70     0.46)  ; 49\r\n              (  107.16    68.39    14.70     0.46)  ; 50\r\n              (  104.49    73.73    14.25     0.46)  ; 51\r\n              (  101.60    76.04    13.90     0.46)  ; 52\r\n              (   98.75    80.15    14.60     0.46)  ; 53\r\n              (   95.99    81.89    14.40     0.46)  ; 54\r\n              (   94.88    84.61    13.80     0.46)  ; 55\r\n              (   91.40    87.38    13.80     0.46)  ; 56\r\n              (   89.53    89.34    13.80     0.46)  ; 57\r\n              (   88.73    92.72    13.15     0.46)  ; 58\r\n              (   85.98    94.47    13.15     0.46)  ; 59\r\n              (   83.79    95.74    12.65     0.46)  ; 60\r\n              (   82.55    99.04    12.65     0.46)  ; 61\r\n              (   79.51   101.91    12.05     0.46)  ; 62\r\n\r\n              (Dot\r\n                (Color Cyan)\r\n                (Name \"Marker 1\")\r\n                (  241.75   -41.48     7.47     0.46)  ; 1\r\n                (  239.33   -43.23     5.30     0.46)  ; 2\r\n                (  226.94   -36.59     7.20     0.46)  ; 3\r\n                (  216.77   -29.42     9.55     0.46)  ; 4\r\n                (  186.07    -4.96    12.30     0.46)  ; 5\r\n                (  148.21    21.87    16.77     0.46)  ; 6\r\n                (  141.13    27.98    16.55     0.46)  ; 7\r\n                (  141.00    28.54    12.67     0.46)  ; 8\r\n                (  122.60    42.73    16.92     0.46)  ; 9\r\n                (  116.81    53.33    16.07     0.46)  ; 10\r\n                (   98.70    78.35    14.60     0.46)  ; 11\r\n                (   88.78    94.53    13.15     0.46)  ; 12\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color RGB (255, 128, 255))\r\n                (Name \"Marker 2\")\r\n                (  214.68   -24.53    10.35     0.46)  ; 1\r\n                (  206.97   -19.77    11.15     0.46)  ; 2\r\n                (  176.32     6.49    14.77     0.46)  ; 3\r\n                (  169.21    10.66    14.52     0.46)  ; 4\r\n                (  102.04    76.14    13.90     0.46)  ; 5\r\n                (   91.40    87.38    13.80     0.46)  ; 6\r\n              )  ;  End of markers\r\n              (\r\n                (   77.33   103.16    13.63     0.46)  ; 1, R-2-2-1-1-2-1-1\r\n                (   75.32   105.68    14.75     0.46)  ; 2\r\n                (   73.77   108.29    15.75     0.46)  ; 3\r\n                (   70.21   113.43    16.17     0.46)  ; 4\r\n                (   67.49   116.98    15.38     0.46)  ; 5\r\n                (   65.10   121.20    14.18     0.46)  ; 6\r\n                (   63.27   124.94    13.05     0.46)  ; 7\r\n                (   62.82   124.84    13.05     0.46)  ; 8\r\n                (   58.27   126.15    12.77     0.46)  ; 9\r\n                (   54.30   127.02    12.77     0.46)  ; 10\r\n                (   51.59   130.57    11.80     0.46)  ; 11\r\n                (   49.90   133.76    11.88     0.46)  ; 12\r\n                (   48.17   135.14    11.88     0.46)  ; 13\r\n                (   45.85   136.99    11.88     0.46)  ; 14\r\n                (   41.65   140.79    12.72     0.46)  ; 15\r\n                (   37.92   144.68    12.45     0.46)  ; 16\r\n                (   34.32   148.02    12.45     0.46)  ; 17\r\n                (   32.18   151.10    10.73     0.46)  ; 18\r\n                (   30.04   154.17    11.13     0.46)  ; 19\r\n                (   28.03   156.69    10.15     0.46)  ; 20\r\n                (   26.03   159.21     8.63     0.46)  ; 21\r\n                (   25.86   163.94     8.45     0.46)  ; 22\r\n                (   26.36   165.85     9.60     0.46)  ; 23\r\n                (   26.32   170.02    10.75     0.46)  ; 24\r\n                (   25.21   172.75    11.05     0.46)  ; 25\r\n                (   25.07   173.32    11.05     0.46)  ; 26\r\n\r\n                (Dot\r\n                  (Color Cyan)\r\n                  (Name \"Marker 1\")\r\n                  (   58.10   124.92    12.77     0.46)  ; 1\r\n                  (   45.67   135.75    11.88     0.46)  ; 2\r\n                  (   41.34   140.10    12.72     0.46)  ; 3\r\n                  (   33.21   150.73    10.73     0.46)  ; 4\r\n                  (   30.93   154.38    11.53     0.46)  ; 5\r\n                  (   27.02   163.03     8.45     0.46)  ; 6\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color RGB (255, 128, 255))\r\n                  (Name \"Marker 2\")\r\n                  (   73.77   108.29    15.75     0.46)  ; 1\r\n                  (   70.79   112.98    16.17     0.46)  ; 2\r\n                  (   49.90   133.76    11.88     0.46)  ; 3\r\n                  (   37.92   144.68    12.45     0.46)  ; 4\r\n                  (   28.48   156.80    10.15     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (   24.72   170.84    12.17     0.46)  ; 1, R-2-2-1-1-2-1-1-1\r\n                  (   24.85   170.28    14.95     0.46)  ; 2\r\n                  (   25.43   169.82    16.20     0.46)  ; 3\r\n                  (   27.48   169.10    17.57     0.46)  ; 4\r\n                  (   28.38   169.31    19.02     0.46)  ; 5\r\n                  (   28.20   168.07    20.20     0.46)  ; 6\r\n                  (   30.29   169.15    21.40     0.46)  ; 7\r\n                  (   31.90   168.34    21.38     0.46)  ; 8\r\n                  (   32.03   167.77    22.02     0.46)  ; 9\r\n                  (   28.20   168.07    23.82     0.46)  ; 10\r\n                  (   27.25   166.06    24.77     0.46)  ; 11\r\n                  (   28.36   163.33    25.20     0.46)  ; 12\r\n                  (   26.34   159.88    25.20     0.46)  ; 13\r\n                  (   26.29   158.07    23.98     0.46)  ; 14\r\n                  (   26.69   156.38    22.57     0.46)  ; 15\r\n                  (   26.96   155.24    21.75     0.46)  ; 16\r\n                  (   26.46   153.34    20.73     0.46)  ; 17\r\n                  (   27.75   151.85    18.42     0.46)  ; 18\r\n                  (   27.75   151.85    18.40     0.46)  ; 19\r\n\r\n                  (OpenCircle\r\n                    (Color RGB (255, 128, 255))\r\n                    (Name \"Marker 2\")\r\n                    (   25.30   170.38    16.20     0.46)  ; 1\r\n                    (   26.25   156.27    21.75     0.46)  ; 2\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (   23.39   176.51     9.25     0.46)  ; 1, R-2-2-1-1-2-1-1-2\r\n                  (   22.94   176.40     9.25     0.46)  ; 2\r\n                  (   23.54   181.91     9.82     0.46)  ; 3\r\n                  (   22.29   185.21    11.10     0.46)  ; 4\r\n                  (   21.76   187.47    12.17     0.46)  ; 5\r\n                  (   20.25   191.90    13.27     0.46)  ; 6\r\n                  (   19.46   195.30    14.67     0.46)  ; 7\r\n                  (   19.42   199.46    15.38     0.46)  ; 8\r\n                  (   16.85   202.43    15.38     0.46)  ; 9\r\n                  (   13.53   204.52    15.38     0.46)  ; 10\r\n                  (   12.03   208.94    16.45     0.46)  ; 11\r\n                  (   11.50   211.22    16.45     0.46)  ; 12\r\n                  (   11.45   215.39    17.30     0.46)  ; 13\r\n                  (   10.62   216.98    18.57     0.46)  ; 14\r\n                  (   10.52   219.35    19.02     0.46)  ; 15\r\n                  (    8.84   222.54    19.50     0.46)  ; 16\r\n                  (    8.18   225.37    17.92     0.46)  ; 17\r\n                  (    6.80   229.22    17.02     0.46)  ; 18\r\n                  (    5.37   231.28    16.47     0.46)  ; 19\r\n                  (    3.41   235.59    16.47     0.46)  ; 20\r\n                  (    2.04   239.45    16.85     0.46)  ; 21\r\n                  (   -0.41   241.87    16.80     0.46)  ; 22\r\n                  (   -2.85   244.27    16.45     0.46)  ; 23\r\n                  (   -3.70   245.87    16.75     0.46)  ; 24\r\n\r\n                  (Dot\r\n                    (Color Cyan)\r\n                    (Name \"Marker 1\")\r\n                    (   24.83   180.43     9.82     0.46)  ; 1\r\n                    (    1.41   238.12    16.85     0.46)  ; 2\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color RGB (255, 128, 255))\r\n                    (Name \"Marker 2\")\r\n                    (   21.76   187.47    12.17     0.46)  ; 1\r\n                    (   20.39   191.33    13.27     0.46)  ; 2\r\n                    (   18.01   201.52    15.38     0.46)  ; 3\r\n                    (   11.25   208.17    16.45     0.46)  ; 4\r\n                    (   11.63   210.65    16.45     0.46)  ; 5\r\n                    (   10.08   219.24    19.02     0.46)  ; 6\r\n                    (    5.37   231.28    16.47     0.46)  ; 7\r\n                    (   -4.28   246.33    16.75     0.46)  ; 8\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (   79.34   106.62    13.10     0.46)  ; 1, R-2-2-1-1-2-1-2\r\n                (   79.70   109.11    14.07     0.46)  ; 2\r\n                (   79.93   112.14    12.50     0.46)  ; 3\r\n                (   80.42   114.05    11.20     0.46)  ; 4\r\n                (   80.20   116.99    10.07     0.46)  ; 5\r\n                (   81.33   120.23     9.40     0.46)  ; 6\r\n                (   80.98   123.73     8.15     0.46)  ; 7\r\n                (   81.08   127.34     6.95     0.46)  ; 8\r\n                (   80.54   129.61     5.57     0.46)  ; 9\r\n                (   81.67   132.85     3.65     0.46)  ; 10\r\n                (   80.88   136.25     2.78     0.46)  ; 11\r\n                (   79.63   139.54     1.90     0.46)  ; 12\r\n                (   79.42   142.48     0.82     0.46)  ; 13\r\n                (   78.89   144.74    -0.43     0.46)  ; 14\r\n                (   78.89   144.74    -0.45     0.46)  ; 15\r\n                (   77.06   148.49    -1.82     0.46)  ; 16\r\n                (   77.06   148.49    -1.85     0.46)  ; 17\r\n                (   76.14   152.46    -2.35     0.46)  ; 18\r\n                (   75.87   153.58    -2.35     0.46)  ; 19\r\n                (   74.63   156.87    -2.92     0.46)  ; 20\r\n                (   74.23   158.58    -3.95     0.46)  ; 21\r\n                (   73.65   159.04    -3.95     0.46)  ; 22\r\n                (   73.43   161.97    -3.95     0.46)  ; 23\r\n                (   72.90   164.24    -5.55     0.46)  ; 24\r\n                (   72.55   167.74    -6.95     0.46)  ; 25\r\n                (   70.99   170.36    -7.15     0.46)  ; 26\r\n                (   71.36   172.83    -9.00     0.46)  ; 27\r\n                (   71.23   173.40   -10.35     0.46)  ; 28\r\n                (   71.23   173.40   -10.38     0.46)  ; 29\r\n                (   71.59   175.88   -11.40     0.46)  ; 30\r\n                (   70.16   177.93   -11.75     0.46)  ; 31\r\n                (   69.37   181.33   -13.35     0.46)  ; 32\r\n                (   70.18   183.90   -15.35     0.46)  ; 33\r\n                (   68.94   187.20   -16.42     0.46)  ; 34\r\n                (   67.83   189.92   -18.23     0.46)  ; 35\r\n                (   68.09   192.84   -19.55     0.46)  ; 36\r\n                (   66.34   194.23   -20.30     0.46)  ; 37\r\n                (   66.34   194.23   -20.33     0.46)  ; 38\r\n                (   65.81   196.50   -21.05     0.46)  ; 39\r\n                (   65.23   196.95   -23.10     0.46)  ; 40\r\n                (   65.28   198.76   -24.52     0.46)  ; 41\r\n                (   65.28   198.76   -24.58     0.46)  ; 42\r\n                (   64.56   199.79   -25.75     0.46)  ; 43\r\n                (   64.56   199.79   -25.77     0.46)  ; 44\r\n                (   62.57   202.30   -27.50     0.46)  ; 45\r\n                (   63.82   204.99   -29.13     0.46)  ; 46\r\n                (   62.39   207.04   -30.60     0.46)  ; 47\r\n                (   60.26   210.13   -31.85     0.46)  ; 48\r\n                (   58.57   213.32   -32.67     0.46)  ; 49\r\n                (   55.68   215.61   -32.92     0.46)  ; 50\r\n                (   53.94   217.00   -33.85     0.46)  ; 51\r\n                (   53.73   219.94   -35.27     0.46)  ; 52\r\n                (   51.66   220.65   -36.52     0.46)  ; 53\r\n                (   48.37   224.66   -37.20     0.46)  ; 54\r\n                (   48.37   224.66   -37.22     0.46)  ; 55\r\n                (   46.68   227.85   -38.33     0.46)  ; 56\r\n                (   46.15   230.10   -40.03     0.46)  ; 57\r\n                (   43.44   233.65   -41.80     0.46)  ; 58\r\n                (   40.99   236.06   -42.77     0.46)  ; 59\r\n                (   40.01   238.22   -44.07     0.46)  ; 60\r\n                (   36.09   240.88   -44.07     0.46)  ; 61\r\n                (   34.54   243.50   -45.35     0.46)  ; 62\r\n                (   32.08   245.91   -46.22     0.46)  ; 63\r\n                (   30.44   250.90   -46.57     0.46)  ; 64\r\n                (   28.80   255.90   -47.05     0.46)  ; 65\r\n                (   28.80   255.90   -47.08     0.46)  ; 66\r\n                (   28.14   258.72   -48.58     0.46)  ; 67\r\n                (   28.68   262.43   -50.00     0.46)  ; 68\r\n                (   27.03   267.42   -51.93     0.46)  ; 69\r\n                (   25.09   271.73   -52.13     0.46)  ; 70\r\n                (   23.98   274.47   -51.75     0.46)  ; 71\r\n                (   21.71   278.11   -52.92     0.46)  ; 72\r\n                (   20.99   279.14   -54.85     0.46)  ; 73\r\n\r\n                (Dot\r\n                  (Color Cyan)\r\n                  (Name \"Marker 1\")\r\n                  (   79.67   119.25     9.40     0.46)  ; 1\r\n                  (   80.67   123.07     8.15     0.46)  ; 2\r\n                  (   79.54   135.94     2.78     0.46)  ; 3\r\n                  (   68.21   182.24   -13.35     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color RGB (255, 128, 255))\r\n                  (Name \"Marker 2\")\r\n                  (   79.57   109.67    14.07     0.46)  ; 1\r\n                  (   80.10   113.38    11.20     0.46)  ; 2\r\n                  (   81.21   126.77     6.95     0.46)  ; 3\r\n                  (   77.06   148.49    -1.85     0.46)  ; 4\r\n                  (   74.94   157.55    -2.92     0.46)  ; 5\r\n                  (   73.43   161.97    -3.95     0.46)  ; 6\r\n                  (   67.83   189.92   -18.23     0.46)  ; 7\r\n                  (   65.94   195.93   -21.05     0.46)  ; 8\r\n                  (   61.81   207.50   -30.60     0.46)  ; 9\r\n                  (   46.29   229.54   -40.03     0.46)  ; 10\r\n                  (   34.09   243.40   -45.35     0.46)  ; 11\r\n                  (   28.23   262.33   -50.00     0.46)  ; 12\r\n                  (   23.98   274.47   -51.75     0.46)  ; 13\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  238.58   -50.00    11.15     0.46)  ; 1, R-2-2-1-1-2-2\r\n              (  238.58   -50.00    11.13     0.46)  ; 2\r\n              (  234.12   -51.05    13.15     0.46)  ; 3\r\n              (  231.48   -49.86    14.92     0.46)  ; 4\r\n              (  228.10   -49.47    16.15     0.46)  ; 5\r\n              (  225.46   -48.29    17.08     0.46)  ; 6\r\n              (  224.43   -47.94    18.25     0.46)  ; 7\r\n              (  221.80   -46.76    19.70     0.46)  ; 8\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  257.39   -59.92    11.55     0.46)  ; 1\r\n                (  246.66   -52.29     9.95     0.46)  ; 2\r\n                (  222.43   -45.42    19.70     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  251.17   -55.41    12.60     0.46)  ; 1\r\n                (  254.50   -57.60    12.60     0.46)  ; 2\r\n              )  ;  End of markers\r\n              (\r\n                (  219.76   -46.05    18.55     0.46)  ; 1, R-2-2-1-1-2-2-1\r\n                (  218.41   -46.37    17.20     0.46)  ; 2\r\n                (  218.41   -46.37    17.15     0.46)  ; 3\r\n                (  216.45   -48.02    15.75     0.46)  ; 4\r\n                (  215.23   -48.89    14.67     0.46)  ; 5\r\n                (  213.01   -49.42    13.63     0.46)  ; 6\r\n                (  210.90   -50.50    12.80     0.46)  ; 7\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  217.47   -48.37    15.75     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  213.14   -49.99    13.63     0.46)  ; 1\r\n                  (  210.46   -50.61    12.80     0.46)  ; 2\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  218.73   -45.69    20.63     0.46)  ; 1, R-2-2-1-1-2-2-2\r\n                (  216.94   -46.12    22.32     0.46)  ; 2\r\n                (  214.26   -46.74    23.65     0.46)  ; 3\r\n                (  212.96   -45.24    24.95     0.46)  ; 4\r\n                (  212.30   -42.41    26.35     0.46)  ; 5\r\n                (  211.77   -40.15    28.63     0.46)  ; 6\r\n                (  210.93   -38.55    30.80     0.46)  ; 7\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  217.39   -46.01    22.32     0.46)  ; 1\r\n                  (  212.43   -42.99    26.35     0.46)  ; 2\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (  272.79   -81.39     7.15     0.46)  ; 1, R-2-2-1-2\r\n          (  269.27   -80.41     7.15     0.46)  ; 2\r\n          (  264.14   -78.65     7.15     0.46)  ; 3\r\n          (  260.04   -77.20     7.77     0.46)  ; 4\r\n          (  255.04   -75.99     7.90     0.46)  ; 5\r\n          (  251.52   -75.03     8.10     0.46)  ; 6\r\n          (  247.09   -74.27     8.10     0.46)  ; 7\r\n          (  242.23   -73.62     8.10     0.46)  ; 8\r\n          (  238.26   -72.77     6.35     0.46)  ; 9\r\n          (  236.34   -72.62     6.18     0.46)  ; 10\r\n          (  235.90   -72.72     6.18     0.46)  ; 11\r\n          (  231.17   -72.64     5.77     0.46)  ; 12\r\n          (  222.51   -69.88     6.65     0.46)  ; 13\r\n          (  219.12   -69.48     7.45     0.46)  ; 14\r\n          (  215.60   -68.53     7.97     0.46)  ; 15\r\n          (  212.07   -67.55     9.67     0.46)  ; 16\r\n          (  206.36   -65.31    10.30     0.46)  ; 17\r\n          (  201.55   -62.86    10.30     0.46)  ; 18\r\n          (  197.05   -59.73    10.85     0.46)  ; 19\r\n          (  195.44   -58.91    11.75     0.46)  ; 20\r\n          (  194.73   -57.88    13.32     0.46)  ; 21\r\n          (  194.73   -57.88    13.30     0.46)  ; 22\r\n          (  192.68   -57.17    13.97     0.46)  ; 23\r\n          (  188.13   -55.85    14.50     0.46)  ; 24\r\n          (  185.68   -53.44    15.12     0.46)  ; 25\r\n          (  182.20   -50.67    15.70     0.46)  ; 26\r\n          (  182.20   -50.67    15.73     0.46)  ; 27\r\n          (  178.23   -49.80    16.20     0.46)  ; 28\r\n          (  176.05   -48.53    16.70     0.46)  ; 29\r\n          (  176.05   -48.53    16.67     0.46)  ; 30\r\n          (  172.52   -47.56    17.27     0.46)  ; 31\r\n          (  169.72   -47.60    18.33     0.46)  ; 32\r\n          (  167.26   -45.19    19.25     0.46)  ; 33\r\n          (  163.29   -44.33    19.25     0.46)  ; 34\r\n          (  162.15   -43.40    19.25     0.46)  ; 35\r\n          (  160.85   -41.92    18.63     0.46)  ; 36\r\n          (  158.04   -41.97    18.63     0.46)  ; 37\r\n          (  155.27   -40.24    17.75     0.46)  ; 38\r\n          (  155.27   -40.24    17.72     0.46)  ; 39\r\n          (  152.96   -38.39    18.55     0.46)  ; 40\r\n          (  149.88   -37.32    19.50     0.46)  ; 41\r\n          (  149.88   -37.32    19.48     0.46)  ; 42\r\n          (  145.33   -36.00    19.73     0.46)  ; 43\r\n          (  141.36   -35.14    20.27     0.46)  ; 44\r\n          (  137.88   -33.48    21.45     0.46)  ; 45\r\n          (\r\n            (  138.86   -35.63    19.55     0.46)  ; 1, R-2-2-1-2-1\r\n            (  139.52   -38.46    18.27     0.46)  ; 2\r\n            (  140.19   -41.30    17.13     0.46)  ; 3\r\n            (  140.19   -41.30    17.08     0.46)  ; 4\r\n            (  141.16   -43.45    15.60     0.46)  ; 5\r\n            (  141.16   -43.45    15.50     0.46)  ; 6\r\n            (  139.09   -48.71    13.47     0.46)  ; 7\r\n            (  139.09   -48.71    13.42     0.46)  ; 8\r\n            (  139.36   -49.86    11.68     0.46)  ; 9\r\n            (  139.13   -52.89     8.45     0.46)  ; 10\r\n            (  139.26   -53.45     6.57     0.46)  ; 11\r\n            (  140.24   -55.61     4.52     0.46)  ; 12\r\n            (  141.13   -55.40     2.42     0.46)  ; 13\r\n            (  142.49   -55.09     0.82     0.46)  ; 14\r\n            (  141.40   -56.54    -5.27     0.46)  ; 15\r\n            (  140.11   -55.04    -6.52     0.46)  ; 16\r\n            (  137.30   -55.11    -7.47     0.46)  ; 17\r\n            (  134.67   -53.93    -7.87     0.46)  ; 18\r\n            (  132.69   -55.59    -8.75     0.46)  ; 19\r\n            (  129.84   -57.45   -10.65     0.46)  ; 20\r\n            (  127.61   -57.98   -11.27     0.46)  ; 21\r\n            (  127.61   -57.98   -11.42     0.46)  ; 22\r\n            (  127.88   -59.11   -13.32     0.46)  ; 23\r\n            (  125.14   -61.54   -15.43     0.46)  ; 24\r\n            (  125.14   -61.54   -15.48     0.46)  ; 25\r\n            (  123.49   -62.53   -19.60     0.46)  ; 26\r\n            (  121.53   -64.18   -22.12     0.46)  ; 27\r\n            (  122.50   -66.34   -24.13     0.46)  ; 28\r\n            (  122.90   -68.04   -26.32     0.46)  ; 29\r\n            (  122.90   -68.04   -26.35     0.46)  ; 30\r\n            (  120.26   -66.86   -27.45     0.46)  ; 31\r\n            (  120.22   -68.67   -29.30     0.46)  ; 32\r\n            (  120.22   -68.67   -29.32     0.46)  ; 33\r\n            (  121.18   -70.82   -31.75     0.46)  ; 34\r\n            (  120.12   -72.28   -32.77     0.46)  ; 35\r\n            (  118.59   -73.83   -33.50     0.46)  ; 36\r\n            (  117.35   -76.51   -34.52     0.46)  ; 37\r\n            (  116.80   -80.21   -35.72     0.46)  ; 38\r\n            (  115.09   -83.00   -36.35     0.46)  ; 39\r\n            (  111.79   -84.97   -37.67     0.46)  ; 40\r\n            (  110.53   -87.66   -38.47     0.46)  ; 41\r\n            (  109.54   -91.47   -40.32     0.46)  ; 42\r\n            (  109.54   -91.47   -40.32     0.46)  ; 43\r\n            (  107.13   -93.23   -41.57     0.46)  ; 44\r\n            (  107.13   -93.23   -41.60     0.46)  ; 45\r\n            (  104.98   -96.12   -43.62     0.46)  ; 46\r\n            (  102.82   -99.02   -45.90     0.46)  ; 47\r\n            (  101.08  -103.60   -47.55     0.46)  ; 48\r\n            (   98.80  -105.93   -47.55     0.46)  ; 49\r\n            (   98.31  -107.84   -49.25     0.46)  ; 50\r\n            (   98.31  -107.84   -49.27     0.46)  ; 51\r\n            (   98.78  -111.91   -50.30     0.46)  ; 52\r\n            (   95.34  -113.32   -52.05     0.46)  ; 53\r\n            (   93.12  -113.83   -54.90     0.46)  ; 54\r\n            (   93.12  -113.83   -54.95     0.46)  ; 55\r\n            (   90.25  -115.69   -58.10     0.46)  ; 56\r\n            (   87.00  -115.86   -61.25     0.46)  ; 57\r\n            (   87.00  -115.86   -61.35     0.46)  ; 58\r\n            (   85.02  -117.52   -64.05     0.46)  ; 59\r\n            (   85.02  -117.52   -64.10     0.46)  ; 60\r\n            (   83.00  -120.98   -64.97     0.46)  ; 61\r\n            (   83.00  -120.98   -65.00     0.46)  ; 62\r\n            (   83.54  -123.25   -67.57     0.46)  ; 63\r\n            (   83.54  -123.25   -67.60     0.46)  ; 64\r\n            (   82.99  -126.95   -69.47     0.46)  ; 65\r\n            (   82.95  -128.76   -72.35     0.46)  ; 66\r\n            (   82.95  -128.76   -72.40     0.46)  ; 67\r\n            (   82.40  -132.47   -74.25     0.46)  ; 68\r\n            (   82.49  -134.83   -74.22     0.46)  ; 69\r\n            (   82.49  -134.83   -74.38     0.46)  ; 70\r\n            (   81.81  -137.98   -78.32     0.46)  ; 71\r\n            (   81.26  -141.69   -81.22     0.46)  ; 72\r\n            (   82.11  -143.28   -84.63     0.46)  ; 73\r\n            (   82.19  -145.66   -87.42     0.46)  ; 74\r\n            (   80.67  -147.21   -89.88     0.46)  ; 75\r\n            (   79.69  -151.02   -89.32     0.46)  ; 76\r\n            (   79.45  -154.06   -93.88     0.46)  ; 77\r\n            (   79.45  -154.06   -94.10     0.46)  ; 78\r\n            (   78.18  -156.73   -97.38     0.46)  ; 79\r\n            (   78.18  -156.73   -97.40     0.46)  ; 80\r\n            (   75.46  -159.17   -99.63     0.46)  ; 81\r\n            (   74.70  -159.94  -101.53     0.46)  ; 82\r\n            (   75.41  -160.97  -103.42     0.46)  ; 83\r\n            (   75.41  -160.97  -103.45     0.46)  ; 84\r\n            (   75.54  -161.53  -105.65     0.46)  ; 85\r\n            (   76.70  -162.46  -106.15     0.46)  ; 86\r\n            (   76.70  -162.46  -106.30     0.46)  ; 87\r\n            (   76.87  -167.20  -108.47     0.46)  ; 88\r\n            (   76.87  -167.20  -109.63     0.46)  ; 89\r\n            (   78.38  -171.62  -109.63     0.46)  ; 90\r\n            (   76.95  -175.54  -111.80     0.46)  ; 91\r\n            (   76.95  -175.54  -111.82     0.46)  ; 92\r\n            (   77.48  -177.80  -115.57     0.46)  ; 93\r\n            (   78.33  -179.40  -118.20     0.46)  ; 94\r\n            (   78.33  -179.40  -118.22     0.46)  ; 95\r\n            (   78.09  -182.43  -119.78     0.46)  ; 96\r\n            (   78.09  -182.43  -119.88     0.46)  ; 97\r\n            (   78.05  -184.24  -122.05     0.46)  ; 98\r\n            (   79.29  -187.54  -123.42     0.46)  ; 99\r\n            (   78.02  -190.21  -125.02     0.46)  ; 100\r\n            (   78.37  -193.71  -127.52     0.46)  ; 101\r\n            (   77.70  -196.86  -129.92     0.46)  ; 102\r\n            (   77.57  -196.30  -129.92     0.46)  ; 103\r\n            (   78.81  -199.58  -130.65     0.46)  ; 104\r\n            (   80.19  -203.43  -131.57     0.46)  ; 105\r\n            (   81.02  -204.92  -132.52     0.46)  ; 106\r\n            (   81.60  -205.40  -134.75     0.46)  ; 107\r\n            (   82.53  -209.36  -136.07     0.46)  ; 108\r\n            (   83.19  -212.18  -137.55     0.46)  ; 109\r\n            (   83.41  -215.12  -138.82     0.46)  ; 110\r\n            (   81.26  -218.01  -141.43     0.46)  ; 111\r\n            (   80.05  -218.90  -145.50     0.46)  ; 112\r\n            (   79.43  -220.23  -148.05     0.46)  ; 113\r\n            (   77.72  -223.02  -150.23     0.46)  ; 114\r\n            (   75.45  -225.35  -150.23     0.46)  ; 115\r\n            (   72.90  -226.54  -150.27     0.46)  ; 116\r\n            (   72.90  -226.54  -150.32     0.46)  ; 117\r\n\r\n            (Dot\r\n              (Color Cyan)\r\n              (Name \"Marker 1\")\r\n              (  140.42   -38.25    18.27     0.46)  ; 1\r\n              (  141.60   -49.33     9.80     0.46)  ; 2\r\n              (  139.89   -52.11     8.05     0.46)  ; 3\r\n              (  140.87   -54.28     4.50     0.46)  ; 4\r\n              (  142.30   -56.33     0.82     0.46)  ; 5\r\n              (  131.18   -57.14   -10.65     0.46)  ; 6\r\n              (  124.23   -67.73   -26.35     0.46)  ; 7\r\n              (   79.16  -180.99  -120.05     0.46)  ; 8\r\n              (   79.74  -203.54  -131.57     0.46)  ; 9\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color RGB (255, 128, 255))\r\n              (Name \"Marker 2\")\r\n              (  118.59   -73.83   -33.50     0.46)  ; 1\r\n              (  113.53   -86.36   -36.88     0.46)  ; 2\r\n              (  107.58   -93.12   -41.97     0.46)  ; 3\r\n              (   80.13  -150.92   -89.32     0.46)  ; 4\r\n              (   76.95  -175.54  -111.82     0.46)  ; 5\r\n              (   79.15  -186.96  -123.42     0.46)  ; 6\r\n            )  ;  End of markers\r\n             Incomplete\r\n          |\r\n            (  138.01   -32.94    21.45     0.46)  ; 1, R-2-2-1-2-2\r\n            (  134.93   -31.87    22.65     0.46)  ; 2\r\n            (  131.91   -28.99    23.67     0.46)  ; 3\r\n            (  128.25   -27.45    24.75     0.46)  ; 4\r\n            (  124.60   -25.93    24.75     0.46)  ; 5\r\n            (  121.56   -23.06    25.05     0.46)  ; 6\r\n            (  118.61   -22.55    25.45     0.46)  ; 7\r\n            (  114.78   -22.26    26.15     0.46)  ; 8\r\n            (  114.78   -22.26    26.13     0.46)  ; 9\r\n            (  112.34   -19.85    26.77     0.46)  ; 10\r\n            (  112.34   -19.85    26.75     0.46)  ; 11\r\n            (  107.33   -18.63    26.92     0.46)  ; 12\r\n            (  105.78   -16.01    27.63     0.46)  ; 13\r\n            (  105.78   -16.01    27.60     0.46)  ; 14\r\n            (  103.02   -14.27    30.33     0.46)  ; 15\r\n            (  101.86   -13.35    31.88     0.46)  ; 16\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  269.53   -81.56     7.15     0.46)  ; 1\r\n              (  256.33   -77.48     7.90     0.46)  ; 2\r\n              (  228.93   -73.16     6.65     0.46)  ; 3\r\n              (  222.91   -71.59     6.65     0.46)  ; 4\r\n              (  211.75   -68.22     9.67     0.46)  ; 5\r\n              (  207.07   -66.34    10.30     0.46)  ; 6\r\n              (  202.83   -64.34    10.30     0.46)  ; 7\r\n              (  196.87   -60.96    10.85     0.46)  ; 8\r\n              (  184.13   -50.82    15.73     0.46)  ; 9\r\n              (  173.14   -46.22    17.27     0.46)  ; 10\r\n              (  161.96   -44.64    19.25     0.46)  ; 11\r\n              (  158.17   -42.55    18.63     0.46)  ; 12\r\n              (  145.28   -37.81    19.73     0.46)  ; 13\r\n              (  105.16   -11.37    27.47     0.46)  ; 14\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  241.79   -73.72     8.10     0.46)  ; 1\r\n              (  248.12   -74.63     8.10     0.46)  ; 2\r\n              (  231.62   -72.53     5.77     0.46)  ; 3\r\n              (  192.81   -57.73    13.97     0.46)  ; 4\r\n              (  188.58   -55.74    14.50     0.46)  ; 5\r\n              (  120.09   -22.80    25.45     0.46)  ; 6\r\n            )  ;  End of markers\r\n             High\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (  281.63   -88.84     4.20     0.46)  ; 1, R-2-2-2\r\n        (  280.91   -87.82     3.10     0.46)  ; 2\r\n        (  280.47   -87.93     3.08     0.46)  ; 3\r\n        (  278.98   -87.67     0.95     0.46)  ; 4\r\n        (  276.49   -87.06    -0.32     0.46)  ; 5\r\n        (  275.91   -86.60    -2.78     0.46)  ; 6\r\n        (  275.64   -85.46    -5.15     0.46)  ; 7\r\n        (  275.51   -84.91    -6.93     0.46)  ; 8\r\n        (  275.07   -85.00    -6.95     0.46)  ; 9\r\n        (  273.95   -82.28    -8.77     0.46)  ; 10\r\n        (  272.49   -82.02   -10.97     0.46)  ; 11\r\n        (  272.49   -82.02   -11.00     0.46)  ; 12\r\n        (  272.22   -80.90   -13.55     0.46)  ; 13\r\n        (  271.78   -81.00   -13.57     0.46)  ; 14\r\n        (  270.04   -79.61   -15.50     0.46)  ; 15\r\n        (  269.31   -78.58   -18.13     0.46)  ; 16\r\n        (  269.31   -78.58   -18.15     0.46)  ; 17\r\n        (  268.11   -79.47   -20.23     0.46)  ; 18\r\n        (  267.00   -76.75   -21.60     0.46)  ; 19\r\n        (  264.51   -76.13   -22.27     0.46)  ; 20\r\n        (  263.35   -75.21   -24.00     0.46)  ; 21\r\n        (  263.35   -75.21   -24.02     0.46)  ; 22\r\n        (  261.43   -75.07   -25.92     0.46)  ; 23\r\n        (  260.09   -75.37   -27.88     0.46)  ; 24\r\n        (  258.61   -75.13   -29.67     0.46)  ; 25\r\n        (  258.61   -75.13   -29.70     0.46)  ; 26\r\n        (  256.25   -75.09   -31.38     0.46)  ; 27\r\n        (  256.25   -75.09   -31.40     0.46)  ; 28\r\n        (  254.20   -74.37   -33.03     0.46)  ; 29\r\n        (  254.20   -74.37   -33.05     0.46)  ; 30\r\n        (  253.05   -73.45   -34.55     0.46)  ; 31\r\n        (  250.42   -72.28   -37.08     0.46)  ; 32\r\n        (  250.42   -72.28   -37.10     0.46)  ; 33\r\n        (  248.81   -71.45   -39.35     0.46)  ; 34\r\n        (  248.81   -71.45   -39.42     0.46)  ; 35\r\n        (  246.17   -70.28   -41.60     0.46)  ; 36\r\n        (  245.59   -69.82   -44.07     0.46)  ; 37\r\n        (  245.59   -69.82   -44.10     0.46)  ; 38\r\n        (  243.41   -68.54   -46.08     0.46)  ; 39\r\n        (  240.90   -67.93   -47.10     0.46)  ; 40\r\n        (  240.90   -67.93   -47.13     0.46)  ; 41\r\n        (  238.41   -67.33   -49.02     0.46)  ; 42\r\n        (  235.79   -66.14   -51.20     0.46)  ; 43\r\n        (  234.31   -65.90   -52.75     0.46)  ; 44\r\n        (  230.92   -65.50   -53.97     0.46)  ; 45\r\n        (  230.92   -65.50   -54.00     0.46)  ; 46\r\n        (  227.98   -64.99   -54.58     0.46)  ; 47\r\n        (  225.66   -63.14   -56.75     0.46)  ; 48\r\n        (  225.66   -63.14   -56.80     0.46)  ; 49\r\n        (  223.16   -62.54   -59.20     0.46)  ; 50\r\n        (  220.26   -60.23   -61.52     0.46)  ; 51\r\n        (  220.26   -60.23   -61.57     0.46)  ; 52\r\n        (  218.66   -59.41   -63.15     0.46)  ; 53\r\n        (  218.66   -59.41   -63.20     0.46)  ; 54\r\n        (  217.23   -57.35   -62.88     0.46)  ; 55\r\n        (  215.82   -55.30   -64.70     0.46)  ; 56\r\n\r\n        (Dot\r\n          (Color Yellow)\r\n          (Name \"Marker 1\")\r\n          (  242.07   -68.85   -47.13     0.46)  ; 1\r\n        )  ;  End of markers\r\n\r\n        (OpenCircle\r\n          (Color Yellow)\r\n          (Name \"Marker 2\")\r\n          (  262.19   -74.29   -25.92     0.46)  ; 1\r\n          (  238.86   -67.22   -49.02     0.46)  ; 2\r\n          (  228.42   -64.89   -54.58     0.46)  ; 3\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  287.43   -87.48     6.15     0.46)  ; 1, R-2-2-3\r\n        (  290.12   -86.85     4.78     0.46)  ; 2\r\n        (\r\n          (  290.48   -84.38     3.47     0.46)  ; 1, R-2-2-3-1\r\n          (  290.65   -83.15     1.75     0.46)  ; 2\r\n          (  290.22   -83.25     1.73     0.46)  ; 3\r\n          (  292.57   -83.30     1.70     0.46)  ; 4\r\n          (  293.65   -81.85     0.50     0.46)  ; 5\r\n          (  293.65   -81.85     0.47     0.46)  ; 6\r\n          (  295.44   -81.43    -1.07     0.46)  ; 7\r\n          (  296.52   -79.98    -3.10     0.46)  ; 8\r\n          (  297.40   -79.77    -4.92     0.46)  ; 9\r\n          (  297.40   -79.77    -4.95     0.46)  ; 10\r\n          (  298.93   -78.22    -7.05     0.46)  ; 11\r\n          (  298.93   -78.22    -7.07     0.46)  ; 12\r\n          (  299.82   -78.01    -8.63     0.46)  ; 13\r\n          (  302.69   -76.15   -10.25     0.46)  ; 14\r\n          (  302.69   -76.15   -10.28     0.46)  ; 15\r\n          (  303.44   -75.38   -12.17     0.46)  ; 16\r\n          (  304.38   -73.36   -13.52     0.46)  ; 17\r\n          (  306.66   -71.04   -14.35     0.46)  ; 18\r\n          (  306.66   -71.04   -14.43     0.46)  ; 19\r\n          (  309.66   -69.74   -14.67     0.46)  ; 20\r\n          (  310.87   -68.85   -14.67     0.46)  ; 21\r\n          (  312.38   -67.30   -14.67     0.46)  ; 22\r\n          (  315.52   -66.57   -15.55     0.46)  ; 23\r\n          (  317.48   -64.92   -15.55     0.46)  ; 24\r\n          (  319.26   -64.50   -16.65     0.46)  ; 25\r\n          (  320.34   -63.06   -18.42     0.46)  ; 26\r\n          (  322.58   -62.53   -19.77     0.46)  ; 27\r\n          (  322.58   -62.53   -19.82     0.46)  ; 28\r\n          (  323.78   -61.64   -21.18     0.46)  ; 29\r\n          (  323.33   -61.75   -21.18     0.46)  ; 30\r\n          (  324.55   -60.87   -22.12     0.46)  ; 31\r\n          (  325.44   -60.66   -22.12     0.46)  ; 32\r\n          (  328.60   -58.13   -21.77     0.46)  ; 33\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  302.50   -77.38   -10.28     0.46)  ; 1\r\n            (  306.17   -72.94   -14.48     0.46)  ; 2\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  294.99   -81.54    -1.07     0.46)  ; 1\r\n            (  299.51   -78.69    -7.07     0.46)  ; 2\r\n            (  303.44   -75.38   -12.17     0.46)  ; 3\r\n            (  315.52   -66.57   -15.55     0.46)  ; 4\r\n          )  ;  End of markers\r\n          (\r\n            (  329.69   -56.68   -23.98     0.46)  ; 1, R-2-2-3-1-1\r\n            (  331.16   -56.94   -23.98     0.46)  ; 2\r\n            (  334.15   -55.64   -25.50     0.46)  ; 3\r\n            (  334.15   -55.64   -25.55     0.46)  ; 4\r\n            (  335.75   -56.45   -27.50     0.46)  ; 5\r\n            (  335.75   -56.45   -27.53     0.46)  ; 6\r\n            (  336.20   -56.35   -27.97     0.46)  ; 7\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  333.39   -56.41   -25.55     0.46)  ; 1\r\n            )  ;  End of markers\r\n            (\r\n              (  339.46   -56.18   -29.27     0.46)  ; 1, R-2-2-3-1-1-1\r\n              (  339.19   -55.05   -30.60     0.46)  ; 2\r\n              (\r\n                (  343.26   -52.31   -31.50     0.46)  ; 1, R-2-2-3-1-1-1-1\r\n                (  344.02   -51.53   -33.03     0.46)  ; 2\r\n                (  345.55   -49.98   -34.08     0.46)  ; 3\r\n                (  347.60   -50.69   -35.27     0.46)  ; 4\r\n                (  348.10   -48.79   -36.50     0.46)  ; 5\r\n                (  351.09   -47.49   -38.57     0.46)  ; 6\r\n                (  353.64   -46.29   -39.85     0.46)  ; 7\r\n                (  353.64   -46.29   -39.88     0.46)  ; 8\r\n                (  353.24   -44.59   -39.88     0.46)  ; 9\r\n                (  356.41   -42.06   -41.08     0.46)  ; 10\r\n                (  357.48   -40.61   -41.97     0.46)  ; 11\r\n                (  358.43   -38.60   -44.10     0.46)  ; 12\r\n                (  360.39   -36.95   -46.10     0.46)  ; 13\r\n                (  363.56   -34.41   -47.80     0.46)  ; 14\r\n                (  363.56   -34.41   -47.82     0.46)  ; 15\r\n                (  367.06   -31.21   -49.32     0.46)  ; 16\r\n                (  367.06   -31.21   -49.38     0.46)  ; 17\r\n                (  371.57   -28.35   -50.72     0.46)  ; 18\r\n                (  373.85   -26.03   -52.45     0.46)  ; 19\r\n                (  373.85   -26.03   -52.47     0.46)  ; 20\r\n                (  376.26   -24.28   -54.40     0.46)  ; 21\r\n                (  378.24   -22.61   -55.60     0.46)  ; 22\r\n                (  381.55   -20.64   -56.57     0.46)  ; 23\r\n                (  384.72   -18.11   -58.45     0.46)  ; 24\r\n                (  385.53   -15.53   -59.75     0.46)  ; 25\r\n                (  385.53   -15.53   -59.78     0.46)  ; 26\r\n                (  387.63   -14.44   -60.85     0.46)  ; 27\r\n                (  390.49   -12.57   -62.28     0.46)  ; 28\r\n                (  392.46   -10.91   -64.30     0.46)  ; 29\r\n                (  392.46   -10.91   -64.32     0.46)  ; 30\r\n                (  395.76    -8.95   -66.17     0.46)  ; 31\r\n                (  395.32    -9.05   -66.17     0.46)  ; 32\r\n                (  398.62    -7.08   -68.47     0.46)  ; 33\r\n                (  400.91    -4.76   -70.55     0.46)  ; 34\r\n                (  400.91    -4.76   -70.57     0.46)  ; 35\r\n                (  402.16    -2.07   -71.15     0.46)  ; 36\r\n                (  404.75     0.92   -72.93     0.46)  ; 37\r\n                (  405.75     4.74   -74.32     0.46)  ; 38\r\n                (  407.50     9.33   -76.07     0.46)  ; 39\r\n                (  409.97    12.89   -78.47     0.46)  ; 40\r\n                (  409.97    12.89   -78.50     0.46)  ; 41\r\n                (  411.62    13.87   -80.60     0.46)  ; 42\r\n                (  411.62    13.87   -80.63     0.46)  ; 43\r\n                (  412.88    16.56   -82.47     0.46)  ; 44\r\n                (  412.88    16.56   -82.55     0.46)  ; 45\r\n                (  415.91    19.66   -85.10     0.46)  ; 46\r\n                (  415.91    19.66   -86.55     0.46)  ; 47\r\n                (  419.36    21.06   -87.72     0.46)  ; 48\r\n                (  420.74    23.18   -89.72     0.46)  ; 49\r\n                (  420.74    23.18   -89.75     0.46)  ; 50\r\n                (  423.39    27.98   -91.22     0.46)  ; 51\r\n                (  425.80    29.73   -93.45     0.46)  ; 52\r\n                (  428.26    33.30   -94.55     0.46)  ; 53\r\n                (  430.55    35.63   -96.75     0.46)  ; 54\r\n                (  432.30    40.20   -98.05     0.46)  ; 55\r\n                (  433.95    41.19   -98.72     0.46)  ; 56\r\n                (  435.35    43.31   -98.72     0.46)  ; 57\r\n                (  435.18    43.27   -98.77     0.46)  ; 58\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  355.91   -43.96   -41.08     0.46)  ; 1\r\n                  (  359.09   -41.44   -41.97     0.46)  ; 2\r\n                  (  378.96   -23.64   -55.60     0.46)  ; 3\r\n                  (  411.98    16.35   -82.55     0.46)  ; 4\r\n                  (  424.52    31.23   -93.45     0.46)  ; 5\r\n                  (  428.62    35.78   -96.75     0.46)  ; 6\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  347.78   -49.45   -36.50     0.46)  ; 1\r\n                  (  376.81   -20.56   -54.70     0.46)  ; 2\r\n                  (  388.20   -14.90   -60.85     0.46)  ; 3\r\n                  (  402.74    -2.54   -71.15     0.46)  ; 4\r\n                  (  405.75     4.74   -74.42     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (  436.75    46.63   -98.77     0.46)  ; 1, R-2-2-3-1-1-1-1-1\r\n                  (  440.06    48.59   -99.85     0.46)  ; 2\r\n                  (  441.94    52.62   -99.17     0.46)  ; 3\r\n                  (  441.94    52.62   -99.22     0.46)  ; 4\r\n                  (  444.46    57.99   -99.78     0.46)  ; 5\r\n                  (  446.34    62.02   -99.78     0.46)  ; 6\r\n                  (  446.57    65.05   -99.78     0.46)  ; 7\r\n                  (  448.58    68.51   -99.78     0.46)  ; 8\r\n                  (  449.89    72.99  -101.10     0.46)  ; 9\r\n                  (  452.34    76.56  -101.60     0.46)  ; 10\r\n                  (  453.29    78.57  -101.67     0.46)  ; 11\r\n                  (  456.97    83.01  -102.20     0.46)  ; 12\r\n                  (  457.02    84.81  -103.07     0.46)  ; 13\r\n                  (  458.00    88.63  -103.97     0.46)  ; 14\r\n                  (  458.36    91.11  -105.40     0.46)  ; 15\r\n                  (  459.32    93.11  -107.05     0.46)  ; 16\r\n                  (  460.52    93.99  -108.47     0.46)  ; 17\r\n                  (  459.85    96.82  -109.65     0.46)  ; 18\r\n                  (  459.85    96.82  -109.67     0.46)  ; 19\r\n                  (  461.81    98.47  -111.82     0.46)  ; 20\r\n                  (  463.83   101.94  -114.03     0.46)  ; 21\r\n                  (  463.83   101.94  -114.22     0.46)  ; 22\r\n                  (  466.43   104.94  -115.60     0.46)  ; 23\r\n                  (  466.43   104.94  -116.17     0.46)  ; 24\r\n                  (  468.71   107.26  -116.45     0.46)  ; 25\r\n                  (  472.12   112.84  -117.50     0.46)  ; 26\r\n                  (  473.82   115.63  -117.50     0.46)  ; 27\r\n                  (  475.30   121.35  -118.15     0.46)  ; 28\r\n                  (  475.99   124.50  -119.15     0.46)  ; 29\r\n                  (  478.49   129.86  -119.52     0.46)  ; 30\r\n                  (  479.92   133.79  -119.52     0.46)  ; 31\r\n                  (  482.66   136.21  -118.27     0.46)  ; 32\r\n                  (  483.78   139.46  -118.27     0.46)  ; 33\r\n                  (  485.53   144.06  -118.82     0.46)  ; 34\r\n                  (  487.23   146.83  -119.85     0.46)  ; 35\r\n                  (  486.89   150.34  -120.42     0.46)  ; 36\r\n                  (  488.46   153.69  -122.35     0.46)  ; 37\r\n                  (  491.11   158.49  -122.85     0.46)  ; 38\r\n                  (  493.57   162.05  -124.07     0.46)  ; 39\r\n                  (  495.58   165.51  -124.82     0.46)  ; 40\r\n                  (  496.84   168.19  -126.20     0.46)  ; 41\r\n                  (  497.46   169.54  -128.38     0.46)  ; 42\r\n                  (  497.46   169.54  -128.40     0.46)  ; 43\r\n                  (  498.54   170.98  -129.85     0.46)  ; 44\r\n                  (  498.54   170.98  -129.88     0.46)  ; 45\r\n                  (  500.50   172.63  -131.27     0.46)  ; 46\r\n                  (  500.50   172.63  -131.30     0.46)  ; 47\r\n                  (  500.24   173.77  -133.90     0.46)  ; 48\r\n                  (  500.24   173.77  -133.95     0.46)  ; 49\r\n                  (  499.97   174.89  -136.00     0.46)  ; 50\r\n                  (  499.97   174.89  -136.05     0.46)  ; 51\r\n                  (  501.18   175.78  -138.77     0.46)  ; 52\r\n                  (  501.18   175.78  -138.80     0.46)  ; 53\r\n                  (  503.60   177.54  -139.38     0.46)  ; 54\r\n                  (  504.99   179.66  -139.38     0.46)  ; 55\r\n                  (  508.16   182.19  -140.55     0.46)  ; 56\r\n                  (  509.87   184.99  -141.60     0.46)  ; 57\r\n                  (  510.10   188.03  -143.13     0.46)  ; 58\r\n                  (  512.06   189.68  -144.35     0.46)  ; 59\r\n                  (  513.00   191.69  -145.68     0.46)  ; 60\r\n                  (  513.62   193.03  -145.98     0.46)  ; 61\r\n                  (  513.62   193.03  -146.00     0.46)  ; 62\r\n                  (  515.07   196.95  -147.27     0.46)  ; 63\r\n                  (  518.11   200.05  -149.40     0.46)  ; 64\r\n                  (  520.40   202.38  -149.40     0.46)  ; 65\r\n                  (  520.40   202.38  -150.15     0.46)  ; 66\r\n                  (  524.38   207.49  -150.25     0.46)  ; 67\r\n                  (  527.29   211.15  -148.80     0.46)  ; 68\r\n                  (  528.23   213.16  -148.80     0.46)  ; 69\r\n                  (  531.89   217.61  -148.80     0.46)  ; 70\r\n                  (  533.34   221.53  -149.22     0.46)  ; 71\r\n                  (  534.28   223.55  -149.50     0.46)  ; 72\r\n                  (  537.00   225.98  -150.57     0.46)  ; 73\r\n                  (  538.03   225.62  -150.20     0.46)  ; 74\r\n                  (  540.85   225.68  -151.27     0.46)  ; 75\r\n                  (  544.33   228.88  -151.27     0.46)  ; 76\r\n                  (  548.26   232.20  -151.27     0.46)  ; 77\r\n                  (  551.00   234.63  -151.27     0.46)  ; 78\r\n                  (  553.02   238.09  -152.38     0.46)  ; 79\r\n                  (  555.74   240.52  -152.38     0.46)  ; 80\r\n                  (  557.84   241.60  -152.38     0.46)  ; 81\r\n                  (  559.87   245.07  -152.38     0.46)  ; 82\r\n                  (  561.83   246.72  -153.10     0.46)  ; 83\r\n                  (  564.28   250.28  -153.40     0.46)  ; 84\r\n                  (  567.46   252.82  -154.05     0.46)  ; 85\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  440.33    47.47   -99.88     0.46)  ; 1\r\n                    (  462.81   102.30  -114.22     0.46)  ; 2\r\n                    (  515.44   199.42  -149.40     0.46)  ; 3\r\n                    (  536.60   227.67  -150.20     0.46)  ; 4\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  448.46    69.07   -99.78     0.46)  ; 1\r\n                    (  453.74    78.67  -101.67     0.46)  ; 2\r\n                    (  475.85   125.06  -119.15     0.46)  ; 3\r\n                    (  486.80   146.73  -119.85     0.46)  ; 4\r\n                    (  487.02   149.77  -120.42     0.46)  ; 5\r\n                    (  493.57   162.05  -124.07     0.46)  ; 6\r\n                    (  501.63   175.88  -138.80     0.46)  ; 7\r\n                    (  510.00   184.42  -141.60     0.46)  ; 8\r\n                    (  513.94   193.71  -146.00     0.46)  ; 9\r\n                    (  525.26   207.69  -150.25     0.46)  ; 10\r\n                    (  562.27   246.82  -153.10     0.46)  ; 11\r\n                    (  564.73   250.39  -153.67     0.46)  ; 12\r\n                    (  567.02   252.72  -154.15     0.46)  ; 13\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  437.68    42.67   -97.40     0.46)  ; 1, R-2-2-3-1-1-1-1-2\r\n                  (  437.68    42.67   -97.32     0.46)  ; 2\r\n                  (  441.34    41.13   -96.27     0.46)  ; 3\r\n                  (  443.07    39.75   -95.15     0.46)  ; 4\r\n                  (  445.39    37.90   -93.20     0.46)  ; 5\r\n                  (  446.10    36.88   -91.27     0.46)  ; 6\r\n                  (  447.57    36.63   -89.50     0.46)  ; 7\r\n                  (  448.81    33.34   -88.40     0.46)  ; 8\r\n                  (  450.70    31.38   -87.45     0.46)  ; 9\r\n                  (  451.54    29.79   -86.00     0.46)  ; 10\r\n                  (  451.54    29.79   -86.03     0.46)  ; 11\r\n                  (  452.70    28.86   -86.03     0.46)  ; 12\r\n                  (  454.88    27.58   -85.15     0.46)  ; 13\r\n                  (  456.44    24.96   -85.15     0.46)  ; 14\r\n                  (  458.17    23.58   -85.15     0.46)  ; 15\r\n                  (  461.66    20.81   -84.08     0.46)  ; 16\r\n                  (  465.84    17.02   -83.07     0.46)  ; 17\r\n                  (  471.31    15.90   -84.30     0.46)  ; 18\r\n                  (  476.44    14.11   -83.80     0.46)  ; 19\r\n                  (  479.96    13.15   -82.90     0.46)  ; 20\r\n                  (  483.35    12.75   -82.15     0.46)  ; 21\r\n                  (  483.35    12.75   -81.62     0.46)  ; 22\r\n                  (  486.56    11.11   -81.67     0.46)  ; 23\r\n                  (  491.43    10.46   -81.30     0.46)  ; 24\r\n                  (  496.87     9.34   -80.80     0.46)  ; 25\r\n                  (  500.09     7.71   -80.57     0.46)  ; 26\r\n                  (  503.16     6.64   -79.50     0.46)  ; 27\r\n                  (  506.69     5.67   -78.47     0.46)  ; 28\r\n                  (  509.01     3.83   -77.22     0.46)  ; 29\r\n                  (  509.01     3.83   -77.30     0.46)  ; 30\r\n                  (  513.42     3.07   -76.50     0.46)  ; 31\r\n                  (  516.69     3.24   -75.78     0.46)  ; 32\r\n                  (  516.69     3.24   -75.80     0.46)  ; 33\r\n                  (  520.21     2.27   -77.70     0.46)  ; 34\r\n                  (  523.10    -0.03   -79.15     0.46)  ; 35\r\n                  (  528.68    -1.71   -79.80     0.46)  ; 36\r\n                  (  528.68    -1.71   -79.82     0.46)  ; 37\r\n                  (  533.81    -3.50   -80.22     0.46)  ; 38\r\n                  (  538.30    -6.63   -78.00     0.46)  ; 39\r\n                  (  541.25    -7.12   -76.47     0.46)  ; 40\r\n                  (  544.52    -6.95   -74.63     0.46)  ; 41\r\n                  (  548.04    -7.93   -73.10     0.46)  ; 42\r\n                  (  550.53    -8.53   -71.77     0.46)  ; 43\r\n                  (  550.53    -8.53   -71.85     0.46)  ; 44\r\n                  (  554.24    -8.26   -70.70     0.46)  ; 45\r\n                  (  554.24    -8.26   -70.72     0.46)  ; 46\r\n                  (  558.07    -8.55   -69.22     0.46)  ; 47\r\n                  (  559.81    -9.94   -67.88     0.46)  ; 48\r\n                  (  563.21   -10.34   -66.65     0.46)  ; 49\r\n                  (  563.21   -10.34   -66.68     0.46)  ; 50\r\n                  (  566.02   -10.28   -65.65     0.46)  ; 51\r\n                  (  570.44   -11.03   -65.05     0.46)  ; 52\r\n                  (  574.09   -12.56   -65.20     0.46)  ; 53\r\n                  (  574.09   -12.56   -65.22     0.46)  ; 54\r\n                  (  578.92   -15.02   -64.92     0.46)  ; 55\r\n                  (  581.72   -14.96   -64.92     0.46)  ; 56\r\n                  (  586.00   -15.15   -64.13     0.46)  ; 57\r\n                  (  589.27   -14.98   -63.38     0.46)  ; 58\r\n                  (  594.38   -16.76   -62.30     0.46)  ; 59\r\n                  (  594.38   -16.76   -62.33     0.46)  ; 60\r\n                  (  597.33   -17.25   -61.42     0.46)  ; 61\r\n                  (  600.41   -18.32   -60.45     0.46)  ; 62\r\n                  (  603.35   -18.82   -59.20     0.46)  ; 63\r\n                  (  604.91   -21.45   -58.55     0.46)  ; 64\r\n                  (  609.47   -22.77   -59.58     0.46)  ; 65\r\n                  (  616.07   -24.81   -59.07     0.46)  ; 66\r\n                  (  616.07   -24.81   -59.10     0.46)  ; 67\r\n                  (  619.90   -25.10   -59.10     0.46)  ; 68\r\n                  (  623.42   -26.07   -59.10     0.46)  ; 69\r\n                  (  626.24   -26.00   -59.85     0.46)  ; 70\r\n                  (  629.18   -26.51   -58.90     0.46)  ; 71\r\n                  (  632.26   -27.57   -57.30     0.46)  ; 72\r\n                  (  632.26   -27.57   -57.33     0.46)  ; 73\r\n                  (  633.72   -27.83   -56.55     0.46)  ; 74\r\n                  (  640.64   -29.19   -55.70     0.46)  ; 75\r\n                  (  646.23   -30.87   -55.70     0.46)  ; 76\r\n                  (  647.78   -33.50   -55.70     0.46)  ; 77\r\n                  (  652.21   -34.25   -55.45     0.46)  ; 78\r\n                  (  655.59   -34.65   -54.75     0.46)  ; 79\r\n                  (  658.98   -35.04   -54.17     0.46)  ; 80\r\n                  (  664.60   -34.94   -54.45     0.46)  ; 81\r\n                  (  669.34   -35.01   -53.67     0.46)  ; 82\r\n                  (  669.34   -35.01   -53.70     0.46)  ; 83\r\n                  (  674.15   -37.47   -52.67     0.46)  ; 84\r\n                  (  676.51   -37.51   -53.95     0.46)  ; 85\r\n                  (  676.51   -37.51   -53.97     0.46)  ; 86\r\n                  (  679.46   -38.01   -54.72     0.46)  ; 87\r\n                  (  681.96   -38.62   -55.97     0.46)  ; 88\r\n                  (  686.70   -38.71   -56.42     0.46)  ; 89\r\n                  (  689.96   -38.54   -56.42     0.46)  ; 90\r\n                  (  693.03   -39.61   -55.92     0.46)  ; 91\r\n                  (  693.03   -39.61   -55.90     0.46)  ; 92\r\n                  (  694.94   -39.75   -54.65     0.46)  ; 93\r\n                  (  698.47   -40.72   -53.82     0.46)  ; 94\r\n                  (  702.17   -40.46   -53.05     0.46)  ; 95\r\n                  (  706.73   -41.77   -53.05     0.46)  ; 96\r\n                  (  712.63   -42.78   -52.28     0.46)  ; 97\r\n                  (  716.73   -44.21   -53.95     0.46)  ; 98\r\n                  (  716.73   -44.21   -53.97     0.46)  ; 99\r\n                  (  718.01   -45.70   -53.97     0.46)  ; 100\r\n                  (  721.59   -44.87   -54.97     0.46)  ; 101\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  499.32     6.94   -80.57     0.46)  ; 1\r\n                    (  553.17    -9.70   -70.72     0.46)  ; 2\r\n                    (  556.56   -10.11   -69.22     0.46)  ; 3\r\n                    (  593.18   -17.63   -62.33     0.46)  ; 4\r\n                    (  651.44   -35.03   -55.45     0.46)  ; 5\r\n                    (  673.84   -38.13   -52.67     0.46)  ; 6\r\n                    (  697.72   -41.50   -53.82     0.46)  ; 7\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  452.70    28.86   -86.03     0.46)  ; 1\r\n                    (  456.57    24.39   -85.15     0.46)  ; 2\r\n                    (  465.84    17.02   -83.07     0.46)  ; 3\r\n                    (  486.56    11.11   -81.67     0.46)  ; 4\r\n                    (  516.69     3.24   -75.80     0.46)  ; 5\r\n                    (  533.81    -3.50   -80.22     0.46)  ; 6\r\n                    (  589.40   -15.55   -63.38     0.46)  ; 7\r\n                    (  600.41   -18.32   -60.45     0.46)  ; 8\r\n                    (  604.78   -20.88   -58.55     0.46)  ; 9\r\n                    (  626.24   -26.00   -59.85     0.46)  ; 10\r\n                    (  632.71   -27.47   -56.55     0.46)  ; 11\r\n                    (  639.18   -28.94   -55.70     0.46)  ; 12\r\n                    (  645.78   -30.98   -55.70     0.46)  ; 13\r\n                    (  681.52   -38.72   -55.97     0.46)  ; 14\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  721.64   -43.07   -54.58     0.46)  ; 1, R-2-2-3-1-1-1-1-2-1\r\n                    (  722.00   -40.58   -53.75     0.46)  ; 2\r\n                    (  721.70   -35.28   -54.27     0.46)  ; 3\r\n                    (  721.92   -32.24   -54.27     0.46)  ; 4\r\n                    (  723.05   -28.99   -53.78     0.46)  ; 5\r\n                    (  723.99   -26.98   -52.92     0.46)  ; 6\r\n                    (  724.94   -24.97   -52.92     0.46)  ; 7\r\n                    (  728.37   -23.56   -52.15     0.46)  ; 8\r\n                    (  729.90   -22.01   -50.77     0.46)  ; 9\r\n                    (  731.74   -19.79   -49.72     0.46)  ; 10\r\n                    (  734.46   -17.36   -49.40     0.46)  ; 11\r\n                    (  736.73   -15.04   -49.10     0.46)  ; 12\r\n                    (  738.58   -12.81   -49.10     0.46)  ; 13\r\n                    (  741.94    -9.04   -48.35     0.46)  ; 14\r\n                    (  743.00    -7.59   -47.67     0.46)  ; 15\r\n                    (  743.18    -6.36   -47.25     0.46)  ; 16\r\n                    (  745.65    -2.80   -46.55     0.46)  ; 17\r\n                    (  747.37     0.00   -44.52     0.46)  ; 18\r\n                    (  750.36     1.31   -44.10     0.46)  ; 19\r\n                    (  752.96     4.29   -44.10     0.46)  ; 20\r\n                    (  755.86     7.97   -43.62     0.46)  ; 21\r\n                    (  756.04     9.20   -46.60     0.46)  ; 22\r\n                    (  755.64    10.90   -48.02     0.46)  ; 23\r\n                    (  755.56    13.27   -49.15     0.46)  ; 24\r\n                    (  755.56    13.27   -49.17     0.46)  ; 25\r\n                    (  756.37    15.85   -50.10     0.46)  ; 26\r\n                    (  755.58    19.25   -50.22     0.46)  ; 27\r\n                    (  755.58    19.25   -50.25     0.46)  ; 28\r\n                    (  756.84    21.93   -49.92     0.46)  ; 29\r\n                    (  758.85    25.39   -48.00     0.46)  ; 30\r\n                    (  759.79    27.39   -47.60     0.46)  ; 31\r\n                    (  762.66    29.26   -48.67     0.46)  ; 32\r\n                    (  764.62    30.91   -48.67     0.46)  ; 33\r\n                    (  766.13    32.46   -49.70     0.46)  ; 34\r\n                    (  767.53    34.59   -50.15     0.46)  ; 35\r\n                    (  769.23    37.37   -50.57     0.46)  ; 36\r\n                    (  770.93    40.16   -50.57     0.46)  ; 37\r\n                    (  772.76    42.38   -50.97     0.46)  ; 38\r\n                    (  775.54    46.61   -50.97     0.46)  ; 39\r\n                    (  776.36    49.19   -50.07     0.46)  ; 40\r\n                    (  777.47    52.44   -49.05     0.46)  ; 41\r\n                    (  781.60    56.99   -49.35     0.46)  ; 42\r\n                    (  784.46    58.85   -49.30     0.46)  ; 43\r\n                    (  786.79    62.98   -48.72     0.46)  ; 44\r\n                    (  789.69    66.66   -48.20     0.46)  ; 45\r\n                    (  791.85    69.54   -49.02     0.46)  ; 46\r\n                    (  793.86    73.00   -50.27     0.46)  ; 47\r\n                    (  794.27    77.25   -49.13     0.46)  ; 48\r\n                    (  796.73    80.82   -48.05     0.46)  ; 49\r\n                    (  796.96    83.86   -46.63     0.46)  ; 50\r\n                    (  797.58    85.21   -45.68     0.46)  ; 51\r\n                    (  797.94    87.68   -44.72     0.46)  ; 52\r\n                    (  799.48    89.23   -43.78     0.46)  ; 53\r\n                    (  799.70    92.27   -43.78     0.46)  ; 54\r\n                    (  801.09    94.38   -43.78     0.46)  ; 55\r\n                    (  803.95    96.24   -43.78     0.46)  ; 56\r\n                    (  806.50    97.43   -43.78     0.46)  ; 57\r\n                    (  809.35    99.31   -43.78     0.46)  ; 58\r\n                    (  810.61   101.99   -43.78     0.46)  ; 59\r\n                    (  811.28   105.13   -43.78     0.46)  ; 60\r\n                    (  811.12   109.87   -44.27     0.46)  ; 61\r\n                    (  811.80   113.02   -42.05     0.46)  ; 62\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  753.62     7.44   -43.62     0.46)  ; 1\r\n                      (  773.32    46.10   -50.97     0.46)  ; 2\r\n                      (  792.08    72.58   -50.27     0.46)  ; 3\r\n                      (  794.94    80.40   -48.05     0.46)  ; 4\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  722.00   -40.58   -53.75     0.46)  ; 1\r\n                      (  728.55   -22.33   -50.77     0.46)  ; 2\r\n                      (  734.01   -17.47   -49.40     0.46)  ; 3\r\n                      (  745.65    -2.80   -46.55     0.46)  ; 4\r\n                      (  758.98    24.82   -48.00     0.46)  ; 5\r\n                      (  760.36    26.94   -47.60     0.46)  ; 6\r\n                      (  777.60    51.87   -49.05     0.46)  ; 7\r\n                      (  781.28    56.32   -49.15     0.46)  ; 8\r\n                      (  784.46    58.85   -49.30     0.46)  ; 9\r\n                      (  786.34    62.88   -48.72     0.46)  ; 10\r\n                      (  798.39    87.78   -44.72     0.46)  ; 11\r\n                      (  800.28    91.80   -43.78     0.46)  ; 12\r\n                      (  803.18    95.47   -43.78     0.46)  ; 13\r\n                      (  805.74    96.66   -43.78     0.46)  ; 14\r\n                      (  811.15   105.70   -43.78     0.46)  ; 15\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  724.23   -46.04   -56.45     0.46)  ; 1, R-2-2-3-1-1-1-1-2-2\r\n                    (  727.62   -46.43   -56.52     0.46)  ; 2\r\n                    (  732.31   -48.32   -56.40     0.46)  ; 3\r\n                    (  736.41   -49.75   -58.05     0.46)  ; 4\r\n                    (  740.20   -51.85   -58.55     0.46)  ; 5\r\n                    (  744.48   -52.05   -58.55     0.46)  ; 6\r\n                    (  746.53   -52.75   -57.37     0.46)  ; 7\r\n                    (  748.32   -52.34   -57.80     0.46)  ; 8\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  747.56   -53.11   -57.80     0.46)  ; 1\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  728.20   -46.90   -56.52     0.46)  ; 1\r\n                      (  740.20   -51.85   -58.55     0.46)  ; 2\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                )  ;  End of split\r\n              |\r\n                (  337.44   -53.80   -30.67     0.46)  ; 1, R-2-2-3-1-1-1-2\r\n                (  337.44   -53.80   -30.73     0.46)  ; 2\r\n                (  337.62   -52.57   -32.83     0.46)  ; 3\r\n                (  337.62   -52.57   -32.85     0.46)  ; 4\r\n                (  336.91   -51.54   -34.67     0.46)  ; 5\r\n                (  336.19   -50.51   -36.40     0.46)  ; 6\r\n                (  336.69   -48.60   -37.50     0.46)  ; 7\r\n                (  335.58   -45.87   -39.22     0.46)  ; 8\r\n                (  333.89   -42.69   -40.13     0.46)  ; 9\r\n                (  333.09   -39.30   -41.57     0.46)  ; 10\r\n                (  332.39   -38.27   -42.88     0.46)  ; 11\r\n                (  332.03   -34.76   -44.30     0.46)  ; 12\r\n                (  331.37   -31.93   -45.22     0.46)  ; 13\r\n                (  328.66   -28.40   -46.77     0.46)  ; 14\r\n                (  327.55   -25.66   -48.45     0.46)  ; 15\r\n                (  328.36   -23.09   -49.35     0.46)  ; 16\r\n                (  328.90   -19.38   -50.52     0.46)  ; 17\r\n                (  329.97   -17.93   -52.67     0.46)  ; 18\r\n                (  330.66   -14.79   -53.47     0.46)  ; 19\r\n                (  330.66   -14.79   -53.50     0.46)  ; 20\r\n                (  330.88   -11.74   -55.97     0.46)  ; 21\r\n                (  331.56    -8.60   -56.17     0.46)  ; 22\r\n                (  331.35    -5.67   -59.27     0.46)  ; 23\r\n                (  331.35    -5.67   -59.30     0.46)  ; 24\r\n                (  331.44    -2.06   -60.30     0.46)  ; 25\r\n                (  331.35     0.30   -62.77     0.46)  ; 26\r\n                (  331.35     0.30   -62.80     0.46)  ; 27\r\n                (  332.08     5.26   -66.68     0.46)  ; 28\r\n                (  332.08     5.26   -66.77     0.46)  ; 29\r\n                (  331.86     8.20   -68.53     0.46)  ; 30\r\n                (  330.76    10.91   -68.85     0.46)  ; 31\r\n                (  331.71    12.93   -69.40     0.46)  ; 32\r\n                (  331.71    12.93   -69.10     0.46)  ; 33\r\n                (  330.46    16.22   -66.43     0.46)  ; 34\r\n                (  329.72    21.42   -64.38     0.46)  ; 35\r\n                (  329.05    24.25   -62.72     0.46)  ; 36\r\n                (  329.64    29.76   -62.72     0.46)  ; 37\r\n                (  329.92    34.61   -61.70     0.46)  ; 38\r\n                (  330.45    38.32   -60.45     0.46)  ; 39\r\n                (  331.09    39.65   -58.90     0.46)  ; 40\r\n                (  331.09    39.65   -58.92     0.46)  ; 41\r\n                (  330.35    44.85   -57.82     0.46)  ; 42\r\n                (  330.44    48.46   -56.42     0.46)  ; 43\r\n                (  330.09    51.96   -55.80     0.46)  ; 44\r\n                (  331.26    57.02   -54.63     0.46)  ; 45\r\n                (  332.11    61.39   -53.50     0.46)  ; 46\r\n                (  332.11    61.39   -53.53     0.46)  ; 47\r\n                (  331.23    67.16   -51.90     0.46)  ; 48\r\n                (  330.87    68.76   -50.57     0.46)  ; 49\r\n                (  330.60    69.88   -48.13     0.46)  ; 50\r\n                (  330.60    69.88   -48.15     0.46)  ; 51\r\n                (  331.58    73.70   -47.08     0.46)  ; 52\r\n                (  331.05    75.95   -45.32     0.46)  ; 53\r\n                (  331.05    75.95   -45.38     0.46)  ; 54\r\n                (  332.05    79.77   -43.62     0.46)  ; 55\r\n                (  332.72    82.92   -41.93     0.46)  ; 56\r\n                (  333.27    86.63   -42.40     0.46)  ; 57\r\n                (  332.34    90.60   -41.70     0.46)  ; 58\r\n                (  331.86    94.66   -41.70     0.46)  ; 59\r\n                (  332.22    97.13   -40.75     0.46)  ; 60\r\n                (  330.09   100.21   -39.05     0.46)  ; 61\r\n                (  330.09   100.21   -39.08     0.46)  ; 62\r\n                (  328.71   104.08   -39.85     0.46)  ; 63\r\n                (  328.71   104.08   -39.88     0.46)  ; 64\r\n                (  327.33   107.93   -38.55     0.46)  ; 65\r\n                (  326.72   112.57   -38.55     0.46)  ; 66\r\n                (  326.64   114.93   -37.58     0.46)  ; 67\r\n                (  327.18   118.64   -36.33     0.46)  ; 68\r\n                (  327.18   118.64   -36.35     0.46)  ; 69\r\n                (  325.67   123.07   -36.35     0.46)  ; 70\r\n                (  326.21   126.77   -36.35     0.46)  ; 71\r\n                (  325.99   129.71   -35.77     0.46)  ; 72\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  335.35   -48.92   -37.50     0.46)  ; 1\r\n                  (  331.10    93.88   -41.70     0.46)  ; 2\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  328.58   -20.06   -50.52     0.46)  ; 1\r\n                  (  329.85    20.85   -64.38     0.46)  ; 2\r\n                  (  331.63    65.46   -51.90     0.46)  ; 3\r\n                  (  332.58    83.48   -43.60     0.46)  ; 4\r\n                  (  333.27    86.63   -42.40     0.46)  ; 5\r\n                  (  328.58   104.63   -39.88     0.46)  ; 6\r\n                  (  327.03   113.23   -38.55     0.46)  ; 7\r\n                  (  326.73   118.53   -36.35     0.46)  ; 8\r\n                  (  326.47   125.64   -36.35     0.46)  ; 9\r\n                  (  326.44   129.81   -36.70     0.46)  ; 10\r\n                )  ;  End of markers\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  328.95   -29.42   -47.67     0.46)  ; 1\r\n                )  ;  End of markers\r\n                (\r\n                  (  326.01   135.69   -36.78     0.46)  ; 1, R-2-2-3-1-1-1-2-1\r\n                  (  327.00   139.51   -36.78     0.46)  ; 2\r\n                  (  327.68   142.65   -38.57     0.46)  ; 3\r\n                  (  327.68   142.65   -38.55     0.46)  ; 4\r\n                  (  327.38   147.96   -39.63     0.46)  ; 5\r\n                  (  328.23   152.34   -39.63     0.46)  ; 6\r\n                  (  328.64   156.61   -40.70     0.46)  ; 7\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  327.23   142.54   -38.55     0.46)  ; 1\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  330.61   158.26   -38.63     0.46)  ; 1, R-2-2-3-1-1-1-2-1-1\r\n                    (  329.15   158.51   -36.15     0.46)  ; 2\r\n                    (  328.56   158.98   -33.90     0.46)  ; 3\r\n                    (  326.95   159.80   -32.20     0.46)  ; 4\r\n                    (  324.64   161.64   -30.25     0.46)  ; 5\r\n                    (  323.04   162.46   -30.17     0.46)  ; 6\r\n                    (  321.83   161.58   -28.30     0.46)  ; 7\r\n                    (  320.54   163.07   -26.82     0.46)  ; 8\r\n                    (  318.35   164.35   -25.03     0.46)  ; 9\r\n                    (  316.87   164.59   -23.70     0.46)  ; 10\r\n                    (  315.27   165.41   -22.17     0.46)  ; 11\r\n                    (  315.27   165.41   -22.20     0.46)  ; 12\r\n                    (  313.93   165.10   -20.90     0.46)  ; 13\r\n                    (  312.78   166.04   -19.58     0.46)  ; 14\r\n                    (  312.52   167.16   -19.50     0.46)  ; 15\r\n                    (  311.04   167.41   -18.52     0.46)  ; 16\r\n                    (  309.16   169.36   -16.73     0.46)  ; 17\r\n                    (  307.25   169.51   -15.60     0.46)  ; 18\r\n                    (  305.06   170.79   -14.05     0.46)  ; 19\r\n                    (  302.17   173.10   -13.42     0.46)  ; 20\r\n                    (  299.41   174.83   -13.15     0.46)  ; 21\r\n                    (  294.99   175.60   -13.13     0.46)  ; 22\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  324.64   161.64   -30.25     0.46)  ; 1\r\n                      (  320.08   162.97   -26.82     0.46)  ; 2\r\n                      (  311.04   167.41   -18.52     0.46)  ; 3\r\n                      (  307.69   169.61   -15.60     0.46)  ; 4\r\n                      (  299.27   175.41   -13.15     0.46)  ; 5\r\n                      (  296.90   175.45   -13.13     0.46)  ; 6\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  328.75   160.21   -39.60     0.46)  ; 1, R-2-2-3-1-1-1-2-1-2\r\n                    (  329.42   163.35   -39.60     0.46)  ; 2\r\n                    (  329.83   167.64   -39.60     0.46)  ; 3\r\n                    (  330.82   171.45   -39.60     0.46)  ; 4\r\n                    (  329.75   175.98   -39.60     0.46)  ; 5\r\n                    (  331.01   178.66   -38.92     0.46)  ; 6\r\n                    (  333.03   182.11   -38.30     0.46)  ; 7\r\n                    (  334.91   186.14   -37.80     0.46)  ; 8\r\n                    (  335.33   190.42   -37.55     0.46)  ; 9\r\n                    (  335.47   195.82   -37.47     0.46)  ; 10\r\n                    (  336.73   198.52   -36.47     0.46)  ; 11\r\n                    (  337.68   200.53   -35.22     0.46)  ; 12\r\n                    (  337.90   203.57   -34.30     0.46)  ; 13\r\n                    (  337.99   207.18   -33.20     0.46)  ; 14\r\n                    (  339.74   211.76   -33.40     0.46)  ; 15\r\n                    (  342.47   214.20   -32.55     0.46)  ; 16\r\n                    (  342.47   214.20   -32.60     0.46)  ; 17\r\n                    (  344.89   215.96   -31.75     0.46)  ; 18\r\n                    (  345.56   219.11   -30.67     0.46)  ; 19\r\n                    (  345.92   221.58   -29.88     0.46)  ; 20\r\n                    (  348.61   222.21   -28.63     0.46)  ; 21\r\n                    (  350.04   226.12   -27.80     0.46)  ; 22\r\n                    (  352.55   231.49   -27.35     0.46)  ; 23\r\n                    (  352.55   231.49   -27.08     0.46)  ; 24\r\n                    (  354.76   236.19   -27.08     0.46)  ; 25\r\n                    (  355.29   239.90   -27.08     0.46)  ; 26\r\n                    (  355.34   241.70   -30.23     0.46)  ; 27\r\n                    (  356.29   243.72   -31.63     0.46)  ; 28\r\n                    (  357.50   244.60   -31.85     0.46)  ; 29\r\n                    (  359.10   243.78   -35.10     0.46)  ; 30\r\n                    (  358.65   243.67   -36.35     0.46)  ; 31\r\n                    (  358.65   243.67   -36.38     0.46)  ; 32\r\n                    (  359.60   245.69   -39.17     0.46)  ; 33\r\n                    (  359.60   245.69   -39.22     0.46)  ; 34\r\n                    (  360.09   247.59   -41.32     0.46)  ; 35\r\n                    (  360.45   250.06   -42.55     0.46)  ; 36\r\n                    (  361.39   252.07   -43.65     0.46)  ; 37\r\n                    (  364.39   253.38   -44.40     0.46)  ; 38\r\n                    (  366.35   255.04   -46.00     0.46)  ; 39\r\n                    (  367.74   257.14   -47.45     0.46)  ; 40\r\n                    (  367.74   257.14   -47.47     0.46)  ; 41\r\n                    (  368.81   258.59   -48.95     0.46)  ; 42\r\n                    (  368.81   258.59   -48.97     0.46)  ; 43\r\n                    (  370.03   259.48   -49.45     0.46)  ; 44\r\n                    (  372.12   260.56   -51.60     0.46)  ; 45\r\n                    (  373.65   262.12   -53.78     0.46)  ; 46\r\n                    (  373.65   262.12   -53.82     0.46)  ; 47\r\n                    (  374.85   262.99   -55.72     0.46)  ; 48\r\n                    (  375.93   264.43   -56.10     0.46)  ; 49\r\n                    (  376.75   267.02   -58.05     0.46)  ; 50\r\n                    (  378.26   268.57   -59.32     0.46)  ; 51\r\n                    (  378.94   271.72   -60.20     0.46)  ; 52\r\n                    (  379.56   273.05   -61.40     0.46)  ; 53\r\n                    (  379.56   273.05   -61.42     0.46)  ; 54\r\n                    (  381.46   277.08   -62.45     0.46)  ; 55\r\n                    (  382.84   279.20   -62.75     0.46)  ; 56\r\n                    (  385.29   282.75   -61.90     0.46)  ; 57\r\n                    (  388.03   285.19   -60.67     0.46)  ; 58\r\n                    (  389.87   287.42   -59.25     0.46)  ; 59\r\n                    (  391.69   289.63   -60.17     0.46)  ; 60\r\n                    (  393.66   291.30   -61.90     0.46)  ; 61\r\n                    (  396.84   293.82   -63.78     0.46)  ; 62\r\n                    (  398.94   294.92   -65.22     0.46)  ; 63\r\n                    (  400.90   296.58   -67.00     0.46)  ; 64\r\n                    (  400.90   296.58   -67.95     0.46)  ; 65\r\n                    (  403.82   300.23   -68.05     0.46)  ; 66\r\n                    (  403.82   300.23   -68.07     0.46)  ; 67\r\n                    (  404.45   301.59   -70.07     0.46)  ; 68\r\n                    (  404.45   301.59   -70.10     0.46)  ; 69\r\n                    (  406.54   302.67   -70.35     0.46)  ; 70\r\n                    (  406.54   302.67   -70.38     0.46)  ; 71\r\n                    (  407.81   305.36   -72.57     0.46)  ; 72\r\n                    (  407.81   305.36   -72.60     0.46)  ; 73\r\n                    (  409.90   306.44   -73.78     0.46)  ; 74\r\n                    (  411.73   308.67   -74.35     0.46)  ; 75\r\n                    (  414.07   312.79   -75.25     0.46)  ; 76\r\n                    (  414.07   312.79   -75.27     0.46)  ; 77\r\n                    (  415.46   314.91   -76.45     0.46)  ; 78\r\n                    (  416.84   317.02   -77.60     0.46)  ; 79\r\n                    (  419.13   319.35   -78.70     0.46)  ; 80\r\n                    (  420.07   321.36   -80.88     0.46)  ; 81\r\n                    (  420.07   321.36   -80.90     0.46)  ; 82\r\n                    (  422.17   322.45   -82.22     0.46)  ; 83\r\n                    (  422.66   324.36   -84.40     0.46)  ; 84\r\n                    (  423.55   324.57   -86.47     0.46)  ; 85\r\n                    (  425.52   326.22   -86.72     0.46)  ; 86\r\n                    (  425.52   326.22   -86.75     0.46)  ; 87\r\n                    (  427.55   329.68   -88.93     0.46)  ; 88\r\n                    (  429.82   332.01   -91.10     0.46)  ; 89\r\n                    (  429.82   332.01   -91.13     0.46)  ; 90\r\n                    (  430.32   333.92   -93.65     0.46)  ; 91\r\n                    (  430.32   333.92   -93.63     0.46)  ; 92\r\n                    (  429.16   334.84   -92.00     0.46)  ; 93\r\n                    (  429.16   334.84   -92.05     0.46)  ; 94\r\n                    (  429.79   336.18   -94.75     0.46)  ; 95\r\n                    (  431.49   338.97   -95.90     0.46)  ; 96\r\n                    (  434.53   342.07   -97.30     0.46)  ; 97\r\n                    (  436.68   344.96   -99.20     0.46)  ; 98\r\n                    (  437.31   346.31  -101.75     0.46)  ; 99\r\n                    (  438.69   348.42  -104.63     0.46)  ; 100\r\n                    (  439.64   350.43  -107.30     0.46)  ; 101\r\n                    (  439.64   350.43  -107.32     0.46)  ; 102\r\n                    (  440.58   352.44  -109.72     0.46)  ; 103\r\n                    (  441.20   353.79  -112.05     0.46)  ; 104\r\n                    (  441.20   353.79  -112.10     0.46)  ; 105\r\n                    (  442.01   356.36  -115.37     0.46)  ; 106\r\n                    (  442.01   356.36  -115.40     0.46)  ; 107\r\n                    (  441.17   357.96  -116.30     0.46)  ; 108\r\n                    (  442.25   359.41  -118.47     0.46)  ; 109\r\n                    (  442.25   359.41  -118.88     0.46)  ; 110\r\n                    (  442.30   361.20  -121.65     0.46)  ; 111\r\n                    (  444.40   362.30  -121.70     0.46)  ; 112\r\n                    (  444.18   365.23  -126.97     0.46)  ; 113\r\n                    (  444.23   367.04  -129.97     0.46)  ; 114\r\n                    (  445.74   368.59  -131.15     0.46)  ; 115\r\n                    (  445.74   368.59  -131.17     0.46)  ; 116\r\n                    (  447.41   369.57  -134.00     0.46)  ; 117\r\n                    (  447.41   369.57  -134.02     0.46)  ; 118\r\n                    (  447.59   370.80  -133.85     0.46)  ; 119\r\n                    (  449.11   372.36  -137.15     0.46)  ; 120\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  338.56   200.73   -35.22     0.46)  ; 1\r\n                      (  379.03   275.32   -62.45     0.46)  ; 2\r\n                      (  442.74   355.33  -115.40     0.46)  ; 3\r\n                      (  442.97   364.35  -126.97     0.46)  ; 4\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  329.83   167.64   -39.60     0.46)  ; 1\r\n                      (  339.74   211.76   -33.40     0.46)  ; 2\r\n                      (  348.47   222.77   -28.63     0.46)  ; 3\r\n                      (  355.29   239.90   -27.08     0.46)  ; 4\r\n                      (  357.63   244.03   -31.85     0.46)  ; 5\r\n                      (  364.39   253.38   -44.40     0.46)  ; 6\r\n                      (  375.61   263.77   -56.10     0.46)  ; 7\r\n                      (  377.81   268.46   -59.35     0.46)  ; 8\r\n                      (  389.73   287.98   -59.25     0.46)  ; 9\r\n                      (  394.11   291.39   -61.90     0.46)  ; 10\r\n                      (  398.81   295.48   -65.22     0.46)  ; 11\r\n                      (  415.32   315.48   -76.45     0.46)  ; 12\r\n                      (  420.52   321.46   -80.90     0.46)  ; 13\r\n                      (  421.46   323.48   -82.22     0.46)  ; 14\r\n                      (  431.18   338.30   -95.90     0.46)  ; 15\r\n                      (  442.75   361.31  -121.65     0.46)  ; 16\r\n                      (  445.08   371.42  -133.85     0.46)  ; 17\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  450.50   374.47  -137.15     0.46)  ; 1, R-2-2-3-1-1-1-2-1-2-1\r\n                      (  451.75   377.17  -138.50     0.46)  ; 2\r\n                      (  453.55   377.58  -140.23     0.46)  ; 3\r\n                      (  453.55   377.58  -140.25     0.46)  ; 4\r\n                      (  455.77   378.10  -141.85     0.46)  ; 5\r\n                      (  457.42   379.09  -144.47     0.46)  ; 6\r\n                      (  459.84   380.85  -147.23     0.46)  ; 7\r\n                      (  460.52   383.99  -148.35     0.46)  ; 8\r\n                      (  463.24   386.42  -149.35     0.46)  ; 9\r\n                      (  465.21   388.07  -151.55     0.46)  ; 10\r\n                      (  467.05   390.30  -153.48     0.46)  ; 11\r\n                      (  468.13   391.74  -155.32     0.46)  ; 12\r\n                      (  469.06   393.76  -156.67     0.46)  ; 13\r\n                      (  469.06   393.76  -156.70     0.46)  ; 14\r\n                      (  470.45   395.87  -158.30     0.46)  ; 15\r\n                      (  470.45   395.87  -158.27     0.46)  ; 16\r\n                      (  471.70   398.56  -160.05     0.46)  ; 17\r\n                      (  473.81   399.65  -162.02     0.46)  ; 18\r\n                      (  473.81   399.65  -162.05     0.46)  ; 19\r\n                      (  476.80   400.95  -164.00     0.46)  ; 20\r\n                      (  476.80   400.95  -164.02     0.46)  ; 21\r\n                      (  478.58   401.37  -165.92     0.46)  ; 22\r\n                      (  481.13   402.56  -166.65     0.46)  ; 23\r\n                      (  485.99   401.91  -167.15     0.46)  ; 24\r\n\r\n                      (OpenCircle\r\n                        (Color Yellow)\r\n                        (Name \"Marker 2\")\r\n                        (  459.26   381.30  -147.23     0.46)  ; 1\r\n                        (  469.19   393.18  -156.70     0.46)  ; 2\r\n                        (  485.86   402.47  -167.15     0.46)  ; 3\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  451.43   370.52  -139.15     0.46)  ; 1, R-2-2-3-1-1-1-2-1-2-2\r\n                      (  454.37   370.01  -140.60     0.46)  ; 2\r\n                      (  456.29   369.86  -142.30     0.46)  ; 3\r\n                      (  456.29   369.86  -142.32     0.46)  ; 4\r\n                      (  460.35   372.60  -142.05     0.46)  ; 5\r\n                      (  464.11   374.67  -142.73     0.46)  ; 6\r\n                      (  464.11   374.67  -142.75     0.46)  ; 7\r\n                      (  464.92   377.26  -143.38     0.46)  ; 8\r\n                      (  464.96   379.06  -145.45     0.46)  ; 9\r\n                      (  464.57   380.76  -147.15     0.46)  ; 10\r\n                      (  465.06   382.67  -148.60     0.46)  ; 11\r\n                      (  466.63   386.03  -149.73     0.46)  ; 12\r\n                      (  466.63   386.03  -149.82     0.46)  ; 13\r\n\r\n                      (Dot\r\n                        (Color Yellow)\r\n                        (Name \"Marker 1\")\r\n                        (  467.16   383.75  -149.82     0.46)  ; 1\r\n                      )  ;  End of markers\r\n\r\n                      (OpenCircle\r\n                        (Color Yellow)\r\n                        (Name \"Marker 2\")\r\n                        (  464.92   377.26  -143.38     0.46)  ; 1\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  )  ;  End of split\r\n                |\r\n                  (  327.25   133.20   -36.90     0.46)  ; 1, R-2-2-3-1-1-1-2-2\r\n                  (  329.22   134.86   -38.33     0.46)  ; 2\r\n                  (  329.22   134.86   -38.35     0.46)  ; 3\r\n                  (  330.47   137.55   -39.88     0.46)  ; 4\r\n                  (  330.47   137.55   -39.90     0.46)  ; 5\r\n                  (  332.62   140.43   -41.35     0.46)  ; 6\r\n                  (  335.48   142.30   -42.95     0.46)  ; 7\r\n                  (  335.48   142.30   -42.98     0.46)  ; 8\r\n                  (  337.89   144.06   -44.97     0.46)  ; 9\r\n                  (  339.68   144.48   -46.82     0.46)  ; 10\r\n                  (  341.79   145.57   -48.35     0.46)  ; 11\r\n                  (  341.79   145.57   -48.38     0.46)  ; 12\r\n                  (  344.77   146.86   -48.55     0.46)  ; 13\r\n                  (  344.77   146.86   -48.60     0.46)  ; 14\r\n                  (  347.19   148.62   -50.38     0.46)  ; 15\r\n                  (  348.14   150.64   -51.50     0.46)  ; 16\r\n                  (  350.10   152.29   -51.72     0.46)  ; 17\r\n                  (  351.31   153.18   -51.75     0.46)  ; 18\r\n                  (  353.98   153.81   -52.77     0.46)  ; 19\r\n                  (  356.71   156.23   -54.15     0.46)  ; 20\r\n                  (  360.46   158.31   -54.20     0.46)  ; 21\r\n                  (  363.60   159.05   -55.20     0.46)  ; 22\r\n                  (  366.00   160.80   -56.40     0.46)  ; 23\r\n                  (  366.00   160.80   -56.42     0.46)  ; 24\r\n                  (  367.26   163.49   -57.65     0.46)  ; 25\r\n                  (  370.26   164.78   -59.45     0.46)  ; 26\r\n                  (  370.26   164.78   -59.47     0.46)  ; 27\r\n                  (  371.83   168.13   -60.07     0.46)  ; 28\r\n                  (  374.11   170.46   -61.10     0.46)  ; 29\r\n                  (  376.21   171.55   -62.13     0.46)  ; 30\r\n                  (  377.56   171.86   -63.62     0.46)  ; 31\r\n                  (  377.42   172.43   -63.62     0.46)  ; 32\r\n                  (  378.36   174.45   -63.62     0.46)  ; 33\r\n                  (  381.80   175.85   -63.42     0.46)  ; 34\r\n                  (  385.87   178.59   -63.72     0.46)  ; 35\r\n                  (  389.63   180.66   -62.47     0.46)  ; 36\r\n                  (  392.35   183.09   -62.47     0.46)  ; 37\r\n                  (  396.29   186.40   -62.47     0.46)  ; 38\r\n                  (  398.52   186.93   -62.47     0.46)  ; 39\r\n                  (  402.60   189.67   -64.35     0.46)  ; 40\r\n                  (  407.68   192.06   -65.27     0.46)  ; 41\r\n                  (  411.39   192.33   -66.17     0.46)  ; 42\r\n                  (  411.39   192.33   -66.20     0.46)  ; 43\r\n                  (  414.11   194.76   -67.05     0.46)  ; 44\r\n                  (  416.58   198.32   -66.60     0.46)  ; 45\r\n                  (  416.58   198.32   -66.63     0.46)  ; 46\r\n                  (  420.20   200.97   -68.40     0.46)  ; 47\r\n                  (  420.20   200.97   -68.42     0.46)  ; 48\r\n                  (  423.06   202.83   -70.70     0.46)  ; 49\r\n                  (  423.06   202.83   -70.72     0.46)  ; 50\r\n                  (  426.06   204.13   -70.05     0.46)  ; 51\r\n                  (  426.06   204.13   -70.07     0.46)  ; 52\r\n                  (  430.40   205.74   -71.03     0.46)  ; 53\r\n                  (  430.40   205.74   -71.07     0.46)  ; 54\r\n                  (  433.25   207.60   -71.07     0.46)  ; 55\r\n                  (  437.77   210.45   -71.50     0.46)  ; 56\r\n                  (  441.38   213.09   -71.50     0.46)  ; 57\r\n                  (  444.83   214.49   -71.50     0.46)  ; 58\r\n                  (  449.03   216.68   -71.25     0.46)  ; 59\r\n                  (  451.43   218.43   -71.25     0.46)  ; 60\r\n                  (  454.30   220.30   -71.25     0.46)  ; 61\r\n                  (  458.81   223.15   -71.25     0.46)  ; 62\r\n                  (  463.82   227.90   -72.55     0.46)  ; 63\r\n                  (  463.82   227.90   -72.57     0.46)  ; 64\r\n                  (  465.41   232.44   -71.22     0.46)  ; 65\r\n                  (  468.86   233.84   -71.22     0.46)  ; 66\r\n                  (  475.10   235.30   -71.70     0.46)  ; 67\r\n                  (  479.04   238.63   -71.68     0.46)  ; 68\r\n                  (  482.35   240.59   -70.82     0.46)  ; 69\r\n                  (  486.10   242.66   -71.97     0.46)  ; 70\r\n                  (  488.65   243.87   -74.40     0.46)  ; 71\r\n                  (  488.78   243.29   -74.40     0.46)  ; 72\r\n                  (  493.38   243.77   -75.72     0.46)  ; 73\r\n                  (  493.38   243.77   -75.75     0.46)  ; 74\r\n                  (  497.00   246.42   -77.10     0.46)  ; 75\r\n                  (  497.00   246.42   -77.13     0.46)  ; 76\r\n                  (  499.91   250.08   -78.20     0.46)  ; 77\r\n                  (  502.19   252.41   -78.30     0.46)  ; 78\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  376.58   174.03   -63.62     0.46)  ; 1\r\n                    (  414.38   193.62   -67.05     0.46)  ; 2\r\n                    (  485.79   242.00   -71.97     0.46)  ; 3\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  351.75   153.28   -51.75     0.46)  ; 1\r\n                    (  363.46   159.60   -55.20     0.46)  ; 2\r\n                    (  385.87   178.59   -63.72     0.46)  ; 3\r\n                    (  422.74   202.16   -70.72     0.46)  ; 4\r\n                    (  463.37   227.81   -72.57     0.46)  ; 5\r\n                    (  475.10   235.30   -71.70     0.46)  ; 6\r\n                    (  501.75   252.31   -78.30     0.46)  ; 7\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            |\r\n              (  336.44   -58.04   -29.25     0.46)  ; 1, R-2-2-3-1-1-2\r\n              (  336.57   -58.61   -30.90     0.46)  ; 2\r\n              (  335.64   -60.61   -30.60     0.46)  ; 3\r\n              (  335.64   -60.61   -30.65     0.46)  ; 4\r\n              (  335.27   -63.09   -32.47     0.46)  ; 5\r\n              (\r\n                (  337.77   -63.70   -32.55     0.46)  ; 1, R-2-2-3-1-1-2-1\r\n                (  337.58   -64.93   -34.30     0.46)  ; 2\r\n                (  337.58   -64.93   -34.35     0.46)  ; 3\r\n                (  337.58   -64.93   -36.58     0.46)  ; 4\r\n                (  337.58   -64.93   -36.60     0.46)  ; 5\r\n                 Normal\r\n              |\r\n                (  334.45   -65.66   -35.08     0.46)  ; 1, R-2-2-3-1-1-2-2\r\n                (  333.65   -68.25   -37.47     0.46)  ; 2\r\n                (  333.47   -69.48   -39.17     0.46)  ; 3\r\n                (  331.63   -71.71   -40.07     0.46)  ; 4\r\n                (  330.52   -74.95   -40.97     0.46)  ; 5\r\n                (  329.57   -76.97   -42.60     0.46)  ; 6\r\n                (  327.47   -78.05   -43.82     0.46)  ; 7\r\n                (  327.47   -78.05   -43.85     0.46)  ; 8\r\n                (  325.55   -77.91   -46.00     0.46)  ; 9\r\n                (  325.55   -77.91   -46.03     0.46)  ; 10\r\n                (  325.06   -79.81   -48.27     0.46)  ; 11\r\n                (  325.06   -79.81   -48.30     0.46)  ; 12\r\n                (  324.74   -80.48   -49.80     0.46)  ; 13\r\n                (  324.43   -81.15   -51.88     0.46)  ; 14\r\n                (  322.34   -82.25   -52.30     0.46)  ; 15\r\n                (  319.64   -82.88   -55.10     0.46)  ; 16\r\n                (  319.16   -84.78   -57.30     0.46)  ; 17\r\n                (  317.50   -85.77   -59.63     0.46)  ; 18\r\n                (  317.00   -87.68   -61.57     0.46)  ; 19\r\n                (  317.00   -87.68   -61.60     0.46)  ; 20\r\n                (  317.10   -90.04   -62.15     0.46)  ; 21\r\n                (  314.98   -91.13   -62.30     0.46)  ; 22\r\n                (  314.98   -91.13   -62.33     0.46)  ; 23\r\n                (  312.13   -93.00   -63.25     0.46)  ; 24\r\n                (  311.14   -96.80   -63.25     0.46)  ; 25\r\n                (  309.24  -100.83   -64.42     0.46)  ; 26\r\n                (  306.21  -103.94   -65.52     0.46)  ; 27\r\n                (  303.82  -109.87   -65.72     0.46)  ; 28\r\n                (  300.79  -112.97   -65.43     0.46)  ; 29\r\n                (  297.20  -119.77   -65.43     0.46)  ; 30\r\n                (  295.14  -125.03   -65.43     0.46)  ; 31\r\n                (  293.25  -129.05   -65.95     0.46)  ; 32\r\n                (  290.48  -133.29   -65.88     0.46)  ; 33\r\n                (  288.16  -137.43   -65.70     0.46)  ; 34\r\n                (  286.32  -139.64   -65.70     0.46)  ; 35\r\n                (  286.22  -143.25   -65.15     0.46)  ; 36\r\n                (  286.22  -143.25   -65.22     0.46)  ; 37\r\n                (  284.65  -146.60   -67.50     0.46)  ; 38\r\n                (  284.65  -146.60   -67.53     0.46)  ; 39\r\n                (  281.82  -152.64   -68.80     0.46)  ; 40\r\n                (  281.82  -152.64   -68.82     0.46)  ; 41\r\n                (  278.16  -157.08   -69.55     0.46)  ; 42\r\n                (  274.69  -164.46   -69.55     0.46)  ; 43\r\n                (  271.61  -169.36   -70.35     0.46)  ; 44\r\n                (  269.40  -174.06   -70.35     0.46)  ; 45\r\n                (  267.84  -177.41   -71.63     0.46)  ; 46\r\n                (  266.59  -180.10   -71.43     0.46)  ; 47\r\n                (  266.59  -180.10   -71.45     0.46)  ; 48\r\n                (  264.96  -185.26   -72.38     0.46)  ; 49\r\n                (  262.37  -194.22   -72.80     0.46)  ; 50\r\n                (  260.43  -200.05   -73.38     0.46)  ; 51\r\n                (  258.86  -203.41   -73.38     0.46)  ; 52\r\n                (  255.81  -206.50   -74.92     0.46)  ; 53\r\n                (  252.59  -210.85   -76.32     0.46)  ; 54\r\n                (  251.02  -214.21   -78.20     0.46)  ; 55\r\n                (  251.02  -214.21   -78.22     0.46)  ; 56\r\n                (  249.27  -218.79   -78.93     0.46)  ; 57\r\n                (  247.94  -223.29   -78.75     0.46)  ; 58\r\n                (  246.83  -226.54   -79.85     0.46)  ; 59\r\n                (  246.83  -226.54   -79.88     0.46)  ; 60\r\n                (  245.26  -229.89   -81.75     0.46)  ; 61\r\n                (  244.71  -233.60   -84.32     0.46)  ; 62\r\n                (  244.71  -233.60   -84.38     0.46)  ; 63\r\n                (  243.63  -235.05   -84.72     0.46)  ; 64\r\n                (  242.83  -237.62   -85.68     0.46)  ; 65\r\n                (  242.83  -237.62   -85.80     0.46)  ; 66\r\n                (  241.84  -241.44   -86.90     0.46)  ; 67\r\n                (  241.84  -241.44   -87.02     0.46)  ; 68\r\n                (  241.75  -245.05   -87.42     0.46)  ; 69\r\n                (  241.75  -245.05   -87.68     0.46)  ; 70\r\n                (  239.23  -250.40   -88.20     0.46)  ; 71\r\n                (  239.23  -250.40   -88.23     0.46)  ; 72\r\n                (  239.00  -253.45   -88.95     0.46)  ; 73\r\n                (  239.00  -253.45   -88.97     0.46)  ; 74\r\n                (  237.43  -256.81   -90.40     0.46)  ; 75\r\n                (  236.43  -260.62   -91.58     0.46)  ; 76\r\n                (  236.43  -260.62   -91.60     0.46)  ; 77\r\n                (  234.29  -263.51   -92.92     0.46)  ; 78\r\n                (  233.48  -266.09   -96.43     0.46)  ; 79\r\n                (  231.97  -267.64   -97.80     0.46)  ; 80\r\n                (  230.38  -270.99   -99.63     0.46)  ; 81\r\n                (  230.25  -270.42   -99.67     0.46)  ; 82\r\n                (  228.11  -273.32  -101.98     0.46)  ; 83\r\n                (  226.54  -276.67  -103.15     0.46)  ; 84\r\n                (  225.54  -280.49  -104.67     0.46)  ; 85\r\n                (  224.69  -284.87  -105.60     0.46)  ; 86\r\n                (  224.69  -284.87  -105.63     0.46)  ; 87\r\n                (  223.25  -288.79  -104.70     0.46)  ; 88\r\n                (  222.35  -294.97  -105.75     0.46)  ; 89\r\n                (  221.22  -298.21  -108.75     0.46)  ; 90\r\n                (  220.86  -300.70  -111.95     0.46)  ; 91\r\n                (  220.86  -300.70  -111.97     0.46)  ; 92\r\n                (  220.31  -304.41  -113.42     0.46)  ; 93\r\n                (  218.80  -305.96  -114.07     0.46)  ; 94\r\n                (  218.35  -306.06  -114.10     0.46)  ; 95\r\n                (  217.08  -308.75  -115.77     0.46)  ; 96\r\n                (  216.28  -311.32  -116.90     0.46)  ; 97\r\n                (  216.28  -311.32  -116.93     0.46)  ; 98\r\n                (  215.29  -315.14  -118.38     0.46)  ; 99\r\n                (  212.83  -318.70  -120.10     0.46)  ; 100\r\n                (  211.89  -320.71  -121.72     0.46)  ; 101\r\n                (  211.89  -320.71  -121.77     0.46)  ; 102\r\n                (  211.39  -322.62  -122.85     0.46)  ; 103\r\n                (  211.21  -323.85  -124.52     0.46)  ; 104\r\n                (  210.80  -328.14  -126.07     0.46)  ; 105\r\n                (  208.02  -332.37  -126.95     0.46)  ; 106\r\n                (  207.21  -334.94  -128.65     0.46)  ; 107\r\n                (  207.21  -334.94  -128.67     0.46)  ; 108\r\n                (  206.76  -335.05  -130.72     0.46)  ; 109\r\n                (  206.09  -338.19  -132.40     0.46)  ; 110\r\n                (  206.09  -338.19  -132.42     0.46)  ; 111\r\n                (  206.04  -339.99  -133.43     0.46)  ; 112\r\n                (  205.37  -343.14  -135.20     0.46)  ; 113\r\n                (  205.14  -346.18  -136.15     0.46)  ; 114\r\n                (  206.69  -348.80  -136.15     0.46)  ; 115\r\n                (  208.20  -353.23  -137.27     0.46)  ; 116\r\n                (  208.64  -359.09  -138.02     0.46)  ; 117\r\n                (  210.19  -361.72  -139.00     0.46)  ; 118\r\n                (  212.01  -365.48  -140.02     0.46)  ; 119\r\n                (  213.83  -369.22  -141.52     0.46)  ; 120\r\n                (  216.56  -372.76  -143.18     0.46)  ; 121\r\n                (  219.13  -375.73  -145.02     0.46)  ; 122\r\n                (  218.83  -376.41  -147.25     0.46)  ; 123\r\n                (  220.78  -380.72  -148.60     0.46)  ; 124\r\n                (  222.08  -382.21  -149.32     0.46)  ; 125\r\n                (  222.74  -385.05  -150.68     0.46)  ; 126\r\n                (  222.50  -388.09  -151.67     0.46)  ; 127\r\n                (  223.79  -389.57  -153.15     0.46)  ; 128\r\n                (  223.79  -389.57  -153.18     0.46)  ; 129\r\n                (  225.23  -391.63  -154.25     0.46)  ; 130\r\n                (  226.52  -393.11  -156.02     0.46)  ; 131\r\n                (  227.05  -395.38  -159.95     0.46)  ; 132\r\n                (  227.05  -395.38  -159.97     0.46)  ; 133\r\n                (  227.58  -397.64  -162.40     0.46)  ; 134\r\n                (  228.42  -399.24  -163.93     0.46)  ; 135\r\n                (  229.40  -401.40  -165.35     0.46)  ; 136\r\n                (  230.37  -403.56  -166.60     0.46)  ; 137\r\n                (  230.37  -403.56  -166.63     0.46)  ; 138\r\n                (  229.44  -405.56  -167.72     0.46)  ; 139\r\n                (  229.39  -407.37  -169.27     0.46)  ; 140\r\n                (  229.33  -409.17  -169.93     0.46)  ; 141\r\n                (  228.97  -411.65  -171.98     0.46)  ; 142\r\n                (  227.45  -413.20  -173.97     0.46)  ; 143\r\n                (  227.45  -413.20  -174.02     0.46)  ; 144\r\n                (  227.54  -415.57  -175.85     0.46)  ; 145\r\n                (  227.54  -415.57  -175.92     0.46)  ; 146\r\n                (  229.35  -419.32  -177.60     0.46)  ; 147\r\n                (  229.35  -419.32  -177.62     0.46)  ; 148\r\n                (  228.42  -421.33  -179.73     0.46)  ; 149\r\n                (  227.34  -422.78  -181.67     0.46)  ; 150\r\n                (  225.32  -426.24  -182.80     0.46)  ; 151\r\n                (  223.80  -427.78  -183.78     0.46)  ; 152\r\n                (  223.80  -427.78  -183.82     0.46)  ; 153\r\n                (  223.89  -430.16  -185.80     0.46)  ; 154\r\n                (  223.39  -432.07  -187.02     0.46)  ; 155\r\n                (  225.52  -435.15  -187.95     0.46)  ; 156\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  304.98  -110.79   -65.72     0.46)  ; 1\r\n                  (  223.79  -285.08  -105.63     0.46)  ; 2\r\n                  (  204.62  -337.94  -132.42     0.46)  ; 3\r\n                  (  225.71  -395.69  -159.97     0.46)  ; 4\r\n                  (  227.89  -419.06  -177.65     0.46)  ; 5\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  329.57   -76.97   -42.60     0.46)  ; 1\r\n                  (  295.14  -125.03   -65.43     0.46)  ; 2\r\n                  (  288.60  -137.32   -65.70     0.46)  ; 3\r\n                  (  269.85  -173.95   -70.35     0.46)  ; 4\r\n                  (  266.59  -180.10   -71.45     0.46)  ; 5\r\n                  (  258.41  -203.51   -73.38     0.46)  ; 6\r\n                  (  239.45  -253.35   -88.97     0.46)  ; 7\r\n                  (  234.29  -263.51   -92.92     0.46)  ; 8\r\n                  (  224.01  -288.01  -104.70     0.46)  ; 9\r\n                  (  216.41  -311.88  -116.93     0.46)  ; 10\r\n                  (  210.80  -328.14  -126.07     0.46)  ; 11\r\n                  (  208.02  -332.37  -126.95     0.46)  ; 12\r\n                  (  205.55  -341.91  -135.20     0.46)  ; 13\r\n                  (  207.76  -353.33  -137.27     0.46)  ; 14\r\n                  (  208.50  -358.53  -138.02     0.46)  ; 15\r\n                  (  212.01  -365.48  -140.02     0.46)  ; 16\r\n                  (  210.58  -369.38  -141.52     0.46)  ; 17\r\n                  (  213.25  -368.76  -141.52     0.46)  ; 18\r\n                  (  219.27  -376.30  -147.25     0.46)  ; 19\r\n                  (  225.10  -391.06  -154.25     0.46)  ; 20\r\n                  (  229.33  -409.17  -169.93     0.46)  ; 21\r\n                  (  225.52  -435.15  -187.95     0.46)  ; 22\r\n                )  ;  End of markers\r\n                 High\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  329.41   -57.06   -20.27     0.46)  ; 1, R-2-2-3-1-2\r\n            (  329.86   -56.95   -18.20     0.46)  ; 2\r\n            (  329.86   -56.95   -18.25     0.46)  ; 3\r\n            (  330.94   -55.51   -16.13     0.46)  ; 4\r\n            (\r\n              (  331.92   -51.69   -15.57     0.46)  ; 1, R-2-2-3-1-2-1\r\n              (  333.19   -49.01   -14.72     0.46)  ; 2\r\n              (  333.54   -46.54   -14.72     0.46)  ; 3\r\n              (  333.33   -43.60   -14.15     0.46)  ; 4\r\n              (  334.32   -39.79   -13.30     0.46)  ; 5\r\n              (  334.99   -36.65   -14.48     0.46)  ; 6\r\n              (  334.06   -32.68   -15.20     0.46)  ; 7\r\n              (  334.06   -32.68   -15.22     0.46)  ; 8\r\n              (  332.95   -29.96   -16.15     0.46)  ; 9\r\n              (  330.82   -26.87   -16.92     0.46)  ; 10\r\n              (  329.40   -24.82   -17.55     0.46)  ; 11\r\n              (  330.34   -22.80   -18.10     0.46)  ; 12\r\n              (  329.28   -18.28   -18.10     0.46)  ; 13\r\n              (  329.01   -17.15   -18.05     0.46)  ; 14\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  332.53   -40.21   -13.30     0.46)  ; 1\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  331.40   -27.34   -16.92     0.46)  ; 1\r\n              )  ;  End of markers\r\n              (\r\n                (  327.64   -13.29   -18.20     0.46)  ; 1, R-2-2-3-1-2-1-1\r\n                (  328.04    -9.01   -18.20     0.46)  ; 2\r\n                (  327.17    -3.25   -18.20     0.46)  ; 3\r\n                (  325.66     1.17   -19.07     0.46)  ; 4\r\n                (  324.55     3.90   -19.88     0.46)  ; 5\r\n                (  324.55     3.90   -19.90     0.46)  ; 6\r\n                (  325.48     5.92   -20.60     0.46)  ; 7\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  324.02     6.17   -20.60     0.46)  ; 1\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  326.51   -16.54   -16.38     0.46)  ; 1, R-2-2-3-1-2-1-2\r\n                (  324.51   -14.02   -14.67     0.46)  ; 2\r\n                (  322.96   -11.40   -13.30     0.46)  ; 3\r\n                (  320.27   -12.03   -11.98     0.46)  ; 4\r\n                (  316.31   -11.16   -12.02     0.46)  ; 5\r\n                (  311.12   -11.19   -11.15     0.46)  ; 6\r\n                (  309.44    -8.00    -9.90     0.46)  ; 7\r\n                (  304.68    -3.75    -9.52     0.46)  ; 8\r\n                (  302.74    -3.59    -8.00     0.46)  ; 9\r\n                (  299.85    -1.29    -7.05     0.46)  ; 10\r\n                (  298.11     0.10    -5.68     0.46)  ; 11\r\n                (  295.36     1.84    -4.70     0.46)  ; 12\r\n                (  293.04     3.68    -3.90     0.46)  ; 13\r\n                (  291.30     5.07    -2.53     0.46)  ; 14\r\n                (  289.56     6.46    -1.60     0.46)  ; 15\r\n                (  287.95     7.27    -0.20     0.46)  ; 16\r\n                (  286.93     7.62     0.43     0.46)  ; 17\r\n                (  282.11    10.08     1.35     0.46)  ; 18\r\n                (  279.26    14.19     0.30     0.46)  ; 19\r\n                (  275.84    18.76     1.07     0.46)  ; 20\r\n                (  273.44    22.97     2.45     0.46)  ; 21\r\n                (  269.19    24.96     4.80     0.46)  ; 22\r\n                (  269.19    24.96     4.75     0.46)  ; 23\r\n                (  266.80    29.18     5.00     0.46)  ; 24\r\n                (  261.85    32.20     5.17     0.46)  ; 25\r\n                (  256.85    33.43     5.68     0.46)  ; 26\r\n                (  254.99    35.38     5.50     0.46)  ; 27\r\n                (  250.35    39.08     5.97     0.46)  ; 28\r\n                (  246.83    40.04     5.95     0.46)  ; 29\r\n                (  243.88    40.54     7.87     0.46)  ; 30\r\n                (  240.66    42.18     8.42     0.46)  ; 31\r\n                (  239.07    43.00    10.15     0.46)  ; 32\r\n                (  237.28    42.57     9.85     0.46)  ; 33\r\n                (  235.93    42.26    12.13     0.46)  ; 34\r\n                (  234.65    43.76    13.70     0.46)  ; 35\r\n                (  233.18    44.00    15.30     0.46)  ; 36\r\n                (  230.91    47.66    16.38     0.46)  ; 37\r\n                (  229.48    49.71    17.38     0.46)  ; 38\r\n                (  228.01    49.96    18.45     0.46)  ; 39\r\n                (  228.01    49.96    18.42     0.46)  ; 40\r\n                (  226.41    50.78    20.60     0.46)  ; 41\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  320.40   -12.59   -11.98     0.46)  ; 1\r\n                  (  299.67    -2.53    -7.05     0.46)  ; 2\r\n                  (  236.96    41.91     9.85     0.46)  ; 3\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  326.38   -15.97   -16.38     0.46)  ; 1\r\n                  (  286.80     8.19     0.43     0.46)  ; 2\r\n                  (  279.52    13.06     0.30     0.46)  ; 3\r\n                  (  281.53    10.54     0.30     0.46)  ; 4\r\n                  (  262.43    31.74     5.17     0.46)  ; 5\r\n                  (  255.57    34.92     5.50     0.46)  ; 6\r\n                  (  251.82    38.82     9.80     0.46)  ; 7\r\n                  (  243.88    40.54     7.85     0.46)  ; 8\r\n                )  ;  End of markers\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  245.33    41.67   -11.90     1.83)  ; 1\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  334.94   -54.56   -15.40     0.46)  ; 1, R-2-2-3-1-2-2\r\n              (  338.78   -54.86   -15.40     0.46)  ; 2\r\n              (  343.78   -56.07   -15.40     0.46)  ; 3\r\n              (  347.03   -55.90   -15.40     0.46)  ; 4\r\n              (  351.77   -56.00   -15.40     0.46)  ; 5\r\n              (  354.45   -55.37   -14.30     0.46)  ; 6\r\n              (  356.81   -55.41   -13.92     0.46)  ; 7\r\n              (  361.10   -55.60   -13.25     0.46)  ; 8\r\n              (  363.55   -58.02   -13.25     0.46)  ; 9\r\n              (  367.20   -59.54   -13.25     0.46)  ; 10\r\n              (  370.87   -61.08   -13.25     0.46)  ; 11\r\n              (  373.64   -62.81   -12.95     0.46)  ; 12\r\n              (  377.82   -66.61   -12.95     0.46)  ; 13\r\n              (  381.88   -69.84   -13.07     0.46)  ; 14\r\n              (  385.09   -71.47   -12.90     0.46)  ; 15\r\n              (  388.75   -73.01   -12.90     0.46)  ; 16\r\n              (  390.94   -74.28   -12.90     0.46)  ; 17\r\n              (  394.59   -75.82   -12.98     0.46)  ; 18\r\n              (  398.37   -77.91   -13.75     0.46)  ; 19\r\n              (  401.01   -79.09   -13.75     0.46)  ; 20\r\n              (  403.95   -79.59   -13.13     0.46)  ; 21\r\n              (  408.06   -81.02   -13.13     0.46)  ; 22\r\n              (  412.16   -82.44   -13.13     0.46)  ; 23\r\n              (  414.21   -83.16   -13.13     0.46)  ; 24\r\n              (  416.39   -84.44   -13.13     0.46)  ; 25\r\n              (  420.32   -87.11   -11.82     0.46)  ; 26\r\n              (  423.08   -88.84   -10.52     0.46)  ; 27\r\n              (  426.42   -91.05    -9.32     0.46)  ; 28\r\n              (  428.74   -92.89    -8.77     0.46)  ; 29\r\n              (  431.38   -94.07    -8.35     0.46)  ; 30\r\n              (  434.18   -94.01    -7.13     0.46)  ; 31\r\n              (  434.18   -94.01    -7.15     0.46)  ; 32\r\n              (  438.43   -96.00    -5.95     0.46)  ; 33\r\n              (  441.94   -96.96    -5.60     0.46)  ; 34\r\n              (  444.71   -98.71    -5.27     0.46)  ; 35\r\n              (  448.11   -99.11    -7.02     0.46)  ; 36\r\n              (  452.08   -99.96    -7.02     0.46)  ; 37\r\n              (  455.34   -99.80    -6.25     0.46)  ; 38\r\n              (  455.34   -99.80    -6.28     0.46)  ; 39\r\n              (  457.70   -99.84    -4.35     0.46)  ; 40\r\n              (  457.70   -99.84    -4.38     0.46)  ; 41\r\n              (  461.04  -102.04    -3.45     0.46)  ; 42\r\n              (  464.17  -101.31    -3.25     0.46)  ; 43\r\n              (  467.12  -101.82    -3.10     0.46)  ; 44\r\n              (  469.79  -101.19    -1.73     0.46)  ; 45\r\n              (  472.61  -101.13    -0.93     0.46)  ; 46\r\n              (  472.61  -101.13    -0.97     0.46)  ; 47\r\n              (  475.55  -101.63    -0.32     0.46)  ; 48\r\n              (  479.56  -100.69     0.55     0.46)  ; 49\r\n              (  481.79  -100.17     1.67     0.46)  ; 50\r\n              (  483.63   -97.95     2.95     0.46)  ; 51\r\n              (  484.38   -97.16     4.45     0.46)  ; 52\r\n              (  485.46   -95.72     5.77     0.46)  ; 53\r\n              (  487.52   -96.44     6.90     0.46)  ; 54\r\n              (  489.25   -97.82     7.67     0.46)  ; 55\r\n              (  490.68   -99.87     8.38     0.46)  ; 56\r\n              (  494.51  -100.17     9.10     0.46)  ; 57\r\n              (  497.19   -99.55     9.77     0.46)  ; 58\r\n              (  502.37   -99.52     9.90     0.46)  ; 59\r\n              (  502.37   -99.52    10.00     0.46)  ; 60\r\n              (  505.05   -98.90    10.33     0.46)  ; 61\r\n              (  507.73   -98.27     9.05     0.46)  ; 62\r\n              (  512.15   -99.02     9.22     0.46)  ; 63\r\n              (  515.54   -99.42     9.98     0.46)  ; 64\r\n              (  515.09   -99.53     9.98     0.46)  ; 65\r\n              (  519.24   -99.15    10.92     0.46)  ; 66\r\n              (  522.64   -99.55    11.30     0.46)  ; 67\r\n              (  522.64   -99.55    11.27     0.46)  ; 68\r\n              (  526.47   -99.84    12.00     0.46)  ; 69\r\n              (  528.83   -99.89    12.20     0.46)  ; 70\r\n              (  530.63   -99.46    12.32     0.46)  ; 71\r\n              (  534.15  -100.43    13.07     0.46)  ; 72\r\n              (  537.86  -100.16    14.20     0.46)  ; 73\r\n              (  540.09   -99.64    15.00     0.46)  ; 74\r\n              (  542.59  -100.24    15.73     0.46)  ; 75\r\n              (  546.12  -101.22    16.50     0.46)  ; 76\r\n              (  550.71  -100.74    17.17     0.46)  ; 77\r\n              (  553.26   -99.54    18.08     0.46)  ; 78\r\n              (  556.20  -100.04    18.65     0.46)  ; 79\r\n              (  560.49  -100.23    18.38     0.46)  ; 80\r\n              (  566.24  -100.68    18.38     0.46)  ; 81\r\n              (  569.63  -101.07    19.85     0.46)  ; 82\r\n              (  574.81  -101.05    20.38     0.46)  ; 83\r\n              (  576.99  -102.34    20.83     0.46)  ; 84\r\n              (  578.95  -106.66    21.15     0.46)  ; 85\r\n              (  581.28  -108.50    22.12     0.46)  ; 86\r\n              (  583.09  -112.25    22.80     0.46)  ; 87\r\n              (  583.09  -112.25    22.77     0.46)  ; 88\r\n              (  586.31  -113.89    22.67     0.46)  ; 89\r\n              (  586.31  -113.89    22.65     0.46)  ; 90\r\n              (  590.14  -114.18    22.65     0.46)  ; 91\r\n              (  593.27  -113.44    23.33     0.46)  ; 92\r\n              (  595.90  -114.63    24.20     0.46)  ; 93\r\n              (  597.11  -113.74    24.52     0.46)  ; 94\r\n              (  597.11  -113.74    24.88     0.46)  ; 95\r\n              (  602.81  -115.99    24.90     0.46)  ; 96\r\n              (  607.36  -117.32    26.05     0.46)  ; 97\r\n              (  608.98  -118.13    26.70     0.46)  ; 98\r\n              (  609.82  -119.72    27.92     0.46)  ; 99\r\n              (  609.82  -119.72    27.90     0.46)  ; 100\r\n              (  612.13  -121.57    29.02     0.46)  ; 101\r\n              (  612.13  -121.57    29.00     0.46)  ; 102\r\n              (  614.36  -121.05    29.75     0.46)  ; 103\r\n              (  616.23  -123.00    30.27     0.46)  ; 104\r\n              (  619.18  -123.50    30.95     0.46)  ; 105\r\n              (  623.28  -124.92    31.82     0.46)  ; 106\r\n              (  624.89  -125.75    33.42     0.46)  ; 107\r\n              (  624.75  -125.18    33.38     0.46)  ; 108\r\n              (  626.68  -125.33    35.45     0.46)  ; 109\r\n              (  626.68  -125.33    35.38     0.46)  ; 110\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  357.57   -54.64   -13.92     0.46)  ; 1\r\n                (  365.99   -60.43   -13.25     0.46)  ; 2\r\n                (  380.54   -70.16   -13.07     0.46)  ; 3\r\n                (  389.27   -75.27   -12.90     0.46)  ; 4\r\n                (  406.85   -81.91   -13.13     0.46)  ; 5\r\n                (  419.57   -87.88   -11.82     0.46)  ; 6\r\n                (  437.22   -96.88    -5.95     0.46)  ; 7\r\n                (  484.78   -98.87     2.95     0.46)  ; 8\r\n                (  496.93   -98.40     9.77     0.46)  ; 9\r\n                (  508.23   -96.36    11.63     0.46)  ; 10\r\n                (  507.28   -98.37     8.15     0.46)  ; 11\r\n                (  520.01   -98.38    10.92     0.46)  ; 12\r\n                (  521.79   -97.96    11.50     0.46)  ; 13\r\n                (  525.18   -98.36    12.30     0.46)  ; 14\r\n                (  533.88   -99.31    13.07     0.46)  ; 15\r\n                (  545.72   -99.52    16.50     0.46)  ; 16\r\n                (  553.52  -100.67    18.08     0.46)  ; 17\r\n                (  568.88  -101.85    19.85     0.46)  ; 18\r\n                (  592.56  -112.42    23.33     0.46)  ; 19\r\n                (  601.17  -116.97    24.90     0.46)  ; 20\r\n                (  622.57  -123.90    31.45     0.46)  ; 21\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  398.37   -77.91   -13.75     0.46)  ; 1\r\n                (  457.70   -99.84    -4.38     0.46)  ; 2\r\n                (  475.10  -101.74    -0.32     0.46)  ; 3\r\n                (  512.15   -99.02     9.22     0.46)  ; 4\r\n                (  561.07  -100.69    18.38     0.46)  ; 5\r\n                (  579.26  -105.99    21.15     0.46)  ; 6\r\n                (  576.42  -101.88    21.15     0.46)  ; 7\r\n                (  582.96  -111.69    22.77     0.46)  ; 8\r\n                (  599.16  -114.46    24.90     0.46)  ; 9\r\n              )  ;  End of markers\r\n               High\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (  293.81   -86.44     3.57     0.46)  ; 1, R-2-2-3-2\r\n          (  296.50   -85.81     2.70     0.46)  ; 2\r\n          (  300.64   -85.43     1.30     0.46)  ; 3\r\n          (  302.12   -85.68     0.47     0.46)  ; 4\r\n          (  304.36   -85.15    -0.12     0.46)  ; 5\r\n          (  306.72   -85.20    -0.12     0.46)  ; 6\r\n          (  310.73   -84.26    -0.82     0.46)  ; 7\r\n          (  315.02   -84.45    -1.73     0.46)  ; 8\r\n          (  319.48   -83.40    -2.05     0.46)  ; 9\r\n          (  323.06   -82.56    -2.27     0.46)  ; 10\r\n          (  326.05   -81.26    -3.72     0.46)  ; 11\r\n          (  329.45   -81.66    -4.70     0.46)  ; 12\r\n          (  330.79   -81.34    -4.52     0.46)  ; 13\r\n          (  334.36   -80.51    -4.03     0.46)  ; 14\r\n          (  337.62   -80.35    -4.03     0.46)  ; 15\r\n          (  341.33   -80.08    -4.03     0.46)  ; 16\r\n          (  344.32   -78.78    -3.13     0.46)  ; 17\r\n          (  344.32   -78.78    -3.15     0.46)  ; 18\r\n          (  348.74   -79.53    -4.60     0.46)  ; 19\r\n          (  348.74   -79.53    -4.63     0.46)  ; 20\r\n          (  351.42   -78.90    -5.95     0.46)  ; 21\r\n          (  353.78   -78.94    -5.95     0.46)  ; 22\r\n          (  356.15   -78.98    -6.93     0.46)  ; 23\r\n          (  356.15   -78.98    -6.95     0.46)  ; 24\r\n          (  357.48   -78.67    -7.35     0.46)  ; 25\r\n          (  358.96   -78.92    -7.35     0.46)  ; 26\r\n          (  360.61   -77.94    -7.35     0.46)  ; 27\r\n          (  362.53   -78.09    -5.97     0.46)  ; 28\r\n          (  364.76   -77.56    -5.97     0.46)  ; 29\r\n          (  367.45   -76.93    -7.05     0.46)  ; 30\r\n          (  368.92   -77.20    -7.85     0.46)  ; 31\r\n          (  368.92   -77.20    -7.87     0.46)  ; 32\r\n          (  370.26   -76.88    -8.60     0.46)  ; 33\r\n          (  370.26   -76.88    -8.63     0.46)  ; 34\r\n          (  372.44   -78.15    -9.60     0.46)  ; 35\r\n          (  372.44   -78.15    -9.65     0.46)  ; 36\r\n          (  373.07   -76.82   -10.95     0.46)  ; 37\r\n          (  372.94   -76.25   -10.97     0.46)  ; 38\r\n          (  374.74   -75.84   -10.97     0.46)  ; 39\r\n          (  375.60   -74.49   -12.17     0.46)  ; 40\r\n          (\r\n            (  375.93   -74.94   -11.70     0.46)  ; 1, R-2-2-3-2-1\r\n            (  377.99   -75.67   -12.17     0.46)  ; 2\r\n            (  382.84   -76.31   -13.00     0.46)  ; 3\r\n            (  385.49   -77.48   -14.38     0.46)  ; 4\r\n            (  387.45   -75.83   -16.00     0.46)  ; 5\r\n            (  387.45   -75.83   -16.02     0.46)  ; 6\r\n            (  388.92   -76.08   -18.40     0.46)  ; 7\r\n            (  391.73   -76.02   -19.42     0.46)  ; 8\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  305.65   -86.64    -0.12     0.46)  ; 1\r\n              (  314.31   -83.42    -1.73     0.46)  ; 2\r\n              (  330.08   -80.32    -4.52     0.46)  ; 3\r\n              (  340.61   -79.04    -4.03     0.46)  ; 4\r\n              (  381.38   -76.06   -12.98     0.46)  ; 5\r\n              (  390.65   -77.47   -19.42     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  296.63   -86.37     2.70     0.46)  ; 1\r\n              (  302.43   -85.01    -0.12     0.46)  ; 2\r\n              (  319.48   -83.40    -2.05     0.46)  ; 3\r\n              (  334.81   -80.41    -4.03     0.46)  ; 4\r\n              (  348.60   -78.96    -4.63     0.46)  ; 5\r\n              (  356.60   -78.88    -6.42     0.46)  ; 6\r\n              (  364.76   -77.56    -5.97     0.46)  ; 7\r\n              (  378.43   -75.56   -12.17     0.46)  ; 8\r\n              (  386.68   -76.61   -16.02     0.46)  ; 9\r\n              (  388.92   -76.08   -18.40     0.46)  ; 10\r\n            )  ;  End of markers\r\n            (\r\n              (  393.25   -74.47   -17.05     0.46)  ; 1, R-2-2-3-2-1-1\r\n              (  396.07   -74.41   -16.70     0.46)  ; 2\r\n              (  401.06   -75.63   -16.40     0.46)  ; 3\r\n              (  401.06   -75.63   -16.42     0.46)  ; 4\r\n              (  403.70   -76.79   -16.15     0.46)  ; 5\r\n              (  408.28   -76.32   -15.35     0.46)  ; 6\r\n              (  413.02   -76.41   -14.52     0.46)  ; 7\r\n              (  417.45   -77.16   -13.82     0.46)  ; 8\r\n              (  419.81   -77.20   -13.82     0.46)  ; 9\r\n              (  422.62   -77.14   -13.82     0.46)  ; 10\r\n              (  425.89   -76.98   -13.10     0.46)  ; 11\r\n              (  429.27   -77.38   -12.40     0.46)  ; 12\r\n              (  431.95   -76.75   -11.80     0.46)  ; 13\r\n              (  434.45   -77.36   -11.13     0.46)  ; 14\r\n              (  439.36   -76.20   -11.13     0.46)  ; 15\r\n              (  439.36   -76.20   -10.55     0.46)  ; 16\r\n              (  443.83   -75.16   -10.05     0.46)  ; 17\r\n              (  447.09   -74.99    -8.97     0.46)  ; 18\r\n              (  450.22   -74.26    -7.92     0.46)  ; 19\r\n              (  450.22   -74.26    -7.95     0.46)  ; 20\r\n              (  452.71   -74.88    -7.17     0.46)  ; 21\r\n              (  457.94   -73.05    -5.30     0.46)  ; 22\r\n              (  462.47   -70.21    -4.35     0.46)  ; 23\r\n              (  465.29   -70.15    -4.82     0.46)  ; 24\r\n              (  468.23   -70.65    -3.50     0.46)  ; 25\r\n              (  470.95   -68.22    -1.88     0.46)  ; 26\r\n              (  473.38   -66.47    -0.45     0.46)  ; 27\r\n              (  475.65   -64.14    -0.45     0.46)  ; 28\r\n              (  477.75   -63.05     0.80     0.46)  ; 29\r\n              (  479.55   -62.62     2.25     0.46)  ; 30\r\n              (  479.55   -62.62     2.17     0.46)  ; 31\r\n              (  483.11   -61.79     2.08     0.46)  ; 32\r\n              (  485.79   -61.16     4.03     0.46)  ; 33\r\n              (  489.05   -60.99     4.72     0.46)  ; 34\r\n              (  492.31   -60.83     5.30     0.46)  ; 35\r\n              (  493.52   -59.95     6.42     0.46)  ; 36\r\n              (  495.93   -58.20     7.22     0.46)  ; 37\r\n              (  498.29   -58.24     9.95     0.46)  ; 38\r\n              (  500.93   -59.40    11.55     0.46)  ; 39\r\n              (  502.27   -59.09    12.55     0.46)  ; 40\r\n              (  502.27   -59.09    12.52     0.46)  ; 41\r\n              (  505.71   -57.69    14.13     0.46)  ; 42\r\n              (  505.71   -57.69    14.10     0.46)  ; 43\r\n              (  507.36   -56.70    14.92     0.46)  ; 44\r\n              (  510.94   -55.87    14.92     0.46)  ; 45\r\n              (  515.84   -54.71    15.92     0.46)  ; 46\r\n              (  517.82   -53.06    17.35     0.46)  ; 47\r\n              (  522.41   -52.59    18.45     0.46)  ; 48\r\n              (  526.11   -51.11    19.45     0.46)  ; 49\r\n              (  529.11   -49.82    19.13     0.46)  ; 50\r\n              (  532.10   -48.53    19.35     0.46)  ; 51\r\n              (  535.22   -47.79    20.60     0.46)  ; 52\r\n              (  535.22   -47.79    20.83     0.46)  ; 53\r\n              (  537.19   -46.14    22.05     0.46)  ; 54\r\n              (  540.45   -45.97    23.47     0.46)  ; 55\r\n              (  540.45   -45.97    23.45     0.46)  ; 56\r\n              (  542.74   -43.65    24.50     0.46)  ; 57\r\n              (  546.71   -44.50    26.92     0.46)  ; 58\r\n              (  549.07   -44.55    28.15     0.46)  ; 59\r\n              (  550.50   -46.61    29.08     0.46)  ; 60\r\n              (  552.49   -49.11    29.70     0.46)  ; 61\r\n              (  553.07   -49.58    31.77     0.46)  ; 62\r\n              (  554.11   -49.94    32.77     0.46)  ; 63\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  462.29   -71.44    -4.35     0.46)  ; 1\r\n                (  481.64   -61.54     2.08     0.46)  ; 2\r\n                (  505.08   -59.04    14.10     0.46)  ; 3\r\n                (  510.63   -56.53    14.92     0.46)  ; 4\r\n                (  525.04   -52.58    19.45     0.46)  ; 5\r\n                (  532.81   -49.55    19.35     0.46)  ; 6\r\n                (  542.02   -42.61    24.50     0.46)  ; 7\r\n                (  545.32   -46.62    26.90     0.46)  ; 8\r\n                (  547.33   -43.17    26.92     0.46)  ; 9\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  413.60   -76.87   -14.52     0.46)  ; 1\r\n                (  457.51   -73.17    -5.30     0.46)  ; 2\r\n                (  458.94   -75.22    -3.88     0.46)  ; 3\r\n                (  470.06   -68.43    -1.88     0.46)  ; 4\r\n                (  475.21   -64.24    -0.45     0.46)  ; 5\r\n                (  492.75   -60.72     6.42     0.46)  ; 6\r\n                (  495.49   -58.30     7.22     0.46)  ; 7\r\n                (  522.54   -53.14    18.45     0.46)  ; 8\r\n              )  ;  End of markers\r\n               High\r\n            |\r\n              (  394.42   -75.41   -20.38     0.46)  ; 1, R-2-2-3-2-1-2\r\n              (  394.42   -75.41   -20.42     0.46)  ; 2\r\n              (  398.44   -74.48   -21.10     0.46)  ; 3\r\n              (  398.44   -74.48   -21.13     0.46)  ; 4\r\n              (  400.23   -74.06   -21.95     0.46)  ; 5\r\n              (  402.19   -72.40   -23.23     0.46)  ; 6\r\n              (  403.98   -71.98   -24.22     0.46)  ; 7\r\n              (  406.66   -71.36   -25.33     0.46)  ; 8\r\n              (  409.02   -71.41   -26.52     0.46)  ; 9\r\n              (  411.53   -72.00   -27.73     0.46)  ; 10\r\n              (  414.08   -70.81   -28.63     0.46)  ; 11\r\n              (  417.20   -70.08   -30.23     0.46)  ; 12\r\n              (  420.05   -68.22   -31.42     0.46)  ; 13\r\n              (  423.77   -67.94   -32.67     0.46)  ; 14\r\n              (  427.15   -68.34   -33.42     0.46)  ; 15\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  400.59   -71.58   -23.23     0.46)  ; 1\r\n                (  405.95   -70.32   -25.33     0.46)  ; 2\r\n                (  422.25   -69.49   -32.67     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  417.07   -69.52   -30.23     0.46)  ; 1\r\n              )  ;  End of markers\r\n              (\r\n                (  431.26   -69.77   -31.20     0.46)  ; 1, R-2-2-3-2-1-2-1\r\n                (  433.90   -70.94   -29.77     0.46)  ; 2\r\n                (  433.90   -70.94   -29.80     0.46)  ; 3\r\n                (  436.52   -72.12   -29.02     0.46)  ; 4\r\n                (  438.94   -70.36   -29.02     0.46)  ; 5\r\n                (  442.96   -69.41   -27.65     0.46)  ; 6\r\n                (  442.96   -69.41   -27.67     0.46)  ; 7\r\n                (  444.92   -67.76   -25.97     0.46)  ; 8\r\n                (  447.43   -68.37   -25.33     0.46)  ; 9\r\n                (  451.00   -67.53   -25.07     0.46)  ; 10\r\n                (  451.00   -67.53   -25.10     0.46)  ; 11\r\n                (  455.28   -67.72   -24.02     0.46)  ; 12\r\n                (  456.93   -66.73   -23.30     0.46)  ; 13\r\n                (  459.03   -65.65   -22.57     0.46)  ; 14\r\n                (  459.03   -65.65   -22.63     0.46)  ; 15\r\n                (  460.50   -65.90   -21.65     0.46)  ; 16\r\n                (  460.50   -65.90   -21.67     0.46)  ; 17\r\n                (  462.43   -66.05   -20.85     0.46)  ; 18\r\n                (  464.40   -64.39   -19.88     0.46)  ; 19\r\n                (  467.52   -63.66   -19.00     0.46)  ; 20\r\n                (  471.99   -62.62   -18.33     0.46)  ; 21\r\n                (  476.18   -60.44   -17.65     0.46)  ; 22\r\n                (  479.49   -58.47   -17.42     0.46)  ; 23\r\n                (  482.08   -55.47   -16.83     0.46)  ; 24\r\n                (  485.08   -54.17   -16.42     0.46)  ; 25\r\n                (  487.05   -52.51   -15.17     0.46)  ; 26\r\n                (  487.05   -52.51   -15.20     0.46)  ; 27\r\n                (  488.27   -50.44   -13.87     0.46)  ; 28\r\n                (  490.81   -49.24   -13.20     0.46)  ; 29\r\n                (  490.81   -49.24   -13.23     0.46)  ; 30\r\n                (  493.53   -46.82   -12.72     0.46)  ; 31\r\n                (  493.53   -46.82   -12.75     0.46)  ; 32\r\n                (  497.60   -44.07   -12.30     0.46)  ; 33\r\n                (  500.73   -43.34   -11.65     0.46)  ; 34\r\n                (  503.59   -41.48   -10.38     0.46)  ; 35\r\n                (  505.38   -41.06    -8.47     0.46)  ; 36\r\n                (  507.74   -41.10    -6.73     0.46)  ; 37\r\n                (  510.16   -39.34    -4.82     0.46)  ; 38\r\n                (  512.26   -38.25    -3.25     0.46)  ; 39\r\n                (  513.78   -36.69    -2.38     0.46)  ; 40\r\n                (  517.36   -35.86    -1.42     0.46)  ; 41\r\n                (  517.22   -35.29    -1.45     0.46)  ; 42\r\n                (  519.50   -32.96    -0.50     0.46)  ; 43\r\n                (  523.97   -31.92     0.70     0.46)  ; 44\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  442.19   -70.19   -27.67     0.46)  ; 1\r\n                  (  472.12   -63.17   -18.33     0.46)  ; 2\r\n                  (  503.41   -42.71   -10.38     0.46)  ; 3\r\n                  (  507.79   -39.30    -4.82     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  436.07   -72.22   -29.02     0.46)  ; 1\r\n                  (  454.71   -67.26   -24.02     0.46)  ; 2\r\n                  (  462.43   -66.05   -20.85     0.46)  ; 3\r\n                  (  487.05   -52.51   -15.20     0.46)  ; 4\r\n                  (  491.25   -49.14   -13.23     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (  527.62   -33.45     0.70     0.46)  ; 1, R-2-2-3-2-1-2-1-1\r\n                  (  529.63   -35.96    -0.93     0.46)  ; 2\r\n                  (  530.65   -36.32    -2.50     0.46)  ; 3\r\n                  (  530.65   -36.32    -2.53     0.46)  ; 4\r\n                  (  531.94   -37.81    -4.47     0.46)  ; 5\r\n                  (  532.07   -38.38    -6.25     0.46)  ; 6\r\n                  (  533.10   -38.74    -8.15     0.46)  ; 7\r\n                  (  533.10   -38.74    -8.18     0.46)  ; 8\r\n                  (  533.81   -39.76   -10.28     0.46)  ; 9\r\n                  (  533.90   -42.14   -11.70     0.46)  ; 10\r\n                  (  533.90   -42.14   -11.73     0.46)  ; 11\r\n                  (  536.17   -45.79   -12.95     0.46)  ; 12\r\n                  (  536.17   -45.79   -12.98     0.46)  ; 13\r\n                  (  537.06   -45.58   -15.17     0.46)  ; 14\r\n                  (  537.51   -45.47   -18.95     0.46)  ; 15\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  534.97   -40.69   -11.73     0.46)  ; 1\r\n                    (  535.01   -44.85   -12.98     0.46)  ; 2\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  526.25   -29.59     0.70     0.46)  ; 1, R-2-2-3-2-1-2-1-2\r\n                  (  529.87   -26.96     1.80     0.46)  ; 2\r\n                  (  532.42   -25.76     3.22     0.46)  ; 3\r\n                  (  534.96   -24.57     3.22     0.46)  ; 4\r\n                  (  538.98   -23.63     3.70     0.46)  ; 5\r\n                  (  541.09   -22.53     4.22     0.46)  ; 6\r\n                  (  543.94   -20.66     4.78     0.46)  ; 7\r\n                  (  543.94   -20.66     4.75     0.46)  ; 8\r\n                  (  547.13   -18.14     5.75     0.46)  ; 9\r\n                  (  547.13   -18.14     5.72     0.46)  ; 10\r\n                  (  548.32   -17.26     7.53     0.46)  ; 11\r\n                  (  549.40   -15.80     9.15     0.46)  ; 12\r\n                  (  551.36   -14.15    10.17     0.46)  ; 13\r\n                  (  551.36   -14.15    10.15     0.46)  ; 14\r\n                  (  553.16   -13.73    11.10     0.46)  ; 15\r\n                  (  553.16   -13.73    11.07     0.46)  ; 16\r\n                  (  554.36   -12.86    12.00     0.46)  ; 17\r\n                  (  554.86   -10.95    12.00     0.46)  ; 18\r\n                  (  557.98   -10.21    12.62     0.46)  ; 19\r\n                  (  559.51    -8.66    12.88     0.46)  ; 20\r\n                  (  561.16    -7.68    12.88     0.46)  ; 21\r\n                  (  562.55    -5.56    12.88     0.46)  ; 22\r\n                  (  564.33    -5.14    12.88     0.46)  ; 23\r\n                  (  568.85    -2.29    11.37     0.46)  ; 24\r\n                  (  571.71    -0.43    12.07     0.46)  ; 25\r\n                  (  575.02     1.54    13.70     0.46)  ; 26\r\n                  (  577.43     3.31    15.12     0.46)  ; 27\r\n                  (  581.64     5.48    16.07     0.46)  ; 28\r\n                  (  586.72     7.87    16.07     0.46)  ; 29\r\n                  (  591.96     9.69    16.47     0.46)  ; 30\r\n                  (  594.51    10.88    16.47     0.46)  ; 31\r\n                  (  597.26     9.15    16.47     0.46)  ; 32\r\n                  (  599.77     8.54    15.20     0.46)  ; 33\r\n                  (  599.77     8.54    15.17     0.46)  ; 34\r\n                  (  603.15     8.14    14.13     0.46)  ; 35\r\n                  (  606.73     8.97    14.13     0.46)  ; 36\r\n                  (  609.14    10.74    13.52     0.46)  ; 37\r\n                  (  610.98    12.96    14.60     0.46)  ; 38\r\n                  (  612.50    14.50    15.75     0.46)  ; 39\r\n                  (  614.96    18.07    16.57     0.46)  ; 40\r\n                  (  617.87    21.74    16.90     0.46)  ; 41\r\n                  (  619.00    24.98    18.05     0.46)  ; 42\r\n                  (  619.36    27.47    19.58     0.46)  ; 43\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  530.71   -28.54     1.80     0.46)  ; 1\r\n                    (  545.65   -17.89     5.72     0.46)  ; 2\r\n                    (  573.86     2.47    13.70     0.46)  ; 3\r\n                    (  577.74     3.98    15.12     0.46)  ; 4\r\n                    (  586.02     8.89    16.07     0.46)  ; 5\r\n                    (  617.66    24.67    18.05     0.46)  ; 6\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  559.96    -8.55    12.88     0.46)  ; 1\r\n                    (  564.33    -5.14    12.88     0.46)  ; 2\r\n                    (  571.71    -0.43    12.07     0.46)  ; 3\r\n                    (  603.15     8.14    14.13     0.46)  ; 4\r\n                    (  606.28     8.86    14.13     0.46)  ; 5\r\n                    (  608.69    10.63    14.13     0.46)  ; 6\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (  431.33   -67.95   -33.67     0.46)  ; 1, R-2-2-3-2-1-2-2\r\n                (  436.19   -68.60   -34.52     0.46)  ; 2\r\n                (  439.32   -67.87   -35.90     0.46)  ; 3\r\n                (  442.44   -67.13   -37.28     0.46)  ; 4\r\n                (  445.83   -67.54   -37.28     0.46)  ; 5\r\n                (  449.67   -67.83   -38.57     0.46)  ; 6\r\n                (  452.61   -68.33   -39.67     0.46)  ; 7\r\n                (  454.14   -66.78   -40.77     0.46)  ; 8\r\n                (  457.85   -66.51   -41.65     0.46)  ; 9\r\n                (  461.42   -65.68   -42.88     0.46)  ; 10\r\n                (  465.57   -65.30   -43.82     0.46)  ; 11\r\n                (  468.38   -65.23   -44.80     0.46)  ; 12\r\n                (  471.65   -65.08   -47.02     0.46)  ; 13\r\n                (  472.71   -63.63   -48.58     0.46)  ; 14\r\n                (  477.44   -63.72   -48.58     0.46)  ; 15\r\n                (  480.13   -63.09   -49.52     0.46)  ; 16\r\n                (  483.69   -62.25   -50.42     0.46)  ; 17\r\n                (  487.59   -60.73   -51.75     0.46)  ; 18\r\n                (  487.59   -60.73   -51.77     0.46)  ; 19\r\n                (  492.32   -59.63   -52.45     0.46)  ; 20\r\n                (  495.31   -58.33   -54.52     0.46)  ; 21\r\n                (  498.30   -57.03   -55.55     0.46)  ; 22\r\n                (  498.30   -57.03   -55.57     0.46)  ; 23\r\n                (  500.99   -56.41   -57.63     0.46)  ; 24\r\n                (  503.08   -55.31   -59.58     0.46)  ; 25\r\n                (  508.85   -55.75   -59.78     0.46)  ; 26\r\n                (  512.54   -55.49   -61.28     0.46)  ; 27\r\n                (  518.17   -55.36   -61.67     0.46)  ; 28\r\n                (  523.09   -54.20   -62.22     0.46)  ; 29\r\n                (  523.09   -54.20   -62.28     0.46)  ; 30\r\n                (  527.42   -52.60   -62.28     0.46)  ; 31\r\n                (  531.76   -50.99   -62.80     0.46)  ; 32\r\n                (  534.88   -50.26   -61.60     0.46)  ; 33\r\n                (  539.85   -47.29   -60.05     0.46)  ; 34\r\n                (  542.97   -46.57   -58.70     0.46)  ; 35\r\n                (  546.23   -46.40   -59.72     0.46)  ; 36\r\n                (  550.82   -45.91   -59.22     0.46)  ; 37\r\n                (  550.82   -45.91   -59.25     0.46)  ; 38\r\n                (  557.35   -45.59   -58.05     0.46)  ; 39\r\n                (  561.81   -44.55   -59.67     0.46)  ; 40\r\n                (  563.07   -41.85   -59.67     0.46)  ; 41\r\n                (  564.18   -38.60   -59.67     0.46)  ; 42\r\n                (  568.89   -34.52   -60.22     0.46)  ; 43\r\n                (  575.64   -31.14   -60.47     0.46)  ; 44\r\n                (  580.41   -29.42   -61.38     0.46)  ; 45\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  470.43   -65.95   -47.02     0.46)  ; 1\r\n                  (  488.17   -61.20   -50.60     0.46)  ; 2\r\n                  (  516.88   -53.87   -61.70     0.46)  ; 3\r\n                  (  580.22   -30.67   -61.38     0.46)  ; 4\r\n                  (  585.44   -34.82   -61.50     0.46)  ; 5\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  439.77   -67.76   -35.90     0.46)  ; 1\r\n                  (  445.83   -67.54   -37.28     0.46)  ; 2\r\n                  (  461.42   -65.68   -42.88     0.46)  ; 3\r\n                  (  498.30   -57.03   -55.57     0.46)  ; 4\r\n                  (  531.76   -50.99   -62.80     0.46)  ; 5\r\n                  (  557.48   -46.15   -58.05     0.46)  ; 6\r\n                )  ;  End of markers\r\n                (\r\n                  (  586.21   -34.04   -61.90     0.46)  ; 1, R-2-2-3-2-1-2-2-1\r\n                  (  588.66   -36.45   -64.25     0.46)  ; 2\r\n                  (  591.55   -38.76   -65.10     0.46)  ; 3\r\n                  (  594.00   -41.18   -65.60     0.46)  ; 4\r\n                  (  594.35   -44.68   -66.65     0.46)  ; 5\r\n                  (  596.04   -47.86   -67.15     0.46)  ; 6\r\n                  (  596.04   -47.86   -67.17     0.46)  ; 7\r\n                  (  597.42   -51.72   -68.17     0.46)  ; 8\r\n                  (  600.57   -55.16   -69.02     0.46)  ; 9\r\n                  (  602.45   -57.11   -70.18     0.46)  ; 10\r\n                  (  604.94   -57.72   -71.28     0.46)  ; 11\r\n                  (  607.27   -59.57   -72.53     0.46)  ; 12\r\n                  (  607.27   -59.57   -72.55     0.46)  ; 13\r\n                  (  608.69   -61.61   -73.63     0.46)  ; 14\r\n                  (  610.42   -63.01   -74.65     0.46)  ; 15\r\n                  (  611.40   -65.16   -76.22     0.46)  ; 16\r\n                  (  611.40   -65.16   -76.25     0.46)  ; 17\r\n                  (  613.89   -65.78   -78.40     0.46)  ; 18\r\n                  (  616.08   -67.05   -80.00     0.46)  ; 19\r\n                  (  618.53   -69.47   -80.30     0.46)  ; 20\r\n                  (  618.53   -69.47   -80.33     0.46)  ; 21\r\n                  (  621.75   -71.10   -81.03     0.46)  ; 22\r\n                  (  624.19   -73.51   -82.07     0.46)  ; 23\r\n                  (  624.59   -75.20   -83.47     0.46)  ; 24\r\n                  (  626.46   -77.16   -84.45     0.46)  ; 25\r\n                  (  628.32   -79.11   -85.07     0.46)  ; 26\r\n                  (  628.32   -79.11   -85.10     0.46)  ; 27\r\n                  (  629.49   -80.03   -85.10     0.46)  ; 28\r\n                  (  633.59   -81.45   -85.10     0.46)  ; 29\r\n                  (  635.78   -82.73   -84.30     0.46)  ; 30\r\n                  (  637.96   -84.02   -84.60     0.46)  ; 31\r\n                  (  637.96   -84.02   -84.80     0.46)  ; 32\r\n                  (  640.05   -88.90   -84.78     0.46)  ; 33\r\n                  (  642.02   -91.30   -84.78     0.46)  ; 34\r\n                  (  644.95   -97.78   -85.65     0.46)  ; 35\r\n                  (  646.84   -99.73   -86.75     0.46)  ; 36\r\n                  (  649.99  -103.18   -87.68     0.46)  ; 37\r\n                  (  650.66  -105.99   -88.65     0.46)  ; 38\r\n                  (  650.66  -105.99   -88.67     0.46)  ; 39\r\n                  (  652.79  -109.08   -89.90     0.46)  ; 40\r\n                  (  652.79  -109.08   -89.92     0.46)  ; 41\r\n                  (  655.10  -110.93   -91.22     0.46)  ; 42\r\n                  (  656.40  -112.41   -92.92     0.46)  ; 43\r\n                  (  658.26  -114.37   -94.25     0.46)  ; 44\r\n                  (  660.54  -118.02   -94.48     0.46)  ; 45\r\n                  (  666.02  -123.30   -94.38     0.46)  ; 46\r\n                  (  666.02  -123.30   -94.40     0.46)  ; 47\r\n                  (  666.15  -123.87   -92.78     0.46)  ; 48\r\n                  (  666.81  -126.70   -91.93     0.46)  ; 49\r\n                  (  669.53  -130.24   -91.45     0.46)  ; 50\r\n                  (  671.08  -132.87   -91.00     0.46)  ; 51\r\n                  (  674.83  -136.77   -90.70     0.46)  ; 52\r\n                  (  675.93  -139.49   -90.70     0.46)  ; 53\r\n                  (  677.31  -143.36   -90.70     0.46)  ; 54\r\n                  (  678.95  -148.34   -89.57     0.46)  ; 55\r\n                  (  680.91  -152.66   -89.05     0.46)  ; 56\r\n                  (  683.31  -156.86   -90.05     0.46)  ; 57\r\n                  (  684.86  -159.50   -89.78     0.46)  ; 58\r\n                  (  685.79  -163.46   -89.78     0.46)  ; 59\r\n                  (  686.27  -167.52   -89.45     0.46)  ; 60\r\n                  (  689.44  -170.96   -89.45     0.46)  ; 61\r\n                  (  690.99  -173.58   -88.55     0.46)  ; 62\r\n                  (  693.85  -177.70   -87.75     0.46)  ; 63\r\n                  (  695.98  -180.78   -86.85     0.46)  ; 64\r\n                  (  695.98  -180.78   -86.88     0.46)  ; 65\r\n                  (  696.51  -183.04   -85.50     0.46)  ; 66\r\n                  (  698.46  -187.36   -85.02     0.46)  ; 67\r\n                  (  700.00  -190.00   -84.50     0.46)  ; 68\r\n                  (  704.13  -195.59   -85.02     0.46)  ; 69\r\n                  (  707.89  -199.49   -85.02     0.46)  ; 70\r\n                  (  711.44  -204.63   -84.48     0.46)  ; 71\r\n                  (  712.60  -205.55   -83.72     0.46)  ; 72\r\n                  (  715.31  -209.09   -83.40     0.46)  ; 73\r\n                  (  718.48  -212.53   -82.57     0.46)  ; 74\r\n                  (  718.48  -212.53   -82.60     0.46)  ; 75\r\n                  (  722.09  -215.87   -81.62     0.46)  ; 76\r\n                  (  724.26  -217.14   -80.22     0.46)  ; 77\r\n                  (  726.14  -219.10   -79.10     0.46)  ; 78\r\n                  (  728.64  -219.71   -77.53     0.46)  ; 79\r\n                  (  728.64  -219.71   -77.55     0.46)  ; 80\r\n                  (  730.38  -221.09   -75.78     0.46)  ; 81\r\n                  (  730.38  -221.09   -75.80     0.46)  ; 82\r\n                  (  732.74  -221.14   -73.85     0.46)  ; 83\r\n                  (  732.74  -221.14   -73.88     0.46)  ; 84\r\n                  (  733.85  -223.85   -72.88     0.46)  ; 85\r\n                  (  735.41  -226.47   -71.40     0.46)  ; 86\r\n                  (  737.27  -228.43   -70.07     0.46)  ; 87\r\n                  (  737.05  -231.46   -66.45     0.46)  ; 88\r\n                  (  740.40  -233.67   -65.03     0.46)  ; 89\r\n                  (  741.56  -234.59   -63.07     0.46)  ; 90\r\n                  (  743.15  -235.41   -61.42     0.46)  ; 91\r\n                  (  744.18  -235.77   -59.35     0.46)  ; 92\r\n                  (  746.09  -235.92   -57.20     0.46)  ; 93\r\n                  (  746.09  -235.92   -57.22     0.46)  ; 94\r\n                  (  749.05  -236.42   -55.52     0.46)  ; 95\r\n                  (  751.99  -236.92   -53.78     0.46)  ; 96\r\n                  (  756.16  -236.54   -53.15     0.46)  ; 97\r\n                  (  761.47  -237.09   -52.67     0.46)  ; 98\r\n                  (  761.02  -237.19   -52.67     0.46)  ; 99\r\n                  (  765.25  -239.18   -51.52     0.46)  ; 100\r\n                  (  768.92  -240.72   -50.27     0.46)  ; 101\r\n                  (  772.26  -242.93   -48.38     0.46)  ; 102\r\n                  (  776.68  -243.67   -46.65     0.46)  ; 103\r\n                  (  777.39  -244.70   -44.43     0.46)  ; 104\r\n                  (  779.13  -246.09   -43.18     0.46)  ; 105\r\n                  (  779.13  -246.09   -43.20     0.46)  ; 106\r\n                  (  780.87  -247.47   -42.08     0.46)  ; 107\r\n                  (  780.87  -247.47   -41.55     0.46)  ; 108\r\n                  (  783.05  -248.75   -41.55     0.46)  ; 109\r\n                  (  786.44  -249.15   -40.88     0.46)  ; 110\r\n                  (  790.36  -251.82   -40.10     0.46)  ; 111\r\n                  (  793.39  -254.68   -39.50     0.46)  ; 112\r\n                  (  795.70  -256.53   -38.42     0.46)  ; 113\r\n                  (  795.70  -256.53   -38.45     0.46)  ; 114\r\n                  (  798.34  -257.70   -37.88     0.46)  ; 115\r\n                  (  800.39  -258.42   -36.58     0.46)  ; 116\r\n                  (  800.39  -258.42   -36.60     0.46)  ; 117\r\n                  (  802.57  -259.70   -35.27     0.46)  ; 118\r\n                  (  805.21  -260.87   -33.70     0.46)  ; 119\r\n                  (  809.71  -264.00   -33.15     0.46)  ; 120\r\n                  (  814.52  -266.45   -33.15     0.46)  ; 121\r\n                  (  817.92  -266.86   -33.15     0.46)  ; 122\r\n                  (  821.13  -268.49   -32.38     0.46)  ; 123\r\n                  (  824.66  -269.46   -30.77     0.46)  ; 124\r\n                  (  827.73  -270.52   -29.48     0.46)  ; 125\r\n                  (  831.51  -272.63   -28.33     0.46)  ; 126\r\n                  (  834.01  -273.23   -27.08     0.46)  ; 127\r\n                  (  837.18  -276.67   -27.08     0.46)  ; 128\r\n                  (  840.83  -278.20   -27.08     0.46)  ; 129\r\n                  (  842.70  -280.15   -27.08     0.46)  ; 130\r\n                  (  846.23  -281.13   -26.80     0.46)  ; 131\r\n                  (  848.86  -282.29   -26.00     0.46)  ; 132\r\n                  (  851.80  -282.80   -25.38     0.46)  ; 133\r\n                  (  851.80  -282.80   -25.40     0.46)  ; 134\r\n                  (  855.74  -285.46   -25.13     0.46)  ; 135\r\n                  (  858.81  -286.53   -25.52     0.46)  ; 136\r\n                  (  860.59  -286.11   -27.17     0.46)  ; 137\r\n                  (  863.72  -285.39   -28.42     0.46)  ; 138\r\n                  (  863.72  -285.39   -28.55     0.46)  ; 139\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  598.66   -55.01   -69.02     0.46)  ; 1\r\n                    (  610.51   -65.37   -76.25     0.46)  ; 2\r\n                    (  665.25  -124.07   -94.40     0.46)  ; 3\r\n                    (  677.22  -146.95   -89.57     0.46)  ; 4\r\n                    (  697.44  -187.00   -85.02     0.46)  ; 5\r\n                    (  750.78  -237.80   -53.78     0.46)  ; 6\r\n                    (  771.24  -242.56   -48.38     0.46)  ; 7\r\n                    (  838.61  -278.73   -27.08     0.46)  ; 8\r\n                    (  853.36  -285.42   -25.13     0.46)  ; 9\r\n                    (  857.91  -286.74   -25.52     0.46)  ; 10\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  593.55   -41.28   -65.60     0.46)  ; 1\r\n                    (  640.77   -89.93   -84.78     0.46)  ; 2\r\n                    (  645.40   -97.67   -85.65     0.46)  ; 3\r\n                    (  653.24  -108.97   -89.92     0.46)  ; 4\r\n                    (  650.08  -105.54   -87.30     0.46)  ; 5\r\n                    (  671.08  -132.87   -91.00     0.46)  ; 6\r\n                    (  712.47  -204.99   -83.72     0.46)  ; 7\r\n                    (  722.09  -215.87   -81.62     0.46)  ; 8\r\n                    (  736.83  -228.54   -70.07     0.46)  ; 9\r\n                    (  768.92  -240.72   -50.27     0.46)  ; 10\r\n                    (  775.78  -243.88   -46.65     0.46)  ; 11\r\n                    (  797.77  -257.24   -37.88     0.46)  ; 12\r\n                    (  805.21  -260.87   -33.70     0.46)  ; 13\r\n                    (  809.27  -264.10   -33.15     0.46)  ; 14\r\n                    (  814.66  -267.03   -33.15     0.46)  ; 15\r\n                    (  817.16  -267.63   -33.15     0.46)  ; 16\r\n                    (  831.65  -273.19   -28.33     0.46)  ; 17\r\n                    (  845.79  -281.23   -26.80     0.46)  ; 18\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  582.04   -27.48   -63.00     0.46)  ; 1, R-2-2-3-2-1-2-2-2\r\n                  (  585.61   -26.65   -64.60     0.46)  ; 2\r\n                  (  587.00   -24.54   -65.52     0.46)  ; 3\r\n                  (  587.00   -24.54   -65.55     0.46)  ; 4\r\n                  (  591.69   -20.44   -66.13     0.46)  ; 5\r\n                  (  594.05   -20.49   -66.40     0.46)  ; 6\r\n                  (  597.18   -19.76   -67.80     0.46)  ; 7\r\n                  (  598.58   -17.64   -69.47     0.46)  ; 8\r\n                  (  600.55   -15.99   -71.20     0.46)  ; 9\r\n                  (  602.38   -13.76   -72.28     0.46)  ; 10\r\n                  (  602.38   -13.76   -72.30     0.46)  ; 11\r\n                  (  602.61   -10.73   -73.35     0.46)  ; 12\r\n                  (  605.51    -7.06   -74.40     0.46)  ; 13\r\n                  (  605.51    -7.06   -74.42     0.46)  ; 14\r\n                  (  608.87    -3.28   -75.57     0.46)  ; 15\r\n                  (  608.87    -3.28   -75.60     0.46)  ; 16\r\n                  (  610.12    -0.61   -76.77     0.46)  ; 17\r\n                  (  609.99    -0.03   -76.80     0.46)  ; 18\r\n                  (  611.38     2.08   -75.82     0.46)  ; 19\r\n                  (  611.38     2.08   -75.85     0.46)  ; 20\r\n                  (  612.20     4.65   -75.85     0.46)  ; 21\r\n                  (  614.03     6.88   -77.95     0.46)  ; 22\r\n                  (  616.39     6.84   -78.65     0.46)  ; 23\r\n                  (  620.19     4.74   -80.97     0.46)  ; 24\r\n                  (  620.19     4.74   -81.03     0.46)  ; 25\r\n                  (  622.14     0.42   -81.98     0.46)  ; 26\r\n                  (  624.15    -2.09   -81.35     0.46)  ; 27\r\n                  (  627.35    -3.72   -81.43     0.46)  ; 28\r\n                  (  629.23    -5.68   -82.47     0.46)  ; 29\r\n                  (  630.33    -8.41   -83.57     0.46)  ; 30\r\n                  (  630.33    -8.41   -83.60     0.46)  ; 31\r\n                  (  631.88   -11.02   -84.63     0.46)  ; 32\r\n                  (  631.88   -11.02   -84.65     0.46)  ; 33\r\n                  (  636.57   -12.92   -85.10     0.46)  ; 34\r\n                  (  637.78   -12.03   -85.10     0.46)  ; 35\r\n                  (  640.41   -13.21   -86.10     0.46)  ; 36\r\n                  (  640.41   -13.21   -86.13     0.46)  ; 37\r\n                  (  646.74   -14.12   -86.72     0.46)  ; 38\r\n                  (  650.77   -13.17   -87.28     0.46)  ; 39\r\n                  (  656.66   -14.18   -87.95     0.46)  ; 40\r\n                  (  660.41   -12.11   -87.97     0.46)  ; 41\r\n                  (  665.27   -12.75   -89.22     0.46)  ; 42\r\n                  (  668.85   -11.91   -88.07     0.46)  ; 43\r\n                  (  668.85   -11.91   -88.10     0.46)  ; 44\r\n                  (  673.00   -11.53   -87.07     0.46)  ; 45\r\n                  (  673.00   -11.53   -87.10     0.46)  ; 46\r\n                  (  679.38   -10.63   -87.10     0.46)  ; 47\r\n                  (  684.87    -9.95   -87.10     0.46)  ; 48\r\n                  (  689.34    -8.90   -86.60     0.46)  ; 49\r\n                  (  693.23    -7.39   -85.70     0.46)  ; 50\r\n                  (  697.29    -4.65   -85.70     0.46)  ; 51\r\n                  (  700.16    -2.78   -84.92     0.46)  ; 52\r\n                  (  705.06    -1.63   -84.45     0.46)  ; 53\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  625.70    -4.71   -81.43     0.46)  ; 1\r\n                    (  664.51   -13.54   -89.22     0.46)  ; 2\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  596.61   -19.30   -67.80     0.46)  ; 1\r\n                    (  624.02    -1.53   -81.35     0.46)  ; 2\r\n                    (  689.34    -8.90   -86.60     0.46)  ; 3\r\n                    (  700.16    -2.78   -84.92     0.46)  ; 4\r\n                    (  705.06    -1.63   -84.45     0.46)  ; 5\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  378.77   -76.13   -11.22     0.46)  ; 1, R-2-2-3-2-2\r\n            (  381.27   -76.74    -9.35     0.46)  ; 2\r\n            (  381.27   -76.74    -9.38     0.46)  ; 3\r\n            (  384.80   -77.70    -7.92     0.46)  ; 4\r\n            (  384.80   -77.70    -7.95     0.46)  ; 5\r\n            (  387.12   -79.54    -7.65     0.46)  ; 6\r\n            (  389.79   -78.91    -8.27     0.46)  ; 7\r\n            (  391.31   -77.37    -6.60     0.46)  ; 8\r\n            (  392.26   -75.35    -5.30     0.46)  ; 9\r\n            (  392.75   -73.44    -4.52     0.46)  ; 10\r\n            (  392.75   -73.44    -4.55     0.46)  ; 11\r\n            (  394.32   -70.09    -3.38     0.46)  ; 12\r\n            (  395.83   -68.54    -2.22     0.46)  ; 13\r\n            (  397.81   -66.89    -0.90     0.46)  ; 14\r\n            (  399.46   -65.91     0.32     0.46)  ; 15\r\n            (  400.54   -64.46     1.55     0.46)  ; 16\r\n            (  401.78   -61.78     3.08     0.46)  ; 17\r\n            (  402.60   -59.20     3.82     0.46)  ; 18\r\n            (  404.43   -56.98     3.82     0.46)  ; 19\r\n            (  406.76   -52.85     4.63     0.46)  ; 20\r\n            (  409.50   -50.41     5.50     0.46)  ; 21\r\n            (  411.02   -48.86     6.75     0.46)  ; 22\r\n            (  411.91   -48.65     8.52     0.46)  ; 23\r\n            (  413.29   -46.54     9.77     0.46)  ; 24\r\n            (  414.96   -45.56    13.72     0.46)  ; 25\r\n            (  416.34   -43.44    15.35     0.46)  ; 26\r\n            (  416.34   -43.44    15.38     0.46)  ; 27\r\n            (  417.28   -41.43    17.02     0.46)  ; 28\r\n            (  417.28   -41.43    17.05     0.46)  ; 29\r\n            (  418.53   -38.74    18.50     0.46)  ; 30\r\n            (  419.16   -37.40    20.17     0.46)  ; 31\r\n            (  419.16   -37.40    20.15     0.46)  ; 32\r\n            (  420.95   -36.98    21.67     0.46)  ; 33\r\n            (  422.21   -34.29    22.83     0.46)  ; 34\r\n            (  426.09   -32.79    24.05     0.46)  ; 35\r\n            (  427.52   -28.86    23.72     0.46)  ; 36\r\n            (  430.34   -28.81    24.35     0.46)  ; 37\r\n            (  432.23   -24.79    24.32     0.46)  ; 38\r\n            (  435.36   -24.05    23.67     0.46)  ; 39\r\n            (  436.96   -24.87    24.48     0.46)  ; 40\r\n            (  438.43   -25.12    25.55     0.46)  ; 41\r\n            (  438.43   -25.12    25.52     0.46)  ; 42\r\n            (  438.61   -23.88    27.45     0.46)  ; 43\r\n            (  438.61   -23.88    27.38     0.46)  ; 44\r\n            (  439.37   -23.12    29.67     0.46)  ; 45\r\n            (  439.37   -23.12    29.70     0.46)  ; 46\r\n\r\n            (Dot\r\n              (Color Cyan)\r\n              (Name \"Marker 1\")\r\n              (  390.05   -80.05    -8.27     0.46)  ; 1\r\n              (  391.73   -73.10    -4.55     0.46)  ; 2\r\n              (  402.50   -62.81     3.08     0.46)  ; 3\r\n              (  413.69   -48.23     9.77     0.46)  ; 4\r\n              (  418.93   -40.45    18.50     0.46)  ; 5\r\n              (  424.26   -35.02    24.05     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color RGB (255, 128, 255))\r\n              (Name \"Marker 2\")\r\n              (  394.76   -69.99    -3.38     0.46)  ; 1\r\n              (  404.30   -56.41     3.82     0.46)  ; 2\r\n              (  428.77   -32.16    21.82     0.46)  ; 3\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Yellow)\r\n  (Dendrite)\r\n  (  254.22    19.87    -2.65     1.38)  ; Root\r\n  (  254.22    19.87    -2.65     1.38)  ; 1, R\r\n  (  253.51    20.90    -4.07     1.38)  ; 2\r\n  (  251.45    21.61    -5.05     1.38)  ; 3\r\n  (  248.96    22.23    -5.40     1.38)  ; 4\r\n  (  247.22    23.60    -6.30     1.38)  ; 5\r\n  (  244.14    24.68    -6.95     1.38)  ; 6\r\n  (  241.46    24.06    -7.27     1.38)  ; 7\r\n  (  239.23    23.53    -8.77     1.38)  ; 8\r\n  (  237.49    24.91    -9.60     0.92)  ; 9\r\n  (  234.90    27.89   -10.45     0.92)  ; 10\r\n  (  232.59    29.73   -11.40     0.92)  ; 11\r\n  (  228.62    30.60   -12.15     0.92)  ; 12\r\n  (  224.92    30.33   -12.27     0.92)  ; 13\r\n  (  222.33    33.30   -11.75     0.92)  ; 14\r\n  (  220.78    35.91   -11.17     0.92)  ; 15\r\n  (  217.56    37.55   -11.17     0.92)  ; 16\r\n  (  214.98    40.53   -11.53     0.92)  ; 17\r\n  (  211.73    40.36   -12.13     0.92)  ; 18\r\n  (  211.73    40.36   -12.15     0.92)  ; 19\r\n  (  209.72    42.89   -12.75     0.92)  ; 20\r\n\r\n  (Cross\r\n    (Color Yellow)\r\n    (Name \"Marker 3\")\r\n    (  243.51    23.33    -6.95     1.38)  ; 1\r\n    (  248.30    25.05    -7.72     1.38)  ; 2\r\n    (  250.74    22.65    -5.48     1.38)  ; 3\r\n    (  253.69    22.13    -5.13     1.38)  ; 4\r\n    (  247.17    21.80    -3.25     1.38)  ; 5\r\n    (  237.44    23.11    -8.77     1.38)  ; 6\r\n    (  231.96    28.39   -11.77     0.92)  ; 7\r\n    (  235.27    30.36    -9.77     0.92)  ; 8\r\n    (  234.28    26.54    -9.57     0.92)  ; 9\r\n    (  228.58    28.79   -11.85     0.92)  ; 10\r\n    (  227.41    29.72   -11.85     0.92)  ; 11\r\n    (  223.80    33.05    -9.95     0.92)  ; 12\r\n    (  218.68    34.83   -11.17     0.92)  ; 13\r\n    (  215.20    37.60   -11.17     0.92)  ; 14\r\n    (  209.94    39.95   -10.60     0.92)  ; 15\r\n    (  209.06    45.71   -10.65     0.92)  ; 16\r\n    (  205.08    46.58   -10.65     0.92)  ; 17\r\n  )  ;  End of markers\r\n  (\r\n    (  209.19    45.15   -13.75     0.46)  ; 1, R-1\r\n    (  207.86    44.84   -15.05     0.46)  ; 2\r\n    (  205.98    46.79   -16.17     0.46)  ; 3\r\n    (  204.68    48.27   -17.72     0.46)  ; 4\r\n    (  204.24    48.17   -17.72     0.46)  ; 5\r\n    (  201.79    50.58   -19.17     0.46)  ; 6\r\n    (  199.74    51.29   -19.55     0.46)  ; 7\r\n    (  196.48    51.13   -19.55     0.46)  ; 8\r\n    (\r\n      (  194.48    53.64   -20.67     0.46)  ; 1, R-1-1\r\n      (  191.45    56.51   -20.87     0.46)  ; 2\r\n      (  191.05    58.21   -23.92     0.46)  ; 3\r\n      (  190.92    58.78   -23.92     0.46)  ; 4\r\n      (  186.19    58.87   -25.13     0.46)  ; 5\r\n      (  186.19    58.87   -25.15     0.46)  ; 6\r\n      (  182.71    61.64   -26.02     0.46)  ; 7\r\n      (  182.71    61.64   -26.10     0.46)  ; 8\r\n      (  180.40    63.48   -27.57     0.46)  ; 9\r\n      (  177.68    67.02   -28.50     0.46)  ; 10\r\n      (  175.89    66.60   -29.13     0.46)  ; 11\r\n      (  174.02    68.56   -30.23     0.46)  ; 12\r\n      (  173.40    67.21   -32.20     0.46)  ; 13\r\n      (  173.40    67.21   -32.22     0.46)  ; 14\r\n      (  170.76    68.39   -33.83     0.46)  ; 15\r\n      (  170.76    68.39   -33.85     0.46)  ; 16\r\n      (  169.35    70.44   -35.47     0.46)  ; 17\r\n      (  166.79    69.24   -37.08     0.46)  ; 18\r\n      (  165.37    71.31   -38.07     0.46)  ; 19\r\n      (  164.08    72.79   -38.88     0.46)  ; 20\r\n      (  162.24    70.58   -39.58     0.46)  ; 21\r\n      (  159.48    72.30   -40.57     0.46)  ; 22\r\n      (  155.51    73.18   -41.97     0.46)  ; 23\r\n      (  155.51    73.18   -42.00     0.46)  ; 24\r\n      (  153.63    75.12   -43.18     0.46)  ; 25\r\n      (  151.46    76.41   -44.05     0.46)  ; 26\r\n      (  151.51    78.21   -46.20     0.46)  ; 27\r\n      (  149.90    79.02   -48.95     0.46)  ; 28\r\n      (  149.18    80.06   -51.20     0.46)  ; 29\r\n      (  147.31    82.00   -52.67     0.46)  ; 30\r\n      (  144.37    82.50   -53.33     0.46)  ; 31\r\n      (  143.13    85.79   -54.87     0.46)  ; 32\r\n      (  142.02    88.53   -56.77     0.46)  ; 33\r\n      (  141.80    91.46   -58.60     0.46)  ; 34\r\n      (  140.38    93.52   -60.22     0.46)  ; 35\r\n      (  137.04    95.71   -62.45     0.46)  ; 36\r\n      (  134.40    96.88   -64.47     0.46)  ; 37\r\n      (  134.40    96.88   -64.50     0.46)  ; 38\r\n      (  134.44    98.69   -68.13     0.46)  ; 39\r\n      (  133.73    99.72   -69.45     0.46)  ; 40\r\n      (  133.73    99.72   -69.60     0.46)  ; 41\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  192.66    57.40   -21.32     0.46)  ; 1\r\n        (  182.67    59.83   -26.10     0.46)  ; 2\r\n        (  184.85    58.55   -26.10     0.46)  ; 3\r\n        (  178.48    63.63   -26.77     0.46)  ; 4\r\n        (  163.76    72.13   -36.42     0.46)  ; 5\r\n        (  160.33    70.72   -38.72     0.46)  ; 6\r\n        (  157.38    71.22   -43.18     0.46)  ; 7\r\n        (  151.14    75.73   -44.05     0.46)  ; 8\r\n        (  190.56    56.31   -20.87     0.46)  ; 9\r\n      )  ;  End of markers\r\n       Normal\r\n    |\r\n      (  193.30    48.59   -19.55     0.46)  ; 1, R-1-2\r\n      (  191.21    47.51   -20.45     0.46)  ; 2\r\n      (  188.08    46.77   -20.10     0.46)  ; 3\r\n      (  185.85    46.25   -21.48     0.46)  ; 4\r\n      (  185.09    45.48   -22.92     0.46)  ; 5\r\n      (  182.09    44.17   -23.17     0.46)  ; 6\r\n      (  176.92    44.16   -23.92     0.46)  ; 7\r\n      (  173.58    46.35   -23.92     0.46)  ; 8\r\n      (  170.00    45.52   -23.92     0.46)  ; 9\r\n      (  164.87    47.29   -23.92     0.46)  ; 10\r\n      (  160.90    48.17   -24.32     0.46)  ; 11\r\n      (  157.38    49.13   -22.83     0.46)  ; 12\r\n      (  152.69    51.02   -23.37     0.46)  ; 13\r\n      (  149.30    51.42   -23.72     0.46)  ; 14\r\n      (  145.19    52.85   -24.10     0.46)  ; 15\r\n      (  140.91    53.04   -24.52     0.46)  ; 16\r\n      (  140.91    53.04   -24.50     0.46)  ; 17\r\n      (  138.14    54.77   -25.35     0.46)  ; 18\r\n      (  135.34    54.71   -25.95     0.46)  ; 19\r\n      (  132.53    54.66   -27.02     0.46)  ; 20\r\n      (  127.26    56.99   -27.35     0.46)  ; 21\r\n      (  124.32    57.51   -28.52     0.46)  ; 22\r\n      (  120.97    59.70   -29.77     0.46)  ; 23\r\n      (  116.73    61.70   -30.95     0.46)  ; 24\r\n      (  113.03    61.42   -32.00     0.46)  ; 25\r\n      (  108.92    62.85   -32.85     0.46)  ; 26\r\n      (  107.77    63.78   -35.22     0.46)  ; 27\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  192.24    47.15   -20.45     0.46)  ; 1\r\n        (  190.71    45.59   -20.10     0.46)  ; 2\r\n        (  182.94    42.58   -23.17     0.46)  ; 3\r\n        (  175.72    43.28   -23.40     0.46)  ; 4\r\n        (  162.64    46.78   -24.32     0.46)  ; 5\r\n        (  162.29    50.28   -23.47     0.46)  ; 6\r\n        (  158.22    47.54   -21.65     0.46)  ; 7\r\n        (  153.80    48.29   -21.50     0.46)  ; 8\r\n        (  148.01    52.90   -21.58     0.46)  ; 9\r\n        (  146.72    54.40   -24.10     0.46)  ; 10\r\n        (  136.00    51.88   -27.02     0.46)  ; 11\r\n        (  126.32    54.98   -28.85     0.46)  ; 12\r\n        (  122.53    57.09   -29.67     0.46)  ; 13\r\n        (  127.67    61.27   -25.95     0.46)  ; 14\r\n        (  115.94    65.09   -32.00     0.46)  ; 15\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  207.40    44.73   -11.95     0.46)  ; 1, R-2\r\n    (  204.46    45.22   -10.20     0.46)  ; 2\r\n    (  203.04    47.29    -9.77     0.46)  ; 3\r\n    (  200.98    48.00   -11.47     0.46)  ; 4\r\n    (  199.74    51.29   -12.77     0.46)  ; 5\r\n    (  199.74    51.29   -12.80     0.46)  ; 6\r\n    (  199.34    53.00   -12.17     0.46)  ; 7\r\n    (  199.34    53.00   -12.20     0.46)  ; 8\r\n    (  196.66    52.37   -10.43     0.46)  ; 9\r\n    (  194.53    55.44   -10.43     0.46)  ; 10\r\n    (  191.81    58.99    -9.38     0.46)  ; 11\r\n    (  190.97    60.58    -8.38     0.46)  ; 12\r\n    (  186.55    61.33    -7.92     0.46)  ; 13\r\n    (  182.31    63.33    -6.05     0.46)  ; 14\r\n    (  179.50    63.27    -4.35     0.46)  ; 15\r\n    (  179.50    63.27    -4.32     0.46)  ; 16\r\n    (  177.13    63.31    -2.97     0.46)  ; 17\r\n    (  177.77    64.65    -1.55     0.46)  ; 18\r\n    (  175.13    65.83     0.07     0.46)  ; 19\r\n    (  173.97    66.75     1.13     0.46)  ; 20\r\n    (  173.22    65.98     3.05     0.46)  ; 21\r\n    (  173.22    65.98     3.15     0.46)  ; 22\r\n    (  173.03    64.74     5.15     0.46)  ; 23\r\n    (  171.69    64.42     7.10     0.46)  ; 24\r\n    (  170.49    63.55     8.95     0.46)  ; 25\r\n    (  168.21    67.20     9.88     0.46)  ; 26\r\n    (  168.27    69.00    11.25     0.46)  ; 27\r\n    (  168.27    69.00    11.27     0.46)  ; 28\r\n    (  168.19    71.37    12.27     0.46)  ; 29\r\n    (  167.21    73.53    12.90     0.46)  ; 30\r\n    (  166.86    77.03    13.25     0.46)  ; 31\r\n    (  167.36    78.93    14.15     0.46)  ; 32\r\n    (  168.42    80.38    16.07     0.46)  ; 33\r\n    (  168.42    80.38    16.05     0.46)  ; 34\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  205.08    46.58    -9.77     0.46)  ; 1\r\n      (  186.95    59.64    -7.92     0.46)  ; 2\r\n      (  181.51    60.75    -6.60     0.46)  ; 3\r\n      (  187.50    63.35    -5.60     0.46)  ; 4\r\n      (  191.15    61.82    -7.85     0.46)  ; 5\r\n      (  182.36    65.13    -4.92     0.46)  ; 6\r\n      (  180.03    61.01    -4.68     0.46)  ; 7\r\n      (  180.58    64.71    -5.53     0.46)  ; 8\r\n      (  174.28    67.42     3.15     0.46)  ; 9\r\n      (  169.14    63.23     8.97     0.46)  ; 10\r\n      (  166.17    73.88    13.25     0.46)  ; 11\r\n      (  166.22    75.69    13.25     0.46)  ; 12\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Green)\r\n  (Dendrite)\r\n  (  253.66    10.19     4.07     3.21)  ; Root\r\n  (  253.66    10.19     4.07     3.21)  ; 1, R\r\n  (  251.83     7.96     4.07     2.75)  ; 2\r\n  (\r\n    (  248.70     7.23     4.07     1.38)  ; 1, R-1\r\n    (  246.61     6.14     2.90     1.38)  ; 2\r\n    (  245.08     4.59     1.63     1.38)  ; 3\r\n    (  242.80     2.27     0.30     1.38)  ; 4\r\n    (  240.87     2.41    -0.70     1.38)  ; 5\r\n    (  239.28     3.23    -1.77     1.38)  ; 6\r\n    (  237.62     2.25    -3.42     1.38)  ; 7\r\n\r\n    (Cross\r\n      (Color Green)\r\n      (Name \"Marker 3\")\r\n      (  244.98     0.98    -0.70     1.38)  ; 1\r\n    )  ;  End of markers\r\n    (\r\n      (  233.96     3.78    -3.42     0.92)  ; 1, R-1-1\r\n      (  229.63     2.16    -2.90     0.92)  ; 2\r\n      (  226.86     3.91    -1.75     0.92)  ; 3\r\n      (  223.61     3.74    -0.45     0.92)  ; 4\r\n      (  220.88     1.31    -0.12     0.92)  ; 5\r\n      (  218.07     1.25     1.75     0.92)  ; 6\r\n      (  215.88     2.53     1.75     0.92)  ; 7\r\n      (  214.28     3.35     1.75     0.92)  ; 8\r\n      (  211.83     5.76     2.63     0.92)  ; 9\r\n      (  209.52     7.60     3.57     0.92)  ; 10\r\n      (  208.35     8.53     4.80     0.92)  ; 11\r\n      (  205.41     9.03     6.22     0.92)  ; 12\r\n      (  202.91     9.64     7.07     0.92)  ; 13\r\n      (  201.00     9.78     8.25     0.92)  ; 14\r\n\r\n      (Cross\r\n        (Color Green)\r\n        (Name \"Marker 3\")\r\n        (  221.86    -0.85    -0.12     0.92)  ; 1\r\n        (  223.79     4.98    -0.12     0.92)  ; 2\r\n        (  226.15     4.94     0.40     0.92)  ; 3\r\n        (  215.98     6.14     0.70     0.92)  ; 4\r\n        (  214.95     0.51     2.65     0.92)  ; 5\r\n        (  211.86     1.58     1.75     0.92)  ; 6\r\n        (  218.33     0.12     1.75     0.92)  ; 7\r\n        (  199.62    13.64     8.25     0.92)  ; 8\r\n        (  194.21    10.59     6.93     0.92)  ; 9\r\n        (  190.63     9.74     8.20     0.92)  ; 10\r\n        (  188.75     5.73     9.75     0.92)  ; 11\r\n        (  199.64     9.47     9.75     0.92)  ; 12\r\n      )  ;  End of markers\r\n      (\r\n        (  198.00     8.48     6.13     0.92)  ; 1, R-1-1-1\r\n        (  196.47     6.94     6.82     0.92)  ; 2\r\n        (  196.47     6.94     6.80     0.92)  ; 3\r\n        (  193.40     8.00     7.53     0.92)  ; 4\r\n        (  191.03     8.05     8.90     0.92)  ; 5\r\n        (  188.53     8.66     9.22     0.92)  ; 6\r\n        (  184.88    10.19    10.10     0.46)  ; 7\r\n        (  181.48    10.60    10.70     0.46)  ; 8\r\n        (  179.44    11.30    12.10     0.46)  ; 9\r\n        (  178.23    10.43    13.38     0.46)  ; 10\r\n        (  178.23    10.43    13.35     0.46)  ; 11\r\n        (  177.15     8.98    14.30     0.46)  ; 12\r\n        (  176.00     9.90    14.90     0.46)  ; 13\r\n        (  174.83    10.82    14.90     0.46)  ; 14\r\n        (  172.16    10.20    15.88     0.46)  ; 15\r\n        (  169.39    11.94    16.50     0.46)  ; 16\r\n        (  167.61    11.52    17.60     0.46)  ; 17\r\n        (  166.12    11.77    18.27     0.46)  ; 18\r\n        (  163.90    11.25    18.48     0.46)  ; 19\r\n        (  161.53    11.29    19.75     0.46)  ; 20\r\n        (  161.53    11.29    19.73     0.46)  ; 21\r\n        (  157.69    11.58    19.85     0.46)  ; 22\r\n        (  154.56    10.85    20.25     0.46)  ; 23\r\n        (  151.71     8.99    20.25     0.46)  ; 24\r\n        (  151.26     8.88    20.25     0.46)  ; 25\r\n        (  149.02     8.35    20.25     0.46)  ; 26\r\n        (  145.51     9.32    20.92     0.46)  ; 27\r\n        (  144.48     9.67    23.10     0.46)  ; 28\r\n        (  140.90     8.84    24.00     0.46)  ; 29\r\n        (  140.90     8.84    23.98     0.46)  ; 30\r\n        (  139.12     8.42    25.13     0.46)  ; 31\r\n        (  136.30     8.36    25.70     0.46)  ; 32\r\n        (  136.30     8.36    25.73     0.46)  ; 33\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  183.62     7.50    10.70     0.46)  ; 1\r\n          (  185.28     8.49    10.70     0.46)  ; 2\r\n          (  174.57    11.95    14.90     0.46)  ; 3\r\n          (  165.19     9.75    18.48     0.46)  ; 4\r\n          (  168.01     9.82    18.48     0.46)  ; 5\r\n          (  160.15     9.17    19.27     0.46)  ; 6\r\n          (  161.18    14.79    21.00     0.46)  ; 7\r\n          (  157.78     9.22    21.25     0.46)  ; 8\r\n          (  150.45     6.30    21.25     0.46)  ; 9\r\n          (  151.52     7.75    21.25     0.46)  ; 10\r\n          (  154.21     8.38    21.25     0.46)  ; 11\r\n          (  155.86     9.36    21.25     0.46)  ; 12\r\n          (  148.21     5.78    21.25     0.46)  ; 13\r\n          (  143.18    11.16    25.73     0.46)  ; 14\r\n          (  141.11     5.91    25.75     0.46)  ; 15\r\n          (  141.08    10.08    24.88     0.46)  ; 16\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  198.48    10.40     6.07     0.92)  ; 1, R-1-1-2\r\n        (  197.15    10.08     4.07     0.92)  ; 2\r\n        (  195.18     8.42     2.15     0.92)  ; 3\r\n        (  193.09     7.34     0.30     0.92)  ; 4\r\n        (  190.00     8.41    -0.32     0.92)  ; 5\r\n        (  188.21     7.99    -0.95     0.46)  ; 6\r\n        (  186.12     6.90    -0.95     0.46)  ; 7\r\n        (  183.17     7.41    -1.60     0.46)  ; 8\r\n        (  183.17     7.41    -1.63     0.46)  ; 9\r\n        (  181.70     7.65    -3.67     0.46)  ; 10\r\n        (  182.09     5.96    -5.80     0.46)  ; 11\r\n        (  179.47     7.13    -7.50     0.46)  ; 12\r\n        (  179.47     7.13    -7.53     0.46)  ; 13\r\n        (  177.23     6.61    -8.13     0.46)  ; 14\r\n        (  174.55     5.98    -8.93     0.46)  ; 15\r\n        (  172.69     7.93   -10.45     0.46)  ; 16\r\n        (  172.69     7.93   -10.50     0.46)  ; 17\r\n        (  170.95     9.31   -11.27     0.46)  ; 18\r\n        (  170.55    11.01   -12.60     0.46)  ; 19\r\n        (  170.55    11.01   -12.67     0.46)  ; 20\r\n        (  171.67    14.26   -14.18     0.46)  ; 21\r\n        (  169.49    15.53   -15.60     0.46)  ; 22\r\n        (  166.73    17.28   -16.80     0.46)  ; 23\r\n        (  165.30    19.34   -18.20     0.46)  ; 24\r\n        (  163.82    19.59   -19.75     0.46)  ; 25\r\n        (  162.71    22.31   -20.35     0.46)  ; 26\r\n        (  162.71    22.31   -20.38     0.46)  ; 27\r\n        (  161.43    23.80   -22.27     0.46)  ; 28\r\n        (  159.69    25.19   -23.60     0.46)  ; 29\r\n        (  158.08    26.01   -25.50     0.46)  ; 30\r\n        (  158.08    26.01   -25.55     0.46)  ; 31\r\n        (  155.59    26.62   -26.92     0.46)  ; 32\r\n        (  153.40    27.89   -28.70     0.46)  ; 33\r\n        (  151.16    27.37   -30.90     0.46)  ; 34\r\n        (  150.76    29.07   -32.80     0.46)  ; 35\r\n        (  150.19    29.53   -35.40     0.46)  ; 36\r\n        (  150.19    29.53   -35.42     0.46)  ; 37\r\n        (  148.72    29.79   -37.90     0.46)  ; 38\r\n        (  148.72    29.79   -37.92     0.46)  ; 39\r\n        (  148.32    31.48   -41.40     0.46)  ; 40\r\n        (  147.88    31.38   -43.78     0.46)  ; 41\r\n        (  147.88    31.38   -43.80     0.46)  ; 42\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  198.67    11.63     4.07     0.92)  ; 1\r\n          (  196.31    11.67     2.15     0.92)  ; 2\r\n          (  184.91     6.01    -1.63     0.46)  ; 3\r\n          (  180.45     4.97    -8.13     0.46)  ; 4\r\n          (  176.60     5.27    -7.90     0.46)  ; 5\r\n          (  175.63     7.42    -7.32     0.46)  ; 6\r\n          (  175.36     8.57    -9.05     0.46)  ; 7\r\n          (  174.63     3.61   -10.95     0.46)  ; 8\r\n          (  170.14     6.73   -12.42     0.46)  ; 9\r\n          (  169.47     9.56    -9.77     0.46)  ; 10\r\n          (  169.57    13.17   -14.18     0.46)  ; 11\r\n          (  168.06    17.59   -15.02     0.46)  ; 12\r\n          (  173.09    12.20   -15.02     0.46)  ; 13\r\n          (  173.14    14.01   -15.60     0.46)  ; 14\r\n          (  166.81    14.91   -15.60     0.46)  ; 15\r\n          (  169.44    13.73   -15.60     0.46)  ; 16\r\n          (  159.80    18.64   -20.38     0.46)  ; 17\r\n          (  164.64    22.16   -21.45     0.46)  ; 18\r\n          (  163.92    23.20   -22.27     0.46)  ; 19\r\n          (  156.12    24.35   -27.08     0.46)  ; 20\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  237.57     0.45    -4.55     1.38)  ; 1, R-1-2\r\n      (\r\n        (  234.22     2.64    -6.25     0.92)  ; 1, R-1-2-1\r\n        (  234.22     2.64    -6.28     0.92)  ; 2\r\n        (  231.91     4.49    -7.55     0.92)  ; 3\r\n        (  231.91     4.49    -7.57     0.92)  ; 4\r\n        (  229.99     4.64    -8.93     0.92)  ; 5\r\n        (  228.03     2.98   -10.17     0.92)  ; 6\r\n        (  225.21     2.92    -9.88     0.92)  ; 7\r\n        (  221.11     4.35   -11.37     0.92)  ; 8\r\n        (  221.95     2.76   -13.23     0.92)  ; 9\r\n        (  221.95     2.76   -13.25     0.92)  ; 10\r\n        (  219.82     5.83   -14.25     0.92)  ; 11\r\n        (  219.82     5.83   -14.27     0.92)  ; 12\r\n        (  217.82     8.36   -13.82     0.92)  ; 13\r\n        (  216.52     9.85   -15.57     0.92)  ; 14\r\n        (  215.50    10.20   -17.67     0.92)  ; 15\r\n        (  215.50    10.20   -17.70     0.92)  ; 16\r\n        (  213.26     9.68   -19.33     0.92)  ; 17\r\n        (  211.61     8.69   -19.60     0.92)  ; 18\r\n        (  210.85     7.91   -21.10     0.92)  ; 19\r\n        (  210.85     7.91   -21.13     0.92)  ; 20\r\n        (  209.65     7.03   -22.92     0.46)  ; 21\r\n        (  209.11     9.30   -24.90     0.46)  ; 22\r\n        (  208.86    10.43   -26.90     0.92)  ; 23\r\n        (  205.14    10.16   -28.13     0.92)  ; 24\r\n        (  205.14    10.16   -28.15     0.92)  ; 25\r\n        (  203.09    10.88   -28.15     0.92)  ; 26\r\n        (  199.88    12.51   -28.15     0.92)  ; 27\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  224.58     1.58    -9.88     0.92)  ; 1\r\n          (  216.74     6.91   -13.82     0.92)  ; 2\r\n          (  214.37     6.95   -19.33     0.92)  ; 3\r\n          (  213.44    10.91   -19.33     0.92)  ; 4\r\n          (  211.70     6.33   -19.60     0.92)  ; 5\r\n          (  202.11    13.03   -28.15     0.92)  ; 6\r\n          (  205.18    11.96   -27.65     0.92)  ; 7\r\n        )  ;  End of markers\r\n        (\r\n          (  195.01    13.16   -27.70     0.46)  ; 1, R-1-2-1-1\r\n          (  191.49    14.13   -29.27     0.46)  ; 2\r\n          (  190.32    15.05   -30.30     0.46)  ; 3\r\n          (  188.33    17.57   -30.83     0.46)  ; 4\r\n          (  186.06    21.21   -32.00     0.46)  ; 5\r\n          (  184.77    22.70   -33.22     0.46)  ; 6\r\n          (  183.61    23.63   -34.52     0.46)  ; 7\r\n          (  183.61    23.63   -34.55     0.46)  ; 8\r\n          (  182.37    26.92   -34.55     0.46)  ; 9\r\n          (  179.97    31.13   -34.60     0.46)  ; 10\r\n          (  179.97    31.13   -34.63     0.46)  ; 11\r\n          (  177.84    34.22   -37.02     0.46)  ; 12\r\n          (  177.84    34.22   -37.05     0.46)  ; 13\r\n          (  175.64    35.50   -38.03     0.46)  ; 14\r\n          (  174.40    38.79   -38.38     0.46)  ; 15\r\n          (  171.19    40.42   -38.60     0.46)  ; 16\r\n          (  169.64    43.04   -39.67     0.46)  ; 17\r\n          (  167.59    43.76   -40.57     0.46)  ; 18\r\n          (  167.59    43.76   -40.60     0.46)  ; 19\r\n\r\n          (Cross\r\n            (Color Green)\r\n            (Name \"Marker 3\")\r\n            (  183.61    23.63   -33.22     0.46)  ; 1\r\n            (  183.17    29.49   -38.35     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  195.86    11.57   -30.10     0.46)  ; 1, R-1-2-1-2\r\n          (  192.74    10.84   -32.25     0.46)  ; 2\r\n          (  192.74    10.84   -32.30     0.46)  ; 3\r\n          (  190.67    11.54   -33.45     0.46)  ; 4\r\n          (  190.67    11.54   -33.47     0.46)  ; 5\r\n          (  189.21    11.81   -35.27     0.46)  ; 6\r\n          (  189.21    11.81   -35.33     0.46)  ; 7\r\n          (  186.71    12.42   -36.92     0.46)  ; 8\r\n          (  186.71    12.42   -36.95     0.46)  ; 9\r\n          (  185.74    14.56   -37.85     0.46)  ; 10\r\n          (  185.74    14.56   -37.88     0.46)  ; 11\r\n          (  182.21    15.54   -39.13     0.46)  ; 12\r\n          (  178.94    15.37   -40.80     0.46)  ; 13\r\n          (  177.13    19.12   -41.82     0.46)  ; 14\r\n          (  177.13    19.12   -41.85     0.46)  ; 15\r\n          (  174.58    17.93   -44.10     0.46)  ; 16\r\n          (  172.53    18.65   -46.25     0.46)  ; 17\r\n          (  170.47    19.35   -48.35     0.46)  ; 18\r\n          (  170.47    19.35   -48.38     0.46)  ; 19\r\n          (  168.56    19.50   -50.35     0.46)  ; 20\r\n          (  168.56    19.50   -50.38     0.46)  ; 21\r\n          (  167.71    21.10   -52.65     0.46)  ; 22\r\n          (  167.71    21.10   -52.70     0.46)  ; 23\r\n          (  167.45    22.22   -54.65     0.46)  ; 24\r\n          (  167.45    22.22   -54.72     0.46)  ; 25\r\n          (  165.71    23.61   -56.42     0.46)  ; 26\r\n          (  165.71    23.61   -56.45     0.46)  ; 27\r\n          (  163.61    22.52   -58.38     0.46)  ; 28\r\n          (  163.61    22.52   -58.40     0.46)  ; 29\r\n          (  165.22    21.71   -60.05     0.46)  ; 30\r\n          (  165.22    21.71   -60.22     0.46)  ; 31\r\n          (  162.84    21.75   -62.70     0.46)  ; 32\r\n          (  162.84    21.75   -62.75     0.46)  ; 33\r\n          (  161.33    20.20   -66.13     0.46)  ; 34\r\n          (  159.99    19.89   -69.00     0.46)  ; 35\r\n          (  158.52    20.14   -69.30     0.46)  ; 36\r\n          (  158.12    21.83   -71.17     0.46)  ; 37\r\n          (  158.12    21.83   -71.20     0.46)  ; 38\r\n          (  156.83    23.33   -73.95     0.46)  ; 39\r\n          (  156.83    23.33   -73.97     0.46)  ; 40\r\n          (  155.77    27.85   -76.28     0.46)  ; 41\r\n          (  155.77    27.85   -76.30     0.46)  ; 42\r\n          (  155.55    30.79   -78.28     0.46)  ; 43\r\n          (  155.55    30.79   -78.32     0.46)  ; 44\r\n          (  155.34    33.71   -80.63     0.46)  ; 45\r\n          (  155.34    33.71   -80.68     0.46)  ; 46\r\n          (  154.62    34.75   -84.12     0.46)  ; 47\r\n          (  154.62    34.75   -84.17     0.46)  ; 48\r\n          (  154.72    38.36   -86.62     0.46)  ; 49\r\n          (  154.72    38.36   -86.65     0.46)  ; 50\r\n          (  153.88    39.95   -89.05     0.46)  ; 51\r\n          (  152.77    42.68   -92.00     0.46)  ; 52\r\n          (  151.61    43.59   -94.80     0.46)  ; 53\r\n          (  151.61    43.59   -94.82     0.46)  ; 54\r\n          (  149.73    45.55   -96.80     0.46)  ; 55\r\n          (  149.92    46.78   -99.05     0.46)  ; 56\r\n          (  147.60    48.62  -102.63     0.46)  ; 57\r\n          (  147.60    48.62  -102.65     0.46)  ; 58\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  147.72    48.06   -50.55     0.46)  ; 1\r\n          )  ;  End of markers\r\n\r\n          (Cross\r\n            (Color Green)\r\n            (Name \"Marker 3\")\r\n            (  193.45     9.81   -32.30     0.46)  ; 1\r\n            (  190.54    12.12   -31.77     0.46)  ; 2\r\n            (  183.19    13.37   -39.13     0.46)  ; 3\r\n            (  179.21    14.24   -41.85     0.46)  ; 4\r\n            (  168.74    20.74   -48.38     0.46)  ; 5\r\n            (  158.79    19.00   -67.32     0.46)  ; 6\r\n            (  157.53    38.41   -86.65     0.46)  ; 7\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  234.31     0.28    -6.78     0.92)  ; 1, R-1-2-2\r\n        (  232.98    -0.04    -7.43     0.92)  ; 2\r\n        (  231.72    -2.72    -8.00     0.92)  ; 3\r\n        (  231.72    -2.72    -8.02     0.92)  ; 4\r\n        (  230.92    -5.30    -8.50     0.92)  ; 5\r\n        (  229.12    -5.72    -8.50     0.92)  ; 6\r\n        (  227.29    -7.94    -8.50     0.92)  ; 7\r\n        (  227.32   -12.11    -9.60     0.92)  ; 8\r\n        (  227.32   -12.11    -9.63     0.92)  ; 9\r\n        (  227.04   -16.95   -10.20     0.92)  ; 10\r\n        (  226.69   -19.43   -10.70     0.92)  ; 11\r\n        (  224.90   -19.85   -11.02     0.92)  ; 12\r\n        (  224.35   -23.55   -11.07     0.92)  ; 13\r\n        (  223.23   -26.79   -11.07     0.92)  ; 14\r\n        (  221.08   -29.70   -11.37     0.92)  ; 15\r\n        (  219.50   -33.06   -11.37     0.92)  ; 16\r\n        (  219.06   -33.16   -11.37     0.92)  ; 17\r\n        (  217.57   -38.87   -11.37     0.92)  ; 18\r\n        (  215.88   -41.66   -11.60     0.92)  ; 19\r\n        (  214.87   -45.48   -11.40     0.92)  ; 20\r\n        (  214.87   -45.48   -11.42     0.92)  ; 21\r\n        (  214.79   -49.08   -13.13     0.92)  ; 22\r\n        (  214.59   -50.33   -13.72     0.92)  ; 23\r\n        (  214.59   -50.33   -13.75     0.92)  ; 24\r\n        (  215.31   -51.36   -15.88     0.92)  ; 25\r\n        (  214.55   -52.14   -17.82     0.92)  ; 26\r\n        (  214.55   -52.14   -17.85     0.92)  ; 27\r\n        (  213.03   -53.69   -19.27     0.92)  ; 28\r\n        (  210.35   -54.31   -20.50     0.92)  ; 29\r\n        (  208.82   -55.86   -22.22     0.92)  ; 30\r\n        (  206.78   -55.15   -24.15     0.92)  ; 31\r\n        (  205.17   -54.33   -25.92     0.92)  ; 32\r\n        (  203.70   -54.09   -27.80     0.92)  ; 33\r\n        (  201.74   -55.74   -29.00     0.92)  ; 34\r\n        (  201.74   -55.74   -29.02     0.92)  ; 35\r\n        (  199.05   -56.36   -30.13     0.92)  ; 36\r\n        (  196.81   -56.89   -30.13     0.92)  ; 37\r\n        (  194.72   -57.97   -30.30     0.46)  ; 38\r\n        (  192.75   -59.63   -30.30     0.46)  ; 39\r\n        (  189.63   -60.36   -30.30     0.46)  ; 40\r\n        (  187.52   -61.46   -32.28     0.46)  ; 41\r\n        (  185.29   -61.97   -33.92     0.46)  ; 42\r\n        (  183.63   -62.97   -35.83     0.46)  ; 43\r\n        (  183.58   -64.77   -38.00     0.46)  ; 44\r\n        (  183.58   -64.77   -38.03     0.46)  ; 45\r\n        (  182.52   -66.21   -38.20     0.46)  ; 46\r\n        (  180.54   -67.87   -39.78     0.46)  ; 47\r\n        (  178.26   -70.20   -40.60     0.46)  ; 48\r\n        (  176.74   -71.75   -40.83     0.46)  ; 49\r\n        (  175.04   -74.53   -43.18     0.46)  ; 50\r\n        (  173.66   -76.65   -44.45     0.46)  ; 51\r\n        (  173.28   -79.12   -46.93     0.46)  ; 52\r\n        (  171.99   -77.63   -49.52     0.46)  ; 53\r\n        (  170.66   -77.94   -52.10     0.46)  ; 54\r\n        (  170.66   -77.94   -52.13     0.46)  ; 55\r\n        (  169.63   -77.59   -55.03     0.46)  ; 56\r\n        (  169.63   -77.59   -55.05     0.46)  ; 57\r\n        (  169.05   -77.13   -58.50     0.46)  ; 58\r\n        (  167.89   -76.21   -62.47     0.46)  ; 59\r\n        (  167.63   -75.08   -67.17     0.46)  ; 60\r\n        (  167.63   -75.08   -67.28     0.46)  ; 61\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  225.37    -7.79    -8.50     0.92)  ; 1\r\n          (  229.07    -7.52    -8.50     0.92)  ; 2\r\n          (  226.89    -6.25    -8.50     0.92)  ; 3\r\n          (  228.09    -5.36    -8.50     0.92)  ; 4\r\n          (  227.97   -20.91   -10.70     0.92)  ; 5\r\n          (  225.10   -28.75   -10.30     0.92)  ; 6\r\n          (  213.49   -47.60   -13.13     0.92)  ; 7\r\n          (  220.98   -33.31    -7.95     0.92)  ; 8\r\n          (  223.44   -29.74    -7.95     0.92)  ; 9\r\n          (  217.64   -31.10    -7.95     0.92)  ; 10\r\n          (  220.16   -35.88    -7.80     0.92)  ; 11\r\n          (  220.54   -33.41    -8.82     0.92)  ; 12\r\n          (  216.54   -44.49    -9.48     0.92)  ; 13\r\n          (  214.01   -55.84   -17.60     0.92)  ; 14\r\n          (  203.38   -54.75   -24.15     0.92)  ; 15\r\n          (  193.02   -60.77   -30.30     0.46)  ; 16\r\n          (  181.95   -59.78   -35.25     0.46)  ; 17\r\n          (  179.87   -71.01   -40.83     0.46)  ; 18\r\n          (  174.69   -71.03   -42.02     0.46)  ; 19\r\n          (  177.46   -72.78   -42.02     0.46)  ; 20\r\n          (  175.40   -78.04   -43.80     0.46)  ; 21\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  236.33    -2.03    -4.55     0.92)  ; 1, R-1-2-3\r\n        (  235.12    -2.92    -4.35     0.92)  ; 2\r\n        (  235.12    -2.92    -4.40     0.92)  ; 3\r\n        (  234.19    -4.93    -3.17     0.92)  ; 4\r\n        (  234.45    -6.06    -1.70     0.92)  ; 5\r\n        (  233.37    -7.50    -0.40     0.92)  ; 6\r\n        (\r\n          (  233.18    -8.74     0.95     0.92)  ; 1, R-1-2-3-1\r\n          (  234.03   -10.34     2.05     0.92)  ; 2\r\n          (  235.20   -11.26     3.38     0.92)  ; 3\r\n          (  235.20   -11.26     3.35     0.92)  ; 4\r\n          (  235.91   -12.28     4.43     0.92)  ; 5\r\n          (  236.75   -13.88     5.15     0.92)  ; 6\r\n          (  237.28   -16.14     5.85     0.92)  ; 7\r\n          (  237.37   -18.51     6.57     0.92)  ; 8\r\n          (  235.84   -20.07     8.35     0.92)  ; 9\r\n          (  235.08   -20.84     9.20     0.92)  ; 10\r\n          (  235.17   -23.21     8.80     0.92)  ; 11\r\n          (  235.25   -25.57    10.50     0.92)  ; 12\r\n          (  235.47   -28.51    11.35     0.92)  ; 13\r\n          (  234.97   -30.41    11.07     0.92)  ; 14\r\n          (  235.95   -32.58    12.25     0.92)  ; 15\r\n          (  235.95   -32.58    12.22     0.92)  ; 16\r\n          (  235.59   -35.06    12.50     0.92)  ; 17\r\n          (  234.20   -37.16    12.07     0.92)  ; 18\r\n          (  234.20   -37.16    12.05     0.92)  ; 19\r\n          (  233.75   -37.27    14.30     0.92)  ; 20\r\n          (  234.65   -37.06    16.57     0.92)  ; 21\r\n          (  235.36   -38.09    18.67     0.92)  ; 22\r\n          (  233.83   -39.65    20.17     0.92)  ; 23\r\n          (  233.60   -42.68    20.97     0.92)  ; 24\r\n          (  232.35   -45.37    21.88     0.92)  ; 25\r\n          (  230.33   -48.83    21.55     0.92)  ; 26\r\n          (  230.33   -48.83    21.52     0.92)  ; 27\r\n          (  229.79   -52.53    22.08     0.92)  ; 28\r\n          (  227.64   -55.43    22.57     0.92)  ; 29\r\n          (  226.57   -56.87    23.33     0.92)  ; 30\r\n          (  224.92   -57.85    23.80     0.92)  ; 31\r\n          (  224.92   -57.85    23.78     0.92)  ; 32\r\n          (  224.11   -60.44    24.38     0.92)  ; 33\r\n          (  224.11   -60.44    24.35     0.92)  ; 34\r\n          (  225.53   -62.48    25.23     0.92)  ; 35\r\n          (  223.64   -66.51    26.45     0.92)  ; 36\r\n          (  223.41   -69.55    27.25     0.92)  ; 37\r\n          (  222.29   -72.80    27.77     0.92)  ; 38\r\n          (  220.27   -76.25    29.13     0.92)  ; 39\r\n          (  218.93   -76.57    30.65     0.92)  ; 40\r\n          (  217.55   -78.69    31.95     0.92)  ; 41\r\n          (  216.47   -80.14    33.85     0.92)  ; 42\r\n          (  216.02   -80.24    34.40     0.92)  ; 43\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  239.02   -17.53     5.85     0.92)  ; 1\r\n            (  233.48   -20.03     9.20     0.92)  ; 2\r\n            (  235.13   -19.04     9.20     0.92)  ; 3\r\n            (  240.05   -17.89    10.33     0.92)  ; 4\r\n            (  232.19   -34.65    12.50     0.92)  ; 5\r\n            (  236.43   -36.65    12.50     0.92)  ; 6\r\n            (  236.00   -30.78    12.50     0.92)  ; 7\r\n            (  237.11   -33.49    12.50     0.92)  ; 8\r\n            (  234.53   -30.52     9.65     0.92)  ; 9\r\n            (  232.99   -38.05    18.67     0.92)  ; 10\r\n            (  236.83   -38.34    20.97     0.92)  ; 11\r\n            (  231.64   -44.34    21.88     0.92)  ; 12\r\n            (  231.87   -41.30    21.88     0.92)  ; 13\r\n            (  230.58   -55.93    22.57     0.92)  ; 14\r\n            (  231.44   -51.55    22.57     0.92)  ; 15\r\n            (  225.59   -54.72    26.58     0.92)  ; 16\r\n            (  226.20   -59.35    23.07     0.92)  ; 17\r\n            (  221.99   -67.50    26.45     0.92)  ; 18\r\n            (  219.80   -72.19    27.30     0.92)  ; 19\r\n            (  225.59   -70.83    27.30     0.92)  ; 20\r\n            (  224.21   -72.95    28.52     0.92)  ; 21\r\n          )  ;  End of markers\r\n           High\r\n        |\r\n          (  231.22   -10.40    -1.42     0.92)  ; 1, R-1-2-3-2\r\n          (  231.89   -13.23    -2.42     0.92)  ; 2\r\n          (  231.89   -13.23    -2.45     0.92)  ; 3\r\n          (  231.66   -16.27    -1.65     0.92)  ; 4\r\n          (  230.85   -18.85    -0.47     0.92)  ; 5\r\n          (  229.60   -21.53     0.17     0.92)  ; 6\r\n          (  227.44   -24.42     0.95     0.92)  ; 7\r\n          (  226.23   -25.31     1.95     0.92)  ; 8\r\n          (  224.98   -27.98     3.08     0.92)  ; 9\r\n          (  223.72   -30.66     4.35     0.92)  ; 10\r\n          (  222.82   -30.87     5.55     0.92)  ; 11\r\n          (  221.71   -34.12     4.38     0.92)  ; 12\r\n          (  220.14   -37.47     4.38     0.92)  ; 13\r\n          (  218.44   -40.27     4.38     0.92)  ; 14\r\n          (  219.10   -43.10     5.37     0.92)  ; 15\r\n          (  220.34   -46.38     6.07     0.92)  ; 16\r\n          (  220.34   -46.38     5.22     0.92)  ; 17\r\n          (  220.87   -48.66     4.50     0.92)  ; 18\r\n          (  220.87   -48.66     4.22     0.92)  ; 19\r\n          (  221.85   -50.82     3.35     0.92)  ; 20\r\n          (  220.54   -55.29     2.83     0.92)  ; 21\r\n          (  217.81   -57.74     1.67     0.92)  ; 22\r\n          (  217.81   -57.74     1.65     0.92)  ; 23\r\n          (  215.53   -60.05     1.42     0.92)  ; 24\r\n          (  215.56   -64.23     0.45     0.92)  ; 25\r\n          (  214.76   -66.81     0.22     0.92)  ; 26\r\n          (  214.76   -66.81     0.17     0.92)  ; 27\r\n          (  213.77   -70.62    -0.67     0.92)  ; 28\r\n          (  213.77   -70.62    -0.72     0.92)  ; 29\r\n          (  213.54   -73.66    -1.17     0.92)  ; 30\r\n          (  214.51   -75.82    -1.17     0.92)  ; 31\r\n          (  213.35   -80.87    -1.47     0.92)  ; 32\r\n          (  212.80   -84.59    -1.60     0.92)  ; 33\r\n          (  212.07   -89.53    -1.60     0.92)  ; 34\r\n          (  212.42   -93.04    -2.75     0.92)  ; 35\r\n          (  210.54   -97.06    -3.78     0.92)  ; 36\r\n          (  209.86  -100.19    -3.78     0.92)  ; 37\r\n          (  209.14  -105.15    -4.78     0.92)  ; 38\r\n          (  209.35  -108.08    -3.60     0.92)  ; 39\r\n          (  208.94  -112.35    -2.22     0.92)  ; 40\r\n          (  208.99  -116.53    -1.32     0.92)  ; 41\r\n          (  210.35  -120.38    -0.45     0.92)  ; 42\r\n          (  211.15  -123.78     0.32     0.92)  ; 43\r\n          (  212.08  -127.75     1.07     0.92)  ; 44\r\n          (  209.80  -130.07     1.90     0.92)  ; 45\r\n          (  207.43  -130.03     2.92     0.92)  ; 46\r\n          (  206.04  -132.15     3.65     0.92)  ; 47\r\n          (  206.04  -132.15     3.70     0.92)  ; 48\r\n          (  204.57  -131.89     4.63     0.92)  ; 49\r\n          (  202.92  -132.88     5.20     0.92)  ; 50\r\n          (  202.56  -135.35     4.63     0.92)  ; 51\r\n          (  202.56  -135.35     4.60     0.92)  ; 52\r\n          (  203.27  -136.37     4.82     0.92)  ; 53\r\n          (  204.51  -139.67     6.02     0.92)  ; 54\r\n          (  202.18  -143.80     7.10     0.92)  ; 55\r\n          (  201.77  -148.07     8.45     0.92)  ; 56\r\n          (  202.30  -150.35     9.77     0.92)  ; 57\r\n          (  203.01  -151.37    12.00     0.92)  ; 58\r\n          (  203.59  -151.83    13.30     0.92)  ; 59\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  230.69    -8.14    -1.42     0.92)  ; 1\r\n            (  233.98   -18.12    -1.42     0.92)  ; 2\r\n            (  233.80   -13.38    -2.97     0.92)  ; 3\r\n            (  231.20   -22.34    -0.55     0.92)  ; 4\r\n            (  227.49   -22.61     1.25     0.92)  ; 5\r\n            (  228.59   -25.35     2.88     0.92)  ; 6\r\n            (  222.25   -52.51     5.13     0.92)  ; 7\r\n            (  219.42   -58.55     5.13     0.92)  ; 8\r\n            (  219.24   -59.78     2.57     0.92)  ; 9\r\n            (  221.43   -55.08     2.57     0.92)  ; 10\r\n            (  217.59   -60.78     2.57     0.92)  ; 11\r\n            (  218.12   -63.03     2.57     0.92)  ; 12\r\n            (  214.10   -58.00     1.13     0.92)  ; 13\r\n            (  217.41   -56.03     1.13     0.92)  ; 14\r\n            (  210.58   -79.14    -1.60     0.92)  ; 15\r\n            (  213.76   -76.59    -1.60     0.92)  ; 16\r\n            (  214.69   -80.56    -1.60     0.92)  ; 17\r\n            (  211.46   -84.89    -1.60     0.92)  ; 18\r\n            (  210.91   -88.61    -1.60     0.92)  ; 19\r\n            (  214.57   -90.13    -1.60     0.92)  ; 20\r\n            (  213.10   -67.79    -1.60     0.92)  ; 21\r\n            (  216.18   -68.86    -1.60     0.92)  ; 22\r\n            (  210.20   -93.55    -3.78     0.92)  ; 23\r\n            (  212.42   -99.00    -1.77     0.92)  ; 24\r\n            (  207.40  -103.76    -4.70     0.92)  ; 25\r\n            (  211.24  -104.05    -5.57     0.92)  ; 26\r\n            (  211.18  -111.83    -2.22     0.92)  ; 27\r\n            (  208.49  -118.44    -1.32     0.92)  ; 28\r\n            (  211.61  -117.71    -1.32     0.92)  ; 29\r\n            (  211.88  -118.83    -1.32     0.92)  ; 30\r\n            (  202.84  -130.51     4.60     0.92)  ; 31\r\n            (  198.85  -135.62     4.60     0.92)  ; 32\r\n            (  207.07  -132.50     4.17     0.92)  ; 33\r\n            (  203.60  -145.85     9.52     0.92)  ; 34\r\n            (  203.24  -148.33     9.52     0.92)  ; 35\r\n            (  213.10  -128.10     3.32     0.92)  ; 36\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (  250.10     9.55     4.75     0.92)  ; 1, R-2\r\n    (  247.61    10.16     6.35     0.92)  ; 2\r\n    (  245.28    12.01     6.97     0.92)  ; 3\r\n    (  243.42    13.95     8.52     0.92)  ; 4\r\n    (  244.49    15.40     9.77     0.92)  ; 5\r\n    (  246.99    14.79    10.12     0.92)  ; 6\r\n    (  245.92    13.35    11.47     0.92)  ; 7\r\n    (  244.26    12.36    14.88     0.92)  ; 8\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  251.31    10.43     6.35     0.92)  ; 1\r\n      (  246.60    16.50     8.85     0.92)  ; 2\r\n      (  248.46    14.54    11.47     0.92)  ; 3\r\n    )  ;  End of markers\r\n    (\r\n      (  245.34    13.80    14.88     0.92)  ; 1, R-2-1\r\n      (  241.54    15.91    14.45     0.92)  ; 2\r\n      (  240.08    16.16    14.85     0.92)  ; 3\r\n      (  240.08    16.16    14.82     0.92)  ; 4\r\n      (  239.86    19.09    16.60     0.92)  ; 5\r\n      (  239.46    20.79    18.38     0.92)  ; 6\r\n      (  238.22    24.08    19.52     0.92)  ; 7\r\n      (  236.21    26.60    20.75     0.92)  ; 8\r\n      (  234.08    29.69    20.92     0.92)  ; 9\r\n      (  231.76    31.53    20.13     0.92)  ; 10\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  238.20    18.11    14.82     0.92)  ; 1\r\n        (  237.23    20.27    18.40     0.92)  ; 2\r\n        (  240.13    23.93    18.40     0.92)  ; 3\r\n        (  238.44    27.12    19.02     0.92)  ; 4\r\n        (  231.53    28.49    18.55     0.92)  ; 5\r\n        (  234.26    30.92    18.55     0.92)  ; 6\r\n        (  232.58    34.11    18.55     0.92)  ; 7\r\n      )  ;  End of markers\r\n      (\r\n        (  229.93    35.27    20.85     0.46)  ; 1, R-2-1-1\r\n        (  227.31    36.46    21.85     0.46)  ; 2\r\n        (  224.86    38.86    22.67     0.46)  ; 3\r\n        (  223.84    39.22    24.20     0.46)  ; 4\r\n        (  223.39    39.12    24.20     0.46)  ; 5\r\n        (  222.63    38.35    26.35     0.46)  ; 6\r\n         Normal\r\n      |\r\n        (  228.42    33.73    19.23     0.92)  ; 1, R-2-1-2\r\n        (  226.23    35.01    19.58     0.92)  ; 2\r\n        (  226.23    35.01    19.55     0.92)  ; 3\r\n        (  224.63    35.83    21.10     0.92)  ; 4\r\n        (  222.00    37.00    23.00     0.92)  ; 5\r\n        (  219.99    39.53    24.08     0.92)  ; 6\r\n        (  217.41    42.50    25.60     0.92)  ; 7\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  214.29    41.77    25.05     0.92)  ; 1\r\n          (  218.09    45.64    25.05     0.92)  ; 2\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  242.34    12.51    14.88     0.92)  ; 1, R-2-2\r\n      (  242.29    10.71    16.38     0.92)  ; 2\r\n      (  242.29    10.71    16.35     0.92)  ; 3\r\n      (  239.40    13.01    18.27     0.92)  ; 4\r\n      (  239.09    12.34    20.00     0.92)  ; 5\r\n      (  237.31    11.93    20.77     0.92)  ; 6\r\n      (  235.32    10.27    22.30     0.92)  ; 7\r\n      (  233.36     8.61    23.25     0.92)  ; 8\r\n      (  233.36     8.61    23.23     0.92)  ; 9\r\n      (  230.61    10.35    24.60     0.92)  ; 10\r\n      (  228.99    11.18    25.47     0.92)  ; 11\r\n      (  227.29     8.38    26.60     0.92)  ; 12\r\n      (  224.08    10.02    27.63     0.92)  ; 13\r\n      (  221.32    11.76    28.02     0.92)  ; 14\r\n      (  218.64    11.14    29.50     0.92)  ; 15\r\n      (  218.64    11.14    29.48     0.92)  ; 16\r\n      (  217.38     8.45    30.27     0.92)  ; 17\r\n      (  217.38     8.45    30.23     0.92)  ; 18\r\n      (  219.12     7.07    31.80     0.92)  ; 19\r\n      (  221.62     6.46    33.70     0.46)  ; 20\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  241.49     8.13    18.27     0.92)  ; 1\r\n        (  239.04    10.54    20.77     0.92)  ; 2\r\n        (  240.61    13.90    20.77     0.92)  ; 3\r\n        (  226.45     9.97    27.63     0.92)  ; 4\r\n        (  225.73    11.01    29.42     0.92)  ; 5\r\n        (  221.64    12.43    30.67     0.92)  ; 6\r\n        (  219.30    14.28    30.67     0.92)  ; 7\r\n        (  229.42    11.07   -25.80     0.92)  ; 8\r\n      )  ;  End of markers\r\n       High\r\n    |\r\n      (  245.82     9.74    15.90     0.46)  ; 1, R-2-3\r\n      (  247.29     9.49    18.80     0.46)  ; 2\r\n      (  245.90     7.37    21.22     0.46)  ; 3\r\n      (  244.56     7.06    23.92     0.46)  ; 4\r\n      (  244.06     5.15    24.80     0.46)  ; 5\r\n      (  243.18     4.94    24.80     0.46)  ; 6\r\n      (\r\n        (  243.13     3.14    25.77     0.46)  ; 1, R-2-3-1\r\n        (  244.64    -1.28    26.52     0.46)  ; 2\r\n        (  246.24    -2.10    28.15     0.46)  ; 3\r\n        (  246.24    -2.10    28.13     0.46)  ; 4\r\n        (  247.13    -1.89    30.63     0.46)  ; 5\r\n        (  247.13    -1.89    30.57     0.46)  ; 6\r\n        (  248.46    -1.57    33.22     0.46)  ; 7\r\n        (  248.46    -1.57    33.27     0.46)  ; 8\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  243.40     7.98    24.80     0.46)  ; 1\r\n          (  244.34     9.99    24.80     0.46)  ; 2\r\n          (  244.86     1.76    26.52     0.46)  ; 3\r\n          (  242.19     1.13    26.52     0.46)  ; 4\r\n          (  242.72    -1.14    26.52     0.46)  ; 5\r\n        )  ;  End of markers\r\n         High\r\n      |\r\n        (  246.70     3.98    24.80     0.46)  ; 1, R-2-3-2\r\n        (  248.17     3.73    23.27     0.46)  ; 2\r\n        (  248.57     2.03    21.77     0.46)  ; 3\r\n        (\r\n          (  250.71    -1.05    22.50     0.46)  ; 1, R-2-3-2-1\r\n          (  253.60    -3.36    22.50     0.46)  ; 2\r\n          (  255.92    -5.21    23.75     0.46)  ; 3\r\n          (  259.50    -4.37    23.75     0.46)  ; 4\r\n          (  260.74    -7.66    23.75     0.46)  ; 5\r\n          (  259.80    -9.67    25.35     0.46)  ; 6\r\n          (  262.60    -9.61    26.35     0.46)  ; 7\r\n          (  264.92   -11.46    27.60     0.46)  ; 8\r\n          (  264.92   -11.46    27.57     0.46)  ; 9\r\n          (  265.94   -11.82    29.58     0.46)  ; 10\r\n          (  266.39   -11.71    32.28     0.46)  ; 11\r\n          (  266.39   -11.71    32.30     0.46)  ; 12\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  252.53    -4.80    22.50     0.46)  ; 1\r\n            (  259.09    -8.65    23.75     0.46)  ; 2\r\n            (  261.68    -5.65    23.75     0.46)  ; 3\r\n            (  259.11   -12.82    23.95     0.46)  ; 4\r\n            (  264.53    -9.75    26.65     0.46)  ; 5\r\n            (  264.43   -13.36    27.10     0.46)  ; 6\r\n          )  ;  End of markers\r\n           High\r\n        |\r\n          (  244.55     1.08    21.77     0.46)  ; 1, R-2-3-2-2\r\n          (  241.74     1.03    22.25     0.46)  ; 2\r\n          (  239.19    -0.17    22.25     0.46)  ; 3\r\n          (  238.74    -0.28    22.25     0.46)  ; 4\r\n          (  236.51    -0.80    22.25     0.46)  ; 5\r\n          (  234.23    -3.13    22.25     0.46)  ; 6\r\n          (  231.59    -1.95    22.45     0.46)  ; 7\r\n          (  230.35     1.34    22.45     0.46)  ; 8\r\n          (  227.94    -0.41    20.73     0.46)  ; 9\r\n          (  225.58    -0.37    19.15     0.46)  ; 10\r\n          (  222.32    -0.54    17.67     0.46)  ; 11\r\n          (  220.35    -2.19    16.17     0.46)  ; 12\r\n          (  219.72    -3.54    15.50     0.46)  ; 13\r\n          (  216.33    -3.14    15.12     0.46)  ; 14\r\n          (  212.88    -4.54    15.12     0.46)  ; 15\r\n          (  209.37    -3.58    14.18     0.46)  ; 16\r\n          (  207.58    -4.00    12.62     0.46)  ; 17\r\n          (  206.23    -4.31    11.07     0.46)  ; 18\r\n          (  204.85    -6.42    10.10     0.46)  ; 19\r\n          (  202.04    -6.49     9.25     0.46)  ; 20\r\n          (  199.62    -8.24     8.85     0.46)  ; 21\r\n          (  196.63    -9.55     8.60     0.46)  ; 22\r\n          (  193.76   -11.41     8.63     0.46)  ; 23\r\n          (  189.56   -13.59     8.63     0.46)  ; 24\r\n          (  187.52   -12.88     8.63     0.46)  ; 25\r\n          (  184.92   -15.87     7.72     0.46)  ; 26\r\n          (  180.27   -18.17     7.72     0.46)  ; 27\r\n          (  177.90   -18.11     7.02     0.46)  ; 28\r\n          (  176.07   -20.34     6.62     0.46)  ; 29\r\n          (  173.08   -21.64     7.55     0.46)  ; 30\r\n          (  173.08   -21.64     7.50     0.46)  ; 31\r\n          (  171.69   -23.75     7.25     0.46)  ; 32\r\n          (  171.69   -23.75     7.22     0.46)  ; 33\r\n          (  168.52   -26.29     6.82     0.46)  ; 34\r\n          (  166.42   -27.37     6.38     0.46)  ; 35\r\n          (  163.73   -28.00     6.38     0.46)  ; 36\r\n          (  161.50   -28.53     6.15     0.46)  ; 37\r\n          (  161.50   -28.53     6.13     0.46)  ; 38\r\n          (  159.67   -30.75     5.75     0.46)  ; 39\r\n          (  157.06   -33.75     4.92     0.46)  ; 40\r\n          (  157.06   -33.75     4.90     0.46)  ; 41\r\n          (  155.10   -35.40     3.45     0.46)  ; 42\r\n          (  153.01   -36.49     1.75     0.46)  ; 43\r\n          (  151.17   -38.72    -0.28     0.46)  ; 44\r\n          (  147.99   -41.25    -1.85     0.46)  ; 45\r\n          (  142.90   -43.64    -3.85     0.46)  ; 46\r\n          (  142.90   -43.64    -3.95     0.46)  ; 47\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  247.49     0.58    21.77     0.46)  ; 1\r\n            (  231.83     1.09    22.45     0.46)  ; 2\r\n            (  229.07     2.84    23.07     0.46)  ; 3\r\n            (  228.52    -0.88    22.60     0.46)  ; 4\r\n            (  225.50     2.00    18.40     0.46)  ; 5\r\n            (  223.91    -1.36    18.33     0.46)  ; 6\r\n            (  225.71    -0.94    18.33     0.46)  ; 7\r\n            (  223.60    -2.03    14.52     0.46)  ; 8\r\n            (  218.65    -4.99    15.12     0.46)  ; 9\r\n            (  216.59    -4.27    15.12     0.46)  ; 10\r\n            (  214.36    -4.79    15.12     0.46)  ; 11\r\n            (  213.28    -6.24    14.40     0.46)  ; 12\r\n            (  212.62    -3.41    16.52     0.46)  ; 13\r\n            (  207.84    -5.13    12.17     0.46)  ; 14\r\n            (  206.32    -6.68    13.15     0.46)  ; 15\r\n            (  199.99    -5.78     8.85     0.46)  ; 16\r\n            (  201.81    -9.53    10.00     0.46)  ; 17\r\n            (  200.46    -9.84    10.00     0.46)  ; 18\r\n            (  194.09   -10.75     8.13     0.46)  ; 19\r\n            (  191.62   -14.30     9.77     0.46)  ; 20\r\n            (  191.58   -10.13     6.70     0.46)  ; 21\r\n            (  184.21   -14.85    10.30     0.46)  ; 22\r\n            (  184.87   -17.68     8.97     0.46)  ; 23\r\n            (  182.33   -18.87     8.93     0.46)  ; 24\r\n            (  177.23   -21.26     6.62     0.46)  ; 25\r\n            (  173.87   -25.04     7.25     0.46)  ; 26\r\n            (  173.13   -19.84     7.25     0.46)  ; 27\r\n            (  166.33   -25.01     7.25     0.46)  ; 28\r\n            (  164.23   -26.10     7.25     0.46)  ; 29\r\n            (  169.49   -28.45     7.82     0.46)  ; 30\r\n            (  161.24   -27.40     5.63     0.46)  ; 31\r\n            (  161.27   -31.57     5.60     0.46)  ; 32\r\n          )  ;  End of markers\r\n           Incomplete\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Yellow)\r\n  (Dendrite)\r\n  (  273.70    23.33     0.80     1.38)  ; Root\r\n  (  273.70    23.33     0.80     1.38)  ; 1, R\r\n  (  276.57    25.19     0.80     1.38)  ; 2\r\n  (  277.82    27.88     0.80     1.38)  ; 3\r\n  (  278.77    29.89     0.80     1.38)  ; 4\r\n  (  281.58    29.95    -0.35     1.38)  ; 5\r\n  (  281.58    29.95    -0.37     1.38)  ; 6\r\n  (  282.42    28.35    -2.08     1.38)  ; 7\r\n\r\n  (Cross\r\n    (Color White)\r\n    (Name \"Marker 3\")\r\n    (  274.62    25.34     2.42     0.46)  ; 1\r\n  )  ;  End of markers\r\n  (\r\n    (  284.52    29.45    -3.25     0.92)  ; 1, R-1\r\n    (  286.31    29.87    -4.65     0.92)  ; 2\r\n    (  289.49    32.40    -6.00     0.92)  ; 3\r\n    (  292.74    32.57    -7.00     0.92)  ; 4\r\n    (  295.24    31.96    -7.63     0.92)  ; 5\r\n    (  299.26    32.91    -7.65     0.92)  ; 6\r\n    (  300.47    33.78    -6.02     0.92)  ; 7\r\n    (  302.25    34.20    -4.82     0.92)  ; 8\r\n    (  302.25    34.20    -4.85     0.92)  ; 9\r\n    (  304.67    35.96    -3.92     0.92)  ; 10\r\n    (  308.10    37.36    -2.90     0.92)  ; 11\r\n    (  308.10    37.36    -2.92     0.92)  ; 12\r\n    (  309.77    38.35    -2.27     0.92)  ; 13\r\n    (  312.76    39.64    -1.22     0.92)  ; 14\r\n    (  316.01    39.81    -3.25     0.92)  ; 15\r\n    (  316.01    39.81    -3.27     0.92)  ; 16\r\n    (  316.25    42.85    -4.13     0.92)  ; 17\r\n    (  316.25    42.85    -4.15     0.92)  ; 18\r\n    (  318.66    44.61    -5.15     0.92)  ; 19\r\n    (  319.99    44.92    -4.97     0.92)  ; 20\r\n    (  322.28    47.26    -5.10     0.92)  ; 21\r\n    (  323.35    48.70    -6.20     0.92)  ; 22\r\n    (  324.75    50.82    -7.72     0.92)  ; 23\r\n    (  329.21    51.86    -8.32     0.92)  ; 24\r\n    (  332.38    54.40    -9.22     0.92)  ; 25\r\n    (  334.98    57.39    -9.77     0.92)  ; 26\r\n    (  336.81    59.62   -10.25     0.92)  ; 27\r\n    (  336.81    59.62   -10.28     0.92)  ; 28\r\n    (  339.10    61.94   -11.55     0.92)  ; 29\r\n    (  339.10    61.94   -11.58     0.92)  ; 30\r\n    (  340.93    64.17   -13.45     0.92)  ; 31\r\n    (  340.93    64.17   -13.47     0.92)  ; 32\r\n    (  341.11    65.40   -15.02     0.92)  ; 33\r\n    (  341.47    67.87   -16.35     0.92)  ; 34\r\n    (  341.47    67.87   -16.38     0.92)  ; 35\r\n    (  342.16    71.02   -16.00     0.92)  ; 36\r\n    (  342.67    74.73   -13.85     0.92)  ; 37\r\n    (  342.67    74.73   -13.87     0.92)  ; 38\r\n    (  343.35    77.87   -12.72     0.92)  ; 39\r\n    (  345.94    80.87   -11.98     0.92)  ; 40\r\n    (  348.10    83.76   -13.00     0.92)  ; 41\r\n    (  350.50    85.52   -14.13     0.92)  ; 42\r\n    (  353.67    88.06   -14.75     0.92)  ; 43\r\n    (  356.42    90.49   -15.73     0.92)  ; 44\r\n    (  359.01    93.49   -16.38     0.92)  ; 45\r\n    (  361.29    95.80   -15.05     0.46)  ; 46\r\n    (  363.88    98.81   -13.32     0.46)  ; 47\r\n    (  363.88    98.81   -13.35     0.46)  ; 48\r\n    (  365.00   102.06   -11.32     0.46)  ; 49\r\n    (  365.00   102.06   -11.30     0.46)  ; 50\r\n    (  365.63   103.40    -9.67     0.46)  ; 51\r\n    (  365.87   106.44    -9.67     0.46)  ; 52\r\n    (  366.19   107.12    -8.25     0.46)  ; 53\r\n    (  367.70   108.66    -7.30     0.46)  ; 54\r\n    (  367.70   108.66    -7.32     0.46)  ; 55\r\n    (  368.46   109.43    -5.50     0.46)  ; 56\r\n    (  369.99   110.99    -3.70     0.46)  ; 57\r\n    (  369.99   110.99    -3.72     0.46)  ; 58\r\n    (  371.05   112.44    -1.60     0.46)  ; 59\r\n    (  371.05   112.44    -1.63     0.46)  ; 60\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  285.92    31.56    -4.65     0.92)  ; 1\r\n      (  297.52    34.28    -7.65     0.92)  ; 2\r\n      (  314.05    38.16    -1.22     0.92)  ; 3\r\n      (  313.21    45.72    -0.12     0.92)  ; 4\r\n      (  323.56    45.76    -5.05     0.92)  ; 5\r\n      (  322.69    51.53    -7.75     0.92)  ; 6\r\n      (  331.28    57.13   -10.00     0.92)  ; 7\r\n      (  334.45    59.66    -9.15     0.92)  ; 8\r\n      (  334.67    62.70   -10.95     0.92)  ; 9\r\n      (  344.20    70.30   -16.02     0.92)  ; 10\r\n      (  341.56    77.45   -11.98     0.92)  ; 11\r\n      (  346.21    79.74   -11.98     0.92)  ; 12\r\n      (  349.54    87.68   -14.75     0.92)  ; 13\r\n      (  355.47    88.48   -14.90     0.92)  ; 14\r\n      (  358.33    90.34   -15.05     0.92)  ; 15\r\n      (  355.57    92.09   -16.38     0.92)  ; 16\r\n      (  366.32   112.52    -5.65     0.46)  ; 17\r\n      (  363.90   104.78    -9.67     0.46)  ; 18\r\n    )  ;  End of markers\r\n     Normal\r\n  |\r\n    (  284.38    30.01    -0.90     0.92)  ; 1, R-2\r\n    (  283.99    31.71    -0.25     0.92)  ; 2\r\n    (  286.22    32.23     0.17     0.92)  ; 3\r\n    (  288.01    32.65     0.17     0.92)  ; 4\r\n    (  290.24    33.18     0.45     0.92)  ; 5\r\n    (  291.76    34.73     1.38     0.92)  ; 6\r\n    (  294.13    34.68     2.63     0.92)  ; 7\r\n    (  295.47    35.00     2.38     0.92)  ; 8\r\n    (  297.65    33.72     3.08     0.92)  ; 9\r\n    (  300.20    34.92     3.08     0.92)  ; 10\r\n    (  302.49    37.23     3.08     0.92)  ; 11\r\n    (  304.58    38.33     3.82     0.92)  ; 12\r\n    (  304.58    38.33     3.80     0.92)  ; 13\r\n    (  306.81    38.85     5.22     0.92)  ; 14\r\n    (  309.32    38.24     6.80     0.92)  ; 15\r\n    (  309.32    38.24     6.78     0.92)  ; 16\r\n    (  310.52    39.11     7.57     0.92)  ; 17\r\n    (  312.04    40.68     7.55     0.92)  ; 18\r\n    (  314.72    41.30     9.82     0.92)  ; 19\r\n    (  316.51    41.72    10.07     0.46)  ; 20\r\n    (  316.37    42.29    10.07     0.46)  ; 21\r\n    (  317.72    42.60    10.82     0.46)  ; 22\r\n    (  317.27    42.49    10.82     0.46)  ; 23\r\n    (  321.86    42.98    11.53     0.46)  ; 24\r\n    (  325.00    43.71    12.10     0.46)  ; 25\r\n    (  327.72    46.14    12.32     0.46)  ; 26\r\n    (  328.08    48.62    13.52     0.46)  ; 27\r\n    (  328.08    48.62    13.47     0.46)  ; 28\r\n    (  330.36    50.94    13.60     0.46)  ; 29\r\n    (  332.74    50.90    14.30     0.46)  ; 30\r\n    (  335.01    53.21    15.55     0.46)  ; 31\r\n    (  334.88    53.79    15.55     0.46)  ; 32\r\n    (  337.19    51.94    16.90     0.46)  ; 33\r\n    (  340.59    51.55    18.45     0.46)  ; 34\r\n    (  340.59    51.55    18.40     0.46)  ; 35\r\n    (  344.12    50.57    20.65     0.46)  ; 36\r\n    (  343.99    51.14    20.65     0.46)  ; 37\r\n    (  347.50    50.18    22.50     0.46)  ; 38\r\n    (  347.06    50.08    22.50     0.46)  ; 39\r\n    (  350.27    48.44    22.05     0.46)  ; 40\r\n    (  353.89    51.08    21.42     0.46)  ; 41\r\n    (  353.89    51.08    21.40     0.46)  ; 42\r\n    (  356.66    49.33    22.50     0.46)  ; 43\r\n    (  356.66    49.33    22.47     0.46)  ; 44\r\n    (  358.00    49.65    20.33     0.46)  ; 45\r\n    (  358.00    49.65    20.30     0.46)  ; 46\r\n    (  359.51    51.20    17.38     0.46)  ; 47\r\n    (  359.07    51.10    17.30     0.46)  ; 48\r\n    (  363.53    52.15    15.82     0.46)  ; 49\r\n    (  366.08    53.34    14.48     0.46)  ; 50\r\n    (  366.08    53.34    14.45     0.46)  ; 51\r\n    (  366.97    53.55    12.20     0.46)  ; 52\r\n    (  366.97    53.55    12.15     0.46)  ; 53\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  297.70    35.52     2.63     0.92)  ; 1\r\n      (  300.42    37.96     5.50     0.92)  ; 2\r\n      (  304.85    37.19     3.65     0.92)  ; 3\r\n      (  307.52    37.82     6.80     0.92)  ; 4\r\n      (  315.51    37.90     6.40     0.92)  ; 5\r\n      (  313.78    39.28     9.95     0.92)  ; 6\r\n      (  316.43    44.09    10.75     0.92)  ; 7\r\n      (  322.68    45.55    10.77     0.92)  ; 8\r\n      (  349.61    51.27    22.73     0.46)  ; 9\r\n      (  334.30    54.26    15.20     0.46)  ; 10\r\n      (  359.29    48.17    18.30     0.46)  ; 11\r\n      (  354.82    47.11    23.70     0.46)  ; 12\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Green)\r\n  (Dendrite)\r\n  (  273.97    18.01     5.45     1.38)  ; Root\r\n  (  273.97    18.01     5.45     1.38)  ; 1, R\r\n  (  276.34    17.97     5.45     1.38)  ; 2\r\n  (  279.28    17.46     5.45     1.38)  ; 3\r\n  (  280.57    15.97     5.80     1.38)  ; 4\r\n  (  281.47    16.18     7.38     1.38)  ; 5\r\n  (  282.10    17.52     9.35     1.38)  ; 6\r\n  (\r\n    (  284.95    19.39     8.75     1.38)  ; 1, R-1\r\n    (  287.94    20.70     8.35     1.38)  ; 2\r\n    (  287.94    20.70     8.32     1.38)  ; 3\r\n    (  289.92    22.35     7.70     1.38)  ; 4\r\n    (\r\n      (  292.60    22.97     7.57     0.92)  ; 1, R-1-1\r\n      (  295.09    22.37     6.73     0.92)  ; 2\r\n      (  296.97    20.41     6.15     0.92)  ; 3\r\n      (  296.97    20.41     6.13     0.92)  ; 4\r\n      (  298.22    17.12     4.50     0.92)  ; 5\r\n      (  300.71    16.51     3.27     0.92)  ; 6\r\n      (  300.71    16.51     3.25     0.92)  ; 7\r\n      (  302.58    14.55     1.95     0.92)  ; 8\r\n      (  302.58    14.55     1.92     0.92)  ; 9\r\n      (  304.89    12.72     1.35     0.92)  ; 10\r\n      (  306.76    10.77     0.90     0.92)  ; 11\r\n      (  306.76    10.77     0.88     0.92)  ; 12\r\n      (  308.20     8.71    -0.05     0.92)  ; 13\r\n      (  308.86     5.88     0.60     0.92)  ; 14\r\n      (  308.86     5.88     0.52     0.92)  ; 15\r\n      (  311.49     4.71    -0.85     0.92)  ; 16\r\n      (  311.49     4.71    -0.88     0.92)  ; 17\r\n      (  314.69     3.07    -1.70     0.92)  ; 18\r\n      (  318.35     1.54    -1.58     0.92)  ; 19\r\n      (  318.35     1.54    -1.60     0.92)  ; 20\r\n      (  321.44     0.47     0.93     0.92)  ; 21\r\n      (  324.64    -1.16     1.70     0.92)  ; 22\r\n      (  327.27    -2.34     2.40     0.92)  ; 23\r\n      (  330.79    -3.30     2.55     0.92)  ; 24\r\n      (  333.87    -4.38     1.70     0.92)  ; 25\r\n      (  333.87    -4.38     1.67     0.92)  ; 26\r\n      (  335.24    -8.24     1.77     0.92)  ; 27\r\n      (  337.38   -11.32     1.77     0.92)  ; 28\r\n      (  341.57   -15.12     1.77     0.92)  ; 29\r\n      (  342.87   -16.60     2.57     0.92)  ; 30\r\n      (  344.55   -19.79     3.57     0.92)  ; 31\r\n      (  344.55   -19.79     3.55     0.92)  ; 32\r\n      (  346.24   -22.98     3.65     0.92)  ; 33\r\n      (  348.38   -26.07     4.50     0.92)  ; 34\r\n      (  351.08   -29.59     5.65     0.92)  ; 35\r\n      (  353.23   -32.69     7.77     0.92)  ; 36\r\n      (  353.23   -32.69     7.75     0.92)  ; 37\r\n      (  353.57   -36.18     9.15     0.92)  ; 38\r\n      (  353.57   -36.18     9.10     0.92)  ; 39\r\n      (  356.30   -39.72    10.15     0.92)  ; 40\r\n      (  359.32   -42.60    10.95     0.92)  ; 41\r\n      (  360.43   -45.33    12.45     0.92)  ; 42\r\n      (  360.43   -45.33    12.35     0.92)  ; 43\r\n      (  361.10   -48.15    14.05     0.92)  ; 44\r\n      (  361.10   -48.15    14.02     0.92)  ; 45\r\n      (  361.13   -52.33    12.95     0.92)  ; 46\r\n      (  362.51   -56.18    14.38     0.92)  ; 47\r\n      (  364.73   -61.64    15.20     0.92)  ; 48\r\n      (  367.62   -63.94    12.70     0.92)  ; 49\r\n      (  367.17   -64.05    12.70     0.92)  ; 50\r\n      (  370.83   -65.59    11.53     0.92)  ; 51\r\n      (  370.83   -65.59    11.50     0.92)  ; 52\r\n      (  373.77   -66.08    10.20     0.92)  ; 53\r\n      (  373.77   -66.08    10.17     0.92)  ; 54\r\n      (  375.83   -66.81     7.85     0.92)  ; 55\r\n      (  375.83   -66.81     7.70     0.92)  ; 56\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  295.04    20.56     6.13     0.92)  ; 1\r\n        (  298.49    21.96     3.85     0.92)  ; 2\r\n        (  299.28    18.57     4.35     0.92)  ; 3\r\n        (  297.05    18.05     6.38     0.92)  ; 4\r\n        (  297.58    15.78     5.57     0.92)  ; 5\r\n        (  299.19    14.96     5.57     0.92)  ; 6\r\n        (  299.64    15.07     1.92     0.92)  ; 7\r\n        (  314.51     1.83    -1.65     0.92)  ; 8\r\n        (  318.75    -0.17     0.17     0.92)  ; 9\r\n        (  320.36    -0.98     0.67     0.92)  ; 10\r\n        (  323.66     0.99     2.38     0.92)  ; 11\r\n        (  320.72     1.50     1.75     0.92)  ; 12\r\n        (  328.22    -0.32     3.08     0.92)  ; 13\r\n        (  332.77    -1.65     0.85     0.92)  ; 14\r\n        (  345.23   -16.64     3.55     0.92)  ; 15\r\n        (  346.07   -18.24     4.60     0.92)  ; 16\r\n        (  347.18   -20.97     5.07     0.92)  ; 17\r\n        (  347.13   -22.77     5.05     0.92)  ; 18\r\n        (  349.76   -23.94     5.70     0.92)  ; 19\r\n        (  346.32   -25.34     3.08     0.92)  ; 20\r\n        (  349.30   -30.01     5.97     0.92)  ; 21\r\n        (  349.30   -30.01     5.65     0.92)  ; 22\r\n        (  358.19   -51.82    12.95     0.92)  ; 23\r\n        (  371.36   -67.85    11.80     0.92)  ; 24\r\n      )  ;  End of markers\r\n       Normal\r\n    |\r\n      (  289.26    25.18     6.70     0.92)  ; 1, R-1-2\r\n      (  291.04    25.60     7.15     0.92)  ; 2\r\n      (  292.43    27.71     7.15     0.92)  ; 3\r\n      (  293.95    29.26     8.75     0.92)  ; 4\r\n      (  293.87    31.63    10.35     0.92)  ; 5\r\n      (  296.23    31.59    11.40     0.92)  ; 6\r\n      (  299.05    31.65    12.35     0.92)  ; 7\r\n      (  300.11    33.09    13.57     0.92)  ; 8\r\n      (  299.58    35.35    16.63     0.92)  ; 9\r\n      (  299.95    37.84    18.05     0.92)  ; 10\r\n      (  299.69    38.96    20.23     0.92)  ; 11\r\n      (  300.59    39.17    23.23     0.92)  ; 12\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  288.10    26.09     8.00     0.92)  ; 1\r\n        (  290.33    26.62     5.82     0.92)  ; 2\r\n        (  292.48    29.51     9.50     0.92)  ; 3\r\n        (  295.83    33.28    12.38     0.92)  ; 4\r\n        (  301.38    35.78    18.05     0.92)  ; 5\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  282.99    17.73    10.48     1.38)  ; 1, R-2\r\n    (  285.48    17.13    12.17     1.38)  ; 2\r\n    (  286.46    14.96    13.57     0.92)  ; 3\r\n    (  285.48    11.15    14.67     0.92)  ; 4\r\n    (  285.88     9.45    16.02     0.92)  ; 5\r\n    (  285.51     6.99    17.72     0.92)  ; 6\r\n    (  286.36     5.39    18.70     0.92)  ; 7\r\n    (  286.49     4.82    18.70     0.92)  ; 8\r\n    (  287.78     3.34    20.00     0.92)  ; 9\r\n    (  290.86     2.26    20.83     0.92)  ; 10\r\n    (  294.20     0.06    21.67     0.92)  ; 11\r\n    (  296.51    -1.79    22.12     0.92)  ; 12\r\n    (  298.25    -3.17    22.97     0.92)  ; 13\r\n    (  298.92    -6.00    23.52     0.92)  ; 14\r\n    (  300.03    -8.73    24.58     0.92)  ; 15\r\n    (  302.79   -10.47    25.70     0.92)  ; 16\r\n    (  306.01   -12.11    26.25     0.92)  ; 17\r\n    (  305.40   -17.61    26.95     0.92)  ; 18\r\n    (  305.89   -21.68    27.22     0.92)  ; 19\r\n    (  306.55   -24.51    28.42     0.92)  ; 20\r\n    (  306.05   -26.42    29.48     0.92)  ; 21\r\n    (  307.61   -29.05    30.60     0.92)  ; 22\r\n    (  311.59   -29.90    31.48     0.92)  ; 23\r\n    (  314.98   -30.31    31.92     0.92)  ; 24\r\n    (  316.76   -29.89    31.92     0.92)  ; 25\r\n    (  318.55   -29.47    33.80     0.92)  ; 26\r\n    (  319.32   -28.70    35.38     0.92)  ; 27\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  281.12    19.69    11.05     1.38)  ; 1\r\n      (  284.04     7.23    18.70     0.92)  ; 2\r\n      (  286.67     6.05    20.00     0.92)  ; 3\r\n      (  290.81     0.46    21.67     0.92)  ; 4\r\n      (  294.12     2.43    21.67     0.92)  ; 5\r\n      (  295.58    -3.80    22.15     0.92)  ; 6\r\n      (  303.13   -13.97    25.15     0.92)  ; 7\r\n      (  307.77   -17.65    27.27     0.92)  ; 8\r\n      (  304.77   -24.93    27.22     0.92)  ; 9\r\n      (  310.20   -32.02    31.92     0.92)  ; 10\r\n      (  311.51   -27.53    31.92     0.92)  ; 11\r\n    )  ;  End of markers\r\n     High\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color RGB (255, 255, 128))\r\n  (Dendrite)\r\n  (  268.25     8.31     8.88     1.38)  ; Root\r\n  (  268.25     8.31     8.88     1.38)  ; 1, R\r\n  (  270.24     5.79     8.90     1.38)  ; 2\r\n  (  271.09     4.20     9.43     1.38)  ; 3\r\n  (  270.28     1.62     9.43     1.38)  ; 4\r\n  (  268.30    -0.03    10.75     1.38)  ; 5\r\n  (  267.36    -2.04    12.83     1.38)  ; 6\r\n\r\n  (Cross\r\n    (Color RGB (255, 255, 128))\r\n    (Name \"Marker 3\")\r\n    (  272.51     2.14     9.43     1.38)  ; 1\r\n  )  ;  End of markers\r\n  (\r\n    (  269.06    -1.19    12.83     0.92)  ; 1, R-1\r\n    (  271.87    -1.13    12.83     0.92)  ; 2\r\n    (  273.62    -2.51    14.30     0.92)  ; 3\r\n    (  273.49    -1.94    14.27     0.92)  ; 4\r\n    (  275.40    -2.09    16.67     0.92)  ; 5\r\n    (  277.00    -2.90    18.35     0.92)  ; 6\r\n    (\r\n      (  276.95    -4.70    19.63     0.92)  ; 1, R-1-1\r\n      (  278.25    -6.21    21.98     0.92)  ; 2\r\n      (  278.25    -6.21    21.95     0.92)  ; 3\r\n      (  278.88    -4.86    24.20     0.92)  ; 4\r\n      (  279.31    -4.76    24.20     0.92)  ; 5\r\n      (  275.21    -3.33    25.10     0.92)  ; 6\r\n      (  275.26    -1.53    25.73     0.92)  ; 7\r\n      (  276.34    -0.07    27.30     0.92)  ; 8\r\n      (  275.31     0.28    28.90     0.92)  ; 9\r\n      (  274.15     1.20    31.17     0.92)  ; 10\r\n      (  273.14     1.56    33.42     0.92)  ; 11\r\n      (  273.14     1.56    33.50     0.92)  ; 12\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  278.39    -0.80    25.23     0.92)  ; 1\r\n      )  ;  End of markers\r\n       High\r\n    |\r\n      (  279.63    -4.08    19.17     0.92)  ; 1, R-1-2\r\n      (  281.05    -6.14    20.35     0.92)  ; 2\r\n      (  283.79    -3.71    20.35     0.92)  ; 3\r\n      (  286.59    -3.65    21.85     0.92)  ; 4\r\n      (  289.23    -4.82    23.05     0.92)  ; 5\r\n      (  292.31    -5.88    23.05     0.92)  ; 6\r\n      (  295.70    -6.29    23.57     0.92)  ; 7\r\n      (  295.70    -6.29    23.55     0.92)  ; 8\r\n      (  298.02    -8.13    23.42     0.92)  ; 9\r\n      (  302.56    -9.46    23.80     0.92)  ; 10\r\n      (  306.40    -9.75    24.62     0.92)  ; 11\r\n      (  310.82   -10.51    24.62     0.92)  ; 12\r\n      (  312.25   -12.56    25.40     0.92)  ; 13\r\n      (  314.12   -14.52    25.52     0.92)  ; 14\r\n      (  318.80   -16.40    26.77     0.92)  ; 15\r\n      (  322.45   -17.94    26.80     0.92)  ; 16\r\n      (  326.11   -19.47    25.35     0.92)  ; 17\r\n      (  330.44   -17.85    25.35     0.92)  ; 18\r\n      (  334.01   -17.01    23.70     0.92)  ; 19\r\n      (  334.01   -17.01    23.67     0.92)  ; 20\r\n      (  336.13   -15.93    21.92     0.92)  ; 21\r\n      (  339.51   -16.33    21.48     0.92)  ; 22\r\n      (  342.68   -13.79    21.20     0.92)  ; 23\r\n      (  346.08   -14.18    21.58     0.92)  ; 24\r\n      (  349.91   -14.48    21.58     0.92)  ; 25\r\n      (  351.70   -14.07    19.63     0.92)  ; 26\r\n      (  353.49   -13.65    17.27     0.92)  ; 27\r\n      (  356.25   -15.39    15.20     0.92)  ; 28\r\n      (  356.25   -15.39    15.17     0.92)  ; 29\r\n      (  357.59   -15.07    12.88     0.92)  ; 30\r\n      (  360.23   -16.25    11.65     0.92)  ; 31\r\n      (  363.30   -17.31    10.97     0.92)  ; 32\r\n      (  367.48   -21.11    13.38     0.92)  ; 33\r\n      (  370.51   -23.98    12.98     0.92)  ; 34\r\n      (  373.10   -26.96    12.98     0.92)  ; 35\r\n      (  372.97   -26.40    12.98     0.92)  ; 36\r\n      (  374.97   -28.91    12.95     0.92)  ; 37\r\n      (  374.97   -28.91    12.93     0.92)  ; 38\r\n      (  377.33   -28.96    12.88     0.46)  ; 39\r\n      (  378.67   -28.64    10.40     0.46)  ; 40\r\n\r\n      (Cross\r\n        (Color RGB (255, 255, 128))\r\n        (Name \"Marker 3\")\r\n        (  280.13    -2.18    19.17     0.92)  ; 1\r\n        (  280.29    -6.91    20.17     0.92)  ; 2\r\n        (  285.92    -6.79    20.38     0.92)  ; 3\r\n        (  292.26    -7.69    20.92     0.92)  ; 4\r\n        (  296.94    -9.58    23.80     0.92)  ; 5\r\n        (  306.80   -11.46    24.62     0.92)  ; 6\r\n      )  ;  End of markers\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  316.21   -13.43    26.80     0.92)  ; 1\r\n        (  321.22   -14.64    26.80     0.92)  ; 2\r\n        (  321.70   -18.70    26.80     0.92)  ; 3\r\n        (  322.95   -16.03    29.08     0.92)  ; 4\r\n        (  326.79   -16.33    28.10     0.92)  ; 5\r\n        (  323.43   -20.09    24.10     0.92)  ; 6\r\n        (  332.63   -19.14    24.08     0.92)  ; 7\r\n        (  331.29   -19.45    21.38     0.92)  ; 8\r\n        (  330.49   -16.05    25.38     0.92)  ; 9\r\n        (  340.94   -12.41    21.58     0.92)  ; 10\r\n        (  348.80   -11.76    21.58     0.92)  ; 11\r\n        (  359.59   -17.59    10.97     0.92)  ; 12\r\n        (  364.54   -20.61    11.25     0.92)  ; 13\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  266.82    -5.76    12.00     1.38)  ; 1, R-2\r\n    (  267.62    -9.15    11.32     1.38)  ; 2\r\n    (  267.62    -9.15    11.30     1.38)  ; 3\r\n    (  269.80   -10.44    11.13     1.38)  ; 4\r\n    (  269.80   -10.44    11.10     1.38)  ; 5\r\n    (  272.25   -12.85    10.02     1.38)  ; 6\r\n    (  272.25   -12.85    10.00     1.38)  ; 7\r\n    (  273.99   -14.24     8.30     1.38)  ; 8\r\n    (  275.02   -14.59     6.60     1.38)  ; 9\r\n    (\r\n      (  275.91   -14.40     4.90     0.92)  ; 1, R-2-1\r\n      (  276.62   -15.42     3.25     0.92)  ; 2\r\n      (  276.62   -15.42     3.22     0.92)  ; 3\r\n      (  277.28   -18.26     1.53     0.92)  ; 4\r\n      (  279.92   -19.43     1.53     0.92)  ; 5\r\n      (  282.29   -19.48     1.53     0.92)  ; 6\r\n      (  283.18   -19.27     1.53     0.92)  ; 7\r\n      (  283.05   -18.69     1.53     0.92)  ; 8\r\n      (  283.93   -18.49     0.15     0.92)  ; 9\r\n      (  284.69   -17.72    -1.53     0.92)  ; 10\r\n      (  286.66   -16.06    -2.88     0.92)  ; 11\r\n      (  289.12   -18.47    -3.47     0.92)  ; 12\r\n      (  291.30   -19.75    -3.65     0.92)  ; 13\r\n      (  291.30   -19.75    -3.67     0.92)  ; 14\r\n      (  293.43   -22.84    -4.47     0.92)  ; 15\r\n      (  295.31   -24.79    -5.37     0.92)  ; 16\r\n      (  296.47   -25.71    -5.27     0.92)  ; 17\r\n      (  298.64   -26.99    -3.45     0.92)  ; 18\r\n\r\n      (Cross\r\n        (Color RGB (255, 255, 128))\r\n        (Name \"Marker 3\")\r\n        (  278.52   -21.56     1.53     0.92)  ; 1\r\n        (  276.40   -18.46     1.53     0.92)  ; 2\r\n        (  278.23   -16.25     1.53     0.92)  ; 3\r\n        (  295.84   -27.05    -2.38     0.92)  ; 4\r\n        (  295.80   -22.87    -2.38     0.92)  ; 5\r\n        (  291.66   -17.28    -1.40     0.92)  ; 6\r\n        (  293.66   -19.79    -1.53     0.92)  ; 7\r\n        (  294.68   -26.13    -6.13     0.92)  ; 8\r\n        (  298.01   -28.33    -5.13     0.92)  ; 9\r\n      )  ;  End of markers\r\n      (\r\n        (  300.56   -27.13    -5.13     0.92)  ; 1, R-2-1-1\r\n        (  302.49   -27.29    -7.07     0.92)  ; 2\r\n        (  302.49   -27.29    -7.10     0.92)  ; 3\r\n        (  306.90   -28.04    -8.57     0.92)  ; 4\r\n        (  309.40   -28.65    -8.90     0.92)  ; 5\r\n        (  310.88   -28.90   -10.70     0.92)  ; 6\r\n        (  312.04   -29.82   -13.55     0.92)  ; 7\r\n        (  312.04   -29.82   -13.57     0.92)  ; 8\r\n        (  314.66   -30.99   -16.67     0.92)  ; 9\r\n        (  314.66   -30.99   -16.70     0.92)  ; 10\r\n        (  316.90   -30.48   -19.23     0.92)  ; 11\r\n        (  316.90   -30.48   -19.27     0.92)  ; 12\r\n        (  320.68   -32.57   -20.65     0.92)  ; 13\r\n        (  323.32   -33.74   -22.97     0.92)  ; 14\r\n        (  323.32   -33.74   -23.02     0.92)  ; 15\r\n        (  324.03   -34.77   -26.30     0.92)  ; 16\r\n        (  324.03   -34.77   -26.32     0.92)  ; 17\r\n        (  323.27   -35.55   -30.42     0.46)  ; 18\r\n        (  323.40   -36.12   -30.45     0.46)  ; 19\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  310.93   -27.10    -7.97     0.92)  ; 1\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  300.83   -28.27    -3.95     0.46)  ; 1, R-2-1-2\r\n        (  304.36   -29.23    -2.73     0.46)  ; 2\r\n        (  306.94   -32.21    -1.77     0.46)  ; 3\r\n        (  308.99   -32.92    -1.17     0.46)  ; 4\r\n        (  312.07   -34.00     2.78     0.46)  ; 5\r\n        (  313.80   -35.38     2.78     0.46)  ; 6\r\n        (  315.86   -36.09     2.78     0.46)  ; 7\r\n        (  318.09   -35.57     2.22     0.46)  ; 8\r\n        (  318.09   -35.57     2.20     0.46)  ; 9\r\n        (  322.11   -34.62     2.60     0.46)  ; 10\r\n        (  324.03   -34.77     2.90     0.46)  ; 11\r\n        (  324.73   -35.80     4.32     0.46)  ; 12\r\n        (  326.22   -36.05     6.22     0.46)  ; 13\r\n        (  329.03   -35.99     8.15     0.46)  ; 14\r\n        (  329.03   -35.99     8.13     0.46)  ; 15\r\n        (  331.13   -34.91     4.17     0.46)  ; 16\r\n        (  335.15   -33.96     2.67     0.46)  ; 17\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  306.62   -32.88    -1.17     0.46)  ; 1\r\n          (  308.15   -31.33    -1.17     0.46)  ; 2\r\n          (  312.70   -32.64     1.40     0.46)  ; 3\r\n          (  309.97   -35.09     2.60     0.46)  ; 4\r\n          (  315.23   -37.43     2.78     0.46)  ; 5\r\n          (  320.60   -36.18     2.90     0.46)  ; 6\r\n          (  318.28   -34.33     2.90     0.46)  ; 7\r\n          (  337.25   -32.87     2.50     0.46)  ; 8\r\n          (  330.23   -35.12     2.50     0.46)  ; 9\r\n          (  324.83   -38.17     7.65     0.46)  ; 10\r\n          (  331.66   -37.16     7.65     0.46)  ; 11\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  274.79   -17.63     6.60     0.92)  ; 1, R-2-2\r\n      (  273.79   -21.44     7.30     0.92)  ; 2\r\n      (  274.14   -24.95     9.05     0.92)  ; 3\r\n      (  274.23   -27.31    10.52     0.92)  ; 4\r\n      (  274.23   -27.31    10.50     0.92)  ; 5\r\n      (  275.97   -28.69    11.68     0.92)  ; 6\r\n      (  277.84   -30.65    12.57     0.92)  ; 7\r\n      (  279.26   -32.70    13.75     0.92)  ; 8\r\n      (  280.11   -34.30    13.95     0.92)  ; 9\r\n      (  280.95   -35.89    14.97     0.92)  ; 10\r\n      (  283.27   -37.74    14.97     0.92)  ; 11\r\n      (  282.91   -40.20    14.67     0.92)  ; 12\r\n      (  283.88   -42.37    15.55     0.92)  ; 13\r\n      (  285.44   -44.98    16.10     0.92)  ; 14\r\n      (  289.22   -47.08    16.10     0.92)  ; 15\r\n      (  291.15   -47.23    16.73     0.92)  ; 16\r\n      (  293.99   -51.34    16.73     0.92)  ; 17\r\n\r\n      (Cross\r\n        (Color RGB (255, 255, 128))\r\n        (Name \"Marker 3\")\r\n        (  275.02   -30.71    10.50     0.92)  ; 1\r\n        (  276.76   -32.09    12.22     0.92)  ; 2\r\n        (  280.33   -37.22    14.97     0.92)  ; 3\r\n        (  282.20   -39.18    14.82     0.92)  ; 4\r\n        (  282.60   -40.87    14.82     0.92)  ; 5\r\n        (  281.65   -42.90    16.90     0.92)  ; 6\r\n        (  285.72   -40.14    17.13     0.92)  ; 7\r\n        (  289.62   -48.79    16.73     0.92)  ; 8\r\n        (  294.09   -47.75    18.35     0.92)  ; 9\r\n        (  292.53   -51.09    15.40     0.92)  ; 10\r\n        (  297.25   -51.19    15.48     0.92)  ; 11\r\n      )  ;  End of markers\r\n      (\r\n        (  293.05   -53.37    16.73     0.92)  ; 1, R-2-2-1\r\n        (  292.69   -55.86    17.45     0.92)  ; 2\r\n        (  293.05   -59.36    17.13     0.92)  ; 3\r\n        (  292.50   -63.06    20.13     0.92)  ; 4\r\n        (  293.35   -64.66    21.18     0.92)  ; 5\r\n        (  289.95   -64.25    22.25     0.92)  ; 6\r\n        (  287.76   -62.98    23.67     0.92)  ; 7\r\n        (  286.17   -62.16    25.35     0.92)  ; 8\r\n        (  286.03   -61.60    25.35     0.92)  ; 9\r\n        (  283.98   -60.88    26.55     0.46)  ; 10\r\n        (  282.94   -60.52    29.02     0.46)  ; 11\r\n        (  281.92   -60.17    31.38     0.46)  ; 12\r\n        (  281.34   -59.71    32.35     0.46)  ; 13\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  287.86   -59.37    17.13     0.92)  ; 1\r\n          (  294.65   -60.17    18.55     0.92)  ; 2\r\n          (  291.46   -62.71    19.95     0.92)  ; 3\r\n          (  293.84   -62.75    19.95     0.92)  ; 4\r\n          (  293.61   -65.79    20.13     0.92)  ; 5\r\n        )  ;  End of markers\r\n         High\r\n      |\r\n        (  296.62   -52.53    18.08     0.92)  ; 1, R-2-2-2\r\n        (  297.68   -57.06    17.70     0.92)  ; 2\r\n        (  298.93   -60.35    17.70     0.92)  ; 3\r\n        (  303.03   -61.77    17.70     0.92)  ; 4\r\n        (  304.01   -63.94    16.47     0.92)  ; 5\r\n        (  304.49   -68.00    16.47     0.92)  ; 6\r\n        (  305.92   -70.05    15.90     0.92)  ; 7\r\n        (  309.21   -74.07    15.22     0.92)  ; 8\r\n        (  309.21   -74.07    15.20     0.92)  ; 9\r\n        (  308.98   -77.10    14.65     0.92)  ; 10\r\n        (  312.09   -82.35    14.65     0.92)  ; 11\r\n        (  314.40   -84.19    12.07     0.92)  ; 12\r\n        (  317.31   -86.49    11.35     0.92)  ; 13\r\n        (  320.02   -90.04    12.22     0.92)  ; 14\r\n        (  321.98   -94.35    11.80     0.92)  ; 15\r\n        (  322.77   -97.75    11.80     0.92)  ; 16\r\n        (  324.78  -100.27    10.63     0.92)  ; 17\r\n        (  324.78  -100.27    10.60     0.92)  ; 18\r\n        (  325.57  -103.67     9.50     0.92)  ; 19\r\n        (  323.87  -106.45     8.05     0.92)  ; 20\r\n        (  323.87  -106.45     8.02     0.92)  ; 21\r\n        (  324.98  -109.19     6.73     0.92)  ; 22\r\n        (  326.94  -113.50     4.60     0.92)  ; 23\r\n        (  326.79  -118.91     4.60     0.92)  ; 24\r\n        (  327.72  -122.87     4.60     0.92)  ; 25\r\n        (  328.78  -127.40     3.42     0.92)  ; 26\r\n        (  330.34  -130.02     2.17     0.92)  ; 27\r\n        (  331.31  -132.18     0.82     0.92)  ; 28\r\n        (  333.14  -135.93     0.28     0.92)  ; 29\r\n        (  333.30  -140.67    -0.75     0.92)  ; 30\r\n        (  334.63  -146.33    -0.80     0.92)  ; 31\r\n        (  335.30  -149.18     0.57     0.92)  ; 32\r\n        (  333.81  -154.89     2.08     0.92)  ; 33\r\n        (  333.44  -157.38     1.63     0.46)  ; 34\r\n        (  333.44  -157.38     1.58     0.46)  ; 35\r\n        (  331.79  -158.36    -0.37     0.46)  ; 36\r\n        (  331.79  -158.36    -0.43     0.46)  ; 37\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  295.55   -53.97    17.70     0.92)  ; 1\r\n          (  296.22   -56.80    17.70     0.92)  ; 2\r\n          (  300.04   -63.06    17.70     0.92)  ; 3\r\n          (  301.52   -63.33    17.70     0.92)  ; 4\r\n          (  304.53   -72.18    15.20     0.92)  ; 5\r\n          (  306.42   -68.14    17.35     0.92)  ; 6\r\n          (  308.04   -79.12    14.65     0.92)  ; 7\r\n          (  312.06   -78.17    14.65     0.92)  ; 8\r\n          (  320.83   -87.46    11.17     0.92)  ; 9\r\n          (  317.73   -92.37    10.22     0.92)  ; 10\r\n          (  310.70   -84.46    10.22     0.92)  ; 11\r\n          (  309.73   -82.30    10.22     0.92)  ; 12\r\n          (  311.61   -78.28    10.22     0.92)  ; 13\r\n          (  321.48   -96.26    11.58     0.92)  ; 14\r\n          (  322.14   -99.10    11.58     0.92)  ; 15\r\n          (  322.44  -110.38     6.73     0.92)  ; 16\r\n          (  325.23  -116.28     5.85     0.92)  ; 17\r\n          (  328.85  -113.65     4.60     0.92)  ; 18\r\n          (  326.19  -108.29     4.60     0.92)  ; 19\r\n          (  329.77  -123.58     4.60     0.92)  ; 20\r\n          (  326.12  -122.06     4.60     0.92)  ; 21\r\n          (  326.28  -126.79     3.42     0.92)  ; 22\r\n          (  328.02  -128.17     3.42     0.92)  ; 23\r\n          (  332.28  -140.31    -1.58     0.92)  ; 24\r\n          (  332.39  -146.86    -0.80     0.92)  ; 25\r\n          (  335.83  -151.43     2.08     0.92)  ; 26\r\n          (  332.46  -155.21     2.08     0.92)  ; 27\r\n          (  330.67  -161.60     0.82     0.92)  ; 28\r\n          (  336.63  -148.86     2.22     0.92)  ; 29\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color RGB (128, 255, 128))\r\n  (Dendrite)\r\n  (  267.79    12.25   -16.13     0.92)  ; Root\r\n  (  267.79    12.25   -16.13     0.92)  ; 1, R\r\n  (  269.84    11.54   -16.13     0.92)  ; 2\r\n  (  270.38     9.27   -16.13     0.92)  ; 3\r\n  (  269.43     7.26   -16.13     0.92)  ; 4\r\n  (  269.30     7.83   -16.13     0.92)  ; 5\r\n  (  268.10     6.96   -18.42     0.92)  ; 6\r\n  (  268.50     5.25   -19.55     0.92)  ; 7\r\n  (  272.34     4.96   -19.55     0.92)  ; 8\r\n  (  274.12     5.38   -21.48     0.92)  ; 9\r\n  (  276.04     5.23   -21.48     0.92)  ; 10\r\n  (  276.31     4.09   -23.10     0.92)  ; 11\r\n  (  276.17     4.66   -23.10     0.92)  ; 12\r\n  (  274.65     3.11   -24.02     0.92)  ; 13\r\n  (  274.65     3.11   -24.05     0.92)  ; 14\r\n  (  274.70     4.91   -25.87     0.92)  ; 15\r\n  (  275.51     7.49   -27.42     0.92)  ; 16\r\n  (  276.75     4.20   -29.22     0.92)  ; 17\r\n  (  278.35     3.38   -30.77     0.92)  ; 18\r\n  (  278.93     2.92   -32.25     0.92)  ; 19\r\n  (  280.01     4.37   -34.17     0.92)  ; 20\r\n  (  279.49     6.63   -36.10     0.92)  ; 21\r\n  (  281.85     6.59   -37.52     0.92)  ; 22\r\n  (  284.02     5.31   -38.88     0.92)  ; 23\r\n  (  286.53     4.69   -39.32     0.92)  ; 24\r\n  (  287.69     3.78   -40.70     0.92)  ; 25\r\n  (  288.89     4.65   -41.22     0.92)  ; 26\r\n  (  288.89     4.65   -41.25     0.92)  ; 27\r\n  (  291.66     2.92   -43.25     0.92)  ; 28\r\n  (  292.95     1.42   -44.95     0.92)  ; 29\r\n  (  294.10     0.51   -47.05     0.92)  ; 30\r\n  (  297.49     0.11   -48.38     0.92)  ; 31\r\n  (  298.79    -1.39   -50.92     0.92)  ; 32\r\n  (  301.15    -1.43   -51.47     0.92)  ; 33\r\n  (  302.26    -4.15   -53.42     0.92)  ; 34\r\n  (  304.32    -4.87   -56.03     0.92)  ; 35\r\n  (  306.23    -5.02   -58.55     0.92)  ; 36\r\n  (  306.23    -5.02   -58.58     0.92)  ; 37\r\n  (  308.43    -6.29   -60.75     0.92)  ; 38\r\n  (  308.43    -6.29   -60.77     0.92)  ; 39\r\n  (  310.34    -6.45   -63.45     0.92)  ; 40\r\n  (  312.26    -6.59   -64.78     0.92)  ; 41\r\n  (  314.62    -6.64   -66.77     0.92)  ; 42\r\n  (  317.84    -8.27   -68.38     0.92)  ; 43\r\n  (  320.82    -6.96   -70.25     0.92)  ; 44\r\n  (  324.27    -5.56   -71.17     0.46)  ; 45\r\n  (  328.42    -5.18   -72.93     0.46)  ; 46\r\n  (  330.47    -5.91   -75.43     0.46)  ; 47\r\n  (  330.47    -5.91   -75.55     0.46)  ; 48\r\n  (  331.09    -4.56   -78.40     0.46)  ; 49\r\n  (  334.22    -3.83   -80.15     0.46)  ; 50\r\n  (  335.43    -2.94   -82.02     0.46)  ; 51\r\n  (  335.43    -2.94   -82.05     0.46)  ; 52\r\n  (  338.37    -3.45   -84.22     0.46)  ; 53\r\n  (  338.37    -3.45   -84.25     0.46)  ; 54\r\n  (  340.49    -2.36   -86.62     0.46)  ; 55\r\n  (  342.71    -1.84   -88.77     0.46)  ; 56\r\n  (  342.71    -1.84   -88.80     0.46)  ; 57\r\n  (  344.63    -1.98   -90.20     0.46)  ; 58\r\n  (  345.57     0.02   -90.20     0.46)  ; 59\r\n  (  347.36     0.45   -91.70     0.46)  ; 60\r\n  (  350.18     0.51   -93.23     0.46)  ; 61\r\n  (  351.95     0.93   -94.40     0.46)  ; 62\r\n  (  352.40     1.03   -94.40     0.46)  ; 63\r\n  (  357.14     0.95   -95.32     0.46)  ; 64\r\n  (  362.00     0.29   -96.47     0.46)  ; 65\r\n  (  364.37     0.25   -97.50     0.46)  ; 66\r\n  (  368.12     2.32   -97.65     0.46)  ; 67\r\n  (  370.22     3.40   -97.63     0.46)  ; 68\r\n  (  373.17     2.91   -99.80     0.46)  ; 69\r\n  (  373.17     2.91   -99.82     0.46)  ; 70\r\n  (  374.91     1.53  -101.13     0.46)  ; 71\r\n\r\n  (Cross\r\n    (Color White)\r\n    (Name \"Marker 3\")\r\n    (  271.54     8.35   -16.13     0.92)  ; 1\r\n    (  287.69     3.78   -39.32     0.92)  ; 2\r\n    (  291.44     5.85   -43.25     0.92)  ; 3\r\n    (  297.84    -3.40   -49.13     0.92)  ; 4\r\n    (  309.36    -4.28   -63.45     0.92)  ; 5\r\n    (  304.19    -4.30   -63.45     0.92)  ; 6\r\n    (  313.23    -8.74   -64.78     0.92)  ; 7\r\n    (  326.01    -6.95   -69.75     0.92)  ; 8\r\n    (  315.47    -8.22   -69.75     0.92)  ; 9\r\n    (  328.87    -5.08   -80.15     0.46)  ; 10\r\n    (  366.37    -2.27   -97.65     0.46)  ; 11\r\n  )  ;  End of markers\r\n   Normal\r\n)  ;  End of tree\r\n\r\n( (Color RGB (128, 255, 255))\r\n  (Dendrite)\r\n  (  260.47     9.34   -15.50     0.92)  ; Root\r\n  (  260.47     9.34   -15.50     0.92)  ; 1, R\r\n  (  258.18     7.02   -15.50     0.92)  ; 2\r\n  (  256.22     5.36   -15.50     0.92)  ; 3\r\n  (  253.80     3.60   -15.50     0.92)  ; 4\r\n  (  252.28     2.05   -16.25     0.92)  ; 5\r\n  (  250.63     1.07   -17.13     0.92)  ; 6\r\n  (  250.63     1.07   -17.15     0.92)  ; 7\r\n  (  250.90    -0.06   -17.80     0.92)  ; 8\r\n  (  250.90    -0.06   -17.82     0.92)  ; 9\r\n  (  252.63    -1.45   -19.42     0.92)  ; 10\r\n  (  252.63    -1.45   -19.45     0.92)  ; 11\r\n  (  252.68     0.36   -21.32     0.92)  ; 12\r\n  (  252.68     0.36   -21.38     0.92)  ; 13\r\n  (  251.21     0.61   -23.23     0.92)  ; 14\r\n  (  249.42     0.19   -25.27     0.92)  ; 15\r\n  (  248.49    -1.83   -26.48     0.92)  ; 16\r\n  (  248.49    -1.83   -26.50     0.92)  ; 17\r\n  (  246.64    -4.04   -26.77     0.92)  ; 18\r\n  (  244.41    -4.57   -27.20     0.92)  ; 19\r\n  (  242.62    -4.99   -28.95     0.92)  ; 20\r\n  (  242.62    -4.99   -28.98     0.92)  ; 21\r\n  (  241.74    -5.20   -32.02     0.92)  ; 22\r\n  (  241.74    -5.20   -32.05     0.92)  ; 23\r\n  (  243.34    -6.01   -36.00     0.92)  ; 24\r\n  (  243.34    -6.01   -36.03     0.92)  ; 25\r\n  (  243.74    -7.71   -38.85     0.92)  ; 26\r\n  (  243.74    -7.71   -38.88     0.92)  ; 27\r\n  (  240.87    -9.58   -38.85     0.92)  ; 28\r\n  (  238.01   -11.44   -39.90     0.92)  ; 29\r\n\r\n  (Cross\r\n    (Color RGB (128, 255, 255))\r\n    (Name \"Marker 3\")\r\n    (  246.59    -5.85   -27.20     0.92)  ; 1\r\n    (  238.46   -11.33   -40.05     0.92)  ; 2\r\n  )  ;  End of markers\r\n  (\r\n    (  238.41   -13.14   -41.93     0.46)  ; 1, R-1\r\n    (  236.76   -14.13   -44.43     0.46)  ; 2\r\n    (  236.76   -14.13   -44.50     0.46)  ; 3\r\n    (  236.58   -15.36   -42.40     0.46)  ; 4\r\n    (  235.41   -14.44   -48.22     0.46)  ; 5\r\n    (  235.41   -14.44   -48.25     0.46)  ; 6\r\n    (  234.20   -15.32   -51.20     0.46)  ; 7\r\n    (  234.20   -15.32   -51.22     0.46)  ; 8\r\n    (  235.68   -15.57   -54.30     0.46)  ; 9\r\n    (  235.68   -15.57   -54.32     0.46)  ; 10\r\n    (  236.40   -16.60   -57.53     0.46)  ; 11\r\n    (  236.40   -16.60   -57.55     0.46)  ; 12\r\n    (  237.87   -16.86   -60.63     0.46)  ; 13\r\n    (  237.87   -16.86   -60.65     0.46)  ; 14\r\n    (  237.96   -19.21   -63.50     0.46)  ; 15\r\n    (  238.09   -19.78   -63.82     0.46)  ; 16\r\n    (  238.09   -19.78   -63.88     0.46)  ; 17\r\n    (  235.58   -19.18   -67.07     0.46)  ; 18\r\n    (  235.58   -19.18   -67.13     0.46)  ; 19\r\n    (  235.53   -20.99   -71.82     0.46)  ; 20\r\n    (  236.11   -21.44   -74.40     0.46)  ; 21\r\n    (  236.11   -21.44   -74.42     0.46)  ; 22\r\n    (  234.46   -22.43   -74.42     0.46)  ; 23\r\n    (  235.61   -23.35   -77.25     0.46)  ; 24\r\n    (  235.61   -23.35   -77.28     0.46)  ; 25\r\n    (  237.09   -23.60   -80.82     0.46)  ; 26\r\n    (  237.09   -23.60   -80.90     0.46)  ; 27\r\n    (  238.25   -24.52   -83.70     0.46)  ; 28\r\n    (  238.25   -24.52   -83.72     0.46)  ; 29\r\n    (  237.49   -25.31   -86.50     0.46)  ; 30\r\n    (  237.04   -25.41   -86.53     0.46)  ; 31\r\n    (  236.15   -25.62   -89.72     0.46)  ; 32\r\n    (  233.78   -25.57   -91.30     0.46)  ; 33\r\n    (  233.34   -25.67   -91.30     0.46)  ; 34\r\n    (  232.14   -26.56   -94.48     0.46)  ; 35\r\n    (  231.51   -27.89   -97.05     0.46)  ; 36\r\n    (  228.64   -29.76   -99.25     0.46)  ; 37\r\n    (  227.88   -30.54  -101.60     0.46)  ; 38\r\n    (  226.67   -31.42  -103.80     0.46)  ; 39\r\n    (  226.67   -31.42  -103.82     0.46)  ; 40\r\n    (  224.49   -30.14  -107.32     0.46)  ; 41\r\n    (  224.49   -30.14  -107.38     0.46)  ; 42\r\n    (  223.78   -29.12  -110.55     0.46)  ; 43\r\n    (  223.78   -29.12  -110.63     0.46)  ; 44\r\n    (  220.65   -29.85  -112.13     0.46)  ; 45\r\n    (  218.87   -30.26  -115.47     0.46)  ; 46\r\n    (  217.22   -31.25  -118.22     0.46)  ; 47\r\n    (  218.95   -32.63  -121.47     0.46)  ; 48\r\n    (  221.63   -32.00  -122.75     0.46)  ; 49\r\n    (  221.94   -31.34  -125.40     0.46)  ; 50\r\n    (  223.41   -31.58  -128.45     0.46)  ; 51\r\n    (  223.41   -31.58  -128.48     0.46)  ; 52\r\n    (  223.87   -31.48  -131.67     0.46)  ; 53\r\n    (  223.87   -31.48  -131.75     0.46)  ; 54\r\n    (  224.18   -30.81  -136.00     0.46)  ; 55\r\n    (  224.94   -30.03  -139.32     0.46)  ; 56\r\n\r\n    (Cross\r\n      (Color RGB (128, 255, 255))\r\n      (Name \"Marker 3\")\r\n      (  238.99   -13.61   -44.50     0.46)  ; 1\r\n      (  240.55   -16.23   -63.50     0.46)  ; 2\r\n      (  229.49   -31.36  -102.40     0.46)  ; 3\r\n    )  ;  End of markers\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  219.35   -34.33  -120.40     0.46)  ; 1\r\n      (  220.60   -31.64  -120.40     0.46)  ; 2\r\n    )  ;  End of markers\r\n     Normal\r\n  |\r\n    (  235.20   -11.50   -40.77     0.92)  ; 1, R-2\r\n    (  232.74   -15.07   -42.35     0.92)  ; 2\r\n    (  231.35   -17.18   -44.27     0.92)  ; 3\r\n    (  231.35   -17.18   -44.30     0.92)  ; 4\r\n    (  231.56   -20.12   -45.72     0.92)  ; 5\r\n    (  230.32   -22.81   -48.07     0.92)  ; 6\r\n    (  230.32   -22.81   -48.10     0.92)  ; 7\r\n    (  227.90   -24.57   -50.60     0.92)  ; 8\r\n    (  227.90   -24.57   -50.65     0.92)  ; 9\r\n    (  224.41   -27.77   -51.70     0.92)  ; 10\r\n    (  222.96   -31.68   -53.22     0.92)  ; 11\r\n    (  220.69   -34.02   -54.52     0.92)  ; 12\r\n    (  219.57   -37.26   -56.08     0.92)  ; 13\r\n    (  219.47   -40.87   -57.60     0.92)  ; 14\r\n    (  219.11   -43.35   -60.10     0.92)  ; 15\r\n    (  217.80   -47.83   -62.88     0.92)  ; 16\r\n    (  216.24   -51.19   -62.57     0.46)  ; 17\r\n    (  216.45   -54.11   -63.17     0.46)  ; 18\r\n    (  216.45   -54.11   -63.30     0.46)  ; 19\r\n    (  215.01   -58.04   -65.82     0.46)  ; 20\r\n    (  215.01   -58.04   -65.85     0.46)  ; 21\r\n    (  214.02   -61.86   -68.20     0.46)  ; 22\r\n    (  214.02   -61.86   -68.22     0.46)  ; 23\r\n    (  211.87   -64.74   -71.57     0.46)  ; 24\r\n    (  211.87   -64.74   -71.63     0.46)  ; 25\r\n    (  209.85   -68.20   -73.47     0.46)  ; 26\r\n    (  205.48   -71.62   -75.27     0.46)  ; 27\r\n    (  205.48   -71.62   -75.32     0.46)  ; 28\r\n    (  203.37   -72.71   -77.13     0.46)  ; 29\r\n    (  203.37   -72.71   -77.15     0.46)  ; 30\r\n    (  201.40   -74.36   -78.55     0.46)  ; 31\r\n    (  201.40   -74.36   -78.57     0.46)  ; 32\r\n    (  200.06   -74.68   -80.65     0.46)  ; 33\r\n    (  198.10   -76.33   -82.97     0.46)  ; 34\r\n    (  198.10   -76.33   -83.00     0.46)  ; 35\r\n    (  198.63   -78.60   -86.53     0.46)  ; 36\r\n    (  198.63   -78.60   -86.62     0.46)  ; 37\r\n\r\n    (Cross\r\n      (Color RGB (128, 255, 255))\r\n      (Name \"Marker 3\")\r\n      (  216.77   -31.36   -54.52     0.92)  ; 1\r\n      (  210.23   -59.75   -71.65     0.46)  ; 2\r\n      (  203.28   -76.32   -78.57     0.46)  ; 3\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Yellow)\r\n  (Dendrite)\r\n  (  264.44     8.49   -15.95     0.92)  ; Root\r\n  (  264.40     6.68   -15.95     0.92)  ; 1, R\r\n  (  265.06     3.85   -15.95     0.92)  ; 2\r\n  (  265.59     1.59   -16.70     0.92)  ; 3\r\n  (  265.53    -0.21   -16.70     0.92)  ; 4\r\n  (  263.34     1.06   -16.70     0.92)  ; 5\r\n  (  263.08     2.19   -19.00     0.92)  ; 6\r\n  (  263.08     2.19   -19.02     0.92)  ; 7\r\n  (  261.43     1.21   -21.30     0.92)  ; 8\r\n  (  261.10     0.66   -21.30     0.92)  ; 9\r\n  (\r\n    (  263.64     1.86   -26.42     0.92)  ; 1, R-1\r\n    (  265.30     2.84   -34.00     0.92)  ; 2\r\n    (  264.14     3.76   -37.75     0.92)  ; 3\r\n    (  264.14     3.76   -37.77     0.92)  ; 4\r\n    (  263.83     3.10   -39.90     0.92)  ; 5\r\n    (  263.83     3.10   -39.92     0.92)  ; 6\r\n    (  264.77     5.12   -42.95     0.92)  ; 7\r\n    (  264.77     5.12   -43.00     0.92)  ; 8\r\n    (  263.30     5.36   -45.90     0.92)  ; 9\r\n    (  263.30     5.36   -46.00     0.92)  ; 10\r\n    (  265.65     5.31   -50.63     0.92)  ; 11\r\n    (  265.65     5.31   -50.65     0.92)  ; 12\r\n    (  266.15     7.22   -54.65     0.92)  ; 13\r\n    (  266.15     7.22   -54.72     0.92)  ; 14\r\n    (  264.11     7.94   -59.22     0.92)  ; 15\r\n    (  264.01    10.30   -63.80     0.92)  ; 16\r\n    (  265.17     9.39   -67.42     0.92)  ; 17\r\n    (  264.33    10.98   -69.72     0.92)  ; 18\r\n    (  264.33    10.98   -69.80     0.92)  ; 19\r\n    (  264.64    11.65   -71.72     0.92)  ; 20\r\n    (\r\n      (  262.20    14.06   -73.80     0.46)  ; 1, R-1-1\r\n      (  262.20    14.06   -73.82     0.46)  ; 2\r\n      (  261.17    14.42   -77.15     0.46)  ; 3\r\n      (  260.73    14.31   -77.17     0.46)  ; 4\r\n      (  259.88    15.90   -80.25     0.46)  ; 5\r\n      (  259.88    15.90   -80.28     0.46)  ; 6\r\n      (  261.67    16.32   -82.92     0.46)  ; 7\r\n      (  261.67    16.32   -82.95     0.46)  ; 8\r\n      (  259.93    17.71   -85.63     0.46)  ; 9\r\n      (  259.93    17.71   -85.65     0.46)  ; 10\r\n      (  258.60    17.39   -89.10     0.46)  ; 11\r\n      (  258.60    17.39   -89.12     0.46)  ; 12\r\n      (  259.26    14.56   -92.87     0.46)  ; 13\r\n      (  259.26    14.56   -92.90     0.46)  ; 14\r\n      (  257.12    17.65   -96.72     0.46)  ; 15\r\n      (  257.12    17.65   -96.77     0.46)  ; 16\r\n      (  259.66    18.83   -99.00     0.46)  ; 17\r\n      (  259.66    18.83   -99.03     0.46)  ; 18\r\n      (  258.33    18.53  -101.50     0.46)  ; 19\r\n      (  258.33    18.53  -101.53     0.46)  ; 20\r\n      (  256.46    20.48  -104.65     0.46)  ; 21\r\n      (  256.46    20.48  -104.67     0.46)  ; 22\r\n      (  258.50    19.76  -108.23     0.46)  ; 23\r\n      (  258.50    19.76  -108.27     0.46)  ; 24\r\n      (  257.17    19.45  -112.70     0.46)  ; 25\r\n      (  256.54    18.11  -116.32     0.46)  ; 26\r\n      (  256.81    16.97  -120.42     0.46)  ; 27\r\n      (  256.81    16.97  -120.45     0.46)  ; 28\r\n      (  254.93    18.92  -124.25     0.46)  ; 29\r\n      (  254.93    18.92  -124.32     0.46)  ; 30\r\n      (  254.66    20.06  -128.10     0.46)  ; 31\r\n      (  254.66    20.06  -128.15     0.46)  ; 32\r\n      (  253.46    19.18  -132.95     0.46)  ; 33\r\n      (  253.46    19.18  -133.00     0.46)  ; 34\r\n      (  253.41    17.37  -137.80     0.46)  ; 35\r\n       Normal\r\n    |\r\n      (  267.46    11.71   -73.07     0.46)  ; 1, R-1-2\r\n      (  268.67    12.59   -73.97     0.46)  ; 2\r\n      (  268.67    12.59   -74.00     0.46)  ; 3\r\n      (  270.59    12.44   -75.57     0.46)  ; 4\r\n      (  270.59    12.44   -75.60     0.46)  ; 5\r\n      (  271.97    14.55   -77.05     0.46)  ; 6\r\n      (  271.97    14.55   -77.10     0.46)  ; 7\r\n      (  274.34    14.51   -79.32     0.46)  ; 8\r\n      (  274.34    14.51   -79.35     0.46)  ; 9\r\n      (  277.16    14.58   -82.35     0.46)  ; 10\r\n      (  279.70    15.77   -84.45     0.46)  ; 11\r\n      (  280.46    16.54   -86.45     0.46)  ; 12\r\n      (  280.46    16.54   -86.47     0.46)  ; 13\r\n      (  282.96    15.94   -87.22     0.46)  ; 14\r\n      (  282.96    15.94   -87.25     0.46)  ; 15\r\n      (  285.24    18.27   -88.80     0.46)  ; 16\r\n      (  286.18    20.28   -91.15     0.46)  ; 17\r\n      (  286.18    20.28   -91.20     0.46)  ; 18\r\n      (  288.15    21.93   -93.60     0.46)  ; 19\r\n      (  288.15    21.93   -93.65     0.46)  ; 20\r\n      (  289.94    22.35   -95.92     0.46)  ; 21\r\n      (  289.94    22.35   -95.95     0.46)  ; 22\r\n      (  290.88    24.37   -97.75     0.46)  ; 23\r\n      (  290.88    24.37   -97.78     0.46)  ; 24\r\n      (  292.22    24.68  -101.40     0.46)  ; 25\r\n      (  294.89    25.31  -104.97     0.46)  ; 26\r\n      (  294.89    25.31  -105.00     0.46)  ; 27\r\n      (  295.79    25.52  -108.15     0.46)  ; 28\r\n      (  297.71    25.37  -110.68     0.46)  ; 29\r\n      (  297.58    25.94  -110.70     0.46)  ; 30\r\n      (  300.00    27.70  -113.25     0.46)  ; 31\r\n      (  301.01    27.34  -116.53     0.46)  ; 32\r\n      (  301.01    27.34  -116.70     0.46)  ; 33\r\n      (  300.75    28.46  -120.52     0.46)  ; 34\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  293.82    23.86   -97.78     0.46)  ; 1\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  260.93    -0.70   -23.60     0.92)  ; 1, R-2\r\n    (  260.93    -0.70   -23.65     0.92)  ; 2\r\n    (  261.91    -2.86   -25.35     0.92)  ; 3\r\n    (  262.49    -3.33   -26.95     0.92)  ; 4\r\n    (  262.44    -5.11   -26.65     0.92)  ; 5\r\n    (  262.84    -6.82   -26.65     0.92)  ; 6\r\n    (\r\n      (  262.75   -10.43   -25.55     0.92)  ; 1, R-2-1\r\n      (  262.31   -10.53   -25.55     0.92)  ; 2\r\n      (  262.40   -12.91   -25.57     0.92)  ; 3\r\n      (  258.64   -14.98   -25.83     0.92)  ; 4\r\n      (  257.43   -15.86   -24.85     0.92)  ; 5\r\n      (  255.73   -18.64   -26.25     0.92)  ; 6\r\n      (  255.73   -18.64   -26.28     0.92)  ; 7\r\n      (  255.55   -19.88   -27.27     0.92)  ; 8\r\n      (  255.55   -19.88   -27.30     0.92)  ; 9\r\n      (  254.29   -22.57   -28.02     0.92)  ; 10\r\n      (  254.29   -22.57   -28.05     0.92)  ; 11\r\n      (  252.82   -22.31   -28.63     0.92)  ; 12\r\n      (  252.27   -26.02   -29.42     0.92)  ; 13\r\n      (  250.13   -28.91   -30.38     0.92)  ; 14\r\n\r\n      (Cross\r\n        (Color Yellow)\r\n        (Name \"Marker 3\")\r\n        (  255.34   -16.94   -24.85     0.92)  ; 1\r\n        (  258.46   -16.21   -24.85     0.92)  ; 2\r\n      )  ;  End of markers\r\n      (\r\n        (  247.00   -29.65   -30.38     0.92)  ; 1, R-2-1-1\r\n        (  244.76   -30.18   -31.42     0.92)  ; 2\r\n        (  244.76   -30.18   -31.45     0.92)  ; 3\r\n        (  243.17   -29.35   -32.83     0.92)  ; 4\r\n        (  243.17   -29.35   -32.88     0.92)  ; 5\r\n        (  240.80   -29.31   -34.45     0.92)  ; 6\r\n        (  240.80   -29.31   -34.47     0.92)  ; 7\r\n        (  238.69   -30.41   -36.42     0.92)  ; 8\r\n        (  237.44   -33.08   -38.05     0.92)  ; 9\r\n        (  237.44   -33.08   -38.07     0.92)  ; 10\r\n        (  236.05   -35.21   -39.95     0.92)  ; 11\r\n        (  236.05   -35.21   -40.07     0.92)  ; 12\r\n        (  234.67   -37.32   -39.38     0.92)  ; 13\r\n        (  232.56   -38.40   -41.00     0.92)  ; 14\r\n        (  232.56   -38.40   -41.03     0.92)  ; 15\r\n        (  232.20   -40.89   -43.07     0.92)  ; 16\r\n        (  231.26   -42.89   -47.22     0.92)  ; 17\r\n        (  231.26   -42.89   -47.28     0.92)  ; 18\r\n\r\n        (Cross\r\n          (Color Yellow)\r\n          (Name \"Marker 3\")\r\n          (  236.71   -38.03   -40.10     0.92)  ; 1\r\n          (  233.38   -35.84   -38.10     0.92)  ; 2\r\n        )  ;  End of markers\r\n        (\r\n          (  233.30   -43.61   -49.10     0.92)  ; 1, R-2-1-1-1\r\n          (  233.30   -43.61   -49.13     0.92)  ; 2\r\n          (  234.02   -44.63   -51.67     0.92)  ; 3\r\n          (  234.02   -44.63   -51.72     0.92)  ; 4\r\n          (  233.40   -45.98   -54.20     0.92)  ; 5\r\n          (  232.37   -45.62   -59.13     0.92)  ; 6\r\n          (  230.27   -46.71   -62.22     0.92)  ; 7\r\n          (  230.27   -46.71   -62.33     0.92)  ; 8\r\n          (  229.38   -46.92   -66.88     0.92)  ; 9\r\n          (  229.91   -49.19   -70.80     0.92)  ; 10\r\n          (  229.91   -49.19   -70.82     0.92)  ; 11\r\n          (  230.04   -49.75   -73.42     0.92)  ; 12\r\n          (  231.11   -48.29   -74.67     0.92)  ; 13\r\n          (  232.77   -47.31   -76.82     0.92)  ; 14\r\n\r\n          (Cross\r\n            (Color Yellow)\r\n            (Name \"Marker 3\")\r\n            (  234.52   -42.73   -54.20     0.92)  ; 1\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  229.16   -43.98   -47.22     0.92)  ; 1, R-2-1-1-2\r\n          (  229.16   -43.98   -47.25     0.92)  ; 2\r\n          (  227.74   -41.93   -48.35     0.92)  ; 3\r\n          (  226.03   -44.71   -50.77     0.92)  ; 4\r\n          (  226.03   -44.71   -50.88     0.92)  ; 5\r\n          (  225.27   -45.50   -54.05     0.92)  ; 6\r\n          (  223.80   -45.23   -55.60     0.92)  ; 7\r\n          (  223.80   -45.23   -55.63     0.92)  ; 8\r\n          (  222.02   -45.65   -58.15     0.92)  ; 9\r\n          (  220.41   -44.84   -61.38     0.92)  ; 10\r\n          (  220.36   -46.64   -63.20     0.92)  ; 11\r\n          (  220.36   -46.64   -63.25     0.92)  ; 12\r\n          (  218.44   -46.49   -66.77     0.92)  ; 13\r\n          (  218.52   -48.86   -70.07     0.92)  ; 14\r\n          (  218.52   -48.86   -70.18     0.92)  ; 15\r\n          (  216.29   -49.39   -72.72     0.92)  ; 16\r\n          (  215.85   -49.49   -72.80     0.92)  ; 17\r\n          (  214.37   -49.24   -73.00     0.92)  ; 18\r\n          (  214.37   -49.24   -73.02     0.92)  ; 19\r\n          (  215.00   -47.91   -76.47     0.92)  ; 20\r\n          (  215.00   -47.91   -76.60     0.92)  ; 21\r\n          (  217.18   -49.18   -80.68     0.92)  ; 22\r\n          (  217.18   -49.18   -80.73     0.92)  ; 23\r\n          (  217.00   -50.41   -85.28     0.92)  ; 24\r\n          (  217.00   -50.41   -85.37     0.92)  ; 25\r\n          (  214.24   -48.67   -89.60     0.92)  ; 26\r\n          (  214.24   -48.67   -89.65     0.92)  ; 27\r\n          (  213.61   -50.02   -92.30     0.92)  ; 28\r\n          (  213.61   -50.02   -92.32     0.92)  ; 29\r\n          (  212.50   -47.29   -93.40     0.92)  ; 30\r\n          (  210.58   -47.14   -94.67     0.92)  ; 31\r\n          (  210.58   -47.14   -94.70     0.92)  ; 32\r\n          (  208.66   -46.99   -96.85     0.92)  ; 33\r\n          (  208.66   -46.99   -96.90     0.92)  ; 34\r\n          (  207.46   -47.87   -99.17     0.92)  ; 35\r\n          (  205.80   -48.85  -100.97     0.92)  ; 36\r\n          (  204.52   -47.37  -102.20     0.92)  ; 37\r\n          (  201.51   -48.67  -104.05     0.92)  ; 38\r\n          (  201.51   -48.67  -104.07     0.92)  ; 39\r\n          (  199.28   -49.19  -104.90     0.92)  ; 40\r\n          (  199.28   -49.19  -104.97     0.92)  ; 41\r\n          (  197.14   -52.09  -105.32     0.92)  ; 42\r\n          (  194.85   -54.41  -106.62     0.92)  ; 43\r\n          (  191.10   -56.48  -108.32     0.92)  ; 44\r\n          (  192.53   -58.54  -111.97     0.92)  ; 45\r\n          (  192.53   -58.54  -112.02     0.92)  ; 46\r\n          (  192.79   -59.67  -113.95     0.92)  ; 47\r\n          (  192.79   -59.67  -113.97     0.92)  ; 48\r\n\r\n          (Cross\r\n            (Color Yellow)\r\n            (Name \"Marker 3\")\r\n            (  222.77   -44.88   -54.07     0.92)  ; 1\r\n            (  216.96   -46.24   -69.75     0.92)  ; 2\r\n            (  198.87   -53.47  -105.32     0.92)  ; 3\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  248.43   -31.71   -30.38     0.92)  ; 1, R-2-1-2\r\n        (  247.61   -34.28   -30.75     0.92)  ; 2\r\n        (  245.79   -36.51   -30.75     0.92)  ; 3\r\n        (  244.66   -39.75   -30.75     0.92)  ; 4\r\n        (  243.15   -41.31   -30.75     0.92)  ; 5\r\n        (  242.70   -41.41   -30.75     0.92)  ; 6\r\n        (  242.14   -45.12   -30.75     0.92)  ; 7\r\n        (  239.55   -48.11   -30.75     0.92)  ; 8\r\n        (  239.90   -51.62   -30.75     0.92)  ; 9\r\n        (  239.93   -55.80   -30.00     0.92)  ; 10\r\n        (  239.40   -59.50   -28.88     0.92)  ; 11\r\n        (  239.40   -59.50   -28.92     0.92)  ; 12\r\n        (  239.93   -61.76   -27.83     0.92)  ; 13\r\n        (  239.93   -61.76   -27.77     0.92)  ; 14\r\n        (  239.37   -65.48   -27.77     0.92)  ; 15\r\n        (  239.27   -69.08   -26.82     0.92)  ; 16\r\n        (  239.85   -69.55   -26.82     0.92)  ; 17\r\n        (  237.84   -73.00   -26.82     0.92)  ; 18\r\n        (  239.09   -76.28   -29.17     0.92)  ; 19\r\n        (  239.30   -79.23   -29.17     0.92)  ; 20\r\n        (  238.63   -82.37   -29.92     0.92)  ; 21\r\n        (  237.90   -87.32   -28.57     0.92)  ; 22\r\n        (  235.94   -88.97   -28.57     0.92)  ; 23\r\n        (  236.91   -91.14   -28.57     0.92)  ; 24\r\n        (  236.63   -95.98   -28.55     0.92)  ; 25\r\n        (  236.17  -102.05   -28.55     0.92)  ; 26\r\n        (  233.44  -104.49   -29.85     0.92)  ; 27\r\n        (  229.38  -107.23   -30.65     0.92)  ; 28\r\n        (  228.44  -109.24   -32.70     0.92)  ; 29\r\n        (  229.42  -111.40   -32.70     0.92)  ; 30\r\n        (  227.75  -112.38   -34.70     0.92)  ; 31\r\n        (  227.66  -115.99   -36.07     0.92)  ; 32\r\n        (  225.96  -118.78   -38.03     0.92)  ; 33\r\n        (  225.96  -118.78   -38.15     0.92)  ; 34\r\n        (  226.35  -120.47   -39.72     0.92)  ; 35\r\n        (  227.33  -122.64   -40.72     0.92)  ; 36\r\n        (  225.19  -125.52   -40.72     0.92)  ; 37\r\n        (  224.77  -129.80   -39.63     0.92)  ; 38\r\n        (  224.77  -129.80   -39.70     0.92)  ; 39\r\n        (  221.46  -131.77   -41.67     0.92)  ; 40\r\n        (  219.05  -133.54   -43.22     0.92)  ; 41\r\n        (  219.05  -133.54   -43.27     0.92)  ; 42\r\n        (  217.27  -133.96   -43.80     0.92)  ; 43\r\n        (  214.71  -135.15   -41.47     0.92)  ; 44\r\n        (  212.93  -135.57   -40.17     0.92)  ; 45\r\n        (  212.93  -135.57   -40.15     0.92)  ; 46\r\n        (  210.65  -137.89   -40.13     0.92)  ; 47\r\n        (  209.21  -141.81   -39.45     0.92)  ; 48\r\n        (  207.25  -143.46   -39.67     0.92)  ; 49\r\n        (  204.71  -144.66   -39.67     0.92)  ; 50\r\n        (  203.17  -146.22   -37.55     0.92)  ; 51\r\n\r\n        (Cross\r\n          (Color Yellow)\r\n          (Name \"Marker 3\")\r\n          (  241.53   -62.58   -27.77     0.92)  ; 1\r\n          (  237.94   -69.40   -26.82     0.92)  ; 2\r\n          (  239.19   -72.69   -26.82     0.92)  ; 3\r\n          (  240.56   -76.54   -29.25     0.92)  ; 4\r\n          (  240.46   -80.14   -29.35     0.92)  ; 5\r\n          (  238.06   -81.91   -29.35     0.92)  ; 6\r\n          (  238.01   -77.74   -29.35     0.92)  ; 7\r\n          (  233.17  -103.35   -29.85     0.92)  ; 8\r\n          (  226.51  -109.09   -29.65     0.92)  ; 9\r\n          (  234.10  -107.31   -29.65     0.92)  ; 10\r\n          (  233.35  -108.10   -29.65     0.92)  ; 11\r\n          (  233.17  -103.35   -30.65     0.92)  ; 12\r\n          (  224.21  -123.38   -40.72     0.92)  ; 13\r\n          (  227.60  -123.77   -40.72     0.92)  ; 14\r\n          (  225.93  -130.72   -38.75     0.92)  ; 15\r\n          (  223.35  -127.75   -41.28     0.92)  ; 16\r\n          (  215.97  -132.46   -43.80     0.92)  ; 17\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  266.05    -8.45   -27.90     0.92)  ; 1, R-2-2\r\n      (  267.80    -9.85   -28.92     0.92)  ; 2\r\n      (  267.80    -9.85   -28.95     0.92)  ; 3\r\n      (  269.35   -12.46   -29.97     0.92)  ; 4\r\n      (  269.35   -12.46   -30.00     0.92)  ; 5\r\n      (  272.29   -12.97   -31.17     0.92)  ; 6\r\n      (  276.13   -13.26   -32.10     0.92)  ; 7\r\n      (  277.92   -12.85   -32.92     0.92)  ; 8\r\n      (  279.21   -14.33   -32.47     0.92)  ; 9\r\n      (  279.21   -14.33   -32.50     0.92)  ; 10\r\n      (  281.22   -16.84   -34.17     0.92)  ; 11\r\n      (  283.39   -18.12   -33.75     0.92)  ; 12\r\n      (  285.62   -17.60   -35.95     0.92)  ; 13\r\n      (  287.87   -17.08   -37.55     0.92)  ; 14\r\n      (  290.54   -16.44   -39.15     0.92)  ; 15\r\n      (  290.54   -16.44   -39.17     0.92)  ; 16\r\n      (  294.96   -17.21   -40.15     0.92)  ; 17\r\n      (  297.90   -17.71   -41.10     0.92)  ; 18\r\n      (  302.14   -19.70   -41.93     0.92)  ; 19\r\n      (  304.14   -22.22   -43.47     0.92)  ; 20\r\n      (  304.14   -22.22   -43.50     0.92)  ; 21\r\n      (  307.36   -23.85   -45.15     0.92)  ; 22\r\n      (  310.51   -27.29   -46.70     0.92)  ; 23\r\n      (  313.15   -28.48   -48.45     0.92)  ; 24\r\n      (  315.38   -27.95   -48.10     0.92)  ; 25\r\n      (  315.38   -27.95   -48.13     0.92)  ; 26\r\n      (  316.67   -29.44   -49.35     0.92)  ; 27\r\n      (  318.40   -30.82   -51.42     0.92)  ; 28\r\n      (  318.40   -30.82   -51.50     0.92)  ; 29\r\n      (  320.65   -30.30   -52.15     0.92)  ; 30\r\n      (  320.65   -30.30   -52.17     0.92)  ; 31\r\n      (  323.87   -31.94   -52.95     0.92)  ; 32\r\n      (  326.04   -33.22   -52.63     0.92)  ; 33\r\n      (  328.94   -35.53   -53.88     0.92)  ; 34\r\n      (  328.94   -35.53   -53.90     0.92)  ; 35\r\n      (  330.67   -36.91   -55.55     0.92)  ; 36\r\n      (  330.67   -36.91   -55.57     0.92)  ; 37\r\n      (  331.79   -39.64   -56.47     0.92)  ; 38\r\n      (  331.96   -44.37   -57.77     0.92)  ; 39\r\n      (  333.91   -48.69   -57.77     0.92)  ; 40\r\n      (  334.36   -48.58   -57.77     0.92)  ; 41\r\n      (  337.40   -51.46   -57.60     0.92)  ; 42\r\n      (  337.40   -51.46   -57.57     0.92)  ; 43\r\n      (  339.39   -53.98   -55.52     0.92)  ; 44\r\n      (  339.39   -53.98   -55.55     0.92)  ; 45\r\n      (  341.71   -55.82   -55.47     0.92)  ; 46\r\n      (  342.82   -58.55   -54.80     0.92)  ; 47\r\n      (  345.27   -60.97   -55.95     0.92)  ; 48\r\n      (  348.03   -62.70   -55.30     0.92)  ; 49\r\n      (  348.03   -62.70   -55.35     0.92)  ; 50\r\n      (  349.15   -65.43   -55.35     0.92)  ; 51\r\n      (  351.20   -66.13   -57.20     0.46)  ; 52\r\n      (  351.20   -66.13   -57.22     0.46)  ; 53\r\n      (  354.85   -67.67   -58.07     0.46)  ; 54\r\n      (  355.30   -67.56   -58.10     0.46)  ; 55\r\n      (  358.38   -68.64   -59.22     0.46)  ; 56\r\n      (  358.38   -68.64   -59.25     0.46)  ; 57\r\n      (  362.48   -70.07   -59.83     0.46)  ; 58\r\n      (  362.48   -70.07   -59.85     0.46)  ; 59\r\n      (  365.87   -70.47   -61.05     0.46)  ; 60\r\n      (  367.67   -70.04   -63.70     0.46)  ; 61\r\n      (  367.67   -70.04   -64.00     0.46)  ; 62\r\n\r\n      (Cross\r\n        (Color Yellow)\r\n        (Name \"Marker 3\")\r\n        (  276.94   -10.68   -32.92     0.92)  ; 1\r\n        (  280.42   -13.45   -32.15     0.92)  ; 2\r\n        (  280.01   -17.73   -33.75     0.92)  ; 3\r\n        (  314.75   -29.29   -48.15     0.92)  ; 4\r\n        (  316.58   -33.05   -51.00     0.92)  ; 5\r\n        (  319.99   -27.47   -52.95     0.92)  ; 6\r\n        (  333.67   -41.59   -57.77     0.92)  ; 7\r\n        (  338.46   -55.99   -55.47     0.92)  ; 8\r\n        (  339.76   -51.50   -58.35     0.92)  ; 9\r\n        (  339.36   -49.80   -58.35     0.92)  ; 10\r\n        (  344.07   -55.86   -54.85     0.92)  ; 11\r\n        (  348.08   -60.90   -54.80     0.92)  ; 12\r\n        (  359.36   -70.79   -58.38     0.46)  ; 13\r\n        (  358.30   -66.27   -59.80     0.46)  ; 14\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Cyan)\r\n  (Dendrite)\r\n  (  265.52    17.95   -15.45     0.92)  ; Root\r\n  (  265.52    17.95   -15.45     0.92)  ; 1, R\r\n  (  267.75    18.48   -15.48     0.92)  ; 2\r\n  (  267.67    20.84   -18.15     0.92)  ; 3\r\n  (  265.57    19.75   -19.20     0.92)  ; 4\r\n  (  262.50    20.83   -20.95     0.92)  ; 5\r\n  (  264.54    20.11   -22.85     0.92)  ; 6\r\n  (  267.48    19.60   -24.72     0.92)  ; 7\r\n  (  267.48    19.60   -24.75     0.92)  ; 8\r\n  (  270.17    20.24   -26.55     0.92)  ; 9\r\n  (  269.73    20.13   -26.55     0.92)  ; 10\r\n  (  273.11    19.73   -28.00     0.92)  ; 11\r\n  (  274.46    20.05   -27.95     0.92)  ; 12\r\n  (\r\n    (  276.23    24.52   -27.35     0.46)  ; 1, R-1\r\n    (  277.89    25.51   -30.13     0.46)  ; 2\r\n    (  282.05    25.89   -31.35     0.46)  ; 3\r\n    (  281.60    25.78   -31.35     0.46)  ; 4\r\n    (  284.54    25.27   -32.13     0.46)  ; 5\r\n    (  285.79    27.95   -33.47     0.46)  ; 6\r\n    (  285.79    27.95   -33.53     0.46)  ; 7\r\n    (  290.26    29.00   -35.72     0.46)  ; 8\r\n    (  293.52    29.17   -36.72     0.46)  ; 9\r\n    (  296.83    31.14   -37.88     0.46)  ; 10\r\n    (  303.84    33.38   -38.55     0.46)  ; 11\r\n    (  303.84    33.38   -38.57     0.46)  ; 12\r\n    (  308.99    37.57   -39.70     0.46)  ; 13\r\n    (  311.99    38.86   -41.45     0.46)  ; 14\r\n    (  311.99    38.86   -41.47     0.46)  ; 15\r\n    (  313.95    40.51   -42.15     0.46)  ; 16\r\n    (  316.81    42.37   -42.15     0.46)  ; 17\r\n    (  319.35    43.58   -42.17     0.46)  ; 18\r\n    (  323.55    45.76   -42.22     0.46)  ; 19\r\n    (  327.49    49.07   -42.22     0.46)  ; 20\r\n    (  327.36    49.64   -42.22     0.46)  ; 21\r\n    (  331.43    52.38   -42.22     0.46)  ; 22\r\n    (  333.71    54.70   -43.80     0.46)  ; 23\r\n    (  335.59    58.73   -45.22     0.46)  ; 24\r\n    (  335.59    58.73   -45.25     0.46)  ; 25\r\n    (  339.67    61.48   -46.57     0.46)  ; 26\r\n    (  341.49    63.69   -48.07     0.46)  ; 27\r\n    (  344.58    62.63   -49.58     0.46)  ; 28\r\n    (  347.61    65.72   -51.28     0.46)  ; 29\r\n    (  347.61    65.72   -51.30     0.46)  ; 30\r\n    (  351.46    65.43   -52.75     0.46)  ; 31\r\n    (  355.03    66.27   -54.20     0.46)  ; 32\r\n    (  355.03    66.27   -54.78     0.46)  ; 33\r\n    (  358.79    68.34   -53.38     0.46)  ; 34\r\n    (  359.86    69.79   -55.63     0.46)  ; 35\r\n    (  359.42    69.68   -55.63     0.46)  ; 36\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  302.14    30.59   -38.57     0.46)  ; 1\r\n      (  303.71    33.94   -38.57     0.46)  ; 2\r\n      (  319.17    42.33   -42.17     0.46)  ; 3\r\n      (  315.24    39.03   -42.17     0.46)  ; 4\r\n      (  315.46    42.07   -42.17     0.46)  ; 5\r\n      (  322.71    47.35   -42.17     0.46)  ; 6\r\n      (  329.09    48.25   -42.90     0.46)  ; 7\r\n      (  330.18    49.69   -42.20     0.46)  ; 8\r\n      (  338.85    58.89   -46.57     0.46)  ; 9\r\n      (  307.47    36.02   -31.70     0.46)  ; 10\r\n    )  ;  End of markers\r\n     Normal\r\n  |\r\n    (  271.69    21.78   -29.82     0.46)  ; 1, R-2\r\n    (  269.91    21.36   -33.03     0.46)  ; 2\r\n    (  269.38    23.64   -32.97     0.46)  ; 3\r\n    (  269.38    23.64   -33.05     0.46)  ; 4\r\n    (  266.88    24.23   -35.67     0.46)  ; 5\r\n    (  266.88    24.23   -35.70     0.46)  ; 6\r\n    (  265.00    26.19   -38.05     0.46)  ; 7\r\n    (  263.13    28.14   -41.08     0.46)  ; 8\r\n    (  261.26    30.09   -43.33     0.46)  ; 9\r\n    (  260.20    34.62   -44.90     0.46)  ; 10\r\n    (  256.41    36.72   -45.95     0.46)  ; 11\r\n    (  255.75    39.55   -47.17     0.46)  ; 12\r\n    (  252.53    41.18   -47.17     0.46)  ; 13\r\n    (  249.64    43.49   -49.15     0.46)  ; 14\r\n    (  249.16    47.56   -50.83     0.46)  ; 15\r\n    (  248.18    49.72   -51.75     0.46)  ; 16\r\n    (  247.74    49.61   -51.75     0.46)  ; 17\r\n    (  247.07    52.44   -52.70     0.46)  ; 18\r\n    (  247.07    52.44   -52.72     0.46)  ; 19\r\n    (  245.78    53.93   -51.72     0.46)  ; 20\r\n    (  244.22    56.55   -54.15     0.46)  ; 21\r\n    (  241.46    58.29   -55.65     0.46)  ; 22\r\n    (  237.98    61.07   -57.53     0.46)  ; 23\r\n    (  237.98    61.07   -57.55     0.46)  ; 24\r\n    (  233.49    64.18   -58.20     0.46)  ; 25\r\n    (  233.49    64.18   -58.22     0.46)  ; 26\r\n    (  230.49    66.94   -57.67     0.46)  ; 27\r\n    (  227.73    68.67   -60.22     0.46)  ; 28\r\n    (  226.11    69.50   -62.70     0.46)  ; 29\r\n    (  223.54    72.48   -64.75     0.46)  ; 30\r\n    (  223.54    72.48   -64.78     0.46)  ; 31\r\n    (  220.90    73.65   -66.95     0.46)  ; 32\r\n    (  220.46    73.55   -66.97     0.46)  ; 33\r\n    (  219.92    75.82   -68.85     0.46)  ; 34\r\n    (  219.92    75.82   -68.90     0.46)  ; 35\r\n    (  217.70    75.29   -71.17     0.46)  ; 36\r\n    (  217.70    75.29   -71.20     0.46)  ; 37\r\n    (  217.22    79.36   -72.40     0.46)  ; 38\r\n    (  216.24    81.51   -75.05     0.46)  ; 39\r\n    (  216.11    82.08   -75.05     0.46)  ; 40\r\n    (  215.53    82.54   -78.30     0.46)  ; 41\r\n    (  215.53    82.54   -78.35     0.46)  ; 42\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  256.29    43.26   -47.17     0.46)  ; 1\r\n      (  245.28    52.02   -51.72     0.46)  ; 2\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color White)\r\n  (Dendrite)\r\n  (  269.00    15.19    12.42     0.92)  ; Root\r\n  (  268.88    15.75    12.42     0.92)  ; 1, R\r\n  (  272.05    18.28    12.42     0.92)  ; 2\r\n  (  269.54    18.89    12.42     0.92)  ; 3\r\n  (  267.18    18.93    14.05     0.92)  ; 4\r\n  (  265.97    18.06    17.32     0.92)  ; 5\r\n  (  264.37    18.87    20.73     0.92)  ; 6\r\n  (  262.76    19.69    23.02     0.92)  ; 7\r\n  (  263.43    16.86    24.60     0.92)  ; 8\r\n  (  261.12    18.70    27.02     0.92)  ; 9\r\n  (  260.67    18.61    27.00     0.92)  ; 10\r\n  (  258.70    16.95    28.90     0.92)  ; 11\r\n  (  259.18    12.88    28.33     0.92)  ; 12\r\n  (  261.68    12.26    30.23     0.92)  ; 13\r\n  (  261.68    12.26    30.20     0.92)  ; 14\r\n  (  263.46    12.68    33.83     0.92)  ; 15\r\n  (  263.46    12.68    33.85     0.92)  ; 16\r\n\r\n  (Cross\r\n    (Color White)\r\n    (Name \"Marker 3\")\r\n    (  262.05    20.71    23.02     0.92)  ; 1\r\n  )  ;  End of markers\r\n   High\r\n)  ;  End of tree\r\n\r\n( (Color RGB (128, 255, 128))\r\n  (Apical)\r\n  (  259.44    31.45    -6.38     6.88)  ; Root\r\n  (  259.44    31.45    -6.38     6.88)  ; 1, R\r\n  (  259.35    33.82    -6.38     6.88)  ; 2\r\n  (  258.93    35.90    -6.38     6.88)  ; 3\r\n  (\r\n    (  259.89    37.53    -6.38     3.67)  ; 1, R-1\r\n    (  260.30    41.81    -6.38     3.67)  ; 2\r\n    (  260.10    44.74    -6.38     3.67)  ; 3\r\n    (\r\n      (  258.08    47.25    -5.92     3.67)  ; 1, R-1-1\r\n      (  256.21    49.21    -4.70     3.67)  ; 2\r\n      (\r\n        (  255.55    52.03    -3.00     3.21)  ; 1, R-1-1-1\r\n        (  254.00    54.66    -1.90     2.75)  ; 2\r\n        (  252.31    57.85    -1.90     2.75)  ; 3\r\n        (  252.36    59.65    -1.90     2.75)  ; 4\r\n        (  254.18    61.88    -1.90     2.75)  ; 5\r\n        (  256.47    64.19    -0.85     2.75)  ; 6\r\n        (  257.46    68.01     0.17     2.75)  ; 7\r\n        (  258.71    70.70     0.55     2.75)  ; 8\r\n        (  258.63    73.06     1.38     2.75)  ; 9\r\n\r\n        (Cross\r\n          (Color RGB (128, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  258.51    51.54    -3.92     1.38)  ; 1\r\n          (  253.84    65.38    -0.85     2.75)  ; 2\r\n        )  ;  End of markers\r\n        (\r\n          (  259.49    75.44    -0.63     2.75)  ; 1, R-1-1-1-1\r\n          (  259.04    77.35    -0.63     2.75)  ; 2\r\n          (  259.42    79.82    -1.22     2.75)  ; 3\r\n          (  259.42    79.82    -1.25     2.75)  ; 4\r\n          (  259.50    83.43    -1.25     2.75)  ; 5\r\n          (  258.63    89.18    -1.25     2.75)  ; 6\r\n          (  257.70    93.15    -2.08     2.75)  ; 7\r\n          (  259.00    97.63    -2.85     2.75)  ; 8\r\n          (  258.47    99.89    -3.60     2.75)  ; 9\r\n          (  258.38   102.27    -5.95     2.75)  ; 10\r\n          (  258.38   102.27    -5.97     2.75)  ; 11\r\n\r\n          (Cross\r\n            (Color RGB (128, 255, 128))\r\n            (Name \"Marker 3\")\r\n            (  261.27    71.90     2.15     2.75)  ; 1\r\n          )  ;  End of markers\r\n          (\r\n            (  255.96   104.56    -4.38     2.75)  ; 1, R-1-1-1-1-1\r\n            (  257.53   107.92    -3.13     2.75)  ; 2\r\n            (  259.11   111.27    -1.80     2.75)  ; 3\r\n            (  258.31   114.66    -0.90     2.75)  ; 4\r\n            (  257.25   119.20     0.12     2.75)  ; 5\r\n            (  255.87   123.05     1.32     2.75)  ; 6\r\n            (\r\n              (  254.55   128.72     1.32     2.75)  ; 1, R-1-1-1-1-1-1\r\n              (\r\n                (  255.26   133.67     0.35     2.75)  ; 1, R-1-1-1-1-1-1-1\r\n                (  255.55   138.51    -0.63     2.75)  ; 2\r\n                (  256.36   141.09    -1.82     2.75)  ; 3\r\n                (  257.93   144.44    -3.20     2.75)  ; 4\r\n                (  257.93   144.44    -3.22     2.75)  ; 5\r\n                (  256.06   146.39    -3.55     2.75)  ; 6\r\n                (  256.20   151.79    -3.55     2.75)  ; 7\r\n                (  256.92   156.74    -3.55     2.75)  ; 8\r\n                (  257.38   162.83    -3.55     2.75)  ; 9\r\n                (  258.82   166.73    -2.55     2.75)  ; 10\r\n                (  262.44   169.39    -2.55     2.75)  ; 11\r\n                (  264.33   173.41    -3.20     2.75)  ; 12\r\n                (  263.09   176.70    -3.85     2.75)  ; 13\r\n                (  259.87   178.33    -4.82     2.75)  ; 14\r\n                (  257.08   184.25    -4.82     2.75)  ; 15\r\n                (  254.73   190.26    -4.82     2.75)  ; 16\r\n                (  251.34   196.64    -4.82     2.75)  ; 17\r\n                (  251.31   200.81    -5.72     2.75)  ; 18\r\n                (  251.86   204.53    -7.35     2.75)  ; 19\r\n                (  253.48   209.68    -8.82     2.75)  ; 20\r\n                (  254.46   213.49   -10.00     2.75)  ; 21\r\n                (  254.11   216.99   -11.25     2.75)  ; 22\r\n                (  251.09   219.87   -12.10     2.75)  ; 23\r\n\r\n                (Cross\r\n                  (Color RGB (128, 255, 128))\r\n                  (Name \"Marker 3\")\r\n                  (  253.56   147.00    -3.55     2.75)  ; 1\r\n                  (  258.38   150.52    -3.55     2.75)  ; 2\r\n                  (  257.60   176.02    -4.82     2.75)  ; 3\r\n                  (  258.91   186.47    -4.82     2.75)  ; 4\r\n                  (  256.22   179.87    -4.82     2.75)  ; 5\r\n                  (  265.90   176.76    -4.82     2.75)  ; 6\r\n                  (  254.62   202.78    -7.35     2.75)  ; 7\r\n                  (  254.27   206.28    -8.82     2.75)  ; 8\r\n                  (  250.79   209.05    -8.82     2.75)  ; 9\r\n                  (  254.95   209.43    -8.82     2.75)  ; 10\r\n                  (  256.65   212.21    -8.82     2.75)  ; 11\r\n                )  ;  End of markers\r\n                (\r\n                  (  250.03   224.39   -12.65     2.75)  ; 1, R-1-1-1-1-1-1-1-1\r\n                  (  248.47   227.01   -11.80     2.75)  ; 2\r\n                  (  245.70   228.76   -11.80     2.75)  ; 3\r\n                  (  243.70   231.27   -12.65     2.75)  ; 4\r\n                  (  243.57   231.83   -12.65     2.75)  ; 5\r\n                  (  245.01   235.76   -13.32     2.75)  ; 6\r\n                  (  245.42   240.04   -14.38     2.75)  ; 7\r\n                  (  244.76   242.87   -16.32     2.75)  ; 8\r\n                  (  245.88   246.12   -18.70     2.75)  ; 9\r\n                  (  245.60   249.49   -19.67     2.75)  ; 10\r\n                  (  246.58   253.31   -19.88     2.75)  ; 11\r\n\r\n                  (Cross\r\n                    (Color RGB (128, 255, 128))\r\n                    (Name \"Marker 3\")\r\n                    (  255.19   218.45   -12.10     2.75)  ; 1\r\n                    (  252.84   224.46   -12.65     2.75)  ; 2\r\n                    (  243.03   244.25   -18.70     2.75)  ; 3\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  248.91   257.44   -19.88     2.29)  ; 1, R-1-1-1-1-1-1-1-1-1\r\n                    (  250.03   260.68   -19.05     2.29)  ; 2\r\n                    (  251.30   263.37   -19.60     2.29)  ; 3\r\n                    (  252.50   264.25   -17.77     2.29)  ; 4\r\n                    (  254.77   266.58   -16.13     2.29)  ; 5\r\n                    (  256.17   268.69   -15.02     2.29)  ; 6\r\n                    (  255.82   272.20   -15.02     2.29)  ; 7\r\n                    (  254.39   274.25   -16.47     2.29)  ; 8\r\n                    (  253.86   276.51   -18.35     2.29)  ; 9\r\n                    (  253.33   278.77   -19.42     2.29)  ; 10\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  251.63   275.99   -18.35     2.29)  ; 1\r\n                      (  256.63   274.76   -18.35     2.29)  ; 2\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  251.06   282.42   -19.42     2.29)  ; 1, R-1-1-1-1-1-1-1-1-1-1\r\n                      (  250.98   284.79   -20.52     2.29)  ; 2\r\n                      (  252.56   288.15   -21.50     2.29)  ; 3\r\n                      (  252.56   288.15   -21.52     2.29)  ; 4\r\n                      (  252.51   292.32   -21.25     2.29)  ; 5\r\n                      (  254.22   295.10   -20.70     2.29)  ; 6\r\n                      (  254.62   299.38   -20.70     2.29)  ; 7\r\n                      (  254.28   302.89   -19.63     2.29)  ; 8\r\n                      (  254.82   306.60   -18.77     2.29)  ; 9\r\n                      (  253.58   309.89   -18.77     2.29)  ; 10\r\n                      (  252.92   312.71   -20.83     2.29)  ; 11\r\n                      (  252.92   312.71   -20.85     2.29)  ; 12\r\n                      (  252.44   316.78   -21.67     2.29)  ; 13\r\n                      (  251.65   320.17   -22.50     2.29)  ; 14\r\n                      (  249.82   323.93   -23.33     2.29)  ; 15\r\n                      (  247.24   326.91   -23.90     2.29)  ; 16\r\n                      (  246.39   328.51   -24.92     2.29)  ; 17\r\n                      (  246.61   331.55   -25.83     2.29)  ; 18\r\n                      (  249.09   335.11   -26.50     2.29)  ; 19\r\n                      (  251.37   337.44   -25.85     2.29)  ; 20\r\n                      (  251.77   341.72   -25.23     2.29)  ; 21\r\n                      (  248.48   345.72   -25.23     2.29)  ; 22\r\n                      (  245.01   348.49   -26.38     2.29)  ; 23\r\n                      (  243.77   351.77   -26.85     2.29)  ; 24\r\n                      (  244.18   356.05   -27.35     2.29)  ; 25\r\n                      (  244.40   359.09   -26.95     2.29)  ; 26\r\n\r\n                      (Cross\r\n                        (Color White)\r\n                        (Name \"Marker 3\")\r\n                        (  247.73   353.23   -26.95     0.92)  ; 1\r\n                      )  ;  End of markers\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  249.73   288.08   -21.25     2.29)  ; 1\r\n                        (  250.78   293.70   -21.25     2.29)  ; 2\r\n                        (  253.40   286.55   -21.25     2.29)  ; 3\r\n                        (  255.10   289.35   -21.25     2.29)  ; 4\r\n                        (  257.05   301.14   -20.83     2.29)  ; 5\r\n                        (  255.55   311.55   -21.67     2.29)  ; 6\r\n                        (  248.38   320.01   -23.33     2.29)  ; 7\r\n                        (  249.18   316.62   -23.33     2.29)  ; 8\r\n                        (  253.21   323.53   -23.33     2.29)  ; 9\r\n                        (  245.00   326.39   -23.90     2.29)  ; 10\r\n                        (  246.08   333.81   -26.50     2.29)  ; 11\r\n                        (  253.10   336.05   -26.72     2.29)  ; 12\r\n                        (  248.70   342.78   -25.23     2.29)  ; 13\r\n                        (  250.89   347.48   -25.23     2.29)  ; 14\r\n                        (  242.46   347.29   -25.23     2.29)  ; 15\r\n                      )  ;  End of markers\r\n                      (\r\n                        (  243.05   361.09   -26.02     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1\r\n                        (  242.09   363.25   -24.32     1.83)  ; 2\r\n                        (  241.68   364.95   -22.80     1.83)  ; 3\r\n                        (  241.73   366.75   -21.32     1.83)  ; 4\r\n                        (  242.22   368.67   -19.65     1.83)  ; 5\r\n                        (  243.04   371.25   -18.72     1.83)  ; 6\r\n                        (  244.28   373.93   -18.80     1.83)  ; 7\r\n                        (  245.10   376.50   -17.60     1.83)  ; 8\r\n                        (  245.01   378.87   -16.67     1.83)  ; 9\r\n                        (  244.75   380.00   -15.90     1.83)  ; 10\r\n                        (  242.74   382.52   -15.40     1.83)  ; 11\r\n                        (  242.52   385.46   -15.10     1.83)  ; 12\r\n                        (  240.25   389.11   -15.10     1.83)  ; 13\r\n                        (  238.39   391.06   -15.10     1.83)  ; 14\r\n                        (  237.73   393.89   -14.13     1.83)  ; 15\r\n                        (  238.66   395.90   -13.42     1.83)  ; 16\r\n                        (  239.60   397.92   -12.62     1.83)  ; 17\r\n                        (  238.54   402.44   -12.40     1.83)  ; 18\r\n                        (  237.93   407.07   -12.40     1.83)  ; 19\r\n                        (  236.19   408.45   -12.40     1.83)  ; 20\r\n                        (  236.69   410.36   -13.32     1.83)  ; 21\r\n                        (  237.23   414.08   -14.52     1.83)  ; 22\r\n                        (  238.04   416.65   -15.48     1.83)  ; 23\r\n                        (  236.53   421.07   -16.38     1.83)  ; 24\r\n                        (  235.04   425.50   -14.65     1.83)  ; 25\r\n                        (  234.06   427.66   -15.40     1.83)  ; 26\r\n                        (  232.63   429.71   -16.35     1.83)  ; 27\r\n                        (  233.48   434.09   -16.77     1.83)  ; 28\r\n                        (  234.21   439.05   -17.25     1.83)  ; 29\r\n                        (  236.04   441.27   -17.30     1.83)  ; 30\r\n                        (  236.54   443.17   -18.10     1.83)  ; 31\r\n                        (  235.16   447.02   -18.92     1.83)  ; 32\r\n                        (  233.30   448.98   -18.92     1.83)  ; 33\r\n                        (  236.18   450.72   -19.73     1.83)  ; 34\r\n                        (  237.25   452.16   -18.72     1.83)  ; 35\r\n                        (  237.29   453.97   -17.27     1.83)  ; 36\r\n                        (  237.29   453.97   -17.30     1.83)  ; 37\r\n\r\n                        (Cross\r\n                          (Color White)\r\n                          (Name \"Marker 3\")\r\n                          (  240.91   402.40   -12.40     1.83)  ; 1\r\n                          (  240.00   412.33   -14.52     1.83)  ; 2\r\n                          (  239.70   417.64   -16.38     1.83)  ; 3\r\n                          (  238.50   422.74   -13.52     1.83)  ; 4\r\n                          (  239.26   439.62   -18.92     1.83)  ; 5\r\n                          (  237.41   447.55   -19.97     1.83)  ; 6\r\n                          (  238.27   451.81   -20.27     1.83)  ; 7\r\n                          (  234.36   444.45   -18.10     1.83)  ; 8\r\n                          (  234.62   443.33   -20.77     1.83)  ; 9\r\n                          (  232.12   437.95   -17.25     1.83)  ; 10\r\n                          (  233.95   424.05   -13.52     1.83)  ; 11\r\n                          (  232.79   424.97   -17.30     1.83)  ; 12\r\n                          (  235.27   434.51   -14.60     1.83)  ; 13\r\n                          (  235.17   430.91   -14.60     1.83)  ; 14\r\n                          (  241.61   395.39   -12.75     1.83)  ; 15\r\n                          (  241.51   391.78   -15.10     1.83)  ; 16\r\n                          (  244.81   387.78   -15.10     1.83)  ; 17\r\n                          (  240.12   383.69   -15.88     1.83)  ; 18\r\n                          (  239.93   382.46   -13.35     1.83)  ; 19\r\n                          (  236.15   390.53   -14.13     1.83)  ; 20\r\n                          (  245.71   371.87   -17.95     1.83)  ; 21\r\n                          (  246.52   374.45   -17.30     1.83)  ; 22\r\n                          (  241.56   371.49   -17.95     1.83)  ; 23\r\n                          (  244.36   365.58   -22.80     1.83)  ; 24\r\n                          (  244.23   366.15   -18.72     1.83)  ; 25\r\n                          (  246.11   370.17   -18.72     1.83)  ; 26\r\n                          (  235.04   415.35   -14.52     1.83)  ; 27\r\n                          (  234.51   411.64   -14.52     1.83)  ; 28\r\n                          (  240.09   393.85   -13.23     1.83)  ; 29\r\n                          (  236.62   396.61   -12.40     1.83)  ; 30\r\n                        )  ;  End of markers\r\n                        (\r\n                          (  238.43   457.22   -16.17     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1\r\n                          (  238.34   459.59   -16.17     1.83)  ; 2\r\n                          (  236.79   462.21   -16.77     1.83)  ; 3\r\n                          (  235.81   464.37   -16.65     1.83)  ; 4\r\n                          (\r\n                            (  234.88   468.33   -16.65     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                            (  234.22   471.17   -17.57     1.83)  ; 2\r\n                            (  235.21   474.98   -18.88     1.83)  ; 3\r\n                            (  235.61   479.26   -18.88     1.83)  ; 4\r\n                            (  234.82   482.65   -18.72     1.83)  ; 5\r\n                            (  234.15   485.47   -19.30     1.83)  ; 6\r\n                            (  236.04   489.51   -19.33     1.83)  ; 7\r\n                            (  237.17   492.76   -19.33     1.83)  ; 8\r\n                            (  238.55   494.87   -19.90     1.83)  ; 9\r\n                            (  239.04   496.78   -19.80     1.83)  ; 10\r\n                            (  236.92   499.87   -19.80     1.83)  ; 11\r\n                            (  235.23   503.05   -20.10     1.83)  ; 12\r\n                            (  233.14   507.94   -20.47     1.83)  ; 13\r\n                            (  233.05   510.30   -19.58     1.83)  ; 14\r\n                            (  233.60   514.01   -20.38     1.83)  ; 15\r\n                            (  231.15   516.43   -20.90     1.83)  ; 16\r\n                            (  230.80   519.93   -20.90     1.83)  ; 17\r\n                            (  231.16   522.40   -20.63     1.83)  ; 18\r\n                            (  232.14   526.21   -21.07     1.83)  ; 19\r\n                            (  231.79   529.72   -21.72     1.83)  ; 20\r\n                            (  230.87   533.68   -21.72     1.83)  ; 21\r\n                            (  229.04   537.43   -22.55     1.83)  ; 22\r\n                            (  229.04   537.43   -22.57     1.83)  ; 23\r\n                            (  228.25   540.83   -23.92     1.83)  ; 24\r\n                            (  228.25   540.83   -23.98     1.83)  ; 25\r\n                            (  228.61   543.30   -24.85     1.83)  ; 26\r\n                            (  230.23   548.46   -25.42     1.83)  ; 27\r\n                            (  230.33   552.06   -26.07     1.83)  ; 28\r\n                            (  232.36   555.42   -24.85     1.83)  ; 29\r\n                            (  232.47   559.02   -24.85     1.83)  ; 30\r\n                            (  231.10   562.87   -25.52     1.83)  ; 31\r\n                            (  231.10   562.87   -25.55     1.83)  ; 32\r\n                            (  230.17   566.84   -26.32     1.83)  ; 33\r\n                            (  230.27   570.44   -27.50     1.83)  ; 34\r\n                            (  231.25   574.26   -27.50     1.83)  ; 35\r\n                            (  231.48   577.30   -28.47     1.83)  ; 36\r\n                            (  232.03   581.02   -29.02     1.83)  ; 37\r\n                            (  231.68   584.50   -29.27     1.83)  ; 38\r\n                            (  231.32   588.00   -29.60     1.83)  ; 39\r\n                            (  230.92   589.71   -30.55     1.83)  ; 40\r\n                            (  232.50   593.05   -31.38     1.83)  ; 41\r\n                            (  234.78   595.39   -32.63     1.83)  ; 42\r\n                            (  233.40   599.25   -32.65     1.83)  ; 43\r\n                            (  232.66   604.45   -31.45     1.83)  ; 44\r\n                            (  233.52   608.83   -32.25     1.83)  ; 45\r\n                            (  232.54   610.99   -32.88     1.83)  ; 46\r\n\r\n                            (Cross\r\n                              (Color White)\r\n                              (Name \"Marker 3\")\r\n                              (  235.75   609.35   -31.77     1.83)  ; 1\r\n                              (  235.15   603.84   -31.70     1.83)  ; 2\r\n                              (  235.81   601.01   -31.45     1.83)  ; 3\r\n                              (  236.44   596.38   -31.52     1.83)  ; 4\r\n                              (  234.26   559.43   -25.97     1.83)  ; 5\r\n                              (  233.90   562.93   -25.00     1.83)  ; 6\r\n                              (  232.84   567.47   -27.50     1.83)  ; 7\r\n                              (  233.44   572.98   -27.50     1.83)  ; 8\r\n                              (  230.15   582.95   -29.27     1.83)  ; 9\r\n                              (  230.82   580.12   -29.27     1.83)  ; 10\r\n                              (  229.67   587.03   -29.27     1.83)  ; 11\r\n                              (  229.27   588.73   -29.27     1.83)  ; 12\r\n                              (  229.64   591.19   -31.40     1.83)  ; 13\r\n                              (  230.12   593.11   -31.40     1.83)  ; 14\r\n                              (  233.88   589.20   -31.40     1.83)  ; 15\r\n                              (  239.86   499.35   -19.80     1.83)  ; 16\r\n                              (  237.45   497.60   -19.80     1.83)  ; 17\r\n                              (  233.69   501.50   -20.47     1.83)  ; 18\r\n                              (  232.72   503.67   -20.47     1.83)  ; 19\r\n                              (  235.49   507.89   -21.50     1.83)  ; 20\r\n                              (  229.73   524.46   -21.15     1.83)  ; 21\r\n                              (  230.23   526.36   -21.15     1.83)  ; 22\r\n                              (  236.62   527.27   -19.97     1.83)  ; 23\r\n                              (  229.75   530.43   -20.10     1.83)  ; 24\r\n                              (  233.36   533.07   -20.50     1.83)  ; 25\r\n                              (  233.42   534.87   -21.70     1.83)  ; 26\r\n                              (  232.11   546.51   -26.07     1.83)  ; 27\r\n                              (  233.85   551.10   -26.07     1.83)  ; 28\r\n                              (  227.84   552.67   -26.07     1.83)  ; 29\r\n                              (  227.63   545.47   -26.07     1.83)  ; 30\r\n                              (  228.31   548.60   -26.07     1.83)  ; 31\r\n                              (  230.00   555.46   -25.97     1.83)  ; 32\r\n                              (  229.89   562.00   -25.97     1.83)  ; 33\r\n                              (  227.85   568.69   -27.50     1.83)  ; 34\r\n                              (  236.90   493.88   -19.90     1.83)  ; 35\r\n                              (  238.41   489.46   -21.35     1.83)  ; 36\r\n                              (  237.32   488.02   -21.95     1.83)  ; 37\r\n                              (  237.92   471.44   -18.88     1.83)  ; 38\r\n                              (  233.23   473.33   -19.30     1.83)  ; 39\r\n                              (  236.81   474.16   -19.35     1.83)  ; 40\r\n                              (  234.15   463.39   -17.92     1.83)  ; 41\r\n                            )  ;  End of markers\r\n                            (\r\n                              (  232.06   615.06   -32.88     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                              (  231.83   617.99   -32.75     1.83)  ; 2\r\n                              (  232.52   621.14   -33.15     1.83)  ; 3\r\n                              (  233.51   624.95   -33.15     1.83)  ; 4\r\n                              (  232.14   628.81   -32.58     1.83)  ; 5\r\n                              (  230.71   630.86   -32.65     1.83)  ; 6\r\n                              (  232.27   634.21   -33.22     1.83)  ; 7\r\n                              (  233.53   636.90   -34.20     1.83)  ; 8\r\n                              (  233.67   642.31   -34.78     1.83)  ; 9\r\n                              (  235.52   644.54   -34.78     1.83)  ; 10\r\n                              (  237.22   647.31   -34.78     1.83)  ; 11\r\n                              (  238.29   648.76   -35.83     1.83)  ; 12\r\n                              (  238.29   648.76   -35.85     1.83)  ; 13\r\n                              (  238.34   650.57   -37.13     1.83)  ; 14\r\n                              (  238.21   651.13   -37.15     1.83)  ; 15\r\n                              (  237.36   652.72   -36.42     1.83)  ; 16\r\n                              (  237.36   652.72   -36.45     1.83)  ; 17\r\n                              (  236.70   655.55   -36.42     1.83)  ; 18\r\n                              (  235.54   656.48   -34.85     1.83)  ; 19\r\n                              (  235.32   659.41   -34.15     1.83)  ; 20\r\n\r\n                              (Cross\r\n                                (Color White)\r\n                                (Name \"Marker 3\")\r\n                                (  233.22   658.33   -35.95     1.83)  ; 1\r\n                                (  237.55   659.94   -35.95     1.83)  ; 2\r\n                                (  237.82   658.80   -33.05     1.83)  ; 3\r\n                                (  238.22   657.10   -36.00     1.83)  ; 4\r\n                                (  236.15   651.84   -34.55     1.83)  ; 5\r\n                                (  239.22   644.81   -34.78     1.83)  ; 6\r\n                                (  236.32   641.13   -34.78     1.83)  ; 7\r\n                                (  230.88   626.12   -34.78     1.83)  ; 8\r\n                                (  230.33   622.41   -32.75     1.83)  ; 9\r\n                                (  234.13   620.32   -32.75     1.83)  ; 10\r\n                                (  234.43   615.00   -31.92     1.83)  ; 11\r\n                                (  235.72   613.52   -32.88     1.83)  ; 12\r\n                                (  235.76   637.42   -34.78     1.83)  ; 13\r\n                                (  228.81   620.86   -32.75     1.83)  ; 14\r\n                              )  ;  End of markers\r\n                              (\r\n                                (  233.23   664.30   -35.47     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                (  234.36   667.55   -35.13     1.83)  ; 2\r\n                                (  234.36   667.55   -35.15     1.83)  ; 3\r\n                                (  233.56   670.95   -35.65     1.83)  ; 4\r\n                                (  232.32   674.24   -35.65     1.83)  ; 5\r\n                                (  232.68   676.71   -34.42     1.83)  ; 6\r\n                                (  232.55   677.27   -34.42     1.83)  ; 7\r\n                                (  233.68   680.52   -33.13     1.83)  ; 8\r\n                                (  233.46   683.47   -33.88     1.83)  ; 9\r\n                                (  233.38   685.82   -33.27     1.83)  ; 10\r\n                                (  232.58   689.22   -33.05     1.83)  ; 11\r\n                                (  232.94   691.70   -33.97     1.83)  ; 12\r\n                                (  232.90   695.87   -35.02     1.83)  ; 13\r\n                                (  232.90   695.87   -35.05     1.83)  ; 14\r\n                                (  230.64   699.52   -36.10     1.83)  ; 15\r\n                                (  231.31   702.66   -37.45     1.83)  ; 16\r\n                                (  230.78   704.93   -37.77     1.83)  ; 17\r\n                                (  229.35   706.99   -36.63     1.83)  ; 18\r\n                                (  229.90   710.69   -35.75     1.83)  ; 19\r\n                                (  230.45   714.40   -34.95     1.83)  ; 20\r\n                                (  231.57   717.65   -34.67     1.83)  ; 21\r\n                                (  230.64   721.62   -35.83     1.83)  ; 22\r\n                                (  230.64   721.62   -35.85     1.83)  ; 23\r\n                                (  230.87   724.66   -36.22     1.83)  ; 24\r\n                                (  230.79   727.03   -37.20     1.83)  ; 25\r\n                                (  231.91   730.28   -38.15     1.83)  ; 26\r\n                                (  231.47   736.14   -39.67     1.83)  ; 27\r\n                                (  229.97   740.57   -39.02     1.83)  ; 28\r\n                                (  231.22   743.25   -38.88     1.83)  ; 29\r\n                                (  230.92   748.55   -38.27     1.83)  ; 30\r\n                                (  231.32   750.94   -35.53     1.83)  ; 31\r\n                                (  231.32   750.94   -35.55     1.83)  ; 32\r\n                                (  231.24   753.30   -34.92     1.83)  ; 33\r\n                                (  229.28   757.62   -34.92     1.83)  ; 34\r\n                                (  229.43   760.49   -34.63     1.83)  ; 35\r\n                                (  230.73   764.97   -33.77     1.83)  ; 36\r\n                                (  231.40   768.12   -34.72     1.83)  ; 37\r\n                                (  231.91   770.02   -33.97     1.83)  ; 38\r\n                                (  231.51   771.73   -33.67     1.83)  ; 39\r\n                                (  229.82   774.90   -33.03     1.83)  ; 40\r\n                                (  229.74   777.28   -33.00     1.83)  ; 41\r\n                                (  230.68   779.28   -33.00     1.83)  ; 42\r\n                                (  231.35   782.44   -32.63     1.83)  ; 43\r\n                                (  231.53   783.67   -33.13     1.83)  ; 44\r\n                                (  230.60   787.62   -32.35     1.83)  ; 45\r\n                                (  231.15   791.34   -31.75     1.83)  ; 46\r\n                                (  231.07   793.72   -31.35     1.83)  ; 47\r\n                                (  232.64   797.06   -31.35     1.83)  ; 48\r\n                                (  233.14   798.98   -31.17     1.83)  ; 49\r\n                                (  232.21   802.93   -30.80     1.83)  ; 50\r\n                                (  232.12   805.30   -31.02     1.83)  ; 51\r\n                                (  232.12   805.30   -31.00     1.83)  ; 52\r\n                                (  234.08   806.96   -30.57     1.83)  ; 53\r\n                                (  234.50   811.25   -29.70     1.83)  ; 54\r\n                                (  233.39   813.96   -29.38     1.83)  ; 55\r\n                                (  233.17   816.90   -29.38     1.83)  ; 56\r\n                                (  233.97   819.48   -30.50     1.83)  ; 57\r\n                                (  233.32   822.30   -31.40     1.83)  ; 58\r\n                                (  233.32   822.30   -31.42     1.83)  ; 59\r\n                                (  234.00   825.45   -32.22     1.83)  ; 60\r\n                                (  234.36   827.93   -31.52     1.83)  ; 61\r\n                                (  234.28   830.29   -30.38     1.83)  ; 62\r\n                                (  234.95   833.44   -30.07     1.83)  ; 63\r\n                                (  235.49   837.15   -30.07     1.83)  ; 64\r\n                                (  236.17   840.28   -30.07     1.83)  ; 65\r\n                                (  236.79   841.64   -30.07     1.83)  ; 66\r\n                                (  235.69   844.36   -29.97     1.83)  ; 67\r\n                                (  235.21   848.42   -30.75     1.83)  ; 68\r\n                                (  236.47   851.11   -30.75     1.83)  ; 69\r\n                                (  237.00   854.82   -29.60     1.83)  ; 70\r\n                                (  235.89   857.54   -28.63     1.83)  ; 71\r\n                                (  235.54   861.04   -29.77     1.83)  ; 72\r\n                                (  236.80   863.73   -30.20     1.83)  ; 73\r\n\r\n                                (Cross\r\n                                  (Color White)\r\n                                  (Name \"Marker 3\")\r\n                                  (  235.10   860.94   -28.50     1.83)  ; 1\r\n                                  (  237.80   851.43   -30.20     1.83)  ; 2\r\n                                  (  238.54   846.23   -32.38     1.83)  ; 3\r\n                                  (  237.13   848.28   -30.55     1.83)  ; 4\r\n                                  (  233.62   839.10   -30.07     1.83)  ; 5\r\n                                  (  238.34   833.04   -30.07     1.83)  ; 6\r\n                                  (  233.30   832.46   -30.07     1.83)  ; 7\r\n                                  (  232.57   827.51   -30.15     1.83)  ; 8\r\n                                  (  236.41   827.21   -33.10     1.83)  ; 9\r\n                                  (  236.57   822.47   -32.22     1.83)  ; 10\r\n                                  (  235.29   823.97   -32.22     1.83)  ; 11\r\n                                  (  234.83   817.88   -32.22     1.83)  ; 12\r\n                                  (  232.89   812.06   -29.20     1.83)  ; 13\r\n                                  (  230.02   804.22   -30.80     1.83)  ; 14\r\n                                  (  231.41   806.33   -30.30     1.83)  ; 15\r\n                                  (  231.64   809.37   -29.52     1.83)  ; 16\r\n                                  (  231.80   760.45   -33.77     1.83)  ; 17\r\n                                  (  228.63   763.89   -33.77     1.83)  ; 18\r\n                                  (  233.42   765.60   -33.77     1.83)  ; 19\r\n                                  (  233.80   780.02   -32.63     1.83)  ; 20\r\n                                  (  233.87   787.79   -32.25     1.83)  ; 21\r\n                                  (  233.12   792.99   -31.35     1.83)  ; 22\r\n                                  (  234.83   795.79   -31.35     1.83)  ; 23\r\n                                  (  231.12   795.52   -33.30     1.83)  ; 24\r\n                                  (  229.49   790.36   -33.13     1.83)  ; 25\r\n                                  (  236.11   810.41   -29.20     1.83)  ; 26\r\n                                  (  235.56   812.69   -29.20     1.83)  ; 27\r\n                                  (  234.22   744.54   -38.27     1.83)  ; 28\r\n                                  (  228.44   733.04   -39.58     1.83)  ; 29\r\n                                  (  227.49   731.02   -38.85     1.83)  ; 30\r\n                                  (  228.47   728.88   -38.17     1.83)  ; 31\r\n                                  (  234.01   731.36   -39.22     1.83)  ; 32\r\n                                  (  233.63   716.95   -34.67     1.83)  ; 33\r\n                                  (  229.28   715.33   -34.67     1.83)  ; 34\r\n                                  (  232.37   714.25   -33.25     1.83)  ; 35\r\n                                  (  228.61   712.18   -33.25     1.83)  ; 36\r\n                                  (  229.13   703.94   -37.92     1.83)  ; 37\r\n                                  (  233.85   703.86   -37.92     1.83)  ; 38\r\n                                  (  233.58   699.01   -34.92     1.83)  ; 39\r\n                                  (  229.75   699.31   -37.52     1.83)  ; 40\r\n                                  (  233.32   722.25   -33.45     1.83)  ; 41\r\n                                  (  230.74   687.00   -33.05     1.83)  ; 42\r\n                                  (  231.37   688.35   -33.05     1.83)  ; 43\r\n                                  (  231.16   691.28   -33.05     1.83)  ; 44\r\n                                  (  230.05   694.01   -36.88     1.83)  ; 45\r\n                                  (  235.92   687.03   -33.05     1.83)  ; 46\r\n                                  (  231.57   679.44   -33.13     1.83)  ; 47\r\n                                  (  234.79   677.80   -33.77     1.83)  ; 48\r\n                                  (  235.90   681.05   -33.77     1.83)  ; 49\r\n                                  (  235.40   673.17   -36.85     1.83)  ; 50\r\n                                  (  235.16   670.13   -36.03     1.83)  ; 51\r\n                                  (  236.02   668.54   -36.03     1.83)  ; 52\r\n                                  (  232.62   668.94   -36.33     1.83)  ; 53\r\n                                )  ;  End of markers\r\n                                (\r\n                                  (  238.51   866.50   -30.20     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                  (  239.13   867.84   -31.58     0.92)  ; 2\r\n                                  (  238.78   871.34   -30.10     0.92)  ; 3\r\n                                  (  239.72   873.35   -30.77     0.92)  ; 4\r\n                                  (  239.59   873.91   -30.77     0.92)  ; 5\r\n                                  (  239.37   876.86   -31.52     0.92)  ; 6\r\n                                  (  239.42   878.66   -33.33     0.92)  ; 7\r\n                                  (  241.13   881.45   -34.45     0.92)  ; 8\r\n                                  (  242.64   882.99   -35.17     0.92)  ; 9\r\n                                  (  242.69   884.79   -36.82     0.92)  ; 10\r\n                                  (  244.71   888.25   -37.55     0.92)  ; 11\r\n                                  (  246.24   889.80   -37.63     0.92)  ; 12\r\n                                  (  247.05   892.39   -38.07     0.92)  ; 13\r\n                                  (  246.82   895.32   -38.90     0.92)  ; 14\r\n                                  (  247.90   896.77   -40.15     0.92)  ; 15\r\n                                  (  249.99   897.85   -40.97     0.92)  ; 16\r\n                                  (  249.64   901.35   -40.38     0.92)  ; 17\r\n                                  (  250.33   904.50   -41.52     0.92)  ; 18\r\n\r\n                                  (Cross\r\n                                    (Color White)\r\n                                    (Name \"Marker 3\")\r\n                                    (  251.76   902.45   -41.52     0.92)  ; 1\r\n                                    (  251.38   899.97   -40.38     0.92)  ; 2\r\n                                    (  248.25   893.26   -39.75     0.92)  ; 3\r\n                                    (  245.57   892.63   -38.88     0.92)  ; 4\r\n                                    (  246.53   884.50   -37.55     0.92)  ; 5\r\n                                    (  241.58   887.52   -32.80     0.92)  ; 6\r\n                                    (  242.66   888.96   -36.90     0.92)  ; 7\r\n                                    (  238.21   877.77   -35.17     0.92)  ; 8\r\n                                    (  240.71   877.16   -31.98     0.92)  ; 9\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  252.48   907.39   -42.55     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                    (  254.31   909.61   -44.17     0.92)  ; 2\r\n                                    (  255.89   912.96   -44.87     0.92)  ; 3\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  256.04   908.22   -44.17     0.92)  ; 1\r\n                                      (  253.32   905.80   -44.17     0.92)  ; 2\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  256.86   914.37   -44.10     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                      (  258.68   916.60   -42.30     0.46)  ; 2\r\n                                      (  258.68   916.60   -42.28     0.46)  ; 3\r\n                                      (  259.89   917.48   -41.45     0.46)  ; 4\r\n                                      (  263.21   919.45   -40.57     0.46)  ; 5\r\n                                      (  265.12   919.30   -41.52     0.46)  ; 6\r\n                                      (  266.91   919.72   -40.22     0.46)  ; 7\r\n                                      (  268.17   922.41   -39.52     0.46)  ; 8\r\n                                      (  270.54   922.36   -39.52     0.46)  ; 9\r\n                                      (  273.21   922.99   -38.55     0.46)  ; 10\r\n                                      (  275.31   924.08   -37.77     0.46)  ; 11\r\n                                      (  276.92   923.26   -36.78     0.46)  ; 12\r\n                                      (  279.10   921.98   -35.60     0.46)  ; 13\r\n                                      (  281.39   924.30   -34.58     0.46)  ; 14\r\n                                      (  282.45   925.75   -33.33     0.46)  ; 15\r\n                                      (  286.16   926.02   -32.75     0.46)  ; 16\r\n                                      (  287.82   927.02   -32.20     0.46)  ; 17\r\n                                      (  289.97   929.90   -31.60     0.46)  ; 18\r\n                                      (  292.20   930.43   -31.00     0.46)  ; 19\r\n                                      (  296.08   931.93   -30.30     0.46)  ; 20\r\n                                      (  297.88   932.35   -29.38     0.46)  ; 21\r\n                                      (  298.82   934.36   -29.38     0.46)  ; 22\r\n                                      (  300.47   935.35   -28.27     0.46)  ; 23\r\n                                      (  302.38   935.20   -27.27     0.46)  ; 24\r\n                                      (  303.51   938.44   -26.72     0.46)  ; 25\r\n                                      (  303.51   938.44   -26.75     0.46)  ; 26\r\n                                      (  305.61   939.53   -26.75     0.46)  ; 27\r\n                                      (  306.56   941.55   -25.73     0.46)  ; 28\r\n                                      (  308.52   943.20   -24.40     0.46)  ; 29\r\n                                      (  310.30   943.62   -23.60     0.46)  ; 30\r\n                                      (  312.41   944.70   -22.97     0.46)  ; 31\r\n                                      (  314.06   945.69   -22.77     0.46)  ; 32\r\n                                      (  317.18   946.42   -22.67     0.46)  ; 33\r\n                                      (  317.18   946.42   -22.70     0.46)  ; 34\r\n                                      (  318.67   946.18   -20.92     0.46)  ; 35\r\n                                      (  318.85   947.42   -20.00     0.46)  ; 36\r\n                                      (  320.45   946.60   -18.40     0.46)  ; 37\r\n                                      (  323.39   946.09   -17.42     0.46)  ; 38\r\n                                      (  323.39   946.09   -17.45     0.46)  ; 39\r\n                                      (  324.28   946.30   -16.20     0.46)  ; 40\r\n                                      (  324.33   948.10   -14.48     0.46)  ; 41\r\n                                      (  327.29   947.60   -14.38     0.46)  ; 42\r\n                                      (  330.72   949.00   -13.05     0.46)  ; 43\r\n                                      (  331.61   949.21   -13.05     0.46)  ; 44\r\n                                      (  332.10   951.12   -13.05     0.46)  ; 45\r\n                                      (  335.42   953.09   -13.05     0.46)  ; 46\r\n                                      (  337.69   955.42   -14.02     0.46)  ; 47\r\n                                      (  340.10   957.18   -14.88     0.46)  ; 48\r\n                                      (  341.45   957.49   -13.72     0.46)  ; 49\r\n                                      (  342.08   958.84   -12.95     0.46)  ; 50\r\n                                      (  344.36   961.15   -12.32     0.46)  ; 51\r\n                                      (  344.36   961.15   -12.35     0.46)  ; 52\r\n                                      (  345.75   963.28   -12.10     0.46)  ; 53\r\n                                      (  345.75   963.28   -12.13     0.46)  ; 54\r\n                                      (  347.59   965.50   -11.17     0.46)  ; 55\r\n                                      (  350.71   966.22   -10.48     0.46)  ; 56\r\n                                      (  351.52   968.81    -9.65     0.46)  ; 57\r\n                                      (  353.17   969.79    -8.42     0.46)  ; 58\r\n                                      (  353.17   969.79    -8.45     0.46)  ; 59\r\n                                      (  354.70   971.34    -6.65     0.46)  ; 60\r\n                                      (  356.35   972.33    -4.17     0.46)  ; 61\r\n                                      (  355.90   972.22    -4.17     0.46)  ; 62\r\n                                      (  356.80   972.43    -1.60     0.46)  ; 63\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  261.91   920.94   -41.45     0.46)  ; 1\r\n                                        (  309.33   945.79   -22.20     0.46)  ; 2\r\n                                        (  305.98   942.01   -24.58     0.46)  ; 3\r\n                                        (  307.94   943.66   -22.43     0.46)  ; 4\r\n                                        (  318.34   945.50   -20.00     0.46)  ; 5\r\n                                        (  325.26   944.14   -18.45     0.46)  ; 6\r\n                                        (  319.83   945.25   -17.20     0.46)  ; 7\r\n                                        (  313.79   946.83   -22.85     0.46)  ; 8\r\n                                        (  316.16   946.79   -20.00     0.46)  ; 9\r\n                                        (  324.38   949.91   -14.38     0.46)  ; 10\r\n                                        (  328.09   950.18   -12.62     0.46)  ; 11\r\n                                        (  332.15   952.92   -12.30     0.46)  ; 12\r\n                                        (  335.05   950.61   -12.32     0.46)  ; 13\r\n                                        (  339.02   949.76   -12.32     0.46)  ; 14\r\n                                        (  341.24   960.43   -12.35     0.46)  ; 15\r\n                                        (  346.28   961.00   -12.52     0.46)  ; 16\r\n                                        (  293.01   933.00   -29.38     0.46)  ; 17\r\n                                        (  286.84   929.16   -31.60     0.46)  ; 18\r\n                                        (  284.12   926.74   -32.75     0.46)  ; 19\r\n                                        (  278.03   920.53   -35.63     0.46)  ; 20\r\n                                        (  275.13   922.84   -35.63     0.46)  ; 21\r\n                                        (  271.47   924.37   -41.05     0.46)  ; 22\r\n                                        (  272.00   922.10   -41.67     0.46)  ; 23\r\n                                        (  268.57   920.70   -39.52     0.46)  ; 24\r\n                                        (  343.83   963.42   -12.57     0.46)  ; 25\r\n                                        (  345.66   965.64   -11.02     0.46)  ; 26\r\n                                        (  350.28   972.10   -12.52     0.46)  ; 27\r\n                                        (  275.23   926.45   -36.78     0.46)  ; 28\r\n                                        (  259.35   913.77   -42.28     0.46)  ; 29\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    |\r\n                                      (  255.99   916.57   -45.75     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                      (  258.40   918.33   -46.67     0.46)  ; 2\r\n                                      (  258.89   920.24   -47.60     0.46)  ; 3\r\n                                      (  258.76   920.81   -48.60     0.46)  ; 4\r\n                                      (  257.28   921.06   -49.05     0.46)  ; 5\r\n                                      (  257.28   921.06   -49.07     0.46)  ; 6\r\n                                      (  257.60   921.72   -49.85     0.46)  ; 7\r\n                                      (  259.43   923.95   -49.85     0.46)  ; 8\r\n                                      (  259.87   924.06   -50.52     0.46)  ; 9\r\n                                      (  258.58   925.54   -52.22     0.46)  ; 10\r\n                                      (  259.43   923.95   -53.60     0.46)  ; 11\r\n                                      (  259.43   923.95   -53.62     0.46)  ; 12\r\n                                      (  259.92   925.86   -55.22     0.46)  ; 13\r\n                                      (  259.92   925.86   -55.30     0.46)  ; 14\r\n                                      (  260.56   927.19   -57.13     0.46)  ; 15\r\n                                      (  262.21   928.18   -58.25     0.46)  ; 16\r\n                                      (  264.18   929.85   -59.38     0.46)  ; 17\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  255.99   922.55   -49.17     0.46)  ; 1\r\n                                        (  259.08   927.46   -57.13     0.46)  ; 2\r\n                                        (  259.84   928.23   -58.22     0.46)  ; 3\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  263.49   930.27   -60.57     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                        (  264.30   932.84   -61.25     0.46)  ; 2\r\n                                        (  262.56   934.23   -62.67     0.46)  ; 3\r\n                                        (  264.23   935.21   -63.75     0.46)  ; 4\r\n                                        (  262.94   936.70   -64.65     0.46)  ; 5\r\n                                        (  263.57   938.04   -65.52     0.46)  ; 6\r\n                                        (  263.34   940.98   -66.20     0.46)  ; 7\r\n                                        (  264.60   943.66   -67.00     0.46)  ; 8\r\n                                        (  264.65   945.46   -67.55     0.46)  ; 9\r\n                                        (  264.65   945.46   -67.57     0.46)  ; 10\r\n                                        (  263.36   946.95   -69.60     0.46)  ; 11\r\n                                        (  263.09   948.09   -70.70     0.46)  ; 12\r\n                                        (  265.18   949.17   -71.85     0.46)  ; 13\r\n                                        (  265.05   949.74   -72.72     0.46)  ; 14\r\n                                        (  265.10   951.53   -73.38     0.46)  ; 15\r\n                                        (  265.10   951.53   -73.40     0.46)  ; 16\r\n                                        (  263.95   952.47   -74.25     0.46)  ; 17\r\n                                        (  262.92   952.82   -75.52     0.46)  ; 18\r\n                                        (  262.97   954.62   -76.80     0.46)  ; 19\r\n                                        (  262.97   954.62   -76.82     0.46)  ; 20\r\n                                        (  262.89   956.99   -78.38     0.46)  ; 21\r\n                                        (  263.64   957.77   -79.75     0.46)  ; 22\r\n                                        (  262.93   958.79   -81.32     0.46)  ; 23\r\n                                        (  259.72   960.43   -82.97     0.46)  ; 24\r\n                                        (  262.40   961.06   -85.85     0.46)  ; 25\r\n                                        (  261.56   962.65   -88.02     0.46)  ; 26\r\n                                        (  260.85   963.68   -89.32     0.46)  ; 27\r\n                                        (  260.90   965.48   -91.07     0.46)  ; 28\r\n                                        (  260.37   967.75   -92.90     0.46)  ; 29\r\n                                        (  259.34   968.11   -95.02     0.46)  ; 30\r\n                                        (  260.99   969.09   -95.82     0.46)  ; 31\r\n                                        (  260.99   969.09   -95.85     0.46)  ; 32\r\n                                        (  259.71   970.58   -96.65     0.46)  ; 33\r\n                                        (  259.93   973.62   -97.80     0.46)  ; 34\r\n                                        (  260.02   977.22   -98.47     0.46)  ; 35\r\n                                        (  260.02   977.22   -98.53     0.46)  ; 36\r\n                                        (  260.98   979.23   -99.45     0.46)  ; 37\r\n                                        (  261.65   982.38  -100.50     0.46)  ; 38\r\n                                        (  262.09   982.48  -100.50     0.46)  ; 39\r\n                                        (  260.98   985.21  -101.27     0.46)  ; 40\r\n                                        (  261.17   986.45  -102.52     0.46)  ; 41\r\n                                        (  262.24   987.89  -103.90     0.46)  ; 42\r\n                                        (  261.45   991.29  -104.82     0.46)  ; 43\r\n                                        (  260.78   994.12  -105.77     0.46)  ; 44\r\n                                        (  261.15   996.59  -107.35     0.46)  ; 45\r\n                                        (  260.92   999.53  -108.72     0.46)  ; 46\r\n                                        (  260.52  1001.22  -110.02     0.46)  ; 47\r\n                                        (  260.44  1003.59  -111.53     0.46)  ; 48\r\n                                        (  258.08  1003.63  -112.75     0.46)  ; 49\r\n                                        (  256.29  1003.22  -114.65     0.46)  ; 50\r\n                                        (  252.58  1002.95  -115.47     0.46)  ; 51\r\n                                        (  251.38  1002.07  -117.40     0.46)  ; 52\r\n                                        (  249.46  1002.21  -118.10     0.46)  ; 53\r\n                                        (  249.46  1002.21  -118.22     0.46)  ; 54\r\n\r\n                                        (Cross\r\n                                          (Color White)\r\n                                          (Name \"Marker 3\")\r\n                                          (  261.82   999.74  -111.53     0.46)  ; 1\r\n                                          (  263.62   990.02  -104.82     0.46)  ; 2\r\n                                          (  263.71   987.64  -104.48     0.46)  ; 3\r\n                                          (  261.08   988.81  -103.47     0.46)  ; 4\r\n                                          (  264.31   954.93   -78.38     0.46)  ; 5\r\n                                          (  259.41   959.76   -84.72     0.46)  ; 6\r\n                                          (  259.69   964.61   -91.07     0.46)  ; 7\r\n                                          (  259.08   975.20   -99.45     0.46)  ; 8\r\n                                          (  259.54   981.29  -100.50     0.46)  ; 9\r\n                                          (  265.15   943.79   -58.53     0.46)  ; 10\r\n                                          (  265.81   944.54   -65.67     0.46)  ; 11\r\n                                          (  264.68   941.29   -67.35     0.46)  ; 12\r\n                                          (  263.34   979.19  -102.65     0.46)  ; 13\r\n                                          (  259.26   992.56  -105.77     0.46)  ; 14\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      |\r\n                                        (  265.88   932.61   -57.90     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                        (  266.11   935.65   -57.02     0.46)  ; 2\r\n                                        (  266.21   939.26   -56.95     0.46)  ; 3\r\n                                        (  267.28   940.71   -57.05     0.46)  ; 4\r\n                                        (  267.51   943.75   -57.05     0.46)  ; 5\r\n                                        (  267.47   947.92   -58.17     0.46)  ; 6\r\n                                        (  268.46   951.74   -59.13     0.46)  ; 7\r\n                                        (  271.38   955.39   -59.67     0.46)  ; 8\r\n                                        (  272.84   955.15   -60.15     0.46)  ; 9\r\n                                        (  274.23   957.27   -60.15     0.46)  ; 10\r\n                                        (  278.13   958.78   -60.95     0.46)  ; 11\r\n                                        (  280.09   960.43   -60.95     0.46)  ; 12\r\n                                        (  282.63   961.62   -61.55     0.46)  ; 13\r\n                                        (  284.48   963.85   -62.28     0.46)  ; 14\r\n                                        (  287.02   965.04   -60.77     0.46)  ; 15\r\n                                        (  289.69   965.67   -59.65     0.46)  ; 16\r\n                                        (  291.35   966.65   -59.83     0.46)  ; 17\r\n                                        (  295.19   966.36   -58.95     0.46)  ; 18\r\n                                        (  296.84   967.35   -58.95     0.46)  ; 19\r\n                                        (  299.79   966.83   -60.67     0.46)  ; 20\r\n                                        (  299.66   967.41   -62.13     0.46)  ; 21\r\n                                        (  303.54   968.91   -62.13     0.46)  ; 22\r\n                                        (  304.66   972.16   -62.35     0.46)  ; 23\r\n                                        (  306.77   973.26   -62.28     0.46)  ; 24\r\n                                        (  308.63   974.88   -62.28     0.46)  ; 25\r\n                                        (  311.50   976.74   -60.92     0.46)  ; 26\r\n                                        (  313.46   978.39   -59.78     0.46)  ; 27\r\n                                        (  316.19   980.84   -58.45     0.46)  ; 28\r\n                                        (  317.84   981.82   -57.20     0.46)  ; 29\r\n                                        (  320.13   984.14   -56.57     0.46)  ; 30\r\n                                        (  322.80   984.77   -55.57     0.46)  ; 31\r\n                                        (  324.73   984.62   -53.95     0.46)  ; 32\r\n                                        (  325.31   984.16   -52.77     0.46)  ; 33\r\n\r\n                                        (Cross\r\n                                          (Color White)\r\n                                          (Name \"Marker 3\")\r\n                                          (  265.94   940.39   -57.40     0.46)  ; 1\r\n                                          (  268.85   944.06   -57.40     0.46)  ; 2\r\n                                          (  266.89   948.38   -56.08     0.46)  ; 3\r\n                                          (  314.09   979.74   -58.53     0.46)  ; 4\r\n                                          (  314.62   977.48   -61.00     0.46)  ; 5\r\n                                          (  305.03   974.64   -62.28     0.46)  ; 6\r\n                                          (  289.17   967.93   -60.75     0.46)  ; 7\r\n                                          (  290.54   964.07   -61.20     0.46)  ; 8\r\n                                          (  288.66   960.05   -59.25     0.46)  ; 9\r\n                                          (  268.70   954.77   -59.67     0.46)  ; 10\r\n                                          (  270.25   952.15   -59.67     0.46)  ; 11\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  248.94   908.14   -41.52     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                    (  248.41   910.40   -42.45     0.92)  ; 2\r\n                                    (  247.75   913.22   -43.50     0.92)  ; 3\r\n                                    (  247.98   916.27   -44.13     0.92)  ; 4\r\n                                    (  246.10   918.22   -44.78     0.92)  ; 5\r\n                                    (  245.89   921.15   -45.32     0.92)  ; 6\r\n                                    (  245.22   923.98   -46.12     0.92)  ; 7\r\n                                    (  245.22   923.98   -46.15     0.92)  ; 8\r\n                                    (  244.30   927.95   -46.60     0.92)  ; 9\r\n                                    (  243.05   931.24   -46.82     0.92)  ; 10\r\n                                    (  241.05   933.76   -47.30     0.92)  ; 11\r\n                                    (  241.01   937.93   -47.33     0.92)  ; 12\r\n                                    (  239.15   939.88   -48.15     0.92)  ; 13\r\n                                    (  237.46   943.07   -48.72     0.92)  ; 14\r\n                                    (  235.77   946.26   -48.72     0.92)  ; 15\r\n                                    (  234.79   948.40   -49.27     0.92)  ; 16\r\n                                    (  233.99   951.80   -49.40     0.92)  ; 17\r\n                                    (  233.46   954.07   -49.80     0.92)  ; 18\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  247.86   906.68   -41.52     0.92)  ; 1\r\n                                      (  249.35   912.41   -44.13     0.92)  ; 2\r\n                                      (  245.48   916.87   -44.13     0.92)  ; 3\r\n                                      (  243.93   925.48   -47.33     0.92)  ; 4\r\n                                      (  243.53   927.17   -47.33     0.92)  ; 5\r\n                                      (  245.06   928.72   -47.82     0.92)  ; 6\r\n                                      (  240.30   932.98   -45.97     0.92)  ; 7\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  233.65   955.31   -50.70     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                      (  231.20   957.72   -50.70     0.46)  ; 2\r\n                                      (  230.22   959.87   -50.60     0.46)  ; 3\r\n                                      (  228.85   963.73   -50.47     0.46)  ; 4\r\n                                      (  227.92   967.70   -50.97     0.46)  ; 5\r\n                                      (  226.04   969.65   -50.97     0.46)  ; 6\r\n                                      (  225.77   970.78   -51.32     0.46)  ; 7\r\n                                      (  223.78   973.30   -51.35     0.46)  ; 8\r\n                                      (  223.06   974.33   -53.00     0.46)  ; 9\r\n                                      (  221.95   977.05   -54.43     0.46)  ; 10\r\n                                      (  220.21   978.43   -55.30     0.46)  ; 11\r\n                                      (  218.93   979.92   -56.03     0.46)  ; 12\r\n                                      (  218.93   979.92   -56.05     0.46)  ; 13\r\n                                      (  218.53   981.62   -56.55     0.46)  ; 14\r\n                                      (  216.61   981.77   -56.90     0.46)  ; 15\r\n                                      (  215.18   983.83   -57.45     0.46)  ; 16\r\n                                      (  215.81   985.16   -58.50     0.46)  ; 17\r\n                                      (  215.42   986.86   -59.38     0.46)  ; 18\r\n                                      (  216.23   989.45   -59.20     0.46)  ; 19\r\n                                      (  215.26   991.50   -59.92     0.46)  ; 20\r\n                                      (  214.73   993.77   -60.95     0.46)  ; 21\r\n                                      (  215.22   995.68   -61.50     0.46)  ; 22\r\n                                      (  214.56   998.50   -62.45     0.46)  ; 23\r\n                                      (  214.78  1001.54   -63.40     0.46)  ; 24\r\n                                      (  214.78  1001.54   -63.42     0.46)  ; 25\r\n                                      (  214.08  1002.57   -63.88     0.46)  ; 26\r\n                                      (  214.08  1002.57   -62.88     0.46)  ; 27\r\n                                      (  214.57  1004.48   -61.88     0.46)  ; 28\r\n                                      (  214.09  1008.55   -60.72     0.46)  ; 29\r\n                                      (  215.04  1010.56   -62.57     0.46)  ; 30\r\n                                      (  214.06  1012.73   -63.42     0.46)  ; 31\r\n                                      (  214.56  1014.63   -64.02     0.46)  ; 32\r\n                                      (  213.71  1016.23   -64.95     0.46)  ; 33\r\n                                      (  212.60  1018.94   -66.17     0.46)  ; 34\r\n                                      (  211.49  1021.67   -67.07     0.46)  ; 35\r\n                                      (  210.20  1023.15   -68.00     0.46)  ; 36\r\n                                      (  209.04  1024.09   -68.75     0.46)  ; 37\r\n                                      (  209.08  1025.89   -69.92     0.46)  ; 38\r\n                                      (  208.68  1027.59   -71.38     0.46)  ; 39\r\n                                      (  211.24  1028.78   -72.60     0.46)  ; 40\r\n                                      (  211.24  1028.78   -72.63     0.46)  ; 41\r\n                                      (  209.95  1030.27   -74.40     0.46)  ; 42\r\n                                      (  209.86  1032.64   -75.30     0.46)  ; 43\r\n                                      (  209.78  1035.00   -76.85     0.46)  ; 44\r\n                                      (  208.94  1036.60   -78.38     0.46)  ; 45\r\n                                      (  208.94  1036.60   -78.35     0.46)  ; 46\r\n                                      (  208.67  1037.74   -81.07     0.46)  ; 47\r\n                                      (  208.67  1037.74   -81.10     0.46)  ; 48\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  229.27   957.87   -50.60     0.46)  ; 1\r\n                                        (  232.00   954.33   -50.60     0.46)  ; 2\r\n                                        (  228.93   961.36   -51.50     0.46)  ; 3\r\n                                        (  227.95   963.52   -50.47     0.46)  ; 4\r\n                                        (  225.24   973.04   -51.35     0.46)  ; 5\r\n                                        (  225.17   975.41   -54.43     0.46)  ; 6\r\n                                        (  213.93   987.12   -59.20     0.46)  ; 7\r\n                                        (  216.73   991.25   -60.95     0.46)  ; 8\r\n                                        (  216.47   992.39   -60.95     0.46)  ; 9\r\n                                        (  216.21   999.49   -63.88     0.46)  ; 10\r\n                                        (  216.28  1007.27   -64.20     0.46)  ; 11\r\n                                        (  217.66  1003.41   -60.72     0.46)  ; 12\r\n                                        (  212.58  1012.97   -61.30     0.46)  ; 13\r\n                                        (  213.28  1022.09   -63.62     0.46)  ; 14\r\n                                        (  208.81  1021.05   -65.95     0.46)  ; 15\r\n                                        (  211.99  1023.57   -69.92     0.46)  ; 16\r\n                                        (  208.46  1024.55   -68.07     0.46)  ; 17\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    |\r\n                                      (  231.74   955.35   -48.00     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                      (  228.98   957.10   -46.12     0.92)  ; 2\r\n                                      (  228.58   958.79   -45.32     0.92)  ; 3\r\n                                      (  226.90   961.98   -45.03     0.92)  ; 4\r\n                                      (  225.91   964.15   -44.00     0.92)  ; 5\r\n                                      (  225.91   964.15   -44.02     0.92)  ; 6\r\n                                      (  224.35   966.76   -42.90     0.92)  ; 7\r\n                                      (  224.35   966.76   -42.92     0.92)  ; 8\r\n                                      (  222.31   967.48   -42.45     0.92)  ; 9\r\n                                      (  222.08   970.41   -42.45     0.92)  ; 10\r\n                                      (  221.38   971.44   -41.15     0.92)  ; 11\r\n                                      (  221.38   971.44   -41.17     0.92)  ; 12\r\n                                      (  219.37   973.95   -40.38     0.92)  ; 13\r\n                                      (  218.08   975.45   -40.40     0.92)  ; 14\r\n                                      (  218.08   975.45   -40.42     0.92)  ; 15\r\n                                      (  217.28   978.85   -39.72     0.92)  ; 16\r\n                                      (  215.87   980.90   -38.80     0.92)  ; 17\r\n                                      (  213.55   982.74   -38.20     0.92)  ; 18\r\n                                      (  212.26   984.22   -37.25     0.92)  ; 19\r\n                                      (  212.35   987.83   -36.27     0.92)  ; 20\r\n                                      (  211.23   990.57   -37.88     0.92)  ; 21\r\n                                      (  208.97   994.22   -37.88     0.92)  ; 22\r\n                                      (  207.86   996.93   -37.88     0.92)  ; 23\r\n                                      (  205.67   998.22   -36.33     0.92)  ; 24\r\n                                      (  203.45  1003.67   -36.33     0.92)  ; 25\r\n                                      (  203.02  1003.56   -36.33     0.92)  ; 26\r\n                                      (  202.17  1005.16   -36.47     0.92)  ; 27\r\n                                      (  201.69  1009.23   -36.52     0.92)  ; 28\r\n                                      (  199.86  1012.98   -37.85     0.92)  ; 29\r\n                                      (  197.15  1016.52   -39.28     0.92)  ; 30\r\n                                      (  196.35  1019.91   -39.92     0.92)  ; 31\r\n                                      (  196.22  1020.49   -39.92     0.92)  ; 32\r\n                                      (  195.95  1021.61   -40.80     0.92)  ; 33\r\n                                      (  193.50  1024.04   -41.73     0.92)  ; 34\r\n                                      (  192.98  1026.29   -40.50     0.92)  ; 35\r\n                                      (  191.87  1029.01   -39.85     0.92)  ; 36\r\n                                      (  189.54  1030.85   -39.85     0.92)  ; 37\r\n                                      (  189.64  1034.46   -40.48     0.92)  ; 38\r\n                                      (  187.95  1037.65   -39.63     0.92)  ; 39\r\n                                      (  186.71  1040.94   -40.55     0.92)  ; 40\r\n                                      (  184.76  1045.27   -40.97     0.92)  ; 41\r\n                                      (  183.02  1046.65   -41.22     0.92)  ; 42\r\n                                      (  183.07  1048.46   -42.05     0.92)  ; 43\r\n                                      (  181.95  1051.18   -42.45     0.92)  ; 44\r\n                                      (  181.16  1054.57   -42.60     0.92)  ; 45\r\n                                      (  179.30  1056.53   -42.60     0.92)  ; 46\r\n                                      (  178.05  1059.82   -41.47     0.92)  ; 47\r\n                                      (  178.10  1061.62   -41.10     0.92)  ; 48\r\n                                      (  176.15  1065.93   -40.48     0.92)  ; 49\r\n                                      (  176.24  1069.54   -39.97     0.92)  ; 50\r\n                                      (  172.45  1071.64   -40.32     0.92)  ; 51\r\n                                      (  171.21  1074.94   -41.60     0.92)  ; 52\r\n                                      (  168.70  1075.54   -42.35     0.92)  ; 53\r\n                                      (  168.36  1079.04   -43.22     0.92)  ; 54\r\n                                      (  168.85  1080.95   -43.72     0.92)  ; 55\r\n                                      (  164.17  1082.84   -43.97     0.92)  ; 56\r\n                                      (  164.17  1082.84   -44.00     0.92)  ; 57\r\n                                      (  162.17  1085.35   -43.10     0.92)  ; 58\r\n                                      (  156.46  1087.59   -43.35     0.92)  ; 59\r\n                                      (  156.46  1087.59   -43.47     0.92)  ; 60\r\n                                      (  153.26  1089.24   -44.63     0.46)  ; 61\r\n                                      (  149.46  1091.34   -45.27     0.46)  ; 62\r\n                                      (  149.46  1091.34   -45.40     0.46)  ; 63\r\n                                      (  146.20  1091.17   -48.22     0.46)  ; 64\r\n                                      (  146.20  1091.17   -48.27     0.46)  ; 65\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  225.00   957.95   -45.32     0.92)  ; 1\r\n                                        (  225.36   960.43   -45.32     0.92)  ; 2\r\n                                        (  230.28   961.58   -45.32     0.92)  ; 3\r\n                                        (  228.23   962.30   -43.78     0.92)  ; 4\r\n                                        (  227.75   966.36   -42.28     0.92)  ; 5\r\n                                        (  223.99   964.30   -45.88     0.92)  ; 6\r\n                                        (  221.86   973.34   -40.38     0.92)  ; 7\r\n                                        (  223.56   970.16   -38.85     0.92)  ; 8\r\n                                        (  219.60   977.00   -38.55     0.92)  ; 9\r\n                                        (  214.66   980.01   -37.80     0.92)  ; 10\r\n                                        (  211.44   981.66   -37.17     0.92)  ; 11\r\n                                        (  210.38   986.18   -37.17     0.92)  ; 12\r\n                                        (  215.03   988.46   -35.70     0.92)  ; 13\r\n                                        (  211.80   984.12   -36.72     0.92)  ; 14\r\n                                        (  211.00   987.52   -33.67     0.92)  ; 15\r\n                                        (  202.65  1001.09   -36.33     0.92)  ; 16\r\n                                        (  206.09  1002.50   -38.60     0.92)  ; 17\r\n                                        (  201.04  1001.91   -38.60     0.92)  ; 18\r\n                                        (  203.37  1006.03   -38.60     0.92)  ; 19\r\n                                        (  201.01  1006.08   -37.32     0.92)  ; 20\r\n                                        (  203.07  1011.34   -36.30     0.92)  ; 21\r\n                                        (  195.62  1014.97   -39.28     0.92)  ; 22\r\n                                        (  195.54  1017.34   -39.28     0.92)  ; 23\r\n                                        (  200.00  1018.38   -39.28     0.92)  ; 24\r\n                                        (  199.52  1016.47   -39.28     0.92)  ; 25\r\n                                        (  201.38  1014.52   -39.28     0.92)  ; 26\r\n                                        (  193.14  1021.55   -41.73     0.92)  ; 27\r\n                                        (  195.65  1026.92   -41.73     0.92)  ; 28\r\n                                        (  191.36  1027.11   -37.28     0.92)  ; 29\r\n                                        (  191.45  1024.74   -36.25     0.92)  ; 30\r\n                                        (  193.65  1029.43   -41.28     0.92)  ; 31\r\n                                        (  190.00  1036.94   -39.63     0.92)  ; 32\r\n                                        (  184.53  1042.22   -39.72     0.92)  ; 33\r\n                                        (  190.41  1035.24   -37.90     0.92)  ; 34\r\n                                        (  182.26  1045.87   -42.45     0.92)  ; 35\r\n                                        (  179.73  1050.65   -42.45     0.92)  ; 36\r\n                                        (  177.95  1056.21   -42.60     0.92)  ; 37\r\n                                        (  179.40  1066.10   -41.47     0.92)  ; 38\r\n                                        (  175.60  1062.22   -40.40     0.92)  ; 39\r\n                                        (  175.55  1060.42   -41.05     0.92)  ; 40\r\n                                        (  174.49  1064.95   -40.13     0.92)  ; 41\r\n                                        (  178.02  1064.00   -40.13     0.92)  ; 42\r\n                                        (  171.06  1069.52   -40.32     0.92)  ; 43\r\n                                        (  173.66  1072.52   -40.32     0.92)  ; 44\r\n                                        (  170.21  1071.12   -42.13     0.92)  ; 45\r\n                                        (  172.10  1075.15   -42.22     0.92)  ; 46\r\n                                        (  165.32  1075.94   -41.35     0.92)  ; 47\r\n                                        (  163.76  1078.56   -41.38     0.92)  ; 48\r\n                                        (  167.87  1083.11   -45.03     0.92)  ; 49\r\n                                        (  171.72  1082.81   -45.05     0.92)  ; 50\r\n                                        (  160.21  1083.69   -41.67     0.92)  ; 51\r\n                                        (  162.39  1082.42   -41.75     0.92)  ; 52\r\n                                        (  166.63  1086.41   -41.75     0.92)  ; 53\r\n                                        (  159.51  1090.70   -44.70     0.92)  ; 54\r\n                                        (  164.90  1087.78   -44.72     0.92)  ; 55\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  )  ;  End of split\r\n                                |\r\n                                  (  235.72   866.34   -28.50     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                  (  234.79   870.30   -29.10     1.38)  ; 2\r\n                                  (  233.09   873.50   -29.10     1.38)  ; 3\r\n                                  (  231.59   877.92   -29.38     1.38)  ; 4\r\n                                  (  230.34   881.21   -28.50     1.38)  ; 5\r\n                                  (  230.58   884.24   -27.97     1.38)  ; 6\r\n                                  (  230.76   885.48   -27.97     1.38)  ; 7\r\n                                  (  228.76   888.00   -27.97     1.38)  ; 8\r\n                                  (  227.02   889.39   -27.22     1.38)  ; 9\r\n                                  (  225.91   892.10   -26.52     1.38)  ; 10\r\n                                  (  224.80   894.84   -25.50     1.38)  ; 11\r\n                                  (  222.34   897.24   -25.20     1.38)  ; 12\r\n                                  (  219.95   901.46   -24.62     1.38)  ; 13\r\n                                  (  218.84   904.19   -26.85     1.38)  ; 14\r\n                                  (  216.88   908.50   -27.27     1.38)  ; 15\r\n                                  (  212.69   912.30   -27.57     1.38)  ; 16\r\n                                  (  213.51   914.88   -28.15     1.38)  ; 17\r\n                                  (  213.43   917.25   -27.97     1.38)  ; 18\r\n                                  (  213.78   919.72   -28.65     1.38)  ; 19\r\n                                  (  213.78   919.72   -28.67     1.38)  ; 20\r\n                                  (  212.23   922.34   -29.08     1.38)  ; 21\r\n                                  (  210.73   926.76   -29.90     1.38)  ; 22\r\n                                  (  211.66   928.78   -30.35     1.38)  ; 23\r\n                                  (  211.66   928.78   -30.38     1.38)  ; 24\r\n                                  (  211.31   932.28   -30.95     1.38)  ; 25\r\n                                  (  210.78   934.54   -31.58     1.38)  ; 26\r\n                                  (  210.78   934.54   -31.60     1.38)  ; 27\r\n                                  (  209.36   936.61   -32.25     1.38)  ; 28\r\n                                  (  207.04   938.44   -32.45     1.38)  ; 29\r\n                                  (  207.04   938.44   -32.47     1.38)  ; 30\r\n                                  (  207.28   941.49   -33.17     1.38)  ; 31\r\n                                  (  206.75   943.74   -34.32     1.38)  ; 32\r\n                                  (  206.75   943.74   -34.35     1.38)  ; 33\r\n                                  (  206.79   945.55   -33.95     1.38)  ; 34\r\n                                  (  207.78   949.37   -34.90     1.38)  ; 35\r\n                                  (  208.09   950.04   -35.85     1.38)  ; 36\r\n                                  (  206.22   951.98   -37.08     1.38)  ; 37\r\n                                  (  204.04   953.27   -37.63     1.38)  ; 38\r\n                                  (  202.80   956.56   -38.42     1.38)  ; 39\r\n                                  (  202.27   958.82   -39.17     1.38)  ; 40\r\n                                  (  199.50   960.56   -40.07     1.38)  ; 41\r\n                                  (  199.29   963.49   -41.03     1.38)  ; 42\r\n                                  (  200.10   966.08   -41.90     1.38)  ; 43\r\n                                  (  199.56   968.34   -42.33     1.38)  ; 44\r\n                                  (  196.58   973.02   -42.57     1.38)  ; 45\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  198.99   962.84   -87.00     0.46)  ; 1\r\n                                    (  206.64   950.31   -79.55     0.46)  ; 2\r\n                                    (  206.07   950.76   -79.55     0.46)  ; 3\r\n                                  )  ;  End of markers\r\n\r\n                                  (Cross\r\n                                    (Color White)\r\n                                    (Name \"Marker 3\")\r\n                                    (  197.51   969.05   -42.57     1.38)  ; 1\r\n                                    (  196.87   961.73   -41.03     1.38)  ; 2\r\n                                    (  199.77   959.44   -38.42     1.38)  ; 3\r\n                                    (  201.58   955.68   -38.42     1.38)  ; 4\r\n                                    (  203.75   942.45   -31.15     1.38)  ; 5\r\n                                    (  213.68   932.23   -31.15     1.38)  ; 6\r\n                                    (  209.75   928.93   -31.00     1.38)  ; 7\r\n                                    (  209.56   927.69   -31.00     1.38)  ; 8\r\n                                    (  214.64   924.10   -30.97     1.38)  ; 9\r\n                                    (  209.86   922.38   -27.00     1.38)  ; 10\r\n                                    (  211.86   919.88   -27.00     1.38)  ; 11\r\n                                    (  220.17   898.52   -24.62     1.38)  ; 12\r\n                                    (  223.29   899.26   -25.27     1.38)  ; 13\r\n                                    (  224.65   889.43   -27.22     1.38)  ; 14\r\n                                    (  234.78   864.32   -29.38     1.38)  ; 15\r\n                                    (  233.98   867.72   -29.38     1.38)  ; 16\r\n                                    (  231.18   873.64   -29.38     1.38)  ; 17\r\n                                    (  231.09   876.00   -29.38     1.38)  ; 18\r\n                                    (  233.86   874.27   -29.58     1.38)  ; 19\r\n                                    (  213.22   910.03   -26.40     1.38)  ; 20\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  196.20   974.70   -43.95     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                    (  194.32   976.64   -44.70     0.92)  ; 2\r\n                                    (  192.85   976.89   -45.52     0.92)  ; 3\r\n                                    (  191.42   978.96   -47.02     0.92)  ; 4\r\n                                    (  190.31   981.67   -47.85     0.92)  ; 5\r\n                                    (  189.20   984.41   -48.90     0.92)  ; 6\r\n                                    (  187.02   985.68   -49.32     0.92)  ; 7\r\n                                    (  184.26   987.43   -49.32     0.92)  ; 8\r\n                                    (  184.26   987.43   -49.35     0.92)  ; 9\r\n                                    (  183.41   989.01   -50.42     0.92)  ; 10\r\n                                    (  183.01   990.72   -50.42     0.92)  ; 11\r\n                                    (  182.75   991.84   -51.35     0.92)  ; 12\r\n                                    (  181.32   993.91   -52.13     0.92)  ; 13\r\n                                    (  181.42   997.50   -52.77     0.92)  ; 14\r\n                                    (  181.42   997.50   -52.80     0.92)  ; 15\r\n                                    (  180.72   998.54   -53.30     0.92)  ; 16\r\n                                    (  179.61  1001.26   -53.58     0.92)  ; 17\r\n                                    (  179.79  1002.50   -54.00     0.92)  ; 18\r\n                                    (  179.26  1004.77   -54.40     0.92)  ; 19\r\n                                    (  181.72  1008.32   -54.90     0.92)  ; 20\r\n                                    (  180.92  1011.72   -54.35     0.92)  ; 21\r\n                                    (  181.14  1014.77   -54.35     0.92)  ; 22\r\n                                    (  177.85  1018.77   -54.90     0.92)  ; 23\r\n                                    (  177.46  1020.46   -55.88     0.92)  ; 24\r\n                                    (  177.46  1020.46   -55.90     0.92)  ; 25\r\n                                    (  176.34  1023.19   -57.00     0.92)  ; 26\r\n                                    (  176.34  1023.19   -57.02     0.92)  ; 27\r\n                                    (  175.23  1025.91   -57.95     0.92)  ; 28\r\n                                    (  173.95  1027.41   -59.00     0.92)  ; 29\r\n                                    (  172.53  1029.46   -60.00     0.92)  ; 30\r\n                                    (  172.53  1029.46   -60.03     0.92)  ; 31\r\n                                    (  171.60  1033.42   -60.70     0.92)  ; 32\r\n                                    (  169.13  1035.83   -61.60     0.92)  ; 33\r\n                                    (  166.82  1037.69   -62.63     0.92)  ; 34\r\n                                    (  164.95  1039.62   -63.35     0.92)  ; 35\r\n                                    (  163.40  1042.25   -64.18     0.92)  ; 36\r\n                                    (  160.82  1045.23   -64.55     0.92)  ; 37\r\n                                    (  159.44  1049.09   -65.43     0.92)  ; 38\r\n                                    (  158.91  1051.34   -65.13     0.92)  ; 39\r\n                                    (  158.38  1053.61   -65.95     0.92)  ; 40\r\n                                    (  157.09  1055.10   -67.07     0.92)  ; 41\r\n                                    (  156.56  1057.37   -68.28     0.92)  ; 42\r\n                                    (  153.52  1060.24   -68.72     0.92)  ; 43\r\n                                    (  153.57  1062.04   -69.47     0.92)  ; 44\r\n                                    (  153.94  1064.53   -70.03     0.92)  ; 45\r\n                                    (  153.42  1066.78   -71.05     0.92)  ; 46\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  151.83  1063.43   -70.03     0.92)  ; 1\r\n                                      (  151.30  1065.69   -70.03     0.92)  ; 2\r\n                                      (  167.77  1039.70   -61.63     0.92)  ; 3\r\n                                      (  165.50  1043.34   -64.18     0.92)  ; 4\r\n                                      (  157.92  1047.53   -65.43     0.92)  ; 5\r\n                                      (  157.12  1050.93   -66.45     0.92)  ; 6\r\n                                      (  159.27  1053.83   -64.13     0.92)  ; 7\r\n                                      (  155.35  1056.49   -68.28     0.92)  ; 8\r\n                                      (  158.66  1058.46   -69.22     0.92)  ; 9\r\n                                      (  178.35  1020.67   -57.02     0.92)  ; 10\r\n                                      (  172.42  1025.86   -60.03     0.92)  ; 11\r\n                                      (  170.38  1032.54   -61.63     0.92)  ; 12\r\n                                      (  176.28  1031.54   -61.63     0.92)  ; 13\r\n                                      (  181.37  1017.80   -53.72     0.92)  ; 14\r\n                                      (  177.67  1017.53   -53.72     0.92)  ; 15\r\n                                      (  182.47   987.01   -49.35     0.92)  ; 16\r\n                                      (  185.23   985.26   -49.35     0.92)  ; 17\r\n                                      (  187.51   987.59   -49.00     0.92)  ; 18\r\n                                      (  182.26   989.94   -50.42     0.92)  ; 19\r\n                                      (  183.43   994.99   -52.80     0.92)  ; 20\r\n                                      (  178.73  1007.03   -54.90     0.92)  ; 21\r\n                                      (  179.66  1009.04   -52.80     0.92)  ; 22\r\n                                      (  181.72  1008.32   -50.97     0.92)  ; 23\r\n                                      (  178.52  1015.94   -50.97     0.92)  ; 24\r\n                                      (  190.61   976.38   -47.02     0.92)  ; 25\r\n                                      (  194.40   974.27   -44.70     0.92)  ; 26\r\n                                      (  195.53   977.53   -43.58     0.92)  ; 27\r\n                                      (  195.54   977.55   -39.05     0.92)  ; 28\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  151.53  1068.73   -71.82     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-1\r\n                                      (  150.51  1069.09   -72.57     0.46)  ; 2\r\n                                      (  149.53  1071.24   -73.28     0.46)  ; 3\r\n                                      (  148.69  1072.85   -74.32     0.46)  ; 4\r\n                                      (  146.77  1072.98   -75.57     0.46)  ; 5\r\n                                      (  145.34  1075.05   -76.92     0.46)  ; 6\r\n                                      (  144.33  1075.41   -78.63     0.46)  ; 7\r\n                                      (  142.01  1077.25   -79.10     0.46)  ; 8\r\n                                      (  140.40  1078.07   -81.25     0.46)  ; 9\r\n                                      (  140.40  1078.07   -81.30     0.46)  ; 10\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  149.49  1069.45   -72.57     0.46)  ; 1\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    |\r\n                                      (  153.64  1069.82   -71.05     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-2\r\n                                      (  152.84  1073.22   -71.05     0.46)  ; 2\r\n                                      (  152.76  1075.58   -71.85     0.46)  ; 3\r\n                                      (  154.86  1076.67   -73.25     0.46)  ; 4\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  197.55   975.03   -41.30     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                    (  198.17   976.38   -40.55     0.92)  ; 2\r\n                                    (  196.18   978.88   -39.60     0.92)  ; 3\r\n                                    (  195.95   981.83   -39.05     0.92)  ; 4\r\n                                    (  194.98   983.99   -38.42     0.92)  ; 5\r\n                                    (  193.69   985.47   -38.42     0.92)  ; 6\r\n                                    (  193.55   986.04   -37.85     0.92)  ; 7\r\n                                    (  193.74   987.27   -37.15     0.92)  ; 8\r\n                                    (  193.74   987.27   -37.17     0.92)  ; 9\r\n                                    (  194.23   989.18   -36.40     0.92)  ; 10\r\n                                    (  194.23   989.18   -36.42     0.92)  ; 11\r\n                                    (  194.59   991.66   -35.45     0.92)  ; 12\r\n                                    (  194.77   992.90   -35.57     0.92)  ; 13\r\n                                    (  194.69   995.27   -34.02     0.92)  ; 14\r\n                                    (  193.66   995.61   -32.40     0.92)  ; 15\r\n                                    (  191.35   997.46   -31.05     0.92)  ; 16\r\n                                    (  188.71   998.64   -31.22     0.92)  ; 17\r\n                                    (  188.31  1000.34   -32.17     0.92)  ; 18\r\n                                    (  188.67  1002.81   -33.25     0.92)  ; 19\r\n                                    (  188.67  1002.81   -33.27     0.92)  ; 20\r\n                                    (  189.58  1003.02   -34.88     0.92)  ; 21\r\n                                    (  189.58  1003.02   -34.90     0.92)  ; 22\r\n                                    (  190.96  1005.14   -35.75     0.92)  ; 23\r\n                                    (  190.96  1005.14   -35.77     0.92)  ; 24\r\n                                    (  192.53  1008.49   -35.75     0.92)  ; 25\r\n                                    (  192.75  1011.53   -36.20     0.92)  ; 26\r\n                                    (  193.12  1014.00   -35.53     0.92)  ; 27\r\n                                    (  194.19  1015.46   -34.25     0.92)  ; 28\r\n                                    (  193.99  1018.39   -33.00     0.92)  ; 29\r\n                                    (  194.21  1021.42   -32.30     0.92)  ; 30\r\n                                    (  194.21  1021.42   -32.32     0.92)  ; 31\r\n                                    (  195.73  1022.98   -31.67     0.92)  ; 32\r\n                                    (  196.98  1025.66   -30.83     0.92)  ; 33\r\n                                    (  196.98  1025.66   -30.85     0.92)  ; 34\r\n                                    (  198.11  1028.91   -30.25     0.92)  ; 35\r\n                                    (  197.70  1030.58   -28.33     0.92)  ; 36\r\n                                    (  198.38  1033.73   -26.88     0.92)  ; 37\r\n                                    (  198.42  1035.53   -26.88     0.92)  ; 38\r\n                                    (  199.50  1036.98   -25.97     0.92)  ; 39\r\n                                    (  200.13  1038.32   -24.17     0.92)  ; 40\r\n                                    (  201.46  1038.63   -23.47     0.92)  ; 41\r\n                                    (  201.75  1043.47   -23.47     0.92)  ; 42\r\n                                    (  202.56  1046.05   -23.47     0.92)  ; 43\r\n                                    (  204.84  1048.38   -22.88     0.92)  ; 44\r\n                                    (  206.04  1049.26   -23.63     0.92)  ; 45\r\n                                    (  206.04  1049.26   -23.60     0.92)  ; 46\r\n                                    (  206.91  1053.64   -22.97     0.92)  ; 47\r\n                                    (  206.56  1057.14   -21.63     0.92)  ; 48\r\n                                    (  208.65  1058.23   -20.20     0.92)  ; 49\r\n                                    (  209.86  1059.11   -18.30     0.92)  ; 50\r\n                                    (  212.27  1060.88   -16.73     0.92)  ; 51\r\n                                    (  212.91  1062.21   -15.27     0.92)  ; 52\r\n                                    (  213.27  1064.69   -14.70     0.92)  ; 53\r\n                                    (  214.25  1068.49   -14.27     0.92)  ; 54\r\n                                    (  216.53  1070.83   -14.27     0.92)  ; 55\r\n                                    (  219.40  1072.69   -14.27     0.92)  ; 56\r\n                                    (  220.39  1076.51   -14.25     0.92)  ; 57\r\n                                    (  220.39  1076.51   -14.32     0.92)  ; 58\r\n                                    (  221.19  1079.08   -15.80     0.92)  ; 59\r\n                                    (  221.19  1079.08   -15.88     0.92)  ; 60\r\n                                    (  223.48  1081.42   -16.67     0.92)  ; 61\r\n                                    (  226.84  1085.18   -16.90     0.92)  ; 62\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  216.10  1064.78     8.55     0.46)  ; 1\r\n                                    )  ;  End of markers\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  200.10   976.22   -39.05     0.92)  ; 1\r\n                                      (  199.25   977.82   -39.05     0.92)  ; 2\r\n                                      (  197.16   982.70   -38.42     0.92)  ; 3\r\n                                      (  194.03   981.98   -38.42     0.92)  ; 4\r\n                                      (  196.18   984.87   -38.42     0.92)  ; 5\r\n                                      (  192.39   986.96   -38.42     0.92)  ; 6\r\n                                      (  196.25   992.64   -35.47     0.92)  ; 7\r\n                                      (  192.37   991.13   -32.72     0.92)  ; 8\r\n                                      (  197.50   995.33   -33.97     0.92)  ; 9\r\n                                      (  196.52   997.49   -32.67     0.92)  ; 10\r\n                                      (  193.05  1000.25   -31.05     0.92)  ; 11\r\n                                      (  188.22   996.74   -31.05     0.92)  ; 12\r\n                                      (  190.42  1001.43   -30.40     0.92)  ; 13\r\n                                      (  187.83  1004.41   -35.77     0.92)  ; 14\r\n                                      (  193.25  1007.46   -35.88     0.92)  ; 15\r\n                                      (  191.55  1010.65   -35.00     0.92)  ; 16\r\n                                      (  192.64  1018.08   -32.32     0.92)  ; 17\r\n                                      (  194.68  1011.38   -33.80     0.92)  ; 18\r\n                                      (  196.89  1022.05   -29.38     0.92)  ; 19\r\n                                      (  196.61  1023.17   -26.88     0.92)  ; 20\r\n                                      (  196.72  1032.74   -26.88     0.92)  ; 21\r\n                                      (  201.01  1032.55   -24.17     0.92)  ; 22\r\n                                      (  200.36  1041.36   -23.47     0.92)  ; 23\r\n                                      (  196.27  1026.68   -30.25     0.92)  ; 24\r\n                                      (  206.03  1043.29   -23.47     0.92)  ; 25\r\n                                      (  207.65  1048.44   -23.47     0.92)  ; 26\r\n                                      (  203.01  1052.13   -26.22     0.92)  ; 27\r\n                                      (  204.54  1053.68   -22.97     0.92)  ; 28\r\n                                      (  204.75  1050.76   -22.30     0.92)  ; 29\r\n                                      (  207.11  1050.71   -22.30     0.92)  ; 30\r\n                                      (  208.73  1055.87   -20.35     0.92)  ; 31\r\n                                      (  209.40  1053.03   -18.25     0.92)  ; 32\r\n                                      (  225.70  1075.96   -17.27     0.92)  ; 33\r\n                                      (  226.43  1080.91   -17.27     0.92)  ; 34\r\n                                      (  219.46  1080.47   -17.32     0.92)  ; 35\r\n                                      (  218.42  1074.86   -14.35     0.92)  ; 36\r\n                                      (  219.05  1076.20   -14.35     0.92)  ; 37\r\n                                      (  217.19  1078.14   -14.32     0.92)  ; 38\r\n                                      (  205.99  1063.58   -17.70     0.92)  ; 39\r\n                                      (  210.00  1064.53   -14.70     0.92)  ; 40\r\n                                      (  221.37  1074.35   -14.35     0.92)  ; 41\r\n                                      (  211.85  1066.74   -14.70     0.92)  ; 42\r\n                                      (  217.36  1063.26   -14.27     0.92)  ; 43\r\n                                      (  218.22  1067.64   -14.27     0.92)  ; 44\r\n                                      (  212.08  1069.79   -14.27     0.92)  ; 45\r\n                                      (  214.04  1071.44   -14.27     0.92)  ; 46\r\n                                      (  221.49  1067.81   -14.27     0.92)  ; 47\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  224.88  1089.51   -16.90     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2-1\r\n                                      (  222.88  1092.01   -18.25     0.92)  ; 2\r\n                                      (  219.53  1094.22   -20.97     0.92)  ; 3\r\n                                      (  219.53  1094.22   -21.02     0.92)  ; 4\r\n                                       Normal\r\n                                    |\r\n                                      (  227.54  1084.15   -16.90     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2-2\r\n                                      (  229.65  1085.25   -17.82     0.92)  ; 2\r\n                                      (  229.65  1085.25   -17.85     0.92)  ; 3\r\n                                      (  230.71  1086.70   -16.47     0.92)  ; 4\r\n                                      (  230.71  1086.70   -16.50     0.92)  ; 5\r\n                                      (  233.09  1086.65   -14.88     0.92)  ; 6\r\n                                      (  235.19  1087.74   -12.83     0.92)  ; 7\r\n                                      (  236.26  1089.18   -11.27     0.92)  ; 8\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  230.46  1087.82   -16.50     0.92)  ; 1\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  )  ;  End of split\r\n                                )  ;  End of split\r\n                              |\r\n                                (  235.39   662.78   -32.52     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                (  236.45   664.22   -31.20     1.38)  ; 2\r\n                                (  237.14   667.36   -30.45     1.38)  ; 3\r\n                                (  237.82   670.52   -29.85     1.38)  ; 4\r\n                                (  238.04   673.55   -29.25     1.38)  ; 5\r\n                                (  238.67   674.90   -27.88     1.38)  ; 6\r\n                                (  237.83   676.49   -26.70     1.38)  ; 7\r\n                                (  237.03   679.88   -26.00     1.38)  ; 8\r\n                                (  237.58   683.60   -26.92     1.38)  ; 9\r\n                                (  238.51   685.60   -26.65     1.38)  ; 10\r\n                                (  238.49   689.78   -26.65     1.38)  ; 11\r\n                                (  239.48   693.59   -27.65     1.38)  ; 12\r\n                                (  239.57   697.20   -27.65     1.38)  ; 13\r\n                                (  239.09   701.26   -27.65     1.38)  ; 14\r\n                                (  239.27   702.50   -27.65     1.38)  ; 15\r\n                                (  239.05   705.44   -27.65     1.38)  ; 16\r\n                                (  236.74   707.28   -27.65     1.38)  ; 17\r\n                                (  236.08   710.11   -27.80     1.38)  ; 18\r\n                                (  235.98   712.48   -27.15     1.38)  ; 19\r\n                                (  237.38   714.60   -26.72     1.38)  ; 20\r\n                                (  238.18   717.18   -26.30     1.38)  ; 21\r\n                                (  237.25   721.14   -26.25     1.38)  ; 22\r\n                                (  236.05   726.23   -26.25     1.38)  ; 23\r\n                                (  236.29   729.27   -27.25     1.38)  ; 24\r\n                                (  236.97   732.42   -27.90     1.38)  ; 25\r\n                                (  237.34   734.89   -27.90     1.38)  ; 26\r\n                                (  237.56   737.93   -28.17     1.38)  ; 27\r\n                                (  238.82   740.62   -26.80     1.38)  ; 28\r\n                                (  238.82   740.62   -26.82     1.38)  ; 29\r\n                                (  238.47   744.12   -26.38     1.38)  ; 30\r\n                                (  238.39   746.48   -26.38     1.38)  ; 31\r\n                                (  238.39   746.48   -26.35     1.38)  ; 32\r\n                                (  239.73   746.79   -26.30     1.38)  ; 33\r\n                                (  239.58   747.37   -26.30     1.38)  ; 34\r\n                                (  237.32   751.02   -25.63     1.38)  ; 35\r\n                                (  236.79   753.27   -25.42     1.38)  ; 36\r\n                                (  237.34   756.99   -25.42     1.38)  ; 37\r\n                                (  238.33   760.81   -25.42     1.38)  ; 38\r\n                                (  239.00   763.95   -26.00     1.38)  ; 39\r\n                                (  238.07   767.91   -26.00     1.38)  ; 40\r\n                                (  238.07   767.91   -26.02     1.38)  ; 41\r\n                                (  236.83   771.20   -26.02     1.38)  ; 42\r\n                                (  236.93   774.80   -26.70     1.38)  ; 43\r\n                                (  236.93   774.80   -26.72     1.38)  ; 44\r\n                                (  236.13   778.20   -27.20     1.38)  ; 45\r\n                                (  236.68   781.91   -27.20     1.38)  ; 46\r\n                                (  236.23   781.81   -27.20     1.38)  ; 47\r\n                                (  238.56   785.94   -27.20     1.38)  ; 48\r\n                                (  237.92   788.65   -25.77     1.38)  ; 49\r\n                                (  238.29   791.14   -24.72     1.38)  ; 50\r\n                                (  237.80   795.20   -24.20     1.38)  ; 51\r\n                                (  237.80   795.20   -24.17     1.38)  ; 52\r\n                                (  237.14   798.03   -23.42     1.38)  ; 53\r\n                                (  237.63   799.93   -22.47     1.38)  ; 54\r\n                                (  238.00   802.41   -21.32     1.38)  ; 55\r\n                                (  237.84   807.14   -20.90     1.38)  ; 56\r\n                                (  238.07   810.19   -21.95     1.38)  ; 57\r\n                                (  239.63   813.54   -21.95     1.38)  ; 58\r\n                                (  239.15   817.61   -22.45     1.38)  ; 59\r\n                                (  239.25   821.22   -23.05     1.38)  ; 60\r\n                                (  239.47   824.25   -22.12     1.38)  ; 61\r\n                                (  239.26   827.18   -21.30     1.38)  ; 62\r\n                                (  240.07   829.77   -20.97     1.38)  ; 63\r\n                                (  242.17   830.86   -22.38     1.38)  ; 64\r\n                                (  241.95   833.78   -21.67     1.38)  ; 65\r\n                                (  242.76   836.37   -21.60     1.38)  ; 66\r\n                                (  242.81   838.17   -20.20     1.38)  ; 67\r\n                                (  244.38   841.52   -18.95     1.38)  ; 68\r\n                                (  244.38   841.52   -18.97     1.38)  ; 69\r\n                                (  245.01   842.86   -18.05     1.38)  ; 70\r\n                                (  244.35   845.69   -18.17     1.38)  ; 71\r\n                                (  244.35   845.69   -18.15     1.38)  ; 72\r\n                                (  244.12   848.64   -16.63     1.38)  ; 73\r\n                                (  243.20   852.59   -16.05     1.38)  ; 74\r\n\r\n                                (Cross\r\n                                  (Color RGB (128, 255, 128))\r\n                                  (Name \"Marker 3\")\r\n                                  (  237.76   662.73   -29.85     1.38)  ; 1\r\n                                  (  239.72   664.39   -29.85     1.38)  ; 2\r\n                                  (  240.81   671.82   -28.63     1.38)  ; 3\r\n                                  (  234.36   679.25   -26.92     1.38)  ; 4\r\n                                  (  239.13   680.97   -26.92     1.38)  ; 5\r\n                                  (  235.92   682.61   -26.92     1.38)  ; 6\r\n                                  (  235.97   684.41   -26.92     1.38)  ; 7\r\n                                  (  237.57   699.72   -27.65     1.38)  ; 8\r\n                                  (  237.97   698.01   -27.65     1.38)  ; 9\r\n                                  (  241.09   698.75   -28.00     1.38)  ; 10\r\n                                  (  240.86   695.71   -28.00     1.38)  ; 11\r\n                                  (  239.37   706.11   -28.00     1.38)  ; 12\r\n                                  (  236.65   693.53   -29.80     1.38)  ; 13\r\n                                  (  240.14   690.76   -29.80     1.38)  ; 14\r\n                                  (  238.17   711.20   -28.75     1.38)  ; 15\r\n                                  (  238.57   709.50   -28.75     1.38)  ; 16\r\n                                  (  240.16   718.84   -27.25     1.38)  ; 17\r\n                                  (  239.02   715.58   -27.25     1.38)  ; 18\r\n                                  (  238.70   725.05   -28.70     1.38)  ; 19\r\n                                  (  237.64   729.59   -28.70     1.38)  ; 20\r\n                                  (  234.99   724.79   -27.08     1.38)  ; 21\r\n                                  (  235.04   726.59   -27.08     1.38)  ; 22\r\n                                  (  235.44   730.87   -27.08     1.38)  ; 23\r\n                                  (  235.96   738.75   -27.08     1.38)  ; 24\r\n                                  (  236.26   733.45   -25.50     1.38)  ; 25\r\n                                  (  239.53   739.58   -25.50     1.38)  ; 26\r\n                                  (  240.15   740.93   -24.72     1.38)  ; 27\r\n                                  (  236.99   744.37   -28.40     1.38)  ; 28\r\n                                  (  241.04   751.29   -25.42     1.38)  ; 29\r\n                                  (  240.19   758.85   -24.90     1.38)  ; 30\r\n                                  (  240.11   761.23   -23.88     1.38)  ; 31\r\n                                  (  237.07   758.11   -22.02     1.38)  ; 32\r\n                                  (  237.85   748.75   -21.92     1.38)  ; 33\r\n                                  (  238.58   753.69   -21.95     1.38)  ; 34\r\n                                  (  235.89   769.19   -26.05     1.38)  ; 35\r\n                                  (  236.24   765.69   -26.05     1.38)  ; 36\r\n                                  (  239.99   767.76   -26.07     1.38)  ; 37\r\n                                  (  237.82   775.01   -28.90     1.38)  ; 38\r\n                                  (  238.27   775.12   -27.20     1.38)  ; 39\r\n                                  (  235.97   792.98   -24.17     1.38)  ; 40\r\n                                  (  240.94   795.94   -24.17     1.38)  ; 41\r\n                                  (  235.35   797.61   -22.65     1.38)  ; 42\r\n                                  (  235.45   801.22   -20.90     1.38)  ; 43\r\n                                  (  236.57   804.47   -20.90     1.38)  ; 44\r\n                                  (  235.82   809.66   -22.55     1.38)  ; 45\r\n                                  (  236.54   808.63   -20.20     1.38)  ; 46\r\n                                  (  237.22   811.78   -23.00     1.38)  ; 47\r\n                                  (  238.02   814.36   -23.00     1.38)  ; 48\r\n                                  (  239.75   806.99   -24.82     1.38)  ; 49\r\n                                  (  240.48   811.94   -24.90     1.38)  ; 50\r\n                                  (  237.86   819.09   -21.95     1.38)  ; 51\r\n                                  (  237.88   831.05   -22.70     1.38)  ; 52\r\n                                  (  245.57   836.43   -18.05     1.38)  ; 53\r\n                                  (  244.18   834.31   -21.18     1.38)  ; 54\r\n                                  (  242.10   839.20   -20.97     1.38)  ; 55\r\n                                )  ;  End of markers\r\n                                (\r\n                                  (  243.43   855.63   -16.05     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                  (  244.37   857.64   -16.05     1.38)  ; 2\r\n                                  (  244.87   859.55   -15.77     1.38)  ; 3\r\n                                  (  244.96   863.16   -15.77     1.38)  ; 4\r\n                                  (  246.41   867.08   -15.77     1.38)  ; 5\r\n                                  (  244.26   870.16   -16.95     1.38)  ; 6\r\n                                  (  242.57   873.35   -17.30     1.38)  ; 7\r\n                                  (  242.09   877.42   -16.85     1.38)  ; 8\r\n                                  (  242.09   877.42   -16.88     1.38)  ; 9\r\n                                  (  241.60   881.48   -15.17     1.38)  ; 10\r\n                                  (  239.47   884.56   -14.57     1.38)  ; 11\r\n                                  (  238.63   886.16   -14.10     1.38)  ; 12\r\n                                  (  237.83   889.56   -14.10     1.38)  ; 13\r\n                                  (  237.62   892.48   -15.40     1.38)  ; 14\r\n                                  (  237.66   894.29   -16.52     1.38)  ; 15\r\n                                  (  237.66   894.29   -16.55     1.38)  ; 16\r\n                                  (  237.77   897.90   -17.30     1.38)  ; 17\r\n                                  (  237.77   897.90   -17.32     1.38)  ; 18\r\n                                  (  238.63   902.29   -17.57     1.38)  ; 19\r\n                                  (  240.51   906.30   -17.57     1.38)  ; 20\r\n                                  (  239.26   909.59   -17.75     1.38)  ; 21\r\n                                  (  239.44   910.83   -18.60     1.38)  ; 22\r\n                                  (  239.44   910.83   -18.63     1.38)  ; 23\r\n                                  (  241.46   914.29   -19.02     1.38)  ; 24\r\n                                  (  243.29   916.51   -17.65     1.38)  ; 25\r\n                                  (  243.97   919.65   -18.20     1.38)  ; 26\r\n                                  (  245.09   922.91   -17.63     1.38)  ; 27\r\n                                  (  245.85   923.68   -17.75     1.38)  ; 28\r\n                                  (  245.90   925.49   -16.17     1.38)  ; 29\r\n                                  (  245.90   925.49   -16.15     1.38)  ; 30\r\n                                  (  245.08   927.07   -14.23     1.38)  ; 31\r\n                                  (  243.33   928.45   -12.10     1.38)  ; 32\r\n                                  (  240.00   930.66   -10.88     1.38)  ; 33\r\n                                  (  238.00   933.18   -10.63     1.38)  ; 34\r\n                                  (  237.20   936.58   -10.33     1.38)  ; 35\r\n                                  (  236.41   939.98   -10.33     1.38)  ; 36\r\n                                  (  236.14   941.10   -11.37     1.38)  ; 37\r\n                                  (  234.90   944.40   -12.42     1.38)  ; 38\r\n                                  (  233.47   946.45   -13.67     1.38)  ; 39\r\n                                  (  233.47   946.45   -13.70     1.38)  ; 40\r\n                                  (  231.65   950.19   -13.95     1.38)  ; 41\r\n                                  (  229.92   951.58   -13.18     1.38)  ; 42\r\n                                  (  228.53   955.44   -12.95     1.38)  ; 43\r\n                                  (  228.95   959.73   -12.07     1.38)  ; 44\r\n                                  (  228.28   962.54   -12.42     1.38)  ; 45\r\n                                  (  226.34   966.86   -12.42     1.38)  ; 46\r\n                                  (  224.65   970.05   -14.10     1.38)  ; 47\r\n                                  (  224.56   972.42   -13.63     1.38)  ; 48\r\n                                  (  224.92   974.89   -13.32     1.38)  ; 49\r\n                                  (  224.39   977.16   -11.93     1.38)  ; 50\r\n                                  (  223.99   978.85   -10.45     1.38)  ; 51\r\n                                  (  223.19   982.25    -9.17     1.38)  ; 52\r\n                                  (  221.78   984.31    -7.32     1.38)  ; 53\r\n                                  (  222.71   986.32    -6.47     1.38)  ; 54\r\n                                  (  226.64   989.64    -6.13     1.38)  ; 55\r\n                                  (  227.01   992.10    -6.18     1.38)  ; 56\r\n                                  (  224.86   995.18    -5.07     1.38)  ; 57\r\n                                  (  222.86   997.70    -3.40     1.38)  ; 58\r\n                                  (  223.09  1000.74    -2.50     1.38)  ; 59\r\n                                  (  222.60  1004.81    -2.25     1.38)  ; 60\r\n                                  (  220.91  1008.00    -1.82     1.38)  ; 61\r\n                                  (  219.80  1010.72    -1.60     1.38)  ; 62\r\n                                  (  219.95  1016.13    -1.02     1.38)  ; 63\r\n                                  (  218.71  1019.42    -0.63     1.38)  ; 64\r\n                                  (  217.15  1022.03    -0.63     1.38)  ; 65\r\n                                  (  216.71  1021.93    -0.63     1.38)  ; 66\r\n                                  (  215.60  1024.66    -0.63     1.38)  ; 67\r\n                                  (  215.56  1028.83    -0.07     1.38)  ; 68\r\n                                  (  214.76  1032.23     0.97     1.38)  ; 69\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  246.76   847.45   -16.05     1.38)  ; 1\r\n                                    (  245.25   851.88   -16.05     1.38)  ; 2\r\n                                    (  246.83   861.21   -15.77     1.38)  ; 3\r\n                                    (  247.20   863.68   -15.77     1.38)  ; 4\r\n                                    (  243.34   858.00   -14.43     1.38)  ; 5\r\n                                    (  246.20   859.86   -16.65     1.38)  ; 6\r\n                                    (  243.09   865.11   -17.82     1.38)  ; 7\r\n                                    (  241.77   870.78   -16.88     1.38)  ; 8\r\n                                    (  245.13   874.54   -18.17     1.38)  ; 9\r\n                                    (  239.73   877.46   -15.10     1.38)  ; 10\r\n                                    (  243.67   880.77   -17.63     1.38)  ; 11\r\n                                    (  236.00   887.34   -14.10     1.38)  ; 12\r\n                                    (  236.09   890.93   -14.10     1.38)  ; 13\r\n                                    (  239.32   895.28   -15.85     1.38)  ; 14\r\n                                    (  236.19   894.54   -16.22     1.38)  ; 15\r\n                                    (  242.06   903.69   -17.57     1.38)  ; 16\r\n                                    (  238.05   902.74   -17.57     1.38)  ; 17\r\n                                    (  236.34   899.95   -14.20     1.38)  ; 18\r\n                                    (  239.24   897.65   -17.15     1.38)  ; 19\r\n                                    (  236.57   902.99   -19.00     1.38)  ; 20\r\n                                    (  237.12   906.71   -16.77     1.38)  ; 21\r\n                                    (  239.03   906.56   -16.77     1.38)  ; 22\r\n                                    (  242.29   906.72   -15.92     1.38)  ; 23\r\n                                    (  239.23   913.77   -19.05     1.38)  ; 24\r\n                                    (  243.07   913.47   -20.50     1.38)  ; 25\r\n                                    (  241.20   915.42   -16.75     1.38)  ; 26\r\n                                    (  241.56   917.90   -16.52     1.38)  ; 27\r\n                                    (  241.43   918.46   -17.63     1.38)  ; 28\r\n                                    (  246.51   920.85   -19.15     1.38)  ; 29\r\n                                    (  248.36   923.08   -16.15     1.38)  ; 30\r\n                                    (  238.17   928.44   -10.88     1.38)  ; 31\r\n                                    (  241.52   932.20   -10.88     1.38)  ; 32\r\n                                    (  244.73   930.57   -10.88     1.38)  ; 33\r\n                                    (  248.07   928.37   -14.55     1.38)  ; 34\r\n                                    (  238.08   930.80   -10.33     1.38)  ; 35\r\n                                    (  237.24   932.40   -10.33     1.38)  ; 36\r\n                                    (  240.81   933.24   -10.33     1.38)  ; 37\r\n                                    (  239.43   937.09   -10.33     1.38)  ; 38\r\n                                    (  234.55   931.77   -10.33     1.38)  ; 39\r\n                                    (  235.80   950.57   -13.95     1.38)  ; 40\r\n                                    (  238.15   944.56   -13.95     1.38)  ; 41\r\n                                    (  237.92   941.52   -15.55     1.38)  ; 42\r\n                                    (  232.98   944.54   -15.55     1.38)  ; 43\r\n                                    (  229.55   949.11   -15.55     1.38)  ; 44\r\n                                    (  232.01   952.68   -15.32     1.38)  ; 45\r\n                                    (  227.23   950.96   -12.27     1.38)  ; 46\r\n                                    (  232.10   950.30   -12.27     1.38)  ; 47\r\n                                    (  231.32   959.67   -12.42     1.38)  ; 48\r\n                                    (  231.27   957.87   -12.42     1.38)  ; 49\r\n                                    (  227.21   961.10   -13.75     1.38)  ; 50\r\n                                    (  224.04   964.54    -9.77     1.38)  ; 51\r\n                                    (  224.94   964.75   -13.18     1.38)  ; 52\r\n                                    (  227.59   969.55   -15.30     1.38)  ; 53\r\n                                    (  222.68   974.37   -12.75     1.38)  ; 54\r\n                                    (  221.93   979.58   -10.15     1.38)  ; 55\r\n                                    (  229.28   988.45    -6.13     1.38)  ; 56\r\n                                    (  227.99   989.95    -6.20     1.38)  ; 57\r\n                                    (  227.87   996.49    -7.07     1.38)  ; 58\r\n                                    (  227.54   995.81    -6.20     1.38)  ; 59\r\n                                    (  223.08   994.77    -6.20     1.38)  ; 60\r\n                                    (  228.36   998.38    -4.20     1.38)  ; 61\r\n                                    (  227.43  1002.35    -4.20     1.38)  ; 62\r\n                                    (  225.73   999.57    -1.58     1.38)  ; 63\r\n                                    (  224.35  1003.42    -1.58     1.38)  ; 64\r\n                                    (  221.22  1002.69    -1.58     1.38)  ; 65\r\n                                    (  220.56  1005.51    -1.58     1.38)  ; 66\r\n                                    (  223.61  1008.62    -1.58     1.38)  ; 67\r\n                                    (  216.02  1012.82    -1.02     1.38)  ; 68\r\n                                    (  218.97  1012.31    -1.02     1.38)  ; 69\r\n                                    (  216.88  1017.19     0.97     1.38)  ; 70\r\n                                    (  214.48  1021.41    -0.75     1.38)  ; 71\r\n                                    (  218.55  1024.16    -0.12     1.38)  ; 72\r\n                                    (  213.68  1024.80     0.00     1.38)  ; 73\r\n                                    (  218.47  1026.53    -0.67     1.38)  ; 74\r\n                                    (  214.36  1027.96    -0.67     1.38)  ; 75\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  213.35  1034.28    -0.47     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-1\r\n                                    (  212.90  1034.17    -0.47     1.38)  ; 2\r\n                                    (  212.37  1036.45     2.13     0.92)  ; 3\r\n                                    (  212.15  1039.38     3.65     0.92)  ; 4\r\n                                    (  212.15  1039.38     3.60     0.92)  ; 5\r\n                                    (  209.96  1040.65     4.32     0.46)  ; 6\r\n                                    (  209.57  1042.35     5.45     0.46)  ; 7\r\n                                    (  209.88  1043.03     5.55     0.46)  ; 8\r\n                                    (  208.01  1044.97     7.55     0.46)  ; 9\r\n                                    (  208.01  1044.97     7.57     0.46)  ; 10\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  214.52  1039.33     5.45     0.46)  ; 1\r\n                                      (  210.66  1033.65     5.45     0.46)  ; 2\r\n                                    )  ;  End of markers\r\n                                     Normal\r\n                                  |\r\n                                    (  216.66  1036.26     2.08     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-2\r\n                                    (  215.55  1038.97     2.97     0.92)  ; 2\r\n                                    (  217.77  1039.50     3.50     0.92)  ; 3\r\n                                    (  219.25  1039.25     4.17     0.92)  ; 4\r\n                                    (  221.08  1041.48     5.07     0.92)  ; 5\r\n                                    (  221.58  1043.38     5.70     0.92)  ; 6\r\n                                    (  222.52  1045.38     6.35     0.92)  ; 7\r\n                                    (  222.17  1048.88     6.80     0.92)  ; 8\r\n                                    (  222.17  1048.88     6.78     0.92)  ; 9\r\n                                    (  224.06  1052.91     5.42     0.92)  ; 10\r\n                                    (  223.83  1055.85     5.17     0.92)  ; 11\r\n                                    (  223.75  1058.22     6.35     0.92)  ; 12\r\n                                    (  223.75  1058.22     6.28     0.92)  ; 13\r\n                                    (  222.33  1060.28     7.67     0.92)  ; 14\r\n                                    (  222.69  1062.74     8.55     0.92)  ; 15\r\n                                    (  223.95  1065.43     8.55     0.92)  ; 16\r\n                                    (  222.84  1068.16     8.55     0.46)  ; 17\r\n                                    (  223.96  1071.40     8.55     0.46)  ; 18\r\n                                    (  224.64  1074.55     8.55     0.46)  ; 19\r\n                                    (  224.19  1074.44     8.55     0.46)  ; 20\r\n                                    (  228.08  1075.95     8.55     0.46)  ; 21\r\n                                    (  228.98  1076.16     9.55     0.46)  ; 22\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  214.73  1036.40     2.97     0.92)  ; 1\r\n                                      (  221.75  1038.64     5.70     0.92)  ; 2\r\n                                      (  219.92  1042.39     5.70     0.92)  ; 3\r\n                                      (  220.74  1044.97     5.70     0.92)  ; 4\r\n                                      (  229.42  1054.17     5.70     0.92)  ; 5\r\n                                      (  226.21  1055.81     5.70     0.92)  ; 6\r\n                                      (  226.63  1066.06     8.55     0.46)  ; 7\r\n                                      (  226.44  1058.85     8.55     0.46)  ; 8\r\n                                    )  ;  End of markers\r\n                                     Normal\r\n                                  )  ;  End of split\r\n                                |\r\n                                  (  241.31   854.66   -17.08     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                  (  241.49   855.89   -18.67     1.38)  ; 2\r\n                                  (  240.91   856.36   -19.80     1.38)  ; 3\r\n                                  (  241.09   857.59   -21.63     1.38)  ; 4\r\n                                  (  241.99   857.81   -23.27     1.38)  ; 5\r\n                                  (  241.59   859.50   -25.05     1.38)  ; 6\r\n                                  (  239.98   860.33   -26.22     1.38)  ; 7\r\n                                  (  238.07   860.48   -27.73     1.38)  ; 8\r\n                                  (  240.48   862.23   -29.70     0.92)  ; 9\r\n                                  (  239.63   863.82   -31.70     0.92)  ; 10\r\n                                  (  238.21   865.87   -32.97     0.92)  ; 11\r\n                                  (  239.02   868.45   -34.17     0.92)  ; 12\r\n                                  (  239.38   870.94   -34.45     0.92)  ; 13\r\n                                  (  237.51   872.88   -35.40     0.92)  ; 14\r\n                                  (  235.60   873.03   -36.75     0.92)  ; 15\r\n                                  (  233.68   873.18   -38.38     0.92)  ; 16\r\n                                  (  233.59   875.54   -39.40     0.92)  ; 17\r\n                                  (  234.09   877.46   -40.75     0.92)  ; 18\r\n                                  (  234.14   879.26   -41.93     0.92)  ; 19\r\n                                  (  231.64   879.87   -43.07     0.92)  ; 20\r\n                                  (  230.84   883.26   -43.47     0.92)  ; 21\r\n                                  (  229.11   884.65   -44.05     0.92)  ; 22\r\n                                  (  229.11   884.65   -44.07     0.92)  ; 23\r\n                                  (  228.45   887.48   -44.60     0.92)  ; 24\r\n                                  (  227.16   888.97   -45.95     0.92)  ; 25\r\n                                  (  227.02   889.53   -47.22     0.92)  ; 26\r\n                                  (  227.25   892.57   -48.45     0.92)  ; 27\r\n                                  (  226.41   894.16   -48.62     0.92)  ; 28\r\n                                  (  226.94   897.87   -49.52     0.92)  ; 29\r\n                                  (  226.94   897.87   -49.55     0.92)  ; 30\r\n                                  (  226.41   900.14   -50.00     0.92)  ; 31\r\n                                  (  226.41   900.14   -50.03     0.92)  ; 32\r\n                                  (  225.13   901.63   -51.28     0.92)  ; 33\r\n                                  (  224.87   902.75   -52.65     0.92)  ; 34\r\n                                  (  224.95   906.36   -53.45     0.92)  ; 35\r\n                                  (  224.87   908.74   -54.17     0.92)  ; 36\r\n                                  (  223.76   911.45   -54.58     0.92)  ; 37\r\n                                  (  222.70   915.98   -54.72     0.92)  ; 38\r\n                                  (  220.97   917.38   -55.52     0.92)  ; 39\r\n                                  (  222.80   919.59   -56.20     0.92)  ; 40\r\n                                  (  222.76   923.76   -56.75     0.92)  ; 41\r\n                                  (  222.05   924.79   -58.55     0.46)  ; 42\r\n                                  (  221.34   925.83   -60.35     0.46)  ; 43\r\n                                  (  221.34   925.83   -60.38     0.46)  ; 44\r\n                                  (  221.39   927.62   -62.13     0.46)  ; 45\r\n                                  (  220.72   930.45   -63.42     0.46)  ; 46\r\n                                  (  218.11   931.49   -64.27     0.46)  ; 47\r\n                                  (  220.08   933.15   -65.00     0.46)  ; 48\r\n                                  (  219.82   934.27   -66.17     0.46)  ; 49\r\n                                  (  218.08   935.66   -67.20     0.46)  ; 50\r\n                                  (  216.02   936.38   -68.13     0.46)  ; 51\r\n                                  (  215.64   938.07   -69.18     0.46)  ; 52\r\n                                  (  215.64   938.07   -69.20     0.46)  ; 53\r\n                                  (  214.15   938.32   -70.45     0.46)  ; 54\r\n                                  (  214.07   940.69   -71.43     0.46)  ; 55\r\n                                  (  214.07   940.69   -71.45     0.46)  ; 56\r\n                                  (  212.46   941.51   -72.32     0.46)  ; 57\r\n                                  (  212.46   941.51   -72.35     0.46)  ; 58\r\n                                  (  210.86   942.33   -72.95     0.46)  ; 59\r\n                                  (  209.89   944.49   -74.15     0.46)  ; 60\r\n                                  (  209.57   943.82   -76.00     0.46)  ; 61\r\n                                  (  209.57   943.82   -76.03     0.46)  ; 62\r\n                                  (  208.28   945.31   -77.47     0.46)  ; 63\r\n                                  (  207.00   946.80   -78.57     0.46)  ; 64\r\n                                  (  206.77   949.74   -79.55     0.46)  ; 65\r\n                                  (  204.53   949.21   -80.70     0.46)  ; 66\r\n                                  (  201.78   950.95   -81.75     0.46)  ; 67\r\n                                  (  200.93   952.55   -82.45     0.46)  ; 68\r\n                                  (  199.50   954.60   -83.00     0.46)  ; 69\r\n                                  (  197.33   955.87   -83.00     0.46)  ; 70\r\n                                  (  196.93   957.58   -84.72     0.46)  ; 71\r\n                                  (  197.10   958.81   -86.32     0.46)  ; 72\r\n                                  (  197.02   961.19   -87.00     0.46)  ; 73\r\n                                  (  194.97   961.90   -87.52     0.46)  ; 74\r\n                                  (  193.50   962.14   -88.42     0.46)  ; 75\r\n                                  (  192.34   963.07   -89.52     0.46)  ; 76\r\n                                  (  191.81   965.33   -90.77     0.46)  ; 77\r\n                                  (  191.81   965.33   -90.80     0.46)  ; 78\r\n                                  (  190.52   966.82   -91.72     0.46)  ; 79\r\n                                  (  189.40   969.55   -92.42     0.46)  ; 80\r\n                                  (  187.35   970.25   -93.47     0.46)  ; 81\r\n                                  (  184.99   970.31   -94.70     0.46)  ; 82\r\n                                  (  183.51   970.56   -95.75     0.46)  ; 83\r\n                                  (  182.23   972.04   -96.80     0.46)  ; 84\r\n                                  (  180.93   973.54   -97.65     0.46)  ; 85\r\n                                  (  180.93   973.54   -97.68     0.46)  ; 86\r\n                                  (  179.64   975.02   -98.53     0.46)  ; 87\r\n                                  (  177.01   976.20   -99.30     0.46)  ; 88\r\n                                  (  173.81   977.84  -100.37     0.46)  ; 89\r\n                                  (  172.07   979.22  -100.37     0.46)  ; 90\r\n                                  (  169.70   979.26  -101.32     0.46)  ; 91\r\n                                  (  168.54   980.19  -102.40     0.46)  ; 92\r\n                                  (  166.40   983.27  -103.60     0.46)  ; 93\r\n                                  (  163.83   986.24  -103.97     0.46)  ; 94\r\n                                  (  162.76   990.78  -103.97     0.46)  ; 95\r\n                                  (  161.39   994.63  -104.85     0.46)  ; 96\r\n                                  (  161.30   997.00  -106.10     0.46)  ; 97\r\n                                  (  159.88   999.06  -107.42     0.46)  ; 98\r\n                                  (  158.68  1004.15  -108.35     0.46)  ; 99\r\n                                  (  156.15  1008.93  -107.78     0.46)  ; 100\r\n                                  (  155.31  1010.53  -106.60     0.46)  ; 101\r\n                                  (  155.35  1012.33  -105.90     0.46)  ; 102\r\n                                  (  155.27  1014.70  -104.10     0.46)  ; 103\r\n                                  (  154.74  1016.96  -102.70     0.46)  ; 104\r\n                                  (  154.29  1016.85  -102.70     0.46)  ; 105\r\n                                  (  154.92  1018.20  -101.88     0.46)  ; 106\r\n                                  (  155.28  1020.67  -100.37     0.46)  ; 107\r\n                                  (  154.75  1022.93   -99.78     0.46)  ; 108\r\n                                  (  153.77  1025.09   -99.03     0.46)  ; 109\r\n                                  (  154.40  1026.44   -98.00     0.46)  ; 110\r\n                                  (  153.56  1028.03   -97.25     0.46)  ; 111\r\n                                  (  153.47  1030.40   -96.07     0.46)  ; 112\r\n                                  (  151.52  1034.72   -95.25     0.46)  ; 113\r\n                                  (  149.83  1037.91   -93.53     0.46)  ; 114\r\n                                  (  148.58  1041.19   -93.15     0.46)  ; 115\r\n                                  (  147.48  1043.92   -92.72     0.46)  ; 116\r\n                                  (  146.95  1046.20   -92.20     0.46)  ; 117\r\n                                  (  144.80  1049.28   -90.42     0.46)  ; 118\r\n\r\n                                  (Cross\r\n                                    (Color White)\r\n                                    (Name \"Marker 3\")\r\n                                    (  234.10   877.30   -31.48     1.38)  ; 1\r\n                                  )  ;  End of markers\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  239.63   857.85   -21.63     1.38)  ; 1\r\n                                    (  237.59   870.51   -34.45     0.92)  ; 2\r\n                                    (  235.33   874.17   -38.38     0.92)  ; 3\r\n                                    (  229.86   879.45   -43.97     0.92)  ; 4\r\n                                    (  235.25   876.53   -42.70     0.92)  ; 5\r\n                                    (  225.28   890.90   -48.45     0.92)  ; 6\r\n                                    (  229.70   890.16   -45.65     0.92)  ; 7\r\n                                    (  223.70   903.69   -53.45     0.92)  ; 8\r\n                                    (  226.83   904.42   -53.45     0.92)  ; 9\r\n                                    (  223.46   916.77   -54.72     0.92)  ; 10\r\n                                    (  223.66   923.97   -64.27     0.46)  ; 11\r\n                                    (  212.77   936.21   -69.22     0.46)  ; 12\r\n                                    (  207.48   942.73   -77.47     0.46)  ; 13\r\n                                    (  202.62   949.36   -81.75     0.46)  ; 14\r\n                                    (  193.65   967.55   -91.72     0.46)  ; 15\r\n                                    (  169.74   981.07  -103.60     0.46)  ; 16\r\n                                    (  160.71   991.49  -104.85     0.46)  ; 17\r\n                                    (  164.19   988.72  -104.85     0.46)  ; 18\r\n                                    (  160.21  1005.70  -108.35     0.46)  ; 19\r\n                                    (  158.27   999.87  -106.27     0.46)  ; 20\r\n                                    (  158.05  1002.81  -109.67     0.46)  ; 21\r\n                                    (  155.47  1005.78  -109.67     0.46)  ; 22\r\n                                    (  156.11  1007.13  -109.67     0.46)  ; 23\r\n                                    (  154.14  1011.44  -105.90     0.46)  ; 24\r\n                                    (  157.41  1011.61  -107.07     0.46)  ; 25\r\n                                    (  153.63  1019.68  -101.88     0.46)  ; 26\r\n                                    (  156.26  1018.52  -101.07     0.46)  ; 27\r\n                                    (  153.99  1022.17   -99.03     0.46)  ; 28\r\n                                    (  153.06  1026.12   -98.40     0.46)  ; 29\r\n                                    (  154.98  1025.98   -97.42     0.46)  ; 30\r\n                                    (  153.57  1034.01   -95.25     0.46)  ; 31\r\n                                    (  146.83  1036.60   -93.95     0.46)  ; 32\r\n                                    (  148.86  1046.05   -90.42     0.46)  ; 33\r\n                                  )  ;  End of markers\r\n                                   Normal\r\n                                )  ;  End of split\r\n                              )  ;  End of split\r\n                            |\r\n                              (  231.42   611.32   -31.42     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                              (  231.21   611.36   -31.42     0.92)  ; 2\r\n                              (  230.58   612.92   -30.00     0.92)  ; 3\r\n                              (  230.31   614.05   -28.57     0.92)  ; 4\r\n                              (  228.85   614.30   -27.15     0.92)  ; 5\r\n                              (  229.48   615.64   -26.32     0.92)  ; 6\r\n                              (  229.96   617.55   -25.07     0.46)  ; 7\r\n                              (  228.41   620.17   -23.60     0.46)  ; 8\r\n                              (  228.77   622.64   -22.57     0.46)  ; 9\r\n                              (  228.77   622.64   -22.60     0.46)  ; 10\r\n                              (  228.06   623.66   -21.75     0.46)  ; 11\r\n                              (  228.37   624.35   -21.75     0.46)  ; 12\r\n                              (  228.30   626.70   -20.90     0.46)  ; 13\r\n                              (  228.79   628.62   -19.85     0.46)  ; 14\r\n                              (  227.18   629.44   -18.97     0.46)  ; 15\r\n                              (  225.89   630.92   -18.08     0.46)  ; 16\r\n                              (  224.42   631.17   -15.97     0.46)  ; 17\r\n                              (  224.42   631.17   -15.95     0.46)  ; 18\r\n\r\n                              (Cross\r\n                                (Color White)\r\n                                (Name \"Marker 3\")\r\n                                (  231.06   611.25   -31.77     1.83)  ; 1\r\n                              )  ;  End of markers\r\n\r\n                              (Cross\r\n                                (Color RGB (128, 255, 128))\r\n                                (Name \"Marker 3\")\r\n                                (  230.74   618.32   -23.60     0.46)  ; 1\r\n                                (  226.41   622.68   -23.60     0.46)  ; 2\r\n                              )  ;  End of markers\r\n                               Normal\r\n                            )  ;  End of split\r\n                          |\r\n                            (  239.41   464.61   -16.32     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                            (  240.94   466.16   -15.48     0.46)  ; 2\r\n                            (  240.86   468.54   -14.90     0.46)  ; 3\r\n                            (  240.60   469.66   -14.15     0.46)  ; 4\r\n                            (  242.24   470.64   -12.62     0.46)  ; 5\r\n                            (  245.23   471.94   -11.63     0.46)  ; 6\r\n                            (  248.37   472.67   -10.70     0.46)  ; 7\r\n                            (  248.15   475.61    -9.77     0.46)  ; 8\r\n                            (  248.69   479.32    -9.13     0.46)  ; 9\r\n                            (  250.97   481.65   -10.07     0.46)  ; 10\r\n                            (  252.95   483.30    -9.22     0.46)  ; 11\r\n                            (  254.46   484.85    -7.87     0.46)  ; 12\r\n                            (  255.09   486.19    -6.67     0.46)  ; 13\r\n                            (  254.25   487.78    -5.13     0.46)  ; 14\r\n                            (  254.25   487.78    -5.15     0.46)  ; 15\r\n                            (  256.42   486.51    -3.57     0.46)  ; 16\r\n                            (  256.42   486.51    -3.60     0.46)  ; 17\r\n                            (  257.90   486.25    -2.45     0.46)  ; 18\r\n                            (  257.63   487.39    -0.63     0.46)  ; 19\r\n                            (  254.83   487.33     0.93     0.46)  ; 20\r\n                            (  254.56   488.46     2.45     0.46)  ; 21\r\n                            (  256.79   488.99     3.85     0.46)  ; 22\r\n                            (  256.75   493.16     3.45     0.46)  ; 23\r\n                            (  255.96   496.55     1.22     0.46)  ; 24\r\n                            (  255.96   496.55     0.88     0.46)  ; 25\r\n\r\n                            (Cross\r\n                              (Color RGB (128, 255, 128))\r\n                              (Name \"Marker 3\")\r\n                              (  238.13   466.10   -16.32     0.46)  ; 1\r\n                              (  238.53   464.40   -16.32     0.46)  ; 2\r\n                              (  240.89   464.36   -15.48     0.46)  ; 3\r\n                              (  243.82   474.00   -11.63     0.46)  ; 4\r\n                              (  243.72   470.39   -11.63     0.46)  ; 5\r\n                              (  246.08   470.36   -11.63     0.46)  ; 6\r\n                              (  246.60   478.24    -9.13     0.46)  ; 7\r\n                              (  247.66   479.68    -9.13     0.46)  ; 8\r\n                              (  252.76   482.07    -8.07     0.46)  ; 9\r\n                              (  250.49   485.71    -8.25     0.46)  ; 10\r\n                              (  250.44   483.92   -10.80     0.46)  ; 11\r\n                              (  252.86   485.67    -6.67     0.46)  ; 12\r\n                              (  257.67   483.21    -6.67     0.46)  ; 13\r\n                              (  253.75   485.88     2.45     0.46)  ; 14\r\n                              (  253.09   488.72     3.85     0.46)  ; 15\r\n                              (  258.71   488.83     3.85     0.46)  ; 16\r\n                              (  255.06   490.37     5.37     0.46)  ; 17\r\n                              (  255.81   491.14     6.22     0.46)  ; 18\r\n                            )  ;  End of markers\r\n                             Normal\r\n                          )  ;  End of split\r\n                        |\r\n                          (  235.28   454.09   -16.50     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-2\r\n                          (  233.82   454.34   -15.32     0.92)  ; 2\r\n                          (  232.08   455.72   -14.05     0.92)  ; 3\r\n                          (  231.94   456.29   -12.62     0.92)  ; 4\r\n                          (  231.58   453.82   -11.55     0.92)  ; 5\r\n                          (  230.51   452.36   -10.20     0.92)  ; 6\r\n                          (  229.44   450.92    -9.67     0.92)  ; 7\r\n                          (  228.68   450.14    -8.23     0.92)  ; 8\r\n                          (  227.47   449.27    -8.23     0.92)  ; 9\r\n                          (  226.52   447.25    -8.23     0.92)  ; 10\r\n                          (  223.58   447.76    -6.97     0.92)  ; 11\r\n                          (  220.90   447.13    -6.55     0.92)  ; 12\r\n                          (  217.64   446.97    -6.85     0.92)  ; 13\r\n                          (  214.83   446.90    -6.02     0.92)  ; 14\r\n                          (  211.83   445.61    -4.92     0.92)  ; 15\r\n                          (  209.97   447.55    -3.65     0.92)  ; 16\r\n                          (  210.45   449.46    -3.20     0.92)  ; 17\r\n                          (  210.19   450.59    -1.70     0.92)  ; 18\r\n                          (  207.51   449.96    -0.97     0.92)  ; 19\r\n                          (  205.20   451.82     0.35     0.92)  ; 20\r\n                          (  205.20   451.82     0.32     0.92)  ; 21\r\n                          (  203.33   453.76     0.67     0.92)  ; 22\r\n                          (  200.38   454.27     0.67     0.92)  ; 23\r\n                          (  196.73   455.79    -0.02     0.92)  ; 24\r\n                          (  193.96   457.54    -1.25     0.92)  ; 25\r\n                          (  191.64   459.38    -0.30     0.92)  ; 26\r\n                          (  190.57   457.94     0.32     0.92)  ; 27\r\n                          (  187.05   458.90     1.22     0.92)  ; 28\r\n                          (  184.54   459.52     0.75     0.92)  ; 29\r\n                          (  181.02   460.47    -0.55     0.92)  ; 30\r\n                          (  178.84   461.75    -0.90     0.92)  ; 31\r\n                          (  175.85   460.45    -2.15     0.92)  ; 32\r\n                          (  174.59   457.78    -2.80     0.92)  ; 33\r\n                          (  171.46   457.04    -3.10     0.92)  ; 34\r\n                          (  167.18   457.23    -3.97     0.92)  ; 35\r\n                          (  167.18   457.23    -4.00     0.92)  ; 36\r\n                          (  164.81   457.27    -3.60     0.92)  ; 37\r\n                          (  164.81   457.27    -3.62     0.92)  ; 38\r\n                          (  163.16   456.29    -1.77     0.92)  ; 39\r\n                          (  160.48   455.66    -1.02     0.92)  ; 40\r\n                          (  158.02   458.07     0.85     0.92)  ; 41\r\n                          (  155.85   459.35     2.85     0.92)  ; 42\r\n                          (  155.85   459.35     2.83     0.92)  ; 43\r\n                          (  152.50   461.56     4.50     0.92)  ; 44\r\n\r\n                          (Cross\r\n                            (Color RGB (128, 255, 128))\r\n                            (Name \"Marker 3\")\r\n                            (  230.17   455.87   -12.62     0.92)  ; 1\r\n                            (  231.04   450.10    -9.67     0.92)  ; 2\r\n                            (  229.04   452.63    -8.30     0.92)  ; 3\r\n                            (  222.19   445.64    -6.55     0.92)  ; 4\r\n                            (  222.16   449.81    -6.55     0.92)  ; 5\r\n                            (  219.95   445.11    -4.82     0.92)  ; 6\r\n                            (  219.92   449.29    -4.85     0.92)  ; 7\r\n                            (  215.95   450.15    -7.43     0.92)  ; 8\r\n                            (  213.55   448.39    -7.75     0.92)  ; 9\r\n                            (  216.25   444.84    -9.13     0.92)  ; 10\r\n                            (  206.84   446.82    -3.65     0.92)  ; 11\r\n                            (  209.60   445.08    -2.30     0.92)  ; 12\r\n                            (  212.12   450.45    -1.40     0.92)  ; 13\r\n                            (  204.97   448.77    -0.97     0.92)  ; 14\r\n                            (  207.56   451.77    -0.97     0.92)  ; 15\r\n                            (  209.79   452.30    -0.97     0.92)  ; 16\r\n                            (  207.03   454.03     1.90     0.92)  ; 17\r\n                            (  201.81   452.22    -1.82     0.92)  ; 18\r\n                            (  199.70   451.12    -0.75     0.92)  ; 19\r\n                            (  180.08   458.46    -0.90     0.92)  ; 20\r\n                            (  182.67   461.46    -0.90     0.92)  ; 21\r\n                            (  183.70   461.10     0.47     0.92)  ; 22\r\n                            (  181.07   462.27    -0.95     0.92)  ; 23\r\n                            (  174.09   455.86    -2.90     0.92)  ; 24\r\n                            (  169.05   455.28    -4.25     0.92)  ; 25\r\n                            (  169.02   459.45    -4.32     0.92)  ; 26\r\n                            (  166.91   458.36    -5.37     0.92)  ; 27\r\n                            (  161.79   460.15    -1.02     0.92)  ; 28\r\n                            (  160.43   453.86    -1.02     0.92)  ; 29\r\n                            (  163.11   454.49    -1.02     0.92)  ; 30\r\n                            (  158.92   458.28    -1.22     0.92)  ; 31\r\n                            (  157.36   460.89     4.50     0.92)  ; 32\r\n                          )  ;  End of markers\r\n                           Normal\r\n                        )  ;  End of split\r\n                      |\r\n                        (  247.39   360.39   -28.37     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2\r\n                        (  247.17   363.33   -29.50     1.83)  ; 2\r\n                        (  245.93   366.62   -30.90     1.83)  ; 3\r\n                        (  244.38   369.24   -32.15     1.83)  ; 4\r\n                        (  242.96   371.30   -33.80     1.83)  ; 5\r\n                        (  242.03   375.25   -34.85     1.83)  ; 6\r\n                        (  240.07   379.58   -34.75     1.83)  ; 7\r\n                        (  237.94   382.66   -35.02     1.83)  ; 8\r\n                        (  237.90   386.83   -35.33     1.83)  ; 9\r\n                        (  237.90   386.83   -35.35     1.83)  ; 10\r\n                        (  236.84   391.36   -35.35     1.83)  ; 11\r\n                        (  236.11   394.40   -36.38     1.83)  ; 12\r\n                        (  235.87   396.36   -36.40     1.83)  ; 13\r\n                        (  235.78   398.72   -36.38     1.83)  ; 14\r\n                        (  235.84   403.66   -36.72     1.83)  ; 15\r\n                        (  232.99   407.77   -37.02     1.83)  ; 16\r\n                        (  233.04   409.57   -38.15     1.83)  ; 17\r\n                        (  231.66   413.43   -39.20     1.83)  ; 18\r\n                        (  229.72   417.76   -40.17     1.83)  ; 19\r\n                        (  229.72   423.72   -41.17     1.83)  ; 20\r\n                        (  229.55   426.58   -41.77     1.83)  ; 21\r\n                        (  230.28   431.52   -41.85     1.83)  ; 22\r\n                        (  229.93   435.02   -40.60     1.83)  ; 23\r\n                        (  228.87   439.55   -41.03     1.83)  ; 24\r\n                        (  228.87   439.55   -41.05     1.83)  ; 25\r\n                        (  226.34   444.33   -41.73     1.83)  ; 26\r\n                        (  224.38   448.65   -42.10     1.83)  ; 27\r\n                        (  224.93   452.36   -43.10     1.83)  ; 28\r\n                        (  225.73   454.93   -43.92     1.83)  ; 29\r\n                        (  225.73   454.93   -43.95     1.83)  ; 30\r\n                        (  224.80   458.90   -44.97     1.83)  ; 31\r\n                        (  223.07   460.29   -45.75     1.83)  ; 32\r\n                        (  223.07   460.29   -45.77     1.83)  ; 33\r\n                        (  221.65   462.34   -47.02     1.83)  ; 34\r\n                        (  222.18   466.04   -47.88     1.83)  ; 35\r\n                        (  221.65   468.31   -47.88     1.83)  ; 36\r\n\r\n                        (Cross\r\n                          (Color White)\r\n                          (Name \"Marker 3\")\r\n                          (  236.18   402.48   -12.40     1.83)  ; 1\r\n                          (  236.30   395.94   -13.23     1.83)  ; 2\r\n                          (  236.22   398.31   -12.40     1.83)  ; 3\r\n                        )  ;  End of markers\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  242.46   369.39   -34.45     1.83)  ; 1\r\n                          (  245.90   370.79   -31.98     1.83)  ; 2\r\n                          (  247.60   367.60   -28.37     1.83)  ; 3\r\n                          (  234.79   392.07   -36.40     1.83)  ; 4\r\n                          (  238.68   393.58   -36.40     1.83)  ; 5\r\n                          (  239.41   398.54   -35.70     1.83)  ; 6\r\n                          (  239.19   401.46   -35.70     1.83)  ; 7\r\n                          (  232.64   405.30   -35.70     1.83)  ; 8\r\n                          (  229.97   410.65   -39.20     1.83)  ; 9\r\n                          (  228.21   422.18   -41.17     1.83)  ; 10\r\n                          (  227.76   426.15   -41.85     1.83)  ; 11\r\n                          (  227.86   429.75   -41.85     1.83)  ; 12\r\n                          (  228.28   434.04   -39.78     1.83)  ; 13\r\n                          (  228.14   456.70   -44.85     1.83)  ; 14\r\n                          (  220.24   460.22   -47.02     1.83)  ; 15\r\n                          (  232.37   426.63   -41.85     1.83)  ; 16\r\n                        )  ;  End of markers\r\n                        (\r\n                          (  223.54   470.57   -46.93     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-1\r\n                          (  225.94   472.34   -46.45     0.92)  ; 2\r\n                          (  228.22   474.66   -46.25     0.92)  ; 3\r\n                          (  230.46   475.19   -46.25     0.92)  ; 4\r\n                          (  231.58   478.44   -46.88     0.92)  ; 5\r\n                          (  232.53   480.45   -47.60     0.92)  ; 6\r\n                          (  235.39   482.30   -47.92     0.92)  ; 7\r\n                          (  236.86   482.06   -47.33     0.92)  ; 8\r\n                          (  238.78   481.91   -48.88     0.92)  ; 9\r\n                          (  238.70   484.28   -50.13     0.92)  ; 10\r\n                          (  238.70   484.28   -50.15     0.92)  ; 11\r\n                          (  238.70   484.28   -51.50     0.92)  ; 12\r\n                          (  238.70   484.28   -51.52     0.92)  ; 13\r\n                          (  237.40   485.76   -52.20     0.92)  ; 14\r\n                          (  237.59   487.01   -53.38     0.92)  ; 15\r\n                          (  239.36   487.41   -54.35     0.92)  ; 16\r\n                          (  241.48   488.51   -55.35     0.92)  ; 17\r\n                          (  241.48   488.51   -55.38     0.92)  ; 18\r\n                          (  241.78   489.19   -55.72     0.92)  ; 19\r\n                          (  241.00   492.57   -55.72     0.92)  ; 20\r\n                          (  242.51   494.14   -56.42     0.92)  ; 21\r\n                          (  243.59   495.58   -57.50     0.92)  ; 22\r\n                          (  244.65   497.02   -58.30     0.92)  ; 23\r\n                          (  245.23   496.56   -58.90     0.92)  ; 24\r\n                          (  246.13   496.77   -58.88     0.92)  ; 25\r\n                          (  245.47   499.60   -59.02     0.92)  ; 26\r\n                          (  245.91   499.70   -59.02     0.92)  ; 27\r\n                          (  248.02   500.80   -59.63     0.92)  ; 28\r\n                          (  248.83   503.38   -59.63     0.92)  ; 29\r\n                          (  249.27   503.48   -60.67     0.92)  ; 30\r\n                          (  248.16   506.21   -61.52     0.92)  ; 31\r\n                          (  251.15   507.50   -62.60     0.92)  ; 32\r\n                          (  253.26   508.59   -63.17     0.92)  ; 33\r\n                          (  255.09   510.82   -63.95     0.92)  ; 34\r\n                          (  255.32   513.86   -65.00     0.92)  ; 35\r\n                          (  256.97   514.84   -65.75     0.92)  ; 36\r\n                          (  259.52   516.03   -66.60     0.92)  ; 37\r\n                          (  262.38   517.90   -64.57     0.92)  ; 38\r\n                          (  264.17   518.32   -65.30     0.92)  ; 39\r\n                          (  264.17   518.32   -65.32     0.92)  ; 40\r\n                          (  266.59   520.08   -65.97     0.92)  ; 41\r\n                          (  268.19   519.25   -66.65     0.92)  ; 42\r\n                          (  268.19   519.25   -66.68     0.92)  ; 43\r\n                          (  270.42   519.78   -67.90     0.92)  ; 44\r\n                          (  270.42   519.78   -67.93     0.92)  ; 45\r\n                          (  272.07   520.76   -69.42     0.92)  ; 46\r\n                          (  272.07   520.76   -69.45     0.92)  ; 47\r\n                          (  273.15   522.21   -70.27     0.92)  ; 48\r\n                          (  272.76   523.91   -71.72     0.92)  ; 49\r\n                          (  272.76   523.91   -71.75     0.92)  ; 50\r\n                          (  276.33   524.75   -72.20     0.92)  ; 51\r\n                          (  280.67   526.36   -73.57     0.92)  ; 52\r\n                          (  283.39   528.80   -74.30     0.92)  ; 53\r\n                          (  286.69   530.77   -74.78     0.92)  ; 54\r\n                          (  289.24   531.95   -75.25     0.92)  ; 55\r\n                          (  293.31   534.69   -75.88     0.92)  ; 56\r\n                          (  295.55   535.22   -76.03     0.92)  ; 57\r\n                          (  298.27   537.66   -75.72     0.92)  ; 58\r\n                          (  301.79   536.68   -76.03     0.92)  ; 59\r\n                          (  305.10   538.66   -76.13     0.92)  ; 60\r\n                          (  308.09   539.96   -77.35     0.92)  ; 61\r\n                          (  309.61   541.51   -78.20     0.92)  ; 62\r\n                          (  313.19   542.34   -79.07     0.92)  ; 63\r\n                          (  316.05   544.20   -80.37     0.92)  ; 64\r\n                          (  318.42   544.16   -82.22     0.92)  ; 65\r\n                          (  318.42   544.16   -82.52     0.92)  ; 66\r\n\r\n                          (Cross\r\n                            (Color White)\r\n                            (Name \"Marker 3\")\r\n                            (  236.69   486.79   -53.38     0.92)  ; 1\r\n                            (  236.46   483.76   -47.33     0.92)  ; 2\r\n                            (  234.70   479.16   -47.92     0.92)  ; 3\r\n                            (  227.51   475.68   -46.25     0.92)  ; 4\r\n                            (  223.72   471.82   -46.25     0.92)  ; 5\r\n                            (  243.44   490.17   -55.72     0.92)  ; 6\r\n                            (  245.40   491.82   -55.92     0.92)  ; 7\r\n                            (  244.87   494.10   -56.42     0.92)  ; 8\r\n                            (  247.65   498.31   -59.02     0.92)  ; 9\r\n                            (  250.24   501.31   -59.63     0.92)  ; 10\r\n                            (  249.86   508.99   -62.60     0.92)  ; 11\r\n                            (  252.88   506.12   -62.60     0.92)  ; 12\r\n                            (  254.10   507.00   -63.95     0.92)  ; 13\r\n                            (  253.94   511.73   -63.95     0.92)  ; 14\r\n                            (  256.51   508.75   -64.13     0.92)  ; 15\r\n                            (  257.64   517.98   -66.60     0.92)  ; 16\r\n                            (  261.00   515.78   -64.57     0.92)  ; 17\r\n                            (  265.10   520.33   -64.57     0.92)  ; 18\r\n                            (  267.48   520.29   -64.42     0.92)  ; 19\r\n                            (  271.75   520.09   -65.70     0.92)  ; 20\r\n                            (  270.91   521.69   -69.20     0.92)  ; 21\r\n                            (  273.56   526.49   -72.20     0.92)  ; 22\r\n                            (  275.07   522.06   -71.47     0.92)  ; 23\r\n                            (  280.03   525.02   -73.57     0.92)  ; 24\r\n                            (  280.00   529.19   -71.57     0.92)  ; 25\r\n                            (  284.01   530.13   -70.97     0.92)  ; 26\r\n                            (  304.89   541.58   -77.35     0.92)  ; 27\r\n                            (  303.85   535.97   -74.78     0.92)  ; 28\r\n                          )  ;  End of markers\r\n                           Normal\r\n                        |\r\n                          (  220.10   470.93   -47.88     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2\r\n                          (  219.93   475.68   -47.88     1.83)  ; 2\r\n                          (  219.00   479.63   -47.13     1.83)  ; 3\r\n                          (  217.94   484.16   -47.67     1.83)  ; 4\r\n                          (  216.57   488.02   -47.55     1.83)  ; 5\r\n                          (  215.46   490.75   -48.75     1.83)  ; 6\r\n                          (  216.44   494.55   -50.05     1.83)  ; 7\r\n                          (  215.02   496.62   -50.05     1.83)  ; 8\r\n                          (  216.14   499.86   -48.62     1.83)  ; 9\r\n                          (  215.66   503.93   -48.62     1.83)  ; 10\r\n                          (  215.63   508.10   -48.62     1.83)  ; 11\r\n                          (  214.96   510.92   -48.62     1.83)  ; 12\r\n                          (\r\n                            (  212.13   513.27   -48.62     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-1\r\n                            (  210.84   514.76   -50.42     0.92)  ; 2\r\n                            (  210.03   512.17   -51.88     0.92)  ; 3\r\n                            (  210.03   512.17   -51.90     0.92)  ; 4\r\n                            (  209.81   515.12   -53.35     0.92)  ; 5\r\n                            (  210.57   515.89   -54.32     0.92)  ; 6\r\n                            (  208.70   517.83   -55.05     0.92)  ; 7\r\n                            (  207.72   520.00   -55.97     0.92)  ; 8\r\n                            (  205.99   521.38   -56.95     0.92)  ; 9\r\n                            (  205.54   521.28   -58.13     0.92)  ; 10\r\n                            (  205.45   523.65   -57.63     0.92)  ; 11\r\n                            (  205.64   524.88   -58.88     0.92)  ; 12\r\n                            (  205.06   525.34   -60.27     0.92)  ; 13\r\n                            (  202.68   525.39   -61.92     0.92)  ; 14\r\n                            (  202.29   527.08   -63.40     0.92)  ; 15\r\n                            (  203.05   527.86   -64.90     0.92)  ; 16\r\n                            (  202.07   530.03   -65.47     0.92)  ; 17\r\n                            (  201.67   531.72   -64.75     0.92)  ; 18\r\n                            (  200.39   533.21   -65.63     0.92)  ; 19\r\n                            (  199.23   534.13   -66.22     0.92)  ; 20\r\n                            (  199.41   535.36   -67.10     0.92)  ; 21\r\n                            (  198.43   537.53   -67.57     0.92)  ; 22\r\n                            (  197.27   538.45   -68.35     0.92)  ; 23\r\n                            (  195.36   538.59   -69.32     0.92)  ; 24\r\n                            (  194.83   540.87   -69.88     0.92)  ; 25\r\n                            (  193.85   543.02   -70.72     0.92)  ; 26\r\n                            (  192.38   543.27   -72.13     0.92)  ; 27\r\n                            (  191.21   544.20   -73.47     0.92)  ; 28\r\n                            (  190.23   546.36   -74.65     0.92)  ; 29\r\n                            (  186.76   549.12   -75.07     0.92)  ; 30\r\n                            (  186.05   550.14   -76.00     0.92)  ; 31\r\n                            (  183.92   553.24   -77.13     0.92)  ; 32\r\n                            (  181.73   554.51   -78.60     0.92)  ; 33\r\n                            (  179.54   555.79   -80.02     0.92)  ; 34\r\n                            (  178.70   557.38   -80.92     0.92)  ; 35\r\n                            (  178.17   559.66   -82.57     0.92)  ; 36\r\n                            (  176.61   562.27   -83.53     0.92)  ; 37\r\n                            (  176.66   564.07   -84.88     0.92)  ; 38\r\n                            (  174.17   564.69   -85.63     0.92)  ; 39\r\n                            (  173.32   566.28   -86.72     0.92)  ; 40\r\n                            (  173.32   566.28   -86.75     0.92)  ; 41\r\n                            (  172.34   568.43   -88.00     0.92)  ; 42\r\n                            (  172.34   568.43   -88.02     0.92)  ; 43\r\n                            (  171.37   570.60   -87.65     0.92)  ; 44\r\n                            (  171.37   570.60   -87.68     0.92)  ; 45\r\n                            (  168.92   573.01   -88.53     0.92)  ; 46\r\n                            (  166.41   573.62   -88.80     0.92)  ; 47\r\n                            (  166.41   573.62   -88.82     0.92)  ; 48\r\n                            (  163.97   576.03   -89.45     0.92)  ; 49\r\n                            (  163.97   576.03   -89.48     0.92)  ; 50\r\n                            (  162.94   576.38   -89.55     0.92)  ; 51\r\n                            (  161.52   578.44   -91.22     0.92)  ; 52\r\n                            (  158.76   580.18   -92.38     0.92)  ; 53\r\n                            (  158.76   580.18   -92.40     0.92)  ; 54\r\n                            (  157.78   582.34   -93.65     0.92)  ; 55\r\n                            (  155.91   584.29   -95.20     0.92)  ; 56\r\n                            (  155.91   584.29   -95.22     0.92)  ; 57\r\n                            (  155.06   585.88   -95.88     0.92)  ; 58\r\n                            (  154.66   587.59   -96.93     0.92)  ; 59\r\n                            (  154.66   587.59   -96.95     0.92)  ; 60\r\n                            (  154.45   590.51   -97.75     0.92)  ; 61\r\n                            (  154.45   590.51   -97.78     0.92)  ; 62\r\n                            (  151.99   592.92   -98.38     0.92)  ; 63\r\n                            (  150.84   593.85   -99.63     0.92)  ; 64\r\n                            (  150.26   594.30  -101.25     0.92)  ; 65\r\n                            (  150.26   594.30  -101.27     0.92)  ; 66\r\n\r\n                            (Cross\r\n                              (Color White)\r\n                              (Name \"Marker 3\")\r\n                              (  206.07   519.01   -56.28     0.92)  ; 1\r\n                              (  209.06   520.32   -53.90     0.92)  ; 2\r\n                              (  206.62   522.73   -57.63     0.92)  ; 3\r\n                              (  207.69   524.17   -58.88     0.92)  ; 4\r\n                              (  201.49   524.51   -61.92     0.92)  ; 5\r\n                              (  203.34   532.70   -63.90     0.92)  ; 6\r\n                              (  197.89   533.82   -66.22     0.92)  ; 7\r\n                              (  195.89   536.33   -67.57     0.92)  ; 8\r\n                              (  199.63   538.41   -67.57     0.92)  ; 9\r\n                              (  197.90   539.79   -67.57     0.92)  ; 10\r\n                              (  211.37   512.49   -50.42     0.92)  ; 11\r\n                              (  163.30   578.86   -89.55     0.92)  ; 12\r\n                              (  158.86   583.79   -95.22     0.92)  ; 13\r\n                              (  155.81   580.69   -95.22     0.92)  ; 14\r\n                              (  153.32   587.27   -95.88     0.92)  ; 15\r\n                              (  163.47   574.12   -88.75     0.92)  ; 16\r\n                              (  211.82   512.60   -54.35     0.92)  ; 17\r\n                            )  ;  End of markers\r\n                             Normal\r\n                          |\r\n                            (  214.35   515.57   -49.25     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2\r\n                            (  214.85   517.48   -49.47     1.83)  ; 2\r\n                            (  215.34   519.38   -49.47     1.83)  ; 3\r\n                            (  213.78   522.00   -49.47     1.83)  ; 4\r\n                            (  214.20   526.28   -49.47     1.83)  ; 5\r\n                            (  213.72   530.35   -49.47     1.83)  ; 6\r\n                            (  213.19   532.61   -49.47     1.83)  ; 7\r\n                            (  214.17   536.42   -50.20     1.83)  ; 8\r\n                            (  214.08   538.80   -49.32     1.83)  ; 9\r\n                            (  212.39   541.98   -49.32     1.83)  ; 10\r\n                            (  212.05   545.48   -49.32     1.83)  ; 11\r\n\r\n                            (Cross\r\n                              (Color RGB (128, 255, 128))\r\n                              (Name \"Marker 3\")\r\n                              (  212.87   515.82   -49.25     1.83)  ; 1\r\n                              (  216.59   522.07   -49.47     1.83)  ; 2\r\n                              (  211.56   527.45   -49.47     1.83)  ; 3\r\n                              (  216.34   529.17   -49.47     1.83)  ; 4\r\n                              (  215.93   524.89   -49.47     1.83)  ; 5\r\n                            )  ;  End of markers\r\n                            (\r\n                              (  209.61   546.12   -47.82     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-1\r\n                              (  209.03   546.59   -46.82     0.92)  ; 2\r\n                              (  208.58   546.48   -46.82     0.92)  ; 3\r\n                              (  208.32   547.61   -46.82     0.92)  ; 4\r\n                              (  205.05   547.44   -45.77     0.92)  ; 5\r\n                              (  205.55   549.35   -45.07     0.92)  ; 6\r\n                              (  207.35   549.77   -43.67     0.92)  ; 7\r\n                              (  205.60   551.15   -42.17     0.92)  ; 8\r\n                              (  204.62   553.31   -41.17     0.92)  ; 9\r\n                              (  203.20   555.37   -40.57     0.92)  ; 10\r\n                              (  201.33   557.31   -41.35     0.92)  ; 11\r\n                              (  199.46   559.27   -41.50     0.92)  ; 12\r\n                              (  197.73   560.65   -40.63     0.92)  ; 13\r\n                              (  196.70   561.01   -39.55     0.92)  ; 14\r\n                              (  194.87   564.77   -39.05     0.92)  ; 15\r\n                              (  194.22   567.59   -38.77     0.92)  ; 16\r\n                              (  192.16   568.31   -38.77     0.92)  ; 17\r\n                              (  189.97   569.59   -37.90     0.92)  ; 18\r\n                              (  187.80   570.86   -37.30     0.92)  ; 19\r\n                              (  185.43   570.90   -35.83     0.92)  ; 20\r\n                              (  183.11   572.76   -35.83     0.92)  ; 21\r\n                              (  180.87   572.23   -35.83     0.92)  ; 22\r\n                              (  178.78   571.14   -37.47     0.92)  ; 23\r\n                              (  178.78   571.14   -37.50     0.92)  ; 24\r\n                              (  176.18   568.15   -38.25     0.92)  ; 25\r\n                              (  173.82   568.18   -36.50     0.92)  ; 26\r\n                              (  172.66   569.11   -36.50     0.92)  ; 27\r\n                              (  170.55   568.02   -36.78     0.92)  ; 28\r\n                              (  167.92   569.20   -37.70     0.92)  ; 29\r\n                              (  164.09   569.48   -38.35     0.92)  ; 30\r\n                              (  160.72   565.72   -38.63     0.92)  ; 31\r\n                              (  159.88   567.31   -39.60     0.92)  ; 32\r\n                              (  158.10   566.89   -40.10     0.92)  ; 33\r\n                              (  155.82   564.57   -39.75     0.92)  ; 34\r\n                              (  150.45   563.31   -39.75     0.92)  ; 35\r\n                              (  146.62   563.61   -41.20     0.92)  ; 36\r\n                              (  144.30   565.45   -40.85     0.92)  ; 37\r\n                              (  144.30   565.45   -40.88     0.92)  ; 38\r\n                              (  142.78   563.89   -40.00     0.92)  ; 39\r\n                              (  140.15   565.08   -39.80     0.92)  ; 40\r\n                              (  137.48   564.45   -39.80     0.92)  ; 41\r\n                              (  134.08   564.85   -38.88     0.92)  ; 42\r\n                              (  131.09   563.55   -37.58     0.92)  ; 43\r\n                              (  127.12   564.41   -36.97     0.92)  ; 44\r\n                              (  125.64   564.66   -35.70     0.92)  ; 45\r\n                              (  126.04   562.97   -34.90     0.92)  ; 46\r\n                              (  127.02   560.80   -34.70     0.92)  ; 47\r\n\r\n                              (Cross\r\n                                (Color White)\r\n                                (Name \"Marker 3\")\r\n                                (  209.71   549.72   -43.67     0.92)  ; 1\r\n                                (  204.13   551.41   -43.67     0.92)  ; 2\r\n                                (  201.38   559.12   -41.08     0.92)  ; 3\r\n                                (  201.86   555.06   -38.97     0.92)  ; 4\r\n                                (  200.09   560.60   -38.97     0.92)  ; 5\r\n                                (  194.77   561.16   -38.53     0.92)  ; 6\r\n                                (  198.66   556.69   -40.42     0.92)  ; 7\r\n                                (  191.62   564.60   -38.77     0.92)  ; 8\r\n                                (  193.66   563.87   -38.77     0.92)  ; 9\r\n                                (  196.27   566.88   -38.77     0.92)  ; 10\r\n                                (  195.64   565.54   -38.77     0.92)  ; 11\r\n                                (  188.63   569.27   -37.30     0.92)  ; 12\r\n                                (  190.02   571.39   -37.30     0.92)  ; 13\r\n                                (  185.92   572.82   -36.13     0.92)  ; 14\r\n                                (  184.76   573.73   -35.80     0.92)  ; 15\r\n                                (  177.80   573.29   -38.25     0.92)  ; 16\r\n                                (  180.07   569.65   -36.80     0.92)  ; 17\r\n                                (  175.42   567.37   -36.78     0.92)  ; 18\r\n                                (  178.55   568.10   -38.88     0.92)  ; 19\r\n                                (  167.42   567.29   -36.92     0.92)  ; 20\r\n                                (  167.98   571.00   -37.25     0.92)  ; 21\r\n                                (  172.97   569.78   -35.83     0.92)  ; 22\r\n                                (  158.15   568.69   -40.10     0.92)  ; 23\r\n                                (  153.37   566.98   -40.32     0.92)  ; 24\r\n                                (  150.10   560.83   -40.10     0.92)  ; 25\r\n                                (  146.27   561.13   -40.72     0.92)  ; 26\r\n                                (  148.46   565.82   -38.70     0.92)  ; 27\r\n                                (  143.31   561.64   -39.40     0.92)  ; 28\r\n                                (  141.84   561.89   -38.83     0.92)  ; 29\r\n                                (  140.55   563.38   -38.53     0.92)  ; 30\r\n                                (  138.76   562.96   -38.53     0.92)  ; 31\r\n                                (  140.33   566.31   -38.47     0.92)  ; 32\r\n                                (  138.76   562.96   -39.80     0.92)  ; 33\r\n                                (  136.39   563.00   -39.80     0.92)  ; 34\r\n                                (  137.66   565.68   -39.80     0.92)  ; 35\r\n                                (  140.33   566.31   -39.80     0.92)  ; 36\r\n                                (  134.25   566.08   -37.58     0.92)  ; 37\r\n                                (  125.06   565.11   -34.70     0.92)  ; 38\r\n                                (  188.85   544.24   -74.65     0.92)  ; 39\r\n                                (  180.53   553.63   -80.02     0.92)  ; 40\r\n                                (  178.00   564.39   -81.67     0.92)  ; 41\r\n                                (  177.14   560.00   -84.92     0.92)  ; 42\r\n                                (  173.37   568.08   -88.02     0.92)  ; 43\r\n                                (  172.38   564.26   -88.02     0.92)  ; 44\r\n                                (  168.87   571.21   -87.22     0.92)  ; 45\r\n                                (  208.09   544.57   -46.82     0.92)  ; 46\r\n                              )  ;  End of markers\r\n                               Normal\r\n                            |\r\n                              (  212.78   550.42   -49.92     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2\r\n                              (  214.30   551.98   -49.83     1.83)  ; 2\r\n                              (  214.66   554.45   -49.83     1.83)  ; 3\r\n                              (  213.68   556.61   -51.22     1.83)  ; 4\r\n                              (  213.33   560.11   -52.02     1.83)  ; 5\r\n                              (  214.45   563.35   -52.77     1.83)  ; 6\r\n                              (  214.28   568.11   -53.62     1.83)  ; 7\r\n                              (  214.84   571.80   -55.27     1.83)  ; 8\r\n                              (  215.69   576.19   -55.27     1.83)  ; 9\r\n                              (  216.24   579.91   -56.20     1.83)  ; 10\r\n                              (  216.46   582.95   -57.40     1.83)  ; 11\r\n                              (  216.11   586.45   -58.65     1.83)  ; 12\r\n                              (  217.96   588.66   -59.40     1.83)  ; 13\r\n                              (  218.89   590.68   -58.80     1.83)  ; 14\r\n                              (  217.07   594.44   -57.47     1.83)  ; 15\r\n                              (  216.85   597.36   -57.92     1.83)  ; 16\r\n                              (  217.84   601.18   -57.92     1.83)  ; 17\r\n                              (  218.34   603.08   -59.10     1.83)  ; 18\r\n                              (  218.83   605.00   -59.10     1.83)  ; 19\r\n                              (  218.66   609.73   -59.80     1.83)  ; 20\r\n                              (  218.76   613.34   -60.60     1.83)  ; 21\r\n                              (  219.30   617.06   -61.55     1.83)  ; 22\r\n                              (  219.40   620.65   -62.40     1.83)  ; 23\r\n                              (  219.40   620.65   -62.42     1.83)  ; 24\r\n                              (  220.08   623.80   -63.40     1.83)  ; 25\r\n                              (  220.08   623.80   -63.42     1.83)  ; 26\r\n                              (  218.84   627.09   -63.42     1.83)  ; 27\r\n                              (  220.45   632.24   -63.88     1.83)  ; 28\r\n                              (  221.46   635.94   -64.05     1.83)  ; 29\r\n                              (  220.35   638.66   -63.05     1.83)  ; 30\r\n                              (  221.65   643.16   -62.45     1.83)  ; 31\r\n                              (  220.99   645.98   -62.08     1.83)  ; 32\r\n\r\n                              (Cross\r\n                                (Color RGB (128, 255, 128))\r\n                                (Name \"Marker 3\")\r\n                                (  212.12   537.13   -49.32     1.83)  ; 1\r\n                                (  216.06   540.45   -49.32     1.83)  ; 2\r\n                                (  211.46   539.97   -49.32     1.83)  ; 3\r\n                                (  214.78   547.92   -48.95     1.83)  ; 4\r\n                                (  212.54   563.51   -51.72     1.83)  ; 5\r\n                                (  217.00   564.56   -51.72     1.83)  ; 6\r\n                                (  212.24   568.82   -55.27     1.83)  ; 7\r\n                                (  216.52   568.63   -55.27     1.83)  ; 8\r\n                                (  217.51   572.43   -55.27     1.83)  ; 9\r\n                                (  217.88   574.92   -55.27     1.83)  ; 10\r\n                                (  213.73   580.50   -55.27     1.83)  ; 11\r\n                                (  214.10   582.99   -57.92     1.83)  ; 12\r\n                                (  219.89   600.46   -57.92     1.83)  ; 13\r\n                                (  216.29   603.80   -58.25     1.83)  ; 14\r\n                                (  217.36   605.25   -58.25     1.83)  ; 15\r\n                                (  218.44   628.79   -63.88     1.83)  ; 16\r\n                                (  219.12   631.93   -64.53     1.83)  ; 17\r\n                                (  223.18   634.68   -63.75     1.83)  ; 18\r\n                              )  ;  End of markers\r\n                              (\r\n                                (  223.32   646.54   -62.08     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-1\r\n                                (  227.43   645.10   -61.15     0.46)  ; 2\r\n                                (  229.21   645.53   -62.35     0.46)  ; 3\r\n                                (  230.81   644.70   -63.53     0.46)  ; 4\r\n                                (  231.84   644.35   -64.85     0.46)  ; 5\r\n                                (  232.60   645.13   -66.82     0.46)  ; 6\r\n                                (  233.23   645.89   -65.95     0.46)  ; 7\r\n                                (  234.03   644.98   -66.57     0.46)  ; 8\r\n                                (  235.28   645.76   -68.20     0.46)  ; 9\r\n                                (  237.78   645.15   -68.85     0.46)  ; 10\r\n                                (  239.57   645.57   -69.60     0.46)  ; 11\r\n                                (  241.16   644.74   -70.00     0.46)  ; 12\r\n                                (  245.27   643.32   -70.78     0.46)  ; 13\r\n                                (  246.96   640.13   -71.63     0.46)  ; 14\r\n                                (  249.46   639.53   -70.72     0.46)  ; 15\r\n                                (  252.73   639.69   -71.75     0.46)  ; 16\r\n                                (  255.80   638.61   -72.53     0.46)  ; 17\r\n                                (  257.27   638.36   -73.47     0.46)  ; 18\r\n                                (  260.00   640.79   -72.85     0.46)  ; 19\r\n                                (  262.60   643.79   -72.95     0.46)  ; 20\r\n                                (  264.25   644.78   -74.05     0.46)  ; 21\r\n                                (  266.04   645.20   -75.70     0.46)  ; 22\r\n                                (  267.11   646.64   -78.42     0.46)  ; 23\r\n                                (  267.11   646.64   -78.53     0.46)  ; 24\r\n\r\n                                (Cross\r\n                                  (Color White)\r\n                                  (Name \"Marker 3\")\r\n                                  (  260.52   638.53   -73.70     0.46)  ; 1\r\n                                  (  255.49   637.95   -70.22     0.46)  ; 2\r\n                                  (  254.06   639.99   -73.25     0.46)  ; 3\r\n                                  (  260.81   643.38   -72.95     0.46)  ; 4\r\n                                  (  264.43   646.01   -72.40     0.46)  ; 5\r\n                                  (  240.63   647.01   -70.00     0.46)  ; 6\r\n                                  (  225.19   644.58   -61.15     0.46)  ; 7\r\n                                  (  233.91   645.35   -34.78     1.83)  ; 8\r\n                                )  ;  End of markers\r\n                                 Normal\r\n                              |\r\n                                (  220.64   649.48   -61.57     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2\r\n                                (  219.71   653.45   -61.57     1.83)  ; 2\r\n                                (  219.23   657.51   -61.57     1.83)  ; 3\r\n                                (  220.36   660.76   -61.57     1.83)  ; 4\r\n                                (  220.90   664.47   -61.57     1.83)  ; 5\r\n                                (  220.24   667.30   -61.57     1.83)  ; 6\r\n                                (  220.51   672.14   -62.65     1.83)  ; 7\r\n                                (  220.92   676.42   -63.53     1.83)  ; 8\r\n                                (  220.57   679.92   -62.35     1.83)  ; 9\r\n                                (  221.11   683.63   -62.35     1.83)  ; 10\r\n                                (  221.72   689.15   -62.97     1.83)  ; 11\r\n                                (  221.68   693.31   -63.72     1.83)  ; 12\r\n                                (  222.17   695.23   -64.13     1.83)  ; 13\r\n                                (  222.71   698.94   -64.40     1.83)  ; 14\r\n                                (  221.60   701.66   -64.40     1.83)  ; 15\r\n                                (  221.97   704.14   -64.85     1.83)  ; 16\r\n                                (  222.56   709.64   -64.85     1.83)  ; 17\r\n                                (  222.66   713.25   -64.27     1.83)  ; 18\r\n                                (  222.58   715.62   -64.27     1.83)  ; 19\r\n                                (  223.83   718.30   -64.60     1.83)  ; 20\r\n                                (  224.42   723.82   -64.32     1.83)  ; 21\r\n                                (  224.34   726.19   -63.05     1.83)  ; 22\r\n                                (  224.57   729.23   -61.88     1.83)  ; 23\r\n                                (  224.08   733.29   -61.10     1.83)  ; 24\r\n                                (  223.41   736.13   -60.25     1.83)  ; 25\r\n                                (  224.68   738.81   -59.27     1.83)  ; 26\r\n                                (  225.09   743.09   -58.82     1.83)  ; 27\r\n                                (  225.63   746.79   -58.82     1.83)  ; 28\r\n                                (  224.70   750.76   -59.95     1.83)  ; 29\r\n                                (  226.41   753.55   -59.85     1.83)  ; 30\r\n                                (  226.31   755.92   -59.85     1.83)  ; 31\r\n                                (  226.29   760.08   -59.85     1.83)  ; 32\r\n                                (  224.99   761.58   -60.45     1.83)  ; 33\r\n                                (  224.96   765.74   -60.45     1.83)  ; 34\r\n                                (  225.33   768.23   -61.00     1.83)  ; 35\r\n                                (  224.80   770.49   -59.97     1.83)  ; 36\r\n                                (  224.00   773.88   -59.32     1.83)  ; 37\r\n\r\n                                (Cross\r\n                                  (Color White)\r\n                                  (Name \"Marker 3\")\r\n                                  (  217.22   650.48   -59.32     0.46)  ; 1\r\n                                  (  218.60   652.58   -59.32     0.46)  ; 2\r\n                                  (  223.07   653.64   -58.45     0.46)  ; 3\r\n                                  (  222.57   651.73   -58.45     0.46)  ; 4\r\n                                  (  221.69   657.49   -57.28     0.46)  ; 5\r\n                                )  ;  End of markers\r\n\r\n                                (Cross\r\n                                  (Color RGB (128, 255, 128))\r\n                                  (Name \"Marker 3\")\r\n                                  (  217.92   647.06   -62.08     1.83)  ; 1\r\n                                  (  223.31   666.23   -59.80     1.83)  ; 2\r\n                                  (  222.20   668.97   -59.80     1.83)  ; 3\r\n                                  (  218.55   670.49   -59.80     1.83)  ; 4\r\n                                  (  223.01   671.53   -59.80     1.83)  ; 5\r\n                                  (  218.83   675.33   -59.80     1.83)  ; 6\r\n                                  (  220.14   685.80   -62.97     1.83)  ; 7\r\n                                  (  219.92   688.73   -62.97     1.83)  ; 8\r\n                                  (  224.29   686.17   -62.97     1.83)  ; 9\r\n                                  (  223.80   684.26   -63.05     1.83)  ; 10\r\n                                  (  224.76   698.22   -64.40     1.83)  ; 11\r\n                                  (  220.75   697.28   -64.40     1.83)  ; 12\r\n                                  (  224.01   703.42   -64.85     1.83)  ; 13\r\n                                  (  221.30   723.08   -64.32     1.83)  ; 14\r\n                                  (  227.49   722.75   -64.32     1.83)  ; 15\r\n                                  (  221.28   717.11   -64.32     1.83)  ; 16\r\n                                  (  226.50   735.05   -59.27     1.83)  ; 17\r\n                                  (  221.50   736.28   -58.27     1.83)  ; 18\r\n                                  (  227.49   738.87   -58.27     1.83)  ; 19\r\n                                  (  228.62   748.10   -59.95     1.83)  ; 20\r\n                                  (  224.17   753.03   -60.63     1.83)  ; 21\r\n                                  (  224.63   759.10   -61.88     1.83)  ; 22\r\n                                  (  223.79   760.69   -61.88     1.83)  ; 23\r\n                                )  ;  End of markers\r\n                                (\r\n                                  (  223.30   779.28   -59.32     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1\r\n                                  (  223.10   782.21   -59.32     1.38)  ; 2\r\n                                  (  223.64   785.92   -59.22     1.38)  ; 3\r\n                                  (  224.58   787.94   -60.57     1.38)  ; 4\r\n                                  (  224.49   790.31   -60.85     1.38)  ; 5\r\n                                  (  225.04   794.01   -60.85     1.38)  ; 6\r\n                                  (  225.08   795.81   -61.30     1.38)  ; 7\r\n                                  (  225.31   798.85   -60.32     1.38)  ; 8\r\n                                  (  225.54   801.90   -59.20     1.38)  ; 9\r\n                                  (  225.32   804.84   -58.70     1.38)  ; 10\r\n                                  (  225.57   807.87   -59.15     1.38)  ; 11\r\n                                  (  225.21   811.37   -59.65     1.38)  ; 12\r\n                                  (  225.17   815.55   -60.07     1.38)  ; 13\r\n                                  (  224.96   818.49   -60.72     1.38)  ; 14\r\n                                  (  224.03   822.44   -61.60     1.38)  ; 15\r\n                                  (  224.26   825.48   -62.25     1.38)  ; 16\r\n                                  (  223.33   829.44   -62.25     1.38)  ; 17\r\n                                  (  222.40   833.41   -62.95     1.38)  ; 18\r\n                                  (  222.63   836.45   -64.02     1.38)  ; 19\r\n                                  (  223.75   839.70   -64.65     1.38)  ; 20\r\n                                  (  222.50   842.99   -64.65     1.38)  ; 21\r\n                                  (  221.26   846.27   -65.13     1.38)  ; 22\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  223.43   794.83   -61.30     1.38)  ; 1\r\n                                    (  222.96   804.88   -58.70     1.38)  ; 2\r\n                                    (  226.44   802.12   -58.70     1.38)  ; 3\r\n                                    (  227.26   810.67   -59.60     1.38)  ; 4\r\n                                    (  224.22   807.55   -59.60     1.38)  ; 5\r\n                                    (  222.65   820.33   -61.60     1.38)  ; 6\r\n                                    (  226.03   819.93   -61.60     1.38)  ; 7\r\n                                    (  221.99   829.12   -62.25     1.38)  ; 8\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  222.29   850.00   -65.80     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1\r\n                                    (  222.38   853.60   -64.88     1.38)  ; 2\r\n                                    (  222.62   856.64   -64.88     1.38)  ; 3\r\n                                    (  221.55   861.17   -66.20     1.38)  ; 4\r\n                                    (  221.47   863.54   -66.50     1.38)  ; 5\r\n                                    (  220.67   866.94   -66.75     1.38)  ; 6\r\n                                    (  220.67   866.94   -66.77     1.38)  ; 7\r\n                                    (  221.34   870.08   -67.28     1.38)  ; 8\r\n                                    (  221.08   871.21   -67.40     1.38)  ; 9\r\n                                    (  221.05   875.38   -67.40     1.38)  ; 10\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  224.74   847.59   -64.88     1.38)  ; 1\r\n                                      (  220.64   849.02   -64.88     1.38)  ; 2\r\n                                      (  224.26   851.65   -62.00     1.38)  ; 3\r\n                                      (  219.33   866.62   -66.38     1.38)  ; 4\r\n                                      (  220.31   864.46   -66.35     1.38)  ; 5\r\n                                      (  219.12   869.56   -67.30     1.38)  ; 6\r\n                                      (  222.79   874.00   -67.40     1.38)  ; 7\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  219.47   878.56   -67.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1-1\r\n                                      (  218.92   880.84   -67.40     0.92)  ; 2\r\n                                      (  219.30   883.30   -67.40     0.92)  ; 3\r\n                                      (  218.77   885.57   -68.17     0.92)  ; 4\r\n                                      (  218.68   887.94   -69.07     0.92)  ; 5\r\n                                      (  218.34   891.44   -69.70     0.92)  ; 6\r\n                                      (  216.64   894.63   -70.20     0.92)  ; 7\r\n                                      (  217.01   897.10   -70.20     0.92)  ; 8\r\n                                      (  216.48   899.37   -70.20     0.92)  ; 9\r\n                                      (  215.81   902.20   -70.70     0.92)  ; 10\r\n                                      (  215.28   904.47   -71.15     0.92)  ; 11\r\n                                      (  214.62   907.29   -71.22     0.92)  ; 12\r\n                                      (  212.75   909.24   -70.78     0.92)  ; 13\r\n                                      (  212.08   912.06   -70.78     0.92)  ; 14\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  213.22   910.03   -27.00     1.38)  ; 1\r\n                                      )  ;  End of markers\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  220.67   879.45   -67.40     0.92)  ; 1\r\n                                        (  217.72   879.95   -67.40     0.92)  ; 2\r\n                                        (  220.64   883.62   -67.40     0.92)  ; 3\r\n                                        (  220.55   885.99   -69.70     0.92)  ; 4\r\n                                        (  217.82   905.66   -71.22     0.92)  ; 5\r\n                                        (  214.80   908.52   -71.22     0.92)  ; 6\r\n                                        (  211.41   908.93   -70.78     0.92)  ; 7\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  213.34   914.76   -71.52     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1-1-1\r\n                                        (  214.01   917.90   -72.17     0.92)  ; 2\r\n                                        (  214.69   921.04   -73.00     0.92)  ; 3\r\n                                        (  216.98   923.37   -73.28     0.92)  ; 4\r\n                                        (  218.23   926.05   -74.13     0.92)  ; 5\r\n                                        (  221.09   927.92   -74.13     0.92)  ; 6\r\n                                        (  220.96   928.49   -74.13     0.92)  ; 7\r\n                                        (  223.96   929.78   -74.97     0.92)  ; 8\r\n                                        (  225.78   932.00   -75.90     0.92)  ; 9\r\n                                        (  226.60   934.58   -76.75     0.92)  ; 10\r\n                                        (  229.15   935.77   -77.50     0.92)  ; 11\r\n                                        (  230.13   939.59   -77.50     0.92)  ; 12\r\n                                        (  231.21   941.03   -78.88     0.92)  ; 13\r\n                                        (  231.52   941.70   -78.65     0.92)  ; 14\r\n                                        (  233.03   943.26   -80.50     0.92)  ; 15\r\n                                        (  233.40   945.72   -81.07     0.92)  ; 16\r\n                                        (  234.04   947.07   -81.53     0.92)  ; 17\r\n                                        (  235.51   946.82   -82.45     0.92)  ; 18\r\n                                        (  237.21   949.61   -83.88     0.92)  ; 19\r\n                                        (  238.72   951.15   -83.80     0.92)  ; 20\r\n                                        (  240.26   952.70   -83.82     0.92)  ; 21\r\n                                        (  240.43   953.95   -85.13     0.92)  ; 22\r\n                                        (  241.82   956.06   -86.85     0.92)  ; 23\r\n                                        (  243.34   957.61   -87.75     0.92)  ; 24\r\n                                        (  243.56   960.65   -88.65     0.92)  ; 25\r\n                                        (  242.90   963.48   -88.65     0.92)  ; 26\r\n                                        (  243.27   965.96   -88.65     0.92)  ; 27\r\n                                        (  244.03   966.73   -89.55     0.92)  ; 28\r\n                                        (  245.15   969.98   -90.65     0.92)  ; 29\r\n                                        (  245.15   969.98   -90.68     0.92)  ; 30\r\n                                        (  246.36   970.86   -90.85     0.92)  ; 31\r\n                                        (  248.77   972.63   -90.88     0.92)  ; 32\r\n                                        (  249.00   975.66   -91.62     0.92)  ; 33\r\n                                        (  248.92   978.04   -92.38     0.92)  ; 34\r\n                                        (  250.26   978.34   -92.97     0.92)  ; 35\r\n                                        (  249.81   978.25   -92.97     0.92)  ; 36\r\n                                        (  251.39   981.60   -93.53     0.92)  ; 37\r\n                                        (  251.60   984.62   -94.90     0.92)  ; 38\r\n                                        (  251.60   984.62   -94.92     0.92)  ; 39\r\n                                        (  251.33   985.76   -95.77     0.92)  ; 40\r\n                                        (  253.44   986.84   -96.43     0.92)  ; 41\r\n                                        (  253.92   988.75   -97.38     0.92)  ; 42\r\n                                        (  253.92   988.75   -97.42     0.92)  ; 43\r\n                                        (  255.01   990.20   -98.15     0.92)  ; 44\r\n                                        (  256.67   991.19   -98.47     0.92)  ; 45\r\n                                        (  257.60   993.20   -98.47     0.92)  ; 46\r\n                                        (  258.41   995.77   -99.63     0.92)  ; 47\r\n                                        (  259.99   999.12   -99.63     0.92)  ; 48\r\n                                        (  259.90  1001.50   -99.63     0.92)  ; 49\r\n                                        (  258.65  1004.79  -100.22     0.92)  ; 50\r\n                                        (  257.98  1007.62  -101.13     0.92)  ; 51\r\n                                        (  258.36  1010.09  -102.13     0.92)  ; 52\r\n                                        (  258.41  1011.88  -103.23     0.92)  ; 53\r\n                                        (  258.46  1013.69  -103.60     0.92)  ; 54\r\n                                        (  259.84  1015.81  -104.88     0.92)  ; 55\r\n                                        (  260.91  1017.26  -105.42     0.92)  ; 56\r\n                                        (  262.21  1021.75  -106.13     0.92)  ; 57\r\n                                        (  262.72  1023.66  -106.32     0.92)  ; 58\r\n                                        (  262.72  1023.66  -106.35     0.92)  ; 59\r\n                                        (  262.77  1025.46  -107.10     0.92)  ; 60\r\n                                        (  262.82  1027.26  -108.42     0.92)  ; 61\r\n                                        (  263.75  1029.27  -109.82     0.92)  ; 62\r\n                                        (  264.43  1032.41  -110.63     0.92)  ; 63\r\n                                        (  264.30  1032.98  -112.15     0.92)  ; 64\r\n                                        (  265.41  1036.23  -113.67     0.92)  ; 65\r\n                                        (  266.95  1037.78  -115.37     0.92)  ; 66\r\n                                        (  266.10  1039.37  -117.02     0.92)  ; 67\r\n                                        (  264.48  1040.20  -118.32     0.92)  ; 68\r\n                                        (  264.48  1040.20  -118.38     0.92)  ; 69\r\n                                        (  263.34  1041.11  -120.65     0.92)  ; 70\r\n                                        (  264.28  1043.12  -122.50     0.92)  ; 71\r\n                                        (  263.74  1045.39  -124.52     0.92)  ; 72\r\n                                        (  263.48  1046.52  -126.65     0.92)  ; 73\r\n                                        (  262.18  1048.01  -130.23     0.92)  ; 74\r\n                                        (  263.85  1049.00  -133.30     0.92)  ; 75\r\n                                        (  263.85  1049.00  -133.32     0.92)  ; 76\r\n                                        (  265.06  1049.88  -132.68     0.92)  ; 77\r\n                                        (  265.06  1049.88  -132.77     0.92)  ; 78\r\n                                        (  265.76  1048.86  -135.93     0.92)  ; 79\r\n                                        (  266.84  1050.30  -138.70     0.92)  ; 80\r\n                                        (  266.84  1050.30  -138.72     0.92)  ; 81\r\n                                        (  267.25  1054.57  -140.47     0.92)  ; 82\r\n                                        (  269.67  1056.33  -140.47     0.92)  ; 83\r\n                                        (  271.41  1060.92  -141.35     0.92)  ; 84\r\n                                        (  274.53  1061.65  -141.38     0.92)  ; 85\r\n                                        (  275.09  1065.37  -142.68     0.92)  ; 86\r\n                                        (  278.21  1066.10  -143.20     0.92)  ; 87\r\n                                        (  279.24  1065.74  -145.63     0.92)  ; 88\r\n                                        (  280.26  1065.39  -148.48     0.92)  ; 89\r\n                                        (  280.26  1065.39  -148.52     0.92)  ; 90\r\n                                        (  282.36  1066.47  -149.73     0.92)  ; 91\r\n                                        (  282.36  1066.47  -149.75     0.92)  ; 92\r\n                                        (  283.30  1068.48  -152.05     0.92)  ; 93\r\n                                        (  283.80  1070.39  -153.82     0.46)  ; 94\r\n                                        (  285.76  1072.05  -156.72     0.46)  ; 95\r\n                                        (  285.76  1072.05  -157.27     0.46)  ; 96\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  215.21   912.80   -71.52     0.92)  ; 1\r\n                                          (  214.96   925.89   -73.28     0.92)  ; 2\r\n                                          (  217.21   926.42   -74.13     0.92)  ; 3\r\n                                          (  218.41   927.29   -74.13     0.92)  ; 4\r\n                                          (  220.38   928.94   -74.13     0.92)  ; 5\r\n                                          (  218.32   923.68   -74.13     0.92)  ; 6\r\n                                          (  224.80   928.19   -76.75     0.92)  ; 7\r\n                                          (  231.95   935.83   -77.50     0.92)  ; 8\r\n                                          (  231.57   943.51   -78.65     0.92)  ; 9\r\n                                          (  235.41   943.21   -79.60     0.92)  ; 10\r\n                                          (  235.20   946.15   -79.60     0.92)  ; 11\r\n                                          (  238.77   946.99   -83.70     0.92)  ; 12\r\n                                          (  236.50   950.64   -82.40     0.92)  ; 13\r\n                                          (  242.66   954.47   -86.85     0.92)  ; 14\r\n                                          (  240.82   968.37   -88.97     0.92)  ; 15\r\n                                          (  242.34   969.91   -88.97     0.92)  ; 16\r\n                                          (  244.14   970.34   -89.90     0.92)  ; 17\r\n                                          (  247.52   969.93   -89.90     0.92)  ; 18\r\n                                          (  247.94   980.18   -93.53     0.92)  ; 19\r\n                                          (  251.07   986.88   -93.40     0.92)  ; 20\r\n                                          (  253.57   986.28   -95.88     0.92)  ; 21\r\n                                          (  259.79   991.91   -97.42     0.92)  ; 22\r\n                                          (  258.94   993.50   -99.63     0.92)  ; 23\r\n                                          (  260.71  1004.08   -99.63     0.92)  ; 24\r\n                                          (  258.28  1002.31   -99.63     0.92)  ; 25\r\n                                          (  256.16  1011.37  -101.77     0.92)  ; 26\r\n                                          (  260.10  1014.68  -105.42     0.92)  ; 27\r\n                                          (  260.87  1015.45  -105.42     0.92)  ; 28\r\n                                          (  259.64  1024.72  -105.63     0.92)  ; 29\r\n                                          (  262.94  1020.71  -105.65     0.92)  ; 30\r\n                                          (  263.56  1022.07  -105.65     0.92)  ; 31\r\n                                          (  262.69  1027.82  -105.65     0.92)  ; 32\r\n                                          (  267.14  1028.88  -108.53     0.92)  ; 33\r\n                                          (  260.32  1027.87  -112.15     0.92)  ; 34\r\n                                          (  262.69  1033.80  -112.15     0.92)  ; 35\r\n                                          (  269.12  1036.50  -112.90     0.92)  ; 36\r\n                                          (  263.10  1038.07  -113.15     0.92)  ; 37\r\n                                          (  262.21  1037.86  -118.45     0.92)  ; 38\r\n                                          (  267.18  1040.83  -118.45     0.92)  ; 39\r\n                                          (  260.78  1039.92  -120.65     0.92)  ; 40\r\n                                          (  266.25  1044.79  -124.52     0.92)  ; 41\r\n                                          (  266.16  1047.15  -124.52     0.92)  ; 42\r\n                                          (  264.79  1051.00  -133.05     0.92)  ; 43\r\n                                          (  260.93  1045.33  -129.75     0.92)  ; 44\r\n                                          (  269.72  1058.14  -141.35     0.92)  ; 45\r\n                                          (  272.71  1065.41  -143.20     0.92)  ; 46\r\n                                          (  280.67  1069.67  -145.63     0.92)  ; 47\r\n                                          (  278.48  1064.97  -145.63     0.92)  ; 48\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    |\r\n                                      (  222.75   878.16   -67.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1-2\r\n                                      (  224.14   880.29   -68.25     0.92)  ; 2\r\n                                      (  225.08   882.30   -68.25     0.92)  ; 3\r\n                                      (  226.20   885.55   -68.25     0.92)  ; 4\r\n                                      (  227.91   888.34   -69.05     0.92)  ; 5\r\n                                      (  228.40   890.24   -68.75     0.92)  ; 6\r\n                                      (  230.06   891.23   -68.67     0.92)  ; 7\r\n                                      (  231.62   894.59   -68.67     0.92)  ; 8\r\n                                      (  234.49   896.45   -69.32     0.92)  ; 9\r\n                                      (  236.90   898.21   -69.32     0.92)  ; 10\r\n                                      (  238.69   898.63   -70.32     0.92)  ; 11\r\n                                      (  240.48   899.04   -71.17     0.92)  ; 12\r\n                                      (  241.55   900.49   -71.85     0.92)  ; 13\r\n                                      (  242.93   902.60   -72.53     0.92)  ; 14\r\n                                      (  245.04   903.70   -72.53     0.92)  ; 15\r\n                                      (  247.01   905.35   -72.28     0.92)  ; 16\r\n                                      (  247.64   906.70   -73.78     0.92)  ; 17\r\n                                      (  251.26   909.33   -74.13     0.92)  ; 18\r\n                                      (  254.57   911.30   -74.13     0.92)  ; 19\r\n                                      (  257.42   913.17   -73.80     0.92)  ; 20\r\n                                      (  259.52   914.26   -73.80     0.92)  ; 21\r\n                                      (  260.34   916.83   -73.80     0.92)  ; 22\r\n                                      (  262.75   918.60   -74.10     0.92)  ; 23\r\n                                      (  264.71   920.25   -74.10     0.92)  ; 24\r\n                                      (  266.56   922.47   -74.10     0.92)  ; 25\r\n                                      (  268.20   923.46   -73.47     0.92)  ; 26\r\n                                      (  270.93   925.89   -73.47     0.92)  ; 27\r\n                                      (  274.24   927.86   -73.47     0.92)  ; 28\r\n                                      (  276.52   930.18   -74.53     0.92)  ; 29\r\n                                      (  278.94   931.94   -74.82     0.92)  ; 30\r\n                                      (  279.88   933.95   -75.10     0.92)  ; 31\r\n                                      (  283.18   935.93   -75.38     0.92)  ; 32\r\n                                      (  285.60   937.69   -75.67     0.92)  ; 33\r\n                                      (  286.81   938.57   -74.67     0.92)  ; 34\r\n                                      (  286.81   938.57   -74.72     0.92)  ; 35\r\n                                      (  289.54   940.99   -73.42     0.92)  ; 36\r\n                                      (  290.03   942.90   -72.20     0.92)  ; 37\r\n                                      (  291.86   945.13   -70.75     0.92)  ; 38\r\n                                      (  292.80   947.14   -69.57     0.92)  ; 39\r\n                                      (  294.64   949.36   -69.57     0.92)  ; 40\r\n                                      (  294.19   949.26   -69.57     0.92)  ; 41\r\n                                      (  296.22   952.71   -68.65     0.92)  ; 42\r\n                                      (  298.04   954.94   -68.20     0.92)  ; 43\r\n                                      (  298.04   954.94   -68.25     0.92)  ; 44\r\n                                      (  298.86   957.52   -67.47     0.92)  ; 45\r\n                                      (  298.86   957.52   -67.50     0.92)  ; 46\r\n                                      (  301.41   958.71   -66.82     0.92)  ; 47\r\n                                      (  303.81   960.47   -66.22     0.46)  ; 48\r\n                                      (  304.71   960.69   -65.52     0.46)  ; 49\r\n                                      (  305.97   963.36   -64.50     0.46)  ; 50\r\n                                      (  308.65   963.99   -63.22     0.46)  ; 51\r\n                                      (  310.30   964.97   -63.17     0.46)  ; 52\r\n                                      (  311.99   967.77   -62.28     0.46)  ; 53\r\n                                      (  314.02   971.23   -61.05     0.46)  ; 54\r\n                                      (  316.88   973.09   -60.15     0.46)  ; 55\r\n                                      (  316.88   973.09   -60.17     0.46)  ; 56\r\n                                      (  319.48   976.08   -59.32     0.46)  ; 57\r\n                                      (  319.48   976.08   -59.30     0.46)  ; 58\r\n                                      (  321.89   977.85   -59.95     0.46)  ; 59\r\n                                      (  322.97   979.29   -59.13     0.46)  ; 60\r\n                                      (  325.12   982.18   -58.58     0.46)  ; 61\r\n                                      (  328.29   984.72   -58.10     0.46)  ; 62\r\n                                      (  328.47   985.95   -57.50     0.46)  ; 63\r\n                                      (  331.06   988.96   -56.82     0.46)  ; 64\r\n                                      (  333.03   990.61   -55.83     0.46)  ; 65\r\n                                      (  335.58   991.80   -54.78     0.46)  ; 66\r\n                                      (  336.78   992.67   -54.45     0.46)  ; 67\r\n                                      (  337.60   995.26   -53.85     0.46)  ; 68\r\n                                      (  338.54   997.27   -54.32     0.46)  ; 69\r\n                                      (  341.26   999.70   -54.85     0.46)  ; 70\r\n                                      (  343.85  1002.70   -55.60     0.46)  ; 71\r\n                                      (  343.85  1002.70   -55.63     0.46)  ; 72\r\n                                      (  344.18  1003.36   -54.40     0.46)  ; 73\r\n                                      (  345.30  1006.61   -52.82     0.46)  ; 74\r\n                                      (  347.40  1007.71   -51.82     0.46)  ; 75\r\n                                      (  349.99  1010.71   -51.52     0.46)  ; 76\r\n                                      (  349.99  1010.71   -51.55     0.46)  ; 77\r\n                                      (  350.62  1012.03   -49.72     0.46)  ; 78\r\n                                      (  350.67  1013.83   -48.65     0.46)  ; 79\r\n                                      (  353.52  1015.70   -48.00     0.46)  ; 80\r\n                                      (  352.99  1017.97   -48.05     0.46)  ; 81\r\n                                      (  355.40  1019.73   -47.33     0.46)  ; 82\r\n                                      (  358.71  1021.69   -47.33     0.46)  ; 83\r\n                                      (  358.55  1026.44   -47.92     0.46)  ; 84\r\n                                      (  359.62  1027.88   -49.13     0.46)  ; 85\r\n                                      (  360.88  1030.56   -49.13     0.46)  ; 86\r\n                                      (  360.67  1033.50   -48.42     0.46)  ; 87\r\n                                      (  360.21  1033.40   -48.42     0.46)  ; 88\r\n                                      (  361.16  1035.40   -47.37     0.46)  ; 89\r\n                                      (  360.95  1038.35   -47.72     0.46)  ; 90\r\n                                      (  360.95  1038.35   -47.75     0.46)  ; 91\r\n                                      (  360.55  1040.04   -47.75     0.46)  ; 92\r\n                                      (  362.64  1041.13   -48.95     0.46)  ; 93\r\n                                      (  365.19  1042.31   -48.58     0.46)  ; 94\r\n                                      (  365.19  1042.31   -48.60     0.46)  ; 95\r\n                                      (  364.80  1044.02   -49.65     0.46)  ; 96\r\n                                      (  364.80  1044.02   -49.67     0.46)  ; 97\r\n                                      (  365.29  1045.92   -49.80     0.46)  ; 98\r\n                                      (  367.39  1047.02   -50.27     0.46)  ; 99\r\n                                      (  369.09  1049.81   -49.95     0.46)  ; 100\r\n                                      (  371.06  1051.46   -51.57     0.46)  ; 101\r\n                                      (  372.72  1052.45   -52.15     0.46)  ; 102\r\n                                      (  373.97  1055.13   -53.65     0.46)  ; 103\r\n                                      (  373.97  1055.13   -53.67     0.46)  ; 104\r\n                                      (  374.91  1057.14   -54.92     0.46)  ; 105\r\n                                      (  374.96  1058.95   -56.77     0.46)  ; 106\r\n                                      (  373.98  1061.10   -59.55     0.46)  ; 107\r\n                                      (  373.98  1061.10   -59.67     0.46)  ; 108\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  223.16   882.44   -67.70     0.92)  ; 1\r\n                                        (  225.62   886.01   -66.38     0.92)  ; 2\r\n                                        (  227.24   891.17   -68.75     0.92)  ; 3\r\n                                        (  227.64   889.47   -71.05     0.92)  ; 4\r\n                                        (  231.04   889.07   -68.67     0.92)  ; 5\r\n                                        (  235.07   895.99   -69.32     0.92)  ; 6\r\n                                        (  242.35   897.10   -71.17     0.92)  ; 7\r\n                                        (  241.60   902.29   -72.53     0.92)  ; 8\r\n                                        (  243.65   901.58   -70.95     0.92)  ; 9\r\n                                        (  249.91   909.02   -71.28     0.92)  ; 10\r\n                                        (  247.56   909.07   -73.53     0.92)  ; 11\r\n                                        (  250.44   906.76   -74.38     0.92)  ; 12\r\n                                        (  257.82   911.46   -72.70     0.92)  ; 13\r\n                                        (  260.82   912.77   -72.55     0.92)  ; 14\r\n                                        (  262.83   916.22   -74.45     0.92)  ; 15\r\n                                        (  274.19   926.06   -71.75     0.92)  ; 16\r\n                                        (  276.16   927.71   -73.35     0.92)  ; 17\r\n                                        (  277.81   928.69   -73.35     0.92)  ; 18\r\n                                        (  277.01   932.09   -73.35     0.92)  ; 19\r\n                                        (  273.53   928.89   -73.90     0.92)  ; 20\r\n                                        (  287.17   941.04   -73.42     0.92)  ; 21\r\n                                        (  298.44   953.24   -68.65     0.92)  ; 22\r\n                                        (  293.85   952.75   -67.15     0.92)  ; 23\r\n                                        (  291.54   944.46   -73.42     0.92)  ; 24\r\n                                        (  300.15   956.03   -68.28     0.92)  ; 25\r\n                                        (  297.89   959.68   -65.05     0.92)  ; 26\r\n                                        (  305.11   958.98   -69.10     0.92)  ; 27\r\n                                        (  320.14   973.26   -58.67     0.46)  ; 28\r\n                                        (  317.37   975.00   -61.67     0.46)  ; 29\r\n                                        (  325.19   979.81   -58.58     0.46)  ; 30\r\n                                        (  329.94   985.70   -59.20     0.46)  ; 31\r\n                                        (  329.46   989.77   -57.88     0.46)  ; 32\r\n                                        (  341.36   997.34   -55.95     0.46)  ; 33\r\n                                        (  339.85  1001.75   -55.50     0.46)  ; 34\r\n                                        (  340.33  1003.67   -53.88     0.46)  ; 35\r\n                                        (  337.43  1000.00   -53.07     0.46)  ; 36\r\n                                        (  343.00   998.31   -53.07     0.46)  ; 37\r\n                                        (  350.20  1007.77   -51.52     0.46)  ; 38\r\n                                        (  349.51  1014.76   -48.00     0.46)  ; 39\r\n                                        (  352.67  1011.33   -48.00     0.46)  ; 40\r\n                                        (  354.32  1012.31   -48.00     0.46)  ; 41\r\n                                        (  354.57  1021.32   -47.08     0.46)  ; 42\r\n                                        (  353.31  1018.63   -49.85     0.46)  ; 43\r\n                                        (  351.93  1016.52   -48.05     0.46)  ; 44\r\n                                        (  354.15  1017.04   -48.05     0.46)  ; 45\r\n                                        (  361.69  1033.14   -46.50     0.46)  ; 46\r\n                                        (  362.09  1031.45   -46.32     0.46)  ; 47\r\n                                        (  362.15  1039.22   -48.45     0.46)  ; 48\r\n                                        (  361.55  1027.73   -49.13     0.46)  ; 49\r\n                                        (  359.72  1031.49   -50.05     0.46)  ; 50\r\n                                        (  365.79  1047.84   -50.27     0.46)  ; 51\r\n                                        (  369.44  1046.30   -50.27     0.46)  ; 52\r\n                                        (  358.49  1040.75   -45.40     0.46)  ; 53\r\n                                        (  358.80  1035.44   -45.42     0.46)  ; 54\r\n                                        (  370.69  1048.99   -51.57     0.46)  ; 55\r\n                                        (  372.36  1049.98   -51.57     0.46)  ; 56\r\n                                        (  369.63  1053.52   -52.35     0.46)  ; 57\r\n                                        (  367.44  1048.82   -49.30     0.46)  ; 58\r\n                                        (  372.41  1051.77   -49.90     0.46)  ; 59\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  218.53   847.92   -63.30     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-2\r\n                                    (  218.57   849.72   -62.45     1.38)  ; 2\r\n                                    (  217.65   853.69   -61.28     1.38)  ; 3\r\n                                    (  216.98   856.52   -61.32     1.38)  ; 4\r\n                                    (  215.88   859.24   -60.85     1.38)  ; 5\r\n                                    (  214.46   861.30   -60.07     1.38)  ; 6\r\n                                    (  213.61   862.88   -59.13     1.38)  ; 7\r\n                                    (  213.97   865.37   -58.45     1.38)  ; 8\r\n                                    (  213.26   866.39   -57.35     1.38)  ; 9\r\n                                    (  211.70   869.02   -56.57     1.38)  ; 10\r\n                                    (  210.99   870.04   -55.25     1.38)  ; 11\r\n                                    (  210.86   870.61   -54.38     1.38)  ; 12\r\n                                    (  208.55   872.46   -53.13     1.38)  ; 13\r\n                                    (  207.26   873.94   -52.60     1.38)  ; 14\r\n                                    (  206.54   874.97   -51.38     1.38)  ; 15\r\n                                    (  207.35   877.55   -50.63     1.38)  ; 16\r\n                                    (  207.09   878.68   -49.60     1.38)  ; 17\r\n                                    (  206.24   880.28   -48.70     1.38)  ; 18\r\n                                    (  205.89   883.78   -47.77     1.38)  ; 19\r\n                                    (  206.25   886.24   -47.95     1.38)  ; 20\r\n                                    (  204.83   888.30   -47.57     1.38)  ; 21\r\n                                    (  200.73   889.72   -47.10     1.38)  ; 22\r\n                                    (  200.07   892.55   -45.50     1.38)  ; 23\r\n                                    (  200.16   896.16   -44.30     1.38)  ; 24\r\n                                    (  199.06   898.90   -43.13     1.38)  ; 25\r\n                                    (  197.26   898.48   -41.75     1.38)  ; 26\r\n                                    (  197.34   896.10   -40.80     1.38)  ; 27\r\n                                    (  197.29   894.30   -39.55     1.38)  ; 28\r\n                                    (  197.74   894.41   -37.63     1.38)  ; 29\r\n                                    (  196.86   894.20   -35.38     1.38)  ; 30\r\n                                    (  194.04   894.13   -33.33     1.38)  ; 31\r\n                                    (  197.66   896.77   -32.13     1.38)  ; 32\r\n                                    (  198.69   896.42   -30.02     1.38)  ; 33\r\n                                    (  195.35   898.63   -29.40     1.38)  ; 34\r\n                                    (  194.95   900.32   -28.55     1.38)  ; 35\r\n                                    (  194.77   905.05   -27.35     1.38)  ; 36\r\n                                    (  196.89   906.14   -26.05     1.38)  ; 37\r\n                                    (  197.64   906.93   -24.35     1.38)  ; 38\r\n                                    (  197.95   907.59   -25.05     1.38)  ; 39\r\n                                    (  194.42   908.56   -23.05     1.38)  ; 40\r\n                                    (  191.48   909.05   -21.85     1.38)  ; 41\r\n                                    (  190.06   911.11   -22.08     1.38)  ; 42\r\n                                    (  189.79   912.24   -20.80     1.38)  ; 43\r\n                                    (  187.93   914.20   -19.75     1.38)  ; 44\r\n                                    (  186.64   915.68   -18.85     1.38)  ; 45\r\n                                    (  186.64   915.68   -18.88     1.38)  ; 46\r\n                                    (  187.45   918.27   -17.05     1.38)  ; 47\r\n                                    (  186.29   919.18   -15.95     1.38)  ; 48\r\n                                    (  186.29   919.18   -15.92     1.38)  ; 49\r\n                                    (  185.26   919.54   -14.60     1.38)  ; 50\r\n                                    (  184.73   921.81   -13.42     1.38)  ; 51\r\n                                    (  184.78   923.61   -12.30     1.38)  ; 52\r\n                                    (  184.78   923.61   -12.32     1.38)  ; 53\r\n                                    (  185.72   925.63   -12.00     1.38)  ; 54\r\n                                    (  184.43   927.12   -12.00     1.38)  ; 55\r\n                                    (  182.06   927.15   -10.12     1.38)  ; 56\r\n                                    (  182.24   928.39    -8.95     1.38)  ; 57\r\n                                    (  183.77   929.95    -8.25     1.38)  ; 58\r\n                                    (  183.10   932.77    -8.35     1.38)  ; 59\r\n                                    (  182.26   934.37    -7.67     1.38)  ; 60\r\n                                    (  179.50   936.10    -6.78     1.38)  ; 61\r\n                                    (  179.59   939.71    -6.28     1.38)  ; 62\r\n                                    (  177.98   940.53    -5.82     1.38)  ; 63\r\n                                    (  176.82   941.46    -4.03     1.38)  ; 64\r\n                                    (  176.82   941.46    -4.05     1.38)  ; 65\r\n                                    (  174.46   941.50    -2.45     1.38)  ; 66\r\n                                    (  170.81   943.03    -1.67     1.38)  ; 67\r\n                                    (  171.17   945.50     0.07     1.38)  ; 68\r\n                                    (  172.61   949.42     0.63     1.38)  ; 69\r\n                                    (  174.25   950.41    -0.50     1.38)  ; 70\r\n                                    (  174.25   950.41    -0.55     1.38)  ; 71\r\n                                    (  173.90   953.90     0.28     1.38)  ; 72\r\n                                    (  176.06   956.80     1.00     1.38)  ; 73\r\n                                    (  177.90   959.03     2.05     1.38)  ; 74\r\n                                    (  177.90   959.03     2.17     1.38)  ; 75\r\n                                    (  179.99   960.11     3.82     1.38)  ; 76\r\n                                    (  179.99   960.11     3.80     1.38)  ; 77\r\n                                    (  182.01   963.57     4.40     1.38)  ; 78\r\n                                    (  182.01   963.57     4.38     1.38)  ; 79\r\n                                    (  184.78   967.79     4.85     1.38)  ; 80\r\n                                    (  187.20   969.56     5.35     1.38)  ; 81\r\n                                    (  186.48   970.59     4.47     1.38)  ; 82\r\n                                    (  186.85   973.06     5.72     0.92)  ; 83\r\n                                    (  186.77   975.43     6.82     0.92)  ; 84\r\n                                    (  188.69   975.29     7.47     0.92)  ; 85\r\n                                    (  188.69   975.29     7.45     0.92)  ; 86\r\n                                    (  190.02   975.60     8.05     0.92)  ; 87\r\n                                    (  190.02   975.60     8.02     0.92)  ; 88\r\n                                    (  191.63   974.78     8.97     0.92)  ; 89\r\n                                    (  191.63   974.78     8.95     0.92)  ; 90\r\n                                    (  193.09   974.54    10.90     0.92)  ; 91\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  217.54   844.11   -63.30     1.38)  ; 1\r\n                                      (  216.45   852.81   -61.28     1.38)  ; 2\r\n                                      (  218.54   853.90   -62.00     1.38)  ; 3\r\n                                      (  210.89   866.43   -56.57     1.38)  ; 4\r\n                                      (  214.79   867.94   -56.57     1.38)  ; 5\r\n                                      (  208.00   868.74   -53.13     1.38)  ; 6\r\n                                      (  205.38   875.89   -50.63     1.38)  ; 7\r\n                                      (  207.49   882.95   -47.22     1.38)  ; 8\r\n                                      (  207.77   887.80   -47.22     1.38)  ; 9\r\n                                      (  199.65   888.28   -47.10     1.38)  ; 10\r\n                                      (  202.28   887.11   -47.10     1.38)  ; 11\r\n                                      (  206.03   889.18   -48.55     1.38)  ; 12\r\n                                      (  203.32   892.72   -46.40     1.38)  ; 13\r\n                                      (  202.35   894.88   -44.30     1.38)  ; 14\r\n                                      (  196.23   898.83   -39.55     1.38)  ; 15\r\n                                      (  195.88   896.35   -39.55     1.38)  ; 16\r\n                                      (  198.45   893.37   -37.63     1.38)  ; 17\r\n                                      (  199.54   894.83   -30.02     1.38)  ; 18\r\n                                      (  200.03   896.73   -29.20     1.38)  ; 19\r\n                                      (  197.71   898.58   -28.27     1.38)  ; 20\r\n                                      (  197.55   903.32   -28.27     1.38)  ; 21\r\n                                      (  192.50   902.73   -27.30     1.38)  ; 22\r\n                                      (  197.11   909.18   -25.05     1.38)  ; 23\r\n                                      (  191.98   910.97   -21.85     1.38)  ; 24\r\n                                      (  189.66   906.84   -20.03     1.38)  ; 25\r\n                                      (  188.33   912.49   -18.70     1.38)  ; 26\r\n                                      (  186.01   914.34   -17.27     1.38)  ; 27\r\n                                      (  190.47   915.40   -16.42     1.38)  ; 28\r\n                                      (  185.34   917.17   -16.47     1.38)  ; 29\r\n                                      (  183.47   919.12   -14.60     1.38)  ; 30\r\n                                      (  188.03   923.78   -12.00     1.38)  ; 31\r\n                                      (  185.15   932.06    -8.35     1.38)  ; 32\r\n                                      (  183.07   936.94    -8.35     1.38)  ; 33\r\n                                      (  181.45   931.79    -6.75     1.38)  ; 34\r\n                                      (  180.92   934.05    -6.67     1.38)  ; 35\r\n                                      (  180.88   938.23    -4.95     1.38)  ; 36\r\n                                      (  179.64   941.52    -7.40     1.38)  ; 37\r\n                                      (  178.33   937.04    -8.35     1.38)  ; 38\r\n                                      (  170.31   941.12    -1.67     1.38)  ; 39\r\n                                      (  176.56   942.58    -1.67     1.38)  ; 40\r\n                                      (  172.19   945.15     0.07     1.38)  ; 41\r\n                                      (  172.90   944.12     0.07     1.38)  ; 42\r\n                                      (  171.00   950.24     0.63     1.38)  ; 43\r\n                                      (  176.54   952.73     0.85     1.38)  ; 44\r\n                                      (  178.19   953.72     0.85     1.38)  ; 45\r\n                                      (  174.41   955.81     2.50     1.38)  ; 46\r\n                                      (  180.85   964.49     4.35     1.38)  ; 47\r\n                                      (  175.56   954.89     3.52     1.38)  ; 48\r\n                                      (  187.60   967.86     5.35     1.38)  ; 49\r\n                                      (  184.88   971.40     4.68     1.38)  ; 50\r\n                                      (  188.01   972.14     1.75     1.38)  ; 51\r\n                                      (  188.54   969.88     1.75     1.38)  ; 52\r\n                                    )  ;  End of markers\r\n                                     Normal\r\n                                  )  ;  End of split\r\n                                |\r\n                                  (  225.26   776.47   -57.92     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2\r\n                                  (  225.89   777.81   -56.15     1.38)  ; 2\r\n                                  (  225.23   780.64   -55.70     1.38)  ; 3\r\n                                  (  225.58   783.11   -55.70     1.38)  ; 4\r\n                                  (  227.16   786.47   -55.70     1.38)  ; 5\r\n                                  (  227.38   789.51   -55.15     1.38)  ; 6\r\n                                  (  226.72   792.33   -55.03     1.38)  ; 7\r\n                                  (  227.66   794.35   -55.03     1.38)  ; 8\r\n                                  (  229.06   796.46   -54.55     1.38)  ; 9\r\n                                  (  229.86   799.04   -54.55     1.38)  ; 10\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  229.49   790.59   -55.03     1.38)  ; 1\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  229.27   802.04   -54.55     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1\r\n                                    (  229.50   805.07   -55.27     1.38)  ; 2\r\n                                    (  229.73   808.11   -55.90     1.38)  ; 3\r\n                                    (  229.07   810.94   -56.70     1.38)  ; 4\r\n                                    (  228.09   813.10   -57.40     1.38)  ; 5\r\n                                    (  227.74   816.60   -57.97     1.38)  ; 6\r\n                                    (  226.36   820.46   -58.55     1.38)  ; 7\r\n                                    (  225.88   824.52   -58.82     1.38)  ; 8\r\n                                    (  224.69   829.62   -58.82     1.38)  ; 9\r\n                                    (  224.47   832.55   -58.82     1.38)  ; 10\r\n                                    (  222.20   836.20   -58.10     1.38)  ; 11\r\n                                    (  221.94   837.33   -58.10     1.38)  ; 12\r\n                                    (  222.93   841.15   -58.10     1.38)  ; 13\r\n                                    (  223.03   844.76   -58.55     1.38)  ; 14\r\n                                    (  221.52   849.18   -58.78     1.38)  ; 15\r\n                                    (  220.99   851.45   -59.20     1.38)  ; 16\r\n                                    (  220.77   854.37   -60.03     1.38)  ; 17\r\n                                    (  220.77   854.37   -60.05     1.38)  ; 18\r\n                                    (  220.86   857.98   -60.47     1.38)  ; 19\r\n                                    (  219.93   861.95   -59.50     1.38)  ; 20\r\n                                    (  220.04   865.56   -58.75     1.38)  ; 21\r\n                                    (  220.85   868.13   -57.25     1.38)  ; 22\r\n                                    (  220.67   872.87   -56.47     1.38)  ; 23\r\n                                    (  221.35   876.01   -58.00     1.38)  ; 24\r\n                                    (  221.77   880.29   -58.80     1.38)  ; 25\r\n                                    (  221.94   881.53   -59.78     1.38)  ; 26\r\n                                    (  221.59   885.02   -59.78     1.38)  ; 27\r\n                                    (  223.30   887.81   -59.78     1.38)  ; 28\r\n                                    (  223.35   889.61   -58.20     1.38)  ; 29\r\n                                    (  222.38   891.78   -56.17     1.38)  ; 30\r\n                                    (  223.58   892.66   -55.20     1.38)  ; 31\r\n                                    (  223.81   895.70   -54.38     1.38)  ; 32\r\n                                    (  225.46   896.68   -53.40     1.38)  ; 33\r\n                                    (  226.53   898.12   -52.10     1.38)  ; 34\r\n                                    (  227.17   899.47   -50.55     1.38)  ; 35\r\n                                    (  225.15   901.98   -49.00     1.38)  ; 36\r\n                                    (  225.07   904.36   -47.40     1.38)  ; 37\r\n                                    (  227.80   906.78   -45.83     1.38)  ; 38\r\n                                    (  228.88   908.22   -45.07     1.38)  ; 39\r\n                                    (  228.66   911.17   -45.07     1.38)  ; 40\r\n                                    (  229.86   912.04   -43.70     1.38)  ; 41\r\n                                    (  231.58   914.84   -43.70     1.38)  ; 42\r\n                                    (  232.51   916.84   -42.75     1.38)  ; 43\r\n                                    (  233.90   918.97   -42.45     1.38)  ; 44\r\n                                    (  233.69   921.89   -42.45     1.38)  ; 45\r\n                                    (  234.27   925.50   -40.03     1.38)  ; 46\r\n                                    (  237.17   929.17   -41.32     1.38)  ; 47\r\n                                    (  238.60   933.09   -41.75     1.38)  ; 48\r\n                                    (  238.83   936.13   -42.67     1.38)  ; 49\r\n                                    (  241.88   939.23   -43.53     1.38)  ; 50\r\n                                    (  240.64   942.52   -43.90     1.38)  ; 51\r\n                                    (  240.42   945.47   -43.90     1.38)  ; 52\r\n                                    (  241.80   947.57   -42.90     1.38)  ; 53\r\n                                    (  242.35   951.29   -42.90     1.38)  ; 54\r\n                                    (  244.50   954.18   -44.10     1.38)  ; 55\r\n                                    (  243.70   957.57   -42.85     1.38)  ; 56\r\n                                    (  243.70   957.57   -42.90     1.38)  ; 57\r\n                                    (  242.73   959.73   -41.73     1.38)  ; 58\r\n                                    (  242.69   963.91   -40.65     1.38)  ; 59\r\n                                    (  243.59   970.09   -41.80     1.38)  ; 60\r\n                                    (  244.27   973.23   -41.30     1.38)  ; 61\r\n                                    (  244.50   976.27   -42.37     1.38)  ; 62\r\n                                    (  243.08   978.33   -42.37     1.38)  ; 63\r\n                                    (  244.20   981.58   -41.95     1.38)  ; 64\r\n                                    (  244.62   985.86   -41.95     1.38)  ; 65\r\n                                    (  243.24   989.71   -41.95     1.38)  ; 66\r\n                                    (  243.47   992.75   -41.95     1.38)  ; 67\r\n                                    (  243.87   997.03   -42.35     1.38)  ; 68\r\n                                    (  244.24   999.50   -43.00     1.38)  ; 69\r\n                                    (  244.39  1004.90   -43.00     1.38)  ; 70\r\n                                    (  245.78  1007.03   -43.90     1.38)  ; 71\r\n                                    (  246.19  1011.31   -44.70     1.38)  ; 72\r\n                                    (  246.42  1014.35   -45.17     1.38)  ; 73\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  224.30   837.30   -58.10     1.38)  ; 1\r\n                                      (  217.89   862.65   -59.50     1.38)  ; 2\r\n                                      (  218.73   861.07   -59.32     1.38)  ; 3\r\n                                      (  223.87   881.37   -58.80     1.38)  ; 4\r\n                                      (  222.92   879.36   -58.80     1.38)  ; 5\r\n                                      (  220.39   884.15   -59.78     1.38)  ; 6\r\n                                      (  220.44   885.96   -59.78     1.38)  ; 7\r\n                                      (  220.92   881.89   -59.78     1.38)  ; 8\r\n                                      (  220.45   891.92   -57.88     1.38)  ; 9\r\n                                      (  220.48   887.75   -56.82     1.38)  ; 10\r\n                                      (  222.43   893.58   -54.38     1.38)  ; 11\r\n                                      (  227.71   903.17   -45.07     1.38)  ; 12\r\n                                      (  224.49   904.81   -45.07     1.38)  ; 13\r\n                                      (  232.73   913.90   -43.70     1.38)  ; 14\r\n                                      (  230.77   918.24   -42.45     1.38)  ; 15\r\n                                      (  229.25   916.67   -41.75     1.38)  ; 16\r\n                                      (  230.70   920.60   -40.57     1.38)  ; 17\r\n                                      (  236.31   920.72   -42.02     1.38)  ; 18\r\n                                      (  235.33   922.88   -40.77     1.38)  ; 19\r\n                                      (  232.94   927.10   -40.00     1.38)  ; 20\r\n                                      (  238.02   927.58   -41.32     1.38)  ; 21\r\n                                      (  236.32   930.77   -40.63     1.38)  ; 22\r\n                                      (  237.26   932.78   -40.63     1.38)  ; 23\r\n                                      (  238.57   937.26   -43.53     1.38)  ; 24\r\n                                      (  243.26   941.35   -44.72     1.38)  ; 25\r\n                                      (  241.65   936.20   -44.72     1.38)  ; 26\r\n                                      (  243.66   939.65   -44.72     1.38)  ; 27\r\n                                      (  238.23   946.74   -43.90     1.38)  ; 28\r\n                                      (  240.34   947.83   -43.90     1.38)  ; 29\r\n                                      (  244.97   966.23   -43.50     1.38)  ; 30\r\n                                      (  241.35   963.60   -37.97     1.38)  ; 31\r\n                                      (  244.87   962.63   -44.72     1.38)  ; 32\r\n                                      (  246.37   974.32   -42.37     1.38)  ; 33\r\n                                      (  245.91   984.38   -41.95     1.38)  ; 34\r\n                                      (  242.29   987.71   -43.65     1.38)  ; 35\r\n                                      (  245.43   988.44   -41.57     1.38)  ; 36\r\n                                      (  246.26   986.84   -41.57     1.38)  ; 37\r\n                                      (  246.59   993.49   -41.90     1.38)  ; 38\r\n                                      (  241.68   992.34   -41.90     1.38)  ; 39\r\n                                      (  246.29   998.79   -43.30     1.38)  ; 40\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  245.44  1016.50   -43.55     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1\r\n                                      (  245.44  1016.50   -43.53     1.38)  ; 2\r\n                                      (  243.12  1018.35   -42.05     1.38)  ; 3\r\n                                      (  243.12  1018.35   -42.10     1.38)  ; 4\r\n                                      (  243.67  1022.06   -40.85     0.92)  ; 5\r\n                                      (  243.59  1024.43   -39.70     0.92)  ; 6\r\n                                      (  242.66  1028.39   -39.15     0.92)  ; 7\r\n                                      (  244.04  1030.50   -39.15     0.92)  ; 8\r\n                                      (  242.93  1033.23   -38.50     0.92)  ; 9\r\n                                      (  242.89  1037.40   -37.15     0.92)  ; 10\r\n                                      (  243.26  1039.88   -37.15     0.92)  ; 11\r\n                                      (  244.91  1040.86   -37.15     0.92)  ; 12\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  241.50  1029.31   -39.15     0.92)  ; 1\r\n                                        (  245.15  1027.79   -39.15     0.92)  ; 2\r\n                                        (  241.98  1025.24   -39.15     0.92)  ; 3\r\n                                        (  242.03  1027.05   -39.15     0.92)  ; 4\r\n                                        (  245.36  1034.99   -37.15     0.92)  ; 5\r\n                                        (  244.11  1038.28   -37.15     0.92)  ; 6\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  243.22  1044.05   -35.80     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1\r\n                                        (  243.15  1046.42   -35.15     0.46)  ; 2\r\n                                        (  243.95  1049.00   -33.95     0.46)  ; 3\r\n                                        (  243.87  1051.37   -33.95     0.46)  ; 4\r\n                                        (  242.49  1055.22   -33.95     0.46)  ; 5\r\n                                        (  241.83  1058.06   -32.75     0.46)  ; 6\r\n                                        (  241.61  1060.99   -31.92     0.46)  ; 7\r\n                                        (  240.32  1062.48   -34.10     0.46)  ; 8\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  240.54  1043.42   -35.15     0.46)  ; 1\r\n                                          (  241.22  1046.56   -33.42     0.46)  ; 2\r\n                                          (  246.40  1046.59   -33.27     0.46)  ; 3\r\n                                          (  241.69  1052.65   -33.95     0.46)  ; 4\r\n                                          (  239.41  1056.30   -33.95     0.46)  ; 5\r\n                                          (  239.92  1058.21   -33.95     0.46)  ; 6\r\n                                          (  245.13  1060.03   -30.25     0.46)  ; 7\r\n                                          (  239.34  1064.64   -34.08     0.46)  ; 8\r\n                                        )  ;  End of markers\r\n                                        (\r\n                                          (  236.47  1062.77   -34.10     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-1\r\n                                          (  233.35  1062.03   -32.25     0.46)  ; 2\r\n                                          (  231.13  1061.51   -31.48     0.46)  ; 3\r\n                                          (  229.83  1063.00   -31.95     0.46)  ; 4\r\n                                          (  227.20  1064.18   -30.73     0.46)  ; 5\r\n                                          (  222.97  1066.17   -29.25     0.46)  ; 6\r\n                                          (  221.49  1066.42   -27.90     0.46)  ; 7\r\n                                          (  217.98  1067.27   -31.52     0.46)  ; 8\r\n                                          (  217.96  1067.40   -31.55     0.46)  ; 9\r\n                                          (  214.84  1066.66   -32.67     0.46)  ; 10\r\n                                          (  211.77  1067.73   -33.97     0.46)  ; 11\r\n\r\n                                          (Cross\r\n                                            (Color RGB (128, 255, 128))\r\n                                            (Name \"Marker 3\")\r\n                                            (  238.03  1060.15   -34.10     0.46)  ; 1\r\n                                            (  234.46  1059.32   -32.52     0.46)  ; 2\r\n                                            (  230.50  1060.17   -31.60     0.46)  ; 3\r\n                                            (  225.63  1060.83   -33.30     0.46)  ; 4\r\n                                            (  231.26  1066.93   -30.25     0.46)  ; 5\r\n                                          )  ;  End of markers\r\n                                           Normal\r\n                                        |\r\n                                          (  242.34  1065.94   -32.40     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2\r\n                                          (  243.22  1066.15   -32.17     0.46)  ; 2\r\n                                          (  243.54  1066.81   -28.72     0.46)  ; 3\r\n\r\n                                          (Cross\r\n                                            (Color RGB (128, 255, 128))\r\n                                            (Name \"Marker 3\")\r\n                                            (  240.91  1067.99   -33.53     0.46)  ; 1\r\n                                          )  ;  End of markers\r\n                                          (\r\n                                            (  246.85  1068.78   -28.72     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-1\r\n                                            (  248.91  1068.08   -27.60     0.46)  ; 2\r\n\r\n                                            (Cross\r\n                                              (Color RGB (128, 255, 128))\r\n                                              (Name \"Marker 3\")\r\n                                              (  246.90  1070.59   -28.72     0.46)  ; 1\r\n                                              (  253.18  1067.89   -25.10     0.46)  ; 2\r\n                                            )  ;  End of markers\r\n                                            (\r\n                                              (  248.41  1066.17   -26.25     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-1-1\r\n                                              (  247.47  1064.15   -24.08     0.46)  ; 2\r\n                                              (  247.47  1064.15   -24.10     0.46)  ; 3\r\n                                              (  248.63  1063.24   -21.67     0.46)  ; 4\r\n                                              (  248.63  1063.24   -21.70     0.46)  ; 5\r\n                                              (  247.37  1060.54   -19.88     0.46)  ; 6\r\n                                              (  247.05  1059.88   -18.70     0.46)  ; 7\r\n                                               Normal\r\n                                            |\r\n                                              (  251.89  1069.37   -26.20     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-1-2\r\n                                              (  255.47  1070.21   -25.52     0.46)  ; 2\r\n                                              (  257.43  1071.86   -23.60     0.46)  ; 3\r\n                                              (  257.43  1071.86   -23.57     0.46)  ; 4\r\n                                               Normal\r\n                                            )  ;  End of split\r\n                                          |\r\n                                            (  241.98  1069.44   -28.72     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-2\r\n                                            (  240.30  1072.63   -28.72     0.46)  ; 2\r\n\r\n                                            (Cross\r\n                                              (Color RGB (128, 255, 128))\r\n                                              (Name \"Marker 3\")\r\n                                              (  236.91  1073.03   -28.72     0.46)  ; 1\r\n                                              (  244.41  1077.16   -28.72     0.46)  ; 2\r\n                                            )  ;  End of markers\r\n                                             Normal\r\n                                          )  ;  End of split\r\n                                        )  ;  End of split\r\n                                      |\r\n                                        (  245.99  1042.31   -37.15     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-2\r\n                                        (  245.33  1045.14   -37.15     0.46)  ; 2\r\n                                        (  248.63  1047.11   -37.15     0.46)  ; 3\r\n                                        (  249.76  1050.36   -36.15     0.46)  ; 4\r\n                                        (  251.73  1052.01   -35.57     0.46)  ; 5\r\n                                        (  253.87  1054.91   -34.85     0.46)  ; 6\r\n                                        (  255.00  1058.15   -34.25     0.46)  ; 7\r\n                                        (  256.65  1059.13   -36.20     0.46)  ; 8\r\n                                        (  258.48  1061.36   -36.20     0.46)  ; 9\r\n                                        (  259.69  1062.24   -38.15     0.46)  ; 10\r\n                                        (  259.92  1065.29   -39.52     0.46)  ; 11\r\n                                        (  261.12  1066.16   -39.82     0.46)  ; 12\r\n                                        (  263.68  1067.36   -41.38     0.46)  ; 13\r\n                                        (  264.88  1068.23   -42.40     0.46)  ; 14\r\n                                        (  264.88  1068.23   -42.42     0.46)  ; 15\r\n                                        (  266.67  1068.65   -43.00     0.46)  ; 16\r\n                                        (  268.32  1069.63   -43.00     0.46)  ; 17\r\n                                        (  269.93  1068.82   -44.43     0.46)  ; 18\r\n                                        (  271.58  1069.80   -45.05     0.46)  ; 19\r\n                                        (  273.19  1068.99   -46.05     0.46)  ; 20\r\n                                        (  273.19  1068.99   -46.08     0.46)  ; 21\r\n                                        (  274.53  1069.31   -47.13     0.46)  ; 22\r\n                                        (  274.53  1069.31   -47.15     0.46)  ; 23\r\n                                        (  276.94  1071.06   -47.17     0.46)  ; 24\r\n                                        (  278.28  1071.37   -48.95     0.46)  ; 25\r\n                                        (  279.13  1069.78   -50.15     0.46)  ; 26\r\n                                        (  281.05  1069.64   -51.67     0.46)  ; 27\r\n                                        (  282.43  1071.75   -53.62     0.46)  ; 28\r\n                                        (  282.43  1071.75   -53.67     0.46)  ; 29\r\n                                        (  283.72  1070.26   -55.72     0.46)  ; 30\r\n                                        (  284.75  1069.90   -58.47     0.46)  ; 31\r\n                                        (  284.75  1069.90   -58.65     0.46)  ; 32\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  251.83  1055.62   -34.25     0.46)  ; 1\r\n                                          (  256.10  1055.43   -34.25     0.46)  ; 2\r\n                                          (  257.18  1056.88   -37.83     0.46)  ; 3\r\n                                          (  257.02  1061.62   -36.25     0.46)  ; 4\r\n                                          (  259.46  1059.20   -36.25     0.46)  ; 5\r\n                                          (  261.47  1062.66   -39.52     0.46)  ; 6\r\n                                          (  263.63  1065.56   -39.82     0.46)  ; 7\r\n                                          (  263.72  1069.15   -43.00     0.46)  ; 8\r\n                                          (  266.72  1070.46   -43.00     0.46)  ; 9\r\n                                          (  266.75  1066.29   -43.00     0.46)  ; 10\r\n                                          (  273.34  1074.40   -47.17     0.46)  ; 11\r\n                                          (  281.00  1073.81   -50.15     0.46)  ; 12\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    |\r\n                                      (  248.12  1017.13   -43.80     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-2\r\n                                      (  249.24  1020.38   -43.80     0.46)  ; 2\r\n                                      (  251.21  1022.03   -43.88     0.46)  ; 3\r\n                                      (  254.78  1022.87   -44.30     0.46)  ; 4\r\n                                      (  254.78  1022.87   -44.32     0.46)  ; 5\r\n                                      (  257.09  1021.03   -44.70     0.46)  ; 6\r\n                                      (  257.09  1021.03   -44.80     0.46)  ; 7\r\n                                      (  259.47  1020.98   -44.90     0.46)  ; 8\r\n                                      (  259.47  1020.98   -45.15     0.46)  ; 9\r\n                                      (  261.57  1022.08   -47.13     0.46)  ; 10\r\n                                      (  261.57  1022.08   -47.20     0.46)  ; 11\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  248.73  1006.53   -45.17     1.38)  ; 1\r\n                                        (  247.30  1008.58   -45.17     1.38)  ; 2\r\n                                        (  248.42  1011.83   -45.17     1.38)  ; 3\r\n                                        (  242.74  1009.90   -43.80     1.38)  ; 4\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  231.44   802.40   -54.55     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2\r\n                                    (  233.80   802.34   -53.30     1.38)  ; 2\r\n                                    (  234.88   803.81   -52.50     1.38)  ; 3\r\n                                    (  234.88   803.81   -52.55     1.38)  ; 4\r\n                                    (  235.10   806.84   -52.55     1.38)  ; 5\r\n                                    (  237.08   808.50   -51.55     1.38)  ; 6\r\n                                    (  238.01   810.51   -50.40     1.38)  ; 7\r\n                                    (  238.19   811.74   -49.60     1.38)  ; 8\r\n                                    (  237.80   813.44   -48.75     1.38)  ; 9\r\n                                    (  241.74   816.75   -48.75     1.38)  ; 10\r\n                                    (  242.94   817.62   -47.67     1.38)  ; 11\r\n                                    (  244.38   821.55   -47.67     1.38)  ; 12\r\n                                    (  246.03   822.53   -47.67     1.38)  ; 13\r\n                                    (  246.84   825.11   -47.67     1.38)  ; 14\r\n                                    (  247.65   827.70   -47.67     1.38)  ; 15\r\n                                    (  250.38   830.12   -47.67     1.38)  ; 16\r\n                                    (  250.34   834.30   -47.67     1.38)  ; 17\r\n                                    (  251.91   837.65   -47.57     1.38)  ; 18\r\n                                    (  252.50   843.16   -47.55     1.38)  ; 19\r\n                                    (  255.10   846.16   -47.15     1.38)  ; 20\r\n                                    (  255.46   848.63   -46.65     1.38)  ; 21\r\n                                    (  255.96   850.53   -46.38     1.38)  ; 22\r\n                                    (  256.45   852.45   -45.65     1.38)  ; 23\r\n                                    (  258.87   854.21   -45.65     1.38)  ; 24\r\n                                    (  259.36   856.12   -44.43     1.38)  ; 25\r\n                                    (  259.36   856.12   -44.45     1.38)  ; 26\r\n                                    (  259.59   859.15   -43.58     1.38)  ; 27\r\n                                    (  262.44   861.02   -43.15     1.38)  ; 28\r\n                                    (  262.44   861.02   -43.18     1.38)  ; 29\r\n                                    (  261.33   863.74   -43.10     1.38)  ; 30\r\n                                    (  263.18   865.97   -42.57     1.38)  ; 31\r\n                                    (  263.18   865.97   -42.60     1.38)  ; 32\r\n                                    (  263.02   870.70   -43.50     1.38)  ; 33\r\n                                    (  265.74   873.13   -42.92     1.38)  ; 34\r\n                                    (  266.09   875.61   -42.05     1.38)  ; 35\r\n                                    (  266.91   878.18   -41.20     1.38)  ; 36\r\n                                    (  269.77   880.06   -40.48     1.38)  ; 37\r\n                                    (  269.77   880.06   -40.50     1.38)  ; 38\r\n                                    (  270.90   883.30   -39.65     1.38)  ; 39\r\n                                    (  273.76   885.17   -39.20     1.38)  ; 40\r\n                                    (  276.35   888.16   -39.02     1.38)  ; 41\r\n                                    (  278.41   893.42   -39.02     1.38)  ; 42\r\n                                    (  278.92   895.33   -37.70     1.38)  ; 43\r\n                                    (  281.64   897.76   -38.03     1.38)  ; 44\r\n                                    (  283.60   899.43   -38.03     1.38)  ; 45\r\n                                    (  284.33   904.37   -38.03     1.38)  ; 46\r\n                                    (  285.77   908.29   -38.03     1.38)  ; 47\r\n                                    (  286.89   911.53   -36.10     1.38)  ; 48\r\n                                    (  286.72   916.27   -36.10     1.38)  ; 49\r\n                                    (  287.22   918.18   -37.20     1.38)  ; 50\r\n                                    (  287.22   918.18   -37.17     1.38)  ; 51\r\n                                    (  287.58   920.65   -38.35     1.38)  ; 52\r\n                                    (  287.58   920.65   -38.38     1.38)  ; 53\r\n                                    (  288.13   924.36   -38.70     1.38)  ; 54\r\n                                    (  288.04   926.73   -38.90     1.38)  ; 55\r\n                                    (  289.17   929.99   -38.90     1.38)  ; 56\r\n                                    (  288.95   932.92   -39.60     1.38)  ; 57\r\n                                    (  291.10   935.81   -40.45     1.38)  ; 58\r\n                                    (  293.12   939.27   -41.00     1.38)  ; 59\r\n                                    (  295.08   940.92   -40.48     1.38)  ; 60\r\n                                    (  295.08   940.92   -40.50     1.38)  ; 61\r\n                                    (  295.31   943.96   -40.50     1.38)  ; 62\r\n                                    (  295.85   947.67   -40.50     1.38)  ; 63\r\n                                    (  298.45   950.67   -40.50     1.38)  ; 64\r\n                                    (  299.70   953.35   -39.72     1.38)  ; 65\r\n                                    (  301.32   958.52   -38.85     1.38)  ; 66\r\n                                    (  302.72   960.62   -38.57     1.38)  ; 67\r\n                                    (  304.86   963.52   -38.57     1.38)  ; 68\r\n                                    (  307.19   967.64   -37.58     1.38)  ; 69\r\n                                    (  308.45   970.33   -36.40     1.38)  ; 70\r\n                                    (  308.10   973.84   -35.15     1.38)  ; 71\r\n                                    (  308.51   978.11   -36.88     1.38)  ; 72\r\n                                    (  309.50   981.93   -35.90     1.38)  ; 73\r\n                                    (  309.42   984.30   -34.70     1.38)  ; 74\r\n                                    (  311.51   985.38   -33.55     1.38)  ; 75\r\n                                    (  313.22   988.17   -32.50     1.38)  ; 76\r\n                                    (  313.85   989.51   -31.80     1.38)  ; 77\r\n                                    (  312.39   995.74   -31.25     1.38)  ; 78\r\n                                    (  313.95   999.09   -31.80     1.38)  ; 79\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  231.13   801.49   -32.20     1.83)  ; 1\r\n                                      (  230.68   801.39   -32.02     1.83)  ; 2\r\n                                    )  ;  End of markers\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  227.32   797.85   -54.55     1.38)  ; 1\r\n                                      (  239.88   808.56   -50.40     1.38)  ; 2\r\n                                      (  235.88   813.59   -48.75     1.38)  ; 3\r\n                                      (  239.72   813.30   -48.75     1.38)  ; 4\r\n                                      (  241.15   817.21   -47.67     1.38)  ; 5\r\n                                      (  252.03   831.11   -46.57     1.38)  ; 6\r\n                                      (  247.76   821.15   -46.57     1.38)  ; 7\r\n                                      (  249.23   837.02   -46.20     1.38)  ; 8\r\n                                      (  254.06   840.54   -46.20     1.38)  ; 9\r\n                                      (  256.88   846.57   -46.20     1.38)  ; 10\r\n                                      (  255.42   852.80   -45.65     1.38)  ; 11\r\n                                      (  256.50   854.25   -45.65     1.38)  ; 12\r\n                                      (  260.50   865.34   -43.95     1.38)  ; 13\r\n                                      (  259.55   863.33   -42.17     1.38)  ; 14\r\n                                      (  262.57   870.60   -45.00     1.38)  ; 15\r\n                                      (  270.63   884.43   -39.65     1.38)  ; 16\r\n                                      (  272.31   881.25   -39.65     1.38)  ; 17\r\n                                      (  267.28   880.66   -39.65     1.38)  ; 18\r\n                                      (  273.45   890.47   -39.02     1.38)  ; 19\r\n                                      (  276.30   886.36   -37.70     1.38)  ; 20\r\n                                      (  282.57   893.80   -39.42     1.38)  ; 21\r\n                                      (  282.92   896.27   -39.90     1.38)  ; 22\r\n                                      (  285.44   901.64   -39.90     1.38)  ; 23\r\n                                      (  286.70   904.33   -40.35     1.38)  ; 24\r\n                                      (  281.96   904.41   -37.38     1.38)  ; 25\r\n                                      (  281.43   906.68   -37.38     1.38)  ; 26\r\n                                      (  289.17   913.86   -36.55     1.38)  ; 27\r\n                                      (  287.18   922.35   -39.50     1.38)  ; 28\r\n                                      (  289.15   924.01   -38.38     1.38)  ; 29\r\n                                      (  290.90   928.59   -38.38     1.38)  ; 30\r\n                                      (  288.11   918.39   -37.47     1.38)  ; 31\r\n                                      (  286.19   918.53   -37.47     1.38)  ; 32\r\n                                      (  291.82   940.76   -41.00     1.38)  ; 33\r\n                                      (  295.03   939.12   -41.00     1.38)  ; 34\r\n                                      (  300.19   949.28   -40.50     1.38)  ; 35\r\n                                      (  297.03   952.72   -40.50     1.38)  ; 36\r\n                                      (  301.63   953.20   -38.85     1.38)  ; 37\r\n                                      (  300.08   961.80   -38.85     1.38)  ; 38\r\n                                      (  302.82   964.23   -38.57     1.38)  ; 39\r\n                                      (  304.33   965.78   -38.57     1.38)  ; 40\r\n                                      (  308.48   966.16   -38.57     1.38)  ; 41\r\n                                      (  308.66   967.40   -38.57     1.38)  ; 42\r\n                                      (  306.39   971.04   -35.15     1.38)  ; 43\r\n                                      (  310.65   975.03   -36.40     1.38)  ; 44\r\n                                      (  315.27   987.46   -31.25     1.38)  ; 45\r\n                                      (  311.31   988.32   -31.25     1.38)  ; 46\r\n                                      (  313.43   985.23   -35.00     1.38)  ; 47\r\n                                      (  311.71   992.60   -35.00     1.38)  ; 48\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  313.70  1000.24   -30.57     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-1\r\n                                      (  313.89  1001.48   -29.17     0.92)  ; 2\r\n                                      (  312.91  1003.64   -27.38     0.92)  ; 3\r\n                                      (  313.90  1007.45   -26.77     0.92)  ; 4\r\n                                      (  315.03  1010.70   -26.15     0.92)  ; 5\r\n                                      (  314.68  1014.20   -25.23     0.92)  ; 6\r\n                                      (  314.91  1017.24   -24.40     0.92)  ; 7\r\n                                      (  315.89  1021.06   -24.40     0.92)  ; 8\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  311.53  1001.53   -27.38     0.92)  ; 1\r\n                                        (  315.01  1004.73   -27.38     0.92)  ; 2\r\n                                        (  312.24  1006.47   -27.38     0.92)  ; 3\r\n                                        (  312.44  1013.67   -24.40     0.92)  ; 4\r\n                                        (  319.04  1011.64   -24.40     0.92)  ; 5\r\n                                        (  317.22  1015.39   -24.40     0.92)  ; 6\r\n                                        (  313.64  1014.56   -24.40     0.92)  ; 7\r\n                                        (  313.67  1020.53   -24.40     0.92)  ; 8\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  318.18  1023.38   -24.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-1-1\r\n                                        (  320.46  1025.71   -24.38     0.92)  ; 2\r\n                                        (  321.00  1029.42   -24.35     0.92)  ; 3\r\n                                        (  322.39  1031.54   -23.67     0.92)  ; 4\r\n                                        (  323.69  1036.02   -22.60     0.92)  ; 5\r\n                                        (  323.61  1038.39   -21.60     0.92)  ; 6\r\n                                        (  326.16  1039.59   -20.92     0.92)  ; 7\r\n                                        (  327.60  1043.50   -20.92     0.92)  ; 8\r\n                                        (  328.35  1044.28   -19.70     0.92)  ; 9\r\n                                        (  330.90  1045.48   -18.50     0.92)  ; 10\r\n                                        (  331.71  1048.05   -17.45     0.92)  ; 11\r\n                                        (  334.00  1050.38   -16.80     0.92)  ; 12\r\n                                        (  335.01  1050.02   -16.73     0.92)  ; 13\r\n                                        (  337.25  1050.55   -16.07     0.92)  ; 14\r\n                                        (  340.43  1053.08   -16.90     0.92)  ; 15\r\n                                        (  340.43  1053.08   -16.88     0.92)  ; 16\r\n                                        (  340.84  1057.36   -15.57     0.92)  ; 17\r\n                                        (  342.62  1057.78   -15.57     0.92)  ; 18\r\n                                        (  341.51  1060.50   -14.75     0.92)  ; 19\r\n                                        (  341.51  1060.50   -14.77     0.92)  ; 20\r\n                                        (  339.91  1061.33   -12.98     0.92)  ; 21\r\n                                        (  339.91  1061.33   -13.00     0.92)  ; 22\r\n                                        (  338.57  1061.01   -11.37     0.92)  ; 23\r\n                                        (  338.57  1061.01   -11.40     0.92)  ; 24\r\n                                        (  336.78  1060.59    -9.93     0.92)  ; 25\r\n                                        (  336.78  1060.59    -9.95     0.92)  ; 26\r\n                                        (  332.94  1060.88    -7.80     0.92)  ; 27\r\n                                        (  332.94  1060.88    -7.72     0.92)  ; 28\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  323.90  1027.12   -24.32     0.92)  ; 1\r\n                                          (  320.62  1020.97   -24.32     0.92)  ; 2\r\n                                          (  325.11  1033.97   -22.60     0.92)  ; 3\r\n                                          (  324.90  1036.90   -22.60     0.92)  ; 4\r\n                                          (  321.11  1039.00   -21.60     0.92)  ; 5\r\n                                          (  323.31  1043.70   -21.60     0.92)  ; 6\r\n                                          (  328.53  1039.54   -20.92     0.92)  ; 7\r\n                                          (  326.70  1043.29   -18.50     0.92)  ; 8\r\n                                          (  328.88  1042.02   -18.50     0.92)  ; 9\r\n                                          (  330.54  1043.00   -17.45     0.92)  ; 10\r\n                                          (  328.00  1047.77   -17.45     0.92)  ; 11\r\n                                          (  338.41  1049.61   -16.88     0.92)  ; 12\r\n                                          (  339.75  1049.93   -16.88     0.92)  ; 13\r\n                                          (  339.77  1055.92   -15.57     0.92)  ; 14\r\n                                          (  335.99  1063.99   -11.40     0.92)  ; 15\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      |\r\n                                        (  314.65  1024.35   -24.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-1-2\r\n                                        (  315.96  1028.83   -24.40     0.92)  ; 2\r\n                                        (  316.19  1031.88   -22.88     0.92)  ; 3\r\n                                        (  316.19  1031.88   -22.90     0.92)  ; 4\r\n                                        (  315.83  1035.37   -22.05     0.92)  ; 5\r\n                                        (  316.51  1038.52   -22.02     0.92)  ; 6\r\n                                        (  313.09  1043.09   -22.77     0.92)  ; 7\r\n                                        (  313.09  1043.09   -22.80     0.92)  ; 8\r\n                                        (  311.48  1043.90   -24.17     0.92)  ; 9\r\n                                        (  310.27  1043.03   -26.00     0.92)  ; 10\r\n                                        (  308.62  1042.04   -28.98     0.92)  ; 11\r\n                                        (  308.62  1042.04   -29.00     0.92)  ; 12\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  314.63  1034.49   -22.05     0.92)  ; 1\r\n                                          (  315.30  1037.64   -22.05     0.92)  ; 2\r\n                                          (  319.77  1038.69   -21.10     0.92)  ; 3\r\n                                          (  315.71  1041.92   -22.02     0.92)  ; 4\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    |\r\n                                      (  316.83  1000.97   -30.90     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-2\r\n                                      (  316.83  1000.97   -30.92     0.92)  ; 2\r\n                                      (  316.61  1003.91   -31.35     0.92)  ; 3\r\n                                      (  316.61  1003.91   -31.38     0.92)  ; 4\r\n                                      (  318.19  1007.27   -30.60     0.92)  ; 5\r\n                                      (  318.19  1007.27   -30.63     0.92)  ; 6\r\n                                      (  319.18  1011.07   -29.67     0.92)  ; 7\r\n                                      (  318.96  1014.01   -29.67     0.92)  ; 8\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  316.29   997.26   -33.13     0.92)  ; 1\r\n                                        (  319.20  1000.93   -30.50     0.92)  ; 2\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  320.92  1015.66   -31.32     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-2-1\r\n                                        (  320.92  1015.66   -31.35     0.92)  ; 2\r\n                                        (  320.84  1018.04   -32.15     0.92)  ; 3\r\n                                        (  321.79  1020.05   -33.13     0.92)  ; 4\r\n                                        (  322.86  1021.50   -34.42     0.92)  ; 5\r\n                                        (  323.22  1023.96   -34.88     0.92)  ; 6\r\n                                        (  323.09  1024.53   -36.40     0.92)  ; 7\r\n                                        (  325.50  1026.30   -37.32     0.92)  ; 8\r\n                                        (  325.50  1026.30   -37.38     0.92)  ; 9\r\n                                        (  325.81  1026.97   -39.02     0.92)  ; 10\r\n                                        (  326.57  1027.74   -40.77     0.92)  ; 11\r\n                                        (  326.57  1027.74   -40.80     0.92)  ; 12\r\n                                        (  326.36  1030.68   -42.20     0.92)  ; 13\r\n                                        (  328.20  1032.90   -42.98     0.92)  ; 14\r\n                                        (  329.19  1036.72   -43.75     0.92)  ; 15\r\n                                        (  331.02  1038.94   -43.75     0.92)  ; 16\r\n                                        (  334.02  1040.23   -44.35     0.92)  ; 17\r\n                                        (  335.99  1041.89   -44.75     0.92)  ; 18\r\n                                        (  335.99  1041.89   -44.78     0.92)  ; 19\r\n                                        (  337.10  1045.13   -46.10     0.92)  ; 20\r\n                                        (  337.10  1045.13   -46.12     0.92)  ; 21\r\n                                        (  340.86  1047.22   -46.63     0.92)  ; 22\r\n                                        (  343.09  1047.74   -46.63     0.92)  ; 23\r\n                                        (  345.15  1047.02   -47.65     0.92)  ; 24\r\n                                        (  345.99  1045.43   -48.07     0.92)  ; 25\r\n                                        (  345.99  1045.43   -48.10     0.92)  ; 26\r\n                                        (  348.99  1046.72   -47.72     0.92)  ; 27\r\n                                        (  352.43  1048.13   -48.42     0.92)  ; 28\r\n                                        (  352.43  1048.13   -48.45     0.92)  ; 29\r\n                                        (  356.81  1051.55   -49.07     0.92)  ; 30\r\n                                        (  360.51  1051.83   -50.07     0.92)  ; 31\r\n                                        (  360.74  1054.86   -50.92     0.46)  ; 32\r\n                                        (  360.74  1054.86   -50.97     0.46)  ; 33\r\n                                        (  358.11  1056.03   -51.77     0.46)  ; 34\r\n                                        (  358.11  1056.03   -51.80     0.46)  ; 35\r\n                                        (  356.06  1056.75   -50.50     0.46)  ; 36\r\n                                        (  356.06  1056.75   -50.52     0.46)  ; 37\r\n                                        (  352.14  1059.41   -53.18     0.46)  ; 38\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  322.27  1015.98   -31.35     0.92)  ; 1\r\n                                          (  322.35  1013.62   -31.35     0.92)  ; 2\r\n                                          (  322.64  1018.46   -30.27     0.92)  ; 3\r\n                                          (  321.44  1023.55   -34.88     0.92)  ; 4\r\n                                          (  321.93  1025.46   -37.38     0.92)  ; 5\r\n                                          (  325.64  1031.71   -42.98     0.92)  ; 6\r\n                                          (  332.00  1036.78   -43.75     0.92)  ; 7\r\n                                          (  335.68  1047.19   -46.12     0.92)  ; 8\r\n                                          (  341.85  1051.03   -46.63     0.92)  ; 9\r\n                                          (  348.81  1045.49   -47.72     0.92)  ; 10\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      |\r\n                                        (  318.74  1016.95   -29.67     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-2-2\r\n                                        (  319.73  1020.76   -29.67     0.92)  ; 2\r\n                                        (  319.11  1025.39   -29.17     0.92)  ; 3\r\n                                        (  320.83  1028.18   -28.02     0.92)  ; 4\r\n                                        (  320.34  1032.24   -26.32     0.92)  ; 5\r\n                                        (  320.70  1034.73   -26.32     0.92)  ; 6\r\n                                        (  321.11  1039.00   -26.17     0.92)  ; 7\r\n                                        (  322.81  1041.79   -26.90     0.92)  ; 8\r\n                                        (  321.12  1044.97   -26.50     0.92)  ; 9\r\n                                        (  323.15  1048.44   -25.87     0.92)  ; 10\r\n                                        (  323.50  1050.90   -24.20     0.92)  ; 11\r\n                                        (  322.92  1051.37   -21.50     0.92)  ; 12\r\n                                        (  321.77  1052.30   -18.67     0.92)  ; 13\r\n                                        (  321.77  1052.30   -18.63     0.92)  ; 14\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  322.02  1023.09   -29.17     0.92)  ; 1\r\n                                          (  322.38  1025.56   -29.17     0.92)  ; 2\r\n                                          (  319.10  1035.55   -26.15     0.92)  ; 3\r\n                                          (  324.14  1036.13   -26.15     0.92)  ; 4\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    )  ;  End of split\r\n                                  )  ;  End of split\r\n                                )  ;  End of split\r\n                              )  ;  End of split\r\n                            )  ;  End of split\r\n                          )  ;  End of split\r\n                        )  ;  End of split\r\n                      )  ;  End of split\r\n                    |\r\n                      (  255.98   279.41   -19.42     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-2\r\n                      (\r\n                        (  257.70   278.01   -18.00     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-2-1\r\n                        (  258.24   275.74   -16.80     0.92)  ; 2\r\n                        (  259.08   274.15   -15.90     0.92)  ; 3\r\n                        (  260.68   273.34   -14.97     0.92)  ; 4\r\n                        (  260.46   270.30   -13.87     0.92)  ; 5\r\n                        (  259.64   267.72   -12.30     0.92)  ; 6\r\n                        (  259.55   264.11   -10.88     0.92)  ; 7\r\n                        (  260.66   261.38    -9.20     0.92)  ; 8\r\n                        (  261.37   260.36    -8.00     0.92)  ; 9\r\n                        (  262.66   258.87    -7.22     0.92)  ; 10\r\n                        (  263.19   256.61    -5.53     0.92)  ; 11\r\n                        (  265.55   256.57    -4.85     0.92)  ; 12\r\n                        (  267.74   255.28    -4.15     0.92)  ; 13\r\n                        (  268.54   251.88    -3.70     0.92)  ; 14\r\n                        (  270.09   249.26    -3.00     0.92)  ; 15\r\n                        (  272.28   247.98    -1.07     0.92)  ; 16\r\n                        (  274.72   245.58    -0.25     0.92)  ; 17\r\n                        (  277.18   243.16     0.30     0.92)  ; 18\r\n                        (  279.17   240.65     1.85     0.92)  ; 19\r\n                        (  280.33   239.72     3.40     0.92)  ; 20\r\n                        (  282.84   239.12     4.80     0.92)  ; 21\r\n                        (  283.68   237.52     4.52     0.92)  ; 22\r\n                        (  284.08   235.83     3.85     0.92)  ; 23\r\n                        (  285.19   233.09     4.07     0.92)  ; 24\r\n                        (  285.19   233.09     4.05     0.92)  ; 25\r\n                        (  284.39   230.53     5.55     0.92)  ; 26\r\n                        (  284.39   230.53     5.48     0.92)  ; 27\r\n                        (  286.25   228.57     6.95     0.92)  ; 28\r\n                        (  288.65   224.36     7.50     0.92)  ; 29\r\n                        (  290.08   222.30     9.88     0.92)  ; 30\r\n                        (  290.08   222.30     9.85     0.92)  ; 31\r\n                        (  291.54   222.05    11.30     0.92)  ; 32\r\n                        (  294.76   220.41    12.83     0.92)  ; 33\r\n                        (  297.84   219.34    13.20     0.92)  ; 34\r\n                        (  299.57   217.95    14.55     0.92)  ; 35\r\n                        (  299.57   217.95    14.52     0.92)  ; 36\r\n                        (  299.97   216.25    16.07     0.92)  ; 37\r\n                        (  299.04   214.25    17.35     0.92)  ; 38\r\n                        (  300.58   211.62    17.92     0.92)  ; 39\r\n                        (  299.60   207.81    18.42     0.92)  ; 40\r\n                        (  299.19   203.53    20.52     0.92)  ; 41\r\n                        (  299.19   203.53    20.60     0.92)  ; 42\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  262.87   272.05   -15.35     0.92)  ; 1\r\n                          (  261.43   268.14   -12.52     0.92)  ; 2\r\n                          (  257.50   264.82   -10.88     0.92)  ; 3\r\n                          (  261.70   267.00   -10.88     0.92)  ; 4\r\n                          (  260.44   264.32   -10.10     0.92)  ; 5\r\n                          (  259.33   261.07    -7.22     0.92)  ; 6\r\n                          (  262.13   261.13    -7.22     0.92)  ; 7\r\n                          (  263.56   259.07    -8.42     0.92)  ; 8\r\n                          (  261.98   255.72    -6.57     0.92)  ; 9\r\n                          (  268.30   248.85    -3.00     0.92)  ; 10\r\n                          (  266.89   256.88    -3.00     0.92)  ; 11\r\n                          (  269.80   254.57    -1.73     0.92)  ; 12\r\n                          (  270.06   247.46    -2.75     0.92)  ; 13\r\n                          (  270.86   250.04    -2.75     0.92)  ; 14\r\n                          (  271.29   244.16    -2.10     0.92)  ; 15\r\n                          (  271.52   247.21    -2.10     0.92)  ; 16\r\n                          (  276.51   245.99     0.30     0.92)  ; 17\r\n                          (  274.06   248.40     0.30     0.92)  ; 18\r\n                          (  278.24   244.60     0.30     0.92)  ; 19\r\n                          (  275.92   240.48     0.30     0.92)  ; 20\r\n                          (  275.26   243.31     2.42     0.92)  ; 21\r\n                          (  279.54   243.12     0.55     0.92)  ; 22\r\n                          (  285.34   238.50     4.52     0.92)  ; 23\r\n                          (  286.66   232.84     4.52     0.92)  ; 24\r\n                          (  284.84   236.60     2.95     0.92)  ; 25\r\n                          (  283.63   229.74     7.50     0.92)  ; 26\r\n                          (  286.60   225.07     7.50     0.92)  ; 27\r\n                          (  284.28   226.92     8.57     0.92)  ; 28\r\n                          (  287.64   230.68     8.57     0.92)  ; 29\r\n                          (  287.28   228.21     8.57     0.92)  ; 30\r\n                          (  291.01   224.32     9.22     0.92)  ; 31\r\n                          (  293.79   222.57    12.83     0.92)  ; 32\r\n                          (  294.26   218.50    13.45     0.92)  ; 33\r\n                          (  295.57   222.98    13.45     0.92)  ; 34\r\n                          (  298.02   220.57    14.52     0.92)  ; 35\r\n                          (  300.70   221.21    14.75     0.92)  ; 36\r\n                          (  297.61   216.29    17.35     0.92)  ; 37\r\n                          (  301.69   208.90    18.92     0.92)  ; 38\r\n                        )  ;  End of markers\r\n                         Normal\r\n                      |\r\n                        (  258.33   279.34   -19.85     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-2-2\r\n                        (  260.96   278.18   -20.52     0.92)  ; 2\r\n                        (  263.29   276.33   -20.80     0.92)  ; 3\r\n                        (  264.70   274.27   -20.80     0.92)  ; 4\r\n                        (  265.73   273.93   -20.80     0.92)  ; 5\r\n                        (  267.87   270.84   -21.45     0.92)  ; 6\r\n                        (  270.05   269.55   -21.70     0.92)  ; 7\r\n                        (  271.92   267.61   -20.30     0.92)  ; 8\r\n                        (  273.33   265.55   -19.48     0.92)  ; 9\r\n                        (  274.00   262.72   -19.33     0.92)  ; 10\r\n                        (  274.98   260.56   -18.70     0.92)  ; 11\r\n                        (  277.75   258.82   -18.10     0.92)  ; 12\r\n                        (  281.27   257.85   -20.65     0.92)  ; 13\r\n                        (  284.49   256.21   -20.73     0.92)  ; 14\r\n                        (  284.49   256.21   -20.75     0.92)  ; 15\r\n                        (  286.63   253.14   -19.52     0.92)  ; 16\r\n                        (  288.17   250.52   -19.23     0.92)  ; 17\r\n                        (  289.42   247.22   -18.57     0.92)  ; 18\r\n                        (  289.42   247.22   -18.63     0.92)  ; 19\r\n                        (  291.11   244.04   -18.15     0.92)  ; 20\r\n                        (  293.55   241.63   -18.15     0.92)  ; 21\r\n                        (  296.00   239.22   -19.40     0.92)  ; 22\r\n                        (  298.46   236.80   -18.20     0.92)  ; 23\r\n                        (  299.57   234.08   -16.83     0.92)  ; 24\r\n                        (  299.57   234.08   -16.85     0.92)  ; 25\r\n                        (  300.50   230.12   -17.30     0.92)  ; 26\r\n                        (  302.05   227.50   -17.08     0.92)  ; 27\r\n                        (  302.05   227.50   -17.10     0.92)  ; 28\r\n                        (  305.26   225.86   -15.70     0.92)  ; 29\r\n                        (  307.31   225.14   -15.60     0.92)  ; 30\r\n                        (  309.57   221.50   -14.88     0.92)  ; 31\r\n                        (  311.14   218.87   -14.40     0.92)  ; 32\r\n                        (  313.33   217.60   -14.40     0.92)  ; 33\r\n                        (  314.13   214.20   -15.35     0.92)  ; 34\r\n                        (  315.73   213.40   -16.45     0.92)  ; 35\r\n                        (  317.86   210.30   -17.13     0.92)  ; 36\r\n                        (  319.73   208.37   -17.20     0.92)  ; 37\r\n                        (  321.47   206.98   -17.05     0.92)  ; 38\r\n                        (  324.37   204.67   -18.05     0.92)  ; 39\r\n                        (  327.89   203.71   -18.77     0.92)  ; 40\r\n                        (  329.63   202.32   -17.02     0.92)  ; 41\r\n                        (  331.23   201.50   -15.00     0.92)  ; 42\r\n                        (  333.61   201.46   -13.87     0.92)  ; 43\r\n                        (  334.59   199.30   -12.85     0.92)  ; 44\r\n                        (  337.34   197.55   -12.30     0.92)  ; 45\r\n                        (  339.48   194.47   -11.95     0.92)  ; 46\r\n                        (  340.45   192.32   -13.63     0.92)  ; 47\r\n                        (  342.90   189.90   -13.00     0.92)  ; 48\r\n                        (  342.46   189.80   -13.00     0.92)  ; 49\r\n                        (  345.08   188.63   -11.53     0.92)  ; 50\r\n                        (  346.96   186.67   -11.45     0.46)  ; 51\r\n                        (  349.45   186.06   -10.68     0.46)  ; 52\r\n                        (  350.75   184.57   -10.68     0.46)  ; 53\r\n                        (  352.04   183.09    -8.50     0.46)  ; 54\r\n                        (  352.04   183.09    -8.45     0.46)  ; 55\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  262.91   273.86   -21.70     0.92)  ; 1\r\n                          (  265.33   275.62   -18.40     0.92)  ; 2\r\n                          (  267.11   270.07   -19.15     0.92)  ; 3\r\n                          (  270.54   271.46   -21.35     0.92)  ; 4\r\n                          (  268.71   269.23   -21.35     0.92)  ; 5\r\n                          (  270.39   266.06   -20.35     0.92)  ; 6\r\n                          (  271.82   264.00   -20.35     0.92)  ; 7\r\n                          (  273.31   269.72   -20.55     0.92)  ; 8\r\n                          (  277.26   256.91   -18.10     0.92)  ; 9\r\n                          (  276.05   256.03   -19.42     0.92)  ; 10\r\n                          (  279.75   256.30   -17.77     0.92)  ; 11\r\n                          (  284.54   258.02   -22.22     0.92)  ; 12\r\n                          (  282.12   256.26   -22.22     0.92)  ; 13\r\n                          (  291.92   252.59   -19.23     0.92)  ; 14\r\n                          (  290.94   248.77   -17.02     0.92)  ; 15\r\n                          (  291.34   247.08   -17.02     0.92)  ; 16\r\n                          (  286.26   250.66   -17.02     0.92)  ; 17\r\n                          (  288.09   246.91   -17.02     0.92)  ; 18\r\n                          (  291.72   239.41   -19.40     0.92)  ; 19\r\n                          (  298.69   239.84   -18.30     0.92)  ; 20\r\n                          (  299.53   238.24   -18.20     0.92)  ; 21\r\n                          (  296.67   236.38   -18.20     0.92)  ; 22\r\n                          (  300.11   237.79   -15.30     0.92)  ; 23\r\n                          (  304.33   229.82   -18.35     0.92)  ; 24\r\n                          (  303.84   227.92   -15.55     0.92)  ; 25\r\n                          (  305.89   227.20   -14.95     0.92)  ; 26\r\n                          (  306.01   220.66   -14.72     0.92)  ; 27\r\n                          (  312.00   223.26   -14.45     0.92)  ; 28\r\n                          (  312.52   215.01   -15.35     0.92)  ; 29\r\n                          (  322.83   213.26   -17.20     0.92)  ; 30\r\n                          (  322.85   209.09   -17.57     0.92)  ; 31\r\n                          (  317.69   209.07   -14.15     0.92)  ; 32\r\n                          (  320.98   205.08   -16.50     0.92)  ; 33\r\n                          (  326.95   201.70   -18.77     0.92)  ; 34\r\n                          (  328.69   200.31   -13.87     0.92)  ; 35\r\n                          (  334.81   202.33   -12.40     0.92)  ; 36\r\n                          (  334.27   198.63   -11.58     0.92)  ; 37\r\n                          (  336.27   196.11   -11.58     0.92)  ; 38\r\n                          (  338.99   198.54   -11.42     0.92)  ; 39\r\n                          (  343.09   191.14   -13.00     0.92)  ; 40\r\n                          (  341.71   195.00   -14.95     0.92)  ; 41\r\n                          (  342.99   187.53   -14.40     0.92)  ; 42\r\n                          (  340.05   188.04   -12.15     0.92)  ; 43\r\n                          (  346.46   184.77   -10.68     0.46)  ; 44\r\n                          (  350.65   180.96   -10.68     0.46)  ; 45\r\n                        )  ;  End of markers\r\n                         Normal\r\n                      )  ;  End of split\r\n                    )  ;  End of split\r\n                  |\r\n                    (  244.93   254.13   -18.75     0.92)  ; 1, R-1-1-1-1-1-1-1-1-2\r\n                    (  244.68   255.26   -16.65     0.92)  ; 2\r\n                    (  246.14   255.02   -14.95     0.92)  ; 3\r\n                    (  247.03   255.22   -12.80     0.92)  ; 4\r\n                    (  242.74   255.42   -11.95     0.92)  ; 5\r\n                    (  240.17   258.39   -11.40     0.92)  ; 6\r\n                    (  236.25   261.06   -11.15     0.92)  ; 7\r\n                    (  236.25   261.06   -11.17     0.92)  ; 8\r\n                    (  234.45   260.64   -10.05     0.92)  ; 9\r\n                    (  231.64   260.57    -9.72     0.92)  ; 10\r\n                    (  230.30   260.26    -8.73     0.92)  ; 11\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  242.84   259.02   -11.40     0.92)  ; 1\r\n                      (  240.83   255.56   -12.13     0.92)  ; 2\r\n                      (  238.96   257.51   -12.13     0.92)  ; 3\r\n                      (  236.33   258.69   -12.13     0.92)  ; 4\r\n                      (  237.58   261.37   -11.63     0.92)  ; 5\r\n                      (  233.37   259.19    -9.72     0.92)  ; 6\r\n                      (  233.65   264.03    -9.72     0.92)  ; 7\r\n                      (  230.80   262.17    -9.72     0.92)  ; 8\r\n                      (  230.25   258.46    -7.43     0.92)  ; 9\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  228.08   259.73    -9.43     0.92)  ; 1, R-1-1-1-1-1-1-1-1-2-1\r\n                      (  224.23   260.04    -9.43     0.92)  ; 2\r\n                      (  223.20   260.39   -10.57     0.92)  ; 3\r\n                      (  223.20   260.39   -10.60     0.92)  ; 4\r\n                      (  220.00   262.02   -11.63     0.92)  ; 5\r\n                      (  217.99   264.54   -12.75     0.92)  ; 6\r\n                      (  215.76   264.02   -13.70     0.92)  ; 7\r\n                      (  213.27   264.62   -14.67     0.92)  ; 8\r\n                      (  210.05   266.26   -14.67     0.92)  ; 9\r\n                      (  210.05   266.26   -14.70     0.92)  ; 10\r\n                      (  209.21   267.85   -15.20     0.92)  ; 11\r\n                      (  207.02   269.14   -15.88     0.92)  ; 12\r\n                      (  207.02   269.14   -15.90     0.92)  ; 13\r\n                      (  204.08   269.64   -16.70     0.92)  ; 14\r\n                      (  202.65   271.70   -17.42     0.92)  ; 15\r\n                      (  198.94   271.43   -17.42     0.92)  ; 16\r\n                      (  194.98   272.28   -17.45     0.92)  ; 17\r\n                      (  193.42   274.91   -18.77     0.92)  ; 18\r\n                      (  193.42   274.91   -18.75     0.92)  ; 19\r\n                      (  191.90   273.35   -20.10     0.92)  ; 20\r\n                      (  189.97   273.50   -21.18     0.92)  ; 21\r\n                      (  187.93   274.22   -22.02     0.92)  ; 22\r\n                      (  185.74   275.49   -22.70     0.92)  ; 23\r\n                      (  183.11   276.68   -23.50     0.92)  ; 24\r\n                      (  181.56   279.29   -23.50     0.92)  ; 25\r\n                      (  179.05   279.91   -23.50     0.92)  ; 26\r\n                      (  175.98   280.97   -23.63     0.92)  ; 27\r\n                      (  172.77   282.61   -23.63     0.92)  ; 28\r\n                      (  169.60   286.05   -23.40     0.46)  ; 29\r\n                      (  166.22   286.44   -24.17     0.46)  ; 30\r\n                      (  162.70   287.41   -25.15     0.46)  ; 31\r\n                      (  160.95   288.80   -25.03     0.46)  ; 32\r\n                      (  160.55   290.49   -24.00     0.46)  ; 33\r\n                      (  157.97   293.47   -24.92     0.46)  ; 34\r\n                      (  157.97   293.47   -24.95     0.46)  ; 35\r\n                      (  156.81   294.38   -26.35     0.46)  ; 36\r\n                      (  156.81   294.38   -26.38     0.46)  ; 37\r\n                      (  154.50   296.24   -27.20     0.46)  ; 38\r\n                      (  151.74   297.97   -27.97     0.46)  ; 39\r\n                      (  150.76   300.14   -28.15     0.46)  ; 40\r\n                      (  148.65   299.06   -28.15     0.46)  ; 41\r\n                      (  144.29   301.61   -28.15     0.46)  ; 42\r\n                      (  140.31   302.46   -28.25     0.46)  ; 43\r\n                      (  140.31   302.46   -28.30     0.46)  ; 44\r\n                      (  137.55   304.21   -28.30     0.46)  ; 45\r\n                      (  134.07   306.99   -28.30     0.46)  ; 46\r\n                      (  130.60   309.74   -27.60     0.46)  ; 47\r\n                      (  127.97   310.92   -26.65     0.46)  ; 48\r\n                      (  125.08   313.23   -26.00     0.46)  ; 49\r\n                      (  123.79   314.72   -25.07     0.46)  ; 50\r\n                      (  121.33   317.13   -24.20     0.46)  ; 51\r\n                      (  118.57   318.87   -22.77     0.46)  ; 52\r\n                      (  117.14   320.93   -22.27     0.46)  ; 53\r\n                      (  114.43   324.47   -21.72     0.46)  ; 54\r\n                      (  114.43   324.47   -21.75     0.46)  ; 55\r\n                      (  114.35   326.83   -20.07     0.46)  ; 56\r\n                      (  114.35   326.83   -19.95     0.46)  ; 57\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  222.00   259.51   -11.63     0.92)  ; 1\r\n                        (  216.39   265.36   -13.50     0.92)  ; 2\r\n                        (  218.89   264.75   -14.35     0.92)  ; 3\r\n                        (  207.50   265.07   -14.32     0.92)  ; 4\r\n                        (  212.29   266.79   -14.27     0.92)  ; 5\r\n                        (  208.81   269.56   -14.27     0.92)  ; 6\r\n                        (  205.86   270.06   -14.27     0.92)  ; 7\r\n                        (  201.80   267.32   -17.45     0.92)  ; 8\r\n                        (  201.49   272.62   -17.45     0.92)  ; 9\r\n                        (  187.25   271.07   -21.18     0.92)  ; 10\r\n                        (  185.79   277.30   -22.70     0.92)  ; 11\r\n                        (  190.03   275.30   -22.70     0.92)  ; 12\r\n                        (  181.16   280.99   -23.50     0.92)  ; 13\r\n                        (  179.40   276.40   -23.50     0.92)  ; 14\r\n                        (  181.33   276.26   -23.50     0.92)  ; 15\r\n                        (  182.32   280.07   -23.50     0.92)  ; 16\r\n                        (  176.25   279.85   -23.63     0.92)  ; 17\r\n                        (  176.78   283.55   -24.77     0.92)  ; 18\r\n                        (  170.40   282.65   -23.27     0.92)  ; 19\r\n                        (  169.55   284.24   -23.85     0.92)  ; 20\r\n                        (  165.68   288.71   -25.17     0.92)  ; 21\r\n                        (  165.68   288.71   -25.15     0.46)  ; 22\r\n                        (  159.75   293.89   -25.27     0.46)  ; 23\r\n                        (  152.85   295.26   -27.97     0.46)  ; 24\r\n                        (  155.25   297.01   -27.97     0.46)  ; 25\r\n                        (  155.34   294.64   -27.97     0.46)  ; 26\r\n                        (  148.12   301.31   -28.15     0.46)  ; 27\r\n                        (  145.37   303.06   -30.73     0.46)  ; 28\r\n                        (  142.64   300.63   -31.45     0.46)  ; 29\r\n                        (  137.47   306.58   -29.42     0.46)  ; 30\r\n                        (  140.95   303.81   -29.45     0.46)  ; 31\r\n                        (  131.28   312.89   -26.65     0.46)  ; 32\r\n                        (  127.21   310.15   -26.00     0.46)  ; 33\r\n                        (  122.78   321.05   -24.20     0.46)  ; 34\r\n                        (  121.52   318.37   -23.15     0.46)  ; 35\r\n                        (  123.16   313.38   -23.50     0.46)  ; 36\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  227.15   263.70    -8.00     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2\r\n                      (\r\n                        (  227.20   265.51    -6.93     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2-1\r\n                        (  225.71   265.76    -5.60     0.46)  ; 2\r\n                        (  223.84   267.71    -4.92     0.46)  ; 3\r\n                        (  222.64   266.82    -4.88     0.46)  ; 4\r\n                        (  223.14   268.74    -2.97     0.46)  ; 5\r\n                        (  223.14   268.74    -2.95     0.46)  ; 6\r\n                        (  222.42   269.76    -1.75     0.46)  ; 7\r\n                        (  221.58   271.35    -0.12     0.46)  ; 8\r\n                        (  222.34   272.14     1.75     0.46)  ; 9\r\n                        (  219.97   272.18     3.82     0.46)  ; 10\r\n                        (  217.03   272.68     4.72     0.46)  ; 11\r\n                        (  217.21   273.91     6.18     0.46)  ; 12\r\n                        (  217.98   274.68     8.10     0.46)  ; 13\r\n                        (  217.44   276.95     9.45     0.46)  ; 14\r\n                        (  218.46   276.60     9.85     0.46)  ; 15\r\n                        (  218.06   278.29    11.98     0.46)  ; 16\r\n                        (  216.15   278.44    14.23     0.46)  ; 17\r\n                        (  217.67   279.99    16.15     0.46)  ; 18\r\n                        (  217.32   283.49    17.35     0.46)  ; 19\r\n                        (  217.32   283.49    17.40     0.46)  ; 20\r\n                        (  218.71   285.61    18.75     0.46)  ; 21\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  223.80   265.91    -4.92     0.46)  ; 1\r\n                          (  226.80   267.20    -4.92     0.46)  ; 2\r\n                          (  224.24   266.01    -2.80     0.46)  ; 3\r\n                          (  220.63   269.35     1.75     0.46)  ; 4\r\n                          (  223.09   272.90     2.63     0.46)  ; 5\r\n                          (  215.66   276.53     8.15     0.46)  ; 6\r\n                          (  219.44   274.43     8.85     0.46)  ; 7\r\n                          (  220.20   275.21     9.90     0.46)  ; 8\r\n                          (  218.69   279.63    17.40     0.46)  ; 9\r\n                        )  ;  End of markers\r\n                        (\r\n                          (  219.38   288.76    18.80     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2-1-1\r\n                          (  219.75   291.23    20.27     0.46)  ; 2\r\n                          (  221.54   291.64    21.92     0.46)  ; 3\r\n                          (  222.56   291.29    23.33     0.46)  ; 4\r\n                          (  222.56   291.29    23.30     0.46)  ; 5\r\n                           Normal\r\n                        |\r\n                          (  220.17   285.36    17.50     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2-1-2\r\n                          (  221.26   286.80    19.13     0.46)  ; 2\r\n                          (  221.26   286.80    19.20     0.46)  ; 3\r\n                          (  221.76   288.71    21.05     0.46)  ; 4\r\n                          (  220.91   290.31    21.80     0.46)  ; 5\r\n                          (  221.54   291.64    23.10     0.46)  ; 6\r\n                          (  223.32   292.06    23.78     0.46)  ; 7\r\n                          (  223.32   292.06    23.95     0.46)  ; 8\r\n                           Normal\r\n                        )  ;  End of split\r\n                      |\r\n                        (  225.08   264.42    -9.15     0.92)  ; 1, R-1-1-1-1-1-1-1-1-2-2-2\r\n                        (  222.46   265.59    -9.40     0.92)  ; 2\r\n                        (  220.15   267.44    -8.32     0.92)  ; 3\r\n                        (  220.15   267.44    -8.35     0.92)  ; 4\r\n                        (  218.36   267.02    -8.20     0.92)  ; 5\r\n                        (  214.38   267.87    -8.32     0.92)  ; 6\r\n                        (  211.04   270.08    -9.43     0.92)  ; 7\r\n                        (  207.91   269.35    -9.43     0.92)  ; 8\r\n                        (  207.25   272.18   -10.17     0.92)  ; 9\r\n                        (  205.38   274.12   -10.17     0.92)  ; 10\r\n                        (  202.88   274.73    -9.25     0.92)  ; 11\r\n                        (  200.21   274.10    -8.15     0.92)  ; 12\r\n                        (  196.99   275.74    -7.95     0.92)  ; 13\r\n                        (  195.17   279.50    -7.47     0.92)  ; 14\r\n                        (  191.51   281.02    -7.47     0.92)  ; 15\r\n                        (  188.11   281.43    -6.55     0.92)  ; 16\r\n                        (  185.22   283.73    -6.55     0.92)  ; 17\r\n                        (  181.97   283.56    -7.90     0.92)  ; 18\r\n                        (  179.33   284.75    -9.15     0.92)  ; 19\r\n                        (  177.02   286.59    -9.60     0.92)  ; 20\r\n                        (  177.02   286.59    -9.63     0.92)  ; 21\r\n                        (  173.10   289.25    -7.15     0.92)  ; 22\r\n                        (  173.10   289.25    -7.17     0.92)  ; 23\r\n                        (  171.67   291.31    -7.17     0.92)  ; 24\r\n                        (  169.23   293.71    -6.52     0.92)  ; 25\r\n                        (  166.77   296.13    -6.52     0.92)  ; 26\r\n                        (  165.61   297.05    -5.80     0.92)  ; 27\r\n                        (  163.83   296.63    -5.80     0.92)  ; 28\r\n                        (  160.18   298.16    -5.03     0.92)  ; 29\r\n                        (  156.91   297.99    -4.15     0.92)  ; 30\r\n                        (  154.28   299.18    -4.10     0.92)  ; 31\r\n                        (  153.61   302.00    -3.57     0.92)  ; 32\r\n                        (  150.72   304.31    -3.57     0.46)  ; 33\r\n                        (  147.38   306.52    -3.57     0.46)  ; 34\r\n                        (  144.44   307.02    -3.57     0.46)  ; 35\r\n                        (  141.01   311.59    -3.95     0.46)  ; 36\r\n                        (  141.01   311.59    -3.97     0.46)  ; 37\r\n                        (  138.96   312.30    -3.27     0.46)  ; 38\r\n                        (  134.59   314.86    -4.35     0.46)  ; 39\r\n                        (  132.40   316.14    -5.13     0.46)  ; 40\r\n                        (  129.77   317.31    -5.13     0.46)  ; 41\r\n                        (  127.89   319.27    -6.22     0.46)  ; 42\r\n                        (  127.18   320.29    -8.30     0.46)  ; 43\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  223.88   263.53    -9.15     0.92)  ; 1\r\n                          (  218.41   268.82    -8.32     0.92)  ; 2\r\n                          (  213.49   267.66    -9.27     0.92)  ; 3\r\n                          (  215.54   266.96   -10.60     0.92)  ; 4\r\n                          (  216.04   268.86    -9.05     0.92)  ; 5\r\n                          (  213.14   271.17   -10.70     0.92)  ; 6\r\n                          (  211.23   271.31    -7.67     0.92)  ; 7\r\n                          (  209.65   267.96    -9.13     0.92)  ; 8\r\n                          (  206.04   271.29    -8.15     0.92)  ; 9\r\n                          (  196.76   272.70    -8.15     0.92)  ; 10\r\n                          (  193.48   282.69    -8.25     0.92)  ; 11\r\n                          (  187.94   280.18    -6.55     0.92)  ; 12\r\n                          (  183.84   281.62    -6.55     0.92)  ; 13\r\n                          (  187.72   283.13    -6.55     0.92)  ; 14\r\n                          (  177.10   284.22    -9.63     0.92)  ; 15\r\n                          (  180.86   286.29    -8.57     0.92)  ; 16\r\n                          (  177.64   287.94    -8.40     0.92)  ; 17\r\n                          (  171.27   293.01    -9.38     0.92)  ; 18\r\n                          (  165.65   292.88    -6.95     0.92)  ; 19\r\n                          (  166.14   294.79    -6.95     0.92)  ; 20\r\n                          (  169.15   296.09    -7.27     0.92)  ; 21\r\n                          (  173.73   290.60    -7.57     0.92)  ; 22\r\n                          (  160.81   299.51    -4.15     0.92)  ; 23\r\n                          (  163.30   298.90    -4.15     0.92)  ; 24\r\n                          (  160.44   297.04    -4.15     0.92)  ; 25\r\n                          (  153.13   300.10    -2.88     0.92)  ; 26\r\n                          (  150.67   302.52    -2.88     0.92)  ; 27\r\n                          (  148.58   307.40    -2.88     0.92)  ; 28\r\n                          (  142.84   313.82    -3.27     0.46)  ; 29\r\n                          (  142.48   311.33    -4.30     0.46)  ; 30\r\n                          (  132.50   319.75    -4.65     0.46)  ; 31\r\n                          (  134.64   316.67    -4.72     0.46)  ; 32\r\n                          (  137.48   312.55    -2.63     0.46)  ; 33\r\n                        )  ;  End of markers\r\n                         Normal\r\n                      )  ;  End of split\r\n                    )  ;  End of split\r\n                  )  ;  End of split\r\n                |\r\n                  (  247.52   217.10   -12.10     1.38)  ; 1, R-1-1-1-1-1-1-1-2\r\n                  (  244.97   215.90   -13.25     1.38)  ; 2\r\n                  (  242.46   216.52   -14.40     1.38)  ; 3\r\n                  (  241.62   218.11   -15.60     1.38)  ; 4\r\n                  (  240.82   221.51   -19.42     1.38)  ; 5\r\n\r\n                  (Cross\r\n                    (Color RGB (128, 255, 128))\r\n                    (Name \"Marker 3\")\r\n                    (  249.43   216.94   -12.10     1.38)  ; 1\r\n                    (  246.54   219.26   -12.10     1.38)  ; 2\r\n                    (  245.45   217.82   -12.10     1.38)  ; 3\r\n                    (  240.15   218.36   -15.60     1.38)  ; 4\r\n                    (  243.76   215.03   -15.60     1.38)  ; 5\r\n                    (  243.09   217.86   -17.90     1.38)  ; 6\r\n                    (  243.01   220.23   -14.35     1.38)  ; 7\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  238.64   222.78   -20.75     1.38)  ; 1, R-1-1-1-1-1-1-1-2-1\r\n                    (  234.35   222.98   -22.30     1.38)  ; 2\r\n                    (  232.13   222.46   -23.25     1.38)  ; 3\r\n                    (  232.13   222.46   -23.27     1.38)  ; 4\r\n                    (  230.78   222.14   -25.00     1.38)  ; 5\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  235.02   220.14   -22.30     1.38)  ; 1\r\n                      (  236.67   221.13   -22.30     1.38)  ; 2\r\n                      (  232.18   224.26   -21.50     1.38)  ; 3\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  228.51   219.81   -25.70     0.92)  ; 1, R-1-1-1-1-1-1-1-2-1-1\r\n                      (  226.09   218.05   -27.15     0.92)  ; 2\r\n                      (  224.48   218.87   -28.85     0.92)  ; 3\r\n                      (  223.59   218.66   -30.23     0.92)  ; 4\r\n                      (  222.97   217.32   -31.67     0.92)  ; 5\r\n                      (  221.63   217.01   -33.10     0.92)  ; 6\r\n                      (  219.65   215.35   -34.17     0.92)  ; 7\r\n                      (  218.71   213.33   -35.92     0.92)  ; 8\r\n                      (  220.05   213.65   -38.53     0.92)  ; 9\r\n                      (  218.94   216.38   -40.95     0.92)  ; 10\r\n                      (  216.13   216.31   -43.05     0.92)  ; 11\r\n                      (  212.99   215.58   -44.35     0.92)  ; 12\r\n                      (  210.59   213.83   -44.25     0.92)  ; 13\r\n                      (  207.86   211.39   -44.75     0.92)  ; 14\r\n                      (  203.79   208.65   -45.15     0.92)  ; 15\r\n                      (  202.14   207.66   -45.15     0.46)  ; 16\r\n                      (  197.62   204.81   -45.97     0.46)  ; 17\r\n                      (  195.26   204.86   -46.93     0.46)  ; 18\r\n                      (  195.26   204.86   -46.95     0.46)  ; 19\r\n                      (  193.52   206.24   -47.80     0.46)  ; 20\r\n                      (  190.84   205.61   -48.70     0.46)  ; 21\r\n                      (  190.84   205.61   -48.72     0.46)  ; 22\r\n                      (  187.27   204.78   -49.58     0.46)  ; 23\r\n                      (  183.88   205.17   -49.38     0.46)  ; 24\r\n                      (  181.38   205.79   -49.38     0.46)  ; 25\r\n                      (  177.99   206.19   -49.38     0.46)  ; 26\r\n                      (  175.94   206.89   -50.32     0.46)  ; 27\r\n                      (  175.94   206.89   -50.35     0.46)  ; 28\r\n                      (  173.75   208.18   -51.25     0.46)  ; 29\r\n                      (  172.90   209.77   -52.08     0.46)  ; 30\r\n                      (  172.52   211.46   -53.38     0.46)  ; 31\r\n                      (  172.52   211.46   -53.40     0.46)  ; 32\r\n                      (  172.11   213.16   -54.67     0.46)  ; 33\r\n                      (  172.11   213.16   -54.83     0.46)  ; 34\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  226.76   221.20   -27.15     0.92)  ; 1\r\n                        (  225.91   216.82   -28.85     0.92)  ; 2\r\n                        (  221.86   220.05   -31.67     0.92)  ; 3\r\n                        (  216.57   216.42   -35.92     0.92)  ; 4\r\n                        (  214.88   213.64   -44.35     0.92)  ; 5\r\n                        (  212.33   212.44   -44.25     0.92)  ; 6\r\n                        (  209.56   214.18   -44.25     0.92)  ; 7\r\n                        (  211.08   215.73   -44.25     0.92)  ; 8\r\n                        (  206.87   207.58   -45.15     0.92)  ; 9\r\n                        (  196.38   208.10   -45.97     0.46)  ; 10\r\n                        (  195.79   202.59   -46.12     0.46)  ; 11\r\n                        (  188.43   203.85   -49.58     0.46)  ; 12\r\n                        (  178.65   203.35   -49.38     0.46)  ; 13\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  226.81   222.99   -25.00     0.92)  ; 1, R-1-1-1-1-1-1-1-2-1-2\r\n                      (  225.08   224.38   -27.63     0.92)  ; 2\r\n                      (  224.94   224.95   -27.63     0.92)  ; 3\r\n                      (  223.60   224.63   -29.77     0.92)  ; 4\r\n                      (  221.51   223.54   -30.60     0.92)  ; 5\r\n                      (  219.64   225.50   -32.38     0.92)  ; 6\r\n                      (  217.27   225.54   -33.45     0.92)  ; 7\r\n                      (  213.96   223.57   -34.32     0.92)  ; 8\r\n                      (  211.96   226.09   -35.25     0.92)  ; 9\r\n                      (  212.01   227.89   -35.25     0.92)  ; 10\r\n                      (  208.93   228.96   -35.25     0.92)  ; 11\r\n                      (  207.50   231.01   -34.28     0.92)  ; 12\r\n                      (  205.36   234.09   -34.35     0.92)  ; 13\r\n                      (  203.68   237.29   -33.35     0.92)  ; 14\r\n                      (  202.07   238.10   -32.13     0.92)  ; 15\r\n                      (  199.58   238.71   -31.67     0.92)  ; 16\r\n                      (  199.05   240.97   -30.73     0.92)  ; 17\r\n                      (  198.52   243.24   -29.77     0.92)  ; 18\r\n                      (  196.90   244.06   -30.55     0.92)  ; 19\r\n                      (  192.89   243.12   -30.55     0.92)  ; 20\r\n                      (  190.13   244.86   -29.40     0.92)  ; 21\r\n                      (  188.02   243.76   -28.33     0.92)  ; 22\r\n                      (  186.10   243.91   -26.17     0.92)  ; 23\r\n                      (  183.30   243.85   -24.70     0.92)  ; 24\r\n                      (  181.64   242.87   -24.32     0.92)  ; 25\r\n                      (  179.72   243.01   -24.32     0.92)  ; 26\r\n                      (  177.43   240.69   -24.42     0.92)  ; 27\r\n                      (  173.16   240.88   -24.13     0.92)  ; 28\r\n                      (  169.05   242.31   -23.23     0.92)  ; 29\r\n                      (  164.95   243.73   -22.53     0.92)  ; 30\r\n                      (  160.98   244.59   -22.00     0.92)  ; 31\r\n                      (  158.39   247.58   -21.18     0.92)  ; 32\r\n                      (  155.77   248.75   -19.85     0.92)  ; 33\r\n                      (  153.53   248.22   -19.85     0.92)  ; 34\r\n                      (  151.21   250.07   -18.55     0.92)  ; 35\r\n                      (  146.80   250.82   -18.95     0.92)  ; 36\r\n                      (  141.78   252.03   -18.15     0.92)  ; 37\r\n                      (  139.46   253.86   -17.55     0.92)  ; 38\r\n                      (  137.78   257.05   -16.67     0.92)  ; 39\r\n                      (  135.86   257.19   -16.07     0.92)  ; 40\r\n                      (  133.76   256.11   -15.65     0.92)  ; 41\r\n                      (  131.17   259.09   -14.65     0.92)  ; 42\r\n                      (  127.39   261.18   -14.13     0.92)  ; 43\r\n                      (  124.36   264.05   -14.13     0.92)  ; 44\r\n                      (  122.94   266.12   -13.15     0.92)  ; 45\r\n                      (  121.32   266.93   -12.30     0.92)  ; 46\r\n                      (  119.28   267.64   -11.30     0.92)  ; 47\r\n                      (  116.39   269.95   -10.30     0.46)  ; 48\r\n                      (  114.38   272.47    -9.88     0.46)  ; 49\r\n                      (  110.95   277.04    -9.22     0.46)  ; 50\r\n                      (  107.47   279.80    -8.25     0.46)  ; 51\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  229.36   224.19   -25.00     0.92)  ; 1\r\n                        (  226.59   225.94   -28.35     0.92)  ; 2\r\n                        (  224.55   226.65   -28.47     0.92)  ; 3\r\n                        (  221.55   225.35   -28.55     0.92)  ; 4\r\n                        (  218.63   221.68   -32.38     0.92)  ; 5\r\n                        (  221.91   221.85   -32.38     0.92)  ; 6\r\n                        (  217.97   224.51   -32.05     0.92)  ; 7\r\n                        (  214.32   226.03   -34.32     0.92)  ; 8\r\n                        (  205.46   237.70   -30.13     0.92)  ; 9\r\n                        (  203.28   238.98   -29.72     0.92)  ; 10\r\n                        (  200.70   241.96   -28.82     0.92)  ; 11\r\n                        (  197.63   243.03   -28.82     0.92)  ; 12\r\n                        (  198.16   240.76   -28.82     0.92)  ; 13\r\n                        (  199.85   243.55   -31.05     0.92)  ; 14\r\n                        (  194.58   245.90   -30.55     0.92)  ; 15\r\n                        (  196.67   241.01   -30.55     0.92)  ; 16\r\n                        (  177.40   244.86   -24.42     0.92)  ; 17\r\n                        (  174.54   243.00   -24.42     0.92)  ; 18\r\n                        (  174.58   238.83   -26.52     0.92)  ; 19\r\n                        (  176.10   240.37   -26.52     0.92)  ; 20\r\n                        (  168.57   246.38   -23.23     0.92)  ; 21\r\n                        (  171.18   239.22   -23.23     0.92)  ; 22\r\n                        (  159.20   250.15   -19.40     0.92)  ; 23\r\n                        (  155.55   251.68   -19.63     0.92)  ; 24\r\n                        (  156.30   246.48   -19.65     0.92)  ; 25\r\n                        (  151.52   250.74   -16.55     0.92)  ; 26\r\n                        (  150.15   254.60   -18.95     0.92)  ; 27\r\n                        (  143.89   253.11   -18.15     0.92)  ; 28\r\n                        (  140.67   254.74   -15.73     0.92)  ; 29\r\n                        (  137.07   258.08   -15.73     0.92)  ; 30\r\n                        (  130.41   258.31   -14.65     0.92)  ; 31\r\n                        (  129.48   262.27   -14.65     0.92)  ; 32\r\n                        (  126.05   260.87   -13.92     0.92)  ; 33\r\n                        (  124.89   261.79   -13.92     0.92)  ; 34\r\n                        (  123.73   262.72   -13.92     0.92)  ; 35\r\n                        (  119.23   265.84   -11.30     0.92)  ; 36\r\n                        (  116.16   266.91   -10.55     0.92)  ; 37\r\n                        (  120.04   268.42   -10.55     0.92)  ; 38\r\n                        (  110.45   275.13    -8.25     0.46)  ; 39\r\n                        (  111.63   280.18    -7.27     0.46)  ; 40\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  |\r\n                    (  242.02   222.35   -22.63     0.92)  ; 1, R-1-1-1-1-1-1-1-2-2\r\n                    (  239.97   223.08   -24.72     0.92)  ; 2\r\n                    (  239.97   223.08   -24.75     0.92)  ; 3\r\n                    (  237.78   224.35   -26.85     0.92)  ; 4\r\n                    (  237.21   224.80   -28.05     0.92)  ; 5\r\n                    (  236.53   221.67   -28.35     0.46)  ; 6\r\n                    (  239.74   220.03   -29.50     0.46)  ; 7\r\n                    (  239.74   220.03   -29.52     0.46)  ; 8\r\n                    (  241.97   220.55   -31.25     0.46)  ; 9\r\n                    (  243.95   222.21   -32.45     0.46)  ; 10\r\n                    (  244.57   223.55   -33.38     0.46)  ; 11\r\n                    (  245.64   224.99   -34.63     0.46)  ; 12\r\n                    (  246.40   225.77   -36.05     0.46)  ; 13\r\n                    (  246.80   224.08   -37.72     0.46)  ; 14\r\n                    (  246.35   223.97   -39.32     0.46)  ; 15\r\n                    (  248.32   225.62   -40.88     0.46)  ; 16\r\n                    (  249.40   227.07   -42.80     0.46)  ; 17\r\n                    (  249.40   227.07   -42.82     0.46)  ; 18\r\n                    (  247.83   229.70   -44.63     0.46)  ; 19\r\n                    (  247.58   230.82   -46.40     0.46)  ; 20\r\n                    (  247.68   234.43   -47.67     0.46)  ; 21\r\n                    (  245.39   232.10   -49.07     0.46)  ; 22\r\n                    (  244.45   230.09   -50.83     0.46)  ; 23\r\n                    (  246.29   232.31   -53.40     0.46)  ; 24\r\n                    (  249.37   231.24   -56.05     0.46)  ; 25\r\n                    (  251.02   232.22   -58.38     0.46)  ; 26\r\n                    (  250.62   233.93   -60.85     0.46)  ; 27\r\n                    (  249.42   233.04   -63.70     0.46)  ; 28\r\n                    (  248.97   232.94   -63.72     0.46)  ; 29\r\n                    (  249.68   231.91   -67.00     0.46)  ; 30\r\n                    (  250.44   232.69   -70.25     0.46)  ; 31\r\n                    (  253.12   233.32   -72.47     0.46)  ; 32\r\n                    (  253.12   233.32   -72.50     0.46)  ; 33\r\n                    (  251.69   235.37   -75.15     0.46)  ; 34\r\n                    (  251.56   235.94   -75.15     0.46)  ; 35\r\n                    (  249.51   236.65   -77.63     0.46)  ; 36\r\n                    (  249.51   236.65   -77.65     0.46)  ; 37\r\n                    (  248.17   236.34   -80.18     0.46)  ; 38\r\n                    (  248.17   236.34   -80.20     0.46)  ; 39\r\n                    (  247.68   234.43   -82.83     0.46)  ; 40\r\n                    (  247.68   234.43   -82.85     0.46)  ; 41\r\n                    (  248.22   238.15   -84.40     0.46)  ; 42\r\n                    (  248.09   238.70   -84.43     0.46)  ; 43\r\n                    (  249.60   240.25   -84.43     0.46)  ; 44\r\n                    (  246.53   241.33   -86.90     0.46)  ; 45\r\n                    (  247.48   243.34   -91.43     0.46)  ; 46\r\n                    (  247.34   243.90   -91.45     0.46)  ; 47\r\n                    (  249.26   243.75   -94.10     0.46)  ; 48\r\n                    (  249.26   243.75   -94.15     0.46)  ; 49\r\n                    (  249.35   247.36   -96.62     0.46)  ; 50\r\n                    (  248.91   247.26   -96.65     0.46)  ; 51\r\n                    (  247.88   247.61  -100.60     0.46)  ; 52\r\n                    (  247.88   247.61  -100.63     0.46)  ; 53\r\n                    (  248.46   247.15  -104.05     0.46)  ; 54\r\n                    (  248.46   247.15  -104.12     0.46)  ; 55\r\n                    (  248.69   250.20  -106.80     0.46)  ; 56\r\n                    (  248.69   250.20  -106.82     0.46)  ; 57\r\n                    (  247.13   252.81  -108.63     0.46)  ; 58\r\n                    (  247.13   252.81  -108.65     0.46)  ; 59\r\n                    (  247.90   253.59  -110.85     0.46)  ; 60\r\n                    (  249.01   256.84  -111.97     0.46)  ; 61\r\n                    (  248.56   256.74  -112.00     0.46)  ; 62\r\n                    (  248.62   258.54  -114.55     0.46)  ; 63\r\n                    (  248.62   258.54  -114.57     0.46)  ; 64\r\n                    (  248.21   260.24  -117.90     0.46)  ; 65\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  247.23   256.42  -112.13     0.46)  ; 1\r\n                      (  247.90   259.57  -113.93     0.46)  ; 2\r\n                      (  250.13   238.00   -79.25     0.46)  ; 3\r\n                      (  253.30   234.56   -70.60     0.46)  ; 4\r\n                      (  252.94   232.08   -61.57     0.46)  ; 5\r\n                      (  246.77   250.34  -102.78     0.46)  ; 6\r\n                    )  ;  End of markers\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  236.93   219.96   -29.52     0.46)  ; 1\r\n                      (  248.16   262.63   -19.60     2.29)  ; 2\r\n                      (  246.06   257.39   -12.80     0.92)  ; 3\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                )  ;  End of split\r\n              |\r\n                (  251.92   127.92     1.32     0.92)  ; 1, R-1-1-1-1-1-1-2\r\n                (  250.64   129.42     2.38     0.92)  ; 2\r\n                (  250.24   131.12     2.38     0.46)  ; 3\r\n                (  251.57   131.43     3.50     0.46)  ; 4\r\n                (  252.33   132.21     4.78     0.46)  ; 5\r\n                (  252.33   132.21     6.42     0.46)  ; 6\r\n                (  251.94   133.90     7.75     0.46)  ; 7\r\n                (  249.31   135.08     7.15     0.46)  ; 8\r\n                (  247.16   138.16     8.07     0.46)  ; 9\r\n                (  244.14   141.04     8.45     0.46)  ; 10\r\n                (  241.95   142.32     9.22     0.46)  ; 11\r\n                (  241.06   142.11    10.05     0.46)  ; 12\r\n                (  238.57   142.71    10.70     0.46)  ; 13\r\n                (  235.62   143.22    11.75     0.46)  ; 14\r\n                (  235.04   143.69    12.85     0.46)  ; 15\r\n                (  233.83   142.80    14.23     0.46)  ; 16\r\n                (  233.69   143.37    14.20     0.46)  ; 17\r\n                (  235.75   142.64    15.90     0.46)  ; 18\r\n                (  235.75   142.64    17.55     0.46)  ; 19\r\n                (  236.59   141.05    19.27     0.46)  ; 20\r\n                (  236.59   141.05    19.25     0.46)  ; 21\r\n                (  234.09   141.66    20.73     0.46)  ; 22\r\n                (  231.02   142.74    21.38     0.46)  ; 23\r\n                (  228.65   142.78    22.08     0.46)  ; 24\r\n                (  227.72   146.75    22.85     0.46)  ; 25\r\n                (  229.56   148.97    23.40     0.46)  ; 26\r\n                (  229.74   150.20    24.60     0.46)  ; 27\r\n                (  229.74   150.20    24.58     0.46)  ; 28\r\n                (  230.06   150.87    26.58     0.46)  ; 29\r\n                (  231.39   151.19    28.70     0.46)  ; 30\r\n                (  232.28   151.40    29.92     0.46)  ; 31\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  248.57   130.13     2.38     0.46)  ; 1\r\n                  (  248.89   130.81     2.38     0.46)  ; 2\r\n                  (  247.54   140.64     7.57     0.46)  ; 3\r\n                  (  244.84   140.00     8.57     0.46)  ; 4\r\n                  (  242.48   140.04     8.57     0.46)  ; 5\r\n                  (  243.03   143.76     8.95     0.46)  ; 6\r\n                  (  240.12   140.10    10.48     0.46)  ; 7\r\n                  (  241.82   142.88    11.45     0.46)  ; 8\r\n                  (  237.72   144.31    11.45     0.46)  ; 9\r\n                  (  230.52   140.82    21.38     0.46)  ; 10\r\n                  (  229.78   146.02    23.55     0.46)  ; 11\r\n                  (  226.33   144.62    23.40     0.46)  ; 12\r\n                  (  226.26   152.96    23.40     0.46)  ; 13\r\n                  (  250.51   135.96     7.15     0.46)  ; 14\r\n                )  ;  End of markers\r\n                 High\r\n              |\r\n                (  258.12   127.60     1.30     0.92)  ; 1, R-1-1-1-1-1-1-3\r\n                (  261.87   129.67     1.73     0.92)  ; 2\r\n                (  266.15   129.48     1.85     0.92)  ; 3\r\n                (  269.11   128.97     2.55     0.92)  ; 4\r\n                (  271.74   127.80     2.08     0.92)  ; 5\r\n                (  274.10   127.76     0.28     0.92)  ; 6\r\n                (  276.48   127.72    -0.70     0.92)  ; 7\r\n                (  278.39   127.57    -0.05     0.92)  ; 8\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  264.64   127.93     1.85     0.92)  ; 1\r\n                  (  261.74   130.23     2.55     0.92)  ; 2\r\n                  (  263.53   130.65     2.55     0.92)  ; 3\r\n                  (  265.89   130.61     2.55     0.92)  ; 4\r\n                  (  268.40   130.01     2.55     0.92)  ; 5\r\n                  (  270.35   125.68     2.55     0.92)  ; 6\r\n                  (  266.55   127.78     2.55     0.92)  ; 7\r\n                )  ;  End of markers\r\n                (\r\n                  (  282.40   128.51     0.90     0.46)  ; 1, R-1-1-1-1-1-1-3-1\r\n                  (  283.58   127.58     2.50     0.46)  ; 2\r\n                  (  283.58   127.58     2.47     0.46)  ; 3\r\n                  (  283.70   127.02     4.22     0.46)  ; 4\r\n                  (  282.89   124.44     5.50     0.46)  ; 5\r\n                  (  282.89   124.44     5.48     0.46)  ; 6\r\n                  (  284.49   123.62     5.95     0.46)  ; 7\r\n                  (  288.07   124.45     6.47     0.46)  ; 8\r\n                  (  290.75   125.09     7.00     0.46)  ; 9\r\n                  (  291.97   125.96     7.75     0.46)  ; 10\r\n                  (  293.75   126.38     6.90     0.46)  ; 11\r\n                  (  294.77   126.02     6.90     0.46)  ; 12\r\n                  (  297.18   127.79     8.35     0.46)  ; 13\r\n                  (  301.16   126.93     9.80     0.46)  ; 14\r\n                  (  301.11   125.13    11.75     0.46)  ; 15\r\n                  (  301.11   125.13    11.70     0.46)  ; 16\r\n                  (  300.17   129.09    14.10     0.46)  ; 17\r\n                  (  300.17   129.09    15.68     0.46)  ; 18\r\n                  (  300.17   129.09    15.65     0.46)  ; 19\r\n                  (  302.37   127.81    17.30     0.46)  ; 20\r\n                  (  302.37   127.81    17.27     0.46)  ; 21\r\n                  (  303.78   125.76    19.23     0.46)  ; 22\r\n                  (  303.78   125.76    19.20     0.46)  ; 23\r\n                  (  305.21   123.70    20.33     0.46)  ; 24\r\n                  (  309.22   124.64    20.33     0.46)  ; 25\r\n                  (  312.80   125.48    21.60     0.46)  ; 26\r\n                  (  312.85   127.29    21.60     0.46)  ; 27\r\n                  (  314.68   129.51    22.67     0.46)  ; 28\r\n                  (  315.71   129.15    24.05     0.46)  ; 29\r\n                  (  315.71   129.15    24.00     0.46)  ; 30\r\n                  (  315.18   131.41    25.13     0.46)  ; 31\r\n                  (  314.20   133.57    26.70     0.46)  ; 32\r\n                  (  313.17   133.92    27.77     0.46)  ; 33\r\n                  (  312.72   133.82    28.05     0.46)  ; 34\r\n                  (  312.46   134.95    29.15     0.46)  ; 35\r\n                  (  312.01   134.84    29.30     0.46)  ; 36\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  288.25   125.71     8.15     0.46)  ; 1\r\n                    (  286.65   126.52     6.47     0.46)  ; 2\r\n                    (  283.29   122.74     8.13     0.46)  ; 3\r\n                    (  294.42   129.53     9.80     0.46)  ; 4\r\n                    (  317.08   125.29    21.28     0.46)  ; 5\r\n                    (  315.86   134.56    21.55     0.46)  ; 6\r\n                    (  318.34   127.97    23.67     0.46)  ; 7\r\n                    (  307.48   126.03    20.33     0.46)  ; 8\r\n                    (  305.57   126.18    20.33     0.46)  ; 9\r\n                    (  303.16   124.41    19.20     0.46)  ; 10\r\n                    (  301.11   125.13    19.20     0.46)  ; 11\r\n                    (  299.45   124.15     9.80     0.46)  ; 12\r\n                    (  296.90   122.94     8.35     0.46)  ; 13\r\n                    (  289.67   123.64     7.87     0.46)  ; 14\r\n                  )  ;  End of markers\r\n                   High\r\n                |\r\n                  (  280.13   126.18    -1.30     0.92)  ; 1, R-1-1-1-1-1-1-3-2\r\n                  (  281.73   125.37    -2.70     0.92)  ; 2\r\n                  (  283.20   125.12    -4.20     0.92)  ; 3\r\n                  (  283.15   123.31    -5.60     0.92)  ; 4\r\n                  (  284.63   123.06    -7.20     0.92)  ; 5\r\n                  (  285.39   123.82    -9.57     0.92)  ; 6\r\n                  (  286.86   123.58   -11.15     0.92)  ; 7\r\n                  (  288.47   122.76   -11.65     0.92)  ; 8\r\n                  (  289.13   119.93   -10.35     0.92)  ; 9\r\n                  (  290.86   118.55    -9.55     0.92)  ; 10\r\n                  (  293.55   119.18   -10.05     0.92)  ; 11\r\n                  (  295.74   117.89   -10.25     0.92)  ; 12\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  286.91   125.39    -9.63     0.92)  ; 1\r\n                    (  285.75   126.31   -11.65     0.92)  ; 2\r\n                    (  289.09   124.10   -10.12     0.92)  ; 3\r\n                    (  290.78   120.91   -11.77     0.92)  ; 4\r\n                    (  281.81   122.99    -5.60     0.92)  ; 5\r\n                    (  292.02   117.62   -11.77     0.92)  ; 6\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  298.10   117.85   -10.25     0.92)  ; 1, R-1-1-1-1-1-1-3-2-1\r\n                    (  301.35   118.02   -11.73     0.92)  ; 2\r\n                    (  303.55   116.74   -12.85     0.92)  ; 3\r\n                    (  303.55   116.74   -12.88     0.92)  ; 4\r\n                    (  306.44   114.43   -12.13     0.92)  ; 5\r\n                    (  310.54   113.00   -14.07     0.92)  ; 6\r\n                    (  314.20   111.47   -17.77     0.92)  ; 7\r\n                    (  317.28   110.40   -18.15     0.92)  ; 8\r\n                    (  319.32   109.69   -18.15     0.92)  ; 9\r\n                    (  323.17   109.40   -17.10     0.92)  ; 10\r\n                    (  325.53   109.36   -15.70     0.92)  ; 11\r\n                    (  327.26   107.97   -16.30     0.92)  ; 12\r\n                    (  327.26   107.97   -16.32     0.92)  ; 13\r\n                    (  330.70   109.37   -16.75     0.92)  ; 14\r\n                    (  332.49   109.79   -17.77     0.92)  ; 15\r\n                    (  335.61   110.51   -18.65     0.92)  ; 16\r\n                    (  338.56   110.02   -17.20     0.92)  ; 17\r\n                    (  341.45   107.71   -18.13     0.92)  ; 18\r\n                    (  345.92   108.76   -18.57     0.92)  ; 19\r\n                    (  349.31   108.36   -17.25     0.92)  ; 20\r\n                    (  352.97   106.83   -17.25     0.92)  ; 21\r\n                    (  354.34   102.97   -18.38     0.92)  ; 22\r\n                    (  358.00   101.43   -18.82     0.92)  ; 23\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  308.71   110.79   -14.07     0.92)  ; 1\r\n                      (  311.74   113.89   -15.12     0.92)  ; 2\r\n                      (  316.75   112.67   -16.75     0.92)  ; 3\r\n                      (  319.37   111.50   -16.75     0.92)  ; 4\r\n                      (  312.54   110.49   -16.25     0.92)  ; 5\r\n                      (  313.67   113.73   -17.85     0.92)  ; 6\r\n                      (  319.72   108.00   -17.10     0.92)  ; 7\r\n                      (  322.53   108.06   -17.10     0.92)  ; 8\r\n                      (  317.55   109.27   -17.10     0.92)  ; 9\r\n                      (  324.73   106.77   -17.10     0.92)  ; 10\r\n                      (  332.27   112.72   -18.65     0.92)  ; 11\r\n                      (  338.30   111.15   -19.05     0.92)  ; 12\r\n                      (  340.21   111.00   -20.33     0.92)  ; 13\r\n                      (  344.54   106.64   -18.57     0.92)  ; 14\r\n                      (  341.72   106.58   -16.98     0.92)  ; 15\r\n                      (  352.30   109.65   -19.23     0.92)  ; 16\r\n                      (  349.94   109.69   -16.67     0.92)  ; 17\r\n                      (  357.92   103.81   -18.82     0.92)  ; 18\r\n                      (  356.61    99.32   -18.82     0.92)  ; 19\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  359.35   101.75   -20.57     0.92)  ; 1, R-1-1-1-1-1-1-3-2-1-1\r\n                      (  358.98    99.27   -22.25     0.92)  ; 2\r\n                      (  360.14    98.35   -24.00     0.92)  ; 3\r\n                      (  362.05    98.20   -24.88     0.92)  ; 4\r\n                      (  363.75    95.02   -25.87     0.92)  ; 5\r\n                      (  366.06    93.18   -25.20     0.92)  ; 6\r\n                      (  369.77    93.45   -25.97     0.92)  ; 7\r\n                      (  372.22    91.04   -26.58     0.92)  ; 8\r\n                      (  374.71    90.43   -27.97     0.46)  ; 9\r\n                      (  379.40    88.54   -29.20     0.46)  ; 10\r\n                      (  382.92    87.57   -30.00     0.46)  ; 11\r\n                      (  384.66    86.19   -30.35     0.46)  ; 12\r\n                      (  386.71    85.47   -30.40     0.46)  ; 13\r\n                      (  388.69    87.13   -31.32     0.46)  ; 14\r\n                      (  388.24    87.02   -31.35     0.46)  ; 15\r\n                      (  392.74    83.90   -32.40     0.46)  ; 16\r\n                      (  394.20    83.64   -33.85     0.46)  ; 17\r\n                      (  394.20    83.64   -33.88     0.46)  ; 18\r\n                      (  395.64    81.59   -35.42     0.46)  ; 19\r\n                      (  398.01    81.55   -36.95     0.46)  ; 20\r\n                      (  398.18    82.79   -39.08     0.46)  ; 21\r\n                      (  398.18    82.79   -39.10     0.46)  ; 22\r\n                      (  398.62    82.90   -41.35     0.46)  ; 23\r\n                      (  400.99    82.85   -43.47     0.46)  ; 24\r\n                      (  400.99    82.85   -43.50     0.46)  ; 25\r\n                      (  401.39    81.15   -45.85     0.46)  ; 26\r\n                      (  402.24    79.56   -49.07     0.46)  ; 27\r\n                      (  402.24    79.56   -49.13     0.46)  ; 28\r\n                      (  402.94    78.53   -52.40     0.46)  ; 29\r\n                      (  402.94    78.53   -52.45     0.46)  ; 30\r\n\r\n                      (Cross\r\n                        (Color White)\r\n                        (Name \"Marker 3\")\r\n                        (  363.79    96.83   -20.47     0.92)  ; 1\r\n                        (  367.46    95.29   -23.35     0.92)  ; 2\r\n                        (  371.42    94.44   -26.05     0.92)  ; 3\r\n                        (  369.59    92.21   -20.47     0.92)  ; 4\r\n                        (  367.35    91.68   -25.85     0.92)  ; 5\r\n                        (  383.55    88.92   -28.35     0.46)  ; 6\r\n                        (  378.11    90.02   -19.13     0.92)  ; 7\r\n                        (  377.89    86.99   -21.02     0.92)  ; 8\r\n                        (  384.03    84.84   -29.67     0.46)  ; 9\r\n                        (  396.45    84.17   -34.67     0.46)  ; 10\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  358.90   101.65   -17.20     0.92)  ; 1, R-1-1-1-1-1-1-3-2-1-2\r\n                      (  358.90   101.65   -17.23     0.92)  ; 2\r\n                      (  360.01    98.92   -17.17     0.92)  ; 3\r\n                      (  360.41    97.23   -16.10     0.92)  ; 4\r\n                      (  362.14    95.84   -19.13     0.92)  ; 5\r\n                      (  362.14    95.84   -19.15     0.92)  ; 6\r\n                      (  363.88    94.45   -19.73     0.92)  ; 7\r\n                      (  363.88    94.45   -19.75     0.92)  ; 8\r\n                      (  367.22    92.25   -20.47     0.92)  ; 9\r\n                      (  368.65    90.20   -20.47     0.92)  ; 10\r\n                      (  371.28    89.02   -20.47     0.92)  ; 11\r\n                      (  374.36    87.95   -20.60     0.46)  ; 12\r\n                      (  376.35    85.43   -21.00     0.46)  ; 13\r\n                      (  378.63    81.78   -21.00     0.46)  ; 14\r\n                      (  380.76    78.71   -22.43     0.46)  ; 15\r\n                      (  383.21    76.30   -22.57     0.46)  ; 16\r\n                      (  385.22    73.78   -22.63     0.46)  ; 17\r\n                      (  389.00    71.67   -23.35     0.46)  ; 18\r\n                      (  390.16    70.76   -24.88     0.46)  ; 19\r\n                      (  390.55    69.06   -26.88     0.46)  ; 20\r\n                      (  390.55    69.06   -26.90     0.46)  ; 21\r\n                      (  391.99    67.00   -28.33     0.46)  ; 22\r\n                      (  392.97    64.85   -30.00     0.46)  ; 23\r\n                      (  394.26    63.35   -31.50     0.46)  ; 24\r\n                      (  394.26    63.35   -31.52     0.46)  ; 25\r\n                      (  392.99    60.68   -33.57     0.46)  ; 26\r\n                      (  392.95    58.89   -37.38     0.46)  ; 27\r\n                      (  392.95    58.89   -37.42     0.46)  ; 28\r\n                      (  393.93    56.72   -39.17     0.46)  ; 29\r\n                      (  393.93    56.72   -39.20     0.46)  ; 30\r\n                      (  395.08    55.80   -41.80     0.46)  ; 31\r\n                      (  395.08    55.80   -41.93     0.46)  ; 32\r\n                      (  395.30    52.87   -43.42     0.46)  ; 33\r\n                      (  395.30    52.87   -43.45     0.46)  ; 34\r\n                      (  394.67    51.53   -45.72     0.46)  ; 35\r\n\r\n                      (Cross\r\n                        (Color White)\r\n                        (Name \"Marker 3\")\r\n                        (  361.07    94.39   -23.40     0.92)  ; 1\r\n                        (  374.36    87.95   -29.40     0.92)  ; 2\r\n                        (  373.46    87.75   -21.02     0.92)  ; 3\r\n                        (  375.42    83.42   -21.02     0.92)  ; 4\r\n                        (  381.75    82.52   -19.52     0.92)  ; 5\r\n                        (  381.84    80.15   -20.67     0.92)  ; 6\r\n                        (  381.42    75.88   -22.57     0.46)  ; 7\r\n                        (  387.31    74.86   -23.57     0.46)  ; 8\r\n                        (  390.93    71.53   -24.97     0.46)  ; 9\r\n                        (  391.04    64.99   -28.33     0.46)  ; 10\r\n                        (  391.71    62.16   -33.57     0.46)  ; 11\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  |\r\n                    (  296.55   116.33   -11.65     0.92)  ; 1, R-1-1-1-1-1-1-3-2-2\r\n                    (  297.08   114.06   -13.57     0.92)  ; 2\r\n                    (  297.08   114.06   -13.60     0.92)  ; 3\r\n                    (  295.35   115.45   -16.02     0.92)  ; 4\r\n                    (  293.69   114.46   -18.05     0.92)  ; 5\r\n                    (  292.05   113.48   -18.72     0.92)  ; 6\r\n                    (  292.05   113.48   -18.75     0.92)  ; 7\r\n                    (  290.44   114.30   -20.10     0.92)  ; 8\r\n                    (  290.06   111.82   -21.45     0.92)  ; 9\r\n                    (  290.91   110.24   -22.97     0.92)  ; 10\r\n                    (  290.24   107.08   -24.52     0.92)  ; 11\r\n                    (  289.70   103.38   -25.10     0.92)  ; 12\r\n                    (  288.75   101.37   -27.75     0.92)  ; 13\r\n                    (  288.22    97.65   -29.95     0.46)  ; 14\r\n                    (  289.06    96.06   -29.95     0.46)  ; 15\r\n                    (  290.08    95.70   -31.17     0.46)  ; 16\r\n                    (  289.41    92.56   -32.60     0.46)  ; 17\r\n                    (  290.52    89.83   -33.53     0.46)  ; 18\r\n                    (  290.60    87.46   -34.75     0.46)  ; 19\r\n                    (  290.60    87.46   -34.78     0.46)  ; 20\r\n                    (  290.95    83.96   -34.47     0.46)  ; 21\r\n                    (  291.48    81.70   -36.52     0.46)  ; 22\r\n                    (  292.41    77.73   -37.58     0.46)  ; 23\r\n                    (  292.25    72.33   -38.05     0.46)  ; 24\r\n                    (  291.80    66.25   -36.63     0.46)  ; 25\r\n                    (  291.12    63.11   -38.25     0.46)  ; 26\r\n                    (  289.56    59.75   -39.55     0.46)  ; 27\r\n                    (  289.56    59.75   -39.60     0.46)  ; 28\r\n                    (  286.96    56.75   -41.05     0.46)  ; 29\r\n                    (  286.96    56.75   -41.08     0.46)  ; 30\r\n                    (  284.99    55.09   -43.00     0.46)  ; 31\r\n                    (  284.24    54.32   -45.68     0.46)  ; 32\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  288.19   107.80   -25.10     0.92)  ; 1\r\n                      (  292.25   104.57   -26.72     0.92)  ; 2\r\n                      (  286.31   103.77   -26.32     0.92)  ; 3\r\n                      (  289.55    97.97   -29.95     0.46)  ; 4\r\n                      (  287.19    98.01   -29.95     0.46)  ; 5\r\n                      (  286.78    93.73   -33.27     0.46)  ; 6\r\n                      (  291.85    90.14   -33.53     0.46)  ; 7\r\n                      (  291.85    84.17   -34.47     0.46)  ; 8\r\n                      (  290.13    81.39   -34.78     0.46)  ; 9\r\n                      (  290.38    68.30   -36.63     0.46)  ; 10\r\n                      (  291.49    62.14   -15.32     0.92)  ; 11\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            |\r\n              (  255.22   125.77     2.25     0.92)  ; 1, R-1-1-1-1-1-2\r\n              (  255.60   128.24     3.50     0.92)  ; 2\r\n              (  257.69   129.32     3.57     0.92)  ; 3\r\n              (  259.93   129.85     4.60     0.92)  ; 4\r\n              (  260.28   132.33     5.13     0.92)  ; 5\r\n              (  261.10   134.90     5.37     0.92)  ; 6\r\n              (  261.33   137.94     5.97     0.92)  ; 7\r\n              (  262.40   139.39     6.00     0.92)  ; 8\r\n\r\n              (Cross\r\n                (Color White)\r\n                (Name \"Marker 3\")\r\n                (  259.91   140.00     6.00     0.92)  ; 1\r\n                (  256.98   130.36     3.57     0.92)  ; 2\r\n              )  ;  End of markers\r\n              (\r\n                (  264.82   141.16     6.65     0.92)  ; 1, R-1-1-1-1-1-2-1\r\n                (  265.13   141.82     8.20     0.92)  ; 2\r\n                (  265.13   141.82     8.18     0.92)  ; 3\r\n                (  267.82   142.45     9.07     0.92)  ; 4\r\n                (  269.20   144.56    10.52     0.92)  ; 5\r\n                (  269.83   145.91    10.60     0.92)  ; 6\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  267.08   137.50     9.07     0.92)  ; 1\r\n                )  ;  End of markers\r\n                (\r\n                  (  270.77   147.92    12.07     0.92)  ; 1, R-1-1-1-1-1-2-1-1\r\n                  (  270.81   149.72    13.52     0.92)  ; 2\r\n                  (  269.84   151.88    14.50     0.92)  ; 3\r\n                  (  270.01   153.12    15.85     0.92)  ; 4\r\n                  (  270.38   155.59    16.77     0.92)  ; 5\r\n                  (  268.95   157.64    18.00     0.92)  ; 6\r\n                  (  268.42   159.90    19.42     0.92)  ; 7\r\n                  (  268.34   162.27    20.42     0.92)  ; 8\r\n                  (  270.19   164.50    21.13     0.92)  ; 9\r\n                  (  273.44   164.67    21.70     0.92)  ; 10\r\n                  (  275.44   162.15    21.07     0.92)  ; 11\r\n                  (  278.48   159.27    22.25     0.92)  ; 12\r\n                  (  280.13   160.26    23.47     0.92)  ; 13\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  272.81   163.32    21.07     0.92)  ; 1\r\n                    (  269.05   155.28    16.52     0.92)  ; 2\r\n                    (  271.59   156.48    16.15     0.92)  ; 3\r\n                    (  271.57   150.50    14.92     0.92)  ; 4\r\n                    (  272.24   147.67    14.50     0.92)  ; 5\r\n                    (  270.06   148.94    14.92     0.92)  ; 6\r\n                    (  270.51   149.05     1.25     0.92)  ; 7\r\n                    (  280.21   157.90    22.32     0.92)  ; 8\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  282.40   162.59    23.47     0.92)  ; 1, R-1-1-1-1-1-2-1-1-1\r\n                    (  284.11   165.38    23.57     0.92)  ; 2\r\n                    (  284.11   165.38    23.55     0.92)  ; 3\r\n                    (  284.88   166.15    24.70     0.46)  ; 4\r\n                    (  284.88   166.15    24.80     0.46)  ; 5\r\n                    (  285.77   166.36    27.42     0.46)  ; 6\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  280.27   165.67    21.32     0.92)  ; 1\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  282.00   158.32    23.47     0.46)  ; 1, R-1-1-1-1-1-2-1-1-2\r\n                    (  282.48   154.25    23.47     0.46)  ; 2\r\n                    (  281.50   150.43    24.70     0.46)  ; 3\r\n                    (  280.86   149.09    26.80     0.46)  ; 4\r\n                    (  280.86   149.09    26.75     0.46)  ; 5\r\n                    (  280.68   147.85    28.42     0.46)  ; 6\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  284.18   157.02    23.57     0.92)  ; 1\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                |\r\n                  (  268.67   146.83    11.77     0.92)  ; 1, R-1-1-1-1-1-2-1-2\r\n                  (  267.07   147.65    13.60     0.92)  ; 2\r\n                  (  264.82   147.12    15.48     0.92)  ; 3\r\n                  (  262.07   148.87    16.30     0.92)  ; 4\r\n                  (  260.19   150.81    17.15     0.92)  ; 5\r\n                  (  257.30   153.12    17.85     0.92)  ; 6\r\n                  (  254.49   153.06    19.27     0.92)  ; 7\r\n                  (  251.63   151.19    20.57     0.92)  ; 8\r\n                  (  249.66   149.53    22.08     0.46)  ; 9\r\n                  (  248.46   148.66    23.67     0.46)  ; 10\r\n                  (  248.53   146.29    25.63     0.46)  ; 11\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  250.39   154.49    20.57     0.92)  ; 1\r\n                    (  257.80   155.02    19.00     0.92)  ; 2\r\n                    (  263.85   149.29    17.42     0.92)  ; 3\r\n                    (  259.84   148.34    17.50     0.92)  ; 4\r\n                    (  259.66   153.08    17.85     0.92)  ; 5\r\n                    (  261.98   151.23    17.85     0.92)  ; 6\r\n                    (  262.55   144.80    15.48     0.92)  ; 7\r\n                    (  256.08   152.24    18.95     0.92)  ; 8\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (  262.76   141.86     4.85     0.92)  ; 1, R-1-1-1-1-1-2-2\r\n                (  263.97   142.74     3.60     0.92)  ; 2\r\n                (  266.12   145.64     2.88     0.92)  ; 3\r\n                (  267.64   147.18     2.27     0.92)  ; 4\r\n                (  268.58   149.19     1.27     0.92)  ; 5\r\n                (  270.59   152.65     0.12     0.92)  ; 6\r\n                (  270.64   154.45    -0.72     0.92)  ; 7\r\n                (  272.05   156.58    -1.40     0.92)  ; 8\r\n                (  274.27   157.10    -2.85     0.92)  ; 9\r\n                (  274.32   158.90    -4.20     0.46)  ; 10\r\n                (  273.87   158.79    -4.22     0.46)  ; 11\r\n                (  275.26   160.92    -5.63     0.46)  ; 12\r\n                (  275.26   160.92    -5.68     0.46)  ; 13\r\n                (  275.88   162.25    -6.93     0.46)  ; 14\r\n                (  276.56   165.40    -8.73     0.92)  ; 15\r\n                (  277.07   167.30    -9.43     0.92)  ; 16\r\n                (  279.39   171.44   -10.12     0.92)  ; 17\r\n                (  279.48   175.04   -11.53     0.92)  ; 18\r\n                (  279.40   177.40   -13.18     0.92)  ; 19\r\n                (  279.18   180.35   -13.70     0.92)  ; 20\r\n                (  281.34   183.24   -12.93     0.92)  ; 21\r\n                (  284.64   185.21   -13.00     0.92)  ; 22\r\n                (  285.32   188.36   -13.13     0.92)  ; 23\r\n                (  285.86   192.06   -14.00     0.92)  ; 24\r\n                (  285.86   192.06   -14.02     0.92)  ; 25\r\n                (  287.70   194.28   -14.60     0.92)  ; 26\r\n                (  292.35   196.56   -15.30     0.92)  ; 27\r\n                (  294.45   197.66   -15.32     0.92)  ; 28\r\n                (  297.11   198.30   -17.63     0.92)  ; 29\r\n                (  300.05   197.79   -18.45     0.92)  ; 30\r\n                (  303.50   199.20   -19.60     0.92)  ; 31\r\n                (  306.99   202.40   -20.60     0.92)  ; 32\r\n                (  308.68   205.19   -21.07     0.92)  ; 33\r\n                (  311.60   208.86   -21.20     0.92)  ; 34\r\n                (  313.88   211.19   -22.63     0.92)  ; 35\r\n                (  313.88   211.19   -22.65     0.92)  ; 36\r\n                (  316.17   213.50   -24.08     0.92)  ; 37\r\n                (  317.68   215.06   -25.55     0.92)  ; 38\r\n                (  317.68   215.06   -25.57     0.92)  ; 39\r\n                (  317.60   217.43   -26.45     0.92)  ; 40\r\n                (  318.63   217.07   -27.67     0.46)  ; 41\r\n                (  318.63   217.07   -27.70     0.46)  ; 42\r\n                (  318.99   219.55   -29.80     0.46)  ; 43\r\n                (  318.51   223.62   -31.32     0.46)  ; 44\r\n                (  318.51   223.62   -31.35     0.46)  ; 45\r\n                (  317.53   225.77   -32.52     0.46)  ; 46\r\n                (  319.23   228.57   -33.40     0.46)  ; 47\r\n                (  321.69   232.11   -33.92     0.46)  ; 48\r\n                (  322.77   233.58   -35.57     0.46)  ; 49\r\n                (  322.77   233.58   -35.60     0.46)  ; 50\r\n                (  323.89   236.82   -36.78     0.46)  ; 51\r\n                (  323.89   236.82   -36.80     0.46)  ; 52\r\n                (  325.28   238.94   -38.47     0.46)  ; 53\r\n                (  325.28   238.94   -38.50     0.46)  ; 54\r\n                (  325.24   243.11   -39.88     0.46)  ; 55\r\n                (  325.24   243.11   -39.90     0.46)  ; 56\r\n                (  324.71   245.37   -41.47     0.46)  ; 57\r\n                (  324.45   246.51   -43.40     0.46)  ; 58\r\n                (  324.45   246.51   -43.42     0.46)  ; 59\r\n                (  324.23   249.43   -45.25     0.46)  ; 60\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  316.64   225.56   -32.47     0.46)  ; 1\r\n                  (  319.76   226.30   -32.77     0.46)  ; 2\r\n                  (  320.45   219.29   -31.35     0.46)  ; 3\r\n                  (  316.00   218.25   -26.45     0.92)  ; 4\r\n                  (  309.29   210.70   -21.07     0.92)  ; 5\r\n                  (  311.82   205.93   -20.13     0.92)  ; 6\r\n                  (  308.92   208.23   -21.07     0.92)  ; 7\r\n                  (  308.11   205.65   -21.07     0.92)  ; 8\r\n                  (  305.87   205.13   -21.07     0.92)  ; 9\r\n                  (  306.41   202.86   -21.07     0.92)  ; 10\r\n                  (  306.62   199.93   -21.07     0.92)  ; 11\r\n                  (  300.14   195.42   -18.45     0.92)  ; 12\r\n                  (  302.42   197.75   -18.45     0.92)  ; 13\r\n                  (  300.10   199.59   -21.07     0.92)  ; 14\r\n                  (  295.74   196.17   -15.32     0.92)  ; 15\r\n                  (  290.51   194.34   -15.32     0.92)  ; 16\r\n                  (  282.46   186.48   -12.88     0.92)  ; 17\r\n                  (  281.52   184.47   -12.88     0.92)  ; 18\r\n                  (  283.13   183.66   -12.88     0.92)  ; 19\r\n                  (  278.28   174.16   -11.53     0.92)  ; 20\r\n                  (  281.94   172.63   -11.00     0.92)  ; 21\r\n                  (  278.02   181.26   -12.77     0.92)  ; 22\r\n                  (  274.07   166.01    -8.18     0.46)  ; 23\r\n                  (  272.89   154.98    -3.50     0.92)  ; 24\r\n                  (  272.39   153.08     2.08     0.92)  ; 25\r\n                  (  271.31   151.63     1.25     0.92)  ; 26\r\n                  (  268.68   152.80    -0.43     0.92)  ; 27\r\n                  (  263.56   138.46     6.00     0.92)  ; 28\r\n                  (  261.43   141.55     6.00     0.92)  ; 29\r\n                  (  274.95   160.24    21.07     0.92)  ; 30\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  259.91   101.78    -7.35     0.92)  ; 1, R-1-1-1-1-2\r\n            (  261.79    99.83    -8.82     0.92)  ; 2\r\n            (  263.04   102.52    -9.43     0.92)  ; 3\r\n            (  263.01   106.69   -10.07     0.92)  ; 4\r\n            (  264.53   108.24   -10.57     0.92)  ; 5\r\n            (  266.76   108.76   -10.85     0.92)  ; 6\r\n            (  267.83   110.21   -11.35     0.92)  ; 7\r\n            (  269.81   111.87   -12.57     0.92)  ; 8\r\n            (  270.57   112.64   -13.72     0.92)  ; 9\r\n            (  273.37   112.71   -14.80     0.92)  ; 10\r\n            (  271.50   114.65   -16.13     0.92)  ; 11\r\n            (  273.29   115.06   -17.95     0.92)  ; 12\r\n            (  275.15   113.13   -18.82     0.92)  ; 13\r\n            (  276.82   114.11   -19.97     0.92)  ; 14\r\n            (  275.84   116.27   -21.28     0.92)  ; 15\r\n            (  275.84   116.27   -21.30     0.92)  ; 16\r\n            (  276.79   118.28   -22.77     0.92)  ; 17\r\n            (  276.79   118.28   -22.80     0.92)  ; 18\r\n            (  277.54   119.05   -23.70     0.92)  ; 19\r\n            (  278.75   119.94   -24.42     0.92)  ; 20\r\n            (  280.49   118.55   -24.42     0.92)  ; 21\r\n            (  282.99   117.95   -26.00     0.92)  ; 22\r\n            (  283.93   119.96   -28.55     0.92)  ; 23\r\n            (  284.11   121.19   -30.67     0.92)  ; 24\r\n            (  285.90   121.61   -32.35     0.92)  ; 25\r\n            (  285.90   121.61   -32.38     0.92)  ; 26\r\n            (  288.70   121.67   -31.60     0.92)  ; 27\r\n            (  288.70   121.67   -31.63     0.92)  ; 28\r\n            (  290.54   123.89   -34.50     0.92)  ; 29\r\n            (  291.22   127.04   -35.42     0.92)  ; 30\r\n            (  291.22   127.04   -35.45     0.92)  ; 31\r\n            (  291.98   127.81   -37.40     0.92)  ; 32\r\n            (  294.03   127.10   -39.47     0.92)  ; 33\r\n            (  294.03   127.10   -39.50     0.92)  ; 34\r\n            (  296.85   127.16   -41.55     0.92)  ; 35\r\n            (  296.85   127.16   -41.57     0.92)  ; 36\r\n            (  298.55   129.95   -44.35     0.92)  ; 37\r\n            (  299.49   131.96   -45.85     0.92)  ; 38\r\n            (  299.49   131.96   -45.88     0.92)  ; 39\r\n            (  301.19   134.74   -47.33     0.92)  ; 40\r\n            (  300.79   136.44   -48.38     0.92)  ; 41\r\n            (  300.79   136.44   -48.40     0.92)  ; 42\r\n            (  303.34   137.63   -49.40     0.92)  ; 43\r\n            (  301.73   138.45   -51.85     0.92)  ; 44\r\n            (  303.70   140.12   -53.70     0.92)  ; 45\r\n            (  306.25   141.31   -55.05     0.92)  ; 46\r\n            (  306.25   141.31   -57.67     0.92)  ; 47\r\n            (  306.25   141.31   -57.70     0.92)  ; 48\r\n            (  306.43   142.55   -59.80     0.92)  ; 49\r\n            (  308.27   144.76   -61.15     0.92)  ; 50\r\n            (  310.24   146.42   -62.85     0.92)  ; 51\r\n            (  311.49   149.10   -64.67     0.92)  ; 52\r\n            (  310.51   151.27   -64.97     0.92)  ; 53\r\n            (  310.51   151.27   -65.00     0.92)  ; 54\r\n            (  309.67   152.88   -66.52     0.46)  ; 55\r\n            (  308.06   153.69   -69.25     0.46)  ; 56\r\n            (  307.08   155.86   -71.17     0.46)  ; 57\r\n            (  307.45   158.33   -73.50     0.46)  ; 58\r\n            (  307.76   159.00   -75.63     0.46)  ; 59\r\n            (  307.76   159.00   -75.65     0.46)  ; 60\r\n            (  308.25   160.91   -77.93     0.46)  ; 61\r\n            (  308.25   160.91   -77.95     0.46)  ; 62\r\n            (  308.03   163.84   -80.02     0.46)  ; 63\r\n            (  308.40   166.31   -82.15     0.46)  ; 64\r\n            (  309.21   168.89   -83.35     0.46)  ; 65\r\n            (  307.34   170.85   -85.05     0.46)  ; 66\r\n            (  308.15   173.42   -86.40     0.46)  ; 67\r\n            (  307.44   174.45   -88.42     0.46)  ; 68\r\n            (  306.59   176.05   -90.80     0.46)  ; 69\r\n            (  306.37   178.97   -91.93     0.46)  ; 70\r\n            (  306.91   182.69   -92.70     0.46)  ; 71\r\n            (  306.51   184.39   -94.07     0.46)  ; 72\r\n            (  306.51   184.39   -94.10     0.46)  ; 73\r\n            (  306.61   188.00   -95.57     0.46)  ; 74\r\n            (  306.61   188.00   -95.63     0.46)  ; 75\r\n            (  306.80   189.23   -98.40     0.46)  ; 76\r\n            (  307.03   192.27  -100.40     0.46)  ; 77\r\n            (  306.10   196.24  -101.55     0.46)  ; 78\r\n            (  305.13   198.38  -103.85     0.46)  ; 79\r\n            (  305.49   200.87  -104.97     0.46)  ; 80\r\n            (  305.49   200.87  -105.00     0.46)  ; 81\r\n            (  305.80   201.54  -106.17     0.46)  ; 82\r\n            (  305.80   201.54  -106.35     0.46)  ; 83\r\n\r\n            (Cross\r\n              (Color White)\r\n              (Name \"Marker 3\")\r\n              (  308.51   197.99  -101.55     0.46)  ; 1\r\n              (  307.91   199.14   -97.07     0.46)  ; 2\r\n              (  308.95   176.00   -90.80     0.46)  ; 3\r\n              (  308.80   177.26   -87.05     0.46)  ; 4\r\n              (  306.26   169.40   -85.05     0.46)  ; 5\r\n              (  305.92   169.42   -81.80     0.46)  ; 6\r\n              (  306.97   146.25   -61.15     0.92)  ; 7\r\n              (  309.39   148.01   -64.67     0.92)  ; 8\r\n              (  312.26   146.42   -65.25     0.46)  ; 9\r\n              (  299.04   131.86   -42.33     0.92)  ; 10\r\n              (  298.65   133.55   -48.20     0.92)  ; 11\r\n              (  260.67   102.56    -7.32     0.92)  ; 12\r\n              (  261.79    99.83    -7.32     0.92)  ; 13\r\n              (  262.02   102.87    -7.32     0.92)  ; 14\r\n              (  274.04   115.85   -16.13     0.92)  ; 15\r\n              (  282.63   115.46   -26.45     0.92)  ; 16\r\n              (  285.17   116.66   -26.45     0.92)  ; 17\r\n              (  287.63   120.23   -31.63     0.92)  ; 18\r\n              (  285.22   122.59     7.53     0.46)  ; 19\r\n              (  290.42   130.44   -34.47     0.92)  ; 20\r\n              (  289.74   127.28   -34.95     0.92)  ; 21\r\n              (  285.36   123.87   -31.63     0.92)  ; 22\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (  255.15    73.83     0.70     0.92)  ; 1, R-1-1-1-2\r\n          (  252.75    72.06     0.70     0.92)  ; 2\r\n          (  251.36    69.94     1.80     0.92)  ; 3\r\n          (  251.36    69.94     1.77     0.92)  ; 4\r\n          (  249.81    72.56     3.70     0.92)  ; 5\r\n          (  250.04    75.60     5.25     0.92)  ; 6\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  255.35    75.06     0.70     0.92)  ; 1\r\n            (  254.72    73.72     7.57     0.92)  ; 2\r\n            (  249.44    70.09     3.78     0.92)  ; 3\r\n            (  249.18    71.22     3.78     0.92)  ; 4\r\n            (  248.51    74.05     8.02     0.92)  ; 5\r\n          )  ;  End of markers\r\n          (\r\n            (  252.26    76.12     4.52     1.38)  ; 1, R-1-1-1-2-1\r\n            (  251.87    77.83     6.42     1.38)  ; 2\r\n            (  252.05    79.06     8.70     1.38)  ; 3\r\n            (  252.05    79.06     8.68     1.38)  ; 4\r\n            (  251.34    80.09    10.82     1.38)  ; 5\r\n            (  252.54    80.97    12.07     1.38)  ; 6\r\n            (  253.89    81.29    14.00     1.38)  ; 7\r\n            (  253.44    81.18    13.95     1.38)  ; 8\r\n            (  255.94    80.57    15.65     1.38)  ; 9\r\n            (  259.51    81.40    16.80     1.38)  ; 10\r\n            (  263.22    81.68    17.95     1.38)  ; 11\r\n            (  265.35    78.59    19.15     1.38)  ; 12\r\n\r\n            (Cross\r\n              (Color White)\r\n              (Name \"Marker 3\")\r\n              (  262.42    85.07    16.80     1.38)  ; 1\r\n              (  257.32    82.69    16.83     1.38)  ; 2\r\n              (  255.31    79.22    15.65     1.38)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  263.70    77.61    19.75     1.38)  ; 1, R-1-1-1-2-1-1\r\n              (  263.70    77.61    19.73     1.38)  ; 2\r\n              (  260.62    78.69    21.45     0.92)  ; 3\r\n              (  260.35    79.81    23.33     0.92)  ; 4\r\n              (  261.03    82.96    24.40     0.92)  ; 5\r\n              (  261.21    84.19    26.13     0.92)  ; 6\r\n              (  259.48    85.58    27.02     0.92)  ; 7\r\n              (  258.31    86.51    28.88     0.92)  ; 8\r\n              (  257.60    87.53    30.23     0.92)  ; 9\r\n\r\n              (Cross\r\n                (Color White)\r\n                (Name \"Marker 3\")\r\n                (  261.47    77.09    21.45     0.92)  ; 1\r\n                (  259.15    78.94    24.40     0.92)  ; 2\r\n              )  ;  End of markers\r\n               High\r\n            |\r\n              (  266.46    75.88    18.17     0.92)  ; 1, R-1-1-1-2-1-2\r\n              (  267.17    74.84    18.57     0.92)  ; 2\r\n              (  269.85    75.47    20.65     0.92)  ; 3\r\n              (  271.02    74.54    21.75     0.92)  ; 4\r\n              (  271.91    74.75    23.42     0.92)  ; 5\r\n              (  273.61    77.55    24.77     0.92)  ; 6\r\n              (  273.61    77.55    24.75     0.92)  ; 7\r\n              (  276.86    77.71    25.38     0.92)  ; 8\r\n              (  279.37    77.10    24.95     0.92)  ; 9\r\n              (  281.28    76.95    25.87     0.92)  ; 10\r\n              (  281.28    76.95    25.85     0.92)  ; 11\r\n              (  281.86    76.49    28.10     0.92)  ; 12\r\n\r\n              (Cross\r\n                (Color White)\r\n                (Name \"Marker 3\")\r\n                (  266.42    80.05    19.15     1.38)  ; 1\r\n                (  268.33    73.92    21.75     0.92)  ; 2\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          |\r\n            (  247.40    76.78     6.15     0.92)  ; 1, R-1-1-1-2-2\r\n            (  244.85    75.58     8.02     0.92)  ; 2\r\n            (  241.33    76.55     8.60     0.92)  ; 3\r\n            (  241.33    76.55     8.57     0.92)  ; 4\r\n            (  236.86    75.50     9.50     0.92)  ; 5\r\n            (  236.86    75.50     9.48     0.92)  ; 6\r\n            (  234.32    74.31    10.85     0.92)  ; 7\r\n            (  234.32    74.31    10.82     0.92)  ; 8\r\n            (  231.59    71.88    10.85     0.92)  ; 9\r\n            (  230.70    71.67    10.85     0.92)  ; 10\r\n            (  230.20    69.76    10.85     0.92)  ; 11\r\n            (  227.12    70.84    12.10     0.92)  ; 12\r\n            (  225.21    70.99    13.07     0.92)  ; 13\r\n            (  225.21    70.99    13.05     0.92)  ; 14\r\n            (  223.10    69.89    12.35     0.92)  ; 15\r\n            (  219.71    70.29    13.40     0.92)  ; 16\r\n            (  217.48    69.76    14.25     0.92)  ; 17\r\n            (  213.83    71.29    14.60     0.92)  ; 18\r\n            (  210.65    68.76    14.10     0.92)  ; 19\r\n            (  209.39    66.07    14.43     0.92)  ; 20\r\n            (  207.55    63.86    15.68     0.92)  ; 21\r\n            (  203.40    63.48    14.97     0.92)  ; 22\r\n            (  201.76    62.49    15.43     0.92)  ; 23\r\n            (  200.50    59.81    17.20     0.92)  ; 24\r\n            (  197.90    56.82    18.45     0.92)  ; 25\r\n            (  194.73    54.28    20.15     0.92)  ; 26\r\n            (  192.94    53.86    21.75     0.92)  ; 27\r\n            (  190.70    53.34    22.95     0.92)  ; 28\r\n            (  190.70    53.34    22.92     0.92)  ; 29\r\n            (  188.03    52.71    24.27     0.92)  ; 30\r\n            (  183.74    52.91    25.20     0.92)  ; 31\r\n            (  182.32    54.95    25.20     0.92)  ; 32\r\n            (  181.79    57.22    24.30     0.92)  ; 33\r\n            (  182.41    58.56    24.67     0.92)  ; 34\r\n            (  182.41    58.56    24.65     0.92)  ; 35\r\n            (  183.50    60.00    25.63     0.92)  ; 36\r\n            (  183.50    60.00    27.35     0.92)  ; 37\r\n            (  183.50    60.00    27.32     0.92)  ; 38\r\n            (  183.94    60.10    29.42     0.92)  ; 39\r\n            (  183.54    61.81    30.60     0.92)  ; 40\r\n            (  183.54    61.81    30.57     0.92)  ; 41\r\n\r\n            (Cross\r\n              (Color White)\r\n              (Name \"Marker 3\")\r\n              (  247.72    77.45     6.82     0.92)  ; 1\r\n              (  241.78    76.66     8.35     0.92)  ; 2\r\n              (  238.26    77.62     8.07     0.92)  ; 3\r\n              (  235.71    76.43     8.30     0.92)  ; 4\r\n              (  227.17    72.64    13.02     0.92)  ; 5\r\n              (  231.51    74.25    14.10     0.92)  ; 6\r\n              (  231.86    70.74    14.20     0.92)  ; 7\r\n              (  230.20    69.76    14.20     0.92)  ; 8\r\n              (  234.71    72.62    11.35     0.92)  ; 9\r\n              (  218.01    67.50    14.60     0.92)  ; 10\r\n              (  213.77    69.49    12.60     0.92)  ; 11\r\n              (  212.39    67.39    15.20     0.92)  ; 12\r\n              (  201.84    60.13    18.45     0.92)  ; 13\r\n              (  201.21    58.78    16.20     0.92)  ; 14\r\n              (  198.58    59.97    16.80     0.92)  ; 15\r\n              (  184.36    54.24    24.65     0.92)  ; 16\r\n              (  192.22    54.90    24.27     0.92)  ; 17\r\n              (  187.89    47.30    25.20     0.92)  ; 18\r\n              (  198.88    54.65    18.45     0.92)  ; 19\r\n            )  ;  End of markers\r\n             High\r\n          )  ;  End of split\r\n        |\r\n          (  259.28    74.90     3.67     0.92)  ; 1, R-1-1-1-3\r\n          (  259.28    74.90     3.65     0.92)  ; 2\r\n          (  260.35    76.34     5.97     0.92)  ; 3\r\n          (  259.95    78.04     7.47     0.92)  ; 4\r\n          (  261.73    78.46     8.38     0.46)  ; 5\r\n          (  263.83    79.56     8.77     0.46)  ; 6\r\n          (  265.68    81.77     8.77     0.46)  ; 7\r\n          (  268.09    83.53     9.57     0.46)  ; 8\r\n          (  267.55    85.80    10.10     0.46)  ; 9\r\n          (  266.44    88.52    11.00     0.46)  ; 10\r\n          (  266.76    89.19    11.90     0.46)  ; 11\r\n          (  268.28    90.74    12.65     0.46)  ; 12\r\n          (  270.96    91.37    13.80     0.46)  ; 13\r\n          (  272.43    91.12    14.75     0.46)  ; 14\r\n          (  273.59    90.19    15.88     0.46)  ; 15\r\n          (  276.00    91.95    16.92     0.46)  ; 16\r\n          (  278.42    93.72    17.95     0.46)  ; 17\r\n          (  279.68    96.40    19.63     0.46)  ; 18\r\n          (  279.68    96.40    19.60     0.46)  ; 19\r\n          (  281.47    96.82    20.90     0.46)  ; 20\r\n          (  281.47    96.82    20.87     0.46)  ; 21\r\n          (  282.04    96.36    22.10     0.46)  ; 22\r\n          (  285.47    97.76    23.47     0.46)  ; 23\r\n          (  287.45    99.41    24.45     0.46)  ; 24\r\n          (  287.01    99.31    24.45     0.46)  ; 25\r\n          (  288.88   103.34    25.30     0.46)  ; 26\r\n          (  289.11   106.38    26.42     0.46)  ; 27\r\n          (  289.16   108.18    24.05     0.46)  ; 28\r\n          (  290.23   109.63    23.17     0.46)  ; 29\r\n          (  289.83   111.32    22.45     0.46)  ; 30\r\n          (  289.83   111.32    22.43     0.46)  ; 31\r\n          (  288.99   112.91    20.55     0.46)  ; 32\r\n          (  288.42   113.38    18.50     0.46)  ; 33\r\n          (  288.42   113.38    18.48     0.46)  ; 34\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  289.62   111.72   -21.60     0.92)  ; 1\r\n            (  286.18    96.74    23.45     0.46)  ; 2\r\n            (  267.12    91.67    12.65     0.46)  ; 3\r\n            (  270.24    92.40    13.80     0.46)  ; 4\r\n            (  270.19    90.59    13.80     0.46)  ; 5\r\n            (  271.01    93.18    14.75     0.46)  ; 6\r\n            (  276.18    93.20    17.95     0.46)  ; 7\r\n            (  277.16    91.04    19.50     0.46)  ; 8\r\n            (  282.00    94.56    22.30     0.46)  ; 9\r\n            (  277.52    93.52    22.32     0.46)  ; 10\r\n            (  279.10    96.87    18.45     0.46)  ; 11\r\n            (  283.64    95.54    23.45     0.46)  ; 12\r\n            (  265.27    80.96    19.15     1.38)  ; 13\r\n            (  266.64    79.62     8.77     0.46)  ; 14\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  251.51    49.77    -3.38     0.92)  ; 1, R-1-1-2\r\n        (  249.56    54.10    -2.95     0.92)  ; 2\r\n        (  246.47    55.17    -2.95     0.92)  ; 3\r\n        (  244.11    55.21    -3.97     0.92)  ; 4\r\n        (  242.82    56.69    -5.65     0.92)  ; 5\r\n        (  241.97    58.29    -6.60     0.92)  ; 6\r\n        (  241.27    59.32    -8.23     0.92)  ; 7\r\n        (  239.67    60.14    -8.95     0.92)  ; 8\r\n        (  236.85    60.07    -9.17     0.92)  ; 9\r\n        (  232.75    61.50    -8.42     0.92)  ; 10\r\n        (  232.75    61.50    -8.45     0.92)  ; 11\r\n        (  231.09    60.51    -9.22     0.92)  ; 12\r\n        (  230.16    58.50   -10.12     0.92)  ; 13\r\n        (  228.81    58.19   -11.25     0.92)  ; 14\r\n        (  225.73    59.26   -12.00     0.92)  ; 15\r\n        (  222.30    57.86   -12.35     0.92)  ; 16\r\n        (  219.17    57.13   -13.00     0.92)  ; 17\r\n        (  215.91    56.96   -13.72     0.92)  ; 18\r\n        (  213.41    57.57   -14.75     0.92)  ; 19\r\n        (  210.81    54.57   -15.22     0.92)  ; 20\r\n        (  207.11    54.30   -15.50     0.92)  ; 21\r\n        (  204.16    54.80   -17.13     0.92)  ; 22\r\n        (  201.61    55.31   -16.57     0.92)  ; 23\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  238.82    61.73    -9.17     0.92)  ; 1\r\n          (  242.87    58.50    -5.70     0.92)  ; 2\r\n          (  240.91    56.84    -7.32     0.92)  ; 3\r\n          (  238.01    59.15    -9.17     0.92)  ; 4\r\n          (  234.60    61.47   -58.22     0.46)  ; 5\r\n          (  228.28    60.45   -11.25     0.92)  ; 6\r\n          (  223.36    59.30   -10.80     0.92)  ; 7\r\n          (  219.79    58.46   -14.65     0.92)  ; 8\r\n          (  221.21    56.41   -10.80     0.92)  ; 9\r\n          (  208.27    53.37   -15.50     0.92)  ; 10\r\n        )  ;  End of markers\r\n        (\r\n          (  200.64    55.77   -16.57     0.92)  ; 1, R-1-1-2-1\r\n          (  198.32    57.62   -15.30     0.92)  ; 2\r\n          (  195.32    56.31   -14.65     0.92)  ; 3\r\n          (  190.34    57.54   -14.27     0.92)  ; 4\r\n          (  187.16    55.00   -13.63     0.92)  ; 5\r\n          (  183.23    51.68   -12.57     0.92)  ; 6\r\n          (  183.31    49.32   -10.82     0.92)  ; 7\r\n          (  180.76    48.13    -8.42     0.92)  ; 8\r\n          (  179.23    46.57    -6.78     0.92)  ; 9\r\n          (  178.48    45.79    -4.63     0.92)  ; 10\r\n          (  174.32    45.41    -2.15     0.46)  ; 11\r\n          (  174.32    45.41    -2.17     0.46)  ; 12\r\n          (  171.87    47.83    -1.67     0.46)  ; 13\r\n          (  171.87    47.83    -1.70     0.46)  ; 14\r\n          (  170.94    51.80    -1.00     0.46)  ; 15\r\n          (  168.63    53.64     0.15     0.46)  ; 16\r\n          (  168.63    53.64     0.12     0.46)  ; 17\r\n          (  167.65    55.80     1.07     0.46)  ; 18\r\n          (  167.65    55.80     1.05     0.46)  ; 19\r\n          (  166.94    56.82     2.45     0.46)  ; 20\r\n          (  166.94    56.82     2.42     0.46)  ; 21\r\n          (  166.36    57.29     4.32     0.46)  ; 22\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  176.11    47.54   -25.90     0.92)  ; 1\r\n            (  178.65    47.02    -4.63     0.92)  ; 2\r\n            (  198.35    55.14   -22.05     0.92)  ; 3\r\n            (  176.16    47.64    -3.47     0.92)  ; 4\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  198.54    56.39   -16.98     0.46)  ; 1, R-1-1-2-2\r\n          (  195.27    56.22   -15.97     0.46)  ; 2\r\n          (  190.86    56.97   -16.45     0.46)  ; 3\r\n          (  188.32    55.78   -15.80     0.46)  ; 4\r\n          (  185.90    54.02   -15.07     0.46)  ; 5\r\n          (  184.51    51.90   -14.63     0.46)  ; 6\r\n          (  183.13    49.78   -13.30     0.46)  ; 7\r\n          (  183.52    48.08   -11.47     0.46)  ; 8\r\n          (  183.52    48.08   -11.50     0.46)  ; 9\r\n          (  182.18    47.77    -9.90     0.46)  ; 10\r\n          (  179.81    47.81    -8.57     0.46)  ; 11\r\n          (  179.81    47.81    -8.55     0.46)  ; 12\r\n          (  179.00    45.23    -7.15     0.46)  ; 13\r\n          (  178.69    44.56    -6.30     0.46)  ; 14\r\n          (  176.77    44.71    -4.70     0.46)  ; 15\r\n          (  172.93    45.01    -3.47     0.46)  ; 16\r\n          (  172.41    47.26    -3.47     0.46)  ; 17\r\n          (  170.71    50.46    -2.95     0.46)  ; 18\r\n          (  170.18    52.72    -1.88     0.46)  ; 19\r\n          (  168.75    54.77    -1.20     0.46)  ; 20\r\n          (  167.47    56.27     0.00     0.46)  ; 21\r\n          (  166.44    56.63     1.63     0.46)  ; 22\r\n          (  166.44    56.63     1.60     0.46)  ; 23\r\n          (  165.42    56.98     2.90     0.46)  ; 24\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  186.92    53.66   -14.63     0.46)  ; 1\r\n            (  172.75    43.77    -4.07     0.46)  ; 2\r\n            (  172.35    43.76    -3.47     0.92)  ; 3\r\n            (  180.92    43.39    -3.47     0.92)  ; 4\r\n            (  179.40    43.54    -4.70     0.46)  ; 5\r\n            (  198.85    55.35   -16.57     0.92)  ; 6\r\n            (  177.44    47.85    -4.70     0.46)  ; 7\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  199.07    54.11   -19.70     0.92)  ; 1, R-1-1-2-3\r\n          (  196.97    53.03   -22.05     0.92)  ; 2\r\n          (  191.92    52.44   -23.00     0.92)  ; 3\r\n          (  189.38    51.24   -23.63     0.92)  ; 4\r\n          (  188.29    49.80   -24.60     0.92)  ; 5\r\n          (  184.46    50.09   -24.60     0.92)  ; 6\r\n          (  181.39    51.17   -25.25     0.92)  ; 7\r\n          (  178.39    49.87   -25.25     0.92)  ; 8\r\n          (  177.89    47.96   -24.82     0.92)  ; 9\r\n          (  177.89    47.96   -24.85     0.92)  ; 10\r\n          (  174.37    48.92   -25.90     0.92)  ; 11\r\n          (  172.27    47.83   -27.67     0.92)  ; 12\r\n          (  171.51    47.06   -29.62     0.92)  ; 13\r\n          (  171.06    46.96   -31.63     0.92)  ; 14\r\n          (  168.20    45.09   -33.10     0.92)  ; 15\r\n          (  165.34    43.23   -34.02     0.92)  ; 16\r\n          (  162.26    44.29   -35.65     0.92)  ; 17\r\n          (  161.55    45.32   -37.77     0.92)  ; 18\r\n          (  160.21    45.01   -39.95     0.92)  ; 19\r\n          (  157.79    43.25   -42.10     0.92)  ; 20\r\n          (  157.79    43.25   -42.13     0.92)  ; 21\r\n          (  154.09    42.98   -43.47     0.92)  ; 22\r\n          (  149.22    43.63   -49.65     0.46)  ; 23\r\n          (  145.38    43.92   -51.22     0.46)  ; 24\r\n          (  145.38    43.92   -51.28     0.46)  ; 25\r\n          (  142.58    43.86   -52.63     0.46)  ; 26\r\n          (  142.58    43.86   -52.65     0.46)  ; 27\r\n          (  141.28    45.36   -54.50     0.46)  ; 28\r\n          (  140.08    44.47   -56.20     0.46)  ; 29\r\n          (  140.08    44.47   -56.22     0.46)  ; 30\r\n          (  137.27    44.42   -57.47     0.46)  ; 31\r\n          (  137.27    44.42   -57.50     0.46)  ; 32\r\n          (  135.61    43.42   -59.47     0.46)  ; 33\r\n          (  135.61    43.42   -59.50     0.46)  ; 34\r\n          (  133.57    44.15   -60.95     0.46)  ; 35\r\n          (  132.54    44.49   -63.05     0.46)  ; 36\r\n          (  132.54    44.49   -63.07     0.46)  ; 37\r\n          (  130.04    45.10   -63.15     0.46)  ; 38\r\n          (  130.08    46.90   -64.70     0.46)  ; 39\r\n          (  127.72    46.96   -66.03     0.46)  ; 40\r\n          (  126.56    47.87   -67.65     0.46)  ; 41\r\n          (  124.46    46.79   -69.70     0.46)  ; 42\r\n          (  121.83    47.96   -71.68     0.46)  ; 43\r\n          (  121.83    47.96   -71.70     0.46)  ; 44\r\n          (  120.49    47.64   -73.78     0.46)  ; 45\r\n          (  120.49    47.64   -73.82     0.46)  ; 46\r\n          (  119.01    47.89   -76.38     0.46)  ; 47\r\n          (  116.97    48.62   -78.07     0.46)  ; 48\r\n          (  116.97    48.62   -78.10     0.46)  ; 49\r\n          (  115.49    48.86   -80.35     0.46)  ; 50\r\n          (  115.49    48.86   -80.40     0.46)  ; 51\r\n          (  114.02    49.11   -82.97     0.46)  ; 52\r\n          (  110.18    49.41   -84.80     0.46)  ; 53\r\n          (  108.89    50.90   -86.67     0.46)  ; 54\r\n          (  108.89    50.90   -86.70     0.46)  ; 55\r\n          (  107.42    51.15   -88.57     0.46)  ; 56\r\n          (  107.42    51.15   -88.60     0.46)  ; 57\r\n          (  104.34    52.22   -90.25     0.46)  ; 58\r\n          (  104.34    52.22   -90.28     0.46)  ; 59\r\n          (  102.46    54.17   -92.15     0.46)  ; 60\r\n          (  101.89    54.63   -93.82     0.46)  ; 61\r\n          (  101.89    54.63   -93.85     0.46)  ; 62\r\n          (   99.13    56.37   -96.10     0.46)  ; 63\r\n          (   97.21    56.53   -98.80     0.46)  ; 64\r\n          (   96.37    58.12  -101.65     0.46)  ; 65\r\n          (   93.55    58.05  -104.25     0.46)  ; 66\r\n          (   91.10    60.46  -106.15     0.46)  ; 67\r\n          (   88.74    60.51  -109.20     0.46)  ; 68\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  192.44    51.95    24.27     0.92)  ; 1\r\n            (  175.89    50.48   -25.90     0.92)  ; 2\r\n            (  175.80    46.87    -3.47     0.46)  ; 3\r\n            (  168.47    43.96   -31.42     0.92)  ; 4\r\n            (  160.29    42.64   -37.77     0.92)  ; 5\r\n            (  155.75    43.96   -39.02     0.46)  ; 6\r\n            (  153.46    41.63   -44.07     0.46)  ; 7\r\n            (  147.97    40.95   -50.55     0.46)  ; 8\r\n            (  165.13    46.16   -32.55     0.92)  ; 9\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  262.09    46.88    -7.07     1.38)  ; 1, R-1-2\r\n      (  266.25    47.26    -7.70     1.38)  ; 2\r\n      (  268.34    48.34    -8.52     1.38)  ; 3\r\n      (  270.17    50.56    -9.20     1.38)  ; 4\r\n      (  271.96    50.98    -9.85     1.38)  ; 5\r\n      (  274.33    50.94    -8.23     1.38)  ; 6\r\n      (  276.25    50.79    -7.32     1.38)  ; 7\r\n      (  278.21    52.45    -7.95     1.38)  ; 8\r\n      (  278.97    53.22    -9.95     0.92)  ; 9\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  274.51    52.17    -9.77     1.38)  ; 1\r\n        (  268.26    50.71    -9.20     1.38)  ; 2\r\n        (  264.76    47.51   -10.57     1.38)  ; 3\r\n      )  ;  End of markers\r\n      (\r\n        (  279.16    54.47   -12.22     0.92)  ; 1, R-1-2-1\r\n        (  280.68    56.01   -13.52     0.92)  ; 2\r\n        (  282.77    57.10   -13.55     0.92)  ; 3\r\n        (  284.12    57.41   -13.35     0.92)  ; 4\r\n        (  288.21    55.99   -13.38     0.92)  ; 5\r\n        (  288.08    56.56   -13.38     0.92)  ; 6\r\n        (  291.13    59.65   -14.00     0.92)  ; 7\r\n        (  290.33    63.05   -13.07     0.92)  ; 8\r\n        (  290.33    63.05   -13.13     0.92)  ; 9\r\n        (  289.67    65.88   -13.52     0.92)  ; 10\r\n        (  287.62    66.60   -14.63     0.92)  ; 11\r\n        (  287.80    67.83   -16.45     0.92)  ; 12\r\n        (  287.80    67.83   -16.47     0.92)  ; 13\r\n        (  288.69    68.04   -18.85     0.92)  ; 14\r\n        (  290.03    68.36   -21.18     0.92)  ; 15\r\n        (  291.42    70.48   -22.45     0.92)  ; 16\r\n        (  291.42    70.48   -22.47     0.92)  ; 17\r\n        (  291.91    72.38   -23.40     0.92)  ; 18\r\n        (  290.93    74.54   -23.33     0.46)  ; 19\r\n        (  291.12    75.78   -24.50     0.46)  ; 20\r\n        (  291.12    75.78   -24.52     0.46)  ; 21\r\n        (  292.47    76.09   -26.00     0.46)  ; 22\r\n        (  295.64    78.63   -27.05     0.46)  ; 23\r\n        (  297.73    79.72   -27.63     0.46)  ; 24\r\n        (  297.12    84.36   -28.90     0.46)  ; 25\r\n        (  297.75    85.69   -30.45     0.46)  ; 26\r\n        (  299.54    86.12   -31.92     0.46)  ; 27\r\n        (  300.75    86.99   -33.80     0.46)  ; 28\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  297.17    86.16   -31.92     0.46)  ; 1\r\n          (  300.30    86.88   -32.25     0.46)  ; 2\r\n          (  301.58    85.40   -31.92     0.46)  ; 3\r\n          (  291.41    73.92   -38.07     0.46)  ; 4\r\n          (  294.15    72.90   -23.40     0.92)  ; 5\r\n          (  288.54    62.63   -11.17     0.92)  ; 6\r\n          (  291.41    64.49   -11.07     0.92)  ; 7\r\n          (  291.13    59.65   -15.32     0.92)  ; 8\r\n          (  277.86    55.95   -13.55     0.92)  ; 9\r\n          (  279.64    56.37   -13.55     0.92)  ; 10\r\n          (  280.86    57.24   -13.55     0.92)  ; 11\r\n        )  ;  End of markers\r\n        (\r\n          (  307.13    87.89   -34.28     0.46)  ; 1, R-1-2-1-1\r\n          (  306.42    88.92   -35.63     0.46)  ; 2\r\n          (  310.39    88.06   -36.63     0.46)  ; 3\r\n          (  312.35    89.72   -37.77     0.46)  ; 4\r\n          (  314.63    92.04   -39.10     0.46)  ; 5\r\n          (  314.63    92.04   -39.13     0.46)  ; 6\r\n          (  316.74    93.14   -40.10     0.46)  ; 7\r\n          (  319.73    94.43   -41.20     0.46)  ; 8\r\n          (  323.80    97.17   -41.52     0.46)  ; 9\r\n          (  326.93    97.90   -41.70     0.46)  ; 10\r\n          (  327.60   101.05   -41.60     0.46)  ; 11\r\n          (  327.60   101.05   -41.63     0.46)  ; 12\r\n          (  328.68   102.49   -43.15     0.46)  ; 13\r\n          (  328.68   102.49   -43.27     0.46)  ; 14\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  303.50    85.25   -33.55     0.46)  ; 1\r\n            (  313.19    88.13   -39.10     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  302.71    88.64   -34.67     0.46)  ; 1, R-1-2-1-2\r\n          (  303.20    90.56   -36.10     0.46)  ; 2\r\n          (  304.28    92.00   -38.83     0.46)  ; 3\r\n          (  303.57    93.02   -39.60     0.46)  ; 4\r\n          (  303.17    94.72   -41.82     0.46)  ; 5\r\n          (  301.43    96.11   -42.37     0.46)  ; 6\r\n          (  301.43    96.11   -42.42     0.46)  ; 7\r\n          (  301.48    97.92   -44.05     0.46)  ; 8\r\n          (  303.27    98.33   -45.72     0.46)  ; 9\r\n          (  300.96   100.17   -46.15     0.46)  ; 10\r\n          (  301.76   102.76   -47.65     0.46)  ; 11\r\n          (  301.68   105.12   -49.47     0.46)  ; 12\r\n          (  301.68   105.12   -49.50     0.46)  ; 13\r\n          (  301.28   106.82   -50.68     0.46)  ; 14\r\n          (  301.82   110.54   -51.15     0.46)  ; 15\r\n          (  302.81   114.35   -51.15     0.46)  ; 16\r\n          (  302.46   117.85   -51.42     0.46)  ; 17\r\n          (  303.27   120.43   -52.80     0.46)  ; 18\r\n          (  303.27   120.43   -52.82     0.46)  ; 19\r\n          (  303.33   122.23   -53.65     0.46)  ; 20\r\n          (  303.33   122.23   -53.67     0.46)  ; 21\r\n          (  302.53   125.62   -54.32     0.46)  ; 22\r\n          (  301.94   126.09   -55.77     0.46)  ; 23\r\n          (  301.94   126.09   -55.80     0.46)  ; 24\r\n          (  302.75   128.66   -57.25     0.46)  ; 25\r\n          (  303.75   132.49   -58.85     0.46)  ; 26\r\n          (  304.38   133.82   -59.75     0.46)  ; 27\r\n          (  304.38   133.82   -59.78     0.46)  ; 28\r\n          (  306.52   136.71   -61.10     0.46)  ; 29\r\n          (  307.33   139.29   -61.38     0.46)  ; 30\r\n          (  307.33   139.29   -61.40     0.46)  ; 31\r\n          (  308.99   140.28   -61.38     0.46)  ; 32\r\n          (  310.15   145.32   -62.95     0.46)  ; 33\r\n          (  311.01   149.71   -63.72     0.46)  ; 34\r\n          (  311.01   149.71   -63.75     0.46)  ; 35\r\n          (  311.82   152.29   -63.05     0.46)  ; 36\r\n          (  313.08   154.97   -61.28     0.46)  ; 37\r\n          (  314.02   156.99   -59.80     0.46)  ; 38\r\n          (  314.02   156.99   -59.83     0.46)  ; 39\r\n          (  317.96   160.29   -59.15     0.46)  ; 40\r\n          (  319.13   165.34   -58.05     0.46)  ; 41\r\n          (  319.04   167.71   -58.62     0.46)  ; 42\r\n          (  317.71   173.37   -60.07     0.46)  ; 43\r\n          (  317.05   176.20   -61.42     0.46)  ; 44\r\n          (  315.63   178.26   -64.27     0.46)  ; 45\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  321.46   169.47   -58.62     0.46)  ; 1\r\n            (  316.51   172.50   -60.07     0.46)  ; 2\r\n            (  305.00   135.16   -58.75     0.46)  ; 3\r\n            (  302.99   129.16    19.20     0.46)  ; 4\r\n            (  301.89   124.29   -53.72     0.46)  ; 5\r\n            (  304.68   112.40   -51.15     0.46)  ; 6\r\n            (  300.88   108.52   -51.15     0.46)  ; 7\r\n            (  304.43    97.41   -45.72     0.46)  ; 8\r\n            (  300.83    90.60   -36.10     0.46)  ; 9\r\n            (  302.18    90.91   -39.10     0.46)  ; 10\r\n            (  301.95    87.87   -33.53     0.46)  ; 11\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  278.18    52.44   -12.95     0.92)  ; 1, R-1-2-2\r\n        (  278.63    52.55   -14.80     0.92)  ; 2\r\n        (  278.63    52.55   -14.92     0.92)  ; 3\r\n        (  278.76    51.98   -17.47     0.92)  ; 4\r\n        (  281.14    51.94   -18.02     0.92)  ; 5\r\n        (  283.28    54.84   -18.50     0.92)  ; 6\r\n        (  284.93    55.82   -18.88     0.92)  ; 7\r\n        (  286.99    55.10   -19.50     0.92)  ; 8\r\n        (  289.57    52.13   -19.50     0.92)  ; 9\r\n        (  291.63    51.41   -20.00     0.92)  ; 10\r\n        (  292.92    49.93   -20.77     0.92)  ; 11\r\n        (  295.28    49.89   -21.88     0.92)  ; 12\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  293.32    48.24   -20.00     0.92)  ; 1\r\n          (  290.28    51.10   -20.00     0.92)  ; 2\r\n        )  ;  End of markers\r\n        (\r\n          (  296.62    50.20   -23.27     0.46)  ; 1, R-1-2-2-1\r\n          (  294.75    52.14   -24.77     0.46)  ; 2\r\n          (  294.30    52.03   -24.80     0.46)  ; 3\r\n          (  293.85    51.93   -26.32     0.46)  ; 4\r\n          (  295.38    53.50   -27.83     0.46)  ; 5\r\n          (  294.80    53.95   -27.85     0.46)  ; 6\r\n          (  295.56    54.73   -29.42     0.46)  ; 7\r\n          (  295.43    55.30   -29.42     0.46)  ; 8\r\n          (  294.85    55.75   -31.30     0.46)  ; 9\r\n          (  293.32    54.20   -31.13     0.46)  ; 10\r\n          (  292.29    54.56   -33.75     0.46)  ; 11\r\n          (  294.00    57.35   -35.55     0.46)  ; 12\r\n          (  293.34    60.18   -36.52     0.46)  ; 13\r\n          (  292.18    61.10   -40.80     0.46)  ; 14\r\n          (  293.52    61.42   -43.72     0.46)  ; 15\r\n          (  293.70    62.65   -45.80     0.46)  ; 16\r\n          (  293.70    62.65   -45.83     0.46)  ; 17\r\n          (  292.09    63.47   -47.02     0.46)  ; 18\r\n          (  291.96    64.03   -49.25     0.46)  ; 19\r\n          (  293.75    64.45   -51.25     0.46)  ; 20\r\n          (  294.51    65.23   -52.97     0.46)  ; 21\r\n          (  294.51    65.23   -53.00     0.46)  ; 22\r\n          (  295.90    67.34   -55.83     0.46)  ; 23\r\n          (  294.93    69.50   -57.85     0.46)  ; 24\r\n          (  295.68    70.29   -59.83     0.46)  ; 25\r\n          (  296.12    70.38   -59.83     0.46)  ; 26\r\n          (  297.28    69.46   -62.10     0.46)  ; 27\r\n          (  297.65    71.94   -63.82     0.46)  ; 28\r\n          (  300.01    71.90   -65.32     0.46)  ; 29\r\n          (  300.78    72.67   -67.25     0.46)  ; 30\r\n          (  300.78    72.67   -67.28     0.46)  ; 31\r\n          (  299.35    74.72   -69.55     0.46)  ; 32\r\n          (  298.76    75.19   -70.57     0.46)  ; 33\r\n          (  299.21    75.30   -72.77     0.46)  ; 34\r\n          (  301.46    75.81   -74.45     0.46)  ; 35\r\n          (  301.28    74.57   -76.60     0.46)  ; 36\r\n          (  301.28    74.57   -76.63     0.46)  ; 37\r\n          (  302.61    74.89   -78.97     0.46)  ; 38\r\n          (  303.72    72.17   -80.52     0.46)  ; 39\r\n          (  303.72    72.17   -80.60     0.46)  ; 40\r\n          (  305.11    74.28   -83.02     0.46)  ; 41\r\n          (  305.46    76.76   -85.02     0.46)  ; 42\r\n          (  304.50    78.92   -87.07     0.46)  ; 43\r\n          (  304.50    78.92   -87.10     0.46)  ; 44\r\n          (  303.65    80.51   -89.65     0.46)  ; 45\r\n          (  303.65    80.51   -89.70     0.46)  ; 46\r\n          (  303.87    83.55   -91.32     0.46)  ; 47\r\n          (  303.79    85.91   -92.15     0.46)  ; 48\r\n          (  303.66    86.48   -95.30     0.46)  ; 49\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  296.80    73.53   -69.55     0.46)  ; 1\r\n            (  297.70    73.74   -65.67     0.46)  ; 2\r\n            (  297.05    66.43   -57.82     0.46)  ; 3\r\n            (  295.04    62.97   -51.25     0.46)  ; 4\r\n            (  291.81    58.62   -36.52     0.46)  ; 5\r\n            (  293.99    51.37   -21.82     0.92)  ; 6\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  296.12    48.29   -21.10     0.46)  ; 1, R-1-2-2-2\r\n          (  295.58    44.58   -22.67     0.46)  ; 2\r\n          (  295.58    44.58   -22.70     0.46)  ; 3\r\n          (  296.60    44.22   -24.40     0.46)  ; 4\r\n          (  298.21    43.41   -25.90     0.46)  ; 5\r\n          (  299.05    41.82   -27.60     0.46)  ; 6\r\n          (  299.01    40.01   -29.17     0.46)  ; 7\r\n          (  302.09    38.94   -30.13     0.46)  ; 8\r\n          (  303.11    38.59   -31.17     0.46)  ; 9\r\n          (  305.25    35.50   -31.70     0.46)  ; 10\r\n          (  307.70    33.09   -32.88     0.46)  ; 11\r\n          (  308.98    31.60   -34.30     0.46)  ; 12\r\n          (  310.67    28.41   -35.45     0.46)  ; 13\r\n          (  311.60    24.45   -36.33     0.46)  ; 14\r\n          (  314.50    22.14   -36.58     0.46)  ; 15\r\n          (  316.90    17.92   -37.00     0.46)  ; 16\r\n          (  318.41    13.50   -37.00     0.46)  ; 17\r\n          (  320.68     9.85   -37.22     0.46)  ; 18\r\n          (  320.68     9.85   -37.25     0.46)  ; 19\r\n          (  320.77     7.48   -38.40     0.46)  ; 20\r\n          (  320.62     2.08   -39.25     0.46)  ; 21\r\n          (  321.37    -3.12   -40.15     0.46)  ; 22\r\n          (  323.64    -6.77   -40.03     0.46)  ; 23\r\n          (  324.17    -9.04   -42.05     0.46)  ; 24\r\n          (  325.99   -12.79   -43.80     0.46)  ; 25\r\n          (  324.29   -15.58   -45.45     0.46)  ; 26\r\n          (  326.19   -21.70   -46.08     0.46)  ; 27\r\n          (  328.20   -24.21   -46.73     0.46)  ; 28\r\n          (  330.99   -30.13   -47.77     0.46)  ; 29\r\n          (  332.25   -31.60   -47.85     0.46)  ; 30\r\n          (  331.62   -32.95   -49.83     0.46)  ; 31\r\n          (  331.62   -32.95   -49.87     0.46)  ; 32\r\n          (  331.57   -34.76   -51.32     0.46)  ; 33\r\n          (  331.57   -34.76   -51.35     0.46)  ; 34\r\n          (  333.13   -37.37   -52.70     0.46)  ; 35\r\n          (  333.13   -37.37   -52.72     0.46)  ; 36\r\n          (  335.97   -41.48   -53.10     0.46)  ; 37\r\n          (  335.97   -41.48   -53.13     0.46)  ; 38\r\n          (  337.53   -44.10   -52.85     0.46)  ; 39\r\n          (  339.80   -47.75   -52.85     0.46)  ; 40\r\n          (  339.80   -47.75   -52.88     0.46)  ; 41\r\n          (  340.28   -51.82   -52.70     0.46)  ; 42\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  295.00    45.05   -24.42     0.46)  ; 1\r\n            (  311.54    32.79   -35.45     0.46)  ; 2\r\n            (  307.87    28.35   -35.45     0.46)  ; 3\r\n            (  317.98    19.37   -37.67     0.46)  ; 4\r\n            (  316.62    13.08   -37.67     0.46)  ; 5\r\n            (  319.43     7.17   -38.40     0.46)  ; 6\r\n            (  321.42    -1.32   -40.03     0.46)  ; 7\r\n            (  330.96   -25.96   -47.77     0.46)  ; 8\r\n            (  336.65   -38.34   -53.13     0.46)  ; 9\r\n            (  338.33   -47.50   -52.70     0.46)  ; 10\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (  256.57    35.95    -7.82     1.83)  ; 1, R-2\r\n    (  253.05    36.90    -9.30     1.83)  ; 2\r\n    (  250.73    38.75    -9.32     1.83)  ; 3\r\n    (  246.88    39.05    -9.32     1.83)  ; 4\r\n    (  245.14    40.44   -10.50     1.83)  ; 5\r\n    (  243.23    40.58   -11.90     1.83)  ; 6\r\n    (  242.78    40.48   -13.27     1.38)  ; 7\r\n    (  241.30    40.73   -14.92     1.38)  ; 8\r\n    (  240.41    40.52   -15.92     1.38)  ; 9\r\n    (  236.35    37.78   -16.20     1.38)  ; 10\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  238.71    37.72   -16.20     1.38)  ; 1\r\n    )  ;  End of markers\r\n    (\r\n      (  235.99    35.30   -17.17     1.38)  ; 1, R-2-1\r\n      (  235.04    33.29   -18.33     1.38)  ; 2\r\n      (  233.20    31.06   -19.33     1.38)  ; 3\r\n      (  232.58    29.73   -21.72     1.38)  ; 4\r\n      (\r\n        (  232.49    26.12   -22.53     0.92)  ; 1, R-2-1-1\r\n        (  231.49    22.30   -23.50     0.92)  ; 2\r\n        (  231.27    19.26   -24.10     0.92)  ; 3\r\n        (  230.90    16.80   -24.85     0.92)  ; 4\r\n        (  231.11    13.85   -25.38     0.92)  ; 5\r\n        (  230.25     9.47   -25.77     0.92)  ; 6\r\n        (  228.82     5.55   -25.80     0.92)  ; 7\r\n        (  227.84     1.75   -26.13     0.92)  ; 8\r\n        (  225.24    -1.26   -24.70     0.92)  ; 9\r\n        (  222.50    -3.68   -23.30     0.92)  ; 10\r\n        (  219.90    -6.69   -22.88     0.92)  ; 11\r\n        (  218.97    -8.69   -23.27     0.92)  ; 12\r\n        (  215.67   -10.67   -23.27     0.92)  ; 13\r\n        (  215.62   -12.47   -23.27     0.46)  ; 14\r\n        (  213.82   -12.89   -23.27     0.46)  ; 15\r\n        (  213.78   -14.69   -23.27     0.46)  ; 16\r\n        (  211.86   -14.54   -23.27     0.46)  ; 17\r\n        (  210.48   -16.66   -23.27     0.46)  ; 18\r\n        (  209.27   -17.54   -24.08     0.46)  ; 19\r\n        (  208.19   -18.99   -24.08     0.46)  ; 20\r\n        (  204.43   -21.05   -24.08     0.46)  ; 21\r\n        (  200.69   -23.14   -24.75     0.46)  ; 22\r\n        (  197.11   -23.98   -24.05     0.46)  ; 23\r\n        (  195.50   -23.15   -24.08     0.46)  ; 24\r\n        (  195.50   -23.15   -24.10     0.46)  ; 25\r\n        (  193.59   -23.00   -24.40     0.46)  ; 26\r\n        (  193.59   -23.00   -24.42     0.46)  ; 27\r\n        (  190.90   -23.63   -25.57     0.46)  ; 28\r\n        (  189.52   -25.76   -27.22     0.46)  ; 29\r\n        (  188.62   -25.97   -29.48     0.46)  ; 30\r\n        (  188.62   -25.97   -29.50     0.46)  ; 31\r\n        (  188.36   -24.82   -31.85     0.46)  ; 32\r\n        (  188.36   -24.82   -31.88     0.46)  ; 33\r\n        (  186.44   -24.67   -34.13     0.46)  ; 34\r\n        (  186.44   -24.67   -34.15     0.46)  ; 35\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  194.65   -27.53   -24.67     0.46)  ; 1\r\n          (  193.23   -25.48   -25.57     0.46)  ; 2\r\n          (  190.98   -26.01   -25.57     0.46)  ; 3\r\n          (  233.18    19.11   -24.10     0.92)  ; 4\r\n          (  216.02    -8.20   -23.27     0.92)  ; 5\r\n          (  229.80     3.40   -26.13     0.92)  ; 6\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  229.63    30.23   -21.72     0.92)  ; 1, R-2-1-2\r\n        (  227.27    30.27   -22.88     0.92)  ; 2\r\n        (  226.64    28.93   -24.30     0.92)  ; 3\r\n        (  227.35    27.91   -26.58     0.92)  ; 4\r\n        (  226.59    27.13   -28.30     0.92)  ; 5\r\n        (  226.85    25.99   -27.80     0.92)  ; 6\r\n        (  225.79    24.55   -29.22     0.92)  ; 7\r\n        (  224.39    22.43   -30.45     0.92)  ; 8\r\n        (  222.66    23.81   -31.60     0.92)  ; 9\r\n        (  222.66    23.81   -31.63     0.92)  ; 10\r\n        (  221.95    24.84   -33.00     0.92)  ; 11\r\n        (  220.79    25.77   -34.88     0.92)  ; 12\r\n        (  219.62    26.69   -36.60     0.92)  ; 13\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  228.78    25.84   -27.80     0.92)  ; 1\r\n        )  ;  End of markers\r\n        (\r\n          (  217.66    25.04   -38.13     0.92)  ; 1, R-2-1-2-1\r\n          (  216.45    24.15   -41.22     0.46)  ; 2\r\n          (  216.45    24.15   -41.20     0.46)  ; 3\r\n          (  214.14    26.00   -42.37     0.46)  ; 4\r\n          (  211.19    26.51   -43.60     0.46)  ; 5\r\n          (  208.38    26.43   -43.72     0.46)  ; 6\r\n          (  206.06    28.28   -44.58     0.46)  ; 7\r\n          (  206.51    28.39   -46.45     0.46)  ; 8\r\n          (  205.93    28.85   -49.10     0.46)  ; 9\r\n          (  203.44    29.46   -50.77     0.46)  ; 10\r\n          (  202.04    27.35   -52.80     0.46)  ; 11\r\n          (  200.39    26.36   -54.38     0.46)  ; 12\r\n          (  200.39    26.36   -54.40     0.46)  ; 13\r\n          (  196.43    27.22   -55.32     0.46)  ; 14\r\n          (  192.85    26.38   -56.80     0.46)  ; 15\r\n          (  191.64    25.51   -58.15     0.46)  ; 16\r\n          (  191.64    25.51   -58.17     0.46)  ; 17\r\n          (  189.73    25.65   -58.90     0.46)  ; 18\r\n          (  187.21    26.26   -59.80     0.46)  ; 19\r\n          (  184.54    25.64   -60.70     0.46)  ; 20\r\n          (  181.82    23.20   -61.38     0.46)  ; 21\r\n          (  178.92    25.50   -62.88     0.46)  ; 22\r\n          (  176.28    26.68   -63.95     0.46)  ; 23\r\n          (  174.54    28.07   -65.30     0.46)  ; 24\r\n          (  172.05    28.68   -66.88     0.46)  ; 25\r\n          (  172.18    28.11   -66.88     0.46)  ; 26\r\n          (  168.65    29.07   -68.20     0.46)  ; 27\r\n          (  166.03    30.25   -69.40     0.46)  ; 28\r\n          (  164.42    31.07   -71.00     0.46)  ; 29\r\n          (  162.77    30.09   -72.93     0.46)  ; 30\r\n          (  160.40    30.12   -74.55     0.46)  ; 31\r\n          (  160.40    30.12   -74.57     0.46)  ; 32\r\n          (  157.32    31.20   -75.63     0.46)  ; 33\r\n          (  156.48    32.79   -77.28     0.46)  ; 34\r\n          (  154.43    33.50   -79.50     0.46)  ; 35\r\n          (  151.93    34.11   -81.22     0.46)  ; 36\r\n          (  150.32    34.93   -83.02     0.46)  ; 37\r\n          (  149.38    32.92   -85.30     0.46)  ; 38\r\n          (  147.02    32.96   -87.70     0.46)  ; 39\r\n          (  147.37    29.46   -89.92     0.46)  ; 40\r\n          (  147.37    29.46   -89.95     0.46)  ; 41\r\n          (  144.95    27.70   -91.13     0.46)  ; 42\r\n          (  142.71    27.17   -92.90     0.46)  ; 43\r\n          (  142.71    27.17   -92.92     0.46)  ; 44\r\n          (  140.31    25.42   -94.10     0.46)  ; 45\r\n          (  140.31    25.42   -94.15     0.46)  ; 46\r\n          (  138.79    23.87   -93.63     0.46)  ; 47\r\n          (  138.79    23.87   -93.88     0.46)  ; 48\r\n          (  136.36    22.10   -94.17     0.46)  ; 49\r\n          (  135.56    19.53   -95.18     0.46)  ; 50\r\n          (  134.04    17.98   -97.28     0.46)  ; 51\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  134.00    22.15   -93.82     0.46)  ; 1\r\n            (  185.22    28.78   -60.65     0.46)  ; 2\r\n            (  181.02    26.60   -62.95     0.46)  ; 3\r\n            (  190.69    23.49   -58.90     0.46)  ; 4\r\n            (  219.40    23.64   -41.20     0.46)  ; 5\r\n            (  160.30    26.52   -74.57     0.46)  ; 6\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  223.20    27.53   -39.55     0.46)  ; 1, R-2-1-2-2\r\n          (  223.65    27.63   -42.47     0.46)  ; 2\r\n          (  223.65    27.63   -42.53     0.46)  ; 3\r\n          (  223.38    28.76   -45.97     0.46)  ; 4\r\n          (  223.38    28.76   -46.10     0.46)  ; 5\r\n          (  225.12    27.38   -48.70     0.46)  ; 6\r\n          (  225.12    27.38   -48.72     0.46)  ; 7\r\n          (  225.17    29.18   -50.95     0.46)  ; 8\r\n          (  224.72    29.07   -50.97     0.46)  ; 9\r\n          (  223.83    28.86   -53.40     0.46)  ; 10\r\n          (  223.70    29.43   -53.42     0.46)  ; 11\r\n          (  223.43    30.57   -55.67     0.46)  ; 12\r\n          (  223.43    30.57   -55.70     0.46)  ; 13\r\n          (  225.34    30.41   -58.42     0.46)  ; 14\r\n          (  225.34    30.41   -58.47     0.46)  ; 15\r\n          (  224.95    32.11   -61.40     0.46)  ; 16\r\n          (  224.95    32.11   -61.47     0.46)  ; 17\r\n          (  224.06    31.91   -63.88     0.46)  ; 18\r\n          (  224.06    31.91   -63.90     0.46)  ; 19\r\n          (  224.86    34.48   -66.65     0.46)  ; 20\r\n          (  225.84    32.32   -69.35     0.46)  ; 21\r\n          (  226.10    31.20   -72.07     0.46)  ; 22\r\n          (  226.10    31.20   -72.17     0.46)  ; 23\r\n          (  228.04    31.04   -76.22     0.46)  ; 24\r\n          (  228.04    31.04   -76.30     0.46)  ; 25\r\n          (  228.04    31.04   -81.53     0.46)  ; 26\r\n          (  228.04    31.04   -81.58     0.46)  ; 27\r\n          (  227.72    30.38   -85.92     0.46)  ; 28\r\n          (  226.50    29.49   -89.05     0.46)  ; 29\r\n          (  226.50    29.49   -89.08     0.46)  ; 30\r\n          (  224.81    26.72   -92.40     0.46)  ; 31\r\n          (  224.76    24.91   -94.75     0.46)  ; 32\r\n          (  225.16    23.21   -97.13     0.46)  ; 33\r\n          (  225.87    22.17   -99.50     0.46)  ; 34\r\n          (  225.87    22.17   -99.52     0.46)  ; 35\r\n          (  224.79    20.73  -102.68     0.46)  ; 36\r\n          (  224.79    20.73  -102.72     0.46)  ; 37\r\n          (  225.24    20.84  -106.25     0.46)  ; 38\r\n          (  225.37    20.27  -110.32     0.46)  ; 39\r\n          (  225.37    20.27  -110.37     0.46)  ; 40\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  228.06    26.87   -88.72     0.46)  ; 1\r\n            (  223.20    27.53   -92.40     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  219.81    27.93   -40.52     0.46)  ; 1, R-2-1-2-3\r\n          (  218.34    28.18   -43.45     0.46)  ; 2\r\n          (  218.34    28.18   -43.47     0.46)  ; 3\r\n          (  216.78    30.80   -45.70     0.46)  ; 4\r\n          (  217.68    31.01   -48.20     0.46)  ; 5\r\n          (  218.62    33.02   -51.08     0.46)  ; 6\r\n          (  219.25    34.37   -54.20     0.46)  ; 7\r\n          (  219.25    34.37   -54.22     0.46)  ; 8\r\n          (  218.17    32.91   -57.15     0.46)  ; 9\r\n          (  218.17    32.91   -57.20     0.46)  ; 10\r\n          (  216.61    35.53   -59.35     0.46)  ; 11\r\n          (  217.87    38.23   -60.27     0.46)  ; 12\r\n          (  217.39    42.29   -61.38     0.46)  ; 13\r\n          (  217.26    42.86   -63.10     0.46)  ; 14\r\n          (  215.77    43.11   -65.45     0.46)  ; 15\r\n          (  215.25    45.36   -67.50     0.46)  ; 16\r\n          (  216.60    45.68   -69.15     0.46)  ; 17\r\n          (  218.24    46.67   -70.45     0.46)  ; 18\r\n          (  216.06    47.95   -70.27     0.46)  ; 19\r\n          (  214.59    48.20   -72.17     0.46)  ; 20\r\n          (  216.11    49.75   -73.95     0.46)  ; 21\r\n          (  216.29    50.99   -74.45     0.46)  ; 22\r\n          (  214.92    54.85   -74.57     0.46)  ; 23\r\n          (  214.92    54.85   -74.60     0.46)  ; 24\r\n          (  214.21    55.87   -76.38     0.46)  ; 25\r\n          (  213.68    58.14   -78.13     0.46)  ; 26\r\n          (  214.17    60.05   -79.55     0.46)  ; 27\r\n          (  213.69    64.11   -80.57     0.46)  ; 28\r\n          (  214.09    68.39   -81.25     0.46)  ; 29\r\n          (  213.56    70.64   -82.75     0.46)  ; 30\r\n          (  213.56    70.64   -82.78     0.46)  ; 31\r\n          (  212.32    73.94   -84.57     0.46)  ; 32\r\n          (  213.58    76.63   -86.10     0.46)  ; 33\r\n          (  214.65    78.07   -87.70     0.46)  ; 34\r\n          (  214.65    78.07   -87.72     0.46)  ; 35\r\n          (  213.40    81.36   -89.25     0.46)  ; 36\r\n          (  212.17    84.66   -90.40     0.46)  ; 37\r\n          (  213.11    86.67   -92.83     0.46)  ; 38\r\n          (  213.11    86.67   -92.90     0.46)  ; 39\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  213.13    54.43   -74.60     0.46)  ; 1\r\n            (  217.89    50.17   -70.25     0.46)  ; 2\r\n            (  218.87    42.03   -61.38     0.46)  ; 3\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  234.11    37.26   -15.38     0.92)  ; 1, R-2-2\r\n      (  232.72    35.13   -15.65     0.92)  ; 2\r\n      (  229.47    34.97   -16.52     0.92)  ; 3\r\n      (  227.02    37.38   -17.25     0.92)  ; 4\r\n      (  224.38    38.55   -18.17     0.92)  ; 5\r\n      (  222.42    36.90   -19.38     0.92)  ; 6\r\n      (  220.81    37.71   -20.38     0.92)  ; 7\r\n      (  218.85    36.06   -21.55     0.92)  ; 8\r\n      (  218.85    36.06   -21.58     0.92)  ; 9\r\n      (  216.65    37.34   -22.30     0.92)  ; 10\r\n      (  215.82    38.93   -23.95     0.92)  ; 11\r\n      (  213.00    38.87   -25.33     0.92)  ; 12\r\n      (  212.87    39.44   -27.15     0.92)  ; 13\r\n      (  210.77    38.34   -28.65     0.92)  ; 14\r\n      (  210.77    38.34   -28.67     0.92)  ; 15\r\n      (  208.22    37.15   -29.70     0.46)  ; 16\r\n      (  208.01    40.09   -30.40     0.46)  ; 17\r\n      (  203.91    41.52   -31.10     0.46)  ; 18\r\n      (  200.56    43.72   -32.08     0.46)  ; 19\r\n      (  198.70    45.67   -33.13     0.46)  ; 20\r\n      (  193.83    46.32   -33.67     0.92)  ; 21\r\n      (  191.07    48.05   -35.60     0.92)  ; 22\r\n      (  188.56    48.67   -37.72     0.92)  ; 23\r\n      (  186.51    49.38   -39.15     0.92)  ; 24\r\n      (  183.84    48.76   -41.05     0.92)  ; 25\r\n      (  183.84    48.76   -41.08     0.92)  ; 26\r\n      (  182.68    49.67   -44.17     0.92)  ; 27\r\n      (  182.68    49.67   -44.20     0.92)  ; 28\r\n      (  180.32    49.72   -45.57     0.92)  ; 29\r\n      (  177.54    51.46   -46.82     0.92)  ; 30\r\n      (  173.84    51.19   -48.35     0.92)  ; 31\r\n      (  171.02    51.12   -49.05     0.92)  ; 32\r\n      (  169.28    52.51   -51.22     0.46)  ; 33\r\n      (  165.90    52.91   -53.35     0.46)  ; 34\r\n      (  165.90    52.91   -53.38     0.46)  ; 35\r\n      (  163.72    54.19   -55.40     0.46)  ; 36\r\n      (  163.72    54.19   -55.42     0.46)  ; 37\r\n      (  161.52    55.47   -57.28     0.46)  ; 38\r\n      (  161.52    55.47   -57.30     0.46)  ; 39\r\n      (  159.03    56.08   -58.22     0.46)  ; 40\r\n      (  155.64    56.48   -59.90     0.46)  ; 41\r\n      (  155.11    58.74   -60.97     0.46)  ; 42\r\n      (  151.90    60.37   -62.45     0.46)  ; 43\r\n      (  149.84    61.10   -64.20     0.46)  ; 44\r\n      (  147.40    63.50   -66.00     0.46)  ; 45\r\n      (  145.53    65.45   -67.95     0.46)  ; 46\r\n      (  145.53    65.45   -67.97     0.46)  ; 47\r\n      (  143.70    69.21   -69.72     0.46)  ; 48\r\n      (  140.76    69.71   -71.50     0.46)  ; 49\r\n      (  140.76    69.71   -71.52     0.46)  ; 50\r\n      (  138.62    72.80   -73.40     0.46)  ; 51\r\n      (  136.44    74.07   -74.97     0.46)  ; 52\r\n      (  134.57    76.03   -76.70     0.46)  ; 53\r\n      (  133.28    77.51   -78.65     0.46)  ; 54\r\n      (  130.51    79.26   -79.80     0.46)  ; 55\r\n      (  126.72    81.35   -82.00     0.46)  ; 56\r\n      (  124.10    82.53   -84.08     0.46)  ; 57\r\n      (  124.10    82.53   -84.10     0.46)  ; 58\r\n      (  121.78    84.37   -86.70     0.46)  ; 59\r\n      (  120.67    87.09   -88.97     0.46)  ; 60\r\n      (  120.67    87.09   -89.00     0.46)  ; 61\r\n      (  118.79    89.05   -90.88     0.46)  ; 62\r\n      (  118.79    89.05   -90.90     0.46)  ; 63\r\n      (  117.10    92.24   -93.50     0.46)  ; 64\r\n      (  117.10    92.24   -93.53     0.46)  ; 65\r\n      (  117.28    93.47   -96.20     0.46)  ; 66\r\n      (  116.57    94.49   -99.92     0.46)  ; 67\r\n      (  116.57    94.49  -100.00     0.46)  ; 68\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  155.03    61.11   -60.97     0.46)  ; 1\r\n        (  150.87    60.74   -60.97     0.46)  ; 2\r\n        (  160.46    54.02   -58.22     0.46)  ; 3\r\n        (  173.30    47.47   -49.05     0.92)  ; 4\r\n        (  124.28    83.76   -84.10     0.46)  ; 5\r\n        (  208.45    40.19   -30.38     0.46)  ; 6\r\n        (  198.01    42.53   -33.13     0.46)  ; 7\r\n        (  194.22    44.61   -33.67     0.46)  ; 8\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/l5pc/C060114A7_axon_replacement.acc",
    "content": "(arbor-component \n  (meta-data \n    (version \"0.9-dev\"))\n  (morphology \n    (branch 0 -1 \n      (segment 0 \n        (point 263.248016 5.356219 -3.380000 0.690000)\n        (point 262.996735 -9.641676 -3.380000 0.690000)\n        2)\n      (segment 1 \n        (point 262.996735 -9.641676 -3.380000 0.690000)\n        (point 262.745453 -24.639572 -3.380000 0.690000)\n        2)\n      (segment 2 \n        (point 262.745453 -24.639572 -3.380000 0.460000)\n        (point 262.494171 -39.637466 -3.380000 0.460000)\n        2)\n      (segment 3 \n        (point 262.494171 -39.637466 -3.380000 0.460000)\n        (point 262.242889 -54.635365 -3.380000 0.460000)\n        2))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/l5pc/C060114A7_modified.acc",
    "content": "(arbor-component \n  (meta-data \n    (version \"0.9-dev\"))\n  (morphology \n    (branch 0 -1 \n      (segment 0 \n        (point 262.132381 19.373333 -3.380000 11.328448)\n        (point 262.132381 8.044885 -3.380000 11.328448)\n        1))\n    (branch 1 -1 \n      (segment 1 \n        (point 262.132381 19.373333 -3.380000 11.328448)\n        (point 262.132381 30.701782 -3.380000 11.328448)\n        1))\n    (branch 2 -1 \n      (segment 2 \n        (point 254.220000 19.870000 -2.650000 0.690000)\n        (point 254.220000 19.870000 -2.650000 0.690000)\n        3)\n      (segment 3 \n        (point 254.220000 19.870000 -2.650000 0.690000)\n        (point 253.510000 20.900000 -4.070000 0.690000)\n        3)\n      (segment 4 \n        (point 253.510000 20.900000 -4.070000 0.690000)\n        (point 251.450000 21.610000 -5.050000 0.690000)\n        3)\n      (segment 5 \n        (point 251.450000 21.610000 -5.050000 0.690000)\n        (point 248.960000 22.230000 -5.400000 0.690000)\n        3)\n      (segment 6 \n        (point 248.960000 22.230000 -5.400000 0.690000)\n        (point 247.220000 23.600000 -6.300000 0.690000)\n        3)\n      (segment 7 \n        (point 247.220000 23.600000 -6.300000 0.690000)\n        (point 244.140000 24.680000 -6.950000 0.690000)\n        3)\n      (segment 8 \n        (point 244.140000 24.680000 -6.950000 0.690000)\n        (point 241.460000 24.060000 -7.270000 0.690000)\n        3)\n      (segment 9 \n        (point 241.460000 24.060000 -7.270000 0.690000)\n        (point 239.230000 23.530000 -8.770000 0.690000)\n        3)\n      (segment 10 \n        (point 239.230000 23.530000 -8.770000 0.690000)\n        (point 237.490000 24.910000 -9.600000 0.460000)\n        3)\n      (segment 11 \n        (point 237.490000 24.910000 -9.600000 0.460000)\n        (point 234.900000 27.890000 -10.450000 0.460000)\n        3)\n      (segment 12 \n        (point 234.900000 27.890000 -10.450000 0.460000)\n        (point 232.590000 29.730000 -11.400000 0.460000)\n        3)\n      (segment 13 \n        (point 232.590000 29.730000 -11.400000 0.460000)\n        (point 228.620000 30.600000 -12.150000 0.460000)\n        3)\n      (segment 14 \n        (point 228.620000 30.600000 -12.150000 0.460000)\n        (point 224.920000 30.330000 -12.270000 0.460000)\n        3)\n      (segment 15 \n        (point 224.920000 30.330000 -12.270000 0.460000)\n        (point 222.330000 33.300000 -11.750000 0.460000)\n        3)\n      (segment 16 \n        (point 222.330000 33.300000 -11.750000 0.460000)\n        (point 220.780000 35.910000 -11.170000 0.460000)\n        3)\n      (segment 17 \n        (point 220.780000 35.910000 -11.170000 0.460000)\n        (point 217.560000 37.550000 -11.170000 0.460000)\n        3)\n      (segment 18 \n        (point 217.560000 37.550000 -11.170000 0.460000)\n        (point 214.980000 40.530000 -11.530000 0.460000)\n        3)\n      (segment 19 \n        (point 214.980000 40.530000 -11.530000 0.460000)\n        (point 211.730000 40.360000 -12.130000 0.460000)\n        3)\n      (segment 20 \n        (point 211.730000 40.360000 -12.130000 0.460000)\n        (point 211.730000 40.360000 -12.150000 0.460000)\n        3)\n      (segment 21 \n        (point 211.730000 40.360000 -12.150000 0.460000)\n        (point 209.720000 42.890000 -12.750000 0.460000)\n        3))\n    (branch 3 2 \n      (segment 22 \n        (point 209.720000 42.890000 -12.750000 0.460000)\n        (point 209.190000 45.150000 -13.750000 0.230000)\n        3)\n      (segment 23 \n        (point 209.190000 45.150000 -13.750000 0.230000)\n        (point 207.860000 44.840000 -15.050000 0.230000)\n        3)\n      (segment 24 \n        (point 207.860000 44.840000 -15.050000 0.230000)\n        (point 205.980000 46.790000 -16.170000 0.230000)\n        3)\n      (segment 25 \n        (point 205.980000 46.790000 -16.170000 0.230000)\n        (point 204.680000 48.270000 -17.720000 0.230000)\n        3)\n      (segment 26 \n        (point 204.680000 48.270000 -17.720000 0.230000)\n        (point 204.240000 48.170000 -17.720000 0.230000)\n        3)\n      (segment 27 \n        (point 204.240000 48.170000 -17.720000 0.230000)\n        (point 201.790000 50.580000 -19.170000 0.230000)\n        3)\n      (segment 28 \n        (point 201.790000 50.580000 -19.170000 0.230000)\n        (point 199.740000 51.290000 -19.550000 0.230000)\n        3)\n      (segment 29 \n        (point 199.740000 51.290000 -19.550000 0.230000)\n        (point 196.480000 51.130000 -19.550000 0.230000)\n        3))\n    (branch 4 3 \n      (segment 30 \n        (point 196.480000 51.130000 -19.550000 0.230000)\n        (point 194.480000 53.640000 -20.670000 0.230000)\n        3)\n      (segment 31 \n        (point 194.480000 53.640000 -20.670000 0.230000)\n        (point 191.450000 56.510000 -20.870000 0.230000)\n        3)\n      (segment 32 \n        (point 191.450000 56.510000 -20.870000 0.230000)\n        (point 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154 \n        (point 202.910000 9.640000 7.070000 0.460000)\n        (point 201.000000 9.780000 8.250000 0.460000)\n        3))\n    (branch 10 9 \n      (segment 155 \n        (point 201.000000 9.780000 8.250000 0.460000)\n        (point 198.000000 8.480000 6.130000 0.460000)\n        3)\n      (segment 156 \n        (point 198.000000 8.480000 6.130000 0.460000)\n        (point 196.470000 6.940000 6.820000 0.460000)\n        3)\n      (segment 157 \n        (point 196.470000 6.940000 6.820000 0.460000)\n        (point 196.470000 6.940000 6.800000 0.460000)\n        3)\n      (segment 158 \n        (point 196.470000 6.940000 6.800000 0.460000)\n        (point 193.400000 8.000000 7.530000 0.460000)\n        3)\n      (segment 159 \n        (point 193.400000 8.000000 7.530000 0.460000)\n        (point 191.030000 8.050000 8.900000 0.460000)\n        3)\n      (segment 160 \n        (point 191.030000 8.050000 8.900000 0.460000)\n        (point 188.530000 8.660000 9.220000 0.460000)\n        3)\n      (segment 161 \n        (point 188.530000 8.660000 9.220000 0.460000)\n        (point 184.880000 10.190000 10.100000 0.230000)\n        3)\n      (segment 162 \n        (point 184.880000 10.190000 10.100000 0.230000)\n        (point 181.480000 10.600000 10.700000 0.230000)\n        3)\n      (segment 163 \n        (point 181.480000 10.600000 10.700000 0.230000)\n        (point 179.440000 11.300000 12.100000 0.230000)\n        3)\n      (segment 164 \n        (point 179.440000 11.300000 12.100000 0.230000)\n        (point 178.230000 10.430000 13.380000 0.230000)\n        3)\n      (segment 165 \n        (point 178.230000 10.430000 13.380000 0.230000)\n        (point 178.230000 10.430000 13.350000 0.230000)\n        3)\n      (segment 166 \n        (point 178.230000 10.430000 13.350000 0.230000)\n        (point 177.150000 8.980000 14.300000 0.230000)\n        3)\n      (segment 167 \n        (point 177.150000 8.980000 14.300000 0.230000)\n        (point 176.000000 9.900000 14.900000 0.230000)\n        3)\n      (segment 168 \n        (point 176.000000 9.900000 14.900000 0.230000)\n        (point 174.830000 10.820000 14.900000 0.230000)\n        3)\n      (segment 169 \n        (point 174.830000 10.820000 14.900000 0.230000)\n        (point 172.160000 10.200000 15.880000 0.230000)\n        3)\n      (segment 170 \n        (point 172.160000 10.200000 15.880000 0.230000)\n        (point 169.390000 11.940000 16.500000 0.230000)\n        3)\n      (segment 171 \n        (point 169.390000 11.940000 16.500000 0.230000)\n        (point 167.610000 11.520000 17.600000 0.230000)\n        3)\n      (segment 172 \n        (point 167.610000 11.520000 17.600000 0.230000)\n        (point 166.120000 11.770000 18.270000 0.230000)\n        3)\n      (segment 173 \n        (point 166.120000 11.770000 18.270000 0.230000)\n        (point 163.900000 11.250000 18.480000 0.230000)\n        3)\n      (segment 174 \n        (point 163.900000 11.250000 18.480000 0.230000)\n        (point 161.530000 11.290000 19.750000 0.230000)\n        3)\n      (segment 175 \n        (point 161.530000 11.290000 19.750000 0.230000)\n        (point 161.530000 11.290000 19.730000 0.230000)\n        3)\n      (segment 176 \n        (point 161.530000 11.290000 19.730000 0.230000)\n        (point 157.690000 11.580000 19.850000 0.230000)\n        3)\n      (segment 177 \n        (point 157.690000 11.580000 19.850000 0.230000)\n        (point 154.560000 10.850000 20.250000 0.230000)\n        3)\n      (segment 178 \n        (point 154.560000 10.850000 20.250000 0.230000)\n        (point 151.710000 8.990000 20.250000 0.230000)\n        3)\n      (segment 179 \n        (point 151.710000 8.990000 20.250000 0.230000)\n        (point 151.260000 8.880000 20.250000 0.230000)\n        3)\n      (segment 180 \n        (point 151.260000 8.880000 20.250000 0.230000)\n        (point 149.020000 8.350000 20.250000 0.230000)\n        3)\n      (segment 181 \n        (point 149.020000 8.350000 20.250000 0.230000)\n        (point 145.510000 9.320000 20.920000 0.230000)\n        3)\n      (segment 182 \n        (point 145.510000 9.320000 20.920000 0.230000)\n        (point 144.480000 9.670000 23.100000 0.230000)\n        3)\n      (segment 183 \n        (point 144.480000 9.670000 23.100000 0.230000)\n        (point 140.900000 8.840000 24.000000 0.230000)\n        3)\n      (segment 184 \n        (point 140.900000 8.840000 24.000000 0.230000)\n        (point 140.900000 8.840000 23.980000 0.230000)\n        3)\n      (segment 185 \n        (point 140.900000 8.840000 23.980000 0.230000)\n        (point 139.120000 8.420000 25.130000 0.230000)\n        3)\n      (segment 186 \n        (point 139.120000 8.420000 25.130000 0.230000)\n        (point 136.300000 8.360000 25.700000 0.230000)\n        3)\n      (segment 187 \n        (point 136.300000 8.360000 25.700000 0.230000)\n        (point 136.300000 8.360000 25.730000 0.230000)\n        3))\n    (branch 11 9 \n      (segment 188 \n        (point 201.000000 9.780000 8.250000 0.460000)\n        (point 198.480000 10.400000 6.070000 0.460000)\n        3)\n      (segment 189 \n        (point 198.480000 10.400000 6.070000 0.460000)\n        (point 197.150000 10.080000 4.070000 0.460000)\n        3)\n      (segment 190 \n        (point 197.150000 10.080000 4.070000 0.460000)\n        (point 195.180000 8.420000 2.150000 0.460000)\n        3)\n      (segment 191 \n        (point 195.180000 8.420000 2.150000 0.460000)\n        (point 193.090000 7.340000 0.300000 0.460000)\n        3)\n      (segment 192 \n        (point 193.090000 7.340000 0.300000 0.460000)\n        (point 190.000000 8.410000 -0.320000 0.460000)\n        3)\n      (segment 193 \n        (point 190.000000 8.410000 -0.320000 0.460000)\n        (point 188.210000 7.990000 -0.950000 0.230000)\n        3)\n      (segment 194 \n        (point 188.210000 7.990000 -0.950000 0.230000)\n        (point 186.120000 6.900000 -0.950000 0.230000)\n        3)\n      (segment 195 \n        (point 186.120000 6.900000 -0.950000 0.230000)\n        (point 183.170000 7.410000 -1.600000 0.230000)\n        3)\n      (segment 196 \n        (point 183.170000 7.410000 -1.600000 0.230000)\n        (point 183.170000 7.410000 -1.630000 0.230000)\n        3)\n      (segment 197 \n        (point 183.170000 7.410000 -1.630000 0.230000)\n        (point 181.700000 7.650000 -3.670000 0.230000)\n        3)\n      (segment 198 \n        (point 181.700000 7.650000 -3.670000 0.230000)\n        (point 182.090000 5.960000 -5.800000 0.230000)\n        3)\n      (segment 199 \n        (point 182.090000 5.960000 -5.800000 0.230000)\n        (point 179.470000 7.130000 -7.500000 0.230000)\n        3)\n      (segment 200 \n        (point 179.470000 7.130000 -7.500000 0.230000)\n        (point 179.470000 7.130000 -7.530000 0.230000)\n        3)\n      (segment 201 \n        (point 179.470000 7.130000 -7.530000 0.230000)\n        (point 177.230000 6.610000 -8.130000 0.230000)\n        3)\n      (segment 202 \n        (point 177.230000 6.610000 -8.130000 0.230000)\n        (point 174.550000 5.980000 -8.930000 0.230000)\n        3)\n      (segment 203 \n        (point 174.550000 5.980000 -8.930000 0.230000)\n        (point 172.690000 7.930000 -10.450000 0.230000)\n        3)\n      (segment 204 \n        (point 172.690000 7.930000 -10.450000 0.230000)\n        (point 172.690000 7.930000 -10.500000 0.230000)\n        3)\n      (segment 205 \n        (point 172.690000 7.930000 -10.500000 0.230000)\n        (point 170.950000 9.310000 -11.270000 0.230000)\n        3)\n      (segment 206 \n        (point 170.950000 9.310000 -11.270000 0.230000)\n        (point 170.550000 11.010000 -12.600000 0.230000)\n        3)\n      (segment 207 \n        (point 170.550000 11.010000 -12.600000 0.230000)\n        (point 170.550000 11.010000 -12.670000 0.230000)\n        3)\n      (segment 208 \n        (point 170.550000 11.010000 -12.670000 0.230000)\n        (point 171.670000 14.260000 -14.180000 0.230000)\n        3)\n      (segment 209 \n        (point 171.670000 14.260000 -14.180000 0.230000)\n        (point 169.490000 15.530000 -15.600000 0.230000)\n        3)\n      (segment 210 \n        (point 169.490000 15.530000 -15.600000 0.230000)\n        (point 166.730000 17.280000 -16.800000 0.230000)\n        3)\n      (segment 211 \n        (point 166.730000 17.280000 -16.800000 0.230000)\n        (point 165.300000 19.340000 -18.200000 0.230000)\n        3)\n      (segment 212 \n        (point 165.300000 19.340000 -18.200000 0.230000)\n        (point 163.820000 19.590000 -19.750000 0.230000)\n        3)\n      (segment 213 \n        (point 163.820000 19.590000 -19.750000 0.230000)\n        (point 162.710000 22.310000 -20.350000 0.230000)\n        3)\n      (segment 214 \n        (point 162.710000 22.310000 -20.350000 0.230000)\n        (point 162.710000 22.310000 -20.380000 0.230000)\n        3)\n      (segment 215 \n        (point 162.710000 22.310000 -20.380000 0.230000)\n        (point 161.430000 23.800000 -22.270000 0.230000)\n        3)\n      (segment 216 \n        (point 161.430000 23.800000 -22.270000 0.230000)\n        (point 159.690000 25.190000 -23.600000 0.230000)\n        3)\n      (segment 217 \n        (point 159.690000 25.190000 -23.600000 0.230000)\n        (point 158.080000 26.010000 -25.500000 0.230000)\n        3)\n      (segment 218 \n        (point 158.080000 26.010000 -25.500000 0.230000)\n        (point 158.080000 26.010000 -25.550000 0.230000)\n        3)\n      (segment 219 \n        (point 158.080000 26.010000 -25.550000 0.230000)\n        (point 155.590000 26.620000 -26.920000 0.230000)\n        3)\n      (segment 220 \n        (point 155.590000 26.620000 -26.920000 0.230000)\n        (point 153.400000 27.890000 -28.700000 0.230000)\n        3)\n      (segment 221 \n        (point 153.400000 27.890000 -28.700000 0.230000)\n        (point 151.160000 27.370000 -30.900000 0.230000)\n        3)\n      (segment 222 \n        (point 151.160000 27.370000 -30.900000 0.230000)\n        (point 150.760000 29.070000 -32.800000 0.230000)\n        3)\n      (segment 223 \n        (point 150.760000 29.070000 -32.800000 0.230000)\n        (point 150.190000 29.530000 -35.400000 0.230000)\n        3)\n      (segment 224 \n        (point 150.190000 29.530000 -35.400000 0.230000)\n        (point 150.190000 29.530000 -35.420000 0.230000)\n        3)\n      (segment 225 \n        (point 150.190000 29.530000 -35.420000 0.230000)\n        (point 148.720000 29.790000 -37.900000 0.230000)\n        3)\n      (segment 226 \n        (point 148.720000 29.790000 -37.900000 0.230000)\n        (point 148.720000 29.790000 -37.920000 0.230000)\n        3)\n      (segment 227 \n        (point 148.720000 29.790000 -37.920000 0.230000)\n        (point 148.320000 31.480000 -41.400000 0.230000)\n        3)\n      (segment 228 \n        (point 148.320000 31.480000 -41.400000 0.230000)\n        (point 147.880000 31.380000 -43.780000 0.230000)\n        3)\n      (segment 229 \n        (point 147.880000 31.380000 -43.780000 0.230000)\n        (point 147.880000 31.380000 -43.800000 0.230000)\n        3))\n    (branch 12 8 \n      (segment 230 \n        (point 237.620000 2.250000 -3.420000 0.690000)\n        (point 237.570000 0.450000 -4.550000 0.690000)\n        3))\n    (branch 13 12 \n      (segment 231 \n        (point 237.570000 0.450000 -4.550000 0.690000)\n        (point 234.220000 2.640000 -6.250000 0.460000)\n        3)\n      (segment 232 \n        (point 234.220000 2.640000 -6.250000 0.460000)\n        (point 234.220000 2.640000 -6.280000 0.460000)\n        3)\n      (segment 233 \n        (point 234.220000 2.640000 -6.280000 0.460000)\n        (point 231.910000 4.490000 -7.550000 0.460000)\n        3)\n      (segment 234 \n        (point 231.910000 4.490000 -7.550000 0.460000)\n        (point 231.910000 4.490000 -7.570000 0.460000)\n        3)\n      (segment 235 \n        (point 231.910000 4.490000 -7.570000 0.460000)\n        (point 229.990000 4.640000 -8.930000 0.460000)\n        3)\n      (segment 236 \n        (point 229.990000 4.640000 -8.930000 0.460000)\n        (point 228.030000 2.980000 -10.170000 0.460000)\n        3)\n      (segment 237 \n        (point 228.030000 2.980000 -10.170000 0.460000)\n        (point 225.210000 2.920000 -9.880000 0.460000)\n        3)\n      (segment 238 \n        (point 225.210000 2.920000 -9.880000 0.460000)\n        (point 221.110000 4.350000 -11.370000 0.460000)\n        3)\n      (segment 239 \n        (point 221.110000 4.350000 -11.370000 0.460000)\n        (point 221.950000 2.760000 -13.230000 0.460000)\n        3)\n      (segment 240 \n        (point 221.950000 2.760000 -13.230000 0.460000)\n        (point 221.950000 2.760000 -13.250000 0.460000)\n        3)\n      (segment 241 \n        (point 221.950000 2.760000 -13.250000 0.460000)\n        (point 219.820000 5.830000 -14.250000 0.460000)\n        3)\n      (segment 242 \n        (point 219.820000 5.830000 -14.250000 0.460000)\n        (point 219.820000 5.830000 -14.270000 0.460000)\n        3)\n      (segment 243 \n        (point 219.820000 5.830000 -14.270000 0.460000)\n        (point 217.820000 8.360000 -13.820000 0.460000)\n        3)\n      (segment 244 \n        (point 217.820000 8.360000 -13.820000 0.460000)\n        (point 216.520000 9.850000 -15.570000 0.460000)\n        3)\n      (segment 245 \n        (point 216.520000 9.850000 -15.570000 0.460000)\n        (point 215.500000 10.200000 -17.670000 0.460000)\n        3)\n      (segment 246 \n        (point 215.500000 10.200000 -17.670000 0.460000)\n        (point 215.500000 10.200000 -17.700000 0.460000)\n        3)\n      (segment 247 \n        (point 215.500000 10.200000 -17.700000 0.460000)\n        (point 213.260000 9.680000 -19.330000 0.460000)\n        3)\n      (segment 248 \n        (point 213.260000 9.680000 -19.330000 0.460000)\n        (point 211.610000 8.690000 -19.600000 0.460000)\n        3)\n      (segment 249 \n        (point 211.610000 8.690000 -19.600000 0.460000)\n        (point 210.850000 7.910000 -21.100000 0.460000)\n        3)\n      (segment 250 \n        (point 210.850000 7.910000 -21.100000 0.460000)\n        (point 210.850000 7.910000 -21.130000 0.460000)\n        3)\n      (segment 251 \n        (point 210.850000 7.910000 -21.130000 0.460000)\n        (point 209.650000 7.030000 -22.920000 0.230000)\n        3)\n      (segment 252 \n        (point 209.650000 7.030000 -22.920000 0.230000)\n        (point 209.110000 9.300000 -24.900000 0.230000)\n        3)\n      (segment 253 \n        (point 209.110000 9.300000 -24.900000 0.230000)\n        (point 208.860000 10.430000 -26.900000 0.460000)\n        3)\n      (segment 254 \n        (point 208.860000 10.430000 -26.900000 0.460000)\n        (point 205.140000 10.160000 -28.130000 0.460000)\n        3)\n      (segment 255 \n        (point 205.140000 10.160000 -28.130000 0.460000)\n        (point 205.140000 10.160000 -28.150000 0.460000)\n        3)\n      (segment 256 \n        (point 205.140000 10.160000 -28.150000 0.460000)\n        (point 203.090000 10.880000 -28.150000 0.460000)\n        3)\n      (segment 257 \n        (point 203.090000 10.880000 -28.150000 0.460000)\n        (point 199.880000 12.510000 -28.150000 0.460000)\n        3))\n    (branch 14 13 \n      (segment 258 \n        (point 199.880000 12.510000 -28.150000 0.460000)\n        (point 195.010000 13.160000 -27.700000 0.230000)\n        3)\n      (segment 259 \n        (point 195.010000 13.160000 -27.700000 0.230000)\n        (point 191.490000 14.130000 -29.270000 0.230000)\n        3)\n      (segment 260 \n        (point 191.490000 14.130000 -29.270000 0.230000)\n        (point 190.320000 15.050000 -30.300000 0.230000)\n        3)\n      (segment 261 \n        (point 190.320000 15.050000 -30.300000 0.230000)\n        (point 188.330000 17.570000 -30.830000 0.230000)\n        3)\n      (segment 262 \n        (point 188.330000 17.570000 -30.830000 0.230000)\n        (point 186.060000 21.210000 -32.000000 0.230000)\n        3)\n      (segment 263 \n        (point 186.060000 21.210000 -32.000000 0.230000)\n        (point 184.770000 22.700000 -33.220000 0.230000)\n        3)\n      (segment 264 \n        (point 184.770000 22.700000 -33.220000 0.230000)\n        (point 183.610000 23.630000 -34.520000 0.230000)\n        3)\n      (segment 265 \n        (point 183.610000 23.630000 -34.520000 0.230000)\n        (point 183.610000 23.630000 -34.550000 0.230000)\n        3)\n      (segment 266 \n        (point 183.610000 23.630000 -34.550000 0.230000)\n        (point 182.370000 26.920000 -34.550000 0.230000)\n        3)\n      (segment 267 \n        (point 182.370000 26.920000 -34.550000 0.230000)\n        (point 179.970000 31.130000 -34.600000 0.230000)\n        3)\n      (segment 268 \n        (point 179.970000 31.130000 -34.600000 0.230000)\n        (point 179.970000 31.130000 -34.630000 0.230000)\n        3)\n      (segment 269 \n        (point 179.970000 31.130000 -34.630000 0.230000)\n        (point 177.840000 34.220000 -37.020000 0.230000)\n        3)\n      (segment 270 \n        (point 177.840000 34.220000 -37.020000 0.230000)\n        (point 177.840000 34.220000 -37.050000 0.230000)\n        3)\n      (segment 271 \n        (point 177.840000 34.220000 -37.050000 0.230000)\n        (point 175.640000 35.500000 -38.030000 0.230000)\n        3)\n      (segment 272 \n        (point 175.640000 35.500000 -38.030000 0.230000)\n        (point 174.400000 38.790000 -38.380000 0.230000)\n        3)\n      (segment 273 \n        (point 174.400000 38.790000 -38.380000 0.230000)\n        (point 171.190000 40.420000 -38.600000 0.230000)\n        3)\n      (segment 274 \n        (point 171.190000 40.420000 -38.600000 0.230000)\n        (point 169.640000 43.040000 -39.670000 0.230000)\n        3)\n      (segment 275 \n        (point 169.640000 43.040000 -39.670000 0.230000)\n        (point 167.590000 43.760000 -40.570000 0.230000)\n        3)\n      (segment 276 \n        (point 167.590000 43.760000 -40.570000 0.230000)\n        (point 167.590000 43.760000 -40.600000 0.230000)\n        3))\n    (branch 15 13 \n      (segment 277 \n        (point 199.880000 12.510000 -28.150000 0.460000)\n        (point 195.860000 11.570000 -30.100000 0.230000)\n        3)\n      (segment 278 \n        (point 195.860000 11.570000 -30.100000 0.230000)\n        (point 192.740000 10.840000 -32.250000 0.230000)\n        3)\n      (segment 279 \n        (point 192.740000 10.840000 -32.250000 0.230000)\n        (point 192.740000 10.840000 -32.300000 0.230000)\n        3)\n      (segment 280 \n        (point 192.740000 10.840000 -32.300000 0.230000)\n        (point 190.670000 11.540000 -33.450000 0.230000)\n        3)\n      (segment 281 \n        (point 190.670000 11.540000 -33.450000 0.230000)\n        (point 190.670000 11.540000 -33.470000 0.230000)\n        3)\n      (segment 282 \n        (point 190.670000 11.540000 -33.470000 0.230000)\n        (point 189.210000 11.810000 -35.270000 0.230000)\n        3)\n      (segment 283 \n        (point 189.210000 11.810000 -35.270000 0.230000)\n        (point 189.210000 11.810000 -35.330000 0.230000)\n        3)\n      (segment 284 \n        (point 189.210000 11.810000 -35.330000 0.230000)\n        (point 186.710000 12.420000 -36.920000 0.230000)\n        3)\n      (segment 285 \n        (point 186.710000 12.420000 -36.920000 0.230000)\n        (point 186.710000 12.420000 -36.950000 0.230000)\n        3)\n      (segment 286 \n        (point 186.710000 12.420000 -36.950000 0.230000)\n        (point 185.740000 14.560000 -37.850000 0.230000)\n        3)\n      (segment 287 \n        (point 185.740000 14.560000 -37.850000 0.230000)\n        (point 185.740000 14.560000 -37.880000 0.230000)\n        3)\n      (segment 288 \n        (point 185.740000 14.560000 -37.880000 0.230000)\n        (point 182.210000 15.540000 -39.130000 0.230000)\n        3)\n      (segment 289 \n        (point 182.210000 15.540000 -39.130000 0.230000)\n        (point 178.940000 15.370000 -40.800000 0.230000)\n        3)\n      (segment 290 \n        (point 178.940000 15.370000 -40.800000 0.230000)\n        (point 177.130000 19.120000 -41.820000 0.230000)\n        3)\n      (segment 291 \n        (point 177.130000 19.120000 -41.820000 0.230000)\n        (point 177.130000 19.120000 -41.850000 0.230000)\n        3)\n      (segment 292 \n        (point 177.130000 19.120000 -41.850000 0.230000)\n        (point 174.580000 17.930000 -44.100000 0.230000)\n        3)\n      (segment 293 \n        (point 174.580000 17.930000 -44.100000 0.230000)\n        (point 172.530000 18.650000 -46.250000 0.230000)\n        3)\n      (segment 294 \n        (point 172.530000 18.650000 -46.250000 0.230000)\n        (point 170.470000 19.350000 -48.350000 0.230000)\n        3)\n      (segment 295 \n        (point 170.470000 19.350000 -48.350000 0.230000)\n        (point 170.470000 19.350000 -48.380000 0.230000)\n        3)\n      (segment 296 \n        (point 170.470000 19.350000 -48.380000 0.230000)\n        (point 168.560000 19.500000 -50.350000 0.230000)\n        3)\n      (segment 297 \n        (point 168.560000 19.500000 -50.350000 0.230000)\n        (point 168.560000 19.500000 -50.380000 0.230000)\n        3)\n      (segment 298 \n        (point 168.560000 19.500000 -50.380000 0.230000)\n        (point 167.710000 21.100000 -52.650000 0.230000)\n        3)\n      (segment 299 \n        (point 167.710000 21.100000 -52.650000 0.230000)\n        (point 167.710000 21.100000 -52.700000 0.230000)\n        3)\n      (segment 300 \n        (point 167.710000 21.100000 -52.700000 0.230000)\n        (point 167.450000 22.220000 -54.650000 0.230000)\n        3)\n      (segment 301 \n        (point 167.450000 22.220000 -54.650000 0.230000)\n        (point 167.450000 22.220000 -54.720000 0.230000)\n        3)\n      (segment 302 \n        (point 167.450000 22.220000 -54.720000 0.230000)\n        (point 165.710000 23.610000 -56.420000 0.230000)\n        3)\n      (segment 303 \n        (point 165.710000 23.610000 -56.420000 0.230000)\n        (point 165.710000 23.610000 -56.450000 0.230000)\n        3)\n      (segment 304 \n        (point 165.710000 23.610000 -56.450000 0.230000)\n        (point 163.610000 22.520000 -58.380000 0.230000)\n        3)\n      (segment 305 \n        (point 163.610000 22.520000 -58.380000 0.230000)\n        (point 163.610000 22.520000 -58.400000 0.230000)\n        3)\n      (segment 306 \n        (point 163.610000 22.520000 -58.400000 0.230000)\n        (point 165.220000 21.710000 -60.050000 0.230000)\n        3)\n      (segment 307 \n        (point 165.220000 21.710000 -60.050000 0.230000)\n        (point 165.220000 21.710000 -60.220000 0.230000)\n        3)\n      (segment 308 \n        (point 165.220000 21.710000 -60.220000 0.230000)\n        (point 162.840000 21.750000 -62.700000 0.230000)\n        3)\n      (segment 309 \n        (point 162.840000 21.750000 -62.700000 0.230000)\n        (point 162.840000 21.750000 -62.750000 0.230000)\n        3)\n      (segment 310 \n        (point 162.840000 21.750000 -62.750000 0.230000)\n        (point 161.330000 20.200000 -66.130000 0.230000)\n        3)\n      (segment 311 \n        (point 161.330000 20.200000 -66.130000 0.230000)\n        (point 159.990000 19.890000 -69.000000 0.230000)\n        3)\n      (segment 312 \n        (point 159.990000 19.890000 -69.000000 0.230000)\n        (point 158.520000 20.140000 -69.300000 0.230000)\n        3)\n      (segment 313 \n        (point 158.520000 20.140000 -69.300000 0.230000)\n        (point 158.120000 21.830000 -71.170000 0.230000)\n        3)\n      (segment 314 \n        (point 158.120000 21.830000 -71.170000 0.230000)\n        (point 158.120000 21.830000 -71.200000 0.230000)\n        3)\n      (segment 315 \n        (point 158.120000 21.830000 -71.200000 0.230000)\n        (point 156.830000 23.330000 -73.950000 0.230000)\n        3)\n      (segment 316 \n        (point 156.830000 23.330000 -73.950000 0.230000)\n        (point 156.830000 23.330000 -73.970000 0.230000)\n        3)\n      (segment 317 \n        (point 156.830000 23.330000 -73.970000 0.230000)\n        (point 155.770000 27.850000 -76.280000 0.230000)\n        3)\n      (segment 318 \n        (point 155.770000 27.850000 -76.280000 0.230000)\n        (point 155.770000 27.850000 -76.300000 0.230000)\n        3)\n      (segment 319 \n        (point 155.770000 27.850000 -76.300000 0.230000)\n        (point 155.550000 30.790000 -78.280000 0.230000)\n        3)\n      (segment 320 \n        (point 155.550000 30.790000 -78.280000 0.230000)\n        (point 155.550000 30.790000 -78.320000 0.230000)\n        3)\n      (segment 321 \n        (point 155.550000 30.790000 -78.320000 0.230000)\n        (point 155.340000 33.710000 -80.630000 0.230000)\n        3)\n      (segment 322 \n        (point 155.340000 33.710000 -80.630000 0.230000)\n        (point 155.340000 33.710000 -80.680000 0.230000)\n        3)\n      (segment 323 \n        (point 155.340000 33.710000 -80.680000 0.230000)\n        (point 154.620000 34.750000 -84.120000 0.230000)\n        3)\n      (segment 324 \n        (point 154.620000 34.750000 -84.120000 0.230000)\n        (point 154.620000 34.750000 -84.170000 0.230000)\n        3)\n      (segment 325 \n        (point 154.620000 34.750000 -84.170000 0.230000)\n        (point 154.720000 38.360000 -86.620000 0.230000)\n        3)\n      (segment 326 \n        (point 154.720000 38.360000 -86.620000 0.230000)\n        (point 154.720000 38.360000 -86.650000 0.230000)\n        3)\n      (segment 327 \n        (point 154.720000 38.360000 -86.650000 0.230000)\n        (point 153.880000 39.950000 -89.050000 0.230000)\n        3)\n      (segment 328 \n        (point 153.880000 39.950000 -89.050000 0.230000)\n        (point 152.770000 42.680000 -92.000000 0.230000)\n        3)\n      (segment 329 \n        (point 152.770000 42.680000 -92.000000 0.230000)\n        (point 151.610000 43.590000 -94.800000 0.230000)\n        3)\n      (segment 330 \n        (point 151.610000 43.590000 -94.800000 0.230000)\n        (point 151.610000 43.590000 -94.820000 0.230000)\n        3)\n      (segment 331 \n        (point 151.610000 43.590000 -94.820000 0.230000)\n        (point 149.730000 45.550000 -96.800000 0.230000)\n        3)\n      (segment 332 \n        (point 149.730000 45.550000 -96.800000 0.230000)\n        (point 149.920000 46.780000 -99.050000 0.230000)\n        3)\n      (segment 333 \n        (point 149.920000 46.780000 -99.050000 0.230000)\n        (point 147.600000 48.620000 -102.630000 0.230000)\n        3)\n      (segment 334 \n        (point 147.600000 48.620000 -102.630000 0.230000)\n        (point 147.600000 48.620000 -102.650000 0.230000)\n        3))\n    (branch 16 12 \n      (segment 335 \n        (point 237.570000 0.450000 -4.550000 0.690000)\n        (point 234.310000 0.280000 -6.780000 0.460000)\n        3)\n      (segment 336 \n        (point 234.310000 0.280000 -6.780000 0.460000)\n        (point 232.980000 -0.040000 -7.430000 0.460000)\n        3)\n      (segment 337 \n        (point 232.980000 -0.040000 -7.430000 0.460000)\n        (point 231.720000 -2.720000 -8.000000 0.460000)\n        3)\n      (segment 338 \n        (point 231.720000 -2.720000 -8.000000 0.460000)\n        (point 231.720000 -2.720000 -8.020000 0.460000)\n        3)\n      (segment 339 \n        (point 231.720000 -2.720000 -8.020000 0.460000)\n        (point 230.920000 -5.300000 -8.500000 0.460000)\n        3)\n      (segment 340 \n        (point 230.920000 -5.300000 -8.500000 0.460000)\n        (point 229.120000 -5.720000 -8.500000 0.460000)\n        3)\n      (segment 341 \n        (point 229.120000 -5.720000 -8.500000 0.460000)\n        (point 227.290000 -7.940000 -8.500000 0.460000)\n        3)\n      (segment 342 \n        (point 227.290000 -7.940000 -8.500000 0.460000)\n        (point 227.320000 -12.110000 -9.600000 0.460000)\n        3)\n      (segment 343 \n        (point 227.320000 -12.110000 -9.600000 0.460000)\n        (point 227.320000 -12.110000 -9.630000 0.460000)\n        3)\n      (segment 344 \n        (point 227.320000 -12.110000 -9.630000 0.460000)\n        (point 227.040000 -16.950000 -10.200000 0.460000)\n        3)\n      (segment 345 \n        (point 227.040000 -16.950000 -10.200000 0.460000)\n        (point 226.690000 -19.430000 -10.700000 0.460000)\n        3)\n      (segment 346 \n        (point 226.690000 -19.430000 -10.700000 0.460000)\n        (point 224.900000 -19.850000 -11.020000 0.460000)\n        3)\n      (segment 347 \n        (point 224.900000 -19.850000 -11.020000 0.460000)\n        (point 224.350000 -23.550000 -11.070000 0.460000)\n        3)\n      (segment 348 \n        (point 224.350000 -23.550000 -11.070000 0.460000)\n        (point 223.230000 -26.790000 -11.070000 0.460000)\n        3)\n      (segment 349 \n        (point 223.230000 -26.790000 -11.070000 0.460000)\n        (point 221.080000 -29.700000 -11.370000 0.460000)\n        3)\n      (segment 350 \n        (point 221.080000 -29.700000 -11.370000 0.460000)\n        (point 219.500000 -33.060000 -11.370000 0.460000)\n        3)\n      (segment 351 \n        (point 219.500000 -33.060000 -11.370000 0.460000)\n        (point 219.060000 -33.160000 -11.370000 0.460000)\n        3)\n      (segment 352 \n        (point 219.060000 -33.160000 -11.370000 0.460000)\n        (point 217.570000 -38.870000 -11.370000 0.460000)\n        3)\n      (segment 353 \n        (point 217.570000 -38.870000 -11.370000 0.460000)\n        (point 215.880000 -41.660000 -11.600000 0.460000)\n        3)\n      (segment 354 \n        (point 215.880000 -41.660000 -11.600000 0.460000)\n        (point 214.870000 -45.480000 -11.400000 0.460000)\n        3)\n      (segment 355 \n        (point 214.870000 -45.480000 -11.400000 0.460000)\n        (point 214.870000 -45.480000 -11.420000 0.460000)\n        3)\n      (segment 356 \n        (point 214.870000 -45.480000 -11.420000 0.460000)\n        (point 214.790000 -49.080000 -13.130000 0.460000)\n        3)\n      (segment 357 \n        (point 214.790000 -49.080000 -13.130000 0.460000)\n        (point 214.590000 -50.330000 -13.720000 0.460000)\n        3)\n      (segment 358 \n        (point 214.590000 -50.330000 -13.720000 0.460000)\n        (point 214.590000 -50.330000 -13.750000 0.460000)\n        3)\n      (segment 359 \n        (point 214.590000 -50.330000 -13.750000 0.460000)\n        (point 215.310000 -51.360000 -15.880000 0.460000)\n        3)\n      (segment 360 \n        (point 215.310000 -51.360000 -15.880000 0.460000)\n        (point 214.550000 -52.140000 -17.820000 0.460000)\n        3)\n      (segment 361 \n        (point 214.550000 -52.140000 -17.820000 0.460000)\n        (point 214.550000 -52.140000 -17.850000 0.460000)\n        3)\n      (segment 362 \n        (point 214.550000 -52.140000 -17.850000 0.460000)\n        (point 213.030000 -53.690000 -19.270000 0.460000)\n        3)\n      (segment 363 \n        (point 213.030000 -53.690000 -19.270000 0.460000)\n        (point 210.350000 -54.310000 -20.500000 0.460000)\n        3)\n      (segment 364 \n        (point 210.350000 -54.310000 -20.500000 0.460000)\n        (point 208.820000 -55.860000 -22.220000 0.460000)\n        3)\n      (segment 365 \n        (point 208.820000 -55.860000 -22.220000 0.460000)\n        (point 206.780000 -55.150000 -24.150000 0.460000)\n        3)\n      (segment 366 \n        (point 206.780000 -55.150000 -24.150000 0.460000)\n        (point 205.170000 -54.330000 -25.920000 0.460000)\n        3)\n      (segment 367 \n        (point 205.170000 -54.330000 -25.920000 0.460000)\n        (point 203.700000 -54.090000 -27.800000 0.460000)\n        3)\n      (segment 368 \n        (point 203.700000 -54.090000 -27.800000 0.460000)\n        (point 201.740000 -55.740000 -29.000000 0.460000)\n        3)\n      (segment 369 \n        (point 201.740000 -55.740000 -29.000000 0.460000)\n        (point 201.740000 -55.740000 -29.020000 0.460000)\n        3)\n      (segment 370 \n        (point 201.740000 -55.740000 -29.020000 0.460000)\n        (point 199.050000 -56.360000 -30.130000 0.460000)\n        3)\n      (segment 371 \n        (point 199.050000 -56.360000 -30.130000 0.460000)\n        (point 196.810000 -56.890000 -30.130000 0.460000)\n        3)\n      (segment 372 \n        (point 196.810000 -56.890000 -30.130000 0.460000)\n        (point 194.720000 -57.970000 -30.300000 0.230000)\n        3)\n      (segment 373 \n        (point 194.720000 -57.970000 -30.300000 0.230000)\n        (point 192.750000 -59.630000 -30.300000 0.230000)\n        3)\n      (segment 374 \n        (point 192.750000 -59.630000 -30.300000 0.230000)\n        (point 189.630000 -60.360000 -30.300000 0.230000)\n        3)\n      (segment 375 \n        (point 189.630000 -60.360000 -30.300000 0.230000)\n        (point 187.520000 -61.460000 -32.280000 0.230000)\n        3)\n      (segment 376 \n        (point 187.520000 -61.460000 -32.280000 0.230000)\n        (point 185.290000 -61.970000 -33.920000 0.230000)\n        3)\n      (segment 377 \n        (point 185.290000 -61.970000 -33.920000 0.230000)\n        (point 183.630000 -62.970000 -35.830000 0.230000)\n        3)\n      (segment 378 \n        (point 183.630000 -62.970000 -35.830000 0.230000)\n        (point 183.580000 -64.770000 -38.000000 0.230000)\n        3)\n      (segment 379 \n        (point 183.580000 -64.770000 -38.000000 0.230000)\n        (point 183.580000 -64.770000 -38.030000 0.230000)\n        3)\n      (segment 380 \n        (point 183.580000 -64.770000 -38.030000 0.230000)\n        (point 182.520000 -66.210000 -38.200000 0.230000)\n        3)\n      (segment 381 \n        (point 182.520000 -66.210000 -38.200000 0.230000)\n        (point 180.540000 -67.870000 -39.780000 0.230000)\n        3)\n      (segment 382 \n        (point 180.540000 -67.870000 -39.780000 0.230000)\n        (point 178.260000 -70.200000 -40.600000 0.230000)\n        3)\n      (segment 383 \n        (point 178.260000 -70.200000 -40.600000 0.230000)\n        (point 176.740000 -71.750000 -40.830000 0.230000)\n        3)\n      (segment 384 \n        (point 176.740000 -71.750000 -40.830000 0.230000)\n        (point 175.040000 -74.530000 -43.180000 0.230000)\n        3)\n      (segment 385 \n        (point 175.040000 -74.530000 -43.180000 0.230000)\n        (point 173.660000 -76.650000 -44.450000 0.230000)\n        3)\n      (segment 386 \n        (point 173.660000 -76.650000 -44.450000 0.230000)\n        (point 173.280000 -79.120000 -46.930000 0.230000)\n        3)\n      (segment 387 \n        (point 173.280000 -79.120000 -46.930000 0.230000)\n        (point 171.990000 -77.630000 -49.520000 0.230000)\n        3)\n      (segment 388 \n        (point 171.990000 -77.630000 -49.520000 0.230000)\n        (point 170.660000 -77.940000 -52.100000 0.230000)\n        3)\n      (segment 389 \n        (point 170.660000 -77.940000 -52.100000 0.230000)\n        (point 170.660000 -77.940000 -52.130000 0.230000)\n        3)\n      (segment 390 \n        (point 170.660000 -77.940000 -52.130000 0.230000)\n        (point 169.630000 -77.590000 -55.030000 0.230000)\n        3)\n      (segment 391 \n        (point 169.630000 -77.590000 -55.030000 0.230000)\n        (point 169.630000 -77.590000 -55.050000 0.230000)\n        3)\n      (segment 392 \n        (point 169.630000 -77.590000 -55.050000 0.230000)\n        (point 169.050000 -77.130000 -58.500000 0.230000)\n        3)\n      (segment 393 \n        (point 169.050000 -77.130000 -58.500000 0.230000)\n        (point 167.890000 -76.210000 -62.470000 0.230000)\n        3)\n      (segment 394 \n        (point 167.890000 -76.210000 -62.470000 0.230000)\n        (point 167.630000 -75.080000 -67.170000 0.230000)\n        3)\n      (segment 395 \n        (point 167.630000 -75.080000 -67.170000 0.230000)\n        (point 167.630000 -75.080000 -67.280000 0.230000)\n        3))\n    (branch 17 12 \n      (segment 396 \n        (point 237.570000 0.450000 -4.550000 0.690000)\n        (point 236.330000 -2.030000 -4.550000 0.460000)\n        3)\n      (segment 397 \n        (point 236.330000 -2.030000 -4.550000 0.460000)\n        (point 235.120000 -2.920000 -4.350000 0.460000)\n        3)\n      (segment 398 \n        (point 235.120000 -2.920000 -4.350000 0.460000)\n        (point 235.120000 -2.920000 -4.400000 0.460000)\n        3)\n      (segment 399 \n        (point 235.120000 -2.920000 -4.400000 0.460000)\n        (point 234.190000 -4.930000 -3.170000 0.460000)\n        3)\n      (segment 400 \n        (point 234.190000 -4.930000 -3.170000 0.460000)\n        (point 234.450000 -6.060000 -1.700000 0.460000)\n        3)\n      (segment 401 \n        (point 234.450000 -6.060000 -1.700000 0.460000)\n        (point 233.370000 -7.500000 -0.400000 0.460000)\n        3))\n    (branch 18 17 \n      (segment 402 \n        (point 233.370000 -7.500000 -0.400000 0.460000)\n        (point 233.180000 -8.740000 0.950000 0.460000)\n        3)\n      (segment 403 \n        (point 233.180000 -8.740000 0.950000 0.460000)\n        (point 234.030000 -10.340000 2.050000 0.460000)\n        3)\n      (segment 404 \n        (point 234.030000 -10.340000 2.050000 0.460000)\n        (point 235.200000 -11.260000 3.380000 0.460000)\n        3)\n      (segment 405 \n        (point 235.200000 -11.260000 3.380000 0.460000)\n        (point 235.200000 -11.260000 3.350000 0.460000)\n        3)\n      (segment 406 \n        (point 235.200000 -11.260000 3.350000 0.460000)\n        (point 235.910000 -12.280000 4.430000 0.460000)\n        3)\n      (segment 407 \n        (point 235.910000 -12.280000 4.430000 0.460000)\n        (point 236.750000 -13.880000 5.150000 0.460000)\n        3)\n      (segment 408 \n        (point 236.750000 -13.880000 5.150000 0.460000)\n        (point 237.280000 -16.140000 5.850000 0.460000)\n        3)\n      (segment 409 \n        (point 237.280000 -16.140000 5.850000 0.460000)\n        (point 237.370000 -18.510000 6.570000 0.460000)\n        3)\n      (segment 410 \n        (point 237.370000 -18.510000 6.570000 0.460000)\n        (point 235.840000 -20.070000 8.350000 0.460000)\n        3)\n      (segment 411 \n        (point 235.840000 -20.070000 8.350000 0.460000)\n        (point 235.080000 -20.840000 9.200000 0.460000)\n        3)\n      (segment 412 \n        (point 235.080000 -20.840000 9.200000 0.460000)\n        (point 235.170000 -23.210000 8.800000 0.460000)\n        3)\n      (segment 413 \n        (point 235.170000 -23.210000 8.800000 0.460000)\n        (point 235.250000 -25.570000 10.500000 0.460000)\n        3)\n      (segment 414 \n        (point 235.250000 -25.570000 10.500000 0.460000)\n        (point 235.470000 -28.510000 11.350000 0.460000)\n        3)\n      (segment 415 \n        (point 235.470000 -28.510000 11.350000 0.460000)\n        (point 234.970000 -30.410000 11.070000 0.460000)\n        3)\n      (segment 416 \n        (point 234.970000 -30.410000 11.070000 0.460000)\n        (point 235.950000 -32.580000 12.250000 0.460000)\n        3)\n      (segment 417 \n        (point 235.950000 -32.580000 12.250000 0.460000)\n        (point 235.950000 -32.580000 12.220000 0.460000)\n        3)\n      (segment 418 \n        (point 235.950000 -32.580000 12.220000 0.460000)\n        (point 235.590000 -35.060000 12.500000 0.460000)\n        3)\n      (segment 419 \n        (point 235.590000 -35.060000 12.500000 0.460000)\n        (point 234.200000 -37.160000 12.070000 0.460000)\n        3)\n      (segment 420 \n        (point 234.200000 -37.160000 12.070000 0.460000)\n        (point 234.200000 -37.160000 12.050000 0.460000)\n        3)\n      (segment 421 \n        (point 234.200000 -37.160000 12.050000 0.460000)\n        (point 233.750000 -37.270000 14.300000 0.460000)\n        3)\n      (segment 422 \n        (point 233.750000 -37.270000 14.300000 0.460000)\n        (point 234.650000 -37.060000 16.570000 0.460000)\n        3)\n      (segment 423 \n        (point 234.650000 -37.060000 16.570000 0.460000)\n        (point 235.360000 -38.090000 18.670000 0.460000)\n        3)\n      (segment 424 \n        (point 235.360000 -38.090000 18.670000 0.460000)\n        (point 233.830000 -39.650000 20.170000 0.460000)\n        3)\n      (segment 425 \n        (point 233.830000 -39.650000 20.170000 0.460000)\n        (point 233.600000 -42.680000 20.970000 0.460000)\n        3)\n      (segment 426 \n        (point 233.600000 -42.680000 20.970000 0.460000)\n        (point 232.350000 -45.370000 21.880000 0.460000)\n        3)\n      (segment 427 \n        (point 232.350000 -45.370000 21.880000 0.460000)\n        (point 230.330000 -48.830000 21.550000 0.460000)\n        3)\n      (segment 428 \n        (point 230.330000 -48.830000 21.550000 0.460000)\n        (point 230.330000 -48.830000 21.520000 0.460000)\n        3)\n      (segment 429 \n        (point 230.330000 -48.830000 21.520000 0.460000)\n        (point 229.790000 -52.530000 22.080000 0.460000)\n        3)\n      (segment 430 \n        (point 229.790000 -52.530000 22.080000 0.460000)\n        (point 227.640000 -55.430000 22.570000 0.460000)\n        3)\n      (segment 431 \n        (point 227.640000 -55.430000 22.570000 0.460000)\n        (point 226.570000 -56.870000 23.330000 0.460000)\n        3)\n      (segment 432 \n        (point 226.570000 -56.870000 23.330000 0.460000)\n        (point 224.920000 -57.850000 23.800000 0.460000)\n        3)\n      (segment 433 \n        (point 224.920000 -57.850000 23.800000 0.460000)\n        (point 224.920000 -57.850000 23.780000 0.460000)\n        3)\n      (segment 434 \n        (point 224.920000 -57.850000 23.780000 0.460000)\n        (point 224.110000 -60.440000 24.380000 0.460000)\n        3)\n      (segment 435 \n        (point 224.110000 -60.440000 24.380000 0.460000)\n        (point 224.110000 -60.440000 24.350000 0.460000)\n        3)\n      (segment 436 \n        (point 224.110000 -60.440000 24.350000 0.460000)\n        (point 225.530000 -62.480000 25.230000 0.460000)\n        3)\n      (segment 437 \n        (point 225.530000 -62.480000 25.230000 0.460000)\n        (point 223.640000 -66.510000 26.450000 0.460000)\n        3)\n      (segment 438 \n        (point 223.640000 -66.510000 26.450000 0.460000)\n        (point 223.410000 -69.550000 27.250000 0.460000)\n        3)\n      (segment 439 \n        (point 223.410000 -69.550000 27.250000 0.460000)\n        (point 222.290000 -72.800000 27.770000 0.460000)\n        3)\n      (segment 440 \n        (point 222.290000 -72.800000 27.770000 0.460000)\n        (point 220.270000 -76.250000 29.130000 0.460000)\n        3)\n      (segment 441 \n        (point 220.270000 -76.250000 29.130000 0.460000)\n        (point 218.930000 -76.570000 30.650000 0.460000)\n        3)\n      (segment 442 \n        (point 218.930000 -76.570000 30.650000 0.460000)\n        (point 217.550000 -78.690000 31.950000 0.460000)\n        3)\n      (segment 443 \n        (point 217.550000 -78.690000 31.950000 0.460000)\n        (point 216.470000 -80.140000 33.850000 0.460000)\n        3)\n      (segment 444 \n        (point 216.470000 -80.140000 33.850000 0.460000)\n        (point 216.020000 -80.240000 34.400000 0.460000)\n        3))\n    (branch 19 17 \n      (segment 445 \n        (point 233.370000 -7.500000 -0.400000 0.460000)\n        (point 231.220000 -10.400000 -1.420000 0.460000)\n        3)\n      (segment 446 \n        (point 231.220000 -10.400000 -1.420000 0.460000)\n        (point 231.890000 -13.230000 -2.420000 0.460000)\n        3)\n      (segment 447 \n        (point 231.890000 -13.230000 -2.420000 0.460000)\n        (point 231.890000 -13.230000 -2.450000 0.460000)\n        3)\n      (segment 448 \n        (point 231.890000 -13.230000 -2.450000 0.460000)\n        (point 231.660000 -16.270000 -1.650000 0.460000)\n        3)\n      (segment 449 \n        (point 231.660000 -16.270000 -1.650000 0.460000)\n        (point 230.850000 -18.850000 -0.470000 0.460000)\n        3)\n      (segment 450 \n        (point 230.850000 -18.850000 -0.470000 0.460000)\n        (point 229.600000 -21.530000 0.170000 0.460000)\n        3)\n      (segment 451 \n        (point 229.600000 -21.530000 0.170000 0.460000)\n        (point 227.440000 -24.420000 0.950000 0.460000)\n        3)\n      (segment 452 \n        (point 227.440000 -24.420000 0.950000 0.460000)\n        (point 226.230000 -25.310000 1.950000 0.460000)\n        3)\n      (segment 453 \n        (point 226.230000 -25.310000 1.950000 0.460000)\n        (point 224.980000 -27.980000 3.080000 0.460000)\n        3)\n      (segment 454 \n        (point 224.980000 -27.980000 3.080000 0.460000)\n        (point 223.720000 -30.660000 4.350000 0.460000)\n        3)\n      (segment 455 \n        (point 223.720000 -30.660000 4.350000 0.460000)\n        (point 222.820000 -30.870000 5.550000 0.460000)\n        3)\n      (segment 456 \n        (point 222.820000 -30.870000 5.550000 0.460000)\n        (point 221.710000 -34.120000 4.380000 0.460000)\n        3)\n      (segment 457 \n        (point 221.710000 -34.120000 4.380000 0.460000)\n        (point 220.140000 -37.470000 4.380000 0.460000)\n        3)\n      (segment 458 \n        (point 220.140000 -37.470000 4.380000 0.460000)\n        (point 218.440000 -40.270000 4.380000 0.460000)\n        3)\n      (segment 459 \n        (point 218.440000 -40.270000 4.380000 0.460000)\n        (point 219.100000 -43.100000 5.370000 0.460000)\n        3)\n      (segment 460 \n        (point 219.100000 -43.100000 5.370000 0.460000)\n        (point 220.340000 -46.380000 6.070000 0.460000)\n        3)\n      (segment 461 \n        (point 220.340000 -46.380000 6.070000 0.460000)\n        (point 220.340000 -46.380000 5.220000 0.460000)\n        3)\n      (segment 462 \n        (point 220.340000 -46.380000 5.220000 0.460000)\n        (point 220.870000 -48.660000 4.500000 0.460000)\n        3)\n      (segment 463 \n        (point 220.870000 -48.660000 4.500000 0.460000)\n        (point 220.870000 -48.660000 4.220000 0.460000)\n        3)\n      (segment 464 \n        (point 220.870000 -48.660000 4.220000 0.460000)\n        (point 221.850000 -50.820000 3.350000 0.460000)\n        3)\n      (segment 465 \n        (point 221.850000 -50.820000 3.350000 0.460000)\n        (point 220.540000 -55.290000 2.830000 0.460000)\n        3)\n      (segment 466 \n        (point 220.540000 -55.290000 2.830000 0.460000)\n        (point 217.810000 -57.740000 1.670000 0.460000)\n        3)\n      (segment 467 \n        (point 217.810000 -57.740000 1.670000 0.460000)\n        (point 217.810000 -57.740000 1.650000 0.460000)\n        3)\n      (segment 468 \n        (point 217.810000 -57.740000 1.650000 0.460000)\n        (point 215.530000 -60.050000 1.420000 0.460000)\n        3)\n      (segment 469 \n        (point 215.530000 -60.050000 1.420000 0.460000)\n        (point 215.560000 -64.230000 0.450000 0.460000)\n        3)\n      (segment 470 \n        (point 215.560000 -64.230000 0.450000 0.460000)\n        (point 214.760000 -66.810000 0.220000 0.460000)\n        3)\n      (segment 471 \n        (point 214.760000 -66.810000 0.220000 0.460000)\n        (point 214.760000 -66.810000 0.170000 0.460000)\n        3)\n      (segment 472 \n        (point 214.760000 -66.810000 0.170000 0.460000)\n        (point 213.770000 -70.620000 -0.670000 0.460000)\n        3)\n      (segment 473 \n        (point 213.770000 -70.620000 -0.670000 0.460000)\n        (point 213.770000 -70.620000 -0.720000 0.460000)\n        3)\n      (segment 474 \n        (point 213.770000 -70.620000 -0.720000 0.460000)\n        (point 213.540000 -73.660000 -1.170000 0.460000)\n        3)\n      (segment 475 \n        (point 213.540000 -73.660000 -1.170000 0.460000)\n        (point 214.510000 -75.820000 -1.170000 0.460000)\n        3)\n      (segment 476 \n        (point 214.510000 -75.820000 -1.170000 0.460000)\n        (point 213.350000 -80.870000 -1.470000 0.460000)\n        3)\n      (segment 477 \n        (point 213.350000 -80.870000 -1.470000 0.460000)\n        (point 212.800000 -84.590000 -1.600000 0.460000)\n        3)\n      (segment 478 \n        (point 212.800000 -84.590000 -1.600000 0.460000)\n        (point 212.070000 -89.530000 -1.600000 0.460000)\n        3)\n      (segment 479 \n        (point 212.070000 -89.530000 -1.600000 0.460000)\n        (point 212.420000 -93.040000 -2.750000 0.460000)\n        3)\n      (segment 480 \n        (point 212.420000 -93.040000 -2.750000 0.460000)\n        (point 210.540000 -97.060000 -3.780000 0.460000)\n        3)\n      (segment 481 \n        (point 210.540000 -97.060000 -3.780000 0.460000)\n        (point 209.860000 -100.190000 -3.780000 0.460000)\n        3)\n      (segment 482 \n        (point 209.860000 -100.190000 -3.780000 0.460000)\n        (point 209.140000 -105.150000 -4.780000 0.460000)\n        3)\n      (segment 483 \n        (point 209.140000 -105.150000 -4.780000 0.460000)\n        (point 209.350000 -108.080000 -3.600000 0.460000)\n        3)\n      (segment 484 \n        (point 209.350000 -108.080000 -3.600000 0.460000)\n        (point 208.940000 -112.350000 -2.220000 0.460000)\n        3)\n      (segment 485 \n        (point 208.940000 -112.350000 -2.220000 0.460000)\n        (point 208.990000 -116.530000 -1.320000 0.460000)\n        3)\n      (segment 486 \n        (point 208.990000 -116.530000 -1.320000 0.460000)\n        (point 210.350000 -120.380000 -0.450000 0.460000)\n        3)\n      (segment 487 \n        (point 210.350000 -120.380000 -0.450000 0.460000)\n        (point 211.150000 -123.780000 0.320000 0.460000)\n        3)\n      (segment 488 \n        (point 211.150000 -123.780000 0.320000 0.460000)\n        (point 212.080000 -127.750000 1.070000 0.460000)\n        3)\n      (segment 489 \n        (point 212.080000 -127.750000 1.070000 0.460000)\n        (point 209.800000 -130.070000 1.900000 0.460000)\n        3)\n      (segment 490 \n        (point 209.800000 -130.070000 1.900000 0.460000)\n        (point 207.430000 -130.030000 2.920000 0.460000)\n        3)\n      (segment 491 \n        (point 207.430000 -130.030000 2.920000 0.460000)\n        (point 206.040000 -132.150000 3.650000 0.460000)\n        3)\n      (segment 492 \n        (point 206.040000 -132.150000 3.650000 0.460000)\n        (point 206.040000 -132.150000 3.700000 0.460000)\n        3)\n      (segment 493 \n        (point 206.040000 -132.150000 3.700000 0.460000)\n        (point 204.570000 -131.890000 4.630000 0.460000)\n        3)\n      (segment 494 \n        (point 204.570000 -131.890000 4.630000 0.460000)\n        (point 202.920000 -132.880000 5.200000 0.460000)\n        3)\n      (segment 495 \n        (point 202.920000 -132.880000 5.200000 0.460000)\n        (point 202.560000 -135.350000 4.630000 0.460000)\n        3)\n      (segment 496 \n        (point 202.560000 -135.350000 4.630000 0.460000)\n        (point 202.560000 -135.350000 4.600000 0.460000)\n        3)\n      (segment 497 \n        (point 202.560000 -135.350000 4.600000 0.460000)\n        (point 203.270000 -136.370000 4.820000 0.460000)\n        3)\n      (segment 498 \n        (point 203.270000 -136.370000 4.820000 0.460000)\n        (point 204.510000 -139.670000 6.020000 0.460000)\n        3)\n      (segment 499 \n        (point 204.510000 -139.670000 6.020000 0.460000)\n        (point 202.180000 -143.800000 7.100000 0.460000)\n        3)\n      (segment 500 \n        (point 202.180000 -143.800000 7.100000 0.460000)\n        (point 201.770000 -148.070000 8.450000 0.460000)\n        3)\n      (segment 501 \n        (point 201.770000 -148.070000 8.450000 0.460000)\n        (point 202.300000 -150.350000 9.770000 0.460000)\n        3)\n      (segment 502 \n        (point 202.300000 -150.350000 9.770000 0.460000)\n        (point 203.010000 -151.370000 12.000000 0.460000)\n        3)\n      (segment 503 \n        (point 203.010000 -151.370000 12.000000 0.460000)\n        (point 203.590000 -151.830000 13.300000 0.460000)\n        3))\n    (branch 20 7 \n      (segment 504 \n        (point 251.830000 7.960000 4.070000 1.375000)\n        (point 250.100000 9.550000 4.750000 0.460000)\n        3)\n      (segment 505 \n        (point 250.100000 9.550000 4.750000 0.460000)\n        (point 247.610000 10.160000 6.350000 0.460000)\n        3)\n      (segment 506 \n        (point 247.610000 10.160000 6.350000 0.460000)\n        (point 245.280000 12.010000 6.970000 0.460000)\n        3)\n      (segment 507 \n        (point 245.280000 12.010000 6.970000 0.460000)\n        (point 243.420000 13.950000 8.520000 0.460000)\n        3)\n      (segment 508 \n        (point 243.420000 13.950000 8.520000 0.460000)\n        (point 244.490000 15.400000 9.770000 0.460000)\n        3)\n      (segment 509 \n        (point 244.490000 15.400000 9.770000 0.460000)\n        (point 246.990000 14.790000 10.120000 0.460000)\n        3)\n      (segment 510 \n        (point 246.990000 14.790000 10.120000 0.460000)\n        (point 245.920000 13.350000 11.470000 0.460000)\n        3)\n      (segment 511 \n        (point 245.920000 13.350000 11.470000 0.460000)\n        (point 244.260000 12.360000 14.880000 0.460000)\n        3))\n    (branch 21 20 \n      (segment 512 \n        (point 244.260000 12.360000 14.880000 0.460000)\n        (point 245.340000 13.800000 14.880000 0.460000)\n        3)\n      (segment 513 \n        (point 245.340000 13.800000 14.880000 0.460000)\n        (point 241.540000 15.910000 14.450000 0.460000)\n        3)\n      (segment 514 \n        (point 241.540000 15.910000 14.450000 0.460000)\n        (point 240.080000 16.160000 14.850000 0.460000)\n        3)\n      (segment 515 \n        (point 240.080000 16.160000 14.850000 0.460000)\n        (point 240.080000 16.160000 14.820000 0.460000)\n        3)\n      (segment 516 \n        (point 240.080000 16.160000 14.820000 0.460000)\n        (point 239.860000 19.090000 16.600000 0.460000)\n        3)\n      (segment 517 \n        (point 239.860000 19.090000 16.600000 0.460000)\n        (point 239.460000 20.790000 18.380000 0.460000)\n        3)\n      (segment 518 \n        (point 239.460000 20.790000 18.380000 0.460000)\n        (point 238.220000 24.080000 19.520000 0.460000)\n        3)\n      (segment 519 \n        (point 238.220000 24.080000 19.520000 0.460000)\n        (point 236.210000 26.600000 20.750000 0.460000)\n        3)\n      (segment 520 \n        (point 236.210000 26.600000 20.750000 0.460000)\n        (point 234.080000 29.690000 20.920000 0.460000)\n        3)\n      (segment 521 \n        (point 234.080000 29.690000 20.920000 0.460000)\n        (point 231.760000 31.530000 20.130000 0.460000)\n        3))\n    (branch 22 21 \n      (segment 522 \n        (point 231.760000 31.530000 20.130000 0.460000)\n        (point 229.930000 35.270000 20.850000 0.230000)\n        3)\n      (segment 523 \n        (point 229.930000 35.270000 20.850000 0.230000)\n        (point 227.310000 36.460000 21.850000 0.230000)\n        3)\n      (segment 524 \n        (point 227.310000 36.460000 21.850000 0.230000)\n        (point 224.860000 38.860000 22.670000 0.230000)\n        3)\n      (segment 525 \n        (point 224.860000 38.860000 22.670000 0.230000)\n        (point 223.840000 39.220000 24.200000 0.230000)\n        3)\n      (segment 526 \n        (point 223.840000 39.220000 24.200000 0.230000)\n        (point 223.390000 39.120000 24.200000 0.230000)\n        3)\n      (segment 527 \n        (point 223.390000 39.120000 24.200000 0.230000)\n        (point 222.630000 38.350000 26.350000 0.230000)\n        3))\n    (branch 23 21 \n      (segment 528 \n        (point 231.760000 31.530000 20.130000 0.460000)\n        (point 228.420000 33.730000 19.230000 0.460000)\n        3)\n      (segment 529 \n        (point 228.420000 33.730000 19.230000 0.460000)\n        (point 226.230000 35.010000 19.580000 0.460000)\n        3)\n      (segment 530 \n        (point 226.230000 35.010000 19.580000 0.460000)\n        (point 226.230000 35.010000 19.550000 0.460000)\n        3)\n      (segment 531 \n        (point 226.230000 35.010000 19.550000 0.460000)\n        (point 224.630000 35.830000 21.100000 0.460000)\n        3)\n      (segment 532 \n        (point 224.630000 35.830000 21.100000 0.460000)\n        (point 222.000000 37.000000 23.000000 0.460000)\n        3)\n      (segment 533 \n        (point 222.000000 37.000000 23.000000 0.460000)\n        (point 219.990000 39.530000 24.080000 0.460000)\n        3)\n      (segment 534 \n        (point 219.990000 39.530000 24.080000 0.460000)\n        (point 217.410000 42.500000 25.600000 0.460000)\n        3))\n    (branch 24 20 \n      (segment 535 \n        (point 244.260000 12.360000 14.880000 0.460000)\n        (point 242.340000 12.510000 14.880000 0.460000)\n        3)\n      (segment 536 \n        (point 242.340000 12.510000 14.880000 0.460000)\n        (point 242.290000 10.710000 16.380000 0.460000)\n        3)\n      (segment 537 \n        (point 242.290000 10.710000 16.380000 0.460000)\n        (point 242.290000 10.710000 16.350000 0.460000)\n        3)\n      (segment 538 \n        (point 242.290000 10.710000 16.350000 0.460000)\n        (point 239.400000 13.010000 18.270000 0.460000)\n        3)\n      (segment 539 \n        (point 239.400000 13.010000 18.270000 0.460000)\n        (point 239.090000 12.340000 20.000000 0.460000)\n        3)\n      (segment 540 \n        (point 239.090000 12.340000 20.000000 0.460000)\n        (point 237.310000 11.930000 20.770000 0.460000)\n        3)\n      (segment 541 \n        (point 237.310000 11.930000 20.770000 0.460000)\n        (point 235.320000 10.270000 22.300000 0.460000)\n        3)\n      (segment 542 \n        (point 235.320000 10.270000 22.300000 0.460000)\n        (point 233.360000 8.610000 23.250000 0.460000)\n        3)\n      (segment 543 \n        (point 233.360000 8.610000 23.250000 0.460000)\n        (point 233.360000 8.610000 23.230000 0.460000)\n        3)\n      (segment 544 \n        (point 233.360000 8.610000 23.230000 0.460000)\n        (point 230.610000 10.350000 24.600000 0.460000)\n        3)\n      (segment 545 \n        (point 230.610000 10.350000 24.600000 0.460000)\n        (point 228.990000 11.180000 25.470000 0.460000)\n        3)\n      (segment 546 \n        (point 228.990000 11.180000 25.470000 0.460000)\n        (point 227.290000 8.380000 26.600000 0.460000)\n        3)\n      (segment 547 \n        (point 227.290000 8.380000 26.600000 0.460000)\n        (point 224.080000 10.020000 27.630000 0.460000)\n        3)\n      (segment 548 \n        (point 224.080000 10.020000 27.630000 0.460000)\n        (point 221.320000 11.760000 28.020000 0.460000)\n        3)\n      (segment 549 \n        (point 221.320000 11.760000 28.020000 0.460000)\n        (point 218.640000 11.140000 29.500000 0.460000)\n        3)\n      (segment 550 \n        (point 218.640000 11.140000 29.500000 0.460000)\n        (point 218.640000 11.140000 29.480000 0.460000)\n        3)\n      (segment 551 \n        (point 218.640000 11.140000 29.480000 0.460000)\n        (point 217.380000 8.450000 30.270000 0.460000)\n        3)\n      (segment 552 \n        (point 217.380000 8.450000 30.270000 0.460000)\n        (point 217.380000 8.450000 30.230000 0.460000)\n        3)\n      (segment 553 \n        (point 217.380000 8.450000 30.230000 0.460000)\n        (point 219.120000 7.070000 31.800000 0.460000)\n        3)\n      (segment 554 \n        (point 219.120000 7.070000 31.800000 0.460000)\n        (point 221.620000 6.460000 33.700000 0.230000)\n        3))\n    (branch 25 20 \n      (segment 555 \n        (point 244.260000 12.360000 14.880000 0.460000)\n        (point 245.820000 9.740000 15.900000 0.230000)\n        3)\n      (segment 556 \n        (point 245.820000 9.740000 15.900000 0.230000)\n        (point 247.290000 9.490000 18.800000 0.230000)\n        3)\n      (segment 557 \n        (point 247.290000 9.490000 18.800000 0.230000)\n        (point 245.900000 7.370000 21.220000 0.230000)\n        3)\n      (segment 558 \n        (point 245.900000 7.370000 21.220000 0.230000)\n        (point 244.560000 7.060000 23.920000 0.230000)\n        3)\n      (segment 559 \n        (point 244.560000 7.060000 23.920000 0.230000)\n        (point 244.060000 5.150000 24.800000 0.230000)\n        3)\n      (segment 560 \n        (point 244.060000 5.150000 24.800000 0.230000)\n        (point 243.180000 4.940000 24.800000 0.230000)\n        3))\n    (branch 26 25 \n      (segment 561 \n        (point 243.180000 4.940000 24.800000 0.230000)\n        (point 243.130000 3.140000 25.770000 0.230000)\n        3)\n      (segment 562 \n        (point 243.130000 3.140000 25.770000 0.230000)\n        (point 244.640000 -1.280000 26.520000 0.230000)\n        3)\n      (segment 563 \n        (point 244.640000 -1.280000 26.520000 0.230000)\n        (point 246.240000 -2.100000 28.150000 0.230000)\n        3)\n      (segment 564 \n        (point 246.240000 -2.100000 28.150000 0.230000)\n        (point 246.240000 -2.100000 28.130000 0.230000)\n        3)\n      (segment 565 \n        (point 246.240000 -2.100000 28.130000 0.230000)\n        (point 247.130000 -1.890000 30.630000 0.230000)\n        3)\n      (segment 566 \n        (point 247.130000 -1.890000 30.630000 0.230000)\n        (point 247.130000 -1.890000 30.570000 0.230000)\n        3)\n      (segment 567 \n        (point 247.130000 -1.890000 30.570000 0.230000)\n        (point 248.460000 -1.570000 33.220000 0.230000)\n        3)\n      (segment 568 \n        (point 248.460000 -1.570000 33.220000 0.230000)\n        (point 248.460000 -1.570000 33.270000 0.230000)\n        3))\n    (branch 27 25 \n      (segment 569 \n        (point 243.180000 4.940000 24.800000 0.230000)\n        (point 246.700000 3.980000 24.800000 0.230000)\n        3)\n      (segment 570 \n        (point 246.700000 3.980000 24.800000 0.230000)\n        (point 248.170000 3.730000 23.270000 0.230000)\n        3)\n      (segment 571 \n        (point 248.170000 3.730000 23.270000 0.230000)\n        (point 248.570000 2.030000 21.770000 0.230000)\n        3))\n    (branch 28 27 \n      (segment 572 \n        (point 248.570000 2.030000 21.770000 0.230000)\n        (point 250.710000 -1.050000 22.500000 0.230000)\n        3)\n      (segment 573 \n        (point 250.710000 -1.050000 22.500000 0.230000)\n        (point 253.600000 -3.360000 22.500000 0.230000)\n        3)\n      (segment 574 \n        (point 253.600000 -3.360000 22.500000 0.230000)\n        (point 255.920000 -5.210000 23.750000 0.230000)\n        3)\n      (segment 575 \n        (point 255.920000 -5.210000 23.750000 0.230000)\n        (point 259.500000 -4.370000 23.750000 0.230000)\n        3)\n     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0.230000)\n        (point 234.230000 -3.130000 22.250000 0.230000)\n        3)\n      (segment 590 \n        (point 234.230000 -3.130000 22.250000 0.230000)\n        (point 231.590000 -1.950000 22.450000 0.230000)\n        3)\n      (segment 591 \n        (point 231.590000 -1.950000 22.450000 0.230000)\n        (point 230.350000 1.340000 22.450000 0.230000)\n        3)\n      (segment 592 \n        (point 230.350000 1.340000 22.450000 0.230000)\n        (point 227.940000 -0.410000 20.730000 0.230000)\n        3)\n      (segment 593 \n        (point 227.940000 -0.410000 20.730000 0.230000)\n        (point 225.580000 -0.370000 19.150000 0.230000)\n        3)\n      (segment 594 \n        (point 225.580000 -0.370000 19.150000 0.230000)\n        (point 222.320000 -0.540000 17.670000 0.230000)\n        3)\n      (segment 595 \n        (point 222.320000 -0.540000 17.670000 0.230000)\n        (point 220.350000 -2.190000 16.170000 0.230000)\n        3)\n      (segment 596 \n        (point 220.350000 -2.190000 16.170000 0.230000)\n        (point 219.720000 -3.540000 15.500000 0.230000)\n        3)\n      (segment 597 \n        (point 219.720000 -3.540000 15.500000 0.230000)\n        (point 216.330000 -3.140000 15.120000 0.230000)\n        3)\n      (segment 598 \n        (point 216.330000 -3.140000 15.120000 0.230000)\n        (point 212.880000 -4.540000 15.120000 0.230000)\n        3)\n      (segment 599 \n        (point 212.880000 -4.540000 15.120000 0.230000)\n        (point 209.370000 -3.580000 14.180000 0.230000)\n        3)\n      (segment 600 \n        (point 209.370000 -3.580000 14.180000 0.230000)\n        (point 207.580000 -4.000000 12.620000 0.230000)\n        3)\n      (segment 601 \n        (point 207.580000 -4.000000 12.620000 0.230000)\n        (point 206.230000 -4.310000 11.070000 0.230000)\n        3)\n      (segment 602 \n        (point 206.230000 -4.310000 11.070000 0.230000)\n        (point 204.850000 -6.420000 10.100000 0.230000)\n        3)\n      (segment 603 \n        (point 204.850000 -6.420000 10.100000 0.230000)\n        (point 202.040000 -6.490000 9.250000 0.230000)\n        3)\n      (segment 604 \n        (point 202.040000 -6.490000 9.250000 0.230000)\n        (point 199.620000 -8.240000 8.850000 0.230000)\n        3)\n      (segment 605 \n        (point 199.620000 -8.240000 8.850000 0.230000)\n        (point 196.630000 -9.550000 8.600000 0.230000)\n        3)\n      (segment 606 \n        (point 196.630000 -9.550000 8.600000 0.230000)\n        (point 193.760000 -11.410000 8.630000 0.230000)\n        3)\n      (segment 607 \n        (point 193.760000 -11.410000 8.630000 0.230000)\n        (point 189.560000 -13.590000 8.630000 0.230000)\n        3)\n      (segment 608 \n        (point 189.560000 -13.590000 8.630000 0.230000)\n        (point 187.520000 -12.880000 8.630000 0.230000)\n        3)\n      (segment 609 \n        (point 187.520000 -12.880000 8.630000 0.230000)\n        (point 184.920000 -15.870000 7.720000 0.230000)\n        3)\n      (segment 610 \n        (point 184.920000 -15.870000 7.720000 0.230000)\n        (point 180.270000 -18.170000 7.720000 0.230000)\n        3)\n      (segment 611 \n        (point 180.270000 -18.170000 7.720000 0.230000)\n        (point 177.900000 -18.110000 7.020000 0.230000)\n        3)\n      (segment 612 \n        (point 177.900000 -18.110000 7.020000 0.230000)\n        (point 176.070000 -20.340000 6.620000 0.230000)\n        3)\n      (segment 613 \n        (point 176.070000 -20.340000 6.620000 0.230000)\n        (point 173.080000 -21.640000 7.550000 0.230000)\n        3)\n      (segment 614 \n        (point 173.080000 -21.640000 7.550000 0.230000)\n        (point 173.080000 -21.640000 7.500000 0.230000)\n        3)\n      (segment 615 \n        (point 173.080000 -21.640000 7.500000 0.230000)\n        (point 171.690000 -23.750000 7.250000 0.230000)\n        3)\n      (segment 616 \n        (point 171.690000 -23.750000 7.250000 0.230000)\n        (point 171.690000 -23.750000 7.220000 0.230000)\n        3)\n      (segment 617 \n        (point 171.690000 -23.750000 7.220000 0.230000)\n        (point 168.520000 -26.290000 6.820000 0.230000)\n        3)\n      (segment 618 \n        (point 168.520000 -26.290000 6.820000 0.230000)\n        (point 166.420000 -27.370000 6.380000 0.230000)\n        3)\n      (segment 619 \n        (point 166.420000 -27.370000 6.380000 0.230000)\n        (point 163.730000 -28.000000 6.380000 0.230000)\n        3)\n      (segment 620 \n        (point 163.730000 -28.000000 6.380000 0.230000)\n        (point 161.500000 -28.530000 6.150000 0.230000)\n        3)\n      (segment 621 \n        (point 161.500000 -28.530000 6.150000 0.230000)\n        (point 161.500000 -28.530000 6.130000 0.230000)\n        3)\n      (segment 622 \n        (point 161.500000 -28.530000 6.130000 0.230000)\n        (point 159.670000 -30.750000 5.750000 0.230000)\n        3)\n      (segment 623 \n        (point 159.670000 -30.750000 5.750000 0.230000)\n        (point 157.060000 -33.750000 4.920000 0.230000)\n        3)\n      (segment 624 \n        (point 157.060000 -33.750000 4.920000 0.230000)\n        (point 157.060000 -33.750000 4.900000 0.230000)\n        3)\n      (segment 625 \n        (point 157.060000 -33.750000 4.900000 0.230000)\n        (point 155.100000 -35.400000 3.450000 0.230000)\n        3)\n      (segment 626 \n        (point 155.100000 -35.400000 3.450000 0.230000)\n        (point 153.010000 -36.490000 1.750000 0.230000)\n        3)\n      (segment 627 \n        (point 153.010000 -36.490000 1.750000 0.230000)\n        (point 151.170000 -38.720000 -0.280000 0.230000)\n        3)\n      (segment 628 \n        (point 151.170000 -38.720000 -0.280000 0.230000)\n        (point 147.990000 -41.250000 -1.850000 0.230000)\n        3)\n      (segment 629 \n        (point 147.990000 -41.250000 -1.850000 0.230000)\n        (point 142.900000 -43.640000 -3.850000 0.230000)\n        3)\n      (segment 630 \n    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0.690000)\n        3)\n      (segment 637 \n        (point 281.580000 29.950000 -0.370000 0.690000)\n        (point 282.420000 28.350000 -2.080000 0.690000)\n        3))\n    (branch 31 30 \n      (segment 638 \n        (point 282.420000 28.350000 -2.080000 0.690000)\n        (point 284.520000 29.450000 -3.250000 0.460000)\n        3)\n      (segment 639 \n        (point 284.520000 29.450000 -3.250000 0.460000)\n        (point 286.310000 29.870000 -4.650000 0.460000)\n        3)\n      (segment 640 \n        (point 286.310000 29.870000 -4.650000 0.460000)\n        (point 289.490000 32.400000 -6.000000 0.460000)\n        3)\n      (segment 641 \n        (point 289.490000 32.400000 -6.000000 0.460000)\n        (point 292.740000 32.570000 -7.000000 0.460000)\n        3)\n      (segment 642 \n        (point 292.740000 32.570000 -7.000000 0.460000)\n        (point 295.240000 31.960000 -7.630000 0.460000)\n        3)\n      (segment 643 \n        (point 295.240000 31.960000 -7.630000 0.460000)\n        (point 299.260000 32.910000 -7.650000 0.460000)\n        3)\n      (segment 644 \n        (point 299.260000 32.910000 -7.650000 0.460000)\n        (point 300.470000 33.780000 -6.020000 0.460000)\n        3)\n      (segment 645 \n        (point 300.470000 33.780000 -6.020000 0.460000)\n        (point 302.250000 34.200000 -4.820000 0.460000)\n        3)\n      (segment 646 \n        (point 302.250000 34.200000 -4.820000 0.460000)\n        (point 302.250000 34.200000 -4.850000 0.460000)\n        3)\n      (segment 647 \n        (point 302.250000 34.200000 -4.850000 0.460000)\n        (point 304.670000 35.960000 -3.920000 0.460000)\n        3)\n      (segment 648 \n        (point 304.670000 35.960000 -3.920000 0.460000)\n        (point 308.100000 37.360000 -2.900000 0.460000)\n        3)\n      (segment 649 \n        (point 308.100000 37.360000 -2.900000 0.460000)\n        (point 308.100000 37.360000 -2.920000 0.460000)\n        3)\n      (segment 650 \n        (point 308.100000 37.360000 -2.920000 0.460000)\n        (point 309.770000 38.350000 -2.270000 0.460000)\n        3)\n      (segment 651 \n        (point 309.770000 38.350000 -2.270000 0.460000)\n        (point 312.760000 39.640000 -1.220000 0.460000)\n        3)\n      (segment 652 \n        (point 312.760000 39.640000 -1.220000 0.460000)\n        (point 316.010000 39.810000 -3.250000 0.460000)\n        3)\n      (segment 653 \n        (point 316.010000 39.810000 -3.250000 0.460000)\n        (point 316.010000 39.810000 -3.270000 0.460000)\n        3)\n      (segment 654 \n        (point 316.010000 39.810000 -3.270000 0.460000)\n        (point 316.250000 42.850000 -4.130000 0.460000)\n        3)\n      (segment 655 \n        (point 316.250000 42.850000 -4.130000 0.460000)\n        (point 316.250000 42.850000 -4.150000 0.460000)\n        3)\n      (segment 656 \n        (point 316.250000 42.850000 -4.150000 0.460000)\n        (point 318.660000 44.610000 -5.150000 0.460000)\n        3)\n      (segment 657 \n        (point 318.660000 44.610000 -5.150000 0.460000)\n        (point 319.990000 44.920000 -4.970000 0.460000)\n        3)\n      (segment 658 \n        (point 319.990000 44.920000 -4.970000 0.460000)\n        (point 322.280000 47.260000 -5.100000 0.460000)\n        3)\n      (segment 659 \n        (point 322.280000 47.260000 -5.100000 0.460000)\n        (point 323.350000 48.700000 -6.200000 0.460000)\n        3)\n      (segment 660 \n        (point 323.350000 48.700000 -6.200000 0.460000)\n        (point 324.750000 50.820000 -7.720000 0.460000)\n        3)\n      (segment 661 \n        (point 324.750000 50.820000 -7.720000 0.460000)\n        (point 329.210000 51.860000 -8.320000 0.460000)\n        3)\n      (segment 662 \n        (point 329.210000 51.860000 -8.320000 0.460000)\n        (point 332.380000 54.400000 -9.220000 0.460000)\n        3)\n      (segment 663 \n        (point 332.380000 54.400000 -9.220000 0.460000)\n        (point 334.980000 57.390000 -9.770000 0.460000)\n        3)\n      (segment 664 \n        (point 334.980000 57.390000 -9.770000 0.460000)\n        (point 336.810000 59.620000 -10.250000 0.460000)\n        3)\n      (segment 665 \n        (point 336.810000 59.620000 -10.250000 0.460000)\n        (point 336.810000 59.620000 -10.280000 0.460000)\n        3)\n      (segment 666 \n        (point 336.810000 59.620000 -10.280000 0.460000)\n        (point 339.100000 61.940000 -11.550000 0.460000)\n        3)\n      (segment 667 \n        (point 339.100000 61.940000 -11.550000 0.460000)\n        (point 339.100000 61.940000 -11.580000 0.460000)\n        3)\n      (segment 668 \n        (point 339.100000 61.940000 -11.580000 0.460000)\n        (point 340.930000 64.170000 -13.450000 0.460000)\n        3)\n      (segment 669 \n        (point 340.930000 64.170000 -13.450000 0.460000)\n        (point 340.930000 64.170000 -13.470000 0.460000)\n        3)\n      (segment 670 \n        (point 340.930000 64.170000 -13.470000 0.460000)\n      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343.350000 77.870000 -12.720000 0.460000)\n        (point 345.940000 80.870000 -11.980000 0.460000)\n        3)\n      (segment 678 \n        (point 345.940000 80.870000 -11.980000 0.460000)\n        (point 348.100000 83.760000 -13.000000 0.460000)\n        3)\n      (segment 679 \n        (point 348.100000 83.760000 -13.000000 0.460000)\n        (point 350.500000 85.520000 -14.130000 0.460000)\n        3)\n      (segment 680 \n        (point 350.500000 85.520000 -14.130000 0.460000)\n        (point 353.670000 88.060000 -14.750000 0.460000)\n        3)\n      (segment 681 \n        (point 353.670000 88.060000 -14.750000 0.460000)\n        (point 356.420000 90.490000 -15.730000 0.460000)\n        3)\n      (segment 682 \n        (point 356.420000 90.490000 -15.730000 0.460000)\n        (point 359.010000 93.490000 -16.380000 0.460000)\n        3)\n      (segment 683 \n        (point 359.010000 93.490000 -16.380000 0.460000)\n        (point 361.290000 95.800000 -15.050000 0.230000)\n        3)\n      (segment 684 \n        (point 361.290000 95.800000 -15.050000 0.230000)\n        (point 363.880000 98.810000 -13.320000 0.230000)\n        3)\n      (segment 685 \n        (point 363.880000 98.810000 -13.320000 0.230000)\n        (point 363.880000 98.810000 -13.350000 0.230000)\n        3)\n      (segment 686 \n        (point 363.880000 98.810000 -13.350000 0.230000)\n        (point 365.000000 102.060000 -11.320000 0.230000)\n        3)\n      (segment 687 \n        (point 365.000000 102.060000 -11.320000 0.230000)\n        (point 365.000000 102.060000 -11.300000 0.230000)\n        3)\n      (segment 688 \n        (point 365.000000 102.060000 -11.300000 0.230000)\n        (point 365.630000 103.400000 -9.670000 0.230000)\n        3)\n      (segment 689 \n        (point 365.630000 103.400000 -9.670000 0.230000)\n        (point 365.870000 106.440000 -9.670000 0.230000)\n        3)\n      (segment 690 \n        (point 365.870000 106.440000 -9.670000 0.230000)\n        (point 366.190000 107.120000 -8.250000 0.230000)\n        3)\n      (segment 691 \n        (point 366.190000 107.120000 -8.250000 0.230000)\n        (point 367.700000 108.660000 -7.300000 0.230000)\n        3)\n      (segment 692 \n        (point 367.700000 108.660000 -7.300000 0.230000)\n        (point 367.700000 108.660000 -7.320000 0.230000)\n        3)\n      (segment 693 \n        (point 367.700000 108.660000 -7.320000 0.230000)\n        (point 368.460000 109.430000 -5.500000 0.230000)\n        3)\n      (segment 694 \n        (point 368.460000 109.430000 -5.500000 0.230000)\n        (point 369.990000 110.990000 -3.700000 0.230000)\n        3)\n      (segment 695 \n        (point 369.990000 110.990000 -3.700000 0.230000)\n        (point 369.990000 110.990000 -3.720000 0.230000)\n        3)\n      (segment 696 \n        (point 369.990000 110.990000 -3.720000 0.230000)\n        (point 371.050000 112.440000 -1.600000 0.230000)\n        3)\n      (segment 697 \n        (point 371.050000 112.440000 -1.600000 0.230000)\n        (point 371.050000 112.440000 -1.630000 0.230000)\n        3))\n    (branch 32 30 \n      (segment 698 \n        (point 282.420000 28.350000 -2.080000 0.690000)\n        (point 284.380000 30.010000 -0.900000 0.460000)\n        3)\n      (segment 699 \n        (point 284.380000 30.010000 -0.900000 0.460000)\n        (point 283.990000 31.710000 -0.250000 0.460000)\n        3)\n      (segment 700 \n        (point 283.990000 31.710000 -0.250000 0.460000)\n        (point 286.220000 32.230000 0.170000 0.460000)\n        3)\n      (segment 701 \n        (point 286.220000 32.230000 0.170000 0.460000)\n        (point 288.010000 32.650000 0.170000 0.460000)\n        3)\n      (segment 702 \n        (point 288.010000 32.650000 0.170000 0.460000)\n        (point 290.240000 33.180000 0.450000 0.460000)\n        3)\n      (segment 703 \n        (point 290.240000 33.180000 0.450000 0.460000)\n        (point 291.760000 34.730000 1.380000 0.460000)\n        3)\n      (segment 704 \n        (point 291.760000 34.730000 1.380000 0.460000)\n        (point 294.130000 34.680000 2.630000 0.460000)\n        3)\n      (segment 705 \n        (point 294.130000 34.680000 2.630000 0.460000)\n        (point 295.470000 35.000000 2.380000 0.460000)\n        3)\n      (segment 706 \n        (point 295.470000 35.000000 2.380000 0.460000)\n        (point 297.650000 33.720000 3.080000 0.460000)\n        3)\n      (segment 707 \n        (point 297.650000 33.720000 3.080000 0.460000)\n        (point 300.200000 34.920000 3.080000 0.460000)\n        3)\n      (segment 708 \n        (point 300.200000 34.920000 3.080000 0.460000)\n        (point 302.490000 37.230000 3.080000 0.460000)\n        3)\n      (segment 709 \n        (point 302.490000 37.230000 3.080000 0.460000)\n        (point 304.580000 38.330000 3.820000 0.460000)\n        3)\n      (segment 710 \n        (point 304.580000 38.330000 3.820000 0.460000)\n        (point 304.580000 38.330000 3.800000 0.460000)\n        3)\n      (segment 711 \n        (point 304.580000 38.330000 3.800000 0.460000)\n        (point 306.810000 38.850000 5.220000 0.460000)\n        3)\n      (segment 712 \n        (point 306.810000 38.850000 5.220000 0.460000)\n        (point 309.320000 38.240000 6.800000 0.460000)\n        3)\n      (segment 713 \n        (point 309.320000 38.240000 6.800000 0.460000)\n        (point 309.320000 38.240000 6.780000 0.460000)\n        3)\n      (segment 714 \n        (point 309.320000 38.240000 6.780000 0.460000)\n        (point 310.520000 39.110000 7.570000 0.460000)\n        3)\n      (segment 715 \n        (point 310.520000 39.110000 7.570000 0.460000)\n        (point 312.040000 40.680000 7.550000 0.460000)\n        3)\n      (segment 716 \n        (point 312.040000 40.680000 7.550000 0.460000)\n        (point 314.720000 41.300000 9.820000 0.460000)\n        3)\n      (segment 717 \n        (point 314.720000 41.300000 9.820000 0.460000)\n        (point 316.510000 41.720000 10.070000 0.230000)\n        3)\n      (segment 718 \n        (point 316.510000 41.720000 10.070000 0.230000)\n        (point 316.370000 42.290000 10.070000 0.230000)\n        3)\n      (segment 719 \n        (point 316.370000 42.290000 10.070000 0.230000)\n        (point 317.720000 42.600000 10.820000 0.230000)\n        3)\n      (segment 720 \n        (point 317.720000 42.600000 10.820000 0.230000)\n        (point 317.270000 42.490000 10.820000 0.230000)\n        3)\n      (segment 721 \n        (point 317.270000 42.490000 10.820000 0.230000)\n        (point 321.860000 42.980000 11.530000 0.230000)\n        3)\n      (segment 722 \n        (point 321.860000 42.980000 11.530000 0.230000)\n        (point 325.000000 43.710000 12.100000 0.230000)\n        3)\n      (segment 723 \n        (point 325.000000 43.710000 12.100000 0.230000)\n        (point 327.720000 46.140000 12.320000 0.230000)\n        3)\n      (segment 724 \n        (point 327.720000 46.140000 12.320000 0.230000)\n        (point 328.080000 48.620000 13.520000 0.230000)\n        3)\n      (segment 725 \n        (point 328.080000 48.620000 13.520000 0.230000)\n        (point 328.080000 48.620000 13.470000 0.230000)\n        3)\n      (segment 726 \n        (point 328.080000 48.620000 13.470000 0.230000)\n        (point 330.360000 50.940000 13.600000 0.230000)\n        3)\n      (segment 727 \n        (point 330.360000 50.940000 13.600000 0.230000)\n        (point 332.740000 50.900000 14.300000 0.230000)\n        3)\n      (segment 728 \n        (point 332.740000 50.900000 14.300000 0.230000)\n        (point 335.010000 53.210000 15.550000 0.230000)\n        3)\n      (segment 729 \n        (point 335.010000 53.210000 15.550000 0.230000)\n        (point 334.880000 53.790000 15.550000 0.230000)\n        3)\n      (segment 730 \n        (point 334.880000 53.790000 15.550000 0.230000)\n        (point 337.190000 51.940000 16.900000 0.230000)\n        3)\n      (segment 731 \n        (point 337.190000 51.940000 16.900000 0.230000)\n        (point 340.590000 51.550000 18.450000 0.230000)\n        3)\n      (segment 732 \n        (point 340.590000 51.550000 18.450000 0.230000)\n        (point 340.590000 51.550000 18.400000 0.230000)\n        3)\n      (segment 733 \n        (point 340.590000 51.550000 18.400000 0.230000)\n        (point 344.120000 50.570000 20.650000 0.230000)\n        3)\n      (segment 734 \n        (point 344.120000 50.570000 20.650000 0.230000)\n        (point 343.990000 51.140000 20.650000 0.230000)\n        3)\n      (segment 735 \n        (point 343.990000 51.140000 20.650000 0.230000)\n        (point 347.500000 50.180000 22.500000 0.230000)\n        3)\n      (segment 736 \n        (point 347.500000 50.180000 22.500000 0.230000)\n        (point 347.060000 50.080000 22.500000 0.230000)\n        3)\n      (segment 737 \n        (point 347.060000 50.080000 22.500000 0.230000)\n        (point 350.270000 48.440000 22.050000 0.230000)\n        3)\n      (segment 738 \n        (point 350.270000 48.440000 22.050000 0.230000)\n        (point 353.890000 51.080000 21.420000 0.230000)\n        3)\n      (segment 739 \n        (point 353.890000 51.080000 21.420000 0.230000)\n        (point 353.890000 51.080000 21.400000 0.230000)\n        3)\n      (segment 740 \n        (point 353.890000 51.080000 21.400000 0.230000)\n        (point 356.660000 49.330000 22.500000 0.230000)\n        3)\n      (segment 741 \n        (point 356.660000 49.330000 22.500000 0.230000)\n        (point 356.660000 49.330000 22.470000 0.230000)\n        3)\n      (segment 742 \n        (point 356.660000 49.330000 22.470000 0.230000)\n        (point 358.000000 49.650000 20.330000 0.230000)\n        3)\n      (segment 743 \n        (point 358.000000 49.650000 20.330000 0.230000)\n        (point 358.000000 49.650000 20.300000 0.230000)\n        3)\n      (segment 744 \n        (point 358.000000 49.650000 20.300000 0.230000)\n        (point 359.510000 51.200000 17.380000 0.230000)\n        3)\n      (segment 745 \n        (point 359.510000 51.200000 17.380000 0.230000)\n        (point 359.070000 51.100000 17.300000 0.230000)\n        3)\n      (segment 746 \n        (point 359.070000 51.100000 17.300000 0.230000)\n        (point 363.530000 52.150000 15.820000 0.230000)\n        3)\n      (segment 747 \n        (point 363.530000 52.150000 15.820000 0.230000)\n        (point 366.080000 53.340000 14.480000 0.230000)\n        3)\n      (segment 748 \n        (point 366.080000 53.340000 14.480000 0.230000)\n        (point 366.080000 53.340000 14.450000 0.230000)\n        3)\n      (segment 749 \n        (point 366.080000 53.340000 14.450000 0.230000)\n        (point 366.970000 53.550000 12.200000 0.230000)\n        3)\n      (segment 750 \n        (point 366.970000 53.550000 12.200000 0.230000)\n        (point 366.970000 53.550000 12.150000 0.230000)\n        3))\n    (branch 33 -1 \n      (segment 751 \n        (point 273.970000 18.010000 5.450000 0.690000)\n        (point 273.970000 18.010000 5.450000 0.690000)\n        3)\n      (segment 752 \n        (point 273.970000 18.010000 5.450000 0.690000)\n        (point 276.340000 17.970000 5.450000 0.690000)\n        3)\n      (segment 753 \n        (point 276.340000 17.970000 5.450000 0.690000)\n        (point 279.280000 17.460000 5.450000 0.690000)\n        3)\n      (segment 754 \n        (point 279.280000 17.460000 5.450000 0.690000)\n        (point 280.570000 15.970000 5.800000 0.690000)\n        3)\n      (segment 755 \n        (point 280.570000 15.970000 5.800000 0.690000)\n        (point 281.470000 16.180000 7.380000 0.690000)\n        3)\n      (segment 756 \n        (point 281.470000 16.180000 7.380000 0.690000)\n        (point 282.100000 17.520000 9.350000 0.690000)\n        3))\n    (branch 34 33 \n      (segment 757 \n        (point 282.100000 17.520000 9.350000 0.690000)\n        (point 284.950000 19.390000 8.750000 0.690000)\n        3)\n      (segment 758 \n        (point 284.950000 19.390000 8.750000 0.690000)\n        (point 287.940000 20.700000 8.350000 0.690000)\n        3)\n      (segment 759 \n        (point 287.940000 20.700000 8.350000 0.690000)\n        (point 287.940000 20.700000 8.320000 0.690000)\n        3)\n      (segment 760 \n        (point 287.940000 20.700000 8.320000 0.690000)\n        (point 289.920000 22.350000 7.700000 0.690000)\n        3))\n    (branch 35 34 \n      (segment 761 \n        (point 289.920000 22.350000 7.700000 0.690000)\n        (point 292.600000 22.970000 7.570000 0.460000)\n        3)\n      (segment 762 \n        (point 292.600000 22.970000 7.570000 0.460000)\n        (point 295.090000 22.370000 6.730000 0.460000)\n        3)\n      (segment 763 \n        (point 295.090000 22.370000 6.730000 0.460000)\n        (point 296.970000 20.410000 6.150000 0.460000)\n        3)\n      (segment 764 \n        (point 296.970000 20.410000 6.150000 0.460000)\n        (point 296.970000 20.410000 6.130000 0.460000)\n        3)\n      (segment 765 \n        (point 296.970000 20.410000 6.130000 0.460000)\n        (point 298.220000 17.120000 4.500000 0.460000)\n        3)\n      (segment 766 \n        (point 298.220000 17.120000 4.500000 0.460000)\n        (point 300.710000 16.510000 3.270000 0.460000)\n        3)\n      (segment 767 \n        (point 300.710000 16.510000 3.270000 0.460000)\n        (point 300.710000 16.510000 3.250000 0.460000)\n        3)\n      (segment 768 \n        (point 300.710000 16.510000 3.250000 0.460000)\n        (point 302.580000 14.550000 1.950000 0.460000)\n        3)\n      (segment 769 \n        (point 302.580000 14.550000 1.950000 0.460000)\n        (point 302.580000 14.550000 1.920000 0.460000)\n        3)\n      (segment 770 \n        (point 302.580000 14.550000 1.920000 0.460000)\n        (point 304.890000 12.720000 1.350000 0.460000)\n        3)\n      (segment 771 \n        (point 304.890000 12.720000 1.350000 0.460000)\n        (point 306.760000 10.770000 0.900000 0.460000)\n        3)\n      (segment 772 \n        (point 306.760000 10.770000 0.900000 0.460000)\n        (point 306.760000 10.770000 0.880000 0.460000)\n        3)\n      (segment 773 \n        (point 306.760000 10.770000 0.880000 0.460000)\n        (point 308.200000 8.710000 -0.050000 0.460000)\n        3)\n      (segment 774 \n        (point 308.200000 8.710000 -0.050000 0.460000)\n        (point 308.860000 5.880000 0.600000 0.460000)\n        3)\n      (segment 775 \n        (point 308.860000 5.880000 0.600000 0.460000)\n        (point 308.860000 5.880000 0.520000 0.460000)\n        3)\n      (segment 776 \n        (point 308.860000 5.880000 0.520000 0.460000)\n        (point 311.490000 4.710000 -0.850000 0.460000)\n        3)\n      (segment 777 \n        (point 311.490000 4.710000 -0.850000 0.460000)\n        (point 311.490000 4.710000 -0.880000 0.460000)\n        3)\n      (segment 778 \n        (point 311.490000 4.710000 -0.880000 0.460000)\n        (point 314.690000 3.070000 -1.700000 0.460000)\n        3)\n      (segment 779 \n        (point 314.690000 3.070000 -1.700000 0.460000)\n        (point 318.350000 1.540000 -1.580000 0.460000)\n        3)\n      (segment 780 \n        (point 318.350000 1.540000 -1.580000 0.460000)\n        (point 318.350000 1.540000 -1.600000 0.460000)\n        3)\n      (segment 781 \n        (point 318.350000 1.540000 -1.600000 0.460000)\n        (point 321.440000 0.470000 0.930000 0.460000)\n        3)\n      (segment 782 \n        (point 321.440000 0.470000 0.930000 0.460000)\n        (point 324.640000 -1.160000 1.700000 0.460000)\n        3)\n      (segment 783 \n        (point 324.640000 -1.160000 1.700000 0.460000)\n        (point 327.270000 -2.340000 2.400000 0.460000)\n        3)\n      (segment 784 \n        (point 327.270000 -2.340000 2.400000 0.460000)\n        (point 330.790000 -3.300000 2.550000 0.460000)\n        3)\n      (segment 785 \n        (point 330.790000 -3.300000 2.550000 0.460000)\n        (point 333.870000 -4.380000 1.700000 0.460000)\n        3)\n      (segment 786 \n        (point 333.870000 -4.380000 1.700000 0.460000)\n        (point 333.870000 -4.380000 1.670000 0.460000)\n        3)\n      (segment 787 \n        (point 333.870000 -4.380000 1.670000 0.460000)\n        (point 335.240000 -8.240000 1.770000 0.460000)\n        3)\n      (segment 788 \n        (point 335.240000 -8.240000 1.770000 0.460000)\n        (point 337.380000 -11.320000 1.770000 0.460000)\n        3)\n      (segment 789 \n        (point 337.380000 -11.320000 1.770000 0.460000)\n        (point 341.570000 -15.120000 1.770000 0.460000)\n        3)\n      (segment 790 \n        (point 341.570000 -15.120000 1.770000 0.460000)\n        (point 342.870000 -16.600000 2.570000 0.460000)\n        3)\n      (segment 791 \n        (point 342.870000 -16.600000 2.570000 0.460000)\n        (point 344.550000 -19.790000 3.570000 0.460000)\n        3)\n      (segment 792 \n        (point 344.550000 -19.790000 3.570000 0.460000)\n        (point 344.550000 -19.790000 3.550000 0.460000)\n        3)\n      (segment 793 \n        (point 344.550000 -19.790000 3.550000 0.460000)\n        (point 346.240000 -22.980000 3.650000 0.460000)\n        3)\n      (segment 794 \n        (point 346.240000 -22.980000 3.650000 0.460000)\n        (point 348.380000 -26.070000 4.500000 0.460000)\n        3)\n      (segment 795 \n        (point 348.380000 -26.070000 4.500000 0.460000)\n        (point 351.080000 -29.590000 5.650000 0.460000)\n        3)\n      (segment 796 \n        (point 351.080000 -29.590000 5.650000 0.460000)\n        (point 353.230000 -32.690000 7.770000 0.460000)\n        3)\n      (segment 797 \n        (point 353.230000 -32.690000 7.770000 0.460000)\n        (point 353.230000 -32.690000 7.750000 0.460000)\n        3)\n      (segment 798 \n        (point 353.230000 -32.690000 7.750000 0.460000)\n        (point 353.570000 -36.180000 9.150000 0.460000)\n        3)\n      (segment 799 \n        (point 353.570000 -36.180000 9.150000 0.460000)\n        (point 353.570000 -36.180000 9.100000 0.460000)\n        3)\n      (segment 800 \n        (point 353.570000 -36.180000 9.100000 0.460000)\n        (point 356.300000 -39.720000 10.150000 0.460000)\n        3)\n      (segment 801 \n        (point 356.300000 -39.720000 10.150000 0.460000)\n        (point 359.320000 -42.600000 10.950000 0.460000)\n        3)\n      (segment 802 \n        (point 359.320000 -42.600000 10.950000 0.460000)\n        (point 360.430000 -45.330000 12.450000 0.460000)\n        3)\n      (segment 803 \n        (point 360.430000 -45.330000 12.450000 0.460000)\n        (point 360.430000 -45.330000 12.350000 0.460000)\n        3)\n      (segment 804 \n        (point 360.430000 -45.330000 12.350000 0.460000)\n        (point 361.100000 -48.150000 14.050000 0.460000)\n        3)\n      (segment 805 \n        (point 361.100000 -48.150000 14.050000 0.460000)\n        (point 361.100000 -48.150000 14.020000 0.460000)\n        3)\n      (segment 806 \n        (point 361.100000 -48.150000 14.020000 0.460000)\n        (point 361.130000 -52.330000 12.950000 0.460000)\n        3)\n      (segment 807 \n        (point 361.130000 -52.330000 12.950000 0.460000)\n        (point 362.510000 -56.180000 14.380000 0.460000)\n        3)\n      (segment 808 \n        (point 362.510000 -56.180000 14.380000 0.460000)\n        (point 364.730000 -61.640000 15.200000 0.460000)\n        3)\n      (segment 809 \n        (point 364.730000 -61.640000 15.200000 0.460000)\n        (point 367.620000 -63.940000 12.700000 0.460000)\n        3)\n      (segment 810 \n        (point 367.620000 -63.940000 12.700000 0.460000)\n        (point 367.170000 -64.050000 12.700000 0.460000)\n        3)\n      (segment 811 \n        (point 367.170000 -64.050000 12.700000 0.460000)\n        (point 370.830000 -65.590000 11.530000 0.460000)\n        3)\n      (segment 812 \n        (point 370.830000 -65.590000 11.530000 0.460000)\n        (point 370.830000 -65.590000 11.500000 0.460000)\n        3)\n      (segment 813 \n        (point 370.830000 -65.590000 11.500000 0.460000)\n        (point 373.770000 -66.080000 10.200000 0.460000)\n        3)\n      (segment 814 \n        (point 373.770000 -66.080000 10.200000 0.460000)\n        (point 373.770000 -66.080000 10.170000 0.460000)\n        3)\n      (segment 815 \n        (point 373.770000 -66.080000 10.170000 0.460000)\n        (point 375.830000 -66.810000 7.850000 0.460000)\n        3)\n      (segment 816 \n        (point 375.830000 -66.810000 7.850000 0.460000)\n        (point 375.830000 -66.810000 7.700000 0.460000)\n        3))\n    (branch 36 34 \n      (segment 817 \n        (point 289.920000 22.350000 7.700000 0.690000)\n        (point 289.260000 25.180000 6.700000 0.460000)\n        3)\n      (segment 818 \n        (point 289.260000 25.180000 6.700000 0.460000)\n        (point 291.040000 25.600000 7.150000 0.460000)\n        3)\n      (segment 819 \n        (point 291.040000 25.600000 7.150000 0.460000)\n        (point 292.430000 27.710000 7.150000 0.460000)\n        3)\n      (segment 820 \n        (point 292.430000 27.710000 7.150000 0.460000)\n        (point 293.950000 29.260000 8.750000 0.460000)\n        3)\n      (segment 821 \n        (point 293.950000 29.260000 8.750000 0.460000)\n        (point 293.870000 31.630000 10.350000 0.460000)\n        3)\n      (segment 822 \n        (point 293.870000 31.630000 10.350000 0.460000)\n        (point 296.230000 31.590000 11.400000 0.460000)\n        3)\n      (segment 823 \n        (point 296.230000 31.590000 11.400000 0.460000)\n        (point 299.050000 31.650000 12.350000 0.460000)\n        3)\n      (segment 824 \n        (point 299.050000 31.650000 12.350000 0.460000)\n        (point 300.110000 33.090000 13.570000 0.460000)\n        3)\n      (segment 825 \n        (point 300.110000 33.090000 13.570000 0.460000)\n        (point 299.580000 35.350000 16.630000 0.460000)\n        3)\n      (segment 826 \n        (point 299.580000 35.350000 16.630000 0.460000)\n        (point 299.950000 37.840000 18.050000 0.460000)\n        3)\n      (segment 827 \n        (point 299.950000 37.840000 18.050000 0.460000)\n        (point 299.690000 38.960000 20.230000 0.460000)\n        3)\n      (segment 828 \n        (point 299.690000 38.960000 20.230000 0.460000)\n        (point 300.590000 39.170000 23.230000 0.460000)\n        3))\n    (branch 37 33 \n      (segment 829 \n        (point 282.100000 17.520000 9.350000 0.690000)\n        (point 282.990000 17.730000 10.480000 0.690000)\n        3)\n      (segment 830 \n        (point 282.990000 17.730000 10.480000 0.690000)\n        (point 285.480000 17.130000 12.170000 0.690000)\n        3)\n      (segment 831 \n        (point 285.480000 17.130000 12.170000 0.690000)\n        (point 286.460000 14.960000 13.570000 0.460000)\n        3)\n      (segment 832 \n        (point 286.460000 14.960000 13.570000 0.460000)\n        (point 285.480000 11.150000 14.670000 0.460000)\n        3)\n      (segment 833 \n        (point 285.480000 11.150000 14.670000 0.460000)\n        (point 285.880000 9.450000 16.020000 0.460000)\n        3)\n      (segment 834 \n        (point 285.880000 9.450000 16.020000 0.460000)\n        (point 285.510000 6.990000 17.720000 0.460000)\n        3)\n      (segment 835 \n        (point 285.510000 6.990000 17.720000 0.460000)\n        (point 286.360000 5.390000 18.700000 0.460000)\n        3)\n      (segment 836 \n        (point 286.360000 5.390000 18.700000 0.460000)\n        (point 286.490000 4.820000 18.700000 0.460000)\n        3)\n      (segment 837 \n        (point 286.490000 4.820000 18.700000 0.460000)\n        (point 287.780000 3.340000 20.000000 0.460000)\n        3)\n      (segment 838 \n        (point 287.780000 3.340000 20.000000 0.460000)\n        (point 290.860000 2.260000 20.830000 0.460000)\n        3)\n      (segment 839 \n        (point 290.860000 2.260000 20.830000 0.460000)\n        (point 294.200000 0.060000 21.670000 0.460000)\n        3)\n      (segment 840 \n        (point 294.200000 0.060000 21.670000 0.460000)\n        (point 296.510000 -1.790000 22.120000 0.460000)\n        3)\n      (segment 841 \n        (point 296.510000 -1.790000 22.120000 0.460000)\n        (point 298.250000 -3.170000 22.970000 0.460000)\n        3)\n      (segment 842 \n        (point 298.250000 -3.170000 22.970000 0.460000)\n        (point 298.920000 -6.000000 23.520000 0.460000)\n        3)\n      (segment 843 \n        (point 298.920000 -6.000000 23.520000 0.460000)\n        (point 300.030000 -8.730000 24.580000 0.460000)\n        3)\n      (segment 844 \n        (point 300.030000 -8.730000 24.580000 0.460000)\n        (point 302.790000 -10.470000 25.700000 0.460000)\n        3)\n      (segment 845 \n        (point 302.790000 -10.470000 25.700000 0.460000)\n        (point 306.010000 -12.110000 26.250000 0.460000)\n        3)\n      (segment 846 \n        (point 306.010000 -12.110000 26.250000 0.460000)\n        (point 305.400000 -17.610000 26.950000 0.460000)\n        3)\n      (segment 847 \n        (point 305.400000 -17.610000 26.950000 0.460000)\n        (point 305.890000 -21.680000 27.220000 0.460000)\n        3)\n      (segment 848 \n        (point 305.890000 -21.680000 27.220000 0.460000)\n        (point 306.550000 -24.510000 28.420000 0.460000)\n        3)\n      (segment 849 \n        (point 306.550000 -24.510000 28.420000 0.460000)\n        (point 306.050000 -26.420000 29.480000 0.460000)\n        3)\n      (segment 850 \n        (point 306.050000 -26.420000 29.480000 0.460000)\n        (point 307.610000 -29.050000 30.600000 0.460000)\n        3)\n      (segment 851 \n        (point 307.610000 -29.050000 30.600000 0.460000)\n        (point 311.590000 -29.900000 31.480000 0.460000)\n        3)\n      (segment 852 \n        (point 311.590000 -29.900000 31.480000 0.460000)\n        (point 314.980000 -30.310000 31.920000 0.460000)\n        3)\n      (segment 853 \n        (point 314.980000 -30.310000 31.920000 0.460000)\n        (point 316.760000 -29.890000 31.920000 0.460000)\n        3)\n      (segment 854 \n        (point 316.760000 -29.890000 31.920000 0.460000)\n        (point 318.550000 -29.470000 33.800000 0.460000)\n        3)\n      (segment 855 \n        (point 318.550000 -29.470000 33.800000 0.460000)\n        (point 319.320000 -28.700000 35.380000 0.460000)\n        3))\n    (branch 38 -1 \n      (segment 856 \n        (point 268.250000 8.310000 8.880000 0.690000)\n        (point 268.250000 8.310000 8.880000 0.690000)\n        3)\n      (segment 857 \n        (point 268.250000 8.310000 8.880000 0.690000)\n        (point 270.240000 5.790000 8.900000 0.690000)\n        3)\n      (segment 858 \n        (point 270.240000 5.790000 8.900000 0.690000)\n        (point 271.090000 4.200000 9.430000 0.690000)\n        3)\n      (segment 859 \n        (point 271.090000 4.200000 9.430000 0.690000)\n        (point 270.280000 1.620000 9.430000 0.690000)\n        3)\n      (segment 860 \n        (point 270.280000 1.620000 9.430000 0.690000)\n        (point 268.300000 -0.030000 10.750000 0.690000)\n        3)\n      (segment 861 \n        (point 268.300000 -0.030000 10.750000 0.690000)\n        (point 267.360000 -2.040000 12.830000 0.690000)\n        3))\n    (branch 39 38 \n      (segment 862 \n        (point 267.360000 -2.040000 12.830000 0.690000)\n        (point 269.060000 -1.190000 12.830000 0.460000)\n        3)\n      (segment 863 \n        (point 269.060000 -1.190000 12.830000 0.460000)\n        (point 271.870000 -1.130000 12.830000 0.460000)\n        3)\n      (segment 864 \n        (point 271.870000 -1.130000 12.830000 0.460000)\n        (point 273.620000 -2.510000 14.300000 0.460000)\n        3)\n      (segment 865 \n        (point 273.620000 -2.510000 14.300000 0.460000)\n        (point 273.490000 -1.940000 14.270000 0.460000)\n        3)\n      (segment 866 \n        (point 273.490000 -1.940000 14.270000 0.460000)\n        (point 275.400000 -2.090000 16.670000 0.460000)\n        3)\n      (segment 867 \n        (point 275.400000 -2.090000 16.670000 0.460000)\n        (point 277.000000 -2.900000 18.350000 0.460000)\n        3))\n    (branch 40 39 \n      (segment 868 \n        (point 277.000000 -2.900000 18.350000 0.460000)\n        (point 276.950000 -4.700000 19.630000 0.460000)\n        3)\n      (segment 869 \n        (point 276.950000 -4.700000 19.630000 0.460000)\n        (point 278.250000 -6.210000 21.980000 0.460000)\n        3)\n      (segment 870 \n        (point 278.250000 -6.210000 21.980000 0.460000)\n        (point 278.250000 -6.210000 21.950000 0.460000)\n        3)\n      (segment 871 \n        (point 278.250000 -6.210000 21.950000 0.460000)\n        (point 278.880000 -4.860000 24.200000 0.460000)\n        3)\n      (segment 872 \n        (point 278.880000 -4.860000 24.200000 0.460000)\n        (point 279.310000 -4.760000 24.200000 0.460000)\n        3)\n      (segment 873 \n        (point 279.310000 -4.760000 24.200000 0.460000)\n        (point 275.210000 -3.330000 25.100000 0.460000)\n        3)\n      (segment 874 \n        (point 275.210000 -3.330000 25.100000 0.460000)\n        (point 275.260000 -1.530000 25.730000 0.460000)\n        3)\n      (segment 875 \n        (point 275.260000 -1.530000 25.730000 0.460000)\n        (point 276.340000 -0.070000 27.300000 0.460000)\n        3)\n      (segment 876 \n        (point 276.340000 -0.070000 27.300000 0.460000)\n        (point 275.310000 0.280000 28.900000 0.460000)\n        3)\n      (segment 877 \n        (point 275.310000 0.280000 28.900000 0.460000)\n        (point 274.150000 1.200000 31.170000 0.460000)\n        3)\n      (segment 878 \n        (point 274.150000 1.200000 31.170000 0.460000)\n        (point 273.140000 1.560000 33.420000 0.460000)\n        3)\n      (segment 879 \n        (point 273.140000 1.560000 33.420000 0.460000)\n        (point 273.140000 1.560000 33.500000 0.460000)\n        3))\n    (branch 41 39 \n      (segment 880 \n        (point 277.000000 -2.900000 18.350000 0.460000)\n        (point 279.630000 -4.080000 19.170000 0.460000)\n        3)\n      (segment 881 \n        (point 279.630000 -4.080000 19.170000 0.460000)\n        (point 281.050000 -6.140000 20.350000 0.460000)\n        3)\n      (segment 882 \n        (point 281.050000 -6.140000 20.350000 0.460000)\n        (point 283.790000 -3.710000 20.350000 0.460000)\n        3)\n      (segment 883 \n        (point 283.790000 -3.710000 20.350000 0.460000)\n        (point 286.590000 -3.650000 21.850000 0.460000)\n        3)\n      (segment 884 \n        (point 286.590000 -3.650000 21.850000 0.460000)\n        (point 289.230000 -4.820000 23.050000 0.460000)\n        3)\n      (segment 885 \n        (point 289.230000 -4.820000 23.050000 0.460000)\n        (point 292.310000 -5.880000 23.050000 0.460000)\n        3)\n      (segment 886 \n        (point 292.310000 -5.880000 23.050000 0.460000)\n        (point 295.700000 -6.290000 23.570000 0.460000)\n        3)\n      (segment 887 \n        (point 295.700000 -6.290000 23.570000 0.460000)\n        (point 295.700000 -6.290000 23.550000 0.460000)\n        3)\n      (segment 888 \n        (point 295.700000 -6.290000 23.550000 0.460000)\n        (point 298.020000 -8.130000 23.420000 0.460000)\n        3)\n      (segment 889 \n        (point 298.020000 -8.130000 23.420000 0.460000)\n        (point 302.560000 -9.460000 23.800000 0.460000)\n        3)\n      (segment 890 \n        (point 302.560000 -9.460000 23.800000 0.460000)\n        (point 306.400000 -9.750000 24.620000 0.460000)\n        3)\n      (segment 891 \n        (point 306.400000 -9.750000 24.620000 0.460000)\n        (point 310.820000 -10.510000 24.620000 0.460000)\n        3)\n      (segment 892 \n        (point 310.820000 -10.510000 24.620000 0.460000)\n        (point 312.250000 -12.560000 25.400000 0.460000)\n        3)\n      (segment 893 \n        (point 312.250000 -12.560000 25.400000 0.460000)\n        (point 314.120000 -14.520000 25.520000 0.460000)\n        3)\n      (segment 894 \n        (point 314.120000 -14.520000 25.520000 0.460000)\n        (point 318.800000 -16.400000 26.770000 0.460000)\n        3)\n      (segment 895 \n        (point 318.800000 -16.400000 26.770000 0.460000)\n        (point 322.450000 -17.940000 26.800000 0.460000)\n        3)\n      (segment 896 \n        (point 322.450000 -17.940000 26.800000 0.460000)\n        (point 326.110000 -19.470000 25.350000 0.460000)\n        3)\n      (segment 897 \n        (point 326.110000 -19.470000 25.350000 0.460000)\n        (point 330.440000 -17.850000 25.350000 0.460000)\n        3)\n      (segment 898 \n        (point 330.440000 -17.850000 25.350000 0.460000)\n        (point 334.010000 -17.010000 23.700000 0.460000)\n        3)\n      (segment 899 \n        (point 334.010000 -17.010000 23.700000 0.460000)\n        (point 334.010000 -17.010000 23.670000 0.460000)\n        3)\n      (segment 900 \n        (point 334.010000 -17.010000 23.670000 0.460000)\n        (point 336.130000 -15.930000 21.920000 0.460000)\n        3)\n      (segment 901 \n        (point 336.130000 -15.930000 21.920000 0.460000)\n        (point 339.510000 -16.330000 21.480000 0.460000)\n        3)\n      (segment 902 \n        (point 339.510000 -16.330000 21.480000 0.460000)\n        (point 342.680000 -13.790000 21.200000 0.460000)\n        3)\n      (segment 903 \n        (point 342.680000 -13.790000 21.200000 0.460000)\n        (point 346.080000 -14.180000 21.580000 0.460000)\n        3)\n      (segment 904 \n        (point 346.080000 -14.180000 21.580000 0.460000)\n        (point 349.910000 -14.480000 21.580000 0.460000)\n        3)\n      (segment 905 \n        (point 349.910000 -14.480000 21.580000 0.460000)\n        (point 351.700000 -14.070000 19.630000 0.460000)\n        3)\n      (segment 906 \n        (point 351.700000 -14.070000 19.630000 0.460000)\n        (point 353.490000 -13.650000 17.270000 0.460000)\n        3)\n      (segment 907 \n        (point 353.490000 -13.650000 17.270000 0.460000)\n        (point 356.250000 -15.390000 15.200000 0.460000)\n        3)\n      (segment 908 \n        (point 356.250000 -15.390000 15.200000 0.460000)\n        (point 356.250000 -15.390000 15.170000 0.460000)\n        3)\n      (segment 909 \n        (point 356.250000 -15.390000 15.170000 0.460000)\n        (point 357.590000 -15.070000 12.880000 0.460000)\n        3)\n      (segment 910 \n        (point 357.590000 -15.070000 12.880000 0.460000)\n        (point 360.230000 -16.250000 11.650000 0.460000)\n        3)\n      (segment 911 \n        (point 360.230000 -16.250000 11.650000 0.460000)\n        (point 363.300000 -17.310000 10.970000 0.460000)\n        3)\n      (segment 912 \n        (point 363.300000 -17.310000 10.970000 0.460000)\n        (point 367.480000 -21.110000 13.380000 0.460000)\n        3)\n      (segment 913 \n        (point 367.480000 -21.110000 13.380000 0.460000)\n        (point 370.510000 -23.980000 12.980000 0.460000)\n        3)\n      (segment 914 \n        (point 370.510000 -23.980000 12.980000 0.460000)\n        (point 373.100000 -26.960000 12.980000 0.460000)\n        3)\n      (segment 915 \n        (point 373.100000 -26.960000 12.980000 0.460000)\n        (point 372.970000 -26.400000 12.980000 0.460000)\n        3)\n      (segment 916 \n        (point 372.970000 -26.400000 12.980000 0.460000)\n        (point 374.970000 -28.910000 12.950000 0.460000)\n        3)\n      (segment 917 \n        (point 374.970000 -28.910000 12.950000 0.460000)\n        (point 374.970000 -28.910000 12.930000 0.460000)\n        3)\n      (segment 918 \n        (point 374.970000 -28.910000 12.930000 0.460000)\n        (point 377.330000 -28.960000 12.880000 0.230000)\n        3)\n      (segment 919 \n        (point 377.330000 -28.960000 12.880000 0.230000)\n        (point 378.670000 -28.640000 10.400000 0.230000)\n        3))\n    (branch 42 38 \n      (segment 920 \n        (point 267.360000 -2.040000 12.830000 0.690000)\n        (point 266.820000 -5.760000 12.000000 0.690000)\n        3)\n      (segment 921 \n        (point 266.820000 -5.760000 12.000000 0.690000)\n        (point 267.620000 -9.150000 11.320000 0.690000)\n        3)\n      (segment 922 \n        (point 267.620000 -9.150000 11.320000 0.690000)\n        (point 267.620000 -9.150000 11.300000 0.690000)\n        3)\n      (segment 923 \n        (point 267.620000 -9.150000 11.300000 0.690000)\n        (point 269.800000 -10.440000 11.130000 0.690000)\n        3)\n      (segment 924 \n        (point 269.800000 -10.440000 11.130000 0.690000)\n        (point 269.800000 -10.440000 11.100000 0.690000)\n        3)\n      (segment 925 \n        (point 269.800000 -10.440000 11.100000 0.690000)\n        (point 272.250000 -12.850000 10.020000 0.690000)\n        3)\n      (segment 926 \n        (point 272.250000 -12.850000 10.020000 0.690000)\n        (point 272.250000 -12.850000 10.000000 0.690000)\n        3)\n      (segment 927 \n        (point 272.250000 -12.850000 10.000000 0.690000)\n        (point 273.990000 -14.240000 8.300000 0.690000)\n        3)\n      (segment 928 \n        (point 273.990000 -14.240000 8.300000 0.690000)\n        (point 275.020000 -14.590000 6.600000 0.690000)\n        3))\n    (branch 43 42 \n      (segment 929 \n        (point 275.020000 -14.590000 6.600000 0.690000)\n        (point 275.910000 -14.400000 4.900000 0.460000)\n        3)\n      (segment 930 \n        (point 275.910000 -14.400000 4.900000 0.460000)\n        (point 276.620000 -15.420000 3.250000 0.460000)\n        3)\n      (segment 931 \n        (point 276.620000 -15.420000 3.250000 0.460000)\n        (point 276.620000 -15.420000 3.220000 0.460000)\n        3)\n      (segment 932 \n        (point 276.620000 -15.420000 3.220000 0.460000)\n        (point 277.280000 -18.260000 1.530000 0.460000)\n        3)\n      (segment 933 \n        (point 277.280000 -18.260000 1.530000 0.460000)\n        (point 279.920000 -19.430000 1.530000 0.460000)\n        3)\n      (segment 934 \n        (point 279.920000 -19.430000 1.530000 0.460000)\n        (point 282.290000 -19.480000 1.530000 0.460000)\n        3)\n      (segment 935 \n        (point 282.290000 -19.480000 1.530000 0.460000)\n        (point 283.180000 -19.270000 1.530000 0.460000)\n        3)\n      (segment 936 \n        (point 283.180000 -19.270000 1.530000 0.460000)\n        (point 283.050000 -18.690000 1.530000 0.460000)\n        3)\n      (segment 937 \n        (point 283.050000 -18.690000 1.530000 0.460000)\n        (point 283.930000 -18.490000 0.150000 0.460000)\n        3)\n      (segment 938 \n        (point 283.930000 -18.490000 0.150000 0.460000)\n        (point 284.690000 -17.720000 -1.530000 0.460000)\n        3)\n      (segment 939 \n        (point 284.690000 -17.720000 -1.530000 0.460000)\n        (point 286.660000 -16.060000 -2.880000 0.460000)\n        3)\n      (segment 940 \n        (point 286.660000 -16.060000 -2.880000 0.460000)\n        (point 289.120000 -18.470000 -3.470000 0.460000)\n        3)\n      (segment 941 \n        (point 289.120000 -18.470000 -3.470000 0.460000)\n        (point 291.300000 -19.750000 -3.650000 0.460000)\n        3)\n      (segment 942 \n        (point 291.300000 -19.750000 -3.650000 0.460000)\n        (point 291.300000 -19.750000 -3.670000 0.460000)\n        3)\n      (segment 943 \n        (point 291.300000 -19.750000 -3.670000 0.460000)\n        (point 293.430000 -22.840000 -4.470000 0.460000)\n        3)\n      (segment 944 \n        (point 293.430000 -22.840000 -4.470000 0.460000)\n        (point 295.310000 -24.790000 -5.370000 0.460000)\n        3)\n      (segment 945 \n        (point 295.310000 -24.790000 -5.370000 0.460000)\n        (point 296.470000 -25.710000 -5.270000 0.460000)\n        3)\n      (segment 946 \n        (point 296.470000 -25.710000 -5.270000 0.460000)\n        (point 298.640000 -26.990000 -3.450000 0.460000)\n        3))\n    (branch 44 43 \n      (segment 947 \n        (point 298.640000 -26.990000 -3.450000 0.460000)\n        (point 300.560000 -27.130000 -5.130000 0.460000)\n        3)\n      (segment 948 \n        (point 300.560000 -27.130000 -5.130000 0.460000)\n        (point 302.490000 -27.290000 -7.070000 0.460000)\n        3)\n      (segment 949 \n        (point 302.490000 -27.290000 -7.070000 0.460000)\n        (point 302.490000 -27.290000 -7.100000 0.460000)\n        3)\n      (segment 950 \n        (point 302.490000 -27.290000 -7.100000 0.460000)\n        (point 306.900000 -28.040000 -8.570000 0.460000)\n        3)\n      (segment 951 \n        (point 306.900000 -28.040000 -8.570000 0.460000)\n        (point 309.400000 -28.650000 -8.900000 0.460000)\n        3)\n      (segment 952 \n        (point 309.400000 -28.650000 -8.900000 0.460000)\n        (point 310.880000 -28.900000 -10.700000 0.460000)\n        3)\n      (segment 953 \n        (point 310.880000 -28.900000 -10.700000 0.460000)\n        (point 312.040000 -29.820000 -13.550000 0.460000)\n        3)\n      (segment 954 \n        (point 312.040000 -29.820000 -13.550000 0.460000)\n        (point 312.040000 -29.820000 -13.570000 0.460000)\n        3)\n      (segment 955 \n        (point 312.040000 -29.820000 -13.570000 0.460000)\n        (point 314.660000 -30.990000 -16.670000 0.460000)\n        3)\n      (segment 956 \n        (point 314.660000 -30.990000 -16.670000 0.460000)\n        (point 314.660000 -30.990000 -16.700000 0.460000)\n        3)\n      (segment 957 \n        (point 314.660000 -30.990000 -16.700000 0.460000)\n        (point 316.900000 -30.480000 -19.230000 0.460000)\n        3)\n      (segment 958 \n        (point 316.900000 -30.480000 -19.230000 0.460000)\n        (point 316.900000 -30.480000 -19.270000 0.460000)\n        3)\n      (segment 959 \n        (point 316.900000 -30.480000 -19.270000 0.460000)\n        (point 320.680000 -32.570000 -20.650000 0.460000)\n        3)\n      (segment 960 \n        (point 320.680000 -32.570000 -20.650000 0.460000)\n        (point 323.320000 -33.740000 -22.970000 0.460000)\n        3)\n      (segment 961 \n        (point 323.320000 -33.740000 -22.970000 0.460000)\n        (point 323.320000 -33.740000 -23.020000 0.460000)\n        3)\n      (segment 962 \n        (point 323.320000 -33.740000 -23.020000 0.460000)\n        (point 324.030000 -34.770000 -26.300000 0.460000)\n        3)\n      (segment 963 \n        (point 324.030000 -34.770000 -26.300000 0.460000)\n        (point 324.030000 -34.770000 -26.320000 0.460000)\n        3)\n      (segment 964 \n        (point 324.030000 -34.770000 -26.320000 0.460000)\n        (point 323.270000 -35.550000 -30.420000 0.230000)\n        3)\n      (segment 965 \n        (point 323.270000 -35.550000 -30.420000 0.230000)\n        (point 323.400000 -36.120000 -30.450000 0.230000)\n        3))\n    (branch 45 43 \n      (segment 966 \n        (point 298.640000 -26.990000 -3.450000 0.460000)\n        (point 300.830000 -28.270000 -3.950000 0.230000)\n        3)\n      (segment 967 \n        (point 300.830000 -28.270000 -3.950000 0.230000)\n        (point 304.360000 -29.230000 -2.730000 0.230000)\n        3)\n      (segment 968 \n        (point 304.360000 -29.230000 -2.730000 0.230000)\n        (point 306.940000 -32.210000 -1.770000 0.230000)\n        3)\n      (segment 969 \n        (point 306.940000 -32.210000 -1.770000 0.230000)\n        (point 308.990000 -32.920000 -1.170000 0.230000)\n        3)\n      (segment 970 \n        (point 308.990000 -32.920000 -1.170000 0.230000)\n        (point 312.070000 -34.000000 2.780000 0.230000)\n        3)\n      (segment 971 \n        (point 312.070000 -34.000000 2.780000 0.230000)\n        (point 313.800000 -35.380000 2.780000 0.230000)\n        3)\n      (segment 972 \n        (point 313.800000 -35.380000 2.780000 0.230000)\n        (point 315.860000 -36.090000 2.780000 0.230000)\n        3)\n      (segment 973 \n        (point 315.860000 -36.090000 2.780000 0.230000)\n        (point 318.090000 -35.570000 2.220000 0.230000)\n        3)\n      (segment 974 \n        (point 318.090000 -35.570000 2.220000 0.230000)\n        (point 318.090000 -35.570000 2.200000 0.230000)\n        3)\n      (segment 975 \n        (point 318.090000 -35.570000 2.200000 0.230000)\n        (point 322.110000 -34.620000 2.600000 0.230000)\n        3)\n      (segment 976 \n        (point 322.110000 -34.620000 2.600000 0.230000)\n        (point 324.030000 -34.770000 2.900000 0.230000)\n        3)\n      (segment 977 \n        (point 324.030000 -34.770000 2.900000 0.230000)\n        (point 324.730000 -35.800000 4.320000 0.230000)\n        3)\n      (segment 978 \n        (point 324.730000 -35.800000 4.320000 0.230000)\n        (point 326.220000 -36.050000 6.220000 0.230000)\n        3)\n      (segment 979 \n        (point 326.220000 -36.050000 6.220000 0.230000)\n        (point 329.030000 -35.990000 8.150000 0.230000)\n        3)\n      (segment 980 \n        (point 329.030000 -35.990000 8.150000 0.230000)\n        (point 329.030000 -35.990000 8.130000 0.230000)\n        3)\n      (segment 981 \n        (point 329.030000 -35.990000 8.130000 0.230000)\n        (point 331.130000 -34.910000 4.170000 0.230000)\n        3)\n      (segment 982 \n        (point 331.130000 -34.910000 4.170000 0.230000)\n        (point 335.150000 -33.960000 2.670000 0.230000)\n        3))\n    (branch 46 42 \n      (segment 983 \n        (point 275.020000 -14.590000 6.600000 0.690000)\n        (point 274.790000 -17.630000 6.600000 0.460000)\n        3)\n      (segment 984 \n        (point 274.790000 -17.630000 6.600000 0.460000)\n        (point 273.790000 -21.440000 7.300000 0.460000)\n        3)\n      (segment 985 \n        (point 273.790000 -21.440000 7.300000 0.460000)\n        (point 274.140000 -24.950000 9.050000 0.460000)\n        3)\n      (segment 986 \n        (point 274.140000 -24.950000 9.050000 0.460000)\n        (point 274.230000 -27.310000 10.520000 0.460000)\n        3)\n      (segment 987 \n        (point 274.230000 -27.310000 10.520000 0.460000)\n        (point 274.230000 -27.310000 10.500000 0.460000)\n        3)\n      (segment 988 \n        (point 274.230000 -27.310000 10.500000 0.460000)\n        (point 275.970000 -28.690000 11.680000 0.460000)\n        3)\n      (segment 989 \n        (point 275.970000 -28.690000 11.680000 0.460000)\n        (point 277.840000 -30.650000 12.570000 0.460000)\n        3)\n      (segment 990 \n        (point 277.840000 -30.650000 12.570000 0.460000)\n        (point 279.260000 -32.700000 13.750000 0.460000)\n        3)\n      (segment 991 \n        (point 279.260000 -32.700000 13.750000 0.460000)\n        (point 280.110000 -34.300000 13.950000 0.460000)\n        3)\n      (segment 992 \n        (point 280.110000 -34.300000 13.950000 0.460000)\n        (point 280.950000 -35.890000 14.970000 0.460000)\n        3)\n      (segment 993 \n        (point 280.950000 -35.890000 14.970000 0.460000)\n        (point 283.270000 -37.740000 14.970000 0.460000)\n        3)\n      (segment 994 \n        (point 283.270000 -37.740000 14.970000 0.460000)\n        (point 282.910000 -40.200000 14.670000 0.460000)\n        3)\n      (segment 995 \n        (point 282.910000 -40.200000 14.670000 0.460000)\n        (point 283.880000 -42.370000 15.550000 0.460000)\n        3)\n      (segment 996 \n        (point 283.880000 -42.370000 15.550000 0.460000)\n        (point 285.440000 -44.980000 16.100000 0.460000)\n        3)\n      (segment 997 \n        (point 285.440000 -44.980000 16.100000 0.460000)\n        (point 289.220000 -47.080000 16.100000 0.460000)\n        3)\n      (segment 998 \n        (point 289.220000 -47.080000 16.100000 0.460000)\n        (point 291.150000 -47.230000 16.730000 0.460000)\n        3)\n      (segment 999 \n        (point 291.150000 -47.230000 16.730000 0.460000)\n        (point 293.990000 -51.340000 16.730000 0.460000)\n        3))\n    (branch 47 46 \n      (segment 1000 \n        (point 293.990000 -51.340000 16.730000 0.460000)\n        (point 293.050000 -53.370000 16.730000 0.460000)\n        3)\n      (segment 1001 \n        (point 293.050000 -53.370000 16.730000 0.460000)\n        (point 292.690000 -55.860000 17.450000 0.460000)\n        3)\n      (segment 1002 \n        (point 292.690000 -55.860000 17.450000 0.460000)\n        (point 293.050000 -59.360000 17.130000 0.460000)\n        3)\n      (segment 1003 \n        (point 293.050000 -59.360000 17.130000 0.460000)\n        (point 292.500000 -63.060000 20.130000 0.460000)\n        3)\n      (segment 1004 \n        (point 292.500000 -63.060000 20.130000 0.460000)\n        (point 293.350000 -64.660000 21.180000 0.460000)\n        3)\n      (segment 1005 \n        (point 293.350000 -64.660000 21.180000 0.460000)\n        (point 289.950000 -64.250000 22.250000 0.460000)\n        3)\n      (segment 1006 \n        (point 289.950000 -64.250000 22.250000 0.460000)\n        (point 287.760000 -62.980000 23.670000 0.460000)\n        3)\n      (segment 1007 \n        (point 287.760000 -62.980000 23.670000 0.460000)\n        (point 286.170000 -62.160000 25.350000 0.460000)\n        3)\n      (segment 1008 \n        (point 286.170000 -62.160000 25.350000 0.460000)\n        (point 286.030000 -61.600000 25.350000 0.460000)\n        3)\n      (segment 1009 \n        (point 286.030000 -61.600000 25.350000 0.460000)\n        (point 283.980000 -60.880000 26.550000 0.230000)\n        3)\n      (segment 1010 \n        (point 283.980000 -60.880000 26.550000 0.230000)\n        (point 282.940000 -60.520000 29.020000 0.230000)\n        3)\n      (segment 1011 \n        (point 282.940000 -60.520000 29.020000 0.230000)\n        (point 281.920000 -60.170000 31.380000 0.230000)\n        3)\n      (segment 1012 \n        (point 281.920000 -60.170000 31.380000 0.230000)\n        (point 281.340000 -59.710000 32.350000 0.230000)\n        3))\n    (branch 48 46 \n      (segment 1013 \n        (point 293.990000 -51.340000 16.730000 0.460000)\n        (point 296.620000 -52.530000 18.080000 0.460000)\n        3)\n      (segment 1014 \n        (point 296.620000 -52.530000 18.080000 0.460000)\n        (point 297.680000 -57.060000 17.700000 0.460000)\n        3)\n      (segment 1015 \n        (point 297.680000 -57.060000 17.700000 0.460000)\n        (point 298.930000 -60.350000 17.700000 0.460000)\n        3)\n      (segment 1016 \n        (point 298.930000 -60.350000 17.700000 0.460000)\n        (point 303.030000 -61.770000 17.700000 0.460000)\n        3)\n      (segment 1017 \n        (point 303.030000 -61.770000 17.700000 0.460000)\n        (point 304.010000 -63.940000 16.470000 0.460000)\n        3)\n      (segment 1018 \n        (point 304.010000 -63.940000 16.470000 0.460000)\n        (point 304.490000 -68.000000 16.470000 0.460000)\n        3)\n      (segment 1019 \n        (point 304.490000 -68.000000 16.470000 0.460000)\n        (point 305.920000 -70.050000 15.900000 0.460000)\n        3)\n      (segment 1020 \n        (point 305.920000 -70.050000 15.900000 0.460000)\n        (point 309.210000 -74.070000 15.220000 0.460000)\n        3)\n      (segment 1021 \n        (point 309.210000 -74.070000 15.220000 0.460000)\n        (point 309.210000 -74.070000 15.200000 0.460000)\n        3)\n      (segment 1022 \n        (point 309.210000 -74.070000 15.200000 0.460000)\n        (point 308.980000 -77.100000 14.650000 0.460000)\n        3)\n      (segment 1023 \n        (point 308.980000 -77.100000 14.650000 0.460000)\n        (point 312.090000 -82.350000 14.650000 0.460000)\n        3)\n      (segment 1024 \n        (point 312.090000 -82.350000 14.650000 0.460000)\n        (point 314.400000 -84.190000 12.070000 0.460000)\n        3)\n      (segment 1025 \n        (point 314.400000 -84.190000 12.070000 0.460000)\n        (point 317.310000 -86.490000 11.350000 0.460000)\n        3)\n      (segment 1026 \n        (point 317.310000 -86.490000 11.350000 0.460000)\n        (point 320.020000 -90.040000 12.220000 0.460000)\n        3)\n      (segment 1027 \n        (point 320.020000 -90.040000 12.220000 0.460000)\n        (point 321.980000 -94.350000 11.800000 0.460000)\n        3)\n      (segment 1028 \n        (point 321.980000 -94.350000 11.800000 0.460000)\n        (point 322.770000 -97.750000 11.800000 0.460000)\n        3)\n      (segment 1029 \n        (point 322.770000 -97.750000 11.800000 0.460000)\n        (point 324.780000 -100.270000 10.630000 0.460000)\n        3)\n      (segment 1030 \n        (point 324.780000 -100.270000 10.630000 0.460000)\n        (point 324.780000 -100.270000 10.600000 0.460000)\n        3)\n      (segment 1031 \n        (point 324.780000 -100.270000 10.600000 0.460000)\n        (point 325.570000 -103.670000 9.500000 0.460000)\n        3)\n      (segment 1032 \n        (point 325.570000 -103.670000 9.500000 0.460000)\n        (point 323.870000 -106.450000 8.050000 0.460000)\n        3)\n      (segment 1033 \n        (point 323.870000 -106.450000 8.050000 0.460000)\n        (point 323.870000 -106.450000 8.020000 0.460000)\n        3)\n      (segment 1034 \n        (point 323.870000 -106.450000 8.020000 0.460000)\n        (point 324.980000 -109.190000 6.730000 0.460000)\n        3)\n      (segment 1035 \n        (point 324.980000 -109.190000 6.730000 0.460000)\n        (point 326.940000 -113.500000 4.600000 0.460000)\n        3)\n      (segment 1036 \n        (point 326.940000 -113.500000 4.600000 0.460000)\n        (point 326.790000 -118.910000 4.600000 0.460000)\n        3)\n      (segment 1037 \n        (point 326.790000 -118.910000 4.600000 0.460000)\n        (point 327.720000 -122.870000 4.600000 0.460000)\n        3)\n      (segment 1038 \n        (point 327.720000 -122.870000 4.600000 0.460000)\n        (point 328.780000 -127.400000 3.420000 0.460000)\n        3)\n      (segment 1039 \n        (point 328.780000 -127.400000 3.420000 0.460000)\n        (point 330.340000 -130.020000 2.170000 0.460000)\n        3)\n      (segment 1040 \n        (point 330.340000 -130.020000 2.170000 0.460000)\n        (point 331.310000 -132.180000 0.820000 0.460000)\n        3)\n      (segment 1041 \n        (point 331.310000 -132.180000 0.820000 0.460000)\n        (point 333.140000 -135.930000 0.280000 0.460000)\n        3)\n      (segment 1042 \n        (point 333.140000 -135.930000 0.280000 0.460000)\n        (point 333.300000 -140.670000 -0.750000 0.460000)\n        3)\n      (segment 1043 \n        (point 333.300000 -140.670000 -0.750000 0.460000)\n        (point 334.630000 -146.330000 -0.800000 0.460000)\n        3)\n      (segment 1044 \n        (point 334.630000 -146.330000 -0.800000 0.460000)\n        (point 335.300000 -149.180000 0.570000 0.460000)\n        3)\n      (segment 1045 \n        (point 335.300000 -149.180000 0.570000 0.460000)\n        (point 333.810000 -154.890000 2.080000 0.460000)\n        3)\n      (segment 1046 \n        (point 333.810000 -154.890000 2.080000 0.460000)\n        (point 333.440000 -157.380000 1.630000 0.230000)\n        3)\n      (segment 1047 \n        (point 333.440000 -157.380000 1.630000 0.230000)\n        (point 333.440000 -157.380000 1.580000 0.230000)\n        3)\n      (segment 1048 \n        (point 333.440000 -157.380000 1.580000 0.230000)\n        (point 331.790000 -158.360000 -0.370000 0.230000)\n        3)\n      (segment 1049 \n        (point 331.790000 -158.360000 -0.370000 0.230000)\n        (point 331.790000 -158.360000 -0.430000 0.230000)\n        3))\n    (branch 49 -1 \n      (segment 1050 \n        (point 267.790000 12.250000 -16.130000 0.460000)\n        (point 267.790000 12.250000 -16.130000 0.460000)\n        3)\n      (segment 1051 \n        (point 267.790000 12.250000 -16.130000 0.460000)\n        (point 269.840000 11.540000 -16.130000 0.460000)\n        3)\n      (segment 1052 \n        (point 269.840000 11.540000 -16.130000 0.460000)\n        (point 270.380000 9.270000 -16.130000 0.460000)\n        3)\n      (segment 1053 \n        (point 270.380000 9.270000 -16.130000 0.460000)\n        (point 269.430000 7.260000 -16.130000 0.460000)\n        3)\n      (segment 1054 \n        (point 269.430000 7.260000 -16.130000 0.460000)\n        (point 269.300000 7.830000 -16.130000 0.460000)\n        3)\n      (segment 1055 \n        (point 269.300000 7.830000 -16.130000 0.460000)\n        (point 268.100000 6.960000 -18.420000 0.460000)\n        3)\n      (segment 1056 \n        (point 268.100000 6.960000 -18.420000 0.460000)\n        (point 268.500000 5.250000 -19.550000 0.460000)\n        3)\n      (segment 1057 \n        (point 268.500000 5.250000 -19.550000 0.460000)\n        (point 272.340000 4.960000 -19.550000 0.460000)\n        3)\n      (segment 1058 \n        (point 272.340000 4.960000 -19.550000 0.460000)\n        (point 274.120000 5.380000 -21.480000 0.460000)\n        3)\n      (segment 1059 \n        (point 274.120000 5.380000 -21.480000 0.460000)\n        (point 276.040000 5.230000 -21.480000 0.460000)\n        3)\n      (segment 1060 \n        (point 276.040000 5.230000 -21.480000 0.460000)\n        (point 276.310000 4.090000 -23.100000 0.460000)\n        3)\n      (segment 1061 \n        (point 276.310000 4.090000 -23.100000 0.460000)\n        (point 276.170000 4.660000 -23.100000 0.460000)\n        3)\n      (segment 1062 \n        (point 276.170000 4.660000 -23.100000 0.460000)\n        (point 274.650000 3.110000 -24.020000 0.460000)\n        3)\n      (segment 1063 \n        (point 274.650000 3.110000 -24.020000 0.460000)\n        (point 274.650000 3.110000 -24.050000 0.460000)\n        3)\n      (segment 1064 \n        (point 274.650000 3.110000 -24.050000 0.460000)\n        (point 274.700000 4.910000 -25.870000 0.460000)\n        3)\n      (segment 1065 \n        (point 274.700000 4.910000 -25.870000 0.460000)\n        (point 275.510000 7.490000 -27.420000 0.460000)\n        3)\n      (segment 1066 \n        (point 275.510000 7.490000 -27.420000 0.460000)\n        (point 276.750000 4.200000 -29.220000 0.460000)\n        3)\n      (segment 1067 \n        (point 276.750000 4.200000 -29.220000 0.460000)\n        (point 278.350000 3.380000 -30.770000 0.460000)\n        3)\n      (segment 1068 \n        (point 278.350000 3.380000 -30.770000 0.460000)\n        (point 278.930000 2.920000 -32.250000 0.460000)\n        3)\n      (segment 1069 \n        (point 278.930000 2.920000 -32.250000 0.460000)\n        (point 280.010000 4.370000 -34.170000 0.460000)\n        3)\n      (segment 1070 \n        (point 280.010000 4.370000 -34.170000 0.460000)\n        (point 279.490000 6.630000 -36.100000 0.460000)\n        3)\n      (segment 1071 \n        (point 279.490000 6.630000 -36.100000 0.460000)\n        (point 281.850000 6.590000 -37.520000 0.460000)\n        3)\n      (segment 1072 \n        (point 281.850000 6.590000 -37.520000 0.460000)\n        (point 284.020000 5.310000 -38.880000 0.460000)\n        3)\n      (segment 1073 \n        (point 284.020000 5.310000 -38.880000 0.460000)\n        (point 286.530000 4.690000 -39.320000 0.460000)\n        3)\n      (segment 1074 \n        (point 286.530000 4.690000 -39.320000 0.460000)\n        (point 287.690000 3.780000 -40.700000 0.460000)\n        3)\n      (segment 1075 \n        (point 287.690000 3.780000 -40.700000 0.460000)\n        (point 288.890000 4.650000 -41.220000 0.460000)\n        3)\n      (segment 1076 \n        (point 288.890000 4.650000 -41.220000 0.460000)\n        (point 288.890000 4.650000 -41.250000 0.460000)\n        3)\n      (segment 1077 \n        (point 288.890000 4.650000 -41.250000 0.460000)\n        (point 291.660000 2.920000 -43.250000 0.460000)\n        3)\n      (segment 1078 \n        (point 291.660000 2.920000 -43.250000 0.460000)\n        (point 292.950000 1.420000 -44.950000 0.460000)\n        3)\n      (segment 1079 \n        (point 292.950000 1.420000 -44.950000 0.460000)\n        (point 294.100000 0.510000 -47.050000 0.460000)\n        3)\n      (segment 1080 \n        (point 294.100000 0.510000 -47.050000 0.460000)\n        (point 297.490000 0.110000 -48.380000 0.460000)\n        3)\n      (segment 1081 \n        (point 297.490000 0.110000 -48.380000 0.460000)\n        (point 298.790000 -1.390000 -50.920000 0.460000)\n        3)\n      (segment 1082 \n        (point 298.790000 -1.390000 -50.920000 0.460000)\n        (point 301.150000 -1.430000 -51.470000 0.460000)\n        3)\n      (segment 1083 \n        (point 301.150000 -1.430000 -51.470000 0.460000)\n        (point 302.260000 -4.150000 -53.420000 0.460000)\n        3)\n      (segment 1084 \n        (point 302.260000 -4.150000 -53.420000 0.460000)\n        (point 304.320000 -4.870000 -56.030000 0.460000)\n        3)\n      (segment 1085 \n        (point 304.320000 -4.870000 -56.030000 0.460000)\n        (point 306.230000 -5.020000 -58.550000 0.460000)\n        3)\n      (segment 1086 \n        (point 306.230000 -5.020000 -58.550000 0.460000)\n        (point 306.230000 -5.020000 -58.580000 0.460000)\n        3)\n      (segment 1087 \n        (point 306.230000 -5.020000 -58.580000 0.460000)\n        (point 308.430000 -6.290000 -60.750000 0.460000)\n        3)\n      (segment 1088 \n        (point 308.430000 -6.290000 -60.750000 0.460000)\n        (point 308.430000 -6.290000 -60.770000 0.460000)\n        3)\n      (segment 1089 \n        (point 308.430000 -6.290000 -60.770000 0.460000)\n        (point 310.340000 -6.450000 -63.450000 0.460000)\n        3)\n      (segment 1090 \n        (point 310.340000 -6.450000 -63.450000 0.460000)\n        (point 312.260000 -6.590000 -64.780000 0.460000)\n        3)\n      (segment 1091 \n        (point 312.260000 -6.590000 -64.780000 0.460000)\n        (point 314.620000 -6.640000 -66.770000 0.460000)\n        3)\n      (segment 1092 \n        (point 314.620000 -6.640000 -66.770000 0.460000)\n        (point 317.840000 -8.270000 -68.380000 0.460000)\n        3)\n      (segment 1093 \n        (point 317.840000 -8.270000 -68.380000 0.460000)\n        (point 320.820000 -6.960000 -70.250000 0.460000)\n        3)\n      (segment 1094 \n        (point 320.820000 -6.960000 -70.250000 0.460000)\n        (point 324.270000 -5.560000 -71.170000 0.230000)\n        3)\n      (segment 1095 \n        (point 324.270000 -5.560000 -71.170000 0.230000)\n        (point 328.420000 -5.180000 -72.930000 0.230000)\n        3)\n      (segment 1096 \n        (point 328.420000 -5.180000 -72.930000 0.230000)\n        (point 330.470000 -5.910000 -75.430000 0.230000)\n        3)\n      (segment 1097 \n        (point 330.470000 -5.910000 -75.430000 0.230000)\n        (point 330.470000 -5.910000 -75.550000 0.230000)\n        3)\n      (segment 1098 \n        (point 330.470000 -5.910000 -75.550000 0.230000)\n        (point 331.090000 -4.560000 -78.400000 0.230000)\n        3)\n      (segment 1099 \n        (point 331.090000 -4.560000 -78.400000 0.230000)\n        (point 334.220000 -3.830000 -80.150000 0.230000)\n        3)\n      (segment 1100 \n        (point 334.220000 -3.830000 -80.150000 0.230000)\n        (point 335.430000 -2.940000 -82.020000 0.230000)\n        3)\n      (segment 1101 \n        (point 335.430000 -2.940000 -82.020000 0.230000)\n        (point 335.430000 -2.940000 -82.050000 0.230000)\n        3)\n      (segment 1102 \n        (point 335.430000 -2.940000 -82.050000 0.230000)\n        (point 338.370000 -3.450000 -84.220000 0.230000)\n        3)\n      (segment 1103 \n        (point 338.370000 -3.450000 -84.220000 0.230000)\n        (point 338.370000 -3.450000 -84.250000 0.230000)\n        3)\n      (segment 1104 \n        (point 338.370000 -3.450000 -84.250000 0.230000)\n        (point 340.490000 -2.360000 -86.620000 0.230000)\n        3)\n      (segment 1105 \n        (point 340.490000 -2.360000 -86.620000 0.230000)\n        (point 342.710000 -1.840000 -88.770000 0.230000)\n        3)\n      (segment 1106 \n        (point 342.710000 -1.840000 -88.770000 0.230000)\n        (point 342.710000 -1.840000 -88.800000 0.230000)\n        3)\n      (segment 1107 \n        (point 342.710000 -1.840000 -88.800000 0.230000)\n        (point 344.630000 -1.980000 -90.200000 0.230000)\n        3)\n      (segment 1108 \n        (point 344.630000 -1.980000 -90.200000 0.230000)\n        (point 345.570000 0.020000 -90.200000 0.230000)\n        3)\n      (segment 1109 \n        (point 345.570000 0.020000 -90.200000 0.230000)\n        (point 347.360000 0.450000 -91.700000 0.230000)\n        3)\n      (segment 1110 \n        (point 347.360000 0.450000 -91.700000 0.230000)\n        (point 350.180000 0.510000 -93.230000 0.230000)\n        3)\n      (segment 1111 \n        (point 350.180000 0.510000 -93.230000 0.230000)\n        (point 351.950000 0.930000 -94.400000 0.230000)\n        3)\n      (segment 1112 \n        (point 351.950000 0.930000 -94.400000 0.230000)\n        (point 352.400000 1.030000 -94.400000 0.230000)\n        3)\n      (segment 1113 \n        (point 352.400000 1.030000 -94.400000 0.230000)\n        (point 357.140000 0.950000 -95.320000 0.230000)\n        3)\n      (segment 1114 \n        (point 357.140000 0.950000 -95.320000 0.230000)\n        (point 362.000000 0.290000 -96.470000 0.230000)\n        3)\n      (segment 1115 \n        (point 362.000000 0.290000 -96.470000 0.230000)\n        (point 364.370000 0.250000 -97.500000 0.230000)\n        3)\n      (segment 1116 \n        (point 364.370000 0.250000 -97.500000 0.230000)\n        (point 368.120000 2.320000 -97.650000 0.230000)\n        3)\n      (segment 1117 \n        (point 368.120000 2.320000 -97.650000 0.230000)\n        (point 370.220000 3.400000 -97.630000 0.230000)\n        3)\n      (segment 1118 \n        (point 370.220000 3.400000 -97.630000 0.230000)\n        (point 373.170000 2.910000 -99.800000 0.230000)\n        3)\n      (segment 1119 \n        (point 373.170000 2.910000 -99.800000 0.230000)\n        (point 373.170000 2.910000 -99.820000 0.230000)\n        3)\n      (segment 1120 \n        (point 373.170000 2.910000 -99.820000 0.230000)\n        (point 374.910000 1.530000 -101.130000 0.230000)\n        3))\n    (branch 50 -1 \n      (segment 1121 \n        (point 260.470000 9.340000 -15.500000 0.460000)\n        (point 260.470000 9.340000 -15.500000 0.460000)\n        3)\n      (segment 1122 \n        (point 260.470000 9.340000 -15.500000 0.460000)\n        (point 258.180000 7.020000 -15.500000 0.460000)\n        3)\n      (segment 1123 \n        (point 258.180000 7.020000 -15.500000 0.460000)\n        (point 256.220000 5.360000 -15.500000 0.460000)\n        3)\n      (segment 1124 \n        (point 256.220000 5.360000 -15.500000 0.460000)\n        (point 253.800000 3.600000 -15.500000 0.460000)\n        3)\n      (segment 1125 \n        (point 253.800000 3.600000 -15.500000 0.460000)\n        (point 252.280000 2.050000 -16.250000 0.460000)\n        3)\n      (segment 1126 \n        (point 252.280000 2.050000 -16.250000 0.460000)\n        (point 250.630000 1.070000 -17.130000 0.460000)\n        3)\n      (segment 1127 \n        (point 250.630000 1.070000 -17.130000 0.460000)\n        (point 250.630000 1.070000 -17.150000 0.460000)\n        3)\n      (segment 1128 \n        (point 250.630000 1.070000 -17.150000 0.460000)\n        (point 250.900000 -0.060000 -17.800000 0.460000)\n        3)\n      (segment 1129 \n        (point 250.900000 -0.060000 -17.800000 0.460000)\n        (point 250.900000 -0.060000 -17.820000 0.460000)\n        3)\n      (segment 1130 \n        (point 250.900000 -0.060000 -17.820000 0.460000)\n        (point 252.630000 -1.450000 -19.420000 0.460000)\n        3)\n      (segment 1131 \n        (point 252.630000 -1.450000 -19.420000 0.460000)\n        (point 252.630000 -1.450000 -19.450000 0.460000)\n        3)\n      (segment 1132 \n        (point 252.630000 -1.450000 -19.450000 0.460000)\n        (point 252.680000 0.360000 -21.320000 0.460000)\n        3)\n      (segment 1133 \n        (point 252.680000 0.360000 -21.320000 0.460000)\n        (point 252.680000 0.360000 -21.380000 0.460000)\n        3)\n      (segment 1134 \n        (point 252.680000 0.360000 -21.380000 0.460000)\n        (point 251.210000 0.610000 -23.230000 0.460000)\n        3)\n      (segment 1135 \n        (point 251.210000 0.610000 -23.230000 0.460000)\n        (point 249.420000 0.190000 -25.270000 0.460000)\n        3)\n      (segment 1136 \n        (point 249.420000 0.190000 -25.270000 0.460000)\n        (point 248.490000 -1.830000 -26.480000 0.460000)\n        3)\n      (segment 1137 \n        (point 248.490000 -1.830000 -26.480000 0.460000)\n        (point 248.490000 -1.830000 -26.500000 0.460000)\n        3)\n      (segment 1138 \n        (point 248.490000 -1.830000 -26.500000 0.460000)\n        (point 246.640000 -4.040000 -26.770000 0.460000)\n        3)\n      (segment 1139 \n        (point 246.640000 -4.040000 -26.770000 0.460000)\n        (point 244.410000 -4.570000 -27.200000 0.460000)\n        3)\n      (segment 1140 \n        (point 244.410000 -4.570000 -27.200000 0.460000)\n        (point 242.620000 -4.990000 -28.950000 0.460000)\n        3)\n      (segment 1141 \n        (point 242.620000 -4.990000 -28.950000 0.460000)\n        (point 242.620000 -4.990000 -28.980000 0.460000)\n        3)\n      (segment 1142 \n        (point 242.620000 -4.990000 -28.980000 0.460000)\n        (point 241.740000 -5.200000 -32.020000 0.460000)\n        3)\n      (segment 1143 \n        (point 241.740000 -5.200000 -32.020000 0.460000)\n        (point 241.740000 -5.200000 -32.050000 0.460000)\n        3)\n      (segment 1144 \n        (point 241.740000 -5.200000 -32.050000 0.460000)\n        (point 243.340000 -6.010000 -36.000000 0.460000)\n        3)\n      (segment 1145 \n        (point 243.340000 -6.010000 -36.000000 0.460000)\n        (point 243.340000 -6.010000 -36.030000 0.460000)\n        3)\n      (segment 1146 \n        (point 243.340000 -6.010000 -36.030000 0.460000)\n        (point 243.740000 -7.710000 -38.850000 0.460000)\n        3)\n      (segment 1147 \n        (point 243.740000 -7.710000 -38.850000 0.460000)\n        (point 243.740000 -7.710000 -38.880000 0.460000)\n        3)\n      (segment 1148 \n        (point 243.740000 -7.710000 -38.880000 0.460000)\n        (point 240.870000 -9.580000 -38.850000 0.460000)\n        3)\n      (segment 1149 \n        (point 240.870000 -9.580000 -38.850000 0.460000)\n        (point 238.010000 -11.440000 -39.900000 0.460000)\n        3))\n    (branch 51 50 \n      (segment 1150 \n        (point 238.010000 -11.440000 -39.900000 0.460000)\n        (point 238.410000 -13.140000 -41.930000 0.230000)\n        3)\n      (segment 1151 \n        (point 238.410000 -13.140000 -41.930000 0.230000)\n        (point 236.760000 -14.130000 -44.430000 0.230000)\n        3)\n      (segment 1152 \n        (point 236.760000 -14.130000 -44.430000 0.230000)\n        (point 236.760000 -14.130000 -44.500000 0.230000)\n        3)\n      (segment 1153 \n        (point 236.760000 -14.130000 -44.500000 0.230000)\n        (point 236.580000 -15.360000 -42.400000 0.230000)\n        3)\n      (segment 1154 \n        (point 236.580000 -15.360000 -42.400000 0.230000)\n        (point 235.410000 -14.440000 -48.220000 0.230000)\n        3)\n      (segment 1155 \n        (point 235.410000 -14.440000 -48.220000 0.230000)\n        (point 235.410000 -14.440000 -48.250000 0.230000)\n        3)\n      (segment 1156 \n        (point 235.410000 -14.440000 -48.250000 0.230000)\n        (point 234.200000 -15.320000 -51.200000 0.230000)\n        3)\n      (segment 1157 \n        (point 234.200000 -15.320000 -51.200000 0.230000)\n        (point 234.200000 -15.320000 -51.220000 0.230000)\n        3)\n      (segment 1158 \n        (point 234.200000 -15.320000 -51.220000 0.230000)\n        (point 235.680000 -15.570000 -54.300000 0.230000)\n        3)\n      (segment 1159 \n        (point 235.680000 -15.570000 -54.300000 0.230000)\n        (point 235.680000 -15.570000 -54.320000 0.230000)\n        3)\n      (segment 1160 \n        (point 235.680000 -15.570000 -54.320000 0.230000)\n        (point 236.400000 -16.600000 -57.530000 0.230000)\n        3)\n      (segment 1161 \n        (point 236.400000 -16.600000 -57.530000 0.230000)\n        (point 236.400000 -16.600000 -57.550000 0.230000)\n        3)\n      (segment 1162 \n        (point 236.400000 -16.600000 -57.550000 0.230000)\n        (point 237.870000 -16.860000 -60.630000 0.230000)\n        3)\n      (segment 1163 \n        (point 237.870000 -16.860000 -60.630000 0.230000)\n        (point 237.870000 -16.860000 -60.650000 0.230000)\n        3)\n      (segment 1164 \n        (point 237.870000 -16.860000 -60.650000 0.230000)\n        (point 237.960000 -19.210000 -63.500000 0.230000)\n        3)\n      (segment 1165 \n        (point 237.960000 -19.210000 -63.500000 0.230000)\n        (point 238.090000 -19.780000 -63.820000 0.230000)\n        3)\n      (segment 1166 \n        (point 238.090000 -19.780000 -63.820000 0.230000)\n        (point 238.090000 -19.780000 -63.880000 0.230000)\n        3)\n      (segment 1167 \n        (point 238.090000 -19.780000 -63.880000 0.230000)\n        (point 235.580000 -19.180000 -67.070000 0.230000)\n        3)\n      (segment 1168 \n        (point 235.580000 -19.180000 -67.070000 0.230000)\n        (point 235.580000 -19.180000 -67.130000 0.230000)\n        3)\n      (segment 1169 \n        (point 235.580000 -19.180000 -67.130000 0.230000)\n        (point 235.530000 -20.990000 -71.820000 0.230000)\n        3)\n      (segment 1170 \n        (point 235.530000 -20.990000 -71.820000 0.230000)\n        (point 236.110000 -21.440000 -74.400000 0.230000)\n        3)\n      (segment 1171 \n        (point 236.110000 -21.440000 -74.400000 0.230000)\n        (point 236.110000 -21.440000 -74.420000 0.230000)\n        3)\n      (segment 1172 \n        (point 236.110000 -21.440000 -74.420000 0.230000)\n        (point 234.460000 -22.430000 -74.420000 0.230000)\n        3)\n      (segment 1173 \n        (point 234.460000 -22.430000 -74.420000 0.230000)\n        (point 235.610000 -23.350000 -77.250000 0.230000)\n        3)\n      (segment 1174 \n        (point 235.610000 -23.350000 -77.250000 0.230000)\n        (point 235.610000 -23.350000 -77.280000 0.230000)\n        3)\n      (segment 1175 \n        (point 235.610000 -23.350000 -77.280000 0.230000)\n        (point 237.090000 -23.600000 -80.820000 0.230000)\n        3)\n      (segment 1176 \n        (point 237.090000 -23.600000 -80.820000 0.230000)\n        (point 237.090000 -23.600000 -80.900000 0.230000)\n        3)\n      (segment 1177 \n        (point 237.090000 -23.600000 -80.900000 0.230000)\n        (point 238.250000 -24.520000 -83.700000 0.230000)\n        3)\n      (segment 1178 \n        (point 238.250000 -24.520000 -83.700000 0.230000)\n        (point 238.250000 -24.520000 -83.720000 0.230000)\n        3)\n      (segment 1179 \n        (point 238.250000 -24.520000 -83.720000 0.230000)\n        (point 237.490000 -25.310000 -86.500000 0.230000)\n        3)\n      (segment 1180 \n        (point 237.490000 -25.310000 -86.500000 0.230000)\n        (point 237.040000 -25.410000 -86.530000 0.230000)\n        3)\n      (segment 1181 \n        (point 237.040000 -25.410000 -86.530000 0.230000)\n        (point 236.150000 -25.620000 -89.720000 0.230000)\n        3)\n      (segment 1182 \n        (point 236.150000 -25.620000 -89.720000 0.230000)\n        (point 233.780000 -25.570000 -91.300000 0.230000)\n        3)\n      (segment 1183 \n        (point 233.780000 -25.570000 -91.300000 0.230000)\n        (point 233.340000 -25.670000 -91.300000 0.230000)\n        3)\n      (segment 1184 \n        (point 233.340000 -25.670000 -91.300000 0.230000)\n        (point 232.140000 -26.560000 -94.480000 0.230000)\n        3)\n      (segment 1185 \n        (point 232.140000 -26.560000 -94.480000 0.230000)\n        (point 231.510000 -27.890000 -97.050000 0.230000)\n        3)\n      (segment 1186 \n        (point 231.510000 -27.890000 -97.050000 0.230000)\n        (point 228.640000 -29.760000 -99.250000 0.230000)\n        3)\n      (segment 1187 \n        (point 228.640000 -29.760000 -99.250000 0.230000)\n        (point 227.880000 -30.540000 -101.600000 0.230000)\n        3)\n      (segment 1188 \n        (point 227.880000 -30.540000 -101.600000 0.230000)\n        (point 226.670000 -31.420000 -103.800000 0.230000)\n        3)\n      (segment 1189 \n        (point 226.670000 -31.420000 -103.800000 0.230000)\n        (point 226.670000 -31.420000 -103.820000 0.230000)\n        3)\n      (segment 1190 \n        (point 226.670000 -31.420000 -103.820000 0.230000)\n        (point 224.490000 -30.140000 -107.320000 0.230000)\n        3)\n      (segment 1191 \n        (point 224.490000 -30.140000 -107.320000 0.230000)\n        (point 224.490000 -30.140000 -107.380000 0.230000)\n        3)\n      (segment 1192 \n        (point 224.490000 -30.140000 -107.380000 0.230000)\n        (point 223.780000 -29.120000 -110.550000 0.230000)\n        3)\n      (segment 1193 \n        (point 223.780000 -29.120000 -110.550000 0.230000)\n        (point 223.780000 -29.120000 -110.630000 0.230000)\n        3)\n      (segment 1194 \n        (point 223.780000 -29.120000 -110.630000 0.230000)\n        (point 220.650000 -29.850000 -112.130000 0.230000)\n        3)\n      (segment 1195 \n        (point 220.650000 -29.850000 -112.130000 0.230000)\n        (point 218.870000 -30.260000 -115.470000 0.230000)\n        3)\n      (segment 1196 \n        (point 218.870000 -30.260000 -115.470000 0.230000)\n        (point 217.220000 -31.250000 -118.220000 0.230000)\n        3)\n      (segment 1197 \n        (point 217.220000 -31.250000 -118.220000 0.230000)\n        (point 218.950000 -32.630000 -121.470000 0.230000)\n        3)\n      (segment 1198 \n        (point 218.950000 -32.630000 -121.470000 0.230000)\n        (point 221.630000 -32.000000 -122.750000 0.230000)\n        3)\n      (segment 1199 \n        (point 221.630000 -32.000000 -122.750000 0.230000)\n        (point 221.940000 -31.340000 -125.400000 0.230000)\n        3)\n      (segment 1200 \n        (point 221.940000 -31.340000 -125.400000 0.230000)\n        (point 223.410000 -31.580000 -128.450000 0.230000)\n        3)\n      (segment 1201 \n        (point 223.410000 -31.580000 -128.450000 0.230000)\n        (point 223.410000 -31.580000 -128.480000 0.230000)\n        3)\n      (segment 1202 \n        (point 223.410000 -31.580000 -128.480000 0.230000)\n        (point 223.870000 -31.480000 -131.670000 0.230000)\n        3)\n      (segment 1203 \n        (point 223.870000 -31.480000 -131.670000 0.230000)\n        (point 223.870000 -31.480000 -131.750000 0.230000)\n        3)\n      (segment 1204 \n        (point 223.870000 -31.480000 -131.750000 0.230000)\n        (point 224.180000 -30.810000 -136.000000 0.230000)\n        3)\n      (segment 1205 \n        (point 224.180000 -30.810000 -136.000000 0.230000)\n        (point 224.940000 -30.030000 -139.320000 0.230000)\n        3))\n    (branch 52 50 \n      (segment 1206 \n        (point 238.010000 -11.440000 -39.900000 0.460000)\n        (point 235.200000 -11.500000 -40.770000 0.460000)\n        3)\n      (segment 1207 \n        (point 235.200000 -11.500000 -40.770000 0.460000)\n        (point 232.740000 -15.070000 -42.350000 0.460000)\n        3)\n      (segment 1208 \n        (point 232.740000 -15.070000 -42.350000 0.460000)\n        (point 231.350000 -17.180000 -44.270000 0.460000)\n        3)\n      (segment 1209 \n        (point 231.350000 -17.180000 -44.270000 0.460000)\n        (point 231.350000 -17.180000 -44.300000 0.460000)\n        3)\n      (segment 1210 \n        (point 231.350000 -17.180000 -44.300000 0.460000)\n        (point 231.560000 -20.120000 -45.720000 0.460000)\n        3)\n      (segment 1211 \n        (point 231.560000 -20.120000 -45.720000 0.460000)\n        (point 230.320000 -22.810000 -48.070000 0.460000)\n        3)\n      (segment 1212 \n        (point 230.320000 -22.810000 -48.070000 0.460000)\n        (point 230.320000 -22.810000 -48.100000 0.460000)\n        3)\n      (segment 1213 \n        (point 230.320000 -22.810000 -48.100000 0.460000)\n        (point 227.900000 -24.570000 -50.600000 0.460000)\n        3)\n      (segment 1214 \n        (point 227.900000 -24.570000 -50.600000 0.460000)\n        (point 227.900000 -24.570000 -50.650000 0.460000)\n        3)\n      (segment 1215 \n        (point 227.900000 -24.570000 -50.650000 0.460000)\n        (point 224.410000 -27.770000 -51.700000 0.460000)\n        3)\n      (segment 1216 \n        (point 224.410000 -27.770000 -51.700000 0.460000)\n        (point 222.960000 -31.680000 -53.220000 0.460000)\n        3)\n      (segment 1217 \n        (point 222.960000 -31.680000 -53.220000 0.460000)\n        (point 220.690000 -34.020000 -54.520000 0.460000)\n        3)\n      (segment 1218 \n        (point 220.690000 -34.020000 -54.520000 0.460000)\n        (point 219.570000 -37.260000 -56.080000 0.460000)\n        3)\n      (segment 1219 \n        (point 219.570000 -37.260000 -56.080000 0.460000)\n        (point 219.470000 -40.870000 -57.600000 0.460000)\n        3)\n      (segment 1220 \n        (point 219.470000 -40.870000 -57.600000 0.460000)\n        (point 219.110000 -43.350000 -60.100000 0.460000)\n        3)\n      (segment 1221 \n        (point 219.110000 -43.350000 -60.100000 0.460000)\n        (point 217.800000 -47.830000 -62.880000 0.460000)\n        3)\n      (segment 1222 \n        (point 217.800000 -47.830000 -62.880000 0.460000)\n        (point 216.240000 -51.190000 -62.570000 0.230000)\n        3)\n      (segment 1223 \n        (point 216.240000 -51.190000 -62.570000 0.230000)\n        (point 216.450000 -54.110000 -63.170000 0.230000)\n        3)\n      (segment 1224 \n        (point 216.450000 -54.110000 -63.170000 0.230000)\n        (point 216.450000 -54.110000 -63.300000 0.230000)\n        3)\n      (segment 1225 \n        (point 216.450000 -54.110000 -63.300000 0.230000)\n        (point 215.010000 -58.040000 -65.820000 0.230000)\n        3)\n      (segment 1226 \n        (point 215.010000 -58.040000 -65.820000 0.230000)\n        (point 215.010000 -58.040000 -65.850000 0.230000)\n        3)\n      (segment 1227 \n        (point 215.010000 -58.040000 -65.850000 0.230000)\n        (point 214.020000 -61.860000 -68.200000 0.230000)\n        3)\n      (segment 1228 \n        (point 214.020000 -61.860000 -68.200000 0.230000)\n        (point 214.020000 -61.860000 -68.220000 0.230000)\n        3)\n      (segment 1229 \n        (point 214.020000 -61.860000 -68.220000 0.230000)\n        (point 211.870000 -64.740000 -71.570000 0.230000)\n        3)\n      (segment 1230 \n        (point 211.870000 -64.740000 -71.570000 0.230000)\n        (point 211.870000 -64.740000 -71.630000 0.230000)\n        3)\n      (segment 1231 \n        (point 211.870000 -64.740000 -71.630000 0.230000)\n        (point 209.850000 -68.200000 -73.470000 0.230000)\n        3)\n      (segment 1232 \n        (point 209.850000 -68.200000 -73.470000 0.230000)\n        (point 205.480000 -71.620000 -75.270000 0.230000)\n        3)\n      (segment 1233 \n        (point 205.480000 -71.620000 -75.270000 0.230000)\n        (point 205.480000 -71.620000 -75.320000 0.230000)\n        3)\n      (segment 1234 \n        (point 205.480000 -71.620000 -75.320000 0.230000)\n        (point 203.370000 -72.710000 -77.130000 0.230000)\n        3)\n      (segment 1235 \n        (point 203.370000 -72.710000 -77.130000 0.230000)\n        (point 203.370000 -72.710000 -77.150000 0.230000)\n        3)\n      (segment 1236 \n        (point 203.370000 -72.710000 -77.150000 0.230000)\n        (point 201.400000 -74.360000 -78.550000 0.230000)\n        3)\n      (segment 1237 \n        (point 201.400000 -74.360000 -78.550000 0.230000)\n        (point 201.400000 -74.360000 -78.570000 0.230000)\n        3)\n      (segment 1238 \n        (point 201.400000 -74.360000 -78.570000 0.230000)\n        (point 200.060000 -74.680000 -80.650000 0.230000)\n        3)\n      (segment 1239 \n        (point 200.060000 -74.680000 -80.650000 0.230000)\n        (point 198.100000 -76.330000 -82.970000 0.230000)\n        3)\n      (segment 1240 \n        (point 198.100000 -76.330000 -82.970000 0.230000)\n        (point 198.100000 -76.330000 -83.000000 0.230000)\n        3)\n      (segment 1241 \n        (point 198.100000 -76.330000 -83.000000 0.230000)\n        (point 198.630000 -78.600000 -86.530000 0.230000)\n        3)\n      (segment 1242 \n        (point 198.630000 -78.600000 -86.530000 0.230000)\n        (point 198.630000 -78.600000 -86.620000 0.230000)\n        3))\n    (branch 53 -1 \n      (segment 1243 \n        (point 264.440000 8.490000 -15.950000 0.460000)\n        (point 264.400000 6.680000 -15.950000 0.460000)\n        3)\n      (segment 1244 \n        (point 264.400000 6.680000 -15.950000 0.460000)\n        (point 265.060000 3.850000 -15.950000 0.460000)\n        3)\n      (segment 1245 \n        (point 265.060000 3.850000 -15.950000 0.460000)\n        (point 265.590000 1.590000 -16.700000 0.460000)\n        3)\n      (segment 1246 \n        (point 265.590000 1.590000 -16.700000 0.460000)\n        (point 265.530000 -0.210000 -16.700000 0.460000)\n        3)\n      (segment 1247 \n        (point 265.530000 -0.210000 -16.700000 0.460000)\n        (point 263.340000 1.060000 -16.700000 0.460000)\n        3)\n      (segment 1248 \n        (point 263.340000 1.060000 -16.700000 0.460000)\n        (point 263.080000 2.190000 -19.000000 0.460000)\n        3)\n      (segment 1249 \n        (point 263.080000 2.190000 -19.000000 0.460000)\n        (point 263.080000 2.190000 -19.020000 0.460000)\n        3)\n      (segment 1250 \n        (point 263.080000 2.190000 -19.020000 0.460000)\n        (point 261.430000 1.210000 -21.300000 0.460000)\n        3)\n      (segment 1251 \n        (point 261.430000 1.210000 -21.300000 0.460000)\n        (point 261.100000 0.660000 -21.300000 0.460000)\n        3))\n    (branch 54 53 \n      (segment 1252 \n        (point 261.100000 0.660000 -21.300000 0.460000)\n        (point 263.640000 1.860000 -26.420000 0.460000)\n        3)\n      (segment 1253 \n        (point 263.640000 1.860000 -26.420000 0.460000)\n        (point 265.300000 2.840000 -34.000000 0.460000)\n        3)\n      (segment 1254 \n        (point 265.300000 2.840000 -34.000000 0.460000)\n        (point 264.140000 3.760000 -37.750000 0.460000)\n        3)\n      (segment 1255 \n        (point 264.140000 3.760000 -37.750000 0.460000)\n        (point 264.140000 3.760000 -37.770000 0.460000)\n        3)\n      (segment 1256 \n        (point 264.140000 3.760000 -37.770000 0.460000)\n        (point 263.830000 3.100000 -39.900000 0.460000)\n        3)\n      (segment 1257 \n        (point 263.830000 3.100000 -39.900000 0.460000)\n        (point 263.830000 3.100000 -39.920000 0.460000)\n        3)\n      (segment 1258 \n        (point 263.830000 3.100000 -39.920000 0.460000)\n        (point 264.770000 5.120000 -42.950000 0.460000)\n        3)\n      (segment 1259 \n        (point 264.770000 5.120000 -42.950000 0.460000)\n        (point 264.770000 5.120000 -43.000000 0.460000)\n        3)\n      (segment 1260 \n        (point 264.770000 5.120000 -43.000000 0.460000)\n        (point 263.300000 5.360000 -45.900000 0.460000)\n        3)\n      (segment 1261 \n        (point 263.300000 5.360000 -45.900000 0.460000)\n        (point 263.300000 5.360000 -46.000000 0.460000)\n        3)\n      (segment 1262 \n        (point 263.300000 5.360000 -46.000000 0.460000)\n        (point 265.650000 5.310000 -50.630000 0.460000)\n        3)\n      (segment 1263 \n        (point 265.650000 5.310000 -50.630000 0.460000)\n        (point 265.650000 5.310000 -50.650000 0.460000)\n        3)\n      (segment 1264 \n        (point 265.650000 5.310000 -50.650000 0.460000)\n        (point 266.150000 7.220000 -54.650000 0.460000)\n        3)\n      (segment 1265 \n        (point 266.150000 7.220000 -54.650000 0.460000)\n        (point 266.150000 7.220000 -54.720000 0.460000)\n        3)\n      (segment 1266 \n        (point 266.150000 7.220000 -54.720000 0.460000)\n        (point 264.110000 7.940000 -59.220000 0.460000)\n        3)\n      (segment 1267 \n        (point 264.110000 7.940000 -59.220000 0.460000)\n        (point 264.010000 10.300000 -63.800000 0.460000)\n        3)\n      (segment 1268 \n        (point 264.010000 10.300000 -63.800000 0.460000)\n        (point 265.170000 9.390000 -67.420000 0.460000)\n        3)\n      (segment 1269 \n        (point 265.170000 9.390000 -67.420000 0.460000)\n        (point 264.330000 10.980000 -69.720000 0.460000)\n        3)\n      (segment 1270 \n        (point 264.330000 10.980000 -69.720000 0.460000)\n        (point 264.330000 10.980000 -69.800000 0.460000)\n        3)\n      (segment 1271 \n        (point 264.330000 10.980000 -69.800000 0.460000)\n        (point 264.640000 11.650000 -71.720000 0.460000)\n        3))\n    (branch 55 54 \n      (segment 1272 \n        (point 264.640000 11.650000 -71.720000 0.460000)\n        (point 262.200000 14.060000 -73.800000 0.230000)\n        3)\n      (segment 1273 \n        (point 262.200000 14.060000 -73.800000 0.230000)\n        (point 262.200000 14.060000 -73.820000 0.230000)\n        3)\n      (segment 1274 \n        (point 262.200000 14.060000 -73.820000 0.230000)\n        (point 261.170000 14.420000 -77.150000 0.230000)\n        3)\n      (segment 1275 \n        (point 261.170000 14.420000 -77.150000 0.230000)\n        (point 260.730000 14.310000 -77.170000 0.230000)\n        3)\n      (segment 1276 \n        (point 260.730000 14.310000 -77.170000 0.230000)\n        (point 259.880000 15.900000 -80.250000 0.230000)\n        3)\n      (segment 1277 \n        (point 259.880000 15.900000 -80.250000 0.230000)\n        (point 259.880000 15.900000 -80.280000 0.230000)\n        3)\n      (segment 1278 \n        (point 259.880000 15.900000 -80.280000 0.230000)\n        (point 261.670000 16.320000 -82.920000 0.230000)\n        3)\n      (segment 1279 \n        (point 261.670000 16.320000 -82.920000 0.230000)\n        (point 261.670000 16.320000 -82.950000 0.230000)\n        3)\n      (segment 1280 \n        (point 261.670000 16.320000 -82.950000 0.230000)\n        (point 259.930000 17.710000 -85.630000 0.230000)\n        3)\n      (segment 1281 \n        (point 259.930000 17.710000 -85.630000 0.230000)\n        (point 259.930000 17.710000 -85.650000 0.230000)\n        3)\n      (segment 1282 \n        (point 259.930000 17.710000 -85.650000 0.230000)\n        (point 258.600000 17.390000 -89.100000 0.230000)\n        3)\n      (segment 1283 \n        (point 258.600000 17.390000 -89.100000 0.230000)\n        (point 258.600000 17.390000 -89.120000 0.230000)\n        3)\n      (segment 1284 \n        (point 258.600000 17.390000 -89.120000 0.230000)\n        (point 259.260000 14.560000 -92.870000 0.230000)\n        3)\n      (segment 1285 \n        (point 259.260000 14.560000 -92.870000 0.230000)\n        (point 259.260000 14.560000 -92.900000 0.230000)\n        3)\n      (segment 1286 \n        (point 259.260000 14.560000 -92.900000 0.230000)\n        (point 257.120000 17.650000 -96.720000 0.230000)\n        3)\n      (segment 1287 \n        (point 257.120000 17.650000 -96.720000 0.230000)\n        (point 257.120000 17.650000 -96.770000 0.230000)\n        3)\n      (segment 1288 \n        (point 257.120000 17.650000 -96.770000 0.230000)\n        (point 259.660000 18.830000 -99.000000 0.230000)\n        3)\n      (segment 1289 \n        (point 259.660000 18.830000 -99.000000 0.230000)\n        (point 259.660000 18.830000 -99.030000 0.230000)\n        3)\n      (segment 1290 \n        (point 259.660000 18.830000 -99.030000 0.230000)\n        (point 258.330000 18.530000 -101.500000 0.230000)\n        3)\n      (segment 1291 \n        (point 258.330000 18.530000 -101.500000 0.230000)\n        (point 258.330000 18.530000 -101.530000 0.230000)\n        3)\n      (segment 1292 \n        (point 258.330000 18.530000 -101.530000 0.230000)\n        (point 256.460000 20.480000 -104.650000 0.230000)\n        3)\n      (segment 1293 \n        (point 256.460000 20.480000 -104.650000 0.230000)\n        (point 256.460000 20.480000 -104.670000 0.230000)\n        3)\n      (segment 1294 \n        (point 256.460000 20.480000 -104.670000 0.230000)\n        (point 258.500000 19.760000 -108.230000 0.230000)\n        3)\n      (segment 1295 \n        (point 258.500000 19.760000 -108.230000 0.230000)\n        (point 258.500000 19.760000 -108.270000 0.230000)\n        3)\n      (segment 1296 \n        (point 258.500000 19.760000 -108.270000 0.230000)\n        (point 257.170000 19.450000 -112.700000 0.230000)\n        3)\n      (segment 1297 \n        (point 257.170000 19.450000 -112.700000 0.230000)\n        (point 256.540000 18.110000 -116.320000 0.230000)\n        3)\n      (segment 1298 \n        (point 256.540000 18.110000 -116.320000 0.230000)\n        (point 256.810000 16.970000 -120.420000 0.230000)\n        3)\n      (segment 1299 \n        (point 256.810000 16.970000 -120.420000 0.230000)\n        (point 256.810000 16.970000 -120.450000 0.230000)\n        3)\n      (segment 1300 \n        (point 256.810000 16.970000 -120.450000 0.230000)\n        (point 254.930000 18.920000 -124.250000 0.230000)\n        3)\n      (segment 1301 \n        (point 254.930000 18.920000 -124.250000 0.230000)\n        (point 254.930000 18.920000 -124.320000 0.230000)\n        3)\n      (segment 1302 \n        (point 254.930000 18.920000 -124.320000 0.230000)\n        (point 254.660000 20.060000 -128.100000 0.230000)\n        3)\n      (segment 1303 \n        (point 254.660000 20.060000 -128.100000 0.230000)\n        (point 254.660000 20.060000 -128.150000 0.230000)\n        3)\n      (segment 1304 \n        (point 254.660000 20.060000 -128.150000 0.230000)\n        (point 253.460000 19.180000 -132.950000 0.230000)\n        3)\n      (segment 1305 \n        (point 253.460000 19.180000 -132.950000 0.230000)\n        (point 253.460000 19.180000 -133.000000 0.230000)\n        3)\n      (segment 1306 \n        (point 253.460000 19.180000 -133.000000 0.230000)\n        (point 253.410000 17.370000 -137.800000 0.230000)\n        3))\n    (branch 56 54 \n      (segment 1307 \n        (point 264.640000 11.650000 -71.720000 0.460000)\n        (point 267.460000 11.710000 -73.070000 0.230000)\n        3)\n      (segment 1308 \n        (point 267.460000 11.710000 -73.070000 0.230000)\n        (point 268.670000 12.590000 -73.970000 0.230000)\n        3)\n      (segment 1309 \n        (point 268.670000 12.590000 -73.970000 0.230000)\n        (point 268.670000 12.590000 -74.000000 0.230000)\n        3)\n      (segment 1310 \n        (point 268.670000 12.590000 -74.000000 0.230000)\n        (point 270.590000 12.440000 -75.570000 0.230000)\n        3)\n      (segment 1311 \n        (point 270.590000 12.440000 -75.570000 0.230000)\n        (point 270.590000 12.440000 -75.600000 0.230000)\n        3)\n      (segment 1312 \n        (point 270.590000 12.440000 -75.600000 0.230000)\n        (point 271.970000 14.550000 -77.050000 0.230000)\n        3)\n      (segment 1313 \n        (point 271.970000 14.550000 -77.050000 0.230000)\n        (point 271.970000 14.550000 -77.100000 0.230000)\n        3)\n      (segment 1314 \n        (point 271.970000 14.550000 -77.100000 0.230000)\n        (point 274.340000 14.510000 -79.320000 0.230000)\n        3)\n      (segment 1315 \n        (point 274.340000 14.510000 -79.320000 0.230000)\n        (point 274.340000 14.510000 -79.350000 0.230000)\n        3)\n      (segment 1316 \n        (point 274.340000 14.510000 -79.350000 0.230000)\n        (point 277.160000 14.580000 -82.350000 0.230000)\n        3)\n      (segment 1317 \n        (point 277.160000 14.580000 -82.350000 0.230000)\n        (point 279.700000 15.770000 -84.450000 0.230000)\n        3)\n      (segment 1318 \n        (point 279.700000 15.770000 -84.450000 0.230000)\n        (point 280.460000 16.540000 -86.450000 0.230000)\n        3)\n      (segment 1319 \n        (point 280.460000 16.540000 -86.450000 0.230000)\n        (point 280.460000 16.540000 -86.470000 0.230000)\n        3)\n      (segment 1320 \n        (point 280.460000 16.540000 -86.470000 0.230000)\n        (point 282.960000 15.940000 -87.220000 0.230000)\n        3)\n      (segment 1321 \n        (point 282.960000 15.940000 -87.220000 0.230000)\n        (point 282.960000 15.940000 -87.250000 0.230000)\n        3)\n      (segment 1322 \n        (point 282.960000 15.940000 -87.250000 0.230000)\n        (point 285.240000 18.270000 -88.800000 0.230000)\n        3)\n      (segment 1323 \n        (point 285.240000 18.270000 -88.800000 0.230000)\n        (point 286.180000 20.280000 -91.150000 0.230000)\n        3)\n      (segment 1324 \n        (point 286.180000 20.280000 -91.150000 0.230000)\n        (point 286.180000 20.280000 -91.200000 0.230000)\n        3)\n      (segment 1325 \n        (point 286.180000 20.280000 -91.200000 0.230000)\n        (point 288.150000 21.930000 -93.600000 0.230000)\n        3)\n      (segment 1326 \n        (point 288.150000 21.930000 -93.600000 0.230000)\n        (point 288.150000 21.930000 -93.650000 0.230000)\n        3)\n      (segment 1327 \n        (point 288.150000 21.930000 -93.650000 0.230000)\n        (point 289.940000 22.350000 -95.920000 0.230000)\n        3)\n      (segment 1328 \n        (point 289.940000 22.350000 -95.920000 0.230000)\n        (point 289.940000 22.350000 -95.950000 0.230000)\n        3)\n      (segment 1329 \n        (point 289.940000 22.350000 -95.950000 0.230000)\n        (point 290.880000 24.370000 -97.750000 0.230000)\n        3)\n      (segment 1330 \n        (point 290.880000 24.370000 -97.750000 0.230000)\n        (point 290.880000 24.370000 -97.780000 0.230000)\n        3)\n      (segment 1331 \n        (point 290.880000 24.370000 -97.780000 0.230000)\n        (point 292.220000 24.680000 -101.400000 0.230000)\n        3)\n      (segment 1332 \n        (point 292.220000 24.680000 -101.400000 0.230000)\n        (point 294.890000 25.310000 -104.970000 0.230000)\n        3)\n      (segment 1333 \n        (point 294.890000 25.310000 -104.970000 0.230000)\n        (point 294.890000 25.310000 -105.000000 0.230000)\n        3)\n      (segment 1334 \n        (point 294.890000 25.310000 -105.000000 0.230000)\n        (point 295.790000 25.520000 -108.150000 0.230000)\n        3)\n      (segment 1335 \n        (point 295.790000 25.520000 -108.150000 0.230000)\n        (point 297.710000 25.370000 -110.680000 0.230000)\n        3)\n      (segment 1336 \n        (point 297.710000 25.370000 -110.680000 0.230000)\n        (point 297.580000 25.940000 -110.700000 0.230000)\n        3)\n      (segment 1337 \n        (point 297.580000 25.940000 -110.700000 0.230000)\n        (point 300.000000 27.700000 -113.250000 0.230000)\n        3)\n      (segment 1338 \n        (point 300.000000 27.700000 -113.250000 0.230000)\n        (point 301.010000 27.340000 -116.530000 0.230000)\n        3)\n      (segment 1339 \n        (point 301.010000 27.340000 -116.530000 0.230000)\n        (point 301.010000 27.340000 -116.700000 0.230000)\n        3)\n      (segment 1340 \n        (point 301.010000 27.340000 -116.700000 0.230000)\n        (point 300.750000 28.460000 -120.520000 0.230000)\n        3))\n    (branch 57 53 \n      (segment 1341 \n        (point 261.100000 0.660000 -21.300000 0.460000)\n        (point 260.930000 -0.700000 -23.600000 0.460000)\n        3)\n      (segment 1342 \n        (point 260.930000 -0.700000 -23.600000 0.460000)\n        (point 260.930000 -0.700000 -23.650000 0.460000)\n        3)\n      (segment 1343 \n        (point 260.930000 -0.700000 -23.650000 0.460000)\n        (point 261.910000 -2.860000 -25.350000 0.460000)\n        3)\n      (segment 1344 \n        (point 261.910000 -2.860000 -25.350000 0.460000)\n        (point 262.490000 -3.330000 -26.950000 0.460000)\n        3)\n      (segment 1345 \n        (point 262.490000 -3.330000 -26.950000 0.460000)\n        (point 262.440000 -5.110000 -26.650000 0.460000)\n        3)\n      (segment 1346 \n        (point 262.440000 -5.110000 -26.650000 0.460000)\n        (point 262.840000 -6.820000 -26.650000 0.460000)\n        3))\n    (branch 58 57 \n      (segment 1347 \n        (point 262.840000 -6.820000 -26.650000 0.460000)\n        (point 262.750000 -10.430000 -25.550000 0.460000)\n        3)\n      (segment 1348 \n        (point 262.750000 -10.430000 -25.550000 0.460000)\n        (point 262.310000 -10.530000 -25.550000 0.460000)\n        3)\n      (segment 1349 \n        (point 262.310000 -10.530000 -25.550000 0.460000)\n        (point 262.400000 -12.910000 -25.570000 0.460000)\n        3)\n      (segment 1350 \n        (point 262.400000 -12.910000 -25.570000 0.460000)\n        (point 258.640000 -14.980000 -25.830000 0.460000)\n        3)\n      (segment 1351 \n        (point 258.640000 -14.980000 -25.830000 0.460000)\n        (point 257.430000 -15.860000 -24.850000 0.460000)\n        3)\n      (segment 1352 \n        (point 257.430000 -15.860000 -24.850000 0.460000)\n        (point 255.730000 -18.640000 -26.250000 0.460000)\n        3)\n      (segment 1353 \n        (point 255.730000 -18.640000 -26.250000 0.460000)\n        (point 255.730000 -18.640000 -26.280000 0.460000)\n        3)\n      (segment 1354 \n        (point 255.730000 -18.640000 -26.280000 0.460000)\n        (point 255.550000 -19.880000 -27.270000 0.460000)\n        3)\n      (segment 1355 \n        (point 255.550000 -19.880000 -27.270000 0.460000)\n        (point 255.550000 -19.880000 -27.300000 0.460000)\n        3)\n      (segment 1356 \n        (point 255.550000 -19.880000 -27.300000 0.460000)\n        (point 254.290000 -22.570000 -28.020000 0.460000)\n        3)\n      (segment 1357 \n        (point 254.290000 -22.570000 -28.020000 0.460000)\n        (point 254.290000 -22.570000 -28.050000 0.460000)\n        3)\n      (segment 1358 \n        (point 254.290000 -22.570000 -28.050000 0.460000)\n        (point 252.820000 -22.310000 -28.630000 0.460000)\n        3)\n      (segment 1359 \n        (point 252.820000 -22.310000 -28.630000 0.460000)\n        (point 252.270000 -26.020000 -29.420000 0.460000)\n        3)\n      (segment 1360 \n        (point 252.270000 -26.020000 -29.420000 0.460000)\n        (point 250.130000 -28.910000 -30.380000 0.460000)\n        3))\n    (branch 59 58 \n      (segment 1361 \n        (point 250.130000 -28.910000 -30.380000 0.460000)\n        (point 247.000000 -29.650000 -30.380000 0.460000)\n        3)\n      (segment 1362 \n        (point 247.000000 -29.650000 -30.380000 0.460000)\n        (point 244.760000 -30.180000 -31.420000 0.460000)\n        3)\n      (segment 1363 \n        (point 244.760000 -30.180000 -31.420000 0.460000)\n        (point 244.760000 -30.180000 -31.450000 0.460000)\n        3)\n      (segment 1364 \n        (point 244.760000 -30.180000 -31.450000 0.460000)\n        (point 243.170000 -29.350000 -32.830000 0.460000)\n        3)\n      (segment 1365 \n        (point 243.170000 -29.350000 -32.830000 0.460000)\n        (point 243.170000 -29.350000 -32.880000 0.460000)\n        3)\n      (segment 1366 \n        (point 243.170000 -29.350000 -32.880000 0.460000)\n        (point 240.800000 -29.310000 -34.450000 0.460000)\n        3)\n      (segment 1367 \n        (point 240.800000 -29.310000 -34.450000 0.460000)\n        (point 240.800000 -29.310000 -34.470000 0.460000)\n        3)\n      (segment 1368 \n        (point 240.800000 -29.310000 -34.470000 0.460000)\n        (point 238.690000 -30.410000 -36.420000 0.460000)\n        3)\n      (segment 1369 \n        (point 238.690000 -30.410000 -36.420000 0.460000)\n        (point 237.440000 -33.080000 -38.050000 0.460000)\n        3)\n      (segment 1370 \n        (point 237.440000 -33.080000 -38.050000 0.460000)\n        (point 237.440000 -33.080000 -38.070000 0.460000)\n        3)\n      (segment 1371 \n        (point 237.440000 -33.080000 -38.070000 0.460000)\n        (point 236.050000 -35.210000 -39.950000 0.460000)\n        3)\n      (segment 1372 \n        (point 236.050000 -35.210000 -39.950000 0.460000)\n        (point 236.050000 -35.210000 -40.070000 0.460000)\n        3)\n      (segment 1373 \n        (point 236.050000 -35.210000 -40.070000 0.460000)\n        (point 234.670000 -37.320000 -39.380000 0.460000)\n        3)\n      (segment 1374 \n        (point 234.670000 -37.320000 -39.380000 0.460000)\n        (point 232.560000 -38.400000 -41.000000 0.460000)\n        3)\n      (segment 1375 \n        (point 232.560000 -38.400000 -41.000000 0.460000)\n        (point 232.560000 -38.400000 -41.030000 0.460000)\n        3)\n      (segment 1376 \n        (point 232.560000 -38.400000 -41.030000 0.460000)\n        (point 232.200000 -40.890000 -43.070000 0.460000)\n        3)\n      (segment 1377 \n        (point 232.200000 -40.890000 -43.070000 0.460000)\n        (point 231.260000 -42.890000 -47.220000 0.460000)\n        3)\n      (segment 1378 \n        (point 231.260000 -42.890000 -47.220000 0.460000)\n        (point 231.260000 -42.890000 -47.280000 0.460000)\n        3))\n    (branch 60 59 \n      (segment 1379 \n        (point 231.260000 -42.890000 -47.280000 0.460000)\n        (point 233.300000 -43.610000 -49.100000 0.460000)\n        3)\n      (segment 1380 \n        (point 233.300000 -43.610000 -49.100000 0.460000)\n        (point 233.300000 -43.610000 -49.130000 0.460000)\n        3)\n      (segment 1381 \n        (point 233.300000 -43.610000 -49.130000 0.460000)\n        (point 234.020000 -44.630000 -51.670000 0.460000)\n        3)\n      (segment 1382 \n        (point 234.020000 -44.630000 -51.670000 0.460000)\n        (point 234.020000 -44.630000 -51.720000 0.460000)\n        3)\n      (segment 1383 \n        (point 234.020000 -44.630000 -51.720000 0.460000)\n        (point 233.400000 -45.980000 -54.200000 0.460000)\n        3)\n      (segment 1384 \n        (point 233.400000 -45.980000 -54.200000 0.460000)\n        (point 232.370000 -45.620000 -59.130000 0.460000)\n        3)\n      (segment 1385 \n        (point 232.370000 -45.620000 -59.130000 0.460000)\n        (point 230.270000 -46.710000 -62.220000 0.460000)\n        3)\n      (segment 1386 \n        (point 230.270000 -46.710000 -62.220000 0.460000)\n        (point 230.270000 -46.710000 -62.330000 0.460000)\n        3)\n      (segment 1387 \n        (point 230.270000 -46.710000 -62.330000 0.460000)\n        (point 229.380000 -46.920000 -66.880000 0.460000)\n        3)\n      (segment 1388 \n        (point 229.380000 -46.920000 -66.880000 0.460000)\n        (point 229.910000 -49.190000 -70.800000 0.460000)\n        3)\n      (segment 1389 \n        (point 229.910000 -49.190000 -70.800000 0.460000)\n        (point 229.910000 -49.190000 -70.820000 0.460000)\n        3)\n      (segment 1390 \n        (point 229.910000 -49.190000 -70.820000 0.460000)\n        (point 230.040000 -49.750000 -73.420000 0.460000)\n        3)\n      (segment 1391 \n        (point 230.040000 -49.750000 -73.420000 0.460000)\n        (point 231.110000 -48.290000 -74.670000 0.460000)\n        3)\n      (segment 1392 \n        (point 231.110000 -48.290000 -74.670000 0.460000)\n        (point 232.770000 -47.310000 -76.820000 0.460000)\n        3))\n    (branch 61 59 \n      (segment 1393 \n        (point 231.260000 -42.890000 -47.280000 0.460000)\n        (point 229.160000 -43.980000 -47.220000 0.460000)\n        3)\n      (segment 1394 \n        (point 229.160000 -43.980000 -47.220000 0.460000)\n        (point 229.160000 -43.980000 -47.250000 0.460000)\n        3)\n      (segment 1395 \n        (point 229.160000 -43.980000 -47.250000 0.460000)\n        (point 227.740000 -41.930000 -48.350000 0.460000)\n        3)\n      (segment 1396 \n        (point 227.740000 -41.930000 -48.350000 0.460000)\n        (point 226.030000 -44.710000 -50.770000 0.460000)\n        3)\n      (segment 1397 \n        (point 226.030000 -44.710000 -50.770000 0.460000)\n        (point 226.030000 -44.710000 -50.880000 0.460000)\n        3)\n      (segment 1398 \n        (point 226.030000 -44.710000 -50.880000 0.460000)\n        (point 225.270000 -45.500000 -54.050000 0.460000)\n        3)\n      (segment 1399 \n        (point 225.270000 -45.500000 -54.050000 0.460000)\n        (point 223.800000 -45.230000 -55.600000 0.460000)\n        3)\n      (segment 1400 \n        (point 223.800000 -45.230000 -55.600000 0.460000)\n        (point 223.800000 -45.230000 -55.630000 0.460000)\n        3)\n      (segment 1401 \n        (point 223.800000 -45.230000 -55.630000 0.460000)\n        (point 222.020000 -45.650000 -58.150000 0.460000)\n        3)\n      (segment 1402 \n        (point 222.020000 -45.650000 -58.150000 0.460000)\n        (point 220.410000 -44.840000 -61.380000 0.460000)\n        3)\n      (segment 1403 \n        (point 220.410000 -44.840000 -61.380000 0.460000)\n        (point 220.360000 -46.640000 -63.200000 0.460000)\n        3)\n      (segment 1404 \n        (point 220.360000 -46.640000 -63.200000 0.460000)\n        (point 220.360000 -46.640000 -63.250000 0.460000)\n        3)\n      (segment 1405 \n        (point 220.360000 -46.640000 -63.250000 0.460000)\n        (point 218.440000 -46.490000 -66.770000 0.460000)\n        3)\n      (segment 1406 \n        (point 218.440000 -46.490000 -66.770000 0.460000)\n        (point 218.520000 -48.860000 -70.070000 0.460000)\n        3)\n      (segment 1407 \n        (point 218.520000 -48.860000 -70.070000 0.460000)\n        (point 218.520000 -48.860000 -70.180000 0.460000)\n        3)\n      (segment 1408 \n        (point 218.520000 -48.860000 -70.180000 0.460000)\n        (point 216.290000 -49.390000 -72.720000 0.460000)\n        3)\n      (segment 1409 \n        (point 216.290000 -49.390000 -72.720000 0.460000)\n        (point 215.850000 -49.490000 -72.800000 0.460000)\n        3)\n      (segment 1410 \n        (point 215.850000 -49.490000 -72.800000 0.460000)\n        (point 214.370000 -49.240000 -73.000000 0.460000)\n        3)\n      (segment 1411 \n        (point 214.370000 -49.240000 -73.000000 0.460000)\n        (point 214.370000 -49.240000 -73.020000 0.460000)\n        3)\n      (segment 1412 \n        (point 214.370000 -49.240000 -73.020000 0.460000)\n        (point 215.000000 -47.910000 -76.470000 0.460000)\n        3)\n      (segment 1413 \n        (point 215.000000 -47.910000 -76.470000 0.460000)\n        (point 215.000000 -47.910000 -76.600000 0.460000)\n        3)\n      (segment 1414 \n        (point 215.000000 -47.910000 -76.600000 0.460000)\n        (point 217.180000 -49.180000 -80.680000 0.460000)\n        3)\n      (segment 1415 \n        (point 217.180000 -49.180000 -80.680000 0.460000)\n        (point 217.180000 -49.180000 -80.730000 0.460000)\n        3)\n      (segment 1416 \n        (point 217.180000 -49.180000 -80.730000 0.460000)\n        (point 217.000000 -50.410000 -85.280000 0.460000)\n        3)\n      (segment 1417 \n        (point 217.000000 -50.410000 -85.280000 0.460000)\n        (point 217.000000 -50.410000 -85.370000 0.460000)\n        3)\n      (segment 1418 \n        (point 217.000000 -50.410000 -85.370000 0.460000)\n        (point 214.240000 -48.670000 -89.600000 0.460000)\n        3)\n      (segment 1419 \n        (point 214.240000 -48.670000 -89.600000 0.460000)\n        (point 214.240000 -48.670000 -89.650000 0.460000)\n        3)\n      (segment 1420 \n        (point 214.240000 -48.670000 -89.650000 0.460000)\n        (point 213.610000 -50.020000 -92.300000 0.460000)\n        3)\n      (segment 1421 \n        (point 213.610000 -50.020000 -92.300000 0.460000)\n        (point 213.610000 -50.020000 -92.320000 0.460000)\n        3)\n      (segment 1422 \n        (point 213.610000 -50.020000 -92.320000 0.460000)\n        (point 212.500000 -47.290000 -93.400000 0.460000)\n        3)\n      (segment 1423 \n        (point 212.500000 -47.290000 -93.400000 0.460000)\n        (point 210.580000 -47.140000 -94.670000 0.460000)\n        3)\n      (segment 1424 \n        (point 210.580000 -47.140000 -94.670000 0.460000)\n        (point 210.580000 -47.140000 -94.700000 0.460000)\n        3)\n      (segment 1425 \n        (point 210.580000 -47.140000 -94.700000 0.460000)\n        (point 208.660000 -46.990000 -96.850000 0.460000)\n        3)\n      (segment 1426 \n        (point 208.660000 -46.990000 -96.850000 0.460000)\n        (point 208.660000 -46.990000 -96.900000 0.460000)\n        3)\n      (segment 1427 \n        (point 208.660000 -46.990000 -96.900000 0.460000)\n        (point 207.460000 -47.870000 -99.170000 0.460000)\n        3)\n      (segment 1428 \n        (point 207.460000 -47.870000 -99.170000 0.460000)\n        (point 205.800000 -48.850000 -100.970000 0.460000)\n        3)\n      (segment 1429 \n        (point 205.800000 -48.850000 -100.970000 0.460000)\n        (point 204.520000 -47.370000 -102.200000 0.460000)\n        3)\n      (segment 1430 \n        (point 204.520000 -47.370000 -102.200000 0.460000)\n        (point 201.510000 -48.670000 -104.050000 0.460000)\n        3)\n      (segment 1431 \n        (point 201.510000 -48.670000 -104.050000 0.460000)\n        (point 201.510000 -48.670000 -104.070000 0.460000)\n        3)\n      (segment 1432 \n        (point 201.510000 -48.670000 -104.070000 0.460000)\n        (point 199.280000 -49.190000 -104.900000 0.460000)\n        3)\n      (segment 1433 \n        (point 199.280000 -49.190000 -104.900000 0.460000)\n        (point 199.280000 -49.190000 -104.970000 0.460000)\n        3)\n      (segment 1434 \n        (point 199.280000 -49.190000 -104.970000 0.460000)\n        (point 197.140000 -52.090000 -105.320000 0.460000)\n        3)\n      (segment 1435 \n        (point 197.140000 -52.090000 -105.320000 0.460000)\n        (point 194.850000 -54.410000 -106.620000 0.460000)\n        3)\n      (segment 1436 \n        (point 194.850000 -54.410000 -106.620000 0.460000)\n        (point 191.100000 -56.480000 -108.320000 0.460000)\n        3)\n      (segment 1437 \n        (point 191.100000 -56.480000 -108.320000 0.460000)\n        (point 192.530000 -58.540000 -111.970000 0.460000)\n        3)\n      (segment 1438 \n        (point 192.530000 -58.540000 -111.970000 0.460000)\n        (point 192.530000 -58.540000 -112.020000 0.460000)\n        3)\n      (segment 1439 \n        (point 192.530000 -58.540000 -112.020000 0.460000)\n        (point 192.790000 -59.670000 -113.950000 0.460000)\n        3)\n      (segment 1440 \n        (point 192.790000 -59.670000 -113.950000 0.460000)\n        (point 192.790000 -59.670000 -113.970000 0.460000)\n        3))\n    (branch 62 58 \n      (segment 1441 \n        (point 250.130000 -28.910000 -30.380000 0.460000)\n        (point 248.430000 -31.710000 -30.380000 0.460000)\n        3)\n      (segment 1442 \n        (point 248.430000 -31.710000 -30.380000 0.460000)\n        (point 247.610000 -34.280000 -30.750000 0.460000)\n        3)\n      (segment 1443 \n        (point 247.610000 -34.280000 -30.750000 0.460000)\n        (point 245.790000 -36.510000 -30.750000 0.460000)\n        3)\n      (segment 1444 \n        (point 245.790000 -36.510000 -30.750000 0.460000)\n        (point 244.660000 -39.750000 -30.750000 0.460000)\n        3)\n      (segment 1445 \n        (point 244.660000 -39.750000 -30.750000 0.460000)\n        (point 243.150000 -41.310000 -30.750000 0.460000)\n        3)\n      (segment 1446 \n        (point 243.150000 -41.310000 -30.750000 0.460000)\n        (point 242.700000 -41.410000 -30.750000 0.460000)\n        3)\n      (segment 1447 \n        (point 242.700000 -41.410000 -30.750000 0.460000)\n        (point 242.140000 -45.120000 -30.750000 0.460000)\n        3)\n      (segment 1448 \n        (point 242.140000 -45.120000 -30.750000 0.460000)\n        (point 239.550000 -48.110000 -30.750000 0.460000)\n        3)\n      (segment 1449 \n        (point 239.550000 -48.110000 -30.750000 0.460000)\n        (point 239.900000 -51.620000 -30.750000 0.460000)\n        3)\n      (segment 1450 \n        (point 239.900000 -51.620000 -30.750000 0.460000)\n        (point 239.930000 -55.800000 -30.000000 0.460000)\n        3)\n      (segment 1451 \n        (point 239.930000 -55.800000 -30.000000 0.460000)\n        (point 239.400000 -59.500000 -28.880000 0.460000)\n        3)\n      (segment 1452 \n        (point 239.400000 -59.500000 -28.880000 0.460000)\n        (point 239.400000 -59.500000 -28.920000 0.460000)\n        3)\n      (segment 1453 \n        (point 239.400000 -59.500000 -28.920000 0.460000)\n        (point 239.930000 -61.760000 -27.830000 0.460000)\n        3)\n      (segment 1454 \n        (point 239.930000 -61.760000 -27.830000 0.460000)\n        (point 239.930000 -61.760000 -27.770000 0.460000)\n        3)\n      (segment 1455 \n        (point 239.930000 -61.760000 -27.770000 0.460000)\n        (point 239.370000 -65.480000 -27.770000 0.460000)\n        3)\n      (segment 1456 \n        (point 239.370000 -65.480000 -27.770000 0.460000)\n        (point 239.270000 -69.080000 -26.820000 0.460000)\n        3)\n      (segment 1457 \n        (point 239.270000 -69.080000 -26.820000 0.460000)\n        (point 239.850000 -69.550000 -26.820000 0.460000)\n        3)\n      (segment 1458 \n        (point 239.850000 -69.550000 -26.820000 0.460000)\n        (point 237.840000 -73.000000 -26.820000 0.460000)\n        3)\n      (segment 1459 \n        (point 237.840000 -73.000000 -26.820000 0.460000)\n        (point 239.090000 -76.280000 -29.170000 0.460000)\n        3)\n      (segment 1460 \n        (point 239.090000 -76.280000 -29.170000 0.460000)\n        (point 239.300000 -79.230000 -29.170000 0.460000)\n        3)\n      (segment 1461 \n        (point 239.300000 -79.230000 -29.170000 0.460000)\n        (point 238.630000 -82.370000 -29.920000 0.460000)\n        3)\n      (segment 1462 \n        (point 238.630000 -82.370000 -29.920000 0.460000)\n        (point 237.900000 -87.320000 -28.570000 0.460000)\n        3)\n      (segment 1463 \n        (point 237.900000 -87.320000 -28.570000 0.460000)\n        (point 235.940000 -88.970000 -28.570000 0.460000)\n        3)\n      (segment 1464 \n        (point 235.940000 -88.970000 -28.570000 0.460000)\n        (point 236.910000 -91.140000 -28.570000 0.460000)\n        3)\n      (segment 1465 \n        (point 236.910000 -91.140000 -28.570000 0.460000)\n        (point 236.630000 -95.980000 -28.550000 0.460000)\n        3)\n      (segment 1466 \n        (point 236.630000 -95.980000 -28.550000 0.460000)\n        (point 236.170000 -102.050000 -28.550000 0.460000)\n        3)\n      (segment 1467 \n        (point 236.170000 -102.050000 -28.550000 0.460000)\n        (point 233.440000 -104.490000 -29.850000 0.460000)\n        3)\n      (segment 1468 \n        (point 233.440000 -104.490000 -29.850000 0.460000)\n        (point 229.380000 -107.230000 -30.650000 0.460000)\n        3)\n      (segment 1469 \n        (point 229.380000 -107.230000 -30.650000 0.460000)\n        (point 228.440000 -109.240000 -32.700000 0.460000)\n        3)\n      (segment 1470 \n        (point 228.440000 -109.240000 -32.700000 0.460000)\n        (point 229.420000 -111.400000 -32.700000 0.460000)\n        3)\n      (segment 1471 \n        (point 229.420000 -111.400000 -32.700000 0.460000)\n        (point 227.750000 -112.380000 -34.700000 0.460000)\n        3)\n      (segment 1472 \n        (point 227.750000 -112.380000 -34.700000 0.460000)\n        (point 227.660000 -115.990000 -36.070000 0.460000)\n        3)\n      (segment 1473 \n        (point 227.660000 -115.990000 -36.070000 0.460000)\n        (point 225.960000 -118.780000 -38.030000 0.460000)\n        3)\n      (segment 1474 \n        (point 225.960000 -118.780000 -38.030000 0.460000)\n        (point 225.960000 -118.780000 -38.150000 0.460000)\n        3)\n      (segment 1475 \n        (point 225.960000 -118.780000 -38.150000 0.460000)\n        (point 226.350000 -120.470000 -39.720000 0.460000)\n        3)\n      (segment 1476 \n        (point 226.350000 -120.470000 -39.720000 0.460000)\n        (point 227.330000 -122.640000 -40.720000 0.460000)\n        3)\n      (segment 1477 \n        (point 227.330000 -122.640000 -40.720000 0.460000)\n        (point 225.190000 -125.520000 -40.720000 0.460000)\n        3)\n      (segment 1478 \n        (point 225.190000 -125.520000 -40.720000 0.460000)\n        (point 224.770000 -129.800000 -39.630000 0.460000)\n        3)\n      (segment 1479 \n        (point 224.770000 -129.800000 -39.630000 0.460000)\n        (point 224.770000 -129.800000 -39.700000 0.460000)\n        3)\n      (segment 1480 \n        (point 224.770000 -129.800000 -39.700000 0.460000)\n        (point 221.460000 -131.770000 -41.670000 0.460000)\n        3)\n      (segment 1481 \n        (point 221.460000 -131.770000 -41.670000 0.460000)\n        (point 219.050000 -133.540000 -43.220000 0.460000)\n        3)\n      (segment 1482 \n        (point 219.050000 -133.540000 -43.220000 0.460000)\n        (point 219.050000 -133.540000 -43.270000 0.460000)\n        3)\n      (segment 1483 \n        (point 219.050000 -133.540000 -43.270000 0.460000)\n        (point 217.270000 -133.960000 -43.800000 0.460000)\n        3)\n      (segment 1484 \n        (point 217.270000 -133.960000 -43.800000 0.460000)\n        (point 214.710000 -135.150000 -41.470000 0.460000)\n        3)\n      (segment 1485 \n        (point 214.710000 -135.150000 -41.470000 0.460000)\n        (point 212.930000 -135.570000 -40.170000 0.460000)\n        3)\n      (segment 1486 \n        (point 212.930000 -135.570000 -40.170000 0.460000)\n        (point 212.930000 -135.570000 -40.150000 0.460000)\n        3)\n      (segment 1487 \n        (point 212.930000 -135.570000 -40.150000 0.460000)\n        (point 210.650000 -137.890000 -40.130000 0.460000)\n        3)\n      (segment 1488 \n        (point 210.650000 -137.890000 -40.130000 0.460000)\n        (point 209.210000 -141.810000 -39.450000 0.460000)\n        3)\n      (segment 1489 \n        (point 209.210000 -141.810000 -39.450000 0.460000)\n        (point 207.250000 -143.460000 -39.670000 0.460000)\n        3)\n      (segment 1490 \n        (point 207.250000 -143.460000 -39.670000 0.460000)\n        (point 204.710000 -144.660000 -39.670000 0.460000)\n        3)\n      (segment 1491 \n        (point 204.710000 -144.660000 -39.670000 0.460000)\n        (point 203.170000 -146.220000 -37.550000 0.460000)\n        3))\n    (branch 63 57 \n      (segment 1492 \n        (point 262.840000 -6.820000 -26.650000 0.460000)\n        (point 266.050000 -8.450000 -27.900000 0.460000)\n        3)\n      (segment 1493 \n        (point 266.050000 -8.450000 -27.900000 0.460000)\n        (point 267.800000 -9.850000 -28.920000 0.460000)\n        3)\n      (segment 1494 \n        (point 267.800000 -9.850000 -28.920000 0.460000)\n        (point 267.800000 -9.850000 -28.950000 0.460000)\n        3)\n      (segment 1495 \n        (point 267.800000 -9.850000 -28.950000 0.460000)\n        (point 269.350000 -12.460000 -29.970000 0.460000)\n        3)\n      (segment 1496 \n        (point 269.350000 -12.460000 -29.970000 0.460000)\n        (point 269.350000 -12.460000 -30.000000 0.460000)\n        3)\n      (segment 1497 \n        (point 269.350000 -12.460000 -30.000000 0.460000)\n        (point 272.290000 -12.970000 -31.170000 0.460000)\n        3)\n      (segment 1498 \n        (point 272.290000 -12.970000 -31.170000 0.460000)\n        (point 276.130000 -13.260000 -32.100000 0.460000)\n        3)\n      (segment 1499 \n        (point 276.130000 -13.260000 -32.100000 0.460000)\n        (point 277.920000 -12.850000 -32.920000 0.460000)\n        3)\n      (segment 1500 \n        (point 277.920000 -12.850000 -32.920000 0.460000)\n        (point 279.210000 -14.330000 -32.470000 0.460000)\n        3)\n      (segment 1501 \n        (point 279.210000 -14.330000 -32.470000 0.460000)\n        (point 279.210000 -14.330000 -32.500000 0.460000)\n        3)\n      (segment 1502 \n        (point 279.210000 -14.330000 -32.500000 0.460000)\n        (point 281.220000 -16.840000 -34.170000 0.460000)\n        3)\n      (segment 1503 \n        (point 281.220000 -16.840000 -34.170000 0.460000)\n        (point 283.390000 -18.120000 -33.750000 0.460000)\n        3)\n      (segment 1504 \n        (point 283.390000 -18.120000 -33.750000 0.460000)\n        (point 285.620000 -17.600000 -35.950000 0.460000)\n        3)\n      (segment 1505 \n        (point 285.620000 -17.600000 -35.950000 0.460000)\n        (point 287.870000 -17.080000 -37.550000 0.460000)\n        3)\n      (segment 1506 \n        (point 287.870000 -17.080000 -37.550000 0.460000)\n        (point 290.540000 -16.440000 -39.150000 0.460000)\n        3)\n      (segment 1507 \n        (point 290.540000 -16.440000 -39.150000 0.460000)\n        (point 290.540000 -16.440000 -39.170000 0.460000)\n        3)\n      (segment 1508 \n        (point 290.540000 -16.440000 -39.170000 0.460000)\n        (point 294.960000 -17.210000 -40.150000 0.460000)\n        3)\n      (segment 1509 \n        (point 294.960000 -17.210000 -40.150000 0.460000)\n        (point 297.900000 -17.710000 -41.100000 0.460000)\n        3)\n      (segment 1510 \n        (point 297.900000 -17.710000 -41.100000 0.460000)\n        (point 302.140000 -19.700000 -41.930000 0.460000)\n        3)\n      (segment 1511 \n        (point 302.140000 -19.700000 -41.930000 0.460000)\n        (point 304.140000 -22.220000 -43.470000 0.460000)\n        3)\n      (segment 1512 \n        (point 304.140000 -22.220000 -43.470000 0.460000)\n        (point 304.140000 -22.220000 -43.500000 0.460000)\n        3)\n      (segment 1513 \n        (point 304.140000 -22.220000 -43.500000 0.460000)\n        (point 307.360000 -23.850000 -45.150000 0.460000)\n        3)\n      (segment 1514 \n        (point 307.360000 -23.850000 -45.150000 0.460000)\n        (point 310.510000 -27.290000 -46.700000 0.460000)\n        3)\n      (segment 1515 \n        (point 310.510000 -27.290000 -46.700000 0.460000)\n        (point 313.150000 -28.480000 -48.450000 0.460000)\n        3)\n      (segment 1516 \n        (point 313.150000 -28.480000 -48.450000 0.460000)\n        (point 315.380000 -27.950000 -48.100000 0.460000)\n        3)\n      (segment 1517 \n        (point 315.380000 -27.950000 -48.100000 0.460000)\n        (point 315.380000 -27.950000 -48.130000 0.460000)\n        3)\n      (segment 1518 \n        (point 315.380000 -27.950000 -48.130000 0.460000)\n        (point 316.670000 -29.440000 -49.350000 0.460000)\n        3)\n      (segment 1519 \n        (point 316.670000 -29.440000 -49.350000 0.460000)\n        (point 318.400000 -30.820000 -51.420000 0.460000)\n        3)\n      (segment 1520 \n        (point 318.400000 -30.820000 -51.420000 0.460000)\n        (point 318.400000 -30.820000 -51.500000 0.460000)\n        3)\n      (segment 1521 \n        (point 318.400000 -30.820000 -51.500000 0.460000)\n        (point 320.650000 -30.300000 -52.150000 0.460000)\n        3)\n      (segment 1522 \n        (point 320.650000 -30.300000 -52.150000 0.460000)\n        (point 320.650000 -30.300000 -52.170000 0.460000)\n        3)\n      (segment 1523 \n        (point 320.650000 -30.300000 -52.170000 0.460000)\n        (point 323.870000 -31.940000 -52.950000 0.460000)\n        3)\n      (segment 1524 \n        (point 323.870000 -31.940000 -52.950000 0.460000)\n        (point 326.040000 -33.220000 -52.630000 0.460000)\n        3)\n      (segment 1525 \n        (point 326.040000 -33.220000 -52.630000 0.460000)\n        (point 328.940000 -35.530000 -53.880000 0.460000)\n        3)\n      (segment 1526 \n        (point 328.940000 -35.530000 -53.880000 0.460000)\n        (point 328.940000 -35.530000 -53.900000 0.460000)\n        3)\n      (segment 1527 \n        (point 328.940000 -35.530000 -53.900000 0.460000)\n        (point 330.670000 -36.910000 -55.550000 0.460000)\n        3)\n      (segment 1528 \n        (point 330.670000 -36.910000 -55.550000 0.460000)\n        (point 330.670000 -36.910000 -55.570000 0.460000)\n        3)\n      (segment 1529 \n        (point 330.670000 -36.910000 -55.570000 0.460000)\n        (point 331.790000 -39.640000 -56.470000 0.460000)\n        3)\n      (segment 1530 \n        (point 331.790000 -39.640000 -56.470000 0.460000)\n        (point 331.960000 -44.370000 -57.770000 0.460000)\n        3)\n      (segment 1531 \n        (point 331.960000 -44.370000 -57.770000 0.460000)\n        (point 333.910000 -48.690000 -57.770000 0.460000)\n        3)\n      (segment 1532 \n        (point 333.910000 -48.690000 -57.770000 0.460000)\n        (point 334.360000 -48.580000 -57.770000 0.460000)\n        3)\n      (segment 1533 \n        (point 334.360000 -48.580000 -57.770000 0.460000)\n        (point 337.400000 -51.460000 -57.600000 0.460000)\n        3)\n      (segment 1534 \n        (point 337.400000 -51.460000 -57.600000 0.460000)\n        (point 337.400000 -51.460000 -57.570000 0.460000)\n        3)\n      (segment 1535 \n        (point 337.400000 -51.460000 -57.570000 0.460000)\n        (point 339.390000 -53.980000 -55.520000 0.460000)\n        3)\n      (segment 1536 \n        (point 339.390000 -53.980000 -55.520000 0.460000)\n        (point 339.390000 -53.980000 -55.550000 0.460000)\n        3)\n      (segment 1537 \n        (point 339.390000 -53.980000 -55.550000 0.460000)\n        (point 341.710000 -55.820000 -55.470000 0.460000)\n        3)\n      (segment 1538 \n        (point 341.710000 -55.820000 -55.470000 0.460000)\n        (point 342.820000 -58.550000 -54.800000 0.460000)\n        3)\n      (segment 1539 \n        (point 342.820000 -58.550000 -54.800000 0.460000)\n        (point 345.270000 -60.970000 -55.950000 0.460000)\n        3)\n      (segment 1540 \n        (point 345.270000 -60.970000 -55.950000 0.460000)\n        (point 348.030000 -62.700000 -55.300000 0.460000)\n        3)\n      (segment 1541 \n        (point 348.030000 -62.700000 -55.300000 0.460000)\n        (point 348.030000 -62.700000 -55.350000 0.460000)\n        3)\n      (segment 1542 \n        (point 348.030000 -62.700000 -55.350000 0.460000)\n        (point 349.150000 -65.430000 -55.350000 0.460000)\n        3)\n      (segment 1543 \n        (point 349.150000 -65.430000 -55.350000 0.460000)\n        (point 351.200000 -66.130000 -57.200000 0.230000)\n        3)\n      (segment 1544 \n        (point 351.200000 -66.130000 -57.200000 0.230000)\n        (point 351.200000 -66.130000 -57.220000 0.230000)\n        3)\n      (segment 1545 \n        (point 351.200000 -66.130000 -57.220000 0.230000)\n        (point 354.850000 -67.670000 -58.070000 0.230000)\n        3)\n      (segment 1546 \n        (point 354.850000 -67.670000 -58.070000 0.230000)\n        (point 355.300000 -67.560000 -58.100000 0.230000)\n        3)\n      (segment 1547 \n        (point 355.300000 -67.560000 -58.100000 0.230000)\n        (point 358.380000 -68.640000 -59.220000 0.230000)\n        3)\n      (segment 1548 \n        (point 358.380000 -68.640000 -59.220000 0.230000)\n        (point 358.380000 -68.640000 -59.250000 0.230000)\n        3)\n      (segment 1549 \n        (point 358.380000 -68.640000 -59.250000 0.230000)\n        (point 362.480000 -70.070000 -59.830000 0.230000)\n        3)\n      (segment 1550 \n        (point 362.480000 -70.070000 -59.830000 0.230000)\n        (point 362.480000 -70.070000 -59.850000 0.230000)\n        3)\n      (segment 1551 \n        (point 362.480000 -70.070000 -59.850000 0.230000)\n        (point 365.870000 -70.470000 -61.050000 0.230000)\n        3)\n      (segment 1552 \n        (point 365.870000 -70.470000 -61.050000 0.230000)\n        (point 367.670000 -70.040000 -63.700000 0.230000)\n        3)\n      (segment 1553 \n        (point 367.670000 -70.040000 -63.700000 0.230000)\n        (point 367.670000 -70.040000 -64.000000 0.230000)\n        3))\n    (branch 64 -1 \n      (segment 1554 \n        (point 265.520000 17.950000 -15.450000 0.460000)\n        (point 265.520000 17.950000 -15.450000 0.460000)\n        3)\n      (segment 1555 \n        (point 265.520000 17.950000 -15.450000 0.460000)\n        (point 267.750000 18.480000 -15.480000 0.460000)\n        3)\n      (segment 1556 \n        (point 267.750000 18.480000 -15.480000 0.460000)\n        (point 267.670000 20.840000 -18.150000 0.460000)\n        3)\n      (segment 1557 \n        (point 267.670000 20.840000 -18.150000 0.460000)\n        (point 265.570000 19.750000 -19.200000 0.460000)\n        3)\n      (segment 1558 \n        (point 265.570000 19.750000 -19.200000 0.460000)\n        (point 262.500000 20.830000 -20.950000 0.460000)\n        3)\n      (segment 1559 \n        (point 262.500000 20.830000 -20.950000 0.460000)\n        (point 264.540000 20.110000 -22.850000 0.460000)\n        3)\n      (segment 1560 \n        (point 264.540000 20.110000 -22.850000 0.460000)\n        (point 267.480000 19.600000 -24.720000 0.460000)\n        3)\n      (segment 1561 \n        (point 267.480000 19.600000 -24.720000 0.460000)\n        (point 267.480000 19.600000 -24.750000 0.460000)\n        3)\n      (segment 1562 \n        (point 267.480000 19.600000 -24.750000 0.460000)\n        (point 270.170000 20.240000 -26.550000 0.460000)\n        3)\n      (segment 1563 \n        (point 270.170000 20.240000 -26.550000 0.460000)\n        (point 269.730000 20.130000 -26.550000 0.460000)\n        3)\n      (segment 1564 \n        (point 269.730000 20.130000 -26.550000 0.460000)\n        (point 273.110000 19.730000 -28.000000 0.460000)\n        3)\n      (segment 1565 \n        (point 273.110000 19.730000 -28.000000 0.460000)\n        (point 274.460000 20.050000 -27.950000 0.460000)\n        3))\n    (branch 65 64 \n      (segment 1566 \n        (point 274.460000 20.050000 -27.950000 0.460000)\n        (point 276.230000 24.520000 -27.350000 0.230000)\n        3)\n      (segment 1567 \n        (point 276.230000 24.520000 -27.350000 0.230000)\n        (point 277.890000 25.510000 -30.130000 0.230000)\n        3)\n      (segment 1568 \n        (point 277.890000 25.510000 -30.130000 0.230000)\n        (point 282.050000 25.890000 -31.350000 0.230000)\n        3)\n      (segment 1569 \n        (point 282.050000 25.890000 -31.350000 0.230000)\n        (point 281.600000 25.780000 -31.350000 0.230000)\n        3)\n      (segment 1570 \n        (point 281.600000 25.780000 -31.350000 0.230000)\n        (point 284.540000 25.270000 -32.130000 0.230000)\n        3)\n      (segment 1571 \n        (point 284.540000 25.270000 -32.130000 0.230000)\n        (point 285.790000 27.950000 -33.470000 0.230000)\n        3)\n      (segment 1572 \n        (point 285.790000 27.950000 -33.470000 0.230000)\n        (point 285.790000 27.950000 -33.530000 0.230000)\n        3)\n      (segment 1573 \n        (point 285.790000 27.950000 -33.530000 0.230000)\n        (point 290.260000 29.000000 -35.720000 0.230000)\n        3)\n      (segment 1574 \n        (point 290.260000 29.000000 -35.720000 0.230000)\n        (point 293.520000 29.170000 -36.720000 0.230000)\n        3)\n      (segment 1575 \n        (point 293.520000 29.170000 -36.720000 0.230000)\n        (point 296.830000 31.140000 -37.880000 0.230000)\n        3)\n      (segment 1576 \n        (point 296.830000 31.140000 -37.880000 0.230000)\n        (point 303.840000 33.380000 -38.550000 0.230000)\n        3)\n      (segment 1577 \n        (point 303.840000 33.380000 -38.550000 0.230000)\n        (point 303.840000 33.380000 -38.570000 0.230000)\n        3)\n      (segment 1578 \n        (point 303.840000 33.380000 -38.570000 0.230000)\n        (point 308.990000 37.570000 -39.700000 0.230000)\n        3)\n      (segment 1579 \n        (point 308.990000 37.570000 -39.700000 0.230000)\n        (point 311.990000 38.860000 -41.450000 0.230000)\n        3)\n      (segment 1580 \n        (point 311.990000 38.860000 -41.450000 0.230000)\n        (point 311.990000 38.860000 -41.470000 0.230000)\n        3)\n      (segment 1581 \n        (point 311.990000 38.860000 -41.470000 0.230000)\n        (point 313.950000 40.510000 -42.150000 0.230000)\n        3)\n      (segment 1582 \n        (point 313.950000 40.510000 -42.150000 0.230000)\n        (point 316.810000 42.370000 -42.150000 0.230000)\n        3)\n      (segment 1583 \n        (point 316.810000 42.370000 -42.150000 0.230000)\n        (point 319.350000 43.580000 -42.170000 0.230000)\n        3)\n      (segment 1584 \n        (point 319.350000 43.580000 -42.170000 0.230000)\n        (point 323.550000 45.760000 -42.220000 0.230000)\n        3)\n      (segment 1585 \n        (point 323.550000 45.760000 -42.220000 0.230000)\n        (point 327.490000 49.070000 -42.220000 0.230000)\n        3)\n      (segment 1586 \n        (point 327.490000 49.070000 -42.220000 0.230000)\n        (point 327.360000 49.640000 -42.220000 0.230000)\n        3)\n      (segment 1587 \n        (point 327.360000 49.640000 -42.220000 0.230000)\n        (point 331.430000 52.380000 -42.220000 0.230000)\n        3)\n      (segment 1588 \n        (point 331.430000 52.380000 -42.220000 0.230000)\n        (point 333.710000 54.700000 -43.800000 0.230000)\n        3)\n      (segment 1589 \n        (point 333.710000 54.700000 -43.800000 0.230000)\n        (point 335.590000 58.730000 -45.220000 0.230000)\n        3)\n      (segment 1590 \n        (point 335.590000 58.730000 -45.220000 0.230000)\n        (point 335.590000 58.730000 -45.250000 0.230000)\n        3)\n      (segment 1591 \n        (point 335.590000 58.730000 -45.250000 0.230000)\n        (point 339.670000 61.480000 -46.570000 0.230000)\n        3)\n      (segment 1592 \n        (point 339.670000 61.480000 -46.570000 0.230000)\n        (point 341.490000 63.690000 -48.070000 0.230000)\n        3)\n      (segment 1593 \n        (point 341.490000 63.690000 -48.070000 0.230000)\n        (point 344.580000 62.630000 -49.580000 0.230000)\n        3)\n      (segment 1594 \n        (point 344.580000 62.630000 -49.580000 0.230000)\n        (point 347.610000 65.720000 -51.280000 0.230000)\n        3)\n      (segment 1595 \n        (point 347.610000 65.720000 -51.280000 0.230000)\n        (point 347.610000 65.720000 -51.300000 0.230000)\n        3)\n      (segment 1596 \n        (point 347.610000 65.720000 -51.300000 0.230000)\n        (point 351.460000 65.430000 -52.750000 0.230000)\n        3)\n      (segment 1597 \n        (point 351.460000 65.430000 -52.750000 0.230000)\n        (point 355.030000 66.270000 -54.200000 0.230000)\n        3)\n      (segment 1598 \n        (point 355.030000 66.270000 -54.200000 0.230000)\n        (point 355.030000 66.270000 -54.780000 0.230000)\n        3)\n      (segment 1599 \n        (point 355.030000 66.270000 -54.780000 0.230000)\n        (point 358.790000 68.340000 -53.380000 0.230000)\n        3)\n      (segment 1600 \n        (point 358.790000 68.340000 -53.380000 0.230000)\n        (point 359.860000 69.790000 -55.630000 0.230000)\n        3)\n      (segment 1601 \n        (point 359.860000 69.790000 -55.630000 0.230000)\n        (point 359.420000 69.680000 -55.630000 0.230000)\n        3))\n    (branch 66 64 \n      (segment 1602 \n        (point 274.460000 20.050000 -27.950000 0.460000)\n        (point 271.690000 21.780000 -29.820000 0.230000)\n        3)\n      (segment 1603 \n        (point 271.690000 21.780000 -29.820000 0.230000)\n        (point 269.910000 21.360000 -33.030000 0.230000)\n        3)\n      (segment 1604 \n        (point 269.910000 21.360000 -33.030000 0.230000)\n        (point 269.380000 23.640000 -32.970000 0.230000)\n        3)\n      (segment 1605 \n        (point 269.380000 23.640000 -32.970000 0.230000)\n        (point 269.380000 23.640000 -33.050000 0.230000)\n        3)\n      (segment 1606 \n        (point 269.380000 23.640000 -33.050000 0.230000)\n        (point 266.880000 24.230000 -35.670000 0.230000)\n        3)\n      (segment 1607 \n        (point 266.880000 24.230000 -35.670000 0.230000)\n        (point 266.880000 24.230000 -35.700000 0.230000)\n        3)\n      (segment 1608 \n        (point 266.880000 24.230000 -35.700000 0.230000)\n        (point 265.000000 26.190000 -38.050000 0.230000)\n        3)\n      (segment 1609 \n        (point 265.000000 26.190000 -38.050000 0.230000)\n        (point 263.130000 28.140000 -41.080000 0.230000)\n        3)\n      (segment 1610 \n        (point 263.130000 28.140000 -41.080000 0.230000)\n        (point 261.260000 30.090000 -43.330000 0.230000)\n        3)\n      (segment 1611 \n        (point 261.260000 30.090000 -43.330000 0.230000)\n        (point 260.200000 34.620000 -44.900000 0.230000)\n        3)\n      (segment 1612 \n        (point 260.200000 34.620000 -44.900000 0.230000)\n        (point 256.410000 36.720000 -45.950000 0.230000)\n        3)\n      (segment 1613 \n        (point 256.410000 36.720000 -45.950000 0.230000)\n        (point 255.750000 39.550000 -47.170000 0.230000)\n        3)\n      (segment 1614 \n        (point 255.750000 39.550000 -47.170000 0.230000)\n        (point 252.530000 41.180000 -47.170000 0.230000)\n        3)\n      (segment 1615 \n        (point 252.530000 41.180000 -47.170000 0.230000)\n        (point 249.640000 43.490000 -49.150000 0.230000)\n        3)\n      (segment 1616 \n        (point 249.640000 43.490000 -49.150000 0.230000)\n        (point 249.160000 47.560000 -50.830000 0.230000)\n        3)\n      (segment 1617 \n        (point 249.160000 47.560000 -50.830000 0.230000)\n        (point 248.180000 49.720000 -51.750000 0.230000)\n        3)\n      (segment 1618 \n        (point 248.180000 49.720000 -51.750000 0.230000)\n        (point 247.740000 49.610000 -51.750000 0.230000)\n        3)\n      (segment 1619 \n        (point 247.740000 49.610000 -51.750000 0.230000)\n        (point 247.070000 52.440000 -52.700000 0.230000)\n        3)\n      (segment 1620 \n        (point 247.070000 52.440000 -52.700000 0.230000)\n        (point 247.070000 52.440000 -52.720000 0.230000)\n        3)\n      (segment 1621 \n        (point 247.070000 52.440000 -52.720000 0.230000)\n        (point 245.780000 53.930000 -51.720000 0.230000)\n        3)\n      (segment 1622 \n        (point 245.780000 53.930000 -51.720000 0.230000)\n        (point 244.220000 56.550000 -54.150000 0.230000)\n        3)\n      (segment 1623 \n        (point 244.220000 56.550000 -54.150000 0.230000)\n        (point 241.460000 58.290000 -55.650000 0.230000)\n        3)\n      (segment 1624 \n        (point 241.460000 58.290000 -55.650000 0.230000)\n        (point 237.980000 61.070000 -57.530000 0.230000)\n        3)\n      (segment 1625 \n        (point 237.980000 61.070000 -57.530000 0.230000)\n        (point 237.980000 61.070000 -57.550000 0.230000)\n        3)\n      (segment 1626 \n        (point 237.980000 61.070000 -57.550000 0.230000)\n        (point 233.490000 64.180000 -58.200000 0.230000)\n        3)\n      (segment 1627 \n        (point 233.490000 64.180000 -58.200000 0.230000)\n        (point 233.490000 64.180000 -58.220000 0.230000)\n        3)\n      (segment 1628 \n        (point 233.490000 64.180000 -58.220000 0.230000)\n        (point 230.490000 66.940000 -57.670000 0.230000)\n        3)\n      (segment 1629 \n        (point 230.490000 66.940000 -57.670000 0.230000)\n        (point 227.730000 68.670000 -60.220000 0.230000)\n        3)\n      (segment 1630 \n        (point 227.730000 68.670000 -60.220000 0.230000)\n        (point 226.110000 69.500000 -62.700000 0.230000)\n        3)\n      (segment 1631 \n        (point 226.110000 69.500000 -62.700000 0.230000)\n        (point 223.540000 72.480000 -64.750000 0.230000)\n        3)\n      (segment 1632 \n        (point 223.540000 72.480000 -64.750000 0.230000)\n        (point 223.540000 72.480000 -64.780000 0.230000)\n        3)\n      (segment 1633 \n        (point 223.540000 72.480000 -64.780000 0.230000)\n        (point 220.900000 73.650000 -66.950000 0.230000)\n        3)\n      (segment 1634 \n        (point 220.900000 73.650000 -66.950000 0.230000)\n        (point 220.460000 73.550000 -66.970000 0.230000)\n        3)\n      (segment 1635 \n        (point 220.460000 73.550000 -66.970000 0.230000)\n        (point 219.920000 75.820000 -68.850000 0.230000)\n        3)\n      (segment 1636 \n        (point 219.920000 75.820000 -68.850000 0.230000)\n        (point 219.920000 75.820000 -68.900000 0.230000)\n        3)\n      (segment 1637 \n        (point 219.920000 75.820000 -68.900000 0.230000)\n        (point 217.700000 75.290000 -71.170000 0.230000)\n        3)\n      (segment 1638 \n        (point 217.700000 75.290000 -71.170000 0.230000)\n        (point 217.700000 75.290000 -71.200000 0.230000)\n        3)\n      (segment 1639 \n        (point 217.700000 75.290000 -71.200000 0.230000)\n        (point 217.220000 79.360000 -72.400000 0.230000)\n        3)\n      (segment 1640 \n        (point 217.220000 79.360000 -72.400000 0.230000)\n        (point 216.240000 81.510000 -75.050000 0.230000)\n        3)\n      (segment 1641 \n        (point 216.240000 81.510000 -75.050000 0.230000)\n        (point 216.110000 82.080000 -75.050000 0.230000)\n        3)\n      (segment 1642 \n        (point 216.110000 82.080000 -75.050000 0.230000)\n        (point 215.530000 82.540000 -78.300000 0.230000)\n        3)\n      (segment 1643 \n        (point 215.530000 82.540000 -78.300000 0.230000)\n        (point 215.530000 82.540000 -78.350000 0.230000)\n        3))\n    (branch 67 -1 \n      (segment 1644 \n        (point 269.000000 15.190000 12.420000 0.460000)\n        (point 268.880000 15.750000 12.420000 0.460000)\n        3)\n      (segment 1645 \n        (point 268.880000 15.750000 12.420000 0.460000)\n        (point 272.050000 18.280000 12.420000 0.460000)\n        3)\n      (segment 1646 \n        (point 272.050000 18.280000 12.420000 0.460000)\n        (point 269.540000 18.890000 12.420000 0.460000)\n        3)\n      (segment 1647 \n        (point 269.540000 18.890000 12.420000 0.460000)\n        (point 267.180000 18.930000 14.050000 0.460000)\n        3)\n      (segment 1648 \n        (point 267.180000 18.930000 14.050000 0.460000)\n        (point 265.970000 18.060000 17.320000 0.460000)\n        3)\n      (segment 1649 \n        (point 265.970000 18.060000 17.320000 0.460000)\n        (point 264.370000 18.870000 20.730000 0.460000)\n        3)\n      (segment 1650 \n        (point 264.370000 18.870000 20.730000 0.460000)\n        (point 262.760000 19.690000 23.020000 0.460000)\n        3)\n      (segment 1651 \n        (point 262.760000 19.690000 23.020000 0.460000)\n        (point 263.430000 16.860000 24.600000 0.460000)\n        3)\n      (segment 1652 \n        (point 263.430000 16.860000 24.600000 0.460000)\n        (point 261.120000 18.700000 27.020000 0.460000)\n        3)\n      (segment 1653 \n        (point 261.120000 18.700000 27.020000 0.460000)\n        (point 260.670000 18.610000 27.000000 0.460000)\n        3)\n      (segment 1654 \n        (point 260.670000 18.610000 27.000000 0.460000)\n        (point 258.700000 16.950000 28.900000 0.460000)\n        3)\n      (segment 1655 \n        (point 258.700000 16.950000 28.900000 0.460000)\n        (point 259.180000 12.880000 28.330000 0.460000)\n        3)\n      (segment 1656 \n        (point 259.180000 12.880000 28.330000 0.460000)\n        (point 261.680000 12.260000 30.230000 0.460000)\n        3)\n      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255.260000 133.670000 0.350000 1.375000)\n        (point 255.550000 138.510000 -0.630000 1.375000)\n        4)\n      (segment 1697 \n        (point 255.550000 138.510000 -0.630000 1.375000)\n        (point 256.360000 141.090000 -1.820000 1.375000)\n        4)\n      (segment 1698 \n        (point 256.360000 141.090000 -1.820000 1.375000)\n        (point 257.930000 144.440000 -3.200000 1.375000)\n        4)\n      (segment 1699 \n        (point 257.930000 144.440000 -3.200000 1.375000)\n        (point 257.930000 144.440000 -3.220000 1.375000)\n        4)\n      (segment 1700 \n        (point 257.930000 144.440000 -3.220000 1.375000)\n        (point 256.060000 146.390000 -3.550000 1.375000)\n        4)\n      (segment 1701 \n        (point 256.060000 146.390000 -3.550000 1.375000)\n        (point 256.200000 151.790000 -3.550000 1.375000)\n        4)\n      (segment 1702 \n        (point 256.200000 151.790000 -3.550000 1.375000)\n        (point 256.920000 156.740000 -3.550000 1.375000)\n 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254.460000 213.490000 -10.000000 1.375000)\n        (point 254.110000 216.990000 -11.250000 1.375000)\n        4)\n      (segment 1717 \n        (point 254.110000 216.990000 -11.250000 1.375000)\n        (point 251.090000 219.870000 -12.100000 1.375000)\n        4))\n    (branch 76 75 \n      (segment 1718 \n        (point 251.090000 219.870000 -12.100000 1.375000)\n        (point 250.030000 224.390000 -12.650000 1.375000)\n        4)\n      (segment 1719 \n        (point 250.030000 224.390000 -12.650000 1.375000)\n        (point 248.470000 227.010000 -11.800000 1.375000)\n        4)\n      (segment 1720 \n        (point 248.470000 227.010000 -11.800000 1.375000)\n        (point 245.700000 228.760000 -11.800000 1.375000)\n        4)\n      (segment 1721 \n        (point 245.700000 228.760000 -11.800000 1.375000)\n        (point 243.700000 231.270000 -12.650000 1.375000)\n        4)\n      (segment 1722 \n        (point 243.700000 231.270000 -12.650000 1.375000)\n        (point 243.570000 231.830000 -12.650000 1.375000)\n        4)\n      (segment 1723 \n        (point 243.570000 231.830000 -12.650000 1.375000)\n        (point 245.010000 235.760000 -13.320000 1.375000)\n        4)\n      (segment 1724 \n        (point 245.010000 235.760000 -13.320000 1.375000)\n        (point 245.420000 240.040000 -14.380000 1.375000)\n        4)\n      (segment 1725 \n        (point 245.420000 240.040000 -14.380000 1.375000)\n        (point 244.760000 242.870000 -16.320000 1.375000)\n        4)\n      (segment 1726 \n        (point 244.760000 242.870000 -16.320000 1.375000)\n        (point 245.880000 246.120000 -18.700000 1.375000)\n        4)\n      (segment 1727 \n        (point 245.880000 246.120000 -18.700000 1.375000)\n        (point 245.600000 249.490000 -19.670000 1.375000)\n        4)\n      (segment 1728 \n        (point 245.600000 249.490000 -19.670000 1.375000)\n        (point 246.580000 253.310000 -19.880000 1.375000)\n        4))\n    (branch 77 76 \n      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(point 255.820000 272.200000 -15.020000 1.145000)\n        4)\n      (segment 1736 \n        (point 255.820000 272.200000 -15.020000 1.145000)\n        (point 254.390000 274.250000 -16.470000 1.145000)\n        4)\n      (segment 1737 \n        (point 254.390000 274.250000 -16.470000 1.145000)\n        (point 253.860000 276.510000 -18.350000 1.145000)\n        4)\n      (segment 1738 \n        (point 253.860000 276.510000 -18.350000 1.145000)\n        (point 253.330000 278.770000 -19.420000 1.145000)\n        4))\n    (branch 78 77 \n      (segment 1739 \n        (point 253.330000 278.770000 -19.420000 1.145000)\n        (point 251.060000 282.420000 -19.420000 1.145000)\n        4)\n      (segment 1740 \n        (point 251.060000 282.420000 -19.420000 1.145000)\n        (point 250.980000 284.790000 -20.520000 1.145000)\n        4)\n      (segment 1741 \n        (point 250.980000 284.790000 -20.520000 1.145000)\n        (point 252.560000 288.150000 -21.500000 1.145000)\n        4)\n      (segment 1742 \n        (point 252.560000 288.150000 -21.500000 1.145000)\n        (point 252.560000 288.150000 -21.520000 1.145000)\n        4)\n      (segment 1743 \n        (point 252.560000 288.150000 -21.520000 1.145000)\n        (point 252.510000 292.320000 -21.250000 1.145000)\n        4)\n      (segment 1744 \n        (point 252.510000 292.320000 -21.250000 1.145000)\n        (point 254.220000 295.100000 -20.700000 1.145000)\n        4)\n      (segment 1745 \n        (point 254.220000 295.100000 -20.700000 1.145000)\n        (point 254.620000 299.380000 -20.700000 1.145000)\n        4)\n      (segment 1746 \n        (point 254.620000 299.380000 -20.700000 1.145000)\n        (point 254.280000 302.890000 -19.630000 1.145000)\n        4)\n      (segment 1747 \n        (point 254.280000 302.890000 -19.630000 1.145000)\n        (point 254.820000 306.600000 -18.770000 1.145000)\n        4)\n      (segment 1748 \n        (point 254.820000 306.600000 -18.770000 1.145000)\n        (point 253.580000 309.890000 -18.770000 1.145000)\n        4)\n      (segment 1749 \n        (point 253.580000 309.890000 -18.770000 1.145000)\n        (point 252.920000 312.710000 -20.830000 1.145000)\n        4)\n      (segment 1750 \n        (point 252.920000 312.710000 -20.830000 1.145000)\n        (point 252.920000 312.710000 -20.850000 1.145000)\n        4)\n      (segment 1751 \n        (point 252.920000 312.710000 -20.850000 1.145000)\n        (point 252.440000 316.780000 -21.670000 1.145000)\n        4)\n      (segment 1752 \n        (point 252.440000 316.780000 -21.670000 1.145000)\n        (point 251.650000 320.170000 -22.500000 1.145000)\n        4)\n      (segment 1753 \n        (point 251.650000 320.170000 -22.500000 1.145000)\n        (point 249.820000 323.930000 -23.330000 1.145000)\n        4)\n      (segment 1754 \n        (point 249.820000 323.930000 -23.330000 1.145000)\n        (point 247.240000 326.910000 -23.900000 1.145000)\n        4)\n      (segment 1755 \n        (point 247.240000 326.910000 -23.900000 1.145000)\n        (point 246.390000 328.510000 -24.920000 1.145000)\n        4)\n      (segment 1756 \n        (point 246.390000 328.510000 -24.920000 1.145000)\n        (point 246.610000 331.550000 -25.830000 1.145000)\n        4)\n      (segment 1757 \n        (point 246.610000 331.550000 -25.830000 1.145000)\n        (point 249.090000 335.110000 -26.500000 1.145000)\n        4)\n      (segment 1758 \n        (point 249.090000 335.110000 -26.500000 1.145000)\n        (point 251.370000 337.440000 -25.850000 1.145000)\n        4)\n      (segment 1759 \n        (point 251.370000 337.440000 -25.850000 1.145000)\n        (point 251.770000 341.720000 -25.230000 1.145000)\n        4)\n      (segment 1760 \n        (point 251.770000 341.720000 -25.230000 1.145000)\n        (point 248.480000 345.720000 -25.230000 1.145000)\n        4)\n      (segment 1761 \n        (point 248.480000 345.720000 -25.230000 1.145000)\n        (point 245.010000 348.490000 -26.380000 1.145000)\n        4)\n      (segment 1762 \n        (point 245.010000 348.490000 -26.380000 1.145000)\n        (point 243.770000 351.770000 -26.850000 1.145000)\n        4)\n      (segment 1763 \n        (point 243.770000 351.770000 -26.850000 1.145000)\n        (point 244.180000 356.050000 -27.350000 1.145000)\n        4)\n      (segment 1764 \n        (point 244.180000 356.050000 -27.350000 1.145000)\n        (point 244.400000 359.090000 -26.950000 1.145000)\n        4))\n    (branch 79 78 \n      (segment 1765 \n        (point 244.400000 359.090000 -26.950000 1.145000)\n        (point 243.050000 361.090000 -26.020000 0.915000)\n        4)\n      (segment 1766 \n        (point 243.050000 361.090000 -26.020000 0.915000)\n        (point 242.090000 363.250000 -24.320000 0.915000)\n        4)\n      (segment 1767 \n        (point 242.090000 363.250000 -24.320000 0.915000)\n        (point 241.680000 364.950000 -22.800000 0.915000)\n        4)\n      (segment 1768 \n        (point 241.680000 364.950000 -22.800000 0.915000)\n        (point 241.730000 366.750000 -21.320000 0.915000)\n        4)\n      (segment 1769 \n        (point 241.730000 366.750000 -21.320000 0.915000)\n        (point 242.220000 368.670000 -19.650000 0.915000)\n        4)\n      (segment 1770 \n        (point 242.220000 368.670000 -19.650000 0.915000)\n        (point 243.040000 371.250000 -18.720000 0.915000)\n        4)\n      (segment 1771 \n        (point 243.040000 371.250000 -18.720000 0.915000)\n        (point 244.280000 373.930000 -18.800000 0.915000)\n        4)\n      (segment 1772 \n        (point 244.280000 373.930000 -18.800000 0.915000)\n        (point 245.100000 376.500000 -17.600000 0.915000)\n        4)\n      (segment 1773 \n        (point 245.100000 376.500000 -17.600000 0.915000)\n        (point 245.010000 378.870000 -16.670000 0.915000)\n        4)\n      (segment 1774 \n        (point 245.010000 378.870000 -16.670000 0.915000)\n        (point 244.750000 380.000000 -15.900000 0.915000)\n        4)\n      (segment 1775 \n        (point 244.750000 380.000000 -15.900000 0.915000)\n        (point 242.740000 382.520000 -15.400000 0.915000)\n        4)\n      (segment 1776 \n        (point 242.740000 382.520000 -15.400000 0.915000)\n        (point 242.520000 385.460000 -15.100000 0.915000)\n        4)\n      (segment 1777 \n        (point 242.520000 385.460000 -15.100000 0.915000)\n        (point 240.250000 389.110000 -15.100000 0.915000)\n        4)\n      (segment 1778 \n        (point 240.250000 389.110000 -15.100000 0.915000)\n        (point 238.390000 391.060000 -15.100000 0.915000)\n        4)\n      (segment 1779 \n        (point 238.390000 391.060000 -15.100000 0.915000)\n        (point 237.730000 393.890000 -14.130000 0.915000)\n        4)\n      (segment 1780 \n        (point 237.730000 393.890000 -14.130000 0.915000)\n        (point 238.660000 395.900000 -13.420000 0.915000)\n        4)\n      (segment 1781 \n        (point 238.660000 395.900000 -13.420000 0.915000)\n        (point 239.600000 397.920000 -12.620000 0.915000)\n        4)\n      (segment 1782 \n        (point 239.600000 397.920000 -12.620000 0.915000)\n        (point 238.540000 402.440000 -12.400000 0.915000)\n        4)\n      (segment 1783 \n        (point 238.540000 402.440000 -12.400000 0.915000)\n        (point 237.930000 407.070000 -12.400000 0.915000)\n        4)\n      (segment 1784 \n        (point 237.930000 407.070000 -12.400000 0.915000)\n        (point 236.190000 408.450000 -12.400000 0.915000)\n        4)\n      (segment 1785 \n        (point 236.190000 408.450000 -12.400000 0.915000)\n        (point 236.690000 410.360000 -13.320000 0.915000)\n        4)\n      (segment 1786 \n        (point 236.690000 410.360000 -13.320000 0.915000)\n        (point 237.230000 414.080000 -14.520000 0.915000)\n        4)\n      (segment 1787 \n        (point 237.230000 414.080000 -14.520000 0.915000)\n        (point 238.040000 416.650000 -15.480000 0.915000)\n        4)\n      (segment 1788 \n        (point 238.040000 416.650000 -15.480000 0.915000)\n        (point 236.530000 421.070000 -16.380000 0.915000)\n        4)\n      (segment 1789 \n        (point 236.530000 421.070000 -16.380000 0.915000)\n        (point 235.040000 425.500000 -14.650000 0.915000)\n        4)\n      (segment 1790 \n        (point 235.040000 425.500000 -14.650000 0.915000)\n        (point 234.060000 427.660000 -15.400000 0.915000)\n        4)\n      (segment 1791 \n        (point 234.060000 427.660000 -15.400000 0.915000)\n        (point 232.630000 429.710000 -16.350000 0.915000)\n        4)\n      (segment 1792 \n        (point 232.630000 429.710000 -16.350000 0.915000)\n        (point 233.480000 434.090000 -16.770000 0.915000)\n        4)\n      (segment 1793 \n        (point 233.480000 434.090000 -16.770000 0.915000)\n        (point 234.210000 439.050000 -17.250000 0.915000)\n        4)\n      (segment 1794 \n        (point 234.210000 439.050000 -17.250000 0.915000)\n        (point 236.040000 441.270000 -17.300000 0.915000)\n        4)\n      (segment 1795 \n        (point 236.040000 441.270000 -17.300000 0.915000)\n        (point 236.540000 443.170000 -18.100000 0.915000)\n        4)\n      (segment 1796 \n        (point 236.540000 443.170000 -18.100000 0.915000)\n        (point 235.160000 447.020000 -18.920000 0.915000)\n        4)\n      (segment 1797 \n        (point 235.160000 447.020000 -18.920000 0.915000)\n        (point 233.300000 448.980000 -18.920000 0.915000)\n        4)\n      (segment 1798 \n        (point 233.300000 448.980000 -18.920000 0.915000)\n        (point 236.180000 450.720000 -19.730000 0.915000)\n        4)\n      (segment 1799 \n        (point 236.180000 450.720000 -19.730000 0.915000)\n        (point 237.250000 452.160000 -18.720000 0.915000)\n        4)\n      (segment 1800 \n        (point 237.250000 452.160000 -18.720000 0.915000)\n        (point 237.290000 453.970000 -17.270000 0.915000)\n        4)\n      (segment 1801 \n        (point 237.290000 453.970000 -17.270000 0.915000)\n        (point 237.290000 453.970000 -17.300000 0.915000)\n        4))\n    (branch 80 79 \n      (segment 1802 \n        (point 237.290000 453.970000 -17.300000 0.915000)\n        (point 238.430000 457.220000 -16.170000 0.915000)\n        4)\n      (segment 1803 \n        (point 238.430000 457.220000 -16.170000 0.915000)\n        (point 238.340000 459.590000 -16.170000 0.915000)\n        4)\n      (segment 1804 \n        (point 238.340000 459.590000 -16.170000 0.915000)\n        (point 236.790000 462.210000 -16.770000 0.915000)\n        4)\n      (segment 1805 \n        (point 236.790000 462.210000 -16.770000 0.915000)\n        (point 235.810000 464.370000 -16.650000 0.915000)\n        4))\n    (branch 81 80 \n      (segment 1806 \n        (point 235.810000 464.370000 -16.650000 0.915000)\n        (point 234.880000 468.330000 -16.650000 0.915000)\n        4)\n      (segment 1807 \n        (point 234.880000 468.330000 -16.650000 0.915000)\n        (point 234.220000 471.170000 -17.570000 0.915000)\n        4)\n      (segment 1808 \n        (point 234.220000 471.170000 -17.570000 0.915000)\n        (point 235.210000 474.980000 -18.880000 0.915000)\n        4)\n      (segment 1809 \n        (point 235.210000 474.980000 -18.880000 0.915000)\n        (point 235.610000 479.260000 -18.880000 0.915000)\n        4)\n      (segment 1810 \n        (point 235.610000 479.260000 -18.880000 0.915000)\n        (point 234.820000 482.650000 -18.720000 0.915000)\n        4)\n      (segment 1811 \n        (point 234.820000 482.650000 -18.720000 0.915000)\n        (point 234.150000 485.470000 -19.300000 0.915000)\n        4)\n      (segment 1812 \n        (point 234.150000 485.470000 -19.300000 0.915000)\n        (point 236.040000 489.510000 -19.330000 0.915000)\n        4)\n      (segment 1813 \n        (point 236.040000 489.510000 -19.330000 0.915000)\n        (point 237.170000 492.760000 -19.330000 0.915000)\n        4)\n      (segment 1814 \n        (point 237.170000 492.760000 -19.330000 0.915000)\n        (point 238.550000 494.870000 -19.900000 0.915000)\n        4)\n      (segment 1815 \n        (point 238.550000 494.870000 -19.900000 0.915000)\n        (point 239.040000 496.780000 -19.800000 0.915000)\n        4)\n      (segment 1816 \n        (point 239.040000 496.780000 -19.800000 0.915000)\n        (point 236.920000 499.870000 -19.800000 0.915000)\n        4)\n      (segment 1817 \n        (point 236.920000 499.870000 -19.800000 0.915000)\n        (point 235.230000 503.050000 -20.100000 0.915000)\n        4)\n      (segment 1818 \n        (point 235.230000 503.050000 -20.100000 0.915000)\n        (point 233.140000 507.940000 -20.470000 0.915000)\n        4)\n      (segment 1819 \n        (point 233.140000 507.940000 -20.470000 0.915000)\n        (point 233.050000 510.300000 -19.580000 0.915000)\n        4)\n      (segment 1820 \n        (point 233.050000 510.300000 -19.580000 0.915000)\n        (point 233.600000 514.010000 -20.380000 0.915000)\n        4)\n      (segment 1821 \n        (point 233.600000 514.010000 -20.380000 0.915000)\n        (point 231.150000 516.430000 -20.900000 0.915000)\n        4)\n      (segment 1822 \n        (point 231.150000 516.430000 -20.900000 0.915000)\n        (point 230.800000 519.930000 -20.900000 0.915000)\n        4)\n      (segment 1823 \n        (point 230.800000 519.930000 -20.900000 0.915000)\n        (point 231.160000 522.400000 -20.630000 0.915000)\n        4)\n      (segment 1824 \n        (point 231.160000 522.400000 -20.630000 0.915000)\n        (point 232.140000 526.210000 -21.070000 0.915000)\n        4)\n      (segment 1825 \n        (point 232.140000 526.210000 -21.070000 0.915000)\n        (point 231.790000 529.720000 -21.720000 0.915000)\n        4)\n      (segment 1826 \n        (point 231.790000 529.720000 -21.720000 0.915000)\n        (point 230.870000 533.680000 -21.720000 0.915000)\n        4)\n      (segment 1827 \n        (point 230.870000 533.680000 -21.720000 0.915000)\n        (point 229.040000 537.430000 -22.550000 0.915000)\n        4)\n      (segment 1828 \n        (point 229.040000 537.430000 -22.550000 0.915000)\n        (point 229.040000 537.430000 -22.570000 0.915000)\n        4)\n      (segment 1829 \n        (point 229.040000 537.430000 -22.570000 0.915000)\n        (point 228.250000 540.830000 -23.920000 0.915000)\n        4)\n      (segment 1830 \n        (point 228.250000 540.830000 -23.920000 0.915000)\n        (point 228.250000 540.830000 -23.980000 0.915000)\n        4)\n      (segment 1831 \n        (point 228.250000 540.830000 -23.980000 0.915000)\n        (point 228.610000 543.300000 -24.850000 0.915000)\n        4)\n      (segment 1832 \n        (point 228.610000 543.300000 -24.850000 0.915000)\n        (point 230.230000 548.460000 -25.420000 0.915000)\n        4)\n      (segment 1833 \n        (point 230.230000 548.460000 -25.420000 0.915000)\n        (point 230.330000 552.060000 -26.070000 0.915000)\n        4)\n      (segment 1834 \n        (point 230.330000 552.060000 -26.070000 0.915000)\n        (point 232.360000 555.420000 -24.850000 0.915000)\n        4)\n      (segment 1835 \n        (point 232.360000 555.420000 -24.850000 0.915000)\n        (point 232.470000 559.020000 -24.850000 0.915000)\n        4)\n      (segment 1836 \n        (point 232.470000 559.020000 -24.850000 0.915000)\n        (point 231.100000 562.870000 -25.520000 0.915000)\n        4)\n      (segment 1837 \n        (point 231.100000 562.870000 -25.520000 0.915000)\n        (point 231.100000 562.870000 -25.550000 0.915000)\n        4)\n      (segment 1838 \n        (point 231.100000 562.870000 -25.550000 0.915000)\n        (point 230.170000 566.840000 -26.320000 0.915000)\n        4)\n      (segment 1839 \n        (point 230.170000 566.840000 -26.320000 0.915000)\n        (point 230.270000 570.440000 -27.500000 0.915000)\n        4)\n      (segment 1840 \n        (point 230.270000 570.440000 -27.500000 0.915000)\n        (point 231.250000 574.260000 -27.500000 0.915000)\n        4)\n      (segment 1841 \n        (point 231.250000 574.260000 -27.500000 0.915000)\n        (point 231.480000 577.300000 -28.470000 0.915000)\n        4)\n      (segment 1842 \n        (point 231.480000 577.300000 -28.470000 0.915000)\n        (point 232.030000 581.020000 -29.020000 0.915000)\n        4)\n      (segment 1843 \n        (point 232.030000 581.020000 -29.020000 0.915000)\n        (point 231.680000 584.500000 -29.270000 0.915000)\n        4)\n      (segment 1844 \n        (point 231.680000 584.500000 -29.270000 0.915000)\n        (point 231.320000 588.000000 -29.600000 0.915000)\n        4)\n      (segment 1845 \n        (point 231.320000 588.000000 -29.600000 0.915000)\n        (point 230.920000 589.710000 -30.550000 0.915000)\n        4)\n      (segment 1846 \n        (point 230.920000 589.710000 -30.550000 0.915000)\n        (point 232.500000 593.050000 -31.380000 0.915000)\n        4)\n      (segment 1847 \n        (point 232.500000 593.050000 -31.380000 0.915000)\n        (point 234.780000 595.390000 -32.630000 0.915000)\n        4)\n      (segment 1848 \n        (point 234.780000 595.390000 -32.630000 0.915000)\n        (point 233.400000 599.250000 -32.650000 0.915000)\n        4)\n      (segment 1849 \n        (point 233.400000 599.250000 -32.650000 0.915000)\n        (point 232.660000 604.450000 -31.450000 0.915000)\n        4)\n      (segment 1850 \n        (point 232.660000 604.450000 -31.450000 0.915000)\n        (point 233.520000 608.830000 -32.250000 0.915000)\n        4)\n      (segment 1851 \n        (point 233.520000 608.830000 -32.250000 0.915000)\n        (point 232.540000 610.990000 -32.880000 0.915000)\n        4))\n    (branch 82 81 \n      (segment 1852 \n        (point 232.540000 610.990000 -32.880000 0.915000)\n        (point 232.060000 615.060000 -32.880000 0.915000)\n        4)\n      (segment 1853 \n        (point 232.060000 615.060000 -32.880000 0.915000)\n        (point 231.830000 617.990000 -32.750000 0.915000)\n        4)\n      (segment 1854 \n        (point 231.830000 617.990000 -32.750000 0.915000)\n        (point 232.520000 621.140000 -33.150000 0.915000)\n        4)\n      (segment 1855 \n        (point 232.520000 621.140000 -33.150000 0.915000)\n        (point 233.510000 624.950000 -33.150000 0.915000)\n        4)\n      (segment 1856 \n        (point 233.510000 624.950000 -33.150000 0.915000)\n        (point 232.140000 628.810000 -32.580000 0.915000)\n        4)\n      (segment 1857 \n        (point 232.140000 628.810000 -32.580000 0.915000)\n        (point 230.710000 630.860000 -32.650000 0.915000)\n        4)\n      (segment 1858 \n        (point 230.710000 630.860000 -32.650000 0.915000)\n        (point 232.270000 634.210000 -33.220000 0.915000)\n        4)\n      (segment 1859 \n        (point 232.270000 634.210000 -33.220000 0.915000)\n        (point 233.530000 636.900000 -34.200000 0.915000)\n        4)\n      (segment 1860 \n        (point 233.530000 636.900000 -34.200000 0.915000)\n        (point 233.670000 642.310000 -34.780000 0.915000)\n        4)\n      (segment 1861 \n        (point 233.670000 642.310000 -34.780000 0.915000)\n        (point 235.520000 644.540000 -34.780000 0.915000)\n        4)\n      (segment 1862 \n        (point 235.520000 644.540000 -34.780000 0.915000)\n        (point 237.220000 647.310000 -34.780000 0.915000)\n        4)\n      (segment 1863 \n        (point 237.220000 647.310000 -34.780000 0.915000)\n        (point 238.290000 648.760000 -35.830000 0.915000)\n        4)\n      (segment 1864 \n        (point 238.290000 648.760000 -35.830000 0.915000)\n        (point 238.290000 648.760000 -35.850000 0.915000)\n        4)\n      (segment 1865 \n        (point 238.290000 648.760000 -35.850000 0.915000)\n        (point 238.340000 650.570000 -37.130000 0.915000)\n        4)\n      (segment 1866 \n        (point 238.340000 650.570000 -37.130000 0.915000)\n        (point 238.210000 651.130000 -37.150000 0.915000)\n        4)\n      (segment 1867 \n        (point 238.210000 651.130000 -37.150000 0.915000)\n        (point 237.360000 652.720000 -36.420000 0.915000)\n        4)\n      (segment 1868 \n        (point 237.360000 652.720000 -36.420000 0.915000)\n        (point 237.360000 652.720000 -36.450000 0.915000)\n        4)\n      (segment 1869 \n        (point 237.360000 652.720000 -36.450000 0.915000)\n        (point 236.700000 655.550000 -36.420000 0.915000)\n        4)\n      (segment 1870 \n        (point 236.700000 655.550000 -36.420000 0.915000)\n        (point 235.540000 656.480000 -34.850000 0.915000)\n        4)\n      (segment 1871 \n        (point 235.540000 656.480000 -34.850000 0.915000)\n        (point 235.320000 659.410000 -34.150000 0.915000)\n        4))\n    (branch 83 82 \n      (segment 1872 \n        (point 235.320000 659.410000 -34.150000 0.915000)\n        (point 233.230000 664.300000 -35.470000 0.915000)\n        4)\n      (segment 1873 \n        (point 233.230000 664.300000 -35.470000 0.915000)\n        (point 234.360000 667.550000 -35.130000 0.915000)\n        4)\n      (segment 1874 \n        (point 234.360000 667.550000 -35.130000 0.915000)\n        (point 234.360000 667.550000 -35.150000 0.915000)\n        4)\n      (segment 1875 \n        (point 234.360000 667.550000 -35.150000 0.915000)\n        (point 233.560000 670.950000 -35.650000 0.915000)\n        4)\n      (segment 1876 \n        (point 233.560000 670.950000 -35.650000 0.915000)\n        (point 232.320000 674.240000 -35.650000 0.915000)\n        4)\n      (segment 1877 \n        (point 232.320000 674.240000 -35.650000 0.915000)\n        (point 232.680000 676.710000 -34.420000 0.915000)\n        4)\n      (segment 1878 \n        (point 232.680000 676.710000 -34.420000 0.915000)\n        (point 232.550000 677.270000 -34.420000 0.915000)\n        4)\n      (segment 1879 \n        (point 232.550000 677.270000 -34.420000 0.915000)\n        (point 233.680000 680.520000 -33.130000 0.915000)\n        4)\n      (segment 1880 \n        (point 233.680000 680.520000 -33.130000 0.915000)\n        (point 233.460000 683.470000 -33.880000 0.915000)\n        4)\n      (segment 1881 \n        (point 233.460000 683.470000 -33.880000 0.915000)\n        (point 233.380000 685.820000 -33.270000 0.915000)\n        4)\n      (segment 1882 \n        (point 233.380000 685.820000 -33.270000 0.915000)\n        (point 232.580000 689.220000 -33.050000 0.915000)\n        4)\n      (segment 1883 \n        (point 232.580000 689.220000 -33.050000 0.915000)\n        (point 232.940000 691.700000 -33.970000 0.915000)\n        4)\n      (segment 1884 \n        (point 232.940000 691.700000 -33.970000 0.915000)\n        (point 232.900000 695.870000 -35.020000 0.915000)\n        4)\n      (segment 1885 \n        (point 232.900000 695.870000 -35.020000 0.915000)\n        (point 232.900000 695.870000 -35.050000 0.915000)\n        4)\n      (segment 1886 \n        (point 232.900000 695.870000 -35.050000 0.915000)\n        (point 230.640000 699.520000 -36.100000 0.915000)\n        4)\n      (segment 1887 \n        (point 230.640000 699.520000 -36.100000 0.915000)\n        (point 231.310000 702.660000 -37.450000 0.915000)\n        4)\n      (segment 1888 \n        (point 231.310000 702.660000 -37.450000 0.915000)\n        (point 230.780000 704.930000 -37.770000 0.915000)\n        4)\n      (segment 1889 \n        (point 230.780000 704.930000 -37.770000 0.915000)\n        (point 229.350000 706.990000 -36.630000 0.915000)\n        4)\n      (segment 1890 \n        (point 229.350000 706.990000 -36.630000 0.915000)\n        (point 229.900000 710.690000 -35.750000 0.915000)\n        4)\n      (segment 1891 \n        (point 229.900000 710.690000 -35.750000 0.915000)\n        (point 230.450000 714.400000 -34.950000 0.915000)\n        4)\n      (segment 1892 \n        (point 230.450000 714.400000 -34.950000 0.915000)\n        (point 231.570000 717.650000 -34.670000 0.915000)\n        4)\n      (segment 1893 \n        (point 231.570000 717.650000 -34.670000 0.915000)\n        (point 230.640000 721.620000 -35.830000 0.915000)\n        4)\n      (segment 1894 \n        (point 230.640000 721.620000 -35.830000 0.915000)\n        (point 230.640000 721.620000 -35.850000 0.915000)\n        4)\n      (segment 1895 \n        (point 230.640000 721.620000 -35.850000 0.915000)\n        (point 230.870000 724.660000 -36.220000 0.915000)\n        4)\n      (segment 1896 \n        (point 230.870000 724.660000 -36.220000 0.915000)\n        (point 230.790000 727.030000 -37.200000 0.915000)\n        4)\n      (segment 1897 \n        (point 230.790000 727.030000 -37.200000 0.915000)\n        (point 231.910000 730.280000 -38.150000 0.915000)\n        4)\n      (segment 1898 \n        (point 231.910000 730.280000 -38.150000 0.915000)\n        (point 231.470000 736.140000 -39.670000 0.915000)\n        4)\n      (segment 1899 \n        (point 231.470000 736.140000 -39.670000 0.915000)\n        (point 229.970000 740.570000 -39.020000 0.915000)\n        4)\n      (segment 1900 \n        (point 229.970000 740.570000 -39.020000 0.915000)\n        (point 231.220000 743.250000 -38.880000 0.915000)\n        4)\n      (segment 1901 \n        (point 231.220000 743.250000 -38.880000 0.915000)\n        (point 230.920000 748.550000 -38.270000 0.915000)\n        4)\n      (segment 1902 \n        (point 230.920000 748.550000 -38.270000 0.915000)\n        (point 231.320000 750.940000 -35.530000 0.915000)\n        4)\n      (segment 1903 \n        (point 231.320000 750.940000 -35.530000 0.915000)\n        (point 231.320000 750.940000 -35.550000 0.915000)\n        4)\n      (segment 1904 \n        (point 231.320000 750.940000 -35.550000 0.915000)\n        (point 231.240000 753.300000 -34.920000 0.915000)\n        4)\n      (segment 1905 \n        (point 231.240000 753.300000 -34.920000 0.915000)\n        (point 229.280000 757.620000 -34.920000 0.915000)\n        4)\n      (segment 1906 \n        (point 229.280000 757.620000 -34.920000 0.915000)\n        (point 229.430000 760.490000 -34.630000 0.915000)\n        4)\n      (segment 1907 \n        (point 229.430000 760.490000 -34.630000 0.915000)\n        (point 230.730000 764.970000 -33.770000 0.915000)\n        4)\n      (segment 1908 \n        (point 230.730000 764.970000 -33.770000 0.915000)\n        (point 231.400000 768.120000 -34.720000 0.915000)\n        4)\n      (segment 1909 \n        (point 231.400000 768.120000 -34.720000 0.915000)\n        (point 231.910000 770.020000 -33.970000 0.915000)\n        4)\n      (segment 1910 \n        (point 231.910000 770.020000 -33.970000 0.915000)\n        (point 231.510000 771.730000 -33.670000 0.915000)\n        4)\n      (segment 1911 \n        (point 231.510000 771.730000 -33.670000 0.915000)\n        (point 229.820000 774.900000 -33.030000 0.915000)\n        4)\n      (segment 1912 \n        (point 229.820000 774.900000 -33.030000 0.915000)\n        (point 229.740000 777.280000 -33.000000 0.915000)\n        4)\n      (segment 1913 \n        (point 229.740000 777.280000 -33.000000 0.915000)\n        (point 230.680000 779.280000 -33.000000 0.915000)\n        4)\n      (segment 1914 \n        (point 230.680000 779.280000 -33.000000 0.915000)\n        (point 231.350000 782.440000 -32.630000 0.915000)\n        4)\n      (segment 1915 \n        (point 231.350000 782.440000 -32.630000 0.915000)\n        (point 231.530000 783.670000 -33.130000 0.915000)\n        4)\n      (segment 1916 \n        (point 231.530000 783.670000 -33.130000 0.915000)\n        (point 230.600000 787.620000 -32.350000 0.915000)\n        4)\n      (segment 1917 \n        (point 230.600000 787.620000 -32.350000 0.915000)\n        (point 231.150000 791.340000 -31.750000 0.915000)\n        4)\n      (segment 1918 \n        (point 231.150000 791.340000 -31.750000 0.915000)\n        (point 231.070000 793.720000 -31.350000 0.915000)\n        4)\n      (segment 1919 \n        (point 231.070000 793.720000 -31.350000 0.915000)\n        (point 232.640000 797.060000 -31.350000 0.915000)\n        4)\n      (segment 1920 \n        (point 232.640000 797.060000 -31.350000 0.915000)\n        (point 233.140000 798.980000 -31.170000 0.915000)\n        4)\n      (segment 1921 \n        (point 233.140000 798.980000 -31.170000 0.915000)\n        (point 232.210000 802.930000 -30.800000 0.915000)\n        4)\n      (segment 1922 \n        (point 232.210000 802.930000 -30.800000 0.915000)\n        (point 232.120000 805.300000 -31.020000 0.915000)\n        4)\n      (segment 1923 \n        (point 232.120000 805.300000 -31.020000 0.915000)\n        (point 232.120000 805.300000 -31.000000 0.915000)\n        4)\n      (segment 1924 \n        (point 232.120000 805.300000 -31.000000 0.915000)\n        (point 234.080000 806.960000 -30.570000 0.915000)\n        4)\n      (segment 1925 \n        (point 234.080000 806.960000 -30.570000 0.915000)\n        (point 234.500000 811.250000 -29.700000 0.915000)\n        4)\n      (segment 1926 \n        (point 234.500000 811.250000 -29.700000 0.915000)\n        (point 233.390000 813.960000 -29.380000 0.915000)\n        4)\n      (segment 1927 \n        (point 233.390000 813.960000 -29.380000 0.915000)\n        (point 233.170000 816.900000 -29.380000 0.915000)\n        4)\n      (segment 1928 \n        (point 233.170000 816.900000 -29.380000 0.915000)\n        (point 233.970000 819.480000 -30.500000 0.915000)\n        4)\n      (segment 1929 \n        (point 233.970000 819.480000 -30.500000 0.915000)\n        (point 233.320000 822.300000 -31.400000 0.915000)\n        4)\n      (segment 1930 \n        (point 233.320000 822.300000 -31.400000 0.915000)\n        (point 233.320000 822.300000 -31.420000 0.915000)\n        4)\n      (segment 1931 \n        (point 233.320000 822.300000 -31.420000 0.915000)\n        (point 234.000000 825.450000 -32.220000 0.915000)\n        4)\n      (segment 1932 \n        (point 234.000000 825.450000 -32.220000 0.915000)\n        (point 234.360000 827.930000 -31.520000 0.915000)\n        4)\n      (segment 1933 \n        (point 234.360000 827.930000 -31.520000 0.915000)\n        (point 234.280000 830.290000 -30.380000 0.915000)\n        4)\n      (segment 1934 \n        (point 234.280000 830.290000 -30.380000 0.915000)\n        (point 234.950000 833.440000 -30.070000 0.915000)\n        4)\n      (segment 1935 \n        (point 234.950000 833.440000 -30.070000 0.915000)\n        (point 235.490000 837.150000 -30.070000 0.915000)\n        4)\n      (segment 1936 \n        (point 235.490000 837.150000 -30.070000 0.915000)\n        (point 236.170000 840.280000 -30.070000 0.915000)\n        4)\n      (segment 1937 \n        (point 236.170000 840.280000 -30.070000 0.915000)\n        (point 236.790000 841.640000 -30.070000 0.915000)\n        4)\n      (segment 1938 \n        (point 236.790000 841.640000 -30.070000 0.915000)\n        (point 235.690000 844.360000 -29.970000 0.915000)\n        4)\n      (segment 1939 \n        (point 235.690000 844.360000 -29.970000 0.915000)\n        (point 235.210000 848.420000 -30.750000 0.915000)\n        4)\n      (segment 1940 \n        (point 235.210000 848.420000 -30.750000 0.915000)\n        (point 236.470000 851.110000 -30.750000 0.915000)\n        4)\n      (segment 1941 \n        (point 236.470000 851.110000 -30.750000 0.915000)\n        (point 237.000000 854.820000 -29.600000 0.915000)\n        4)\n      (segment 1942 \n        (point 237.000000 854.820000 -29.600000 0.915000)\n        (point 235.890000 857.540000 -28.630000 0.915000)\n        4)\n      (segment 1943 \n        (point 235.890000 857.540000 -28.630000 0.915000)\n        (point 235.540000 861.040000 -29.770000 0.915000)\n        4)\n      (segment 1944 \n        (point 235.540000 861.040000 -29.770000 0.915000)\n        (point 236.800000 863.730000 -30.200000 0.915000)\n        4))\n    (branch 84 83 \n      (segment 1945 \n        (point 236.800000 863.730000 -30.200000 0.915000)\n        (point 238.510000 866.500000 -30.200000 0.460000)\n        4)\n      (segment 1946 \n        (point 238.510000 866.500000 -30.200000 0.460000)\n        (point 239.130000 867.840000 -31.580000 0.460000)\n        4)\n      (segment 1947 \n        (point 239.130000 867.840000 -31.580000 0.460000)\n        (point 238.780000 871.340000 -30.100000 0.460000)\n        4)\n      (segment 1948 \n        (point 238.780000 871.340000 -30.100000 0.460000)\n        (point 239.720000 873.350000 -30.770000 0.460000)\n        4)\n      (segment 1949 \n        (point 239.720000 873.350000 -30.770000 0.460000)\n        (point 239.590000 873.910000 -30.770000 0.460000)\n        4)\n      (segment 1950 \n        (point 239.590000 873.910000 -30.770000 0.460000)\n        (point 239.370000 876.860000 -31.520000 0.460000)\n        4)\n      (segment 1951 \n        (point 239.370000 876.860000 -31.520000 0.460000)\n        (point 239.420000 878.660000 -33.330000 0.460000)\n        4)\n      (segment 1952 \n        (point 239.420000 878.660000 -33.330000 0.460000)\n        (point 241.130000 881.450000 -34.450000 0.460000)\n        4)\n      (segment 1953 \n        (point 241.130000 881.450000 -34.450000 0.460000)\n        (point 242.640000 882.990000 -35.170000 0.460000)\n        4)\n      (segment 1954 \n        (point 242.640000 882.990000 -35.170000 0.460000)\n        (point 242.690000 884.790000 -36.820000 0.460000)\n        4)\n      (segment 1955 \n        (point 242.690000 884.790000 -36.820000 0.460000)\n        (point 244.710000 888.250000 -37.550000 0.460000)\n        4)\n      (segment 1956 \n        (point 244.710000 888.250000 -37.550000 0.460000)\n        (point 246.240000 889.800000 -37.630000 0.460000)\n        4)\n      (segment 1957 \n        (point 246.240000 889.800000 -37.630000 0.460000)\n        (point 247.050000 892.390000 -38.070000 0.460000)\n        4)\n      (segment 1958 \n        (point 247.050000 892.390000 -38.070000 0.460000)\n        (point 246.820000 895.320000 -38.900000 0.460000)\n        4)\n      (segment 1959 \n        (point 246.820000 895.320000 -38.900000 0.460000)\n        (point 247.900000 896.770000 -40.150000 0.460000)\n        4)\n      (segment 1960 \n        (point 247.900000 896.770000 -40.150000 0.460000)\n        (point 249.990000 897.850000 -40.970000 0.460000)\n        4)\n      (segment 1961 \n        (point 249.990000 897.850000 -40.970000 0.460000)\n        (point 249.640000 901.350000 -40.380000 0.460000)\n        4)\n      (segment 1962 \n        (point 249.640000 901.350000 -40.380000 0.460000)\n        (point 250.330000 904.500000 -41.520000 0.460000)\n        4))\n    (branch 85 84 \n      (segment 1963 \n        (point 250.330000 904.500000 -41.520000 0.460000)\n        (point 252.480000 907.390000 -42.550000 0.460000)\n        4)\n      (segment 1964 \n        (point 252.480000 907.390000 -42.550000 0.460000)\n        (point 254.310000 909.610000 -44.170000 0.460000)\n        4)\n      (segment 1965 \n        (point 254.310000 909.610000 -44.170000 0.460000)\n        (point 255.890000 912.960000 -44.870000 0.460000)\n        4))\n    (branch 86 85 \n      (segment 1966 \n        (point 255.890000 912.960000 -44.870000 0.460000)\n        (point 256.860000 914.370000 -44.100000 0.230000)\n        4)\n      (segment 1967 \n        (point 256.860000 914.370000 -44.100000 0.230000)\n        (point 258.680000 916.600000 -42.300000 0.230000)\n        4)\n      (segment 1968 \n        (point 258.680000 916.600000 -42.300000 0.230000)\n        (point 258.680000 916.600000 -42.280000 0.230000)\n        4)\n      (segment 1969 \n        (point 258.680000 916.600000 -42.280000 0.230000)\n        (point 259.890000 917.480000 -41.450000 0.230000)\n        4)\n      (segment 1970 \n        (point 259.890000 917.480000 -41.450000 0.230000)\n        (point 263.210000 919.450000 -40.570000 0.230000)\n        4)\n      (segment 1971 \n        (point 263.210000 919.450000 -40.570000 0.230000)\n        (point 265.120000 919.300000 -41.520000 0.230000)\n        4)\n      (segment 1972 \n        (point 265.120000 919.300000 -41.520000 0.230000)\n        (point 266.910000 919.720000 -40.220000 0.230000)\n        4)\n      (segment 1973 \n        (point 266.910000 919.720000 -40.220000 0.230000)\n        (point 268.170000 922.410000 -39.520000 0.230000)\n        4)\n      (segment 1974 \n        (point 268.170000 922.410000 -39.520000 0.230000)\n        (point 270.540000 922.360000 -39.520000 0.230000)\n        4)\n      (segment 1975 \n        (point 270.540000 922.360000 -39.520000 0.230000)\n        (point 273.210000 922.990000 -38.550000 0.230000)\n        4)\n      (segment 1976 \n        (point 273.210000 922.990000 -38.550000 0.230000)\n        (point 275.310000 924.080000 -37.770000 0.230000)\n        4)\n      (segment 1977 \n        (point 275.310000 924.080000 -37.770000 0.230000)\n        (point 276.920000 923.260000 -36.780000 0.230000)\n        4)\n      (segment 1978 \n        (point 276.920000 923.260000 -36.780000 0.230000)\n        (point 279.100000 921.980000 -35.600000 0.230000)\n        4)\n      (segment 1979 \n        (point 279.100000 921.980000 -35.600000 0.230000)\n        (point 281.390000 924.300000 -34.580000 0.230000)\n        4)\n      (segment 1980 \n        (point 281.390000 924.300000 -34.580000 0.230000)\n        (point 282.450000 925.750000 -33.330000 0.230000)\n        4)\n      (segment 1981 \n        (point 282.450000 925.750000 -33.330000 0.230000)\n        (point 286.160000 926.020000 -32.750000 0.230000)\n        4)\n      (segment 1982 \n        (point 286.160000 926.020000 -32.750000 0.230000)\n        (point 287.820000 927.020000 -32.200000 0.230000)\n        4)\n      (segment 1983 \n        (point 287.820000 927.020000 -32.200000 0.230000)\n        (point 289.970000 929.900000 -31.600000 0.230000)\n        4)\n      (segment 1984 \n        (point 289.970000 929.900000 -31.600000 0.230000)\n        (point 292.200000 930.430000 -31.000000 0.230000)\n        4)\n      (segment 1985 \n        (point 292.200000 930.430000 -31.000000 0.230000)\n        (point 296.080000 931.930000 -30.300000 0.230000)\n        4)\n      (segment 1986 \n        (point 296.080000 931.930000 -30.300000 0.230000)\n        (point 297.880000 932.350000 -29.380000 0.230000)\n        4)\n      (segment 1987 \n        (point 297.880000 932.350000 -29.380000 0.230000)\n        (point 298.820000 934.360000 -29.380000 0.230000)\n        4)\n      (segment 1988 \n        (point 298.820000 934.360000 -29.380000 0.230000)\n        (point 300.470000 935.350000 -28.270000 0.230000)\n        4)\n      (segment 1989 \n        (point 300.470000 935.350000 -28.270000 0.230000)\n        (point 302.380000 935.200000 -27.270000 0.230000)\n        4)\n      (segment 1990 \n        (point 302.380000 935.200000 -27.270000 0.230000)\n        (point 303.510000 938.440000 -26.720000 0.230000)\n        4)\n      (segment 1991 \n        (point 303.510000 938.440000 -26.720000 0.230000)\n        (point 303.510000 938.440000 -26.750000 0.230000)\n        4)\n      (segment 1992 \n        (point 303.510000 938.440000 -26.750000 0.230000)\n        (point 305.610000 939.530000 -26.750000 0.230000)\n        4)\n      (segment 1993 \n        (point 305.610000 939.530000 -26.750000 0.230000)\n        (point 306.560000 941.550000 -25.730000 0.230000)\n        4)\n      (segment 1994 \n        (point 306.560000 941.550000 -25.730000 0.230000)\n        (point 308.520000 943.200000 -24.400000 0.230000)\n        4)\n      (segment 1995 \n        (point 308.520000 943.200000 -24.400000 0.230000)\n        (point 310.300000 943.620000 -23.600000 0.230000)\n        4)\n      (segment 1996 \n        (point 310.300000 943.620000 -23.600000 0.230000)\n        (point 312.410000 944.700000 -22.970000 0.230000)\n        4)\n      (segment 1997 \n        (point 312.410000 944.700000 -22.970000 0.230000)\n        (point 314.060000 945.690000 -22.770000 0.230000)\n        4)\n      (segment 1998 \n        (point 314.060000 945.690000 -22.770000 0.230000)\n        (point 317.180000 946.420000 -22.670000 0.230000)\n        4)\n      (segment 1999 \n        (point 317.180000 946.420000 -22.670000 0.230000)\n        (point 317.180000 946.420000 -22.700000 0.230000)\n        4)\n      (segment 2000 \n        (point 317.180000 946.420000 -22.700000 0.230000)\n        (point 318.670000 946.180000 -20.920000 0.230000)\n        4)\n      (segment 2001 \n        (point 318.670000 946.180000 -20.920000 0.230000)\n        (point 318.850000 947.420000 -20.000000 0.230000)\n        4)\n      (segment 2002 \n        (point 318.850000 947.420000 -20.000000 0.230000)\n        (point 320.450000 946.600000 -18.400000 0.230000)\n        4)\n      (segment 2003 \n        (point 320.450000 946.600000 -18.400000 0.230000)\n        (point 323.390000 946.090000 -17.420000 0.230000)\n        4)\n      (segment 2004 \n        (point 323.390000 946.090000 -17.420000 0.230000)\n        (point 323.390000 946.090000 -17.450000 0.230000)\n        4)\n      (segment 2005 \n        (point 323.390000 946.090000 -17.450000 0.230000)\n        (point 324.280000 946.300000 -16.200000 0.230000)\n        4)\n      (segment 2006 \n        (point 324.280000 946.300000 -16.200000 0.230000)\n        (point 324.330000 948.100000 -14.480000 0.230000)\n        4)\n      (segment 2007 \n        (point 324.330000 948.100000 -14.480000 0.230000)\n        (point 327.290000 947.600000 -14.380000 0.230000)\n        4)\n      (segment 2008 \n        (point 327.290000 947.600000 -14.380000 0.230000)\n        (point 330.720000 949.000000 -13.050000 0.230000)\n        4)\n      (segment 2009 \n        (point 330.720000 949.000000 -13.050000 0.230000)\n        (point 331.610000 949.210000 -13.050000 0.230000)\n        4)\n      (segment 2010 \n        (point 331.610000 949.210000 -13.050000 0.230000)\n        (point 332.100000 951.120000 -13.050000 0.230000)\n        4)\n      (segment 2011 \n        (point 332.100000 951.120000 -13.050000 0.230000)\n        (point 335.420000 953.090000 -13.050000 0.230000)\n        4)\n      (segment 2012 \n        (point 335.420000 953.090000 -13.050000 0.230000)\n        (point 337.690000 955.420000 -14.020000 0.230000)\n        4)\n      (segment 2013 \n        (point 337.690000 955.420000 -14.020000 0.230000)\n        (point 340.100000 957.180000 -14.880000 0.230000)\n        4)\n      (segment 2014 \n        (point 340.100000 957.180000 -14.880000 0.230000)\n        (point 341.450000 957.490000 -13.720000 0.230000)\n        4)\n      (segment 2015 \n        (point 341.450000 957.490000 -13.720000 0.230000)\n        (point 342.080000 958.840000 -12.950000 0.230000)\n        4)\n      (segment 2016 \n        (point 342.080000 958.840000 -12.950000 0.230000)\n        (point 344.360000 961.150000 -12.320000 0.230000)\n        4)\n      (segment 2017 \n        (point 344.360000 961.150000 -12.320000 0.230000)\n        (point 344.360000 961.150000 -12.350000 0.230000)\n        4)\n      (segment 2018 \n        (point 344.360000 961.150000 -12.350000 0.230000)\n        (point 345.750000 963.280000 -12.100000 0.230000)\n        4)\n      (segment 2019 \n        (point 345.750000 963.280000 -12.100000 0.230000)\n        (point 345.750000 963.280000 -12.130000 0.230000)\n        4)\n      (segment 2020 \n        (point 345.750000 963.280000 -12.130000 0.230000)\n        (point 347.590000 965.500000 -11.170000 0.230000)\n        4)\n      (segment 2021 \n        (point 347.590000 965.500000 -11.170000 0.230000)\n        (point 350.710000 966.220000 -10.480000 0.230000)\n        4)\n      (segment 2022 \n        (point 350.710000 966.220000 -10.480000 0.230000)\n        (point 351.520000 968.810000 -9.650000 0.230000)\n        4)\n      (segment 2023 \n        (point 351.520000 968.810000 -9.650000 0.230000)\n        (point 353.170000 969.790000 -8.420000 0.230000)\n        4)\n      (segment 2024 \n        (point 353.170000 969.790000 -8.420000 0.230000)\n        (point 353.170000 969.790000 -8.450000 0.230000)\n        4)\n      (segment 2025 \n        (point 353.170000 969.790000 -8.450000 0.230000)\n        (point 354.700000 971.340000 -6.650000 0.230000)\n        4)\n      (segment 2026 \n        (point 354.700000 971.340000 -6.650000 0.230000)\n        (point 356.350000 972.330000 -4.170000 0.230000)\n        4)\n      (segment 2027 \n        (point 356.350000 972.330000 -4.170000 0.230000)\n        (point 355.900000 972.220000 -4.170000 0.230000)\n        4)\n      (segment 2028 \n        (point 355.900000 972.220000 -4.170000 0.230000)\n        (point 356.800000 972.430000 -1.600000 0.230000)\n        4))\n    (branch 87 85 \n      (segment 2029 \n        (point 255.890000 912.960000 -44.870000 0.460000)\n        (point 255.990000 916.570000 -45.750000 0.230000)\n        4)\n      (segment 2030 \n        (point 255.990000 916.570000 -45.750000 0.230000)\n        (point 258.400000 918.330000 -46.670000 0.230000)\n        4)\n      (segment 2031 \n        (point 258.400000 918.330000 -46.670000 0.230000)\n        (point 258.890000 920.240000 -47.600000 0.230000)\n        4)\n      (segment 2032 \n        (point 258.890000 920.240000 -47.600000 0.230000)\n        (point 258.760000 920.810000 -48.600000 0.230000)\n        4)\n      (segment 2033 \n        (point 258.760000 920.810000 -48.600000 0.230000)\n        (point 257.280000 921.060000 -49.050000 0.230000)\n        4)\n      (segment 2034 \n        (point 257.280000 921.060000 -49.050000 0.230000)\n        (point 257.280000 921.060000 -49.070000 0.230000)\n        4)\n      (segment 2035 \n        (point 257.280000 921.060000 -49.070000 0.230000)\n        (point 257.600000 921.720000 -49.850000 0.230000)\n        4)\n      (segment 2036 \n        (point 257.600000 921.720000 -49.850000 0.230000)\n        (point 259.430000 923.950000 -49.850000 0.230000)\n        4)\n      (segment 2037 \n        (point 259.430000 923.950000 -49.850000 0.230000)\n        (point 259.870000 924.060000 -50.520000 0.230000)\n        4)\n      (segment 2038 \n        (point 259.870000 924.060000 -50.520000 0.230000)\n        (point 258.580000 925.540000 -52.220000 0.230000)\n        4)\n      (segment 2039 \n        (point 258.580000 925.540000 -52.220000 0.230000)\n        (point 259.430000 923.950000 -53.600000 0.230000)\n        4)\n      (segment 2040 \n        (point 259.430000 923.950000 -53.600000 0.230000)\n        (point 259.430000 923.950000 -53.620000 0.230000)\n        4)\n      (segment 2041 \n        (point 259.430000 923.950000 -53.620000 0.230000)\n        (point 259.920000 925.860000 -55.220000 0.230000)\n        4)\n      (segment 2042 \n        (point 259.920000 925.860000 -55.220000 0.230000)\n        (point 259.920000 925.860000 -55.300000 0.230000)\n        4)\n      (segment 2043 \n        (point 259.920000 925.860000 -55.300000 0.230000)\n        (point 260.560000 927.190000 -57.130000 0.230000)\n        4)\n      (segment 2044 \n        (point 260.560000 927.190000 -57.130000 0.230000)\n        (point 262.210000 928.180000 -58.250000 0.230000)\n        4)\n      (segment 2045 \n        (point 262.210000 928.180000 -58.250000 0.230000)\n        (point 264.180000 929.850000 -59.380000 0.230000)\n        4))\n    (branch 88 87 \n      (segment 2046 \n        (point 264.180000 929.850000 -59.380000 0.230000)\n        (point 263.490000 930.270000 -60.570000 0.230000)\n        4)\n      (segment 2047 \n        (point 263.490000 930.270000 -60.570000 0.230000)\n        (point 264.300000 932.840000 -61.250000 0.230000)\n        4)\n      (segment 2048 \n        (point 264.300000 932.840000 -61.250000 0.230000)\n        (point 262.560000 934.230000 -62.670000 0.230000)\n        4)\n      (segment 2049 \n        (point 262.560000 934.230000 -62.670000 0.230000)\n        (point 264.230000 935.210000 -63.750000 0.230000)\n        4)\n      (segment 2050 \n        (point 264.230000 935.210000 -63.750000 0.230000)\n        (point 262.940000 936.700000 -64.650000 0.230000)\n        4)\n      (segment 2051 \n        (point 262.940000 936.700000 -64.650000 0.230000)\n        (point 263.570000 938.040000 -65.520000 0.230000)\n        4)\n      (segment 2052 \n        (point 263.570000 938.040000 -65.520000 0.230000)\n        (point 263.340000 940.980000 -66.200000 0.230000)\n        4)\n      (segment 2053 \n        (point 263.340000 940.980000 -66.200000 0.230000)\n        (point 264.600000 943.660000 -67.000000 0.230000)\n        4)\n      (segment 2054 \n        (point 264.600000 943.660000 -67.000000 0.230000)\n        (point 264.650000 945.460000 -67.550000 0.230000)\n        4)\n      (segment 2055 \n        (point 264.650000 945.460000 -67.550000 0.230000)\n        (point 264.650000 945.460000 -67.570000 0.230000)\n        4)\n      (segment 2056 \n        (point 264.650000 945.460000 -67.570000 0.230000)\n        (point 263.360000 946.950000 -69.600000 0.230000)\n        4)\n      (segment 2057 \n        (point 263.360000 946.950000 -69.600000 0.230000)\n        (point 263.090000 948.090000 -70.700000 0.230000)\n        4)\n      (segment 2058 \n        (point 263.090000 948.090000 -70.700000 0.230000)\n        (point 265.180000 949.170000 -71.850000 0.230000)\n        4)\n      (segment 2059 \n        (point 265.180000 949.170000 -71.850000 0.230000)\n        (point 265.050000 949.740000 -72.720000 0.230000)\n        4)\n      (segment 2060 \n        (point 265.050000 949.740000 -72.720000 0.230000)\n        (point 265.100000 951.530000 -73.380000 0.230000)\n        4)\n      (segment 2061 \n        (point 265.100000 951.530000 -73.380000 0.230000)\n        (point 265.100000 951.530000 -73.400000 0.230000)\n        4)\n      (segment 2062 \n        (point 265.100000 951.530000 -73.400000 0.230000)\n        (point 263.950000 952.470000 -74.250000 0.230000)\n        4)\n      (segment 2063 \n        (point 263.950000 952.470000 -74.250000 0.230000)\n        (point 262.920000 952.820000 -75.520000 0.230000)\n        4)\n      (segment 2064 \n        (point 262.920000 952.820000 -75.520000 0.230000)\n        (point 262.970000 954.620000 -76.800000 0.230000)\n        4)\n      (segment 2065 \n        (point 262.970000 954.620000 -76.800000 0.230000)\n        (point 262.970000 954.620000 -76.820000 0.230000)\n        4)\n      (segment 2066 \n        (point 262.970000 954.620000 -76.820000 0.230000)\n        (point 262.890000 956.990000 -78.380000 0.230000)\n        4)\n      (segment 2067 \n        (point 262.890000 956.990000 -78.380000 0.230000)\n        (point 263.640000 957.770000 -79.750000 0.230000)\n        4)\n      (segment 2068 \n        (point 263.640000 957.770000 -79.750000 0.230000)\n        (point 262.930000 958.790000 -81.320000 0.230000)\n        4)\n      (segment 2069 \n        (point 262.930000 958.790000 -81.320000 0.230000)\n        (point 259.720000 960.430000 -82.970000 0.230000)\n        4)\n      (segment 2070 \n        (point 259.720000 960.430000 -82.970000 0.230000)\n        (point 262.400000 961.060000 -85.850000 0.230000)\n        4)\n      (segment 2071 \n        (point 262.400000 961.060000 -85.850000 0.230000)\n        (point 261.560000 962.650000 -88.020000 0.230000)\n        4)\n      (segment 2072 \n        (point 261.560000 962.650000 -88.020000 0.230000)\n        (point 260.850000 963.680000 -89.320000 0.230000)\n        4)\n      (segment 2073 \n        (point 260.850000 963.680000 -89.320000 0.230000)\n        (point 260.900000 965.480000 -91.070000 0.230000)\n        4)\n      (segment 2074 \n        (point 260.900000 965.480000 -91.070000 0.230000)\n        (point 260.370000 967.750000 -92.900000 0.230000)\n        4)\n      (segment 2075 \n        (point 260.370000 967.750000 -92.900000 0.230000)\n        (point 259.340000 968.110000 -95.020000 0.230000)\n        4)\n      (segment 2076 \n        (point 259.340000 968.110000 -95.020000 0.230000)\n        (point 260.990000 969.090000 -95.820000 0.230000)\n        4)\n      (segment 2077 \n        (point 260.990000 969.090000 -95.820000 0.230000)\n        (point 260.990000 969.090000 -95.850000 0.230000)\n        4)\n      (segment 2078 \n        (point 260.990000 969.090000 -95.850000 0.230000)\n        (point 259.710000 970.580000 -96.650000 0.230000)\n        4)\n      (segment 2079 \n        (point 259.710000 970.580000 -96.650000 0.230000)\n        (point 259.930000 973.620000 -97.800000 0.230000)\n        4)\n      (segment 2080 \n        (point 259.930000 973.620000 -97.800000 0.230000)\n        (point 260.020000 977.220000 -98.470000 0.230000)\n        4)\n      (segment 2081 \n        (point 260.020000 977.220000 -98.470000 0.230000)\n        (point 260.020000 977.220000 -98.530000 0.230000)\n        4)\n      (segment 2082 \n        (point 260.020000 977.220000 -98.530000 0.230000)\n        (point 260.980000 979.230000 -99.450000 0.230000)\n        4)\n      (segment 2083 \n        (point 260.980000 979.230000 -99.450000 0.230000)\n        (point 261.650000 982.380000 -100.500000 0.230000)\n        4)\n      (segment 2084 \n        (point 261.650000 982.380000 -100.500000 0.230000)\n        (point 262.090000 982.480000 -100.500000 0.230000)\n        4)\n      (segment 2085 \n        (point 262.090000 982.480000 -100.500000 0.230000)\n        (point 260.980000 985.210000 -101.270000 0.230000)\n        4)\n      (segment 2086 \n        (point 260.980000 985.210000 -101.270000 0.230000)\n        (point 261.170000 986.450000 -102.520000 0.230000)\n        4)\n      (segment 2087 \n        (point 261.170000 986.450000 -102.520000 0.230000)\n        (point 262.240000 987.890000 -103.900000 0.230000)\n        4)\n      (segment 2088 \n        (point 262.240000 987.890000 -103.900000 0.230000)\n        (point 261.450000 991.290000 -104.820000 0.230000)\n        4)\n      (segment 2089 \n        (point 261.450000 991.290000 -104.820000 0.230000)\n        (point 260.780000 994.120000 -105.770000 0.230000)\n        4)\n      (segment 2090 \n        (point 260.780000 994.120000 -105.770000 0.230000)\n        (point 261.150000 996.590000 -107.350000 0.230000)\n        4)\n      (segment 2091 \n        (point 261.150000 996.590000 -107.350000 0.230000)\n        (point 260.920000 999.530000 -108.720000 0.230000)\n        4)\n      (segment 2092 \n        (point 260.920000 999.530000 -108.720000 0.230000)\n        (point 260.520000 1001.220000 -110.020000 0.230000)\n        4)\n      (segment 2093 \n        (point 260.520000 1001.220000 -110.020000 0.230000)\n        (point 260.440000 1003.590000 -111.530000 0.230000)\n        4)\n      (segment 2094 \n        (point 260.440000 1003.590000 -111.530000 0.230000)\n        (point 258.080000 1003.630000 -112.750000 0.230000)\n        4)\n      (segment 2095 \n        (point 258.080000 1003.630000 -112.750000 0.230000)\n        (point 256.290000 1003.220000 -114.650000 0.230000)\n        4)\n      (segment 2096 \n        (point 256.290000 1003.220000 -114.650000 0.230000)\n        (point 252.580000 1002.950000 -115.470000 0.230000)\n        4)\n      (segment 2097 \n        (point 252.580000 1002.950000 -115.470000 0.230000)\n        (point 251.380000 1002.070000 -117.400000 0.230000)\n        4)\n      (segment 2098 \n        (point 251.380000 1002.070000 -117.400000 0.230000)\n        (point 249.460000 1002.210000 -118.100000 0.230000)\n        4)\n      (segment 2099 \n        (point 249.460000 1002.210000 -118.100000 0.230000)\n        (point 249.460000 1002.210000 -118.220000 0.230000)\n        4))\n    (branch 89 87 \n      (segment 2100 \n        (point 264.180000 929.850000 -59.380000 0.230000)\n        (point 265.880000 932.610000 -57.900000 0.230000)\n        4)\n      (segment 2101 \n        (point 265.880000 932.610000 -57.900000 0.230000)\n        (point 266.110000 935.650000 -57.020000 0.230000)\n        4)\n      (segment 2102 \n        (point 266.110000 935.650000 -57.020000 0.230000)\n        (point 266.210000 939.260000 -56.950000 0.230000)\n        4)\n      (segment 2103 \n        (point 266.210000 939.260000 -56.950000 0.230000)\n        (point 267.280000 940.710000 -57.050000 0.230000)\n        4)\n      (segment 2104 \n        (point 267.280000 940.710000 -57.050000 0.230000)\n        (point 267.510000 943.750000 -57.050000 0.230000)\n        4)\n      (segment 2105 \n        (point 267.510000 943.750000 -57.050000 0.230000)\n        (point 267.470000 947.920000 -58.170000 0.230000)\n        4)\n      (segment 2106 \n        (point 267.470000 947.920000 -58.170000 0.230000)\n        (point 268.460000 951.740000 -59.130000 0.230000)\n        4)\n      (segment 2107 \n        (point 268.460000 951.740000 -59.130000 0.230000)\n        (point 271.380000 955.390000 -59.670000 0.230000)\n        4)\n      (segment 2108 \n        (point 271.380000 955.390000 -59.670000 0.230000)\n        (point 272.840000 955.150000 -60.150000 0.230000)\n        4)\n      (segment 2109 \n        (point 272.840000 955.150000 -60.150000 0.230000)\n        (point 274.230000 957.270000 -60.150000 0.230000)\n        4)\n      (segment 2110 \n        (point 274.230000 957.270000 -60.150000 0.230000)\n        (point 278.130000 958.780000 -60.950000 0.230000)\n        4)\n      (segment 2111 \n        (point 278.130000 958.780000 -60.950000 0.230000)\n        (point 280.090000 960.430000 -60.950000 0.230000)\n        4)\n      (segment 2112 \n        (point 280.090000 960.430000 -60.950000 0.230000)\n        (point 282.630000 961.620000 -61.550000 0.230000)\n        4)\n      (segment 2113 \n        (point 282.630000 961.620000 -61.550000 0.230000)\n        (point 284.480000 963.850000 -62.280000 0.230000)\n        4)\n      (segment 2114 \n        (point 284.480000 963.850000 -62.280000 0.230000)\n        (point 287.020000 965.040000 -60.770000 0.230000)\n        4)\n      (segment 2115 \n        (point 287.020000 965.040000 -60.770000 0.230000)\n        (point 289.690000 965.670000 -59.650000 0.230000)\n        4)\n      (segment 2116 \n        (point 289.690000 965.670000 -59.650000 0.230000)\n        (point 291.350000 966.650000 -59.830000 0.230000)\n        4)\n      (segment 2117 \n        (point 291.350000 966.650000 -59.830000 0.230000)\n        (point 295.190000 966.360000 -58.950000 0.230000)\n        4)\n      (segment 2118 \n        (point 295.190000 966.360000 -58.950000 0.230000)\n        (point 296.840000 967.350000 -58.950000 0.230000)\n        4)\n      (segment 2119 \n        (point 296.840000 967.350000 -58.950000 0.230000)\n        (point 299.790000 966.830000 -60.670000 0.230000)\n        4)\n      (segment 2120 \n        (point 299.790000 966.830000 -60.670000 0.230000)\n        (point 299.660000 967.410000 -62.130000 0.230000)\n        4)\n      (segment 2121 \n        (point 299.660000 967.410000 -62.130000 0.230000)\n        (point 303.540000 968.910000 -62.130000 0.230000)\n        4)\n      (segment 2122 \n        (point 303.540000 968.910000 -62.130000 0.230000)\n        (point 304.660000 972.160000 -62.350000 0.230000)\n        4)\n      (segment 2123 \n        (point 304.660000 972.160000 -62.350000 0.230000)\n        (point 306.770000 973.260000 -62.280000 0.230000)\n        4)\n      (segment 2124 \n        (point 306.770000 973.260000 -62.280000 0.230000)\n        (point 308.630000 974.880000 -62.280000 0.230000)\n        4)\n      (segment 2125 \n        (point 308.630000 974.880000 -62.280000 0.230000)\n        (point 311.500000 976.740000 -60.920000 0.230000)\n        4)\n      (segment 2126 \n        (point 311.500000 976.740000 -60.920000 0.230000)\n        (point 313.460000 978.390000 -59.780000 0.230000)\n        4)\n      (segment 2127 \n        (point 313.460000 978.390000 -59.780000 0.230000)\n        (point 316.190000 980.840000 -58.450000 0.230000)\n        4)\n      (segment 2128 \n        (point 316.190000 980.840000 -58.450000 0.230000)\n        (point 317.840000 981.820000 -57.200000 0.230000)\n        4)\n      (segment 2129 \n        (point 317.840000 981.820000 -57.200000 0.230000)\n        (point 320.130000 984.140000 -56.570000 0.230000)\n        4)\n      (segment 2130 \n        (point 320.130000 984.140000 -56.570000 0.230000)\n        (point 322.800000 984.770000 -55.570000 0.230000)\n        4)\n      (segment 2131 \n        (point 322.800000 984.770000 -55.570000 0.230000)\n        (point 324.730000 984.620000 -53.950000 0.230000)\n        4)\n      (segment 2132 \n        (point 324.730000 984.620000 -53.950000 0.230000)\n        (point 325.310000 984.160000 -52.770000 0.230000)\n        4))\n    (branch 90 84 \n      (segment 2133 \n        (point 250.330000 904.500000 -41.520000 0.460000)\n        (point 248.940000 908.140000 -41.520000 0.460000)\n        4)\n      (segment 2134 \n        (point 248.940000 908.140000 -41.520000 0.460000)\n        (point 248.410000 910.400000 -42.450000 0.460000)\n        4)\n      (segment 2135 \n        (point 248.410000 910.400000 -42.450000 0.460000)\n        (point 247.750000 913.220000 -43.500000 0.460000)\n        4)\n      (segment 2136 \n        (point 247.750000 913.220000 -43.500000 0.460000)\n        (point 247.980000 916.270000 -44.130000 0.460000)\n        4)\n      (segment 2137 \n        (point 247.980000 916.270000 -44.130000 0.460000)\n        (point 246.100000 918.220000 -44.780000 0.460000)\n        4)\n      (segment 2138 \n        (point 246.100000 918.220000 -44.780000 0.460000)\n        (point 245.890000 921.150000 -45.320000 0.460000)\n        4)\n      (segment 2139 \n        (point 245.890000 921.150000 -45.320000 0.460000)\n        (point 245.220000 923.980000 -46.120000 0.460000)\n        4)\n      (segment 2140 \n        (point 245.220000 923.980000 -46.120000 0.460000)\n        (point 245.220000 923.980000 -46.150000 0.460000)\n        4)\n      (segment 2141 \n        (point 245.220000 923.980000 -46.150000 0.460000)\n        (point 244.300000 927.950000 -46.600000 0.460000)\n        4)\n      (segment 2142 \n        (point 244.300000 927.950000 -46.600000 0.460000)\n        (point 243.050000 931.240000 -46.820000 0.460000)\n        4)\n      (segment 2143 \n        (point 243.050000 931.240000 -46.820000 0.460000)\n        (point 241.050000 933.760000 -47.300000 0.460000)\n        4)\n      (segment 2144 \n        (point 241.050000 933.760000 -47.300000 0.460000)\n        (point 241.010000 937.930000 -47.330000 0.460000)\n        4)\n      (segment 2145 \n        (point 241.010000 937.930000 -47.330000 0.460000)\n        (point 239.150000 939.880000 -48.150000 0.460000)\n        4)\n      (segment 2146 \n        (point 239.150000 939.880000 -48.150000 0.460000)\n        (point 237.460000 943.070000 -48.720000 0.460000)\n        4)\n      (segment 2147 \n        (point 237.460000 943.070000 -48.720000 0.460000)\n        (point 235.770000 946.260000 -48.720000 0.460000)\n        4)\n      (segment 2148 \n        (point 235.770000 946.260000 -48.720000 0.460000)\n        (point 234.790000 948.400000 -49.270000 0.460000)\n        4)\n      (segment 2149 \n        (point 234.790000 948.400000 -49.270000 0.460000)\n        (point 233.990000 951.800000 -49.400000 0.460000)\n        4)\n      (segment 2150 \n        (point 233.990000 951.800000 -49.400000 0.460000)\n        (point 233.460000 954.070000 -49.800000 0.460000)\n        4))\n    (branch 91 90 \n      (segment 2151 \n        (point 233.460000 954.070000 -49.800000 0.460000)\n        (point 233.650000 955.310000 -50.700000 0.230000)\n        4)\n      (segment 2152 \n        (point 233.650000 955.310000 -50.700000 0.230000)\n        (point 231.200000 957.720000 -50.700000 0.230000)\n        4)\n      (segment 2153 \n        (point 231.200000 957.720000 -50.700000 0.230000)\n        (point 230.220000 959.870000 -50.600000 0.230000)\n        4)\n      (segment 2154 \n        (point 230.220000 959.870000 -50.600000 0.230000)\n        (point 228.850000 963.730000 -50.470000 0.230000)\n        4)\n      (segment 2155 \n        (point 228.850000 963.730000 -50.470000 0.230000)\n        (point 227.920000 967.700000 -50.970000 0.230000)\n        4)\n      (segment 2156 \n        (point 227.920000 967.700000 -50.970000 0.230000)\n        (point 226.040000 969.650000 -50.970000 0.230000)\n        4)\n      (segment 2157 \n        (point 226.040000 969.650000 -50.970000 0.230000)\n        (point 225.770000 970.780000 -51.320000 0.230000)\n        4)\n      (segment 2158 \n        (point 225.770000 970.780000 -51.320000 0.230000)\n        (point 223.780000 973.300000 -51.350000 0.230000)\n        4)\n      (segment 2159 \n        (point 223.780000 973.300000 -51.350000 0.230000)\n        (point 223.060000 974.330000 -53.000000 0.230000)\n        4)\n      (segment 2160 \n        (point 223.060000 974.330000 -53.000000 0.230000)\n        (point 221.950000 977.050000 -54.430000 0.230000)\n        4)\n      (segment 2161 \n        (point 221.950000 977.050000 -54.430000 0.230000)\n        (point 220.210000 978.430000 -55.300000 0.230000)\n        4)\n      (segment 2162 \n        (point 220.210000 978.430000 -55.300000 0.230000)\n        (point 218.930000 979.920000 -56.030000 0.230000)\n        4)\n      (segment 2163 \n        (point 218.930000 979.920000 -56.030000 0.230000)\n        (point 218.930000 979.920000 -56.050000 0.230000)\n        4)\n      (segment 2164 \n        (point 218.930000 979.920000 -56.050000 0.230000)\n        (point 218.530000 981.620000 -56.550000 0.230000)\n        4)\n      (segment 2165 \n        (point 218.530000 981.620000 -56.550000 0.230000)\n        (point 216.610000 981.770000 -56.900000 0.230000)\n        4)\n      (segment 2166 \n        (point 216.610000 981.770000 -56.900000 0.230000)\n        (point 215.180000 983.830000 -57.450000 0.230000)\n        4)\n      (segment 2167 \n        (point 215.180000 983.830000 -57.450000 0.230000)\n        (point 215.810000 985.160000 -58.500000 0.230000)\n        4)\n      (segment 2168 \n        (point 215.810000 985.160000 -58.500000 0.230000)\n        (point 215.420000 986.860000 -59.380000 0.230000)\n        4)\n      (segment 2169 \n        (point 215.420000 986.860000 -59.380000 0.230000)\n        (point 216.230000 989.450000 -59.200000 0.230000)\n        4)\n      (segment 2170 \n        (point 216.230000 989.450000 -59.200000 0.230000)\n        (point 215.260000 991.500000 -59.920000 0.230000)\n        4)\n      (segment 2171 \n        (point 215.260000 991.500000 -59.920000 0.230000)\n        (point 214.730000 993.770000 -60.950000 0.230000)\n        4)\n      (segment 2172 \n        (point 214.730000 993.770000 -60.950000 0.230000)\n        (point 215.220000 995.680000 -61.500000 0.230000)\n        4)\n      (segment 2173 \n        (point 215.220000 995.680000 -61.500000 0.230000)\n        (point 214.560000 998.500000 -62.450000 0.230000)\n        4)\n      (segment 2174 \n        (point 214.560000 998.500000 -62.450000 0.230000)\n        (point 214.780000 1001.540000 -63.400000 0.230000)\n        4)\n      (segment 2175 \n        (point 214.780000 1001.540000 -63.400000 0.230000)\n        (point 214.780000 1001.540000 -63.420000 0.230000)\n        4)\n      (segment 2176 \n        (point 214.780000 1001.540000 -63.420000 0.230000)\n        (point 214.080000 1002.570000 -63.880000 0.230000)\n        4)\n      (segment 2177 \n        (point 214.080000 1002.570000 -63.880000 0.230000)\n        (point 214.080000 1002.570000 -62.880000 0.230000)\n        4)\n      (segment 2178 \n        (point 214.080000 1002.570000 -62.880000 0.230000)\n        (point 214.570000 1004.480000 -61.880000 0.230000)\n        4)\n      (segment 2179 \n        (point 214.570000 1004.480000 -61.880000 0.230000)\n        (point 214.090000 1008.550000 -60.720000 0.230000)\n        4)\n      (segment 2180 \n        (point 214.090000 1008.550000 -60.720000 0.230000)\n        (point 215.040000 1010.560000 -62.570000 0.230000)\n        4)\n      (segment 2181 \n        (point 215.040000 1010.560000 -62.570000 0.230000)\n        (point 214.060000 1012.730000 -63.420000 0.230000)\n        4)\n      (segment 2182 \n        (point 214.060000 1012.730000 -63.420000 0.230000)\n        (point 214.560000 1014.630000 -64.020000 0.230000)\n        4)\n      (segment 2183 \n        (point 214.560000 1014.630000 -64.020000 0.230000)\n        (point 213.710000 1016.230000 -64.950000 0.230000)\n        4)\n      (segment 2184 \n        (point 213.710000 1016.230000 -64.950000 0.230000)\n        (point 212.600000 1018.940000 -66.170000 0.230000)\n        4)\n      (segment 2185 \n        (point 212.600000 1018.940000 -66.170000 0.230000)\n        (point 211.490000 1021.670000 -67.070000 0.230000)\n        4)\n      (segment 2186 \n        (point 211.490000 1021.670000 -67.070000 0.230000)\n        (point 210.200000 1023.150000 -68.000000 0.230000)\n        4)\n      (segment 2187 \n        (point 210.200000 1023.150000 -68.000000 0.230000)\n        (point 209.040000 1024.090000 -68.750000 0.230000)\n        4)\n      (segment 2188 \n        (point 209.040000 1024.090000 -68.750000 0.230000)\n        (point 209.080000 1025.890000 -69.920000 0.230000)\n        4)\n      (segment 2189 \n        (point 209.080000 1025.890000 -69.920000 0.230000)\n        (point 208.680000 1027.590000 -71.380000 0.230000)\n        4)\n      (segment 2190 \n        (point 208.680000 1027.590000 -71.380000 0.230000)\n        (point 211.240000 1028.780000 -72.600000 0.230000)\n        4)\n      (segment 2191 \n        (point 211.240000 1028.780000 -72.600000 0.230000)\n        (point 211.240000 1028.780000 -72.630000 0.230000)\n        4)\n      (segment 2192 \n        (point 211.240000 1028.780000 -72.630000 0.230000)\n        (point 209.950000 1030.270000 -74.400000 0.230000)\n        4)\n      (segment 2193 \n        (point 209.950000 1030.270000 -74.400000 0.230000)\n        (point 209.860000 1032.640000 -75.300000 0.230000)\n        4)\n      (segment 2194 \n        (point 209.860000 1032.640000 -75.300000 0.230000)\n        (point 209.780000 1035.000000 -76.850000 0.230000)\n        4)\n      (segment 2195 \n        (point 209.780000 1035.000000 -76.850000 0.230000)\n        (point 208.940000 1036.600000 -78.380000 0.230000)\n        4)\n      (segment 2196 \n        (point 208.940000 1036.600000 -78.380000 0.230000)\n        (point 208.940000 1036.600000 -78.350000 0.230000)\n        4)\n      (segment 2197 \n        (point 208.940000 1036.600000 -78.350000 0.230000)\n        (point 208.670000 1037.740000 -81.070000 0.230000)\n        4)\n      (segment 2198 \n        (point 208.670000 1037.740000 -81.070000 0.230000)\n        (point 208.670000 1037.740000 -81.100000 0.230000)\n        4))\n    (branch 92 90 \n      (segment 2199 \n        (point 233.460000 954.070000 -49.800000 0.460000)\n        (point 231.740000 955.350000 -48.000000 0.460000)\n        4)\n      (segment 2200 \n        (point 231.740000 955.350000 -48.000000 0.460000)\n        (point 228.980000 957.100000 -46.120000 0.460000)\n        4)\n      (segment 2201 \n        (point 228.980000 957.100000 -46.120000 0.460000)\n        (point 228.580000 958.790000 -45.320000 0.460000)\n        4)\n      (segment 2202 \n        (point 228.580000 958.790000 -45.320000 0.460000)\n        (point 226.900000 961.980000 -45.030000 0.460000)\n        4)\n      (segment 2203 \n        (point 226.900000 961.980000 -45.030000 0.460000)\n        (point 225.910000 964.150000 -44.000000 0.460000)\n        4)\n      (segment 2204 \n        (point 225.910000 964.150000 -44.000000 0.460000)\n        (point 225.910000 964.150000 -44.020000 0.460000)\n        4)\n      (segment 2205 \n        (point 225.910000 964.150000 -44.020000 0.460000)\n        (point 224.350000 966.760000 -42.900000 0.460000)\n        4)\n      (segment 2206 \n        (point 224.350000 966.760000 -42.900000 0.460000)\n        (point 224.350000 966.760000 -42.920000 0.460000)\n        4)\n      (segment 2207 \n        (point 224.350000 966.760000 -42.920000 0.460000)\n        (point 222.310000 967.480000 -42.450000 0.460000)\n        4)\n      (segment 2208 \n        (point 222.310000 967.480000 -42.450000 0.460000)\n        (point 222.080000 970.410000 -42.450000 0.460000)\n        4)\n      (segment 2209 \n        (point 222.080000 970.410000 -42.450000 0.460000)\n        (point 221.380000 971.440000 -41.150000 0.460000)\n        4)\n      (segment 2210 \n        (point 221.380000 971.440000 -41.150000 0.460000)\n        (point 221.380000 971.440000 -41.170000 0.460000)\n        4)\n      (segment 2211 \n        (point 221.380000 971.440000 -41.170000 0.460000)\n        (point 219.370000 973.950000 -40.380000 0.460000)\n        4)\n      (segment 2212 \n        (point 219.370000 973.950000 -40.380000 0.460000)\n        (point 218.080000 975.450000 -40.400000 0.460000)\n        4)\n      (segment 2213 \n        (point 218.080000 975.450000 -40.400000 0.460000)\n        (point 218.080000 975.450000 -40.420000 0.460000)\n        4)\n      (segment 2214 \n        (point 218.080000 975.450000 -40.420000 0.460000)\n        (point 217.280000 978.850000 -39.720000 0.460000)\n        4)\n      (segment 2215 \n        (point 217.280000 978.850000 -39.720000 0.460000)\n        (point 215.870000 980.900000 -38.800000 0.460000)\n        4)\n      (segment 2216 \n        (point 215.870000 980.900000 -38.800000 0.460000)\n        (point 213.550000 982.740000 -38.200000 0.460000)\n        4)\n      (segment 2217 \n        (point 213.550000 982.740000 -38.200000 0.460000)\n        (point 212.260000 984.220000 -37.250000 0.460000)\n        4)\n      (segment 2218 \n        (point 212.260000 984.220000 -37.250000 0.460000)\n        (point 212.350000 987.830000 -36.270000 0.460000)\n        4)\n      (segment 2219 \n        (point 212.350000 987.830000 -36.270000 0.460000)\n        (point 211.230000 990.570000 -37.880000 0.460000)\n        4)\n      (segment 2220 \n        (point 211.230000 990.570000 -37.880000 0.460000)\n        (point 208.970000 994.220000 -37.880000 0.460000)\n        4)\n      (segment 2221 \n        (point 208.970000 994.220000 -37.880000 0.460000)\n        (point 207.860000 996.930000 -37.880000 0.460000)\n        4)\n      (segment 2222 \n        (point 207.860000 996.930000 -37.880000 0.460000)\n        (point 205.670000 998.220000 -36.330000 0.460000)\n        4)\n      (segment 2223 \n        (point 205.670000 998.220000 -36.330000 0.460000)\n        (point 203.450000 1003.670000 -36.330000 0.460000)\n        4)\n      (segment 2224 \n        (point 203.450000 1003.670000 -36.330000 0.460000)\n        (point 203.020000 1003.560000 -36.330000 0.460000)\n        4)\n      (segment 2225 \n        (point 203.020000 1003.560000 -36.330000 0.460000)\n        (point 202.170000 1005.160000 -36.470000 0.460000)\n        4)\n      (segment 2226 \n        (point 202.170000 1005.160000 -36.470000 0.460000)\n        (point 201.690000 1009.230000 -36.520000 0.460000)\n        4)\n      (segment 2227 \n        (point 201.690000 1009.230000 -36.520000 0.460000)\n        (point 199.860000 1012.980000 -37.850000 0.460000)\n        4)\n      (segment 2228 \n        (point 199.860000 1012.980000 -37.850000 0.460000)\n        (point 197.150000 1016.520000 -39.280000 0.460000)\n        4)\n      (segment 2229 \n        (point 197.150000 1016.520000 -39.280000 0.460000)\n        (point 196.350000 1019.910000 -39.920000 0.460000)\n        4)\n      (segment 2230 \n        (point 196.350000 1019.910000 -39.920000 0.460000)\n        (point 196.220000 1020.490000 -39.920000 0.460000)\n        4)\n      (segment 2231 \n        (point 196.220000 1020.490000 -39.920000 0.460000)\n        (point 195.950000 1021.610000 -40.800000 0.460000)\n        4)\n      (segment 2232 \n        (point 195.950000 1021.610000 -40.800000 0.460000)\n        (point 193.500000 1024.040000 -41.730000 0.460000)\n        4)\n      (segment 2233 \n        (point 193.500000 1024.040000 -41.730000 0.460000)\n        (point 192.980000 1026.290000 -40.500000 0.460000)\n        4)\n      (segment 2234 \n        (point 192.980000 1026.290000 -40.500000 0.460000)\n        (point 191.870000 1029.010000 -39.850000 0.460000)\n        4)\n      (segment 2235 \n        (point 191.870000 1029.010000 -39.850000 0.460000)\n        (point 189.540000 1030.850000 -39.850000 0.460000)\n        4)\n      (segment 2236 \n        (point 189.540000 1030.850000 -39.850000 0.460000)\n        (point 189.640000 1034.460000 -40.480000 0.460000)\n        4)\n      (segment 2237 \n        (point 189.640000 1034.460000 -40.480000 0.460000)\n        (point 187.950000 1037.650000 -39.630000 0.460000)\n        4)\n      (segment 2238 \n        (point 187.950000 1037.650000 -39.630000 0.460000)\n        (point 186.710000 1040.940000 -40.550000 0.460000)\n        4)\n      (segment 2239 \n        (point 186.710000 1040.940000 -40.550000 0.460000)\n        (point 184.760000 1045.270000 -40.970000 0.460000)\n        4)\n      (segment 2240 \n        (point 184.760000 1045.270000 -40.970000 0.460000)\n        (point 183.020000 1046.650000 -41.220000 0.460000)\n        4)\n      (segment 2241 \n        (point 183.020000 1046.650000 -41.220000 0.460000)\n        (point 183.070000 1048.460000 -42.050000 0.460000)\n        4)\n      (segment 2242 \n        (point 183.070000 1048.460000 -42.050000 0.460000)\n        (point 181.950000 1051.180000 -42.450000 0.460000)\n        4)\n      (segment 2243 \n        (point 181.950000 1051.180000 -42.450000 0.460000)\n        (point 181.160000 1054.570000 -42.600000 0.460000)\n        4)\n      (segment 2244 \n        (point 181.160000 1054.570000 -42.600000 0.460000)\n        (point 179.300000 1056.530000 -42.600000 0.460000)\n        4)\n      (segment 2245 \n        (point 179.300000 1056.530000 -42.600000 0.460000)\n        (point 178.050000 1059.820000 -41.470000 0.460000)\n        4)\n      (segment 2246 \n        (point 178.050000 1059.820000 -41.470000 0.460000)\n        (point 178.100000 1061.620000 -41.100000 0.460000)\n        4)\n      (segment 2247 \n        (point 178.100000 1061.620000 -41.100000 0.460000)\n        (point 176.150000 1065.930000 -40.480000 0.460000)\n        4)\n      (segment 2248 \n        (point 176.150000 1065.930000 -40.480000 0.460000)\n        (point 176.240000 1069.540000 -39.970000 0.460000)\n        4)\n      (segment 2249 \n        (point 176.240000 1069.540000 -39.970000 0.460000)\n        (point 172.450000 1071.640000 -40.320000 0.460000)\n        4)\n      (segment 2250 \n        (point 172.450000 1071.640000 -40.320000 0.460000)\n        (point 171.210000 1074.940000 -41.600000 0.460000)\n        4)\n      (segment 2251 \n        (point 171.210000 1074.940000 -41.600000 0.460000)\n        (point 168.700000 1075.540000 -42.350000 0.460000)\n        4)\n      (segment 2252 \n        (point 168.700000 1075.540000 -42.350000 0.460000)\n        (point 168.360000 1079.040000 -43.220000 0.460000)\n        4)\n      (segment 2253 \n        (point 168.360000 1079.040000 -43.220000 0.460000)\n        (point 168.850000 1080.950000 -43.720000 0.460000)\n        4)\n      (segment 2254 \n        (point 168.850000 1080.950000 -43.720000 0.460000)\n        (point 164.170000 1082.840000 -43.970000 0.460000)\n        4)\n      (segment 2255 \n        (point 164.170000 1082.840000 -43.970000 0.460000)\n        (point 164.170000 1082.840000 -44.000000 0.460000)\n        4)\n      (segment 2256 \n        (point 164.170000 1082.840000 -44.000000 0.460000)\n        (point 162.170000 1085.350000 -43.100000 0.460000)\n        4)\n      (segment 2257 \n        (point 162.170000 1085.350000 -43.100000 0.460000)\n        (point 156.460000 1087.590000 -43.350000 0.460000)\n        4)\n      (segment 2258 \n        (point 156.460000 1087.590000 -43.350000 0.460000)\n        (point 156.460000 1087.590000 -43.470000 0.460000)\n        4)\n      (segment 2259 \n        (point 156.460000 1087.590000 -43.470000 0.460000)\n        (point 153.260000 1089.240000 -44.630000 0.230000)\n        4)\n      (segment 2260 \n        (point 153.260000 1089.240000 -44.630000 0.230000)\n        (point 149.460000 1091.340000 -45.270000 0.230000)\n        4)\n      (segment 2261 \n        (point 149.460000 1091.340000 -45.270000 0.230000)\n        (point 149.460000 1091.340000 -45.400000 0.230000)\n        4)\n      (segment 2262 \n        (point 149.460000 1091.340000 -45.400000 0.230000)\n        (point 146.200000 1091.170000 -48.220000 0.230000)\n        4)\n      (segment 2263 \n        (point 146.200000 1091.170000 -48.220000 0.230000)\n        (point 146.200000 1091.170000 -48.270000 0.230000)\n        4))\n    (branch 93 83 \n      (segment 2264 \n        (point 236.800000 863.730000 -30.200000 0.915000)\n        (point 235.720000 866.340000 -28.500000 0.690000)\n        4)\n      (segment 2265 \n        (point 235.720000 866.340000 -28.500000 0.690000)\n        (point 234.790000 870.300000 -29.100000 0.690000)\n        4)\n      (segment 2266 \n        (point 234.790000 870.300000 -29.100000 0.690000)\n        (point 233.090000 873.500000 -29.100000 0.690000)\n        4)\n      (segment 2267 \n        (point 233.090000 873.500000 -29.100000 0.690000)\n        (point 231.590000 877.920000 -29.380000 0.690000)\n        4)\n      (segment 2268 \n        (point 231.590000 877.920000 -29.380000 0.690000)\n        (point 230.340000 881.210000 -28.500000 0.690000)\n        4)\n      (segment 2269 \n        (point 230.340000 881.210000 -28.500000 0.690000)\n     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       (point 222.340000 897.240000 -25.200000 0.690000)\n        (point 219.950000 901.460000 -24.620000 0.690000)\n        4)\n      (segment 2277 \n        (point 219.950000 901.460000 -24.620000 0.690000)\n        (point 218.840000 904.190000 -26.850000 0.690000)\n        4)\n      (segment 2278 \n        (point 218.840000 904.190000 -26.850000 0.690000)\n        (point 216.880000 908.500000 -27.270000 0.690000)\n        4)\n      (segment 2279 \n        (point 216.880000 908.500000 -27.270000 0.690000)\n        (point 212.690000 912.300000 -27.570000 0.690000)\n        4)\n      (segment 2280 \n        (point 212.690000 912.300000 -27.570000 0.690000)\n        (point 213.510000 914.880000 -28.150000 0.690000)\n        4)\n      (segment 2281 \n        (point 213.510000 914.880000 -28.150000 0.690000)\n        (point 213.430000 917.250000 -27.970000 0.690000)\n        4)\n      (segment 2282 \n        (point 213.430000 917.250000 -27.970000 0.690000)\n        (point 213.780000 919.720000 -28.650000 0.690000)\n        4)\n      (segment 2283 \n        (point 213.780000 919.720000 -28.650000 0.690000)\n        (point 213.780000 919.720000 -28.670000 0.690000)\n        4)\n      (segment 2284 \n        (point 213.780000 919.720000 -28.670000 0.690000)\n        (point 212.230000 922.340000 -29.080000 0.690000)\n        4)\n      (segment 2285 \n        (point 212.230000 922.340000 -29.080000 0.690000)\n        (point 210.730000 926.760000 -29.900000 0.690000)\n        4)\n      (segment 2286 \n        (point 210.730000 926.760000 -29.900000 0.690000)\n        (point 211.660000 928.780000 -30.350000 0.690000)\n        4)\n      (segment 2287 \n        (point 211.660000 928.780000 -30.350000 0.690000)\n        (point 211.660000 928.780000 -30.380000 0.690000)\n        4)\n      (segment 2288 \n        (point 211.660000 928.780000 -30.380000 0.690000)\n        (point 211.310000 932.280000 -30.950000 0.690000)\n        4)\n      (segment 2289 \n        (point 211.310000 932.280000 -30.950000 0.690000)\n        (point 210.780000 934.540000 -31.580000 0.690000)\n        4)\n      (segment 2290 \n        (point 210.780000 934.540000 -31.580000 0.690000)\n        (point 210.780000 934.540000 -31.600000 0.690000)\n        4)\n      (segment 2291 \n        (point 210.780000 934.540000 -31.600000 0.690000)\n        (point 209.360000 936.610000 -32.250000 0.690000)\n        4)\n      (segment 2292 \n        (point 209.360000 936.610000 -32.250000 0.690000)\n        (point 207.040000 938.440000 -32.450000 0.690000)\n        4)\n      (segment 2293 \n        (point 207.040000 938.440000 -32.450000 0.690000)\n        (point 207.040000 938.440000 -32.470000 0.690000)\n        4)\n      (segment 2294 \n        (point 207.040000 938.440000 -32.470000 0.690000)\n        (point 207.280000 941.490000 -33.170000 0.690000)\n        4)\n      (segment 2295 \n        (point 207.280000 941.490000 -33.170000 0.690000)\n        (point 206.750000 943.740000 -34.320000 0.690000)\n        4)\n      (segment 2296 \n        (point 206.750000 943.740000 -34.320000 0.690000)\n        (point 206.750000 943.740000 -34.350000 0.690000)\n        4)\n      (segment 2297 \n        (point 206.750000 943.740000 -34.350000 0.690000)\n        (point 206.790000 945.550000 -33.950000 0.690000)\n        4)\n      (segment 2298 \n        (point 206.790000 945.550000 -33.950000 0.690000)\n        (point 207.780000 949.370000 -34.900000 0.690000)\n        4)\n      (segment 2299 \n        (point 207.780000 949.370000 -34.900000 0.690000)\n        (point 208.090000 950.040000 -35.850000 0.690000)\n        4)\n      (segment 2300 \n        (point 208.090000 950.040000 -35.850000 0.690000)\n        (point 206.220000 951.980000 -37.080000 0.690000)\n        4)\n      (segment 2301 \n        (point 206.220000 951.980000 -37.080000 0.690000)\n        (point 204.040000 953.270000 -37.630000 0.690000)\n        4)\n      (segment 2302 \n        (point 204.040000 953.270000 -37.630000 0.690000)\n        (point 202.800000 956.560000 -38.420000 0.690000)\n        4)\n      (segment 2303 \n        (point 202.800000 956.560000 -38.420000 0.690000)\n        (point 202.270000 958.820000 -39.170000 0.690000)\n        4)\n      (segment 2304 \n        (point 202.270000 958.820000 -39.170000 0.690000)\n        (point 199.500000 960.560000 -40.070000 0.690000)\n        4)\n      (segment 2305 \n        (point 199.500000 960.560000 -40.070000 0.690000)\n        (point 199.290000 963.490000 -41.030000 0.690000)\n        4)\n      (segment 2306 \n        (point 199.290000 963.490000 -41.030000 0.690000)\n        (point 200.100000 966.080000 -41.900000 0.690000)\n        4)\n      (segment 2307 \n        (point 200.100000 966.080000 -41.900000 0.690000)\n        (point 199.560000 968.340000 -42.330000 0.690000)\n        4)\n      (segment 2308 \n        (point 199.560000 968.340000 -42.330000 0.690000)\n        (point 196.580000 973.020000 -42.570000 0.690000)\n        4))\n    (branch 94 93 \n      (segment 2309 \n        (point 196.580000 973.020000 -42.570000 0.690000)\n        (point 196.200000 974.700000 -43.950000 0.460000)\n        4)\n      (segment 2310 \n        (point 196.200000 974.700000 -43.950000 0.460000)\n        (point 194.320000 976.640000 -44.700000 0.460000)\n        4)\n      (segment 2311 \n        (point 194.320000 976.640000 -44.700000 0.460000)\n        (point 192.850000 976.890000 -45.520000 0.460000)\n        4)\n      (segment 2312 \n        (point 192.850000 976.890000 -45.520000 0.460000)\n        (point 191.420000 978.960000 -47.020000 0.460000)\n        4)\n      (segment 2313 \n        (point 191.420000 978.960000 -47.020000 0.460000)\n        (point 190.310000 981.670000 -47.850000 0.460000)\n        4)\n      (segment 2314 \n        (point 190.310000 981.670000 -47.850000 0.460000)\n        (point 189.200000 984.410000 -48.900000 0.460000)\n        4)\n      (segment 2315 \n        (point 189.200000 984.410000 -48.900000 0.460000)\n        (point 187.020000 985.680000 -49.320000 0.460000)\n        4)\n      (segment 2316 \n        (point 187.020000 985.680000 -49.320000 0.460000)\n        (point 184.260000 987.430000 -49.320000 0.460000)\n        4)\n      (segment 2317 \n        (point 184.260000 987.430000 -49.320000 0.460000)\n        (point 184.260000 987.430000 -49.350000 0.460000)\n        4)\n      (segment 2318 \n        (point 184.260000 987.430000 -49.350000 0.460000)\n        (point 183.410000 989.010000 -50.420000 0.460000)\n        4)\n      (segment 2319 \n        (point 183.410000 989.010000 -50.420000 0.460000)\n        (point 183.010000 990.720000 -50.420000 0.460000)\n        4)\n      (segment 2320 \n        (point 183.010000 990.720000 -50.420000 0.460000)\n        (point 182.750000 991.840000 -51.350000 0.460000)\n        4)\n      (segment 2321 \n        (point 182.750000 991.840000 -51.350000 0.460000)\n        (point 181.320000 993.910000 -52.130000 0.460000)\n        4)\n      (segment 2322 \n        (point 181.320000 993.910000 -52.130000 0.460000)\n        (point 181.420000 997.500000 -52.770000 0.460000)\n        4)\n      (segment 2323 \n        (point 181.420000 997.500000 -52.770000 0.460000)\n        (point 181.420000 997.500000 -52.800000 0.460000)\n        4)\n      (segment 2324 \n        (point 181.420000 997.500000 -52.800000 0.460000)\n        (point 180.720000 998.540000 -53.300000 0.460000)\n        4)\n      (segment 2325 \n        (point 180.720000 998.540000 -53.300000 0.460000)\n        (point 179.610000 1001.260000 -53.580000 0.460000)\n        4)\n      (segment 2326 \n        (point 179.610000 1001.260000 -53.580000 0.460000)\n        (point 179.790000 1002.500000 -54.000000 0.460000)\n        4)\n      (segment 2327 \n        (point 179.790000 1002.500000 -54.000000 0.460000)\n        (point 179.260000 1004.770000 -54.400000 0.460000)\n        4)\n      (segment 2328 \n        (point 179.260000 1004.770000 -54.400000 0.460000)\n        (point 181.720000 1008.320000 -54.900000 0.460000)\n        4)\n      (segment 2329 \n        (point 181.720000 1008.320000 -54.900000 0.460000)\n        (point 180.920000 1011.720000 -54.350000 0.460000)\n        4)\n      (segment 2330 \n        (point 180.920000 1011.720000 -54.350000 0.460000)\n        (point 181.140000 1014.770000 -54.350000 0.460000)\n        4)\n      (segment 2331 \n        (point 181.140000 1014.770000 -54.350000 0.460000)\n        (point 177.850000 1018.770000 -54.900000 0.460000)\n        4)\n      (segment 2332 \n        (point 177.850000 1018.770000 -54.900000 0.460000)\n        (point 177.460000 1020.460000 -55.880000 0.460000)\n        4)\n      (segment 2333 \n        (point 177.460000 1020.460000 -55.880000 0.460000)\n        (point 177.460000 1020.460000 -55.900000 0.460000)\n        4)\n      (segment 2334 \n        (point 177.460000 1020.460000 -55.900000 0.460000)\n        (point 176.340000 1023.190000 -57.000000 0.460000)\n        4)\n      (segment 2335 \n        (point 176.340000 1023.190000 -57.000000 0.460000)\n        (point 176.340000 1023.190000 -57.020000 0.460000)\n        4)\n      (segment 2336 \n        (point 176.340000 1023.190000 -57.020000 0.460000)\n        (point 175.230000 1025.910000 -57.950000 0.460000)\n        4)\n      (segment 2337 \n        (point 175.230000 1025.910000 -57.950000 0.460000)\n        (point 173.950000 1027.410000 -59.000000 0.460000)\n        4)\n      (segment 2338 \n        (point 173.950000 1027.410000 -59.000000 0.460000)\n        (point 172.530000 1029.460000 -60.000000 0.460000)\n        4)\n      (segment 2339 \n        (point 172.530000 1029.460000 -60.000000 0.460000)\n        (point 172.530000 1029.460000 -60.030000 0.460000)\n        4)\n      (segment 2340 \n        (point 172.530000 1029.460000 -60.030000 0.460000)\n        (point 171.600000 1033.420000 -60.700000 0.460000)\n        4)\n      (segment 2341 \n        (point 171.600000 1033.420000 -60.700000 0.460000)\n        (point 169.130000 1035.830000 -61.600000 0.460000)\n        4)\n      (segment 2342 \n        (point 169.130000 1035.830000 -61.600000 0.460000)\n        (point 166.820000 1037.690000 -62.630000 0.460000)\n        4)\n      (segment 2343 \n        (point 166.820000 1037.690000 -62.630000 0.460000)\n        (point 164.950000 1039.620000 -63.350000 0.460000)\n        4)\n      (segment 2344 \n        (point 164.950000 1039.620000 -63.350000 0.460000)\n        (point 163.400000 1042.250000 -64.180000 0.460000)\n        4)\n      (segment 2345 \n        (point 163.400000 1042.250000 -64.180000 0.460000)\n        (point 160.820000 1045.230000 -64.550000 0.460000)\n        4)\n      (segment 2346 \n        (point 160.820000 1045.230000 -64.550000 0.460000)\n        (point 159.440000 1049.090000 -65.430000 0.460000)\n        4)\n      (segment 2347 \n        (point 159.440000 1049.090000 -65.430000 0.460000)\n        (point 158.910000 1051.340000 -65.130000 0.460000)\n        4)\n      (segment 2348 \n        (point 158.910000 1051.340000 -65.130000 0.460000)\n        (point 158.380000 1053.610000 -65.950000 0.460000)\n        4)\n      (segment 2349 \n        (point 158.380000 1053.610000 -65.950000 0.460000)\n        (point 157.090000 1055.100000 -67.070000 0.460000)\n        4)\n      (segment 2350 \n        (point 157.090000 1055.100000 -67.070000 0.460000)\n        (point 156.560000 1057.370000 -68.280000 0.460000)\n        4)\n      (segment 2351 \n        (point 156.560000 1057.370000 -68.280000 0.460000)\n        (point 153.520000 1060.240000 -68.720000 0.460000)\n        4)\n      (segment 2352 \n        (point 153.520000 1060.240000 -68.720000 0.460000)\n        (point 153.570000 1062.040000 -69.470000 0.460000)\n        4)\n      (segment 2353 \n        (point 153.570000 1062.040000 -69.470000 0.460000)\n        (point 153.940000 1064.530000 -70.030000 0.460000)\n        4)\n      (segment 2354 \n        (point 153.940000 1064.530000 -70.030000 0.460000)\n        (point 153.420000 1066.780000 -71.050000 0.460000)\n        4))\n    (branch 95 94 \n      (segment 2355 \n        (point 153.420000 1066.780000 -71.050000 0.460000)\n        (point 151.530000 1068.730000 -71.820000 0.230000)\n        4)\n      (segment 2356 \n        (point 151.530000 1068.730000 -71.820000 0.230000)\n        (point 150.510000 1069.090000 -72.570000 0.230000)\n        4)\n      (segment 2357 \n        (point 150.510000 1069.090000 -72.570000 0.230000)\n        (point 149.530000 1071.240000 -73.280000 0.230000)\n        4)\n      (segment 2358 \n        (point 149.530000 1071.240000 -73.280000 0.230000)\n        (point 148.690000 1072.850000 -74.320000 0.230000)\n        4)\n      (segment 2359 \n        (point 148.690000 1072.850000 -74.320000 0.230000)\n        (point 146.770000 1072.980000 -75.570000 0.230000)\n        4)\n      (segment 2360 \n        (point 146.770000 1072.980000 -75.570000 0.230000)\n        (point 145.340000 1075.050000 -76.920000 0.230000)\n        4)\n      (segment 2361 \n        (point 145.340000 1075.050000 -76.920000 0.230000)\n        (point 144.330000 1075.410000 -78.630000 0.230000)\n        4)\n      (segment 2362 \n        (point 144.330000 1075.410000 -78.630000 0.230000)\n        (point 142.010000 1077.250000 -79.100000 0.230000)\n        4)\n      (segment 2363 \n        (point 142.010000 1077.250000 -79.100000 0.230000)\n        (point 140.400000 1078.070000 -81.250000 0.230000)\n        4)\n      (segment 2364 \n        (point 140.400000 1078.070000 -81.250000 0.230000)\n        (point 140.400000 1078.070000 -81.300000 0.230000)\n        4))\n    (branch 96 94 \n      (segment 2365 \n        (point 153.420000 1066.780000 -71.050000 0.460000)\n        (point 153.640000 1069.820000 -71.050000 0.230000)\n        4)\n      (segment 2366 \n        (point 153.640000 1069.820000 -71.050000 0.230000)\n        (point 152.840000 1073.220000 -71.050000 0.230000)\n        4)\n      (segment 2367 \n        (point 152.840000 1073.220000 -71.050000 0.230000)\n        (point 152.760000 1075.580000 -71.850000 0.230000)\n        4)\n      (segment 2368 \n        (point 152.760000 1075.580000 -71.850000 0.230000)\n        (point 154.860000 1076.670000 -73.250000 0.230000)\n        4))\n    (branch 97 93 \n      (segment 2369 \n        (point 196.580000 973.020000 -42.570000 0.690000)\n        (point 197.550000 975.030000 -41.300000 0.460000)\n        4)\n      (segment 2370 \n        (point 197.550000 975.030000 -41.300000 0.460000)\n        (point 198.170000 976.380000 -40.550000 0.460000)\n        4)\n      (segment 2371 \n        (point 198.170000 976.380000 -40.550000 0.460000)\n        (point 196.180000 978.880000 -39.600000 0.460000)\n        4)\n      (segment 2372 \n        (point 196.180000 978.880000 -39.600000 0.460000)\n        (point 195.950000 981.830000 -39.050000 0.460000)\n        4)\n      (segment 2373 \n        (point 195.950000 981.830000 -39.050000 0.460000)\n        (point 194.980000 983.990000 -38.420000 0.460000)\n        4)\n      (segment 2374 \n        (point 194.980000 983.990000 -38.420000 0.460000)\n        (point 193.690000 985.470000 -38.420000 0.460000)\n        4)\n      (segment 2375 \n        (point 193.690000 985.470000 -38.420000 0.460000)\n        (point 193.550000 986.040000 -37.850000 0.460000)\n        4)\n      (segment 2376 \n        (point 193.550000 986.040000 -37.850000 0.460000)\n        (point 193.740000 987.270000 -37.150000 0.460000)\n        4)\n      (segment 2377 \n        (point 193.740000 987.270000 -37.150000 0.460000)\n        (point 193.740000 987.270000 -37.170000 0.460000)\n        4)\n      (segment 2378 \n        (point 193.740000 987.270000 -37.170000 0.460000)\n        (point 194.230000 989.180000 -36.400000 0.460000)\n        4)\n      (segment 2379 \n        (point 194.230000 989.180000 -36.400000 0.460000)\n        (point 194.230000 989.180000 -36.420000 0.460000)\n        4)\n      (segment 2380 \n        (point 194.230000 989.180000 -36.420000 0.460000)\n        (point 194.590000 991.660000 -35.450000 0.460000)\n        4)\n      (segment 2381 \n        (point 194.590000 991.660000 -35.450000 0.460000)\n        (point 194.770000 992.900000 -35.570000 0.460000)\n        4)\n      (segment 2382 \n        (point 194.770000 992.900000 -35.570000 0.460000)\n        (point 194.690000 995.270000 -34.020000 0.460000)\n        4)\n      (segment 2383 \n        (point 194.690000 995.270000 -34.020000 0.460000)\n        (point 193.660000 995.610000 -32.400000 0.460000)\n        4)\n      (segment 2384 \n        (point 193.660000 995.610000 -32.400000 0.460000)\n        (point 191.350000 997.460000 -31.050000 0.460000)\n        4)\n      (segment 2385 \n        (point 191.350000 997.460000 -31.050000 0.460000)\n        (point 188.710000 998.640000 -31.220000 0.460000)\n        4)\n      (segment 2386 \n        (point 188.710000 998.640000 -31.220000 0.460000)\n        (point 188.310000 1000.340000 -32.170000 0.460000)\n        4)\n      (segment 2387 \n        (point 188.310000 1000.340000 -32.170000 0.460000)\n        (point 188.670000 1002.810000 -33.250000 0.460000)\n        4)\n      (segment 2388 \n        (point 188.670000 1002.810000 -33.250000 0.460000)\n        (point 188.670000 1002.810000 -33.270000 0.460000)\n        4)\n      (segment 2389 \n        (point 188.670000 1002.810000 -33.270000 0.460000)\n        (point 189.580000 1003.020000 -34.880000 0.460000)\n        4)\n      (segment 2390 \n        (point 189.580000 1003.020000 -34.880000 0.460000)\n        (point 189.580000 1003.020000 -34.900000 0.460000)\n        4)\n      (segment 2391 \n        (point 189.580000 1003.020000 -34.900000 0.460000)\n        (point 190.960000 1005.140000 -35.750000 0.460000)\n        4)\n      (segment 2392 \n        (point 190.960000 1005.140000 -35.750000 0.460000)\n        (point 190.960000 1005.140000 -35.770000 0.460000)\n        4)\n      (segment 2393 \n        (point 190.960000 1005.140000 -35.770000 0.460000)\n        (point 192.530000 1008.490000 -35.750000 0.460000)\n        4)\n      (segment 2394 \n        (point 192.530000 1008.490000 -35.750000 0.460000)\n        (point 192.750000 1011.530000 -36.200000 0.460000)\n        4)\n      (segment 2395 \n        (point 192.750000 1011.530000 -36.200000 0.460000)\n        (point 193.120000 1014.000000 -35.530000 0.460000)\n        4)\n      (segment 2396 \n        (point 193.120000 1014.000000 -35.530000 0.460000)\n        (point 194.190000 1015.460000 -34.250000 0.460000)\n        4)\n      (segment 2397 \n        (point 194.190000 1015.460000 -34.250000 0.460000)\n        (point 193.990000 1018.390000 -33.000000 0.460000)\n        4)\n      (segment 2398 \n        (point 193.990000 1018.390000 -33.000000 0.460000)\n        (point 194.210000 1021.420000 -32.300000 0.460000)\n        4)\n      (segment 2399 \n        (point 194.210000 1021.420000 -32.300000 0.460000)\n        (point 194.210000 1021.420000 -32.320000 0.460000)\n        4)\n      (segment 2400 \n        (point 194.210000 1021.420000 -32.320000 0.460000)\n        (point 195.730000 1022.980000 -31.670000 0.460000)\n        4)\n      (segment 2401 \n        (point 195.730000 1022.980000 -31.670000 0.460000)\n        (point 196.980000 1025.660000 -30.830000 0.460000)\n        4)\n      (segment 2402 \n        (point 196.980000 1025.660000 -30.830000 0.460000)\n        (point 196.980000 1025.660000 -30.850000 0.460000)\n        4)\n      (segment 2403 \n        (point 196.980000 1025.660000 -30.850000 0.460000)\n        (point 198.110000 1028.910000 -30.250000 0.460000)\n        4)\n      (segment 2404 \n        (point 198.110000 1028.910000 -30.250000 0.460000)\n        (point 197.700000 1030.580000 -28.330000 0.460000)\n        4)\n      (segment 2405 \n        (point 197.700000 1030.580000 -28.330000 0.460000)\n        (point 198.380000 1033.730000 -26.880000 0.460000)\n        4)\n      (segment 2406 \n        (point 198.380000 1033.730000 -26.880000 0.460000)\n        (point 198.420000 1035.530000 -26.880000 0.460000)\n        4)\n      (segment 2407 \n        (point 198.420000 1035.530000 -26.880000 0.460000)\n        (point 199.500000 1036.980000 -25.970000 0.460000)\n        4)\n      (segment 2408 \n        (point 199.500000 1036.980000 -25.970000 0.460000)\n        (point 200.130000 1038.320000 -24.170000 0.460000)\n        4)\n      (segment 2409 \n        (point 200.130000 1038.320000 -24.170000 0.460000)\n        (point 201.460000 1038.630000 -23.470000 0.460000)\n        4)\n      (segment 2410 \n        (point 201.460000 1038.630000 -23.470000 0.460000)\n        (point 201.750000 1043.470000 -23.470000 0.460000)\n        4)\n      (segment 2411 \n        (point 201.750000 1043.470000 -23.470000 0.460000)\n        (point 202.560000 1046.050000 -23.470000 0.460000)\n        4)\n      (segment 2412 \n        (point 202.560000 1046.050000 -23.470000 0.460000)\n        (point 204.840000 1048.380000 -22.880000 0.460000)\n        4)\n      (segment 2413 \n        (point 204.840000 1048.380000 -22.880000 0.460000)\n        (point 206.040000 1049.260000 -23.630000 0.460000)\n        4)\n      (segment 2414 \n        (point 206.040000 1049.260000 -23.630000 0.460000)\n        (point 206.040000 1049.260000 -23.600000 0.460000)\n        4)\n      (segment 2415 \n        (point 206.040000 1049.260000 -23.600000 0.460000)\n        (point 206.910000 1053.640000 -22.970000 0.460000)\n        4)\n      (segment 2416 \n        (point 206.910000 1053.640000 -22.970000 0.460000)\n        (point 206.560000 1057.140000 -21.630000 0.460000)\n        4)\n      (segment 2417 \n        (point 206.560000 1057.140000 -21.630000 0.460000)\n        (point 208.650000 1058.230000 -20.200000 0.460000)\n        4)\n      (segment 2418 \n        (point 208.650000 1058.230000 -20.200000 0.460000)\n        (point 209.860000 1059.110000 -18.300000 0.460000)\n        4)\n      (segment 2419 \n        (point 209.860000 1059.110000 -18.300000 0.460000)\n        (point 212.270000 1060.880000 -16.730000 0.460000)\n        4)\n      (segment 2420 \n        (point 212.270000 1060.880000 -16.730000 0.460000)\n        (point 212.910000 1062.210000 -15.270000 0.460000)\n        4)\n      (segment 2421 \n        (point 212.910000 1062.210000 -15.270000 0.460000)\n        (point 213.270000 1064.690000 -14.700000 0.460000)\n        4)\n      (segment 2422 \n        (point 213.270000 1064.690000 -14.700000 0.460000)\n        (point 214.250000 1068.490000 -14.270000 0.460000)\n        4)\n      (segment 2423 \n        (point 214.250000 1068.490000 -14.270000 0.460000)\n        (point 216.530000 1070.830000 -14.270000 0.460000)\n        4)\n      (segment 2424 \n        (point 216.530000 1070.830000 -14.270000 0.460000)\n        (point 219.400000 1072.690000 -14.270000 0.460000)\n        4)\n      (segment 2425 \n        (point 219.400000 1072.690000 -14.270000 0.460000)\n        (point 220.390000 1076.510000 -14.250000 0.460000)\n        4)\n      (segment 2426 \n        (point 220.390000 1076.510000 -14.250000 0.460000)\n        (point 220.390000 1076.510000 -14.320000 0.460000)\n        4)\n      (segment 2427 \n        (point 220.390000 1076.510000 -14.320000 0.460000)\n        (point 221.190000 1079.080000 -15.800000 0.460000)\n        4)\n      (segment 2428 \n        (point 221.190000 1079.080000 -15.800000 0.460000)\n        (point 221.190000 1079.080000 -15.880000 0.460000)\n        4)\n      (segment 2429 \n        (point 221.190000 1079.080000 -15.880000 0.460000)\n        (point 223.480000 1081.420000 -16.670000 0.460000)\n        4)\n      (segment 2430 \n        (point 223.480000 1081.420000 -16.670000 0.460000)\n        (point 226.840000 1085.180000 -16.900000 0.460000)\n        4))\n    (branch 98 97 \n      (segment 2431 \n        (point 226.840000 1085.180000 -16.900000 0.460000)\n        (point 224.880000 1089.510000 -16.900000 0.460000)\n        4)\n      (segment 2432 \n        (point 224.880000 1089.510000 -16.900000 0.460000)\n        (point 222.880000 1092.010000 -18.250000 0.460000)\n        4)\n      (segment 2433 \n        (point 222.880000 1092.010000 -18.250000 0.460000)\n        (point 219.530000 1094.220000 -20.970000 0.460000)\n        4)\n      (segment 2434 \n        (point 219.530000 1094.220000 -20.970000 0.460000)\n        (point 219.530000 1094.220000 -21.020000 0.460000)\n        4))\n    (branch 99 97 \n      (segment 2435 \n        (point 226.840000 1085.180000 -16.900000 0.460000)\n        (point 227.540000 1084.150000 -16.900000 0.460000)\n        4)\n      (segment 2436 \n        (point 227.540000 1084.150000 -16.900000 0.460000)\n        (point 229.650000 1085.250000 -17.820000 0.460000)\n        4)\n      (segment 2437 \n        (point 229.650000 1085.250000 -17.820000 0.460000)\n        (point 229.650000 1085.250000 -17.850000 0.460000)\n        4)\n      (segment 2438 \n        (point 229.650000 1085.250000 -17.850000 0.460000)\n        (point 230.710000 1086.700000 -16.470000 0.460000)\n        4)\n      (segment 2439 \n        (point 230.710000 1086.700000 -16.470000 0.460000)\n        (point 230.710000 1086.700000 -16.500000 0.460000)\n        4)\n      (segment 2440 \n        (point 230.710000 1086.700000 -16.500000 0.460000)\n        (point 233.090000 1086.650000 -14.880000 0.460000)\n        4)\n      (segment 2441 \n        (point 233.090000 1086.650000 -14.880000 0.460000)\n        (point 235.190000 1087.740000 -12.830000 0.460000)\n        4)\n      (segment 2442 \n        (point 235.190000 1087.740000 -12.830000 0.460000)\n        (point 236.260000 1089.180000 -11.270000 0.460000)\n        4))\n    (branch 100 82 \n      (segment 2443 \n        (point 235.320000 659.410000 -34.150000 0.915000)\n        (point 235.390000 662.780000 -32.520000 0.690000)\n        4)\n      (segment 2444 \n        (point 235.390000 662.780000 -32.520000 0.690000)\n        (point 236.450000 664.220000 -31.200000 0.690000)\n        4)\n      (segment 2445 \n        (point 236.450000 664.220000 -31.200000 0.690000)\n        (point 237.140000 667.360000 -30.450000 0.690000)\n        4)\n      (segment 2446 \n        (point 237.140000 667.360000 -30.450000 0.690000)\n        (point 237.820000 670.520000 -29.850000 0.690000)\n        4)\n      (segment 2447 \n        (point 237.820000 670.520000 -29.850000 0.690000)\n        (point 238.040000 673.550000 -29.250000 0.690000)\n        4)\n      (segment 2448 \n        (point 238.040000 673.550000 -29.250000 0.690000)\n        (point 238.670000 674.900000 -27.880000 0.690000)\n        4)\n      (segment 2449 \n        (point 238.670000 674.900000 -27.880000 0.690000)\n        (point 237.830000 676.490000 -26.700000 0.690000)\n        4)\n      (segment 2450 \n        (point 237.830000 676.490000 -26.700000 0.690000)\n        (point 237.030000 679.880000 -26.000000 0.690000)\n        4)\n      (segment 2451 \n        (point 237.030000 679.880000 -26.000000 0.690000)\n        (point 237.580000 683.600000 -26.920000 0.690000)\n        4)\n      (segment 2452 \n        (point 237.580000 683.600000 -26.920000 0.690000)\n        (point 238.510000 685.600000 -26.650000 0.690000)\n        4)\n      (segment 2453 \n        (point 238.510000 685.600000 -26.650000 0.690000)\n        (point 238.490000 689.780000 -26.650000 0.690000)\n        4)\n      (segment 2454 \n        (point 238.490000 689.780000 -26.650000 0.690000)\n        (point 239.480000 693.590000 -27.650000 0.690000)\n        4)\n      (segment 2455 \n        (point 239.480000 693.590000 -27.650000 0.690000)\n        (point 239.570000 697.200000 -27.650000 0.690000)\n        4)\n      (segment 2456 \n        (point 239.570000 697.200000 -27.650000 0.690000)\n        (point 239.090000 701.260000 -27.650000 0.690000)\n        4)\n      (segment 2457 \n        (point 239.090000 701.260000 -27.650000 0.690000)\n        (point 239.270000 702.500000 -27.650000 0.690000)\n        4)\n      (segment 2458 \n        (point 239.270000 702.500000 -27.650000 0.690000)\n        (point 239.050000 705.440000 -27.650000 0.690000)\n        4)\n      (segment 2459 \n        (point 239.050000 705.440000 -27.650000 0.690000)\n        (point 236.740000 707.280000 -27.650000 0.690000)\n        4)\n      (segment 2460 \n        (point 236.740000 707.280000 -27.650000 0.690000)\n        (point 236.080000 710.110000 -27.800000 0.690000)\n        4)\n      (segment 2461 \n        (point 236.080000 710.110000 -27.800000 0.690000)\n        (point 235.980000 712.480000 -27.150000 0.690000)\n        4)\n      (segment 2462 \n        (point 235.980000 712.480000 -27.150000 0.690000)\n        (point 237.380000 714.600000 -26.720000 0.690000)\n        4)\n      (segment 2463 \n        (point 237.380000 714.600000 -26.720000 0.690000)\n        (point 238.180000 717.180000 -26.300000 0.690000)\n        4)\n      (segment 2464 \n        (point 238.180000 717.180000 -26.300000 0.690000)\n        (point 237.250000 721.140000 -26.250000 0.690000)\n        4)\n      (segment 2465 \n        (point 237.250000 721.140000 -26.250000 0.690000)\n        (point 236.050000 726.230000 -26.250000 0.690000)\n        4)\n      (segment 2466 \n        (point 236.050000 726.230000 -26.250000 0.690000)\n        (point 236.290000 729.270000 -27.250000 0.690000)\n        4)\n      (segment 2467 \n        (point 236.290000 729.270000 -27.250000 0.690000)\n        (point 236.970000 732.420000 -27.900000 0.690000)\n        4)\n      (segment 2468 \n        (point 236.970000 732.420000 -27.900000 0.690000)\n        (point 237.340000 734.890000 -27.900000 0.690000)\n        4)\n      (segment 2469 \n        (point 237.340000 734.890000 -27.900000 0.690000)\n        (point 237.560000 737.930000 -28.170000 0.690000)\n        4)\n      (segment 2470 \n        (point 237.560000 737.930000 -28.170000 0.690000)\n        (point 238.820000 740.620000 -26.800000 0.690000)\n        4)\n      (segment 2471 \n        (point 238.820000 740.620000 -26.800000 0.690000)\n        (point 238.820000 740.620000 -26.820000 0.690000)\n        4)\n      (segment 2472 \n        (point 238.820000 740.620000 -26.820000 0.690000)\n        (point 238.470000 744.120000 -26.380000 0.690000)\n        4)\n      (segment 2473 \n        (point 238.470000 744.120000 -26.380000 0.690000)\n        (point 238.390000 746.480000 -26.380000 0.690000)\n        4)\n      (segment 2474 \n        (point 238.390000 746.480000 -26.380000 0.690000)\n        (point 238.390000 746.480000 -26.350000 0.690000)\n        4)\n      (segment 2475 \n        (point 238.390000 746.480000 -26.350000 0.690000)\n        (point 239.730000 746.790000 -26.300000 0.690000)\n        4)\n      (segment 2476 \n        (point 239.730000 746.790000 -26.300000 0.690000)\n        (point 239.580000 747.370000 -26.300000 0.690000)\n        4)\n      (segment 2477 \n        (point 239.580000 747.370000 -26.300000 0.690000)\n        (point 237.320000 751.020000 -25.630000 0.690000)\n        4)\n      (segment 2478 \n        (point 237.320000 751.020000 -25.630000 0.690000)\n        (point 236.790000 753.270000 -25.420000 0.690000)\n        4)\n      (segment 2479 \n        (point 236.790000 753.270000 -25.420000 0.690000)\n        (point 237.340000 756.990000 -25.420000 0.690000)\n        4)\n      (segment 2480 \n        (point 237.340000 756.990000 -25.420000 0.690000)\n        (point 238.330000 760.810000 -25.420000 0.690000)\n        4)\n      (segment 2481 \n        (point 238.330000 760.810000 -25.420000 0.690000)\n        (point 239.000000 763.950000 -26.000000 0.690000)\n        4)\n      (segment 2482 \n        (point 239.000000 763.950000 -26.000000 0.690000)\n        (point 238.070000 767.910000 -26.000000 0.690000)\n        4)\n      (segment 2483 \n        (point 238.070000 767.910000 -26.000000 0.690000)\n        (point 238.070000 767.910000 -26.020000 0.690000)\n        4)\n      (segment 2484 \n        (point 238.070000 767.910000 -26.020000 0.690000)\n        (point 236.830000 771.200000 -26.020000 0.690000)\n        4)\n      (segment 2485 \n        (point 236.830000 771.200000 -26.020000 0.690000)\n        (point 236.930000 774.800000 -26.700000 0.690000)\n        4)\n      (segment 2486 \n        (point 236.930000 774.800000 -26.700000 0.690000)\n        (point 236.930000 774.800000 -26.720000 0.690000)\n        4)\n      (segment 2487 \n        (point 236.930000 774.800000 -26.720000 0.690000)\n        (point 236.130000 778.200000 -27.200000 0.690000)\n        4)\n      (segment 2488 \n        (point 236.130000 778.200000 -27.200000 0.690000)\n        (point 236.680000 781.910000 -27.200000 0.690000)\n        4)\n      (segment 2489 \n        (point 236.680000 781.910000 -27.200000 0.690000)\n        (point 236.230000 781.810000 -27.200000 0.690000)\n        4)\n      (segment 2490 \n        (point 236.230000 781.810000 -27.200000 0.690000)\n        (point 238.560000 785.940000 -27.200000 0.690000)\n        4)\n      (segment 2491 \n        (point 238.560000 785.940000 -27.200000 0.690000)\n        (point 237.920000 788.650000 -25.770000 0.690000)\n        4)\n      (segment 2492 \n        (point 237.920000 788.650000 -25.770000 0.690000)\n        (point 238.290000 791.140000 -24.720000 0.690000)\n        4)\n      (segment 2493 \n        (point 238.290000 791.140000 -24.720000 0.690000)\n        (point 237.800000 795.200000 -24.200000 0.690000)\n        4)\n      (segment 2494 \n        (point 237.800000 795.200000 -24.200000 0.690000)\n        (point 237.800000 795.200000 -24.170000 0.690000)\n        4)\n      (segment 2495 \n        (point 237.800000 795.200000 -24.170000 0.690000)\n        (point 237.140000 798.030000 -23.420000 0.690000)\n        4)\n      (segment 2496 \n        (point 237.140000 798.030000 -23.420000 0.690000)\n        (point 237.630000 799.930000 -22.470000 0.690000)\n        4)\n      (segment 2497 \n        (point 237.630000 799.930000 -22.470000 0.690000)\n        (point 238.000000 802.410000 -21.320000 0.690000)\n        4)\n      (segment 2498 \n        (point 238.000000 802.410000 -21.320000 0.690000)\n        (point 237.840000 807.140000 -20.900000 0.690000)\n        4)\n      (segment 2499 \n        (point 237.840000 807.140000 -20.900000 0.690000)\n        (point 238.070000 810.190000 -21.950000 0.690000)\n        4)\n      (segment 2500 \n        (point 238.070000 810.190000 -21.950000 0.690000)\n        (point 239.630000 813.540000 -21.950000 0.690000)\n        4)\n      (segment 2501 \n        (point 239.630000 813.540000 -21.950000 0.690000)\n        (point 239.150000 817.610000 -22.450000 0.690000)\n        4)\n      (segment 2502 \n        (point 239.150000 817.610000 -22.450000 0.690000)\n        (point 239.250000 821.220000 -23.050000 0.690000)\n        4)\n      (segment 2503 \n        (point 239.250000 821.220000 -23.050000 0.690000)\n        (point 239.470000 824.250000 -22.120000 0.690000)\n        4)\n      (segment 2504 \n        (point 239.470000 824.250000 -22.120000 0.690000)\n        (point 239.260000 827.180000 -21.300000 0.690000)\n        4)\n      (segment 2505 \n        (point 239.260000 827.180000 -21.300000 0.690000)\n        (point 240.070000 829.770000 -20.970000 0.690000)\n        4)\n      (segment 2506 \n        (point 240.070000 829.770000 -20.970000 0.690000)\n        (point 242.170000 830.860000 -22.380000 0.690000)\n        4)\n      (segment 2507 \n        (point 242.170000 830.860000 -22.380000 0.690000)\n        (point 241.950000 833.780000 -21.670000 0.690000)\n        4)\n      (segment 2508 \n        (point 241.950000 833.780000 -21.670000 0.690000)\n        (point 242.760000 836.370000 -21.600000 0.690000)\n        4)\n      (segment 2509 \n        (point 242.760000 836.370000 -21.600000 0.690000)\n        (point 242.810000 838.170000 -20.200000 0.690000)\n        4)\n      (segment 2510 \n        (point 242.810000 838.170000 -20.200000 0.690000)\n        (point 244.380000 841.520000 -18.950000 0.690000)\n        4)\n      (segment 2511 \n        (point 244.380000 841.520000 -18.950000 0.690000)\n        (point 244.380000 841.520000 -18.970000 0.690000)\n        4)\n      (segment 2512 \n        (point 244.380000 841.520000 -18.970000 0.690000)\n        (point 245.010000 842.860000 -18.050000 0.690000)\n        4)\n      (segment 2513 \n        (point 245.010000 842.860000 -18.050000 0.690000)\n        (point 244.350000 845.690000 -18.170000 0.690000)\n        4)\n      (segment 2514 \n        (point 244.350000 845.690000 -18.170000 0.690000)\n        (point 244.350000 845.690000 -18.150000 0.690000)\n        4)\n      (segment 2515 \n        (point 244.350000 845.690000 -18.150000 0.690000)\n        (point 244.120000 848.640000 -16.630000 0.690000)\n        4)\n      (segment 2516 \n        (point 244.120000 848.640000 -16.630000 0.690000)\n        (point 243.200000 852.590000 -16.050000 0.690000)\n        4))\n    (branch 101 100 \n      (segment 2517 \n        (point 243.200000 852.590000 -16.050000 0.690000)\n        (point 243.430000 855.630000 -16.050000 0.690000)\n        4)\n      (segment 2518 \n        (point 243.430000 855.630000 -16.050000 0.690000)\n        (point 244.370000 857.640000 -16.050000 0.690000)\n        4)\n      (segment 2519 \n        (point 244.370000 857.640000 -16.050000 0.690000)\n        (point 244.870000 859.550000 -15.770000 0.690000)\n        4)\n      (segment 2520 \n        (point 244.870000 859.550000 -15.770000 0.690000)\n        (point 244.960000 863.160000 -15.770000 0.690000)\n        4)\n      (segment 2521 \n        (point 244.960000 863.160000 -15.770000 0.690000)\n        (point 246.410000 867.080000 -15.770000 0.690000)\n        4)\n      (segment 2522 \n        (point 246.410000 867.080000 -15.770000 0.690000)\n        (point 244.260000 870.160000 -16.950000 0.690000)\n        4)\n      (segment 2523 \n        (point 244.260000 870.160000 -16.950000 0.690000)\n        (point 242.570000 873.350000 -17.300000 0.690000)\n        4)\n      (segment 2524 \n        (point 242.570000 873.350000 -17.300000 0.690000)\n        (point 242.090000 877.420000 -16.850000 0.690000)\n        4)\n      (segment 2525 \n        (point 242.090000 877.420000 -16.850000 0.690000)\n        (point 242.090000 877.420000 -16.880000 0.690000)\n        4)\n      (segment 2526 \n        (point 242.090000 877.420000 -16.880000 0.690000)\n        (point 241.600000 881.480000 -15.170000 0.690000)\n        4)\n      (segment 2527 \n        (point 241.600000 881.480000 -15.170000 0.690000)\n        (point 239.470000 884.560000 -14.570000 0.690000)\n        4)\n      (segment 2528 \n        (point 239.470000 884.560000 -14.570000 0.690000)\n        (point 238.630000 886.160000 -14.100000 0.690000)\n        4)\n      (segment 2529 \n        (point 238.630000 886.160000 -14.100000 0.690000)\n        (point 237.830000 889.560000 -14.100000 0.690000)\n        4)\n      (segment 2530 \n        (point 237.830000 889.560000 -14.100000 0.690000)\n        (point 237.620000 892.480000 -15.400000 0.690000)\n        4)\n      (segment 2531 \n        (point 237.620000 892.480000 -15.400000 0.690000)\n        (point 237.660000 894.290000 -16.520000 0.690000)\n        4)\n      (segment 2532 \n        (point 237.660000 894.290000 -16.520000 0.690000)\n        (point 237.660000 894.290000 -16.550000 0.690000)\n        4)\n      (segment 2533 \n        (point 237.660000 894.290000 -16.550000 0.690000)\n        (point 237.770000 897.900000 -17.300000 0.690000)\n        4)\n      (segment 2534 \n        (point 237.770000 897.900000 -17.300000 0.690000)\n        (point 237.770000 897.900000 -17.320000 0.690000)\n        4)\n      (segment 2535 \n        (point 237.770000 897.900000 -17.320000 0.690000)\n        (point 238.630000 902.290000 -17.570000 0.690000)\n        4)\n      (segment 2536 \n        (point 238.630000 902.290000 -17.570000 0.690000)\n        (point 240.510000 906.300000 -17.570000 0.690000)\n        4)\n      (segment 2537 \n        (point 240.510000 906.300000 -17.570000 0.690000)\n        (point 239.260000 909.590000 -17.750000 0.690000)\n        4)\n      (segment 2538 \n        (point 239.260000 909.590000 -17.750000 0.690000)\n        (point 239.440000 910.830000 -18.600000 0.690000)\n        4)\n      (segment 2539 \n        (point 239.440000 910.830000 -18.600000 0.690000)\n        (point 239.440000 910.830000 -18.630000 0.690000)\n        4)\n      (segment 2540 \n        (point 239.440000 910.830000 -18.630000 0.690000)\n        (point 241.460000 914.290000 -19.020000 0.690000)\n        4)\n      (segment 2541 \n        (point 241.460000 914.290000 -19.020000 0.690000)\n        (point 243.290000 916.510000 -17.650000 0.690000)\n        4)\n      (segment 2542 \n        (point 243.290000 916.510000 -17.650000 0.690000)\n        (point 243.970000 919.650000 -18.200000 0.690000)\n        4)\n      (segment 2543 \n        (point 243.970000 919.650000 -18.200000 0.690000)\n        (point 245.090000 922.910000 -17.630000 0.690000)\n        4)\n      (segment 2544 \n        (point 245.090000 922.910000 -17.630000 0.690000)\n        (point 245.850000 923.680000 -17.750000 0.690000)\n        4)\n      (segment 2545 \n        (point 245.850000 923.680000 -17.750000 0.690000)\n        (point 245.900000 925.490000 -16.170000 0.690000)\n        4)\n      (segment 2546 \n        (point 245.900000 925.490000 -16.170000 0.690000)\n        (point 245.900000 925.490000 -16.150000 0.690000)\n        4)\n      (segment 2547 \n        (point 245.900000 925.490000 -16.150000 0.690000)\n        (point 245.080000 927.070000 -14.230000 0.690000)\n        4)\n      (segment 2548 \n        (point 245.080000 927.070000 -14.230000 0.690000)\n        (point 243.330000 928.450000 -12.100000 0.690000)\n        4)\n      (segment 2549 \n        (point 243.330000 928.450000 -12.100000 0.690000)\n        (point 240.000000 930.660000 -10.880000 0.690000)\n        4)\n      (segment 2550 \n        (point 240.000000 930.660000 -10.880000 0.690000)\n        (point 238.000000 933.180000 -10.630000 0.690000)\n        4)\n      (segment 2551 \n        (point 238.000000 933.180000 -10.630000 0.690000)\n        (point 237.200000 936.580000 -10.330000 0.690000)\n        4)\n      (segment 2552 \n        (point 237.200000 936.580000 -10.330000 0.690000)\n        (point 236.410000 939.980000 -10.330000 0.690000)\n        4)\n      (segment 2553 \n        (point 236.410000 939.980000 -10.330000 0.690000)\n        (point 236.140000 941.100000 -11.370000 0.690000)\n        4)\n      (segment 2554 \n        (point 236.140000 941.100000 -11.370000 0.690000)\n        (point 234.900000 944.400000 -12.420000 0.690000)\n        4)\n      (segment 2555 \n        (point 234.900000 944.400000 -12.420000 0.690000)\n        (point 233.470000 946.450000 -13.670000 0.690000)\n        4)\n      (segment 2556 \n        (point 233.470000 946.450000 -13.670000 0.690000)\n        (point 233.470000 946.450000 -13.700000 0.690000)\n        4)\n      (segment 2557 \n        (point 233.470000 946.450000 -13.700000 0.690000)\n        (point 231.650000 950.190000 -13.950000 0.690000)\n        4)\n      (segment 2558 \n        (point 231.650000 950.190000 -13.950000 0.690000)\n        (point 229.920000 951.580000 -13.180000 0.690000)\n        4)\n      (segment 2559 \n        (point 229.920000 951.580000 -13.180000 0.690000)\n        (point 228.530000 955.440000 -12.950000 0.690000)\n        4)\n      (segment 2560 \n        (point 228.530000 955.440000 -12.950000 0.690000)\n        (point 228.950000 959.730000 -12.070000 0.690000)\n        4)\n      (segment 2561 \n        (point 228.950000 959.730000 -12.070000 0.690000)\n        (point 228.280000 962.540000 -12.420000 0.690000)\n        4)\n      (segment 2562 \n        (point 228.280000 962.540000 -12.420000 0.690000)\n        (point 226.340000 966.860000 -12.420000 0.690000)\n        4)\n      (segment 2563 \n        (point 226.340000 966.860000 -12.420000 0.690000)\n        (point 224.650000 970.050000 -14.100000 0.690000)\n        4)\n      (segment 2564 \n        (point 224.650000 970.050000 -14.100000 0.690000)\n        (point 224.560000 972.420000 -13.630000 0.690000)\n        4)\n      (segment 2565 \n        (point 224.560000 972.420000 -13.630000 0.690000)\n        (point 224.920000 974.890000 -13.320000 0.690000)\n        4)\n      (segment 2566 \n        (point 224.920000 974.890000 -13.320000 0.690000)\n        (point 224.390000 977.160000 -11.930000 0.690000)\n        4)\n      (segment 2567 \n        (point 224.390000 977.160000 -11.930000 0.690000)\n        (point 223.990000 978.850000 -10.450000 0.690000)\n        4)\n      (segment 2568 \n        (point 223.990000 978.850000 -10.450000 0.690000)\n        (point 223.190000 982.250000 -9.170000 0.690000)\n        4)\n      (segment 2569 \n        (point 223.190000 982.250000 -9.170000 0.690000)\n        (point 221.780000 984.310000 -7.320000 0.690000)\n        4)\n      (segment 2570 \n        (point 221.780000 984.310000 -7.320000 0.690000)\n        (point 222.710000 986.320000 -6.470000 0.690000)\n        4)\n      (segment 2571 \n        (point 222.710000 986.320000 -6.470000 0.690000)\n        (point 226.640000 989.640000 -6.130000 0.690000)\n        4)\n      (segment 2572 \n        (point 226.640000 989.640000 -6.130000 0.690000)\n        (point 227.010000 992.100000 -6.180000 0.690000)\n        4)\n      (segment 2573 \n        (point 227.010000 992.100000 -6.180000 0.690000)\n        (point 224.860000 995.180000 -5.070000 0.690000)\n        4)\n      (segment 2574 \n        (point 224.860000 995.180000 -5.070000 0.690000)\n        (point 222.860000 997.700000 -3.400000 0.690000)\n        4)\n      (segment 2575 \n        (point 222.860000 997.700000 -3.400000 0.690000)\n        (point 223.090000 1000.740000 -2.500000 0.690000)\n        4)\n      (segment 2576 \n        (point 223.090000 1000.740000 -2.500000 0.690000)\n        (point 222.600000 1004.810000 -2.250000 0.690000)\n        4)\n      (segment 2577 \n        (point 222.600000 1004.810000 -2.250000 0.690000)\n        (point 220.910000 1008.000000 -1.820000 0.690000)\n        4)\n      (segment 2578 \n        (point 220.910000 1008.000000 -1.820000 0.690000)\n        (point 219.800000 1010.720000 -1.600000 0.690000)\n        4)\n      (segment 2579 \n        (point 219.800000 1010.720000 -1.600000 0.690000)\n        (point 219.950000 1016.130000 -1.020000 0.690000)\n        4)\n      (segment 2580 \n        (point 219.950000 1016.130000 -1.020000 0.690000)\n        (point 218.710000 1019.420000 -0.630000 0.690000)\n        4)\n      (segment 2581 \n        (point 218.710000 1019.420000 -0.630000 0.690000)\n        (point 217.150000 1022.030000 -0.630000 0.690000)\n        4)\n      (segment 2582 \n        (point 217.150000 1022.030000 -0.630000 0.690000)\n        (point 216.710000 1021.930000 -0.630000 0.690000)\n        4)\n      (segment 2583 \n        (point 216.710000 1021.930000 -0.630000 0.690000)\n        (point 215.600000 1024.660000 -0.630000 0.690000)\n        4)\n      (segment 2584 \n        (point 215.600000 1024.660000 -0.630000 0.690000)\n        (point 215.560000 1028.830000 -0.070000 0.690000)\n        4)\n      (segment 2585 \n        (point 215.560000 1028.830000 -0.070000 0.690000)\n        (point 214.760000 1032.230000 0.970000 0.690000)\n        4))\n    (branch 102 101 \n      (segment 2586 \n        (point 214.760000 1032.230000 0.970000 0.690000)\n        (point 213.350000 1034.280000 -0.470000 0.690000)\n        4)\n      (segment 2587 \n        (point 213.350000 1034.280000 -0.470000 0.690000)\n        (point 212.900000 1034.170000 -0.470000 0.690000)\n        4)\n      (segment 2588 \n        (point 212.900000 1034.170000 -0.470000 0.690000)\n        (point 212.370000 1036.450000 2.130000 0.460000)\n        4)\n      (segment 2589 \n        (point 212.370000 1036.450000 2.130000 0.460000)\n        (point 212.150000 1039.380000 3.650000 0.460000)\n        4)\n      (segment 2590 \n        (point 212.150000 1039.380000 3.650000 0.460000)\n        (point 212.150000 1039.380000 3.600000 0.460000)\n        4)\n      (segment 2591 \n        (point 212.150000 1039.380000 3.600000 0.460000)\n        (point 209.960000 1040.650000 4.320000 0.230000)\n        4)\n      (segment 2592 \n        (point 209.960000 1040.650000 4.320000 0.230000)\n        (point 209.570000 1042.350000 5.450000 0.230000)\n        4)\n      (segment 2593 \n        (point 209.570000 1042.350000 5.450000 0.230000)\n        (point 209.880000 1043.030000 5.550000 0.230000)\n        4)\n      (segment 2594 \n        (point 209.880000 1043.030000 5.550000 0.230000)\n        (point 208.010000 1044.970000 7.550000 0.230000)\n        4)\n      (segment 2595 \n        (point 208.010000 1044.970000 7.550000 0.230000)\n        (point 208.010000 1044.970000 7.570000 0.230000)\n        4))\n    (branch 103 101 \n      (segment 2596 \n        (point 214.760000 1032.230000 0.970000 0.690000)\n        (point 216.660000 1036.260000 2.080000 0.460000)\n        4)\n      (segment 2597 \n        (point 216.660000 1036.260000 2.080000 0.460000)\n        (point 215.550000 1038.970000 2.970000 0.460000)\n        4)\n      (segment 2598 \n        (point 215.550000 1038.970000 2.970000 0.460000)\n        (point 217.770000 1039.500000 3.500000 0.460000)\n        4)\n      (segment 2599 \n        (point 217.770000 1039.500000 3.500000 0.460000)\n        (point 219.250000 1039.250000 4.170000 0.460000)\n        4)\n      (segment 2600 \n        (point 219.250000 1039.250000 4.170000 0.460000)\n        (point 221.080000 1041.480000 5.070000 0.460000)\n        4)\n      (segment 2601 \n        (point 221.080000 1041.480000 5.070000 0.460000)\n        (point 221.580000 1043.380000 5.700000 0.460000)\n        4)\n      (segment 2602 \n        (point 221.580000 1043.380000 5.700000 0.460000)\n        (point 222.520000 1045.380000 6.350000 0.460000)\n        4)\n      (segment 2603 \n        (point 222.520000 1045.380000 6.350000 0.460000)\n        (point 222.170000 1048.880000 6.800000 0.460000)\n        4)\n      (segment 2604 \n        (point 222.170000 1048.880000 6.800000 0.460000)\n        (point 222.170000 1048.880000 6.780000 0.460000)\n        4)\n      (segment 2605 \n        (point 222.170000 1048.880000 6.780000 0.460000)\n        (point 224.060000 1052.910000 5.420000 0.460000)\n        4)\n      (segment 2606 \n        (point 224.060000 1052.910000 5.420000 0.460000)\n        (point 223.830000 1055.850000 5.170000 0.460000)\n        4)\n      (segment 2607 \n        (point 223.830000 1055.850000 5.170000 0.460000)\n        (point 223.750000 1058.220000 6.350000 0.460000)\n        4)\n      (segment 2608 \n        (point 223.750000 1058.220000 6.350000 0.460000)\n        (point 223.750000 1058.220000 6.280000 0.460000)\n        4)\n      (segment 2609 \n        (point 223.750000 1058.220000 6.280000 0.460000)\n        (point 222.330000 1060.280000 7.670000 0.460000)\n        4)\n      (segment 2610 \n        (point 222.330000 1060.280000 7.670000 0.460000)\n        (point 222.690000 1062.740000 8.550000 0.460000)\n        4)\n      (segment 2611 \n        (point 222.690000 1062.740000 8.550000 0.460000)\n        (point 223.950000 1065.430000 8.550000 0.460000)\n        4)\n      (segment 2612 \n        (point 223.950000 1065.430000 8.550000 0.460000)\n        (point 222.840000 1068.160000 8.550000 0.230000)\n        4)\n      (segment 2613 \n        (point 222.840000 1068.160000 8.550000 0.230000)\n        (point 223.960000 1071.400000 8.550000 0.230000)\n        4)\n      (segment 2614 \n        (point 223.960000 1071.400000 8.550000 0.230000)\n        (point 224.640000 1074.550000 8.550000 0.230000)\n        4)\n      (segment 2615 \n        (point 224.640000 1074.550000 8.550000 0.230000)\n        (point 224.190000 1074.440000 8.550000 0.230000)\n        4)\n      (segment 2616 \n        (point 224.190000 1074.440000 8.550000 0.230000)\n        (point 228.080000 1075.950000 8.550000 0.230000)\n        4)\n      (segment 2617 \n        (point 228.080000 1075.950000 8.550000 0.230000)\n        (point 228.980000 1076.160000 9.550000 0.230000)\n        4))\n    (branch 104 100 \n      (segment 2618 \n        (point 243.200000 852.590000 -16.050000 0.690000)\n        (point 241.310000 854.660000 -17.080000 0.690000)\n        4)\n      (segment 2619 \n        (point 241.310000 854.660000 -17.080000 0.690000)\n        (point 241.490000 855.890000 -18.670000 0.690000)\n        4)\n      (segment 2620 \n        (point 241.490000 855.890000 -18.670000 0.690000)\n        (point 240.910000 856.360000 -19.800000 0.690000)\n        4)\n      (segment 2621 \n        (point 240.910000 856.360000 -19.800000 0.690000)\n        (point 241.090000 857.590000 -21.630000 0.690000)\n        4)\n      (segment 2622 \n        (point 241.090000 857.590000 -21.630000 0.690000)\n        (point 241.990000 857.810000 -23.270000 0.690000)\n        4)\n      (segment 2623 \n        (point 241.990000 857.810000 -23.270000 0.690000)\n        (point 241.590000 859.500000 -25.050000 0.690000)\n        4)\n      (segment 2624 \n        (point 241.590000 859.500000 -25.050000 0.690000)\n        (point 239.980000 860.330000 -26.220000 0.690000)\n        4)\n      (segment 2625 \n        (point 239.980000 860.330000 -26.220000 0.690000)\n        (point 238.070000 860.480000 -27.730000 0.690000)\n        4)\n      (segment 2626 \n        (point 238.070000 860.480000 -27.730000 0.690000)\n        (point 240.480000 862.230000 -29.700000 0.460000)\n        4)\n      (segment 2627 \n        (point 240.480000 862.230000 -29.700000 0.460000)\n        (point 239.630000 863.820000 -31.700000 0.460000)\n        4)\n      (segment 2628 \n        (point 239.630000 863.820000 -31.700000 0.460000)\n        (point 238.210000 865.870000 -32.970000 0.460000)\n        4)\n      (segment 2629 \n        (point 238.210000 865.870000 -32.970000 0.460000)\n        (point 239.020000 868.450000 -34.170000 0.460000)\n        4)\n      (segment 2630 \n        (point 239.020000 868.450000 -34.170000 0.460000)\n        (point 239.380000 870.940000 -34.450000 0.460000)\n        4)\n      (segment 2631 \n        (point 239.380000 870.940000 -34.450000 0.460000)\n        (point 237.510000 872.880000 -35.400000 0.460000)\n        4)\n      (segment 2632 \n        (point 237.510000 872.880000 -35.400000 0.460000)\n        (point 235.600000 873.030000 -36.750000 0.460000)\n        4)\n      (segment 2633 \n        (point 235.600000 873.030000 -36.750000 0.460000)\n        (point 233.680000 873.180000 -38.380000 0.460000)\n        4)\n      (segment 2634 \n        (point 233.680000 873.180000 -38.380000 0.460000)\n        (point 233.590000 875.540000 -39.400000 0.460000)\n        4)\n      (segment 2635 \n        (point 233.590000 875.540000 -39.400000 0.460000)\n        (point 234.090000 877.460000 -40.750000 0.460000)\n        4)\n      (segment 2636 \n        (point 234.090000 877.460000 -40.750000 0.460000)\n        (point 234.140000 879.260000 -41.930000 0.460000)\n        4)\n      (segment 2637 \n        (point 234.140000 879.260000 -41.930000 0.460000)\n        (point 231.640000 879.870000 -43.070000 0.460000)\n        4)\n      (segment 2638 \n        (point 231.640000 879.870000 -43.070000 0.460000)\n        (point 230.840000 883.260000 -43.470000 0.460000)\n        4)\n      (segment 2639 \n        (point 230.840000 883.260000 -43.470000 0.460000)\n        (point 229.110000 884.650000 -44.050000 0.460000)\n        4)\n      (segment 2640 \n        (point 229.110000 884.650000 -44.050000 0.460000)\n        (point 229.110000 884.650000 -44.070000 0.460000)\n        4)\n      (segment 2641 \n        (point 229.110000 884.650000 -44.070000 0.460000)\n        (point 228.450000 887.480000 -44.600000 0.460000)\n        4)\n      (segment 2642 \n        (point 228.450000 887.480000 -44.600000 0.460000)\n        (point 227.160000 888.970000 -45.950000 0.460000)\n        4)\n      (segment 2643 \n        (point 227.160000 888.970000 -45.950000 0.460000)\n        (point 227.020000 889.530000 -47.220000 0.460000)\n        4)\n      (segment 2644 \n        (point 227.020000 889.530000 -47.220000 0.460000)\n        (point 227.250000 892.570000 -48.450000 0.460000)\n        4)\n      (segment 2645 \n        (point 227.250000 892.570000 -48.450000 0.460000)\n        (point 226.410000 894.160000 -48.620000 0.460000)\n        4)\n      (segment 2646 \n        (point 226.410000 894.160000 -48.620000 0.460000)\n        (point 226.940000 897.870000 -49.520000 0.460000)\n        4)\n      (segment 2647 \n        (point 226.940000 897.870000 -49.520000 0.460000)\n        (point 226.940000 897.870000 -49.550000 0.460000)\n        4)\n      (segment 2648 \n        (point 226.940000 897.870000 -49.550000 0.460000)\n        (point 226.410000 900.140000 -50.000000 0.460000)\n        4)\n      (segment 2649 \n        (point 226.410000 900.140000 -50.000000 0.460000)\n        (point 226.410000 900.140000 -50.030000 0.460000)\n        4)\n      (segment 2650 \n        (point 226.410000 900.140000 -50.030000 0.460000)\n        (point 225.130000 901.630000 -51.280000 0.460000)\n        4)\n      (segment 2651 \n        (point 225.130000 901.630000 -51.280000 0.460000)\n        (point 224.870000 902.750000 -52.650000 0.460000)\n        4)\n      (segment 2652 \n        (point 224.870000 902.750000 -52.650000 0.460000)\n        (point 224.950000 906.360000 -53.450000 0.460000)\n        4)\n      (segment 2653 \n        (point 224.950000 906.360000 -53.450000 0.460000)\n        (point 224.870000 908.740000 -54.170000 0.460000)\n        4)\n      (segment 2654 \n        (point 224.870000 908.740000 -54.170000 0.460000)\n        (point 223.760000 911.450000 -54.580000 0.460000)\n        4)\n      (segment 2655 \n        (point 223.760000 911.450000 -54.580000 0.460000)\n        (point 222.700000 915.980000 -54.720000 0.460000)\n        4)\n      (segment 2656 \n        (point 222.700000 915.980000 -54.720000 0.460000)\n        (point 220.970000 917.380000 -55.520000 0.460000)\n        4)\n      (segment 2657 \n        (point 220.970000 917.380000 -55.520000 0.460000)\n        (point 222.800000 919.590000 -56.200000 0.460000)\n        4)\n      (segment 2658 \n        (point 222.800000 919.590000 -56.200000 0.460000)\n        (point 222.760000 923.760000 -56.750000 0.460000)\n        4)\n      (segment 2659 \n        (point 222.760000 923.760000 -56.750000 0.460000)\n        (point 222.050000 924.790000 -58.550000 0.230000)\n        4)\n      (segment 2660 \n        (point 222.050000 924.790000 -58.550000 0.230000)\n        (point 221.340000 925.830000 -60.350000 0.230000)\n        4)\n      (segment 2661 \n        (point 221.340000 925.830000 -60.350000 0.230000)\n        (point 221.340000 925.830000 -60.380000 0.230000)\n        4)\n      (segment 2662 \n        (point 221.340000 925.830000 -60.380000 0.230000)\n        (point 221.390000 927.620000 -62.130000 0.230000)\n        4)\n      (segment 2663 \n        (point 221.390000 927.620000 -62.130000 0.230000)\n        (point 220.720000 930.450000 -63.420000 0.230000)\n        4)\n      (segment 2664 \n        (point 220.720000 930.450000 -63.420000 0.230000)\n        (point 218.110000 931.490000 -64.270000 0.230000)\n        4)\n      (segment 2665 \n        (point 218.110000 931.490000 -64.270000 0.230000)\n        (point 220.080000 933.150000 -65.000000 0.230000)\n        4)\n      (segment 2666 \n        (point 220.080000 933.150000 -65.000000 0.230000)\n        (point 219.820000 934.270000 -66.170000 0.230000)\n        4)\n      (segment 2667 \n        (point 219.820000 934.270000 -66.170000 0.230000)\n        (point 218.080000 935.660000 -67.200000 0.230000)\n        4)\n      (segment 2668 \n        (point 218.080000 935.660000 -67.200000 0.230000)\n        (point 216.020000 936.380000 -68.130000 0.230000)\n        4)\n      (segment 2669 \n        (point 216.020000 936.380000 -68.130000 0.230000)\n        (point 215.640000 938.070000 -69.180000 0.230000)\n        4)\n      (segment 2670 \n        (point 215.640000 938.070000 -69.180000 0.230000)\n        (point 215.640000 938.070000 -69.200000 0.230000)\n        4)\n      (segment 2671 \n        (point 215.640000 938.070000 -69.200000 0.230000)\n        (point 214.150000 938.320000 -70.450000 0.230000)\n        4)\n      (segment 2672 \n        (point 214.150000 938.320000 -70.450000 0.230000)\n        (point 214.070000 940.690000 -71.430000 0.230000)\n        4)\n      (segment 2673 \n        (point 214.070000 940.690000 -71.430000 0.230000)\n        (point 214.070000 940.690000 -71.450000 0.230000)\n        4)\n      (segment 2674 \n        (point 214.070000 940.690000 -71.450000 0.230000)\n        (point 212.460000 941.510000 -72.320000 0.230000)\n        4)\n      (segment 2675 \n        (point 212.460000 941.510000 -72.320000 0.230000)\n        (point 212.460000 941.510000 -72.350000 0.230000)\n        4)\n      (segment 2676 \n        (point 212.460000 941.510000 -72.350000 0.230000)\n        (point 210.860000 942.330000 -72.950000 0.230000)\n        4)\n      (segment 2677 \n        (point 210.860000 942.330000 -72.950000 0.230000)\n        (point 209.890000 944.490000 -74.150000 0.230000)\n        4)\n      (segment 2678 \n        (point 209.890000 944.490000 -74.150000 0.230000)\n        (point 209.570000 943.820000 -76.000000 0.230000)\n        4)\n      (segment 2679 \n        (point 209.570000 943.820000 -76.000000 0.230000)\n        (point 209.570000 943.820000 -76.030000 0.230000)\n        4)\n      (segment 2680 \n        (point 209.570000 943.820000 -76.030000 0.230000)\n        (point 208.280000 945.310000 -77.470000 0.230000)\n        4)\n      (segment 2681 \n        (point 208.280000 945.310000 -77.470000 0.230000)\n        (point 207.000000 946.800000 -78.570000 0.230000)\n        4)\n      (segment 2682 \n        (point 207.000000 946.800000 -78.570000 0.230000)\n        (point 206.770000 949.740000 -79.550000 0.230000)\n        4)\n      (segment 2683 \n        (point 206.770000 949.740000 -79.550000 0.230000)\n        (point 204.530000 949.210000 -80.700000 0.230000)\n        4)\n      (segment 2684 \n        (point 204.530000 949.210000 -80.700000 0.230000)\n        (point 201.780000 950.950000 -81.750000 0.230000)\n        4)\n      (segment 2685 \n        (point 201.780000 950.950000 -81.750000 0.230000)\n        (point 200.930000 952.550000 -82.450000 0.230000)\n        4)\n      (segment 2686 \n        (point 200.930000 952.550000 -82.450000 0.230000)\n        (point 199.500000 954.600000 -83.000000 0.230000)\n        4)\n      (segment 2687 \n        (point 199.500000 954.600000 -83.000000 0.230000)\n        (point 197.330000 955.870000 -83.000000 0.230000)\n        4)\n      (segment 2688 \n        (point 197.330000 955.870000 -83.000000 0.230000)\n        (point 196.930000 957.580000 -84.720000 0.230000)\n        4)\n      (segment 2689 \n        (point 196.930000 957.580000 -84.720000 0.230000)\n        (point 197.100000 958.810000 -86.320000 0.230000)\n        4)\n      (segment 2690 \n        (point 197.100000 958.810000 -86.320000 0.230000)\n        (point 197.020000 961.190000 -87.000000 0.230000)\n        4)\n      (segment 2691 \n        (point 197.020000 961.190000 -87.000000 0.230000)\n        (point 194.970000 961.900000 -87.520000 0.230000)\n        4)\n      (segment 2692 \n        (point 194.970000 961.900000 -87.520000 0.230000)\n        (point 193.500000 962.140000 -88.420000 0.230000)\n        4)\n      (segment 2693 \n        (point 193.500000 962.140000 -88.420000 0.230000)\n        (point 192.340000 963.070000 -89.520000 0.230000)\n        4)\n      (segment 2694 \n        (point 192.340000 963.070000 -89.520000 0.230000)\n        (point 191.810000 965.330000 -90.770000 0.230000)\n        4)\n      (segment 2695 \n        (point 191.810000 965.330000 -90.770000 0.230000)\n        (point 191.810000 965.330000 -90.800000 0.230000)\n        4)\n      (segment 2696 \n        (point 191.810000 965.330000 -90.800000 0.230000)\n        (point 190.520000 966.820000 -91.720000 0.230000)\n        4)\n      (segment 2697 \n        (point 190.520000 966.820000 -91.720000 0.230000)\n        (point 189.400000 969.550000 -92.420000 0.230000)\n        4)\n      (segment 2698 \n        (point 189.400000 969.550000 -92.420000 0.230000)\n        (point 187.350000 970.250000 -93.470000 0.230000)\n        4)\n      (segment 2699 \n        (point 187.350000 970.250000 -93.470000 0.230000)\n        (point 184.990000 970.310000 -94.700000 0.230000)\n        4)\n      (segment 2700 \n        (point 184.990000 970.310000 -94.700000 0.230000)\n        (point 183.510000 970.560000 -95.750000 0.230000)\n        4)\n      (segment 2701 \n        (point 183.510000 970.560000 -95.750000 0.230000)\n        (point 182.230000 972.040000 -96.800000 0.230000)\n        4)\n      (segment 2702 \n        (point 182.230000 972.040000 -96.800000 0.230000)\n        (point 180.930000 973.540000 -97.650000 0.230000)\n        4)\n      (segment 2703 \n        (point 180.930000 973.540000 -97.650000 0.230000)\n        (point 180.930000 973.540000 -97.680000 0.230000)\n        4)\n      (segment 2704 \n        (point 180.930000 973.540000 -97.680000 0.230000)\n        (point 179.640000 975.020000 -98.530000 0.230000)\n        4)\n      (segment 2705 \n        (point 179.640000 975.020000 -98.530000 0.230000)\n        (point 177.010000 976.200000 -99.300000 0.230000)\n        4)\n      (segment 2706 \n        (point 177.010000 976.200000 -99.300000 0.230000)\n        (point 173.810000 977.840000 -100.370000 0.230000)\n        4)\n      (segment 2707 \n        (point 173.810000 977.840000 -100.370000 0.230000)\n        (point 172.070000 979.220000 -100.370000 0.230000)\n        4)\n      (segment 2708 \n        (point 172.070000 979.220000 -100.370000 0.230000)\n        (point 169.700000 979.260000 -101.320000 0.230000)\n        4)\n      (segment 2709 \n        (point 169.700000 979.260000 -101.320000 0.230000)\n        (point 168.540000 980.190000 -102.400000 0.230000)\n        4)\n      (segment 2710 \n        (point 168.540000 980.190000 -102.400000 0.230000)\n        (point 166.400000 983.270000 -103.600000 0.230000)\n        4)\n      (segment 2711 \n        (point 166.400000 983.270000 -103.600000 0.230000)\n        (point 163.830000 986.240000 -103.970000 0.230000)\n        4)\n      (segment 2712 \n        (point 163.830000 986.240000 -103.970000 0.230000)\n        (point 162.760000 990.780000 -103.970000 0.230000)\n        4)\n      (segment 2713 \n        (point 162.760000 990.780000 -103.970000 0.230000)\n        (point 161.390000 994.630000 -104.850000 0.230000)\n        4)\n      (segment 2714 \n        (point 161.390000 994.630000 -104.850000 0.230000)\n        (point 161.300000 997.000000 -106.100000 0.230000)\n        4)\n      (segment 2715 \n        (point 161.300000 997.000000 -106.100000 0.230000)\n        (point 159.880000 999.060000 -107.420000 0.230000)\n        4)\n      (segment 2716 \n        (point 159.880000 999.060000 -107.420000 0.230000)\n        (point 158.680000 1004.150000 -108.350000 0.230000)\n        4)\n      (segment 2717 \n        (point 158.680000 1004.150000 -108.350000 0.230000)\n        (point 156.150000 1008.930000 -107.780000 0.230000)\n        4)\n      (segment 2718 \n        (point 156.150000 1008.930000 -107.780000 0.230000)\n        (point 155.310000 1010.530000 -106.600000 0.230000)\n        4)\n      (segment 2719 \n        (point 155.310000 1010.530000 -106.600000 0.230000)\n        (point 155.350000 1012.330000 -105.900000 0.230000)\n        4)\n      (segment 2720 \n        (point 155.350000 1012.330000 -105.900000 0.230000)\n        (point 155.270000 1014.700000 -104.100000 0.230000)\n        4)\n      (segment 2721 \n        (point 155.270000 1014.700000 -104.100000 0.230000)\n        (point 154.740000 1016.960000 -102.700000 0.230000)\n        4)\n      (segment 2722 \n        (point 154.740000 1016.960000 -102.700000 0.230000)\n        (point 154.290000 1016.850000 -102.700000 0.230000)\n        4)\n      (segment 2723 \n        (point 154.290000 1016.850000 -102.700000 0.230000)\n        (point 154.920000 1018.200000 -101.880000 0.230000)\n        4)\n      (segment 2724 \n        (point 154.920000 1018.200000 -101.880000 0.230000)\n        (point 155.280000 1020.670000 -100.370000 0.230000)\n        4)\n      (segment 2725 \n        (point 155.280000 1020.670000 -100.370000 0.230000)\n        (point 154.750000 1022.930000 -99.780000 0.230000)\n        4)\n      (segment 2726 \n        (point 154.750000 1022.930000 -99.780000 0.230000)\n        (point 153.770000 1025.090000 -99.030000 0.230000)\n        4)\n      (segment 2727 \n        (point 153.770000 1025.090000 -99.030000 0.230000)\n        (point 154.400000 1026.440000 -98.000000 0.230000)\n        4)\n      (segment 2728 \n        (point 154.400000 1026.440000 -98.000000 0.230000)\n        (point 153.560000 1028.030000 -97.250000 0.230000)\n        4)\n      (segment 2729 \n        (point 153.560000 1028.030000 -97.250000 0.230000)\n        (point 153.470000 1030.400000 -96.070000 0.230000)\n        4)\n      (segment 2730 \n        (point 153.470000 1030.400000 -96.070000 0.230000)\n        (point 151.520000 1034.720000 -95.250000 0.230000)\n        4)\n      (segment 2731 \n        (point 151.520000 1034.720000 -95.250000 0.230000)\n        (point 149.830000 1037.910000 -93.530000 0.230000)\n        4)\n      (segment 2732 \n        (point 149.830000 1037.910000 -93.530000 0.230000)\n        (point 148.580000 1041.190000 -93.150000 0.230000)\n        4)\n      (segment 2733 \n        (point 148.580000 1041.190000 -93.150000 0.230000)\n        (point 147.480000 1043.920000 -92.720000 0.230000)\n        4)\n      (segment 2734 \n        (point 147.480000 1043.920000 -92.720000 0.230000)\n        (point 146.950000 1046.200000 -92.200000 0.230000)\n        4)\n      (segment 2735 \n        (point 146.950000 1046.200000 -92.200000 0.230000)\n        (point 144.800000 1049.280000 -90.420000 0.230000)\n        4))\n    (branch 105 81 \n      (segment 2736 \n        (point 232.540000 610.990000 -32.880000 0.915000)\n        (point 231.420000 611.320000 -31.420000 0.460000)\n        4)\n      (segment 2737 \n        (point 231.420000 611.320000 -31.420000 0.460000)\n        (point 231.210000 611.360000 -31.420000 0.460000)\n        4)\n      (segment 2738 \n        (point 231.210000 611.360000 -31.420000 0.460000)\n        (point 230.580000 612.920000 -30.000000 0.460000)\n        4)\n      (segment 2739 \n        (point 230.580000 612.920000 -30.000000 0.460000)\n        (point 230.310000 614.050000 -28.570000 0.460000)\n        4)\n      (segment 2740 \n        (point 230.310000 614.050000 -28.570000 0.460000)\n        (point 228.850000 614.300000 -27.150000 0.460000)\n        4)\n      (segment 2741 \n        (point 228.850000 614.300000 -27.150000 0.460000)\n        (point 229.480000 615.640000 -26.320000 0.460000)\n        4)\n      (segment 2742 \n        (point 229.480000 615.640000 -26.320000 0.460000)\n        (point 229.960000 617.550000 -25.070000 0.230000)\n        4)\n      (segment 2743 \n        (point 229.960000 617.550000 -25.070000 0.230000)\n        (point 228.410000 620.170000 -23.600000 0.230000)\n        4)\n      (segment 2744 \n        (point 228.410000 620.170000 -23.600000 0.230000)\n        (point 228.770000 622.640000 -22.570000 0.230000)\n        4)\n      (segment 2745 \n        (point 228.770000 622.640000 -22.570000 0.230000)\n        (point 228.770000 622.640000 -22.600000 0.230000)\n        4)\n      (segment 2746 \n        (point 228.770000 622.640000 -22.600000 0.230000)\n        (point 228.060000 623.660000 -21.750000 0.230000)\n        4)\n      (segment 2747 \n        (point 228.060000 623.660000 -21.750000 0.230000)\n        (point 228.370000 624.350000 -21.750000 0.230000)\n        4)\n      (segment 2748 \n        (point 228.370000 624.350000 -21.750000 0.230000)\n        (point 228.300000 626.700000 -20.900000 0.230000)\n        4)\n      (segment 2749 \n        (point 228.300000 626.700000 -20.900000 0.230000)\n        (point 228.790000 628.620000 -19.850000 0.230000)\n        4)\n      (segment 2750 \n        (point 228.790000 628.620000 -19.850000 0.230000)\n        (point 227.180000 629.440000 -18.970000 0.230000)\n        4)\n      (segment 2751 \n        (point 227.180000 629.440000 -18.970000 0.230000)\n        (point 225.890000 630.920000 -18.080000 0.230000)\n        4)\n      (segment 2752 \n        (point 225.890000 630.920000 -18.080000 0.230000)\n        (point 224.420000 631.170000 -15.970000 0.230000)\n        4)\n      (segment 2753 \n        (point 224.420000 631.170000 -15.970000 0.230000)\n        (point 224.420000 631.170000 -15.950000 0.230000)\n        4))\n    (branch 106 80 \n      (segment 2754 \n        (point 235.810000 464.370000 -16.650000 0.915000)\n        (point 239.410000 464.610000 -16.320000 0.230000)\n        4)\n      (segment 2755 \n        (point 239.410000 464.610000 -16.320000 0.230000)\n        (point 240.940000 466.160000 -15.480000 0.230000)\n        4)\n      (segment 2756 \n        (point 240.940000 466.160000 -15.480000 0.230000)\n        (point 240.860000 468.540000 -14.900000 0.230000)\n        4)\n      (segment 2757 \n        (point 240.860000 468.540000 -14.900000 0.230000)\n        (point 240.600000 469.660000 -14.150000 0.230000)\n        4)\n      (segment 2758 \n        (point 240.600000 469.660000 -14.150000 0.230000)\n        (point 242.240000 470.640000 -12.620000 0.230000)\n        4)\n      (segment 2759 \n        (point 242.240000 470.640000 -12.620000 0.230000)\n        (point 245.230000 471.940000 -11.630000 0.230000)\n        4)\n      (segment 2760 \n        (point 245.230000 471.940000 -11.630000 0.230000)\n        (point 248.370000 472.670000 -10.700000 0.230000)\n        4)\n      (segment 2761 \n        (point 248.370000 472.670000 -10.700000 0.230000)\n        (point 248.150000 475.610000 -9.770000 0.230000)\n        4)\n      (segment 2762 \n        (point 248.150000 475.610000 -9.770000 0.230000)\n        (point 248.690000 479.320000 -9.130000 0.230000)\n        4)\n      (segment 2763 \n        (point 248.690000 479.320000 -9.130000 0.230000)\n        (point 250.970000 481.650000 -10.070000 0.230000)\n        4)\n      (segment 2764 \n        (point 250.970000 481.650000 -10.070000 0.230000)\n        (point 252.950000 483.300000 -9.220000 0.230000)\n        4)\n      (segment 2765 \n        (point 252.950000 483.300000 -9.220000 0.230000)\n        (point 254.460000 484.850000 -7.870000 0.230000)\n        4)\n      (segment 2766 \n        (point 254.460000 484.850000 -7.870000 0.230000)\n        (point 255.090000 486.190000 -6.670000 0.230000)\n        4)\n      (segment 2767 \n        (point 255.090000 486.190000 -6.670000 0.230000)\n        (point 254.250000 487.780000 -5.130000 0.230000)\n        4)\n      (segment 2768 \n        (point 254.250000 487.780000 -5.130000 0.230000)\n        (point 254.250000 487.780000 -5.150000 0.230000)\n        4)\n      (segment 2769 \n        (point 254.250000 487.780000 -5.150000 0.230000)\n        (point 256.420000 486.510000 -3.570000 0.230000)\n        4)\n      (segment 2770 \n        (point 256.420000 486.510000 -3.570000 0.230000)\n        (point 256.420000 486.510000 -3.600000 0.230000)\n        4)\n      (segment 2771 \n        (point 256.420000 486.510000 -3.600000 0.230000)\n        (point 257.900000 486.250000 -2.450000 0.230000)\n        4)\n      (segment 2772 \n        (point 257.900000 486.250000 -2.450000 0.230000)\n        (point 257.630000 487.390000 -0.630000 0.230000)\n        4)\n      (segment 2773 \n        (point 257.630000 487.390000 -0.630000 0.230000)\n        (point 254.830000 487.330000 0.930000 0.230000)\n        4)\n      (segment 2774 \n        (point 254.830000 487.330000 0.930000 0.230000)\n        (point 254.560000 488.460000 2.450000 0.230000)\n        4)\n      (segment 2775 \n        (point 254.560000 488.460000 2.450000 0.230000)\n        (point 256.790000 488.990000 3.850000 0.230000)\n        4)\n      (segment 2776 \n        (point 256.790000 488.990000 3.850000 0.230000)\n        (point 256.750000 493.160000 3.450000 0.230000)\n        4)\n      (segment 2777 \n        (point 256.750000 493.160000 3.450000 0.230000)\n        (point 255.960000 496.550000 1.220000 0.230000)\n        4)\n      (segment 2778 \n        (point 255.960000 496.550000 1.220000 0.230000)\n        (point 255.960000 496.550000 0.880000 0.230000)\n        4))\n    (branch 107 79 \n      (segment 2779 \n        (point 237.290000 453.970000 -17.300000 0.915000)\n        (point 235.280000 454.090000 -16.500000 0.460000)\n        4)\n      (segment 2780 \n        (point 235.280000 454.090000 -16.500000 0.460000)\n        (point 233.820000 454.340000 -15.320000 0.460000)\n        4)\n      (segment 2781 \n        (point 233.820000 454.340000 -15.320000 0.460000)\n        (point 232.080000 455.720000 -14.050000 0.460000)\n        4)\n      (segment 2782 \n        (point 232.080000 455.720000 -14.050000 0.460000)\n        (point 231.940000 456.290000 -12.620000 0.460000)\n        4)\n      (segment 2783 \n        (point 231.940000 456.290000 -12.620000 0.460000)\n        (point 231.580000 453.820000 -11.550000 0.460000)\n        4)\n      (segment 2784 \n        (point 231.580000 453.820000 -11.550000 0.460000)\n        (point 230.510000 452.360000 -10.200000 0.460000)\n        4)\n      (segment 2785 \n        (point 230.510000 452.360000 -10.200000 0.460000)\n        (point 229.440000 450.920000 -9.670000 0.460000)\n        4)\n      (segment 2786 \n        (point 229.440000 450.920000 -9.670000 0.460000)\n        (point 228.680000 450.140000 -8.230000 0.460000)\n        4)\n      (segment 2787 \n        (point 228.680000 450.140000 -8.230000 0.460000)\n        (point 227.470000 449.270000 -8.230000 0.460000)\n        4)\n      (segment 2788 \n        (point 227.470000 449.270000 -8.230000 0.460000)\n        (point 226.520000 447.250000 -8.230000 0.460000)\n        4)\n      (segment 2789 \n        (point 226.520000 447.250000 -8.230000 0.460000)\n        (point 223.580000 447.760000 -6.970000 0.460000)\n        4)\n      (segment 2790 \n        (point 223.580000 447.760000 -6.970000 0.460000)\n        (point 220.900000 447.130000 -6.550000 0.460000)\n        4)\n      (segment 2791 \n        (point 220.900000 447.130000 -6.550000 0.460000)\n        (point 217.640000 446.970000 -6.850000 0.460000)\n        4)\n      (segment 2792 \n        (point 217.640000 446.970000 -6.850000 0.460000)\n        (point 214.830000 446.900000 -6.020000 0.460000)\n        4)\n      (segment 2793 \n        (point 214.830000 446.900000 -6.020000 0.460000)\n        (point 211.830000 445.610000 -4.920000 0.460000)\n        4)\n      (segment 2794 \n        (point 211.830000 445.610000 -4.920000 0.460000)\n        (point 209.970000 447.550000 -3.650000 0.460000)\n        4)\n      (segment 2795 \n        (point 209.970000 447.550000 -3.650000 0.460000)\n        (point 210.450000 449.460000 -3.200000 0.460000)\n        4)\n      (segment 2796 \n        (point 210.450000 449.460000 -3.200000 0.460000)\n        (point 210.190000 450.590000 -1.700000 0.460000)\n        4)\n      (segment 2797 \n        (point 210.190000 450.590000 -1.700000 0.460000)\n        (point 207.510000 449.960000 -0.970000 0.460000)\n        4)\n      (segment 2798 \n        (point 207.510000 449.960000 -0.970000 0.460000)\n        (point 205.200000 451.820000 0.350000 0.460000)\n        4)\n      (segment 2799 \n        (point 205.200000 451.820000 0.350000 0.460000)\n        (point 205.200000 451.820000 0.320000 0.460000)\n        4)\n      (segment 2800 \n        (point 205.200000 451.820000 0.320000 0.460000)\n        (point 203.330000 453.760000 0.670000 0.460000)\n        4)\n      (segment 2801 \n        (point 203.330000 453.760000 0.670000 0.460000)\n        (point 200.380000 454.270000 0.670000 0.460000)\n        4)\n      (segment 2802 \n        (point 200.380000 454.270000 0.670000 0.460000)\n        (point 196.730000 455.790000 -0.020000 0.460000)\n        4)\n      (segment 2803 \n        (point 196.730000 455.790000 -0.020000 0.460000)\n        (point 193.960000 457.540000 -1.250000 0.460000)\n        4)\n      (segment 2804 \n        (point 193.960000 457.540000 -1.250000 0.460000)\n        (point 191.640000 459.380000 -0.300000 0.460000)\n        4)\n      (segment 2805 \n        (point 191.640000 459.380000 -0.300000 0.460000)\n        (point 190.570000 457.940000 0.320000 0.460000)\n        4)\n      (segment 2806 \n        (point 190.570000 457.940000 0.320000 0.460000)\n        (point 187.050000 458.900000 1.220000 0.460000)\n        4)\n      (segment 2807 \n        (point 187.050000 458.900000 1.220000 0.460000)\n        (point 184.540000 459.520000 0.750000 0.460000)\n        4)\n      (segment 2808 \n        (point 184.540000 459.520000 0.750000 0.460000)\n        (point 181.020000 460.470000 -0.550000 0.460000)\n        4)\n      (segment 2809 \n        (point 181.020000 460.470000 -0.550000 0.460000)\n        (point 178.840000 461.750000 -0.900000 0.460000)\n        4)\n      (segment 2810 \n        (point 178.840000 461.750000 -0.900000 0.460000)\n        (point 175.850000 460.450000 -2.150000 0.460000)\n        4)\n      (segment 2811 \n        (point 175.850000 460.450000 -2.150000 0.460000)\n        (point 174.590000 457.780000 -2.800000 0.460000)\n        4)\n      (segment 2812 \n        (point 174.590000 457.780000 -2.800000 0.460000)\n        (point 171.460000 457.040000 -3.100000 0.460000)\n        4)\n      (segment 2813 \n        (point 171.460000 457.040000 -3.100000 0.460000)\n        (point 167.180000 457.230000 -3.970000 0.460000)\n        4)\n      (segment 2814 \n        (point 167.180000 457.230000 -3.970000 0.460000)\n        (point 167.180000 457.230000 -4.000000 0.460000)\n        4)\n      (segment 2815 \n        (point 167.180000 457.230000 -4.000000 0.460000)\n        (point 164.810000 457.270000 -3.600000 0.460000)\n        4)\n      (segment 2816 \n        (point 164.810000 457.270000 -3.600000 0.460000)\n        (point 164.810000 457.270000 -3.620000 0.460000)\n        4)\n      (segment 2817 \n        (point 164.810000 457.270000 -3.620000 0.460000)\n        (point 163.160000 456.290000 -1.770000 0.460000)\n        4)\n      (segment 2818 \n        (point 163.160000 456.290000 -1.770000 0.460000)\n        (point 160.480000 455.660000 -1.020000 0.460000)\n        4)\n      (segment 2819 \n        (point 160.480000 455.660000 -1.020000 0.460000)\n        (point 158.020000 458.070000 0.850000 0.460000)\n        4)\n      (segment 2820 \n        (point 158.020000 458.070000 0.850000 0.460000)\n        (point 155.850000 459.350000 2.850000 0.460000)\n        4)\n      (segment 2821 \n        (point 155.850000 459.350000 2.850000 0.460000)\n        (point 155.850000 459.350000 2.830000 0.460000)\n        4)\n      (segment 2822 \n        (point 155.850000 459.350000 2.830000 0.460000)\n        (point 152.500000 461.560000 4.500000 0.460000)\n        4))\n    (branch 108 78 \n      (segment 2823 \n        (point 244.400000 359.090000 -26.950000 1.145000)\n        (point 247.390000 360.390000 -28.370000 0.915000)\n        4)\n      (segment 2824 \n        (point 247.390000 360.390000 -28.370000 0.915000)\n        (point 247.170000 363.330000 -29.500000 0.915000)\n        4)\n      (segment 2825 \n        (point 247.170000 363.330000 -29.500000 0.915000)\n        (point 245.930000 366.620000 -30.900000 0.915000)\n        4)\n      (segment 2826 \n        (point 245.930000 366.620000 -30.900000 0.915000)\n        (point 244.380000 369.240000 -32.150000 0.915000)\n        4)\n      (segment 2827 \n        (point 244.380000 369.240000 -32.150000 0.915000)\n        (point 242.960000 371.300000 -33.800000 0.915000)\n        4)\n      (segment 2828 \n        (point 242.960000 371.300000 -33.800000 0.915000)\n        (point 242.030000 375.250000 -34.850000 0.915000)\n        4)\n      (segment 2829 \n        (point 242.030000 375.250000 -34.850000 0.915000)\n        (point 240.070000 379.580000 -34.750000 0.915000)\n        4)\n      (segment 2830 \n        (point 240.070000 379.580000 -34.750000 0.915000)\n        (point 237.940000 382.660000 -35.020000 0.915000)\n        4)\n      (segment 2831 \n        (point 237.940000 382.660000 -35.020000 0.915000)\n        (point 237.900000 386.830000 -35.330000 0.915000)\n        4)\n      (segment 2832 \n        (point 237.900000 386.830000 -35.330000 0.915000)\n        (point 237.900000 386.830000 -35.350000 0.915000)\n        4)\n      (segment 2833 \n        (point 237.900000 386.830000 -35.350000 0.915000)\n        (point 236.840000 391.360000 -35.350000 0.915000)\n        4)\n      (segment 2834 \n        (point 236.840000 391.360000 -35.350000 0.915000)\n        (point 236.110000 394.400000 -36.380000 0.915000)\n        4)\n      (segment 2835 \n        (point 236.110000 394.400000 -36.380000 0.915000)\n        (point 235.870000 396.360000 -36.400000 0.915000)\n        4)\n      (segment 2836 \n        (point 235.870000 396.360000 -36.400000 0.915000)\n        (point 235.780000 398.720000 -36.380000 0.915000)\n        4)\n      (segment 2837 \n        (point 235.780000 398.720000 -36.380000 0.915000)\n        (point 235.840000 403.660000 -36.720000 0.915000)\n        4)\n      (segment 2838 \n        (point 235.840000 403.660000 -36.720000 0.915000)\n        (point 232.990000 407.770000 -37.020000 0.915000)\n        4)\n      (segment 2839 \n        (point 232.990000 407.770000 -37.020000 0.915000)\n        (point 233.040000 409.570000 -38.150000 0.915000)\n        4)\n      (segment 2840 \n        (point 233.040000 409.570000 -38.150000 0.915000)\n        (point 231.660000 413.430000 -39.200000 0.915000)\n        4)\n      (segment 2841 \n        (point 231.660000 413.430000 -39.200000 0.915000)\n        (point 229.720000 417.760000 -40.170000 0.915000)\n        4)\n      (segment 2842 \n        (point 229.720000 417.760000 -40.170000 0.915000)\n        (point 229.720000 423.720000 -41.170000 0.915000)\n        4)\n      (segment 2843 \n        (point 229.720000 423.720000 -41.170000 0.915000)\n        (point 229.550000 426.580000 -41.770000 0.915000)\n        4)\n      (segment 2844 \n        (point 229.550000 426.580000 -41.770000 0.915000)\n        (point 230.280000 431.520000 -41.850000 0.915000)\n        4)\n      (segment 2845 \n        (point 230.280000 431.520000 -41.850000 0.915000)\n        (point 229.930000 435.020000 -40.600000 0.915000)\n        4)\n      (segment 2846 \n        (point 229.930000 435.020000 -40.600000 0.915000)\n        (point 228.870000 439.550000 -41.030000 0.915000)\n        4)\n      (segment 2847 \n        (point 228.870000 439.550000 -41.030000 0.915000)\n        (point 228.870000 439.550000 -41.050000 0.915000)\n        4)\n      (segment 2848 \n        (point 228.870000 439.550000 -41.050000 0.915000)\n        (point 226.340000 444.330000 -41.730000 0.915000)\n        4)\n      (segment 2849 \n        (point 226.340000 444.330000 -41.730000 0.915000)\n        (point 224.380000 448.650000 -42.100000 0.915000)\n        4)\n      (segment 2850 \n        (point 224.380000 448.650000 -42.100000 0.915000)\n        (point 224.930000 452.360000 -43.100000 0.915000)\n        4)\n      (segment 2851 \n        (point 224.930000 452.360000 -43.100000 0.915000)\n        (point 225.730000 454.930000 -43.920000 0.915000)\n        4)\n      (segment 2852 \n        (point 225.730000 454.930000 -43.920000 0.915000)\n        (point 225.730000 454.930000 -43.950000 0.915000)\n        4)\n      (segment 2853 \n        (point 225.730000 454.930000 -43.950000 0.915000)\n        (point 224.800000 458.900000 -44.970000 0.915000)\n        4)\n      (segment 2854 \n        (point 224.800000 458.900000 -44.970000 0.915000)\n        (point 223.070000 460.290000 -45.750000 0.915000)\n        4)\n      (segment 2855 \n        (point 223.070000 460.290000 -45.750000 0.915000)\n        (point 223.070000 460.290000 -45.770000 0.915000)\n        4)\n      (segment 2856 \n        (point 223.070000 460.290000 -45.770000 0.915000)\n        (point 221.650000 462.340000 -47.020000 0.915000)\n        4)\n      (segment 2857 \n        (point 221.650000 462.340000 -47.020000 0.915000)\n        (point 222.180000 466.040000 -47.880000 0.915000)\n        4)\n      (segment 2858 \n        (point 222.180000 466.040000 -47.880000 0.915000)\n        (point 221.650000 468.310000 -47.880000 0.915000)\n        4))\n    (branch 109 108 \n      (segment 2859 \n        (point 221.650000 468.310000 -47.880000 0.915000)\n        (point 223.540000 470.570000 -46.930000 0.460000)\n        4)\n      (segment 2860 \n        (point 223.540000 470.570000 -46.930000 0.460000)\n        (point 225.940000 472.340000 -46.450000 0.460000)\n        4)\n      (segment 2861 \n        (point 225.940000 472.340000 -46.450000 0.460000)\n        (point 228.220000 474.660000 -46.250000 0.460000)\n        4)\n      (segment 2862 \n        (point 228.220000 474.660000 -46.250000 0.460000)\n        (point 230.460000 475.190000 -46.250000 0.460000)\n        4)\n      (segment 2863 \n        (point 230.460000 475.190000 -46.250000 0.460000)\n        (point 231.580000 478.440000 -46.880000 0.460000)\n        4)\n      (segment 2864 \n        (point 231.580000 478.440000 -46.880000 0.460000)\n        (point 232.530000 480.450000 -47.600000 0.460000)\n        4)\n      (segment 2865 \n        (point 232.530000 480.450000 -47.600000 0.460000)\n        (point 235.390000 482.300000 -47.920000 0.460000)\n        4)\n      (segment 2866 \n        (point 235.390000 482.300000 -47.920000 0.460000)\n        (point 236.860000 482.060000 -47.330000 0.460000)\n        4)\n      (segment 2867 \n        (point 236.860000 482.060000 -47.330000 0.460000)\n        (point 238.780000 481.910000 -48.880000 0.460000)\n        4)\n      (segment 2868 \n        (point 238.780000 481.910000 -48.880000 0.460000)\n        (point 238.700000 484.280000 -50.130000 0.460000)\n        4)\n      (segment 2869 \n        (point 238.700000 484.280000 -50.130000 0.460000)\n        (point 238.700000 484.280000 -50.150000 0.460000)\n        4)\n      (segment 2870 \n        (point 238.700000 484.280000 -50.150000 0.460000)\n        (point 238.700000 484.280000 -51.500000 0.460000)\n        4)\n      (segment 2871 \n        (point 238.700000 484.280000 -51.500000 0.460000)\n        (point 238.700000 484.280000 -51.520000 0.460000)\n        4)\n      (segment 2872 \n        (point 238.700000 484.280000 -51.520000 0.460000)\n        (point 237.400000 485.760000 -52.200000 0.460000)\n        4)\n      (segment 2873 \n        (point 237.400000 485.760000 -52.200000 0.460000)\n        (point 237.590000 487.010000 -53.380000 0.460000)\n        4)\n      (segment 2874 \n        (point 237.590000 487.010000 -53.380000 0.460000)\n        (point 239.360000 487.410000 -54.350000 0.460000)\n        4)\n      (segment 2875 \n        (point 239.360000 487.410000 -54.350000 0.460000)\n        (point 241.480000 488.510000 -55.350000 0.460000)\n        4)\n      (segment 2876 \n        (point 241.480000 488.510000 -55.350000 0.460000)\n        (point 241.480000 488.510000 -55.380000 0.460000)\n        4)\n      (segment 2877 \n        (point 241.480000 488.510000 -55.380000 0.460000)\n        (point 241.780000 489.190000 -55.720000 0.460000)\n        4)\n      (segment 2878 \n        (point 241.780000 489.190000 -55.720000 0.460000)\n        (point 241.000000 492.570000 -55.720000 0.460000)\n        4)\n      (segment 2879 \n        (point 241.000000 492.570000 -55.720000 0.460000)\n        (point 242.510000 494.140000 -56.420000 0.460000)\n        4)\n      (segment 2880 \n        (point 242.510000 494.140000 -56.420000 0.460000)\n        (point 243.590000 495.580000 -57.500000 0.460000)\n        4)\n      (segment 2881 \n        (point 243.590000 495.580000 -57.500000 0.460000)\n        (point 244.650000 497.020000 -58.300000 0.460000)\n        4)\n      (segment 2882 \n        (point 244.650000 497.020000 -58.300000 0.460000)\n        (point 245.230000 496.560000 -58.900000 0.460000)\n        4)\n      (segment 2883 \n        (point 245.230000 496.560000 -58.900000 0.460000)\n        (point 246.130000 496.770000 -58.880000 0.460000)\n        4)\n      (segment 2884 \n        (point 246.130000 496.770000 -58.880000 0.460000)\n        (point 245.470000 499.600000 -59.020000 0.460000)\n        4)\n      (segment 2885 \n        (point 245.470000 499.600000 -59.020000 0.460000)\n        (point 245.910000 499.700000 -59.020000 0.460000)\n        4)\n      (segment 2886 \n        (point 245.910000 499.700000 -59.020000 0.460000)\n        (point 248.020000 500.800000 -59.630000 0.460000)\n        4)\n      (segment 2887 \n        (point 248.020000 500.800000 -59.630000 0.460000)\n        (point 248.830000 503.380000 -59.630000 0.460000)\n        4)\n      (segment 2888 \n        (point 248.830000 503.380000 -59.630000 0.460000)\n        (point 249.270000 503.480000 -60.670000 0.460000)\n        4)\n      (segment 2889 \n        (point 249.270000 503.480000 -60.670000 0.460000)\n        (point 248.160000 506.210000 -61.520000 0.460000)\n        4)\n      (segment 2890 \n        (point 248.160000 506.210000 -61.520000 0.460000)\n        (point 251.150000 507.500000 -62.600000 0.460000)\n        4)\n      (segment 2891 \n        (point 251.150000 507.500000 -62.600000 0.460000)\n        (point 253.260000 508.590000 -63.170000 0.460000)\n        4)\n      (segment 2892 \n        (point 253.260000 508.590000 -63.170000 0.460000)\n        (point 255.090000 510.820000 -63.950000 0.460000)\n        4)\n      (segment 2893 \n        (point 255.090000 510.820000 -63.950000 0.460000)\n        (point 255.320000 513.860000 -65.000000 0.460000)\n        4)\n      (segment 2894 \n        (point 255.320000 513.860000 -65.000000 0.460000)\n        (point 256.970000 514.840000 -65.750000 0.460000)\n        4)\n      (segment 2895 \n        (point 256.970000 514.840000 -65.750000 0.460000)\n        (point 259.520000 516.030000 -66.600000 0.460000)\n        4)\n      (segment 2896 \n        (point 259.520000 516.030000 -66.600000 0.460000)\n        (point 262.380000 517.900000 -64.570000 0.460000)\n        4)\n      (segment 2897 \n        (point 262.380000 517.900000 -64.570000 0.460000)\n        (point 264.170000 518.320000 -65.300000 0.460000)\n        4)\n      (segment 2898 \n        (point 264.170000 518.320000 -65.300000 0.460000)\n        (point 264.170000 518.320000 -65.320000 0.460000)\n        4)\n      (segment 2899 \n        (point 264.170000 518.320000 -65.320000 0.460000)\n        (point 266.590000 520.080000 -65.970000 0.460000)\n        4)\n      (segment 2900 \n        (point 266.590000 520.080000 -65.970000 0.460000)\n        (point 268.190000 519.250000 -66.650000 0.460000)\n        4)\n      (segment 2901 \n        (point 268.190000 519.250000 -66.650000 0.460000)\n        (point 268.190000 519.250000 -66.680000 0.460000)\n        4)\n      (segment 2902 \n        (point 268.190000 519.250000 -66.680000 0.460000)\n        (point 270.420000 519.780000 -67.900000 0.460000)\n        4)\n      (segment 2903 \n        (point 270.420000 519.780000 -67.900000 0.460000)\n        (point 270.420000 519.780000 -67.930000 0.460000)\n        4)\n      (segment 2904 \n        (point 270.420000 519.780000 -67.930000 0.460000)\n        (point 272.070000 520.760000 -69.420000 0.460000)\n        4)\n      (segment 2905 \n        (point 272.070000 520.760000 -69.420000 0.460000)\n        (point 272.070000 520.760000 -69.450000 0.460000)\n        4)\n      (segment 2906 \n        (point 272.070000 520.760000 -69.450000 0.460000)\n        (point 273.150000 522.210000 -70.270000 0.460000)\n        4)\n      (segment 2907 \n        (point 273.150000 522.210000 -70.270000 0.460000)\n        (point 272.760000 523.910000 -71.720000 0.460000)\n        4)\n      (segment 2908 \n        (point 272.760000 523.910000 -71.720000 0.460000)\n        (point 272.760000 523.910000 -71.750000 0.460000)\n        4)\n      (segment 2909 \n        (point 272.760000 523.910000 -71.750000 0.460000)\n        (point 276.330000 524.750000 -72.200000 0.460000)\n        4)\n      (segment 2910 \n        (point 276.330000 524.750000 -72.200000 0.460000)\n        (point 280.670000 526.360000 -73.570000 0.460000)\n        4)\n      (segment 2911 \n        (point 280.670000 526.360000 -73.570000 0.460000)\n        (point 283.390000 528.800000 -74.300000 0.460000)\n        4)\n      (segment 2912 \n        (point 283.390000 528.800000 -74.300000 0.460000)\n        (point 286.690000 530.770000 -74.780000 0.460000)\n        4)\n      (segment 2913 \n        (point 286.690000 530.770000 -74.780000 0.460000)\n        (point 289.240000 531.950000 -75.250000 0.460000)\n        4)\n      (segment 2914 \n        (point 289.240000 531.950000 -75.250000 0.460000)\n        (point 293.310000 534.690000 -75.880000 0.460000)\n        4)\n      (segment 2915 \n        (point 293.310000 534.690000 -75.880000 0.460000)\n        (point 295.550000 535.220000 -76.030000 0.460000)\n        4)\n      (segment 2916 \n        (point 295.550000 535.220000 -76.030000 0.460000)\n        (point 298.270000 537.660000 -75.720000 0.460000)\n        4)\n      (segment 2917 \n        (point 298.270000 537.660000 -75.720000 0.460000)\n        (point 301.790000 536.680000 -76.030000 0.460000)\n        4)\n      (segment 2918 \n        (point 301.790000 536.680000 -76.030000 0.460000)\n        (point 305.100000 538.660000 -76.130000 0.460000)\n        4)\n      (segment 2919 \n        (point 305.100000 538.660000 -76.130000 0.460000)\n        (point 308.090000 539.960000 -77.350000 0.460000)\n        4)\n      (segment 2920 \n        (point 308.090000 539.960000 -77.350000 0.460000)\n        (point 309.610000 541.510000 -78.200000 0.460000)\n        4)\n      (segment 2921 \n        (point 309.610000 541.510000 -78.200000 0.460000)\n        (point 313.190000 542.340000 -79.070000 0.460000)\n        4)\n      (segment 2922 \n        (point 313.190000 542.340000 -79.070000 0.460000)\n        (point 316.050000 544.200000 -80.370000 0.460000)\n        4)\n      (segment 2923 \n        (point 316.050000 544.200000 -80.370000 0.460000)\n        (point 318.420000 544.160000 -82.220000 0.460000)\n        4)\n      (segment 2924 \n        (point 318.420000 544.160000 -82.220000 0.460000)\n        (point 318.420000 544.160000 -82.520000 0.460000)\n        4))\n    (branch 110 108 \n      (segment 2925 \n        (point 221.650000 468.310000 -47.880000 0.915000)\n        (point 220.100000 470.930000 -47.880000 0.915000)\n        4)\n      (segment 2926 \n        (point 220.100000 470.930000 -47.880000 0.915000)\n        (point 219.930000 475.680000 -47.880000 0.915000)\n        4)\n      (segment 2927 \n        (point 219.930000 475.680000 -47.880000 0.915000)\n        (point 219.000000 479.630000 -47.130000 0.915000)\n        4)\n      (segment 2928 \n        (point 219.000000 479.630000 -47.130000 0.915000)\n        (point 217.940000 484.160000 -47.670000 0.915000)\n        4)\n      (segment 2929 \n        (point 217.940000 484.160000 -47.670000 0.915000)\n        (point 216.570000 488.020000 -47.550000 0.915000)\n        4)\n      (segment 2930 \n        (point 216.570000 488.020000 -47.550000 0.915000)\n        (point 215.460000 490.750000 -48.750000 0.915000)\n        4)\n      (segment 2931 \n        (point 215.460000 490.750000 -48.750000 0.915000)\n        (point 216.440000 494.550000 -50.050000 0.915000)\n        4)\n      (segment 2932 \n        (point 216.440000 494.550000 -50.050000 0.915000)\n        (point 215.020000 496.620000 -50.050000 0.915000)\n        4)\n      (segment 2933 \n        (point 215.020000 496.620000 -50.050000 0.915000)\n        (point 216.140000 499.860000 -48.620000 0.915000)\n        4)\n      (segment 2934 \n        (point 216.140000 499.860000 -48.620000 0.915000)\n        (point 215.660000 503.930000 -48.620000 0.915000)\n        4)\n      (segment 2935 \n        (point 215.660000 503.930000 -48.620000 0.915000)\n        (point 215.630000 508.100000 -48.620000 0.915000)\n        4)\n      (segment 2936 \n        (point 215.630000 508.100000 -48.620000 0.915000)\n        (point 214.960000 510.920000 -48.620000 0.915000)\n        4))\n    (branch 111 110 \n      (segment 2937 \n        (point 214.960000 510.920000 -48.620000 0.915000)\n        (point 212.130000 513.270000 -48.620000 0.460000)\n        4)\n      (segment 2938 \n        (point 212.130000 513.270000 -48.620000 0.460000)\n        (point 210.840000 514.760000 -50.420000 0.460000)\n        4)\n      (segment 2939 \n        (point 210.840000 514.760000 -50.420000 0.460000)\n        (point 210.030000 512.170000 -51.880000 0.460000)\n        4)\n      (segment 2940 \n        (point 210.030000 512.170000 -51.880000 0.460000)\n        (point 210.030000 512.170000 -51.900000 0.460000)\n        4)\n      (segment 2941 \n        (point 210.030000 512.170000 -51.900000 0.460000)\n        (point 209.810000 515.120000 -53.350000 0.460000)\n        4)\n      (segment 2942 \n        (point 209.810000 515.120000 -53.350000 0.460000)\n        (point 210.570000 515.890000 -54.320000 0.460000)\n        4)\n      (segment 2943 \n        (point 210.570000 515.890000 -54.320000 0.460000)\n        (point 208.700000 517.830000 -55.050000 0.460000)\n        4)\n      (segment 2944 \n        (point 208.700000 517.830000 -55.050000 0.460000)\n        (point 207.720000 520.000000 -55.970000 0.460000)\n        4)\n      (segment 2945 \n        (point 207.720000 520.000000 -55.970000 0.460000)\n        (point 205.990000 521.380000 -56.950000 0.460000)\n        4)\n      (segment 2946 \n        (point 205.990000 521.380000 -56.950000 0.460000)\n        (point 205.540000 521.280000 -58.130000 0.460000)\n        4)\n      (segment 2947 \n        (point 205.540000 521.280000 -58.130000 0.460000)\n        (point 205.450000 523.650000 -57.630000 0.460000)\n        4)\n      (segment 2948 \n        (point 205.450000 523.650000 -57.630000 0.460000)\n        (point 205.640000 524.880000 -58.880000 0.460000)\n        4)\n      (segment 2949 \n        (point 205.640000 524.880000 -58.880000 0.460000)\n        (point 205.060000 525.340000 -60.270000 0.460000)\n        4)\n      (segment 2950 \n        (point 205.060000 525.340000 -60.270000 0.460000)\n        (point 202.680000 525.390000 -61.920000 0.460000)\n        4)\n      (segment 2951 \n        (point 202.680000 525.390000 -61.920000 0.460000)\n        (point 202.290000 527.080000 -63.400000 0.460000)\n        4)\n      (segment 2952 \n        (point 202.290000 527.080000 -63.400000 0.460000)\n        (point 203.050000 527.860000 -64.900000 0.460000)\n        4)\n      (segment 2953 \n        (point 203.050000 527.860000 -64.900000 0.460000)\n        (point 202.070000 530.030000 -65.470000 0.460000)\n        4)\n      (segment 2954 \n        (point 202.070000 530.030000 -65.470000 0.460000)\n        (point 201.670000 531.720000 -64.750000 0.460000)\n        4)\n      (segment 2955 \n        (point 201.670000 531.720000 -64.750000 0.460000)\n        (point 200.390000 533.210000 -65.630000 0.460000)\n        4)\n      (segment 2956 \n        (point 200.390000 533.210000 -65.630000 0.460000)\n        (point 199.230000 534.130000 -66.220000 0.460000)\n        4)\n      (segment 2957 \n        (point 199.230000 534.130000 -66.220000 0.460000)\n        (point 199.410000 535.360000 -67.100000 0.460000)\n        4)\n      (segment 2958 \n        (point 199.410000 535.360000 -67.100000 0.460000)\n        (point 198.430000 537.530000 -67.570000 0.460000)\n        4)\n      (segment 2959 \n        (point 198.430000 537.530000 -67.570000 0.460000)\n        (point 197.270000 538.450000 -68.350000 0.460000)\n        4)\n      (segment 2960 \n        (point 197.270000 538.450000 -68.350000 0.460000)\n        (point 195.360000 538.590000 -69.320000 0.460000)\n        4)\n      (segment 2961 \n        (point 195.360000 538.590000 -69.320000 0.460000)\n        (point 194.830000 540.870000 -69.880000 0.460000)\n        4)\n      (segment 2962 \n        (point 194.830000 540.870000 -69.880000 0.460000)\n        (point 193.850000 543.020000 -70.720000 0.460000)\n        4)\n      (segment 2963 \n        (point 193.850000 543.020000 -70.720000 0.460000)\n        (point 192.380000 543.270000 -72.130000 0.460000)\n        4)\n      (segment 2964 \n        (point 192.380000 543.270000 -72.130000 0.460000)\n        (point 191.210000 544.200000 -73.470000 0.460000)\n        4)\n      (segment 2965 \n        (point 191.210000 544.200000 -73.470000 0.460000)\n        (point 190.230000 546.360000 -74.650000 0.460000)\n        4)\n      (segment 2966 \n        (point 190.230000 546.360000 -74.650000 0.460000)\n        (point 186.760000 549.120000 -75.070000 0.460000)\n        4)\n      (segment 2967 \n        (point 186.760000 549.120000 -75.070000 0.460000)\n        (point 186.050000 550.140000 -76.000000 0.460000)\n        4)\n      (segment 2968 \n        (point 186.050000 550.140000 -76.000000 0.460000)\n        (point 183.920000 553.240000 -77.130000 0.460000)\n        4)\n      (segment 2969 \n        (point 183.920000 553.240000 -77.130000 0.460000)\n        (point 181.730000 554.510000 -78.600000 0.460000)\n        4)\n      (segment 2970 \n        (point 181.730000 554.510000 -78.600000 0.460000)\n        (point 179.540000 555.790000 -80.020000 0.460000)\n        4)\n      (segment 2971 \n        (point 179.540000 555.790000 -80.020000 0.460000)\n        (point 178.700000 557.380000 -80.920000 0.460000)\n        4)\n      (segment 2972 \n        (point 178.700000 557.380000 -80.920000 0.460000)\n        (point 178.170000 559.660000 -82.570000 0.460000)\n        4)\n      (segment 2973 \n        (point 178.170000 559.660000 -82.570000 0.460000)\n        (point 176.610000 562.270000 -83.530000 0.460000)\n        4)\n      (segment 2974 \n        (point 176.610000 562.270000 -83.530000 0.460000)\n        (point 176.660000 564.070000 -84.880000 0.460000)\n        4)\n      (segment 2975 \n        (point 176.660000 564.070000 -84.880000 0.460000)\n        (point 174.170000 564.690000 -85.630000 0.460000)\n        4)\n      (segment 2976 \n        (point 174.170000 564.690000 -85.630000 0.460000)\n        (point 173.320000 566.280000 -86.720000 0.460000)\n        4)\n      (segment 2977 \n        (point 173.320000 566.280000 -86.720000 0.460000)\n        (point 173.320000 566.280000 -86.750000 0.460000)\n        4)\n      (segment 2978 \n        (point 173.320000 566.280000 -86.750000 0.460000)\n        (point 172.340000 568.430000 -88.000000 0.460000)\n        4)\n      (segment 2979 \n        (point 172.340000 568.430000 -88.000000 0.460000)\n        (point 172.340000 568.430000 -88.020000 0.460000)\n        4)\n      (segment 2980 \n        (point 172.340000 568.430000 -88.020000 0.460000)\n        (point 171.370000 570.600000 -87.650000 0.460000)\n        4)\n      (segment 2981 \n        (point 171.370000 570.600000 -87.650000 0.460000)\n        (point 171.370000 570.600000 -87.680000 0.460000)\n        4)\n      (segment 2982 \n        (point 171.370000 570.600000 -87.680000 0.460000)\n        (point 168.920000 573.010000 -88.530000 0.460000)\n        4)\n      (segment 2983 \n        (point 168.920000 573.010000 -88.530000 0.460000)\n        (point 166.410000 573.620000 -88.800000 0.460000)\n        4)\n      (segment 2984 \n        (point 166.410000 573.620000 -88.800000 0.460000)\n        (point 166.410000 573.620000 -88.820000 0.460000)\n        4)\n      (segment 2985 \n        (point 166.410000 573.620000 -88.820000 0.460000)\n        (point 163.970000 576.030000 -89.450000 0.460000)\n        4)\n      (segment 2986 \n        (point 163.970000 576.030000 -89.450000 0.460000)\n        (point 163.970000 576.030000 -89.480000 0.460000)\n        4)\n      (segment 2987 \n        (point 163.970000 576.030000 -89.480000 0.460000)\n        (point 162.940000 576.380000 -89.550000 0.460000)\n        4)\n      (segment 2988 \n        (point 162.940000 576.380000 -89.550000 0.460000)\n        (point 161.520000 578.440000 -91.220000 0.460000)\n        4)\n      (segment 2989 \n        (point 161.520000 578.440000 -91.220000 0.460000)\n        (point 158.760000 580.180000 -92.380000 0.460000)\n        4)\n      (segment 2990 \n        (point 158.760000 580.180000 -92.380000 0.460000)\n        (point 158.760000 580.180000 -92.400000 0.460000)\n        4)\n      (segment 2991 \n        (point 158.760000 580.180000 -92.400000 0.460000)\n        (point 157.780000 582.340000 -93.650000 0.460000)\n        4)\n      (segment 2992 \n        (point 157.780000 582.340000 -93.650000 0.460000)\n        (point 155.910000 584.290000 -95.200000 0.460000)\n        4)\n      (segment 2993 \n        (point 155.910000 584.290000 -95.200000 0.460000)\n        (point 155.910000 584.290000 -95.220000 0.460000)\n        4)\n      (segment 2994 \n        (point 155.910000 584.290000 -95.220000 0.460000)\n        (point 155.060000 585.880000 -95.880000 0.460000)\n        4)\n      (segment 2995 \n        (point 155.060000 585.880000 -95.880000 0.460000)\n        (point 154.660000 587.590000 -96.930000 0.460000)\n        4)\n      (segment 2996 \n        (point 154.660000 587.590000 -96.930000 0.460000)\n        (point 154.660000 587.590000 -96.950000 0.460000)\n        4)\n      (segment 2997 \n        (point 154.660000 587.590000 -96.950000 0.460000)\n        (point 154.450000 590.510000 -97.750000 0.460000)\n        4)\n      (segment 2998 \n        (point 154.450000 590.510000 -97.750000 0.460000)\n        (point 154.450000 590.510000 -97.780000 0.460000)\n        4)\n      (segment 2999 \n        (point 154.450000 590.510000 -97.780000 0.460000)\n        (point 151.990000 592.920000 -98.380000 0.460000)\n        4)\n      (segment 3000 \n        (point 151.990000 592.920000 -98.380000 0.460000)\n        (point 150.840000 593.850000 -99.630000 0.460000)\n        4)\n      (segment 3001 \n        (point 150.840000 593.850000 -99.630000 0.460000)\n        (point 150.260000 594.300000 -101.250000 0.460000)\n        4)\n      (segment 3002 \n        (point 150.260000 594.300000 -101.250000 0.460000)\n        (point 150.260000 594.300000 -101.270000 0.460000)\n        4))\n    (branch 112 110 \n      (segment 3003 \n        (point 214.960000 510.920000 -48.620000 0.915000)\n        (point 214.350000 515.570000 -49.250000 0.915000)\n        4)\n      (segment 3004 \n        (point 214.350000 515.570000 -49.250000 0.915000)\n        (point 214.850000 517.480000 -49.470000 0.915000)\n        4)\n      (segment 3005 \n        (point 214.850000 517.480000 -49.470000 0.915000)\n        (point 215.340000 519.380000 -49.470000 0.915000)\n        4)\n      (segment 3006 \n        (point 215.340000 519.380000 -49.470000 0.915000)\n        (point 213.780000 522.000000 -49.470000 0.915000)\n        4)\n      (segment 3007 \n        (point 213.780000 522.000000 -49.470000 0.915000)\n        (point 214.200000 526.280000 -49.470000 0.915000)\n        4)\n      (segment 3008 \n        (point 214.200000 526.280000 -49.470000 0.915000)\n        (point 213.720000 530.350000 -49.470000 0.915000)\n        4)\n      (segment 3009 \n        (point 213.720000 530.350000 -49.470000 0.915000)\n        (point 213.190000 532.610000 -49.470000 0.915000)\n        4)\n      (segment 3010 \n        (point 213.190000 532.610000 -49.470000 0.915000)\n        (point 214.170000 536.420000 -50.200000 0.915000)\n        4)\n      (segment 3011 \n        (point 214.170000 536.420000 -50.200000 0.915000)\n        (point 214.080000 538.800000 -49.320000 0.915000)\n        4)\n      (segment 3012 \n        (point 214.080000 538.800000 -49.320000 0.915000)\n        (point 212.390000 541.980000 -49.320000 0.915000)\n        4)\n      (segment 3013 \n        (point 212.390000 541.980000 -49.320000 0.915000)\n        (point 212.050000 545.480000 -49.320000 0.915000)\n        4))\n    (branch 113 112 \n      (segment 3014 \n        (point 212.050000 545.480000 -49.320000 0.915000)\n        (point 209.610000 546.120000 -47.820000 0.460000)\n        4)\n      (segment 3015 \n        (point 209.610000 546.120000 -47.820000 0.460000)\n        (point 209.030000 546.590000 -46.820000 0.460000)\n        4)\n      (segment 3016 \n        (point 209.030000 546.590000 -46.820000 0.460000)\n        (point 208.580000 546.480000 -46.820000 0.460000)\n        4)\n      (segment 3017 \n        (point 208.580000 546.480000 -46.820000 0.460000)\n        (point 208.320000 547.610000 -46.820000 0.460000)\n        4)\n      (segment 3018 \n        (point 208.320000 547.610000 -46.820000 0.460000)\n        (point 205.050000 547.440000 -45.770000 0.460000)\n        4)\n      (segment 3019 \n        (point 205.050000 547.440000 -45.770000 0.460000)\n        (point 205.550000 549.350000 -45.070000 0.460000)\n        4)\n      (segment 3020 \n        (point 205.550000 549.350000 -45.070000 0.460000)\n        (point 207.350000 549.770000 -43.670000 0.460000)\n        4)\n      (segment 3021 \n        (point 207.350000 549.770000 -43.670000 0.460000)\n        (point 205.600000 551.150000 -42.170000 0.460000)\n        4)\n      (segment 3022 \n        (point 205.600000 551.150000 -42.170000 0.460000)\n        (point 204.620000 553.310000 -41.170000 0.460000)\n        4)\n      (segment 3023 \n        (point 204.620000 553.310000 -41.170000 0.460000)\n        (point 203.200000 555.370000 -40.570000 0.460000)\n        4)\n      (segment 3024 \n        (point 203.200000 555.370000 -40.570000 0.460000)\n        (point 201.330000 557.310000 -41.350000 0.460000)\n        4)\n      (segment 3025 \n        (point 201.330000 557.310000 -41.350000 0.460000)\n        (point 199.460000 559.270000 -41.500000 0.460000)\n        4)\n      (segment 3026 \n        (point 199.460000 559.270000 -41.500000 0.460000)\n        (point 197.730000 560.650000 -40.630000 0.460000)\n        4)\n      (segment 3027 \n        (point 197.730000 560.650000 -40.630000 0.460000)\n        (point 196.700000 561.010000 -39.550000 0.460000)\n        4)\n      (segment 3028 \n        (point 196.700000 561.010000 -39.550000 0.460000)\n        (point 194.870000 564.770000 -39.050000 0.460000)\n        4)\n      (segment 3029 \n        (point 194.870000 564.770000 -39.050000 0.460000)\n        (point 194.220000 567.590000 -38.770000 0.460000)\n        4)\n      (segment 3030 \n        (point 194.220000 567.590000 -38.770000 0.460000)\n        (point 192.160000 568.310000 -38.770000 0.460000)\n        4)\n      (segment 3031 \n        (point 192.160000 568.310000 -38.770000 0.460000)\n        (point 189.970000 569.590000 -37.900000 0.460000)\n        4)\n      (segment 3032 \n        (point 189.970000 569.590000 -37.900000 0.460000)\n        (point 187.800000 570.860000 -37.300000 0.460000)\n        4)\n      (segment 3033 \n        (point 187.800000 570.860000 -37.300000 0.460000)\n        (point 185.430000 570.900000 -35.830000 0.460000)\n        4)\n      (segment 3034 \n        (point 185.430000 570.900000 -35.830000 0.460000)\n        (point 183.110000 572.760000 -35.830000 0.460000)\n        4)\n      (segment 3035 \n        (point 183.110000 572.760000 -35.830000 0.460000)\n        (point 180.870000 572.230000 -35.830000 0.460000)\n        4)\n      (segment 3036 \n        (point 180.870000 572.230000 -35.830000 0.460000)\n        (point 178.780000 571.140000 -37.470000 0.460000)\n        4)\n      (segment 3037 \n        (point 178.780000 571.140000 -37.470000 0.460000)\n        (point 178.780000 571.140000 -37.500000 0.460000)\n        4)\n      (segment 3038 \n        (point 178.780000 571.140000 -37.500000 0.460000)\n        (point 176.180000 568.150000 -38.250000 0.460000)\n        4)\n      (segment 3039 \n        (point 176.180000 568.150000 -38.250000 0.460000)\n        (point 173.820000 568.180000 -36.500000 0.460000)\n        4)\n      (segment 3040 \n        (point 173.820000 568.180000 -36.500000 0.460000)\n        (point 172.660000 569.110000 -36.500000 0.460000)\n        4)\n      (segment 3041 \n        (point 172.660000 569.110000 -36.500000 0.460000)\n        (point 170.550000 568.020000 -36.780000 0.460000)\n        4)\n      (segment 3042 \n        (point 170.550000 568.020000 -36.780000 0.460000)\n        (point 167.920000 569.200000 -37.700000 0.460000)\n        4)\n      (segment 3043 \n        (point 167.920000 569.200000 -37.700000 0.460000)\n        (point 164.090000 569.480000 -38.350000 0.460000)\n        4)\n      (segment 3044 \n        (point 164.090000 569.480000 -38.350000 0.460000)\n        (point 160.720000 565.720000 -38.630000 0.460000)\n        4)\n      (segment 3045 \n        (point 160.720000 565.720000 -38.630000 0.460000)\n        (point 159.880000 567.310000 -39.600000 0.460000)\n        4)\n      (segment 3046 \n        (point 159.880000 567.310000 -39.600000 0.460000)\n        (point 158.100000 566.890000 -40.100000 0.460000)\n        4)\n      (segment 3047 \n        (point 158.100000 566.890000 -40.100000 0.460000)\n        (point 155.820000 564.570000 -39.750000 0.460000)\n        4)\n      (segment 3048 \n        (point 155.820000 564.570000 -39.750000 0.460000)\n        (point 150.450000 563.310000 -39.750000 0.460000)\n        4)\n      (segment 3049 \n        (point 150.450000 563.310000 -39.750000 0.460000)\n        (point 146.620000 563.610000 -41.200000 0.460000)\n        4)\n      (segment 3050 \n        (point 146.620000 563.610000 -41.200000 0.460000)\n        (point 144.300000 565.450000 -40.850000 0.460000)\n        4)\n      (segment 3051 \n        (point 144.300000 565.450000 -40.850000 0.460000)\n        (point 144.300000 565.450000 -40.880000 0.460000)\n        4)\n      (segment 3052 \n        (point 144.300000 565.450000 -40.880000 0.460000)\n        (point 142.780000 563.890000 -40.000000 0.460000)\n        4)\n      (segment 3053 \n        (point 142.780000 563.890000 -40.000000 0.460000)\n        (point 140.150000 565.080000 -39.800000 0.460000)\n        4)\n      (segment 3054 \n        (point 140.150000 565.080000 -39.800000 0.460000)\n        (point 137.480000 564.450000 -39.800000 0.460000)\n        4)\n      (segment 3055 \n        (point 137.480000 564.450000 -39.800000 0.460000)\n        (point 134.080000 564.850000 -38.880000 0.460000)\n        4)\n      (segment 3056 \n        (point 134.080000 564.850000 -38.880000 0.460000)\n        (point 131.090000 563.550000 -37.580000 0.460000)\n        4)\n      (segment 3057 \n        (point 131.090000 563.550000 -37.580000 0.460000)\n        (point 127.120000 564.410000 -36.970000 0.460000)\n        4)\n      (segment 3058 \n        (point 127.120000 564.410000 -36.970000 0.460000)\n        (point 125.640000 564.660000 -35.700000 0.460000)\n        4)\n      (segment 3059 \n        (point 125.640000 564.660000 -35.700000 0.460000)\n        (point 126.040000 562.970000 -34.900000 0.460000)\n        4)\n      (segment 3060 \n        (point 126.040000 562.970000 -34.900000 0.460000)\n        (point 127.020000 560.800000 -34.700000 0.460000)\n        4))\n    (branch 114 112 \n      (segment 3061 \n        (point 212.050000 545.480000 -49.320000 0.915000)\n        (point 212.780000 550.420000 -49.920000 0.915000)\n        4)\n      (segment 3062 \n        (point 212.780000 550.420000 -49.920000 0.915000)\n        (point 214.300000 551.980000 -49.830000 0.915000)\n        4)\n      (segment 3063 \n        (point 214.300000 551.980000 -49.830000 0.915000)\n        (point 214.660000 554.450000 -49.830000 0.915000)\n        4)\n      (segment 3064 \n        (point 214.660000 554.450000 -49.830000 0.915000)\n        (point 213.680000 556.610000 -51.220000 0.915000)\n        4)\n      (segment 3065 \n        (point 213.680000 556.610000 -51.220000 0.915000)\n        (point 213.330000 560.110000 -52.020000 0.915000)\n        4)\n      (segment 3066 \n        (point 213.330000 560.110000 -52.020000 0.915000)\n        (point 214.450000 563.350000 -52.770000 0.915000)\n        4)\n      (segment 3067 \n        (point 214.450000 563.350000 -52.770000 0.915000)\n        (point 214.280000 568.110000 -53.620000 0.915000)\n        4)\n      (segment 3068 \n        (point 214.280000 568.110000 -53.620000 0.915000)\n        (point 214.840000 571.800000 -55.270000 0.915000)\n        4)\n      (segment 3069 \n        (point 214.840000 571.800000 -55.270000 0.915000)\n        (point 215.690000 576.190000 -55.270000 0.915000)\n        4)\n      (segment 3070 \n        (point 215.690000 576.190000 -55.270000 0.915000)\n        (point 216.240000 579.910000 -56.200000 0.915000)\n        4)\n      (segment 3071 \n        (point 216.240000 579.910000 -56.200000 0.915000)\n        (point 216.460000 582.950000 -57.400000 0.915000)\n        4)\n      (segment 3072 \n        (point 216.460000 582.950000 -57.400000 0.915000)\n        (point 216.110000 586.450000 -58.650000 0.915000)\n        4)\n      (segment 3073 \n        (point 216.110000 586.450000 -58.650000 0.915000)\n        (point 217.960000 588.660000 -59.400000 0.915000)\n        4)\n      (segment 3074 \n        (point 217.960000 588.660000 -59.400000 0.915000)\n        (point 218.890000 590.680000 -58.800000 0.915000)\n        4)\n      (segment 3075 \n        (point 218.890000 590.680000 -58.800000 0.915000)\n        (point 217.070000 594.440000 -57.470000 0.915000)\n        4)\n      (segment 3076 \n        (point 217.070000 594.440000 -57.470000 0.915000)\n        (point 216.850000 597.360000 -57.920000 0.915000)\n        4)\n      (segment 3077 \n        (point 216.850000 597.360000 -57.920000 0.915000)\n        (point 217.840000 601.180000 -57.920000 0.915000)\n        4)\n      (segment 3078 \n        (point 217.840000 601.180000 -57.920000 0.915000)\n        (point 218.340000 603.080000 -59.100000 0.915000)\n        4)\n      (segment 3079 \n        (point 218.340000 603.080000 -59.100000 0.915000)\n        (point 218.830000 605.000000 -59.100000 0.915000)\n        4)\n      (segment 3080 \n        (point 218.830000 605.000000 -59.100000 0.915000)\n        (point 218.660000 609.730000 -59.800000 0.915000)\n        4)\n      (segment 3081 \n        (point 218.660000 609.730000 -59.800000 0.915000)\n        (point 218.760000 613.340000 -60.600000 0.915000)\n        4)\n      (segment 3082 \n        (point 218.760000 613.340000 -60.600000 0.915000)\n        (point 219.300000 617.060000 -61.550000 0.915000)\n        4)\n      (segment 3083 \n        (point 219.300000 617.060000 -61.550000 0.915000)\n        (point 219.400000 620.650000 -62.400000 0.915000)\n        4)\n      (segment 3084 \n        (point 219.400000 620.650000 -62.400000 0.915000)\n        (point 219.400000 620.650000 -62.420000 0.915000)\n        4)\n      (segment 3085 \n        (point 219.400000 620.650000 -62.420000 0.915000)\n        (point 220.080000 623.800000 -63.400000 0.915000)\n        4)\n      (segment 3086 \n        (point 220.080000 623.800000 -63.400000 0.915000)\n        (point 220.080000 623.800000 -63.420000 0.915000)\n        4)\n      (segment 3087 \n        (point 220.080000 623.800000 -63.420000 0.915000)\n        (point 218.840000 627.090000 -63.420000 0.915000)\n        4)\n      (segment 3088 \n        (point 218.840000 627.090000 -63.420000 0.915000)\n        (point 220.450000 632.240000 -63.880000 0.915000)\n        4)\n      (segment 3089 \n        (point 220.450000 632.240000 -63.880000 0.915000)\n        (point 221.460000 635.940000 -64.050000 0.915000)\n        4)\n      (segment 3090 \n        (point 221.460000 635.940000 -64.050000 0.915000)\n        (point 220.350000 638.660000 -63.050000 0.915000)\n        4)\n      (segment 3091 \n        (point 220.350000 638.660000 -63.050000 0.915000)\n        (point 221.650000 643.160000 -62.450000 0.915000)\n        4)\n      (segment 3092 \n        (point 221.650000 643.160000 -62.450000 0.915000)\n        (point 220.990000 645.980000 -62.080000 0.915000)\n        4))\n    (branch 115 114 \n      (segment 3093 \n        (point 220.990000 645.980000 -62.080000 0.915000)\n        (point 223.320000 646.540000 -62.080000 0.230000)\n        4)\n      (segment 3094 \n        (point 223.320000 646.540000 -62.080000 0.230000)\n        (point 227.430000 645.100000 -61.150000 0.230000)\n        4)\n      (segment 3095 \n        (point 227.430000 645.100000 -61.150000 0.230000)\n        (point 229.210000 645.530000 -62.350000 0.230000)\n        4)\n      (segment 3096 \n        (point 229.210000 645.530000 -62.350000 0.230000)\n        (point 230.810000 644.700000 -63.530000 0.230000)\n        4)\n      (segment 3097 \n        (point 230.810000 644.700000 -63.530000 0.230000)\n        (point 231.840000 644.350000 -64.850000 0.230000)\n        4)\n      (segment 3098 \n        (point 231.840000 644.350000 -64.850000 0.230000)\n        (point 232.600000 645.130000 -66.820000 0.230000)\n        4)\n      (segment 3099 \n        (point 232.600000 645.130000 -66.820000 0.230000)\n        (point 233.230000 645.890000 -65.950000 0.230000)\n        4)\n      (segment 3100 \n        (point 233.230000 645.890000 -65.950000 0.230000)\n        (point 234.030000 644.980000 -66.570000 0.230000)\n        4)\n      (segment 3101 \n        (point 234.030000 644.980000 -66.570000 0.230000)\n        (point 235.280000 645.760000 -68.200000 0.230000)\n        4)\n      (segment 3102 \n        (point 235.280000 645.760000 -68.200000 0.230000)\n        (point 237.780000 645.150000 -68.850000 0.230000)\n        4)\n      (segment 3103 \n        (point 237.780000 645.150000 -68.850000 0.230000)\n        (point 239.570000 645.570000 -69.600000 0.230000)\n        4)\n      (segment 3104 \n        (point 239.570000 645.570000 -69.600000 0.230000)\n        (point 241.160000 644.740000 -70.000000 0.230000)\n        4)\n      (segment 3105 \n        (point 241.160000 644.740000 -70.000000 0.230000)\n        (point 245.270000 643.320000 -70.780000 0.230000)\n        4)\n      (segment 3106 \n        (point 245.270000 643.320000 -70.780000 0.230000)\n        (point 246.960000 640.130000 -71.630000 0.230000)\n        4)\n      (segment 3107 \n        (point 246.960000 640.130000 -71.630000 0.230000)\n        (point 249.460000 639.530000 -70.720000 0.230000)\n        4)\n      (segment 3108 \n        (point 249.460000 639.530000 -70.720000 0.230000)\n        (point 252.730000 639.690000 -71.750000 0.230000)\n        4)\n      (segment 3109 \n        (point 252.730000 639.690000 -71.750000 0.230000)\n        (point 255.800000 638.610000 -72.530000 0.230000)\n        4)\n      (segment 3110 \n        (point 255.800000 638.610000 -72.530000 0.230000)\n        (point 257.270000 638.360000 -73.470000 0.230000)\n        4)\n      (segment 3111 \n        (point 257.270000 638.360000 -73.470000 0.230000)\n        (point 260.000000 640.790000 -72.850000 0.230000)\n        4)\n      (segment 3112 \n        (point 260.000000 640.790000 -72.850000 0.230000)\n        (point 262.600000 643.790000 -72.950000 0.230000)\n        4)\n      (segment 3113 \n        (point 262.600000 643.790000 -72.950000 0.230000)\n        (point 264.250000 644.780000 -74.050000 0.230000)\n        4)\n      (segment 3114 \n        (point 264.250000 644.780000 -74.050000 0.230000)\n        (point 266.040000 645.200000 -75.700000 0.230000)\n        4)\n      (segment 3115 \n        (point 266.040000 645.200000 -75.700000 0.230000)\n        (point 267.110000 646.640000 -78.420000 0.230000)\n        4)\n      (segment 3116 \n        (point 267.110000 646.640000 -78.420000 0.230000)\n        (point 267.110000 646.640000 -78.530000 0.230000)\n        4))\n    (branch 116 114 \n      (segment 3117 \n        (point 220.990000 645.980000 -62.080000 0.915000)\n        (point 220.640000 649.480000 -61.570000 0.915000)\n        4)\n      (segment 3118 \n        (point 220.640000 649.480000 -61.570000 0.915000)\n        (point 219.710000 653.450000 -61.570000 0.915000)\n        4)\n      (segment 3119 \n        (point 219.710000 653.450000 -61.570000 0.915000)\n        (point 219.230000 657.510000 -61.570000 0.915000)\n        4)\n      (segment 3120 \n        (point 219.230000 657.510000 -61.570000 0.915000)\n        (point 220.360000 660.760000 -61.570000 0.915000)\n        4)\n      (segment 3121 \n        (point 220.360000 660.760000 -61.570000 0.915000)\n        (point 220.900000 664.470000 -61.570000 0.915000)\n        4)\n      (segment 3122 \n        (point 220.900000 664.470000 -61.570000 0.915000)\n        (point 220.240000 667.300000 -61.570000 0.915000)\n        4)\n      (segment 3123 \n        (point 220.240000 667.300000 -61.570000 0.915000)\n        (point 220.510000 672.140000 -62.650000 0.915000)\n        4)\n      (segment 3124 \n        (point 220.510000 672.140000 -62.650000 0.915000)\n        (point 220.920000 676.420000 -63.530000 0.915000)\n        4)\n      (segment 3125 \n        (point 220.920000 676.420000 -63.530000 0.915000)\n        (point 220.570000 679.920000 -62.350000 0.915000)\n        4)\n      (segment 3126 \n        (point 220.570000 679.920000 -62.350000 0.915000)\n        (point 221.110000 683.630000 -62.350000 0.915000)\n        4)\n      (segment 3127 \n        (point 221.110000 683.630000 -62.350000 0.915000)\n        (point 221.720000 689.150000 -62.970000 0.915000)\n        4)\n      (segment 3128 \n        (point 221.720000 689.150000 -62.970000 0.915000)\n        (point 221.680000 693.310000 -63.720000 0.915000)\n        4)\n      (segment 3129 \n        (point 221.680000 693.310000 -63.720000 0.915000)\n        (point 222.170000 695.230000 -64.130000 0.915000)\n        4)\n      (segment 3130 \n        (point 222.170000 695.230000 -64.130000 0.915000)\n        (point 222.710000 698.940000 -64.400000 0.915000)\n        4)\n      (segment 3131 \n        (point 222.710000 698.940000 -64.400000 0.915000)\n        (point 221.600000 701.660000 -64.400000 0.915000)\n        4)\n      (segment 3132 \n        (point 221.600000 701.660000 -64.400000 0.915000)\n        (point 221.970000 704.140000 -64.850000 0.915000)\n        4)\n      (segment 3133 \n        (point 221.970000 704.140000 -64.850000 0.915000)\n        (point 222.560000 709.640000 -64.850000 0.915000)\n        4)\n      (segment 3134 \n        (point 222.560000 709.640000 -64.850000 0.915000)\n        (point 222.660000 713.250000 -64.270000 0.915000)\n        4)\n      (segment 3135 \n        (point 222.660000 713.250000 -64.270000 0.915000)\n        (point 222.580000 715.620000 -64.270000 0.915000)\n        4)\n      (segment 3136 \n        (point 222.580000 715.620000 -64.270000 0.915000)\n        (point 223.830000 718.300000 -64.600000 0.915000)\n        4)\n      (segment 3137 \n        (point 223.830000 718.300000 -64.600000 0.915000)\n        (point 224.420000 723.820000 -64.320000 0.915000)\n        4)\n      (segment 3138 \n        (point 224.420000 723.820000 -64.320000 0.915000)\n        (point 224.340000 726.190000 -63.050000 0.915000)\n        4)\n      (segment 3139 \n        (point 224.340000 726.190000 -63.050000 0.915000)\n        (point 224.570000 729.230000 -61.880000 0.915000)\n        4)\n      (segment 3140 \n        (point 224.570000 729.230000 -61.880000 0.915000)\n        (point 224.080000 733.290000 -61.100000 0.915000)\n        4)\n      (segment 3141 \n        (point 224.080000 733.290000 -61.100000 0.915000)\n        (point 223.410000 736.130000 -60.250000 0.915000)\n        4)\n      (segment 3142 \n        (point 223.410000 736.130000 -60.250000 0.915000)\n        (point 224.680000 738.810000 -59.270000 0.915000)\n        4)\n      (segment 3143 \n        (point 224.680000 738.810000 -59.270000 0.915000)\n        (point 225.090000 743.090000 -58.820000 0.915000)\n        4)\n      (segment 3144 \n        (point 225.090000 743.090000 -58.820000 0.915000)\n        (point 225.630000 746.790000 -58.820000 0.915000)\n        4)\n      (segment 3145 \n        (point 225.630000 746.790000 -58.820000 0.915000)\n        (point 224.700000 750.760000 -59.950000 0.915000)\n        4)\n      (segment 3146 \n        (point 224.700000 750.760000 -59.950000 0.915000)\n        (point 226.410000 753.550000 -59.850000 0.915000)\n        4)\n      (segment 3147 \n        (point 226.410000 753.550000 -59.850000 0.915000)\n        (point 226.310000 755.920000 -59.850000 0.915000)\n        4)\n      (segment 3148 \n        (point 226.310000 755.920000 -59.850000 0.915000)\n        (point 226.290000 760.080000 -59.850000 0.915000)\n        4)\n      (segment 3149 \n        (point 226.290000 760.080000 -59.850000 0.915000)\n        (point 224.990000 761.580000 -60.450000 0.915000)\n        4)\n      (segment 3150 \n        (point 224.990000 761.580000 -60.450000 0.915000)\n        (point 224.960000 765.740000 -60.450000 0.915000)\n        4)\n      (segment 3151 \n        (point 224.960000 765.740000 -60.450000 0.915000)\n        (point 225.330000 768.230000 -61.000000 0.915000)\n        4)\n      (segment 3152 \n        (point 225.330000 768.230000 -61.000000 0.915000)\n        (point 224.800000 770.490000 -59.970000 0.915000)\n        4)\n      (segment 3153 \n        (point 224.800000 770.490000 -59.970000 0.915000)\n        (point 224.000000 773.880000 -59.320000 0.915000)\n        4))\n    (branch 117 116 \n      (segment 3154 \n        (point 224.000000 773.880000 -59.320000 0.915000)\n        (point 223.300000 779.280000 -59.320000 0.690000)\n        4)\n      (segment 3155 \n        (point 223.300000 779.280000 -59.320000 0.690000)\n        (point 223.100000 782.210000 -59.320000 0.690000)\n        4)\n      (segment 3156 \n        (point 223.100000 782.210000 -59.320000 0.690000)\n        (point 223.640000 785.920000 -59.220000 0.690000)\n        4)\n      (segment 3157 \n        (point 223.640000 785.920000 -59.220000 0.690000)\n        (point 224.580000 787.940000 -60.570000 0.690000)\n        4)\n      (segment 3158 \n        (point 224.580000 787.940000 -60.570000 0.690000)\n        (point 224.490000 790.310000 -60.850000 0.690000)\n        4)\n      (segment 3159 \n        (point 224.490000 790.310000 -60.850000 0.690000)\n        (point 225.040000 794.010000 -60.850000 0.690000)\n        4)\n      (segment 3160 \n        (point 225.040000 794.010000 -60.850000 0.690000)\n        (point 225.080000 795.810000 -61.300000 0.690000)\n        4)\n      (segment 3161 \n        (point 225.080000 795.810000 -61.300000 0.690000)\n        (point 225.310000 798.850000 -60.320000 0.690000)\n        4)\n      (segment 3162 \n        (point 225.310000 798.850000 -60.320000 0.690000)\n        (point 225.540000 801.900000 -59.200000 0.690000)\n        4)\n      (segment 3163 \n        (point 225.540000 801.900000 -59.200000 0.690000)\n        (point 225.320000 804.840000 -58.700000 0.690000)\n        4)\n      (segment 3164 \n        (point 225.320000 804.840000 -58.700000 0.690000)\n        (point 225.570000 807.870000 -59.150000 0.690000)\n        4)\n      (segment 3165 \n        (point 225.570000 807.870000 -59.150000 0.690000)\n        (point 225.210000 811.370000 -59.650000 0.690000)\n        4)\n      (segment 3166 \n        (point 225.210000 811.370000 -59.650000 0.690000)\n        (point 225.170000 815.550000 -60.070000 0.690000)\n        4)\n      (segment 3167 \n        (point 225.170000 815.550000 -60.070000 0.690000)\n        (point 224.960000 818.490000 -60.720000 0.690000)\n        4)\n      (segment 3168 \n        (point 224.960000 818.490000 -60.720000 0.690000)\n        (point 224.030000 822.440000 -61.600000 0.690000)\n        4)\n      (segment 3169 \n        (point 224.030000 822.440000 -61.600000 0.690000)\n        (point 224.260000 825.480000 -62.250000 0.690000)\n        4)\n      (segment 3170 \n        (point 224.260000 825.480000 -62.250000 0.690000)\n        (point 223.330000 829.440000 -62.250000 0.690000)\n        4)\n      (segment 3171 \n        (point 223.330000 829.440000 -62.250000 0.690000)\n        (point 222.400000 833.410000 -62.950000 0.690000)\n        4)\n      (segment 3172 \n        (point 222.400000 833.410000 -62.950000 0.690000)\n        (point 222.630000 836.450000 -64.020000 0.690000)\n        4)\n      (segment 3173 \n        (point 222.630000 836.450000 -64.020000 0.690000)\n        (point 223.750000 839.700000 -64.650000 0.690000)\n        4)\n      (segment 3174 \n        (point 223.750000 839.700000 -64.650000 0.690000)\n        (point 222.500000 842.990000 -64.650000 0.690000)\n        4)\n      (segment 3175 \n        (point 222.500000 842.990000 -64.650000 0.690000)\n        (point 221.260000 846.270000 -65.130000 0.690000)\n        4))\n    (branch 118 117 \n      (segment 3176 \n        (point 221.260000 846.270000 -65.130000 0.690000)\n        (point 222.290000 850.000000 -65.800000 0.690000)\n        4)\n      (segment 3177 \n        (point 222.290000 850.000000 -65.800000 0.690000)\n        (point 222.380000 853.600000 -64.880000 0.690000)\n        4)\n      (segment 3178 \n        (point 222.380000 853.600000 -64.880000 0.690000)\n        (point 222.620000 856.640000 -64.880000 0.690000)\n        4)\n      (segment 3179 \n        (point 222.620000 856.640000 -64.880000 0.690000)\n        (point 221.550000 861.170000 -66.200000 0.690000)\n        4)\n      (segment 3180 \n        (point 221.550000 861.170000 -66.200000 0.690000)\n        (point 221.470000 863.540000 -66.500000 0.690000)\n        4)\n      (segment 3181 \n        (point 221.470000 863.540000 -66.500000 0.690000)\n        (point 220.670000 866.940000 -66.750000 0.690000)\n        4)\n      (segment 3182 \n        (point 220.670000 866.940000 -66.750000 0.690000)\n        (point 220.670000 866.940000 -66.770000 0.690000)\n        4)\n      (segment 3183 \n        (point 220.670000 866.940000 -66.770000 0.690000)\n        (point 221.340000 870.080000 -67.280000 0.690000)\n        4)\n      (segment 3184 \n        (point 221.340000 870.080000 -67.280000 0.690000)\n        (point 221.080000 871.210000 -67.400000 0.690000)\n        4)\n      (segment 3185 \n        (point 221.080000 871.210000 -67.400000 0.690000)\n        (point 221.050000 875.380000 -67.400000 0.690000)\n        4))\n    (branch 119 118 \n      (segment 3186 \n        (point 221.050000 875.380000 -67.400000 0.690000)\n        (point 219.470000 878.560000 -67.400000 0.460000)\n        4)\n      (segment 3187 \n        (point 219.470000 878.560000 -67.400000 0.460000)\n        (point 218.920000 880.840000 -67.400000 0.460000)\n        4)\n      (segment 3188 \n        (point 218.920000 880.840000 -67.400000 0.460000)\n        (point 219.300000 883.300000 -67.400000 0.460000)\n        4)\n      (segment 3189 \n        (point 219.300000 883.300000 -67.400000 0.460000)\n        (point 218.770000 885.570000 -68.170000 0.460000)\n        4)\n      (segment 3190 \n        (point 218.770000 885.570000 -68.170000 0.460000)\n        (point 218.680000 887.940000 -69.070000 0.460000)\n        4)\n      (segment 3191 \n        (point 218.680000 887.940000 -69.070000 0.460000)\n        (point 218.340000 891.440000 -69.700000 0.460000)\n        4)\n      (segment 3192 \n        (point 218.340000 891.440000 -69.700000 0.460000)\n        (point 216.640000 894.630000 -70.200000 0.460000)\n        4)\n      (segment 3193 \n        (point 216.640000 894.630000 -70.200000 0.460000)\n        (point 217.010000 897.100000 -70.200000 0.460000)\n        4)\n      (segment 3194 \n        (point 217.010000 897.100000 -70.200000 0.460000)\n        (point 216.480000 899.370000 -70.200000 0.460000)\n        4)\n      (segment 3195 \n        (point 216.480000 899.370000 -70.200000 0.460000)\n        (point 215.810000 902.200000 -70.700000 0.460000)\n        4)\n      (segment 3196 \n        (point 215.810000 902.200000 -70.700000 0.460000)\n        (point 215.280000 904.470000 -71.150000 0.460000)\n        4)\n      (segment 3197 \n        (point 215.280000 904.470000 -71.150000 0.460000)\n        (point 214.620000 907.290000 -71.220000 0.460000)\n        4)\n      (segment 3198 \n        (point 214.620000 907.290000 -71.220000 0.460000)\n        (point 212.750000 909.240000 -70.780000 0.460000)\n        4)\n      (segment 3199 \n        (point 212.750000 909.240000 -70.780000 0.460000)\n        (point 212.080000 912.060000 -70.780000 0.460000)\n        4)\n      (segment 3200 \n        (point 212.080000 912.060000 -70.780000 0.460000)\n        (point 213.340000 914.760000 -71.520000 0.460000)\n        4)\n      (segment 3201 \n        (point 213.340000 914.760000 -71.520000 0.460000)\n        (point 214.010000 917.900000 -72.170000 0.460000)\n        4)\n      (segment 3202 \n        (point 214.010000 917.900000 -72.170000 0.460000)\n        (point 214.690000 921.040000 -73.000000 0.460000)\n        4)\n      (segment 3203 \n        (point 214.690000 921.040000 -73.000000 0.460000)\n        (point 216.980000 923.370000 -73.280000 0.460000)\n        4)\n      (segment 3204 \n        (point 216.980000 923.370000 -73.280000 0.460000)\n        (point 218.230000 926.050000 -74.130000 0.460000)\n        4)\n      (segment 3205 \n        (point 218.230000 926.050000 -74.130000 0.460000)\n        (point 221.090000 927.920000 -74.130000 0.460000)\n        4)\n      (segment 3206 \n        (point 221.090000 927.920000 -74.130000 0.460000)\n        (point 220.960000 928.490000 -74.130000 0.460000)\n        4)\n      (segment 3207 \n        (point 220.960000 928.490000 -74.130000 0.460000)\n        (point 223.960000 929.780000 -74.970000 0.460000)\n        4)\n      (segment 3208 \n        (point 223.960000 929.780000 -74.970000 0.460000)\n        (point 225.780000 932.000000 -75.900000 0.460000)\n        4)\n      (segment 3209 \n        (point 225.780000 932.000000 -75.900000 0.460000)\n        (point 226.600000 934.580000 -76.750000 0.460000)\n        4)\n      (segment 3210 \n        (point 226.600000 934.580000 -76.750000 0.460000)\n        (point 229.150000 935.770000 -77.500000 0.460000)\n        4)\n      (segment 3211 \n        (point 229.150000 935.770000 -77.500000 0.460000)\n        (point 230.130000 939.590000 -77.500000 0.460000)\n        4)\n      (segment 3212 \n        (point 230.130000 939.590000 -77.500000 0.460000)\n        (point 231.210000 941.030000 -78.880000 0.460000)\n        4)\n      (segment 3213 \n        (point 231.210000 941.030000 -78.880000 0.460000)\n        (point 231.520000 941.700000 -78.650000 0.460000)\n        4)\n      (segment 3214 \n        (point 231.520000 941.700000 -78.650000 0.460000)\n        (point 233.030000 943.260000 -80.500000 0.460000)\n        4)\n      (segment 3215 \n        (point 233.030000 943.260000 -80.500000 0.460000)\n        (point 233.400000 945.720000 -81.070000 0.460000)\n        4)\n      (segment 3216 \n        (point 233.400000 945.720000 -81.070000 0.460000)\n        (point 234.040000 947.070000 -81.530000 0.460000)\n        4)\n      (segment 3217 \n        (point 234.040000 947.070000 -81.530000 0.460000)\n        (point 235.510000 946.820000 -82.450000 0.460000)\n        4)\n      (segment 3218 \n        (point 235.510000 946.820000 -82.450000 0.460000)\n        (point 237.210000 949.610000 -83.880000 0.460000)\n        4)\n      (segment 3219 \n        (point 237.210000 949.610000 -83.880000 0.460000)\n        (point 238.720000 951.150000 -83.800000 0.460000)\n        4)\n      (segment 3220 \n        (point 238.720000 951.150000 -83.800000 0.460000)\n        (point 240.260000 952.700000 -83.820000 0.460000)\n        4)\n      (segment 3221 \n        (point 240.260000 952.700000 -83.820000 0.460000)\n        (point 240.430000 953.950000 -85.130000 0.460000)\n        4)\n      (segment 3222 \n        (point 240.430000 953.950000 -85.130000 0.460000)\n        (point 241.820000 956.060000 -86.850000 0.460000)\n        4)\n      (segment 3223 \n        (point 241.820000 956.060000 -86.850000 0.460000)\n        (point 243.340000 957.610000 -87.750000 0.460000)\n        4)\n      (segment 3224 \n        (point 243.340000 957.610000 -87.750000 0.460000)\n        (point 243.560000 960.650000 -88.650000 0.460000)\n        4)\n      (segment 3225 \n        (point 243.560000 960.650000 -88.650000 0.460000)\n        (point 242.900000 963.480000 -88.650000 0.460000)\n        4)\n      (segment 3226 \n        (point 242.900000 963.480000 -88.650000 0.460000)\n        (point 243.270000 965.960000 -88.650000 0.460000)\n        4)\n      (segment 3227 \n        (point 243.270000 965.960000 -88.650000 0.460000)\n        (point 244.030000 966.730000 -89.550000 0.460000)\n        4)\n      (segment 3228 \n        (point 244.030000 966.730000 -89.550000 0.460000)\n        (point 245.150000 969.980000 -90.650000 0.460000)\n        4)\n      (segment 3229 \n        (point 245.150000 969.980000 -90.650000 0.460000)\n        (point 245.150000 969.980000 -90.680000 0.460000)\n        4)\n      (segment 3230 \n        (point 245.150000 969.980000 -90.680000 0.460000)\n        (point 246.360000 970.860000 -90.850000 0.460000)\n        4)\n      (segment 3231 \n        (point 246.360000 970.860000 -90.850000 0.460000)\n        (point 248.770000 972.630000 -90.880000 0.460000)\n        4)\n      (segment 3232 \n        (point 248.770000 972.630000 -90.880000 0.460000)\n        (point 249.000000 975.660000 -91.620000 0.460000)\n        4)\n      (segment 3233 \n        (point 249.000000 975.660000 -91.620000 0.460000)\n        (point 248.920000 978.040000 -92.380000 0.460000)\n        4)\n      (segment 3234 \n        (point 248.920000 978.040000 -92.380000 0.460000)\n        (point 250.260000 978.340000 -92.970000 0.460000)\n        4)\n      (segment 3235 \n        (point 250.260000 978.340000 -92.970000 0.460000)\n        (point 249.810000 978.250000 -92.970000 0.460000)\n        4)\n      (segment 3236 \n        (point 249.810000 978.250000 -92.970000 0.460000)\n        (point 251.390000 981.600000 -93.530000 0.460000)\n        4)\n      (segment 3237 \n        (point 251.390000 981.600000 -93.530000 0.460000)\n        (point 251.600000 984.620000 -94.900000 0.460000)\n        4)\n      (segment 3238 \n        (point 251.600000 984.620000 -94.900000 0.460000)\n        (point 251.600000 984.620000 -94.920000 0.460000)\n        4)\n      (segment 3239 \n        (point 251.600000 984.620000 -94.920000 0.460000)\n        (point 251.330000 985.760000 -95.770000 0.460000)\n        4)\n      (segment 3240 \n        (point 251.330000 985.760000 -95.770000 0.460000)\n        (point 253.440000 986.840000 -96.430000 0.460000)\n        4)\n      (segment 3241 \n        (point 253.440000 986.840000 -96.430000 0.460000)\n        (point 253.920000 988.750000 -97.380000 0.460000)\n        4)\n      (segment 3242 \n        (point 253.920000 988.750000 -97.380000 0.460000)\n        (point 253.920000 988.750000 -97.420000 0.460000)\n        4)\n      (segment 3243 \n        (point 253.920000 988.750000 -97.420000 0.460000)\n        (point 255.010000 990.200000 -98.150000 0.460000)\n        4)\n      (segment 3244 \n        (point 255.010000 990.200000 -98.150000 0.460000)\n        (point 256.670000 991.190000 -98.470000 0.460000)\n        4)\n      (segment 3245 \n        (point 256.670000 991.190000 -98.470000 0.460000)\n        (point 257.600000 993.200000 -98.470000 0.460000)\n        4)\n      (segment 3246 \n        (point 257.600000 993.200000 -98.470000 0.460000)\n        (point 258.410000 995.770000 -99.630000 0.460000)\n        4)\n      (segment 3247 \n        (point 258.410000 995.770000 -99.630000 0.460000)\n        (point 259.990000 999.120000 -99.630000 0.460000)\n        4)\n      (segment 3248 \n        (point 259.990000 999.120000 -99.630000 0.460000)\n        (point 259.900000 1001.500000 -99.630000 0.460000)\n        4)\n      (segment 3249 \n        (point 259.900000 1001.500000 -99.630000 0.460000)\n        (point 258.650000 1004.790000 -100.220000 0.460000)\n        4)\n      (segment 3250 \n        (point 258.650000 1004.790000 -100.220000 0.460000)\n        (point 257.980000 1007.620000 -101.130000 0.460000)\n        4)\n      (segment 3251 \n        (point 257.980000 1007.620000 -101.130000 0.460000)\n        (point 258.360000 1010.090000 -102.130000 0.460000)\n        4)\n      (segment 3252 \n        (point 258.360000 1010.090000 -102.130000 0.460000)\n        (point 258.410000 1011.880000 -103.230000 0.460000)\n        4)\n      (segment 3253 \n        (point 258.410000 1011.880000 -103.230000 0.460000)\n        (point 258.460000 1013.690000 -103.600000 0.460000)\n        4)\n      (segment 3254 \n        (point 258.460000 1013.690000 -103.600000 0.460000)\n        (point 259.840000 1015.810000 -104.880000 0.460000)\n        4)\n      (segment 3255 \n        (point 259.840000 1015.810000 -104.880000 0.460000)\n        (point 260.910000 1017.260000 -105.420000 0.460000)\n        4)\n      (segment 3256 \n        (point 260.910000 1017.260000 -105.420000 0.460000)\n        (point 262.210000 1021.750000 -106.130000 0.460000)\n        4)\n      (segment 3257 \n        (point 262.210000 1021.750000 -106.130000 0.460000)\n        (point 262.720000 1023.660000 -106.320000 0.460000)\n        4)\n      (segment 3258 \n        (point 262.720000 1023.660000 -106.320000 0.460000)\n        (point 262.720000 1023.660000 -106.350000 0.460000)\n        4)\n      (segment 3259 \n        (point 262.720000 1023.660000 -106.350000 0.460000)\n        (point 262.770000 1025.460000 -107.100000 0.460000)\n        4)\n      (segment 3260 \n        (point 262.770000 1025.460000 -107.100000 0.460000)\n        (point 262.820000 1027.260000 -108.420000 0.460000)\n        4)\n      (segment 3261 \n        (point 262.820000 1027.260000 -108.420000 0.460000)\n        (point 263.750000 1029.270000 -109.820000 0.460000)\n        4)\n      (segment 3262 \n        (point 263.750000 1029.270000 -109.820000 0.460000)\n        (point 264.430000 1032.410000 -110.630000 0.460000)\n        4)\n      (segment 3263 \n        (point 264.430000 1032.410000 -110.630000 0.460000)\n        (point 264.300000 1032.980000 -112.150000 0.460000)\n        4)\n      (segment 3264 \n        (point 264.300000 1032.980000 -112.150000 0.460000)\n        (point 265.410000 1036.230000 -113.670000 0.460000)\n        4)\n      (segment 3265 \n        (point 265.410000 1036.230000 -113.670000 0.460000)\n        (point 266.950000 1037.780000 -115.370000 0.460000)\n        4)\n      (segment 3266 \n        (point 266.950000 1037.780000 -115.370000 0.460000)\n        (point 266.100000 1039.370000 -117.020000 0.460000)\n        4)\n      (segment 3267 \n        (point 266.100000 1039.370000 -117.020000 0.460000)\n        (point 264.480000 1040.200000 -118.320000 0.460000)\n        4)\n      (segment 3268 \n        (point 264.480000 1040.200000 -118.320000 0.460000)\n        (point 264.480000 1040.200000 -118.380000 0.460000)\n        4)\n      (segment 3269 \n        (point 264.480000 1040.200000 -118.380000 0.460000)\n        (point 263.340000 1041.110000 -120.650000 0.460000)\n        4)\n      (segment 3270 \n        (point 263.340000 1041.110000 -120.650000 0.460000)\n        (point 264.280000 1043.120000 -122.500000 0.460000)\n        4)\n      (segment 3271 \n        (point 264.280000 1043.120000 -122.500000 0.460000)\n        (point 263.740000 1045.390000 -124.520000 0.460000)\n        4)\n      (segment 3272 \n        (point 263.740000 1045.390000 -124.520000 0.460000)\n        (point 263.480000 1046.520000 -126.650000 0.460000)\n        4)\n      (segment 3273 \n        (point 263.480000 1046.520000 -126.650000 0.460000)\n        (point 262.180000 1048.010000 -130.230000 0.460000)\n        4)\n      (segment 3274 \n        (point 262.180000 1048.010000 -130.230000 0.460000)\n        (point 263.850000 1049.000000 -133.300000 0.460000)\n        4)\n      (segment 3275 \n        (point 263.850000 1049.000000 -133.300000 0.460000)\n        (point 263.850000 1049.000000 -133.320000 0.460000)\n        4)\n      (segment 3276 \n        (point 263.850000 1049.000000 -133.320000 0.460000)\n        (point 265.060000 1049.880000 -132.680000 0.460000)\n        4)\n      (segment 3277 \n        (point 265.060000 1049.880000 -132.680000 0.460000)\n        (point 265.060000 1049.880000 -132.770000 0.460000)\n        4)\n      (segment 3278 \n        (point 265.060000 1049.880000 -132.770000 0.460000)\n        (point 265.760000 1048.860000 -135.930000 0.460000)\n        4)\n      (segment 3279 \n        (point 265.760000 1048.860000 -135.930000 0.460000)\n        (point 266.840000 1050.300000 -138.700000 0.460000)\n        4)\n      (segment 3280 \n        (point 266.840000 1050.300000 -138.700000 0.460000)\n        (point 266.840000 1050.300000 -138.720000 0.460000)\n        4)\n      (segment 3281 \n        (point 266.840000 1050.300000 -138.720000 0.460000)\n        (point 267.250000 1054.570000 -140.470000 0.460000)\n        4)\n      (segment 3282 \n        (point 267.250000 1054.570000 -140.470000 0.460000)\n        (point 269.670000 1056.330000 -140.470000 0.460000)\n        4)\n      (segment 3283 \n        (point 269.670000 1056.330000 -140.470000 0.460000)\n        (point 271.410000 1060.920000 -141.350000 0.460000)\n        4)\n      (segment 3284 \n        (point 271.410000 1060.920000 -141.350000 0.460000)\n        (point 274.530000 1061.650000 -141.380000 0.460000)\n        4)\n      (segment 3285 \n        (point 274.530000 1061.650000 -141.380000 0.460000)\n        (point 275.090000 1065.370000 -142.680000 0.460000)\n        4)\n      (segment 3286 \n        (point 275.090000 1065.370000 -142.680000 0.460000)\n        (point 278.210000 1066.100000 -143.200000 0.460000)\n        4)\n      (segment 3287 \n        (point 278.210000 1066.100000 -143.200000 0.460000)\n        (point 279.240000 1065.740000 -145.630000 0.460000)\n        4)\n      (segment 3288 \n        (point 279.240000 1065.740000 -145.630000 0.460000)\n        (point 280.260000 1065.390000 -148.480000 0.460000)\n        4)\n      (segment 3289 \n        (point 280.260000 1065.390000 -148.480000 0.460000)\n        (point 280.260000 1065.390000 -148.520000 0.460000)\n        4)\n      (segment 3290 \n        (point 280.260000 1065.390000 -148.520000 0.460000)\n        (point 282.360000 1066.470000 -149.730000 0.460000)\n        4)\n      (segment 3291 \n        (point 282.360000 1066.470000 -149.730000 0.460000)\n        (point 282.360000 1066.470000 -149.750000 0.460000)\n        4)\n      (segment 3292 \n        (point 282.360000 1066.470000 -149.750000 0.460000)\n        (point 283.300000 1068.480000 -152.050000 0.460000)\n        4)\n      (segment 3293 \n        (point 283.300000 1068.480000 -152.050000 0.460000)\n        (point 283.800000 1070.390000 -153.820000 0.230000)\n        4)\n      (segment 3294 \n        (point 283.800000 1070.390000 -153.820000 0.230000)\n        (point 285.760000 1072.050000 -156.720000 0.230000)\n        4)\n      (segment 3295 \n        (point 285.760000 1072.050000 -156.720000 0.230000)\n        (point 285.760000 1072.050000 -157.270000 0.230000)\n        4))\n    (branch 120 118 \n      (segment 3296 \n        (point 221.050000 875.380000 -67.400000 0.690000)\n        (point 222.750000 878.160000 -67.400000 0.460000)\n        4)\n      (segment 3297 \n        (point 222.750000 878.160000 -67.400000 0.460000)\n        (point 224.140000 880.290000 -68.250000 0.460000)\n        4)\n      (segment 3298 \n        (point 224.140000 880.290000 -68.250000 0.460000)\n        (point 225.080000 882.300000 -68.250000 0.460000)\n        4)\n      (segment 3299 \n        (point 225.080000 882.300000 -68.250000 0.460000)\n        (point 226.200000 885.550000 -68.250000 0.460000)\n        4)\n      (segment 3300 \n        (point 226.200000 885.550000 -68.250000 0.460000)\n        (point 227.910000 888.340000 -69.050000 0.460000)\n        4)\n      (segment 3301 \n        (point 227.910000 888.340000 -69.050000 0.460000)\n        (point 228.400000 890.240000 -68.750000 0.460000)\n        4)\n      (segment 3302 \n        (point 228.400000 890.240000 -68.750000 0.460000)\n        (point 230.060000 891.230000 -68.670000 0.460000)\n        4)\n      (segment 3303 \n        (point 230.060000 891.230000 -68.670000 0.460000)\n        (point 231.620000 894.590000 -68.670000 0.460000)\n        4)\n      (segment 3304 \n        (point 231.620000 894.590000 -68.670000 0.460000)\n        (point 234.490000 896.450000 -69.320000 0.460000)\n        4)\n      (segment 3305 \n        (point 234.490000 896.450000 -69.320000 0.460000)\n        (point 236.900000 898.210000 -69.320000 0.460000)\n        4)\n      (segment 3306 \n        (point 236.900000 898.210000 -69.320000 0.460000)\n        (point 238.690000 898.630000 -70.320000 0.460000)\n        4)\n      (segment 3307 \n        (point 238.690000 898.630000 -70.320000 0.460000)\n        (point 240.480000 899.040000 -71.170000 0.460000)\n        4)\n      (segment 3308 \n        (point 240.480000 899.040000 -71.170000 0.460000)\n        (point 241.550000 900.490000 -71.850000 0.460000)\n        4)\n      (segment 3309 \n        (point 241.550000 900.490000 -71.850000 0.460000)\n        (point 242.930000 902.600000 -72.530000 0.460000)\n        4)\n      (segment 3310 \n        (point 242.930000 902.600000 -72.530000 0.460000)\n        (point 245.040000 903.700000 -72.530000 0.460000)\n        4)\n      (segment 3311 \n        (point 245.040000 903.700000 -72.530000 0.460000)\n        (point 247.010000 905.350000 -72.280000 0.460000)\n        4)\n      (segment 3312 \n        (point 247.010000 905.350000 -72.280000 0.460000)\n        (point 247.640000 906.700000 -73.780000 0.460000)\n        4)\n      (segment 3313 \n        (point 247.640000 906.700000 -73.780000 0.460000)\n        (point 251.260000 909.330000 -74.130000 0.460000)\n        4)\n      (segment 3314 \n        (point 251.260000 909.330000 -74.130000 0.460000)\n        (point 254.570000 911.300000 -74.130000 0.460000)\n        4)\n      (segment 3315 \n        (point 254.570000 911.300000 -74.130000 0.460000)\n        (point 257.420000 913.170000 -73.800000 0.460000)\n        4)\n      (segment 3316 \n        (point 257.420000 913.170000 -73.800000 0.460000)\n        (point 259.520000 914.260000 -73.800000 0.460000)\n        4)\n      (segment 3317 \n        (point 259.520000 914.260000 -73.800000 0.460000)\n        (point 260.340000 916.830000 -73.800000 0.460000)\n        4)\n      (segment 3318 \n        (point 260.340000 916.830000 -73.800000 0.460000)\n        (point 262.750000 918.600000 -74.100000 0.460000)\n        4)\n      (segment 3319 \n        (point 262.750000 918.600000 -74.100000 0.460000)\n        (point 264.710000 920.250000 -74.100000 0.460000)\n        4)\n      (segment 3320 \n        (point 264.710000 920.250000 -74.100000 0.460000)\n        (point 266.560000 922.470000 -74.100000 0.460000)\n        4)\n      (segment 3321 \n        (point 266.560000 922.470000 -74.100000 0.460000)\n        (point 268.200000 923.460000 -73.470000 0.460000)\n        4)\n      (segment 3322 \n        (point 268.200000 923.460000 -73.470000 0.460000)\n        (point 270.930000 925.890000 -73.470000 0.460000)\n        4)\n      (segment 3323 \n        (point 270.930000 925.890000 -73.470000 0.460000)\n        (point 274.240000 927.860000 -73.470000 0.460000)\n        4)\n      (segment 3324 \n        (point 274.240000 927.860000 -73.470000 0.460000)\n        (point 276.520000 930.180000 -74.530000 0.460000)\n        4)\n      (segment 3325 \n        (point 276.520000 930.180000 -74.530000 0.460000)\n        (point 278.940000 931.940000 -74.820000 0.460000)\n        4)\n      (segment 3326 \n        (point 278.940000 931.940000 -74.820000 0.460000)\n        (point 279.880000 933.950000 -75.100000 0.460000)\n        4)\n      (segment 3327 \n        (point 279.880000 933.950000 -75.100000 0.460000)\n        (point 283.180000 935.930000 -75.380000 0.460000)\n        4)\n      (segment 3328 \n        (point 283.180000 935.930000 -75.380000 0.460000)\n        (point 285.600000 937.690000 -75.670000 0.460000)\n        4)\n      (segment 3329 \n        (point 285.600000 937.690000 -75.670000 0.460000)\n        (point 286.810000 938.570000 -74.670000 0.460000)\n        4)\n      (segment 3330 \n        (point 286.810000 938.570000 -74.670000 0.460000)\n        (point 286.810000 938.570000 -74.720000 0.460000)\n        4)\n      (segment 3331 \n        (point 286.810000 938.570000 -74.720000 0.460000)\n        (point 289.540000 940.990000 -73.420000 0.460000)\n        4)\n      (segment 3332 \n        (point 289.540000 940.990000 -73.420000 0.460000)\n        (point 290.030000 942.900000 -72.200000 0.460000)\n        4)\n      (segment 3333 \n        (point 290.030000 942.900000 -72.200000 0.460000)\n        (point 291.860000 945.130000 -70.750000 0.460000)\n        4)\n      (segment 3334 \n        (point 291.860000 945.130000 -70.750000 0.460000)\n        (point 292.800000 947.140000 -69.570000 0.460000)\n        4)\n      (segment 3335 \n        (point 292.800000 947.140000 -69.570000 0.460000)\n        (point 294.640000 949.360000 -69.570000 0.460000)\n        4)\n      (segment 3336 \n        (point 294.640000 949.360000 -69.570000 0.460000)\n        (point 294.190000 949.260000 -69.570000 0.460000)\n        4)\n      (segment 3337 \n        (point 294.190000 949.260000 -69.570000 0.460000)\n        (point 296.220000 952.710000 -68.650000 0.460000)\n        4)\n      (segment 3338 \n        (point 296.220000 952.710000 -68.650000 0.460000)\n        (point 298.040000 954.940000 -68.200000 0.460000)\n        4)\n      (segment 3339 \n        (point 298.040000 954.940000 -68.200000 0.460000)\n        (point 298.040000 954.940000 -68.250000 0.460000)\n        4)\n      (segment 3340 \n        (point 298.040000 954.940000 -68.250000 0.460000)\n        (point 298.860000 957.520000 -67.470000 0.460000)\n        4)\n      (segment 3341 \n        (point 298.860000 957.520000 -67.470000 0.460000)\n        (point 298.860000 957.520000 -67.500000 0.460000)\n        4)\n      (segment 3342 \n        (point 298.860000 957.520000 -67.500000 0.460000)\n        (point 301.410000 958.710000 -66.820000 0.460000)\n        4)\n      (segment 3343 \n        (point 301.410000 958.710000 -66.820000 0.460000)\n        (point 303.810000 960.470000 -66.220000 0.230000)\n        4)\n      (segment 3344 \n        (point 303.810000 960.470000 -66.220000 0.230000)\n        (point 304.710000 960.690000 -65.520000 0.230000)\n        4)\n      (segment 3345 \n        (point 304.710000 960.690000 -65.520000 0.230000)\n        (point 305.970000 963.360000 -64.500000 0.230000)\n        4)\n      (segment 3346 \n        (point 305.970000 963.360000 -64.500000 0.230000)\n        (point 308.650000 963.990000 -63.220000 0.230000)\n        4)\n      (segment 3347 \n        (point 308.650000 963.990000 -63.220000 0.230000)\n        (point 310.300000 964.970000 -63.170000 0.230000)\n        4)\n      (segment 3348 \n        (point 310.300000 964.970000 -63.170000 0.230000)\n        (point 311.990000 967.770000 -62.280000 0.230000)\n        4)\n      (segment 3349 \n        (point 311.990000 967.770000 -62.280000 0.230000)\n        (point 314.020000 971.230000 -61.050000 0.230000)\n        4)\n      (segment 3350 \n        (point 314.020000 971.230000 -61.050000 0.230000)\n        (point 316.880000 973.090000 -60.150000 0.230000)\n        4)\n      (segment 3351 \n        (point 316.880000 973.090000 -60.150000 0.230000)\n        (point 316.880000 973.090000 -60.170000 0.230000)\n        4)\n      (segment 3352 \n        (point 316.880000 973.090000 -60.170000 0.230000)\n        (point 319.480000 976.080000 -59.320000 0.230000)\n        4)\n      (segment 3353 \n        (point 319.480000 976.080000 -59.320000 0.230000)\n        (point 319.480000 976.080000 -59.300000 0.230000)\n        4)\n      (segment 3354 \n        (point 319.480000 976.080000 -59.300000 0.230000)\n        (point 321.890000 977.850000 -59.950000 0.230000)\n        4)\n      (segment 3355 \n        (point 321.890000 977.850000 -59.950000 0.230000)\n        (point 322.970000 979.290000 -59.130000 0.230000)\n        4)\n      (segment 3356 \n        (point 322.970000 979.290000 -59.130000 0.230000)\n        (point 325.120000 982.180000 -58.580000 0.230000)\n        4)\n      (segment 3357 \n        (point 325.120000 982.180000 -58.580000 0.230000)\n        (point 328.290000 984.720000 -58.100000 0.230000)\n        4)\n      (segment 3358 \n        (point 328.290000 984.720000 -58.100000 0.230000)\n        (point 328.470000 985.950000 -57.500000 0.230000)\n        4)\n      (segment 3359 \n        (point 328.470000 985.950000 -57.500000 0.230000)\n        (point 331.060000 988.960000 -56.820000 0.230000)\n        4)\n      (segment 3360 \n        (point 331.060000 988.960000 -56.820000 0.230000)\n        (point 333.030000 990.610000 -55.830000 0.230000)\n        4)\n      (segment 3361 \n        (point 333.030000 990.610000 -55.830000 0.230000)\n        (point 335.580000 991.800000 -54.780000 0.230000)\n        4)\n      (segment 3362 \n        (point 335.580000 991.800000 -54.780000 0.230000)\n        (point 336.780000 992.670000 -54.450000 0.230000)\n        4)\n      (segment 3363 \n        (point 336.780000 992.670000 -54.450000 0.230000)\n        (point 337.600000 995.260000 -53.850000 0.230000)\n        4)\n      (segment 3364 \n        (point 337.600000 995.260000 -53.850000 0.230000)\n        (point 338.540000 997.270000 -54.320000 0.230000)\n        4)\n      (segment 3365 \n        (point 338.540000 997.270000 -54.320000 0.230000)\n        (point 341.260000 999.700000 -54.850000 0.230000)\n        4)\n      (segment 3366 \n        (point 341.260000 999.700000 -54.850000 0.230000)\n        (point 343.850000 1002.700000 -55.600000 0.230000)\n        4)\n      (segment 3367 \n        (point 343.850000 1002.700000 -55.600000 0.230000)\n        (point 343.850000 1002.700000 -55.630000 0.230000)\n        4)\n      (segment 3368 \n        (point 343.850000 1002.700000 -55.630000 0.230000)\n        (point 344.180000 1003.360000 -54.400000 0.230000)\n        4)\n      (segment 3369 \n        (point 344.180000 1003.360000 -54.400000 0.230000)\n        (point 345.300000 1006.610000 -52.820000 0.230000)\n        4)\n      (segment 3370 \n        (point 345.300000 1006.610000 -52.820000 0.230000)\n        (point 347.400000 1007.710000 -51.820000 0.230000)\n        4)\n      (segment 3371 \n        (point 347.400000 1007.710000 -51.820000 0.230000)\n        (point 349.990000 1010.710000 -51.520000 0.230000)\n        4)\n      (segment 3372 \n        (point 349.990000 1010.710000 -51.520000 0.230000)\n        (point 349.990000 1010.710000 -51.550000 0.230000)\n        4)\n      (segment 3373 \n        (point 349.990000 1010.710000 -51.550000 0.230000)\n        (point 350.620000 1012.030000 -49.720000 0.230000)\n        4)\n      (segment 3374 \n        (point 350.620000 1012.030000 -49.720000 0.230000)\n        (point 350.670000 1013.830000 -48.650000 0.230000)\n        4)\n      (segment 3375 \n        (point 350.670000 1013.830000 -48.650000 0.230000)\n        (point 353.520000 1015.700000 -48.000000 0.230000)\n        4)\n      (segment 3376 \n        (point 353.520000 1015.700000 -48.000000 0.230000)\n        (point 352.990000 1017.970000 -48.050000 0.230000)\n        4)\n      (segment 3377 \n        (point 352.990000 1017.970000 -48.050000 0.230000)\n        (point 355.400000 1019.730000 -47.330000 0.230000)\n        4)\n      (segment 3378 \n        (point 355.400000 1019.730000 -47.330000 0.230000)\n        (point 358.710000 1021.690000 -47.330000 0.230000)\n        4)\n      (segment 3379 \n        (point 358.710000 1021.690000 -47.330000 0.230000)\n        (point 358.550000 1026.440000 -47.920000 0.230000)\n        4)\n      (segment 3380 \n        (point 358.550000 1026.440000 -47.920000 0.230000)\n        (point 359.620000 1027.880000 -49.130000 0.230000)\n        4)\n      (segment 3381 \n        (point 359.620000 1027.880000 -49.130000 0.230000)\n        (point 360.880000 1030.560000 -49.130000 0.230000)\n        4)\n      (segment 3382 \n        (point 360.880000 1030.560000 -49.130000 0.230000)\n        (point 360.670000 1033.500000 -48.420000 0.230000)\n        4)\n      (segment 3383 \n        (point 360.670000 1033.500000 -48.420000 0.230000)\n        (point 360.210000 1033.400000 -48.420000 0.230000)\n        4)\n      (segment 3384 \n        (point 360.210000 1033.400000 -48.420000 0.230000)\n        (point 361.160000 1035.400000 -47.370000 0.230000)\n        4)\n      (segment 3385 \n        (point 361.160000 1035.400000 -47.370000 0.230000)\n        (point 360.950000 1038.350000 -47.720000 0.230000)\n        4)\n      (segment 3386 \n        (point 360.950000 1038.350000 -47.720000 0.230000)\n        (point 360.950000 1038.350000 -47.750000 0.230000)\n        4)\n      (segment 3387 \n        (point 360.950000 1038.350000 -47.750000 0.230000)\n        (point 360.550000 1040.040000 -47.750000 0.230000)\n        4)\n      (segment 3388 \n        (point 360.550000 1040.040000 -47.750000 0.230000)\n        (point 362.640000 1041.130000 -48.950000 0.230000)\n        4)\n      (segment 3389 \n        (point 362.640000 1041.130000 -48.950000 0.230000)\n        (point 365.190000 1042.310000 -48.580000 0.230000)\n        4)\n      (segment 3390 \n        (point 365.190000 1042.310000 -48.580000 0.230000)\n        (point 365.190000 1042.310000 -48.600000 0.230000)\n        4)\n      (segment 3391 \n        (point 365.190000 1042.310000 -48.600000 0.230000)\n        (point 364.800000 1044.020000 -49.650000 0.230000)\n        4)\n      (segment 3392 \n        (point 364.800000 1044.020000 -49.650000 0.230000)\n        (point 364.800000 1044.020000 -49.670000 0.230000)\n        4)\n      (segment 3393 \n        (point 364.800000 1044.020000 -49.670000 0.230000)\n        (point 365.290000 1045.920000 -49.800000 0.230000)\n        4)\n      (segment 3394 \n        (point 365.290000 1045.920000 -49.800000 0.230000)\n        (point 367.390000 1047.020000 -50.270000 0.230000)\n        4)\n      (segment 3395 \n        (point 367.390000 1047.020000 -50.270000 0.230000)\n        (point 369.090000 1049.810000 -49.950000 0.230000)\n        4)\n      (segment 3396 \n        (point 369.090000 1049.810000 -49.950000 0.230000)\n        (point 371.060000 1051.460000 -51.570000 0.230000)\n        4)\n      (segment 3397 \n        (point 371.060000 1051.460000 -51.570000 0.230000)\n        (point 372.720000 1052.450000 -52.150000 0.230000)\n        4)\n      (segment 3398 \n        (point 372.720000 1052.450000 -52.150000 0.230000)\n        (point 373.970000 1055.130000 -53.650000 0.230000)\n        4)\n      (segment 3399 \n        (point 373.970000 1055.130000 -53.650000 0.230000)\n        (point 373.970000 1055.130000 -53.670000 0.230000)\n        4)\n      (segment 3400 \n        (point 373.970000 1055.130000 -53.670000 0.230000)\n        (point 374.910000 1057.140000 -54.920000 0.230000)\n        4)\n      (segment 3401 \n        (point 374.910000 1057.140000 -54.920000 0.230000)\n        (point 374.960000 1058.950000 -56.770000 0.230000)\n        4)\n      (segment 3402 \n        (point 374.960000 1058.950000 -56.770000 0.230000)\n        (point 373.980000 1061.100000 -59.550000 0.230000)\n        4)\n      (segment 3403 \n        (point 373.980000 1061.100000 -59.550000 0.230000)\n        (point 373.980000 1061.100000 -59.670000 0.230000)\n        4))\n    (branch 121 117 \n      (segment 3404 \n        (point 221.260000 846.270000 -65.130000 0.690000)\n        (point 218.530000 847.920000 -63.300000 0.690000)\n        4)\n      (segment 3405 \n        (point 218.530000 847.920000 -63.300000 0.690000)\n        (point 218.570000 849.720000 -62.450000 0.690000)\n        4)\n      (segment 3406 \n        (point 218.570000 849.720000 -62.450000 0.690000)\n        (point 217.650000 853.690000 -61.280000 0.690000)\n        4)\n      (segment 3407 \n        (point 217.650000 853.690000 -61.280000 0.690000)\n        (point 216.980000 856.520000 -61.320000 0.690000)\n        4)\n      (segment 3408 \n        (point 216.980000 856.520000 -61.320000 0.690000)\n        (point 215.880000 859.240000 -60.850000 0.690000)\n        4)\n      (segment 3409 \n        (point 215.880000 859.240000 -60.850000 0.690000)\n        (point 214.460000 861.300000 -60.070000 0.690000)\n        4)\n      (segment 3410 \n        (point 214.460000 861.300000 -60.070000 0.690000)\n        (point 213.610000 862.880000 -59.130000 0.690000)\n        4)\n      (segment 3411 \n        (point 213.610000 862.880000 -59.130000 0.690000)\n        (point 213.970000 865.370000 -58.450000 0.690000)\n        4)\n      (segment 3412 \n        (point 213.970000 865.370000 -58.450000 0.690000)\n        (point 213.260000 866.390000 -57.350000 0.690000)\n        4)\n      (segment 3413 \n        (point 213.260000 866.390000 -57.350000 0.690000)\n        (point 211.700000 869.020000 -56.570000 0.690000)\n        4)\n      (segment 3414 \n        (point 211.700000 869.020000 -56.570000 0.690000)\n        (point 210.990000 870.040000 -55.250000 0.690000)\n        4)\n      (segment 3415 \n        (point 210.990000 870.040000 -55.250000 0.690000)\n        (point 210.860000 870.610000 -54.380000 0.690000)\n        4)\n      (segment 3416 \n        (point 210.860000 870.610000 -54.380000 0.690000)\n        (point 208.550000 872.460000 -53.130000 0.690000)\n        4)\n      (segment 3417 \n        (point 208.550000 872.460000 -53.130000 0.690000)\n        (point 207.260000 873.940000 -52.600000 0.690000)\n        4)\n      (segment 3418 \n        (point 207.260000 873.940000 -52.600000 0.690000)\n        (point 206.540000 874.970000 -51.380000 0.690000)\n        4)\n      (segment 3419 \n        (point 206.540000 874.970000 -51.380000 0.690000)\n        (point 207.350000 877.550000 -50.630000 0.690000)\n        4)\n      (segment 3420 \n        (point 207.350000 877.550000 -50.630000 0.690000)\n        (point 207.090000 878.680000 -49.600000 0.690000)\n        4)\n      (segment 3421 \n        (point 207.090000 878.680000 -49.600000 0.690000)\n        (point 206.240000 880.280000 -48.700000 0.690000)\n        4)\n      (segment 3422 \n        (point 206.240000 880.280000 -48.700000 0.690000)\n        (point 205.890000 883.780000 -47.770000 0.690000)\n        4)\n      (segment 3423 \n        (point 205.890000 883.780000 -47.770000 0.690000)\n        (point 206.250000 886.240000 -47.950000 0.690000)\n        4)\n      (segment 3424 \n        (point 206.250000 886.240000 -47.950000 0.690000)\n        (point 204.830000 888.300000 -47.570000 0.690000)\n        4)\n      (segment 3425 \n        (point 204.830000 888.300000 -47.570000 0.690000)\n        (point 200.730000 889.720000 -47.100000 0.690000)\n        4)\n      (segment 3426 \n        (point 200.730000 889.720000 -47.100000 0.690000)\n        (point 200.070000 892.550000 -45.500000 0.690000)\n        4)\n      (segment 3427 \n        (point 200.070000 892.550000 -45.500000 0.690000)\n        (point 200.160000 896.160000 -44.300000 0.690000)\n        4)\n      (segment 3428 \n        (point 200.160000 896.160000 -44.300000 0.690000)\n        (point 199.060000 898.900000 -43.130000 0.690000)\n        4)\n      (segment 3429 \n        (point 199.060000 898.900000 -43.130000 0.690000)\n        (point 197.260000 898.480000 -41.750000 0.690000)\n        4)\n      (segment 3430 \n        (point 197.260000 898.480000 -41.750000 0.690000)\n        (point 197.340000 896.100000 -40.800000 0.690000)\n        4)\n      (segment 3431 \n        (point 197.340000 896.100000 -40.800000 0.690000)\n        (point 197.290000 894.300000 -39.550000 0.690000)\n        4)\n      (segment 3432 \n        (point 197.290000 894.300000 -39.550000 0.690000)\n        (point 197.740000 894.410000 -37.630000 0.690000)\n        4)\n      (segment 3433 \n        (point 197.740000 894.410000 -37.630000 0.690000)\n        (point 196.860000 894.200000 -35.380000 0.690000)\n        4)\n      (segment 3434 \n        (point 196.860000 894.200000 -35.380000 0.690000)\n        (point 194.040000 894.130000 -33.330000 0.690000)\n        4)\n      (segment 3435 \n        (point 194.040000 894.130000 -33.330000 0.690000)\n        (point 197.660000 896.770000 -32.130000 0.690000)\n        4)\n      (segment 3436 \n        (point 197.660000 896.770000 -32.130000 0.690000)\n        (point 198.690000 896.420000 -30.020000 0.690000)\n        4)\n      (segment 3437 \n        (point 198.690000 896.420000 -30.020000 0.690000)\n        (point 195.350000 898.630000 -29.400000 0.690000)\n        4)\n      (segment 3438 \n        (point 195.350000 898.630000 -29.400000 0.690000)\n        (point 194.950000 900.320000 -28.550000 0.690000)\n        4)\n      (segment 3439 \n        (point 194.950000 900.320000 -28.550000 0.690000)\n        (point 194.770000 905.050000 -27.350000 0.690000)\n        4)\n      (segment 3440 \n        (point 194.770000 905.050000 -27.350000 0.690000)\n        (point 196.890000 906.140000 -26.050000 0.690000)\n        4)\n      (segment 3441 \n        (point 196.890000 906.140000 -26.050000 0.690000)\n        (point 197.640000 906.930000 -24.350000 0.690000)\n        4)\n      (segment 3442 \n        (point 197.640000 906.930000 -24.350000 0.690000)\n        (point 197.950000 907.590000 -25.050000 0.690000)\n        4)\n      (segment 3443 \n        (point 197.950000 907.590000 -25.050000 0.690000)\n        (point 194.420000 908.560000 -23.050000 0.690000)\n        4)\n      (segment 3444 \n        (point 194.420000 908.560000 -23.050000 0.690000)\n        (point 191.480000 909.050000 -21.850000 0.690000)\n        4)\n      (segment 3445 \n        (point 191.480000 909.050000 -21.850000 0.690000)\n        (point 190.060000 911.110000 -22.080000 0.690000)\n        4)\n      (segment 3446 \n        (point 190.060000 911.110000 -22.080000 0.690000)\n        (point 189.790000 912.240000 -20.800000 0.690000)\n        4)\n      (segment 3447 \n        (point 189.790000 912.240000 -20.800000 0.690000)\n        (point 187.930000 914.200000 -19.750000 0.690000)\n        4)\n      (segment 3448 \n        (point 187.930000 914.200000 -19.750000 0.690000)\n        (point 186.640000 915.680000 -18.850000 0.690000)\n        4)\n      (segment 3449 \n        (point 186.640000 915.680000 -18.850000 0.690000)\n        (point 186.640000 915.680000 -18.880000 0.690000)\n        4)\n      (segment 3450 \n        (point 186.640000 915.680000 -18.880000 0.690000)\n        (point 187.450000 918.270000 -17.050000 0.690000)\n        4)\n      (segment 3451 \n        (point 187.450000 918.270000 -17.050000 0.690000)\n        (point 186.290000 919.180000 -15.950000 0.690000)\n        4)\n      (segment 3452 \n        (point 186.290000 919.180000 -15.950000 0.690000)\n        (point 186.290000 919.180000 -15.920000 0.690000)\n        4)\n      (segment 3453 \n        (point 186.290000 919.180000 -15.920000 0.690000)\n        (point 185.260000 919.540000 -14.600000 0.690000)\n        4)\n      (segment 3454 \n        (point 185.260000 919.540000 -14.600000 0.690000)\n        (point 184.730000 921.810000 -13.420000 0.690000)\n        4)\n      (segment 3455 \n        (point 184.730000 921.810000 -13.420000 0.690000)\n        (point 184.780000 923.610000 -12.300000 0.690000)\n        4)\n      (segment 3456 \n        (point 184.780000 923.610000 -12.300000 0.690000)\n        (point 184.780000 923.610000 -12.320000 0.690000)\n        4)\n      (segment 3457 \n        (point 184.780000 923.610000 -12.320000 0.690000)\n        (point 185.720000 925.630000 -12.000000 0.690000)\n        4)\n      (segment 3458 \n        (point 185.720000 925.630000 -12.000000 0.690000)\n        (point 184.430000 927.120000 -12.000000 0.690000)\n        4)\n      (segment 3459 \n        (point 184.430000 927.120000 -12.000000 0.690000)\n        (point 182.060000 927.150000 -10.120000 0.690000)\n        4)\n      (segment 3460 \n        (point 182.060000 927.150000 -10.120000 0.690000)\n        (point 182.240000 928.390000 -8.950000 0.690000)\n        4)\n      (segment 3461 \n        (point 182.240000 928.390000 -8.950000 0.690000)\n        (point 183.770000 929.950000 -8.250000 0.690000)\n        4)\n      (segment 3462 \n        (point 183.770000 929.950000 -8.250000 0.690000)\n        (point 183.100000 932.770000 -8.350000 0.690000)\n        4)\n      (segment 3463 \n        (point 183.100000 932.770000 -8.350000 0.690000)\n        (point 182.260000 934.370000 -7.670000 0.690000)\n        4)\n      (segment 3464 \n        (point 182.260000 934.370000 -7.670000 0.690000)\n        (point 179.500000 936.100000 -6.780000 0.690000)\n        4)\n      (segment 3465 \n        (point 179.500000 936.100000 -6.780000 0.690000)\n        (point 179.590000 939.710000 -6.280000 0.690000)\n        4)\n      (segment 3466 \n        (point 179.590000 939.710000 -6.280000 0.690000)\n        (point 177.980000 940.530000 -5.820000 0.690000)\n        4)\n      (segment 3467 \n        (point 177.980000 940.530000 -5.820000 0.690000)\n        (point 176.820000 941.460000 -4.030000 0.690000)\n        4)\n      (segment 3468 \n        (point 176.820000 941.460000 -4.030000 0.690000)\n        (point 176.820000 941.460000 -4.050000 0.690000)\n        4)\n      (segment 3469 \n        (point 176.820000 941.460000 -4.050000 0.690000)\n        (point 174.460000 941.500000 -2.450000 0.690000)\n        4)\n      (segment 3470 \n        (point 174.460000 941.500000 -2.450000 0.690000)\n        (point 170.810000 943.030000 -1.670000 0.690000)\n        4)\n      (segment 3471 \n        (point 170.810000 943.030000 -1.670000 0.690000)\n        (point 171.170000 945.500000 0.070000 0.690000)\n        4)\n      (segment 3472 \n        (point 171.170000 945.500000 0.070000 0.690000)\n        (point 172.610000 949.420000 0.630000 0.690000)\n        4)\n      (segment 3473 \n        (point 172.610000 949.420000 0.630000 0.690000)\n        (point 174.250000 950.410000 -0.500000 0.690000)\n        4)\n      (segment 3474 \n        (point 174.250000 950.410000 -0.500000 0.690000)\n        (point 174.250000 950.410000 -0.550000 0.690000)\n        4)\n      (segment 3475 \n        (point 174.250000 950.410000 -0.550000 0.690000)\n        (point 173.900000 953.900000 0.280000 0.690000)\n        4)\n      (segment 3476 \n        (point 173.900000 953.900000 0.280000 0.690000)\n        (point 176.060000 956.800000 1.000000 0.690000)\n        4)\n      (segment 3477 \n        (point 176.060000 956.800000 1.000000 0.690000)\n        (point 177.900000 959.030000 2.050000 0.690000)\n        4)\n      (segment 3478 \n        (point 177.900000 959.030000 2.050000 0.690000)\n        (point 177.900000 959.030000 2.170000 0.690000)\n        4)\n      (segment 3479 \n        (point 177.900000 959.030000 2.170000 0.690000)\n        (point 179.990000 960.110000 3.820000 0.690000)\n        4)\n      (segment 3480 \n        (point 179.990000 960.110000 3.820000 0.690000)\n        (point 179.990000 960.110000 3.800000 0.690000)\n        4)\n      (segment 3481 \n        (point 179.990000 960.110000 3.800000 0.690000)\n        (point 182.010000 963.570000 4.400000 0.690000)\n        4)\n      (segment 3482 \n        (point 182.010000 963.570000 4.400000 0.690000)\n        (point 182.010000 963.570000 4.380000 0.690000)\n        4)\n      (segment 3483 \n        (point 182.010000 963.570000 4.380000 0.690000)\n        (point 184.780000 967.790000 4.850000 0.690000)\n        4)\n      (segment 3484 \n        (point 184.780000 967.790000 4.850000 0.690000)\n        (point 187.200000 969.560000 5.350000 0.690000)\n        4)\n      (segment 3485 \n        (point 187.200000 969.560000 5.350000 0.690000)\n        (point 186.480000 970.590000 4.470000 0.690000)\n        4)\n      (segment 3486 \n        (point 186.480000 970.590000 4.470000 0.690000)\n        (point 186.850000 973.060000 5.720000 0.460000)\n        4)\n      (segment 3487 \n        (point 186.850000 973.060000 5.720000 0.460000)\n        (point 186.770000 975.430000 6.820000 0.460000)\n        4)\n      (segment 3488 \n        (point 186.770000 975.430000 6.820000 0.460000)\n        (point 188.690000 975.290000 7.470000 0.460000)\n        4)\n      (segment 3489 \n        (point 188.690000 975.290000 7.470000 0.460000)\n        (point 188.690000 975.290000 7.450000 0.460000)\n        4)\n      (segment 3490 \n        (point 188.690000 975.290000 7.450000 0.460000)\n        (point 190.020000 975.600000 8.050000 0.460000)\n        4)\n      (segment 3491 \n        (point 190.020000 975.600000 8.050000 0.460000)\n        (point 190.020000 975.600000 8.020000 0.460000)\n        4)\n      (segment 3492 \n        (point 190.020000 975.600000 8.020000 0.460000)\n        (point 191.630000 974.780000 8.970000 0.460000)\n        4)\n      (segment 3493 \n        (point 191.630000 974.780000 8.970000 0.460000)\n        (point 191.630000 974.780000 8.950000 0.460000)\n        4)\n      (segment 3494 \n        (point 191.630000 974.780000 8.950000 0.460000)\n        (point 193.090000 974.540000 10.900000 0.460000)\n        4))\n    (branch 122 116 \n      (segment 3495 \n        (point 224.000000 773.880000 -59.320000 0.915000)\n        (point 225.260000 776.470000 -57.920000 0.690000)\n        4)\n      (segment 3496 \n        (point 225.260000 776.470000 -57.920000 0.690000)\n        (point 225.890000 777.810000 -56.150000 0.690000)\n        4)\n      (segment 3497 \n        (point 225.890000 777.810000 -56.150000 0.690000)\n        (point 225.230000 780.640000 -55.700000 0.690000)\n        4)\n      (segment 3498 \n        (point 225.230000 780.640000 -55.700000 0.690000)\n        (point 225.580000 783.110000 -55.700000 0.690000)\n        4)\n      (segment 3499 \n        (point 225.580000 783.110000 -55.700000 0.690000)\n        (point 227.160000 786.470000 -55.700000 0.690000)\n        4)\n      (segment 3500 \n        (point 227.160000 786.470000 -55.700000 0.690000)\n        (point 227.380000 789.510000 -55.150000 0.690000)\n        4)\n      (segment 3501 \n        (point 227.380000 789.510000 -55.150000 0.690000)\n        (point 226.720000 792.330000 -55.030000 0.690000)\n        4)\n      (segment 3502 \n        (point 226.720000 792.330000 -55.030000 0.690000)\n        (point 227.660000 794.350000 -55.030000 0.690000)\n        4)\n      (segment 3503 \n        (point 227.660000 794.350000 -55.030000 0.690000)\n        (point 229.060000 796.460000 -54.550000 0.690000)\n        4)\n      (segment 3504 \n        (point 229.060000 796.460000 -54.550000 0.690000)\n        (point 229.860000 799.040000 -54.550000 0.690000)\n        4))\n    (branch 123 122 \n      (segment 3505 \n        (point 229.860000 799.040000 -54.550000 0.690000)\n        (point 229.270000 802.040000 -54.550000 0.690000)\n        4)\n      (segment 3506 \n        (point 229.270000 802.040000 -54.550000 0.690000)\n        (point 229.500000 805.070000 -55.270000 0.690000)\n        4)\n      (segment 3507 \n        (point 229.500000 805.070000 -55.270000 0.690000)\n        (point 229.730000 808.110000 -55.900000 0.690000)\n        4)\n      (segment 3508 \n        (point 229.730000 808.110000 -55.900000 0.690000)\n        (point 229.070000 810.940000 -56.700000 0.690000)\n        4)\n      (segment 3509 \n        (point 229.070000 810.940000 -56.700000 0.690000)\n        (point 228.090000 813.100000 -57.400000 0.690000)\n        4)\n      (segment 3510 \n        (point 228.090000 813.100000 -57.400000 0.690000)\n        (point 227.740000 816.600000 -57.970000 0.690000)\n        4)\n      (segment 3511 \n        (point 227.740000 816.600000 -57.970000 0.690000)\n        (point 226.360000 820.460000 -58.550000 0.690000)\n        4)\n      (segment 3512 \n        (point 226.360000 820.460000 -58.550000 0.690000)\n        (point 225.880000 824.520000 -58.820000 0.690000)\n        4)\n      (segment 3513 \n        (point 225.880000 824.520000 -58.820000 0.690000)\n        (point 224.690000 829.620000 -58.820000 0.690000)\n        4)\n      (segment 3514 \n        (point 224.690000 829.620000 -58.820000 0.690000)\n        (point 224.470000 832.550000 -58.820000 0.690000)\n        4)\n      (segment 3515 \n        (point 224.470000 832.550000 -58.820000 0.690000)\n        (point 222.200000 836.200000 -58.100000 0.690000)\n        4)\n      (segment 3516 \n        (point 222.200000 836.200000 -58.100000 0.690000)\n        (point 221.940000 837.330000 -58.100000 0.690000)\n        4)\n      (segment 3517 \n        (point 221.940000 837.330000 -58.100000 0.690000)\n        (point 222.930000 841.150000 -58.100000 0.690000)\n        4)\n      (segment 3518 \n        (point 222.930000 841.150000 -58.100000 0.690000)\n        (point 223.030000 844.760000 -58.550000 0.690000)\n        4)\n      (segment 3519 \n        (point 223.030000 844.760000 -58.550000 0.690000)\n        (point 221.520000 849.180000 -58.780000 0.690000)\n        4)\n      (segment 3520 \n        (point 221.520000 849.180000 -58.780000 0.690000)\n        (point 220.990000 851.450000 -59.200000 0.690000)\n        4)\n      (segment 3521 \n        (point 220.990000 851.450000 -59.200000 0.690000)\n        (point 220.770000 854.370000 -60.030000 0.690000)\n        4)\n      (segment 3522 \n        (point 220.770000 854.370000 -60.030000 0.690000)\n        (point 220.770000 854.370000 -60.050000 0.690000)\n        4)\n      (segment 3523 \n        (point 220.770000 854.370000 -60.050000 0.690000)\n        (point 220.860000 857.980000 -60.470000 0.690000)\n        4)\n      (segment 3524 \n        (point 220.860000 857.980000 -60.470000 0.690000)\n        (point 219.930000 861.950000 -59.500000 0.690000)\n        4)\n      (segment 3525 \n        (point 219.930000 861.950000 -59.500000 0.690000)\n        (point 220.040000 865.560000 -58.750000 0.690000)\n        4)\n      (segment 3526 \n        (point 220.040000 865.560000 -58.750000 0.690000)\n        (point 220.850000 868.130000 -57.250000 0.690000)\n        4)\n      (segment 3527 \n        (point 220.850000 868.130000 -57.250000 0.690000)\n        (point 220.670000 872.870000 -56.470000 0.690000)\n        4)\n      (segment 3528 \n        (point 220.670000 872.870000 -56.470000 0.690000)\n        (point 221.350000 876.010000 -58.000000 0.690000)\n        4)\n      (segment 3529 \n        (point 221.350000 876.010000 -58.000000 0.690000)\n        (point 221.770000 880.290000 -58.800000 0.690000)\n        4)\n      (segment 3530 \n        (point 221.770000 880.290000 -58.800000 0.690000)\n        (point 221.940000 881.530000 -59.780000 0.690000)\n        4)\n      (segment 3531 \n        (point 221.940000 881.530000 -59.780000 0.690000)\n        (point 221.590000 885.020000 -59.780000 0.690000)\n        4)\n      (segment 3532 \n        (point 221.590000 885.020000 -59.780000 0.690000)\n        (point 223.300000 887.810000 -59.780000 0.690000)\n        4)\n      (segment 3533 \n        (point 223.300000 887.810000 -59.780000 0.690000)\n        (point 223.350000 889.610000 -58.200000 0.690000)\n        4)\n      (segment 3534 \n        (point 223.350000 889.610000 -58.200000 0.690000)\n        (point 222.380000 891.780000 -56.170000 0.690000)\n        4)\n      (segment 3535 \n        (point 222.380000 891.780000 -56.170000 0.690000)\n        (point 223.580000 892.660000 -55.200000 0.690000)\n        4)\n      (segment 3536 \n        (point 223.580000 892.660000 -55.200000 0.690000)\n        (point 223.810000 895.700000 -54.380000 0.690000)\n        4)\n      (segment 3537 \n        (point 223.810000 895.700000 -54.380000 0.690000)\n        (point 225.460000 896.680000 -53.400000 0.690000)\n        4)\n      (segment 3538 \n        (point 225.460000 896.680000 -53.400000 0.690000)\n        (point 226.530000 898.120000 -52.100000 0.690000)\n        4)\n      (segment 3539 \n        (point 226.530000 898.120000 -52.100000 0.690000)\n        (point 227.170000 899.470000 -50.550000 0.690000)\n        4)\n      (segment 3540 \n        (point 227.170000 899.470000 -50.550000 0.690000)\n        (point 225.150000 901.980000 -49.000000 0.690000)\n        4)\n      (segment 3541 \n        (point 225.150000 901.980000 -49.000000 0.690000)\n        (point 225.070000 904.360000 -47.400000 0.690000)\n        4)\n      (segment 3542 \n        (point 225.070000 904.360000 -47.400000 0.690000)\n        (point 227.800000 906.780000 -45.830000 0.690000)\n        4)\n      (segment 3543 \n        (point 227.800000 906.780000 -45.830000 0.690000)\n        (point 228.880000 908.220000 -45.070000 0.690000)\n        4)\n      (segment 3544 \n        (point 228.880000 908.220000 -45.070000 0.690000)\n        (point 228.660000 911.170000 -45.070000 0.690000)\n        4)\n      (segment 3545 \n        (point 228.660000 911.170000 -45.070000 0.690000)\n        (point 229.860000 912.040000 -43.700000 0.690000)\n        4)\n      (segment 3546 \n        (point 229.860000 912.040000 -43.700000 0.690000)\n        (point 231.580000 914.840000 -43.700000 0.690000)\n        4)\n      (segment 3547 \n        (point 231.580000 914.840000 -43.700000 0.690000)\n        (point 232.510000 916.840000 -42.750000 0.690000)\n        4)\n      (segment 3548 \n        (point 232.510000 916.840000 -42.750000 0.690000)\n        (point 233.900000 918.970000 -42.450000 0.690000)\n        4)\n      (segment 3549 \n        (point 233.900000 918.970000 -42.450000 0.690000)\n        (point 233.690000 921.890000 -42.450000 0.690000)\n        4)\n      (segment 3550 \n        (point 233.690000 921.890000 -42.450000 0.690000)\n        (point 234.270000 925.500000 -40.030000 0.690000)\n        4)\n      (segment 3551 \n        (point 234.270000 925.500000 -40.030000 0.690000)\n        (point 237.170000 929.170000 -41.320000 0.690000)\n        4)\n      (segment 3552 \n        (point 237.170000 929.170000 -41.320000 0.690000)\n        (point 238.600000 933.090000 -41.750000 0.690000)\n        4)\n      (segment 3553 \n        (point 238.600000 933.090000 -41.750000 0.690000)\n        (point 238.830000 936.130000 -42.670000 0.690000)\n        4)\n      (segment 3554 \n        (point 238.830000 936.130000 -42.670000 0.690000)\n        (point 241.880000 939.230000 -43.530000 0.690000)\n        4)\n      (segment 3555 \n        (point 241.880000 939.230000 -43.530000 0.690000)\n        (point 240.640000 942.520000 -43.900000 0.690000)\n        4)\n      (segment 3556 \n        (point 240.640000 942.520000 -43.900000 0.690000)\n        (point 240.420000 945.470000 -43.900000 0.690000)\n        4)\n      (segment 3557 \n        (point 240.420000 945.470000 -43.900000 0.690000)\n        (point 241.800000 947.570000 -42.900000 0.690000)\n        4)\n      (segment 3558 \n        (point 241.800000 947.570000 -42.900000 0.690000)\n        (point 242.350000 951.290000 -42.900000 0.690000)\n        4)\n      (segment 3559 \n        (point 242.350000 951.290000 -42.900000 0.690000)\n        (point 244.500000 954.180000 -44.100000 0.690000)\n        4)\n      (segment 3560 \n        (point 244.500000 954.180000 -44.100000 0.690000)\n        (point 243.700000 957.570000 -42.850000 0.690000)\n        4)\n      (segment 3561 \n        (point 243.700000 957.570000 -42.850000 0.690000)\n        (point 243.700000 957.570000 -42.900000 0.690000)\n        4)\n      (segment 3562 \n        (point 243.700000 957.570000 -42.900000 0.690000)\n        (point 242.730000 959.730000 -41.730000 0.690000)\n        4)\n      (segment 3563 \n        (point 242.730000 959.730000 -41.730000 0.690000)\n        (point 242.690000 963.910000 -40.650000 0.690000)\n        4)\n      (segment 3564 \n        (point 242.690000 963.910000 -40.650000 0.690000)\n        (point 243.590000 970.090000 -41.800000 0.690000)\n        4)\n      (segment 3565 \n        (point 243.590000 970.090000 -41.800000 0.690000)\n        (point 244.270000 973.230000 -41.300000 0.690000)\n        4)\n      (segment 3566 \n        (point 244.270000 973.230000 -41.300000 0.690000)\n        (point 244.500000 976.270000 -42.370000 0.690000)\n        4)\n      (segment 3567 \n        (point 244.500000 976.270000 -42.370000 0.690000)\n        (point 243.080000 978.330000 -42.370000 0.690000)\n        4)\n      (segment 3568 \n        (point 243.080000 978.330000 -42.370000 0.690000)\n        (point 244.200000 981.580000 -41.950000 0.690000)\n        4)\n      (segment 3569 \n        (point 244.200000 981.580000 -41.950000 0.690000)\n        (point 244.620000 985.860000 -41.950000 0.690000)\n        4)\n      (segment 3570 \n        (point 244.620000 985.860000 -41.950000 0.690000)\n        (point 243.240000 989.710000 -41.950000 0.690000)\n        4)\n      (segment 3571 \n        (point 243.240000 989.710000 -41.950000 0.690000)\n        (point 243.470000 992.750000 -41.950000 0.690000)\n        4)\n      (segment 3572 \n        (point 243.470000 992.750000 -41.950000 0.690000)\n        (point 243.870000 997.030000 -42.350000 0.690000)\n        4)\n      (segment 3573 \n        (point 243.870000 997.030000 -42.350000 0.690000)\n        (point 244.240000 999.500000 -43.000000 0.690000)\n        4)\n      (segment 3574 \n        (point 244.240000 999.500000 -43.000000 0.690000)\n        (point 244.390000 1004.900000 -43.000000 0.690000)\n        4)\n      (segment 3575 \n        (point 244.390000 1004.900000 -43.000000 0.690000)\n        (point 245.780000 1007.030000 -43.900000 0.690000)\n        4)\n      (segment 3576 \n        (point 245.780000 1007.030000 -43.900000 0.690000)\n        (point 246.190000 1011.310000 -44.700000 0.690000)\n        4)\n      (segment 3577 \n        (point 246.190000 1011.310000 -44.700000 0.690000)\n        (point 246.420000 1014.350000 -45.170000 0.690000)\n        4))\n    (branch 124 123 \n      (segment 3578 \n        (point 246.420000 1014.350000 -45.170000 0.690000)\n        (point 245.440000 1016.500000 -43.550000 0.690000)\n        4)\n      (segment 3579 \n        (point 245.440000 1016.500000 -43.550000 0.690000)\n        (point 245.440000 1016.500000 -43.530000 0.690000)\n        4)\n      (segment 3580 \n        (point 245.440000 1016.500000 -43.530000 0.690000)\n        (point 243.120000 1018.350000 -42.050000 0.690000)\n        4)\n      (segment 3581 \n        (point 243.120000 1018.350000 -42.050000 0.690000)\n        (point 243.120000 1018.350000 -42.100000 0.690000)\n        4)\n      (segment 3582 \n        (point 243.120000 1018.350000 -42.100000 0.690000)\n        (point 243.670000 1022.060000 -40.850000 0.460000)\n        4)\n      (segment 3583 \n        (point 243.670000 1022.060000 -40.850000 0.460000)\n        (point 243.590000 1024.430000 -39.700000 0.460000)\n        4)\n      (segment 3584 \n        (point 243.590000 1024.430000 -39.700000 0.460000)\n        (point 242.660000 1028.390000 -39.150000 0.460000)\n        4)\n      (segment 3585 \n        (point 242.660000 1028.390000 -39.150000 0.460000)\n        (point 244.040000 1030.500000 -39.150000 0.460000)\n        4)\n      (segment 3586 \n        (point 244.040000 1030.500000 -39.150000 0.460000)\n        (point 242.930000 1033.230000 -38.500000 0.460000)\n        4)\n      (segment 3587 \n        (point 242.930000 1033.230000 -38.500000 0.460000)\n        (point 242.890000 1037.400000 -37.150000 0.460000)\n        4)\n      (segment 3588 \n        (point 242.890000 1037.400000 -37.150000 0.460000)\n        (point 243.260000 1039.880000 -37.150000 0.460000)\n        4)\n      (segment 3589 \n        (point 243.260000 1039.880000 -37.150000 0.460000)\n        (point 244.910000 1040.860000 -37.150000 0.460000)\n        4))\n    (branch 125 124 \n      (segment 3590 \n        (point 244.910000 1040.860000 -37.150000 0.460000)\n        (point 243.220000 1044.050000 -35.800000 0.230000)\n        4)\n      (segment 3591 \n        (point 243.220000 1044.050000 -35.800000 0.230000)\n        (point 243.150000 1046.420000 -35.150000 0.230000)\n        4)\n      (segment 3592 \n        (point 243.150000 1046.420000 -35.150000 0.230000)\n        (point 243.950000 1049.000000 -33.950000 0.230000)\n        4)\n      (segment 3593 \n        (point 243.950000 1049.000000 -33.950000 0.230000)\n        (point 243.870000 1051.370000 -33.950000 0.230000)\n        4)\n      (segment 3594 \n        (point 243.870000 1051.370000 -33.950000 0.230000)\n        (point 242.490000 1055.220000 -33.950000 0.230000)\n        4)\n      (segment 3595 \n        (point 242.490000 1055.220000 -33.950000 0.230000)\n        (point 241.830000 1058.060000 -32.750000 0.230000)\n        4)\n      (segment 3596 \n        (point 241.830000 1058.060000 -32.750000 0.230000)\n        (point 241.610000 1060.990000 -31.920000 0.230000)\n        4)\n      (segment 3597 \n        (point 241.610000 1060.990000 -31.920000 0.230000)\n        (point 240.320000 1062.480000 -34.100000 0.230000)\n        4))\n    (branch 126 125 \n      (segment 3598 \n        (point 240.320000 1062.480000 -34.100000 0.230000)\n        (point 236.470000 1062.770000 -34.100000 0.230000)\n        4)\n      (segment 3599 \n        (point 236.470000 1062.770000 -34.100000 0.230000)\n        (point 233.350000 1062.030000 -32.250000 0.230000)\n        4)\n      (segment 3600 \n        (point 233.350000 1062.030000 -32.250000 0.230000)\n        (point 231.130000 1061.510000 -31.480000 0.230000)\n        4)\n      (segment 3601 \n        (point 231.130000 1061.510000 -31.480000 0.230000)\n        (point 229.830000 1063.000000 -31.950000 0.230000)\n        4)\n      (segment 3602 \n        (point 229.830000 1063.000000 -31.950000 0.230000)\n        (point 227.200000 1064.180000 -30.730000 0.230000)\n        4)\n      (segment 3603 \n        (point 227.200000 1064.180000 -30.730000 0.230000)\n        (point 222.970000 1066.170000 -29.250000 0.230000)\n        4)\n      (segment 3604 \n        (point 222.970000 1066.170000 -29.250000 0.230000)\n        (point 221.490000 1066.420000 -27.900000 0.230000)\n        4)\n      (segment 3605 \n        (point 221.490000 1066.420000 -27.900000 0.230000)\n        (point 217.980000 1067.270000 -31.520000 0.230000)\n        4)\n      (segment 3606 \n        (point 217.980000 1067.270000 -31.520000 0.230000)\n        (point 217.960000 1067.400000 -31.550000 0.230000)\n        4)\n      (segment 3607 \n        (point 217.960000 1067.400000 -31.550000 0.230000)\n        (point 214.840000 1066.660000 -32.670000 0.230000)\n        4)\n      (segment 3608 \n        (point 214.840000 1066.660000 -32.670000 0.230000)\n        (point 211.770000 1067.730000 -33.970000 0.230000)\n        4))\n    (branch 127 125 \n      (segment 3609 \n        (point 240.320000 1062.480000 -34.100000 0.230000)\n        (point 242.340000 1065.940000 -32.400000 0.230000)\n        4)\n      (segment 3610 \n        (point 242.340000 1065.940000 -32.400000 0.230000)\n        (point 243.220000 1066.150000 -32.170000 0.230000)\n        4)\n      (segment 3611 \n        (point 243.220000 1066.150000 -32.170000 0.230000)\n        (point 243.540000 1066.810000 -28.720000 0.230000)\n        4))\n    (branch 128 127 \n      (segment 3612 \n        (point 243.540000 1066.810000 -28.720000 0.230000)\n        (point 246.850000 1068.780000 -28.720000 0.230000)\n        4)\n      (segment 3613 \n        (point 246.850000 1068.780000 -28.720000 0.230000)\n        (point 248.910000 1068.080000 -27.600000 0.230000)\n        4))\n    (branch 129 128 \n      (segment 3614 \n        (point 248.910000 1068.080000 -27.600000 0.230000)\n        (point 248.410000 1066.170000 -26.250000 0.230000)\n        4)\n      (segment 3615 \n        (point 248.410000 1066.170000 -26.250000 0.230000)\n        (point 247.470000 1064.150000 -24.080000 0.230000)\n        4)\n      (segment 3616 \n        (point 247.470000 1064.150000 -24.080000 0.230000)\n        (point 247.470000 1064.150000 -24.100000 0.230000)\n        4)\n      (segment 3617 \n        (point 247.470000 1064.150000 -24.100000 0.230000)\n        (point 248.630000 1063.240000 -21.670000 0.230000)\n        4)\n      (segment 3618 \n        (point 248.630000 1063.240000 -21.670000 0.230000)\n        (point 248.630000 1063.240000 -21.700000 0.230000)\n        4)\n      (segment 3619 \n        (point 248.630000 1063.240000 -21.700000 0.230000)\n        (point 247.370000 1060.540000 -19.880000 0.230000)\n        4)\n      (segment 3620 \n        (point 247.370000 1060.540000 -19.880000 0.230000)\n        (point 247.050000 1059.880000 -18.700000 0.230000)\n        4))\n    (branch 130 128 \n      (segment 3621 \n        (point 248.910000 1068.080000 -27.600000 0.230000)\n        (point 251.890000 1069.370000 -26.200000 0.230000)\n        4)\n      (segment 3622 \n        (point 251.890000 1069.370000 -26.200000 0.230000)\n        (point 255.470000 1070.210000 -25.520000 0.230000)\n        4)\n      (segment 3623 \n        (point 255.470000 1070.210000 -25.520000 0.230000)\n        (point 257.430000 1071.860000 -23.600000 0.230000)\n        4)\n      (segment 3624 \n        (point 257.430000 1071.860000 -23.600000 0.230000)\n        (point 257.430000 1071.860000 -23.570000 0.230000)\n        4))\n    (branch 131 127 \n      (segment 3625 \n        (point 243.540000 1066.810000 -28.720000 0.230000)\n        (point 241.980000 1069.440000 -28.720000 0.230000)\n        4)\n      (segment 3626 \n        (point 241.980000 1069.440000 -28.720000 0.230000)\n        (point 240.300000 1072.630000 -28.720000 0.230000)\n        4))\n    (branch 132 124 \n      (segment 3627 \n        (point 244.910000 1040.860000 -37.150000 0.460000)\n        (point 245.990000 1042.310000 -37.150000 0.230000)\n        4)\n      (segment 3628 \n        (point 245.990000 1042.310000 -37.150000 0.230000)\n        (point 245.330000 1045.140000 -37.150000 0.230000)\n        4)\n      (segment 3629 \n        (point 245.330000 1045.140000 -37.150000 0.230000)\n        (point 248.630000 1047.110000 -37.150000 0.230000)\n        4)\n      (segment 3630 \n        (point 248.630000 1047.110000 -37.150000 0.230000)\n        (point 249.760000 1050.360000 -36.150000 0.230000)\n        4)\n      (segment 3631 \n        (point 249.760000 1050.360000 -36.150000 0.230000)\n        (point 251.730000 1052.010000 -35.570000 0.230000)\n        4)\n      (segment 3632 \n        (point 251.730000 1052.010000 -35.570000 0.230000)\n        (point 253.870000 1054.910000 -34.850000 0.230000)\n        4)\n      (segment 3633 \n        (point 253.870000 1054.910000 -34.850000 0.230000)\n        (point 255.000000 1058.150000 -34.250000 0.230000)\n        4)\n      (segment 3634 \n        (point 255.000000 1058.150000 -34.250000 0.230000)\n        (point 256.650000 1059.130000 -36.200000 0.230000)\n        4)\n      (segment 3635 \n        (point 256.650000 1059.130000 -36.200000 0.230000)\n        (point 258.480000 1061.360000 -36.200000 0.230000)\n        4)\n      (segment 3636 \n        (point 258.480000 1061.360000 -36.200000 0.230000)\n        (point 259.690000 1062.240000 -38.150000 0.230000)\n        4)\n      (segment 3637 \n        (point 259.690000 1062.240000 -38.150000 0.230000)\n        (point 259.920000 1065.290000 -39.520000 0.230000)\n        4)\n      (segment 3638 \n        (point 259.920000 1065.290000 -39.520000 0.230000)\n        (point 261.120000 1066.160000 -39.820000 0.230000)\n        4)\n      (segment 3639 \n        (point 261.120000 1066.160000 -39.820000 0.230000)\n        (point 263.680000 1067.360000 -41.380000 0.230000)\n        4)\n      (segment 3640 \n        (point 263.680000 1067.360000 -41.380000 0.230000)\n        (point 264.880000 1068.230000 -42.400000 0.230000)\n        4)\n      (segment 3641 \n        (point 264.880000 1068.230000 -42.400000 0.230000)\n        (point 264.880000 1068.230000 -42.420000 0.230000)\n        4)\n      (segment 3642 \n        (point 264.880000 1068.230000 -42.420000 0.230000)\n        (point 266.670000 1068.650000 -43.000000 0.230000)\n        4)\n      (segment 3643 \n        (point 266.670000 1068.650000 -43.000000 0.230000)\n        (point 268.320000 1069.630000 -43.000000 0.230000)\n        4)\n      (segment 3644 \n        (point 268.320000 1069.630000 -43.000000 0.230000)\n        (point 269.930000 1068.820000 -44.430000 0.230000)\n        4)\n      (segment 3645 \n        (point 269.930000 1068.820000 -44.430000 0.230000)\n        (point 271.580000 1069.800000 -45.050000 0.230000)\n        4)\n      (segment 3646 \n        (point 271.580000 1069.800000 -45.050000 0.230000)\n        (point 273.190000 1068.990000 -46.050000 0.230000)\n        4)\n      (segment 3647 \n        (point 273.190000 1068.990000 -46.050000 0.230000)\n        (point 273.190000 1068.990000 -46.080000 0.230000)\n        4)\n      (segment 3648 \n        (point 273.190000 1068.990000 -46.080000 0.230000)\n        (point 274.530000 1069.310000 -47.130000 0.230000)\n        4)\n      (segment 3649 \n        (point 274.530000 1069.310000 -47.130000 0.230000)\n        (point 274.530000 1069.310000 -47.150000 0.230000)\n        4)\n      (segment 3650 \n        (point 274.530000 1069.310000 -47.150000 0.230000)\n        (point 276.940000 1071.060000 -47.170000 0.230000)\n        4)\n      (segment 3651 \n        (point 276.940000 1071.060000 -47.170000 0.230000)\n        (point 278.280000 1071.370000 -48.950000 0.230000)\n        4)\n      (segment 3652 \n        (point 278.280000 1071.370000 -48.950000 0.230000)\n        (point 279.130000 1069.780000 -50.150000 0.230000)\n        4)\n      (segment 3653 \n        (point 279.130000 1069.780000 -50.150000 0.230000)\n        (point 281.050000 1069.640000 -51.670000 0.230000)\n        4)\n      (segment 3654 \n        (point 281.050000 1069.640000 -51.670000 0.230000)\n        (point 282.430000 1071.750000 -53.620000 0.230000)\n        4)\n      (segment 3655 \n        (point 282.430000 1071.750000 -53.620000 0.230000)\n        (point 282.430000 1071.750000 -53.670000 0.230000)\n        4)\n      (segment 3656 \n        (point 282.430000 1071.750000 -53.670000 0.230000)\n        (point 283.720000 1070.260000 -55.720000 0.230000)\n        4)\n      (segment 3657 \n        (point 283.720000 1070.260000 -55.720000 0.230000)\n        (point 284.750000 1069.900000 -58.470000 0.230000)\n        4)\n      (segment 3658 \n        (point 284.750000 1069.900000 -58.470000 0.230000)\n        (point 284.750000 1069.900000 -58.650000 0.230000)\n        4))\n    (branch 133 123 \n      (segment 3659 \n        (point 246.420000 1014.350000 -45.170000 0.690000)\n        (point 248.120000 1017.130000 -43.800000 0.230000)\n        4)\n      (segment 3660 \n        (point 248.120000 1017.130000 -43.800000 0.230000)\n        (point 249.240000 1020.380000 -43.800000 0.230000)\n        4)\n      (segment 3661 \n        (point 249.240000 1020.380000 -43.800000 0.230000)\n        (point 251.210000 1022.030000 -43.880000 0.230000)\n        4)\n      (segment 3662 \n        (point 251.210000 1022.030000 -43.880000 0.230000)\n        (point 254.780000 1022.870000 -44.300000 0.230000)\n        4)\n      (segment 3663 \n        (point 254.780000 1022.870000 -44.300000 0.230000)\n        (point 254.780000 1022.870000 -44.320000 0.230000)\n        4)\n      (segment 3664 \n        (point 254.780000 1022.870000 -44.320000 0.230000)\n        (point 257.090000 1021.030000 -44.700000 0.230000)\n        4)\n      (segment 3665 \n        (point 257.090000 1021.030000 -44.700000 0.230000)\n        (point 257.090000 1021.030000 -44.800000 0.230000)\n        4)\n      (segment 3666 \n        (point 257.090000 1021.030000 -44.800000 0.230000)\n        (point 259.470000 1020.980000 -44.900000 0.230000)\n        4)\n      (segment 3667 \n        (point 259.470000 1020.980000 -44.900000 0.230000)\n        (point 259.470000 1020.980000 -45.150000 0.230000)\n        4)\n      (segment 3668 \n        (point 259.470000 1020.980000 -45.150000 0.230000)\n        (point 261.570000 1022.080000 -47.130000 0.230000)\n        4)\n      (segment 3669 \n        (point 261.570000 1022.080000 -47.130000 0.230000)\n        (point 261.570000 1022.080000 -47.200000 0.230000)\n        4))\n    (branch 134 122 \n      (segment 3670 \n        (point 229.860000 799.040000 -54.550000 0.690000)\n        (point 231.440000 802.400000 -54.550000 0.690000)\n        4)\n      (segment 3671 \n        (point 231.440000 802.400000 -54.550000 0.690000)\n        (point 233.800000 802.340000 -53.300000 0.690000)\n        4)\n      (segment 3672 \n        (point 233.800000 802.340000 -53.300000 0.690000)\n        (point 234.880000 803.810000 -52.500000 0.690000)\n        4)\n      (segment 3673 \n        (point 234.880000 803.810000 -52.500000 0.690000)\n        (point 234.880000 803.810000 -52.550000 0.690000)\n        4)\n      (segment 3674 \n        (point 234.880000 803.810000 -52.550000 0.690000)\n        (point 235.100000 806.840000 -52.550000 0.690000)\n        4)\n      (segment 3675 \n        (point 235.100000 806.840000 -52.550000 0.690000)\n        (point 237.080000 808.500000 -51.550000 0.690000)\n        4)\n      (segment 3676 \n        (point 237.080000 808.500000 -51.550000 0.690000)\n        (point 238.010000 810.510000 -50.400000 0.690000)\n        4)\n      (segment 3677 \n        (point 238.010000 810.510000 -50.400000 0.690000)\n        (point 238.190000 811.740000 -49.600000 0.690000)\n        4)\n      (segment 3678 \n        (point 238.190000 811.740000 -49.600000 0.690000)\n        (point 237.800000 813.440000 -48.750000 0.690000)\n        4)\n      (segment 3679 \n        (point 237.800000 813.440000 -48.750000 0.690000)\n        (point 241.740000 816.750000 -48.750000 0.690000)\n        4)\n      (segment 3680 \n        (point 241.740000 816.750000 -48.750000 0.690000)\n        (point 242.940000 817.620000 -47.670000 0.690000)\n        4)\n      (segment 3681 \n        (point 242.940000 817.620000 -47.670000 0.690000)\n        (point 244.380000 821.550000 -47.670000 0.690000)\n        4)\n      (segment 3682 \n        (point 244.380000 821.550000 -47.670000 0.690000)\n        (point 246.030000 822.530000 -47.670000 0.690000)\n        4)\n      (segment 3683 \n        (point 246.030000 822.530000 -47.670000 0.690000)\n        (point 246.840000 825.110000 -47.670000 0.690000)\n        4)\n      (segment 3684 \n        (point 246.840000 825.110000 -47.670000 0.690000)\n        (point 247.650000 827.700000 -47.670000 0.690000)\n        4)\n      (segment 3685 \n        (point 247.650000 827.700000 -47.670000 0.690000)\n        (point 250.380000 830.120000 -47.670000 0.690000)\n        4)\n      (segment 3686 \n        (point 250.380000 830.120000 -47.670000 0.690000)\n        (point 250.340000 834.300000 -47.670000 0.690000)\n        4)\n      (segment 3687 \n        (point 250.340000 834.300000 -47.670000 0.690000)\n        (point 251.910000 837.650000 -47.570000 0.690000)\n        4)\n      (segment 3688 \n        (point 251.910000 837.650000 -47.570000 0.690000)\n        (point 252.500000 843.160000 -47.550000 0.690000)\n        4)\n      (segment 3689 \n        (point 252.500000 843.160000 -47.550000 0.690000)\n        (point 255.100000 846.160000 -47.150000 0.690000)\n        4)\n      (segment 3690 \n        (point 255.100000 846.160000 -47.150000 0.690000)\n        (point 255.460000 848.630000 -46.650000 0.690000)\n        4)\n      (segment 3691 \n        (point 255.460000 848.630000 -46.650000 0.690000)\n        (point 255.960000 850.530000 -46.380000 0.690000)\n        4)\n      (segment 3692 \n        (point 255.960000 850.530000 -46.380000 0.690000)\n        (point 256.450000 852.450000 -45.650000 0.690000)\n        4)\n      (segment 3693 \n        (point 256.450000 852.450000 -45.650000 0.690000)\n        (point 258.870000 854.210000 -45.650000 0.690000)\n        4)\n      (segment 3694 \n        (point 258.870000 854.210000 -45.650000 0.690000)\n        (point 259.360000 856.120000 -44.430000 0.690000)\n        4)\n      (segment 3695 \n        (point 259.360000 856.120000 -44.430000 0.690000)\n        (point 259.360000 856.120000 -44.450000 0.690000)\n        4)\n      (segment 3696 \n        (point 259.360000 856.120000 -44.450000 0.690000)\n        (point 259.590000 859.150000 -43.580000 0.690000)\n        4)\n      (segment 3697 \n        (point 259.590000 859.150000 -43.580000 0.690000)\n        (point 262.440000 861.020000 -43.150000 0.690000)\n        4)\n      (segment 3698 \n        (point 262.440000 861.020000 -43.150000 0.690000)\n        (point 262.440000 861.020000 -43.180000 0.690000)\n        4)\n      (segment 3699 \n        (point 262.440000 861.020000 -43.180000 0.690000)\n        (point 261.330000 863.740000 -43.100000 0.690000)\n        4)\n      (segment 3700 \n        (point 261.330000 863.740000 -43.100000 0.690000)\n        (point 263.180000 865.970000 -42.570000 0.690000)\n        4)\n      (segment 3701 \n        (point 263.180000 865.970000 -42.570000 0.690000)\n        (point 263.180000 865.970000 -42.600000 0.690000)\n        4)\n      (segment 3702 \n        (point 263.180000 865.970000 -42.600000 0.690000)\n        (point 263.020000 870.700000 -43.500000 0.690000)\n        4)\n      (segment 3703 \n        (point 263.020000 870.700000 -43.500000 0.690000)\n        (point 265.740000 873.130000 -42.920000 0.690000)\n        4)\n      (segment 3704 \n        (point 265.740000 873.130000 -42.920000 0.690000)\n        (point 266.090000 875.610000 -42.050000 0.690000)\n        4)\n      (segment 3705 \n        (point 266.090000 875.610000 -42.050000 0.690000)\n        (point 266.910000 878.180000 -41.200000 0.690000)\n        4)\n      (segment 3706 \n        (point 266.910000 878.180000 -41.200000 0.690000)\n        (point 269.770000 880.060000 -40.480000 0.690000)\n        4)\n      (segment 3707 \n        (point 269.770000 880.060000 -40.480000 0.690000)\n        (point 269.770000 880.060000 -40.500000 0.690000)\n        4)\n      (segment 3708 \n        (point 269.770000 880.060000 -40.500000 0.690000)\n        (point 270.900000 883.300000 -39.650000 0.690000)\n        4)\n      (segment 3709 \n        (point 270.900000 883.300000 -39.650000 0.690000)\n        (point 273.760000 885.170000 -39.200000 0.690000)\n        4)\n      (segment 3710 \n        (point 273.760000 885.170000 -39.200000 0.690000)\n        (point 276.350000 888.160000 -39.020000 0.690000)\n        4)\n      (segment 3711 \n        (point 276.350000 888.160000 -39.020000 0.690000)\n        (point 278.410000 893.420000 -39.020000 0.690000)\n        4)\n      (segment 3712 \n        (point 278.410000 893.420000 -39.020000 0.690000)\n        (point 278.920000 895.330000 -37.700000 0.690000)\n        4)\n      (segment 3713 \n        (point 278.920000 895.330000 -37.700000 0.690000)\n        (point 281.640000 897.760000 -38.030000 0.690000)\n        4)\n      (segment 3714 \n        (point 281.640000 897.760000 -38.030000 0.690000)\n        (point 283.600000 899.430000 -38.030000 0.690000)\n        4)\n      (segment 3715 \n        (point 283.600000 899.430000 -38.030000 0.690000)\n        (point 284.330000 904.370000 -38.030000 0.690000)\n        4)\n      (segment 3716 \n        (point 284.330000 904.370000 -38.030000 0.690000)\n        (point 285.770000 908.290000 -38.030000 0.690000)\n        4)\n      (segment 3717 \n        (point 285.770000 908.290000 -38.030000 0.690000)\n        (point 286.890000 911.530000 -36.100000 0.690000)\n        4)\n      (segment 3718 \n        (point 286.890000 911.530000 -36.100000 0.690000)\n        (point 286.720000 916.270000 -36.100000 0.690000)\n        4)\n      (segment 3719 \n        (point 286.720000 916.270000 -36.100000 0.690000)\n        (point 287.220000 918.180000 -37.200000 0.690000)\n        4)\n      (segment 3720 \n        (point 287.220000 918.180000 -37.200000 0.690000)\n        (point 287.220000 918.180000 -37.170000 0.690000)\n        4)\n      (segment 3721 \n        (point 287.220000 918.180000 -37.170000 0.690000)\n        (point 287.580000 920.650000 -38.350000 0.690000)\n        4)\n      (segment 3722 \n        (point 287.580000 920.650000 -38.350000 0.690000)\n        (point 287.580000 920.650000 -38.380000 0.690000)\n        4)\n      (segment 3723 \n        (point 287.580000 920.650000 -38.380000 0.690000)\n        (point 288.130000 924.360000 -38.700000 0.690000)\n        4)\n      (segment 3724 \n        (point 288.130000 924.360000 -38.700000 0.690000)\n        (point 288.040000 926.730000 -38.900000 0.690000)\n        4)\n      (segment 3725 \n        (point 288.040000 926.730000 -38.900000 0.690000)\n        (point 289.170000 929.990000 -38.900000 0.690000)\n        4)\n      (segment 3726 \n        (point 289.170000 929.990000 -38.900000 0.690000)\n        (point 288.950000 932.920000 -39.600000 0.690000)\n        4)\n      (segment 3727 \n        (point 288.950000 932.920000 -39.600000 0.690000)\n        (point 291.100000 935.810000 -40.450000 0.690000)\n        4)\n      (segment 3728 \n        (point 291.100000 935.810000 -40.450000 0.690000)\n        (point 293.120000 939.270000 -41.000000 0.690000)\n        4)\n      (segment 3729 \n        (point 293.120000 939.270000 -41.000000 0.690000)\n        (point 295.080000 940.920000 -40.480000 0.690000)\n        4)\n      (segment 3730 \n        (point 295.080000 940.920000 -40.480000 0.690000)\n        (point 295.080000 940.920000 -40.500000 0.690000)\n        4)\n      (segment 3731 \n        (point 295.080000 940.920000 -40.500000 0.690000)\n        (point 295.310000 943.960000 -40.500000 0.690000)\n        4)\n      (segment 3732 \n        (point 295.310000 943.960000 -40.500000 0.690000)\n        (point 295.850000 947.670000 -40.500000 0.690000)\n        4)\n      (segment 3733 \n        (point 295.850000 947.670000 -40.500000 0.690000)\n        (point 298.450000 950.670000 -40.500000 0.690000)\n        4)\n      (segment 3734 \n        (point 298.450000 950.670000 -40.500000 0.690000)\n        (point 299.700000 953.350000 -39.720000 0.690000)\n        4)\n      (segment 3735 \n        (point 299.700000 953.350000 -39.720000 0.690000)\n        (point 301.320000 958.520000 -38.850000 0.690000)\n        4)\n      (segment 3736 \n        (point 301.320000 958.520000 -38.850000 0.690000)\n        (point 302.720000 960.620000 -38.570000 0.690000)\n        4)\n      (segment 3737 \n        (point 302.720000 960.620000 -38.570000 0.690000)\n        (point 304.860000 963.520000 -38.570000 0.690000)\n        4)\n      (segment 3738 \n        (point 304.860000 963.520000 -38.570000 0.690000)\n        (point 307.190000 967.640000 -37.580000 0.690000)\n        4)\n      (segment 3739 \n        (point 307.190000 967.640000 -37.580000 0.690000)\n        (point 308.450000 970.330000 -36.400000 0.690000)\n        4)\n      (segment 3740 \n        (point 308.450000 970.330000 -36.400000 0.690000)\n        (point 308.100000 973.840000 -35.150000 0.690000)\n        4)\n      (segment 3741 \n        (point 308.100000 973.840000 -35.150000 0.690000)\n        (point 308.510000 978.110000 -36.880000 0.690000)\n        4)\n      (segment 3742 \n        (point 308.510000 978.110000 -36.880000 0.690000)\n        (point 309.500000 981.930000 -35.900000 0.690000)\n        4)\n      (segment 3743 \n        (point 309.500000 981.930000 -35.900000 0.690000)\n        (point 309.420000 984.300000 -34.700000 0.690000)\n        4)\n      (segment 3744 \n        (point 309.420000 984.300000 -34.700000 0.690000)\n        (point 311.510000 985.380000 -33.550000 0.690000)\n        4)\n      (segment 3745 \n        (point 311.510000 985.380000 -33.550000 0.690000)\n        (point 313.220000 988.170000 -32.500000 0.690000)\n        4)\n      (segment 3746 \n        (point 313.220000 988.170000 -32.500000 0.690000)\n        (point 313.850000 989.510000 -31.800000 0.690000)\n        4)\n      (segment 3747 \n        (point 313.850000 989.510000 -31.800000 0.690000)\n        (point 312.390000 995.740000 -31.250000 0.690000)\n        4)\n      (segment 3748 \n        (point 312.390000 995.740000 -31.250000 0.690000)\n        (point 313.950000 999.090000 -31.800000 0.690000)\n        4))\n    (branch 135 134 \n      (segment 3749 \n        (point 313.950000 999.090000 -31.800000 0.690000)\n        (point 313.700000 1000.240000 -30.570000 0.460000)\n        4)\n      (segment 3750 \n        (point 313.700000 1000.240000 -30.570000 0.460000)\n        (point 313.890000 1001.480000 -29.170000 0.460000)\n        4)\n      (segment 3751 \n        (point 313.890000 1001.480000 -29.170000 0.460000)\n        (point 312.910000 1003.640000 -27.380000 0.460000)\n        4)\n      (segment 3752 \n        (point 312.910000 1003.640000 -27.380000 0.460000)\n        (point 313.900000 1007.450000 -26.770000 0.460000)\n        4)\n      (segment 3753 \n        (point 313.900000 1007.450000 -26.770000 0.460000)\n        (point 315.030000 1010.700000 -26.150000 0.460000)\n        4)\n      (segment 3754 \n        (point 315.030000 1010.700000 -26.150000 0.460000)\n        (point 314.680000 1014.200000 -25.230000 0.460000)\n        4)\n      (segment 3755 \n        (point 314.680000 1014.200000 -25.230000 0.460000)\n        (point 314.910000 1017.240000 -24.400000 0.460000)\n        4)\n      (segment 3756 \n        (point 314.910000 1017.240000 -24.400000 0.460000)\n        (point 315.890000 1021.060000 -24.400000 0.460000)\n        4))\n    (branch 136 135 \n      (segment 3757 \n        (point 315.890000 1021.060000 -24.400000 0.460000)\n        (point 318.180000 1023.380000 -24.400000 0.460000)\n        4)\n      (segment 3758 \n        (point 318.180000 1023.380000 -24.400000 0.460000)\n        (point 320.460000 1025.710000 -24.380000 0.460000)\n        4)\n      (segment 3759 \n        (point 320.460000 1025.710000 -24.380000 0.460000)\n        (point 321.000000 1029.420000 -24.350000 0.460000)\n        4)\n      (segment 3760 \n        (point 321.000000 1029.420000 -24.350000 0.460000)\n        (point 322.390000 1031.540000 -23.670000 0.460000)\n        4)\n      (segment 3761 \n        (point 322.390000 1031.540000 -23.670000 0.460000)\n        (point 323.690000 1036.020000 -22.600000 0.460000)\n        4)\n      (segment 3762 \n        (point 323.690000 1036.020000 -22.600000 0.460000)\n        (point 323.610000 1038.390000 -21.600000 0.460000)\n        4)\n      (segment 3763 \n        (point 323.610000 1038.390000 -21.600000 0.460000)\n        (point 326.160000 1039.590000 -20.920000 0.460000)\n        4)\n      (segment 3764 \n        (point 326.160000 1039.590000 -20.920000 0.460000)\n        (point 327.600000 1043.500000 -20.920000 0.460000)\n        4)\n      (segment 3765 \n        (point 327.600000 1043.500000 -20.920000 0.460000)\n        (point 328.350000 1044.280000 -19.700000 0.460000)\n        4)\n      (segment 3766 \n        (point 328.350000 1044.280000 -19.700000 0.460000)\n        (point 330.900000 1045.480000 -18.500000 0.460000)\n        4)\n      (segment 3767 \n        (point 330.900000 1045.480000 -18.500000 0.460000)\n        (point 331.710000 1048.050000 -17.450000 0.460000)\n        4)\n      (segment 3768 \n        (point 331.710000 1048.050000 -17.450000 0.460000)\n        (point 334.000000 1050.380000 -16.800000 0.460000)\n        4)\n      (segment 3769 \n        (point 334.000000 1050.380000 -16.800000 0.460000)\n        (point 335.010000 1050.020000 -16.730000 0.460000)\n        4)\n      (segment 3770 \n        (point 335.010000 1050.020000 -16.730000 0.460000)\n        (point 337.250000 1050.550000 -16.070000 0.460000)\n        4)\n      (segment 3771 \n        (point 337.250000 1050.550000 -16.070000 0.460000)\n        (point 340.430000 1053.080000 -16.900000 0.460000)\n        4)\n      (segment 3772 \n        (point 340.430000 1053.080000 -16.900000 0.460000)\n        (point 340.430000 1053.080000 -16.880000 0.460000)\n        4)\n      (segment 3773 \n        (point 340.430000 1053.080000 -16.880000 0.460000)\n        (point 340.840000 1057.360000 -15.570000 0.460000)\n        4)\n      (segment 3774 \n        (point 340.840000 1057.360000 -15.570000 0.460000)\n        (point 342.620000 1057.780000 -15.570000 0.460000)\n        4)\n      (segment 3775 \n        (point 342.620000 1057.780000 -15.570000 0.460000)\n        (point 341.510000 1060.500000 -14.750000 0.460000)\n        4)\n      (segment 3776 \n        (point 341.510000 1060.500000 -14.750000 0.460000)\n        (point 341.510000 1060.500000 -14.770000 0.460000)\n        4)\n      (segment 3777 \n        (point 341.510000 1060.500000 -14.770000 0.460000)\n        (point 339.910000 1061.330000 -12.980000 0.460000)\n        4)\n      (segment 3778 \n        (point 339.910000 1061.330000 -12.980000 0.460000)\n        (point 339.910000 1061.330000 -13.000000 0.460000)\n        4)\n      (segment 3779 \n        (point 339.910000 1061.330000 -13.000000 0.460000)\n        (point 338.570000 1061.010000 -11.370000 0.460000)\n        4)\n      (segment 3780 \n        (point 338.570000 1061.010000 -11.370000 0.460000)\n        (point 338.570000 1061.010000 -11.400000 0.460000)\n        4)\n      (segment 3781 \n        (point 338.570000 1061.010000 -11.400000 0.460000)\n        (point 336.780000 1060.590000 -9.930000 0.460000)\n        4)\n      (segment 3782 \n        (point 336.780000 1060.590000 -9.930000 0.460000)\n        (point 336.780000 1060.590000 -9.950000 0.460000)\n        4)\n      (segment 3783 \n        (point 336.780000 1060.590000 -9.950000 0.460000)\n        (point 332.940000 1060.880000 -7.800000 0.460000)\n        4)\n      (segment 3784 \n        (point 332.940000 1060.880000 -7.800000 0.460000)\n        (point 332.940000 1060.880000 -7.720000 0.460000)\n        4))\n    (branch 137 135 \n      (segment 3785 \n        (point 315.890000 1021.060000 -24.400000 0.460000)\n        (point 314.650000 1024.350000 -24.400000 0.460000)\n        4)\n      (segment 3786 \n        (point 314.650000 1024.350000 -24.400000 0.460000)\n        (point 315.960000 1028.830000 -24.400000 0.460000)\n        4)\n      (segment 3787 \n        (point 315.960000 1028.830000 -24.400000 0.460000)\n        (point 316.190000 1031.880000 -22.880000 0.460000)\n        4)\n      (segment 3788 \n        (point 316.190000 1031.880000 -22.880000 0.460000)\n        (point 316.190000 1031.880000 -22.900000 0.460000)\n        4)\n      (segment 3789 \n        (point 316.190000 1031.880000 -22.900000 0.460000)\n        (point 315.830000 1035.370000 -22.050000 0.460000)\n        4)\n      (segment 3790 \n        (point 315.830000 1035.370000 -22.050000 0.460000)\n        (point 316.510000 1038.520000 -22.020000 0.460000)\n        4)\n      (segment 3791 \n        (point 316.510000 1038.520000 -22.020000 0.460000)\n        (point 313.090000 1043.090000 -22.770000 0.460000)\n        4)\n      (segment 3792 \n        (point 313.090000 1043.090000 -22.770000 0.460000)\n        (point 313.090000 1043.090000 -22.800000 0.460000)\n        4)\n      (segment 3793 \n        (point 313.090000 1043.090000 -22.800000 0.460000)\n        (point 311.480000 1043.900000 -24.170000 0.460000)\n        4)\n      (segment 3794 \n        (point 311.480000 1043.900000 -24.170000 0.460000)\n        (point 310.270000 1043.030000 -26.000000 0.460000)\n        4)\n      (segment 3795 \n        (point 310.270000 1043.030000 -26.000000 0.460000)\n        (point 308.620000 1042.040000 -28.980000 0.460000)\n        4)\n      (segment 3796 \n        (point 308.620000 1042.040000 -28.980000 0.460000)\n        (point 308.620000 1042.040000 -29.000000 0.460000)\n        4))\n    (branch 138 134 \n      (segment 3797 \n        (point 313.950000 999.090000 -31.800000 0.690000)\n        (point 316.830000 1000.970000 -30.900000 0.460000)\n        4)\n      (segment 3798 \n        (point 316.830000 1000.970000 -30.900000 0.460000)\n        (point 316.830000 1000.970000 -30.920000 0.460000)\n        4)\n      (segment 3799 \n        (point 316.830000 1000.970000 -30.920000 0.460000)\n        (point 316.610000 1003.910000 -31.350000 0.460000)\n        4)\n      (segment 3800 \n        (point 316.610000 1003.910000 -31.350000 0.460000)\n        (point 316.610000 1003.910000 -31.380000 0.460000)\n        4)\n      (segment 3801 \n        (point 316.610000 1003.910000 -31.380000 0.460000)\n        (point 318.190000 1007.270000 -30.600000 0.460000)\n        4)\n      (segment 3802 \n        (point 318.190000 1007.270000 -30.600000 0.460000)\n        (point 318.190000 1007.270000 -30.630000 0.460000)\n        4)\n      (segment 3803 \n        (point 318.190000 1007.270000 -30.630000 0.460000)\n        (point 319.180000 1011.070000 -29.670000 0.460000)\n        4)\n      (segment 3804 \n        (point 319.180000 1011.070000 -29.670000 0.460000)\n        (point 318.960000 1014.010000 -29.670000 0.460000)\n        4))\n    (branch 139 138 \n      (segment 3805 \n        (point 318.960000 1014.010000 -29.670000 0.460000)\n        (point 320.920000 1015.660000 -31.320000 0.460000)\n        4)\n      (segment 3806 \n        (point 320.920000 1015.660000 -31.320000 0.460000)\n        (point 320.920000 1015.660000 -31.350000 0.460000)\n        4)\n      (segment 3807 \n        (point 320.920000 1015.660000 -31.350000 0.460000)\n        (point 320.840000 1018.040000 -32.150000 0.460000)\n        4)\n      (segment 3808 \n        (point 320.840000 1018.040000 -32.150000 0.460000)\n        (point 321.790000 1020.050000 -33.130000 0.460000)\n        4)\n      (segment 3809 \n        (point 321.790000 1020.050000 -33.130000 0.460000)\n        (point 322.860000 1021.500000 -34.420000 0.460000)\n        4)\n      (segment 3810 \n        (point 322.860000 1021.500000 -34.420000 0.460000)\n        (point 323.220000 1023.960000 -34.880000 0.460000)\n        4)\n      (segment 3811 \n        (point 323.220000 1023.960000 -34.880000 0.460000)\n        (point 323.090000 1024.530000 -36.400000 0.460000)\n        4)\n      (segment 3812 \n        (point 323.090000 1024.530000 -36.400000 0.460000)\n        (point 325.500000 1026.300000 -37.320000 0.460000)\n        4)\n      (segment 3813 \n        (point 325.500000 1026.300000 -37.320000 0.460000)\n        (point 325.500000 1026.300000 -37.380000 0.460000)\n        4)\n      (segment 3814 \n        (point 325.500000 1026.300000 -37.380000 0.460000)\n        (point 325.810000 1026.970000 -39.020000 0.460000)\n        4)\n      (segment 3815 \n        (point 325.810000 1026.970000 -39.020000 0.460000)\n        (point 326.570000 1027.740000 -40.770000 0.460000)\n        4)\n      (segment 3816 \n        (point 326.570000 1027.740000 -40.770000 0.460000)\n        (point 326.570000 1027.740000 -40.800000 0.460000)\n        4)\n      (segment 3817 \n        (point 326.570000 1027.740000 -40.800000 0.460000)\n        (point 326.360000 1030.680000 -42.200000 0.460000)\n        4)\n      (segment 3818 \n        (point 326.360000 1030.680000 -42.200000 0.460000)\n        (point 328.200000 1032.900000 -42.980000 0.460000)\n        4)\n      (segment 3819 \n        (point 328.200000 1032.900000 -42.980000 0.460000)\n        (point 329.190000 1036.720000 -43.750000 0.460000)\n        4)\n      (segment 3820 \n        (point 329.190000 1036.720000 -43.750000 0.460000)\n        (point 331.020000 1038.940000 -43.750000 0.460000)\n        4)\n      (segment 3821 \n        (point 331.020000 1038.940000 -43.750000 0.460000)\n        (point 334.020000 1040.230000 -44.350000 0.460000)\n        4)\n      (segment 3822 \n        (point 334.020000 1040.230000 -44.350000 0.460000)\n        (point 335.990000 1041.890000 -44.750000 0.460000)\n        4)\n      (segment 3823 \n        (point 335.990000 1041.890000 -44.750000 0.460000)\n        (point 335.990000 1041.890000 -44.780000 0.460000)\n        4)\n      (segment 3824 \n        (point 335.990000 1041.890000 -44.780000 0.460000)\n        (point 337.100000 1045.130000 -46.100000 0.460000)\n        4)\n      (segment 3825 \n        (point 337.100000 1045.130000 -46.100000 0.460000)\n        (point 337.100000 1045.130000 -46.120000 0.460000)\n        4)\n      (segment 3826 \n        (point 337.100000 1045.130000 -46.120000 0.460000)\n        (point 340.860000 1047.220000 -46.630000 0.460000)\n        4)\n      (segment 3827 \n        (point 340.860000 1047.220000 -46.630000 0.460000)\n        (point 343.090000 1047.740000 -46.630000 0.460000)\n        4)\n      (segment 3828 \n        (point 343.090000 1047.740000 -46.630000 0.460000)\n        (point 345.150000 1047.020000 -47.650000 0.460000)\n        4)\n      (segment 3829 \n        (point 345.150000 1047.020000 -47.650000 0.460000)\n        (point 345.990000 1045.430000 -48.070000 0.460000)\n        4)\n      (segment 3830 \n        (point 345.990000 1045.430000 -48.070000 0.460000)\n        (point 345.990000 1045.430000 -48.100000 0.460000)\n        4)\n      (segment 3831 \n        (point 345.990000 1045.430000 -48.100000 0.460000)\n        (point 348.990000 1046.720000 -47.720000 0.460000)\n        4)\n      (segment 3832 \n        (point 348.990000 1046.720000 -47.720000 0.460000)\n        (point 352.430000 1048.130000 -48.420000 0.460000)\n        4)\n      (segment 3833 \n        (point 352.430000 1048.130000 -48.420000 0.460000)\n        (point 352.430000 1048.130000 -48.450000 0.460000)\n        4)\n      (segment 3834 \n        (point 352.430000 1048.130000 -48.450000 0.460000)\n        (point 356.810000 1051.550000 -49.070000 0.460000)\n        4)\n      (segment 3835 \n        (point 356.810000 1051.550000 -49.070000 0.460000)\n        (point 360.510000 1051.830000 -50.070000 0.460000)\n        4)\n      (segment 3836 \n        (point 360.510000 1051.830000 -50.070000 0.460000)\n        (point 360.740000 1054.860000 -50.920000 0.230000)\n        4)\n      (segment 3837 \n        (point 360.740000 1054.860000 -50.920000 0.230000)\n        (point 360.740000 1054.860000 -50.970000 0.230000)\n        4)\n      (segment 3838 \n        (point 360.740000 1054.860000 -50.970000 0.230000)\n        (point 358.110000 1056.030000 -51.770000 0.230000)\n        4)\n      (segment 3839 \n        (point 358.110000 1056.030000 -51.770000 0.230000)\n        (point 358.110000 1056.030000 -51.800000 0.230000)\n        4)\n      (segment 3840 \n        (point 358.110000 1056.030000 -51.800000 0.230000)\n        (point 356.060000 1056.750000 -50.500000 0.230000)\n        4)\n      (segment 3841 \n        (point 356.060000 1056.750000 -50.500000 0.230000)\n        (point 356.060000 1056.750000 -50.520000 0.230000)\n        4)\n      (segment 3842 \n        (point 356.060000 1056.750000 -50.520000 0.230000)\n        (point 352.140000 1059.410000 -53.180000 0.230000)\n        4))\n    (branch 140 138 \n      (segment 3843 \n        (point 318.960000 1014.010000 -29.670000 0.460000)\n        (point 318.740000 1016.950000 -29.670000 0.460000)\n        4)\n      (segment 3844 \n        (point 318.740000 1016.950000 -29.670000 0.460000)\n        (point 319.730000 1020.760000 -29.670000 0.460000)\n        4)\n      (segment 3845 \n        (point 319.730000 1020.760000 -29.670000 0.460000)\n        (point 319.110000 1025.390000 -29.170000 0.460000)\n        4)\n      (segment 3846 \n        (point 319.110000 1025.390000 -29.170000 0.460000)\n        (point 320.830000 1028.180000 -28.020000 0.460000)\n        4)\n      (segment 3847 \n        (point 320.830000 1028.180000 -28.020000 0.460000)\n        (point 320.340000 1032.240000 -26.320000 0.460000)\n        4)\n      (segment 3848 \n        (point 320.340000 1032.240000 -26.320000 0.460000)\n        (point 320.700000 1034.730000 -26.320000 0.460000)\n        4)\n      (segment 3849 \n        (point 320.700000 1034.730000 -26.320000 0.460000)\n        (point 321.110000 1039.000000 -26.170000 0.460000)\n        4)\n      (segment 3850 \n        (point 321.110000 1039.000000 -26.170000 0.460000)\n        (point 322.810000 1041.790000 -26.900000 0.460000)\n        4)\n      (segment 3851 \n        (point 322.810000 1041.790000 -26.900000 0.460000)\n        (point 321.120000 1044.970000 -26.500000 0.460000)\n        4)\n      (segment 3852 \n        (point 321.120000 1044.970000 -26.500000 0.460000)\n        (point 323.150000 1048.440000 -25.870000 0.460000)\n        4)\n      (segment 3853 \n        (point 323.150000 1048.440000 -25.870000 0.460000)\n        (point 323.500000 1050.900000 -24.200000 0.460000)\n        4)\n      (segment 3854 \n        (point 323.500000 1050.900000 -24.200000 0.460000)\n        (point 322.920000 1051.370000 -21.500000 0.460000)\n        4)\n      (segment 3855 \n        (point 322.920000 1051.370000 -21.500000 0.460000)\n        (point 321.770000 1052.300000 -18.670000 0.460000)\n        4)\n      (segment 3856 \n        (point 321.770000 1052.300000 -18.670000 0.460000)\n        (point 321.770000 1052.300000 -18.630000 0.460000)\n        4))\n    (branch 141 77 \n      (segment 3857 \n        (point 253.330000 278.770000 -19.420000 1.145000)\n        (point 255.980000 279.410000 -19.420000 0.460000)\n        4))\n    (branch 142 141 \n      (segment 3858 \n        (point 255.980000 279.410000 -19.420000 0.460000)\n        (point 257.700000 278.010000 -18.000000 0.460000)\n        4)\n      (segment 3859 \n        (point 257.700000 278.010000 -18.000000 0.460000)\n        (point 258.240000 275.740000 -16.800000 0.460000)\n        4)\n      (segment 3860 \n        (point 258.240000 275.740000 -16.800000 0.460000)\n        (point 259.080000 274.150000 -15.900000 0.460000)\n        4)\n      (segment 3861 \n        (point 259.080000 274.150000 -15.900000 0.460000)\n        (point 260.680000 273.340000 -14.970000 0.460000)\n        4)\n      (segment 3862 \n        (point 260.680000 273.340000 -14.970000 0.460000)\n        (point 260.460000 270.300000 -13.870000 0.460000)\n        4)\n      (segment 3863 \n        (point 260.460000 270.300000 -13.870000 0.460000)\n        (point 259.640000 267.720000 -12.300000 0.460000)\n        4)\n      (segment 3864 \n        (point 259.640000 267.720000 -12.300000 0.460000)\n        (point 259.550000 264.110000 -10.880000 0.460000)\n        4)\n      (segment 3865 \n        (point 259.550000 264.110000 -10.880000 0.460000)\n        (point 260.660000 261.380000 -9.200000 0.460000)\n        4)\n      (segment 3866 \n        (point 260.660000 261.380000 -9.200000 0.460000)\n        (point 261.370000 260.360000 -8.000000 0.460000)\n        4)\n      (segment 3867 \n        (point 261.370000 260.360000 -8.000000 0.460000)\n        (point 262.660000 258.870000 -7.220000 0.460000)\n        4)\n      (segment 3868 \n        (point 262.660000 258.870000 -7.220000 0.460000)\n        (point 263.190000 256.610000 -5.530000 0.460000)\n        4)\n      (segment 3869 \n        (point 263.190000 256.610000 -5.530000 0.460000)\n        (point 265.550000 256.570000 -4.850000 0.460000)\n        4)\n      (segment 3870 \n        (point 265.550000 256.570000 -4.850000 0.460000)\n        (point 267.740000 255.280000 -4.150000 0.460000)\n        4)\n      (segment 3871 \n        (point 267.740000 255.280000 -4.150000 0.460000)\n        (point 268.540000 251.880000 -3.700000 0.460000)\n        4)\n      (segment 3872 \n        (point 268.540000 251.880000 -3.700000 0.460000)\n        (point 270.090000 249.260000 -3.000000 0.460000)\n        4)\n      (segment 3873 \n        (point 270.090000 249.260000 -3.000000 0.460000)\n        (point 272.280000 247.980000 -1.070000 0.460000)\n        4)\n      (segment 3874 \n        (point 272.280000 247.980000 -1.070000 0.460000)\n        (point 274.720000 245.580000 -0.250000 0.460000)\n        4)\n      (segment 3875 \n        (point 274.720000 245.580000 -0.250000 0.460000)\n        (point 277.180000 243.160000 0.300000 0.460000)\n        4)\n      (segment 3876 \n        (point 277.180000 243.160000 0.300000 0.460000)\n        (point 279.170000 240.650000 1.850000 0.460000)\n        4)\n      (segment 3877 \n        (point 279.170000 240.650000 1.850000 0.460000)\n        (point 280.330000 239.720000 3.400000 0.460000)\n        4)\n      (segment 3878 \n        (point 280.330000 239.720000 3.400000 0.460000)\n        (point 282.840000 239.120000 4.800000 0.460000)\n        4)\n      (segment 3879 \n        (point 282.840000 239.120000 4.800000 0.460000)\n        (point 283.680000 237.520000 4.520000 0.460000)\n        4)\n      (segment 3880 \n        (point 283.680000 237.520000 4.520000 0.460000)\n        (point 284.080000 235.830000 3.850000 0.460000)\n        4)\n      (segment 3881 \n        (point 284.080000 235.830000 3.850000 0.460000)\n        (point 285.190000 233.090000 4.070000 0.460000)\n        4)\n      (segment 3882 \n        (point 285.190000 233.090000 4.070000 0.460000)\n        (point 285.190000 233.090000 4.050000 0.460000)\n        4)\n      (segment 3883 \n        (point 285.190000 233.090000 4.050000 0.460000)\n        (point 284.390000 230.530000 5.550000 0.460000)\n        4)\n      (segment 3884 \n        (point 284.390000 230.530000 5.550000 0.460000)\n        (point 284.390000 230.530000 5.480000 0.460000)\n        4)\n      (segment 3885 \n        (point 284.390000 230.530000 5.480000 0.460000)\n        (point 286.250000 228.570000 6.950000 0.460000)\n        4)\n      (segment 3886 \n        (point 286.250000 228.570000 6.950000 0.460000)\n        (point 288.650000 224.360000 7.500000 0.460000)\n        4)\n      (segment 3887 \n        (point 288.650000 224.360000 7.500000 0.460000)\n        (point 290.080000 222.300000 9.880000 0.460000)\n        4)\n      (segment 3888 \n        (point 290.080000 222.300000 9.880000 0.460000)\n        (point 290.080000 222.300000 9.850000 0.460000)\n        4)\n      (segment 3889 \n        (point 290.080000 222.300000 9.850000 0.460000)\n        (point 291.540000 222.050000 11.300000 0.460000)\n        4)\n      (segment 3890 \n        (point 291.540000 222.050000 11.300000 0.460000)\n        (point 294.760000 220.410000 12.830000 0.460000)\n        4)\n      (segment 3891 \n        (point 294.760000 220.410000 12.830000 0.460000)\n        (point 297.840000 219.340000 13.200000 0.460000)\n        4)\n      (segment 3892 \n        (point 297.840000 219.340000 13.200000 0.460000)\n        (point 299.570000 217.950000 14.550000 0.460000)\n        4)\n      (segment 3893 \n        (point 299.570000 217.950000 14.550000 0.460000)\n        (point 299.570000 217.950000 14.520000 0.460000)\n        4)\n      (segment 3894 \n        (point 299.570000 217.950000 14.520000 0.460000)\n        (point 299.970000 216.250000 16.070000 0.460000)\n        4)\n      (segment 3895 \n        (point 299.970000 216.250000 16.070000 0.460000)\n        (point 299.040000 214.250000 17.350000 0.460000)\n        4)\n      (segment 3896 \n        (point 299.040000 214.250000 17.350000 0.460000)\n        (point 300.580000 211.620000 17.920000 0.460000)\n        4)\n      (segment 3897 \n        (point 300.580000 211.620000 17.920000 0.460000)\n        (point 299.600000 207.810000 18.420000 0.460000)\n        4)\n      (segment 3898 \n        (point 299.600000 207.810000 18.420000 0.460000)\n        (point 299.190000 203.530000 20.520000 0.460000)\n        4)\n      (segment 3899 \n        (point 299.190000 203.530000 20.520000 0.460000)\n        (point 299.190000 203.530000 20.600000 0.460000)\n        4))\n    (branch 143 141 \n      (segment 3900 \n        (point 255.980000 279.410000 -19.420000 0.460000)\n        (point 258.330000 279.340000 -19.850000 0.460000)\n        4)\n      (segment 3901 \n        (point 258.330000 279.340000 -19.850000 0.460000)\n        (point 260.960000 278.180000 -20.520000 0.460000)\n        4)\n      (segment 3902 \n        (point 260.960000 278.180000 -20.520000 0.460000)\n        (point 263.290000 276.330000 -20.800000 0.460000)\n        4)\n      (segment 3903 \n        (point 263.290000 276.330000 -20.800000 0.460000)\n        (point 264.700000 274.270000 -20.800000 0.460000)\n        4)\n      (segment 3904 \n        (point 264.700000 274.270000 -20.800000 0.460000)\n        (point 265.730000 273.930000 -20.800000 0.460000)\n        4)\n      (segment 3905 \n        (point 265.730000 273.930000 -20.800000 0.460000)\n        (point 267.870000 270.840000 -21.450000 0.460000)\n        4)\n      (segment 3906 \n        (point 267.870000 270.840000 -21.450000 0.460000)\n        (point 270.050000 269.550000 -21.700000 0.460000)\n        4)\n      (segment 3907 \n        (point 270.050000 269.550000 -21.700000 0.460000)\n        (point 271.920000 267.610000 -20.300000 0.460000)\n        4)\n      (segment 3908 \n        (point 271.920000 267.610000 -20.300000 0.460000)\n        (point 273.330000 265.550000 -19.480000 0.460000)\n        4)\n      (segment 3909 \n        (point 273.330000 265.550000 -19.480000 0.460000)\n        (point 274.000000 262.720000 -19.330000 0.460000)\n        4)\n      (segment 3910 \n        (point 274.000000 262.720000 -19.330000 0.460000)\n        (point 274.980000 260.560000 -18.700000 0.460000)\n        4)\n      (segment 3911 \n        (point 274.980000 260.560000 -18.700000 0.460000)\n        (point 277.750000 258.820000 -18.100000 0.460000)\n        4)\n      (segment 3912 \n        (point 277.750000 258.820000 -18.100000 0.460000)\n        (point 281.270000 257.850000 -20.650000 0.460000)\n        4)\n      (segment 3913 \n        (point 281.270000 257.850000 -20.650000 0.460000)\n        (point 284.490000 256.210000 -20.730000 0.460000)\n        4)\n      (segment 3914 \n        (point 284.490000 256.210000 -20.730000 0.460000)\n        (point 284.490000 256.210000 -20.750000 0.460000)\n        4)\n      (segment 3915 \n        (point 284.490000 256.210000 -20.750000 0.460000)\n        (point 286.630000 253.140000 -19.520000 0.460000)\n        4)\n      (segment 3916 \n        (point 286.630000 253.140000 -19.520000 0.460000)\n        (point 288.170000 250.520000 -19.230000 0.460000)\n        4)\n      (segment 3917 \n        (point 288.170000 250.520000 -19.230000 0.460000)\n        (point 289.420000 247.220000 -18.570000 0.460000)\n        4)\n      (segment 3918 \n        (point 289.420000 247.220000 -18.570000 0.460000)\n        (point 289.420000 247.220000 -18.630000 0.460000)\n        4)\n      (segment 3919 \n        (point 289.420000 247.220000 -18.630000 0.460000)\n        (point 291.110000 244.040000 -18.150000 0.460000)\n        4)\n      (segment 3920 \n        (point 291.110000 244.040000 -18.150000 0.460000)\n        (point 293.550000 241.630000 -18.150000 0.460000)\n        4)\n      (segment 3921 \n        (point 293.550000 241.630000 -18.150000 0.460000)\n        (point 296.000000 239.220000 -19.400000 0.460000)\n        4)\n      (segment 3922 \n        (point 296.000000 239.220000 -19.400000 0.460000)\n        (point 298.460000 236.800000 -18.200000 0.460000)\n        4)\n      (segment 3923 \n        (point 298.460000 236.800000 -18.200000 0.460000)\n        (point 299.570000 234.080000 -16.830000 0.460000)\n        4)\n      (segment 3924 \n        (point 299.570000 234.080000 -16.830000 0.460000)\n        (point 299.570000 234.080000 -16.850000 0.460000)\n        4)\n      (segment 3925 \n        (point 299.570000 234.080000 -16.850000 0.460000)\n        (point 300.500000 230.120000 -17.300000 0.460000)\n        4)\n      (segment 3926 \n        (point 300.500000 230.120000 -17.300000 0.460000)\n        (point 302.050000 227.500000 -17.080000 0.460000)\n        4)\n      (segment 3927 \n        (point 302.050000 227.500000 -17.080000 0.460000)\n        (point 302.050000 227.500000 -17.100000 0.460000)\n        4)\n      (segment 3928 \n        (point 302.050000 227.500000 -17.100000 0.460000)\n        (point 305.260000 225.860000 -15.700000 0.460000)\n        4)\n      (segment 3929 \n        (point 305.260000 225.860000 -15.700000 0.460000)\n        (point 307.310000 225.140000 -15.600000 0.460000)\n        4)\n      (segment 3930 \n        (point 307.310000 225.140000 -15.600000 0.460000)\n        (point 309.570000 221.500000 -14.880000 0.460000)\n        4)\n      (segment 3931 \n        (point 309.570000 221.500000 -14.880000 0.460000)\n        (point 311.140000 218.870000 -14.400000 0.460000)\n        4)\n      (segment 3932 \n        (point 311.140000 218.870000 -14.400000 0.460000)\n        (point 313.330000 217.600000 -14.400000 0.460000)\n        4)\n      (segment 3933 \n        (point 313.330000 217.600000 -14.400000 0.460000)\n        (point 314.130000 214.200000 -15.350000 0.460000)\n        4)\n      (segment 3934 \n        (point 314.130000 214.200000 -15.350000 0.460000)\n        (point 315.730000 213.400000 -16.450000 0.460000)\n        4)\n      (segment 3935 \n        (point 315.730000 213.400000 -16.450000 0.460000)\n        (point 317.860000 210.300000 -17.130000 0.460000)\n        4)\n      (segment 3936 \n        (point 317.860000 210.300000 -17.130000 0.460000)\n        (point 319.730000 208.370000 -17.200000 0.460000)\n        4)\n      (segment 3937 \n        (point 319.730000 208.370000 -17.200000 0.460000)\n        (point 321.470000 206.980000 -17.050000 0.460000)\n        4)\n      (segment 3938 \n        (point 321.470000 206.980000 -17.050000 0.460000)\n        (point 324.370000 204.670000 -18.050000 0.460000)\n        4)\n      (segment 3939 \n        (point 324.370000 204.670000 -18.050000 0.460000)\n        (point 327.890000 203.710000 -18.770000 0.460000)\n        4)\n      (segment 3940 \n        (point 327.890000 203.710000 -18.770000 0.460000)\n        (point 329.630000 202.320000 -17.020000 0.460000)\n        4)\n      (segment 3941 \n        (point 329.630000 202.320000 -17.020000 0.460000)\n        (point 331.230000 201.500000 -15.000000 0.460000)\n        4)\n      (segment 3942 \n        (point 331.230000 201.500000 -15.000000 0.460000)\n        (point 333.610000 201.460000 -13.870000 0.460000)\n        4)\n      (segment 3943 \n        (point 333.610000 201.460000 -13.870000 0.460000)\n        (point 334.590000 199.300000 -12.850000 0.460000)\n        4)\n      (segment 3944 \n        (point 334.590000 199.300000 -12.850000 0.460000)\n        (point 337.340000 197.550000 -12.300000 0.460000)\n        4)\n      (segment 3945 \n        (point 337.340000 197.550000 -12.300000 0.460000)\n        (point 339.480000 194.470000 -11.950000 0.460000)\n        4)\n      (segment 3946 \n        (point 339.480000 194.470000 -11.950000 0.460000)\n        (point 340.450000 192.320000 -13.630000 0.460000)\n        4)\n      (segment 3947 \n        (point 340.450000 192.320000 -13.630000 0.460000)\n        (point 342.900000 189.900000 -13.000000 0.460000)\n        4)\n      (segment 3948 \n        (point 342.900000 189.900000 -13.000000 0.460000)\n        (point 342.460000 189.800000 -13.000000 0.460000)\n        4)\n      (segment 3949 \n        (point 342.460000 189.800000 -13.000000 0.460000)\n        (point 345.080000 188.630000 -11.530000 0.460000)\n        4)\n      (segment 3950 \n        (point 345.080000 188.630000 -11.530000 0.460000)\n        (point 346.960000 186.670000 -11.450000 0.230000)\n        4)\n      (segment 3951 \n        (point 346.960000 186.670000 -11.450000 0.230000)\n        (point 349.450000 186.060000 -10.680000 0.230000)\n        4)\n      (segment 3952 \n        (point 349.450000 186.060000 -10.680000 0.230000)\n        (point 350.750000 184.570000 -10.680000 0.230000)\n        4)\n      (segment 3953 \n        (point 350.750000 184.570000 -10.680000 0.230000)\n        (point 352.040000 183.090000 -8.500000 0.230000)\n        4)\n      (segment 3954 \n        (point 352.040000 183.090000 -8.500000 0.230000)\n        (point 352.040000 183.090000 -8.450000 0.230000)\n        4))\n    (branch 144 76 \n      (segment 3955 \n        (point 246.580000 253.310000 -19.880000 1.375000)\n        (point 244.930000 254.130000 -18.750000 0.460000)\n        4)\n      (segment 3956 \n        (point 244.930000 254.130000 -18.750000 0.460000)\n        (point 244.680000 255.260000 -16.650000 0.460000)\n        4)\n      (segment 3957 \n        (point 244.680000 255.260000 -16.650000 0.460000)\n        (point 246.140000 255.020000 -14.950000 0.460000)\n        4)\n      (segment 3958 \n        (point 246.140000 255.020000 -14.950000 0.460000)\n        (point 247.030000 255.220000 -12.800000 0.460000)\n        4)\n      (segment 3959 \n        (point 247.030000 255.220000 -12.800000 0.460000)\n        (point 242.740000 255.420000 -11.950000 0.460000)\n        4)\n      (segment 3960 \n        (point 242.740000 255.420000 -11.950000 0.460000)\n        (point 240.170000 258.390000 -11.400000 0.460000)\n        4)\n      (segment 3961 \n        (point 240.170000 258.390000 -11.400000 0.460000)\n        (point 236.250000 261.060000 -11.150000 0.460000)\n        4)\n      (segment 3962 \n        (point 236.250000 261.060000 -11.150000 0.460000)\n        (point 236.250000 261.060000 -11.170000 0.460000)\n        4)\n      (segment 3963 \n        (point 236.250000 261.060000 -11.170000 0.460000)\n        (point 234.450000 260.640000 -10.050000 0.460000)\n        4)\n      (segment 3964 \n        (point 234.450000 260.640000 -10.050000 0.460000)\n        (point 231.640000 260.570000 -9.720000 0.460000)\n        4)\n      (segment 3965 \n        (point 231.640000 260.570000 -9.720000 0.460000)\n        (point 230.300000 260.260000 -8.730000 0.460000)\n        4))\n    (branch 145 144 \n      (segment 3966 \n        (point 230.300000 260.260000 -8.730000 0.460000)\n        (point 228.080000 259.730000 -9.430000 0.460000)\n        4)\n      (segment 3967 \n        (point 228.080000 259.730000 -9.430000 0.460000)\n        (point 224.230000 260.040000 -9.430000 0.460000)\n        4)\n      (segment 3968 \n        (point 224.230000 260.040000 -9.430000 0.460000)\n        (point 223.200000 260.390000 -10.570000 0.460000)\n        4)\n      (segment 3969 \n        (point 223.200000 260.390000 -10.570000 0.460000)\n        (point 223.200000 260.390000 -10.600000 0.460000)\n        4)\n      (segment 3970 \n        (point 223.200000 260.390000 -10.600000 0.460000)\n        (point 220.000000 262.020000 -11.630000 0.460000)\n        4)\n      (segment 3971 \n        (point 220.000000 262.020000 -11.630000 0.460000)\n        (point 217.990000 264.540000 -12.750000 0.460000)\n        4)\n      (segment 3972 \n        (point 217.990000 264.540000 -12.750000 0.460000)\n        (point 215.760000 264.020000 -13.700000 0.460000)\n        4)\n      (segment 3973 \n        (point 215.760000 264.020000 -13.700000 0.460000)\n        (point 213.270000 264.620000 -14.670000 0.460000)\n        4)\n      (segment 3974 \n        (point 213.270000 264.620000 -14.670000 0.460000)\n        (point 210.050000 266.260000 -14.670000 0.460000)\n        4)\n      (segment 3975 \n        (point 210.050000 266.260000 -14.670000 0.460000)\n        (point 210.050000 266.260000 -14.700000 0.460000)\n        4)\n      (segment 3976 \n        (point 210.050000 266.260000 -14.700000 0.460000)\n        (point 209.210000 267.850000 -15.200000 0.460000)\n        4)\n      (segment 3977 \n        (point 209.210000 267.850000 -15.200000 0.460000)\n        (point 207.020000 269.140000 -15.880000 0.460000)\n        4)\n      (segment 3978 \n        (point 207.020000 269.140000 -15.880000 0.460000)\n        (point 207.020000 269.140000 -15.900000 0.460000)\n        4)\n      (segment 3979 \n        (point 207.020000 269.140000 -15.900000 0.460000)\n        (point 204.080000 269.640000 -16.700000 0.460000)\n        4)\n      (segment 3980 \n        (point 204.080000 269.640000 -16.700000 0.460000)\n        (point 202.650000 271.700000 -17.420000 0.460000)\n        4)\n      (segment 3981 \n        (point 202.650000 271.700000 -17.420000 0.460000)\n        (point 198.940000 271.430000 -17.420000 0.460000)\n        4)\n      (segment 3982 \n        (point 198.940000 271.430000 -17.420000 0.460000)\n        (point 194.980000 272.280000 -17.450000 0.460000)\n        4)\n      (segment 3983 \n        (point 194.980000 272.280000 -17.450000 0.460000)\n        (point 193.420000 274.910000 -18.770000 0.460000)\n        4)\n      (segment 3984 \n        (point 193.420000 274.910000 -18.770000 0.460000)\n        (point 193.420000 274.910000 -18.750000 0.460000)\n        4)\n      (segment 3985 \n        (point 193.420000 274.910000 -18.750000 0.460000)\n        (point 191.900000 273.350000 -20.100000 0.460000)\n        4)\n      (segment 3986 \n        (point 191.900000 273.350000 -20.100000 0.460000)\n        (point 189.970000 273.500000 -21.180000 0.460000)\n        4)\n      (segment 3987 \n        (point 189.970000 273.500000 -21.180000 0.460000)\n        (point 187.930000 274.220000 -22.020000 0.460000)\n        4)\n      (segment 3988 \n        (point 187.930000 274.220000 -22.020000 0.460000)\n        (point 185.740000 275.490000 -22.700000 0.460000)\n        4)\n      (segment 3989 \n        (point 185.740000 275.490000 -22.700000 0.460000)\n        (point 183.110000 276.680000 -23.500000 0.460000)\n        4)\n      (segment 3990 \n        (point 183.110000 276.680000 -23.500000 0.460000)\n        (point 181.560000 279.290000 -23.500000 0.460000)\n        4)\n      (segment 3991 \n        (point 181.560000 279.290000 -23.500000 0.460000)\n        (point 179.050000 279.910000 -23.500000 0.460000)\n        4)\n      (segment 3992 \n        (point 179.050000 279.910000 -23.500000 0.460000)\n        (point 175.980000 280.970000 -23.630000 0.460000)\n        4)\n      (segment 3993 \n        (point 175.980000 280.970000 -23.630000 0.460000)\n        (point 172.770000 282.610000 -23.630000 0.460000)\n        4)\n      (segment 3994 \n        (point 172.770000 282.610000 -23.630000 0.460000)\n        (point 169.600000 286.050000 -23.400000 0.230000)\n        4)\n      (segment 3995 \n        (point 169.600000 286.050000 -23.400000 0.230000)\n        (point 166.220000 286.440000 -24.170000 0.230000)\n        4)\n      (segment 3996 \n        (point 166.220000 286.440000 -24.170000 0.230000)\n        (point 162.700000 287.410000 -25.150000 0.230000)\n        4)\n      (segment 3997 \n        (point 162.700000 287.410000 -25.150000 0.230000)\n        (point 160.950000 288.800000 -25.030000 0.230000)\n        4)\n      (segment 3998 \n        (point 160.950000 288.800000 -25.030000 0.230000)\n        (point 160.550000 290.490000 -24.000000 0.230000)\n        4)\n      (segment 3999 \n        (point 160.550000 290.490000 -24.000000 0.230000)\n        (point 157.970000 293.470000 -24.920000 0.230000)\n        4)\n      (segment 4000 \n        (point 157.970000 293.470000 -24.920000 0.230000)\n        (point 157.970000 293.470000 -24.950000 0.230000)\n        4)\n      (segment 4001 \n        (point 157.970000 293.470000 -24.950000 0.230000)\n        (point 156.810000 294.380000 -26.350000 0.230000)\n        4)\n      (segment 4002 \n        (point 156.810000 294.380000 -26.350000 0.230000)\n        (point 156.810000 294.380000 -26.380000 0.230000)\n        4)\n      (segment 4003 \n        (point 156.810000 294.380000 -26.380000 0.230000)\n        (point 154.500000 296.240000 -27.200000 0.230000)\n        4)\n      (segment 4004 \n        (point 154.500000 296.240000 -27.200000 0.230000)\n        (point 151.740000 297.970000 -27.970000 0.230000)\n        4)\n      (segment 4005 \n        (point 151.740000 297.970000 -27.970000 0.230000)\n        (point 150.760000 300.140000 -28.150000 0.230000)\n        4)\n      (segment 4006 \n        (point 150.760000 300.140000 -28.150000 0.230000)\n        (point 148.650000 299.060000 -28.150000 0.230000)\n        4)\n      (segment 4007 \n        (point 148.650000 299.060000 -28.150000 0.230000)\n        (point 144.290000 301.610000 -28.150000 0.230000)\n        4)\n      (segment 4008 \n        (point 144.290000 301.610000 -28.150000 0.230000)\n        (point 140.310000 302.460000 -28.250000 0.230000)\n        4)\n      (segment 4009 \n        (point 140.310000 302.460000 -28.250000 0.230000)\n        (point 140.310000 302.460000 -28.300000 0.230000)\n        4)\n      (segment 4010 \n        (point 140.310000 302.460000 -28.300000 0.230000)\n        (point 137.550000 304.210000 -28.300000 0.230000)\n        4)\n      (segment 4011 \n        (point 137.550000 304.210000 -28.300000 0.230000)\n        (point 134.070000 306.990000 -28.300000 0.230000)\n        4)\n      (segment 4012 \n        (point 134.070000 306.990000 -28.300000 0.230000)\n        (point 130.600000 309.740000 -27.600000 0.230000)\n        4)\n      (segment 4013 \n        (point 130.600000 309.740000 -27.600000 0.230000)\n        (point 127.970000 310.920000 -26.650000 0.230000)\n        4)\n      (segment 4014 \n        (point 127.970000 310.920000 -26.650000 0.230000)\n        (point 125.080000 313.230000 -26.000000 0.230000)\n        4)\n      (segment 4015 \n        (point 125.080000 313.230000 -26.000000 0.230000)\n        (point 123.790000 314.720000 -25.070000 0.230000)\n        4)\n      (segment 4016 \n        (point 123.790000 314.720000 -25.070000 0.230000)\n        (point 121.330000 317.130000 -24.200000 0.230000)\n        4)\n      (segment 4017 \n        (point 121.330000 317.130000 -24.200000 0.230000)\n        (point 118.570000 318.870000 -22.770000 0.230000)\n        4)\n      (segment 4018 \n        (point 118.570000 318.870000 -22.770000 0.230000)\n        (point 117.140000 320.930000 -22.270000 0.230000)\n        4)\n      (segment 4019 \n        (point 117.140000 320.930000 -22.270000 0.230000)\n        (point 114.430000 324.470000 -21.720000 0.230000)\n        4)\n      (segment 4020 \n        (point 114.430000 324.470000 -21.720000 0.230000)\n        (point 114.430000 324.470000 -21.750000 0.230000)\n        4)\n      (segment 4021 \n        (point 114.430000 324.470000 -21.750000 0.230000)\n        (point 114.350000 326.830000 -20.070000 0.230000)\n        4)\n      (segment 4022 \n        (point 114.350000 326.830000 -20.070000 0.230000)\n        (point 114.350000 326.830000 -19.950000 0.230000)\n        4))\n    (branch 146 144 \n      (segment 4023 \n        (point 230.300000 260.260000 -8.730000 0.460000)\n        (point 227.150000 263.700000 -8.000000 0.230000)\n        4))\n    (branch 147 146 \n      (segment 4024 \n        (point 227.150000 263.700000 -8.000000 0.230000)\n        (point 227.200000 265.510000 -6.930000 0.230000)\n        4)\n      (segment 4025 \n        (point 227.200000 265.510000 -6.930000 0.230000)\n        (point 225.710000 265.760000 -5.600000 0.230000)\n        4)\n      (segment 4026 \n        (point 225.710000 265.760000 -5.600000 0.230000)\n        (point 223.840000 267.710000 -4.920000 0.230000)\n        4)\n      (segment 4027 \n        (point 223.840000 267.710000 -4.920000 0.230000)\n        (point 222.640000 266.820000 -4.880000 0.230000)\n        4)\n      (segment 4028 \n        (point 222.640000 266.820000 -4.880000 0.230000)\n        (point 223.140000 268.740000 -2.970000 0.230000)\n        4)\n      (segment 4029 \n        (point 223.140000 268.740000 -2.970000 0.230000)\n        (point 223.140000 268.740000 -2.950000 0.230000)\n        4)\n      (segment 4030 \n        (point 223.140000 268.740000 -2.950000 0.230000)\n        (point 222.420000 269.760000 -1.750000 0.230000)\n        4)\n      (segment 4031 \n        (point 222.420000 269.760000 -1.750000 0.230000)\n        (point 221.580000 271.350000 -0.120000 0.230000)\n        4)\n      (segment 4032 \n        (point 221.580000 271.350000 -0.120000 0.230000)\n        (point 222.340000 272.140000 1.750000 0.230000)\n        4)\n      (segment 4033 \n        (point 222.340000 272.140000 1.750000 0.230000)\n        (point 219.970000 272.180000 3.820000 0.230000)\n        4)\n      (segment 4034 \n        (point 219.970000 272.180000 3.820000 0.230000)\n        (point 217.030000 272.680000 4.720000 0.230000)\n        4)\n      (segment 4035 \n        (point 217.030000 272.680000 4.720000 0.230000)\n        (point 217.210000 273.910000 6.180000 0.230000)\n        4)\n      (segment 4036 \n        (point 217.210000 273.910000 6.180000 0.230000)\n        (point 217.980000 274.680000 8.100000 0.230000)\n        4)\n      (segment 4037 \n        (point 217.980000 274.680000 8.100000 0.230000)\n        (point 217.440000 276.950000 9.450000 0.230000)\n        4)\n      (segment 4038 \n        (point 217.440000 276.950000 9.450000 0.230000)\n        (point 218.460000 276.600000 9.850000 0.230000)\n        4)\n      (segment 4039 \n        (point 218.460000 276.600000 9.850000 0.230000)\n        (point 218.060000 278.290000 11.980000 0.230000)\n        4)\n      (segment 4040 \n        (point 218.060000 278.290000 11.980000 0.230000)\n        (point 216.150000 278.440000 14.230000 0.230000)\n        4)\n      (segment 4041 \n        (point 216.150000 278.440000 14.230000 0.230000)\n        (point 217.670000 279.990000 16.150000 0.230000)\n        4)\n      (segment 4042 \n        (point 217.670000 279.990000 16.150000 0.230000)\n        (point 217.320000 283.490000 17.350000 0.230000)\n        4)\n      (segment 4043 \n        (point 217.320000 283.490000 17.350000 0.230000)\n        (point 217.320000 283.490000 17.400000 0.230000)\n        4)\n      (segment 4044 \n        (point 217.320000 283.490000 17.400000 0.230000)\n        (point 218.710000 285.610000 18.750000 0.230000)\n        4))\n    (branch 148 147 \n      (segment 4045 \n        (point 218.710000 285.610000 18.750000 0.230000)\n        (point 219.380000 288.760000 18.800000 0.230000)\n        4)\n      (segment 4046 \n        (point 219.380000 288.760000 18.800000 0.230000)\n        (point 219.750000 291.230000 20.270000 0.230000)\n        4)\n      (segment 4047 \n        (point 219.750000 291.230000 20.270000 0.230000)\n        (point 221.540000 291.640000 21.920000 0.230000)\n        4)\n      (segment 4048 \n        (point 221.540000 291.640000 21.920000 0.230000)\n        (point 222.560000 291.290000 23.330000 0.230000)\n        4)\n      (segment 4049 \n        (point 222.560000 291.290000 23.330000 0.230000)\n        (point 222.560000 291.290000 23.300000 0.230000)\n        4))\n    (branch 149 147 \n      (segment 4050 \n        (point 218.710000 285.610000 18.750000 0.230000)\n        (point 220.170000 285.360000 17.500000 0.230000)\n        4)\n      (segment 4051 \n        (point 220.170000 285.360000 17.500000 0.230000)\n        (point 221.260000 286.800000 19.130000 0.230000)\n        4)\n      (segment 4052 \n        (point 221.260000 286.800000 19.130000 0.230000)\n        (point 221.260000 286.800000 19.200000 0.230000)\n        4)\n      (segment 4053 \n        (point 221.260000 286.800000 19.200000 0.230000)\n        (point 221.760000 288.710000 21.050000 0.230000)\n        4)\n      (segment 4054 \n        (point 221.760000 288.710000 21.050000 0.230000)\n        (point 220.910000 290.310000 21.800000 0.230000)\n        4)\n      (segment 4055 \n        (point 220.910000 290.310000 21.800000 0.230000)\n        (point 221.540000 291.640000 23.100000 0.230000)\n        4)\n      (segment 4056 \n        (point 221.540000 291.640000 23.100000 0.230000)\n        (point 223.320000 292.060000 23.780000 0.230000)\n        4)\n      (segment 4057 \n        (point 223.320000 292.060000 23.780000 0.230000)\n        (point 223.320000 292.060000 23.950000 0.230000)\n        4))\n    (branch 150 146 \n      (segment 4058 \n        (point 227.150000 263.700000 -8.000000 0.230000)\n        (point 225.080000 264.420000 -9.150000 0.460000)\n        4)\n      (segment 4059 \n        (point 225.080000 264.420000 -9.150000 0.460000)\n        (point 222.460000 265.590000 -9.400000 0.460000)\n        4)\n      (segment 4060 \n        (point 222.460000 265.590000 -9.400000 0.460000)\n        (point 220.150000 267.440000 -8.320000 0.460000)\n        4)\n      (segment 4061 \n        (point 220.150000 267.440000 -8.320000 0.460000)\n        (point 220.150000 267.440000 -8.350000 0.460000)\n        4)\n      (segment 4062 \n        (point 220.150000 267.440000 -8.350000 0.460000)\n        (point 218.360000 267.020000 -8.200000 0.460000)\n        4)\n      (segment 4063 \n        (point 218.360000 267.020000 -8.200000 0.460000)\n        (point 214.380000 267.870000 -8.320000 0.460000)\n        4)\n      (segment 4064 \n        (point 214.380000 267.870000 -8.320000 0.460000)\n        (point 211.040000 270.080000 -9.430000 0.460000)\n        4)\n      (segment 4065 \n        (point 211.040000 270.080000 -9.430000 0.460000)\n        (point 207.910000 269.350000 -9.430000 0.460000)\n        4)\n      (segment 4066 \n        (point 207.910000 269.350000 -9.430000 0.460000)\n        (point 207.250000 272.180000 -10.170000 0.460000)\n        4)\n      (segment 4067 \n        (point 207.250000 272.180000 -10.170000 0.460000)\n        (point 205.380000 274.120000 -10.170000 0.460000)\n        4)\n      (segment 4068 \n        (point 205.380000 274.120000 -10.170000 0.460000)\n        (point 202.880000 274.730000 -9.250000 0.460000)\n        4)\n      (segment 4069 \n        (point 202.880000 274.730000 -9.250000 0.460000)\n        (point 200.210000 274.100000 -8.150000 0.460000)\n        4)\n      (segment 4070 \n        (point 200.210000 274.100000 -8.150000 0.460000)\n        (point 196.990000 275.740000 -7.950000 0.460000)\n        4)\n      (segment 4071 \n        (point 196.990000 275.740000 -7.950000 0.460000)\n        (point 195.170000 279.500000 -7.470000 0.460000)\n        4)\n      (segment 4072 \n        (point 195.170000 279.500000 -7.470000 0.460000)\n        (point 191.510000 281.020000 -7.470000 0.460000)\n        4)\n      (segment 4073 \n        (point 191.510000 281.020000 -7.470000 0.460000)\n        (point 188.110000 281.430000 -6.550000 0.460000)\n        4)\n      (segment 4074 \n        (point 188.110000 281.430000 -6.550000 0.460000)\n        (point 185.220000 283.730000 -6.550000 0.460000)\n        4)\n      (segment 4075 \n        (point 185.220000 283.730000 -6.550000 0.460000)\n        (point 181.970000 283.560000 -7.900000 0.460000)\n        4)\n      (segment 4076 \n        (point 181.970000 283.560000 -7.900000 0.460000)\n        (point 179.330000 284.750000 -9.150000 0.460000)\n        4)\n      (segment 4077 \n        (point 179.330000 284.750000 -9.150000 0.460000)\n        (point 177.020000 286.590000 -9.600000 0.460000)\n        4)\n      (segment 4078 \n        (point 177.020000 286.590000 -9.600000 0.460000)\n        (point 177.020000 286.590000 -9.630000 0.460000)\n        4)\n      (segment 4079 \n        (point 177.020000 286.590000 -9.630000 0.460000)\n        (point 173.100000 289.250000 -7.150000 0.460000)\n        4)\n      (segment 4080 \n        (point 173.100000 289.250000 -7.150000 0.460000)\n        (point 173.100000 289.250000 -7.170000 0.460000)\n        4)\n      (segment 4081 \n        (point 173.100000 289.250000 -7.170000 0.460000)\n        (point 171.670000 291.310000 -7.170000 0.460000)\n        4)\n      (segment 4082 \n        (point 171.670000 291.310000 -7.170000 0.460000)\n        (point 169.230000 293.710000 -6.520000 0.460000)\n        4)\n      (segment 4083 \n        (point 169.230000 293.710000 -6.520000 0.460000)\n        (point 166.770000 296.130000 -6.520000 0.460000)\n        4)\n      (segment 4084 \n        (point 166.770000 296.130000 -6.520000 0.460000)\n        (point 165.610000 297.050000 -5.800000 0.460000)\n        4)\n      (segment 4085 \n        (point 165.610000 297.050000 -5.800000 0.460000)\n        (point 163.830000 296.630000 -5.800000 0.460000)\n        4)\n      (segment 4086 \n        (point 163.830000 296.630000 -5.800000 0.460000)\n        (point 160.180000 298.160000 -5.030000 0.460000)\n        4)\n      (segment 4087 \n        (point 160.180000 298.160000 -5.030000 0.460000)\n        (point 156.910000 297.990000 -4.150000 0.460000)\n        4)\n      (segment 4088 \n        (point 156.910000 297.990000 -4.150000 0.460000)\n        (point 154.280000 299.180000 -4.100000 0.460000)\n        4)\n      (segment 4089 \n        (point 154.280000 299.180000 -4.100000 0.460000)\n        (point 153.610000 302.000000 -3.570000 0.460000)\n        4)\n      (segment 4090 \n        (point 153.610000 302.000000 -3.570000 0.460000)\n        (point 150.720000 304.310000 -3.570000 0.230000)\n        4)\n      (segment 4091 \n        (point 150.720000 304.310000 -3.570000 0.230000)\n        (point 147.380000 306.520000 -3.570000 0.230000)\n        4)\n      (segment 4092 \n        (point 147.380000 306.520000 -3.570000 0.230000)\n        (point 144.440000 307.020000 -3.570000 0.230000)\n        4)\n      (segment 4093 \n        (point 144.440000 307.020000 -3.570000 0.230000)\n        (point 141.010000 311.590000 -3.950000 0.230000)\n        4)\n      (segment 4094 \n        (point 141.010000 311.590000 -3.950000 0.230000)\n        (point 141.010000 311.590000 -3.970000 0.230000)\n        4)\n      (segment 4095 \n        (point 141.010000 311.590000 -3.970000 0.230000)\n        (point 138.960000 312.300000 -3.270000 0.230000)\n        4)\n      (segment 4096 \n        (point 138.960000 312.300000 -3.270000 0.230000)\n        (point 134.590000 314.860000 -4.350000 0.230000)\n        4)\n      (segment 4097 \n        (point 134.590000 314.860000 -4.350000 0.230000)\n        (point 132.400000 316.140000 -5.130000 0.230000)\n        4)\n      (segment 4098 \n        (point 132.400000 316.140000 -5.130000 0.230000)\n        (point 129.770000 317.310000 -5.130000 0.230000)\n        4)\n      (segment 4099 \n        (point 129.770000 317.310000 -5.130000 0.230000)\n        (point 127.890000 319.270000 -6.220000 0.230000)\n        4)\n      (segment 4100 \n        (point 127.890000 319.270000 -6.220000 0.230000)\n        (point 127.180000 320.290000 -8.300000 0.230000)\n        4))\n    (branch 151 75 \n      (segment 4101 \n        (point 251.090000 219.870000 -12.100000 1.375000)\n        (point 247.520000 217.100000 -12.100000 0.690000)\n        4)\n      (segment 4102 \n        (point 247.520000 217.100000 -12.100000 0.690000)\n        (point 244.970000 215.900000 -13.250000 0.690000)\n        4)\n      (segment 4103 \n        (point 244.970000 215.900000 -13.250000 0.690000)\n        (point 242.460000 216.520000 -14.400000 0.690000)\n        4)\n      (segment 4104 \n        (point 242.460000 216.520000 -14.400000 0.690000)\n        (point 241.620000 218.110000 -15.600000 0.690000)\n        4)\n      (segment 4105 \n        (point 241.620000 218.110000 -15.600000 0.690000)\n        (point 240.820000 221.510000 -19.420000 0.690000)\n        4))\n    (branch 152 151 \n      (segment 4106 \n        (point 240.820000 221.510000 -19.420000 0.690000)\n        (point 238.640000 222.780000 -20.750000 0.690000)\n        4)\n      (segment 4107 \n        (point 238.640000 222.780000 -20.750000 0.690000)\n        (point 234.350000 222.980000 -22.300000 0.690000)\n        4)\n      (segment 4108 \n        (point 234.350000 222.980000 -22.300000 0.690000)\n        (point 232.130000 222.460000 -23.250000 0.690000)\n        4)\n      (segment 4109 \n        (point 232.130000 222.460000 -23.250000 0.690000)\n        (point 232.130000 222.460000 -23.270000 0.690000)\n        4)\n      (segment 4110 \n        (point 232.130000 222.460000 -23.270000 0.690000)\n        (point 230.780000 222.140000 -25.000000 0.690000)\n        4))\n    (branch 153 152 \n      (segment 4111 \n        (point 230.780000 222.140000 -25.000000 0.690000)\n        (point 228.510000 219.810000 -25.700000 0.460000)\n        4)\n      (segment 4112 \n        (point 228.510000 219.810000 -25.700000 0.460000)\n        (point 226.090000 218.050000 -27.150000 0.460000)\n        4)\n      (segment 4113 \n        (point 226.090000 218.050000 -27.150000 0.460000)\n        (point 224.480000 218.870000 -28.850000 0.460000)\n        4)\n      (segment 4114 \n        (point 224.480000 218.870000 -28.850000 0.460000)\n        (point 223.590000 218.660000 -30.230000 0.460000)\n        4)\n      (segment 4115 \n        (point 223.590000 218.660000 -30.230000 0.460000)\n        (point 222.970000 217.320000 -31.670000 0.460000)\n        4)\n      (segment 4116 \n        (point 222.970000 217.320000 -31.670000 0.460000)\n        (point 221.630000 217.010000 -33.100000 0.460000)\n        4)\n      (segment 4117 \n        (point 221.630000 217.010000 -33.100000 0.460000)\n        (point 219.650000 215.350000 -34.170000 0.460000)\n        4)\n      (segment 4118 \n        (point 219.650000 215.350000 -34.170000 0.460000)\n        (point 218.710000 213.330000 -35.920000 0.460000)\n        4)\n      (segment 4119 \n        (point 218.710000 213.330000 -35.920000 0.460000)\n        (point 220.050000 213.650000 -38.530000 0.460000)\n        4)\n      (segment 4120 \n        (point 220.050000 213.650000 -38.530000 0.460000)\n        (point 218.940000 216.380000 -40.950000 0.460000)\n        4)\n      (segment 4121 \n        (point 218.940000 216.380000 -40.950000 0.460000)\n        (point 216.130000 216.310000 -43.050000 0.460000)\n        4)\n      (segment 4122 \n        (point 216.130000 216.310000 -43.050000 0.460000)\n        (point 212.990000 215.580000 -44.350000 0.460000)\n        4)\n      (segment 4123 \n        (point 212.990000 215.580000 -44.350000 0.460000)\n        (point 210.590000 213.830000 -44.250000 0.460000)\n        4)\n      (segment 4124 \n        (point 210.590000 213.830000 -44.250000 0.460000)\n        (point 207.860000 211.390000 -44.750000 0.460000)\n        4)\n      (segment 4125 \n        (point 207.860000 211.390000 -44.750000 0.460000)\n        (point 203.790000 208.650000 -45.150000 0.460000)\n        4)\n      (segment 4126 \n        (point 203.790000 208.650000 -45.150000 0.460000)\n        (point 202.140000 207.660000 -45.150000 0.230000)\n        4)\n      (segment 4127 \n        (point 202.140000 207.660000 -45.150000 0.230000)\n        (point 197.620000 204.810000 -45.970000 0.230000)\n        4)\n      (segment 4128 \n        (point 197.620000 204.810000 -45.970000 0.230000)\n        (point 195.260000 204.860000 -46.930000 0.230000)\n        4)\n      (segment 4129 \n        (point 195.260000 204.860000 -46.930000 0.230000)\n        (point 195.260000 204.860000 -46.950000 0.230000)\n        4)\n      (segment 4130 \n        (point 195.260000 204.860000 -46.950000 0.230000)\n        (point 193.520000 206.240000 -47.800000 0.230000)\n        4)\n      (segment 4131 \n        (point 193.520000 206.240000 -47.800000 0.230000)\n        (point 190.840000 205.610000 -48.700000 0.230000)\n        4)\n      (segment 4132 \n        (point 190.840000 205.610000 -48.700000 0.230000)\n        (point 190.840000 205.610000 -48.720000 0.230000)\n        4)\n      (segment 4133 \n        (point 190.840000 205.610000 -48.720000 0.230000)\n        (point 187.270000 204.780000 -49.580000 0.230000)\n        4)\n      (segment 4134 \n        (point 187.270000 204.780000 -49.580000 0.230000)\n        (point 183.880000 205.170000 -49.380000 0.230000)\n        4)\n      (segment 4135 \n        (point 183.880000 205.170000 -49.380000 0.230000)\n        (point 181.380000 205.790000 -49.380000 0.230000)\n        4)\n      (segment 4136 \n        (point 181.380000 205.790000 -49.380000 0.230000)\n        (point 177.990000 206.190000 -49.380000 0.230000)\n        4)\n      (segment 4137 \n        (point 177.990000 206.190000 -49.380000 0.230000)\n        (point 175.940000 206.890000 -50.320000 0.230000)\n        4)\n      (segment 4138 \n        (point 175.940000 206.890000 -50.320000 0.230000)\n        (point 175.940000 206.890000 -50.350000 0.230000)\n        4)\n      (segment 4139 \n        (point 175.940000 206.890000 -50.350000 0.230000)\n        (point 173.750000 208.180000 -51.250000 0.230000)\n        4)\n      (segment 4140 \n        (point 173.750000 208.180000 -51.250000 0.230000)\n        (point 172.900000 209.770000 -52.080000 0.230000)\n        4)\n      (segment 4141 \n        (point 172.900000 209.770000 -52.080000 0.230000)\n        (point 172.520000 211.460000 -53.380000 0.230000)\n        4)\n      (segment 4142 \n        (point 172.520000 211.460000 -53.380000 0.230000)\n        (point 172.520000 211.460000 -53.400000 0.230000)\n        4)\n      (segment 4143 \n        (point 172.520000 211.460000 -53.400000 0.230000)\n        (point 172.110000 213.160000 -54.670000 0.230000)\n        4)\n      (segment 4144 \n        (point 172.110000 213.160000 -54.670000 0.230000)\n        (point 172.110000 213.160000 -54.830000 0.230000)\n        4))\n    (branch 154 152 \n      (segment 4145 \n        (point 230.780000 222.140000 -25.000000 0.690000)\n        (point 226.810000 222.990000 -25.000000 0.460000)\n        4)\n      (segment 4146 \n        (point 226.810000 222.990000 -25.000000 0.460000)\n        (point 225.080000 224.380000 -27.630000 0.460000)\n        4)\n      (segment 4147 \n        (point 225.080000 224.380000 -27.630000 0.460000)\n        (point 224.940000 224.950000 -27.630000 0.460000)\n        4)\n      (segment 4148 \n        (point 224.940000 224.950000 -27.630000 0.460000)\n        (point 223.600000 224.630000 -29.770000 0.460000)\n        4)\n      (segment 4149 \n        (point 223.600000 224.630000 -29.770000 0.460000)\n        (point 221.510000 223.540000 -30.600000 0.460000)\n        4)\n      (segment 4150 \n        (point 221.510000 223.540000 -30.600000 0.460000)\n        (point 219.640000 225.500000 -32.380000 0.460000)\n        4)\n      (segment 4151 \n        (point 219.640000 225.500000 -32.380000 0.460000)\n        (point 217.270000 225.540000 -33.450000 0.460000)\n        4)\n      (segment 4152 \n        (point 217.270000 225.540000 -33.450000 0.460000)\n        (point 213.960000 223.570000 -34.320000 0.460000)\n        4)\n      (segment 4153 \n        (point 213.960000 223.570000 -34.320000 0.460000)\n        (point 211.960000 226.090000 -35.250000 0.460000)\n        4)\n      (segment 4154 \n        (point 211.960000 226.090000 -35.250000 0.460000)\n        (point 212.010000 227.890000 -35.250000 0.460000)\n        4)\n      (segment 4155 \n        (point 212.010000 227.890000 -35.250000 0.460000)\n        (point 208.930000 228.960000 -35.250000 0.460000)\n        4)\n      (segment 4156 \n        (point 208.930000 228.960000 -35.250000 0.460000)\n        (point 207.500000 231.010000 -34.280000 0.460000)\n        4)\n      (segment 4157 \n        (point 207.500000 231.010000 -34.280000 0.460000)\n        (point 205.360000 234.090000 -34.350000 0.460000)\n        4)\n      (segment 4158 \n        (point 205.360000 234.090000 -34.350000 0.460000)\n        (point 203.680000 237.290000 -33.350000 0.460000)\n        4)\n      (segment 4159 \n        (point 203.680000 237.290000 -33.350000 0.460000)\n        (point 202.070000 238.100000 -32.130000 0.460000)\n        4)\n      (segment 4160 \n        (point 202.070000 238.100000 -32.130000 0.460000)\n        (point 199.580000 238.710000 -31.670000 0.460000)\n        4)\n      (segment 4161 \n        (point 199.580000 238.710000 -31.670000 0.460000)\n        (point 199.050000 240.970000 -30.730000 0.460000)\n        4)\n      (segment 4162 \n        (point 199.050000 240.970000 -30.730000 0.460000)\n        (point 198.520000 243.240000 -29.770000 0.460000)\n        4)\n      (segment 4163 \n        (point 198.520000 243.240000 -29.770000 0.460000)\n        (point 196.900000 244.060000 -30.550000 0.460000)\n        4)\n      (segment 4164 \n        (point 196.900000 244.060000 -30.550000 0.460000)\n        (point 192.890000 243.120000 -30.550000 0.460000)\n        4)\n      (segment 4165 \n        (point 192.890000 243.120000 -30.550000 0.460000)\n        (point 190.130000 244.860000 -29.400000 0.460000)\n        4)\n      (segment 4166 \n        (point 190.130000 244.860000 -29.400000 0.460000)\n        (point 188.020000 243.760000 -28.330000 0.460000)\n        4)\n      (segment 4167 \n        (point 188.020000 243.760000 -28.330000 0.460000)\n        (point 186.100000 243.910000 -26.170000 0.460000)\n        4)\n      (segment 4168 \n        (point 186.100000 243.910000 -26.170000 0.460000)\n        (point 183.300000 243.850000 -24.700000 0.460000)\n        4)\n      (segment 4169 \n        (point 183.300000 243.850000 -24.700000 0.460000)\n        (point 181.640000 242.870000 -24.320000 0.460000)\n        4)\n      (segment 4170 \n        (point 181.640000 242.870000 -24.320000 0.460000)\n        (point 179.720000 243.010000 -24.320000 0.460000)\n        4)\n      (segment 4171 \n        (point 179.720000 243.010000 -24.320000 0.460000)\n        (point 177.430000 240.690000 -24.420000 0.460000)\n        4)\n      (segment 4172 \n        (point 177.430000 240.690000 -24.420000 0.460000)\n        (point 173.160000 240.880000 -24.130000 0.460000)\n        4)\n      (segment 4173 \n        (point 173.160000 240.880000 -24.130000 0.460000)\n        (point 169.050000 242.310000 -23.230000 0.460000)\n        4)\n      (segment 4174 \n        (point 169.050000 242.310000 -23.230000 0.460000)\n        (point 164.950000 243.730000 -22.530000 0.460000)\n        4)\n      (segment 4175 \n        (point 164.950000 243.730000 -22.530000 0.460000)\n        (point 160.980000 244.590000 -22.000000 0.460000)\n        4)\n      (segment 4176 \n        (point 160.980000 244.590000 -22.000000 0.460000)\n        (point 158.390000 247.580000 -21.180000 0.460000)\n        4)\n      (segment 4177 \n        (point 158.390000 247.580000 -21.180000 0.460000)\n        (point 155.770000 248.750000 -19.850000 0.460000)\n        4)\n      (segment 4178 \n        (point 155.770000 248.750000 -19.850000 0.460000)\n        (point 153.530000 248.220000 -19.850000 0.460000)\n        4)\n      (segment 4179 \n        (point 153.530000 248.220000 -19.850000 0.460000)\n        (point 151.210000 250.070000 -18.550000 0.460000)\n        4)\n      (segment 4180 \n        (point 151.210000 250.070000 -18.550000 0.460000)\n        (point 146.800000 250.820000 -18.950000 0.460000)\n        4)\n      (segment 4181 \n        (point 146.800000 250.820000 -18.950000 0.460000)\n        (point 141.780000 252.030000 -18.150000 0.460000)\n        4)\n      (segment 4182 \n        (point 141.780000 252.030000 -18.150000 0.460000)\n        (point 139.460000 253.860000 -17.550000 0.460000)\n        4)\n      (segment 4183 \n        (point 139.460000 253.860000 -17.550000 0.460000)\n        (point 137.780000 257.050000 -16.670000 0.460000)\n        4)\n      (segment 4184 \n        (point 137.780000 257.050000 -16.670000 0.460000)\n        (point 135.860000 257.190000 -16.070000 0.460000)\n        4)\n      (segment 4185 \n        (point 135.860000 257.190000 -16.070000 0.460000)\n        (point 133.760000 256.110000 -15.650000 0.460000)\n        4)\n      (segment 4186 \n        (point 133.760000 256.110000 -15.650000 0.460000)\n        (point 131.170000 259.090000 -14.650000 0.460000)\n        4)\n      (segment 4187 \n        (point 131.170000 259.090000 -14.650000 0.460000)\n        (point 127.390000 261.180000 -14.130000 0.460000)\n        4)\n      (segment 4188 \n        (point 127.390000 261.180000 -14.130000 0.460000)\n        (point 124.360000 264.050000 -14.130000 0.460000)\n        4)\n      (segment 4189 \n        (point 124.360000 264.050000 -14.130000 0.460000)\n        (point 122.940000 266.120000 -13.150000 0.460000)\n        4)\n      (segment 4190 \n        (point 122.940000 266.120000 -13.150000 0.460000)\n        (point 121.320000 266.930000 -12.300000 0.460000)\n        4)\n      (segment 4191 \n        (point 121.320000 266.930000 -12.300000 0.460000)\n        (point 119.280000 267.640000 -11.300000 0.460000)\n        4)\n      (segment 4192 \n        (point 119.280000 267.640000 -11.300000 0.460000)\n        (point 116.390000 269.950000 -10.300000 0.230000)\n        4)\n      (segment 4193 \n        (point 116.390000 269.950000 -10.300000 0.230000)\n        (point 114.380000 272.470000 -9.880000 0.230000)\n        4)\n      (segment 4194 \n        (point 114.380000 272.470000 -9.880000 0.230000)\n        (point 110.950000 277.040000 -9.220000 0.230000)\n        4)\n      (segment 4195 \n        (point 110.950000 277.040000 -9.220000 0.230000)\n        (point 107.470000 279.800000 -8.250000 0.230000)\n        4))\n    (branch 155 151 \n      (segment 4196 \n        (point 240.820000 221.510000 -19.420000 0.690000)\n        (point 242.020000 222.350000 -22.630000 0.460000)\n        4)\n      (segment 4197 \n        (point 242.020000 222.350000 -22.630000 0.460000)\n        (point 239.970000 223.080000 -24.720000 0.460000)\n        4)\n      (segment 4198 \n        (point 239.970000 223.080000 -24.720000 0.460000)\n        (point 239.970000 223.080000 -24.750000 0.460000)\n        4)\n      (segment 4199 \n        (point 239.970000 223.080000 -24.750000 0.460000)\n        (point 237.780000 224.350000 -26.850000 0.460000)\n        4)\n      (segment 4200 \n        (point 237.780000 224.350000 -26.850000 0.460000)\n        (point 237.210000 224.800000 -28.050000 0.460000)\n        4)\n      (segment 4201 \n        (point 237.210000 224.800000 -28.050000 0.460000)\n        (point 236.530000 221.670000 -28.350000 0.230000)\n        4)\n      (segment 4202 \n        (point 236.530000 221.670000 -28.350000 0.230000)\n        (point 239.740000 220.030000 -29.500000 0.230000)\n        4)\n      (segment 4203 \n        (point 239.740000 220.030000 -29.500000 0.230000)\n        (point 239.740000 220.030000 -29.520000 0.230000)\n        4)\n      (segment 4204 \n        (point 239.740000 220.030000 -29.520000 0.230000)\n        (point 241.970000 220.550000 -31.250000 0.230000)\n        4)\n      (segment 4205 \n        (point 241.970000 220.550000 -31.250000 0.230000)\n        (point 243.950000 222.210000 -32.450000 0.230000)\n        4)\n      (segment 4206 \n        (point 243.950000 222.210000 -32.450000 0.230000)\n        (point 244.570000 223.550000 -33.380000 0.230000)\n        4)\n      (segment 4207 \n        (point 244.570000 223.550000 -33.380000 0.230000)\n        (point 245.640000 224.990000 -34.630000 0.230000)\n        4)\n      (segment 4208 \n        (point 245.640000 224.990000 -34.630000 0.230000)\n        (point 246.400000 225.770000 -36.050000 0.230000)\n        4)\n      (segment 4209 \n        (point 246.400000 225.770000 -36.050000 0.230000)\n        (point 246.800000 224.080000 -37.720000 0.230000)\n        4)\n      (segment 4210 \n        (point 246.800000 224.080000 -37.720000 0.230000)\n        (point 246.350000 223.970000 -39.320000 0.230000)\n        4)\n      (segment 4211 \n        (point 246.350000 223.970000 -39.320000 0.230000)\n        (point 248.320000 225.620000 -40.880000 0.230000)\n        4)\n      (segment 4212 \n        (point 248.320000 225.620000 -40.880000 0.230000)\n        (point 249.400000 227.070000 -42.800000 0.230000)\n        4)\n      (segment 4213 \n        (point 249.400000 227.070000 -42.800000 0.230000)\n        (point 249.400000 227.070000 -42.820000 0.230000)\n        4)\n      (segment 4214 \n        (point 249.400000 227.070000 -42.820000 0.230000)\n        (point 247.830000 229.700000 -44.630000 0.230000)\n        4)\n      (segment 4215 \n        (point 247.830000 229.700000 -44.630000 0.230000)\n        (point 247.580000 230.820000 -46.400000 0.230000)\n        4)\n      (segment 4216 \n        (point 247.580000 230.820000 -46.400000 0.230000)\n        (point 247.680000 234.430000 -47.670000 0.230000)\n        4)\n      (segment 4217 \n        (point 247.680000 234.430000 -47.670000 0.230000)\n        (point 245.390000 232.100000 -49.070000 0.230000)\n        4)\n      (segment 4218 \n        (point 245.390000 232.100000 -49.070000 0.230000)\n        (point 244.450000 230.090000 -50.830000 0.230000)\n        4)\n      (segment 4219 \n        (point 244.450000 230.090000 -50.830000 0.230000)\n        (point 246.290000 232.310000 -53.400000 0.230000)\n        4)\n      (segment 4220 \n        (point 246.290000 232.310000 -53.400000 0.230000)\n        (point 249.370000 231.240000 -56.050000 0.230000)\n        4)\n      (segment 4221 \n        (point 249.370000 231.240000 -56.050000 0.230000)\n        (point 251.020000 232.220000 -58.380000 0.230000)\n        4)\n      (segment 4222 \n        (point 251.020000 232.220000 -58.380000 0.230000)\n        (point 250.620000 233.930000 -60.850000 0.230000)\n        4)\n      (segment 4223 \n        (point 250.620000 233.930000 -60.850000 0.230000)\n        (point 249.420000 233.040000 -63.700000 0.230000)\n        4)\n      (segment 4224 \n        (point 249.420000 233.040000 -63.700000 0.230000)\n        (point 248.970000 232.940000 -63.720000 0.230000)\n        4)\n      (segment 4225 \n        (point 248.970000 232.940000 -63.720000 0.230000)\n        (point 249.680000 231.910000 -67.000000 0.230000)\n        4)\n      (segment 4226 \n        (point 249.680000 231.910000 -67.000000 0.230000)\n        (point 250.440000 232.690000 -70.250000 0.230000)\n        4)\n      (segment 4227 \n        (point 250.440000 232.690000 -70.250000 0.230000)\n        (point 253.120000 233.320000 -72.470000 0.230000)\n        4)\n      (segment 4228 \n        (point 253.120000 233.320000 -72.470000 0.230000)\n        (point 253.120000 233.320000 -72.500000 0.230000)\n        4)\n      (segment 4229 \n        (point 253.120000 233.320000 -72.500000 0.230000)\n        (point 251.690000 235.370000 -75.150000 0.230000)\n        4)\n      (segment 4230 \n        (point 251.690000 235.370000 -75.150000 0.230000)\n        (point 251.560000 235.940000 -75.150000 0.230000)\n        4)\n      (segment 4231 \n        (point 251.560000 235.940000 -75.150000 0.230000)\n        (point 249.510000 236.650000 -77.630000 0.230000)\n        4)\n      (segment 4232 \n        (point 249.510000 236.650000 -77.630000 0.230000)\n        (point 249.510000 236.650000 -77.650000 0.230000)\n        4)\n      (segment 4233 \n        (point 249.510000 236.650000 -77.650000 0.230000)\n        (point 248.170000 236.340000 -80.180000 0.230000)\n        4)\n      (segment 4234 \n        (point 248.170000 236.340000 -80.180000 0.230000)\n        (point 248.170000 236.340000 -80.200000 0.230000)\n        4)\n      (segment 4235 \n        (point 248.170000 236.340000 -80.200000 0.230000)\n        (point 247.680000 234.430000 -82.830000 0.230000)\n        4)\n      (segment 4236 \n        (point 247.680000 234.430000 -82.830000 0.230000)\n        (point 247.680000 234.430000 -82.850000 0.230000)\n        4)\n      (segment 4237 \n        (point 247.680000 234.430000 -82.850000 0.230000)\n        (point 248.220000 238.150000 -84.400000 0.230000)\n        4)\n      (segment 4238 \n        (point 248.220000 238.150000 -84.400000 0.230000)\n        (point 248.090000 238.700000 -84.430000 0.230000)\n        4)\n      (segment 4239 \n        (point 248.090000 238.700000 -84.430000 0.230000)\n        (point 249.600000 240.250000 -84.430000 0.230000)\n        4)\n      (segment 4240 \n        (point 249.600000 240.250000 -84.430000 0.230000)\n        (point 246.530000 241.330000 -86.900000 0.230000)\n        4)\n      (segment 4241 \n        (point 246.530000 241.330000 -86.900000 0.230000)\n        (point 247.480000 243.340000 -91.430000 0.230000)\n        4)\n      (segment 4242 \n        (point 247.480000 243.340000 -91.430000 0.230000)\n        (point 247.340000 243.900000 -91.450000 0.230000)\n        4)\n      (segment 4243 \n        (point 247.340000 243.900000 -91.450000 0.230000)\n        (point 249.260000 243.750000 -94.100000 0.230000)\n        4)\n      (segment 4244 \n        (point 249.260000 243.750000 -94.100000 0.230000)\n        (point 249.260000 243.750000 -94.150000 0.230000)\n        4)\n      (segment 4245 \n        (point 249.260000 243.750000 -94.150000 0.230000)\n        (point 249.350000 247.360000 -96.620000 0.230000)\n        4)\n      (segment 4246 \n        (point 249.350000 247.360000 -96.620000 0.230000)\n        (point 248.910000 247.260000 -96.650000 0.230000)\n        4)\n      (segment 4247 \n        (point 248.910000 247.260000 -96.650000 0.230000)\n        (point 247.880000 247.610000 -100.600000 0.230000)\n        4)\n      (segment 4248 \n        (point 247.880000 247.610000 -100.600000 0.230000)\n        (point 247.880000 247.610000 -100.630000 0.230000)\n        4)\n      (segment 4249 \n        (point 247.880000 247.610000 -100.630000 0.230000)\n        (point 248.460000 247.150000 -104.050000 0.230000)\n        4)\n      (segment 4250 \n        (point 248.460000 247.150000 -104.050000 0.230000)\n        (point 248.460000 247.150000 -104.120000 0.230000)\n        4)\n      (segment 4251 \n        (point 248.460000 247.150000 -104.120000 0.230000)\n        (point 248.690000 250.200000 -106.800000 0.230000)\n        4)\n      (segment 4252 \n        (point 248.690000 250.200000 -106.800000 0.230000)\n        (point 248.690000 250.200000 -106.820000 0.230000)\n        4)\n      (segment 4253 \n        (point 248.690000 250.200000 -106.820000 0.230000)\n        (point 247.130000 252.810000 -108.630000 0.230000)\n        4)\n      (segment 4254 \n        (point 247.130000 252.810000 -108.630000 0.230000)\n        (point 247.130000 252.810000 -108.650000 0.230000)\n        4)\n      (segment 4255 \n        (point 247.130000 252.810000 -108.650000 0.230000)\n        (point 247.900000 253.590000 -110.850000 0.230000)\n        4)\n      (segment 4256 \n        (point 247.900000 253.590000 -110.850000 0.230000)\n        (point 249.010000 256.840000 -111.970000 0.230000)\n        4)\n      (segment 4257 \n        (point 249.010000 256.840000 -111.970000 0.230000)\n        (point 248.560000 256.740000 -112.000000 0.230000)\n        4)\n      (segment 4258 \n        (point 248.560000 256.740000 -112.000000 0.230000)\n        (point 248.620000 258.540000 -114.550000 0.230000)\n        4)\n      (segment 4259 \n        (point 248.620000 258.540000 -114.550000 0.230000)\n        (point 248.620000 258.540000 -114.570000 0.230000)\n        4)\n      (segment 4260 \n        (point 248.620000 258.540000 -114.570000 0.230000)\n        (point 248.210000 260.240000 -117.900000 0.230000)\n        4))\n    (branch 156 74 \n      (segment 4261 \n        (point 254.550000 128.720000 1.320000 1.375000)\n        (point 251.920000 127.920000 1.320000 0.460000)\n        4)\n      (segment 4262 \n        (point 251.920000 127.920000 1.320000 0.460000)\n        (point 250.640000 129.420000 2.380000 0.460000)\n        4)\n      (segment 4263 \n        (point 250.640000 129.420000 2.380000 0.460000)\n        (point 250.240000 131.120000 2.380000 0.230000)\n        4)\n      (segment 4264 \n        (point 250.240000 131.120000 2.380000 0.230000)\n        (point 251.570000 131.430000 3.500000 0.230000)\n        4)\n      (segment 4265 \n        (point 251.570000 131.430000 3.500000 0.230000)\n        (point 252.330000 132.210000 4.780000 0.230000)\n        4)\n      (segment 4266 \n        (point 252.330000 132.210000 4.780000 0.230000)\n        (point 252.330000 132.210000 6.420000 0.230000)\n        4)\n      (segment 4267 \n        (point 252.330000 132.210000 6.420000 0.230000)\n        (point 251.940000 133.900000 7.750000 0.230000)\n        4)\n      (segment 4268 \n        (point 251.940000 133.900000 7.750000 0.230000)\n        (point 249.310000 135.080000 7.150000 0.230000)\n        4)\n      (segment 4269 \n        (point 249.310000 135.080000 7.150000 0.230000)\n        (point 247.160000 138.160000 8.070000 0.230000)\n        4)\n      (segment 4270 \n        (point 247.160000 138.160000 8.070000 0.230000)\n        (point 244.140000 141.040000 8.450000 0.230000)\n        4)\n      (segment 4271 \n        (point 244.140000 141.040000 8.450000 0.230000)\n        (point 241.950000 142.320000 9.220000 0.230000)\n        4)\n      (segment 4272 \n        (point 241.950000 142.320000 9.220000 0.230000)\n        (point 241.060000 142.110000 10.050000 0.230000)\n        4)\n      (segment 4273 \n        (point 241.060000 142.110000 10.050000 0.230000)\n        (point 238.570000 142.710000 10.700000 0.230000)\n        4)\n      (segment 4274 \n        (point 238.570000 142.710000 10.700000 0.230000)\n        (point 235.620000 143.220000 11.750000 0.230000)\n        4)\n      (segment 4275 \n        (point 235.620000 143.220000 11.750000 0.230000)\n        (point 235.040000 143.690000 12.850000 0.230000)\n        4)\n      (segment 4276 \n        (point 235.040000 143.690000 12.850000 0.230000)\n        (point 233.830000 142.800000 14.230000 0.230000)\n        4)\n      (segment 4277 \n        (point 233.830000 142.800000 14.230000 0.230000)\n        (point 233.690000 143.370000 14.200000 0.230000)\n        4)\n      (segment 4278 \n        (point 233.690000 143.370000 14.200000 0.230000)\n        (point 235.750000 142.640000 15.900000 0.230000)\n        4)\n      (segment 4279 \n        (point 235.750000 142.640000 15.900000 0.230000)\n        (point 235.750000 142.640000 17.550000 0.230000)\n        4)\n      (segment 4280 \n        (point 235.750000 142.640000 17.550000 0.230000)\n        (point 236.590000 141.050000 19.270000 0.230000)\n        4)\n      (segment 4281 \n        (point 236.590000 141.050000 19.270000 0.230000)\n        (point 236.590000 141.050000 19.250000 0.230000)\n        4)\n      (segment 4282 \n        (point 236.590000 141.050000 19.250000 0.230000)\n        (point 234.090000 141.660000 20.730000 0.230000)\n        4)\n      (segment 4283 \n        (point 234.090000 141.660000 20.730000 0.230000)\n        (point 231.020000 142.740000 21.380000 0.230000)\n        4)\n      (segment 4284 \n        (point 231.020000 142.740000 21.380000 0.230000)\n        (point 228.650000 142.780000 22.080000 0.230000)\n        4)\n      (segment 4285 \n        (point 228.650000 142.780000 22.080000 0.230000)\n        (point 227.720000 146.750000 22.850000 0.230000)\n        4)\n      (segment 4286 \n        (point 227.720000 146.750000 22.850000 0.230000)\n        (point 229.560000 148.970000 23.400000 0.230000)\n        4)\n      (segment 4287 \n        (point 229.560000 148.970000 23.400000 0.230000)\n        (point 229.740000 150.200000 24.600000 0.230000)\n        4)\n      (segment 4288 \n        (point 229.740000 150.200000 24.600000 0.230000)\n        (point 229.740000 150.200000 24.580000 0.230000)\n        4)\n      (segment 4289 \n        (point 229.740000 150.200000 24.580000 0.230000)\n        (point 230.060000 150.870000 26.580000 0.230000)\n        4)\n      (segment 4290 \n        (point 230.060000 150.870000 26.580000 0.230000)\n        (point 231.390000 151.190000 28.700000 0.230000)\n        4)\n      (segment 4291 \n        (point 231.390000 151.190000 28.700000 0.230000)\n        (point 232.280000 151.400000 29.920000 0.230000)\n        4))\n    (branch 157 74 \n      (segment 4292 \n        (point 254.550000 128.720000 1.320000 1.375000)\n        (point 258.120000 127.600000 1.300000 0.460000)\n        4)\n      (segment 4293 \n        (point 258.120000 127.600000 1.300000 0.460000)\n        (point 261.870000 129.670000 1.730000 0.460000)\n        4)\n      (segment 4294 \n        (point 261.870000 129.670000 1.730000 0.460000)\n        (point 266.150000 129.480000 1.850000 0.460000)\n        4)\n      (segment 4295 \n        (point 266.150000 129.480000 1.850000 0.460000)\n        (point 269.110000 128.970000 2.550000 0.460000)\n        4)\n      (segment 4296 \n        (point 269.110000 128.970000 2.550000 0.460000)\n        (point 271.740000 127.800000 2.080000 0.460000)\n        4)\n      (segment 4297 \n        (point 271.740000 127.800000 2.080000 0.460000)\n        (point 274.100000 127.760000 0.280000 0.460000)\n        4)\n      (segment 4298 \n        (point 274.100000 127.760000 0.280000 0.460000)\n        (point 276.480000 127.720000 -0.700000 0.460000)\n        4)\n      (segment 4299 \n        (point 276.480000 127.720000 -0.700000 0.460000)\n        (point 278.390000 127.570000 -0.050000 0.460000)\n        4))\n    (branch 158 157 \n      (segment 4300 \n        (point 278.390000 127.570000 -0.050000 0.460000)\n        (point 282.400000 128.510000 0.900000 0.230000)\n        4)\n      (segment 4301 \n        (point 282.400000 128.510000 0.900000 0.230000)\n        (point 283.580000 127.580000 2.500000 0.230000)\n        4)\n      (segment 4302 \n        (point 283.580000 127.580000 2.500000 0.230000)\n        (point 283.580000 127.580000 2.470000 0.230000)\n        4)\n      (segment 4303 \n        (point 283.580000 127.580000 2.470000 0.230000)\n        (point 283.700000 127.020000 4.220000 0.230000)\n        4)\n      (segment 4304 \n        (point 283.700000 127.020000 4.220000 0.230000)\n        (point 282.890000 124.440000 5.500000 0.230000)\n        4)\n      (segment 4305 \n        (point 282.890000 124.440000 5.500000 0.230000)\n        (point 282.890000 124.440000 5.480000 0.230000)\n        4)\n      (segment 4306 \n        (point 282.890000 124.440000 5.480000 0.230000)\n        (point 284.490000 123.620000 5.950000 0.230000)\n        4)\n      (segment 4307 \n        (point 284.490000 123.620000 5.950000 0.230000)\n        (point 288.070000 124.450000 6.470000 0.230000)\n        4)\n      (segment 4308 \n        (point 288.070000 124.450000 6.470000 0.230000)\n        (point 290.750000 125.090000 7.000000 0.230000)\n        4)\n      (segment 4309 \n        (point 290.750000 125.090000 7.000000 0.230000)\n        (point 291.970000 125.960000 7.750000 0.230000)\n        4)\n      (segment 4310 \n        (point 291.970000 125.960000 7.750000 0.230000)\n        (point 293.750000 126.380000 6.900000 0.230000)\n        4)\n      (segment 4311 \n        (point 293.750000 126.380000 6.900000 0.230000)\n        (point 294.770000 126.020000 6.900000 0.230000)\n        4)\n      (segment 4312 \n        (point 294.770000 126.020000 6.900000 0.230000)\n        (point 297.180000 127.790000 8.350000 0.230000)\n        4)\n      (segment 4313 \n        (point 297.180000 127.790000 8.350000 0.230000)\n        (point 301.160000 126.930000 9.800000 0.230000)\n        4)\n      (segment 4314 \n        (point 301.160000 126.930000 9.800000 0.230000)\n        (point 301.110000 125.130000 11.750000 0.230000)\n        4)\n      (segment 4315 \n        (point 301.110000 125.130000 11.750000 0.230000)\n        (point 301.110000 125.130000 11.700000 0.230000)\n        4)\n      (segment 4316 \n        (point 301.110000 125.130000 11.700000 0.230000)\n        (point 300.170000 129.090000 14.100000 0.230000)\n        4)\n      (segment 4317 \n        (point 300.170000 129.090000 14.100000 0.230000)\n        (point 300.170000 129.090000 15.680000 0.230000)\n        4)\n      (segment 4318 \n        (point 300.170000 129.090000 15.680000 0.230000)\n        (point 300.170000 129.090000 15.650000 0.230000)\n        4)\n      (segment 4319 \n        (point 300.170000 129.090000 15.650000 0.230000)\n        (point 302.370000 127.810000 17.300000 0.230000)\n        4)\n      (segment 4320 \n        (point 302.370000 127.810000 17.300000 0.230000)\n        (point 302.370000 127.810000 17.270000 0.230000)\n        4)\n      (segment 4321 \n        (point 302.370000 127.810000 17.270000 0.230000)\n        (point 303.780000 125.760000 19.230000 0.230000)\n        4)\n      (segment 4322 \n        (point 303.780000 125.760000 19.230000 0.230000)\n        (point 303.780000 125.760000 19.200000 0.230000)\n        4)\n      (segment 4323 \n        (point 303.780000 125.760000 19.200000 0.230000)\n        (point 305.210000 123.700000 20.330000 0.230000)\n        4)\n      (segment 4324 \n        (point 305.210000 123.700000 20.330000 0.230000)\n        (point 309.220000 124.640000 20.330000 0.230000)\n        4)\n      (segment 4325 \n        (point 309.220000 124.640000 20.330000 0.230000)\n        (point 312.800000 125.480000 21.600000 0.230000)\n        4)\n      (segment 4326 \n        (point 312.800000 125.480000 21.600000 0.230000)\n        (point 312.850000 127.290000 21.600000 0.230000)\n        4)\n      (segment 4327 \n        (point 312.850000 127.290000 21.600000 0.230000)\n        (point 314.680000 129.510000 22.670000 0.230000)\n        4)\n      (segment 4328 \n        (point 314.680000 129.510000 22.670000 0.230000)\n        (point 315.710000 129.150000 24.050000 0.230000)\n        4)\n      (segment 4329 \n        (point 315.710000 129.150000 24.050000 0.230000)\n        (point 315.710000 129.150000 24.000000 0.230000)\n        4)\n      (segment 4330 \n        (point 315.710000 129.150000 24.000000 0.230000)\n        (point 315.180000 131.410000 25.130000 0.230000)\n        4)\n      (segment 4331 \n        (point 315.180000 131.410000 25.130000 0.230000)\n        (point 314.200000 133.570000 26.700000 0.230000)\n        4)\n      (segment 4332 \n        (point 314.200000 133.570000 26.700000 0.230000)\n        (point 313.170000 133.920000 27.770000 0.230000)\n        4)\n      (segment 4333 \n        (point 313.170000 133.920000 27.770000 0.230000)\n        (point 312.720000 133.820000 28.050000 0.230000)\n        4)\n      (segment 4334 \n        (point 312.720000 133.820000 28.050000 0.230000)\n        (point 312.460000 134.950000 29.150000 0.230000)\n        4)\n      (segment 4335 \n        (point 312.460000 134.950000 29.150000 0.230000)\n        (point 312.010000 134.840000 29.300000 0.230000)\n        4))\n    (branch 159 157 \n      (segment 4336 \n        (point 278.390000 127.570000 -0.050000 0.460000)\n        (point 280.130000 126.180000 -1.300000 0.460000)\n        4)\n      (segment 4337 \n        (point 280.130000 126.180000 -1.300000 0.460000)\n        (point 281.730000 125.370000 -2.700000 0.460000)\n        4)\n      (segment 4338 \n        (point 281.730000 125.370000 -2.700000 0.460000)\n        (point 283.200000 125.120000 -4.200000 0.460000)\n        4)\n      (segment 4339 \n        (point 283.200000 125.120000 -4.200000 0.460000)\n        (point 283.150000 123.310000 -5.600000 0.460000)\n        4)\n      (segment 4340 \n        (point 283.150000 123.310000 -5.600000 0.460000)\n        (point 284.630000 123.060000 -7.200000 0.460000)\n        4)\n      (segment 4341 \n        (point 284.630000 123.060000 -7.200000 0.460000)\n        (point 285.390000 123.820000 -9.570000 0.460000)\n        4)\n      (segment 4342 \n        (point 285.390000 123.820000 -9.570000 0.460000)\n        (point 286.860000 123.580000 -11.150000 0.460000)\n        4)\n      (segment 4343 \n        (point 286.860000 123.580000 -11.150000 0.460000)\n        (point 288.470000 122.760000 -11.650000 0.460000)\n        4)\n      (segment 4344 \n        (point 288.470000 122.760000 -11.650000 0.460000)\n        (point 289.130000 119.930000 -10.350000 0.460000)\n        4)\n      (segment 4345 \n        (point 289.130000 119.930000 -10.350000 0.460000)\n        (point 290.860000 118.550000 -9.550000 0.460000)\n        4)\n      (segment 4346 \n        (point 290.860000 118.550000 -9.550000 0.460000)\n        (point 293.550000 119.180000 -10.050000 0.460000)\n        4)\n      (segment 4347 \n        (point 293.550000 119.180000 -10.050000 0.460000)\n        (point 295.740000 117.890000 -10.250000 0.460000)\n        4))\n    (branch 160 159 \n      (segment 4348 \n        (point 295.740000 117.890000 -10.250000 0.460000)\n        (point 298.100000 117.850000 -10.250000 0.460000)\n        4)\n      (segment 4349 \n        (point 298.100000 117.850000 -10.250000 0.460000)\n        (point 301.350000 118.020000 -11.730000 0.460000)\n        4)\n      (segment 4350 \n        (point 301.350000 118.020000 -11.730000 0.460000)\n        (point 303.550000 116.740000 -12.850000 0.460000)\n        4)\n      (segment 4351 \n        (point 303.550000 116.740000 -12.850000 0.460000)\n        (point 303.550000 116.740000 -12.880000 0.460000)\n        4)\n      (segment 4352 \n        (point 303.550000 116.740000 -12.880000 0.460000)\n        (point 306.440000 114.430000 -12.130000 0.460000)\n        4)\n      (segment 4353 \n        (point 306.440000 114.430000 -12.130000 0.460000)\n        (point 310.540000 113.000000 -14.070000 0.460000)\n        4)\n      (segment 4354 \n        (point 310.540000 113.000000 -14.070000 0.460000)\n        (point 314.200000 111.470000 -17.770000 0.460000)\n        4)\n      (segment 4355 \n        (point 314.200000 111.470000 -17.770000 0.460000)\n        (point 317.280000 110.400000 -18.150000 0.460000)\n        4)\n      (segment 4356 \n        (point 317.280000 110.400000 -18.150000 0.460000)\n        (point 319.320000 109.690000 -18.150000 0.460000)\n        4)\n      (segment 4357 \n        (point 319.320000 109.690000 -18.150000 0.460000)\n        (point 323.170000 109.400000 -17.100000 0.460000)\n        4)\n      (segment 4358 \n        (point 323.170000 109.400000 -17.100000 0.460000)\n        (point 325.530000 109.360000 -15.700000 0.460000)\n        4)\n      (segment 4359 \n        (point 325.530000 109.360000 -15.700000 0.460000)\n        (point 327.260000 107.970000 -16.300000 0.460000)\n        4)\n      (segment 4360 \n        (point 327.260000 107.970000 -16.300000 0.460000)\n        (point 327.260000 107.970000 -16.320000 0.460000)\n        4)\n      (segment 4361 \n        (point 327.260000 107.970000 -16.320000 0.460000)\n        (point 330.700000 109.370000 -16.750000 0.460000)\n        4)\n      (segment 4362 \n        (point 330.700000 109.370000 -16.750000 0.460000)\n        (point 332.490000 109.790000 -17.770000 0.460000)\n        4)\n      (segment 4363 \n        (point 332.490000 109.790000 -17.770000 0.460000)\n        (point 335.610000 110.510000 -18.650000 0.460000)\n        4)\n      (segment 4364 \n        (point 335.610000 110.510000 -18.650000 0.460000)\n        (point 338.560000 110.020000 -17.200000 0.460000)\n        4)\n      (segment 4365 \n        (point 338.560000 110.020000 -17.200000 0.460000)\n        (point 341.450000 107.710000 -18.130000 0.460000)\n        4)\n      (segment 4366 \n        (point 341.450000 107.710000 -18.130000 0.460000)\n        (point 345.920000 108.760000 -18.570000 0.460000)\n        4)\n      (segment 4367 \n        (point 345.920000 108.760000 -18.570000 0.460000)\n        (point 349.310000 108.360000 -17.250000 0.460000)\n        4)\n      (segment 4368 \n        (point 349.310000 108.360000 -17.250000 0.460000)\n        (point 352.970000 106.830000 -17.250000 0.460000)\n        4)\n      (segment 4369 \n        (point 352.970000 106.830000 -17.250000 0.460000)\n        (point 354.340000 102.970000 -18.380000 0.460000)\n        4)\n      (segment 4370 \n        (point 354.340000 102.970000 -18.380000 0.460000)\n        (point 358.000000 101.430000 -18.820000 0.460000)\n        4))\n    (branch 161 160 \n      (segment 4371 \n        (point 358.000000 101.430000 -18.820000 0.460000)\n        (point 359.350000 101.750000 -20.570000 0.460000)\n        4)\n      (segment 4372 \n        (point 359.350000 101.750000 -20.570000 0.460000)\n        (point 358.980000 99.270000 -22.250000 0.460000)\n        4)\n      (segment 4373 \n        (point 358.980000 99.270000 -22.250000 0.460000)\n        (point 360.140000 98.350000 -24.000000 0.460000)\n        4)\n      (segment 4374 \n        (point 360.140000 98.350000 -24.000000 0.460000)\n        (point 362.050000 98.200000 -24.880000 0.460000)\n        4)\n      (segment 4375 \n        (point 362.050000 98.200000 -24.880000 0.460000)\n        (point 363.750000 95.020000 -25.870000 0.460000)\n        4)\n      (segment 4376 \n        (point 363.750000 95.020000 -25.870000 0.460000)\n        (point 366.060000 93.180000 -25.200000 0.460000)\n        4)\n      (segment 4377 \n        (point 366.060000 93.180000 -25.200000 0.460000)\n        (point 369.770000 93.450000 -25.970000 0.460000)\n        4)\n      (segment 4378 \n        (point 369.770000 93.450000 -25.970000 0.460000)\n        (point 372.220000 91.040000 -26.580000 0.460000)\n        4)\n      (segment 4379 \n        (point 372.220000 91.040000 -26.580000 0.460000)\n        (point 374.710000 90.430000 -27.970000 0.230000)\n        4)\n      (segment 4380 \n        (point 374.710000 90.430000 -27.970000 0.230000)\n        (point 379.400000 88.540000 -29.200000 0.230000)\n        4)\n      (segment 4381 \n        (point 379.400000 88.540000 -29.200000 0.230000)\n        (point 382.920000 87.570000 -30.000000 0.230000)\n        4)\n      (segment 4382 \n        (point 382.920000 87.570000 -30.000000 0.230000)\n        (point 384.660000 86.190000 -30.350000 0.230000)\n        4)\n      (segment 4383 \n        (point 384.660000 86.190000 -30.350000 0.230000)\n        (point 386.710000 85.470000 -30.400000 0.230000)\n        4)\n      (segment 4384 \n        (point 386.710000 85.470000 -30.400000 0.230000)\n        (point 388.690000 87.130000 -31.320000 0.230000)\n        4)\n      (segment 4385 \n        (point 388.690000 87.130000 -31.320000 0.230000)\n        (point 388.240000 87.020000 -31.350000 0.230000)\n        4)\n      (segment 4386 \n        (point 388.240000 87.020000 -31.350000 0.230000)\n        (point 392.740000 83.900000 -32.400000 0.230000)\n        4)\n      (segment 4387 \n        (point 392.740000 83.900000 -32.400000 0.230000)\n        (point 394.200000 83.640000 -33.850000 0.230000)\n        4)\n      (segment 4388 \n        (point 394.200000 83.640000 -33.850000 0.230000)\n        (point 394.200000 83.640000 -33.880000 0.230000)\n        4)\n      (segment 4389 \n        (point 394.200000 83.640000 -33.880000 0.230000)\n        (point 395.640000 81.590000 -35.420000 0.230000)\n        4)\n      (segment 4390 \n        (point 395.640000 81.590000 -35.420000 0.230000)\n        (point 398.010000 81.550000 -36.950000 0.230000)\n        4)\n      (segment 4391 \n        (point 398.010000 81.550000 -36.950000 0.230000)\n        (point 398.180000 82.790000 -39.080000 0.230000)\n        4)\n      (segment 4392 \n        (point 398.180000 82.790000 -39.080000 0.230000)\n        (point 398.180000 82.790000 -39.100000 0.230000)\n        4)\n      (segment 4393 \n        (point 398.180000 82.790000 -39.100000 0.230000)\n        (point 398.620000 82.900000 -41.350000 0.230000)\n        4)\n      (segment 4394 \n        (point 398.620000 82.900000 -41.350000 0.230000)\n        (point 400.990000 82.850000 -43.470000 0.230000)\n        4)\n      (segment 4395 \n        (point 400.990000 82.850000 -43.470000 0.230000)\n        (point 400.990000 82.850000 -43.500000 0.230000)\n        4)\n      (segment 4396 \n        (point 400.990000 82.850000 -43.500000 0.230000)\n        (point 401.390000 81.150000 -45.850000 0.230000)\n        4)\n      (segment 4397 \n        (point 401.390000 81.150000 -45.850000 0.230000)\n        (point 402.240000 79.560000 -49.070000 0.230000)\n        4)\n      (segment 4398 \n        (point 402.240000 79.560000 -49.070000 0.230000)\n        (point 402.240000 79.560000 -49.130000 0.230000)\n        4)\n      (segment 4399 \n        (point 402.240000 79.560000 -49.130000 0.230000)\n        (point 402.940000 78.530000 -52.400000 0.230000)\n        4)\n      (segment 4400 \n        (point 402.940000 78.530000 -52.400000 0.230000)\n        (point 402.940000 78.530000 -52.450000 0.230000)\n        4))\n    (branch 162 160 \n      (segment 4401 \n        (point 358.000000 101.430000 -18.820000 0.460000)\n        (point 358.900000 101.650000 -17.200000 0.460000)\n        4)\n      (segment 4402 \n        (point 358.900000 101.650000 -17.200000 0.460000)\n        (point 358.900000 101.650000 -17.230000 0.460000)\n        4)\n      (segment 4403 \n        (point 358.900000 101.650000 -17.230000 0.460000)\n        (point 360.010000 98.920000 -17.170000 0.460000)\n        4)\n      (segment 4404 \n        (point 360.010000 98.920000 -17.170000 0.460000)\n        (point 360.410000 97.230000 -16.100000 0.460000)\n        4)\n      (segment 4405 \n        (point 360.410000 97.230000 -16.100000 0.460000)\n        (point 362.140000 95.840000 -19.130000 0.460000)\n        4)\n      (segment 4406 \n        (point 362.140000 95.840000 -19.130000 0.460000)\n        (point 362.140000 95.840000 -19.150000 0.460000)\n        4)\n      (segment 4407 \n        (point 362.140000 95.840000 -19.150000 0.460000)\n        (point 363.880000 94.450000 -19.730000 0.460000)\n        4)\n      (segment 4408 \n        (point 363.880000 94.450000 -19.730000 0.460000)\n        (point 363.880000 94.450000 -19.750000 0.460000)\n        4)\n      (segment 4409 \n        (point 363.880000 94.450000 -19.750000 0.460000)\n        (point 367.220000 92.250000 -20.470000 0.460000)\n        4)\n      (segment 4410 \n        (point 367.220000 92.250000 -20.470000 0.460000)\n        (point 368.650000 90.200000 -20.470000 0.460000)\n        4)\n      (segment 4411 \n        (point 368.650000 90.200000 -20.470000 0.460000)\n        (point 371.280000 89.020000 -20.470000 0.460000)\n        4)\n      (segment 4412 \n        (point 371.280000 89.020000 -20.470000 0.460000)\n        (point 374.360000 87.950000 -20.600000 0.230000)\n        4)\n      (segment 4413 \n        (point 374.360000 87.950000 -20.600000 0.230000)\n        (point 376.350000 85.430000 -21.000000 0.230000)\n        4)\n      (segment 4414 \n        (point 376.350000 85.430000 -21.000000 0.230000)\n        (point 378.630000 81.780000 -21.000000 0.230000)\n        4)\n      (segment 4415 \n        (point 378.630000 81.780000 -21.000000 0.230000)\n        (point 380.760000 78.710000 -22.430000 0.230000)\n        4)\n      (segment 4416 \n        (point 380.760000 78.710000 -22.430000 0.230000)\n        (point 383.210000 76.300000 -22.570000 0.230000)\n        4)\n      (segment 4417 \n        (point 383.210000 76.300000 -22.570000 0.230000)\n        (point 385.220000 73.780000 -22.630000 0.230000)\n        4)\n      (segment 4418 \n        (point 385.220000 73.780000 -22.630000 0.230000)\n        (point 389.000000 71.670000 -23.350000 0.230000)\n        4)\n      (segment 4419 \n        (point 389.000000 71.670000 -23.350000 0.230000)\n        (point 390.160000 70.760000 -24.880000 0.230000)\n        4)\n      (segment 4420 \n        (point 390.160000 70.760000 -24.880000 0.230000)\n        (point 390.550000 69.060000 -26.880000 0.230000)\n        4)\n      (segment 4421 \n        (point 390.550000 69.060000 -26.880000 0.230000)\n        (point 390.550000 69.060000 -26.900000 0.230000)\n        4)\n      (segment 4422 \n        (point 390.550000 69.060000 -26.900000 0.230000)\n        (point 391.990000 67.000000 -28.330000 0.230000)\n        4)\n      (segment 4423 \n        (point 391.990000 67.000000 -28.330000 0.230000)\n        (point 392.970000 64.850000 -30.000000 0.230000)\n        4)\n      (segment 4424 \n        (point 392.970000 64.850000 -30.000000 0.230000)\n        (point 394.260000 63.350000 -31.500000 0.230000)\n        4)\n      (segment 4425 \n        (point 394.260000 63.350000 -31.500000 0.230000)\n        (point 394.260000 63.350000 -31.520000 0.230000)\n        4)\n      (segment 4426 \n        (point 394.260000 63.350000 -31.520000 0.230000)\n        (point 392.990000 60.680000 -33.570000 0.230000)\n        4)\n      (segment 4427 \n        (point 392.990000 60.680000 -33.570000 0.230000)\n        (point 392.950000 58.890000 -37.380000 0.230000)\n        4)\n      (segment 4428 \n        (point 392.950000 58.890000 -37.380000 0.230000)\n        (point 392.950000 58.890000 -37.420000 0.230000)\n        4)\n      (segment 4429 \n        (point 392.950000 58.890000 -37.420000 0.230000)\n        (point 393.930000 56.720000 -39.170000 0.230000)\n        4)\n      (segment 4430 \n        (point 393.930000 56.720000 -39.170000 0.230000)\n        (point 393.930000 56.720000 -39.200000 0.230000)\n        4)\n      (segment 4431 \n        (point 393.930000 56.720000 -39.200000 0.230000)\n        (point 395.080000 55.800000 -41.800000 0.230000)\n        4)\n      (segment 4432 \n        (point 395.080000 55.800000 -41.800000 0.230000)\n        (point 395.080000 55.800000 -41.930000 0.230000)\n        4)\n      (segment 4433 \n        (point 395.080000 55.800000 -41.930000 0.230000)\n        (point 395.300000 52.870000 -43.420000 0.230000)\n        4)\n      (segment 4434 \n        (point 395.300000 52.870000 -43.420000 0.230000)\n        (point 395.300000 52.870000 -43.450000 0.230000)\n        4)\n      (segment 4435 \n        (point 395.300000 52.870000 -43.450000 0.230000)\n        (point 394.670000 51.530000 -45.720000 0.230000)\n        4))\n    (branch 163 159 \n      (segment 4436 \n        (point 295.740000 117.890000 -10.250000 0.460000)\n        (point 296.550000 116.330000 -11.650000 0.460000)\n        4)\n      (segment 4437 \n        (point 296.550000 116.330000 -11.650000 0.460000)\n        (point 297.080000 114.060000 -13.570000 0.460000)\n        4)\n      (segment 4438 \n        (point 297.080000 114.060000 -13.570000 0.460000)\n        (point 297.080000 114.060000 -13.600000 0.460000)\n        4)\n      (segment 4439 \n        (point 297.080000 114.060000 -13.600000 0.460000)\n        (point 295.350000 115.450000 -16.020000 0.460000)\n        4)\n      (segment 4440 \n        (point 295.350000 115.450000 -16.020000 0.460000)\n        (point 293.690000 114.460000 -18.050000 0.460000)\n        4)\n      (segment 4441 \n        (point 293.690000 114.460000 -18.050000 0.460000)\n        (point 292.050000 113.480000 -18.720000 0.460000)\n        4)\n      (segment 4442 \n        (point 292.050000 113.480000 -18.720000 0.460000)\n        (point 292.050000 113.480000 -18.750000 0.460000)\n        4)\n      (segment 4443 \n        (point 292.050000 113.480000 -18.750000 0.460000)\n        (point 290.440000 114.300000 -20.100000 0.460000)\n        4)\n      (segment 4444 \n        (point 290.440000 114.300000 -20.100000 0.460000)\n        (point 290.060000 111.820000 -21.450000 0.460000)\n        4)\n      (segment 4445 \n        (point 290.060000 111.820000 -21.450000 0.460000)\n        (point 290.910000 110.240000 -22.970000 0.460000)\n        4)\n      (segment 4446 \n        (point 290.910000 110.240000 -22.970000 0.460000)\n        (point 290.240000 107.080000 -24.520000 0.460000)\n        4)\n      (segment 4447 \n        (point 290.240000 107.080000 -24.520000 0.460000)\n        (point 289.700000 103.380000 -25.100000 0.460000)\n        4)\n      (segment 4448 \n        (point 289.700000 103.380000 -25.100000 0.460000)\n        (point 288.750000 101.370000 -27.750000 0.460000)\n        4)\n      (segment 4449 \n        (point 288.750000 101.370000 -27.750000 0.460000)\n        (point 288.220000 97.650000 -29.950000 0.230000)\n        4)\n      (segment 4450 \n        (point 288.220000 97.650000 -29.950000 0.230000)\n        (point 289.060000 96.060000 -29.950000 0.230000)\n        4)\n      (segment 4451 \n        (point 289.060000 96.060000 -29.950000 0.230000)\n        (point 290.080000 95.700000 -31.170000 0.230000)\n        4)\n      (segment 4452 \n        (point 290.080000 95.700000 -31.170000 0.230000)\n        (point 289.410000 92.560000 -32.600000 0.230000)\n        4)\n      (segment 4453 \n        (point 289.410000 92.560000 -32.600000 0.230000)\n        (point 290.520000 89.830000 -33.530000 0.230000)\n        4)\n      (segment 4454 \n        (point 290.520000 89.830000 -33.530000 0.230000)\n        (point 290.600000 87.460000 -34.750000 0.230000)\n        4)\n      (segment 4455 \n        (point 290.600000 87.460000 -34.750000 0.230000)\n        (point 290.600000 87.460000 -34.780000 0.230000)\n        4)\n      (segment 4456 \n        (point 290.600000 87.460000 -34.780000 0.230000)\n        (point 290.950000 83.960000 -34.470000 0.230000)\n        4)\n      (segment 4457 \n        (point 290.950000 83.960000 -34.470000 0.230000)\n        (point 291.480000 81.700000 -36.520000 0.230000)\n        4)\n      (segment 4458 \n        (point 291.480000 81.700000 -36.520000 0.230000)\n        (point 292.410000 77.730000 -37.580000 0.230000)\n        4)\n      (segment 4459 \n        (point 292.410000 77.730000 -37.580000 0.230000)\n        (point 292.250000 72.330000 -38.050000 0.230000)\n        4)\n      (segment 4460 \n        (point 292.250000 72.330000 -38.050000 0.230000)\n        (point 291.800000 66.250000 -36.630000 0.230000)\n        4)\n      (segment 4461 \n        (point 291.800000 66.250000 -36.630000 0.230000)\n        (point 291.120000 63.110000 -38.250000 0.230000)\n        4)\n      (segment 4462 \n        (point 291.120000 63.110000 -38.250000 0.230000)\n        (point 289.560000 59.750000 -39.550000 0.230000)\n        4)\n      (segment 4463 \n        (point 289.560000 59.750000 -39.550000 0.230000)\n        (point 289.560000 59.750000 -39.600000 0.230000)\n        4)\n      (segment 4464 \n        (point 289.560000 59.750000 -39.600000 0.230000)\n        (point 286.960000 56.750000 -41.050000 0.230000)\n        4)\n      (segment 4465 \n        (point 286.960000 56.750000 -41.050000 0.230000)\n        (point 286.960000 56.750000 -41.080000 0.230000)\n        4)\n      (segment 4466 \n        (point 286.960000 56.750000 -41.080000 0.230000)\n        (point 284.990000 55.090000 -43.000000 0.230000)\n        4)\n      (segment 4467 \n        (point 284.990000 55.090000 -43.000000 0.230000)\n        (point 284.240000 54.320000 -45.680000 0.230000)\n        4))\n    (branch 164 73 \n      (segment 4468 \n        (point 255.870000 123.050000 1.320000 1.375000)\n        (point 255.220000 125.770000 2.250000 0.460000)\n        4)\n      (segment 4469 \n        (point 255.220000 125.770000 2.250000 0.460000)\n        (point 255.600000 128.240000 3.500000 0.460000)\n        4)\n      (segment 4470 \n        (point 255.600000 128.240000 3.500000 0.460000)\n        (point 257.690000 129.320000 3.570000 0.460000)\n        4)\n      (segment 4471 \n        (point 257.690000 129.320000 3.570000 0.460000)\n        (point 259.930000 129.850000 4.600000 0.460000)\n        4)\n      (segment 4472 \n        (point 259.930000 129.850000 4.600000 0.460000)\n        (point 260.280000 132.330000 5.130000 0.460000)\n        4)\n      (segment 4473 \n        (point 260.280000 132.330000 5.130000 0.460000)\n        (point 261.100000 134.900000 5.370000 0.460000)\n        4)\n      (segment 4474 \n        (point 261.100000 134.900000 5.370000 0.460000)\n        (point 261.330000 137.940000 5.970000 0.460000)\n        4)\n      (segment 4475 \n        (point 261.330000 137.940000 5.970000 0.460000)\n        (point 262.400000 139.390000 6.000000 0.460000)\n        4))\n    (branch 165 164 \n      (segment 4476 \n        (point 262.400000 139.390000 6.000000 0.460000)\n        (point 264.820000 141.160000 6.650000 0.460000)\n        4)\n      (segment 4477 \n        (point 264.820000 141.160000 6.650000 0.460000)\n        (point 265.130000 141.820000 8.200000 0.460000)\n        4)\n      (segment 4478 \n        (point 265.130000 141.820000 8.200000 0.460000)\n        (point 265.130000 141.820000 8.180000 0.460000)\n        4)\n      (segment 4479 \n        (point 265.130000 141.820000 8.180000 0.460000)\n        (point 267.820000 142.450000 9.070000 0.460000)\n        4)\n      (segment 4480 \n        (point 267.820000 142.450000 9.070000 0.460000)\n        (point 269.200000 144.560000 10.520000 0.460000)\n        4)\n      (segment 4481 \n        (point 269.200000 144.560000 10.520000 0.460000)\n        (point 269.830000 145.910000 10.600000 0.460000)\n        4))\n    (branch 166 165 \n      (segment 4482 \n        (point 269.830000 145.910000 10.600000 0.460000)\n        (point 270.770000 147.920000 12.070000 0.460000)\n        4)\n      (segment 4483 \n        (point 270.770000 147.920000 12.070000 0.460000)\n        (point 270.810000 149.720000 13.520000 0.460000)\n        4)\n      (segment 4484 \n        (point 270.810000 149.720000 13.520000 0.460000)\n        (point 269.840000 151.880000 14.500000 0.460000)\n        4)\n      (segment 4485 \n        (point 269.840000 151.880000 14.500000 0.460000)\n        (point 270.010000 153.120000 15.850000 0.460000)\n        4)\n      (segment 4486 \n        (point 270.010000 153.120000 15.850000 0.460000)\n        (point 270.380000 155.590000 16.770000 0.460000)\n        4)\n      (segment 4487 \n        (point 270.380000 155.590000 16.770000 0.460000)\n        (point 268.950000 157.640000 18.000000 0.460000)\n        4)\n      (segment 4488 \n        (point 268.950000 157.640000 18.000000 0.460000)\n        (point 268.420000 159.900000 19.420000 0.460000)\n        4)\n      (segment 4489 \n        (point 268.420000 159.900000 19.420000 0.460000)\n        (point 268.340000 162.270000 20.420000 0.460000)\n        4)\n      (segment 4490 \n        (point 268.340000 162.270000 20.420000 0.460000)\n        (point 270.190000 164.500000 21.130000 0.460000)\n        4)\n      (segment 4491 \n        (point 270.190000 164.500000 21.130000 0.460000)\n        (point 273.440000 164.670000 21.700000 0.460000)\n        4)\n      (segment 4492 \n        (point 273.440000 164.670000 21.700000 0.460000)\n        (point 275.440000 162.150000 21.070000 0.460000)\n        4)\n      (segment 4493 \n        (point 275.440000 162.150000 21.070000 0.460000)\n        (point 278.480000 159.270000 22.250000 0.460000)\n        4)\n      (segment 4494 \n        (point 278.480000 159.270000 22.250000 0.460000)\n        (point 280.130000 160.260000 23.470000 0.460000)\n        4))\n    (branch 167 166 \n      (segment 4495 \n        (point 280.130000 160.260000 23.470000 0.460000)\n        (point 282.400000 162.590000 23.470000 0.460000)\n        4)\n      (segment 4496 \n        (point 282.400000 162.590000 23.470000 0.460000)\n        (point 284.110000 165.380000 23.570000 0.460000)\n        4)\n      (segment 4497 \n        (point 284.110000 165.380000 23.570000 0.460000)\n        (point 284.110000 165.380000 23.550000 0.460000)\n        4)\n      (segment 4498 \n        (point 284.110000 165.380000 23.550000 0.460000)\n        (point 284.880000 166.150000 24.700000 0.230000)\n        4)\n      (segment 4499 \n        (point 284.880000 166.150000 24.700000 0.230000)\n        (point 284.880000 166.150000 24.800000 0.230000)\n        4)\n      (segment 4500 \n        (point 284.880000 166.150000 24.800000 0.230000)\n        (point 285.770000 166.360000 27.420000 0.230000)\n        4))\n    (branch 168 166 \n      (segment 4501 \n        (point 280.130000 160.260000 23.470000 0.460000)\n        (point 282.000000 158.320000 23.470000 0.230000)\n        4)\n      (segment 4502 \n        (point 282.000000 158.320000 23.470000 0.230000)\n        (point 282.480000 154.250000 23.470000 0.230000)\n        4)\n      (segment 4503 \n        (point 282.480000 154.250000 23.470000 0.230000)\n        (point 281.500000 150.430000 24.700000 0.230000)\n        4)\n      (segment 4504 \n        (point 281.500000 150.430000 24.700000 0.230000)\n        (point 280.860000 149.090000 26.800000 0.230000)\n        4)\n      (segment 4505 \n        (point 280.860000 149.090000 26.800000 0.230000)\n        (point 280.860000 149.090000 26.750000 0.230000)\n        4)\n      (segment 4506 \n        (point 280.860000 149.090000 26.750000 0.230000)\n        (point 280.680000 147.850000 28.420000 0.230000)\n        4))\n    (branch 169 165 \n      (segment 4507 \n        (point 269.830000 145.910000 10.600000 0.460000)\n        (point 268.670000 146.830000 11.770000 0.460000)\n        4)\n      (segment 4508 \n        (point 268.670000 146.830000 11.770000 0.460000)\n        (point 267.070000 147.650000 13.600000 0.460000)\n        4)\n      (segment 4509 \n        (point 267.070000 147.650000 13.600000 0.460000)\n        (point 264.820000 147.120000 15.480000 0.460000)\n        4)\n      (segment 4510 \n        (point 264.820000 147.120000 15.480000 0.460000)\n        (point 262.070000 148.870000 16.300000 0.460000)\n        4)\n      (segment 4511 \n        (point 262.070000 148.870000 16.300000 0.460000)\n        (point 260.190000 150.810000 17.150000 0.460000)\n        4)\n      (segment 4512 \n        (point 260.190000 150.810000 17.150000 0.460000)\n        (point 257.300000 153.120000 17.850000 0.460000)\n        4)\n      (segment 4513 \n        (point 257.300000 153.120000 17.850000 0.460000)\n        (point 254.490000 153.060000 19.270000 0.460000)\n        4)\n      (segment 4514 \n        (point 254.490000 153.060000 19.270000 0.460000)\n        (point 251.630000 151.190000 20.570000 0.460000)\n        4)\n      (segment 4515 \n        (point 251.630000 151.190000 20.570000 0.460000)\n        (point 249.660000 149.530000 22.080000 0.230000)\n        4)\n      (segment 4516 \n        (point 249.660000 149.530000 22.080000 0.230000)\n        (point 248.460000 148.660000 23.670000 0.230000)\n        4)\n      (segment 4517 \n        (point 248.460000 148.660000 23.670000 0.230000)\n        (point 248.530000 146.290000 25.630000 0.230000)\n        4))\n    (branch 170 164 \n      (segment 4518 \n        (point 262.400000 139.390000 6.000000 0.460000)\n        (point 262.760000 141.860000 4.850000 0.460000)\n        4)\n      (segment 4519 \n        (point 262.760000 141.860000 4.850000 0.460000)\n        (point 263.970000 142.740000 3.600000 0.460000)\n        4)\n      (segment 4520 \n        (point 263.970000 142.740000 3.600000 0.460000)\n        (point 266.120000 145.640000 2.880000 0.460000)\n        4)\n      (segment 4521 \n        (point 266.120000 145.640000 2.880000 0.460000)\n        (point 267.640000 147.180000 2.270000 0.460000)\n        4)\n      (segment 4522 \n        (point 267.640000 147.180000 2.270000 0.460000)\n        (point 268.580000 149.190000 1.270000 0.460000)\n        4)\n      (segment 4523 \n        (point 268.580000 149.190000 1.270000 0.460000)\n        (point 270.590000 152.650000 0.120000 0.460000)\n        4)\n      (segment 4524 \n        (point 270.590000 152.650000 0.120000 0.460000)\n        (point 270.640000 154.450000 -0.720000 0.460000)\n        4)\n      (segment 4525 \n        (point 270.640000 154.450000 -0.720000 0.460000)\n        (point 272.050000 156.580000 -1.400000 0.460000)\n        4)\n      (segment 4526 \n        (point 272.050000 156.580000 -1.400000 0.460000)\n        (point 274.270000 157.100000 -2.850000 0.460000)\n        4)\n      (segment 4527 \n        (point 274.270000 157.100000 -2.850000 0.460000)\n        (point 274.320000 158.900000 -4.200000 0.230000)\n        4)\n      (segment 4528 \n        (point 274.320000 158.900000 -4.200000 0.230000)\n        (point 273.870000 158.790000 -4.220000 0.230000)\n        4)\n      (segment 4529 \n        (point 273.870000 158.790000 -4.220000 0.230000)\n        (point 275.260000 160.920000 -5.630000 0.230000)\n        4)\n      (segment 4530 \n        (point 275.260000 160.920000 -5.630000 0.230000)\n        (point 275.260000 160.920000 -5.680000 0.230000)\n        4)\n      (segment 4531 \n        (point 275.260000 160.920000 -5.680000 0.230000)\n        (point 275.880000 162.250000 -6.930000 0.230000)\n        4)\n      (segment 4532 \n        (point 275.880000 162.250000 -6.930000 0.230000)\n        (point 276.560000 165.400000 -8.730000 0.460000)\n        4)\n      (segment 4533 \n        (point 276.560000 165.400000 -8.730000 0.460000)\n        (point 277.070000 167.300000 -9.430000 0.460000)\n        4)\n      (segment 4534 \n        (point 277.070000 167.300000 -9.430000 0.460000)\n        (point 279.390000 171.440000 -10.120000 0.460000)\n        4)\n      (segment 4535 \n        (point 279.390000 171.440000 -10.120000 0.460000)\n        (point 279.480000 175.040000 -11.530000 0.460000)\n        4)\n      (segment 4536 \n        (point 279.480000 175.040000 -11.530000 0.460000)\n        (point 279.400000 177.400000 -13.180000 0.460000)\n        4)\n      (segment 4537 \n        (point 279.400000 177.400000 -13.180000 0.460000)\n        (point 279.180000 180.350000 -13.700000 0.460000)\n        4)\n      (segment 4538 \n        (point 279.180000 180.350000 -13.700000 0.460000)\n        (point 281.340000 183.240000 -12.930000 0.460000)\n        4)\n      (segment 4539 \n        (point 281.340000 183.240000 -12.930000 0.460000)\n        (point 284.640000 185.210000 -13.000000 0.460000)\n        4)\n      (segment 4540 \n        (point 284.640000 185.210000 -13.000000 0.460000)\n        (point 285.320000 188.360000 -13.130000 0.460000)\n        4)\n      (segment 4541 \n        (point 285.320000 188.360000 -13.130000 0.460000)\n        (point 285.860000 192.060000 -14.000000 0.460000)\n        4)\n      (segment 4542 \n        (point 285.860000 192.060000 -14.000000 0.460000)\n        (point 285.860000 192.060000 -14.020000 0.460000)\n        4)\n      (segment 4543 \n        (point 285.860000 192.060000 -14.020000 0.460000)\n        (point 287.700000 194.280000 -14.600000 0.460000)\n        4)\n      (segment 4544 \n        (point 287.700000 194.280000 -14.600000 0.460000)\n        (point 292.350000 196.560000 -15.300000 0.460000)\n        4)\n      (segment 4545 \n        (point 292.350000 196.560000 -15.300000 0.460000)\n        (point 294.450000 197.660000 -15.320000 0.460000)\n        4)\n      (segment 4546 \n        (point 294.450000 197.660000 -15.320000 0.460000)\n        (point 297.110000 198.300000 -17.630000 0.460000)\n        4)\n      (segment 4547 \n        (point 297.110000 198.300000 -17.630000 0.460000)\n        (point 300.050000 197.790000 -18.450000 0.460000)\n        4)\n      (segment 4548 \n        (point 300.050000 197.790000 -18.450000 0.460000)\n        (point 303.500000 199.200000 -19.600000 0.460000)\n        4)\n      (segment 4549 \n        (point 303.500000 199.200000 -19.600000 0.460000)\n        (point 306.990000 202.400000 -20.600000 0.460000)\n        4)\n      (segment 4550 \n        (point 306.990000 202.400000 -20.600000 0.460000)\n        (point 308.680000 205.190000 -21.070000 0.460000)\n        4)\n      (segment 4551 \n        (point 308.680000 205.190000 -21.070000 0.460000)\n        (point 311.600000 208.860000 -21.200000 0.460000)\n        4)\n      (segment 4552 \n        (point 311.600000 208.860000 -21.200000 0.460000)\n        (point 313.880000 211.190000 -22.630000 0.460000)\n        4)\n      (segment 4553 \n        (point 313.880000 211.190000 -22.630000 0.460000)\n        (point 313.880000 211.190000 -22.650000 0.460000)\n        4)\n      (segment 4554 \n        (point 313.880000 211.190000 -22.650000 0.460000)\n        (point 316.170000 213.500000 -24.080000 0.460000)\n        4)\n      (segment 4555 \n        (point 316.170000 213.500000 -24.080000 0.460000)\n        (point 317.680000 215.060000 -25.550000 0.460000)\n        4)\n      (segment 4556 \n        (point 317.680000 215.060000 -25.550000 0.460000)\n        (point 317.680000 215.060000 -25.570000 0.460000)\n        4)\n      (segment 4557 \n        (point 317.680000 215.060000 -25.570000 0.460000)\n        (point 317.600000 217.430000 -26.450000 0.460000)\n        4)\n      (segment 4558 \n        (point 317.600000 217.430000 -26.450000 0.460000)\n        (point 318.630000 217.070000 -27.670000 0.230000)\n        4)\n      (segment 4559 \n        (point 318.630000 217.070000 -27.670000 0.230000)\n        (point 318.630000 217.070000 -27.700000 0.230000)\n        4)\n      (segment 4560 \n        (point 318.630000 217.070000 -27.700000 0.230000)\n        (point 318.990000 219.550000 -29.800000 0.230000)\n        4)\n      (segment 4561 \n        (point 318.990000 219.550000 -29.800000 0.230000)\n        (point 318.510000 223.620000 -31.320000 0.230000)\n        4)\n      (segment 4562 \n        (point 318.510000 223.620000 -31.320000 0.230000)\n        (point 318.510000 223.620000 -31.350000 0.230000)\n        4)\n      (segment 4563 \n        (point 318.510000 223.620000 -31.350000 0.230000)\n        (point 317.530000 225.770000 -32.520000 0.230000)\n        4)\n      (segment 4564 \n        (point 317.530000 225.770000 -32.520000 0.230000)\n        (point 319.230000 228.570000 -33.400000 0.230000)\n        4)\n      (segment 4565 \n        (point 319.230000 228.570000 -33.400000 0.230000)\n        (point 321.690000 232.110000 -33.920000 0.230000)\n        4)\n      (segment 4566 \n        (point 321.690000 232.110000 -33.920000 0.230000)\n        (point 322.770000 233.580000 -35.570000 0.230000)\n        4)\n      (segment 4567 \n        (point 322.770000 233.580000 -35.570000 0.230000)\n        (point 322.770000 233.580000 -35.600000 0.230000)\n        4)\n      (segment 4568 \n        (point 322.770000 233.580000 -35.600000 0.230000)\n        (point 323.890000 236.820000 -36.780000 0.230000)\n        4)\n      (segment 4569 \n        (point 323.890000 236.820000 -36.780000 0.230000)\n        (point 323.890000 236.820000 -36.800000 0.230000)\n        4)\n      (segment 4570 \n        (point 323.890000 236.820000 -36.800000 0.230000)\n        (point 325.280000 238.940000 -38.470000 0.230000)\n        4)\n      (segment 4571 \n        (point 325.280000 238.940000 -38.470000 0.230000)\n        (point 325.280000 238.940000 -38.500000 0.230000)\n        4)\n      (segment 4572 \n        (point 325.280000 238.940000 -38.500000 0.230000)\n        (point 325.240000 243.110000 -39.880000 0.230000)\n        4)\n      (segment 4573 \n        (point 325.240000 243.110000 -39.880000 0.230000)\n        (point 325.240000 243.110000 -39.900000 0.230000)\n        4)\n      (segment 4574 \n        (point 325.240000 243.110000 -39.900000 0.230000)\n        (point 324.710000 245.370000 -41.470000 0.230000)\n        4)\n      (segment 4575 \n        (point 324.710000 245.370000 -41.470000 0.230000)\n        (point 324.450000 246.510000 -43.400000 0.230000)\n        4)\n      (segment 4576 \n        (point 324.450000 246.510000 -43.400000 0.230000)\n        (point 324.450000 246.510000 -43.420000 0.230000)\n        4)\n      (segment 4577 \n        (point 324.450000 246.510000 -43.420000 0.230000)\n        (point 324.230000 249.430000 -45.250000 0.230000)\n        4))\n    (branch 171 72 \n      (segment 4578 \n        (point 258.380000 102.270000 -5.970000 1.375000)\n        (point 259.910000 101.780000 -7.350000 0.460000)\n        4)\n      (segment 4579 \n        (point 259.910000 101.780000 -7.350000 0.460000)\n        (point 261.790000 99.830000 -8.820000 0.460000)\n        4)\n      (segment 4580 \n        (point 261.790000 99.830000 -8.820000 0.460000)\n        (point 263.040000 102.520000 -9.430000 0.460000)\n        4)\n      (segment 4581 \n        (point 263.040000 102.520000 -9.430000 0.460000)\n        (point 263.010000 106.690000 -10.070000 0.460000)\n        4)\n      (segment 4582 \n        (point 263.010000 106.690000 -10.070000 0.460000)\n        (point 264.530000 108.240000 -10.570000 0.460000)\n        4)\n      (segment 4583 \n        (point 264.530000 108.240000 -10.570000 0.460000)\n        (point 266.760000 108.760000 -10.850000 0.460000)\n        4)\n      (segment 4584 \n        (point 266.760000 108.760000 -10.850000 0.460000)\n        (point 267.830000 110.210000 -11.350000 0.460000)\n        4)\n      (segment 4585 \n        (point 267.830000 110.210000 -11.350000 0.460000)\n        (point 269.810000 111.870000 -12.570000 0.460000)\n        4)\n      (segment 4586 \n        (point 269.810000 111.870000 -12.570000 0.460000)\n        (point 270.570000 112.640000 -13.720000 0.460000)\n        4)\n      (segment 4587 \n        (point 270.570000 112.640000 -13.720000 0.460000)\n        (point 273.370000 112.710000 -14.800000 0.460000)\n        4)\n      (segment 4588 \n        (point 273.370000 112.710000 -14.800000 0.460000)\n        (point 271.500000 114.650000 -16.130000 0.460000)\n        4)\n      (segment 4589 \n        (point 271.500000 114.650000 -16.130000 0.460000)\n        (point 273.290000 115.060000 -17.950000 0.460000)\n        4)\n      (segment 4590 \n        (point 273.290000 115.060000 -17.950000 0.460000)\n        (point 275.150000 113.130000 -18.820000 0.460000)\n        4)\n      (segment 4591 \n        (point 275.150000 113.130000 -18.820000 0.460000)\n        (point 276.820000 114.110000 -19.970000 0.460000)\n        4)\n      (segment 4592 \n        (point 276.820000 114.110000 -19.970000 0.460000)\n        (point 275.840000 116.270000 -21.280000 0.460000)\n        4)\n      (segment 4593 \n        (point 275.840000 116.270000 -21.280000 0.460000)\n        (point 275.840000 116.270000 -21.300000 0.460000)\n        4)\n      (segment 4594 \n        (point 275.840000 116.270000 -21.300000 0.460000)\n        (point 276.790000 118.280000 -22.770000 0.460000)\n        4)\n      (segment 4595 \n        (point 276.790000 118.280000 -22.770000 0.460000)\n        (point 276.790000 118.280000 -22.800000 0.460000)\n        4)\n      (segment 4596 \n        (point 276.790000 118.280000 -22.800000 0.460000)\n        (point 277.540000 119.050000 -23.700000 0.460000)\n        4)\n      (segment 4597 \n        (point 277.540000 119.050000 -23.700000 0.460000)\n        (point 278.750000 119.940000 -24.420000 0.460000)\n        4)\n      (segment 4598 \n        (point 278.750000 119.940000 -24.420000 0.460000)\n        (point 280.490000 118.550000 -24.420000 0.460000)\n        4)\n      (segment 4599 \n        (point 280.490000 118.550000 -24.420000 0.460000)\n        (point 282.990000 117.950000 -26.000000 0.460000)\n        4)\n      (segment 4600 \n        (point 282.990000 117.950000 -26.000000 0.460000)\n        (point 283.930000 119.960000 -28.550000 0.460000)\n        4)\n      (segment 4601 \n        (point 283.930000 119.960000 -28.550000 0.460000)\n        (point 284.110000 121.190000 -30.670000 0.460000)\n        4)\n      (segment 4602 \n        (point 284.110000 121.190000 -30.670000 0.460000)\n        (point 285.900000 121.610000 -32.350000 0.460000)\n        4)\n      (segment 4603 \n        (point 285.900000 121.610000 -32.350000 0.460000)\n        (point 285.900000 121.610000 -32.380000 0.460000)\n        4)\n      (segment 4604 \n        (point 285.900000 121.610000 -32.380000 0.460000)\n        (point 288.700000 121.670000 -31.600000 0.460000)\n        4)\n      (segment 4605 \n        (point 288.700000 121.670000 -31.600000 0.460000)\n        (point 288.700000 121.670000 -31.630000 0.460000)\n        4)\n      (segment 4606 \n        (point 288.700000 121.670000 -31.630000 0.460000)\n        (point 290.540000 123.890000 -34.500000 0.460000)\n        4)\n      (segment 4607 \n        (point 290.540000 123.890000 -34.500000 0.460000)\n        (point 291.220000 127.040000 -35.420000 0.460000)\n        4)\n      (segment 4608 \n        (point 291.220000 127.040000 -35.420000 0.460000)\n        (point 291.220000 127.040000 -35.450000 0.460000)\n        4)\n      (segment 4609 \n        (point 291.220000 127.040000 -35.450000 0.460000)\n        (point 291.980000 127.810000 -37.400000 0.460000)\n        4)\n      (segment 4610 \n        (point 291.980000 127.810000 -37.400000 0.460000)\n        (point 294.030000 127.100000 -39.470000 0.460000)\n        4)\n      (segment 4611 \n        (point 294.030000 127.100000 -39.470000 0.460000)\n        (point 294.030000 127.100000 -39.500000 0.460000)\n        4)\n      (segment 4612 \n        (point 294.030000 127.100000 -39.500000 0.460000)\n        (point 296.850000 127.160000 -41.550000 0.460000)\n        4)\n      (segment 4613 \n        (point 296.850000 127.160000 -41.550000 0.460000)\n        (point 296.850000 127.160000 -41.570000 0.460000)\n        4)\n      (segment 4614 \n        (point 296.850000 127.160000 -41.570000 0.460000)\n        (point 298.550000 129.950000 -44.350000 0.460000)\n        4)\n      (segment 4615 \n        (point 298.550000 129.950000 -44.350000 0.460000)\n        (point 299.490000 131.960000 -45.850000 0.460000)\n        4)\n      (segment 4616 \n        (point 299.490000 131.960000 -45.850000 0.460000)\n        (point 299.490000 131.960000 -45.880000 0.460000)\n        4)\n      (segment 4617 \n        (point 299.490000 131.960000 -45.880000 0.460000)\n        (point 301.190000 134.740000 -47.330000 0.460000)\n        4)\n      (segment 4618 \n        (point 301.190000 134.740000 -47.330000 0.460000)\n        (point 300.790000 136.440000 -48.380000 0.460000)\n        4)\n      (segment 4619 \n        (point 300.790000 136.440000 -48.380000 0.460000)\n        (point 300.790000 136.440000 -48.400000 0.460000)\n        4)\n      (segment 4620 \n        (point 300.790000 136.440000 -48.400000 0.460000)\n        (point 303.340000 137.630000 -49.400000 0.460000)\n        4)\n      (segment 4621 \n        (point 303.340000 137.630000 -49.400000 0.460000)\n        (point 301.730000 138.450000 -51.850000 0.460000)\n        4)\n      (segment 4622 \n        (point 301.730000 138.450000 -51.850000 0.460000)\n        (point 303.700000 140.120000 -53.700000 0.460000)\n        4)\n      (segment 4623 \n        (point 303.700000 140.120000 -53.700000 0.460000)\n        (point 306.250000 141.310000 -55.050000 0.460000)\n        4)\n      (segment 4624 \n        (point 306.250000 141.310000 -55.050000 0.460000)\n        (point 306.250000 141.310000 -57.670000 0.460000)\n        4)\n      (segment 4625 \n        (point 306.250000 141.310000 -57.670000 0.460000)\n        (point 306.250000 141.310000 -57.700000 0.460000)\n        4)\n      (segment 4626 \n        (point 306.250000 141.310000 -57.700000 0.460000)\n        (point 306.430000 142.550000 -59.800000 0.460000)\n        4)\n      (segment 4627 \n        (point 306.430000 142.550000 -59.800000 0.460000)\n        (point 308.270000 144.760000 -61.150000 0.460000)\n        4)\n      (segment 4628 \n        (point 308.270000 144.760000 -61.150000 0.460000)\n        (point 310.240000 146.420000 -62.850000 0.460000)\n        4)\n      (segment 4629 \n        (point 310.240000 146.420000 -62.850000 0.460000)\n        (point 311.490000 149.100000 -64.670000 0.460000)\n        4)\n      (segment 4630 \n        (point 311.490000 149.100000 -64.670000 0.460000)\n        (point 310.510000 151.270000 -64.970000 0.460000)\n        4)\n      (segment 4631 \n        (point 310.510000 151.270000 -64.970000 0.460000)\n        (point 310.510000 151.270000 -65.000000 0.460000)\n        4)\n      (segment 4632 \n        (point 310.510000 151.270000 -65.000000 0.460000)\n        (point 309.670000 152.880000 -66.520000 0.230000)\n        4)\n      (segment 4633 \n        (point 309.670000 152.880000 -66.520000 0.230000)\n        (point 308.060000 153.690000 -69.250000 0.230000)\n        4)\n      (segment 4634 \n        (point 308.060000 153.690000 -69.250000 0.230000)\n        (point 307.080000 155.860000 -71.170000 0.230000)\n        4)\n      (segment 4635 \n        (point 307.080000 155.860000 -71.170000 0.230000)\n        (point 307.450000 158.330000 -73.500000 0.230000)\n        4)\n      (segment 4636 \n        (point 307.450000 158.330000 -73.500000 0.230000)\n        (point 307.760000 159.000000 -75.630000 0.230000)\n        4)\n      (segment 4637 \n        (point 307.760000 159.000000 -75.630000 0.230000)\n        (point 307.760000 159.000000 -75.650000 0.230000)\n        4)\n      (segment 4638 \n        (point 307.760000 159.000000 -75.650000 0.230000)\n        (point 308.250000 160.910000 -77.930000 0.230000)\n        4)\n      (segment 4639 \n        (point 308.250000 160.910000 -77.930000 0.230000)\n        (point 308.250000 160.910000 -77.950000 0.230000)\n        4)\n      (segment 4640 \n        (point 308.250000 160.910000 -77.950000 0.230000)\n        (point 308.030000 163.840000 -80.020000 0.230000)\n        4)\n      (segment 4641 \n        (point 308.030000 163.840000 -80.020000 0.230000)\n        (point 308.400000 166.310000 -82.150000 0.230000)\n        4)\n      (segment 4642 \n        (point 308.400000 166.310000 -82.150000 0.230000)\n        (point 309.210000 168.890000 -83.350000 0.230000)\n        4)\n      (segment 4643 \n        (point 309.210000 168.890000 -83.350000 0.230000)\n        (point 307.340000 170.850000 -85.050000 0.230000)\n        4)\n      (segment 4644 \n        (point 307.340000 170.850000 -85.050000 0.230000)\n        (point 308.150000 173.420000 -86.400000 0.230000)\n        4)\n      (segment 4645 \n        (point 308.150000 173.420000 -86.400000 0.230000)\n        (point 307.440000 174.450000 -88.420000 0.230000)\n        4)\n      (segment 4646 \n        (point 307.440000 174.450000 -88.420000 0.230000)\n        (point 306.590000 176.050000 -90.800000 0.230000)\n        4)\n      (segment 4647 \n        (point 306.590000 176.050000 -90.800000 0.230000)\n        (point 306.370000 178.970000 -91.930000 0.230000)\n        4)\n      (segment 4648 \n        (point 306.370000 178.970000 -91.930000 0.230000)\n        (point 306.910000 182.690000 -92.700000 0.230000)\n        4)\n      (segment 4649 \n        (point 306.910000 182.690000 -92.700000 0.230000)\n        (point 306.510000 184.390000 -94.070000 0.230000)\n        4)\n      (segment 4650 \n        (point 306.510000 184.390000 -94.070000 0.230000)\n        (point 306.510000 184.390000 -94.100000 0.230000)\n        4)\n      (segment 4651 \n        (point 306.510000 184.390000 -94.100000 0.230000)\n        (point 306.610000 188.000000 -95.570000 0.230000)\n        4)\n      (segment 4652 \n        (point 306.610000 188.000000 -95.570000 0.230000)\n        (point 306.610000 188.000000 -95.630000 0.230000)\n        4)\n      (segment 4653 \n        (point 306.610000 188.000000 -95.630000 0.230000)\n        (point 306.800000 189.230000 -98.400000 0.230000)\n        4)\n      (segment 4654 \n        (point 306.800000 189.230000 -98.400000 0.230000)\n        (point 307.030000 192.270000 -100.400000 0.230000)\n        4)\n      (segment 4655 \n        (point 307.030000 192.270000 -100.400000 0.230000)\n        (point 306.100000 196.240000 -101.550000 0.230000)\n        4)\n      (segment 4656 \n        (point 306.100000 196.240000 -101.550000 0.230000)\n        (point 305.130000 198.380000 -103.850000 0.230000)\n        4)\n      (segment 4657 \n        (point 305.130000 198.380000 -103.850000 0.230000)\n        (point 305.490000 200.870000 -104.970000 0.230000)\n        4)\n      (segment 4658 \n        (point 305.490000 200.870000 -104.970000 0.230000)\n        (point 305.490000 200.870000 -105.000000 0.230000)\n        4)\n      (segment 4659 \n        (point 305.490000 200.870000 -105.000000 0.230000)\n        (point 305.800000 201.540000 -106.170000 0.230000)\n        4)\n      (segment 4660 \n        (point 305.800000 201.540000 -106.170000 0.230000)\n        (point 305.800000 201.540000 -106.350000 0.230000)\n        4))\n    (branch 172 71 \n      (segment 4661 \n        (point 258.630000 73.060000 1.380000 1.375000)\n        (point 255.150000 73.830000 0.700000 0.460000)\n        4)\n      (segment 4662 \n        (point 255.150000 73.830000 0.700000 0.460000)\n        (point 252.750000 72.060000 0.700000 0.460000)\n        4)\n      (segment 4663 \n        (point 252.750000 72.060000 0.700000 0.460000)\n        (point 251.360000 69.940000 1.800000 0.460000)\n        4)\n      (segment 4664 \n        (point 251.360000 69.940000 1.800000 0.460000)\n        (point 251.360000 69.940000 1.770000 0.460000)\n        4)\n    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80.090000 10.820000 0.690000)\n        4)\n      (segment 4672 \n        (point 251.340000 80.090000 10.820000 0.690000)\n        (point 252.540000 80.970000 12.070000 0.690000)\n        4)\n      (segment 4673 \n        (point 252.540000 80.970000 12.070000 0.690000)\n        (point 253.890000 81.290000 14.000000 0.690000)\n        4)\n      (segment 4674 \n        (point 253.890000 81.290000 14.000000 0.690000)\n        (point 253.440000 81.180000 13.950000 0.690000)\n        4)\n      (segment 4675 \n        (point 253.440000 81.180000 13.950000 0.690000)\n        (point 255.940000 80.570000 15.650000 0.690000)\n        4)\n      (segment 4676 \n        (point 255.940000 80.570000 15.650000 0.690000)\n        (point 259.510000 81.400000 16.800000 0.690000)\n        4)\n      (segment 4677 \n        (point 259.510000 81.400000 16.800000 0.690000)\n        (point 263.220000 81.680000 17.950000 0.690000)\n        4)\n      (segment 4678 \n        (point 263.220000 81.680000 17.950000 0.690000)\n        (point 265.350000 78.590000 19.150000 0.690000)\n        4))\n    (branch 174 173 \n      (segment 4679 \n        (point 265.350000 78.590000 19.150000 0.690000)\n        (point 263.700000 77.610000 19.750000 0.690000)\n        4)\n      (segment 4680 \n        (point 263.700000 77.610000 19.750000 0.690000)\n        (point 263.700000 77.610000 19.730000 0.690000)\n        4)\n      (segment 4681 \n        (point 263.700000 77.610000 19.730000 0.690000)\n        (point 260.620000 78.690000 21.450000 0.460000)\n        4)\n      (segment 4682 \n        (point 260.620000 78.690000 21.450000 0.460000)\n        (point 260.350000 79.810000 23.330000 0.460000)\n        4)\n      (segment 4683 \n        (point 260.350000 79.810000 23.330000 0.460000)\n        (point 261.030000 82.960000 24.400000 0.460000)\n        4)\n      (segment 4684 \n        (point 261.030000 82.960000 24.400000 0.460000)\n        (point 261.210000 84.190000 26.130000 0.460000)\n        4)\n      (segment 4685 \n        (point 261.210000 84.190000 26.130000 0.460000)\n        (point 259.480000 85.580000 27.020000 0.460000)\n        4)\n      (segment 4686 \n        (point 259.480000 85.580000 27.020000 0.460000)\n        (point 258.310000 86.510000 28.880000 0.460000)\n        4)\n      (segment 4687 \n        (point 258.310000 86.510000 28.880000 0.460000)\n        (point 257.600000 87.530000 30.230000 0.460000)\n        4))\n    (branch 175 173 \n      (segment 4688 \n        (point 265.350000 78.590000 19.150000 0.690000)\n        (point 266.460000 75.880000 18.170000 0.460000)\n        4)\n      (segment 4689 \n        (point 266.460000 75.880000 18.170000 0.460000)\n        (point 267.170000 74.840000 18.570000 0.460000)\n        4)\n      (segment 4690 \n        (point 267.170000 74.840000 18.570000 0.460000)\n        (point 269.850000 75.470000 20.650000 0.460000)\n        4)\n      (segment 4691 \n        (point 269.850000 75.470000 20.650000 0.460000)\n        (point 271.020000 74.540000 21.750000 0.460000)\n        4)\n      (segment 4692 \n        (point 271.020000 74.540000 21.750000 0.460000)\n        (point 271.910000 74.750000 23.420000 0.460000)\n        4)\n      (segment 4693 \n        (point 271.910000 74.750000 23.420000 0.460000)\n        (point 273.610000 77.550000 24.770000 0.460000)\n        4)\n      (segment 4694 \n        (point 273.610000 77.550000 24.770000 0.460000)\n        (point 273.610000 77.550000 24.750000 0.460000)\n        4)\n      (segment 4695 \n        (point 273.610000 77.550000 24.750000 0.460000)\n        (point 276.860000 77.710000 25.380000 0.460000)\n        4)\n      (segment 4696 \n        (point 276.860000 77.710000 25.380000 0.460000)\n        (point 279.370000 77.100000 24.950000 0.460000)\n        4)\n      (segment 4697 \n        (point 279.370000 77.100000 24.950000 0.460000)\n        (point 281.280000 76.950000 25.870000 0.460000)\n        4)\n      (segment 4698 \n        (point 281.280000 76.950000 25.870000 0.460000)\n        (point 281.280000 76.950000 25.850000 0.460000)\n        4)\n      (segment 4699 \n        (point 281.280000 76.950000 25.850000 0.460000)\n        (point 281.860000 76.490000 28.100000 0.460000)\n        4))\n    (branch 176 172 \n      (segment 4700 \n        (point 250.040000 75.600000 5.250000 0.460000)\n        (point 247.400000 76.780000 6.150000 0.460000)\n        4)\n      (segment 4701 \n        (point 247.400000 76.780000 6.150000 0.460000)\n        (point 244.850000 75.580000 8.020000 0.460000)\n        4)\n      (segment 4702 \n        (point 244.850000 75.580000 8.020000 0.460000)\n        (point 241.330000 76.550000 8.600000 0.460000)\n        4)\n      (segment 4703 \n        (point 241.330000 76.550000 8.600000 0.460000)\n        (point 241.330000 76.550000 8.570000 0.460000)\n        4)\n      (segment 4704 \n        (point 241.330000 76.550000 8.570000 0.460000)\n        (point 236.860000 75.500000 9.500000 0.460000)\n        4)\n      (segment 4705 \n        (point 236.860000 75.500000 9.500000 0.460000)\n        (point 236.860000 75.500000 9.480000 0.460000)\n        4)\n      (segment 4706 \n        (point 236.860000 75.500000 9.480000 0.460000)\n        (point 234.320000 74.310000 10.850000 0.460000)\n        4)\n      (segment 4707 \n        (point 234.320000 74.310000 10.850000 0.460000)\n        (point 234.320000 74.310000 10.820000 0.460000)\n        4)\n      (segment 4708 \n        (point 234.320000 74.310000 10.820000 0.460000)\n        (point 231.590000 71.880000 10.850000 0.460000)\n        4)\n      (segment 4709 \n        (point 231.590000 71.880000 10.850000 0.460000)\n        (point 230.700000 71.670000 10.850000 0.460000)\n        4)\n      (segment 4710 \n        (point 230.700000 71.670000 10.850000 0.460000)\n        (point 230.200000 69.760000 10.850000 0.460000)\n        4)\n      (segment 4711 \n        (point 230.200000 69.760000 10.850000 0.460000)\n        (point 227.120000 70.840000 12.100000 0.460000)\n        4)\n      (segment 4712 \n        (point 227.120000 70.840000 12.100000 0.460000)\n        (point 225.210000 70.990000 13.070000 0.460000)\n        4)\n      (segment 4713 \n        (point 225.210000 70.990000 13.070000 0.460000)\n        (point 225.210000 70.990000 13.050000 0.460000)\n        4)\n      (segment 4714 \n        (point 225.210000 70.990000 13.050000 0.460000)\n        (point 223.100000 69.890000 12.350000 0.460000)\n        4)\n      (segment 4715 \n        (point 223.100000 69.890000 12.350000 0.460000)\n        (point 219.710000 70.290000 13.400000 0.460000)\n        4)\n      (segment 4716 \n        (point 219.710000 70.290000 13.400000 0.460000)\n        (point 217.480000 69.760000 14.250000 0.460000)\n        4)\n      (segment 4717 \n        (point 217.480000 69.760000 14.250000 0.460000)\n        (point 213.830000 71.290000 14.600000 0.460000)\n        4)\n      (segment 4718 \n        (point 213.830000 71.290000 14.600000 0.460000)\n        (point 210.650000 68.760000 14.100000 0.460000)\n        4)\n      (segment 4719 \n        (point 210.650000 68.760000 14.100000 0.460000)\n        (point 209.390000 66.070000 14.430000 0.460000)\n        4)\n      (segment 4720 \n        (point 209.390000 66.070000 14.430000 0.460000)\n        (point 207.550000 63.860000 15.680000 0.460000)\n        4)\n      (segment 4721 \n        (point 207.550000 63.860000 15.680000 0.460000)\n        (point 203.400000 63.480000 14.970000 0.460000)\n        4)\n      (segment 4722 \n        (point 203.400000 63.480000 14.970000 0.460000)\n        (point 201.760000 62.490000 15.430000 0.460000)\n        4)\n      (segment 4723 \n        (point 201.760000 62.490000 15.430000 0.460000)\n        (point 200.500000 59.810000 17.200000 0.460000)\n        4)\n      (segment 4724 \n        (point 200.500000 59.810000 17.200000 0.460000)\n        (point 197.900000 56.820000 18.450000 0.460000)\n        4)\n      (segment 4725 \n        (point 197.900000 56.820000 18.450000 0.460000)\n        (point 194.730000 54.280000 20.150000 0.460000)\n        4)\n      (segment 4726 \n        (point 194.730000 54.280000 20.150000 0.460000)\n        (point 192.940000 53.860000 21.750000 0.460000)\n        4)\n      (segment 4727 \n        (point 192.940000 53.860000 21.750000 0.460000)\n        (point 190.700000 53.340000 22.950000 0.460000)\n        4)\n      (segment 4728 \n        (point 190.700000 53.340000 22.950000 0.460000)\n        (point 190.700000 53.340000 22.920000 0.460000)\n        4)\n      (segment 4729 \n        (point 190.700000 53.340000 22.920000 0.460000)\n        (point 188.030000 52.710000 24.270000 0.460000)\n        4)\n      (segment 4730 \n        (point 188.030000 52.710000 24.270000 0.460000)\n        (point 183.740000 52.910000 25.200000 0.460000)\n        4)\n      (segment 4731 \n        (point 183.740000 52.910000 25.200000 0.460000)\n        (point 182.320000 54.950000 25.200000 0.460000)\n        4)\n      (segment 4732 \n        (point 182.320000 54.950000 25.200000 0.460000)\n        (point 181.790000 57.220000 24.300000 0.460000)\n        4)\n      (segment 4733 \n        (point 181.790000 57.220000 24.300000 0.460000)\n        (point 182.410000 58.560000 24.670000 0.460000)\n        4)\n      (segment 4734 \n        (point 182.410000 58.560000 24.670000 0.460000)\n        (point 182.410000 58.560000 24.650000 0.460000)\n        4)\n      (segment 4735 \n        (point 182.410000 58.560000 24.650000 0.460000)\n        (point 183.500000 60.000000 25.630000 0.460000)\n        4)\n      (segment 4736 \n        (point 183.500000 60.000000 25.630000 0.460000)\n        (point 183.500000 60.000000 27.350000 0.460000)\n        4)\n      (segment 4737 \n        (point 183.500000 60.000000 27.350000 0.460000)\n        (point 183.500000 60.000000 27.320000 0.460000)\n        4)\n      (segment 4738 \n        (point 183.500000 60.000000 27.320000 0.460000)\n        (point 183.940000 60.100000 29.420000 0.460000)\n        4)\n      (segment 4739 \n        (point 183.940000 60.100000 29.420000 0.460000)\n        (point 183.540000 61.810000 30.600000 0.460000)\n        4)\n      (segment 4740 \n        (point 183.540000 61.810000 30.600000 0.460000)\n        (point 183.540000 61.810000 30.570000 0.460000)\n        4))\n    (branch 177 71 \n      (segment 4741 \n        (point 258.630000 73.060000 1.380000 1.375000)\n        (point 259.280000 74.900000 3.670000 0.460000)\n        4)\n      (segment 4742 \n        (point 259.280000 74.900000 3.670000 0.460000)\n        (point 259.280000 74.900000 3.650000 0.460000)\n        4)\n      (segment 4743 \n        (point 259.280000 74.900000 3.650000 0.460000)\n        (point 260.350000 76.340000 5.970000 0.460000)\n        4)\n      (segment 4744 \n        (point 260.350000 76.340000 5.970000 0.460000)\n        (point 259.950000 78.040000 7.470000 0.460000)\n        4)\n      (segment 4745 \n        (point 259.950000 78.040000 7.470000 0.460000)\n        (point 261.730000 78.460000 8.380000 0.230000)\n        4)\n      (segment 4746 \n        (point 261.730000 78.460000 8.380000 0.230000)\n        (point 263.830000 79.560000 8.770000 0.230000)\n        4)\n      (segment 4747 \n        (point 263.830000 79.560000 8.770000 0.230000)\n        (point 265.680000 81.770000 8.770000 0.230000)\n        4)\n      (segment 4748 \n        (point 265.680000 81.770000 8.770000 0.230000)\n        (point 268.090000 83.530000 9.570000 0.230000)\n        4)\n      (segment 4749 \n        (point 268.090000 83.530000 9.570000 0.230000)\n        (point 267.550000 85.800000 10.100000 0.230000)\n        4)\n      (segment 4750 \n        (point 267.550000 85.800000 10.100000 0.230000)\n        (point 266.440000 88.520000 11.000000 0.230000)\n        4)\n      (segment 4751 \n        (point 266.440000 88.520000 11.000000 0.230000)\n        (point 266.760000 89.190000 11.900000 0.230000)\n        4)\n      (segment 4752 \n        (point 266.760000 89.190000 11.900000 0.230000)\n        (point 268.280000 90.740000 12.650000 0.230000)\n        4)\n      (segment 4753 \n        (point 268.280000 90.740000 12.650000 0.230000)\n        (point 270.960000 91.370000 13.800000 0.230000)\n        4)\n      (segment 4754 \n        (point 270.960000 91.370000 13.800000 0.230000)\n        (point 272.430000 91.120000 14.750000 0.230000)\n        4)\n      (segment 4755 \n        (point 272.430000 91.120000 14.750000 0.230000)\n        (point 273.590000 90.190000 15.880000 0.230000)\n        4)\n      (segment 4756 \n        (point 273.590000 90.190000 15.880000 0.230000)\n        (point 276.000000 91.950000 16.920000 0.230000)\n        4)\n      (segment 4757 \n        (point 276.000000 91.950000 16.920000 0.230000)\n        (point 278.420000 93.720000 17.950000 0.230000)\n        4)\n      (segment 4758 \n        (point 278.420000 93.720000 17.950000 0.230000)\n        (point 279.680000 96.400000 19.630000 0.230000)\n        4)\n      (segment 4759 \n        (point 279.680000 96.400000 19.630000 0.230000)\n        (point 279.680000 96.400000 19.600000 0.230000)\n        4)\n      (segment 4760 \n        (point 279.680000 96.400000 19.600000 0.230000)\n        (point 281.470000 96.820000 20.900000 0.230000)\n        4)\n      (segment 4761 \n        (point 281.470000 96.820000 20.900000 0.230000)\n        (point 281.470000 96.820000 20.870000 0.230000)\n        4)\n      (segment 4762 \n        (point 281.470000 96.820000 20.870000 0.230000)\n        (point 282.040000 96.360000 22.100000 0.230000)\n        4)\n      (segment 4763 \n        (point 282.040000 96.360000 22.100000 0.230000)\n        (point 285.470000 97.760000 23.470000 0.230000)\n        4)\n      (segment 4764 \n        (point 285.470000 97.760000 23.470000 0.230000)\n        (point 287.450000 99.410000 24.450000 0.230000)\n        4)\n      (segment 4765 \n        (point 287.450000 99.410000 24.450000 0.230000)\n        (point 287.010000 99.310000 24.450000 0.230000)\n        4)\n      (segment 4766 \n        (point 287.010000 99.310000 24.450000 0.230000)\n        (point 288.880000 103.340000 25.300000 0.230000)\n        4)\n      (segment 4767 \n        (point 288.880000 103.340000 25.300000 0.230000)\n        (point 289.110000 106.380000 26.420000 0.230000)\n        4)\n      (segment 4768 \n        (point 289.110000 106.380000 26.420000 0.230000)\n        (point 289.160000 108.180000 24.050000 0.230000)\n        4)\n      (segment 4769 \n        (point 289.160000 108.180000 24.050000 0.230000)\n        (point 290.230000 109.630000 23.170000 0.230000)\n        4)\n      (segment 4770 \n        (point 290.230000 109.630000 23.170000 0.230000)\n        (point 289.830000 111.320000 22.450000 0.230000)\n        4)\n      (segment 4771 \n        (point 289.830000 111.320000 22.450000 0.230000)\n        (point 289.830000 111.320000 22.430000 0.230000)\n        4)\n      (segment 4772 \n        (point 289.830000 111.320000 22.430000 0.230000)\n        (point 288.990000 112.910000 20.550000 0.230000)\n        4)\n      (segment 4773 \n        (point 288.990000 112.910000 20.550000 0.230000)\n        (point 288.420000 113.380000 18.500000 0.230000)\n        4)\n      (segment 4774 \n        (point 288.420000 113.380000 18.500000 0.230000)\n        (point 288.420000 113.380000 18.480000 0.230000)\n        4))\n    (branch 178 70 \n      (segment 4775 \n        (point 256.210000 49.210000 -4.700000 1.835000)\n        (point 251.510000 49.770000 -3.380000 0.460000)\n        4)\n      (segment 4776 \n        (point 251.510000 49.770000 -3.380000 0.460000)\n        (point 249.560000 54.100000 -2.950000 0.460000)\n        4)\n      (segment 4777 \n        (point 249.560000 54.100000 -2.950000 0.460000)\n        (point 246.470000 55.170000 -2.950000 0.460000)\n        4)\n      (segment 4778 \n        (point 246.470000 55.170000 -2.950000 0.460000)\n        (point 244.110000 55.210000 -3.970000 0.460000)\n        4)\n      (segment 4779 \n        (point 244.110000 55.210000 -3.970000 0.460000)\n        (point 242.820000 56.690000 -5.650000 0.460000)\n        4)\n      (segment 4780 \n        (point 242.820000 56.690000 -5.650000 0.460000)\n        (point 241.970000 58.290000 -6.600000 0.460000)\n        4)\n      (segment 4781 \n        (point 241.970000 58.290000 -6.600000 0.460000)\n        (point 241.270000 59.320000 -8.230000 0.460000)\n        4)\n      (segment 4782 \n        (point 241.270000 59.320000 -8.230000 0.460000)\n        (point 239.670000 60.140000 -8.950000 0.460000)\n        4)\n      (segment 4783 \n        (point 239.670000 60.140000 -8.950000 0.460000)\n        (point 236.850000 60.070000 -9.170000 0.460000)\n        4)\n      (segment 4784 \n        (point 236.850000 60.070000 -9.170000 0.460000)\n        (point 232.750000 61.500000 -8.420000 0.460000)\n        4)\n      (segment 4785 \n        (point 232.750000 61.500000 -8.420000 0.460000)\n        (point 232.750000 61.500000 -8.450000 0.460000)\n        4)\n      (segment 4786 \n        (point 232.750000 61.500000 -8.450000 0.460000)\n        (point 231.090000 60.510000 -9.220000 0.460000)\n        4)\n      (segment 4787 \n        (point 231.090000 60.510000 -9.220000 0.460000)\n        (point 230.160000 58.500000 -10.120000 0.460000)\n        4)\n      (segment 4788 \n        (point 230.160000 58.500000 -10.120000 0.460000)\n        (point 228.810000 58.190000 -11.250000 0.460000)\n        4)\n      (segment 4789 \n        (point 228.810000 58.190000 -11.250000 0.460000)\n        (point 225.730000 59.260000 -12.000000 0.460000)\n        4)\n      (segment 4790 \n        (point 225.730000 59.260000 -12.000000 0.460000)\n        (point 222.300000 57.860000 -12.350000 0.460000)\n        4)\n      (segment 4791 \n        (point 222.300000 57.860000 -12.350000 0.460000)\n        (point 219.170000 57.130000 -13.000000 0.460000)\n        4)\n      (segment 4792 \n        (point 219.170000 57.130000 -13.000000 0.460000)\n        (point 215.910000 56.960000 -13.720000 0.460000)\n        4)\n      (segment 4793 \n        (point 215.910000 56.960000 -13.720000 0.460000)\n        (point 213.410000 57.570000 -14.750000 0.460000)\n        4)\n      (segment 4794 \n        (point 213.410000 57.570000 -14.750000 0.460000)\n        (point 210.810000 54.570000 -15.220000 0.460000)\n        4)\n      (segment 4795 \n        (point 210.810000 54.570000 -15.220000 0.460000)\n        (point 207.110000 54.300000 -15.500000 0.460000)\n        4)\n      (segment 4796 \n        (point 207.110000 54.300000 -15.500000 0.460000)\n        (point 204.160000 54.800000 -17.130000 0.460000)\n        4)\n      (segment 4797 \n        (point 204.160000 54.800000 -17.130000 0.460000)\n        (point 201.610000 55.310000 -16.570000 0.460000)\n        4))\n    (branch 179 178 \n      (segment 4798 \n        (point 201.610000 55.310000 -16.570000 0.460000)\n        (point 200.640000 55.770000 -16.570000 0.460000)\n        4)\n      (segment 4799 \n        (point 200.640000 55.770000 -16.570000 0.460000)\n        (point 198.320000 57.620000 -15.300000 0.460000)\n        4)\n      (segment 4800 \n        (point 198.320000 57.620000 -15.300000 0.460000)\n        (point 195.320000 56.310000 -14.650000 0.460000)\n        4)\n      (segment 4801 \n        (point 195.320000 56.310000 -14.650000 0.460000)\n        (point 190.340000 57.540000 -14.270000 0.460000)\n        4)\n      (segment 4802 \n        (point 190.340000 57.540000 -14.270000 0.460000)\n        (point 187.160000 55.000000 -13.630000 0.460000)\n        4)\n      (segment 4803 \n        (point 187.160000 55.000000 -13.630000 0.460000)\n        (point 183.230000 51.680000 -12.570000 0.460000)\n        4)\n      (segment 4804 \n        (point 183.230000 51.680000 -12.570000 0.460000)\n        (point 183.310000 49.320000 -10.820000 0.460000)\n        4)\n      (segment 4805 \n        (point 183.310000 49.320000 -10.820000 0.460000)\n        (point 180.760000 48.130000 -8.420000 0.460000)\n        4)\n      (segment 4806 \n        (point 180.760000 48.130000 -8.420000 0.460000)\n        (point 179.230000 46.570000 -6.780000 0.460000)\n        4)\n      (segment 4807 \n        (point 179.230000 46.570000 -6.780000 0.460000)\n        (point 178.480000 45.790000 -4.630000 0.460000)\n        4)\n      (segment 4808 \n        (point 178.480000 45.790000 -4.630000 0.460000)\n        (point 174.320000 45.410000 -2.150000 0.230000)\n        4)\n      (segment 4809 \n        (point 174.320000 45.410000 -2.150000 0.230000)\n        (point 174.320000 45.410000 -2.170000 0.230000)\n        4)\n      (segment 4810 \n        (point 174.320000 45.410000 -2.170000 0.230000)\n        (point 171.870000 47.830000 -1.670000 0.230000)\n        4)\n      (segment 4811 \n        (point 171.870000 47.830000 -1.670000 0.230000)\n        (point 171.870000 47.830000 -1.700000 0.230000)\n        4)\n      (segment 4812 \n        (point 171.870000 47.830000 -1.700000 0.230000)\n        (point 170.940000 51.800000 -1.000000 0.230000)\n        4)\n      (segment 4813 \n        (point 170.940000 51.800000 -1.000000 0.230000)\n        (point 168.630000 53.640000 0.150000 0.230000)\n        4)\n      (segment 4814 \n        (point 168.630000 53.640000 0.150000 0.230000)\n        (point 168.630000 53.640000 0.120000 0.230000)\n        4)\n      (segment 4815 \n        (point 168.630000 53.640000 0.120000 0.230000)\n        (point 167.650000 55.800000 1.070000 0.230000)\n        4)\n      (segment 4816 \n        (point 167.650000 55.800000 1.070000 0.230000)\n        (point 167.650000 55.800000 1.050000 0.230000)\n        4)\n      (segment 4817 \n        (point 167.650000 55.800000 1.050000 0.230000)\n        (point 166.940000 56.820000 2.450000 0.230000)\n        4)\n      (segment 4818 \n        (point 166.940000 56.820000 2.450000 0.230000)\n        (point 166.940000 56.820000 2.420000 0.230000)\n        4)\n      (segment 4819 \n        (point 166.940000 56.820000 2.420000 0.230000)\n        (point 166.360000 57.290000 4.320000 0.230000)\n        4))\n    (branch 180 178 \n      (segment 4820 \n        (point 201.610000 55.310000 -16.570000 0.460000)\n        (point 198.540000 56.390000 -16.980000 0.230000)\n        4)\n      (segment 4821 \n        (point 198.540000 56.390000 -16.980000 0.230000)\n        (point 195.270000 56.220000 -15.970000 0.230000)\n        4)\n      (segment 4822 \n        (point 195.270000 56.220000 -15.970000 0.230000)\n        (point 190.860000 56.970000 -16.450000 0.230000)\n        4)\n      (segment 4823 \n        (point 190.860000 56.970000 -16.450000 0.230000)\n        (point 188.320000 55.780000 -15.800000 0.230000)\n        4)\n      (segment 4824 \n        (point 188.320000 55.780000 -15.800000 0.230000)\n        (point 185.900000 54.020000 -15.070000 0.230000)\n        4)\n      (segment 4825 \n        (point 185.900000 54.020000 -15.070000 0.230000)\n        (point 184.510000 51.900000 -14.630000 0.230000)\n        4)\n      (segment 4826 \n        (point 184.510000 51.900000 -14.630000 0.230000)\n        (point 183.130000 49.780000 -13.300000 0.230000)\n        4)\n      (segment 4827 \n        (point 183.130000 49.780000 -13.300000 0.230000)\n        (point 183.520000 48.080000 -11.470000 0.230000)\n        4)\n      (segment 4828 \n        (point 183.520000 48.080000 -11.470000 0.230000)\n        (point 183.520000 48.080000 -11.500000 0.230000)\n        4)\n      (segment 4829 \n        (point 183.520000 48.080000 -11.500000 0.230000)\n        (point 182.180000 47.770000 -9.900000 0.230000)\n        4)\n      (segment 4830 \n        (point 182.180000 47.770000 -9.900000 0.230000)\n        (point 179.810000 47.810000 -8.570000 0.230000)\n        4)\n      (segment 4831 \n        (point 179.810000 47.810000 -8.570000 0.230000)\n        (point 179.810000 47.810000 -8.550000 0.230000)\n        4)\n      (segment 4832 \n        (point 179.810000 47.810000 -8.550000 0.230000)\n        (point 179.000000 45.230000 -7.150000 0.230000)\n        4)\n      (segment 4833 \n        (point 179.000000 45.230000 -7.150000 0.230000)\n        (point 178.690000 44.560000 -6.300000 0.230000)\n        4)\n      (segment 4834 \n        (point 178.690000 44.560000 -6.300000 0.230000)\n        (point 176.770000 44.710000 -4.700000 0.230000)\n        4)\n      (segment 4835 \n        (point 176.770000 44.710000 -4.700000 0.230000)\n        (point 172.930000 45.010000 -3.470000 0.230000)\n        4)\n      (segment 4836 \n        (point 172.930000 45.010000 -3.470000 0.230000)\n        (point 172.410000 47.260000 -3.470000 0.230000)\n        4)\n      (segment 4837 \n        (point 172.410000 47.260000 -3.470000 0.230000)\n        (point 170.710000 50.460000 -2.950000 0.230000)\n        4)\n      (segment 4838 \n        (point 170.710000 50.460000 -2.950000 0.230000)\n        (point 170.180000 52.720000 -1.880000 0.230000)\n        4)\n      (segment 4839 \n        (point 170.180000 52.720000 -1.880000 0.230000)\n        (point 168.750000 54.770000 -1.200000 0.230000)\n        4)\n      (segment 4840 \n        (point 168.750000 54.770000 -1.200000 0.230000)\n        (point 167.470000 56.270000 0.000000 0.230000)\n        4)\n      (segment 4841 \n        (point 167.470000 56.270000 0.000000 0.230000)\n        (point 166.440000 56.630000 1.630000 0.230000)\n        4)\n      (segment 4842 \n        (point 166.440000 56.630000 1.630000 0.230000)\n        (point 166.440000 56.630000 1.600000 0.230000)\n        4)\n      (segment 4843 \n        (point 166.440000 56.630000 1.600000 0.230000)\n        (point 165.420000 56.980000 2.900000 0.230000)\n        4))\n    (branch 181 178 \n      (segment 4844 \n        (point 201.610000 55.310000 -16.570000 0.460000)\n        (point 199.070000 54.110000 -19.700000 0.460000)\n        4)\n      (segment 4845 \n        (point 199.070000 54.110000 -19.700000 0.460000)\n        (point 196.970000 53.030000 -22.050000 0.460000)\n        4)\n      (segment 4846 \n        (point 196.970000 53.030000 -22.050000 0.460000)\n        (point 191.920000 52.440000 -23.000000 0.460000)\n        4)\n      (segment 4847 \n        (point 191.920000 52.440000 -23.000000 0.460000)\n        (point 189.380000 51.240000 -23.630000 0.460000)\n        4)\n      (segment 4848 \n        (point 189.380000 51.240000 -23.630000 0.460000)\n        (point 188.290000 49.800000 -24.600000 0.460000)\n        4)\n      (segment 4849 \n        (point 188.290000 49.800000 -24.600000 0.460000)\n        (point 184.460000 50.090000 -24.600000 0.460000)\n        4)\n      (segment 4850 \n        (point 184.460000 50.090000 -24.600000 0.460000)\n        (point 181.390000 51.170000 -25.250000 0.460000)\n        4)\n      (segment 4851 \n        (point 181.390000 51.170000 -25.250000 0.460000)\n        (point 178.390000 49.870000 -25.250000 0.460000)\n        4)\n      (segment 4852 \n        (point 178.390000 49.870000 -25.250000 0.460000)\n        (point 177.890000 47.960000 -24.820000 0.460000)\n        4)\n      (segment 4853 \n        (point 177.890000 47.960000 -24.820000 0.460000)\n        (point 177.890000 47.960000 -24.850000 0.460000)\n        4)\n      (segment 4854 \n        (point 177.890000 47.960000 -24.850000 0.460000)\n        (point 174.370000 48.920000 -25.900000 0.460000)\n        4)\n      (segment 4855 \n        (point 174.370000 48.920000 -25.900000 0.460000)\n        (point 172.270000 47.830000 -27.670000 0.460000)\n        4)\n      (segment 4856 \n        (point 172.270000 47.830000 -27.670000 0.460000)\n        (point 171.510000 47.060000 -29.620000 0.460000)\n        4)\n      (segment 4857 \n        (point 171.510000 47.060000 -29.620000 0.460000)\n        (point 171.060000 46.960000 -31.630000 0.460000)\n        4)\n      (segment 4858 \n        (point 171.060000 46.960000 -31.630000 0.460000)\n        (point 168.200000 45.090000 -33.100000 0.460000)\n        4)\n      (segment 4859 \n        (point 168.200000 45.090000 -33.100000 0.460000)\n        (point 165.340000 43.230000 -34.020000 0.460000)\n        4)\n      (segment 4860 \n        (point 165.340000 43.230000 -34.020000 0.460000)\n        (point 162.260000 44.290000 -35.650000 0.460000)\n        4)\n      (segment 4861 \n        (point 162.260000 44.290000 -35.650000 0.460000)\n        (point 161.550000 45.320000 -37.770000 0.460000)\n        4)\n      (segment 4862 \n        (point 161.550000 45.320000 -37.770000 0.460000)\n        (point 160.210000 45.010000 -39.950000 0.460000)\n        4)\n      (segment 4863 \n        (point 160.210000 45.010000 -39.950000 0.460000)\n        (point 157.790000 43.250000 -42.100000 0.460000)\n        4)\n      (segment 4864 \n        (point 157.790000 43.250000 -42.100000 0.460000)\n        (point 157.790000 43.250000 -42.130000 0.460000)\n        4)\n      (segment 4865 \n        (point 157.790000 43.250000 -42.130000 0.460000)\n        (point 154.090000 42.980000 -43.470000 0.460000)\n        4)\n      (segment 4866 \n        (point 154.090000 42.980000 -43.470000 0.460000)\n        (point 149.220000 43.630000 -49.650000 0.230000)\n        4)\n      (segment 4867 \n        (point 149.220000 43.630000 -49.650000 0.230000)\n        (point 145.380000 43.920000 -51.220000 0.230000)\n        4)\n      (segment 4868 \n        (point 145.380000 43.920000 -51.220000 0.230000)\n        (point 145.380000 43.920000 -51.280000 0.230000)\n        4)\n      (segment 4869 \n        (point 145.380000 43.920000 -51.280000 0.230000)\n        (point 142.580000 43.860000 -52.630000 0.230000)\n        4)\n      (segment 4870 \n        (point 142.580000 43.860000 -52.630000 0.230000)\n        (point 142.580000 43.860000 -52.650000 0.230000)\n        4)\n      (segment 4871 \n        (point 142.580000 43.860000 -52.650000 0.230000)\n        (point 141.280000 45.360000 -54.500000 0.230000)\n        4)\n      (segment 4872 \n        (point 141.280000 45.360000 -54.500000 0.230000)\n        (point 140.080000 44.470000 -56.200000 0.230000)\n        4)\n      (segment 4873 \n        (point 140.080000 44.470000 -56.200000 0.230000)\n        (point 140.080000 44.470000 -56.220000 0.230000)\n        4)\n      (segment 4874 \n        (point 140.080000 44.470000 -56.220000 0.230000)\n        (point 137.270000 44.420000 -57.470000 0.230000)\n        4)\n      (segment 4875 \n        (point 137.270000 44.420000 -57.470000 0.230000)\n        (point 137.270000 44.420000 -57.500000 0.230000)\n        4)\n      (segment 4876 \n        (point 137.270000 44.420000 -57.500000 0.230000)\n        (point 135.610000 43.420000 -59.470000 0.230000)\n        4)\n      (segment 4877 \n        (point 135.610000 43.420000 -59.470000 0.230000)\n        (point 135.610000 43.420000 -59.500000 0.230000)\n        4)\n      (segment 4878 \n        (point 135.610000 43.420000 -59.500000 0.230000)\n        (point 133.570000 44.150000 -60.950000 0.230000)\n        4)\n      (segment 4879 \n        (point 133.570000 44.150000 -60.950000 0.230000)\n        (point 132.540000 44.490000 -63.050000 0.230000)\n        4)\n      (segment 4880 \n        (point 132.540000 44.490000 -63.050000 0.230000)\n        (point 132.540000 44.490000 -63.070000 0.230000)\n        4)\n      (segment 4881 \n        (point 132.540000 44.490000 -63.070000 0.230000)\n        (point 130.040000 45.100000 -63.150000 0.230000)\n        4)\n      (segment 4882 \n        (point 130.040000 45.100000 -63.150000 0.230000)\n        (point 130.080000 46.900000 -64.700000 0.230000)\n        4)\n      (segment 4883 \n        (point 130.080000 46.900000 -64.700000 0.230000)\n        (point 127.720000 46.960000 -66.030000 0.230000)\n        4)\n      (segment 4884 \n        (point 127.720000 46.960000 -66.030000 0.230000)\n        (point 126.560000 47.870000 -67.650000 0.230000)\n        4)\n      (segment 4885 \n        (point 126.560000 47.870000 -67.650000 0.230000)\n        (point 124.460000 46.790000 -69.700000 0.230000)\n        4)\n      (segment 4886 \n        (point 124.460000 46.790000 -69.700000 0.230000)\n        (point 121.830000 47.960000 -71.680000 0.230000)\n        4)\n      (segment 4887 \n        (point 121.830000 47.960000 -71.680000 0.230000)\n        (point 121.830000 47.960000 -71.700000 0.230000)\n        4)\n      (segment 4888 \n        (point 121.830000 47.960000 -71.700000 0.230000)\n        (point 120.490000 47.640000 -73.780000 0.230000)\n        4)\n      (segment 4889 \n        (point 120.490000 47.640000 -73.780000 0.230000)\n        (point 120.490000 47.640000 -73.820000 0.230000)\n        4)\n      (segment 4890 \n        (point 120.490000 47.640000 -73.820000 0.230000)\n        (point 119.010000 47.890000 -76.380000 0.230000)\n        4)\n      (segment 4891 \n        (point 119.010000 47.890000 -76.380000 0.230000)\n        (point 116.970000 48.620000 -78.070000 0.230000)\n        4)\n      (segment 4892 \n        (point 116.970000 48.620000 -78.070000 0.230000)\n        (point 116.970000 48.620000 -78.100000 0.230000)\n        4)\n      (segment 4893 \n        (point 116.970000 48.620000 -78.100000 0.230000)\n        (point 115.490000 48.860000 -80.350000 0.230000)\n        4)\n      (segment 4894 \n        (point 115.490000 48.860000 -80.350000 0.230000)\n        (point 115.490000 48.860000 -80.400000 0.230000)\n        4)\n      (segment 4895 \n        (point 115.490000 48.860000 -80.400000 0.230000)\n        (point 114.020000 49.110000 -82.970000 0.230000)\n        4)\n      (segment 4896 \n        (point 114.020000 49.110000 -82.970000 0.230000)\n        (point 110.180000 49.410000 -84.800000 0.230000)\n        4)\n      (segment 4897 \n        (point 110.180000 49.410000 -84.800000 0.230000)\n        (point 108.890000 50.900000 -86.670000 0.230000)\n        4)\n      (segment 4898 \n        (point 108.890000 50.900000 -86.670000 0.230000)\n        (point 108.890000 50.900000 -86.700000 0.230000)\n        4)\n      (segment 4899 \n        (point 108.890000 50.900000 -86.700000 0.230000)\n        (point 107.420000 51.150000 -88.570000 0.230000)\n        4)\n      (segment 4900 \n        (point 107.420000 51.150000 -88.570000 0.230000)\n        (point 107.420000 51.150000 -88.600000 0.230000)\n        4)\n      (segment 4901 \n        (point 107.420000 51.150000 -88.600000 0.230000)\n        (point 104.340000 52.220000 -90.250000 0.230000)\n        4)\n      (segment 4902 \n        (point 104.340000 52.220000 -90.250000 0.230000)\n        (point 104.340000 52.220000 -90.280000 0.230000)\n        4)\n      (segment 4903 \n        (point 104.340000 52.220000 -90.280000 0.230000)\n        (point 102.460000 54.170000 -92.150000 0.230000)\n        4)\n      (segment 4904 \n        (point 102.460000 54.170000 -92.150000 0.230000)\n        (point 101.890000 54.630000 -93.820000 0.230000)\n        4)\n      (segment 4905 \n        (point 101.890000 54.630000 -93.820000 0.230000)\n        (point 101.890000 54.630000 -93.850000 0.230000)\n        4)\n      (segment 4906 \n        (point 101.890000 54.630000 -93.850000 0.230000)\n        (point 99.130000 56.370000 -96.100000 0.230000)\n        4)\n      (segment 4907 \n        (point 99.130000 56.370000 -96.100000 0.230000)\n        (point 97.210000 56.530000 -98.800000 0.230000)\n        4)\n      (segment 4908 \n        (point 97.210000 56.530000 -98.800000 0.230000)\n        (point 96.370000 58.120000 -101.650000 0.230000)\n        4)\n      (segment 4909 \n        (point 96.370000 58.120000 -101.650000 0.230000)\n        (point 93.550000 58.050000 -104.250000 0.230000)\n        4)\n      (segment 4910 \n        (point 93.550000 58.050000 -104.250000 0.230000)\n        (point 91.100000 60.460000 -106.150000 0.230000)\n        4)\n      (segment 4911 \n        (point 91.100000 60.460000 -106.150000 0.230000)\n        (point 88.740000 60.510000 -109.200000 0.230000)\n        4))\n    (branch 182 69 \n      (segment 4912 \n        (point 260.100000 44.740000 -6.380000 1.835000)\n        (point 262.090000 46.880000 -7.070000 0.690000)\n        4)\n      (segment 4913 \n        (point 262.090000 46.880000 -7.070000 0.690000)\n        (point 266.250000 47.260000 -7.700000 0.690000)\n        4)\n      (segment 4914 \n        (point 266.250000 47.260000 -7.700000 0.690000)\n        (point 268.340000 48.340000 -8.520000 0.690000)\n        4)\n      (segment 4915 \n        (point 268.340000 48.340000 -8.520000 0.690000)\n        (point 270.170000 50.560000 -9.200000 0.690000)\n        4)\n      (segment 4916 \n        (point 270.170000 50.560000 -9.200000 0.690000)\n        (point 271.960000 50.980000 -9.850000 0.690000)\n        4)\n      (segment 4917 \n        (point 271.960000 50.980000 -9.850000 0.690000)\n        (point 274.330000 50.940000 -8.230000 0.690000)\n        4)\n      (segment 4918 \n        (point 274.330000 50.940000 -8.230000 0.690000)\n        (point 276.250000 50.790000 -7.320000 0.690000)\n        4)\n      (segment 4919 \n        (point 276.250000 50.790000 -7.320000 0.690000)\n        (point 278.210000 52.450000 -7.950000 0.690000)\n        4)\n      (segment 4920 \n        (point 278.210000 52.450000 -7.950000 0.690000)\n        (point 278.970000 53.220000 -9.950000 0.460000)\n        4))\n    (branch 183 182 \n      (segment 4921 \n        (point 278.970000 53.220000 -9.950000 0.460000)\n        (point 279.160000 54.470000 -12.220000 0.460000)\n        4)\n      (segment 4922 \n        (point 279.160000 54.470000 -12.220000 0.460000)\n        (point 280.680000 56.010000 -13.520000 0.460000)\n        4)\n      (segment 4923 \n        (point 280.680000 56.010000 -13.520000 0.460000)\n        (point 282.770000 57.100000 -13.550000 0.460000)\n        4)\n      (segment 4924 \n        (point 282.770000 57.100000 -13.550000 0.460000)\n        (point 284.120000 57.410000 -13.350000 0.460000)\n        4)\n      (segment 4925 \n        (point 284.120000 57.410000 -13.350000 0.460000)\n        (point 288.210000 55.990000 -13.380000 0.460000)\n        4)\n      (segment 4926 \n        (point 288.210000 55.990000 -13.380000 0.460000)\n        (point 288.080000 56.560000 -13.380000 0.460000)\n        4)\n      (segment 4927 \n        (point 288.080000 56.560000 -13.380000 0.460000)\n        (point 291.130000 59.650000 -14.000000 0.460000)\n        4)\n      (segment 4928 \n        (point 291.130000 59.650000 -14.000000 0.460000)\n        (point 290.330000 63.050000 -13.070000 0.460000)\n        4)\n      (segment 4929 \n        (point 290.330000 63.050000 -13.070000 0.460000)\n        (point 290.330000 63.050000 -13.130000 0.460000)\n        4)\n      (segment 4930 \n        (point 290.330000 63.050000 -13.130000 0.460000)\n        (point 289.670000 65.880000 -13.520000 0.460000)\n        4)\n      (segment 4931 \n        (point 289.670000 65.880000 -13.520000 0.460000)\n        (point 287.620000 66.600000 -14.630000 0.460000)\n        4)\n      (segment 4932 \n        (point 287.620000 66.600000 -14.630000 0.460000)\n        (point 287.800000 67.830000 -16.450000 0.460000)\n        4)\n      (segment 4933 \n        (point 287.800000 67.830000 -16.450000 0.460000)\n        (point 287.800000 67.830000 -16.470000 0.460000)\n        4)\n      (segment 4934 \n        (point 287.800000 67.830000 -16.470000 0.460000)\n        (point 288.690000 68.040000 -18.850000 0.460000)\n        4)\n      (segment 4935 \n        (point 288.690000 68.040000 -18.850000 0.460000)\n        (point 290.030000 68.360000 -21.180000 0.460000)\n        4)\n      (segment 4936 \n        (point 290.030000 68.360000 -21.180000 0.460000)\n        (point 291.420000 70.480000 -22.450000 0.460000)\n        4)\n      (segment 4937 \n        (point 291.420000 70.480000 -22.450000 0.460000)\n        (point 291.420000 70.480000 -22.470000 0.460000)\n        4)\n      (segment 4938 \n        (point 291.420000 70.480000 -22.470000 0.460000)\n        (point 291.910000 72.380000 -23.400000 0.460000)\n        4)\n      (segment 4939 \n        (point 291.910000 72.380000 -23.400000 0.460000)\n        (point 290.930000 74.540000 -23.330000 0.230000)\n        4)\n      (segment 4940 \n        (point 290.930000 74.540000 -23.330000 0.230000)\n        (point 291.120000 75.780000 -24.500000 0.230000)\n        4)\n      (segment 4941 \n        (point 291.120000 75.780000 -24.500000 0.230000)\n        (point 291.120000 75.780000 -24.520000 0.230000)\n        4)\n      (segment 4942 \n        (point 291.120000 75.780000 -24.520000 0.230000)\n        (point 292.470000 76.090000 -26.000000 0.230000)\n        4)\n      (segment 4943 \n        (point 292.470000 76.090000 -26.000000 0.230000)\n        (point 295.640000 78.630000 -27.050000 0.230000)\n        4)\n      (segment 4944 \n        (point 295.640000 78.630000 -27.050000 0.230000)\n        (point 297.730000 79.720000 -27.630000 0.230000)\n        4)\n      (segment 4945 \n        (point 297.730000 79.720000 -27.630000 0.230000)\n        (point 297.120000 84.360000 -28.900000 0.230000)\n        4)\n      (segment 4946 \n        (point 297.120000 84.360000 -28.900000 0.230000)\n        (point 297.750000 85.690000 -30.450000 0.230000)\n        4)\n      (segment 4947 \n        (point 297.750000 85.690000 -30.450000 0.230000)\n        (point 299.540000 86.120000 -31.920000 0.230000)\n        4)\n      (segment 4948 \n        (point 299.540000 86.120000 -31.920000 0.230000)\n        (point 300.750000 86.990000 -33.800000 0.230000)\n        4))\n    (branch 184 183 \n      (segment 4949 \n        (point 300.750000 86.990000 -33.800000 0.230000)\n        (point 307.130000 87.890000 -34.280000 0.230000)\n        4)\n      (segment 4950 \n        (point 307.130000 87.890000 -34.280000 0.230000)\n        (point 306.420000 88.920000 -35.630000 0.230000)\n        4)\n      (segment 4951 \n        (point 306.420000 88.920000 -35.630000 0.230000)\n        (point 310.390000 88.060000 -36.630000 0.230000)\n        4)\n      (segment 4952 \n        (point 310.390000 88.060000 -36.630000 0.230000)\n        (point 312.350000 89.720000 -37.770000 0.230000)\n        4)\n      (segment 4953 \n        (point 312.350000 89.720000 -37.770000 0.230000)\n        (point 314.630000 92.040000 -39.100000 0.230000)\n        4)\n      (segment 4954 \n        (point 314.630000 92.040000 -39.100000 0.230000)\n        (point 314.630000 92.040000 -39.130000 0.230000)\n        4)\n      (segment 4955 \n        (point 314.630000 92.040000 -39.130000 0.230000)\n        (point 316.740000 93.140000 -40.100000 0.230000)\n        4)\n      (segment 4956 \n        (point 316.740000 93.140000 -40.100000 0.230000)\n        (point 319.730000 94.430000 -41.200000 0.230000)\n        4)\n      (segment 4957 \n        (point 319.730000 94.430000 -41.200000 0.230000)\n        (point 323.800000 97.170000 -41.520000 0.230000)\n        4)\n      (segment 4958 \n        (point 323.800000 97.170000 -41.520000 0.230000)\n        (point 326.930000 97.900000 -41.700000 0.230000)\n        4)\n      (segment 4959 \n        (point 326.930000 97.900000 -41.700000 0.230000)\n        (point 327.600000 101.050000 -41.600000 0.230000)\n        4)\n      (segment 4960 \n        (point 327.600000 101.050000 -41.600000 0.230000)\n        (point 327.600000 101.050000 -41.630000 0.230000)\n        4)\n      (segment 4961 \n        (point 327.600000 101.050000 -41.630000 0.230000)\n        (point 328.680000 102.490000 -43.150000 0.230000)\n        4)\n      (segment 4962 \n        (point 328.680000 102.490000 -43.150000 0.230000)\n        (point 328.680000 102.490000 -43.270000 0.230000)\n        4))\n    (branch 185 183 \n      (segment 4963 \n        (point 300.750000 86.990000 -33.800000 0.230000)\n        (point 302.710000 88.640000 -34.670000 0.230000)\n        4)\n      (segment 4964 \n        (point 302.710000 88.640000 -34.670000 0.230000)\n        (point 303.200000 90.560000 -36.100000 0.230000)\n        4)\n      (segment 4965 \n        (point 303.200000 90.560000 -36.100000 0.230000)\n        (point 304.280000 92.000000 -38.830000 0.230000)\n        4)\n      (segment 4966 \n        (point 304.280000 92.000000 -38.830000 0.230000)\n        (point 303.570000 93.020000 -39.600000 0.230000)\n        4)\n      (segment 4967 \n        (point 303.570000 93.020000 -39.600000 0.230000)\n        (point 303.170000 94.720000 -41.820000 0.230000)\n        4)\n      (segment 4968 \n        (point 303.170000 94.720000 -41.820000 0.230000)\n        (point 301.430000 96.110000 -42.370000 0.230000)\n        4)\n      (segment 4969 \n        (point 301.430000 96.110000 -42.370000 0.230000)\n        (point 301.430000 96.110000 -42.420000 0.230000)\n        4)\n      (segment 4970 \n        (point 301.430000 96.110000 -42.420000 0.230000)\n        (point 301.480000 97.920000 -44.050000 0.230000)\n        4)\n      (segment 4971 \n        (point 301.480000 97.920000 -44.050000 0.230000)\n        (point 303.270000 98.330000 -45.720000 0.230000)\n        4)\n      (segment 4972 \n        (point 303.270000 98.330000 -45.720000 0.230000)\n        (point 300.960000 100.170000 -46.150000 0.230000)\n        4)\n      (segment 4973 \n        (point 300.960000 100.170000 -46.150000 0.230000)\n        (point 301.760000 102.760000 -47.650000 0.230000)\n        4)\n      (segment 4974 \n        (point 301.760000 102.760000 -47.650000 0.230000)\n        (point 301.680000 105.120000 -49.470000 0.230000)\n        4)\n      (segment 4975 \n        (point 301.680000 105.120000 -49.470000 0.230000)\n        (point 301.680000 105.120000 -49.500000 0.230000)\n        4)\n      (segment 4976 \n        (point 301.680000 105.120000 -49.500000 0.230000)\n        (point 301.280000 106.820000 -50.680000 0.230000)\n        4)\n      (segment 4977 \n        (point 301.280000 106.820000 -50.680000 0.230000)\n        (point 301.820000 110.540000 -51.150000 0.230000)\n        4)\n      (segment 4978 \n        (point 301.820000 110.540000 -51.150000 0.230000)\n        (point 302.810000 114.350000 -51.150000 0.230000)\n        4)\n      (segment 4979 \n        (point 302.810000 114.350000 -51.150000 0.230000)\n        (point 302.460000 117.850000 -51.420000 0.230000)\n        4)\n      (segment 4980 \n        (point 302.460000 117.850000 -51.420000 0.230000)\n        (point 303.270000 120.430000 -52.800000 0.230000)\n        4)\n      (segment 4981 \n        (point 303.270000 120.430000 -52.800000 0.230000)\n        (point 303.270000 120.430000 -52.820000 0.230000)\n        4)\n      (segment 4982 \n        (point 303.270000 120.430000 -52.820000 0.230000)\n        (point 303.330000 122.230000 -53.650000 0.230000)\n        4)\n      (segment 4983 \n        (point 303.330000 122.230000 -53.650000 0.230000)\n        (point 303.330000 122.230000 -53.670000 0.230000)\n        4)\n      (segment 4984 \n        (point 303.330000 122.230000 -53.670000 0.230000)\n        (point 302.530000 125.620000 -54.320000 0.230000)\n        4)\n      (segment 4985 \n        (point 302.530000 125.620000 -54.320000 0.230000)\n        (point 301.940000 126.090000 -55.770000 0.230000)\n        4)\n      (segment 4986 \n        (point 301.940000 126.090000 -55.770000 0.230000)\n        (point 301.940000 126.090000 -55.800000 0.230000)\n        4)\n      (segment 4987 \n        (point 301.940000 126.090000 -55.800000 0.230000)\n        (point 302.750000 128.660000 -57.250000 0.230000)\n        4)\n      (segment 4988 \n        (point 302.750000 128.660000 -57.250000 0.230000)\n        (point 303.750000 132.490000 -58.850000 0.230000)\n        4)\n      (segment 4989 \n        (point 303.750000 132.490000 -58.850000 0.230000)\n        (point 304.380000 133.820000 -59.750000 0.230000)\n        4)\n      (segment 4990 \n        (point 304.380000 133.820000 -59.750000 0.230000)\n        (point 304.380000 133.820000 -59.780000 0.230000)\n        4)\n      (segment 4991 \n        (point 304.380000 133.820000 -59.780000 0.230000)\n        (point 306.520000 136.710000 -61.100000 0.230000)\n        4)\n      (segment 4992 \n        (point 306.520000 136.710000 -61.100000 0.230000)\n        (point 307.330000 139.290000 -61.380000 0.230000)\n        4)\n      (segment 4993 \n        (point 307.330000 139.290000 -61.380000 0.230000)\n        (point 307.330000 139.290000 -61.400000 0.230000)\n        4)\n      (segment 4994 \n        (point 307.330000 139.290000 -61.400000 0.230000)\n        (point 308.990000 140.280000 -61.380000 0.230000)\n        4)\n      (segment 4995 \n        (point 308.990000 140.280000 -61.380000 0.230000)\n        (point 310.150000 145.320000 -62.950000 0.230000)\n        4)\n      (segment 4996 \n        (point 310.150000 145.320000 -62.950000 0.230000)\n        (point 311.010000 149.710000 -63.720000 0.230000)\n        4)\n      (segment 4997 \n        (point 311.010000 149.710000 -63.720000 0.230000)\n        (point 311.010000 149.710000 -63.750000 0.230000)\n        4)\n      (segment 4998 \n        (point 311.010000 149.710000 -63.750000 0.230000)\n        (point 311.820000 152.290000 -63.050000 0.230000)\n        4)\n      (segment 4999 \n        (point 311.820000 152.290000 -63.050000 0.230000)\n        (point 313.080000 154.970000 -61.280000 0.230000)\n        4)\n      (segment 5000 \n        (point 313.080000 154.970000 -61.280000 0.230000)\n        (point 314.020000 156.990000 -59.800000 0.230000)\n        4)\n      (segment 5001 \n        (point 314.020000 156.990000 -59.800000 0.230000)\n        (point 314.020000 156.990000 -59.830000 0.230000)\n        4)\n      (segment 5002 \n        (point 314.020000 156.990000 -59.830000 0.230000)\n        (point 317.960000 160.290000 -59.150000 0.230000)\n        4)\n      (segment 5003 \n        (point 317.960000 160.290000 -59.150000 0.230000)\n        (point 319.130000 165.340000 -58.050000 0.230000)\n        4)\n      (segment 5004 \n        (point 319.130000 165.340000 -58.050000 0.230000)\n        (point 319.040000 167.710000 -58.620000 0.230000)\n        4)\n      (segment 5005 \n        (point 319.040000 167.710000 -58.620000 0.230000)\n        (point 317.710000 173.370000 -60.070000 0.230000)\n        4)\n      (segment 5006 \n        (point 317.710000 173.370000 -60.070000 0.230000)\n        (point 317.050000 176.200000 -61.420000 0.230000)\n        4)\n      (segment 5007 \n        (point 317.050000 176.200000 -61.420000 0.230000)\n        (point 315.630000 178.260000 -64.270000 0.230000)\n        4))\n    (branch 186 182 \n      (segment 5008 \n        (point 278.970000 53.220000 -9.950000 0.460000)\n        (point 278.180000 52.440000 -12.950000 0.460000)\n        4)\n      (segment 5009 \n        (point 278.180000 52.440000 -12.950000 0.460000)\n        (point 278.630000 52.550000 -14.800000 0.460000)\n        4)\n      (segment 5010 \n        (point 278.630000 52.550000 -14.800000 0.460000)\n        (point 278.630000 52.550000 -14.920000 0.460000)\n        4)\n      (segment 5011 \n        (point 278.630000 52.550000 -14.920000 0.460000)\n        (point 278.760000 51.980000 -17.470000 0.460000)\n        4)\n      (segment 5012 \n        (point 278.760000 51.980000 -17.470000 0.460000)\n        (point 281.140000 51.940000 -18.020000 0.460000)\n        4)\n      (segment 5013 \n        (point 281.140000 51.940000 -18.020000 0.460000)\n        (point 283.280000 54.840000 -18.500000 0.460000)\n        4)\n      (segment 5014 \n        (point 283.280000 54.840000 -18.500000 0.460000)\n        (point 284.930000 55.820000 -18.880000 0.460000)\n        4)\n      (segment 5015 \n        (point 284.930000 55.820000 -18.880000 0.460000)\n        (point 286.990000 55.100000 -19.500000 0.460000)\n        4)\n      (segment 5016 \n        (point 286.990000 55.100000 -19.500000 0.460000)\n        (point 289.570000 52.130000 -19.500000 0.460000)\n        4)\n      (segment 5017 \n        (point 289.570000 52.130000 -19.500000 0.460000)\n        (point 291.630000 51.410000 -20.000000 0.460000)\n        4)\n      (segment 5018 \n        (point 291.630000 51.410000 -20.000000 0.460000)\n        (point 292.920000 49.930000 -20.770000 0.460000)\n        4)\n      (segment 5019 \n        (point 292.920000 49.930000 -20.770000 0.460000)\n        (point 295.280000 49.890000 -21.880000 0.460000)\n        4))\n    (branch 187 186 \n      (segment 5020 \n        (point 295.280000 49.890000 -21.880000 0.460000)\n        (point 296.620000 50.200000 -23.270000 0.230000)\n        4)\n      (segment 5021 \n        (point 296.620000 50.200000 -23.270000 0.230000)\n        (point 294.750000 52.140000 -24.770000 0.230000)\n        4)\n      (segment 5022 \n        (point 294.750000 52.140000 -24.770000 0.230000)\n        (point 294.300000 52.030000 -24.800000 0.230000)\n        4)\n      (segment 5023 \n        (point 294.300000 52.030000 -24.800000 0.230000)\n        (point 293.850000 51.930000 -26.320000 0.230000)\n        4)\n      (segment 5024 \n        (point 293.850000 51.930000 -26.320000 0.230000)\n        (point 295.380000 53.500000 -27.830000 0.230000)\n        4)\n      (segment 5025 \n        (point 295.380000 53.500000 -27.830000 0.230000)\n        (point 294.800000 53.950000 -27.850000 0.230000)\n        4)\n      (segment 5026 \n        (point 294.800000 53.950000 -27.850000 0.230000)\n        (point 295.560000 54.730000 -29.420000 0.230000)\n        4)\n      (segment 5027 \n        (point 295.560000 54.730000 -29.420000 0.230000)\n        (point 295.430000 55.300000 -29.420000 0.230000)\n        4)\n      (segment 5028 \n        (point 295.430000 55.300000 -29.420000 0.230000)\n        (point 294.850000 55.750000 -31.300000 0.230000)\n        4)\n      (segment 5029 \n        (point 294.850000 55.750000 -31.300000 0.230000)\n        (point 293.320000 54.200000 -31.130000 0.230000)\n        4)\n      (segment 5030 \n        (point 293.320000 54.200000 -31.130000 0.230000)\n        (point 292.290000 54.560000 -33.750000 0.230000)\n        4)\n      (segment 5031 \n        (point 292.290000 54.560000 -33.750000 0.230000)\n        (point 294.000000 57.350000 -35.550000 0.230000)\n        4)\n      (segment 5032 \n        (point 294.000000 57.350000 -35.550000 0.230000)\n        (point 293.340000 60.180000 -36.520000 0.230000)\n        4)\n      (segment 5033 \n        (point 293.340000 60.180000 -36.520000 0.230000)\n        (point 292.180000 61.100000 -40.800000 0.230000)\n        4)\n      (segment 5034 \n        (point 292.180000 61.100000 -40.800000 0.230000)\n        (point 293.520000 61.420000 -43.720000 0.230000)\n        4)\n      (segment 5035 \n        (point 293.520000 61.420000 -43.720000 0.230000)\n        (point 293.700000 62.650000 -45.800000 0.230000)\n        4)\n      (segment 5036 \n        (point 293.700000 62.650000 -45.800000 0.230000)\n        (point 293.700000 62.650000 -45.830000 0.230000)\n        4)\n      (segment 5037 \n        (point 293.700000 62.650000 -45.830000 0.230000)\n        (point 292.090000 63.470000 -47.020000 0.230000)\n        4)\n      (segment 5038 \n        (point 292.090000 63.470000 -47.020000 0.230000)\n        (point 291.960000 64.030000 -49.250000 0.230000)\n        4)\n      (segment 5039 \n        (point 291.960000 64.030000 -49.250000 0.230000)\n        (point 293.750000 64.450000 -51.250000 0.230000)\n        4)\n      (segment 5040 \n        (point 293.750000 64.450000 -51.250000 0.230000)\n        (point 294.510000 65.230000 -52.970000 0.230000)\n        4)\n      (segment 5041 \n        (point 294.510000 65.230000 -52.970000 0.230000)\n        (point 294.510000 65.230000 -53.000000 0.230000)\n        4)\n      (segment 5042 \n        (point 294.510000 65.230000 -53.000000 0.230000)\n        (point 295.900000 67.340000 -55.830000 0.230000)\n        4)\n      (segment 5043 \n        (point 295.900000 67.340000 -55.830000 0.230000)\n        (point 294.930000 69.500000 -57.850000 0.230000)\n        4)\n      (segment 5044 \n        (point 294.930000 69.500000 -57.850000 0.230000)\n        (point 295.680000 70.290000 -59.830000 0.230000)\n        4)\n      (segment 5045 \n        (point 295.680000 70.290000 -59.830000 0.230000)\n        (point 296.120000 70.380000 -59.830000 0.230000)\n        4)\n      (segment 5046 \n        (point 296.120000 70.380000 -59.830000 0.230000)\n        (point 297.280000 69.460000 -62.100000 0.230000)\n        4)\n      (segment 5047 \n        (point 297.280000 69.460000 -62.100000 0.230000)\n        (point 297.650000 71.940000 -63.820000 0.230000)\n        4)\n      (segment 5048 \n        (point 297.650000 71.940000 -63.820000 0.230000)\n        (point 300.010000 71.900000 -65.320000 0.230000)\n        4)\n      (segment 5049 \n        (point 300.010000 71.900000 -65.320000 0.230000)\n        (point 300.780000 72.670000 -67.250000 0.230000)\n        4)\n      (segment 5050 \n        (point 300.780000 72.670000 -67.250000 0.230000)\n        (point 300.780000 72.670000 -67.280000 0.230000)\n        4)\n      (segment 5051 \n        (point 300.780000 72.670000 -67.280000 0.230000)\n        (point 299.350000 74.720000 -69.550000 0.230000)\n        4)\n      (segment 5052 \n        (point 299.350000 74.720000 -69.550000 0.230000)\n        (point 298.760000 75.190000 -70.570000 0.230000)\n        4)\n      (segment 5053 \n        (point 298.760000 75.190000 -70.570000 0.230000)\n        (point 299.210000 75.300000 -72.770000 0.230000)\n        4)\n      (segment 5054 \n        (point 299.210000 75.300000 -72.770000 0.230000)\n        (point 301.460000 75.810000 -74.450000 0.230000)\n        4)\n      (segment 5055 \n        (point 301.460000 75.810000 -74.450000 0.230000)\n        (point 301.280000 74.570000 -76.600000 0.230000)\n        4)\n      (segment 5056 \n        (point 301.280000 74.570000 -76.600000 0.230000)\n        (point 301.280000 74.570000 -76.630000 0.230000)\n        4)\n      (segment 5057 \n        (point 301.280000 74.570000 -76.630000 0.230000)\n        (point 302.610000 74.890000 -78.970000 0.230000)\n        4)\n      (segment 5058 \n        (point 302.610000 74.890000 -78.970000 0.230000)\n        (point 303.720000 72.170000 -80.520000 0.230000)\n        4)\n      (segment 5059 \n        (point 303.720000 72.170000 -80.520000 0.230000)\n        (point 303.720000 72.170000 -80.600000 0.230000)\n        4)\n      (segment 5060 \n        (point 303.720000 72.170000 -80.600000 0.230000)\n        (point 305.110000 74.280000 -83.020000 0.230000)\n        4)\n      (segment 5061 \n        (point 305.110000 74.280000 -83.020000 0.230000)\n        (point 305.460000 76.760000 -85.020000 0.230000)\n        4)\n      (segment 5062 \n        (point 305.460000 76.760000 -85.020000 0.230000)\n        (point 304.500000 78.920000 -87.070000 0.230000)\n        4)\n      (segment 5063 \n        (point 304.500000 78.920000 -87.070000 0.230000)\n        (point 304.500000 78.920000 -87.100000 0.230000)\n        4)\n      (segment 5064 \n        (point 304.500000 78.920000 -87.100000 0.230000)\n        (point 303.650000 80.510000 -89.650000 0.230000)\n        4)\n      (segment 5065 \n        (point 303.650000 80.510000 -89.650000 0.230000)\n        (point 303.650000 80.510000 -89.700000 0.230000)\n        4)\n      (segment 5066 \n        (point 303.650000 80.510000 -89.700000 0.230000)\n        (point 303.870000 83.550000 -91.320000 0.230000)\n        4)\n      (segment 5067 \n        (point 303.870000 83.550000 -91.320000 0.230000)\n        (point 303.790000 85.910000 -92.150000 0.230000)\n        4)\n      (segment 5068 \n        (point 303.790000 85.910000 -92.150000 0.230000)\n        (point 303.660000 86.480000 -95.300000 0.230000)\n        4))\n    (branch 188 186 \n      (segment 5069 \n        (point 295.280000 49.890000 -21.880000 0.460000)\n        (point 296.120000 48.290000 -21.100000 0.230000)\n        4)\n      (segment 5070 \n        (point 296.120000 48.290000 -21.100000 0.230000)\n        (point 295.580000 44.580000 -22.670000 0.230000)\n        4)\n      (segment 5071 \n        (point 295.580000 44.580000 -22.670000 0.230000)\n        (point 295.580000 44.580000 -22.700000 0.230000)\n        4)\n      (segment 5072 \n        (point 295.580000 44.580000 -22.700000 0.230000)\n        (point 296.600000 44.220000 -24.400000 0.230000)\n        4)\n      (segment 5073 \n        (point 296.600000 44.220000 -24.400000 0.230000)\n        (point 298.210000 43.410000 -25.900000 0.230000)\n        4)\n      (segment 5074 \n        (point 298.210000 43.410000 -25.900000 0.230000)\n        (point 299.050000 41.820000 -27.600000 0.230000)\n        4)\n      (segment 5075 \n        (point 299.050000 41.820000 -27.600000 0.230000)\n        (point 299.010000 40.010000 -29.170000 0.230000)\n        4)\n      (segment 5076 \n        (point 299.010000 40.010000 -29.170000 0.230000)\n        (point 302.090000 38.940000 -30.130000 0.230000)\n        4)\n      (segment 5077 \n        (point 302.090000 38.940000 -30.130000 0.230000)\n        (point 303.110000 38.590000 -31.170000 0.230000)\n        4)\n      (segment 5078 \n        (point 303.110000 38.590000 -31.170000 0.230000)\n        (point 305.250000 35.500000 -31.700000 0.230000)\n        4)\n      (segment 5079 \n        (point 305.250000 35.500000 -31.700000 0.230000)\n        (point 307.700000 33.090000 -32.880000 0.230000)\n        4)\n      (segment 5080 \n        (point 307.700000 33.090000 -32.880000 0.230000)\n        (point 308.980000 31.600000 -34.300000 0.230000)\n        4)\n      (segment 5081 \n        (point 308.980000 31.600000 -34.300000 0.230000)\n        (point 310.670000 28.410000 -35.450000 0.230000)\n        4)\n      (segment 5082 \n        (point 310.670000 28.410000 -35.450000 0.230000)\n        (point 311.600000 24.450000 -36.330000 0.230000)\n        4)\n      (segment 5083 \n        (point 311.600000 24.450000 -36.330000 0.230000)\n        (point 314.500000 22.140000 -36.580000 0.230000)\n        4)\n      (segment 5084 \n        (point 314.500000 22.140000 -36.580000 0.230000)\n        (point 316.900000 17.920000 -37.000000 0.230000)\n        4)\n      (segment 5085 \n        (point 316.900000 17.920000 -37.000000 0.230000)\n        (point 318.410000 13.500000 -37.000000 0.230000)\n        4)\n      (segment 5086 \n        (point 318.410000 13.500000 -37.000000 0.230000)\n        (point 320.680000 9.850000 -37.220000 0.230000)\n        4)\n      (segment 5087 \n        (point 320.680000 9.850000 -37.220000 0.230000)\n        (point 320.680000 9.850000 -37.250000 0.230000)\n        4)\n      (segment 5088 \n        (point 320.680000 9.850000 -37.250000 0.230000)\n        (point 320.770000 7.480000 -38.400000 0.230000)\n        4)\n      (segment 5089 \n        (point 320.770000 7.480000 -38.400000 0.230000)\n        (point 320.620000 2.080000 -39.250000 0.230000)\n        4)\n      (segment 5090 \n        (point 320.620000 2.080000 -39.250000 0.230000)\n        (point 321.370000 -3.120000 -40.150000 0.230000)\n        4)\n      (segment 5091 \n        (point 321.370000 -3.120000 -40.150000 0.230000)\n        (point 323.640000 -6.770000 -40.030000 0.230000)\n        4)\n      (segment 5092 \n        (point 323.640000 -6.770000 -40.030000 0.230000)\n        (point 324.170000 -9.040000 -42.050000 0.230000)\n        4)\n      (segment 5093 \n        (point 324.170000 -9.040000 -42.050000 0.230000)\n        (point 325.990000 -12.790000 -43.800000 0.230000)\n        4)\n      (segment 5094 \n        (point 325.990000 -12.790000 -43.800000 0.230000)\n        (point 324.290000 -15.580000 -45.450000 0.230000)\n        4)\n      (segment 5095 \n        (point 324.290000 -15.580000 -45.450000 0.230000)\n        (point 326.190000 -21.700000 -46.080000 0.230000)\n        4)\n      (segment 5096 \n        (point 326.190000 -21.700000 -46.080000 0.230000)\n        (point 328.200000 -24.210000 -46.730000 0.230000)\n        4)\n      (segment 5097 \n        (point 328.200000 -24.210000 -46.730000 0.230000)\n        (point 330.990000 -30.130000 -47.770000 0.230000)\n        4)\n      (segment 5098 \n        (point 330.990000 -30.130000 -47.770000 0.230000)\n        (point 332.250000 -31.600000 -47.850000 0.230000)\n        4)\n      (segment 5099 \n        (point 332.250000 -31.600000 -47.850000 0.230000)\n        (point 331.620000 -32.950000 -49.830000 0.230000)\n        4)\n      (segment 5100 \n        (point 331.620000 -32.950000 -49.830000 0.230000)\n        (point 331.620000 -32.950000 -49.870000 0.230000)\n        4)\n      (segment 5101 \n        (point 331.620000 -32.950000 -49.870000 0.230000)\n        (point 331.570000 -34.760000 -51.320000 0.230000)\n        4)\n      (segment 5102 \n        (point 331.570000 -34.760000 -51.320000 0.230000)\n        (point 331.570000 -34.760000 -51.350000 0.230000)\n        4)\n      (segment 5103 \n        (point 331.570000 -34.760000 -51.350000 0.230000)\n        (point 333.130000 -37.370000 -52.700000 0.230000)\n        4)\n      (segment 5104 \n        (point 333.130000 -37.370000 -52.700000 0.230000)\n        (point 333.130000 -37.370000 -52.720000 0.230000)\n        4)\n      (segment 5105 \n        (point 333.130000 -37.370000 -52.720000 0.230000)\n        (point 335.970000 -41.480000 -53.100000 0.230000)\n        4)\n      (segment 5106 \n        (point 335.970000 -41.480000 -53.100000 0.230000)\n        (point 335.970000 -41.480000 -53.130000 0.230000)\n        4)\n      (segment 5107 \n        (point 335.970000 -41.480000 -53.130000 0.230000)\n        (point 337.530000 -44.100000 -52.850000 0.230000)\n        4)\n      (segment 5108 \n        (point 337.530000 -44.100000 -52.850000 0.230000)\n        (point 339.800000 -47.750000 -52.850000 0.230000)\n        4)\n      (segment 5109 \n        (point 339.800000 -47.750000 -52.850000 0.230000)\n        (point 339.800000 -47.750000 -52.880000 0.230000)\n        4)\n      (segment 5110 \n        (point 339.800000 -47.750000 -52.880000 0.230000)\n        (point 340.280000 -51.820000 -52.700000 0.230000)\n        4))\n    (branch 189 68 \n      (segment 5111 \n        (point 258.930000 35.900000 -6.380000 3.440000)\n        (point 256.570000 35.950000 -7.820000 0.915000)\n        4)\n      (segment 5112 \n        (point 256.570000 35.950000 -7.820000 0.915000)\n        (point 253.050000 36.900000 -9.300000 0.915000)\n        4)\n      (segment 5113 \n        (point 253.050000 36.900000 -9.300000 0.915000)\n        (point 250.730000 38.750000 -9.320000 0.915000)\n        4)\n      (segment 5114 \n        (point 250.730000 38.750000 -9.320000 0.915000)\n        (point 246.880000 39.050000 -9.320000 0.915000)\n        4)\n      (segment 5115 \n        (point 246.880000 39.050000 -9.320000 0.915000)\n        (point 245.140000 40.440000 -10.500000 0.915000)\n        4)\n      (segment 5116 \n        (point 245.140000 40.440000 -10.500000 0.915000)\n        (point 243.230000 40.580000 -11.900000 0.915000)\n        4)\n      (segment 5117 \n        (point 243.230000 40.580000 -11.900000 0.915000)\n        (point 242.780000 40.480000 -13.270000 0.690000)\n        4)\n      (segment 5118 \n        (point 242.780000 40.480000 -13.270000 0.690000)\n        (point 241.300000 40.730000 -14.920000 0.690000)\n        4)\n      (segment 5119 \n        (point 241.300000 40.730000 -14.920000 0.690000)\n        (point 240.410000 40.520000 -15.920000 0.690000)\n        4)\n      (segment 5120 \n        (point 240.410000 40.520000 -15.920000 0.690000)\n        (point 236.350000 37.780000 -16.200000 0.690000)\n        4))\n    (branch 190 189 \n      (segment 5121 \n        (point 236.350000 37.780000 -16.200000 0.690000)\n        (point 235.990000 35.300000 -17.170000 0.690000)\n        4)\n      (segment 5122 \n        (point 235.990000 35.300000 -17.170000 0.690000)\n        (point 235.040000 33.290000 -18.330000 0.690000)\n        4)\n      (segment 5123 \n        (point 235.040000 33.290000 -18.330000 0.690000)\n        (point 233.200000 31.060000 -19.330000 0.690000)\n        4)\n      (segment 5124 \n        (point 233.200000 31.060000 -19.330000 0.690000)\n        (point 232.580000 29.730000 -21.720000 0.690000)\n        4))\n    (branch 191 190 \n      (segment 5125 \n        (point 232.580000 29.730000 -21.720000 0.690000)\n        (point 232.490000 26.120000 -22.530000 0.460000)\n        4)\n      (segment 5126 \n        (point 232.490000 26.120000 -22.530000 0.460000)\n        (point 231.490000 22.300000 -23.500000 0.460000)\n        4)\n      (segment 5127 \n        (point 231.490000 22.300000 -23.500000 0.460000)\n        (point 231.270000 19.260000 -24.100000 0.460000)\n        4)\n      (segment 5128 \n        (point 231.270000 19.260000 -24.100000 0.460000)\n        (point 230.900000 16.800000 -24.850000 0.460000)\n        4)\n      (segment 5129 \n        (point 230.900000 16.800000 -24.850000 0.460000)\n        (point 231.110000 13.850000 -25.380000 0.460000)\n        4)\n      (segment 5130 \n        (point 231.110000 13.850000 -25.380000 0.460000)\n        (point 230.250000 9.470000 -25.770000 0.460000)\n        4)\n      (segment 5131 \n        (point 230.250000 9.470000 -25.770000 0.460000)\n        (point 228.820000 5.550000 -25.800000 0.460000)\n        4)\n      (segment 5132 \n        (point 228.820000 5.550000 -25.800000 0.460000)\n        (point 227.840000 1.750000 -26.130000 0.460000)\n        4)\n      (segment 5133 \n        (point 227.840000 1.750000 -26.130000 0.460000)\n        (point 225.240000 -1.260000 -24.700000 0.460000)\n        4)\n      (segment 5134 \n        (point 225.240000 -1.260000 -24.700000 0.460000)\n        (point 222.500000 -3.680000 -23.300000 0.460000)\n        4)\n      (segment 5135 \n        (point 222.500000 -3.680000 -23.300000 0.460000)\n        (point 219.900000 -6.690000 -22.880000 0.460000)\n        4)\n      (segment 5136 \n        (point 219.900000 -6.690000 -22.880000 0.460000)\n        (point 218.970000 -8.690000 -23.270000 0.460000)\n        4)\n      (segment 5137 \n        (point 218.970000 -8.690000 -23.270000 0.460000)\n        (point 215.670000 -10.670000 -23.270000 0.460000)\n        4)\n      (segment 5138 \n        (point 215.670000 -10.670000 -23.270000 0.460000)\n        (point 215.620000 -12.470000 -23.270000 0.230000)\n        4)\n      (segment 5139 \n        (point 215.620000 -12.470000 -23.270000 0.230000)\n        (point 213.820000 -12.890000 -23.270000 0.230000)\n        4)\n      (segment 5140 \n        (point 213.820000 -12.890000 -23.270000 0.230000)\n        (point 213.780000 -14.690000 -23.270000 0.230000)\n        4)\n      (segment 5141 \n        (point 213.780000 -14.690000 -23.270000 0.230000)\n        (point 211.860000 -14.540000 -23.270000 0.230000)\n        4)\n      (segment 5142 \n        (point 211.860000 -14.540000 -23.270000 0.230000)\n        (point 210.480000 -16.660000 -23.270000 0.230000)\n        4)\n      (segment 5143 \n        (point 210.480000 -16.660000 -23.270000 0.230000)\n        (point 209.270000 -17.540000 -24.080000 0.230000)\n        4)\n      (segment 5144 \n        (point 209.270000 -17.540000 -24.080000 0.230000)\n        (point 208.190000 -18.990000 -24.080000 0.230000)\n        4)\n      (segment 5145 \n        (point 208.190000 -18.990000 -24.080000 0.230000)\n        (point 204.430000 -21.050000 -24.080000 0.230000)\n        4)\n      (segment 5146 \n        (point 204.430000 -21.050000 -24.080000 0.230000)\n        (point 200.690000 -23.140000 -24.750000 0.230000)\n        4)\n      (segment 5147 \n        (point 200.690000 -23.140000 -24.750000 0.230000)\n        (point 197.110000 -23.980000 -24.050000 0.230000)\n        4)\n      (segment 5148 \n        (point 197.110000 -23.980000 -24.050000 0.230000)\n        (point 195.500000 -23.150000 -24.080000 0.230000)\n        4)\n      (segment 5149 \n        (point 195.500000 -23.150000 -24.080000 0.230000)\n        (point 195.500000 -23.150000 -24.100000 0.230000)\n        4)\n      (segment 5150 \n        (point 195.500000 -23.150000 -24.100000 0.230000)\n        (point 193.590000 -23.000000 -24.400000 0.230000)\n        4)\n      (segment 5151 \n        (point 193.590000 -23.000000 -24.400000 0.230000)\n        (point 193.590000 -23.000000 -24.420000 0.230000)\n        4)\n      (segment 5152 \n        (point 193.590000 -23.000000 -24.420000 0.230000)\n        (point 190.900000 -23.630000 -25.570000 0.230000)\n        4)\n      (segment 5153 \n        (point 190.900000 -23.630000 -25.570000 0.230000)\n        (point 189.520000 -25.760000 -27.220000 0.230000)\n        4)\n      (segment 5154 \n        (point 189.520000 -25.760000 -27.220000 0.230000)\n        (point 188.620000 -25.970000 -29.480000 0.230000)\n        4)\n      (segment 5155 \n        (point 188.620000 -25.970000 -29.480000 0.230000)\n        (point 188.620000 -25.970000 -29.500000 0.230000)\n        4)\n      (segment 5156 \n        (point 188.620000 -25.970000 -29.500000 0.230000)\n        (point 188.360000 -24.820000 -31.850000 0.230000)\n        4)\n      (segment 5157 \n        (point 188.360000 -24.820000 -31.850000 0.230000)\n        (point 188.360000 -24.820000 -31.880000 0.230000)\n        4)\n      (segment 5158 \n        (point 188.360000 -24.820000 -31.880000 0.230000)\n        (point 186.440000 -24.670000 -34.130000 0.230000)\n        4)\n      (segment 5159 \n        (point 186.440000 -24.670000 -34.130000 0.230000)\n        (point 186.440000 -24.670000 -34.150000 0.230000)\n        4))\n    (branch 192 190 \n      (segment 5160 \n        (point 232.580000 29.730000 -21.720000 0.690000)\n        (point 229.630000 30.230000 -21.720000 0.460000)\n        4)\n      (segment 5161 \n        (point 229.630000 30.230000 -21.720000 0.460000)\n        (point 227.270000 30.270000 -22.880000 0.460000)\n        4)\n   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222.660000 23.810000 -31.600000 0.460000)\n        4)\n      (segment 5169 \n        (point 222.660000 23.810000 -31.600000 0.460000)\n        (point 222.660000 23.810000 -31.630000 0.460000)\n        4)\n      (segment 5170 \n        (point 222.660000 23.810000 -31.630000 0.460000)\n        (point 221.950000 24.840000 -33.000000 0.460000)\n        4)\n      (segment 5171 \n        (point 221.950000 24.840000 -33.000000 0.460000)\n        (point 220.790000 25.770000 -34.880000 0.460000)\n        4)\n      (segment 5172 \n        (point 220.790000 25.770000 -34.880000 0.460000)\n        (point 219.620000 26.690000 -36.600000 0.460000)\n        4))\n    (branch 193 192 \n      (segment 5173 \n        (point 219.620000 26.690000 -36.600000 0.460000)\n        (point 217.660000 25.040000 -38.130000 0.460000)\n        4)\n      (segment 5174 \n        (point 217.660000 25.040000 -38.130000 0.460000)\n        (point 216.450000 24.150000 -41.220000 0.230000)\n        4)\n      (segment 5175 \n        (point 216.450000 24.150000 -41.220000 0.230000)\n        (point 216.450000 24.150000 -41.200000 0.230000)\n        4)\n      (segment 5176 \n        (point 216.450000 24.150000 -41.200000 0.230000)\n        (point 214.140000 26.000000 -42.370000 0.230000)\n        4)\n      (segment 5177 \n        (point 214.140000 26.000000 -42.370000 0.230000)\n        (point 211.190000 26.510000 -43.600000 0.230000)\n        4)\n      (segment 5178 \n        (point 211.190000 26.510000 -43.600000 0.230000)\n        (point 208.380000 26.430000 -43.720000 0.230000)\n        4)\n      (segment 5179 \n        (point 208.380000 26.430000 -43.720000 0.230000)\n        (point 206.060000 28.280000 -44.580000 0.230000)\n        4)\n      (segment 5180 \n        (point 206.060000 28.280000 -44.580000 0.230000)\n        (point 206.510000 28.390000 -46.450000 0.230000)\n        4)\n      (segment 5181 \n        (point 206.510000 28.390000 -46.450000 0.230000)\n        (point 205.930000 28.850000 -49.100000 0.230000)\n        4)\n      (segment 5182 \n        (point 205.930000 28.850000 -49.100000 0.230000)\n        (point 203.440000 29.460000 -50.770000 0.230000)\n        4)\n      (segment 5183 \n        (point 203.440000 29.460000 -50.770000 0.230000)\n        (point 202.040000 27.350000 -52.800000 0.230000)\n        4)\n      (segment 5184 \n        (point 202.040000 27.350000 -52.800000 0.230000)\n        (point 200.390000 26.360000 -54.380000 0.230000)\n        4)\n      (segment 5185 \n        (point 200.390000 26.360000 -54.380000 0.230000)\n        (point 200.390000 26.360000 -54.400000 0.230000)\n        4)\n      (segment 5186 \n        (point 200.390000 26.360000 -54.400000 0.230000)\n        (point 196.430000 27.220000 -55.320000 0.230000)\n        4)\n      (segment 5187 \n        (point 196.430000 27.220000 -55.320000 0.230000)\n        (point 192.850000 26.380000 -56.800000 0.230000)\n        4)\n      (segment 5188 \n        (point 192.850000 26.380000 -56.800000 0.230000)\n        (point 191.640000 25.510000 -58.150000 0.230000)\n        4)\n      (segment 5189 \n        (point 191.640000 25.510000 -58.150000 0.230000)\n        (point 191.640000 25.510000 -58.170000 0.230000)\n        4)\n      (segment 5190 \n        (point 191.640000 25.510000 -58.170000 0.230000)\n        (point 189.730000 25.650000 -58.900000 0.230000)\n        4)\n      (segment 5191 \n        (point 189.730000 25.650000 -58.900000 0.230000)\n        (point 187.210000 26.260000 -59.800000 0.230000)\n        4)\n      (segment 5192 \n        (point 187.210000 26.260000 -59.800000 0.230000)\n        (point 184.540000 25.640000 -60.700000 0.230000)\n        4)\n      (segment 5193 \n        (point 184.540000 25.640000 -60.700000 0.230000)\n        (point 181.820000 23.200000 -61.380000 0.230000)\n        4)\n      (segment 5194 \n        (point 181.820000 23.200000 -61.380000 0.230000)\n        (point 178.920000 25.500000 -62.880000 0.230000)\n        4)\n      (segment 5195 \n        (point 178.920000 25.500000 -62.880000 0.230000)\n        (point 176.280000 26.680000 -63.950000 0.230000)\n        4)\n      (segment 5196 \n        (point 176.280000 26.680000 -63.950000 0.230000)\n        (point 174.540000 28.070000 -65.300000 0.230000)\n        4)\n      (segment 5197 \n        (point 174.540000 28.070000 -65.300000 0.230000)\n        (point 172.050000 28.680000 -66.880000 0.230000)\n        4)\n      (segment 5198 \n        (point 172.050000 28.680000 -66.880000 0.230000)\n        (point 172.180000 28.110000 -66.880000 0.230000)\n        4)\n      (segment 5199 \n        (point 172.180000 28.110000 -66.880000 0.230000)\n        (point 168.650000 29.070000 -68.200000 0.230000)\n        4)\n      (segment 5200 \n        (point 168.650000 29.070000 -68.200000 0.230000)\n        (point 166.030000 30.250000 -69.400000 0.230000)\n        4)\n      (segment 5201 \n        (point 166.030000 30.250000 -69.400000 0.230000)\n        (point 164.420000 31.070000 -71.000000 0.230000)\n        4)\n      (segment 5202 \n        (point 164.420000 31.070000 -71.000000 0.230000)\n        (point 162.770000 30.090000 -72.930000 0.230000)\n        4)\n      (segment 5203 \n        (point 162.770000 30.090000 -72.930000 0.230000)\n        (point 160.400000 30.120000 -74.550000 0.230000)\n        4)\n      (segment 5204 \n        (point 160.400000 30.120000 -74.550000 0.230000)\n        (point 160.400000 30.120000 -74.570000 0.230000)\n        4)\n      (segment 5205 \n        (point 160.400000 30.120000 -74.570000 0.230000)\n        (point 157.320000 31.200000 -75.630000 0.230000)\n        4)\n      (segment 5206 \n        (point 157.320000 31.200000 -75.630000 0.230000)\n        (point 156.480000 32.790000 -77.280000 0.230000)\n        4)\n      (segment 5207 \n        (point 156.480000 32.790000 -77.280000 0.230000)\n        (point 154.430000 33.500000 -79.500000 0.230000)\n        4)\n      (segment 5208 \n        (point 154.430000 33.500000 -79.500000 0.230000)\n        (point 151.930000 34.110000 -81.220000 0.230000)\n        4)\n      (segment 5209 \n        (point 151.930000 34.110000 -81.220000 0.230000)\n        (point 150.320000 34.930000 -83.020000 0.230000)\n        4)\n      (segment 5210 \n        (point 150.320000 34.930000 -83.020000 0.230000)\n        (point 149.380000 32.920000 -85.300000 0.230000)\n        4)\n      (segment 5211 \n        (point 149.380000 32.920000 -85.300000 0.230000)\n        (point 147.020000 32.960000 -87.700000 0.230000)\n        4)\n      (segment 5212 \n        (point 147.020000 32.960000 -87.700000 0.230000)\n        (point 147.370000 29.460000 -89.920000 0.230000)\n        4)\n      (segment 5213 \n        (point 147.370000 29.460000 -89.920000 0.230000)\n        (point 147.370000 29.460000 -89.950000 0.230000)\n        4)\n      (segment 5214 \n        (point 147.370000 29.460000 -89.950000 0.230000)\n        (point 144.950000 27.700000 -91.130000 0.230000)\n        4)\n      (segment 5215 \n        (point 144.950000 27.700000 -91.130000 0.230000)\n        (point 142.710000 27.170000 -92.900000 0.230000)\n        4)\n      (segment 5216 \n        (point 142.710000 27.170000 -92.900000 0.230000)\n        (point 142.710000 27.170000 -92.920000 0.230000)\n        4)\n      (segment 5217 \n        (point 142.710000 27.170000 -92.920000 0.230000)\n        (point 140.310000 25.420000 -94.100000 0.230000)\n        4)\n      (segment 5218 \n        (point 140.310000 25.420000 -94.100000 0.230000)\n        (point 140.310000 25.420000 -94.150000 0.230000)\n        4)\n      (segment 5219 \n        (point 140.310000 25.420000 -94.150000 0.230000)\n        (point 138.790000 23.870000 -93.630000 0.230000)\n        4)\n      (segment 5220 \n        (point 138.790000 23.870000 -93.630000 0.230000)\n        (point 138.790000 23.870000 -93.880000 0.230000)\n        4)\n      (segment 5221 \n        (point 138.790000 23.870000 -93.880000 0.230000)\n        (point 136.360000 22.100000 -94.170000 0.230000)\n        4)\n      (segment 5222 \n        (point 136.360000 22.100000 -94.170000 0.230000)\n        (point 135.560000 19.530000 -95.180000 0.230000)\n        4)\n      (segment 5223 \n        (point 135.560000 19.530000 -95.180000 0.230000)\n        (point 134.040000 17.980000 -97.280000 0.230000)\n        4))\n    (branch 194 192 \n      (segment 5224 \n        (point 219.620000 26.690000 -36.600000 0.460000)\n        (point 223.200000 27.530000 -39.550000 0.230000)\n        4)\n      (segment 5225 \n        (point 223.200000 27.530000 -39.550000 0.230000)\n        (point 223.650000 27.630000 -42.470000 0.230000)\n        4)\n      (segment 5226 \n        (point 223.650000 27.630000 -42.470000 0.230000)\n        (point 223.650000 27.630000 -42.530000 0.230000)\n        4)\n      (segment 5227 \n        (point 223.650000 27.630000 -42.530000 0.230000)\n        (point 223.380000 28.760000 -45.970000 0.230000)\n        4)\n      (segment 5228 \n        (point 223.380000 28.760000 -45.970000 0.230000)\n        (point 223.380000 28.760000 -46.100000 0.230000)\n        4)\n      (segment 5229 \n        (point 223.380000 28.760000 -46.100000 0.230000)\n        (point 225.120000 27.380000 -48.700000 0.230000)\n        4)\n      (segment 5230 \n        (point 225.120000 27.380000 -48.700000 0.230000)\n        (point 225.120000 27.380000 -48.720000 0.230000)\n        4)\n      (segment 5231 \n        (point 225.120000 27.380000 -48.720000 0.230000)\n        (point 225.170000 29.180000 -50.950000 0.230000)\n        4)\n      (segment 5232 \n        (point 225.170000 29.180000 -50.950000 0.230000)\n        (point 224.720000 29.070000 -50.970000 0.230000)\n        4)\n      (segment 5233 \n        (point 224.720000 29.070000 -50.970000 0.230000)\n        (point 223.830000 28.860000 -53.400000 0.230000)\n        4)\n      (segment 5234 \n        (point 223.830000 28.860000 -53.400000 0.230000)\n        (point 223.700000 29.430000 -53.420000 0.230000)\n        4)\n      (segment 5235 \n        (point 223.700000 29.430000 -53.420000 0.230000)\n        (point 223.430000 30.570000 -55.670000 0.230000)\n        4)\n      (segment 5236 \n        (point 223.430000 30.570000 -55.670000 0.230000)\n        (point 223.430000 30.570000 -55.700000 0.230000)\n        4)\n      (segment 5237 \n        (point 223.430000 30.570000 -55.700000 0.230000)\n        (point 225.340000 30.410000 -58.420000 0.230000)\n        4)\n      (segment 5238 \n        (point 225.340000 30.410000 -58.420000 0.230000)\n        (point 225.340000 30.410000 -58.470000 0.230000)\n        4)\n      (segment 5239 \n        (point 225.340000 30.410000 -58.470000 0.230000)\n        (point 224.950000 32.110000 -61.400000 0.230000)\n        4)\n      (segment 5240 \n        (point 224.950000 32.110000 -61.400000 0.230000)\n        (point 224.950000 32.110000 -61.470000 0.230000)\n        4)\n      (segment 5241 \n        (point 224.950000 32.110000 -61.470000 0.230000)\n        (point 224.060000 31.910000 -63.880000 0.230000)\n        4)\n      (segment 5242 \n        (point 224.060000 31.910000 -63.880000 0.230000)\n        (point 224.060000 31.910000 -63.900000 0.230000)\n        4)\n      (segment 5243 \n        (point 224.060000 31.910000 -63.900000 0.230000)\n        (point 224.860000 34.480000 -66.650000 0.230000)\n        4)\n      (segment 5244 \n        (point 224.860000 34.480000 -66.650000 0.230000)\n        (point 225.840000 32.320000 -69.350000 0.230000)\n        4)\n      (segment 5245 \n        (point 225.840000 32.320000 -69.350000 0.230000)\n        (point 226.100000 31.200000 -72.070000 0.230000)\n        4)\n      (segment 5246 \n        (point 226.100000 31.200000 -72.070000 0.230000)\n        (point 226.100000 31.200000 -72.170000 0.230000)\n        4)\n      (segment 5247 \n        (point 226.100000 31.200000 -72.170000 0.230000)\n        (point 228.040000 31.040000 -76.220000 0.230000)\n        4)\n      (segment 5248 \n        (point 228.040000 31.040000 -76.220000 0.230000)\n        (point 228.040000 31.040000 -76.300000 0.230000)\n        4)\n      (segment 5249 \n        (point 228.040000 31.040000 -76.300000 0.230000)\n        (point 228.040000 31.040000 -81.530000 0.230000)\n        4)\n      (segment 5250 \n        (point 228.040000 31.040000 -81.530000 0.230000)\n        (point 228.040000 31.040000 -81.580000 0.230000)\n        4)\n      (segment 5251 \n        (point 228.040000 31.040000 -81.580000 0.230000)\n        (point 227.720000 30.380000 -85.920000 0.230000)\n        4)\n      (segment 5252 \n        (point 227.720000 30.380000 -85.920000 0.230000)\n        (point 226.500000 29.490000 -89.050000 0.230000)\n        4)\n      (segment 5253 \n        (point 226.500000 29.490000 -89.050000 0.230000)\n        (point 226.500000 29.490000 -89.080000 0.230000)\n        4)\n      (segment 5254 \n        (point 226.500000 29.490000 -89.080000 0.230000)\n        (point 224.810000 26.720000 -92.400000 0.230000)\n        4)\n      (segment 5255 \n        (point 224.810000 26.720000 -92.400000 0.230000)\n        (point 224.760000 24.910000 -94.750000 0.230000)\n        4)\n      (segment 5256 \n        (point 224.760000 24.910000 -94.750000 0.230000)\n        (point 225.160000 23.210000 -97.130000 0.230000)\n        4)\n      (segment 5257 \n        (point 225.160000 23.210000 -97.130000 0.230000)\n        (point 225.870000 22.170000 -99.500000 0.230000)\n        4)\n      (segment 5258 \n        (point 225.870000 22.170000 -99.500000 0.230000)\n        (point 225.870000 22.170000 -99.520000 0.230000)\n        4)\n      (segment 5259 \n        (point 225.870000 22.170000 -99.520000 0.230000)\n        (point 224.790000 20.730000 -102.680000 0.230000)\n        4)\n      (segment 5260 \n        (point 224.790000 20.730000 -102.680000 0.230000)\n        (point 224.790000 20.730000 -102.720000 0.230000)\n        4)\n      (segment 5261 \n        (point 224.790000 20.730000 -102.720000 0.230000)\n        (point 225.240000 20.840000 -106.250000 0.230000)\n        4)\n      (segment 5262 \n        (point 225.240000 20.840000 -106.250000 0.230000)\n        (point 225.370000 20.270000 -110.320000 0.230000)\n        4)\n      (segment 5263 \n        (point 225.370000 20.270000 -110.320000 0.230000)\n        (point 225.370000 20.270000 -110.370000 0.230000)\n        4))\n    (branch 195 192 \n      (segment 5264 \n        (point 219.620000 26.690000 -36.600000 0.460000)\n        (point 219.810000 27.930000 -40.520000 0.230000)\n        4)\n      (segment 5265 \n        (point 219.810000 27.930000 -40.520000 0.230000)\n        (point 218.340000 28.180000 -43.450000 0.230000)\n        4)\n      (segment 5266 \n        (point 218.340000 28.180000 -43.450000 0.230000)\n        (point 218.340000 28.180000 -43.470000 0.230000)\n        4)\n      (segment 5267 \n        (point 218.340000 28.180000 -43.470000 0.230000)\n        (point 216.780000 30.800000 -45.700000 0.230000)\n        4)\n      (segment 5268 \n        (point 216.780000 30.800000 -45.700000 0.230000)\n        (point 217.680000 31.010000 -48.200000 0.230000)\n        4)\n      (segment 5269 \n        (point 217.680000 31.010000 -48.200000 0.230000)\n        (point 218.620000 33.020000 -51.080000 0.230000)\n        4)\n      (segment 5270 \n        (point 218.620000 33.020000 -51.080000 0.230000)\n        (point 219.250000 34.370000 -54.200000 0.230000)\n        4)\n      (segment 5271 \n        (point 219.250000 34.370000 -54.200000 0.230000)\n        (point 219.250000 34.370000 -54.220000 0.230000)\n        4)\n      (segment 5272 \n        (point 219.250000 34.370000 -54.220000 0.230000)\n        (point 218.170000 32.910000 -57.150000 0.230000)\n        4)\n      (segment 5273 \n        (point 218.170000 32.910000 -57.150000 0.230000)\n        (point 218.170000 32.910000 -57.200000 0.230000)\n        4)\n      (segment 5274 \n        (point 218.170000 32.910000 -57.200000 0.230000)\n        (point 216.610000 35.530000 -59.350000 0.230000)\n        4)\n      (segment 5275 \n        (point 216.610000 35.530000 -59.350000 0.230000)\n        (point 217.870000 38.230000 -60.270000 0.230000)\n        4)\n      (segment 5276 \n        (point 217.870000 38.230000 -60.270000 0.230000)\n        (point 217.390000 42.290000 -61.380000 0.230000)\n        4)\n      (segment 5277 \n        (point 217.390000 42.290000 -61.380000 0.230000)\n        (point 217.260000 42.860000 -63.100000 0.230000)\n        4)\n      (segment 5278 \n        (point 217.260000 42.860000 -63.100000 0.230000)\n        (point 215.770000 43.110000 -65.450000 0.230000)\n        4)\n      (segment 5279 \n        (point 215.770000 43.110000 -65.450000 0.230000)\n        (point 215.250000 45.360000 -67.500000 0.230000)\n        4)\n      (segment 5280 \n        (point 215.250000 45.360000 -67.500000 0.230000)\n        (point 216.600000 45.680000 -69.150000 0.230000)\n        4)\n      (segment 5281 \n        (point 216.600000 45.680000 -69.150000 0.230000)\n        (point 218.240000 46.670000 -70.450000 0.230000)\n        4)\n      (segment 5282 \n        (point 218.240000 46.670000 -70.450000 0.230000)\n        (point 216.060000 47.950000 -70.270000 0.230000)\n        4)\n      (segment 5283 \n        (point 216.060000 47.950000 -70.270000 0.230000)\n        (point 214.590000 48.200000 -72.170000 0.230000)\n        4)\n      (segment 5284 \n        (point 214.590000 48.200000 -72.170000 0.230000)\n        (point 216.110000 49.750000 -73.950000 0.230000)\n        4)\n      (segment 5285 \n        (point 216.110000 49.750000 -73.950000 0.230000)\n        (point 216.290000 50.990000 -74.450000 0.230000)\n        4)\n      (segment 5286 \n        (point 216.290000 50.990000 -74.450000 0.230000)\n        (point 214.920000 54.850000 -74.570000 0.230000)\n        4)\n      (segment 5287 \n        (point 214.920000 54.850000 -74.570000 0.230000)\n        (point 214.920000 54.850000 -74.600000 0.230000)\n        4)\n      (segment 5288 \n        (point 214.920000 54.850000 -74.600000 0.230000)\n        (point 214.210000 55.870000 -76.380000 0.230000)\n        4)\n      (segment 5289 \n        (point 214.210000 55.870000 -76.380000 0.230000)\n        (point 213.680000 58.140000 -78.130000 0.230000)\n        4)\n      (segment 5290 \n        (point 213.680000 58.140000 -78.130000 0.230000)\n        (point 214.170000 60.050000 -79.550000 0.230000)\n        4)\n      (segment 5291 \n        (point 214.170000 60.050000 -79.550000 0.230000)\n        (point 213.690000 64.110000 -80.570000 0.230000)\n        4)\n      (segment 5292 \n        (point 213.690000 64.110000 -80.570000 0.230000)\n        (point 214.090000 68.390000 -81.250000 0.230000)\n        4)\n      (segment 5293 \n        (point 214.090000 68.390000 -81.250000 0.230000)\n        (point 213.560000 70.640000 -82.750000 0.230000)\n        4)\n      (segment 5294 \n        (point 213.560000 70.640000 -82.750000 0.230000)\n        (point 213.560000 70.640000 -82.780000 0.230000)\n        4)\n      (segment 5295 \n        (point 213.560000 70.640000 -82.780000 0.230000)\n        (point 212.320000 73.940000 -84.570000 0.230000)\n        4)\n      (segment 5296 \n        (point 212.320000 73.940000 -84.570000 0.230000)\n        (point 213.580000 76.630000 -86.100000 0.230000)\n        4)\n      (segment 5297 \n        (point 213.580000 76.630000 -86.100000 0.230000)\n        (point 214.650000 78.070000 -87.700000 0.230000)\n        4)\n      (segment 5298 \n        (point 214.650000 78.070000 -87.700000 0.230000)\n        (point 214.650000 78.070000 -87.720000 0.230000)\n        4)\n      (segment 5299 \n        (point 214.650000 78.070000 -87.720000 0.230000)\n        (point 213.400000 81.360000 -89.250000 0.230000)\n        4)\n      (segment 5300 \n        (point 213.400000 81.360000 -89.250000 0.230000)\n        (point 212.170000 84.660000 -90.400000 0.230000)\n        4)\n      (segment 5301 \n        (point 212.170000 84.660000 -90.400000 0.230000)\n        (point 213.110000 86.670000 -92.830000 0.230000)\n        4)\n      (segment 5302 \n        (point 213.110000 86.670000 -92.830000 0.230000)\n        (point 213.110000 86.670000 -92.900000 0.230000)\n        4))\n    (branch 196 189 \n      (segment 5303 \n        (point 236.350000 37.780000 -16.200000 0.690000)\n        (point 234.110000 37.260000 -15.380000 0.460000)\n        4)\n      (segment 5304 \n        (point 234.110000 37.260000 -15.380000 0.460000)\n        (point 232.720000 35.130000 -15.650000 0.460000)\n        4)\n      (segment 5305 \n        (point 232.720000 35.130000 -15.650000 0.460000)\n        (point 229.470000 34.970000 -16.520000 0.460000)\n        4)\n      (segment 5306 \n        (point 229.470000 34.970000 -16.520000 0.460000)\n        (point 227.020000 37.380000 -17.250000 0.460000)\n        4)\n      (segment 5307 \n        (point 227.020000 37.380000 -17.250000 0.460000)\n        (point 224.380000 38.550000 -18.170000 0.460000)\n        4)\n      (segment 5308 \n        (point 224.380000 38.550000 -18.170000 0.460000)\n        (point 222.420000 36.900000 -19.380000 0.460000)\n        4)\n      (segment 5309 \n        (point 222.420000 36.900000 -19.380000 0.460000)\n        (point 220.810000 37.710000 -20.380000 0.460000)\n        4)\n      (segment 5310 \n        (point 220.810000 37.710000 -20.380000 0.460000)\n        (point 218.850000 36.060000 -21.550000 0.460000)\n        4)\n      (segment 5311 \n        (point 218.850000 36.060000 -21.550000 0.460000)\n        (point 218.850000 36.060000 -21.580000 0.460000)\n        4)\n      (segment 5312 \n        (point 218.850000 36.060000 -21.580000 0.460000)\n        (point 216.650000 37.340000 -22.300000 0.460000)\n        4)\n      (segment 5313 \n        (point 216.650000 37.340000 -22.300000 0.460000)\n        (point 215.820000 38.930000 -23.950000 0.460000)\n        4)\n      (segment 5314 \n        (point 215.820000 38.930000 -23.950000 0.460000)\n        (point 213.000000 38.870000 -25.330000 0.460000)\n        4)\n      (segment 5315 \n        (point 213.000000 38.870000 -25.330000 0.460000)\n        (point 212.870000 39.440000 -27.150000 0.460000)\n        4)\n      (segment 5316 \n        (point 212.870000 39.440000 -27.150000 0.460000)\n        (point 210.770000 38.340000 -28.650000 0.460000)\n        4)\n      (segment 5317 \n        (point 210.770000 38.340000 -28.650000 0.460000)\n        (point 210.770000 38.340000 -28.670000 0.460000)\n        4)\n      (segment 5318 \n        (point 210.770000 38.340000 -28.670000 0.460000)\n        (point 208.220000 37.150000 -29.700000 0.230000)\n        4)\n      (segment 5319 \n        (point 208.220000 37.150000 -29.700000 0.230000)\n        (point 208.010000 40.090000 -30.400000 0.230000)\n        4)\n      (segment 5320 \n        (point 208.010000 40.090000 -30.400000 0.230000)\n        (point 203.910000 41.520000 -31.100000 0.230000)\n        4)\n      (segment 5321 \n        (point 203.910000 41.520000 -31.100000 0.230000)\n        (point 200.560000 43.720000 -32.080000 0.230000)\n        4)\n      (segment 5322 \n        (point 200.560000 43.720000 -32.080000 0.230000)\n        (point 198.700000 45.670000 -33.130000 0.230000)\n        4)\n      (segment 5323 \n        (point 198.700000 45.670000 -33.130000 0.230000)\n        (point 193.830000 46.320000 -33.670000 0.460000)\n        4)\n      (segment 5324 \n        (point 193.830000 46.320000 -33.670000 0.460000)\n        (point 191.070000 48.050000 -35.600000 0.460000)\n        4)\n      (segment 5325 \n        (point 191.070000 48.050000 -35.600000 0.460000)\n        (point 188.560000 48.670000 -37.720000 0.460000)\n        4)\n      (segment 5326 \n        (point 188.560000 48.670000 -37.720000 0.460000)\n        (point 186.510000 49.380000 -39.150000 0.460000)\n        4)\n      (segment 5327 \n        (point 186.510000 49.380000 -39.150000 0.460000)\n        (point 183.840000 48.760000 -41.050000 0.460000)\n        4)\n      (segment 5328 \n        (point 183.840000 48.760000 -41.050000 0.460000)\n        (point 183.840000 48.760000 -41.080000 0.460000)\n        4)\n      (segment 5329 \n        (point 183.840000 48.760000 -41.080000 0.460000)\n        (point 182.680000 49.670000 -44.170000 0.460000)\n        4)\n      (segment 5330 \n        (point 182.680000 49.670000 -44.170000 0.460000)\n        (point 182.680000 49.670000 -44.200000 0.460000)\n        4)\n      (segment 5331 \n        (point 182.680000 49.670000 -44.200000 0.460000)\n        (point 180.320000 49.720000 -45.570000 0.460000)\n        4)\n      (segment 5332 \n        (point 180.320000 49.720000 -45.570000 0.460000)\n        (point 177.540000 51.460000 -46.820000 0.460000)\n        4)\n      (segment 5333 \n        (point 177.540000 51.460000 -46.820000 0.460000)\n        (point 173.840000 51.190000 -48.350000 0.460000)\n        4)\n      (segment 5334 \n        (point 173.840000 51.190000 -48.350000 0.460000)\n        (point 171.020000 51.120000 -49.050000 0.460000)\n        4)\n      (segment 5335 \n        (point 171.020000 51.120000 -49.050000 0.460000)\n        (point 169.280000 52.510000 -51.220000 0.230000)\n        4)\n      (segment 5336 \n        (point 169.280000 52.510000 -51.220000 0.230000)\n        (point 165.900000 52.910000 -53.350000 0.230000)\n        4)\n      (segment 5337 \n        (point 165.900000 52.910000 -53.350000 0.230000)\n        (point 165.900000 52.910000 -53.380000 0.230000)\n        4)\n      (segment 5338 \n        (point 165.900000 52.910000 -53.380000 0.230000)\n        (point 163.720000 54.190000 -55.400000 0.230000)\n        4)\n      (segment 5339 \n        (point 163.720000 54.190000 -55.400000 0.230000)\n        (point 163.720000 54.190000 -55.420000 0.230000)\n        4)\n      (segment 5340 \n        (point 163.720000 54.190000 -55.420000 0.230000)\n        (point 161.520000 55.470000 -57.280000 0.230000)\n        4)\n      (segment 5341 \n        (point 161.520000 55.470000 -57.280000 0.230000)\n        (point 161.520000 55.470000 -57.300000 0.230000)\n        4)\n      (segment 5342 \n        (point 161.520000 55.470000 -57.300000 0.230000)\n        (point 159.030000 56.080000 -58.220000 0.230000)\n        4)\n      (segment 5343 \n        (point 159.030000 56.080000 -58.220000 0.230000)\n        (point 155.640000 56.480000 -59.900000 0.230000)\n        4)\n      (segment 5344 \n        (point 155.640000 56.480000 -59.900000 0.230000)\n        (point 155.110000 58.740000 -60.970000 0.230000)\n        4)\n      (segment 5345 \n        (point 155.110000 58.740000 -60.970000 0.230000)\n        (point 151.900000 60.370000 -62.450000 0.230000)\n        4)\n      (segment 5346 \n        (point 151.900000 60.370000 -62.450000 0.230000)\n        (point 149.840000 61.100000 -64.200000 0.230000)\n        4)\n      (segment 5347 \n        (point 149.840000 61.100000 -64.200000 0.230000)\n        (point 147.400000 63.500000 -66.000000 0.230000)\n        4)\n      (segment 5348 \n        (point 147.400000 63.500000 -66.000000 0.230000)\n        (point 145.530000 65.450000 -67.950000 0.230000)\n        4)\n      (segment 5349 \n        (point 145.530000 65.450000 -67.950000 0.230000)\n        (point 145.530000 65.450000 -67.970000 0.230000)\n        4)\n      (segment 5350 \n        (point 145.530000 65.450000 -67.970000 0.230000)\n        (point 143.700000 69.210000 -69.720000 0.230000)\n        4)\n      (segment 5351 \n        (point 143.700000 69.210000 -69.720000 0.230000)\n        (point 140.760000 69.710000 -71.500000 0.230000)\n        4)\n      (segment 5352 \n        (point 140.760000 69.710000 -71.500000 0.230000)\n        (point 140.760000 69.710000 -71.520000 0.230000)\n        4)\n      (segment 5353 \n        (point 140.760000 69.710000 -71.520000 0.230000)\n        (point 138.620000 72.800000 -73.400000 0.230000)\n        4)\n      (segment 5354 \n        (point 138.620000 72.800000 -73.400000 0.230000)\n        (point 136.440000 74.070000 -74.970000 0.230000)\n        4)\n      (segment 5355 \n        (point 136.440000 74.070000 -74.970000 0.230000)\n        (point 134.570000 76.030000 -76.700000 0.230000)\n        4)\n      (segment 5356 \n        (point 134.570000 76.030000 -76.700000 0.230000)\n        (point 133.280000 77.510000 -78.650000 0.230000)\n        4)\n      (segment 5357 \n        (point 133.280000 77.510000 -78.650000 0.230000)\n        (point 130.510000 79.260000 -79.800000 0.230000)\n        4)\n      (segment 5358 \n        (point 130.510000 79.260000 -79.800000 0.230000)\n        (point 126.720000 81.350000 -82.000000 0.230000)\n        4)\n      (segment 5359 \n        (point 126.720000 81.350000 -82.000000 0.230000)\n        (point 124.100000 82.530000 -84.080000 0.230000)\n        4)\n      (segment 5360 \n        (point 124.100000 82.530000 -84.080000 0.230000)\n        (point 124.100000 82.530000 -84.100000 0.230000)\n        4)\n      (segment 5361 \n        (point 124.100000 82.530000 -84.100000 0.230000)\n        (point 121.780000 84.370000 -86.700000 0.230000)\n        4)\n      (segment 5362 \n        (point 121.780000 84.370000 -86.700000 0.230000)\n        (point 120.670000 87.090000 -88.970000 0.230000)\n        4)\n      (segment 5363 \n        (point 120.670000 87.090000 -88.970000 0.230000)\n        (point 120.670000 87.090000 -89.000000 0.230000)\n        4)\n      (segment 5364 \n        (point 120.670000 87.090000 -89.000000 0.230000)\n        (point 118.790000 89.050000 -90.880000 0.230000)\n        4)\n      (segment 5365 \n        (point 118.790000 89.050000 -90.880000 0.230000)\n        (point 118.790000 89.050000 -90.900000 0.230000)\n        4)\n      (segment 5366 \n        (point 118.790000 89.050000 -90.900000 0.230000)\n        (point 117.100000 92.240000 -93.500000 0.230000)\n        4)\n      (segment 5367 \n        (point 117.100000 92.240000 -93.500000 0.230000)\n        (point 117.100000 92.240000 -93.530000 0.230000)\n        4)\n      (segment 5368 \n        (point 117.100000 92.240000 -93.530000 0.230000)\n        (point 117.280000 93.470000 -96.200000 0.230000)\n        4)\n      (segment 5369 \n        (point 117.280000 93.470000 -96.200000 0.230000)\n        (point 116.570000 94.490000 -99.920000 0.230000)\n        4)\n      (segment 5370 \n        (point 116.570000 94.490000 -99.920000 0.230000)\n        (point 116.570000 94.490000 -100.000000 0.230000)\n        4))\n    (branch 197 -1 \n      (segment 5371 \n        (point 263.248016 5.356219 -3.380000 0.690000)\n        (point 262.996735 -9.641676 -3.380000 0.690000)\n        2)\n      (segment 5372 \n        (point 262.996735 -9.641676 -3.380000 0.690000)\n        (point 262.745453 -24.639572 -3.380000 0.690000)\n        2)\n      (segment 5373 \n        (point 262.745453 -24.639572 -3.380000 0.460000)\n        (point 262.494171 -39.637466 -3.380000 0.460000)\n        2)\n      (segment 5374 \n        (point 262.494171 -39.637466 -3.380000 0.460000)\n        (point 262.242889 -54.635365 -3.380000 0.460000)\n        2))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/l5pc/l5pc.json",
    "content": "{ \n  \"cell_model_name\": \"l5pc\",\n  \"produced_by\": \"Created by BluePyOpt(1.12.113) at 2022-11-06 18:21:20.822883\", \n  \"morphology\": {\n     \"original\": \"C060114A7.asc\",\n     \"replace_axon\": \"C060114A7_axon_replacement.acc\",\n     \"modified\": \"C060114A7_modified.acc\"\n  },\n  \"label_dict\": \"l5pc_label_dict.acc\",\n  \"decor\": \"l5pc_decor.acc\"\n}\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/l5pc/l5pc_decor.acc",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (decor\n    (default (membrane-potential -65 (scalar 1.0)))\n    (default (temperature-kelvin 307.14999999999998 (scalar 1.0)))\n    (default (membrane-capacitance 0.01 (scalar 1.0)))\n    (default (axial-resistivity 100 (scalar 1.0)))\n    (paint (region \"all\") (density (mechanism \"default::pas/e=-75\" (\"g\" 3.0000000000000001e-05))))\n    (paint (region \"soma\") (ion-reversal-potential \"na\" 50 (scalar 1.0)))\n    (paint (region \"soma\") (ion-reversal-potential \"k\" -85 (scalar 1.0)))\n    (paint (region \"soma\") (density (mechanism \"BBP::NaTs2_t\" (\"gNaTs2_tbar\" 0.98395500000000002))))\n    (paint (region \"soma\") (density (mechanism \"BBP::SKv3_1\" (\"gSKv3_1bar\" 0.30347200000000002))))\n    (paint (region \"soma\") (density (mechanism \"BBP::SK_E2\" (\"gSK_E2bar\" 0.0084069999999999995))))\n    (paint (region \"soma\") (density (mechanism \"BBP::Ca_HVA\" (\"gCa_HVAbar\" 0.00099400000000000009))))\n    (paint (region \"soma\") (density (mechanism \"BBP::Ca_LVAst\" (\"gCa_LVAstbar\" 0.00033300000000000002))))\n    (paint (region \"soma\") (density (mechanism \"BBP::CaDynamics_E2\" (\"gamma\" 0.00060899999999999995) (\"decay\" 210.48528400000001))))\n    (paint (region \"soma\") (density (mechanism \"BBP::Ih\" (\"gIhbar\" 8.0000000000000007e-05))))\n    (paint (region \"axon\") (ion-reversal-potential \"na\" 50 (scalar 1.0)))\n    (paint (region \"axon\") (ion-reversal-potential \"k\" -85 (scalar 1.0)))\n    (paint (region \"axon\") (density (mechanism \"BBP::NaTa_t\" (\"gNaTa_tbar\" 3.1379679999999999))))\n    (paint (region \"axon\") (density (mechanism \"BBP::Nap_Et2\" (\"gNap_Et2bar\" 0.0068269999999999997))))\n    (paint (region \"axon\") (density (mechanism \"BBP::K_Pst\" (\"gK_Pstbar\" 0.97353800000000001))))\n    (paint (region \"axon\") (density (mechanism \"BBP::K_Tst\" (\"gK_Tstbar\" 0.089259000000000005))))\n    (paint (region \"axon\") (density (mechanism \"BBP::SK_E2\" (\"gSK_E2bar\" 0.0071040000000000001))))\n    (paint (region \"axon\") (density (mechanism \"BBP::SKv3_1\" (\"gSKv3_1bar\" 1.0219450000000001))))\n    (paint (region \"axon\") (density (mechanism \"BBP::Ca_HVA\" (\"gCa_HVAbar\" 0.00098999999999999999))))\n    (paint (region \"axon\") (density (mechanism \"BBP::Ca_LVAst\" (\"gCa_LVAstbar\" 0.0087519999999999994))))\n    (paint (region \"axon\") (density (mechanism \"BBP::CaDynamics_E2\" (\"gamma\" 0.0029099999999999998) (\"decay\" 287.19873100000001))))\n    (paint (region \"dend\") (membrane-capacitance 0.02 (scalar 1.0)))\n    (paint (region \"dend\") (density (mechanism \"BBP::Ih\" (\"gIhbar\" 8.0000000000000007e-05))))\n    (paint (region \"apic\") (ion-reversal-potential \"na\" 50 (scalar 1.0)))\n    (paint (region \"apic\") (ion-reversal-potential \"k\" -85 (scalar 1.0)))\n    (paint (region \"apic\") (membrane-capacitance 0.02 (scalar 1.0)))\n    (paint (region \"apic\") (density (mechanism \"BBP::NaTs2_t\" (\"gNaTs2_tbar\" 0.026145000000000002))))\n    (paint (region \"apic\") (density (mechanism \"BBP::SKv3_1\" (\"gSKv3_1bar\" 0.0042259999999999997))))\n    (paint (region \"apic\") (density (mechanism \"BBP::Im\" (\"gImbar\" 0.00014300000000000001))))\n    (paint (region \"apic\") (scaled-mechanism (density (mechanism \"BBP::Ih\" (\"gIhbar\" 8.0000000000000007e-05))) (\"gIhbar\" (add (scalar -0.86960000000000004) (mul (scalar 2.0870000000000002) (exp (mul (distance (region \"soma\")) (scalar 0.0030999999999999999) ) ) ) ))))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/l5pc/l5pc_label_dict.acc",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (label-dict\n    (region-def \"all\" (all))\n    (region-def \"soma\" (tag 1))\n    (region-def \"axon\" (tag 2))\n    (region-def \"dend\" (tag 3))\n    (region-def \"apic\" (tag 4))\n    (region-def \"myelin\" (tag 5))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/l5pc_py37/l5pc_decor.acc",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (decor\n    (default (membrane-potential -65 (scalar 1.0)))\n    (default (temperature-kelvin 307.14999999999998 (scalar 1.0)))\n    (default (membrane-capacitance 0.01 (scalar 1.0)))\n    (default (axial-resistivity 100 (scalar 1.0)))\n    (paint (region \"all\") (density (mechanism \"default::pas/e=-75\" (\"g\" 3.0000000000000001e-05))))\n    (paint (region \"soma\") (ion-reversal-potential \"na\" 50 (scalar 1.0)))\n    (paint (region \"soma\") (ion-reversal-potential \"k\" -85 (scalar 1.0)))\n    (paint (region \"soma\") (density (mechanism \"BBP::NaTs2_t\" (\"gNaTs2_tbar\" 0.98395500000000002))))\n    (paint (region \"soma\") (density (mechanism \"BBP::SKv3_1\" (\"gSKv3_1bar\" 0.30347200000000002))))\n    (paint (region \"soma\") (density (mechanism \"BBP::SK_E2\" (\"gSK_E2bar\" 0.0084069999999999995))))\n    (paint (region \"soma\") (density (mechanism \"BBP::Ca_HVA\" (\"gCa_HVAbar\" 0.00099400000000000009))))\n    (paint (region \"soma\") (density (mechanism \"BBP::Ca_LVAst\" (\"gCa_LVAstbar\" 0.00033300000000000002))))\n    (paint (region \"soma\") (density (mechanism \"BBP::CaDynamics_E2\" (\"gamma\" 0.00060899999999999995) (\"decay\" 210.48528400000001))))\n    (paint (region \"soma\") (density (mechanism \"BBP::Ih\" (\"gIhbar\" 8.0000000000000007e-05))))\n    (paint (region \"axon\") (ion-reversal-potential \"na\" 50 (scalar 1.0)))\n    (paint (region \"axon\") (ion-reversal-potential \"k\" -85 (scalar 1.0)))\n    (paint (region \"axon\") (density (mechanism \"BBP::NaTa_t\" (\"gNaTa_tbar\" 3.1379679999999999))))\n    (paint (region \"axon\") (density (mechanism \"BBP::Nap_Et2\" (\"gNap_Et2bar\" 0.0068269999999999997))))\n    (paint (region \"axon\") (density (mechanism \"BBP::K_Pst\" (\"gK_Pstbar\" 0.97353800000000001))))\n    (paint (region \"axon\") (density (mechanism \"BBP::K_Tst\" (\"gK_Tstbar\" 0.089259000000000005))))\n    (paint (region \"axon\") (density (mechanism \"BBP::SK_E2\" (\"gSK_E2bar\" 0.0071040000000000001))))\n    (paint (region \"axon\") (density (mechanism \"BBP::SKv3_1\" (\"gSKv3_1bar\" 1.0219450000000001))))\n    (paint (region \"axon\") (density (mechanism \"BBP::Ca_HVA\" (\"gCa_HVAbar\" 0.00098999999999999999))))\n    (paint (region \"axon\") (density (mechanism \"BBP::Ca_LVAst\" (\"gCa_LVAstbar\" 0.0087519999999999994))))\n    (paint (region \"axon\") (density (mechanism \"BBP::CaDynamics_E2\" (\"gamma\" 0.0029099999999999998) (\"decay\" 287.19873100000001))))\n    (paint (region \"dend\") (membrane-capacitance 0.02 (scalar 1.0)))\n    (paint (region \"dend\") (density (mechanism \"BBP::Ih\" (\"gIhbar\" 8.0000000000000007e-05))))\n    (paint (region \"apic\") (ion-reversal-potential \"na\" 50 (scalar 1.0)))\n    (paint (region \"apic\") (ion-reversal-potential \"k\" -85 (scalar 1.0)))\n    (paint (region \"apic\") (membrane-capacitance 0.02 (scalar 1.0)))\n    (paint (region \"apic\") (density (mechanism \"BBP::NaTs2_t\" (\"gNaTs2_tbar\" 0.026145000000000002))))\n    (paint (region \"apic\") (density (mechanism \"BBP::SKv3_1\" (\"gSKv3_1bar\" 0.0042259999999999997))))\n    (paint (region \"apic\") (density (mechanism \"BBP::Im\" (\"gImbar\" 0.00014300000000000001))))\n    (paint (region \"apic\") (scaled-mechanism (density (mechanism \"BBP::Ih\" (\"gIhbar\" 8.0000000000000007e-05))) (\"gIhbar\" (add (mul (scalar -1) (scalar 0.86960000000000004) ) (mul (scalar 2.0870000000000002) (exp (mul (distance (region \"soma\")) (scalar 0.0030999999999999999) ) ) ) ))))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple_axon_replacement.acc",
    "content": "(arbor-component \n  (meta-data \n    (version \"0.9-dev\"))\n  (morphology \n    (branch 0 -1 \n      (segment 0 \n        (point 5.000000 0.000000 0.000000 0.500000)\n        (point 20.000000 0.000000 0.000000 0.500000)\n        2)\n      (segment 1 \n        (point 20.000000 0.000000 0.000000 0.500000)\n        (point 35.000000 0.000000 0.000000 0.500000)\n        2)\n      (segment 2 \n        (point 35.000000 0.000000 0.000000 0.500000)\n        (point 50.000000 0.000000 0.000000 0.500000)\n        2)\n      (segment 3 \n        (point 50.000000 0.000000 0.000000 0.500000)\n        (point 65.000000 0.000000 0.000000 0.500000)\n        2))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple_cell.json",
    "content": "{ \n  \"cell_model_name\": \"simple_cell\",\n  \"produced_by\": \"Created by BluePyOpt(1.12.113) at 2022-11-06 18:29:03.845296\", \n  \"morphology\": {\n     \"original\": \"simple.swc\",\n     \"replace_axon\": \"simple_axon_replacement.acc\",\n     \"modified\": \"simple_modified.acc\"\n  },\n  \"label_dict\": \"simple_cell_label_dict.acc\",\n  \"decor\": \"simple_cell_decor.acc\"\n}\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple_cell_decor.acc",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (decor\n    (paint (region \"soma\") (membrane-capacitance 0.01 (scalar 1.0)))\n    (paint (region \"soma\") (density (mechanism \"default::hh\" (\"gnabar\" 0.10299326453483033) (\"gkbar\" 0.027124836082684685))))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple_cell_label_dict.acc",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (label-dict\n    (region-def \"all\" (all))\n    (region-def \"soma\" (tag 1))\n    (region-def \"axon\" (tag 2))\n    (region-def \"dend\" (tag 3))\n    (region-def \"apic\" (tag 4))\n    (region-def \"myelin\" (tag 5))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple_modified.acc",
    "content": "(arbor-component \n  (meta-data \n    (version \"0.9-dev\"))\n  (morphology \n    (branch 0 -1 \n      (segment 0 \n        (point -5.000000 0.000000 0.000000 5.000000)\n        (point 0.000000 0.000000 0.000000 5.000000)\n        1)\n      (segment 1 \n        (point 0.000000 0.000000 0.000000 5.000000)\n        (point 5.000000 0.000000 0.000000 5.000000)\n        1))\n    (branch 1 -1 \n      (segment 2 \n        (point 5.000000 0.000000 0.000000 0.500000)\n        (point 20.000000 0.000000 0.000000 0.500000)\n        2)\n      (segment 3 \n        (point 20.000000 0.000000 0.000000 0.500000)\n        (point 35.000000 0.000000 0.000000 0.500000)\n        2)\n      (segment 4 \n        (point 35.000000 0.000000 0.000000 0.500000)\n        (point 50.000000 0.000000 0.000000 0.500000)\n        2)\n      (segment 5 \n        (point 50.000000 0.000000 0.000000 0.500000)\n        (point 65.000000 0.000000 0.000000 0.500000)\n        2))))"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/templates/cell_json_template.jinja2",
    "content": "{ \n  \"cell_model_name\": \"{{template_name}}\",\n  {%- if banner %}\n  \"produced_by\": \"{{banner}} (from {{ custom_param }})\",\n  {%- endif %}\n  {%- if morphology %} {# feed morphology separately as a SWC/ASC file #}\n  {%- if replace_axon is not none %}\n  \"morphology\": {\n     \"original\": \"{{morphology}}\",\n     \"replace_axon\": {{replace_axon}},\n     \"modified\": \"{{modified_morphology}}\"\n  },\n  {%- else %}\n  \"morphology\": {\n     \"original\": \"{{morphology}}\"\n  },\n  {%- endif %}\n  {%- else %}\n    execerror(\"Template {{template_name}} requires morphology name to instantiate\")\n  {%- endif %}\n  \"label_dict\": \"{{filenames['label_dict.acc']}}\",\n  \"decor\": \"{{filenames['decor.acc']}}\"\n}"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/templates/decor_acc_template.jinja2",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (meta-data (info \"test-decor\"))\n  (decor\n    {%- for mech, params in global_mechs.items() %}\n    {%- if mech is not none %}\n    {%- if mech in global_scaled_mechs %}\n    (default (scaled-mechanism (density (mechanism \"{{ mech }}\" {%- for param in params if param.value is not none %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})){%- for param in global_scaled_mechs[mech] %} (\"{{ param.name }}\" {{ param.scale }}){%- endfor %}))\n    {%- else %}\n    (default (density (mechanism \"{{ mech }}\" {%- for param in params %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})))\n    {%- endif %}\n    {%- else %}\n    {%- for param in params %}\n    (default ({{ param.name }} {{ param.value }} (scalar 1.0)))\n    {%- endfor %}\n    {%- endif %}\n    {%- endfor %}\n\n    {%- for loc, mech_parameters in local_mechs.items() %}{# paint-to-region instead of default #}\n    {%- for mech, params in mech_parameters.items() %}\n    {%- if mech is not none %}\n    {%- if mech in local_scaled_mechs[loc] %}\n    (paint {{loc.ref}} (scaled-mechanism (density (mechanism \"{{ mech }}\" {%- for param in params if param.value is not none %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})){%- for param in local_scaled_mechs[loc][mech] %} (\"{{ param.name }}\" {{ param.scale }}){%- endfor %}))\n    {%- else %}\n    (paint {{loc.ref}} (density (mechanism \"{{ mech }}\" {%- for param in params %} (\"{{ param.name }}\" {{ param.value }}){%- endfor %})))\n    {%- endif %}\n    {%- else %}\n    {%- for param in params %}\n    (paint {{loc.ref}} ({{ param.name }} {{ param.value }} (scalar 1.0)))\n    {%- endfor %}\n    {%- endif %}\n    {%- endfor %}\n    {%- endfor %}))\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/acc/templates/label_dict_acc_template.jinja2",
    "content": "(arbor-component\n  (meta-data (version \"0.9-dev\"))\n  (meta-data (info \"test-label-dict\"))\n  (label-dict\n  {%- for loc, label in label_dict.items() %}{# this is a comment #}\n    {{ label.defn }}\n  {%- endfor %}))\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/apic.swc",
    "content": "# Dummy cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n4 4 5.0 0.0 0.0 1.0 3\n5 4 20.0 0.0 0.0 1.0 4\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/simple.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/simple.wrong",
    "content": ""
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/simple_ax1.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n4 2 5.0 0.0 0.0 1.0 3\n5 2 10.0 0.0 0.0 1.0 4\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/simple_ax2.asc",
    "content": ";\tV3 text file written for MicroBrightField products.\n\n(\"CellBody\"\n  (CellBody)\n  (  -5    0    0     0)  ;  1, 1\n  (  0     -5    0     0)  ;  1, 2\n  (  5     0    0     0)  ;  1, 3\n  (  0     5    0     0)  ;  1, 4\n)\n\n( (Axon)\n  (  5     0    0     .5)  ;  Root\n  (  10     0    0     .5)  ;  1, R\n  (\n    (  20     0    0     .5)  ;  1, R-1\n    (  1000     0    0     .5)  ;  2\n  )\n)\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/simple_ax2.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n4 2 5.0 0.0 0.0 1.0 3\n5 2 10.0 0.0 0.0 1.0 4\n6 2 100.0 0.0 0.0 1.0 5\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testdata/test.jinja2",
    "content": "/* \nTest template\n\n{%- if banner %}\n{{banner}} \n{%- endif %}\n*/\n{load_file(\"stdrun.hoc\")}\n{load_file(\"import3d.hoc\")}\n\n{%- if global_params %}\n/*\n * Check that global parameters are the same as with the optimization\n */\nproc check_parameter(/* name, expected_value, value */){\n  strdef error\n  if($2 != $3){\n    sprint(error, \"Parameter %s has different value %f != %f\", $s1, $2, $3)\n    execerror(error)\n  }\n}\nproc check_simulator() {\n  {%- for param, value in global_params.items() %}\n  check_parameter(\"{{param}}\", {{value}}, {{param}})\n  {%- endfor %}\n}\n{%- endif %}\n{%- if ignored_global_params %}\n/* The following global parameters were set in BluePyOpt\n{%- for param, value in ignored_global_params.items() %}\n * {{param}} = {{value}}\n{%- endfor %}\n */\n{%- endif %}\n\nbegintemplate {{template_name}}\n  public init, morphology, geom_nseg_fixed, geom_nsec\n  public soma, dend, apic, axon, myelin\n  create soma[1], dend[1], apic[1], axon[1], myelin[1]\n\n  objref this, CellRef, segCounts\n\n  public all, somatic, apical, axonal, basal, myelinated, APC\n  objref all, somatic, apical, axonal, basal, myelinated, APC\n\nproc init(/* args: morphology_dir, morphology_name */) {\n  all = new SectionList()\n  apical = new SectionList()\n  axonal = new SectionList()\n  basal = new SectionList()\n  somatic = new SectionList()\n  myelinated = new SectionList()\n\n  //For compatibility with BBP CCells\n  CellRef = this\n\n  forall delete_section()\n\n  if(numarg() >= 2) {\n    load_morphology($s1, $s2)\n  } else {\n  {%- if morphology %}\n    load_morphology($s1, \"{{morphology}}\")\n  {%- else %}\n    execerror(\"Template {{template_name}} requires morphology name to instantiate\")\n  {%- endif %}\n  }\n\n  geom_nseg()\n  {%- if replace_axon %}\n    replace_axon()\n  {%- endif %}\n  insertChannel()\n  biophys()\n  re_init_rng()\n}\n\nproc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension\n  strdef morph_path\n  sprint(morph_path, \"%s/%s\", $s1, $s2)\n\n  sf = new StringFunctions()\n  extension = new String()\n\n  sscanf(morph_path, \"%s\", extension.s)\n  sf.right(extension.s, sf.len(extension.s)-4)\n\n  if( strcmp(extension.s, \".asc\") == 0 ) {\n    morph = new Import3d_Neurolucida3()\n  } else if( strcmp(extension.s, \".swc\" ) == 0) {\n    morph = new Import3d_SWC_read()\n  } else {\n    printf(\"Unsupported file format: Morphology file has to end with .asc or .swc\" )\n    quit()\n  }\n\n  morph.quiet = 1\n  morph.input(morph_path)\n\n  import = new Import3d_GUI(morph, 0)\n  import.instantiate(this)\n}\n\n/*\n * Assignment of mechanism values based on distance from the soma\n * Matches the BluePyOpt method\n */\nproc distribute_distance(){local x localobj sl\n  strdef stmp, distfunc, mech\n\n  sl = $o1\n  mech = $s2\n  distfunc = $s3\n  this.soma[0] distance(0, 0.5)\n  sprint(distfunc, \"%%s %s(%%f) = %s\", mech, distfunc)\n  forsec sl for(x, 0) {\n    sprint(stmp, distfunc, secname(), x, distance(x))\n    execute(stmp)\n  }\n}\n\nproc geom_nseg() {\n  this.geom_nsec() //To count all sections\n  //TODO: geom_nseg_fixed depends on segCounts which is calculated by\n  //  geom_nsec.  Can this be collapsed?\n  this.geom_nseg_fixed(40)\n  this.geom_nsec() //To count all sections\n}\n\nproc insertChannel() {\n  {%- for location, names in channels.items() %}\n  forsec this.{{location}} {\n  {%- for channel in names %}\n    insert {{channel}}\n  {%- endfor %}\n  }\n  {%- endfor %}\n}\n\nproc biophys() {\n  {% for loc, parameters in section_params %}\n  forsec CellRef.{{ loc }} {\n  {%- for param in parameters %}\n    {{ param.name }} = {{ param.value }}\n  {%- endfor %}\n  }\n  {% endfor %}\n  {%- for location, param_name, value in range_params %}\n  distribute_distance(CellRef.{{location}}, \"{{param_name}}\", \"{{value}}\")\n  {%- endfor %}\n}\n\nfunc sec_count(/* SectionList */) { local nSec\n  nSec = 0\n  forsec $o1 {\n      nSec += 1\n  }\n  return nSec\n}\n\n/*\n * Iterate over the section and compute how many segments should be allocate to\n * each.\n */\nproc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize\n  chunkSize = $1\n  soma area(.5) // make sure diam reflects 3d points\n  secIndex = 0\n  forsec all {\n    nseg = 1 + 2*int(L/chunkSize)\n    segCounts.x[secIndex] = nseg\n    secIndex += 1\n  }\n}\n\n/*\n * Count up the number of sections\n */\nproc geom_nsec() { local nSec\n  nSecAll = sec_count(all)\n  nSecSoma = sec_count(somatic)\n  nSecApical = sec_count(apical)\n  nSecBasal = sec_count(basal)\n  nSecMyelinated = sec_count(myelinated)\n  nSecAxonalOrig = nSecAxonal = sec_count(axonal)\n\n  segCounts = new Vector()\n  segCounts.resize(nSecAll)\n  nSec = 0\n  forsec all {\n    segCounts.x[nSec] = nseg\n    nSec += 1\n  }\n}\n\n/*\n * Replace the axon built from the original morphology file with a stub axon\n */\n{%- if replace_axon %}\n    {{replace_axon}}\n{%- endif %}\n\n{%- if custom_param %}\n    {{custom_param}}\n{%- endif %}\n\n\n{{re_init_rng}}\n\nendtemplate {{template_name}}\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/testmodels/__init__.py",
    "content": ""
  },
  {
    "path": "bluepyopt/tests/test_ephys/testmodels/dummycells.py",
    "content": "\"\"\"Dummy cell model used for testing\"\"\"\n\nimport bluepyopt.ephys as ephys\n\n\nclass DummyCellModel1(ephys.models.Model):\n\n    \"\"\"Dummy cell model 1\"\"\"\n\n    def __init__(self, name=None):\n        \"\"\"Constructor\"\"\"\n\n        super(DummyCellModel1, self).__init__(name)\n        self.persistent = []\n        self.icell = None\n\n    def freeze(self, param_values):\n        \"\"\"Freeze model\"\"\"\n        pass\n\n    def unfreeze(self, param_names):\n        \"\"\"Freeze model\"\"\"\n        pass\n\n    def instantiate(self, sim=None):\n        \"\"\"Instantiate cell in simulator\"\"\"\n\n        class Cell(object):\n\n            \"\"\"Empty cell class\"\"\"\n\n            def __init__(self):\n                \"\"\"Constructor\"\"\"\n                self.soma = None\n                self.somatic = None\n\n        self.icell = Cell()\n\n        self.icell.soma = [sim.neuron.h.Section(name='soma', cell=self.icell)]\n        self.icell.apic = [\n            sim.neuron.h.Section(\n                name='apic1',\n                cell=self.icell)]\n\n        self.icell.somatic = sim.neuron.h.SectionList(\n        )  # pylint: disable = W0201\n        self.icell.somatic.append(sec=self.icell.soma[0])\n\n        self.icell.apical = sim.neuron.h.SectionList()\n        self.icell.apical.append(sec=self.icell.apic[0])\n\n        self.persistent.append(self.icell)\n        self.persistent.append(self.icell.soma[0])\n\n        return self.icell\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy cell from simulator\"\"\"\n\n        self.persistent = []\n\n\nclass DummyLFPyCellModel1(ephys.models.Model):\n\n    \"\"\"Dummy LFPy cell model 1\"\"\"\n\n    def __init__(self, name=None):\n        \"\"\"Constructor\"\"\"\n\n        super(DummyLFPyCellModel1, self).__init__(name)\n        self.persistent = []\n        self.icell = None\n        self.lfpy_cell = None\n        self.electrode = None\n        self.lfpy_electrode = None\n\n    def freeze(self, param_values):\n        \"\"\"Freeze model\"\"\"\n        pass\n\n    def unfreeze(self, param_names):\n        \"\"\"Freeze model\"\"\"\n        pass\n\n    def instantiate(self, sim=None):\n        \"\"\"Instantiate cell in simulator\"\"\"\n        import LFPy\n\n        class Cell(object):\n\n            \"\"\"Empty cell class\"\"\"\n\n            def __init__(self):\n                \"\"\"Constructor\"\"\"\n                self.soma = None\n                self.somatic = None\n\n        self.icell = Cell()\n\n        self.icell.soma = [sim.neuron.h.Section(name='soma', cell=self.icell)]\n        self.icell.apic = [\n            sim.neuron.h.Section(\n                name='apic1',\n                cell=self.icell)]\n\n        self.icell.somatic = sim.neuron.h.SectionList(\n        )  # pylint: disable = W0201\n        self.icell.somatic.append(sec=self.icell.soma[0])\n\n        self.icell.apical = sim.neuron.h.SectionList()\n        self.icell.apical.append(sec=self.icell.apic[0])\n\n        self.persistent.append(self.icell)\n        self.persistent.append(self.icell.soma[0])\n\n        self.lfpy_cell = LFPy.Cell(\n            morphology=sim.neuron.h.allsec(),\n            dt=0.025,\n            v_init=-65,\n            pt3d=True,\n            delete_sections=False,\n            nsegs_method=None,\n        )\n\n        return self.icell, self.lfpy_cell\n\n    def destroy(self, sim=None):\n        \"\"\"Destroy cell from simulator\"\"\"\n\n        self.persistent = []\n"
  },
  {
    "path": "bluepyopt/tests/test_ephys/utils.py",
    "content": "\"\"\"EPhys test utils\"\"\"\n\nfrom bluepyopt import ephys\nfrom bluepyopt.ephys.parameters import (\n    NrnGlobalParameter, NrnSectionParameter, NrnRangeParameter,)\nfrom bluepyopt.ephys.locations import NrnSeclistLocation\n\n\ndef make_mech():\n    \"\"\"Create mechanism\"\"\"\n    basal = ephys.locations.NrnSeclistLocation('basal', seclist_name='basal')\n    apical = ephys.locations.NrnSeclistLocation(\n        'apical', seclist_name='apical')\n    return ephys.mechanisms.NrnMODMechanism(\n        'Ih',\n        suffix='Ih',\n        locations=[\n            basal,\n            apical,\n        ])\n\n\ndef make_parameters():\n    \"\"\"Create parameters\"\"\"\n    value, frozen, bounds, param_name = 65, False, [\n        0, 100.0], 'gSKv3_1bar_SKv3_1'\n    value_scaler = ephys.parameterscalers.NrnSegmentLinearScaler()\n    locations = (NrnSeclistLocation('Location0', 'somatic'),\n                 NrnSeclistLocation('Location1', 'apical'),)\n    parameters = (\n        NrnGlobalParameter(\n            'NrnGlobalParameter',\n            value,\n            frozen,\n            bounds,\n            param_name),\n        NrnSectionParameter(\n            'NrnSectionParameter',\n            value,\n            frozen,\n            bounds,\n            param_name,\n            value_scaler,\n            locations),\n        NrnRangeParameter(\n            'NrnRangeParameter',\n            value,\n            frozen,\n            bounds,\n            param_name,\n            value_scaler,\n            locations),\n    )\n    return parameters\n"
  },
  {
    "path": "bluepyopt/tests/test_evaluators.py",
    "content": "\"\"\"bluepyopt.evaluators tests\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\n\nimport pytest\nimport numpy\nimport bluepyopt\n\n\n@pytest.mark.unit\ndef test_evaluator_init():\n    \"\"\"bluepyopt.evaluators: test Evaluator init\"\"\"\n\n    evaluator = bluepyopt.evaluators.Evaluator()\n    assert isinstance(evaluator, bluepyopt.evaluators.Evaluator)\n"
  },
  {
    "path": "bluepyopt/tests/test_l5pc.py",
    "content": "\"\"\"Test l5pc example\"\"\"\n\nimport json\nimport os\nimport sys\nfrom contextlib import contextmanager\n\nimport bluepyopt\nfrom bluepyopt import ephys\n\nif sys.version_info[0] < 3:\n    from StringIO import StringIO\nelse:\n    from io import StringIO\n\n\nimport pytest\n\nL5PC_PATH = os.path.abspath(\n    os.path.join(os.path.dirname(__file__), \"../../examples/l5pc\")\n)\nSCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))\n\nsys.path.insert(0, L5PC_PATH)\n\n\nneuron_sim = ephys.simulators.NrnSimulator()\n\nneuron_sim.neuron.h.nrn_load_dll(\n    os.path.join(L5PC_PATH, \"x86_64/.libs/libnrnmech.so\")\n)\n\n\n# Parameters in release circuit model\nrelease_parameters = {\n    \"gNaTs2_tbar_NaTs2_t.apical\": 0.026145,\n    \"gSKv3_1bar_SKv3_1.apical\": 0.004226,\n    \"gImbar_Im.apical\": 0.000143,\n    \"gNaTa_tbar_NaTa_t.axonal\": 3.137968,\n    \"gK_Tstbar_K_Tst.axonal\": 0.089259,\n    \"gamma_CaDynamics_E2.axonal\": 0.002910,\n    \"gNap_Et2bar_Nap_Et2.axonal\": 0.006827,\n    \"gSK_E2bar_SK_E2.axonal\": 0.007104,\n    \"gCa_HVAbar_Ca_HVA.axonal\": 0.000990,\n    \"gK_Pstbar_K_Pst.axonal\": 0.973538,\n    \"gSKv3_1bar_SKv3_1.axonal\": 1.021945,\n    \"decay_CaDynamics_E2.axonal\": 287.198731,\n    \"gCa_LVAstbar_Ca_LVAst.axonal\": 0.008752,\n    \"gamma_CaDynamics_E2.somatic\": 0.000609,\n    \"gSKv3_1bar_SKv3_1.somatic\": 0.303472,\n    \"gSK_E2bar_SK_E2.somatic\": 0.008407,\n    \"gCa_HVAbar_Ca_HVA.somatic\": 0.000994,\n    \"gNaTs2_tbar_NaTs2_t.somatic\": 0.983955,\n    \"decay_CaDynamics_E2.somatic\": 210.485284,\n    \"gCa_LVAstbar_Ca_LVAst.somatic\": 0.000333,\n}\n\n\ndef load_from_json(filename):\n    \"\"\"Load structure from json\"\"\"\n\n    with open(filename) as json_file:\n        return json.load(json_file)\n\n\ndef dump_to_json(content, filename):\n    \"\"\"Dump structure to json\"\"\"\n\n    with open(filename, \"w\") as json_file:\n        return json.dump(content, json_file, indent=4, separators=(\",\", \": \"))\n\n\ndef test_import():\n    \"\"\"L5PC: test import\"\"\"\n\n    import l5pc_model  # NOQA\n    import l5pc_evaluator  # NOQA\n    import opt_l5pc  # NOQA\n\n\nclass TestL5PCModel(object):\n    \"\"\"Test L5PC model\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up class\"\"\"\n        sys.path.insert(0, L5PC_PATH)\n\n        import l5pc_model  # NOQA\n\n        self.l5pc_cell = l5pc_model.create()\n        assert isinstance(self.l5pc_cell, bluepyopt.ephys.models.CellModel)\n        self.nrn = ephys.simulators.NrnSimulator()\n\n    def test_instantiate(self):\n        \"\"\"L5PC: test instantiation of l5pc cell model\"\"\"\n        self.l5pc_cell.freeze(release_parameters)\n        self.l5pc_cell.instantiate(sim=self.nrn)\n\n    def teardown_method(self):\n        \"\"\"Teardown\"\"\"\n        self.l5pc_cell.destroy(sim=self.nrn)\n\n\nclass TestL5PCEvaluator(object):\n    \"\"\"Test L5PC evaluator\"\"\"\n\n    def setup_method(self):\n        \"\"\"Set up class\"\"\"\n\n        import l5pc_evaluator  # NOQA\n\n        self.l5pc_evaluator = l5pc_evaluator.create()\n\n        assert isinstance(\n            self.l5pc_evaluator, bluepyopt.ephys.evaluators.CellEvaluator\n        )\n\n    @pytest.mark.slow\n    def test_eval(self):\n        \"\"\"L5PC: test evaluation of l5pc evaluator\"\"\"\n        result = self.l5pc_evaluator.evaluate_with_dicts(\n            param_dict=release_parameters\n        )\n\n        expected_results = load_from_json(\n            os.path.join(SCRIPT_DIR, \"expected_results.json\")\n        )\n\n        # Use two lines below to update expected result\n        # expected_results['TestL5PCEvaluator.test_eval'] = result\n        # dump_to_json(expected_results, 'expected_results.json')\n\n        assert set(result.keys()) == set(\n            expected_results[\"TestL5PCEvaluator.test_eval\"].keys()\n        )\n\n    def teardown_method(self):\n        \"\"\"Teardown\"\"\"\n        pass\n\n\n# backport from python 3.4\n@contextmanager\ndef stdout_redirector(stream):\n    \"\"\"Stdout redirector\"\"\"\n    old_stdout = sys.stdout\n    sys.stdout = stream\n    try:\n        yield\n    finally:\n        sys.stdout = old_stdout\n\n\n@pytest.mark.slow\ndef test_exec():\n    \"\"\"L5PC Notebook: test execution\"\"\"\n    import numpy\n\n    numpy.seterr(all=\"raise\")\n    old_cwd = os.getcwd()\n    output = StringIO()\n    try:\n        os.chdir(L5PC_PATH)\n        with stdout_redirector(output):\n            # When using import instead of execfile this doesn't work\n            # Probably because multiprocessing doesn't work correctly during\n            # import\n            if sys.version_info[0] < 3:\n                execfile(\"L5PC.py\")  # NOQA\n            else:\n                with open(\"L5PC.py\") as l5pc_file:\n                    exec(compile(l5pc_file.read(), \"L5PC.py\", \"exec\"))  # NOQA\n        stdout = output.getvalue()\n        # first and last values of optimal individual\n        assert \"0.001017834439738432\" in stdout\n        assert \"202.18814057682334\" in stdout\n        assert \"'gamma_CaDynamics_E2.somatic': 0.03229357096515606\" in stdout\n    finally:\n        os.chdir(old_cwd)\n        output.close()\n\n\n@pytest.mark.slow\ndef test_l5pc_validate_neuron_arbor():\n    \"\"\"L5PC Neuron/Arbor validation Notebook: test execution\"\"\"\n    import numpy\n\n    numpy.seterr(all=\"raise\")\n    old_cwd = os.getcwd()\n    output = StringIO()\n    try:\n        os.chdir(L5PC_PATH)\n        with stdout_redirector(output):\n            # When using import instead of execfile this doesn't work\n            # Probably because multiprocessing doesn't work correctly during\n            # import\n            if sys.version_info[0] < 3:\n                execfile(\"l5pc_validate_neuron_arbor_somatic.py\")  # NOQA\n            else:\n                with open(\n                    \"l5pc_validate_neuron_arbor_somatic.py\"\n                ) as l5pc_file:\n                    l5pc_globals = {}\n                    exec(\n                        compile(\n                            l5pc_file.read(),\n                            \"l5pc_validate_neuron_arbor_somatic.py\",\n                            \"exec\",\n                        ),\n                        l5pc_globals,\n                        l5pc_globals,\n                    )  # NOQA\n        stdout = output.getvalue()\n        # mean relative L1-deviation between Arbor and Neuron below tolerance\n        assert (\n            \"Default dt ({:,.3g}): test_l5pc OK!\".format(0.025)\n            + \" The mean relative Arbor-Neuron L1-deviation and error\"\n            in stdout\n        )\n    finally:\n        os.chdir(old_cwd)\n        output.close()\n"
  },
  {
    "path": "bluepyopt/tests/test_lfpy.py",
    "content": "\"\"\"Functional LFPy test\"\"\"\n\nimport os\nimport sys\n\nimport pytest\n\nL5PC_LFPY_PATH = os.path.abspath(\n    os.path.join(os.path.dirname(__file__), \"../../examples/l5pc_lfpy\")\n)\nSCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))\n\nsys.path.insert(0, L5PC_LFPY_PATH)\n\nimport l5pc_lfpy_evaluator\nfrom generate_extra_features import release_params\n\n\n@pytest.mark.slow\ndef test_lfpy_evaluator():\n    \"\"\"Test CellEvaluator with an LFPy cell and LFPy simulator\"\"\"\n\n    evaluator = l5pc_lfpy_evaluator.create(\n        feature_file=L5PC_LFPY_PATH + \"/extra_features.json\",\n        cvode_active=False,\n        dt=0.025,\n    )\n\n    responses = evaluator.run_protocols(\n        protocols=evaluator.fitness_protocols.values(),\n        param_values=release_params,\n    )\n    values = evaluator.fitness_calculator.calculate_values(responses)\n\n    assert len(values) == 21\n    assert abs(values[\"Step1.soma.AP_height\"] - 27.85963902931001) < 1e-5\n    assert len(responses[\"Step1.MEA.v\"][\"voltage\"]) == 40\n"
  },
  {
    "path": "bluepyopt/tests/test_neuroml_fcts.py",
    "content": "\"\"\"Test neuroml functions\"\"\"\n\nimport os\nimport sys\n\nimport efel\nimport neuroml\nimport numpy\nimport pytest\nfrom pyneuroml import pynml\nfrom bluepyopt import ephys\nfrom bluepyopt.neuroml import biophys\nfrom bluepyopt.neuroml import cell\nfrom bluepyopt.neuroml import morphology\nfrom bluepyopt.neuroml import simulation\n\nL5PC_PATH = os.path.abspath(\n    os.path.join(os.path.dirname(__file__), \"../../examples/l5pc\")\n)\n\nsys.path.insert(0, L5PC_PATH)\n\nimport l5pc_evaluator\nimport l5pc_model  # NOQA\n\nl5pc_cell = l5pc_model.create()\nprotocols = l5pc_evaluator.define_protocols()\n\nrelease_params = {\n    \"gNaTs2_tbar_NaTs2_t.apical\": 0.026145,\n    \"gSKv3_1bar_SKv3_1.apical\": 0.004226,\n    \"gImbar_Im.apical\": 0.000143,\n    \"gNaTa_tbar_NaTa_t.axonal\": 3.137968,\n    \"gK_Tstbar_K_Tst.axonal\": 0.089259,\n    \"gamma_CaDynamics_E2.axonal\": 0.002910,\n    \"gNap_Et2bar_Nap_Et2.axonal\": 0.006827,\n    \"gSK_E2bar_SK_E2.axonal\": 0.007104,\n    \"gCa_HVAbar_Ca_HVA.axonal\": 0.000990,\n    \"gK_Pstbar_K_Pst.axonal\": 0.973538,\n    \"gSKv3_1bar_SKv3_1.axonal\": 1.021945,\n    \"decay_CaDynamics_E2.axonal\": 287.198731,\n    \"gCa_LVAstbar_Ca_LVAst.axonal\": 0.008752,\n    \"gamma_CaDynamics_E2.somatic\": 0.000609,\n    \"gSKv3_1bar_SKv3_1.somatic\": 0.303472,\n    \"gSK_E2bar_SK_E2.somatic\": 0.008407,\n    \"gCa_HVAbar_Ca_HVA.somatic\": 0.000994,\n    \"gNaTs2_tbar_NaTs2_t.somatic\": 0.983955,\n    \"decay_CaDynamics_E2.somatic\": 210.485284,\n    \"gCa_LVAstbar_Ca_LVAst.somatic\": 0.000333,\n}\n\n\n@pytest.mark.unit\ndef test_get_nml_mech_dir():\n    \"\"\"biophys.get_nml_mech_dir: Test get_nml_mech_dir\"\"\"\n    channels = [\n        \"Ih\",\n        \"NaTa_t\",\n        \"NaTs2_t\",\n        \"Nap_Et2\",\n        \"K_Tst\",\n        \"K_Pst\",\n        \"SKv3_1\",\n        \"SK_E2\",\n        \"StochKv_deterministic\",\n        \"KdShu2007\",\n        \"Im\",\n        \"Ca\",\n        \"Ca_HVA\",\n        \"Ca_LVAst\",\n        \"pas\",\n    ]\n\n    for channel in channels:\n        assert os.path.isfile(\n            os.path.join(biophys.get_nml_mech_dir(), f\"{channel}.channel.nml\")\n        )\n    assert os.path.isfile(\n        os.path.join(biophys.get_nml_mech_dir(), \"baseCaDynamics_E2_NML2.nml\")\n    )\n\n\n@pytest.mark.unit\ndef test_get_channel_from_param_name():\n    \"\"\"biophys.get_channel_from_param_name:\n\n    Test get_channel_from_param_name\n    \"\"\"\n    # 3 underscores case\n    assert biophys.get_channel_from_param_name(\n        \"gNaTs2_tbar_NaTs2_t\"\n    ) == \"NaTs2_t\"\n    # 2 underscores case\n    assert biophys.get_channel_from_param_name(\n        \"gamma_CaDynamics_E2\"\n    ) == \"CaDynamics_E2\"\n    # 1 underscore case\n    assert biophys.get_channel_from_param_name(\"gIhbar_Ih\") == \"Ih\"\n\n\n@pytest.mark.unit\ndef test_format_dist_fun():\n    \"\"\"biophys.format_dist_fun: Test format_dist_fun\"\"\"\n    raw_expr = \"(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}\"\n    formatted_expr = \"(-0.8696 + 2.087*exp(p*0.0031))*8e-05\"\n    assert biophys.format_dist_fun(raw_expr, 8e-05, None) == formatted_expr\n\n\n@pytest.mark.unit\ndef test_add_nml_channel_to_nml_cell_file():\n    \"\"\"biophys.add_nml_channel_to_nml_cell_file:\n\n    Test add_nml_channel_to_nml_cell_file\n    \"\"\"\n    empty_cell_doc = neuroml.NeuroMLDocument(id=\"test_nml_cell\")\n    included_channels = []\n\n    pytest.raises(\n        ValueError,\n        biophys.add_nml_channel_to_nml_cell_file,\n        empty_cell_doc,\n        included_channels,\n    )\n\n    channel_name = \"K_Pst\"\n    biophys.add_nml_channel_to_nml_cell_file(\n        empty_cell_doc,\n        included_channels,\n        channel_name=channel_name,\n        skip_channels_copy=True,\n    )\n    assert len(empty_cell_doc.includes) == 1\n    assert channel_name in str(empty_cell_doc.includes[0].href)\n    assert len(included_channels) == 1\n    assert f\"{channel_name}.channel.nml\" in included_channels\n\n    # test that we cannot add the same channel twice\n    biophys.add_nml_channel_to_nml_cell_file(\n        empty_cell_doc,\n        included_channels,\n        channel_name=channel_name,\n        skip_channels_copy=True,\n    )\n    assert len(empty_cell_doc.includes) == 1\n    assert len(included_channels) == 1\n\n\n@pytest.mark.unit\ndef test_get_arguments():\n    \"\"\"biophys.get_arguments: Test get_arguments\"\"\"\n    parameter_name = \"gSKv3_1bar_SKv3_1\"\n    section_list = \"somatic\"\n    channel = \"SKv3_1\"\n    variable_parameters = None\n    cond_density = \"0.303472 S_per_cm2\"\n    arguments, channel_class = biophys.get_arguments(\n        l5pc_cell.params,\n        parameter_name,\n        section_list,\n        channel,\n        channel,\n        variable_parameters,\n        cond_density,\n        release_params,\n    )\n    assert arguments[\"ion\"] == \"k\"\n    assert arguments[\"segment_groups\"] == section_list\n    assert arguments[\"erev\"] == \"-85.0 mV\"\n    assert arguments[\"id\"] == \"somatic_gSKv3_1bar_SKv3_1\"\n    assert arguments[\"ion_channel\"] == channel\n    assert arguments[\"cond_density\"] == cond_density\n    assert \"variable_parameters\" not in arguments\n    assert channel_class == \"ChannelDensity\"\n\n    # pas case\n    parameter_name = \"g_pas\"\n    section_list = \"all\"\n    channel = \"pas\"\n    cond_density = \"3e-05 S_per_cm2\"\n    arguments, channel_class = biophys.get_arguments(\n        l5pc_cell.params,\n        parameter_name,\n        section_list,\n        channel,\n        channel,\n        variable_parameters,\n        cond_density,\n        release_params,\n    )\n    assert arguments[\"ion\"] == \"non_specific\"\n    assert arguments[\"segment_groups\"] == section_list\n    assert arguments[\"erev\"] == \"-75 mV\"\n    assert arguments[\"id\"] == \"all_g_pas\"\n    assert arguments[\"ion_channel\"] == channel\n    assert arguments[\"cond_density\"] == cond_density\n    assert \"variable_parameters\" not in arguments\n    assert channel_class == \"ChannelDensity\"\n\n    # nernst case\n    parameter_name = \"gCa_HVAbar_Ca_HVA\"\n    section_list = \"axonal\"\n    channel = \"Ca_HVA\"\n    cond_density = \"0.00099 S_per_cm2\"\n    arguments, channel_class = biophys.get_arguments(\n        l5pc_cell.params,\n        parameter_name,\n        section_list,\n        channel,\n        channel,\n        variable_parameters,\n        cond_density,\n        release_params,\n    )\n    assert arguments[\"ion\"] == \"ca\"\n    assert arguments[\"segment_groups\"] == section_list\n    assert \"erev\" not in arguments\n    assert arguments[\"id\"] == \"axonal_gCa_HVAbar_Ca_HVA\"\n    assert arguments[\"ion_channel\"] == channel\n    assert arguments[\"cond_density\"] == cond_density\n    assert \"variable_parameters\" not in arguments\n    assert channel_class == \"ChannelDensityNernst\"\n\n\n@pytest.mark.unit\ndef test_extract_parameter_value():\n    \"\"\"biophys.extract_parameter_value: Test extract_parameter_value\"\"\"\n    # uniform parameter case\n    param_name = \"gSKv3_1bar_SKv3_1\"\n    section_list = \"apical\"\n    channel = \"SKv3_1\"\n    cond_density, variable_parameters = biophys.extract_parameter_value(\n        l5pc_cell.params[\".\".join((param_name, section_list))],\n        section_list,\n        channel,\n        True,\n        release_params,\n    )\n    assert cond_density == \"0.004226 S_per_cm2\"\n    assert variable_parameters is None\n\n    # skipped non uniform parameter case\n    param_name = \"gIhbar_Ih\"\n    section_list = \"apical\"\n    channel = \"Ih\"\n    cond_density, variable_parameters = biophys.extract_parameter_value(\n        l5pc_cell.params[\".\".join((param_name, section_list))],\n        section_list,\n        channel,\n        True,\n        release_params,\n    )\n    assert cond_density is None\n    assert variable_parameters is None\n\n    # non uniform parameter case (unskipped)\n    param_name = \"gIhbar_Ih\"\n    section_list = \"apical\"\n    channel = \"Ih\"\n    cond_density, variable_parameters = biophys.extract_parameter_value(\n        l5pc_cell.params[\".\".join((param_name, section_list))],\n        section_list,\n        channel,\n        False,\n        release_params,\n    )\n    assert cond_density is None\n    assert variable_parameters[0].segment_groups == section_list\n    assert variable_parameters[0].parameter == \"condDensity\"\n    assert (\n        variable_parameters[0].inhomogeneous_value.inhomogeneous_parameters\n        == \"PathLengthOver_apical\"\n    )\n    assert (\n        variable_parameters[0].inhomogeneous_value.value\n        == \"(-0.8696 + 2.087*exp(p*0.0031))*8e-05\"\n    )\n\n\n@pytest.mark.unit\ndef test_get_density():\n    \"\"\"biophys.get_density: Test get_density\"\"\"\n    empty_cell_doc = neuroml.NeuroMLDocument(id=\"test_nml_cell\")\n    param_name = \"gSK_E2bar_SK_E2\"\n    section_list = \"axonal\"\n    density, channel_class = biophys.get_density(\n        empty_cell_doc,\n        l5pc_cell,\n        l5pc_cell.params[\".\".join((param_name, section_list))],\n        section_list,\n        included_channels=[],\n        skip_non_uniform=True,\n        release_params=release_params,\n        skip_channels_copy=True,\n    )\n\n    assert channel_class == \"ChannelDensity\"\n    assert density.id == \"axonal_gSK_E2bar_SK_E2\"\n    assert density.ion_channel == \"SK_E2\"\n    assert density.cond_density == \"0.007104 S_per_cm2\"\n    assert density.erev == \"-85.0 mV\"\n    assert density.segment_groups == \"axonal\"\n    assert density.ion == \"k\"\n\n\n@pytest.mark.unit\ndef test_get_specific_capacitance():\n    \"\"\"biophys.get_specific_capacitance: Test get_specific_capacitance\"\"\"\n    # case: default\n    specific_capacitances = biophys.get_specific_capacitance({})\n    assert neuroml.SpecificCapacitance(\n        value=\"1.0 uF_per_cm2\", segment_groups=\"axonal\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"1.0 uF_per_cm2\", segment_groups=\"somatic\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"1.0 uF_per_cm2\", segment_groups=\"basal\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"1.0 uF_per_cm2\", segment_groups=\"apical\"\n    ) in specific_capacitances\n\n    # case: all\n    specific_capacitances = biophys.get_specific_capacitance(\n        {\"all\": \"2.0 uF_per_cm2\"}\n    )\n    assert neuroml.SpecificCapacitance(\n        value=\"2.0 uF_per_cm2\", segment_groups=\"axonal\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"2.0 uF_per_cm2\", segment_groups=\"somatic\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"2.0 uF_per_cm2\", segment_groups=\"basal\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"2.0 uF_per_cm2\", segment_groups=\"apical\"\n    ) in specific_capacitances\n\n    # case: specific section(s)\n    specific_capacitances = biophys.get_specific_capacitance(\n        {\"somatic\": \"2.0 uF_per_cm2\", \"axonal\": \"3.0 uF_per_cm2\"}\n    )\n    assert neuroml.SpecificCapacitance(\n        value=\"3.0 uF_per_cm2\", segment_groups=\"axonal\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"2.0 uF_per_cm2\", segment_groups=\"somatic\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"1.0 uF_per_cm2\", segment_groups=\"basal\"\n    ) in specific_capacitances\n    assert neuroml.SpecificCapacitance(\n        value=\"1.0 uF_per_cm2\", segment_groups=\"apical\"\n    ) in specific_capacitances\n\n\n@pytest.mark.unit\ndef test_get_biophys():\n    \"\"\"biophys.get_biophys: Test get_biophys\"\"\"\n    empty_cell_doc = neuroml.NeuroMLDocument(id=\"test_nml_cell\")\n    bio_prop = biophys.get_biophys(\n        l5pc_cell,\n        empty_cell_doc,\n        release_params,\n        skip_non_uniform=True,\n        skip_channels_copy=True,\n    )\n    membrane_props = bio_prop.membrane_properties\n    intracell_props = bio_prop.intracellular_properties\n\n    assert bio_prop.id == \"biophys\"\n    assert membrane_props.init_memb_potentials[0].value == \"-65 mV\"\n    assert len(membrane_props.specific_capacitances) == 4\n    assert len(membrane_props.channel_density_nernsts) == 4\n    assert len(membrane_props.channel_densities) == 15\n    assert intracell_props.resistivities[0].value == \"100 ohm_cm\"\n    assert len(intracell_props.species) == 2\n\n\n@pytest.mark.unit\ndef test_add_segment_groups():\n    \"\"\"morphology.add_segment_groups: Test add_segment_groups\"\"\"\n    # creates a neuroml cell with a morphology\n    cell = neuroml.Cell(id=\"nml_cell\")\n    morph = neuroml.Morphology(id=\"test_nml_cell_morph\")\n    for loc in [\"soma\", \"axon\", \"dend\", \"apic\"]:\n        seg = neuroml.Segment(id=0, name=loc)\n        morph.segments.append(seg)\n    cell.morphology = morph\n\n    morphology.add_segment_groups(cell)\n    segment_group_names = [\n        group.id for group in cell.morphology.segment_groups\n    ]\n    assert \"somatic\" in segment_group_names\n    assert \"axonal\" in segment_group_names\n    assert \"basal\" in segment_group_names\n    assert \"apical\" in segment_group_names\n    assert \"soma_group\" in segment_group_names\n    assert \"axon_group\" in segment_group_names\n    assert \"dendrite_group\" in segment_group_names\n\n\n@pytest.mark.slow\n@pytest.mark.neuroml\ndef test_neuroml_run():\n    \"\"\"Test neuroml conversion and simulation\"\"\"\n    # replace axon is not supported yet\n    l5pc_cell.morphology.do_replace_axon = False\n    dt = 0.025\n    protocol_name = \"Step3\"\n    bpo_test_protocol = protocols[protocol_name]\n\n    os.system(\"nrnivmodl examples/l5pc/mechanisms/\")\n\n    # create neuroml cell\n    cell.create_neuroml_cell(\n        l5pc_cell, release_params, skip_channels_copy=False\n    )\n\n    # create LEMS simulation\n    network_filename = f\"{l5pc_cell.name}.net.nml\"\n    lems_filename = f\"LEMS_{l5pc_cell.name}.xml\"\n    simulation.create_neuroml_simulation(\n        network_filename, bpo_test_protocol, dt, l5pc_cell.name, lems_filename\n    )\n\n    # remove compiled mechanisms if any before running LEMS simulation\n    os.system(\"rm -rf x86_64/\")\n\n    # run the simulation with the NEURON simulator,\n    # since the default jNeuroML simulator can only simulate single\n    # compartment cells\n    pynml.run_lems_with_jneuroml_neuron(\n        lems_filename, nogui=True, plot=False)\n\n    # re-compile the mechanisms before running with bluepyopt\n    os.system(\"rm -rf x86_64/\")\n    os.system(\"nrnivmodl examples/l5pc/mechanisms/\")\n\n    lems_output = numpy.loadtxt(\"l5pc.Pop_l5pc_0_0.v.dat\")\n    lems_voltage = lems_output[:, 1] * 1000  # *1000 -> mV\n    lems_time = lems_output[:, 0] * 1000  # *1000 -> ms\n\n    # remove non uniform parameter to be coherent with LEMS simulation\n    to_remove = []\n    for key, param in l5pc_cell.params.items():\n        if hasattr(param, \"value_scaler\"):\n            if isinstance(\n                param.value_scaler,\n                ephys.parameterscalers.NrnSegmentSomaDistanceScaler,\n            ):\n                to_remove.append(key)\n\n    for param_name in to_remove:\n        l5pc_cell.params.pop(param_name)\n\n    # run with regular bluepyopt\n    sim = ephys.simulators.NrnSimulator(dt=dt, cvode_active=False)\n    bpo_output = bpo_test_protocol.run(\n        cell_model=l5pc_cell, param_values=release_params, sim=sim\n    )\n\n    response_name = f\"{protocol_name}.soma.v\"\n    bpo_voltage = bpo_output[response_name][\"voltage\"]\n    bpo_time = bpo_output[response_name][\"time\"]\n\n    # we don't expect the two traces to be exactly the same,\n    # but to be pretty similar\n    # we consider it satisfactory if they fire within 3 ms respectively\n    trace_lems = {\n        \"T\": lems_time,\n        \"V\": lems_voltage,\n        \"stim_start\": [700],\n        \"stim_end\": [2700]\n    }\n    trace_bpo = {\n        \"T\": bpo_time,\n        \"V\": bpo_voltage,\n        \"stim_start\": [700],\n        \"stim_end\": [2700]\n    }\n    traces = [trace_lems, trace_bpo]\n    features = [\"peak_time\"]\n    feature_values = efel.getFeatureValues(\n        traces, features, raise_warnings=False\n    )\n    lems_peak_time = feature_values[0][\"peak_time\"]\n    bpo_peak_time = feature_values[1][\"peak_time\"]\n\n    numpy.testing.assert_allclose(lems_peak_time, bpo_peak_time, atol=3)\n"
  },
  {
    "path": "bluepyopt/tests/test_parameters.py",
    "content": "\"\"\"bluepyopt.parameters tests\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint:disable=W0612\n\n\nimport pytest\n\nimport bluepyopt\n\n\n@pytest.mark.unit\ndef test_parameters_init():\n    \"\"\"bluepyopt.parameters: test Parameter init\"\"\"\n\n    param = bluepyopt.parameters.Parameter(name='test')\n    assert isinstance(param, bluepyopt.parameters.Parameter)\n    assert param.name == 'test'\n\n\n@pytest.mark.unit\ndef test_parameters_fields():\n    \"\"\"bluepyopt.parameters: test Parameter fields\"\"\"\n\n    param = bluepyopt.parameters.Parameter(name='test')\n\n    assert param.lower_bound is None\n    assert param.upper_bound is None\n\n    param.freeze(5)\n    pytest.raises(Exception, setattr, param, \"value\", 5)\n\n    param = bluepyopt.parameters.Parameter(name='test', bounds=[2, 5])\n    pytest.raises(ValueError, param.freeze, 1)\n\n\n@pytest.mark.unit\ndef test_parameters_str():\n    \"\"\"bluepyopt.parameters: test Parameter str conversion\"\"\"\n\n    param = bluepyopt.parameters.Parameter(name='test')\n\n    assert str(param) == 'test: value = None'\n\n    param.freeze(5.5)\n\n    assert str(param) == 'test: value = 5.5'\n\n\n@pytest.mark.unit\ndef test_MetaListEqualParameter_init():\n    \"\"\"bluepyopt.parameters: test MetaListEqualParameter init\"\"\"\n\n    sub_params = [\n        bluepyopt.parameters.Parameter(\n            name='sub1', value=1), bluepyopt.parameters.Parameter(\n            name='sub2', value=2)]\n\n    assert sub_params[0].value == 1\n    assert sub_params[1].value == 2\n\n    param = bluepyopt.parameters.MetaListEqualParameter(\n        name='param', value=0, frozen=True, sub_parameters=sub_params)\n    assert isinstance(param, bluepyopt.parameters.Parameter)\n    assert isinstance(param, bluepyopt.parameters.MetaListEqualParameter)\n\n    assert param.name == 'param'\n    assert param.sub_parameters[0].name == 'sub1'\n    assert param.sub_parameters[1].name == 'sub2'\n\n    assert param.value == 0\n    assert sub_params[0].value == 0\n    assert sub_params[1].value == 0\n\n\n@pytest.mark.unit\ndef test_MetaListEqualParameter_freeze_unfreeze():\n    \"\"\"bluepyopt.parameters: test MetaListEqualParameter freeze and unfreeze\"\"\"\n\n    sub_params = [\n        bluepyopt.parameters.Parameter(\n            name='sub1', value=1), bluepyopt.parameters.Parameter(\n            name='sub2', value=2)]\n\n    param = bluepyopt.parameters.MetaListEqualParameter(\n        name='param', sub_parameters=sub_params)\n\n    assert param.value is None\n    assert sub_params[0].value == 1\n    assert sub_params[1].value == 2\n\n    param.freeze(0)\n\n    assert param.value == 0\n    assert sub_params[0].value == 0\n    assert sub_params[1].value == 0\n\n    param.unfreeze()\n\n    sub_params[0].freeze(1)\n    pytest.raises(Exception, param.freeze, 0)\n\n\n@pytest.mark.unit\ndef test_MetaListEqualParamete_str():\n    \"\"\"bluepyopt.parameters: test MetaListEqualParamete str conversion\"\"\"\n\n    sub_params = [\n        bluepyopt.parameters.Parameter(\n            name='sub1', value=1), bluepyopt.parameters.Parameter(\n            name='sub2', value=2)]\n\n    param = bluepyopt.parameters.MetaListEqualParameter(\n        name='param', sub_parameters=sub_params)\n\n    assert (\n        str(param)\n        == 'param (sub_params: sub1: value = None,sub2: value = None): '\n        'value = None')\n\n    param.freeze(5.5)\n\n    assert (\n        str(param)\n        == 'param (sub_params: sub1: value = 5.5,sub2: value = 5.5): '\n        'value = 5.5')\n"
  },
  {
    "path": "bluepyopt/tests/test_simplecell.py",
    "content": "\"\"\"Simple cell example test class\"\"\"\n\nimport os\nimport sys\n\nSIMPLECELL_PATH = os.path.abspath(\n    os.path.join(os.path.dirname(__file__), \"../../examples/simplecell\")\n)\n\n# sys.path.insert(0, SIMPLECELL_PATH)\n\n\nclass TestSimpleCellClass(object):\n    \"\"\"Simple cell example test class for NEURON\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.old_cwd = os.getcwd()\n        self.old_stdout = sys.stdout\n\n        os.chdir(SIMPLECELL_PATH)\n        sys.stdout = open(os.devnull, \"w\")\n\n    def test_exec(self):\n        \"\"\"Simplecell NEURON: test execution\"\"\"\n        # When using import instead of execfile this doesn't work\n        # Probably because multiprocessing doesn't work correctly during\n        # import\n        if sys.version_info[0] < 3:\n            execfile(\"simplecell.py\")  # NOQA\n        else:\n            with open(\"simplecell.py\") as sc_file:\n                exec(compile(sc_file.read(), \"simplecell.py\", \"exec\"))  # NOQA\n\n    def teardown_method(self):\n        \"\"\"Tear down\"\"\"\n\n        sys.stdout = self.old_stdout\n        os.chdir(self.old_cwd)\n\n\nclass TestSimpleCellArborClass(object):\n    \"\"\"Simple cell example test class for Arbor\"\"\"\n\n    def setup_method(self):\n        \"\"\"Setup\"\"\"\n        self.old_cwd = os.getcwd()\n        self.old_stdout = sys.stdout\n\n        os.chdir(SIMPLECELL_PATH)\n        sys.stdout = open(os.devnull, \"w\")\n\n    def test_exec(self):\n        \"\"\"Simplecell Arbor: test execution\"\"\"\n        # When using import instead of execfile this doesn't work\n        # Probably because multiprocessing doesn't work correctly during\n        # import\n        if sys.version_info[0] < 3:\n            execfile(\"simplecell_arbor.py\")  # NOQA\n        else:\n            with open(\"simplecell_arbor.py\") as sc_file:\n                exec(compile(sc_file.read(), \"simplecell_arbor.py\", \"exec\"))  # NOQA\n\n    def teardown_method(self):\n        \"\"\"Tear down\"\"\"\n\n        sys.stdout = self.old_stdout\n        os.chdir(self.old_cwd)\n"
  },
  {
    "path": "bluepyopt/tests/test_stochkv.py",
    "content": "\"\"\"Test l5pc example\"\"\"\n\nimport os\nimport sys\nimport difflib\n\nSTOCHKV_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__),\n                                            '../../examples/stochkv'))\n\nsys.path.insert(0, STOCHKV_PATH)\n\nfrom bluepyopt import ephys\n\nneuron_sim = ephys.simulators.NrnSimulator()\n\nneuron_sim.neuron.h.nrn_load_dll(\n    os.path.join(\n        STOCHKV_PATH,\n        'x86_64/.libs/libnrnmech.so'))\n\n\ndef compare_strings(s1, s2):\n    \"\"\"Compare two strings\"\"\"\n\n    diff = list(difflib.unified_diff(s1.splitlines(1), s2.splitlines(1)))\n\n    if len(diff) > 0:\n        print(''.join(diff))\n        return False\n    else:\n        return True\n\n\ndef test_import():\n    \"\"\"StochKv example: test import\"\"\"\n\n    import stochkvcell  # NOQA\n\n\ndef test_run():\n    \"\"\"StochKv example: test run\"\"\"\n\n    import stochkvcell  # NOQA\n    for deterministic in [True, False]:\n        py_response, hoc_response, different_seed_response, hoc_string = \\\n            stochkvcell.run_stochkv_model(deterministic=deterministic)\n\n        assert py_response['Step.soma.v']['time'].equals(\n            hoc_response['Step.soma.v']['time'])\n        assert py_response['Step.soma.v']['voltage'].equals(\n            hoc_response['Step.soma.v']['voltage'])\n        if deterministic:\n            assert py_response['Step.soma.v']['voltage'].equals(\n                different_seed_response['Step.soma.v']['voltage'])\n        else:\n            assert not py_response['Step.soma.v']['voltage'].equals(\n                different_seed_response['Step.soma.v']['voltage'])\n\n        expected_hoc_filename = os.path.join(\n            STOCHKV_PATH,\n            stochkvcell.stochkv_hoc_filename(deterministic=deterministic))\n\n        # with open(expected_hoc_filename, 'w') as expected_hoc_file:\n        #     expected_hoc_file.write(hoc_string)\n\n        with open(expected_hoc_filename) as expected_hoc_file:\n            expected_hoc_string = expected_hoc_file.read()\n\n        assert compare_strings(expected_hoc_string, hoc_string)\n\n\ndef test_run_stochkv3():\n    \"\"\"StochKv3 example: test run\"\"\"\n\n    import stochkv3cell  # NOQA\n    for deterministic in [True, False]:\n        py_response, hoc_response, different_seed_response, hoc_string = \\\n            stochkv3cell.run_stochkv3_model(deterministic=deterministic)\n\n        assert py_response['Step.soma.v']['time'].equals(\n            hoc_response['Step.soma.v']['time'])\n        assert py_response['Step.soma.v']['voltage'].equals(\n            hoc_response['Step.soma.v']['voltage'])\n        if deterministic:\n            assert py_response['Step.soma.v']['voltage'].equals(\n                different_seed_response['Step.soma.v']['voltage'])\n        else:\n            assert not py_response['Step.soma.v']['voltage'].equals(\n                different_seed_response['Step.soma.v']['voltage'])\n\n        expected_hoc_filename = os.path.join(\n            STOCHKV_PATH,\n            stochkv3cell.stochkv3_hoc_filename(deterministic=deterministic))\n\n        # with open(expected_hoc_filename, 'w') as expected_hoc_file:\n        #     expected_hoc_file.write(hoc_string)\n\n        with open(expected_hoc_filename) as expected_hoc_file:\n            expected_hoc_string = expected_hoc_file.read()\n\n        assert compare_strings(expected_hoc_string, hoc_string)\n"
  },
  {
    "path": "bluepyopt/tests/test_tools.py",
    "content": "\"\"\"Test bluepyopt.tools\"\"\"\n\nimport pytest\n\n\n@pytest.mark.unit\ndef test_load():\n    \"\"\"bluepyopt.tools: test import\"\"\"\n\n    import bluepyopt.tools  # NOQA\n\n\n@pytest.mark.unit\ndef test_uint32_seed():\n    \"\"\"bluepyopt.tools: test uint32_seed\"\"\"\n\n    import bluepyopt.tools as bpoptools\n\n    assert bpoptools.uint32_seed(\"test\") == 640136438\n\n    import random\n    random.seed(1)\n\n    hashes = []\n    strings = []\n    for _ in range(1000):\n        string = ''.join(\n            (chr(random.randint(0, 127)) for x in\n             range(random.randint(10, 255))))\n        strings.append(string)\n        hashes.append(bpoptools.uint32_seed(string))\n\n    assert len(strings) == len(set(strings))\n    assert len(hashes) == len(set(hashes))\n\n    import numpy\n    for hash_value in hashes:\n        assert hash_value == numpy.uint32(hash_value)\n"
  },
  {
    "path": "bluepyopt/tests/testdata/l5pc_validate_neuron_arbor/param_values.json",
    "content": "[\n    {\n        \"gNaTs2_tbar_NaTs2_t.apical\": 0.024728378969164945,\n        \"gSKv3_1bar_SKv3_1.apical\": 0.03798941330025154,\n        \"gImbar_Im.apical\": 0.0002539253963547741,\n        \"gNaTa_tbar_NaTa_t.axonal\": 1.4077868264399775,\n        \"gNap_Et2bar_Nap_Et2.axonal\": 0.4883696674077984,\n        \"gK_Pstbar_K_Pst.axonal\": 0.2094093687043327,\n        \"gK_Tstbar_K_Tst.axonal\": 0.014394661257078424,\n        \"gSK_E2bar_SK_E2.axonal\": 0.08531938445280127,\n        \"gSKv3_1bar_SKv3_1.axonal\": 1.9458756462007896,\n        \"gCa_HVAbar_Ca_HVA.axonal\": 0.00047482720978403063,\n        \"gCa_LVAstbar_Ca_LVAst.axonal\": 0.003298061450166594,\n        \"gamma_CaDynamics_E2.axonal\": 0.0016672175965771645,\n        \"decay_CaDynamics_E2.axonal\": 107.99372196248892,\n        \"gNaTs2_tbar_NaTs2_t.somatic\": 0.016894566815433776,\n        \"gSKv3_1bar_SKv3_1.somatic\": 0.5504181382385894,\n        \"gSK_E2bar_SK_E2.somatic\": 0.0014684200840784367,\n        \"gCa_HVAbar_Ca_HVA.somatic\": 0.0009900677505308665,\n        \"gCa_LVAstbar_Ca_LVAst.somatic\": 0.004837611369100268,\n        \"gamma_CaDynamics_E2.somatic\": 0.04468460763916868,\n        \"decay_CaDynamics_E2.somatic\": 700.3244890996936\n    },\n    {\n        \"gNaTs2_tbar_NaTs2_t.apical\": 0.012563715900342314,\n        \"gSKv3_1bar_SKv3_1.apical\": 0.030532088135424608,\n        \"gImbar_Im.apical\": 0.0003896322863148641,\n        \"gNaTa_tbar_NaTa_t.axonal\": 0.8703921802368879,\n        \"gNap_Et2bar_Nap_Et2.axonal\": 2.709684677548464,\n        \"gK_Pstbar_K_Pst.axonal\": 0.5684212625540379,\n        \"gK_Tstbar_K_Tst.axonal\": 0.046099219765027045,\n        \"gSK_E2bar_SK_E2.axonal\": 0.043249366076373946,\n        \"gSKv3_1bar_SKv3_1.axonal\": 0.9098265241750978,\n        \"gCa_HVAbar_Ca_HVA.axonal\": 0.0008372326923436864,\n        \"gCa_LVAstbar_Ca_LVAst.axonal\": 0.004471958629720602,\n        \"gamma_CaDynamics_E2.axonal\": 0.028712619127496453,\n        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\"gNap_Et2bar_Nap_Et2.axonal\": 1.4061498137665431,\n        \"gK_Pstbar_K_Pst.axonal\": 0.3943872359829874,\n        \"gK_Tstbar_K_Tst.axonal\": 0.035561350976474206,\n        \"gSK_E2bar_SK_E2.axonal\": 0.02171178703228345,\n        \"gSKv3_1bar_SKv3_1.axonal\": 1.7696510942499797,\n        \"gCa_HVAbar_Ca_HVA.axonal\": 0.0006580710297293586,\n        \"gCa_LVAstbar_Ca_LVAst.axonal\": 0.0063160804680408205,\n        \"gamma_CaDynamics_E2.axonal\": 0.023213952061165458,\n        \"decay_CaDynamics_E2.axonal\": 967.0051783210414,\n        \"gNaTs2_tbar_NaTs2_t.somatic\": 0.6021516164385508,\n        \"gSKv3_1bar_SKv3_1.somatic\": 0.6231231198067643,\n        \"gSK_E2bar_SK_E2.somatic\": 0.031180502899348185,\n        \"gCa_HVAbar_Ca_HVA.somatic\": 1.8482399998351218e-05,\n        \"gCa_LVAstbar_Ca_LVAst.somatic\": 0.0006089857296922108,\n        \"gamma_CaDynamics_E2.somatic\": 0.014921182004896194,\n        \"decay_CaDynamics_E2.somatic\": 220.06396960215915\n    },\n    {\n        \"gNaTs2_tbar_NaTs2_t.apical\": 0.03414645624289801,\n        \"gSKv3_1bar_SKv3_1.apical\": 0.03716027643455195,\n        \"gImbar_Im.apical\": 0.0006141579238080173,\n        \"gNaTa_tbar_NaTa_t.axonal\": 1.3903172305839067,\n        \"gNap_Et2bar_Nap_Et2.axonal\": 0.8426141730398107,\n        \"gK_Pstbar_K_Pst.axonal\": 0.6391552213781772,\n        \"gK_Tstbar_K_Tst.axonal\": 0.09532353467899872,\n        \"gSK_E2bar_SK_E2.axonal\": 0.0003580425070283666,\n        \"gSKv3_1bar_SKv3_1.axonal\": 1.6453573616166768,\n        \"gCa_HVAbar_Ca_HVA.axonal\": 0.0008843062363314521,\n        \"gCa_LVAstbar_Ca_LVAst.axonal\": 0.004350888951090779,\n        \"gamma_CaDynamics_E2.axonal\": 0.013015501138335075,\n        \"decay_CaDynamics_E2.axonal\": 317.84821369565793,\n        \"gNaTs2_tbar_NaTs2_t.somatic\": 0.5411660208241308,\n        \"gSKv3_1bar_SKv3_1.somatic\": 0.16267060224572238,\n        \"gSK_E2bar_SK_E2.somatic\": 0.019600545141054505,\n        \"gCa_HVAbar_Ca_HVA.somatic\": 0.0001971878789850715,\n        \"gCa_LVAstbar_Ca_LVAst.somatic\": 0.003305523187140309,\n        \"gamma_CaDynamics_E2.somatic\": 0.03067667559799945,\n        \"decay_CaDynamics_E2.somatic\": 984.7729355155103\n    }\n]"
  },
  {
    "path": "bluepyopt/tools.py",
    "content": "\"\"\"BluePyOpt tools\"\"\"\n\nimport hashlib\n\n\ndef uint32_seed(string):\n    \"\"\"Get unsigned int seed of a string\"\"\"\n\n    hex_value = hashlib.md5(string.encode('utf-8')).hexdigest()\n\n    return int(hex_value, 16) & 0xFFFFFFFF\n"
  },
  {
    "path": "cloud-config/README.md",
    "content": "# Single Cell Optimization\n\n## Introduction\n\nThis documentation outlines how to setup distributed single cell optimization\nusing [eFEL](https://github.com/BlueBrain/eFEL), [DEAP](http://deap.readthedocs.org/en/master/)\nand [SCOOP](scoop.readthedocs.org) along with [NEURON](www.neuron.yale.edu).\n\nThe scope of this documentation is on setup, not on the process of\noptimization.\n\n## Setup\n\nThe three sorts of distributed systems that this documentation targets are:\n\n1. [Vagrant](https://www.vagrantup.com/) machines, usually used for testing\n  as all the machines likely run on one local machine.  Detailed\n  information [here](config/vagrant/README.md).\n\n2. A cluster with a shared filesystem.  The optimization is configured\n  within the home directory of the user such that it can be launched by\n  [SLURM](https://computing.llnl.gov/linux/slurm/) or another cluster\n  resource manager.  Detailed information [here](config/cluster-user/README.md).\n\n3. An [Amazon Web Services](https://aws.amazon.com/) Detailed\n  information [here](config/amazon/README.md).\n\n\n## Ansible\n\nThroughout this guide, [Ansible](http://www.ansible.com/) is used for the\nautomatic installation and configuration of the required software.\n\nIt is recommended to install it into a [Virtualenv](https://virtualenv.readthedocs.org/en/latest/)\nso as to separate it from the rest of the system.\n```\n$ virtualenv venv-ansible\n$ ./venv-ansible/bin/pip install ansible\n```\n\nFrom then on, one can activate the environment, and have access to Ansible:\n```\n$ source ./venv-ansible/bin/activate\n$ ansible-playbook --version\n```\n\nNote: Ansible is not required, and all the commands can be run manually by\nexamining the `roles/*/tasks/main.yaml` files.\n\n\n## Ansible Information\n\nThe three sorts of configurations share [Ansible Roles](http://docs.ansible.com/ansible/playbooks_roles.html#roles) but have different configuration information and parameters.\n\nThe easiest way to setup one of the three systems is to:\n\n1. Change to the correct directory (`config/vagrant`, `config/cluster-user/`, or\n   `config/amazon/`) and create a symbolic link to the `roles` directory:\n\n   `$ ln -s ../../roles .`\n\n2. Change any config information in `ansible.cfg` and `vars.yaml`\n\n3. Edit the `hosts` file to include the correct hosts (in the Amazon case,\n   this is handled by the `gather_config.py` script.\n\n\n## Ansible configuration options\n\nThe following options are defined in the `vars.yaml` file, which should\ncontain the only options that could potentially need configuration.  They are:\n\n- *user_name*: the name of the user under who's name the install will be run\n- *workspace*: the base directory of the installation\n- *venv*: the location of the virtualenvironment\n- *build_dir*: the location where software is built\n- *install_dir*: the base location where software is installed\n- *add_bin_path*: Boolean on if the path to neuron and the python environment\n should be added to the users' `.bashrc`\n- *using_headnode*: Set to true when there is a headnode that will control\n  everything, used for when an SSH has to be distributed to the workers\n- *neuron_build*: location to build NEURON\n- *neuron_url*: URL to download the NEURON source\n- *neuron_version*: NEURON version\n- *neuron_config*: command to configure neuron\n- *python27_build*: false - whether a local python version should be compiled\n- *python27_url*: URL to get Python source\n- *python27_version*: Python version\n\nVersions for various python software that is installed\n- *pip_version*: 7.1.2\n- *numpy_version*: 1.10.4\n- *efel_version*: 2.10.7\n- *scoop_version*: 0.7.1.1\n- *jupyter_version*: 1.0.0\n- *matplotlib_version*: 1.5.1\n\n## Installation Information\n\nThe installation is composed of several components:\n\n1. Compiled version of NEURON in `~/workspace/install/nrnpython`.  This has\n   the python bindings built in, and should also allow for dynamic loading of\n   compiled `.modl` files\n\n2. A python virtual environment with eFEL as well as DEAP and SCOOP.\n\n\n## Ansible Tips\n\n1. Test that your system is working and you can reach the hosts with `ansible -m ping all`\n\n2. Use the 'Dry-run' command `ansible-playbook site.yml --check` to see what\n   would happen if ansible was to run, this can be augmented with `--diff` to\n   show differences\n\n3. Use `-vv` and `-vvvv` to increase verbosity\n\n\n## Running an Optimization Example\n\nNote: Every optimization is different, this is an example on how to launch one\nthat already exists.\n\n### eFEL Example\n\nIf there are multiple hosts, this has to be done on each of them.  If there is \na shared file system, it only needs to be done once.\n```\n# Get latest eFEL\n$ git clone https://github.com/BlueBrain/eFEL.git\n\n# Compile the modl files with the nrnivmodl located in ~/workspace/install/. (for instance here ~/workspace/install/nrnpython/x86_64/bin/nrnivmodl)\n$ cd ~/eFEL/examples/deap/GranuleCell1\n$ ~/workspace/install/nrnpython/x86_64/bin/nrnivmodl mechanisms\n```\n\n\n#### Launch it:\nThen, on the head node:\n```\n$ /home/neuron/workspace/venv/bin/python -m scoop -vvv --hostfile ~/scoop_workers GranuleCellDeap1-scoop.py\n```\n### DEAP Example\n\nGet the [deap/examples/ga/onemax_island_scoop.py](https://github.com/DEAP/deap/blob/master/examples/ga/onemax_island_scoop.py) example.  You may want to\nhave it output data by editing the call to main with:\n\n```\nislands = main()\nwith open('output.dat', 'w') as fd:\n    fd.write(str(islands))\n    fd.write('\\n')\n```\n\nThen, distribute it to all the worker nodes, if the node are not sharing their file system:\n    (as the 'neuron' user: 'sudo su neuron')\n```\n    $ cd workspace\n    $ for host in `cat ~/scoop_workers`; do scp onemax_island_scoop.py $i:workspace; done\n```\n\n#### Launch it:\n```\n    $ cd workspace\n    $ venv/bin/python -m scoop --hostfile ~/scoop_workers onemax_island_scoop.py\n    $ look at the output in 'output.dat'\n```\n"
  },
  {
    "path": "cloud-config/config/amazon/README.md",
    "content": "# Amazon Setup\n\nThis documents how an Amazon Web Services cluster can be created.\n\nNote: It is quite easy to run up a large bill on AWS, so the following is only\nguidance for setting it up.  Make sure that the costs for setting up a cluster\nare fully understood.  It is recommended to often check the AWS console's\n`Estimated Billing` to keep track of theses costs.\n\n## Installation\n\nIt is assumed that a `virtualenv` exists for `Ansible`.  It is worth reading\nthe description of `Ansible` and the initial setup in [here](../../README.md).\n\nTo access the AWS and `Elastic Computing` components (EC2), the following \nextra components should be installed:\n\n```\n$ source ./venv-ansible/activate\n$ pip install boto boto3 awscli\n```\n\n[Boto](https://pypi.python.org/pypi/boto) is the older generation of AWS/EC2\nmanagement utilities, however it is need for Ansible\n\n[boto3](https://github.com/boto/boto3) and [awscli](https://pypi.python.org/pypi/awscli) are useful for interfacing with AWS.\n\n\n## Creating AWS Cloud\n\nFirst, one should configure the following in the create_instance.yaml\nPlaybook:\n```\n    region: us-west-2\n    ami_image: ami-187c9978 # http://cloud-images.ubuntu.com/trusty/current/\n    instance_type: t2.nano\n    worker_instances: 2\n```\n\nWhere:\n\n- *region*: is which [AWS Region](http://docs.aws.amazon.com/general/latest/gr/rande.html) that should be used.  This impacts the cost of resources, where the data is stored and which ami's can be used\n\n- *ami_image*: the image that is used as the base for the configuration.\n  Generally, a Ubuntu LTS image will work, and the correct one for the chosen\n  region must be chosen [from here](http://cloud-images.ubuntu.com/trusty/current/)  A `hvm` instance works.  One may get better performance, at a cost, by using SSD instances or other types.\n\n- *instance_type*: A description of the different instance types is [available\n  here](https://aws.amazon.com/ec2/instance-types/).  Usually, the costs grows\n  with the computing power.  For the example, the cheapest instance type is\n  created, but in a real optimization scenario, the computing resources are\n  likely too small to be effective.\n\n- *worker_instances*: How many worker instances to launch, in addition to the\n  head node.\n\nTo run the creation scrips, the following must exist in the terminal\nenvironment so that the AWS infrastructure can authenticate the client.\n```\nexport AWS_ACCESS_KEY_ID= replace with access id\nexport AWS_SECRET_ACCESS_KEY= replace with access key\nexport AWS_REGION= replace with default AWS region\n```\n\nIt's also a good idea to create the credentials in the ~/.aws/ directory as\ndescribed [here.](http://boto3.readthedocs.org/en/latest/guide/quickstart.html#configuration)\n\nNow the simple cloud can be created and launched with:\n```\n$ source ./venv-ansible/activate\n$ ansible-playbook create_instance.yaml\n```\n\n### Cloud Details\n\nThe above cloud has the following details:\n\n- An [AWS Keypair](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-key-pairs.html) with the name `single_cell_opt`.  It will have created a `single_cell_opt.pem` file in the directory it ran, and this file should only be given to people who are allowed to login to the cloud instances.\n\n- An [AWS Virtual Private Cloud](https://aws.amazon.com/vpc/) (VPC) is created\n  with the resource tag `single_cell_opt`.  By default, a 10.0.0.0/24 block is\n  reserved, and an [Internet Gateway](http://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/VPC_Internet_Gateway.html) is created so the instances can access the internet.\n\n- An [AWS Security Group](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-network-security.html) is created by the name of `single_cell_opt`.  By default, it only allows access via SSH from the full internet (0.0.0.0/0), and allows the instances to access the internet.\n\n## Configuring AWS Cloud\n\nOnce the instances are created, then it's a matter of configuring them using\nthe normal `Ansible` methods.  However, a couple configuration steps must\nfirst be performed:\n\n1. The `hosts` and `amazon_ssh_config` files must be created.  This can be\n   accomplished by running the following script:\n```\n# Note: this can be run with the --dry-run argument to see the output first\n$ python gather_config.py\n```\n\n2. Then, ansible will take care of the rest of the configuration, as described\n   [here.](../../README.md)\n```\n$ ansible-playbook site.yaml\n```\n\n# AWS Tips\n\nFrom the command line, one can check the running instances:\n```\n$ aws ec2 describe-instances\n```\n\nOr list the current IP addresses\n```\n$ aws ec2 describe-addresses\n```\n"
  },
  {
    "path": "cloud-config/config/amazon/ansible.cfg",
    "content": "[defaults]\ninventory = ./hosts\nprivate_key_file = single_cell_opt.pem\nremote_user = ubuntu\nhost_key_checking = False\n\n[ssh_connection]\nssh_args = -F amazon_ssh_config\n"
  },
  {
    "path": "cloud-config/config/amazon/create_instance.yaml",
    "content": "- name: Setup Ubuntu machines\n  vars:\n    region: us-west-2\n    ami_image: ami-187c9978 # http://cloud-images.ubuntu.com/trusty/current/\n    instance_type: t2.nano\n    worker_instances: 2\n\n  hosts: localhost\n  tasks:\n    - name: Create Keypair\n      ec2_key: \n        name: single_cell_opt\n        region: \"{{ region }}\"\n      register: keypair\n\n    - name: Write key to file\n      copy:\n        dest: ./single_cell_opt.pem\n        content: \"{{ keypair.key.private_key }}\"\n        mode: 0600\n      when: keypair.changed\n\n    - name: Setup VPC\n      ec2_vpc:\n        state: present\n        cidr_block: 10.0.0.0/24\n        region: \"{{ region }}\"\n        resource_tags: \n          Environment: single_cell_opt\n        subnets:\n          - cidr: 10.0.0.0/24\n        internet_gateway: yes\n        route_tables:\n          - subnets:\n              - 10.0.0.0/24\n            routes:\n              - dest: 0.0.0.0/0\n                gw: igw\n        wait: yes\n      register: vpc\n\n    - name: Single Cell Optimization Security group\n      ec2_group:\n        name: single_cell_opt\n        description: Allow SSH, all exit and all intra-vpc\n        vpc_id: \"{{ vpc.vpc_id }}\"\n        region: \"{{ region }}\"\n        rules:\n          - proto: tcp\n            from_port: 22\n            to_port: 22\n            cidr_ip: 0.0.0.0/0\n          - proto: all\n            cidr_ip: 10.0.0.0/24\n        rules_egress:\n          - proto: all\n            cidr_ip: 0.0.0.0/0\n\n    - name: Start the head instance\n      ec2:\n        image: \"{{ ami_image }}\"\n        region: \"{{ region }}\"\n        instance_type: \"{{ instance_type }}\"\n        key_name: \"{{ keypair.key.name }}\"\n        vpc_subnet_id: \"{{ vpc.subnets[0].id }}\"\n        group: [single_cell_opt, ]\n        assign_public_ip: yes\n        instance_tags: { type: single_cell_opt_head }\n        exact_count: 1\n        count_tag: { type: single_cell_opt_head }\n        wait: yes\n      register: ec2head\n\n    - name: Wait for SSH server\n      wait_for: host={{ item.public_dns_name }} port=22 search_regex=OpenSSH\n      with_items: ec2head.instances\n\n    - name: Start the worker instances\n      ec2:\n        image: \"{{ ami_image }}\"\n        region: \"{{ region }}\"\n        instance_type: \"{{ instance_type }}\"\n        key_name: \"{{ keypair.key.name }}\"\n        vpc_subnet_id: \"{{ vpc.subnets[0].id }}\"\n        group: [single_cell_opt, ]\n        assign_public_ip: yes\n        instance_tags: { type: single_cell_opt_worker }\n        exact_count: \"{{ worker_instances }}\"\n        count_tag: { type: single_cell_opt_worker }\n        wait: yes\n      register: ec2workers\n\n    - name: Wait for SSH server\n      wait_for: host={{ item.public_dns_name }} port=22 search_regex=OpenSSH\n      with_items: ec2workers.instances\n"
  },
  {
    "path": "cloud-config/config/amazon/gather_config.py",
    "content": "#!/usr/bin/env python\nimport argparse\n\nimport boto3\nfrom jinja2 import Environment\n\n\nKEY_NAME = 'single_cell_opt.pem'\nHEAD_INSTANCE_NAME = 'single_cell_opt_head'\nWORKER_INSTANCE_NAME = 'single_cell_opt_worker'\n\n\nhosts_template = '''\n[neuron-optimizer-head]\n{{ head_public_ip }}\n\n[neuron-optimizer-worker]\n{% for ip in worker_private_ips %}\n{{ ip }}{% endfor %}\n'''\n\n\nssh_config_template = '''\nControlMaster auto\nControlPersist 60s\n\nHost 10.0.0.*\n  ProxyCommand ssh -i {{ private_key }} -W %h:%p %r@{{ head_public_ip }}\n'''\n\n\ndef _get_instances_by_tag(ec2, tag):\n    '''filter on all instances, and get the ones tagged with 'type: tag' '''\n    filters = [{'Name': 'tag:type', 'Values': [tag]},\n               {'Name': 'instance-state-name', 'Values': ['running']},\n               ]\n    return list(ec2.instances.filter(Filters=filters))\n\n\ndef get_head_public_ip(ec2):\n    '''returns string value of the public IP of the head instance'''\n    instance = _get_instances_by_tag(ec2, HEAD_INSTANCE_NAME)\n    assert len(instance) == 1, 'Only expect one head node'\n    instance = instance[0]\n    return instance.public_ip_address\n\n\ndef get_work_private_ips(ec2, tag=WORKER_INSTANCE_NAME, include_head=True):\n    '''get the internal IPs of the workers, including the head by default'''\n    instances = _get_instances_by_tag(ec2, tag)\n    if include_head:\n        head_instance = _get_instances_by_tag(ec2, HEAD_INSTANCE_NAME)\n        assert len(head_instance) == 1, 'Only expect one head node'\n        head_ip = head_instance[0].private_ip_address\n    return [head_ip] + [i.private_ip_address for i in instances]\n\n\ndef get_parser():\n    '''return the argument parser'''\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        '--dry-run', action='store_true',\n        help='Output the results of the templates without writing them')\n    return parser\n\n\ndef main():\n    '''main function'''\n    args = get_parser().parse_args()\n\n    ec2 = boto3.resource('ec2')\n\n    print('Instances:', list(i.id for i in ec2.instances.all()))\n\n    env = Environment()\n    hosts = env.from_string(hosts_template)\n    ssh_config = env.from_string(ssh_config_template)\n\n    head_public_ip = get_head_public_ip(ec2)\n    worker_private_ips = get_work_private_ips(ec2)\n\n    hosts_rendered = hosts.render(head_public_ip=head_public_ip,\n                                  worker_private_ips=worker_private_ips)\n\n    ssh_config_rendered = ssh_config.render(head_public_ip=head_public_ip,\n                                            private_key=KEY_NAME)\n\n    if args.dry_run:\n        print('{:*^30}'.format(' Hosts '))\n        print(hosts_rendered)\n        print('{:*^30}'.format(' ssh_config '))\n        print(ssh_config_rendered)\n    else:\n        with open('hosts', 'w') as fd:\n            fd.write(hosts_rendered)\n        with open('amazon_ssh_config', 'w') as fd:\n            fd.write(ssh_config_rendered)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "cloud-config/config/amazon/site.yaml",
    "content": "---\n\n- name: Install Neuron Optimizer Framework Head\n  hosts: neuron-optimizer-head\n  sudo: true\n\n  vars_files:\n   - vars.yaml\n\n  roles:\n    - scoop-master\n\n- name: Install Neuron Optimizer Framework Worker\n  hosts: neuron-optimizer-worker\n  sudo: true\n\n  vars_files:\n   - vars.yaml\n\n  roles:\n    - base\n\n- name: Install Neuron Optimizer Framework Worker\n  hosts: neuron-optimizer-worker\n  become: yes\n  become_user: \"{{ user_name }}\"\n\n  vars_files:\n   - vars.yaml\n\n  roles:\n    - neuron\n    - deap\n\n#- name: Granule Example\n#  hosts: neuron-optimizer-worker-granule\n#  sudo: true\n#\n#  vars_files:\n#   - vars.yaml\n#\n#  roles:\n#    - granule-example\n"
  },
  {
    "path": "cloud-config/config/amazon/vars.yaml",
    "content": "user_name: neuron\nworkspace: ~/workspace\nvenv: \"{{workspace}}/venv\"\nbuild_dir: \"{{workspace}}/build\"\ninstall_dir: \"{{workspace}}/install\"\nadd_bin_path: true\n\nusing_headnode: true\n\nneuron_build: \"{{workspace}}/build/neuron\"\nneuron_url: http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.tar.gz\nneuron_version: 7.4\nneuron_config: >-\n                ./configure\n                --prefix=`readlink -f {{ install_dir }}`/nrnpython\n                --with-nrnpython\n                --without-paranrn\n                --without-x\n                --without-iv\n                have_cython=no\n                BUILD_RX3D=0\n\n# if only an old version of python is available, you may need to set this true:\npython27_build: false\npython27_url: https://www.python.org/ftp/python/2.7.11/Python-2.7.11.tgz\npython27_version: 2.7.11\n\npip_version: 7.1.2\nnumpy_version: 1.10.4\nefel_version: 2.10.7\nscoop_version: 0.7.1.1\njupyter_version: 1.0.0\nmatplotlib_version: 1.5.1\n"
  },
  {
    "path": "cloud-config/config/cluster-user/README.md",
    "content": "# Cluster User\n\nIf you are not the administrator of a cluster, but have access to one, this\ndocument outlines how to setup a working environment that can be used for\noptimization.\n\nNote: It is assumed that on this cluster, there is a shared file-system where the\nsoftware can be installed once and then used by all allocation of resources.\n\n## Basic Configuration\n\n1. Make sure that there is a virtual environment from which you will run\n   `Ansible`, read the documentation [here](../../README.md) to set one up.\n\n2. In the cluster-user directory, modify the `hosts` file to include the cluster head node,\n   the contents should be a single stanza with a single line:\n    ```\n    [neuron-optimizer-worker]\n    dns.name.of.head.node\n    ```\n\n3. If the username is different for the cluster from on the host it's being\n   configured from, the `user_name` in `vars.yaml` will have to be changed to\n   this new name.  One can also set an absolute path in for `workspace` in\n   `vars.yaml` to modify the installation path.\n\n4. If the python version available on the cluster isn't at least 2.7, then it\n   is recommended to modify `python27_build` in `vars.yaml` to be 'true' such\n   that a local version of Python will be compiled.\n\n## Installation Information\n\nRun the installation by issuing:\n```\n$ source ./venv-ansible/bin/activate\n$ ansible-playbook site.yaml\n```\n\nOnce it has run, there should be a `~/workspace` directory on the cluster with\nall the required software as described [here](../README.md) under\n'Installation Information'.\n"
  },
  {
    "path": "cloud-config/config/cluster-user/ansible.cfg",
    "content": "[defaults]\ninventory = ./hosts\n"
  },
  {
    "path": "cloud-config/config/cluster-user/hosts",
    "content": "[neuron-optimizer-worker]\nviz1\n"
  },
  {
    "path": "cloud-config/config/cluster-user/site.yaml",
    "content": "---\n- name: Install Neuron Optimizer Framework Worker\n  hosts: neuron-optimizer-worker\n\n  vars_files:\n   - vars.yaml\n\n  roles:\n    - neuron\n    - deap\n\n#- name: Granule Example\n#  hosts: neuron-optimizer-worker-granule\n#  sudo: true\n#\n#  vars_files:\n#   - vars.yaml\n#\n#  roles:\n#    - granule-example\n"
  },
  {
    "path": "cloud-config/config/cluster-user/vars.yaml",
    "content": "#Note: you may need to change this\nuser_name: \"{{ ansible_user_id }}\"\nworkspace: ~/workspace\nvenv: \"{{workspace}}/venv\"\nbuild_dir: \"{{workspace}}/build\"\ninstall_dir: \"{{workspace}}/install\"\nadd_bin_path: false\n\nusing_headnode: false\n\nneuron_build: \"{{workspace}}/build/neuron\"\nneuron_url: http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.tar.gz\nneuron_version: 7.4\nneuron_config: >-\n                ./configure\n                --prefix=`readlink -f {{ install_dir }}`/nrnpython\n                --with-nrnpython\n                --without-paranrn\n                --without-x\n                --without-iv\n                have_cython=no\n                BUILD_RX3D=0\n\n# if only an old version of python is available, you may need to set this true:\npython27_build: false\npython27_url: https://www.python.org/ftp/python/2.7.11/Python-2.7.11.tgz\npython27_version: 2.7.11\n\npip_version: 7.1.2\nnumpy_version: 1.10.4\nefel_version: 2.10.7\nscoop_version: 0.7.1.1\njupyter_version: 1.0.0\nmatplotlib_version: 1.5.1\n"
  },
  {
    "path": "cloud-config/config/vagrant/README.md",
    "content": "# Vagrant Setup\n\n 1. Install vagrant\n\n 2. Get the trusty64 box:\n\n    `$ vagrant box add ubuntu/trusty64`\n\n 3. Use the included `Vagrantfile` to bring up 4 machines:\n    This includes:\n      - *head*: 192.168.61.10\n      - *worker0*: 192.168.61.20\n      - *worker1*: 192.168.61.21\n      - *worker2*: 192.168.61.22\n\n 4. Boot the vagrant instances:\n\n    `$ vagrant up`\n\n## Configure with Ansible\n\nNote: Make sure that there is a virtual environment from which you will run\n`Ansible`, read the documentation [here](../../README.md) to set one up.\n\n```\n$ source ./venv-ansible/activate\n$ ansible-playbook site.yaml\n```\n\nNotes:\n - `hosts.example` defines the host groups, and uses the running Vagrant instances.\n - `ansible.cfg` defines some defaults (like `hosts.example` and which ssh key to use)\n - One can `vagrant destroy` and then `vagrant up` to start from a clean slate\n"
  },
  {
    "path": "cloud-config/config/vagrant/Vagrantfile",
    "content": "# -*- mode: ruby -*-\n# vi: set ft=ruby :\n\n# Vagrantfile API/syntax version. Don't touch unless you know what you're doing!\nVAGRANTFILE_API_VERSION = \"2\"\n\nVagrant.configure(VAGRANTFILE_API_VERSION) do |config|\n    config.vm.define \"head\" do |inventory|\n        inventory.vm.box = \"ubuntu/trusty64\"\n        inventory.vm.network \"private_network\", ip: \"192.168.61.10\"\n    end\n\n    config.vm.define \"worker0\" do |inventory|\n        inventory.vm.box = \"ubuntu/trusty64\"\n        inventory.vm.network \"private_network\", ip: \"192.168.61.20\"\n    end\n    config.vm.define \"worker1\" do |inventory|\n        inventory.vm.box = \"ubuntu/trusty64\"\n        inventory.vm.network \"private_network\", ip: \"192.168.61.21\"\n    end\n    config.vm.define \"worker2\" do |inventory|\n        inventory.vm.box = \"ubuntu/trusty64\"\n        inventory.vm.network \"private_network\", ip: \"192.168.61.22\"\n    end\nend\n"
  },
  {
    "path": "cloud-config/config/vagrant/ansible.cfg",
    "content": "[defaults]\ninventory = ./hosts\nprivate_key_file = ~/.vagrant.d/insecure_private_key\nremote_user = vagrant\nhost_key_checking = False\n"
  },
  {
    "path": "cloud-config/config/vagrant/hosts",
    "content": "[neuron-optimizer-head]\n192.168.61.10\n\n[neuron-optimizer-worker]\n192.168.61.10\n192.168.61.20\n192.168.61.21\n192.168.61.22\n\n[neuron-optimizer-worker-granule]\n192.168.61.10\n192.168.61.20\n192.168.61.21\n192.168.61.22\n"
  },
  {
    "path": "cloud-config/config/vagrant/site.yaml",
    "content": "---\n\n- name: Install Neuron Optimizer Framework Head\n  hosts: neuron-optimizer-head\n  sudo: true\n\n  vars_files:\n   - vars.yaml\n\n  roles:\n    - scoop-master\n\n- name: Install Neuron Optimizer Framework Worker\n  hosts: neuron-optimizer-worker\n  sudo: true\n\n  vars_files:\n   - vars.yaml\n\n  roles:\n    - base\n\n- name: Install Neuron Optimizer Framework Worker\n  hosts: neuron-optimizer-worker\n  become: yes\n  become_user: \"{{ user_name }}\"\n\n  vars_files:\n   - vars.yaml\n\n  roles:\n    - neuron\n    - deap\n\n#- name: Granule Example\n#  hosts: neuron-optimizer-worker-granule\n#  sudo: true\n#\n#  vars_files:\n#   - vars.yaml\n#\n#  roles:\n#    - granule-example\n"
  },
  {
    "path": "cloud-config/config/vagrant/vars.yaml",
    "content": "user_name: neuron\nworkspace: ~/workspace\nvenv: \"{{workspace}}/venv\"\nbuild_dir: \"{{workspace}}/build\"\ninstall_dir: \"{{workspace}}/install\"\nadd_bin_path: true\n\nusing_headnode: true\n\nneuron_build: \"{{workspace}}/build/neuron\"\nneuron_url: http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.tar.gz\nneuron_version: 7.4\nneuron_config: >-\n                ./configure\n                --prefix=`readlink -f {{ install_dir }}`/nrnpython\n                --with-nrnpython\n                --without-paranrn\n                --without-x\n                --without-iv\n                have_cython=no\n                BUILD_RX3D=0\n\n# if only an old version of python is available, you may need to set this true:\npython27_build: true\npython27_url: https://www.python.org/ftp/python/2.7.11/Python-2.7.11.tgz\npython27_version: 2.7.11\n\npip_version: 7.1.2\nnumpy_version: 1.10.4\nefel_version: 2.10.7\nscoop_version: 0.7.1.1\njupyter_version: 1.0.0\nmatplotlib_version: 1.5.1\n"
  },
  {
    "path": "cloud-config/roles/base/tasks/main.yaml",
    "content": "---\n#note: this is for debian and ubuntu distributions\n- name: update apt cache\n  apt: update_cache=yes\n\n- name: Install base packages\n  apt: name={{ item }} force=yes state=installed\n  with_items:\n    - build-essential\n    - git\n    - htop\n    - libreadline-dev\n    - libzmq3-dev\n    - ntp\n    - python-dev\n    - python-pip\n    - python-virtualenv\n    - unzip\n    #for matplotlib\n    - pkg-config\n    - libfreetype6-dev\n  tags: packages\n\n- name: Configure User\n  user: name={{ user_name }}\n"
  },
  {
    "path": "cloud-config/roles/deap/tasks/main.yaml",
    "content": "---\n\n- name: Upgrade pip\n  pip: name=pip version=\"{{ pip_version }}\" virtualenv={{ venv }}\n\n- name: Install numpy\n  pip: name=numpy version=\"{{ numpy_version }}\" virtualenv={{ venv }}\n\n- name: Install Jupyter\n  pip: name=jupyter version=\"{{ jupyter_version }}\" virtualenv={{ venv }}\n\n- name: Install matplotlib\n  pip: name=matplotlib version=\"{{ matplotlib_version }}\" virtualenv={{ venv }}\n\n#Note: using the BBP version of DEAP, as it includes the updated IBEA tools\n- name: Install deap\n  pip: name='git+https://github.com/BlueBrain/deap#egg=deap' virtualenv={{ venv }}\n\n- name: Install efel\n  pip: name=efel version=\"{{ efel_version }}\" virtualenv={{ venv }}\n\n- name: Install scoop\n  pip: name=scoop version=\"{{ scoop_version }}\" virtualenv={{ venv }}\n\n#Use the latest version\n- name: Install BluePyOpt\n  pip: name=bluepyopt virtualenv={{ venv }}\n\n- name: Add virtualenv to setup.sh file\n  lineinfile:\n    dest=\"{{ workspace }}/setup.sh\"\n    create=yes\n    line=\"source {{ venv }}/bin/activate\"\n\n- name: Install ssh key\n  authorized_key:\n    user: \"{{ user_name }}\"\n    key: \"{{ lookup('file', 'id_rsa.tmp') }}\"\n  when: \"{{ using_headnode }}\"\n"
  },
  {
    "path": "cloud-config/roles/granule-example/tasks/main.yaml",
    "content": "- name: Get eFEL Source\n  git: repo=https://github.com/BlueBrain/eFEL.git dest=~/workspace/eFEL\n\n- name: Compile models\n  shell: \"{{ install_dir }}/nrnpython/x86_64/bin/nrnivmodl mechanisms\"\n  args:\n    chdir: \"{{ workspace }}/eFEL/examples/deap/GranuleCell1\"\n    creates: \"{{ workspace }}/eFEL/examples/deap/GranuleCell1/x86_64\"\n"
  },
  {
    "path": "cloud-config/roles/neuron/tasks/main.yaml",
    "content": "---\n- include: python27.yaml\n  when: \"{{ python27_build }}\"\n\n- set_fact: extra_path=\"{{ pythonbin.stdout + ':' }}\"\n  when: python27_build\n\n- set_fact: extra_path=\"\"\n  when: not python27_build\n\n- name: Create directories\n  file: path={{ item }} state=directory\n  with_items:\n    - \"{{ workspace }}\"\n    - \"{{ neuron_build }}\"\n\n- name: Get Source\n  get_url: url={{ neuron_url }} dest={{ neuron_build }}/nrn-{{ neuron_version }}.tar.gz\n\n- name: Untar source\n  unarchive: copy=no src={{ neuron_build }}/nrn-{{ neuron_version }}.tar.gz dest={{ neuron_build }}\n  args:\n    creates: \"{{ neuron_build }}/nrn-{{ neuron_version }}/configure\"\n\n- name: Configure Neuron\n  shell: \"{{ neuron_config }}\"\n  environment:\n    PATH: \"{{ extra_path }}{{ ansible_env.PATH }}\"\n  args:\n    chdir: \"{{ neuron_build }}/nrn-{{ neuron_version }}\"\n    creates: \"{{ neuron_build }}/nrn-{{ neuron_version }}/config.log\"\n\n- name: Build Neuron\n  #Note: could use -j here, but this gets oom-killed often\n  shell: make install\n  args:\n    chdir: \"{{ neuron_build }}/nrn-{{ neuron_version }}\"\n    creates: \"{{ install_dir }}/nrnpython\"\n\n- name: Install numpy\n  pip: name=numpy version=\"{{ numpy_version }}\" virtualenv=\"{{ venv }}\"\n\n- name: Install nrnpython\n  shell: \"{{ venv }}/bin/python setup.py install\"\n  args:\n    chdir: \"{{ neuron_build }}/nrn-{{ neuron_version }}/src/nrnpython\"\n    creates: \"{{ venv }}/lib/python2.7/site-packages/neuron\"\n\n- name: Add neuron and python virtualenv to path\n  lineinfile: >\n    dest=~/.bashrc\n    state=present\n    line=\"export PATH={{ venv }}/bin:{{ install_dir }}/nrnpython/x86_64/bin/:$PATH\"\n  when: \"{{ add_bin_path }}\"\n\n- name: Add paths to setup.sh file\n  lineinfile:\n    dest=\"{{ workspace }}/setup.sh\"\n    create=yes\n    line=\"export PATH={{ venv }}/bin:{{ install_dir }}/nrnpython/x86_64/bin/:$PATH\"\n"
  },
  {
    "path": "cloud-config/roles/neuron/tasks/python27.yaml",
    "content": "---\n#NOTE: need zlib1g-dev, libssl-dev package on ubuntu/debian\n\n- name: Create directories\n  file: path={{ item }} state=directory\n  with_items:\n    - \"{{ workspace }}\"\n    - \"{{ build_dir }}/python27\"\n\n- name: Get Source\n  get_url: url={{ python27_url }} dest=\"{{ build_dir }}/python27/python-{{ python27_version }}.tar.gz\"\n\n- name: Untar source\n  unarchive: copy=no src=\"{{ build_dir }}/python27/python-{{ python27_version }}.tar.gz\" dest=\"{{ build_dir }}/python27\"\n  args:\n    creates: \"{{ build_dir }}/python27/Python-{{ python27_version }}/configure\"\n\n- name: Configure Python\n  shell: ./configure --prefix=`readlink -f {{ install_dir }}`\n  args:\n    chdir: \"{{ build_dir }}/python27/Python-{{ python27_version }}\"\n    creates: \"{{ build_dir }}/python27/Python-{{ python27_version }}/config.log\"\n\n- name: Build & Install Python\n  #Note: could use -j here, but this gets oom-killed often\n  shell: make install\n  args:\n    chdir: \"{{ build_dir }}/python27/Python-{{ python27_version }}\"\n    creates: \"{{ install_dir }}/bin/python2.7\"\n\n- name: Create virtualenv\n  shell: \"/usr/bin/virtualenv -p {{ install_dir }}/bin/python {{ venv }}\"\n  args:\n    creates: \"{{ venv }}/bin/python2.7\"\n\n- name: Register pythonbin\n  shell: readlink -f {{ install_dir }}/bin/\n  register: pythonbin\n"
  },
  {
    "path": "cloud-config/roles/scoop-master/tasks/main.yaml",
    "content": "---\n- name: Configure User\n  user: name={{ user_name }} generate_ssh_key=yes\n\n- name: Downloading pub key\n  fetch: src=/home/{{ user_name }}/.ssh/id_rsa.pub dest=id_rsa.tmp flat=yes\n\n- name: Save SCOOP hostlist\n  become: yes\n  become_user: \"{{ user_name }}\"\n  copy: content=\"{{ groups['neuron-optimizer-worker'] | join('\\n') }}\" dest=/home/{{ user_name }}/scoop_workers\n"
  },
  {
    "path": "codecov.yml",
    "content": "coverage:\n  range: \"90...100\"\n    \n  status:\n    project:\n      default:\n        target: \"90%\" \n        threshold: \"5%\"\n    patch: false\n"
  },
  {
    "path": "docs/.gitignore",
    "content": "/build\n"
  },
  {
    "path": "docs/Makefile",
    "content": "# Makefile for Sphinx documentation\n#\n\n# You can set these variables from the command line.\nSPHINXOPTS    =\nSPHINXBUILD   = sphinx-build\nPAPER         =\nBUILDDIR      = build\n\n# User-friendly check for sphinx-build\nifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)\n$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)\nendif\n\n# Internal variables.\nPAPEROPT_a4     = -D latex_paper_size=a4\nPAPEROPT_letter = -D latex_paper_size=letter\nALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source\n# the i18n builder cannot share the environment and doctrees with the others\nI18NSPHINXOPTS  = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source\n\n.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext\n\nhelp:\n\t@echo \"Please use \\`make <target>' where <target> is one of\"\n\t@echo \"  html       to make standalone HTML files\"\n\t@echo \"  dirhtml    to make HTML files named index.html in directories\"\n\t@echo \"  singlehtml to make a single large HTML file\"\n\t@echo \"  pickle     to make pickle files\"\n\t@echo \"  json       to make JSON files\"\n\t@echo \"  htmlhelp   to make HTML files and a HTML help project\"\n\t@echo \"  qthelp     to make HTML files and a qthelp project\"\n\t@echo \"  applehelp  to make an Apple Help Book\"\n\t@echo \"  devhelp    to make HTML files and a Devhelp project\"\n\t@echo \"  epub       to make an epub\"\n\t@echo \"  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter\"\n\t@echo \"  latexpdf   to make LaTeX files and run them through pdflatex\"\n\t@echo \"  latexpdfja to make LaTeX files and run them through platex/dvipdfmx\"\n\t@echo \"  text       to make text files\"\n\t@echo \"  man        to make manual pages\"\n\t@echo \"  texinfo    to make Texinfo files\"\n\t@echo \"  info       to make Texinfo files and run them through makeinfo\"\n\t@echo \"  gettext    to make PO message catalogs\"\n\t@echo \"  changes    to make an overview of all changed/added/deprecated items\"\n\t@echo \"  xml        to make Docutils-native XML files\"\n\t@echo \"  pseudoxml  to make pseudoxml-XML files for display purposes\"\n\t@echo \"  linkcheck  to check all external links for integrity\"\n\t@echo \"  doctest    to run all doctests embedded in the documentation (if enabled)\"\n\t@echo \"  coverage   to run coverage check of the documentation (if enabled)\"\n\nclean:\n\trm -rf $(BUILDDIR)/*\n\nhtml:\n\t$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html\n\t@echo\n\t@echo \"Build finished. 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The help book is in $(BUILDDIR)/applehelp.\"\n\t@echo \"N.B. You won't be able to view it unless you put it in\" \\\n\t      \"~/Library/Documentation/Help or install it in your application\" \\\n\t      \"bundle.\"\n\ndevhelp:\n\t$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp\n\t@echo\n\t@echo \"Build finished.\"\n\t@echo \"To view the help file:\"\n\t@echo \"# mkdir -p $$HOME/.local/share/devhelp/eFEL\"\n\t@echo \"# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/eFEL\"\n\t@echo \"# devhelp\"\n\nepub:\n\t$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub\n\t@echo\n\t@echo \"Build finished. The epub file is in $(BUILDDIR)/epub.\"\n\nlatex:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo\n\t@echo \"Build finished; the LaTeX files are in $(BUILDDIR)/latex.\"\n\t@echo \"Run \\`make' in that directory to run these through (pdf)latex\" \\\n\t      \"(use \\`make latexpdf' here to do that automatically).\"\n\nlatexpdf:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo \"Running LaTeX files through pdflatex...\"\n\t$(MAKE) -C $(BUILDDIR)/latex all-pdf\n\t@echo \"pdflatex finished; the PDF files are in $(BUILDDIR)/latex.\"\n\nlatexpdfja:\n\t$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex\n\t@echo \"Running LaTeX files through platex and dvipdfmx...\"\n\t$(MAKE) -C $(BUILDDIR)/latex all-pdf-ja\n\t@echo \"pdflatex finished; the PDF files are in $(BUILDDIR)/latex.\"\n\ntext:\n\t$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text\n\t@echo\n\t@echo \"Build finished. The text files are in $(BUILDDIR)/text.\"\n\nman:\n\t$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man\n\t@echo\n\t@echo \"Build finished. The manual pages are in $(BUILDDIR)/man.\"\n\ntexinfo:\n\t$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo\n\t@echo\n\t@echo \"Build finished. The Texinfo files are in $(BUILDDIR)/texinfo.\"\n\t@echo \"Run \\`make' in that directory to run these through makeinfo\" \\\n\t      \"(use \\`make info' here to do that automatically).\"\n\ninfo:\n\t$(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo\n\t@echo \"Running Texinfo files through makeinfo...\"\n\tmake -C $(BUILDDIR)/texinfo info\n\t@echo \"makeinfo finished; the Info files are in $(BUILDDIR)/texinfo.\"\n\ngettext:\n\t$(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale\n\t@echo\n\t@echo \"Build finished. The message catalogs are in $(BUILDDIR)/locale.\"\n\nchanges:\n\t$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes\n\t@echo\n\t@echo \"The overview file is in $(BUILDDIR)/changes.\"\n\nlinkcheck:\n\t$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck\n\t@echo\n\t@echo \"Link check complete; look for any errors in the above output \" \\\n\t      \"or in $(BUILDDIR)/linkcheck/output.txt.\"\n\ndoctest:\n\t$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest\n\t@echo \"Testing of doctests in the sources finished, look at the \" \\\n\t      \"results in $(BUILDDIR)/doctest/output.txt.\"\n\ncoverage:\n\t$(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage\n\t@echo \"Testing of coverage in the sources finished, look at the \" \\\n\t      \"results in $(BUILDDIR)/coverage/python.txt.\"\n\nxml:\n\t$(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml\n\t@echo\n\t@echo \"Build finished. The XML files are in $(BUILDDIR)/xml.\"\n\npseudoxml:\n\t$(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml\n\t@echo\n\t@echo \"Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml.\"\n"
  },
  {
    "path": "docs/source/.gitignore",
    "content": "/ephys/\n/optimisations/\n/deapext/\n"
  },
  {
    "path": "docs/source/_templates/module.rst",
    "content": "{{ fullname }}\n{{ underline }}\n\n.. automodule:: {{ fullname }}\n   :members:           \n"
  },
  {
    "path": "docs/source/api.rst",
    "content": ".. BluePyOpt documentation master file, created by\n   sphinx-quickstart on Mon May 11 14:40:15 2015.\n   You can adapt this file completely to your liking, but it should at least\n   contain the root `toctree` directive.\n\nPython API\n==========\n\n.. toctree::\n   :maxdepth: 3\n\n   optimisations\n   ephys\n   deapext\n"
  },
  {
    "path": "docs/source/conf.py",
    "content": "# -*- coding: utf-8 -*-\n#\n# BluePyOpt documentation build configuration file, created by\n# sphinx-quickstart on Mon May 11 14:40:15 2015.\n#\n# This file is execfile()d with the current directory set to its 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\nimport sys\nimport os\nimport bluepyopt\nimport bluepyopt.ephys\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.\nsys.path.insert(0, os.path.abspath('.'))\n\n# -- General configuration -----------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\nneeds_sphinx = '1.3'\n\n# Add any Sphinx extension module names here, as strings. They can be extensions\n# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.\nextensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode',\n              'sphinx.ext.autosummary', 'sphinx.ext.napoleon']\n\n# napoleon_numpy_docstring = True\nnapoleon_google_docstring = True\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = ['_templates']\n\n# The suffix of source filenames.\nsource_suffix = '.rst'\n\n# The encoding of source files.\n#source_encoding = 'utf-8-sig'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = u'bluepyopt'\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 = bluepyopt.__version__\n# The full version, including alpha/beta/rc tags.\nrelease = bluepyopt.__version__\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#language = None\n\n# There are two options for replacing |today|: either, you set today to some\n# non-false value, then it is used:\n#today = ''\n# Else, today_fmt is used as the format for a strftime call.\n#today_fmt = '%B %d, %Y'\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\nexclude_patterns = []\n\n# The reST default role (used for this markup: `text`) to use for all documents.\n#default_role = None\n\n# If true, '()' will be appended to :func: etc. cross-reference text.\n#add_function_parentheses = True\n\n# If true, the current module name will be prepended to all description\n# unit titles (such as .. function::).\n#add_module_names = True\n\n# If true, sectionauthor and moduleauthor directives will be shown in the\n# output. They are ignored by default.\n#show_authors = False\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'sphinx'\n\n# A list of ignored prefixes for module index sorting.\n#modindex_common_prefix = []\n\nautosummary_generate = True\nautodoc_default_flags = ['show-inheritance']\nautoclass_content = 'both'\ntolerate_sphinx_warnings = True\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.\nhtml_theme = 'sphinx-bluebrain-theme'\nhtml_title = 'BluepyOpt'\nhtml_show_sourcelink = False\nhtml_theme_options = {\n    \"repo_url\": \"https://github.com/BlueBrain/BluePyOpt/\",\n    \"repo_name\": \"BlueBrain/BluePyOpt\"\n}\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#html_theme_options = {}\n\n# Add any paths that contain custom themes here, relative to this directory.\nhtml_theme_path = ['./']\n\n# The name for this set of Sphinx documents.  If None, it defaults to\n# \"<project> v<release> documentation\".\n#html_title = None\n\n# A shorter title for the navigation bar.  Default is the same as html_title.\n#html_short_title = None\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar.\nhtml_logo = \"_static/bbp.jpg\"\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\n#html_favicon = None\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\".\nhtml_static_path = ['_static']\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\nhtml_last_updated_fmt = '%b %d, %Y'\n#html_last_updated_fmt = '%b %d, %Y'\n\n# If true, SmartyPants will be used to convert quotes and dashes to\n# typographically correct entities.\n#html_use_smartypants = True\n\n# Custom sidebar templates, maps document names to template names.\n#html_sidebars = {}\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\n#html_additional_pages = {}\n\n# If false, no module index is generated.\n#html_domain_indices = True\n\n# If false, no index is generated.\n#html_use_index = True\n\n# If true, the index is split into individual pages for each letter.\n#html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n#html_show_sourcelink = True\n\n# If true, \"Created using Sphinx\" is shown in the HTML footer. Default is True.\n#html_show_sphinx = True\n\n# If true, \"(C) Copyright ...\" is shown in the HTML footer. Default is True.\n#html_show_copyright = True\n\n# If true, an OpenSearch description file will be output, and all pages will\n# contain a <link> tag referring to it.  The value of this option must be the\n# base URL from which the finished HTML is served.\n#html_use_opensearch = ''\n\n# This is the file name suffix for HTML files (e.g. \".xhtml\").\n#html_file_suffix = None\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'bluepyoptdoc'\n\n\n# -- Options for LaTeX output --------------------------------------------------\n\nlatex_elements = {\n    # The paper size ('letterpaper' or 'a4paper').\n    #'papersize': 'letterpaper',\n\n    # The font size ('10pt', '11pt' or '12pt').\n    #'pointsize': '10pt',\n\n    # Additional stuff for the LaTeX preamble.\n    #'preamble': '',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title, author, documentclass [howto/manual]).\nlatex_documents = [\n    ('index', 'bluepyopt.tex', u'BluePyOpt Documentation',\n     u'BBP, EPFL', 'manual'),\n]\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\n#latex_logo = None\n\n# For \"manual\" documents, if this is true, then toplevel headings are parts,\n# not chapters.\n#latex_use_parts = False\n\n# If true, show page references after internal links.\n#latex_show_pagerefs = False\n\n# If true, show URL addresses after external links.\n#latex_show_urls = False\n\n# Documents to append as an appendix to all manuals.\n#latex_appendices = []\n\n# If false, no module index is generated.\n#latex_domain_indices = True\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 = [\n    ('index', 'bluepyopt', u'BluePyOpt Documentation',\n     [u'BBP, EPFL'], 1)\n]\n\n# If true, show URL addresses after external links.\n#man_show_urls = False\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    ('index', 'bluepyopt', u'BluePyOpt Documentation',\n     u'BBP, EPFL', 'bluepyopt', 'One line description of project.',\n     'Miscellaneous'),\n]\n\n# Documents to append as an appendix to all manuals.\n#texinfo_appendices = []\n\n# If false, no module index is generated.\n#texinfo_domain_indices = True\n\n# How to display URL addresses: 'footnote', 'no', or 'inline'.\n#texinfo_show_urls = 'footnote'\n\n\n# -- Options for Epub output ---------------------------------------------------\n\n# Bibliographic Dublin Core info.\nepub_title = u'bluepyopt'\nepub_author = u'BBP, EPFL'\nepub_publisher = u'BBP, EPFL'\nepub_copyright = u'2015, BBP, EPFL'\n\n# The language of the text. It defaults to the language option\n# or en if the language is not set.\n#epub_language = ''\n\n# The scheme of the identifier. Typical schemes are ISBN or URL.\n#epub_scheme = ''\n\n# The unique identifier of the text. This can be a ISBN number\n# or the project homepage.\n#epub_identifier = ''\n\n# A unique identification for the text.\n#epub_uid = ''\n\n# A tuple containing the cover image and cover page html template filenames.\n#epub_cover = ()\n\n# HTML files that should be inserted before the pages created by sphinx.\n# The format is a list of tuples containing the path and title.\n#epub_pre_files = []\n\n# HTML files shat should be inserted after the pages created by sphinx.\n# The format is a list of tuples containing the path and title.\n#epub_post_files = []\n\n# A list of files that should not be packed into the epub file.\n#epub_exclude_files = []\n\n# The depth of the table of contents in toc.ncx.\n#epub_tocdepth = 3\n\n# Allow duplicate toc entries.\n#epub_tocdup = True\n"
  },
  {
    "path": "docs/source/deapext.rst",
    "content": "Deap extension API\n==================\n\n.. autosummary::\n    :toctree: deapext\n    :template: module.rst\n    \n    bluepyopt.deapext.optimisations\n"
  },
  {
    "path": "docs/source/ephys.rst",
    "content": "EPhys model API\n===============\n\n.. autosummary::\n    :toctree: ephys\n    :template: module.rst\n    \n    bluepyopt.ephys.evaluators\n    bluepyopt.ephys.models\n    bluepyopt.ephys.efeatures\n    bluepyopt.ephys.locations\n    bluepyopt.ephys.mechanisms\n    bluepyopt.ephys.morphologies\n    bluepyopt.ephys.objectives\n    bluepyopt.ephys.parameters\n    bluepyopt.ephys.parameterscalers\n    bluepyopt.ephys.protocols\n    bluepyopt.ephys.recordings\n    bluepyopt.ephys.responses\n    bluepyopt.ephys.objectivescalculators\n    bluepyopt.ephys.stimuli\n"
  },
  {
    "path": "docs/source/index.rst",
    "content": ".. BluePyOpt documentation master file, created by\n   sphinx-quickstart on Mon May 11 14:40:15 2015.\n   You can adapt this file completely to your liking, but it should at least\n   contain the root `toctree` directive.\n\n\n\n.. include:: ../../README.rst\n   :end-before: .. substitutions\n\n.. toctree::\n   :maxdepth: 3\n\n   Home <self>\n   api.rst\n\nIndices and tables\n==================\n\n* :ref:`genindex`\n* :ref:`modindex`\n* :ref:`search`\n\n.. |banner| image:: /logo/BluePyOptBanner.png\n.. |landscape_example| image:: ../../examples/simplecell/figures/landscape_example.png\n"
  },
  {
    "path": "docs/source/optimisations.rst",
    "content": "General optimisation API\n========================\n\n.. autosummary::\n    :toctree: optimisations\n    :template: module.rst\n    \n    bluepyopt.optimisations\n    bluepyopt.parameters\n    bluepyopt.objectives\n    bluepyopt.evaluators\n"
  },
  {
    "path": "examples/BluePyOpt-ipyparallel.md",
    "content": "# BluePyOpt Parallelization with ipyparallel\n\nBy default, when the optimization is being run, it only uses a single core.\nIf you have access to a multicore machine, or a cluster of multicore machines, the extra processing power can easily be leveraged by using `BluePyOpt` in combination with [ipyparallel](https://ipyparallel.readthedocs.io/en/latest/).\n\n# Quick Introduction to ipyparallel\n\nThe `ipyparallel` project is uses the IPython/Jupyter protocol for simplifying distributing work over many cores.\nIn the simplest terms, a worker (called an `ipengine`) is started per core.\nIt is directed by a master process (called an `ipcontroller`) to perform work.\nThe `ipcontroller` gets its work from clients that connect to it.\n\n## Installation\n\n`ipyparallel` can be installed using pip:\n\n    # create and activate virtualenv\n    $ venv venv-ipyparellel; source venv-ipyparellel/bin/activate\n\n    # install ipyparallel\n    (venv-ipyparellel)$ pip install ipyparallel\n\n    #check that it installed correctly\n    (venv-ipyparellel)$ ipcontroller -V\n    [returns a version number]\n\nWith `ipyparallel` installed, let's perform a simple parallel operation.\nTo fully understand what is happening, it is probably best to open three windows.\nOne for the `ipcontroller`, one for the `ipengines`, and lastly one for the client.\n\nIn the `ipcontroller` window:\n\n    # activate the virtualenv\n    $ source venv-ipyparellel/bin/activate\n\n    # run the ipcontroller\n    (venv-ipyparellel)$ ipcontroller\n\nIn the `ipengine` window:\n\n    # activate the virtualenv\n    $ source venv-ipyparellel/bin/activate\n\n    # start two ipengines\n    (venv-ipyparellel)$ ipengine &; ipengine &\n\nIn the client window:\n\n    # activate the virtualenv\n    $ source venv-ipyparellel/bin/activate\n\n    #start python\n    (venv-ipyparellel)$ python\n\n    >>> from ipyparallel import Client\n    >>> c = Client()  # creates a connection to the server\n    >>> view = c[:] # creates a 'view' of all the workers\n    >>> import socket\n    >>> view.apply_sync(socket.gethostname)  # run the function gethostname on all ipengines\n    ['your_hostname', 'your_hostname']\n\nIf that works, it means that you can parallelize work on a single computer, but across every processor: just start one `ipengine` per processor, and `ipyparallel` will handle the rest.\nSeveral things should be noted here:\n* `ipyparallel` will attempt to use an [ipython profile](http://ipython.readthedocs.io/en/stable/config/intro.html#profiles) to coordinate the initial startup between the controller, its workers, and the client.  That is why the above the above commands never included explicit host information\n* using the `ipcluster` command one can simplify starting multiple instances on the same machine, and will require only a single window instead of one for the `ipcluster` and the `ipengines`.  Ex:\n\n        # start an ipyparellel cluster with 1 head node, and as many processors as the current machine has\n        (venv-ipyparellel)$ ipcluster start\n\n* With a cluster of machines, if there is a shared file system, parallelizing across all the machines and their processors is a matter of starting `ipengine`s on each of the machines\n\n# Running the L5_PC example\n\nWith some experience with `ipyparallel`, it's time to try and run an optimization using it.\n\n## Installation\n\nIf you already have a working environment, with `BluePyOpt`, [NEURON](http://www.neuron.yale.edu/neuron/) and `ipyparallel` installed, you can skip this step.\n\nThere are multiple ways to install the full stack to perform this optimization: using `ansible` (documented [here](https://github.com/BlueBrain/BluePyOpt/tree/master/cloud-config)) as well as using [conda](https://conda.io/docs/).  For simplicity's sake, we will be using the latter to setup an environment that includes all the requirements, including a `NEURON` installation that includes all required mechanisms compiled in.  Note: this only works for `Linux` and `macOS`.\n\n\nFollow [these instructions](https://conda.io/docs/install/quick.html) to install `conda`.\nMake sure that the `conda` command runs (make sure that the install location is included on your path.)\n\nInstall the `anaconda` environment manager:\n    \n    $ conda install anaconda-client\n\nInstall the BluePyOpt suite:\n\n    $ conda env create bluepyopt/gecco2017\n\nActivate it, and check that it works:\n\n    $ source activate gecco2017\n    $ python\n    >>> import neuron\n\nIf that works, you should have a properly working environment.\n\n## Running\n\nStart a cluster of `ipengines` (in one window):\n\n    (gecco2017)$ ipcluster start\n\nIn the `BluePyOpt` [git](https://github.com/BlueBrain/BluePyOpt/) repository, there is an `examples/l5pc` directory.\nFrom there, one can launch the optimization like so (in another window):\n\n    (gecco2017)$ ./opt_l5pc.py \\\n        -vv                    \\\n        --checkpoint check.pkl \\\n        --offspring_size=50    \\\n        --max_ngen=2           \\\n        --ipyparallel          \\\n        --start\n\nThis will run, and based on the number of generations (2 in the example above) and offspring (50 in the example above) the amount of work (2*50 = 100 units) will be distributed across the workers.\n\nOne should get output something along the lines of:\n\n    DEBUG:root:Using ipyparallel with 8 engines\n    DEBUG:root:Doing start or continue\n    DEBUG:traitlets:Importing canning map\n    DEBUG:root:Generation took 0:01:10.813599\n    DEBUG:root:Generation took 0:01:33.446949\n    INFO:__main__:gen       nevals  avg     std     min     max\n    1       2       4862.45 123.749 4738.7  4986.2\n    2       2       4068.58 1604.07 1307.39 5242.02\n"
  },
  {
    "path": "examples/README.md",
    "content": "# BluePyOpt Examples\n\nThis directory contains examples of optimizations that can be performed with `BluePyOpt`.\nThey can be used to learn the concepts behind the package, and also as a starting point for other optimizations\n\n* expsyn: Example optimization of a synapse (a point process) in NEURON\n\n* graupnerbrunelstdp: Graupner-Brunel STDP model fitting\n\n* l5pc: Layer 5 pyramidal neuron parameter optimization\n\n* simplecell: optimisation of simple single compartmental cell with two free parameters\n\n* stochkv: simple cell optimization with stochastic channels\n\n* tsodyksmarkramstp: optimizing parameters of the Tsodyks-Markram model of short-term synaptic plasticity\n\nThe expsyn, l5pc and simplecell examples contain an implementation for [Arbor](https://arbor-sim.org/) as an alternative simulator backend to NEURON.\n\n# Documentation\n\n[Parallelization with ipyparallel](BluePyOpt-ipyparallel.md)\n"
  },
  {
    "path": "examples/__init__.py",
    "content": "\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n"
  },
  {
    "path": "examples/cma_strategy/cma.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optimisation using the CMA evolutionary strategy\\n\",\n    \"\\n\",\n    \"This notebook will explain how to optimise a model using the covariance matrix adaptation (CMA) optimisation strategy. \\n\",\n    \"BluePyOpt includes two flavors of CMA: a single objective one and a hybrid single/multi objective one.\\n\",\n    \"\\n\",\n    \"For a tutorial on the theory and algorithm behind CMA, please refer to https://arxiv.org/abs/1604.00772.\\n\",\n    \"\\n\",\n    \"This notebook uses the simple cell model defined in examples/simplecell. Please refer to this notebook for a first introduction to model fitting.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: matplotlib in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (3.4.3)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (from matplotlib) (2.8.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (from matplotlib) (0.11.0)\\n\",\n      \"Requirement already satisfied: numpy>=1.16 in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (from matplotlib) (1.21.4)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (from matplotlib) (8.4.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (from matplotlib) (2.4.7)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (from matplotlib) (1.3.2)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\\n\",\n      \"\\u001b[33mWARNING: You are using pip version 20.1.1; however, version 22.0.3 is available.\\n\",\n      \"You should consider upgrading via the '/gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/bin/python3 -m pip install --upgrade pip' command.\\u001b[0m\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Install matplotlib if needed\\n\",\n    \"!pip install matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy\\n\",\n    \"\\n\",\n    \"%load_ext autoreload\\n\",\n    \"%autoreload\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_fitness(logbook):\\n\",\n    \"    gen_numbers = logbook.select('gen')\\n\",\n    \"    min_fitness = logbook.select('min')\\n\",\n    \"    max_fitness = logbook.select('max')\\n\",\n    \"    plt.plot(gen_numbers, min_fitness, label='min fitness')\\n\",\n    \"    plt.xlabel('generation #')\\n\",\n    \"    plt.ylabel('score (# std)')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \\n\",\n    \"    plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_responses(responses):\\n\",\n    \"    plt.subplot(2,1,1)\\n\",\n    \"    plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.subplot(2,1,2)\\n\",\n    \"    plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.tight_layout()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Setting up the cell template and evaluator\\n\",\n    \"-------------------------\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, we instantiate the cell template and evaluator as defined in the simplecell example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import bluepyopt.ephys.examples.simplecell\\n\",\n    \"\\n\",\n    \"simple_cell = bluepyopt.ephys.examples.simplecell.SimpleCell()\\n\",\n    \"evaluator = simple_cell.cell_evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Optimisation using single objective CMA (SO-CMA)\\n\",\n    \"-------------------------\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First we will present the single objective version of the CMA strategy.\\n\",\n    \"\\n\",\n    \"In this version of CMA, the optimizer aims at minimizing a single fitness value computed as the sum of the scores of the objectives.\\n\",\n    \"\\n\",\n    \"Note that in CMA, informing the offspring_size is optional as by default, it is automatically set to\\n\",\n    \"int(4 + 3 * log(dimension_parameter_space)).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 53,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimiser = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA\\n\",\n    \"optimisation = optimiser(evaluator=evaluator, seed=1)\\n\",\n    \"pop, hof, log, hist = optimisation.run(max_ngen=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 54,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best individual:  [0.11513442416272959, 0.038816802452611765]\\n\",\n      \"Fitness values:  (0.0,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind = hof[0]\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 55,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_fitness(log)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If one wishes to optimize starting form a known solution, or fine-tune a past mdodel, the argument `centroid` can be used to initialise the center of the CMA search. In this case, it might be useful to also specify `sigma`, which is the initial standard deviation of the distribution from which the models are drawn.\\n\",\n    \"\\n\",\n    \"For example here,we can restart from the final results of the previous optimisation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 56,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = optimiser(centroids=[list(hof[0])], sigma=0.01, evaluator=evaluator, seed=2)\\n\",\n    \"pop, hof, log, hist = optimisation.run(max_ngen=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 57,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best individual:  [0.11485248564327624, 0.039145905479049836]\\n\",\n      \"Fitness values:  (0.0,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind = hof[0]\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Optimisation using multi objective CMA (MO-CMA)\\n\",\n    \"-------------------------\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Second is the hybrid single/multi objective CMA strategy. It is tasked with both:\\n\",\n    \"- minimizing the fitness computed as the sum of the scores \\n\",\n    \"- maximizing the hyper-volume of the Pareto front formed by the current population of models.\\n\",\n    \"\\n\",\n    \"At each generation, all models in the population are ranked for both criteria, and a mixed rank is obtained following the formula:\\n\",\n    \"\\n\",\n    \"rank_mixed = w_hv * rank_hv. + ((1 - w_hv) * rank_fitness).\\n\",\n    \"\\n\",\n    \"Following this ranking, the best models are selected to update the CMA kernel for the next generation.\\n\",\n    \"\\n\",\n    \"By default, the weight assigned to the hyper-volume ranking (w_hv) is set to 0.5. The case w_hv=1  would lead to a pure multi-objective optimisation aiming at maximizing the hypervolume while w_hv would aim at minimizing the raw fitness (note that in the latter, the result would differ from using the SO-CMA as the MO-CMA uses a slightly different evolutionary logic).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 58,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = optimiser(weight_hv=0.5, offspring_size=3, selector_name=\\\"multi_objective\\\", evaluator=evaluator, seed=2)\\n\",\n    \"pop, hof, log, hist = optimisation.run(max_ngen=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 59,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best individual:  [0.11309639259118459, 0.034266363909519156]\\n\",\n      \"Fitness values:  (0.0,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind = hof[0]\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 60,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_fitness(log)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"CMA versus IBEA \\n\",\n    \"-------------------------\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As the present package proposes both the IBEA and CMA evolutionary strategies, one might ask: Which one is the best?\\n\",\n    \"\\n\",\n    \"Unfortunately, there is no definitive answer to this question.\\n\",\n    \"\\n\",\n    \"From a theoretical point of view, the advantages of the CMA strategy seem strong:\\n\",\n    \"- In IBEA, the creation of a new generation is performed through random mating and mutation. However, due to the lack of a learning rate, this leads to a lack of convergence in the latter stage of the optimisation as the models will jump around an optimal solution without being able to reach it. In CMA, the `sigma` parameter, which is the width of the distribution from which the models should be drawn, decreases once the optimisation finds a bassin in the fitness landscape, leading to smoother convergence.\\n\",\n    \"- In IBEA, as the new generation only depends on the latest one, the knowledge contained in the previous generations is almost completely lost. In CMA, the covariance matrix continuously evolves, taking into account the results of each generation, leading to an accumulation of past knowledge about the shape of the local fitness landscape.\\n\",\n    \"- The ideal CMA population size, computed as int(4 + 3 * log(dimension_parameter_space)) is often one or two order of magnitude smaller than the population size needed by IBEA to reach the same results. This results in less compute per generation for the CMA strategy.\\n\",\n    \"\\n\",\n    \"However, CMA is not without drawbacks:\\n\",\n    \"- It is frequent for the CMA strategy (especially the SO-CMA) to converge too quickly and thus get stuck in sub-optimal minima. Therefore, to achieve the exploration level displayed by the IBEA strategy, it might be needed to run several CMA optimisations in parallel and pool the results.\\n\",\n    \"- Although the population size is much smaller when using the CMA strategy, a proper convergence might require many more generations than for the IBEA strategy, thus nullifying the advantage of the small generation in term of compute.\\n\",\n    \"\\n\",\n    \"Overall, CMA makes a more clever use of the information available, but IBEA is not to be neglected, especially if more compute power is available.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"lfpyenv_new\",\n   \"language\": \"python\",\n   \"name\": \"lfpyenv_new\"\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\": \"3.8.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/expsyn/.gitignore",
    "content": ".ipynb_checkpoints/\n"
  },
  {
    "path": "examples/expsyn/ExpSyn.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optimising synaptic parameters \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook shows how the parameters of a NEURON point process (in this case a synapse), can be optimised using BluePyOpt.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First some initial setup:\"\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    \"%reload_ext autoreload\\n\",\n    \"%autoreload\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"import bluepyopt as bpopt\\n\",\n    \"import bluepyopt.ephys as ephys\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We need a Simulator (NEURON), Morphology (one compartment) and two Location objects (the 'somatic' sectionlist and the center of the soma).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# NEURON simulator\\n\",\n    \"nrn_sim = ephys.simulators.NrnSimulator()\\n\",\n    \"\\n\",\n    \"# Single compartment\\n\",\n    \"morph = ephys.morphologies.NrnFileMorphology('simple.swc')\\n\",\n    \"\\n\",\n    \"# Object that points to sectionlist somatic\\n\",\n    \"somatic_loc = ephys.locations.NrnSeclistLocation('somatic',seclist_name='somatic')\\n\",\n    \"\\n\",\n    \"# Object that points to the center of the soma\\n\",\n    \"somacenter_loc = ephys.locations.NrnSeclistCompLocation(\\n\",\n    \"    name='somacenter',\\n\",\n    \"    seclist_name='somatic',\\n\",\n    \"    sec_index=0,\\n\",\n    \"    comp_x=0.5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will also add a leak channel:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pas_mech = ephys.mechanisms.NrnMODMechanism(                                    \\n\",\n    \"    name='pas',                                                                 \\n\",\n    \"    suffix='pas',                                                               \\n\",\n    \"    locations=[somatic_loc])                                                    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now comes the code which will add the synapse. We specify the suffix of the point process MOD file, and the location (or the list of locations) where to add it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Add ExpSyn synapse pointprocess at the center of the soma\\n\",\n    \"expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism(                     \\n\",\n    \"    name='expsyn',                                                              \\n\",\n    \"    suffix='ExpSyn',                                                            \\n\",\n    \"    locations=[somacenter_loc])                                                 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once we have defined a point process, we can create a Location object that points to it\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"expsyn_loc = ephys.locations.NrnPointProcessLocation(                           \\n\",\n    \"    'expsyn_loc',                                                               \\n\",\n    \"    pprocess_mech=expsyn_mech)                                                  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Using this location, we can specify the parameters of the synapse. Let's fit the decay time constant:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"expsyn_tau_param = ephys.parameters.NrnPointProcessParameter(                   \\n\",\n    \"    name='expsyn_tau',                                                          \\n\",\n    \"    param_name='tau',                                                           \\n\",\n    \"    value=2,                                                                    \\n\",\n    \"    bounds=[0, 50],                                                             \\n\",\n    \"    locations=[expsyn_loc])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's put all these concepts together in a cell model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cell = ephys.models.CellModel(                                               \\n\",\n    \"    name='simple_cell',                                                      \\n\",\n    \"    morph=morph,                                                             \\n\",\n    \"    mechs=[pas_mech, expsyn_mech],                                           \\n\",\n    \"    params=[expsyn_tau_param])                                     \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need to define the fitness function. The idea is to stimulate the synapse 5 times, and let the resulting train of EPSPs reach exactly -50 mV.\\n\",\n    \"\\n\",\n    \"We first create a stimulus that injects the presynaptic events:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"stim_start = 20\\n\",\n    \"number = 5\\n\",\n    \"interval = 5\\n\",\n    \"\\n\",\n    \"netstim = ephys.stimuli.NrnNetStimStimulus(                                  \\n\",\n    \"    total_duration=200,                                                      \\n\",\n    \"    number=5,                                                                \\n\",\n    \"    interval=5,                                                              \\n\",\n    \"    start=stim_start,                                                        \\n\",\n    \"    weight=5e-4,                                                             \\n\",\n    \"    locations=[expsyn_loc])\\n\",\n    \"\\n\",\n    \"stim_end = stim_start + interval * number\\n\",\n    \"\\n\",\n    \"rec = ephys.recordings.CompRecording(\\n\",\n    \"    name='soma.v', \\n\",\n    \"    location=somacenter_loc,\\n\",\n    \"    variable='v')\\n\",\n    \"\\n\",\n    \"protocol = ephys.protocols.SweepProtocol('netstim_protocol', [netstim], [rec])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then we define an eFELFeature that will target the maximum voltage and we put everything in an evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"max_volt_feature = ephys.efeatures.eFELFeature(                              \\n\",\n    \"    'maximum_voltage',                                                       \\n\",\n    \"    efel_feature_name='maximum_voltage',                                     \\n\",\n    \"    recording_names={'': 'soma.v'},                                          \\n\",\n    \"    stim_start=stim_start,                                                   \\n\",\n    \"    stim_end=stim_end,                                                       \\n\",\n    \"    exp_mean=-50,                                                            \\n\",\n    \"    exp_std=.1)\\n\",\n    \"\\n\",\n    \"max_volt_objective = ephys.objectives.SingletonObjective(                    \\n\",\n    \"    max_volt_feature.name,                                                   \\n\",\n    \"    max_volt_feature)                       \\n\",\n    \"\\n\",\n    \"score_calc = ephys.objectivescalculators.ObjectivesCalculator(               \\n\",\n    \"    [max_volt_objective])                                                    \\n\",\n    \"\\n\",\n    \"cell_evaluator = ephys.evaluators.CellEvaluator(                             \\n\",\n    \"    cell_model=cell,                                                         \\n\",\n    \"    param_names=['expsyn_tau'],                                              \\n\",\n    \"    fitness_protocols={protocol.name: protocol},                             \\n\",\n    \"    fitness_calculator=score_calc,                                           \\n\",\n    \"    sim=nrn_sim)                                                             \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's try out the evaluator with a decay time constant of 10.0\"\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      \"{'maximum_voltage': 29.716831912606736}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"default_param_values = {'expsyn_tau': 10.0}                                  \\n\",\n    \"\\n\",\n    \"print(cell_evaluator.evaluate_with_dicts(default_param_values))              \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can run the optimisation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = bpopt.optimisations.DEAPOptimisation(                         \\n\",\n    \"    evaluator=cell_evaluator,                                                \\n\",\n    \"    offspring_size=10)                                                       \\n\",\n    \"\\n\",\n    \"_, hall_of_fame, _, _ = optimisation.run(max_ngen=5)                         \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And then we can plot the best individual:\"\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      \"Best individual:  [14.03202650928813]\\n\",\n      \"Fitness values:  (1.181341617527849,)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"best_ind = hall_of_fame[0]                                                   \\n\",\n    \"\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\\n\",\n    \"\\n\",\n    \"best_ind_dict = cell_evaluator.param_dict(best_ind)                          \\n\",\n    \"responses = protocol.run(                                                    \\n\",\n    \"    cell_model=cell,                                                         \\n\",\n    \"    param_values=best_ind_dict,                                              \\n\",\n    \"    sim=nrn_sim)                                                             \\n\",\n    \"\\n\",\n    \"time = responses['soma.v']['time']                                           \\n\",\n    \"voltage = responses['soma.v']['voltage']                                     \\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt                                              \\n\",\n    \"plt.style.use('ggplot')                                                      \\n\",\n    \"plt.plot(time, voltage)                                                      \\n\",\n    \"plt.xlabel('Time (ms)')                                                      \\n\",\n    \"plt.ylabel('Voltage (mV)')                                                   \\n\",\n    \"plt.show()                                                                   \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\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\": \"3.8.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/expsyn/ExpSyn_arbor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optimising synaptic parameters in Arbor\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook shows how the parameters of an Arbor point process (in this case a synapse), can be optimised using BluePyOpt.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First some initial setup:\"\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    \"%reload_ext autoreload\\n\",\n    \"%autoreload\\n\",\n    \"\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"import bluepyopt as bpopt\\n\",\n    \"import bluepyopt.ephys as ephys\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We need a Simulator (Arbor), Morphology (one compartment) and two Location objects (the 'somatic' sectionlist and the center of the soma).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Arbor simulator\\n\",\n    \"arb_sim = ephys.simulators.ArbSimulator()\\n\",\n    \"\\n\",\n    \"# Single compartment\\n\",\n    \"morph = ephys.morphologies.NrnFileMorphology('simple.swc')\\n\",\n    \"\\n\",\n    \"# Object that points to sectionlist somatic\\n\",\n    \"somatic_loc = ephys.locations.NrnSeclistLocation('somatic',seclist_name='somatic')\\n\",\n    \"\\n\",\n    \"# Object that points to the center of the soma\\n\",\n    \"somacenter_loc = ephys.locations.ArbLocsetLocation(\\n\",\n    \"    name='somacenter',\\n\",\n    \"    locset='(location 0 0.5)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We will also add a leak channel:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pas_mech = ephys.mechanisms.NrnMODMechanism(                                    \\n\",\n    \"    name='pas',                                                                 \\n\",\n    \"    suffix='pas',                                                               \\n\",\n    \"    locations=[somatic_loc])                                                    \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now comes the code which will add the synapse. We specify the suffix of the point process MOD file, and the location (or the list of locations) where to add it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Add ExpSyn synapse pointprocess at the center of the soma\\n\",\n    \"expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism(                     \\n\",\n    \"    name='expsyn',                                                              \\n\",\n    \"    suffix='ExpSyn',                                                            \\n\",\n    \"    locations=[somacenter_loc])                                                 \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Once we have defined a point process, we can create a Location object that points to it\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"expsyn_loc = ephys.locations.NrnPointProcessLocation(                           \\n\",\n    \"    'expsyn_loc',                                                               \\n\",\n    \"    pprocess_mech=expsyn_mech)                                                  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Using this location, we can specify the parameters of the synapse. Let's fit the decay time constant:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"expsyn_tau_param = ephys.parameters.NrnPointProcessParameter(                   \\n\",\n    \"    name='expsyn_tau',                                                          \\n\",\n    \"    param_name='tau',                                                           \\n\",\n    \"    value=2,                                                                    \\n\",\n    \"    bounds=[0, 50],                                                             \\n\",\n    \"    locations=[expsyn_loc])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's put all these concepts together in a cell model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cell = ephys.models.CellModel(                                               \\n\",\n    \"    name='simple_cell',                                                      \\n\",\n    \"    morph=morph,                                                             \\n\",\n    \"    mechs=[pas_mech, expsyn_mech],                                           \\n\",\n    \"    params=[expsyn_tau_param])                                     \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we need to define the fitness function. The idea is to stimulate the synapse 5 times, and let the resulting train of EPSPs reach exactly -50 mV.\\n\",\n    \"\\n\",\n    \"We first create a stimulus that injects the presynaptic events:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"stim_start = 20\\n\",\n    \"number = 5\\n\",\n    \"interval = 5\\n\",\n    \"\\n\",\n    \"netstim = ephys.stimuli.NrnNetStimStimulus(                                  \\n\",\n    \"    total_duration=200,                                                      \\n\",\n    \"    number=5,                                                                \\n\",\n    \"    interval=5,                                                              \\n\",\n    \"    start=stim_start,                                                        \\n\",\n    \"    weight=5e-4,                                                             \\n\",\n    \"    locations=[expsyn_loc])\\n\",\n    \"\\n\",\n    \"stim_end = stim_start + interval * number\\n\",\n    \"\\n\",\n    \"rec = ephys.recordings.CompRecording(\\n\",\n    \"    name='soma.v', \\n\",\n    \"    location=somacenter_loc,\\n\",\n    \"    variable='v')\\n\",\n    \"\\n\",\n    \"protocol = ephys.protocols.ArbSweepProtocol('netstim_protocol', [netstim], [rec])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then we define an eFELFeature that will target the maximum voltage and we put everything in an evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"max_volt_feature = ephys.efeatures.eFELFeature(                              \\n\",\n    \"    'maximum_voltage',                                                       \\n\",\n    \"    efel_feature_name='maximum_voltage',                                     \\n\",\n    \"    recording_names={'': 'soma.v'},                                          \\n\",\n    \"    stim_start=stim_start,                                                   \\n\",\n    \"    stim_end=stim_end,                                                       \\n\",\n    \"    exp_mean=-50,                                                            \\n\",\n    \"    exp_std=.1)\\n\",\n    \"\\n\",\n    \"max_volt_objective = ephys.objectives.SingletonObjective(                    \\n\",\n    \"    max_volt_feature.name,                                                   \\n\",\n    \"    max_volt_feature)                       \\n\",\n    \"\\n\",\n    \"score_calc = ephys.objectivescalculators.ObjectivesCalculator(               \\n\",\n    \"    [max_volt_objective])                                                    \\n\",\n    \"\\n\",\n    \"cell_evaluator = ephys.evaluators.CellEvaluator(                             \\n\",\n    \"    cell_model=cell,                                                         \\n\",\n    \"    param_names=['expsyn_tau'],                                              \\n\",\n    \"    fitness_protocols={protocol.name: protocol},                             \\n\",\n    \"    fitness_calculator=score_calc,                                           \\n\",\n    \"    sim=arb_sim)                                                             \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's try out the evaluator with a decay time constant of 10.0\"\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      \"{'maximum_voltage': 29.690040424145465}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"default_param_values = {'expsyn_tau': 10.0}                                  \\n\",\n    \"\\n\",\n    \"print(cell_evaluator.evaluate_with_dicts(default_param_values))              \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can run the optimisation:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = bpopt.optimisations.DEAPOptimisation(                         \\n\",\n    \"    evaluator=cell_evaluator,                                                \\n\",\n    \"    offspring_size=10)                                                       \\n\",\n    \"\\n\",\n    \"_, hall_of_fame, _, _ = optimisation.run(max_ngen=5)                         \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And then we can plot the best individual:\"\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      \"Best individual:  [14.03202650928813]\\n\",\n      \"Fitness values:  (1.1134864832433067,)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"best_ind = hall_of_fame[0]                                                   \\n\",\n    \"\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\\n\",\n    \"\\n\",\n    \"best_ind_dict = cell_evaluator.param_dict(best_ind)                          \\n\",\n    \"responses = protocol.run(                                                    \\n\",\n    \"    cell_model=cell,                                                         \\n\",\n    \"    param_values=best_ind_dict,                                              \\n\",\n    \"    sim=arb_sim)                                                             \\n\",\n    \"\\n\",\n    \"time = responses['soma.v']['time']                                           \\n\",\n    \"voltage = responses['soma.v']['voltage']                                     \\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt                                              \\n\",\n    \"plt.style.use('ggplot')                                                      \\n\",\n    \"plt.plot(time, voltage)                                                      \\n\",\n    \"plt.xlabel('Time (ms)')                                                      \\n\",\n    \"plt.ylabel('Voltage (mV)')                                                   \\n\",\n    \"plt.show()                                                                   \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\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\": \"3.8.10\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"581988038cf9ce8838e7faf3da7c29f4ff88d898cd43cb17e0086e389d8deda2\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/expsyn/expsyn.py",
    "content": "\"\"\"Expsyn synapse parameter fitting\"\"\"\n\n# pylint: disable=R0914\n\n\nimport os\nimport argparse\n\nimport bluepyopt as bpopt\nimport bluepyopt.ephys as ephys\n\n\ndef create_model(sim, do_replace_axon, return_locations=False):\n    \"\"\"Create model and optionally return locations dict\"\"\"\n    if sim not in ['nrn', 'arb']:\n        raise ValueError(\"Invalid simulator %s.\" % sim)\n\n    locations = dict()\n\n    morph = ephys.morphologies.NrnFileMorphology(\n        os.path.join(\n            os.path.dirname(os.path.abspath(__file__)),\n            'simple.swc'),\n        do_replace_axon=do_replace_axon)\n    somatic_loc = ephys.locations.NrnSeclistLocation(\n        'somatic',\n        seclist_name='somatic')\n    locations['somatic_loc'] = somatic_loc\n\n    if sim == 'nrn':\n        somacenter_loc = ephys.locations.NrnSeclistCompLocation(\n            name='somacenter',\n            seclist_name='somatic',\n            sec_index=0,\n            comp_x=0.5)\n    else:\n        somacenter_loc = ephys.locations.ArbLocsetLocation(\n            name='somacenter',\n            locset='(location 0 0.5)')\n    locations['somacenter_loc'] = somacenter_loc\n\n    pas_mech = ephys.mechanisms.NrnMODMechanism(\n        name='pas',\n        suffix='pas',\n        locations=[somatic_loc])\n\n    expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism(\n        name='expsyn',\n        suffix='ExpSyn',\n        locations=[somacenter_loc])\n\n    expsyn_loc = ephys.locations.NrnPointProcessLocation(\n        'expsyn_loc',\n        pprocess_mech=expsyn_mech)\n    locations['expsyn_loc'] = expsyn_loc\n\n    expsyn_tau_param = ephys.parameters.NrnPointProcessParameter(\n        name='expsyn_tau',\n        param_name='tau',\n        value=2,\n        bounds=[0, 50],\n        locations=[expsyn_loc])\n\n    cm_param = ephys.parameters.NrnSectionParameter(\n        name='cm',\n        param_name='cm',\n        value=1.0,\n        locations=[somatic_loc],\n        frozen=True)\n\n    cell = ephys.models.CellModel(\n        name='simple_cell',\n        morph=morph,\n        mechs=[pas_mech, expsyn_mech],\n        params=[cm_param, expsyn_tau_param])\n\n    if return_locations is True:\n        return cell, locations\n    else:\n        return cell\n\n\ndef main(args):\n    \"\"\"Main\"\"\"\n    if args.sim == 'nrn':\n        sim = ephys.simulators.NrnSimulator()\n    else:\n        sim = ephys.simulators.ArbSimulator()\n\n    cell, locations = create_model(sim=args.sim,\n                                   do_replace_axon=False,\n                                   return_locations=True)\n\n    stim_start = 20\n    number = 5\n    interval = 5\n\n    netstim = ephys.stimuli.NrnNetStimStimulus(\n        total_duration=200,\n        number=5,\n        interval=5,\n        start=stim_start,\n        weight=5e-4,\n        locations=[locations['expsyn_loc']])\n\n    stim_end = stim_start + interval * number\n\n    rec = ephys.recordings.CompRecording(\n        name='soma.v',\n        location=locations['somacenter_loc'],\n        variable='v')\n\n    if args.sim == 'nrn':\n        protocol = ephys.protocols.SweepProtocol(\n            'netstim_protocol',\n            [netstim],\n            [rec])\n    else:\n        protocol = ephys.protocols.ArbSweepProtocol(\n            'netstim_protocol',\n            [netstim],\n            [rec])\n\n    max_volt_feature = ephys.efeatures.eFELFeature(\n        'maximum_voltage',\n        efel_feature_name='maximum_voltage',\n        recording_names={'': 'soma.v'},\n        stim_start=stim_start,\n        stim_end=stim_end,\n        exp_mean=-50,\n        exp_std=.1)\n    max_volt_objective = ephys.objectives.SingletonObjective(\n        max_volt_feature.name,\n        max_volt_feature)\n\n    score_calc = ephys.objectivescalculators.ObjectivesCalculator(\n        [max_volt_objective])\n\n    cell_evaluator = ephys.evaluators.CellEvaluator(\n        cell_model=cell,\n        param_names=['expsyn_tau'],\n        fitness_protocols={protocol.name: protocol},\n        fitness_calculator=score_calc,\n        sim=sim)\n\n    default_param_values = {'expsyn_tau': 10.0}\n\n    print(cell_evaluator.evaluate_with_dicts(default_param_values))\n\n    optimisation = bpopt.optimisations.DEAPOptimisation(\n        evaluator=cell_evaluator,\n        offspring_size=10)\n\n    _, hall_of_fame, _, _ = optimisation.run(max_ngen=5)\n\n    best_ind = hall_of_fame[0]\n\n    print('Best individual: ', best_ind)\n    print('Fitness values: ', best_ind.fitness.values)\n\n    best_ind_dict = cell_evaluator.param_dict(best_ind)\n    responses = protocol.run(\n        cell_model=cell,\n        param_values=best_ind_dict,\n        sim=sim)\n\n    time = responses['soma.v']['time']\n    voltage = responses['soma.v']['voltage']\n\n    import matplotlib.pyplot as plt\n    plt.style.use('ggplot')\n    plt.plot(time, voltage)\n    plt.xlabel('Time (ms)')\n    plt.ylabel('Voltage (ms)')\n\n    if args.output is not None:\n        if not os.path.exists(args.output):\n            output_dir = os.path.dirname(args.output)\n            if len(output_dir) > 0:\n                os.makedirs(output_dir, exist_ok=True)\n            plt.savefig(args.output)\n\n    plt.show()\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='expsyn')\n    parser.add_argument('--sim', default='nrn', choices=['nrn', 'arb'],\n                        help='Simulator (choose either nrn or arb)')\n    parser.add_argument('-o', '--output',\n                        help='Path to store voltage trace plot to')\n    args = parser.parse_args()\n\n    if args.sim not in ['nrn', 'arb']:\n        raise argparse.ArgumentError('Simulator must be either nrn or arb')\n\n    main(args)\n"
  },
  {
    "path": "examples/expsyn/generate_acc.py",
    "content": "#!/usr/bin/env python\n\n'''Example for generating a mixed JSON/ACC Arbor cable cell description (with optional axon-replacement)\n\n $ python generate_acc.py --output-dir test_acc/ --replace-axon\n\n Will save 'simple_cell.json', 'simple_cell_label_dict.acc' and 'simple_cell_decor.acc'\n into the folder 'test_acc' that can be loaded in Arbor with:\n     'cell_json, morpho, decor, labels = \\\n        ephys.create_acc.read_acc(\"test_acc/simple_cell_cell.json\")'\n An Arbor cable cell can then be created with\n     'cell = arbor.cable_cell(morphology=morpho, decor=decor, labels=labels)'\n The resulting cable cell can be output to ACC for visual inspection \n and e.g. validating/deriving custom Arbor locset/region/iexpr \n expressions in the Arbor GUI (File > Cable cell > Load) using\n     'arbor.write_component(cell, \"simple_cell_cable_cell.acc\")'\n'''\nimport argparse\n\nfrom bluepyopt import ephys\n\nimport expsyn\n\n\ndef main():\n    '''main'''\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        description=__doc__)\n    parser.add_argument('-o', '--output-dir', dest='output_dir',\n                        help='Output directory for JSON/ACC files')\n    parser.add_argument('-ra', '--replace-axon', action='store_true',\n                        help='Replace axon with Neuron-dependent policy')\n    args = parser.parse_args()\n\n    cell = expsyn.create_model(sim='arb', do_replace_axon=args.replace_axon)\n    if args.replace_axon:\n        nrn_sim = ephys.simulators.NrnSimulator()\n        cell.instantiate_morphology_3d(nrn_sim)\n\n    param_values = {'expsyn_tau': 10.0}\n\n    # Add modcc-compiled external mechanisms catalogues here\n    # ext_catalogues = {'cat-name': 'path/to/nmodl-dir', ...}\n\n    if args.output_dir is not None:\n        cell.write_acc(args.output_dir,\n                       param_values,\n                       # ext_catalogues=ext_catalogues,\n                       create_mod_morph=True)\n    else:\n        output = cell.create_acc(\n            param_values,\n            template='acc/*_template.jinja2',\n            # ext_catalogues=ext_catalogues,\n            create_mod_morph=True)\n        for el, val in output.items():\n            print(\"%s:\\n%s\\n\" % (el, val))\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/expsyn/simple.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n"
  },
  {
    "path": "examples/graupnerbrunelstdp/checkpoints/.gitignore",
    "content": "/*.pkl\n/checkpoint.*\n"
  },
  {
    "path": "examples/graupnerbrunelstdp/figures/.gitignore",
    "content": "/*.eps\n"
  },
  {
    "path": "examples/graupnerbrunelstdp/gbevaluator.py",
    "content": "\"\"\"Main Graupner-Brunel STDP example script\"\"\"\n\nimport numpy\n\nimport bluepyopt as bpop\nimport stdputil\n\n\ndef gbParam(params):\n    \"\"\"Create the parameter set for Graupner-Brunel model from an *individual*.\n\n    :param individual: iterable\n    :rtype : dict\n    \"\"\"\n    gbparam = dict(\n        theta_d=1.0,\n        theta_p=1.3,\n        rho_star=0.5,\n        beta=0.75)  # Fixed params\n\n    for param_name, param_value in params:\n        gbparam[param_name] = param_value\n\n    return gbparam\n\n\nclass GraupnerBrunelEvaluator(bpop.evaluators.Evaluator):\n\n    \"\"\"Graupner-Brunel Evaluator\"\"\"\n\n    def __init__(self):\n        \"\"\"Constructor\"\"\"\n\n        super(GraupnerBrunelEvaluator, self).__init__()\n        # Graupner-Brunel model parameters and boundaries,\n        # from (Graupner and Brunel, 2012)\n        self.graup_params = [('tau_ca', 1e-3, 100e-3),\n                             ('C_pre', 0.1, 20.0),\n                             ('C_post', 0.1, 50.0),\n                             ('gamma_d', 5.0, 5000.0),\n                             ('gamma_p', 5.0, 2500.0),\n                             ('sigma', 0.35, 70.7),\n                             ('tau', 2.5, 2500.0),\n                             ('D', 0.0, 50e-3),\n                             ('b', 1.0, 100.0)]\n\n        self.params = [bpop.parameters.Parameter\n                       (param_name, bounds=(min_bound, max_bound))\n                       for param_name, min_bound, max_bound in self.\n                       graup_params]\n\n        self.param_names = [param.name for param in self.params]\n\n        self.protocols, self.sg, self.stdev, self.stderr = \\\n            stdputil.load_neviansakmann()\n\n        self.objectives = [bpop.objectives.Objective(protocol.prot_id)\n                           for protocol in self.protocols]\n\n    def get_param_dict(self, param_values):\n        \"\"\"Build dictionary of parameters for the Graupner-Brunel model from an\n        ordered list of values (i.e. an individual).\n\n        :param param_values: iterable\n            Parameters list\n        \"\"\"\n        return gbParam(zip(self.param_names, param_values))\n\n    def compute_synaptic_gain_with_lists(self, param_values):\n        \"\"\"Compute synaptic gain for all protocols.\n\n        :param param_values: iterable\n            Parameters list\n        \"\"\"\n        param_dict = self.get_param_dict(param_values)\n\n        syn_gain = [stdputil.protocol_outcome(protocol, param_dict)\n                    for protocol in self.protocols]\n\n        return syn_gain\n\n    def evaluate_with_lists(self, param_values):\n        \"\"\"Evaluate individual\n\n        :param param_values: iterable\n            Parameters list\n        \"\"\"\n        param_dict = self.get_param_dict(param_values)\n\n        err = []\n        for protocol, sg, stderr in \\\n                zip(self.protocols, self.sg, self.stderr):\n            res = stdputil.protocol_outcome(protocol, param_dict)\n\n            err.append(numpy.abs(sg - res) / stderr)\n\n        return err\n"
  },
  {
    "path": "examples/graupnerbrunelstdp/graupnerbrunelstdp.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import bluepyopt as bpop\\n\",\n    \"import gbevaluator\\n\",\n    \"import stdputil\\n\",\n    \"import numpy as np\\n\",\n    \"import run_fit\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"evaluator = gbevaluator.GraupnerBrunelEvaluator()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"opt = bpop.optimisations.DEAPOptimisation(evaluator, offspring_size=100,                                                                                                                                            \\n\",\n    \"                                          eta=20, mutpb=0.3, cxpb=0.7)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"_, hof, log, hst = opt.run(max_ngen=200)   \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'C_post': 0.48314077418341633,\\n\",\n       \" 'C_pre': 0.7897761163017487,\\n\",\n       \" 'D': 0.043495200524733116,\\n\",\n       \" 'b': 21.765640344877976,\\n\",\n       \" 'beta': 0.75,\\n\",\n       \" 'gamma_d': 1178.419751433852,\\n\",\n       \" 'gamma_p': 2302.4928211408605,\\n\",\n       \" 'rho_star': 0.5,\\n\",\n       \" 'sigma': 14.567793393352874,\\n\",\n       \" 'tau': 2499.1107653632594,\\n\",\n       \" 'tau_ca': 0.06578285231852728,\\n\",\n       \" 'theta_d': 1.0,\\n\",\n       \" 'theta_p': 1.3}\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"best_ind_dict = evaluator.get_param_dict(hof[0])\\n\",\n    \"best_ind_dict\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"good_solutions = [evaluator.get_param_dict(ind) for ind in hst.genealogy_history.itervalues() \\n\",\n    \"                  if np.all(np.array(ind.fitness.values) < 1)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"protocols, sg, _, stderr = stdputil.load_neviansakmann()\\n\",\n    \"dt = np.array([float(p.prot_id[:3]) for p in protocols])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Sl/93ff4O/+jmlvbHYm2zYMgy9/+cvDj91uNxdccAE/+MEP+KM/+qMR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\\nAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fe3e89fec10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"run_fit.plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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uhSAQAAAKHHp+C9bds2paenFy5ffvmlFixYoMTERL377rv+LiP8wDVQ\\nt2ghrVkjLVzo7Aly4oSUmirdfLP3sRJQdRQUuH+fliZ16WJC+E8/BaZMAAAAQCjyKXi3a9fObenU\\nqZNGjhyp2bNna968ef4uI/zANUSFhUk2m3T//dLOndKgQc5tH38stW0rTZ0q5eRUfDkRPFwf1sTG\\nmq8FBWZU/ORk6d//Dky5AAAAgFDjU/D2JCkpSVu2bPHnKeEnriHK5jJQVpMm0vvvm8Xxan5urvTn\\nP5sA/uGHFVtOBA/Xe2bZMunpp6WICPP94cPSkCHSLbfQQwIAAAAojU/BOysry23JzMzU7t27NXny\\n5OLTjSAoeAreDoMGmdbvxx5zDiL43Xdm7u9f/1rat69iyongVKOGuTe++UZyTOErSStXSldfLU2a\\nRPdzAAAAwBOfgne9evVUv379wiUmJkbJycnavHmzXn75ZX+XEX5QWvCWpNq1Tavm9u1Sjx7O9f/v\\n/0lt2kiPP064qkpKGmivRQtp1SozEJ9jBoe8POnZZ6XERDNwX9F3wwEAAICqzqfgvX79eq1bt65w\\n+eSTT7Rz50599913uv7668t8vo0bN2rQoEFq0qSJwsLCtHz5cl+KBS+KvuPtTXKytH69Ge3cMQVe\\nbq70zDPSlVdKixaZ0a5RuXl6WGOzSXfeKe3ZI/3pT1LNmmb98eNmarquXSXeOAEAAACcfArePXr0\\ncFu6d++u1q1bK7y0ic49OHfunNq3b6/58+fL5qk5FuVyKS3ermw2adgw8/7upEmmq7FkRj+/7z6p\\nc2dp40ZryorgUNo9U7u2md97505p8GDn+i+/NKOf3323tH+/9eUEAAAAgt0lJ+Xly5drwIABql69\\neqkt0oNch8m+BP3791f//v0lSXYmkrZEWYO3Q1SUael+4AHpj380g2xJ0rZt0o03SrfdJv3lL6aV\\nHJXLpd4zLVqY1xHWrJEeeUTatcusf/ttaelSafRo0zIeE2NteQEAAIBgdcnBe/DgwTp+/LgaNWqk\\nwa7NW0XYbDZdpB9y0ClLV/OStGghvfee9MknZh7n7dvN+vffl/7v/0wX4xkzpGbN/FJcBJlLeVjT\\nt6+5L15+WZo5UzpzxryiMHeueT3hiSekhx92jowOAAAAVBWXHMEKCgrUqFGjws+eFkJ3cPK1xbuo\\nnj2ltDRp4UIpPt6sKyiQXn/dvP/96KMmcCH0+dL5pHp1acwYMyL+4487Q/aPP0oTJ0pJSWbsAP4z\\nAQAAgKrEt5eyg0D/N/urxic1StyW2CBR64av83p878W9tfeM5wmIx18/XuOvH+9x+57Te9TnH328\\nXmPtsLVKaphUbP3Fi1K1atLczXM1d/Ncj8f7sx6egrcv9ahWTbr/ful3v5PmzZNmfDRX5zvM1QVJ\\nc+zS3KelunWlOnXMtYL99+FQkb8PT4KpHl813Cv9UtQBa6Tqnzi3l1aP43l79I+GfVRvppSVJWVn\\nm/UHJd3zX+n+J6RZV63V2KFJHntg8Ptwoh4G9XCyuh4FuQXqcKCD7rjuDm3fvl1z5xa/VijUQ6oc\\nvw+JeriiHgb1cKIeTtTDKE89cg/nej3OVz4F7zFjxqhVq1YaM2aM2/oXX3xRGRkZeuGFF/xSOG9O\\nvXdKKtplta1ZoiOiSz3+xLkTOvLTEY/bsy5keT0+vyDf6/GOfYp65x3TLTspSbpyZJbXc/izHp66\\nmvtaD0mqVcvM7Xy2XZae/cp5DrukLElZP5vv69YI3t9H0WtU1O/Dk2Cqx7lqR6Qo8/3J85LOu1+j\\ntDIWliFchecp3C5pwqP5WvSsNH26mSu+aADn9+FEPQzq4X4NS+vxtjTk8SEaPny4Bg4cqL179yox\\nMbFYGYO+Hqokvw9Rj6L7eEM9nKiHQT3cr0E9jBPnTujI50ekHUU2nC9x93LzKXi/9957JQ6w1rVr\\nVz3zzDMVErxjfxOrGk1LbvGOqx1X6vFxteOUeT7T4/aomlEet0lSeFi4mtRtUuo+Rb3xhnThgvTf\\n/0r/fTtK4d2bKCpKiowsuYyludR6eGrx9rUebmWoF6UmdZvo4kX31k2HjENxmjVL+sMfTCt4eerh\\nrYzlrUdUzSiv5wjm+6roNfxRjwPHM3XunPm+USPTjdz1GqWVsaQy5OZKmZnmqwrC9c030u23S+3a\\nmTECBg1y3p/8Ptz3oR7Uo+g1rKrHuS/OKceeo5ZJLSVJ2dnZ2rdvX7HgHez1cL2GN9TD/RrUw7kP\\n9aAeRa9BPZz7VJp6dM2Uurqvzz2cq1PPnyr1+LKy2X0YRjwiIkJff/21WrVq5bY+IyNDV199tc6f\\nL9tjgnPnzikjI0N2u10dO3bU3Llz1atXL8XExKhZkdG60tPTlZKSorS0NHXs2LGsRQ+4fv2kjz4q\\nvr51a2nyZDM/so+zsnk1YoR5D1uSvvnG2lHId+8200wtWeLe0t6ggRkZ3VsAR/AYNUp65RXz+T//\\nMeHYH+x2ad06acoUafNm920dOphB2IYMMa80AKh4rVq10p///GelpqaqoKBADRo00Icffqhrr702\\n0EUDAMByVuVNn+bxbtWqlT788MNi61etWqUWLVqU+Xxbt25Vhw4dlJKSIpvNpgkTJqhjx46aNm2a\\nL8ULaq5BtHNn5+fdu6WhQ00g/vvff2kR9CPXxyu+jGpeFq1bS2++aQL+3Xc7r3fmjOmanpBgWjcZ\\nhC24+WtAvqJsNqlPH2nTJmnVKqlTJ+e2bdtMC3hysnlQ5O+/AwDebd26VYcOHVJGRoZmzZqlcePG\\nKS8vT+389eQNAIAqyqcINn78eE2cOFHTpk3Thg0btGHDBk2dOlWPPfaYxo0bV+bz9ejRo3BEdNdl\\n0aJFvhQvqLmGmXXrpI8/lrp3d6779lvp3nvN9F1z5piu2/7gGvj9GaK88RbAp0+XLr/czPt84EDF\\nlAdlY1Xwdj1n//7SV19Jy5dLrg8U9+41vTRatTID+BV9fQGANbZs2aKuXbtqypQpmjRpkho0aKDU\\n1FRFuMwDmJGRoU6uT8wAAECpfAreI0aM0Jw5c/Taa6+pV69e6tWrl9588029/PLLGjlypL/LWKkU\\nbXnu00f69FNp/XozVZfDkSNmaq7LLzfTMh075r/rVlTwdnAE8J07pd//3tmFODvbhKqWLc36HUUH\\nNkBAVdQ9Y7NJv/qVtHWrtHq11KOHc9uhQ+bhTPPm5vWF06etKwcAKSsrS+3bt5dkpg594403ig2k\\nGhsbqzZt2gSieAAAhCyfOx0/+OCDOnz4sE6cOKGsrCzt27dPw4YN82fZKiVPLc89e5rw/dln0m23\\nOddnZkrPPGO6Z99/v+mS7ouK7GruSVKS9I9/SBkZ0sMPOweUu3jRBPNrrpFuucX0AvBlDmlYpyIe\\n1ths0s03S598Yrqh33qrc9vp0+ad8GbNzLvnu3ZZXx6gKmrZsqVq1aolSVq4cKGGDh2qtm3bSpJe\\nffVVrVq1Sk888YT69u0byGICABByyh3BYmNjVYeRsi5Zaa2IN9wg/fvfpnX4vvukGr8M3J6bK732\\nmtSmjTRggLRypXuIL00gupp7kpBgWroPHpSmTZNiYpzbVq6U+vaV2rY1A3vRxThwAvnwo2tX6f/+\\nT9q+XUpNdT4sOn/e3BfJyebv4KOPeEgD+NOQIUN05swZLVy4UDk5OYVjraxcuVJnzpzRgAEDlJOT\\no379+gW4pAAAhJZLHj+7Q4cOsl1iYktPT/e5QJXdpbY8t2kjvfqqNHOmCakvv+x83/vDD83SqpU0\\nerR0zz1SdClT1QWyq7knDRuad73/+EfzUGHOHBPGJfNe+KhRZjC2kSPNSOiXXx7Q4lY5wXDPXHON\\n9Pbb0lNPmb+D116TfvrJbHP8HVx1lTR2rPS735m55QH4rlq1alqwYEGx9StWrNADDzwgSTp16pQu\\nXLhQ0UUDACCkXXLwHjx4sJXlqDLKGmYaNzZdzZ94Qlq4UHrxRen77822jAwTOCZPloYPNyG8dWv/\\nXLci1a4tjRkjPfigae3/3/81XY0l6exZ6dlnpdmzzRRTo0ebd4CDrQ6VUTDdM1dcIT3/vBkN/7XX\\nTAh3/B188415OPPHP5q/g//5H89/BwB8M2TIEG3evFkHDhxQixYttHPnTl3O01AAAC6ZT/N4B1Ko\\nz+PdrZszVObllX3O7osXpRUrTPBYu7b49t69pQcekAYPlmrWdK6/807pX/8yn7//3gxWFczS0kwd\\n//nP4lNKJSWZOg4bZlrNYY177pEWLzafd+0KrjCbny+9/74J446/J1e9epkHOYMHS9WrV3z5AAAA\\nEJqCah5v+K68rYjVqkmDBpkByL7+2rTuuXavXbdOuusuqWlTMyr6nj3+uW5FS0kxoe/gQdMdPS7O\\nuW3PHmnCBKlJEzNN2YYNvOdrtWC7Z8LDpd/8xgxG+NVX5iGBy2xHWr9euuMO83rC5MlMWQcAAIDA\\n8il4X7x4UbNnz1aXLl102WWXKSYmxm2BZ/4cXfyqq8y734cPm/ejr7zSue30abOudWvTNfvdd53b\\ngi1EeRMXZwZgO3jQvOvrOuVabq5zXZs20ty50smTgSpp5RMqDzM6d5Zef91MwTd3rpSY6Nx2/Lh5\\nP/yKK6SbbpLeekvKyQlcWQEAAFA1+RT9ZsyYoblz5+rOO+9UZmamxo8fr1//+tcKCwvT9OnT/VzE\\nysWK0cXr15fGjzctwevXm1GgHaOhS2aecFeBmk6sPGrUMPVav95MqTZhgtSggXO7ayv4oEHSe+9J\\njP1TPqHWSyImRho3ztwfa9dKv/2t81UOu92sGzpUuuwyM3DfF1+EzsMFAAAAhDafIthbb72lhQsX\\nasKECQoPD1dqaqpeffVVTZ06VV988YW/y1ipWPkPfZvNtP6+/baz9a9Nm+L7ub77HYqSksxga4cP\\nF28Fz88301D99rdmYLo//MF0RSZglV2oBW8Hm82MdfDuu6anxJNPSi1bOrdnZZkpya6/3vQaefZZ\\n6ejRwJUXAAAAlZ9Pwfv48eNq27atJKlOnTrKzMyUJN16661asWKF/0pXCTnCjNWtzg0bmta/b76R\\nNm40A5E1amRGfa4sA5JFRLi3gk+aZMK2ww8/SC+9JF17rZn3+emnnSNho3ShGrxdxcdLf/qT9O23\\npufHvfeaUfQddu0y903Tpiasv/qqGUkfAAAA8Cef4l/Tpk117NgxSVLLli310UcfSZK2bNmimqHe\\nnGoxR1fzigoyNpsZSX3xYunECenvf6+Y61a0pCQz7drBg9JHH5lB1yIjndt37zZTsl1xhXTddWY0\\n7CNHAlfeUBOqwdvBZpO6d5cWLTLvfS9aZL53sNvNA5yRI824ArfdZkbUP3cucGUGAABA5eFT8B4y\\nZIjW/jKX1cMPP6wpU6boyiuv1LBhwzRixAi/FrCycbQihnqQCVbVqkl9+0pvvmkC1muvSTfe6L7P\\nl1+ad+KbNTPb5s83DyXgrrJ2z69Tx7R8f/qpaQmfMkVq1cq5PS9PWr7c9KaIizMPcZYvl86fD1yZ\\nAQAAENr8Mo/3F198oc8//1xXXnmlfvWrX/mjXB6F+jzeHTtK27aZuYWLzk8N6+zfLy1ZIr3zjvTf\\n/xbfHhZm3hW//XYzOJtrl/Wq6ne/Mz8zSfruO6lFi8CWx0p2u5k7/u23zT1S0jvfdepIAweaacwG\\nDJDq1q34cgIAAMBaQTWP99NPP61FixYVfn/ddddp/PjxOnXqlGbNmuW3wlVGFfWON9xdcYXpar59\\nu3mvd/p0M9WaQ0GBmQP9wQfNyOjXXy/NmuWcB70qqgzveF8qm03q1MkMSHjwoLPbef36zn1+/ln6\\n17+kO++UYmPNA5rFi81YAgAAAIA3PsW/v/3tb2rtmlp+cdVVV2nBggXlLlRlVtHveKO41q3N3OA7\\nd5og/sQT7qNeS2aqqcceM/u2aSM9/rjpou46HVxlV5WCt6tq1Uzvh1deMa8rfPCB6ZoeE+Pc58IF\\nM3r+PfeY7uh9+0rz5pmeAQAAAEBRPo9qHh8fX2x9bGxs4aBrwe7kSfP+7+bN0sWLFXdd3vEOHjab\\ndM010lNPmXd909LM+76/DNhfaPduM3DbddeZ98Lvv9/ME/7LYP5VQlW9X2vUkG65xQzGduKEmQv8\\noYfMaOkO+fnSxx9Ljzxi3hVv00b64x+lTz4x74sDAAAAPgXvZs2aadOmTcXWb9q0SY1D5OXY2283\\nAaprV9OIWZ3UAAAgAElEQVRt9K67zIjfVj83oKt5cLLZzPv3M2ead8AzMqQ5c8zI166h8+hR88Dm\\nt78107L17Gnmgd6xo/INRlbZ6lNe4eFmyrH5880c8ps2SRMmmNcYXO3ebeaZ79XL/LflzjulN96Q\\nTp0KTLkBAAAQeD7Fv5EjR2rs2LF6/fXXdeDAAR04cECLFi3SuHHjNHLkSH+X0RK7dzs/nz1rBlS6\\n914zqFb79qZr8YYN/m+xoqt5aGjZ0ox8/umnztHRb73VzB3ukJ9v7pFJk0zL+eWXSw88IC1bVjnm\\ngq6qXc0vRViYeWg3e7bpXr5jh5knvls394dqmZnmvfBhw6RGjczDnUmTTAs5o6QDAABUHT6Nam63\\n2/XYY49p3rx5yv1laO6IiAhNmjRJU6dO9XshXflrlLlGjUwLVI0aZr5nT92G69aVevQwLV29e5tu\\nyOVprU5ONoN71a0rZWX5fh4ERk6OCdsrV0qrVpmW8ZKEhZmQ1aePWW64QapVq2LLWl633y4tXWo+\\nHzxoutmjdGfOSKtXm3fDP/zQ80OYiAjTo6JvX7Nccw09YQAAAALNqlHNyzWd2M8//6xdu3YpMjJS\\nV155pWrWrOm3gnnirx9EbKx0+rSZImnPHjNw1ocfmmXrVs/HNWhguhc7gnhSUtlaA9u0Ma3t0dHS\\njz/6XHwEiW+/NQF81SozEvaFCyXvV6OGaSF1BPHOnU3X5WD229+ad9kl6dAhqWnTwJYnFOXnm3Ek\\nVqyQPvrITCXoSWysuTd69jQP+8r63xYAAACUX1AG70Dw1w+iQQMzDVDLlsVbLU+eNP9I/vBD89Xb\\nu5nx8SaA9+hhupm2bu39H8tJSdLevVK9epWjOzKcsrPNgFpr1phBuHbs8Lxv3bqmFbx7d3PfdOni\\n3o09GLgG78OHzTRrKJ9Tp8y98dFH5j45fNjzvo0aSTfeaP7b0qOHdNVVtIgDAABYjeD9C3/9IGJi\\nTPC98koThD2x26VvvjFzPK9fb4KVt5bqBg1MoOrWzSwdO0quHQESE00raf36zP9b2Z04Ye6ZtWvN\\nsn+/531r1DCt4I4gfsMN5uFMIP3mN+Z9dYngbQW73fS2WbPGLOvXm7nCPYmJMfeH4yFfu3bmvgEA\\nAID/ELx/4a8fRP36JkAnJpp//F6qixel//zHBPF168zgW9nZnvePiDCtmY4wPmyYeQc0JsZ8RdWx\\nf7+5Z9auNV9PnPC8r81mxhPo1s1MY3btteYhUUV2Pf71r6X/9//M5yNHzMCDsE5enpSebsYQ2LBB\\n+uwz7+NARERInTpJ11/vXC67rOLKCwAAUBkRvH/hrx9EdLT5R21SkvsI52WVmytt2WKmFvrsM7Nc\\nShfyhg2ZXqgqs9vNaNgbNzoXTwO1OdSvbx7iXHutWbp0MfeRVYYMkf79b/P56FH3uathPcdDvk8/\\nNUH8009L/29LQoIzhF93Ha3iAAAAZWVV8A7y4Z2s46/5tGvUMK3ZN9wgTZxopgvbvdsZwj/7rOQu\\nxhUwDh2CmM0mtWpllnvvNeuOHTMPcBxBfPt25/Rzkgldq1ebxaFlS2cQT0kxQatOHWvKi4pVrZr5\\nnaakSOPGmXvh669NAN+82SxF/9vy/fdmWbLEfF+jhhkt3XGeTp3Mu+KEcQAAgIpV5YO3vwNFWJiZ\\nMiw52czpLJluups2meWLL8w/lseO9e91Efri482AZr/9rfk+K0v66isz4v4XX5ivRXtJfPedWd5+\\n23xvs5nXJzp0MOMLdOhglgYNyl6e0OoLU/mFhZkQfc010ujRZt2JE84Q/sUXpvdNTo7zmNxcM0uD\\n60wNjjDeqZMzkF99tVS9esXWBwAAoCqpssHb0ZJYES15TZpId9xhFuBSRUVJN91kFskE4e+/NwHc\\nsaSnu09h5hiwa88e6Z//dK5v3twZxtu3N++PX3659x4frsGbFu/gFBcnDR5sFsm8J/7f/zqDeFqa\\nuRdcf5eewnhysjPYO5a4uIqtDwAAQGVVZYO3v7qaAxXFZpOuuMIsd91l1uXmmqC1ZYuZI3rbNvN9\\nbq77sQcOmMXxzrZkuqRffbVZ2rZ1fm7UyGwneIee6tWdrdiOVvGffjL3xdatJog7wrir3FzzPvl/\\n/uO+vlEj8/qCaxhv04ZXZQAAAMqqygdvAgVCWY0apstwp07OdXl50s6dJmylpzsD+blz7sf+/LNp\\nFf3iC/f1jRqZAL5unXMdfyehq25dMx/4jTc612VlmXsiLc0E8v/8x4Rx1zEFJOnkSed0Zw7Vqkkt\\nWpgAXnSpW7di6gQAABBqCN4EClQy1aubVsp27aR77jHrCgrMqOnp6aZF/OuvpR07TNf1ok6edA/d\\nEn8nlU1UlJkPvEcP57qcHGnXLnN/OJbt26XTp92PvXhR+vZbsyxf7r6tadPiYTwpyXRZ5x4CAABV\\nWZUN3o6WHbqaoyoICzODriUmOrupS6Yb8s6dJoQ7wvjXX5vw7RAbK9WrV/FlRsWKjDRjALjOmmG3\\nmwHcXIP4N9+YmRtcB3FzOHzYLK4t5JJ5raFVKzMXvWM0f8fnyy4jlAMAgMqvygZvWrwB0zXYMR2Z\\nq5MnTQDfv990UQ6vsv+lqNpsNhOML7tMuvlm5/qCAungQdNC7lh27jRfS5pr/OefS36HXJJq13aG\\nccdyxRVmTvJmzZj6DAAAVA5V9p/TBG/As0aNpN69A10KBKuwMBOMExKkAQOc6+1289DGNZB/+615\\nzWH/ftNNvahz50xL+vbtxbfZbGZWCMe1ii4EcwAAECqqbPCuyOnEAKAqsNnM+9xxcVLPnu7b8vLM\\nyPqOIJ6R4R7K8/OLn89ud3Zf/+yzkq/nCObNmpl3zB1Lkybm62WXmQHhAAAAAqnKBm+mEwOAilO9\\nurMreVH5+SaUOwL5gQNm4D/HcupUyed0DeaeVKsmxce7h3LXYN64sQnntWr5oZIAAAAeVMngzfzE\\nABA8wsOlli3N0q9f8e3nzhUP45cSzCXTvb20cC6Z8Q4c77N7Wxo1YswDAABQdlXynw8EbwAIHbVr\\nS8nJZinJuXPSkSMmXDu+ui5HjpjR2b356SezfPut9/1sNqlhQ2cIj40133v62rAhQR0AABC86WoO\\nACGudm3ndHmeXLggHTvmHsYPHZKOH3dfMjO9X8tuNy3s3lrZi6pf33Moj4kx2+vXd/9cuzYPhgEA\\nqEyqfPDmHzYAUPnVrOkcDd2bnBzTOl40kLsux46Zr7m5l3bts2fNUlpruqvw8OJhvKSA7liio6Wo\\nKOdSvfqlXwsAAFivSgZvx4jmEsEbAOAUGXlpAd1ul7KypNOnTeu3t6+Oz6W1prvKzy97y3rRejhC\\nuGsoLxrQS/q+bl3T4l6njjkP/58EAKD8qmTwpqs5AKA8bDYTUqOjzaBwlyI3Vzpzxj2Unz0r/fCD\\ns1Xcsbiuy8oqe/lycpyt9+VhszlDuOOr6+eyrKtVywR5xxIRQagHAFQdQRO858+fr9mzZ+v48eNq\\n166d/vrXv6pz586WXIuu5gCAilajhpnaLD6+bMfl50s//ug9nP/0k2lRz8pyLq7fu/b0Kgu7Xfr5\\nZ7P4m81mwrdrGC8azr2tLynI16xpFtfPJS08dAcAVLSgCN7vvPOOJkyYoFdeeUVdunTR888/r379\\n+mnv3r1q2LCh369HV3MAQKgID3cOxuYLu13KznYP4p5CemamCdnnzjkDt+Oz4+uFC/6pl93ubJmv\\naOHhlxbQvQX5GjXMu/SOxYrvw8P5dwoAVBZBEbyff/55jRo1SsOGDZMkLViwQCtWrNCiRYs0ceJE\\nv1+PruYAgKrC0V28dm2pcePyny8/v3gY9xbUHV+zs51BOyfH8/fnz5e/jJdSB0c9gl14eMnBvHp1\\nqVo1sz08/NI++3u/ko6pVs3828rx1fVzRW7jgQWAYBPw4J2Xl6e0tDQ98cQThetsNptuuukmbd68\\n2euxZRkh1lV2tvMz/2EGAODShYc732+3QkGBaVUvKZyXFtgvXPC8lLbddQkmjocEgegZEOrKGtht\\nNs9ffd1W3uOtPLfk3MfTZ3/sV5HXCtR+RfNEeb4P1LGUw/n97t2yRMCD9+nTp3Xx4kXFxcW5rY+L\\ni9OePXu8HuttztZLs0Q2W2p5TwIUs2TJEqWmcm/B/7i3YJVgubfCwpzvbcfEVPz17XYzEF5p4Tw3\\nV8rLcy4V+X1urnTxognkjq+Oz76+z2+tJZIq/t66eNEseXkVfmlUmMDcW4AvAh68PbHb7bJ5aY4e\\nN26cpKKP21NVtj++JWrenD9W+F+w/AMWlQ/3FqzCvWXYbM73uENRQYHnUF7S50vd71KPcVy/oMD5\\n+e23l+j221NL3FbSV2/bynu8t212u1kcn4t+vdRtqEgEb5TXkl8WV2WY/7MMAh68GzZsqGrVqulE\\nkTlPTp48WawV3NXzzz+vF17oWK5rf/65NGNGuU4BAAAQNBzdpqtXD3RJnNLTpaeeCnQpKo6nkO6P\\nUO+P4x29IhzbPH32tu1S9/PHObzt9/zz0tixFX/dkra5/v59/T5Qx1btcpiGW9ftJ0+m6913U+Rv\\nAQ/e1atXV0pKitauXatBgwZJMq3da9eu1ZgxY7we+49/lO/agwZJTZqU7xwAAACAg+u71bDW0qXS\\n8OGBLgUqm/R06d13/X/egAdvSRo/fryGDx+ulJSUwunEsrOzdc899wS6aAAAAAAAlEtQBO877rhD\\np0+f1tSpU3XixAm1b99eq1evVmxsbLF9c34Z1nPXrl3lvm5mZqbS09PLfR6gKO4tWIV7C1bh3oJV\\nuLdgFe4tWMGRM3P8PJ2EzW4PrWEg3nrrLQ0dOjTQxQAAAAAAVFJvvvmm7r77br+dL+SC9+nTp7V6\\n9WolJCQoMjIy0MUBAAAAAFQSOTk5+v7779WvXz81bNjQb+cNueANAAAAAEAoYbxFAAAAAAAsRPAG\\nAAAAAMBCBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBC\\nBG8AAAAAACxE8AYAAAAAwEIEbwAAAAAALETwBgAAAADAQgRvAAAAAAAsRPAGAAAAAMBCBG8AAAAA\\nACxE8AYAAAAAwEIEbwAAgtDixYsVFhZWbKlWrZpOnjxZbP/ly5crJSVFkZGRat68uaZPn66LFy8G\\noOQAAKCo8EAXAAAAlMxms+nPf/6zEhIS3NbXq1fP7ftVq1ZpyJAh6t27t1588UXt2LFDTz75pE6d\\nOqX58+dXYIkBAEBJCN4AAASx/v37q2PHjl73mTBhgtq3b6/Vq1crLMx0Zqtbt66efvppPfLII0pM\\nTKyIogIAAA/oag4AQJD7+eefVVBQUOK2Xbt2affu3XrggQcKQ7ckPfTQQyooKNDSpUsL191zzz2q\\nW7euDh06pFtvvVV169ZVs2bN9NJLL0mSduzYoT59+qhOnTpKSEjQkiVL3K6Vn5+vGTNmKDExUZGR\\nkWrYsKG6d++utWvXWlBrAAAqD4I3AABBym63q2fPnoqKilKtWrV02223KSMjw22fbdu2yWazKSUl\\nxW19fHy8mjZtqm3bthWus9lsKigo0IABA9S8eXM999xzSkhI0MMPP6zFixdrwIAB6ty5s5599llF\\nRUVp+PDhOnDgQOHx06ZN08yZM9WnTx/Nnz9fkydPVvPmzZWenm7tDwIAgBBHV3MAAIJQrVq1dO+9\\n96pXr16KiopSWlqa5syZoxtuuEHp6elq0qSJJOnYsWOSTNAuKj4+XkePHnVbd/78eQ0bNkwTJ06U\\nJKWmpqpx48a677779M477+g3v/mNJOmmm25S69attXjxYk2dOlWStHLlSt1yyy16+eWXLas3AACV\\nEcEbAIAgdPvtt+v2228v/H7QoEG6+eabdeONN+qpp54q7B6ek5MjSapZs2axc0REROinn34qtv6+\\n++4r/BwdHa2kpCR99913haFbkhITE1WvXj3t27evcF29evX0zTffKCMjQ61atSp/JQEAqCLoag4A\\nQADl5eXpxIkTboun97lvuOEGXXvttfr4448L10VGRkqSLly4UGz/8+fPF253iIiIUIMGDdzWRUdH\\nq2nTpsWOj46O1tmzZwu/nzlzpn788UclJibqmmuu0aRJk7Rjx45LrywAAFUUwRsAgAD6/PPPFR8f\\nr8aNGxd+PXz4sMf9mzVrph9++KHwe0cXc0eXc1fHjh1T48aN3dZVq1atxPN6Wm+32ws/d+/eXd99\\n951ef/11tW3bVq+++qo6duyoRYsWea4gAACgqzkAAIHUvn17txZsSbrssss87r9v3z7Fxsa6HW+3\\n27V161Z16tSpcP2xY8d0+PBhjRo1yq/lrVevnoYPH67hw4crOztb3bt31/Tp0zVixAi/XgcAgMqE\\n4A0AQABFR0erd+/exdafPn1aDRs2dFu3cuVKpaWlaezYsYXrkpOT1bp1a73yyisaNWqUbDabJOml\\nl15SWFiY23vb5fXDDz8oJiam8PtatWqpVatWXlvoAQAAwRsAgKDUtWtXdejQQZ06dVJ0dLTS0tL0\\n+uuvq3nz5nr88cfd9n3uued02223qW/fvrrrrru0Y8cOzZ8/XyNHjlTr1q39Vqbk5GT17NlTKSkp\\niomJ0ZYtW7R06VKNGTPGb9cAAKAyIngDABCE7rrrLq1YsUJr1qxRdna24uPjNWrUKE2dOtWtq7kk\\n3XLLLVq2bJlmzJihMWPGKDY2VpMnT9aUKVOKndfRIn4p6202m9v6Rx55RMuXL9eaNWt04cIFNW/e\\nXH/5y1/06KOPlrO2AABUbja766gpAAAAAADArxjVHAAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwB\\nAAAAALBQyI1qfvr0aa1evVoJCQmKjIwMdHEAAAAAAJVETk6Ovv/+e/Xr108NGzb023lDLnivXr1a\\nQ4cODXQxAAAAAACV1Jtvvqm7777bb+cLueCdkJAgyfwg2rRpU65zjRs3Ts8//7wfSgW4496CVbi3\\nYBXuLViFewtW4d6CFXbt2qWhQ4cW5k5/Cbng7ehe3qZNG3Xs2LFc54qOji73OYCScG/BKtxbsAr3\\nFqzCvQWrcG/BSv5+rZnB1QAAAAAAsBDBGwAAAAAACwU8eD/99NPq0qWLoqKiFBcXpyFDhmjv3r2B\\nLhYAAAAAAH4R8OC9ceNGPfzww/ryyy/18ccfKy8vTzfffLNycnIsv3Zqaqrl10DVxL0Fq3BvwSrc\\nW7AK9xaswr2FUGKz2+32QBfC1enTp9WoUSN9+umn6tatW7Ht6enpSklJUVpaGoMpAAAAAAD8xqq8\\nGfAW76J+/PFH2Ww2xcTEBLooAAAAAACUW1AFb7vdrrFjx6pbt25KTk4OdHEAAAAAACi3oJrH+6GH\\nHtLOnTu1adOmQBcFAAAAAAC/CJrgPXr0aK1cuVIbN25UfHx8qfuPGzdO0dHRbutSU1MZZAEAAAAA\\nUKolS5ZoyZIlbusyMzMtuVZQDK42evRovf/++9qwYYNatGjhdV8GVwMAAAAAWMGqvBnwFu+HHnpI\\nS5Ys0fLly1W7dm2dOHFCkhQdHa2IiIgAlw4AAAAAgPIJ+OBqCxYsUFZWlnr27KnGjRsXLv/6178C\\nXTQAAAAAAMot4C3eBQUFgS4CAAAAAACWCXiLNwAAAAAAlRnBGwAAAAAACxG8AQAAAACwEMEbAAAA\\nAAALEbwBAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwB\\nAAAAALAQwRsAAAAAAAsRvAEAAAAAsBDBGwAAAAAACxG8AQAAAACwEMEbAAAAAAALEbwBAAAAALAQ\\nwRsAAAAAAAsRvAEAAAAAsFB4oAsAAACCS3Z2tubNm6f4+Hht375dc+fODXSRAAAIabR4A5dowQIp\\nMVGaMUPKzw90aQDAOnfeeacGDRqk4cOHa/fu3dq7d2+giwQAQEjzqcXbbrdr6dKlWr9+vU6ePKmC\\nggK37cuWLfNL4YBg8uST0pEj0vTp0iefSG+/LcXHB7pUAOBfr776qs6fP6/k5GRJpvV73759SkxM\\nDHDJAAAIXT61eI8dO1a///3vtX//ftWpU0fR0dFuC1AZnTvn/PzJJ1KHDtL69QErDgBY4plnntGI\\nESMkSQUFBdq+fbvq168f4FIBABDafGrxfuONN7Rs2TINHDjQ3+UBgpbd7v79iRPSTTeZrudPPCGF\\n8eIGgBC3detWHTp0SBkZGZo1a5aOHj2qvLw8tWvXLtBFAwAgpPkUFaKjo9WiRQt/lwUICU2aSDff\\nbD4XFEhTpkgDB0qnTgW2XABQXlu2bFHXrl01ZcoUTZo0SQ0aNFBqaqoiIiICXTQAAEKaT8F7+vTp\\nmjFjhnJycvxdHiBoOVq869aVVq6UZs50tnKvXi21ayetWRO48gFAeWVlZal9+/aSTDfzN954Q2PG\\njAlwqQAACH0+dTW/4447tGTJEjVq1EgJCQmqXr262/b09HS/FA4IJo7gbbNJ1aqZlu4bbpBSU6WT\\nJ6Vjx0xL+IQJ0lNPSTVrBra8AFBWLVu2VFZWliRp4cKFGjp0qNq2bavPPvtMH3zwgTp37qyaNWsq\\nMzNTd999d4BLCwBA6PApeA8fPlxpaWkaOnSo4uLiZLPZ/F0uIOi4Bm+H3r2l//xHuuce6aOPzLo5\\nc6S1a82o523aVHgxAcBnQ4YM0ccff6yFCxcqJydH06ZNkyQ1a9ZMp06d0sCBA5Wdna17772X4A0A\\nQBn4FLxXrFih1atXq1u3bv4uDxC0SgrekplSbNUqad48adIkKTfXhPGUFGnuXGnUqOLHAEAwqlat\\nmhYsWFBsffPmzXX+/HlFRkZq2bJl6tu3bwBKBwBA6PLpHe9mzZopKirK32UBQlZYmDR2rPTVV9Iv\\nU98qJ0d68EFp0CDTDR0AQtWuXbuUl5en1atX69ChQxo9enSgiwQAQEjxqcV7zpw5mjhxohYsWKCE\\nhAQ/F+kS9e8v1ahR8rbERGndOu/H9+4t7d3refv48WbxZM8eqU8f79dYu1ZKSvK8fe5cs3ji53oc\\nPSpFR0u1a7tsD8F6lKgC6rEiJ1E9tc5r63W7cb2148e9yqztMu/3B5K9iXSunlTrT+Nlm1C57qsS\\nUQ8n6uFEPYwQrMe6des0ZswYdevWTf369TPbQ7AeJaIeTtTDiXoY1MOJejhV9nrk5no/zkc+Be+h\\nQ4cqOztbLVu2VK1atYoNrvbDDz/4pXBeeZu7KTq69ONPnJCOHPG8/ZfBZTzKz/d+vGMfb7KyvJ/D\\nj/VYv94M/FWzpvT009If/vDLiNwhVg+PKqAesTZTD6/dxk+cUNjRI6ovqb7reruks9I/F2ap593S\\nZZd5KSO/D+c1qIdBPQzq4X6NCqrH0aNH9fbbb+vKK68sXsYQqodH1MP9GtTDoB4G9XC/BvUwqko9\\n/Myn4P3CCy/4uxxlFxvrucU7Lq704+PipMxMz9tL60ofHm4mdC5tH2+ioryfw4/1+Ogjc//m50tj\\nxkjvvCO99pqUFGL18KgC6nHyWJxkLyV4F6nHxQLpxx9Nt3NJ+mpPlP5wlfTXv5rR0Iudi9+H+zWo\\nh3Mf6kE9il6jgurRuHFjbdq0qeQyhlA9PKIe7tegHs59qAf1KHoN6uHcpzLXIzfXeyOvj2x2u2PI\\nqNCQnp6ulJQUpaWlqWPHjoEuTsiYOFF67jn3dTVrStOnS48+Wvq9DSkiQrpwwczX/Z//lO3YZcuk\\n//kf97/hwYOl+fOlxo39W04AAAAAvrEqb/o0uNrBgwe9Lgg+ro9XHA+hLlyQHn9cuvZaafv2wJSr\\nqvj1r6WdO6U773Su+/e/zXRjL70kFRQErmwAAAAArOVT8E5ISNAVV1zhcUHwcQ3e774rTZjwyzve\\nktLTpU6dzFRYhQOCoRhP04ldqoYNpX/+0/z8Y2PNuqws8779DTdIO3b4p5wAAAAAgotPwXvbtm1K\\nT08vXL788kstWLBAiYmJevfdd/1dRviBa/CuXVuaPVvavFm66iqzLj9fevZZMxXW++8HpozBrrzB\\n2+G3v5V27ZJGjHCu++ILqWNH6bHHpOzs8p0fAAAAQHDxKXi3a9fObenUqZNGjhyp2bNna968ef4u\\nI/zAtSuzo6W7SxcpLc285+0Yp+7gQfPu8aBB0vffV3Qpg5u/grckNWhgBrf75BPnTAn5+dKsWdLV\\nV0urVpX/GgAAAACCg0/B25OkpCRt2bLFn6eEn7i2eLsGx5o1pWnTpK+/lvr2da7/v/8zrd9/+Ytl\\nU9mFHCuGIezRw7xf7/rwY/9+aeBA6Ve/kjIy/H9NAAAAABXLp+CdlZXltmRmZmr37t2aPHly8Xk+\\nERQ8BW+HK6+UVq8204zFx5t1OTnSn/4ktW0rrVhhTfAMRf5o8XblePixfbsJ4g4ffGBeBfjTn6Sf\\nf/bvNQEAAABUHJ+Cd7169VS/fv3CJSYmRsnJydq8ebNefvllf5cRflBSV/OibDbpjjuk3bulRx5x\\n7rd3r3TrrVL//mZk7qrKn13NS9K6tbR+vfT2284pxnJzTa+D1q2lJUt4+AEAAACEIp+C9/r167Vu\\n3brC5ZNPPtHOnTv13Xff6frrry/z+TZu3KhBgwapSZMmCgsL0/Lly30pFrworcXbVVSU9MIL0tat\\nZrRth48+kq65Rnr4YenMGWvKGcysDt6Oc6emSnv2mKneHN3PjxyRfvc70yK+dat11wcAAADgfz4F\\n7x49ergt3bt3V+vWrRUeHu5TIc6dO6f27dtr/vz5slmZaqqwsgRvhw4dpI0bTffzyy836y5elF58\\n0XRNnzdPysvzf1mDVUUEb4c6dUxL9zffmN4GDhs3Sp07mxC+f7/15QAAAABQfpeclJcvX64BAwao\\nevXqpbZIDxo0qEyF6N+/v/r37y9JstOX1hKX0tW8JI7u57/6lTRnjvT002a6q7NnTXf0v/5VevJJ\\n6WTRfc4AACAASURBVPbby3ZeXJpWrcxAdytXSmPHSt9+a9YvWSK99540erR5BzwmJrDlBAAAAODZ\\nJQfvwYMH6/jx42rUqJEGDx7scT+bzaaLFy/6pXDwH19avF1FRkqTJ5u5px9/XPrHP8z6jAzprrvM\\nHODPPOM+MnplFYhOGQMHmp/t3/4mzZghnT5t3v+eO1datMiE79GjpYiIii8bAAAAAO8uuY2yoKBA\\njRo1KvzsaSF0B6fyBm+Hxo2lxYulr76SevVyrk9Pl26+Wbrppsr5DrK/fn7lUb26CdcZGdITTzhD\\n9o8/Sn/8o5kP/LXXqlb3fwDWyM7O1jPPPKPFixdr/PjxgS4OAAAhz7eXsoNA/zf7q8YnNUrcltgg\\nUeuGr/N6fO/FvbX3zF6P28dfP17jr/f8j409p/eozz/6eL3G2mFrldQwyW2d3W5GBm/aVHpt51zN\\n3TzX4/H+rIdrcHTtEu5rPTp3ltauNQOujVg4V0ebm3qsldR5iRT5bzNIW3h4cP8+XM3d7P33oeGJ\\n0uJ1XoN3RdXjqaeS9OCD0pQp5kGI3S4dPCjdf7806d9zVXDtXNWqVfLxleX3QT2cqIcT9TDKW48z\\nr53Rw396WI/97jENHDhQe/fuVWJiots+oVAPqXL8PiTq4Yp6GNTDiXo4UQ+jPPXIPZzr9Thf+RS8\\nx4wZo1atWmnMmDFu61988UVlZGTohRde8EvhvDn13impaLfatmaJjogu9fgT507oyE9HPG7PupDl\\n9fj8gnyvxzv2KWr2bGniRDN4VruxWToS7vkc/qyH6zversHR13o4ztOvn3R/jSzN/NT9HDky84BL\\nUoQteH8fRa/h9Ry1TT28Be+KrEfTptLrr0vjxkmPPSatWmW2n/k5S7p4RGd/Kvn4YP77KHoNb+eg\\nHu7X8IZ6uF+Dehge65Em6bxUq7F5epedna19+/YVC95BXw+Xa3hDPdyvQT0M6mFQD/drUA+jUtXj\\n8yPSjiIbzpd6qE98Ct7vvfdeiQOsde3aVc8880yFBO/Y38SqRtOSW7zjaseVenxc7Thlns/0uD2q\\nZpTX48PDwtWkbpNS9ylq7Vrz9eefpU1ro2Tr2kS1a0t16xYfnMyf9fDUVdrXeriKjogqPMe5c1JW\\nlnvQ/25/nO66y7TOXnVV+erhrYzlrUdUzSiv5ziyPzjvq2uuMYOvff65NHWqtDY7SspyniM8XIqK\\nliIjnGUsTSj8PqiH+zW8oR7u16Aezn1Kqsfxz48rqn+UompGqaCgQNu3b1f9+vVLLGMw18P1Gt5Q\\nD/drUA/nPtSDehS9BvVw7lNp6tE1U+rqvj73cK5OPX+q1OPLymb3YRjxiIgIff3112rVqpXb+oyM\\nDF199dU6f75sjwnOnTunjIwM2e12dezYUXPnzlWvXr0UExOjZs2aue2bnp6ulJQUpaWlqWPHjmUt\\nesDdfLO0Zk3x9bVqSQ8+aN7VjSv9PimzESNM66hkurq3aeP/azicOyctWGAGXDt50rneMUK6twAe\\nrPLzzTvWkpnb/LPPAlsebz75xPyMi5bxqqvMwHh33mnCOAAUtXXrVt1www2aPHmyatSooaNHj+q1\\n117T6dOnFcHojQCAKsCqvOnTBFCtWrXShx9+WGz9qlWr1KJFizKfb+vWrerQoYNSUlJks9k0YcIE\\ndezYUdOmTfOleEHN9THHffdJNWuaz9nZZrquK64w03QdOODf63rqam6F2rWlCROkfftM1/pfxuST\\n3W7mBL/6amnwYGnzZmvL4U/BMLjaperZU/r0U2n1aqlLF+f6b76Rhg6VEhPN6OhlfD4GoArYsmWL\\nunbtqilTpmjSpElq0KCBUlNT3UJ3RkaGOnXqFMBSAgAQenwK3uPHj9fEiRM1bdo0bdiwQRs2bNDU\\nqVP12GOPady4cWU+X48ePQpHRHddFi1a5EvxgpprgPvf/zXh9JFHnCNU5+RI8+ZJLVuakLR9u/+v\\nW1HB0VMAl6T335e6dpVuvFFascK9fMEolIK3ZMp4883SF1+Yn29Xly40+/dL//M/UosWZjqyn38O\\nXDkBBJesrCy1b99ekpnB5I033ig2nktsbKzaWNltCgCASsin4D1ixAjNmTNHr732mnr16qVevXrp\\nzTff1Msvv6yRI0f6u4yVStGW58aNpRdeMOF03DgzX7YkXbwovfWW1L691L+/tG5d+cJpIIOjI4Dv\\n32+CXhOX1zE2/n/27jy8qTJh//id7qXYQulCERBBdpBCAUGLCyIIo7ziKFipODg/dGRQWRQcVARn\\nFBmRqrwoijIuMIgL7+gIWmUR2UQEURRkR6isBSxQSiltfn88pkm6N02apP1+ruu5kpxzcs5zSoDe\\nebZV0k03mXHK77zju0th+VvwtrFYzBrgq1dLK1eayfBsDh0yfy5Nm5rlyQ4e9F49AfiGFi1aqM7v\\nSyLMmTNHqamp6tixoyTp9ddf16effqqJEyfqhhtu8GY1AQDwOy4Fb0m6//77lZGRoSNHjujUqVPa\\ns2ePhg0b5s661UilBbiEBBNKf/lFevJJqUED+770dOn6680SXgsXuhZOHQN/0UncqkudOubLhT17\\npLlzpTZt7Pt+/FEaNsy09D/3nHTypHfqWBpfb5Evj8Viehd89pm0YYN06632fSdPSlOnSs2aSXfd\\nZdZkB1A7DRo0SMePH9ecOXOUk5NTOORryZIlOn78uPr376+cnBz1c/wWDwAAlKvKESw2NlZ169Z1\\nR11qhdLW07aJjZUmTzZrMv/v/5ox3zYbN0p33GG2PfOMdKwSk+35UottSIg0fLgZc/yf/0g9e9r3\\nHThglltr3NhMNrdtm/fq6ciXfn5V1bWr9OGH5uc/bJh90ri8PGnePCkpyYwT//hj5y9sANR8gYGB\\nmj17tkaMGKHRo0cXbl+8eLFuvPFGSdKxY8eUm5vrrSoCAOCXKhy8O3furC5dulSooHQVDXB16kh/\\n/au0Y4f07ruS44/111+lxx6TmjQxAbYiLZS+GBwDAqT/+R9pzRozGdgf/mDfd/asmRm9XTszVnnx\\nYt8Jgb7y86uqdu2kt96S9u0zn6foaPu+lSvNn03r1mYohK/1QABQvQYNGqR169bp448/VvPmzbV1\\n61ZvVwkAAL9S4eXEpkyZUuGTenI2cn9fTqxXL/syT+fP21sby2O1SitWmInXPv64eNfnK6+UHnzQ\\ndCEu6ZwpKSbAS2asdbNmLt+CR+3YIc2cKb35ZvFJvy67TBo5Urr7bueQWB3OnjVj1SXpuuvMmPua\\n5uxZ6e23TdDevt15X3i4+QyNHGlaxAEAAICayFN506V1vL3J34N3crJp4ZVM115X1lPeu1d6+WXp\\n9del335z3hcXZ1rB/9//M0HVZsgQ6b33zPN9+6RLLnGp+tUmK8usOz5zphkT7ig0VLr9dunee83P\\nszpaoLOzJduIit69pWXLPH9NbykokD79VEpLK/k+u3UzwwCGDDE9MwAAAICawqfW8YbryhvjXRGX\\nXmomIMvIMOsxt29v33f0qDRtmtSypZmQbeFCKTfXN7ualyUqSho92rSAf/SRuReb3FwzFvnqq829\\nv/CCdOKEZ+vjX19PVU1AgOn2v3SpGQf+wANSZKR9/4YN0j33mHH4Y8ZIW7Z4r64AAACAP3Ap+uXn\\n52v69Onq3r27GjZsqOjoaKeC0hVdTqwqIiJMq++WLabr8+DBzt3Mly83k7E1biy9/759u7dmNXdF\\nYKA0cKAJgVu3mqDn+BHbts1sa9TIzMi9fLlnxoL72xcX7tKunRnecPCg9NprUufO9n0nT5ovPS6/\\n3LSCv/IKY8EBAACAkrgUwaZMmaIZM2ZoyJAhysrK0tixY3XrrbcqICBAkydPdnMVaxZPBDiLxYw7\\nXrjQtIL/85+mxdsmM7P48f6obVuz5Nqvv5oW71697PtsreDXX296BDz+uGkt9wR//flVRUSENGKE\\nmVn/66/NbOihofb9335rxn8nJEh33il98YXvTIYHAAAAeJtLwXv+/PmaM2eOxo0bp6CgIKWkpOj1\\n11/XpEmT9PXXX7u7jjWKLXh7KrzFxUmPPGImx1qxwoSgkBD7/oAA+1hlfxUWJg0damZC37rVdEmv\\nX9++f/9+6emnzYzcV15pZkevaktsbW3xLspika64wsyGfvCgWfLOcehLbq60YIGZib5ZM/MFCJMf\\nAwAAoLZzKXgfPnxYHTt2lCTVrVtXWVlZkqSbbrpJixcvdl/taiBbK6Cnw5vFYtZinj/fBKS0NKlf\\nP9MaHhXl2WtXp7Ztzb0dPGhmbe/f37kr/bp1ZiKwhATTFf8//5HOnav8dQjexUVHmyXvNm6UNm+W\\nHnpIatDAvv/AAfMFSPv2UmKi+ezt3++9+gIAAADe4lLwbty4sQ4dOiRJatGihT7//HNJ0oYNGxTq\\n2P8UxXi6xbskDRqYVuHPPpPGjau+61ansDAzy/aSJaa7/fTp0u/fDUkyLbHvvy8NGiTFx5uu0kuW\\nmCXdKqI2Ta7mik6dzHjvgwelDz4wk7M5fgHy/ffShAlmNv2rrza9EIoOgQAAAABqKpeC96BBg7Ts\\n93WGHnjgAT3xxBNq2bKlhg0bpnvuucetFaxpvBG8a5uEBPMFw/ffS5s2mS8dYmPt+0+dkt55x4TD\\nhg3N0mtffCFduFD6OWnxrpiQEOmPf5Q++cSMxX/xRdM13dGqVfZeCAMGSG+8QQgHAABAzeaWdby/\\n/vprrV27Vi1bttTNN9/sjnqVyt/X8e7SRfruOxNQcnO9XZvaIy/PzIy+cKH0f/9nwndRsbGmRfyW\\nW8xa3Y6dN06etM+mfuONZp1rVNzu3Wbs9/z50s8/F98fGChdc40J7YMGmVAOAAAAVDefWsd76tSp\\nmjt3buHrHj16aOzYsTp27JimTZvmtsrVRNU1xhvOgoPN+O833zRrnX/0kZSSYmbrtjl2zCyZNWCA\\nCeF33GHGjWdl0eJdVS1a2Cda++47MwFg48b2/fn5Zim4v/5Vuvhi6aqrzAz2+/Z5rcoAAACA27gU\\nvF999VW1adOm2Pb27dtr9uzZVa5UTUZXc+8LDTVrg//73yaEv/++dNttZpy4zenTpnU8JcWE8Ftv\\nte/jz851Fot9orVffjFLkz3yiNS8uf0Yq1Vau9YMF7j0UjN+/LHHzER5+fneqzsAAADgKpdnNU8o\\noS9obGxs4aRrvm7TJmnUKOnll6W9e6vvurbgHeDSTx7uVqeOCd3vv2/GGS9aZCZec1yeLC9PWrnS\\ne3WsqQICzPjvf/5T2rXLzIz+xBNSu3bOx/3wg/TMM2ZpuIYNpbvvlt57z/REAAAAAPxBkCtvatKk\\nidasWaNLL73UafuaNWvUqFEjt1TM04YPN7/Q27RqZcbu3nijWYYrPNwz16Wrue+KiDDjiwcNMmF7\\n9Wqz/Nh//uO8DJbjRG1wD4vFtGx36iQ99ZQZB75okRkS8M039uMyM6W33zYlKEjq1ctMkte3r9Sh\\nA3+vAAAA4JtcCt4jRozQ6NGjlZeXp969e0uSli1bpvHjx2ucn6xXdfiw8+sdO0x56SXT5fiaa+xB\\nvHVr9/1CT1dz/xAcLF13nSkvvGDGJf/nP9KhQ9LEid6uXc3Xpo35OU+cKB05Yiaz++QTKT1dOnPG\\nHHPhgrRihSmSmZCtTx/phhvMIxO0AQAAwFe4NKu51WrVo48+qpdeeknnf18IOSwsTBMmTNCkSZPc\\nXklH7pplLi7OTKZVt66ZaXzNmtLHj158sZnlundvE8QuucTly6pdO2nbNikykq6yQGXl5prlyD75\\nRPrvf6U9e0o/tmNHE8JvuMGsHV6nTvXVEwAAAP7JU7OaV2k5sTNnzmjbtm0KDw9Xy5YtFeq4/pKH\\nuOsHERtruq02b26WOsrKkpYtMy1rn30mZWSU/t4WLUwAtwXxhg0rft02baTt26WoKOm331yuPlDr\\nWa3m71J6ulmH/csvpezsko8NCTHjya+5xpSePZ1ntAcAAAAkHw3e3uCuH0SDBtKJE9Jll0k7dzrv\\ns1rNskeffSZ9/rlpYcvJKf1c7dqZX+aTk01p2rT0Y1u3Nl3a69Uza0MDcI/z583M5198YcqGDc7L\\nwDkKCpK6dTN/b6+91kzcdtFF1VpdAAAA+CCC9+/c9YOIjjbBt2VLE4TLcv68tH69WWd4xQrzy/3v\\nPexL1KSJPYQnJ0vt20uBgWZfq1Ym6Nevb4I/AM84ccL8nf3iC9ObZffu0o8NDJSSksxkbT17muIn\\n80QCAADAjTwVvF2aXK0mqMwkZyEh5hfyXr2kJ5+Uzp416wwvX27Khg322col6cABacECUyTTrbxn\\nTxPCba3rLCcGeFZ0tFkq7rbbzOtffzXLwtnK9u32Y/PzzezpjjOoN21qWsJtQTwx0Uy6BwAAAFRW\\nrQ3etqDsSgCuU8fMmtynj3l9+rT09ddm+anVq83zs2ftx2dlmW7rn31m38as5kD1uvhi6c47TZHM\\nygZffWVC+JdfmuEljvbvN+Xdd83rsDCpa1cTwnv0MC3kTZvydxkAAADlq7XB253Lel10kX32ZMms\\nAb15sz2Ir14tHT3q/J6oqKpfF4DrGjaUBg82RTKTLa5bZy/ffOP8Bdq5c/a/zzYxMSaMd+1qgnjX\\nribgE8YBAADgiODtgV+Qg4PNxE3dukljxphr7dplliz7+mtp715p5Ej3XxeA62JipJtvNkUy64T/\\n8IMJ4WvXmse9e53fk5lZvDdLfLw9hCclSZ060TIOAABQ29Xa4F2VruaVZbGYSdxatpT+9CfPXw9A\\n1QUFSV26mPLXv5pthw+bL8++/daUjRtN+HZ05Ii0ZIkpNlFR0uWX20unTlKHDixpBgAAUFvU2uDt\\nyRZvADVTw4bSLbeYIpl/R/bvNwHcMYwXXbEgK8ssS7hqlX2bxSK1aGFC+OWXSx07mqUJW7QwoR8A\\nAAA1R6399Y7gDaCqLBbpkktMufVWs81qlfbtMyF882bp++9Nl/UDB5zfaxuCsmuX9OGH9u0hIaZ3\\nTNu2JojbHlu1MhO8AQAAwP/U2uBt62pO8AbgThaLdOmlptx+u337yZMmgNvK999LP/4o5eQ4v//8\\neemnn0xxFBBgzmkL461b24ewxMfzbxkAAIAvq7XB29bizXraAKpD/frSNdeYYpOfL+3ebQ/h27aZ\\nZc127DCrIzgqKDDH7t4t/fe/zvvq1pUuu8yE8KKPhHIAAADvq/XBm19IAXhLYKDpQt6qlXPr+IUL\\n0p49JoTbwvi2bdLPP0vZ2cXPc+aM6da+eXPxfbZQftllpsW8WTNTLr3UdJGvU8dTdwcAAACbWhu8\\n6WoOwFcFBdkDuW0iN8n8u3XggAnhO3fay65dZqmz/Pzi5yorlEtSXJw9iNtCue1106ZSeLj77w8A\\nAKC2qbXBm67mAPxNQIB9Mrcbb3Tel5dnJnXbtcsexssL5ZJ09Kgp33xT8v6YGKlJE6lx49ILreYA\\nAABlq/XBmxZvADVBcLB9srX+/Z335eWZZc/27bOXvXvtzw8etP+bWFRmpinffVf6tevXNwHcFtAv\\nvlhKSDDLr9ke4+NNHQEAAGqjWhm8HX/BJHgDqOmCg8364C1alLw/N7d4MLeVjAwTzC9cKP38J0+a\\nsmVL2fWIiTEh3FZsobzo83r1+LcZAADULLU+eNPVHEBtFxpqby0vSX6+6Y6ekVFyOXBA+vVXsxRa\\nWWyt5z/+WPZxQUEmpMfG2h9tpaTXMTHmPQAAAL6qVv6qQos3AFRcYKBpkU5IkLp1K/mYggITqm1h\\n/MgR6dAh6fBhU2zPDx0yLexluXDB/r6Kql/fHsQbNJCio822sh7r1SOwAwCA6lErf+WwzWguEbwB\\nwB0CAswM6XFxUpcupR9ntUqnTjmHcsdgfviwdOyYvZQX0m1s3d137KhcvSMjSw7m9eubYB4ZKUVF\\nmUfH57bHkJDKXQ8AANROtTJ409UcALzDYjGhNSpKatOm7GOtVrNuuWMQz8ws+/WpU5Wrz6lTpvzy\\ni2v3ExZWfjiPipIuusisqW4rERHOr+vWNefiy2AAAGomnwnes2bN0vTp03X48GF16tRJM2fOVLfS\\n+jRWEV3NAcD3WSz2UHrppRV7T26uvfX75EnpxImyHx2f5+VVvo7nzply9Gjl31uU4/2WFM5Lel2n\\njinh4aaU9Nz2SLAHAMB7fCJ4L1y4UOPGjdNrr72m7t27Ky0tTf369dOOHTsUExPj9uvR1RwAaqbQ\\nUPvs6JVha113DOJZWaY1vOhjSdtsj6Wtl17ROpw+bYqn2EJ5eSHd9jwszPxMbaUqr4OC+D8XAFB7\\n+UTwTktL03333adhw4ZJkmbPnq3Fixdr7ty5Gj9+vNuvR4s3AMCRY2tzkyauncNqlc6eLT2sZ2dL\\nZ86Y4vjcsThuP3267GXcXJGTY4o3WCzlB/XgYDNuPji4Ys8rc2x57wsKKl4CA81jQAC/LwAAqsbr\\nwTsvL08bN27UxIkTC7dZLBb16dNH69at88g1GeMNAHA3i8V0/46IMDPAu8P58yWH9OxsE8xtQfrs\\n2eLPS9pW0vOKTmBXVVard4N/VZUUyEsL6u46NjDQlIAAzz66+5y2YrG4/mgrAFBTeD14Z2ZmKj8/\\nX/Hx8U7b4+PjtX379jLf+957rl3z3Dn7c/5RBwD4qpAQU+rX99w1CgrM/4slhfNz50wwtz3aSnmv\\nK/uec+ecvxT3RRcuuL8HAspX1QDv6UfHLwjc9dqd5/Kn144cX5e1z5PvpR6+U4+yeOLYffsqfs7K\\n8HrwLo3VapWlnJ/OkCFVvcoCWSwpVT0JUMyCBQuUksJnC+7HZwvuFhBgxnN/9JH3PltWqxkfn5dn\\nWvnz8ir/vKrvswXr/Hz787K2VWS7KxP21UwLJLn22bLNy1OV+RNQk7n+2QKqm9eDd0xMjAIDA3Xk\\nyBGn7UePHi3WCu5ozJgxkqKKbE1R5f7yLVBiIn9Z4X6EI3gKny14ijc/WxaLvXt1eLhXquAxBQWV\\nD++O2woKzOuqPrrjHJV5tFrtj+vXL1DXrinFtvvjI3wNwRtVteD34ijLI1fyevAODg5WUlKSli1b\\npoEDB0oyrd3Lli3Tgw8+WOr70tLStHJllypde9486amnqnQKAACAUgUEmOECtdnAgdLHH3u7Fu5h\\ntdqLLZAX3e7O1548ty+9LvozLul5Sa8nTZKmTHHtvZV57a33Uo+yuefY4g23v/yySVOnJlX85BXk\\n9eAtSWPHjtXdd9+tpKSkwuXEzp49qz/96U9lvm/MmKpdd8WKmvfNOgAAADzDcVxyYKB36wJp5kyp\\nb19v1wI1zaZN0tSp7j+vTwTvwYMHKzMzU5MmTdKRI0eUmJio9PR0xcbGertqAAAAAABUiU8Eb0ka\\nOXKkRo4cWe5xOb+vQ7Jt27YqXzMrK0ubNm2q8nmAovhswVP4bMFT+GzBU/hswVP4bMETbDkzx83r\\nX1qs1sr0jve++fPnKzU11dvVAAAAAADUUPPmzdPQoUPddj6/C96ZmZlKT09Xs2bNFM4AbQAAAACA\\nm+Tk5Gjfvn3q16+fYmJi3HZevwveAAAAAAD4kwBvVwAAAAAAgJqM4A0AAAAAgAcRvAEAAAAA8CCC\\nNwAAAAAAHkTwBgAAAADAgwjeAAAAAAB4EMEbAAAAAAAPIngDAAAAAOBBBG8AAAAAADyI4A0AAAAA\\ngAcRvAEAAAAA8CCCNwAAAAAAHkTwBgAAAADAgwjeAAAAAAB4EMEbAAAAAAAPIngDAAAAAOBBBG8A\\nAHzQqlWr9D//8z9q2rSpwsPDlZCQoP79+2vt2rUlHr927VolJycrIiJCCQkJeuihh5SdnV3NtQYA\\nACUJ8nYFAABAcTt27FBgYKDuv/9+NWzYUCdPntS8efN09dVXa8mSJerbt2/hsZs3b1afPn3Url07\\npaWlKSMjQ88995x27dqlxYsXe/EuAACAJFmsVqvV25UAAADly8nJUfPmzdW5c2ctWbKkcPuAAQP0\\nww8/aPv27YqIiJAkvfHGG7r33nuVnp6uPn36eKvKAABAdDUHAMBvhIeHKzY2Vr/99lvhttOnT2vp\\n0qW66667CkO3JA0bNkwRERF67733CrdNnjxZAQEB2rlzp1JTU1WvXj3FxcVp0qRJkqQDBw7olltu\\nUVRUlBISEjRjxoxidZg5c6Y6dOigiIgIRUdHq1u3bnr33Xc9eNcAAPg/gjcAAD7s9OnTOn78uLZv\\n366JEyfqp59+cmrB3rJliy5cuKCkpCSn9wUHBysxMVHfffdd4TaLxSJJGjJkiCRp2rRp6tGjh55+\\n+mm98MIL6tu3rxo3bqxp06apZcuWeuSRR7R69erC98+ZM0cPPfSQOnTooBdffFFPPfWUOnfurPXr\\n13vyRwAAgN9jjDcAAD5s8ODBSk9PlySFhITovvvu0+OPP164/9ChQ7JYLEpISCj23oSEBKfgbNOj\\nRw+9/PLLkqQRI0aoWbNmevjhhzVt2jSNGzdOkpSSkqJGjRpp7ty5Sk5OliQtWbJEHTp0oIUbAIBK\\nosUbAAAfNm3aNH3xxReaO3euevbsqfPnzysvL69wf05OjiQpNDS02HvDwsIK99tYLBb9+c9/Lnwd\\nEBCgrl27ymq1avjw4YXbo6Ki1Lp1a+3Zs6dwW7169ZSRkaFvv/3WbfcHAEBtQIs3AABelJeXpxMn\\nTjhti42NVUCA+W788ssvL9w+dOhQdenSRcOHDy8cux0eHi5Jys3NLXbuc+fOFe531LRpU6fXUVFR\\nCgsLU3R0dLHtjnWbMGGCli1bpu7du+uyyy5T3759deedd+rKK6+szC0DAFDr0OINAIAXrV27VgkJ\\nCWrUqFHhY0ZGRonHBgcHa+DAgVq0aFFh0E5ISJDVatWhQ4eKHX/o0CE1atSo2PbAwMAKbZMkx8VP\\n2rRpo+3bt2vhwoXq1auXFi1apOTkZE2ZMqVC9woAQG1F8AYAwIsSExO1dOlSffHFF4WPDRs2QOpz\\n9gAAIABJREFULPX4s2fPymq16vTp05KkDh06KCgoqFj377y8PG3evFmJiYlurW94eLhuv/12vfHG\\nG9q/f7/+8Ic/6Omnn9b58+fdeh0AAGoSgjcAAF4UFRWl3r17O5WQkBAdO3as2LG//fabPvzwQzVt\\n2lQxMTGSpMjISPXp00fz5s1TdnZ24bFvv/22srOzNXjwYLfVtWiX+KCgILVt21YFBQVO484BAIAz\\nxngDAOCD+vfvr8aNG+uKK65QXFycfvnlF7355ps6dOiQ09rckvT000/rqquu0tVXX617771XGRkZ\\nev7559WvXz/dcMMNbqtT37591bBhQ1111VWKj4/X1q1bNWvWLN18881Oa4gDAABnBG8AAHzQn//8\\nZ7377rt64YUX9Ntvv6l+/frq2bOnHnnkkWKTmXXu3FlLly7VhAkTNHbsWF100UUaMWKEnnnmmQpf\\nz7bGd1nb//KXv2j+/PlKS0vTmTNn1LhxY40ePVqPPfaYazcJAEAtYbE6zpoCAAAAAADcijHeAAAA\\nAAB4EMEbAAAAAAAPIngDAAAAAOBBBG8AAAAAADzI72Y1z8zMVHp6upo1a6bw8HBvVwcAAAAAUEPk\\n5ORo37596tevn2JiYtx2Xr8L3unp6UpNTfV2NQAAAAAANdS8efM0dOhQt53P74J3s2bNJJkfRNu2\\nbat0rjFjxigtLc0NtQKc8dmCp/DZgqfw2YKn8NmCp/DZgids27ZNqamphbnTXfwueNu6l7dt21Zd\\nunSp0rmioqKqfA6gJHy24Cl8tuApfLbgKXy24Cl8tuBJ7h7WzORqAAAAAAB4EMEbAAAAAAAP8nrw\\nnjp1qrp3767IyEjFx8dr0KBB2rFjh7erBQAAAACAW3g9eK9atUoPPPCA1q9fr6VLlyovL099+/ZV\\nTk6Ox6+dkpLi8WugduKzBU/hswVP4bMFT+GzBU/hswV/YrFarVZvV8JRZmam4uLi9NVXXyk5ObnY\\n/k2bNikpKUkbN25kMgUAAAAAgNt4Km96vcW7qN9++00Wi0XR0dHergoAAAAAAFXmU8HbarVq9OjR\\nSk5OVrt27bxdHQAAAAAAqsyn1vEeOXKktm7dqjVr1ni7KgAAAAAAuIXPBO9Ro0ZpyZIlWrVqlRIS\\nEso9fsyYMYqKinLalpKSwiQLAAAAAIByLViwQAsWLHDalpWV5ZFr+cTkaqNGjdJHH32klStXqnnz\\n5mUey+RqAAAAAABP8FTe9HqL98iRI7VgwQJ9/PHHioiI0JEjRyRJUVFRCgsL83LtAAAAAACoGq9P\\nrjZ79mydOnVK1157rRo1alRY3nvvPW9XDQAAAACAKvN6i3dBQYG3qwAAAAAAgMd4vcUbAAAAAICa\\njOANAAAAAIAHEbwBAAAAAPAggjcAAAAAAB5E8AYAAAAAwIMI3gAAAAAAeBDBGwAAAAAADyJ4AwAA\\nAADgQQRvAAAAAAA8iOANv/PBB9KoUdLOnd6uCQAAAACUj+ANv3LqlJSaKs2aJV1xhbRypbdrBAAA\\nAABlI3jDr5w8KeXm2p/fcIP0zjverRMAAAAAlIXgDb+WlycNGyZNnixZrd6uDQAAAAAUR/CGX3EM\\n13Xq2J9PmSLdfbe9NRwAAAAAfAXBG37FMXgPHCg9/7xksZjX77wj9esnnTjhnboBAAAAQEkI3vAr\\njsHbYpHGjjWznIeFmW0rV0rdukk//uid+gEAAABAUQRv+JWSxnHfeqv05ZdSXJx5vWeP1KOH9H//\\nV61VAwAAAIASEbzht2xdzCWztNiGDVKXLuZ1drYJ5E89JRUUeKd+AAAAACARvOFninY1d9S0qbRq\\nlZSSYt/25JPS7bdLZ85UT/0AAAAAoCiCN/xKWcFbMjOdz58vTZtm379okdSzp7RzZ/XUEQD83dmz\\nZ/Xss8/qrbfe0tixY71dHQAA/F6QK2+yWq364IMPtGLFCh09elQFRfryLlq0yC2VA4oqL3jbto8f\\nL3XoIN15p5SVZSZbS0qS/vUv6Y9/rJ66AoC/GjJkiKZNm6Z27dppwIAB2rFjh1q1auXtagEA4Ldc\\navEePXq07rrrLu3du1d169ZVVFSUUwE8paTJ1UozYIC0fr3Upo15ffq0dNttZib0vDzP1A8A/N3r\\nr7+uc+fOqV27dpJM6/eePXu8XCsAAPybSy3e77zzjhYtWqQBAwa4uz5AhZXW4u2odWsz6dqIEdK7\\n75ptaWkmkC9cKDVu7Nk6AoC/efbZZ/X3v/9dklRQUKDvv/9e9evX93KtAADwby61eEdFRal58+bu\\nrgtQrop0NS+qbl3p3/+WZs2SgoPNtrVrpc6dpS++cH8dAcBfffvttzpw4IB27dqladOmacyYMcrL\\ny1OnTp28XTUAAPyaS8F78uTJmjJlinJyctxdH6BMrgRv27EjR0pr1pjZzyUpM1Pq10+aOJGu5wAg\\nSRs2bNCVV16pJ554QhMmTFCDBg2UkpKisLAwb1cNAAC/5lLwHjx4sE6ePKm4uDh17NhRXbp0cSqA\\np7gavG26dZM2bTLjv23nmzpVSk6Wdu92Tx0BwF+dOnVKiYmJkkw383feeUcPPvigl2sFAID/c2mM\\n9913362NGzcqNTVV8fHxsriSgAAXVGZytdI0aCD997/Sc89Jjz8uXbggffONlJgovfyydNddVb8G\\nAPijFi1a6NSpU5KkOXPmKDU1VR07dtTq1av1ySefqFu3bgoNDVVWVpaGDh3q5doCAOA/XAreixcv\\nVnp6upKTk91dH6DCqvJ9T0CANGGC1Lu3WXJs1y7pzBlp2DApPd2MB2eCfgC1zaBBg7R06VLNmTNH\\nOTk5evLJJyVJTZo00bFjxzRgwACdPXtWw4cPJ3gDAFAJLgXvJk2aKDIy0t11AcpV1a7mRdm6nj/4\\noPTmm2bb/Plm8rW33zZd0AGgtggMDNTs2bOLbb/kkkt07tw5hYeHa9GiRbrhhhu8UDsAAPyXS2O8\\nn3/+eY0fP1779u1zc3WAsrk7eEvSRRdJ//qXtGCBZPs+ae9e6eqrpYcflphDEEBtt23bNuXl5Sk9\\nPV0HDhzQqFGjvF0lAAD8ikst3qmpqTp79qxatGihOnXqKNi2RtPvTpw44ZbKlenGG6WQkJL3tWol\\nLV9e9vt795Z27Ch9/9ixppRm+3bp+uvLvsayZWYh6dLMmGFKadx4H1artHSpFBNjltEq5If3YeMU\\nvN1wH3ccnKHb6szQiXPS+fOSrJKel7JelALrSyEd+FwV4j7suA+D+7CrgfexfPlyPfjgg0pOTla/\\nfv3Mfj+8jxJxH3bchx33YXAfdtyHXU2/j/Pny36fi1wK3i+88IK761F5x46Vvq8ig3OPHJF+/bX0\\n/b9PLlOqCxfKfr/tmLKcOlX2Odx4H+++a8YyS9Lw4dLzz0v168vv7qPUydXcdB9Bh39VXLH3STom\\nHdkSpXq5UmhoGeeoZZ+rUnEfztfgPgzuw/Cz+zh48KD+/e9/q2XLlsXr6Ef3USruw/ka3IfBfRjc\\nh/M1uA+jttyHm7k8q7nXxcaW3uIdH1/+++Pjpays0veXN4Y9KEi6+OLyjylLZGTZ53DjfWzebN/0\\nr39Jn34qvfKKdEtb/7qPUlu8PfDnkXdBOnHCvsb3jsx4je5mxoKXumpeLftclYr7cL4G92E/hvvw\\nu/to1KiR1qxZU3Id/eg+SsV9OF+D+7Afw31wH0WvwX3Yj6nJ93H+fNmNvC6yWK2VX6Bp//79Ze5v\\n2rSpyxUqz6ZNm5SUlKSNGzeyZngljB9vls8qavBgaeZMKa5YM69v2rzZ3lX+vvukEuYAcqsLF6Rn\\nn5WeesoewAMDpTFjpMmTpYgIz14fAAAAQPXxVN50aXK1Zs2a6dJLLy21wPc4fr3Srp39+Xvvmdfz\\n57tnjWxP88TkamUJCjJrfW/YIF1+udmWny9Nny517GiWHgMAAACAsrgUvL/77jtt2rSpsKxfv16z\\nZ89Wq1at9P7777u7jnADx8D66qvSvHlSgwbm9fHjUmqqma9u507v1K+iqjt423TqZML3P/5hH+O9\\nd6/5maWmSkePVl9dAAAAAPgXl4J3p06dnErXrl01YsQITZ8+XS+99JK76wg3KCiwPw8IkIYOlbZu\\nNV3NbT7/3LTiTp4snTtX7VWsEG+2yoeESI89Jv3wg3Tttfbt8+dLbduasfP+0GsAAAAAQPVyKXiX\\npnXr1tqwYYM7Twk3KamlOC5OWrhQ+ugjyTYsPzdXmjJF6tDBN7tRe6vF25FtdYI33vh9ZniZSdju\\nuUfq1ct5IjsAAAAAcCl4nzp1yqlkZWXp559/1uOPP158uRH4hLIC68CBpvV7wgT7BIK7d5tu1Lff\\nXq2z7FeKt4K37dr33CP9/LOUkmLfvmaNlJQk/fWvJowDAAAAgEvBu169eqpfv35hiY6OVrt27bRu\\n3Tq98sor7q4j3KBoV/OiIiLM7N2bN0tXX23f/sEHpoX3H/+QcnI8X8/y+EKLt6O4OOnf/za9A1q1\\nMtsKCqSXXzavX3vNTMYGAAAAoPZyKXivWLFCy5cvLyxffvmltm7dqt27d6tnz57uriPcoKKBtX17\\n6csvzVrVMTFm29mz0hNPSG3amK7p3hzH7GvB26ZvX2nLFmnaNPsSY8ePmyXPrrhCWrvWu/UDAAAA\\n4D0uBe9rrrnGqfTq1Utt2rRRUHkLnZdi1apVGjhwoC6++GIFBATo448/duk8KF1lAqvFIt19t7R9\\nuzRqlFm3WpL275fuuMO0iG/c6Lm6lsWXJy8LCTHrpW/fLt15p337xo3SVVeZiex27/Ze/QAAAAB4\\nR4WD98cff6y8vLzC52WVysrOzlZiYqJmzZoliy81Y9Yg5XU1L0l0tDRzpvT996ZF12b1aqlbN2n4\\n8Oof/+2rLd6OLr7YzHT+5Zdmlnib9983s5+PHcv4bwAAAKA2qXAT9S233KLDhw8rLi5Ot9xyS6nH\\nWSwW5VdyUOuNN96oG2+8UZJk9eUmTT9WlcDavr302WfSkiUmNO7YYc735pvSu+9KDz1kJmazzfBd\\nXXw1eNtcc420aZM0Z4705JPSsWNSXp6UlmaWHnviCTMJm21dcAAAAAA1U4VbvAsKChQXF1f4vLRS\\n2dCN6lHVlmKLRfrDH8w45rQ0qV49s/3cOTOuuXlz6Z//9PwEbP7Q4u0oKEi6/35p1y6zBnhYmNn+\\n22/SuHGmBfzf/3bukQAAAACgZnHrOt7wXY6BtaJdzUsSEiKNHm2C5Nix5rVkguSECVLLltLrr0sX\\nLlStvqXxt+BtExlpZobfudOMn7fVfe9eaehQqVMn6T//8e0x7ABqj7Nnz+rZZ5/VW2+9pbFjx3q7\\nOgAA+D2XZkN78MEHddlll+nBBx902v6///u/2rVrl1544QW3VK4sN867USFfhpS4r1WDVlp+9/Iy\\n39/7rd7acXxHqfvH9hyrsT1L/2Vje+Z2Xf/29WVeY9mwZWod09pp27lzZomutm2llednaMa6GaW+\\n35334dii6hhYXb2PBg2k55833cxve36GNgSZ+/hV0oht0v2PmbAZHu7e+ygtmLp6H45mrKueP483\\n3xyr0aOlhx+Wli0z23/8URo0SOpwzXYd6nt9Ycu4L9+HJ/5+OOI+7LgPg/uw8/R9HH/juB547AE9\\neuejGjBggHbs2KFWtjUTf+cP9yHVjD8PiftwxH0Y3Icd92HHfRhVuY/zGefLfJ+rXAreH374YYmT\\nqF155ZV69tlnqyV4H/vwmFQ0nHQ0JSosqtz3H8k+ol9Plz4z2KncU2W+/0LBhTLfbzumqEcflV58\\n0Txv8edT+rVJ6edw532U1lLs6n3YNG0qDRh0ShtWOp/jgqQTFySdlpQbpfx8++zoJfH2fdiuUdY5\\n3PnnkZgoffGFtHy56YK+fr3Z/+PWC9J1v0p5pV/Dl+6jNP7251Ea7sP5GtyHUePvY6Okc1KdRnUk\\nmdbvPXv2FAvePn8fDtcoC/fhfA3uw+A+DO7D+Rrch1Gj7mPtr9KWIjvOlftWl7gUvI8fP66oqOI3\\nExkZqczMzCpXqiJi/xirkMYlt3jHR8SX+/74iHhlncsqdX9kaGSZ7w8KCNLFF11c7jFFbdtmf757\\na6QUdbFCQ03rcEiR23HnfZQWWF29j6LXsJ3j/HkpK8s82vx6PF4dOkiTJpkltUoK4K78eXjyPkri\\n7s+VxSJdf73Uu7e0eLH0+OPS9xlB0il7HUJCpciLnCdg87X7KIk//nmUhPtwvgb3YT+mJt/H4bWH\\nFXljpCJDI1VQUKDvv/9e9UuYPdPX78PxGmXhPpyvwX3Yj+E+uI+i1+A+7MfUmPu4Mku60nn7+Yzz\\nOpZ2rNz3V5bF6sI04h06dNBf/vIXjRo1ymn7zJkz9corr2jr1q2VOl92drZ27dolq9WqLl26aMaM\\nGbruuusUHR2tJk2aOB27adMmJSUlaePGjerSpUtlq+51ffuals6S9O5tZr+++mr3X3f4cDMLuSRt\\n3Wq6unuK1SqtWGHuZfVq531t2pjZvAcPNhOPVdaqVfafz8MPS889V/X6+oqCAmnRIvMFheMXNJJ0\\nxRWmZfymm/xrbDsA//Ltt9/qqquu0uOPP66QkBAdPHhQb7zxhjIzMxVW1hgYAABqCE/lTZem2Ro7\\ndqzGjx+vJ598UitXrtTKlSs1adIkPfrooxozZkylz/ftt9+qc+fOSkpKksVi0bhx49SlSxc9+eST\\nrlTPpzl+zfHSS9Kll9pfL19ulqC6+mrTAurOma6rc1Iyi8V8ifDVV2YMc69e9n0//2wmE2vVSnr5\\n5crPgu6vk6tVRECAdNttZub4t982PyOb9eulgQNNF/X33pNYPACAJ2zYsEFXXnmlnnjiCU2YMEEN\\nGjRQSkqKU+jetWuXunbt6sVaAgDgf1wK3vfcc4+ef/55vfHGG7ruuut03XXXad68eXrllVc0YsSI\\nSp/vmmuuKVyKzLHMnTvXler5NMfgeM890vbtZk3nFi3s21etMi2bl18uvfWWc7dtd1y3ugKrLYCv\\nXFk8gO/da9awvuQSM9v3iRMVO2dtmPU7MFC66y7TM+Hdd83nwOaHH6QhQ8za6m+8YSbrAwB3OXXq\\nlBITEyWZpUPfeeedYhOpxsbGqq0nu00BAFADubyw1P3336+MjAwdOXJEp06d0p49ezRs2DB31q1G\\nKjq7eHCw9Kc/mZbgt982XbFtfvrJ7GvRQpoxQzp92j3XrcpyYq5wDOArVkj9+tn3HTtmup43bWqW\\nJztwoOxz1eQW76ICA03I3rxZ+vhjqXt3+77t26X/9//MFxd//7tUTVMrAKjhWrRooTp1zKRqc+bM\\nUWpqqjp27ChJev311/Xpp59q4sSJuuGGG7xZTQAA/E6VI1hsbKzq1q3rjrrUCqVOchZkWjl/+kn6\\n6CPpSodB/hkZ0rhxUpMmZlb08sJpZa5bnSwW6dprpc8+kzZtku64w/4lQHa2lJYmNW8u3XmnfZbv\\n8s5XG1gs0s03S19/beYHuPZa+76jR82Y8CZNpL/8xQRyAHDVoEGDdPz4cc2ZM0c5OTmFQ76WLFmi\\n48ePq3///srJyVE/x29QAQBAuSo8vVXnzp1lqWDS2bRpk8sVqukcA3BJLc8BAWYs78CB0po10j//\\naVo7JTNb+LRp0vTpZs3nBx4w3bcr8sfiC8HbUefO0oIF0tNPm/XA58413aYvXDDbFywwE4o99JD0\\nxz/aZ3z3tfuoThaL1KePKevXm14QH3xgejOcOye9+qopN91keg9ce23t+xkBqJrAwEDNnj272PbF\\nixfr3nvvlSQdO3ZMubm51V01AAD8WoWD9y233OLJetQaRbual+Wqq0zr97ZtJmy/846Ul2cm1vrg\\nA1M6dTIB/M47pfDw0s9VXuD3lubNpVmzzAzoM2dKr7wiHT9u9q1fb+4rIUEaOVK6997aHbwdXXGF\\ntHChtG+fmaRvzhzpzBmz75NPTGnXzvzc7rrLLFcHAK4aNGiQ1q1bp19++UXNmzfX1q1b1bRpU29X\\nCwAAv+HScmLe5O/LifXqZV9i6/x5M8a7og4dMi2as2dLR44474uOlkaMkO67z3mmdJs77jBBTTIT\\nmzVr5lL1PS4nx7R2v/iimUjMUWio1K2b/ef3t79JzzxT/XX0RVlZJny/+KIZmuCobl0Tvu+/X/p9\\nqCYAAACAEvjUcmJwXVVanhMSpMmTpf37pXnzTKunzYkTpht6ixZm8rIPPnCeDd1fWorDw81s75s3\\nS19+abrU235OubnO64L78n1Ut6gos675nj3mi4vkZPu+M2dMT4LLLzdL1b37rntmygcAAABQMS4F\\n7/z8fE2fPl3du3dXw4YNFR0d7VRQusp0NS9NSIhZC/vrr0137NRUe8u51Sp9/rl0++32ydh27fLd\\nrualsVjMmuaLFkm7d5tQWa+e8zFlda2vrYKDTe+GVauk7783E65FRNj3r1olpaRIF19sxoH/+KP3\\n6goAAADUFi5FsClTpmjGjBkaMmSIsrKyNHbsWN16660KCAjQ5MmT3VzFmsXdLc/du5ux3wcOSFOn\\nmjHTNkePmlbwli2l999373WrU7Nm0nPPSb/+aiZhS042Xc7vvNPbNfNtl19uWroPHjTj5x2X3c3M\\nNLPId+xoek68+qrprg4AAADA/VwK3vPnz9ecOXM0btw4BQUFKSUlRa+//romTZqkr7/+2t11rFE8\\n1eU7Pt60bu/caZacuv320seP+1vwtqlTRxo+3LTafvON85cMKF1kpDRqlFmqbsUK0yIeGmrf/803\\npmU8IUEaNsx08XfsmQEAAACgalwK3ocPH1bH32dpqlu3rrJ+byq76aabtHjxYvfVrgayBW9Phd+A\\nALPc1HvvmUm2/vlP0+JtExYm1a/vmWvDt9nWUV+wwN4K3rmzfX9Ojuk9cd11ZoK+Rx+VtmzxWnUB\\nAACAGsOl4N24cWMdOnRIktSiRQt9/vnnkqQNGzYo1LEpDcXYWhKro9U5Lk565BFp+3bT0vnoo2bM\\ndJ06nr82fFt0tGkF37TJlFGjnL+Q2b/fDFO4/HJTpk0z2wAAAABUnkvBe9CgQVq2bJkk6YEHHtAT\\nTzyhli1batiwYbrnnnvcWsGaxtMt3iWxtXROnSr1719914V/6NzZtH4fPGhaw/v3lwID7fu3bDFf\\n2lxyiZkV/dVXzRhxAAAAABUT5Mqbnn322cLnQ4YM0SWXXKK1a9eqZcuWuvnmm91WuZrIFrz9YWZx\\n1C5hYWb89x13mIn53n9fmj9fWrfOfsyqVab89a9m1vnbbjNLvjVs6L16AwAAAL7Opfg3depUzZ07\\nt/B1jx49NHbsWB07dkzTpk1zW+Vqoursag64Ki7OhOu1a81ybn//u9SmjX1/fr60fLk0cqTUqJFp\\nCX/xRTO7PgAAAABnLgXvV199VW0cfwv/Xfv27TV79uwqV6om80ZXc6AqmjeXHn9c2rrVjAcfP15q\\n0cK+32o1reCjR0tNm0o9ephJ/bZtc57FHwAAAKitXJ7VPCEhodj22NjYwknXUDK6msNfWSxmPPi0\\naWbZuu++M4G86Hdw69dLEyZI7dqZGfXHjJGWLZPOn/dOvQEAAABvcyn+NWnSRGvWrCm2fc2aNWrU\\nqFGVK1UdPvhAuuoqacQIM9P37yuieRxdzVETWCxSYqLpgr5tm1kjfMoUMwO6o927pRdeMEvcxcZK\\ngwebJcuYnA0AAAC1iUuTq40YMUKjR49WXl6eevfuLUlatmyZxo8fr3Hjxrm1gp7yt79Ju3aZMayv\\nv25mcb7ySunGG82szp06eaZVmq7mqInatZMmTTJl1y7pv/81ZdUq6cIFc8ypU2bCtvffN3+3uneX\\n+vY15YorpCCX/jUCAAAAfJ/Faq38KEyr1apHH31UL730ks7/3n80LCxMEyZM0KRJk9xeSUebNm1S\\nUlKSNm7cqC5durh8nvh4M3NzWfv79TNB/PrrzWRT7tC+vRkrW7eudPq0e84J+KrffpPS000IX7JE\\nOnmy5OMiI6Xeve1B3HEMOQAAAFBd3JU3i3IpeNucOXNG27ZtU3h4uFq2bKnQ0FC3Vaw07vpBxMVJ\\nx45JDRpIQ4dKn30m7dhR+vEdOphg0Lu3WUapXj3XrtuunemaGxlZfd3bAV9w4YJZmuyTT0zZurX0\\nY5s3NwG8Tx8zY3psbPXVEwAAALWXTwZvb3DXDyImRjp+3LSs7dpltu3ZY1rnPv3ULJWUnV3yewMC\\npC5dpOuuM0E8Odm0YFdEmzbS9u1SVJRpDQRqq4wM6YsvpM8/N4/Hj5d+bIcO5guva681jwRxAAAA\\neALB+3fu+kE0aCCdOCFddpmZobmo3FxpzRoTClaskL791j4xWlFBQVLXriaAJyebSdtiYko+tnVr\\n07Jer17p3W6B2qagwMyS/vnnpqxZI+XllX58+/b2EH7NNe4bCgIAAIDazVPBu9ZOZ1TeJGehofau\\n5ZLpFv7VV6YlfPly6Ycf7MdeuCB9/bUp06ebbW3a2IN4crLpOmuxsJwYUJKAACkpyZS//U06c8b8\\nffvyS1M2bnT+4uunn0yZNcu8btnSTI7Ys6d5bNfOTJgIAAAA+IJaG7xtv8RXNABHRUk332yKZMaH\\nf/mlPYgXHR/+88+mvP66eR0fbwK4rXWdWc2B0tWtKw0YYIpkZkRfvVpaudIexPPz7cfv3GnKW2+Z\\n15GRUo8e9jB+xRXm7zAAAADgDbU2eFd1Wa/YWOn2202RzAzpa9eacLB6tQkGtmWUJOnIEenDD+2v\\nCd5AxUVGFg/ia9aYEL56tRkK8vsCC4X7bd3WJfP3rX17qVs3U7p2NWuOV8N8kAAAAADB210BOC5O\\nuuUWUyTp7Fnpm2/sQXztWuflwxIS3HNdoDaKjJT69zdFMnMybNpk/p6tW2dC+eHD9uOtVunHH035\\n17/MtuBgqWNHE8JtYbx9e7MdAAAAcKdaG7xtXc091fJcp46Z/Onaa83r/HzzS//XX0tqnj0XAAAg\\nAElEQVR790qpqZ65LlAbhYaaLuU9e5rXVqv0yy/OQfyHH5y7p+flmbC+aZP02mv28yQmSp07S506\\nmVbxjh2liy6q/nsCAABAzVFrg3d1T3IWGGh+ke/UqXquB9RmFovUrJkpd95ptuXkSN9/b7qlf/ut\\ntGGDtG2b/d8CybScr19viqMWLexB3Pb3uFkzhowAAACgYmp98OYXZ6B2CA83E6716GHfduaMWcbM\\nMYyXtLzg7t2mLFpk3xYZaVrDO3aU2rY1M6m3a2eGkfDvCgAAABzV2uDt6a7mAHxf3bpSr16m2GRl\\nSVu2mNZxW9myxbSYO7JN8LZmjfP2qCh7CHcM5E2asIwgAABAbVVrgzfraQMoSVSUWfovOdm+LT/f\\ntHg7hvHvv5cOHCj+/qwsM6583Trn7RERUps2Zs1xx9KqlRQd7dl7AgAAgHfV+uBNizeA8gQGmoDc\\nqpV9CUFJOnnSjBPftk3autVe9u8vfo7sbLPM4MaNxfdFRxcP5LbC+uMAAAD+r9YGb7qaA6iq+vWl\\nK680xdGZM9LPPzuH8a1bpT17nCdzszlxouRJ3SQTyps1ky691BTbc9vkcXXquP++AAAA4F61NnjT\\n1RyAp9Sta9YF79rVeXturgnfO3cWLyV1W5dMKD9xwix7VpL4+OLBvGlTM6a8cWNazAEAAHxBrQ/e\\ntHgDqC6hoWbCtbZti+/LyTHjyHfulHbsMI+7dkn79plQbuulU9SRI6aU1FoumTXIGze2B/Gij4Rz\\nAAAAzyN4E7wB+IDwcKlDB1OKyssz4XvfPmnvXlNsz/ftkw4eLP28p0/bx6GXxhbOGzUyy6E1bGge\\nHZ83bGgCOv9mAgAAVF6tDN6OYyzpag7A1wUHS82bm1KSc+ekX36xh/GMDBPUHR+LLofmqCLhXJLC\\nwoqHcVtAj411LpGRhHQAAACbWh+8+cUQgL8LC5NatzalJFarGSeekVFyKK9IOJdMwLe1uJcnJESK\\nibEH8bi44uHcsdSrxxehAACg5qqVwdtxrCTBG0BNZ7FIDRqY0qlTycdYrdKpU9KhQ9Lhw86PRbcd\\nP17+Nc+fN13gy+oGX7SO9eubWdxtj0Wfl/Y6NLTiPwsAAABvqJXBmxZvAHBmsZgx3FFRUps2ZR+b\\nmysdPeocyo8elY4dK14yM80Y9fLYWuVPnKh83evUsYdw2z3YSmRk+a8jI6WgWvm/IQAAqC618lcN\\nxngDgOtCQ82s6E2alH+s1SplZZUcyh2LLXSfOCH99lvJ652X5uxZUzIyXL+niIjiwfyii8zScK4U\\ngjwAAHDkM78azJo1S9OnT9fhw4fVqVMnzZw5U926dfPItehqDgDVw2Ix47fr1ZNatqzYewoKTFi3\\nBfGTJ52DeVmvc3Ndq2d2tikV7RpfntDQkgN5RIRpoQ8PL/uxIvuCg91TVwAA4Hk+EbwXLlyocePG\\n6bXXXlP37t2Vlpamfv36aceOHYqJiXH79ehqDgC+KyDAdBuvX19q0aJy783NNWPVs7LspazXJe07\\nfbrq95Cba0pFxsO7KiioeCgPCzMlNLT4Y0nbKrKvtGNCQkwd+H8UAIDy+UTwTktL03333adhw4ZJ\\nkmbPnq3Fixdr7ty5Gj9+vNuvR1dzAKiZQkPtM6W7Kj/fhO8zZ9xXsrPdd482Fy6Yerrji4KqCA42\\nIbzoY0nbKvpY3jHBwSb0V7UEBhbfFhDAlwkAAPfzevDOy8vTxo0bNXHixMJtFotFffr00bp16zxy\\nTbqaAwBKExho7x7vLgUF9u7sOTlmTHppj2XtK+uYs2fNkm/5+e6rd0Xk5VVsAj1/4kqAt5WAgLKf\\nV2Wbu87j+GixmEfH50Uf/W0fv9sB8EVeD96ZmZnKz89XfHy80/b4+Hht3769zPf+7W+uXfP8eftz\\n/nEGAHhaQICZrO2iizx/rfx808393LmSH8vaV5FjbM/PnzclL6/8R38L5hcumAL/5RjKbb/r2Z5X\\ndlttfr/jz7Poz7ey+9x9HNfy7LVKUpHcVBPOcfRo+ed3hdeDd2msVqss5fzEnn22qldZoICAlKqe\\nBChmwYIFSknhswX347OF8gQG2idhqwxPfrasVhNkKxPWS3vMzTXnys+3B2R3laqcs7p7GviXBZKq\\n998tq5U/k9qh+j9bgKu8HrxjYmIUGBioI0eOOG0/evRosVZwR2PGjJEUVWRriir3l2+B+vfnLyvc\\nj3AET+GzBU/x5GfLYrGPzY6I8MglfEJBgSn5+fZHx+cV3ebKe1w5j9VqntseHZ9XdltZ+9LTF6hP\\nnxS3n7e8bbbnknl0LDVhW3nH1A4Eb1TVgt+LoyyPXMnrwTs4OFhJSUlatmyZBg4cKMm0di9btkwP\\nPvhgqe9LS0vTqVNdqnTtp56Sxoyp0ikAAAAk2ccZs467s4EDpblzvV2L2qmqwd7xHCW9rshzdx/n\\n+Pzuu6U33/RuHWriuctSkePceS7PX7N4w+3PP2/S8OFJFTthJfjEfw1jx47V3XffraSkpMLlxM6e\\nPas//elPZb7v2murdt0ZM6r2fgAAAMBXOY7brolCQ6WEBG/XAjVNSIhnzusTwXvw4MHKzMzUpEmT\\ndOTIESUmJio9PV2xVVkPBgAAAAAAH+ATwVuSRo4cqZEjR5Z7XE5OjiRp27ZtVb5mVlaWNm3aVOXz\\nAEXx2YKn8NmCp/DZgqfw2YKn8NmCJ9hypi13uovFavWv6Rfmz5+v1NRUb1cDAAAAAFBDzZs3T0OH\\nDnXb+fwueGdmZio9PV3NmjVTeHi4t6sDAAAAAKghcnJytG/fPvXr108xMTFuO6/fBW8AAAAAAPxJ\\ngLcrAAAAAABATUbwBgAAAADAgwjeAAAAAAB4EMEbAAAAAAAPIngDAAAAAOBBBG8AAAAAADyI4A0A\\nAAAAgAcRvAEAAAAA8CCCNwAAAAAAHkTwBgAAAADAgwjeAAAAAAB4EMEbAAAAAAAPIngDAAAAAOBB\\nBG8AAAAAADyI4A0AAAAAgAcRvAEA8EGHDx/Wo48+qt69eysyMlIBAQH66quvSj1+7dq1Sk5OVkRE\\nhBISEvTQQw8pOzu7GmsMAABKQ/AGAMAHbd++Xc8995wOHjyoyy+/XBaLpdRjN2/erD59+ujcuXNK\\nS0vTiBEj9Nprr2nw4MHVWGMAAFCaIG9XAAAAFNe1a1cdP35c9erV04cffqh169aVeuzEiRMVHR2t\\nlStXKiIiQpJ0ySWX6N5779XSpUvVp0+f6qo2AAAoAS3eAAD4oIiICNWrV6/c406fPq2lS5fqrrvu\\nKgzdkjRs2DBFRETovffeK9w2efJkBQQEaOfOnUpNTVW9evUUFxenSZMmSZIOHDigW265RVFRUUpI\\nSNCMGTOKXW/mzJnq0KGDIiIiFB0drW7duundd991wx0DAFBzEbwBAPBjW7Zs0YULF5SUlOS0PTg4\\nWImJifruu+8Kt9m6qw8ZMkSSNG3aNPXo0UNPP/20XnjhBfXt21eNGzfWtGnT1LJlSz3yyCNavXp1\\n4fvnzJmjhx56SB06dNCLL76op556Sp07d9b69eur4U4BAPBfdDUHAMCPHTp0SBaLRQkJCcX2JSQk\\nOAVnmx49eujll1+WJI0YMULNmjXTww8/rGnTpmncuHGSpJSUFDVq1Ehz585VcnKyJGnJkiXq0KED\\nLdwAAFQSLd4AAPixnJwcSVJoaGixfWFhYYX7bSwWi/785z8Xvg4ICFDXrl1ltVo1fPjwwu1RUVFq\\n3bq19uzZU7itXr16ysjI0Lfffuvu2wAAoEYjeAMA4EV5eXk6cuSIUykoKKjw+8PDwyVJubm5xfad\\nO3eucL+jpk2bOr2OiopSWFiYoqOji20/efJk4esJEyaobt266t69u1q1aqVRo0Zp7dq1Fa4rAAC1\\nFcEbAAAvWrt2rRISEtSoUaPCx4yMjAq/PyEhQVarVYcOHSq279ChQ2rUqFGx7YGBgRXaJklWq7Xw\\neZs2bbR9+3YtXLhQvXr10qJFi5ScnKwpU6ZUuL4AANRGBG8AALwoMTFRS5cu1RdffFH42LBhwwq/\\nv0OHDgoKCirW/TsvL0+bN29WYmKiW+sbHh6u22+/XW+88Yb279+vP/zhD3r66ad1/vx5t14HAICa\\nhOANAIAXRUVFqXfv3k4lJCSkwu+PjIxUnz59NG/ePGVnZxduf/vtt5Wdna3Bgwe7ra4nTpxweh0U\\nFKS2bduqoKBAeXl5brsOAAA1DbOaAwDgo/7xj3/IYrHop59+ktVq1dtvv61Vq1ZJkh577LHC455+\\n+mldddVVuvrqq3XvvfcqIyNDzz//vPr166cbbrjBbfXp27evGjZsqKuuukrx8fHaunWrZs2apZtv\\nvtlpDXEAAOCM4A0AgI+aNGlS4drbFotF//rXvwqfOwbvzp07a+nSpZowYYLGjh2riy66SCNGjNAz\\nzzxT4WvZrlPW9r/85S+aP3++0tLSdObMGTVu3FijR492qgsAACjOYnWcNQUAAAAAALgVY7wBAAAA\\nAPAggjcAAAAAAB5E8AYAAAAAwIMI3gAAAAAAeBDBGwAAAAAAD/K75cQyMzOVnp6uZs2aKTw83NvV\\nAQAAAADUEDk5Odq3b5/69eunmJgYt53X74J3enq6UlNTvV0NAAAAAEANNW/ePA0dOtRt5/O74N2s\\nWTNJ5gfRtm3bKp1rzJgxSktLc0OtAGd8tuApfLbgKXy24Cl8tuApfLbgCdu2bVNqamph7nQXvwve\\ntu7lbdu2VZcuXap0rqioqCqfAygJny14Cp8teAqfLXgKny14Cp8teJK7hzUzuRoAAAAAAB5E8AYA\\nAAAAwIO8HrynTp2q7t27KzIyUvHx8Ro0aJB27Njh7WoBAAAAAOAWXg/eq1at0gMPPKD169dr6dKl\\nysvLU9++fZWTk+Pxa6ekpHj8Gvj/7N15eJNV2sfxb0qhZWuhUMousoMgq4iACiiyKAguYKWC4ouO\\nDCKUARRlc4WRRceFjii+IouDyjuiyDKssskAFRwH2QRkL4vSYmmhpXn/OIQkbdMlTZqk/X2u61xN\\nzvPkOfchKXDnnOec4kmfLfEWfbbEW/TZEm/RZ0u8RZ8tCSQWq9Vq9XUQjs6dO0eVKlX47rvv6NSp\\nU5bj8fHxtGnThp07d2oxBREREREREfEYb+WbPh/xzuzChQtYLBYiIiJ8HYqIiIiIiIhIgflV4m21\\nWhk5ciSdOnWiadOmvg5HREREREREpMD8ah/vYcOGsWfPHjZv3pzruaNGjSI8PNypLjo6Wvd6FKLU\\nVLh8GTK9DSIiIiIiIn5v0aJFLFq0yKkuMTHRK235zT3ew4cP5+uvv2bjxo3Url3b5Xm6x9s/JCZC\\no0Zw8SLExcFjj/k6IhERERERkYLxVr7pFyPew4cP56uvvmLDhg05Jt3iPzZuhIQE83jwYLBYICbG\\ntzGJiIiIiIj4I58n3sOGDWPRokUsXbqUsmXLknAtmwsPDyc0NNTH0YkrV6/aH1ut9uR74EDfxSQi\\nIiIiIuKPfL64WlxcHElJSXTu3Jnq1atfL4sXL/Z1aJKDzDcoZGTAoEGwcKFv4hEREREREfFXPh/x\\nzsjI8HUI4gbHxLtqVTh92iTftnu9H33UN3GJiIiIiIj4G5+PeEvge+45+NOfzGNb8p1pcUARERER\\nEZFiS4m3uMVxxDsoCN57D55+2jzPyDALrc2d65vYRERERERE/IkSb3FL5nu8g4Lg/fedk+8nn4S/\\n/a3wYxMREREREfEnSrzFLY6Jt8VifgYFwezZMHKk/dhzz8Frr2VN1EVERERERIoLJd7iluwSb9vj\\nmTNhwgR73UsvwQsvKPkWEREREZHiSYm3FJhj4m17/vLL8Ne/2uumTYPhw80UdBERERERkeJEibe4\\nxdWIt6MxYyAuzn78/ffNiudXrng/PhEREREREX+hxFvckpfEG8xia/PmQYkS5vnChXDvvXDxonfj\\nExERERER8RdKvMUt+blfOyYGliyB0FDzfPVquPNOOH3aO7GJiIiIiIj4EyXe4pa8jnjb9OkDa9ZA\\nRIR5/sMP0KED7N/vnfhERERERET8hRJvcUt+E28wifamTVC7tnl++DB07Ajbtnk+PhEREREREX+h\\nxFsKLK+JN0CTJrB1K9x8s3l+7hx07QpLl3onNhEREREREV9T4i1ucWfE26Z6dfjuO+jc2Ty/dAn6\\n9oXp07XXt4iIiIiIFD1KvMUtBU2Qw8NhxQqIjrZfb8wYGDpU242JiIiIiEjRosRb3FKQEW+bkBBY\\nsACmTLHXffQRdO8Ov/1WsPhERERERET8hRJvcYsnEm/baydOhEWLTCIOsH49tG+vFc9FRERERKRo\\nUOItBVaQxNvmkUdMwl2linl+4IBJvletKvi1RUREREREfEmJt7jFUyPejtq3h3//G5o3N89//x16\\n9ICpU7XomoiIiIiIBC4l3uIWbyXCN9wAmzdDnz72dl54Afr3h4sXvdOmiIiIiIiINynxFrd4Y8Tb\\npnx5+L//g5dftl/7iy/MiPiBA55tS0REsrp06RJTp07lk08+ITY21tfhiIiIBLxgd15ktVr54osv\\nWLduHWfOnCEjI8Pp+JIlSzwSnPgvbybeAEFBMGECtG4NAwdCYiLs2QO33ALz58N993m+TRERMQYM\\nGMC0adNo2rQpvXr1Yv/+/TRs2NDXYYmIiAQst0a8R44cyWOPPcbhw4cpV64c4eHhTkWKPm8n3jb3\\n3gvbt0PTpuZ5YiL07m2mn6ene69dEZHi6sMPPyQ1NZWm1/7ivXTpEocOHfJxVCIiIoHNrRHvTz/9\\nlCVLltCrVy9PxyMByJuJN0CDBvD99/DEE/Dll6Zu6lTYtMlsQ1azpnfbFxEpTqZOncorr7wCQEZG\\nBrt376ZixYo+jkpERCSwuTXiHR4eTt26dT0diwSQwl5lvHx5+PxzmD4dgq99XbRpE7RsCcuXF24s\\nIiJF1Y4dOzh27BgHDx5k2rRpjBo1irS0NFq0aOHr0ERERAKaW4n35MmTmTJlCikpKZ6ORwJEYU01\\nd2SxwOjRsHEj1K5t6s6fh1694PnnIS2tcOIQESmqtm/fTocOHZgwYQLjxo2jUqVKREdHExoa6uvQ\\nREREAppbiXf//v35/fffqVKlCs2bN6d169ZORYo+XyTeNu3bww8/mHu9baZNg86d4fDhwo1FRKQo\\nSUpKomXLloCZZv7pp58yYsQIH0clIiIS+Ny6x3vw4MHs3LmTmJgYoqKisBR25iU+58vEGyAiAr76\\nCt56C8aONQutbdkCLVrAe+9BTIxv4hIRCWT16tUjKSkJgDlz5hATE0Pz5s3ZtGkT33zzDbfccgsh\\nISEkJiYycOBAH0crIiISONxKvJctW8bKlSvp1KmTp+ORAOSrBNdigVGjoEMHeOQROHIELl6EQYPg\\nm28gLg60HpCISN7169eP1atXM2fOHFJSUpg0aRIAtWrV4uzZs/Tq1YtLly7xxBNPKPEWERHJB7cS\\n71q1ahEWFubpWCSA+HrE29Gtt8KuXTBiBMybZ+oWLzYj4J98Al27+jY+EZFAUaJECeLi4rLU33DD\\nDaSmplK6dGmWLFlCt27dfBCdiIhI4HLrHu8ZM2YwduxYjhw54uFwJFAU9qrmuQkPN0n2P/5hH+U+\\nfhzuvhvGjIHUVN/GJyISyH7++WfS0tJYuXIlx44dY/jw4b4OSUREJKC4NeIdExPDpUuXqFevHmXK\\nlKFkyZJOx3/77TePBJejHj2gVKnsjzVsCGvX5vz6rl1h/37Xx2NjTXFl3z64666c21izBho1cn18\\n5kxTXPFgPy5dgnffhchIeOwx+5Zc7vbDMfG+aeVMeKlw+uHStX70Bx4Igd9D4PJlwApMhwtvQcKC\\nNbTo7x/vR279yJEffa5cUj/s1A879cMIwH6sXbuWESNG0KlTJ7p3726OB2A/sqV+2KkfduqHoX7Y\\nqR92Rb0fV67k/Do3uZV4v/XWW56OI//OnnV9LDw899cnJMCJE66PX1tcxqX09JxfbzsnJ0lJOV/D\\ng/343/+FceNM1fvvw0cfwc0343Y/HBPvUqmF1w+XHPoRDERmOQ7dHkmnx3Z4+WUoXdpFG37UjxzP\\nyYn6Yad+GOqHcxvqh5HHfpw8eZKFCxfSoEGDrDEGUD9cUj+c21A/DPXDUD+c21A/jOLSDw9ze1Vz\\nn4uMdD3iHRWV++ujoiAx0fXx3O5hDw6GGjVyPycnYWE5X8OD/fj1V3vVjh3Qpg2MHw8vPhRMKTf6\\n4Zh4p5UuvH645OL9SEuD3343P69Yg5k+HZYuhblzoWPHbNrw035kOScn6ofzOeqH+pG5DfXDfk4e\\n+lG9enU2b96cfYwB1A+X1A/nNtQP+znqh/qRuQ31w35OUe7HlSs5D/K6yWK15v9u3aNHj+Z4vHbt\\n2m4HlJv4+HjatGnDzp07tWd4PowdC2++mbX+pptMEtquXf6u9+678Oyz5vGnn5rtu/xVejpMnw6T\\nJtlnjlgs8Nxz8NprUKaMb+MTERERERH/4K18063F1erUqcONN97osoj/cfx65f777V8i/fe/cNtt\\n8Je/QHKye9fzd8HB8PzzZuXzW281dVar2QP8ppvg2299G5+IiIiIiBRtbiXeP/zwA/Hx8dfLtm3b\\niIuLo2HDhnz++eeejlE8ICPD/njMGDPd3PYFTkYGzJhhktClS/N2PX/aTiyvmjSBzZvN6HdoqKk7\\ncgTuvRcefrhQb/EQEREREZFixK3Eu0WLFk6lbdu2DB06lOnTp/O3v/3N0zGKB2ROlFu0gG3b4I03\\nICTE1P/6qxkNv/9+53vC83K9QFGiBIweDbt3O+/v/cUXJjH/29/g6lXfxSciIiIiIkWPW4m3K40a\\nNWL79u2evKR4SHaJsm0K9o8/mv2ubZYuNUno1KmuV9MP1MTbpmFDWL3a3J8eeW0J9IsXzX3ft95q\\nZgSIiIiIiIh4gluJd1JSklNJTExk7969vPTSS1m3GxG/4DjVPCjTu96wIaxaBZ99BtWqmbqUFHjh\\nBWjZEtaty/nagZh4g4k7Jgb27oWhQ+31O3eaxeaeesorCxqKiIiIiEgx41biXaFCBSpWrHi9RERE\\n0LRpU7Zu3crs2bM9HaN4QG4j1BYLDBgAP/8MI0bYk/OffzZTsh98EA4dyv56gS4iAj74wNz/3ayZ\\nqbNaYc4caNAAZs0y25GJiIiIiIi4w63Ee926daxdu/Z6Wb9+PXv27OGXX37htttu83SM4gF5nRoe\\nHg5vvw3btztvMbZkiZl+/vzzZr/6QJ9qnp0OHSA+3iy+Vr68qUtMhNhYuPlmWLHCt/GJiIiIiEhg\\ncivxvvPOO53K7bffTuPGjQnObaNzFzZu3EifPn2oUaMGQUFBLM3r0tqSZ46Jcuap5tlp3Rq2boWP\\nPrLvP3/lCkybZqamf/SR/dyikngDlCxpFl87cACGDLH3be9e6NkTeveG/ft9G6OIiIiIiASWPGfK\\nS5cupWfPnpQsWTLXxLhPnz75CiI5OZmWLVsyZMgQHnzwwXy9VvLG8R7vvCbKQUEm+Xz4YXj9dZg5\\n0yTfCQmm5Pd6gSQqyny58MwzZsG1LVtM/TffwPLl5v7vSZPsX0qIiIiIiIi4kufEu2/fvpw+fZoq\\nVarQt29fl+dZLBau5nM/ph49etCjRw8ArEXp5mE/UpCp4eXLm23Hhg6FsWPhyy+djxfFxNumbVvY\\ntAkWLTJ9P3HCbDc2ezbMmwd/+YsZIbdNTRcREREREcksz1PNMzIyqFKlyvXHrkp+k24pHJ64J7tu\\nXbPf9bp1Zh9wm5tuKlhs/s5igUcfhX37YMoUKFfO1Ccnm+f168N772kBNhERERERyZ5H9/EW/5XT\\ndmL51bmz2XJr1Sr497/NomvFQdmyMHEi/PILDB9u9kEHOHPGPG/a1IyM67snEQl0ly5dYurUqXzy\\nySfExsb6OhwREZGA59ZqaCNGjKB+/fqMGDHCqf7dd9/l4MGDvPXWWx4JLic95veg1PpS2R5rWKkh\\nawevzfH1XT/pyv7zrlfJir0tltjbXP9nY9+5fdw1764c21gzaA2NKjdyqjt+3GxP1aQJ/NZoJn/b\\nPtPl6z3ZD1cj3u72o0QJ6NbNPJ65dSYztxZOP1xxtx+O8tqPKlXgnXfMvd8vvgiLF5vjBw/Coyu7\\nMmjXfsLCoHRp/+5HTgLp/ciJ+mGoH3bqh11O/Tj/0XmeffFZnn/0eXr16sX+/ftp2LCh0zmB0A8o\\nGu8HqB+O1A9D/bBTP+zUD6Mg/bhy/EqOr3OXW4n3l19+me0Cax06dGDq1KmFknif/fIshGaqbG5K\\neGh4rq9PSE7gxMUTLo8nXU7K8fXpGek5vt52TmYvvQSffGIeh/VJIqm162t4sh+uEm93+5G5jZyu\\n4c/vR+Y28tOP+vXhH/8w93mPHQvr1wNlE0gvc4Lf0oGL2beRW4x6P+xtqB+G+mGoH85teK0fO4FU\\nKFO9DGBGvw8dOpQl8fb7fji0kRP1w7kN9cNQPwz1w7kN9cMoUv3YcgL+k+lAaq4vdYtbiff58+cJ\\nD8/ambCwMM6dO1fgoPIi8sFIStXMfsQ7qmzuS01HlY0iMTXR5fGwkLAcXx8cFEyN8jVyPSezU6fs\\nj5POhkFSDYKCICwMypRxToo92Q9XU83d7UfmNnK6hj+/H5nbcKcft9wCa9ea8uDSKBKTnPtRsiSU\\nD4PSof7dj8znBOr7kfkc9UP9yNyG+mE/J7t+nN5ymrAeYYSFhJGRkcHu3bupWLFitjH6cz8c28iJ\\n+uHchvphP0f9UD8yt6F+2M8pMv3okAgdnOuvHL/C2Vlnc319flmsbiwj3qxZM/70pz8xfPhwp/p3\\n3nmH2bNns2fPnnxdLzk5mYMHD2K1WmndujUzZ86kS5cuREREUKtWLadz4+PjadOmDTt37qR169b5\\nDd3n7rkH/vWv7I9VqwbjxpmtqrKbplwQTzwB//u/5vGePcXnvuzCZrWa93fSJLZDhu4AACAASURB\\nVPj+e+djzZqZ9/eRR+z3h4uI+JMdO3bQsWNHXnrpJUqVKsXJkyf56KOPOHfuHKGhmaeZiYiIFD3e\\nyjfdWmYrNjaWsWPHMmnSJDZs2MCGDRuYOHEizz//PKNGjcr39Xbs2EGrVq1o06YNFouF0aNH07p1\\nayZNmuROeH7N8WuO9evh/vvtz0+dgpEjoU4deO01+O0377RblLf/8jWLxXy5smULfPutGQ23+ekn\\neOwxaNAA3n8fUlJ8F6eISHa2b99Ohw4dmDBhAuPGjaNSpUpER0c7Jd0HDx6kbdu2PoxSREQk8LiV\\neA8ZMoQZM2bw0Ucf0aVLF7p06cL8+fOZPXs2Q4cOzff17rzzzutbkTmWuXPnuhOeX3Oc8t22Lfzz\\nn/DDD/DAA/b6M2fMveC1a8OoUXD0aMHbdUy8C7qqueTOYoGePWHbNvj6a2jf3n7syBH485/NFyxv\\nvAGJrmfqiIgUqqSkJFq2bAmYrUM//fTTLAupRkZG0kTTpkRERPLF7RTsmWee4fjx4yQkJJCUlMSh\\nQ4cYNGiQJ2MrkrIbeW7ZEr78EnbvhgED7IlxcjK89ZbZP/uxx+DHH91v1zHh14h34bFY4L77zAj4\\n+vXQvbv92JkzMH68+YJlzBjPfMEiIlIQ9erVo0wZs6janDlziImJoXnz5gB8+OGHLF++nPHjx9PN\\ntq2FiIiI5EmBxz4jIyMpV66cJ2IpFnIaeb75ZvjsMzhwwIyI2u7zvnoV5s+HFi2gRw9Yvtw5kc5v\\nu0q8C5/FAnfeCStWQHw89O9vfx+SkmD6dPMFS//+JknP/8oLIiIF169fP86fP8+cOXNISUm5fsvX\\nt99+y/nz5+nZsycpKSl0d/wWUURERHKV5yWeWrVqhSWPGVt8fLzbARV1eRl5rlsX3n3XLND13nvm\\n8fnz5tjKlaY0aGCS88cfh2wWmM9CU839R6tWZhuyV1+FN98028tduWK+YPn8c1NuucXc7//QQ1Aq\\n+8X7RUQ8rkSJEsTFxWWpX7ZsGU899RQAZ8+e5fLly4UdmoiISEDLc+Ldt29fb8ZRbORn5DkyEiZP\\nNtOQP/4YZsww9weDGRUfOdLcCz54MAwfDo0bu76Wppr7nwYN4IMP4JVX4O9/NwuuJSSYY9u3w8CB\\n5r0fNgyefBKqVvVtvCJSfPXr14+tW7fy66+/UrduXfbs2UPt2rV9HZaIiEjAcGs7MV8K9O3Ebr8d\\nNm0yj9PS8ret1NWr8M038M47sGZN1uP33APPPAP33mv2jnb0yCNmlBXg8GGzsJf4l8uXzXs0axbs\\n2uV8LDgY+vWDp5+GLl00a0FERERExBv8ajsxcV9BRp5LlDDbj61ebbam+tOf4NoaOACsWmWSsxtu\\nMCPhhw/bj2mquf8LCYFBg8w94Bs2mPfS9hlJTzdT0O++28xsmDHDfvuBiIiIiIj4N7dSsKtXrzJ9\\n+nTatWtH1apViYiIcCrimqcWObvpJpg9G44fN0lY3br2Y6dOmX3A69Uzq2h/8YUZTfVEu+J9Fgvc\\ncQcsWQKHDpmVz6tUsR8/cAD+8heoUQNiYkySHljzVkREREREihe3Eu8pU6Ywc+ZMBgwYQGJiIrGx\\nsTzwwAMEBQUxefJkD4dYtHh6dfGKFSE2FvbvNytmP/CAffq61WpGwR9+GL76yrPtSuGoU8d8iXLs\\nGCxeDF272o9dvgwLFkDnzlC/Prz8sn0NABERERER8R9uJd4LFixgzpw5jB49muDgYKKjo/nwww+Z\\nOHEi33//vadjLFK8ta1XiRJmdPvLL02S9sYbzqPgjpR4B55SpcwXKGvWwN695ssWx8klhw6ZVfBv\\nvNEk559+avaBFxERERER33Mr8T59+jTNmzcHoFy5ciQmJgJw3333sWzZMs9FVwTZ7vH2ZvJbtSo8\\n/7yZkvyvf5mEzbbYWqVKztOWJfA0amRuLzhxwuzv3q2b8+dp3Tpzr3jVqmY19HXrzMJ8IiIiIiLi\\nG24l3jVr1uTUqVMA1KtXj1WrVgGwfft2QkJCPBddEWQb8S6MUeegILMY1+LFJkn7xz9g27asK55L\\nYAoNNVuOrVoFv/5qpqQ3aGA//scfMHeuGQGvVctsP7dtm+4HFxEREREpbG4l3v369WPNtf2snn32\\nWSZMmECDBg0YNGgQQ4YM8WiARY0t6SnslcUjI6F/f7PgmhQ9tWqZRdj27YPNm2HoUChf3n781Cl4\\n+21o3958BsaPhx9/VBIuIiIiIlIYPLKP9/fff8+WLVto0KABvXv39kRcLgX6Pt6tW8MPP5hR5ytX\\nfB2NFGWXLsHSpfDZZ7B8efaft6ZNzRcy/fpB8+a6/19EREREije/2sf7jTfeYO7cudeft2/fntjY\\nWM6ePcu0adM8FlxRVJhTzaV4K1MGHnkE/vlPSEgw0867dXOebbFnD0yeDC1amGnqY8bA1q3O+82L\\niIiIiEjBuJV4//3vf6dx48ZZ6m+66Sbi4uIKHFRR5qup5lK8VagATzxh7gc/eRLefRc6dnQ+55df\\nYPp06NABataEYcPM4nxpab6JWURERESkqHB7VfNq1aplqY+MjLy+6JpkrzBWNRfJSVQU/PnPsGmT\\n2XrunXfMAmwlStjPOXUKZs+Ge+4x6wMMGADz5sHZs76LW0REREQkULmVeNeqVYvNmzdnqd+8eTPV\\nq1cvcFCF4Y03zFTctm1hwgSzIFV6uvfb1VRz8Sc1a8Lw4WZ/8IQE+Phj6N0bHDcnSEw0K+MPHmyS\\n9vbt4eWXYedOTUkXEREREcmLYHdeNHToUEaOHElaWhpdu3YFYM2aNYwdO5bRo0d7NEBvef99SEkx\\nycPOnfDqqxAebu6B7dkTuneHGjU8364Sb/FXlSrB44+b8scfsGIFLFliFma7cMGcY7WaLcm2bYNJ\\nk8xe4b16md+Zrl0hIsKXPRARERER8U9uJd5jxozh/PnzDBs2jCvXlkoODQ1l3LhxvPDCCx4N0Fuy\\nW+E5MRG++MIUMKs89+hh9sLu2BHKli14u7rHWwJBuXLw0EOmpKebBdeWLTPlp5/s550+bRZtmzvX\\nfJnUpo35fbH9zoSG+q4PIiIiIiL+okDbif3xxx/8/PPPlC5dmgYNGhDiOD/VSzy1vHtkJJw7B9Wr\\nw+uvm1G9Vavg99+zP79kSTPFtksXM7LXvr3zdNy8atIE9u6FsDCT6IsEmqNH4dtvTRK+Zo2ZOZKd\\n0FC4/XZ7It6ypb5wEhERERH/5q3txDyyj3dh8tQfROXKcP481KsHBw+auqtXYft2k4SvWGEeu/rT\\nKV3ajOh17WpKmzYQnIf5A40bw759Zlq7bfquSKBKTYX162H1alN273Z9bkSEScTvuMOUli3z9jsj\\nIiIiIlJYvJV4F9v/9mZ3r3WJEmYku317mDLFjIivWQPr1sHatXDggP3clBR7sgFmGnr79tCpkynt\\n25vpuq7a1cifFAWhoeZ2jB49zPOEBPO7snq12Yrs2DH7ub/9Bl99ZQqY34+OHeHOO00i3rate7NI\\nRERERET8XbFNvPOyrVflymYbpQEDzPNjx+xJ+Jo1cPy4/dzkZFO3Zo15XqKEGdGzJeIdO0K1atpO\\nTIq2qCiIjjbFajWzSf71L5OIb9hgkm+bP/6AlStNAZPEt29vflduuw1uvdX8DoqIiIiIBLpim3i7\\nM/JcqxYMGmSK1Qq//GKS8LVrzZ7IJ07Yz7161b5i+ttvm7q6deHQIfNYibcUdRYLNGhgyrBh5kun\\nPXvgu+9M2bDBLM5mY5u2vn69va5+fZOM33ab+dm8uVlvQUREREQkkBT7xNvdBNhiMUlB/frw1FPm\\nekePmgTcVhxXfwZ70l2QdkUCVVAQNGtmyrBh9i+vbEn4d9/BkSPOrzl40JT5883z0qXNlPT27aFd\\nO7O2Qp06+n0SEREREf9WbBNvT0/5tljghhtMGTjQ1P32m9mGadMm2LwZ/v1vuHzZHGvWzDPtigQq\\nxy+vhgwxdceOwfff28vOnfbfGTBrK2zcaIpNxYrQurVJwm0/69bVOgoiIiIi4j+KbeJdGIucRUTA\\nvfeaAiaB2L0bDh+Gbt28165IoKpVy5SHHzbPr1yBXbuck/HDh51f8/vvzusrgNk1oFUrezLeogU0\\nbKhp6iIiIiLiG8U+8S7MKaohIWZ6bLt2hdemSCArVcr+OzNihKlLSIBt22DHDoiPN6PijveKAyQm\\nZr1fvFQps53fzTebe8VtP6tX11R1EREREfGuYpt4a3VxkcAUFQV9+phic/KkPQnfudM8dlzsEMzo\\n+Y8/muIoIsIk4LZkvGlTk6BXquT9voiIiIhI8VBsE2/tpy1SdFSvbsp999nrEhJMAh4fD//5jyn7\\n9pkdBxz99ptZ3G3DBuf6yEiTgDdp4vyzdm39vSEiIiIi+VPsE2+NeIsUTVFR0LOnKTaXL8PPP9sT\\n8R9/ND9Pnsz6+rNnTXFcyA3MyuqNGpkkvHFjs12abZG4iAjv9klEREREAlOxTbw11Vyk+AkJgZYt\\nTXF0/rw9Gd+71yTne/fCqVNZr5GSYhZ827Ur67GKFaFePXsi7liqVNHfNyIiIiLFVbFNvDXVXERs\\nKlWCzp1NcXThgpmebkvEbT9/+SXrlHUwK6zv2GFKZmXLmgS8bl2z7WCdOvYtCOvUgQoVlJiLiIiI\\nFFXFPvHWf3RFxJUKFeDWW01xdPmySb5tSfjBg/afR4/a/35xlJxsthPcvTv7tsqXz5qM2x7fcIO5\\n51xfFIqIiIgEpmKbeGuquYi4KyTErH7etGnWY5cvm73GHZNxWzlyBNLTs7/mxYv26e7ZKVnSLCBX\\nowbUrOn80/a4enWzbZqIiIiI+Jdim3jbaARJRDwpJMS+8Fpm6elmm7NffzVJeOafR49CWlr2101L\\nM+f9+mvO7VepYk/Ga9SAqlXtJSrK/rNMmQJ2VERERETyrFgm3o7TQDXiLSKFJTjYPnX8jjuyHs/I\\ngNOnsyblR4+ahP3ECbMQXE7OnDHlhx9yPq98eedkPHNiXrWqSeIrVzZJuv6uFBEREXFfsUy8bdPM\\nQf+ZFBH/ERRk35O8Q4fsz0lJMdufnTgBx487/7Q9PnUq+8XfHF28aMqBA7nHFRpqEvCcSmSk/XGl\\nSmbkX0RERESMYpl4a8RbRAJV6dJmy7J69Vyfc/WqGfU+cQISEswoeuaftseJibm3mZpqEvrjx/Me\\nZ/nyJgGvWDH7UqGC6/rgYvkvk4iIiBRlxfK/N46Jt+7xFpGipkQJqFbNlNykprpOzs+dg7NnzU9b\\ncXUPema2EfUjR/Iff7lyWRPysLC8l/LlzYi7vlgVERERf1EsE29NNRcRMUJD7fed58ZqNcm0YyLu\\nWDIn6efOmb3Nc5v2ntkff5hy7Jh7fQKzCnxOiXnZsibBL1s262NXx7RivIiIiLjLbxLv9957j+nT\\np3P69GlatGjBO++8wy233OKVtjTVXEQk/ywWe/Jat27eXmO1mj3Mf//ddblwwfWxK1fcizUtzSxE\\nl9tidPkRHJxzsl62rLkVwN0SGmr/qX+bREREiha/SLz/8Y9/MHr0aD744APatWvHrFmz6N69O/v3\\n76dy5coeb09TzUVECofFYhLUcuWgVq38vdZqNYvJXbgASUlZy8WL2ddnLomJeZ8in5P0dBPLhQsF\\nv1ZubEl4dsl5SIgppUrZHzuW/Nbn9poSJbzfXxERkaLOLxLvWbNm8fTTTzNo0CAA4uLiWLZsGXPn\\nzmXs2LEeb08j3iIi/s9iMVuZlSljVnoviMuXnZPx5GTn8scf2T/O6dgff+R/Gn1epaaa8vvv3rl+\\nflgsZuq+O6VUKfdfayvBwaaUKJH1cX5/5naOvowXERFv8XninZaWxs6dOxk/fvz1OovFwt13383W\\nrVu90qbu8RYRKV5CQsyWZ5GRnrum1Wqmwjsm6SkpeS+pqfk7PyXFc7G70093p/0HEosl70m7rQQF\\n2X/aiuPzvDz29Hl5fY3FYoqnHnvyWt5qz7HY3nNXj3M7np9z83JcRIo2nyfe586d4+rVq0RFRTnV\\nR0VFsW/fvhxf26OHe22mp9sf69ttERFxh8Vin5IdEeH99qxWM2X+8mXncuVK1jp36rM7duWKaTOv\\nxVszAAqL1Wr+j+D4/wSRwuTphN9X57rql6vnnq7z9vWLS5v+3Ke8cue1Fy+6315OfJ54u2K1WrHk\\n8ie1cmVBW1lEqVLRBb2ISBaLFi0iOlqfLfE8fbaKL4vFTN0uVcqszO5pnvhsZWTkL1F3VRwT/qtX\\nTSLs7k9vvjYjw/7T8bHjLW0CsAjQ31t5Yfvs6DOUV/psSeDweeJduXJlSpQoQUJCglP9mTNnsoyC\\nOxo1ahQQnqk2mvz88pUosYgnn9Qvq3iekiPxFn22xFs88dkKCrLPAijOrFZTskvKMz/O6Zi3X2OL\\n02q1f2FQ0MfZHVu0aBEDBkR7rI38tO3qPMf3KbvHuR3Pz7mevFYgnJtZ5rq8nJPXuosXF1GuXHSO\\n5xRGXVFts3hYdK04SvRKSz5PvEuWLEmbNm1Ys2YNffr0Acxo95o1axgxYoTL182aNYu6dVsXqO2Y\\nGHjggQJdQkRERMSJ473FAj/8AK+84usopCjq0weWLvV1FMWHv3yZkFd5e23Wgdv4+Hg6dmzjfsMu\\n+DzxBoiNjWXw4MG0adPm+nZily5d4vHHH8/xdRUqFKxd/YMoIiIiIiKSO0/ff+2vQkO9c12/SLz7\\n9+/PuXPnmDhxIgkJCbRs2ZKVK1cS6cnlZ0VERERERER8wC8Sb4Bhw4YxbNiwXM9Lubafys8//1zg\\nNhMTE4mPjy/wdUQy02dLvEWfLfEWfbbEW/TZEm/RZ0u8wZZnpnh4H0+L1RpYt9IvWLCAmJgYX4ch\\nIiIiIiIiRdT8+fMZOHCgx64XcIn3uXPnWLlyJXXq1KF06dK+DkdERERERESKiJSUFI4cOUL37t2p\\nXLmyx64bcIm3iIiIiIiISCDRut4iIiIiIiIiXqTEW0RERERERMSLlHiLiIiIiIiIeJESbxERERER\\nEREvUuItIiIiIiIi4kVKvEVERERERES8SIm3iIiIiIiIiBcp8RYRERERERHxIiXeIiIiIiIiIl6k\\nxFtERERERETEi5R4i4iIiIiIiHiREm8RERERERERL1LiLSIiIiIiIuJFSrxFREREREREvEiJt4iI\\niIiIiIgXKfEWEREJAKdPn+b555+na9euhIWFERQUxHfffefy/C1bttCpUyfKli1LtWrVeO6550hO\\nTi7EiEVERMRGibeIiEgA2LdvH2+++SYnT57k5ptvxmKxuDx3165d3H333aSmpjJr1iyGDh3KBx98\\nQP/+/QsxYhEREbEJ9nUAIiIiAl26dOHGG29k7ty52R5v27Yt58+fp0KFCnz55Zds3brV5bXGjx9P\\nREQEGzZsoGzZsgDccMMNPPXUU6xevZq7777bK30QERGR7GnEW0REJACULVuWChUq5HrexYsXWb16\\nNY899tj1pBtg0KBBlC1blsWLF1+vmzx5MkFBQRw4cICYmBgqVKhAlSpVmDhxIgDHjh2jb9++hIeH\\nU61aNWbOnJmlvXfeeYdmzZpRtmxZIiIiuOWWW/jss8880GMREZGiQ4m3iIhIEfKf//yH9PR02rRp\\n41RfsmRJWrZsyQ8//HC9zjZdfcCAAQBMmzaN9u3b89prr/HWW29xzz33ULNmTaZNm0aDBg0YM2YM\\nmzZtuv76OXPm8Nxzz9GsWTPefvttXn75ZVq1asW2bdsKoaciIiKBQ1PNRUREipBTp05hsVioVq1a\\nlmPVqlVzSpxt2rdvz/vvvw/A0KFDqVOnDn/5y1+YNm0ao0ePBiA6Oprq1aszd+5cOnXqBMC3335L\\ns2bNNMItIiKSC414i4iIFLL09HTOnz9/vZw7d460tDQuX77sVH/+/HmsVmu+rp2SkgJASEhIlmOh\\noaHXj9tYLBaefPLJ68+DgoJo27YtVquVJ5544np9eHg4jRo14tChQ9frKlSowPHjx9mxY0e+YhQR\\nESlulHiLiIgUss2bNxMZGXm9VKlShS1btrBo0aIs9ceOHcvXtUuXLg3A5cuXsxxLTU29ftxR7dq1\\nnZ6Hh4cTGhpKRERElvrff//9+vNx48ZRrlw52rVrR8OGDRk+fDhbtmzJV7wiIiLFgaaai4iIFLKW\\nLVuyevVqp7rY2FiqVavGmDFjnOqrVq2ar2tXq1YNq9XKqVOnshw7deoU1atXz1JfokSJPNUBTiPw\\njRs3Zt++fXzzzTesWLGCJUuW8P777zNp0iQmTZqUr7hFRESKMiXeIiIihSw8PJyuXbs61VWsWJFq\\n1aplqc+vZs2aERwczI4dO3jooYeu16elpbFr167rC6l5SunSpXn44Yd5+OGHSU9Pp1+/frz22mu8\\n8MILlCpVyqNtiYiIBCpNNRcRESlCwsLCuPvuu5k/fz7JycnX6+fNm0dycjL9+/f3WFu//fab0/Pg\\n4GCaNGlCRkYGaWlpHmtHREQk0GnEW0REJEC8+uqrWCwW/vvf/2K1Wpk3bx4bN24E4MUXX7x+3muv\\nvUbHjh254447eOqppzh+/DgzZsyge/fudOvWzWPx3HPPPVStWpWOHTsSFRXFnj17eO+99+jdu7fT\\nHuIiIiLFnRJvERGRADFx4sTre29bLBY+/vjj648dE+9WrVqxevVqxo0bR2xsLOXLl2fo0KG8/vrr\\neW7L1k5O9X/6059YsGABs2bN4o8//qBmzZqMHDnSKRYREREBizW/+5SIiIiIiIiISJ7pHm8RERER\\nERERL1LiLSIiIiIiIuJFSrxFREREREREvEiJt4iIiIiIiIgXKfEWERERERER8aKA207s3LlzrFy5\\nkjp16lC6dGlfhyMiIiIiIiJFREpKCkeOHKF79+5UrlzZY9cNuMR75cqVxMTE+DoMERERERERKaLm\\nz5/PwIEDPXa9gEu869SpA5g/iCZNmhToWqNGjWLWrFkeiErEmT5b4i36bIm36LMl3qLPlniLPlvi\\nDT///DMxMTHX805PCbjE2za9vEmTJrRu3bpA1woPDy/wNYqLK1fAYoGSJX0dSWDQZ0u8RZ8t8RZ9\\ntsRb9NkSb9FnS7zJ07c1a3E1ydWRI1CjBtStCzt2+DoaERERERGRwKLEW3L1zTdw7hwcPw49e8Le\\nvb6OSEREREREJHD4PPF+4403aNeuHWFhYURFRdGvXz/279/v67DEQVqa/fG5c9CtGxw96rt4RERE\\nREREAonPE++NGzfy7LPPsm3bNlavXk1aWhr33HMPKSkpXm87Ojra620UBRkZzs+PH4d77oGzZ30T\\nTyDQZ0u8RZ8t8RZ9tsRb9NkSb9FnSwKJxWq1Wn0dhKNz585RpUoVvvvuOzp16pTleHx8PG3atGHn\\nzp1aTKGQTJ8OY8aYx8HBkJ5uHrduDevWQViY72ITERERERHxFG/lmz4f8c7swoULWCwWIiIifB2K\\nXOP41cy0aWahNYD4eLj/fkhN9U1cIiIiIiIigcCvEm+r1crIkSPp1KkTTZs29XU4co3jVPM6dWDV\\nKrB9L7J+PTz8sNluTERERERERLLyq328hw0bxp49e9i8eXOu544aNYrw8HCnuujoaN3r4QWOI95B\\nQdC0KSxfDl27QnKyWfX8kUfgH//QPt8iIiIiIhIYFi1axKJFi5zqEhMTvdKW3yTew4cP59tvv2Xj\\nxo1Uq1Yt1/NnzZqle7wLiWPibbGYn+3awddfw733QkoK/N//wcCBsHChuQ9cRERERETEn2U3cGu7\\nx9vT/GKq+fDhw/nqq69Yt24dtWvX9nU4konjVHNb4g3QpQt89RWEhJjnn38OgwfD1auFG5+IiIiI\\niIg/83niPWzYMBYsWMDChQspW7YsCQkJJCQkkKoVu/xG5qnmjrp1M6PdpUqZ5wsXwpNPZt2CTERE\\nREREpLjyeeIdFxdHUlISnTt3pnr16tfL4sWLfR2aXJPdVHNHPXvCF1/Yp5h/8gk8/bSSbxERERER\\nEfCDe7wzlJ35vdwSb4Devc3iav37m6nmH35ofs6ZAyVKFE6cIiIiIiIi/sjnI97i/xy/G8k81dzR\\nAw+Yqea2RPvjj2HQIEhP9258IiIiIiIi/kyJt+QqLyPeNv37m5Fv27TzhQthwADt8y0iIiIiIsWX\\nEm/JVX4Sb4AHH4QlS+wLri1ZYkbDtV6eiIiIiIgUR0q8JVd5nWruqHdvs8936dLm+bJlpi452fPx\\niYiIiIiI+DMl3pKr/I5429xzDyxfDmXLmuerV0OPHnDhgmfjExERERER8WdKvCVX7ibeAHfeCatW\\nQViYeb5pE9xxB5w65bn4RERERERE/JkSb8mVY+Kd16nmjjp0gHXrIDLSPP/Pf0zdgQOeiU9ERERE\\nRMSfKfEu5n76Cf7735zPcbzHO78j3jatW8PmzVCnjnl+5Ah07Ajx8e5dT0REREREJFAo8S7Gdu2C\\n5s2hWTOYNMl5ZNtRQaaaO2rQwCTfzZub52fPQufOsHat+9cUERERERHxd0q8i7F//9v++OWX4fHH\\ns99vu6BTzR1Vrw7ffQedOpnnFy9Cz55m728REREREZGiSIl3MZZ5hHvePLj3XkhMdK73xFRzRxUq\\nmAXXevc2z69cgUcegTfecD3qLiIiIiIiEqiUeBdj2SW5q1fD7bfD8ePZn+eJxBvM/t5LlsCTT9rr\\nxo+HoUMhLc0zbYiIiIiIiPgDJd7FmGNC/fTTUKmSefyf/0D79rB7d9bzCjrV3FFwMMyZA6+/bq/7\\n6CMz9Vx7fYuIiIiISFGhxLsYc0yob7sNtm6FevXM8xMnzH3Y33zj+anmjiwWeOEF+OwzCAkxdWvW\\nmBXPjxzxbFsiIiIiIiK+oMRbAJMAN2hgku9bbzV1f/wBffrA1187n+cNAwaYhNs26r5nj4nj+++9\\n056IiIiIiEhhUeJdjGV373ZkpNneq39/+zmHD9vP8+RU88w6djSJdsOG3Z2MCgAAIABJREFU5vmZ\\nM3DnnfDxx95rU0RERERExNuUeBdjrhZNK1PGTP2ePDnra7w14m1Tv74Zde/c2Ty/cgWGDIERI7To\\nmoiIiIiIBCYl3sVYTlt3WSwwaRIsXmxWILepUMH7cUVEmO3G/vxne90770D37nDunPfbFxERERER\\n8SQl3sVYXrYJe/hh2LjR7Ln90kv2xde8rWRJePdds+p5yZKmbt06uOUW+PHHwolBRERERETEE4J9\\nHYD4h5ymkLdpA0uXFl4sjv7nf6BpU3jgAUhIMCud33abScgffdQ3MYmIFHWXLl3ib3/7G9WqVWP3\\n7t3MnDnT1yGJiIgENLcSb6vVyhdffMG6des4c+YMGY77TQFLlizxSHDiXXkZ8fYHHTrAjh3Qr5/5\\neekSDBwImzbBrFn2bchERMQzBgwYwLRp02jatCm9evVi//79NLStfCkiIiL55tZU85EjR/LYY49x\\n+PBhypUrR3h4uFORwBAoiTdAzZrw3Xfw+OP2utmzzUrojquui4hIwXz44YekpqbStGlTwIx+Hzp0\\nyMdRiYiIBDa3Rrw//fRTlixZQq9evTwdjxSinBZX80elS5utxW6/3Sy8lpoKO3dC69Ywb565D11E\\nRApm6tSpvPLKKwBkZGSwe/duKlas6OOoREREAptbI97h4eHUrVvX07FIIQukEW9HQ4aYLcdsC71d\\nuAB9+sC4cdpyTESkIHbs2MGxY8c4ePAg06ZNY9SoUaSlpdGiRQtfhyYiIhLQ3Eq8J0+ezJQpU0hJ\\nSfF0POIjgZR4A7RsaUa7H3jAXvfXv8Idd4BmRIqIuGf79u106NCBCRMmMG7cOCpVqkR0dDShoaG+\\nDk1ERCSguZV49+/fn99//50qVarQvHlzWrdu7VQkMATqiLdNeDh88YVZYC342k0T339vkvIFC3wb\\nm4hIIEpKSqJly5aAmWb+6aefMmLECB9HJSIiEvjcusd78ODB7Ny5k5iYGKKiorAEYtYmAZ94g4l7\\n5Eizxdijj5rR7osXISYGVqyA996DsDBfRykiEhjq1atHUlISAHPmzCEmJobmzZuzadMmvvnmG265\\n5RZCQkJITExk4MCBPo5WREQkcLiVeC9btoyVK1fSqVMnT8cjhSjQFlfLya23wg8/wLPPmoXWAObP\\nh82bYeFCaN/et/GJiASCfv36sXr1aubMmUNKSgqTJk0CoFatWpw9e5ZevXpx6dIlnnjiCSXeIiIi\\n+eBW4l2rVi3CNIwY8IrCiLejsDD45BPo3h2eeQaSksxWY506wYsvmlKqlK+jFBHxXyVKlCAuLi5L\\n/Q033EBqaiqlS5dmyZIldOvWzQfRiYiIBC637vGeMWMGY8eO5ciRIx4OR3ylKCTeNo8+Crt2menn\\nAFevwssvm1HxH3/0bWwiIoHo559/Ji0tjZUrV3Ls2DGGDx/u65BEREQCilsj3jExMVy6dIl69epR\\npkwZSpYs6XT8t99+80hwOerRw/XwZcOGsHZtzq/v2hX273d9PDbWFFf27YO77sq5jTVroFEj18dn\\nzjTFFS/3w2qFhuxjDXdRaSjgav0cP+8HkOX9uBHYDFwsD0kXTd1du9bQtm0jJk0yW48FZ/70+2E/\\nshWA70e21A9D/bBTP+z8rB9r165lxIgRdOrUie7du5vjAdiPbKkfduqHnfphqB926oddUe/HlSs5\\nv85NbiXeb731lqfjyL+zZ10fCw/P/fUJCXDihOvj1xaXcSk9PefX287JSVJSztfwcj+sVggmnZqc\\ngJy+K/HzfgDZvh8WIOxaAdPXtDR46SX46iszLb1Jk0xt+GE/sj0nJ+qHnfphqB/ObagfRh77cfLk\\nSRYuXEiDBg2yxhhA/XBJ/XBuQ/0w1A9D/XBuQ/0wiks/PMztVc19LjLS9Yh3VFTur4+KgsRE18dz\\nu4c9OBhq1Mj9nJyEheV8DS/3w2qFdII5Tg0qVYLSrrZp9fN+ALm+H1bgyXuDGfMhZGTA9u3QqhVM\\nmWK+kCtZkoDox/VzcqJ+OJ+jfqgfmdtQP+zn5KEf1atXZ/PmzdnHGED9cEn9cG5D/bCfo36oH5nb\\nUD/s5xTlfly5kvMgr5ssVmv+17Y+evRojsdr167tdkC5iY+Pp02bNuzcuVN7hhfQ66+bBccA/vlP\\nuP9+38ZTGLZtg8GDzQwXmxYtYM4cuOUW38UlIiIiIiK+5618060R7zp16uS4d/fVq1fdDkgKT1Fb\\n1TwvbNuOvfQSzJpl/gx27zbbjT37LLzyCpQv7+soRURERESkKHFrVfMffviB+Pj462Xbtm3ExcXR\\nsGFDPv/8c0/HKIWguCTeAKVLw4wZ8P33ZrQbzPTzt9+Gm26Cr7/2bXwiIiIiIlK0uDXi3cKWrTho\\n27Yt1atX58033+SBBx4ocGDifcVxxNtRu3bmXu9Zs2DyZEhJgWPHoE8feOghU1+zpq+jFBERERGR\\nQOfWiLcrjRo1Yvv27Z68pHhRcU+8wSyqNnYs/PQTdOtmr//iC2jcGKZOhcuXfRefiIiIiIgEPrcS\\n76SkJKeSmJjI3r17eemll7JuNyJ+K//L6hVddevCypUwfz5UrmzqkpPhhRegeXP49lvfxiciIiIi\\nIoHLrcS7QoUKVKxY8XqJiIigadOmbN26ldmzZ3s6RvESjXg7s1hg4ECz4vmf/wxB1347DhyAe+81\\nU9B/+cW3MYqIiIiISOBx6x7vdevWOT0PCgoiMjKS+vXrE5zbnmvil5R420VEwLvvwtChMHw4bNpk\\n6r/+Glatgr/8BcaN0+rnIiIiIiKSN26NeN95551O5fbbb6dx48ZuJ90bN26kT58+1KhRg6CgIJYu\\nXerWdSR/NOKdsxYt4LvvYMECqFbN1F2+DK+9BvXrQ1wcpKf7NkYREREREfF/ec6Uly5dSs+ePSlZ\\nsmSuiXGfPn3yFURycjItW7ZkyJAhPPjgg/l6rbhPiXfuLBZ49FHo3RtefdWsdJ6WBmfOwDPPmC3I\\n3nzTTEXXn6GIiIiIiGQnz4l33759OX36NFWqVKFv374uz7NYLFy9ejVfQfTo0YMePXoAYNWKX4VG\\nf9R5V748TJsGTz1lFlyzbVe/d69Jyjt3hunToU0bn4YpIiIiIiJ+KM9TzTMyMqhSpcr1x65KfpNu\\n8R2NeOdfvXqweDFs2QIdOtjr16+Htm0hOhr27/dZeCIiIiIi4ocCdiW0HvN7UGp9qWyPNazUkLWD\\n1+b4+q6fdGX/edcZUuxtscTeFuvy+L5z+7hr3l05trFm0BoaVW7k8vjMrTOZuXWmy+OF0Q8q7YPB\\ndzFwJ4T8lP0pgdCPwn4/brvNLLq2ZAk8/zwcPGjO+SykK5/9fT9lykBYGJQo4d/9cCXQ3g9X1A9D\\n/bBTP+xy6kfGlQxa/dqK/u37s3v3bmbOzNpWIPQDisb7AeqHI/XDUD/s1A879cMoSD+uHL+S4+vc\\n5VbiPWLECOrXr8+IESOc6t99910OHjzIW2+95ZHgcnL2y7MQmqmyuSnhoeG5vj4hOYETF0+4PJ50\\nOSnH16dnpOf4ets5OUm6nJTjNTL3Y98+ePZZqFULxoyBxo0L1g+rFQhKh7ATnLsCuPiMebof2QnE\\n98NigQcfNFPN4+LMPeBnyyZA2AkuAZcuZd9GbjEWdj+yE4jvR3bUD0P9cG5D/TBy7MdC6PdCPwYP\\nHkyvXr3Yv38/DRs2zBKj3/eDIvJ+oH5kPicn6oed+mGoH85tqB9GQnICJ7acgP9kOpCa60vd4lbi\\n/eWXX2a7wFqHDh2YOnVqoSTekQ9GUqpm9iPeUWWjcn19VNkoElMTXR4PCwnL8fXBQcHUKF8j13Ny\\nEhYSluM1MvfjvffgX/8yjz/+GAYMgHK3R1GjvHv9sFqBjGBIqkHlyhASkv15nu6Hq3MC7f2wKVUK\\nRoyAIUOgxcwoDiclZrl/vlw5c5+4P/cj8zmB+n5kPkf9UD8yt6F+2M/Jrh/J3yeTYk2hXqN6AFy6\\ndIlDhw5lSbz9vR+ObeRE/XBuQ/2wn6N+qB+Z21A/7OcUmX50SIQOzvVXjl/h7Kyzub4+vyxWN1Yz\\nCw0N5aeffqJ+/fpO9QcPHqRZs2akprr/NUFQUBD//Oc/Xa6MHh8fT5s2bdi5cyetW7d2u51ANHgw\\nzJvnXGexwMMPw4QJ0KxZ/q43fjy88YZ5vHo13JXzjA/JowsXYOZMswL6H3/Y60ND4X/+x8xWqF3b\\nd/GJiOSkfv36vPLKK0RHR5ORkUGlSpVYsWIFt956q69DExER8Tpv5Ztu7eNdv359VqxYkaV++fLl\\n1K1bN9/XS05OZvfu3ezatQuAQ4cOsXv3bo4dO+ZOeEWW41ckZcva6xYvhubN4aGHYPdu966nxdU8\\np0IFePllOHzYJNmlS5v61FR4912zQNuTT8KBA76NU0Qksx07dnDs2DEOHjzItGnTGDVqFGlpabRo\\n0cLXoYmIiAQ0txLv2NhYxo4dy6RJk9iwYQMbNmxg4sSJPP/884waNSrf19uxYwetWrWiTZs2WCwW\\nRo8eTevWrZk0aZI74RVZGRn2x5s3m+2rri00D8CXX0LLltCzJ6xbl7/twpR4e17lyvDXv8Ivv0Bs\\nLJQpY+rT02HuXHOP/qOPwk8uFrUTESls27dvp0OHDkyYMIFx48ZRqVIloqOjCQ21L6py8OBB2rZt\\n68MoRUREAo9bifeQIUOYMWMGH330EV26dKFLly7Mnz+f2bNnM3To0Hxf784777y+FZljmTt3rjvh\\nFVmOiXS5cjB6tBlVnTULqla1H1uxArp2hXbtzGh4uou1CTTiXTiqVYMZM+DXX+GllyD82loPGRmw\\naJGZrdC7t9mSTHuri4gvJSUl0bJlS8BsHfrpp59mWUg1MjKSJk2a+CI8ERGRgOVW4g3wzDPPcPz4\\ncRISEkhKSuLQoUMMGjTIk7FJJo5JWdC1d65MGRg5Eg4dgnfegRtvtJ+zY4dZgK1RI3j//ayrbCvx\\nLlyVK8Mrr5gE/PXXzXObb76BLl3MXuALFkBamu/iFJHiq169epS5Nj1nzpw5xMTE0Lx5cwA+/PBD\\nli9fzvjx4+nWrZsvwxQREQk4bifeNpGRkZQrV84TsUguHKeaZ06US5eG4cNh/3747DNwXAfg0CH4\\n85/hhhvgxRfhxLWV9zW66hvh4fDCCyYBf+stsz2cTXw8xMSYL1D++lezUJuISGHp168f58+fZ86c\\nOaSkpFy/5evbb7/l/Pnz9OzZk5SUFLp37+7jSEVERAJLnrcTa9WqFZY8DovGx8e7HZC4lpcR6uBg\\nM8rdvz+sXWuSt1WrzLFz58xI61//ahZiS07O/XriPWXKwHPPwbBh5v78GTPMLAUwX46MG2cWaRsy\\nxHxx0qiRb+MVkaKvRIkSxMXFZalftmwZTz31FABnz57l8uXLhR2aiIhIQMtz4t23b19vxiF5kN1U\\nc1csFrM92F13wa5d8Oab9vu909PNqHjm88U3SpaERx4xX5hs3Gi2Ilu61LzfycnmFoJ33jHv5bBh\\n0KeP+YJFRKSw9OvXj61bt/Lrr79St25d9uzZQ23tiygiIpJnbu3j7UvFeR/vhx+GL74wj48dg5o1\\n8/f6kychLs6Us5n2hN+4ETp18kycUnD798Pbb8PHH0NKivOxGjXgqadg6FCzcJuIiIiIiHiGX+3j\\nLb6R0z3eeVG9upm6fPSoSeiuLVxLpUpw882eiVE8o2FDeO89OH7cjIDXr28/duIETJoEtWubWwpW\\nr3b+bIiIiIiIiH9xK/G+evUq06dPp127dlStWpWIiAinIt7hqVXIQ0Ph8cfNQl5795oSFlbg8MQL\\nIiJg1CjYtw9WroT777ffZpCeDp9/Dt26Qd26MGWKWbBNRERERET8i1uJ95QpU5g5cyYDBgwgMTGR\\n2NhYHnjgAYKCgpg8ebKHQxSb/NzjnRcWi1mwy3FbK/FPQUFwzz3wz3+avdtffBGqVLEf//VXmDzZ\\nrIZ+zz3mHv7UVJ+FKyIiIiIiDtxK3xYsWMCcOXMYPXo0wcHBREdH8+GHHzJx4kS+//57T8co1xR0\\nqrkUDbVrw6uvmvv8Fy+GHj3snwerFf71L4iONvd/Dx8O//63to4TEREREfEltxLv06dP07x5cwDK\\nlStHYmIiAPfddx/Lli3zXHTixFNTzaVoKFXKLLi3fLkZ8X71VTPl3ObCBXOf+K23mnvGJ00yU9ZF\\nRERERKRwuZV416xZk1OnTgFQr149Vl3bKHr79u2EhIR4Ljpx4ump5lJ01Kplpp8fOADr1sFjj0Hp\\n0vbjBw+ahfUaN4a2bc2CbSdP+i5eEREREZHixK30rV+/fqxZswaAZ599lgkTJtCgQQMGDRrEkCFD\\nPBqg2GmqueQmKAg6d4Z58+DUKfjoI+ja1fnzsnMnjB5ttqO76y744AM4c8ZnIYuIiIiIFHnB7rxo\\n6tSp1x8PGDCAG264gS1bttCgQQN69+7tseDEmaaaS36Eh8OQIaacPGkWXFu40CTeYD5Pa9ea8swz\\ncMcd8NBD0K+f2XpOREREREQ8w60R7zfeeIO5c+def96+fXtiY2M5e/Ys06ZN81hw4kyJt7irenWI\\njYUdO+Dnn2HCBKhXz348IwPWrzeLsdWsCR07wqxZ2p5MRERERMQT3Eq8//73v9O4ceMs9TfddBNx\\ncXEFDkqyp3u8xRMaNzb3ex84ANu3w/PPQ/369uNWK2zZYhL1OnXMPeFTpph937U6uoiIiIhI/rm9\\nqnm1atWy1EdGRl5fdK0os1pN0pKcXLjt6h5v8SSLxSTVb7wB+/fD7t0wcSI0bep83s6dZo/wNm3M\\naPjTT8PXX8OlSz4JW0REREQk4LiVeNeqVYvNmzdnqd+8eTPVi8HNoePHm+2ZIiKgWzeYMQP27PH+\\naKCmmou3WCxw881mZPu//zXT0V99FVq1cj7v5EmzGFufPlCpEtx3H8TFwdGjvolbRERERCQQuLW4\\n2tChQxk5ciRpaWl07doVgDVr1jD2/9u78/CoqsON4+9MEiALCYGEsBP2gCJLBAVRFhFEKxUXFEFQ\\nW7SliAIqP0VZqq2gKK2KoCBKgaLWpVIBo0akiiCQIAUBkR0EAkFMIBtZ5vfHcTKZ7JnMZDLJ9/M8\\n55mZM3fuPYdcIO+cc8997DFNnTrVrQ2sjj75xDxevCh9/rkpjzxibul0/fXSsGFmtejQUPcel6nm\\nqCoxMeb2ZNOnS8eOSWvWSB9/LMXHS5mZZpvMTFO/Zo3jM0OGmC+jBgyQQkK81nwAAACgWnEpeD/6\\n6KM6e/asJkyYoIsXL0qS6tWrp2nTpunxxx93awOro9zc4uuPHZMWLzbF31/q21caPNjczql3bykg\\noHLHZao5vKFlS+kPfzAlLc2sgv6f/5ggXvDKkr17TXnpJXOu9+1rgviQIVLPnnxZBAAAgNrLYrO5\\nPkH6woUL2rNnjwIDA9WhQwfVrVvXnW0rVmJiomJjY5WQkKCePXt6/HjF6dpV2rVLCgw0C0598okp\\nX34pZWUV/5ngYOnqq00IHzRI6t5d8vOr2HEHDjTHkMz1tYGBlekFUDl5edL27WbE+9NPpc2bS/5S\\nqlEjc/4OGGBKly58eQQAAIDqx1N5s1LB2xuqQ/C+9FJzHWxwsHThgqM+PV3asMGE8HXrzAJsJWnQ\\nQOrf34TwAQOkSy4pO4gPGGD2L5lpvlXwPQdQbikp0vr1JoR/+ql04EDJ20ZEmPN/wADzeMkljIgD\\nAADA+zyVN12aal7b2ad8Fx6xCwoy13cPGyb9/e/S4cMmiHzxhbk2tuC03F9+kT76yBRJCgszU3P7\\n9TP3UO7du+iINlPNUZ2FhUk332yKZIL3Z5+ZEh9vgrldcrL0/vumSGZEvH9/U666yiz0VtlLMwAA\\nAIDqguDtAvscgbJG6KKjpXvvNcVmM7ds+uILU9avl86edWybkmJGydetM68DAsztm/r1c4RxVjWH\\nL2nXzpQ//MFMQf/uOzNj48svpf/+1zmInz0rffCBKZL50ql3b/NlVJ8+pkREeKUbAAAAQKUx1dwF\\nMTHSDz+YEb5ffnFtH3l50s6dJoR//bUpp0+X//PZ2WYBN8AX5eZK//ufCeEbNphS1t+ljh1NALeH\\n8S5dKr5OAgAAAFAapppXIyVNNa8Iq1Xq1s2UyZPNaPb+/dLGjY4g/sMPJX+eEW/4Mj8/c4/wHj3M\\n+Z+ba76I+vpr6ZtvpE2bzKUaBe3bZ8qyZeZ1UJBZLf3yyx2lQweuFQcAAED1Q/B2QXmnmleExWJC\\nQ4cO0j33mLrTp00IsQfx7dvNvcOvu46RPtQsfn5mpf/u3aWJE03diRMmgNuDeEKCOf/t0tMdfzfs\\n6tc3l2gUDONt2/JFFQAAALyL4O0Ce/D29C/zjRs7L1aVlWVGAdu08exxgeqgWTPp1ltNkcxK/omJ\\nJohv2SJt2yYdOuT8mfPnzfR1+233JCk01CzWdtlljlkml15q7koAAAAAVAWCtwvcMdXcFXXrSp06\\nVe0xgeqiXj1zfXffvo66s2fNSPi2bY5y7Jjz51JTi46M22eYFAzj3bpJLVsyOg4AAAD3I3i7wBNT\\nzQFUXKNG0pAhptglJZkwvnWrCeL/+5909Kjz5+x3Gdi3T3rvPUd9/fpS585m4TZ76dzZ3KGAv+8A\\nAABwFcHbBVU11RxAxUVFSTfcYIrduXMmgO/YYcr//ift2mWmrxd0/ryZxr5li3N9YKC5m4E9iNsf\\n27aV6tTxfJ8AAADg2wjeLvDWVHMArgkPl/r3N8UuJ0f68UfnML57d9HV1CUpI8Msbrh9u3O91WpG\\nw+0LIxYs0dHc8g8AAAAGvxa6gKnmgO/z9zej1p07S3fe6ahPSzO38tu925Q9e8zj/v2OL93s8vKk\\ngwdNiYsruv82bZzDeNu2pq51azOKDgAAgNqB4O0CppoDNVdwsLk/eM+ezvVZWWaE3B7I9+41r3/8\\n0UxRL8w+ov7jj8Ufp0kTMyrepk3Rx1atmMIOAABQkxC8XUDwBmqfunXNbcguvdS53maTTp92hOyC\\nZf9+M4JenFOnTNm8ueh7FovUvLkJ4q1bSy1amNKypeN5ZCSzbgAAAHwFwdsFXOMNwM5iMQu6RUVJ\\n/fo5v2ezSSdPmgD+44/mvuOHDzseT5wofp82m3T8uCkFb4NWUJ06JpwXF8pbtjT3QW/cmOvMAQAA\\nqgN+JXMB13gDKA+LxQTgZs2ka64p+n5mpnTkiHMYL/h45kzJ+7540Wxz6FDpx4+MlJo2NVPbCz4W\\nrgsJqWRnAQAAUCKCtwuYag7AHerVkzp1MqU4aWlm1PvYMccIuP25/fHcuZL3b58Gf/q0Wbm9NCEh\\njiAeFWUCe0SEeSxcIiKkgADX+w0AAFDbELxdwFRzAFUhOLj0YC45wnnBYH7smJnifuqU4zEnp/Rj\\nXbhgpsTv31++tjVoUHowb9jQlPBwxyMLxgEAgNqK4O0CppoDqC7KE87z8qSff3Ys6FY4lBd8TEkp\\n33F/+cWU8gZ1e1uLC+SF6+zPGzSQwsKk0FBG2AEAgG8jeLuAqeYAfInVakahIyKKrspeWEaGmZp+\\n5oyUnGweSyvlDeqSGZ1PSzMj8hUVGGhCuD2Il/d5wcegIL4wBQAA3kHwdgFTzQHUVIGB5hZmrVuX\\nb/uLF6WzZ53DeHKyufb8559NKe75xYsVa1dGhimnTlW8T3YWixl1Dw4217RXtgQHmzBfpw7/HwAA\\ngNJVm+C9YMECzZs3T6dOnVK3bt308ssvq1evXt5uVrGYag4ARp06jlXSy8tmMyG6tGD+889mNN1e\\nUlMdz8+fd/w7XBE2m7mW/cIFKSmp4p8vidVqvrAICirfY0W2rVfP3EPe/mgvBH0AAHxLtQje77zz\\njqZOnarXX39dvXv31vz58zV06FDt27dPERER3m5eEUw1BwDXWSwmWAYFmfuOV1RengnPBQN5wWBe\\n3PPUVDPN3R687SUrq/L9yctzTKOvKnXqOAfygsG8rOclvV+3rrmWPiDA7L88j8XV+flV3Z8DAAC+\\noloE7/nz5+uBBx7Q2LFjJUmLFi3SmjVrtHTpUj322GNebl1RTDUHAO+xWs0126Ghld9Xdnbxgbxg\\nKen98+fNyH16evGP2dmVb19JLl405fx5zx3DVVZr2eG8uADv71968fNzzzYV2ZfVah4LPi/8WNJ7\\n/I4AACjI68E7OztbCQkJeuKJJ/LrLBaLBg8erE2bNnmxZSVjqjkA1AwBAWb19AYN3L/vnJzSg3lp\\n76Wnm5KVJWVmmsfyPi/r1nGelpfnaEttZrG4Ftgr857V6igWS+mPrr5XHbYpXOx/3qXVeWobbx67\\nsu0reK4WPncr+p479lGb2oXayevBOzk5Wbm5uYqKinKqj4qK0g8//FDqZwt9pMrYpxPyFwgAUBJ/\\nf6l+fVOqUm5uxQN7ZqYZob94sXyP7tgmN7dq/1yqms3m/S9BAPgGb34hUN52uWtbX9inp/5/8nrw\\nLonNZpOllD+ZyZMn6/TpsEK1o34t5bWqgts7Cyt8eOBXq1at0qhRrp9bQEk4t1AWPz/HNfQVUdXn\\nVl6eCab2MJ6TY37ZyckpvZRnG3ftKy/PbFP4sbg6T75X8NE3Ve73LaBkNePcKrhgqCuLh6IyVv1a\\nCqrAvVIrwOvBOyIiQn5+fkoqtMTs6dOni4yCFzR//nzdemvPSh07KWmVoqJc+8saHi7NmFGpw6MG\\nIxzBUzi34ClVfW5ZreYa7zp1quyQPs9mM6Wk4J6b69gmL6/kx9Le88Q2zzyzSo8/Psqtx7KXgn8u\\nJdV5c5vq2r6C51Thc6ys59Xpve3bV6l791Flbucr71X1sUtTkS8AfHufRQduMzISdeRIbPl3XE5e\\nD94BAQGKjY1VfHy8hg8fLkmy2WyKj4/XpEmTSv3soUOVO/bw4dLq1ZXbBwAAQFWwX6Pra2vMLFki\\njRjh7VagJuJ3eXhCYqIU6/7c7f3gLUlTpkzRuHHjFBsbm387sfQQ76AOAAAgAElEQVT0dN1zzz3e\\nbhoAAAAAAJVSLYL3yJEjlZycrBkzZigpKUndu3dXXFycIiMjvd00AAAAAAAqpVoEb0maMGGCJkyY\\nUOZ2GRkZkqQ9e/ZU+pgpKSlKTEys9H6Awji34CmcW/AUzi14CucWPIVzC55gz5n23OkuFputIpek\\ne9/KlSs1ZswYbzcDAAAAAFBDrVixQqNHj3bb/nwueCcnJysuLk7R0dEKDAz0dnMAAAAAADVERkaG\\nDh8+rKFDhyoiIsJt+/W54A0AAAAAgC/xsRtSAAAAAADgWwjeAAAAAAB4EMEbAAAAAAAPIngDAAAA\\nAOBBBG8AAAAAADyI4A0AAAAAgAcRvAEAAAAA8CCCNwAAAAAAHkTwBgAAAADAgwjeAAAAAAB4EMEb\\nAAAAAAAPIngDAAAAAOBBBG8AAAAAADyI4A0AAAAAgAcRvAEAAAAA8CCCNwAAPmDZsmWyWq1Fip+f\\nn06fPl1k+9WrVys2NlaBgYFq3bq1Zs2apdzcXC+0HAAA+Hu7AQAAoHwsFouefvppRUdHO9U3aNDA\\n6fW6des0YsQIDRo0SK+88op27typZ555RmfOnNGCBQuqsMUAAEAieAMAUC0MHDhQbdq00dKlS0vd\\n7vrrr1fPnj1L3Wbq1Knq3r274uLiZLWayW3169fXs88+q4ceekgdO3Z0W7sBAEDZmGoOAICPuXDh\\ngvLy8op9b8+ePdq7d6/uv//+/NAtSRMmTFBeXp7ee++9/Lp77rlH9evX17Fjx/Sb3/xG9evXV8uW\\nLfXqq69Kknbu3Klrr71WISEhio6O1qpVq5yOlZOTo9mzZ6tjx44KDAxURESErr76asXHx3ug1wAA\\n+C6CNwAAPsJms2nAgAEKDQ1VUFCQfvvb32r//v1O22zfvl0Wi0WxsbFO9U2bNlWLFi20ffv2/DqL\\nxaK8vDwNGzZMrVu31vPPP6/o6Gg9+OCDWrZsmYYNG6ZevXrpueeeU2hoqMaNG6cjR47kf37mzJn6\\n85//rGuvvVYLFizQk08+qdatWysxMdGzfxAAAPgYppoDAOADgoKCdO+992rgwIEKDQ1VQkKCXnjh\\nBV111VVKTExU8+bNJUknT56UZIJ2YU2bNtWJEyec6jIzMzV27Fg99thjkqRRo0apWbNm+t3vfqd3\\n3nlHt956qyRp8ODBiomJ0bJlyzRjxgxJ0tq1a3XjjTdq4cKFHus3AAA1AcEbAIAqlpOTo5SUlPzX\\nNptN2dnZysrK0tmzZ522bdiwoSwWi26//Xbdfvvt+fXDhw/XkCFDdM011+gvf/lL/vTwjIwMSVLd\\nunWLHLdevXo6f/58kfrf/e53+c/DwsLUqVMnHThwID90S1LHjh3VoEEDHTx4ML+uQYMG+v7777V/\\n/361b9++on8MAADUGkw1BwCgim3cuFGRkZH5pXHjxvrmm2+0atWqIvXHjh0rcT9XXXWVrrjiCn3+\\n+ef5dYGBgZKkrKysIttnZmbmv29Xr149NWrUyKkuLCxMLVq0KPL5sLAwnTt3Lv/1n//8Z/3yyy/q\\n2LGjLrvsMk2bNk07d+4s3x8CAAC1CCPeAABUse7duzuFZUmaMmWKmjZtqkcffdSpvkmTJqXuq2XL\\nltq3b1/+a/sU85MnT+ZPP7c7efKkrrjiCqc6Pz+/YvdbUr3NZst/fvXVV+vAgQP66KOP9Omnn2rJ\\nkiV68cUX9dprr+m+++4rtd0AANQmBG8AAKpYWFiYBg0a5FQXHh6upk2bFqkvy8GDBxUZGZn/unv3\\n7rLZbNq2bZsuv/zy/PqTJ0/q+PHjeuCBByrX+EIaNGigcePGady4cUpPT9fVV1+tWbNmEbwBACiA\\nqeYAAPiA5OTkInVr165VQkKChg0bll/XpUsXxcTE6PXXX3canX711VdltVqdrtuurJ9//tnpdVBQ\\nkNq3b1/sNHcAAGozRrwBAPABffv2VY8ePXT55ZcrLCxMCQkJevPNN9W6dWs9/vjjTts+//zz+u1v\\nf6vrrrtOd955p3bu3KkFCxZo/PjxiomJcVubunTpogEDBig2NlYNGzbU1q1b9d5772nSpEluOwYA\\nADUBwRsAAB9w5513as2aNfrss8+Unp6upk2b6oEHHtCMGTOcpppL0o033qgPPvhAs2fP1qRJkxQZ\\nGaknn3xSTz31VJH9WiyWYo9XXL3FYnGqf+ihh7R69Wp99tlnysrKUuvWrfXXv/5VjzzySCV7CwBA\\nzWKxFZyHBgAAAAAA3IprvAEAAAAA8CCCNwAAAAAAHkTwBgAAAADAgwjeAAAAAAB4EMEbAAAAAAAP\\n8rnbiSUnJysuLk7R0dEKDAz0dnMAAAAAADVERkaGDh8+rKFDhyoiIsJt+/W54B0XF6cxY8Z4uxkA\\nAAAAgBpqxYoVGj16tNv253PBOzo6WpL5g+jcuXOl9jV58mTNnz/fDa0CnHFuwVM4t+ApnFvwFM4t\\neArnFjxhz549GjNmTH7udBefC9726eWdO3dWz549K7WvsLCwSu8DKA7nFjyFcwuewrkFT+Hcgqdw\\nbsGT3H1ZM4urAQAAAADgQV4P3s8++6x69+6t0NBQRUVFacSIEdq3b5+3mwUAAAAAgFt4PXh/9dVX\\nevDBB/Xtt9/q888/V3Z2toYMGaKMjAxvNw0AAAAAgErz+jXea9eudXr91ltvqXHjxkpISFC/fv08\\neuxRo0Z5dP+ovTi34CmcW/AUzi14CucWPIVzC77EYrPZbN5uREH79+9Xp06dtHPnTnXp0qXI+4mJ\\niYqNjVVCQgKLKQAAAAAA3MZTedPrU80Lstlsevjhh9WvX79iQzcAAAAAAL7G61PNC5owYYJ2796t\\njRs3erspAAAAAAC4RbUJ3hMnTtTatWv11VdfqWnTpmVuP3nyZIWFhTnVjRo1ims9AAAAAABlWrVq\\nlVatWuVUl5KS4pFjVYtrvCdOnKiPPvpIGzZsUNu2bUvdlmu8AQAAAACe4Km86fUR7wkTJmjVqlVa\\nvXq1goODlZSUJEkKCwtTvXr1vNw6AAAAAAAqx+uLqy1atEipqakaMGCAmjVrll/effddbzcNAAAA\\nAIBK8/qId15enrebAAAAAACAx3h9xBsAAAAAgJqM4A0AAAAAgAcRvAEAAAAA8CCCNwAAAAAAHkTw\\nBgAAAADAgwjeAAAAAAB4EMEbAAAAAAAPIngDxdiyRXr2WenECW+3BAAAAICvI3gDhWRnS7/5jfTE\\nE9Lll0s7dni7RQAAAAB8GcEbKOTCBenMGfP85Enp6qulzz/3bpsAAAAA+C6CN1CIzeb8+vx5adgw\\nacUK77QHAAAAgG8jeAOFFA7ekpSTI919tzRnTvHvAwAAAEBJCN5AIXl5jufDhkl/+IPj9eOPSxMn\\nmiAOAAAAAOVB8AYKKTii7e8vvfqq9Ne/OupefVUaPlxKSan6tgEAAADwPQRvVHtVPbW74PGsVsli\\nMSPd//iHCeKStG6d1KePdOBA1bYNAAAAgO8heKPaysmRhgyR2reX/v3vqjtuwanmFovj+d13S599\\nJjVsaF7v2SNdcYX03/9WXdsAAAAA+B6CN6qtrVtN0D14UBoxQpo92zkUe0rBEe+CwVuSBgyQvv1W\\niokxr8+elQYPlt54w/PtAgAAAOCbCN6otjIznV/PmiXdequ5vZcnlRa8JTMCv3mzNHSoeZ2dLf3+\\n99LUqSy6BgAAAKAogjeqreJGt//9b89fW13wuNYS/oaEhUkffyxNmuSoe/FF6frrpTNnPNc2AAAA\\nAL6H4I1qq+DIc//+JuxK0vffS716mWnonj5ucSPedv7+0t//Li1a5Fh0LT5eio010+QBAAAAQCJ4\\noxorGICvvlrassVxbfW5c2Z0+dln3X/dd3mDt90DD0jr10tNmpjXx45J/fpJS5a4t10AUFXS09M1\\nZ84cLVu2TFOmTPF2cwAA8Hn+rnzIZrPpvffe0/r163X69GnlFUo+H3zwgVsah9qt8OriHTuahc3G\\njJH+8x/z/hNPSF9/LS1f7lhtvLIK306sPPr1kxISpNtvl775Rrp4URo/3nxZ8PLLUt267mkbAFSF\\nO+64Q3PnzlWXLl10ww03aN++ferYsaO3mwUAgM9yacT74Ycf1t13361Dhw4pJCREYWFhTgVwh+JG\\nnkNDzXXes2Y56taulXr2dN/07pJuJ1aWZs3MyPfEiY66xYvNaP3hw+5pGwB42pIlS5SZmakuXbpI\\nMqPfBw8e9HKrAADwbS6NeC9fvlwffPCBbrjhBne3B8hX0siz1SrNnGkWWRs9WkpOlo4cka66Spo/\\nX5owoWKBubTjVnQ/deqYEe7evaX77zcrs2/dKnXvbm45duutrrcLAKrCnDlz9PTTT0uS8vLytGPH\\nDoWHh3u5VQAA+DaXRrzDwsLUtm1bd7cFcFJWAB4yRNq+3QRwydzWa+JE6a67pNRU9xy3vFPNC7v7\\nbmnTJsn+1yQlRbrtNulPfyp6mzQAqC62bdumY8eOaf/+/Zo7d64mT56s7OxsdevWzdtNAwDAp7kU\\nK2bNmqXZs2crIyPD3e1BNZSeLj30kPTII9LZs1V33PJM+W7RQtqwQZo82VH39ttmhHnzZs8dtzy6\\nd5cSE6WRIx11r74qXXmltG+f6/sFAE/ZunWr+vbtq6eeekrTpk1To0aNNGrUKNWrV8/bTQMAwKe5\\nFLxHjhypc+fOqXHjxuratat69uzpVFCzfPih9NJL0gsvSJddJn3+edUct7wjzwEB5h7a779vrgGX\\npEOHzIJnzzwj5ea6ftzKBG/J3ALt7bel116T7L+37thhrklfvrxy+wYAd0tNTVX37t0lmWnmy5cv\\n16RJk7zcKgAAfJ9L13iPGzdOCQkJGjNmjKKiomSpbDpBtXb6tOP5iRPSdddJU6dKf/mLZ1frrmgA\\nvuUWqUcPc933pk0mcD/1lLnf9/LlUqtWnjluWSwWc713nz5m9HvvXiktTRo71iwM9+qrEpdPAqgO\\n2rVrp9Rfr9VZvHixxowZo65du+rrr7/Wxx9/rF69eqlu3bpKSUnR6NGjvdxaAAB8h0vBe82aNYqL\\ni1O/fv3c3R5UQwWDqN0LL5iR73/+U/p14Vu3c2XKd5s20n//Kz39tBntzsszr7t1k15/3dzuqyLH\\ndfUa7+J07Spt2yY9+KD05pum7u23pa++kt56Sxo82H3HAgBXjBgxQp9//rkWL16sjIwMzZw5U5LU\\nsmVLnTlzRjfccIPS09N17733ErwBAKgAl2JFy5YtFWqf04sar2AQveEGs3K3ZKZMx8ZKCxYUH84r\\ny9VFzvz9pdmzzbXf9lHuX34xo81jx0rnzpX/uO6ezBEcLC1dKq1aJTVoYOp++snMInj4YYllEwB4\\nk5+fnxYtWqTx48fr4Ycfzq9v3bq1MjMzFRgYqE8++UTXXXedF1sJAIDvcSl4v/DCC3rsscd0mJsT\\n1woFg+h990lbtjhGuTMzzUriQ4aYW3p56riuBOB+/cyXAwUXN1u+XLr0UmndOs8dtzzuvFPauVO6\\n9lpH3d//br7ISEz0zDEBwFV79uxRdna24uLidOzYMU2cONHbTQIAwKdYbLaKj1WGh4crPT1dOTk5\\nCgoKUkBAgNP7P//8s9saWFhiYqJiY2OVEBmpnvah18I6dpS++KL0HQ0aVPrS0lOmmFKSH35wTk3F\\niY+XOnUq+f0XXzSlJNWkH0tGxWv8PNOP998311JnZEjTppl7Vk/Wi5qiF2WxmMXEgoMlp7zqYj/S\\nMyT7qfTjb6Zo4H9c64dNZmX2/tnxSrjg+Hn87ndmynxY2K8Vv/48LmY7rmsPDpbCG1SuH04K/Tzy\\n8syf4bRpUlaW1FE/KF7XKrS+VD+00J+jXQ05r+jHr+iHA/1wqGb9WLBggbp16+Z8iZkP9qNY9MOB\\nfjjQD4N+ONAPhxrej8SLFxV75owSEhLcunC4S9d4/+1vf3NbA1x25kzJ7+UnqVIkJZk5viUp60bQ\\nOTmlf96+TWlSU0vfRzXphyXX0Q/7CHBgoFnp/MYbpe9vT1WL8z+ZhPvLr6UgF/sR9GuRpKNZrvfD\\nIilY0sef5+ie56W4OFP/xhvSp5+aqd+DByv/51FHUgv7h9N+LZXoh5NCPw+r1dyq7brrpDFjpKzt\\nOWqhn6TzMqWkvpbGR84r+vEr+uF8jNLQD+djVFE/Tpw4oX/+85/q0KFD0Tb6UD9KRD+cj0E/DPph\\n0A/nY9APo7b0w81cXtXc6yIjHRcbFxYVVfbno6KklJSS3y/rGnZ/f6l587K3KU1oaOn7qCb9yClw\\nmhS+1nroUOmaaaE699fmSkt31DuNfrvYj4Ij3hcDK9+PJi38tW6dtGSJ+RLuwgXp2DETeu+/X/pb\\nq1AFNm9e8oi3B38eXbqY+46/+pC/ji9y7kf9EPOx/GnvNeS8oh+/oh/OxygN/XA+RhX1o1mzZtq4\\ncWPxbfShfpSIfjgfg344tqEf9KPwMeiHY5ua3I+LF0sf5HWRS1PNjx49Wur7rcp73yYX5E81d/PQ\\nP0r2179K06eb5x99JA0fXvx2cXHS738vHT/uqBswQFq0qPSZIiV55x1zLbRkZpJMnlzxfZTk8GFz\\nvfr69Y66pk3NtO82bcy11pL0xz+a231Vpe3bTdu++85R16GDGZnnRgIAAACA53gqb7q0uFp0dLTa\\ntGlTYkHNUt7FxoYOlXbtMuHb7ssvpcsuM6uMZ2VV7Lieuq2XJEVHm9uhvfKKGdWWpJMnpdtuM6Pf\\ndt64RX2PHmYBu2eecUzq+PFH6eqrpQkTzArtAAAAAHyHS3Fm+/btSkxMzC/ffvutFi1apI4dO+pf\\n//qXu9sIL6tIAA4LkxYvlj75xIRbyczWmDXLBPAvvyz/cT29urjVKv3pT9Lu3eZadbuEBM8etzwC\\nAswsg+3bpSuucNQvXGhmD6xY4ZlbuAEAAABwP5eCd7du3ZzK5ZdfrvHjx2vevHl66aWX3N1GeJkr\\nAXjoUOn7781q3fZLNPbtkwYOlO65R0pO9sxxXdGqlfSf/5ip7YUvB/FW8Lbr0kXauNFMtbePzJ8+\\nLd19t1kMcu9e77YPAAAAQNncOoG3U6dO2rp1qzt3iWrA1QAcFCTNmWPuS92nj6N+2TIzartwoZSb\\nW/LnPTnVvDCLxdzve88e56nyl13m2eOWh5+fub59zx5pxAhH/fr1pn3Tp5vbpQEAAAConlyKM6mp\\nqU4lJSVFe/fu1ZNPPln0diPweQWDtysBuGtX6euvzSJrDX5dIfznn831yrGx0n//W/Zxq2rkOTzc\\nTJXfsUNat845hHtby5bSBx9IH3/smMafnW0Wv7vkEnOPdaafAwAAANWPS8G7QYMGCg8Pzy8NGzZU\\nly5dtGnTJi1cuNDdbYSXFRx5djUAW63SAw+YqdFjxjjqd+yQ+vc3q5cfO+b8GW8Eb7vLLpOuv977\\nU82Lc+ONZhr/9OnmWnDJrNJ+221mKv/27V5tHgAAAIBCXAre69ev1xdffJFfvvzyS+3evVsHDhxQ\\nn4JzilEjuDMAR0VJy5ebEfCCq/O/846Zfv7001JGhqmryqnmviYoyKx6vnOnNHiwo37DBjOLYPx4\\nKSnJe+0DAAAA4OBSnOnfv79TufrqqxUTEyP/sm50XoKvvvpKw4cPV/PmzWW1WrV69WqX9gPPqOxU\\n8+JcdZW5ZdbixVJEhKnLyJBmzJBiYqSVK90z0l7Tdeokffqpub96+/amzmaTliwx9/5+7rmK38YN\\nAAAAgHuVOymvXr1aw4YNU0BAQJnBePjw4RVqRFpamrp376777rtPt956a4U+C8/zVAD28zPXUN96\\nq7nP9yuvmMXWjh4109FDQz1z3JrGYpGGDzcryb/yivTnP0upqdL582ZV+YULzUyCu+5i5gAAAADg\\nDeUO3jfffLNOnTqlxo0b6+abby5xO4vFotzSlqouxvXXX6/rr79ekmRjdahqx9PXWoeHS3/7m5ke\\n/eijZlEzyYRHOwJj2erWlaZONbcae+opM+qdl2eu/777bmnePOnZZ6vvtesAAABATVXuOJOXl6fG\\njRvnPy+pVDR0o/qrqkXOLrlEWrvWTJ3u1s35vbp1PXfcmqZxY+m118xt3K67zlG/Y4d0ww1mAbbN\\nm73XPgAAAKC2ce2i7Grg+hXXq86XdYp9r2Ojjvpi3Belfn7QskHad3Zfie9P6TNFU/pMKfH9H5J/\\n0LX/uLbUY8SPjVeniE4lvv/iphf14qYXS3y/uvTjesVLMv0obuTZI/0YK4Wnm+nSFot0qMkUSfw8\\npAr243opYqCUkmJuPSZJG852VJ8+X2jECLNAW5cuPtCPYvjkz6MY9MOBfjh4ux95F/PU40gPjbxy\\npHbs2KEXXyx6LF/oh1Qzfh4S/SiIfhj0w4F+ONAPozL9uHj8Yqmfc5VLwXvSpElq3769Jk2a5FT/\\nyiuvaP/+/frb3/7mlsaV5sz7Z6R6hSq7mhJWL6zMzyelJemn8z+V+H5qVmqJ70lSTl5OqZ+3b1Oa\\n1KzUUvdRXfqRa3P0o7gRb4/2I+jXNvjz8yi4TWmK7Ufgr0WSMk0/PvxQ+ve/pZEjzdT0Sy5xbF5t\\n+1GAT/88CqAfzscoDf1wPoZH+/FPacTjIzRu3DjdcMMN2rdvnzp27FikjdW+H6ohPw/Rj8LblIZ+\\nONAPg344H4N+GElpSfrpm5+knYXeyCzzoy5xKXi///77xS6w1rdvX82ZM6dKgnfkrZGq06L4Ee+o\\n4KgyPx8VHKWUzJQS3w+tG1rie5Lkb/VX8/rNy9ymNKF1Q0vdR3Xph+WCox/FBW9f6UdN+XlUth/B\\nAVFKbSKdOmUuI3jnHendd50DuC/0o6b8POiH8zFKQz+cj+GpfqRtTlOGLUPtOrWTJKWnp+vgwYNF\\ngnd170fBY5SGfjgfg344tqEf9KPwMeiHY5sa04++KVJf5/qLxy/qzPwzZX6+oiw2F1Yzq1evnnbt\\n2qX29vsX/Wr//v269NJLlZnp+tcEVqtV//73v0tcGT0xMVGxsbFKSEhQz4I3gobHTJ0q2WcZbtwo\\n9e1b+vao/tLSzGrnzz0nnSnw74rFUvwIOIDao3379nr66ac1atQo5eXlqVGjRvrkk090xRVXeLtp\\nAAB4nKfypktrRbdv316ffPJJkfp169apbdu2Fd5fWlqaduzYoe+++06SdPDgQe3YsUPHjh1zpXko\\np1OnpK5dzf2f581zXkW8IO6nXfMEB0uPPCIdOiQ9/7wUGWnq7SPgXbtKt90mbd3q3XYCqFrbtm3T\\nsWPHtH//fs2dO1eTJ09Wdna2uhVe8RIAAFSIS8F7ypQpeuyxxzRz5kxt2LBBGzZs0IwZM/R///d/\\nmjx5coX3t23bNvXo0UOxsbGyWCyaOnWqevbsqZkzZ7rSPJTTJ59Iu3ZJBw6Y23i1aiU98YSUlOS8\\nXVWtao6qV1oAf/99qXdvadAgs9I8d/oDar6tW7eqb9++euqppzRt2jQ1atRIo0aNUr16jkVV9u/f\\nr8svv9yLrQQAwPe4FLzvu+8+vfDCC3rjjTc0cOBADRw4UCtWrNDChQs1fvz4Cu+vf//++bciK1iW\\nLl3qSvNQTllZzq9TUsx9nlu3lv74RxPIJefAxf20a6bCAbxJE8d769dLQ4dKsbHS229LOaWvdQHA\\nh6Wmpqp79+6SzK1Dly9fXmQh1cjISHXu3NkbzQMAwGe5HKP++Mc/6vjx40pKSlJqaqoOHjyosWPH\\nurNt8LCCgbpbNykgwDzPypIWLZI6dpTuuEP64QfHdox412wFA/jrr0sdOjje275dGjVK6tRJeuUV\\nc6s3ADVLu3btFBRkbiexePFijRkzRl27dpUkLVmyROvWrdMTTzyh6667zpvNBADA51R6/DIyMlIh\\nISHuaAu8aPJkE7YeeUSy/zjz8sxq13Fxju0I3rVDvXrS+PHSnj3Se+9JvXo53jt4UHrwQalFC2nK\\nFPMaQM0wYsQInT17VosXL1ZGRkb+JV9r167V2bNnNWzYMGVkZGjo0KFebikAAL6l3LcT69Gjhyzl\\nTF2JiYkuNwhVp/C1282bm2nG06ebFa//9jfp9GnnzzDVvHbx85NuvVW65Rbpyy+luXMdX8Skpkrz\\n55vz5KabpIcekgYO5MsZwJf5+flp0aJFRerXrFmj+++/X5J05swZZRW+VgkAAJSq3MH75ptv9mQ7\\n4AUlLZbVoIH0+ONmFHzlShOuvv9eatxY4rK+2sliMaF64EBp507ppZekFSukzExzHq1ebcqll0qT\\nJpkp6UyEAWqOESNGaNOmTTpy5Ijatm2r3bt3q1WrVt5uFgAAPsOl+3h7E/fxdp8FC6SJE83zf/xD\\nuvvu4rez2aTdu6WoKCkiourah+otOVlavNicRz/95Pxe/frS6NHSAw9Iv67TBAAAAFR71eo+3qgZ\\nynubMItFuuQSQjecRUSYmRGHDpl7f/ft63jv/HmzQF+PHtIVV0hvvCGlpXmvrQAAAIA3uRS8c3Nz\\nNW/ePPXu3VtNmjRRw4YNnQp8D9flwlUBAdLIkdLGjdK2bWZRtuBgx/tbtki//73UtKk0YYJZHd23\\n5tkAAAAAleNS8J49e7ZefPFF3XHHHUpJSdGUKVN0yy23yGq1atasWW5uIjylvCPeQHnFxprbkJ04\\nYRboKzjN/Px5U9ezp7l93bx50smT3msrAAAAUFVcCt4rV67U4sWLNXXqVPn7+2vUqFFasmSJZsyY\\noc2bN7u7jfAQRh3hKaGh0h/+ICUmOka8C46C79wpPfqouSXZDTeYqeqZmd5rLwAAAOBJLgXvU6dO\\nqWvXrpKkkJAQpaSkSJJ+85vfaM2aNe5rHTyKEW94msVi7gG+eLEZBV+0yPla8Lw8ad066c47pSZN\\npPvvl776ytQDAAAANYVLwbtFixY6+esc0Xbt2unTTz+VJG3dulV169Z1X+vgUQRvVKXQULPK+caN\\n0r590pNPSgXvRpSSYgL6NdeY+ilTpG+/ZWYGAAAAfJ9LwW+dix4AABgvSURBVHvEiBGKj4+XJD34\\n4IN66qmn1KFDB40dO1b33XefWxuIqkHwRlXq0EF6+mmzIvoXX0jjxjlPRf/pJ3P/+CuvlNq2lf7v\\n/1iUDQAAAL7L35UPzZkzJ//5HXfcodatW+ubb75Rhw4ddNNNN7mtcfAsRrzhbVarNHCgKQsWSB9+\\naK73jouTsrPNNocPS3PnmtKhg3THHdKIEeZWZZy3AAAA8AUujXg/++yzWrp0af7rK6+8UlOmTNGZ\\nM2c0d+5ctzUOnsXoIaqT4GBpzBjpP/+RkpLMvb+HDJH8/Bzb/Pij9MwzZvX06GjpoYek9eulnByv\\nNRsAAAAok0vB+7XXXlNMTEyR+ksuuUSLFi2qdKNQNRjxRnUVHi7dd58Z+T550tyGbMAA5/P06FHp\\npZekQYOkqCjpnnukf/9bSk/3VqsBAACA4rm8qnnTpk2L1EdGRuYvuobqj+ANXxAZaW5Ntn69ufZ7\\n0SJp6FApIMCxzc8/S8uWmSnoERHSTTeZsH74sNeaDQAAAORzKXi3bNlSGzduLFK/ceNGNWvWrNKN\\nQtUjeMMXNG1qVkb/5BPpzBlp1SpzzXf9+o5tMjKkjz+WJkyQ2rSROneWpk6VPv9cysryXtsBAABQ\\ne7m0uNr48eP18MMPKzs7W4MGDZIkxcfH67HHHtPUqVPd2kB4Dtd4w5eFhZn7f995pwnUX3xhppqv\\nXi2dOuXYbu9eU1580VxHPmiQdMMN5vrxtm29134AAADUHi4F70cffVRnz57VhAkTdPHiRUlSvXr1\\nNG3aND3++ONubSA8h6nmqCnq1pWGDTNl4UJpxw5p7Vpp3Tpp0yYpL89sl5ZmFm/7z3/M6+ho6dpr\\nTbFfKw4AAAC4m0vB22KxaO7cuXrqqae0Z88eBQYGqkOHDqpbt6672wcPInijJrJaza3GevSQpk83\\n139/9pkJ4evWSadPO7Y9fNisnv7GG+b1pZc6gnj//lJoqFe6AAAAgBrGpeBtFxISol69ermrLfAi\\ngjdqqoYNzXXgd9xhRr63bzfXiMfHS99843zd965dpvz97+Y2Zj17SldfLV1zjdSvn9Sokff6AQAA\\nAN9VqeAN38aIN2obq9XcAzw21oyGZ2RIGzeaEB4fLyUkOKal5+ZKW7ea8uKLpq5LFxPE7aVVK+/1\\nBQAAAL6D4F2LsbgaarvAQGnwYFMk6ZdfpC+/NCH8iy+k3budt9+925TXXjOvW7UyAbxPH+mKK6TL\\nLpPq1KnSLgAAAMAHELxrMUa8AWcNGkg332yKJCUnmxHxr74yJSHBjITbHT0qrVxpimQWeevZ04Rw\\ne4mO5u8XAABAbUfwrsUI3kDpIiKk3/7WFMmsir55syOIb9pkpqvbZWWZuk2bHHWNGztC+OWXm0Xf\\nGjeu2n4AAADAuwjekETwBsojONix6rkkZWdL330nffutCeTffivt3+/8mdOnnW9hJknNm5uR8R49\\nzGPPnlKLFvw9BAAAqKkI3rUYI95A5QQESL16mTJxoqk7e1bassURxLdskc6dc/7cTz+ZUjCMN2rk\\nCOE9ekhdu0odOphjAAAAwLcRvGsxFlcD3K9RI2nYMFMk8/fsxx9NAE9MNLczS0yUUlOdP3f2rLnf\\n+GefOerq1JFiYkwIv/RSx2OrVnxZBgAA4EsI3rUYI96A51ksUseOpowZY+ry8qRDhxwh3F7OnHH+\\n7MWL0v/+Z0pB9es7B/HOnU1Ab96cv8sAAADVEcG7FiN4A95htUrt2ply222mzmaTTpwwAfy776Rd\\nu0z54QfnldQl6fz5oou4SVJIiAn4MTFSp07mMSbGTFkPDKyavgEAAKAogjckEbwBb7NYzIh18+bS\\nTTc56rOyTPjetUvaudMRyA8fLrqPCxcco+eF9926tQnjnTpJ7ds7gn90tLkNGgAAADyH4F2LcY03\\nUP3VrStddpkpBaWmSrt3mxC+d68J53v3SgcPmqnsBdlsJqgfPizFxTm/Z7FILVs6gnjhEhbmyd4B\\nAADUDgTvWoyp5oDvCg2VrrzSlIKysqQDB5zDuP0xJaXofmw26ehRU9avL/p+o0ZS27ZmxLxVK/NY\\n8Hl4OP9+AAAAlIXgXYsRvIGap25dqUsXUwqy2aSkJBPCDxwwI+MHDjjKzz8Xv7+zZ03ZurX490NC\\nig/krVubafPNmpnV2QEAAGozgnctRvAGag+LRWrSxJT+/Yu+/8svzkG8YDl+vORLUy5cMFPed+8u\\n+diRkSaA269hL+55RAT/DgEAgJqL4A1J/MIL1HYNGkixsaYUdvGi9NNP0pEjjnL0qPPzrKyS933m\\njCk7dpS8TZ06JoTbS1SUKY0bO57bS3Bw5fsLAABQlQjetRiLqwEojzp1pDZtTCmOzSadPl00kJ84\\nYQL7iROm5OSUfIyLFx0LwJUlOLjkUB4VZUbPIyLM9emNGjHVHQAAeB/BuxZjqjkAd7BYHKG3d+/i\\nt8nLM6PeBcP4Tz85Pz9xQkpOLvt4aWnmGvWDB8vXvvr1HUG8vI/16pW//wAAAGUheNdiBG8AVcVq\\ndYTzHj1K3i4z0ywCV7icPl20rqQF4Qo7f96UQ4fK39569cz0+wYNzMrt9ueFXxf3XliY5M//rgAA\\noAB+NajFCN4Aqpt69Ryropfl4kUzil44kJ89a0bOCz7aS3kvscnMlE6dMsUVISEmiIeFmRH30NDi\\nH8uqCwri32cAAGoCgjck8YsdAN9Tp45jdfTyyMszq7cXDuUFw7n9+S+/SOfOmcfz5yvetgsXTDl+\\nvOKfLchqdQ7jISHmGveSSlBQ+d8LCKhc2wAAQPlVm+C9YMECzZs3T6dOnVK3bt308ssvq1evXt5u\\nVo3G4moAahOrVWrY0JSKyMmRUlJMCLcXeygv7rX9+blzUmqqlJ7uepvz8syxU1Jc30dJAgKcQ3lQ\\nkJlxEBjonsfi6gj7AIDaqloE73feeUdTp07V66+/rt69e2v+/PkaOnSo9u3bp4iICG83r8ZiqjkA\\nlM3f37FCuitycszo9/nzJojbrzm3Py/psbi6tDT39Ss72/FlQVWxWs1Mhbp1q+4xIMD8DAMCHKU8\\nr/39+b8RAOA+1SJ4z58/Xw888IDGjh0rSVq0aJHWrFmjpUuX6rHHHvNy62ougjcAeJ6/v2PxtcrK\\nyzMj6OnpJoQXLJWpS0sz17VnZla+jWW1vyqO4y72QO5KcC/83M/PFH//sp974z0/P/PFSEmlrPf5\\nPQIASuf14J2dna2EhAQ98cQT+XUWi0WDBw/Wpk2bSv3syZOebl3NduGC4zn/YQJA9We1muu8Q0I8\\ns3+bTcrKkjIyTDh25bG097KyzKJ4pT1WJzk5pd9/Hs7KE9DLG+Rd2ZfFYkrB5wWLJ+u9cczS6u2/\\n1xV+XtFHPuv6Zwsrrr4q6jhOxeuOHSv+uJXl9eCdnJys3NxcRUVFOdVHRUXphx9+KPWzzZp5smUA\\nANQuFovj+mxvsNlM0C1PQC/usXBdVpaZUp+TYx7txZ2v7c9zc73zZ1ad5OWZAgAoyuvBuyQ2m02W\\nkr7mkDR58mRJYYVqR/1aymtVBbevufgSw71WrVqlUaM4t+B+nFvwlOpwblksjinaviYvzzFCXlxI\\nz801JSfH8bzw65KeV8V79tBcuJT2Xnm3++WXVQoJGeW2/QEO/C6Pylr1aynIAyuaqhoE74iICPn5\\n+SkpKcmp/vTp00VGwQuaP3++nnmmZ6WOvWnTKvXpU7v/slos0o03Su3aebslNUt1+AUWNRPnFjyF\\nc6ty7AvH1anj7ZZUP8OHr9Lq1e47t2y2kgO6zeZ43/68YHFHvSf37a56+59TwecVffSFzy5fvkpj\\nxoyqdm0urLj6qqjjOOWpKzpwe/Zsotatiy2+AZXg9eAdEBCg2NhYxcfHa/jw4ZLMaHd8fLwmTZpU\\n6mc/+KByxx4+vPL7AAAAQNWxWBwLwvni7Ai4z+bN0vTp3m4FaprERGndOvfv1+vBW5KmTJmicePG\\nKTY2Nv92Yunp6brnnnu83TQAAAAAACqlWgTvkSNHKjk5WTNmzFBSUpK6d++uuLg4RUZGertpAAAA\\nAABUSrUI3pI0YcIETZgwocztMjIyJEl79uyp9DFTUlKUmJhY6f0AhXFuwVM4t+ApnFvwFM4teArn\\nFjzBnjPtudNdLDZbSZeuV08rV67UmDFjvN0MAAAAAEANtWLFCo0ePdpt+/O54J2cnKy4uDhFR0cr\\nMDDQ280BAAAAANQQGRkZOnz4sIYOHaqIiAi37dfngjcAAAAAAL7E6u0GAAAAAABQkxG8AQAAAADw\\nIII3AAAAAAAeVKOD94IFC9SmTRsFBgbqyiuv1NatW0vd/l//+pc6d+6swMBAdevWTevWrauilsLX\\nVOTc2r17t2677Ta1adNGVqtVL730UhW2FL6mIufWkiVLdM0116hhw4Zq2LChrrvuujL/nUPtVZFz\\n68MPP1SvXr0UHh6ukJAQ9ejRQytWrKjC1sKXVPT3Lbu3335bVqtVt9xyi4dbCF9VkXNr2bJlslqt\\n8vPzk9VqldVqVVBQUBW2FihdjQ3e77zzjqZOnarZs2dr+/bt6tatm4YOHark5ORit9+0aZPuuusu\\njR8/Xt99951uvvlm3Xzzzdq9e3cVtxzVXUXPrfT0dLVr105z585V06ZNq7i18CUVPbc2bNigu+66\\nS19++aU2b96sli1basiQITp58mQVtxzVXUXPrUaNGunJJ5/U5s2btXPnTt17772699579dlnn1Vx\\ny1HdVfTcsjty5IgeffRRXXPNNVXUUvgaV86tsLAwnTp1Kr8cOXKkClsMlMFWQ11xxRW2SZMm5b/O\\ny8uzNW/e3DZ37txit7/jjjtsN910k1PdlVdeafvjH//o0XbC91T03CooOjra9ve//92TzYMPq8y5\\nZbPZbLm5ubbQ0FDb8uXLPdVE+KjKnls2m83Ws2dP24wZMzzRPPgwV86t3NxcW79+/WxLly613XPP\\nPbYRI0ZURVPhYyp6br311lu28PDwqmoeUGE1csQ7OztbCQkJuvbaa/PrLBaLBg8erE2bNhX7mU2b\\nNmnw4MFOdUOHDi1xe9ROrpxbQHm449xKS0tTdna2GjZs6Klmwge549yKj4/Xvn371L9/f081Ez7I\\n1XNr9uzZaty4se69996qaCZ8kKvn1oULFxQdHa1WrVoxcxXVTo0M3snJycrNzVVUVJRTfVRUlE6d\\nOlXsZ06dOlWh7VE7uXJuAeXhjnNr2rRpat68eZEvEVG7uXpupaamqn79+qpTp45uuukmvfzyyxo0\\naJCnmwsf4sq5tXHjRr355ptasmRJVTQRPsqVc6tTp05aunSpVq9erZUrVyovL099+/bVTz/9VBVN\\nBsrk7+0GVCWbzSaLxeKx7VF7ca7AU8p7bs2ZM0fvvvuuNmzYoDp16lRBy+Dryjq36tevrx07dujC\\nhQuKj4/X5MmT1bZtW67JRZlKOrcuXLigu+++W4sXL1Z4eLgXWgZfV9q/W1deeaWuvPLK/Nd9+vRR\\n586d9frrr2v27NlV1USgRDUyeEdERMjPz09JSUlO9adPny7yzZldkyZNKrQ9aidXzi2gPCpzbs2b\\nN0/PPfec4uPjdckll3iymfBBrp5bFotFbdu2lSRddtll2r17t5599lmCN/JV9Nw6cOCAjhw5optu\\nukk2m02SlJeXJ0mqU6eOfvjhB7Vp08bzDUe1547ft/z9/dWjRw/t37/fE00EKqxGTjUPCAhQbGys\\n4uPj8+tsNpvi4+PVt2/fYj/Tp08fp+0l6bPPPlOfPn082lb4FlfOLaA8XD23nn/+ef3lL39RXFyc\\nevToURVNhY9x179beXl5ysrK8kQT4aMqem517txZO3fu1HfffacdO3Zox44dGj58uAYNGqQdO3ao\\nZcuWVdl8VGPu+HcrLy9Pu3bt4o4yqDb8Zs2aNcvbjfCE0NBQPfXUU2rVqpXq1q2rJ598Ujt27NCS\\nJUsUHByssWPHauvWrfmLNjRv3lzTp09XcHCwGjZsqFdeeUX/+te/9MYbbygyMtLLvUF1UtFzKzs7\\nW7t27dKpU6e0fPlytWnTRo0bN1ZaWhqLYMFJRc+t5557TjNmzNDy5ct16aWXKi0tTWlpabJYLEw3\\nh5OKnltz5sxRZmamLBaLkpKStGzZMs2fP1/Tp0/nCx44qci55efnp8jISKcSFxcnm82miRMnymqt\\nkeNBcFFF/916+umndfHiRVmtVh0+fFhTp07Vli1b9NprrykiIsLLvQFq6FRzSRo5cqSSk5M1Y8YM\\nJSUlqXv37oqLi8sP0cePH5e/v6P7ffr00apVqzR9+nRNnz5dHTp00EcffaQuXbp4qwuopip6bp04\\ncUI9evTIvyZp3rx5mjdvnvr3768vvvjCK31A9VTRc2vhwoXKzs7Wbbfd5rSfmTNnasaMGVXadlRv\\nFT230tLS9Kc//UnHjx9XYGCgYmJitHLlyiLnGlDRcwsor4qeW+fOndP999+vU6dOKTw8XLGxsdq0\\naZNiYmK81QXAicVmv8gGAAAAAAC4HXN6AAAAAADwIII3AAAAAAAeRPAGAAAAAMCDCN4AAAAAAHgQ\\nwRsAAAAAAA8ieAMAAAAA4EEEbwAAAAAAPIjgDQAAAACABxG8AQAAAADwIII3AAAAAAAeRPAGAKAa\\n2rBhg/z8/JSamuqV48fHx6tLly7l2vaTTz5Rjx49PNwiAAB8F8EbAIAqZrVa5efnJ6vVWqT4+fnp\\nz3/+s6666iqdPHlSoaGhXmnjtGnTNGPGjHJte/3116tOnTpauXKlh1sFAIBvsthsNpu3GwEAQG1y\\n+vTp/Odvv/22Zs6cqX379sn+X3JISIiCgoK81Tx9/fXXGj58uE6dOqU6deqU6zOvvvqq3nrrLW3Z\\nssXDrQMAwPcw4g0AQBVr3LhxfgkLC5PFYlFkZGR+XVBQkDZs2CCr1Zo/1XzZsmUKDw/XmjVrFBMT\\no+DgYI0cOVIZGRlatmyZ2rRpo4YNG+qhhx5Swe/UL168qEceeUQtWrRQSEiI+vTpow0bNpTavnfe\\neUdDhgxxCt3/+9//NGjQIIWGhiosLEy9evVSYmJi/vs33XSTtm3bpkOHDrn5TwsAAN/n7+0GAACA\\n4lksFqfX6enpevnll/Xuu+8qNTVVI0aM0IgRIxQeHq5169bp4MGDuuWWW9SvXz/dfvvtkqQ//elP\\n2rt3r9599101bdpUH374oYYNG6adO3eqXbt2xR73q6++0pgxY5zqRo8erZ49e+q1116T1WrVd999\\np4CAgPz3W7ZsqaioKH311Vdq06aNm/8kAADwbQRvAAB8RE5OjhYtWqTo6GhJ0m233aYVK1bo9OnT\\nCgwMVExMjAYOHKj169fr9ttv19GjR/XWW2/p2LFjatKkiSRpypQpWrdund58800988wzxR7nyJEj\\natq0qVPd0aNH9dhjj6lDhw6SVGxob9asmY4cOeLGHgMAUDMQvAEA8BFBQUH5oVuSoqKiFB0drcDA\\nQKc6+zXku3btUm5urjp27Fhk+nlERESJx8nIyFC9evWc6qZMmaLf/e53+sc//qHBgwfr9ttvV9u2\\nbZ22CQwMVHp6emW6CABAjUTwBgDARxSc2i2ZqejF1eXl5UmSLly4IH9/fyUmJspqdV7WJSQkpMTj\\nRERE6Ny5c051M2fO1OjRo7VmzRqtXbtWs2bN0ttvv63f/va3+dv8/PPPioyMdKlvAADUZCyuBgBA\\nDdWjRw/l5uYqKSlJbdu2dSqNGzcu9XO7d+8uUt++fXs99NBDiouL04gRI/Tmm2/mv5eVlaUDBw5w\\nP28AAIpB8AYAoJqq7B0/O3TooLvuuktjx47Vhx9+qMOHD2vLli2aM2eO1q1bV+Lnhg4dqq+//jr/\\ndWZmph588EFt2LBBR48e1caNG7V161Z16dIlf5tNmzapXr166tOnT6XaDABATUTwBgCgmiq8qrkr\\n3nrrLY0dO1aPPPKIYmJiNGLECG3btk2tWrUq8TOjR4/W999/rx9//FGS5Ofnp7Nnz2rcuHHq1KmT\\n7rzzTt14442aNWtW/mfefvttjR49usi14QAAQLLYKvt1OgAAqHGmTZum1NRULVy4sMxtz549q5iY\\nGG3btk2tW7eugtYBAOBbGPEGAABFPPHEE2rdunW5prsfPnxYr776KqEbAIASMOINAAAAAIAHMeIN\\nAAAAAIAHEbwBAAAAAPAggjcAAAAAAB5E8AYAAAAAwIMI3gAAAAAAeBDBGwAAAAAADyJ4AwAAAADg\\nQQRvAAAAAAA8iOANAAAAAIAH/T831d3zLQytLwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fe3e9042e90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"run_fit.plot_calcium_transients(protocols, best_ind_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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CZMmKC5c+fajVu2bJliY2O1cuVKbdmyRfXr19eUKVOKLOmWrt55e8GC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\\n3URERERERERe8v8BLQMuaLUbmBEAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x7fe3e6cfaa50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"run_fit.plot_log(log)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "examples/graupnerbrunelstdp/run_fit.py",
    "content": "\"\"\"Main Graupner-Brunel STDP example script\"\"\"\n\n# pylint: disable=R0914\n\nimport pickle\nimport bluepyopt as bpop\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport gbevaluator\nimport stdputil\n\ncp_filename = 'checkpoints/checkpoint.pkl'\n\nevaluator = gbevaluator.GraupnerBrunelEvaluator()\n\nopt = bpop.optimisations.DEAPOptimisation(evaluator, offspring_size=100,\n                                          eta=20, mutpb=0.3, cxpb=0.7)\n\n\ndef run_model():\n    \"\"\"Run model\"\"\"\n\n    _, _, _, _ = opt.run(\n        max_ngen=200, cp_filename=cp_filename, cp_frequency=100)\n\n\ndef plot_log(log):\n    \"\"\"Plot logbook\"\"\"\n\n    fig, axes = plt.subplots(figsize=(10, 10), facecolor='white')\n\n    gen_numbers = log.select('gen')\n    mean = np.array(log.select('avg'))\n    std = np.array(log.select('std'))\n    minimum = log.select('min')\n    # maximum = log.select('max')\n\n    stdminus = mean - std\n    stdplus = mean + std\n    axes.plot(\n        gen_numbers,\n        mean,\n        color='black',\n        linewidth=2,\n        label='population average')\n\n    axes.fill_between(\n        gen_numbers,\n        stdminus,\n        stdplus,\n        color='lightgray',\n        linewidth=2,\n        label=r'population standard deviation')\n\n    axes.plot(\n        gen_numbers,\n        minimum,\n        color='red',\n        linewidth=2,\n        label='population minimum')\n\n    axes.set_xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)\n    axes.set_xlabel('Generation #')\n    axes.set_ylabel('Sum of objectives')\n    axes.set_ylim([0, max(stdplus)])\n    axes.legend()\n\n    fig.tight_layout()\n    fig.savefig('figures/graupner_evolution.eps')\n\n\ndef plot_epspamp_discrete(dt, model_sg, sg, stderr):\n    \"\"\"Plot EPSP amplitude change for discrete points\"\"\"\n    # Plot result summary\n    fig1, ax1 = plt.subplots(figsize=(10, 10), facecolor='white')\n\n    ax1.errorbar(dt, model_sg, marker='o', label='Model')\n    ax1.errorbar(dt, sg, yerr=stderr, marker='o', label='In vitro')\n\n    ax1.axhline(y=1, color='k', linestyle='--')\n    ax1.axvline(color='k', linestyle='--')\n\n    ax1.set_xlabel(r'$\\Delta t$ (ms)')\n    ax1.set_ylabel('change in EPSP amplitude')\n    ax1.legend()\n\n    fig1.savefig('figures/graupner_fit.eps')\n\n\ndef plot_calcium_transients(protocols, best_ind_dict):\n    \"\"\"Plot calcium transients\"\"\"\n\n    # Plot calcium transients for each protocol\n    fig2, axarr2 = plt.subplots(\n        len(protocols), 1, sharex=True, figsize=(\n            10, 10), facecolor='white')\n    for i, protocol in enumerate(protocols):\n        calcium = stdputil.CalciumTrace(protocol, best_ind_dict)\n        time, ca = calcium.materializetrace()\n\n        axarr2[i].plot(time, ca)\n        axarr2[i].axhline(y=best_ind_dict['theta_d'], color='g', linestyle='--')\n        axarr2[i].annotate(\n            r'$\\theta_d$', xy=(\n                0.3, best_ind_dict['theta_d'] - 0.15))\n        axarr2[i].axhline(y=best_ind_dict['theta_p'], color='r', linestyle='--')\n        axarr2[i].annotate(\n            r'$\\theta_p$', xy=(\n                0.3, best_ind_dict['theta_p'] + 0.05))\n        axarr2[i].set_title(protocol.prot_id)\n        axarr2[i].set_xlim(-0.012, 0.6)\n        axarr2[i].set_ylim(-0.1, 2.2)\n        axarr2[i].set_ylabel('calcium')\n\n        yloc = plt.MaxNLocator(3)\n        axarr2[i].yaxis.set_major_locator(yloc)\n\n    axarr2[i].set_xlabel('Time (s)')\n\n    fig2.tight_layout()\n\n    fig2.savefig('figures/graupner_ca_traces.eps')\n\n\ndef plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr):\n    \"\"\"Plot dt scan\"\"\"\n    dt_vec = np.linspace(-90e-3, 50e-3, 100)\n    sg_vec = []\n    for model_dt in dt_vec:\n        protocol = stdputil.Protocol(\n            ['pre', 'post', 'post', 'post'], [model_dt, 20e-3, 20e-3], 0.1,\n            60.0, prot_id='%.2fms' % model_dt)\n        model_sg = stdputil.protocol_outcome(protocol, best_ind_dict)\n        sg_vec.append(model_sg)\n\n    try:\n        sg_good_sol_vec = pickle.load(open(\"sg_good_sol_vec.pkl\", \"rb\"))\n    except IOError:\n        sg_good_sol_vec = []\n        for _, good_sol in enumerate(good_solutions):\n            sg_ind = []\n            for model_dt in dt_vec:\n                protocol = stdputil.Protocol(\n                    ['pre', 'post', 'post', 'post'], [model_dt, 20e-3, 20e-3],\n                    0.1, 60.0, prot_id='%.2fms' % model_dt)\n                model_sg = stdputil.protocol_outcome(protocol, good_sol)\n                sg_ind.append(model_sg)\n            sg_good_sol_vec.append(sg_ind)\n        pickle.dump(sg_good_sol_vec, open(\"sg_good_sol_vec.pkl\", \"wb\"))\n\n    fig3, ax3 = plt.subplots(figsize=(10, 10), facecolor='white')\n    ax3.set_rasterization_zorder(1)\n\n    for sg_ind in sg_good_sol_vec:\n        ax3.plot(dt_vec * 1000.0, sg_ind, lw=1, color='lightblue', zorder=0)\n    ax3.plot(dt_vec * 1000.0, sg_vec, marker='o', lw=1, color='darkblue',\n             label='Best model')\n    ax3.errorbar(dt, sg, yerr=stderr, fmt='o', color='red', ms=10,\n                 ecolor='red', elinewidth=3, capsize=5, capthick=3,\n                 zorder=10000, label='In vitro')\n\n    ax3.axhline(y=1, color='k', linestyle='--')\n    ax3.axvline(color='k', linestyle='--')\n\n    ax3.set_xlabel(r'$\\Delta t$ (ms)')\n    ax3.set_ylabel('EPSP amplitude change')\n    ax3.legend()\n\n    fig3.tight_layout()\n\n    fig3.savefig('figures/graupner_dtscan.eps', rasterized=True, dpi=72)\n\n\ndef analyse():\n    \"\"\"Generate plot\"\"\"\n\n    cp = pickle.load(open(cp_filename, \"r\"))\n    results = (\n        cp['population'],\n        cp['halloffame'],\n        cp['history'],\n        cp['logbook'])\n\n    _, hof, hst, log = results\n\n    best_ind = hof[0]\n    best_ind_dict = evaluator.get_param_dict(best_ind)\n\n    print('Best Individual')\n    for attribute, value in best_ind_dict.items():\n        print('\\t{} : {}'.format(attribute, value))\n\n    good_solutions = [\n        evaluator.get_param_dict\n        (ind)\n        for ind in hst.genealogy_history.itervalues\n        () if np.all(np.array(ind.fitness.values) < 1)]\n\n    # model_sg = evaluator.compute_synaptic_gain_with_lists(best_ind)\n\n    # Load data\n    protocols, sg, _, stderr = stdputil.load_neviansakmann()\n    dt = np.array([float(p.prot_id[:3]) for p in protocols])\n\n    plt.rcParams['lines.linewidth'] = 2\n    # plot_epspamp_discrete(dt, model_sg, sg, stderr)\n    plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr)\n    plot_calcium_transients(protocols, best_ind_dict)\n\n    plot_log(log)\n\n    plt.show()\n\n\ndef main():\n    \"\"\"Main\"\"\"\n    import argparse\n    parser = argparse.ArgumentParser(description='Graupner-Brunel STDP')\n    parser.add_argument('--start', action=\"store_true\")\n    parser.add_argument('--continue_cp', action=\"store_true\")\n    parser.add_argument('--analyse', action=\"store_true\")\n\n    args = parser.parse_args()\n    if args.analyse:\n        analyse()\n    elif args.start:\n        run_model()\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/graupnerbrunelstdp/stdputil.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 12 17:33:41 2013.\n\nUtility functions for calcium-based STDP using simplified calcium model as in\n(Graupner and Brunel, 2012).\n\n@author: Giuseppe Chindemi\n@remark: Copyright © BBP/EPFL 2005-2016; All rights reserved.\n         Do not distribute without further notice.\n\"\"\"\n\n# pylint: disable=R0914, R0912\n\nimport logging\nimport numpy as np\nfrom scipy.special import erf  # NOQA\n\ntry:\n    xrange\nexcept NameError:\n    xrange = range\n\nlogging.basicConfig(level=logging.WARN)\n\n# Note: having debug logging statements increases the run time by ~ 25%,\n# because they exist in tight loops, and expand their outputs, even when\n# debug is off, so we disable logging if possible.  Set this to true if\n# verbose output is needed\nLOGGING_DEBUG = False\n\n\ndef logging_debug_vec(fmt, vec):\n    '''log to debug a vector'''\n    if LOGGING_DEBUG:\n        logging.debug(fmt, ', '.join(map(str, vec)))\n\n\ndef logging_debug(*args):\n    '''wrapper to log to debug a vector'''\n    if LOGGING_DEBUG:\n        logging.debug(*args)\n\n\n# Parameters for cortical slices (Sjostrom et al., 2001)\n# From SI, Graupner and Brunel (2012)\nparam_cortical = {\n    'tau_ca': 22.6936e-3,  # [s]\n    'C_pre': 0.5617539,\n    'C_post': 1.23964,\n    'theta_d': 1.0,\n    'theta_p': 1.3,\n    'gamma_d': 331.909,\n    'gamma_p': 725.085,\n    'sigma': 3.3501,\n    'tau': 346.3615,  # [s]\n    'rho_star': 0.5,\n    'D': 4.6098e-3,  # [s]\n    'beta': 0.5,\n    'b': 5.40988}\n\n\n# Parameters for hippocampal slices (Wittenberg and Wang, 2006)\n# From SI, Graupner and Brunel (2012)\nparam_hippocampal = {\n    'tau_ca': 48.8373e-3,  # [s]\n    'C_pre': 1.0,\n    'C_post': 0.275865,\n    'theta_d': 1.0,\n    'theta_p': 1.3,\n    'gamma_d': 313.0965,\n    'gamma_p': 1645.59,\n    'sigma': 9.1844,\n    'tau': 688.355,  # [s]\n    'rho_star': 0.5,\n    'D': 18.8008e-3,  # [s]\n    'beta': 0.7,\n    'b': 5.28145}\n\n\nclass Protocol(object):\n\n    \"\"\"Protocol\"\"\"\n\n    def __init__(self, stim_vec, delta_vec, f, n, prot_id=None):\n        \"\"\"A stimulation protocol.\n\n        :param stim_vec: list\n            List of stimuli ['pre'|'post'|'burst']\n        :param delta_vec: list\n            List of time deltas between stimuli\n        :param f: float\n            Frequency of the protocol in Hz\n        :param n: int\n            Number of repetitions of the protocol\n        :param prot_id: string\n            ID of the protocol\n        \"\"\"\n        # Check stim strings\n        valid_stim = set(['pre', 'post'])\n        for s in stim_vec:\n            assert s in valid_stim, \\\n                '\\'{0}\\' is not a recognised stimulus'.format(s)\n\n        self.stim_vec = np.array(stim_vec, dtype='a10')\n        self.delta_vec = np.array(delta_vec)\n        self.f = f\n        self.n = n\n        self.prot_id = prot_id\n\n        # Compute time of stimuli\n        self.stim_t = np.zeros(len(self.stim_vec))\n        for i in xrange(len(self.delta_vec)):\n            self.stim_t[i + 1] = self.stim_t[i] + self.delta_vec[i]\n\n    def sort(self):\n        \"\"\"Sort stimuli in place.\"\"\"\n        logging_debug('Protocol.sort()')\n\n        # Check if the stimuli are already sorted\n        if np.all(self.delta_vec >= 0.0):\n            logging_debug('Protocol is already sorted.')\n        else:\n            logging_debug('Before sorting:')\n            logging_debug_vec('stim_vec = [%s]', self.stim_vec)\n            logging_debug_vec('delta_vec = [%s]', self.delta_vec)\n\n            # Sort stimuli\n            index_vec = np.argsort(self.stim_t)\n            self.stim_vec = self.stim_vec[index_vec]\n            self.delta_vec = np.diff(self.stim_t[index_vec])\n            self.stim_t = self.stim_t[index_vec]\n\n            logging_debug('After sorting:')\n            logging_debug_vec('stim_vec = [%s]', self.stim_vec)\n            logging_debug_vec('delta_vec = [%s]', self.delta_vec)\n\n\nclass CalciumTrace(object):\n\n    \"\"\"CalciumTrace\"\"\"\n\n    def __init__(self, protocol, model):\n        \"\"\"Calcium trace produced by **model** when stimulated by **protocol**.\n\n        :param protocol: stdputil.Protocol\n            The stimulation protocol.\n        :param model: dict\n            Parameters of the Graupner-Brunel model\n        \"\"\"\n        self.protocol = protocol\n        self.model = model\n\n        # Generate the events corresponding to the configuration\n        n_stim = len(protocol.stim_vec)\n        curr_time = 0.0\n\n        event = []\n        time = []\n        amplitude = []\n        for i in xrange(n_stim):\n            if protocol.stim_vec[i] == 'pre':\n                event.append('Cpre')\n                time.append(curr_time + model['D'])\n                amplitude.append(model['C_pre'])\n            elif protocol.stim_vec[i] == 'post':\n                event.append('Cpost')\n                time.append(curr_time)\n                amplitude.append(model['C_post'])\n\n            if i < n_stim - 1:\n                # Delta vector is shorter than Stimulus vector\n                curr_time += protocol.delta_vec[i]\n\n        # Convert into numpy arrays for convenience\n        event = np.array(event, dtype='a10')\n        time = np.array(time)\n        amplitude = np.array(amplitude)\n\n        # Sort calcium events\n        index_vec = np.argsort(time)\n\n        logging_debug_vec('Sorted indices = [%s]', index_vec)\n        logging_debug_vec('Calcium event = [%s]', event[index_vec])\n        logging_debug_vec('Calcium time = [%s]', time[index_vec])\n        logging_debug_vec('Calcium amplitude = [%s]', amplitude[index_vec])\n\n        self.__evnt = event[index_vec]\n        self.__t = time[index_vec]\n        self.__amp = amplitude[index_vec]\n\n    def materializetrace(self):\n        \"\"\"Materialize trace\"\"\"\n        # Create exemplary traces for plotting\n        period = 1.0 / self.protocol.f\n        tstart = -0.01\n        tstop = tstart + period\n        dt = 0.0001\n        n = int((tstop - tstart) / dt)\n        tvec = np.linspace(tstart, tstop, n)\n\n        trace = np.zeros(n)\n        for j in xrange(len(self.__evnt)):\n            offset = int((self.time[j] - tstart) / dt)\n            component = self.amplitude[\n                j] * np.exp(-(tvec[:n - offset] / self.model['tau_ca']))\n            trace[offset:] += component\n\n        return tvec, trace\n\n    @property\n    def event(self):\n        \"\"\"Event\"\"\"\n        return self.__evnt\n\n    @property\n    def time(self):\n        \"\"\"Time\"\"\"\n        return self.__t\n\n    @property\n    def amplitude(self):\n        \"\"\"Amplitude\"\"\"\n        return self.__amp\n\n\ndef load_neviansakmann():\n    \"\"\"Load in vitro data, from figure 2B in (Nevian and Sakmann, 2006).\"\"\"\n    protocols = [\n        Protocol(['post', 'post', 'post', 'pre'],\n                 [20e-3, 20e-3, 50e-3], 0.1, 60.0, prot_id='-90ms'),\n        Protocol(['post', 'post', 'post', 'pre'],\n                 [20e-3, 20e-3, 10e-3], 0.1, 60.0, prot_id='-50ms'),\n        Protocol(['post', 'post', 'pre', 'post'],\n                 [20e-3, 10e-3, 10e-3], 0.1, 60.0, prot_id='-30ms'),\n        Protocol(['post', 'pre', 'post', 'post'],\n                 [10e-3, 10e-3, 20e-3], 0.1, 60.0, prot_id='-10ms'),\n        Protocol(['pre', 'post', 'post', 'post'],\n                 [10e-3, 20e-3, 20e-3], 0.1, 60.0, prot_id='+10ms'),\n        Protocol(['pre', 'post', 'post', 'post'],\n                 [50e-3, 20e-3, 20e-3], 0.1, 60.0, prot_id='+50ms')]\n\n    sg = [1.0, 0.68, 0.98, 1.42, 2.01, 0.92]\n\n    stdev = None\n\n    stderr = [0.09, 0.05, 0.12, 0.19, 0.22, 0.11]\n\n    return protocols, sg, stdev, stderr\n\n\ndef time_above_threshold(protocol, param):\n    \"\"\"Compute time spent by calcium above the potentiation and depression\n    thresholds.\n\n    :param protocol: stdputil.Protocol\n        The stimulation protocol.\n    :param model: dict\n        Parameters of the Graupner-Brunel model\n    \"\"\"\n    # Generate calcium trace\n    calcium_trace = CalciumTrace(protocol, param)\n\n    ca_event_t = calcium_trace.time\n    ca_event_amp = calcium_trace.amplitude\n\n    # Sort the protocol if not already sorted\n    protocol.sort()\n\n    # Compute calcium rise time/delta\n    ca_event_delta = np.diff(np.append(ca_event_t, 1.0 / protocol.f))\n\n    # Compute calcium baseline\n    A_f = (1.0 / (1.0 - np.exp(-1.0 / (protocol.f * param['tau_ca']))))\n    if A_f != 1.0:\n        baseline = (A_f - 1.0) * (ca_event_amp[0])\n        for i in xrange(len(ca_event_amp) - 1):\n            baseline += (A_f - 1.0) * \\\n                (ca_event_amp[i + 1] * np.exp(\n                    np.sum(np.abs(ca_event_delta[:i + 1])) / param['tau_ca']))\n    else:\n        baseline = 0.0\n\n    # Calcium amplitudes\n    logging_debug('Calcium amplitudes')\n    n_events = len(ca_event_amp)\n    C_amp = np.zeros(2 * n_events)\n    for i in xrange(n_events - 1):\n        C_amp[i] = baseline * \\\n            np.exp(-np.sum(np.abs(ca_event_delta[:i + 1])) / param['tau_ca'])\n        logging_debug('C_amp[%d] = 0.0', i)\n        for j in xrange(i + 1):\n            C_amp[i] += \\\n                ca_event_amp[j] * \\\n                np.exp(-np.sum(\n                    np.abs(ca_event_delta[j:i + 1])) / param['tau_ca'])\n            logging_debug('C_amp[%d] += %s * exp(-sum(abs(deltas[%d:%d])))',\n                          i, protocol.stim_vec[j], j, i + 1)\n        logging_debug('C_amp[%d] = %f', i, C_amp[i])\n    C_amp[n_events - 1] = baseline\n    logging_debug('C_amp[%d] = 0.0', n_events - 1)\n    C_amp[n_events] = baseline + ca_event_amp[0]  # For convenience\n    logging_debug('C_amp[%d] = %f', n_events, ca_event_amp[0])\n    for i in xrange(n_events + 1, 2 * n_events):\n        C_amp[i] = C_amp[i - n_events - 1] + ca_event_amp[i - n_events]\n        logging_debug('C_amp[%d] = C_amp[%d] + %s',\n                      i, i - n_events - 1, ca_event_amp[i - n_events])\n\n    # Time spent above depression threshold\n    t_d = np.zeros(n_events)\n    for i in xrange(n_events):\n        if C_amp[i] > param['theta_d']:\n            t_d[i] = ca_event_delta[i]\n        elif C_amp[i] <= param['theta_d'] < C_amp[i + n_events]:\n            t_d[i] = param['tau_ca'] * \\\n                np.log(C_amp[i + n_events] / param['theta_d'])\n        else:\n            t_d[i] = 0.0\n    t_d_tot = np.sum(t_d)\n    logging_debug('Time above depression threshold = %f', t_d_tot)\n\n    # Time spent above potentiation threshold\n    t_p = np.zeros(n_events)\n    for i in xrange(n_events):\n        if C_amp[i] > param['theta_p']:\n            t_p[i] = ca_event_delta[i]\n        elif C_amp[i] <= param['theta_p'] < C_amp[i + n_events]:\n            t_p[i] = param['tau_ca'] * \\\n                np.log(C_amp[i + n_events] / param['theta_p'])\n        else:\n            t_p[i] = 0.0\n    t_p_tot = np.sum(t_p)\n    logging_debug('Time above potentiation threshold = %f', t_p_tot)\n\n    return t_d_tot, t_p_tot\n\n\ndef transition_prob(protocol, param=None):\n    \"\"\"Compute transition probabilities for the given protocol and model\n    parameters.\n\n    :param protocol: stdputil.Protocol\n        The stimulation protocol.\n    :param model: dict\n        Parameters of the Graupner-Brunel model\n    \"\"\"\n\n    if param is None:\n        param = param_cortical\n    # Sort the protocol if not already sorted\n    protocol.sort()\n\n    t_d_tot, t_p_tot = time_above_threshold(protocol, param)\n\n    # TODO Ask Michael\n    if t_d_tot == 0.0 and t_p_tot == 0.0:\n        up = 0.0\n        down = 0.0\n        return up, down\n\n    # Define aliases for convenience\n    f = protocol.f\n    n = protocol.n\n\n    # Compute alpha depression and alpha potentiation\n    alpha_d = t_d_tot * f\n    alpha_p = t_p_tot * f\n\n    # Compute Gamma depression and potentiation\n    big_gamma_d = alpha_d * param['gamma_d']\n    big_gamma_p = alpha_p * param['gamma_p']\n\n    # Compute rho bar\n    rho_bar = big_gamma_p / (big_gamma_p + big_gamma_d)\n    sigma_rho_sq = param['sigma'] ** 2 * \\\n        (alpha_p + alpha_d) / (big_gamma_p + big_gamma_d)\n    tau_eff = param['tau'] / (big_gamma_p + big_gamma_d)\n\n    # Up transition\n    rho_0 = 0.0\n    erf_arg = -((param['rho_star'] - rho_bar +\n                 (rho_bar - rho_0) * np.exp(-n / (tau_eff * f))) /\n                (np.sqrt(sigma_rho_sq * (1.0 -\n                                         np.exp(-2.0 * n / (tau_eff * f))))))\n    up = 0.5 * (1.0 + erf(erf_arg))\n\n    # Down transition\n    rho_0 = 1.0\n    erf_arg = -((param['rho_star'] - rho_bar +\n                 (rho_bar - rho_0) * np.exp(-n / (tau_eff * f))) /\n                (np.sqrt(sigma_rho_sq * (1.0 -\n                                         np.exp(-2.0 * n / (tau_eff * f))))))\n    down = 0.5 * (1.0 - erf(erf_arg))\n\n    return up, down\n\n\ndef protocol_outcome(protocol, param=None):\n    \"\"\"Compute the average synaptic gain for a given stimulation protocol and\n    model parameters.\n\n    :param protocol: stdputil.Protocol\n        The stimulation protocol.\n    :param model: dict\n        Parameters of the Graupner-Brunel model\n    \"\"\"\n\n    if param is None:\n        param = param_cortical\n    # Compute Up and Down transition probabilities\n    up, down = transition_prob(protocol, param)\n\n    # Compute synaptic gain\n    sg = ((1.0 - up) * param['beta'] + down * (1.0 - param['beta']) +\n          param['b'] * (up * param['beta'] +\n                        (1.0 - down) * (1.0 - param['beta']))) / \\\n         (param['beta'] + (1.0 - param['beta']) * param['b'])\n\n    return sg\n"
  },
  {
    "path": "examples/graupnerbrunelstdp/test_stdputil.py",
    "content": "import stdputil\nimport numpy as np\nimport numpy.testing as npt\n\ntry:\n    xrange\nexcept NameError:\n    xrange = range\n\n\ndef test_protocol_outcome():\n    \"\"\"\n    Test against Fig. 3 of Graupner and Brunel (2012).\n    TODO, Ask permission to Michael to add testing files.\n\n    Notes\n    -----\n    Data used for the test were kindly provprot_ided by Dr. M. Graupner.\n    Fig. 3F cannot be reproduced because generated using an approximate\n    solution.\n    \"\"\"\n    # Case 1: Fig. 3B\n    mgspike = np.loadtxt(\n        '/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/post_spike.dat')\n    dt = mgspike[:, 0]\n    outcome = np.zeros(len(dt))\n    for i in xrange(len(dt)):\n        p = stdputil.Protocol(['pre', 'post'], [dt[i] * 1e-3], 5.0, 200.0)\n        outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)\n    npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)\n\n    # Case 2: Fig. 3D\n    mgspike = np.loadtxt(\n        '/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/'\n        'post_burst_100.dat')\n    dt = mgspike[:, 0]\n    outcome = np.zeros(len(dt))\n    for i in xrange(len(dt)):\n        p = stdputil.Protocol(\n            ['post', 'post', 'pre'],\n            [11.5e-3, -dt[i] * 1e-3],\n            5.0, 100.0)\n        outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)\n    npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)\n\n    # Case 3: Fig. 3E\n    mgspike = np.loadtxt(\n        '/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/'\n        'post_burst_30.dat')\n    dt = mgspike[:, 0]\n    outcome = np.zeros(len(dt))\n    for i in xrange(len(dt)):\n        p = stdputil.Protocol(\n            ['post', 'post', 'pre'],\n            [11.5e-3, -dt[i] * 1e-3],\n            5.0, 30.0)\n        outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal)\n    npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05)\n"
  },
  {
    "path": "examples/l5pc/.gitignore",
    "content": "/*.pkl\n/.ipynb_checkpoints/\n/L5PC.py\n/.ipython\n"
  },
  {
    "path": "examples/l5pc/L5PC.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optimisation of a Neocortical Layer 5 Pyramidal Cell\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook shows you how to optimise the maximal conductance of Neocortical Layer 5 Pyramidal Cell as used in Markram et al. 2015.\\n\",\n    \"\\n\",\n    \"Author of this script: Werner Van Geit @ Blue Brain Project\\n\",\n    \"\\n\",\n    \"Choice of parameters, protocols and other settings was done by Etay Hay @ HUJI\\n\",\n    \"\\n\",\n    \"What's described here is a more advanced use of BluePyOpt. We suggest to first go through the introductary example here: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb\\n\",\n    \"\\n\",\n    \"**If you use the methods in this notebook, we ask you to cite the following publications when publishing your research:**\\n\",\n    \"\\n\",\n    \"Van Geit, W., M. Gevaert, G. Chindemi, C. Rössert, J.-D. Courcol, E. Muller, F. Schürmann, I. Segev, and H. Markram (2016, March). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. ArXiv e-prints.\\n\",\n    \"http://arxiv.org/abs/1603.00500\\n\",\n    \"\\n\",\n    \"Markram, H., E. Muller, S. Ramaswamy, M. W. Reimann, M. Abdellah, C. A. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492.\\n\",\n    \"http://www.cell.com/abstract/S0092-8674%2815%2901191-5\\n\",\n    \"\\n\",\n    \"Some of the modules loaded in this script are located in the L5PC example folder: https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We first load the bluepyopt python module, the ephys submodule and some helper functionality\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/werner/src/bpopt/examples/l5pc\\n\",\n      \"mechanisms/CaDynamics_E2.mod mechanisms/Ca_HVA.mod mechanisms/Ca_LVAst.mod mechanisms/Ih.mod mechanisms/Im.mod mechanisms/K_Pst.mod mechanisms/K_Tst.mod mechanisms/NaTa_t.mod mechanisms/NaTs2_t.mod mechanisms/Nap_Et2.mod mechanisms/ProbAMPANMDA_EMS.mod mechanisms/ProbGABAAB_EMS.mod mechanisms/SK_E2.mod mechanisms/SKv3_1.mod\\n\",\n      \"CaDynamics_E2.mod Ca_HVA.mod Ca_LVAst.mod Ih.mod Im.mod K_Pst.mod K_Tst.mod NaTa_t.mod NaTs2_t.mod Nap_Et2.mod ProbAMPANMDA_EMS.mod ProbGABAAB_EMS.mod SK_E2.mod SKv3_1.mod\\n\",\n      \"\\\"/Users/werner/local/nrnnogui/share/nrn/libtool\\\"  --mode=compile gcc -DHAVE_CONFIG_H  -I. -I.. -I\\\"/Users/werner/local/nrnnogui/include/nrn\\\" -I\\\"/Users/werner/local/nrnnogui/x86_64/lib\\\"      -g -O2 -c -o mod_func.lo mod_func.c\\n\",\n      \"libtool: compile:  gcc -DHAVE_CONFIG_H -I. -I.. -I/Users/werner/local/nrnnogui/include/nrn -I/Users/werner/local/nrnnogui/x86_64/lib -g -O2 -c mod_func.c  -fno-common -DPIC -o .libs/mod_func.o\\n\",\n      \"\\\"/Users/werner/local/nrnnogui/share/nrn/libtool\\\"  --mode=link gcc -module  -g -O2    -headerpad_max_install_names -o libnrnmech.la -rpath \\\"/Users/werner/local/nrnnogui/x86_64/lib\\\"  CaDynamics_E2.lo Ca_HVA.lo Ca_LVAst.lo Ih.lo Im.lo K_Pst.lo K_Tst.lo NaTa_t.lo NaTs2_t.lo Nap_Et2.lo ProbAMPANMDA_EMS.lo ProbGABAAB_EMS.lo SK_E2.lo SKv3_1.lo mod_func.lo  -L\\\"/Users/werner/local/nrnnogui/x86_64/lib\\\" -lnrnoc -loc -lmemacs -lnrnmpi -lscopmath -lsparse13 -lreadline -lncurses -L\\\"/Users/werner/local/nrnnogui/x86_64/lib\\\" \\\"/Users/werner/local/nrnnogui/x86_64/lib/libnrniv.la\\\" -livoc -lneuron_gnu -lmeschach -lsundials -livos      \\n\",\n      \"libtool: link: rm -fr  .libs/libnrnmech.0.so .libs/libnrnmech.0.so.dSYM .libs/libnrnmech.la .libs/libnrnmech.lai .libs/libnrnmech.so\\n\",\n      \"libtool: link: gcc -Wl,-undefined -Wl,dynamic_lookup -o .libs/libnrnmech.0.so -bundle  .libs/CaDynamics_E2.o .libs/Ca_HVA.o .libs/Ca_LVAst.o .libs/Ih.o .libs/Im.o .libs/K_Pst.o .libs/K_Tst.o .libs/NaTa_t.o .libs/NaTs2_t.o .libs/Nap_Et2.o .libs/ProbAMPANMDA_EMS.o .libs/ProbGABAAB_EMS.o .libs/SK_E2.o .libs/SKv3_1.o .libs/mod_func.o   -L/Users/werner/local/nrnnogui/x86_64/lib /Users/werner/local/nrnnogui/x86_64/lib/libnrnoc.dylib /Users/werner/local/nrnnogui/x86_64/lib/liboc.dylib /Users/werner/local/nrnnogui/x86_64/lib/libmemacs.dylib /Users/werner/local/nrnnogui/x86_64/lib/libnrnmpi.dylib /Users/werner/local/nrnnogui/x86_64/lib/libscopmath.dylib /Users/werner/local/nrnnogui/x86_64/lib/libsparse13.dylib -lreadline -lncurses /Users/werner/local/nrnnogui/x86_64/lib/libnrniv.dylib /Users/werner/local/nrnnogui/x86_64/lib/libivoc.dylib /Users/werner/local/nrnnogui/x86_64/lib/libneuron_gnu.dylib /Users/werner/local/nrnnogui/x86_64/lib/libmeschach.dylib /Users/werner/local/nrnnogui/x86_64/lib/libsundials.dylib /Users/werner/local/nrnnogui/x86_64/lib/libivos.dylib   \\n\",\n      \"libtool: link: dsymutil .libs/libnrnmech.0.so || :\\n\",\n      \"libtool: link: (cd \\\".libs\\\" && rm -f \\\"libnrnmech.so\\\" && ln -s \\\"libnrnmech.0.so\\\" \\\"libnrnmech.so\\\")\\n\",\n      \"libtool: link: ( cd \\\".libs\\\" && rm -f \\\"libnrnmech.la\\\" && ln -s \\\"../libnrnmech.la\\\" \\\"libnrnmech.la\\\" )\\n\",\n      \"Successfully created x86_64/special\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext autoreload\\n\",\n    \"%autoreload\\n\",\n    \"\\n\",\n    \"!nrnivmodl mechanisms\\n\",\n    \"import bluepyopt as bpopt\\n\",\n    \"import bluepyopt.ephys as ephys\\n\",\n    \"\\n\",\n    \"import pprint\\n\",\n    \"pp = pprint.PrettyPrinter(indent=2)\\n\",\n    \"\\n\",\n    \"%matplotlib notebook\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Enable the code below to enable debug level logging\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# import logging                                                                      \\n\",\n    \"# logging.basicConfig()                                                               \\n\",\n    \"# logger = logging.getLogger()                                                        \\n\",\n    \"# logger.setLevel(logging.DEBUG)   \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model description\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Morphology\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We're using a complex reconstructed morphology of an L5PC cell. Let's visualise this with the BlueBrain NeuroM software:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already up-to-date: neurom in /usr/local/lib/python2.7/site-packages\\n\",\n      \"Requirement already up-to-date: pyyaml>=3.10 in /usr/local/lib/python2.7/site-packages (from neurom)\\n\",\n      \"Requirement already up-to-date: enum34>=1.0.4 in /usr/local/lib/python2.7/site-packages (from neurom)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.segments is deprecated. \\n\",\n      \"  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\\n\",\n      \"/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.sections is deprecated. \\n\",\n      \"  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\\n\",\n      \"/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.bifurcations is deprecated. \\n\",\n      \"  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\\n\",\n      \"/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.points is deprecated. \\n\",\n      \"  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\\n\",\n      \"/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.ezy is deprecated. \\n\",\n      \"  _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\\n\",\n      \"/usr/local/lib/python2.7/site-packages/neurom/io/neurolucida.py:254: UserWarning: This is an experimental reader. There are no guarantees regarding ability to parse Neurolucida .asc files or correctness of output.\\n\",\n      \"  warnings.warn(msg)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": \"/* Put everything inside the global mpl namespace */\\nwindow.mpl = {};\\n\\nmpl.get_websocket_type = function() {\\n    if (typeof(WebSocket) !== 'undefined') {\\n        return WebSocket;\\n    } else if (typeof(MozWebSocket) !== 'undefined') {\\n        return MozWebSocket;\\n    } else {\\n        alert('Your browser does not have WebSocket support.' +\\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n              'Firefox 4 and 5 are also supported but you ' +\\n              'have to enable WebSockets in about:config.');\\n    };\\n}\\n\\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n    this.id = figure_id;\\n\\n    this.ws = websocket;\\n\\n    this.supports_binary = (this.ws.binaryType != undefined);\\n\\n    if (!this.supports_binary) {\\n        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n        if (warnings) {\\n            warnings.style.display = 'block';\\n            warnings.textContent = (\\n                \\\"This browser does not support binary websocket messages. \\\" +\\n                    \\\"Performance may be slow.\\\");\\n        }\\n    }\\n\\n    this.imageObj = new Image();\\n\\n    this.context = undefined;\\n    this.message = undefined;\\n    this.canvas = undefined;\\n    this.rubberband_canvas = undefined;\\n    this.rubberband_context = undefined;\\n    this.format_dropdown = undefined;\\n\\n    this.image_mode = 'full';\\n\\n    this.root = $('<div/>');\\n    this._root_extra_style(this.root)\\n    this.root.attr('style', 'display: inline-block');\\n\\n    $(parent_element).append(this.root);\\n\\n    this._init_header(this);\\n    this._init_canvas(this);\\n    this._init_toolbar(this);\\n\\n    var fig = this;\\n\\n    this.waiting = false;\\n\\n    this.ws.onopen =  function () {\\n            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n            fig.send_message(\\\"send_image_mode\\\", {});\\n            fig.send_message(\\\"refresh\\\", {});\\n        }\\n\\n    this.imageObj.onload = function() {\\n            if (fig.image_mode == 'full') {\\n                // Full images could contain transparency (where diff images\\n                // almost always do), so we need to clear the canvas so that\\n                // there is no ghosting.\\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n            }\\n            fig.context.drawImage(fig.imageObj, 0, 0);\\n        };\\n\\n    this.imageObj.onunload = function() {\\n        this.ws.close();\\n    }\\n\\n    this.ws.onmessage = this._make_on_message_function(this);\\n\\n    this.ondownload = ondownload;\\n}\\n\\nmpl.figure.prototype._init_header = function() {\\n    var titlebar = $(\\n        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n        'ui-helper-clearfix\\\"/>');\\n    var titletext = $(\\n        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n        'text-align: center; padding: 3px;\\\"/>');\\n    titlebar.append(titletext)\\n    this.root.append(titlebar);\\n    this.header = titletext[0];\\n}\\n\\n\\n\\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\\n}\\n\\n\\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\\n}\\n\\nmpl.figure.prototype._init_canvas = function() {\\n    var fig = this;\\n\\n    var canvas_div = $('<div/>');\\n\\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\\n    function canvas_keyboard_event(event) {\\n        return fig.key_event(event, event['data']);\\n    }\\n\\n    canvas_div.keydown('key_press', canvas_keyboard_event);\\n    canvas_div.keyup('key_release', canvas_keyboard_event);\\n    this.canvas_div = canvas_div\\n    this._canvas_extra_style(canvas_div)\\n    this.root.append(canvas_div);\\n\\n    var canvas = $('<canvas/>');\\n    canvas.addClass('mpl-canvas');\\n    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\\n    this.canvas = canvas[0];\\n    this.context = canvas[0].getContext(\\\"2d\\\");\\n\\n    var rubberband = $('<canvas/>');\\n    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\\n    var pass_mouse_events = true;\\n\\n    canvas_div.resizable({\\n        start: function(event, ui) {\\n            pass_mouse_events = false;\\n        },\\n        resize: function(event, ui) {\\n            fig.request_resize(ui.size.width, ui.size.height);\\n        },\\n        stop: function(event, ui) {\\n            pass_mouse_events = true;\\n            fig.request_resize(ui.size.width, ui.size.height);\\n        },\\n    });\\n\\n    function mouse_event_fn(event) {\\n        if (pass_mouse_events)\\n            return fig.mouse_event(event, event['data']);\\n    }\\n\\n    rubberband.mousedown('button_press', mouse_event_fn);\\n    rubberband.mouseup('button_release', mouse_event_fn);\\n    // Throttle sequential mouse events to 1 every 20ms.\\n    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\\n    canvas_div.on(\\\"wheel\\\", function (event) {\\n        event = event.originalEvent;\\n        event['data'] = 'scroll'\\n        if (event.deltaY < 0) {\\n            event.step = 1;\\n        } else {\\n            event.step = -1;\\n        }\\n        mouse_event_fn(event);\\n    });\\n\\n    canvas_div.append(canvas);\\n    canvas_div.append(rubberband);\\n\\n    this.rubberband = rubberband;\\n    this.rubberband_canvas = rubberband[0];\\n    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\\n    this._resize_canvas = function(width, height) {\\n        // Keep the size of the canvas, canvas container, and rubber band\\n        // canvas in synch.\\n        canvas_div.css('width', width)\\n        canvas_div.css('height', height)\\n\\n        canvas.attr('width', width);\\n        canvas.attr('height', height);\\n\\n        rubberband.attr('width', width);\\n        rubberband.attr('height', height);\\n    }\\n\\n    // Set the figure to an initial 600x600px, this will subsequently be updated\\n    // upon first draw.\\n    this._resize_canvas(600, 600);\\n\\n    // Disable right mouse context menu.\\n    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n        return false;\\n    });\\n\\n    function set_focus () {\\n        canvas.focus();\\n        canvas_div.focus();\\n    }\\n\\n    window.setTimeout(set_focus, 100);\\n}\\n\\nmpl.figure.prototype._init_toolbar = function() {\\n    var fig = this;\\n\\n    var nav_element = $('<div/>')\\n    nav_element.attr('style', 'width: 100%');\\n    this.root.append(nav_element);\\n\\n    // Define a callback function for later on.\\n    function toolbar_event(event) {\\n        return fig.toolbar_button_onclick(event['data']);\\n    }\\n    function toolbar_mouse_event(event) {\\n        return fig.toolbar_button_onmouseover(event['data']);\\n    }\\n\\n    for(var toolbar_ind in mpl.toolbar_items) {\\n        var name = mpl.toolbar_items[toolbar_ind][0];\\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n        var image = mpl.toolbar_items[toolbar_ind][2];\\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\\n        if (!name) {\\n            // put a spacer in here.\\n            continue;\\n        }\\n        var button = $('<button/>');\\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n                        'ui-button-icon-only');\\n        button.attr('role', 'button');\\n        button.attr('aria-disabled', 'false');\\n        button.click(method_name, toolbar_event);\\n        button.mouseover(tooltip, toolbar_mouse_event);\\n\\n        var icon_img = $('<span/>');\\n        icon_img.addClass('ui-button-icon-primary ui-icon');\\n        icon_img.addClass(image);\\n        icon_img.addClass('ui-corner-all');\\n\\n        var tooltip_span = $('<span/>');\\n        tooltip_span.addClass('ui-button-text');\\n        tooltip_span.html(tooltip);\\n\\n        button.append(icon_img);\\n        button.append(tooltip_span);\\n\\n        nav_element.append(button);\\n    }\\n\\n    var fmt_picker_span = $('<span/>');\\n\\n    var fmt_picker = $('<select/>');\\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n    fmt_picker_span.append(fmt_picker);\\n    nav_element.append(fmt_picker_span);\\n    this.format_dropdown = fmt_picker[0];\\n\\n    for (var ind in mpl.extensions) {\\n        var fmt = mpl.extensions[ind];\\n        var option = $(\\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n        fmt_picker.append(option)\\n    }\\n\\n    // Add hover states to the ui-buttons\\n    $( \\\".ui-button\\\" ).hover(\\n        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n    );\\n\\n    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n    nav_element.append(status_bar);\\n    this.message = status_bar[0];\\n}\\n\\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n    // which will in turn request a refresh of the image.\\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n}\\n\\nmpl.figure.prototype.send_message = function(type, properties) {\\n    properties['type'] = type;\\n    properties['figure_id'] = this.id;\\n    this.ws.send(JSON.stringify(properties));\\n}\\n\\nmpl.figure.prototype.send_draw_message = function() {\\n    if (!this.waiting) {\\n        this.waiting = true;\\n        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n    }\\n}\\n\\n\\nmpl.figure.prototype.handle_save = function(fig, msg) {\\n    var format_dropdown = fig.format_dropdown;\\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n    fig.ondownload(fig, format);\\n}\\n\\n\\nmpl.figure.prototype.handle_resize = function(fig, msg) {\\n    var size = msg['size'];\\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n        fig._resize_canvas(size[0], size[1]);\\n        fig.send_message(\\\"refresh\\\", {});\\n    };\\n}\\n\\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n    var x0 = msg['x0'];\\n    var y0 = fig.canvas.height - msg['y0'];\\n    var x1 = msg['x1'];\\n    var y1 = fig.canvas.height - msg['y1'];\\n    x0 = Math.floor(x0) + 0.5;\\n    y0 = Math.floor(y0) + 0.5;\\n    x1 = Math.floor(x1) + 0.5;\\n    y1 = Math.floor(y1) + 0.5;\\n    var min_x = Math.min(x0, x1);\\n    var min_y = Math.min(y0, y1);\\n    var width = Math.abs(x1 - x0);\\n    var height = Math.abs(y1 - y0);\\n\\n    fig.rubberband_context.clearRect(\\n        0, 0, fig.canvas.width, fig.canvas.height);\\n\\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n}\\n\\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n    // Updates the figure title.\\n    fig.header.textContent = msg['label'];\\n}\\n\\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\\n    var cursor = msg['cursor'];\\n    switch(cursor)\\n    {\\n    case 0:\\n        cursor = 'pointer';\\n        break;\\n    case 1:\\n        cursor = 'default';\\n        break;\\n    case 2:\\n        cursor = 'crosshair';\\n        break;\\n    case 3:\\n        cursor = 'move';\\n        break;\\n    }\\n    fig.rubberband_canvas.style.cursor = cursor;\\n}\\n\\nmpl.figure.prototype.handle_message = function(fig, msg) {\\n    fig.message.textContent = msg['message'];\\n}\\n\\nmpl.figure.prototype.handle_draw = function(fig, msg) {\\n    // Request the server to send over a new figure.\\n    fig.send_draw_message();\\n}\\n\\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n    fig.image_mode = msg['mode'];\\n}\\n\\nmpl.figure.prototype.updated_canvas_event = function() {\\n    // Called whenever the canvas gets updated.\\n    this.send_message(\\\"ack\\\", {});\\n}\\n\\n// A function to construct a web socket function for onmessage handling.\\n// Called in the figure constructor.\\nmpl.figure.prototype._make_on_message_function = function(fig) {\\n    return function socket_on_message(evt) {\\n        if (evt.data instanceof Blob) {\\n            /* FIXME: We get \\\"Resource interpreted as Image but\\n             * transferred with MIME type text/plain:\\\" errors on\\n             * Chrome.  But how to set the MIME type?  It doesn't seem\\n             * to be part of the websocket stream */\\n            evt.data.type = \\\"image/png\\\";\\n\\n            /* Free the memory for the previous frames */\\n            if (fig.imageObj.src) {\\n                (window.URL || window.webkitURL).revokeObjectURL(\\n                    fig.imageObj.src);\\n            }\\n\\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n                evt.data);\\n            fig.updated_canvas_event();\\n            fig.waiting = false;\\n            return;\\n        }\\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n            fig.imageObj.src = evt.data;\\n            fig.updated_canvas_event();\\n            fig.waiting = false;\\n            return;\\n        }\\n\\n        var msg = JSON.parse(evt.data);\\n        var msg_type = msg['type'];\\n\\n        // Call the  \\\"handle_{type}\\\" callback, which takes\\n        // the figure and JSON message as its only arguments.\\n        try {\\n            var callback = fig[\\\"handle_\\\" + msg_type];\\n        } catch (e) {\\n            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n            return;\\n        }\\n\\n        if (callback) {\\n            try {\\n                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n                callback(fig, msg);\\n            } catch (e) {\\n                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n            }\\n        }\\n    };\\n}\\n\\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\nmpl.findpos = function(e) {\\n    //this section is from http://www.quirksmode.org/js/events_properties.html\\n    var targ;\\n    if (!e)\\n        e = window.event;\\n    if (e.target)\\n        targ = e.target;\\n    else if (e.srcElement)\\n        targ = e.srcElement;\\n    if (targ.nodeType == 3) // defeat Safari bug\\n        targ = targ.parentNode;\\n\\n    // jQuery normalizes the pageX and pageY\\n    // pageX,Y are the mouse positions relative to the document\\n    // offset() returns the position of the element relative to the document\\n    var x = e.pageX - $(targ).offset().left;\\n    var y = e.pageY - $(targ).offset().top;\\n\\n    return {\\\"x\\\": x, \\\"y\\\": y};\\n};\\n\\n/*\\n * return a copy of an object with only non-object keys\\n * we need this to avoid circular references\\n * http://stackoverflow.com/a/24161582/3208463\\n */\\nfunction simpleKeys (original) {\\n  return Object.keys(original).reduce(function (obj, key) {\\n    if (typeof original[key] !== 'object')\\n        obj[key] = original[key]\\n    return obj;\\n  }, {});\\n}\\n\\nmpl.figure.prototype.mouse_event = function(event, name) {\\n    var canvas_pos = mpl.findpos(event)\\n\\n    if (name === 'button_press')\\n    {\\n        this.canvas.focus();\\n        this.canvas_div.focus();\\n    }\\n\\n    var x = canvas_pos.x;\\n    var y = canvas_pos.y;\\n\\n    this.send_message(name, {x: x, y: y, button: event.button,\\n                             step: event.step,\\n                             guiEvent: simpleKeys(event)});\\n\\n    /* This prevents the web browser from automatically changing to\\n     * the text insertion cursor when the button is pressed.  We want\\n     * to control all of the cursor setting manually through the\\n     * 'cursor' event from matplotlib */\\n    event.preventDefault();\\n    return false;\\n}\\n\\nmpl.figure.prototype._key_event_extra = function(event, name) {\\n    // Handle any extra behaviour associated with a key event\\n}\\n\\nmpl.figure.prototype.key_event = function(event, name) {\\n\\n    // Prevent repeat events\\n    if (name == 'key_press')\\n    {\\n        if (event.which === this._key)\\n            return;\\n        else\\n            this._key = event.which;\\n    }\\n    if (name == 'key_release')\\n        this._key = null;\\n\\n    var value = '';\\n    if (event.ctrlKey && event.which != 17)\\n        value += \\\"ctrl+\\\";\\n    if (event.altKey && event.which != 18)\\n        value += \\\"alt+\\\";\\n    if (event.shiftKey && event.which != 16)\\n        value += \\\"shift+\\\";\\n\\n    value += 'k';\\n    value += event.which.toString();\\n\\n    this._key_event_extra(event, name);\\n\\n    this.send_message(name, {key: value,\\n                             guiEvent: simpleKeys(event)});\\n    return false;\\n}\\n\\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\\n    if (name == 'download') {\\n        this.handle_save(this, null);\\n    } else {\\n        this.send_message(\\\"toolbar_button\\\", {name: name});\\n    }\\n};\\n\\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n    this.message.textContent = tooltip;\\n};\\nmpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\\nmpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\\nmpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n    // object with the appropriate methods. Currently this is a non binary\\n    // socket, so there is still some room for performance tuning.\\n    var ws = {};\\n\\n    ws.close = function() {\\n        comm.close()\\n    };\\n    ws.send = function(m) {\\n        //console.log('sending', m);\\n        comm.send(m);\\n    };\\n    // Register the callback with on_msg.\\n    comm.on_msg(function(msg) {\\n        //console.log('receiving', msg['content']['data'], msg);\\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n        ws.onmessage(msg['content']['data'])\\n    });\\n    return ws;\\n}\\n\\nmpl.mpl_figure_comm = function(comm, msg) {\\n    // This is the function which gets called when the mpl process\\n    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\\n    var id = msg.content.data.id;\\n    // Get hold of the div created by the display call when the Comm\\n    // socket was opened in Python.\\n    var element = $(\\\"#\\\" + id);\\n    var ws_proxy = comm_websocket_adapter(comm)\\n\\n    function ondownload(figure, format) {\\n        window.open(figure.imageObj.src);\\n    }\\n\\n    var fig = new mpl.figure(id, ws_proxy,\\n                           ondownload,\\n                           element.get(0));\\n\\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n    // web socket which is closed, not our websocket->open comm proxy.\\n    ws_proxy.onopen();\\n\\n    fig.parent_element = element.get(0);\\n    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n    if (!fig.cell_info) {\\n        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n        return;\\n    }\\n\\n    var output_index = fig.cell_info[2]\\n    var cell = fig.cell_info[0];\\n\\n};\\n\\nmpl.figure.prototype.handle_close = function(fig, msg) {\\n    fig.root.unbind('remove')\\n\\n    // Update the output cell to use the data from the current canvas.\\n    fig.push_to_output();\\n    var dataURL = fig.canvas.toDataURL();\\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n    // the notebook keyboard shortcuts fail.\\n    IPython.keyboard_manager.enable()\\n    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n    fig.close_ws(fig, msg);\\n}\\n\\nmpl.figure.prototype.close_ws = function(fig, msg){\\n    fig.send_message('closing', msg);\\n    // fig.ws.close()\\n}\\n\\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\\n    // Turn the data on the canvas into data in the output cell.\\n    var dataURL = this.canvas.toDataURL();\\n    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n}\\n\\nmpl.figure.prototype.updated_canvas_event = function() {\\n    // Tell IPython that the notebook contents must change.\\n    IPython.notebook.set_dirty(true);\\n    this.send_message(\\\"ack\\\", {});\\n    var fig = this;\\n    // Wait a second, then push the new image to the DOM so\\n    // that it is saved nicely (might be nice to debounce this).\\n    setTimeout(function () { fig.push_to_output() }, 1000);\\n}\\n\\nmpl.figure.prototype._init_toolbar = function() {\\n    var fig = this;\\n\\n    var nav_element = $('<div/>')\\n    nav_element.attr('style', 'width: 100%');\\n    this.root.append(nav_element);\\n\\n    // Define a callback function for later on.\\n    function toolbar_event(event) {\\n        return fig.toolbar_button_onclick(event['data']);\\n    }\\n    function toolbar_mouse_event(event) {\\n        return fig.toolbar_button_onmouseover(event['data']);\\n    }\\n\\n    for(var toolbar_ind in mpl.toolbar_items){\\n        var name = mpl.toolbar_items[toolbar_ind][0];\\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n        var image = mpl.toolbar_items[toolbar_ind][2];\\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\\n        if (!name) { continue; };\\n\\n        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n        button.click(method_name, toolbar_event);\\n        button.mouseover(tooltip, toolbar_mouse_event);\\n        nav_element.append(button);\\n    }\\n\\n    // Add the status bar.\\n    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n    nav_element.append(status_bar);\\n    this.message = status_bar[0];\\n\\n    // Add the close button to the window.\\n    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n    buttongrp.append(button);\\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n    titlebar.prepend(buttongrp);\\n}\\n\\nmpl.figure.prototype._root_extra_style = function(el){\\n    var fig = this\\n    el.on(\\\"remove\\\", function(){\\n\\tfig.close_ws(fig, {});\\n    });\\n}\\n\\nmpl.figure.prototype._canvas_extra_style = function(el){\\n    // this is important to make the div 'focusable\\n    el.attr('tabindex', 0)\\n    // reach out to IPython and tell the keyboard manager to turn it's self\\n    // off when our div gets focus\\n\\n    // location in version 3\\n    if (IPython.notebook.keyboard_manager) {\\n        IPython.notebook.keyboard_manager.register_events(el);\\n    }\\n    else {\\n        // location in version 2\\n        IPython.keyboard_manager.register_events(el);\\n    }\\n\\n}\\n\\nmpl.figure.prototype._key_event_extra = function(event, name) {\\n    var manager = IPython.notebook.keyboard_manager;\\n    if (!manager)\\n        manager = IPython.keyboard_manager;\\n\\n    // Check for shift+enter\\n    if (event.shiftKey && event.which == 13) {\\n        this.canvas_div.blur();\\n        // select the cell after this one\\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n        IPython.notebook.select(index + 1);\\n    }\\n}\\n\\nmpl.figure.prototype.handle_save = function(fig, msg) {\\n    fig.ondownload(fig, null);\\n}\\n\\n\\nmpl.find_output_cell = function(html_output) {\\n    // Return the cell and output element which can be found *uniquely* in the notebook.\\n    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n    // IPython event is triggered only after the cells have been serialised, which for\\n    // our purposes (turning an active figure into a static one), is too late.\\n    var cells = IPython.notebook.get_cells();\\n    var ncells = cells.length;\\n    for (var i=0; i<ncells; i++) {\\n        var cell = cells[i];\\n        if (cell.cell_type === 'code'){\\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n                var data = cell.output_area.outputs[j];\\n                if (data.data) {\\n                    // IPython >= 3 moved mimebundle to data attribute of output\\n                    data = data.data;\\n                }\\n                if (data['text/html'] == html_output) {\\n                    return [cell, data, j];\\n                }\\n            }\\n        }\\n    }\\n}\\n\\n// Register the function which deals with the matplotlib target/channel.\\n// The kernel may be null if the page has been refreshed.\\nif (IPython.notebook.kernel != null) {\\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n}\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _comm_id_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:29: DeprecationWarning: Comm._iopub_socket_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _iopub_socket_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _kernel_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _session_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _topic_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _kernel_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _comm_id_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/manager.py:37: DeprecationWarning: CommManager._iopub_socket_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _iopub_socket_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _session_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _topic_default(self):\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install neurom --upgrade\\n\",\n    \"import neurom.viewer\\n\",\n    \"neurom.viewer.draw(neurom.load_neuron('morphology/C060114A7.asc'));\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To load the morphology we create a NrnFileMorphology object. We set 'do_replace_axon' to True to replace the axon with a AIS.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"morphology/C060114A7.asc\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"morphology = ephys.morphologies.NrnFileMorphology('morphology/C060114A7.asc', do_replace_axon=True)\\n\",\n    \"print(str(morphology))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Parameters\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since we have many parameters in this model, they are stored in a json file: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/config/parameters.json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[u'g_pas', u'e_pas', u'cm', u'Ra', u'v_init', u'celsius', u'ena', u'ek', u'cm', u'ena', u'ek', u'cm', u'ena', u'ek', u'gIhbar_Ih', u'gNaTs2_tbar_NaTs2_t', u'gSKv3_1bar_SKv3_1', u'gImbar_Im', u'gIhbar_Ih', u'gNaTa_tbar_NaTa_t', u'gNap_Et2bar_Nap_Et2', u'gK_Pstbar_K_Pst', u'gK_Tstbar_K_Tst', u'gSK_E2bar_SK_E2', u'gSKv3_1bar_SKv3_1', u'gCa_HVAbar_Ca_HVA', u'gCa_LVAstbar_Ca_LVAst', u'gamma_CaDynamics_E2', u'decay_CaDynamics_E2', u'gNaTs2_tbar_NaTs2_t', u'gSKv3_1bar_SKv3_1', u'gSK_E2bar_SK_E2', u'gCa_HVAbar_Ca_HVA', u'gCa_LVAstbar_Ca_LVAst', u'gamma_CaDynamics_E2', u'decay_CaDynamics_E2', u'gIhbar_Ih']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import json\\n\",\n    \"param_configs = json.load(open('config/parameters.json'))\\n\",\n    \"print([param_config['param_name'] for param_config in param_configs])\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The directory that contains this notebook has a module that will load all the parameters in BluePyOpt Parameter objects\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"g_pas.all: ['all'] g_pas = 3e-05\\n\",\n      \"e_pas.all: ['all'] e_pas = -75\\n\",\n      \"cm.all: ['all'] cm = 1\\n\",\n      \"Ra.all: ['all'] Ra = 100\\n\",\n      \"v_init: v_init = -65\\n\",\n      \"celsius: celsius = 34\\n\",\n      \"ena.apical: ['apical'] ena = 50\\n\",\n      \"ek.apical: ['apical'] ek = -85\\n\",\n      \"cm.apical: ['apical'] cm = 2\\n\",\n      \"ena.somatic: ['somatic'] ena = 50\\n\",\n      \"ek.somatic: ['somatic'] ek = -85\\n\",\n      \"cm.basal: ['basal'] cm = 2\\n\",\n      \"ena.axonal: ['axonal'] ena = 50\\n\",\n      \"ek.axonal: ['axonal'] ek = -85\\n\",\n      \"gIhbar_Ih.basal: ['basal'] gIhbar_Ih = 8e-05\\n\",\n      \"gNaTs2_tbar_NaTs2_t.apical: ['apical'] gNaTs2_tbar_NaTs2_t = [0, 0.04]\\n\",\n      \"gSKv3_1bar_SKv3_1.apical: ['apical'] gSKv3_1bar_SKv3_1 = [0, 0.04]\\n\",\n      \"gImbar_Im.apical: ['apical'] gImbar_Im = [0, 0.001]\\n\",\n      \"gIhbar_Ih.apical: ['apical'] gIhbar_Ih = 8e-05\\n\",\n      \"gNaTa_tbar_NaTa_t.axonal: ['axonal'] gNaTa_tbar_NaTa_t = [0, 4]\\n\",\n      \"gNap_Et2bar_Nap_Et2.axonal: ['axonal'] gNap_Et2bar_Nap_Et2 = [0, 4]\\n\",\n      \"gK_Pstbar_K_Pst.axonal: ['axonal'] gK_Pstbar_K_Pst = [0, 1]\\n\",\n      \"gK_Tstbar_K_Tst.axonal: ['axonal'] gK_Tstbar_K_Tst = [0, 0.1]\\n\",\n      \"gSK_E2bar_SK_E2.axonal: ['axonal'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"gSKv3_1bar_SKv3_1.axonal: ['axonal'] gSKv3_1bar_SKv3_1 = [0, 2]\\n\",\n      \"gCa_HVAbar_Ca_HVA.axonal: ['axonal'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"gCa_LVAstbar_Ca_LVAst.axonal: ['axonal'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"gamma_CaDynamics_E2.axonal: ['axonal'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"decay_CaDynamics_E2.axonal: ['axonal'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"gNaTs2_tbar_NaTs2_t.somatic: ['somatic'] gNaTs2_tbar_NaTs2_t = [0, 1]\\n\",\n      \"gSKv3_1bar_SKv3_1.somatic: ['somatic'] gSKv3_1bar_SKv3_1 = [0, 1]\\n\",\n      \"gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"gCa_HVAbar_Ca_HVA.somatic: ['somatic'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"gCa_LVAstbar_Ca_LVAst.somatic: ['somatic'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"gamma_CaDynamics_E2.somatic: ['somatic'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"decay_CaDynamics_E2.somatic: ['somatic'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"gIhbar_Ih.somatic: ['somatic'] gIhbar_Ih = 8e-05\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import l5pc_model\\n\",\n    \"parameters = l5pc_model.define_parameters()\\n\",\n    \"print('\\\\n'.join('%s' % param for param in parameters))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see there are two types of parameters, parameters with a fixed value and parameters with bounds. The latter will be optimised by the algorithm.\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Mechanism\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We also need to add all the necessary mechanisms, like ion channels to the model. \\n\",\n    \"The configuration of the mechanisms is also stored in a json file, and can be loaded in a similar way.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Ih.basal: Ih at ['basal']\\n\",\n      \"pas.all: pas at ['all']\\n\",\n      \"NaTs2_t.somatic: NaTs2_t at ['somatic']\\n\",\n      \"SKv3_1.somatic: SKv3_1 at ['somatic']\\n\",\n      \"SK_E2.somatic: SK_E2 at ['somatic']\\n\",\n      \"CaDynamics_E2.somatic: CaDynamics_E2 at ['somatic']\\n\",\n      \"Ca_HVA.somatic: Ca_HVA at ['somatic']\\n\",\n      \"Ca_LVAst.somatic: Ca_LVAst at ['somatic']\\n\",\n      \"Ih.somatic: Ih at ['somatic']\\n\",\n      \"Ih.apical: Ih at ['apical']\\n\",\n      \"Im.apical: Im at ['apical']\\n\",\n      \"SKv3_1.apical: SKv3_1 at ['apical']\\n\",\n      \"NaTs2_t.apical: NaTs2_t at ['apical']\\n\",\n      \"Ca_LVAst.axonal: Ca_LVAst at ['axonal']\\n\",\n      \"Ca_HVA.axonal: Ca_HVA at ['axonal']\\n\",\n      \"CaDynamics_E2.axonal: CaDynamics_E2 at ['axonal']\\n\",\n      \"SKv3_1.axonal: SKv3_1 at ['axonal']\\n\",\n      \"SK_E2.axonal: SK_E2 at ['axonal']\\n\",\n      \"K_Tst.axonal: K_Tst at ['axonal']\\n\",\n      \"K_Pst.axonal: K_Pst at ['axonal']\\n\",\n      \"Nap_Et2.axonal: Nap_Et2 at ['axonal']\\n\",\n      \"NaTa_t.axonal: NaTa_t at ['axonal']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mechanisms = l5pc_model.define_mechanisms()\\n\",\n    \"print('\\\\n'.join('%s' % mech for mech in mechanisms))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Cell model\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With the morphology, mechanisms and parameters we can build the cell model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"l5pc:\\n\",\n      \"  morphology:\\n\",\n      \"    morphology/C060114A7.asc\\n\",\n      \"  mechanisms:\\n\",\n      \"    Ih.basal: Ih at ['basal']\\n\",\n      \"    pas.all: pas at ['all']\\n\",\n      \"    NaTs2_t.somatic: NaTs2_t at ['somatic']\\n\",\n      \"    SKv3_1.somatic: SKv3_1 at ['somatic']\\n\",\n      \"    SK_E2.somatic: SK_E2 at ['somatic']\\n\",\n      \"    CaDynamics_E2.somatic: CaDynamics_E2 at ['somatic']\\n\",\n      \"    Ca_HVA.somatic: Ca_HVA at ['somatic']\\n\",\n      \"    Ca_LVAst.somatic: Ca_LVAst at ['somatic']\\n\",\n      \"    Ih.somatic: Ih at ['somatic']\\n\",\n      \"    Ih.apical: Ih at ['apical']\\n\",\n      \"    Im.apical: Im at ['apical']\\n\",\n      \"    SKv3_1.apical: SKv3_1 at ['apical']\\n\",\n      \"    NaTs2_t.apical: NaTs2_t at ['apical']\\n\",\n      \"    Ca_LVAst.axonal: Ca_LVAst at ['axonal']\\n\",\n      \"    Ca_HVA.axonal: Ca_HVA at ['axonal']\\n\",\n      \"    CaDynamics_E2.axonal: CaDynamics_E2 at ['axonal']\\n\",\n      \"    SKv3_1.axonal: SKv3_1 at ['axonal']\\n\",\n      \"    SK_E2.axonal: SK_E2 at ['axonal']\\n\",\n      \"    K_Tst.axonal: K_Tst at ['axonal']\\n\",\n      \"    K_Pst.axonal: K_Pst at ['axonal']\\n\",\n      \"    Nap_Et2.axonal: Nap_Et2 at ['axonal']\\n\",\n      \"    NaTa_t.axonal: NaTa_t at ['axonal']\\n\",\n      \"  params:\\n\",\n      \"    g_pas.all: ['all'] g_pas = 3e-05\\n\",\n      \"    e_pas.all: ['all'] e_pas = -75\\n\",\n      \"    cm.all: ['all'] cm = 1\\n\",\n      \"    Ra.all: ['all'] Ra = 100\\n\",\n      \"    v_init: v_init = -65\\n\",\n      \"    celsius: celsius = 34\\n\",\n      \"    ena.apical: ['apical'] ena = 50\\n\",\n      \"    ek.apical: ['apical'] ek = -85\\n\",\n      \"    cm.apical: ['apical'] cm = 2\\n\",\n      \"    ena.somatic: ['somatic'] ena = 50\\n\",\n      \"    ek.somatic: ['somatic'] ek = -85\\n\",\n      \"    cm.basal: ['basal'] cm = 2\\n\",\n      \"    ena.axonal: ['axonal'] ena = 50\\n\",\n      \"    ek.axonal: ['axonal'] ek = -85\\n\",\n      \"    gIhbar_Ih.basal: ['basal'] gIhbar_Ih = 8e-05\\n\",\n      \"    gNaTs2_tbar_NaTs2_t.apical: ['apical'] gNaTs2_tbar_NaTs2_t = [0, 0.04]\\n\",\n      \"    gSKv3_1bar_SKv3_1.apical: ['apical'] gSKv3_1bar_SKv3_1 = [0, 0.04]\\n\",\n      \"    gImbar_Im.apical: ['apical'] gImbar_Im = [0, 0.001]\\n\",\n      \"    gIhbar_Ih.apical: ['apical'] gIhbar_Ih = 8e-05\\n\",\n      \"    gNaTa_tbar_NaTa_t.axonal: ['axonal'] gNaTa_tbar_NaTa_t = [0, 4]\\n\",\n      \"    gNap_Et2bar_Nap_Et2.axonal: ['axonal'] gNap_Et2bar_Nap_Et2 = [0, 4]\\n\",\n      \"    gK_Pstbar_K_Pst.axonal: ['axonal'] gK_Pstbar_K_Pst = [0, 1]\\n\",\n      \"    gK_Tstbar_K_Tst.axonal: ['axonal'] gK_Tstbar_K_Tst = [0, 0.1]\\n\",\n      \"    gSK_E2bar_SK_E2.axonal: ['axonal'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"    gSKv3_1bar_SKv3_1.axonal: ['axonal'] gSKv3_1bar_SKv3_1 = [0, 2]\\n\",\n      \"    gCa_HVAbar_Ca_HVA.axonal: ['axonal'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"    gCa_LVAstbar_Ca_LVAst.axonal: ['axonal'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"    gamma_CaDynamics_E2.axonal: ['axonal'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"    decay_CaDynamics_E2.axonal: ['axonal'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"    gNaTs2_tbar_NaTs2_t.somatic: ['somatic'] gNaTs2_tbar_NaTs2_t = [0, 1]\\n\",\n      \"    gSKv3_1bar_SKv3_1.somatic: ['somatic'] gSKv3_1bar_SKv3_1 = [0, 1]\\n\",\n      \"    gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"    gCa_HVAbar_Ca_HVA.somatic: ['somatic'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"    gCa_LVAstbar_Ca_LVAst.somatic: ['somatic'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"    gamma_CaDynamics_E2.somatic: ['somatic'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"    decay_CaDynamics_E2.somatic: ['somatic'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"    gIhbar_Ih.somatic: ['somatic'] gIhbar_Ih = 8e-05\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"l5pc_cell = ephys.models.CellModel('l5pc', morph=morphology, mechs=mechanisms, params=parameters)\\n\",\n    \"print(l5pc_cell)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For use in the cell evaluator later, we need to make a list of the name of the parameters we are going to optimise.\\n\",\n    \"These are the parameters that are not frozen.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"param_names = [param.name for param in l5pc_cell.params.values() if not param.frozen]      \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Protocols\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we have a cell model, we can apply protocols to it. The protocols are also stored in a json file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{u'Step1': {u'stimuli': [{u'delay': 700, u'amp': 0.458, u'duration': 2000, u'totduration': 3000}, {u'delay': 0, u'amp': -0.126, u'duration': 3000, u'totduration': 3000}]}, u'Step3': {u'stimuli': [{u'delay': 700, u'amp': 0.95, u'duration': 2000, u'totduration': 3000}, {u'delay': 0, u'amp': -0.126, u'duration': 3000, u'totduration': 3000}]}, u'Step2': {u'stimuli': [{u'delay': 700, u'amp': 0.562, u'duration': 2000, u'totduration': 3000}, {u'delay': 0, u'amp': -0.126, u'duration': 3000, u'totduration': 3000}]}, u'bAP': {u'stimuli': [{u'delay': 295, u'amp': 1.9, u'duration': 5, u'totduration': 600}], u'extra_recordings': [{u'var': u'v', u'somadistance': 660, u'type': u'somadistance', u'name': u'dend1', u'seclist_name': u'apical'}, {u'var': u'v', u'somadistance': 800, u'type': u'somadistance', u'name': u'dend2', u'seclist_name': u'apical'}]}}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"proto_configs = json.load(open('config/protocols.json'))\\n\",\n    \"print(proto_configs)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And they can be automatically loaded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"bAP:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 1.900000 delay 295.000000 duration 5.000000 totdur 600.000000 at somatic[0](0.5)\\n\",\n      \"  recordings:\\n\",\n      \"    bAP.soma.v: v at somatic[0](0.5)\\n\",\n      \"    bAP.dend1.v: v at 660.000000 micron from soma in apical\\n\",\n      \"    bAP.dend2.v: v at 800.000000 micron from soma in apical\\n\",\n      \"\\n\",\n      \"Step3:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 0.950000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at somatic[0](0.5)\\n\",\n      \"    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at somatic[0](0.5)\\n\",\n      \"  recordings:\\n\",\n      \"    Step3.soma.v: v at somatic[0](0.5)\\n\",\n      \"\\n\",\n      \"Step2:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 0.562000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at somatic[0](0.5)\\n\",\n      \"    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at somatic[0](0.5)\\n\",\n      \"  recordings:\\n\",\n      \"    Step2.soma.v: v at somatic[0](0.5)\\n\",\n      \"\\n\",\n      \"Step1:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 0.458000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at somatic[0](0.5)\\n\",\n      \"    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at somatic[0](0.5)\\n\",\n      \"  recordings:\\n\",\n      \"    Step1.soma.v: v at somatic[0](0.5)\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import l5pc_evaluator\\n\",\n    \"fitness_protocols = l5pc_evaluator.define_protocols()\\n\",\n    \"print('\\\\n'.join('%s' % protocol for protocol in fitness_protocols.values()))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## eFeatures\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For every protocol we need to define which eFeatures will be used as objectives of the optimisation algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{ u'Step1': { u'soma': { u'AHP_depth_abs': [-60.3636, 2.3018],\\n\",\n      \"                         u'AHP_depth_abs_slow': [-61.1513, 2.3385],\\n\",\n      \"                         u'AHP_slow_time': [0.1599, 0.0483],\\n\",\n      \"                         u'AP_height': [25.0141, 3.1463],\\n\",\n      \"                         u'AP_width': [3.5312, 0.8592],\\n\",\n      \"                         u'ISI_CV': [0.109, 0.1217],\\n\",\n      \"                         u'adaptation_index2': [0.0047, 0.0514],\\n\",\n      \"                         u'doublet_ISI': [62.75, 9.6667],\\n\",\n      \"                         u'mean_frequency': [6, 1.2222],\\n\",\n      \"                         u'time_to_first_spike': [27.25, 5.7222]}},\\n\",\n      \"  u'Step2': { u'soma': { u'AHP_depth_abs': [-59.9055, 1.8329],\\n\",\n      \"                         u'AHP_depth_abs_slow': [-60.2471, 1.8972],\\n\",\n      \"                         u'AHP_slow_time': [0.1676, 0.0339],\\n\",\n      \"                         u'AP_height': [27.1003, 3.1463],\\n\",\n      \"                         u'AP_width': [2.7917, 0.7499],\\n\",\n      \"                         u'ISI_CV': [0.0674, 0.075],\\n\",\n      \"                         u'adaptation_index2': [0.005, 0.0067],\\n\",\n      \"                         u'doublet_ISI': [44.0, 7.1327],\\n\",\n      \"                         u'mean_frequency': [8.5, 0.9796],\\n\",\n      \"                         u'time_to_first_spike': [19.75, 2.8776]}},\\n\",\n      \"  u'Step3': { u'soma': { u'AHP_depth_abs': [-57.0905, 2.3427],\\n\",\n      \"                         u'AHP_depth_abs_slow': [-61.1513, 2.3385],\\n\",\n      \"                         u'AHP_slow_time': [0.1968, 0.0112],\\n\",\n      \"                         u'AP_height': [19.7207, 3.7204],\\n\",\n      \"                         u'AP_width': [3.5347, 0.8788],\\n\",\n      \"                         u'ISI_CV': [0.0737, 0.0292],\\n\",\n      \"                         u'adaptation_index2': [0.0055, 0.0015],\\n\",\n      \"                         u'doublet_ISI': [22.75, 4.14],\\n\",\n      \"                         u'mean_frequency': [17.5, 0.8],\\n\",\n      \"                         u'time_to_first_spike': [10.5, 1.36]}},\\n\",\n      \"  u'bAP': { u'dend1': { u'AP_amplitude_from_voltagebase': [45, 10]},\\n\",\n      \"            u'dend2': { u'AP_amplitude_from_voltagebase': [36, 9.33]},\\n\",\n      \"            u'soma': { u'AP_height': [25.0, 5.0],\\n\",\n      \"                       u'AP_width': [2.0, 0.5],\\n\",\n      \"                       u'Spikecount': [1.0, 0.01]}}}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"feature_configs = json.load(open('config/features.json'))\\n\",\n    \"pp.pprint(feature_configs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"objectives:\\n\",\n      \"  ( AP_height for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 25.0141 and std 3.1463 and AP threshold override -20 )\\n\",\n      \"  ( AHP_slow_time for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.1599 and std 0.0483 and AP threshold override -20 )\\n\",\n      \"  ( ISI_CV for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.109 and std 0.1217 and AP threshold override -20 )\\n\",\n      \"  ( doublet_ISI for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 62.75 and std 9.6667 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs_slow for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean -61.1513 and std 2.3385 and AP threshold override -20 )\\n\",\n      \"  ( AP_width for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 3.5312 and std 0.8592 and AP threshold override -20 )\\n\",\n      \"  ( time_to_first_spike for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 27.25 and std 5.7222 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean -60.3636 and std 2.3018 and AP threshold override -20 )\\n\",\n      \"  ( adaptation_index2 for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.0047 and std 0.0514 and AP threshold override -20 )\\n\",\n      \"  ( mean_frequency for {'': u'Step1.soma.v'} with stim start 700 and end 2700, exp mean 6 and std 1.2222 and AP threshold override -20 )\\n\",\n      \"  ( AP_height for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 19.7207 and std 3.7204 and AP threshold override -20 )\\n\",\n      \"  ( AHP_slow_time for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.1968 and std 0.0112 and AP threshold override -20 )\\n\",\n      \"  ( ISI_CV for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.0737 and std 0.0292 and AP threshold override -20 )\\n\",\n      \"  ( doublet_ISI for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 22.75 and std 4.14 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs_slow for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean -61.1513 and std 2.3385 and AP threshold override -20 )\\n\",\n      \"  ( AP_width for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 3.5347 and std 0.8788 and AP threshold override -20 )\\n\",\n      \"  ( time_to_first_spike for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 10.5 and std 1.36 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean -57.0905 and std 2.3427 and AP threshold override -20 )\\n\",\n      \"  ( adaptation_index2 for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.0055 and std 0.0015 and AP threshold override -20 )\\n\",\n      \"  ( mean_frequency for {'': u'Step3.soma.v'} with stim start 700 and end 2700, exp mean 17.5 and std 0.8 and AP threshold override -20 )\\n\",\n      \"  ( AP_height for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 27.1003 and std 3.1463 and AP threshold override -20 )\\n\",\n      \"  ( AHP_slow_time for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.1676 and std 0.0339 and AP threshold override -20 )\\n\",\n      \"  ( ISI_CV for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.0674 and std 0.075 and AP threshold override -20 )\\n\",\n      \"  ( doublet_ISI for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 44.0 and std 7.1327 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs_slow for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean -60.2471 and std 1.8972 and AP threshold override -20 )\\n\",\n      \"  ( AP_width for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 2.7917 and std 0.7499 and AP threshold override -20 )\\n\",\n      \"  ( time_to_first_spike for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 19.75 and std 2.8776 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean -59.9055 and std 1.8329 and AP threshold override -20 )\\n\",\n      \"  ( adaptation_index2 for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.005 and std 0.0067 and AP threshold override -20 )\\n\",\n      \"  ( mean_frequency for {'': u'Step2.soma.v'} with stim start 700 and end 2700, exp mean 8.5 and std 0.9796 and AP threshold override -20 )\\n\",\n      \"  ( AP_width for {'': u'bAP.soma.v'} with stim start 295 and end 600, exp mean 2.0 and std 0.5 and AP threshold override -20 )\\n\",\n      \"  ( AP_height for {'': u'bAP.soma.v'} with stim start 295 and end 600, exp mean 25.0 and std 5.0 and AP threshold override -20 )\\n\",\n      \"  ( Spikecount for {'': u'bAP.soma.v'} with stim start 295 and end 600, exp mean 1.0 and std 0.01 and AP threshold override -20 )\\n\",\n      \"  ( AP_amplitude_from_voltagebase for {'': u'bAP.dend1.v'} with stim start 295 and end 600, exp mean 45 and std 10 and AP threshold override -55 )\\n\",\n      \"  ( AP_amplitude_from_voltagebase for {'': u'bAP.dend2.v'} with stim start 295 and end 600, exp mean 36 and std 9.33 and AP threshold override -55 )\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fitness_calculator = l5pc_evaluator.define_fitness_calculator(fitness_protocols)\\n\",\n    \"print(fitness_calculator)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Simulator\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We need to define which simulator we will use. In this case it will be Neuron, i.e. the NrnSimulator class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sim = ephys.simulators.NrnSimulator()\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluator\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With all the components defined above we can build a cell evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"evaluator = ephys.evaluators.CellEvaluator(                                          \\n\",\n    \"        cell_model=l5pc_cell,                                                       \\n\",\n    \"        param_names=param_names,                                                    \\n\",\n    \"        fitness_protocols=fitness_protocols,                                        \\n\",\n    \"        fitness_calculator=fitness_calculator,                                      \\n\",\n    \"        sim=sim)  \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This evaluator can be used to run the protocols. The original parameter values for the Markram et al. 2015 L5PC model are:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"release_params = {\\n\",\n    \"    'gNaTs2_tbar_NaTs2_t.apical': 0.026145,\\n\",\n    \"    'gSKv3_1bar_SKv3_1.apical': 0.004226,\\n\",\n    \"    'gImbar_Im.apical': 0.000143,\\n\",\n    \"    'gNaTa_tbar_NaTa_t.axonal': 3.137968,\\n\",\n    \"    'gK_Tstbar_K_Tst.axonal': 0.089259,\\n\",\n    \"    'gamma_CaDynamics_E2.axonal': 0.002910,\\n\",\n    \"    'gNap_Et2bar_Nap_Et2.axonal': 0.006827,\\n\",\n    \"    'gSK_E2bar_SK_E2.axonal': 0.007104,\\n\",\n    \"    'gCa_HVAbar_Ca_HVA.axonal': 0.000990,\\n\",\n    \"    'gK_Pstbar_K_Pst.axonal': 0.973538,\\n\",\n    \"    'gSKv3_1bar_SKv3_1.axonal': 1.021945,\\n\",\n    \"    'decay_CaDynamics_E2.axonal': 287.198731,\\n\",\n    \"    'gCa_LVAstbar_Ca_LVAst.axonal': 0.008752,\\n\",\n    \"    'gamma_CaDynamics_E2.somatic': 0.000609,\\n\",\n    \"    'gSKv3_1bar_SKv3_1.somatic': 0.303472,\\n\",\n    \"    'gSK_E2bar_SK_E2.somatic': 0.008407,\\n\",\n    \"    'gCa_HVAbar_Ca_HVA.somatic': 0.000994,\\n\",\n    \"    'gNaTs2_tbar_NaTs2_t.somatic': 0.983955,\\n\",\n    \"    'decay_CaDynamics_E2.somatic': 210.485284,\\n\",\n    \"    'gCa_LVAstbar_Ca_LVAst.somatic': 0.000333\\n\",\n    \"}\\n\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Running the responses is as easy as passing the protocols and parameters to the evaluator. (The line below will take some time to execute)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"release_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=release_params)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now plot all the responses\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": \"/* Put everything inside the global mpl namespace */\\nwindow.mpl = {};\\n\\nmpl.get_websocket_type = function() {\\n    if (typeof(WebSocket) !== 'undefined') {\\n        return WebSocket;\\n    } else if (typeof(MozWebSocket) !== 'undefined') {\\n        return MozWebSocket;\\n    } else {\\n        alert('Your browser does not have WebSocket support.' +\\n              'Please try Chrome, Safari or Firefox ≥ 6. 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    event.step = -1;\\n        }\\n        mouse_event_fn(event);\\n    });\\n\\n    canvas_div.append(canvas);\\n    canvas_div.append(rubberband);\\n\\n    this.rubberband = rubberband;\\n    this.rubberband_canvas = rubberband[0];\\n    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\\n    this._resize_canvas = function(width, height) {\\n        // Keep the size of the canvas, canvas container, and rubber band\\n        // canvas in synch.\\n        canvas_div.css('width', width)\\n        canvas_div.css('height', height)\\n\\n        canvas.attr('width', width);\\n        canvas.attr('height', height);\\n\\n        rubberband.attr('width', width);\\n        rubberband.attr('height', height);\\n    }\\n\\n    // Set the figure to an initial 600x600px, this will subsequently be updated\\n    // upon first draw.\\n    this._resize_canvas(600, 600);\\n\\n    // Disable right mouse context menu.\\n    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n        return false;\\n    });\\n\\n    function set_focus () {\\n        canvas.focus();\\n        canvas_div.focus();\\n    }\\n\\n    window.setTimeout(set_focus, 100);\\n}\\n\\nmpl.figure.prototype._init_toolbar = function() {\\n    var fig = this;\\n\\n    var nav_element = $('<div/>')\\n    nav_element.attr('style', 'width: 100%');\\n    this.root.append(nav_element);\\n\\n    // Define a callback function for later on.\\n    function toolbar_event(event) {\\n        return fig.toolbar_button_onclick(event['data']);\\n    }\\n    function toolbar_mouse_event(event) {\\n        return fig.toolbar_button_onmouseover(event['data']);\\n    }\\n\\n    for(var toolbar_ind in mpl.toolbar_items) {\\n        var name = mpl.toolbar_items[toolbar_ind][0];\\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n        var image = mpl.toolbar_items[toolbar_ind][2];\\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\\n        if (!name) {\\n            // put a spacer in here.\\n            continue;\\n        }\\n        var button = $('<button/>');\\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n                        'ui-button-icon-only');\\n        button.attr('role', 'button');\\n        button.attr('aria-disabled', 'false');\\n        button.click(method_name, toolbar_event);\\n        button.mouseover(tooltip, toolbar_mouse_event);\\n\\n        var icon_img = $('<span/>');\\n        icon_img.addClass('ui-button-icon-primary ui-icon');\\n        icon_img.addClass(image);\\n        icon_img.addClass('ui-corner-all');\\n\\n        var tooltip_span = $('<span/>');\\n        tooltip_span.addClass('ui-button-text');\\n        tooltip_span.html(tooltip);\\n\\n        button.append(icon_img);\\n        button.append(tooltip_span);\\n\\n        nav_element.append(button);\\n    }\\n\\n    var fmt_picker_span = $('<span/>');\\n\\n    var fmt_picker = $('<select/>');\\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n    fmt_picker_span.append(fmt_picker);\\n    nav_element.append(fmt_picker_span);\\n    this.format_dropdown = fmt_picker[0];\\n\\n    for (var ind in mpl.extensions) {\\n        var fmt = mpl.extensions[ind];\\n        var option = $(\\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n        fmt_picker.append(option)\\n    }\\n\\n    // Add hover states to the ui-buttons\\n    $( \\\".ui-button\\\" ).hover(\\n        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n    );\\n\\n    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n    nav_element.append(status_bar);\\n    this.message = status_bar[0];\\n}\\n\\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n    // which will in turn request a refresh of the image.\\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n}\\n\\nmpl.figure.prototype.send_message = function(type, properties) {\\n    properties['type'] = type;\\n    properties['figure_id'] = this.id;\\n    this.ws.send(JSON.stringify(properties));\\n}\\n\\nmpl.figure.prototype.send_draw_message = function() {\\n    if (!this.waiting) {\\n        this.waiting = true;\\n        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n    }\\n}\\n\\n\\nmpl.figure.prototype.handle_save = function(fig, msg) {\\n    var format_dropdown = fig.format_dropdown;\\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n    fig.ondownload(fig, format);\\n}\\n\\n\\nmpl.figure.prototype.handle_resize = function(fig, msg) {\\n    var size = msg['size'];\\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n        fig._resize_canvas(size[0], size[1]);\\n        fig.send_message(\\\"refresh\\\", {});\\n    };\\n}\\n\\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n    var x0 = msg['x0'];\\n    var y0 = fig.canvas.height - msg['y0'];\\n    var x1 = msg['x1'];\\n    var y1 = fig.canvas.height - msg['y1'];\\n    x0 = Math.floor(x0) + 0.5;\\n    y0 = Math.floor(y0) + 0.5;\\n    x1 = Math.floor(x1) + 0.5;\\n    y1 = Math.floor(y1) + 0.5;\\n    var min_x = Math.min(x0, x1);\\n    var min_y = Math.min(y0, y1);\\n    var width = Math.abs(x1 - x0);\\n    var height = Math.abs(y1 - y0);\\n\\n    fig.rubberband_context.clearRect(\\n        0, 0, fig.canvas.width, fig.canvas.height);\\n\\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n}\\n\\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n    // Updates the figure title.\\n    fig.header.textContent = msg['label'];\\n}\\n\\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\\n    var cursor = msg['cursor'];\\n    switch(cursor)\\n    {\\n    case 0:\\n        cursor = 'pointer';\\n        break;\\n    case 1:\\n        cursor = 'default';\\n        break;\\n    case 2:\\n        cursor = 'crosshair';\\n        break;\\n    case 3:\\n        cursor = 'move';\\n        break;\\n    }\\n    fig.rubberband_canvas.style.cursor = cursor;\\n}\\n\\nmpl.figure.prototype.handle_message = function(fig, msg) {\\n    fig.message.textContent = msg['message'];\\n}\\n\\nmpl.figure.prototype.handle_draw = function(fig, msg) {\\n    // Request the server to send over a new figure.\\n    fig.send_draw_message();\\n}\\n\\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n    fig.image_mode = msg['mode'];\\n}\\n\\nmpl.figure.prototype.updated_canvas_event = function() {\\n    // Called whenever the canvas gets updated.\\n    this.send_message(\\\"ack\\\", {});\\n}\\n\\n// A function to construct a web socket function for onmessage handling.\\n// Called in the figure constructor.\\nmpl.figure.prototype._make_on_message_function = function(fig) {\\n    return function socket_on_message(evt) {\\n        if (evt.data instanceof Blob) {\\n            /* FIXME: We get \\\"Resource interpreted as Image but\\n             * transferred with MIME type text/plain:\\\" errors on\\n             * Chrome.  But how to set the MIME type?  It doesn't seem\\n             * to be part of the websocket stream */\\n            evt.data.type = \\\"image/png\\\";\\n\\n            /* Free the memory for the previous frames */\\n            if (fig.imageObj.src) {\\n                (window.URL || window.webkitURL).revokeObjectURL(\\n                    fig.imageObj.src);\\n            }\\n\\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n                evt.data);\\n            fig.updated_canvas_event();\\n            fig.waiting = false;\\n            return;\\n        }\\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n            fig.imageObj.src = evt.data;\\n            fig.updated_canvas_event();\\n            fig.waiting = false;\\n            return;\\n        }\\n\\n        var msg = JSON.parse(evt.data);\\n        var msg_type = msg['type'];\\n\\n        // Call the  \\\"handle_{type}\\\" callback, which takes\\n        // the figure and JSON message as its only arguments.\\n        try {\\n            var callback = fig[\\\"handle_\\\" + msg_type];\\n        } catch (e) {\\n            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n            return;\\n        }\\n\\n        if (callback) {\\n            try {\\n                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n                callback(fig, msg);\\n            } catch (e) {\\n                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n            }\\n        }\\n    };\\n}\\n\\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\nmpl.findpos = function(e) {\\n    //this section is from http://www.quirksmode.org/js/events_properties.html\\n    var targ;\\n    if (!e)\\n        e = window.event;\\n    if (e.target)\\n        targ = e.target;\\n    else if (e.srcElement)\\n        targ = e.srcElement;\\n    if (targ.nodeType == 3) // defeat Safari bug\\n        targ = targ.parentNode;\\n\\n    // jQuery normalizes the pageX and pageY\\n    // pageX,Y are the mouse positions relative to the document\\n    // offset() returns the position of the element relative to the document\\n    var x = e.pageX - $(targ).offset().left;\\n    var y = e.pageY - $(targ).offset().top;\\n\\n    return {\\\"x\\\": x, \\\"y\\\": y};\\n};\\n\\n/*\\n * return a copy of an object with only non-object keys\\n * we need this to avoid circular references\\n * http://stackoverflow.com/a/24161582/3208463\\n */\\nfunction simpleKeys (original) {\\n  return Object.keys(original).reduce(function (obj, key) {\\n    if (typeof original[key] !== 'object')\\n        obj[key] = original[key]\\n    return obj;\\n  }, {});\\n}\\n\\nmpl.figure.prototype.mouse_event = function(event, name) {\\n    var canvas_pos = mpl.findpos(event)\\n\\n    if (name === 'button_press')\\n    {\\n        this.canvas.focus();\\n        this.canvas_div.focus();\\n    }\\n\\n    var x = canvas_pos.x;\\n    var y = canvas_pos.y;\\n\\n    this.send_message(name, {x: x, y: y, button: event.button,\\n                             step: event.step,\\n                             guiEvent: simpleKeys(event)});\\n\\n    /* This prevents the web browser from automatically changing to\\n     * the text insertion cursor when the button is pressed.  We want\\n     * to control all of the cursor setting manually through the\\n     * 'cursor' event from matplotlib */\\n    event.preventDefault();\\n    return false;\\n}\\n\\nmpl.figure.prototype._key_event_extra = function(event, name) {\\n    // Handle any extra behaviour associated with a key event\\n}\\n\\nmpl.figure.prototype.key_event = function(event, name) {\\n\\n    // Prevent repeat events\\n    if (name == 'key_press')\\n    {\\n        if (event.which === this._key)\\n            return;\\n        else\\n            this._key = event.which;\\n    }\\n    if (name == 'key_release')\\n        this._key = null;\\n\\n    var value = '';\\n    if (event.ctrlKey && event.which != 17)\\n        value += \\\"ctrl+\\\";\\n    if (event.altKey && event.which != 18)\\n        value += \\\"alt+\\\";\\n    if (event.shiftKey && event.which != 16)\\n        value += \\\"shift+\\\";\\n\\n    value += 'k';\\n    value += event.which.toString();\\n\\n    this._key_event_extra(event, name);\\n\\n    this.send_message(name, {key: value,\\n                             guiEvent: simpleKeys(event)});\\n    return false;\\n}\\n\\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\\n    if (name == 'download') {\\n        this.handle_save(this, null);\\n    } else {\\n        this.send_message(\\\"toolbar_button\\\", {name: name});\\n    }\\n};\\n\\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n    this.message.textContent = tooltip;\\n};\\nmpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\\nmpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\\nmpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n    // object with the appropriate methods. Currently this is a non binary\\n    // socket, so there is still some room for performance tuning.\\n    var ws = {};\\n\\n    ws.close = function() {\\n        comm.close()\\n    };\\n    ws.send = function(m) {\\n        //console.log('sending', m);\\n        comm.send(m);\\n    };\\n    // Register the callback with on_msg.\\n    comm.on_msg(function(msg) {\\n        //console.log('receiving', msg['content']['data'], msg);\\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n        ws.onmessage(msg['content']['data'])\\n    });\\n    return ws;\\n}\\n\\nmpl.mpl_figure_comm = function(comm, msg) {\\n    // This is the function which gets called when the mpl process\\n    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\\n    var id = msg.content.data.id;\\n    // Get hold of the div created by the display call when the Comm\\n    // socket was opened in Python.\\n    var element = $(\\\"#\\\" + id);\\n    var ws_proxy = comm_websocket_adapter(comm)\\n\\n    function ondownload(figure, format) {\\n        window.open(figure.imageObj.src);\\n    }\\n\\n    var fig = new mpl.figure(id, ws_proxy,\\n                           ondownload,\\n                           element.get(0));\\n\\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n    // web socket which is closed, not our websocket->open comm proxy.\\n    ws_proxy.onopen();\\n\\n    fig.parent_element = element.get(0);\\n    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n    if (!fig.cell_info) {\\n        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n        return;\\n    }\\n\\n    var output_index = fig.cell_info[2]\\n    var cell = fig.cell_info[0];\\n\\n};\\n\\nmpl.figure.prototype.handle_close = function(fig, msg) {\\n    fig.root.unbind('remove')\\n\\n    // Update the output cell to use the data from the current canvas.\\n    fig.push_to_output();\\n    var dataURL = fig.canvas.toDataURL();\\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n    // the notebook keyboard shortcuts fail.\\n    IPython.keyboard_manager.enable()\\n    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n    fig.close_ws(fig, msg);\\n}\\n\\nmpl.figure.prototype.close_ws = function(fig, msg){\\n    fig.send_message('closing', msg);\\n    // fig.ws.close()\\n}\\n\\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\\n    // Turn the data on the canvas into data in the output cell.\\n    var dataURL = this.canvas.toDataURL();\\n    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n}\\n\\nmpl.figure.prototype.updated_canvas_event = function() {\\n    // Tell IPython that the notebook contents must change.\\n    IPython.notebook.set_dirty(true);\\n    this.send_message(\\\"ack\\\", {});\\n    var fig = this;\\n    // Wait a second, then push the new image to the DOM so\\n    // that it is saved nicely (might be nice to debounce this).\\n    setTimeout(function () { fig.push_to_output() }, 1000);\\n}\\n\\nmpl.figure.prototype._init_toolbar = function() {\\n    var fig = this;\\n\\n    var nav_element = $('<div/>')\\n    nav_element.attr('style', 'width: 100%');\\n    this.root.append(nav_element);\\n\\n    // Define a callback function for later on.\\n    function toolbar_event(event) {\\n        return fig.toolbar_button_onclick(event['data']);\\n    }\\n    function toolbar_mouse_event(event) {\\n        return fig.toolbar_button_onmouseover(event['data']);\\n    }\\n\\n    for(var toolbar_ind in mpl.toolbar_items){\\n        var name = mpl.toolbar_items[toolbar_ind][0];\\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n        var image = mpl.toolbar_items[toolbar_ind][2];\\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\\n        if (!name) { continue; };\\n\\n        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n        button.click(method_name, toolbar_event);\\n        button.mouseover(tooltip, toolbar_mouse_event);\\n        nav_element.append(button);\\n    }\\n\\n    // Add the status bar.\\n    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n    nav_element.append(status_bar);\\n    this.message = status_bar[0];\\n\\n    // Add the close button to the window.\\n    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n    buttongrp.append(button);\\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n    titlebar.prepend(buttongrp);\\n}\\n\\nmpl.figure.prototype._root_extra_style = function(el){\\n    var fig = this\\n    el.on(\\\"remove\\\", function(){\\n\\tfig.close_ws(fig, {});\\n    });\\n}\\n\\nmpl.figure.prototype._canvas_extra_style = function(el){\\n    // this is important to make the div 'focusable\\n    el.attr('tabindex', 0)\\n    // reach out to IPython and tell the keyboard manager to turn it's self\\n    // off when our div gets focus\\n\\n    // location in version 3\\n    if (IPython.notebook.keyboard_manager) {\\n        IPython.notebook.keyboard_manager.register_events(el);\\n    }\\n    else {\\n        // location in version 2\\n        IPython.keyboard_manager.register_events(el);\\n    }\\n\\n}\\n\\nmpl.figure.prototype._key_event_extra = function(event, name) {\\n    var manager = IPython.notebook.keyboard_manager;\\n    if (!manager)\\n        manager = IPython.keyboard_manager;\\n\\n    // Check for shift+enter\\n    if (event.shiftKey && event.which == 13) {\\n        this.canvas_div.blur();\\n        // select the cell after this one\\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n        IPython.notebook.select(index + 1);\\n    }\\n}\\n\\nmpl.figure.prototype.handle_save = function(fig, msg) {\\n    fig.ondownload(fig, null);\\n}\\n\\n\\nmpl.find_output_cell = function(html_output) {\\n    // Return the cell and output element which can be found *uniquely* in the notebook.\\n    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n    // IPython event is triggered only after the cells have been serialised, which for\\n    // our purposes (turning an active figure into a static one), is too late.\\n    var cells = IPython.notebook.get_cells();\\n    var ncells = cells.length;\\n    for (var i=0; i<ncells; i++) {\\n        var cell = cells[i];\\n        if (cell.cell_type === 'code'){\\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n                var data = cell.output_area.outputs[j];\\n                if (data.data) {\\n                    // IPython >= 3 moved mimebundle to data attribute of output\\n                    data = data.data;\\n                }\\n                if (data['text/html'] == html_output) {\\n                    return [cell, data, j];\\n                }\\n            }\\n        }\\n    }\\n}\\n\\n// Register the function which deals with the matplotlib target/channel.\\n// The kernel may be null if the page has been refreshed.\\nif (IPython.notebook.kernel != null) {\\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n}\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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     ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _comm_id_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:29: DeprecationWarning: Comm._iopub_socket_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _iopub_socket_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _kernel_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _session_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _topic_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _kernel_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _comm_id_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _session_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _topic_default(self):\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def plot_responses(responses):\\n\",\n    \"    fig, axes = plt.subplots(len(responses), figsize=(10,10))\\n\",\n    \"    for index, (resp_name, response) in enumerate(sorted(responses.items())):\\n\",\n    \"        axes[index].plot(response['time'], response['voltage'], label=resp_name)\\n\",\n    \"        axes[index].set_title(resp_name)\\n\",\n    \"    fig.tight_layout()\\n\",\n    \"    fig.show()\\n\",\n    \"plot_responses(release_responses)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Running an optimisation of the parameters now has become very easy. \\n\",\n    \"Of course running the L5PC optimisation will require quite some computing resources. \\n\",\n    \"\\n\",\n    \"To show a proof-of-concept, we will only run 2 generations, with 2 offspring individuals per generations.\\n\",\n    \"If you want to run all full optimisation, you should run for 100 generations with an offspring size of 100 individuals. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"opt = bpopt.optimisations.DEAPOptimisation(                                     \\n\",\n    \"    evaluator=evaluator,                                                            \\n\",\n    \"    offspring_size=2) \\n\",\n    \"final_pop, halloffame, log, hist = opt.run(max_ngen=2, cp_filename='checkpoints/checkpoint.pkl')\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first individual in the hall of fame will contain the best solution found.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.0007314357233762349, 0.03367469445915072, 0.0009391491627785106, 1.5248169507528497, 1.1187461922146316, 0.3599561642296192, 0.0029040787574867947, 0.022169166627303505, 0.8757751873011441, 0.00048095122076782077, 0.008079463408928685, 0.016843185951228554, 234.40541659093483, 0.19334802048394228, 0.3527777415382436, 0.0005121897764114314, 0.00022936710285097395, 0.007891413026508394, 0.03254436076094874, 150.32985893616308]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(halloffame[0])\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"These are the raw parameter values. \\n\",\n    \"The evaluator object can convert this in a dictionary, so that we can see the parameter names corresponding to these values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{ u'decay_CaDynamics_E2.axonal': 234.40541659093483,\\n\",\n      \"  u'decay_CaDynamics_E2.somatic': 150.32985893616308,\\n\",\n      \"  u'gCa_HVAbar_Ca_HVA.axonal': 0.00048095122076782077,\\n\",\n      \"  u'gCa_HVAbar_Ca_HVA.somatic': 0.00022936710285097395,\\n\",\n      \"  u'gCa_LVAstbar_Ca_LVAst.axonal': 0.008079463408928685,\\n\",\n      \"  u'gCa_LVAstbar_Ca_LVAst.somatic': 0.007891413026508394,\\n\",\n      \"  u'gImbar_Im.apical': 0.0009391491627785106,\\n\",\n      \"  u'gK_Pstbar_K_Pst.axonal': 0.3599561642296192,\\n\",\n      \"  u'gK_Tstbar_K_Tst.axonal': 0.0029040787574867947,\\n\",\n      \"  u'gNaTa_tbar_NaTa_t.axonal': 1.5248169507528497,\\n\",\n      \"  u'gNaTs2_tbar_NaTs2_t.apical': 0.0007314357233762349,\\n\",\n      \"  u'gNaTs2_tbar_NaTs2_t.somatic': 0.19334802048394228,\\n\",\n      \"  u'gNap_Et2bar_Nap_Et2.axonal': 1.1187461922146316,\\n\",\n      \"  u'gSK_E2bar_SK_E2.axonal': 0.022169166627303505,\\n\",\n      \"  u'gSK_E2bar_SK_E2.somatic': 0.0005121897764114314,\\n\",\n      \"  u'gSKv3_1bar_SKv3_1.apical': 0.03367469445915072,\\n\",\n      \"  u'gSKv3_1bar_SKv3_1.axonal': 0.8757751873011441,\\n\",\n      \"  u'gSKv3_1bar_SKv3_1.somatic': 0.3527777415382436,\\n\",\n      \"  u'gamma_CaDynamics_E2.axonal': 0.016843185951228554,\\n\",\n      \"  u'gamma_CaDynamics_E2.somatic': 0.03254436076094874}\\n\",\n      \"None\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_params = evaluator.param_dict(halloffame[0])\\n\",\n    \"print(pp.pprint(best_params))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then we can run the fitness protocols on the model with these parameter values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"best_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=best_params)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And then we can also plot these responses. \\n\",\n    \"\\n\",\n    \"When you ran the above optimisation with only 2 individuals and 2 generations, this 'best' model will of course be very low quality.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": \"/* Put everything inside the global mpl namespace */\\nwindow.mpl = {};\\n\\nmpl.get_websocket_type = function() {\\n    if (typeof(WebSocket) !== 'undefined') {\\n        return WebSocket;\\n    } else if (typeof(MozWebSocket) !== 'undefined') {\\n        return MozWebSocket;\\n    } else {\\n        alert('Your browser does not have WebSocket support.' +\\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n              'Firefox 4 and 5 are also supported but you ' +\\n              'have to enable WebSockets in about:config.');\\n    };\\n}\\n\\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n    this.id = figure_id;\\n\\n    this.ws = websocket;\\n\\n    this.supports_binary = (this.ws.binaryType != undefined);\\n\\n    if (!this.supports_binary) {\\n        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n        if (warnings) {\\n            warnings.style.display = 'block';\\n            warnings.textContent = (\\n                \\\"This browser does not support binary websocket messages. \\\" +\\n                    \\\"Performance may be slow.\\\");\\n        }\\n    }\\n\\n    this.imageObj = new Image();\\n\\n    this.context = undefined;\\n    this.message = undefined;\\n    this.canvas = undefined;\\n    this.rubberband_canvas = undefined;\\n    this.rubberband_context = undefined;\\n    this.format_dropdown = undefined;\\n\\n    this.image_mode = 'full';\\n\\n    this.root = $('<div/>');\\n    this._root_extra_style(this.root)\\n    this.root.attr('style', 'display: inline-block');\\n\\n    $(parent_element).append(this.root);\\n\\n    this._init_header(this);\\n    this._init_canvas(this);\\n    this._init_toolbar(this);\\n\\n    var fig = this;\\n\\n    this.waiting = false;\\n\\n    this.ws.onopen =  function () {\\n            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n            fig.send_message(\\\"send_image_mode\\\", {});\\n            fig.send_message(\\\"refresh\\\", {});\\n        }\\n\\n    this.imageObj.onload = function() {\\n            if (fig.image_mode == 'full') {\\n                // Full images could contain transparency (where diff images\\n                // almost always do), so we need to clear the canvas so that\\n                // there is no ghosting.\\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n            }\\n            fig.context.drawImage(fig.imageObj, 0, 0);\\n        };\\n\\n    this.imageObj.onunload = function() {\\n        this.ws.close();\\n    }\\n\\n    this.ws.onmessage = this._make_on_message_function(this);\\n\\n    this.ondownload = ondownload;\\n}\\n\\nmpl.figure.prototype._init_header = function() {\\n    var titlebar = $(\\n        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n        'ui-helper-clearfix\\\"/>');\\n    var titletext = $(\\n        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n        'text-align: center; padding: 3px;\\\"/>');\\n    titlebar.append(titletext)\\n    this.root.append(titlebar);\\n    this.header = titletext[0];\\n}\\n\\n\\n\\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\\n}\\n\\n\\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\\n}\\n\\nmpl.figure.prototype._init_canvas = function() {\\n    var fig = this;\\n\\n    var canvas_div = $('<div/>');\\n\\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\\n    function canvas_keyboard_event(event) {\\n        return fig.key_event(event, event['data']);\\n    }\\n\\n    canvas_div.keydown('key_press', canvas_keyboard_event);\\n    canvas_div.keyup('key_release', canvas_keyboard_event);\\n    this.canvas_div = canvas_div\\n    this._canvas_extra_style(canvas_div)\\n    this.root.append(canvas_div);\\n\\n    var canvas = $('<canvas/>');\\n    canvas.addClass('mpl-canvas');\\n    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\\n    this.canvas = canvas[0];\\n    this.context = canvas[0].getContext(\\\"2d\\\");\\n\\n    var rubberband = $('<canvas/>');\\n    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\\n    var pass_mouse_events = true;\\n\\n    canvas_div.resizable({\\n        start: function(event, ui) {\\n            pass_mouse_events = false;\\n        },\\n        resize: function(event, ui) {\\n            fig.request_resize(ui.size.width, ui.size.height);\\n        },\\n        stop: function(event, ui) {\\n            pass_mouse_events = true;\\n            fig.request_resize(ui.size.width, ui.size.height);\\n        },\\n    });\\n\\n    function mouse_event_fn(event) {\\n        if (pass_mouse_events)\\n            return fig.mouse_event(event, event['data']);\\n    }\\n\\n    rubberband.mousedown('button_press', mouse_event_fn);\\n    rubberband.mouseup('button_release', mouse_event_fn);\\n    // Throttle sequential mouse events to 1 every 20ms.\\n    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\\n    canvas_div.on(\\\"wheel\\\", function (event) {\\n        event = event.originalEvent;\\n        event['data'] = 'scroll'\\n        if (event.deltaY < 0) {\\n            event.step = 1;\\n        } else {\\n            event.step = -1;\\n        }\\n        mouse_event_fn(event);\\n    });\\n\\n    canvas_div.append(canvas);\\n    canvas_div.append(rubberband);\\n\\n    this.rubberband = rubberband;\\n    this.rubberband_canvas = rubberband[0];\\n    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\\n    this._resize_canvas = function(width, height) {\\n        // Keep the size of the canvas, canvas container, and rubber band\\n        // canvas in synch.\\n        canvas_div.css('width', width)\\n        canvas_div.css('height', height)\\n\\n        canvas.attr('width', width);\\n        canvas.attr('height', height);\\n\\n        rubberband.attr('width', width);\\n        rubberband.attr('height', height);\\n    }\\n\\n    // Set the figure to an initial 600x600px, this will subsequently be updated\\n    // upon first draw.\\n    this._resize_canvas(600, 600);\\n\\n    // Disable right mouse context menu.\\n    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n        return false;\\n    });\\n\\n    function set_focus () {\\n        canvas.focus();\\n        canvas_div.focus();\\n    }\\n\\n    window.setTimeout(set_focus, 100);\\n}\\n\\nmpl.figure.prototype._init_toolbar = function() {\\n    var fig = this;\\n\\n    var nav_element = $('<div/>')\\n    nav_element.attr('style', 'width: 100%');\\n    this.root.append(nav_element);\\n\\n    // Define a callback function for later on.\\n    function toolbar_event(event) {\\n        return fig.toolbar_button_onclick(event['data']);\\n    }\\n    function toolbar_mouse_event(event) {\\n        return fig.toolbar_button_onmouseover(event['data']);\\n    }\\n\\n    for(var toolbar_ind in mpl.toolbar_items) {\\n        var name = mpl.toolbar_items[toolbar_ind][0];\\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n        var image = mpl.toolbar_items[toolbar_ind][2];\\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\\n        if (!name) {\\n            // put a spacer in here.\\n            continue;\\n        }\\n        var button = $('<button/>');\\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n                        'ui-button-icon-only');\\n        button.attr('role', 'button');\\n        button.attr('aria-disabled', 'false');\\n        button.click(method_name, toolbar_event);\\n        button.mouseover(tooltip, toolbar_mouse_event);\\n\\n        var icon_img = $('<span/>');\\n        icon_img.addClass('ui-button-icon-primary ui-icon');\\n        icon_img.addClass(image);\\n        icon_img.addClass('ui-corner-all');\\n\\n        var tooltip_span = $('<span/>');\\n        tooltip_span.addClass('ui-button-text');\\n        tooltip_span.html(tooltip);\\n\\n        button.append(icon_img);\\n        button.append(tooltip_span);\\n\\n        nav_element.append(button);\\n    }\\n\\n    var fmt_picker_span = $('<span/>');\\n\\n    var fmt_picker = $('<select/>');\\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n    fmt_picker_span.append(fmt_picker);\\n    nav_element.append(fmt_picker_span);\\n    this.format_dropdown = fmt_picker[0];\\n\\n    for (var ind in mpl.extensions) {\\n        var fmt = mpl.extensions[ind];\\n        var option = $(\\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n        fmt_picker.append(option)\\n    }\\n\\n    // Add hover states to the ui-buttons\\n    $( \\\".ui-button\\\" ).hover(\\n        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n    );\\n\\n    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n    nav_element.append(status_bar);\\n    this.message = status_bar[0];\\n}\\n\\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n    // which will in turn request a refresh of the image.\\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n}\\n\\nmpl.figure.prototype.send_message = function(type, properties) {\\n    properties['type'] = type;\\n    properties['figure_id'] = this.id;\\n    this.ws.send(JSON.stringify(properties));\\n}\\n\\nmpl.figure.prototype.send_draw_message = function() {\\n    if (!this.waiting) {\\n        this.waiting = true;\\n        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n    }\\n}\\n\\n\\nmpl.figure.prototype.handle_save = function(fig, msg) {\\n    var format_dropdown = fig.format_dropdown;\\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n    fig.ondownload(fig, format);\\n}\\n\\n\\nmpl.figure.prototype.handle_resize = function(fig, msg) {\\n    var size = msg['size'];\\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n        fig._resize_canvas(size[0], size[1]);\\n        fig.send_message(\\\"refresh\\\", {});\\n    };\\n}\\n\\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n    var x0 = msg['x0'];\\n    var y0 = fig.canvas.height - msg['y0'];\\n    var x1 = msg['x1'];\\n    var y1 = fig.canvas.height - msg['y1'];\\n    x0 = Math.floor(x0) + 0.5;\\n    y0 = Math.floor(y0) + 0.5;\\n    x1 = Math.floor(x1) + 0.5;\\n    y1 = Math.floor(y1) + 0.5;\\n    var min_x = Math.min(x0, x1);\\n    var min_y = Math.min(y0, y1);\\n    var width = Math.abs(x1 - x0);\\n    var height = Math.abs(y1 - y0);\\n\\n    fig.rubberband_context.clearRect(\\n        0, 0, fig.canvas.width, fig.canvas.height);\\n\\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n}\\n\\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n    // Updates the figure title.\\n    fig.header.textContent = msg['label'];\\n}\\n\\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\\n    var cursor = msg['cursor'];\\n    switch(cursor)\\n    {\\n    case 0:\\n        cursor = 'pointer';\\n        break;\\n    case 1:\\n        cursor = 'default';\\n        break;\\n    case 2:\\n        cursor = 'crosshair';\\n        break;\\n    case 3:\\n        cursor = 'move';\\n        break;\\n    }\\n    fig.rubberband_canvas.style.cursor = cursor;\\n}\\n\\nmpl.figure.prototype.handle_message = function(fig, msg) {\\n    fig.message.textContent = msg['message'];\\n}\\n\\nmpl.figure.prototype.handle_draw = function(fig, msg) {\\n    // Request the server to send over a new figure.\\n    fig.send_draw_message();\\n}\\n\\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n    fig.image_mode = msg['mode'];\\n}\\n\\nmpl.figure.prototype.updated_canvas_event = function() {\\n    // Called whenever the canvas gets updated.\\n    this.send_message(\\\"ack\\\", {});\\n}\\n\\n// A function to construct a web socket function for onmessage handling.\\n// Called in the figure constructor.\\nmpl.figure.prototype._make_on_message_function = function(fig) {\\n    return function socket_on_message(evt) {\\n        if (evt.data instanceof Blob) {\\n            /* FIXME: We get \\\"Resource interpreted as Image but\\n             * transferred with MIME type text/plain:\\\" errors on\\n             * Chrome.  But how to set the MIME type?  It doesn't seem\\n             * to be part of the websocket stream */\\n            evt.data.type = \\\"image/png\\\";\\n\\n            /* Free the memory for the previous frames */\\n            if (fig.imageObj.src) {\\n                (window.URL || window.webkitURL).revokeObjectURL(\\n                    fig.imageObj.src);\\n            }\\n\\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n                evt.data);\\n            fig.updated_canvas_event();\\n            fig.waiting = false;\\n            return;\\n        }\\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n            fig.imageObj.src = evt.data;\\n            fig.updated_canvas_event();\\n            fig.waiting = false;\\n            return;\\n        }\\n\\n        var msg = JSON.parse(evt.data);\\n        var msg_type = msg['type'];\\n\\n        // Call the  \\\"handle_{type}\\\" callback, which takes\\n        // the figure and JSON message as its only arguments.\\n        try {\\n            var callback = fig[\\\"handle_\\\" + msg_type];\\n        } catch (e) {\\n            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n            return;\\n        }\\n\\n        if (callback) {\\n            try {\\n                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n                callback(fig, msg);\\n            } catch (e) {\\n                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n            }\\n        }\\n    };\\n}\\n\\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\nmpl.findpos = function(e) {\\n    //this section is from http://www.quirksmode.org/js/events_properties.html\\n    var targ;\\n    if (!e)\\n        e = window.event;\\n    if (e.target)\\n        targ = e.target;\\n    else if (e.srcElement)\\n        targ = e.srcElement;\\n    if (targ.nodeType == 3) // defeat Safari bug\\n        targ = targ.parentNode;\\n\\n    // jQuery normalizes the pageX and pageY\\n    // pageX,Y are the mouse positions relative to the document\\n    // offset() returns the position of the element relative to the document\\n    var x = e.pageX - $(targ).offset().left;\\n    var y = e.pageY - $(targ).offset().top;\\n\\n    return {\\\"x\\\": x, \\\"y\\\": y};\\n};\\n\\n/*\\n * return a copy of an object with only non-object keys\\n * we need this to avoid circular references\\n * http://stackoverflow.com/a/24161582/3208463\\n */\\nfunction simpleKeys (original) {\\n  return Object.keys(original).reduce(function (obj, key) {\\n    if (typeof original[key] !== 'object')\\n        obj[key] = original[key]\\n    return obj;\\n  }, {});\\n}\\n\\nmpl.figure.prototype.mouse_event = function(event, name) {\\n    var canvas_pos = mpl.findpos(event)\\n\\n    if (name === 'button_press')\\n    {\\n        this.canvas.focus();\\n        this.canvas_div.focus();\\n    }\\n\\n    var x = canvas_pos.x;\\n    var y = canvas_pos.y;\\n\\n    this.send_message(name, {x: x, y: y, button: event.button,\\n                             step: event.step,\\n                             guiEvent: simpleKeys(event)});\\n\\n    /* This prevents the web browser from automatically changing to\\n     * the text insertion cursor when the button is pressed.  We want\\n     * to control all of the cursor setting manually through the\\n     * 'cursor' event from matplotlib */\\n    event.preventDefault();\\n    return false;\\n}\\n\\nmpl.figure.prototype._key_event_extra = function(event, name) {\\n    // Handle any extra behaviour associated with a key event\\n}\\n\\nmpl.figure.prototype.key_event = function(event, name) {\\n\\n    // Prevent repeat events\\n    if (name == 'key_press')\\n    {\\n        if (event.which === this._key)\\n            return;\\n        else\\n            this._key = event.which;\\n    }\\n    if (name == 'key_release')\\n        this._key = null;\\n\\n    var value = '';\\n    if (event.ctrlKey && event.which != 17)\\n        value += \\\"ctrl+\\\";\\n    if (event.altKey && event.which != 18)\\n        value += \\\"alt+\\\";\\n    if (event.shiftKey && event.which != 16)\\n        value += \\\"shift+\\\";\\n\\n    value += 'k';\\n    value += event.which.toString();\\n\\n    this._key_event_extra(event, name);\\n\\n    this.send_message(name, {key: value,\\n                             guiEvent: simpleKeys(event)});\\n    return false;\\n}\\n\\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\\n    if (name == 'download') {\\n        this.handle_save(this, null);\\n    } else {\\n        this.send_message(\\\"toolbar_button\\\", {name: name});\\n    }\\n};\\n\\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n    this.message.textContent = tooltip;\\n};\\nmpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\\nmpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\\nmpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n    // object with the appropriate methods. Currently this is a non binary\\n    // socket, so there is still some room for performance tuning.\\n    var ws = {};\\n\\n    ws.close = function() {\\n        comm.close()\\n    };\\n    ws.send = function(m) {\\n        //console.log('sending', m);\\n        comm.send(m);\\n    };\\n    // Register the callback with on_msg.\\n    comm.on_msg(function(msg) {\\n        //console.log('receiving', msg['content']['data'], msg);\\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n        ws.onmessage(msg['content']['data'])\\n    });\\n    return ws;\\n}\\n\\nmpl.mpl_figure_comm = function(comm, msg) {\\n    // This is the function which gets called when the mpl process\\n    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\\n    var id = msg.content.data.id;\\n    // Get hold of the div created by the display call when the Comm\\n    // socket was opened in Python.\\n    var element = $(\\\"#\\\" + id);\\n    var ws_proxy = comm_websocket_adapter(comm)\\n\\n    function ondownload(figure, format) {\\n        window.open(figure.imageObj.src);\\n    }\\n\\n    var fig = new mpl.figure(id, ws_proxy,\\n                           ondownload,\\n                           element.get(0));\\n\\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n    // web socket which is closed, not our websocket->open comm proxy.\\n    ws_proxy.onopen();\\n\\n    fig.parent_element = element.get(0);\\n    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n    if (!fig.cell_info) {\\n        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n        return;\\n    }\\n\\n    var output_index = fig.cell_info[2]\\n    var cell = fig.cell_info[0];\\n\\n};\\n\\nmpl.figure.prototype.handle_close = function(fig, msg) {\\n    fig.root.unbind('remove')\\n\\n    // Update the output cell to use the data from the current canvas.\\n    fig.push_to_output();\\n    var dataURL = fig.canvas.toDataURL();\\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n    // the notebook keyboard shortcuts fail.\\n    IPython.keyboard_manager.enable()\\n    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n    fig.close_ws(fig, msg);\\n}\\n\\nmpl.figure.prototype.close_ws = function(fig, msg){\\n    fig.send_message('closing', msg);\\n    // fig.ws.close()\\n}\\n\\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\\n    // Turn the data on the canvas into data in the output cell.\\n    var dataURL = this.canvas.toDataURL();\\n    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n}\\n\\nmpl.figure.prototype.updated_canvas_event = function() {\\n    // Tell IPython that the notebook contents must change.\\n    IPython.notebook.set_dirty(true);\\n    this.send_message(\\\"ack\\\", {});\\n    var fig = this;\\n    // Wait a second, then push the new image to the DOM so\\n    // that it is saved nicely (might be nice to debounce this).\\n    setTimeout(function () { fig.push_to_output() }, 1000);\\n}\\n\\nmpl.figure.prototype._init_toolbar = function() {\\n    var fig = this;\\n\\n    var nav_element = $('<div/>')\\n    nav_element.attr('style', 'width: 100%');\\n    this.root.append(nav_element);\\n\\n    // Define a callback function for later on.\\n    function toolbar_event(event) {\\n        return fig.toolbar_button_onclick(event['data']);\\n    }\\n    function toolbar_mouse_event(event) {\\n        return fig.toolbar_button_onmouseover(event['data']);\\n    }\\n\\n    for(var toolbar_ind in mpl.toolbar_items){\\n        var name = mpl.toolbar_items[toolbar_ind][0];\\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n        var image = mpl.toolbar_items[toolbar_ind][2];\\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\\n        if (!name) { continue; };\\n\\n        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n        button.click(method_name, toolbar_event);\\n        button.mouseover(tooltip, toolbar_mouse_event);\\n        nav_element.append(button);\\n    }\\n\\n    // Add the status bar.\\n    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n    nav_element.append(status_bar);\\n    this.message = status_bar[0];\\n\\n    // Add the close button to the window.\\n    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n    buttongrp.append(button);\\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n    titlebar.prepend(buttongrp);\\n}\\n\\nmpl.figure.prototype._root_extra_style = function(el){\\n    var fig = this\\n    el.on(\\\"remove\\\", function(){\\n\\tfig.close_ws(fig, {});\\n    });\\n}\\n\\nmpl.figure.prototype._canvas_extra_style = function(el){\\n    // this is important to make the div 'focusable\\n    el.attr('tabindex', 0)\\n    // reach out to IPython and tell the keyboard manager to turn it's self\\n    // off when our div gets focus\\n\\n    // location in version 3\\n    if (IPython.notebook.keyboard_manager) {\\n        IPython.notebook.keyboard_manager.register_events(el);\\n    }\\n    else {\\n        // location in version 2\\n        IPython.keyboard_manager.register_events(el);\\n    }\\n\\n}\\n\\nmpl.figure.prototype._key_event_extra = function(event, name) {\\n    var manager = IPython.notebook.keyboard_manager;\\n    if (!manager)\\n        manager = IPython.keyboard_manager;\\n\\n    // Check for shift+enter\\n    if (event.shiftKey && event.which == 13) {\\n        this.canvas_div.blur();\\n        // select the cell after this one\\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n        IPython.notebook.select(index + 1);\\n    }\\n}\\n\\nmpl.figure.prototype.handle_save = function(fig, msg) {\\n    fig.ondownload(fig, null);\\n}\\n\\n\\nmpl.find_output_cell = function(html_output) {\\n    // Return the cell and output element which can be found *uniquely* in the notebook.\\n    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n    // IPython event is triggered only after the cells have been serialised, which for\\n    // our purposes (turning an active figure into a static one), is too late.\\n    var cells = IPython.notebook.get_cells();\\n    var ncells = cells.length;\\n    for (var i=0; i<ncells; i++) {\\n        var cell = cells[i];\\n        if (cell.cell_type === 'code'){\\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n                var data = cell.output_area.outputs[j];\\n                if (data.data) {\\n                    // IPython >= 3 moved mimebundle to data attribute of output\\n                    data = data.data;\\n                }\\n                if (data['text/html'] == html_output) {\\n                    return [cell, data, j];\\n                }\\n            }\\n        }\\n    }\\n}\\n\\n// Register the function which deals with the matplotlib target/channel.\\n// The kernel may be null if the page has been refreshed.\\nif (IPython.notebook.kernel != null) {\\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n}\\n\",\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _comm_id_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:29: DeprecationWarning: Comm._iopub_socket_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _iopub_socket_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _kernel_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _session_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _topic_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _kernel_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _comm_id_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _session_default(self):\\n\",\n      \"/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\\n\",\n      \"  def _topic_default(self):\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"plot_responses(best_responses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "examples/l5pc/L5PC_arbor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optimisation of a Neocortical Layer 5 Pyramidal Cell in Arbor\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook shows you how to optimise the maximal conductance of Neocortical Layer 5 Pyramidal Cell as used in Markram et al. 2015 using Arbor as the simulator.\\n\",\n    \"\\n\",\n    \"Author of this script: Werner Van Geit @ Blue Brain Project\\n\",\n    \"\\n\",\n    \"Choice of parameters, protocols and other settings was done by Etay Hay @ HUJI\\n\",\n    \"\\n\",\n    \"What's described here is a more advanced use of BluePyOpt. We suggest to first go through the introductary example here: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb\\n\",\n    \"\\n\",\n    \"**If you use the methods in this notebook, we ask you to cite the following publications when publishing your research:**\\n\",\n    \"\\n\",\n    \"Van Geit, W., M. Gevaert, G. Chindemi, C. Rössert, J.-D. Courcol, E. Muller, F. Schürmann, I. Segev, and H. Markram (2016, March). BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. ArXiv e-prints.\\n\",\n    \"http://arxiv.org/abs/1603.00500\\n\",\n    \"\\n\",\n    \"Markram, H., E. Muller, S. Ramaswamy, M. W. Reimann, M. Abdellah, C. A. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever, et al. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell 163(2), 456–492.\\n\",\n    \"http://www.cell.com/abstract/S0092-8674%2815%2901191-5\\n\",\n    \"\\n\",\n    \"Some of the modules loaded in this script are located in the L5PC example folder: https://github.com/BlueBrain/BluePyOpt/tree/master/examples/l5pc \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We first load the bluepyopt python module, the ephys submodule and some helper functionality\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc\\n\",\n      \"Mod files: \\\"mechanisms/CaDynamics_E2.mod\\\" \\\"mechanisms/Ca_HVA.mod\\\" \\\"mechanisms/Ca_LVAst.mod\\\" \\\"mechanisms/Ih.mod\\\" \\\"mechanisms/Im.mod\\\" \\\"mechanisms/K_Pst.mod\\\" \\\"mechanisms/K_Tst.mod\\\" \\\"mechanisms/Nap_Et2.mod\\\" \\\"mechanisms/NaTa_t.mod\\\" \\\"mechanisms/NaTs2_t.mod\\\" \\\"mechanisms/SK_E2.mod\\\" \\\"mechanisms/SKv3_1.mod\\\"\\n\",\n      \"\\n\",\n      \"Creating x86_64 directory for .o files.\\n\",\n      \"\\n\",\n      \"COBJS=''\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m mod_func.c\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/CaDynamics_E2.mod\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c mod_func.c -o mod_func.o\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl CaDynamics_E2.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/Ca_HVA.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl Ca_HVA.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/Ca_LVAst.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl Ca_LVAst.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \"Translating CaDynamics_E2.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/CaDynamics_E2.c\\n\",\n      \"Translating Ca_HVA.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/Ca_HVA.c\\n\",\n      \"Translating Ca_LVAst.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/Ca_LVAst.c\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/Ih.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl Ih.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \"Translating Ih.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/Ih.c\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/Im.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl Im.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \"Translating Im.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/Im.c\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/K_Pst.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl K_Pst.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/K_Tst.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl K_Tst.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/Nap_Et2.mod\\n\",\n      \"Translating K_Pst.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/K_Pst.c\\n\",\n      \"Thread Safe\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl Nap_Et2.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \"Translating K_Tst.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/K_Tst.c\\n\",\n      \"Thread Safe\\n\",\n      \"Translating Nap_Et2.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/Nap_Et2.c\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/NaTa_t.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl NaTa_t.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/NaTs2_t.mod\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/SK_E2.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl NaTs2_t.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl SK_E2.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \"Translating NaTa_t.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/NaTa_t.c\\n\",\n      \"Translating NaTs2_t.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/NaTs2_t.c\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../mechanisms/SKv3_1.mod\\n\",\n      \"(cd \\\"../mechanisms\\\"; MODLUNIT=/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/share/nrn/lib/nrnunits.lib /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/bin/nocmodl SKv3_1.mod -o \\\"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64\\\")\\n\",\n      \"Translating SK_E2.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/SK_E2.c\\n\",\n      \"Thread Safe\\n\",\n      \"Translating SKv3_1.mod into /home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc/x86_64/SKv3_1.c\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m CaDynamics_E2.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c CaDynamics_E2.c -o CaDynamics_E2.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Ca_HVA.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c Ca_HVA.c -o Ca_HVA.o\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Ca_LVAst.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c Ca_LVAst.c -o Ca_LVAst.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Ih.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c Ih.c -o Ih.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Im.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c Im.c -o Im.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m K_Pst.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c K_Pst.c -o K_Pst.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m K_Tst.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c K_Tst.c -o K_Tst.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Nap_Et2.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c Nap_Et2.c -o Nap_Et2.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m NaTa_t.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c NaTa_t.c -o NaTa_t.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m NaTs2_t.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c NaTs2_t.c -o NaTs2_t.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m SK_E2.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c SK_E2.c -o SK_E2.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m SKv3_1.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I\\\"../mechanisms\\\" -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c SKv3_1.c -o SKv3_1.o\\n\",\n      \" => \\u001b[32mLINKING\\u001b[0m shared library ./libnrnmech.so\\n\",\n      \"x86_64-linux-gnu-g++ -O2 -DVERSION_INFO='8.0.2' -std=c++11 -shared -fPIC  -I /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include -o ./libnrnmech.so -Wl,-soname,libnrnmech.so \\\\\\n\",\n      \"  ./mod_func.o ./CaDynamics_E2.o ./Ca_HVA.o ./Ca_LVAst.o ./Ih.o ./Im.o ./K_Pst.o ./K_Tst.o ./Nap_Et2.o ./NaTa_t.o ./NaTs2_t.o ./SK_E2.o ./SKv3_1.o  -L/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/lib -lnrniv -Wl,-rpath,/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/lib   \\n\",\n      \"rm -f ./.libs/libnrnmech.so ; mkdir -p ./.libs ; cp ./libnrnmech.so ./.libs/libnrnmech.so\\n\",\n      \"Successfully created x86_64/special\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext autoreload\\n\",\n    \"%autoreload\\n\",\n    \"\\n\",\n    \"!nrnivmodl mechanisms\\n\",\n    \"import bluepyopt as bpopt\\n\",\n    \"import bluepyopt.ephys as ephys\\n\",\n    \"\\n\",\n    \"import pprint\\n\",\n    \"pp = pprint.PrettyPrinter(indent=2)\\n\",\n    \"\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Enable the code below to enable debug level logging\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# import logging                                                                      \\n\",\n    \"# logging.basicConfig()                                                               \\n\",\n    \"# logger = logging.getLogger()                                                        \\n\",\n    \"# logger.setLevel(logging.DEBUG)   \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model description\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Morphology\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We're using a complex reconstructed morphology of an L5PC cell. Let's visualise this with the BlueBrain NeuroM software. Alternatively, the cell model can be exported to JSON/ACC by running\\n\",\n    \"\\n\",\n    \"```shell\\n\",\n    \"./generate_acc.py --replace-axon --output <output-dir>\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"The output can be visualized with the Arbor GUI (graphical user interface) as shown in the [documentation](https://docs.arbor-sim.org/en/latest/tutorial/single_cell_bluepyopt.html).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: neurom in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (3.2.2)\\n\",\n      \"Requirement already satisfied: morphio>=3.1.1 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (3.3.3)\\n\",\n      \"Requirement already satisfied: numpy>=1.8.0 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (1.22.3)\\n\",\n      \"Requirement already satisfied: scipy>=1.2.0 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (1.8.0)\\n\",\n      \"Requirement already satisfied: matplotlib>=3.2.1 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (3.5.1)\\n\",\n      \"Requirement already satisfied: pandas>=1.0.5 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (1.4.1)\\n\",\n      \"Requirement already satisfied: tqdm>=4.8.4 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (4.63.1)\\n\",\n      \"Requirement already satisfied: click>=7.0 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (8.1.3)\\n\",\n      \"Requirement already satisfied: pyyaml>=3.10 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from neurom) (6.0)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from matplotlib>=3.2.1->neurom) (9.0.1)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from matplotlib>=3.2.1->neurom) (21.3)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from matplotlib>=3.2.1->neurom) (3.0.7)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from matplotlib>=3.2.1->neurom) (2.8.2)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from matplotlib>=3.2.1->neurom) (4.31.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from matplotlib>=3.2.1->neurom) (0.11.0)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from matplotlib>=3.2.1->neurom) (1.4.2)\\n\",\n      \"Requirement already satisfied: pytz>=2020.1 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from pandas>=1.0.5->neurom) (2022.1)\\n\",\n      \"Requirement already satisfied: six>=1.5 in /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib>=3.2.1->neurom) (1.16.0)\\n\",\n      \"\\n\",\n      \"\\u001b[1m[\\u001b[0m\\u001b[34;49mnotice\\u001b[0m\\u001b[1;39;49m]\\u001b[0m\\u001b[39;49m A new release of pip available: \\u001b[0m\\u001b[31;49m22.2.2\\u001b[0m\\u001b[39;49m -> \\u001b[0m\\u001b[32;49m22.3\\u001b[0m\\n\",\n      \"\\u001b[1m[\\u001b[0m\\u001b[34;49mnotice\\u001b[0m\\u001b[1;39;49m]\\u001b[0m\\u001b[39;49m To update, run: \\u001b[0m\\u001b[32;49mpip install --upgrade pip\\u001b[0m\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/tmp/ipykernel_466037/1031438697.py:3: NeuroMDeprecationWarning: `neurom.io.utils.load_neuron` is deprecated in favor of `neurom.io.utils.load_morphology`\\n\",\n      \"  neurom.viewer.draw(neurom.load_neuron('morphology/C060114A7.asc'));\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"!pip install neurom --upgrade\\n\",\n    \"import neurom.viewer\\n\",\n    \"neurom.viewer.draw(neurom.load_neuron('morphology/C060114A7.asc'));\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To load the morphology we create a NrnFileMorphology object. We set 'do_replace_axon' to True to replace the axon with a AIS.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"morphology/C060114A7.asc\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"morphology = ephys.morphologies.NrnFileMorphology('morphology/C060114A7.asc', do_replace_axon=True)\\n\",\n    \"print(str(morphology))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Parameters\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since we have many parameters in this model, they are stored in a json file: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/config/parameters.json\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"['g_pas', 'e_pas', 'cm', 'Ra', 'v_init', 'celsius', 'ena', 'ek', 'cm', 'ena', 'ek', 'cm', 'ena', 'ek', 'gIhbar_Ih', 'gNaTs2_tbar_NaTs2_t', 'gSKv3_1bar_SKv3_1', 'gImbar_Im', 'gIhbar_Ih', 'gNaTa_tbar_NaTa_t', 'gNap_Et2bar_Nap_Et2', 'gK_Pstbar_K_Pst', 'gK_Tstbar_K_Tst', 'gSK_E2bar_SK_E2', 'gSKv3_1bar_SKv3_1', 'gCa_HVAbar_Ca_HVA', 'gCa_LVAstbar_Ca_LVAst', 'gamma_CaDynamics_E2', 'decay_CaDynamics_E2', 'gNaTs2_tbar_NaTs2_t', 'gSKv3_1bar_SKv3_1', 'gSK_E2bar_SK_E2', 'gCa_HVAbar_Ca_HVA', 'gCa_LVAstbar_Ca_LVAst', 'gamma_CaDynamics_E2', 'decay_CaDynamics_E2', 'gIhbar_Ih']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import json\\n\",\n    \"param_configs = json.load(open('config/parameters.json'))\\n\",\n    \"print([param_config['param_name'] for param_config in param_configs])\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The directory that contains this notebook has a module that will load all the parameters in BluePyOpt Parameter objects\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"g_pas.all: ['all'] g_pas = 3e-05\\n\",\n      \"e_pas.all: ['all'] e_pas = -75\\n\",\n      \"cm.all: ['all'] cm = 1\\n\",\n      \"Ra.all: ['all'] Ra = 100\\n\",\n      \"v_init: v_init = -65\\n\",\n      \"celsius: celsius = 34\\n\",\n      \"ena.apical: ['apical'] ena = 50\\n\",\n      \"ek.apical: ['apical'] ek = -85\\n\",\n      \"cm.apical: ['apical'] cm = 2\\n\",\n      \"ena.somatic: ['somatic'] ena = 50\\n\",\n      \"ek.somatic: ['somatic'] ek = -85\\n\",\n      \"cm.basal: ['basal'] cm = 2\\n\",\n      \"ena.axonal: ['axonal'] ena = 50\\n\",\n      \"ek.axonal: ['axonal'] ek = -85\\n\",\n      \"gIhbar_Ih.basal: ['basal'] gIhbar_Ih = 8e-05\\n\",\n      \"gNaTs2_tbar_NaTs2_t.apical: ['apical'] gNaTs2_tbar_NaTs2_t = [0, 0.04]\\n\",\n      \"gSKv3_1bar_SKv3_1.apical: ['apical'] gSKv3_1bar_SKv3_1 = [0, 0.04]\\n\",\n      \"gImbar_Im.apical: ['apical'] gImbar_Im = [0, 0.001]\\n\",\n      \"gIhbar_Ih.apical: ['apical'] gIhbar_Ih = 8e-05\\n\",\n      \"gNaTa_tbar_NaTa_t.axonal: ['axonal'] gNaTa_tbar_NaTa_t = [0, 4]\\n\",\n      \"gNap_Et2bar_Nap_Et2.axonal: ['axonal'] gNap_Et2bar_Nap_Et2 = [0, 4]\\n\",\n      \"gK_Pstbar_K_Pst.axonal: ['axonal'] gK_Pstbar_K_Pst = [0, 1]\\n\",\n      \"gK_Tstbar_K_Tst.axonal: ['axonal'] gK_Tstbar_K_Tst = [0, 0.1]\\n\",\n      \"gSK_E2bar_SK_E2.axonal: ['axonal'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"gSKv3_1bar_SKv3_1.axonal: ['axonal'] gSKv3_1bar_SKv3_1 = [0, 2]\\n\",\n      \"gCa_HVAbar_Ca_HVA.axonal: ['axonal'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"gCa_LVAstbar_Ca_LVAst.axonal: ['axonal'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"gamma_CaDynamics_E2.axonal: ['axonal'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"decay_CaDynamics_E2.axonal: ['axonal'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"gNaTs2_tbar_NaTs2_t.somatic: ['somatic'] gNaTs2_tbar_NaTs2_t = [0, 1]\\n\",\n      \"gSKv3_1bar_SKv3_1.somatic: ['somatic'] gSKv3_1bar_SKv3_1 = [0, 1]\\n\",\n      \"gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"gCa_HVAbar_Ca_HVA.somatic: ['somatic'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"gCa_LVAstbar_Ca_LVAst.somatic: ['somatic'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"gamma_CaDynamics_E2.somatic: ['somatic'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"decay_CaDynamics_E2.somatic: ['somatic'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"gIhbar_Ih.somatic: ['somatic'] gIhbar_Ih = 8e-05\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import l5pc_model\\n\",\n    \"parameters = l5pc_model.define_parameters()\\n\",\n    \"print('\\\\n'.join('%s' % param for param in parameters))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see there are two types of parameters, parameters with a fixed value and parameters with bounds. The latter will be optimised by the algorithm.\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Mechanism\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We also need to add all the necessary mechanisms, like ion channels to the model. \\n\",\n    \"The configuration of the mechanisms is also stored in a json file, and can be loaded in a similar way.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Ih.basal: Ih at ['basal']\\n\",\n      \"pas.all: pas at ['all']\\n\",\n      \"Ih.apical: Ih at ['apical']\\n\",\n      \"Im.apical: Im at ['apical']\\n\",\n      \"SKv3_1.apical: SKv3_1 at ['apical']\\n\",\n      \"NaTs2_t.apical: NaTs2_t at ['apical']\\n\",\n      \"Ca_LVAst.axonal: Ca_LVAst at ['axonal']\\n\",\n      \"Ca_HVA.axonal: Ca_HVA at ['axonal']\\n\",\n      \"CaDynamics_E2.axonal: CaDynamics_E2 at ['axonal']\\n\",\n      \"SKv3_1.axonal: SKv3_1 at ['axonal']\\n\",\n      \"SK_E2.axonal: SK_E2 at ['axonal']\\n\",\n      \"K_Tst.axonal: K_Tst at ['axonal']\\n\",\n      \"K_Pst.axonal: K_Pst at ['axonal']\\n\",\n      \"Nap_Et2.axonal: Nap_Et2 at ['axonal']\\n\",\n      \"NaTa_t.axonal: NaTa_t at ['axonal']\\n\",\n      \"NaTs2_t.somatic: NaTs2_t at ['somatic']\\n\",\n      \"SKv3_1.somatic: SKv3_1 at ['somatic']\\n\",\n      \"SK_E2.somatic: SK_E2 at ['somatic']\\n\",\n      \"CaDynamics_E2.somatic: CaDynamics_E2 at ['somatic']\\n\",\n      \"Ca_HVA.somatic: Ca_HVA at ['somatic']\\n\",\n      \"Ca_LVAst.somatic: Ca_LVAst at ['somatic']\\n\",\n      \"Ih.somatic: Ih at ['somatic']\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"mechanisms = l5pc_model.define_mechanisms()\\n\",\n    \"print('\\\\n'.join('%s' % mech for mech in mechanisms))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Cell model\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With the morphology, mechanisms and parameters we can build the cell model. If we use axon-replacement, we must 3d-instantiate the morphology first in the Neuron simulator to obtain a faithful representation in Arbor.\\n\",\n    \"\\n\",\n    \"Note that before `l5pc_cell` can subsequently be used in a Neuron protocol, `l5pc_cell.destroy(sim=nrn_sim)` must be invoked. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"l5pc:\\n\",\n      \"  morphology:\\n\",\n      \"    morphology/C060114A7.asc\\n\",\n      \"  mechanisms:\\n\",\n      \"    Ih.basal: Ih at ['basal']\\n\",\n      \"    pas.all: pas at ['all']\\n\",\n      \"    Ih.apical: Ih at ['apical']\\n\",\n      \"    Im.apical: Im at ['apical']\\n\",\n      \"    SKv3_1.apical: SKv3_1 at ['apical']\\n\",\n      \"    NaTs2_t.apical: NaTs2_t at ['apical']\\n\",\n      \"    Ca_LVAst.axonal: Ca_LVAst at ['axonal']\\n\",\n      \"    Ca_HVA.axonal: Ca_HVA at ['axonal']\\n\",\n      \"    CaDynamics_E2.axonal: CaDynamics_E2 at ['axonal']\\n\",\n      \"    SKv3_1.axonal: SKv3_1 at ['axonal']\\n\",\n      \"    SK_E2.axonal: SK_E2 at ['axonal']\\n\",\n      \"    K_Tst.axonal: K_Tst at ['axonal']\\n\",\n      \"    K_Pst.axonal: K_Pst at ['axonal']\\n\",\n      \"    Nap_Et2.axonal: Nap_Et2 at ['axonal']\\n\",\n      \"    NaTa_t.axonal: NaTa_t at ['axonal']\\n\",\n      \"    NaTs2_t.somatic: NaTs2_t at ['somatic']\\n\",\n      \"    SKv3_1.somatic: SKv3_1 at ['somatic']\\n\",\n      \"    SK_E2.somatic: SK_E2 at ['somatic']\\n\",\n      \"    CaDynamics_E2.somatic: CaDynamics_E2 at ['somatic']\\n\",\n      \"    Ca_HVA.somatic: Ca_HVA at ['somatic']\\n\",\n      \"    Ca_LVAst.somatic: Ca_LVAst at ['somatic']\\n\",\n      \"    Ih.somatic: Ih at ['somatic']\\n\",\n      \"  params:\\n\",\n      \"    g_pas.all: ['all'] g_pas = 3e-05\\n\",\n      \"    e_pas.all: ['all'] e_pas = -75\\n\",\n      \"    cm.all: ['all'] cm = 1\\n\",\n      \"    Ra.all: ['all'] Ra = 100\\n\",\n      \"    v_init: v_init = -65\\n\",\n      \"    celsius: celsius = 34\\n\",\n      \"    ena.apical: ['apical'] ena = 50\\n\",\n      \"    ek.apical: ['apical'] ek = -85\\n\",\n      \"    cm.apical: ['apical'] cm = 2\\n\",\n      \"    ena.somatic: ['somatic'] ena = 50\\n\",\n      \"    ek.somatic: ['somatic'] ek = -85\\n\",\n      \"    cm.basal: ['basal'] cm = 2\\n\",\n      \"    ena.axonal: ['axonal'] ena = 50\\n\",\n      \"    ek.axonal: ['axonal'] ek = -85\\n\",\n      \"    gIhbar_Ih.basal: ['basal'] gIhbar_Ih = 8e-05\\n\",\n      \"    gNaTs2_tbar_NaTs2_t.apical: ['apical'] gNaTs2_tbar_NaTs2_t = [0, 0.04]\\n\",\n      \"    gSKv3_1bar_SKv3_1.apical: ['apical'] gSKv3_1bar_SKv3_1 = [0, 0.04]\\n\",\n      \"    gImbar_Im.apical: ['apical'] gImbar_Im = [0, 0.001]\\n\",\n      \"    gIhbar_Ih.apical: ['apical'] gIhbar_Ih = 8e-05\\n\",\n      \"    gNaTa_tbar_NaTa_t.axonal: ['axonal'] gNaTa_tbar_NaTa_t = [0, 4]\\n\",\n      \"    gNap_Et2bar_Nap_Et2.axonal: ['axonal'] gNap_Et2bar_Nap_Et2 = [0, 4]\\n\",\n      \"    gK_Pstbar_K_Pst.axonal: ['axonal'] gK_Pstbar_K_Pst = [0, 1]\\n\",\n      \"    gK_Tstbar_K_Tst.axonal: ['axonal'] gK_Tstbar_K_Tst = [0, 0.1]\\n\",\n      \"    gSK_E2bar_SK_E2.axonal: ['axonal'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"    gSKv3_1bar_SKv3_1.axonal: ['axonal'] gSKv3_1bar_SKv3_1 = [0, 2]\\n\",\n      \"    gCa_HVAbar_Ca_HVA.axonal: ['axonal'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"    gCa_LVAstbar_Ca_LVAst.axonal: ['axonal'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"    gamma_CaDynamics_E2.axonal: ['axonal'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"    decay_CaDynamics_E2.axonal: ['axonal'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"    gNaTs2_tbar_NaTs2_t.somatic: ['somatic'] gNaTs2_tbar_NaTs2_t = [0, 1]\\n\",\n      \"    gSKv3_1bar_SKv3_1.somatic: ['somatic'] gSKv3_1bar_SKv3_1 = [0, 1]\\n\",\n      \"    gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.1]\\n\",\n      \"    gCa_HVAbar_Ca_HVA.somatic: ['somatic'] gCa_HVAbar_Ca_HVA = [0, 0.001]\\n\",\n      \"    gCa_LVAstbar_Ca_LVAst.somatic: ['somatic'] gCa_LVAstbar_Ca_LVAst = [0, 0.01]\\n\",\n      \"    gamma_CaDynamics_E2.somatic: ['somatic'] gamma_CaDynamics_E2 = [0.0005, 0.05]\\n\",\n      \"    decay_CaDynamics_E2.somatic: ['somatic'] decay_CaDynamics_E2 = [20, 1000]\\n\",\n      \"    gIhbar_Ih.somatic: ['somatic'] gIhbar_Ih = 8e-05\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"l5pc_cell = ephys.models.CellModel('l5pc', morph=morphology, mechs=mechanisms, params=parameters)\\n\",\n    \"\\n\",\n    \"if morphology.do_replace_axon:\\n\",\n    \"    nrn_sim = ephys.simulators.NrnSimulator()\\n\",\n    \"    # invoke this before exporting an axon-replaced morphology to JSON/ACC-format\\n\",\n    \"    l5pc_cell.instantiate_morphology_3d(nrn_sim)\\n\",\n    \"\\n\",\n    \"print(l5pc_cell)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For use in the cell evaluator later, we need to make a list of the name of the parameters we are going to optimise.\\n\",\n    \"These are the parameters that are not frozen.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"param_names = [param.name for param in l5pc_cell.params.values() if not param.frozen]      \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Protocols\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we have a cell model, we can apply protocols to it. The protocols are also stored in a json file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'bAP': {'stimuli': [{'delay': 295, 'amp': 1.9, 'duration': 5, 'totduration': 600}], 'extra_recordings': [{'var': 'v', 'somadistance': 660, 'type': 'somadistance', 'name': 'dend1', 'seclist_name': 'apical', 'arbor_branch_index': 251, 'arbor_branch_index_with_replaced_axon': 123}, {'var': 'v', 'somadistance': 800, 'type': 'somadistance', 'name': 'dend2', 'seclist_name': 'apical', 'arbor_branch_index': 251, 'arbor_branch_index_with_replaced_axon': 123}]}, 'Step3': {'stimuli': [{'delay': 700, 'amp': 0.95, 'duration': 2000, 'totduration': 3000}, {'delay': 0, 'amp': -0.126, 'duration': 3000, 'totduration': 3000}]}, 'Step2': {'stimuli': [{'delay': 700, 'amp': 0.562, 'duration': 2000, 'totduration': 3000}, {'delay': 0, 'amp': -0.126, 'duration': 3000, 'totduration': 3000}]}, 'Step1': {'stimuli': [{'delay': 700, 'amp': 0.458, 'duration': 2000, 'totduration': 3000}, {'delay': 0, 'amp': -0.126, 'duration': 3000, 'totduration': 3000}]}}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"proto_configs = json.load(open('config/protocols.json'))\\n\",\n    \"print(proto_configs)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And they can be automatically loaded\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"bAP:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 1.900000 delay 295.000000 duration 5.000000 totdur 600.000000 at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"  recordings:\\n\",\n      \"    bAP.soma.v: v at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"    bAP.dend1.v: v at ArbLocsetLocation (locset-def \\\"dend1\\\" (restrict (distal-translate (proximal (region \\\"apic\\\")) 660) (proximal-interval (distal (branch 123)))))\\n\",\n      \"    bAP.dend2.v: v at ArbLocsetLocation (locset-def \\\"dend2\\\" (restrict (distal-translate (proximal (region \\\"apic\\\")) 800) (proximal-interval (distal (branch 123)))))\\n\",\n      \"\\n\",\n      \"Step3:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 0.950000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"  recordings:\\n\",\n      \"    Step3.soma.v: v at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"\\n\",\n      \"Step2:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 0.562000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"  recordings:\\n\",\n      \"    Step2.soma.v: v at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"\\n\",\n      \"Step1:\\n\",\n      \"  stimuli:\\n\",\n      \"    Square pulse amp 0.458000 delay 700.000000 duration 2000.000000 totdur 3000.000000 at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"    Square pulse amp -0.126000 delay 0.000000 duration 3000.000000 totdur 3000.000000 at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"  recordings:\\n\",\n      \"    Step1.soma.v: v at ArbBranchRelLocation '(locset-def \\\"soma\\\" (location 0 0.5))'\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import l5pc_evaluator\\n\",\n    \"fitness_protocols = l5pc_evaluator.define_protocols(do_replace_axon=morphology.do_replace_axon, sim='arb')\\n\",\n    \"print('\\\\n'.join('%s' % protocol for protocol in fitness_protocols.values()))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## eFeatures\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For every protocol we need to define which eFeatures will be used as objectives of the optimisation algorithm.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{ 'Step1': { 'soma': { 'AHP_depth_abs': [-60.3636, 2.3018],\\n\",\n      \"                       'AHP_depth_abs_slow': [-61.1513, 2.3385],\\n\",\n      \"                       'AHP_slow_time': [0.1599, 0.0483],\\n\",\n      \"                       'AP_height': [25.0141, 3.1463],\\n\",\n      \"                       'AP_width': [3.5312, 0.8592],\\n\",\n      \"                       'ISI_CV': [0.109, 0.1217],\\n\",\n      \"                       'adaptation_index2': [0.0047, 0.0514],\\n\",\n      \"                       'doublet_ISI': [62.75, 9.6667],\\n\",\n      \"                       'mean_frequency': [6, 1.2222],\\n\",\n      \"                       'time_to_first_spike': [27.25, 5.7222]}},\\n\",\n      \"  'Step2': { 'soma': { 'AHP_depth_abs': [-59.9055, 1.8329],\\n\",\n      \"                       'AHP_depth_abs_slow': [-60.2471, 1.8972],\\n\",\n      \"                       'AHP_slow_time': [0.1676, 0.0339],\\n\",\n      \"                       'AP_height': [27.1003, 3.1463],\\n\",\n      \"                       'AP_width': [2.7917, 0.7499],\\n\",\n      \"                       'ISI_CV': [0.0674, 0.075],\\n\",\n      \"                       'adaptation_index2': [0.005, 0.0067],\\n\",\n      \"                       'doublet_ISI': [44.0, 7.1327],\\n\",\n      \"                       'mean_frequency': [8.5, 0.9796],\\n\",\n      \"                       'time_to_first_spike': [19.75, 2.8776]}},\\n\",\n      \"  'Step3': { 'soma': { 'AHP_depth_abs': [-57.0905, 2.3427],\\n\",\n      \"                       'AHP_depth_abs_slow': [-61.1513, 2.3385],\\n\",\n      \"                       'AHP_slow_time': [0.1968, 0.0112],\\n\",\n      \"                       'AP_height': [19.7207, 3.7204],\\n\",\n      \"                       'AP_width': [3.5347, 0.8788],\\n\",\n      \"                       'ISI_CV': [0.0737, 0.0292],\\n\",\n      \"                       'adaptation_index2': [0.0055, 0.0015],\\n\",\n      \"                       'doublet_ISI': [22.75, 4.14],\\n\",\n      \"                       'mean_frequency': [17.5, 0.8],\\n\",\n      \"                       'time_to_first_spike': [10.5, 1.36]}},\\n\",\n      \"  'bAP': { 'dend1': {'AP_amplitude_from_voltagebase': [45, 10]},\\n\",\n      \"           'dend2': {'AP_amplitude_from_voltagebase': [36, 9.33]},\\n\",\n      \"           'soma': { 'AP_height': [25.0, 5.0],\\n\",\n      \"                     'AP_width': [2.0, 0.5],\\n\",\n      \"                     'Spikecount': [1.0, 0.01]}}}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"feature_configs = json.load(open('config/features.json'))\\n\",\n    \"pp.pprint(feature_configs)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"objectives:\\n\",\n      \"  ( AP_width for {'': 'bAP.soma.v'} with stim start 295 and end 600, exp mean 2.0 and std 0.5 and AP threshold override -20 )\\n\",\n      \"  ( AP_height for {'': 'bAP.soma.v'} with stim start 295 and end 600, exp mean 25.0 and std 5.0 and AP threshold override -20 )\\n\",\n      \"  ( Spikecount for {'': 'bAP.soma.v'} with stim start 295 and end 600, exp mean 1.0 and std 0.01 and AP threshold override -20 )\\n\",\n      \"  ( AP_amplitude_from_voltagebase for {'': 'bAP.dend1.v'} with stim start 295 and end 600, exp mean 45 and std 10 and AP threshold override -55 )\\n\",\n      \"  ( AP_amplitude_from_voltagebase for {'': 'bAP.dend2.v'} with stim start 295 and end 600, exp mean 36 and std 9.33 and AP threshold override -55 )\\n\",\n      \"  ( AP_height for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 19.7207 and std 3.7204 and AP threshold override -20 )\\n\",\n      \"  ( AHP_slow_time for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.1968 and std 0.0112 and AP threshold override -20 )\\n\",\n      \"  ( ISI_CV for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.0737 and std 0.0292 and AP threshold override -20 )\\n\",\n      \"  ( doublet_ISI for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 22.75 and std 4.14 and AP threshold override -20 )\\n\",\n      \"  ( adaptation_index2 for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 0.0055 and std 0.0015 and AP threshold override -20 )\\n\",\n      \"  ( mean_frequency for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 17.5 and std 0.8 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs_slow for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean -61.1513 and std 2.3385 and AP threshold override -20 )\\n\",\n      \"  ( AP_width for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 3.5347 and std 0.8788 and AP threshold override -20 )\\n\",\n      \"  ( time_to_first_spike for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean 10.5 and std 1.36 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs for {'': 'Step3.soma.v'} with stim start 700 and end 2700, exp mean -57.0905 and std 2.3427 and AP threshold override -20 )\\n\",\n      \"  ( AP_height for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 27.1003 and std 3.1463 and AP threshold override -20 )\\n\",\n      \"  ( AHP_slow_time for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.1676 and std 0.0339 and AP threshold override -20 )\\n\",\n      \"  ( ISI_CV for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.0674 and std 0.075 and AP threshold override -20 )\\n\",\n      \"  ( doublet_ISI for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 44.0 and std 7.1327 and AP threshold override -20 )\\n\",\n      \"  ( adaptation_index2 for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 0.005 and std 0.0067 and AP threshold override -20 )\\n\",\n      \"  ( mean_frequency for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 8.5 and std 0.9796 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs_slow for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean -60.2471 and std 1.8972 and AP threshold override -20 )\\n\",\n      \"  ( AP_width for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 2.7917 and std 0.7499 and AP threshold override -20 )\\n\",\n      \"  ( time_to_first_spike for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean 19.75 and std 2.8776 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs for {'': 'Step2.soma.v'} with stim start 700 and end 2700, exp mean -59.9055 and std 1.8329 and AP threshold override -20 )\\n\",\n      \"  ( AP_height for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 25.0141 and std 3.1463 and AP threshold override -20 )\\n\",\n      \"  ( AHP_slow_time for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.1599 and std 0.0483 and AP threshold override -20 )\\n\",\n      \"  ( ISI_CV for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.109 and std 0.1217 and AP threshold override -20 )\\n\",\n      \"  ( doublet_ISI for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 62.75 and std 9.6667 and AP threshold override -20 )\\n\",\n      \"  ( adaptation_index2 for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 0.0047 and std 0.0514 and AP threshold override -20 )\\n\",\n      \"  ( mean_frequency for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 6 and std 1.2222 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs_slow for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean -61.1513 and std 2.3385 and AP threshold override -20 )\\n\",\n      \"  ( AP_width for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 3.5312 and std 0.8592 and AP threshold override -20 )\\n\",\n      \"  ( time_to_first_spike for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean 27.25 and std 5.7222 and AP threshold override -20 )\\n\",\n      \"  ( AHP_depth_abs for {'': 'Step1.soma.v'} with stim start 700 and end 2700, exp mean -60.3636 and std 2.3018 and AP threshold override -20 )\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"fitness_calculator = l5pc_evaluator.define_fitness_calculator(fitness_protocols)\\n\",\n    \"print(fitness_calculator)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Simulator\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We need to define which simulator we will use. In this case it will be Arbor, i.e. the ArbSimulator class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sim = ephys.simulators.ArbSimulator()\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluator\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With all the components defined above we can build a cell evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"evaluator = ephys.evaluators.CellEvaluator(                                          \\n\",\n    \"        cell_model=l5pc_cell,                                                       \\n\",\n    \"        param_names=param_names,                                                    \\n\",\n    \"        fitness_protocols=fitness_protocols,                                        \\n\",\n    \"        fitness_calculator=fitness_calculator,                                      \\n\",\n    \"        sim=sim)  \"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This evaluator can be used to run the protocols. The original parameter values for the Markram et al. 2015 L5PC model are:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"release_params = {\\n\",\n    \"    'gNaTs2_tbar_NaTs2_t.apical': 0.026145,\\n\",\n    \"    'gSKv3_1bar_SKv3_1.apical': 0.004226,\\n\",\n    \"    'gImbar_Im.apical': 0.000143,\\n\",\n    \"    'gNaTa_tbar_NaTa_t.axonal': 3.137968,\\n\",\n    \"    'gK_Tstbar_K_Tst.axonal': 0.089259,\\n\",\n    \"    'gamma_CaDynamics_E2.axonal': 0.002910,\\n\",\n    \"    'gNap_Et2bar_Nap_Et2.axonal': 0.006827,\\n\",\n    \"    'gSK_E2bar_SK_E2.axonal': 0.007104,\\n\",\n    \"    'gCa_HVAbar_Ca_HVA.axonal': 0.000990,\\n\",\n    \"    'gK_Pstbar_K_Pst.axonal': 0.973538,\\n\",\n    \"    'gSKv3_1bar_SKv3_1.axonal': 1.021945,\\n\",\n    \"    'decay_CaDynamics_E2.axonal': 287.198731,\\n\",\n    \"    'gCa_LVAstbar_Ca_LVAst.axonal': 0.008752,\\n\",\n    \"    'gamma_CaDynamics_E2.somatic': 0.000609,\\n\",\n    \"    'gSKv3_1bar_SKv3_1.somatic': 0.303472,\\n\",\n    \"    'gSK_E2bar_SK_E2.somatic': 0.008407,\\n\",\n    \"    'gCa_HVAbar_Ca_HVA.somatic': 0.000994,\\n\",\n    \"    'gNaTs2_tbar_NaTs2_t.somatic': 0.983955,\\n\",\n    \"    'decay_CaDynamics_E2.somatic': 210.485284,\\n\",\n    \"    'gCa_LVAstbar_Ca_LVAst.somatic': 0.000333\\n\",\n    \"}\\n\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Running the responses is as easy as passing the protocols and parameters to the evaluator. (The line below will take some time to execute)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"release_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=release_params)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now plot all the responses\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_responses(responses):\\n\",\n    \"    fig, axes = plt.subplots(len(responses), figsize=(10,10))\\n\",\n    \"    for index, (resp_name, response) in enumerate(sorted(responses.items())):\\n\",\n    \"        axes[index].plot(response['time'], response['voltage'], label=resp_name)\\n\",\n    \"        axes[index].set_title(resp_name)\\n\",\n    \"    fig.tight_layout()\\n\",\n    \"plot_responses(release_responses)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Running an optimisation of the parameters now has become very easy. \\n\",\n    \"Of course running the L5PC optimisation will require quite some computing resources. \\n\",\n    \"\\n\",\n    \"To show a proof-of-concept, we will only run 2 generations, with 2 offspring individuals per generations.\\n\",\n    \"If you want to run all full optimisation, you should run for 100 generations with an offspring size of 100 individuals. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"opt = bpopt.optimisations.DEAPOptimisation(                                     \\n\",\n    \"    evaluator=evaluator,                                                            \\n\",\n    \"    offspring_size=2) \\n\",\n    \"final_pop, halloffame, log, hist = opt.run(max_ngen=2, cp_filename='checkpoints/checkpoint.pkl')\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first individual in the hall of fame will contain the best solution found.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[0.001017834439738432, 0.021656498911739864, 0.0009391491627785106, 1.5248169507528497, 0.8663975885224535, 0.4221165755827173, 0.0029040787574867947, 0.022169166627303505, 0.8757751873011441, 0.0004958122413818507, 0.002330844502575726, 0.011927893806278723, 234.40541659093483, 0.4596034657377336, 0.28978161459048557, 0.0021489705265908877, 0.0008375779756625729, 0.005564543226524335, 0.03229357096515606, 202.18814057682334]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(halloffame[0])\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"These are the raw parameter values. \\n\",\n    \"The evaluator object can convert this in a dictionary, so that we can see the parameter names corresponding to these values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{ 'decay_CaDynamics_E2.axonal': 234.40541659093483,\\n\",\n      \"  'decay_CaDynamics_E2.somatic': 202.18814057682334,\\n\",\n      \"  'gCa_HVAbar_Ca_HVA.axonal': 0.0004958122413818507,\\n\",\n      \"  'gCa_HVAbar_Ca_HVA.somatic': 0.0008375779756625729,\\n\",\n      \"  'gCa_LVAstbar_Ca_LVAst.axonal': 0.002330844502575726,\\n\",\n      \"  'gCa_LVAstbar_Ca_LVAst.somatic': 0.005564543226524335,\\n\",\n      \"  'gImbar_Im.apical': 0.0009391491627785106,\\n\",\n      \"  'gK_Pstbar_K_Pst.axonal': 0.4221165755827173,\\n\",\n      \"  'gK_Tstbar_K_Tst.axonal': 0.0029040787574867947,\\n\",\n      \"  'gNaTa_tbar_NaTa_t.axonal': 1.5248169507528497,\\n\",\n      \"  'gNaTs2_tbar_NaTs2_t.apical': 0.001017834439738432,\\n\",\n      \"  'gNaTs2_tbar_NaTs2_t.somatic': 0.4596034657377336,\\n\",\n      \"  'gNap_Et2bar_Nap_Et2.axonal': 0.8663975885224535,\\n\",\n      \"  'gSK_E2bar_SK_E2.axonal': 0.022169166627303505,\\n\",\n      \"  'gSK_E2bar_SK_E2.somatic': 0.0021489705265908877,\\n\",\n      \"  'gSKv3_1bar_SKv3_1.apical': 0.021656498911739864,\\n\",\n      \"  'gSKv3_1bar_SKv3_1.axonal': 0.8757751873011441,\\n\",\n      \"  'gSKv3_1bar_SKv3_1.somatic': 0.28978161459048557,\\n\",\n      \"  'gamma_CaDynamics_E2.axonal': 0.011927893806278723,\\n\",\n      \"  'gamma_CaDynamics_E2.somatic': 0.03229357096515606}\\n\",\n      \"None\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_params = evaluator.param_dict(halloffame[0])\\n\",\n    \"print(pp.pprint(best_params))\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then we can run the fitness protocols on the model with these parameter values\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"best_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=best_params)\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And then we can also plot these responses. \\n\",\n    \"\\n\",\n    \"When you ran the above optimisation with only 2 individuals and 2 generations, this 'best' model will of course be very low quality.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_responses(best_responses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\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\": \"3.8.10\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"581988038cf9ce8838e7faf3da7c29f4ff88d898cd43cb17e0086e389d8deda2\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/l5pc/benchmark/get_stats.py",
    "content": "\nimport datetime\nfrom ipyparallel import Client\nrc = Client()\n# rc.get_result('77cdf201-5925-4199-bdd8-0eae0d833d2a')\n# incomplete = rc.db_query({'completed' : None}, keys=['msg_id', 'started'])\ncompleted = rc.db_query(\n    {'completed': {'$ne': None}}, keys=['msg_id', 'started', 'completed',\n                                        'engine_uuid'])\ntotal_time = datetime.timedelta()\nfor task in completed:\n    # print task['started'], task['completed'] - task['started']\n    total_time += task['completed'] - task['started']\nprint(total_time, total_time / len(completed))\nprint(len(set(task['engine_uuid'] for task in completed)), len(completed))\n"
  },
  {
    "path": "examples/l5pc/benchmark/l5pc_benchmark.sbatch",
    "content": "#!/bin/bash\n\n#SBATCH --ntasks=50\n#SBATCH --partition=prod\n#SBATCH --job-name=l5pc_benchmark\n#SBATCH --time=1-00:00:00\n#SBATCH --error=logs/l5pc_benchmark.stdout\n#SBATCH --output=logs/l5pc_benchmark.stdout\n\nset -e\nset -x\n\n./start.sh\n"
  },
  {
    "path": "examples/l5pc/benchmark/logs/.gitignore",
    "content": "*\n!.gitignore\n"
  },
  {
    "path": "examples/l5pc/benchmark/run_benchmark.sh",
    "content": "#!/bin/bash\n\n# Script to run a benchmark optimisation on CSCS viz cluster\n\nLOGFILENAME=logs/l5pc_benchmark.stdout\n\nrm -rf ${LOGFILENAME}\n\nsbatch -A proj37 l5pc_benchmark.sbatch\n\ntail -f --retry ${LOGFILENAME}\n"
  },
  {
    "path": "examples/l5pc/benchmark/start.sh",
    "content": "#!/bin/bash\n\nset -e\nset -x\n\nPWD=$(pwd)\nLOGS=$PWD/logs\nmkdir -p $LOGS\n\ncd ..\n\nOFFSPRING_SIZE=100\nMAX_NGEN=100\n\nexport IPYTHONDIR=${PWD}/.ipython\nexport IPYTHON_PROFILE=benchmark.${SLURM_JOBID}\n\nipcontroller --init --ip='*' --sqlitedb --ping=30000 --profile=${IPYTHON_PROFILE} &\nsleep 10\nsrun --output=\"${LOGS}/engine_%j_%2t.out\" ipengine --timeout=300 --profile=${IPYTHON_PROFILE} &\nsleep 10\n\nCHECKPOINTS_DIR=\"checkpoints/run.${SLURM_JOBID}\"\nmkdir -p ${CHECKPOINTS_DIR}\n\npids=\"\"\nfor seed in {1..4}; do\n    python opt_l5pc.py                     \\\n        -vv                                \\\n        --offspring_size=${OFFSPRING_SIZE} \\\n        --max_ngen=${MAX_NGEN}             \\\n        --seed=${seed}                     \\\n        --ipyparallel                      \\\n        --start                            \\\n        --checkpoint \"${CHECKPOINTS_DIR}/seed${seed}.pkl\" &\n    pids+=\"$! \"\ndone\n\nwait $pids\n"
  },
  {
    "path": "examples/l5pc/benchmark/task_stats.py",
    "content": "import sqlite3\nimport collections\nfrom datetime import datetime, timedelta\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('ggplot')\n\n\ndef get_engine_data():\n    \"\"\"Main\"\"\"\n    conn = sqlite3.connect('tasks.db')\n\n    cursor = conn.cursor()\n\n    tasks = collections.defaultdict(list)\n\n    SQL = 'SELECT started, completed, engine_uuid FROM \"ipython-tasks\";'\n\n    TIME_FORMAT = '%Y-%m-%d %H:%M:%S.%f'\n    for started, completed, engine_uuid in cursor.execute(SQL).fetchall():\n        # TODO hardcoded 2016 for the moment, sometimes started contains strange\n        # character (and is empty for the rest)\n        if started and '2016' in started and completed:\n            started = datetime.strptime(started, TIME_FORMAT)\n            completed = datetime.strptime(completed, TIME_FORMAT)\n\n            duration = (\n                completed -\n                started).total_seconds() if completed else None\n            task = {'started': started,\n                    'completed': completed,\n                    'duration': duration,\n                    'engine_uuid': engine_uuid,\n                    }\n\n            tasks[engine_uuid].append(task)\n\n    # drop engines that only resolved a few tasks\n    # TODO: figure out why there are 'ghost' engines that only exist at the\n    # start\n    for engine_uuid in tasks.keys():\n        if len(tasks[engine_uuid]) < 10:\n            del tasks[engine_uuid]\n\n    engine_number_map = dict(zip(tasks.keys(), range(len(tasks.keys()))))\n    return tasks, engine_number_map\n\n\ndef plot_usage(tasks, engine_number_map):\n    fig, ax = plt.subplots(1, 1, facecolor='white')\n\n    for engine_uuid, task_list in tasks.items():\n        engine_number = engine_number_map[engine_uuid]\n        number_list = [engine_number for _ in task_list]\n        start_list = [task['started'] for task in task_list]\n        completed_list = [task['completed'] for task in task_list]\n        ax.plot(\n            [number_list, number_list],\n            [start_list, completed_list], linewidth=10,\n            solid_capstyle=\"butt\")\n\n    ax.set_xlim(min(engine_number_map.values()) - 1,\n                max(engine_number_map.values()) + 1)\n    ax.set_xlabel('Compute engine number')\n    ax.set_ylabel('Compute time')\n    plt.show()\n\n\ndef plot_duration_histogram(tasks):\n    durations = np.fromiter((t['duration']\n                             for task_list in tasks.values()\n                             for t in task_list),\n                            dtype=np.float64)\n    plt.hist(durations, 100, range=(1, 160))\n\n    plt.xlabel('Duration (s)')\n    plt.ylabel('Count')\n    plt.title('Histogram of task execution')\n    plt.grid(True)\n    plt.show()\n\n\ndef filter_start_time(start_time, tasks):\n    ret = collections.defaultdict(list)\n    for engine_uuid, task_list in tasks.items():\n        for task in task_list:\n            if task['started'] > start_time:\n                ret[engine_uuid].append(task)\n    return ret\n\n\ndef calculate_unused_compute(tasks):\n    start_time, end_time = datetime.max, datetime.min\n    total_time = timedelta()\n    for task_lists in tasks.values():\n        for task in task_lists:\n            start_time = min(start_time, task['started'])\n            end_time = max(end_time, task['completed'])\n            total_time += timedelta(seconds=task['duration'])\n    engines = len(tasks.keys())\n    return engines * (end_time - start_time) - total_time\n\n\ndef main():\n    tasks, engine_number_map = get_engine_data()\n    plot_usage(tasks, engine_number_map)\n    print('Unused compute total:', calculate_unused_compute(tasks))\n\n    plot_duration_histogram(tasks)\n    filtered_tasks = filter_start_time(datetime(2016, 4, 13, 13), tasks)\n    plot_usage(filtered_tasks, engine_number_map)\n    print('Unused compute last 30 minutes:', calculate_unused_compute(\n        filtered_tasks))\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/l5pc/cADpyr_76.hoc",
    "content": "//runL5PC.run91.dend3-0.3\n\n{load_file(\"Cell.hoc\")}\n{load_file(\"TDistFunc.hoc\")}\nbegintemplate CCell\npublic init, printInfo, delete_axon,  getCell, init_biophys, insertChannel\npublic gid, CellRef, getThreshold, geom_nseg, gmechdistribute, biophys\nobjref this, CellRef, gmechdistribute\n\nproc init() { local ind localobj strMorphName, strTmp, sf\n    strMorphName  = new String(\"C060114A7.asc\")\n    strTmp        = new String()\n    sf            = new StringFunctions()\n    if(numarg() == 2){\n        sscanf($s2, \"%s\", strTmp.s)\n        ind      = sf.substr(strTmp.s, \".asc\")\n        if((ind>0) && (ind == (sf.len(strTmp.s)-4))){\n            CellRef = new Cell($1, $s2)\n        }else{\n            sprint(strMorphName.s, \"%s/%s\", $s2, strMorphName.s)\n            CellRef = new Cell($1, strMorphName.s)\n        }\n    }\n    gmechdistribute = new TDistFunc()\n    CellRef.setCCell(this)\n    gid = CellRef.gid\n    geom_nseg()   //This function is called to have count of actual axon sections\n    delete_axon()\n    insertChannel()\n    init_biophys()\n    biophys()\n}\n\nfunc getThreshold() { return 0.0 }\n\nproc geom_nseg() {\n    CellRef.geom_nseg_fixed(40)\n    CellRef.geom_nsec() //To count all sections\n}\n\nobfunc getCell(){\n    return CellRef\n}\n\nproc delete_axon(){\n    CellRef.delete_axon()\n}\n\nproc init_biophys() {\n    forsec CellRef.all { cm = 1.0 }\n    forsec CellRef.all { Ra = 100.0 }\n\n    CellRef.soma[0] distance()\n}\n\nproc insertChannel() {\n\n}\n\nproc biophys() {\n    CellRef.insertChannel(\"axonal\",\"NaTa_t\")\n    CellRef.insertChannel(\"axonal\",\"Nap_Et2\")\n    CellRef.insertChannel(\"axonal\",\"K_Pst\")\n    CellRef.insertChannel(\"axonal\",\"K_Tst\")\n    CellRef.insertChannel(\"axonal\",\"SK_E2\")\n    CellRef.insertChannel(\"axonal\",\"SKv3_1\")\n    CellRef.insertChannel(\"axonal\",\"CaDynamics_E2\")\n    CellRef.insertChannel(\"axonal\",\"Ca_HVA\")\n    CellRef.insertChannel(\"axonal\",\"Ca_LVAst\")\n    CellRef.insertChannel(\"somatic\",\"NaTs2_t\")\n    CellRef.insertChannel(\"somatic\",\"SKv3_1\")\n    CellRef.insertChannel(\"somatic\",\"SK_E2\")\n    CellRef.insertChannel(\"somatic\",\"CaDynamics_E2\")\n    CellRef.insertChannel(\"somatic\",\"Ca_HVA\")\n    CellRef.insertChannel(\"somatic\",\"Ca_LVAst\")\n    CellRef.insertChannel(\"apical\",\"NaTs2_t\")\n    CellRef.insertChannel(\"apical\",\"SKv3_1\")\n    CellRef.insertChannel(\"apical\",\"Im\")\n    CellRef.insertChannel(\"apical\",\"Ih\")\n    CellRef.insertChannel(\"basal\",\"Ih\")\n    CellRef.insertChannel(\"apical\",\"Ih\")\n    CellRef.insertChannel(\"somatic\",\"Ih\")\n    CellRef.insertChannel(\"all\",\"pas\")\n\n    { CellRef.soma[0] distance() }\n    { forsec CellRef.all { e_pas = -75 } }\n    { forsec CellRef.all { g_pas = 3e-5 } }\n    { forsec CellRef.all { cm = 1 } }\n    { forsec CellRef.all { Ra = 100 } }\n    { forsec CellRef.somatic { ek = -85 } }\n    { forsec CellRef.somatic { ena = 50 } }\n    { forsec CellRef.axonal { ek = -85 } }\n    { forsec CellRef.axonal { ena = 50 } }\n    { forsec CellRef.apical { ek = -85 } }\n    { forsec CellRef.apical { ena = 50 } }\n    { forsec CellRef.apical { cm = 2 } }\n    { forsec CellRef.basal { cm = 2 } }\n    { forsec CellRef.apical { cm = 2 } }\n    gmechdistribute.distribute(CellRef.axonal,\"gNaTa_tbar_NaTa_t\",\"( 0.000000 * %g  + 1.000000 ) * 3.137968\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gNap_Et2bar_Nap_Et2\",\"( 0.000000 * %g  + 1.000000 ) * 0.006827\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gK_Pstbar_K_Pst\",\"( 0.000000 * %g  + 1.000000 ) * 0.973538\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gK_Tstbar_K_Tst\",\"( 0.000000 * %g  + 1.000000 ) * 0.089259\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gSK_E2bar_SK_E2\",\"( 0.000000 * %g  + 1.000000 ) * 0.007104\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gSKv3_1bar_SKv3_1\",\"( 0.000000 * %g  + 1.000000 ) * 1.021945\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gCa_HVAbar_Ca_HVA\",\"( 0.000000 * %g  + 1.000000 ) * 0.000990\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gCa_LVAstbar_Ca_LVAst\",\"( 0.000000 * %g  + 1.000000 ) * 0.008752\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"gamma_CaDynamics_E2\",\"( 0.000000 * %g  + 1.000000 ) * 0.002910\",1)\n    gmechdistribute.distribute(CellRef.axonal,\"decay_CaDynamics_E2\",\"( 0.000000 * %g  + 1.000000 ) * 287.198731\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"gNaTs2_tbar_NaTs2_t\",\"( 0.000000 * %g  + 1.000000 ) * 0.983955\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"gSKv3_1bar_SKv3_1\",\"( 0.000000 * %g  + 1.000000 ) * 0.303472\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"gSK_E2bar_SK_E2\",\"( 0.000000 * %g  + 1.000000 ) * 0.008407\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"gCa_HVAbar_Ca_HVA\",\"( 0.000000 * %g  + 1.000000 ) * 0.000994\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"gCa_LVAstbar_Ca_LVAst\",\"( 0.000000 * %g  + 1.000000 ) * 0.000333\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"gamma_CaDynamics_E2\",\"( 0.000000 * %g  + 1.000000 ) * 0.000609\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"decay_CaDynamics_E2\",\"( 0.000000 * %g  + 1.000000 ) * 210.485284\",1)\n    gmechdistribute.distribute(CellRef.apical,\"gNaTs2_tbar_NaTs2_t\",\"( 0.000000 * %g  + 1.000000 ) * 0.026145\",1)\n    gmechdistribute.distribute(CellRef.apical,\"gSKv3_1bar_SKv3_1\",\"( 0.000000 * %g  + 1.000000 ) * 0.004226\",1)\n    gmechdistribute.distribute(CellRef.apical,\"gImbar_Im\",\"( 0.000000 * %g  + 1.000000 ) * 0.000143\",1)\n    gmechdistribute.distribute(CellRef.apical,\"gIhbar_Ih\",\"(-0.869600 + 2.087000*exp((%g - 0.000000) * 0.003100)) * 0.000080\",1)\n    gmechdistribute.distribute(CellRef.somatic,\"gIhbar_Ih\",\"( 0.000000 * %g  + 1.000000 ) * 0.000080\",1)\n    gmechdistribute.distribute(CellRef.basal,\"gIhbar_Ih\",\"( 0.000000 * %g  + 1.000000 ) * 0.000080\",1)\n\n\n\n}\n\nendtemplate CCell\n"
  },
  {
    "path": "examples/l5pc/checkpoints/.gitignore",
    "content": "/*\n!.gitignore\n"
  },
  {
    "path": "examples/l5pc/config/features.json",
    "content": "{\n    \"bAP\": {\n        \"soma\": {\n            \"AP_width\": [\n                2.0,\n                0.5\n            ],\n            \"AP_height\": [\n                25.0,\n                5.0\n            ],\n            \"Spikecount\": [\n                1.0,\n                0.01\n            ]\n        },\n        \"dend1\": {\n            \"AP_amplitude_from_voltagebase\": [\n                45,\n                10\n            ]\n        },\n        \"dend2\": {\n            \"AP_amplitude_from_voltagebase\": [\n                36,\n                9.33\n            ]\n        }\n    },\n    \"Step3\": {\n        \"soma\": {\n            \"AP_height\": [\n                19.7207,\n                3.7204\n            ],\n            \"AHP_slow_time\": [\n                0.1968,\n                0.0112\n            ],\n            \"ISI_CV\": [\n                0.0737,\n                0.0292\n            ],\n            \"doublet_ISI\": [\n                22.75,\n                4.14\n            ],\n            \"adaptation_index2\": [\n                0.0055,\n                0.0015\n            ],\n            \"mean_frequency\": [\n                17.5,\n                0.8\n            ],\n            \"AHP_depth_abs_slow\": [\n                -61.1513,\n                2.3385\n            ],\n            \"AP_width\": [\n                3.5347,\n                0.8788\n            ],\n            \"time_to_first_spike\": [\n                10.5,\n                1.36\n            ],\n            \"AHP_depth_abs\": [\n                -57.0905,\n                2.3427\n            ]\n        }\n    },\n    \"Step2\": {\n        \"soma\": {\n            \"AP_height\": [\n                27.1003,\n                3.1463\n            ],\n            \"AHP_slow_time\": [\n                0.1676,\n                0.0339\n            ],\n            \"ISI_CV\": [\n                0.0674,\n                0.075\n            ],\n            \"doublet_ISI\": [\n                44.0,\n                7.1327\n            ],\n            \"adaptation_index2\": [\n                0.005,\n                0.0067\n            ],\n            \"mean_frequency\": [\n                8.5,\n                0.9796\n            ],\n            \"AHP_depth_abs_slow\": [\n                -60.2471,\n                1.8972\n            ],\n            \"AP_width\": [\n                2.7917,\n                0.7499\n            ],\n            \"time_to_first_spike\": [\n                19.75,\n                2.8776\n            ],\n            \"AHP_depth_abs\": [\n                -59.9055,\n                1.8329\n            ]\n        }\n    },\n    \"Step1\": {\n        \"soma\": {\n            \"AP_height\": [\n                25.0141,\n                3.1463\n            ],\n            \"AHP_slow_time\": [\n                0.1599,\n                0.0483\n            ],\n            \"ISI_CV\": [\n                0.109,\n                0.1217\n            ],\n            \"doublet_ISI\": [\n                62.75,\n                9.6667\n            ],\n            \"adaptation_index2\": [\n                0.0047,\n                0.0514\n            ],\n            \"mean_frequency\": [\n                6,\n                1.2222\n            ],\n            \"AHP_depth_abs_slow\": [\n                -61.1513,\n                2.3385\n            ],\n            \"AP_width\": [\n                3.5312,\n                0.8592\n            ],\n            \"time_to_first_spike\": [\n                27.25,\n                5.7222\n            ],\n            \"AHP_depth_abs\": [\n                -60.3636,\n                2.3018\n            ]\n        }\n    }\n}\n"
  },
  {
    "path": "examples/l5pc/config/fixed_params.json",
    "content": "{\n    \"global\": [\n        [\n            \"v_init\",\n            -65\n        ],\n        [\n            \"celsius\",\n            34\n        ]\n    ],\n    \"all\": [\n        [\n            \"g_pas\",\n            3e-05,\n            \"uniform\"\n        ],\n        [\n            \"e_pas\",\n            -75,\n            \"uniform\"\n        ],\n        [\n            \"cm\",\n            1,\n            \"uniform\"\n        ],\n        [\n            \"Ra\",\n            100,\n            \"uniform\"\n        ]\n    ],\n    \"apical\": [\n        [\n            \"ena\",\n            50,\n            \"uniform\"\n        ],\n        [\n            \"ek\",\n            -85,\n            \"uniform\"\n        ],\n        [\n            \"cm\",\n            2,\n            \"uniform\"\n        ]\n    ],\n    \"somatic\": [\n        [\n            \"ena\",\n            50,\n            \"uniform\"\n        ],\n        [\n            \"ek\",\n            -85,\n            \"uniform\"\n        ]\n    ],\n    \"basal\": [\n        [\n            \"cm\",\n            2,\n            \"uniform\"\n        ]\n    ],\n    \"axonal\": [\n        [\n            \"ena\",\n            50,\n            \"uniform\"\n        ],\n        [\n            \"ek\",\n            -85,\n            \"uniform\"\n        ]\n    ]\n}\n"
  },
  {
    "path": "examples/l5pc/config/mechanisms.json",
    "content": "{\n    \"basal\": [\n        \"Ih\"\n    ],\n    \"all\": [\n        \"pas\"\n    ],\n    \"apical\": [\n        \"Ih\",\n        \"Im\",\n        \"SKv3_1\",\n        \"NaTs2_t\"\n    ],\n    \"axonal\": [\n        \"Ca_LVAst\",\n        \"Ca_HVA\",\n        \"CaDynamics_E2\",\n        \"SKv3_1\",\n        \"SK_E2\",\n        \"K_Tst\",\n        \"K_Pst\",\n        \"Nap_Et2\",\n        \"NaTa_t\"\n    ],\n    \"somatic\": [\n        \"NaTs2_t\",\n        \"SKv3_1\",\n        \"SK_E2\",\n        \"CaDynamics_E2\",\n        \"Ca_HVA\",\n        \"Ca_LVAst\",\n        \"Ih\"\n    ]\n}\n"
  },
  {
    "path": "examples/l5pc/config/parameters.json",
    "content": "[\n    {\n        \"param_name\": \"g_pas\",\n        \"sectionlist\": \"all\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 3e-05\n    },\n    {\n        \"param_name\": \"e_pas\",\n        \"sectionlist\": \"all\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": -75\n    },\n    {\n        \"param_name\": \"cm\",\n        \"sectionlist\": \"all\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 1\n    },\n    {\n        \"param_name\": \"Ra\",\n        \"sectionlist\": \"all\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 100\n    },\n    {\n        \"param_name\": \"v_init\",\n        \"type\": \"global\",\n        \"value\": -65\n    },\n    {\n        \"param_name\": \"celsius\",\n        \"type\": \"global\",\n        \"value\": 34\n    },\n    {\n        \"param_name\": \"ena\",\n        \"sectionlist\": \"apical\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 50\n    },\n    {\n        \"param_name\": \"ek\",\n        \"sectionlist\": \"apical\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": -85\n    },\n    {\n        \"param_name\": \"cm\",\n        \"sectionlist\": \"apical\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 2\n    },\n    {\n        \"param_name\": \"ena\",\n        \"sectionlist\": \"somatic\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 50\n    },\n    {\n        \"param_name\": \"ek\",\n        \"sectionlist\": \"somatic\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": -85\n    },\n    {\n        \"param_name\": \"cm\",\n        \"sectionlist\": \"basal\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 2\n    },\n    {\n        \"param_name\": \"ena\",\n        \"sectionlist\": \"axonal\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": 50\n    },\n    {\n        \"param_name\": \"ek\",\n        \"sectionlist\": \"axonal\",\n        \"type\": \"section\",\n        \"dist_type\": \"uniform\",\n        \"value\": -85\n    },\n    {\n        \"param_name\": \"gIhbar_Ih\",\n        \"mech\": \"Ih\",\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gIhbar\",\n        \"value\": 8e-05,\n        \"type\": \"range\",\n        \"sectionlist\": \"basal\"\n    },\n    {\n        \"param_name\": \"gNaTs2_tbar_NaTs2_t\",\n        \"mech\": \"NaTs2_t\",\n        \"bounds\": [\n            0,\n            0.04\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gNaTs2_tbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"apical\"\n    },\n    {\n        \"param_name\": \"gSKv3_1bar_SKv3_1\",\n        \"mech\": \"SKv3_1\",\n        \"bounds\": [\n            0,\n            0.04\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gSKv3_1bar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"apical\"\n    },\n    {\n        \"param_name\": \"gImbar_Im\",\n        \"mech\": \"Im\",\n        \"bounds\": [\n            0,\n            0.001\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gImbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"apical\"\n    },\n    {\n        \"param_name\": \"gIhbar_Ih\",\n        \"mech\": \"Ih\",\n        \"dist\": \"(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}\",\n        \"dist_type\": \"exp\",\n        \"mech_param\": \"gIhbar\",\n        \"value\": 8e-05,\n        \"type\": \"range\",\n        \"sectionlist\": \"apical\"\n    },\n    {\n        \"param_name\": \"gNaTa_tbar_NaTa_t\",\n        \"mech\": \"NaTa_t\",\n        \"bounds\": [\n            0,\n            4\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gNaTa_tbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gNap_Et2bar_Nap_Et2\",\n        \"mech\": \"Nap_Et2\",\n        \"bounds\": [\n            0,\n            4\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gNap_Et2bar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gK_Pstbar_K_Pst\",\n        \"mech\": \"K_Pst\",\n        \"bounds\": [\n            0,\n            1\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gK_Pstbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gK_Tstbar_K_Tst\",\n        \"mech\": \"K_Tst\",\n        \"bounds\": [\n            0,\n            0.1\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gK_Tstbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gSK_E2bar_SK_E2\",\n        \"mech\": \"SK_E2\",\n        \"bounds\": [\n            0,\n            0.1\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gSK_E2bar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gSKv3_1bar_SKv3_1\",\n        \"mech\": \"SKv3_1\",\n        \"bounds\": [\n            0,\n            2\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gSKv3_1bar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gCa_HVAbar_Ca_HVA\",\n        \"mech\": \"Ca_HVA\",\n        \"bounds\": [\n            0,\n            0.001\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gCa_HVAbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gCa_LVAstbar_Ca_LVAst\",\n        \"mech\": \"Ca_LVAst\",\n        \"bounds\": [\n            0,\n            0.01\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gCa_LVAstbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gamma_CaDynamics_E2\",\n        \"mech\": \"CaDynamics_E2\",\n        \"bounds\": [\n            0.0005,\n            0.05\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gamma\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"decay_CaDynamics_E2\",\n        \"mech\": \"CaDynamics_E2\",\n        \"bounds\": [\n            20,\n            1000\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"decay\",\n        \"type\": \"range\",\n        \"sectionlist\": \"axonal\"\n    },\n    {\n        \"param_name\": \"gNaTs2_tbar_NaTs2_t\",\n        \"mech\": \"NaTs2_t\",\n        \"bounds\": [\n            0,\n            1\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gNaTs2_tbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    },\n    {\n        \"param_name\": \"gSKv3_1bar_SKv3_1\",\n        \"mech\": \"SKv3_1\",\n        \"bounds\": [\n            0,\n            1\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gSKv3_1bar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    },\n    {\n        \"param_name\": \"gSK_E2bar_SK_E2\",\n        \"mech\": \"SK_E2\",\n        \"bounds\": [\n            0,\n            0.1\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gSK_E2bar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    },\n    {\n        \"param_name\": \"gCa_HVAbar_Ca_HVA\",\n        \"mech\": \"Ca_HVA\",\n        \"bounds\": [\n            0,\n            0.001\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gCa_HVAbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    },\n    {\n        \"param_name\": \"gCa_LVAstbar_Ca_LVAst\",\n        \"mech\": \"Ca_LVAst\",\n        \"bounds\": [\n            0,\n            0.01\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gCa_LVAstbar\",\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    },\n    {\n        \"param_name\": \"gamma_CaDynamics_E2\",\n        \"mech\": \"CaDynamics_E2\",\n        \"bounds\": [\n            0.0005,\n            0.05\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gamma\",\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    },\n    {\n        \"param_name\": \"decay_CaDynamics_E2\",\n        \"mech\": \"CaDynamics_E2\",\n        \"bounds\": [\n            20,\n            1000\n        ],\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"decay\",\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    },\n    {\n        \"param_name\": \"gIhbar_Ih\",\n        \"mech\": \"Ih\",\n        \"dist_type\": \"uniform\",\n        \"mech_param\": \"gIhbar\",\n        \"value\": 8e-05,\n        \"type\": \"range\",\n        \"sectionlist\": \"somatic\"\n    }\n]"
  },
  {
    "path": "examples/l5pc/config/params.json",
    "content": "{\n    \"basal\": [\n        [\n            \"Ih\",\n            \"gIhbar\",\n            7.999e-05,\n            8.001e-05,\n            \"uniform\"\n        ]\n    ],\n    \"apical\": [\n        [\n            \"NaTs2_t\",\n            \"gNaTs2_tbar\",\n            0,\n            0.04,\n            \"uniform\"\n        ],\n        [\n            \"SKv3_1\",\n            \"gSKv3_1bar\",\n            0,\n            0.04,\n            \"uniform\"\n        ],\n        [\n            \"Im\",\n            \"gImbar\",\n            0,\n            0.001,\n            \"uniform\"\n        ],\n        [\n            \"Ih\",\n            \"gIhbar\",\n            7.999e-05,\n            8.001e-05,\n            \"exp\"\n        ]\n    ],\n    \"axonal\": [\n        [\n            \"NaTa_t\",\n            \"gNaTa_tbar\",\n            0,\n            4,\n            \"uniform\"\n        ],\n        [\n            \"Nap_Et2\",\n            \"gNap_Et2bar\",\n            0,\n            4,\n            \"uniform\"\n        ],\n        [\n            \"K_Pst\",\n            \"gK_Pstbar\",\n            0,\n            1,\n            \"uniform\"\n        ],\n        [\n            \"K_Tst\",\n            \"gK_Tstbar\",\n            0,\n            0.1,\n            \"uniform\"\n        ],\n        [\n            \"SK_E2\",\n            \"gSK_E2bar\",\n            0,\n            0.1,\n            \"uniform\"\n        ],\n        [\n            \"SKv3_1\",\n            \"gSKv3_1bar\",\n            0,\n            2,\n            \"uniform\"\n        ],\n        [\n            \"Ca_HVA\",\n            \"gCa_HVAbar\",\n            0,\n            0.001,\n            \"uniform\"\n        ],\n        [\n            \"Ca_LVAst\",\n            \"gCa_LVAstbar\",\n            0,\n            0.01,\n            \"uniform\"\n        ],\n        [\n            \"CaDynamics_E2\",\n            \"gamma\",\n            0.0005,\n            0.05,\n            \"uniform\"\n        ],\n        [\n            \"CaDynamics_E2\",\n            \"decay\",\n            20,\n            1000,\n            \"uniform\"\n        ]\n    ],\n    \"somatic\": [\n        [\n            \"NaTs2_t\",\n            \"gNaTs2_tbar\",\n            0,\n            1,\n            \"uniform\"\n        ],\n        [\n            \"SKv3_1\",\n            \"gSKv3_1bar\",\n            0,\n            1,\n            \"uniform\"\n        ],\n        [\n            \"SK_E2\",\n            \"gSK_E2bar\",\n            0,\n            0.1,\n            \"uniform\"\n        ],\n        [\n            \"Ca_HVA\",\n            \"gCa_HVAbar\",\n            0,\n            0.001,\n            \"uniform\"\n        ],\n        [\n            \"Ca_LVAst\",\n            \"gCa_LVAstbar\",\n            0,\n            0.01,\n            \"uniform\"\n        ],\n        [\n            \"CaDynamics_E2\",\n            \"gamma\",\n            0.0005,\n            0.05,\n            \"uniform\"\n        ],\n        [\n            \"CaDynamics_E2\",\n            \"decay\",\n            20,\n            1000,\n            \"uniform\"\n        ],\n        [\n            \"Ih\",\n            \"gIhbar\",\n            7.999e-05,\n            8.001e-05,\n            \"uniform\"\n        ]\n    ]\n}\n"
  },
  {
    "path": "examples/l5pc/config/protocols.json",
    "content": "{\n    \"bAP\": {\n        \"stimuli\": [\n            {\n                \"delay\": 295,\n                \"amp\": 1.9,\n                \"duration\": 5,\n                \"totduration\": 600\n            }\n        ],\n        \"extra_recordings\": [\n            {\n                \"var\": \"v\",\n                \"somadistance\": 660,\n                \"type\": \"somadistance\",\n                \"name\": \"dend1\",\n                \"seclist_name\": \"apical\",\n                \"arbor_branch_index\": 251,\n                \"arbor_branch_index_with_replaced_axon\": 123\n            },\n            {\n                \"var\": \"v\",\n                \"somadistance\": 800,\n                \"type\": \"somadistance\",\n                \"name\": \"dend2\",\n                \"seclist_name\": \"apical\",\n                \"arbor_branch_index\": 251,\n                \"arbor_branch_index_with_replaced_axon\": 123\n            }\n        ]\n    },\n    \"Step3\": {\n        \"stimuli\": [\n            {\n                \"delay\": 700,\n                \"amp\": 0.95,\n                \"duration\": 2000,\n                \"totduration\": 3000\n            },\n            {\n                \"delay\": 0,\n                \"amp\": -0.126,\n                \"duration\": 3000,\n                \"totduration\": 3000\n            }\n        ]\n    },\n    \"Step2\": {\n        \"stimuli\": [\n            {\n                \"delay\": 700,\n                \"amp\": 0.562,\n                \"duration\": 2000,\n                \"totduration\": 3000\n            },\n            {\n                \"delay\": 0,\n                \"amp\": -0.126,\n                \"duration\": 3000,\n                \"totduration\": 3000\n            }\n        ]\n    },\n    \"Step1\": {\n        \"stimuli\": [\n            {\n                \"delay\": 700,\n                \"amp\": 0.458,\n                \"duration\": 2000,\n                \"totduration\": 3000\n            },\n            {\n                \"delay\": 0,\n                \"amp\": -0.126,\n                \"duration\": 3000,\n                \"totduration\": 3000\n            }\n        ]\n    }\n}\n"
  },
  {
    "path": "examples/l5pc/convert_noise_exp.py",
    "content": "\"\"\"Read exp data for validation\"\"\"\n\nimport igorpy\nimport numpy\n\nvoltages = []\n\n# import matplotlib.pyplot as plt\n# fig, axes = plt.subplots(11)\n\nfor i in range(436, 447):\n    # header, voltage = igorpy.read('exp_data/X_NoiseSpiking_ch1_%d.ibw' % i)\n    header, current = igorpy.read('exp_data/X_NoiseSpiking_ch0_%d.ibw' % i)\n    # axes[i - 436].plot(voltage)\n# header, voltage = igorpy.read('exp_data/X_APThreshold_ch1_262.ibw')\n# header, current = igorpy.read('exp_data/X_APThreshold_ch0_262.ibw')\n# plt.show()\n# voltage *= 1000\n# voltage -= 14.0\ncurrent *= 1e9\n\ntime = numpy.arange(len(current)) * header.dx * 1000\n\nnumpy.savetxt('exp_data/noise_i.txt', numpy.vstack((time, current)).T)\n\n# import matplotlib.pyplot as plt\n\n# fig, ax = plt.subplots(2)\n# ax[0].plot(time, current)\n# ax[1].plot(time, voltage)\n\n# plt.show()\n"
  },
  {
    "path": "examples/l5pc/convert_params.py",
    "content": "\"\"\"Convert params.json and fixed_params.json to parameters.json format\"\"\"\n\nimport json\n\n\ndef main():\n    \"\"\"Main\"\"\"\n\n    fixed_params = json.load(open('fixed_params.json'))\n    params = json.load(open('params.json'))\n\n    parameters = []\n\n    for sectionlist in fixed_params:\n        if sectionlist == 'global':\n            for param_name, value in fixed_params[sectionlist]:\n                param = {\n                    'value': value,\n                    'param_name': param_name,\n                    'type': 'global'}\n                parameters.append(param)\n        else:\n            for param_name, value, dist_type in fixed_params[sectionlist]:\n                param = {\n                    'value': value,\n                    'param_name': param_name,\n                    'type': 'section',\n                    'dist_type': dist_type,\n                    'sectionlist': sectionlist\n                }\n                parameters.append(param)\n\n    for sectionlist in params:\n        for mech, param_name, min_bound, max_bound, dist_type in \\\n                params[sectionlist]:\n            param = {\n                'bounds': [min_bound, max_bound],\n                'mech': mech,\n                'mech_param': param_name,\n                'param_name': '%s_%s' % (param_name, mech),\n                'type': 'range',\n                'dist_type': dist_type,\n                'sectionlist': sectionlist\n            }\n\n            if mech == 'Ih':\n                del param['bounds']\n                param['value'] = 8e-5\n\n            if dist_type == 'exp':\n                param['dist'] = \\\n                    '(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}'\n\n            parameters.append(param)\n\n    json.dump(parameters, open('parameters.json', 'w'),\n              indent=4,\n              separators=(',', ': '))\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/l5pc/create_tables.py",
    "content": "\"\"\"Create tables for BluePyOpt paper\"\"\"\n\nimport json\n\n\ndef load_json(filename):\n    \"\"\"Load struct from json\"\"\"\n\n    return json.load(open(filename))\n\n\ndef create_feature_fields():\n    \"\"\"Create fields for param.json\"\"\"\n\n    features = json.load(open('features.json'))\n\n    fields_content = ''\n\n    print(features)\n\n    fields_content += \\\n        '\\t\\tStimulus & Location & eFeature & Mean & Std \\\\\\\\ \\n'\n    fields_content += '\\t\\t\\\\midrule\\n'\n    for stimulus, loc_list in sorted(features.items()):\n        stim_field = stimulus\n        for location, features in sorted(loc_list.items()):\n            loc_field = location\n            for feature_name, (mean, std) in sorted(features.items()):\n                feature_name = feature_name.replace('_', '{\\\\_}')\n                fields_content += '\\t\\t%s \\\\\\\\\\n' % ' & '.join([stim_field,\n                                                                loc_field,\n                                                                feature_name,\n                                                                str(mean),\n                                                                str(std)])\n                if loc_field != '':\n                    loc_field = ''\n                if stim_field != '':\n                    stim_field = ''\n    fields_content += '\\t\\t\\\\botrule\\n'\n\n    return fields_content, 6\n\n\ndef create_param_fields_list():\n    \"\"\"Create fields for param.json\"\"\"\n\n    import collections\n    param_configs = json.load(open('parameters.json'))\n\n    fields_list = []\n    for param_config in param_configs:\n        fields = collections.OrderedDict(\n            (('location', ''),\n             ('mech', ''),\n             ('param', ''),\n             ('dist', ''),\n             ('units', ''),\n             ('lbound', ''),\n             ('ubound', ''),\n             ('value', '')))\n\n        if 'value' in param_config:\n            fields['value'] = str(param_config['value'])\n        elif 'bounds' in param_config:\n            fields['lbound'] = str(param_config['bounds'][0])\n            fields['ubound'] = str(param_config['bounds'][1])\n\n        if param_config['type'] == 'global':\n            fields['param'] = param_config['param_name']\n            fields['location'] = 'global'\n        elif param_config['type'] in ['section', 'range']:\n            fields['dist'] = param_config['dist_type']\n            fields['location'] = param_config['sectionlist']\n            if param_config['type'] == 'range':\n                fields['mech'] = param_config['mech']\n                fields['param'] = param_config['mech_param']\n            elif param_config['type'] == 'section':\n                fields['param'] = param_config['param_name']\n\n        if 'bar' in fields['param'] or fields['param'] == 'g_pas':\n            fields['units'] = '\\\\SI{}{\\\\siemens\\\\per\\\\cm\\\\squared}'\n        elif fields['param'] == 'gamma':\n            fields['units'] = ''\n        elif fields['param'] == 'decay':\n            fields['units'] = ' \\\\SI{}{\\\\milli\\\\second}'\n        elif 'celsius' == fields['param']:\n            fields['units'] = ' \\\\SI{}{\\\\celsius}'\n        elif fields['param'] in ['v_init', 'e_pas', 'ek', 'ena']:\n            fields['units'] = ' \\\\SI{}{\\\\milli\\\\volt}'\n        elif 'Ra' == fields['param']:\n            fields['units'] = ' \\\\SI{}{\\\\ohm\\\\cm}'\n        elif 'cm' == fields['param']:\n            fields['units'] = ' \\\\SI{}{\\\\micro\\\\farad\\\\per\\\\cm\\\\squared}'\n\n        for key in fields:\n            fields[key] = fields[key].replace('_', '{\\\\_}')\n\n        fields_list.append(fields)\n\n    return fields_list\n\n\ndef create_param_fields_string():\n    \"\"\"Create parameter fields string\"\"\"\n\n    fields_list = create_param_fields_list()\n\n    fields_content = ''\n    fields_content += \\\n        '\\t\\tLocation & Mechanism & Parameter name & ' \\\n        'Distribution & Units & Lower bound & Upper bound \\\\\\\\\\n'\n    fields_content += '\\t\\t\\\\midrule\\n'\n\n    opt_fields = [field for field in fields_list if field['value'] == '']\n    fixed_fields = [field for field in fields_list if field['value'] != '']\n    global_fixed_fields = [\n        field\n        for field in fixed_fields if field['location'] == 'global']\n    nonglobal_fixed_fields = [\n        field\n        for field in\n        fixed_fields if field['location'] != 'global']\n\n    for fields in opt_fields:\n        del fields['value']\n        fields_content += '\\t\\t%s \\\\\\\\\\n' % ' & '.join(fields.values())\n    fields_content += '\\t\\t\\\\midrule\\n'\n    fields_content += \\\n        '\\t\\tLocation & Mechanism & Parameter name & ' \\\n        'Distribution & Units & Value & \\\\\\\\\\n'\n    fields_content += '\\t\\t\\\\midrule\\n'\n    for fields in global_fixed_fields + nonglobal_fixed_fields:\n        del fields['lbound']\n        del fields['ubound']\n        fields_content += '\\t\\t%s \\\\\\\\\\n' % ' & '.join(\n            fields.values() + [' '])\n\n    fields_content += '\\t\\t\\\\botrule\\n'\n\n    return fields_content, 7\n\n\ndef create_table(field_content, n_of_cols):\n    \"\"\"Surround fiels with table creation\"\"\"\n\n    table_content = '\\\\begin{tabular}{%s}\\n\\t\\\\toprule\\n' % \\\n        ('l' * n_of_cols)\n\n    table_content += field_content\n\n    table_content += '\\n\\\\end{tabular}'\n\n    return table_content\n\n\ndef main():\n    \"\"\"Main\"\"\"\n\n    param_fields, n_of_cols = create_param_fields_string()\n    param_content = create_table(param_fields, n_of_cols)\n    open('tables/params.tex', 'w').write(param_content)\n    print(param_content)\n\n    feature_fields, n_of_cols = create_feature_fields()\n    feature_content = create_table(feature_fields, n_of_cols)\n    open('tables/features.tex', 'w').write(feature_content)\n    print(feature_content)\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/l5pc/exp_data/.gitignore",
    "content": "/*.ibw\n"
  },
  {
    "path": "examples/l5pc/exp_data/noise_i.txt",
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  },
  {
    "path": "examples/l5pc/figures/.gitignore",
    "content": "/*.eps\n"
  },
  {
    "path": "examples/l5pc/generate_acc.py",
    "content": "#!/usr/bin/env python\n\n'''Example for generating a mixed JSON/ACC Arbor cable cell description (with optional axon-replacement)\n\n $ python generate_acc.py --output-dir test_acc/ --replace-axon\n\n Will save 'l5pc.json', 'l5pc_label_dict.acc' and 'l5pc_decor.acc'\n into the folder 'test_acc' that can be loaded in Arbor with:\n     'cell_json, morpho, decor, labels = \\\n        ephys.create_acc.read_acc(\"test_acc/l5pc_cell.json\")'\n An Arbor cable cell can then be created with\n     'cell = arbor.cable_cell(morphology=morpho, decor=decor, labels=labels)'\n The resulting cable cell can be output to ACC for visual inspection \n and e.g. validating/deriving custom Arbor locset/region/iexpr \n expressions in the Arbor GUI (File > Cable cell > Load) using\n     'arbor.write_component(cell, \"l5pc_cable_cell.acc\")'\n'''\nimport argparse\n\nfrom bluepyopt import ephys\n\nimport l5pc_model\nfrom generate_hoc import param_values\n\n\ndef main():\n    '''main'''\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        description=__doc__)\n    parser.add_argument('-o', '--output-dir', dest='output_dir',\n                        help='Output directory for JSON/ACC files')\n    parser.add_argument('-ra', '--replace-axon', action='store_true',\n                        help='Replace axon with Neuron-dependent policy')\n    args = parser.parse_args()\n\n    cell = l5pc_model.create(do_replace_axon=args.replace_axon)\n    if args.replace_axon:\n        nrn_sim = ephys.simulators.NrnSimulator()\n        cell.instantiate_morphology_3d(nrn_sim)\n\n    # Add modcc-compiled external mechanisms catalogues here\n    # ext_catalogues = {'cat-name': 'path/to/nmodl-dir', ...}\n\n    if args.output_dir is not None:\n        cell.write_acc(args.output_dir,\n                       param_values,\n                       # ext_catalogues=ext_catalogues,\n                       create_mod_morph=True)\n    else:\n        output = cell.create_acc(\n            param_values,\n            template='acc/*_template.jinja2',\n            # ext_catalogues=ext_catalogues,\n            create_mod_morph=True)\n        for el, val in output.items():\n            print(\"%s:\\n%s\\n\" % (el, val))\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/l5pc/generate_hoc.py",
    "content": "#!/usr/bin/env python\n\n'''Example for generating a hoc template\n\n $ python generate_hoc.py > test.hoc\n\n Will save 'test.hoc' file, which can be loaded in neuron with:\n     'load_file(\"test.hoc\")'\n Then the hoc template needs to be instantiated with a morphology\n     CCell(\"ignored\", \"path/to/morphology.asc\")\n'''\nimport sys\n\nimport l5pc_model\n\nparam_values = {\n    'gNaTs2_tbar_NaTs2_t.apical': 0.026145,\n    'gSKv3_1bar_SKv3_1.apical': 0.004226,\n    'gImbar_Im.apical': 0.000143,\n    'gNaTa_tbar_NaTa_t.axonal': 3.137968,\n    'gK_Tstbar_K_Tst.axonal': 0.089259,\n    'gamma_CaDynamics_E2.axonal': 0.002910,\n    'gNap_Et2bar_Nap_Et2.axonal': 0.006827,\n    'gSK_E2bar_SK_E2.axonal': 0.007104,\n    'gCa_HVAbar_Ca_HVA.axonal': 0.000990,\n    'gK_Pstbar_K_Pst.axonal': 0.973538,\n    'gSKv3_1bar_SKv3_1.axonal': 1.021945,\n    'decay_CaDynamics_E2.axonal': 287.198731,\n    'gCa_LVAstbar_Ca_LVAst.axonal': 0.008752,\n    'gamma_CaDynamics_E2.somatic': 0.000609,\n    'gSKv3_1bar_SKv3_1.somatic': 0.303472,\n    'gSK_E2bar_SK_E2.somatic': 0.008407,\n    'gCa_HVAbar_Ca_HVA.somatic': 0.000994,\n    'gNaTs2_tbar_NaTs2_t.somatic': 0.983955,\n    'decay_CaDynamics_E2.somatic': 210.485284,\n    'gCa_LVAstbar_Ca_LVAst.somatic': 0.000333,\n}\n\n\ndef main():\n    '''main'''\n    cell = l5pc_model.create()\n    print(cell.create_hoc(param_values))\n\n\nif __name__ == '__main__':\n    if '-h' in sys.argv or '--help' in sys.argv:\n        print(__doc__)\n    else:\n        main()\n"
  },
  {
    "path": "examples/l5pc/hocmodel.py",
    "content": "\n#from bluepyopt.ephys.models import CellModel\nimport bglibpy\nfrom bglibpy.importer import neuron\n\nimport collections\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass HocModel(object):\n\n    \"\"\"Neurodamus model class\"\"\"\n\n    def __init__(\n            self,\n            morphname=None,\n            template=None):\n        \"\"\"Constructor\"\"\"\n\n        self.name = None\n\n        # morphology\n        self.morphology = morphname\n        self.template = template\n\n        self.mechanisms = None\n        self.params = None\n\n        # Cell instantiation in simulator\n        self.icell = None\n        self.param_values = None\n\n\n    def instantiate(self):\n        \"\"\"Instantiate model in simulator\"\"\"\n\n        self.cell = bglibpy.Cell(self.template, self.morphology)\n        self.name = self.cell.cellname\n\n        #self.sim = bglibpy.Simulation()\n        #self.sim.add_cell(cell)\n\n        self.icell = self.cell.cell.getCell()\n\n\n    def run_protocol(self, protocol):\n        \"\"\"Run protocol\"\"\"\n\n        self.instantiate()\n        protocol.instantiate(self)\n\n        neuron.h.tstop = protocol.total_duration\n        neuron.h.cvode_active(1)\n        logger.debug(\n            'Running protocol %s for %.6g ms',\n            protocol.name,\n            protocol.total_duration)\n        neuron.h.run()\n        responses = protocol.responses\n\n        protocol.destroy()\n        self.destroy()\n\n        logger.debug('Protocol finished, returning responses')\n        return responses\n\n    def destroy(self):\n        \"\"\"Destroy instantiated model in simulator\"\"\"\n\n        self.icell = None\n\n\n    def run_protocols(self, protocols, param_values=None):\n        \"\"\"Run stimulus protocols\"\"\"\n\n        # TODO Put all of this in a decorator ?\n        import traceback\n        import sys\n        try:\n\n            responses = {}\n            for protocol in protocols.itervalues():\n                protocol_responses = self.run_protocol(protocol)\n                for response_name, response in protocol_responses.items():\n                    if response_name in responses:\n                        raise Exception(\n                            'CellModel: response name used twice: %s' %\n                            response.name)\n                    responses[response_name] = response\n\n        except:\n            raise Exception(\n                \"\".join(\n                    traceback.format_exception(\n                        *\n                        sys.exc_info())))\n\n        return responses\n\n    def __str__(self):\n        \"\"\"Return string representation\"\"\"\n\n        content = '%s:\\n' % self.name\n\n        content += '  morphology:\\n'\n        content += '    %s\\n' % self.morphology\n\n        content += '  template:\\n'\n        content += '    %s\\n' % self.template\n\n        return content\n"
  },
  {
    "path": "examples/l5pc/l5pc_analysis.py",
    "content": "\"\"\"Run simple cell optimisation\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\n# pylint: disable=R0914, W0633\n\nimport os\nimport pickle\nimport numpy as np\nimport bluepyopt.ephys as ephys\n\n# Parameters in release circuit model\nrelease_params = {\n    'gNaTs2_tbar_NaTs2_t.apical': 0.026145,\n    'gSKv3_1bar_SKv3_1.apical': 0.004226,\n    'gImbar_Im.apical': 0.000143,\n    'gNaTa_tbar_NaTa_t.axonal': 3.137968,\n    'gK_Tstbar_K_Tst.axonal': 0.089259,\n    'gamma_CaDynamics_E2.axonal': 0.002910,\n    'gNap_Et2bar_Nap_Et2.axonal': 0.006827,\n    'gSK_E2bar_SK_E2.axonal': 0.007104,\n    'gCa_HVAbar_Ca_HVA.axonal': 0.000990,\n    'gK_Pstbar_K_Pst.axonal': 0.973538,\n    'gSKv3_1bar_SKv3_1.axonal': 1.021945,\n    'decay_CaDynamics_E2.axonal': 287.198731,\n    'gCa_LVAstbar_Ca_LVAst.axonal': 0.008752,\n    'gamma_CaDynamics_E2.somatic': 0.000609,\n    'gSKv3_1bar_SKv3_1.somatic': 0.303472,\n    'gSK_E2bar_SK_E2.somatic': 0.008407,\n    'gCa_HVAbar_Ca_HVA.somatic': 0.000994,\n    'gNaTs2_tbar_NaTs2_t.somatic': 0.983955,\n    'decay_CaDynamics_E2.somatic': 210.485284,\n    'gCa_LVAstbar_Ca_LVAst.somatic': 0.000333\n}\n\n\ndef set_rcoptions(func):\n    '''decorator to apply custom matplotlib rc params to function,undo after'''\n    import matplotlib\n\n    def wrap(*args, **kwargs):\n        \"\"\"Wrap\"\"\"\n        options = {'axes.linewidth': 2, }\n        with matplotlib.rc_context(rc=options):\n            func(*args, **kwargs)\n    return wrap\n\n\ndef get_responses(cell_evaluator, individuals, filename):\n\n    responses = []\n    if filename and os.path.exists(filename):\n        with open(filename, 'rb') as fd:\n            return pickle.load(fd)\n\n    for individual in individuals:\n        individual_dict = cell_evaluator.param_dict(individual)\n        responses.append(\n            cell_evaluator.run_protocols(\n                cell_evaluator.fitness_protocols.values(),\n                param_values=individual_dict))\n\n    if filename:\n        with open(filename, 'wb') as fd:\n            pickle.dump(responses, fd)\n\n    return responses\n\n\n@set_rcoptions\ndef analyse_cp(opt, cp_filename, responses_filename, figs, sim='nrn'):\n    \"\"\"Analyse optimisation results\"\"\"\n    (model_fig, model_box), (objectives_fig, objectives_box), (\n        evol_fig, evol_box) = figs\n\n    cp = pickle.load(open(cp_filename, \"rb\"))\n    hof = cp['halloffame']\n\n    responses = get_responses(opt.evaluator, hof, responses_filename)\n    plot_multiple_responses(responses, fig=model_fig)\n\n    # objectives\n    parameter_values = opt.evaluator.param_dict(hof[0])\n    fitness_protocols = opt.evaluator.fitness_protocols\n    responses = {}\n\n    if sim == 'nrn':\n        simulator = ephys.simulators.NrnSimulator()\n    elif sim == 'arb':\n        simulator = ephys.simulators.ArbSimulator()\n    else:\n        raise ValueError('sim must be either \\'nrn\\' or \\'arb\\'.')\n\n    for protocol in fitness_protocols.values():\n        response = protocol.run(\n            cell_model=opt.evaluator.cell_model,\n            param_values=parameter_values,\n            sim=simulator)\n        responses.update(response)\n\n    objectives = opt.evaluator.fitness_calculator.calculate_scores(responses)\n    plot_objectives(objectives, fig=objectives_fig, box=objectives_box)\n    # objectives\n\n    plot_log(cp['logbook'], fig=evol_fig, box=evol_box)\n\n\ndef plot_log(log, fig=None, box=None):\n    \"\"\"Plot logbook\"\"\"\n\n    gen_numbers = log.select('gen')\n    mean = np.array(log.select('avg'))\n    std = np.array(log.select('std'))\n    minimum = np.array(log.select('min'))\n\n    left_margin = box['width'] * 0.1\n    right_margin = box['width'] * 0.05\n    top_margin = box['height'] * 0.05\n    bottom_margin = box['height'] * 0.1\n\n    axes = fig.add_axes(\n        (box['left'] + left_margin,\n         box['bottom'] + bottom_margin,\n         box['width'] - left_margin - right_margin,\n         box['height'] - bottom_margin - top_margin))\n\n    stdminus = mean - std\n    stdplus = mean + std\n    axes.plot(\n        gen_numbers,\n        mean,\n        color='black',\n        linewidth=2,\n        label='population average')\n\n    axes.fill_between(\n        gen_numbers,\n        stdminus,\n        stdplus,\n        color='lightgray',\n        linewidth=2,\n        label=r'population standard deviation')\n\n    axes.plot(\n        gen_numbers,\n        minimum,\n        color='red',\n        linewidth=2,\n        label='population minimum')\n\n    axes.set_xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)\n    axes.set_xlabel('Generation #')\n    axes.set_ylabel('Sum of objectives')\n    axes.set_ylim([0, max(stdplus)])\n    axes.legend()\n\n\ndef plot_history(history):\n    \"\"\"Plot the history of the individuals\"\"\"\n\n    import networkx\n\n    import matplotlib.pyplot as plt\n    plt.figure()\n\n    graph = networkx.DiGraph(history.genealogy_tree)\n    graph = graph.reverse()     # Make the grah top-down\n    # colors = [\\\n    #        toolbox.evaluate(history.genealogy_history[i])[0] for i in graph]\n    positions = networkx.graphviz_layout(graph, prog=\"dot\")\n    networkx.draw(graph, positions)\n\n\ndef plot_objectives(objectives, fig=None, box=None):\n    \"\"\"Plot objectives of the cell model\"\"\"\n\n    import collections\n    objectives = collections.OrderedDict(sorted(objectives.items()))\n    left_margin = box['width'] * 0.4\n    right_margin = box['width'] * 0.05\n    top_margin = box['height'] * 0.05\n    bottom_margin = box['height'] * 0.1\n\n    axes = fig.add_axes(\n        (box['left'] + left_margin,\n         box['bottom'] + bottom_margin,\n         box['width'] - left_margin - right_margin,\n         box['height'] - bottom_margin - top_margin))\n\n    ytick_pos = [x + 0.5 for x in range(len(objectives.keys()))]\n\n    axes.barh(ytick_pos,\n              objectives.values(),\n              height=0.5,\n              align='center',\n              color='#779ECB')\n    axes.set_yticks(ytick_pos)\n    axes.set_yticklabels(objectives.keys(), size='x-small')\n    axes.set_ylim(-0.5, len(objectives.values()) + 0.5)\n    axes.set_xlabel('Objective value (# std)')\n    axes.set_ylabel('Objectives')\n\n\ndef plot_responses(responses, fig=None, box=None):\n    \"\"\"Plot responses of the cell model\"\"\"\n    rec_rect = {}\n    rec_rect['left'] = box['left']\n    rec_rect['width'] = box['width']\n    rec_rect['height'] = float(box['height']) / len(responses)\n    rec_rect['bottom'] = box['bottom'] + \\\n        box['height'] - rec_rect['height']\n    last = len(responses) - 1\n    for i, (_, recording) in enumerate(sorted(responses.items())):\n        plot_recording(recording, fig=fig, box=rec_rect, xlabel=(last == i))\n        rec_rect['bottom'] -= rec_rect['height']\n\n\ndef get_slice(start, end, data):\n    return slice(np.searchsorted(data, start),\n                 np.searchsorted(data, end))\n\n\ndef plot_multiple_responses(responses, fig):\n    '''creates 6 subplots for step{1,2,3} / dAP traces, plots the responses'''\n    traces = ('Step1.soma.v', 'Step2.soma.v', 'Step3.soma.v',\n              'bAP.dend1.v', 'bAP.dend2.v', 'bAP.soma.v', )\n    plot_count = len(traces)\n    ax = [fig.add_subplot(plot_count, 1, i + 1) for i in range(plot_count)]\n\n    overlay_count = len(responses)\n    for i, response in enumerate(reversed(responses[:overlay_count])):\n        color = 'lightblue'\n        if i == overlay_count - 1:\n            color = 'blue'\n\n        for i, name in enumerate(traces):\n            sl = get_slice(0, 3000, response[name]['time'])\n            ax[i].plot(\n                response[name]['time'][sl],\n                response[name]['voltage'][sl],\n                color=color,\n                linewidth=1)\n            ax[i].set_ylabel(name + '\\nVoltage (mV)')\n            ax[i].set_autoscaley_on(True)\n            ax[i].set_autoscalex_on(True)\n            ax[i].set_ylim((-85, 50))\n\n        ax[-1].set_xlabel('Time (ms)')\n\n\ndef plot_recording(recording, fig=None, box=None, xlabel=False):\n    \"\"\"Plot responses of the cell model\"\"\"\n\n    import matplotlib.pyplot as plt\n\n    left_margin = box['width'] * 0.25\n    right_margin = box['width'] * 0.05\n    top_margin = box['height'] * 0.1\n    bottom_margin = box['height'] * 0.25\n\n    axes = fig.add_axes(\n        (box['left'] + left_margin,\n         box['bottom'] + bottom_margin,\n         box['width'] - left_margin - right_margin,\n         box['height'] - bottom_margin - top_margin))\n\n    recording.plot(axes)\n    axes.set_ylim(-100, 40)\n    axes.spines['top'].set_visible(False)\n    axes.spines['right'].set_visible(False)\n    axes.tick_params(\n        axis='both',\n        bottom='on',\n        top='off',\n        left='on',\n        right='off')\n\n    name = recording.name\n    if name.endswith('.v'):\n        name = name[:-2]\n\n    axes.set_ylabel(name + '\\n(mV)', labelpad=25)\n    yloc = plt.MaxNLocator(2)\n    axes.yaxis.set_major_locator(yloc)\n\n    if xlabel:\n        axes.set_xlabel('Time (ms)')\n\n\ndef plot_validation(opt, parameters):\n    \"\"\"Plot validation\"\"\"\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=0.5)\n\n    validation_recording = ephys.recordings.CompRecording(\n        name='validation.soma.v',\n        location=soma_loc,\n        variable='v')\n\n    validation_i_data = np.loadtxt('exp_data/noise_i.txt')\n    # validation_v_data = np.loadtxt('exp_data/noise_v.txt')\n    validation_time = validation_i_data[:, 0] + 200.0\n    validation_current = validation_i_data[:, 1]\n    # validation_voltage = validation_v_data[:, 1]\n    validation_stimulus = ephys.stimuli.NrnCurrentPlayStimulus(\n        current_points=validation_current,\n        time_points=validation_time,\n        location=soma_loc)\n    hypamp_stimulus = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=-0.126,\n        step_delay=0,\n        step_duration=max(validation_time),\n        location=soma_loc,\n        total_duration=max(validation_time))\n\n    validation_protocol = ephys.protocols.SweepProtocol(\n        'validation',\n        [validation_stimulus, hypamp_stimulus],\n        [validation_recording])\n\n    validation_responses = {}\n    write_pickle = False\n\n    paramsets = {}\n    paramsets['release'] = release_params\n    for index, param_values in enumerate(parameters):\n        paramsets['model%d' % index] = param_values\n\n    if write_pickle:\n        for paramset_name, paramset in paramsets.items():\n            validation_responses[paramset_name] = opt.evaluator.run_protocols(\n                [validation_protocol],\n                param_values=paramset)\n\n        pickle.dump(validation_responses, open('validation_response.pkl', 'wb'))\n    else:\n        validation_responses = pickle.load(open('validation_response.pkl', 'rb'))\n    # print validation_responses['validation.soma.v']['time']\n\n    peaktimes = {}\n    import efel\n    for index, model_name in enumerate(validation_responses.keys()):\n        trace = {}\n        trace['T'] = validation_responses[\n            model_name]['validation.soma.v']['time']\n        trace['V'] = validation_responses[model_name][\n            'validation.soma.v']['voltage']\n        trace['stim_start'] = [500]\n        trace['stim_end'] = [max(validation_time)]\n        peaktimes[model_name] = efel.getFeatureValues(\n            [trace],\n            ['peak_time'])[0]['peak_time']\n\n    import matplotlib.pyplot as plt\n    fig, ax = plt.subplots(3, figsize=(10, 7), facecolor='white', sharex=True)\n\n    ax[0].plot(validation_time, validation_current, 'k')\n    ax[0].spines['top'].set_visible(False)\n    ax[0].spines['right'].set_visible(False)\n    ax[0].spines['bottom'].set_visible(False)\n    ax[0].tick_params(\n        axis='both',\n        bottom='off',\n        top='off',\n        left='on',\n        right='off')\n    ax[0].set_ylabel('Current\\n (nA)', rotation=0, labelpad=25)\n\n    for index, (model_name, peak_time) in enumerate(sorted(peaktimes.items())):\n        print(model_name)\n        if model_name == 'release':\n            color = 'red'\n            print(color, peak_time)\n        elif model_name == 'model0':\n            color = 'darkblue'\n        else:\n            color = 'lightblue'\n        ax[1].scatter(\n            peak_time,\n            np.array(\n                [100] *\n                len(peak_time)) +\n            10 *\n            index,\n            color=color,\n            s=10)\n    ax[1].spines['top'].set_visible(False)\n    ax[1].spines['right'].set_visible(False)\n    ax[1].spines['bottom'].set_visible(False)\n    ax[1].spines['left'].set_visible(False)\n    ax[1].tick_params(\n        bottom='off',\n        top='off',\n        left='off',\n        right='off')\n    ax[1].set_yticks([])\n\n    ax[2].plot(\n        validation_responses['release']['validation.soma.v']['time'],\n        validation_responses['release']['validation.soma.v']['voltage'], 'r',\n        linewidth=1)\n\n    ax[2].plot(\n        validation_responses[\n            'model0']['validation.soma.v']['time'],\n        validation_responses[\n            'model0']['validation.soma.v']['voltage'],\n        color='darkblue',\n        linewidth=1)\n\n    ax[2].spines['top'].set_visible(False)\n    ax[2].spines['right'].set_visible(False)\n    ax[2].tick_params(\n        axis='both',\n        bottom='on',\n        top='off',\n        left='on',\n        right='off')\n\n    ax[2].set_yticks([-100, 0.0])\n    ax[2].set_ylabel('Voltage\\n (mV)', rotation=0, labelpad=25)\n    ax[2].set_xlabel('Time (ms)')\n    ax[2].set_xlim(min(validation_time), max(validation_time))\n\n    fig.tight_layout()\n\n    fig.savefig('figures/l5pc_valid.eps')\n\n\n@set_rcoptions\ndef analyse_releasecircuit_model(opt, figs, box=None, sim='nrn'):\n    \"\"\"Analyse L5PC model from release circuit\"\"\"\n    (release_responses_fig, response_box), (\n        release_objectives_fig, objectives_box) = figs\n\n    fitness_protocols = opt.evaluator.fitness_protocols\n\n    if sim == 'nrn':\n        simulator = ephys.simulators.NrnSimulator()\n    elif sim == 'arb':\n        simulator = ephys.simulators.ArbSimulator()\n    else:\n        raise ValueError('sim must be either \\'nrn\\' or \\'arb\\'.')\n\n    responses = {}\n    for protocol in fitness_protocols.values():\n        response = protocol.run(\n            cell_model=opt.evaluator.cell_model,\n            param_values=release_params,\n            sim=simulator)\n        responses.update(response)\n\n    plot_multiple_responses([responses], fig=release_responses_fig)\n\n    objectives = opt.evaluator.fitness_calculator.calculate_scores(responses)\n    plot_objectives(objectives, fig=release_objectives_fig, box=objectives_box)\n\n\ndef analyse_releasecircuit_hocmodel(opt, fig=None, box=None):\n    \"\"\"Analyse L5PC model from release circuit from .hoc template\"\"\"\n\n    fitness_protocols = opt.evaluator.fitness_protocols\n\n    from hocmodel import HocModel  # NOQA\n\n    template_model = HocModel(morphname=\"./morphology/C060114A7.asc\",\n                              template=\"./cADpyr_76.hoc\")\n\n    responses = template_model.run_protocols(\n        fitness_protocols)\n\n    objectives = opt.evaluator.fitness_calculator.calculate_scores(\n        responses)\n\n    # template_model.instantiate()\n    # for section in template_model.icell.axonal:\n    #    print section.L, section.diam, section.nseg\n\n    plot_responses(responses, fig=fig,\n                   box={\n                       'left': box['left'],\n                       'bottom': box['bottom'] + float(box['height']) / 2.0,\n                       'width': box['width'],\n                       'height': float(box['height']) / 2.0})\n\n    plot_objectives(objectives, fig=fig,\n                    box={\n                        'left': box['left'],\n                        'bottom': box['bottom'],\n                        'width': box['width'],\n                        'height': float(box['height']) / 2.0})\n\n\nFITNESS_CUT_OFF = 5\n\n\ndef plot_individual_params(\n        opt,\n        ax,\n        params,\n        marker,\n        color,\n        markersize=40,\n        plot_bounds=False,\n        fitness_cut_off=FITNESS_CUT_OFF):\n    '''plot the individual parameter values'''\n    observations_count = len(params)\n    param_count = len(params[0])\n\n    results = np.zeros((observations_count, param_count))\n    good_fitness = 0\n    for i, param in enumerate(params):\n        if fitness_cut_off < max(param.fitness.values):\n            continue\n        results[good_fitness] = param\n        good_fitness += 1\n\n    results = results\n\n    for c in range(good_fitness):\n        x = np.arange(param_count)\n        y = results[c, :]\n        ax.scatter(x=x, y=y, s=float(markersize), marker=marker, color=color)\n\n    if plot_bounds:\n        def plot_tick(column, y):\n            col_width = 0.25\n            x = [column - col_width,\n                 column + col_width]\n            y = [y, y]\n            ax.plot(x, y, color='black')\n\n        # plot min and max\n        for i, parameter in enumerate(opt.evaluator.params):\n            min_value = parameter.lower_bound\n            max_value = parameter.upper_bound\n            plot_tick(i, min_value)\n            plot_tick(i, max_value)\n\n\ndef plot_diversity(opt, checkpoint_file, fig, param_names):\n    '''plot the whole history, the hall of fame, and the best individual\n    from a unpickled checkpoint\n    '''\n    import matplotlib.pyplot as plt\n    checkpoint = pickle.load(open(checkpoint_file, \"rb\"))\n\n    ax = fig.add_subplot(1, 1, 1)\n\n    import copy\n    release_individual = copy.deepcopy(checkpoint['halloffame'][0])\n    for index, param_name in enumerate(opt.evaluator.param_names):\n        release_individual[index] = release_params[param_name]\n    plot_individual_params(\n        opt,\n        ax,\n        checkpoint['history'].genealogy_history.values(),\n        marker='.',\n        color='grey',\n        plot_bounds=True)\n    plot_individual_params(opt, ax, checkpoint['halloffame'],\n                           marker='o', color='black')\n    plot_individual_params(opt,\n                           ax,\n                           [checkpoint['halloffame'][0]],\n                           markersize=150,\n                           marker='x',\n                           color='blue')\n    plot_individual_params(opt, ax, [release_individual], markersize=150,\n                           marker='x', color='red')\n\n    labels = [name.replace('.', '\\n') for name in param_names]\n\n    param_count = len(checkpoint['halloffame'][0])\n    x = range(param_count)\n    for xline in x:\n        ax.axvline(xline, linewidth=1, color='grey', linestyle=':')\n\n    plt.xticks(x, labels, rotation=80, ha='center', size='small')\n    ax.set_xlabel('Parameter names')\n    ax.set_ylabel('Parameter values')\n    ax.set_yscale('log')\n    ax.set_ylim(bottom=1e-7)\n\n    plt.tight_layout()\n    plt.plot()\n    ax.set_autoscalex_on(True)\n"
  },
  {
    "path": "examples/l5pc/l5pc_evaluator.py",
    "content": "\"\"\"Run simple cell optimisation\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n# pylint: disable=R0914\n\nimport os\nimport json\n\nimport l5pc_model  # NOQA\n\nimport bluepyopt.ephys as ephys\n\nscript_dir = os.path.dirname(__file__)\nconfig_dir = os.path.join(script_dir, 'config')\n\n# TODO store definition dicts in json\n# TODO rename 'score' into 'objective'\n# TODO add functionality to read settings of every object from config format\n\n\ndef define_protocols(do_replace_axon=True, sim='nrn'):\n    \"\"\"Define protocols\"\"\"\n    protocol_definitions = load_protocols()\n    return create_protocols(protocol_definitions, do_replace_axon, sim=sim)\n\n\ndef load_protocols():\n\n    return json.load(\n        open(\n            os.path.join(\n                config_dir,\n                'protocols.json')))\n\n\ndef create_protocols(protocol_definitions, do_replace_axon=None, sim='nrn'):\n\n    protocols = {}\n\n    if sim == 'nrn':\n        soma_loc = ephys.locations.NrnSeclistCompLocation(\n            name='soma',\n            seclist_name='somatic',\n            sec_index=0,\n            comp_x=0.5)\n    elif sim == 'arb':\n        soma_loc = ephys.locations.ArbBranchRelLocation(\n            name='soma',\n            branch=0,\n            pos=0.5)\n    else:\n        raise ValueError('Simulator must be either nrn or arb, not %s' % sim)\n\n    for protocol_name, protocol_definition in protocol_definitions.items():\n        # By default include somatic recording\n        somav_recording = ephys.recordings.CompRecording(\n            name='%s.soma.v' %\n            protocol_name,\n            location=soma_loc,\n            variable='v')\n\n        recordings = [somav_recording]\n\n        if 'extra_recordings' in protocol_definition:\n            for recording_definition in protocol_definition['extra_recordings']:\n                if recording_definition['type'] == 'somadistance':\n                    if sim == 'nrn':\n                        location = ephys.locations.NrnSomaDistanceCompLocation(\n                            name=recording_definition['name'],\n                            soma_distance=recording_definition['somadistance'],\n                            seclist_name=recording_definition['seclist_name'])\n                    else:\n                        # L5PC has disconnected topology\n                        location = ephys.locations.ArbLocsetLocation(\n                            name=recording_definition['name'],\n                            locset='(restrict (distal-translate (proximal %s) %s) (proximal-interval (distal (branch %s))))' %\n                            (ephys.morphologies.ArbFileMorphology.region_labels[recording_definition['seclist_name']].ref,\n                             recording_definition['somadistance'],\n                             recording_definition['arbor_branch_index_with_replaced_axon'] if do_replace_axon else\n                             recording_definition['arbor_branch_index']))\n                    var = recording_definition['var']\n                    recording = ephys.recordings.CompRecording(\n                        name='%s.%s.%s' % (protocol_name, location.name, var),\n                        location=location,\n                        variable=recording_definition['var'])\n\n                    recordings.append(recording)\n                else:\n                    raise Exception(\n                        'Recording type %s not supported' %\n                        recording_definition['type'])\n\n        stimuli = []\n        for stimulus_definition in protocol_definition['stimuli']:\n            stimuli.append(ephys.stimuli.NrnSquarePulse(\n                step_amplitude=stimulus_definition['amp'],\n                step_delay=stimulus_definition['delay'],\n                step_duration=stimulus_definition['duration'],\n                location=soma_loc,\n                total_duration=stimulus_definition['totduration']))\n\n        if sim == 'nrn':\n            protocols[protocol_name] = ephys.protocols.SweepProtocol(\n                protocol_name,\n                stimuli,\n                recordings)\n        else:\n            protocols[protocol_name] = ephys.protocols.ArbSweepProtocol(\n                protocol_name,\n                stimuli,\n                recordings)\n\n    return protocols\n\n\ndef define_fitness_calculator(protocols):\n    \"\"\"Define fitness calculator\"\"\"\n\n    feature_definitions = json.load(\n        open(\n            os.path.join(\n                config_dir,\n                'features.json')))\n\n    # TODO: add bAP stimulus\n    objectives = []\n\n    for protocol_name, locations in feature_definitions.items():\n        for location, features in locations.items():\n            for efel_feature_name, meanstd in features.items():\n                feature_name = '%s.%s.%s' % (\n                    protocol_name, location, efel_feature_name)\n                recording_names = {'': '%s.%s.v' % (protocol_name, location)}\n                stimulus = protocols[protocol_name].stimuli[0]\n\n                stim_start = stimulus.step_delay\n\n                if location == 'soma':\n                    threshold = -20\n                elif 'dend' in location:\n                    threshold = -55\n\n                if protocol_name == 'bAP':\n                    stim_end = stimulus.total_duration\n                else:\n                    stim_end = stimulus.step_delay + stimulus.step_duration\n\n                feature = ephys.efeatures.eFELFeature(\n                    feature_name,\n                    efel_feature_name=efel_feature_name,\n                    recording_names=recording_names,\n                    stim_start=stim_start,\n                    stim_end=stim_end,\n                    exp_mean=meanstd[0],\n                    exp_std=meanstd[1],\n                    threshold=threshold)\n                objective = ephys.objectives.SingletonObjective(\n                    feature_name,\n                    feature)\n                objectives.append(objective)\n\n    fitcalc = ephys.objectivescalculators.ObjectivesCalculator(objectives)\n\n    return fitcalc\n\n\ndef create(do_replace_axon=True, sim='nrn'):\n    \"\"\"Setup\"\"\"\n\n    l5pc_cell = l5pc_model.create(do_replace_axon=do_replace_axon)\n\n    fitness_protocols = define_protocols(\n        do_replace_axon=do_replace_axon, sim=sim)\n    fitness_calculator = define_fitness_calculator(fitness_protocols)\n\n    param_names = [param.name\n                   for param in l5pc_cell.params.values()\n                   if not param.frozen]\n\n    if sim == 'nrn':\n        simulator = ephys.simulators.NrnSimulator()\n    elif sim == 'arb':\n        simulator = ephys.simulators.ArbSimulator()\n        if do_replace_axon:\n            nrn_sim = ephys.simulators.NrnSimulator()\n            l5pc_cell.instantiate_morphology_3d(nrn_sim)\n    else:\n        raise ValueError('Simulator must be either \\'nrn\\' or \\'arb\\'.')\n\n    return ephys.evaluators.CellEvaluator(\n        cell_model=l5pc_cell,\n        param_names=param_names,\n        fitness_protocols=fitness_protocols,\n        fitness_calculator=fitness_calculator,\n        sim=simulator)\n"
  },
  {
    "path": "examples/l5pc/l5pc_model.py",
    "content": "\"\"\"Run simple cell optimisation\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n# pylint: disable=R0914\n\nimport os\nimport json\n\nimport bluepyopt.ephys as ephys\n\nscript_dir = os.path.dirname(__file__)\nconfig_dir = os.path.join(script_dir, 'config')\n\n# TODO store definition dicts in json\n# TODO rename 'score' into 'objective'\n# TODO add functionality to read settings of every object from config format\n\n\ndef define_mechanisms():\n    \"\"\"Define mechanisms\"\"\"\n\n    mech_definitions = load_mechanisms()\n    return create_mechanisms(mech_definitions)\n\n\ndef load_mechanisms():\n\n    return json.load(\n        open(\n            os.path.join(\n                config_dir,\n                'mechanisms.json')))\n\n\ndef create_mechanisms(mech_definitions):\n\n    mechanisms = []\n    for sectionlist, channels in mech_definitions.items():\n        seclist_loc = ephys.locations.NrnSeclistLocation(\n            sectionlist,\n            seclist_name=sectionlist)\n        for channel in channels:\n            mechanisms.append(ephys.mechanisms.NrnMODMechanism(\n                name='%s.%s' % (channel, sectionlist),\n                mod_path=None,\n                suffix=channel,\n                locations=[seclist_loc],\n                preloaded=True))\n\n    return mechanisms\n\n\ndef define_parameters():\n    \"\"\"Define parameters\"\"\"\n\n    param_configs = load_parameters()\n    return create_parameters(param_configs)\n\n\ndef load_parameters():\n    return json.load(open(os.path.join(config_dir, 'parameters.json')))\n\n\ndef create_parameters(param_configs):\n    parameters = []\n\n    for param_config in param_configs:\n        if 'value' in param_config:\n            frozen = True\n            value = param_config['value']\n            bounds = None\n        elif 'bounds' in param_config:\n            frozen = False\n            bounds = param_config['bounds']\n            value = None\n        else:\n            raise Exception(\n                'Parameter config has to have bounds or value: %s'\n                % param_config)\n\n        if param_config['type'] == 'global':\n            parameters.append(\n                ephys.parameters.NrnGlobalParameter(\n                    name=param_config['param_name'],\n                    param_name=param_config['param_name'],\n                    frozen=frozen,\n                    bounds=bounds,\n                    value=value))\n        elif param_config['type'] in ['section', 'range']:\n            if param_config['dist_type'] == 'uniform':\n                scaler = ephys.parameterscalers.NrnSegmentLinearScaler()\n            elif param_config['dist_type'] == 'exp':\n                scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n                    distribution=param_config['dist'])\n            seclist_loc = ephys.locations.NrnSeclistLocation(\n                param_config['sectionlist'],\n                seclist_name=param_config['sectionlist'])\n\n            name = '%s.%s' % (param_config['param_name'],\n                              param_config['sectionlist'])\n\n            if param_config['type'] == 'section':\n                parameters.append(\n                    ephys.parameters.NrnSectionParameter(\n                        name=name,\n                        param_name=param_config['param_name'],\n                        value_scaler=scaler,\n                        value=value,\n                        frozen=frozen,\n                        bounds=bounds,\n                        locations=[seclist_loc]))\n            elif param_config['type'] == 'range':\n                parameters.append(\n                    ephys.parameters.NrnRangeParameter(\n                        name=name,\n                        param_name=param_config['param_name'],\n                        value_scaler=scaler,\n                        value=value,\n                        frozen=frozen,\n                        bounds=bounds,\n                        locations=[seclist_loc]))\n        else:\n            raise Exception(\n                'Param config type has to be global, section or range: %s' %\n                param_config)\n\n    return parameters\n\n\ndef define_morphology(do_replace_axon):\n    \"\"\"Define morphology\"\"\"\n\n    return ephys.morphologies.NrnFileMorphology(\n        os.path.join(\n            script_dir,\n            'morphology/C060114A7.asc'),\n        do_replace_axon=do_replace_axon)\n\n\ndef create(do_replace_axon=True):\n    \"\"\"Create cell model\"\"\"\n\n    cell = ephys.models.CellModel(\n        'l5pc',\n        morph=define_morphology(do_replace_axon),\n        mechs=define_mechanisms(),\n        params=define_parameters())\n\n    return cell\n"
  },
  {
    "path": "examples/l5pc/l5pc_validate_neuron_arbor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Simulating optimized cell models in Arbor and cross-validation with Neuron\\n\",\n    \"\\n\",\n    \"This notebook demonstrates how to run a simulation of a simple single compartmental cell with fixed/optimized parameters in Arbor. We follow the standard BluePyOpt flow of setting up an electrophysiological experiment and export the cell model to a mixed JSON/ACC-format. We then cross-validate voltage traces obtained with Arbor with those from a Neuron simulation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"tags\": [\n     \"parameters\"\n    ]\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Choose subset of L5PC mechanisms to run (all regional mechs get re-mapped to soma)\\n\",\n    \"mechanism_defs = dict(\\n\",\n    \"    all=['pas'],\\n\",\n    \"    somatic=['hh'])\\n\",\n    \"\\n\",\n    \"extra_params = dict(\\n\",\n    \"    v_init='global',\\n\",\n    \"    # celsius='global',\\n\",\n    \"    cm=['all'],\\n\",\n    \"    Ra=['all'],\\n\",\n    \"    g_pas=['all'],  # add 'pas' to mechs on all above\\n\",\n    \"    e_pas=['all'],  # add 'pas' to mechs on all above\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"param_values_json = None\\n\",\n    \"\\n\",\n    \"default_dt = 0.025\\n\",\n    \"\\n\",\n    \"fine_dt = 0.001\\n\",\n    \"\\n\",\n    \"voltage_residual_rel_l1_tolerance = 5e-2\\n\",\n    \"\\n\",\n    \"run_spike_time_analysis = True\\n\",\n    \"\\n\",\n    \"run_fine_dt = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%load_ext autoreload\\n\",\n    \"%autoreload\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"First we need to compile the L5PC mechanisms and import the module that contains all the functionality to create electrical cell models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    },\n    \"tags\": []\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/lukasd/src/arbor/dev/BluePyOpt/examples/l5pc\\n\",\n      \"Mod files: \\\"mechanisms/CaDynamics_E2.mod\\\" \\\"mechanisms/Ca_HVA.mod\\\" \\\"mechanisms/Ca_LVAst.mod\\\" \\\"mechanisms/Ih.mod\\\" \\\"mechanisms/Im.mod\\\" \\\"mechanisms/K_Pst.mod\\\" \\\"mechanisms/K_Tst.mod\\\" \\\"mechanisms/Nap_Et2.mod\\\" \\\"mechanisms/NaTa_t.mod\\\" \\\"mechanisms/NaTs2_t.mod\\\" \\\"mechanisms/SK_E2.mod\\\" \\\"mechanisms/SKv3_1.mod\\\"\\n\",\n      \"\\n\",\n      \"COBJS=''\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m mod_func.c\\n\",\n      \"x86_64-linux-gnu-gcc -O2   -I.   -I/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include  -I/nrnwheel/openmpi/include -fPIC -c mod_func.c -o mod_func.o\\n\",\n      \" => \\u001b[32mLINKING\\u001b[0m shared library ./libnrnmech.so\\n\",\n      \"x86_64-linux-gnu-g++ -O2 -DVERSION_INFO='8.0.2' -std=c++11 -shared -fPIC  -I /home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/include -o ./libnrnmech.so -Wl,-soname,libnrnmech.so \\\\\\n\",\n      \"  ./mod_func.o ./CaDynamics_E2.o ./Ca_HVA.o ./Ca_LVAst.o ./Ih.o ./Im.o ./K_Pst.o ./K_Tst.o ./Nap_Et2.o ./NaTa_t.o ./NaTs2_t.o ./SK_E2.o ./SKv3_1.o  -L/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/lib -lnrniv -Wl,-rpath,/home/lukasd/src/arbor/dev/venv/lib/python3.8/site-packages/neuron/.data/lib   \\n\",\n      \"rm -f ./.libs/libnrnmech.so ; mkdir -p ./.libs ; cp ./libnrnmech.so ./.libs/libnrnmech.so\\n\",\n      \"Successfully created x86_64/special\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!nrnivmodl mechanisms\\n\",\n    \"import bluepyopt as bpop\\n\",\n    \"import bluepyopt.ephys as ephys\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import tempfile\\n\",\n    \"from dataclasses import dataclass\\n\",\n    \"import typing\\n\",\n    \"import warnings\\n\",\n    \"import multiprocessing\\n\",\n    \"\\n\",\n    \"import numpy\\n\",\n    \"import pandas\\n\",\n    \"import scipy.integrate\\n\",\n    \"import scipy.interpolate\\n\",\n    \"\\n\",\n    \"import arbor\\n\",\n    \"\\n\",\n    \"from IPython.display import display\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"If you want to see a lot of information about the internals, \\n\",\n    \"the verbose level can be set to 'debug' by commenting out\\n\",\n    \"the following lines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    },\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# import logging\\n\",\n    \"# logger = logging.getLogger()\\n\",\n    \"# logger.setLevel(logging.DEBUG)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Setting up the cell model\\n\",\n    \"\\n\",\n    \"We use a single-compartimental cell model with the same morphology as in the `simplecell` example, but mechanisms from the `l5pc` model. They are instantiated with different options for axon replacement policy and (by default randomly sampled) mechanism parameter values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"jupyter\": {\n     \"outputs_hidden\": false\n    },\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# parameter randomization\\n\",\n    \"import json\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"# os.chdir('../../../BluePyOpt/examples/l5pc')\\n\",\n    \"import l5pc_model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Creating locations for Arbor\\n\",\n    \"\\n\",\n    \"A protocol consists of a set of stimuli and recordings. These responses will later be used to compare voltage traces from simulations between Arbor and Neuron for different parameter values and axon replacement configurations.\\n\",\n    \"\\n\",\n    \"For the protocols, we apply stimuli centrally at the soma and probe the membrane voltage at a slightly displaced location. For this purpose, we introduce locations in Arbor, which are specified by a relative position `pos` on a `branch` of the morphology.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Define locations on branch 0 of the morphology (soma)\\n\",\n    \"arb_locations = dict(\\n\",\n    \"    stim_loc=ephys.locations.ArbBranchRelLocation(\\n\",\n    \"        name='stim_loc',\\n\",\n    \"        branch=0,\\n\",\n    \"        pos=0.5\\n\",\n    \"    ),\\n\",\n    \"    probe_loc=ephys.locations.ArbBranchRelLocation(\\n\",\n    \"        name='probe_loc',\\n\",\n    \"        branch=0,\\n\",\n    \"        pos=0.75\\n\",\n    \"    )\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"nrn_locations = dict(\\n\",\n    \"    stim_loc=ephys.locations.NrnSeclistCompLocation(\\n\",\n    \"        name='soma',\\n\",\n    \"        seclist_name='somatic',\\n\",\n    \"        sec_index=0,\\n\",\n    \"        comp_x=0.5),\\n\",\n    \"    probe_loc=ephys.locations.NrnSeclistCompLocation(\\n\",\n    \"        name='probe',\\n\",\n    \"        seclist_name='somatic',\\n\",\n    \"        sec_index=0,\\n\",\n    \"        comp_x=0.75)\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"We use a modified version of the L5PC protocols with shortened stimulus duration and reduced amplitudes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Protocol prots configuration\\n\",\n    \"protocol_steps = []\\n\",\n    \"for name, amplitude, duration in [('bAP', 0.19, 5), ('Step1', 0.0458, 50), ('Step3', 0.095, 50)]:\\n\",\n    \"    protocol_steps.append(\\n\",\n    \"        dict(name=name,\\n\",\n    \"             total_duration=120,\\n\",\n    \"             stimuli=[\\n\",\n    \"                 dict(name='%s.iclamp' % name,\\n\",\n    \"                      location='stim_loc',\\n\",\n    \"                      amplitude=amplitude,\\n\",\n    \"                      delay=40,\\n\",\n    \"                      duration=duration)],\\n\",\n    \"             recordings=[\\n\",\n    \"                 dict(name='%s.soma.v' % name,\\n\",\n    \"                      location='probe_loc')]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining protocols to run with Arbor\\n\",\n    \"\\n\",\n    \"To define a protocol in Arbor, we perform the same steps as for Neuron. That is we create stimuli and recordings and initialize an `ArbSweepProtocol` with it as a basic building block (analogous to `SweepProtocol` for Neuron). Note that we use location objects specific to Arbor, where the corresponding ones for Neuron are not well-defined on an Arbor morphology. The sweep protocol can then be assembled into other protocols as usual.\\n\",\n    \"\\n\",\n    \"The protocols defined in the following are analogous to those of the L5PC model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"nrn_sweep_protocols = []\\n\",\n    \"arb_sweep_protocols = []\\n\",\n    \"\\n\",\n    \"for prot_def in protocol_steps:\\n\",\n    \"    nrn_stims = []\\n\",\n    \"    arb_stims = []\\n\",\n    \"    for stim in prot_def['stimuli']:\\n\",\n    \"        stim_args = dict(step_amplitude=stim['amplitude'],\\n\",\n    \"                         step_delay=stim['delay'],\\n\",\n    \"                         step_duration=stim['duration'],\\n\",\n    \"                         total_duration=prot_def['total_duration'])\\n\",\n    \"\\n\",\n    \"        nrn_stims.append(ephys.stimuli.NrnSquarePulse(location=nrn_locations[stim['location']],\\n\",\n    \"                                                      **stim_args))\\n\",\n    \"\\n\",\n    \"        arb_stims.append(ephys.stimuli.NrnSquarePulse(location=arb_locations[stim['location']],\\n\",\n    \"                                                      **stim_args))\\n\",\n    \"\\n\",\n    \"    nrn_recs = []\\n\",\n    \"    arb_recs = []\\n\",\n    \"    for rec in prot_def['recordings']:\\n\",\n    \"        rec_args = dict(name=rec['name'],\\n\",\n    \"                        variable='v')\\n\",\n    \"\\n\",\n    \"        nrn_recs.append(ephys.recordings.CompRecording(location=nrn_locations[rec['location']],\\n\",\n    \"                                                       **rec_args))\\n\",\n    \"\\n\",\n    \"        arb_recs.append(ephys.recordings.CompRecording(location=arb_locations[rec['location']],\\n\",\n    \"                                                       **rec_args))\\n\",\n    \"\\n\",\n    \"    nrn_protocol = ephys.protocols.SweepProtocol(prot_def['name'], nrn_stims, nrn_recs)\\n\",\n    \"    nrn_sweep_protocols.append(nrn_protocol)\\n\",\n    \"\\n\",\n    \"    arb_protocol = ephys.protocols.ArbSweepProtocol(prot_def['name'], arb_stims, arb_recs)\\n\",\n    \"    arb_sweep_protocols.append(arb_protocol)\\n\",\n    \"\\n\",\n    \"nrn_protocol = ephys.protocols.SequenceProtocol('multistep', protocols=nrn_sweep_protocols)\\n\",\n    \"arb_protocol = ephys.protocols.SequenceProtocol('multistep', protocols=arb_sweep_protocols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Mechanisms and parameters for cross-validation of Arbor and Neuron\\n\",\n    \"\\n\",\n    \"To validate Arbor's simulation output with that of Neuron, we run the protocols over a set of parameter values, either loaded from a JSON-file or randomly sampled from the parameter bounds (using `random.uniform(*bounds)`) and both with and without axon replacement.\\n\",\n    \"\\n\",\n    \"First, we gather L5PC mechanisms and parameters according to `mechanism_defs` and `extra_params` in the top cell of this notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"['g_pas.all',\\n\",\n       \" 'e_pas.all',\\n\",\n       \" 'cm.all',\\n\",\n       \" 'Ra.all',\\n\",\n       \" 'v_init',\\n\",\n       \" 'gnabar_hh.somatic',\\n\",\n       \" 'gkbar_hh.somatic']\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"mechanisms = l5pc_model.create_mechanisms(\\n\",\n    \"    dict(all=mechanism_defs['all'],\\n\",\n    \"         somatic=[mech for loc, mechs in mechanism_defs.items() \\n\",\n    \"                  if loc != 'all' for mech in mechs]))\\n\",\n    \"\\n\",\n    \"l5pc_param_configs = l5pc_model.load_parameters()\\n\",\n    \"l5pc_param_names = []\\n\",\n    \"for p in l5pc_param_configs:\\n\",\n    \"    if 'mech' in p and p['mech'] in mechanism_defs.get(p['sectionlist'], []):\\n\",\n    \"        l5pc_param_names.append(p['param_name'] + '.' + p['sectionlist'])\\n\",\n    \"\\n\",\n    \"param_configs = []\\n\",\n    \"for p in l5pc_param_configs:\\n\",\n    \"    if 'mech' not in p:\\n\",\n    \"        if p['param_name'] in extra_params and (p['type'] == 'global' or \\\\\\n\",\n    \"            p['sectionlist'] in extra_params[p['param_name']]):\\n\",\n    \"            if p['type'] != 'global' and p['sectionlist'] != 'all':\\n\",\n    \"                p['sectionlist'] = 'somatic'  # remap to soma\\n\",\n    \"            param_configs.append(p)\\n\",\n    \"\\n\",\n    \"    elif p['mech'] in mechanism_defs.get(p['sectionlist'], []):\\n\",\n    \"        p['sectionlist'] = 'somatic'  # remap to soma\\n\",\n    \"        param_configs.append(p)\\n\",\n    \"\\n\",\n    \"if 'somatic' in mechanism_defs and 'hh' in mechanism_defs['somatic']:\\n\",\n    \"    for param_name, bounds in [('gnabar', (0.05, 0.125)),\\n\",\n    \"                               ('gkbar', (0.01, 0.075))]:\\n\",\n    \"        param_configs.append({\\n\",\n    \"            \\\"param_name\\\": param_name + \\\"_hh\\\",\\n\",\n    \"            \\\"mech\\\": \\\"hh\\\",\\n\",\n    \"            \\\"bounds\\\": bounds,\\n\",\n    \"            \\\"dist_type\\\": \\\"uniform\\\",\\n\",\n    \"            \\\"type\\\": \\\"range\\\",\\n\",\n    \"            \\\"sectionlist\\\": \\\"somatic\\\"\\n\",\n    \"        })\\n\",\n    \"\\n\",\n    \"parameters = l5pc_model.create_parameters(param_configs)\\n\",\n    \"\\n\",\n    \"# Print the names of all parameters\\n\",\n    \"[p.name for p in parameters]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We either randomly sample or read the values of non-frozen parameters from a file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'gnabar_hh.somatic': 0.11973877406449063,\\n\",\n       \"  'gkbar_hh.somatic': 0.04055126228540966},\\n\",\n       \" {'gnabar_hh.somatic': 0.05919475965492274,\\n\",\n       \"  'gkbar_hh.somatic': 0.029459071503543953},\\n\",\n       \" {'gnabar_hh.somatic': 0.07691598000952081,\\n\",\n       \"  'gkbar_hh.somatic': 0.06899888218472573},\\n\",\n       \" {'gnabar_hh.somatic': 0.09502944351022523,\\n\",\n       \"  'gkbar_hh.somatic': 0.06782567113386834},\\n\",\n       \" {'gnabar_hh.somatic': 0.05531450463602975,\\n\",\n       \"  'gkbar_hh.somatic': 0.021221419554995388},\\n\",\n       \" {'gnabar_hh.somatic': 0.07993122992703557,\\n\",\n       \"  'gkbar_hh.somatic': 0.01889628263996689},\\n\",\n       \" {'gnabar_hh.somatic': 0.05622552264031967,\\n\",\n       \"  'gkbar_hh.somatic': 0.012782122091769259},\\n\",\n       \" {'gnabar_hh.somatic': 0.05885834962722003,\\n\",\n       \"  'gkbar_hh.somatic': 0.0664357042663082},\\n\",\n       \" {'gnabar_hh.somatic': 0.07084270624040735,\\n\",\n       \"  'gkbar_hh.somatic': 0.02674180928477552},\\n\",\n       \" {'gnabar_hh.somatic': 0.07305000390848138,\\n\",\n       \"  'gkbar_hh.somatic': 0.07406120832799508}]\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"if not hasattr(arbor.segment_tree, 'tag_roots'):  # skip axon replacement if not yet supported\\n\",\n    \"    replace_axon = [False]\\n\",\n    \"else:\\n\",\n    \"    replace_axon = [False, True]\\n\",\n    \"\\n\",\n    \"non_frozen_parameters = [param for param in parameters if not param.frozen]\\n\",\n    \"param_names = [param.name for param in non_frozen_parameters]\\n\",\n    \"\\n\",\n    \"if param_values_json is not None:\\n\",\n    \"    with open(param_values_json) as f:\\n\",\n    \"        params = []\\n\",\n    \"        for param_sample in json.load(f):\\n\",\n    \"            ps = dict()\\n\",\n    \"            for p_name, p_value in param_sample.items():\\n\",\n    \"                if p_name in l5pc_param_names:\\n\",\n    \"                    ps[p_name.split('.')[0] + '.somatic'] = p_value\\n\",\n    \"            params.append(ps)\\n\",\n    \"else:\\n\",\n    \"    params = [{param.name: random.uniform(*param.bounds)\\n\",\n    \"               for param in non_frozen_parameters}\\n\",\n    \"               for i in range(10)]\\n\",\n    \"params\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Running an Arbor protocol\\n\",\n    \"\\n\",\n    \"The interface of an Arbor protocol is the same as that for Neuron with the only difference that an instance of `ArbSimulator` must be supplied to the `run` method. In addition, if axon-replacement is used, the morphology must be instantiated on the cell model.\\n\",\n    \"\\n\",\n    \"Upon invoking the `protocol.run` method, the cell model is exported to a mixed JSON/ACC-format (cf. [generate_acc.py](generate_acc.py)) and an Arbor cable cell is assembled. Protocol stimuli and recordings are instantiated on this cell and an Arbor simulation is run.\\n\",\n    \"\\n\",\n    \"To create cell models with different configurations for the cross-validation, we use a factory in the following. This enables us to run both Arbor and Neuron simulations for the defined sequence protocols and each combination of axon replacement and parameter values...\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"@dataclass\\n\",\n    \"class CellModelFactory:\\n\",\n    \"    mechanisms: typing.List[ephys.mechanisms.NrnMODMechanism]\\n\",\n    \"    parameters: typing.List[ephys.parameters.NrnParameter]\\n\",\n    \"\\n\",\n    \"    def create_cell_model(self, do_replace_axon):\\n\",\n    \"\\n\",\n    \"        morphology = ephys.morphologies.NrnFileMorphology(\\n\",\n    \"            '../simplecell/simple.swc', do_replace_axon=do_replace_axon)\\n\",\n    \"\\n\",\n    \"        return ephys.models.CellModel(\\n\",\n    \"            'simple_cell',\\n\",\n    \"            morph=morphology,\\n\",\n    \"            mechs=self.mechanisms,\\n\",\n    \"            params=self.parameters)\\n\",\n    \"\\n\",\n    \"@dataclass\\n\",\n    \"class SimulationRunner:\\n\",\n    \"    cell_factory: CellModelFactory\\n\",\n    \"    arb_protocol: ephys.protocols.SequenceProtocol\\n\",\n    \"    nrn_protocol: ephys.protocols.SequenceProtocol\\n\",\n    \"\\n\",\n    \"    def run_all(self, replace_axon_policies, param_list, dt=0.025):\\n\",\n    \"        arb_resp = dict()\\n\",\n    \"        nrn_resp = dict()\\n\",\n    \"\\n\",\n    \"        nrn_sim = ephys.simulators.NrnSimulator(dt=dt)\\n\",\n    \"        arb_sim = ephys.simulators.ArbSimulator(dt=dt)\\n\",\n    \"\\n\",\n    \"        for do_replace_axon in replace_axon_policies:\\n\",\n    \"            for param_i in range(len(param_list)):\\n\",\n    \"\\n\",\n    \"                cell_model = self.cell_factory.create_cell_model(do_replace_axon=do_replace_axon)\\n\",\n    \"\\n\",\n    \"                # calculate morphology with axon-replacement in Neuron\\n\",\n    \"                cell_model.instantiate_morphology_3d(nrn_sim)\\n\",\n    \"\\n\",\n    \"                key = (do_replace_axon, param_i)\\n\",\n    \"                arb_resp[key] = self.arb_protocol.run(cell_model, param_list[param_i], arb_sim)\\n\",\n    \"\\n\",\n    \"                # need to destroy instantiated cell model first to avoid Hoc serialization error\\n\",\n    \"                cell_model.destroy(sim=nrn_sim)\\n\",\n    \"\\n\",\n    \"                nrn_resp[key] = self.nrn_protocol.run(cell_model, param_list[param_i], nrn_sim)\\n\",\n    \"        return arb_resp, nrn_resp\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cell_factory = CellModelFactory(mechanisms, parameters)\\n\",\n    \"simulation_runner = SimulationRunner(cell_factory, arb_protocol, nrn_protocol)\\n\",\n    \"arb_responses, nrn_responses = simulation_runner.run_all(replace_axon, params, default_dt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"...and to plot the responses for visual validation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def plot_response_comparison(arb_resp, nrn_resp, title):\\n\",\n    \"    num_protocols = len(protocol_steps)\\n\",\n    \"    num_recs = max([len(step['recordings']) for step in protocol_steps])\\n\",\n    \"    assert num_recs == 1  # add j as a second index with multiple recordings\\n\",\n    \"    fig, ax = plt.subplots(num_protocols, num_recs, figsize=(12,7))\\n\",\n    \"    for i, step in enumerate(protocol_steps):\\n\",\n    \"        for j, rec in enumerate(step['recordings']):\\n\",\n    \"            rec_name = rec['name']\\n\",\n    \"            ax[i].plot(nrn_resp[rec_name]['time'], nrn_resp[rec_name]['voltage'], label='Neuron ' + rec_name)\\n\",\n    \"            ax[i].plot(arb_resp[rec_name]['time'], arb_resp[rec_name]['voltage'], label='Arbor ' + rec_name)\\n\",\n    \"            ax[i].set_title(title)\\n\",\n    \"            ax[i].legend(loc='upper left')\\n\",\n    \"    plt.tight_layout()\\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### The cross-validation metric\\n\",\n    \"\\n\",\n    \"To analyze the responses, we compare the difference of voltage traces between Arbor and Neuron in the L1-norm. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.107</td>\\n\",\n       \"      <td>6.9e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0947</td>\\n\",\n       \"      <td>1e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0923</td>\\n\",\n       \"      <td>7.65e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0866</td>\\n\",\n       \"      <td>1.42e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0863</td>\\n\",\n       \"      <td>5.83e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"2          False        0               0.12            0.0406  Step3.soma.v   \\n\",\n       \"25         False        8             0.0708            0.0267  Step1.soma.v   \\n\",\n       \"17         False        5             0.0799            0.0189  Step3.soma.v   \\n\",\n       \"19         False        6             0.0562            0.0128  Step1.soma.v   \\n\",\n       \"26         False        8             0.0708            0.0267  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"2                  0.107                6.9e-07  \\n\",\n       \"25                0.0947                  1e-07  \\n\",\n       \"17                0.0923               7.65e-07  \\n\",\n       \"19                0.0866               1.42e-06  \\n\",\n       \"26                0.0863               5.83e-07  \"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def voltage_trace_residual_l1_norm(int_resp, ref_resp):\\n\",\n    \"    int_resp_func = scipy.interpolate.interp1d(int_resp['time'], int_resp['voltage'], kind='cubic')\\n\",\n    \"    ref_resp_func = scipy.interpolate.interp1d(ref_resp['time'], ref_resp['voltage'], kind='cubic')\\n\",\n    \"    abs_diff = lambda t: abs(int_resp_func(t)-ref_resp_func(t))\\n\",\n    \"    with warnings.catch_warnings():\\n\",\n    \"        warnings.filterwarnings(\\\"ignore\\\", category=scipy.integrate.IntegrationWarning)\\n\",\n    \"        if isinstance(ref_resp, pandas.DataFrame):\\n\",\n    \"            time_start, time_end = (ref_resp['time'].iloc[0], ref_resp['time'].iloc[-1])\\n\",\n    \"        else:\\n\",\n    \"            time_start, time_end = (ref_resp['time'][0], ref_resp['time'][-1])\\n\",\n    \"        return scipy.integrate.quad(abs_diff, time_start, time_end, limit=400)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def voltage_trace_residual_l1_norm_sweep_protocol(args):\\n\",\n    \"    arb_resp = args['arb_resp'].response\\n\",\n    \"    nrn_resp = args['nrn_resp'].response\\n\",\n    \"\\n\",\n    \"    residual_l1_norm, residual_error = voltage_trace_residual_l1_norm(\\n\",\n    \"        nrn_resp, arb_resp)\\n\",\n    \"\\n\",\n    \"    nrn_to_min_l1_norm, nrn_to_min_error = \\\\\\n\",\n    \"        voltage_trace_residual_l1_norm(\\n\",\n    \"            nrn_resp,\\n\",\n    \"            dict(time=nrn_resp['time'].values,\\n\",\n    \"                 voltage=numpy.full(nrn_resp['voltage'].shape,\\n\",\n    \"                                    numpy.min(nrn_resp['voltage']))))\\n\",\n    \"    return dict(\\n\",\n    \"        residual_rel_l1_norm=residual_l1_norm/nrn_to_min_l1_norm,\\n\",\n    \"        residual_rel_l1_error=residual_error/residual_l1_norm + \\\\\\n\",\n    \"                            nrn_to_min_error/nrn_to_min_l1_norm\\n\",\n    \"    )\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def analyze_voltage_traces_l1(arb_resp, nrn_resp):\\n\",\n    \"    indices = [(key,step) for key in arb_resp for step in arb_resp[key]]\\n\",\n    \"\\n\",\n    \"    # test_l5pc: insert if True:\\n\",\n    \"    with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:\\n\",\n    \"        l1_task_results = pool.map(voltage_trace_residual_l1_norm_sweep_protocol,\\n\",\n    \"                                   [dict(arb_resp=arb_resp[key][step],\\n\",\n    \"                                         nrn_resp=nrn_resp[key][step])\\n\",\n    \"                                    for key, step in indices])\\n\",\n    \"\\n\",\n    \"    l1_results = []\\n\",\n    \"    for l1_task_result, (key, step) in zip(l1_task_results, indices):\\n\",\n    \"            l1_results.append(\\n\",\n    \"                dict(replace_axon=key[0],\\n\",\n    \"                     param_i=key[1],\\n\",\n    \"                     **params[key[1]],\\n\",\n    \"                     protocol=step,\\n\",\n    \"                     **l1_task_result))\\n\",\n    \"\\n\",\n    \"    return pandas.DataFrame(l1_results)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"l1_results = analyze_voltage_traces_l1(arb_responses, nrn_responses)\\n\",\n    \"pandas.options.display.float_format = '{:,.3g}'.format\\n\",\n    \"l1_results.sort_values(by='residual_rel_l1_norm', ascending=False).head(5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Default dt (0.025): test_l5pc OK! The mean relative Arbor-Neuron L1-deviation and error (tol in brackets) are 0.0292 (0.05), 2.42e-06 (0.0005).\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def print_voltage_trace_l1_results(config_str, l1_results):\\n\",\n    \"    voltage_residual_rel_l1_error_tolerance = 1e-2 * voltage_residual_rel_l1_tolerance\\n\",\n    \"\\n\",\n    \"    mean_rel_l1_norm = l1_results['residual_rel_l1_norm'].mean()\\n\",\n    \"    mean_rel_l1_error = l1_results['residual_rel_l1_error'].mean()\\n\",\n    \"    # max_l1_norm_record = l1_results.loc[l1_results['residual_rel_l1_norm'].idxmax()]\\n\",\n    \"\\n\",\n    \"    message = '{}: test_l5pc %s! The mean relative Arbor-Neuron L1-deviation and error (tol in brackets) are {:,.3g} ({:,.3g}), {:,.3g} ({:,.3g}).'.format(\\n\",\n    \"        config_str, mean_rel_l1_norm, voltage_residual_rel_l1_tolerance, mean_rel_l1_error, voltage_residual_rel_l1_error_tolerance)\\n\",\n    \"    if mean_rel_l1_norm < voltage_residual_rel_l1_tolerance and mean_rel_l1_error < voltage_residual_rel_l1_error_tolerance:\\n\",\n    \"        print(message % 'OK')\\n\",\n    \"    else:\\n\",\n    \"        print(message % 'ERROR')\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"print_voltage_trace_l1_results('Default dt ({:,.3g})'.format(default_dt), l1_results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00838</td>\\n\",\n       \"      <td>5.52e-08</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0835</td>\\n\",\n       \"      <td>2.07e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.107</td>\\n\",\n       \"      <td>6.9e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"0         False        0               0.12            0.0406    bAP.soma.v   \\n\",\n       \"1         False        0               0.12            0.0406  Step1.soma.v   \\n\",\n       \"2         False        0               0.12            0.0406  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"   residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"0               0.00838               5.52e-08  \\n\",\n       \"1                0.0835               2.07e-07  \\n\",\n       \"2                 0.107                6.9e-07  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>30</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00663</td>\\n\",\n       \"      <td>8.57e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>31</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0707</td>\\n\",\n       \"      <td>3.9e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>32</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0723</td>\\n\",\n       \"      <td>1.11e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"30          True        0               0.12            0.0406    bAP.soma.v   \\n\",\n       \"31          True        0               0.12            0.0406  Step1.soma.v   \\n\",\n       \"32          True        0               0.12            0.0406  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"30               0.00663               8.57e-06  \\n\",\n       \"31                0.0707                3.9e-07  \\n\",\n       \"32                0.0723               1.11e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00794</td>\\n\",\n       \"      <td>5.48e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00912</td>\\n\",\n       \"      <td>6.42e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0103</td>\\n\",\n       \"      <td>1.64e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"3         False        1             0.0592            0.0295    bAP.soma.v   \\n\",\n       \"4         False        1             0.0592            0.0295  Step1.soma.v   \\n\",\n       \"5         False        1             0.0592            0.0295  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"   residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"3               0.00794               5.48e-06  \\n\",\n       \"4               0.00912               6.42e-07  \\n\",\n       \"5                0.0103               1.64e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>33</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00615</td>\\n\",\n       \"      <td>3.32e-08</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>34</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00752</td>\\n\",\n       \"      <td>3.02e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>35</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00875</td>\\n\",\n       \"      <td>1.01e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"33          True        1             0.0592            0.0295    bAP.soma.v   \\n\",\n       \"34          True        1             0.0592            0.0295  Step1.soma.v   \\n\",\n       \"35          True        1             0.0592            0.0295  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"33               0.00615               3.32e-08  \\n\",\n       \"34               0.00752               3.02e-06  \\n\",\n       \"35               0.00875               1.01e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00979</td>\\n\",\n       \"      <td>1.15e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.011</td>\\n\",\n       \"      <td>8.94e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00961</td>\\n\",\n       \"      <td>7.11e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"6         False        2             0.0769             0.069    bAP.soma.v   \\n\",\n       \"7         False        2             0.0769             0.069  Step1.soma.v   \\n\",\n       \"8         False        2             0.0769             0.069  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"   residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"6               0.00979               1.15e-07  \\n\",\n       \"7                 0.011               8.94e-06  \\n\",\n       \"8               0.00961               7.11e-07  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>36</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00774</td>\\n\",\n       \"      <td>6.1e-08</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>37</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00953</td>\\n\",\n       \"      <td>4.58e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>38</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00778</td>\\n\",\n       \"      <td>6.73e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"36          True        2             0.0769             0.069    bAP.soma.v   \\n\",\n       \"37          True        2             0.0769             0.069  Step1.soma.v   \\n\",\n       \"38          True        2             0.0769             0.069  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"36               0.00774                6.1e-08  \\n\",\n       \"37               0.00953               4.58e-06  \\n\",\n       \"38               0.00778               6.73e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00976</td>\\n\",\n       \"      <td>2.33e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0108</td>\\n\",\n       \"      <td>1.56e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00954</td>\\n\",\n       \"      <td>8.79e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"9          False        3              0.095            0.0678    bAP.soma.v   \\n\",\n       \"10         False        3              0.095            0.0678  Step1.soma.v   \\n\",\n       \"11         False        3              0.095            0.0678  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"9                0.00976               2.33e-07  \\n\",\n       \"10                0.0108               1.56e-05  \\n\",\n       \"11               0.00954               8.79e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00768</td>\\n\",\n       \"      <td>1.12e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>40</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00896</td>\\n\",\n       \"      <td>2e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>41</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00766</td>\\n\",\n       \"      <td>1.95e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"39          True        3              0.095            0.0678    bAP.soma.v   \\n\",\n       \"40          True        3              0.095            0.0678  Step1.soma.v   \\n\",\n       \"41          True        3              0.095            0.0678  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"39               0.00768               1.12e-06  \\n\",\n       \"40               0.00896                  2e-06  \\n\",\n       \"41               0.00766               1.95e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00735</td>\\n\",\n       \"      <td>5.06e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>8.95e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0655</td>\\n\",\n       \"      <td>3.39e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"12         False        4             0.0553            0.0212    bAP.soma.v   \\n\",\n       \"13         False        4             0.0553            0.0212  Step1.soma.v   \\n\",\n       \"14         False        4             0.0553            0.0212  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"12               0.00735               5.06e-07  \\n\",\n       \"13                0.0678               8.95e-07  \\n\",\n       \"14                0.0655               3.39e-07  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00575</td>\\n\",\n       \"      <td>1.63e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>43</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0231</td>\\n\",\n       \"      <td>2.3e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>44</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>1.25e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"42          True        4             0.0553            0.0212    bAP.soma.v   \\n\",\n       \"43          True        4             0.0553            0.0212  Step1.soma.v   \\n\",\n       \"44          True        4             0.0553            0.0212  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"42               0.00575               1.63e-06  \\n\",\n       \"43                0.0231                2.3e-07  \\n\",\n       \"44                0.0406               1.25e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.0072</td>\\n\",\n       \"      <td>1.35e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0811</td>\\n\",\n       \"      <td>3.35e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0923</td>\\n\",\n       \"      <td>7.65e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"15         False        5             0.0799            0.0189    bAP.soma.v   \\n\",\n       \"16         False        5             0.0799            0.0189  Step1.soma.v   \\n\",\n       \"17         False        5             0.0799            0.0189  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"15                0.0072               1.35e-07  \\n\",\n       \"16                0.0811               3.35e-07  \\n\",\n       \"17                0.0923               7.65e-07  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>45</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00585</td>\\n\",\n       \"      <td>2.4e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>46</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0578</td>\\n\",\n       \"      <td>5.03e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>47</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0784</td>\\n\",\n       \"      <td>1.02e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"45          True        5             0.0799            0.0189    bAP.soma.v   \\n\",\n       \"46          True        5             0.0799            0.0189  Step1.soma.v   \\n\",\n       \"47          True        5             0.0799            0.0189  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"45               0.00585                2.4e-07  \\n\",\n       \"46                0.0578               5.03e-07  \\n\",\n       \"47                0.0784               1.02e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00694</td>\\n\",\n       \"      <td>2.58e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0866</td>\\n\",\n       \"      <td>1.42e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0828</td>\\n\",\n       \"      <td>1.2e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"18         False        6             0.0562            0.0128    bAP.soma.v   \\n\",\n       \"19         False        6             0.0562            0.0128  Step1.soma.v   \\n\",\n       \"20         False        6             0.0562            0.0128  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"18               0.00694               2.58e-07  \\n\",\n       \"19                0.0866               1.42e-06  \\n\",\n       \"20                0.0828                1.2e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00554</td>\\n\",\n       \"      <td>2.01e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>49</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0844</td>\\n\",\n       \"      <td>1.03e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0555</td>\\n\",\n       \"      <td>1.1e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"48          True        6             0.0562            0.0128    bAP.soma.v   \\n\",\n       \"49          True        6             0.0562            0.0128  Step1.soma.v   \\n\",\n       \"50          True        6             0.0562            0.0128  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"48               0.00554               2.01e-07  \\n\",\n       \"49                0.0844               1.03e-07  \\n\",\n       \"50                0.0555                1.1e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00966</td>\\n\",\n       \"      <td>2.95e-08</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0113</td>\\n\",\n       \"      <td>2.8e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0096</td>\\n\",\n       \"      <td>8.35e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"21         False        7             0.0589            0.0664    bAP.soma.v   \\n\",\n       \"22         False        7             0.0589            0.0664  Step1.soma.v   \\n\",\n       \"23         False        7             0.0589            0.0664  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"21               0.00966               2.95e-08  \\n\",\n       \"22                0.0113                2.8e-06  \\n\",\n       \"23                0.0096               8.35e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>51</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.0075</td>\\n\",\n       \"      <td>9.92e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>52</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0109</td>\\n\",\n       \"      <td>7.61e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>53</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00785</td>\\n\",\n       \"      <td>1.24e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"51          True        7             0.0589            0.0664    bAP.soma.v   \\n\",\n       \"52          True        7             0.0589            0.0664  Step1.soma.v   \\n\",\n       \"53          True        7             0.0589            0.0664  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"51                0.0075               9.92e-06  \\n\",\n       \"52                0.0109               7.61e-07  \\n\",\n       \"53               0.00785               1.24e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00774</td>\\n\",\n       \"      <td>4.38e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0947</td>\\n\",\n       \"      <td>1e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0863</td>\\n\",\n       \"      <td>5.83e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"24         False        8             0.0708            0.0267    bAP.soma.v   \\n\",\n       \"25         False        8             0.0708            0.0267  Step1.soma.v   \\n\",\n       \"26         False        8             0.0708            0.0267  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"24               0.00774               4.38e-07  \\n\",\n       \"25                0.0947                  1e-07  \\n\",\n       \"26                0.0863               5.83e-07  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>54</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00604</td>\\n\",\n       \"      <td>3.15e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>55</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0244</td>\\n\",\n       \"      <td>9.16e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>56</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>1.18e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"54          True        8             0.0708            0.0267    bAP.soma.v   \\n\",\n       \"55          True        8             0.0708            0.0267  Step1.soma.v   \\n\",\n       \"56          True        8             0.0708            0.0267  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"54               0.00604               3.15e-07  \\n\",\n       \"55                0.0244               9.16e-07  \\n\",\n       \"56                0.0553               1.18e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.01</td>\\n\",\n       \"      <td>2.04e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0115</td>\\n\",\n       \"      <td>4.11e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00971</td>\\n\",\n       \"      <td>9.06e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"27         False        9             0.0731            0.0741    bAP.soma.v   \\n\",\n       \"28         False        9             0.0731            0.0741  Step1.soma.v   \\n\",\n       \"29         False        9             0.0731            0.0741  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"27                  0.01               2.04e-06  \\n\",\n       \"28                0.0115               4.11e-06  \\n\",\n       \"29               0.00971               9.06e-07  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>57</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00788</td>\\n\",\n       \"      <td>3.73e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>58</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0109</td>\\n\",\n       \"      <td>8.39e-08</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>59</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00791</td>\\n\",\n       \"      <td>1.39e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"57          True        9             0.0731            0.0741    bAP.soma.v   \\n\",\n       \"58          True        9             0.0731            0.0741  Step1.soma.v   \\n\",\n       \"59          True        9             0.0731            0.0741  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"57               0.00788               3.73e-07  \\n\",\n       \"58                0.0109               8.39e-08  \\n\",\n       \"59               0.00791               1.39e-05  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def compare_responses(arb_resp, nrn_resp, l1_results, *key):\\n\",\n    \"    if key in arb_resp:\\n\",\n    \"        plot_response_comparison(arb_resp[key], nrn_resp[key], 'replace_axon = %s, param_i = %s ' % (key[0], key[1]))\\n\",\n    \"        display(l1_results[(l1_results['replace_axon'] == key[0]) & (l1_results['param_i'] == key[1])])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for param_i in range(len(params)):  # test_l5pc: skip\\n\",\n    \"    for do_replace_axon in replace_axon:  # test_l5pc: skip\\n\",\n    \"        compare_responses(arb_responses, nrn_responses, l1_results, do_replace_axon, param_i)  # test_l5pc: skip\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The voltage traces look mostly similar between Arbor and Neuron. Under certain conditions, we can perform spike time analysis to understand this quantitatively. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Spike time cross-validation\\n\",\n    \"\\n\",\n    \"To compare Arbor and Neuron voltage traces further, we analyze the spike counts and times with the eFEL library. Note that in contrast to eFEL that measures the `peak_time`, Arbor's built-in spike detector measures the time when the voltage surpasses a given voltage threshold.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Neuron</th>\\n\",\n       \"      <th>Arbor</th>\\n\",\n       \"      <th>abs_diff Arbor to Neuron</th>\\n\",\n       \"      <th>rel_abs_diff Arbor to Neuron [%]</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>efel</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"5\\\" valign=\\\"top\\\">False</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">bAP</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.12</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.0406</th>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>0.9</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>11.1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>0.9</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>11.1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">Step1</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.12</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.0406</th>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>1.8</td>\\n\",\n       \"      <td>1.9</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>5.56</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"5\\\" valign=\\\"top\\\">True</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">Step1</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.0731</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.0741</th>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>3.8</td>\\n\",\n       \"      <td>3.9</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>2.63</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>3.8</td>\\n\",\n       \"      <td>3.9</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>2.63</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">Step3</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.0731</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.0741</th>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>200 rows × 4 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                                              Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                          \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount                1   \\n\",\n       \"                                                         time_to_first_spike     0.9   \\n\",\n       \"                                                         time_to_last_spike      0.9   \\n\",\n       \"             Step1    0.12              0.0406           Spikecount                4   \\n\",\n       \"                                                         time_to_first_spike     1.8   \\n\",\n       \"...                                                                              ...   \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike     3.8   \\n\",\n       \"                                                         time_to_last_spike      3.8   \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount                1   \\n\",\n       \"                                                         time_to_first_spike     2.1   \\n\",\n       \"                                                         time_to_last_spike      2.1   \\n\",\n       \"\\n\",\n       \"                                                                              Arbor  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                         \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount               1   \\n\",\n       \"                                                         time_to_first_spike      1   \\n\",\n       \"                                                         time_to_last_spike       1   \\n\",\n       \"             Step1    0.12              0.0406           Spikecount               4   \\n\",\n       \"                                                         time_to_first_spike    1.9   \\n\",\n       \"...                                                                             ...   \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike    3.9   \\n\",\n       \"                                                         time_to_last_spike     3.9   \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount               1   \\n\",\n       \"                                                         time_to_first_spike    2.1   \\n\",\n       \"                                                         time_to_last_spike     2.1   \\n\",\n       \"\\n\",\n       \"                                                                              abs_diff Arbor to Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                            \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount                                  0   \\n\",\n       \"                                                         time_to_first_spike                       0.1   \\n\",\n       \"                                                         time_to_last_spike                        0.1   \\n\",\n       \"             Step1    0.12              0.0406           Spikecount                                  0   \\n\",\n       \"                                                         time_to_first_spike                       0.1   \\n\",\n       \"...                                                                                                ...   \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike                       0.1   \\n\",\n       \"                                                         time_to_last_spike                        0.1   \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount                                  0   \\n\",\n       \"                                                         time_to_first_spike                         0   \\n\",\n       \"                                                         time_to_last_spike                          0   \\n\",\n       \"\\n\",\n       \"                                                                              rel_abs_diff Arbor to Neuron [%]  \\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                                   \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount                                          0  \\n\",\n       \"                                                         time_to_first_spike                              11.1  \\n\",\n       \"                                                         time_to_last_spike                               11.1  \\n\",\n       \"             Step1    0.12              0.0406           Spikecount                                          0  \\n\",\n       \"                                                         time_to_first_spike                              5.56  \\n\",\n       \"...                                                                                                        ...  \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike                              2.63  \\n\",\n       \"                                                         time_to_last_spike                               2.63  \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount                                          0  \\n\",\n       \"                                                         time_to_first_spike                                 0  \\n\",\n       \"                                                         time_to_last_spike                                  0  \\n\",\n       \"\\n\",\n       \"[200 rows x 4 columns]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_spike_time_analysis:\\n\",\n    \"\\n\",\n    \"    efel_features = ['Spikecount',\\n\",\n    \"                    'time_to_first_spike',\\n\",\n    \"                    'time_to_second_spike',\\n\",\n    \"                    'time_to_last_spike']\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    # Extract spike observables from protocol simulation responses\\n\",\n    \"    def get_spike_data(protocols, do_replace_axon, param_values,\\n\",\n    \"                       arb_resp, nrn_resp):\\n\",\n    \"        spike_res = []\\n\",\n    \"\\n\",\n    \"        for step in protocols:\\n\",\n    \"            recording_name = step['recordings'][0]['name'] # use only first recording\\n\",\n    \"            stim_start = min([stim['delay'] for stim in step['stimuli']])\\n\",\n    \"            stim_end = max([stim['delay'] + stim['duration'] for stim in step['stimuli']])\\n\",\n    \"\\n\",\n    \"            for efel_feature_name in efel_features:\\n\",\n    \"                # Calculate spike observables with eFEL\\n\",\n    \"                feature_name = '%s.%s' % (step['name'], efel_feature_name)\\n\",\n    \"                feature = ephys.efeatures.eFELFeature(\\n\",\n    \"                            feature_name,\\n\",\n    \"                            efel_feature_name=efel_feature_name,\\n\",\n    \"                            recording_names={'': recording_name},\\n\",\n    \"                            stim_start=stim_start,\\n\",\n    \"                            stim_end=stim_end)\\n\",\n    \"\\n\",\n    \"                spike_res.append(dict(\\n\",\n    \"                    replace_axon=do_replace_axon,\\n\",\n    \"                    protocol=step['name'],\\n\",\n    \"                    **param_values,\\n\",\n    \"                    efel=efel_feature_name,\\n\",\n    \"                    Neuron=feature.calculate_feature(nrn_resp),\\n\",\n    \"                    Arbor=feature.calculate_feature(arb_resp)))\\n\",\n    \"        return spike_res\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    # Compare spike observables between Arbor and Neuron\\n\",\n    \"    def analyze_spikes(spike_res):\\n\",\n    \"        spike_res_df = pandas.DataFrame(spike_res)\\n\",\n    \"        spike_res_df.set_index(\\n\",\n    \"            ['replace_axon', 'protocol',\\n\",\n    \"             *param_names, 'efel'], inplace=True)\\n\",\n    \"        spike_res_df.dropna(how='all', inplace=True)  # drop all-NaN rows\\n\",\n    \"\\n\",\n    \"        # Arbor to Neuron cross-validation with eFEL\\n\",\n    \"        spike_res_df['abs_diff Arbor to Neuron'] = \\\\\\n\",\n    \"            spike_res_df.apply(\\n\",\n    \"                lambda r: abs(r['Arbor']-r['Neuron']), axis=1)\\n\",\n    \"        spike_res_df['rel_abs_diff Arbor to Neuron [%]'] = \\\\\\n\",\n    \"            spike_res_df.apply(\\n\",\n    \"                lambda r: 100.*abs(r['Arbor']-r['Neuron'])/r['Neuron']\\n\",\n    \"                          if r['Neuron'] != 0 else numpy.nan, axis=1)\\n\",\n    \"\\n\",\n    \"        return spike_res_df\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    # Aggregate all simulations into a single data frame \\n\",\n    \"    def joint_spike_analysis(arb_resp, nrn_resp, replace_axon_policies, param_list):\\n\",\n    \"        return pandas.concat(\\n\",\n    \"            [analyze_spikes(get_spike_data(protocol_steps,\\n\",\n    \"                                           replace_axon_policies[key[0]],\\n\",\n    \"                                           param_list[key[1]],\\n\",\n    \"                                           arb_resp[key],\\n\",\n    \"                                           nrn_resp[key]))\\n\",\n    \"             for key in arb_resp], axis=0)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    pandas.options.display.float_format = '{:,.3g}'.format\\n\",\n    \"    # pandas.options.display.max_rows = None  # uncomment for full view\\n\",\n    \"    spike_results = joint_spike_analysis(arb_responses, nrn_responses, replace_axon, params)\\n\",\n    \"    display(spike_results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To understand the deviations over the entire parameter set and different axon replacement policies, we explore the per eFEL-observable statistics. Compare the `Spikecount`s that are usually fully consistent between Arbor and Neuron, whereas `time_to_last_spike` are the least consistent of these variables.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead tr th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead tr:last-of-type th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th colspan=\\\"8\\\" halign=\\\"left\\\">abs_diff Arbor to Neuron</th>\\n\",\n       \"      <th colspan=\\\"8\\\" halign=\\\"left\\\">rel_abs_diff Arbor to Neuron [%]</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>efel</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0.0167</td>\\n\",\n       \"      <td>0.129</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0.417</td>\\n\",\n       \"      <td>3.23</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>25</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0.0467</td>\\n\",\n       \"      <td>0.0503</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>2.92</td>\\n\",\n       \"      <td>3.54</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>5.56</td>\\n\",\n       \"      <td>11.1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0.307</td>\\n\",\n       \"      <td>1.31</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>10.2</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>2.91</td>\\n\",\n       \"      <td>4.92</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.67</td>\\n\",\n       \"      <td>4.55</td>\\n\",\n       \"      <td>30.9</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_second_spike</th>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>0.115</td>\\n\",\n       \"      <td>0.0366</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>0.812</td>\\n\",\n       \"      <td>0.246</td>\\n\",\n       \"      <td>0.521</td>\\n\",\n       \"      <td>0.657</td>\\n\",\n       \"      <td>0.746</td>\\n\",\n       \"      <td>0.852</td>\\n\",\n       \"      <td>1.59</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                     abs_diff Arbor to Neuron                                \\\\\\n\",\n       \"                                        count   mean    std min 25% 50% 75%   \\n\",\n       \"efel                                                                          \\n\",\n       \"Spikecount                                 60 0.0167  0.129   0   0   0   0   \\n\",\n       \"time_to_first_spike                        60 0.0467 0.0503   0   0   0 0.1   \\n\",\n       \"time_to_last_spike                         60  0.307   1.31   0   0 0.1 0.3   \\n\",\n       \"time_to_second_spike                       20  0.115 0.0366 0.1 0.1 0.1 0.1   \\n\",\n       \"\\n\",\n       \"                          rel_abs_diff Arbor to Neuron [%]                    \\\\\\n\",\n       \"                      max                            count  mean   std   min   \\n\",\n       \"efel                                                                           \\n\",\n       \"Spikecount              1                               60 0.417  3.23     0   \\n\",\n       \"time_to_first_spike   0.1                               60  2.92  3.54     0   \\n\",\n       \"time_to_last_spike   10.2                               60  2.91  4.92     0   \\n\",\n       \"time_to_second_spike  0.2                               20 0.812 0.246 0.521   \\n\",\n       \"\\n\",\n       \"                                             \\n\",\n       \"                       25%   50%   75%  max  \\n\",\n       \"efel                                         \\n\",\n       \"Spikecount               0     0     0   25  \\n\",\n       \"time_to_first_spike      0     0  5.56 11.1  \\n\",\n       \"time_to_last_spike       0  0.67  4.55 30.9  \\n\",\n       \"time_to_second_spike 0.657 0.746 0.852 1.59  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_spike_time_analysis:\\n\",\n    \"    display(spike_results[['abs_diff Arbor to Neuron',\\n\",\n    \"                           'rel_abs_diff Arbor to Neuron [%]']].groupby('efel').describe())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can inspect the traces with highest difference in `time_to_last_spike` to identify outliers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Neuron</th>\\n\",\n       \"      <th>Arbor</th>\\n\",\n       \"      <th>abs_diff Arbor to Neuron</th>\\n\",\n       \"      <th>rel_abs_diff Arbor to Neuron [%]</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>efel</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>False</th>\\n\",\n       \"      <th>Step3</th>\\n\",\n       \"      <th>0.0553</th>\\n\",\n       \"      <th>0.0212</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>33</td>\\n\",\n       \"      <td>22.8</td>\\n\",\n       \"      <td>10.2</td>\\n\",\n       \"      <td>30.9</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>True</th>\\n\",\n       \"      <th>Step1</th>\\n\",\n       \"      <th>0.0562</th>\\n\",\n       \"      <th>0.0128</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>51.7</td>\\n\",\n       \"      <td>52.4</td>\\n\",\n       \"      <td>0.7</td>\\n\",\n       \"      <td>1.35</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">False</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">Step1</th>\\n\",\n       \"      <th>0.0553</th>\\n\",\n       \"      <th>0.0212</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>31.8</td>\\n\",\n       \"      <td>32.4</td>\\n\",\n       \"      <td>0.6</td>\\n\",\n       \"      <td>1.89</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0.0708</th>\\n\",\n       \"      <th>0.0267</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>46.7</td>\\n\",\n       \"      <td>47.3</td>\\n\",\n       \"      <td>0.6</td>\\n\",\n       \"      <td>1.28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>True</th>\\n\",\n       \"      <th>Step3</th>\\n\",\n       \"      <th>0.0799</th>\\n\",\n       \"      <th>0.0189</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>50.8</td>\\n\",\n       \"      <td>51.2</td>\\n\",\n       \"      <td>0.4</td>\\n\",\n       \"      <td>0.787</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                                             Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                         \\n\",\n       \"False        Step3    0.0553            0.0212           time_to_last_spike      33   \\n\",\n       \"True         Step1    0.0562            0.0128           time_to_last_spike    51.7   \\n\",\n       \"False        Step1    0.0553            0.0212           time_to_last_spike    31.8   \\n\",\n       \"                      0.0708            0.0267           time_to_last_spike    46.7   \\n\",\n       \"True         Step3    0.0799            0.0189           time_to_last_spike    50.8   \\n\",\n       \"\\n\",\n       \"                                                                             Arbor  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                        \\n\",\n       \"False        Step3    0.0553            0.0212           time_to_last_spike   22.8   \\n\",\n       \"True         Step1    0.0562            0.0128           time_to_last_spike   52.4   \\n\",\n       \"False        Step1    0.0553            0.0212           time_to_last_spike   32.4   \\n\",\n       \"                      0.0708            0.0267           time_to_last_spike   47.3   \\n\",\n       \"True         Step3    0.0799            0.0189           time_to_last_spike   51.2   \\n\",\n       \"\\n\",\n       \"                                                                             abs_diff Arbor to Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                           \\n\",\n       \"False        Step3    0.0553            0.0212           time_to_last_spike                      10.2   \\n\",\n       \"True         Step1    0.0562            0.0128           time_to_last_spike                       0.7   \\n\",\n       \"False        Step1    0.0553            0.0212           time_to_last_spike                       0.6   \\n\",\n       \"                      0.0708            0.0267           time_to_last_spike                       0.6   \\n\",\n       \"True         Step3    0.0799            0.0189           time_to_last_spike                       0.4   \\n\",\n       \"\\n\",\n       \"                                                                             rel_abs_diff Arbor to Neuron [%]  \\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                                  \\n\",\n       \"False        Step3    0.0553            0.0212           time_to_last_spike                              30.9  \\n\",\n       \"True         Step1    0.0562            0.0128           time_to_last_spike                              1.35  \\n\",\n       \"False        Step1    0.0553            0.0212           time_to_last_spike                              1.89  \\n\",\n       \"                      0.0708            0.0267           time_to_last_spike                              1.28  \\n\",\n       \"True         Step3    0.0799            0.0189           time_to_last_spike                             0.787  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_spike_time_analysis:\\n\",\n    \"    display(spike_results[ [el[spike_results.index.names.index('efel')] == 'time_to_last_spike'\\n\",\n    \"                            for el in spike_results.index] ].sort_values(\\n\",\n    \"                            by='abs_diff Arbor to Neuron', ascending=False).head(5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Running protocols with a finer time step\\n\",\n    \"\\n\",\n    \"To rule out the discretization as a possible source of the above error in `time_to_last_spike`, we can re-run the simulations at a smaller `dt` of 0.001 ms (default is 0.025 ms).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0319</td>\\n\",\n       \"      <td>1.12e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0279</td>\\n\",\n       \"      <td>4.75e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0132</td>\\n\",\n       \"      <td>1.07e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0109</td>\\n\",\n       \"      <td>3.88e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00924</td>\\n\",\n       \"      <td>1.05e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"25         False        8             0.0708            0.0267  Step1.soma.v   \\n\",\n       \"13         False        4             0.0553            0.0212  Step1.soma.v   \\n\",\n       \"2          False        0               0.12            0.0406  Step3.soma.v   \\n\",\n       \"1          False        0               0.12            0.0406  Step1.soma.v   \\n\",\n       \"16         False        5             0.0799            0.0189  Step1.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"25                0.0319               1.12e-06  \\n\",\n       \"13                0.0279               4.75e-07  \\n\",\n       \"2                 0.0132               1.07e-05  \\n\",\n       \"1                 0.0109               3.88e-06  \\n\",\n       \"16               0.00924               1.05e-05  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_fine_dt:\\n\",\n    \"    arb_responses_fine_dt, nrn_responses_fine_dt = simulation_runner.run_all(replace_axon, params, dt=fine_dt)\\n\",\n    \"\\n\",\n    \"    l1_results_fine_dt = analyze_voltage_traces_l1(arb_responses_fine_dt, nrn_responses_fine_dt)\\n\",\n    \"\\n\",\n    \"    display(l1_results_fine_dt.sort_values(by='residual_rel_l1_norm', ascending=False).head(5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Fine dt (0.001): test_l5pc OK! The mean relative Arbor-Neuron L1-deviation and error (tol in brackets) are 0.00378 (0.05), 0.00015 (0.0005).\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"if run_fine_dt:\\n\",\n    \"    print_voltage_trace_l1_results('Fine dt ({:,.3g})'.format(fine_dt), l1_results_fine_dt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.000729</td>\\n\",\n       \"      <td>2.09e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0109</td>\\n\",\n       \"      <td>3.88e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.0132</td>\\n\",\n       \"      <td>1.07e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"0         False        0               0.12            0.0406    bAP.soma.v   \\n\",\n       \"1         False        0               0.12            0.0406  Step1.soma.v   \\n\",\n       \"2         False        0               0.12            0.0406  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"   residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"0              0.000729               2.09e-05  \\n\",\n       \"1                0.0109               3.88e-06  \\n\",\n       \"2                0.0132               1.07e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>30</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00164</td>\\n\",\n       \"      <td>2.03e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>31</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00922</td>\\n\",\n       \"      <td>3.45e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>32</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.12</td>\\n\",\n       \"      <td>0.0406</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00295</td>\\n\",\n       \"      <td>0.000155</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"30          True        0               0.12            0.0406    bAP.soma.v   \\n\",\n       \"31          True        0               0.12            0.0406  Step1.soma.v   \\n\",\n       \"32          True        0               0.12            0.0406  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"30               0.00164               2.03e-05  \\n\",\n       \"31               0.00922               3.45e-06  \\n\",\n       \"32               0.00295               0.000155  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00063</td>\\n\",\n       \"      <td>2.49e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.000959</td>\\n\",\n       \"      <td>1.5e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00141</td>\\n\",\n       \"      <td>0.000209</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"3         False        1             0.0592            0.0295    bAP.soma.v   \\n\",\n       \"4         False        1             0.0592            0.0295  Step1.soma.v   \\n\",\n       \"5         False        1             0.0592            0.0295  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"   residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"3               0.00063               2.49e-05  \\n\",\n       \"4              0.000959                1.5e-05  \\n\",\n       \"5               0.00141               0.000209  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>33</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00151</td>\\n\",\n       \"      <td>3.17e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>34</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00166</td>\\n\",\n       \"      <td>3.09e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>35</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0.0592</td>\\n\",\n       \"      <td>0.0295</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00257</td>\\n\",\n       \"      <td>1.49e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"33          True        1             0.0592            0.0295    bAP.soma.v   \\n\",\n       \"34          True        1             0.0592            0.0295  Step1.soma.v   \\n\",\n       \"35          True        1             0.0592            0.0295  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"33               0.00151               3.17e-07  \\n\",\n       \"34               0.00166               3.09e-05  \\n\",\n       \"35               0.00257               1.49e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.000689</td>\\n\",\n       \"      <td>0.00164</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.000847</td>\\n\",\n       \"      <td>0.000212</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.000715</td>\\n\",\n       \"      <td>0.00011</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"6         False        2             0.0769             0.069    bAP.soma.v   \\n\",\n       \"7         False        2             0.0769             0.069  Step1.soma.v   \\n\",\n       \"8         False        2             0.0769             0.069  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"   residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"6              0.000689                0.00164  \\n\",\n       \"7              0.000847               0.000212  \\n\",\n       \"8              0.000715                0.00011  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>36</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00178</td>\\n\",\n       \"      <td>6.03e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>37</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00159</td>\\n\",\n       \"      <td>5.43e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>38</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0.0769</td>\\n\",\n       \"      <td>0.069</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00176</td>\\n\",\n       \"      <td>1.68e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"36          True        2             0.0769             0.069    bAP.soma.v   \\n\",\n       \"37          True        2             0.0769             0.069  Step1.soma.v   \\n\",\n       \"38          True        2             0.0769             0.069  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"36               0.00178               6.03e-05  \\n\",\n       \"37               0.00159               5.43e-06  \\n\",\n       \"38               0.00176               1.68e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.000701</td>\\n\",\n       \"      <td>7.94e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.000948</td>\\n\",\n       \"      <td>4.03e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.000783</td>\\n\",\n       \"      <td>0.00152</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"9          False        3              0.095            0.0678    bAP.soma.v   \\n\",\n       \"10         False        3              0.095            0.0678  Step1.soma.v   \\n\",\n       \"11         False        3              0.095            0.0678  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"9               0.000701               7.94e-05  \\n\",\n       \"10              0.000948               4.03e-05  \\n\",\n       \"11              0.000783                0.00152  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>39</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00177</td>\\n\",\n       \"      <td>0.000205</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>40</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00166</td>\\n\",\n       \"      <td>6.4e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>41</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>3</td>\\n\",\n       \"      <td>0.095</td>\\n\",\n       \"      <td>0.0678</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00177</td>\\n\",\n       \"      <td>3.01e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"39          True        3              0.095            0.0678    bAP.soma.v   \\n\",\n       \"40          True        3              0.095            0.0678  Step1.soma.v   \\n\",\n       \"41          True        3              0.095            0.0678  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"39               0.00177               0.000205  \\n\",\n       \"40               0.00166                6.4e-06  \\n\",\n       \"41               0.00177               3.01e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>12</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00064</td>\\n\",\n       \"      <td>6.72e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>13</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0279</td>\\n\",\n       \"      <td>4.75e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>14</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00573</td>\\n\",\n       \"      <td>1.71e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"12         False        4             0.0553            0.0212    bAP.soma.v   \\n\",\n       \"13         False        4             0.0553            0.0212  Step1.soma.v   \\n\",\n       \"14         False        4             0.0553            0.0212  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"12               0.00064               6.72e-05  \\n\",\n       \"13                0.0279               4.75e-07  \\n\",\n       \"14               0.00573               1.71e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>42</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00152</td>\\n\",\n       \"      <td>0.000961</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>43</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00562</td>\\n\",\n       \"      <td>1.23e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>44</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0.0553</td>\\n\",\n       \"      <td>0.0212</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00433</td>\\n\",\n       \"      <td>4.84e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"42          True        4             0.0553            0.0212    bAP.soma.v   \\n\",\n       \"43          True        4             0.0553            0.0212  Step1.soma.v   \\n\",\n       \"44          True        4             0.0553            0.0212  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"42               0.00152               0.000961  \\n\",\n       \"43               0.00562               1.23e-06  \\n\",\n       \"44               0.00433               4.84e-06  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>15</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00084</td>\\n\",\n       \"      <td>8.6e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>16</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00924</td>\\n\",\n       \"      <td>1.05e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>17</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00782</td>\\n\",\n       \"      <td>3e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"15         False        5             0.0799            0.0189    bAP.soma.v   \\n\",\n       \"16         False        5             0.0799            0.0189  Step1.soma.v   \\n\",\n       \"17         False        5             0.0799            0.0189  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"15               0.00084                8.6e-06  \\n\",\n       \"16               0.00924               1.05e-05  \\n\",\n       \"17               0.00782                  3e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>45</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00174</td>\\n\",\n       \"      <td>1.1e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>46</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00427</td>\\n\",\n       \"      <td>1.27e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>47</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>5</td>\\n\",\n       \"      <td>0.0799</td>\\n\",\n       \"      <td>0.0189</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00258</td>\\n\",\n       \"      <td>5.22e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"45          True        5             0.0799            0.0189    bAP.soma.v   \\n\",\n       \"46          True        5             0.0799            0.0189  Step1.soma.v   \\n\",\n       \"47          True        5             0.0799            0.0189  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"45               0.00174                1.1e-06  \\n\",\n       \"46               0.00427               1.27e-05  \\n\",\n       \"47               0.00258               5.22e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>18</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00102</td>\\n\",\n       \"      <td>3.19e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>19</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00923</td>\\n\",\n       \"      <td>1.11e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>20</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00765</td>\\n\",\n       \"      <td>1.28e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"18         False        6             0.0562            0.0128    bAP.soma.v   \\n\",\n       \"19         False        6             0.0562            0.0128  Step1.soma.v   \\n\",\n       \"20         False        6             0.0562            0.0128  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"18               0.00102               3.19e-05  \\n\",\n       \"19               0.00923               1.11e-05  \\n\",\n       \"20               0.00765               1.28e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>48</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00166</td>\\n\",\n       \"      <td>1.37e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>49</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00312</td>\\n\",\n       \"      <td>2.16e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>6</td>\\n\",\n       \"      <td>0.0562</td>\\n\",\n       \"      <td>0.0128</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00289</td>\\n\",\n       \"      <td>2.39e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"48          True        6             0.0562            0.0128    bAP.soma.v   \\n\",\n       \"49          True        6             0.0562            0.0128  Step1.soma.v   \\n\",\n       \"50          True        6             0.0562            0.0128  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"48               0.00166               1.37e-06  \\n\",\n       \"49               0.00312               2.16e-05  \\n\",\n       \"50               0.00289               2.39e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>21</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00073</td>\\n\",\n       \"      <td>5.21e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>22</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.000964</td>\\n\",\n       \"      <td>1.84e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>23</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.000679</td>\\n\",\n       \"      <td>0.00121</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"21         False        7             0.0589            0.0664    bAP.soma.v   \\n\",\n       \"22         False        7             0.0589            0.0664  Step1.soma.v   \\n\",\n       \"23         False        7             0.0589            0.0664  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"21               0.00073               5.21e-06  \\n\",\n       \"22              0.000964               1.84e-05  \\n\",\n       \"23              0.000679                0.00121  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>51</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00168</td>\\n\",\n       \"      <td>0.00105</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>52</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00105</td>\\n\",\n       \"      <td>0.000202</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>53</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0.0589</td>\\n\",\n       \"      <td>0.0664</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00163</td>\\n\",\n       \"      <td>0.000394</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"51          True        7             0.0589            0.0664    bAP.soma.v   \\n\",\n       \"52          True        7             0.0589            0.0664  Step1.soma.v   \\n\",\n       \"53          True        7             0.0589            0.0664  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"51               0.00168                0.00105  \\n\",\n       \"52               0.00105               0.000202  \\n\",\n       \"53               0.00163               0.000394  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>24</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.000726</td>\\n\",\n       \"      <td>2.44e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.0319</td>\\n\",\n       \"      <td>1.12e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>26</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00768</td>\\n\",\n       \"      <td>2.59e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"24         False        8             0.0708            0.0267    bAP.soma.v   \\n\",\n       \"25         False        8             0.0708            0.0267  Step1.soma.v   \\n\",\n       \"26         False        8             0.0708            0.0267  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"24              0.000726               2.44e-05  \\n\",\n       \"25                0.0319               1.12e-06  \\n\",\n       \"26               0.00768               2.59e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>54</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00158</td>\\n\",\n       \"      <td>3.19e-07</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>55</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00751</td>\\n\",\n       \"      <td>1.78e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>56</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>8</td>\\n\",\n       \"      <td>0.0708</td>\\n\",\n       \"      <td>0.0267</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00359</td>\\n\",\n       \"      <td>8.12e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"54          True        8             0.0708            0.0267    bAP.soma.v   \\n\",\n       \"55          True        8             0.0708            0.0267  Step1.soma.v   \\n\",\n       \"56          True        8             0.0708            0.0267  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"54               0.00158               3.19e-07  \\n\",\n       \"55               0.00751               1.78e-06  \\n\",\n       \"56               0.00359               8.12e-05  \"\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       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>27</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.000788</td>\\n\",\n       \"      <td>5.35e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>28</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.000962</td>\\n\",\n       \"      <td>0.000178</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>29</th>\\n\",\n       \"      <td>False</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.000688</td>\\n\",\n       \"      <td>1.73e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"27         False        9             0.0731            0.0741    bAP.soma.v   \\n\",\n       \"28         False        9             0.0731            0.0741  Step1.soma.v   \\n\",\n       \"29         False        9             0.0731            0.0741  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"27              0.000788               5.35e-05  \\n\",\n       \"28              0.000962               0.000178  \\n\",\n       \"29              0.000688               1.73e-05  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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lzuPnmm4HYNVg7pv81jHvevHm8/PLLPProo9x111288sorLYozFArVizkUClFTU8MXX3zBbbfdxnvvvUePHj24+OKLG72X1amnnsqPf/xjCgoKWLBgAccee+xu+xVpC5XlsRUESe3GNutBavmm5AYkIiIinUrCFrkws27AY8DV7l5vHp/HLh5q9AIiM7vczOab2fz8/I59U8+ePXty1llncc8999TtO/7447nzzjvrthctWgTAsGHDWLhwIQALFy7kiy8av1D+iCOO4OGHH6a2tpb8/Hxee+01pkyZskdx7Rj1eeONN8jJySEnJ4c5c+bwr3/9q+66rwULFjR5HVa8kpISioqK+OpXv8rtt9/O4sWLAfjyl79cd/yDDz7IEUcc0eL4iouLycrKIicnh02bNvH88883Wq9bt25MnjyZq666ipNPPplwONziPkQSpbK8LPYgJYPt4R6kVW5NbkAiIiLSqSQkwTKzFGLJ1YPu/niwe5OZ9Q/K+wObGzvW3e9290nuPmnHdUAd2bXXXltvQYg77riD+fPnM2HCBMaMGcPs2bMBOOOMMygoKGDs2LHcddddjBw5stH2Tj/9dCZMmMBBBx3Esccey6233kq/fv32KKb09HQOPvhgrrjiCu655x5WrlzJqlWr6i3PPnz4cHJycnj33XcbbeOrX/0q69evZ/v27Zx88slMmDCBadOm8etf/xqAO++8k7/+9a9MmDCB+++/n9/+9rctju+ggw7i4IMP5sADD+Tcc8/l8MMPryu78cYb6y0TP3PmTB544AFND5Skqa6MjWBZaialqXl0qy5IckQiIiLSmVhTK9O1uIHY/LT7gAJ3vzpu/6+ArXGLXPR09xuaaAaASZMmecN7Oi1btozRo0e3KkbpWvQ7IW3pwyXzGff4cSw97P9R/snLDCp4h76ztFS7iIiI7GRmC9x9UmNliRjBOhy4ADjWzBYFP18FbgH+y8w+A74SbIuIdGi1wQhWKDWL2sw+9PQiqpu4/lFERESkoVYvcuHubwDWRPFxrW1fRKQ91VSWAxBJz8S69SHFatm8dTN9+g5IcmQiIiLSGSRskQsRka6gtiI2ghVJyyQlJ3Y9ZNFm3QtLREREWkYJlohInGhVbBXBSFoW6bn9ASjbtjGZIYmIiEgnktD7YImIdHa1VbEpgqkZWXRLjd1ou3Lb+mSGJCIiIp2IEiwRkXjVsRGslLRM0rNyAajdrpsNi4iISMtoimALPfnkk5gZH3/8cZN1Vq5cybhx4xLW58UXX8yjjz7aZPnVV1/NwIEDiUajdfvuvfdeevfuzcSJExkzZgx/+tOfEhaPyL7AgymCqRndyO6eR5VHoLTR2/iJiIiI7EIJVgvNmTOHadOmMWfOnEbLaxKwjHNtbW2L60ajUZ544gkGDx7Mf/7zn3plM2fOZNGiRcydO5cf//jHbNqkv76LtJTXVACQlpGFhUIUWC6RsvwkRyUiIiKdhRKsFigpKeGNN97gnnvu4aGHHqrbP3fuXI444ghOPfVUxowZA8QSrfPOO4/Ro0dz5plnUlYW+2v4yy+/zMEHH8z48eO59NJLqaysBGDYsGH84Ac/4JBDDuEf//jHLn2/9NJLTJo0iZEjR/LPf/6zXt9jx47lyiuvbDLp69OnD/vvvz+rVq2q23fHHXcwZswYJkyYwNlnnw1AQUEBp512GhMmTGDq1KksWbIEgFmzZnHRRRdxxBFHMHToUB5//HFuuOEGxo8fz/Tp06murgbgpptuYvLkyYwbN47LL7+chjevjkajDBs2jMLCwrp9I0aMUOInHZJV71zkAqA40pO0yq3JDElEREQ6kc51DdbzP4SNHyS2zX7j4cTd3wP5qaeeYvr06YwcOZK8vDwWLFjAl770JQAWLlzIhx9+yPDhw1m5ciWffPIJ99xzD4cffjiXXnopv//97/nOd77DxRdfzMsvv8zIkSO58MIL+cMf/sDVV18NQF5eHgsXLmy075UrVzJv3jxWrFjBMcccw/Lly0lPT2fOnDmcc845zJgxgx//+MdUV1eTkpJS79jPP/+czz//nAMOOKBu3y233MIXX3xBWlpaXcLz85//nIMPPpgnn3ySV155hQsvvJBFixYBsGLFCl599VWWLl3KYYcdxmOPPcatt97K6aefzrPPPstpp53Gd77zHW688UYALrjgAv75z39yyimn1PUZCoWYMWMGTzzxBJdccgnvvvsuQ4cOpW/fvi0+TSLtxWoqqPIIqaEwAKUpeeRUaRVBERERaRmNYLXAnDlz6kZ7zj777HojRlOmTGH48OF124MHD+bwww8H4Pzzz+eNN97gk08+Yfjw4YwcORKAiy66iNdee63umJkzZzbZ91lnnUUoFGLEiBHst99+fPzxx1RVVfHcc89x2mmn0b17dw499FD+/e9/1x3z8MMPM3HiRM455xz++Mc/0rNnz7qyCRMmcN555/HAAw8QicTy6zfeeIMLLrgAgGOPPZatW7dSXFwMwIknnkhKSgrjx4+ntraW6dOnAzB+/HhWrlwJwKuvvsqhhx7K+PHjeeWVV/joo492eR4zZ87k4YcfBuChhx7a7XMWSSarKafC0uq2q9Ly6F67LYkRiYiISGfSuUawmhlpagsFBQW88sorfPDBB5gZtbW1mBm/+tWvAMjKyqpX38x2u92Yhm00196///1vCgsLGT9+PABlZWVkZGRw8sknA7Fk5q677mq0vWeffZbXXnuNZ555hptvvpkPPtj9iGBaWuyLZigUIiUlpS6eUChETU0NFRUVfOtb32L+/PkMHjyYWbNmUVFRsUs7hx12GMuXLyc/P58nn3ySn/70p7vtVyRZQjUVVJFat12b1Zse24qI1tQQinSuj0wRERFpfxrBasajjz7KBRdcwKpVq1i5ciVr1qxh+PDhvP76643WX716NW+//TYAf//735k2bRqjRo1i5cqVLF++HID777+fo446qkX9/+Mf/yAajbJixQo+//xzRo0axZw5c/jzn//MypUrWblyJV988QUvvvhi3fVeTYlGo6xZs4ZjjjmG//u//6OoqIiSkhKOOOIIHnzwQSB2bVevXr3o3r17i+LbkUz16tWLkpKSJlc9NDNOP/10vv/97zN69Gjy8vJa1L5IewvVVlAZN4JFt76EzSku0DWDIiIi0jwlWM2YM2cOp59+er19Z5xxRpMLS4waNYrf/e53jB49mm3btnHllVeSnp7OX//6V77+9a8zfvx4QqEQV1xxRYv6HzJkCFOmTOHEE09k9uzZRKNR/vWvf3HSSSfV1cnKymLatGk888wzjbZx2WWXMX/+fGprazn//PMZP348Bx98MN/73vfIzc1l1qxZLFiwgAkTJvDDH/6Q++67r4WvDuTm5vLNb36TcePGccIJJzB58uS6stmzZzN79uy67ZkzZ/LAAw9oeqB0aJHaCqpC6XXbKTn9ACjcsjZZIYmIiEgnYg1XfEt4B2bTgd8CYeDP7t7kPL9Jkyb5/Pnz6+1btmwZo0ePbtMYpXPR74S0pSW3HEd6TTEjf/oeAB+89TzjXzibj469l7FHnt7M0SIiIrIvMLMF7j6psbI2HcEyszDwO+BEYAxwjpmNacs+RURaI1JbSXXcCFZ23gAAKgq1kqCIiIg0r62nCE4Blrv75+5eBTwEzGjjPkVE9lqKV1AT3plg5fQZDEBtsRIsERERaV5bJ1gDgTVx22uDfXXM7HIzm29m8/Pz8xttpK2nMUrnod8FaWup0Qpq40awcrrnUu6pULo5iVGJiIhIZ5H0RS7c/W53n+Tuk3r37r1LeXp6Olu3btUXa8Hd2bp1K+np6c1XFtlLKV5VbwQrFA5RYD2IlDX+ByARERGReG19U5d1wOC47UHBvhYbNGgQa9eupanRLdm3pKenM2jQoGSHIV1YmlcSDWfU27c9kkta5dYkRSQiIiKdSVsnWO8BI8xsOLHE6mzg3D1pICUlheHDh7dFbCIiu0ijEo+k1dtXmtKLvCot0y4iIiLNa9Mpgu5eA3wH+DewDHjE3T9qyz5FRFojzauIRuqPYFWl59G9dluSIhIREZHOpK1HsHD354Dn2rofEZHW8poqUqwWT8mst782sw+5Bdvx2mosnJKk6ERERKQzSPoiFyIiHUVleSkAllJ/IRXL7kPInOKtG5IRloiIiHQiSrBERAJVQYJFgxGslO79ACjK36M1ekRERGQfpARLRCRQVVECQCi1foKV0SOWYJVuXd/uMYmIiEjnogRLRCRQVREbwQql1l/koluv2P3RKwo3tntMIiIi0rkowRIRCVRXlAG7jmDlBAlWbbESLBEREdk9JVgiIoHqYAQrnFY/wcrNyaXE0/GSzckIS0RERDoRJVgiIoGayliCFWkwghUKGdtCPQiXKcESERGR3VOCJSISqK0qByCSnrVLWUmkB2mVW9s7JBEREelklGCJiARqgxGslEYSrPLUXnSrVoIlIiIiu6cES0QkEK2KLXKRkp65S1lNRi9yagvbOSIRERHpbJRgiYgEPJgimJrRbdfC7L7kWgklpaXtHJWIiIh0JkqwREQCXh1LsNIamSKY0r0vAFs2rm3XmERERKRzaVWCZWa/MrOPzWyJmT1hZrlxZT8ys+Vm9omZndDqSEVE2lp1GbVupKdn7FKU0WMAAEVb1rV3VCIiItKJtHYE60VgnLtPAD4FfgRgZmOAs4GxwHTg92YWbmVfIiJtyqsrqCCV9NTILmXZvWM3Gy7dohEsERERaVqrEix3f8Hda4LNd4BBweMZwEPuXunuXwDLgSmt6UtEpK1ZdTkVpBEO2S5lPfsNA6B6mxIsERERaVoir8G6FHg+eDwQWBNXtjbYtwszu9zM5pvZ/Pz8/ASGIyKyZ6y6lEpLa7Qso0d/qglj2zVFUERERJq26zyYBszsJaBfI0U/cfengjo/AWqAB/c0AHe/G7gbYNKkSb6nx4uIJEq4ppRy23WJdgBCYbZaHqmlG9o3KBEREelUmk2w3P0ruys3s4uBk4Hj3H1HgrQOGBxXbVCwT0Skw4pUl1IR2nUFwR2KUvqQVbG5HSMSERGRzqa1qwhOB24ATnX3sriip4GzzSzNzIYDI4B5relLRKStpdaWUBluOsEqy+hHjxolWCIiItK01l6DdReQDbxoZovMbDaAu38EPAIsBf4FfNvda1vZl4hIm0qvLaMm0sQUQaA6qz+9fSs1NTVN1hEREZF9W7NTBHfH3Q/YTdnNwM2taV9EpD2leyk1Kd2arpAziLT1NWzevJ4+A4a0X2AiIiLSaSRyFUERkU4t08up3U2CldYzdmlpwcYv2iskERER6WSUYImIAERryaQCT8tuskq3vkMBKNu8qr2iEhERkU5GCZaICFBTXhx7kNp0gtWz33AAqgrWNFlHRERE9m1KsEREgLLthQBYevcm6+T26k+lp0Cx7johIiIijVOCJSIClJdsAyCc0XSCZaEQW0J5pJSub6+wREREpJNRgiUiApQHI1iR3SRYAIUpfciq2NQOEYmIiEhnpARLRAQoL4zdQDgjp/du65Vl9CO3WjcbFhERkcYpwRIRASqLY0lTVs9+u61XkzWAXl5ArW42LCIiIo1QgiUiAkRL8gHonrf7BMtyBxKxKNs2aSVBERER2ZUSLBERwEu3UOpp5HbP2W29tJ5DANimmw2LiIhII5RgiYgAkfItbLMcIuHdfyx26zMMgJJ83WxYREREdqUES0QESKksYHto96NXAHkDYjcbrtbNhkVERKQRCUmwzOxaM3Mz6xVsm5ndYWbLzWyJmR2SiH5ERNpKelUB5ak9m63Xo2dvSj0NinSzYREREdlVqxMsMxsMHA+sjtt9IjAi+Lkc+ENr+xERaUu5NVupSt/9Eu2w42bDvXSzYREREWlUIkawbgduADxu3wzgbx7zDpBrZv0T0JeISMLVVJTSkyJqswe2qH5RSh+yKnWzYREREdlVqxIsM5sBrHP3xQ2KBgLxFyisDfY11sblZjbfzObn5+e3JhwRkb2ydUNsRcBQz6Etql+R0Zecmi1tGZKIiIh0UpHmKpjZS0BjN4b5CfBjYtMD95q73w3cDTBp0iRvprqISMIVbVhBXyC9V8sSrJqsfuQVbiNaU0Mo0uzHqIiIiOxDmv1m4O5faWy/mY0HhgOLzQxgELDQzKYA64DBcdUHBftERDqc0s2xJddz+u3XovrhnIFE1kfZkr+WXv2HtWFkIiIi0tns9RRBd//A3fu4+zB3H0ZsGuAh7r4ReBq4MFhNcCpQ5O4bEhOyiEhiVeavoNrDDBqyf4vqp/UcBMC2jboXloiIiNTXVnNbngO+CiwHyoBL2qgfEZFWSylcwcZwPwanpbWofrfesQH67Zt1LywRERGpL2EJVjCKteOxA99OVNsiIm2pR9kqtmUOrTeveXd69o9dq1W1TQmWiIiI1JeQGw2LiHRWZRWVDIxuoDq3ZdMDAXr0Hki1h4kWa+aziIiI1KcES0T2aatXLCPNqkntN6rFx1goTEGoB5ESJVgiIiJSnxIsEdmnbf38fQB67nfIHh1XGOlNRsXmtghJREREOjElWCKyT6vd8CFRN/odMHGPjitP70P3at0cXUREROpTgiUi+7SMbR+zIdyfcFrWHh1XndWPvOhWolHdH11ERER2UoIlIvu0vuUr2JI1Yo+PC3UfQDeroKBgaxtEJSIiIp2VEiwR2WcVFRYyyDdSlTd6j49N6TEQgIINKxMclYiIiHRmSrBEZJ+17rOFhMxJHzR+j4/t1jt2L6zi/FWJDktEREQ6MSVYIrLPKlq1GIA+B+zZCoIAuf2GAFCxVTcbFhERkZ2UYInIPss3fkSZp9FnSMvvgbVDbu9YglVbpHthiYiIyE5KsERkn5Vd/BnrUoZiofAeHxtKy6SIboR1s2ERERGJowRLRPZJ7s7Aqs8pzN7zFQR3KIz0Ir1iUwKjEhERkc6u1QmWmX3XzD42s4/M7Na4/T8ys+Vm9omZndDafkREEmnLxjX0pBjvvecrCO5QmtaH7KrNCYxKREREOrtIaw42s2OAGcBB7l5pZn2C/WOAs4GxwADgJTMb6e61rQ1YRCQRNn62kN5A1pAJe91GZWY/BpV8grtjZokLTkRERDqt1o5gXQnc4u6VAO6+40+5M4CH3L3S3b8AlgNTWtmXiEjClK5ZAkC/EV/a+0a69SePYgq3lyYoKhEREensWptgjQSOMLN3zew/ZjY52D8QiF+7eG2wbxdmdrmZzTez+fn5+a0MR0SkZSJblrGVHPL6Dtr7NnoMJGTOlg2rExiZiIiIdGbNThE0s5eAfo0U/SQ4vicwFZgMPGJm++1JAO5+N3A3wKRJk3xPjhUR2Vu52z9jXepw8lrRRkbeYACKN6+EUWMSEpeIiIh0bs0mWO7+labKzOxK4HF3d2CemUWBXsA6YHBc1UHBPhGRpPPaGgbWrGJhr9Na1U5O//0BqMj/IgFRiYiISFfQ2imCTwLHAJjZSCAV2AI8DZxtZmlmNhwYAcxrZV8iIglRsPYzMqiCPnu/giBAjwEHxB5sW5WAqERERKQraNUqgsBfgL+Y2YdAFXBRMJr1kZk9AiwFaoBvd8YVBIu3bqKwYBNDRuz9KmMi0vFsWLmMPKD7oNYlWJH0LLbQg3CxrsESERGRmFYlWO5eBZzfRNnNwM2taT/Zlv39BiZueZa3B1/E6DN+RG6PXskOSUQSoGTjcgD6DT2w1W1tTR1AVunaVrcjIiIiXUOrbzTcle1/5k0syT6Cw9b+mZTfjOWd31/Oqk8XJzssEWkl37aSSk+hV/8hrW6rrNtQ+levxqPRBEQmIiIinV1rpwh2ab36D6XXdU/wxZI3yX/pdr606VFS/v4wK8L7sWnQdPocNJ1h4w8jkpKa7FATw51obQ3V1ZXUVFdTU1NDTU0VNdVV1NZUU1NdTW1tDdHqKjxag0droLYGr63GvRaiTtQd92jwEzyO+s7HHnuMOxD8uBO7ReuOx80vJtnam7ruerg1+rAFLe1tBHt52I7j2uOmtq2Ncc/4Xp/TPT+ue8ESNoX7MCQU3ss+d6ruM55eBc+xacNK+g7co0VUpbXcqamuorKynKrKCqoqy6mprKCqqoKaynKi1RWxz6loDdFoFK+tJRqtiSXD0drYPq+F2hqiHsWjtRCtxaPRep9CtuO/wa9a/c+foDTY5XXbRsPfzZ2HGZjVL22w7bvpY9ff+Qbb8ceaNajR8LNuN8c2PKrBe3TX96w13o+ISCtVZPVn1Pgp5GSkJDuUFlGC1QLDJxzO8AmHU7BxFR+/fB95K5/ly6t+D6t+T8lTGaxOG0FJzgjoPZr0vvuTlTeQnN6D6dGrH+Hw3g8S1tZGqagoo6K8lMryUqoqSqkqL6e6spTqyjJqKkqJVpVTW1lGbXU5XlUO1eVQU47VVGDV5VhtBeHaCkK1lUSiFURqK0nxSlKilaR6JWlUkuZVpFFFulUTAtKCH5Gu7P2sabR+/Apy958CH8OaD9/ebYJVUV7K4qfvpN8n95M/7jImfe2aBPTeOXk0SmlJEUVbN7N92ybKi/KpKdmClxbgFYVYVQlUlRKqLiFSXUqktoyU2jLSouWkR8vJoJxUryaNaiLmRICsZD8pERFpMw/UHEdq/78wcXBuskNpEYutSdExTJo0yefPn5/sMFokf+NqVi14kegXr5NduIxB1avItvJ6dao8TJllUEkaVZZGZSgdt3BshMYdBwzH3Il4NalUkeJVpFJFmleTZtV7HV8VESpIo8pSqbI0qi2N6lAaNaE0akPp1IbTqA1nUBtOIxpOJxpOIxpOw8OphMIRCEWwcAoWjsR+QhFC4VRCkdhjQhEIp0AoHKsbimAGZiEsZLF/LUTIDCyEhSAU7LNYxZ1/Pa37S6/hZrjbLn8pbZEdv8vN/Er7LhV2bu/Z22Hv3ju21+85b0Wve9NdK+Lcq2P3sr+96MtxBo44mOycnnvXZ5zq8u1EbxnOwj6ncdi3/7xL+fbCLSz9553st/w+erONWjc+zDqUg274d6v77mjKtxeyZeNqivLXULZlLVWFGwiVbiK1fDOZlflk1Wwjq7aY7r6dVGt63aMKT6HMMii3DCotg6pwJtXhTGoimdREsohGMohG0iGcBpE0bMdPShqhSBrhlHRCKWkQSQ8+yyKxz59wmFA4EnxOhQmFw7F/Q8H+YF8oFPusgtivl+F1nxsejX8fBv+t+x30umMa/l7uqLPjc3/nx9XO/x/UicZvNXzfN/i8atBP/RkAcfFQv66z62yBek15wx4bvM+8wZTYes8hrq6DmdeN7ImI7I2a9F4MHzGWrLSOMzZkZgvcfVJjZR0nyk6md78h9D7pG8A3AIjWRtmwbgVFG1ZQXrCe6qIN+PZNRCtKCNWWE6mtIFJbHpuCAsF0EAtmnRg1oVRqQul4OA2PBF8MIulYSjqhlHRCqemEUjMJp2YSSYv9pKRnkZqeSUp6JqnpWaRldCMtPZNQagapoTBdZOKiSIeWkpHN/KypjMx/gYqyEtIzuwGwfvkS1rxwJ+M2Pc2hVsGHqQex6cg7KXn7r+xX1vmu5YxGnc0FW9m06lNKNi4nuvVzIkWrySxbQ4/K9eRFt5BFBYOpfxPECk9ha6gnReE88tOGsD61B57RA8vsSbhbHmnZeWR0701GTm/Sc3vRrXtv0tPTSG/lNGAREZFkUYKVIKFwiP5DRtB/yIhkhyIi7Sxj2rfIe+EcPv7tiZT1HE33/AUcULOc3h5mUc6x5Bx7NeMmTgPgjeXv0mfly2zbspEevfolOfL6qqpr2LBuJVvXfkrphuVEC74gtXg12RVr6VOzkX5WSHzEJWSwKdyf/PRhrM08DLL7k5LTn8y8gXTvM5i8/kPJzO7JQDMGJu1ZiYiItC8lWCIirTT2y1/l9ZU/Ytwnd5G+/mO+iOzHm/tfwwHHXszkgcPq1e077ihYeSefvfYPpnztu+0e6/btxWxc9QlF6z+jYvPn2LYvyCiNjUL1j25kqFUzNKgbdSM/1IvCtAGs73EE63oMI6PP/nQfMIKeg0bSrXtvummkSUREpB5dgyUikiDVtVFqa6Okpzb9tyuPRlnzy3FUk8qQH80jJcGrkEZro2zZtJYtaz6hZONyarZ8TqRoFd3K19KregN9KKhXv4x0NkX6sz19IFXdhxDO24+svgeQN3gkPQfsj6WkJzQ+ERGRrkDXYImItIOUcIiUZlYOtVCIgsnXMfHda3j3nu8x5fLfYXuwXLxHoxQV5FOw7jNKNq6gIv9zKFxNeulacirX07d2E32smj5xx2wij4LUAazuMZWVucNI7TWc7gNG0GfIKLr17M9wjUKJiIgkjBIsEZF2NnH6Jby9/HUO2ziHL/7nHfKHnkx6v1FYaiZhMyrLiqkuKyZaVkCodBPh0k1kVGymW/VW8qIF5FoFuXHtFXkW+ZF+bM0YzobsIwn1GEpmn/3oMWgkvQePpG96Jn2T9WRFRET2MZoiKCKSBNHaKO889Qd6fXgPI6MrmqxX7qlsDfWkONKL8vTe1Gb2xbsPJJI3nG799qPPkFHk9shr9c23RUREpOV2N0VQCZaISBK5O1u35LNl7SdEqyqJRmtJ65ZLVnYu2bl5dOveEwvt/Q3LRUREJPHa7BosM5sIzAbSgRrgW+4+z2J/Sv0t8FWgDLjY3Re2pi8Rka7IzOjVuw+9evdpvrKIiIh0eK39s+itwC/cfSJwY7ANcCIwIvi5HPhDK/sRERERERHp8FqbYDnQPXicA6wPHs8A/uYx7wC5Zta/lX2JiIiIiIh0aK1dRfBq4N9mdhuxZO3Lwf6BwJq4emuDfRsaNmBmlxMb5WLIkCGtDEdERERERCR5mk2wzOwloF8jRT8BjgOucffHzOws4B7gK3sSgLvfDdwd9JVvZqv25Ph20AvYkuwgpN3ofO87dK73HTrX+xad732HzvW+oyOe66FNFbRqFUEzKwJy3d2DhS2K3L27mf0RmOvuc4J6nwBHu/suI1gdnZnNb2qFEOl6dL73HTrX+w6d632Lzve+Q+d639HZznVrr8FaDxwVPD4W+Cx4/DRwocVMJZZ4dbrkSkREREREZE+09hqsbwK/NbMIUEFwLRXwHLEl2pcTW6b9klb2IyIiIiIi0uG1KsFy9zeALzWy34Fvt6btDuTuZAcg7Urne9+hc73v0Lnet+h87zt0rvcdnepct+oaLBEREREREdmptddgiYiIiIiISEAJloiIiIiISIIowdoNM5tuZp+Y2XIz+2Gy45HEMbPBZvaqmS01s4/M7Kpgf08ze9HMPgv+7ZHsWCUxzCxsZu+b2T+D7eFm9m7w/n7YzFKTHaMkhpnlmtmjZvaxmS0zs8P03u6azOya4DP8QzObY2bpem93HWb2FzPbbGYfxu1r9L0crFx9R3Del5jZIcmLXPZUE+f6V8Hn+BIze8LMcuPKfhSc60/M7ISkBL0bSrCaYGZh4HfAicAY4BwzG5PcqCSBaoBr3X0MMBX4dnB+fwi87O4jgJeDbekargKWxW3/H3C7ux8AbAO+kZSopC38FviXux8IHETsvOu93cWY2UDge8Akdx8HhIGz0Xu7K7kXmN5gX1Pv5ROBEcHP5cAf2ilGSYx72fVcvwiMc/cJwKfAjwCC72tnA2ODY34ffG/vMJRgNW0KsNzdP3f3KuAhYEaSY5IEcfcN7r4weLyd2BewgcTO8X1BtfuA05ISoCSUmQ0CTgL+HGwbsXv3PRpU0bnuIswsBzgSuAfA3avcvRC9t7uqCJAR3C4mE9iA3ttdhru/BhQ02N3Ue3kG8DePeQfINbP+7RKotFpj59rdX3D3mmDzHWBQ8HgG8JC7V7r7F8RuCzWl3YJtASVYTRsIrInbXhvsky7GzIYBBwPvAn3jboq9EeibrLgkoX4D3ABEg+08oDDug1vv765jOJAP/DWYEvpnM8tC7+0ux93XAbcBq4klVkXAAvTe7uqaei/re1vXdinwfPC4w59rJViyTzOzbsBjwNXuXhxfFtzPTfcx6OTM7GRgs7svSHYs0i4iwCHAH9z9YKCUBtMB9d7uGoJrb2YQS6oHAFnsOsVIujC9l/cNZvYTYpd2PJjsWFpKCVbT1gGD47YHBfukizCzFGLJ1YPu/niwe9OOKQXBv5uTFZ8kzOHAqWa2kthU32OJXaOTG0wrAr2/u5K1wFp3fzfYfpRYwqX3dtfzFeALd89392rgcWLvd723u7am3sv63tYFmdnFwMnAeb7z5r0d/lwrwWrae8CIYDWiVGIX0z2d5JgkQYJrcO4Blrn7r+OKngYuCh5fBDzV3rFJYrn7j9x9kLsPI/Y+fsXdzwNeBc4MqulcdxHuvhFYY2ajgl3HAUvRe7srWg1MNbPM4DN9x7nWe7tra+q9/DRwYbCa4FSgKG4qoXRCZjad2PT+U929LK7oaeBsM0szs+HEFjaZl4wYm2I7k0FpyMy+SuzajTDwF3e/ObkRSaKY2TTgdeADdl6X82Ni12E9AgwBVgFnuXvDC2ylkzKzo4Hr3P1kM9uP2IhWT+B94Hx3r0xieJIgZjaR2IImqcDnwCXE/qCo93YXY2a/AGYSmz70PnAZsWsx9N7uAsxsDnA00AvYBPwceJJG3stBkn0XsWmiZcAl7j4/CWHLXmjiXP8ISAO2BtXecfcrgvo/IXZdVg2xyzyeb9hmMinBEhERERERSRBNERQREREREUkQJVgiIiIiIiIJogRLREREREQkQZRgiYiIiIiIJIgSLBERERERkQRRgiUiIiIiIpIgSrBEREREREQSRAmWiIiIiIhIgijBEhERERERSRAlWCIiIiIiIgmiBEtERERERCRBlGCJiIiIiIgkiBIsEZEOxsyGmZmbWSTZsci+wcw+MrOjkx2HiEhXoARLREQ6PTObbWYlwU+VmVXHbT+f7Pg6Oncf6+5zE9mmmQ00s6fMrMDM1prZFYlsX0SkozJ3T3YMIiJdiplF3L2mFccPA74AUlrTzr7KzGYBB7j7+Y2UterctKfOFGtjzOxVYDFwPTAGeBU4w91fTWpgIiJtTCNYIiIJYGYrzewHZrYEKDWziJlNNbO3zKzQzBbHT8Eys7lm9r9mNs/MioO/9Pdsou1LzGyZmW03s8/N7L8blM8ws0VBOyvMbHqwP8fM7jGzDWa2zsx+aWbhZp7H/mb2ipltNbMtZvagmeXGlRWY2SHB9gAzy9/xvMzs1GCqWWHw/EY3eH2uM7MlZlZkZg+bWfqev9J7rolz42Z2QFyde83sl3HbJwevaWFwDie0sK+jg9GaHwev30ozOy+u/CQzez84V2uCZHBH2Y6pod8ws9XAK8H+f5jZxuB1e83MxjaI+/dm9nwwWvemmfUzs9+Y2TYz+9jMDm7ha/SVljzHFr4O3YCjgZvdvdrdFwOPApcmqg8RkY5KCZaISOKcA5wE5AJ9gWeBXwI9geuAx8ysd1z9C4l94ewP1AB3NNHuZuBkoDtwCXB7XJIzBfgbsVGCXOBIYGVw3L1BuwcABwPHA5c18xwM+F9gADAaGAzMAnD3FcAPgAfMLBP4K3Cfu881s5HAHOBqoDfwHPCMmaXGtX0WMB0YDkwALm40ALNpQWLT1M+0Zp5DY+rOTXOjQkFC8hfgv4E84I/A02aW1sK++gG9gIHARcDdZjYqKCsldt5zg3iuNLPTGhx/FLHX/oRg+3lgBNAHWAg82KD+WcBPgz4rgbeDer2IJTW/bmHcjTKzH+7ufDR1WIN/dzwe15pYREQ6AyVYIiKJc4e7r3H3cuB84Dl3f87do+7+IjAf+Gpc/fvd/UN3LwV+BpzV2AiTuz/r7is85j/AC8ARQfE3gL+4+4tBP+vc/WMz6xv0dbW7l7r7ZuB24OzdPQF3Xx60Venu+cS+nB8VV/4nYDnwLrHE8CdB0Uzg2eDYauA2IAP4coPXZ727FwDPABObiOENd8/dzc8bu3sOTYg/N825HPiju7/r7rXufh+xxGXqHvT3s+A1/A+xRPssAHef6+4fBOdqCbGk9KgGx84Kzll5cMxf3H27u1cSS3YPMrOcuPpPuPsCd68AngAq3P1v7l4LPEwsud5r7n7L7s5HE8dsB94EfmZm6cEfBM4AMlsTi4hIZ6AES0QkcdbEPR4KfL3BX/qnEUtKGqu/CkghNupQj5mdaGbvBNPzCoklTjvqDQZWNBLL0KC9DXH9/5HYKEiTzKyvmT0UTCksBh5oJKY/ERuJuDP40g+xEa9VOyq4ezR4fgPjjtsY97gM6La7WBJsTfNV6gwFrm1w7gYTe44tsS1ImndYteNYMzvUzF4NplYWAVew6+tbF6uZhc3sFotN/Sxm5+hk/DGb4h6XN7Ldnq9zvPOIjVauAf5A7HdpbZJiERFpN0qwREQSJ37VoDXERqji/9qf5e63xNUZHPd4CFANbIlvMJiW9hixEaG+wYjBc+ycerUG2L+RWNYQG3XpFdd/d3cf20jdeP8TPI/x7t6d2Ehc3TSv4Nqa3wD3ALNs53Vj64klJjvqWfD81jXT3y7M7AjbuQJgYz9HNN/KLhqu6FRG/dGUfnGP1xC7dij+3GW6+5wW9tXDzLLitocQe30A/g48DQx29xxgNvWn0TWM9VxgBvAVIAcYFuxveEybCa4na/J8NHWcu69y95Pdvbe7H0osKZzXXnGLiCSLEiwRkbbxAHCKmZ0QjEKkBwsgDIqrc76ZjQmuZ7oJeDSY1hUvFUgD8oEaMzuR2LVUO9wDXGJmx5lZyGJLYx/o7huITSX8f2bWPSjb38waTkdrKBsoAYrMbCCxa7vi/RaY7+6XEZv6NjvY/whwUhBHCnAtsQTvreZeqIbc/XV377abn9f3tM1GLALODc7NdOpP0/sTcEUw2mRmlmWxxSmyoW5hiXubaf8XZpYaJIMnA/8I9mcDBe5eEVw/d24z7WQTex23EksI/2cPnmNCuPv/7O58NHWcmY02s+zgdTif2O9tq64HExHpDJRgiYi0AXdfQ2zk4cfEkqM1xJKV+M/d+4ktRLERSAe+10g724P9jwDbiH0hfzqufB7BwhdAEfAfdo4kXUgsQVsaHPso9acoNuYXwCFBW88Cj+8oMLMZxBapuDLY9X3gEDM7z90/ITbadSexUbhTgFPcvaqZ/pLlKmIxFhKbyvbkjgJ3nw98E7iL2Ou2nPoLcgwmdn1RUzYGx60ntiDFFe7+cVD2LeAmM9sO3EjsvO7O34hNMVxH7Dy+09wT60BOAD4n9lpcAUwPrusTEenSdB8sEZEkMLO5wAPu/udkxyItF6yKuBiYECzm0bD8aGLndVDDMhER2TdEkh2AiIhIZxGMyI1utqKIiOyzWj1F0MwGBysiLbXYDSavCvb3NLMXzeyz4N8erQ9XRERay8xmN7Fgwezmj5bOyMyG7GahiiHJjk9EpCtp9RRBM+sP9Hf3hcEFwAuA04jNVy9w91vM7IdAD3f/QSvjFRERERER6bBaPYLl7hvcfWHweDuwjNh9T2YA9wXV7iOWdImIiIiIiHRZCV3kwsyGAa8RuwHl6h13eA/uh7KtsTu+m9nlwOUAWVlZXzrwwAMTFo+IiIiIiEiiLViwYIu7926sLGEJVnDzyf8Quznj42ZWGJ9Qmdk2d9/tdViTJk3y+fPnJyQeERERERGRtmBmC9x9UmNlCbkPVnBTyceAB919xz1TNgXXZ+24TmtzIvoSERERERHpqBKxiqAB9wDL3D3+Du1PAxcFjy8CnmptXyIiIiIiIh1ZIkawDgcuAI41s0XBz1eBW4D/MrPPgK8E2yIiXZq78/iC1RSV7XIPWhEREdkHtPpGw+7+BmBNFB/X2varq6tZu3YtFRUVrW1K9jHp6ekMGjSIlJSUZIci+5AP3nyer710Dr/5cDZXX3ROssMRERGRdtbqBKutrV27luzsbIYNG0ZsNqJI89ydrVu3snbtWoYPH57scGQfUvrpqwB8ZdVvACVYIiIi+5qELHLRlioqKsjLy1NyJXvEzMjLy9PIp7S72tJtAPSszSeRt8EQERGRzqHDJ1iAkivZK/q9kWRIL98IQD8K2La9NMnRiIiISHvrFAmWiEhn0b1qEwAhc/LXrEhyNCIiItLelGC1gJlx7bXX1m3fdtttzJo1K3kBxXnnnXc49NBDmThxIqNHj66La+7cubz11lutanv69Onk5uZy8sknJyBSkX1Dz9p81lk/AIo3Lk9yNCIiItLelGC1QFpaGo8//jhbtmxJaLvuTjQabVUbF110EXfffTeLFi3iww8/5KyzzgISk2Bdf/313H///a1qQ2Sf4k5OtJgN2eMAqNyyMrnxiIiISLvr8KsIxvvFMx+xdH1xQtscM6A7Pz9l7G7rRCIRLr/8cm6//XZuvvnmemX5+flcccUVrF69GoDf/OY3HH744cyaNYtu3bpx3XXXATBu3Dj++c9/AnDCCSdw6KGHsmDBAp577jnuuusunn/+ecyMn/70p8ycOZO5c+cya9YsevXqxYcffsiXvvQlHnjggV2uK9q8eTP9+/cHIBwOM2bMGFauXMns2bMJh8M88MAD3HnnnRx44IFNxrlixQqWL1/Oli1buOGGG/jmN78JwHHHHcfcuXN3+9r84x//4Be/+AXhcJicnBxee+01KioquPLKK5k/fz6RSIRf//rXHHPMMdx77708+eSTlJaW8tlnn3HddddRVVXF/fffT1paGs899xw9e/bkT3/6E3fffTdVVVUccMAB3H///WRmZtbrd+rUqdxzzz2MHRs7d0cffTS33XYbkyZN2m28Im3JaypJsVpqug+FYqjdvjnZIYmIiEg70whWC33729/mwQcfpKioqN7+q666imuuuYb33nuPxx57jMsuu6zZtj777DO+9a1v8dFHHzF//nwWLVrE4sWLeemll7j++uvZsGEDAO+//z6/+c1vWLp0KZ9//jlvvvnmLm1dc801jBo1itNPP50//vGPVFRUMGzYMK644gquueYaFi1axBFHHLHbOJcsWcIrr7zC22+/zU033cT69etb/LrcdNNN/Pvf/2bx4sU8/fTTAPzud7/DzPjggw+YM2cOF110Ud1qfh9++CGPP/447733Hj/5yU/IzMzk/fff57DDDuNvf/sbAF/72td47733WLx4MaNHj+aee+7Zpd+ZM2fyyCOPALBhwwY2bNig5EqSrqKsBICa9B5sJ5NQWX6SIxIREZH21qlGsJobaWpL3bt358ILL+SOO+4gIyOjbv9LL73E0qVL67aLi4spKSnZbVtDhw5l6tSpALzxxhucc845hMNh+vbty1FHHcV7771H9+7dmTJlCoMGDQJg4sSJrFy5kmnTptVr68Ybb+S8887jhRde4O9//ztz5sxpdNRpd3HOmDGDjIwMMjIyOOaYY5g3bx6nnXZai16Xww8/nIsvvpizzjqLr33ta3XP6bvf/S4ABx54IEOHDuXTTz8F4JhjjiE7O5vs7GxycnI45ZRTABg/fjxLliwBYknYT3/6UwoLCykpKeGEE07Ypd+zzjqL448/nl/84hc88sgjnHnmmS2KV6QtVZQWkwGEUrMoDuWSWrE12SGJiIhIO+tUCVayXX311RxyyCFccskldfui0SjvvPMO6enp9epGIpF611fF348pKyurRf2lpaXVPQ6Hw9TU1DRab//99+fKK6/km9/8Jr1792br1l2/1DUVJ+y6nPmeLG8+e/Zs3n33XZ599lm+9KUvsWDBgt3Wj39OoVCobjsUCtU9v4svvpgnn3ySgw46iHvvvbfRhHHgwIHk5eWxZMkSHn74YWbPnt3imEXaSkXZdgAsLYuSlJ5kVCnBEhER2ddoiuAe6NmzJ2eddVa9KWvHH388d955Z932okWLABg2bBgLFy4EYOHChXzxxReNtnnEEUfw8MMPU1tbS35+Pq+99hpTpkxpcUzPPvts3c1MP/vsM8LhMLm5uWRnZ7N9+/Zm4wR46qmnqKioYOvWrcydO5fJkye3uP8VK1Zw6KGHctNNN9G7d2/WrFnDEUccwYMPPgjAp59+yurVqxk1alSL29y+fTv9+/enurq6rp3GzJw5k1tvvZWioiImTJjQ4vZF2kpVRWxUOJKWRWVqT7rVFiY3IBEREWl3SrD20LXXXltvNcE77riD+fPnM2HCBMaMGVM3knLGGWdQUFDA2LFjueuuuxg5cmSj7Z1++ulMmDCBgw46iGOPPZZbb72Vfv36tTie+++/n1GjRjFx4kQuuOACHnzwQcLhMKeccgpPPPEEEydO5PXXX28yToAJEyZwzDHHMHXqVH72s58xYMAAIJb8ff3rX+fll19m0KBB/Pvf/wZi0xJ3XG91/fXXM378eMaNG8eXv/xlDjroIL71rW8RjUYZP348M2fO5N577603ctWc/+//+/849NBDOfzwwznwwAPr9j/99NPceOONddtnnnkmDz30UN3KiSLJVhWMYIXTu1GT0Yse0cK6P4CIiIjIvsE60v/8J02a5PPnz6+3b9myZYwePTpJEXV9DVc77Gr0+yPtadlrjzL6lW+wZPpjVH/yIl/6YjZF120kp1tG8weLiIhIp2FmC9y90RXWNIIlIpIgNcEUwdSMbMLZfQDYtqXlq3KKiIhI56dFLvZxs2bNSnYIIl1GbWUpAKmZ3Yjm9AVg+5b1MGz/ZIYlIiIi7ajNR7DMbLqZfWJmy83sh23dn4hIskSDBCstI5vMHrFrKcu2bUxmSCIiItLO2jTBMrMw8DvgRGAMcI6ZjWnLPkVEksUrY1ME07Oyye4VWyymulgJloiIyL6kracITgGWu/vnAGb2EDADWLrbo0REOiGvKiPqRmZmNilpsfvJ1W7PT3JUIiIi0p7aeorgQGBN3PbaYJ+ISNdTVUo5qaSnholk5FBJCqEyJVgiIiL7kqSvImhml5vZfDObn5/fcb+IPPnkk5gZH3/8cZN1Vq5cybhx4xLW5yeffMLRRx/NxIkTGT16NJdffjkQu0nwc88916q2L730Uvr06ZPQeEX2dVZTRjnpmBmYUWi5pJRvaf5AERER6TLaOsFaBwyO2x4U7Kvj7ne7+yR3n9S7d+82DmfvzZkzh2nTpjFnzpxGy2tqalrdR21tbb3t733ve1xzzTUsWrSIZcuW8d3vfhdITIJ18cUX869//atVbYhIfaGacips5021t0dySa3alsSIREREpL219TVY7wEjzGw4scTqbODcvW7t+R/Cxg8SFFqg33g48ZbdVikpKeGNN97g1Vdf5ZRTTuEXv/gFAHPnzuVnP/sZPXr04OOPP+aFF16gpqaG8847j4ULFzJ27Fj+9re/kZmZycsvv8x1111HTU0NkydP5g9/+ANpaWkMGzaMmTNn8uKLL3LDDTdw9tln1/W7YcMGBg0aVLc9fvx4qqqquPHGGykvL+eNN97gRz/6ESeffDLf/e53+fDDD6murmbWrFnMmDGDe++9lyeeeIKioiLWrVvH+eefz89//nMAjjzySFauXLnb5/2f//yHq666CgAz47XXXqNbt27ccMMNPP/885gZP/3pT5k5cyZz587l5z//Obm5uXzwwQecddZZjB8/nt/+9reUl5fz5JNPsv/++/PMM8/wy1/+kqqqKvLy8njwwQfp27dvvX7PPvtsLrjgAk466SQglgyefPLJnHnmmS07pyJJEq4upcJ23lS4IqUHmRUFSYxIRERE2lubjmC5ew3wHeDfwDLgEXf/qC37bAtPPfUU06dPZ+TIkeTl5bFgwYK6soULF/Lb3/6WTz/9FIhN6/vWt77FsmXL6N69O7///e+pqKjg4osv5uGHH+aDDz6gpqaGP/zhD3Vt5OXlsXDhwnrJFcA111zDsccey4knnsjtt99OYWEhqamp3HTTTcycOZNFixYxc+ZMbr75Zo499ljmzZvHq6++yvXXX09paWy56Hnz5vHYY4+xZMkS/vGPfzB//vwWP+/bbruN3/3udyxatIjXX3+djIwMHn/8cRYtWsTixYt56aWXuP7669mwYQMAixcvZvbs2Sxbtoz777+fTz/9lHnz5nHZZZdx5513AjBt2jTeeecd3n//fc4++2xuvfXWXfqdOXMmjzzyCABVVVW8/PLLdcmWSEcWqS2nKpRet12Vlkf3aGHyAhIREZF21+Y3Gnb354DWzWfboZmRprYyZ86cupGcs88+mzlz5vClL30JgClTpjB8+PC6uoMHD+bwww8H4Pzzz+eOO+7gv/7rvxg+fDgjR44E4KKLLuJ3v/sdV199NRBLKBpzySWXcMIJJ/Cvf/2Lp556ij/+8Y8sXrx4l3ovvPACTz/9NLfddhsAFRUVrF69GoD/+q//Ii8vD4Cvfe1rvPHGG0yaNKlFz/vwww/n+9//Pueddx5f+9rXGDRoEG+88QbnnHMO4XCYvn37ctRRR/Hee+/RvXt3Jk+eTP/+/QHYf//9Of7444HYyNurr74KwNq1a5k5cyYbNmygqqqq3mu3w4knnshVV11FZWUl//rXvzjyyCPJyMjYpZ5IRxOpLac8tPN3NZrZi54FRVTX1JISCScxMhEREWkvSV/koqMrKCjglVde4bLLLmPYsGH86le/4pFHHsHdAcjKyqpX38x2u92Yhm3EGzBgAJdeeilPPfUUkUiEDz/8cJc67s5jjz3GokWLWLRoEatXr2b06NF7Hc8OP/zhD/nzn/9MeXk5hx9++G4X+ABIS9t57UkoFKrbDoVCddeoffe73+U73/kOH3zwAX/84x+pqKjYpZ309HSOPvpo/v3vf/Pwww83mYCKdDSp0QqqwztHsKxbL9KtmsJCXYclIiKyr1CC1YxHH32UCy64gFWrVrFy5UrWrFnD8OHDef311xutv3r1at5++20A/v73vzNt2jRGjRrFypUrWb58OQD3338/Rx11VLN9/+tf/6K6uhqAjRs3snXrVgYOHEh2djbbt2+vq3fCCSdw55131iV977//fl3Ziy++SEFBQd11UDtG11pixYoVjB8/nh/84AdMnjyZjz/+mCOOOIKHH36Y2tpa8vPzee2115gyZUqL2ywqKmLgwNhK/ffdd1+T9WbOnMlf//pXXn/9daZPn97i9kWSKSVaTk145whWSnYfAIq2bkhWSCIiItLOlGA1Y86cOZx++un19p1xxhlNriY4atQofve73zF69Gi2bdvGlVdeSXp6On/961/5+te/zvjx4wmFQlxxxRXN9v3CCy8wbtw4DjroIE444QR+9atf0a9fP4455hiWLl3KxIkTefjhh/nZz35GdXU1EyZMYOzYsfzsZz+ra2PKlCmcccYZTJgwgTPOOKNueuA555zDYYcdxieffMKgQYO45557AJg9ezazZ88G4De/+Q3jxo1jwoQJpKSkcOKJJ3L66aczYcIEDjroII499lhuvfVW+vXr1+LXc9asWXz961/nS1/6Er169arbP3/+fC677LK67eOPP57//Oc/fOUrXyE1NbXF7YskU7pXUBvJrNtOzYkt4FJasDFZIYmIiEg7sx2jHh3BpEmTvOEiDMuWLaub7iZ75t5772X+/PncddddyQ4lafT7I+2pdFY/FuadzBHf/TMAaz58i8GPnsi7h97FoSdekOToREREJFHMbIG7N7qwgUawREQSwZ10KvCUnSNY3XvFRnerijvuTdRFREQksdp8FUFJnosvvpiLL7442WGI7BtqKgnjROOmCGb3iCVY0ZLNyYpKRERE2lmnGMHqSNMYpfPQ7420p+qKktiD1J2rgobSMikhg3DZliRFJSIiIu2twydY6enpbN26VV+WZY+4O1u3biU9Pb35yiIJUF5aDEAorf5tF7aHcohUFCQjJBEREUmCDj9FcNCgQaxdu5b8fF3DIHsmPT2dQYMGJTsM2UdUlsVundAwwSqJ9CC9SgmWiIjIvqLDJ1gpKSkMHz482WGIiOxWVZBghRskWBWpPckqXZeMkERERCQJOvwUQRGRzqCqPHYNViS9W7391el55ESLkhGSiIiIJIESLBGRBKgKFrlIyeheb79n5NGDYiqqapIRloiIiLQzJVgiIglQUx6bIpiaUX8EK5TdmxSrpaBA15GKiIjsC5RgiYgkQG1lKQCpmfUTrEh2XwC2b1nf7jGJiIhI+1OCJSKSALXBFMGMzOx6+zNy+wBQWrCx3WMSERGR9qcES0QkAaJVsRGs9AYJVree/QGoKNrU7jGJiIhI+2tVgmVmvzKzj81siZk9YWa5cWU/MrPlZvaJmZ3Q6khFRDowryql1o2srPpTBLv3iiVYNdt1DZaIiMi+oLUjWC8C49x9AvAp8CMAMxsDnA2MBaYDvzezcCv7EhHpuKpKKSOdtJT6H3UZObEpgpRuTkJQIiIi0t5alWC5+wvuvmPt4XeAQcHjGcBD7l7p7l8Ay4EprelLRKQjs+pyKkjDzOrvj6RSRDdCZVuSFJmIiIi0p0Reg3Up8HzweCCwJq5sbbBvF2Z2uZnNN7P5+fmaQiMinVOouowKS2+0rDjcg7SKre0ckYiIiCRDpLkKZvYS0K+Rop+4+1NBnZ8ANcCDexqAu98N3A0wadIk39PjRUQ6gnBtGVWhxhOskpSeZFYrwRIREdkXNJtguftXdlduZhcDJwPHufuOBGkdMDiu2qBgn4hIlxSuKafSMhotq0zNo2flsnaOSERERJKhtasITgduAE5197K4oqeBs80szcyGAyOAea3pS0SkI0upLac63PgIVk1GL3pEC9n5NygRERHpqpodwWrGXUAa8GJwYfc77n6Fu39kZo8AS4lNHfy2u9e2si8RkQ4rNVpGcWrPRsu8Wx+yrZyikhJysrMbrSMiIiJdQ6sSLHc/YDdlNwM3t6Z9EZHOIiNaSnVK48lTJLsvANs2ryMn+8D2DEtERETaWSJXERQR2Wd18xJqU3MaLUvLja0TtH3r+vYMSURERJJACZaISCt5bTVZVBBN695oeVZefwDKt21oz7BEREQkCZRgiYi0UmVJIQCW0fgIVk6v2G0Aqwo3tldIIiIikiRKsEREWqm0cAsAoYwejZZ3D0awvGRzu8UkIiIiyaEES0Sklcq2x24iHMlqPMEKpWawnUysNL89wxIREZEkUIIlItJK5dsLAEjNym2yTlGoB6kVW9opIhEREUkWJVgiIq1UtX0bAOnZjd8HC6A0pScZVQXtFZKIiIgkiRIsEZFWqi6NJU6ZOXlN1qlIyyO7VgmWiIhIV6cES0SklaqDa7B69OrbZJ2ajF7kRgtx9/YKS0RERJJACZaISCtZ6WZKPIPsbo3fBwuArN7kWinFJWXtF5iIiIi0OyVYIiKtFCnPZ1soFzNrsk44uw8ABfnr2issERERSQIlWCIirZRWuZXicNMLXACk5/YDYPuWDe0RkoiIiCSJEiwRkVbqVr2V8tSmF7gAyOo5AICybUqwREREujIlWCIirZQb3UZVeq/d1uneK5ZgVRVtbI+QREREJEmUYImItEJ1ZRndKcWzeu+2XnZeLMGKbt/cHmGJiIhIkiQkwTKza83MzaxXsG1mdoeZLTezJWZ2SCL6ERHpaPLXrAAg0nPIbuuF0rtRRjqhsvz2CEtERESSpNUJlpkNBo4HVsftPhEYEfxcDvyhtf2IiHREhes/BSCz7wHN1i0K5ZJSvqWtQxIREZEkSsQI1u3ADUD83TNnAH/zmHeAXDPrn4C+REQ6lPJNsRGsnoNGNlu3NKUnGdVb2zokERERSaJWJVhmNgNY5+6LGxQNBNbEba8N9jXWxuVmNt/M5ufna+qMiHQu0YIvqPAU+gzY/RRBgIrUPLrVbGuHqERERCRZIs1VMLOXgH6NFP0E+DGx6YF7zd3vBu4GmDRpkjdTXUSkQ0kt+oKN4X4MizT7cUpNRi96FC8mGnVCoaZvSiwiIiKdV7PfCNz9K43tN7PxwHBgsZkBDAIWmtkUYB0wOK76oGCfiEiX0rd8OWuyxjGsBXU9qzc92E5RaTk9sjPbOjQRERFJgr2eIujuH7h7H3cf5u7DiE0DPMTdNwJPAxcGqwlOBYrcXXfXFJEupbxoK/08n8q8MS2qH+7el5A527bo41BERKSraqv7YD0HfA4sB/4EfKuN+hERSZqVS98FIGvIxBbVT8uJzbYu3rK+rUISERGRJGv+ooEWCkaxdjx24NuJaltEpCPa/unrAAydcESL6mflxRZTLdumESwREZGuqq1GsEREuryMDe/yeWgoPXu37C4UOXmxxVSriza1ZVgiIiKSREqwRET2QmnJdvYv/5Atvaa0+JhuvWKJWO12JVgiIiJdlRIsEZG98PFbT5NplWRNOKXFx1hadypJJVSqe/6JiIh0VUqwRET2xgePUkQWI6ec0PJjzCgK5RCp2Np2cYmIiEhSKcESEdlDBZvXM674dT7ufSIpqel7dGxJpCcZVUqwREREuiolWCIie2jFM7eSQg19j9vzxVIr0vLoVrOtDaISERGRjkAJlojIHijeuokxa+Ywv9tRDDvwkD0+viajF7nRbUSj3gbRiYiISLIpwRIR2QPL/n4DGV5J3ld/ulfHe1Zv8iimsKwywZGJiIhIR6AES0SkhT5463kO3fok7/U9i/3HTt6rNsLZfYlYlIItWqpdRESkK1KCJSLSAoX5G+j9wnfYYH2YcOGv9rqdtJx+ABRvWZeo0ERERKQDUYIlItKMmqpK1vz5XHp6IcWn3ENGt5y9biujRyzBqijcmKjwREREpANRgiUishsejfL+Hy5lfOVCFh90I6MOObJV7XXrNRCAaiVYIiIiXZISLBGR3XjrLz9g8rZ/8tbAS5j8tata3V73vAEAREs2t7otERER6XiUYImINOHtOf/L4WvvZkGP6Rz2jV8npM1QZg+qiWCl+QlpT0RERDoWJVgiIo2Y//RsDvvkFhZlHs7Ebz+AhRL0cWlGoeWQUrElMe2JiIhIh9Lqbwxm9l0z+9jMPjKzW+P2/8jMlpvZJ2Z2Qmv7ERFpLx++8hATF/yIj1IP4sDv/oNwJCWh7W+P9CC9cmtC2xQREZGOIdKag83sGGAGcJC7V5pZn2D/GOBsYCwwAHjJzEa6e21rAxYRaUvL5/2LA/7zHT6P7M/gbz9JekZWwvsoT8mjm0awREREuqTWjmBdCdzi7pUA7r7jqu0ZwEPuXunuXwDLgSmt7EtEpE2t+WQhfZ+7hI2hPvS4/Cm65/Rsk36q0/PIjha1SdsiIiKSXK1NsEYCR5jZu2b2HzObHOwfCKyJq7c22LcLM7vczOab2fz8fF30LSLJsS1/A6GHzqaSVEIXPkHvvo1+ZCVEbWYeeV5EVbUG9UVERLqaZqcImtlLQL9Gin4SHN8TmApMBh4xs/32JAB3vxu4G2DSpEm+J8eKiCRCbXUlG//0dfaLFrDq1EcYOXxU23bYrQ9pVs3mwgL69O7dtn11Al5bQ2VZMZUV5VRVVVJVWUlVVSW1NVWEvAYzMAtjZlgojIXCYCFCodDOx+Ew4XAk9m8ogkUihMNhIuEIoXCkrsxC4WQ/XRER6eKaTbDc/StNlZnZlcDj7u7APDOLAr2AdcDguKqDgn0iIh3O+3/+DpOqPuCdg/+PqV86ts37S8nuA0DRlvVdNsHyaJQt+evYsnIp2zevIlq0ntD2DaSUbyKjcgvp1cVkREvI8jK6WTnpQHo7xVbjIaKEqCVE1OIe1/2E6/ZHsXrHGrv+HdB22QM0Vs/r79t5nP62uK+xxn9pdq3XklKDslA23b/5DD37DmplZCKSCK1a5AJ4EjgGeNXMRgKpwBbgaeDvZvZrYotcjADmtbIvEZGEW/Ly35m06RHe7PV1Dj/tinbpMy23LwClBRuBg9qlz7ZUuG0rqz96m7JVCwhvXkp26Rf0r15LbyslPn0s8zS2hHpSHM5ja/pgPDWbaHoOtSnZRFO7YZF0wimphCKpRFJSIZyKWziW5ESj4FHwWvAo7rUQdXzHvmgtHt35r0dr6/bXlXkUi9bGjg3KLGgv/rF5LRatDdIrx+O+5hqNp0Pe6DfmJvbZ7ut4M1+rpXNzfJdfIg/+4zu36j/yJh7jmDtTS17k3cdv5stX/qGNohaRPdHaBOsvwF/M7EOgCrgoGM36yMweAZYCNcC3O+sKgh6NJu7+NyLSoWzbuJIhr1/P8vB+TLrsjnbrN6tHfwAqiza2W5+J4tEoq1d8xPrFLxFZ8xb9ij9ksK8nNyjPpweb04byae7xRPMOIL3fKHr034+cvkPpntOTIfo8FUm4Bb89m8kbH2bZwnMZfcgRyQ5HZJ/XqgTL3auA85souxm4uTXtJ9v7z/8V//BxDvjGn+nes2+ywxGRBPJolDV/u4IRXoWd+VfS0jPbre/ueQMAqO4kCVbBptWseOsp+PxVhm5/n6EUMBQooDurMsezrs8MsodNov/oQ+nddzBdc9KjSMc18sI72PbbqWQ+cwWFw18jt0deskMS2ae1dgSrS6vavpmDS96k6I5DmTf+Kg4+5VukpKYlOywRSYBF/76Pg8ve5q39r+LLoye2a9/ZvQZQ4yFs+4Z27belPFrLyg/fJn/B0/RY9yojaj6lJ7CFXFZnH8zqIYfT/6D/YsABE+ipESmRpMvu0Yf10+9k/+fP58M/ns2Y7z9LampqssMS2WeZe8e5uHbSpEk+f/78ZIdRz/IlbxF95mpGVn/COuvLmtGXMX76ZWR1b5v744hI2ystLqDi1wdTEM5j+A/fiV3v0842ztqfNTlfYvI1j7R7342pra1l2bwXKVnwCAdseYVebCPqxqcpo9g68Bj6HHIq+4+bSiishEqko1rw2G186YP/j3lZx3DQVQ+Tpj8Ki7QZM1vg7pMaK9MIVjMOmPBlfNw7LJ77CFlv3cbUpTdT9tFtvJtzNGnjTmH/Q08iu41uRtpeorW1VFWWUV1ZRk1VJTXVVVRXVVFbU0VNddy/weNobTXUVkO0pu5fd8ejUdwdvLbBduzf2IXl1P1rHiV2gW60Xjzx14o3TP+twcXfOy4s37F/l/oWK613DDuOqd9GfC+7XmPeeBtNlcd13il0nkgTI7R2HpMpZPP0vyYluQIoTOlDRnlypwh6NMrHC+dSOO8h9tv8EuPYSoWn8FHWVJYfMJ39v3waB/bTqmQincWXzriOBZXbmfLpb1jy/05k0GV/p2fvxu6008W4E62ppqKilMryMqoqyqiqLKOyoozqynKiVRVQU0k0Wku0thqvrcFra4hGa/DaWjxaE1scp3bnv0ZssZtduiK2IqjHf+NoMFixY2uXeg0YVu87SKP/L274XaKx7ywNthr9XrMHbTT+/aVBP3sYV0u+W+2ujeJu+3PwESfSq1vn+KOBRrD2QOzLyGtsf+seDix4ie6UUe1hvkg5gMIe47D+E8jqN5KcASPIGzCM9NSUhPRbU1NDWWkxlaXbKS8toqq8hOry7VSXb6e2YjvRyhKilWV4ZQlUlWLVpVhNOVZdTqi2gnDwE4lWkhKtJMUrSdvxQxXpVp2QOEU6k8UZh3LQD15IWv8Lf306vbcvY/DPP27Xfj0aZcUHb5H/zhyGbnyBAb6ZKo+wNGsK0bGnc+CRXyczu0e7xiQiifXe47/hoMU3UWTd2XDYzxn/Xxd12AW7qqprKSwsYHvBBiqK8qku3ky0JJ/askKiFduhajtUlhCuLiFSU0pKTSnp0TIyvIwMLyedSlK9irB1nO+zkngP1BzHuP/+CxMH5yY7lDq7G8FSgrWXKisr+GzBq5R++Bzd8t9nWNVnZFlFXXm1h9luWZSGsikNZVMVzgQLQyhM1MLUEgJ3QtEaIl5F2GP/hqLVhL2a1GglGZST7pVkWmWL44q6UUYaFZZGFWlUhdKotjSqQ+nUhNOoDacTDacTDWfgKel4OB2PZBCNpEMknVAkFQunYJEUQpFUQuHg30gK4Ugq4UgqFknBwimEwikQTsFCYULhCCEDQmFCFiIUCmMGofCO7RBmIULhECEzzMJghluI2F81DIIFkXfyhk+uwbNtsKBtw78gue/ahje5CG5ccePHNBVX45/pe/6+2t1fudpUEj8CktW1O/TsMyBpo1cA7/3x20xY/zDhn20iEmnjm9+6s2rZe2x4aw6D1j3PIN9AtYdZmnEIVQeexqijz6Z7bq+2jUFE2tWKxa8RfeoqRkQ/59PISLZPvJxRR59Nt27ZbdpvbW2Uwm1bKN66gZKCDVQUbqZ6ez7Rknwo20pKxVbSqraRVVNIdrSIHl5M2m7+0FvqaZRZJhWh2E91OJOqcBZVkW7URLLw4PsLkXQsJfYTSskgnJpOODX2byg1g1AkDQtHCIcihCKxn9jjFMLBzcjD4WB/OALhFLBQvUGVHaM2FnxniR9vsVD8jBrbedzuZrPEfb9o7PuHN5jh0/A7e3PbLaqzy3b9PlvW7+7jBN/1q1Qz3/EafifySAa5PXuTGuk4fyhQgtUOamtqWL/qE4rWfUZl/gqihWuIlm0jVFFIRk0RqdHyunuwhKglQi2YUU0KtZZCjaVQG0ohGkrFQylEIxnUpmQSTcnCUjIhtRuWlkUorRuhtGwi6VmkZGSTkplNWmZ30jKzSc/KJiOjGylt/WVNRFpt3qP/jykf3sT6i95lwPAD26SPdZ8tYt0bf6fvmucYGl1DrRtL0ydSNnIGo446h9xe+8DUIZF9WFVVFYueuoP+S+9hsK+n3FP5OONgqvsdTPqgCfQYPJrcPoPoltNrlxEuj0apqq6krLSUypJtVBTlU1a4mYriLVSXbKG2ZCtWXkBKRQFpVQVk1hTSPVpEjheTao3fmaeUdIoth9JILuWpPahJ60k0Mw+yehPu1puU7N6kdO9DRm5fsnLy6JadS2qCZgOJJJoSLBGRDubj917mwGe/xqLDf8fE/2r0bhd7ZfVnH7D+rTn0Xf0cw2u/IOrG0tRxbN//FA44+lx69xucsL5EpHOoqanh03eepWzJ0/TNf4uB0Q2E4qZf1LpRRQo1hKm1MKlUk9aCaXfbyaTYulMS6UF5Sg+q03viGXlYVi8i2b1Jy+lDVs9+dO/Zn+55/Qintd/tMETamha5EBHpYIaMnkT0n0b56sU0cTvBFoldU/U2m+Y/SZ91LzMiuoIhwLLIaN4ccT3DjzyXcYP3S1jcItL5RCIRxkybAdNmAFBUtI0Nny6geOPn1BRvhrItWG0VFq3GojXUhlKxlAwsJYNQagaW1h2yepLevTdZub3p3rMvuT37kJ2aRttOOBTpnJRgiYgkQWa3HFaGB9F983t7fGxFeSmfvvs8lR89y5Atr3GAb2E/N5anjmLe/t9nyBHnMnrIiDaIWkS6gpycHuRM/kqywxDpspRgiYgkydq+x3HY+vsoyl9LTu+ml0OvqqzkiyWvUbT0Fbqtf5P9K5Yywaop8zQ+zprEqgOu4oDDT2dkX03/ExERSTYlWCIiSdL/yIvxh/7Gqge+y7hvzyGUmk60ppr1qz4j/9N3qV6zgMwtSxhe+TGjgtVEl4eG836/M8g88DhGHHoSh2RmJflZiIiISDwlWCIiSbL/6IN5ZdDlHLtuNhX/M5hSMsnxYgZZlEFApUdYFdmPj/qcTGT/oxhyyPEc0Kc/ByQ7cBEREWmSEiwRkSQ65rJbePvlKVQve4FITQnRzF6k9hpG7n6TGDJ6EiPTM5IdooiIiOwBJVgiIklkZhz2lTPgK2ckOxQRERFJgI5zO2QREREREZFOTgmWiIiIiIhIgijBEhERERERSRBz92THUMfM8oFVyY6jgV7AlmQHIe1G53vfoXO979C53rfofO87dK73HR3xXA91996NFXSoBKsjMrP57j4p2XFI+9D53nfoXO87dK73LTrf+w6d631HZzvXmiIoIiIiIiKSIEqwREREREREEkQJVvPuTnYA0q50vvcdOtf7Dp3rfYvO975D53rf0anOta7BEhERERERSRCNYImIiIiIiCSIEiwREREREZEEUYK1G2Y23cw+MbPlZvbDZMcjiWNmg83sVTNbamYfmdlVwf6eZvaimX0W/Nsj2bFKYphZ2MzeN7N/BtvDzezd4P39sJmlJjtGSQwzyzWzR83sYzNbZmaH6b3dNZnZNcFn+IdmNsfM0vXe7jrM7C9mttnMPozb1+h72WLuCM77EjM7JHmRy55q4lz/KvgcX2JmT5hZblzZj4Jz/YmZnZCUoHdDCVYTzCwM/A44ERgDnGNmY5IblSRQDXCtu48BpgLfDs7vD4GX3X0E8HKwLV3DVcCyuO3/A2539wOAbcA3khKVtIXfAv9y9wOBg4idd723uxgzGwh8D5jk7uOAMHA2em93JfcC0xvsa+q9fCIwIvi5HPhDO8UoiXEvu57rF4Fx7j4B+BT4EUDwfe1sYGxwzO+D7+0dhhKspk0Blrv75+5eBTwEzEhyTJIg7r7B3RcGj7cT+wI2kNg5vi+odh9wWlIClIQys0HAScCfg20DjgUeDaroXHcRZpYDHAncA+DuVe5eiN7bXVUEyDCzCJAJbEDv7S7D3V8DChrsbuq9PAP4m8e8A+SaWf92CVRarbFz7e4vuHtNsPkOMCh4PAN4yN0r3f0LYDmx7+0dhhKspg0E1sRtrw32SRdjZsOAg4F3gb7uviEo2gj0TVZcklC/AW4AosF2HlAY98Gt93fXMRzIB/4aTAn9s5llofd2l+Pu64DbgNXEEqsiYAF6b3d1Tb2X9b2ta7sUeD543OHPtRIs2aeZWTfgMeBqdy+OL/PYPQx0H4NOzsxOBja7+4JkxyLtIgIcAvzB3Q8GSmkwHVDv7a4huPZmBrGkegCQxa5TjKQL03t532BmPyF2aceDyY6lpZRgNW0dMDhue1CwT7oIM0shllw96O6PB7s37ZhSEPy7OVnxScIcDpxqZiuJTfU9ltg1OrnBtCLQ+7srWQusdfd3g+1HiSVcem93PV8BvnD3fHevBh4n9n7Xe7tra+q9rO9tXZCZXQycDJznO2/e2+HPtRKspr0HjAhWI0oldjHd00mOSRIkuAbnHmCZu/86ruhp4KLg8UXAU+0dmySWu//I3Qe5+zBi7+NX3P084FXgzKCaznUX4e4bgTVmNirYdRywFL23u6LVwFQzyww+03eca723u7am3stPAxcGqwlOBYriphJKJ2Rm04lN7z/V3cviip4GzjazNDMbTmxhk3nJiLEptjMZlIbM7KvErt0IA39x95uTG5EkiplNA14HPmDndTk/JnYd1iPAEGAVcJa7N7zAVjopMzsauM7dTzaz/YiNaPUE3gfOd/fKJIYnCWJmE4ktaJIKfA5cQuwPinpvdzFm9gtgJrHpQ+8DlxG7FkPv7S7AzOYARwO9gE3Az4EnaeS9HCTZdxGbJloGXOLu85MQtuyFJs71j4A0YGtQ7R13vyKo/xNi12XVELvM4/mGbSaTEiwREREREZEE0RRBERERERGRBFGCJSIiIiIikiBKsERERERERBJECZaIiIiIiEiCKMESERERERFJECVYIiIiIiIiCaIES0REREREJEGUYImIiIiIiCSIEiwREREREZEEUYIlIiIiIiKSIEqwREREREREEkQJloiIiIiISIIowRIR6WDMbJiZuZlFkh2L7BvM7CMzOzrZcYiIdAVKsEREpNMzs9lmVhL8VJlZddz288mOr6Nz97HuPjeRbZrZQDN7yswKzGytmV2RyPZFRDoqc/dkxyAi0qWYWcTda1px/DDgCyClNe3sq8xsFnCAu5/fSFmrzk176kyxNsbMXgUWA9cDY4BXgTPc/dWkBiYi0sY0giUikgBmttLMfmBmS4BSM4uY2VQze8vMCs1scfwULDOba2b/a2bzzKw4+Et/zybavsTMlpnZdjP73Mz+u0H5DDNbFLSzwsymB/tzzOweM9tgZuvM7JdmFm7meexvZq+Y2VYz22JmD5pZblxZgZkdEmwPMLP8Hc/LzE4NppoVBs9vdIPX5zozW2JmRWb2sJml7/krveeaODduZgfE1bnXzH4Zt31y8JoWBudwQgv7OjoYrflx8PqtNLPz4spPMrP3g3O1JkgGd5TtmBr6DTNbDbwS7P+HmW0MXrfXzGxsg7h/b2bPB6N1b5pZPzP7jZltM7OPzezgFr5GX2nJc2zh69ANOBq42d2r3X0x8ChwaaL6EBHpqJRgiYgkzjnASUAu0Bd4Fvgl0BO4DnjMzHrH1b+Q2BfO/kANcEcT7W4GTga6A5cAt8clOVOAvxEbJcgFjgRWBsfdG7R7AHAwcDxwWTPPwYD/BQYAo4HBwCwAd18B/AB4wMwygb8C97n7XDMbCcwBrgZ6A88Bz5hZalzbZwHTgeHABODiRgMwmxYkNk39TGvmOTSm7tw0NyoUJCR/Af4byAP+CDxtZmkt7Ksf0AsYCFwE3G1mo4KyUmLnPTeI50ozO63B8UcRe+1PCLafB0YAfYCFwIMN6p8F/DTosxJ4O6jXi1hS8+sWxt0oM/vh7s5HU4c1+HfH43GtiUVEpDNQgiUikjh3uPsady8Hzgeec/fn3D3q7i8C84GvxtW/390/dPdS4GfAWY2NMLn7s+6+wmP+A7wAHBEUfwP4i7u/GPSzzt0/NrO+QV9Xu3upu28GbgfO3t0TcPflQVuV7p5P7Mv5UXHlfwKWA+8SSwx/EhTNBJ4Njq0GbgMygC83eH3Wu3sB8AwwsYkY3nD33N38vLG759CE+HPTnMuBP7r7u+5e6+73EUtcpu5Bfz8LXsP/EEu0zwJw97nu/kFwrpYQS0qPanDsrOCclQfH/MXdt7t7JbFk9yAzy4mr/4S7L3D3CuAJoMLd/+butcDDxJLrvebut+zufDRxzHbgTeBnZpYe/EHgDCCzNbGIiHQGSrBERBJnTdzjocDXG/ylfxqxpKSx+quAFGKjDvWY2Ylm9k4wPa+QWOK0o95gYEUjsQwN2tsQ1/8fiY2CNMnM+prZQ8GUwmLggUZi+hOxkYg7gy/9EBvxWrWjgrtHg+c3MO64jXGPy4Buu4slwdY0X6XOUODaBuduMLHn2BLbgqR5h1U7jjWzQ83s1WBqZRFwBbu+vnWxmlnYzG6x2NTPYnaOTsYfsynucXkj2+35Osc7j9ho5RrgD8R+l9YmKRYRkXajBEtEJHHiVw1aQ2yEKv6v/VnufktcncFxj4cA1cCW+AaDaWmPERsR6huMGDzHzqlXa4D9G4llDbFRl15x/Xd397GN1I33P8HzGO/u3YmNxNVN8wqurfkNcA8wy3ZeN7aeWGKyo54Fz29dM/3twsyOsJ0rADb2c0Tzreyi4YpOZdQfTekX93gNsWuH4s9dprvPaWFfPcwsK257CLHXB+DvwNPAYHfPAWZTfxpdw1jPBWYAXwFygGHB/obHtJngerImz0dTx7n7Knc/2d17u/uhxJLCee0Vt4hIsijBEhFpGw8Ap5jZCcEoRHqwAMKguDrnm9mY4Hqmm4BHg2ld8VKBNCAfqDGzE4ldS7XDPcAlZnacmYUstjT2ge6+gdhUwv9nZt2Dsv3NrOF0tIaygRKgyMwGEru2K95vgfnufhmxqW+zg/2PACcFcaQA1xJL8N5q7oVqyN1fd/duu/l5fU/bbMQi4Nzg3Eyn/jS9PwFXBKNNZmZZFlucIhvqFpa4t5n2f2FmqUEyeDLwj2B/NlDg7hXB9XPnNtNONrHXcSuxhPB/9uA5JoS7/8/uzkdTx5nZaDPLDl6H84n93rbqejARkc5ACZaISBtw9zXERh5+TCw5WkMsWYn/3L2f2EIUG4F04HuNtLM92P8IsI3YF/Kn48rnESx8ARQB/2HnSNKFxBK0pcGxj1J/imJjfgEcErT1LPD4jgIzm0FskYorg13fBw4xs/Pc/RNio113EhuFOwU4xd2rmukvWa4iFmMhsalsT+4ocPf5wDeBu4i9bsupvyDHYGLXFzVlY3DcemILUlzh7h8HZd8CbjKz7cCNxM7r7vyN2BTDdcTO4zvNPbEO5ATgc2KvxRXA9OC6PhGRLk33wRIRSQIzmws84O5/TnYs0nLBqoiLgQnBYh4Ny48mdl4HNSwTEZF9QyTZAYiIiHQWwYjc6GYriojIPktTBEVE9jFmNruJBQtmN3+0dEZmNmQ3C1UMSXZ8IiJdiaYIioiIiIiIJIhGsERERERERBKkQ12D1atXLx82bFiywxAREREREWnSggULtrh778bKOlSCNWzYMObPn5/sMERERERERJpkZquaKtMUQRERERERkQRRgiUiIiIiIpIgSrBERFqgNupo1VURERFpToe6Bqsx1dXVrF27loqKimSHIp1Meno6gwYNIiUlJdmhSCdXVRPlr7d8h16DDuCMi7+f7HBERESkA+vwCdbatWvJzs5m2LBhmFmyw5FOwt3ZunUra9euZfjw4ckORzq5LzZs4r9rHoSVEI1eQyikzyIRERFpXIefIlhRUUFeXp6SK9kjZkZeXp5GPiUhSpa9Uvd4c1FZEiMRERGRjq7VCZaZDTazV81sqZl9ZGZXBft7mtmLZvZZ8G+PVvTR2jBlH6TfG0mUUP7HdY83rVyaxEhERESko0vECFYNcK27jwGmAt82szHAD4GX3X0E8HKwLSLS+VRtr3u4fe1HSQxEREREOrpWJ1juvsHdFwaPtwPLgIHADOC+oNp9wGmt7StZzIxrr722bvu2225j1qxZyQsozjvvvMOhhx7KxIkTGT16dF1cc+fO5a233trrdletWsUhhxzCxIkTGTt2LLNnz05QxCKdT6iqpO5xVeG6JEYiIiIiHV1CF7kws2HAwcC7QF933xAUbQT6NnHM5cDlAEOGDElkOAmTlpbG448/zo9+9CN69eqVsHbdY8s+h0J7n+dedNFFPPLIIxx00EHU1tbyySefALEEq1u3bnz5y1/eq3b79+/P22+/TVpaGiUlJYwbN45TTz2VAQMG7HWsIp1VuGo7a70X/awA276h+QNERERkn5WwRS7MrBvwGHC1uxfHl3ns5jGN3kDG3e9290nuPql3796JCiehIpEIl19+ObfffvsuZfn5+ZxxxhlMnjyZyZMn8+abbwIwa9Ysbrvttrp648aNY+XKlaxcuZJRo0Zx4YUXMm7cONasWcP111/PuHHjGD9+PA8//DAQS5COPvpozjzzTA488EDOO++8Ru/Bs3nzZvr37w9AOBxmzJgxrFy5ktmzZ3P77bczceJEXn/99d3GecEFF3DYYYcxYsQI/vSnPwGQmppKWloaAJWVlUSj0UZfmzvuuIMxY8YwYcIEzj77bAAKCgo47bTTmDBhAlOnTmXJkiV1fV100UUcccQRDB06lMcff5wbbriB8ePHM336dKqrqwG46aabmDx5MuPGjePyyy/f5XlHo1GGDRtGYWFh3b4RI0awadOm3Z1Gkb0WqSmhxLLYZj1ILdPvmYiIiDQtISNYZpZCLLl60N0fD3ZvMrP+7r7BzPoDm1vbzy+e+Yil64ubr7gHxgzozs9PGdtsvW9/+9tMmDCBG264od7+q666imuuuYZp06axevVqTjjhBJYtW7bbtj777DPuu+8+pk6dymOPPcaiRYtYvHgxW7ZsYfLkyRx55JEAvP/++3z00UcMGDCAww8/nDfffJNp06bVa+uaa65h1KhRHH300UyfPp2LLrqIYcOGccUVV9CtWzeuu+46AM4999wm41yyZAnvvPMOpaWlHHzwwZx00kkMGDCANWvWcNJJJ7F8+XJ+9atfNTp6dcstt/DFF1+QlpZWl/D8/Oc/5+CDD+bJJ5/klVde4cILL2TRokUArFixgldffZWlS5dy2GGH8dhjj3Hrrbdy+umn8+yzz3Laaafxne98hxtvvBGACy64gH/+85+ccsopdX2GQiFmzJjBE088wSWXXMK7777L0KFD6du30UFSkVZLqSml2LIoTskgszI/2eGIiIhIB5aIVQQNuAdY5u6/jit6GrgoeHwR8FRr+0qm7t27c+GFF3LHHXfU2//SSy/xne98h4kTJ3LqqadSXFxMSUlJE63EDB06lKlTpwLwxhtvcM455xAOh+nbty9HHXUU7733HgBTpkxh0KBBhEIhJk6cyMqVK3dp68Ybb2T+/Pkcf/zx/P3vf2f69OmN9rm7OGfMmEFGRga9evXimGOOYd68eQAMHjyYJUuWsHz5cu67775GR4gmTJjAeeedxwMPPEAkEql7ThdccAEAxx57LFu3bqW4OJYYn3jiiaSkpDB+/Hhqa2vr4h0/fnzd83v11Vc59NBDGT9+PK+88goffbTrogIzZ86sG+176KGHmDlz5m5fc5HWSK0ppSKUSVlab3JqtiQ7HBEREenAEjGCdThwAfCBmS0K9v0YuAV4xMy+AawCzmptRy0ZaWpLV199NYcccgiXXHJJ3b5oNMo777xDenp6vbqRSKTetLr4+zFlZWW1qL8dU/QgNv2vpqam0Xr7778/V155Jd/85jfp3bs3W7du3aVOU3HCrsuZN9weMGAA48aN4/XXX+fMM8+sV/bss8/y2muv8cwzz3DzzTfzwf/f3p3HyVXVeR///GrtNd2d7s6ekCBJyNJZSGfBEEmQJYwCYoDgCBii8ogoI+qMooKIj89LdB7nGQVl1CiikV0CMyKCLGJm2JIQSEISiCQkgSy9p7eqruU8f1Sl03s66equXr7v16vTde85955f9+1bub86956zZUu3fiaPx4Pf729uy+PxEI1GCYVCfP7zn2fDhg2MHz+e2267rcO5rM4880x27dpFWVkZ69at41vf+laX7Yr0RCBWT8gzmoysIoYf2UQs7vBqsmERERHpQCpGEVzvnDPn3Czn3Jzk1xPOuQrn3Iedc5Odc+c65ypTEXA6DR8+nCuuuII1a9Y0rzv//PP5yU9+0rx89Fa4iRMnsmnTJgA2bdrE7t27O9znkiVLeOCBB4jFYpSVlfHCCy+wYMGCbsf0xz/+sfkZpbfffhuv10t+fj65ubnU1h4bWrqzOAEee+wxQqEQFRUVPP/888yfP5/9+/fT2NgIQFVVFevXr2fq1Kmt2o7H4+zbt49ly5Zxxx13UFNTQ11dHUuWLGHt2rVA4lmyoqIihg0b1q2f52gyVVRURF1dHQ8//HCH9cyMSy+9lC9/+ctMmzaNwsLCbu1f5GRkxOtp8mZB9gjyrZ6q2q57qUVERGToStkgF0PFV77yFcrLj90i9OMf/5gNGzYwa9Yspk+f3jyc+YoVK6isrGTGjBnceeedTJkypcP9XXrppcyaNYvZs2dzzjnn8IMf/IBRo0Z1O57f/va3TJ06lTlz5nD11Vezdu1avF4vF110EY8++mjzIBedxQmJ2/yWLVvGokWLuOWWWxgzZgzbt29n4cKFzJ49m7PPPpuvfvWrlJSUAPCZz3yGDRs2EIvFuOqqqygpKWHu3LnceOON5Ofnc9ttt7Fx40ZmzZrF17/+dX7zm990Fn47+fn5fPazn2XmzJlccMEFzJ8/v7ns7rvvbhX3ypUr+d3vfqfbA6XXZcQbiPhy8A5LPOdXU6aRBEVERKRj1tHIdOlSWlrqNmzY0Grd9u3bmTZtWpoiGvxuu+22VoNhDDb6+5EeizbB/y7m0fxVTJ+9kKl/vZ7NFz7GnIVL0x2ZiIiIpImZbXTOlXZUph4sEZGuJCcZjvmzyR6emBKhsVo9WCIiItKxlE40LAPPbbfdlu4QRPq3SOJZRHyZDCtKTFXQVKO5sERERKRj6sESEelKNDHwigUyyS1M9GDFazUXloiIiHRMCZaISFeOJlj+TCyYS4gAnoYez5suIiIig5QSLBGRLsSbErcIevyZYEa1pwB/qP1ccyIiIiKgBEtEpEuRUAMAnmAmAPW+AjKaBvy0fiIiItJLlGB107p16zAzduzY0WmdPXv2MHPmzJS1uXPnTpYuXcqcOXOYNm0a1113HZCYJPiJJ5446f2GQiEWLFjA7NmzmTFjBt/+9rdTFbLIoNMUrgfAF8gCIBQcTk60Kp0hiYiISD+mBKub7rvvPs466yzuu+++Dsuj0WiP24jFYq2Wb7zxRm666SY2b97M9u3b+eIXvwj0PMEKBoM8++yzvP7662zevJknn3ySl156qUexiwxWTckerKMJViSjmPx4Nf1pDkERERHpP5RgdUNdXR3r169nzZo13H///c3rn3/+eZYsWcLFF1/M9OnTgUSi9clPfpJp06Zx2WWX0dCQuDh75plnmDt3LiUlJaxevZpwOAzAxIkT+drXvsYZZ5zBQw891KrdAwcOMG7cuOblkpISmpqauPXWW3nggQeYM2cODzzwAPX19axevZoFCxYwd+5cHnvsMQDuueceLrnkEpYuXcrkyZP5zne+A4CZkZOTA0AkEiESiWBm7X7uhx56iJkzZzJ79mw+9KEPAYner2uvvZaSkhLmzp3Lc88919zWxz72Mc477zwmTpzInXfeyY9+9CPmzp3LokWLqKxM3FL1i1/8gvnz5zN79mxWrFjR/PtpadGiRWzbtq15eenSpbSdgFqkrxy9RdCXkbhFkOwihnOEIw1NaYxKRERE+quBNQ/Wn74OB7ekdp+jSuDC73dZ5bHHHmP58uVMmTKFwsJCNm7cyLx58wDYtGkTW7duZdKkSezZs4edO3eyZs0aFi9ezOrVq/npT3/KF77wBVatWsUzzzzDlClTuOaaa/jZz37Gl770JQAKCwvZtGlTu3ZvuukmzjnnHD74wQ9y/vnnc+2115Kfn8/tt9/Ohg0buPPOOwH4xje+wTnnnMOvfvUrqqurWbBgAeeeey4Ar7zyClu3biUrK4v58+fzkY98hNLSUmKxGPPmzWPXrl3ccMMNLFy4sF37t99+O3/+858ZO3Ys1dXVANx1112YGVu2bGHHjh2cf/75vPXWWwBs3bqV1157jVAoxGmnncYdd9zBa6+9xk033cS9997Ll770JT7+8Y/z2c9+FoBvfetbrFmzprln7qiVK1fy4IMP8p3vfIcDBw5w4MABSks7nChbpNdFw4kEyx9M9GB5ckbgszgV5YfIy56QztBERESkH1IPVjfcd999XHnllQBceeWVrW4TXLBgAZMmTWpeHj9+PIsXLwbgqquuYv369ezcuZNJkyYxZcoUAD71qU/xwgsvNG+zcuXKDtu99tpr2b59O5dffjnPP/88ixYtau75aumpp57i+9//PnPmzGHp0qWEQiH27t0LwHnnnUdhYSGZmZl8/OMfZ/369QB4vV42b97M/v37m5OwthYvXsyqVav4xS9+0Xz74vr167nqqqsAOP300znllFOaE6xly5aRm5tLcXExeXl5XHTRRUCi523Pnj1AIglbsmQJJSUlrF27tlVP1VFXXHEFDz/8MAAPPvggl112WYe/H5G+cDTBCmQkEqxA3igAaiveS1tMIiIi0n/1eg+WmS0H/h3wAr90znXdXdSV4/Q09YbKykqeffZZtmzZgpkRi8UwM374wx8CkJ2d3ap+21vtOrr1rq22+2hpzJgxrF69mtWrVzNz5swOEyHnHI888ghTp05ttf7ll18+bjz5+fksW7aMJ598st0AHXfffTcvv/wyf/zjH5k3bx4bN27s8ucIBoPNrz0eT/Oyx+NpfkZt1apVrFu3jtmzZ3PPPffw/PPPt9vP2LFjKSws5I033uCBBx7g7rvv7rJdkd4USw7THshMJFhZwxMJVkPlgbTFJCIiIv1Xr/ZgmZkXuAu4EJgOfMLMpvdmm6n28MMPc/XVV/Puu++yZ88e9u3bx6RJk/jb3/7WYf29e/fy4osvAvD73/+es846i6lTp7Jnzx527doFwG9/+1vOPvvs47b95JNPEolEADh48CAVFRWMHTuW3Nxcamtrm+tdcMEF/OQnP2l+6P61115rLnv66aeprKyksbGRdevWsXjxYsrKyppv+WtsbOTpp5/m9NNPb9f+3//+dxYuXMjtt99OcXEx+/btY8mSJaxduxaAt956i71797ZL7LpSW1vL6NGjiUQizfvpyMqVK/nBD35ATU0Ns2bN6vb+RVLt6DxYgYzEByG5hWMAaKo5lLaYREREpP/q7VsEFwC7nHPvOOeagPuBS3q5zZS67777uPTSS1utW7FiRaejCU6dOpW77rqLadOmUVVVxfXXX09GRga//vWvufzyyykpKcHj8fC5z33uuG0/9dRTzYNMXHDBBfzwhz9k1KhRLFu2jDfffLN5kItbbrmFSCTCrFmzmDFjBrfcckvzPhYsWMCKFSuYNWsWK1asoLS0lAMHDrBs2TJmzZrF/PnzOe+88/joRz8KwK233srjjz8OwD//8z9TUlLCzJkz+eAHP8js2bP5/Oc/Tzwep6SkhJUrV3LPPfe06rk6nu9+97ssXLiQxYsXt0rqHn/8cW699dbm5csuu4z777+fK664otv7FukN8UgjEeclM/l3PqxoLACx2sPpDEtERET6KevNoYbN7DJguXPuM8nlq4GFzrkvtKhzHXAdwIQJE+a9++67rfaxfft2pk2b1msxDmb33HNPq8EwhiL9/UhP7bjnBsbufoS6L+9mdF4mxONEby/kb8X/yLIb7kp3eCIiIpIGZrbROdfhKGxpH+TCOfdz51ypc660uLg43eGIiLQWCRHCT6bfm1j2eKixfHyNFemNS0RERPql3h7k4j1gfIvlccl10gdWrVrFqlWr0h2GyMAWbSRMgNyjCRZQ58sno0kJloiIiLTX2z1YrwKTzWySmQWAK4HHT3QnvXkbowxe+ruRVLBoiLDzE/Qde7tsDBSSHalKY1QiIiLSX/VqguWciwJfAP4MbAcedM61n/ioCxkZGVRUVOhiWU6Ic46KigoyMjLSHYoMcBYN0WTBVlMcNGUUkhev0vuSiIiItNPr82A5554AnjjZ7ceNG8f+/fspKytLYVQyFGRkZDBu3Lh0hyEDnCcWotECrdbFs4oppIb6cJScDH+aIhMREZH+qNcTrJ7y+/1MmjQp3WGIyBDljYWJeFpPReDJGUGGRXi3spKcMSPTFJmIiIj0R2kfRVBEpD/zxsNE2yRY/rxEUnWkXGP2iIiISGtKsEREuuCNh4m1SbAy80cBUF95IB0hiYiISD+mBEtEpAv+eJiot/VgKblFYwAI1xxKR0giIiLSjynBEhHpgj/eRLxNgjUsmWBFjyjBEhERkdaUYImIdCHgwsS9bZ7Byh2ReFF/OA0RiYiISH+mBEtEpAsBmsDXZj41r58jlou3oSI9QYmIiEi/pQRLRKQz8Rh+ori2CRZwxFtAMFyehqBERESkP1OCJSLSmUhj4rsvs11Ro7+A7EhlHwckIiIi/Z0SLBGRzkRDie/+9j1Y4WARubHqvo1HRERE+j0lWCIinWgKNQBg/vY9WPHsIgqpIRSJ9XVYIiIi0o8pwRIR6US4sR4Ab6B9gmXZxQyzBsqrj/R1WCIiItKPKcESEelEYzLB8gWz2pX5ho0EoKb8QJ/GJCIiIv1bjxIsM/uhme0wszfM7FEzy29RdrOZ7TKznWZ2QY8jFRHpY809WMH2PVgZ+aMBqKt4v09jEhERkf6tpz1YTwMznXOzgLeAmwHMbDpwJTADWA781My8PWxLRKRPNYUSCVYgmN2uLLswkWCFqg/2aUwiIiLSv/UowXLOPeWciyYXXwLGJV9fAtzvnAs753YDu4AFPWlLRKSvHR3kwp/Rvgcrr2gMANEjh/o0JhEREenfUvkM1mrgT8nXY4F9Lcr2J9eJiAwY0VAdAIHM3HZlwbzEM1iu7nCfxiQiIiL9m+94FczsL8CoDoq+6Zx7LFnnm0AUWHuiAZjZdcB1ABMmTDjRzUVEek2ssRaAYHZe+8JANo1k4Gko7+OoREREpD87boLlnDu3q3IzWwV8FPiwc84lV78HjG9RbVxyXUf7/znwc4DS0lLXUR0RkXRw4cQQ7MGcDhIsoMZbQDCsBEtERESO6ekogsuBfwEuds41tCh6HLjSzIJmNgmYDLzSk7ZERPqaCyduEczM6jjBqvUXkdOkBEtERESOOW4P1nHcCQSBp80M4CXn3Oecc9vM7EHgTRK3Dt7gnIv1sC0Rkb4VriPk/GRlZXRYHMooJj+0vY+DEhERkf6sRwmWc+60Lsq+B3yvJ/sXEUknT1Mt9WQy3NtxZ380aySFVf9DJBbH30kdERERGVp0RSAi0glPpJ56yyTZQ9+Oyx1FjoWoqKzo48hERESkv1KCJSLSCV+0jkbL6rQ8kJ+YC6vq0L5O64iIiMjQogRLRKQTvkg9IU/nCVZWYWJu9boyJVgiIiKSoARLRKQTvlg9UW92p+V5xYkEK1T1fl+FJCIiIv2cEiwRkU4EYg1E/Z0nWPkjE5Ojx2oO9FVIIiIi0s8pwRIR6URmvAEXyOm03JuZR4gAnjolWCIiIpKgBEtEpCPOketqiQXzO69jRpWnEH9jWZ+FJSIiIv2bEiwRkQ6E6qoJWAyyCrusV+svIjusBEtEREQSlGCJiHSgrvIgAJZT1GW9UEYxw2KVfRGSiIiIDABKsEREOlBXfQgAX86ILuvFskdS5CoJR2N9EZaIiIj0c0qwREQ6EKo+DEDGsOIu63mGjSLHQpRXVPRFWCIiItLPKcESEelA5EgywSrougfLnz8WgKpDmmxYRERElGCJiHQoWpcYuCKvcHSX9bILEwlWfbkSLBEREVGCJSLSoeiRwzS6AEUFBV3WyxsxHoBQ5ft9EZaIiIj0cylJsMzsK2bmzKwouWxm9mMz22Vmb5jZGaloR0Skr/jr3qfMU4TX2/Xb5LDiRIIVP6LJhkVERCQFCZaZjQfOB/a2WH0hMDn5dR3ws562IyLSl7JDB6jyjzxuPU9mHiECWN2hPohKRERE+rtU9GD9G/AvgGux7hLgXpfwEpBvZl0/yCAi0o8MjxymIbMbb1tmVHkLCYQO935QIiIi0u/1KMEys0uA95xzr7cpGgu0fOJ7f3JdR/u4zsw2mNmGsrKynoQjIpIS8aZGCqkimjuuW/Xr/EVkhct7OSoREREZCHzHq2BmfwFGdVD0TeAbJG4PPGnOuZ8DPwcoLS11x6kuItLrKg+8QxHgyR/frfrhjGLyQ9t7NygREREZEI6bYDnnzu1ovZmVAJOA180MYBywycwWAO8BLa9MxiXXiYj0e4ff2UIRkDt2Wrfqx7JHUlT1P4QiMTL83t4NTkRERPq1k75F0Dm3xTk3wjk30Tk3kcRtgGc45w4CjwPXJEcTXATUOOc0xJaIDAj1720DYOyUud2qb8NGk2MhysorejMsERERGQB6ax6sJ4B3gF3AL4DP91I7IiIp5ynfyUEKKSws6lb9YH5iMIyqw3uPU1NEREQGu+PeIthdyV6so68dcEOq9i0i0peG177FweDEDh8+7UhWUeKO6Lry/UBpr8UlIiIi/V9v9WCJiAxIofoaJkT3UFs4u9vb5I+YAEC46v3eCktEREQGCCVYIiIt7H7jv/GaI/vURd3eJqdoDACxGj1qKiIiMtQpwRIRaaFm5wvEnXHqnLO7vY1l5BMigKf+YC9GJiIiIgOBEiwRkRbyDvw3u7ynkl/U3SewADOqvcMJNh7uvcBERERkQFCCJSKSVF9TwQdC2ygrPvOEt63zF5PVVN4LUYmIiMhAogRLRCTpnb+uJWAxhs1bccLbhjOKyY9qHiwREZGhTgmWiEhSxo5H2MNopp3R/eevjopmj6SIahqbYr0QmYiIiAwUSrBERIBQxV4+0PA6O4ovxOfznvD2ljuaXGukvKKyF6ITERGRgUIJlogIsPu5e/HgKDrzkye1vb9gNADVZXtTGZaIiIgMMEqwRESAnJ2PsNUmM3fOvJPaPmv4OAAayvenMiwREREZYJRgiciQV7l7M+Mj73DglIvxeuyk9jGsOJFghaveT2VoIiIiMsAowRKRIW/fX39D1Hk4benVJ72PvOLxAMSOaLJhERGRoUwJlogMbfE4o/f+J5sDZzBp4qST3o0nK58wfqz+UAqDExERkYGmxwmWmX3RzHaY2TYz+0GL9Teb2S4z22lmF/S0HRGR3rD39WcZES+jYeqlPduRGdWeAgKNh1MTmIiIiAxIvp5sbGbLgEuA2c65sJmNSK6fDlwJzADGAH8xsynOOU0QIyL9SuWLv6XIBZl+zid6vK9afyFZ4fIURNWPOEdDbRXlB97lSPl+og01xEK1xEL1uHAdLtYEgOGS9cEM4ubDefzg9YM3gPkCye9+8Abx+AJ4fEHwBfD6Anj8iWWP14fP68Hn9eH1evF6Da/Xh9/rxeP1YObFYWCeoy3imps+FsPRb811kq9cizJzDnd0xcn9cnqwbYtg0qkHMZgZR38H1mp9m3otSluWWdv2W5W1rdjxYrv9dVavs4Iu9tH+5+ikrM2vsO12nbfV4vfSZiet27IO17faXyAHyyrovGER6VM9SrCA64HvO+fCAM65ox/dXgLcn1y/28x2AQuAF3vYnohIysQjYU49/Bdez17MmcMLe7y/ULCY3CO7UxBZ36utqeS97S9TvXcr8cM7ya3bTWHTexTEK8iiiQnpDlBEOhXBS9WnX2LE+CnpDkVE6HmCNQVYYmbfA0LAV51zrwJjgZda1NufXNeOmV0HXAcwYYL+CxeRvvP2/6xjKnUw6/KU7C+aNYLhNZuIxd1Jj0bYV97bvZMDm5+EvS9RWLOVU2L7ON0Sn6I3EOR97zj2Z07lnazR2LCRBPLHkFkwhmDOcILZuWRk5ZGRk0sgEAQs+VG6HeshikeIRyPEIiGiTU3EIiFikTCxaBPxSJhYpAkXbSIWC+MiTcSjTbhomHg8RiweIx6L4+Ix4vE48XicWDyxbM5hxDHiAHT22f6xD/2t056IRP+Lte8WOCE9PM5ddXd0g+tp+ynQMoauOsRcJ0sOa1XYWb12S0f/1sxaNdwuhE723XmfUdufo6uIjq6zdgWOLmJqs8OO99n5ipZlFqrm3P138u4r/8WI8V/uqiUR6SPHTbDM7C/AqA6KvpncfjiwCJgPPGhmp55IAM65nwM/BygtLe0H90uIyFDR8NpDVLsc5pz98ZTsz+WMpMDqOFxzhBEFeSnZZ6qEGht466X/IrztCUZXvMQ4d4CxQBW57MucxqsjlhOcUMqoKfMYOXYSp3m8PWwxIxVhi8hxuHicw7f/Ht75K6AES6Q/OG6C5Zw7t7MyM7se+INL3MT+ipnFgSLgPWB8i6rjkutERPqFUGM9k6vXs61gGQszU5MM+PMSn0VVH97fLxKsUEMdO/57HZEt6zi9Zj2zrJF6F+TtrDm8P+FqimdfwClTz6DAqwFlRQYq83jYV/whZh5+guqKMvILi9MdksiQ19NbBNcBy4DnzGwKEADKgceB35vZj0gMcjEZeKWHbYmIpMyO9Y8xh0YyZ69I2T6DBYk7oWvL98PUGSnb74lwzrFty0ZqXribkrInmGP1VJPDzuHLCJR8jClnXsSczKy0xCYivSN/yWfJ/MM6Xn/qlyz6xM3pDkdkyOtpgvUr4FdmthVoAj6V7M3aZmYPAm8CUeCGgTiCYKj+CLVHqikerWfDRAab2NZHqSGb0z94Ucr2mVuUSLAaqw6kbJ/dVXWknlf//DuKdvyOM2Jv0OS8bMtbinfe1Uw78x8oDQT7PCYR6Runlixm1+OnMeKt3xOLfQ2veqVF0qpHCZZzrgm4qpOy7wHf68n+0+21e/+F6Yce55XZ32Dexdfj9fb0mQQR6Q+i4Uam1PyNbXlLWRRMXeKRPyJxZ3S0pu8SrJ1v7WDf0z9j1uHHON+qKPOMYMvUGzn1guuZO3xMn8UhIuljZtTN/jRzNt7Mpqd/xxnLr0l3SCJDWk97sAa1cR/+HO89+DoLXv8me7bcTdms65l14acJBvXwtshA9taGvzCdRnwzLk7pfjPyRxHDsNqDKd1vW5FolI3PPoptXMO80MtMxvH2sIWEz/pfjJ9/CcU9HqBCRAaamcs/w97X7mT4Kz8g8uFP4Pf70x2SyJClBKsL46fMwX1jPZuf/BXDNvyE+Zu/QcXmO9g84nyGzf8EU89Yike9WsflnCMed8RdYrhl5xwunpwC1Dlw8WOvce0mXOyO5pGO2wx5bF0MYZyo2r78uIMen9Swyt3bpnlCyV5so02DJ7jlybdxYkUn2M4J/r5qtj9HzBlTFy4/sXaOx+PlsBWTWbc3tftNKjv0Ptv/dDcT9zzAIg5SzTC2TbyGScu/yNTRp/VKmyIyMPj8AaoXfo1ZL97Iiw98jzOvui3dIQ1czhGPRojHo62uX+LOJa5j4hCPx4i7eKvrGbPk9xbTnB+9Dmm+TPFYi/VH/89vW5b819osc3RGDDu233bzULSfYvvYNVKrim0m9G69XevFlhNidLyNWYufp902dFjWcjqNlmWdxtXDaS36kvVsFvvUKi0tdRs2bEh3GB1y8TjbXniE8Kv3MrPuRYIWoZpc/p5zBtEx8xl2yhzGTp3PsKKORrTvPfG4IxSJ0FhfR7ixjqZQPU2NDTSF6omE6omG6ok1NRBvaiAWbsBFGiHSAE2NEG3EEwvhi4Xwxhrxx8P44iE8LoYnHsHjYpiL4XVRPMTwuBheF8PLsS/f0dcuhrd5XhqX/AKP9Z+/L5GW3vadxuRvbUz5frd+/xwCkRqm3JKafbt4nB2bnuPIC//BnJpnCVqEt4IziJ5xLacvuxpPQD3qIpLg4nFe/9ePML3+FfZe9kdOK1mU7pBSJh6LUVtbTX11OfVHqgjXVhKuryLWUA2hGuJNjbhIA0QasEgjFm3EGw3hjYXwuxD+eAh/PIzXRfG6GB6izdc0PhfFRwwfUbzE8duAGzZg0Ptd9MPMuG4NcycUpDuUZma20TlX2lGZerC6yTweZi69HJZeTm11OVteeIj4359nYs2rjHjrr/AW8DTUkE2Fp4gjgRFEgsMhkEPcn0M8kA3+bHzeRIYfd464M5xzEI/iiYWxWBiioeRXYtkbC+GJJRIfXyxEwIWbv4IuTCZNZFmEkxkTLESAMAHCFqTJgoQtSMSCxMxH1ALEvV6c+XDmxXl8iS/z4TyJ9fHm10freTDzJD5hSH4CYUdf27GJSJvrAM48yVQsuXw0NevgUwrXWc+Wa/fi+FzH9Y+3BzupDyR6P8k8mV6/LmcE7Xyj/tcGJ/HzOyic+5ETbqc7GoedyqRD/0k8FsfTgwfNG+uOsPXPayh487dMi/2dejLYMuIiRn/480w5fX4KIxaRwcI8Hk5Z9UtqfrqYYY98gveyn2DsqdPSHVaHIk0hqg6/z5GK92moPEi45hDR2sNQX4a3sYJAuJKsSBU58RpyXD05roE8cxxvAoxGl7iuOXpt02QZRDxBwp4sGnz5xD1+nPlx3sQ1jXl9OI8fPH5Irosnr3fweI9dx7S4fjl2beNpvrY52l/lmpet3eVJq+uYNv8/tuzwsLbTVDdv36aseRvX6lsHC63aa/9/Zsuy1rHQSczWpoZ18bN1to/2bbtWxS3byMk5nVF5A+cDRfVgpcDh9/fy/tsbadz7Ot6adwk2HiQ3fIis2BEy4w1kEer2pyGNLkCT+WkiQMQCRM1PxJN4c4h5Moh6M4j7Mon7MnDeDPBngT8TC2Rh/kw8gSy8wSx8wSy8wWz8GdkEMrMJZGQTzMwhmJV4bb5M8GiUIZFU2/DgHZS++X9479pNjD3lAye8/Ttb/ofyv/4H08v/TA6NvOOZSPnpn2TG8s+SPaz/fHInIv3X7m0vU/jQpYQJcGj5fzBz0QV92n60KUTFgT1UH9xDXdk+olX74Mh7BBoOkhM+REG0jOGupsO7XMLOT5XlUefLp95XQFOggFhwGATzIDMPb2Ye/uwCAjkFZOQMJyOngEBOPlk5w8jIyO7RB1siJ0I9WL1sxJgJjBgzAbi0w3IXj9PUFCbUWEs4kki0fB7Da4bHHB5fAI8/A58/SKbPS2Yfxi4iqVUweSG8Ce9vfa7bCdaR6nJ2PvMb8rffx+To24xxfrbkn0PWotVMX3g+p+rDEBE5AZNmLOQd9yjBR65h+p9W8urLH2Hkhf/MhClzerzvWFMjFQfepfrQHurL9hKp2p9InuoPJJOncgqpZiQwssV2R1wWFd4iagMjqMydyts5Y/AMG4U/byRZBaPIHT6KvOIxZOfkM0rveTLAqQdLRCSFYtEI9d+dwI7Cc1hw49pO64Uaatn+14ewrQ8zve5lAhblHc8pHJz8Caaf/2nyC0f0YdQiMhg1HKlk8+9upvTQQwQsxl7vBA4OX0C8eCrZIz5A5rDh+DJz8Xk8RCIhwo2NRBpqiNWVE6mrIFpXgdUfJqPxMLlNhymIlTGcI+3aqXHZieTJX0xj1mjiOaPx5I0jo2g8eSMnMnz0RIblFbQfkEFkAOuqB0sJlohIim38v5cyufYl7J9eJ7fgWKJUVfY+77z4GLz9Z04/8iLZFqKcfHYVn0/+on9k6tyzMX1yKyIpVn5oPzuf+gXZ+15gangrmdbU7W2ryaHCW0xtYAShjJFEk8lTZtE4ho2YSOGYieQpeZIhSAmWiEgf2vn6S3zgDxeyzz+RytFLoKGCgpo3mRjZjccc5eTz9/yzyJq3kmmLLsSn+WpEpI+4eIzyA3s4vPdtwvU1uKY6onHw+oMEA0H8WcPw5RSRO3wkhUUj8PkD6Q5ZpF9SgiUi0sf++4/3MuHV7zHCldNAJnuDk2kYVUrhnIs4bfZizaEnIiIygGmQCxGRPrb4I9cQv/BqmmJx8n0eCnT7jIiIyJCgBEtEpJd4PEaGRz1VIiIiQ4mephYREREREUkRJVgiIiIiIiIpogRLREREREQkRfrVKIJmVga8m+442igCytMdhPQZHe+hQ8d66NCxHlp0vIcOHeuhoz8e61Occ8UdFfSrBKs/MrMNnQ3BKIOPjvfQoWM9dOhYDy063kOHjvXQMdCOtW4RFBERERERSRElWCIiIiIiIimiBOv4fp7uAKRP6XgPHTrWQ4eO9dCi4z106FgPHQPqWOsZLBERERERkRRRD5aIiIiIiEiKKMESERERERFJESVYXTCz5Wa208x2mdnX0x2PpI6ZjTez58zsTTPbZmb/lFw/3MyeNrO3k98L0h2rpIaZec3sNTP7r+TyJDN7OXl+P2BmgXTHKKlhZvlm9rCZ7TCz7WZ2ps7twcnMbkq+h281s/vMLEPn9uBhZr8ys8NmtrXFug7PZUv4cfK4v2FmZ6QvcjlRnRzrHybfx98ws0fNLL9F2c3JY73TzC5IS9BdUILVCTPzAncBFwLTgU+Y2fT0RiUpFAW+4pybDiwCbkge368DzzjnJgPPJJdlcPgnYHuL5TuAf3POnQZUAZ9OS1TSG/4deNI5dzowm8Rx17k9yJjZWOBGoNQ5NxPwAleic3swuQdY3mZdZ+fyhcDk5Nd1wM/6KEZJjXtof6yfBmY652YBbwE3AySv164EZiS3+Wnyur3fUILVuQXALufcO865JuB+4JI0xyQp4pw74JzblHxdS+ICbCyJY/ybZLXfAB9LS4CSUmY2DvgI8MvksgHnAA8nq+hYDxJmlgd8CFgD4Jxrcs5Vo3N7sPIBmWbmA7KAA+jcHjSccy8AlW1Wd3YuXwLc6xJeAvLNbHSfBCo91tGxds495ZyLJhdfAsYlX18C3O+cCzvndgO7SFy39xtKsDo3FtjXYnl/cp0MMmY2EZgLvAyMdM4dSBYdBEamKy5Jqf8H/AsQTy4XAtUt3rh1fg8ek4Ay4NfJW0J/aWbZ6NwedJxz7wH/CuwlkVjVABvRuT3YdXYu67ptcFsN/Cn5ut8fayVYMqSZWQ7wCPAl59yRlmUuMYeB5jEY4Mzso8Bh59zGdMcifcIHnAH8zDk3F6inze2AOrcHh+SzN5eQSKrHANm0v8VIBjGdy0ODmX2TxKMda9MdS3cpwerce8D4FsvjkutkkDAzP4nkaq1z7g/J1YeO3lKQ/H44XfFJyiwGLjazPSRu9T2HxDM6+cnbikDn92CyH9jvnHs5ufwwiYRL5/bgcy6w2zlX5pyLAH8gcb7r3B7cOjuXdd02CJnZKuCjwCfdscl7+/2xVoLVuVeBycnRiAIkHqZ7PM0xSYokn8FZA2x3zv2oRdHjwKeSrz8FPNbXsUlqOeduds6Nc85NJHEeP+uc+yTwHHBZspqO9SDhnDsI7DOzqclVHwbeROf2YLQXWGRmWcn39KPHWuf24NbZufw4cE1yNMFFQE2LWwllADKz5SRu77/YOdfQouhx4EozC5rZJBIDm7ySjhg7Y8eSQWnLzP6BxLMbXuBXzrnvpTciSRUzOwv4G7CFY8/lfIPEc1gPAhOAd4ErnHNtH7CVAcrMlgJfdc591MxOJdGjNRx4DbjKORdOY3iSImY2h8SAJgHgHeBaEh8o6tweZMzsO8BKErcPvQZ8hsSzGDq3BwEzuw9YChQBh4BvA+vo4FxOJtl3krhNtAG41jm3IQ1hy0no5FjfDASBimS1l5xzn0vW/yaJ57KiJB7z+FPbfaaTEiwREREREZEU0S2CIiIiIiIiKaIES0REREREJEWUYImIiIiIiKSIEiwREREREZEUUYIlIiIiIiKSIkqwREREREREUkQJloiIiIiISIr8f5G9+c2PRRXlAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>param_i</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>residual_rel_l1_norm</th>\\n\",\n       \"      <th>residual_rel_l1_error</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>57</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>bAP.soma.v</td>\\n\",\n       \"      <td>0.00177</td>\\n\",\n       \"      <td>1.05e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>58</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step1.soma.v</td>\\n\",\n       \"      <td>0.00091</td>\\n\",\n       \"      <td>2.8e-06</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>59</th>\\n\",\n       \"      <td>True</td>\\n\",\n       \"      <td>9</td>\\n\",\n       \"      <td>0.0731</td>\\n\",\n       \"      <td>0.0741</td>\\n\",\n       \"      <td>Step3.soma.v</td>\\n\",\n       \"      <td>0.00174</td>\\n\",\n       \"      <td>2.78e-05</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    replace_axon  param_i  gnabar_hh.somatic  gkbar_hh.somatic      protocol  \\\\\\n\",\n       \"57          True        9             0.0731            0.0741    bAP.soma.v   \\n\",\n       \"58          True        9             0.0731            0.0741  Step1.soma.v   \\n\",\n       \"59          True        9             0.0731            0.0741  Step3.soma.v   \\n\",\n       \"\\n\",\n       \"    residual_rel_l1_norm  residual_rel_l1_error  \\n\",\n       \"57               0.00177               1.05e-06  \\n\",\n       \"58               0.00091                2.8e-06  \\n\",\n       \"59               0.00174               2.78e-05  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_fine_dt:\\n\",\n    \"    for param_i in range(len(params)):\\n\",\n    \"        for do_replace_axon in replace_axon:\\n\",\n    \"            compare_responses(arb_responses_fine_dt,\\n\",\n    \"                              nrn_responses_fine_dt,\\n\",\n    \"                              l1_results_fine_dt,\\n\",\n    \"                              do_replace_axon, param_i)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Neuron</th>\\n\",\n       \"      <th>Arbor</th>\\n\",\n       \"      <th>abs_diff Arbor to Neuron</th>\\n\",\n       \"      <th>rel_abs_diff Arbor to Neuron [%]</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>efel</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"5\\\" valign=\\\"top\\\">False</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">bAP</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.12</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.0406</th>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>0.9</td>\\n\",\n       \"      <td>0.9</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>0.9</td>\\n\",\n       \"      <td>0.9</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">Step1</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.12</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.0406</th>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>1.8</td>\\n\",\n       \"      <td>1.8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"5\\\" valign=\\\"top\\\">True</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">Step1</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.0731</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">0.0741</th>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>3.8</td>\\n\",\n       \"      <td>3.8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>3.8</td>\\n\",\n       \"      <td>3.8</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">Step3</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.0731</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">0.0741</th>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>2.1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>200 rows × 4 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                                              Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                          \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount                1   \\n\",\n       \"                                                         time_to_first_spike     0.9   \\n\",\n       \"                                                         time_to_last_spike      0.9   \\n\",\n       \"             Step1    0.12              0.0406           Spikecount                4   \\n\",\n       \"                                                         time_to_first_spike     1.8   \\n\",\n       \"...                                                                              ...   \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike     3.8   \\n\",\n       \"                                                         time_to_last_spike      3.8   \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount                1   \\n\",\n       \"                                                         time_to_first_spike     2.1   \\n\",\n       \"                                                         time_to_last_spike      2.1   \\n\",\n       \"\\n\",\n       \"                                                                              Arbor  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                         \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount               1   \\n\",\n       \"                                                         time_to_first_spike    0.9   \\n\",\n       \"                                                         time_to_last_spike     0.9   \\n\",\n       \"             Step1    0.12              0.0406           Spikecount               4   \\n\",\n       \"                                                         time_to_first_spike    1.8   \\n\",\n       \"...                                                                             ...   \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike    3.8   \\n\",\n       \"                                                         time_to_last_spike     3.8   \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount               1   \\n\",\n       \"                                                         time_to_first_spike    2.1   \\n\",\n       \"                                                         time_to_last_spike     2.1   \\n\",\n       \"\\n\",\n       \"                                                                              abs_diff Arbor to Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                            \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount                                  0   \\n\",\n       \"                                                         time_to_first_spike                         0   \\n\",\n       \"                                                         time_to_last_spike                          0   \\n\",\n       \"             Step1    0.12              0.0406           Spikecount                                  0   \\n\",\n       \"                                                         time_to_first_spike                         0   \\n\",\n       \"...                                                                                                ...   \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike                         0   \\n\",\n       \"                                                         time_to_last_spike                          0   \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount                                  0   \\n\",\n       \"                                                         time_to_first_spike                         0   \\n\",\n       \"                                                         time_to_last_spike                          0   \\n\",\n       \"\\n\",\n       \"                                                                              rel_abs_diff Arbor to Neuron [%]  \\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                                   \\n\",\n       \"False        bAP      0.12              0.0406           Spikecount                                          0  \\n\",\n       \"                                                         time_to_first_spike                                 0  \\n\",\n       \"                                                         time_to_last_spike                                  0  \\n\",\n       \"             Step1    0.12              0.0406           Spikecount                                          0  \\n\",\n       \"                                                         time_to_first_spike                                 0  \\n\",\n       \"...                                                                                                        ...  \\n\",\n       \"True         Step1    0.0731            0.0741           time_to_first_spike                                 0  \\n\",\n       \"                                                         time_to_last_spike                                  0  \\n\",\n       \"             Step3    0.0731            0.0741           Spikecount                                          0  \\n\",\n       \"                                                         time_to_first_spike                                 0  \\n\",\n       \"                                                         time_to_last_spike                                  0  \\n\",\n       \"\\n\",\n       \"[200 rows x 4 columns]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_spike_time_analysis and run_fine_dt:\\n\",\n    \"    spike_results_fine_dt = joint_spike_analysis(arb_responses_fine_dt, nrn_responses_fine_dt, replace_axon, params)\\n\",\n    \"    display(spike_results_fine_dt)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead tr th {\\n\",\n       \"        text-align: left;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead tr:last-of-type th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th colspan=\\\"8\\\" halign=\\\"left\\\">abs_diff Arbor to Neuron</th>\\n\",\n       \"      <th colspan=\\\"8\\\" halign=\\\"left\\\">rel_abs_diff Arbor to Neuron [%]</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <th>max</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>efel</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0.0117</td>\\n\",\n       \"      <td>0.0555</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>60</td>\\n\",\n       \"      <td>0.0302</td>\\n\",\n       \"      <td>0.148</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.943</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_second_spike</th>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>0.01</td>\\n\",\n       \"      <td>0.0308</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>20</td>\\n\",\n       \"      <td>0.0775</td>\\n\",\n       \"      <td>0.245</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0.943</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                     abs_diff Arbor to Neuron                                \\\\\\n\",\n       \"                                        count   mean    std min 25% 50% 75%   \\n\",\n       \"efel                                                                          \\n\",\n       \"Spikecount                                 60      0      0   0   0   0   0   \\n\",\n       \"time_to_first_spike                        60      0      0   0   0   0   0   \\n\",\n       \"time_to_last_spike                         60 0.0117 0.0555   0   0   0   0   \\n\",\n       \"time_to_second_spike                       20   0.01 0.0308   0   0   0   0   \\n\",\n       \"\\n\",\n       \"                         rel_abs_diff Arbor to Neuron [%]                   \\\\\\n\",\n       \"                     max                            count   mean   std min   \\n\",\n       \"efel                                                                         \\n\",\n       \"Spikecount             0                               60      0     0   0   \\n\",\n       \"time_to_first_spike    0                               60      0     0   0   \\n\",\n       \"time_to_last_spike   0.3                               60 0.0302 0.148   0   \\n\",\n       \"time_to_second_spike 0.1                               20 0.0775 0.245   0   \\n\",\n       \"\\n\",\n       \"                                        \\n\",\n       \"                     25% 50% 75%   max  \\n\",\n       \"efel                                    \\n\",\n       \"Spikecount             0   0   0     0  \\n\",\n       \"time_to_first_spike    0   0   0     0  \\n\",\n       \"time_to_last_spike     0   0   0 0.943  \\n\",\n       \"time_to_second_spike   0   0   0 0.943  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_spike_time_analysis and run_fine_dt:\\n\",\n    \"    display(spike_results_fine_dt[['abs_diff Arbor to Neuron',\\n\",\n    \"                                   'rel_abs_diff Arbor to Neuron [%]']].groupby('efel').describe())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The outlier in `time_to_last_spike` is gone now, both visually and quantitatively.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Neuron</th>\\n\",\n       \"      <th>Arbor</th>\\n\",\n       \"      <th>abs_diff Arbor to Neuron</th>\\n\",\n       \"      <th>rel_abs_diff Arbor to Neuron [%]</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>replace_axon</th>\\n\",\n       \"      <th>protocol</th>\\n\",\n       \"      <th>gnabar_hh.somatic</th>\\n\",\n       \"      <th>gkbar_hh.somatic</th>\\n\",\n       \"      <th>efel</th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">False</th>\\n\",\n       \"      <th rowspan=\\\"3\\\" valign=\\\"top\\\">Step1</th>\\n\",\n       \"      <th>0.0708</th>\\n\",\n       \"      <th>0.0267</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>46.7</td>\\n\",\n       \"      <td>47</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.642</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0.0553</th>\\n\",\n       \"      <th>0.0212</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>31.8</td>\\n\",\n       \"      <td>32.1</td>\\n\",\n       \"      <td>0.3</td>\\n\",\n       \"      <td>0.943</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0.12</th>\\n\",\n       \"      <th>0.0406</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>44.7</td>\\n\",\n       \"      <td>44.8</td>\\n\",\n       \"      <td>0.1</td>\\n\",\n       \"      <td>0.224</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">True</th>\\n\",\n       \"      <th rowspan=\\\"2\\\" valign=\\\"top\\\">bAP</th>\\n\",\n       \"      <th>0.0799</th>\\n\",\n       \"      <th>0.0189</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>1.3</td>\\n\",\n       \"      <td>1.3</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0.0592</th>\\n\",\n       \"      <th>0.0295</th>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                                                                             Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                         \\n\",\n       \"False        Step1    0.0708            0.0267           time_to_last_spike    46.7   \\n\",\n       \"                      0.0553            0.0212           time_to_last_spike    31.8   \\n\",\n       \"                      0.12              0.0406           time_to_last_spike    44.7   \\n\",\n       \"True         bAP      0.0799            0.0189           time_to_last_spike     1.3   \\n\",\n       \"                      0.0592            0.0295           time_to_last_spike     1.4   \\n\",\n       \"\\n\",\n       \"                                                                             Arbor  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                        \\n\",\n       \"False        Step1    0.0708            0.0267           time_to_last_spike     47   \\n\",\n       \"                      0.0553            0.0212           time_to_last_spike   32.1   \\n\",\n       \"                      0.12              0.0406           time_to_last_spike   44.8   \\n\",\n       \"True         bAP      0.0799            0.0189           time_to_last_spike    1.3   \\n\",\n       \"                      0.0592            0.0295           time_to_last_spike    1.4   \\n\",\n       \"\\n\",\n       \"                                                                             abs_diff Arbor to Neuron  \\\\\\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                           \\n\",\n       \"False        Step1    0.0708            0.0267           time_to_last_spike                       0.3   \\n\",\n       \"                      0.0553            0.0212           time_to_last_spike                       0.3   \\n\",\n       \"                      0.12              0.0406           time_to_last_spike                       0.1   \\n\",\n       \"True         bAP      0.0799            0.0189           time_to_last_spike                         0   \\n\",\n       \"                      0.0592            0.0295           time_to_last_spike                         0   \\n\",\n       \"\\n\",\n       \"                                                                             rel_abs_diff Arbor to Neuron [%]  \\n\",\n       \"replace_axon protocol gnabar_hh.somatic gkbar_hh.somatic efel                                                  \\n\",\n       \"False        Step1    0.0708            0.0267           time_to_last_spike                             0.642  \\n\",\n       \"                      0.0553            0.0212           time_to_last_spike                             0.943  \\n\",\n       \"                      0.12              0.0406           time_to_last_spike                             0.224  \\n\",\n       \"True         bAP      0.0799            0.0189           time_to_last_spike                                 0  \\n\",\n       \"                      0.0592            0.0295           time_to_last_spike                                 0  \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_spike_time_analysis and run_fine_dt:\\n\",\n    \"    display(spike_results_fine_dt[ [el[spike_results_fine_dt.index.names.index('efel')] == 'time_to_last_spike'\\n\",\n    \"                                    for el in spike_results_fine_dt.index] ].sort_values(\\n\",\n    \"                                    by='abs_diff Arbor to Neuron', ascending=False).head(5))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Furthermore, the mean deviation between Arbor and Neuron for eFEL spike times is significantly reduced.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>ratio of mean abs_diff Arbor to Neuron for fine dt vs. default dt</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>efel</th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Spikecount</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_first_spike</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_last_spike</th>\\n\",\n       \"      <td>0.038</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>time_to_second_spike</th>\\n\",\n       \"      <td>0.087</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"                      ratio of mean abs_diff Arbor to Neuron for fine dt vs. default dt\\n\",\n       \"efel                                                                                   \\n\",\n       \"Spikecount                                                            0                \\n\",\n       \"time_to_first_spike                                                   0                \\n\",\n       \"time_to_last_spike                                                0.038                \\n\",\n       \"time_to_second_spike                                              0.087                \"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"if run_spike_time_analysis and run_fine_dt:\\n\",\n    \"    display((spike_results_fine_dt[['abs_diff Arbor to Neuron']].groupby('efel').mean()/\\n\",\n    \"             spike_results[['abs_diff Arbor to Neuron']].groupby('efel').mean()).rename(\\n\",\n    \"                 columns={'abs_diff Arbor to Neuron': \\n\",\n    \"                         'ratio of mean abs_diff Arbor to Neuron for fine dt vs. default dt'}))\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\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\": \"3.8.10\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"581988038cf9ce8838e7faf3da7c29f4ff88d898cd43cb17e0086e389d8deda2\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/l5pc/l5pc_validate_neuron_arbor_pm.py",
    "content": "#!/usr/bin/env python\n\nimport os\nimport sys\nimport traceback\nimport json\nimport argparse\nimport random\nimport itertools\ntry:\n    import papermill\nexcept ImportError:\n    raise ImportError('Please install papermill to batch-process'\n                      ' l5pc_validate_neuron_arbor notebook.')\n\n\nimport logging\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger()\n\nSCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))\n\nparser = argparse.ArgumentParser(description=\n    'Run l5pc_validate_neuron_arbor notebook with papermill using different options.')\nparser.add_argument('--output-dir', type=str, default='.',\n                    help='Output directory')\nparser.add_argument('--regions', type=str, nargs='+',\n                    help='L5PC mechanisms to use: region[:mech1,mech2,...].')\nparser.add_argument('--powerset', type=int,\n                    help='Process powerset of local mechs up to this size.')\nparser.add_argument('--param-values', type=str,\n                    help='JSON file with parameter values'\n                         ' (instead of using random sampling).')\nparser.add_argument('--prepare-only', action='store_true',\n                    help='Prepare notebooks only, do not run them.)')\nparser.add_argument('--default-dt', type=float, default=0.025,\n                    help='dt used for time-integration by default.')\nparser.add_argument('--run-fine-dt', action='store_true',\n                    help='Run time-integration with fine dt (0.001).')\nparser.add_argument('--rel-l1-tolerance', type=float, default=0.05,\n                    help='Tolerance for rel. Arbor-Neuron L1-difference.')\nargs = parser.parse_args()\n\noutput_dir = os.path.abspath(args.output_dir)\nos.makedirs(output_dir, exist_ok=True)\noutput_dir = os.path.relpath(output_dir, start=SCRIPT_DIR)\n\nparam_values_json = args.param_values\nif param_values_json is not None:\n    param_values_json = os.path.relpath(param_values_json, start=SCRIPT_DIR)\n\nos.chdir(SCRIPT_DIR)\n\nimport l5pc_model\n\n# load all mechs and params of L5PC\nall_mechanisms = l5pc_model.load_mechanisms()\n\nall_parameters = l5pc_model.load_parameters()\n\nif param_values_json is None:\n    param_values_json = os.path.join(output_dir, 'param_values.json')\n\n    num_samples = 5\n    with open(param_values_json, 'w') as f:\n        param_values = [{param['param_name'] + '.' + param['sectionlist']:\n                            random.uniform(*param['bounds'])\n                        for param in all_parameters\n                        if 'bounds' in param}\n                        for i in range(num_samples)]\n\n        if 'hh' in all_mechanisms.get('somatic', []):\n            for i in range(len(param_values)):\n                param_values[i].update({\n                    'gnabar_hh.somatic': random.uniform(0.05, 0.125),\n                    'gkbar_hh.somatic': random.uniform(0.01, 0.075)})\n        json.dump(param_values, f, indent=4)\n\n    logger.info('Dumped parameter values to %s.' % param_values_json)\n\n\ndef powerset(mechs):  # from itertools docs\n    mechs = list(mechs)\n    for count in range(len(mechs) + 1):\n        for mech_comb in itertools.combinations(mechs, count):\n            yield list(mech_comb)\n\n\ndef get_extra_params(loc, mechs):\n    extra_params = {\n        p['param_name']: p['type'] for p in all_parameters\n        if p['type'] == 'global'\n        }\n\n    for p in all_parameters:\n        if 'sectionlist' in p and \\\n        p['sectionlist'] in ['all', loc] and \\\n        'mech' not in p:\n            if p['param_name'] == 'ena' and \\\n                not any([m[:2] in ['Na'] for m in mechs]):\n                continue\n            if p['param_name'] == 'ek' and \\\n                not any([m[:2] in ['Im', 'K_', 'SK'] for m in mechs]):\n                continue\n            if p['param_name'] == 'eca' and \\\n                not any([m[:2] in ['Ca'] for m in mechs]):\n                continue\n            if p['param_name'] not in extra_params:\n                extra_params[p['param_name']] = [p['sectionlist']]\n            else:\n                extra_params[p['param_name']].append(p['sectionlist'])\n    return extra_params\n\n\nfor loc, loc_mechs in all_mechanisms.items():\n\n    if args.regions is not None:\n        region = [r for r in args.regions if r.startswith(loc)]\n        if len(region) == 0:\n            continue\n        elif len(region) > 1:\n            raise ValueError('Multiple values supplied for region %s.' % loc)\n        else:\n            region = region[0]\n            if ':' in region:  # filter for selected mechs\n                loc_mechs_subset = region.split(':')[1].split(',')\n                for mech in loc_mechs_subset:\n                    if mech not in loc_mechs:\n                        raise ValueError('Mechanism %s not in region %s.'\n                                        % (mech, loc))\n                logger.info('Reducing local mechs on %s from %s to %s.',\n                    region, loc_mechs, loc_mechs_subset)\n                loc_mechs = loc_mechs_subset\n\n\n    # First test the entire region\n    mechanism_defs = {\n        'all': ['pas'],\n        loc: loc_mechs\n    }\n\n    extra_params = get_extra_params(loc, loc_mechs)\n\n    target_file = os.path.join(output_dir, 'l5pc_validate_neuron_arbor_%s.ipynb' % loc)\n    if os.path.exists(target_file):\n        raise FileExistsError('Invalid target file - exists already: ',\n                              target_file)\n\n    logger.info('Outputting l5pc_validate_neuron_arbor notebook to %s '\n                'with all local mechs/params...\\n'\n                'mechs = %s\\nextra_params = %s',\n                target_file, mechanism_defs, extra_params)\n\n    try:\n        papermill.execute_notebook(\n            'l5pc_validate_neuron_arbor.ipynb',\n            target_file,\n            parameters=dict(mechanism_defs=mechanism_defs,\n                            extra_params=extra_params,\n                            param_values_json=param_values_json,\n                            default_dt=args.default_dt,\n                            run_spike_time_analysis=False,\n                            run_fine_dt=args.run_fine_dt,\n                            voltage_residual_rel_l1_tolerance=\n                                args.rel_l1_tolerance),\n            prepare_only=args.prepare_only\n        )\n    except papermill.exceptions.PapermillException:\n        traceback.print_exception(*sys.exc_info())\n\n    # Test subsets of local mechanisms in ascending size\n    if args.powerset is not None:\n        for mechs in powerset(loc_mechs):\n            if len(mechs) < 1 or len(mechs) > args.powerset or \\\n                len(mechs) == len(loc_mechs):\n                continue\n\n            mechanism_defs = {\n                'all': ['pas'],\n                loc: mechs\n            }\n\n            extra_params = get_extra_params(loc, mechs)\n\n            target_file = os.path.join(output_dir, 'l5pc_validate_neuron_arbor_%s_%s.ipynb' % \\\n                (loc, '_'.join(mechs)))\n            if os.path.exists(target_file):\n                raise FileExistsError('Invalid target file - exists already: ',\n                                      target_file)\n            logger.info('Outputting l5pc_validate_neuron_arbor notebook to %s'\n                        ' with...\\nmechs = %s\\nextra_params = %s',\n                        target_file, mechanism_defs, extra_params)\n\n            try:\n                papermill.execute_notebook(\n                    'l5pc_validate_neuron_arbor.ipynb',\n                    target_file,\n                    parameters=dict(mechanism_defs=mechanism_defs,\n                                    extra_params=extra_params,\n                                    param_values_json=param_values_json,\n                                    default_dt=args.default_dt,\n                                    run_spike_time_analysis=False,\n                                    run_fine_dt=args.run_fine_dt,\n                                    voltage_residual_rel_l1_tolerance=\n                                        args.rel_l1_tolerance),\n                    prepare_only=args.prepare_only\n                )\n            except papermill.exceptions.PapermillException:\n                traceback.print_exception(*sys.exc_info())\n\n"
  },
  {
    "path": "examples/l5pc/mechanisms/CaDynamics_E2.mod",
    "content": ": Dynamics that track inside calcium concentration\n: modified from Destexhe et al. 1994\n\nNEURON\t{\n\tSUFFIX CaDynamics_E2\n\tUSEION ca READ ica WRITE cai\n\tRANGE decay, gamma, minCai, depth\n}\n\nUNITS\t{\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n\tFARADAY = (faraday) (coulombs)\n\t(molar) = (1/liter)\n\t(mM) = (millimolar)\n\t(um)\t= (micron)\n}\n\nPARAMETER\t{\n\tgamma = 0.05 : percent of free calcium (not buffered)\n\tdecay = 80 (ms) : rate of removal of calcium\n\tdepth = 0.1 (um) : depth of shell\n\tminCai = 1e-4 (mM)\n}\n\nASSIGNED\t{ica (mA/cm2)}\n\nSTATE\t{\n\tcai (mM)\n\t}\n\nBREAKPOINT\t{ SOLVE states METHOD cnexp }\n\nDERIVATIVE states\t{\n\tcai' = -(10000)*(ica*gamma/(2*FARADAY*depth)) - (cai - minCai)/decay\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/Ca_HVA.mod",
    "content": ":Comment :\n:Reference : :\t\tReuveni, Friedman, Amitai, and Gutnick, J.Neurosci. 1993\n\nNEURON\t{\n\tSUFFIX Ca_HVA\n\tUSEION ca READ eca WRITE ica\n\tRANGE gCa_HVAbar, gCa_HVA, ica \n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgCa_HVAbar = 0.00001 (S/cm2) \n}\n\nASSIGNED\t{\n\tv\t(mV)\n\teca\t(mV)\n\tica\t(mA/cm2)\n\tgCa\t(S/cm2)\n\tmInf\n\tmTau\n\tmAlpha\n\tmBeta\n\thInf\n\thTau\n\thAlpha\n\thBeta\n}\n\nSTATE\t{ \n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgCa = gCa_HVAbar*m*m*h\n\tica = gCa*(v-eca)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n\tUNITSOFF\n        if((v == -27) ){        \n            v = v+0.0001\n        }\n\t\tmAlpha =  (0.055*(-27-v))/(exp((-27-v)/3.8) - 1)        \n\t\tmBeta  =  (0.94*exp((-75-v)/17))\n\t\tmInf = mAlpha/(mAlpha + mBeta)\n\t\tmTau = 1/(mAlpha + mBeta)\n\t\thAlpha =  (0.000457*exp((-13-v)/50))\n\t\thBeta  =  (0.0065/(exp((-v-15)/28)+1))\n\t\thInf = hAlpha/(hAlpha + hBeta)\n\t\thTau = 1/(hAlpha + hBeta)\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/Ca_LVAst.mod",
    "content": ":Comment : LVA ca channel. Note: mtau is an approximation from the plots\n:Reference : :\t\tAvery and Johnston 1996, tau from Randall 1997\n:Comment: shifted by -10 mv to correct for junction potential\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21\n\nNEURON\t{\n\tSUFFIX Ca_LVAst\n\tUSEION ca READ eca WRITE ica\n\tRANGE gCa_LVAstbar, gCa_LVAst, ica\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgCa_LVAstbar = 0.00001 (S/cm2)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\teca\t(mV)\n\tica\t(mA/cm2)\n\tgCa_LVAst\t(S/cm2)\n\tmInf\n\tmTau\n\thInf\n\thTau\n}\n\nSTATE\t{\n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgCa_LVAst = gCa_LVAstbar*m*m*h\n\tica = gCa_LVAst*(v-eca)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\n\tUNITSOFF\n\t\tv = v + 10\n\t\tmInf = 1.0000/(1+ exp((v - -30.000)/-6))\n\t\tmTau = (5.0000 + 20.0000/(1+exp((v - -25.000)/5)))/qt\n\t\thInf = 1.0000/(1+ exp((v - -80.000)/6.4))\n\t\thTau = (20.0000 + 50.0000/(1+exp((v - -40.000)/7)))/qt\n\t\tv = v - 10\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/Ih.mod",
    "content": ":Comment :\n:Reference : :\t\tKole,Hallermann,and Stuart, J. Neurosci. 2006\n\nNEURON\t{\n\tSUFFIX Ih\n\tNONSPECIFIC_CURRENT ihcn\n\tRANGE gIhbar, gIh, ihcn \n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgIhbar = 0.00001 (S/cm2) \n\tehcn =  -45.0 (mV)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tihcn\t(mA/cm2)\n\tgIh\t(S/cm2)\n\tmInf\n\tmTau\n\tmAlpha\n\tmBeta\n}\n\nSTATE\t{ \n\tm\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgIh = gIhbar*m\n\tihcn = gIh*(v-ehcn)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n}\n\nPROCEDURE rates(){\n\tUNITSOFF\n        if(v == -154.9){\n            v = v + 0.0001\n        }\n\t\tmAlpha =  0.001*6.43*(v+154.9)/(exp((v+154.9)/11.9)-1)\n\t\tmBeta  =  0.001*193*exp(v/33.1)\n\t\tmInf = mAlpha/(mAlpha + mBeta)\n\t\tmTau = 1/(mAlpha + mBeta)\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/Im.mod",
    "content": ":Reference : :\t\tAdams et al. 1982 - M-currents and other potassium currents in bullfrog sympathetic neurones\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21\n\nNEURON\t{\n\tSUFFIX Im\n\tUSEION k READ ek WRITE ik\n\tRANGE gImbar, gIm, ik\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgImbar = 0.00001 (S/cm2) \n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tek\t(mV)\n\tik\t(mA/cm2)\n\tgIm\t(S/cm2)\n\tmInf\n\tmTau\n\tmAlpha\n\tmBeta\n}\n\nSTATE\t{ \n\tm\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgIm = gImbar*m\n\tik = gIm*(v-ek)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\n\tUNITSOFF\n\t\tmAlpha = 3.3e-3*exp(2.5*0.04*(v - -35))\n\t\tmBeta = 3.3e-3*exp(-2.5*0.04*(v - -35))\n\t\tmInf = mAlpha/(mAlpha + mBeta)\n\t\tmTau = (1/(mAlpha + mBeta))/qt\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/K_Pst.mod",
    "content": ":Comment : The persistent component of the K current\n:Reference : :\t\tVoltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000\n:Comment : shifted -10 mv to correct for junction potential\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21\n\n\nNEURON\t{\n\tSUFFIX K_Pst\n\tUSEION k READ ek WRITE ik\n\tRANGE gK_Pstbar, gK_Pst, ik\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgK_Pstbar = 0.00001 (S/cm2)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tek\t(mV)\n\tik\t(mA/cm2)\n\tgK_Pst\t(S/cm2)\n\tmInf\n\tmTau\n\thInf\n\thTau\n}\n\nSTATE\t{\n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgK_Pst = gK_Pstbar*m*m*h\n\tik = gK_Pst*(v-ek)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\tUNITSOFF\n\t\tv = v + 10\n\t\tmInf =  (1/(1 + exp(-(v+1)/12)))\n        if(v<-50){\n\t\t    mTau =  (1.25+175.03*exp(-v * -0.026))/qt\n        }else{\n            mTau = ((1.25+13*exp(-v*0.026)))/qt\n        }\n\t\thInf =  1/(1 + exp(-(v+54)/-11))\n\t\thTau =  (360+(1010+24*(v+55))*exp(-((v+75)/48)^2))/qt\n\t\tv = v - 10\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/K_Tst.mod",
    "content": ":Comment : The transient component of the K current\n:Reference : :\t\tVoltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000\n:Comment : shifted -10 mv to correct for junction potential\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21\n\nNEURON\t{\n\tSUFFIX K_Tst\n\tUSEION k READ ek WRITE ik\n\tRANGE gK_Tstbar, gK_Tst, ik\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgK_Tstbar = 0.00001 (S/cm2)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tek\t(mV)\n\tik\t(mA/cm2)\n\tgK_Tst\t(S/cm2)\n\tmInf\n\tmTau\n\thInf\n\thTau\n}\n\nSTATE\t{\n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgK_Tst = gK_Tstbar*(m^4)*h\n\tik = gK_Tst*(v-ek)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\n\tUNITSOFF\n\t\tv = v + 10\n\t\tmInf =  1/(1 + exp(-(v+0)/19))\n\t\tmTau =  (0.34+0.92*exp(-((v+71)/59)^2))/qt\n\t\thInf =  1/(1 + exp(-(v+66)/-10))\n\t\thTau =  (8+49*exp(-((v+73)/23)^2))/qt\n\t\tv = v - 10\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/LICENSE",
    "content": "The CC-BY-NC-SA license applies, as indicated by headers in the\nrespective source files.\n\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/\n\nThe detailed text is available here\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/legalcode\n\nor in this tarball in the seperate file LICENSE_CC-BY-CA-SA-4.0\n\n\nThe HOC code, Python code, synapse MOD code and cell morphology are licensed with the above mentioned CC-BY-NC-SA license.\n\n\nFor models for which the original source is available on ModelDB, any\nspecific licenses on mentioned on ModelDB, or the generic License of ModelDB \napply:\n\n1) Ih model\nAuthor: Stefan Hallermann\nOriginal URL: http://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=144526&file=\\HallermannEtAl2012\\h.mod\n\n2) StochKv model\nAuthors: Zach Mainen Adaptations: Kamran Diba, Mickey London, Peter N. Steinmetz, Werner Van Geit\nOriginal URL: http://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=125385&file=\\Sbpap_code\\mod\\skm.mod\n\n3) D-type K current model\nAuthors: Yuguo Yu\nOriginal URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=135898&file=\\YuEtAlPNAS2007\\kd.mod\n\n4) Internal calcium concentration model\nAuthor: Alain Destexhe\nOriginal URL: http://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=3670&file=\\NTW_NEW\\capump.mod\n\n5) Ca_LVAst, Im, K_Tst, NaTa_t, SK_E2, Ca_HVA, Ih, K_Pst, Nap_Et2, NaTs2_t, SKv3_1            \nAuthor: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit\nOriginal URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=139653&file=\\L5bPCmodelsEH\\mod\\\n"
  },
  {
    "path": "examples/l5pc/mechanisms/NaTa_t.mod",
    "content": ":Reference :Colbert and Pan 2002\n\nNEURON\t{\n\tSUFFIX NaTa_t\n\tUSEION na READ ena WRITE ina\n\tRANGE gNaTa_tbar, gNaTa_t, ina\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgNaTa_tbar = 0.00001 (S/cm2)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tena\t(mV)\n\tina\t(mA/cm2)\n\tgNaTa_t\t(S/cm2)\n\tmInf\n\tmTau\n\tmAlpha\n\tmBeta\n\thInf\n\thTau\n\thAlpha\n\thBeta\n}\n\nSTATE\t{\n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgNaTa_t = gNaTa_tbar*m*m*m*h\n\tina = gNaTa_t*(v-ena)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\t\n  UNITSOFF\n    if(v == -38){\n    \tv = v+0.0001\n    }\n\t\tmAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))\n\t\tmBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))\n\t\tmTau = (1/(mAlpha + mBeta))/qt\n\t\tmInf = mAlpha/(mAlpha + mBeta)\n\n    if(v == -66){\n      v = v + 0.0001\n    }\n\n\t\thAlpha = (-0.015 * (v- -66))/(1-(exp((v- -66)/6)))\n\t\thBeta  = (-0.015 * (-v -66))/(1-(exp((-v -66)/6)))\n\t\thTau = (1/(hAlpha + hBeta))/qt\n\t\thInf = hAlpha/(hAlpha + hBeta)\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/NaTs2_t.mod",
    "content": ":Reference :Colbert and Pan 2002\n:comment: took the NaTa and shifted both activation/inactivation by 6 mv\n\nNEURON\t{\n\tSUFFIX NaTs2_t\n\tUSEION na READ ena WRITE ina\n\tRANGE gNaTs2_tbar, gNaTs2_t, ina\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgNaTs2_tbar = 0.00001 (S/cm2)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tena\t(mV)\n\tina\t(mA/cm2)\n\tgNaTs2_t\t(S/cm2)\n\tmInf\n\tmTau\n\tmAlpha\n\tmBeta\n\thInf\n\thTau\n\thAlpha\n\thBeta\n}\n\nSTATE\t{\n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgNaTs2_t = gNaTs2_tbar*m*m*m*h\n\tina = gNaTs2_t*(v-ena)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\n\tUNITSOFF\n    if(v == -32){\n    \tv = v+0.0001\n    }\n\t\tmAlpha = (0.182 * (v- -32))/(1-(exp(-(v- -32)/6)))\n\t\tmBeta  = (0.124 * (-v -32))/(1-(exp(-(-v -32)/6)))\n\t\tmInf = mAlpha/(mAlpha + mBeta)\n\t\tmTau = (1/(mAlpha + mBeta))/qt\n\n    if(v == -60){\n      v = v + 0.0001\n    }\n\t\thAlpha = (-0.015 * (v- -60))/(1-(exp((v- -60)/6)))\n\t\thBeta  = (-0.015 * (-v -60))/(1-(exp((-v -60)/6)))\n\t\thInf = hAlpha/(hAlpha + hBeta)\n\t\thTau = (1/(hAlpha + hBeta))/qt\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/Nap_Et2.mod",
    "content": ":Comment : mtau deduced from text (said to be 6 times faster than for NaTa)\n:Comment : so I used the equations from NaT and multiplied by 6\n:Reference : Modeled according to kinetics derived from Magistretti & Alonso 1999\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21\n\nNEURON\t{\n\tSUFFIX Nap_Et2\n\tUSEION na READ ena WRITE ina\n\tRANGE gNap_Et2bar, gNap_Et2, ina\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgNap_Et2bar = 0.00001 (S/cm2)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tena\t(mV)\n\tina\t(mA/cm2)\n\tgNap_Et2\t(S/cm2)\n\tmInf\n\tmTau\n\tmAlpha\n\tmBeta\n\thInf\n\thTau\n\thAlpha\n\thBeta\n}\n\nSTATE\t{\n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgNap_Et2 = gNap_Et2bar*m*m*m*h\n\tina = gNap_Et2*(v-ena)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\n\tUNITSOFF\n\t\tmInf = 1.0/(1+exp((v- -52.6)/-4.6))\n    if(v == -38){\n    \tv = v+0.0001\n    }\n\t\tmAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))\n\t\tmBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))\n\t\tmTau = 6*(1/(mAlpha + mBeta))/qt\n\n  \tif(v == -17){\n   \t\tv = v + 0.0001\n  \t}\n    if(v == -64.4){\n      v = v+0.0001\n    }\n\n\t\thInf = 1.0/(1+exp((v- -48.8)/10))\n    hAlpha = -2.88e-6 * (v + 17) / (1 - exp((v + 17)/4.63))\n    hBeta = 6.94e-6 * (v + 64.4) / (1 - exp(-(v + 64.4)/2.63))\n\t\thTau = (1/(hAlpha + hBeta))/qt\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/SK_E2.mod",
    "content": ": SK-type calcium-activated potassium current\n: Reference : Kohler et al. 1996\n\nNEURON {\n       SUFFIX SK_E2\n       USEION k READ ek WRITE ik\n       USEION ca READ cai\n       RANGE gSK_E2bar, gSK_E2, ik\n}\n\nUNITS {\n      (mV) = (millivolt)\n      (mA) = (milliamp)\n      (mM) = (milli/liter)\n}\n\nPARAMETER {\n          v            (mV)\n          gSK_E2bar = .000001 (mho/cm2)\n          zTau = 1              (ms)\n          ek           (mV)\n          cai          (mM)\n}\n\nASSIGNED {\n         zInf\n         ik            (mA/cm2)\n         gSK_E2\t       (S/cm2)\n}\n\nSTATE {\n      z   FROM 0 TO 1\n}\n\nBREAKPOINT {\n           SOLVE states METHOD cnexp\n           gSK_E2  = gSK_E2bar * z\n           ik   =  gSK_E2 * (v - ek)\n}\n\nDERIVATIVE states {\n        rates(cai)\n        z' = (zInf - z) / zTau\n}\n\nPROCEDURE rates(ca(mM)) {\n          if(ca < 1e-7){\n\t              ca = ca + 1e-07\n          }\n          zInf = 1/(1 + (0.00043 / ca)^4.8)\n}\n\nINITIAL {\n        rates(cai)\n        z = zInf\n}\n"
  },
  {
    "path": "examples/l5pc/mechanisms/SKv3_1.mod",
    "content": ":Comment :\r\n:Reference : :\t\tCharacterization of a Shaw-related potassium channel family in rat brain, The EMBO Journal, vol.11, no.7,2473-2486 (1992)\r\n\r\nNEURON\t{\r\n\tSUFFIX SKv3_1\r\n\tUSEION k READ ek WRITE ik\r\n\tRANGE gSKv3_1bar, gSKv3_1, ik \r\n}\r\n\r\nUNITS\t{\r\n\t(S) = (siemens)\r\n\t(mV) = (millivolt)\r\n\t(mA) = (milliamp)\r\n}\r\n\r\nPARAMETER\t{\r\n\tgSKv3_1bar = 0.00001 (S/cm2) \r\n}\r\n\r\nASSIGNED\t{\r\n\tv\t(mV)\r\n\tek\t(mV)\r\n\tik\t(mA/cm2)\r\n\tgSKv3_1\t(S/cm2)\r\n\tmInf\r\n\tmTau\r\n}\r\n\r\nSTATE\t{ \r\n\tm\r\n}\r\n\r\nBREAKPOINT\t{\r\n\tSOLVE states METHOD cnexp\r\n\tgSKv3_1 = gSKv3_1bar*m\r\n\tik = gSKv3_1*(v-ek)\r\n}\r\n\r\nDERIVATIVE states\t{\r\n\trates()\r\n\tm' = (mInf-m)/mTau\r\n}\r\n\r\nINITIAL{\r\n\trates()\r\n\tm = mInf\r\n}\r\n\r\nPROCEDURE rates(){\r\n\tUNITSOFF\r\n\t\tmInf =  1/(1+exp(((v -(18.700))/(-9.700))))\r\n\t\tmTau =  0.2*20.000/(1+exp(((v -(-46.560))/(-44.140))))\r\n\tUNITSON\r\n}\r\n\r\n"
  },
  {
    "path": "examples/l5pc/mechanisms/dummy.inc",
    "content": ""
  },
  {
    "path": "examples/l5pc/morphology/C060114A7.asc",
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3\r\n            (  255.74  -213.88     4.60     0.46)  ; 4\r\n            (  233.48  -211.33     1.75     0.46)  ; 5\r\n            (  219.54  -212.22     0.82     0.46)  ; 6\r\n            (  211.42  -211.72    -2.55     0.46)  ; 7\r\n            (  171.11  -214.61    -0.47     0.46)  ; 8\r\n            (  149.78  -204.08    -0.35     0.46)  ; 9\r\n            (  145.36  -203.31    -0.95     0.46)  ; 10\r\n            (  115.35  -191.84    -1.45     0.46)  ; 11\r\n            (   59.04  -169.93   -21.98     0.46)  ; 12\r\n            (   42.98  -167.72   -26.30     0.46)  ; 13\r\n            (   17.85  -167.04   -15.63     0.46)  ; 14\r\n            (   11.38  -165.57   -12.17     0.46)  ; 15\r\n            (  -10.60  -152.22   -18.27     0.46)  ; 16\r\n            (  -19.34  -147.09   -26.63     0.46)  ; 17\r\n            (  -45.81  -146.73   -39.70     0.46)  ; 18\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  249.58  -211.73     2.85     0.46)  ; 1\r\n            (  186.55  -212.18    -5.60     0.46)  ; 2\r\n            (  160.23  -206.40    -1.00     0.46)  ; 3\r\n            (  126.50  -195.20     0.80     0.46)  ; 4\r\n            (  102.64  -185.86    -9.35     0.46)  ; 5\r\n            (  104.74  -184.77    -9.35     0.46)  ; 6\r\n            (   99.56  -184.79    -8.13     0.46)  ; 7\r\n            (   81.76  -172.37   -14.35     0.46)  ; 8\r\n            (   71.72  -171.73   -19.02     0.46)  ; 9\r\n            (   54.93  -168.50   -23.10     0.46)  ; 10\r\n            (   37.53  -166.61   -21.30     0.46)  ; 11\r\n            (   32.99  -165.28   -18.97     0.46)  ; 12\r\n            (    6.43  -162.55    -8.68     0.46)  ; 13\r\n            (    3.62  -162.61    -8.95     0.46)  ; 14\r\n            (  -31.66  -148.79   -33.67     0.46)  ; 15\r\n            (  -77.19  -131.39   -65.38     0.46)  ; 16\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (    1.85  -163.78    -8.52     0.46)  ; 1, R-1-1-1-2\r\n          (   -0.07  -163.64    -6.22     0.46)  ; 2\r\n          (   -0.07  -163.64    -6.20     0.46)  ; 3\r\n          (   -0.82  -164.42    -1.47     0.46)  ; 4\r\n          (   -2.30  -164.16     0.60     0.46)  ; 5\r\n          (   -4.53  -164.69     2.78     0.46)  ; 6\r\n          (   -7.49  -164.19     2.85     0.46)  ; 7\r\n          (   -7.22  -165.31     3.97     0.46)  ; 8\r\n          (   -7.22  -165.31     3.95     0.46)  ; 9\r\n          (   -7.39  -166.55     6.07     0.46)  ; 10\r\n          (   -8.60  -167.44     7.90     0.46)  ; 11\r\n          (   -8.60  -167.44     7.87     0.46)  ; 12\r\n          (   -9.89  -165.94     8.75     0.46)  ; 13\r\n          (  -10.52  -167.28    10.00     0.46)  ; 14\r\n          (  -11.99  -167.03    12.07     0.46)  ; 15\r\n          (  -11.99  -167.03    12.05     0.46)  ; 16\r\n          (  -13.34  -167.34    13.80     0.46)  ; 17\r\n          (  -14.67  -167.66    14.72     0.46)  ; 18\r\n          (  -16.51  -169.89    15.17     0.46)  ; 19\r\n          (  -16.43  -172.25    15.17     0.46)  ; 20\r\n          (  -19.42  -173.55    15.17     0.46)  ; 21\r\n          (  -20.31  -173.76    17.10     0.46)  ; 22\r\n\r\n          (Dot\r\n            (Color Cyan)\r\n            (Name \"Marker 1\")\r\n            (  -13.81  -163.28    12.20     0.46)  ; 1\r\n            (  -17.94  -173.80    15.17     0.46)  ; 2\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color RGB (255, 128, 255))\r\n            (Name \"Marker 2\")\r\n            (   -0.38  -164.31    -6.20     0.46)  ; 1\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  164.12  -212.93    -1.70     0.46)  ; 1, R-1-1-2\r\n        (  161.76  -212.88    -1.70     0.46)  ; 2\r\n        (  158.82  -212.38    -2.73     0.46)  ; 3\r\n        (  156.00  -212.44    -3.80     0.46)  ; 4\r\n        (  152.48  -211.47    -3.80     0.46)  ; 5\r\n        (  149.36  -212.21    -2.90     0.46)  ; 6\r\n        (  146.66  -212.83    -2.03     0.46)  ; 7\r\n        (  143.41  -213.00    -2.03     0.46)  ; 8\r\n        (  140.15  -213.17    -1.27     0.46)  ; 9\r\n        (  137.03  -213.90    -1.27     0.46)  ; 10\r\n        (  133.77  -214.07    -2.03     0.46)  ; 11\r\n        (  131.22  -215.26    -2.03     0.46)  ; 12\r\n        (  130.77  -215.37    -2.03     0.46)  ; 13\r\n        (  128.85  -215.22    -2.90     0.46)  ; 14\r\n        (  125.15  -215.49    -2.90     0.46)  ; 15\r\n        (  118.95  -215.16    -4.00     0.46)  ; 16\r\n        (  114.21  -215.07    -4.55     0.46)  ; 17\r\n        (  109.17  -215.65    -4.55     0.46)  ; 18\r\n        (  105.78  -215.26    -4.55     0.46)  ; 19\r\n        (  103.41  -215.22    -4.00     0.46)  ; 20\r\n\r\n        (Dot\r\n          (Color Cyan)\r\n          (Name \"Marker 1\")\r\n          (  129.12  -216.36    -2.90     0.46)  ; 1\r\n          (  125.86  -216.53    -2.90     0.46)  ; 2\r\n          (  108.99  -216.89    -4.55     0.46)  ; 3\r\n        )  ;  End of markers\r\n\r\n        (OpenCircle\r\n          (Color RGB (255, 128, 255))\r\n          (Name \"Marker 2\")\r\n          (  152.61  -212.05    -3.80     0.46)  ; 1\r\n          (  143.86  -212.90    -2.03     0.46)  ; 2\r\n          (  137.47  -213.80    -1.27     0.46)  ; 3\r\n          (  118.95  -215.16    -4.00     0.46)  ; 4\r\n        )  ;  End of markers\r\n        (\r\n          (  101.93  -216.16    -1.75     0.46)  ; 1, R-1-1-2-1\r\n          (  102.06  -216.73     0.40     0.46)  ; 2\r\n          (  101.30  -217.51     2.47     0.46)  ; 3\r\n          (  100.24  -218.96     4.30     0.46)  ; 4\r\n          (  100.24  -218.96     5.90     0.46)  ; 5\r\n          (   97.87  -218.91     8.10     0.46)  ; 6\r\n          (   98.41  -215.20     7.72     0.46)  ; 7\r\n          (   99.54  -211.95     7.02     0.46)  ; 8\r\n          (  100.02  -210.05     6.78     0.46)  ; 9\r\n          (  100.12  -206.44     7.50     0.46)  ; 10\r\n          (   99.92  -203.50     8.40     0.46)  ; 11\r\n          (   99.78  -202.93     9.38     0.46)  ; 12\r\n          (   99.12  -200.10    10.40     0.46)  ; 13\r\n          (   98.32  -196.70    12.17     0.46)  ; 14\r\n          (   99.67  -196.39    13.85     0.46)  ; 15\r\n          (   99.67  -196.39    13.82     0.46)  ; 16\r\n          (   99.00  -193.57    15.57     0.46)  ; 17\r\n\r\n          (Dot\r\n            (Color Cyan)\r\n            (Name \"Marker 1\")\r\n            (   99.05  -207.88     7.50     0.46)  ; 1\r\n            (   97.11  -197.59    12.17     0.46)  ; 2\r\n            (   98.56  -193.67    15.57     0.46)  ; 3\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color RGB (255, 128, 255))\r\n            (Name \"Marker 2\")\r\n            (  101.75  -217.40     2.47     0.46)  ; 1\r\n            (   99.12  -200.10    10.40     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (   97.70  -214.17    -4.63     0.46)  ; 1, R-1-1-2-2\r\n          (   94.71  -215.47    -4.63     0.46)  ; 2\r\n          (   91.89  -215.53    -4.63     0.46)  ; 3\r\n          (   88.64  -215.70    -4.63     0.46)  ; 4\r\n          (   84.48  -216.07    -5.90     0.46)  ; 5\r\n          (   82.69  -216.49    -5.90     0.46)  ; 6\r\n          (   79.17  -215.53    -5.97     0.46)  ; 7\r\n          (   77.38  -215.95    -6.05     0.46)  ; 8\r\n          (   77.38  -215.95    -6.07     0.46)  ; 9\r\n          (   74.31  -214.87    -5.32     0.46)  ; 10\r\n          (   71.63  -215.50    -5.32     0.46)  ; 11\r\n          (   68.81  -215.56    -5.03     0.46)  ; 12\r\n          (   66.58  -216.09    -5.03     0.46)  ; 13\r\n          (   63.68  -213.77    -8.02     0.46)  ; 14\r\n          (   61.76  -213.63    -8.27     0.46)  ; 15\r\n          (   59.17  -216.63    -9.00     0.46)  ; 16\r\n          (   57.25  -216.49    -9.00     0.46)  ; 17\r\n          (   54.13  -217.22    -7.25     0.46)  ; 18\r\n          (   50.60  -216.25    -5.40     0.46)  ; 19\r\n          (   50.60  -216.25    -5.42     0.46)  ; 20\r\n          (   48.55  -215.54    -3.95     0.46)  ; 21\r\n          (   48.55  -215.54    -3.97     0.46)  ; 22\r\n          (   45.04  -214.57    -3.22     0.46)  ; 23\r\n          (   41.63  -214.17    -3.78     0.46)  ; 24\r\n          (   37.80  -213.88    -4.75     0.46)  ; 25\r\n          (   34.27  -212.91    -5.72     0.46)  ; 26\r\n          (   31.77  -212.30    -6.67     0.46)  ; 27\r\n          (   28.83  -211.80    -6.52     0.46)  ; 28\r\n          (   25.26  -212.64    -6.52     0.46)  ; 29\r\n          (   21.60  -211.11    -6.52     0.46)  ; 30\r\n          (   17.89  -211.37    -5.80     0.46)  ; 31\r\n          (   15.08  -211.44    -5.80     0.46)  ; 32\r\n          (   10.99  -210.02    -5.80     0.46)  ; 33\r\n          (    8.47  -209.40    -4.47     0.46)  ; 34\r\n          (    6.30  -208.12    -4.47     0.46)  ; 35\r\n          (    4.37  -207.98    -3.70     0.46)  ; 36\r\n          (    2.28  -209.06    -2.05     0.46)  ; 37\r\n          (   -1.56  -208.78    -2.05     0.46)  ; 38\r\n          (   -5.35  -206.67    -0.97     0.46)  ; 39\r\n          (   -8.88  -205.71    -0.02     0.46)  ; 40\r\n          (  -10.53  -206.69     0.93     0.46)  ; 41\r\n          (  -13.87  -204.48     1.42     0.46)  ; 42\r\n          (  -17.40  -203.53     1.65     0.46)  ; 43\r\n          (  -21.55  -203.90     1.65     0.46)  ; 44\r\n          (  -27.53  -200.52     0.57     0.46)  ; 45\r\n          (  -29.76  -201.05     0.57     0.46)  ; 46\r\n          (  -34.05  -200.86    -0.35     0.46)  ; 47\r\n          (  -36.41  -200.81     1.00     0.46)  ; 48\r\n          (  -39.03  -199.64     2.17     0.46)  ; 49\r\n          (  -41.14  -200.73     3.35     0.46)  ; 50\r\n          (  -43.90  -198.98     4.17     0.46)  ; 51\r\n          (  -47.87  -198.13     4.68     0.46)  ; 52\r\n          (  -51.00  -198.86     5.40     0.46)  ; 53\r\n          (  -54.97  -198.00     5.92     0.46)  ; 54\r\n          (  -57.34  -197.95     6.67     0.46)  ; 55\r\n          (  -58.62  -196.47     7.38     0.46)  ; 56\r\n          (  -61.27  -195.29     8.42     0.46)  ; 57\r\n          (  -63.89  -194.11     9.57     0.46)  ; 58\r\n          (  -68.90  -192.89     9.57     0.46)  ; 59\r\n          (  -72.28  -192.50     8.60     0.46)  ; 60\r\n          (  -75.35  -191.43     7.35     0.46)  ; 61\r\n          (  -79.83  -192.48     7.35     0.46)  ; 62\r\n          (  -84.24  -191.72     8.25     0.46)  ; 63\r\n          (  -83.79  -191.61     8.23     0.46)  ; 64\r\n          (  -87.90  -190.19     9.15     0.46)  ; 65\r\n          (  -91.42  -189.22    10.15     0.46)  ; 66\r\n          (  -94.18  -187.49    11.45     0.46)  ; 67\r\n          (  -98.16  -186.62    12.62     0.46)  ; 68\r\n          ( -101.99  -186.33    13.92     0.46)  ; 69\r\n          ( -108.34  -185.42    14.07     0.46)  ; 70\r\n          ( -113.33  -184.20    14.50     0.46)  ; 71\r\n          ( -116.98  -182.67    13.95     0.46)  ; 72\r\n          ( -119.04  -181.96    14.55     0.46)  ; 73\r\n          ( -121.66  -180.78    13.85     0.46)  ; 74\r\n          ( -124.17  -180.18    12.57     0.46)  ; 75\r\n          ( -127.43  -180.34    13.07     0.46)  ; 76\r\n          ( -130.23  -180.41    13.07     0.46)  ; 77\r\n          ( -132.42  -179.13    14.70     0.46)  ; 78\r\n          ( -137.42  -177.90    15.57     0.46)  ; 79\r\n          ( -140.94  -176.94    16.27     0.46)  ; 80\r\n          ( -145.18  -174.95    17.47     0.46)  ; 81\r\n          ( -147.10  -174.81    18.48     0.46)  ; 82\r\n          ( -150.18  -173.73    19.58     0.46)  ; 83\r\n          ( -152.94  -171.99    19.23     0.46)  ; 84\r\n          ( -157.17  -169.99    19.23     0.46)  ; 85\r\n          ( -161.46  -169.81    19.23     0.46)  ; 86\r\n          ( -164.22  -168.07    19.73     0.46)  ; 87\r\n          ( -166.14  -167.92    19.73     0.46)  ; 88\r\n          ( -170.17  -168.86    20.25     0.46)  ; 89\r\n          ( -174.13  -168.00    21.00     0.46)  ; 90\r\n          ( -177.22  -166.93    22.38     0.46)  ; 91\r\n          ( -180.16  -166.43    22.92     0.46)  ; 92\r\n          ( -183.31  -164.18    23.33     0.46)  ; 93\r\n          ( -186.70  -163.79    23.33     0.46)  ; 94\r\n          ( -190.35  -162.26    22.83     0.46)  ; 95\r\n          ( -190.81  -162.36    22.83     0.46)  ; 96\r\n          ( -194.01  -160.73    24.15     0.46)  ; 97\r\n          ( -195.80  -161.14    25.57     0.46)  ; 98\r\n          ( -198.74  -160.65    27.20     0.46)  ; 99\r\n          ( -201.69  -160.13    28.33     0.46)  ; 100\r\n          ( -204.63  -159.63    29.25     0.46)  ; 101\r\n          ( -207.76  -160.36    29.52     0.46)  ; 102\r\n          ( -211.99  -158.37    28.88     0.46)  ; 103\r\n          ( -213.60  -157.56    29.92     0.46)  ; 104\r\n          ( -213.60  -157.56    29.90     0.46)  ; 105\r\n          ( -216.29  -158.18    31.13     0.46)  ; 106\r\n          ( -218.52  -158.70    31.95     0.46)  ; 107\r\n          ( -221.59  -157.64    31.95     0.46)  ; 108\r\n          ( -224.58  -158.93    32.02     0.46)  ; 109\r\n          ( -227.63  -162.03    31.20     0.46)  ; 110\r\n          ( -228.31  -165.19    31.20     0.46)  ; 111\r\n          ( -228.48  -166.42    32.22     0.46)  ; 112\r\n          ( -227.65  -168.02    33.85     0.46)  ; 113\r\n\r\n          (Dot\r\n            (Color Cyan)\r\n            (Name \"Marker 1\")\r\n            (   89.35  -216.73    -4.63     0.46)  ; 1\r\n            (   69.31  -213.66    -5.03     0.46)  ; 2\r\n            (   42.39  -213.39    -3.78     0.46)  ; 3\r\n            (   18.47  -211.84    -5.80     0.46)  ; 4\r\n            (    4.24  -207.41    -3.70     0.46)  ; 5\r\n            (    0.22  -208.36    -2.05     0.46)  ; 6\r\n            (  -20.97  -204.37     1.65     0.46)  ; 7\r\n            (  -32.89  -201.77    -0.35     0.46)  ; 8\r\n            (  -44.09  -200.22     4.17     0.46)  ; 9\r\n            (  -50.41  -199.32     5.40     0.46)  ; 10\r\n            (  -63.89  -194.11     9.57     0.46)  ; 11\r\n            (  -90.35  -187.78    10.15     0.46)  ; 12\r\n            ( -113.06  -185.34    14.50     0.46)  ; 13\r\n            ( -123.91  -181.31    12.57     0.46)  ; 14\r\n            ( -129.53  -181.43    13.07     0.46)  ; 15\r\n            ( -144.78  -176.64    17.47     0.46)  ; 16\r\n            ( -169.01  -169.78    20.25     0.46)  ; 17\r\n            ( -177.08  -167.49    22.38     0.46)  ; 18\r\n            ( -189.95  -163.96    22.83     0.46)  ; 19\r\n            ( -197.45  -162.13    27.20     0.46)  ; 20\r\n            ( -211.73  -159.51    28.88     0.46)  ; 21\r\n            ( -217.36  -159.63    31.95     0.46)  ; 22\r\n            ( -226.70  -166.00    33.85     0.46)  ; 23\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color RGB (255, 128, 255))\r\n            (Name \"Marker 2\")\r\n            (   32.22  -212.20    -6.67     0.46)  ; 1\r\n            (   11.12  -210.57    -5.80     0.46)  ; 2\r\n            (    6.74  -208.02    -4.47     0.46)  ; 3\r\n            (  -58.62  -196.47     7.38     0.46)  ; 4\r\n            (  -72.41  -191.94     8.60     0.46)  ; 5\r\n            (  -77.59  -191.96     7.35     0.46)  ; 6\r\n            (  -94.18  -187.49    11.45     0.46)  ; 7\r\n            ( -118.59  -181.86    14.55     0.46)  ; 8\r\n            ( -152.49  -171.88    19.23     0.46)  ; 9\r\n            ( -166.14  -167.92    19.73     0.46)  ; 10\r\n            ( -180.16  -166.43    22.92     0.46)  ; 11\r\n          )  ;  End of markers\r\n           High\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  292.80  -219.06     8.60     0.92)  ; 1, R-1-2\r\n      (  295.55  -226.77     8.60     0.92)  ; 2\r\n      (  296.22  -229.60     7.25     0.92)  ; 3\r\n      (\r\n        (  294.71  -230.43     4.75     0.46)  ; 1, R-1-2-1\r\n        (  294.71  -230.43     4.72     0.46)  ; 2\r\n        (  294.21  -232.34     2.33     0.46)  ; 3\r\n        (  291.98  -232.86     0.82     0.46)  ; 4\r\n        (  291.98  -232.86     0.80     0.46)  ; 5\r\n        (  290.50  -232.61    -0.95     0.46)  ; 6\r\n        (  290.50  -232.61    -0.97     0.46)  ; 7\r\n        (  290.18  -233.27    -3.27     0.46)  ; 8\r\n        (  289.03  -232.36    -5.37     0.46)  ; 9\r\n        (  289.03  -232.36    -5.40     0.46)  ; 10\r\n        (  287.82  -233.23    -6.93     0.46)  ; 11\r\n        (  286.17  -234.22    -9.27     0.46)  ; 12\r\n        (  285.02  -233.29   -11.37     0.46)  ; 13\r\n        (  284.39  -234.64   -13.57     0.46)  ; 14\r\n        (  282.32  -233.93   -16.10     0.46)  ; 15\r\n        (  282.32  -233.93   -16.13     0.46)  ; 16\r\n        (  282.46  -234.49   -16.67     0.46)  ; 17\r\n        (  280.68  -234.91   -19.77     0.46)  ; 18\r\n        (  279.78  -235.12   -22.55     0.46)  ; 19\r\n        (  279.78  -235.12   -22.57     0.46)  ; 20\r\n        (  279.15  -236.47   -23.47     0.46)  ; 21\r\n        (  277.95  -237.35   -25.87     0.46)  ; 22\r\n        (  277.95  -237.35   -25.90     0.46)  ; 23\r\n        (  275.71  -237.87   -26.75     0.46)  ; 24\r\n        (  275.09  -239.21   -29.08     0.46)  ; 25\r\n        (  274.14  -241.22   -31.10     0.46)  ; 26\r\n        (  274.14  -241.22   -31.13     0.46)  ; 27\r\n        (  272.50  -242.21   -33.25     0.46)  ; 28\r\n        (  269.38  -242.94   -34.25     0.46)  ; 29\r\n\r\n        (OpenCircle\r\n          (Color Yellow)\r\n          (Name \"Marker 2\")\r\n          (  286.04  -233.65   -11.37     0.46)  ; 1\r\n          (  282.02  -234.60   -16.67     0.46)  ; 2\r\n          (  279.34  -235.22   -23.47     0.46)  ; 3\r\n          (  275.71  -237.87   -26.75     0.46)  ; 4\r\n        )  ;  End of markers\r\n        (\r\n          (  271.07  -243.13   -36.58     0.46)  ; 1, R-1-2-1-1\r\n          (  271.07  -243.13   -36.63     0.46)  ; 2\r\n          (  271.65  -243.59   -38.83     0.46)  ; 3\r\n          (  271.65  -243.59   -38.85     0.46)  ; 4\r\n          (  270.00  -244.57   -40.88     0.46)  ; 5\r\n          (  270.00  -244.57   -40.90     0.46)  ; 6\r\n          (  269.29  -243.55   -43.45     0.46)  ; 7\r\n          (  269.29  -243.55   -43.47     0.46)  ; 8\r\n          (  267.77  -245.09   -44.90     0.46)  ; 9\r\n          (  267.14  -246.44   -46.70     0.46)  ; 10\r\n          (  266.51  -247.79   -48.88     0.46)  ; 11\r\n          (  266.51  -247.79   -48.90     0.46)  ; 12\r\n          (  265.88  -249.12   -51.35     0.46)  ; 13\r\n          (  265.88  -249.12   -51.38     0.46)  ; 14\r\n          (  265.38  -251.03   -53.53     0.46)  ; 15\r\n          (  265.38  -251.03   -53.55     0.46)  ; 16\r\n          (  264.05  -251.33   -56.67     0.46)  ; 17\r\n          (  264.05  -251.33   -56.70     0.46)  ; 18\r\n          (  266.28  -250.82   -60.03     0.46)  ; 19\r\n          (  267.62  -250.50   -63.33     0.46)  ; 20\r\n          (  267.62  -250.50   -63.35     0.46)  ; 21\r\n          (  268.78  -251.43   -65.75     0.46)  ; 22\r\n          (  268.78  -251.43   -65.80     0.46)  ; 23\r\n          (  269.36  -251.89   -68.35     0.46)  ; 24\r\n          (  270.52  -252.81   -70.45     0.46)  ; 25\r\n          (  270.52  -252.81   -70.50     0.46)  ; 26\r\n          (  270.34  -254.05   -72.95     0.46)  ; 27\r\n          (  271.76  -256.10   -87.90     0.46)  ; 28\r\n          (  271.76  -256.10   -87.95     0.46)  ; 29\r\n          (  272.03  -257.24   -96.57     0.46)  ; 30\r\n          (  272.52  -255.33  -102.65     0.46)  ; 31\r\n          (  272.97  -255.23  -106.43     0.46)  ; 32\r\n          (  271.63  -255.54  -112.20     0.46)  ; 33\r\n          (  271.63  -255.54  -116.75     0.46)  ; 34\r\n          (  272.79  -256.46  -120.82     0.46)  ; 35\r\n          (  271.89  -256.67  -125.55     0.46)  ; 36\r\n          (  270.65  -259.36  -128.70     0.46)  ; 37\r\n          (  270.65  -259.36  -128.72     0.46)  ; 38\r\n          (  273.01  -259.39  -133.75     0.46)  ; 39\r\n          (  274.47  -259.65  -136.17     0.46)  ; 40\r\n          (  274.47  -259.65  -136.25     0.46)  ; 41\r\n          (  275.18  -260.68  -139.90     0.46)  ; 42\r\n          (  275.18  -260.68  -139.95     0.46)  ; 43\r\n          (  274.87  -261.34  -143.65     0.46)  ; 44\r\n          (  275.94  -259.90  -147.10     0.46)  ; 45\r\n          (  275.94  -259.90  -147.20     0.46)  ; 46\r\n          (  276.84  -259.69  -149.32     0.46)  ; 47\r\n          (  277.55  -260.72  -152.00     0.46)  ; 48\r\n          (  278.57  -261.08  -154.75     0.46)  ; 49\r\n          (  278.57  -261.08  -154.82     0.46)  ; 50\r\n          (  279.92  -260.77  -157.73     0.46)  ; 51\r\n          (  279.92  -260.77  -157.95     0.46)  ; 52\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  268.38  -249.72   -65.82     0.46)  ; 1\r\n            (  272.71  -254.10  -106.43     0.46)  ; 2\r\n            (  271.53  -259.15  -128.72     0.46)  ; 3\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  276.71  -259.13  -149.32     0.46)  ; 1\r\n            (  275.02  -255.94  -151.15     0.46)  ; 2\r\n          )  ;  End of markers\r\n          (\r\n            (  282.11  -262.04  -160.50     0.46)  ; 1, R-1-2-1-1-1\r\n            (  282.11  -262.04  -160.52     0.46)  ; 2\r\n            (  282.81  -263.07  -163.70     0.46)  ; 3\r\n            (  282.81  -263.07  -163.75     0.46)  ; 4\r\n            (  284.20  -260.95  -166.10     0.46)  ; 5\r\n            (  288.10  -259.44  -167.68     0.46)  ; 6\r\n            (  289.92  -257.22  -169.40     0.46)  ; 7\r\n            (  292.28  -257.26  -171.65     0.46)  ; 8\r\n            (  292.28  -257.26  -171.70     0.46)  ; 9\r\n            (  292.60  -256.59  -174.58     0.46)  ; 10\r\n            (  293.86  -253.91  -176.50     0.46)  ; 11\r\n            (  293.86  -253.91  -176.52     0.46)  ; 12\r\n            (  295.96  -252.83  -178.52     0.46)  ; 13\r\n            (  295.51  -252.93  -178.55     0.46)  ; 14\r\n            (  297.30  -252.51  -181.43     0.46)  ; 15\r\n            (  299.84  -251.31  -184.60     0.46)  ; 16\r\n            (  301.05  -250.43  -187.50     0.46)  ; 17\r\n            (  301.05  -250.43  -187.57     0.46)  ; 18\r\n            (  300.08  -248.28  -189.70     0.46)  ; 19\r\n            (  300.08  -248.28  -189.80     0.46)  ; 20\r\n            (  300.26  -247.03  -192.55     0.46)  ; 21\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  297.17  -251.93  -181.43     0.46)  ; 1\r\n            )  ;  End of markers\r\n             Low\r\n          |\r\n            (  281.03  -263.49  -160.15     0.46)  ; 1, R-1-2-1-1-2\r\n            (  283.84  -263.42  -163.48     0.46)  ; 2\r\n            (  283.84  -263.42  -163.50     0.46)  ; 3\r\n            (  285.63  -263.00  -167.32     0.46)  ; 4\r\n            (  285.98  -266.50  -169.50     0.46)  ; 5\r\n            (  285.98  -266.50  -169.52     0.46)  ; 6\r\n            (  287.57  -267.34  -172.45     0.46)  ; 7\r\n            (  288.15  -267.79  -176.02     0.46)  ; 8\r\n            (  288.15  -267.79  -176.05     0.46)  ; 9\r\n            (  289.63  -268.04  -179.73     0.46)  ; 10\r\n            (  292.00  -268.08  -182.45     0.46)  ; 11\r\n            (  292.00  -268.08  -182.50     0.46)  ; 12\r\n            (  293.16  -269.00  -186.22     0.46)  ; 13\r\n            (  293.16  -269.00  -186.25     0.46)  ; 14\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  292.84  -269.67  -183.50     0.46)  ; 1\r\n            )  ;  End of markers\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (  266.68  -243.58   -33.27     0.46)  ; 1, R-1-2-1-2\r\n          (  266.68  -243.58   -33.35     0.46)  ; 2\r\n          (  261.01  -245.50   -32.05     0.46)  ; 3\r\n          (  259.10  -245.35   -30.45     0.46)  ; 4\r\n          (  256.99  -246.44   -30.45     0.46)  ; 5\r\n          (  254.64  -246.39   -28.85     0.46)  ; 6\r\n          (  254.64  -246.39   -28.88     0.46)  ; 7\r\n          (  254.00  -247.73   -29.88     0.46)  ; 8\r\n          (  251.90  -248.83   -28.42     0.46)  ; 9\r\n          (  248.91  -250.12   -26.52     0.46)  ; 10\r\n          (  245.90  -251.42   -26.52     0.46)  ; 11\r\n          (  244.39  -252.97   -24.80     0.46)  ; 12\r\n          (  244.39  -252.97   -24.82     0.46)  ; 13\r\n          (  241.85  -254.17   -23.63     0.46)  ; 14\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  254.87  -243.35   -27.45     0.46)  ; 1\r\n            (  255.98  -240.10   -26.52     0.46)  ; 2\r\n            (  252.48  -249.28   -28.42     0.46)  ; 3\r\n            (  246.93  -251.77   -26.50     0.46)  ; 4\r\n            (  242.12  -255.30   -23.63     0.46)  ; 5\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  261.46  -245.39   -32.05     0.46)  ; 1\r\n          )  ;  End of markers\r\n          (\r\n            (  240.87  -254.99   -27.35     0.46)  ; 1, R-1-2-1-2-1\r\n            (  240.87  -254.99   -27.38     0.46)  ; 2\r\n            (  238.73  -257.88   -28.95     0.46)  ; 3\r\n            (  237.78  -259.89   -30.27     0.46)  ; 4\r\n            (  236.39  -262.00   -31.45     0.46)  ; 5\r\n            (  236.39  -262.00   -31.48     0.46)  ; 6\r\n            (  235.31  -263.46   -32.83     0.46)  ; 7\r\n            (  235.31  -263.46   -32.85     0.46)  ; 8\r\n            (  233.48  -265.67   -34.28     0.46)  ; 9\r\n            (  233.12  -268.15   -35.00     0.46)  ; 10\r\n            (  231.86  -270.82   -35.00     0.46)  ; 11\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  232.77  -264.65   -34.28     0.46)  ; 1\r\n              (  230.83  -270.47   -35.00     0.46)  ; 2\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  238.28  -257.98   -28.98     0.46)  ; 1\r\n            )  ;  End of markers\r\n             Normal\r\n          |\r\n            (  239.88  -255.82   -23.50     0.46)  ; 1, R-1-2-1-2-2\r\n            (  239.88  -255.82   -23.52     0.46)  ; 2\r\n            (  237.91  -257.48   -23.52     0.46)  ; 3\r\n            (  235.23  -258.11   -23.52     0.46)  ; 4\r\n            (  232.68  -259.30   -23.52     0.46)  ; 5\r\n            (  231.29  -261.42   -23.52     0.46)  ; 6\r\n            (  227.85  -262.82   -22.25     0.46)  ; 7\r\n            (  225.12  -265.25   -23.23     0.46)  ; 8\r\n            (  223.47  -266.23   -21.18     0.46)  ; 9\r\n            (  219.81  -264.71   -19.97     0.46)  ; 10\r\n            (  219.81  -264.71   -20.03     0.46)  ; 11\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  234.65  -257.64   -23.52     0.46)  ; 1\r\n              (  225.12  -265.25   -23.23     0.46)  ; 2\r\n            )  ;  End of markers\r\n            (\r\n              (  217.19  -263.54   -20.03     0.46)  ; 1, R-1-2-1-2-2-1\r\n              (  214.37  -263.59   -18.92     0.46)  ; 2\r\n              (  214.37  -263.59   -18.95     0.46)  ; 3\r\n              (  212.18  -262.32   -17.63     0.46)  ; 4\r\n              (  209.38  -262.38   -16.40     0.46)  ; 5\r\n              (  207.90  -262.13   -14.43     0.46)  ; 6\r\n              (  205.98  -261.98   -13.02     0.46)  ; 7\r\n              (  203.47  -261.36   -12.30     0.46)  ; 8\r\n              (  200.20  -261.52   -11.75     0.46)  ; 9\r\n              (  197.88  -259.69   -11.75     0.46)  ; 10\r\n              (  195.08  -259.75   -11.50     0.46)  ; 11\r\n              (  192.63  -257.33   -10.95     0.46)  ; 12\r\n              (  189.11  -256.37   -10.12     0.46)  ; 13\r\n              (  185.72  -255.96    -8.88     0.46)  ; 14\r\n              (  182.19  -255.01    -8.07     0.46)  ; 15\r\n              (  179.82  -254.97    -7.22     0.46)  ; 16\r\n              (  178.21  -254.14    -6.00     0.46)  ; 17\r\n              (  178.08  -253.58    -6.00     0.46)  ; 18\r\n              (  177.06  -253.22    -4.60     0.46)  ; 19\r\n              (  177.06  -253.22    -4.63     0.46)  ; 20\r\n              (  176.35  -252.19    -3.05     0.46)  ; 21\r\n              (  176.35  -252.19    -3.08     0.46)  ; 22\r\n              (  171.98  -249.63    -1.88     0.46)  ; 23\r\n              (  169.43  -250.82    -1.32     0.46)  ; 24\r\n              (  167.86  -254.18    -1.13     0.46)  ; 25\r\n              (  166.61  -256.86    -1.00     0.46)  ; 26\r\n              (  165.66  -258.88    -1.00     0.46)  ; 27\r\n              (  164.54  -262.13    -1.32     0.46)  ; 28\r\n              (  162.27  -264.45     0.12     0.46)  ; 29\r\n              (  160.79  -264.20     0.95     0.46)  ; 30\r\n              (  158.37  -265.95     1.55     0.46)  ; 31\r\n              (  155.52  -267.81     1.50     0.46)  ; 32\r\n              (  151.76  -269.90     1.50     0.46)  ; 33\r\n              (  148.06  -270.17     2.20     0.46)  ; 34\r\n              (  145.82  -270.70     2.20     0.46)  ; 35\r\n              (  144.43  -272.81     2.20     0.46)  ; 36\r\n              (  142.64  -273.23     2.67     0.46)  ; 37\r\n              (  137.68  -276.18     4.20     0.46)  ; 38\r\n              (  135.72  -277.84     4.68     0.46)  ; 39\r\n              (  133.48  -278.37     5.07     0.46)  ; 40\r\n              (  131.33  -281.25     5.55     0.46)  ; 41\r\n              (  129.81  -282.80     6.20     0.46)  ; 42\r\n              (  128.34  -282.55     6.20     0.46)  ; 43\r\n              (  126.38  -284.21     6.47     0.46)  ; 44\r\n              (  122.35  -285.15     6.47     0.46)  ; 45\r\n              (  119.93  -286.91     6.50     0.46)  ; 46\r\n              (  118.60  -287.23     4.63     0.46)  ; 47\r\n              (  118.60  -287.23     4.60     0.46)  ; 48\r\n              (  116.94  -288.21     3.22     0.46)  ; 49\r\n              (  112.30  -290.49     2.73     0.46)  ; 50\r\n              (  109.61  -291.12     2.73     0.46)  ; 51\r\n              (  109.16  -291.23     2.73     0.46)  ; 52\r\n              (  105.08  -293.98     1.90     0.46)  ; 53\r\n              (  103.25  -296.20     1.02     0.46)  ; 54\r\n              (  101.99  -298.88     0.77     0.46)  ; 55\r\n              (   98.25  -300.95     0.35     0.46)  ; 56\r\n              (   97.30  -302.96     0.35     0.46)  ; 57\r\n              (   95.33  -304.62     0.35     0.46)  ; 58\r\n              (   93.81  -306.17    -0.32     0.46)  ; 59\r\n              (   92.47  -306.49    -0.32     0.46)  ; 60\r\n              (   90.64  -308.71    -0.32     0.46)  ; 61\r\n              (   87.32  -310.67    -0.32     0.46)  ; 62\r\n              (   85.68  -311.66    -0.32     0.46)  ; 63\r\n              (   83.26  -313.42    -0.32     0.46)  ; 64\r\n              (   80.13  -314.16    -0.37     0.46)  ; 65\r\n              (   77.45  -314.78    -0.93     0.46)  ; 66\r\n              (   74.91  -315.98    -0.93     0.46)  ; 67\r\n              (   73.38  -317.53    -2.63     0.46)  ; 68\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  192.45  -258.56   -10.95     0.46)  ; 1\r\n                (  159.39  -266.31     1.55     0.46)  ; 2\r\n                (  152.77  -270.26     1.50     0.46)  ; 3\r\n                (  118.78  -285.99     4.60     0.46)  ; 4\r\n                (  112.57  -291.63     2.73     0.46)  ; 5\r\n                (  100.70  -297.38     0.77     0.46)  ; 6\r\n                (   94.93  -302.92     0.35     0.46)  ; 7\r\n                (   81.29  -315.07    -0.37     0.46)  ; 8\r\n                (   75.18  -317.11    -0.93     0.46)  ; 9\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  206.56  -262.44   -13.02     0.46)  ; 1\r\n                (  176.48  -252.76    -3.08     0.46)  ; 2\r\n                (  174.74  -251.38    -1.88     0.46)  ; 3\r\n                (  167.41  -254.28    -1.13     0.46)  ; 4\r\n                (  142.20  -273.33     2.67     0.46)  ; 5\r\n                (   98.25  -300.95     0.35     0.46)  ; 6\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  216.76  -267.80   -19.07     0.46)  ; 1, R-1-2-1-2-2-2\r\n              (  216.15  -269.14   -17.90     0.46)  ; 2\r\n              (  216.15  -269.14   -17.92     0.46)  ; 3\r\n              (  215.78  -271.62   -17.08     0.46)  ; 4\r\n              (  215.65  -271.05   -17.08     0.46)  ; 5\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  214.75  -271.26   -17.08     0.46)  ; 1\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (  296.30  -231.98     7.38     0.92)  ; 1, R-1-2-2\r\n        (\r\n          (  294.90  -233.85     5.40     0.46)  ; 1, R-1-2-2-1\r\n          (  294.28  -235.20     4.07     0.46)  ; 2\r\n          (  294.28  -235.20     4.05     0.46)  ; 3\r\n          (  294.41  -235.77     1.05     0.46)  ; 4\r\n          (  294.41  -235.77     1.02     0.46)  ; 5\r\n          (  293.07  -236.08    -0.95     0.46)  ; 6\r\n          (  293.07  -236.08    -0.97     0.46)  ; 7\r\n          (  293.33  -237.21    -4.05     0.46)  ; 8\r\n          (  293.33  -237.21    -4.07     0.46)  ; 9\r\n          (  291.99  -237.53    -6.70     0.46)  ; 10\r\n          (  291.99  -237.53    -6.73     0.46)  ; 11\r\n          (  290.65  -237.84    -9.38     0.46)  ; 12\r\n          (  290.91  -238.97   -11.50     0.46)  ; 13\r\n          (  290.15  -239.75   -13.15     0.46)  ; 14\r\n          (  289.09  -241.20   -15.15     0.46)  ; 15\r\n          (  288.77  -241.87   -17.25     0.46)  ; 16\r\n          (  288.19  -241.41   -20.00     0.46)  ; 17\r\n          (  288.46  -242.53   -22.60     0.46)  ; 18\r\n          (  287.69  -243.31   -25.60     0.46)  ; 19\r\n          (  288.27  -243.78   -27.65     0.46)  ; 20\r\n          (  288.27  -243.78   -27.67     0.46)  ; 21\r\n          (  287.25  -243.42   -29.92     0.46)  ; 22\r\n          (  285.99  -246.10   -32.60     0.46)  ; 23\r\n          (  285.67  -246.77   -35.22     0.46)  ; 24\r\n          (  285.23  -246.88   -38.20     0.46)  ; 25\r\n          (  285.23  -246.88   -38.22     0.46)  ; 26\r\n          (  284.60  -248.22   -41.22     0.46)  ; 27\r\n          (  283.84  -248.99   -43.20     0.46)  ; 28\r\n          (  283.84  -248.99   -43.22     0.46)  ; 29\r\n          (  283.98  -249.55   -45.70     0.46)  ; 30\r\n          (  283.80  -250.79   -47.90     0.46)  ; 31\r\n          (  283.35  -250.90   -51.08     0.46)  ; 32\r\n          (  284.06  -251.93   -54.52     0.46)  ; 33\r\n          (  284.06  -251.93   -54.58     0.46)  ; 34\r\n          (  282.01  -251.21   -58.13     0.46)  ; 35\r\n          (  282.22  -254.15   -60.95     0.46)  ; 36\r\n          (  280.83  -256.27   -64.57     0.46)  ; 37\r\n          (  280.83  -256.27   -64.60     0.46)  ; 38\r\n          (  279.94  -256.48   -67.13     0.46)  ; 39\r\n          (  279.94  -256.48   -67.15     0.46)  ; 40\r\n          (  279.89  -258.27   -69.92     0.46)  ; 41\r\n          (  279.89  -258.27   -69.95     0.46)  ; 42\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  285.14  -244.50   -32.60     0.46)  ; 1\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  290.47  -239.07   -11.50     0.46)  ; 1\r\n            (  288.01  -242.64   -22.60     0.46)  ; 2\r\n            (  283.21  -250.33   -47.90     0.46)  ; 3\r\n            (  280.70  -255.70   -64.60     0.46)  ; 4\r\n          )  ;  End of markers\r\n          (\r\n            (  276.48  -257.99   -71.35     0.46)  ; 1, R-1-2-2-1-1\r\n            (  273.81  -258.61   -72.03     0.46)  ; 2\r\n            (  271.44  -258.57   -74.90     0.46)  ; 3\r\n            (  269.20  -259.08   -76.97     0.46)  ; 4\r\n            (  265.63  -259.93   -79.60     0.46)  ; 5\r\n            (  263.26  -259.88   -79.65     0.46)  ; 6\r\n            (  260.59  -260.51   -81.00     0.46)  ; 7\r\n            (  257.65  -260.01   -83.20     0.46)  ; 8\r\n            (  254.96  -260.64   -85.28     0.46)  ; 9\r\n            (  253.89  -262.08   -88.25     0.46)  ; 10\r\n            (  252.73  -261.16   -91.58     0.46)  ; 11\r\n            (  250.99  -259.78   -93.65     0.46)  ; 12\r\n            (  250.99  -259.78   -93.75     0.46)  ; 13\r\n            (  248.36  -258.60   -96.47     0.46)  ; 14\r\n            (  248.36  -258.60   -96.50     0.46)  ; 15\r\n            (  246.00  -258.56   -99.20     0.46)  ; 16\r\n            (  246.00  -258.56   -99.25     0.46)  ; 17\r\n            (  243.94  -257.84  -102.02     0.46)  ; 18\r\n            (  243.94  -257.84  -102.07     0.46)  ; 19\r\n            (  241.58  -257.81  -104.85     0.46)  ; 20\r\n            (  238.05  -256.83  -107.60     0.46)  ; 21\r\n            (  233.37  -254.94  -108.57     0.46)  ; 22\r\n            (  233.37  -254.94  -108.60     0.46)  ; 23\r\n            (  231.32  -254.23  -110.35     0.46)  ; 24\r\n            (  231.32  -254.23  -110.37     0.46)  ; 25\r\n            (  230.12  -255.11  -113.75     0.46)  ; 26\r\n            (  226.46  -253.58  -115.90     0.46)  ; 27\r\n            (  222.36  -252.15  -117.85     0.46)  ; 28\r\n            (  218.02  -253.76  -119.25     0.46)  ; 29\r\n            (  215.08  -253.27  -121.75     0.46)  ; 30\r\n            (  212.44  -252.10  -125.80     0.46)  ; 31\r\n            (  210.47  -253.75  -126.60     0.46)  ; 32\r\n            (  210.47  -253.75  -126.62     0.46)  ; 33\r\n            (  208.11  -253.71  -127.90     0.46)  ; 34\r\n            (  205.69  -255.47  -128.75     0.46)  ; 35\r\n            (  205.69  -255.47  -128.77     0.46)  ; 36\r\n            (  204.54  -254.54  -130.77     0.46)  ; 37\r\n            (  204.54  -254.54  -130.80     0.46)  ; 38\r\n            (  201.36  -257.08  -131.48     0.46)  ; 39\r\n            (  198.41  -256.57  -132.18     0.46)  ; 40\r\n            (  198.41  -256.57  -132.20     0.46)  ; 41\r\n            (  195.28  -257.31  -134.05     0.46)  ; 42\r\n            (  195.28  -257.31  -134.13     0.46)  ; 43\r\n            (  193.06  -257.82  -135.65     0.46)  ; 44\r\n            (  191.83  -259.91  -137.27     0.46)  ; 45\r\n            (  191.83  -259.91  -137.30     0.46)  ; 46\r\n            (  189.29  -261.11  -139.32     0.46)  ; 47\r\n            (  186.74  -262.31  -141.07     0.46)  ; 48\r\n            (  186.74  -262.31  -141.10     0.46)  ; 49\r\n            (  184.52  -262.83  -143.32     0.46)  ; 50\r\n            (  184.33  -264.06  -146.30     0.46)  ; 51\r\n            (  183.25  -265.51  -149.20     0.46)  ; 52\r\n            (  181.28  -267.16  -150.55     0.46)  ; 53\r\n            (  178.88  -268.92  -152.85     0.46)  ; 54\r\n            (  178.88  -268.92  -152.88     0.46)  ; 55\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  273.81  -258.61   -72.03     0.46)  ; 1\r\n              (  241.58  -257.81  -104.85     0.46)  ; 2\r\n              (  213.55  -254.81  -125.82     0.46)  ; 3\r\n              (  212.44  -252.10  -125.82     0.46)  ; 4\r\n              (  206.46  -254.69  -128.50     0.46)  ; 5\r\n              (  192.61  -257.93  -135.65     0.46)  ; 6\r\n              (  181.15  -266.60  -150.55     0.46)  ; 7\r\n            )  ;  End of markers\r\n             Normal\r\n          |\r\n            (  278.82  -259.72   -72.38     0.46)  ; 1, R-1-2-2-1-2\r\n            (  279.09  -260.86   -74.82     0.46)  ; 2\r\n            (  279.04  -262.66   -76.45     0.46)  ; 3\r\n            (  279.17  -263.22   -82.63     0.46)  ; 4\r\n            (  278.85  -263.89   -85.97     0.46)  ; 5\r\n            (  278.85  -263.89   -86.00     0.46)  ; 6\r\n            (  279.12  -265.03   -89.72     0.46)  ; 7\r\n            (  279.12  -265.03   -89.75     0.46)  ; 8\r\n            (  277.20  -264.87   -93.05     0.46)  ; 9\r\n            (  276.45  -265.66   -96.25     0.46)  ; 10\r\n            (  276.45  -265.66   -96.27     0.46)  ; 11\r\n            (  275.67  -266.43  -100.68     0.46)  ; 12\r\n            (  273.13  -267.63  -103.15     0.46)  ; 13\r\n            (  272.95  -268.86  -107.22     0.46)  ; 14\r\n            (  273.35  -270.56  -110.47     0.46)  ; 15\r\n            (  273.35  -270.56  -110.50     0.46)  ; 16\r\n            (  272.32  -270.20  -113.57     0.46)  ; 17\r\n            (  272.32  -270.20  -113.60     0.46)  ; 18\r\n            (  271.56  -270.99  -116.15     0.46)  ; 19\r\n            (  271.56  -270.99  -116.17     0.46)  ; 20\r\n            (  271.38  -272.22  -119.27     0.46)  ; 21\r\n            (  271.38  -272.22  -119.30     0.46)  ; 22\r\n            (  270.63  -272.99  -121.77     0.46)  ; 23\r\n            (  270.63  -272.99  -121.80     0.46)  ; 24\r\n            (  271.60  -275.15  -124.20     0.46)  ; 25\r\n            (  271.60  -275.15  -124.30     0.46)  ; 26\r\n            (  272.75  -276.08  -128.15     0.46)  ; 27\r\n            (  273.02  -277.20  -131.10     0.46)  ; 28\r\n            (  272.97  -279.01  -133.60     0.46)  ; 29\r\n            (  273.68  -280.03  -135.98     0.46)  ; 30\r\n            (  271.82  -278.08  -138.67     0.46)  ; 31\r\n            (  271.90  -280.45  -141.35     0.46)  ; 32\r\n            (  271.90  -280.45  -141.38     0.46)  ; 33\r\n            (  273.06  -281.37  -143.63     0.46)  ; 34\r\n            (  273.06  -281.37  -143.65     0.46)  ; 35\r\n            (  275.16  -280.29  -146.17     0.46)  ; 36\r\n            (  275.16  -280.29  -146.22     0.46)  ; 37\r\n            (  276.18  -280.64  -149.00     0.46)  ; 38\r\n            (  276.13  -282.44  -151.32     0.46)  ; 39\r\n            (  276.45  -281.77  -153.63     0.46)  ; 40\r\n            (  276.40  -283.58  -157.23     0.46)  ; 41\r\n            (  276.53  -284.15  -160.57     0.46)  ; 42\r\n            (  276.53  -284.15  -160.63     0.46)  ; 43\r\n            (  277.11  -284.60  -163.15     0.46)  ; 44\r\n            (  275.72  -286.72  -164.50     0.46)  ; 45\r\n            (  276.70  -288.88  -167.25     0.46)  ; 46\r\n            (  277.91  -288.00  -169.07     0.46)  ; 47\r\n            (  278.62  -289.03  -171.92     0.46)  ; 48\r\n            (  278.62  -289.03  -172.55     0.46)  ; 49\r\n            (  278.17  -289.13  -174.85     0.46)  ; 50\r\n            (  279.02  -290.73  -177.62     0.46)  ; 51\r\n            (  279.02  -290.73  -177.68     0.46)  ; 52\r\n            (  279.02  -290.73  -181.15     0.46)  ; 53\r\n            (  279.42  -292.43  -185.30     0.46)  ; 54\r\n            (  280.70  -293.91  -187.85     0.46)  ; 55\r\n            (  280.97  -295.05  -190.42     0.46)  ; 56\r\n            (  280.97  -295.05  -190.45     0.46)  ; 57\r\n            (  282.26  -296.53  -193.60     0.46)  ; 58\r\n            (  283.37  -299.27  -198.27     0.46)  ; 59\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  278.14  -262.86   -76.45     0.46)  ; 1\r\n              (  271.29  -269.84  -110.55     0.46)  ; 2\r\n              (  271.55  -276.95  -131.10     0.46)  ; 3\r\n              (  274.43  -285.23  -164.50     0.46)  ; 4\r\n              (  277.54  -290.47  -181.15     0.46)  ; 5\r\n              (  279.10  -293.09  -185.27     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  273.68  -280.03  -135.98     0.46)  ; 1\r\n              (  275.74  -280.75  -149.00     0.46)  ; 2\r\n            )  ;  End of markers\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (  297.10  -235.38     8.38     0.92)  ; 1, R-1-2-2-2\r\n          (  298.21  -238.09     9.48     0.92)  ; 2\r\n          (  298.87  -240.93    10.70     0.92)  ; 3\r\n          (  300.30  -242.97    11.63     0.92)  ; 4\r\n          (  300.30  -242.97    11.60     0.92)  ; 5\r\n          (  301.14  -244.57    12.67     0.92)  ; 6\r\n          (\r\n            (  303.39  -244.87    14.15     0.46)  ; 1, R-1-2-2-2-1\r\n            (  306.78  -245.28    15.38     0.46)  ; 2\r\n            (  309.59  -245.22    15.38     0.46)  ; 3\r\n            (  313.30  -244.94    15.38     0.46)  ; 4\r\n            (  317.14  -245.24    15.38     0.46)  ; 5\r\n            (  321.87  -245.31    15.30     0.46)  ; 6\r\n            (  324.55  -244.69    14.57     0.46)  ; 7\r\n            (  328.26  -244.43    14.57     0.46)  ; 8\r\n            (  331.38  -243.69    14.57     0.46)  ; 9\r\n            (  334.19  -243.63    15.65     0.46)  ; 10\r\n            (  338.35  -243.25    16.70     0.46)  ; 11\r\n            (  342.49  -242.87    16.70     0.46)  ; 12\r\n            (  345.76  -242.70    16.70     0.46)  ; 13\r\n            (  350.35  -242.23    17.35     0.46)  ; 14\r\n            (  352.58  -241.70    18.13     0.46)  ; 15\r\n            (  356.87  -241.90    18.77     0.46)  ; 16\r\n            (  360.57  -241.63    19.40     0.46)  ; 17\r\n            (  364.28  -241.35    19.35     0.46)  ; 18\r\n            (  367.09  -241.29    20.47     0.46)  ; 19\r\n            (  369.46  -241.33    21.38     0.46)  ; 20\r\n            (  371.88  -239.58    22.27     0.46)  ; 21\r\n            (  375.40  -240.53    22.27     0.46)  ; 22\r\n            (  380.14  -240.63    22.32     0.46)  ; 23\r\n            (  383.21  -241.69    21.18     0.46)  ; 24\r\n            (  387.05  -241.98    21.18     0.46)  ; 25\r\n            (  388.96  -242.13    21.18     0.46)  ; 26\r\n            (  390.31  -241.82    21.18     0.46)  ; 27\r\n            (  394.48  -241.47    21.18     0.46)  ; 28\r\n            (\r\n              (  395.42  -239.46    18.63     0.46)  ; 1, R-1-2-2-2-1-1\r\n              (  396.90  -239.71    17.47     0.46)  ; 2\r\n              (  396.45  -239.81    17.42     0.46)  ; 3\r\n              (  398.68  -239.29    16.42     0.46)  ; 4\r\n              (  400.91  -238.77    15.52     0.46)  ; 5\r\n              (  402.84  -238.91    14.95     0.46)  ; 6\r\n              (  404.93  -237.82    14.13     0.46)  ; 7\r\n              (  406.98  -238.54    13.02     0.46)  ; 8\r\n              (  406.98  -238.54    13.00     0.46)  ; 9\r\n              (  408.28  -240.03    12.98     0.46)  ; 10\r\n              (  410.78  -240.63    12.42     0.46)  ; 11\r\n              (  410.78  -240.63    12.40     0.46)  ; 12\r\n              (  413.14  -240.67    12.38     0.46)  ; 13\r\n              (  416.39  -240.51    12.60     0.46)  ; 14\r\n              (  419.52  -239.78    12.60     0.46)  ; 15\r\n              (  422.60  -240.84    11.68     0.46)  ; 16\r\n              (  425.69  -241.92    10.35     0.46)  ; 17\r\n              (  425.69  -241.92    10.33     0.46)  ; 18\r\n              (  429.07  -242.32     9.20     0.46)  ; 19\r\n              (  429.07  -242.32     9.17     0.46)  ; 20\r\n              (  432.33  -242.15     8.55     0.46)  ; 21\r\n              (  432.33  -242.15     8.52     0.46)  ; 22\r\n              (  434.53  -243.43     7.02     0.46)  ; 23\r\n              (  439.87  -242.18     6.73     0.46)  ; 24\r\n              (  442.56  -241.55     5.80     0.46)  ; 25\r\n              (  445.50  -242.05     5.30     0.46)  ; 26\r\n              (  445.50  -242.05     5.27     0.46)  ; 27\r\n              (  447.10  -242.86     4.88     0.46)  ; 28\r\n              (  447.10  -242.86     4.82     0.46)  ; 29\r\n              (  449.78  -242.24     3.65     0.46)  ; 30\r\n              (  449.64  -241.67     3.65     0.46)  ; 31\r\n              (  452.59  -242.18     3.65     0.46)  ; 32\r\n              (  455.15  -240.98     3.65     0.46)  ; 33\r\n              (  459.44  -241.18     2.70     0.46)  ; 34\r\n              (  459.44  -241.18     2.67     0.46)  ; 35\r\n              (  463.27  -241.47     1.88     0.46)  ; 36\r\n              (  466.08  -241.41     1.00     0.46)  ; 37\r\n              (  469.61  -242.37    -0.90     0.46)  ; 38\r\n              (  471.39  -241.95    -2.05     0.46)  ; 39\r\n              (  473.71  -243.80    -3.10     0.46)  ; 40\r\n              (  473.71  -243.80    -3.13     0.46)  ; 41\r\n              (  477.42  -243.52    -2.83     0.46)  ; 42\r\n              (  477.42  -243.52    -3.17     0.46)  ; 43\r\n              (  480.49  -244.61    -4.13     0.46)  ; 44\r\n              (  483.30  -244.54    -5.50     0.46)  ; 45\r\n              (  487.14  -244.83    -6.65     0.46)  ; 46\r\n              (  490.97  -245.12    -8.05     0.46)  ; 47\r\n              (  493.35  -245.16    -9.25     0.46)  ; 48\r\n              (  493.35  -245.16    -9.27     0.46)  ; 49\r\n              (  496.29  -245.68   -10.80     0.46)  ; 50\r\n              (  500.39  -247.10   -11.80     0.46)  ; 51\r\n              (  503.87  -249.87   -13.05     0.46)  ; 52\r\n              (  503.87  -249.87   -13.07     0.46)  ; 53\r\n              (  508.42  -251.19   -15.05     0.46)  ; 54\r\n              (  511.37  -251.70   -15.40     0.46)  ; 55\r\n              (  514.57  -253.33   -15.43     0.46)  ; 56\r\n              (  517.39  -253.27   -16.17     0.46)  ; 57\r\n              (  517.39  -253.27   -16.20     0.46)  ; 58\r\n              (  521.80  -254.02   -16.70     0.46)  ; 59\r\n              (  524.75  -254.53   -16.70     0.46)  ; 60\r\n              (  529.17  -255.28   -16.73     0.46)  ; 61\r\n              (  531.50  -255.92   -15.52     0.46)  ; 62\r\n              (  535.46  -256.78   -14.75     0.46)  ; 63\r\n              (  537.65  -258.06   -14.75     0.46)  ; 64\r\n              (  542.83  -258.04   -14.75     0.46)  ; 65\r\n              (  545.91  -259.11   -14.05     0.46)  ; 66\r\n              (  548.14  -258.59   -13.52     0.46)  ; 67\r\n              (  551.52  -258.99   -13.52     0.46)  ; 68\r\n              (  553.46  -259.14   -13.52     0.46)  ; 69\r\n              (  556.44  -257.83   -13.20     0.46)  ; 70\r\n              (  559.56  -257.10   -13.20     0.46)  ; 71\r\n              (  563.99  -257.86   -13.20     0.46)  ; 72\r\n              (  568.40  -258.62   -13.20     0.46)  ; 73\r\n              (  574.03  -258.50   -12.55     0.46)  ; 74\r\n              (  574.03  -258.50   -12.57     0.46)  ; 75\r\n              (  579.16  -260.27   -12.57     0.46)  ; 76\r\n              (  584.16  -261.50   -12.57     0.46)  ; 77\r\n              (  587.82  -263.02   -12.57     0.46)  ; 78\r\n              (  593.12  -263.57   -12.57     0.46)  ; 79\r\n              (  595.75  -264.74   -12.13     0.46)  ; 80\r\n              (  597.99  -264.22   -12.13     0.46)  ; 81\r\n              (  605.04  -266.15   -11.75     0.46)  ; 82\r\n              (  609.01  -267.02   -11.75     0.46)  ; 83\r\n              (  612.52  -267.98   -13.65     0.46)  ; 84\r\n              (  612.52  -267.98   -13.67     0.46)  ; 85\r\n              (  616.63  -269.40   -15.55     0.46)  ; 86\r\n              (  621.49  -270.06   -17.17     0.46)  ; 87\r\n              (  626.50  -271.27   -18.45     0.46)  ; 88\r\n              (  631.81  -271.83   -18.45     0.46)  ; 89\r\n              (  636.67  -272.47   -18.45     0.46)  ; 90\r\n              (  639.48  -272.41   -20.47     0.46)  ; 91\r\n              (  643.90  -273.17   -21.63     0.46)  ; 92\r\n              (  643.90  -273.17   -21.65     0.46)  ; 93\r\n              (  647.15  -273.01   -22.80     0.46)  ; 94\r\n              (  647.15  -273.01   -22.83     0.46)  ; 95\r\n              (  654.12  -272.56   -23.33     0.46)  ; 96\r\n              (  658.72  -272.08   -23.33     0.46)  ; 97\r\n              (  662.16  -270.68   -22.22     0.46)  ; 98\r\n              (  662.16  -270.68   -22.25     0.46)  ; 99\r\n              (  664.26  -269.59   -19.85     0.46)  ; 100\r\n              (  666.19  -269.73   -17.85     0.46)  ; 101\r\n              (  669.57  -270.14   -17.15     0.46)  ; 102\r\n              (  671.94  -270.19   -18.00     0.46)  ; 103\r\n              (  676.67  -270.27   -18.02     0.46)  ; 104\r\n              (  682.29  -270.14   -18.90     0.46)  ; 105\r\n              (  685.69  -270.54   -19.73     0.46)  ; 106\r\n              (  690.60  -269.40   -20.52     0.46)  ; 107\r\n              (  694.30  -269.12   -21.28     0.46)  ; 108\r\n              (  697.43  -268.39   -21.28     0.46)  ; 109\r\n              (  701.72  -268.57   -21.95     0.46)  ; 110\r\n              (  706.01  -268.76   -23.52     0.46)  ; 111\r\n              (  706.01  -268.76   -23.57     0.46)  ; 112\r\n              (  710.60  -268.28   -25.03     0.46)  ; 113\r\n              (  715.02  -269.05   -26.13     0.46)  ; 114\r\n              (  721.67  -268.08   -26.13     0.46)  ; 115\r\n              (  723.91  -267.55   -27.15     0.46)  ; 116\r\n              (  726.72  -267.49   -27.15     0.46)  ; 117\r\n              (  728.19  -267.74   -29.50     0.46)  ; 118\r\n              (  730.42  -267.22   -30.92     0.46)  ; 119\r\n              (  733.55  -266.49   -31.32     0.46)  ; 120\r\n              (  733.55  -266.49   -31.35     0.46)  ; 121\r\n              (  736.50  -266.98   -33.05     0.46)  ; 122\r\n              (  740.02  -267.96   -34.32     0.46)  ; 123\r\n              (  740.02  -267.96   -34.35     0.46)  ; 124\r\n              (  745.33  -268.50   -35.65     0.46)  ; 125\r\n              (  751.98  -268.74   -35.97     0.46)  ; 126\r\n              (  755.69  -268.47   -37.52     0.46)  ; 127\r\n              (  755.69  -268.47   -37.55     0.46)  ; 128\r\n              (  760.42  -268.55   -37.55     0.46)  ; 129\r\n              (  765.72  -269.11   -37.55     0.46)  ; 130\r\n              (  768.99  -268.94   -38.45     0.46)  ; 131\r\n              (  774.03  -268.35   -39.63     0.46)  ; 132\r\n              (  778.19  -267.97   -41.93     0.46)  ; 133\r\n              (  782.77  -267.49   -41.93     0.46)  ; 134\r\n              (  789.44  -267.73   -41.93     0.46)  ; 135\r\n              (  794.03  -267.24   -42.92     0.46)  ; 136\r\n              (  796.40  -267.29   -43.30     0.46)  ; 137\r\n              (  796.40  -267.29   -43.33     0.46)  ; 138\r\n              (  799.47  -268.35   -42.65     0.46)  ; 139\r\n              (  799.47  -268.35   -42.67     0.46)  ; 140\r\n              (  802.24  -270.10   -43.00     0.46)  ; 141\r\n              (  805.49  -269.93   -44.30     0.46)  ; 142\r\n              (  808.63  -269.20   -45.83     0.46)  ; 143\r\n              (  812.64  -268.25   -47.33     0.46)  ; 144\r\n              (  816.03  -268.65   -47.42     0.46)  ; 145\r\n              (  816.03  -268.65   -47.45     0.46)  ; 146\r\n              (  818.41  -268.69   -49.02     0.46)  ; 147\r\n              (  821.21  -268.63   -50.38     0.46)  ; 148\r\n              (  821.21  -268.63   -50.40     0.46)  ; 149\r\n              (  824.33  -267.90   -51.57     0.46)  ; 150\r\n              (  829.07  -267.99   -52.88     0.46)  ; 151\r\n              (  829.07  -267.99   -52.90     0.46)  ; 152\r\n              (  834.50  -269.10   -53.97     0.46)  ; 153\r\n              (  837.33  -269.03   -54.27     0.46)  ; 154\r\n              (  837.33  -269.03   -54.30     0.46)  ; 155\r\n              (  841.17  -269.33   -54.30     0.46)  ; 156\r\n              (  844.88  -269.06   -55.42     0.46)  ; 157\r\n              (  844.88  -269.06   -55.45     0.46)  ; 158\r\n              (  849.46  -268.58   -57.13     0.46)  ; 159\r\n              (  854.60  -270.37   -55.97     0.46)  ; 160\r\n              (  856.79  -270.45   -55.00     0.46)  ; 161\r\n              (  860.18  -270.85   -57.28     0.46)  ; 162\r\n              (  863.12  -271.35   -59.22     0.46)  ; 163\r\n              (  868.12  -272.57   -58.05     0.46)  ; 164\r\n              (  871.54  -277.14   -57.17     0.46)  ; 165\r\n              (  873.55  -279.66   -56.28     0.46)  ; 166\r\n              (  876.89  -281.87   -55.57     0.46)  ; 167\r\n              (  880.24  -284.06   -55.57     0.46)  ; 168\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  409.62  -239.72    12.40     0.46)  ; 1\r\n                (  449.06  -241.21     3.65     0.46)  ; 2\r\n                (  487.27  -245.40    -6.65     0.46)  ; 3\r\n                (  625.74  -272.06   -18.45     0.46)  ; 4\r\n                (  653.50  -273.91   -23.33     0.46)  ; 5\r\n                (  714.40  -270.38   -26.13     0.46)  ; 6\r\n                (  815.41  -269.99   -47.45     0.46)  ; 7\r\n                (  875.56  -282.18   -55.57     0.46)  ; 8\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  400.02  -238.97    15.52     0.46)  ; 1\r\n                (  413.14  -240.67    12.38     0.46)  ; 2\r\n                (  434.96  -243.33     7.02     0.46)  ; 3\r\n                (  466.08  -241.41     1.00     0.46)  ; 4\r\n                (  468.13  -242.11    -0.90     0.46)  ; 5\r\n                (  473.71  -243.80    -3.13     0.46)  ; 6\r\n                (  496.29  -245.68   -10.80     0.46)  ; 7\r\n                (  498.78  -246.28   -11.77     0.46)  ; 8\r\n                (  595.75  -264.74   -12.13     0.46)  ; 9\r\n                (  598.70  -265.25   -12.13     0.46)  ; 10\r\n                (  602.09  -265.65   -11.75     0.46)  ; 11\r\n                (  608.56  -267.13   -11.75     0.46)  ; 12\r\n                (  669.57  -270.14   -17.15     0.46)  ; 13\r\n                (  768.54  -269.04   -38.45     0.46)  ; 14\r\n                (  778.77  -268.43   -41.93     0.46)  ; 15\r\n                (  796.40  -267.29   -43.33     0.46)  ; 16\r\n                (  800.72  -271.65   -41.35     0.46)  ; 17\r\n                (  855.05  -270.26   -56.00     0.46)  ; 18\r\n                (  854.68  -272.73   -55.00     0.46)  ; 19\r\n                (  863.56  -271.25   -59.22     0.46)  ; 20\r\n                (  880.24  -284.06   -55.57     0.46)  ; 21\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  394.60  -242.01    21.18     0.46)  ; 1, R-1-2-2-2-1-2\r\n              (  396.83  -241.49    21.18     0.46)  ; 2\r\n              (  399.65  -241.43    22.15     0.46)  ; 3\r\n              (  401.18  -239.88    22.15     0.46)  ; 4\r\n              (  404.13  -240.39    22.73     0.46)  ; 5\r\n              (  407.25  -239.65    23.27     0.46)  ; 6\r\n              (  410.51  -239.49    22.55     0.46)  ; 7\r\n              (  413.72  -241.12    23.33     0.46)  ; 8\r\n              (  415.06  -240.80    23.50     0.46)  ; 9\r\n              (  419.40  -239.19    24.45     0.46)  ; 10\r\n              (  422.83  -237.79    24.45     0.46)  ; 11\r\n              (  425.19  -237.83    24.48     0.46)  ; 12\r\n              (  428.59  -238.23    24.88     0.46)  ; 13\r\n              (  432.29  -237.96    26.13     0.46)  ; 14\r\n              (  435.24  -238.47    26.55     0.46)  ; 15\r\n              (  437.48  -237.94    26.55     0.46)  ; 16\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  321.11  -246.10    15.30     0.46)  ; 1\r\n                (  330.76  -245.03    14.57     0.46)  ; 2\r\n                (  355.98  -242.11    18.77     0.46)  ; 3\r\n                (  394.78  -240.77    21.18     0.46)  ; 4\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  306.78  -245.28    15.38     0.46)  ; 1\r\n                (  337.90  -243.35    16.70     0.46)  ; 2\r\n                (  345.76  -242.70    16.70     0.46)  ; 3\r\n                (  360.71  -242.19    19.40     0.46)  ; 4\r\n                (  363.39  -241.56    19.35     0.46)  ; 5\r\n                (  369.33  -240.76    21.38     0.46)  ; 6\r\n                (  372.59  -240.60    22.27     0.46)  ; 7\r\n                (  375.40  -240.53    22.27     0.46)  ; 8\r\n                (  380.14  -240.63    22.32     0.46)  ; 9\r\n                (  406.80  -239.76    23.27     0.46)  ; 10\r\n                (  415.06  -240.80    23.50     0.46)  ; 11\r\n                (  428.59  -238.23    24.95     0.46)  ; 12\r\n              )  ;  End of markers\r\n              (\r\n                (  439.04  -234.60    24.80     0.46)  ; 1, R-1-2-2-2-1-2-1\r\n                (  442.67  -231.97    23.75     0.46)  ; 2\r\n                (  442.67  -231.97    23.72     0.46)  ; 3\r\n                (  445.13  -228.40    23.05     0.46)  ; 4\r\n                (  448.44  -226.44    22.05     0.46)  ; 5\r\n                (  450.72  -224.10    22.05     0.46)  ; 6\r\n                (  451.53  -221.53    21.60     0.46)  ; 7\r\n                (  453.05  -219.98    21.60     0.46)  ; 8\r\n                (  456.04  -218.68    21.60     0.46)  ; 9\r\n                (  458.76  -216.25    22.27     0.46)  ; 10\r\n                (  461.23  -212.69    23.25     0.46)  ; 11\r\n                (  464.27  -209.58    24.15     0.46)  ; 12\r\n                (  467.90  -206.94    24.42     0.46)  ; 13\r\n                (  471.07  -204.41    24.45     0.46)  ; 14\r\n                (  475.14  -201.66    23.42     0.46)  ; 15\r\n                (  476.09  -199.65    22.60     0.46)  ; 16\r\n                (  478.76  -199.03    21.90     0.46)  ; 17\r\n                (  478.81  -197.22    20.95     0.46)  ; 18\r\n                (  480.91  -196.12    20.95     0.46)  ; 19\r\n                (  483.06  -193.24    19.75     0.46)  ; 20\r\n                (  484.58  -191.68    19.02     0.46)  ; 21\r\n                (  486.55  -190.03    19.02     0.46)  ; 22\r\n                (  488.38  -187.81    18.72     0.46)  ; 23\r\n                (  490.35  -186.15    20.15     0.46)  ; 24\r\n                (  492.18  -183.93    21.35     0.46)  ; 25\r\n                (  495.05  -182.08    22.38     0.46)  ; 26\r\n                (  496.31  -179.38    24.30     0.46)  ; 27\r\n                (  497.95  -178.40    25.55     0.46)  ; 28\r\n                (  498.00  -176.60    25.45     0.46)  ; 29\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  445.39  -229.53    23.05     0.46)  ; 1\r\n                  (  449.02  -226.90    22.05     0.46)  ; 2\r\n                  (  458.18  -215.79    22.27     0.46)  ; 3\r\n                  (  463.12  -208.66    24.15     0.46)  ; 4\r\n                  (  473.45  -204.46    23.42     0.46)  ; 5\r\n                  (  478.58  -200.26    21.90     0.46)  ; 6\r\n                  (  496.79  -177.48    25.45     0.46)  ; 7\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  452.10  -222.00    21.60     0.46)  ; 1\r\n                  (  466.57  -207.25    24.42     0.46)  ; 2\r\n                  (  470.75  -205.08    24.45     0.46)  ; 3\r\n                  (  483.06  -193.24    19.75     0.46)  ; 4\r\n                  (  486.11  -190.14    19.02     0.46)  ; 5\r\n                  (  488.51  -188.38    18.72     0.46)  ; 6\r\n                  (  490.49  -186.73    20.17     0.46)  ; 7\r\n                  (  494.78  -180.93    22.38     0.46)  ; 8\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  441.44  -238.80    26.55     0.46)  ; 1, R-1-2-2-2-1-2-2\r\n                (  445.46  -237.86    25.25     0.46)  ; 2\r\n                (  447.70  -237.34    25.25     0.46)  ; 3\r\n                (  450.07  -237.39    26.15     0.46)  ; 4\r\n                (  451.41  -237.07    26.15     0.46)  ; 5\r\n                (  455.69  -237.25    26.70     0.46)  ; 6\r\n                (  458.94  -237.09    26.70     0.46)  ; 7\r\n                (  464.25  -237.63    27.35     0.46)  ; 8\r\n                (  468.15  -236.13    28.17     0.46)  ; 9\r\n                (  470.02  -236.87    28.82     0.46)  ; 10\r\n                (  474.00  -237.75    29.05     0.46)  ; 11\r\n                (  476.67  -237.12    29.05     0.46)  ; 12\r\n                (  479.16  -237.73    29.72     0.46)  ; 13\r\n                (  482.29  -236.99    30.23     0.46)  ; 14\r\n                (  485.69  -237.39    30.57     0.46)  ; 15\r\n                (  489.71  -236.44    30.57     0.46)  ; 16\r\n                (  495.33  -236.33    31.00     0.46)  ; 17\r\n                (  498.99  -237.85    31.52     0.46)  ; 18\r\n                (  501.80  -237.79    30.83     0.46)  ; 19\r\n                (  506.09  -237.99    30.83     0.46)  ; 20\r\n                (  512.55  -239.45    29.88     0.46)  ; 21\r\n                (  516.26  -239.19    29.58     0.46)  ; 22\r\n                (  519.78  -240.15    29.58     0.46)  ; 23\r\n                (  522.02  -239.63    29.58     0.46)  ; 24\r\n                (  525.73  -239.35    29.58     0.46)  ; 25\r\n                (  528.53  -239.29    30.33     0.46)  ; 26\r\n                (  528.53  -239.29    30.30     0.46)  ; 27\r\n                (  530.76  -238.77    31.30     0.46)  ; 28\r\n                (  536.97  -239.11    32.50     0.46)  ; 29\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  512.60  -237.65    29.88     0.46)  ; 1\r\n                  (  533.32  -237.58    32.50     0.46)  ; 2\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  447.70  -237.34    25.25     0.46)  ; 1\r\n                  (  468.15  -236.13    28.17     0.46)  ; 2\r\n                  (  499.44  -237.75    31.52     0.46)  ; 3\r\n                  (  505.64  -238.09    30.83     0.46)  ; 4\r\n                  (  522.46  -239.52    29.58     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (  537.38  -234.83    30.07     0.46)  ; 1, R-1-2-2-2-1-2-2-1\r\n                  (  537.38  -234.83    30.05     0.46)  ; 2\r\n                  (  539.18  -228.43    29.32     0.46)  ; 3\r\n                  (  539.28  -224.82    29.70     0.46)  ; 4\r\n                  (  539.28  -224.82    29.65     0.46)  ; 5\r\n                  (  538.22  -220.30    28.45     0.46)  ; 6\r\n                  (  540.54  -216.18    27.97     0.46)  ; 7\r\n                  (  540.54  -216.18    27.95     0.46)  ; 8\r\n                  (  542.83  -213.85    28.45     0.46)  ; 9\r\n                  (  542.83  -213.85    28.52     0.46)  ; 10\r\n                  (  546.63  -209.97    28.95     0.46)  ; 11\r\n                  (  549.10  -206.41    28.70     0.46)  ; 12\r\n                  (  549.10  -206.41    28.67     0.46)  ; 13\r\n                  (  549.38  -201.57    27.97     0.46)  ; 14\r\n                  (  550.76  -199.45    26.95     0.46)  ; 15\r\n                  (  550.76  -199.45    26.92     0.46)  ; 16\r\n                  (  550.86  -195.84    26.25     0.46)  ; 17\r\n                  (  551.68  -193.27    26.25     0.46)  ; 18\r\n                  (  555.12  -191.87    25.67     0.46)  ; 19\r\n                  (  555.12  -191.87    25.65     0.46)  ; 20\r\n                  (  556.99  -187.84    25.13     0.46)  ; 21\r\n                  (  560.75  -185.77    24.60     0.46)  ; 22\r\n                  (  560.75  -185.77    24.58     0.46)  ; 23\r\n                  (  563.16  -184.00    25.87     0.46)  ; 24\r\n                  (  563.16  -184.00    25.85     0.46)  ; 25\r\n                  (  564.99  -181.78    26.32     0.46)  ; 26\r\n                  (  569.46  -180.74    25.63     0.46)  ; 27\r\n                  (  569.46  -180.74    25.60     0.46)  ; 28\r\n                  (  570.01  -177.02    24.52     0.46)  ; 29\r\n                  (  570.01  -177.02    24.50     0.46)  ; 30\r\n                  (  572.74  -174.59    23.13     0.46)  ; 31\r\n                  (  574.08  -174.29    21.50     0.46)  ; 32\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  545.83  -212.54    28.95     0.46)  ; 1\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  537.26  -234.27    30.05     0.46)  ; 1\r\n                    (  538.43  -229.21    29.32     0.46)  ; 2\r\n                    (  549.64  -202.70    27.95     0.46)  ; 3\r\n                    (  552.33  -196.09    27.92     0.46)  ; 4\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  575.64  -170.95    22.92     0.46)  ; 1, R-1-2-2-2-1-2-2-1-1\r\n                    (  575.56  -168.57    21.55     0.46)  ; 2\r\n                    (  578.60  -165.47    20.30     0.46)  ; 3\r\n                    (  580.96  -165.52    19.50     0.46)  ; 4\r\n                    (  582.67  -162.72    19.50     0.46)  ; 5\r\n                    (  585.72  -159.62    19.50     0.46)  ; 6\r\n                    (  587.68  -157.97    19.50     0.46)  ; 7\r\n                    (  588.44  -157.20    20.35     0.46)  ; 8\r\n                    (  588.44  -157.20    20.33     0.46)  ; 9\r\n                    (  590.28  -154.98    19.17     0.46)  ; 10\r\n                    (  591.98  -152.19    18.23     0.46)  ; 11\r\n                    (  593.81  -149.97    17.13     0.46)  ; 12\r\n                    (  595.64  -147.74    15.95     0.46)  ; 13\r\n                    (  595.64  -147.74    15.92     0.46)  ; 14\r\n                    (  597.76  -146.65    15.15     0.46)  ; 15\r\n                    (  600.16  -144.90    14.75     0.46)  ; 16\r\n                    (  603.30  -144.16    14.65     0.46)  ; 17\r\n                    (  608.46  -144.14    14.07     0.46)  ; 18\r\n                    (  610.96  -144.75    13.72     0.46)  ; 19\r\n                    (  613.73  -146.50    13.80     0.46)  ; 20\r\n                    (  613.73  -146.50    13.72     0.46)  ; 21\r\n                    (  616.94  -148.13    13.47     0.46)  ; 22\r\n                    (  618.86  -148.27    10.95     0.46)  ; 23\r\n                    (  618.86  -148.27    10.77     0.46)  ; 24\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  583.80  -159.47    19.50     0.46)  ; 1\r\n                      (  596.05  -149.44    15.92     0.46)  ; 2\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  579.18  -165.94    20.30     0.46)  ; 1\r\n                      (  581.15  -164.28    19.50     0.46)  ; 2\r\n                      (  590.28  -154.98    19.17     0.46)  ; 3\r\n                      (  599.85  -145.56    14.75     0.46)  ; 4\r\n                      (  608.33  -143.57    14.07     0.46)  ; 5\r\n                      (  610.83  -144.18    13.72     0.46)  ; 6\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  575.68  -175.10    20.45     0.46)  ; 1, R-1-2-2-2-1-2-2-1-2\r\n                    (  576.16  -179.17    19.63     0.46)  ; 2\r\n                    (  577.59  -181.21    18.85     0.46)  ; 3\r\n                    (  577.59  -181.21    18.80     0.46)  ; 4\r\n                    (  577.72  -181.79    18.50     0.46)  ; 5\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  576.48  -178.50    19.63     0.46)  ; 1\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  575.62  -182.89    18.15     0.46)  ; 1, R-1-2-2-2-1-2-2-1-2-1\r\n                      (  575.62  -182.89    18.13     0.46)  ; 2\r\n                      (  576.33  -183.92    15.90     0.46)  ; 3\r\n                      (  577.48  -184.84    13.13     0.46)  ; 4\r\n                      (  577.05  -184.94    13.10     0.46)  ; 5\r\n                      (  580.36  -182.98    11.50     0.46)  ; 6\r\n                      (  580.36  -182.98    11.42     0.46)  ; 7\r\n\r\n                      (Dot\r\n                        (Color Yellow)\r\n                        (Name \"Marker 1\")\r\n                        (  580.30  -184.78     9.85     0.46)  ; 1\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  577.49  -184.82    18.50     0.46)  ; 1, R-1-2-2-2-1-2-2-1-2-2\r\n                      (  577.26  -187.87    16.65     0.46)  ; 2\r\n                      (  578.06  -191.27    14.97     0.46)  ; 3\r\n                      (  578.45  -192.96    12.90     0.46)  ; 4\r\n                      (  580.64  -194.25    11.55     0.46)  ; 5\r\n                      (  581.04  -195.94     9.65     0.46)  ; 6\r\n                      (  581.04  -195.94     9.60     0.46)  ; 7\r\n                      (  583.08  -196.65     7.02     0.46)  ; 8\r\n                      (  584.82  -198.03     6.25     0.46)  ; 9\r\n                      (  585.09  -199.17     5.20     0.46)  ; 10\r\n                      (  586.38  -200.65     3.90     0.46)  ; 11\r\n                      (  586.38  -200.65     3.88     0.46)  ; 12\r\n                      (  587.76  -204.50     2.55     0.46)  ; 13\r\n                      (  589.05  -206.01     0.50     0.46)  ; 14\r\n                      (  589.05  -206.01     0.47     0.46)  ; 15\r\n                      (  590.66  -206.82    -1.25     0.46)  ; 16\r\n                      (  592.35  -210.01    -2.50     0.46)  ; 17\r\n                      (  591.22  -213.26    -4.25     0.46)  ; 18\r\n                      (  590.86  -215.73    -6.02     0.46)  ; 19\r\n                      (  590.86  -215.73    -6.05     0.46)  ; 20\r\n                      (  592.60  -217.12    -7.60     0.46)  ; 21\r\n                      (  592.60  -217.12    -7.63     0.46)  ; 22\r\n                      (  593.13  -219.37    -8.75     0.46)  ; 23\r\n                      (  596.73  -222.71    -9.40     0.46)  ; 24\r\n                      (  597.98  -226.00    -9.43     0.46)  ; 25\r\n                      (  599.40  -228.06   -10.60     0.46)  ; 26\r\n                      (  601.84  -230.47   -11.88     0.46)  ; 27\r\n                      (  603.73  -232.43   -13.42     0.46)  ; 28\r\n                      (  603.73  -232.43   -13.45     0.46)  ; 29\r\n                      (  606.22  -233.03   -15.15     0.46)  ; 30\r\n                      (  606.22  -233.03   -15.17     0.46)  ; 31\r\n                      (  609.88  -234.56   -16.05     0.46)  ; 32\r\n                      (  612.06  -235.84   -15.07     0.46)  ; 33\r\n                      (  615.09  -238.72   -16.05     0.46)  ; 34\r\n                      (  615.09  -238.72   -16.07     0.46)  ; 35\r\n                      (  616.25  -239.63   -17.63     0.46)  ; 36\r\n                      (  616.25  -239.63   -17.65     0.46)  ; 37\r\n                      (  617.15  -239.42   -19.73     0.46)  ; 38\r\n                      (  617.15  -239.42   -19.75     0.46)  ; 39\r\n                      (  619.27  -242.51   -21.50     0.46)  ; 40\r\n                      (  619.27  -242.51   -21.55     0.46)  ; 41\r\n                      (  621.78  -243.12   -23.25     0.46)  ; 42\r\n                      (  625.26  -245.88   -24.65     0.46)  ; 43\r\n                      (  625.26  -245.88   -24.67     0.46)  ; 44\r\n                      (  627.18  -246.03   -26.15     0.46)  ; 45\r\n                      (  629.04  -247.99   -27.27     0.46)  ; 46\r\n                      (  631.10  -248.69   -29.22     0.46)  ; 47\r\n                      (  634.31  -250.33   -30.10     0.46)  ; 48\r\n                      (  635.60  -251.82   -31.88     0.46)  ; 49\r\n                      (  635.60  -251.82   -31.90     0.46)  ; 50\r\n                      (  637.20  -252.64   -33.33     0.46)  ; 51\r\n                      (  640.10  -254.94   -34.63     0.46)  ; 52\r\n                      (  640.10  -254.94   -34.65     0.46)  ; 53\r\n                      (  643.90  -257.04   -36.55     0.46)  ; 54\r\n                      (  647.23  -259.25   -37.72     0.46)  ; 55\r\n                      (  647.23  -259.25   -37.75     0.46)  ; 56\r\n                      (  649.86  -260.42   -40.45     0.46)  ; 57\r\n                      (  649.86  -260.42   -41.35     0.46)  ; 58\r\n                      (  653.06  -262.05   -41.90     0.46)  ; 59\r\n                      (  655.31  -261.53   -42.85     0.46)  ; 60\r\n                      (  658.38  -262.61   -42.85     0.46)  ; 61\r\n                      (  662.80  -263.36   -44.23     0.46)  ; 62\r\n                      (  662.80  -263.36   -44.25     0.46)  ; 63\r\n                      (  666.27  -266.13   -44.25     0.46)  ; 64\r\n                      (  671.09  -268.59   -44.60     0.46)  ; 65\r\n                      (  674.62  -269.54   -44.60     0.46)  ; 66\r\n                      (  677.56  -270.06   -46.00     0.46)  ; 67\r\n                      (  677.56  -270.06   -46.08     0.46)  ; 68\r\n                      (  679.88  -271.90   -47.65     0.46)  ; 69\r\n                      (  679.88  -271.90   -47.67     0.46)  ; 70\r\n                      (  684.56  -273.78   -48.67     0.46)  ; 71\r\n                      (  687.95  -274.18   -50.25     0.46)  ; 72\r\n                      (  690.72  -275.93   -52.15     0.46)  ; 73\r\n                      (  690.72  -275.93   -52.17     0.46)  ; 74\r\n                      (  694.64  -278.59   -54.70     0.46)  ; 75\r\n                      (  694.64  -278.59   -54.72     0.46)  ; 76\r\n                      (  700.03  -281.51   -56.28     0.46)  ; 77\r\n                      (  702.66  -282.68   -58.50     0.46)  ; 78\r\n                      (  702.66  -282.68   -58.60     0.46)  ; 79\r\n                      (  705.74  -283.75   -59.80     0.46)  ; 80\r\n                      (  707.91  -283.74   -60.60     0.46)  ; 81\r\n                      (  710.98  -284.81   -62.60     0.46)  ; 82\r\n                      (  710.98  -284.81   -62.65     0.46)  ; 83\r\n                      (  715.39  -285.56   -64.05     0.46)  ; 84\r\n                      (  715.39  -285.56   -64.07     0.46)  ; 85\r\n                      (  718.74  -287.77   -65.00     0.46)  ; 86\r\n                      (  718.74  -287.77   -65.03     0.46)  ; 87\r\n                      (  720.74  -290.29   -66.40     0.46)  ; 88\r\n                      (  723.55  -290.22   -68.07     0.46)  ; 89\r\n                      (  727.03  -292.99   -68.88     0.46)  ; 90\r\n                      (  727.03  -292.99   -68.90     0.46)  ; 91\r\n                      (  731.18  -292.61   -70.20     0.46)  ; 92\r\n                      (  733.10  -292.76   -70.07     0.46)  ; 93\r\n                      (  733.37  -293.89   -71.43     0.46)  ; 94\r\n                      (  736.76  -294.30   -72.03     0.46)  ; 95\r\n                      (  739.65  -296.60   -72.03     0.46)  ; 96\r\n                      (  742.59  -297.12   -73.85     0.46)  ; 97\r\n                      (  745.28  -296.48   -76.80     0.46)  ; 98\r\n                      (  748.09  -296.42   -79.32     0.46)  ; 99\r\n                      (  748.09  -296.42   -79.35     0.46)  ; 100\r\n                      (  751.74  -297.95   -80.92     0.46)  ; 101\r\n                      (  755.27  -298.91   -82.72     0.46)  ; 102\r\n                      (  755.27  -298.91   -82.95     0.46)  ; 103\r\n\r\n                      (Dot\r\n                        (Color Yellow)\r\n                        (Name \"Marker 1\")\r\n                        (  583.61  -198.92     5.20     0.46)  ; 1\r\n                        (  595.98  -223.48    -9.43     0.46)  ; 2\r\n                        (  638.63  -254.70   -34.65     0.46)  ; 3\r\n                      )  ;  End of markers\r\n\r\n                      (OpenCircle\r\n                        (Color Yellow)\r\n                        (Name \"Marker 2\")\r\n                        (  577.57  -187.20    16.65     0.46)  ; 1\r\n                        (  578.45  -192.96    12.90     0.46)  ; 2\r\n                        (  582.95  -196.09     7.02     0.46)  ; 3\r\n                        (  591.80  -213.72    -4.27     0.46)  ; 4\r\n                        (  609.88  -234.56   -16.05     0.46)  ; 5\r\n                        (  628.91  -247.42   -27.27     0.46)  ; 6\r\n                        (  655.44  -262.11   -42.85     0.46)  ; 7\r\n                        (  659.14  -261.83   -42.85     0.46)  ; 8\r\n                        (  684.56  -273.78   -48.67     0.46)  ; 9\r\n                        (  718.74  -287.77   -65.03     0.46)  ; 10\r\n                        (  744.84  -296.59   -76.80     0.46)  ; 11\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  )  ;  End of split\r\n                |\r\n                  (  540.82  -239.41    30.23     0.46)  ; 1, R-1-2-2-2-1-2-2-2\r\n                  (  544.78  -240.26    30.63     0.46)  ; 2\r\n                  (  548.35  -239.42    32.05     0.46)  ; 3\r\n                  (  551.75  -239.83    33.25     0.46)  ; 4\r\n                  (  554.11  -239.87    32.95     0.46)  ; 5\r\n                  (  556.30  -241.14    33.10     0.46)  ; 6\r\n                  (  559.43  -240.41    33.47     0.46)  ; 7\r\n                  (  564.74  -240.96    33.88     0.46)  ; 8\r\n                  (  567.54  -240.90    33.88     0.46)  ; 9\r\n                  (  568.89  -240.58    33.88     0.46)  ; 10\r\n                  (  571.83  -241.09    34.72     0.46)  ; 11\r\n                  (  576.12  -241.28    34.72     0.46)  ; 12\r\n                  (  577.72  -242.10    35.50     0.46)  ; 13\r\n                  (  577.28  -242.20    35.50     0.46)  ; 14\r\n                  (  579.02  -243.58    37.70     0.46)  ; 15\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  565.00  -242.10    33.88     0.46)  ; 1\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  540.82  -239.41    30.23     0.46)  ; 1\r\n                    (  551.17  -239.36    33.25     0.46)  ; 2\r\n                  )  ;  End of markers\r\n                   High\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  301.49  -248.08    12.65     0.92)  ; 1, R-1-2-2-2-2\r\n            (  301.34  -253.49    12.65     0.92)  ; 2\r\n            (  302.27  -257.45    12.65     0.92)  ; 3\r\n            (  304.10  -261.20    11.50     0.92)  ; 4\r\n            (  305.34  -264.49    10.02     0.92)  ; 5\r\n            (  306.18  -266.08     9.20     0.92)  ; 6\r\n            (  306.18  -266.08     9.17     0.92)  ; 7\r\n            (  304.61  -269.44     8.10     0.92)  ; 8\r\n            (  306.75  -272.53     7.38     0.92)  ; 9\r\n            (  307.63  -278.28     7.38     0.92)  ; 10\r\n            (  307.27  -280.77     8.93     0.92)  ; 11\r\n            (  307.17  -284.36    10.28     0.92)  ; 12\r\n            (  307.38  -287.30    11.68     0.92)  ; 13\r\n            (  309.52  -290.38    12.57     0.92)  ; 14\r\n            (  312.42  -292.69    12.57     0.92)  ; 15\r\n            (  312.32  -296.30    12.90     0.92)  ; 16\r\n            (  310.70  -301.45    12.90     0.92)  ; 17\r\n            (  310.68  -307.43    11.98     0.92)  ; 18\r\n            (  311.43  -312.63    10.45     0.92)  ; 19\r\n            (  311.43  -312.63    10.43     0.92)  ; 20\r\n            (  312.23  -316.02    10.43     0.92)  ; 21\r\n            (\r\n              (  309.50  -317.45    10.43     0.46)  ; 1, R-1-2-2-2-2-1\r\n              (  307.71  -317.87     9.00     0.46)  ; 2\r\n              (  305.62  -318.97     7.77     0.46)  ; 3\r\n              (  304.27  -319.28     6.18     0.46)  ; 4\r\n              (  303.52  -320.06     4.63     0.46)  ; 5\r\n              (  303.02  -321.96     3.32     0.46)  ; 6\r\n              (  303.02  -321.96     3.30     0.46)  ; 7\r\n              (  302.70  -322.62     1.67     0.46)  ; 8\r\n              (  301.36  -322.94     0.20     0.46)  ; 9\r\n              (  302.20  -324.54     0.20     0.46)  ; 10\r\n              (  300.42  -324.96    -0.37     0.46)  ; 11\r\n              (  300.37  -326.76    -1.75     0.46)  ; 12\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  303.02  -321.96     3.30     0.46)  ; 1\r\n              )  ;  End of markers\r\n              (\r\n                (  300.33  -328.57    -3.45     0.46)  ; 1, R-1-2-2-2-2-1-1\r\n                (  299.43  -328.78    -5.07     0.46)  ; 2\r\n                (  297.91  -330.33    -6.45     0.46)  ; 3\r\n                (  297.16  -331.09    -8.00     0.46)  ; 4\r\n                (  295.80  -331.41   -10.30     0.46)  ; 5\r\n                (  296.33  -333.68   -12.22     0.46)  ; 6\r\n                (  294.69  -334.66   -14.25     0.46)  ; 7\r\n                (  293.79  -334.87   -16.55     0.46)  ; 8\r\n                (  293.79  -334.87   -16.57     0.46)  ; 9\r\n                (  292.90  -335.08   -18.63     0.46)  ; 10\r\n                (  292.00  -335.29   -21.72     0.46)  ; 11\r\n                (  290.67  -335.61   -23.45     0.46)  ; 12\r\n                (  290.67  -335.61   -23.47     0.46)  ; 13\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  297.51  -328.63    -6.45     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  300.33  -328.57    -3.45     0.46)  ; 1\r\n                  (  294.99  -334.00   -14.25     0.46)  ; 2\r\n                  (  291.12  -335.50   -23.50     0.46)  ; 3\r\n                )  ;  End of markers\r\n                (\r\n                  (  288.47  -340.30   -25.47     0.46)  ; 1, R-1-2-2-2-2-1-1-1\r\n                  (  286.95  -341.85   -28.05     0.46)  ; 2\r\n                  (  285.61  -342.16   -29.77     0.46)  ; 3\r\n                  (  284.54  -343.61   -31.80     0.46)  ; 4\r\n                  (  284.54  -343.61   -31.82     0.46)  ; 5\r\n                  (  282.43  -344.71   -33.33     0.46)  ; 6\r\n                  (  280.64  -345.12   -35.70     0.46)  ; 7\r\n                  (  280.73  -347.49   -36.82     0.46)  ; 8\r\n                  (  279.21  -349.04   -39.00     0.46)  ; 9\r\n                  (  279.21  -349.04   -39.02     0.46)  ; 10\r\n                  (  279.48  -350.17   -40.85     0.46)  ; 11\r\n                  (  279.48  -350.17   -40.88     0.46)  ; 12\r\n                  (  279.30  -351.41   -42.75     0.46)  ; 13\r\n                  (  278.67  -352.75   -45.03     0.46)  ; 14\r\n                  (  278.67  -352.75   -45.05     0.46)  ; 15\r\n                  (  277.91  -353.54   -48.02     0.46)  ; 16\r\n                  (  276.56  -353.84   -49.83     0.46)  ; 17\r\n                  (  276.12  -353.95   -49.90     0.46)  ; 18\r\n                  (  275.62  -355.85   -52.88     0.46)  ; 19\r\n                  (  274.72  -356.06   -55.25     0.46)  ; 20\r\n                  (  273.53  -356.95   -56.72     0.46)  ; 21\r\n                  (  273.53  -356.95   -56.75     0.46)  ; 22\r\n                  (  271.87  -357.93   -59.35     0.46)  ; 23\r\n                  (  271.87  -357.93   -59.40     0.46)  ; 24\r\n                  (  269.58  -360.26   -60.55     0.46)  ; 25\r\n                  (  269.58  -360.26   -60.63     0.46)  ; 26\r\n                  (  268.07  -361.81   -63.97     0.46)  ; 27\r\n                  (  265.97  -362.89   -66.55     0.46)  ; 28\r\n                  (  265.97  -362.89   -66.63     0.46)  ; 29\r\n                  (  263.87  -363.99   -69.18     0.46)  ; 30\r\n                  (  262.83  -363.63   -72.07     0.46)  ; 31\r\n                  (  261.19  -364.61   -74.40     0.46)  ; 32\r\n                  (  261.19  -364.61   -74.42     0.46)  ; 33\r\n                  (  260.56  -365.94   -77.47     0.46)  ; 34\r\n                  (  257.70  -367.82   -79.53     0.46)  ; 35\r\n                  (  256.04  -368.81   -80.88     0.46)  ; 36\r\n                  (  253.63  -370.56   -83.18     0.46)  ; 37\r\n                  (  252.60  -370.21   -86.00     0.46)  ; 38\r\n                  (  252.60  -370.21   -86.03     0.46)  ; 39\r\n                  (  250.51  -371.29   -89.22     0.46)  ; 40\r\n                  (  248.27  -371.81   -91.30     0.46)  ; 41\r\n                  (  248.27  -371.81   -91.32     0.46)  ; 42\r\n                  (  247.91  -374.30   -94.10     0.46)  ; 43\r\n                  (  247.91  -374.30   -94.12     0.46)  ; 44\r\n                  (  246.83  -375.74   -96.32     0.46)  ; 45\r\n                  (  246.83  -375.74   -96.35     0.46)  ; 46\r\n                  (  245.18  -376.72   -98.77     0.46)  ; 47\r\n                  (  243.34  -378.95  -100.30     0.46)  ; 48\r\n                  (  240.79  -380.14  -101.57     0.46)  ; 49\r\n                  (  240.79  -380.14  -101.60     0.46)  ; 50\r\n                  (  236.73  -382.88  -104.12     0.46)  ; 51\r\n                  (  235.38  -383.20  -106.43     0.46)  ; 52\r\n                  (  232.21  -385.74  -108.77     0.46)  ; 53\r\n                  (  229.27  -385.23  -110.90     0.46)  ; 54\r\n                  (  228.91  -387.70  -113.72     0.46)  ; 55\r\n                  (  229.94  -388.06  -116.38     0.46)  ; 56\r\n                  (  230.20  -389.19  -119.95     0.46)  ; 57\r\n                  (  229.58  -390.53  -122.72     0.46)  ; 58\r\n                  (  229.58  -390.53  -122.75     0.46)  ; 59\r\n                  (  228.36  -391.42  -125.52     0.46)  ; 60\r\n                  (  228.36  -391.42  -125.55     0.46)  ; 61\r\n                  (  225.50  -393.28  -127.82     0.46)  ; 62\r\n                  (  221.80  -393.55  -129.22     0.46)  ; 63\r\n                  (  218.40  -393.15  -130.50     0.46)  ; 64\r\n                  (  218.40  -393.15  -130.52     0.46)  ; 65\r\n                  (  215.59  -393.21  -132.52     0.46)  ; 66\r\n                  (  213.36  -393.73  -134.63     0.46)  ; 67\r\n                  (  213.36  -393.73  -134.68     0.46)  ; 68\r\n                  (  213.36  -393.73  -138.07     0.46)  ; 69\r\n                  (  211.70  -394.72  -140.07     0.46)  ; 70\r\n                  (  209.79  -394.56  -141.63     0.46)  ; 71\r\n                  (  209.79  -394.56  -141.65     0.46)  ; 72\r\n                  (  207.69  -395.67  -143.77     0.46)  ; 73\r\n                  (  207.69  -395.67  -143.80     0.46)  ; 74\r\n                  (  206.21  -395.41  -146.52     0.46)  ; 75\r\n                  (  206.17  -397.22  -149.70     0.46)  ; 76\r\n                  (  206.17  -397.22  -149.73     0.46)  ; 77\r\n                  (  204.06  -398.30  -151.27     0.46)  ; 78\r\n                  (  202.28  -398.72  -154.40     0.46)  ; 79\r\n                  (  202.28  -398.72  -154.43     0.46)  ; 80\r\n                  (  199.68  -401.71  -156.07     0.46)  ; 81\r\n                  (  199.55  -401.15  -156.07     0.46)  ; 82\r\n                  (  197.89  -402.13  -158.35     0.46)  ; 83\r\n                  (  196.74  -401.21  -160.63     0.46)  ; 84\r\n                  (  196.29  -401.32  -160.63     0.46)  ; 85\r\n                  (  195.21  -402.76  -162.15     0.46)  ; 86\r\n                  (  195.21  -402.76  -162.23     0.46)  ; 87\r\n                  (  191.37  -402.47  -164.45     0.46)  ; 88\r\n                  (  189.37  -399.96  -164.45     0.46)  ; 89\r\n                  (  187.82  -397.33  -164.45     0.46)  ; 90\r\n                  (  184.48  -395.12  -164.45     0.46)  ; 91\r\n                  (  180.73  -391.23  -165.10     0.46)  ; 92\r\n                  (  180.73  -391.23  -165.12     0.46)  ; 93\r\n                  (  176.36  -388.67  -165.95     0.46)  ; 94\r\n                  (  171.86  -385.54  -164.77     0.46)  ; 95\r\n                  (  168.33  -384.58  -162.68     0.46)  ; 96\r\n                  (  165.08  -384.75  -159.90     0.46)  ; 97\r\n                  (  162.44  -383.58  -158.48     0.46)  ; 98\r\n                  (  158.19  -381.60  -158.48     0.46)  ; 99\r\n                  (  154.99  -379.95  -158.48     0.46)  ; 100\r\n                  (  150.57  -379.21  -158.07     0.46)  ; 101\r\n                  (  145.84  -379.11  -157.68     0.46)  ; 102\r\n                  (  140.58  -376.76  -157.68     0.46)  ; 103\r\n                  (  137.63  -376.26  -156.95     0.46)  ; 104\r\n                  (  134.16  -373.49  -156.32     0.46)  ; 105\r\n                  (  132.60  -370.87  -155.38     0.46)  ; 106\r\n                  (  130.86  -369.49  -153.98     0.46)  ; 107\r\n                  (  130.86  -369.49  -154.00     0.46)  ; 108\r\n                  (  129.12  -368.11  -153.07     0.46)  ; 109\r\n                  (  128.14  -365.94  -151.85     0.46)  ; 110\r\n                  (  124.22  -363.28  -151.65     0.46)  ; 111\r\n                  (  118.95  -360.93  -151.65     0.46)  ; 112\r\n                  (  115.75  -359.29  -150.55     0.46)  ; 113\r\n                  (  111.52  -357.30  -150.55     0.46)  ; 114\r\n                  (  110.79  -356.27  -149.35     0.46)  ; 115\r\n                  (  109.51  -354.79  -148.18     0.46)  ; 116\r\n                  (  107.77  -353.40  -146.43     0.46)  ; 117\r\n                  (  105.46  -351.56  -144.82     0.46)  ; 118\r\n                  (  105.46  -351.56  -144.85     0.46)  ; 119\r\n                  (  103.14  -349.71  -143.22     0.46)  ; 120\r\n                  (  103.14  -349.71  -143.25     0.46)  ; 121\r\n                  (   99.48  -348.18  -142.10     0.46)  ; 122\r\n                  (   95.96  -347.21  -140.88     0.46)  ; 123\r\n                  (   93.46  -346.60  -139.60     0.46)  ; 124\r\n                  (   90.56  -344.30  -138.50     0.46)  ; 125\r\n                  (   87.48  -343.23  -137.77     0.46)  ; 126\r\n                  (   84.72  -341.50  -137.77     0.46)  ; 127\r\n                  (   80.34  -338.93  -137.77     0.46)  ; 128\r\n                  (   74.64  -336.69  -137.38     0.46)  ; 129\r\n                  (   71.11  -335.72  -139.13     0.46)  ; 130\r\n                  (   65.23  -334.72  -140.15     0.46)  ; 131\r\n                  (   65.23  -334.72  -140.18     0.46)  ; 132\r\n                  (   61.43  -332.61  -141.07     0.46)  ; 133\r\n                  (   56.18  -330.27  -142.70     0.46)  ; 134\r\n                  (   56.18  -330.27  -142.73     0.46)  ; 135\r\n                  (   50.33  -327.46  -143.72     0.46)  ; 136\r\n                  (   44.68  -323.41  -143.72     0.46)  ; 137\r\n                  (   39.46  -319.26  -142.30     0.46)  ; 138\r\n                  (   39.59  -319.82  -142.32     0.46)  ; 139\r\n                  (   37.27  -317.99  -140.75     0.46)  ; 140\r\n                  (   35.27  -315.47  -139.43     0.46)  ; 141\r\n                  (   31.03  -313.47  -138.67     0.46)  ; 142\r\n                  (   26.53  -310.35  -138.67     0.46)  ; 143\r\n                  (   23.32  -308.72  -140.07     0.46)  ; 144\r\n                  (   23.32  -308.72  -140.10     0.46)  ; 145\r\n                  (   20.29  -305.85  -141.38     0.46)  ; 146\r\n                  (   20.29  -305.85  -141.40     0.46)  ; 147\r\n                  (   16.24  -302.62  -142.00     0.46)  ; 148\r\n                  (   16.24  -302.62  -142.02     0.46)  ; 149\r\n                  (   12.64  -299.28  -141.67     0.46)  ; 150\r\n                  (    9.01  -295.95  -140.55     0.46)  ; 151\r\n                  (    4.38  -292.25  -140.20     0.46)  ; 152\r\n                  (    1.18  -290.61  -140.20     0.46)  ; 153\r\n                  (   -2.03  -288.98  -140.20     0.46)  ; 154\r\n                  (   -3.15  -286.26  -139.30     0.46)  ; 155\r\n                  (   -8.09  -283.24  -139.02     0.46)  ; 156\r\n                  (  -12.47  -280.68  -139.02     0.46)  ; 157\r\n                  (  -14.64  -279.40  -138.72     0.46)  ; 158\r\n                  (  -17.01  -279.36  -138.15     0.46)  ; 159\r\n                  (  -21.19  -275.57  -138.15     0.46)  ; 160\r\n                  (  -23.46  -271.92  -138.88     0.46)  ; 161\r\n                  (  -25.92  -269.50  -137.82     0.46)  ; 162\r\n                  (  -27.91  -266.98  -136.85     0.46)  ; 163\r\n                  (  -31.00  -265.92  -135.50     0.46)  ; 164\r\n                  (  -34.26  -266.09  -133.77     0.46)  ; 165\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  230.12  -386.83  -110.90     0.46)  ; 1\r\n                    (  201.60  -401.87  -156.07     0.46)  ; 2\r\n                    (  129.35  -365.07  -151.85     0.46)  ; 3\r\n                    (   12.59  -301.09  -141.67     0.46)  ; 4\r\n                    (    3.32  -293.69  -140.20     0.46)  ; 5\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  288.34  -339.73   -25.47     0.46)  ; 1\r\n                    (  269.45  -359.68   -60.63     0.46)  ; 2\r\n                    (  225.50  -393.28  -127.82     0.46)  ; 3\r\n                    (  213.36  -393.73  -138.07     0.46)  ; 4\r\n                    (  206.35  -395.98  -149.73     0.46)  ; 5\r\n                    (  191.51  -403.04  -164.45     0.46)  ; 6\r\n                    (  176.36  -388.67  -165.95     0.46)  ; 7\r\n                    (  165.08  -384.75  -159.90     0.46)  ; 8\r\n                    (  150.57  -379.21  -158.07     0.46)  ; 9\r\n                    (  146.15  -378.44  -157.68     0.46)  ; 10\r\n                    (  119.40  -360.83  -151.65     0.46)  ; 11\r\n                    (  111.52  -357.30  -150.55     0.46)  ; 12\r\n                    (   95.83  -346.65  -140.88     0.46)  ; 13\r\n                    (   85.17  -341.40  -137.77     0.46)  ; 14\r\n                    (   61.75  -331.95  -141.07     0.46)  ; 15\r\n                    (   50.33  -327.46  -143.72     0.46)  ; 16\r\n                    (   39.46  -319.26  -142.32     0.46)  ; 17\r\n                    (  -14.51  -279.96  -138.72     0.46)  ; 18\r\n                    (  -21.19  -275.57  -138.15     0.46)  ; 19\r\n                    (  -22.75  -272.94  -138.15     0.46)  ; 20\r\n                    (  -24.76  -270.42  -137.07     0.46)  ; 21\r\n                    (  -27.34  -267.45  -136.32     0.46)  ; 22\r\n                    (  -33.81  -265.98  -133.77     0.46)  ; 23\r\n                    (  -37.33  -265.00  -133.02     0.46)  ; 24\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  288.34  -333.76   -25.15     0.46)  ; 1, R-1-2-2-2-2-1-1-2\r\n                  (  287.81  -331.50   -26.85     0.46)  ; 2\r\n                  (  286.08  -330.12   -28.15     0.46)  ; 3\r\n                  (  285.63  -330.22   -28.17     0.46)  ; 4\r\n                  (  283.63  -327.71   -29.58     0.46)  ; 5\r\n                  (  282.34  -326.21   -31.15     0.46)  ; 6\r\n                  (  281.19  -325.29   -33.00     0.46)  ; 7\r\n                  (  278.42  -323.55   -34.17     0.46)  ; 8\r\n                  (  276.10  -321.70   -36.07     0.46)  ; 9\r\n                  (  273.78  -319.86   -37.30     0.46)  ; 10\r\n                  (  272.36  -317.81   -38.92     0.46)  ; 11\r\n                  (  271.78  -317.34   -41.00     0.46)  ; 12\r\n                  (  271.78  -317.34   -41.03     0.46)  ; 13\r\n                  (  270.94  -315.74   -43.10     0.46)  ; 14\r\n                  (  268.75  -314.47   -46.15     0.46)  ; 15\r\n                  (  267.90  -312.87   -48.15     0.46)  ; 16\r\n                  (  265.40  -312.26   -50.40     0.46)  ; 17\r\n                  (  265.40  -312.26   -50.42     0.46)  ; 18\r\n                  (  263.41  -309.76   -52.57     0.46)  ; 19\r\n                  (  261.35  -309.03   -54.97     0.46)  ; 20\r\n                  (  259.71  -310.02   -57.92     0.46)  ; 21\r\n                  (  257.19  -309.41   -60.75     0.46)  ; 22\r\n                  (  256.13  -310.85   -62.42     0.46)  ; 23\r\n                  (  253.81  -309.01   -63.85     0.46)  ; 24\r\n                  (  251.71  -310.11   -65.80     0.46)  ; 25\r\n                  (  249.08  -308.94   -68.82     0.46)  ; 26\r\n                  (  249.08  -308.94   -68.85     0.46)  ; 27\r\n                  (  249.08  -308.94   -71.35     0.46)  ; 28\r\n                  (  249.08  -308.94   -71.38     0.46)  ; 29\r\n                  (  247.60  -308.68   -73.05     0.46)  ; 30\r\n                  (  245.42  -307.40   -76.50     0.46)  ; 31\r\n                  (  243.95  -307.15   -79.65     0.46)  ; 32\r\n                  (  243.95  -307.15   -79.70     0.46)  ; 33\r\n                  (  242.34  -306.33   -83.70     0.46)  ; 34\r\n                  (  241.90  -306.43   -83.72     0.46)  ; 35\r\n                  (  237.47  -305.68   -86.00     0.46)  ; 36\r\n                  (  234.53  -305.17   -88.42     0.46)  ; 37\r\n                  (  233.06  -304.92   -91.13     0.46)  ; 38\r\n                  (  233.06  -304.92   -91.15     0.46)  ; 39\r\n                  (  231.33  -303.53   -95.82     0.46)  ; 40\r\n                  (  231.33  -303.53   -95.85     0.46)  ; 41\r\n                  (  228.96  -303.49   -98.42     0.46)  ; 42\r\n                  (  225.26  -303.76  -102.50     0.46)  ; 43\r\n                  (  221.85  -303.36  -104.48     0.46)  ; 44\r\n                  (  219.18  -303.99  -105.63     0.46)  ; 45\r\n                  (  219.18  -303.99  -105.65     0.46)  ; 46\r\n                  (  215.79  -303.59  -107.90     0.46)  ; 47\r\n                  (  215.79  -303.59  -107.92     0.46)  ; 48\r\n                  (  214.32  -303.34  -110.77     0.46)  ; 49\r\n                  (  214.32  -303.34  -110.85     0.46)  ; 50\r\n                  (  212.26  -302.63  -113.70     0.46)  ; 51\r\n                  (  212.26  -302.63  -113.75     0.46)  ; 52\r\n                  (  208.74  -301.67  -116.15     0.46)  ; 53\r\n                  (  205.67  -300.59  -118.97     0.46)  ; 54\r\n                  (  203.16  -299.99  -121.10     0.46)  ; 55\r\n                  (  202.01  -299.06  -123.65     0.46)  ; 56\r\n                  (  200.98  -298.71  -125.95     0.46)  ; 57\r\n                  (  198.61  -298.66  -128.70     0.46)  ; 58\r\n                  (  198.61  -298.66  -128.72     0.46)  ; 59\r\n                  (  196.12  -298.05  -132.32     0.46)  ; 60\r\n                  (  194.95  -297.14  -133.90     0.46)  ; 61\r\n                  (  191.74  -295.50  -136.10     0.46)  ; 62\r\n                  (  190.19  -292.87  -137.45     0.46)  ; 63\r\n                  (  188.77  -290.81  -139.00     0.46)  ; 64\r\n                  (  187.74  -290.47  -140.88     0.46)  ; 65\r\n                  (  186.00  -289.08  -144.05     0.46)  ; 66\r\n                  (  184.46  -286.46  -145.42     0.46)  ; 67\r\n                  (  181.41  -283.59  -147.80     0.46)  ; 68\r\n                  (  180.88  -281.31  -149.75     0.46)  ; 69\r\n                  (  178.97  -281.16  -152.05     0.46)  ; 70\r\n                  (  178.34  -282.51  -154.70     0.46)  ; 71\r\n                  (  178.34  -282.51  -154.73     0.46)  ; 72\r\n                  (  177.18  -281.59  -157.63     0.46)  ; 73\r\n                  (  177.18  -281.59  -157.65     0.46)  ; 74\r\n                  (  174.36  -281.65  -159.52     0.46)  ; 75\r\n                  (  174.36  -281.65  -159.55     0.46)  ; 76\r\n                  (  173.20  -280.73  -162.27     0.46)  ; 77\r\n                  (  170.98  -281.25  -162.20     0.46)  ; 78\r\n                  (  169.19  -281.67  -164.82     0.46)  ; 79\r\n                  (  169.19  -281.67  -164.85     0.46)  ; 80\r\n                  (  167.59  -280.86  -167.57     0.46)  ; 81\r\n                  (  167.59  -280.86  -168.10     0.46)  ; 82\r\n                  (  165.21  -280.81  -169.67     0.46)  ; 83\r\n                  (  163.61  -279.99  -171.47     0.46)  ; 84\r\n                  (  162.09  -281.53  -171.47     0.46)  ; 85\r\n                  (  159.47  -280.37  -172.90     0.46)  ; 86\r\n                  (  159.47  -280.37  -172.92     0.46)  ; 87\r\n                  (  157.86  -279.56  -174.75     0.46)  ; 88\r\n                  (  157.86  -279.56  -174.77     0.46)  ; 89\r\n                  (  156.78  -281.00  -177.35     0.46)  ; 90\r\n                  (  156.78  -281.00  -177.45     0.46)  ; 91\r\n                  (  153.98  -281.06  -180.63     0.46)  ; 92\r\n                  (  151.79  -279.78  -182.85     0.46)  ; 93\r\n                  (  149.69  -280.87  -184.88     0.46)  ; 94\r\n                  (  148.03  -281.86  -186.45     0.46)  ; 95\r\n                  (  147.27  -282.63  -188.85     0.46)  ; 96\r\n                  (  143.75  -281.66  -191.13     0.46)  ; 97\r\n                  (  140.66  -280.59  -192.02     0.46)  ; 98\r\n                  (  140.58  -278.22  -193.77     0.46)  ; 99\r\n                  (  140.58  -278.22  -193.80     0.46)  ; 100\r\n                  (  136.93  -276.69  -195.83     0.46)  ; 101\r\n                  (  130.59  -275.78  -196.22     0.46)  ; 102\r\n                  (  128.09  -275.18  -198.00     0.46)  ; 103\r\n                  (  125.47  -274.01  -200.20     0.46)  ; 104\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  155.62  -280.07  -177.45     0.46)  ; 1\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  283.63  -327.71   -29.58     0.46)  ; 1\r\n                    (  281.63  -325.18   -33.00     0.46)  ; 2\r\n                    (  276.10  -321.70   -36.07     0.46)  ; 3\r\n                    (  221.73  -302.81  -104.48     0.46)  ; 4\r\n                    (  205.35  -301.26  -118.97     0.46)  ; 5\r\n                    (  201.43  -298.61  -125.95     0.46)  ; 6\r\n                    (  186.00  -289.08  -144.05     0.46)  ; 7\r\n                    (  171.43  -281.15  -162.20     0.46)  ; 8\r\n                  )  ;  End of markers\r\n                   Low\r\n                )  ;  End of split\r\n              |\r\n                (  301.62  -330.05    -1.75     0.46)  ; 1, R-1-2-2-2-2-1-2\r\n                (  301.70  -332.42    -1.75     0.46)  ; 2\r\n                (  301.60  -336.02    -0.70     0.46)  ; 3\r\n                (  301.60  -336.02    -0.72     0.46)  ; 4\r\n                (  300.97  -337.38    -0.72     0.46)  ; 5\r\n                (  300.57  -341.65     0.15     0.46)  ; 6\r\n                (  300.46  -345.25    -0.02     0.46)  ; 7\r\n                (  299.39  -346.70    -0.02     0.46)  ; 8\r\n                (  300.50  -349.42    -0.02     0.46)  ; 9\r\n                (  300.14  -351.89    -0.02     0.46)  ; 10\r\n                (  299.46  -355.04    -0.97     0.46)  ; 11\r\n                (  300.39  -359.01    -0.97     0.46)  ; 12\r\n                (  300.34  -360.81    -1.45     0.46)  ; 13\r\n                (  300.11  -363.85    -1.45     0.46)  ; 14\r\n                (  299.98  -363.28    -1.45     0.46)  ; 15\r\n                (  301.85  -365.23    -2.42     0.46)  ; 16\r\n                (  303.73  -367.18    -4.32     0.46)  ; 17\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  299.78  -332.27    -1.75     0.46)  ; 1\r\n                  (  301.68  -344.36    -0.02     0.46)  ; 2\r\n                  (  301.67  -350.35    -0.02     0.46)  ; 3\r\n                  (  301.15  -358.22    -0.97     0.46)  ; 4\r\n                )  ;  End of markers\r\n                (\r\n                  (  306.39  -366.55    -5.63     0.46)  ; 1, R-1-2-2-2-2-1-2-1\r\n                  (  308.31  -366.70    -5.63     0.46)  ; 2\r\n                  (  312.01  -366.43    -5.63     0.46)  ; 3\r\n                  (  314.37  -366.47    -5.63     0.46)  ; 4\r\n                  (  318.66  -366.66    -6.15     0.46)  ; 5\r\n                  (  323.09  -367.42    -6.18     0.46)  ; 6\r\n                  (  325.84  -369.15    -6.25     0.46)  ; 7\r\n                  (  328.35  -369.77    -6.40     0.46)  ; 8\r\n                  (  332.72  -372.33    -6.85     0.46)  ; 9\r\n                  (  333.42  -373.36    -7.97     0.46)  ; 10\r\n                  (  336.06  -374.53    -9.35     0.46)  ; 11\r\n                  (  338.95  -376.84   -10.55     0.46)  ; 12\r\n                  (  342.42  -379.61   -11.27     0.46)  ; 13\r\n                  (  345.91  -382.37   -12.35     0.46)  ; 14\r\n                  (  345.91  -382.37   -12.38     0.46)  ; 15\r\n                  (  347.77  -384.32   -13.35     0.46)  ; 16\r\n                  (  349.64  -386.28   -14.32     0.46)  ; 17\r\n                  (  351.82  -387.55   -13.82     0.46)  ; 18\r\n                  (  354.47  -388.73   -12.57     0.46)  ; 19\r\n                  (  355.75  -390.21   -11.58     0.46)  ; 20\r\n                  (  358.38  -391.39   -11.05     0.46)  ; 21\r\n                  (  362.17  -393.49   -10.75     0.46)  ; 22\r\n                  (  363.46  -394.97   -10.57     0.46)  ; 23\r\n                  (  365.52  -395.69   -10.57     0.46)  ; 24\r\n                  (  367.31  -395.27   -10.05     0.46)  ; 25\r\n                  (  368.14  -396.86   -10.05     0.46)  ; 26\r\n                  (  367.70  -396.97   -10.05     0.46)  ; 27\r\n                  (  370.46  -398.71   -10.77     0.46)  ; 28\r\n                  (  372.77  -400.56   -10.77     0.46)  ; 29\r\n                  (  376.00  -402.20   -11.55     0.46)  ; 30\r\n                  (  379.33  -404.41   -11.82     0.46)  ; 31\r\n                  (  381.25  -404.54   -12.02     0.46)  ; 32\r\n                  (  383.31  -405.26   -12.15     0.46)  ; 33\r\n                  (  390.53  -405.95   -12.65     0.46)  ; 34\r\n                  (  392.72  -407.24   -13.52     0.46)  ; 35\r\n                  (  392.72  -407.24   -13.55     0.46)  ; 36\r\n                  (  394.72  -409.75   -14.18     0.46)  ; 37\r\n                  (  396.91  -411.02   -14.18     0.46)  ; 38\r\n                  (  399.27  -411.06   -14.18     0.46)  ; 39\r\n                  (  401.90  -412.24   -14.18     0.46)  ; 40\r\n                  (  407.21  -412.79   -14.60     0.46)  ; 41\r\n                  (  409.44  -412.26   -14.82     0.46)  ; 42\r\n                  (  411.37  -412.41   -14.82     0.46)  ; 43\r\n                  (  415.21  -412.70   -14.82     0.46)  ; 44\r\n                  (  415.07  -412.14   -14.82     0.46)  ; 45\r\n                  (  418.28  -413.78   -14.82     0.46)  ; 46\r\n                  (  422.12  -414.07   -15.43     0.46)  ; 47\r\n                  (  424.17  -414.79   -15.45     0.46)  ; 48\r\n                  (  426.09  -414.94   -16.17     0.46)  ; 49\r\n                  (  429.93  -415.23   -17.20     0.46)  ; 50\r\n                  (  434.40  -414.19   -17.80     0.46)  ; 51\r\n                  (  438.81  -414.94   -18.05     0.46)  ; 52\r\n                  (  443.23  -415.70   -19.35     0.46)  ; 53\r\n                  (  444.71  -414.75   -19.65     0.46)  ; 54\r\n                  (  444.71  -414.75   -19.70     0.46)  ; 55\r\n                  (  447.97  -414.59   -20.97     0.46)  ; 56\r\n                  (  447.97  -414.59   -21.00     0.46)  ; 57\r\n                  (  451.81  -414.88   -21.15     0.46)  ; 58\r\n                  (  456.10  -415.08   -22.22     0.46)  ; 59\r\n                  (  458.79  -414.45   -23.30     0.46)  ; 60\r\n                  (  462.04  -414.28   -24.00     0.46)  ; 61\r\n                  (  466.45  -415.04   -24.55     0.46)  ; 62\r\n                  (  470.47  -414.09   -25.45     0.46)  ; 63\r\n                  (  473.60  -413.35   -26.60     0.46)  ; 64\r\n                  (  476.86  -413.19   -27.55     0.46)  ; 65\r\n                  (  481.19  -411.58   -28.47     0.46)  ; 66\r\n                  (  483.87  -410.95   -28.47     0.46)  ; 67\r\n                  (  487.13  -410.79   -29.20     0.46)  ; 68\r\n                  (  489.37  -410.26   -30.07     0.46)  ; 69\r\n                  (  492.04  -409.63   -31.10     0.46)  ; 70\r\n                  (  495.48  -408.23   -31.10     0.46)  ; 71\r\n                  (  500.22  -408.31   -32.02     0.46)  ; 72\r\n                  (  501.11  -408.10   -32.38     0.46)  ; 73\r\n                  (  503.79  -407.47   -33.70     0.46)  ; 74\r\n                  (  503.79  -407.47   -33.72     0.46)  ; 75\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  330.79  -372.18    -6.85     0.46)  ; 1\r\n                    (  350.04  -387.97   -13.82     0.46)  ; 2\r\n                    (  406.00  -413.67   -14.60     0.46)  ; 3\r\n                    (  471.18  -415.11   -25.45     0.46)  ; 4\r\n                    (  487.75  -409.44   -30.07     0.46)  ; 5\r\n                    (  500.93  -409.34   -31.88     0.46)  ; 6\r\n                    (  503.60  -408.72   -33.67     0.46)  ; 7\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  319.11  -366.55    -6.15     0.46)  ; 1\r\n                    (  358.25  -390.82   -11.05     0.46)  ; 2\r\n                    (  365.38  -395.13   -10.57     0.46)  ; 3\r\n                    (  372.77  -400.56   -10.77     0.46)  ; 4\r\n                    (  395.17  -409.64   -14.18     0.46)  ; 5\r\n                    (  448.56  -415.05   -21.00     0.46)  ; 6\r\n                    (  459.35  -414.91   -23.30     0.46)  ; 7\r\n                    (  475.84  -412.84   -27.55     0.46)  ; 8\r\n                    (  495.61  -408.80   -31.10     0.46)  ; 9\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  305.27  -369.79    -2.55     0.46)  ; 1, R-1-2-2-2-2-1-2-2\r\n                  (  307.14  -371.75    -2.55     0.46)  ; 2\r\n                  (  306.77  -374.22    -3.08     0.46)  ; 3\r\n                  (  306.55  -377.26    -3.08     0.46)  ; 4\r\n                  (  306.27  -382.10    -3.75     0.46)  ; 5\r\n                  (  306.17  -385.71    -4.57     0.46)  ; 6\r\n                  (  307.42  -389.00    -4.57     0.46)  ; 7\r\n                  (  308.53  -391.72    -5.00     0.46)  ; 8\r\n                  (  308.29  -394.77    -5.00     0.46)  ; 9\r\n                  (  309.09  -398.16    -5.00     0.46)  ; 10\r\n                  (  309.49  -399.85    -5.53     0.46)  ; 11\r\n                  (  309.97  -403.93    -6.22     0.46)  ; 12\r\n                  (  310.33  -407.43    -6.65     0.46)  ; 13\r\n                  (  311.13  -410.83    -7.02     0.46)  ; 14\r\n                  (  311.92  -414.22    -7.02     0.46)  ; 15\r\n                  (  311.99  -416.58    -7.02     0.46)  ; 16\r\n                  (  313.12  -419.32    -7.02     0.46)  ; 17\r\n                  (  312.43  -422.46    -7.97     0.46)  ; 18\r\n                  (  312.64  -425.40    -9.02     0.46)  ; 19\r\n                  (  312.86  -428.32    -9.72     0.46)  ; 20\r\n                  (  313.71  -429.92    -9.72     0.46)  ; 21\r\n                  (  313.79  -432.29    -8.77     0.46)  ; 22\r\n                  (  313.11  -435.44    -8.77     0.46)  ; 23\r\n                  (  313.46  -438.94    -8.88     0.46)  ; 24\r\n                  (  312.96  -440.85    -9.05     0.46)  ; 25\r\n                  (  312.03  -442.86    -9.80     0.46)  ; 26\r\n                  (  312.03  -442.86    -9.82     0.46)  ; 27\r\n                  (  312.43  -444.56   -10.95     0.46)  ; 28\r\n                  (  310.14  -446.88   -12.35     0.46)  ; 29\r\n                  (  309.69  -446.99   -12.35     0.46)  ; 30\r\n                  (  308.88  -449.57   -13.18     0.46)  ; 31\r\n                  (  308.57  -450.24   -15.27     0.46)  ; 32\r\n                  (  308.57  -450.24   -15.30     0.46)  ; 33\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  310.58  -392.44    -5.00     0.46)  ; 1\r\n                    (  312.13  -433.29    -8.77     0.46)  ; 2\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  306.56  -371.30    -2.55     0.46)  ; 1\r\n                    (  306.55  -377.26    -3.08     0.46)  ; 2\r\n                    (  306.27  -382.10    -3.75     0.46)  ; 3\r\n                    (  307.28  -388.44    -4.57     0.46)  ; 4\r\n                    (  309.62  -400.43    -5.53     0.46)  ; 5\r\n                    (  310.77  -407.32    -6.65     0.46)  ; 6\r\n                    (  311.92  -414.22    -7.02     0.46)  ; 7\r\n                    (  312.29  -421.90    -7.97     0.46)  ; 8\r\n                    (  312.25  -423.69    -9.02     0.46)  ; 9\r\n                    (  312.73  -427.77    -9.72     0.46)  ; 10\r\n                    (  313.71  -429.92    -9.72     0.46)  ; 11\r\n                    (  309.69  -446.99   -12.35     0.46)  ; 12\r\n                    (  308.88  -449.57   -13.18     0.46)  ; 13\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            |\r\n              (  313.90  -319.09    11.85     0.92)  ; 1, R-1-2-2-2-2-2\r\n              (  315.59  -322.28    13.05     0.92)  ; 2\r\n              (  315.49  -325.89    14.07     0.92)  ; 3\r\n              (  316.15  -328.72    15.15     0.92)  ; 4\r\n              (  317.08  -332.69    16.25     0.92)  ; 5\r\n              (  318.32  -335.98    17.42     0.92)  ; 6\r\n              (  319.70  -339.83    18.02     0.46)  ; 7\r\n              (  321.12  -341.88    19.10     0.46)  ; 8\r\n              (  319.82  -346.37    19.07     0.46)  ; 9\r\n              (  318.29  -347.92    19.07     0.46)  ; 10\r\n              (  319.26  -350.08    19.07     0.46)  ; 11\r\n              (  319.49  -353.01    19.35     0.46)  ; 12\r\n              (  319.61  -359.56    19.35     0.46)  ; 13\r\n              (  319.82  -362.50    19.35     0.46)  ; 14\r\n              (  319.79  -368.36    15.85     0.46)  ; 15\r\n              (  320.43  -377.17    17.20     0.46)  ; 16\r\n              (  321.39  -391.28    18.08     0.46)  ; 17\r\n              (  324.69  -411.41    18.08     0.46)  ; 18\r\n              (  328.03  -435.70    18.92     0.46)  ; 19\r\n              (  328.03  -435.70    19.10     0.46)  ; 20\r\n              (  331.25  -453.46    19.90     0.46)  ; 21\r\n              (  331.25  -453.46    19.95     0.46)  ; 22\r\n              (  332.28  -475.91    21.45     0.46)  ; 23\r\n              (  330.87  -483.99    22.50     0.46)  ; 24\r\n              (  332.33  -490.23    22.95     0.46)  ; 25\r\n              (  332.00  -496.87    23.25     0.46)  ; 26\r\n              (  331.01  -500.69    23.57     0.46)  ; 27\r\n              (  331.49  -504.75    23.25     0.46)  ; 28\r\n              (  330.81  -507.90    23.25     0.46)  ; 29\r\n              (  329.20  -513.05    23.25     0.46)  ; 30\r\n              (  328.88  -513.73    20.95     0.46)  ; 31\r\n              (  327.77  -516.98    19.02     0.46)  ; 32\r\n              (  327.77  -516.98    19.00     0.46)  ; 33\r\n              (  326.23  -518.53    17.05     0.46)  ; 34\r\n              (  326.23  -518.53    17.00     0.46)  ; 35\r\n              (  326.58  -522.03    16.67     0.46)  ; 36\r\n              (  326.89  -527.33    16.67     0.46)  ; 37\r\n              (  324.96  -533.16    15.75     0.46)  ; 38\r\n              (  324.96  -533.16    15.60     0.46)  ; 39\r\n              (  323.63  -542.80    16.50     0.46)  ; 40\r\n              (  322.77  -553.14    14.15     0.46)  ; 41\r\n              (  322.77  -553.14    14.07     0.46)  ; 42\r\n              (  322.27  -571.17    13.75     0.46)  ; 43\r\n              (  322.27  -571.17    13.47     0.46)  ; 44\r\n              (  326.57  -603.60    12.75     0.46)  ; 45\r\n              (  326.73  -614.32    14.55     0.46)  ; 46\r\n              (  326.70  -626.27    14.55     0.46)  ; 47\r\n              (  326.27  -626.38    14.55     0.46)  ; 48\r\n              (  325.74  -640.23    14.55     0.46)  ; 49\r\n              (  323.99  -644.82    13.32     0.46)  ; 50\r\n              (  323.99  -644.82    13.30     0.46)  ; 51\r\n              (  323.90  -648.43    11.35     0.46)  ; 52\r\n              (  322.95  -650.44     9.80     0.46)  ; 53\r\n              (  322.18  -651.21     7.55     0.46)  ; 54\r\n              (  322.58  -652.91     5.42     0.46)  ; 55\r\n              (  321.20  -655.03     3.47     0.46)  ; 56\r\n              (  320.34  -659.41     1.95     0.46)  ; 57\r\n              (  320.34  -659.41     1.90     0.46)  ; 58\r\n              (  319.30  -665.02     0.05     0.46)  ; 59\r\n              (  319.91  -669.65    -2.85     0.46)  ; 60\r\n              (  319.91  -669.65    -2.92     0.46)  ; 61\r\n              (  319.86  -671.46    -4.72     0.46)  ; 62\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  327.47  -511.68    23.25     0.46)  ; 1\r\n                (  322.11  -648.85     9.80     0.46)  ; 2\r\n              )  ;  End of markers\r\n               Incomplete\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (  280.96  -100.13     5.05     0.46)  ; 1, R-2\r\n    (  282.62   -99.15     6.13     0.46)  ; 2\r\n    (  284.58   -97.49     6.62     0.46)  ; 3\r\n    (  287.32   -95.05     6.90     0.46)  ; 4\r\n    (\r\n      (  290.57   -94.90     7.80     0.46)  ; 1, R-2-1\r\n      (  292.09   -93.34     7.80     0.46)  ; 2\r\n      (  293.62   -91.79     8.38     0.46)  ; 3\r\n      (  293.67   -89.99     8.82     0.46)  ; 4\r\n      (  293.53   -89.42     8.82     0.46)  ; 5\r\n      (  295.37   -87.20     9.00     0.46)  ; 6\r\n      (  298.23   -85.34     8.55     0.46)  ; 7\r\n      (  300.91   -84.71    10.48     0.46)  ; 8\r\n      (  302.43   -83.15    10.63     0.46)  ; 9\r\n      (  304.08   -82.17     9.65     0.46)  ; 10\r\n      (  306.31   -81.65     9.65     0.46)  ; 11\r\n      (  308.11   -81.23     9.32     0.46)  ; 12\r\n      (  311.99   -79.72    10.10     0.46)  ; 13\r\n      (  313.32   -79.41    11.00     0.46)  ; 14\r\n      (  316.32   -78.11    11.93     0.46)  ; 15\r\n      (  317.85   -76.56    11.93     0.46)  ; 16\r\n      (  320.12   -74.23    12.30     0.46)  ; 17\r\n      (  323.75   -71.60    12.75     0.46)  ; 18\r\n\r\n      (Dot\r\n        (Color Yellow)\r\n        (Name \"Marker 1\")\r\n        (  299.75   -83.78    10.63     0.46)  ; 1\r\n      )  ;  End of markers\r\n\r\n      (OpenCircle\r\n        (Color Yellow)\r\n        (Name \"Marker 2\")\r\n        (  292.09   -93.34     7.80     0.46)  ; 1\r\n        (  295.19   -88.43     9.00     0.46)  ; 2\r\n        (  297.02   -86.21     8.55     0.46)  ; 3\r\n        (  308.11   -81.23     9.32     0.46)  ; 4\r\n      )  ;  End of markers\r\n      (\r\n        (  327.47   -71.33    11.95     0.46)  ; 1, R-2-1-1\r\n        (  329.98   -71.95    11.47     0.46)  ; 2\r\n        (  331.77   -71.53    11.13     0.46)  ; 3\r\n        (  332.83   -70.08    11.13     0.46)  ; 4\r\n        (\r\n          (  337.57   -70.17     9.95     0.46)  ; 1, R-2-1-1-1\r\n          (  341.22   -71.70     9.27     0.46)  ; 2\r\n          (  341.22   -71.70     9.25     0.46)  ; 3\r\n          (  344.62   -72.10     8.40     0.46)  ; 4\r\n          (  347.43   -72.05     8.60     0.46)  ; 5\r\n          (  347.43   -72.05     8.55     0.46)  ; 6\r\n          (  350.38   -72.54     9.48     0.46)  ; 7\r\n          (  353.32   -73.05    10.60     0.46)  ; 8\r\n          (  356.58   -72.88    11.70     0.46)  ; 9\r\n          (  358.50   -73.02    10.15     0.46)  ; 10\r\n          (  360.23   -74.41     8.35     0.46)  ; 11\r\n          (  363.31   -75.48     7.38     0.46)  ; 12\r\n          (  367.91   -74.99     6.65     0.46)  ; 13\r\n          (  372.20   -75.19     6.65     0.46)  ; 14\r\n          (  375.72   -76.16     6.38     0.46)  ; 15\r\n          (  378.66   -76.65     5.60     0.46)  ; 16\r\n          (  380.62   -77.05     5.04     0.46)  ; 17\r\n          (  382.50   -76.96     4.60     0.46)  ; 18\r\n          (  382.50   -76.96     4.57     0.46)  ; 19\r\n          (  385.14   -78.13     3.80     0.46)  ; 20\r\n          (  389.73   -77.65     3.45     0.46)  ; 21\r\n          (  391.03   -79.15     3.45     0.46)  ; 22\r\n          (  395.57   -80.46     3.15     0.46)  ; 23\r\n          (  397.05   -80.72     6.13     0.46)  ; 24\r\n          (  399.99   -81.22     6.95     0.46)  ; 25\r\n          (  405.43   -82.34     6.73     0.46)  ; 26\r\n          (  407.93   -82.94     5.70     0.46)  ; 27\r\n          (  407.93   -82.94     5.68     0.46)  ; 28\r\n          (  410.82   -85.26     4.47     0.46)  ; 29\r\n          (  411.67   -86.85     2.70     0.46)  ; 30\r\n          (  411.67   -86.85     2.67     0.46)  ; 31\r\n          (  413.22   -89.47     1.38     0.46)  ; 32\r\n          (  416.57   -91.68     0.37     0.46)  ; 33\r\n          (  419.96   -92.07    -0.12     0.46)  ; 34\r\n          (  424.33   -94.63    -0.12     0.46)  ; 35\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  336.81   -70.95     9.95     0.46)  ; 1\r\n            (  354.48   -73.96    11.70     0.46)  ; 2\r\n            (  373.85   -74.21     6.38     0.46)  ; 3\r\n            (  394.59   -78.30     3.15     0.46)  ; 4\r\n            (  409.84   -83.09     4.47     0.46)  ; 5\r\n            (  417.20   -90.33     0.37     0.46)  ; 6\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  347.43   -72.05     8.55     0.46)  ; 1\r\n            (  360.10   -73.84     8.35     0.46)  ; 2\r\n            (  363.31   -75.48     7.38     0.46)  ; 3\r\n            (  385.57   -78.02     3.80     0.46)  ; 4\r\n          )  ;  End of markers\r\n          (\r\n            (  427.09   -96.37    -0.28     0.46)  ; 1, R-2-1-1-1-1\r\n            (  427.09   -96.37    -0.32     0.46)  ; 2\r\n            (  429.99   -98.67    -1.17     0.46)  ; 3\r\n            (  431.77   -98.26    -1.17     0.46)  ; 4\r\n            (  434.15   -98.29    -2.10     0.46)  ; 5\r\n            (  435.56  -100.36    -2.65     0.46)  ; 6\r\n            (  438.51  -100.86    -1.80     0.46)  ; 7\r\n            (  441.90  -101.26    -1.80     0.46)  ; 8\r\n            (  446.76  -101.91    -2.88     0.46)  ; 9\r\n            (  448.82  -102.63    -3.67     0.46)  ; 10\r\n            (  451.05  -102.10    -4.47     0.46)  ; 11\r\n            (  453.29  -101.58    -5.30     0.46)  ; 12\r\n            (  456.18  -103.89    -6.50     0.46)  ; 13\r\n            (  459.52  -106.08    -7.05     0.46)  ; 14\r\n            (  462.78  -105.92    -6.93     0.46)  ; 15\r\n            (  464.84  -106.64    -6.10     0.46)  ; 16\r\n            (  466.75  -106.78    -5.55     0.46)  ; 17\r\n            (  470.01  -106.62    -5.37     0.46)  ; 18\r\n            (  473.08  -107.68    -5.63     0.46)  ; 19\r\n            (  476.29  -109.32    -6.52     0.46)  ; 20\r\n            (  476.29  -109.32    -6.55     0.46)  ; 21\r\n            (  477.90  -110.14    -6.78     0.46)  ; 22\r\n            (  479.45  -112.76    -6.88     0.46)  ; 23\r\n            (  481.82  -112.80    -6.75     0.46)  ; 24\r\n            (  483.24  -114.86    -6.75     0.46)  ; 25\r\n            (  486.06  -114.80    -7.77     0.46)  ; 26\r\n            (  488.24  -116.07    -7.77     0.46)  ; 27\r\n            (  490.74  -116.68    -8.25     0.46)  ; 28\r\n            (  494.57  -116.98    -8.95     0.46)  ; 29\r\n            (  497.21  -118.16    -9.60     0.46)  ; 30\r\n            (  498.81  -118.97   -10.88     0.46)  ; 31\r\n            (  498.81  -118.97   -10.92     0.46)  ; 32\r\n            (  500.55  -120.36   -11.53     0.46)  ; 33\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  446.14  -103.25    -2.88     0.46)  ; 1\r\n              (  467.01  -107.91    -5.55     0.46)  ; 2\r\n              (  473.93  -109.28    -6.55     0.46)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  502.25  -117.57   -12.38     0.46)  ; 1, R-2-1-1-1-1-1\r\n              (  505.69  -116.17   -13.20     0.46)  ; 2\r\n              (  506.27  -116.62   -14.43     0.46)  ; 3\r\n              (  507.80  -115.07   -14.43     0.46)  ; 4\r\n              (  510.02  -114.56   -15.02     0.46)  ; 5\r\n              (  511.68  -113.57   -16.77     0.46)  ; 6\r\n              (  513.02  -113.25   -17.75     0.46)  ; 7\r\n              (  514.36  -112.94   -19.52     0.46)  ; 8\r\n              (  516.09  -114.33   -21.63     0.46)  ; 9\r\n              (  518.33  -113.80   -23.52     0.46)  ; 10\r\n              (  520.24  -113.95   -25.20     0.46)  ; 11\r\n              (  522.80  -112.75   -26.40     0.46)  ; 12\r\n              (  525.92  -112.02   -28.15     0.46)  ; 13\r\n              (  525.92  -112.02   -28.17     0.46)  ; 14\r\n              (  531.10  -112.01   -29.25     0.46)  ; 15\r\n              (  534.54  -110.59   -30.10     0.46)  ; 16\r\n              (  537.98  -109.19   -30.85     0.46)  ; 17\r\n              (  541.68  -108.92   -31.38     0.46)  ; 18\r\n              (  544.78  -110.01   -32.13     0.46)  ; 19\r\n              (  547.45  -109.38   -32.92     0.46)  ; 20\r\n              (  550.09  -110.55   -33.42     0.46)  ; 21\r\n              (  552.14  -111.27   -34.80     0.46)  ; 22\r\n              (  554.06  -111.41   -35.02     0.46)  ; 23\r\n              (  555.53  -111.66   -37.77     0.46)  ; 24\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  518.60  -114.93   -23.52     0.46)  ; 1\r\n                (  530.70  -110.31   -29.25     0.46)  ; 2\r\n                (  550.92  -112.15   -34.80     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  541.68  -108.92   -31.38     0.46)  ; 1\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  502.47  -120.51   -11.75     0.46)  ; 1, R-2-1-1-1-1-2\r\n              (  504.35  -122.47   -11.75     0.46)  ; 2\r\n              (  506.58  -121.94   -11.32     0.46)  ; 3\r\n              (  509.66  -123.00   -11.37     0.46)  ; 4\r\n              (  512.47  -122.95   -11.77     0.46)  ; 5\r\n              (  514.21  -124.34   -11.77     0.46)  ; 6\r\n              (  516.32  -123.25   -11.77     0.46)  ; 7\r\n              (  519.70  -123.65   -12.72     0.46)  ; 8\r\n              (  521.43  -125.04   -13.57     0.46)  ; 9\r\n              (  524.96  -125.99   -13.87     0.46)  ; 10\r\n              (  528.93  -126.85   -14.23     0.46)  ; 11\r\n              (  531.44  -127.46   -15.50     0.46)  ; 12\r\n              (  533.35  -127.61   -16.88     0.46)  ; 13\r\n              (  535.41  -128.33   -17.65     0.46)  ; 14\r\n              (  537.63  -127.80   -18.90     0.46)  ; 15\r\n              (  540.90  -127.63   -19.58     0.46)  ; 16\r\n              (  544.87  -128.50   -18.20     0.46)  ; 17\r\n              (  544.87  -128.50   -18.23     0.46)  ; 18\r\n              (  547.37  -129.10   -16.73     0.46)  ; 19\r\n              (  549.82  -131.52   -16.27     0.46)  ; 20\r\n              (  552.58  -133.26   -17.30     0.46)  ; 21\r\n              (  555.08  -133.86   -17.98     0.46)  ; 22\r\n              (  557.97  -136.18   -18.77     0.46)  ; 23\r\n              (  560.87  -138.48   -18.35     0.46)  ; 24\r\n              (  565.23  -141.05   -18.55     0.46)  ; 25\r\n              (  569.08  -141.33   -18.55     0.46)  ; 26\r\n              (  572.29  -142.97   -19.13     0.46)  ; 27\r\n              (  572.29  -142.97   -19.15     0.46)  ; 28\r\n              (  576.52  -144.96   -19.92     0.46)  ; 29\r\n              (  579.34  -144.90   -20.57     0.46)  ; 30\r\n              (  583.76  -145.66   -20.17     0.46)  ; 31\r\n              (  583.76  -145.66   -20.23     0.46)  ; 32\r\n              (  586.57  -145.60   -18.48     0.46)  ; 33\r\n              (  589.33  -147.34   -19.50     0.46)  ; 34\r\n              (  589.33  -147.34   -19.52     0.46)  ; 35\r\n              (  591.25  -147.48   -21.52     0.46)  ; 36\r\n              (  591.25  -147.48   -21.55     0.46)  ; 37\r\n              (  592.98  -148.87   -21.77     0.46)  ; 38\r\n              (  596.98  -149.71   -22.15     0.46)  ; 39\r\n              (  600.94  -150.57   -22.15     0.46)  ; 40\r\n              (  603.25  -152.42   -21.20     0.46)  ; 41\r\n              (  603.25  -152.42   -20.70     0.46)  ; 42\r\n              (  606.32  -153.48   -20.57     0.46)  ; 43\r\n              (  610.57  -155.48   -19.95     0.46)  ; 44\r\n              (  613.96  -155.88   -21.28     0.46)  ; 45\r\n              (  617.05  -156.94   -22.27     0.46)  ; 46\r\n              (  616.91  -156.37   -22.30     0.46)  ; 47\r\n              (  619.36  -158.79   -22.85     0.46)  ; 48\r\n              (  623.45  -160.21   -23.60     0.46)  ; 49\r\n              (  626.54  -161.29   -23.82     0.46)  ; 50\r\n              (  629.17  -162.46   -23.70     0.46)  ; 51\r\n              (  631.08  -162.61   -23.75     0.46)  ; 52\r\n              (  634.43  -164.81   -23.65     0.46)  ; 53\r\n              (  637.07  -165.99   -24.90     0.46)  ; 54\r\n              (  640.14  -167.06   -26.38     0.46)  ; 55\r\n              (  643.66  -168.02   -27.08     0.46)  ; 56\r\n              (  646.17  -168.64   -26.38     0.46)  ; 57\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  509.04  -124.36   -11.37     0.46)  ; 1\r\n                (  519.97  -124.77   -12.72     0.46)  ; 2\r\n                (  565.50  -142.17   -18.55     0.46)  ; 3\r\n                (  569.34  -142.47   -18.55     0.46)  ; 4\r\n                (  591.65  -149.18   -21.77     0.46)  ; 5\r\n                (  622.84  -161.56   -23.60     0.46)  ; 6\r\n                (  626.23  -161.96   -23.82     0.46)  ; 7\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  531.44  -127.46   -15.50     0.46)  ; 1\r\n                (  549.37  -131.63   -16.27     0.46)  ; 2\r\n                (  552.44  -132.69   -17.30     0.46)  ; 3\r\n                (  557.84  -135.61   -18.77     0.46)  ; 4\r\n                (  611.15  -155.94   -19.95     0.46)  ; 5\r\n                (  630.77  -163.29   -23.75     0.46)  ; 6\r\n              )  ;  End of markers\r\n              (\r\n                (  646.79  -167.28   -25.17     0.46)  ; 1, R-2-1-1-1-1-2-1\r\n                (  648.40  -168.11   -22.65     0.46)  ; 2\r\n                (  648.08  -168.79   -20.50     0.46)  ; 3\r\n                (  649.69  -169.60   -17.20     0.46)  ; 4\r\n                (  648.35  -169.91   -14.88     0.46)  ; 5\r\n                (  648.79  -169.81   -11.95     0.46)  ; 6\r\n                (  648.21  -169.35   -11.95     0.46)  ; 7\r\n                (  650.40  -170.62    -9.93     0.46)  ; 8\r\n                (  650.80  -172.32    -7.97     0.46)  ; 9\r\n                (  651.95  -173.25    -6.42     0.46)  ; 10\r\n                (  652.93  -175.40    -5.15     0.46)  ; 11\r\n                (  654.68  -176.79    -3.75     0.46)  ; 12\r\n                (  655.52  -178.38    -2.53     0.46)  ; 13\r\n                (  658.41  -180.69    -1.38     0.46)  ; 14\r\n                (  662.07  -182.22    -0.77     0.46)  ; 15\r\n                (  668.23  -184.37    -1.05     0.46)  ; 16\r\n                (  673.03  -186.81    -1.05     0.46)  ; 17\r\n                (  675.41  -186.86    -0.15     0.46)  ; 18\r\n                (  676.97  -189.48     1.27     0.46)  ; 19\r\n                (  678.88  -189.63     2.33     0.46)  ; 20\r\n                (  680.36  -189.88     3.85     0.46)  ; 21\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  649.11  -169.13   -20.50     0.46)  ; 1\r\n                  (  650.17  -173.67    -6.42     0.46)  ; 2\r\n                  (  653.33  -177.10    -3.75     0.46)  ; 3\r\n                  (  680.17  -191.12     3.85     0.46)  ; 4\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  649.24  -169.70   -27.95     0.46)  ; 1, R-2-1-1-1-1-2-2\r\n                (  652.31  -170.78   -29.17     0.46)  ; 2\r\n                (  654.51  -172.05   -29.92     0.46)  ; 3\r\n                (  654.51  -172.05   -30.92     0.46)  ; 4\r\n                (  655.98  -172.30   -31.35     0.46)  ; 5\r\n                (  655.98  -172.30   -31.38     0.46)  ; 6\r\n                (  659.50  -173.28   -32.00     0.46)  ; 7\r\n                (  659.50  -173.28   -32.08     0.46)  ; 8\r\n                (  661.68  -174.55   -33.10     0.46)  ; 9\r\n                (  664.18  -175.15   -32.30     0.46)  ; 10\r\n                (  668.11  -176.60   -33.35     0.46)  ; 11\r\n                (  670.93  -176.54   -33.35     0.46)  ; 12\r\n                (  674.00  -177.62   -33.77     0.46)  ; 13\r\n                (  676.64  -178.79   -34.65     0.46)  ; 14\r\n                (  679.72  -179.86   -35.17     0.46)  ; 15\r\n                (  682.08  -179.91   -35.57     0.46)  ; 16\r\n                (  685.73  -181.43   -36.88     0.46)  ; 17\r\n                (  685.28  -181.54   -36.90     0.46)  ; 18\r\n                (  688.81  -182.52   -37.55     0.46)  ; 19\r\n                (  688.81  -182.52   -37.58     0.46)  ; 20\r\n                (  691.05  -181.99   -38.47     0.46)  ; 21\r\n                (  692.65  -182.80   -38.47     0.46)  ; 22\r\n                (  695.86  -184.44   -39.67     0.46)  ; 23\r\n                (  698.05  -185.71   -40.55     0.46)  ; 24\r\n                (  700.85  -185.65   -41.47     0.46)  ; 25\r\n                (  704.83  -186.52   -43.00     0.46)  ; 26\r\n                (  704.83  -186.52   -43.02     0.46)  ; 27\r\n                (  709.06  -188.51   -44.02     0.46)  ; 28\r\n                (  713.48  -189.27   -44.87     0.46)  ; 29\r\n                (  713.48  -189.27   -44.90     0.46)  ; 30\r\n                (  715.09  -190.08   -45.60     0.46)  ; 31\r\n                (  719.91  -192.54   -45.92     0.46)  ; 32\r\n                (  722.08  -193.81   -45.92     0.46)  ; 33\r\n                (  723.51  -195.88   -45.92     0.46)  ; 34\r\n                (  725.88  -195.92   -44.80     0.46)  ; 35\r\n                (  730.42  -197.23   -44.00     0.46)  ; 36\r\n                (  730.42  -197.23   -44.05     0.46)  ; 37\r\n                (  734.72  -197.43   -43.47     0.46)  ; 38\r\n                (  734.72  -197.43   -43.50     0.46)  ; 39\r\n                (  740.91  -197.76   -41.97     0.46)  ; 40\r\n                (  740.91  -197.76   -42.00     0.46)  ; 41\r\n                (  744.49  -196.93   -41.82     0.46)  ; 42\r\n                (  744.49  -196.93   -41.85     0.46)  ; 43\r\n                (  747.74  -196.76   -43.30     0.46)  ; 44\r\n                (  747.74  -196.76   -43.33     0.46)  ; 45\r\n                (  749.80  -197.47   -44.58     0.46)  ; 46\r\n                (  752.12  -199.32   -44.78     0.46)  ; 47\r\n                (  758.33  -199.66   -45.10     0.46)  ; 48\r\n                (  763.94  -199.54   -45.10     0.46)  ; 49\r\n                (  768.37  -200.28   -45.10     0.46)  ; 50\r\n                (  772.33  -201.16   -45.88     0.46)  ; 51\r\n                (  774.56  -200.63   -43.67     0.46)  ; 52\r\n                (  777.34  -202.36   -43.05     0.46)  ; 53\r\n                (  780.99  -203.90   -41.88     0.46)  ; 54\r\n                (  785.60  -203.42   -41.88     0.46)  ; 55\r\n                (  790.45  -204.07   -41.88     0.46)  ; 56\r\n                (  795.14  -205.96   -43.07     0.46)  ; 57\r\n                (  798.92  -208.06   -43.22     0.46)  ; 58\r\n                (  803.15  -210.05   -43.22     0.46)  ; 59\r\n                (  808.03  -210.70   -43.75     0.46)  ; 60\r\n                (  808.03  -210.70   -43.78     0.46)  ; 61\r\n                (  810.16  -213.79   -44.30     0.46)  ; 62\r\n                (  815.60  -214.90   -44.30     0.46)  ; 63\r\n                (  821.05  -216.01   -44.52     0.46)  ; 64\r\n                (  826.04  -217.22   -43.22     0.46)  ; 65\r\n                (  831.49  -218.34   -42.17     0.46)  ; 66\r\n                (  831.49  -218.34   -42.20     0.46)  ; 67\r\n                (  833.90  -216.59   -41.32     0.46)  ; 68\r\n                (  837.02  -215.85   -40.45     0.46)  ; 69\r\n                (  839.85  -215.79   -39.63     0.46)  ; 70\r\n                (  844.70  -216.43   -39.63     0.46)  ; 71\r\n                (  848.49  -218.53   -39.15     0.46)  ; 72\r\n                (  852.64  -218.16   -41.57     0.46)  ; 73\r\n                (  858.94  -220.86   -41.42     0.46)  ; 74\r\n                (  863.49  -222.19   -41.42     0.46)  ; 75\r\n                (  866.88  -222.58   -40.55     0.46)  ; 76\r\n                (  871.34  -221.54   -39.30     0.46)  ; 77\r\n                (  874.15  -221.50   -37.00     0.46)  ; 78\r\n                (  878.62  -220.45   -35.97     0.46)  ; 79\r\n                (  878.17  -220.55   -35.97     0.46)  ; 80\r\n                (  881.12  -221.06   -34.95     0.46)  ; 81\r\n                (  883.93  -220.99   -33.85     0.46)  ; 82\r\n                (  888.08  -220.62   -34.47     0.46)  ; 83\r\n                (  891.02  -221.13   -34.47     0.46)  ; 84\r\n                (  895.18  -220.75   -35.92     0.46)  ; 85\r\n                (  899.91  -220.83   -35.92     0.46)  ; 86\r\n                (  904.34  -221.59   -37.02     0.46)  ; 87\r\n                (  904.34  -221.59   -37.05     0.46)  ; 88\r\n                (  907.14  -221.53   -37.05     0.46)  ; 89\r\n                (  909.63  -222.14   -37.05     0.46)  ; 90\r\n                (  911.74  -221.04   -37.05     0.46)  ; 91\r\n                (  916.60  -221.70   -37.05     0.46)  ; 92\r\n                (  918.39  -221.28   -35.97     0.46)  ; 93\r\n                (  918.39  -221.28   -36.10     0.46)  ; 94\r\n                (  921.92  -222.25   -36.17     0.46)  ; 95\r\n                (  929.01  -222.37   -36.17     0.46)  ; 96\r\n                (  931.24  -221.85   -36.17     0.46)  ; 97\r\n                (  936.42  -221.83   -36.17     0.46)  ; 98\r\n                (  941.47  -221.25   -37.28     0.46)  ; 99\r\n                (  945.50  -220.31   -38.25     0.46)  ; 100\r\n                (  945.50  -220.31   -38.27     0.46)  ; 101\r\n                (  949.77  -220.50   -39.13     0.46)  ; 102\r\n                (  952.09  -222.34   -40.32     0.46)  ; 103\r\n                (  952.09  -222.34   -40.35     0.46)  ; 104\r\n                (  952.93  -223.94   -40.97     0.46)  ; 105\r\n                (  952.93  -223.94   -41.00     0.46)  ; 106\r\n                (  955.35  -222.18   -41.52     0.46)  ; 107\r\n                (  955.35  -222.18   -41.55     0.46)  ; 108\r\n                (  954.69  -219.35   -40.90     0.46)  ; 109\r\n                (  953.89  -215.95   -39.65     0.46)  ; 110\r\n                (  953.36  -213.68   -39.65     0.46)  ; 111\r\n                (  952.65  -212.66   -38.38     0.46)  ; 112\r\n                (  950.86  -213.07   -36.25     0.46)  ; 113\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  678.96  -180.64   -35.17     0.46)  ; 1\r\n                  (  883.31  -222.34   -33.85     0.46)  ; 2\r\n                  (  890.40  -222.47   -34.47     0.46)  ; 3\r\n                  (  899.73  -222.07   -35.92     0.46)  ; 4\r\n                  (  955.30  -223.98   -41.57     0.46)  ; 5\r\n                  (  954.26  -213.47   -41.70     0.46)  ; 6\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  664.18  -175.15   -32.30     0.46)  ; 1\r\n                  (  692.65  -182.80   -38.47     0.46)  ; 2\r\n                  (  715.09  -190.08   -45.60     0.46)  ; 3\r\n                  (  772.91  -201.62   -45.88     0.46)  ; 4\r\n                  (  821.50  -215.90   -44.52     0.46)  ; 5\r\n                  (  839.85  -215.79   -39.63     0.46)  ; 6\r\n                  (  858.62  -221.53   -41.42     0.46)  ; 7\r\n                  (  863.49  -222.19   -41.42     0.46)  ; 8\r\n                  (  870.89  -221.65   -39.30     0.46)  ; 9\r\n                  (  951.18  -212.40   -36.25     0.46)  ; 10\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  427.40   -91.87    -0.88     0.46)  ; 1, R-2-1-1-1-2\r\n            (  428.66   -89.19    -1.40     0.46)  ; 2\r\n            (  429.29   -87.85    -1.65     0.46)  ; 3\r\n            (  431.69   -86.08    -1.65     0.46)  ; 4\r\n            (  434.42   -83.66    -2.45     0.46)  ; 5\r\n            (  434.47   -81.85    -3.88     0.46)  ; 6\r\n            (  437.96   -78.65    -4.57     0.46)  ; 7\r\n            (  437.96   -78.65    -4.60     0.46)  ; 8\r\n            (  438.72   -77.87    -5.27     0.46)  ; 9\r\n            (  441.45   -75.44    -6.07     0.46)  ; 10\r\n            (  444.18   -73.01    -6.80     0.46)  ; 11\r\n            (  447.04   -71.15    -7.35     0.46)  ; 12\r\n            (  449.64   -68.15    -7.35     0.46)  ; 13\r\n            (  451.91   -65.83    -8.18     0.46)  ; 14\r\n            (  452.91   -62.01    -8.18     0.46)  ; 15\r\n            (  453.84   -60.00    -8.52     0.46)  ; 16\r\n            (  455.56   -57.21    -8.52     0.46)  ; 17\r\n            (  455.11   -57.31    -8.52     0.46)  ; 18\r\n            (  456.76   -56.33    -8.52     0.46)  ; 19\r\n            (  456.67   -53.96    -8.52     0.46)  ; 20\r\n            (  458.06   -51.84    -8.88     0.46)  ; 21\r\n            (  460.66   -48.84    -9.17     0.46)  ; 22\r\n            (  460.66   -48.84    -9.20     0.46)  ; 23\r\n            (  460.27   -47.15    -9.80     0.46)  ; 24\r\n            (  462.99   -44.71   -10.57     0.46)  ; 25\r\n            (  463.84   -40.33   -11.42     0.46)  ; 26\r\n            (  465.56   -37.55   -12.52     0.46)  ; 27\r\n            (  465.56   -37.55   -12.55     0.46)  ; 28\r\n            (  466.80   -34.86   -13.67     0.46)  ; 29\r\n            (  466.80   -34.86   -13.70     0.46)  ; 30\r\n            (  468.06   -32.19   -14.30     0.46)  ; 31\r\n            (  470.02   -30.53   -14.57     0.46)  ; 32\r\n            (  471.29   -27.84   -14.57     0.46)  ; 33\r\n            (  472.67   -25.73   -15.17     0.46)  ; 34\r\n            (  473.93   -23.04   -15.05     0.46)  ; 35\r\n            (  475.63   -20.26   -15.68     0.46)  ; 36\r\n            (  477.15   -18.71   -15.68     0.46)  ; 37\r\n            (  479.17   -15.25   -16.52     0.46)  ; 38\r\n            (  481.81   -10.44   -17.05     0.46)  ; 39\r\n            (  482.62    -7.87   -17.80     0.46)  ; 40\r\n            (  485.21    -4.87   -17.13     0.46)  ; 41\r\n            (  486.52    -0.38   -16.27     0.46)  ; 42\r\n            (  488.23     2.40   -15.90     0.46)  ; 43\r\n            (  490.15     8.24   -16.25     0.46)  ; 44\r\n            (  491.28    11.48   -16.27     0.46)  ; 45\r\n            (  491.77    13.39   -17.15     0.46)  ; 46\r\n            (  493.35    16.74   -17.80     0.46)  ; 47\r\n            (  495.49    19.63   -18.63     0.46)  ; 48\r\n            (  497.95    23.20   -19.33     0.46)  ; 49\r\n            (  500.47    28.56   -20.20     0.46)  ; 50\r\n            (  500.47    28.56   -20.25     0.46)  ; 51\r\n            (  504.59    33.11   -20.85     0.46)  ; 52\r\n            (  504.59    33.11   -20.87     0.46)  ; 53\r\n            (  508.56    38.22   -21.32     0.46)  ; 54\r\n            (  509.42    42.61   -21.32     0.46)  ; 55\r\n            (  512.39    48.07   -21.42     0.46)  ; 56\r\n            (  513.40    51.91   -19.58     0.46)  ; 57\r\n            (  515.86    55.47   -20.13     0.46)  ; 58\r\n            (  518.64    59.71   -20.42     0.46)  ; 59\r\n            (  520.52    63.73   -19.58     0.46)  ; 60\r\n            (  520.88    66.21   -19.58     0.46)  ; 61\r\n            (  523.80    69.88   -19.58     0.46)  ; 62\r\n            (  524.65    74.25   -19.58     0.46)  ; 63\r\n            (  528.32    78.70   -19.75     0.46)  ; 64\r\n            (  529.89    82.06   -20.57     0.46)  ; 65\r\n            (  530.75    86.44   -21.48     0.46)  ; 66\r\n            (  530.75    86.44   -21.50     0.46)  ; 67\r\n            (  533.22    89.99   -22.08     0.46)  ; 68\r\n            (  533.22    89.99   -22.10     0.46)  ; 69\r\n            (  534.07    94.37   -22.32     0.46)  ; 70\r\n            (  535.19    97.63   -22.12     0.46)  ; 71\r\n            (  535.19    97.63   -22.20     0.46)  ; 72\r\n            (  538.06    99.49   -23.72     0.46)  ; 73\r\n            (  538.06    99.49   -23.78     0.46)  ; 74\r\n            (  537.71   102.99   -24.62     0.46)  ; 75\r\n            (  537.71   102.99   -24.65     0.46)  ; 76\r\n            (  537.81   106.60   -25.57     0.46)  ; 77\r\n            (  539.37   109.95   -26.17     0.46)  ; 78\r\n            (  539.20   114.70   -26.82     0.46)  ; 79\r\n            (  539.20   114.70   -26.85     0.46)  ; 80\r\n            (  538.41   118.10   -27.47     0.46)  ; 81\r\n            (  538.91   120.01   -24.77     0.46)  ; 82\r\n            (  538.83   122.37   -25.95     0.46)  ; 83\r\n            (  539.23   126.65   -27.12     0.46)  ; 84\r\n            (  539.08   131.39   -28.10     0.46)  ; 85\r\n            (  539.76   134.53   -28.52     0.46)  ; 86\r\n            (  539.54   137.47   -29.85     0.46)  ; 87\r\n            (  539.54   137.47   -29.88     0.46)  ; 88\r\n            (  539.31   140.39   -30.42     0.46)  ; 89\r\n            (  539.31   140.39   -30.45     0.46)  ; 90\r\n            (  539.59   145.25   -30.63     0.46)  ; 91\r\n            (  538.71   151.01   -30.63     0.46)  ; 92\r\n            (  536.81   157.12   -30.13     0.46)  ; 93\r\n            (  535.61   162.22   -29.30     0.46)  ; 94\r\n            (  535.26   165.73   -28.88     0.46)  ; 95\r\n            (  535.99   170.67   -28.02     0.46)  ; 96\r\n            (  536.99   174.48   -28.02     0.46)  ; 97\r\n            (  536.82   179.23   -27.42     0.46)  ; 98\r\n            (  536.77   183.40   -26.85     0.46)  ; 99\r\n            (  536.56   186.33   -26.98     0.46)  ; 100\r\n            (  536.56   186.33   -27.00     0.46)  ; 101\r\n            (  533.93   187.51   -27.00     0.46)  ; 102\r\n            (  530.98   188.01   -27.80     0.46)  ; 103\r\n            (  526.44   189.34   -28.27     0.46)  ; 104\r\n            (  524.83   190.16   -29.32     0.46)  ; 105\r\n            (  524.30   192.41   -30.25     0.46)  ; 106\r\n            (  523.32   194.58   -31.48     0.46)  ; 107\r\n            (  522.34   196.73   -32.67     0.46)  ; 108\r\n            (  522.34   196.73   -32.70     0.46)  ; 109\r\n            (  521.64   197.77   -34.35     0.46)  ; 110\r\n            (  521.64   197.77   -34.40     0.46)  ; 111\r\n            (  520.48   198.68   -36.25     0.46)  ; 112\r\n            (  520.48   198.68   -36.27     0.46)  ; 113\r\n            (  519.77   199.71   -38.10     0.46)  ; 114\r\n            (  519.24   201.97   -40.13     0.46)  ; 115\r\n            (  519.24   201.97   -40.17     0.46)  ; 116\r\n            (  518.84   203.67   -42.30     0.46)  ; 117\r\n            (  518.84   203.67   -42.42     0.46)  ; 118\r\n            (  518.56   204.81   -44.97     0.46)  ; 119\r\n            (  519.25   207.95   -46.50     0.46)  ; 120\r\n            (  519.03   210.89   -47.67     0.46)  ; 121\r\n            (  519.21   212.12   -49.02     0.46)  ; 122\r\n            (  520.47   214.81   -49.45     0.46)  ; 123\r\n            (  521.27   217.38   -51.77     0.46)  ; 124\r\n            (  520.17   220.11   -53.15     0.46)  ; 125\r\n            (  520.17   220.11   -53.18     0.46)  ; 126\r\n            (  519.68   224.17   -54.02     0.46)  ; 127\r\n            (  519.24   224.07   -54.02     0.46)  ; 128\r\n            (  519.59   226.55   -54.78     0.46)  ; 129\r\n            (  514.94   228.34   -54.32     0.46)  ; 130\r\n            (  514.94   228.34   -54.35     0.46)  ; 131\r\n            (  511.20   232.24   -54.35     0.46)  ; 132\r\n            (  508.80   236.46   -55.07     0.46)  ; 133\r\n            (  507.03   242.00   -55.83     0.46)  ; 134\r\n            (  505.70   247.66   -56.92     0.46)  ; 135\r\n            (  505.70   247.66   -56.95     0.46)  ; 136\r\n            (  503.82   249.62   -58.05     0.46)  ; 137\r\n            (  502.98   251.21   -59.22     0.46)  ; 138\r\n            (  499.51   253.98   -59.95     0.46)  ; 139\r\n            (  497.68   257.73   -60.95     0.46)  ; 140\r\n            (  497.68   257.73   -60.97     0.46)  ; 141\r\n            (  495.82   259.69   -62.28     0.46)  ; 142\r\n            (  493.32   260.29   -63.42     0.46)  ; 143\r\n            (  491.31   262.81   -64.80     0.46)  ; 144\r\n            (  491.31   262.81   -64.82     0.46)  ; 145\r\n            (  489.12   264.09   -66.70     0.46)  ; 146\r\n            (  487.44   267.28   -68.10     0.46)  ; 147\r\n            (  487.48   269.07   -69.88     0.46)  ; 148\r\n            (  487.53   270.88   -71.92     0.46)  ; 149\r\n            (  486.25   272.37   -73.22     0.46)  ; 150\r\n            (  485.89   275.87   -75.18     0.46)  ; 151\r\n            (  485.89   275.87   -75.20     0.46)  ; 152\r\n            (  486.57   279.02   -76.90     0.46)  ; 153\r\n            (  486.57   279.02   -76.92     0.46)  ; 154\r\n            (  486.80   282.06   -78.20     0.46)  ; 155\r\n            (  486.80   282.06   -78.22     0.46)  ; 156\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  427.22   -93.11    -0.88     0.46)  ; 1\r\n              (  437.83   -78.08    -5.27     0.46)  ; 2\r\n              (  459.18   -48.59    -9.20     0.46)  ; 3\r\n              (  483.78    -8.79   -17.80     0.46)  ; 4\r\n              (  487.60     1.06   -14.85     0.46)  ; 5\r\n              (  491.18     7.87   -16.25     0.46)  ; 6\r\n              (  511.23    49.00   -21.42     0.46)  ; 7\r\n              (  519.35    58.68   -20.42     0.46)  ; 8\r\n              (  529.04    77.67   -19.75     0.46)  ; 9\r\n              (  537.51   117.88   -27.47     0.46)  ; 10\r\n              (  540.55   131.13   -28.10     0.46)  ; 11\r\n              (  506.60   247.88   -56.97     0.46)  ; 12\r\n              (  499.02   258.05   -60.97     0.46)  ; 13\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  426.82   -85.44    -1.65     0.46)  ; 1\r\n              (  431.25   -86.19    -1.65     0.46)  ; 2\r\n              (  443.60   -72.55    -6.80     0.46)  ; 3\r\n              (  449.32   -68.82    -7.35     0.46)  ; 4\r\n              (  455.69   -57.78    -8.52     0.46)  ; 5\r\n              (  463.53   -41.01   -11.42     0.46)  ; 6\r\n              (  468.64   -32.64   -14.30     0.46)  ; 7\r\n              (  474.37   -22.94   -15.05     0.46)  ; 8\r\n              (  484.77    -4.98   -17.13     0.46)  ; 9\r\n              (  538.25   106.70   -25.60     0.46)  ; 10\r\n              (  539.76   140.50   -30.45     0.46)  ; 11\r\n              (  536.81   157.12   -30.13     0.46)  ; 12\r\n              (  535.99   170.67   -28.02     0.46)  ; 13\r\n              (  536.69   185.76   -27.00     0.46)  ; 14\r\n              (  525.99   189.23   -28.27     0.46)  ; 15\r\n              (  518.44   205.37   -44.97     0.46)  ; 16\r\n              (  520.02   214.70   -49.45     0.46)  ; 17\r\n              (  519.59   226.55   -54.78     0.46)  ; 18\r\n              (  502.98   251.21   -59.25     0.46)  ; 19\r\n              (  489.44   264.76   -66.72     0.46)  ; 20\r\n              (  487.48   269.07   -71.92     0.46)  ; 21\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (  334.92   -67.41    12.88     0.46)  ; 1, R-2-1-1-2\r\n          (  334.92   -67.41    12.85     0.46)  ; 2\r\n          (  336.39   -67.66    15.02     0.46)  ; 3\r\n          (  337.86   -67.91    16.92     0.46)  ; 4\r\n          (  338.81   -65.90    17.38     0.46)  ; 5\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  334.73   -68.64    12.85     0.46)  ; 1\r\n          )  ;  End of markers\r\n          (\r\n            (  340.85   -66.61    17.38     0.46)  ; 1, R-2-1-1-2-1\r\n            (  343.80   -67.11    18.72     0.46)  ; 2\r\n            (  345.58   -66.69    20.00     0.46)  ; 3\r\n            (  347.25   -65.71    21.50     0.46)  ; 4\r\n            (  348.98   -67.10    22.27     0.46)  ; 5\r\n            (  350.45   -67.35    22.92     0.46)  ; 6\r\n            (  352.50   -68.06    23.17     0.46)  ; 7\r\n            (  354.16   -67.08    23.17     0.46)  ; 8\r\n            (  356.21   -67.78    23.90     0.46)  ; 9\r\n            (  358.18   -66.13    24.52     0.46)  ; 10\r\n            (  359.20   -66.49    25.80     0.46)  ; 11\r\n            (  359.20   -66.49    25.77     0.46)  ; 12\r\n            (  360.67   -66.74    27.92     0.46)  ; 13\r\n            (  363.04   -66.78    29.20     0.46)  ; 14\r\n            (  366.39   -68.98    30.00     0.46)  ; 15\r\n            (  370.79   -69.74    30.87     0.46)  ; 16\r\n            (  373.12   -71.59    30.87     0.46)  ; 17\r\n            (  376.20   -72.65    31.65     0.46)  ; 18\r\n            (  378.25   -73.37    32.55     0.46)  ; 19\r\n            (  379.98   -74.76    34.50     0.46)  ; 20\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  347.38   -66.27    21.50     0.46)  ; 1\r\n              (  360.36   -67.41    27.92     0.46)  ; 2\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  343.80   -67.11    18.72     0.46)  ; 1\r\n              (  351.03   -67.81    22.92     0.46)  ; 2\r\n              (  365.36   -68.64    30.00     0.46)  ; 3\r\n            )  ;  End of markers\r\n             High\r\n          |\r\n            (  339.89   -64.45    15.80     0.46)  ; 1, R-2-1-1-2-2\r\n            (  343.49   -61.81    15.07     0.46)  ; 2\r\n            (  345.66   -58.92    16.30     0.46)  ; 3\r\n            (  347.80   -56.02    16.30     0.46)  ; 4\r\n            (  352.31   -53.18    15.00     0.46)  ; 5\r\n            (  355.05   -50.74    15.00     0.46)  ; 6\r\n            (  358.93   -49.23    15.77     0.46)  ; 7\r\n            (  358.93   -49.23    15.73     0.46)  ; 8\r\n            (  360.14   -48.36    17.08     0.46)  ; 9\r\n            (  362.55   -46.60    17.60     0.46)  ; 10\r\n            (  364.65   -45.50    18.60     0.46)  ; 11\r\n            (  365.86   -44.63    19.60     0.46)  ; 12\r\n            (  365.86   -44.63    19.58     0.46)  ; 13\r\n            (  368.27   -42.87    20.35     0.46)  ; 14\r\n            (  370.06   -42.45    20.27     0.46)  ; 15\r\n            (  372.92   -40.59    20.27     0.46)  ; 16\r\n            (  374.40   -40.84    20.77     0.46)  ; 17\r\n            (  378.14   -38.76    20.35     0.46)  ; 18\r\n            (  379.66   -37.21    20.85     0.46)  ; 19\r\n            (  382.35   -36.58    20.85     0.46)  ; 20\r\n            (  384.58   -36.06    20.85     0.46)  ; 21\r\n            (  386.55   -34.41    21.25     0.46)  ; 22\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  343.32   -63.04    15.07     0.46)  ; 1\r\n              (  351.29   -52.81    15.00     0.46)  ; 2\r\n              (  361.52   -46.24    17.60     0.46)  ; 3\r\n              (  368.53   -44.00    20.35     0.46)  ; 4\r\n              (  372.42   -42.49    20.27     0.46)  ; 5\r\n              (  380.83   -38.13    20.85     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  339.89   -64.45    15.80     0.46)  ; 1\r\n              (  345.66   -58.92    16.30     0.46)  ; 2\r\n              (  353.84   -51.62    15.00     0.46)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  385.88   -31.57    20.25     0.46)  ; 1, R-2-1-1-2-2-1\r\n              (  385.88   -31.57    20.20     0.46)  ; 2\r\n              (  382.99   -29.27    19.13     0.46)  ; 3\r\n              (  382.99   -29.27    19.10     0.46)  ; 4\r\n              (  380.85   -26.19    18.27     0.46)  ; 5\r\n              (  378.98   -24.23    17.60     0.46)  ; 6\r\n              (  378.98   -24.23    17.57     0.46)  ; 7\r\n              (  377.38   -23.41    16.38     0.46)  ; 8\r\n              (  376.67   -22.39    14.80     0.46)  ; 9\r\n              (  376.67   -22.39    14.75     0.46)  ; 10\r\n              (  375.24   -20.33    13.30     0.46)  ; 11\r\n              (  375.24   -20.33    13.27     0.46)  ; 12\r\n              (  373.19   -19.63    11.50     0.46)  ; 13\r\n              (  370.61   -16.64    10.80     0.46)  ; 14\r\n              (  370.61   -16.64    10.77     0.46)  ; 15\r\n              (  368.21   -12.43     9.95     0.46)  ; 16\r\n              (  368.21   -12.43     9.93     0.46)  ; 17\r\n              (  365.89   -10.58     9.05     0.46)  ; 18\r\n              (  365.89   -10.58     9.02     0.46)  ; 19\r\n              (  363.32    -7.61    10.10     0.46)  ; 20\r\n              (  361.30    -5.09    10.97     0.46)  ; 21\r\n              (  358.85    -2.68    11.85     0.46)  ; 22\r\n              (  356.72     0.41    12.70     0.46)  ; 23\r\n              (  355.74     2.56    13.40     0.46)  ; 24\r\n              (  352.01     6.47    14.40     0.46)  ; 25\r\n              (  349.10     8.77    14.90     0.46)  ; 26\r\n              (  347.23    10.73    14.40     0.46)  ; 27\r\n              (  345.54    13.90    15.15     0.46)  ; 28\r\n              (  343.23    15.76    14.67     0.46)  ; 29\r\n              (  341.54    18.94    15.32     0.46)  ; 30\r\n              (  339.85    22.13    15.63     0.46)  ; 31\r\n              (  339.40    22.03    15.63     0.46)  ; 32\r\n              (  338.29    24.74    15.12     0.46)  ; 33\r\n              (  337.22    29.29    16.05     0.46)  ; 34\r\n              (  337.20    33.44    15.07     0.46)  ; 35\r\n              (  335.77    35.50    13.95     0.46)  ; 36\r\n              (  335.77    35.50    13.92     0.46)  ; 37\r\n              (  333.19    38.48    13.25     0.46)  ; 38\r\n              (  332.66    40.75    12.77     0.46)  ; 39\r\n              (  331.36    42.24    12.77     0.46)  ; 40\r\n              (  330.65    43.26    12.22     0.46)  ; 41\r\n              (  330.65    43.26    11.95     0.46)  ; 42\r\n              (  328.74    43.41    12.13     0.46)  ; 43\r\n              (  326.33    45.71    12.13     0.46)  ; 44\r\n              (  325.09    49.00    11.30     0.46)  ; 45\r\n              (  322.50    51.98    11.30     0.46)  ; 46\r\n              (  320.29    57.43    11.10     0.46)  ; 47\r\n              (  318.91    61.29    10.52     0.46)  ; 48\r\n              (  317.09    65.04    10.28     0.46)  ; 49\r\n              (  315.36    66.43     9.35     0.46)  ; 50\r\n              (  313.79    69.05     8.68     0.46)  ; 51\r\n              (  313.26    71.31     8.05     0.46)  ; 52\r\n              (  312.41    72.90     7.50     0.46)  ; 53\r\n              (  312.33    75.28     6.55     0.46)  ; 54\r\n              (  312.33    75.28     6.52     0.46)  ; 55\r\n              (  310.64    78.46     5.27     0.46)  ; 56\r\n              (  308.28    78.51     4.27     0.46)  ; 57\r\n              (  308.28    78.51     4.25     0.46)  ; 58\r\n              (  307.93    82.01     3.57     0.46)  ; 59\r\n              (  307.48    81.90     3.55     0.46)  ; 60\r\n              (  306.82    84.73     3.02     0.46)  ; 61\r\n              (  306.82    84.73     3.00     0.46)  ; 62\r\n              (  305.08    86.11     2.47     0.46)  ; 63\r\n              (  304.68    87.81     1.80     0.46)  ; 64\r\n              (  303.57    90.54     1.38     0.46)  ; 65\r\n              (  302.28    92.02     0.70     0.46)  ; 66\r\n              (  301.88    93.73    -0.30     0.46)  ; 67\r\n              (  301.05    95.32    -1.63     0.46)  ; 68\r\n              (  301.05    95.32    -1.65     0.46)  ; 69\r\n              (  299.04    97.83    -2.38     0.46)  ; 70\r\n              (  299.04    97.83    -2.40     0.46)  ; 71\r\n              (  297.75    99.32    -3.80     0.46)  ; 72\r\n              (  298.24   101.23    -5.32     0.46)  ; 73\r\n              (  296.76   101.48    -6.55     0.46)  ; 74\r\n              (  296.95   102.71    -6.88     0.46)  ; 75\r\n              (  296.95   102.71    -6.90     0.46)  ; 76\r\n              (  296.23   103.76    -8.00     0.46)  ; 77\r\n              (  296.23   103.76    -8.02     0.46)  ; 78\r\n              (  294.50   105.13    -8.70     0.46)  ; 79\r\n              (  294.10   106.83    -9.90     0.46)  ; 80\r\n              (  294.10   106.83    -9.93     0.46)  ; 81\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  358.10    -3.44    11.85     0.46)  ; 1\r\n                (  351.95     4.67    14.40     0.46)  ; 2\r\n                (  338.63    21.25    15.63     0.46)  ; 3\r\n                (  336.47    28.50    16.00     0.46)  ; 4\r\n                (  333.14    36.67    13.25     0.46)  ; 5\r\n                (  319.39    57.22    11.10     0.46)  ; 6\r\n                (  313.03    68.27     8.68     0.46)  ; 7\r\n                (  308.55    83.34     3.00     0.46)  ; 8\r\n                (  303.08    88.64     1.38     0.46)  ; 9\r\n                (  295.57   106.58    -9.93     0.46)  ; 10\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  376.41   -21.26    13.27     0.46)  ; 1\r\n                (  365.89   -10.58     9.02     0.46)  ; 2\r\n                (  363.32    -7.61    10.10     0.46)  ; 3\r\n                (  347.68    10.83    14.40     0.46)  ; 4\r\n                (  343.67    15.86    14.67     0.46)  ; 5\r\n                (  328.74    43.41    12.13     0.46)  ; 6\r\n                (  315.22    67.00     9.35     0.46)  ; 7\r\n                (  298.33    98.86    -3.80     0.46)  ; 8\r\n              )  ;  End of markers\r\n              (\r\n                (  292.12   109.25   -10.60     0.46)  ; 1, R-2-1-1-2-2-1-1\r\n                (  292.12   109.25   -10.63     0.46)  ; 2\r\n                (  290.69   111.29   -12.22     0.46)  ; 3\r\n                (  290.69   111.29   -12.25     0.46)  ; 4\r\n                (  289.28   113.35   -13.72     0.46)  ; 5\r\n                (  289.28   113.35   -13.75     0.46)  ; 6\r\n                (  288.30   115.52   -15.45     0.46)  ; 7\r\n                (  287.14   116.43   -17.35     0.46)  ; 8\r\n                (  287.06   118.80   -18.82     0.46)  ; 9\r\n                (  285.50   121.42   -20.23     0.46)  ; 10\r\n                (  285.73   124.46   -21.45     0.46)  ; 11\r\n                (  283.14   127.44   -21.92     0.46)  ; 12\r\n                (  280.97   128.72   -22.85     0.46)  ; 13\r\n                (  280.97   128.72   -22.88     0.46)  ; 14\r\n                (  278.69   132.36   -24.20     0.46)  ; 15\r\n                (  278.69   132.36   -24.22     0.46)  ; 16\r\n                (  276.42   136.01   -24.70     0.46)  ; 17\r\n                (  276.42   136.01   -24.72     0.46)  ; 18\r\n                (  275.58   137.60   -25.95     0.46)  ; 19\r\n                (  273.57   140.12   -27.32     0.46)  ; 20\r\n                (  273.57   140.12   -27.35     0.46)  ; 21\r\n                (  271.84   141.50   -28.55     0.46)  ; 22\r\n                (  271.71   142.08   -28.55     0.46)  ; 23\r\n                (  270.29   144.12   -29.70     0.46)  ; 24\r\n                (  270.29   144.12   -29.72     0.46)  ; 25\r\n                (  266.10   147.92   -30.38     0.46)  ; 26\r\n                (  264.80   149.42   -31.88     0.46)  ; 27\r\n                (  264.80   149.42   -31.90     0.46)  ; 28\r\n                (  263.70   152.13   -33.57     0.46)  ; 29\r\n                (  261.68   154.65   -34.28     0.46)  ; 30\r\n                (  259.95   156.04   -35.13     0.46)  ; 31\r\n                (  259.51   155.94   -35.15     0.46)  ; 32\r\n                (  256.35   159.37   -36.20     0.46)  ; 33\r\n                (  256.27   161.75   -37.32     0.46)  ; 34\r\n                (  256.27   161.75   -37.35     0.46)  ; 35\r\n                (  255.02   165.04   -38.27     0.46)  ; 36\r\n                (  255.02   165.04   -38.30     0.46)  ; 37\r\n                (  251.15   169.50   -39.25     0.46)  ; 38\r\n                (  249.01   172.58   -38.20     0.46)  ; 39\r\n                (  245.71   176.59   -36.65     0.46)  ; 40\r\n                (  242.37   178.79   -35.27     0.46)  ; 41\r\n                (  237.82   180.11   -35.13     0.46)  ; 42\r\n                (  235.82   182.63   -36.63     0.46)  ; 43\r\n                (  234.39   184.69   -35.80     0.46)  ; 44\r\n                (  234.39   184.69   -35.88     0.46)  ; 45\r\n                (  231.19   186.32   -37.17     0.46)  ; 46\r\n                (  230.74   186.22   -37.17     0.46)  ; 47\r\n                (  227.39   188.42   -38.92     0.46)  ; 48\r\n                (  227.39   188.42   -38.95     0.46)  ; 49\r\n                (  225.52   190.36   -40.88     0.46)  ; 50\r\n                (  225.52   190.36   -40.90     0.46)  ; 51\r\n                (  223.03   190.98   -42.45     0.46)  ; 52\r\n                (  219.68   193.17   -43.65     0.46)  ; 53\r\n                (  218.04   198.17   -44.65     0.46)  ; 54\r\n                (  214.43   201.50   -44.70     0.46)  ; 55\r\n                (  212.25   202.78   -44.70     0.46)  ; 56\r\n                (  209.36   205.09   -45.50     0.46)  ; 57\r\n                (  207.66   208.28   -46.20     0.46)  ; 58\r\n                (  204.94   211.83   -46.32     0.46)  ; 59\r\n                (  201.16   213.92   -47.88     0.46)  ; 60\r\n                (  201.16   213.92   -47.90     0.46)  ; 61\r\n                (  198.85   215.76   -48.95     0.46)  ; 62\r\n                (  195.23   219.10   -49.65     0.46)  ; 63\r\n                (  191.04   222.89   -50.27     0.46)  ; 64\r\n                (  187.84   224.53   -51.25     0.46)  ; 65\r\n                (  185.52   226.38   -52.22     0.46)  ; 66\r\n                (  182.62   228.68   -52.22     0.46)  ; 67\r\n                (  179.86   230.42   -53.42     0.46)  ; 68\r\n                (  177.14   233.96   -53.92     0.46)  ; 69\r\n                (  175.59   236.59   -54.32     0.46)  ; 70\r\n                (  174.08   241.01   -55.85     0.46)  ; 71\r\n                (  171.94   244.09   -57.42     0.46)  ; 72\r\n                (  169.81   247.18   -58.40     0.46)  ; 73\r\n                (  168.75   251.71   -59.45     0.46)  ; 74\r\n                (  168.75   251.71   -59.47     0.46)  ; 75\r\n                (  168.53   254.65   -61.40     0.46)  ; 76\r\n                (  167.15   258.51   -62.55     0.46)  ; 77\r\n                (  167.15   258.51   -62.57     0.46)  ; 78\r\n                (  165.96   263.59   -63.02     0.46)  ; 79\r\n                (  163.47   270.17   -63.02     0.46)  ; 80\r\n                (  160.62   274.29   -63.13     0.46)  ; 81\r\n                (  157.52   279.53   -63.13     0.46)  ; 82\r\n                (  156.54   281.69   -61.65     0.46)  ; 83\r\n                (  156.09   281.59   -61.65     0.46)  ; 84\r\n                (  154.10   284.10   -58.07     0.46)  ; 85\r\n                (  151.63   286.52   -56.60     0.46)  ; 86\r\n                (  149.32   288.35   -55.32     0.46)  ; 87\r\n                (  149.32   288.35   -55.35     0.46)  ; 88\r\n                (  146.92   292.58   -54.40     0.46)  ; 89\r\n                (  146.92   292.58   -54.05     0.46)  ; 90\r\n                (  144.08   296.68   -53.62     0.46)  ; 91\r\n                (  141.86   302.13   -53.05     0.46)  ; 92\r\n                (  139.13   305.67   -52.50     0.46)  ; 93\r\n                (  136.64   306.18   -51.32     0.46)  ; 94\r\n                (  133.08   311.32   -50.92     0.46)  ; 95\r\n                (  131.53   313.95   -49.72     0.46)  ; 96\r\n                (  129.92   314.76   -48.15     0.46)  ; 97\r\n                (  128.37   317.39   -47.17     0.46)  ; 98\r\n                (  125.97   321.60   -46.25     0.46)  ; 99\r\n                (  122.93   324.47   -45.45     0.46)  ; 100\r\n                (  122.93   324.47   -45.50     0.46)  ; 101\r\n                (  121.06   326.42   -44.30     0.46)  ; 102\r\n                (  118.74   328.27   -43.88     0.46)  ; 103\r\n                (  118.74   328.27   -43.90     0.46)  ; 104\r\n                (  116.88   330.22   -42.72     0.46)  ; 105\r\n                (  115.01   332.16   -41.50     0.46)  ; 106\r\n                (  111.66   334.36   -40.32     0.46)  ; 107\r\n                (  109.79   336.32   -39.17     0.46)  ; 108\r\n                (  108.50   337.80   -37.15     0.46)  ; 109\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  288.38   113.14   -13.75     0.46)  ; 1\r\n                  (  198.09   214.99   -48.95     0.46)  ; 2\r\n                  (  183.02   226.98   -52.22     0.46)  ; 3\r\n                  (  119.01   327.13   -43.90     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  286.48   119.25   -18.82     0.46)  ; 1\r\n                  (  246.29   176.13   -36.65     0.46)  ; 2\r\n                  (  219.68   193.17   -43.65     0.46)  ; 3\r\n                  (  176.04   236.70   -54.32     0.46)  ; 4\r\n                  (  157.78   278.39   -63.13     0.46)  ; 5\r\n                  (  139.13   305.67   -52.50     0.46)  ; 6\r\n                  (  131.66   313.37   -49.72     0.46)  ; 7\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  292.93   108.96   -12.15     0.46)  ; 1, R-2-1-1-2-2-1-2\r\n                (  291.82   111.69   -12.67     0.46)  ; 2\r\n                (  292.20   114.16   -13.72     0.46)  ; 3\r\n                (  291.92   115.30   -15.63     0.46)  ; 4\r\n                (  292.47   119.00   -17.82     0.46)  ; 5\r\n                (  294.04   122.36   -20.07     0.46)  ; 6\r\n                (  294.04   122.36   -20.13     0.46)  ; 7\r\n                (  295.11   123.80   -22.55     0.46)  ; 8\r\n                (  295.11   123.80   -22.57     0.46)  ; 9\r\n                (  295.29   125.04   -24.22     0.46)  ; 10\r\n                (  295.29   125.04   -24.25     0.46)  ; 11\r\n                (  295.47   126.27   -25.67     0.46)  ; 12\r\n                (  293.43   126.99   -27.12     0.46)  ; 13\r\n                (  291.42   129.50   -31.72     0.46)  ; 14\r\n                (  291.42   129.50   -31.75     0.46)  ; 15\r\n                (  289.86   132.13   -33.38     0.46)  ; 16\r\n                (  289.07   135.53   -36.72     0.46)  ; 17\r\n                (  286.62   137.93   -37.60     0.46)  ; 18\r\n                (  284.29   139.78   -39.75     0.46)  ; 19\r\n                (  284.29   139.78   -39.80     0.46)  ; 20\r\n                (  282.56   141.16   -41.97     0.46)  ; 21\r\n                (  282.56   141.16   -42.05     0.46)  ; 22\r\n                (  282.35   144.10   -43.55     0.46)  ; 23\r\n                (  282.35   144.10   -43.58     0.46)  ; 24\r\n                (  280.47   146.04   -45.72     0.46)  ; 25\r\n                (  279.68   149.44   -46.82     0.46)  ; 26\r\n                (  279.68   149.44   -46.85     0.46)  ; 27\r\n                (  278.57   152.17   -48.25     0.46)  ; 28\r\n                (  278.57   152.17   -48.30     0.46)  ; 29\r\n                (  277.72   153.76   -50.38     0.46)  ; 30\r\n                (  276.74   155.92   -52.30     0.46)  ; 31\r\n                (  276.74   155.92   -52.35     0.46)  ; 32\r\n                (  276.08   158.76   -54.52     0.46)  ; 33\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  292.40   111.22   -12.67     0.46)  ; 1\r\n                  (  295.29   125.04   -24.25     0.46)  ; 2\r\n                  (  289.20   134.95   -36.72     0.46)  ; 3\r\n                  (  275.63   158.65   -54.52     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  276.37   158.19    -7.22     0.46)  ; 1\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  390.27   -32.93    21.42     0.46)  ; 1, R-2-1-1-2-2-2\r\n              (  390.27   -32.93    21.40     0.46)  ; 2\r\n              (  392.87   -29.93    21.40     0.46)  ; 3\r\n              (  395.99   -29.20    22.70     0.46)  ; 4\r\n              (  398.67   -28.58    23.88     0.46)  ; 5\r\n              (  400.32   -27.59    25.15     0.46)  ; 6\r\n              (  402.74   -25.83    25.83     0.46)  ; 7\r\n              (  406.62   -24.32    25.83     0.46)  ; 8\r\n              (  407.39   -23.54    26.70     0.46)  ; 9\r\n              (  409.62   -23.02    27.45     0.46)  ; 10\r\n              (  410.95   -22.71    29.15     0.46)  ; 11\r\n              (  410.95   -22.71    29.10     0.46)  ; 12\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  409.89   -24.16    29.10     0.46)  ; 1\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  396.44   -29.09    22.70     0.46)  ; 1\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (  321.59   -67.56    11.70     0.46)  ; 1, R-2-1-2\r\n        (  320.79   -64.16    10.60     0.46)  ; 2\r\n        (  320.79   -64.16    10.57     0.46)  ; 3\r\n        (  320.71   -61.79     9.82     0.46)  ; 4\r\n        (  320.71   -61.79     9.80     0.46)  ; 5\r\n        (  321.39   -58.64     9.32     0.46)  ; 6\r\n        (  321.44   -56.84     8.05     0.46)  ; 7\r\n        (  321.04   -55.15     6.78     0.46)  ; 8\r\n        (  320.19   -53.55     5.97     0.46)  ; 9\r\n        (  320.19   -53.55     5.95     0.46)  ; 10\r\n        (  320.24   -51.75     5.15     0.46)  ; 11\r\n        (  320.24   -51.75     5.13     0.46)  ; 12\r\n        (  318.95   -50.25     4.60     0.46)  ; 13\r\n        (  318.95   -50.25     4.57     0.46)  ; 14\r\n        (  318.16   -46.87     4.72     0.46)  ; 15\r\n        (  316.60   -44.25     4.92     0.46)  ; 16\r\n        (  316.33   -43.11     4.92     0.46)  ; 17\r\n        (  317.14   -40.53     4.47     0.46)  ; 18\r\n\r\n        (Dot\r\n          (Color Yellow)\r\n          (Name \"Marker 1\")\r\n          (  319.43   -60.30     9.32     0.46)  ; 1\r\n          (  317.66   -48.77     4.72     0.46)  ; 2\r\n        )  ;  End of markers\r\n\r\n        (OpenCircle\r\n          (Color Yellow)\r\n          (Name \"Marker 2\")\r\n          (  320.48   -48.71     4.00     0.46)  ; 1\r\n        )  ;  End of markers\r\n        (\r\n          (  316.35   -37.13     3.92     0.46)  ; 1, R-2-1-2-1\r\n          (  316.45   -33.52     3.02     0.46)  ; 2\r\n          (  314.89   -30.91     1.70     0.46)  ; 3\r\n          (  314.89   -30.91     1.67     0.46)  ; 4\r\n          (  316.41   -29.36     0.32     0.46)  ; 5\r\n          (  316.41   -29.36     0.28     0.46)  ; 6\r\n          (  316.33   -26.99    -0.52     0.46)  ; 7\r\n          (  314.46   -25.03    -1.55     0.46)  ; 8\r\n          (  315.53   -23.59    -3.10     0.46)  ; 9\r\n          (  315.45   -21.21    -4.05     0.46)  ; 10\r\n          (  315.68   -18.18    -4.68     0.46)  ; 11\r\n          (  315.33   -14.68    -5.22     0.46)  ; 12\r\n          (  316.26   -12.67    -6.38     0.46)  ; 13\r\n          (  316.13   -12.10    -7.70     0.46)  ; 14\r\n          (  314.98   -11.18    -8.95     0.46)  ; 15\r\n          (  314.76    -8.24   -10.22     0.46)  ; 16\r\n          (  314.81    -6.44    -9.77     0.46)  ; 17\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  317.37   -37.49     3.92     0.46)  ; 1\r\n            (  315.58   -27.77    -0.52     0.46)  ; 2\r\n            (  316.74   -22.72    -4.05     0.46)  ; 3\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  321.45   -28.77    -0.52     0.46)  ; 1\r\n            (  316.94    -9.53   -10.22     0.46)  ; 2\r\n          )  ;  End of markers\r\n          (\r\n            (  315.62    -3.86   -11.70     0.46)  ; 1, R-2-1-2-1-1\r\n            (  316.97    -3.55   -12.98     0.46)  ; 2\r\n            (  315.86    -0.83   -13.85     0.46)  ; 3\r\n            (  315.86    -0.83   -13.87     0.46)  ; 4\r\n            (  316.21     1.65   -14.40     0.46)  ; 5\r\n            (  316.49     6.49   -14.82     0.46)  ; 6\r\n            (  314.93     9.11   -14.65     0.46)  ; 7\r\n            (  314.14    12.51   -15.40     0.46)  ; 8\r\n            (  314.14    12.51   -15.43     0.46)  ; 9\r\n            (  314.50    14.98   -16.90     0.46)  ; 10\r\n            (  314.73    18.02   -18.48     0.46)  ; 11\r\n            (  314.73    18.02   -18.50     0.46)  ; 12\r\n            (  316.69    19.67   -20.03     0.46)  ; 13\r\n            (  318.14    23.60   -20.97     0.46)  ; 14\r\n            (  318.95    26.17   -21.77     0.46)  ; 15\r\n            (  320.52    29.53   -22.65     0.46)  ; 16\r\n            (  321.10    35.03   -23.30     0.46)  ; 17\r\n            (  321.10    35.03   -23.33     0.46)  ; 18\r\n            (  320.76    38.54   -23.67     0.46)  ; 19\r\n            (  321.48    43.48   -24.45     0.46)  ; 20\r\n            (  321.13    46.99   -25.00     0.46)  ; 21\r\n            (  321.86    51.93   -25.67     0.46)  ; 22\r\n            (  322.27    56.21   -26.42     0.46)  ; 23\r\n            (  321.69    56.67   -27.97     0.46)  ; 24\r\n            (  321.42    57.81   -28.82     0.46)  ; 25\r\n            (  321.42    57.81   -28.85     0.46)  ; 26\r\n            (  322.10    60.95   -30.30     0.46)  ; 27\r\n            (  323.59    66.67   -31.40     0.46)  ; 28\r\n            (  324.71    69.92   -31.40     0.46)  ; 29\r\n            (  325.52    72.50   -31.40     0.46)  ; 30\r\n            (  326.91    74.62   -31.88     0.46)  ; 31\r\n            (  326.74    79.35   -32.50     0.46)  ; 32\r\n            (  327.02    84.20   -33.03     0.46)  ; 33\r\n            (  327.12    87.80   -34.20     0.46)  ; 34\r\n            (  328.06    89.82   -35.13     0.46)  ; 35\r\n            (  327.40    92.65   -35.20     0.46)  ; 36\r\n            (  327.94    96.35   -35.65     0.46)  ; 37\r\n            (  328.39   100.54   -35.42     0.46)  ; 38\r\n            (  328.45   102.34   -36.85     0.46)  ; 39\r\n            (  328.22   105.27   -37.60     0.46)  ; 40\r\n            (  328.22   105.27   -37.63     0.46)  ; 41\r\n            (  329.80   108.63   -38.67     0.46)  ; 42\r\n            (  329.27   110.88   -38.15     0.46)  ; 43\r\n            (  329.54   115.73   -38.15     0.46)  ; 44\r\n            (  329.63   119.33   -38.15     0.46)  ; 45\r\n            (  332.10   122.90   -37.17     0.46)  ; 46\r\n            (  332.51   127.18   -37.10     0.46)  ; 47\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  314.69     0.10   -14.40     0.46)  ; 1\r\n              (  322.74    46.18   -25.00     0.46)  ; 2\r\n              (  333.13   122.55   -37.17     0.46)  ; 3\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  315.38     9.21   -14.65     0.46)  ; 1\r\n              (  313.65    10.60   -17.05     0.46)  ; 2\r\n              (  314.27    11.94   -15.27     0.46)  ; 3\r\n              (  318.28    23.03   -20.97     0.46)  ; 4\r\n              (  322.10    60.95   -30.30     0.46)  ; 5\r\n              (  326.91    74.62   -31.88     0.46)  ; 6\r\n              (  328.07    95.79   -35.67     0.46)  ; 7\r\n            )  ;  End of markers\r\n            (\r\n              (  334.92   128.94   -35.83     0.46)  ; 1, R-2-1-2-1-1-1\r\n              (  334.92   128.94   -35.88     0.46)  ; 2\r\n              (  338.82   130.44   -34.78     0.46)  ; 3\r\n              (  338.82   130.44   -34.80     0.46)  ; 4\r\n              (  342.20   130.04   -33.92     0.46)  ; 5\r\n              (  344.49   132.37   -33.10     0.46)  ; 6\r\n              (  348.06   133.21   -32.42     0.46)  ; 7\r\n              (  350.33   135.54   -31.00     0.46)  ; 8\r\n              (  352.27   135.39   -30.65     0.46)  ; 9\r\n              (  354.36   136.48   -29.97     0.46)  ; 10\r\n              (  357.66   138.45   -28.77     0.46)  ; 11\r\n              (  360.35   139.07   -28.77     0.46)  ; 12\r\n              (  363.21   140.93   -28.77     0.46)  ; 13\r\n              (  364.86   141.92   -28.77     0.46)  ; 14\r\n              (  367.54   142.54   -28.77     0.46)  ; 15\r\n              (  370.09   143.75   -28.05     0.46)  ; 16\r\n              (  373.65   144.58   -27.22     0.46)  ; 17\r\n              (  377.28   147.23   -26.88     0.46)  ; 18\r\n              (  379.97   147.85   -26.07     0.46)  ; 19\r\n              (  381.49   149.41   -25.05     0.46)  ; 20\r\n              (  383.01   150.96   -24.20     0.46)  ; 21\r\n              (  385.42   152.72   -22.67     0.46)  ; 22\r\n              (  385.42   152.72   -22.70     0.46)  ; 23\r\n              (  388.29   154.58   -22.47     0.46)  ; 24\r\n              (  390.38   155.67   -22.47     0.46)  ; 25\r\n              (  391.14   156.44   -22.47     0.46)  ; 26\r\n              (  395.75   156.92   -22.47     0.46)  ; 27\r\n              (  398.60   158.79   -23.57     0.46)  ; 28\r\n              (  402.44   158.50   -23.67     0.46)  ; 29\r\n              (  406.02   159.33   -23.72     0.46)  ; 30\r\n              (  408.69   159.96   -22.12     0.46)  ; 31\r\n              (  411.25   161.15   -20.60     0.46)  ; 32\r\n              (  414.77   160.19   -19.92     0.46)  ; 33\r\n              (  418.92   160.57   -20.27     0.46)  ; 34\r\n              (  422.63   160.84   -20.27     0.46)  ; 35\r\n              (  425.75   161.57   -20.27     0.46)  ; 36\r\n              (  428.44   162.20   -19.52     0.46)  ; 37\r\n              (  432.90   163.24   -19.52     0.46)  ; 38\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  341.49   131.07   -33.92     0.46)  ; 1\r\n                (  351.05   134.51   -31.00     0.46)  ; 2\r\n                (  405.70   158.66   -23.72     0.46)  ; 3\r\n                (  417.40   159.01   -20.27     0.46)  ; 4\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  354.23   137.04   -29.97     0.46)  ; 1\r\n                (  385.29   153.28   -22.70     0.46)  ; 2\r\n                (  383.01   150.96   -22.70     0.46)  ; 3\r\n                (  398.47   159.36   -23.57     0.46)  ; 4\r\n                (  402.89   158.60   -23.67     0.46)  ; 5\r\n                (  411.12   161.72   -20.60     0.46)  ; 6\r\n                (  428.44   162.20   -19.52     0.46)  ; 7\r\n              )  ;  End of markers\r\n              (\r\n                (  433.75   161.65   -19.67     0.46)  ; 1, R-2-1-2-1-1-1-1\r\n                (  435.29   159.02   -20.10     0.46)  ; 2\r\n                (  435.29   159.02   -20.13     0.46)  ; 3\r\n                (  436.27   156.87   -20.55     0.46)  ; 4\r\n                (  436.94   154.04   -21.50     0.46)  ; 5\r\n                (  434.84   152.95   -22.92     0.46)  ; 6\r\n                (  431.59   152.79   -23.35     0.46)  ; 7\r\n                (  428.01   151.94   -24.22     0.46)  ; 8\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  436.46   158.10   -20.55     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  436.22   155.07   -21.50     0.46)  ; 1\r\n                  (  428.46   152.05   -24.22     0.46)  ; 2\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  434.87   164.90   -19.52     0.46)  ; 1, R-2-1-2-1-1-1-2\r\n                (  435.50   166.24   -19.52     0.46)  ; 2\r\n                (  437.55   165.53   -18.67     0.46)  ; 3\r\n                (  439.65   166.61   -18.05     0.46)  ; 4\r\n                (  441.69   165.91   -16.95     0.46)  ; 5\r\n                (  443.93   166.43   -15.88     0.46)  ; 6\r\n                (  447.63   166.70   -15.88     0.46)  ; 7\r\n                (  451.04   166.30   -16.98     0.46)  ; 8\r\n                (  452.95   166.15   -18.08     0.46)  ; 9\r\n                (  454.47   167.70   -18.50     0.46)  ; 10\r\n                (  457.73   167.87   -19.70     0.46)  ; 11\r\n                (  460.41   168.50   -20.42     0.46)  ; 12\r\n                (  462.77   168.45   -21.75     0.46)  ; 13\r\n                (  462.77   168.45   -22.22     0.46)  ; 14\r\n                (  466.16   168.05   -22.92     0.46)  ; 15\r\n                (  469.29   168.79   -23.60     0.46)  ; 16\r\n                (  469.29   168.79   -23.63     0.46)  ; 17\r\n                (  472.86   169.62   -24.27     0.46)  ; 18\r\n                (  477.60   169.54   -24.52     0.46)  ; 19\r\n                (  478.88   169.24   -23.23     0.46)  ; 20\r\n                (  481.13   169.76   -21.82     0.46)  ; 21\r\n                (  481.13   169.76   -21.80     0.46)  ; 22\r\n                (  482.33   170.65   -20.55     0.46)  ; 23\r\n                (  481.88   170.55   -20.55     0.46)  ; 24\r\n                (  484.96   169.48   -20.10     0.46)  ; 25\r\n                (  487.51   170.67   -19.50     0.46)  ; 26\r\n                (  489.29   171.08   -18.77     0.46)  ; 27\r\n                (  491.80   170.48   -18.33     0.46)  ; 28\r\n                (  496.26   171.53   -17.70     0.46)  ; 29\r\n                (  499.83   172.36   -17.70     0.46)  ; 30\r\n                (  503.40   173.20   -17.70     0.46)  ; 31\r\n                (  506.49   172.13   -17.72     0.46)  ; 32\r\n                (  509.43   171.62   -18.82     0.46)  ; 33\r\n                (  510.59   170.70   -19.05     0.46)  ; 34\r\n                (  510.59   170.70   -19.15     0.46)  ; 35\r\n                (  512.24   171.69   -20.20     0.46)  ; 36\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  508.67   170.84   -18.82     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  439.20   166.52   -18.05     0.46)  ; 1\r\n                  (  443.93   166.43   -15.88     0.46)  ; 2\r\n                  (  457.28   167.76   -19.70     0.46)  ; 3\r\n                  (  477.15   169.43   -24.60     0.46)  ; 4\r\n                  (  479.79   169.44   -21.80     0.46)  ; 5\r\n                  (  491.80   170.48   -18.33     0.46)  ; 6\r\n                  (  499.52   171.70   -17.70     0.46)  ; 7\r\n                  (  512.24   171.69   -20.20     0.46)  ; 8\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  332.11   128.87   -38.33     0.46)  ; 1, R-2-1-2-1-1-2\r\n              (  331.58   131.14   -39.52     0.46)  ; 2\r\n              (  332.12   134.85   -40.40     0.46)  ; 3\r\n              (  332.92   137.43   -40.90     0.46)  ; 4\r\n              (  333.56   138.76   -41.97     0.46)  ; 5\r\n              (  333.79   141.81   -42.75     0.46)  ; 6\r\n              (  334.02   144.85   -43.38     0.46)  ; 7\r\n              (  334.02   144.85   -43.40     0.46)  ; 8\r\n              (  334.11   148.45   -44.07     0.46)  ; 9\r\n              (  334.03   150.82   -44.75     0.46)  ; 10\r\n              (  334.21   152.05   -45.52     0.46)  ; 11\r\n              (  332.97   155.35   -45.90     0.46)  ; 12\r\n              (  333.83   159.72   -46.08     0.46)  ; 13\r\n              (  333.48   163.23   -46.77     0.46)  ; 14\r\n              (  332.82   166.07   -47.22     0.46)  ; 15\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  334.44   155.09   -45.90     0.46)  ; 1\r\n                (  334.72   159.93   -46.08     0.46)  ; 2\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  329.03   129.95   -39.52     0.46)  ; 1\r\n                (  331.84   130.00   -39.52     0.46)  ; 2\r\n              )  ;  End of markers\r\n              (\r\n                (  330.50   167.91   -47.22     0.46)  ; 1, R-2-1-2-1-1-2-1\r\n                (  327.56   168.41   -46.65     0.46)  ; 2\r\n                (  324.48   169.49   -46.10     0.46)  ; 3\r\n                (  322.16   171.33   -45.25     0.46)  ; 4\r\n                (  320.43   172.72   -44.23     0.46)  ; 5\r\n                (  320.43   172.72   -44.25     0.46)  ; 6\r\n                (  318.69   174.10   -43.50     0.46)  ; 7\r\n                (  316.68   176.61   -42.37     0.46)  ; 8\r\n                (  314.94   178.00   -41.10     0.46)  ; 9\r\n                (  310.89   181.23   -41.10     0.46)  ; 10\r\n                (  306.26   184.92   -40.75     0.46)  ; 11\r\n                (  304.65   185.73   -42.88     0.46)  ; 12\r\n                (  301.62   188.61   -43.58     0.46)  ; 13\r\n                (  300.91   189.64   -44.70     0.46)  ; 14\r\n                (  300.91   189.64   -44.72     0.46)  ; 15\r\n                (  298.72   190.92   -46.05     0.46)  ; 16\r\n                (  298.72   190.92   -46.08     0.46)  ; 17\r\n                (  297.04   194.10   -47.00     0.46)  ; 18\r\n                (  293.15   198.58   -47.55     0.46)  ; 19\r\n                (  293.15   198.58   -47.57     0.46)  ; 20\r\n                (  289.41   202.47   -47.35     0.46)  ; 21\r\n                (  285.00   203.23   -46.42     0.46)  ; 22\r\n                (  281.47   204.19   -47.37     0.46)  ; 23\r\n                (  281.47   204.19   -47.40     0.46)  ; 24\r\n                (  277.92   209.33   -46.88     0.46)  ; 25\r\n                (  273.29   213.02   -46.28     0.46)  ; 26\r\n                (  273.29   213.02   -46.30     0.46)  ; 27\r\n                (  270.72   215.86   -45.92     0.46)  ; 28\r\n                (  270.50   218.80   -44.45     0.46)  ; 29\r\n                (  270.05   218.69   -44.20     0.46)  ; 30\r\n                (  267.88   219.98   -43.35     0.46)  ; 31\r\n                (  267.43   219.87   -43.35     0.46)  ; 32\r\n                (  265.82   220.69   -41.97     0.46)  ; 33\r\n                (  263.50   222.53   -40.57     0.46)  ; 34\r\n                (  263.50   222.53   -40.60     0.46)  ; 35\r\n                (  262.97   224.79   -39.70     0.46)  ; 36\r\n                (  262.90   227.16   -38.55     0.46)  ; 37\r\n                (  261.79   229.89   -37.17     0.46)  ; 38\r\n                (  261.31   233.96   -36.22     0.46)  ; 39\r\n                (  259.80   238.38   -35.42     0.46)  ; 40\r\n                (  259.80   238.38   -35.45     0.46)  ; 41\r\n                (  259.97   239.63   -34.22     0.46)  ; 42\r\n                (  259.63   243.11   -33.25     0.46)  ; 43\r\n                (  258.20   245.17   -32.42     0.46)  ; 44\r\n                (  256.50   248.36   -31.85     0.46)  ; 45\r\n                (  252.64   252.83   -31.85     0.46)  ; 46\r\n                (  251.39   256.13   -31.60     0.46)  ; 47\r\n                (  250.15   259.42   -31.60     0.46)  ; 48\r\n                (  247.97   260.69   -31.05     0.46)  ; 49\r\n                (  246.28   263.88   -30.05     0.46)  ; 50\r\n                (  243.68   266.86   -30.05     0.46)  ; 51\r\n                (  240.98   270.40   -30.05     0.46)  ; 52\r\n                (  238.71   274.05   -30.05     0.46)  ; 53\r\n                (  235.73   278.73   -30.05     0.46)  ; 54\r\n                (  231.86   283.19   -30.85     0.46)  ; 55\r\n                (  229.14   286.73   -30.85     0.46)  ; 56\r\n                (  229.00   287.30   -30.85     0.46)  ; 57\r\n                (  227.31   290.49   -30.77     0.46)  ; 58\r\n                (  224.56   292.24   -30.77     0.46)  ; 59\r\n                (  224.02   294.49   -30.77     0.46)  ; 60\r\n                (  220.47   299.63   -30.85     0.46)  ; 61\r\n                (  218.02   302.04   -30.02     0.46)  ; 62\r\n                (  214.99   304.91   -30.02     0.46)  ; 63\r\n                (  212.14   309.02   -29.80     0.46)  ; 64\r\n                (  210.06   313.91   -29.85     0.46)  ; 65\r\n                (  208.23   317.66   -29.25     0.46)  ; 66\r\n                (  205.65   320.64   -27.95     0.46)  ; 67\r\n                (  203.19   323.06   -26.75     0.46)  ; 68\r\n                (  202.13   327.59   -25.63     0.46)  ; 69\r\n                (  201.20   331.55   -25.63     0.46)  ; 70\r\n                (  198.35   335.65   -25.63     0.46)  ; 71\r\n                (  196.22   338.74   -25.63     0.46)  ; 72\r\n                (  194.27   343.06   -25.63     0.46)  ; 73\r\n                (  194.05   345.99   -25.63     0.46)  ; 74\r\n                (  190.71   348.20   -25.63     0.46)  ; 75\r\n                (  189.34   352.05   -25.63     0.46)  ; 76\r\n                (  189.30   356.22   -26.32     0.46)  ; 77\r\n                (  187.57   357.60   -27.05     0.46)  ; 78\r\n                (  185.36   358.25   -27.05     0.46)  ; 79\r\n                (\r\n                  (  185.87   360.79   -27.63     0.46)  ; 1, R-2-1-2-1-1-2-1-1\r\n                  (  185.08   364.19   -27.20     0.46)  ; 2\r\n                  (  184.28   367.59   -26.75     0.46)  ; 3\r\n                  (  183.09   372.68   -26.75     0.46)  ; 4\r\n                  (  183.00   375.06   -26.75     0.46)  ; 5\r\n                  (  182.92   377.42   -26.75     0.46)  ; 6\r\n                  (  181.99   381.38   -27.32     0.46)  ; 7\r\n                  (  180.43   384.00   -25.92     0.46)  ; 8\r\n                  (  180.80   386.48   -24.75     0.46)  ; 9\r\n                  (  179.69   389.20   -24.15     0.46)  ; 10\r\n                  (  180.21   391.00   -22.57     0.46)  ; 11\r\n                  (  180.26   392.80   -21.00     0.46)  ; 12\r\n                  (  180.30   394.60   -19.58     0.46)  ; 13\r\n                  (  180.53   397.65   -18.38     0.46)  ; 14\r\n                  (  178.98   400.27   -17.17     0.46)  ; 15\r\n                  (  177.87   402.99   -16.20     0.46)  ; 16\r\n                  (  177.52   406.49   -15.35     0.46)  ; 17\r\n                  (  176.85   409.32   -14.82     0.46)  ; 18\r\n                  (  176.90   411.12   -13.57     0.46)  ; 19\r\n                  (  177.58   414.27   -13.07     0.46)  ; 20\r\n                  (  177.23   417.76   -12.10     0.46)  ; 21\r\n                  (  176.52   418.79   -10.73     0.46)  ; 22\r\n                  (  176.31   421.73    -9.43     0.46)  ; 23\r\n                  (  175.64   424.56    -8.82     0.46)  ; 24\r\n                  (  175.64   424.56    -8.80     0.46)  ; 25\r\n                  (  175.11   426.83    -8.40     0.46)  ; 26\r\n                  (  173.69   428.87    -7.35     0.46)  ; 27\r\n                  (  173.69   428.87    -7.38     0.46)  ; 28\r\n                  (  172.26   430.93    -6.73     0.46)  ; 29\r\n                  (  169.81   433.36    -5.75     0.46)  ; 30\r\n                  (  168.38   435.40    -4.63     0.46)  ; 31\r\n                  (  166.25   438.49    -3.57     0.46)  ; 32\r\n                  (  166.25   438.49    -3.00     0.46)  ; 33\r\n                  (  164.56   441.68    -2.27     0.46)  ; 34\r\n                  (  163.72   443.27    -1.02     0.46)  ; 35\r\n                  (  163.00   444.29     0.57     0.46)  ; 36\r\n                  (  163.05   446.10     1.50     0.46)  ; 37\r\n                  (  162.66   447.79     2.60     0.46)  ; 38\r\n                  (  163.15   449.71     2.60     0.46)  ; 39\r\n                  (  162.62   451.96     3.72     0.46)  ; 40\r\n                  (  161.78   453.56     5.22     0.46)  ; 41\r\n                  (  161.25   455.82     6.02     0.46)  ; 42\r\n                  (  160.00   459.11     5.65     0.46)  ; 43\r\n                  (  159.65   462.61     7.35     0.46)  ; 44\r\n                  (  159.65   462.61     7.32     0.46)  ; 45\r\n                  (  158.41   465.91     8.77     0.46)  ; 46\r\n                  (  157.43   468.06    10.80     0.46)  ; 47\r\n                  (  157.35   470.43    11.93     0.46)  ; 48\r\n                  (  155.92   472.48    11.93     0.46)  ; 49\r\n                  (  154.36   475.10    12.80     0.46)  ; 50\r\n                  (  152.50   477.06    13.72     0.46)  ; 51\r\n                  (  152.47   481.24    13.82     0.46)  ; 52\r\n                  (  152.47   481.24    13.80     0.46)  ; 53\r\n                  (  150.60   483.18    15.07     0.46)  ; 54\r\n                  (  150.60   483.18    15.05     0.46)  ; 55\r\n                  (  150.20   484.89    16.32     0.46)  ; 56\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  306.66   183.22   -40.75     0.46)  ; 1\r\n                    (  263.12   230.20   -37.17     0.46)  ; 2\r\n                    (  258.28   242.81   -33.25     0.46)  ; 3\r\n                    (  191.65   350.20   -25.63     0.46)  ; 4\r\n                    (  186.02   366.21   -26.75     0.46)  ; 5\r\n                    (  163.80   440.90    -2.27     0.46)  ; 6\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  220.47   299.63   -30.85     0.46)  ; 1\r\n                    (  215.57   304.45   -30.02     0.46)  ; 2\r\n                    (  210.63   313.45   -29.85     0.46)  ; 3\r\n                    (  208.37   317.09   -29.25     0.46)  ; 4\r\n                    (  202.13   327.59   -25.63     0.46)  ; 5\r\n                    (  182.34   377.88   -26.75     0.46)  ; 6\r\n                    (  181.99   381.38   -27.32     0.46)  ; 7\r\n                    (  179.69   389.20   -24.15     0.46)  ; 8\r\n                    (  179.55   399.80   -17.15     0.46)  ; 9\r\n                    (  176.90   411.12   -13.57     0.46)  ; 10\r\n                    (  172.71   431.04    -6.73     0.46)  ; 11\r\n                    (  170.25   433.46    -5.07     0.46)  ; 12\r\n                    (  168.96   434.94    -4.10     0.46)  ; 13\r\n                    (  163.50   446.20     1.50     0.46)  ; 14\r\n                    (  157.88   468.17    10.80     0.46)  ; 15\r\n                    (  156.06   471.92    11.93     0.46)  ; 16\r\n                    (  152.95   477.17    13.72     0.46)  ; 17\r\n                    (  150.33   484.31    16.32     0.46)  ; 18\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  183.19   359.54   -24.92     0.46)  ; 1, R-2-1-2-1-1-2-1-2\r\n                  (  180.12   360.60   -22.97     0.46)  ; 2\r\n                  (  177.55   359.41   -21.20     0.46)  ; 3\r\n                  (  174.61   359.91   -19.40     0.46)  ; 4\r\n                  (  172.57   360.62   -17.47     0.46)  ; 5\r\n                  (  170.70   362.58   -15.27     0.46)  ; 6\r\n                  (  168.38   364.43   -15.02     0.46)  ; 7\r\n                  (  165.51   366.63   -13.47     0.46)  ; 8\r\n                  (  163.77   368.02   -12.27     0.46)  ; 9\r\n                  (  161.91   369.97   -10.95     0.46)  ; 10\r\n                  (  160.61   371.46    -9.60     0.46)  ; 11\r\n                  (  159.63   373.61    -7.87     0.46)  ; 12\r\n                  (  158.21   375.67    -6.10     0.46)  ; 13\r\n                  (  156.73   375.92    -4.85     0.46)  ; 14\r\n                  (  155.26   376.18    -3.62     0.46)  ; 15\r\n                  (  153.52   377.55    -2.55     0.46)  ; 16\r\n                  (  152.19   377.24    -1.10     0.46)  ; 17\r\n                  (  151.47   378.27     0.93     0.46)  ; 18\r\n                  (  150.18   379.76     2.40     0.46)  ; 19\r\n                  (  149.16   380.11     4.00     0.46)  ; 20\r\n                  (  147.10   380.84     5.10     0.46)  ; 21\r\n                  (  146.25   382.42     6.35     0.46)  ; 22\r\n                  (  146.76   384.33     7.57     0.46)  ; 23\r\n                  (  146.81   386.14     8.80     0.46)  ; 24\r\n                  (  145.97   387.73    10.55     0.46)  ; 25\r\n                  (  144.17   387.31    12.50     0.46)  ; 26\r\n                  (  144.17   387.31    12.47     0.46)  ; 27\r\n                  (  146.59   389.06    14.38     0.46)  ; 28\r\n                  (  146.33   390.20    16.15     0.46)  ; 29\r\n                  (  146.33   390.20    16.13     0.46)  ; 30\r\n                  (  144.90   392.25    17.42     0.46)  ; 31\r\n                  (  143.93   394.42    18.52     0.46)  ; 32\r\n                  (  142.10   398.16    19.45     0.46)  ; 33\r\n                  (  142.10   398.16    19.42     0.46)  ; 34\r\n                  (  140.23   400.11    20.17     0.46)  ; 35\r\n                  (  138.94   401.61    21.88     0.46)  ; 36\r\n\r\n                  (Dot\r\n                    (Color Cyan)\r\n                    (Name \"Marker 1\")\r\n                    (  171.46   363.35   -15.27     0.46)  ; 1\r\n                    (  154.94   375.50    -3.62     0.46)  ; 2\r\n                    (  148.98   378.88     4.00     0.46)  ; 3\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color RGB (255, 128, 255))\r\n                    (Name \"Marker 2\")\r\n                    (  168.38   364.43   -15.02     0.46)  ; 1\r\n                    (  161.91   369.97   -10.77     0.46)  ; 2\r\n                    (  159.63   373.61    -7.87     0.46)  ; 3\r\n                    (  146.45   383.66     7.57     0.46)  ; 4\r\n                    (  144.24   395.08    18.52     0.46)  ; 5\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (  333.75   168.06   -48.58     0.46)  ; 1, R-2-1-2-1-1-2-2\r\n                (  335.33   171.40   -49.83     0.46)  ; 2\r\n                (  334.53   174.80   -51.63     0.46)  ; 3\r\n                (  334.67   180.22   -51.63     0.46)  ; 4\r\n                (  335.84   185.27   -51.90     0.46)  ; 5\r\n                (  335.81   189.45   -52.00     0.46)  ; 6\r\n                (  335.64   194.18   -52.35     0.46)  ; 7\r\n                (  335.16   198.24   -52.95     0.46)  ; 8\r\n                (  335.16   198.24   -52.97     0.46)  ; 9\r\n                (  335.96   200.82   -53.42     0.46)  ; 10\r\n                (  335.48   204.88   -53.42     0.46)  ; 11\r\n                (  335.58   208.49   -53.33     0.46)  ; 12\r\n                (  336.39   211.08   -54.15     0.46)  ; 13\r\n                (  337.07   214.21   -54.15     0.46)  ; 14\r\n                (  337.75   217.36   -54.95     0.46)  ; 15\r\n                (  337.66   219.74   -56.20     0.46)  ; 16\r\n                (  337.89   222.77   -57.60     0.46)  ; 17\r\n                (  339.11   229.63   -57.92     0.46)  ; 18\r\n                (  339.07   233.80   -58.42     0.46)  ; 19\r\n                (  339.07   233.80   -58.45     0.46)  ; 20\r\n                (  338.99   236.16   -58.67     0.46)  ; 21\r\n                (  341.06   241.43   -59.17     0.46)  ; 22\r\n                (  342.18   244.68   -58.75     0.46)  ; 23\r\n                (  343.04   249.05   -58.60     0.46)  ; 24\r\n                (  343.14   252.66   -58.60     0.46)  ; 25\r\n                (  344.44   257.15   -59.80     0.46)  ; 26\r\n                (  344.80   259.63   -60.63     0.46)  ; 27\r\n                (  345.61   262.20   -61.63     0.46)  ; 28\r\n                (  345.61   262.20   -61.67     0.46)  ; 29\r\n                (  346.59   266.01   -62.20     0.46)  ; 30\r\n                (  347.59   269.83   -63.10     0.46)  ; 31\r\n                (  348.01   274.11   -63.95     0.46)  ; 32\r\n                (  349.12   277.35   -64.65     0.46)  ; 33\r\n                (  349.12   277.35   -64.67     0.46)  ; 34\r\n                (  349.84   282.31   -63.95     0.46)  ; 35\r\n                (  351.47   287.47   -64.53     0.46)  ; 36\r\n                (  350.73   292.66   -64.80     0.46)  ; 37\r\n                (  351.89   297.71   -62.80     0.46)  ; 38\r\n                (  351.89   297.71   -62.82     0.46)  ; 39\r\n                (  352.36   301.85   -59.70     0.46)  ; 40\r\n                (  351.88   305.92   -58.25     0.46)  ; 41\r\n                (  351.88   305.92   -58.27     0.46)  ; 42\r\n                (  352.28   310.19   -60.22     0.46)  ; 43\r\n                (  353.09   312.78   -62.37     0.46)  ; 44\r\n                (  353.09   312.78   -62.40     0.46)  ; 45\r\n                (  352.79   318.09   -63.70     0.46)  ; 46\r\n                (  352.79   318.09   -63.75     0.46)  ; 47\r\n                (  353.07   322.93   -65.32     0.46)  ; 48\r\n                (  353.07   322.93   -65.35     0.46)  ; 49\r\n                (  352.64   328.80   -65.45     0.46)  ; 50\r\n                (  353.49   333.18   -65.55     0.46)  ; 51\r\n                (  354.48   337.00   -65.55     0.46)  ; 52\r\n                (  353.64   338.59   -65.55     0.46)  ; 53\r\n                (  354.00   341.06   -66.50     0.46)  ; 54\r\n                (  355.00   344.87   -67.57     0.46)  ; 55\r\n                (  356.17   349.92   -68.42     0.46)  ; 56\r\n                (  356.39   352.96   -67.85     0.46)  ; 57\r\n                (  356.39   352.96   -67.88     0.46)  ; 58\r\n                (  357.07   356.11   -68.82     0.46)  ; 59\r\n                (  357.04   360.29   -69.30     0.46)  ; 60\r\n                (  356.24   363.69   -69.53     0.46)  ; 61\r\n                (  356.21   367.85   -69.53     0.46)  ; 62\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  337.96   214.42   -54.15     0.46)  ; 1\r\n                  (  355.73   355.79   -68.82     0.46)  ; 2\r\n                  (  355.00   366.98   -69.53     0.46)  ; 3\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  336.26   189.55   -52.00     0.46)  ; 1\r\n                  (  335.89   209.16   -53.33     0.46)  ; 2\r\n                  (  339.56   229.73   -57.92     0.46)  ; 3\r\n                  (  342.18   244.68   -58.75     0.46)  ; 4\r\n                  (  343.14   252.66   -58.62     0.46)  ; 5\r\n                  (  352.77   328.24   -65.45     0.46)  ; 6\r\n                  (  354.45   341.17   -66.50     0.46)  ; 7\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  314.04    -3.06    -9.05     0.46)  ; 1, R-2-1-2-1-2\r\n            (  311.85    -1.79    -7.07     0.46)  ; 2\r\n            (  308.34    -0.82    -6.02     0.46)  ; 3\r\n            (  306.29    -0.10    -4.47     0.46)  ; 4\r\n            (  303.91    -0.06    -3.60     0.46)  ; 5\r\n            (  300.84     1.00    -2.35     0.46)  ; 6\r\n            (  298.92     1.15    -1.82     0.46)  ; 7\r\n            (  298.92     1.15    -1.88     0.46)  ; 8\r\n            (  294.68     3.15    -0.72     0.46)  ; 9\r\n            (  289.82     3.79    -0.72     0.46)  ; 10\r\n            (  286.16     5.33     0.22     0.46)  ; 11\r\n            (  281.75     6.08     0.80     0.46)  ; 12\r\n            (  278.68     7.16    -0.28     0.46)  ; 13\r\n            (  278.68     7.16    -0.30     0.46)  ; 14\r\n            (  274.43     9.15    -2.50     0.46)  ; 15\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  301.87     0.65    -2.35     0.46)  ; 1\r\n              (  286.61     5.43     0.22     0.46)  ; 2\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (  319.61   -38.80     6.40     0.46)  ; 1, R-2-1-2-2\r\n          (  319.61   -38.80     6.42     0.46)  ; 2\r\n          (  321.70   -37.71     7.22     0.46)  ; 3\r\n          (  322.65   -35.70     8.42     0.46)  ; 4\r\n          (  325.64   -34.40    10.07     0.46)  ; 5\r\n          (  328.05   -32.63    10.07     0.46)  ; 6\r\n          (  331.41   -28.87    10.07     0.46)  ; 7\r\n          (  334.14   -26.44    10.07     0.46)  ; 8\r\n          (  337.94   -22.56     8.77     0.46)  ; 9\r\n          (  340.17   -22.04     8.35     0.46)  ; 10\r\n          (  343.54   -18.26     7.55     0.46)  ; 11\r\n          (  346.97   -16.86     7.05     0.46)  ; 12\r\n          (  346.97   -16.86     7.02     0.46)  ; 13\r\n          (  348.85   -12.83     7.95     0.46)  ; 14\r\n          (  351.45    -9.83     7.80     0.46)  ; 15\r\n          (  354.93    -6.63     7.80     0.46)  ; 16\r\n          (  357.09    -3.74     8.68     0.46)  ; 17\r\n          (  360.97    -2.24     9.10     0.46)  ; 18\r\n          (  363.33    -2.27     9.10     0.46)  ; 19\r\n          (  365.44    -1.18    10.25     0.46)  ; 20\r\n          (  365.31    -0.61    10.25     0.46)  ; 21\r\n          (  367.99     0.01    11.45     0.46)  ; 22\r\n          (  370.21     0.53    13.25     0.46)  ; 23\r\n          (  372.06     2.75    14.97     0.46)  ; 24\r\n          (  373.71     3.74    14.97     0.46)  ; 25\r\n          (  375.23     5.29    15.65     0.46)  ; 26\r\n          (  378.41     7.82    16.80     0.46)  ; 27\r\n          (  378.63    10.86    17.42     0.46)  ; 28\r\n          (  380.60    12.52    17.75     0.46)  ; 29\r\n          (  382.57    14.18    19.15     0.46)  ; 30\r\n          (  382.57    14.18    19.13     0.46)  ; 31\r\n          (  384.80    14.70    21.52     0.46)  ; 32\r\n          (  385.56    15.47    23.42     0.46)  ; 33\r\n          (  385.12    15.37    23.40     0.46)  ; 34\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  337.45   -24.47     8.77     0.46)  ; 1\r\n            (  346.76   -13.92     7.95     0.46)  ; 2\r\n            (  350.29    -8.92     7.80     0.46)  ; 3\r\n            (  360.66    -2.90     9.10     0.46)  ; 4\r\n            (  370.03    -0.70    13.25     0.46)  ; 5\r\n            (  372.63     2.29    14.97     0.46)  ; 6\r\n            (  377.04    11.69    19.05     0.46)  ; 7\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  319.61   -38.80     6.42     0.46)  ; 1\r\n            (  322.65   -35.70     8.42     0.46)  ; 2\r\n            (  328.81   -31.86    10.07     0.46)  ; 3\r\n            (  321.63   -35.35    10.07     0.46)  ; 4\r\n            (  356.64    -3.85     8.68     0.46)  ; 5\r\n            (  375.23     5.29    15.65     0.46)  ; 6\r\n            (  378.41     7.82    16.80     0.46)  ; 7\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  286.67   -92.36     6.90     0.46)  ; 1, R-2-2\r\n      (  285.12   -89.75     6.90     0.46)  ; 2\r\n      (  283.69   -87.69     6.15     0.46)  ; 3\r\n      (\r\n        (  282.14   -85.06     7.22     0.46)  ; 1, R-2-2-1\r\n        (  281.69   -85.17     7.22     0.46)  ; 2\r\n        (  279.83   -83.23     7.87     0.46)  ; 3\r\n        (  276.60   -81.59     7.53     0.46)  ; 4\r\n        (\r\n          (  274.47   -78.50     7.53     0.46)  ; 1, R-2-2-1-1\r\n          (  272.10   -78.46     7.53     0.46)  ; 2\r\n          (  270.16   -74.14     7.53     0.46)  ; 3\r\n          (  268.52   -69.15     7.92     0.46)  ; 4\r\n          (\r\n            (  269.63   -65.90     8.75     0.46)  ; 1, R-2-2-1-1-1\r\n            (  269.41   -62.97     9.50     0.46)  ; 2\r\n            (  269.51   -59.37     9.88     0.46)  ; 3\r\n            (  268.90   -54.73    11.45     0.46)  ; 4\r\n            (  268.37   -52.46    12.47     0.46)  ; 5\r\n            (  268.42   -50.65    13.80     0.46)  ; 6\r\n            (  268.28   -50.09    15.17     0.46)  ; 7\r\n            (  265.88   -45.88    15.90     0.46)  ; 8\r\n            (  262.91   -41.21    15.90     0.46)  ; 9\r\n            (  261.66   -37.91    14.85     0.46)  ; 10\r\n            (  260.02   -32.93    14.30     0.46)  ; 11\r\n            (  258.65   -29.07    14.88     0.46)  ; 12\r\n            (  257.54   -26.34    14.70     0.46)  ; 13\r\n            (  255.85   -23.15    14.40     0.46)  ; 14\r\n            (  254.43   -21.10    14.40     0.46)  ; 15\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  268.14   -55.51    11.45     0.46)  ; 1\r\n              (  265.12   -46.66    15.90     0.46)  ; 2\r\n              (  259.26   -33.70    14.30     0.46)  ; 3\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  267.97   -50.76    15.17     0.46)  ; 1\r\n              (  263.61   -42.23    15.90     0.46)  ; 2\r\n              (  257.57   -30.51    16.70     0.46)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  254.39   -16.92    14.40     0.46)  ; 1, R-2-2-1-1-1-1\r\n              (  253.46   -12.97    14.40     0.46)  ; 2\r\n              (  252.66    -9.57    15.35     0.46)  ; 3\r\n              (  251.68    -7.40    16.00     0.46)  ; 4\r\n              (  251.68    -7.40    15.97     0.46)  ; 5\r\n              (  250.73    -5.37    17.20     0.46)  ; 6\r\n              (  250.08    -2.55    18.02     0.46)  ; 7\r\n              (  249.09    -0.39    17.52     0.46)  ; 8\r\n              (  249.02     1.97    19.10     0.46)  ; 9\r\n              (  247.58     4.03    19.70     0.46)  ; 10\r\n              (  247.18     5.73    20.20     0.46)  ; 11\r\n              (  247.56     8.20    21.15     0.46)  ; 12\r\n              (  245.85    11.39    22.50     0.46)  ; 13\r\n              (  244.89    13.55    22.75     0.46)  ; 14\r\n              (  242.03    17.67    22.27     0.46)  ; 15\r\n              (  241.24    21.06    23.27     0.46)  ; 16\r\n              (  239.11    24.14    24.10     0.46)  ; 17\r\n              (  237.11    26.65    24.67     0.46)  ; 18\r\n              (  233.01    28.08    24.67     0.46)  ; 19\r\n              (  232.07    32.04    23.72     0.46)  ; 20\r\n              (  230.38    35.23    22.75     0.46)  ; 21\r\n              (  230.38    35.23    22.73     0.46)  ; 22\r\n              (  227.17    36.87    21.55     0.46)  ; 23\r\n              (  227.17    36.87    21.52     0.46)  ; 24\r\n              (  225.12    37.59    22.63     0.46)  ; 25\r\n              (  222.93    38.87    21.25     0.46)  ; 26\r\n              (  222.93    38.87    21.02     0.46)  ; 27\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  250.93    -8.19    15.97     0.46)  ; 1\r\n                (  237.87    27.43    24.67     0.46)  ; 2\r\n                (  224.72    39.29    20.92     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  250.26    -1.31    17.52     0.46)  ; 1\r\n                (  245.47    13.08    22.75     0.46)  ; 2\r\n              )  ;  End of markers\r\n               Normal\r\n            |\r\n              (  250.43   -22.17    13.32     0.46)  ; 1, R-2-2-1-1-1-2\r\n              (  245.97   -23.22    11.88     0.46)  ; 2\r\n              (  241.50   -24.26    11.98     0.46)  ; 3\r\n              (  238.50   -25.56    11.17     0.46)  ; 4\r\n              (  236.27   -26.08     9.90     0.46)  ; 5\r\n              (  232.52   -28.15     9.17     0.46)  ; 6\r\n              (  227.79   -28.07     8.57     0.46)  ; 7\r\n              (  225.56   -28.59     6.97     0.46)  ; 8\r\n              (  221.98   -29.44     6.28     0.46)  ; 9\r\n              (  221.98   -29.44     6.25     0.46)  ; 10\r\n              (  219.43   -30.63     5.55     0.46)  ; 11\r\n              (  216.30   -31.36     4.90     0.46)  ; 12\r\n              (  213.63   -31.98     4.15     0.46)  ; 13\r\n              (  213.63   -31.98     4.13     0.46)  ; 14\r\n              (  211.27   -31.94     2.78     0.46)  ; 15\r\n              (  209.79   -31.70     2.10     0.46)  ; 16\r\n              (  207.42   -31.66     1.20     0.46)  ; 17\r\n              (  203.27   -32.03     0.57     0.46)  ; 18\r\n              (  199.43   -31.73    -0.43     0.46)  ; 19\r\n              (  199.43   -31.73    -0.45     0.46)  ; 20\r\n              (  196.31   -32.46     0.52     0.46)  ; 21\r\n              (  192.68   -35.11    -0.22     0.46)  ; 22\r\n              (  187.64   -35.70    -1.13     0.46)  ; 23\r\n              (  185.68   -37.35    -1.82     0.46)  ; 24\r\n              (  182.05   -39.98    -2.63     0.46)  ; 25\r\n              (  178.93   -40.72    -2.42     0.46)  ; 26\r\n              (  174.59   -42.33    -2.50     0.46)  ; 27\r\n              (  170.97   -44.97    -2.83     0.46)  ; 28\r\n              (  167.71   -45.14    -4.32     0.46)  ; 29\r\n              (  167.71   -45.14    -4.35     0.46)  ; 30\r\n              (  165.61   -46.23    -5.75     0.46)  ; 31\r\n              (  163.70   -46.08    -4.90     0.46)  ; 32\r\n              (  161.01   -46.72    -6.55     0.46)  ; 33\r\n              (  158.78   -47.23    -7.82     0.46)  ; 34\r\n              (  158.15   -48.57    -8.60     0.46)  ; 35\r\n              (  154.57   -49.41    -9.25     0.46)  ; 36\r\n              (  150.72   -49.10    -9.25     0.46)  ; 37\r\n              (  148.57   -52.00    -9.25     0.46)  ; 38\r\n              (  144.77   -55.87    -9.48     0.46)  ; 39\r\n              (  139.86   -57.02   -10.38     0.46)  ; 40\r\n              (  137.19   -57.65   -11.40     0.46)  ; 41\r\n              (  133.74   -59.06   -12.72     0.46)  ; 42\r\n              (  133.74   -59.06   -12.75     0.46)  ; 43\r\n              (  132.40   -59.38   -15.00     0.46)  ; 44\r\n              (  129.54   -61.24   -15.70     0.46)  ; 45\r\n              (  128.47   -62.68   -15.70     0.46)  ; 46\r\n              (  124.89   -63.51   -16.57     0.46)  ; 47\r\n              (  121.45   -64.92   -16.90     0.46)  ; 48\r\n              (  118.01   -66.32   -16.90     0.46)  ; 49\r\n              (  112.39   -66.45   -18.35     0.46)  ; 50\r\n              (  107.79   -66.93   -18.35     0.46)  ; 51\r\n              (  105.43   -66.89   -20.23     0.46)  ; 52\r\n              (  105.43   -66.89   -20.25     0.46)  ; 53\r\n              (  101.85   -67.72   -22.77     0.46)  ; 54\r\n              (   98.15   -68.00   -22.77     0.46)  ; 55\r\n              (   95.65   -68.56   -24.25     0.46)  ; 56\r\n              (   93.87   -68.97   -25.70     0.46)  ; 57\r\n              (   93.87   -68.97   -25.73     0.46)  ; 58\r\n              (   91.50   -68.93   -26.98     0.46)  ; 59\r\n              (   90.11   -71.05   -28.70     0.46)  ; 60\r\n              (   86.72   -70.66   -30.02     0.46)  ; 61\r\n              (   86.59   -70.09   -30.02     0.46)  ; 62\r\n              (   83.64   -69.59   -31.45     0.46)  ; 63\r\n              (   81.85   -70.01   -33.10     0.46)  ; 64\r\n              (   80.21   -70.99   -35.02     0.46)  ; 65\r\n              (   79.58   -72.33   -37.20     0.46)  ; 66\r\n              (   76.89   -72.95   -39.58     0.46)  ; 67\r\n              (   76.89   -72.95   -39.60     0.46)  ; 68\r\n              (   74.22   -73.58   -41.47     0.46)  ; 69\r\n              (   70.52   -73.86   -41.28     0.46)  ; 70\r\n              (   67.24   -74.03   -43.33     0.46)  ; 71\r\n              (   63.82   -75.42   -44.35     0.46)  ; 72\r\n              (   59.39   -74.68   -46.00     0.46)  ; 73\r\n              (   56.98   -76.44   -47.47     0.46)  ; 74\r\n              (   49.74   -75.73   -47.47     0.46)  ; 75\r\n              (   48.46   -74.25   -49.70     0.46)  ; 76\r\n              (   47.20   -76.93   -50.52     0.46)  ; 77\r\n              (   44.21   -78.23   -51.70     0.46)  ; 78\r\n              (   38.59   -78.35   -52.85     0.46)  ; 79\r\n              (   35.50   -77.29   -53.70     0.46)  ; 80\r\n              (   30.52   -76.06   -54.45     0.46)  ; 81\r\n              (   30.52   -76.06   -54.47     0.46)  ; 82\r\n              (   25.51   -74.85   -53.67     0.46)  ; 83\r\n              (   22.26   -75.02   -53.65     0.46)  ; 84\r\n              (   19.18   -73.94   -53.47     0.46)  ; 85\r\n              (   15.61   -74.78   -52.15     0.46)  ; 86\r\n              (   12.79   -74.85   -51.50     0.46)  ; 87\r\n              (    8.77   -75.79   -51.50     0.46)  ; 88\r\n              (    4.63   -76.16   -50.40     0.46)  ; 89\r\n              (   -0.69   -75.62   -49.67     0.46)  ; 90\r\n              (   -3.81   -76.35   -49.27     0.46)  ; 91\r\n              (   -8.11   -76.16   -49.72     0.46)  ; 92\r\n              (  -12.65   -74.84   -49.72     0.46)  ; 93\r\n              (  -18.77   -76.87   -49.45     0.46)  ; 94\r\n              (  -22.30   -77.10   -48.42     0.46)  ; 95\r\n              (  -26.19   -78.61   -48.42     0.46)  ; 96\r\n              (  -31.50   -78.05   -48.13     0.46)  ; 97\r\n              (  -35.21   -78.32   -47.75     0.46)  ; 98\r\n              (  -39.04   -78.04   -47.75     0.46)  ; 99\r\n              (  -41.58   -79.23   -46.52     0.46)  ; 100\r\n              (  -44.53   -78.72   -45.60     0.46)  ; 101\r\n              (  -47.03   -78.12   -45.60     0.46)  ; 102\r\n              (  -50.47   -79.52   -45.60     0.46)  ; 103\r\n              (  -55.38   -80.67   -45.20     0.46)  ; 104\r\n              (  -59.67   -80.47   -42.80     0.46)  ; 105\r\n              (  -63.64   -79.62   -42.22     0.46)  ; 106\r\n              (  -68.11   -80.67   -41.32     0.46)  ; 107\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  246.68   -24.24    11.88     0.46)  ; 1\r\n                (  205.32   -32.74     0.57     0.46)  ; 2\r\n                (  192.81   -35.67    -0.22     0.46)  ; 3\r\n                (  182.10   -38.18    -2.63     0.46)  ; 4\r\n                (  175.56   -44.50    -2.50     0.46)  ; 5\r\n                (   83.91   -70.72   -31.45     0.46)  ; 6\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  219.88   -30.52     5.55     0.46)  ; 1\r\n                (  216.43   -31.92     4.90     0.46)  ; 2\r\n                (  209.34   -31.80     2.10     0.46)  ; 3\r\n                (  200.01   -32.18    -0.45     0.46)  ; 4\r\n                (  163.83   -46.64    -4.90     0.46)  ; 5\r\n                (  155.02   -49.30    -9.25     0.46)  ; 6\r\n                (  144.32   -55.97    -9.48     0.46)  ; 7\r\n                (  118.45   -66.22   -16.90     0.46)  ; 8\r\n                (  121.45   -64.92   -16.90     0.46)  ; 9\r\n                (  108.23   -66.83   -18.33     0.46)  ; 10\r\n                (   98.73   -68.46   -22.77     0.46)  ; 11\r\n                (  102.30   -67.62   -22.77     0.46)  ; 12\r\n                (   91.64   -69.50   -26.98     0.46)  ; 13\r\n                (   70.96   -73.75   -41.28     0.46)  ; 14\r\n                (   64.12   -74.76   -44.35     0.46)  ; 15\r\n                (   23.37   -77.74   -53.65     0.46)  ; 16\r\n                (   22.71   -74.91   -53.65     0.46)  ; 17\r\n                (   15.61   -74.78   -52.15     0.46)  ; 18\r\n                (    9.66   -75.58   -51.50     0.46)  ; 19\r\n                (  -18.46   -76.20   -49.45     0.46)  ; 20\r\n                (  -54.93   -80.56   -45.20     0.46)  ; 21\r\n                (  -60.12   -80.58   -42.80     0.46)  ; 22\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          |\r\n            (  265.02   -68.28     7.43     0.46)  ; 1, R-2-2-1-1-2\r\n            (  262.56   -65.87     8.47     0.46)  ; 2\r\n            (  261.13   -63.82     9.75     0.46)  ; 3\r\n            (  259.98   -62.89    10.33     0.46)  ; 4\r\n            (  257.92   -62.19    10.95     0.46)  ; 5\r\n            (  257.92   -62.19    10.92     0.46)  ; 6\r\n            (  256.50   -60.13    11.55     0.46)  ; 7\r\n            (  252.90   -56.79    12.60     0.46)  ; 8\r\n            (  250.58   -54.94    12.60     0.46)  ; 9\r\n            (  248.57   -52.43    11.00     0.46)  ; 10\r\n            (  245.81   -50.69     9.95     0.46)  ; 11\r\n            (  242.03   -48.60     9.67     0.46)  ; 12\r\n            (  241.41   -48.12     9.67     0.46)  ; 13\r\n            (\r\n              (  240.44   -45.97     7.47     0.46)  ; 1, R-2-2-1-1-2-1\r\n              (  239.01   -43.91     6.15     0.46)  ; 2\r\n              (  236.39   -42.74     5.30     0.46)  ; 3\r\n              (  233.75   -41.55     6.32     0.46)  ; 4\r\n              (  231.31   -39.15     6.90     0.46)  ; 5\r\n              (  228.85   -36.73     6.90     0.46)  ; 6\r\n              (  225.64   -35.09     7.20     0.46)  ; 7\r\n              (  223.95   -31.91     7.77     0.46)  ; 8\r\n              (  219.26   -30.03     8.42     0.46)  ; 9\r\n              (  217.14   -26.94     9.55     0.46)  ; 10\r\n              (  214.10   -24.07    10.45     0.46)  ; 11\r\n              (  212.19   -23.92    10.75     0.46)  ; 12\r\n              (  211.53   -21.09    10.75     0.46)  ; 13\r\n              (  208.58   -20.58    11.15     0.46)  ; 14\r\n              (  206.71   -18.64    11.15     0.46)  ; 15\r\n              (  203.63   -17.56    10.77     0.46)  ; 16\r\n              (  201.05   -14.59    12.40     0.46)  ; 17\r\n              (  201.05   -14.59    12.42     0.46)  ; 18\r\n              (  196.99   -11.36    12.42     0.46)  ; 19\r\n              (  192.62    -8.80    12.42     0.46)  ; 20\r\n              (  189.60    -5.93    11.93     0.46)  ; 21\r\n              (  186.12    -3.16    12.30     0.46)  ; 22\r\n              (  183.22    -0.86    13.18     0.46)  ; 23\r\n              (  180.06     2.58    13.27     0.46)  ; 24\r\n              (  175.74     6.95    14.77     0.46)  ; 25\r\n              (  171.98     8.93    14.52     0.46)  ; 26\r\n              (  168.76    10.56    14.52     0.46)  ; 27\r\n              (  165.16    13.89    15.38     0.46)  ; 28\r\n              (  159.59    15.58    15.38     0.46)  ; 29\r\n              (  157.00    18.55    16.10     0.46)  ; 30\r\n              (  154.51    19.16    16.10     0.46)  ; 31\r\n              (  151.47    22.03    16.77     0.46)  ; 32\r\n              (  146.80    23.93    16.77     0.46)  ; 33\r\n              (  144.03    25.66    17.27     0.46)  ; 34\r\n              (  142.92    28.39    17.45     0.46)  ; 35\r\n              (  141.00    28.54    17.63     0.46)  ; 36\r\n              (  138.41    31.51    17.63     0.46)  ; 37\r\n              (  135.84    34.50    17.63     0.46)  ; 38\r\n              (  133.12    38.04    18.02     0.46)  ; 39\r\n              (  130.93    39.32    18.02     0.46)  ; 40\r\n              (  129.19    40.71    16.98     0.46)  ; 41\r\n              (  125.09    42.13    16.92     0.46)  ; 42\r\n              (  121.48    45.46    16.92     0.46)  ; 43\r\n              (  119.79    48.65    17.20     0.46)  ; 44\r\n              (  118.68    51.38    16.45     0.46)  ; 45\r\n              (  116.73    55.70    16.07     0.46)  ; 46\r\n              (  114.99    57.09    15.50     0.46)  ; 47\r\n              (  113.43    59.70    15.50     0.46)  ; 48\r\n              (  110.77    65.05    14.70     0.46)  ; 49\r\n              (  107.16    68.39    14.70     0.46)  ; 50\r\n              (  104.49    73.73    14.25     0.46)  ; 51\r\n              (  101.60    76.04    13.90     0.46)  ; 52\r\n              (   98.75    80.15    14.60     0.46)  ; 53\r\n              (   95.99    81.89    14.40     0.46)  ; 54\r\n              (   94.88    84.61    13.80     0.46)  ; 55\r\n              (   91.40    87.38    13.80     0.46)  ; 56\r\n              (   89.53    89.34    13.80     0.46)  ; 57\r\n              (   88.73    92.72    13.15     0.46)  ; 58\r\n              (   85.98    94.47    13.15     0.46)  ; 59\r\n              (   83.79    95.74    12.65     0.46)  ; 60\r\n              (   82.55    99.04    12.65     0.46)  ; 61\r\n              (   79.51   101.91    12.05     0.46)  ; 62\r\n\r\n              (Dot\r\n                (Color Cyan)\r\n                (Name \"Marker 1\")\r\n                (  241.75   -41.48     7.47     0.46)  ; 1\r\n                (  239.33   -43.23     5.30     0.46)  ; 2\r\n                (  226.94   -36.59     7.20     0.46)  ; 3\r\n                (  216.77   -29.42     9.55     0.46)  ; 4\r\n                (  186.07    -4.96    12.30     0.46)  ; 5\r\n                (  148.21    21.87    16.77     0.46)  ; 6\r\n                (  141.13    27.98    16.55     0.46)  ; 7\r\n                (  141.00    28.54    12.67     0.46)  ; 8\r\n                (  122.60    42.73    16.92     0.46)  ; 9\r\n                (  116.81    53.33    16.07     0.46)  ; 10\r\n                (   98.70    78.35    14.60     0.46)  ; 11\r\n                (   88.78    94.53    13.15     0.46)  ; 12\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color RGB (255, 128, 255))\r\n                (Name \"Marker 2\")\r\n                (  214.68   -24.53    10.35     0.46)  ; 1\r\n                (  206.97   -19.77    11.15     0.46)  ; 2\r\n                (  176.32     6.49    14.77     0.46)  ; 3\r\n                (  169.21    10.66    14.52     0.46)  ; 4\r\n                (  102.04    76.14    13.90     0.46)  ; 5\r\n                (   91.40    87.38    13.80     0.46)  ; 6\r\n              )  ;  End of markers\r\n              (\r\n                (   77.33   103.16    13.63     0.46)  ; 1, R-2-2-1-1-2-1-1\r\n                (   75.32   105.68    14.75     0.46)  ; 2\r\n                (   73.77   108.29    15.75     0.46)  ; 3\r\n                (   70.21   113.43    16.17     0.46)  ; 4\r\n                (   67.49   116.98    15.38     0.46)  ; 5\r\n                (   65.10   121.20    14.18     0.46)  ; 6\r\n                (   63.27   124.94    13.05     0.46)  ; 7\r\n                (   62.82   124.84    13.05     0.46)  ; 8\r\n                (   58.27   126.15    12.77     0.46)  ; 9\r\n                (   54.30   127.02    12.77     0.46)  ; 10\r\n                (   51.59   130.57    11.80     0.46)  ; 11\r\n                (   49.90   133.76    11.88     0.46)  ; 12\r\n                (   48.17   135.14    11.88     0.46)  ; 13\r\n                (   45.85   136.99    11.88     0.46)  ; 14\r\n                (   41.65   140.79    12.72     0.46)  ; 15\r\n                (   37.92   144.68    12.45     0.46)  ; 16\r\n                (   34.32   148.02    12.45     0.46)  ; 17\r\n                (   32.18   151.10    10.73     0.46)  ; 18\r\n                (   30.04   154.17    11.13     0.46)  ; 19\r\n                (   28.03   156.69    10.15     0.46)  ; 20\r\n                (   26.03   159.21     8.63     0.46)  ; 21\r\n                (   25.86   163.94     8.45     0.46)  ; 22\r\n                (   26.36   165.85     9.60     0.46)  ; 23\r\n                (   26.32   170.02    10.75     0.46)  ; 24\r\n                (   25.21   172.75    11.05     0.46)  ; 25\r\n                (   25.07   173.32    11.05     0.46)  ; 26\r\n\r\n                (Dot\r\n                  (Color Cyan)\r\n                  (Name \"Marker 1\")\r\n                  (   58.10   124.92    12.77     0.46)  ; 1\r\n                  (   45.67   135.75    11.88     0.46)  ; 2\r\n                  (   41.34   140.10    12.72     0.46)  ; 3\r\n                  (   33.21   150.73    10.73     0.46)  ; 4\r\n                  (   30.93   154.38    11.53     0.46)  ; 5\r\n                  (   27.02   163.03     8.45     0.46)  ; 6\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color RGB (255, 128, 255))\r\n                  (Name \"Marker 2\")\r\n                  (   73.77   108.29    15.75     0.46)  ; 1\r\n                  (   70.79   112.98    16.17     0.46)  ; 2\r\n                  (   49.90   133.76    11.88     0.46)  ; 3\r\n                  (   37.92   144.68    12.45     0.46)  ; 4\r\n                  (   28.48   156.80    10.15     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (   24.72   170.84    12.17     0.46)  ; 1, R-2-2-1-1-2-1-1-1\r\n                  (   24.85   170.28    14.95     0.46)  ; 2\r\n                  (   25.43   169.82    16.20     0.46)  ; 3\r\n                  (   27.48   169.10    17.57     0.46)  ; 4\r\n                  (   28.38   169.31    19.02     0.46)  ; 5\r\n                  (   28.20   168.07    20.20     0.46)  ; 6\r\n                  (   30.29   169.15    21.40     0.46)  ; 7\r\n                  (   31.90   168.34    21.38     0.46)  ; 8\r\n                  (   32.03   167.77    22.02     0.46)  ; 9\r\n                  (   28.20   168.07    23.82     0.46)  ; 10\r\n                  (   27.25   166.06    24.77     0.46)  ; 11\r\n                  (   28.36   163.33    25.20     0.46)  ; 12\r\n                  (   26.34   159.88    25.20     0.46)  ; 13\r\n                  (   26.29   158.07    23.98     0.46)  ; 14\r\n                  (   26.69   156.38    22.57     0.46)  ; 15\r\n                  (   26.96   155.24    21.75     0.46)  ; 16\r\n                  (   26.46   153.34    20.73     0.46)  ; 17\r\n                  (   27.75   151.85    18.42     0.46)  ; 18\r\n                  (   27.75   151.85    18.40     0.46)  ; 19\r\n\r\n                  (OpenCircle\r\n                    (Color RGB (255, 128, 255))\r\n                    (Name \"Marker 2\")\r\n                    (   25.30   170.38    16.20     0.46)  ; 1\r\n                    (   26.25   156.27    21.75     0.46)  ; 2\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (   23.39   176.51     9.25     0.46)  ; 1, R-2-2-1-1-2-1-1-2\r\n                  (   22.94   176.40     9.25     0.46)  ; 2\r\n                  (   23.54   181.91     9.82     0.46)  ; 3\r\n                  (   22.29   185.21    11.10     0.46)  ; 4\r\n                  (   21.76   187.47    12.17     0.46)  ; 5\r\n                  (   20.25   191.90    13.27     0.46)  ; 6\r\n                  (   19.46   195.30    14.67     0.46)  ; 7\r\n                  (   19.42   199.46    15.38     0.46)  ; 8\r\n                  (   16.85   202.43    15.38     0.46)  ; 9\r\n                  (   13.53   204.52    15.38     0.46)  ; 10\r\n                  (   12.03   208.94    16.45     0.46)  ; 11\r\n                  (   11.50   211.22    16.45     0.46)  ; 12\r\n                  (   11.45   215.39    17.30     0.46)  ; 13\r\n                  (   10.62   216.98    18.57     0.46)  ; 14\r\n                  (   10.52   219.35    19.02     0.46)  ; 15\r\n                  (    8.84   222.54    19.50     0.46)  ; 16\r\n                  (    8.18   225.37    17.92     0.46)  ; 17\r\n                  (    6.80   229.22    17.02     0.46)  ; 18\r\n                  (    5.37   231.28    16.47     0.46)  ; 19\r\n                  (    3.41   235.59    16.47     0.46)  ; 20\r\n                  (    2.04   239.45    16.85     0.46)  ; 21\r\n                  (   -0.41   241.87    16.80     0.46)  ; 22\r\n                  (   -2.85   244.27    16.45     0.46)  ; 23\r\n                  (   -3.70   245.87    16.75     0.46)  ; 24\r\n\r\n                  (Dot\r\n                    (Color Cyan)\r\n                    (Name \"Marker 1\")\r\n                    (   24.83   180.43     9.82     0.46)  ; 1\r\n                    (    1.41   238.12    16.85     0.46)  ; 2\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color RGB (255, 128, 255))\r\n                    (Name \"Marker 2\")\r\n                    (   21.76   187.47    12.17     0.46)  ; 1\r\n                    (   20.39   191.33    13.27     0.46)  ; 2\r\n                    (   18.01   201.52    15.38     0.46)  ; 3\r\n                    (   11.25   208.17    16.45     0.46)  ; 4\r\n                    (   11.63   210.65    16.45     0.46)  ; 5\r\n                    (   10.08   219.24    19.02     0.46)  ; 6\r\n                    (    5.37   231.28    16.47     0.46)  ; 7\r\n                    (   -4.28   246.33    16.75     0.46)  ; 8\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (   79.34   106.62    13.10     0.46)  ; 1, R-2-2-1-1-2-1-2\r\n                (   79.70   109.11    14.07     0.46)  ; 2\r\n                (   79.93   112.14    12.50     0.46)  ; 3\r\n                (   80.42   114.05    11.20     0.46)  ; 4\r\n                (   80.20   116.99    10.07     0.46)  ; 5\r\n                (   81.33   120.23     9.40     0.46)  ; 6\r\n                (   80.98   123.73     8.15     0.46)  ; 7\r\n                (   81.08   127.34     6.95     0.46)  ; 8\r\n                (   80.54   129.61     5.57     0.46)  ; 9\r\n                (   81.67   132.85     3.65     0.46)  ; 10\r\n                (   80.88   136.25     2.78     0.46)  ; 11\r\n                (   79.63   139.54     1.90     0.46)  ; 12\r\n                (   79.42   142.48     0.82     0.46)  ; 13\r\n                (   78.89   144.74    -0.43     0.46)  ; 14\r\n                (   78.89   144.74    -0.45     0.46)  ; 15\r\n                (   77.06   148.49    -1.82     0.46)  ; 16\r\n                (   77.06   148.49    -1.85     0.46)  ; 17\r\n                (   76.14   152.46    -2.35     0.46)  ; 18\r\n                (   75.87   153.58    -2.35     0.46)  ; 19\r\n                (   74.63   156.87    -2.92     0.46)  ; 20\r\n                (   74.23   158.58    -3.95     0.46)  ; 21\r\n                (   73.65   159.04    -3.95     0.46)  ; 22\r\n                (   73.43   161.97    -3.95     0.46)  ; 23\r\n                (   72.90   164.24    -5.55     0.46)  ; 24\r\n                (   72.55   167.74    -6.95     0.46)  ; 25\r\n                (   70.99   170.36    -7.15     0.46)  ; 26\r\n                (   71.36   172.83    -9.00     0.46)  ; 27\r\n                (   71.23   173.40   -10.35     0.46)  ; 28\r\n                (   71.23   173.40   -10.38     0.46)  ; 29\r\n                (   71.59   175.88   -11.40     0.46)  ; 30\r\n                (   70.16   177.93   -11.75     0.46)  ; 31\r\n                (   69.37   181.33   -13.35     0.46)  ; 32\r\n                (   70.18   183.90   -15.35     0.46)  ; 33\r\n                (   68.94   187.20   -16.42     0.46)  ; 34\r\n                (   67.83   189.92   -18.23     0.46)  ; 35\r\n                (   68.09   192.84   -19.55     0.46)  ; 36\r\n                (   66.34   194.23   -20.30     0.46)  ; 37\r\n                (   66.34   194.23   -20.33     0.46)  ; 38\r\n                (   65.81   196.50   -21.05     0.46)  ; 39\r\n                (   65.23   196.95   -23.10     0.46)  ; 40\r\n                (   65.28   198.76   -24.52     0.46)  ; 41\r\n                (   65.28   198.76   -24.58     0.46)  ; 42\r\n                (   64.56   199.79   -25.75     0.46)  ; 43\r\n                (   64.56   199.79   -25.77     0.46)  ; 44\r\n                (   62.57   202.30   -27.50     0.46)  ; 45\r\n                (   63.82   204.99   -29.13     0.46)  ; 46\r\n                (   62.39   207.04   -30.60     0.46)  ; 47\r\n                (   60.26   210.13   -31.85     0.46)  ; 48\r\n                (   58.57   213.32   -32.67     0.46)  ; 49\r\n                (   55.68   215.61   -32.92     0.46)  ; 50\r\n                (   53.94   217.00   -33.85     0.46)  ; 51\r\n                (   53.73   219.94   -35.27     0.46)  ; 52\r\n                (   51.66   220.65   -36.52     0.46)  ; 53\r\n                (   48.37   224.66   -37.20     0.46)  ; 54\r\n                (   48.37   224.66   -37.22     0.46)  ; 55\r\n                (   46.68   227.85   -38.33     0.46)  ; 56\r\n                (   46.15   230.10   -40.03     0.46)  ; 57\r\n                (   43.44   233.65   -41.80     0.46)  ; 58\r\n                (   40.99   236.06   -42.77     0.46)  ; 59\r\n                (   40.01   238.22   -44.07     0.46)  ; 60\r\n                (   36.09   240.88   -44.07     0.46)  ; 61\r\n                (   34.54   243.50   -45.35     0.46)  ; 62\r\n                (   32.08   245.91   -46.22     0.46)  ; 63\r\n                (   30.44   250.90   -46.57     0.46)  ; 64\r\n                (   28.80   255.90   -47.05     0.46)  ; 65\r\n                (   28.80   255.90   -47.08     0.46)  ; 66\r\n                (   28.14   258.72   -48.58     0.46)  ; 67\r\n                (   28.68   262.43   -50.00     0.46)  ; 68\r\n                (   27.03   267.42   -51.93     0.46)  ; 69\r\n                (   25.09   271.73   -52.13     0.46)  ; 70\r\n                (   23.98   274.47   -51.75     0.46)  ; 71\r\n                (   21.71   278.11   -52.92     0.46)  ; 72\r\n                (   20.99   279.14   -54.85     0.46)  ; 73\r\n\r\n                (Dot\r\n                  (Color Cyan)\r\n                  (Name \"Marker 1\")\r\n                  (   79.67   119.25     9.40     0.46)  ; 1\r\n                  (   80.67   123.07     8.15     0.46)  ; 2\r\n                  (   79.54   135.94     2.78     0.46)  ; 3\r\n                  (   68.21   182.24   -13.35     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color RGB (255, 128, 255))\r\n                  (Name \"Marker 2\")\r\n                  (   79.57   109.67    14.07     0.46)  ; 1\r\n                  (   80.10   113.38    11.20     0.46)  ; 2\r\n                  (   81.21   126.77     6.95     0.46)  ; 3\r\n                  (   77.06   148.49    -1.85     0.46)  ; 4\r\n                  (   74.94   157.55    -2.92     0.46)  ; 5\r\n                  (   73.43   161.97    -3.95     0.46)  ; 6\r\n                  (   67.83   189.92   -18.23     0.46)  ; 7\r\n                  (   65.94   195.93   -21.05     0.46)  ; 8\r\n                  (   61.81   207.50   -30.60     0.46)  ; 9\r\n                  (   46.29   229.54   -40.03     0.46)  ; 10\r\n                  (   34.09   243.40   -45.35     0.46)  ; 11\r\n                  (   28.23   262.33   -50.00     0.46)  ; 12\r\n                  (   23.98   274.47   -51.75     0.46)  ; 13\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  238.58   -50.00    11.15     0.46)  ; 1, R-2-2-1-1-2-2\r\n              (  238.58   -50.00    11.13     0.46)  ; 2\r\n              (  234.12   -51.05    13.15     0.46)  ; 3\r\n              (  231.48   -49.86    14.92     0.46)  ; 4\r\n              (  228.10   -49.47    16.15     0.46)  ; 5\r\n              (  225.46   -48.29    17.08     0.46)  ; 6\r\n              (  224.43   -47.94    18.25     0.46)  ; 7\r\n              (  221.80   -46.76    19.70     0.46)  ; 8\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  257.39   -59.92    11.55     0.46)  ; 1\r\n                (  246.66   -52.29     9.95     0.46)  ; 2\r\n                (  222.43   -45.42    19.70     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  251.17   -55.41    12.60     0.46)  ; 1\r\n                (  254.50   -57.60    12.60     0.46)  ; 2\r\n              )  ;  End of markers\r\n              (\r\n                (  219.76   -46.05    18.55     0.46)  ; 1, R-2-2-1-1-2-2-1\r\n                (  218.41   -46.37    17.20     0.46)  ; 2\r\n                (  218.41   -46.37    17.15     0.46)  ; 3\r\n                (  216.45   -48.02    15.75     0.46)  ; 4\r\n                (  215.23   -48.89    14.67     0.46)  ; 5\r\n                (  213.01   -49.42    13.63     0.46)  ; 6\r\n                (  210.90   -50.50    12.80     0.46)  ; 7\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  217.47   -48.37    15.75     0.46)  ; 1\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  213.14   -49.99    13.63     0.46)  ; 1\r\n                  (  210.46   -50.61    12.80     0.46)  ; 2\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  218.73   -45.69    20.63     0.46)  ; 1, R-2-2-1-1-2-2-2\r\n                (  216.94   -46.12    22.32     0.46)  ; 2\r\n                (  214.26   -46.74    23.65     0.46)  ; 3\r\n                (  212.96   -45.24    24.95     0.46)  ; 4\r\n                (  212.30   -42.41    26.35     0.46)  ; 5\r\n                (  211.77   -40.15    28.63     0.46)  ; 6\r\n                (  210.93   -38.55    30.80     0.46)  ; 7\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  217.39   -46.01    22.32     0.46)  ; 1\r\n                  (  212.43   -42.99    26.35     0.46)  ; 2\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (  272.79   -81.39     7.15     0.46)  ; 1, R-2-2-1-2\r\n          (  269.27   -80.41     7.15     0.46)  ; 2\r\n          (  264.14   -78.65     7.15     0.46)  ; 3\r\n          (  260.04   -77.20     7.77     0.46)  ; 4\r\n          (  255.04   -75.99     7.90     0.46)  ; 5\r\n          (  251.52   -75.03     8.10     0.46)  ; 6\r\n          (  247.09   -74.27     8.10     0.46)  ; 7\r\n          (  242.23   -73.62     8.10     0.46)  ; 8\r\n          (  238.26   -72.77     6.35     0.46)  ; 9\r\n          (  236.34   -72.62     6.18     0.46)  ; 10\r\n          (  235.90   -72.72     6.18     0.46)  ; 11\r\n          (  231.17   -72.64     5.77     0.46)  ; 12\r\n          (  222.51   -69.88     6.65     0.46)  ; 13\r\n          (  219.12   -69.48     7.45     0.46)  ; 14\r\n          (  215.60   -68.53     7.97     0.46)  ; 15\r\n          (  212.07   -67.55     9.67     0.46)  ; 16\r\n          (  206.36   -65.31    10.30     0.46)  ; 17\r\n          (  201.55   -62.86    10.30     0.46)  ; 18\r\n          (  197.05   -59.73    10.85     0.46)  ; 19\r\n          (  195.44   -58.91    11.75     0.46)  ; 20\r\n          (  194.73   -57.88    13.32     0.46)  ; 21\r\n          (  194.73   -57.88    13.30     0.46)  ; 22\r\n          (  192.68   -57.17    13.97     0.46)  ; 23\r\n          (  188.13   -55.85    14.50     0.46)  ; 24\r\n          (  185.68   -53.44    15.12     0.46)  ; 25\r\n          (  182.20   -50.67    15.70     0.46)  ; 26\r\n          (  182.20   -50.67    15.73     0.46)  ; 27\r\n          (  178.23   -49.80    16.20     0.46)  ; 28\r\n          (  176.05   -48.53    16.70     0.46)  ; 29\r\n          (  176.05   -48.53    16.67     0.46)  ; 30\r\n          (  172.52   -47.56    17.27     0.46)  ; 31\r\n          (  169.72   -47.60    18.33     0.46)  ; 32\r\n          (  167.26   -45.19    19.25     0.46)  ; 33\r\n          (  163.29   -44.33    19.25     0.46)  ; 34\r\n          (  162.15   -43.40    19.25     0.46)  ; 35\r\n          (  160.85   -41.92    18.63     0.46)  ; 36\r\n          (  158.04   -41.97    18.63     0.46)  ; 37\r\n          (  155.27   -40.24    17.75     0.46)  ; 38\r\n          (  155.27   -40.24    17.72     0.46)  ; 39\r\n          (  152.96   -38.39    18.55     0.46)  ; 40\r\n          (  149.88   -37.32    19.50     0.46)  ; 41\r\n          (  149.88   -37.32    19.48     0.46)  ; 42\r\n          (  145.33   -36.00    19.73     0.46)  ; 43\r\n          (  141.36   -35.14    20.27     0.46)  ; 44\r\n          (  137.88   -33.48    21.45     0.46)  ; 45\r\n          (\r\n            (  138.86   -35.63    19.55     0.46)  ; 1, R-2-2-1-2-1\r\n            (  139.52   -38.46    18.27     0.46)  ; 2\r\n            (  140.19   -41.30    17.13     0.46)  ; 3\r\n            (  140.19   -41.30    17.08     0.46)  ; 4\r\n            (  141.16   -43.45    15.60     0.46)  ; 5\r\n            (  141.16   -43.45    15.50     0.46)  ; 6\r\n            (  139.09   -48.71    13.47     0.46)  ; 7\r\n            (  139.09   -48.71    13.42     0.46)  ; 8\r\n            (  139.36   -49.86    11.68     0.46)  ; 9\r\n            (  139.13   -52.89     8.45     0.46)  ; 10\r\n            (  139.26   -53.45     6.57     0.46)  ; 11\r\n            (  140.24   -55.61     4.52     0.46)  ; 12\r\n            (  141.13   -55.40     2.42     0.46)  ; 13\r\n            (  142.49   -55.09     0.82     0.46)  ; 14\r\n            (  141.40   -56.54    -5.27     0.46)  ; 15\r\n            (  140.11   -55.04    -6.52     0.46)  ; 16\r\n            (  137.30   -55.11    -7.47     0.46)  ; 17\r\n            (  134.67   -53.93    -7.87     0.46)  ; 18\r\n            (  132.69   -55.59    -8.75     0.46)  ; 19\r\n            (  129.84   -57.45   -10.65     0.46)  ; 20\r\n            (  127.61   -57.98   -11.27     0.46)  ; 21\r\n            (  127.61   -57.98   -11.42     0.46)  ; 22\r\n            (  127.88   -59.11   -13.32     0.46)  ; 23\r\n            (  125.14   -61.54   -15.43     0.46)  ; 24\r\n            (  125.14   -61.54   -15.48     0.46)  ; 25\r\n            (  123.49   -62.53   -19.60     0.46)  ; 26\r\n            (  121.53   -64.18   -22.12     0.46)  ; 27\r\n            (  122.50   -66.34   -24.13     0.46)  ; 28\r\n            (  122.90   -68.04   -26.32     0.46)  ; 29\r\n            (  122.90   -68.04   -26.35     0.46)  ; 30\r\n            (  120.26   -66.86   -27.45     0.46)  ; 31\r\n            (  120.22   -68.67   -29.30     0.46)  ; 32\r\n            (  120.22   -68.67   -29.32     0.46)  ; 33\r\n            (  121.18   -70.82   -31.75     0.46)  ; 34\r\n            (  120.12   -72.28   -32.77     0.46)  ; 35\r\n            (  118.59   -73.83   -33.50     0.46)  ; 36\r\n            (  117.35   -76.51   -34.52     0.46)  ; 37\r\n            (  116.80   -80.21   -35.72     0.46)  ; 38\r\n            (  115.09   -83.00   -36.35     0.46)  ; 39\r\n            (  111.79   -84.97   -37.67     0.46)  ; 40\r\n            (  110.53   -87.66   -38.47     0.46)  ; 41\r\n            (  109.54   -91.47   -40.32     0.46)  ; 42\r\n            (  109.54   -91.47   -40.32     0.46)  ; 43\r\n            (  107.13   -93.23   -41.57     0.46)  ; 44\r\n            (  107.13   -93.23   -41.60     0.46)  ; 45\r\n            (  104.98   -96.12   -43.62     0.46)  ; 46\r\n            (  102.82   -99.02   -45.90     0.46)  ; 47\r\n            (  101.08  -103.60   -47.55     0.46)  ; 48\r\n            (   98.80  -105.93   -47.55     0.46)  ; 49\r\n            (   98.31  -107.84   -49.25     0.46)  ; 50\r\n            (   98.31  -107.84   -49.27     0.46)  ; 51\r\n            (   98.78  -111.91   -50.30     0.46)  ; 52\r\n            (   95.34  -113.32   -52.05     0.46)  ; 53\r\n            (   93.12  -113.83   -54.90     0.46)  ; 54\r\n            (   93.12  -113.83   -54.95     0.46)  ; 55\r\n            (   90.25  -115.69   -58.10     0.46)  ; 56\r\n            (   87.00  -115.86   -61.25     0.46)  ; 57\r\n            (   87.00  -115.86   -61.35     0.46)  ; 58\r\n            (   85.02  -117.52   -64.05     0.46)  ; 59\r\n            (   85.02  -117.52   -64.10     0.46)  ; 60\r\n            (   83.00  -120.98   -64.97     0.46)  ; 61\r\n            (   83.00  -120.98   -65.00     0.46)  ; 62\r\n            (   83.54  -123.25   -67.57     0.46)  ; 63\r\n            (   83.54  -123.25   -67.60     0.46)  ; 64\r\n            (   82.99  -126.95   -69.47     0.46)  ; 65\r\n            (   82.95  -128.76   -72.35     0.46)  ; 66\r\n            (   82.95  -128.76   -72.40     0.46)  ; 67\r\n            (   82.40  -132.47   -74.25     0.46)  ; 68\r\n            (   82.49  -134.83   -74.22     0.46)  ; 69\r\n            (   82.49  -134.83   -74.38     0.46)  ; 70\r\n            (   81.81  -137.98   -78.32     0.46)  ; 71\r\n            (   81.26  -141.69   -81.22     0.46)  ; 72\r\n            (   82.11  -143.28   -84.63     0.46)  ; 73\r\n            (   82.19  -145.66   -87.42     0.46)  ; 74\r\n            (   80.67  -147.21   -89.88     0.46)  ; 75\r\n            (   79.69  -151.02   -89.32     0.46)  ; 76\r\n            (   79.45  -154.06   -93.88     0.46)  ; 77\r\n            (   79.45  -154.06   -94.10     0.46)  ; 78\r\n            (   78.18  -156.73   -97.38     0.46)  ; 79\r\n            (   78.18  -156.73   -97.40     0.46)  ; 80\r\n            (   75.46  -159.17   -99.63     0.46)  ; 81\r\n            (   74.70  -159.94  -101.53     0.46)  ; 82\r\n            (   75.41  -160.97  -103.42     0.46)  ; 83\r\n            (   75.41  -160.97  -103.45     0.46)  ; 84\r\n            (   75.54  -161.53  -105.65     0.46)  ; 85\r\n            (   76.70  -162.46  -106.15     0.46)  ; 86\r\n            (   76.70  -162.46  -106.30     0.46)  ; 87\r\n            (   76.87  -167.20  -108.47     0.46)  ; 88\r\n            (   76.87  -167.20  -109.63     0.46)  ; 89\r\n            (   78.38  -171.62  -109.63     0.46)  ; 90\r\n            (   76.95  -175.54  -111.80     0.46)  ; 91\r\n            (   76.95  -175.54  -111.82     0.46)  ; 92\r\n            (   77.48  -177.80  -115.57     0.46)  ; 93\r\n            (   78.33  -179.40  -118.20     0.46)  ; 94\r\n            (   78.33  -179.40  -118.22     0.46)  ; 95\r\n            (   78.09  -182.43  -119.78     0.46)  ; 96\r\n            (   78.09  -182.43  -119.88     0.46)  ; 97\r\n            (   78.05  -184.24  -122.05     0.46)  ; 98\r\n            (   79.29  -187.54  -123.42     0.46)  ; 99\r\n            (   78.02  -190.21  -125.02     0.46)  ; 100\r\n            (   78.37  -193.71  -127.52     0.46)  ; 101\r\n            (   77.70  -196.86  -129.92     0.46)  ; 102\r\n            (   77.57  -196.30  -129.92     0.46)  ; 103\r\n            (   78.81  -199.58  -130.65     0.46)  ; 104\r\n            (   80.19  -203.43  -131.57     0.46)  ; 105\r\n            (   81.02  -204.92  -132.52     0.46)  ; 106\r\n            (   81.60  -205.40  -134.75     0.46)  ; 107\r\n            (   82.53  -209.36  -136.07     0.46)  ; 108\r\n            (   83.19  -212.18  -137.55     0.46)  ; 109\r\n            (   83.41  -215.12  -138.82     0.46)  ; 110\r\n            (   81.26  -218.01  -141.43     0.46)  ; 111\r\n            (   80.05  -218.90  -145.50     0.46)  ; 112\r\n            (   79.43  -220.23  -148.05     0.46)  ; 113\r\n            (   77.72  -223.02  -150.23     0.46)  ; 114\r\n            (   75.45  -225.35  -150.23     0.46)  ; 115\r\n            (   72.90  -226.54  -150.27     0.46)  ; 116\r\n            (   72.90  -226.54  -150.32     0.46)  ; 117\r\n\r\n            (Dot\r\n              (Color Cyan)\r\n              (Name \"Marker 1\")\r\n              (  140.42   -38.25    18.27     0.46)  ; 1\r\n              (  141.60   -49.33     9.80     0.46)  ; 2\r\n              (  139.89   -52.11     8.05     0.46)  ; 3\r\n              (  140.87   -54.28     4.50     0.46)  ; 4\r\n              (  142.30   -56.33     0.82     0.46)  ; 5\r\n              (  131.18   -57.14   -10.65     0.46)  ; 6\r\n              (  124.23   -67.73   -26.35     0.46)  ; 7\r\n              (   79.16  -180.99  -120.05     0.46)  ; 8\r\n              (   79.74  -203.54  -131.57     0.46)  ; 9\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color RGB (255, 128, 255))\r\n              (Name \"Marker 2\")\r\n              (  118.59   -73.83   -33.50     0.46)  ; 1\r\n              (  113.53   -86.36   -36.88     0.46)  ; 2\r\n              (  107.58   -93.12   -41.97     0.46)  ; 3\r\n              (   80.13  -150.92   -89.32     0.46)  ; 4\r\n              (   76.95  -175.54  -111.82     0.46)  ; 5\r\n              (   79.15  -186.96  -123.42     0.46)  ; 6\r\n            )  ;  End of markers\r\n             Incomplete\r\n          |\r\n            (  138.01   -32.94    21.45     0.46)  ; 1, R-2-2-1-2-2\r\n            (  134.93   -31.87    22.65     0.46)  ; 2\r\n            (  131.91   -28.99    23.67     0.46)  ; 3\r\n            (  128.25   -27.45    24.75     0.46)  ; 4\r\n            (  124.60   -25.93    24.75     0.46)  ; 5\r\n            (  121.56   -23.06    25.05     0.46)  ; 6\r\n            (  118.61   -22.55    25.45     0.46)  ; 7\r\n            (  114.78   -22.26    26.15     0.46)  ; 8\r\n            (  114.78   -22.26    26.13     0.46)  ; 9\r\n            (  112.34   -19.85    26.77     0.46)  ; 10\r\n            (  112.34   -19.85    26.75     0.46)  ; 11\r\n            (  107.33   -18.63    26.92     0.46)  ; 12\r\n            (  105.78   -16.01    27.63     0.46)  ; 13\r\n            (  105.78   -16.01    27.60     0.46)  ; 14\r\n            (  103.02   -14.27    30.33     0.46)  ; 15\r\n            (  101.86   -13.35    31.88     0.46)  ; 16\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  269.53   -81.56     7.15     0.46)  ; 1\r\n              (  256.33   -77.48     7.90     0.46)  ; 2\r\n              (  228.93   -73.16     6.65     0.46)  ; 3\r\n              (  222.91   -71.59     6.65     0.46)  ; 4\r\n              (  211.75   -68.22     9.67     0.46)  ; 5\r\n              (  207.07   -66.34    10.30     0.46)  ; 6\r\n              (  202.83   -64.34    10.30     0.46)  ; 7\r\n              (  196.87   -60.96    10.85     0.46)  ; 8\r\n              (  184.13   -50.82    15.73     0.46)  ; 9\r\n              (  173.14   -46.22    17.27     0.46)  ; 10\r\n              (  161.96   -44.64    19.25     0.46)  ; 11\r\n              (  158.17   -42.55    18.63     0.46)  ; 12\r\n              (  145.28   -37.81    19.73     0.46)  ; 13\r\n              (  105.16   -11.37    27.47     0.46)  ; 14\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  241.79   -73.72     8.10     0.46)  ; 1\r\n              (  248.12   -74.63     8.10     0.46)  ; 2\r\n              (  231.62   -72.53     5.77     0.46)  ; 3\r\n              (  192.81   -57.73    13.97     0.46)  ; 4\r\n              (  188.58   -55.74    14.50     0.46)  ; 5\r\n              (  120.09   -22.80    25.45     0.46)  ; 6\r\n            )  ;  End of markers\r\n             High\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (  281.63   -88.84     4.20     0.46)  ; 1, R-2-2-2\r\n        (  280.91   -87.82     3.10     0.46)  ; 2\r\n        (  280.47   -87.93     3.08     0.46)  ; 3\r\n        (  278.98   -87.67     0.95     0.46)  ; 4\r\n        (  276.49   -87.06    -0.32     0.46)  ; 5\r\n        (  275.91   -86.60    -2.78     0.46)  ; 6\r\n        (  275.64   -85.46    -5.15     0.46)  ; 7\r\n        (  275.51   -84.91    -6.93     0.46)  ; 8\r\n        (  275.07   -85.00    -6.95     0.46)  ; 9\r\n        (  273.95   -82.28    -8.77     0.46)  ; 10\r\n        (  272.49   -82.02   -10.97     0.46)  ; 11\r\n        (  272.49   -82.02   -11.00     0.46)  ; 12\r\n        (  272.22   -80.90   -13.55     0.46)  ; 13\r\n        (  271.78   -81.00   -13.57     0.46)  ; 14\r\n        (  270.04   -79.61   -15.50     0.46)  ; 15\r\n        (  269.31   -78.58   -18.13     0.46)  ; 16\r\n        (  269.31   -78.58   -18.15     0.46)  ; 17\r\n        (  268.11   -79.47   -20.23     0.46)  ; 18\r\n        (  267.00   -76.75   -21.60     0.46)  ; 19\r\n        (  264.51   -76.13   -22.27     0.46)  ; 20\r\n        (  263.35   -75.21   -24.00     0.46)  ; 21\r\n        (  263.35   -75.21   -24.02     0.46)  ; 22\r\n        (  261.43   -75.07   -25.92     0.46)  ; 23\r\n        (  260.09   -75.37   -27.88     0.46)  ; 24\r\n        (  258.61   -75.13   -29.67     0.46)  ; 25\r\n        (  258.61   -75.13   -29.70     0.46)  ; 26\r\n        (  256.25   -75.09   -31.38     0.46)  ; 27\r\n        (  256.25   -75.09   -31.40     0.46)  ; 28\r\n        (  254.20   -74.37   -33.03     0.46)  ; 29\r\n        (  254.20   -74.37   -33.05     0.46)  ; 30\r\n        (  253.05   -73.45   -34.55     0.46)  ; 31\r\n        (  250.42   -72.28   -37.08     0.46)  ; 32\r\n        (  250.42   -72.28   -37.10     0.46)  ; 33\r\n        (  248.81   -71.45   -39.35     0.46)  ; 34\r\n        (  248.81   -71.45   -39.42     0.46)  ; 35\r\n        (  246.17   -70.28   -41.60     0.46)  ; 36\r\n        (  245.59   -69.82   -44.07     0.46)  ; 37\r\n        (  245.59   -69.82   -44.10     0.46)  ; 38\r\n        (  243.41   -68.54   -46.08     0.46)  ; 39\r\n        (  240.90   -67.93   -47.10     0.46)  ; 40\r\n        (  240.90   -67.93   -47.13     0.46)  ; 41\r\n        (  238.41   -67.33   -49.02     0.46)  ; 42\r\n        (  235.79   -66.14   -51.20     0.46)  ; 43\r\n        (  234.31   -65.90   -52.75     0.46)  ; 44\r\n        (  230.92   -65.50   -53.97     0.46)  ; 45\r\n        (  230.92   -65.50   -54.00     0.46)  ; 46\r\n        (  227.98   -64.99   -54.58     0.46)  ; 47\r\n        (  225.66   -63.14   -56.75     0.46)  ; 48\r\n        (  225.66   -63.14   -56.80     0.46)  ; 49\r\n        (  223.16   -62.54   -59.20     0.46)  ; 50\r\n        (  220.26   -60.23   -61.52     0.46)  ; 51\r\n        (  220.26   -60.23   -61.57     0.46)  ; 52\r\n        (  218.66   -59.41   -63.15     0.46)  ; 53\r\n        (  218.66   -59.41   -63.20     0.46)  ; 54\r\n        (  217.23   -57.35   -62.88     0.46)  ; 55\r\n        (  215.82   -55.30   -64.70     0.46)  ; 56\r\n\r\n        (Dot\r\n          (Color Yellow)\r\n          (Name \"Marker 1\")\r\n          (  242.07   -68.85   -47.13     0.46)  ; 1\r\n        )  ;  End of markers\r\n\r\n        (OpenCircle\r\n          (Color Yellow)\r\n          (Name \"Marker 2\")\r\n          (  262.19   -74.29   -25.92     0.46)  ; 1\r\n          (  238.86   -67.22   -49.02     0.46)  ; 2\r\n          (  228.42   -64.89   -54.58     0.46)  ; 3\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  287.43   -87.48     6.15     0.46)  ; 1, R-2-2-3\r\n        (  290.12   -86.85     4.78     0.46)  ; 2\r\n        (\r\n          (  290.48   -84.38     3.47     0.46)  ; 1, R-2-2-3-1\r\n          (  290.65   -83.15     1.75     0.46)  ; 2\r\n          (  290.22   -83.25     1.73     0.46)  ; 3\r\n          (  292.57   -83.30     1.70     0.46)  ; 4\r\n          (  293.65   -81.85     0.50     0.46)  ; 5\r\n          (  293.65   -81.85     0.47     0.46)  ; 6\r\n          (  295.44   -81.43    -1.07     0.46)  ; 7\r\n          (  296.52   -79.98    -3.10     0.46)  ; 8\r\n          (  297.40   -79.77    -4.92     0.46)  ; 9\r\n          (  297.40   -79.77    -4.95     0.46)  ; 10\r\n          (  298.93   -78.22    -7.05     0.46)  ; 11\r\n          (  298.93   -78.22    -7.07     0.46)  ; 12\r\n          (  299.82   -78.01    -8.63     0.46)  ; 13\r\n          (  302.69   -76.15   -10.25     0.46)  ; 14\r\n          (  302.69   -76.15   -10.28     0.46)  ; 15\r\n          (  303.44   -75.38   -12.17     0.46)  ; 16\r\n          (  304.38   -73.36   -13.52     0.46)  ; 17\r\n          (  306.66   -71.04   -14.35     0.46)  ; 18\r\n          (  306.66   -71.04   -14.43     0.46)  ; 19\r\n          (  309.66   -69.74   -14.67     0.46)  ; 20\r\n          (  310.87   -68.85   -14.67     0.46)  ; 21\r\n          (  312.38   -67.30   -14.67     0.46)  ; 22\r\n          (  315.52   -66.57   -15.55     0.46)  ; 23\r\n          (  317.48   -64.92   -15.55     0.46)  ; 24\r\n          (  319.26   -64.50   -16.65     0.46)  ; 25\r\n          (  320.34   -63.06   -18.42     0.46)  ; 26\r\n          (  322.58   -62.53   -19.77     0.46)  ; 27\r\n          (  322.58   -62.53   -19.82     0.46)  ; 28\r\n          (  323.78   -61.64   -21.18     0.46)  ; 29\r\n          (  323.33   -61.75   -21.18     0.46)  ; 30\r\n          (  324.55   -60.87   -22.12     0.46)  ; 31\r\n          (  325.44   -60.66   -22.12     0.46)  ; 32\r\n          (  328.60   -58.13   -21.77     0.46)  ; 33\r\n\r\n          (Dot\r\n            (Color Yellow)\r\n            (Name \"Marker 1\")\r\n            (  302.50   -77.38   -10.28     0.46)  ; 1\r\n            (  306.17   -72.94   -14.48     0.46)  ; 2\r\n          )  ;  End of markers\r\n\r\n          (OpenCircle\r\n            (Color Yellow)\r\n            (Name \"Marker 2\")\r\n            (  294.99   -81.54    -1.07     0.46)  ; 1\r\n            (  299.51   -78.69    -7.07     0.46)  ; 2\r\n            (  303.44   -75.38   -12.17     0.46)  ; 3\r\n            (  315.52   -66.57   -15.55     0.46)  ; 4\r\n          )  ;  End of markers\r\n          (\r\n            (  329.69   -56.68   -23.98     0.46)  ; 1, R-2-2-3-1-1\r\n            (  331.16   -56.94   -23.98     0.46)  ; 2\r\n            (  334.15   -55.64   -25.50     0.46)  ; 3\r\n            (  334.15   -55.64   -25.55     0.46)  ; 4\r\n            (  335.75   -56.45   -27.50     0.46)  ; 5\r\n            (  335.75   -56.45   -27.53     0.46)  ; 6\r\n            (  336.20   -56.35   -27.97     0.46)  ; 7\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  333.39   -56.41   -25.55     0.46)  ; 1\r\n            )  ;  End of markers\r\n            (\r\n              (  339.46   -56.18   -29.27     0.46)  ; 1, R-2-2-3-1-1-1\r\n              (  339.19   -55.05   -30.60     0.46)  ; 2\r\n              (\r\n                (  343.26   -52.31   -31.50     0.46)  ; 1, R-2-2-3-1-1-1-1\r\n                (  344.02   -51.53   -33.03     0.46)  ; 2\r\n                (  345.55   -49.98   -34.08     0.46)  ; 3\r\n                (  347.60   -50.69   -35.27     0.46)  ; 4\r\n                (  348.10   -48.79   -36.50     0.46)  ; 5\r\n                (  351.09   -47.49   -38.57     0.46)  ; 6\r\n                (  353.64   -46.29   -39.85     0.46)  ; 7\r\n                (  353.64   -46.29   -39.88     0.46)  ; 8\r\n                (  353.24   -44.59   -39.88     0.46)  ; 9\r\n                (  356.41   -42.06   -41.08     0.46)  ; 10\r\n                (  357.48   -40.61   -41.97     0.46)  ; 11\r\n                (  358.43   -38.60   -44.10     0.46)  ; 12\r\n                (  360.39   -36.95   -46.10     0.46)  ; 13\r\n                (  363.56   -34.41   -47.80     0.46)  ; 14\r\n                (  363.56   -34.41   -47.82     0.46)  ; 15\r\n                (  367.06   -31.21   -49.32     0.46)  ; 16\r\n                (  367.06   -31.21   -49.38     0.46)  ; 17\r\n                (  371.57   -28.35   -50.72     0.46)  ; 18\r\n                (  373.85   -26.03   -52.45     0.46)  ; 19\r\n                (  373.85   -26.03   -52.47     0.46)  ; 20\r\n                (  376.26   -24.28   -54.40     0.46)  ; 21\r\n                (  378.24   -22.61   -55.60     0.46)  ; 22\r\n                (  381.55   -20.64   -56.57     0.46)  ; 23\r\n                (  384.72   -18.11   -58.45     0.46)  ; 24\r\n                (  385.53   -15.53   -59.75     0.46)  ; 25\r\n                (  385.53   -15.53   -59.78     0.46)  ; 26\r\n                (  387.63   -14.44   -60.85     0.46)  ; 27\r\n                (  390.49   -12.57   -62.28     0.46)  ; 28\r\n                (  392.46   -10.91   -64.30     0.46)  ; 29\r\n                (  392.46   -10.91   -64.32     0.46)  ; 30\r\n                (  395.76    -8.95   -66.17     0.46)  ; 31\r\n                (  395.32    -9.05   -66.17     0.46)  ; 32\r\n                (  398.62    -7.08   -68.47     0.46)  ; 33\r\n                (  400.91    -4.76   -70.55     0.46)  ; 34\r\n                (  400.91    -4.76   -70.57     0.46)  ; 35\r\n                (  402.16    -2.07   -71.15     0.46)  ; 36\r\n                (  404.75     0.92   -72.93     0.46)  ; 37\r\n                (  405.75     4.74   -74.32     0.46)  ; 38\r\n                (  407.50     9.33   -76.07     0.46)  ; 39\r\n                (  409.97    12.89   -78.47     0.46)  ; 40\r\n                (  409.97    12.89   -78.50     0.46)  ; 41\r\n                (  411.62    13.87   -80.60     0.46)  ; 42\r\n                (  411.62    13.87   -80.63     0.46)  ; 43\r\n                (  412.88    16.56   -82.47     0.46)  ; 44\r\n                (  412.88    16.56   -82.55     0.46)  ; 45\r\n                (  415.91    19.66   -85.10     0.46)  ; 46\r\n                (  415.91    19.66   -86.55     0.46)  ; 47\r\n                (  419.36    21.06   -87.72     0.46)  ; 48\r\n                (  420.74    23.18   -89.72     0.46)  ; 49\r\n                (  420.74    23.18   -89.75     0.46)  ; 50\r\n                (  423.39    27.98   -91.22     0.46)  ; 51\r\n                (  425.80    29.73   -93.45     0.46)  ; 52\r\n                (  428.26    33.30   -94.55     0.46)  ; 53\r\n                (  430.55    35.63   -96.75     0.46)  ; 54\r\n                (  432.30    40.20   -98.05     0.46)  ; 55\r\n                (  433.95    41.19   -98.72     0.46)  ; 56\r\n                (  435.35    43.31   -98.72     0.46)  ; 57\r\n                (  435.18    43.27   -98.77     0.46)  ; 58\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  355.91   -43.96   -41.08     0.46)  ; 1\r\n                  (  359.09   -41.44   -41.97     0.46)  ; 2\r\n                  (  378.96   -23.64   -55.60     0.46)  ; 3\r\n                  (  411.98    16.35   -82.55     0.46)  ; 4\r\n                  (  424.52    31.23   -93.45     0.46)  ; 5\r\n                  (  428.62    35.78   -96.75     0.46)  ; 6\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  347.78   -49.45   -36.50     0.46)  ; 1\r\n                  (  376.81   -20.56   -54.70     0.46)  ; 2\r\n                  (  388.20   -14.90   -60.85     0.46)  ; 3\r\n                  (  402.74    -2.54   -71.15     0.46)  ; 4\r\n                  (  405.75     4.74   -74.42     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (  436.75    46.63   -98.77     0.46)  ; 1, R-2-2-3-1-1-1-1-1\r\n                  (  440.06    48.59   -99.85     0.46)  ; 2\r\n                  (  441.94    52.62   -99.17     0.46)  ; 3\r\n                  (  441.94    52.62   -99.22     0.46)  ; 4\r\n                  (  444.46    57.99   -99.78     0.46)  ; 5\r\n                  (  446.34    62.02   -99.78     0.46)  ; 6\r\n                  (  446.57    65.05   -99.78     0.46)  ; 7\r\n                  (  448.58    68.51   -99.78     0.46)  ; 8\r\n                  (  449.89    72.99  -101.10     0.46)  ; 9\r\n                  (  452.34    76.56  -101.60     0.46)  ; 10\r\n                  (  453.29    78.57  -101.67     0.46)  ; 11\r\n                  (  456.97    83.01  -102.20     0.46)  ; 12\r\n                  (  457.02    84.81  -103.07     0.46)  ; 13\r\n                  (  458.00    88.63  -103.97     0.46)  ; 14\r\n                  (  458.36    91.11  -105.40     0.46)  ; 15\r\n                  (  459.32    93.11  -107.05     0.46)  ; 16\r\n                  (  460.52    93.99  -108.47     0.46)  ; 17\r\n                  (  459.85    96.82  -109.65     0.46)  ; 18\r\n                  (  459.85    96.82  -109.67     0.46)  ; 19\r\n                  (  461.81    98.47  -111.82     0.46)  ; 20\r\n                  (  463.83   101.94  -114.03     0.46)  ; 21\r\n                  (  463.83   101.94  -114.22     0.46)  ; 22\r\n                  (  466.43   104.94  -115.60     0.46)  ; 23\r\n                  (  466.43   104.94  -116.17     0.46)  ; 24\r\n                  (  468.71   107.26  -116.45     0.46)  ; 25\r\n                  (  472.12   112.84  -117.50     0.46)  ; 26\r\n                  (  473.82   115.63  -117.50     0.46)  ; 27\r\n                  (  475.30   121.35  -118.15     0.46)  ; 28\r\n                  (  475.99   124.50  -119.15     0.46)  ; 29\r\n                  (  478.49   129.86  -119.52     0.46)  ; 30\r\n                  (  479.92   133.79  -119.52     0.46)  ; 31\r\n                  (  482.66   136.21  -118.27     0.46)  ; 32\r\n                  (  483.78   139.46  -118.27     0.46)  ; 33\r\n                  (  485.53   144.06  -118.82     0.46)  ; 34\r\n                  (  487.23   146.83  -119.85     0.46)  ; 35\r\n                  (  486.89   150.34  -120.42     0.46)  ; 36\r\n                  (  488.46   153.69  -122.35     0.46)  ; 37\r\n                  (  491.11   158.49  -122.85     0.46)  ; 38\r\n                  (  493.57   162.05  -124.07     0.46)  ; 39\r\n                  (  495.58   165.51  -124.82     0.46)  ; 40\r\n                  (  496.84   168.19  -126.20     0.46)  ; 41\r\n                  (  497.46   169.54  -128.38     0.46)  ; 42\r\n                  (  497.46   169.54  -128.40     0.46)  ; 43\r\n                  (  498.54   170.98  -129.85     0.46)  ; 44\r\n                  (  498.54   170.98  -129.88     0.46)  ; 45\r\n                  (  500.50   172.63  -131.27     0.46)  ; 46\r\n                  (  500.50   172.63  -131.30     0.46)  ; 47\r\n                  (  500.24   173.77  -133.90     0.46)  ; 48\r\n                  (  500.24   173.77  -133.95     0.46)  ; 49\r\n                  (  499.97   174.89  -136.00     0.46)  ; 50\r\n                  (  499.97   174.89  -136.05     0.46)  ; 51\r\n                  (  501.18   175.78  -138.77     0.46)  ; 52\r\n                  (  501.18   175.78  -138.80     0.46)  ; 53\r\n                  (  503.60   177.54  -139.38     0.46)  ; 54\r\n                  (  504.99   179.66  -139.38     0.46)  ; 55\r\n                  (  508.16   182.19  -140.55     0.46)  ; 56\r\n                  (  509.87   184.99  -141.60     0.46)  ; 57\r\n                  (  510.10   188.03  -143.13     0.46)  ; 58\r\n                  (  512.06   189.68  -144.35     0.46)  ; 59\r\n                  (  513.00   191.69  -145.68     0.46)  ; 60\r\n                  (  513.62   193.03  -145.98     0.46)  ; 61\r\n                  (  513.62   193.03  -146.00     0.46)  ; 62\r\n                  (  515.07   196.95  -147.27     0.46)  ; 63\r\n                  (  518.11   200.05  -149.40     0.46)  ; 64\r\n                  (  520.40   202.38  -149.40     0.46)  ; 65\r\n                  (  520.40   202.38  -150.15     0.46)  ; 66\r\n                  (  524.38   207.49  -150.25     0.46)  ; 67\r\n                  (  527.29   211.15  -148.80     0.46)  ; 68\r\n                  (  528.23   213.16  -148.80     0.46)  ; 69\r\n                  (  531.89   217.61  -148.80     0.46)  ; 70\r\n                  (  533.34   221.53  -149.22     0.46)  ; 71\r\n                  (  534.28   223.55  -149.50     0.46)  ; 72\r\n                  (  537.00   225.98  -150.57     0.46)  ; 73\r\n                  (  538.03   225.62  -150.20     0.46)  ; 74\r\n                  (  540.85   225.68  -151.27     0.46)  ; 75\r\n                  (  544.33   228.88  -151.27     0.46)  ; 76\r\n                  (  548.26   232.20  -151.27     0.46)  ; 77\r\n                  (  551.00   234.63  -151.27     0.46)  ; 78\r\n                  (  553.02   238.09  -152.38     0.46)  ; 79\r\n                  (  555.74   240.52  -152.38     0.46)  ; 80\r\n                  (  557.84   241.60  -152.38     0.46)  ; 81\r\n                  (  559.87   245.07  -152.38     0.46)  ; 82\r\n                  (  561.83   246.72  -153.10     0.46)  ; 83\r\n                  (  564.28   250.28  -153.40     0.46)  ; 84\r\n                  (  567.46   252.82  -154.05     0.46)  ; 85\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  440.33    47.47   -99.88     0.46)  ; 1\r\n                    (  462.81   102.30  -114.22     0.46)  ; 2\r\n                    (  515.44   199.42  -149.40     0.46)  ; 3\r\n                    (  536.60   227.67  -150.20     0.46)  ; 4\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  448.46    69.07   -99.78     0.46)  ; 1\r\n                    (  453.74    78.67  -101.67     0.46)  ; 2\r\n                    (  475.85   125.06  -119.15     0.46)  ; 3\r\n                    (  486.80   146.73  -119.85     0.46)  ; 4\r\n                    (  487.02   149.77  -120.42     0.46)  ; 5\r\n                    (  493.57   162.05  -124.07     0.46)  ; 6\r\n                    (  501.63   175.88  -138.80     0.46)  ; 7\r\n                    (  510.00   184.42  -141.60     0.46)  ; 8\r\n                    (  513.94   193.71  -146.00     0.46)  ; 9\r\n                    (  525.26   207.69  -150.25     0.46)  ; 10\r\n                    (  562.27   246.82  -153.10     0.46)  ; 11\r\n                    (  564.73   250.39  -153.67     0.46)  ; 12\r\n                    (  567.02   252.72  -154.15     0.46)  ; 13\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  437.68    42.67   -97.40     0.46)  ; 1, R-2-2-3-1-1-1-1-2\r\n                  (  437.68    42.67   -97.32     0.46)  ; 2\r\n                  (  441.34    41.13   -96.27     0.46)  ; 3\r\n                  (  443.07    39.75   -95.15     0.46)  ; 4\r\n                  (  445.39    37.90   -93.20     0.46)  ; 5\r\n                  (  446.10    36.88   -91.27     0.46)  ; 6\r\n                  (  447.57    36.63   -89.50     0.46)  ; 7\r\n                  (  448.81    33.34   -88.40     0.46)  ; 8\r\n                  (  450.70    31.38   -87.45     0.46)  ; 9\r\n                  (  451.54    29.79   -86.00     0.46)  ; 10\r\n                  (  451.54    29.79   -86.03     0.46)  ; 11\r\n                  (  452.70    28.86   -86.03     0.46)  ; 12\r\n                  (  454.88    27.58   -85.15     0.46)  ; 13\r\n                  (  456.44    24.96   -85.15     0.46)  ; 14\r\n                  (  458.17    23.58   -85.15     0.46)  ; 15\r\n                  (  461.66    20.81   -84.08     0.46)  ; 16\r\n                  (  465.84    17.02   -83.07     0.46)  ; 17\r\n                  (  471.31    15.90   -84.30     0.46)  ; 18\r\n                  (  476.44    14.11   -83.80     0.46)  ; 19\r\n                  (  479.96    13.15   -82.90     0.46)  ; 20\r\n                  (  483.35    12.75   -82.15     0.46)  ; 21\r\n                  (  483.35    12.75   -81.62     0.46)  ; 22\r\n                  (  486.56    11.11   -81.67     0.46)  ; 23\r\n                  (  491.43    10.46   -81.30     0.46)  ; 24\r\n                  (  496.87     9.34   -80.80     0.46)  ; 25\r\n                  (  500.09     7.71   -80.57     0.46)  ; 26\r\n                  (  503.16     6.64   -79.50     0.46)  ; 27\r\n                  (  506.69     5.67   -78.47     0.46)  ; 28\r\n                  (  509.01     3.83   -77.22     0.46)  ; 29\r\n                  (  509.01     3.83   -77.30     0.46)  ; 30\r\n                  (  513.42     3.07   -76.50     0.46)  ; 31\r\n                  (  516.69     3.24   -75.78     0.46)  ; 32\r\n                  (  516.69     3.24   -75.80     0.46)  ; 33\r\n                  (  520.21     2.27   -77.70     0.46)  ; 34\r\n                  (  523.10    -0.03   -79.15     0.46)  ; 35\r\n                  (  528.68    -1.71   -79.80     0.46)  ; 36\r\n                  (  528.68    -1.71   -79.82     0.46)  ; 37\r\n                  (  533.81    -3.50   -80.22     0.46)  ; 38\r\n                  (  538.30    -6.63   -78.00     0.46)  ; 39\r\n                  (  541.25    -7.12   -76.47     0.46)  ; 40\r\n                  (  544.52    -6.95   -74.63     0.46)  ; 41\r\n                  (  548.04    -7.93   -73.10     0.46)  ; 42\r\n                  (  550.53    -8.53   -71.77     0.46)  ; 43\r\n                  (  550.53    -8.53   -71.85     0.46)  ; 44\r\n                  (  554.24    -8.26   -70.70     0.46)  ; 45\r\n                  (  554.24    -8.26   -70.72     0.46)  ; 46\r\n                  (  558.07    -8.55   -69.22     0.46)  ; 47\r\n                  (  559.81    -9.94   -67.88     0.46)  ; 48\r\n                  (  563.21   -10.34   -66.65     0.46)  ; 49\r\n                  (  563.21   -10.34   -66.68     0.46)  ; 50\r\n                  (  566.02   -10.28   -65.65     0.46)  ; 51\r\n                  (  570.44   -11.03   -65.05     0.46)  ; 52\r\n                  (  574.09   -12.56   -65.20     0.46)  ; 53\r\n                  (  574.09   -12.56   -65.22     0.46)  ; 54\r\n                  (  578.92   -15.02   -64.92     0.46)  ; 55\r\n                  (  581.72   -14.96   -64.92     0.46)  ; 56\r\n                  (  586.00   -15.15   -64.13     0.46)  ; 57\r\n                  (  589.27   -14.98   -63.38     0.46)  ; 58\r\n                  (  594.38   -16.76   -62.30     0.46)  ; 59\r\n                  (  594.38   -16.76   -62.33     0.46)  ; 60\r\n                  (  597.33   -17.25   -61.42     0.46)  ; 61\r\n                  (  600.41   -18.32   -60.45     0.46)  ; 62\r\n                  (  603.35   -18.82   -59.20     0.46)  ; 63\r\n                  (  604.91   -21.45   -58.55     0.46)  ; 64\r\n                  (  609.47   -22.77   -59.58     0.46)  ; 65\r\n                  (  616.07   -24.81   -59.07     0.46)  ; 66\r\n                  (  616.07   -24.81   -59.10     0.46)  ; 67\r\n                  (  619.90   -25.10   -59.10     0.46)  ; 68\r\n                  (  623.42   -26.07   -59.10     0.46)  ; 69\r\n                  (  626.24   -26.00   -59.85     0.46)  ; 70\r\n                  (  629.18   -26.51   -58.90     0.46)  ; 71\r\n                  (  632.26   -27.57   -57.30     0.46)  ; 72\r\n                  (  632.26   -27.57   -57.33     0.46)  ; 73\r\n                  (  633.72   -27.83   -56.55     0.46)  ; 74\r\n                  (  640.64   -29.19   -55.70     0.46)  ; 75\r\n                  (  646.23   -30.87   -55.70     0.46)  ; 76\r\n                  (  647.78   -33.50   -55.70     0.46)  ; 77\r\n                  (  652.21   -34.25   -55.45     0.46)  ; 78\r\n                  (  655.59   -34.65   -54.75     0.46)  ; 79\r\n                  (  658.98   -35.04   -54.17     0.46)  ; 80\r\n                  (  664.60   -34.94   -54.45     0.46)  ; 81\r\n                  (  669.34   -35.01   -53.67     0.46)  ; 82\r\n                  (  669.34   -35.01   -53.70     0.46)  ; 83\r\n                  (  674.15   -37.47   -52.67     0.46)  ; 84\r\n                  (  676.51   -37.51   -53.95     0.46)  ; 85\r\n                  (  676.51   -37.51   -53.97     0.46)  ; 86\r\n                  (  679.46   -38.01   -54.72     0.46)  ; 87\r\n                  (  681.96   -38.62   -55.97     0.46)  ; 88\r\n                  (  686.70   -38.71   -56.42     0.46)  ; 89\r\n                  (  689.96   -38.54   -56.42     0.46)  ; 90\r\n                  (  693.03   -39.61   -55.92     0.46)  ; 91\r\n                  (  693.03   -39.61   -55.90     0.46)  ; 92\r\n                  (  694.94   -39.75   -54.65     0.46)  ; 93\r\n                  (  698.47   -40.72   -53.82     0.46)  ; 94\r\n                  (  702.17   -40.46   -53.05     0.46)  ; 95\r\n                  (  706.73   -41.77   -53.05     0.46)  ; 96\r\n                  (  712.63   -42.78   -52.28     0.46)  ; 97\r\n                  (  716.73   -44.21   -53.95     0.46)  ; 98\r\n                  (  716.73   -44.21   -53.97     0.46)  ; 99\r\n                  (  718.01   -45.70   -53.97     0.46)  ; 100\r\n                  (  721.59   -44.87   -54.97     0.46)  ; 101\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  499.32     6.94   -80.57     0.46)  ; 1\r\n                    (  553.17    -9.70   -70.72     0.46)  ; 2\r\n                    (  556.56   -10.11   -69.22     0.46)  ; 3\r\n                    (  593.18   -17.63   -62.33     0.46)  ; 4\r\n                    (  651.44   -35.03   -55.45     0.46)  ; 5\r\n                    (  673.84   -38.13   -52.67     0.46)  ; 6\r\n                    (  697.72   -41.50   -53.82     0.46)  ; 7\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  452.70    28.86   -86.03     0.46)  ; 1\r\n                    (  456.57    24.39   -85.15     0.46)  ; 2\r\n                    (  465.84    17.02   -83.07     0.46)  ; 3\r\n                    (  486.56    11.11   -81.67     0.46)  ; 4\r\n                    (  516.69     3.24   -75.80     0.46)  ; 5\r\n                    (  533.81    -3.50   -80.22     0.46)  ; 6\r\n                    (  589.40   -15.55   -63.38     0.46)  ; 7\r\n                    (  600.41   -18.32   -60.45     0.46)  ; 8\r\n                    (  604.78   -20.88   -58.55     0.46)  ; 9\r\n                    (  626.24   -26.00   -59.85     0.46)  ; 10\r\n                    (  632.71   -27.47   -56.55     0.46)  ; 11\r\n                    (  639.18   -28.94   -55.70     0.46)  ; 12\r\n                    (  645.78   -30.98   -55.70     0.46)  ; 13\r\n                    (  681.52   -38.72   -55.97     0.46)  ; 14\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  721.64   -43.07   -54.58     0.46)  ; 1, R-2-2-3-1-1-1-1-2-1\r\n                    (  722.00   -40.58   -53.75     0.46)  ; 2\r\n                    (  721.70   -35.28   -54.27     0.46)  ; 3\r\n                    (  721.92   -32.24   -54.27     0.46)  ; 4\r\n                    (  723.05   -28.99   -53.78     0.46)  ; 5\r\n                    (  723.99   -26.98   -52.92     0.46)  ; 6\r\n                    (  724.94   -24.97   -52.92     0.46)  ; 7\r\n                    (  728.37   -23.56   -52.15     0.46)  ; 8\r\n                    (  729.90   -22.01   -50.77     0.46)  ; 9\r\n                    (  731.74   -19.79   -49.72     0.46)  ; 10\r\n                    (  734.46   -17.36   -49.40     0.46)  ; 11\r\n                    (  736.73   -15.04   -49.10     0.46)  ; 12\r\n                    (  738.58   -12.81   -49.10     0.46)  ; 13\r\n                    (  741.94    -9.04   -48.35     0.46)  ; 14\r\n                    (  743.00    -7.59   -47.67     0.46)  ; 15\r\n                    (  743.18    -6.36   -47.25     0.46)  ; 16\r\n                    (  745.65    -2.80   -46.55     0.46)  ; 17\r\n                    (  747.37     0.00   -44.52     0.46)  ; 18\r\n                    (  750.36     1.31   -44.10     0.46)  ; 19\r\n                    (  752.96     4.29   -44.10     0.46)  ; 20\r\n                    (  755.86     7.97   -43.62     0.46)  ; 21\r\n                    (  756.04     9.20   -46.60     0.46)  ; 22\r\n                    (  755.64    10.90   -48.02     0.46)  ; 23\r\n                    (  755.56    13.27   -49.15     0.46)  ; 24\r\n                    (  755.56    13.27   -49.17     0.46)  ; 25\r\n                    (  756.37    15.85   -50.10     0.46)  ; 26\r\n                    (  755.58    19.25   -50.22     0.46)  ; 27\r\n                    (  755.58    19.25   -50.25     0.46)  ; 28\r\n                    (  756.84    21.93   -49.92     0.46)  ; 29\r\n                    (  758.85    25.39   -48.00     0.46)  ; 30\r\n                    (  759.79    27.39   -47.60     0.46)  ; 31\r\n                    (  762.66    29.26   -48.67     0.46)  ; 32\r\n                    (  764.62    30.91   -48.67     0.46)  ; 33\r\n                    (  766.13    32.46   -49.70     0.46)  ; 34\r\n                    (  767.53    34.59   -50.15     0.46)  ; 35\r\n                    (  769.23    37.37   -50.57     0.46)  ; 36\r\n                    (  770.93    40.16   -50.57     0.46)  ; 37\r\n                    (  772.76    42.38   -50.97     0.46)  ; 38\r\n                    (  775.54    46.61   -50.97     0.46)  ; 39\r\n                    (  776.36    49.19   -50.07     0.46)  ; 40\r\n                    (  777.47    52.44   -49.05     0.46)  ; 41\r\n                    (  781.60    56.99   -49.35     0.46)  ; 42\r\n                    (  784.46    58.85   -49.30     0.46)  ; 43\r\n                    (  786.79    62.98   -48.72     0.46)  ; 44\r\n                    (  789.69    66.66   -48.20     0.46)  ; 45\r\n                    (  791.85    69.54   -49.02     0.46)  ; 46\r\n                    (  793.86    73.00   -50.27     0.46)  ; 47\r\n                    (  794.27    77.25   -49.13     0.46)  ; 48\r\n                    (  796.73    80.82   -48.05     0.46)  ; 49\r\n                    (  796.96    83.86   -46.63     0.46)  ; 50\r\n                    (  797.58    85.21   -45.68     0.46)  ; 51\r\n                    (  797.94    87.68   -44.72     0.46)  ; 52\r\n                    (  799.48    89.23   -43.78     0.46)  ; 53\r\n                    (  799.70    92.27   -43.78     0.46)  ; 54\r\n                    (  801.09    94.38   -43.78     0.46)  ; 55\r\n                    (  803.95    96.24   -43.78     0.46)  ; 56\r\n                    (  806.50    97.43   -43.78     0.46)  ; 57\r\n                    (  809.35    99.31   -43.78     0.46)  ; 58\r\n                    (  810.61   101.99   -43.78     0.46)  ; 59\r\n                    (  811.28   105.13   -43.78     0.46)  ; 60\r\n                    (  811.12   109.87   -44.27     0.46)  ; 61\r\n                    (  811.80   113.02   -42.05     0.46)  ; 62\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  753.62     7.44   -43.62     0.46)  ; 1\r\n                      (  773.32    46.10   -50.97     0.46)  ; 2\r\n                      (  792.08    72.58   -50.27     0.46)  ; 3\r\n                      (  794.94    80.40   -48.05     0.46)  ; 4\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  722.00   -40.58   -53.75     0.46)  ; 1\r\n                      (  728.55   -22.33   -50.77     0.46)  ; 2\r\n                      (  734.01   -17.47   -49.40     0.46)  ; 3\r\n                      (  745.65    -2.80   -46.55     0.46)  ; 4\r\n                      (  758.98    24.82   -48.00     0.46)  ; 5\r\n                      (  760.36    26.94   -47.60     0.46)  ; 6\r\n                      (  777.60    51.87   -49.05     0.46)  ; 7\r\n                      (  781.28    56.32   -49.15     0.46)  ; 8\r\n                      (  784.46    58.85   -49.30     0.46)  ; 9\r\n                      (  786.34    62.88   -48.72     0.46)  ; 10\r\n                      (  798.39    87.78   -44.72     0.46)  ; 11\r\n                      (  800.28    91.80   -43.78     0.46)  ; 12\r\n                      (  803.18    95.47   -43.78     0.46)  ; 13\r\n                      (  805.74    96.66   -43.78     0.46)  ; 14\r\n                      (  811.15   105.70   -43.78     0.46)  ; 15\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  724.23   -46.04   -56.45     0.46)  ; 1, R-2-2-3-1-1-1-1-2-2\r\n                    (  727.62   -46.43   -56.52     0.46)  ; 2\r\n                    (  732.31   -48.32   -56.40     0.46)  ; 3\r\n                    (  736.41   -49.75   -58.05     0.46)  ; 4\r\n                    (  740.20   -51.85   -58.55     0.46)  ; 5\r\n                    (  744.48   -52.05   -58.55     0.46)  ; 6\r\n                    (  746.53   -52.75   -57.37     0.46)  ; 7\r\n                    (  748.32   -52.34   -57.80     0.46)  ; 8\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  747.56   -53.11   -57.80     0.46)  ; 1\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  728.20   -46.90   -56.52     0.46)  ; 1\r\n                      (  740.20   -51.85   -58.55     0.46)  ; 2\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                )  ;  End of split\r\n              |\r\n                (  337.44   -53.80   -30.67     0.46)  ; 1, R-2-2-3-1-1-1-2\r\n                (  337.44   -53.80   -30.73     0.46)  ; 2\r\n                (  337.62   -52.57   -32.83     0.46)  ; 3\r\n                (  337.62   -52.57   -32.85     0.46)  ; 4\r\n                (  336.91   -51.54   -34.67     0.46)  ; 5\r\n                (  336.19   -50.51   -36.40     0.46)  ; 6\r\n                (  336.69   -48.60   -37.50     0.46)  ; 7\r\n                (  335.58   -45.87   -39.22     0.46)  ; 8\r\n                (  333.89   -42.69   -40.13     0.46)  ; 9\r\n                (  333.09   -39.30   -41.57     0.46)  ; 10\r\n                (  332.39   -38.27   -42.88     0.46)  ; 11\r\n                (  332.03   -34.76   -44.30     0.46)  ; 12\r\n                (  331.37   -31.93   -45.22     0.46)  ; 13\r\n                (  328.66   -28.40   -46.77     0.46)  ; 14\r\n                (  327.55   -25.66   -48.45     0.46)  ; 15\r\n                (  328.36   -23.09   -49.35     0.46)  ; 16\r\n                (  328.90   -19.38   -50.52     0.46)  ; 17\r\n                (  329.97   -17.93   -52.67     0.46)  ; 18\r\n                (  330.66   -14.79   -53.47     0.46)  ; 19\r\n                (  330.66   -14.79   -53.50     0.46)  ; 20\r\n                (  330.88   -11.74   -55.97     0.46)  ; 21\r\n                (  331.56    -8.60   -56.17     0.46)  ; 22\r\n                (  331.35    -5.67   -59.27     0.46)  ; 23\r\n                (  331.35    -5.67   -59.30     0.46)  ; 24\r\n                (  331.44    -2.06   -60.30     0.46)  ; 25\r\n                (  331.35     0.30   -62.77     0.46)  ; 26\r\n                (  331.35     0.30   -62.80     0.46)  ; 27\r\n                (  332.08     5.26   -66.68     0.46)  ; 28\r\n                (  332.08     5.26   -66.77     0.46)  ; 29\r\n                (  331.86     8.20   -68.53     0.46)  ; 30\r\n                (  330.76    10.91   -68.85     0.46)  ; 31\r\n                (  331.71    12.93   -69.40     0.46)  ; 32\r\n                (  331.71    12.93   -69.10     0.46)  ; 33\r\n                (  330.46    16.22   -66.43     0.46)  ; 34\r\n                (  329.72    21.42   -64.38     0.46)  ; 35\r\n                (  329.05    24.25   -62.72     0.46)  ; 36\r\n                (  329.64    29.76   -62.72     0.46)  ; 37\r\n                (  329.92    34.61   -61.70     0.46)  ; 38\r\n                (  330.45    38.32   -60.45     0.46)  ; 39\r\n                (  331.09    39.65   -58.90     0.46)  ; 40\r\n                (  331.09    39.65   -58.92     0.46)  ; 41\r\n                (  330.35    44.85   -57.82     0.46)  ; 42\r\n                (  330.44    48.46   -56.42     0.46)  ; 43\r\n                (  330.09    51.96   -55.80     0.46)  ; 44\r\n                (  331.26    57.02   -54.63     0.46)  ; 45\r\n                (  332.11    61.39   -53.50     0.46)  ; 46\r\n                (  332.11    61.39   -53.53     0.46)  ; 47\r\n                (  331.23    67.16   -51.90     0.46)  ; 48\r\n                (  330.87    68.76   -50.57     0.46)  ; 49\r\n                (  330.60    69.88   -48.13     0.46)  ; 50\r\n                (  330.60    69.88   -48.15     0.46)  ; 51\r\n                (  331.58    73.70   -47.08     0.46)  ; 52\r\n                (  331.05    75.95   -45.32     0.46)  ; 53\r\n                (  331.05    75.95   -45.38     0.46)  ; 54\r\n                (  332.05    79.77   -43.62     0.46)  ; 55\r\n                (  332.72    82.92   -41.93     0.46)  ; 56\r\n                (  333.27    86.63   -42.40     0.46)  ; 57\r\n                (  332.34    90.60   -41.70     0.46)  ; 58\r\n                (  331.86    94.66   -41.70     0.46)  ; 59\r\n                (  332.22    97.13   -40.75     0.46)  ; 60\r\n                (  330.09   100.21   -39.05     0.46)  ; 61\r\n                (  330.09   100.21   -39.08     0.46)  ; 62\r\n                (  328.71   104.08   -39.85     0.46)  ; 63\r\n                (  328.71   104.08   -39.88     0.46)  ; 64\r\n                (  327.33   107.93   -38.55     0.46)  ; 65\r\n                (  326.72   112.57   -38.55     0.46)  ; 66\r\n                (  326.64   114.93   -37.58     0.46)  ; 67\r\n                (  327.18   118.64   -36.33     0.46)  ; 68\r\n                (  327.18   118.64   -36.35     0.46)  ; 69\r\n                (  325.67   123.07   -36.35     0.46)  ; 70\r\n                (  326.21   126.77   -36.35     0.46)  ; 71\r\n                (  325.99   129.71   -35.77     0.46)  ; 72\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  335.35   -48.92   -37.50     0.46)  ; 1\r\n                  (  331.10    93.88   -41.70     0.46)  ; 2\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  328.58   -20.06   -50.52     0.46)  ; 1\r\n                  (  329.85    20.85   -64.38     0.46)  ; 2\r\n                  (  331.63    65.46   -51.90     0.46)  ; 3\r\n                  (  332.58    83.48   -43.60     0.46)  ; 4\r\n                  (  333.27    86.63   -42.40     0.46)  ; 5\r\n                  (  328.58   104.63   -39.88     0.46)  ; 6\r\n                  (  327.03   113.23   -38.55     0.46)  ; 7\r\n                  (  326.73   118.53   -36.35     0.46)  ; 8\r\n                  (  326.47   125.64   -36.35     0.46)  ; 9\r\n                  (  326.44   129.81   -36.70     0.46)  ; 10\r\n                )  ;  End of markers\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  328.95   -29.42   -47.67     0.46)  ; 1\r\n                )  ;  End of markers\r\n                (\r\n                  (  326.01   135.69   -36.78     0.46)  ; 1, R-2-2-3-1-1-1-2-1\r\n                  (  327.00   139.51   -36.78     0.46)  ; 2\r\n                  (  327.68   142.65   -38.57     0.46)  ; 3\r\n                  (  327.68   142.65   -38.55     0.46)  ; 4\r\n                  (  327.38   147.96   -39.63     0.46)  ; 5\r\n                  (  328.23   152.34   -39.63     0.46)  ; 6\r\n                  (  328.64   156.61   -40.70     0.46)  ; 7\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  327.23   142.54   -38.55     0.46)  ; 1\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  330.61   158.26   -38.63     0.46)  ; 1, R-2-2-3-1-1-1-2-1-1\r\n                    (  329.15   158.51   -36.15     0.46)  ; 2\r\n                    (  328.56   158.98   -33.90     0.46)  ; 3\r\n                    (  326.95   159.80   -32.20     0.46)  ; 4\r\n                    (  324.64   161.64   -30.25     0.46)  ; 5\r\n                    (  323.04   162.46   -30.17     0.46)  ; 6\r\n                    (  321.83   161.58   -28.30     0.46)  ; 7\r\n                    (  320.54   163.07   -26.82     0.46)  ; 8\r\n                    (  318.35   164.35   -25.03     0.46)  ; 9\r\n                    (  316.87   164.59   -23.70     0.46)  ; 10\r\n                    (  315.27   165.41   -22.17     0.46)  ; 11\r\n                    (  315.27   165.41   -22.20     0.46)  ; 12\r\n                    (  313.93   165.10   -20.90     0.46)  ; 13\r\n                    (  312.78   166.04   -19.58     0.46)  ; 14\r\n                    (  312.52   167.16   -19.50     0.46)  ; 15\r\n                    (  311.04   167.41   -18.52     0.46)  ; 16\r\n                    (  309.16   169.36   -16.73     0.46)  ; 17\r\n                    (  307.25   169.51   -15.60     0.46)  ; 18\r\n                    (  305.06   170.79   -14.05     0.46)  ; 19\r\n                    (  302.17   173.10   -13.42     0.46)  ; 20\r\n                    (  299.41   174.83   -13.15     0.46)  ; 21\r\n                    (  294.99   175.60   -13.13     0.46)  ; 22\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  324.64   161.64   -30.25     0.46)  ; 1\r\n                      (  320.08   162.97   -26.82     0.46)  ; 2\r\n                      (  311.04   167.41   -18.52     0.46)  ; 3\r\n                      (  307.69   169.61   -15.60     0.46)  ; 4\r\n                      (  299.27   175.41   -13.15     0.46)  ; 5\r\n                      (  296.90   175.45   -13.13     0.46)  ; 6\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  328.75   160.21   -39.60     0.46)  ; 1, R-2-2-3-1-1-1-2-1-2\r\n                    (  329.42   163.35   -39.60     0.46)  ; 2\r\n                    (  329.83   167.64   -39.60     0.46)  ; 3\r\n                    (  330.82   171.45   -39.60     0.46)  ; 4\r\n                    (  329.75   175.98   -39.60     0.46)  ; 5\r\n                    (  331.01   178.66   -38.92     0.46)  ; 6\r\n                    (  333.03   182.11   -38.30     0.46)  ; 7\r\n                    (  334.91   186.14   -37.80     0.46)  ; 8\r\n                    (  335.33   190.42   -37.55     0.46)  ; 9\r\n                    (  335.47   195.82   -37.47     0.46)  ; 10\r\n                    (  336.73   198.52   -36.47     0.46)  ; 11\r\n                    (  337.68   200.53   -35.22     0.46)  ; 12\r\n                    (  337.90   203.57   -34.30     0.46)  ; 13\r\n                    (  337.99   207.18   -33.20     0.46)  ; 14\r\n                    (  339.74   211.76   -33.40     0.46)  ; 15\r\n                    (  342.47   214.20   -32.55     0.46)  ; 16\r\n                    (  342.47   214.20   -32.60     0.46)  ; 17\r\n                    (  344.89   215.96   -31.75     0.46)  ; 18\r\n                    (  345.56   219.11   -30.67     0.46)  ; 19\r\n                    (  345.92   221.58   -29.88     0.46)  ; 20\r\n                    (  348.61   222.21   -28.63     0.46)  ; 21\r\n                    (  350.04   226.12   -27.80     0.46)  ; 22\r\n                    (  352.55   231.49   -27.35     0.46)  ; 23\r\n                    (  352.55   231.49   -27.08     0.46)  ; 24\r\n                    (  354.76   236.19   -27.08     0.46)  ; 25\r\n                    (  355.29   239.90   -27.08     0.46)  ; 26\r\n                    (  355.34   241.70   -30.23     0.46)  ; 27\r\n                    (  356.29   243.72   -31.63     0.46)  ; 28\r\n                    (  357.50   244.60   -31.85     0.46)  ; 29\r\n                    (  359.10   243.78   -35.10     0.46)  ; 30\r\n                    (  358.65   243.67   -36.35     0.46)  ; 31\r\n                    (  358.65   243.67   -36.38     0.46)  ; 32\r\n                    (  359.60   245.69   -39.17     0.46)  ; 33\r\n                    (  359.60   245.69   -39.22     0.46)  ; 34\r\n                    (  360.09   247.59   -41.32     0.46)  ; 35\r\n                    (  360.45   250.06   -42.55     0.46)  ; 36\r\n                    (  361.39   252.07   -43.65     0.46)  ; 37\r\n                    (  364.39   253.38   -44.40     0.46)  ; 38\r\n                    (  366.35   255.04   -46.00     0.46)  ; 39\r\n                    (  367.74   257.14   -47.45     0.46)  ; 40\r\n                    (  367.74   257.14   -47.47     0.46)  ; 41\r\n                    (  368.81   258.59   -48.95     0.46)  ; 42\r\n                    (  368.81   258.59   -48.97     0.46)  ; 43\r\n                    (  370.03   259.48   -49.45     0.46)  ; 44\r\n                    (  372.12   260.56   -51.60     0.46)  ; 45\r\n                    (  373.65   262.12   -53.78     0.46)  ; 46\r\n                    (  373.65   262.12   -53.82     0.46)  ; 47\r\n                    (  374.85   262.99   -55.72     0.46)  ; 48\r\n                    (  375.93   264.43   -56.10     0.46)  ; 49\r\n                    (  376.75   267.02   -58.05     0.46)  ; 50\r\n                    (  378.26   268.57   -59.32     0.46)  ; 51\r\n                    (  378.94   271.72   -60.20     0.46)  ; 52\r\n                    (  379.56   273.05   -61.40     0.46)  ; 53\r\n                    (  379.56   273.05   -61.42     0.46)  ; 54\r\n                    (  381.46   277.08   -62.45     0.46)  ; 55\r\n                    (  382.84   279.20   -62.75     0.46)  ; 56\r\n                    (  385.29   282.75   -61.90     0.46)  ; 57\r\n                    (  388.03   285.19   -60.67     0.46)  ; 58\r\n                    (  389.87   287.42   -59.25     0.46)  ; 59\r\n                    (  391.69   289.63   -60.17     0.46)  ; 60\r\n                    (  393.66   291.30   -61.90     0.46)  ; 61\r\n                    (  396.84   293.82   -63.78     0.46)  ; 62\r\n                    (  398.94   294.92   -65.22     0.46)  ; 63\r\n                    (  400.90   296.58   -67.00     0.46)  ; 64\r\n                    (  400.90   296.58   -67.95     0.46)  ; 65\r\n                    (  403.82   300.23   -68.05     0.46)  ; 66\r\n                    (  403.82   300.23   -68.07     0.46)  ; 67\r\n                    (  404.45   301.59   -70.07     0.46)  ; 68\r\n                    (  404.45   301.59   -70.10     0.46)  ; 69\r\n                    (  406.54   302.67   -70.35     0.46)  ; 70\r\n                    (  406.54   302.67   -70.38     0.46)  ; 71\r\n                    (  407.81   305.36   -72.57     0.46)  ; 72\r\n                    (  407.81   305.36   -72.60     0.46)  ; 73\r\n                    (  409.90   306.44   -73.78     0.46)  ; 74\r\n                    (  411.73   308.67   -74.35     0.46)  ; 75\r\n                    (  414.07   312.79   -75.25     0.46)  ; 76\r\n                    (  414.07   312.79   -75.27     0.46)  ; 77\r\n                    (  415.46   314.91   -76.45     0.46)  ; 78\r\n                    (  416.84   317.02   -77.60     0.46)  ; 79\r\n                    (  419.13   319.35   -78.70     0.46)  ; 80\r\n                    (  420.07   321.36   -80.88     0.46)  ; 81\r\n                    (  420.07   321.36   -80.90     0.46)  ; 82\r\n                    (  422.17   322.45   -82.22     0.46)  ; 83\r\n                    (  422.66   324.36   -84.40     0.46)  ; 84\r\n                    (  423.55   324.57   -86.47     0.46)  ; 85\r\n                    (  425.52   326.22   -86.72     0.46)  ; 86\r\n                    (  425.52   326.22   -86.75     0.46)  ; 87\r\n                    (  427.55   329.68   -88.93     0.46)  ; 88\r\n                    (  429.82   332.01   -91.10     0.46)  ; 89\r\n                    (  429.82   332.01   -91.13     0.46)  ; 90\r\n                    (  430.32   333.92   -93.65     0.46)  ; 91\r\n                    (  430.32   333.92   -93.63     0.46)  ; 92\r\n                    (  429.16   334.84   -92.00     0.46)  ; 93\r\n                    (  429.16   334.84   -92.05     0.46)  ; 94\r\n                    (  429.79   336.18   -94.75     0.46)  ; 95\r\n                    (  431.49   338.97   -95.90     0.46)  ; 96\r\n                    (  434.53   342.07   -97.30     0.46)  ; 97\r\n                    (  436.68   344.96   -99.20     0.46)  ; 98\r\n                    (  437.31   346.31  -101.75     0.46)  ; 99\r\n                    (  438.69   348.42  -104.63     0.46)  ; 100\r\n                    (  439.64   350.43  -107.30     0.46)  ; 101\r\n                    (  439.64   350.43  -107.32     0.46)  ; 102\r\n                    (  440.58   352.44  -109.72     0.46)  ; 103\r\n                    (  441.20   353.79  -112.05     0.46)  ; 104\r\n                    (  441.20   353.79  -112.10     0.46)  ; 105\r\n                    (  442.01   356.36  -115.37     0.46)  ; 106\r\n                    (  442.01   356.36  -115.40     0.46)  ; 107\r\n                    (  441.17   357.96  -116.30     0.46)  ; 108\r\n                    (  442.25   359.41  -118.47     0.46)  ; 109\r\n                    (  442.25   359.41  -118.88     0.46)  ; 110\r\n                    (  442.30   361.20  -121.65     0.46)  ; 111\r\n                    (  444.40   362.30  -121.70     0.46)  ; 112\r\n                    (  444.18   365.23  -126.97     0.46)  ; 113\r\n                    (  444.23   367.04  -129.97     0.46)  ; 114\r\n                    (  445.74   368.59  -131.15     0.46)  ; 115\r\n                    (  445.74   368.59  -131.17     0.46)  ; 116\r\n                    (  447.41   369.57  -134.00     0.46)  ; 117\r\n                    (  447.41   369.57  -134.02     0.46)  ; 118\r\n                    (  447.59   370.80  -133.85     0.46)  ; 119\r\n                    (  449.11   372.36  -137.15     0.46)  ; 120\r\n\r\n                    (Dot\r\n                      (Color Yellow)\r\n                      (Name \"Marker 1\")\r\n                      (  338.56   200.73   -35.22     0.46)  ; 1\r\n                      (  379.03   275.32   -62.45     0.46)  ; 2\r\n                      (  442.74   355.33  -115.40     0.46)  ; 3\r\n                      (  442.97   364.35  -126.97     0.46)  ; 4\r\n                    )  ;  End of markers\r\n\r\n                    (OpenCircle\r\n                      (Color Yellow)\r\n                      (Name \"Marker 2\")\r\n                      (  329.83   167.64   -39.60     0.46)  ; 1\r\n                      (  339.74   211.76   -33.40     0.46)  ; 2\r\n                      (  348.47   222.77   -28.63     0.46)  ; 3\r\n                      (  355.29   239.90   -27.08     0.46)  ; 4\r\n                      (  357.63   244.03   -31.85     0.46)  ; 5\r\n                      (  364.39   253.38   -44.40     0.46)  ; 6\r\n                      (  375.61   263.77   -56.10     0.46)  ; 7\r\n                      (  377.81   268.46   -59.35     0.46)  ; 8\r\n                      (  389.73   287.98   -59.25     0.46)  ; 9\r\n                      (  394.11   291.39   -61.90     0.46)  ; 10\r\n                      (  398.81   295.48   -65.22     0.46)  ; 11\r\n                      (  415.32   315.48   -76.45     0.46)  ; 12\r\n                      (  420.52   321.46   -80.90     0.46)  ; 13\r\n                      (  421.46   323.48   -82.22     0.46)  ; 14\r\n                      (  431.18   338.30   -95.90     0.46)  ; 15\r\n                      (  442.75   361.31  -121.65     0.46)  ; 16\r\n                      (  445.08   371.42  -133.85     0.46)  ; 17\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  450.50   374.47  -137.15     0.46)  ; 1, R-2-2-3-1-1-1-2-1-2-1\r\n                      (  451.75   377.17  -138.50     0.46)  ; 2\r\n                      (  453.55   377.58  -140.23     0.46)  ; 3\r\n                      (  453.55   377.58  -140.25     0.46)  ; 4\r\n                      (  455.77   378.10  -141.85     0.46)  ; 5\r\n                      (  457.42   379.09  -144.47     0.46)  ; 6\r\n                      (  459.84   380.85  -147.23     0.46)  ; 7\r\n                      (  460.52   383.99  -148.35     0.46)  ; 8\r\n                      (  463.24   386.42  -149.35     0.46)  ; 9\r\n                      (  465.21   388.07  -151.55     0.46)  ; 10\r\n                      (  467.05   390.30  -153.48     0.46)  ; 11\r\n                      (  468.13   391.74  -155.32     0.46)  ; 12\r\n                      (  469.06   393.76  -156.67     0.46)  ; 13\r\n                      (  469.06   393.76  -156.70     0.46)  ; 14\r\n                      (  470.45   395.87  -158.30     0.46)  ; 15\r\n                      (  470.45   395.87  -158.27     0.46)  ; 16\r\n                      (  471.70   398.56  -160.05     0.46)  ; 17\r\n                      (  473.81   399.65  -162.02     0.46)  ; 18\r\n                      (  473.81   399.65  -162.05     0.46)  ; 19\r\n                      (  476.80   400.95  -164.00     0.46)  ; 20\r\n                      (  476.80   400.95  -164.02     0.46)  ; 21\r\n                      (  478.58   401.37  -165.92     0.46)  ; 22\r\n                      (  481.13   402.56  -166.65     0.46)  ; 23\r\n                      (  485.99   401.91  -167.15     0.46)  ; 24\r\n\r\n                      (OpenCircle\r\n                        (Color Yellow)\r\n                        (Name \"Marker 2\")\r\n                        (  459.26   381.30  -147.23     0.46)  ; 1\r\n                        (  469.19   393.18  -156.70     0.46)  ; 2\r\n                        (  485.86   402.47  -167.15     0.46)  ; 3\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  451.43   370.52  -139.15     0.46)  ; 1, R-2-2-3-1-1-1-2-1-2-2\r\n                      (  454.37   370.01  -140.60     0.46)  ; 2\r\n                      (  456.29   369.86  -142.30     0.46)  ; 3\r\n                      (  456.29   369.86  -142.32     0.46)  ; 4\r\n                      (  460.35   372.60  -142.05     0.46)  ; 5\r\n                      (  464.11   374.67  -142.73     0.46)  ; 6\r\n                      (  464.11   374.67  -142.75     0.46)  ; 7\r\n                      (  464.92   377.26  -143.38     0.46)  ; 8\r\n                      (  464.96   379.06  -145.45     0.46)  ; 9\r\n                      (  464.57   380.76  -147.15     0.46)  ; 10\r\n                      (  465.06   382.67  -148.60     0.46)  ; 11\r\n                      (  466.63   386.03  -149.73     0.46)  ; 12\r\n                      (  466.63   386.03  -149.82     0.46)  ; 13\r\n\r\n                      (Dot\r\n                        (Color Yellow)\r\n                        (Name \"Marker 1\")\r\n                        (  467.16   383.75  -149.82     0.46)  ; 1\r\n                      )  ;  End of markers\r\n\r\n                      (OpenCircle\r\n                        (Color Yellow)\r\n                        (Name \"Marker 2\")\r\n                        (  464.92   377.26  -143.38     0.46)  ; 1\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  )  ;  End of split\r\n                |\r\n                  (  327.25   133.20   -36.90     0.46)  ; 1, R-2-2-3-1-1-1-2-2\r\n                  (  329.22   134.86   -38.33     0.46)  ; 2\r\n                  (  329.22   134.86   -38.35     0.46)  ; 3\r\n                  (  330.47   137.55   -39.88     0.46)  ; 4\r\n                  (  330.47   137.55   -39.90     0.46)  ; 5\r\n                  (  332.62   140.43   -41.35     0.46)  ; 6\r\n                  (  335.48   142.30   -42.95     0.46)  ; 7\r\n                  (  335.48   142.30   -42.98     0.46)  ; 8\r\n                  (  337.89   144.06   -44.97     0.46)  ; 9\r\n                  (  339.68   144.48   -46.82     0.46)  ; 10\r\n                  (  341.79   145.57   -48.35     0.46)  ; 11\r\n                  (  341.79   145.57   -48.38     0.46)  ; 12\r\n                  (  344.77   146.86   -48.55     0.46)  ; 13\r\n                  (  344.77   146.86   -48.60     0.46)  ; 14\r\n                  (  347.19   148.62   -50.38     0.46)  ; 15\r\n                  (  348.14   150.64   -51.50     0.46)  ; 16\r\n                  (  350.10   152.29   -51.72     0.46)  ; 17\r\n                  (  351.31   153.18   -51.75     0.46)  ; 18\r\n                  (  353.98   153.81   -52.77     0.46)  ; 19\r\n                  (  356.71   156.23   -54.15     0.46)  ; 20\r\n                  (  360.46   158.31   -54.20     0.46)  ; 21\r\n                  (  363.60   159.05   -55.20     0.46)  ; 22\r\n                  (  366.00   160.80   -56.40     0.46)  ; 23\r\n                  (  366.00   160.80   -56.42     0.46)  ; 24\r\n                  (  367.26   163.49   -57.65     0.46)  ; 25\r\n                  (  370.26   164.78   -59.45     0.46)  ; 26\r\n                  (  370.26   164.78   -59.47     0.46)  ; 27\r\n                  (  371.83   168.13   -60.07     0.46)  ; 28\r\n                  (  374.11   170.46   -61.10     0.46)  ; 29\r\n                  (  376.21   171.55   -62.13     0.46)  ; 30\r\n                  (  377.56   171.86   -63.62     0.46)  ; 31\r\n                  (  377.42   172.43   -63.62     0.46)  ; 32\r\n                  (  378.36   174.45   -63.62     0.46)  ; 33\r\n                  (  381.80   175.85   -63.42     0.46)  ; 34\r\n                  (  385.87   178.59   -63.72     0.46)  ; 35\r\n                  (  389.63   180.66   -62.47     0.46)  ; 36\r\n                  (  392.35   183.09   -62.47     0.46)  ; 37\r\n                  (  396.29   186.40   -62.47     0.46)  ; 38\r\n                  (  398.52   186.93   -62.47     0.46)  ; 39\r\n                  (  402.60   189.67   -64.35     0.46)  ; 40\r\n                  (  407.68   192.06   -65.27     0.46)  ; 41\r\n                  (  411.39   192.33   -66.17     0.46)  ; 42\r\n                  (  411.39   192.33   -66.20     0.46)  ; 43\r\n                  (  414.11   194.76   -67.05     0.46)  ; 44\r\n                  (  416.58   198.32   -66.60     0.46)  ; 45\r\n                  (  416.58   198.32   -66.63     0.46)  ; 46\r\n                  (  420.20   200.97   -68.40     0.46)  ; 47\r\n                  (  420.20   200.97   -68.42     0.46)  ; 48\r\n                  (  423.06   202.83   -70.70     0.46)  ; 49\r\n                  (  423.06   202.83   -70.72     0.46)  ; 50\r\n                  (  426.06   204.13   -70.05     0.46)  ; 51\r\n                  (  426.06   204.13   -70.07     0.46)  ; 52\r\n                  (  430.40   205.74   -71.03     0.46)  ; 53\r\n                  (  430.40   205.74   -71.07     0.46)  ; 54\r\n                  (  433.25   207.60   -71.07     0.46)  ; 55\r\n                  (  437.77   210.45   -71.50     0.46)  ; 56\r\n                  (  441.38   213.09   -71.50     0.46)  ; 57\r\n                  (  444.83   214.49   -71.50     0.46)  ; 58\r\n                  (  449.03   216.68   -71.25     0.46)  ; 59\r\n                  (  451.43   218.43   -71.25     0.46)  ; 60\r\n                  (  454.30   220.30   -71.25     0.46)  ; 61\r\n                  (  458.81   223.15   -71.25     0.46)  ; 62\r\n                  (  463.82   227.90   -72.55     0.46)  ; 63\r\n                  (  463.82   227.90   -72.57     0.46)  ; 64\r\n                  (  465.41   232.44   -71.22     0.46)  ; 65\r\n                  (  468.86   233.84   -71.22     0.46)  ; 66\r\n                  (  475.10   235.30   -71.70     0.46)  ; 67\r\n                  (  479.04   238.63   -71.68     0.46)  ; 68\r\n                  (  482.35   240.59   -70.82     0.46)  ; 69\r\n                  (  486.10   242.66   -71.97     0.46)  ; 70\r\n                  (  488.65   243.87   -74.40     0.46)  ; 71\r\n                  (  488.78   243.29   -74.40     0.46)  ; 72\r\n                  (  493.38   243.77   -75.72     0.46)  ; 73\r\n                  (  493.38   243.77   -75.75     0.46)  ; 74\r\n                  (  497.00   246.42   -77.10     0.46)  ; 75\r\n                  (  497.00   246.42   -77.13     0.46)  ; 76\r\n                  (  499.91   250.08   -78.20     0.46)  ; 77\r\n                  (  502.19   252.41   -78.30     0.46)  ; 78\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  376.58   174.03   -63.62     0.46)  ; 1\r\n                    (  414.38   193.62   -67.05     0.46)  ; 2\r\n                    (  485.79   242.00   -71.97     0.46)  ; 3\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  351.75   153.28   -51.75     0.46)  ; 1\r\n                    (  363.46   159.60   -55.20     0.46)  ; 2\r\n                    (  385.87   178.59   -63.72     0.46)  ; 3\r\n                    (  422.74   202.16   -70.72     0.46)  ; 4\r\n                    (  463.37   227.81   -72.57     0.46)  ; 5\r\n                    (  475.10   235.30   -71.70     0.46)  ; 6\r\n                    (  501.75   252.31   -78.30     0.46)  ; 7\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            |\r\n              (  336.44   -58.04   -29.25     0.46)  ; 1, R-2-2-3-1-1-2\r\n              (  336.57   -58.61   -30.90     0.46)  ; 2\r\n              (  335.64   -60.61   -30.60     0.46)  ; 3\r\n              (  335.64   -60.61   -30.65     0.46)  ; 4\r\n              (  335.27   -63.09   -32.47     0.46)  ; 5\r\n              (\r\n                (  337.77   -63.70   -32.55     0.46)  ; 1, R-2-2-3-1-1-2-1\r\n                (  337.58   -64.93   -34.30     0.46)  ; 2\r\n                (  337.58   -64.93   -34.35     0.46)  ; 3\r\n                (  337.58   -64.93   -36.58     0.46)  ; 4\r\n                (  337.58   -64.93   -36.60     0.46)  ; 5\r\n                 Normal\r\n              |\r\n                (  334.45   -65.66   -35.08     0.46)  ; 1, R-2-2-3-1-1-2-2\r\n                (  333.65   -68.25   -37.47     0.46)  ; 2\r\n                (  333.47   -69.48   -39.17     0.46)  ; 3\r\n                (  331.63   -71.71   -40.07     0.46)  ; 4\r\n                (  330.52   -74.95   -40.97     0.46)  ; 5\r\n                (  329.57   -76.97   -42.60     0.46)  ; 6\r\n                (  327.47   -78.05   -43.82     0.46)  ; 7\r\n                (  327.47   -78.05   -43.85     0.46)  ; 8\r\n                (  325.55   -77.91   -46.00     0.46)  ; 9\r\n                (  325.55   -77.91   -46.03     0.46)  ; 10\r\n                (  325.06   -79.81   -48.27     0.46)  ; 11\r\n                (  325.06   -79.81   -48.30     0.46)  ; 12\r\n                (  324.74   -80.48   -49.80     0.46)  ; 13\r\n                (  324.43   -81.15   -51.88     0.46)  ; 14\r\n                (  322.34   -82.25   -52.30     0.46)  ; 15\r\n                (  319.64   -82.88   -55.10     0.46)  ; 16\r\n                (  319.16   -84.78   -57.30     0.46)  ; 17\r\n                (  317.50   -85.77   -59.63     0.46)  ; 18\r\n                (  317.00   -87.68   -61.57     0.46)  ; 19\r\n                (  317.00   -87.68   -61.60     0.46)  ; 20\r\n                (  317.10   -90.04   -62.15     0.46)  ; 21\r\n                (  314.98   -91.13   -62.30     0.46)  ; 22\r\n                (  314.98   -91.13   -62.33     0.46)  ; 23\r\n                (  312.13   -93.00   -63.25     0.46)  ; 24\r\n                (  311.14   -96.80   -63.25     0.46)  ; 25\r\n                (  309.24  -100.83   -64.42     0.46)  ; 26\r\n                (  306.21  -103.94   -65.52     0.46)  ; 27\r\n                (  303.82  -109.87   -65.72     0.46)  ; 28\r\n                (  300.79  -112.97   -65.43     0.46)  ; 29\r\n                (  297.20  -119.77   -65.43     0.46)  ; 30\r\n                (  295.14  -125.03   -65.43     0.46)  ; 31\r\n                (  293.25  -129.05   -65.95     0.46)  ; 32\r\n                (  290.48  -133.29   -65.88     0.46)  ; 33\r\n                (  288.16  -137.43   -65.70     0.46)  ; 34\r\n                (  286.32  -139.64   -65.70     0.46)  ; 35\r\n                (  286.22  -143.25   -65.15     0.46)  ; 36\r\n                (  286.22  -143.25   -65.22     0.46)  ; 37\r\n                (  284.65  -146.60   -67.50     0.46)  ; 38\r\n                (  284.65  -146.60   -67.53     0.46)  ; 39\r\n                (  281.82  -152.64   -68.80     0.46)  ; 40\r\n                (  281.82  -152.64   -68.82     0.46)  ; 41\r\n                (  278.16  -157.08   -69.55     0.46)  ; 42\r\n                (  274.69  -164.46   -69.55     0.46)  ; 43\r\n                (  271.61  -169.36   -70.35     0.46)  ; 44\r\n                (  269.40  -174.06   -70.35     0.46)  ; 45\r\n                (  267.84  -177.41   -71.63     0.46)  ; 46\r\n                (  266.59  -180.10   -71.43     0.46)  ; 47\r\n                (  266.59  -180.10   -71.45     0.46)  ; 48\r\n                (  264.96  -185.26   -72.38     0.46)  ; 49\r\n                (  262.37  -194.22   -72.80     0.46)  ; 50\r\n                (  260.43  -200.05   -73.38     0.46)  ; 51\r\n                (  258.86  -203.41   -73.38     0.46)  ; 52\r\n                (  255.81  -206.50   -74.92     0.46)  ; 53\r\n                (  252.59  -210.85   -76.32     0.46)  ; 54\r\n                (  251.02  -214.21   -78.20     0.46)  ; 55\r\n                (  251.02  -214.21   -78.22     0.46)  ; 56\r\n                (  249.27  -218.79   -78.93     0.46)  ; 57\r\n                (  247.94  -223.29   -78.75     0.46)  ; 58\r\n                (  246.83  -226.54   -79.85     0.46)  ; 59\r\n                (  246.83  -226.54   -79.88     0.46)  ; 60\r\n                (  245.26  -229.89   -81.75     0.46)  ; 61\r\n                (  244.71  -233.60   -84.32     0.46)  ; 62\r\n                (  244.71  -233.60   -84.38     0.46)  ; 63\r\n                (  243.63  -235.05   -84.72     0.46)  ; 64\r\n                (  242.83  -237.62   -85.68     0.46)  ; 65\r\n                (  242.83  -237.62   -85.80     0.46)  ; 66\r\n                (  241.84  -241.44   -86.90     0.46)  ; 67\r\n                (  241.84  -241.44   -87.02     0.46)  ; 68\r\n                (  241.75  -245.05   -87.42     0.46)  ; 69\r\n                (  241.75  -245.05   -87.68     0.46)  ; 70\r\n                (  239.23  -250.40   -88.20     0.46)  ; 71\r\n                (  239.23  -250.40   -88.23     0.46)  ; 72\r\n                (  239.00  -253.45   -88.95     0.46)  ; 73\r\n                (  239.00  -253.45   -88.97     0.46)  ; 74\r\n                (  237.43  -256.81   -90.40     0.46)  ; 75\r\n                (  236.43  -260.62   -91.58     0.46)  ; 76\r\n                (  236.43  -260.62   -91.60     0.46)  ; 77\r\n                (  234.29  -263.51   -92.92     0.46)  ; 78\r\n                (  233.48  -266.09   -96.43     0.46)  ; 79\r\n                (  231.97  -267.64   -97.80     0.46)  ; 80\r\n                (  230.38  -270.99   -99.63     0.46)  ; 81\r\n                (  230.25  -270.42   -99.67     0.46)  ; 82\r\n                (  228.11  -273.32  -101.98     0.46)  ; 83\r\n                (  226.54  -276.67  -103.15     0.46)  ; 84\r\n                (  225.54  -280.49  -104.67     0.46)  ; 85\r\n                (  224.69  -284.87  -105.60     0.46)  ; 86\r\n                (  224.69  -284.87  -105.63     0.46)  ; 87\r\n                (  223.25  -288.79  -104.70     0.46)  ; 88\r\n                (  222.35  -294.97  -105.75     0.46)  ; 89\r\n                (  221.22  -298.21  -108.75     0.46)  ; 90\r\n                (  220.86  -300.70  -111.95     0.46)  ; 91\r\n                (  220.86  -300.70  -111.97     0.46)  ; 92\r\n                (  220.31  -304.41  -113.42     0.46)  ; 93\r\n                (  218.80  -305.96  -114.07     0.46)  ; 94\r\n                (  218.35  -306.06  -114.10     0.46)  ; 95\r\n                (  217.08  -308.75  -115.77     0.46)  ; 96\r\n                (  216.28  -311.32  -116.90     0.46)  ; 97\r\n                (  216.28  -311.32  -116.93     0.46)  ; 98\r\n                (  215.29  -315.14  -118.38     0.46)  ; 99\r\n                (  212.83  -318.70  -120.10     0.46)  ; 100\r\n                (  211.89  -320.71  -121.72     0.46)  ; 101\r\n                (  211.89  -320.71  -121.77     0.46)  ; 102\r\n                (  211.39  -322.62  -122.85     0.46)  ; 103\r\n                (  211.21  -323.85  -124.52     0.46)  ; 104\r\n                (  210.80  -328.14  -126.07     0.46)  ; 105\r\n                (  208.02  -332.37  -126.95     0.46)  ; 106\r\n                (  207.21  -334.94  -128.65     0.46)  ; 107\r\n                (  207.21  -334.94  -128.67     0.46)  ; 108\r\n                (  206.76  -335.05  -130.72     0.46)  ; 109\r\n                (  206.09  -338.19  -132.40     0.46)  ; 110\r\n                (  206.09  -338.19  -132.42     0.46)  ; 111\r\n                (  206.04  -339.99  -133.43     0.46)  ; 112\r\n                (  205.37  -343.14  -135.20     0.46)  ; 113\r\n                (  205.14  -346.18  -136.15     0.46)  ; 114\r\n                (  206.69  -348.80  -136.15     0.46)  ; 115\r\n                (  208.20  -353.23  -137.27     0.46)  ; 116\r\n                (  208.64  -359.09  -138.02     0.46)  ; 117\r\n                (  210.19  -361.72  -139.00     0.46)  ; 118\r\n                (  212.01  -365.48  -140.02     0.46)  ; 119\r\n                (  213.83  -369.22  -141.52     0.46)  ; 120\r\n                (  216.56  -372.76  -143.18     0.46)  ; 121\r\n                (  219.13  -375.73  -145.02     0.46)  ; 122\r\n                (  218.83  -376.41  -147.25     0.46)  ; 123\r\n                (  220.78  -380.72  -148.60     0.46)  ; 124\r\n                (  222.08  -382.21  -149.32     0.46)  ; 125\r\n                (  222.74  -385.05  -150.68     0.46)  ; 126\r\n                (  222.50  -388.09  -151.67     0.46)  ; 127\r\n                (  223.79  -389.57  -153.15     0.46)  ; 128\r\n                (  223.79  -389.57  -153.18     0.46)  ; 129\r\n                (  225.23  -391.63  -154.25     0.46)  ; 130\r\n                (  226.52  -393.11  -156.02     0.46)  ; 131\r\n                (  227.05  -395.38  -159.95     0.46)  ; 132\r\n                (  227.05  -395.38  -159.97     0.46)  ; 133\r\n                (  227.58  -397.64  -162.40     0.46)  ; 134\r\n                (  228.42  -399.24  -163.93     0.46)  ; 135\r\n                (  229.40  -401.40  -165.35     0.46)  ; 136\r\n                (  230.37  -403.56  -166.60     0.46)  ; 137\r\n                (  230.37  -403.56  -166.63     0.46)  ; 138\r\n                (  229.44  -405.56  -167.72     0.46)  ; 139\r\n                (  229.39  -407.37  -169.27     0.46)  ; 140\r\n                (  229.33  -409.17  -169.93     0.46)  ; 141\r\n                (  228.97  -411.65  -171.98     0.46)  ; 142\r\n                (  227.45  -413.20  -173.97     0.46)  ; 143\r\n                (  227.45  -413.20  -174.02     0.46)  ; 144\r\n                (  227.54  -415.57  -175.85     0.46)  ; 145\r\n                (  227.54  -415.57  -175.92     0.46)  ; 146\r\n                (  229.35  -419.32  -177.60     0.46)  ; 147\r\n                (  229.35  -419.32  -177.62     0.46)  ; 148\r\n                (  228.42  -421.33  -179.73     0.46)  ; 149\r\n                (  227.34  -422.78  -181.67     0.46)  ; 150\r\n                (  225.32  -426.24  -182.80     0.46)  ; 151\r\n                (  223.80  -427.78  -183.78     0.46)  ; 152\r\n                (  223.80  -427.78  -183.82     0.46)  ; 153\r\n                (  223.89  -430.16  -185.80     0.46)  ; 154\r\n                (  223.39  -432.07  -187.02     0.46)  ; 155\r\n                (  225.52  -435.15  -187.95     0.46)  ; 156\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  304.98  -110.79   -65.72     0.46)  ; 1\r\n                  (  223.79  -285.08  -105.63     0.46)  ; 2\r\n                  (  204.62  -337.94  -132.42     0.46)  ; 3\r\n                  (  225.71  -395.69  -159.97     0.46)  ; 4\r\n                  (  227.89  -419.06  -177.65     0.46)  ; 5\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  329.57   -76.97   -42.60     0.46)  ; 1\r\n                  (  295.14  -125.03   -65.43     0.46)  ; 2\r\n                  (  288.60  -137.32   -65.70     0.46)  ; 3\r\n                  (  269.85  -173.95   -70.35     0.46)  ; 4\r\n                  (  266.59  -180.10   -71.45     0.46)  ; 5\r\n                  (  258.41  -203.51   -73.38     0.46)  ; 6\r\n                  (  239.45  -253.35   -88.97     0.46)  ; 7\r\n                  (  234.29  -263.51   -92.92     0.46)  ; 8\r\n                  (  224.01  -288.01  -104.70     0.46)  ; 9\r\n                  (  216.41  -311.88  -116.93     0.46)  ; 10\r\n                  (  210.80  -328.14  -126.07     0.46)  ; 11\r\n                  (  208.02  -332.37  -126.95     0.46)  ; 12\r\n                  (  205.55  -341.91  -135.20     0.46)  ; 13\r\n                  (  207.76  -353.33  -137.27     0.46)  ; 14\r\n                  (  208.50  -358.53  -138.02     0.46)  ; 15\r\n                  (  212.01  -365.48  -140.02     0.46)  ; 16\r\n                  (  210.58  -369.38  -141.52     0.46)  ; 17\r\n                  (  213.25  -368.76  -141.52     0.46)  ; 18\r\n                  (  219.27  -376.30  -147.25     0.46)  ; 19\r\n                  (  225.10  -391.06  -154.25     0.46)  ; 20\r\n                  (  229.33  -409.17  -169.93     0.46)  ; 21\r\n                  (  225.52  -435.15  -187.95     0.46)  ; 22\r\n                )  ;  End of markers\r\n                 High\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  329.41   -57.06   -20.27     0.46)  ; 1, R-2-2-3-1-2\r\n            (  329.86   -56.95   -18.20     0.46)  ; 2\r\n            (  329.86   -56.95   -18.25     0.46)  ; 3\r\n            (  330.94   -55.51   -16.13     0.46)  ; 4\r\n            (\r\n              (  331.92   -51.69   -15.57     0.46)  ; 1, R-2-2-3-1-2-1\r\n              (  333.19   -49.01   -14.72     0.46)  ; 2\r\n              (  333.54   -46.54   -14.72     0.46)  ; 3\r\n              (  333.33   -43.60   -14.15     0.46)  ; 4\r\n              (  334.32   -39.79   -13.30     0.46)  ; 5\r\n              (  334.99   -36.65   -14.48     0.46)  ; 6\r\n              (  334.06   -32.68   -15.20     0.46)  ; 7\r\n              (  334.06   -32.68   -15.22     0.46)  ; 8\r\n              (  332.95   -29.96   -16.15     0.46)  ; 9\r\n              (  330.82   -26.87   -16.92     0.46)  ; 10\r\n              (  329.40   -24.82   -17.55     0.46)  ; 11\r\n              (  330.34   -22.80   -18.10     0.46)  ; 12\r\n              (  329.28   -18.28   -18.10     0.46)  ; 13\r\n              (  329.01   -17.15   -18.05     0.46)  ; 14\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  332.53   -40.21   -13.30     0.46)  ; 1\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  331.40   -27.34   -16.92     0.46)  ; 1\r\n              )  ;  End of markers\r\n              (\r\n                (  327.64   -13.29   -18.20     0.46)  ; 1, R-2-2-3-1-2-1-1\r\n                (  328.04    -9.01   -18.20     0.46)  ; 2\r\n                (  327.17    -3.25   -18.20     0.46)  ; 3\r\n                (  325.66     1.17   -19.07     0.46)  ; 4\r\n                (  324.55     3.90   -19.88     0.46)  ; 5\r\n                (  324.55     3.90   -19.90     0.46)  ; 6\r\n                (  325.48     5.92   -20.60     0.46)  ; 7\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  324.02     6.17   -20.60     0.46)  ; 1\r\n                )  ;  End of markers\r\n                 Normal\r\n              |\r\n                (  326.51   -16.54   -16.38     0.46)  ; 1, R-2-2-3-1-2-1-2\r\n                (  324.51   -14.02   -14.67     0.46)  ; 2\r\n                (  322.96   -11.40   -13.30     0.46)  ; 3\r\n                (  320.27   -12.03   -11.98     0.46)  ; 4\r\n                (  316.31   -11.16   -12.02     0.46)  ; 5\r\n                (  311.12   -11.19   -11.15     0.46)  ; 6\r\n                (  309.44    -8.00    -9.90     0.46)  ; 7\r\n                (  304.68    -3.75    -9.52     0.46)  ; 8\r\n                (  302.74    -3.59    -8.00     0.46)  ; 9\r\n                (  299.85    -1.29    -7.05     0.46)  ; 10\r\n                (  298.11     0.10    -5.68     0.46)  ; 11\r\n                (  295.36     1.84    -4.70     0.46)  ; 12\r\n                (  293.04     3.68    -3.90     0.46)  ; 13\r\n                (  291.30     5.07    -2.53     0.46)  ; 14\r\n                (  289.56     6.46    -1.60     0.46)  ; 15\r\n                (  287.95     7.27    -0.20     0.46)  ; 16\r\n                (  286.93     7.62     0.43     0.46)  ; 17\r\n                (  282.11    10.08     1.35     0.46)  ; 18\r\n                (  279.26    14.19     0.30     0.46)  ; 19\r\n                (  275.84    18.76     1.07     0.46)  ; 20\r\n                (  273.44    22.97     2.45     0.46)  ; 21\r\n                (  269.19    24.96     4.80     0.46)  ; 22\r\n                (  269.19    24.96     4.75     0.46)  ; 23\r\n                (  266.80    29.18     5.00     0.46)  ; 24\r\n                (  261.85    32.20     5.17     0.46)  ; 25\r\n                (  256.85    33.43     5.68     0.46)  ; 26\r\n                (  254.99    35.38     5.50     0.46)  ; 27\r\n                (  250.35    39.08     5.97     0.46)  ; 28\r\n                (  246.83    40.04     5.95     0.46)  ; 29\r\n                (  243.88    40.54     7.87     0.46)  ; 30\r\n                (  240.66    42.18     8.42     0.46)  ; 31\r\n                (  239.07    43.00    10.15     0.46)  ; 32\r\n                (  237.28    42.57     9.85     0.46)  ; 33\r\n                (  235.93    42.26    12.13     0.46)  ; 34\r\n                (  234.65    43.76    13.70     0.46)  ; 35\r\n                (  233.18    44.00    15.30     0.46)  ; 36\r\n                (  230.91    47.66    16.38     0.46)  ; 37\r\n                (  229.48    49.71    17.38     0.46)  ; 38\r\n                (  228.01    49.96    18.45     0.46)  ; 39\r\n                (  228.01    49.96    18.42     0.46)  ; 40\r\n                (  226.41    50.78    20.60     0.46)  ; 41\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  320.40   -12.59   -11.98     0.46)  ; 1\r\n                  (  299.67    -2.53    -7.05     0.46)  ; 2\r\n                  (  236.96    41.91     9.85     0.46)  ; 3\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  326.38   -15.97   -16.38     0.46)  ; 1\r\n                  (  286.80     8.19     0.43     0.46)  ; 2\r\n                  (  279.52    13.06     0.30     0.46)  ; 3\r\n                  (  281.53    10.54     0.30     0.46)  ; 4\r\n                  (  262.43    31.74     5.17     0.46)  ; 5\r\n                  (  255.57    34.92     5.50     0.46)  ; 6\r\n                  (  251.82    38.82     9.80     0.46)  ; 7\r\n                  (  243.88    40.54     7.85     0.46)  ; 8\r\n                )  ;  End of markers\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  245.33    41.67   -11.90     1.83)  ; 1\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            |\r\n              (  334.94   -54.56   -15.40     0.46)  ; 1, R-2-2-3-1-2-2\r\n              (  338.78   -54.86   -15.40     0.46)  ; 2\r\n              (  343.78   -56.07   -15.40     0.46)  ; 3\r\n              (  347.03   -55.90   -15.40     0.46)  ; 4\r\n              (  351.77   -56.00   -15.40     0.46)  ; 5\r\n              (  354.45   -55.37   -14.30     0.46)  ; 6\r\n              (  356.81   -55.41   -13.92     0.46)  ; 7\r\n              (  361.10   -55.60   -13.25     0.46)  ; 8\r\n              (  363.55   -58.02   -13.25     0.46)  ; 9\r\n              (  367.20   -59.54   -13.25     0.46)  ; 10\r\n              (  370.87   -61.08   -13.25     0.46)  ; 11\r\n              (  373.64   -62.81   -12.95     0.46)  ; 12\r\n              (  377.82   -66.61   -12.95     0.46)  ; 13\r\n              (  381.88   -69.84   -13.07     0.46)  ; 14\r\n              (  385.09   -71.47   -12.90     0.46)  ; 15\r\n              (  388.75   -73.01   -12.90     0.46)  ; 16\r\n              (  390.94   -74.28   -12.90     0.46)  ; 17\r\n              (  394.59   -75.82   -12.98     0.46)  ; 18\r\n              (  398.37   -77.91   -13.75     0.46)  ; 19\r\n              (  401.01   -79.09   -13.75     0.46)  ; 20\r\n              (  403.95   -79.59   -13.13     0.46)  ; 21\r\n              (  408.06   -81.02   -13.13     0.46)  ; 22\r\n              (  412.16   -82.44   -13.13     0.46)  ; 23\r\n              (  414.21   -83.16   -13.13     0.46)  ; 24\r\n              (  416.39   -84.44   -13.13     0.46)  ; 25\r\n              (  420.32   -87.11   -11.82     0.46)  ; 26\r\n              (  423.08   -88.84   -10.52     0.46)  ; 27\r\n              (  426.42   -91.05    -9.32     0.46)  ; 28\r\n              (  428.74   -92.89    -8.77     0.46)  ; 29\r\n              (  431.38   -94.07    -8.35     0.46)  ; 30\r\n              (  434.18   -94.01    -7.13     0.46)  ; 31\r\n              (  434.18   -94.01    -7.15     0.46)  ; 32\r\n              (  438.43   -96.00    -5.95     0.46)  ; 33\r\n              (  441.94   -96.96    -5.60     0.46)  ; 34\r\n              (  444.71   -98.71    -5.27     0.46)  ; 35\r\n              (  448.11   -99.11    -7.02     0.46)  ; 36\r\n              (  452.08   -99.96    -7.02     0.46)  ; 37\r\n              (  455.34   -99.80    -6.25     0.46)  ; 38\r\n              (  455.34   -99.80    -6.28     0.46)  ; 39\r\n              (  457.70   -99.84    -4.35     0.46)  ; 40\r\n              (  457.70   -99.84    -4.38     0.46)  ; 41\r\n              (  461.04  -102.04    -3.45     0.46)  ; 42\r\n              (  464.17  -101.31    -3.25     0.46)  ; 43\r\n              (  467.12  -101.82    -3.10     0.46)  ; 44\r\n              (  469.79  -101.19    -1.73     0.46)  ; 45\r\n              (  472.61  -101.13    -0.93     0.46)  ; 46\r\n              (  472.61  -101.13    -0.97     0.46)  ; 47\r\n              (  475.55  -101.63    -0.32     0.46)  ; 48\r\n              (  479.56  -100.69     0.55     0.46)  ; 49\r\n              (  481.79  -100.17     1.67     0.46)  ; 50\r\n              (  483.63   -97.95     2.95     0.46)  ; 51\r\n              (  484.38   -97.16     4.45     0.46)  ; 52\r\n              (  485.46   -95.72     5.77     0.46)  ; 53\r\n              (  487.52   -96.44     6.90     0.46)  ; 54\r\n              (  489.25   -97.82     7.67     0.46)  ; 55\r\n              (  490.68   -99.87     8.38     0.46)  ; 56\r\n              (  494.51  -100.17     9.10     0.46)  ; 57\r\n              (  497.19   -99.55     9.77     0.46)  ; 58\r\n              (  502.37   -99.52     9.90     0.46)  ; 59\r\n              (  502.37   -99.52    10.00     0.46)  ; 60\r\n              (  505.05   -98.90    10.33     0.46)  ; 61\r\n              (  507.73   -98.27     9.05     0.46)  ; 62\r\n              (  512.15   -99.02     9.22     0.46)  ; 63\r\n              (  515.54   -99.42     9.98     0.46)  ; 64\r\n              (  515.09   -99.53     9.98     0.46)  ; 65\r\n              (  519.24   -99.15    10.92     0.46)  ; 66\r\n              (  522.64   -99.55    11.30     0.46)  ; 67\r\n              (  522.64   -99.55    11.27     0.46)  ; 68\r\n              (  526.47   -99.84    12.00     0.46)  ; 69\r\n              (  528.83   -99.89    12.20     0.46)  ; 70\r\n              (  530.63   -99.46    12.32     0.46)  ; 71\r\n              (  534.15  -100.43    13.07     0.46)  ; 72\r\n              (  537.86  -100.16    14.20     0.46)  ; 73\r\n              (  540.09   -99.64    15.00     0.46)  ; 74\r\n              (  542.59  -100.24    15.73     0.46)  ; 75\r\n              (  546.12  -101.22    16.50     0.46)  ; 76\r\n              (  550.71  -100.74    17.17     0.46)  ; 77\r\n              (  553.26   -99.54    18.08     0.46)  ; 78\r\n              (  556.20  -100.04    18.65     0.46)  ; 79\r\n              (  560.49  -100.23    18.38     0.46)  ; 80\r\n              (  566.24  -100.68    18.38     0.46)  ; 81\r\n              (  569.63  -101.07    19.85     0.46)  ; 82\r\n              (  574.81  -101.05    20.38     0.46)  ; 83\r\n              (  576.99  -102.34    20.83     0.46)  ; 84\r\n              (  578.95  -106.66    21.15     0.46)  ; 85\r\n              (  581.28  -108.50    22.12     0.46)  ; 86\r\n              (  583.09  -112.25    22.80     0.46)  ; 87\r\n              (  583.09  -112.25    22.77     0.46)  ; 88\r\n              (  586.31  -113.89    22.67     0.46)  ; 89\r\n              (  586.31  -113.89    22.65     0.46)  ; 90\r\n              (  590.14  -114.18    22.65     0.46)  ; 91\r\n              (  593.27  -113.44    23.33     0.46)  ; 92\r\n              (  595.90  -114.63    24.20     0.46)  ; 93\r\n              (  597.11  -113.74    24.52     0.46)  ; 94\r\n              (  597.11  -113.74    24.88     0.46)  ; 95\r\n              (  602.81  -115.99    24.90     0.46)  ; 96\r\n              (  607.36  -117.32    26.05     0.46)  ; 97\r\n              (  608.98  -118.13    26.70     0.46)  ; 98\r\n              (  609.82  -119.72    27.92     0.46)  ; 99\r\n              (  609.82  -119.72    27.90     0.46)  ; 100\r\n              (  612.13  -121.57    29.02     0.46)  ; 101\r\n              (  612.13  -121.57    29.00     0.46)  ; 102\r\n              (  614.36  -121.05    29.75     0.46)  ; 103\r\n              (  616.23  -123.00    30.27     0.46)  ; 104\r\n              (  619.18  -123.50    30.95     0.46)  ; 105\r\n              (  623.28  -124.92    31.82     0.46)  ; 106\r\n              (  624.89  -125.75    33.42     0.46)  ; 107\r\n              (  624.75  -125.18    33.38     0.46)  ; 108\r\n              (  626.68  -125.33    35.45     0.46)  ; 109\r\n              (  626.68  -125.33    35.38     0.46)  ; 110\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  357.57   -54.64   -13.92     0.46)  ; 1\r\n                (  365.99   -60.43   -13.25     0.46)  ; 2\r\n                (  380.54   -70.16   -13.07     0.46)  ; 3\r\n                (  389.27   -75.27   -12.90     0.46)  ; 4\r\n                (  406.85   -81.91   -13.13     0.46)  ; 5\r\n                (  419.57   -87.88   -11.82     0.46)  ; 6\r\n                (  437.22   -96.88    -5.95     0.46)  ; 7\r\n                (  484.78   -98.87     2.95     0.46)  ; 8\r\n                (  496.93   -98.40     9.77     0.46)  ; 9\r\n                (  508.23   -96.36    11.63     0.46)  ; 10\r\n                (  507.28   -98.37     8.15     0.46)  ; 11\r\n                (  520.01   -98.38    10.92     0.46)  ; 12\r\n                (  521.79   -97.96    11.50     0.46)  ; 13\r\n                (  525.18   -98.36    12.30     0.46)  ; 14\r\n                (  533.88   -99.31    13.07     0.46)  ; 15\r\n                (  545.72   -99.52    16.50     0.46)  ; 16\r\n                (  553.52  -100.67    18.08     0.46)  ; 17\r\n                (  568.88  -101.85    19.85     0.46)  ; 18\r\n                (  592.56  -112.42    23.33     0.46)  ; 19\r\n                (  601.17  -116.97    24.90     0.46)  ; 20\r\n                (  622.57  -123.90    31.45     0.46)  ; 21\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  398.37   -77.91   -13.75     0.46)  ; 1\r\n                (  457.70   -99.84    -4.38     0.46)  ; 2\r\n                (  475.10  -101.74    -0.32     0.46)  ; 3\r\n                (  512.15   -99.02     9.22     0.46)  ; 4\r\n                (  561.07  -100.69    18.38     0.46)  ; 5\r\n                (  579.26  -105.99    21.15     0.46)  ; 6\r\n                (  576.42  -101.88    21.15     0.46)  ; 7\r\n                (  582.96  -111.69    22.77     0.46)  ; 8\r\n                (  599.16  -114.46    24.90     0.46)  ; 9\r\n              )  ;  End of markers\r\n               High\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (  293.81   -86.44     3.57     0.46)  ; 1, R-2-2-3-2\r\n          (  296.50   -85.81     2.70     0.46)  ; 2\r\n          (  300.64   -85.43     1.30     0.46)  ; 3\r\n          (  302.12   -85.68     0.47     0.46)  ; 4\r\n          (  304.36   -85.15    -0.12     0.46)  ; 5\r\n          (  306.72   -85.20    -0.12     0.46)  ; 6\r\n          (  310.73   -84.26    -0.82     0.46)  ; 7\r\n          (  315.02   -84.45    -1.73     0.46)  ; 8\r\n          (  319.48   -83.40    -2.05     0.46)  ; 9\r\n          (  323.06   -82.56    -2.27     0.46)  ; 10\r\n          (  326.05   -81.26    -3.72     0.46)  ; 11\r\n          (  329.45   -81.66    -4.70     0.46)  ; 12\r\n          (  330.79   -81.34    -4.52     0.46)  ; 13\r\n          (  334.36   -80.51    -4.03     0.46)  ; 14\r\n          (  337.62   -80.35    -4.03     0.46)  ; 15\r\n          (  341.33   -80.08    -4.03     0.46)  ; 16\r\n          (  344.32   -78.78    -3.13     0.46)  ; 17\r\n          (  344.32   -78.78    -3.15     0.46)  ; 18\r\n          (  348.74   -79.53    -4.60     0.46)  ; 19\r\n          (  348.74   -79.53    -4.63     0.46)  ; 20\r\n          (  351.42   -78.90    -5.95     0.46)  ; 21\r\n          (  353.78   -78.94    -5.95     0.46)  ; 22\r\n          (  356.15   -78.98    -6.93     0.46)  ; 23\r\n          (  356.15   -78.98    -6.95     0.46)  ; 24\r\n          (  357.48   -78.67    -7.35     0.46)  ; 25\r\n          (  358.96   -78.92    -7.35     0.46)  ; 26\r\n          (  360.61   -77.94    -7.35     0.46)  ; 27\r\n          (  362.53   -78.09    -5.97     0.46)  ; 28\r\n          (  364.76   -77.56    -5.97     0.46)  ; 29\r\n          (  367.45   -76.93    -7.05     0.46)  ; 30\r\n          (  368.92   -77.20    -7.85     0.46)  ; 31\r\n          (  368.92   -77.20    -7.87     0.46)  ; 32\r\n          (  370.26   -76.88    -8.60     0.46)  ; 33\r\n          (  370.26   -76.88    -8.63     0.46)  ; 34\r\n          (  372.44   -78.15    -9.60     0.46)  ; 35\r\n          (  372.44   -78.15    -9.65     0.46)  ; 36\r\n          (  373.07   -76.82   -10.95     0.46)  ; 37\r\n          (  372.94   -76.25   -10.97     0.46)  ; 38\r\n          (  374.74   -75.84   -10.97     0.46)  ; 39\r\n          (  375.60   -74.49   -12.17     0.46)  ; 40\r\n          (\r\n            (  375.93   -74.94   -11.70     0.46)  ; 1, R-2-2-3-2-1\r\n            (  377.99   -75.67   -12.17     0.46)  ; 2\r\n            (  382.84   -76.31   -13.00     0.46)  ; 3\r\n            (  385.49   -77.48   -14.38     0.46)  ; 4\r\n            (  387.45   -75.83   -16.00     0.46)  ; 5\r\n            (  387.45   -75.83   -16.02     0.46)  ; 6\r\n            (  388.92   -76.08   -18.40     0.46)  ; 7\r\n            (  391.73   -76.02   -19.42     0.46)  ; 8\r\n\r\n            (Dot\r\n              (Color Yellow)\r\n              (Name \"Marker 1\")\r\n              (  305.65   -86.64    -0.12     0.46)  ; 1\r\n              (  314.31   -83.42    -1.73     0.46)  ; 2\r\n              (  330.08   -80.32    -4.52     0.46)  ; 3\r\n              (  340.61   -79.04    -4.03     0.46)  ; 4\r\n              (  381.38   -76.06   -12.98     0.46)  ; 5\r\n              (  390.65   -77.47   -19.42     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color Yellow)\r\n              (Name \"Marker 2\")\r\n              (  296.63   -86.37     2.70     0.46)  ; 1\r\n              (  302.43   -85.01    -0.12     0.46)  ; 2\r\n              (  319.48   -83.40    -2.05     0.46)  ; 3\r\n              (  334.81   -80.41    -4.03     0.46)  ; 4\r\n              (  348.60   -78.96    -4.63     0.46)  ; 5\r\n              (  356.60   -78.88    -6.42     0.46)  ; 6\r\n              (  364.76   -77.56    -5.97     0.46)  ; 7\r\n              (  378.43   -75.56   -12.17     0.46)  ; 8\r\n              (  386.68   -76.61   -16.02     0.46)  ; 9\r\n              (  388.92   -76.08   -18.40     0.46)  ; 10\r\n            )  ;  End of markers\r\n            (\r\n              (  393.25   -74.47   -17.05     0.46)  ; 1, R-2-2-3-2-1-1\r\n              (  396.07   -74.41   -16.70     0.46)  ; 2\r\n              (  401.06   -75.63   -16.40     0.46)  ; 3\r\n              (  401.06   -75.63   -16.42     0.46)  ; 4\r\n              (  403.70   -76.79   -16.15     0.46)  ; 5\r\n              (  408.28   -76.32   -15.35     0.46)  ; 6\r\n              (  413.02   -76.41   -14.52     0.46)  ; 7\r\n              (  417.45   -77.16   -13.82     0.46)  ; 8\r\n              (  419.81   -77.20   -13.82     0.46)  ; 9\r\n              (  422.62   -77.14   -13.82     0.46)  ; 10\r\n              (  425.89   -76.98   -13.10     0.46)  ; 11\r\n              (  429.27   -77.38   -12.40     0.46)  ; 12\r\n              (  431.95   -76.75   -11.80     0.46)  ; 13\r\n              (  434.45   -77.36   -11.13     0.46)  ; 14\r\n              (  439.36   -76.20   -11.13     0.46)  ; 15\r\n              (  439.36   -76.20   -10.55     0.46)  ; 16\r\n              (  443.83   -75.16   -10.05     0.46)  ; 17\r\n              (  447.09   -74.99    -8.97     0.46)  ; 18\r\n              (  450.22   -74.26    -7.92     0.46)  ; 19\r\n              (  450.22   -74.26    -7.95     0.46)  ; 20\r\n              (  452.71   -74.88    -7.17     0.46)  ; 21\r\n              (  457.94   -73.05    -5.30     0.46)  ; 22\r\n              (  462.47   -70.21    -4.35     0.46)  ; 23\r\n              (  465.29   -70.15    -4.82     0.46)  ; 24\r\n              (  468.23   -70.65    -3.50     0.46)  ; 25\r\n              (  470.95   -68.22    -1.88     0.46)  ; 26\r\n              (  473.38   -66.47    -0.45     0.46)  ; 27\r\n              (  475.65   -64.14    -0.45     0.46)  ; 28\r\n              (  477.75   -63.05     0.80     0.46)  ; 29\r\n              (  479.55   -62.62     2.25     0.46)  ; 30\r\n              (  479.55   -62.62     2.17     0.46)  ; 31\r\n              (  483.11   -61.79     2.08     0.46)  ; 32\r\n              (  485.79   -61.16     4.03     0.46)  ; 33\r\n              (  489.05   -60.99     4.72     0.46)  ; 34\r\n              (  492.31   -60.83     5.30     0.46)  ; 35\r\n              (  493.52   -59.95     6.42     0.46)  ; 36\r\n              (  495.93   -58.20     7.22     0.46)  ; 37\r\n              (  498.29   -58.24     9.95     0.46)  ; 38\r\n              (  500.93   -59.40    11.55     0.46)  ; 39\r\n              (  502.27   -59.09    12.55     0.46)  ; 40\r\n              (  502.27   -59.09    12.52     0.46)  ; 41\r\n              (  505.71   -57.69    14.13     0.46)  ; 42\r\n              (  505.71   -57.69    14.10     0.46)  ; 43\r\n              (  507.36   -56.70    14.92     0.46)  ; 44\r\n              (  510.94   -55.87    14.92     0.46)  ; 45\r\n              (  515.84   -54.71    15.92     0.46)  ; 46\r\n              (  517.82   -53.06    17.35     0.46)  ; 47\r\n              (  522.41   -52.59    18.45     0.46)  ; 48\r\n              (  526.11   -51.11    19.45     0.46)  ; 49\r\n              (  529.11   -49.82    19.13     0.46)  ; 50\r\n              (  532.10   -48.53    19.35     0.46)  ; 51\r\n              (  535.22   -47.79    20.60     0.46)  ; 52\r\n              (  535.22   -47.79    20.83     0.46)  ; 53\r\n              (  537.19   -46.14    22.05     0.46)  ; 54\r\n              (  540.45   -45.97    23.47     0.46)  ; 55\r\n              (  540.45   -45.97    23.45     0.46)  ; 56\r\n              (  542.74   -43.65    24.50     0.46)  ; 57\r\n              (  546.71   -44.50    26.92     0.46)  ; 58\r\n              (  549.07   -44.55    28.15     0.46)  ; 59\r\n              (  550.50   -46.61    29.08     0.46)  ; 60\r\n              (  552.49   -49.11    29.70     0.46)  ; 61\r\n              (  553.07   -49.58    31.77     0.46)  ; 62\r\n              (  554.11   -49.94    32.77     0.46)  ; 63\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  462.29   -71.44    -4.35     0.46)  ; 1\r\n                (  481.64   -61.54     2.08     0.46)  ; 2\r\n                (  505.08   -59.04    14.10     0.46)  ; 3\r\n                (  510.63   -56.53    14.92     0.46)  ; 4\r\n                (  525.04   -52.58    19.45     0.46)  ; 5\r\n                (  532.81   -49.55    19.35     0.46)  ; 6\r\n                (  542.02   -42.61    24.50     0.46)  ; 7\r\n                (  545.32   -46.62    26.90     0.46)  ; 8\r\n                (  547.33   -43.17    26.92     0.46)  ; 9\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  413.60   -76.87   -14.52     0.46)  ; 1\r\n                (  457.51   -73.17    -5.30     0.46)  ; 2\r\n                (  458.94   -75.22    -3.88     0.46)  ; 3\r\n                (  470.06   -68.43    -1.88     0.46)  ; 4\r\n                (  475.21   -64.24    -0.45     0.46)  ; 5\r\n                (  492.75   -60.72     6.42     0.46)  ; 6\r\n                (  495.49   -58.30     7.22     0.46)  ; 7\r\n                (  522.54   -53.14    18.45     0.46)  ; 8\r\n              )  ;  End of markers\r\n               High\r\n            |\r\n              (  394.42   -75.41   -20.38     0.46)  ; 1, R-2-2-3-2-1-2\r\n              (  394.42   -75.41   -20.42     0.46)  ; 2\r\n              (  398.44   -74.48   -21.10     0.46)  ; 3\r\n              (  398.44   -74.48   -21.13     0.46)  ; 4\r\n              (  400.23   -74.06   -21.95     0.46)  ; 5\r\n              (  402.19   -72.40   -23.23     0.46)  ; 6\r\n              (  403.98   -71.98   -24.22     0.46)  ; 7\r\n              (  406.66   -71.36   -25.33     0.46)  ; 8\r\n              (  409.02   -71.41   -26.52     0.46)  ; 9\r\n              (  411.53   -72.00   -27.73     0.46)  ; 10\r\n              (  414.08   -70.81   -28.63     0.46)  ; 11\r\n              (  417.20   -70.08   -30.23     0.46)  ; 12\r\n              (  420.05   -68.22   -31.42     0.46)  ; 13\r\n              (  423.77   -67.94   -32.67     0.46)  ; 14\r\n              (  427.15   -68.34   -33.42     0.46)  ; 15\r\n\r\n              (Dot\r\n                (Color Yellow)\r\n                (Name \"Marker 1\")\r\n                (  400.59   -71.58   -23.23     0.46)  ; 1\r\n                (  405.95   -70.32   -25.33     0.46)  ; 2\r\n                (  422.25   -69.49   -32.67     0.46)  ; 3\r\n              )  ;  End of markers\r\n\r\n              (OpenCircle\r\n                (Color Yellow)\r\n                (Name \"Marker 2\")\r\n                (  417.07   -69.52   -30.23     0.46)  ; 1\r\n              )  ;  End of markers\r\n              (\r\n                (  431.26   -69.77   -31.20     0.46)  ; 1, R-2-2-3-2-1-2-1\r\n                (  433.90   -70.94   -29.77     0.46)  ; 2\r\n                (  433.90   -70.94   -29.80     0.46)  ; 3\r\n                (  436.52   -72.12   -29.02     0.46)  ; 4\r\n                (  438.94   -70.36   -29.02     0.46)  ; 5\r\n                (  442.96   -69.41   -27.65     0.46)  ; 6\r\n                (  442.96   -69.41   -27.67     0.46)  ; 7\r\n                (  444.92   -67.76   -25.97     0.46)  ; 8\r\n                (  447.43   -68.37   -25.33     0.46)  ; 9\r\n                (  451.00   -67.53   -25.07     0.46)  ; 10\r\n                (  451.00   -67.53   -25.10     0.46)  ; 11\r\n                (  455.28   -67.72   -24.02     0.46)  ; 12\r\n                (  456.93   -66.73   -23.30     0.46)  ; 13\r\n                (  459.03   -65.65   -22.57     0.46)  ; 14\r\n                (  459.03   -65.65   -22.63     0.46)  ; 15\r\n                (  460.50   -65.90   -21.65     0.46)  ; 16\r\n                (  460.50   -65.90   -21.67     0.46)  ; 17\r\n                (  462.43   -66.05   -20.85     0.46)  ; 18\r\n                (  464.40   -64.39   -19.88     0.46)  ; 19\r\n                (  467.52   -63.66   -19.00     0.46)  ; 20\r\n                (  471.99   -62.62   -18.33     0.46)  ; 21\r\n                (  476.18   -60.44   -17.65     0.46)  ; 22\r\n                (  479.49   -58.47   -17.42     0.46)  ; 23\r\n                (  482.08   -55.47   -16.83     0.46)  ; 24\r\n                (  485.08   -54.17   -16.42     0.46)  ; 25\r\n                (  487.05   -52.51   -15.17     0.46)  ; 26\r\n                (  487.05   -52.51   -15.20     0.46)  ; 27\r\n                (  488.27   -50.44   -13.87     0.46)  ; 28\r\n                (  490.81   -49.24   -13.20     0.46)  ; 29\r\n                (  490.81   -49.24   -13.23     0.46)  ; 30\r\n                (  493.53   -46.82   -12.72     0.46)  ; 31\r\n                (  493.53   -46.82   -12.75     0.46)  ; 32\r\n                (  497.60   -44.07   -12.30     0.46)  ; 33\r\n                (  500.73   -43.34   -11.65     0.46)  ; 34\r\n                (  503.59   -41.48   -10.38     0.46)  ; 35\r\n                (  505.38   -41.06    -8.47     0.46)  ; 36\r\n                (  507.74   -41.10    -6.73     0.46)  ; 37\r\n                (  510.16   -39.34    -4.82     0.46)  ; 38\r\n                (  512.26   -38.25    -3.25     0.46)  ; 39\r\n                (  513.78   -36.69    -2.38     0.46)  ; 40\r\n                (  517.36   -35.86    -1.42     0.46)  ; 41\r\n                (  517.22   -35.29    -1.45     0.46)  ; 42\r\n                (  519.50   -32.96    -0.50     0.46)  ; 43\r\n                (  523.97   -31.92     0.70     0.46)  ; 44\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  442.19   -70.19   -27.67     0.46)  ; 1\r\n                  (  472.12   -63.17   -18.33     0.46)  ; 2\r\n                  (  503.41   -42.71   -10.38     0.46)  ; 3\r\n                  (  507.79   -39.30    -4.82     0.46)  ; 4\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  436.07   -72.22   -29.02     0.46)  ; 1\r\n                  (  454.71   -67.26   -24.02     0.46)  ; 2\r\n                  (  462.43   -66.05   -20.85     0.46)  ; 3\r\n                  (  487.05   -52.51   -15.20     0.46)  ; 4\r\n                  (  491.25   -49.14   -13.23     0.46)  ; 5\r\n                )  ;  End of markers\r\n                (\r\n                  (  527.62   -33.45     0.70     0.46)  ; 1, R-2-2-3-2-1-2-1-1\r\n                  (  529.63   -35.96    -0.93     0.46)  ; 2\r\n                  (  530.65   -36.32    -2.50     0.46)  ; 3\r\n                  (  530.65   -36.32    -2.53     0.46)  ; 4\r\n                  (  531.94   -37.81    -4.47     0.46)  ; 5\r\n                  (  532.07   -38.38    -6.25     0.46)  ; 6\r\n                  (  533.10   -38.74    -8.15     0.46)  ; 7\r\n                  (  533.10   -38.74    -8.18     0.46)  ; 8\r\n                  (  533.81   -39.76   -10.28     0.46)  ; 9\r\n                  (  533.90   -42.14   -11.70     0.46)  ; 10\r\n                  (  533.90   -42.14   -11.73     0.46)  ; 11\r\n                  (  536.17   -45.79   -12.95     0.46)  ; 12\r\n                  (  536.17   -45.79   -12.98     0.46)  ; 13\r\n                  (  537.06   -45.58   -15.17     0.46)  ; 14\r\n                  (  537.51   -45.47   -18.95     0.46)  ; 15\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  534.97   -40.69   -11.73     0.46)  ; 1\r\n                    (  535.01   -44.85   -12.98     0.46)  ; 2\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  526.25   -29.59     0.70     0.46)  ; 1, R-2-2-3-2-1-2-1-2\r\n                  (  529.87   -26.96     1.80     0.46)  ; 2\r\n                  (  532.42   -25.76     3.22     0.46)  ; 3\r\n                  (  534.96   -24.57     3.22     0.46)  ; 4\r\n                  (  538.98   -23.63     3.70     0.46)  ; 5\r\n                  (  541.09   -22.53     4.22     0.46)  ; 6\r\n                  (  543.94   -20.66     4.78     0.46)  ; 7\r\n                  (  543.94   -20.66     4.75     0.46)  ; 8\r\n                  (  547.13   -18.14     5.75     0.46)  ; 9\r\n                  (  547.13   -18.14     5.72     0.46)  ; 10\r\n                  (  548.32   -17.26     7.53     0.46)  ; 11\r\n                  (  549.40   -15.80     9.15     0.46)  ; 12\r\n                  (  551.36   -14.15    10.17     0.46)  ; 13\r\n                  (  551.36   -14.15    10.15     0.46)  ; 14\r\n                  (  553.16   -13.73    11.10     0.46)  ; 15\r\n                  (  553.16   -13.73    11.07     0.46)  ; 16\r\n                  (  554.36   -12.86    12.00     0.46)  ; 17\r\n                  (  554.86   -10.95    12.00     0.46)  ; 18\r\n                  (  557.98   -10.21    12.62     0.46)  ; 19\r\n                  (  559.51    -8.66    12.88     0.46)  ; 20\r\n                  (  561.16    -7.68    12.88     0.46)  ; 21\r\n                  (  562.55    -5.56    12.88     0.46)  ; 22\r\n                  (  564.33    -5.14    12.88     0.46)  ; 23\r\n                  (  568.85    -2.29    11.37     0.46)  ; 24\r\n                  (  571.71    -0.43    12.07     0.46)  ; 25\r\n                  (  575.02     1.54    13.70     0.46)  ; 26\r\n                  (  577.43     3.31    15.12     0.46)  ; 27\r\n                  (  581.64     5.48    16.07     0.46)  ; 28\r\n                  (  586.72     7.87    16.07     0.46)  ; 29\r\n                  (  591.96     9.69    16.47     0.46)  ; 30\r\n                  (  594.51    10.88    16.47     0.46)  ; 31\r\n                  (  597.26     9.15    16.47     0.46)  ; 32\r\n                  (  599.77     8.54    15.20     0.46)  ; 33\r\n                  (  599.77     8.54    15.17     0.46)  ; 34\r\n                  (  603.15     8.14    14.13     0.46)  ; 35\r\n                  (  606.73     8.97    14.13     0.46)  ; 36\r\n                  (  609.14    10.74    13.52     0.46)  ; 37\r\n                  (  610.98    12.96    14.60     0.46)  ; 38\r\n                  (  612.50    14.50    15.75     0.46)  ; 39\r\n                  (  614.96    18.07    16.57     0.46)  ; 40\r\n                  (  617.87    21.74    16.90     0.46)  ; 41\r\n                  (  619.00    24.98    18.05     0.46)  ; 42\r\n                  (  619.36    27.47    19.58     0.46)  ; 43\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  530.71   -28.54     1.80     0.46)  ; 1\r\n                    (  545.65   -17.89     5.72     0.46)  ; 2\r\n                    (  573.86     2.47    13.70     0.46)  ; 3\r\n                    (  577.74     3.98    15.12     0.46)  ; 4\r\n                    (  586.02     8.89    16.07     0.46)  ; 5\r\n                    (  617.66    24.67    18.05     0.46)  ; 6\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  559.96    -8.55    12.88     0.46)  ; 1\r\n                    (  564.33    -5.14    12.88     0.46)  ; 2\r\n                    (  571.71    -0.43    12.07     0.46)  ; 3\r\n                    (  603.15     8.14    14.13     0.46)  ; 4\r\n                    (  606.28     8.86    14.13     0.46)  ; 5\r\n                    (  608.69    10.63    14.13     0.46)  ; 6\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (  431.33   -67.95   -33.67     0.46)  ; 1, R-2-2-3-2-1-2-2\r\n                (  436.19   -68.60   -34.52     0.46)  ; 2\r\n                (  439.32   -67.87   -35.90     0.46)  ; 3\r\n                (  442.44   -67.13   -37.28     0.46)  ; 4\r\n                (  445.83   -67.54   -37.28     0.46)  ; 5\r\n                (  449.67   -67.83   -38.57     0.46)  ; 6\r\n                (  452.61   -68.33   -39.67     0.46)  ; 7\r\n                (  454.14   -66.78   -40.77     0.46)  ; 8\r\n                (  457.85   -66.51   -41.65     0.46)  ; 9\r\n                (  461.42   -65.68   -42.88     0.46)  ; 10\r\n                (  465.57   -65.30   -43.82     0.46)  ; 11\r\n                (  468.38   -65.23   -44.80     0.46)  ; 12\r\n                (  471.65   -65.08   -47.02     0.46)  ; 13\r\n                (  472.71   -63.63   -48.58     0.46)  ; 14\r\n                (  477.44   -63.72   -48.58     0.46)  ; 15\r\n                (  480.13   -63.09   -49.52     0.46)  ; 16\r\n                (  483.69   -62.25   -50.42     0.46)  ; 17\r\n                (  487.59   -60.73   -51.75     0.46)  ; 18\r\n                (  487.59   -60.73   -51.77     0.46)  ; 19\r\n                (  492.32   -59.63   -52.45     0.46)  ; 20\r\n                (  495.31   -58.33   -54.52     0.46)  ; 21\r\n                (  498.30   -57.03   -55.55     0.46)  ; 22\r\n                (  498.30   -57.03   -55.57     0.46)  ; 23\r\n                (  500.99   -56.41   -57.63     0.46)  ; 24\r\n                (  503.08   -55.31   -59.58     0.46)  ; 25\r\n                (  508.85   -55.75   -59.78     0.46)  ; 26\r\n                (  512.54   -55.49   -61.28     0.46)  ; 27\r\n                (  518.17   -55.36   -61.67     0.46)  ; 28\r\n                (  523.09   -54.20   -62.22     0.46)  ; 29\r\n                (  523.09   -54.20   -62.28     0.46)  ; 30\r\n                (  527.42   -52.60   -62.28     0.46)  ; 31\r\n                (  531.76   -50.99   -62.80     0.46)  ; 32\r\n                (  534.88   -50.26   -61.60     0.46)  ; 33\r\n                (  539.85   -47.29   -60.05     0.46)  ; 34\r\n                (  542.97   -46.57   -58.70     0.46)  ; 35\r\n                (  546.23   -46.40   -59.72     0.46)  ; 36\r\n                (  550.82   -45.91   -59.22     0.46)  ; 37\r\n                (  550.82   -45.91   -59.25     0.46)  ; 38\r\n                (  557.35   -45.59   -58.05     0.46)  ; 39\r\n                (  561.81   -44.55   -59.67     0.46)  ; 40\r\n                (  563.07   -41.85   -59.67     0.46)  ; 41\r\n                (  564.18   -38.60   -59.67     0.46)  ; 42\r\n                (  568.89   -34.52   -60.22     0.46)  ; 43\r\n                (  575.64   -31.14   -60.47     0.46)  ; 44\r\n                (  580.41   -29.42   -61.38     0.46)  ; 45\r\n\r\n                (Dot\r\n                  (Color Yellow)\r\n                  (Name \"Marker 1\")\r\n                  (  470.43   -65.95   -47.02     0.46)  ; 1\r\n                  (  488.17   -61.20   -50.60     0.46)  ; 2\r\n                  (  516.88   -53.87   -61.70     0.46)  ; 3\r\n                  (  580.22   -30.67   -61.38     0.46)  ; 4\r\n                  (  585.44   -34.82   -61.50     0.46)  ; 5\r\n                )  ;  End of markers\r\n\r\n                (OpenCircle\r\n                  (Color Yellow)\r\n                  (Name \"Marker 2\")\r\n                  (  439.77   -67.76   -35.90     0.46)  ; 1\r\n                  (  445.83   -67.54   -37.28     0.46)  ; 2\r\n                  (  461.42   -65.68   -42.88     0.46)  ; 3\r\n                  (  498.30   -57.03   -55.57     0.46)  ; 4\r\n                  (  531.76   -50.99   -62.80     0.46)  ; 5\r\n                  (  557.48   -46.15   -58.05     0.46)  ; 6\r\n                )  ;  End of markers\r\n                (\r\n                  (  586.21   -34.04   -61.90     0.46)  ; 1, R-2-2-3-2-1-2-2-1\r\n                  (  588.66   -36.45   -64.25     0.46)  ; 2\r\n                  (  591.55   -38.76   -65.10     0.46)  ; 3\r\n                  (  594.00   -41.18   -65.60     0.46)  ; 4\r\n                  (  594.35   -44.68   -66.65     0.46)  ; 5\r\n                  (  596.04   -47.86   -67.15     0.46)  ; 6\r\n                  (  596.04   -47.86   -67.17     0.46)  ; 7\r\n                  (  597.42   -51.72   -68.17     0.46)  ; 8\r\n                  (  600.57   -55.16   -69.02     0.46)  ; 9\r\n                  (  602.45   -57.11   -70.18     0.46)  ; 10\r\n                  (  604.94   -57.72   -71.28     0.46)  ; 11\r\n                  (  607.27   -59.57   -72.53     0.46)  ; 12\r\n                  (  607.27   -59.57   -72.55     0.46)  ; 13\r\n                  (  608.69   -61.61   -73.63     0.46)  ; 14\r\n                  (  610.42   -63.01   -74.65     0.46)  ; 15\r\n                  (  611.40   -65.16   -76.22     0.46)  ; 16\r\n                  (  611.40   -65.16   -76.25     0.46)  ; 17\r\n                  (  613.89   -65.78   -78.40     0.46)  ; 18\r\n                  (  616.08   -67.05   -80.00     0.46)  ; 19\r\n                  (  618.53   -69.47   -80.30     0.46)  ; 20\r\n                  (  618.53   -69.47   -80.33     0.46)  ; 21\r\n                  (  621.75   -71.10   -81.03     0.46)  ; 22\r\n                  (  624.19   -73.51   -82.07     0.46)  ; 23\r\n                  (  624.59   -75.20   -83.47     0.46)  ; 24\r\n                  (  626.46   -77.16   -84.45     0.46)  ; 25\r\n                  (  628.32   -79.11   -85.07     0.46)  ; 26\r\n                  (  628.32   -79.11   -85.10     0.46)  ; 27\r\n                  (  629.49   -80.03   -85.10     0.46)  ; 28\r\n                  (  633.59   -81.45   -85.10     0.46)  ; 29\r\n                  (  635.78   -82.73   -84.30     0.46)  ; 30\r\n                  (  637.96   -84.02   -84.60     0.46)  ; 31\r\n                  (  637.96   -84.02   -84.80     0.46)  ; 32\r\n                  (  640.05   -88.90   -84.78     0.46)  ; 33\r\n                  (  642.02   -91.30   -84.78     0.46)  ; 34\r\n                  (  644.95   -97.78   -85.65     0.46)  ; 35\r\n                  (  646.84   -99.73   -86.75     0.46)  ; 36\r\n                  (  649.99  -103.18   -87.68     0.46)  ; 37\r\n                  (  650.66  -105.99   -88.65     0.46)  ; 38\r\n                  (  650.66  -105.99   -88.67     0.46)  ; 39\r\n                  (  652.79  -109.08   -89.90     0.46)  ; 40\r\n                  (  652.79  -109.08   -89.92     0.46)  ; 41\r\n                  (  655.10  -110.93   -91.22     0.46)  ; 42\r\n                  (  656.40  -112.41   -92.92     0.46)  ; 43\r\n                  (  658.26  -114.37   -94.25     0.46)  ; 44\r\n                  (  660.54  -118.02   -94.48     0.46)  ; 45\r\n                  (  666.02  -123.30   -94.38     0.46)  ; 46\r\n                  (  666.02  -123.30   -94.40     0.46)  ; 47\r\n                  (  666.15  -123.87   -92.78     0.46)  ; 48\r\n                  (  666.81  -126.70   -91.93     0.46)  ; 49\r\n                  (  669.53  -130.24   -91.45     0.46)  ; 50\r\n                  (  671.08  -132.87   -91.00     0.46)  ; 51\r\n                  (  674.83  -136.77   -90.70     0.46)  ; 52\r\n                  (  675.93  -139.49   -90.70     0.46)  ; 53\r\n                  (  677.31  -143.36   -90.70     0.46)  ; 54\r\n                  (  678.95  -148.34   -89.57     0.46)  ; 55\r\n                  (  680.91  -152.66   -89.05     0.46)  ; 56\r\n                  (  683.31  -156.86   -90.05     0.46)  ; 57\r\n                  (  684.86  -159.50   -89.78     0.46)  ; 58\r\n                  (  685.79  -163.46   -89.78     0.46)  ; 59\r\n                  (  686.27  -167.52   -89.45     0.46)  ; 60\r\n                  (  689.44  -170.96   -89.45     0.46)  ; 61\r\n                  (  690.99  -173.58   -88.55     0.46)  ; 62\r\n                  (  693.85  -177.70   -87.75     0.46)  ; 63\r\n                  (  695.98  -180.78   -86.85     0.46)  ; 64\r\n                  (  695.98  -180.78   -86.88     0.46)  ; 65\r\n                  (  696.51  -183.04   -85.50     0.46)  ; 66\r\n                  (  698.46  -187.36   -85.02     0.46)  ; 67\r\n                  (  700.00  -190.00   -84.50     0.46)  ; 68\r\n                  (  704.13  -195.59   -85.02     0.46)  ; 69\r\n                  (  707.89  -199.49   -85.02     0.46)  ; 70\r\n                  (  711.44  -204.63   -84.48     0.46)  ; 71\r\n                  (  712.60  -205.55   -83.72     0.46)  ; 72\r\n                  (  715.31  -209.09   -83.40     0.46)  ; 73\r\n                  (  718.48  -212.53   -82.57     0.46)  ; 74\r\n                  (  718.48  -212.53   -82.60     0.46)  ; 75\r\n                  (  722.09  -215.87   -81.62     0.46)  ; 76\r\n                  (  724.26  -217.14   -80.22     0.46)  ; 77\r\n                  (  726.14  -219.10   -79.10     0.46)  ; 78\r\n                  (  728.64  -219.71   -77.53     0.46)  ; 79\r\n                  (  728.64  -219.71   -77.55     0.46)  ; 80\r\n                  (  730.38  -221.09   -75.78     0.46)  ; 81\r\n                  (  730.38  -221.09   -75.80     0.46)  ; 82\r\n                  (  732.74  -221.14   -73.85     0.46)  ; 83\r\n                  (  732.74  -221.14   -73.88     0.46)  ; 84\r\n                  (  733.85  -223.85   -72.88     0.46)  ; 85\r\n                  (  735.41  -226.47   -71.40     0.46)  ; 86\r\n                  (  737.27  -228.43   -70.07     0.46)  ; 87\r\n                  (  737.05  -231.46   -66.45     0.46)  ; 88\r\n                  (  740.40  -233.67   -65.03     0.46)  ; 89\r\n                  (  741.56  -234.59   -63.07     0.46)  ; 90\r\n                  (  743.15  -235.41   -61.42     0.46)  ; 91\r\n                  (  744.18  -235.77   -59.35     0.46)  ; 92\r\n                  (  746.09  -235.92   -57.20     0.46)  ; 93\r\n                  (  746.09  -235.92   -57.22     0.46)  ; 94\r\n                  (  749.05  -236.42   -55.52     0.46)  ; 95\r\n                  (  751.99  -236.92   -53.78     0.46)  ; 96\r\n                  (  756.16  -236.54   -53.15     0.46)  ; 97\r\n                  (  761.47  -237.09   -52.67     0.46)  ; 98\r\n                  (  761.02  -237.19   -52.67     0.46)  ; 99\r\n                  (  765.25  -239.18   -51.52     0.46)  ; 100\r\n                  (  768.92  -240.72   -50.27     0.46)  ; 101\r\n                  (  772.26  -242.93   -48.38     0.46)  ; 102\r\n                  (  776.68  -243.67   -46.65     0.46)  ; 103\r\n                  (  777.39  -244.70   -44.43     0.46)  ; 104\r\n                  (  779.13  -246.09   -43.18     0.46)  ; 105\r\n                  (  779.13  -246.09   -43.20     0.46)  ; 106\r\n                  (  780.87  -247.47   -42.08     0.46)  ; 107\r\n                  (  780.87  -247.47   -41.55     0.46)  ; 108\r\n                  (  783.05  -248.75   -41.55     0.46)  ; 109\r\n                  (  786.44  -249.15   -40.88     0.46)  ; 110\r\n                  (  790.36  -251.82   -40.10     0.46)  ; 111\r\n                  (  793.39  -254.68   -39.50     0.46)  ; 112\r\n                  (  795.70  -256.53   -38.42     0.46)  ; 113\r\n                  (  795.70  -256.53   -38.45     0.46)  ; 114\r\n                  (  798.34  -257.70   -37.88     0.46)  ; 115\r\n                  (  800.39  -258.42   -36.58     0.46)  ; 116\r\n                  (  800.39  -258.42   -36.60     0.46)  ; 117\r\n                  (  802.57  -259.70   -35.27     0.46)  ; 118\r\n                  (  805.21  -260.87   -33.70     0.46)  ; 119\r\n                  (  809.71  -264.00   -33.15     0.46)  ; 120\r\n                  (  814.52  -266.45   -33.15     0.46)  ; 121\r\n                  (  817.92  -266.86   -33.15     0.46)  ; 122\r\n                  (  821.13  -268.49   -32.38     0.46)  ; 123\r\n                  (  824.66  -269.46   -30.77     0.46)  ; 124\r\n                  (  827.73  -270.52   -29.48     0.46)  ; 125\r\n                  (  831.51  -272.63   -28.33     0.46)  ; 126\r\n                  (  834.01  -273.23   -27.08     0.46)  ; 127\r\n                  (  837.18  -276.67   -27.08     0.46)  ; 128\r\n                  (  840.83  -278.20   -27.08     0.46)  ; 129\r\n                  (  842.70  -280.15   -27.08     0.46)  ; 130\r\n                  (  846.23  -281.13   -26.80     0.46)  ; 131\r\n                  (  848.86  -282.29   -26.00     0.46)  ; 132\r\n                  (  851.80  -282.80   -25.38     0.46)  ; 133\r\n                  (  851.80  -282.80   -25.40     0.46)  ; 134\r\n                  (  855.74  -285.46   -25.13     0.46)  ; 135\r\n                  (  858.81  -286.53   -25.52     0.46)  ; 136\r\n                  (  860.59  -286.11   -27.17     0.46)  ; 137\r\n                  (  863.72  -285.39   -28.42     0.46)  ; 138\r\n                  (  863.72  -285.39   -28.55     0.46)  ; 139\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  598.66   -55.01   -69.02     0.46)  ; 1\r\n                    (  610.51   -65.37   -76.25     0.46)  ; 2\r\n                    (  665.25  -124.07   -94.40     0.46)  ; 3\r\n                    (  677.22  -146.95   -89.57     0.46)  ; 4\r\n                    (  697.44  -187.00   -85.02     0.46)  ; 5\r\n                    (  750.78  -237.80   -53.78     0.46)  ; 6\r\n                    (  771.24  -242.56   -48.38     0.46)  ; 7\r\n                    (  838.61  -278.73   -27.08     0.46)  ; 8\r\n                    (  853.36  -285.42   -25.13     0.46)  ; 9\r\n                    (  857.91  -286.74   -25.52     0.46)  ; 10\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  593.55   -41.28   -65.60     0.46)  ; 1\r\n                    (  640.77   -89.93   -84.78     0.46)  ; 2\r\n                    (  645.40   -97.67   -85.65     0.46)  ; 3\r\n                    (  653.24  -108.97   -89.92     0.46)  ; 4\r\n                    (  650.08  -105.54   -87.30     0.46)  ; 5\r\n                    (  671.08  -132.87   -91.00     0.46)  ; 6\r\n                    (  712.47  -204.99   -83.72     0.46)  ; 7\r\n                    (  722.09  -215.87   -81.62     0.46)  ; 8\r\n                    (  736.83  -228.54   -70.07     0.46)  ; 9\r\n                    (  768.92  -240.72   -50.27     0.46)  ; 10\r\n                    (  775.78  -243.88   -46.65     0.46)  ; 11\r\n                    (  797.77  -257.24   -37.88     0.46)  ; 12\r\n                    (  805.21  -260.87   -33.70     0.46)  ; 13\r\n                    (  809.27  -264.10   -33.15     0.46)  ; 14\r\n                    (  814.66  -267.03   -33.15     0.46)  ; 15\r\n                    (  817.16  -267.63   -33.15     0.46)  ; 16\r\n                    (  831.65  -273.19   -28.33     0.46)  ; 17\r\n                    (  845.79  -281.23   -26.80     0.46)  ; 18\r\n                  )  ;  End of markers\r\n                   Normal\r\n                |\r\n                  (  582.04   -27.48   -63.00     0.46)  ; 1, R-2-2-3-2-1-2-2-2\r\n                  (  585.61   -26.65   -64.60     0.46)  ; 2\r\n                  (  587.00   -24.54   -65.52     0.46)  ; 3\r\n                  (  587.00   -24.54   -65.55     0.46)  ; 4\r\n                  (  591.69   -20.44   -66.13     0.46)  ; 5\r\n                  (  594.05   -20.49   -66.40     0.46)  ; 6\r\n                  (  597.18   -19.76   -67.80     0.46)  ; 7\r\n                  (  598.58   -17.64   -69.47     0.46)  ; 8\r\n                  (  600.55   -15.99   -71.20     0.46)  ; 9\r\n                  (  602.38   -13.76   -72.28     0.46)  ; 10\r\n                  (  602.38   -13.76   -72.30     0.46)  ; 11\r\n                  (  602.61   -10.73   -73.35     0.46)  ; 12\r\n                  (  605.51    -7.06   -74.40     0.46)  ; 13\r\n                  (  605.51    -7.06   -74.42     0.46)  ; 14\r\n                  (  608.87    -3.28   -75.57     0.46)  ; 15\r\n                  (  608.87    -3.28   -75.60     0.46)  ; 16\r\n                  (  610.12    -0.61   -76.77     0.46)  ; 17\r\n                  (  609.99    -0.03   -76.80     0.46)  ; 18\r\n                  (  611.38     2.08   -75.82     0.46)  ; 19\r\n                  (  611.38     2.08   -75.85     0.46)  ; 20\r\n                  (  612.20     4.65   -75.85     0.46)  ; 21\r\n                  (  614.03     6.88   -77.95     0.46)  ; 22\r\n                  (  616.39     6.84   -78.65     0.46)  ; 23\r\n                  (  620.19     4.74   -80.97     0.46)  ; 24\r\n                  (  620.19     4.74   -81.03     0.46)  ; 25\r\n                  (  622.14     0.42   -81.98     0.46)  ; 26\r\n                  (  624.15    -2.09   -81.35     0.46)  ; 27\r\n                  (  627.35    -3.72   -81.43     0.46)  ; 28\r\n                  (  629.23    -5.68   -82.47     0.46)  ; 29\r\n                  (  630.33    -8.41   -83.57     0.46)  ; 30\r\n                  (  630.33    -8.41   -83.60     0.46)  ; 31\r\n                  (  631.88   -11.02   -84.63     0.46)  ; 32\r\n                  (  631.88   -11.02   -84.65     0.46)  ; 33\r\n                  (  636.57   -12.92   -85.10     0.46)  ; 34\r\n                  (  637.78   -12.03   -85.10     0.46)  ; 35\r\n                  (  640.41   -13.21   -86.10     0.46)  ; 36\r\n                  (  640.41   -13.21   -86.13     0.46)  ; 37\r\n                  (  646.74   -14.12   -86.72     0.46)  ; 38\r\n                  (  650.77   -13.17   -87.28     0.46)  ; 39\r\n                  (  656.66   -14.18   -87.95     0.46)  ; 40\r\n                  (  660.41   -12.11   -87.97     0.46)  ; 41\r\n                  (  665.27   -12.75   -89.22     0.46)  ; 42\r\n                  (  668.85   -11.91   -88.07     0.46)  ; 43\r\n                  (  668.85   -11.91   -88.10     0.46)  ; 44\r\n                  (  673.00   -11.53   -87.07     0.46)  ; 45\r\n                  (  673.00   -11.53   -87.10     0.46)  ; 46\r\n                  (  679.38   -10.63   -87.10     0.46)  ; 47\r\n                  (  684.87    -9.95   -87.10     0.46)  ; 48\r\n                  (  689.34    -8.90   -86.60     0.46)  ; 49\r\n                  (  693.23    -7.39   -85.70     0.46)  ; 50\r\n                  (  697.29    -4.65   -85.70     0.46)  ; 51\r\n                  (  700.16    -2.78   -84.92     0.46)  ; 52\r\n                  (  705.06    -1.63   -84.45     0.46)  ; 53\r\n\r\n                  (Dot\r\n                    (Color Yellow)\r\n                    (Name \"Marker 1\")\r\n                    (  625.70    -4.71   -81.43     0.46)  ; 1\r\n                    (  664.51   -13.54   -89.22     0.46)  ; 2\r\n                  )  ;  End of markers\r\n\r\n                  (OpenCircle\r\n                    (Color Yellow)\r\n                    (Name \"Marker 2\")\r\n                    (  596.61   -19.30   -67.80     0.46)  ; 1\r\n                    (  624.02    -1.53   -81.35     0.46)  ; 2\r\n                    (  689.34    -8.90   -86.60     0.46)  ; 3\r\n                    (  700.16    -2.78   -84.92     0.46)  ; 4\r\n                    (  705.06    -1.63   -84.45     0.46)  ; 5\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  378.77   -76.13   -11.22     0.46)  ; 1, R-2-2-3-2-2\r\n            (  381.27   -76.74    -9.35     0.46)  ; 2\r\n            (  381.27   -76.74    -9.38     0.46)  ; 3\r\n            (  384.80   -77.70    -7.92     0.46)  ; 4\r\n            (  384.80   -77.70    -7.95     0.46)  ; 5\r\n            (  387.12   -79.54    -7.65     0.46)  ; 6\r\n            (  389.79   -78.91    -8.27     0.46)  ; 7\r\n            (  391.31   -77.37    -6.60     0.46)  ; 8\r\n            (  392.26   -75.35    -5.30     0.46)  ; 9\r\n            (  392.75   -73.44    -4.52     0.46)  ; 10\r\n            (  392.75   -73.44    -4.55     0.46)  ; 11\r\n            (  394.32   -70.09    -3.38     0.46)  ; 12\r\n            (  395.83   -68.54    -2.22     0.46)  ; 13\r\n            (  397.81   -66.89    -0.90     0.46)  ; 14\r\n            (  399.46   -65.91     0.32     0.46)  ; 15\r\n            (  400.54   -64.46     1.55     0.46)  ; 16\r\n            (  401.78   -61.78     3.08     0.46)  ; 17\r\n            (  402.60   -59.20     3.82     0.46)  ; 18\r\n            (  404.43   -56.98     3.82     0.46)  ; 19\r\n            (  406.76   -52.85     4.63     0.46)  ; 20\r\n            (  409.50   -50.41     5.50     0.46)  ; 21\r\n            (  411.02   -48.86     6.75     0.46)  ; 22\r\n            (  411.91   -48.65     8.52     0.46)  ; 23\r\n            (  413.29   -46.54     9.77     0.46)  ; 24\r\n            (  414.96   -45.56    13.72     0.46)  ; 25\r\n            (  416.34   -43.44    15.35     0.46)  ; 26\r\n            (  416.34   -43.44    15.38     0.46)  ; 27\r\n            (  417.28   -41.43    17.02     0.46)  ; 28\r\n            (  417.28   -41.43    17.05     0.46)  ; 29\r\n            (  418.53   -38.74    18.50     0.46)  ; 30\r\n            (  419.16   -37.40    20.17     0.46)  ; 31\r\n            (  419.16   -37.40    20.15     0.46)  ; 32\r\n            (  420.95   -36.98    21.67     0.46)  ; 33\r\n            (  422.21   -34.29    22.83     0.46)  ; 34\r\n            (  426.09   -32.79    24.05     0.46)  ; 35\r\n            (  427.52   -28.86    23.72     0.46)  ; 36\r\n            (  430.34   -28.81    24.35     0.46)  ; 37\r\n            (  432.23   -24.79    24.32     0.46)  ; 38\r\n            (  435.36   -24.05    23.67     0.46)  ; 39\r\n            (  436.96   -24.87    24.48     0.46)  ; 40\r\n            (  438.43   -25.12    25.55     0.46)  ; 41\r\n            (  438.43   -25.12    25.52     0.46)  ; 42\r\n            (  438.61   -23.88    27.45     0.46)  ; 43\r\n            (  438.61   -23.88    27.38     0.46)  ; 44\r\n            (  439.37   -23.12    29.67     0.46)  ; 45\r\n            (  439.37   -23.12    29.70     0.46)  ; 46\r\n\r\n            (Dot\r\n              (Color Cyan)\r\n              (Name \"Marker 1\")\r\n              (  390.05   -80.05    -8.27     0.46)  ; 1\r\n              (  391.73   -73.10    -4.55     0.46)  ; 2\r\n              (  402.50   -62.81     3.08     0.46)  ; 3\r\n              (  413.69   -48.23     9.77     0.46)  ; 4\r\n              (  418.93   -40.45    18.50     0.46)  ; 5\r\n              (  424.26   -35.02    24.05     0.46)  ; 6\r\n            )  ;  End of markers\r\n\r\n            (OpenCircle\r\n              (Color RGB (255, 128, 255))\r\n              (Name \"Marker 2\")\r\n              (  394.76   -69.99    -3.38     0.46)  ; 1\r\n              (  404.30   -56.41     3.82     0.46)  ; 2\r\n              (  428.77   -32.16    21.82     0.46)  ; 3\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Yellow)\r\n  (Dendrite)\r\n  (  254.22    19.87    -2.65     1.38)  ; Root\r\n  (  254.22    19.87    -2.65     1.38)  ; 1, R\r\n  (  253.51    20.90    -4.07     1.38)  ; 2\r\n  (  251.45    21.61    -5.05     1.38)  ; 3\r\n  (  248.96    22.23    -5.40     1.38)  ; 4\r\n  (  247.22    23.60    -6.30     1.38)  ; 5\r\n  (  244.14    24.68    -6.95     1.38)  ; 6\r\n  (  241.46    24.06    -7.27     1.38)  ; 7\r\n  (  239.23    23.53    -8.77     1.38)  ; 8\r\n  (  237.49    24.91    -9.60     0.92)  ; 9\r\n  (  234.90    27.89   -10.45     0.92)  ; 10\r\n  (  232.59    29.73   -11.40     0.92)  ; 11\r\n  (  228.62    30.60   -12.15     0.92)  ; 12\r\n  (  224.92    30.33   -12.27     0.92)  ; 13\r\n  (  222.33    33.30   -11.75     0.92)  ; 14\r\n  (  220.78    35.91   -11.17     0.92)  ; 15\r\n  (  217.56    37.55   -11.17     0.92)  ; 16\r\n  (  214.98    40.53   -11.53     0.92)  ; 17\r\n  (  211.73    40.36   -12.13     0.92)  ; 18\r\n  (  211.73    40.36   -12.15     0.92)  ; 19\r\n  (  209.72    42.89   -12.75     0.92)  ; 20\r\n\r\n  (Cross\r\n    (Color Yellow)\r\n    (Name \"Marker 3\")\r\n    (  243.51    23.33    -6.95     1.38)  ; 1\r\n    (  248.30    25.05    -7.72     1.38)  ; 2\r\n    (  250.74    22.65    -5.48     1.38)  ; 3\r\n    (  253.69    22.13    -5.13     1.38)  ; 4\r\n    (  247.17    21.80    -3.25     1.38)  ; 5\r\n    (  237.44    23.11    -8.77     1.38)  ; 6\r\n    (  231.96    28.39   -11.77     0.92)  ; 7\r\n    (  235.27    30.36    -9.77     0.92)  ; 8\r\n    (  234.28    26.54    -9.57     0.92)  ; 9\r\n    (  228.58    28.79   -11.85     0.92)  ; 10\r\n    (  227.41    29.72   -11.85     0.92)  ; 11\r\n    (  223.80    33.05    -9.95     0.92)  ; 12\r\n    (  218.68    34.83   -11.17     0.92)  ; 13\r\n    (  215.20    37.60   -11.17     0.92)  ; 14\r\n    (  209.94    39.95   -10.60     0.92)  ; 15\r\n    (  209.06    45.71   -10.65     0.92)  ; 16\r\n    (  205.08    46.58   -10.65     0.92)  ; 17\r\n  )  ;  End of markers\r\n  (\r\n    (  209.19    45.15   -13.75     0.46)  ; 1, R-1\r\n    (  207.86    44.84   -15.05     0.46)  ; 2\r\n    (  205.98    46.79   -16.17     0.46)  ; 3\r\n    (  204.68    48.27   -17.72     0.46)  ; 4\r\n    (  204.24    48.17   -17.72     0.46)  ; 5\r\n    (  201.79    50.58   -19.17     0.46)  ; 6\r\n    (  199.74    51.29   -19.55     0.46)  ; 7\r\n    (  196.48    51.13   -19.55     0.46)  ; 8\r\n    (\r\n      (  194.48    53.64   -20.67     0.46)  ; 1, R-1-1\r\n      (  191.45    56.51   -20.87     0.46)  ; 2\r\n      (  191.05    58.21   -23.92     0.46)  ; 3\r\n      (  190.92    58.78   -23.92     0.46)  ; 4\r\n      (  186.19    58.87   -25.13     0.46)  ; 5\r\n      (  186.19    58.87   -25.15     0.46)  ; 6\r\n      (  182.71    61.64   -26.02     0.46)  ; 7\r\n      (  182.71    61.64   -26.10     0.46)  ; 8\r\n      (  180.40    63.48   -27.57     0.46)  ; 9\r\n      (  177.68    67.02   -28.50     0.46)  ; 10\r\n      (  175.89    66.60   -29.13     0.46)  ; 11\r\n      (  174.02    68.56   -30.23     0.46)  ; 12\r\n      (  173.40    67.21   -32.20     0.46)  ; 13\r\n      (  173.40    67.21   -32.22     0.46)  ; 14\r\n      (  170.76    68.39   -33.83     0.46)  ; 15\r\n      (  170.76    68.39   -33.85     0.46)  ; 16\r\n      (  169.35    70.44   -35.47     0.46)  ; 17\r\n      (  166.79    69.24   -37.08     0.46)  ; 18\r\n      (  165.37    71.31   -38.07     0.46)  ; 19\r\n      (  164.08    72.79   -38.88     0.46)  ; 20\r\n      (  162.24    70.58   -39.58     0.46)  ; 21\r\n      (  159.48    72.30   -40.57     0.46)  ; 22\r\n      (  155.51    73.18   -41.97     0.46)  ; 23\r\n      (  155.51    73.18   -42.00     0.46)  ; 24\r\n      (  153.63    75.12   -43.18     0.46)  ; 25\r\n      (  151.46    76.41   -44.05     0.46)  ; 26\r\n      (  151.51    78.21   -46.20     0.46)  ; 27\r\n      (  149.90    79.02   -48.95     0.46)  ; 28\r\n      (  149.18    80.06   -51.20     0.46)  ; 29\r\n      (  147.31    82.00   -52.67     0.46)  ; 30\r\n      (  144.37    82.50   -53.33     0.46)  ; 31\r\n      (  143.13    85.79   -54.87     0.46)  ; 32\r\n      (  142.02    88.53   -56.77     0.46)  ; 33\r\n      (  141.80    91.46   -58.60     0.46)  ; 34\r\n      (  140.38    93.52   -60.22     0.46)  ; 35\r\n      (  137.04    95.71   -62.45     0.46)  ; 36\r\n      (  134.40    96.88   -64.47     0.46)  ; 37\r\n      (  134.40    96.88   -64.50     0.46)  ; 38\r\n      (  134.44    98.69   -68.13     0.46)  ; 39\r\n      (  133.73    99.72   -69.45     0.46)  ; 40\r\n      (  133.73    99.72   -69.60     0.46)  ; 41\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  192.66    57.40   -21.32     0.46)  ; 1\r\n        (  182.67    59.83   -26.10     0.46)  ; 2\r\n        (  184.85    58.55   -26.10     0.46)  ; 3\r\n        (  178.48    63.63   -26.77     0.46)  ; 4\r\n        (  163.76    72.13   -36.42     0.46)  ; 5\r\n        (  160.33    70.72   -38.72     0.46)  ; 6\r\n        (  157.38    71.22   -43.18     0.46)  ; 7\r\n        (  151.14    75.73   -44.05     0.46)  ; 8\r\n        (  190.56    56.31   -20.87     0.46)  ; 9\r\n      )  ;  End of markers\r\n       Normal\r\n    |\r\n      (  193.30    48.59   -19.55     0.46)  ; 1, R-1-2\r\n      (  191.21    47.51   -20.45     0.46)  ; 2\r\n      (  188.08    46.77   -20.10     0.46)  ; 3\r\n      (  185.85    46.25   -21.48     0.46)  ; 4\r\n      (  185.09    45.48   -22.92     0.46)  ; 5\r\n      (  182.09    44.17   -23.17     0.46)  ; 6\r\n      (  176.92    44.16   -23.92     0.46)  ; 7\r\n      (  173.58    46.35   -23.92     0.46)  ; 8\r\n      (  170.00    45.52   -23.92     0.46)  ; 9\r\n      (  164.87    47.29   -23.92     0.46)  ; 10\r\n      (  160.90    48.17   -24.32     0.46)  ; 11\r\n      (  157.38    49.13   -22.83     0.46)  ; 12\r\n      (  152.69    51.02   -23.37     0.46)  ; 13\r\n      (  149.30    51.42   -23.72     0.46)  ; 14\r\n      (  145.19    52.85   -24.10     0.46)  ; 15\r\n      (  140.91    53.04   -24.52     0.46)  ; 16\r\n      (  140.91    53.04   -24.50     0.46)  ; 17\r\n      (  138.14    54.77   -25.35     0.46)  ; 18\r\n      (  135.34    54.71   -25.95     0.46)  ; 19\r\n      (  132.53    54.66   -27.02     0.46)  ; 20\r\n      (  127.26    56.99   -27.35     0.46)  ; 21\r\n      (  124.32    57.51   -28.52     0.46)  ; 22\r\n      (  120.97    59.70   -29.77     0.46)  ; 23\r\n      (  116.73    61.70   -30.95     0.46)  ; 24\r\n      (  113.03    61.42   -32.00     0.46)  ; 25\r\n      (  108.92    62.85   -32.85     0.46)  ; 26\r\n      (  107.77    63.78   -35.22     0.46)  ; 27\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  192.24    47.15   -20.45     0.46)  ; 1\r\n        (  190.71    45.59   -20.10     0.46)  ; 2\r\n        (  182.94    42.58   -23.17     0.46)  ; 3\r\n        (  175.72    43.28   -23.40     0.46)  ; 4\r\n        (  162.64    46.78   -24.32     0.46)  ; 5\r\n        (  162.29    50.28   -23.47     0.46)  ; 6\r\n        (  158.22    47.54   -21.65     0.46)  ; 7\r\n        (  153.80    48.29   -21.50     0.46)  ; 8\r\n        (  148.01    52.90   -21.58     0.46)  ; 9\r\n        (  146.72    54.40   -24.10     0.46)  ; 10\r\n        (  136.00    51.88   -27.02     0.46)  ; 11\r\n        (  126.32    54.98   -28.85     0.46)  ; 12\r\n        (  122.53    57.09   -29.67     0.46)  ; 13\r\n        (  127.67    61.27   -25.95     0.46)  ; 14\r\n        (  115.94    65.09   -32.00     0.46)  ; 15\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  207.40    44.73   -11.95     0.46)  ; 1, R-2\r\n    (  204.46    45.22   -10.20     0.46)  ; 2\r\n    (  203.04    47.29    -9.77     0.46)  ; 3\r\n    (  200.98    48.00   -11.47     0.46)  ; 4\r\n    (  199.74    51.29   -12.77     0.46)  ; 5\r\n    (  199.74    51.29   -12.80     0.46)  ; 6\r\n    (  199.34    53.00   -12.17     0.46)  ; 7\r\n    (  199.34    53.00   -12.20     0.46)  ; 8\r\n    (  196.66    52.37   -10.43     0.46)  ; 9\r\n    (  194.53    55.44   -10.43     0.46)  ; 10\r\n    (  191.81    58.99    -9.38     0.46)  ; 11\r\n    (  190.97    60.58    -8.38     0.46)  ; 12\r\n    (  186.55    61.33    -7.92     0.46)  ; 13\r\n    (  182.31    63.33    -6.05     0.46)  ; 14\r\n    (  179.50    63.27    -4.35     0.46)  ; 15\r\n    (  179.50    63.27    -4.32     0.46)  ; 16\r\n    (  177.13    63.31    -2.97     0.46)  ; 17\r\n    (  177.77    64.65    -1.55     0.46)  ; 18\r\n    (  175.13    65.83     0.07     0.46)  ; 19\r\n    (  173.97    66.75     1.13     0.46)  ; 20\r\n    (  173.22    65.98     3.05     0.46)  ; 21\r\n    (  173.22    65.98     3.15     0.46)  ; 22\r\n    (  173.03    64.74     5.15     0.46)  ; 23\r\n    (  171.69    64.42     7.10     0.46)  ; 24\r\n    (  170.49    63.55     8.95     0.46)  ; 25\r\n    (  168.21    67.20     9.88     0.46)  ; 26\r\n    (  168.27    69.00    11.25     0.46)  ; 27\r\n    (  168.27    69.00    11.27     0.46)  ; 28\r\n    (  168.19    71.37    12.27     0.46)  ; 29\r\n    (  167.21    73.53    12.90     0.46)  ; 30\r\n    (  166.86    77.03    13.25     0.46)  ; 31\r\n    (  167.36    78.93    14.15     0.46)  ; 32\r\n    (  168.42    80.38    16.07     0.46)  ; 33\r\n    (  168.42    80.38    16.05     0.46)  ; 34\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  205.08    46.58    -9.77     0.46)  ; 1\r\n      (  186.95    59.64    -7.92     0.46)  ; 2\r\n      (  181.51    60.75    -6.60     0.46)  ; 3\r\n      (  187.50    63.35    -5.60     0.46)  ; 4\r\n      (  191.15    61.82    -7.85     0.46)  ; 5\r\n      (  182.36    65.13    -4.92     0.46)  ; 6\r\n      (  180.03    61.01    -4.68     0.46)  ; 7\r\n      (  180.58    64.71    -5.53     0.46)  ; 8\r\n      (  174.28    67.42     3.15     0.46)  ; 9\r\n      (  169.14    63.23     8.97     0.46)  ; 10\r\n      (  166.17    73.88    13.25     0.46)  ; 11\r\n      (  166.22    75.69    13.25     0.46)  ; 12\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Green)\r\n  (Dendrite)\r\n  (  253.66    10.19     4.07     3.21)  ; Root\r\n  (  253.66    10.19     4.07     3.21)  ; 1, R\r\n  (  251.83     7.96     4.07     2.75)  ; 2\r\n  (\r\n    (  248.70     7.23     4.07     1.38)  ; 1, R-1\r\n    (  246.61     6.14     2.90     1.38)  ; 2\r\n    (  245.08     4.59     1.63     1.38)  ; 3\r\n    (  242.80     2.27     0.30     1.38)  ; 4\r\n    (  240.87     2.41    -0.70     1.38)  ; 5\r\n    (  239.28     3.23    -1.77     1.38)  ; 6\r\n    (  237.62     2.25    -3.42     1.38)  ; 7\r\n\r\n    (Cross\r\n      (Color Green)\r\n      (Name \"Marker 3\")\r\n      (  244.98     0.98    -0.70     1.38)  ; 1\r\n    )  ;  End of markers\r\n    (\r\n      (  233.96     3.78    -3.42     0.92)  ; 1, R-1-1\r\n      (  229.63     2.16    -2.90     0.92)  ; 2\r\n      (  226.86     3.91    -1.75     0.92)  ; 3\r\n      (  223.61     3.74    -0.45     0.92)  ; 4\r\n      (  220.88     1.31    -0.12     0.92)  ; 5\r\n      (  218.07     1.25     1.75     0.92)  ; 6\r\n      (  215.88     2.53     1.75     0.92)  ; 7\r\n      (  214.28     3.35     1.75     0.92)  ; 8\r\n      (  211.83     5.76     2.63     0.92)  ; 9\r\n      (  209.52     7.60     3.57     0.92)  ; 10\r\n      (  208.35     8.53     4.80     0.92)  ; 11\r\n      (  205.41     9.03     6.22     0.92)  ; 12\r\n      (  202.91     9.64     7.07     0.92)  ; 13\r\n      (  201.00     9.78     8.25     0.92)  ; 14\r\n\r\n      (Cross\r\n        (Color Green)\r\n        (Name \"Marker 3\")\r\n        (  221.86    -0.85    -0.12     0.92)  ; 1\r\n        (  223.79     4.98    -0.12     0.92)  ; 2\r\n        (  226.15     4.94     0.40     0.92)  ; 3\r\n        (  215.98     6.14     0.70     0.92)  ; 4\r\n        (  214.95     0.51     2.65     0.92)  ; 5\r\n        (  211.86     1.58     1.75     0.92)  ; 6\r\n        (  218.33     0.12     1.75     0.92)  ; 7\r\n        (  199.62    13.64     8.25     0.92)  ; 8\r\n        (  194.21    10.59     6.93     0.92)  ; 9\r\n        (  190.63     9.74     8.20     0.92)  ; 10\r\n        (  188.75     5.73     9.75     0.92)  ; 11\r\n        (  199.64     9.47     9.75     0.92)  ; 12\r\n      )  ;  End of markers\r\n      (\r\n        (  198.00     8.48     6.13     0.92)  ; 1, R-1-1-1\r\n        (  196.47     6.94     6.82     0.92)  ; 2\r\n        (  196.47     6.94     6.80     0.92)  ; 3\r\n        (  193.40     8.00     7.53     0.92)  ; 4\r\n        (  191.03     8.05     8.90     0.92)  ; 5\r\n        (  188.53     8.66     9.22     0.92)  ; 6\r\n        (  184.88    10.19    10.10     0.46)  ; 7\r\n        (  181.48    10.60    10.70     0.46)  ; 8\r\n        (  179.44    11.30    12.10     0.46)  ; 9\r\n        (  178.23    10.43    13.38     0.46)  ; 10\r\n        (  178.23    10.43    13.35     0.46)  ; 11\r\n        (  177.15     8.98    14.30     0.46)  ; 12\r\n        (  176.00     9.90    14.90     0.46)  ; 13\r\n        (  174.83    10.82    14.90     0.46)  ; 14\r\n        (  172.16    10.20    15.88     0.46)  ; 15\r\n        (  169.39    11.94    16.50     0.46)  ; 16\r\n        (  167.61    11.52    17.60     0.46)  ; 17\r\n        (  166.12    11.77    18.27     0.46)  ; 18\r\n        (  163.90    11.25    18.48     0.46)  ; 19\r\n        (  161.53    11.29    19.75     0.46)  ; 20\r\n        (  161.53    11.29    19.73     0.46)  ; 21\r\n        (  157.69    11.58    19.85     0.46)  ; 22\r\n        (  154.56    10.85    20.25     0.46)  ; 23\r\n        (  151.71     8.99    20.25     0.46)  ; 24\r\n        (  151.26     8.88    20.25     0.46)  ; 25\r\n        (  149.02     8.35    20.25     0.46)  ; 26\r\n        (  145.51     9.32    20.92     0.46)  ; 27\r\n        (  144.48     9.67    23.10     0.46)  ; 28\r\n        (  140.90     8.84    24.00     0.46)  ; 29\r\n        (  140.90     8.84    23.98     0.46)  ; 30\r\n        (  139.12     8.42    25.13     0.46)  ; 31\r\n        (  136.30     8.36    25.70     0.46)  ; 32\r\n        (  136.30     8.36    25.73     0.46)  ; 33\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  183.62     7.50    10.70     0.46)  ; 1\r\n          (  185.28     8.49    10.70     0.46)  ; 2\r\n          (  174.57    11.95    14.90     0.46)  ; 3\r\n          (  165.19     9.75    18.48     0.46)  ; 4\r\n          (  168.01     9.82    18.48     0.46)  ; 5\r\n          (  160.15     9.17    19.27     0.46)  ; 6\r\n          (  161.18    14.79    21.00     0.46)  ; 7\r\n          (  157.78     9.22    21.25     0.46)  ; 8\r\n          (  150.45     6.30    21.25     0.46)  ; 9\r\n          (  151.52     7.75    21.25     0.46)  ; 10\r\n          (  154.21     8.38    21.25     0.46)  ; 11\r\n          (  155.86     9.36    21.25     0.46)  ; 12\r\n          (  148.21     5.78    21.25     0.46)  ; 13\r\n          (  143.18    11.16    25.73     0.46)  ; 14\r\n          (  141.11     5.91    25.75     0.46)  ; 15\r\n          (  141.08    10.08    24.88     0.46)  ; 16\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  198.48    10.40     6.07     0.92)  ; 1, R-1-1-2\r\n        (  197.15    10.08     4.07     0.92)  ; 2\r\n        (  195.18     8.42     2.15     0.92)  ; 3\r\n        (  193.09     7.34     0.30     0.92)  ; 4\r\n        (  190.00     8.41    -0.32     0.92)  ; 5\r\n        (  188.21     7.99    -0.95     0.46)  ; 6\r\n        (  186.12     6.90    -0.95     0.46)  ; 7\r\n        (  183.17     7.41    -1.60     0.46)  ; 8\r\n        (  183.17     7.41    -1.63     0.46)  ; 9\r\n        (  181.70     7.65    -3.67     0.46)  ; 10\r\n        (  182.09     5.96    -5.80     0.46)  ; 11\r\n        (  179.47     7.13    -7.50     0.46)  ; 12\r\n        (  179.47     7.13    -7.53     0.46)  ; 13\r\n        (  177.23     6.61    -8.13     0.46)  ; 14\r\n        (  174.55     5.98    -8.93     0.46)  ; 15\r\n        (  172.69     7.93   -10.45     0.46)  ; 16\r\n        (  172.69     7.93   -10.50     0.46)  ; 17\r\n        (  170.95     9.31   -11.27     0.46)  ; 18\r\n        (  170.55    11.01   -12.60     0.46)  ; 19\r\n        (  170.55    11.01   -12.67     0.46)  ; 20\r\n        (  171.67    14.26   -14.18     0.46)  ; 21\r\n        (  169.49    15.53   -15.60     0.46)  ; 22\r\n        (  166.73    17.28   -16.80     0.46)  ; 23\r\n        (  165.30    19.34   -18.20     0.46)  ; 24\r\n        (  163.82    19.59   -19.75     0.46)  ; 25\r\n        (  162.71    22.31   -20.35     0.46)  ; 26\r\n        (  162.71    22.31   -20.38     0.46)  ; 27\r\n        (  161.43    23.80   -22.27     0.46)  ; 28\r\n        (  159.69    25.19   -23.60     0.46)  ; 29\r\n        (  158.08    26.01   -25.50     0.46)  ; 30\r\n        (  158.08    26.01   -25.55     0.46)  ; 31\r\n        (  155.59    26.62   -26.92     0.46)  ; 32\r\n        (  153.40    27.89   -28.70     0.46)  ; 33\r\n        (  151.16    27.37   -30.90     0.46)  ; 34\r\n        (  150.76    29.07   -32.80     0.46)  ; 35\r\n        (  150.19    29.53   -35.40     0.46)  ; 36\r\n        (  150.19    29.53   -35.42     0.46)  ; 37\r\n        (  148.72    29.79   -37.90     0.46)  ; 38\r\n        (  148.72    29.79   -37.92     0.46)  ; 39\r\n        (  148.32    31.48   -41.40     0.46)  ; 40\r\n        (  147.88    31.38   -43.78     0.46)  ; 41\r\n        (  147.88    31.38   -43.80     0.46)  ; 42\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  198.67    11.63     4.07     0.92)  ; 1\r\n          (  196.31    11.67     2.15     0.92)  ; 2\r\n          (  184.91     6.01    -1.63     0.46)  ; 3\r\n          (  180.45     4.97    -8.13     0.46)  ; 4\r\n          (  176.60     5.27    -7.90     0.46)  ; 5\r\n          (  175.63     7.42    -7.32     0.46)  ; 6\r\n          (  175.36     8.57    -9.05     0.46)  ; 7\r\n          (  174.63     3.61   -10.95     0.46)  ; 8\r\n          (  170.14     6.73   -12.42     0.46)  ; 9\r\n          (  169.47     9.56    -9.77     0.46)  ; 10\r\n          (  169.57    13.17   -14.18     0.46)  ; 11\r\n          (  168.06    17.59   -15.02     0.46)  ; 12\r\n          (  173.09    12.20   -15.02     0.46)  ; 13\r\n          (  173.14    14.01   -15.60     0.46)  ; 14\r\n          (  166.81    14.91   -15.60     0.46)  ; 15\r\n          (  169.44    13.73   -15.60     0.46)  ; 16\r\n          (  159.80    18.64   -20.38     0.46)  ; 17\r\n          (  164.64    22.16   -21.45     0.46)  ; 18\r\n          (  163.92    23.20   -22.27     0.46)  ; 19\r\n          (  156.12    24.35   -27.08     0.46)  ; 20\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  237.57     0.45    -4.55     1.38)  ; 1, R-1-2\r\n      (\r\n        (  234.22     2.64    -6.25     0.92)  ; 1, R-1-2-1\r\n        (  234.22     2.64    -6.28     0.92)  ; 2\r\n        (  231.91     4.49    -7.55     0.92)  ; 3\r\n        (  231.91     4.49    -7.57     0.92)  ; 4\r\n        (  229.99     4.64    -8.93     0.92)  ; 5\r\n        (  228.03     2.98   -10.17     0.92)  ; 6\r\n        (  225.21     2.92    -9.88     0.92)  ; 7\r\n        (  221.11     4.35   -11.37     0.92)  ; 8\r\n        (  221.95     2.76   -13.23     0.92)  ; 9\r\n        (  221.95     2.76   -13.25     0.92)  ; 10\r\n        (  219.82     5.83   -14.25     0.92)  ; 11\r\n        (  219.82     5.83   -14.27     0.92)  ; 12\r\n        (  217.82     8.36   -13.82     0.92)  ; 13\r\n        (  216.52     9.85   -15.57     0.92)  ; 14\r\n        (  215.50    10.20   -17.67     0.92)  ; 15\r\n        (  215.50    10.20   -17.70     0.92)  ; 16\r\n        (  213.26     9.68   -19.33     0.92)  ; 17\r\n        (  211.61     8.69   -19.60     0.92)  ; 18\r\n        (  210.85     7.91   -21.10     0.92)  ; 19\r\n        (  210.85     7.91   -21.13     0.92)  ; 20\r\n        (  209.65     7.03   -22.92     0.46)  ; 21\r\n        (  209.11     9.30   -24.90     0.46)  ; 22\r\n        (  208.86    10.43   -26.90     0.92)  ; 23\r\n        (  205.14    10.16   -28.13     0.92)  ; 24\r\n        (  205.14    10.16   -28.15     0.92)  ; 25\r\n        (  203.09    10.88   -28.15     0.92)  ; 26\r\n        (  199.88    12.51   -28.15     0.92)  ; 27\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  224.58     1.58    -9.88     0.92)  ; 1\r\n          (  216.74     6.91   -13.82     0.92)  ; 2\r\n          (  214.37     6.95   -19.33     0.92)  ; 3\r\n          (  213.44    10.91   -19.33     0.92)  ; 4\r\n          (  211.70     6.33   -19.60     0.92)  ; 5\r\n          (  202.11    13.03   -28.15     0.92)  ; 6\r\n          (  205.18    11.96   -27.65     0.92)  ; 7\r\n        )  ;  End of markers\r\n        (\r\n          (  195.01    13.16   -27.70     0.46)  ; 1, R-1-2-1-1\r\n          (  191.49    14.13   -29.27     0.46)  ; 2\r\n          (  190.32    15.05   -30.30     0.46)  ; 3\r\n          (  188.33    17.57   -30.83     0.46)  ; 4\r\n          (  186.06    21.21   -32.00     0.46)  ; 5\r\n          (  184.77    22.70   -33.22     0.46)  ; 6\r\n          (  183.61    23.63   -34.52     0.46)  ; 7\r\n          (  183.61    23.63   -34.55     0.46)  ; 8\r\n          (  182.37    26.92   -34.55     0.46)  ; 9\r\n          (  179.97    31.13   -34.60     0.46)  ; 10\r\n          (  179.97    31.13   -34.63     0.46)  ; 11\r\n          (  177.84    34.22   -37.02     0.46)  ; 12\r\n          (  177.84    34.22   -37.05     0.46)  ; 13\r\n          (  175.64    35.50   -38.03     0.46)  ; 14\r\n          (  174.40    38.79   -38.38     0.46)  ; 15\r\n          (  171.19    40.42   -38.60     0.46)  ; 16\r\n          (  169.64    43.04   -39.67     0.46)  ; 17\r\n          (  167.59    43.76   -40.57     0.46)  ; 18\r\n          (  167.59    43.76   -40.60     0.46)  ; 19\r\n\r\n          (Cross\r\n            (Color Green)\r\n            (Name \"Marker 3\")\r\n            (  183.61    23.63   -33.22     0.46)  ; 1\r\n            (  183.17    29.49   -38.35     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  195.86    11.57   -30.10     0.46)  ; 1, R-1-2-1-2\r\n          (  192.74    10.84   -32.25     0.46)  ; 2\r\n          (  192.74    10.84   -32.30     0.46)  ; 3\r\n          (  190.67    11.54   -33.45     0.46)  ; 4\r\n          (  190.67    11.54   -33.47     0.46)  ; 5\r\n          (  189.21    11.81   -35.27     0.46)  ; 6\r\n          (  189.21    11.81   -35.33     0.46)  ; 7\r\n          (  186.71    12.42   -36.92     0.46)  ; 8\r\n          (  186.71    12.42   -36.95     0.46)  ; 9\r\n          (  185.74    14.56   -37.85     0.46)  ; 10\r\n          (  185.74    14.56   -37.88     0.46)  ; 11\r\n          (  182.21    15.54   -39.13     0.46)  ; 12\r\n          (  178.94    15.37   -40.80     0.46)  ; 13\r\n          (  177.13    19.12   -41.82     0.46)  ; 14\r\n          (  177.13    19.12   -41.85     0.46)  ; 15\r\n          (  174.58    17.93   -44.10     0.46)  ; 16\r\n          (  172.53    18.65   -46.25     0.46)  ; 17\r\n          (  170.47    19.35   -48.35     0.46)  ; 18\r\n          (  170.47    19.35   -48.38     0.46)  ; 19\r\n          (  168.56    19.50   -50.35     0.46)  ; 20\r\n          (  168.56    19.50   -50.38     0.46)  ; 21\r\n          (  167.71    21.10   -52.65     0.46)  ; 22\r\n          (  167.71    21.10   -52.70     0.46)  ; 23\r\n          (  167.45    22.22   -54.65     0.46)  ; 24\r\n          (  167.45    22.22   -54.72     0.46)  ; 25\r\n          (  165.71    23.61   -56.42     0.46)  ; 26\r\n          (  165.71    23.61   -56.45     0.46)  ; 27\r\n          (  163.61    22.52   -58.38     0.46)  ; 28\r\n          (  163.61    22.52   -58.40     0.46)  ; 29\r\n          (  165.22    21.71   -60.05     0.46)  ; 30\r\n          (  165.22    21.71   -60.22     0.46)  ; 31\r\n          (  162.84    21.75   -62.70     0.46)  ; 32\r\n          (  162.84    21.75   -62.75     0.46)  ; 33\r\n          (  161.33    20.20   -66.13     0.46)  ; 34\r\n          (  159.99    19.89   -69.00     0.46)  ; 35\r\n          (  158.52    20.14   -69.30     0.46)  ; 36\r\n          (  158.12    21.83   -71.17     0.46)  ; 37\r\n          (  158.12    21.83   -71.20     0.46)  ; 38\r\n          (  156.83    23.33   -73.95     0.46)  ; 39\r\n          (  156.83    23.33   -73.97     0.46)  ; 40\r\n          (  155.77    27.85   -76.28     0.46)  ; 41\r\n          (  155.77    27.85   -76.30     0.46)  ; 42\r\n          (  155.55    30.79   -78.28     0.46)  ; 43\r\n          (  155.55    30.79   -78.32     0.46)  ; 44\r\n          (  155.34    33.71   -80.63     0.46)  ; 45\r\n          (  155.34    33.71   -80.68     0.46)  ; 46\r\n          (  154.62    34.75   -84.12     0.46)  ; 47\r\n          (  154.62    34.75   -84.17     0.46)  ; 48\r\n          (  154.72    38.36   -86.62     0.46)  ; 49\r\n          (  154.72    38.36   -86.65     0.46)  ; 50\r\n          (  153.88    39.95   -89.05     0.46)  ; 51\r\n          (  152.77    42.68   -92.00     0.46)  ; 52\r\n          (  151.61    43.59   -94.80     0.46)  ; 53\r\n          (  151.61    43.59   -94.82     0.46)  ; 54\r\n          (  149.73    45.55   -96.80     0.46)  ; 55\r\n          (  149.92    46.78   -99.05     0.46)  ; 56\r\n          (  147.60    48.62  -102.63     0.46)  ; 57\r\n          (  147.60    48.62  -102.65     0.46)  ; 58\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  147.72    48.06   -50.55     0.46)  ; 1\r\n          )  ;  End of markers\r\n\r\n          (Cross\r\n            (Color Green)\r\n            (Name \"Marker 3\")\r\n            (  193.45     9.81   -32.30     0.46)  ; 1\r\n            (  190.54    12.12   -31.77     0.46)  ; 2\r\n            (  183.19    13.37   -39.13     0.46)  ; 3\r\n            (  179.21    14.24   -41.85     0.46)  ; 4\r\n            (  168.74    20.74   -48.38     0.46)  ; 5\r\n            (  158.79    19.00   -67.32     0.46)  ; 6\r\n            (  157.53    38.41   -86.65     0.46)  ; 7\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  234.31     0.28    -6.78     0.92)  ; 1, R-1-2-2\r\n        (  232.98    -0.04    -7.43     0.92)  ; 2\r\n        (  231.72    -2.72    -8.00     0.92)  ; 3\r\n        (  231.72    -2.72    -8.02     0.92)  ; 4\r\n        (  230.92    -5.30    -8.50     0.92)  ; 5\r\n        (  229.12    -5.72    -8.50     0.92)  ; 6\r\n        (  227.29    -7.94    -8.50     0.92)  ; 7\r\n        (  227.32   -12.11    -9.60     0.92)  ; 8\r\n        (  227.32   -12.11    -9.63     0.92)  ; 9\r\n        (  227.04   -16.95   -10.20     0.92)  ; 10\r\n        (  226.69   -19.43   -10.70     0.92)  ; 11\r\n        (  224.90   -19.85   -11.02     0.92)  ; 12\r\n        (  224.35   -23.55   -11.07     0.92)  ; 13\r\n        (  223.23   -26.79   -11.07     0.92)  ; 14\r\n        (  221.08   -29.70   -11.37     0.92)  ; 15\r\n        (  219.50   -33.06   -11.37     0.92)  ; 16\r\n        (  219.06   -33.16   -11.37     0.92)  ; 17\r\n        (  217.57   -38.87   -11.37     0.92)  ; 18\r\n        (  215.88   -41.66   -11.60     0.92)  ; 19\r\n        (  214.87   -45.48   -11.40     0.92)  ; 20\r\n        (  214.87   -45.48   -11.42     0.92)  ; 21\r\n        (  214.79   -49.08   -13.13     0.92)  ; 22\r\n        (  214.59   -50.33   -13.72     0.92)  ; 23\r\n        (  214.59   -50.33   -13.75     0.92)  ; 24\r\n        (  215.31   -51.36   -15.88     0.92)  ; 25\r\n        (  214.55   -52.14   -17.82     0.92)  ; 26\r\n        (  214.55   -52.14   -17.85     0.92)  ; 27\r\n        (  213.03   -53.69   -19.27     0.92)  ; 28\r\n        (  210.35   -54.31   -20.50     0.92)  ; 29\r\n        (  208.82   -55.86   -22.22     0.92)  ; 30\r\n        (  206.78   -55.15   -24.15     0.92)  ; 31\r\n        (  205.17   -54.33   -25.92     0.92)  ; 32\r\n        (  203.70   -54.09   -27.80     0.92)  ; 33\r\n        (  201.74   -55.74   -29.00     0.92)  ; 34\r\n        (  201.74   -55.74   -29.02     0.92)  ; 35\r\n        (  199.05   -56.36   -30.13     0.92)  ; 36\r\n        (  196.81   -56.89   -30.13     0.92)  ; 37\r\n        (  194.72   -57.97   -30.30     0.46)  ; 38\r\n        (  192.75   -59.63   -30.30     0.46)  ; 39\r\n        (  189.63   -60.36   -30.30     0.46)  ; 40\r\n        (  187.52   -61.46   -32.28     0.46)  ; 41\r\n        (  185.29   -61.97   -33.92     0.46)  ; 42\r\n        (  183.63   -62.97   -35.83     0.46)  ; 43\r\n        (  183.58   -64.77   -38.00     0.46)  ; 44\r\n        (  183.58   -64.77   -38.03     0.46)  ; 45\r\n        (  182.52   -66.21   -38.20     0.46)  ; 46\r\n        (  180.54   -67.87   -39.78     0.46)  ; 47\r\n        (  178.26   -70.20   -40.60     0.46)  ; 48\r\n        (  176.74   -71.75   -40.83     0.46)  ; 49\r\n        (  175.04   -74.53   -43.18     0.46)  ; 50\r\n        (  173.66   -76.65   -44.45     0.46)  ; 51\r\n        (  173.28   -79.12   -46.93     0.46)  ; 52\r\n        (  171.99   -77.63   -49.52     0.46)  ; 53\r\n        (  170.66   -77.94   -52.10     0.46)  ; 54\r\n        (  170.66   -77.94   -52.13     0.46)  ; 55\r\n        (  169.63   -77.59   -55.03     0.46)  ; 56\r\n        (  169.63   -77.59   -55.05     0.46)  ; 57\r\n        (  169.05   -77.13   -58.50     0.46)  ; 58\r\n        (  167.89   -76.21   -62.47     0.46)  ; 59\r\n        (  167.63   -75.08   -67.17     0.46)  ; 60\r\n        (  167.63   -75.08   -67.28     0.46)  ; 61\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  225.37    -7.79    -8.50     0.92)  ; 1\r\n          (  229.07    -7.52    -8.50     0.92)  ; 2\r\n          (  226.89    -6.25    -8.50     0.92)  ; 3\r\n          (  228.09    -5.36    -8.50     0.92)  ; 4\r\n          (  227.97   -20.91   -10.70     0.92)  ; 5\r\n          (  225.10   -28.75   -10.30     0.92)  ; 6\r\n          (  213.49   -47.60   -13.13     0.92)  ; 7\r\n          (  220.98   -33.31    -7.95     0.92)  ; 8\r\n          (  223.44   -29.74    -7.95     0.92)  ; 9\r\n          (  217.64   -31.10    -7.95     0.92)  ; 10\r\n          (  220.16   -35.88    -7.80     0.92)  ; 11\r\n          (  220.54   -33.41    -8.82     0.92)  ; 12\r\n          (  216.54   -44.49    -9.48     0.92)  ; 13\r\n          (  214.01   -55.84   -17.60     0.92)  ; 14\r\n          (  203.38   -54.75   -24.15     0.92)  ; 15\r\n          (  193.02   -60.77   -30.30     0.46)  ; 16\r\n          (  181.95   -59.78   -35.25     0.46)  ; 17\r\n          (  179.87   -71.01   -40.83     0.46)  ; 18\r\n          (  174.69   -71.03   -42.02     0.46)  ; 19\r\n          (  177.46   -72.78   -42.02     0.46)  ; 20\r\n          (  175.40   -78.04   -43.80     0.46)  ; 21\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  236.33    -2.03    -4.55     0.92)  ; 1, R-1-2-3\r\n        (  235.12    -2.92    -4.35     0.92)  ; 2\r\n        (  235.12    -2.92    -4.40     0.92)  ; 3\r\n        (  234.19    -4.93    -3.17     0.92)  ; 4\r\n        (  234.45    -6.06    -1.70     0.92)  ; 5\r\n        (  233.37    -7.50    -0.40     0.92)  ; 6\r\n        (\r\n          (  233.18    -8.74     0.95     0.92)  ; 1, R-1-2-3-1\r\n          (  234.03   -10.34     2.05     0.92)  ; 2\r\n          (  235.20   -11.26     3.38     0.92)  ; 3\r\n          (  235.20   -11.26     3.35     0.92)  ; 4\r\n          (  235.91   -12.28     4.43     0.92)  ; 5\r\n          (  236.75   -13.88     5.15     0.92)  ; 6\r\n          (  237.28   -16.14     5.85     0.92)  ; 7\r\n          (  237.37   -18.51     6.57     0.92)  ; 8\r\n          (  235.84   -20.07     8.35     0.92)  ; 9\r\n          (  235.08   -20.84     9.20     0.92)  ; 10\r\n          (  235.17   -23.21     8.80     0.92)  ; 11\r\n          (  235.25   -25.57    10.50     0.92)  ; 12\r\n          (  235.47   -28.51    11.35     0.92)  ; 13\r\n          (  234.97   -30.41    11.07     0.92)  ; 14\r\n          (  235.95   -32.58    12.25     0.92)  ; 15\r\n          (  235.95   -32.58    12.22     0.92)  ; 16\r\n          (  235.59   -35.06    12.50     0.92)  ; 17\r\n          (  234.20   -37.16    12.07     0.92)  ; 18\r\n          (  234.20   -37.16    12.05     0.92)  ; 19\r\n          (  233.75   -37.27    14.30     0.92)  ; 20\r\n          (  234.65   -37.06    16.57     0.92)  ; 21\r\n          (  235.36   -38.09    18.67     0.92)  ; 22\r\n          (  233.83   -39.65    20.17     0.92)  ; 23\r\n          (  233.60   -42.68    20.97     0.92)  ; 24\r\n          (  232.35   -45.37    21.88     0.92)  ; 25\r\n          (  230.33   -48.83    21.55     0.92)  ; 26\r\n          (  230.33   -48.83    21.52     0.92)  ; 27\r\n          (  229.79   -52.53    22.08     0.92)  ; 28\r\n          (  227.64   -55.43    22.57     0.92)  ; 29\r\n          (  226.57   -56.87    23.33     0.92)  ; 30\r\n          (  224.92   -57.85    23.80     0.92)  ; 31\r\n          (  224.92   -57.85    23.78     0.92)  ; 32\r\n          (  224.11   -60.44    24.38     0.92)  ; 33\r\n          (  224.11   -60.44    24.35     0.92)  ; 34\r\n          (  225.53   -62.48    25.23     0.92)  ; 35\r\n          (  223.64   -66.51    26.45     0.92)  ; 36\r\n          (  223.41   -69.55    27.25     0.92)  ; 37\r\n          (  222.29   -72.80    27.77     0.92)  ; 38\r\n          (  220.27   -76.25    29.13     0.92)  ; 39\r\n          (  218.93   -76.57    30.65     0.92)  ; 40\r\n          (  217.55   -78.69    31.95     0.92)  ; 41\r\n          (  216.47   -80.14    33.85     0.92)  ; 42\r\n          (  216.02   -80.24    34.40     0.92)  ; 43\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  239.02   -17.53     5.85     0.92)  ; 1\r\n            (  233.48   -20.03     9.20     0.92)  ; 2\r\n            (  235.13   -19.04     9.20     0.92)  ; 3\r\n            (  240.05   -17.89    10.33     0.92)  ; 4\r\n            (  232.19   -34.65    12.50     0.92)  ; 5\r\n            (  236.43   -36.65    12.50     0.92)  ; 6\r\n            (  236.00   -30.78    12.50     0.92)  ; 7\r\n            (  237.11   -33.49    12.50     0.92)  ; 8\r\n            (  234.53   -30.52     9.65     0.92)  ; 9\r\n            (  232.99   -38.05    18.67     0.92)  ; 10\r\n            (  236.83   -38.34    20.97     0.92)  ; 11\r\n            (  231.64   -44.34    21.88     0.92)  ; 12\r\n            (  231.87   -41.30    21.88     0.92)  ; 13\r\n            (  230.58   -55.93    22.57     0.92)  ; 14\r\n            (  231.44   -51.55    22.57     0.92)  ; 15\r\n            (  225.59   -54.72    26.58     0.92)  ; 16\r\n            (  226.20   -59.35    23.07     0.92)  ; 17\r\n            (  221.99   -67.50    26.45     0.92)  ; 18\r\n            (  219.80   -72.19    27.30     0.92)  ; 19\r\n            (  225.59   -70.83    27.30     0.92)  ; 20\r\n            (  224.21   -72.95    28.52     0.92)  ; 21\r\n          )  ;  End of markers\r\n           High\r\n        |\r\n          (  231.22   -10.40    -1.42     0.92)  ; 1, R-1-2-3-2\r\n          (  231.89   -13.23    -2.42     0.92)  ; 2\r\n          (  231.89   -13.23    -2.45     0.92)  ; 3\r\n          (  231.66   -16.27    -1.65     0.92)  ; 4\r\n          (  230.85   -18.85    -0.47     0.92)  ; 5\r\n          (  229.60   -21.53     0.17     0.92)  ; 6\r\n          (  227.44   -24.42     0.95     0.92)  ; 7\r\n          (  226.23   -25.31     1.95     0.92)  ; 8\r\n          (  224.98   -27.98     3.08     0.92)  ; 9\r\n          (  223.72   -30.66     4.35     0.92)  ; 10\r\n          (  222.82   -30.87     5.55     0.92)  ; 11\r\n          (  221.71   -34.12     4.38     0.92)  ; 12\r\n          (  220.14   -37.47     4.38     0.92)  ; 13\r\n          (  218.44   -40.27     4.38     0.92)  ; 14\r\n          (  219.10   -43.10     5.37     0.92)  ; 15\r\n          (  220.34   -46.38     6.07     0.92)  ; 16\r\n          (  220.34   -46.38     5.22     0.92)  ; 17\r\n          (  220.87   -48.66     4.50     0.92)  ; 18\r\n          (  220.87   -48.66     4.22     0.92)  ; 19\r\n          (  221.85   -50.82     3.35     0.92)  ; 20\r\n          (  220.54   -55.29     2.83     0.92)  ; 21\r\n          (  217.81   -57.74     1.67     0.92)  ; 22\r\n          (  217.81   -57.74     1.65     0.92)  ; 23\r\n          (  215.53   -60.05     1.42     0.92)  ; 24\r\n          (  215.56   -64.23     0.45     0.92)  ; 25\r\n          (  214.76   -66.81     0.22     0.92)  ; 26\r\n          (  214.76   -66.81     0.17     0.92)  ; 27\r\n          (  213.77   -70.62    -0.67     0.92)  ; 28\r\n          (  213.77   -70.62    -0.72     0.92)  ; 29\r\n          (  213.54   -73.66    -1.17     0.92)  ; 30\r\n          (  214.51   -75.82    -1.17     0.92)  ; 31\r\n          (  213.35   -80.87    -1.47     0.92)  ; 32\r\n          (  212.80   -84.59    -1.60     0.92)  ; 33\r\n          (  212.07   -89.53    -1.60     0.92)  ; 34\r\n          (  212.42   -93.04    -2.75     0.92)  ; 35\r\n          (  210.54   -97.06    -3.78     0.92)  ; 36\r\n          (  209.86  -100.19    -3.78     0.92)  ; 37\r\n          (  209.14  -105.15    -4.78     0.92)  ; 38\r\n          (  209.35  -108.08    -3.60     0.92)  ; 39\r\n          (  208.94  -112.35    -2.22     0.92)  ; 40\r\n          (  208.99  -116.53    -1.32     0.92)  ; 41\r\n          (  210.35  -120.38    -0.45     0.92)  ; 42\r\n          (  211.15  -123.78     0.32     0.92)  ; 43\r\n          (  212.08  -127.75     1.07     0.92)  ; 44\r\n          (  209.80  -130.07     1.90     0.92)  ; 45\r\n          (  207.43  -130.03     2.92     0.92)  ; 46\r\n          (  206.04  -132.15     3.65     0.92)  ; 47\r\n          (  206.04  -132.15     3.70     0.92)  ; 48\r\n          (  204.57  -131.89     4.63     0.92)  ; 49\r\n          (  202.92  -132.88     5.20     0.92)  ; 50\r\n          (  202.56  -135.35     4.63     0.92)  ; 51\r\n          (  202.56  -135.35     4.60     0.92)  ; 52\r\n          (  203.27  -136.37     4.82     0.92)  ; 53\r\n          (  204.51  -139.67     6.02     0.92)  ; 54\r\n          (  202.18  -143.80     7.10     0.92)  ; 55\r\n          (  201.77  -148.07     8.45     0.92)  ; 56\r\n          (  202.30  -150.35     9.77     0.92)  ; 57\r\n          (  203.01  -151.37    12.00     0.92)  ; 58\r\n          (  203.59  -151.83    13.30     0.92)  ; 59\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  230.69    -8.14    -1.42     0.92)  ; 1\r\n            (  233.98   -18.12    -1.42     0.92)  ; 2\r\n            (  233.80   -13.38    -2.97     0.92)  ; 3\r\n            (  231.20   -22.34    -0.55     0.92)  ; 4\r\n            (  227.49   -22.61     1.25     0.92)  ; 5\r\n            (  228.59   -25.35     2.88     0.92)  ; 6\r\n            (  222.25   -52.51     5.13     0.92)  ; 7\r\n            (  219.42   -58.55     5.13     0.92)  ; 8\r\n            (  219.24   -59.78     2.57     0.92)  ; 9\r\n            (  221.43   -55.08     2.57     0.92)  ; 10\r\n            (  217.59   -60.78     2.57     0.92)  ; 11\r\n            (  218.12   -63.03     2.57     0.92)  ; 12\r\n            (  214.10   -58.00     1.13     0.92)  ; 13\r\n            (  217.41   -56.03     1.13     0.92)  ; 14\r\n            (  210.58   -79.14    -1.60     0.92)  ; 15\r\n            (  213.76   -76.59    -1.60     0.92)  ; 16\r\n            (  214.69   -80.56    -1.60     0.92)  ; 17\r\n            (  211.46   -84.89    -1.60     0.92)  ; 18\r\n            (  210.91   -88.61    -1.60     0.92)  ; 19\r\n            (  214.57   -90.13    -1.60     0.92)  ; 20\r\n            (  213.10   -67.79    -1.60     0.92)  ; 21\r\n            (  216.18   -68.86    -1.60     0.92)  ; 22\r\n            (  210.20   -93.55    -3.78     0.92)  ; 23\r\n            (  212.42   -99.00    -1.77     0.92)  ; 24\r\n            (  207.40  -103.76    -4.70     0.92)  ; 25\r\n            (  211.24  -104.05    -5.57     0.92)  ; 26\r\n            (  211.18  -111.83    -2.22     0.92)  ; 27\r\n            (  208.49  -118.44    -1.32     0.92)  ; 28\r\n            (  211.61  -117.71    -1.32     0.92)  ; 29\r\n            (  211.88  -118.83    -1.32     0.92)  ; 30\r\n            (  202.84  -130.51     4.60     0.92)  ; 31\r\n            (  198.85  -135.62     4.60     0.92)  ; 32\r\n            (  207.07  -132.50     4.17     0.92)  ; 33\r\n            (  203.60  -145.85     9.52     0.92)  ; 34\r\n            (  203.24  -148.33     9.52     0.92)  ; 35\r\n            (  213.10  -128.10     3.32     0.92)  ; 36\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (  250.10     9.55     4.75     0.92)  ; 1, R-2\r\n    (  247.61    10.16     6.35     0.92)  ; 2\r\n    (  245.28    12.01     6.97     0.92)  ; 3\r\n    (  243.42    13.95     8.52     0.92)  ; 4\r\n    (  244.49    15.40     9.77     0.92)  ; 5\r\n    (  246.99    14.79    10.12     0.92)  ; 6\r\n    (  245.92    13.35    11.47     0.92)  ; 7\r\n    (  244.26    12.36    14.88     0.92)  ; 8\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  251.31    10.43     6.35     0.92)  ; 1\r\n      (  246.60    16.50     8.85     0.92)  ; 2\r\n      (  248.46    14.54    11.47     0.92)  ; 3\r\n    )  ;  End of markers\r\n    (\r\n      (  245.34    13.80    14.88     0.92)  ; 1, R-2-1\r\n      (  241.54    15.91    14.45     0.92)  ; 2\r\n      (  240.08    16.16    14.85     0.92)  ; 3\r\n      (  240.08    16.16    14.82     0.92)  ; 4\r\n      (  239.86    19.09    16.60     0.92)  ; 5\r\n      (  239.46    20.79    18.38     0.92)  ; 6\r\n      (  238.22    24.08    19.52     0.92)  ; 7\r\n      (  236.21    26.60    20.75     0.92)  ; 8\r\n      (  234.08    29.69    20.92     0.92)  ; 9\r\n      (  231.76    31.53    20.13     0.92)  ; 10\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  238.20    18.11    14.82     0.92)  ; 1\r\n        (  237.23    20.27    18.40     0.92)  ; 2\r\n        (  240.13    23.93    18.40     0.92)  ; 3\r\n        (  238.44    27.12    19.02     0.92)  ; 4\r\n        (  231.53    28.49    18.55     0.92)  ; 5\r\n        (  234.26    30.92    18.55     0.92)  ; 6\r\n        (  232.58    34.11    18.55     0.92)  ; 7\r\n      )  ;  End of markers\r\n      (\r\n        (  229.93    35.27    20.85     0.46)  ; 1, R-2-1-1\r\n        (  227.31    36.46    21.85     0.46)  ; 2\r\n        (  224.86    38.86    22.67     0.46)  ; 3\r\n        (  223.84    39.22    24.20     0.46)  ; 4\r\n        (  223.39    39.12    24.20     0.46)  ; 5\r\n        (  222.63    38.35    26.35     0.46)  ; 6\r\n         Normal\r\n      |\r\n        (  228.42    33.73    19.23     0.92)  ; 1, R-2-1-2\r\n        (  226.23    35.01    19.58     0.92)  ; 2\r\n        (  226.23    35.01    19.55     0.92)  ; 3\r\n        (  224.63    35.83    21.10     0.92)  ; 4\r\n        (  222.00    37.00    23.00     0.92)  ; 5\r\n        (  219.99    39.53    24.08     0.92)  ; 6\r\n        (  217.41    42.50    25.60     0.92)  ; 7\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  214.29    41.77    25.05     0.92)  ; 1\r\n          (  218.09    45.64    25.05     0.92)  ; 2\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  242.34    12.51    14.88     0.92)  ; 1, R-2-2\r\n      (  242.29    10.71    16.38     0.92)  ; 2\r\n      (  242.29    10.71    16.35     0.92)  ; 3\r\n      (  239.40    13.01    18.27     0.92)  ; 4\r\n      (  239.09    12.34    20.00     0.92)  ; 5\r\n      (  237.31    11.93    20.77     0.92)  ; 6\r\n      (  235.32    10.27    22.30     0.92)  ; 7\r\n      (  233.36     8.61    23.25     0.92)  ; 8\r\n      (  233.36     8.61    23.23     0.92)  ; 9\r\n      (  230.61    10.35    24.60     0.92)  ; 10\r\n      (  228.99    11.18    25.47     0.92)  ; 11\r\n      (  227.29     8.38    26.60     0.92)  ; 12\r\n      (  224.08    10.02    27.63     0.92)  ; 13\r\n      (  221.32    11.76    28.02     0.92)  ; 14\r\n      (  218.64    11.14    29.50     0.92)  ; 15\r\n      (  218.64    11.14    29.48     0.92)  ; 16\r\n      (  217.38     8.45    30.27     0.92)  ; 17\r\n      (  217.38     8.45    30.23     0.92)  ; 18\r\n      (  219.12     7.07    31.80     0.92)  ; 19\r\n      (  221.62     6.46    33.70     0.46)  ; 20\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  241.49     8.13    18.27     0.92)  ; 1\r\n        (  239.04    10.54    20.77     0.92)  ; 2\r\n        (  240.61    13.90    20.77     0.92)  ; 3\r\n        (  226.45     9.97    27.63     0.92)  ; 4\r\n        (  225.73    11.01    29.42     0.92)  ; 5\r\n        (  221.64    12.43    30.67     0.92)  ; 6\r\n        (  219.30    14.28    30.67     0.92)  ; 7\r\n        (  229.42    11.07   -25.80     0.92)  ; 8\r\n      )  ;  End of markers\r\n       High\r\n    |\r\n      (  245.82     9.74    15.90     0.46)  ; 1, R-2-3\r\n      (  247.29     9.49    18.80     0.46)  ; 2\r\n      (  245.90     7.37    21.22     0.46)  ; 3\r\n      (  244.56     7.06    23.92     0.46)  ; 4\r\n      (  244.06     5.15    24.80     0.46)  ; 5\r\n      (  243.18     4.94    24.80     0.46)  ; 6\r\n      (\r\n        (  243.13     3.14    25.77     0.46)  ; 1, R-2-3-1\r\n        (  244.64    -1.28    26.52     0.46)  ; 2\r\n        (  246.24    -2.10    28.15     0.46)  ; 3\r\n        (  246.24    -2.10    28.13     0.46)  ; 4\r\n        (  247.13    -1.89    30.63     0.46)  ; 5\r\n        (  247.13    -1.89    30.57     0.46)  ; 6\r\n        (  248.46    -1.57    33.22     0.46)  ; 7\r\n        (  248.46    -1.57    33.27     0.46)  ; 8\r\n\r\n        (Cross\r\n          (Color Green)\r\n          (Name \"Marker 3\")\r\n          (  243.40     7.98    24.80     0.46)  ; 1\r\n          (  244.34     9.99    24.80     0.46)  ; 2\r\n          (  244.86     1.76    26.52     0.46)  ; 3\r\n          (  242.19     1.13    26.52     0.46)  ; 4\r\n          (  242.72    -1.14    26.52     0.46)  ; 5\r\n        )  ;  End of markers\r\n         High\r\n      |\r\n        (  246.70     3.98    24.80     0.46)  ; 1, R-2-3-2\r\n        (  248.17     3.73    23.27     0.46)  ; 2\r\n        (  248.57     2.03    21.77     0.46)  ; 3\r\n        (\r\n          (  250.71    -1.05    22.50     0.46)  ; 1, R-2-3-2-1\r\n          (  253.60    -3.36    22.50     0.46)  ; 2\r\n          (  255.92    -5.21    23.75     0.46)  ; 3\r\n          (  259.50    -4.37    23.75     0.46)  ; 4\r\n          (  260.74    -7.66    23.75     0.46)  ; 5\r\n          (  259.80    -9.67    25.35     0.46)  ; 6\r\n          (  262.60    -9.61    26.35     0.46)  ; 7\r\n          (  264.92   -11.46    27.60     0.46)  ; 8\r\n          (  264.92   -11.46    27.57     0.46)  ; 9\r\n          (  265.94   -11.82    29.58     0.46)  ; 10\r\n          (  266.39   -11.71    32.28     0.46)  ; 11\r\n          (  266.39   -11.71    32.30     0.46)  ; 12\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  252.53    -4.80    22.50     0.46)  ; 1\r\n            (  259.09    -8.65    23.75     0.46)  ; 2\r\n            (  261.68    -5.65    23.75     0.46)  ; 3\r\n            (  259.11   -12.82    23.95     0.46)  ; 4\r\n            (  264.53    -9.75    26.65     0.46)  ; 5\r\n            (  264.43   -13.36    27.10     0.46)  ; 6\r\n          )  ;  End of markers\r\n           High\r\n        |\r\n          (  244.55     1.08    21.77     0.46)  ; 1, R-2-3-2-2\r\n          (  241.74     1.03    22.25     0.46)  ; 2\r\n          (  239.19    -0.17    22.25     0.46)  ; 3\r\n          (  238.74    -0.28    22.25     0.46)  ; 4\r\n          (  236.51    -0.80    22.25     0.46)  ; 5\r\n          (  234.23    -3.13    22.25     0.46)  ; 6\r\n          (  231.59    -1.95    22.45     0.46)  ; 7\r\n          (  230.35     1.34    22.45     0.46)  ; 8\r\n          (  227.94    -0.41    20.73     0.46)  ; 9\r\n          (  225.58    -0.37    19.15     0.46)  ; 10\r\n          (  222.32    -0.54    17.67     0.46)  ; 11\r\n          (  220.35    -2.19    16.17     0.46)  ; 12\r\n          (  219.72    -3.54    15.50     0.46)  ; 13\r\n          (  216.33    -3.14    15.12     0.46)  ; 14\r\n          (  212.88    -4.54    15.12     0.46)  ; 15\r\n          (  209.37    -3.58    14.18     0.46)  ; 16\r\n          (  207.58    -4.00    12.62     0.46)  ; 17\r\n          (  206.23    -4.31    11.07     0.46)  ; 18\r\n          (  204.85    -6.42    10.10     0.46)  ; 19\r\n          (  202.04    -6.49     9.25     0.46)  ; 20\r\n          (  199.62    -8.24     8.85     0.46)  ; 21\r\n          (  196.63    -9.55     8.60     0.46)  ; 22\r\n          (  193.76   -11.41     8.63     0.46)  ; 23\r\n          (  189.56   -13.59     8.63     0.46)  ; 24\r\n          (  187.52   -12.88     8.63     0.46)  ; 25\r\n          (  184.92   -15.87     7.72     0.46)  ; 26\r\n          (  180.27   -18.17     7.72     0.46)  ; 27\r\n          (  177.90   -18.11     7.02     0.46)  ; 28\r\n          (  176.07   -20.34     6.62     0.46)  ; 29\r\n          (  173.08   -21.64     7.55     0.46)  ; 30\r\n          (  173.08   -21.64     7.50     0.46)  ; 31\r\n          (  171.69   -23.75     7.25     0.46)  ; 32\r\n          (  171.69   -23.75     7.22     0.46)  ; 33\r\n          (  168.52   -26.29     6.82     0.46)  ; 34\r\n          (  166.42   -27.37     6.38     0.46)  ; 35\r\n          (  163.73   -28.00     6.38     0.46)  ; 36\r\n          (  161.50   -28.53     6.15     0.46)  ; 37\r\n          (  161.50   -28.53     6.13     0.46)  ; 38\r\n          (  159.67   -30.75     5.75     0.46)  ; 39\r\n          (  157.06   -33.75     4.92     0.46)  ; 40\r\n          (  157.06   -33.75     4.90     0.46)  ; 41\r\n          (  155.10   -35.40     3.45     0.46)  ; 42\r\n          (  153.01   -36.49     1.75     0.46)  ; 43\r\n          (  151.17   -38.72    -0.28     0.46)  ; 44\r\n          (  147.99   -41.25    -1.85     0.46)  ; 45\r\n          (  142.90   -43.64    -3.85     0.46)  ; 46\r\n          (  142.90   -43.64    -3.95     0.46)  ; 47\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  247.49     0.58    21.77     0.46)  ; 1\r\n            (  231.83     1.09    22.45     0.46)  ; 2\r\n            (  229.07     2.84    23.07     0.46)  ; 3\r\n            (  228.52    -0.88    22.60     0.46)  ; 4\r\n            (  225.50     2.00    18.40     0.46)  ; 5\r\n            (  223.91    -1.36    18.33     0.46)  ; 6\r\n            (  225.71    -0.94    18.33     0.46)  ; 7\r\n            (  223.60    -2.03    14.52     0.46)  ; 8\r\n            (  218.65    -4.99    15.12     0.46)  ; 9\r\n            (  216.59    -4.27    15.12     0.46)  ; 10\r\n            (  214.36    -4.79    15.12     0.46)  ; 11\r\n            (  213.28    -6.24    14.40     0.46)  ; 12\r\n            (  212.62    -3.41    16.52     0.46)  ; 13\r\n            (  207.84    -5.13    12.17     0.46)  ; 14\r\n            (  206.32    -6.68    13.15     0.46)  ; 15\r\n            (  199.99    -5.78     8.85     0.46)  ; 16\r\n            (  201.81    -9.53    10.00     0.46)  ; 17\r\n            (  200.46    -9.84    10.00     0.46)  ; 18\r\n            (  194.09   -10.75     8.13     0.46)  ; 19\r\n            (  191.62   -14.30     9.77     0.46)  ; 20\r\n            (  191.58   -10.13     6.70     0.46)  ; 21\r\n            (  184.21   -14.85    10.30     0.46)  ; 22\r\n            (  184.87   -17.68     8.97     0.46)  ; 23\r\n            (  182.33   -18.87     8.93     0.46)  ; 24\r\n            (  177.23   -21.26     6.62     0.46)  ; 25\r\n            (  173.87   -25.04     7.25     0.46)  ; 26\r\n            (  173.13   -19.84     7.25     0.46)  ; 27\r\n            (  166.33   -25.01     7.25     0.46)  ; 28\r\n            (  164.23   -26.10     7.25     0.46)  ; 29\r\n            (  169.49   -28.45     7.82     0.46)  ; 30\r\n            (  161.24   -27.40     5.63     0.46)  ; 31\r\n            (  161.27   -31.57     5.60     0.46)  ; 32\r\n          )  ;  End of markers\r\n           Incomplete\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Yellow)\r\n  (Dendrite)\r\n  (  273.70    23.33     0.80     1.38)  ; Root\r\n  (  273.70    23.33     0.80     1.38)  ; 1, R\r\n  (  276.57    25.19     0.80     1.38)  ; 2\r\n  (  277.82    27.88     0.80     1.38)  ; 3\r\n  (  278.77    29.89     0.80     1.38)  ; 4\r\n  (  281.58    29.95    -0.35     1.38)  ; 5\r\n  (  281.58    29.95    -0.37     1.38)  ; 6\r\n  (  282.42    28.35    -2.08     1.38)  ; 7\r\n\r\n  (Cross\r\n    (Color White)\r\n    (Name \"Marker 3\")\r\n    (  274.62    25.34     2.42     0.46)  ; 1\r\n  )  ;  End of markers\r\n  (\r\n    (  284.52    29.45    -3.25     0.92)  ; 1, R-1\r\n    (  286.31    29.87    -4.65     0.92)  ; 2\r\n    (  289.49    32.40    -6.00     0.92)  ; 3\r\n    (  292.74    32.57    -7.00     0.92)  ; 4\r\n    (  295.24    31.96    -7.63     0.92)  ; 5\r\n    (  299.26    32.91    -7.65     0.92)  ; 6\r\n    (  300.47    33.78    -6.02     0.92)  ; 7\r\n    (  302.25    34.20    -4.82     0.92)  ; 8\r\n    (  302.25    34.20    -4.85     0.92)  ; 9\r\n    (  304.67    35.96    -3.92     0.92)  ; 10\r\n    (  308.10    37.36    -2.90     0.92)  ; 11\r\n    (  308.10    37.36    -2.92     0.92)  ; 12\r\n    (  309.77    38.35    -2.27     0.92)  ; 13\r\n    (  312.76    39.64    -1.22     0.92)  ; 14\r\n    (  316.01    39.81    -3.25     0.92)  ; 15\r\n    (  316.01    39.81    -3.27     0.92)  ; 16\r\n    (  316.25    42.85    -4.13     0.92)  ; 17\r\n    (  316.25    42.85    -4.15     0.92)  ; 18\r\n    (  318.66    44.61    -5.15     0.92)  ; 19\r\n    (  319.99    44.92    -4.97     0.92)  ; 20\r\n    (  322.28    47.26    -5.10     0.92)  ; 21\r\n    (  323.35    48.70    -6.20     0.92)  ; 22\r\n    (  324.75    50.82    -7.72     0.92)  ; 23\r\n    (  329.21    51.86    -8.32     0.92)  ; 24\r\n    (  332.38    54.40    -9.22     0.92)  ; 25\r\n    (  334.98    57.39    -9.77     0.92)  ; 26\r\n    (  336.81    59.62   -10.25     0.92)  ; 27\r\n    (  336.81    59.62   -10.28     0.92)  ; 28\r\n    (  339.10    61.94   -11.55     0.92)  ; 29\r\n    (  339.10    61.94   -11.58     0.92)  ; 30\r\n    (  340.93    64.17   -13.45     0.92)  ; 31\r\n    (  340.93    64.17   -13.47     0.92)  ; 32\r\n    (  341.11    65.40   -15.02     0.92)  ; 33\r\n    (  341.47    67.87   -16.35     0.92)  ; 34\r\n    (  341.47    67.87   -16.38     0.92)  ; 35\r\n    (  342.16    71.02   -16.00     0.92)  ; 36\r\n    (  342.67    74.73   -13.85     0.92)  ; 37\r\n    (  342.67    74.73   -13.87     0.92)  ; 38\r\n    (  343.35    77.87   -12.72     0.92)  ; 39\r\n    (  345.94    80.87   -11.98     0.92)  ; 40\r\n    (  348.10    83.76   -13.00     0.92)  ; 41\r\n    (  350.50    85.52   -14.13     0.92)  ; 42\r\n    (  353.67    88.06   -14.75     0.92)  ; 43\r\n    (  356.42    90.49   -15.73     0.92)  ; 44\r\n    (  359.01    93.49   -16.38     0.92)  ; 45\r\n    (  361.29    95.80   -15.05     0.46)  ; 46\r\n    (  363.88    98.81   -13.32     0.46)  ; 47\r\n    (  363.88    98.81   -13.35     0.46)  ; 48\r\n    (  365.00   102.06   -11.32     0.46)  ; 49\r\n    (  365.00   102.06   -11.30     0.46)  ; 50\r\n    (  365.63   103.40    -9.67     0.46)  ; 51\r\n    (  365.87   106.44    -9.67     0.46)  ; 52\r\n    (  366.19   107.12    -8.25     0.46)  ; 53\r\n    (  367.70   108.66    -7.30     0.46)  ; 54\r\n    (  367.70   108.66    -7.32     0.46)  ; 55\r\n    (  368.46   109.43    -5.50     0.46)  ; 56\r\n    (  369.99   110.99    -3.70     0.46)  ; 57\r\n    (  369.99   110.99    -3.72     0.46)  ; 58\r\n    (  371.05   112.44    -1.60     0.46)  ; 59\r\n    (  371.05   112.44    -1.63     0.46)  ; 60\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  285.92    31.56    -4.65     0.92)  ; 1\r\n      (  297.52    34.28    -7.65     0.92)  ; 2\r\n      (  314.05    38.16    -1.22     0.92)  ; 3\r\n      (  313.21    45.72    -0.12     0.92)  ; 4\r\n      (  323.56    45.76    -5.05     0.92)  ; 5\r\n      (  322.69    51.53    -7.75     0.92)  ; 6\r\n      (  331.28    57.13   -10.00     0.92)  ; 7\r\n      (  334.45    59.66    -9.15     0.92)  ; 8\r\n      (  334.67    62.70   -10.95     0.92)  ; 9\r\n      (  344.20    70.30   -16.02     0.92)  ; 10\r\n      (  341.56    77.45   -11.98     0.92)  ; 11\r\n      (  346.21    79.74   -11.98     0.92)  ; 12\r\n      (  349.54    87.68   -14.75     0.92)  ; 13\r\n      (  355.47    88.48   -14.90     0.92)  ; 14\r\n      (  358.33    90.34   -15.05     0.92)  ; 15\r\n      (  355.57    92.09   -16.38     0.92)  ; 16\r\n      (  366.32   112.52    -5.65     0.46)  ; 17\r\n      (  363.90   104.78    -9.67     0.46)  ; 18\r\n    )  ;  End of markers\r\n     Normal\r\n  |\r\n    (  284.38    30.01    -0.90     0.92)  ; 1, R-2\r\n    (  283.99    31.71    -0.25     0.92)  ; 2\r\n    (  286.22    32.23     0.17     0.92)  ; 3\r\n    (  288.01    32.65     0.17     0.92)  ; 4\r\n    (  290.24    33.18     0.45     0.92)  ; 5\r\n    (  291.76    34.73     1.38     0.92)  ; 6\r\n    (  294.13    34.68     2.63     0.92)  ; 7\r\n    (  295.47    35.00     2.38     0.92)  ; 8\r\n    (  297.65    33.72     3.08     0.92)  ; 9\r\n    (  300.20    34.92     3.08     0.92)  ; 10\r\n    (  302.49    37.23     3.08     0.92)  ; 11\r\n    (  304.58    38.33     3.82     0.92)  ; 12\r\n    (  304.58    38.33     3.80     0.92)  ; 13\r\n    (  306.81    38.85     5.22     0.92)  ; 14\r\n    (  309.32    38.24     6.80     0.92)  ; 15\r\n    (  309.32    38.24     6.78     0.92)  ; 16\r\n    (  310.52    39.11     7.57     0.92)  ; 17\r\n    (  312.04    40.68     7.55     0.92)  ; 18\r\n    (  314.72    41.30     9.82     0.92)  ; 19\r\n    (  316.51    41.72    10.07     0.46)  ; 20\r\n    (  316.37    42.29    10.07     0.46)  ; 21\r\n    (  317.72    42.60    10.82     0.46)  ; 22\r\n    (  317.27    42.49    10.82     0.46)  ; 23\r\n    (  321.86    42.98    11.53     0.46)  ; 24\r\n    (  325.00    43.71    12.10     0.46)  ; 25\r\n    (  327.72    46.14    12.32     0.46)  ; 26\r\n    (  328.08    48.62    13.52     0.46)  ; 27\r\n    (  328.08    48.62    13.47     0.46)  ; 28\r\n    (  330.36    50.94    13.60     0.46)  ; 29\r\n    (  332.74    50.90    14.30     0.46)  ; 30\r\n    (  335.01    53.21    15.55     0.46)  ; 31\r\n    (  334.88    53.79    15.55     0.46)  ; 32\r\n    (  337.19    51.94    16.90     0.46)  ; 33\r\n    (  340.59    51.55    18.45     0.46)  ; 34\r\n    (  340.59    51.55    18.40     0.46)  ; 35\r\n    (  344.12    50.57    20.65     0.46)  ; 36\r\n    (  343.99    51.14    20.65     0.46)  ; 37\r\n    (  347.50    50.18    22.50     0.46)  ; 38\r\n    (  347.06    50.08    22.50     0.46)  ; 39\r\n    (  350.27    48.44    22.05     0.46)  ; 40\r\n    (  353.89    51.08    21.42     0.46)  ; 41\r\n    (  353.89    51.08    21.40     0.46)  ; 42\r\n    (  356.66    49.33    22.50     0.46)  ; 43\r\n    (  356.66    49.33    22.47     0.46)  ; 44\r\n    (  358.00    49.65    20.33     0.46)  ; 45\r\n    (  358.00    49.65    20.30     0.46)  ; 46\r\n    (  359.51    51.20    17.38     0.46)  ; 47\r\n    (  359.07    51.10    17.30     0.46)  ; 48\r\n    (  363.53    52.15    15.82     0.46)  ; 49\r\n    (  366.08    53.34    14.48     0.46)  ; 50\r\n    (  366.08    53.34    14.45     0.46)  ; 51\r\n    (  366.97    53.55    12.20     0.46)  ; 52\r\n    (  366.97    53.55    12.15     0.46)  ; 53\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  297.70    35.52     2.63     0.92)  ; 1\r\n      (  300.42    37.96     5.50     0.92)  ; 2\r\n      (  304.85    37.19     3.65     0.92)  ; 3\r\n      (  307.52    37.82     6.80     0.92)  ; 4\r\n      (  315.51    37.90     6.40     0.92)  ; 5\r\n      (  313.78    39.28     9.95     0.92)  ; 6\r\n      (  316.43    44.09    10.75     0.92)  ; 7\r\n      (  322.68    45.55    10.77     0.92)  ; 8\r\n      (  349.61    51.27    22.73     0.46)  ; 9\r\n      (  334.30    54.26    15.20     0.46)  ; 10\r\n      (  359.29    48.17    18.30     0.46)  ; 11\r\n      (  354.82    47.11    23.70     0.46)  ; 12\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Green)\r\n  (Dendrite)\r\n  (  273.97    18.01     5.45     1.38)  ; Root\r\n  (  273.97    18.01     5.45     1.38)  ; 1, R\r\n  (  276.34    17.97     5.45     1.38)  ; 2\r\n  (  279.28    17.46     5.45     1.38)  ; 3\r\n  (  280.57    15.97     5.80     1.38)  ; 4\r\n  (  281.47    16.18     7.38     1.38)  ; 5\r\n  (  282.10    17.52     9.35     1.38)  ; 6\r\n  (\r\n    (  284.95    19.39     8.75     1.38)  ; 1, R-1\r\n    (  287.94    20.70     8.35     1.38)  ; 2\r\n    (  287.94    20.70     8.32     1.38)  ; 3\r\n    (  289.92    22.35     7.70     1.38)  ; 4\r\n    (\r\n      (  292.60    22.97     7.57     0.92)  ; 1, R-1-1\r\n      (  295.09    22.37     6.73     0.92)  ; 2\r\n      (  296.97    20.41     6.15     0.92)  ; 3\r\n      (  296.97    20.41     6.13     0.92)  ; 4\r\n      (  298.22    17.12     4.50     0.92)  ; 5\r\n      (  300.71    16.51     3.27     0.92)  ; 6\r\n      (  300.71    16.51     3.25     0.92)  ; 7\r\n      (  302.58    14.55     1.95     0.92)  ; 8\r\n      (  302.58    14.55     1.92     0.92)  ; 9\r\n      (  304.89    12.72     1.35     0.92)  ; 10\r\n      (  306.76    10.77     0.90     0.92)  ; 11\r\n      (  306.76    10.77     0.88     0.92)  ; 12\r\n      (  308.20     8.71    -0.05     0.92)  ; 13\r\n      (  308.86     5.88     0.60     0.92)  ; 14\r\n      (  308.86     5.88     0.52     0.92)  ; 15\r\n      (  311.49     4.71    -0.85     0.92)  ; 16\r\n      (  311.49     4.71    -0.88     0.92)  ; 17\r\n      (  314.69     3.07    -1.70     0.92)  ; 18\r\n      (  318.35     1.54    -1.58     0.92)  ; 19\r\n      (  318.35     1.54    -1.60     0.92)  ; 20\r\n      (  321.44     0.47     0.93     0.92)  ; 21\r\n      (  324.64    -1.16     1.70     0.92)  ; 22\r\n      (  327.27    -2.34     2.40     0.92)  ; 23\r\n      (  330.79    -3.30     2.55     0.92)  ; 24\r\n      (  333.87    -4.38     1.70     0.92)  ; 25\r\n      (  333.87    -4.38     1.67     0.92)  ; 26\r\n      (  335.24    -8.24     1.77     0.92)  ; 27\r\n      (  337.38   -11.32     1.77     0.92)  ; 28\r\n      (  341.57   -15.12     1.77     0.92)  ; 29\r\n      (  342.87   -16.60     2.57     0.92)  ; 30\r\n      (  344.55   -19.79     3.57     0.92)  ; 31\r\n      (  344.55   -19.79     3.55     0.92)  ; 32\r\n      (  346.24   -22.98     3.65     0.92)  ; 33\r\n      (  348.38   -26.07     4.50     0.92)  ; 34\r\n      (  351.08   -29.59     5.65     0.92)  ; 35\r\n      (  353.23   -32.69     7.77     0.92)  ; 36\r\n      (  353.23   -32.69     7.75     0.92)  ; 37\r\n      (  353.57   -36.18     9.15     0.92)  ; 38\r\n      (  353.57   -36.18     9.10     0.92)  ; 39\r\n      (  356.30   -39.72    10.15     0.92)  ; 40\r\n      (  359.32   -42.60    10.95     0.92)  ; 41\r\n      (  360.43   -45.33    12.45     0.92)  ; 42\r\n      (  360.43   -45.33    12.35     0.92)  ; 43\r\n      (  361.10   -48.15    14.05     0.92)  ; 44\r\n      (  361.10   -48.15    14.02     0.92)  ; 45\r\n      (  361.13   -52.33    12.95     0.92)  ; 46\r\n      (  362.51   -56.18    14.38     0.92)  ; 47\r\n      (  364.73   -61.64    15.20     0.92)  ; 48\r\n      (  367.62   -63.94    12.70     0.92)  ; 49\r\n      (  367.17   -64.05    12.70     0.92)  ; 50\r\n      (  370.83   -65.59    11.53     0.92)  ; 51\r\n      (  370.83   -65.59    11.50     0.92)  ; 52\r\n      (  373.77   -66.08    10.20     0.92)  ; 53\r\n      (  373.77   -66.08    10.17     0.92)  ; 54\r\n      (  375.83   -66.81     7.85     0.92)  ; 55\r\n      (  375.83   -66.81     7.70     0.92)  ; 56\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  295.04    20.56     6.13     0.92)  ; 1\r\n        (  298.49    21.96     3.85     0.92)  ; 2\r\n        (  299.28    18.57     4.35     0.92)  ; 3\r\n        (  297.05    18.05     6.38     0.92)  ; 4\r\n        (  297.58    15.78     5.57     0.92)  ; 5\r\n        (  299.19    14.96     5.57     0.92)  ; 6\r\n        (  299.64    15.07     1.92     0.92)  ; 7\r\n        (  314.51     1.83    -1.65     0.92)  ; 8\r\n        (  318.75    -0.17     0.17     0.92)  ; 9\r\n        (  320.36    -0.98     0.67     0.92)  ; 10\r\n        (  323.66     0.99     2.38     0.92)  ; 11\r\n        (  320.72     1.50     1.75     0.92)  ; 12\r\n        (  328.22    -0.32     3.08     0.92)  ; 13\r\n        (  332.77    -1.65     0.85     0.92)  ; 14\r\n        (  345.23   -16.64     3.55     0.92)  ; 15\r\n        (  346.07   -18.24     4.60     0.92)  ; 16\r\n        (  347.18   -20.97     5.07     0.92)  ; 17\r\n        (  347.13   -22.77     5.05     0.92)  ; 18\r\n        (  349.76   -23.94     5.70     0.92)  ; 19\r\n        (  346.32   -25.34     3.08     0.92)  ; 20\r\n        (  349.30   -30.01     5.97     0.92)  ; 21\r\n        (  349.30   -30.01     5.65     0.92)  ; 22\r\n        (  358.19   -51.82    12.95     0.92)  ; 23\r\n        (  371.36   -67.85    11.80     0.92)  ; 24\r\n      )  ;  End of markers\r\n       Normal\r\n    |\r\n      (  289.26    25.18     6.70     0.92)  ; 1, R-1-2\r\n      (  291.04    25.60     7.15     0.92)  ; 2\r\n      (  292.43    27.71     7.15     0.92)  ; 3\r\n      (  293.95    29.26     8.75     0.92)  ; 4\r\n      (  293.87    31.63    10.35     0.92)  ; 5\r\n      (  296.23    31.59    11.40     0.92)  ; 6\r\n      (  299.05    31.65    12.35     0.92)  ; 7\r\n      (  300.11    33.09    13.57     0.92)  ; 8\r\n      (  299.58    35.35    16.63     0.92)  ; 9\r\n      (  299.95    37.84    18.05     0.92)  ; 10\r\n      (  299.69    38.96    20.23     0.92)  ; 11\r\n      (  300.59    39.17    23.23     0.92)  ; 12\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  288.10    26.09     8.00     0.92)  ; 1\r\n        (  290.33    26.62     5.82     0.92)  ; 2\r\n        (  292.48    29.51     9.50     0.92)  ; 3\r\n        (  295.83    33.28    12.38     0.92)  ; 4\r\n        (  301.38    35.78    18.05     0.92)  ; 5\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  282.99    17.73    10.48     1.38)  ; 1, R-2\r\n    (  285.48    17.13    12.17     1.38)  ; 2\r\n    (  286.46    14.96    13.57     0.92)  ; 3\r\n    (  285.48    11.15    14.67     0.92)  ; 4\r\n    (  285.88     9.45    16.02     0.92)  ; 5\r\n    (  285.51     6.99    17.72     0.92)  ; 6\r\n    (  286.36     5.39    18.70     0.92)  ; 7\r\n    (  286.49     4.82    18.70     0.92)  ; 8\r\n    (  287.78     3.34    20.00     0.92)  ; 9\r\n    (  290.86     2.26    20.83     0.92)  ; 10\r\n    (  294.20     0.06    21.67     0.92)  ; 11\r\n    (  296.51    -1.79    22.12     0.92)  ; 12\r\n    (  298.25    -3.17    22.97     0.92)  ; 13\r\n    (  298.92    -6.00    23.52     0.92)  ; 14\r\n    (  300.03    -8.73    24.58     0.92)  ; 15\r\n    (  302.79   -10.47    25.70     0.92)  ; 16\r\n    (  306.01   -12.11    26.25     0.92)  ; 17\r\n    (  305.40   -17.61    26.95     0.92)  ; 18\r\n    (  305.89   -21.68    27.22     0.92)  ; 19\r\n    (  306.55   -24.51    28.42     0.92)  ; 20\r\n    (  306.05   -26.42    29.48     0.92)  ; 21\r\n    (  307.61   -29.05    30.60     0.92)  ; 22\r\n    (  311.59   -29.90    31.48     0.92)  ; 23\r\n    (  314.98   -30.31    31.92     0.92)  ; 24\r\n    (  316.76   -29.89    31.92     0.92)  ; 25\r\n    (  318.55   -29.47    33.80     0.92)  ; 26\r\n    (  319.32   -28.70    35.38     0.92)  ; 27\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  281.12    19.69    11.05     1.38)  ; 1\r\n      (  284.04     7.23    18.70     0.92)  ; 2\r\n      (  286.67     6.05    20.00     0.92)  ; 3\r\n      (  290.81     0.46    21.67     0.92)  ; 4\r\n      (  294.12     2.43    21.67     0.92)  ; 5\r\n      (  295.58    -3.80    22.15     0.92)  ; 6\r\n      (  303.13   -13.97    25.15     0.92)  ; 7\r\n      (  307.77   -17.65    27.27     0.92)  ; 8\r\n      (  304.77   -24.93    27.22     0.92)  ; 9\r\n      (  310.20   -32.02    31.92     0.92)  ; 10\r\n      (  311.51   -27.53    31.92     0.92)  ; 11\r\n    )  ;  End of markers\r\n     High\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color RGB (255, 255, 128))\r\n  (Dendrite)\r\n  (  268.25     8.31     8.88     1.38)  ; Root\r\n  (  268.25     8.31     8.88     1.38)  ; 1, R\r\n  (  270.24     5.79     8.90     1.38)  ; 2\r\n  (  271.09     4.20     9.43     1.38)  ; 3\r\n  (  270.28     1.62     9.43     1.38)  ; 4\r\n  (  268.30    -0.03    10.75     1.38)  ; 5\r\n  (  267.36    -2.04    12.83     1.38)  ; 6\r\n\r\n  (Cross\r\n    (Color RGB (255, 255, 128))\r\n    (Name \"Marker 3\")\r\n    (  272.51     2.14     9.43     1.38)  ; 1\r\n  )  ;  End of markers\r\n  (\r\n    (  269.06    -1.19    12.83     0.92)  ; 1, R-1\r\n    (  271.87    -1.13    12.83     0.92)  ; 2\r\n    (  273.62    -2.51    14.30     0.92)  ; 3\r\n    (  273.49    -1.94    14.27     0.92)  ; 4\r\n    (  275.40    -2.09    16.67     0.92)  ; 5\r\n    (  277.00    -2.90    18.35     0.92)  ; 6\r\n    (\r\n      (  276.95    -4.70    19.63     0.92)  ; 1, R-1-1\r\n      (  278.25    -6.21    21.98     0.92)  ; 2\r\n      (  278.25    -6.21    21.95     0.92)  ; 3\r\n      (  278.88    -4.86    24.20     0.92)  ; 4\r\n      (  279.31    -4.76    24.20     0.92)  ; 5\r\n      (  275.21    -3.33    25.10     0.92)  ; 6\r\n      (  275.26    -1.53    25.73     0.92)  ; 7\r\n      (  276.34    -0.07    27.30     0.92)  ; 8\r\n      (  275.31     0.28    28.90     0.92)  ; 9\r\n      (  274.15     1.20    31.17     0.92)  ; 10\r\n      (  273.14     1.56    33.42     0.92)  ; 11\r\n      (  273.14     1.56    33.50     0.92)  ; 12\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  278.39    -0.80    25.23     0.92)  ; 1\r\n      )  ;  End of markers\r\n       High\r\n    |\r\n      (  279.63    -4.08    19.17     0.92)  ; 1, R-1-2\r\n      (  281.05    -6.14    20.35     0.92)  ; 2\r\n      (  283.79    -3.71    20.35     0.92)  ; 3\r\n      (  286.59    -3.65    21.85     0.92)  ; 4\r\n      (  289.23    -4.82    23.05     0.92)  ; 5\r\n      (  292.31    -5.88    23.05     0.92)  ; 6\r\n      (  295.70    -6.29    23.57     0.92)  ; 7\r\n      (  295.70    -6.29    23.55     0.92)  ; 8\r\n      (  298.02    -8.13    23.42     0.92)  ; 9\r\n      (  302.56    -9.46    23.80     0.92)  ; 10\r\n      (  306.40    -9.75    24.62     0.92)  ; 11\r\n      (  310.82   -10.51    24.62     0.92)  ; 12\r\n      (  312.25   -12.56    25.40     0.92)  ; 13\r\n      (  314.12   -14.52    25.52     0.92)  ; 14\r\n      (  318.80   -16.40    26.77     0.92)  ; 15\r\n      (  322.45   -17.94    26.80     0.92)  ; 16\r\n      (  326.11   -19.47    25.35     0.92)  ; 17\r\n      (  330.44   -17.85    25.35     0.92)  ; 18\r\n      (  334.01   -17.01    23.70     0.92)  ; 19\r\n      (  334.01   -17.01    23.67     0.92)  ; 20\r\n      (  336.13   -15.93    21.92     0.92)  ; 21\r\n      (  339.51   -16.33    21.48     0.92)  ; 22\r\n      (  342.68   -13.79    21.20     0.92)  ; 23\r\n      (  346.08   -14.18    21.58     0.92)  ; 24\r\n      (  349.91   -14.48    21.58     0.92)  ; 25\r\n      (  351.70   -14.07    19.63     0.92)  ; 26\r\n      (  353.49   -13.65    17.27     0.92)  ; 27\r\n      (  356.25   -15.39    15.20     0.92)  ; 28\r\n      (  356.25   -15.39    15.17     0.92)  ; 29\r\n      (  357.59   -15.07    12.88     0.92)  ; 30\r\n      (  360.23   -16.25    11.65     0.92)  ; 31\r\n      (  363.30   -17.31    10.97     0.92)  ; 32\r\n      (  367.48   -21.11    13.38     0.92)  ; 33\r\n      (  370.51   -23.98    12.98     0.92)  ; 34\r\n      (  373.10   -26.96    12.98     0.92)  ; 35\r\n      (  372.97   -26.40    12.98     0.92)  ; 36\r\n      (  374.97   -28.91    12.95     0.92)  ; 37\r\n      (  374.97   -28.91    12.93     0.92)  ; 38\r\n      (  377.33   -28.96    12.88     0.46)  ; 39\r\n      (  378.67   -28.64    10.40     0.46)  ; 40\r\n\r\n      (Cross\r\n        (Color RGB (255, 255, 128))\r\n        (Name \"Marker 3\")\r\n        (  280.13    -2.18    19.17     0.92)  ; 1\r\n        (  280.29    -6.91    20.17     0.92)  ; 2\r\n        (  285.92    -6.79    20.38     0.92)  ; 3\r\n        (  292.26    -7.69    20.92     0.92)  ; 4\r\n        (  296.94    -9.58    23.80     0.92)  ; 5\r\n        (  306.80   -11.46    24.62     0.92)  ; 6\r\n      )  ;  End of markers\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  316.21   -13.43    26.80     0.92)  ; 1\r\n        (  321.22   -14.64    26.80     0.92)  ; 2\r\n        (  321.70   -18.70    26.80     0.92)  ; 3\r\n        (  322.95   -16.03    29.08     0.92)  ; 4\r\n        (  326.79   -16.33    28.10     0.92)  ; 5\r\n        (  323.43   -20.09    24.10     0.92)  ; 6\r\n        (  332.63   -19.14    24.08     0.92)  ; 7\r\n        (  331.29   -19.45    21.38     0.92)  ; 8\r\n        (  330.49   -16.05    25.38     0.92)  ; 9\r\n        (  340.94   -12.41    21.58     0.92)  ; 10\r\n        (  348.80   -11.76    21.58     0.92)  ; 11\r\n        (  359.59   -17.59    10.97     0.92)  ; 12\r\n        (  364.54   -20.61    11.25     0.92)  ; 13\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  266.82    -5.76    12.00     1.38)  ; 1, R-2\r\n    (  267.62    -9.15    11.32     1.38)  ; 2\r\n    (  267.62    -9.15    11.30     1.38)  ; 3\r\n    (  269.80   -10.44    11.13     1.38)  ; 4\r\n    (  269.80   -10.44    11.10     1.38)  ; 5\r\n    (  272.25   -12.85    10.02     1.38)  ; 6\r\n    (  272.25   -12.85    10.00     1.38)  ; 7\r\n    (  273.99   -14.24     8.30     1.38)  ; 8\r\n    (  275.02   -14.59     6.60     1.38)  ; 9\r\n    (\r\n      (  275.91   -14.40     4.90     0.92)  ; 1, R-2-1\r\n      (  276.62   -15.42     3.25     0.92)  ; 2\r\n      (  276.62   -15.42     3.22     0.92)  ; 3\r\n      (  277.28   -18.26     1.53     0.92)  ; 4\r\n      (  279.92   -19.43     1.53     0.92)  ; 5\r\n      (  282.29   -19.48     1.53     0.92)  ; 6\r\n      (  283.18   -19.27     1.53     0.92)  ; 7\r\n      (  283.05   -18.69     1.53     0.92)  ; 8\r\n      (  283.93   -18.49     0.15     0.92)  ; 9\r\n      (  284.69   -17.72    -1.53     0.92)  ; 10\r\n      (  286.66   -16.06    -2.88     0.92)  ; 11\r\n      (  289.12   -18.47    -3.47     0.92)  ; 12\r\n      (  291.30   -19.75    -3.65     0.92)  ; 13\r\n      (  291.30   -19.75    -3.67     0.92)  ; 14\r\n      (  293.43   -22.84    -4.47     0.92)  ; 15\r\n      (  295.31   -24.79    -5.37     0.92)  ; 16\r\n      (  296.47   -25.71    -5.27     0.92)  ; 17\r\n      (  298.64   -26.99    -3.45     0.92)  ; 18\r\n\r\n      (Cross\r\n        (Color RGB (255, 255, 128))\r\n        (Name \"Marker 3\")\r\n        (  278.52   -21.56     1.53     0.92)  ; 1\r\n        (  276.40   -18.46     1.53     0.92)  ; 2\r\n        (  278.23   -16.25     1.53     0.92)  ; 3\r\n        (  295.84   -27.05    -2.38     0.92)  ; 4\r\n        (  295.80   -22.87    -2.38     0.92)  ; 5\r\n        (  291.66   -17.28    -1.40     0.92)  ; 6\r\n        (  293.66   -19.79    -1.53     0.92)  ; 7\r\n        (  294.68   -26.13    -6.13     0.92)  ; 8\r\n        (  298.01   -28.33    -5.13     0.92)  ; 9\r\n      )  ;  End of markers\r\n      (\r\n        (  300.56   -27.13    -5.13     0.92)  ; 1, R-2-1-1\r\n        (  302.49   -27.29    -7.07     0.92)  ; 2\r\n        (  302.49   -27.29    -7.10     0.92)  ; 3\r\n        (  306.90   -28.04    -8.57     0.92)  ; 4\r\n        (  309.40   -28.65    -8.90     0.92)  ; 5\r\n        (  310.88   -28.90   -10.70     0.92)  ; 6\r\n        (  312.04   -29.82   -13.55     0.92)  ; 7\r\n        (  312.04   -29.82   -13.57     0.92)  ; 8\r\n        (  314.66   -30.99   -16.67     0.92)  ; 9\r\n        (  314.66   -30.99   -16.70     0.92)  ; 10\r\n        (  316.90   -30.48   -19.23     0.92)  ; 11\r\n        (  316.90   -30.48   -19.27     0.92)  ; 12\r\n        (  320.68   -32.57   -20.65     0.92)  ; 13\r\n        (  323.32   -33.74   -22.97     0.92)  ; 14\r\n        (  323.32   -33.74   -23.02     0.92)  ; 15\r\n        (  324.03   -34.77   -26.30     0.92)  ; 16\r\n        (  324.03   -34.77   -26.32     0.92)  ; 17\r\n        (  323.27   -35.55   -30.42     0.46)  ; 18\r\n        (  323.40   -36.12   -30.45     0.46)  ; 19\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  310.93   -27.10    -7.97     0.92)  ; 1\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  300.83   -28.27    -3.95     0.46)  ; 1, R-2-1-2\r\n        (  304.36   -29.23    -2.73     0.46)  ; 2\r\n        (  306.94   -32.21    -1.77     0.46)  ; 3\r\n        (  308.99   -32.92    -1.17     0.46)  ; 4\r\n        (  312.07   -34.00     2.78     0.46)  ; 5\r\n        (  313.80   -35.38     2.78     0.46)  ; 6\r\n        (  315.86   -36.09     2.78     0.46)  ; 7\r\n        (  318.09   -35.57     2.22     0.46)  ; 8\r\n        (  318.09   -35.57     2.20     0.46)  ; 9\r\n        (  322.11   -34.62     2.60     0.46)  ; 10\r\n        (  324.03   -34.77     2.90     0.46)  ; 11\r\n        (  324.73   -35.80     4.32     0.46)  ; 12\r\n        (  326.22   -36.05     6.22     0.46)  ; 13\r\n        (  329.03   -35.99     8.15     0.46)  ; 14\r\n        (  329.03   -35.99     8.13     0.46)  ; 15\r\n        (  331.13   -34.91     4.17     0.46)  ; 16\r\n        (  335.15   -33.96     2.67     0.46)  ; 17\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  306.62   -32.88    -1.17     0.46)  ; 1\r\n          (  308.15   -31.33    -1.17     0.46)  ; 2\r\n          (  312.70   -32.64     1.40     0.46)  ; 3\r\n          (  309.97   -35.09     2.60     0.46)  ; 4\r\n          (  315.23   -37.43     2.78     0.46)  ; 5\r\n          (  320.60   -36.18     2.90     0.46)  ; 6\r\n          (  318.28   -34.33     2.90     0.46)  ; 7\r\n          (  337.25   -32.87     2.50     0.46)  ; 8\r\n          (  330.23   -35.12     2.50     0.46)  ; 9\r\n          (  324.83   -38.17     7.65     0.46)  ; 10\r\n          (  331.66   -37.16     7.65     0.46)  ; 11\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  274.79   -17.63     6.60     0.92)  ; 1, R-2-2\r\n      (  273.79   -21.44     7.30     0.92)  ; 2\r\n      (  274.14   -24.95     9.05     0.92)  ; 3\r\n      (  274.23   -27.31    10.52     0.92)  ; 4\r\n      (  274.23   -27.31    10.50     0.92)  ; 5\r\n      (  275.97   -28.69    11.68     0.92)  ; 6\r\n      (  277.84   -30.65    12.57     0.92)  ; 7\r\n      (  279.26   -32.70    13.75     0.92)  ; 8\r\n      (  280.11   -34.30    13.95     0.92)  ; 9\r\n      (  280.95   -35.89    14.97     0.92)  ; 10\r\n      (  283.27   -37.74    14.97     0.92)  ; 11\r\n      (  282.91   -40.20    14.67     0.92)  ; 12\r\n      (  283.88   -42.37    15.55     0.92)  ; 13\r\n      (  285.44   -44.98    16.10     0.92)  ; 14\r\n      (  289.22   -47.08    16.10     0.92)  ; 15\r\n      (  291.15   -47.23    16.73     0.92)  ; 16\r\n      (  293.99   -51.34    16.73     0.92)  ; 17\r\n\r\n      (Cross\r\n        (Color RGB (255, 255, 128))\r\n        (Name \"Marker 3\")\r\n        (  275.02   -30.71    10.50     0.92)  ; 1\r\n        (  276.76   -32.09    12.22     0.92)  ; 2\r\n        (  280.33   -37.22    14.97     0.92)  ; 3\r\n        (  282.20   -39.18    14.82     0.92)  ; 4\r\n        (  282.60   -40.87    14.82     0.92)  ; 5\r\n        (  281.65   -42.90    16.90     0.92)  ; 6\r\n        (  285.72   -40.14    17.13     0.92)  ; 7\r\n        (  289.62   -48.79    16.73     0.92)  ; 8\r\n        (  294.09   -47.75    18.35     0.92)  ; 9\r\n        (  292.53   -51.09    15.40     0.92)  ; 10\r\n        (  297.25   -51.19    15.48     0.92)  ; 11\r\n      )  ;  End of markers\r\n      (\r\n        (  293.05   -53.37    16.73     0.92)  ; 1, R-2-2-1\r\n        (  292.69   -55.86    17.45     0.92)  ; 2\r\n        (  293.05   -59.36    17.13     0.92)  ; 3\r\n        (  292.50   -63.06    20.13     0.92)  ; 4\r\n        (  293.35   -64.66    21.18     0.92)  ; 5\r\n        (  289.95   -64.25    22.25     0.92)  ; 6\r\n        (  287.76   -62.98    23.67     0.92)  ; 7\r\n        (  286.17   -62.16    25.35     0.92)  ; 8\r\n        (  286.03   -61.60    25.35     0.92)  ; 9\r\n        (  283.98   -60.88    26.55     0.46)  ; 10\r\n        (  282.94   -60.52    29.02     0.46)  ; 11\r\n        (  281.92   -60.17    31.38     0.46)  ; 12\r\n        (  281.34   -59.71    32.35     0.46)  ; 13\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  287.86   -59.37    17.13     0.92)  ; 1\r\n          (  294.65   -60.17    18.55     0.92)  ; 2\r\n          (  291.46   -62.71    19.95     0.92)  ; 3\r\n          (  293.84   -62.75    19.95     0.92)  ; 4\r\n          (  293.61   -65.79    20.13     0.92)  ; 5\r\n        )  ;  End of markers\r\n         High\r\n      |\r\n        (  296.62   -52.53    18.08     0.92)  ; 1, R-2-2-2\r\n        (  297.68   -57.06    17.70     0.92)  ; 2\r\n        (  298.93   -60.35    17.70     0.92)  ; 3\r\n        (  303.03   -61.77    17.70     0.92)  ; 4\r\n        (  304.01   -63.94    16.47     0.92)  ; 5\r\n        (  304.49   -68.00    16.47     0.92)  ; 6\r\n        (  305.92   -70.05    15.90     0.92)  ; 7\r\n        (  309.21   -74.07    15.22     0.92)  ; 8\r\n        (  309.21   -74.07    15.20     0.92)  ; 9\r\n        (  308.98   -77.10    14.65     0.92)  ; 10\r\n        (  312.09   -82.35    14.65     0.92)  ; 11\r\n        (  314.40   -84.19    12.07     0.92)  ; 12\r\n        (  317.31   -86.49    11.35     0.92)  ; 13\r\n        (  320.02   -90.04    12.22     0.92)  ; 14\r\n        (  321.98   -94.35    11.80     0.92)  ; 15\r\n        (  322.77   -97.75    11.80     0.92)  ; 16\r\n        (  324.78  -100.27    10.63     0.92)  ; 17\r\n        (  324.78  -100.27    10.60     0.92)  ; 18\r\n        (  325.57  -103.67     9.50     0.92)  ; 19\r\n        (  323.87  -106.45     8.05     0.92)  ; 20\r\n        (  323.87  -106.45     8.02     0.92)  ; 21\r\n        (  324.98  -109.19     6.73     0.92)  ; 22\r\n        (  326.94  -113.50     4.60     0.92)  ; 23\r\n        (  326.79  -118.91     4.60     0.92)  ; 24\r\n        (  327.72  -122.87     4.60     0.92)  ; 25\r\n        (  328.78  -127.40     3.42     0.92)  ; 26\r\n        (  330.34  -130.02     2.17     0.92)  ; 27\r\n        (  331.31  -132.18     0.82     0.92)  ; 28\r\n        (  333.14  -135.93     0.28     0.92)  ; 29\r\n        (  333.30  -140.67    -0.75     0.92)  ; 30\r\n        (  334.63  -146.33    -0.80     0.92)  ; 31\r\n        (  335.30  -149.18     0.57     0.92)  ; 32\r\n        (  333.81  -154.89     2.08     0.92)  ; 33\r\n        (  333.44  -157.38     1.63     0.46)  ; 34\r\n        (  333.44  -157.38     1.58     0.46)  ; 35\r\n        (  331.79  -158.36    -0.37     0.46)  ; 36\r\n        (  331.79  -158.36    -0.43     0.46)  ; 37\r\n\r\n        (Cross\r\n          (Color RGB (255, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  295.55   -53.97    17.70     0.92)  ; 1\r\n          (  296.22   -56.80    17.70     0.92)  ; 2\r\n          (  300.04   -63.06    17.70     0.92)  ; 3\r\n          (  301.52   -63.33    17.70     0.92)  ; 4\r\n          (  304.53   -72.18    15.20     0.92)  ; 5\r\n          (  306.42   -68.14    17.35     0.92)  ; 6\r\n          (  308.04   -79.12    14.65     0.92)  ; 7\r\n          (  312.06   -78.17    14.65     0.92)  ; 8\r\n          (  320.83   -87.46    11.17     0.92)  ; 9\r\n          (  317.73   -92.37    10.22     0.92)  ; 10\r\n          (  310.70   -84.46    10.22     0.92)  ; 11\r\n          (  309.73   -82.30    10.22     0.92)  ; 12\r\n          (  311.61   -78.28    10.22     0.92)  ; 13\r\n          (  321.48   -96.26    11.58     0.92)  ; 14\r\n          (  322.14   -99.10    11.58     0.92)  ; 15\r\n          (  322.44  -110.38     6.73     0.92)  ; 16\r\n          (  325.23  -116.28     5.85     0.92)  ; 17\r\n          (  328.85  -113.65     4.60     0.92)  ; 18\r\n          (  326.19  -108.29     4.60     0.92)  ; 19\r\n          (  329.77  -123.58     4.60     0.92)  ; 20\r\n          (  326.12  -122.06     4.60     0.92)  ; 21\r\n          (  326.28  -126.79     3.42     0.92)  ; 22\r\n          (  328.02  -128.17     3.42     0.92)  ; 23\r\n          (  332.28  -140.31    -1.58     0.92)  ; 24\r\n          (  332.39  -146.86    -0.80     0.92)  ; 25\r\n          (  335.83  -151.43     2.08     0.92)  ; 26\r\n          (  332.46  -155.21     2.08     0.92)  ; 27\r\n          (  330.67  -161.60     0.82     0.92)  ; 28\r\n          (  336.63  -148.86     2.22     0.92)  ; 29\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color RGB (128, 255, 128))\r\n  (Dendrite)\r\n  (  267.79    12.25   -16.13     0.92)  ; Root\r\n  (  267.79    12.25   -16.13     0.92)  ; 1, R\r\n  (  269.84    11.54   -16.13     0.92)  ; 2\r\n  (  270.38     9.27   -16.13     0.92)  ; 3\r\n  (  269.43     7.26   -16.13     0.92)  ; 4\r\n  (  269.30     7.83   -16.13     0.92)  ; 5\r\n  (  268.10     6.96   -18.42     0.92)  ; 6\r\n  (  268.50     5.25   -19.55     0.92)  ; 7\r\n  (  272.34     4.96   -19.55     0.92)  ; 8\r\n  (  274.12     5.38   -21.48     0.92)  ; 9\r\n  (  276.04     5.23   -21.48     0.92)  ; 10\r\n  (  276.31     4.09   -23.10     0.92)  ; 11\r\n  (  276.17     4.66   -23.10     0.92)  ; 12\r\n  (  274.65     3.11   -24.02     0.92)  ; 13\r\n  (  274.65     3.11   -24.05     0.92)  ; 14\r\n  (  274.70     4.91   -25.87     0.92)  ; 15\r\n  (  275.51     7.49   -27.42     0.92)  ; 16\r\n  (  276.75     4.20   -29.22     0.92)  ; 17\r\n  (  278.35     3.38   -30.77     0.92)  ; 18\r\n  (  278.93     2.92   -32.25     0.92)  ; 19\r\n  (  280.01     4.37   -34.17     0.92)  ; 20\r\n  (  279.49     6.63   -36.10     0.92)  ; 21\r\n  (  281.85     6.59   -37.52     0.92)  ; 22\r\n  (  284.02     5.31   -38.88     0.92)  ; 23\r\n  (  286.53     4.69   -39.32     0.92)  ; 24\r\n  (  287.69     3.78   -40.70     0.92)  ; 25\r\n  (  288.89     4.65   -41.22     0.92)  ; 26\r\n  (  288.89     4.65   -41.25     0.92)  ; 27\r\n  (  291.66     2.92   -43.25     0.92)  ; 28\r\n  (  292.95     1.42   -44.95     0.92)  ; 29\r\n  (  294.10     0.51   -47.05     0.92)  ; 30\r\n  (  297.49     0.11   -48.38     0.92)  ; 31\r\n  (  298.79    -1.39   -50.92     0.92)  ; 32\r\n  (  301.15    -1.43   -51.47     0.92)  ; 33\r\n  (  302.26    -4.15   -53.42     0.92)  ; 34\r\n  (  304.32    -4.87   -56.03     0.92)  ; 35\r\n  (  306.23    -5.02   -58.55     0.92)  ; 36\r\n  (  306.23    -5.02   -58.58     0.92)  ; 37\r\n  (  308.43    -6.29   -60.75     0.92)  ; 38\r\n  (  308.43    -6.29   -60.77     0.92)  ; 39\r\n  (  310.34    -6.45   -63.45     0.92)  ; 40\r\n  (  312.26    -6.59   -64.78     0.92)  ; 41\r\n  (  314.62    -6.64   -66.77     0.92)  ; 42\r\n  (  317.84    -8.27   -68.38     0.92)  ; 43\r\n  (  320.82    -6.96   -70.25     0.92)  ; 44\r\n  (  324.27    -5.56   -71.17     0.46)  ; 45\r\n  (  328.42    -5.18   -72.93     0.46)  ; 46\r\n  (  330.47    -5.91   -75.43     0.46)  ; 47\r\n  (  330.47    -5.91   -75.55     0.46)  ; 48\r\n  (  331.09    -4.56   -78.40     0.46)  ; 49\r\n  (  334.22    -3.83   -80.15     0.46)  ; 50\r\n  (  335.43    -2.94   -82.02     0.46)  ; 51\r\n  (  335.43    -2.94   -82.05     0.46)  ; 52\r\n  (  338.37    -3.45   -84.22     0.46)  ; 53\r\n  (  338.37    -3.45   -84.25     0.46)  ; 54\r\n  (  340.49    -2.36   -86.62     0.46)  ; 55\r\n  (  342.71    -1.84   -88.77     0.46)  ; 56\r\n  (  342.71    -1.84   -88.80     0.46)  ; 57\r\n  (  344.63    -1.98   -90.20     0.46)  ; 58\r\n  (  345.57     0.02   -90.20     0.46)  ; 59\r\n  (  347.36     0.45   -91.70     0.46)  ; 60\r\n  (  350.18     0.51   -93.23     0.46)  ; 61\r\n  (  351.95     0.93   -94.40     0.46)  ; 62\r\n  (  352.40     1.03   -94.40     0.46)  ; 63\r\n  (  357.14     0.95   -95.32     0.46)  ; 64\r\n  (  362.00     0.29   -96.47     0.46)  ; 65\r\n  (  364.37     0.25   -97.50     0.46)  ; 66\r\n  (  368.12     2.32   -97.65     0.46)  ; 67\r\n  (  370.22     3.40   -97.63     0.46)  ; 68\r\n  (  373.17     2.91   -99.80     0.46)  ; 69\r\n  (  373.17     2.91   -99.82     0.46)  ; 70\r\n  (  374.91     1.53  -101.13     0.46)  ; 71\r\n\r\n  (Cross\r\n    (Color White)\r\n    (Name \"Marker 3\")\r\n    (  271.54     8.35   -16.13     0.92)  ; 1\r\n    (  287.69     3.78   -39.32     0.92)  ; 2\r\n    (  291.44     5.85   -43.25     0.92)  ; 3\r\n    (  297.84    -3.40   -49.13     0.92)  ; 4\r\n    (  309.36    -4.28   -63.45     0.92)  ; 5\r\n    (  304.19    -4.30   -63.45     0.92)  ; 6\r\n    (  313.23    -8.74   -64.78     0.92)  ; 7\r\n    (  326.01    -6.95   -69.75     0.92)  ; 8\r\n    (  315.47    -8.22   -69.75     0.92)  ; 9\r\n    (  328.87    -5.08   -80.15     0.46)  ; 10\r\n    (  366.37    -2.27   -97.65     0.46)  ; 11\r\n  )  ;  End of markers\r\n   Normal\r\n)  ;  End of tree\r\n\r\n( (Color RGB (128, 255, 255))\r\n  (Dendrite)\r\n  (  260.47     9.34   -15.50     0.92)  ; Root\r\n  (  260.47     9.34   -15.50     0.92)  ; 1, R\r\n  (  258.18     7.02   -15.50     0.92)  ; 2\r\n  (  256.22     5.36   -15.50     0.92)  ; 3\r\n  (  253.80     3.60   -15.50     0.92)  ; 4\r\n  (  252.28     2.05   -16.25     0.92)  ; 5\r\n  (  250.63     1.07   -17.13     0.92)  ; 6\r\n  (  250.63     1.07   -17.15     0.92)  ; 7\r\n  (  250.90    -0.06   -17.80     0.92)  ; 8\r\n  (  250.90    -0.06   -17.82     0.92)  ; 9\r\n  (  252.63    -1.45   -19.42     0.92)  ; 10\r\n  (  252.63    -1.45   -19.45     0.92)  ; 11\r\n  (  252.68     0.36   -21.32     0.92)  ; 12\r\n  (  252.68     0.36   -21.38     0.92)  ; 13\r\n  (  251.21     0.61   -23.23     0.92)  ; 14\r\n  (  249.42     0.19   -25.27     0.92)  ; 15\r\n  (  248.49    -1.83   -26.48     0.92)  ; 16\r\n  (  248.49    -1.83   -26.50     0.92)  ; 17\r\n  (  246.64    -4.04   -26.77     0.92)  ; 18\r\n  (  244.41    -4.57   -27.20     0.92)  ; 19\r\n  (  242.62    -4.99   -28.95     0.92)  ; 20\r\n  (  242.62    -4.99   -28.98     0.92)  ; 21\r\n  (  241.74    -5.20   -32.02     0.92)  ; 22\r\n  (  241.74    -5.20   -32.05     0.92)  ; 23\r\n  (  243.34    -6.01   -36.00     0.92)  ; 24\r\n  (  243.34    -6.01   -36.03     0.92)  ; 25\r\n  (  243.74    -7.71   -38.85     0.92)  ; 26\r\n  (  243.74    -7.71   -38.88     0.92)  ; 27\r\n  (  240.87    -9.58   -38.85     0.92)  ; 28\r\n  (  238.01   -11.44   -39.90     0.92)  ; 29\r\n\r\n  (Cross\r\n    (Color RGB (128, 255, 255))\r\n    (Name \"Marker 3\")\r\n    (  246.59    -5.85   -27.20     0.92)  ; 1\r\n    (  238.46   -11.33   -40.05     0.92)  ; 2\r\n  )  ;  End of markers\r\n  (\r\n    (  238.41   -13.14   -41.93     0.46)  ; 1, R-1\r\n    (  236.76   -14.13   -44.43     0.46)  ; 2\r\n    (  236.76   -14.13   -44.50     0.46)  ; 3\r\n    (  236.58   -15.36   -42.40     0.46)  ; 4\r\n    (  235.41   -14.44   -48.22     0.46)  ; 5\r\n    (  235.41   -14.44   -48.25     0.46)  ; 6\r\n    (  234.20   -15.32   -51.20     0.46)  ; 7\r\n    (  234.20   -15.32   -51.22     0.46)  ; 8\r\n    (  235.68   -15.57   -54.30     0.46)  ; 9\r\n    (  235.68   -15.57   -54.32     0.46)  ; 10\r\n    (  236.40   -16.60   -57.53     0.46)  ; 11\r\n    (  236.40   -16.60   -57.55     0.46)  ; 12\r\n    (  237.87   -16.86   -60.63     0.46)  ; 13\r\n    (  237.87   -16.86   -60.65     0.46)  ; 14\r\n    (  237.96   -19.21   -63.50     0.46)  ; 15\r\n    (  238.09   -19.78   -63.82     0.46)  ; 16\r\n    (  238.09   -19.78   -63.88     0.46)  ; 17\r\n    (  235.58   -19.18   -67.07     0.46)  ; 18\r\n    (  235.58   -19.18   -67.13     0.46)  ; 19\r\n    (  235.53   -20.99   -71.82     0.46)  ; 20\r\n    (  236.11   -21.44   -74.40     0.46)  ; 21\r\n    (  236.11   -21.44   -74.42     0.46)  ; 22\r\n    (  234.46   -22.43   -74.42     0.46)  ; 23\r\n    (  235.61   -23.35   -77.25     0.46)  ; 24\r\n    (  235.61   -23.35   -77.28     0.46)  ; 25\r\n    (  237.09   -23.60   -80.82     0.46)  ; 26\r\n    (  237.09   -23.60   -80.90     0.46)  ; 27\r\n    (  238.25   -24.52   -83.70     0.46)  ; 28\r\n    (  238.25   -24.52   -83.72     0.46)  ; 29\r\n    (  237.49   -25.31   -86.50     0.46)  ; 30\r\n    (  237.04   -25.41   -86.53     0.46)  ; 31\r\n    (  236.15   -25.62   -89.72     0.46)  ; 32\r\n    (  233.78   -25.57   -91.30     0.46)  ; 33\r\n    (  233.34   -25.67   -91.30     0.46)  ; 34\r\n    (  232.14   -26.56   -94.48     0.46)  ; 35\r\n    (  231.51   -27.89   -97.05     0.46)  ; 36\r\n    (  228.64   -29.76   -99.25     0.46)  ; 37\r\n    (  227.88   -30.54  -101.60     0.46)  ; 38\r\n    (  226.67   -31.42  -103.80     0.46)  ; 39\r\n    (  226.67   -31.42  -103.82     0.46)  ; 40\r\n    (  224.49   -30.14  -107.32     0.46)  ; 41\r\n    (  224.49   -30.14  -107.38     0.46)  ; 42\r\n    (  223.78   -29.12  -110.55     0.46)  ; 43\r\n    (  223.78   -29.12  -110.63     0.46)  ; 44\r\n    (  220.65   -29.85  -112.13     0.46)  ; 45\r\n    (  218.87   -30.26  -115.47     0.46)  ; 46\r\n    (  217.22   -31.25  -118.22     0.46)  ; 47\r\n    (  218.95   -32.63  -121.47     0.46)  ; 48\r\n    (  221.63   -32.00  -122.75     0.46)  ; 49\r\n    (  221.94   -31.34  -125.40     0.46)  ; 50\r\n    (  223.41   -31.58  -128.45     0.46)  ; 51\r\n    (  223.41   -31.58  -128.48     0.46)  ; 52\r\n    (  223.87   -31.48  -131.67     0.46)  ; 53\r\n    (  223.87   -31.48  -131.75     0.46)  ; 54\r\n    (  224.18   -30.81  -136.00     0.46)  ; 55\r\n    (  224.94   -30.03  -139.32     0.46)  ; 56\r\n\r\n    (Cross\r\n      (Color RGB (128, 255, 255))\r\n      (Name \"Marker 3\")\r\n      (  238.99   -13.61   -44.50     0.46)  ; 1\r\n      (  240.55   -16.23   -63.50     0.46)  ; 2\r\n      (  229.49   -31.36  -102.40     0.46)  ; 3\r\n    )  ;  End of markers\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  219.35   -34.33  -120.40     0.46)  ; 1\r\n      (  220.60   -31.64  -120.40     0.46)  ; 2\r\n    )  ;  End of markers\r\n     Normal\r\n  |\r\n    (  235.20   -11.50   -40.77     0.92)  ; 1, R-2\r\n    (  232.74   -15.07   -42.35     0.92)  ; 2\r\n    (  231.35   -17.18   -44.27     0.92)  ; 3\r\n    (  231.35   -17.18   -44.30     0.92)  ; 4\r\n    (  231.56   -20.12   -45.72     0.92)  ; 5\r\n    (  230.32   -22.81   -48.07     0.92)  ; 6\r\n    (  230.32   -22.81   -48.10     0.92)  ; 7\r\n    (  227.90   -24.57   -50.60     0.92)  ; 8\r\n    (  227.90   -24.57   -50.65     0.92)  ; 9\r\n    (  224.41   -27.77   -51.70     0.92)  ; 10\r\n    (  222.96   -31.68   -53.22     0.92)  ; 11\r\n    (  220.69   -34.02   -54.52     0.92)  ; 12\r\n    (  219.57   -37.26   -56.08     0.92)  ; 13\r\n    (  219.47   -40.87   -57.60     0.92)  ; 14\r\n    (  219.11   -43.35   -60.10     0.92)  ; 15\r\n    (  217.80   -47.83   -62.88     0.92)  ; 16\r\n    (  216.24   -51.19   -62.57     0.46)  ; 17\r\n    (  216.45   -54.11   -63.17     0.46)  ; 18\r\n    (  216.45   -54.11   -63.30     0.46)  ; 19\r\n    (  215.01   -58.04   -65.82     0.46)  ; 20\r\n    (  215.01   -58.04   -65.85     0.46)  ; 21\r\n    (  214.02   -61.86   -68.20     0.46)  ; 22\r\n    (  214.02   -61.86   -68.22     0.46)  ; 23\r\n    (  211.87   -64.74   -71.57     0.46)  ; 24\r\n    (  211.87   -64.74   -71.63     0.46)  ; 25\r\n    (  209.85   -68.20   -73.47     0.46)  ; 26\r\n    (  205.48   -71.62   -75.27     0.46)  ; 27\r\n    (  205.48   -71.62   -75.32     0.46)  ; 28\r\n    (  203.37   -72.71   -77.13     0.46)  ; 29\r\n    (  203.37   -72.71   -77.15     0.46)  ; 30\r\n    (  201.40   -74.36   -78.55     0.46)  ; 31\r\n    (  201.40   -74.36   -78.57     0.46)  ; 32\r\n    (  200.06   -74.68   -80.65     0.46)  ; 33\r\n    (  198.10   -76.33   -82.97     0.46)  ; 34\r\n    (  198.10   -76.33   -83.00     0.46)  ; 35\r\n    (  198.63   -78.60   -86.53     0.46)  ; 36\r\n    (  198.63   -78.60   -86.62     0.46)  ; 37\r\n\r\n    (Cross\r\n      (Color RGB (128, 255, 255))\r\n      (Name \"Marker 3\")\r\n      (  216.77   -31.36   -54.52     0.92)  ; 1\r\n      (  210.23   -59.75   -71.65     0.46)  ; 2\r\n      (  203.28   -76.32   -78.57     0.46)  ; 3\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Yellow)\r\n  (Dendrite)\r\n  (  264.44     8.49   -15.95     0.92)  ; Root\r\n  (  264.40     6.68   -15.95     0.92)  ; 1, R\r\n  (  265.06     3.85   -15.95     0.92)  ; 2\r\n  (  265.59     1.59   -16.70     0.92)  ; 3\r\n  (  265.53    -0.21   -16.70     0.92)  ; 4\r\n  (  263.34     1.06   -16.70     0.92)  ; 5\r\n  (  263.08     2.19   -19.00     0.92)  ; 6\r\n  (  263.08     2.19   -19.02     0.92)  ; 7\r\n  (  261.43     1.21   -21.30     0.92)  ; 8\r\n  (  261.10     0.66   -21.30     0.92)  ; 9\r\n  (\r\n    (  263.64     1.86   -26.42     0.92)  ; 1, R-1\r\n    (  265.30     2.84   -34.00     0.92)  ; 2\r\n    (  264.14     3.76   -37.75     0.92)  ; 3\r\n    (  264.14     3.76   -37.77     0.92)  ; 4\r\n    (  263.83     3.10   -39.90     0.92)  ; 5\r\n    (  263.83     3.10   -39.92     0.92)  ; 6\r\n    (  264.77     5.12   -42.95     0.92)  ; 7\r\n    (  264.77     5.12   -43.00     0.92)  ; 8\r\n    (  263.30     5.36   -45.90     0.92)  ; 9\r\n    (  263.30     5.36   -46.00     0.92)  ; 10\r\n    (  265.65     5.31   -50.63     0.92)  ; 11\r\n    (  265.65     5.31   -50.65     0.92)  ; 12\r\n    (  266.15     7.22   -54.65     0.92)  ; 13\r\n    (  266.15     7.22   -54.72     0.92)  ; 14\r\n    (  264.11     7.94   -59.22     0.92)  ; 15\r\n    (  264.01    10.30   -63.80     0.92)  ; 16\r\n    (  265.17     9.39   -67.42     0.92)  ; 17\r\n    (  264.33    10.98   -69.72     0.92)  ; 18\r\n    (  264.33    10.98   -69.80     0.92)  ; 19\r\n    (  264.64    11.65   -71.72     0.92)  ; 20\r\n    (\r\n      (  262.20    14.06   -73.80     0.46)  ; 1, R-1-1\r\n      (  262.20    14.06   -73.82     0.46)  ; 2\r\n      (  261.17    14.42   -77.15     0.46)  ; 3\r\n      (  260.73    14.31   -77.17     0.46)  ; 4\r\n      (  259.88    15.90   -80.25     0.46)  ; 5\r\n      (  259.88    15.90   -80.28     0.46)  ; 6\r\n      (  261.67    16.32   -82.92     0.46)  ; 7\r\n      (  261.67    16.32   -82.95     0.46)  ; 8\r\n      (  259.93    17.71   -85.63     0.46)  ; 9\r\n      (  259.93    17.71   -85.65     0.46)  ; 10\r\n      (  258.60    17.39   -89.10     0.46)  ; 11\r\n      (  258.60    17.39   -89.12     0.46)  ; 12\r\n      (  259.26    14.56   -92.87     0.46)  ; 13\r\n      (  259.26    14.56   -92.90     0.46)  ; 14\r\n      (  257.12    17.65   -96.72     0.46)  ; 15\r\n      (  257.12    17.65   -96.77     0.46)  ; 16\r\n      (  259.66    18.83   -99.00     0.46)  ; 17\r\n      (  259.66    18.83   -99.03     0.46)  ; 18\r\n      (  258.33    18.53  -101.50     0.46)  ; 19\r\n      (  258.33    18.53  -101.53     0.46)  ; 20\r\n      (  256.46    20.48  -104.65     0.46)  ; 21\r\n      (  256.46    20.48  -104.67     0.46)  ; 22\r\n      (  258.50    19.76  -108.23     0.46)  ; 23\r\n      (  258.50    19.76  -108.27     0.46)  ; 24\r\n      (  257.17    19.45  -112.70     0.46)  ; 25\r\n      (  256.54    18.11  -116.32     0.46)  ; 26\r\n      (  256.81    16.97  -120.42     0.46)  ; 27\r\n      (  256.81    16.97  -120.45     0.46)  ; 28\r\n      (  254.93    18.92  -124.25     0.46)  ; 29\r\n      (  254.93    18.92  -124.32     0.46)  ; 30\r\n      (  254.66    20.06  -128.10     0.46)  ; 31\r\n      (  254.66    20.06  -128.15     0.46)  ; 32\r\n      (  253.46    19.18  -132.95     0.46)  ; 33\r\n      (  253.46    19.18  -133.00     0.46)  ; 34\r\n      (  253.41    17.37  -137.80     0.46)  ; 35\r\n       Normal\r\n    |\r\n      (  267.46    11.71   -73.07     0.46)  ; 1, R-1-2\r\n      (  268.67    12.59   -73.97     0.46)  ; 2\r\n      (  268.67    12.59   -74.00     0.46)  ; 3\r\n      (  270.59    12.44   -75.57     0.46)  ; 4\r\n      (  270.59    12.44   -75.60     0.46)  ; 5\r\n      (  271.97    14.55   -77.05     0.46)  ; 6\r\n      (  271.97    14.55   -77.10     0.46)  ; 7\r\n      (  274.34    14.51   -79.32     0.46)  ; 8\r\n      (  274.34    14.51   -79.35     0.46)  ; 9\r\n      (  277.16    14.58   -82.35     0.46)  ; 10\r\n      (  279.70    15.77   -84.45     0.46)  ; 11\r\n      (  280.46    16.54   -86.45     0.46)  ; 12\r\n      (  280.46    16.54   -86.47     0.46)  ; 13\r\n      (  282.96    15.94   -87.22     0.46)  ; 14\r\n      (  282.96    15.94   -87.25     0.46)  ; 15\r\n      (  285.24    18.27   -88.80     0.46)  ; 16\r\n      (  286.18    20.28   -91.15     0.46)  ; 17\r\n      (  286.18    20.28   -91.20     0.46)  ; 18\r\n      (  288.15    21.93   -93.60     0.46)  ; 19\r\n      (  288.15    21.93   -93.65     0.46)  ; 20\r\n      (  289.94    22.35   -95.92     0.46)  ; 21\r\n      (  289.94    22.35   -95.95     0.46)  ; 22\r\n      (  290.88    24.37   -97.75     0.46)  ; 23\r\n      (  290.88    24.37   -97.78     0.46)  ; 24\r\n      (  292.22    24.68  -101.40     0.46)  ; 25\r\n      (  294.89    25.31  -104.97     0.46)  ; 26\r\n      (  294.89    25.31  -105.00     0.46)  ; 27\r\n      (  295.79    25.52  -108.15     0.46)  ; 28\r\n      (  297.71    25.37  -110.68     0.46)  ; 29\r\n      (  297.58    25.94  -110.70     0.46)  ; 30\r\n      (  300.00    27.70  -113.25     0.46)  ; 31\r\n      (  301.01    27.34  -116.53     0.46)  ; 32\r\n      (  301.01    27.34  -116.70     0.46)  ; 33\r\n      (  300.75    28.46  -120.52     0.46)  ; 34\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  293.82    23.86   -97.78     0.46)  ; 1\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (  260.93    -0.70   -23.60     0.92)  ; 1, R-2\r\n    (  260.93    -0.70   -23.65     0.92)  ; 2\r\n    (  261.91    -2.86   -25.35     0.92)  ; 3\r\n    (  262.49    -3.33   -26.95     0.92)  ; 4\r\n    (  262.44    -5.11   -26.65     0.92)  ; 5\r\n    (  262.84    -6.82   -26.65     0.92)  ; 6\r\n    (\r\n      (  262.75   -10.43   -25.55     0.92)  ; 1, R-2-1\r\n      (  262.31   -10.53   -25.55     0.92)  ; 2\r\n      (  262.40   -12.91   -25.57     0.92)  ; 3\r\n      (  258.64   -14.98   -25.83     0.92)  ; 4\r\n      (  257.43   -15.86   -24.85     0.92)  ; 5\r\n      (  255.73   -18.64   -26.25     0.92)  ; 6\r\n      (  255.73   -18.64   -26.28     0.92)  ; 7\r\n      (  255.55   -19.88   -27.27     0.92)  ; 8\r\n      (  255.55   -19.88   -27.30     0.92)  ; 9\r\n      (  254.29   -22.57   -28.02     0.92)  ; 10\r\n      (  254.29   -22.57   -28.05     0.92)  ; 11\r\n      (  252.82   -22.31   -28.63     0.92)  ; 12\r\n      (  252.27   -26.02   -29.42     0.92)  ; 13\r\n      (  250.13   -28.91   -30.38     0.92)  ; 14\r\n\r\n      (Cross\r\n        (Color Yellow)\r\n        (Name \"Marker 3\")\r\n        (  255.34   -16.94   -24.85     0.92)  ; 1\r\n        (  258.46   -16.21   -24.85     0.92)  ; 2\r\n      )  ;  End of markers\r\n      (\r\n        (  247.00   -29.65   -30.38     0.92)  ; 1, R-2-1-1\r\n        (  244.76   -30.18   -31.42     0.92)  ; 2\r\n        (  244.76   -30.18   -31.45     0.92)  ; 3\r\n        (  243.17   -29.35   -32.83     0.92)  ; 4\r\n        (  243.17   -29.35   -32.88     0.92)  ; 5\r\n        (  240.80   -29.31   -34.45     0.92)  ; 6\r\n        (  240.80   -29.31   -34.47     0.92)  ; 7\r\n        (  238.69   -30.41   -36.42     0.92)  ; 8\r\n        (  237.44   -33.08   -38.05     0.92)  ; 9\r\n        (  237.44   -33.08   -38.07     0.92)  ; 10\r\n        (  236.05   -35.21   -39.95     0.92)  ; 11\r\n        (  236.05   -35.21   -40.07     0.92)  ; 12\r\n        (  234.67   -37.32   -39.38     0.92)  ; 13\r\n        (  232.56   -38.40   -41.00     0.92)  ; 14\r\n        (  232.56   -38.40   -41.03     0.92)  ; 15\r\n        (  232.20   -40.89   -43.07     0.92)  ; 16\r\n        (  231.26   -42.89   -47.22     0.92)  ; 17\r\n        (  231.26   -42.89   -47.28     0.92)  ; 18\r\n\r\n        (Cross\r\n          (Color Yellow)\r\n          (Name \"Marker 3\")\r\n          (  236.71   -38.03   -40.10     0.92)  ; 1\r\n          (  233.38   -35.84   -38.10     0.92)  ; 2\r\n        )  ;  End of markers\r\n        (\r\n          (  233.30   -43.61   -49.10     0.92)  ; 1, R-2-1-1-1\r\n          (  233.30   -43.61   -49.13     0.92)  ; 2\r\n          (  234.02   -44.63   -51.67     0.92)  ; 3\r\n          (  234.02   -44.63   -51.72     0.92)  ; 4\r\n          (  233.40   -45.98   -54.20     0.92)  ; 5\r\n          (  232.37   -45.62   -59.13     0.92)  ; 6\r\n          (  230.27   -46.71   -62.22     0.92)  ; 7\r\n          (  230.27   -46.71   -62.33     0.92)  ; 8\r\n          (  229.38   -46.92   -66.88     0.92)  ; 9\r\n          (  229.91   -49.19   -70.80     0.92)  ; 10\r\n          (  229.91   -49.19   -70.82     0.92)  ; 11\r\n          (  230.04   -49.75   -73.42     0.92)  ; 12\r\n          (  231.11   -48.29   -74.67     0.92)  ; 13\r\n          (  232.77   -47.31   -76.82     0.92)  ; 14\r\n\r\n          (Cross\r\n            (Color Yellow)\r\n            (Name \"Marker 3\")\r\n            (  234.52   -42.73   -54.20     0.92)  ; 1\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  229.16   -43.98   -47.22     0.92)  ; 1, R-2-1-1-2\r\n          (  229.16   -43.98   -47.25     0.92)  ; 2\r\n          (  227.74   -41.93   -48.35     0.92)  ; 3\r\n          (  226.03   -44.71   -50.77     0.92)  ; 4\r\n          (  226.03   -44.71   -50.88     0.92)  ; 5\r\n          (  225.27   -45.50   -54.05     0.92)  ; 6\r\n          (  223.80   -45.23   -55.60     0.92)  ; 7\r\n          (  223.80   -45.23   -55.63     0.92)  ; 8\r\n          (  222.02   -45.65   -58.15     0.92)  ; 9\r\n          (  220.41   -44.84   -61.38     0.92)  ; 10\r\n          (  220.36   -46.64   -63.20     0.92)  ; 11\r\n          (  220.36   -46.64   -63.25     0.92)  ; 12\r\n          (  218.44   -46.49   -66.77     0.92)  ; 13\r\n          (  218.52   -48.86   -70.07     0.92)  ; 14\r\n          (  218.52   -48.86   -70.18     0.92)  ; 15\r\n          (  216.29   -49.39   -72.72     0.92)  ; 16\r\n          (  215.85   -49.49   -72.80     0.92)  ; 17\r\n          (  214.37   -49.24   -73.00     0.92)  ; 18\r\n          (  214.37   -49.24   -73.02     0.92)  ; 19\r\n          (  215.00   -47.91   -76.47     0.92)  ; 20\r\n          (  215.00   -47.91   -76.60     0.92)  ; 21\r\n          (  217.18   -49.18   -80.68     0.92)  ; 22\r\n          (  217.18   -49.18   -80.73     0.92)  ; 23\r\n          (  217.00   -50.41   -85.28     0.92)  ; 24\r\n          (  217.00   -50.41   -85.37     0.92)  ; 25\r\n          (  214.24   -48.67   -89.60     0.92)  ; 26\r\n          (  214.24   -48.67   -89.65     0.92)  ; 27\r\n          (  213.61   -50.02   -92.30     0.92)  ; 28\r\n          (  213.61   -50.02   -92.32     0.92)  ; 29\r\n          (  212.50   -47.29   -93.40     0.92)  ; 30\r\n          (  210.58   -47.14   -94.67     0.92)  ; 31\r\n          (  210.58   -47.14   -94.70     0.92)  ; 32\r\n          (  208.66   -46.99   -96.85     0.92)  ; 33\r\n          (  208.66   -46.99   -96.90     0.92)  ; 34\r\n          (  207.46   -47.87   -99.17     0.92)  ; 35\r\n          (  205.80   -48.85  -100.97     0.92)  ; 36\r\n          (  204.52   -47.37  -102.20     0.92)  ; 37\r\n          (  201.51   -48.67  -104.05     0.92)  ; 38\r\n          (  201.51   -48.67  -104.07     0.92)  ; 39\r\n          (  199.28   -49.19  -104.90     0.92)  ; 40\r\n          (  199.28   -49.19  -104.97     0.92)  ; 41\r\n          (  197.14   -52.09  -105.32     0.92)  ; 42\r\n          (  194.85   -54.41  -106.62     0.92)  ; 43\r\n          (  191.10   -56.48  -108.32     0.92)  ; 44\r\n          (  192.53   -58.54  -111.97     0.92)  ; 45\r\n          (  192.53   -58.54  -112.02     0.92)  ; 46\r\n          (  192.79   -59.67  -113.95     0.92)  ; 47\r\n          (  192.79   -59.67  -113.97     0.92)  ; 48\r\n\r\n          (Cross\r\n            (Color Yellow)\r\n            (Name \"Marker 3\")\r\n            (  222.77   -44.88   -54.07     0.92)  ; 1\r\n            (  216.96   -46.24   -69.75     0.92)  ; 2\r\n            (  198.87   -53.47  -105.32     0.92)  ; 3\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  248.43   -31.71   -30.38     0.92)  ; 1, R-2-1-2\r\n        (  247.61   -34.28   -30.75     0.92)  ; 2\r\n        (  245.79   -36.51   -30.75     0.92)  ; 3\r\n        (  244.66   -39.75   -30.75     0.92)  ; 4\r\n        (  243.15   -41.31   -30.75     0.92)  ; 5\r\n        (  242.70   -41.41   -30.75     0.92)  ; 6\r\n        (  242.14   -45.12   -30.75     0.92)  ; 7\r\n        (  239.55   -48.11   -30.75     0.92)  ; 8\r\n        (  239.90   -51.62   -30.75     0.92)  ; 9\r\n        (  239.93   -55.80   -30.00     0.92)  ; 10\r\n        (  239.40   -59.50   -28.88     0.92)  ; 11\r\n        (  239.40   -59.50   -28.92     0.92)  ; 12\r\n        (  239.93   -61.76   -27.83     0.92)  ; 13\r\n        (  239.93   -61.76   -27.77     0.92)  ; 14\r\n        (  239.37   -65.48   -27.77     0.92)  ; 15\r\n        (  239.27   -69.08   -26.82     0.92)  ; 16\r\n        (  239.85   -69.55   -26.82     0.92)  ; 17\r\n        (  237.84   -73.00   -26.82     0.92)  ; 18\r\n        (  239.09   -76.28   -29.17     0.92)  ; 19\r\n        (  239.30   -79.23   -29.17     0.92)  ; 20\r\n        (  238.63   -82.37   -29.92     0.92)  ; 21\r\n        (  237.90   -87.32   -28.57     0.92)  ; 22\r\n        (  235.94   -88.97   -28.57     0.92)  ; 23\r\n        (  236.91   -91.14   -28.57     0.92)  ; 24\r\n        (  236.63   -95.98   -28.55     0.92)  ; 25\r\n        (  236.17  -102.05   -28.55     0.92)  ; 26\r\n        (  233.44  -104.49   -29.85     0.92)  ; 27\r\n        (  229.38  -107.23   -30.65     0.92)  ; 28\r\n        (  228.44  -109.24   -32.70     0.92)  ; 29\r\n        (  229.42  -111.40   -32.70     0.92)  ; 30\r\n        (  227.75  -112.38   -34.70     0.92)  ; 31\r\n        (  227.66  -115.99   -36.07     0.92)  ; 32\r\n        (  225.96  -118.78   -38.03     0.92)  ; 33\r\n        (  225.96  -118.78   -38.15     0.92)  ; 34\r\n        (  226.35  -120.47   -39.72     0.92)  ; 35\r\n        (  227.33  -122.64   -40.72     0.92)  ; 36\r\n        (  225.19  -125.52   -40.72     0.92)  ; 37\r\n        (  224.77  -129.80   -39.63     0.92)  ; 38\r\n        (  224.77  -129.80   -39.70     0.92)  ; 39\r\n        (  221.46  -131.77   -41.67     0.92)  ; 40\r\n        (  219.05  -133.54   -43.22     0.92)  ; 41\r\n        (  219.05  -133.54   -43.27     0.92)  ; 42\r\n        (  217.27  -133.96   -43.80     0.92)  ; 43\r\n        (  214.71  -135.15   -41.47     0.92)  ; 44\r\n        (  212.93  -135.57   -40.17     0.92)  ; 45\r\n        (  212.93  -135.57   -40.15     0.92)  ; 46\r\n        (  210.65  -137.89   -40.13     0.92)  ; 47\r\n        (  209.21  -141.81   -39.45     0.92)  ; 48\r\n        (  207.25  -143.46   -39.67     0.92)  ; 49\r\n        (  204.71  -144.66   -39.67     0.92)  ; 50\r\n        (  203.17  -146.22   -37.55     0.92)  ; 51\r\n\r\n        (Cross\r\n          (Color Yellow)\r\n          (Name \"Marker 3\")\r\n          (  241.53   -62.58   -27.77     0.92)  ; 1\r\n          (  237.94   -69.40   -26.82     0.92)  ; 2\r\n          (  239.19   -72.69   -26.82     0.92)  ; 3\r\n          (  240.56   -76.54   -29.25     0.92)  ; 4\r\n          (  240.46   -80.14   -29.35     0.92)  ; 5\r\n          (  238.06   -81.91   -29.35     0.92)  ; 6\r\n          (  238.01   -77.74   -29.35     0.92)  ; 7\r\n          (  233.17  -103.35   -29.85     0.92)  ; 8\r\n          (  226.51  -109.09   -29.65     0.92)  ; 9\r\n          (  234.10  -107.31   -29.65     0.92)  ; 10\r\n          (  233.35  -108.10   -29.65     0.92)  ; 11\r\n          (  233.17  -103.35   -30.65     0.92)  ; 12\r\n          (  224.21  -123.38   -40.72     0.92)  ; 13\r\n          (  227.60  -123.77   -40.72     0.92)  ; 14\r\n          (  225.93  -130.72   -38.75     0.92)  ; 15\r\n          (  223.35  -127.75   -41.28     0.92)  ; 16\r\n          (  215.97  -132.46   -43.80     0.92)  ; 17\r\n        )  ;  End of markers\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (  266.05    -8.45   -27.90     0.92)  ; 1, R-2-2\r\n      (  267.80    -9.85   -28.92     0.92)  ; 2\r\n      (  267.80    -9.85   -28.95     0.92)  ; 3\r\n      (  269.35   -12.46   -29.97     0.92)  ; 4\r\n      (  269.35   -12.46   -30.00     0.92)  ; 5\r\n      (  272.29   -12.97   -31.17     0.92)  ; 6\r\n      (  276.13   -13.26   -32.10     0.92)  ; 7\r\n      (  277.92   -12.85   -32.92     0.92)  ; 8\r\n      (  279.21   -14.33   -32.47     0.92)  ; 9\r\n      (  279.21   -14.33   -32.50     0.92)  ; 10\r\n      (  281.22   -16.84   -34.17     0.92)  ; 11\r\n      (  283.39   -18.12   -33.75     0.92)  ; 12\r\n      (  285.62   -17.60   -35.95     0.92)  ; 13\r\n      (  287.87   -17.08   -37.55     0.92)  ; 14\r\n      (  290.54   -16.44   -39.15     0.92)  ; 15\r\n      (  290.54   -16.44   -39.17     0.92)  ; 16\r\n      (  294.96   -17.21   -40.15     0.92)  ; 17\r\n      (  297.90   -17.71   -41.10     0.92)  ; 18\r\n      (  302.14   -19.70   -41.93     0.92)  ; 19\r\n      (  304.14   -22.22   -43.47     0.92)  ; 20\r\n      (  304.14   -22.22   -43.50     0.92)  ; 21\r\n      (  307.36   -23.85   -45.15     0.92)  ; 22\r\n      (  310.51   -27.29   -46.70     0.92)  ; 23\r\n      (  313.15   -28.48   -48.45     0.92)  ; 24\r\n      (  315.38   -27.95   -48.10     0.92)  ; 25\r\n      (  315.38   -27.95   -48.13     0.92)  ; 26\r\n      (  316.67   -29.44   -49.35     0.92)  ; 27\r\n      (  318.40   -30.82   -51.42     0.92)  ; 28\r\n      (  318.40   -30.82   -51.50     0.92)  ; 29\r\n      (  320.65   -30.30   -52.15     0.92)  ; 30\r\n      (  320.65   -30.30   -52.17     0.92)  ; 31\r\n      (  323.87   -31.94   -52.95     0.92)  ; 32\r\n      (  326.04   -33.22   -52.63     0.92)  ; 33\r\n      (  328.94   -35.53   -53.88     0.92)  ; 34\r\n      (  328.94   -35.53   -53.90     0.92)  ; 35\r\n      (  330.67   -36.91   -55.55     0.92)  ; 36\r\n      (  330.67   -36.91   -55.57     0.92)  ; 37\r\n      (  331.79   -39.64   -56.47     0.92)  ; 38\r\n      (  331.96   -44.37   -57.77     0.92)  ; 39\r\n      (  333.91   -48.69   -57.77     0.92)  ; 40\r\n      (  334.36   -48.58   -57.77     0.92)  ; 41\r\n      (  337.40   -51.46   -57.60     0.92)  ; 42\r\n      (  337.40   -51.46   -57.57     0.92)  ; 43\r\n      (  339.39   -53.98   -55.52     0.92)  ; 44\r\n      (  339.39   -53.98   -55.55     0.92)  ; 45\r\n      (  341.71   -55.82   -55.47     0.92)  ; 46\r\n      (  342.82   -58.55   -54.80     0.92)  ; 47\r\n      (  345.27   -60.97   -55.95     0.92)  ; 48\r\n      (  348.03   -62.70   -55.30     0.92)  ; 49\r\n      (  348.03   -62.70   -55.35     0.92)  ; 50\r\n      (  349.15   -65.43   -55.35     0.92)  ; 51\r\n      (  351.20   -66.13   -57.20     0.46)  ; 52\r\n      (  351.20   -66.13   -57.22     0.46)  ; 53\r\n      (  354.85   -67.67   -58.07     0.46)  ; 54\r\n      (  355.30   -67.56   -58.10     0.46)  ; 55\r\n      (  358.38   -68.64   -59.22     0.46)  ; 56\r\n      (  358.38   -68.64   -59.25     0.46)  ; 57\r\n      (  362.48   -70.07   -59.83     0.46)  ; 58\r\n      (  362.48   -70.07   -59.85     0.46)  ; 59\r\n      (  365.87   -70.47   -61.05     0.46)  ; 60\r\n      (  367.67   -70.04   -63.70     0.46)  ; 61\r\n      (  367.67   -70.04   -64.00     0.46)  ; 62\r\n\r\n      (Cross\r\n        (Color Yellow)\r\n        (Name \"Marker 3\")\r\n        (  276.94   -10.68   -32.92     0.92)  ; 1\r\n        (  280.42   -13.45   -32.15     0.92)  ; 2\r\n        (  280.01   -17.73   -33.75     0.92)  ; 3\r\n        (  314.75   -29.29   -48.15     0.92)  ; 4\r\n        (  316.58   -33.05   -51.00     0.92)  ; 5\r\n        (  319.99   -27.47   -52.95     0.92)  ; 6\r\n        (  333.67   -41.59   -57.77     0.92)  ; 7\r\n        (  338.46   -55.99   -55.47     0.92)  ; 8\r\n        (  339.76   -51.50   -58.35     0.92)  ; 9\r\n        (  339.36   -49.80   -58.35     0.92)  ; 10\r\n        (  344.07   -55.86   -54.85     0.92)  ; 11\r\n        (  348.08   -60.90   -54.80     0.92)  ; 12\r\n        (  359.36   -70.79   -58.38     0.46)  ; 13\r\n        (  358.30   -66.27   -59.80     0.46)  ; 14\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Cyan)\r\n  (Dendrite)\r\n  (  265.52    17.95   -15.45     0.92)  ; Root\r\n  (  265.52    17.95   -15.45     0.92)  ; 1, R\r\n  (  267.75    18.48   -15.48     0.92)  ; 2\r\n  (  267.67    20.84   -18.15     0.92)  ; 3\r\n  (  265.57    19.75   -19.20     0.92)  ; 4\r\n  (  262.50    20.83   -20.95     0.92)  ; 5\r\n  (  264.54    20.11   -22.85     0.92)  ; 6\r\n  (  267.48    19.60   -24.72     0.92)  ; 7\r\n  (  267.48    19.60   -24.75     0.92)  ; 8\r\n  (  270.17    20.24   -26.55     0.92)  ; 9\r\n  (  269.73    20.13   -26.55     0.92)  ; 10\r\n  (  273.11    19.73   -28.00     0.92)  ; 11\r\n  (  274.46    20.05   -27.95     0.92)  ; 12\r\n  (\r\n    (  276.23    24.52   -27.35     0.46)  ; 1, R-1\r\n    (  277.89    25.51   -30.13     0.46)  ; 2\r\n    (  282.05    25.89   -31.35     0.46)  ; 3\r\n    (  281.60    25.78   -31.35     0.46)  ; 4\r\n    (  284.54    25.27   -32.13     0.46)  ; 5\r\n    (  285.79    27.95   -33.47     0.46)  ; 6\r\n    (  285.79    27.95   -33.53     0.46)  ; 7\r\n    (  290.26    29.00   -35.72     0.46)  ; 8\r\n    (  293.52    29.17   -36.72     0.46)  ; 9\r\n    (  296.83    31.14   -37.88     0.46)  ; 10\r\n    (  303.84    33.38   -38.55     0.46)  ; 11\r\n    (  303.84    33.38   -38.57     0.46)  ; 12\r\n    (  308.99    37.57   -39.70     0.46)  ; 13\r\n    (  311.99    38.86   -41.45     0.46)  ; 14\r\n    (  311.99    38.86   -41.47     0.46)  ; 15\r\n    (  313.95    40.51   -42.15     0.46)  ; 16\r\n    (  316.81    42.37   -42.15     0.46)  ; 17\r\n    (  319.35    43.58   -42.17     0.46)  ; 18\r\n    (  323.55    45.76   -42.22     0.46)  ; 19\r\n    (  327.49    49.07   -42.22     0.46)  ; 20\r\n    (  327.36    49.64   -42.22     0.46)  ; 21\r\n    (  331.43    52.38   -42.22     0.46)  ; 22\r\n    (  333.71    54.70   -43.80     0.46)  ; 23\r\n    (  335.59    58.73   -45.22     0.46)  ; 24\r\n    (  335.59    58.73   -45.25     0.46)  ; 25\r\n    (  339.67    61.48   -46.57     0.46)  ; 26\r\n    (  341.49    63.69   -48.07     0.46)  ; 27\r\n    (  344.58    62.63   -49.58     0.46)  ; 28\r\n    (  347.61    65.72   -51.28     0.46)  ; 29\r\n    (  347.61    65.72   -51.30     0.46)  ; 30\r\n    (  351.46    65.43   -52.75     0.46)  ; 31\r\n    (  355.03    66.27   -54.20     0.46)  ; 32\r\n    (  355.03    66.27   -54.78     0.46)  ; 33\r\n    (  358.79    68.34   -53.38     0.46)  ; 34\r\n    (  359.86    69.79   -55.63     0.46)  ; 35\r\n    (  359.42    69.68   -55.63     0.46)  ; 36\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  302.14    30.59   -38.57     0.46)  ; 1\r\n      (  303.71    33.94   -38.57     0.46)  ; 2\r\n      (  319.17    42.33   -42.17     0.46)  ; 3\r\n      (  315.24    39.03   -42.17     0.46)  ; 4\r\n      (  315.46    42.07   -42.17     0.46)  ; 5\r\n      (  322.71    47.35   -42.17     0.46)  ; 6\r\n      (  329.09    48.25   -42.90     0.46)  ; 7\r\n      (  330.18    49.69   -42.20     0.46)  ; 8\r\n      (  338.85    58.89   -46.57     0.46)  ; 9\r\n      (  307.47    36.02   -31.70     0.46)  ; 10\r\n    )  ;  End of markers\r\n     Normal\r\n  |\r\n    (  271.69    21.78   -29.82     0.46)  ; 1, R-2\r\n    (  269.91    21.36   -33.03     0.46)  ; 2\r\n    (  269.38    23.64   -32.97     0.46)  ; 3\r\n    (  269.38    23.64   -33.05     0.46)  ; 4\r\n    (  266.88    24.23   -35.67     0.46)  ; 5\r\n    (  266.88    24.23   -35.70     0.46)  ; 6\r\n    (  265.00    26.19   -38.05     0.46)  ; 7\r\n    (  263.13    28.14   -41.08     0.46)  ; 8\r\n    (  261.26    30.09   -43.33     0.46)  ; 9\r\n    (  260.20    34.62   -44.90     0.46)  ; 10\r\n    (  256.41    36.72   -45.95     0.46)  ; 11\r\n    (  255.75    39.55   -47.17     0.46)  ; 12\r\n    (  252.53    41.18   -47.17     0.46)  ; 13\r\n    (  249.64    43.49   -49.15     0.46)  ; 14\r\n    (  249.16    47.56   -50.83     0.46)  ; 15\r\n    (  248.18    49.72   -51.75     0.46)  ; 16\r\n    (  247.74    49.61   -51.75     0.46)  ; 17\r\n    (  247.07    52.44   -52.70     0.46)  ; 18\r\n    (  247.07    52.44   -52.72     0.46)  ; 19\r\n    (  245.78    53.93   -51.72     0.46)  ; 20\r\n    (  244.22    56.55   -54.15     0.46)  ; 21\r\n    (  241.46    58.29   -55.65     0.46)  ; 22\r\n    (  237.98    61.07   -57.53     0.46)  ; 23\r\n    (  237.98    61.07   -57.55     0.46)  ; 24\r\n    (  233.49    64.18   -58.20     0.46)  ; 25\r\n    (  233.49    64.18   -58.22     0.46)  ; 26\r\n    (  230.49    66.94   -57.67     0.46)  ; 27\r\n    (  227.73    68.67   -60.22     0.46)  ; 28\r\n    (  226.11    69.50   -62.70     0.46)  ; 29\r\n    (  223.54    72.48   -64.75     0.46)  ; 30\r\n    (  223.54    72.48   -64.78     0.46)  ; 31\r\n    (  220.90    73.65   -66.95     0.46)  ; 32\r\n    (  220.46    73.55   -66.97     0.46)  ; 33\r\n    (  219.92    75.82   -68.85     0.46)  ; 34\r\n    (  219.92    75.82   -68.90     0.46)  ; 35\r\n    (  217.70    75.29   -71.17     0.46)  ; 36\r\n    (  217.70    75.29   -71.20     0.46)  ; 37\r\n    (  217.22    79.36   -72.40     0.46)  ; 38\r\n    (  216.24    81.51   -75.05     0.46)  ; 39\r\n    (  216.11    82.08   -75.05     0.46)  ; 40\r\n    (  215.53    82.54   -78.30     0.46)  ; 41\r\n    (  215.53    82.54   -78.35     0.46)  ; 42\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  256.29    43.26   -47.17     0.46)  ; 1\r\n      (  245.28    52.02   -51.72     0.46)  ; 2\r\n    )  ;  End of markers\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color White)\r\n  (Dendrite)\r\n  (  269.00    15.19    12.42     0.92)  ; Root\r\n  (  268.88    15.75    12.42     0.92)  ; 1, R\r\n  (  272.05    18.28    12.42     0.92)  ; 2\r\n  (  269.54    18.89    12.42     0.92)  ; 3\r\n  (  267.18    18.93    14.05     0.92)  ; 4\r\n  (  265.97    18.06    17.32     0.92)  ; 5\r\n  (  264.37    18.87    20.73     0.92)  ; 6\r\n  (  262.76    19.69    23.02     0.92)  ; 7\r\n  (  263.43    16.86    24.60     0.92)  ; 8\r\n  (  261.12    18.70    27.02     0.92)  ; 9\r\n  (  260.67    18.61    27.00     0.92)  ; 10\r\n  (  258.70    16.95    28.90     0.92)  ; 11\r\n  (  259.18    12.88    28.33     0.92)  ; 12\r\n  (  261.68    12.26    30.23     0.92)  ; 13\r\n  (  261.68    12.26    30.20     0.92)  ; 14\r\n  (  263.46    12.68    33.83     0.92)  ; 15\r\n  (  263.46    12.68    33.85     0.92)  ; 16\r\n\r\n  (Cross\r\n    (Color White)\r\n    (Name \"Marker 3\")\r\n    (  262.05    20.71    23.02     0.92)  ; 1\r\n  )  ;  End of markers\r\n   High\r\n)  ;  End of tree\r\n\r\n( (Color RGB (128, 255, 128))\r\n  (Apical)\r\n  (  259.44    31.45    -6.38     6.88)  ; Root\r\n  (  259.44    31.45    -6.38     6.88)  ; 1, R\r\n  (  259.35    33.82    -6.38     6.88)  ; 2\r\n  (  258.93    35.90    -6.38     6.88)  ; 3\r\n  (\r\n    (  259.89    37.53    -6.38     3.67)  ; 1, R-1\r\n    (  260.30    41.81    -6.38     3.67)  ; 2\r\n    (  260.10    44.74    -6.38     3.67)  ; 3\r\n    (\r\n      (  258.08    47.25    -5.92     3.67)  ; 1, R-1-1\r\n      (  256.21    49.21    -4.70     3.67)  ; 2\r\n      (\r\n        (  255.55    52.03    -3.00     3.21)  ; 1, R-1-1-1\r\n        (  254.00    54.66    -1.90     2.75)  ; 2\r\n        (  252.31    57.85    -1.90     2.75)  ; 3\r\n        (  252.36    59.65    -1.90     2.75)  ; 4\r\n        (  254.18    61.88    -1.90     2.75)  ; 5\r\n        (  256.47    64.19    -0.85     2.75)  ; 6\r\n        (  257.46    68.01     0.17     2.75)  ; 7\r\n        (  258.71    70.70     0.55     2.75)  ; 8\r\n        (  258.63    73.06     1.38     2.75)  ; 9\r\n\r\n        (Cross\r\n          (Color RGB (128, 255, 128))\r\n          (Name \"Marker 3\")\r\n          (  258.51    51.54    -3.92     1.38)  ; 1\r\n          (  253.84    65.38    -0.85     2.75)  ; 2\r\n        )  ;  End of markers\r\n        (\r\n          (  259.49    75.44    -0.63     2.75)  ; 1, R-1-1-1-1\r\n          (  259.04    77.35    -0.63     2.75)  ; 2\r\n          (  259.42    79.82    -1.22     2.75)  ; 3\r\n          (  259.42    79.82    -1.25     2.75)  ; 4\r\n          (  259.50    83.43    -1.25     2.75)  ; 5\r\n          (  258.63    89.18    -1.25     2.75)  ; 6\r\n          (  257.70    93.15    -2.08     2.75)  ; 7\r\n          (  259.00    97.63    -2.85     2.75)  ; 8\r\n          (  258.47    99.89    -3.60     2.75)  ; 9\r\n          (  258.38   102.27    -5.95     2.75)  ; 10\r\n          (  258.38   102.27    -5.97     2.75)  ; 11\r\n\r\n          (Cross\r\n            (Color RGB (128, 255, 128))\r\n            (Name \"Marker 3\")\r\n            (  261.27    71.90     2.15     2.75)  ; 1\r\n          )  ;  End of markers\r\n          (\r\n            (  255.96   104.56    -4.38     2.75)  ; 1, R-1-1-1-1-1\r\n            (  257.53   107.92    -3.13     2.75)  ; 2\r\n            (  259.11   111.27    -1.80     2.75)  ; 3\r\n            (  258.31   114.66    -0.90     2.75)  ; 4\r\n            (  257.25   119.20     0.12     2.75)  ; 5\r\n            (  255.87   123.05     1.32     2.75)  ; 6\r\n            (\r\n              (  254.55   128.72     1.32     2.75)  ; 1, R-1-1-1-1-1-1\r\n              (\r\n                (  255.26   133.67     0.35     2.75)  ; 1, R-1-1-1-1-1-1-1\r\n                (  255.55   138.51    -0.63     2.75)  ; 2\r\n                (  256.36   141.09    -1.82     2.75)  ; 3\r\n                (  257.93   144.44    -3.20     2.75)  ; 4\r\n                (  257.93   144.44    -3.22     2.75)  ; 5\r\n                (  256.06   146.39    -3.55     2.75)  ; 6\r\n                (  256.20   151.79    -3.55     2.75)  ; 7\r\n                (  256.92   156.74    -3.55     2.75)  ; 8\r\n                (  257.38   162.83    -3.55     2.75)  ; 9\r\n                (  258.82   166.73    -2.55     2.75)  ; 10\r\n                (  262.44   169.39    -2.55     2.75)  ; 11\r\n                (  264.33   173.41    -3.20     2.75)  ; 12\r\n                (  263.09   176.70    -3.85     2.75)  ; 13\r\n                (  259.87   178.33    -4.82     2.75)  ; 14\r\n                (  257.08   184.25    -4.82     2.75)  ; 15\r\n                (  254.73   190.26    -4.82     2.75)  ; 16\r\n                (  251.34   196.64    -4.82     2.75)  ; 17\r\n                (  251.31   200.81    -5.72     2.75)  ; 18\r\n                (  251.86   204.53    -7.35     2.75)  ; 19\r\n                (  253.48   209.68    -8.82     2.75)  ; 20\r\n                (  254.46   213.49   -10.00     2.75)  ; 21\r\n                (  254.11   216.99   -11.25     2.75)  ; 22\r\n                (  251.09   219.87   -12.10     2.75)  ; 23\r\n\r\n                (Cross\r\n                  (Color RGB (128, 255, 128))\r\n                  (Name \"Marker 3\")\r\n                  (  253.56   147.00    -3.55     2.75)  ; 1\r\n                  (  258.38   150.52    -3.55     2.75)  ; 2\r\n                  (  257.60   176.02    -4.82     2.75)  ; 3\r\n                  (  258.91   186.47    -4.82     2.75)  ; 4\r\n                  (  256.22   179.87    -4.82     2.75)  ; 5\r\n                  (  265.90   176.76    -4.82     2.75)  ; 6\r\n                  (  254.62   202.78    -7.35     2.75)  ; 7\r\n                  (  254.27   206.28    -8.82     2.75)  ; 8\r\n                  (  250.79   209.05    -8.82     2.75)  ; 9\r\n                  (  254.95   209.43    -8.82     2.75)  ; 10\r\n                  (  256.65   212.21    -8.82     2.75)  ; 11\r\n                )  ;  End of markers\r\n                (\r\n                  (  250.03   224.39   -12.65     2.75)  ; 1, R-1-1-1-1-1-1-1-1\r\n                  (  248.47   227.01   -11.80     2.75)  ; 2\r\n                  (  245.70   228.76   -11.80     2.75)  ; 3\r\n                  (  243.70   231.27   -12.65     2.75)  ; 4\r\n                  (  243.57   231.83   -12.65     2.75)  ; 5\r\n                  (  245.01   235.76   -13.32     2.75)  ; 6\r\n                  (  245.42   240.04   -14.38     2.75)  ; 7\r\n                  (  244.76   242.87   -16.32     2.75)  ; 8\r\n                  (  245.88   246.12   -18.70     2.75)  ; 9\r\n                  (  245.60   249.49   -19.67     2.75)  ; 10\r\n                  (  246.58   253.31   -19.88     2.75)  ; 11\r\n\r\n                  (Cross\r\n                    (Color RGB (128, 255, 128))\r\n                    (Name \"Marker 3\")\r\n                    (  255.19   218.45   -12.10     2.75)  ; 1\r\n                    (  252.84   224.46   -12.65     2.75)  ; 2\r\n                    (  243.03   244.25   -18.70     2.75)  ; 3\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  248.91   257.44   -19.88     2.29)  ; 1, R-1-1-1-1-1-1-1-1-1\r\n                    (  250.03   260.68   -19.05     2.29)  ; 2\r\n                    (  251.30   263.37   -19.60     2.29)  ; 3\r\n                    (  252.50   264.25   -17.77     2.29)  ; 4\r\n                    (  254.77   266.58   -16.13     2.29)  ; 5\r\n                    (  256.17   268.69   -15.02     2.29)  ; 6\r\n                    (  255.82   272.20   -15.02     2.29)  ; 7\r\n                    (  254.39   274.25   -16.47     2.29)  ; 8\r\n                    (  253.86   276.51   -18.35     2.29)  ; 9\r\n                    (  253.33   278.77   -19.42     2.29)  ; 10\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  251.63   275.99   -18.35     2.29)  ; 1\r\n                      (  256.63   274.76   -18.35     2.29)  ; 2\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  251.06   282.42   -19.42     2.29)  ; 1, R-1-1-1-1-1-1-1-1-1-1\r\n                      (  250.98   284.79   -20.52     2.29)  ; 2\r\n                      (  252.56   288.15   -21.50     2.29)  ; 3\r\n                      (  252.56   288.15   -21.52     2.29)  ; 4\r\n                      (  252.51   292.32   -21.25     2.29)  ; 5\r\n                      (  254.22   295.10   -20.70     2.29)  ; 6\r\n                      (  254.62   299.38   -20.70     2.29)  ; 7\r\n                      (  254.28   302.89   -19.63     2.29)  ; 8\r\n                      (  254.82   306.60   -18.77     2.29)  ; 9\r\n                      (  253.58   309.89   -18.77     2.29)  ; 10\r\n                      (  252.92   312.71   -20.83     2.29)  ; 11\r\n                      (  252.92   312.71   -20.85     2.29)  ; 12\r\n                      (  252.44   316.78   -21.67     2.29)  ; 13\r\n                      (  251.65   320.17   -22.50     2.29)  ; 14\r\n                      (  249.82   323.93   -23.33     2.29)  ; 15\r\n                      (  247.24   326.91   -23.90     2.29)  ; 16\r\n                      (  246.39   328.51   -24.92     2.29)  ; 17\r\n                      (  246.61   331.55   -25.83     2.29)  ; 18\r\n                      (  249.09   335.11   -26.50     2.29)  ; 19\r\n                      (  251.37   337.44   -25.85     2.29)  ; 20\r\n                      (  251.77   341.72   -25.23     2.29)  ; 21\r\n                      (  248.48   345.72   -25.23     2.29)  ; 22\r\n                      (  245.01   348.49   -26.38     2.29)  ; 23\r\n                      (  243.77   351.77   -26.85     2.29)  ; 24\r\n                      (  244.18   356.05   -27.35     2.29)  ; 25\r\n                      (  244.40   359.09   -26.95     2.29)  ; 26\r\n\r\n                      (Cross\r\n                        (Color White)\r\n                        (Name \"Marker 3\")\r\n                        (  247.73   353.23   -26.95     0.92)  ; 1\r\n                      )  ;  End of markers\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  249.73   288.08   -21.25     2.29)  ; 1\r\n                        (  250.78   293.70   -21.25     2.29)  ; 2\r\n                        (  253.40   286.55   -21.25     2.29)  ; 3\r\n                        (  255.10   289.35   -21.25     2.29)  ; 4\r\n                        (  257.05   301.14   -20.83     2.29)  ; 5\r\n                        (  255.55   311.55   -21.67     2.29)  ; 6\r\n                        (  248.38   320.01   -23.33     2.29)  ; 7\r\n                        (  249.18   316.62   -23.33     2.29)  ; 8\r\n                        (  253.21   323.53   -23.33     2.29)  ; 9\r\n                        (  245.00   326.39   -23.90     2.29)  ; 10\r\n                        (  246.08   333.81   -26.50     2.29)  ; 11\r\n                        (  253.10   336.05   -26.72     2.29)  ; 12\r\n                        (  248.70   342.78   -25.23     2.29)  ; 13\r\n                        (  250.89   347.48   -25.23     2.29)  ; 14\r\n                        (  242.46   347.29   -25.23     2.29)  ; 15\r\n                      )  ;  End of markers\r\n                      (\r\n                        (  243.05   361.09   -26.02     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1\r\n                        (  242.09   363.25   -24.32     1.83)  ; 2\r\n                        (  241.68   364.95   -22.80     1.83)  ; 3\r\n                        (  241.73   366.75   -21.32     1.83)  ; 4\r\n                        (  242.22   368.67   -19.65     1.83)  ; 5\r\n                        (  243.04   371.25   -18.72     1.83)  ; 6\r\n                        (  244.28   373.93   -18.80     1.83)  ; 7\r\n                        (  245.10   376.50   -17.60     1.83)  ; 8\r\n                        (  245.01   378.87   -16.67     1.83)  ; 9\r\n                        (  244.75   380.00   -15.90     1.83)  ; 10\r\n                        (  242.74   382.52   -15.40     1.83)  ; 11\r\n                        (  242.52   385.46   -15.10     1.83)  ; 12\r\n                        (  240.25   389.11   -15.10     1.83)  ; 13\r\n                        (  238.39   391.06   -15.10     1.83)  ; 14\r\n                        (  237.73   393.89   -14.13     1.83)  ; 15\r\n                        (  238.66   395.90   -13.42     1.83)  ; 16\r\n                        (  239.60   397.92   -12.62     1.83)  ; 17\r\n                        (  238.54   402.44   -12.40     1.83)  ; 18\r\n                        (  237.93   407.07   -12.40     1.83)  ; 19\r\n                        (  236.19   408.45   -12.40     1.83)  ; 20\r\n                        (  236.69   410.36   -13.32     1.83)  ; 21\r\n                        (  237.23   414.08   -14.52     1.83)  ; 22\r\n                        (  238.04   416.65   -15.48     1.83)  ; 23\r\n                        (  236.53   421.07   -16.38     1.83)  ; 24\r\n                        (  235.04   425.50   -14.65     1.83)  ; 25\r\n                        (  234.06   427.66   -15.40     1.83)  ; 26\r\n                        (  232.63   429.71   -16.35     1.83)  ; 27\r\n                        (  233.48   434.09   -16.77     1.83)  ; 28\r\n                        (  234.21   439.05   -17.25     1.83)  ; 29\r\n                        (  236.04   441.27   -17.30     1.83)  ; 30\r\n                        (  236.54   443.17   -18.10     1.83)  ; 31\r\n                        (  235.16   447.02   -18.92     1.83)  ; 32\r\n                        (  233.30   448.98   -18.92     1.83)  ; 33\r\n                        (  236.18   450.72   -19.73     1.83)  ; 34\r\n                        (  237.25   452.16   -18.72     1.83)  ; 35\r\n                        (  237.29   453.97   -17.27     1.83)  ; 36\r\n                        (  237.29   453.97   -17.30     1.83)  ; 37\r\n\r\n                        (Cross\r\n                          (Color White)\r\n                          (Name \"Marker 3\")\r\n                          (  240.91   402.40   -12.40     1.83)  ; 1\r\n                          (  240.00   412.33   -14.52     1.83)  ; 2\r\n                          (  239.70   417.64   -16.38     1.83)  ; 3\r\n                          (  238.50   422.74   -13.52     1.83)  ; 4\r\n                          (  239.26   439.62   -18.92     1.83)  ; 5\r\n                          (  237.41   447.55   -19.97     1.83)  ; 6\r\n                          (  238.27   451.81   -20.27     1.83)  ; 7\r\n                          (  234.36   444.45   -18.10     1.83)  ; 8\r\n                          (  234.62   443.33   -20.77     1.83)  ; 9\r\n                          (  232.12   437.95   -17.25     1.83)  ; 10\r\n                          (  233.95   424.05   -13.52     1.83)  ; 11\r\n                          (  232.79   424.97   -17.30     1.83)  ; 12\r\n                          (  235.27   434.51   -14.60     1.83)  ; 13\r\n                          (  235.17   430.91   -14.60     1.83)  ; 14\r\n                          (  241.61   395.39   -12.75     1.83)  ; 15\r\n                          (  241.51   391.78   -15.10     1.83)  ; 16\r\n                          (  244.81   387.78   -15.10     1.83)  ; 17\r\n                          (  240.12   383.69   -15.88     1.83)  ; 18\r\n                          (  239.93   382.46   -13.35     1.83)  ; 19\r\n                          (  236.15   390.53   -14.13     1.83)  ; 20\r\n                          (  245.71   371.87   -17.95     1.83)  ; 21\r\n                          (  246.52   374.45   -17.30     1.83)  ; 22\r\n                          (  241.56   371.49   -17.95     1.83)  ; 23\r\n                          (  244.36   365.58   -22.80     1.83)  ; 24\r\n                          (  244.23   366.15   -18.72     1.83)  ; 25\r\n                          (  246.11   370.17   -18.72     1.83)  ; 26\r\n                          (  235.04   415.35   -14.52     1.83)  ; 27\r\n                          (  234.51   411.64   -14.52     1.83)  ; 28\r\n                          (  240.09   393.85   -13.23     1.83)  ; 29\r\n                          (  236.62   396.61   -12.40     1.83)  ; 30\r\n                        )  ;  End of markers\r\n                        (\r\n                          (  238.43   457.22   -16.17     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1\r\n                          (  238.34   459.59   -16.17     1.83)  ; 2\r\n                          (  236.79   462.21   -16.77     1.83)  ; 3\r\n                          (  235.81   464.37   -16.65     1.83)  ; 4\r\n                          (\r\n                            (  234.88   468.33   -16.65     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                            (  234.22   471.17   -17.57     1.83)  ; 2\r\n                            (  235.21   474.98   -18.88     1.83)  ; 3\r\n                            (  235.61   479.26   -18.88     1.83)  ; 4\r\n                            (  234.82   482.65   -18.72     1.83)  ; 5\r\n                            (  234.15   485.47   -19.30     1.83)  ; 6\r\n                            (  236.04   489.51   -19.33     1.83)  ; 7\r\n                            (  237.17   492.76   -19.33     1.83)  ; 8\r\n                            (  238.55   494.87   -19.90     1.83)  ; 9\r\n                            (  239.04   496.78   -19.80     1.83)  ; 10\r\n                            (  236.92   499.87   -19.80     1.83)  ; 11\r\n                            (  235.23   503.05   -20.10     1.83)  ; 12\r\n                            (  233.14   507.94   -20.47     1.83)  ; 13\r\n                            (  233.05   510.30   -19.58     1.83)  ; 14\r\n                            (  233.60   514.01   -20.38     1.83)  ; 15\r\n                            (  231.15   516.43   -20.90     1.83)  ; 16\r\n                            (  230.80   519.93   -20.90     1.83)  ; 17\r\n                            (  231.16   522.40   -20.63     1.83)  ; 18\r\n                            (  232.14   526.21   -21.07     1.83)  ; 19\r\n                            (  231.79   529.72   -21.72     1.83)  ; 20\r\n                            (  230.87   533.68   -21.72     1.83)  ; 21\r\n                            (  229.04   537.43   -22.55     1.83)  ; 22\r\n                            (  229.04   537.43   -22.57     1.83)  ; 23\r\n                            (  228.25   540.83   -23.92     1.83)  ; 24\r\n                            (  228.25   540.83   -23.98     1.83)  ; 25\r\n                            (  228.61   543.30   -24.85     1.83)  ; 26\r\n                            (  230.23   548.46   -25.42     1.83)  ; 27\r\n                            (  230.33   552.06   -26.07     1.83)  ; 28\r\n                            (  232.36   555.42   -24.85     1.83)  ; 29\r\n                            (  232.47   559.02   -24.85     1.83)  ; 30\r\n                            (  231.10   562.87   -25.52     1.83)  ; 31\r\n                            (  231.10   562.87   -25.55     1.83)  ; 32\r\n                            (  230.17   566.84   -26.32     1.83)  ; 33\r\n                            (  230.27   570.44   -27.50     1.83)  ; 34\r\n                            (  231.25   574.26   -27.50     1.83)  ; 35\r\n                            (  231.48   577.30   -28.47     1.83)  ; 36\r\n                            (  232.03   581.02   -29.02     1.83)  ; 37\r\n                            (  231.68   584.50   -29.27     1.83)  ; 38\r\n                            (  231.32   588.00   -29.60     1.83)  ; 39\r\n                            (  230.92   589.71   -30.55     1.83)  ; 40\r\n                            (  232.50   593.05   -31.38     1.83)  ; 41\r\n                            (  234.78   595.39   -32.63     1.83)  ; 42\r\n                            (  233.40   599.25   -32.65     1.83)  ; 43\r\n                            (  232.66   604.45   -31.45     1.83)  ; 44\r\n                            (  233.52   608.83   -32.25     1.83)  ; 45\r\n                            (  232.54   610.99   -32.88     1.83)  ; 46\r\n\r\n                            (Cross\r\n                              (Color White)\r\n                              (Name \"Marker 3\")\r\n                              (  235.75   609.35   -31.77     1.83)  ; 1\r\n                              (  235.15   603.84   -31.70     1.83)  ; 2\r\n                              (  235.81   601.01   -31.45     1.83)  ; 3\r\n                              (  236.44   596.38   -31.52     1.83)  ; 4\r\n                              (  234.26   559.43   -25.97     1.83)  ; 5\r\n                              (  233.90   562.93   -25.00     1.83)  ; 6\r\n                              (  232.84   567.47   -27.50     1.83)  ; 7\r\n                              (  233.44   572.98   -27.50     1.83)  ; 8\r\n                              (  230.15   582.95   -29.27     1.83)  ; 9\r\n                              (  230.82   580.12   -29.27     1.83)  ; 10\r\n                              (  229.67   587.03   -29.27     1.83)  ; 11\r\n                              (  229.27   588.73   -29.27     1.83)  ; 12\r\n                              (  229.64   591.19   -31.40     1.83)  ; 13\r\n                              (  230.12   593.11   -31.40     1.83)  ; 14\r\n                              (  233.88   589.20   -31.40     1.83)  ; 15\r\n                              (  239.86   499.35   -19.80     1.83)  ; 16\r\n                              (  237.45   497.60   -19.80     1.83)  ; 17\r\n                              (  233.69   501.50   -20.47     1.83)  ; 18\r\n                              (  232.72   503.67   -20.47     1.83)  ; 19\r\n                              (  235.49   507.89   -21.50     1.83)  ; 20\r\n                              (  229.73   524.46   -21.15     1.83)  ; 21\r\n                              (  230.23   526.36   -21.15     1.83)  ; 22\r\n                              (  236.62   527.27   -19.97     1.83)  ; 23\r\n                              (  229.75   530.43   -20.10     1.83)  ; 24\r\n                              (  233.36   533.07   -20.50     1.83)  ; 25\r\n                              (  233.42   534.87   -21.70     1.83)  ; 26\r\n                              (  232.11   546.51   -26.07     1.83)  ; 27\r\n                              (  233.85   551.10   -26.07     1.83)  ; 28\r\n                              (  227.84   552.67   -26.07     1.83)  ; 29\r\n                              (  227.63   545.47   -26.07     1.83)  ; 30\r\n                              (  228.31   548.60   -26.07     1.83)  ; 31\r\n                              (  230.00   555.46   -25.97     1.83)  ; 32\r\n                              (  229.89   562.00   -25.97     1.83)  ; 33\r\n                              (  227.85   568.69   -27.50     1.83)  ; 34\r\n                              (  236.90   493.88   -19.90     1.83)  ; 35\r\n                              (  238.41   489.46   -21.35     1.83)  ; 36\r\n                              (  237.32   488.02   -21.95     1.83)  ; 37\r\n                              (  237.92   471.44   -18.88     1.83)  ; 38\r\n                              (  233.23   473.33   -19.30     1.83)  ; 39\r\n                              (  236.81   474.16   -19.35     1.83)  ; 40\r\n                              (  234.15   463.39   -17.92     1.83)  ; 41\r\n                            )  ;  End of markers\r\n                            (\r\n                              (  232.06   615.06   -32.88     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                              (  231.83   617.99   -32.75     1.83)  ; 2\r\n                              (  232.52   621.14   -33.15     1.83)  ; 3\r\n                              (  233.51   624.95   -33.15     1.83)  ; 4\r\n                              (  232.14   628.81   -32.58     1.83)  ; 5\r\n                              (  230.71   630.86   -32.65     1.83)  ; 6\r\n                              (  232.27   634.21   -33.22     1.83)  ; 7\r\n                              (  233.53   636.90   -34.20     1.83)  ; 8\r\n                              (  233.67   642.31   -34.78     1.83)  ; 9\r\n                              (  235.52   644.54   -34.78     1.83)  ; 10\r\n                              (  237.22   647.31   -34.78     1.83)  ; 11\r\n                              (  238.29   648.76   -35.83     1.83)  ; 12\r\n                              (  238.29   648.76   -35.85     1.83)  ; 13\r\n                              (  238.34   650.57   -37.13     1.83)  ; 14\r\n                              (  238.21   651.13   -37.15     1.83)  ; 15\r\n                              (  237.36   652.72   -36.42     1.83)  ; 16\r\n                              (  237.36   652.72   -36.45     1.83)  ; 17\r\n                              (  236.70   655.55   -36.42     1.83)  ; 18\r\n                              (  235.54   656.48   -34.85     1.83)  ; 19\r\n                              (  235.32   659.41   -34.15     1.83)  ; 20\r\n\r\n                              (Cross\r\n                                (Color White)\r\n                                (Name \"Marker 3\")\r\n                                (  233.22   658.33   -35.95     1.83)  ; 1\r\n                                (  237.55   659.94   -35.95     1.83)  ; 2\r\n                                (  237.82   658.80   -33.05     1.83)  ; 3\r\n                                (  238.22   657.10   -36.00     1.83)  ; 4\r\n                                (  236.15   651.84   -34.55     1.83)  ; 5\r\n                                (  239.22   644.81   -34.78     1.83)  ; 6\r\n                                (  236.32   641.13   -34.78     1.83)  ; 7\r\n                                (  230.88   626.12   -34.78     1.83)  ; 8\r\n                                (  230.33   622.41   -32.75     1.83)  ; 9\r\n                                (  234.13   620.32   -32.75     1.83)  ; 10\r\n                                (  234.43   615.00   -31.92     1.83)  ; 11\r\n                                (  235.72   613.52   -32.88     1.83)  ; 12\r\n                                (  235.76   637.42   -34.78     1.83)  ; 13\r\n                                (  228.81   620.86   -32.75     1.83)  ; 14\r\n                              )  ;  End of markers\r\n                              (\r\n                                (  233.23   664.30   -35.47     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                (  234.36   667.55   -35.13     1.83)  ; 2\r\n                                (  234.36   667.55   -35.15     1.83)  ; 3\r\n                                (  233.56   670.95   -35.65     1.83)  ; 4\r\n                                (  232.32   674.24   -35.65     1.83)  ; 5\r\n                                (  232.68   676.71   -34.42     1.83)  ; 6\r\n                                (  232.55   677.27   -34.42     1.83)  ; 7\r\n                                (  233.68   680.52   -33.13     1.83)  ; 8\r\n                                (  233.46   683.47   -33.88     1.83)  ; 9\r\n                                (  233.38   685.82   -33.27     1.83)  ; 10\r\n                                (  232.58   689.22   -33.05     1.83)  ; 11\r\n                                (  232.94   691.70   -33.97     1.83)  ; 12\r\n                                (  232.90   695.87   -35.02     1.83)  ; 13\r\n                                (  232.90   695.87   -35.05     1.83)  ; 14\r\n                                (  230.64   699.52   -36.10     1.83)  ; 15\r\n                                (  231.31   702.66   -37.45     1.83)  ; 16\r\n                                (  230.78   704.93   -37.77     1.83)  ; 17\r\n                                (  229.35   706.99   -36.63     1.83)  ; 18\r\n                                (  229.90   710.69   -35.75     1.83)  ; 19\r\n                                (  230.45   714.40   -34.95     1.83)  ; 20\r\n                                (  231.57   717.65   -34.67     1.83)  ; 21\r\n                                (  230.64   721.62   -35.83     1.83)  ; 22\r\n                                (  230.64   721.62   -35.85     1.83)  ; 23\r\n                                (  230.87   724.66   -36.22     1.83)  ; 24\r\n                                (  230.79   727.03   -37.20     1.83)  ; 25\r\n                                (  231.91   730.28   -38.15     1.83)  ; 26\r\n                                (  231.47   736.14   -39.67     1.83)  ; 27\r\n                                (  229.97   740.57   -39.02     1.83)  ; 28\r\n                                (  231.22   743.25   -38.88     1.83)  ; 29\r\n                                (  230.92   748.55   -38.27     1.83)  ; 30\r\n                                (  231.32   750.94   -35.53     1.83)  ; 31\r\n                                (  231.32   750.94   -35.55     1.83)  ; 32\r\n                                (  231.24   753.30   -34.92     1.83)  ; 33\r\n                                (  229.28   757.62   -34.92     1.83)  ; 34\r\n                                (  229.43   760.49   -34.63     1.83)  ; 35\r\n                                (  230.73   764.97   -33.77     1.83)  ; 36\r\n                                (  231.40   768.12   -34.72     1.83)  ; 37\r\n                                (  231.91   770.02   -33.97     1.83)  ; 38\r\n                                (  231.51   771.73   -33.67     1.83)  ; 39\r\n                                (  229.82   774.90   -33.03     1.83)  ; 40\r\n                                (  229.74   777.28   -33.00     1.83)  ; 41\r\n                                (  230.68   779.28   -33.00     1.83)  ; 42\r\n                                (  231.35   782.44   -32.63     1.83)  ; 43\r\n                                (  231.53   783.67   -33.13     1.83)  ; 44\r\n                                (  230.60   787.62   -32.35     1.83)  ; 45\r\n                                (  231.15   791.34   -31.75     1.83)  ; 46\r\n                                (  231.07   793.72   -31.35     1.83)  ; 47\r\n                                (  232.64   797.06   -31.35     1.83)  ; 48\r\n                                (  233.14   798.98   -31.17     1.83)  ; 49\r\n                                (  232.21   802.93   -30.80     1.83)  ; 50\r\n                                (  232.12   805.30   -31.02     1.83)  ; 51\r\n                                (  232.12   805.30   -31.00     1.83)  ; 52\r\n                                (  234.08   806.96   -30.57     1.83)  ; 53\r\n                                (  234.50   811.25   -29.70     1.83)  ; 54\r\n                                (  233.39   813.96   -29.38     1.83)  ; 55\r\n                                (  233.17   816.90   -29.38     1.83)  ; 56\r\n                                (  233.97   819.48   -30.50     1.83)  ; 57\r\n                                (  233.32   822.30   -31.40     1.83)  ; 58\r\n                                (  233.32   822.30   -31.42     1.83)  ; 59\r\n                                (  234.00   825.45   -32.22     1.83)  ; 60\r\n                                (  234.36   827.93   -31.52     1.83)  ; 61\r\n                                (  234.28   830.29   -30.38     1.83)  ; 62\r\n                                (  234.95   833.44   -30.07     1.83)  ; 63\r\n                                (  235.49   837.15   -30.07     1.83)  ; 64\r\n                                (  236.17   840.28   -30.07     1.83)  ; 65\r\n                                (  236.79   841.64   -30.07     1.83)  ; 66\r\n                                (  235.69   844.36   -29.97     1.83)  ; 67\r\n                                (  235.21   848.42   -30.75     1.83)  ; 68\r\n                                (  236.47   851.11   -30.75     1.83)  ; 69\r\n                                (  237.00   854.82   -29.60     1.83)  ; 70\r\n                                (  235.89   857.54   -28.63     1.83)  ; 71\r\n                                (  235.54   861.04   -29.77     1.83)  ; 72\r\n                                (  236.80   863.73   -30.20     1.83)  ; 73\r\n\r\n                                (Cross\r\n                                  (Color White)\r\n                                  (Name \"Marker 3\")\r\n                                  (  235.10   860.94   -28.50     1.83)  ; 1\r\n                                  (  237.80   851.43   -30.20     1.83)  ; 2\r\n                                  (  238.54   846.23   -32.38     1.83)  ; 3\r\n                                  (  237.13   848.28   -30.55     1.83)  ; 4\r\n                                  (  233.62   839.10   -30.07     1.83)  ; 5\r\n                                  (  238.34   833.04   -30.07     1.83)  ; 6\r\n                                  (  233.30   832.46   -30.07     1.83)  ; 7\r\n                                  (  232.57   827.51   -30.15     1.83)  ; 8\r\n                                  (  236.41   827.21   -33.10     1.83)  ; 9\r\n                                  (  236.57   822.47   -32.22     1.83)  ; 10\r\n                                  (  235.29   823.97   -32.22     1.83)  ; 11\r\n                                  (  234.83   817.88   -32.22     1.83)  ; 12\r\n                                  (  232.89   812.06   -29.20     1.83)  ; 13\r\n                                  (  230.02   804.22   -30.80     1.83)  ; 14\r\n                                  (  231.41   806.33   -30.30     1.83)  ; 15\r\n                                  (  231.64   809.37   -29.52     1.83)  ; 16\r\n                                  (  231.80   760.45   -33.77     1.83)  ; 17\r\n                                  (  228.63   763.89   -33.77     1.83)  ; 18\r\n                                  (  233.42   765.60   -33.77     1.83)  ; 19\r\n                                  (  233.80   780.02   -32.63     1.83)  ; 20\r\n                                  (  233.87   787.79   -32.25     1.83)  ; 21\r\n                                  (  233.12   792.99   -31.35     1.83)  ; 22\r\n                                  (  234.83   795.79   -31.35     1.83)  ; 23\r\n                                  (  231.12   795.52   -33.30     1.83)  ; 24\r\n                                  (  229.49   790.36   -33.13     1.83)  ; 25\r\n                                  (  236.11   810.41   -29.20     1.83)  ; 26\r\n                                  (  235.56   812.69   -29.20     1.83)  ; 27\r\n                                  (  234.22   744.54   -38.27     1.83)  ; 28\r\n                                  (  228.44   733.04   -39.58     1.83)  ; 29\r\n                                  (  227.49   731.02   -38.85     1.83)  ; 30\r\n                                  (  228.47   728.88   -38.17     1.83)  ; 31\r\n                                  (  234.01   731.36   -39.22     1.83)  ; 32\r\n                                  (  233.63   716.95   -34.67     1.83)  ; 33\r\n                                  (  229.28   715.33   -34.67     1.83)  ; 34\r\n                                  (  232.37   714.25   -33.25     1.83)  ; 35\r\n                                  (  228.61   712.18   -33.25     1.83)  ; 36\r\n                                  (  229.13   703.94   -37.92     1.83)  ; 37\r\n                                  (  233.85   703.86   -37.92     1.83)  ; 38\r\n                                  (  233.58   699.01   -34.92     1.83)  ; 39\r\n                                  (  229.75   699.31   -37.52     1.83)  ; 40\r\n                                  (  233.32   722.25   -33.45     1.83)  ; 41\r\n                                  (  230.74   687.00   -33.05     1.83)  ; 42\r\n                                  (  231.37   688.35   -33.05     1.83)  ; 43\r\n                                  (  231.16   691.28   -33.05     1.83)  ; 44\r\n                                  (  230.05   694.01   -36.88     1.83)  ; 45\r\n                                  (  235.92   687.03   -33.05     1.83)  ; 46\r\n                                  (  231.57   679.44   -33.13     1.83)  ; 47\r\n                                  (  234.79   677.80   -33.77     1.83)  ; 48\r\n                                  (  235.90   681.05   -33.77     1.83)  ; 49\r\n                                  (  235.40   673.17   -36.85     1.83)  ; 50\r\n                                  (  235.16   670.13   -36.03     1.83)  ; 51\r\n                                  (  236.02   668.54   -36.03     1.83)  ; 52\r\n                                  (  232.62   668.94   -36.33     1.83)  ; 53\r\n                                )  ;  End of markers\r\n                                (\r\n                                  (  238.51   866.50   -30.20     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                  (  239.13   867.84   -31.58     0.92)  ; 2\r\n                                  (  238.78   871.34   -30.10     0.92)  ; 3\r\n                                  (  239.72   873.35   -30.77     0.92)  ; 4\r\n                                  (  239.59   873.91   -30.77     0.92)  ; 5\r\n                                  (  239.37   876.86   -31.52     0.92)  ; 6\r\n                                  (  239.42   878.66   -33.33     0.92)  ; 7\r\n                                  (  241.13   881.45   -34.45     0.92)  ; 8\r\n                                  (  242.64   882.99   -35.17     0.92)  ; 9\r\n                                  (  242.69   884.79   -36.82     0.92)  ; 10\r\n                                  (  244.71   888.25   -37.55     0.92)  ; 11\r\n                                  (  246.24   889.80   -37.63     0.92)  ; 12\r\n                                  (  247.05   892.39   -38.07     0.92)  ; 13\r\n                                  (  246.82   895.32   -38.90     0.92)  ; 14\r\n                                  (  247.90   896.77   -40.15     0.92)  ; 15\r\n                                  (  249.99   897.85   -40.97     0.92)  ; 16\r\n                                  (  249.64   901.35   -40.38     0.92)  ; 17\r\n                                  (  250.33   904.50   -41.52     0.92)  ; 18\r\n\r\n                                  (Cross\r\n                                    (Color White)\r\n                                    (Name \"Marker 3\")\r\n                                    (  251.76   902.45   -41.52     0.92)  ; 1\r\n                                    (  251.38   899.97   -40.38     0.92)  ; 2\r\n                                    (  248.25   893.26   -39.75     0.92)  ; 3\r\n                                    (  245.57   892.63   -38.88     0.92)  ; 4\r\n                                    (  246.53   884.50   -37.55     0.92)  ; 5\r\n                                    (  241.58   887.52   -32.80     0.92)  ; 6\r\n                                    (  242.66   888.96   -36.90     0.92)  ; 7\r\n                                    (  238.21   877.77   -35.17     0.92)  ; 8\r\n                                    (  240.71   877.16   -31.98     0.92)  ; 9\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  252.48   907.39   -42.55     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                    (  254.31   909.61   -44.17     0.92)  ; 2\r\n                                    (  255.89   912.96   -44.87     0.92)  ; 3\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  256.04   908.22   -44.17     0.92)  ; 1\r\n                                      (  253.32   905.80   -44.17     0.92)  ; 2\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  256.86   914.37   -44.10     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1\r\n                                      (  258.68   916.60   -42.30     0.46)  ; 2\r\n                                      (  258.68   916.60   -42.28     0.46)  ; 3\r\n                                      (  259.89   917.48   -41.45     0.46)  ; 4\r\n                                      (  263.21   919.45   -40.57     0.46)  ; 5\r\n                                      (  265.12   919.30   -41.52     0.46)  ; 6\r\n                                      (  266.91   919.72   -40.22     0.46)  ; 7\r\n                                      (  268.17   922.41   -39.52     0.46)  ; 8\r\n                                      (  270.54   922.36   -39.52     0.46)  ; 9\r\n                                      (  273.21   922.99   -38.55     0.46)  ; 10\r\n                                      (  275.31   924.08   -37.77     0.46)  ; 11\r\n                                      (  276.92   923.26   -36.78     0.46)  ; 12\r\n                                      (  279.10   921.98   -35.60     0.46)  ; 13\r\n                                      (  281.39   924.30   -34.58     0.46)  ; 14\r\n                                      (  282.45   925.75   -33.33     0.46)  ; 15\r\n                                      (  286.16   926.02   -32.75     0.46)  ; 16\r\n                                      (  287.82   927.02   -32.20     0.46)  ; 17\r\n                                      (  289.97   929.90   -31.60     0.46)  ; 18\r\n                                      (  292.20   930.43   -31.00     0.46)  ; 19\r\n                                      (  296.08   931.93   -30.30     0.46)  ; 20\r\n                                      (  297.88   932.35   -29.38     0.46)  ; 21\r\n                                      (  298.82   934.36   -29.38     0.46)  ; 22\r\n                                      (  300.47   935.35   -28.27     0.46)  ; 23\r\n                                      (  302.38   935.20   -27.27     0.46)  ; 24\r\n                                      (  303.51   938.44   -26.72     0.46)  ; 25\r\n                                      (  303.51   938.44   -26.75     0.46)  ; 26\r\n                                      (  305.61   939.53   -26.75     0.46)  ; 27\r\n                                      (  306.56   941.55   -25.73     0.46)  ; 28\r\n                                      (  308.52   943.20   -24.40     0.46)  ; 29\r\n                                      (  310.30   943.62   -23.60     0.46)  ; 30\r\n                                      (  312.41   944.70   -22.97     0.46)  ; 31\r\n                                      (  314.06   945.69   -22.77     0.46)  ; 32\r\n                                      (  317.18   946.42   -22.67     0.46)  ; 33\r\n                                      (  317.18   946.42   -22.70     0.46)  ; 34\r\n                                      (  318.67   946.18   -20.92     0.46)  ; 35\r\n                                      (  318.85   947.42   -20.00     0.46)  ; 36\r\n                                      (  320.45   946.60   -18.40     0.46)  ; 37\r\n                                      (  323.39   946.09   -17.42     0.46)  ; 38\r\n                                      (  323.39   946.09   -17.45     0.46)  ; 39\r\n                                      (  324.28   946.30   -16.20     0.46)  ; 40\r\n                                      (  324.33   948.10   -14.48     0.46)  ; 41\r\n                                      (  327.29   947.60   -14.38     0.46)  ; 42\r\n                                      (  330.72   949.00   -13.05     0.46)  ; 43\r\n                                      (  331.61   949.21   -13.05     0.46)  ; 44\r\n                                      (  332.10   951.12   -13.05     0.46)  ; 45\r\n                                      (  335.42   953.09   -13.05     0.46)  ; 46\r\n                                      (  337.69   955.42   -14.02     0.46)  ; 47\r\n                                      (  340.10   957.18   -14.88     0.46)  ; 48\r\n                                      (  341.45   957.49   -13.72     0.46)  ; 49\r\n                                      (  342.08   958.84   -12.95     0.46)  ; 50\r\n                                      (  344.36   961.15   -12.32     0.46)  ; 51\r\n                                      (  344.36   961.15   -12.35     0.46)  ; 52\r\n                                      (  345.75   963.28   -12.10     0.46)  ; 53\r\n                                      (  345.75   963.28   -12.13     0.46)  ; 54\r\n                                      (  347.59   965.50   -11.17     0.46)  ; 55\r\n                                      (  350.71   966.22   -10.48     0.46)  ; 56\r\n                                      (  351.52   968.81    -9.65     0.46)  ; 57\r\n                                      (  353.17   969.79    -8.42     0.46)  ; 58\r\n                                      (  353.17   969.79    -8.45     0.46)  ; 59\r\n                                      (  354.70   971.34    -6.65     0.46)  ; 60\r\n                                      (  356.35   972.33    -4.17     0.46)  ; 61\r\n                                      (  355.90   972.22    -4.17     0.46)  ; 62\r\n                                      (  356.80   972.43    -1.60     0.46)  ; 63\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  261.91   920.94   -41.45     0.46)  ; 1\r\n                                        (  309.33   945.79   -22.20     0.46)  ; 2\r\n                                        (  305.98   942.01   -24.58     0.46)  ; 3\r\n                                        (  307.94   943.66   -22.43     0.46)  ; 4\r\n                                        (  318.34   945.50   -20.00     0.46)  ; 5\r\n                                        (  325.26   944.14   -18.45     0.46)  ; 6\r\n                                        (  319.83   945.25   -17.20     0.46)  ; 7\r\n                                        (  313.79   946.83   -22.85     0.46)  ; 8\r\n                                        (  316.16   946.79   -20.00     0.46)  ; 9\r\n                                        (  324.38   949.91   -14.38     0.46)  ; 10\r\n                                        (  328.09   950.18   -12.62     0.46)  ; 11\r\n                                        (  332.15   952.92   -12.30     0.46)  ; 12\r\n                                        (  335.05   950.61   -12.32     0.46)  ; 13\r\n                                        (  339.02   949.76   -12.32     0.46)  ; 14\r\n                                        (  341.24   960.43   -12.35     0.46)  ; 15\r\n                                        (  346.28   961.00   -12.52     0.46)  ; 16\r\n                                        (  293.01   933.00   -29.38     0.46)  ; 17\r\n                                        (  286.84   929.16   -31.60     0.46)  ; 18\r\n                                        (  284.12   926.74   -32.75     0.46)  ; 19\r\n                                        (  278.03   920.53   -35.63     0.46)  ; 20\r\n                                        (  275.13   922.84   -35.63     0.46)  ; 21\r\n                                        (  271.47   924.37   -41.05     0.46)  ; 22\r\n                                        (  272.00   922.10   -41.67     0.46)  ; 23\r\n                                        (  268.57   920.70   -39.52     0.46)  ; 24\r\n                                        (  343.83   963.42   -12.57     0.46)  ; 25\r\n                                        (  345.66   965.64   -11.02     0.46)  ; 26\r\n                                        (  350.28   972.10   -12.52     0.46)  ; 27\r\n                                        (  275.23   926.45   -36.78     0.46)  ; 28\r\n                                        (  259.35   913.77   -42.28     0.46)  ; 29\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    |\r\n                                      (  255.99   916.57   -45.75     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                      (  258.40   918.33   -46.67     0.46)  ; 2\r\n                                      (  258.89   920.24   -47.60     0.46)  ; 3\r\n                                      (  258.76   920.81   -48.60     0.46)  ; 4\r\n                                      (  257.28   921.06   -49.05     0.46)  ; 5\r\n                                      (  257.28   921.06   -49.07     0.46)  ; 6\r\n                                      (  257.60   921.72   -49.85     0.46)  ; 7\r\n                                      (  259.43   923.95   -49.85     0.46)  ; 8\r\n                                      (  259.87   924.06   -50.52     0.46)  ; 9\r\n                                      (  258.58   925.54   -52.22     0.46)  ; 10\r\n                                      (  259.43   923.95   -53.60     0.46)  ; 11\r\n                                      (  259.43   923.95   -53.62     0.46)  ; 12\r\n                                      (  259.92   925.86   -55.22     0.46)  ; 13\r\n                                      (  259.92   925.86   -55.30     0.46)  ; 14\r\n                                      (  260.56   927.19   -57.13     0.46)  ; 15\r\n                                      (  262.21   928.18   -58.25     0.46)  ; 16\r\n                                      (  264.18   929.85   -59.38     0.46)  ; 17\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  255.99   922.55   -49.17     0.46)  ; 1\r\n                                        (  259.08   927.46   -57.13     0.46)  ; 2\r\n                                        (  259.84   928.23   -58.22     0.46)  ; 3\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  263.49   930.27   -60.57     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                        (  264.30   932.84   -61.25     0.46)  ; 2\r\n                                        (  262.56   934.23   -62.67     0.46)  ; 3\r\n                                        (  264.23   935.21   -63.75     0.46)  ; 4\r\n                                        (  262.94   936.70   -64.65     0.46)  ; 5\r\n                                        (  263.57   938.04   -65.52     0.46)  ; 6\r\n                                        (  263.34   940.98   -66.20     0.46)  ; 7\r\n                                        (  264.60   943.66   -67.00     0.46)  ; 8\r\n                                        (  264.65   945.46   -67.55     0.46)  ; 9\r\n                                        (  264.65   945.46   -67.57     0.46)  ; 10\r\n                                        (  263.36   946.95   -69.60     0.46)  ; 11\r\n                                        (  263.09   948.09   -70.70     0.46)  ; 12\r\n                                        (  265.18   949.17   -71.85     0.46)  ; 13\r\n                                        (  265.05   949.74   -72.72     0.46)  ; 14\r\n                                        (  265.10   951.53   -73.38     0.46)  ; 15\r\n                                        (  265.10   951.53   -73.40     0.46)  ; 16\r\n                                        (  263.95   952.47   -74.25     0.46)  ; 17\r\n                                        (  262.92   952.82   -75.52     0.46)  ; 18\r\n                                        (  262.97   954.62   -76.80     0.46)  ; 19\r\n                                        (  262.97   954.62   -76.82     0.46)  ; 20\r\n                                        (  262.89   956.99   -78.38     0.46)  ; 21\r\n                                        (  263.64   957.77   -79.75     0.46)  ; 22\r\n                                        (  262.93   958.79   -81.32     0.46)  ; 23\r\n                                        (  259.72   960.43   -82.97     0.46)  ; 24\r\n                                        (  262.40   961.06   -85.85     0.46)  ; 25\r\n                                        (  261.56   962.65   -88.02     0.46)  ; 26\r\n                                        (  260.85   963.68   -89.32     0.46)  ; 27\r\n                                        (  260.90   965.48   -91.07     0.46)  ; 28\r\n                                        (  260.37   967.75   -92.90     0.46)  ; 29\r\n                                        (  259.34   968.11   -95.02     0.46)  ; 30\r\n                                        (  260.99   969.09   -95.82     0.46)  ; 31\r\n                                        (  260.99   969.09   -95.85     0.46)  ; 32\r\n                                        (  259.71   970.58   -96.65     0.46)  ; 33\r\n                                        (  259.93   973.62   -97.80     0.46)  ; 34\r\n                                        (  260.02   977.22   -98.47     0.46)  ; 35\r\n                                        (  260.02   977.22   -98.53     0.46)  ; 36\r\n                                        (  260.98   979.23   -99.45     0.46)  ; 37\r\n                                        (  261.65   982.38  -100.50     0.46)  ; 38\r\n                                        (  262.09   982.48  -100.50     0.46)  ; 39\r\n                                        (  260.98   985.21  -101.27     0.46)  ; 40\r\n                                        (  261.17   986.45  -102.52     0.46)  ; 41\r\n                                        (  262.24   987.89  -103.90     0.46)  ; 42\r\n                                        (  261.45   991.29  -104.82     0.46)  ; 43\r\n                                        (  260.78   994.12  -105.77     0.46)  ; 44\r\n                                        (  261.15   996.59  -107.35     0.46)  ; 45\r\n                                        (  260.92   999.53  -108.72     0.46)  ; 46\r\n                                        (  260.52  1001.22  -110.02     0.46)  ; 47\r\n                                        (  260.44  1003.59  -111.53     0.46)  ; 48\r\n                                        (  258.08  1003.63  -112.75     0.46)  ; 49\r\n                                        (  256.29  1003.22  -114.65     0.46)  ; 50\r\n                                        (  252.58  1002.95  -115.47     0.46)  ; 51\r\n                                        (  251.38  1002.07  -117.40     0.46)  ; 52\r\n                                        (  249.46  1002.21  -118.10     0.46)  ; 53\r\n                                        (  249.46  1002.21  -118.22     0.46)  ; 54\r\n\r\n                                        (Cross\r\n                                          (Color White)\r\n                                          (Name \"Marker 3\")\r\n                                          (  261.82   999.74  -111.53     0.46)  ; 1\r\n                                          (  263.62   990.02  -104.82     0.46)  ; 2\r\n                                          (  263.71   987.64  -104.48     0.46)  ; 3\r\n                                          (  261.08   988.81  -103.47     0.46)  ; 4\r\n                                          (  264.31   954.93   -78.38     0.46)  ; 5\r\n                                          (  259.41   959.76   -84.72     0.46)  ; 6\r\n                                          (  259.69   964.61   -91.07     0.46)  ; 7\r\n                                          (  259.08   975.20   -99.45     0.46)  ; 8\r\n                                          (  259.54   981.29  -100.50     0.46)  ; 9\r\n                                          (  265.15   943.79   -58.53     0.46)  ; 10\r\n                                          (  265.81   944.54   -65.67     0.46)  ; 11\r\n                                          (  264.68   941.29   -67.35     0.46)  ; 12\r\n                                          (  263.34   979.19  -102.65     0.46)  ; 13\r\n                                          (  259.26   992.56  -105.77     0.46)  ; 14\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      |\r\n                                        (  265.88   932.61   -57.90     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                        (  266.11   935.65   -57.02     0.46)  ; 2\r\n                                        (  266.21   939.26   -56.95     0.46)  ; 3\r\n                                        (  267.28   940.71   -57.05     0.46)  ; 4\r\n                                        (  267.51   943.75   -57.05     0.46)  ; 5\r\n                                        (  267.47   947.92   -58.17     0.46)  ; 6\r\n                                        (  268.46   951.74   -59.13     0.46)  ; 7\r\n                                        (  271.38   955.39   -59.67     0.46)  ; 8\r\n                                        (  272.84   955.15   -60.15     0.46)  ; 9\r\n                                        (  274.23   957.27   -60.15     0.46)  ; 10\r\n                                        (  278.13   958.78   -60.95     0.46)  ; 11\r\n                                        (  280.09   960.43   -60.95     0.46)  ; 12\r\n                                        (  282.63   961.62   -61.55     0.46)  ; 13\r\n                                        (  284.48   963.85   -62.28     0.46)  ; 14\r\n                                        (  287.02   965.04   -60.77     0.46)  ; 15\r\n                                        (  289.69   965.67   -59.65     0.46)  ; 16\r\n                                        (  291.35   966.65   -59.83     0.46)  ; 17\r\n                                        (  295.19   966.36   -58.95     0.46)  ; 18\r\n                                        (  296.84   967.35   -58.95     0.46)  ; 19\r\n                                        (  299.79   966.83   -60.67     0.46)  ; 20\r\n                                        (  299.66   967.41   -62.13     0.46)  ; 21\r\n                                        (  303.54   968.91   -62.13     0.46)  ; 22\r\n                                        (  304.66   972.16   -62.35     0.46)  ; 23\r\n                                        (  306.77   973.26   -62.28     0.46)  ; 24\r\n                                        (  308.63   974.88   -62.28     0.46)  ; 25\r\n                                        (  311.50   976.74   -60.92     0.46)  ; 26\r\n                                        (  313.46   978.39   -59.78     0.46)  ; 27\r\n                                        (  316.19   980.84   -58.45     0.46)  ; 28\r\n                                        (  317.84   981.82   -57.20     0.46)  ; 29\r\n                                        (  320.13   984.14   -56.57     0.46)  ; 30\r\n                                        (  322.80   984.77   -55.57     0.46)  ; 31\r\n                                        (  324.73   984.62   -53.95     0.46)  ; 32\r\n                                        (  325.31   984.16   -52.77     0.46)  ; 33\r\n\r\n                                        (Cross\r\n                                          (Color White)\r\n                                          (Name \"Marker 3\")\r\n                                          (  265.94   940.39   -57.40     0.46)  ; 1\r\n                                          (  268.85   944.06   -57.40     0.46)  ; 2\r\n                                          (  266.89   948.38   -56.08     0.46)  ; 3\r\n                                          (  314.09   979.74   -58.53     0.46)  ; 4\r\n                                          (  314.62   977.48   -61.00     0.46)  ; 5\r\n                                          (  305.03   974.64   -62.28     0.46)  ; 6\r\n                                          (  289.17   967.93   -60.75     0.46)  ; 7\r\n                                          (  290.54   964.07   -61.20     0.46)  ; 8\r\n                                          (  288.66   960.05   -59.25     0.46)  ; 9\r\n                                          (  268.70   954.77   -59.67     0.46)  ; 10\r\n                                          (  270.25   952.15   -59.67     0.46)  ; 11\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  248.94   908.14   -41.52     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                    (  248.41   910.40   -42.45     0.92)  ; 2\r\n                                    (  247.75   913.22   -43.50     0.92)  ; 3\r\n                                    (  247.98   916.27   -44.13     0.92)  ; 4\r\n                                    (  246.10   918.22   -44.78     0.92)  ; 5\r\n                                    (  245.89   921.15   -45.32     0.92)  ; 6\r\n                                    (  245.22   923.98   -46.12     0.92)  ; 7\r\n                                    (  245.22   923.98   -46.15     0.92)  ; 8\r\n                                    (  244.30   927.95   -46.60     0.92)  ; 9\r\n                                    (  243.05   931.24   -46.82     0.92)  ; 10\r\n                                    (  241.05   933.76   -47.30     0.92)  ; 11\r\n                                    (  241.01   937.93   -47.33     0.92)  ; 12\r\n                                    (  239.15   939.88   -48.15     0.92)  ; 13\r\n                                    (  237.46   943.07   -48.72     0.92)  ; 14\r\n                                    (  235.77   946.26   -48.72     0.92)  ; 15\r\n                                    (  234.79   948.40   -49.27     0.92)  ; 16\r\n                                    (  233.99   951.80   -49.40     0.92)  ; 17\r\n                                    (  233.46   954.07   -49.80     0.92)  ; 18\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  247.86   906.68   -41.52     0.92)  ; 1\r\n                                      (  249.35   912.41   -44.13     0.92)  ; 2\r\n                                      (  245.48   916.87   -44.13     0.92)  ; 3\r\n                                      (  243.93   925.48   -47.33     0.92)  ; 4\r\n                                      (  243.53   927.17   -47.33     0.92)  ; 5\r\n                                      (  245.06   928.72   -47.82     0.92)  ; 6\r\n                                      (  240.30   932.98   -45.97     0.92)  ; 7\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  233.65   955.31   -50.70     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                      (  231.20   957.72   -50.70     0.46)  ; 2\r\n                                      (  230.22   959.87   -50.60     0.46)  ; 3\r\n                                      (  228.85   963.73   -50.47     0.46)  ; 4\r\n                                      (  227.92   967.70   -50.97     0.46)  ; 5\r\n                                      (  226.04   969.65   -50.97     0.46)  ; 6\r\n                                      (  225.77   970.78   -51.32     0.46)  ; 7\r\n                                      (  223.78   973.30   -51.35     0.46)  ; 8\r\n                                      (  223.06   974.33   -53.00     0.46)  ; 9\r\n                                      (  221.95   977.05   -54.43     0.46)  ; 10\r\n                                      (  220.21   978.43   -55.30     0.46)  ; 11\r\n                                      (  218.93   979.92   -56.03     0.46)  ; 12\r\n                                      (  218.93   979.92   -56.05     0.46)  ; 13\r\n                                      (  218.53   981.62   -56.55     0.46)  ; 14\r\n                                      (  216.61   981.77   -56.90     0.46)  ; 15\r\n                                      (  215.18   983.83   -57.45     0.46)  ; 16\r\n                                      (  215.81   985.16   -58.50     0.46)  ; 17\r\n                                      (  215.42   986.86   -59.38     0.46)  ; 18\r\n                                      (  216.23   989.45   -59.20     0.46)  ; 19\r\n                                      (  215.26   991.50   -59.92     0.46)  ; 20\r\n                                      (  214.73   993.77   -60.95     0.46)  ; 21\r\n                                      (  215.22   995.68   -61.50     0.46)  ; 22\r\n                                      (  214.56   998.50   -62.45     0.46)  ; 23\r\n                                      (  214.78  1001.54   -63.40     0.46)  ; 24\r\n                                      (  214.78  1001.54   -63.42     0.46)  ; 25\r\n                                      (  214.08  1002.57   -63.88     0.46)  ; 26\r\n                                      (  214.08  1002.57   -62.88     0.46)  ; 27\r\n                                      (  214.57  1004.48   -61.88     0.46)  ; 28\r\n                                      (  214.09  1008.55   -60.72     0.46)  ; 29\r\n                                      (  215.04  1010.56   -62.57     0.46)  ; 30\r\n                                      (  214.06  1012.73   -63.42     0.46)  ; 31\r\n                                      (  214.56  1014.63   -64.02     0.46)  ; 32\r\n                                      (  213.71  1016.23   -64.95     0.46)  ; 33\r\n                                      (  212.60  1018.94   -66.17     0.46)  ; 34\r\n                                      (  211.49  1021.67   -67.07     0.46)  ; 35\r\n                                      (  210.20  1023.15   -68.00     0.46)  ; 36\r\n                                      (  209.04  1024.09   -68.75     0.46)  ; 37\r\n                                      (  209.08  1025.89   -69.92     0.46)  ; 38\r\n                                      (  208.68  1027.59   -71.38     0.46)  ; 39\r\n                                      (  211.24  1028.78   -72.60     0.46)  ; 40\r\n                                      (  211.24  1028.78   -72.63     0.46)  ; 41\r\n                                      (  209.95  1030.27   -74.40     0.46)  ; 42\r\n                                      (  209.86  1032.64   -75.30     0.46)  ; 43\r\n                                      (  209.78  1035.00   -76.85     0.46)  ; 44\r\n                                      (  208.94  1036.60   -78.38     0.46)  ; 45\r\n                                      (  208.94  1036.60   -78.35     0.46)  ; 46\r\n                                      (  208.67  1037.74   -81.07     0.46)  ; 47\r\n                                      (  208.67  1037.74   -81.10     0.46)  ; 48\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  229.27   957.87   -50.60     0.46)  ; 1\r\n                                        (  232.00   954.33   -50.60     0.46)  ; 2\r\n                                        (  228.93   961.36   -51.50     0.46)  ; 3\r\n                                        (  227.95   963.52   -50.47     0.46)  ; 4\r\n                                        (  225.24   973.04   -51.35     0.46)  ; 5\r\n                                        (  225.17   975.41   -54.43     0.46)  ; 6\r\n                                        (  213.93   987.12   -59.20     0.46)  ; 7\r\n                                        (  216.73   991.25   -60.95     0.46)  ; 8\r\n                                        (  216.47   992.39   -60.95     0.46)  ; 9\r\n                                        (  216.21   999.49   -63.88     0.46)  ; 10\r\n                                        (  216.28  1007.27   -64.20     0.46)  ; 11\r\n                                        (  217.66  1003.41   -60.72     0.46)  ; 12\r\n                                        (  212.58  1012.97   -61.30     0.46)  ; 13\r\n                                        (  213.28  1022.09   -63.62     0.46)  ; 14\r\n                                        (  208.81  1021.05   -65.95     0.46)  ; 15\r\n                                        (  211.99  1023.57   -69.92     0.46)  ; 16\r\n                                        (  208.46  1024.55   -68.07     0.46)  ; 17\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    |\r\n                                      (  231.74   955.35   -48.00     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                      (  228.98   957.10   -46.12     0.92)  ; 2\r\n                                      (  228.58   958.79   -45.32     0.92)  ; 3\r\n                                      (  226.90   961.98   -45.03     0.92)  ; 4\r\n                                      (  225.91   964.15   -44.00     0.92)  ; 5\r\n                                      (  225.91   964.15   -44.02     0.92)  ; 6\r\n                                      (  224.35   966.76   -42.90     0.92)  ; 7\r\n                                      (  224.35   966.76   -42.92     0.92)  ; 8\r\n                                      (  222.31   967.48   -42.45     0.92)  ; 9\r\n                                      (  222.08   970.41   -42.45     0.92)  ; 10\r\n                                      (  221.38   971.44   -41.15     0.92)  ; 11\r\n                                      (  221.38   971.44   -41.17     0.92)  ; 12\r\n                                      (  219.37   973.95   -40.38     0.92)  ; 13\r\n                                      (  218.08   975.45   -40.40     0.92)  ; 14\r\n                                      (  218.08   975.45   -40.42     0.92)  ; 15\r\n                                      (  217.28   978.85   -39.72     0.92)  ; 16\r\n                                      (  215.87   980.90   -38.80     0.92)  ; 17\r\n                                      (  213.55   982.74   -38.20     0.92)  ; 18\r\n                                      (  212.26   984.22   -37.25     0.92)  ; 19\r\n                                      (  212.35   987.83   -36.27     0.92)  ; 20\r\n                                      (  211.23   990.57   -37.88     0.92)  ; 21\r\n                                      (  208.97   994.22   -37.88     0.92)  ; 22\r\n                                      (  207.86   996.93   -37.88     0.92)  ; 23\r\n                                      (  205.67   998.22   -36.33     0.92)  ; 24\r\n                                      (  203.45  1003.67   -36.33     0.92)  ; 25\r\n                                      (  203.02  1003.56   -36.33     0.92)  ; 26\r\n                                      (  202.17  1005.16   -36.47     0.92)  ; 27\r\n                                      (  201.69  1009.23   -36.52     0.92)  ; 28\r\n                                      (  199.86  1012.98   -37.85     0.92)  ; 29\r\n                                      (  197.15  1016.52   -39.28     0.92)  ; 30\r\n                                      (  196.35  1019.91   -39.92     0.92)  ; 31\r\n                                      (  196.22  1020.49   -39.92     0.92)  ; 32\r\n                                      (  195.95  1021.61   -40.80     0.92)  ; 33\r\n                                      (  193.50  1024.04   -41.73     0.92)  ; 34\r\n                                      (  192.98  1026.29   -40.50     0.92)  ; 35\r\n                                      (  191.87  1029.01   -39.85     0.92)  ; 36\r\n                                      (  189.54  1030.85   -39.85     0.92)  ; 37\r\n                                      (  189.64  1034.46   -40.48     0.92)  ; 38\r\n                                      (  187.95  1037.65   -39.63     0.92)  ; 39\r\n                                      (  186.71  1040.94   -40.55     0.92)  ; 40\r\n                                      (  184.76  1045.27   -40.97     0.92)  ; 41\r\n                                      (  183.02  1046.65   -41.22     0.92)  ; 42\r\n                                      (  183.07  1048.46   -42.05     0.92)  ; 43\r\n                                      (  181.95  1051.18   -42.45     0.92)  ; 44\r\n                                      (  181.16  1054.57   -42.60     0.92)  ; 45\r\n                                      (  179.30  1056.53   -42.60     0.92)  ; 46\r\n                                      (  178.05  1059.82   -41.47     0.92)  ; 47\r\n                                      (  178.10  1061.62   -41.10     0.92)  ; 48\r\n                                      (  176.15  1065.93   -40.48     0.92)  ; 49\r\n                                      (  176.24  1069.54   -39.97     0.92)  ; 50\r\n                                      (  172.45  1071.64   -40.32     0.92)  ; 51\r\n                                      (  171.21  1074.94   -41.60     0.92)  ; 52\r\n                                      (  168.70  1075.54   -42.35     0.92)  ; 53\r\n                                      (  168.36  1079.04   -43.22     0.92)  ; 54\r\n                                      (  168.85  1080.95   -43.72     0.92)  ; 55\r\n                                      (  164.17  1082.84   -43.97     0.92)  ; 56\r\n                                      (  164.17  1082.84   -44.00     0.92)  ; 57\r\n                                      (  162.17  1085.35   -43.10     0.92)  ; 58\r\n                                      (  156.46  1087.59   -43.35     0.92)  ; 59\r\n                                      (  156.46  1087.59   -43.47     0.92)  ; 60\r\n                                      (  153.26  1089.24   -44.63     0.46)  ; 61\r\n                                      (  149.46  1091.34   -45.27     0.46)  ; 62\r\n                                      (  149.46  1091.34   -45.40     0.46)  ; 63\r\n                                      (  146.20  1091.17   -48.22     0.46)  ; 64\r\n                                      (  146.20  1091.17   -48.27     0.46)  ; 65\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  225.00   957.95   -45.32     0.92)  ; 1\r\n                                        (  225.36   960.43   -45.32     0.92)  ; 2\r\n                                        (  230.28   961.58   -45.32     0.92)  ; 3\r\n                                        (  228.23   962.30   -43.78     0.92)  ; 4\r\n                                        (  227.75   966.36   -42.28     0.92)  ; 5\r\n                                        (  223.99   964.30   -45.88     0.92)  ; 6\r\n                                        (  221.86   973.34   -40.38     0.92)  ; 7\r\n                                        (  223.56   970.16   -38.85     0.92)  ; 8\r\n                                        (  219.60   977.00   -38.55     0.92)  ; 9\r\n                                        (  214.66   980.01   -37.80     0.92)  ; 10\r\n                                        (  211.44   981.66   -37.17     0.92)  ; 11\r\n                                        (  210.38   986.18   -37.17     0.92)  ; 12\r\n                                        (  215.03   988.46   -35.70     0.92)  ; 13\r\n                                        (  211.80   984.12   -36.72     0.92)  ; 14\r\n                                        (  211.00   987.52   -33.67     0.92)  ; 15\r\n                                        (  202.65  1001.09   -36.33     0.92)  ; 16\r\n                                        (  206.09  1002.50   -38.60     0.92)  ; 17\r\n                                        (  201.04  1001.91   -38.60     0.92)  ; 18\r\n                                        (  203.37  1006.03   -38.60     0.92)  ; 19\r\n                                        (  201.01  1006.08   -37.32     0.92)  ; 20\r\n                                        (  203.07  1011.34   -36.30     0.92)  ; 21\r\n                                        (  195.62  1014.97   -39.28     0.92)  ; 22\r\n                                        (  195.54  1017.34   -39.28     0.92)  ; 23\r\n                                        (  200.00  1018.38   -39.28     0.92)  ; 24\r\n                                        (  199.52  1016.47   -39.28     0.92)  ; 25\r\n                                        (  201.38  1014.52   -39.28     0.92)  ; 26\r\n                                        (  193.14  1021.55   -41.73     0.92)  ; 27\r\n                                        (  195.65  1026.92   -41.73     0.92)  ; 28\r\n                                        (  191.36  1027.11   -37.28     0.92)  ; 29\r\n                                        (  191.45  1024.74   -36.25     0.92)  ; 30\r\n                                        (  193.65  1029.43   -41.28     0.92)  ; 31\r\n                                        (  190.00  1036.94   -39.63     0.92)  ; 32\r\n                                        (  184.53  1042.22   -39.72     0.92)  ; 33\r\n                                        (  190.41  1035.24   -37.90     0.92)  ; 34\r\n                                        (  182.26  1045.87   -42.45     0.92)  ; 35\r\n                                        (  179.73  1050.65   -42.45     0.92)  ; 36\r\n                                        (  177.95  1056.21   -42.60     0.92)  ; 37\r\n                                        (  179.40  1066.10   -41.47     0.92)  ; 38\r\n                                        (  175.60  1062.22   -40.40     0.92)  ; 39\r\n                                        (  175.55  1060.42   -41.05     0.92)  ; 40\r\n                                        (  174.49  1064.95   -40.13     0.92)  ; 41\r\n                                        (  178.02  1064.00   -40.13     0.92)  ; 42\r\n                                        (  171.06  1069.52   -40.32     0.92)  ; 43\r\n                                        (  173.66  1072.52   -40.32     0.92)  ; 44\r\n                                        (  170.21  1071.12   -42.13     0.92)  ; 45\r\n                                        (  172.10  1075.15   -42.22     0.92)  ; 46\r\n                                        (  165.32  1075.94   -41.35     0.92)  ; 47\r\n                                        (  163.76  1078.56   -41.38     0.92)  ; 48\r\n                                        (  167.87  1083.11   -45.03     0.92)  ; 49\r\n                                        (  171.72  1082.81   -45.05     0.92)  ; 50\r\n                                        (  160.21  1083.69   -41.67     0.92)  ; 51\r\n                                        (  162.39  1082.42   -41.75     0.92)  ; 52\r\n                                        (  166.63  1086.41   -41.75     0.92)  ; 53\r\n                                        (  159.51  1090.70   -44.70     0.92)  ; 54\r\n                                        (  164.90  1087.78   -44.72     0.92)  ; 55\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  )  ;  End of split\r\n                                |\r\n                                  (  235.72   866.34   -28.50     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                  (  234.79   870.30   -29.10     1.38)  ; 2\r\n                                  (  233.09   873.50   -29.10     1.38)  ; 3\r\n                                  (  231.59   877.92   -29.38     1.38)  ; 4\r\n                                  (  230.34   881.21   -28.50     1.38)  ; 5\r\n                                  (  230.58   884.24   -27.97     1.38)  ; 6\r\n                                  (  230.76   885.48   -27.97     1.38)  ; 7\r\n                                  (  228.76   888.00   -27.97     1.38)  ; 8\r\n                                  (  227.02   889.39   -27.22     1.38)  ; 9\r\n                                  (  225.91   892.10   -26.52     1.38)  ; 10\r\n                                  (  224.80   894.84   -25.50     1.38)  ; 11\r\n                                  (  222.34   897.24   -25.20     1.38)  ; 12\r\n                                  (  219.95   901.46   -24.62     1.38)  ; 13\r\n                                  (  218.84   904.19   -26.85     1.38)  ; 14\r\n                                  (  216.88   908.50   -27.27     1.38)  ; 15\r\n                                  (  212.69   912.30   -27.57     1.38)  ; 16\r\n                                  (  213.51   914.88   -28.15     1.38)  ; 17\r\n                                  (  213.43   917.25   -27.97     1.38)  ; 18\r\n                                  (  213.78   919.72   -28.65     1.38)  ; 19\r\n                                  (  213.78   919.72   -28.67     1.38)  ; 20\r\n                                  (  212.23   922.34   -29.08     1.38)  ; 21\r\n                                  (  210.73   926.76   -29.90     1.38)  ; 22\r\n                                  (  211.66   928.78   -30.35     1.38)  ; 23\r\n                                  (  211.66   928.78   -30.38     1.38)  ; 24\r\n                                  (  211.31   932.28   -30.95     1.38)  ; 25\r\n                                  (  210.78   934.54   -31.58     1.38)  ; 26\r\n                                  (  210.78   934.54   -31.60     1.38)  ; 27\r\n                                  (  209.36   936.61   -32.25     1.38)  ; 28\r\n                                  (  207.04   938.44   -32.45     1.38)  ; 29\r\n                                  (  207.04   938.44   -32.47     1.38)  ; 30\r\n                                  (  207.28   941.49   -33.17     1.38)  ; 31\r\n                                  (  206.75   943.74   -34.32     1.38)  ; 32\r\n                                  (  206.75   943.74   -34.35     1.38)  ; 33\r\n                                  (  206.79   945.55   -33.95     1.38)  ; 34\r\n                                  (  207.78   949.37   -34.90     1.38)  ; 35\r\n                                  (  208.09   950.04   -35.85     1.38)  ; 36\r\n                                  (  206.22   951.98   -37.08     1.38)  ; 37\r\n                                  (  204.04   953.27   -37.63     1.38)  ; 38\r\n                                  (  202.80   956.56   -38.42     1.38)  ; 39\r\n                                  (  202.27   958.82   -39.17     1.38)  ; 40\r\n                                  (  199.50   960.56   -40.07     1.38)  ; 41\r\n                                  (  199.29   963.49   -41.03     1.38)  ; 42\r\n                                  (  200.10   966.08   -41.90     1.38)  ; 43\r\n                                  (  199.56   968.34   -42.33     1.38)  ; 44\r\n                                  (  196.58   973.02   -42.57     1.38)  ; 45\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  198.99   962.84   -87.00     0.46)  ; 1\r\n                                    (  206.64   950.31   -79.55     0.46)  ; 2\r\n                                    (  206.07   950.76   -79.55     0.46)  ; 3\r\n                                  )  ;  End of markers\r\n\r\n                                  (Cross\r\n                                    (Color White)\r\n                                    (Name \"Marker 3\")\r\n                                    (  197.51   969.05   -42.57     1.38)  ; 1\r\n                                    (  196.87   961.73   -41.03     1.38)  ; 2\r\n                                    (  199.77   959.44   -38.42     1.38)  ; 3\r\n                                    (  201.58   955.68   -38.42     1.38)  ; 4\r\n                                    (  203.75   942.45   -31.15     1.38)  ; 5\r\n                                    (  213.68   932.23   -31.15     1.38)  ; 6\r\n                                    (  209.75   928.93   -31.00     1.38)  ; 7\r\n                                    (  209.56   927.69   -31.00     1.38)  ; 8\r\n                                    (  214.64   924.10   -30.97     1.38)  ; 9\r\n                                    (  209.86   922.38   -27.00     1.38)  ; 10\r\n                                    (  211.86   919.88   -27.00     1.38)  ; 11\r\n                                    (  220.17   898.52   -24.62     1.38)  ; 12\r\n                                    (  223.29   899.26   -25.27     1.38)  ; 13\r\n                                    (  224.65   889.43   -27.22     1.38)  ; 14\r\n                                    (  234.78   864.32   -29.38     1.38)  ; 15\r\n                                    (  233.98   867.72   -29.38     1.38)  ; 16\r\n                                    (  231.18   873.64   -29.38     1.38)  ; 17\r\n                                    (  231.09   876.00   -29.38     1.38)  ; 18\r\n                                    (  233.86   874.27   -29.58     1.38)  ; 19\r\n                                    (  213.22   910.03   -26.40     1.38)  ; 20\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  196.20   974.70   -43.95     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                    (  194.32   976.64   -44.70     0.92)  ; 2\r\n                                    (  192.85   976.89   -45.52     0.92)  ; 3\r\n                                    (  191.42   978.96   -47.02     0.92)  ; 4\r\n                                    (  190.31   981.67   -47.85     0.92)  ; 5\r\n                                    (  189.20   984.41   -48.90     0.92)  ; 6\r\n                                    (  187.02   985.68   -49.32     0.92)  ; 7\r\n                                    (  184.26   987.43   -49.32     0.92)  ; 8\r\n                                    (  184.26   987.43   -49.35     0.92)  ; 9\r\n                                    (  183.41   989.01   -50.42     0.92)  ; 10\r\n                                    (  183.01   990.72   -50.42     0.92)  ; 11\r\n                                    (  182.75   991.84   -51.35     0.92)  ; 12\r\n                                    (  181.32   993.91   -52.13     0.92)  ; 13\r\n                                    (  181.42   997.50   -52.77     0.92)  ; 14\r\n                                    (  181.42   997.50   -52.80     0.92)  ; 15\r\n                                    (  180.72   998.54   -53.30     0.92)  ; 16\r\n                                    (  179.61  1001.26   -53.58     0.92)  ; 17\r\n                                    (  179.79  1002.50   -54.00     0.92)  ; 18\r\n                                    (  179.26  1004.77   -54.40     0.92)  ; 19\r\n                                    (  181.72  1008.32   -54.90     0.92)  ; 20\r\n                                    (  180.92  1011.72   -54.35     0.92)  ; 21\r\n                                    (  181.14  1014.77   -54.35     0.92)  ; 22\r\n                                    (  177.85  1018.77   -54.90     0.92)  ; 23\r\n                                    (  177.46  1020.46   -55.88     0.92)  ; 24\r\n                                    (  177.46  1020.46   -55.90     0.92)  ; 25\r\n                                    (  176.34  1023.19   -57.00     0.92)  ; 26\r\n                                    (  176.34  1023.19   -57.02     0.92)  ; 27\r\n                                    (  175.23  1025.91   -57.95     0.92)  ; 28\r\n                                    (  173.95  1027.41   -59.00     0.92)  ; 29\r\n                                    (  172.53  1029.46   -60.00     0.92)  ; 30\r\n                                    (  172.53  1029.46   -60.03     0.92)  ; 31\r\n                                    (  171.60  1033.42   -60.70     0.92)  ; 32\r\n                                    (  169.13  1035.83   -61.60     0.92)  ; 33\r\n                                    (  166.82  1037.69   -62.63     0.92)  ; 34\r\n                                    (  164.95  1039.62   -63.35     0.92)  ; 35\r\n                                    (  163.40  1042.25   -64.18     0.92)  ; 36\r\n                                    (  160.82  1045.23   -64.55     0.92)  ; 37\r\n                                    (  159.44  1049.09   -65.43     0.92)  ; 38\r\n                                    (  158.91  1051.34   -65.13     0.92)  ; 39\r\n                                    (  158.38  1053.61   -65.95     0.92)  ; 40\r\n                                    (  157.09  1055.10   -67.07     0.92)  ; 41\r\n                                    (  156.56  1057.37   -68.28     0.92)  ; 42\r\n                                    (  153.52  1060.24   -68.72     0.92)  ; 43\r\n                                    (  153.57  1062.04   -69.47     0.92)  ; 44\r\n                                    (  153.94  1064.53   -70.03     0.92)  ; 45\r\n                                    (  153.42  1066.78   -71.05     0.92)  ; 46\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  151.83  1063.43   -70.03     0.92)  ; 1\r\n                                      (  151.30  1065.69   -70.03     0.92)  ; 2\r\n                                      (  167.77  1039.70   -61.63     0.92)  ; 3\r\n                                      (  165.50  1043.34   -64.18     0.92)  ; 4\r\n                                      (  157.92  1047.53   -65.43     0.92)  ; 5\r\n                                      (  157.12  1050.93   -66.45     0.92)  ; 6\r\n                                      (  159.27  1053.83   -64.13     0.92)  ; 7\r\n                                      (  155.35  1056.49   -68.28     0.92)  ; 8\r\n                                      (  158.66  1058.46   -69.22     0.92)  ; 9\r\n                                      (  178.35  1020.67   -57.02     0.92)  ; 10\r\n                                      (  172.42  1025.86   -60.03     0.92)  ; 11\r\n                                      (  170.38  1032.54   -61.63     0.92)  ; 12\r\n                                      (  176.28  1031.54   -61.63     0.92)  ; 13\r\n                                      (  181.37  1017.80   -53.72     0.92)  ; 14\r\n                                      (  177.67  1017.53   -53.72     0.92)  ; 15\r\n                                      (  182.47   987.01   -49.35     0.92)  ; 16\r\n                                      (  185.23   985.26   -49.35     0.92)  ; 17\r\n                                      (  187.51   987.59   -49.00     0.92)  ; 18\r\n                                      (  182.26   989.94   -50.42     0.92)  ; 19\r\n                                      (  183.43   994.99   -52.80     0.92)  ; 20\r\n                                      (  178.73  1007.03   -54.90     0.92)  ; 21\r\n                                      (  179.66  1009.04   -52.80     0.92)  ; 22\r\n                                      (  181.72  1008.32   -50.97     0.92)  ; 23\r\n                                      (  178.52  1015.94   -50.97     0.92)  ; 24\r\n                                      (  190.61   976.38   -47.02     0.92)  ; 25\r\n                                      (  194.40   974.27   -44.70     0.92)  ; 26\r\n                                      (  195.53   977.53   -43.58     0.92)  ; 27\r\n                                      (  195.54   977.55   -39.05     0.92)  ; 28\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  151.53  1068.73   -71.82     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-1\r\n                                      (  150.51  1069.09   -72.57     0.46)  ; 2\r\n                                      (  149.53  1071.24   -73.28     0.46)  ; 3\r\n                                      (  148.69  1072.85   -74.32     0.46)  ; 4\r\n                                      (  146.77  1072.98   -75.57     0.46)  ; 5\r\n                                      (  145.34  1075.05   -76.92     0.46)  ; 6\r\n                                      (  144.33  1075.41   -78.63     0.46)  ; 7\r\n                                      (  142.01  1077.25   -79.10     0.46)  ; 8\r\n                                      (  140.40  1078.07   -81.25     0.46)  ; 9\r\n                                      (  140.40  1078.07   -81.30     0.46)  ; 10\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  149.49  1069.45   -72.57     0.46)  ; 1\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    |\r\n                                      (  153.64  1069.82   -71.05     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-2\r\n                                      (  152.84  1073.22   -71.05     0.46)  ; 2\r\n                                      (  152.76  1075.58   -71.85     0.46)  ; 3\r\n                                      (  154.86  1076.67   -73.25     0.46)  ; 4\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  197.55   975.03   -41.30     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                    (  198.17   976.38   -40.55     0.92)  ; 2\r\n                                    (  196.18   978.88   -39.60     0.92)  ; 3\r\n                                    (  195.95   981.83   -39.05     0.92)  ; 4\r\n                                    (  194.98   983.99   -38.42     0.92)  ; 5\r\n                                    (  193.69   985.47   -38.42     0.92)  ; 6\r\n                                    (  193.55   986.04   -37.85     0.92)  ; 7\r\n                                    (  193.74   987.27   -37.15     0.92)  ; 8\r\n                                    (  193.74   987.27   -37.17     0.92)  ; 9\r\n                                    (  194.23   989.18   -36.40     0.92)  ; 10\r\n                                    (  194.23   989.18   -36.42     0.92)  ; 11\r\n                                    (  194.59   991.66   -35.45     0.92)  ; 12\r\n                                    (  194.77   992.90   -35.57     0.92)  ; 13\r\n                                    (  194.69   995.27   -34.02     0.92)  ; 14\r\n                                    (  193.66   995.61   -32.40     0.92)  ; 15\r\n                                    (  191.35   997.46   -31.05     0.92)  ; 16\r\n                                    (  188.71   998.64   -31.22     0.92)  ; 17\r\n                                    (  188.31  1000.34   -32.17     0.92)  ; 18\r\n                                    (  188.67  1002.81   -33.25     0.92)  ; 19\r\n                                    (  188.67  1002.81   -33.27     0.92)  ; 20\r\n                                    (  189.58  1003.02   -34.88     0.92)  ; 21\r\n                                    (  189.58  1003.02   -34.90     0.92)  ; 22\r\n                                    (  190.96  1005.14   -35.75     0.92)  ; 23\r\n                                    (  190.96  1005.14   -35.77     0.92)  ; 24\r\n                                    (  192.53  1008.49   -35.75     0.92)  ; 25\r\n                                    (  192.75  1011.53   -36.20     0.92)  ; 26\r\n                                    (  193.12  1014.00   -35.53     0.92)  ; 27\r\n                                    (  194.19  1015.46   -34.25     0.92)  ; 28\r\n                                    (  193.99  1018.39   -33.00     0.92)  ; 29\r\n                                    (  194.21  1021.42   -32.30     0.92)  ; 30\r\n                                    (  194.21  1021.42   -32.32     0.92)  ; 31\r\n                                    (  195.73  1022.98   -31.67     0.92)  ; 32\r\n                                    (  196.98  1025.66   -30.83     0.92)  ; 33\r\n                                    (  196.98  1025.66   -30.85     0.92)  ; 34\r\n                                    (  198.11  1028.91   -30.25     0.92)  ; 35\r\n                                    (  197.70  1030.58   -28.33     0.92)  ; 36\r\n                                    (  198.38  1033.73   -26.88     0.92)  ; 37\r\n                                    (  198.42  1035.53   -26.88     0.92)  ; 38\r\n                                    (  199.50  1036.98   -25.97     0.92)  ; 39\r\n                                    (  200.13  1038.32   -24.17     0.92)  ; 40\r\n                                    (  201.46  1038.63   -23.47     0.92)  ; 41\r\n                                    (  201.75  1043.47   -23.47     0.92)  ; 42\r\n                                    (  202.56  1046.05   -23.47     0.92)  ; 43\r\n                                    (  204.84  1048.38   -22.88     0.92)  ; 44\r\n                                    (  206.04  1049.26   -23.63     0.92)  ; 45\r\n                                    (  206.04  1049.26   -23.60     0.92)  ; 46\r\n                                    (  206.91  1053.64   -22.97     0.92)  ; 47\r\n                                    (  206.56  1057.14   -21.63     0.92)  ; 48\r\n                                    (  208.65  1058.23   -20.20     0.92)  ; 49\r\n                                    (  209.86  1059.11   -18.30     0.92)  ; 50\r\n                                    (  212.27  1060.88   -16.73     0.92)  ; 51\r\n                                    (  212.91  1062.21   -15.27     0.92)  ; 52\r\n                                    (  213.27  1064.69   -14.70     0.92)  ; 53\r\n                                    (  214.25  1068.49   -14.27     0.92)  ; 54\r\n                                    (  216.53  1070.83   -14.27     0.92)  ; 55\r\n                                    (  219.40  1072.69   -14.27     0.92)  ; 56\r\n                                    (  220.39  1076.51   -14.25     0.92)  ; 57\r\n                                    (  220.39  1076.51   -14.32     0.92)  ; 58\r\n                                    (  221.19  1079.08   -15.80     0.92)  ; 59\r\n                                    (  221.19  1079.08   -15.88     0.92)  ; 60\r\n                                    (  223.48  1081.42   -16.67     0.92)  ; 61\r\n                                    (  226.84  1085.18   -16.90     0.92)  ; 62\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  216.10  1064.78     8.55     0.46)  ; 1\r\n                                    )  ;  End of markers\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  200.10   976.22   -39.05     0.92)  ; 1\r\n                                      (  199.25   977.82   -39.05     0.92)  ; 2\r\n                                      (  197.16   982.70   -38.42     0.92)  ; 3\r\n                                      (  194.03   981.98   -38.42     0.92)  ; 4\r\n                                      (  196.18   984.87   -38.42     0.92)  ; 5\r\n                                      (  192.39   986.96   -38.42     0.92)  ; 6\r\n                                      (  196.25   992.64   -35.47     0.92)  ; 7\r\n                                      (  192.37   991.13   -32.72     0.92)  ; 8\r\n                                      (  197.50   995.33   -33.97     0.92)  ; 9\r\n                                      (  196.52   997.49   -32.67     0.92)  ; 10\r\n                                      (  193.05  1000.25   -31.05     0.92)  ; 11\r\n                                      (  188.22   996.74   -31.05     0.92)  ; 12\r\n                                      (  190.42  1001.43   -30.40     0.92)  ; 13\r\n                                      (  187.83  1004.41   -35.77     0.92)  ; 14\r\n                                      (  193.25  1007.46   -35.88     0.92)  ; 15\r\n                                      (  191.55  1010.65   -35.00     0.92)  ; 16\r\n                                      (  192.64  1018.08   -32.32     0.92)  ; 17\r\n                                      (  194.68  1011.38   -33.80     0.92)  ; 18\r\n                                      (  196.89  1022.05   -29.38     0.92)  ; 19\r\n                                      (  196.61  1023.17   -26.88     0.92)  ; 20\r\n                                      (  196.72  1032.74   -26.88     0.92)  ; 21\r\n                                      (  201.01  1032.55   -24.17     0.92)  ; 22\r\n                                      (  200.36  1041.36   -23.47     0.92)  ; 23\r\n                                      (  196.27  1026.68   -30.25     0.92)  ; 24\r\n                                      (  206.03  1043.29   -23.47     0.92)  ; 25\r\n                                      (  207.65  1048.44   -23.47     0.92)  ; 26\r\n                                      (  203.01  1052.13   -26.22     0.92)  ; 27\r\n                                      (  204.54  1053.68   -22.97     0.92)  ; 28\r\n                                      (  204.75  1050.76   -22.30     0.92)  ; 29\r\n                                      (  207.11  1050.71   -22.30     0.92)  ; 30\r\n                                      (  208.73  1055.87   -20.35     0.92)  ; 31\r\n                                      (  209.40  1053.03   -18.25     0.92)  ; 32\r\n                                      (  225.70  1075.96   -17.27     0.92)  ; 33\r\n                                      (  226.43  1080.91   -17.27     0.92)  ; 34\r\n                                      (  219.46  1080.47   -17.32     0.92)  ; 35\r\n                                      (  218.42  1074.86   -14.35     0.92)  ; 36\r\n                                      (  219.05  1076.20   -14.35     0.92)  ; 37\r\n                                      (  217.19  1078.14   -14.32     0.92)  ; 38\r\n                                      (  205.99  1063.58   -17.70     0.92)  ; 39\r\n                                      (  210.00  1064.53   -14.70     0.92)  ; 40\r\n                                      (  221.37  1074.35   -14.35     0.92)  ; 41\r\n                                      (  211.85  1066.74   -14.70     0.92)  ; 42\r\n                                      (  217.36  1063.26   -14.27     0.92)  ; 43\r\n                                      (  218.22  1067.64   -14.27     0.92)  ; 44\r\n                                      (  212.08  1069.79   -14.27     0.92)  ; 45\r\n                                      (  214.04  1071.44   -14.27     0.92)  ; 46\r\n                                      (  221.49  1067.81   -14.27     0.92)  ; 47\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  224.88  1089.51   -16.90     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2-1\r\n                                      (  222.88  1092.01   -18.25     0.92)  ; 2\r\n                                      (  219.53  1094.22   -20.97     0.92)  ; 3\r\n                                      (  219.53  1094.22   -21.02     0.92)  ; 4\r\n                                       Normal\r\n                                    |\r\n                                      (  227.54  1084.15   -16.90     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2-2\r\n                                      (  229.65  1085.25   -17.82     0.92)  ; 2\r\n                                      (  229.65  1085.25   -17.85     0.92)  ; 3\r\n                                      (  230.71  1086.70   -16.47     0.92)  ; 4\r\n                                      (  230.71  1086.70   -16.50     0.92)  ; 5\r\n                                      (  233.09  1086.65   -14.88     0.92)  ; 6\r\n                                      (  235.19  1087.74   -12.83     0.92)  ; 7\r\n                                      (  236.26  1089.18   -11.27     0.92)  ; 8\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  230.46  1087.82   -16.50     0.92)  ; 1\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  )  ;  End of split\r\n                                )  ;  End of split\r\n                              |\r\n                                (  235.39   662.78   -32.52     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                                (  236.45   664.22   -31.20     1.38)  ; 2\r\n                                (  237.14   667.36   -30.45     1.38)  ; 3\r\n                                (  237.82   670.52   -29.85     1.38)  ; 4\r\n                                (  238.04   673.55   -29.25     1.38)  ; 5\r\n                                (  238.67   674.90   -27.88     1.38)  ; 6\r\n                                (  237.83   676.49   -26.70     1.38)  ; 7\r\n                                (  237.03   679.88   -26.00     1.38)  ; 8\r\n                                (  237.58   683.60   -26.92     1.38)  ; 9\r\n                                (  238.51   685.60   -26.65     1.38)  ; 10\r\n                                (  238.49   689.78   -26.65     1.38)  ; 11\r\n                                (  239.48   693.59   -27.65     1.38)  ; 12\r\n                                (  239.57   697.20   -27.65     1.38)  ; 13\r\n                                (  239.09   701.26   -27.65     1.38)  ; 14\r\n                                (  239.27   702.50   -27.65     1.38)  ; 15\r\n                                (  239.05   705.44   -27.65     1.38)  ; 16\r\n                                (  236.74   707.28   -27.65     1.38)  ; 17\r\n                                (  236.08   710.11   -27.80     1.38)  ; 18\r\n                                (  235.98   712.48   -27.15     1.38)  ; 19\r\n                                (  237.38   714.60   -26.72     1.38)  ; 20\r\n                                (  238.18   717.18   -26.30     1.38)  ; 21\r\n                                (  237.25   721.14   -26.25     1.38)  ; 22\r\n                                (  236.05   726.23   -26.25     1.38)  ; 23\r\n                                (  236.29   729.27   -27.25     1.38)  ; 24\r\n                                (  236.97   732.42   -27.90     1.38)  ; 25\r\n                                (  237.34   734.89   -27.90     1.38)  ; 26\r\n                                (  237.56   737.93   -28.17     1.38)  ; 27\r\n                                (  238.82   740.62   -26.80     1.38)  ; 28\r\n                                (  238.82   740.62   -26.82     1.38)  ; 29\r\n                                (  238.47   744.12   -26.38     1.38)  ; 30\r\n                                (  238.39   746.48   -26.38     1.38)  ; 31\r\n                                (  238.39   746.48   -26.35     1.38)  ; 32\r\n                                (  239.73   746.79   -26.30     1.38)  ; 33\r\n                                (  239.58   747.37   -26.30     1.38)  ; 34\r\n                                (  237.32   751.02   -25.63     1.38)  ; 35\r\n                                (  236.79   753.27   -25.42     1.38)  ; 36\r\n                                (  237.34   756.99   -25.42     1.38)  ; 37\r\n                                (  238.33   760.81   -25.42     1.38)  ; 38\r\n                                (  239.00   763.95   -26.00     1.38)  ; 39\r\n                                (  238.07   767.91   -26.00     1.38)  ; 40\r\n                                (  238.07   767.91   -26.02     1.38)  ; 41\r\n                                (  236.83   771.20   -26.02     1.38)  ; 42\r\n                                (  236.93   774.80   -26.70     1.38)  ; 43\r\n                                (  236.93   774.80   -26.72     1.38)  ; 44\r\n                                (  236.13   778.20   -27.20     1.38)  ; 45\r\n                                (  236.68   781.91   -27.20     1.38)  ; 46\r\n                                (  236.23   781.81   -27.20     1.38)  ; 47\r\n                                (  238.56   785.94   -27.20     1.38)  ; 48\r\n                                (  237.92   788.65   -25.77     1.38)  ; 49\r\n                                (  238.29   791.14   -24.72     1.38)  ; 50\r\n                                (  237.80   795.20   -24.20     1.38)  ; 51\r\n                                (  237.80   795.20   -24.17     1.38)  ; 52\r\n                                (  237.14   798.03   -23.42     1.38)  ; 53\r\n                                (  237.63   799.93   -22.47     1.38)  ; 54\r\n                                (  238.00   802.41   -21.32     1.38)  ; 55\r\n                                (  237.84   807.14   -20.90     1.38)  ; 56\r\n                                (  238.07   810.19   -21.95     1.38)  ; 57\r\n                                (  239.63   813.54   -21.95     1.38)  ; 58\r\n                                (  239.15   817.61   -22.45     1.38)  ; 59\r\n                                (  239.25   821.22   -23.05     1.38)  ; 60\r\n                                (  239.47   824.25   -22.12     1.38)  ; 61\r\n                                (  239.26   827.18   -21.30     1.38)  ; 62\r\n                                (  240.07   829.77   -20.97     1.38)  ; 63\r\n                                (  242.17   830.86   -22.38     1.38)  ; 64\r\n                                (  241.95   833.78   -21.67     1.38)  ; 65\r\n                                (  242.76   836.37   -21.60     1.38)  ; 66\r\n                                (  242.81   838.17   -20.20     1.38)  ; 67\r\n                                (  244.38   841.52   -18.95     1.38)  ; 68\r\n                                (  244.38   841.52   -18.97     1.38)  ; 69\r\n                                (  245.01   842.86   -18.05     1.38)  ; 70\r\n                                (  244.35   845.69   -18.17     1.38)  ; 71\r\n                                (  244.35   845.69   -18.15     1.38)  ; 72\r\n                                (  244.12   848.64   -16.63     1.38)  ; 73\r\n                                (  243.20   852.59   -16.05     1.38)  ; 74\r\n\r\n                                (Cross\r\n                                  (Color RGB (128, 255, 128))\r\n                                  (Name \"Marker 3\")\r\n                                  (  237.76   662.73   -29.85     1.38)  ; 1\r\n                                  (  239.72   664.39   -29.85     1.38)  ; 2\r\n                                  (  240.81   671.82   -28.63     1.38)  ; 3\r\n                                  (  234.36   679.25   -26.92     1.38)  ; 4\r\n                                  (  239.13   680.97   -26.92     1.38)  ; 5\r\n                                  (  235.92   682.61   -26.92     1.38)  ; 6\r\n                                  (  235.97   684.41   -26.92     1.38)  ; 7\r\n                                  (  237.57   699.72   -27.65     1.38)  ; 8\r\n                                  (  237.97   698.01   -27.65     1.38)  ; 9\r\n                                  (  241.09   698.75   -28.00     1.38)  ; 10\r\n                                  (  240.86   695.71   -28.00     1.38)  ; 11\r\n                                  (  239.37   706.11   -28.00     1.38)  ; 12\r\n                                  (  236.65   693.53   -29.80     1.38)  ; 13\r\n                                  (  240.14   690.76   -29.80     1.38)  ; 14\r\n                                  (  238.17   711.20   -28.75     1.38)  ; 15\r\n                                  (  238.57   709.50   -28.75     1.38)  ; 16\r\n                                  (  240.16   718.84   -27.25     1.38)  ; 17\r\n                                  (  239.02   715.58   -27.25     1.38)  ; 18\r\n                                  (  238.70   725.05   -28.70     1.38)  ; 19\r\n                                  (  237.64   729.59   -28.70     1.38)  ; 20\r\n                                  (  234.99   724.79   -27.08     1.38)  ; 21\r\n                                  (  235.04   726.59   -27.08     1.38)  ; 22\r\n                                  (  235.44   730.87   -27.08     1.38)  ; 23\r\n                                  (  235.96   738.75   -27.08     1.38)  ; 24\r\n                                  (  236.26   733.45   -25.50     1.38)  ; 25\r\n                                  (  239.53   739.58   -25.50     1.38)  ; 26\r\n                                  (  240.15   740.93   -24.72     1.38)  ; 27\r\n                                  (  236.99   744.37   -28.40     1.38)  ; 28\r\n                                  (  241.04   751.29   -25.42     1.38)  ; 29\r\n                                  (  240.19   758.85   -24.90     1.38)  ; 30\r\n                                  (  240.11   761.23   -23.88     1.38)  ; 31\r\n                                  (  237.07   758.11   -22.02     1.38)  ; 32\r\n                                  (  237.85   748.75   -21.92     1.38)  ; 33\r\n                                  (  238.58   753.69   -21.95     1.38)  ; 34\r\n                                  (  235.89   769.19   -26.05     1.38)  ; 35\r\n                                  (  236.24   765.69   -26.05     1.38)  ; 36\r\n                                  (  239.99   767.76   -26.07     1.38)  ; 37\r\n                                  (  237.82   775.01   -28.90     1.38)  ; 38\r\n                                  (  238.27   775.12   -27.20     1.38)  ; 39\r\n                                  (  235.97   792.98   -24.17     1.38)  ; 40\r\n                                  (  240.94   795.94   -24.17     1.38)  ; 41\r\n                                  (  235.35   797.61   -22.65     1.38)  ; 42\r\n                                  (  235.45   801.22   -20.90     1.38)  ; 43\r\n                                  (  236.57   804.47   -20.90     1.38)  ; 44\r\n                                  (  235.82   809.66   -22.55     1.38)  ; 45\r\n                                  (  236.54   808.63   -20.20     1.38)  ; 46\r\n                                  (  237.22   811.78   -23.00     1.38)  ; 47\r\n                                  (  238.02   814.36   -23.00     1.38)  ; 48\r\n                                  (  239.75   806.99   -24.82     1.38)  ; 49\r\n                                  (  240.48   811.94   -24.90     1.38)  ; 50\r\n                                  (  237.86   819.09   -21.95     1.38)  ; 51\r\n                                  (  237.88   831.05   -22.70     1.38)  ; 52\r\n                                  (  245.57   836.43   -18.05     1.38)  ; 53\r\n                                  (  244.18   834.31   -21.18     1.38)  ; 54\r\n                                  (  242.10   839.20   -20.97     1.38)  ; 55\r\n                                )  ;  End of markers\r\n                                (\r\n                                  (  243.43   855.63   -16.05     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1\r\n                                  (  244.37   857.64   -16.05     1.38)  ; 2\r\n                                  (  244.87   859.55   -15.77     1.38)  ; 3\r\n                                  (  244.96   863.16   -15.77     1.38)  ; 4\r\n                                  (  246.41   867.08   -15.77     1.38)  ; 5\r\n                                  (  244.26   870.16   -16.95     1.38)  ; 6\r\n                                  (  242.57   873.35   -17.30     1.38)  ; 7\r\n                                  (  242.09   877.42   -16.85     1.38)  ; 8\r\n                                  (  242.09   877.42   -16.88     1.38)  ; 9\r\n                                  (  241.60   881.48   -15.17     1.38)  ; 10\r\n                                  (  239.47   884.56   -14.57     1.38)  ; 11\r\n                                  (  238.63   886.16   -14.10     1.38)  ; 12\r\n                                  (  237.83   889.56   -14.10     1.38)  ; 13\r\n                                  (  237.62   892.48   -15.40     1.38)  ; 14\r\n                                  (  237.66   894.29   -16.52     1.38)  ; 15\r\n                                  (  237.66   894.29   -16.55     1.38)  ; 16\r\n                                  (  237.77   897.90   -17.30     1.38)  ; 17\r\n                                  (  237.77   897.90   -17.32     1.38)  ; 18\r\n                                  (  238.63   902.29   -17.57     1.38)  ; 19\r\n                                  (  240.51   906.30   -17.57     1.38)  ; 20\r\n                                  (  239.26   909.59   -17.75     1.38)  ; 21\r\n                                  (  239.44   910.83   -18.60     1.38)  ; 22\r\n                                  (  239.44   910.83   -18.63     1.38)  ; 23\r\n                                  (  241.46   914.29   -19.02     1.38)  ; 24\r\n                                  (  243.29   916.51   -17.65     1.38)  ; 25\r\n                                  (  243.97   919.65   -18.20     1.38)  ; 26\r\n                                  (  245.09   922.91   -17.63     1.38)  ; 27\r\n                                  (  245.85   923.68   -17.75     1.38)  ; 28\r\n                                  (  245.90   925.49   -16.17     1.38)  ; 29\r\n                                  (  245.90   925.49   -16.15     1.38)  ; 30\r\n                                  (  245.08   927.07   -14.23     1.38)  ; 31\r\n                                  (  243.33   928.45   -12.10     1.38)  ; 32\r\n                                  (  240.00   930.66   -10.88     1.38)  ; 33\r\n                                  (  238.00   933.18   -10.63     1.38)  ; 34\r\n                                  (  237.20   936.58   -10.33     1.38)  ; 35\r\n                                  (  236.41   939.98   -10.33     1.38)  ; 36\r\n                                  (  236.14   941.10   -11.37     1.38)  ; 37\r\n                                  (  234.90   944.40   -12.42     1.38)  ; 38\r\n                                  (  233.47   946.45   -13.67     1.38)  ; 39\r\n                                  (  233.47   946.45   -13.70     1.38)  ; 40\r\n                                  (  231.65   950.19   -13.95     1.38)  ; 41\r\n                                  (  229.92   951.58   -13.18     1.38)  ; 42\r\n                                  (  228.53   955.44   -12.95     1.38)  ; 43\r\n                                  (  228.95   959.73   -12.07     1.38)  ; 44\r\n                                  (  228.28   962.54   -12.42     1.38)  ; 45\r\n                                  (  226.34   966.86   -12.42     1.38)  ; 46\r\n                                  (  224.65   970.05   -14.10     1.38)  ; 47\r\n                                  (  224.56   972.42   -13.63     1.38)  ; 48\r\n                                  (  224.92   974.89   -13.32     1.38)  ; 49\r\n                                  (  224.39   977.16   -11.93     1.38)  ; 50\r\n                                  (  223.99   978.85   -10.45     1.38)  ; 51\r\n                                  (  223.19   982.25    -9.17     1.38)  ; 52\r\n                                  (  221.78   984.31    -7.32     1.38)  ; 53\r\n                                  (  222.71   986.32    -6.47     1.38)  ; 54\r\n                                  (  226.64   989.64    -6.13     1.38)  ; 55\r\n                                  (  227.01   992.10    -6.18     1.38)  ; 56\r\n                                  (  224.86   995.18    -5.07     1.38)  ; 57\r\n                                  (  222.86   997.70    -3.40     1.38)  ; 58\r\n                                  (  223.09  1000.74    -2.50     1.38)  ; 59\r\n                                  (  222.60  1004.81    -2.25     1.38)  ; 60\r\n                                  (  220.91  1008.00    -1.82     1.38)  ; 61\r\n                                  (  219.80  1010.72    -1.60     1.38)  ; 62\r\n                                  (  219.95  1016.13    -1.02     1.38)  ; 63\r\n                                  (  218.71  1019.42    -0.63     1.38)  ; 64\r\n                                  (  217.15  1022.03    -0.63     1.38)  ; 65\r\n                                  (  216.71  1021.93    -0.63     1.38)  ; 66\r\n                                  (  215.60  1024.66    -0.63     1.38)  ; 67\r\n                                  (  215.56  1028.83    -0.07     1.38)  ; 68\r\n                                  (  214.76  1032.23     0.97     1.38)  ; 69\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  246.76   847.45   -16.05     1.38)  ; 1\r\n                                    (  245.25   851.88   -16.05     1.38)  ; 2\r\n                                    (  246.83   861.21   -15.77     1.38)  ; 3\r\n                                    (  247.20   863.68   -15.77     1.38)  ; 4\r\n                                    (  243.34   858.00   -14.43     1.38)  ; 5\r\n                                    (  246.20   859.86   -16.65     1.38)  ; 6\r\n                                    (  243.09   865.11   -17.82     1.38)  ; 7\r\n                                    (  241.77   870.78   -16.88     1.38)  ; 8\r\n                                    (  245.13   874.54   -18.17     1.38)  ; 9\r\n                                    (  239.73   877.46   -15.10     1.38)  ; 10\r\n                                    (  243.67   880.77   -17.63     1.38)  ; 11\r\n                                    (  236.00   887.34   -14.10     1.38)  ; 12\r\n                                    (  236.09   890.93   -14.10     1.38)  ; 13\r\n                                    (  239.32   895.28   -15.85     1.38)  ; 14\r\n                                    (  236.19   894.54   -16.22     1.38)  ; 15\r\n                                    (  242.06   903.69   -17.57     1.38)  ; 16\r\n                                    (  238.05   902.74   -17.57     1.38)  ; 17\r\n                                    (  236.34   899.95   -14.20     1.38)  ; 18\r\n                                    (  239.24   897.65   -17.15     1.38)  ; 19\r\n                                    (  236.57   902.99   -19.00     1.38)  ; 20\r\n                                    (  237.12   906.71   -16.77     1.38)  ; 21\r\n                                    (  239.03   906.56   -16.77     1.38)  ; 22\r\n                                    (  242.29   906.72   -15.92     1.38)  ; 23\r\n                                    (  239.23   913.77   -19.05     1.38)  ; 24\r\n                                    (  243.07   913.47   -20.50     1.38)  ; 25\r\n                                    (  241.20   915.42   -16.75     1.38)  ; 26\r\n                                    (  241.56   917.90   -16.52     1.38)  ; 27\r\n                                    (  241.43   918.46   -17.63     1.38)  ; 28\r\n                                    (  246.51   920.85   -19.15     1.38)  ; 29\r\n                                    (  248.36   923.08   -16.15     1.38)  ; 30\r\n                                    (  238.17   928.44   -10.88     1.38)  ; 31\r\n                                    (  241.52   932.20   -10.88     1.38)  ; 32\r\n                                    (  244.73   930.57   -10.88     1.38)  ; 33\r\n                                    (  248.07   928.37   -14.55     1.38)  ; 34\r\n                                    (  238.08   930.80   -10.33     1.38)  ; 35\r\n                                    (  237.24   932.40   -10.33     1.38)  ; 36\r\n                                    (  240.81   933.24   -10.33     1.38)  ; 37\r\n                                    (  239.43   937.09   -10.33     1.38)  ; 38\r\n                                    (  234.55   931.77   -10.33     1.38)  ; 39\r\n                                    (  235.80   950.57   -13.95     1.38)  ; 40\r\n                                    (  238.15   944.56   -13.95     1.38)  ; 41\r\n                                    (  237.92   941.52   -15.55     1.38)  ; 42\r\n                                    (  232.98   944.54   -15.55     1.38)  ; 43\r\n                                    (  229.55   949.11   -15.55     1.38)  ; 44\r\n                                    (  232.01   952.68   -15.32     1.38)  ; 45\r\n                                    (  227.23   950.96   -12.27     1.38)  ; 46\r\n                                    (  232.10   950.30   -12.27     1.38)  ; 47\r\n                                    (  231.32   959.67   -12.42     1.38)  ; 48\r\n                                    (  231.27   957.87   -12.42     1.38)  ; 49\r\n                                    (  227.21   961.10   -13.75     1.38)  ; 50\r\n                                    (  224.04   964.54    -9.77     1.38)  ; 51\r\n                                    (  224.94   964.75   -13.18     1.38)  ; 52\r\n                                    (  227.59   969.55   -15.30     1.38)  ; 53\r\n                                    (  222.68   974.37   -12.75     1.38)  ; 54\r\n                                    (  221.93   979.58   -10.15     1.38)  ; 55\r\n                                    (  229.28   988.45    -6.13     1.38)  ; 56\r\n                                    (  227.99   989.95    -6.20     1.38)  ; 57\r\n                                    (  227.87   996.49    -7.07     1.38)  ; 58\r\n                                    (  227.54   995.81    -6.20     1.38)  ; 59\r\n                                    (  223.08   994.77    -6.20     1.38)  ; 60\r\n                                    (  228.36   998.38    -4.20     1.38)  ; 61\r\n                                    (  227.43  1002.35    -4.20     1.38)  ; 62\r\n                                    (  225.73   999.57    -1.58     1.38)  ; 63\r\n                                    (  224.35  1003.42    -1.58     1.38)  ; 64\r\n                                    (  221.22  1002.69    -1.58     1.38)  ; 65\r\n                                    (  220.56  1005.51    -1.58     1.38)  ; 66\r\n                                    (  223.61  1008.62    -1.58     1.38)  ; 67\r\n                                    (  216.02  1012.82    -1.02     1.38)  ; 68\r\n                                    (  218.97  1012.31    -1.02     1.38)  ; 69\r\n                                    (  216.88  1017.19     0.97     1.38)  ; 70\r\n                                    (  214.48  1021.41    -0.75     1.38)  ; 71\r\n                                    (  218.55  1024.16    -0.12     1.38)  ; 72\r\n                                    (  213.68  1024.80     0.00     1.38)  ; 73\r\n                                    (  218.47  1026.53    -0.67     1.38)  ; 74\r\n                                    (  214.36  1027.96    -0.67     1.38)  ; 75\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  213.35  1034.28    -0.47     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-1\r\n                                    (  212.90  1034.17    -0.47     1.38)  ; 2\r\n                                    (  212.37  1036.45     2.13     0.92)  ; 3\r\n                                    (  212.15  1039.38     3.65     0.92)  ; 4\r\n                                    (  212.15  1039.38     3.60     0.92)  ; 5\r\n                                    (  209.96  1040.65     4.32     0.46)  ; 6\r\n                                    (  209.57  1042.35     5.45     0.46)  ; 7\r\n                                    (  209.88  1043.03     5.55     0.46)  ; 8\r\n                                    (  208.01  1044.97     7.55     0.46)  ; 9\r\n                                    (  208.01  1044.97     7.57     0.46)  ; 10\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  214.52  1039.33     5.45     0.46)  ; 1\r\n                                      (  210.66  1033.65     5.45     0.46)  ; 2\r\n                                    )  ;  End of markers\r\n                                     Normal\r\n                                  |\r\n                                    (  216.66  1036.26     2.08     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-2\r\n                                    (  215.55  1038.97     2.97     0.92)  ; 2\r\n                                    (  217.77  1039.50     3.50     0.92)  ; 3\r\n                                    (  219.25  1039.25     4.17     0.92)  ; 4\r\n                                    (  221.08  1041.48     5.07     0.92)  ; 5\r\n                                    (  221.58  1043.38     5.70     0.92)  ; 6\r\n                                    (  222.52  1045.38     6.35     0.92)  ; 7\r\n                                    (  222.17  1048.88     6.80     0.92)  ; 8\r\n                                    (  222.17  1048.88     6.78     0.92)  ; 9\r\n                                    (  224.06  1052.91     5.42     0.92)  ; 10\r\n                                    (  223.83  1055.85     5.17     0.92)  ; 11\r\n                                    (  223.75  1058.22     6.35     0.92)  ; 12\r\n                                    (  223.75  1058.22     6.28     0.92)  ; 13\r\n                                    (  222.33  1060.28     7.67     0.92)  ; 14\r\n                                    (  222.69  1062.74     8.55     0.92)  ; 15\r\n                                    (  223.95  1065.43     8.55     0.92)  ; 16\r\n                                    (  222.84  1068.16     8.55     0.46)  ; 17\r\n                                    (  223.96  1071.40     8.55     0.46)  ; 18\r\n                                    (  224.64  1074.55     8.55     0.46)  ; 19\r\n                                    (  224.19  1074.44     8.55     0.46)  ; 20\r\n                                    (  228.08  1075.95     8.55     0.46)  ; 21\r\n                                    (  228.98  1076.16     9.55     0.46)  ; 22\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  214.73  1036.40     2.97     0.92)  ; 1\r\n                                      (  221.75  1038.64     5.70     0.92)  ; 2\r\n                                      (  219.92  1042.39     5.70     0.92)  ; 3\r\n                                      (  220.74  1044.97     5.70     0.92)  ; 4\r\n                                      (  229.42  1054.17     5.70     0.92)  ; 5\r\n                                      (  226.21  1055.81     5.70     0.92)  ; 6\r\n                                      (  226.63  1066.06     8.55     0.46)  ; 7\r\n                                      (  226.44  1058.85     8.55     0.46)  ; 8\r\n                                    )  ;  End of markers\r\n                                     Normal\r\n                                  )  ;  End of split\r\n                                |\r\n                                  (  241.31   854.66   -17.08     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-2\r\n                                  (  241.49   855.89   -18.67     1.38)  ; 2\r\n                                  (  240.91   856.36   -19.80     1.38)  ; 3\r\n                                  (  241.09   857.59   -21.63     1.38)  ; 4\r\n                                  (  241.99   857.81   -23.27     1.38)  ; 5\r\n                                  (  241.59   859.50   -25.05     1.38)  ; 6\r\n                                  (  239.98   860.33   -26.22     1.38)  ; 7\r\n                                  (  238.07   860.48   -27.73     1.38)  ; 8\r\n                                  (  240.48   862.23   -29.70     0.92)  ; 9\r\n                                  (  239.63   863.82   -31.70     0.92)  ; 10\r\n                                  (  238.21   865.87   -32.97     0.92)  ; 11\r\n                                  (  239.02   868.45   -34.17     0.92)  ; 12\r\n                                  (  239.38   870.94   -34.45     0.92)  ; 13\r\n                                  (  237.51   872.88   -35.40     0.92)  ; 14\r\n                                  (  235.60   873.03   -36.75     0.92)  ; 15\r\n                                  (  233.68   873.18   -38.38     0.92)  ; 16\r\n                                  (  233.59   875.54   -39.40     0.92)  ; 17\r\n                                  (  234.09   877.46   -40.75     0.92)  ; 18\r\n                                  (  234.14   879.26   -41.93     0.92)  ; 19\r\n                                  (  231.64   879.87   -43.07     0.92)  ; 20\r\n                                  (  230.84   883.26   -43.47     0.92)  ; 21\r\n                                  (  229.11   884.65   -44.05     0.92)  ; 22\r\n                                  (  229.11   884.65   -44.07     0.92)  ; 23\r\n                                  (  228.45   887.48   -44.60     0.92)  ; 24\r\n                                  (  227.16   888.97   -45.95     0.92)  ; 25\r\n                                  (  227.02   889.53   -47.22     0.92)  ; 26\r\n                                  (  227.25   892.57   -48.45     0.92)  ; 27\r\n                                  (  226.41   894.16   -48.62     0.92)  ; 28\r\n                                  (  226.94   897.87   -49.52     0.92)  ; 29\r\n                                  (  226.94   897.87   -49.55     0.92)  ; 30\r\n                                  (  226.41   900.14   -50.00     0.92)  ; 31\r\n                                  (  226.41   900.14   -50.03     0.92)  ; 32\r\n                                  (  225.13   901.63   -51.28     0.92)  ; 33\r\n                                  (  224.87   902.75   -52.65     0.92)  ; 34\r\n                                  (  224.95   906.36   -53.45     0.92)  ; 35\r\n                                  (  224.87   908.74   -54.17     0.92)  ; 36\r\n                                  (  223.76   911.45   -54.58     0.92)  ; 37\r\n                                  (  222.70   915.98   -54.72     0.92)  ; 38\r\n                                  (  220.97   917.38   -55.52     0.92)  ; 39\r\n                                  (  222.80   919.59   -56.20     0.92)  ; 40\r\n                                  (  222.76   923.76   -56.75     0.92)  ; 41\r\n                                  (  222.05   924.79   -58.55     0.46)  ; 42\r\n                                  (  221.34   925.83   -60.35     0.46)  ; 43\r\n                                  (  221.34   925.83   -60.38     0.46)  ; 44\r\n                                  (  221.39   927.62   -62.13     0.46)  ; 45\r\n                                  (  220.72   930.45   -63.42     0.46)  ; 46\r\n                                  (  218.11   931.49   -64.27     0.46)  ; 47\r\n                                  (  220.08   933.15   -65.00     0.46)  ; 48\r\n                                  (  219.82   934.27   -66.17     0.46)  ; 49\r\n                                  (  218.08   935.66   -67.20     0.46)  ; 50\r\n                                  (  216.02   936.38   -68.13     0.46)  ; 51\r\n                                  (  215.64   938.07   -69.18     0.46)  ; 52\r\n                                  (  215.64   938.07   -69.20     0.46)  ; 53\r\n                                  (  214.15   938.32   -70.45     0.46)  ; 54\r\n                                  (  214.07   940.69   -71.43     0.46)  ; 55\r\n                                  (  214.07   940.69   -71.45     0.46)  ; 56\r\n                                  (  212.46   941.51   -72.32     0.46)  ; 57\r\n                                  (  212.46   941.51   -72.35     0.46)  ; 58\r\n                                  (  210.86   942.33   -72.95     0.46)  ; 59\r\n                                  (  209.89   944.49   -74.15     0.46)  ; 60\r\n                                  (  209.57   943.82   -76.00     0.46)  ; 61\r\n                                  (  209.57   943.82   -76.03     0.46)  ; 62\r\n                                  (  208.28   945.31   -77.47     0.46)  ; 63\r\n                                  (  207.00   946.80   -78.57     0.46)  ; 64\r\n                                  (  206.77   949.74   -79.55     0.46)  ; 65\r\n                                  (  204.53   949.21   -80.70     0.46)  ; 66\r\n                                  (  201.78   950.95   -81.75     0.46)  ; 67\r\n                                  (  200.93   952.55   -82.45     0.46)  ; 68\r\n                                  (  199.50   954.60   -83.00     0.46)  ; 69\r\n                                  (  197.33   955.87   -83.00     0.46)  ; 70\r\n                                  (  196.93   957.58   -84.72     0.46)  ; 71\r\n                                  (  197.10   958.81   -86.32     0.46)  ; 72\r\n                                  (  197.02   961.19   -87.00     0.46)  ; 73\r\n                                  (  194.97   961.90   -87.52     0.46)  ; 74\r\n                                  (  193.50   962.14   -88.42     0.46)  ; 75\r\n                                  (  192.34   963.07   -89.52     0.46)  ; 76\r\n                                  (  191.81   965.33   -90.77     0.46)  ; 77\r\n                                  (  191.81   965.33   -90.80     0.46)  ; 78\r\n                                  (  190.52   966.82   -91.72     0.46)  ; 79\r\n                                  (  189.40   969.55   -92.42     0.46)  ; 80\r\n                                  (  187.35   970.25   -93.47     0.46)  ; 81\r\n                                  (  184.99   970.31   -94.70     0.46)  ; 82\r\n                                  (  183.51   970.56   -95.75     0.46)  ; 83\r\n                                  (  182.23   972.04   -96.80     0.46)  ; 84\r\n                                  (  180.93   973.54   -97.65     0.46)  ; 85\r\n                                  (  180.93   973.54   -97.68     0.46)  ; 86\r\n                                  (  179.64   975.02   -98.53     0.46)  ; 87\r\n                                  (  177.01   976.20   -99.30     0.46)  ; 88\r\n                                  (  173.81   977.84  -100.37     0.46)  ; 89\r\n                                  (  172.07   979.22  -100.37     0.46)  ; 90\r\n                                  (  169.70   979.26  -101.32     0.46)  ; 91\r\n                                  (  168.54   980.19  -102.40     0.46)  ; 92\r\n                                  (  166.40   983.27  -103.60     0.46)  ; 93\r\n                                  (  163.83   986.24  -103.97     0.46)  ; 94\r\n                                  (  162.76   990.78  -103.97     0.46)  ; 95\r\n                                  (  161.39   994.63  -104.85     0.46)  ; 96\r\n                                  (  161.30   997.00  -106.10     0.46)  ; 97\r\n                                  (  159.88   999.06  -107.42     0.46)  ; 98\r\n                                  (  158.68  1004.15  -108.35     0.46)  ; 99\r\n                                  (  156.15  1008.93  -107.78     0.46)  ; 100\r\n                                  (  155.31  1010.53  -106.60     0.46)  ; 101\r\n                                  (  155.35  1012.33  -105.90     0.46)  ; 102\r\n                                  (  155.27  1014.70  -104.10     0.46)  ; 103\r\n                                  (  154.74  1016.96  -102.70     0.46)  ; 104\r\n                                  (  154.29  1016.85  -102.70     0.46)  ; 105\r\n                                  (  154.92  1018.20  -101.88     0.46)  ; 106\r\n                                  (  155.28  1020.67  -100.37     0.46)  ; 107\r\n                                  (  154.75  1022.93   -99.78     0.46)  ; 108\r\n                                  (  153.77  1025.09   -99.03     0.46)  ; 109\r\n                                  (  154.40  1026.44   -98.00     0.46)  ; 110\r\n                                  (  153.56  1028.03   -97.25     0.46)  ; 111\r\n                                  (  153.47  1030.40   -96.07     0.46)  ; 112\r\n                                  (  151.52  1034.72   -95.25     0.46)  ; 113\r\n                                  (  149.83  1037.91   -93.53     0.46)  ; 114\r\n                                  (  148.58  1041.19   -93.15     0.46)  ; 115\r\n                                  (  147.48  1043.92   -92.72     0.46)  ; 116\r\n                                  (  146.95  1046.20   -92.20     0.46)  ; 117\r\n                                  (  144.80  1049.28   -90.42     0.46)  ; 118\r\n\r\n                                  (Cross\r\n                                    (Color White)\r\n                                    (Name \"Marker 3\")\r\n                                    (  234.10   877.30   -31.48     1.38)  ; 1\r\n                                  )  ;  End of markers\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  239.63   857.85   -21.63     1.38)  ; 1\r\n                                    (  237.59   870.51   -34.45     0.92)  ; 2\r\n                                    (  235.33   874.17   -38.38     0.92)  ; 3\r\n                                    (  229.86   879.45   -43.97     0.92)  ; 4\r\n                                    (  235.25   876.53   -42.70     0.92)  ; 5\r\n                                    (  225.28   890.90   -48.45     0.92)  ; 6\r\n                                    (  229.70   890.16   -45.65     0.92)  ; 7\r\n                                    (  223.70   903.69   -53.45     0.92)  ; 8\r\n                                    (  226.83   904.42   -53.45     0.92)  ; 9\r\n                                    (  223.46   916.77   -54.72     0.92)  ; 10\r\n                                    (  223.66   923.97   -64.27     0.46)  ; 11\r\n                                    (  212.77   936.21   -69.22     0.46)  ; 12\r\n                                    (  207.48   942.73   -77.47     0.46)  ; 13\r\n                                    (  202.62   949.36   -81.75     0.46)  ; 14\r\n                                    (  193.65   967.55   -91.72     0.46)  ; 15\r\n                                    (  169.74   981.07  -103.60     0.46)  ; 16\r\n                                    (  160.71   991.49  -104.85     0.46)  ; 17\r\n                                    (  164.19   988.72  -104.85     0.46)  ; 18\r\n                                    (  160.21  1005.70  -108.35     0.46)  ; 19\r\n                                    (  158.27   999.87  -106.27     0.46)  ; 20\r\n                                    (  158.05  1002.81  -109.67     0.46)  ; 21\r\n                                    (  155.47  1005.78  -109.67     0.46)  ; 22\r\n                                    (  156.11  1007.13  -109.67     0.46)  ; 23\r\n                                    (  154.14  1011.44  -105.90     0.46)  ; 24\r\n                                    (  157.41  1011.61  -107.07     0.46)  ; 25\r\n                                    (  153.63  1019.68  -101.88     0.46)  ; 26\r\n                                    (  156.26  1018.52  -101.07     0.46)  ; 27\r\n                                    (  153.99  1022.17   -99.03     0.46)  ; 28\r\n                                    (  153.06  1026.12   -98.40     0.46)  ; 29\r\n                                    (  154.98  1025.98   -97.42     0.46)  ; 30\r\n                                    (  153.57  1034.01   -95.25     0.46)  ; 31\r\n                                    (  146.83  1036.60   -93.95     0.46)  ; 32\r\n                                    (  148.86  1046.05   -90.42     0.46)  ; 33\r\n                                  )  ;  End of markers\r\n                                   Normal\r\n                                )  ;  End of split\r\n                              )  ;  End of split\r\n                            |\r\n                              (  231.42   611.32   -31.42     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                              (  231.21   611.36   -31.42     0.92)  ; 2\r\n                              (  230.58   612.92   -30.00     0.92)  ; 3\r\n                              (  230.31   614.05   -28.57     0.92)  ; 4\r\n                              (  228.85   614.30   -27.15     0.92)  ; 5\r\n                              (  229.48   615.64   -26.32     0.92)  ; 6\r\n                              (  229.96   617.55   -25.07     0.46)  ; 7\r\n                              (  228.41   620.17   -23.60     0.46)  ; 8\r\n                              (  228.77   622.64   -22.57     0.46)  ; 9\r\n                              (  228.77   622.64   -22.60     0.46)  ; 10\r\n                              (  228.06   623.66   -21.75     0.46)  ; 11\r\n                              (  228.37   624.35   -21.75     0.46)  ; 12\r\n                              (  228.30   626.70   -20.90     0.46)  ; 13\r\n                              (  228.79   628.62   -19.85     0.46)  ; 14\r\n                              (  227.18   629.44   -18.97     0.46)  ; 15\r\n                              (  225.89   630.92   -18.08     0.46)  ; 16\r\n                              (  224.42   631.17   -15.97     0.46)  ; 17\r\n                              (  224.42   631.17   -15.95     0.46)  ; 18\r\n\r\n                              (Cross\r\n                                (Color White)\r\n                                (Name \"Marker 3\")\r\n                                (  231.06   611.25   -31.77     1.83)  ; 1\r\n                              )  ;  End of markers\r\n\r\n                              (Cross\r\n                                (Color RGB (128, 255, 128))\r\n                                (Name \"Marker 3\")\r\n                                (  230.74   618.32   -23.60     0.46)  ; 1\r\n                                (  226.41   622.68   -23.60     0.46)  ; 2\r\n                              )  ;  End of markers\r\n                               Normal\r\n                            )  ;  End of split\r\n                          |\r\n                            (  239.41   464.61   -16.32     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-2\r\n                            (  240.94   466.16   -15.48     0.46)  ; 2\r\n                            (  240.86   468.54   -14.90     0.46)  ; 3\r\n                            (  240.60   469.66   -14.15     0.46)  ; 4\r\n                            (  242.24   470.64   -12.62     0.46)  ; 5\r\n                            (  245.23   471.94   -11.63     0.46)  ; 6\r\n                            (  248.37   472.67   -10.70     0.46)  ; 7\r\n                            (  248.15   475.61    -9.77     0.46)  ; 8\r\n                            (  248.69   479.32    -9.13     0.46)  ; 9\r\n                            (  250.97   481.65   -10.07     0.46)  ; 10\r\n                            (  252.95   483.30    -9.22     0.46)  ; 11\r\n                            (  254.46   484.85    -7.87     0.46)  ; 12\r\n                            (  255.09   486.19    -6.67     0.46)  ; 13\r\n                            (  254.25   487.78    -5.13     0.46)  ; 14\r\n                            (  254.25   487.78    -5.15     0.46)  ; 15\r\n                            (  256.42   486.51    -3.57     0.46)  ; 16\r\n                            (  256.42   486.51    -3.60     0.46)  ; 17\r\n                            (  257.90   486.25    -2.45     0.46)  ; 18\r\n                            (  257.63   487.39    -0.63     0.46)  ; 19\r\n                            (  254.83   487.33     0.93     0.46)  ; 20\r\n                            (  254.56   488.46     2.45     0.46)  ; 21\r\n                            (  256.79   488.99     3.85     0.46)  ; 22\r\n                            (  256.75   493.16     3.45     0.46)  ; 23\r\n                            (  255.96   496.55     1.22     0.46)  ; 24\r\n                            (  255.96   496.55     0.88     0.46)  ; 25\r\n\r\n                            (Cross\r\n                              (Color RGB (128, 255, 128))\r\n                              (Name \"Marker 3\")\r\n                              (  238.13   466.10   -16.32     0.46)  ; 1\r\n                              (  238.53   464.40   -16.32     0.46)  ; 2\r\n                              (  240.89   464.36   -15.48     0.46)  ; 3\r\n                              (  243.82   474.00   -11.63     0.46)  ; 4\r\n                              (  243.72   470.39   -11.63     0.46)  ; 5\r\n                              (  246.08   470.36   -11.63     0.46)  ; 6\r\n                              (  246.60   478.24    -9.13     0.46)  ; 7\r\n                              (  247.66   479.68    -9.13     0.46)  ; 8\r\n                              (  252.76   482.07    -8.07     0.46)  ; 9\r\n                              (  250.49   485.71    -8.25     0.46)  ; 10\r\n                              (  250.44   483.92   -10.80     0.46)  ; 11\r\n                              (  252.86   485.67    -6.67     0.46)  ; 12\r\n                              (  257.67   483.21    -6.67     0.46)  ; 13\r\n                              (  253.75   485.88     2.45     0.46)  ; 14\r\n                              (  253.09   488.72     3.85     0.46)  ; 15\r\n                              (  258.71   488.83     3.85     0.46)  ; 16\r\n                              (  255.06   490.37     5.37     0.46)  ; 17\r\n                              (  255.81   491.14     6.22     0.46)  ; 18\r\n                            )  ;  End of markers\r\n                             Normal\r\n                          )  ;  End of split\r\n                        |\r\n                          (  235.28   454.09   -16.50     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-1-2\r\n                          (  233.82   454.34   -15.32     0.92)  ; 2\r\n                          (  232.08   455.72   -14.05     0.92)  ; 3\r\n                          (  231.94   456.29   -12.62     0.92)  ; 4\r\n                          (  231.58   453.82   -11.55     0.92)  ; 5\r\n                          (  230.51   452.36   -10.20     0.92)  ; 6\r\n                          (  229.44   450.92    -9.67     0.92)  ; 7\r\n                          (  228.68   450.14    -8.23     0.92)  ; 8\r\n                          (  227.47   449.27    -8.23     0.92)  ; 9\r\n                          (  226.52   447.25    -8.23     0.92)  ; 10\r\n                          (  223.58   447.76    -6.97     0.92)  ; 11\r\n                          (  220.90   447.13    -6.55     0.92)  ; 12\r\n                          (  217.64   446.97    -6.85     0.92)  ; 13\r\n                          (  214.83   446.90    -6.02     0.92)  ; 14\r\n                          (  211.83   445.61    -4.92     0.92)  ; 15\r\n                          (  209.97   447.55    -3.65     0.92)  ; 16\r\n                          (  210.45   449.46    -3.20     0.92)  ; 17\r\n                          (  210.19   450.59    -1.70     0.92)  ; 18\r\n                          (  207.51   449.96    -0.97     0.92)  ; 19\r\n                          (  205.20   451.82     0.35     0.92)  ; 20\r\n                          (  205.20   451.82     0.32     0.92)  ; 21\r\n                          (  203.33   453.76     0.67     0.92)  ; 22\r\n                          (  200.38   454.27     0.67     0.92)  ; 23\r\n                          (  196.73   455.79    -0.02     0.92)  ; 24\r\n                          (  193.96   457.54    -1.25     0.92)  ; 25\r\n                          (  191.64   459.38    -0.30     0.92)  ; 26\r\n                          (  190.57   457.94     0.32     0.92)  ; 27\r\n                          (  187.05   458.90     1.22     0.92)  ; 28\r\n                          (  184.54   459.52     0.75     0.92)  ; 29\r\n                          (  181.02   460.47    -0.55     0.92)  ; 30\r\n                          (  178.84   461.75    -0.90     0.92)  ; 31\r\n                          (  175.85   460.45    -2.15     0.92)  ; 32\r\n                          (  174.59   457.78    -2.80     0.92)  ; 33\r\n                          (  171.46   457.04    -3.10     0.92)  ; 34\r\n                          (  167.18   457.23    -3.97     0.92)  ; 35\r\n                          (  167.18   457.23    -4.00     0.92)  ; 36\r\n                          (  164.81   457.27    -3.60     0.92)  ; 37\r\n                          (  164.81   457.27    -3.62     0.92)  ; 38\r\n                          (  163.16   456.29    -1.77     0.92)  ; 39\r\n                          (  160.48   455.66    -1.02     0.92)  ; 40\r\n                          (  158.02   458.07     0.85     0.92)  ; 41\r\n                          (  155.85   459.35     2.85     0.92)  ; 42\r\n                          (  155.85   459.35     2.83     0.92)  ; 43\r\n                          (  152.50   461.56     4.50     0.92)  ; 44\r\n\r\n                          (Cross\r\n                            (Color RGB (128, 255, 128))\r\n                            (Name \"Marker 3\")\r\n                            (  230.17   455.87   -12.62     0.92)  ; 1\r\n                            (  231.04   450.10    -9.67     0.92)  ; 2\r\n                            (  229.04   452.63    -8.30     0.92)  ; 3\r\n                            (  222.19   445.64    -6.55     0.92)  ; 4\r\n                            (  222.16   449.81    -6.55     0.92)  ; 5\r\n                            (  219.95   445.11    -4.82     0.92)  ; 6\r\n                            (  219.92   449.29    -4.85     0.92)  ; 7\r\n                            (  215.95   450.15    -7.43     0.92)  ; 8\r\n                            (  213.55   448.39    -7.75     0.92)  ; 9\r\n                            (  216.25   444.84    -9.13     0.92)  ; 10\r\n                            (  206.84   446.82    -3.65     0.92)  ; 11\r\n                            (  209.60   445.08    -2.30     0.92)  ; 12\r\n                            (  212.12   450.45    -1.40     0.92)  ; 13\r\n                            (  204.97   448.77    -0.97     0.92)  ; 14\r\n                            (  207.56   451.77    -0.97     0.92)  ; 15\r\n                            (  209.79   452.30    -0.97     0.92)  ; 16\r\n                            (  207.03   454.03     1.90     0.92)  ; 17\r\n                            (  201.81   452.22    -1.82     0.92)  ; 18\r\n                            (  199.70   451.12    -0.75     0.92)  ; 19\r\n                            (  180.08   458.46    -0.90     0.92)  ; 20\r\n                            (  182.67   461.46    -0.90     0.92)  ; 21\r\n                            (  183.70   461.10     0.47     0.92)  ; 22\r\n                            (  181.07   462.27    -0.95     0.92)  ; 23\r\n                            (  174.09   455.86    -2.90     0.92)  ; 24\r\n                            (  169.05   455.28    -4.25     0.92)  ; 25\r\n                            (  169.02   459.45    -4.32     0.92)  ; 26\r\n                            (  166.91   458.36    -5.37     0.92)  ; 27\r\n                            (  161.79   460.15    -1.02     0.92)  ; 28\r\n                            (  160.43   453.86    -1.02     0.92)  ; 29\r\n                            (  163.11   454.49    -1.02     0.92)  ; 30\r\n                            (  158.92   458.28    -1.22     0.92)  ; 31\r\n                            (  157.36   460.89     4.50     0.92)  ; 32\r\n                          )  ;  End of markers\r\n                           Normal\r\n                        )  ;  End of split\r\n                      |\r\n                        (  247.39   360.39   -28.37     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2\r\n                        (  247.17   363.33   -29.50     1.83)  ; 2\r\n                        (  245.93   366.62   -30.90     1.83)  ; 3\r\n                        (  244.38   369.24   -32.15     1.83)  ; 4\r\n                        (  242.96   371.30   -33.80     1.83)  ; 5\r\n                        (  242.03   375.25   -34.85     1.83)  ; 6\r\n                        (  240.07   379.58   -34.75     1.83)  ; 7\r\n                        (  237.94   382.66   -35.02     1.83)  ; 8\r\n                        (  237.90   386.83   -35.33     1.83)  ; 9\r\n                        (  237.90   386.83   -35.35     1.83)  ; 10\r\n                        (  236.84   391.36   -35.35     1.83)  ; 11\r\n                        (  236.11   394.40   -36.38     1.83)  ; 12\r\n                        (  235.87   396.36   -36.40     1.83)  ; 13\r\n                        (  235.78   398.72   -36.38     1.83)  ; 14\r\n                        (  235.84   403.66   -36.72     1.83)  ; 15\r\n                        (  232.99   407.77   -37.02     1.83)  ; 16\r\n                        (  233.04   409.57   -38.15     1.83)  ; 17\r\n                        (  231.66   413.43   -39.20     1.83)  ; 18\r\n                        (  229.72   417.76   -40.17     1.83)  ; 19\r\n                        (  229.72   423.72   -41.17     1.83)  ; 20\r\n                        (  229.55   426.58   -41.77     1.83)  ; 21\r\n                        (  230.28   431.52   -41.85     1.83)  ; 22\r\n                        (  229.93   435.02   -40.60     1.83)  ; 23\r\n                        (  228.87   439.55   -41.03     1.83)  ; 24\r\n                        (  228.87   439.55   -41.05     1.83)  ; 25\r\n                        (  226.34   444.33   -41.73     1.83)  ; 26\r\n                        (  224.38   448.65   -42.10     1.83)  ; 27\r\n                        (  224.93   452.36   -43.10     1.83)  ; 28\r\n                        (  225.73   454.93   -43.92     1.83)  ; 29\r\n                        (  225.73   454.93   -43.95     1.83)  ; 30\r\n                        (  224.80   458.90   -44.97     1.83)  ; 31\r\n                        (  223.07   460.29   -45.75     1.83)  ; 32\r\n                        (  223.07   460.29   -45.77     1.83)  ; 33\r\n                        (  221.65   462.34   -47.02     1.83)  ; 34\r\n                        (  222.18   466.04   -47.88     1.83)  ; 35\r\n                        (  221.65   468.31   -47.88     1.83)  ; 36\r\n\r\n                        (Cross\r\n                          (Color White)\r\n                          (Name \"Marker 3\")\r\n                          (  236.18   402.48   -12.40     1.83)  ; 1\r\n                          (  236.30   395.94   -13.23     1.83)  ; 2\r\n                          (  236.22   398.31   -12.40     1.83)  ; 3\r\n                        )  ;  End of markers\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  242.46   369.39   -34.45     1.83)  ; 1\r\n                          (  245.90   370.79   -31.98     1.83)  ; 2\r\n                          (  247.60   367.60   -28.37     1.83)  ; 3\r\n                          (  234.79   392.07   -36.40     1.83)  ; 4\r\n                          (  238.68   393.58   -36.40     1.83)  ; 5\r\n                          (  239.41   398.54   -35.70     1.83)  ; 6\r\n                          (  239.19   401.46   -35.70     1.83)  ; 7\r\n                          (  232.64   405.30   -35.70     1.83)  ; 8\r\n                          (  229.97   410.65   -39.20     1.83)  ; 9\r\n                          (  228.21   422.18   -41.17     1.83)  ; 10\r\n                          (  227.76   426.15   -41.85     1.83)  ; 11\r\n                          (  227.86   429.75   -41.85     1.83)  ; 12\r\n                          (  228.28   434.04   -39.78     1.83)  ; 13\r\n                          (  228.14   456.70   -44.85     1.83)  ; 14\r\n                          (  220.24   460.22   -47.02     1.83)  ; 15\r\n                          (  232.37   426.63   -41.85     1.83)  ; 16\r\n                        )  ;  End of markers\r\n                        (\r\n                          (  223.54   470.57   -46.93     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-1\r\n                          (  225.94   472.34   -46.45     0.92)  ; 2\r\n                          (  228.22   474.66   -46.25     0.92)  ; 3\r\n                          (  230.46   475.19   -46.25     0.92)  ; 4\r\n                          (  231.58   478.44   -46.88     0.92)  ; 5\r\n                          (  232.53   480.45   -47.60     0.92)  ; 6\r\n                          (  235.39   482.30   -47.92     0.92)  ; 7\r\n                          (  236.86   482.06   -47.33     0.92)  ; 8\r\n                          (  238.78   481.91   -48.88     0.92)  ; 9\r\n                          (  238.70   484.28   -50.13     0.92)  ; 10\r\n                          (  238.70   484.28   -50.15     0.92)  ; 11\r\n                          (  238.70   484.28   -51.50     0.92)  ; 12\r\n                          (  238.70   484.28   -51.52     0.92)  ; 13\r\n                          (  237.40   485.76   -52.20     0.92)  ; 14\r\n                          (  237.59   487.01   -53.38     0.92)  ; 15\r\n                          (  239.36   487.41   -54.35     0.92)  ; 16\r\n                          (  241.48   488.51   -55.35     0.92)  ; 17\r\n                          (  241.48   488.51   -55.38     0.92)  ; 18\r\n                          (  241.78   489.19   -55.72     0.92)  ; 19\r\n                          (  241.00   492.57   -55.72     0.92)  ; 20\r\n                          (  242.51   494.14   -56.42     0.92)  ; 21\r\n                          (  243.59   495.58   -57.50     0.92)  ; 22\r\n                          (  244.65   497.02   -58.30     0.92)  ; 23\r\n                          (  245.23   496.56   -58.90     0.92)  ; 24\r\n                          (  246.13   496.77   -58.88     0.92)  ; 25\r\n                          (  245.47   499.60   -59.02     0.92)  ; 26\r\n                          (  245.91   499.70   -59.02     0.92)  ; 27\r\n                          (  248.02   500.80   -59.63     0.92)  ; 28\r\n                          (  248.83   503.38   -59.63     0.92)  ; 29\r\n                          (  249.27   503.48   -60.67     0.92)  ; 30\r\n                          (  248.16   506.21   -61.52     0.92)  ; 31\r\n                          (  251.15   507.50   -62.60     0.92)  ; 32\r\n                          (  253.26   508.59   -63.17     0.92)  ; 33\r\n                          (  255.09   510.82   -63.95     0.92)  ; 34\r\n                          (  255.32   513.86   -65.00     0.92)  ; 35\r\n                          (  256.97   514.84   -65.75     0.92)  ; 36\r\n                          (  259.52   516.03   -66.60     0.92)  ; 37\r\n                          (  262.38   517.90   -64.57     0.92)  ; 38\r\n                          (  264.17   518.32   -65.30     0.92)  ; 39\r\n                          (  264.17   518.32   -65.32     0.92)  ; 40\r\n                          (  266.59   520.08   -65.97     0.92)  ; 41\r\n                          (  268.19   519.25   -66.65     0.92)  ; 42\r\n                          (  268.19   519.25   -66.68     0.92)  ; 43\r\n                          (  270.42   519.78   -67.90     0.92)  ; 44\r\n                          (  270.42   519.78   -67.93     0.92)  ; 45\r\n                          (  272.07   520.76   -69.42     0.92)  ; 46\r\n                          (  272.07   520.76   -69.45     0.92)  ; 47\r\n                          (  273.15   522.21   -70.27     0.92)  ; 48\r\n                          (  272.76   523.91   -71.72     0.92)  ; 49\r\n                          (  272.76   523.91   -71.75     0.92)  ; 50\r\n                          (  276.33   524.75   -72.20     0.92)  ; 51\r\n                          (  280.67   526.36   -73.57     0.92)  ; 52\r\n                          (  283.39   528.80   -74.30     0.92)  ; 53\r\n                          (  286.69   530.77   -74.78     0.92)  ; 54\r\n                          (  289.24   531.95   -75.25     0.92)  ; 55\r\n                          (  293.31   534.69   -75.88     0.92)  ; 56\r\n                          (  295.55   535.22   -76.03     0.92)  ; 57\r\n                          (  298.27   537.66   -75.72     0.92)  ; 58\r\n                          (  301.79   536.68   -76.03     0.92)  ; 59\r\n                          (  305.10   538.66   -76.13     0.92)  ; 60\r\n                          (  308.09   539.96   -77.35     0.92)  ; 61\r\n                          (  309.61   541.51   -78.20     0.92)  ; 62\r\n                          (  313.19   542.34   -79.07     0.92)  ; 63\r\n                          (  316.05   544.20   -80.37     0.92)  ; 64\r\n                          (  318.42   544.16   -82.22     0.92)  ; 65\r\n                          (  318.42   544.16   -82.52     0.92)  ; 66\r\n\r\n                          (Cross\r\n                            (Color White)\r\n                            (Name \"Marker 3\")\r\n                            (  236.69   486.79   -53.38     0.92)  ; 1\r\n                            (  236.46   483.76   -47.33     0.92)  ; 2\r\n                            (  234.70   479.16   -47.92     0.92)  ; 3\r\n                            (  227.51   475.68   -46.25     0.92)  ; 4\r\n                            (  223.72   471.82   -46.25     0.92)  ; 5\r\n                            (  243.44   490.17   -55.72     0.92)  ; 6\r\n                            (  245.40   491.82   -55.92     0.92)  ; 7\r\n                            (  244.87   494.10   -56.42     0.92)  ; 8\r\n                            (  247.65   498.31   -59.02     0.92)  ; 9\r\n                            (  250.24   501.31   -59.63     0.92)  ; 10\r\n                            (  249.86   508.99   -62.60     0.92)  ; 11\r\n                            (  252.88   506.12   -62.60     0.92)  ; 12\r\n                            (  254.10   507.00   -63.95     0.92)  ; 13\r\n                            (  253.94   511.73   -63.95     0.92)  ; 14\r\n                            (  256.51   508.75   -64.13     0.92)  ; 15\r\n                            (  257.64   517.98   -66.60     0.92)  ; 16\r\n                            (  261.00   515.78   -64.57     0.92)  ; 17\r\n                            (  265.10   520.33   -64.57     0.92)  ; 18\r\n                            (  267.48   520.29   -64.42     0.92)  ; 19\r\n                            (  271.75   520.09   -65.70     0.92)  ; 20\r\n                            (  270.91   521.69   -69.20     0.92)  ; 21\r\n                            (  273.56   526.49   -72.20     0.92)  ; 22\r\n                            (  275.07   522.06   -71.47     0.92)  ; 23\r\n                            (  280.03   525.02   -73.57     0.92)  ; 24\r\n                            (  280.00   529.19   -71.57     0.92)  ; 25\r\n                            (  284.01   530.13   -70.97     0.92)  ; 26\r\n                            (  304.89   541.58   -77.35     0.92)  ; 27\r\n                            (  303.85   535.97   -74.78     0.92)  ; 28\r\n                          )  ;  End of markers\r\n                           Normal\r\n                        |\r\n                          (  220.10   470.93   -47.88     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2\r\n                          (  219.93   475.68   -47.88     1.83)  ; 2\r\n                          (  219.00   479.63   -47.13     1.83)  ; 3\r\n                          (  217.94   484.16   -47.67     1.83)  ; 4\r\n                          (  216.57   488.02   -47.55     1.83)  ; 5\r\n                          (  215.46   490.75   -48.75     1.83)  ; 6\r\n                          (  216.44   494.55   -50.05     1.83)  ; 7\r\n                          (  215.02   496.62   -50.05     1.83)  ; 8\r\n                          (  216.14   499.86   -48.62     1.83)  ; 9\r\n                          (  215.66   503.93   -48.62     1.83)  ; 10\r\n                          (  215.63   508.10   -48.62     1.83)  ; 11\r\n                          (  214.96   510.92   -48.62     1.83)  ; 12\r\n                          (\r\n                            (  212.13   513.27   -48.62     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-1\r\n                            (  210.84   514.76   -50.42     0.92)  ; 2\r\n                            (  210.03   512.17   -51.88     0.92)  ; 3\r\n                            (  210.03   512.17   -51.90     0.92)  ; 4\r\n                            (  209.81   515.12   -53.35     0.92)  ; 5\r\n                            (  210.57   515.89   -54.32     0.92)  ; 6\r\n                            (  208.70   517.83   -55.05     0.92)  ; 7\r\n                            (  207.72   520.00   -55.97     0.92)  ; 8\r\n                            (  205.99   521.38   -56.95     0.92)  ; 9\r\n                            (  205.54   521.28   -58.13     0.92)  ; 10\r\n                            (  205.45   523.65   -57.63     0.92)  ; 11\r\n                            (  205.64   524.88   -58.88     0.92)  ; 12\r\n                            (  205.06   525.34   -60.27     0.92)  ; 13\r\n                            (  202.68   525.39   -61.92     0.92)  ; 14\r\n                            (  202.29   527.08   -63.40     0.92)  ; 15\r\n                            (  203.05   527.86   -64.90     0.92)  ; 16\r\n                            (  202.07   530.03   -65.47     0.92)  ; 17\r\n                            (  201.67   531.72   -64.75     0.92)  ; 18\r\n                            (  200.39   533.21   -65.63     0.92)  ; 19\r\n                            (  199.23   534.13   -66.22     0.92)  ; 20\r\n                            (  199.41   535.36   -67.10     0.92)  ; 21\r\n                            (  198.43   537.53   -67.57     0.92)  ; 22\r\n                            (  197.27   538.45   -68.35     0.92)  ; 23\r\n                            (  195.36   538.59   -69.32     0.92)  ; 24\r\n                            (  194.83   540.87   -69.88     0.92)  ; 25\r\n                            (  193.85   543.02   -70.72     0.92)  ; 26\r\n                            (  192.38   543.27   -72.13     0.92)  ; 27\r\n                            (  191.21   544.20   -73.47     0.92)  ; 28\r\n                            (  190.23   546.36   -74.65     0.92)  ; 29\r\n                            (  186.76   549.12   -75.07     0.92)  ; 30\r\n                            (  186.05   550.14   -76.00     0.92)  ; 31\r\n                            (  183.92   553.24   -77.13     0.92)  ; 32\r\n                            (  181.73   554.51   -78.60     0.92)  ; 33\r\n                            (  179.54   555.79   -80.02     0.92)  ; 34\r\n                            (  178.70   557.38   -80.92     0.92)  ; 35\r\n                            (  178.17   559.66   -82.57     0.92)  ; 36\r\n                            (  176.61   562.27   -83.53     0.92)  ; 37\r\n                            (  176.66   564.07   -84.88     0.92)  ; 38\r\n                            (  174.17   564.69   -85.63     0.92)  ; 39\r\n                            (  173.32   566.28   -86.72     0.92)  ; 40\r\n                            (  173.32   566.28   -86.75     0.92)  ; 41\r\n                            (  172.34   568.43   -88.00     0.92)  ; 42\r\n                            (  172.34   568.43   -88.02     0.92)  ; 43\r\n                            (  171.37   570.60   -87.65     0.92)  ; 44\r\n                            (  171.37   570.60   -87.68     0.92)  ; 45\r\n                            (  168.92   573.01   -88.53     0.92)  ; 46\r\n                            (  166.41   573.62   -88.80     0.92)  ; 47\r\n                            (  166.41   573.62   -88.82     0.92)  ; 48\r\n                            (  163.97   576.03   -89.45     0.92)  ; 49\r\n                            (  163.97   576.03   -89.48     0.92)  ; 50\r\n                            (  162.94   576.38   -89.55     0.92)  ; 51\r\n                            (  161.52   578.44   -91.22     0.92)  ; 52\r\n                            (  158.76   580.18   -92.38     0.92)  ; 53\r\n                            (  158.76   580.18   -92.40     0.92)  ; 54\r\n                            (  157.78   582.34   -93.65     0.92)  ; 55\r\n                            (  155.91   584.29   -95.20     0.92)  ; 56\r\n                            (  155.91   584.29   -95.22     0.92)  ; 57\r\n                            (  155.06   585.88   -95.88     0.92)  ; 58\r\n                            (  154.66   587.59   -96.93     0.92)  ; 59\r\n                            (  154.66   587.59   -96.95     0.92)  ; 60\r\n                            (  154.45   590.51   -97.75     0.92)  ; 61\r\n                            (  154.45   590.51   -97.78     0.92)  ; 62\r\n                            (  151.99   592.92   -98.38     0.92)  ; 63\r\n                            (  150.84   593.85   -99.63     0.92)  ; 64\r\n                            (  150.26   594.30  -101.25     0.92)  ; 65\r\n                            (  150.26   594.30  -101.27     0.92)  ; 66\r\n\r\n                            (Cross\r\n                              (Color White)\r\n                              (Name \"Marker 3\")\r\n                              (  206.07   519.01   -56.28     0.92)  ; 1\r\n                              (  209.06   520.32   -53.90     0.92)  ; 2\r\n                              (  206.62   522.73   -57.63     0.92)  ; 3\r\n                              (  207.69   524.17   -58.88     0.92)  ; 4\r\n                              (  201.49   524.51   -61.92     0.92)  ; 5\r\n                              (  203.34   532.70   -63.90     0.92)  ; 6\r\n                              (  197.89   533.82   -66.22     0.92)  ; 7\r\n                              (  195.89   536.33   -67.57     0.92)  ; 8\r\n                              (  199.63   538.41   -67.57     0.92)  ; 9\r\n                              (  197.90   539.79   -67.57     0.92)  ; 10\r\n                              (  211.37   512.49   -50.42     0.92)  ; 11\r\n                              (  163.30   578.86   -89.55     0.92)  ; 12\r\n                              (  158.86   583.79   -95.22     0.92)  ; 13\r\n                              (  155.81   580.69   -95.22     0.92)  ; 14\r\n                              (  153.32   587.27   -95.88     0.92)  ; 15\r\n                              (  163.47   574.12   -88.75     0.92)  ; 16\r\n                              (  211.82   512.60   -54.35     0.92)  ; 17\r\n                            )  ;  End of markers\r\n                             Normal\r\n                          |\r\n                            (  214.35   515.57   -49.25     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2\r\n                            (  214.85   517.48   -49.47     1.83)  ; 2\r\n                            (  215.34   519.38   -49.47     1.83)  ; 3\r\n                            (  213.78   522.00   -49.47     1.83)  ; 4\r\n                            (  214.20   526.28   -49.47     1.83)  ; 5\r\n                            (  213.72   530.35   -49.47     1.83)  ; 6\r\n                            (  213.19   532.61   -49.47     1.83)  ; 7\r\n                            (  214.17   536.42   -50.20     1.83)  ; 8\r\n                            (  214.08   538.80   -49.32     1.83)  ; 9\r\n                            (  212.39   541.98   -49.32     1.83)  ; 10\r\n                            (  212.05   545.48   -49.32     1.83)  ; 11\r\n\r\n                            (Cross\r\n                              (Color RGB (128, 255, 128))\r\n                              (Name \"Marker 3\")\r\n                              (  212.87   515.82   -49.25     1.83)  ; 1\r\n                              (  216.59   522.07   -49.47     1.83)  ; 2\r\n                              (  211.56   527.45   -49.47     1.83)  ; 3\r\n                              (  216.34   529.17   -49.47     1.83)  ; 4\r\n                              (  215.93   524.89   -49.47     1.83)  ; 5\r\n                            )  ;  End of markers\r\n                            (\r\n                              (  209.61   546.12   -47.82     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-1\r\n                              (  209.03   546.59   -46.82     0.92)  ; 2\r\n                              (  208.58   546.48   -46.82     0.92)  ; 3\r\n                              (  208.32   547.61   -46.82     0.92)  ; 4\r\n                              (  205.05   547.44   -45.77     0.92)  ; 5\r\n                              (  205.55   549.35   -45.07     0.92)  ; 6\r\n                              (  207.35   549.77   -43.67     0.92)  ; 7\r\n                              (  205.60   551.15   -42.17     0.92)  ; 8\r\n                              (  204.62   553.31   -41.17     0.92)  ; 9\r\n                              (  203.20   555.37   -40.57     0.92)  ; 10\r\n                              (  201.33   557.31   -41.35     0.92)  ; 11\r\n                              (  199.46   559.27   -41.50     0.92)  ; 12\r\n                              (  197.73   560.65   -40.63     0.92)  ; 13\r\n                              (  196.70   561.01   -39.55     0.92)  ; 14\r\n                              (  194.87   564.77   -39.05     0.92)  ; 15\r\n                              (  194.22   567.59   -38.77     0.92)  ; 16\r\n                              (  192.16   568.31   -38.77     0.92)  ; 17\r\n                              (  189.97   569.59   -37.90     0.92)  ; 18\r\n                              (  187.80   570.86   -37.30     0.92)  ; 19\r\n                              (  185.43   570.90   -35.83     0.92)  ; 20\r\n                              (  183.11   572.76   -35.83     0.92)  ; 21\r\n                              (  180.87   572.23   -35.83     0.92)  ; 22\r\n                              (  178.78   571.14   -37.47     0.92)  ; 23\r\n                              (  178.78   571.14   -37.50     0.92)  ; 24\r\n                              (  176.18   568.15   -38.25     0.92)  ; 25\r\n                              (  173.82   568.18   -36.50     0.92)  ; 26\r\n                              (  172.66   569.11   -36.50     0.92)  ; 27\r\n                              (  170.55   568.02   -36.78     0.92)  ; 28\r\n                              (  167.92   569.20   -37.70     0.92)  ; 29\r\n                              (  164.09   569.48   -38.35     0.92)  ; 30\r\n                              (  160.72   565.72   -38.63     0.92)  ; 31\r\n                              (  159.88   567.31   -39.60     0.92)  ; 32\r\n                              (  158.10   566.89   -40.10     0.92)  ; 33\r\n                              (  155.82   564.57   -39.75     0.92)  ; 34\r\n                              (  150.45   563.31   -39.75     0.92)  ; 35\r\n                              (  146.62   563.61   -41.20     0.92)  ; 36\r\n                              (  144.30   565.45   -40.85     0.92)  ; 37\r\n                              (  144.30   565.45   -40.88     0.92)  ; 38\r\n                              (  142.78   563.89   -40.00     0.92)  ; 39\r\n                              (  140.15   565.08   -39.80     0.92)  ; 40\r\n                              (  137.48   564.45   -39.80     0.92)  ; 41\r\n                              (  134.08   564.85   -38.88     0.92)  ; 42\r\n                              (  131.09   563.55   -37.58     0.92)  ; 43\r\n                              (  127.12   564.41   -36.97     0.92)  ; 44\r\n                              (  125.64   564.66   -35.70     0.92)  ; 45\r\n                              (  126.04   562.97   -34.90     0.92)  ; 46\r\n                              (  127.02   560.80   -34.70     0.92)  ; 47\r\n\r\n                              (Cross\r\n                                (Color White)\r\n                                (Name \"Marker 3\")\r\n                                (  209.71   549.72   -43.67     0.92)  ; 1\r\n                                (  204.13   551.41   -43.67     0.92)  ; 2\r\n                                (  201.38   559.12   -41.08     0.92)  ; 3\r\n                                (  201.86   555.06   -38.97     0.92)  ; 4\r\n                                (  200.09   560.60   -38.97     0.92)  ; 5\r\n                                (  194.77   561.16   -38.53     0.92)  ; 6\r\n                                (  198.66   556.69   -40.42     0.92)  ; 7\r\n                                (  191.62   564.60   -38.77     0.92)  ; 8\r\n                                (  193.66   563.87   -38.77     0.92)  ; 9\r\n                                (  196.27   566.88   -38.77     0.92)  ; 10\r\n                                (  195.64   565.54   -38.77     0.92)  ; 11\r\n                                (  188.63   569.27   -37.30     0.92)  ; 12\r\n                                (  190.02   571.39   -37.30     0.92)  ; 13\r\n                                (  185.92   572.82   -36.13     0.92)  ; 14\r\n                                (  184.76   573.73   -35.80     0.92)  ; 15\r\n                                (  177.80   573.29   -38.25     0.92)  ; 16\r\n                                (  180.07   569.65   -36.80     0.92)  ; 17\r\n                                (  175.42   567.37   -36.78     0.92)  ; 18\r\n                                (  178.55   568.10   -38.88     0.92)  ; 19\r\n                                (  167.42   567.29   -36.92     0.92)  ; 20\r\n                                (  167.98   571.00   -37.25     0.92)  ; 21\r\n                                (  172.97   569.78   -35.83     0.92)  ; 22\r\n                                (  158.15   568.69   -40.10     0.92)  ; 23\r\n                                (  153.37   566.98   -40.32     0.92)  ; 24\r\n                                (  150.10   560.83   -40.10     0.92)  ; 25\r\n                                (  146.27   561.13   -40.72     0.92)  ; 26\r\n                                (  148.46   565.82   -38.70     0.92)  ; 27\r\n                                (  143.31   561.64   -39.40     0.92)  ; 28\r\n                                (  141.84   561.89   -38.83     0.92)  ; 29\r\n                                (  140.55   563.38   -38.53     0.92)  ; 30\r\n                                (  138.76   562.96   -38.53     0.92)  ; 31\r\n                                (  140.33   566.31   -38.47     0.92)  ; 32\r\n                                (  138.76   562.96   -39.80     0.92)  ; 33\r\n                                (  136.39   563.00   -39.80     0.92)  ; 34\r\n                                (  137.66   565.68   -39.80     0.92)  ; 35\r\n                                (  140.33   566.31   -39.80     0.92)  ; 36\r\n                                (  134.25   566.08   -37.58     0.92)  ; 37\r\n                                (  125.06   565.11   -34.70     0.92)  ; 38\r\n                                (  188.85   544.24   -74.65     0.92)  ; 39\r\n                                (  180.53   553.63   -80.02     0.92)  ; 40\r\n                                (  178.00   564.39   -81.67     0.92)  ; 41\r\n                                (  177.14   560.00   -84.92     0.92)  ; 42\r\n                                (  173.37   568.08   -88.02     0.92)  ; 43\r\n                                (  172.38   564.26   -88.02     0.92)  ; 44\r\n                                (  168.87   571.21   -87.22     0.92)  ; 45\r\n                                (  208.09   544.57   -46.82     0.92)  ; 46\r\n                              )  ;  End of markers\r\n                               Normal\r\n                            |\r\n                              (  212.78   550.42   -49.92     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2\r\n                              (  214.30   551.98   -49.83     1.83)  ; 2\r\n                              (  214.66   554.45   -49.83     1.83)  ; 3\r\n                              (  213.68   556.61   -51.22     1.83)  ; 4\r\n                              (  213.33   560.11   -52.02     1.83)  ; 5\r\n                              (  214.45   563.35   -52.77     1.83)  ; 6\r\n                              (  214.28   568.11   -53.62     1.83)  ; 7\r\n                              (  214.84   571.80   -55.27     1.83)  ; 8\r\n                              (  215.69   576.19   -55.27     1.83)  ; 9\r\n                              (  216.24   579.91   -56.20     1.83)  ; 10\r\n                              (  216.46   582.95   -57.40     1.83)  ; 11\r\n                              (  216.11   586.45   -58.65     1.83)  ; 12\r\n                              (  217.96   588.66   -59.40     1.83)  ; 13\r\n                              (  218.89   590.68   -58.80     1.83)  ; 14\r\n                              (  217.07   594.44   -57.47     1.83)  ; 15\r\n                              (  216.85   597.36   -57.92     1.83)  ; 16\r\n                              (  217.84   601.18   -57.92     1.83)  ; 17\r\n                              (  218.34   603.08   -59.10     1.83)  ; 18\r\n                              (  218.83   605.00   -59.10     1.83)  ; 19\r\n                              (  218.66   609.73   -59.80     1.83)  ; 20\r\n                              (  218.76   613.34   -60.60     1.83)  ; 21\r\n                              (  219.30   617.06   -61.55     1.83)  ; 22\r\n                              (  219.40   620.65   -62.40     1.83)  ; 23\r\n                              (  219.40   620.65   -62.42     1.83)  ; 24\r\n                              (  220.08   623.80   -63.40     1.83)  ; 25\r\n                              (  220.08   623.80   -63.42     1.83)  ; 26\r\n                              (  218.84   627.09   -63.42     1.83)  ; 27\r\n                              (  220.45   632.24   -63.88     1.83)  ; 28\r\n                              (  221.46   635.94   -64.05     1.83)  ; 29\r\n                              (  220.35   638.66   -63.05     1.83)  ; 30\r\n                              (  221.65   643.16   -62.45     1.83)  ; 31\r\n                              (  220.99   645.98   -62.08     1.83)  ; 32\r\n\r\n                              (Cross\r\n                                (Color RGB (128, 255, 128))\r\n                                (Name \"Marker 3\")\r\n                                (  212.12   537.13   -49.32     1.83)  ; 1\r\n                                (  216.06   540.45   -49.32     1.83)  ; 2\r\n                                (  211.46   539.97   -49.32     1.83)  ; 3\r\n                                (  214.78   547.92   -48.95     1.83)  ; 4\r\n                                (  212.54   563.51   -51.72     1.83)  ; 5\r\n                                (  217.00   564.56   -51.72     1.83)  ; 6\r\n                                (  212.24   568.82   -55.27     1.83)  ; 7\r\n                                (  216.52   568.63   -55.27     1.83)  ; 8\r\n                                (  217.51   572.43   -55.27     1.83)  ; 9\r\n                                (  217.88   574.92   -55.27     1.83)  ; 10\r\n                                (  213.73   580.50   -55.27     1.83)  ; 11\r\n                                (  214.10   582.99   -57.92     1.83)  ; 12\r\n                                (  219.89   600.46   -57.92     1.83)  ; 13\r\n                                (  216.29   603.80   -58.25     1.83)  ; 14\r\n                                (  217.36   605.25   -58.25     1.83)  ; 15\r\n                                (  218.44   628.79   -63.88     1.83)  ; 16\r\n                                (  219.12   631.93   -64.53     1.83)  ; 17\r\n                                (  223.18   634.68   -63.75     1.83)  ; 18\r\n                              )  ;  End of markers\r\n                              (\r\n                                (  223.32   646.54   -62.08     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-1\r\n                                (  227.43   645.10   -61.15     0.46)  ; 2\r\n                                (  229.21   645.53   -62.35     0.46)  ; 3\r\n                                (  230.81   644.70   -63.53     0.46)  ; 4\r\n                                (  231.84   644.35   -64.85     0.46)  ; 5\r\n                                (  232.60   645.13   -66.82     0.46)  ; 6\r\n                                (  233.23   645.89   -65.95     0.46)  ; 7\r\n                                (  234.03   644.98   -66.57     0.46)  ; 8\r\n                                (  235.28   645.76   -68.20     0.46)  ; 9\r\n                                (  237.78   645.15   -68.85     0.46)  ; 10\r\n                                (  239.57   645.57   -69.60     0.46)  ; 11\r\n                                (  241.16   644.74   -70.00     0.46)  ; 12\r\n                                (  245.27   643.32   -70.78     0.46)  ; 13\r\n                                (  246.96   640.13   -71.63     0.46)  ; 14\r\n                                (  249.46   639.53   -70.72     0.46)  ; 15\r\n                                (  252.73   639.69   -71.75     0.46)  ; 16\r\n                                (  255.80   638.61   -72.53     0.46)  ; 17\r\n                                (  257.27   638.36   -73.47     0.46)  ; 18\r\n                                (  260.00   640.79   -72.85     0.46)  ; 19\r\n                                (  262.60   643.79   -72.95     0.46)  ; 20\r\n                                (  264.25   644.78   -74.05     0.46)  ; 21\r\n                                (  266.04   645.20   -75.70     0.46)  ; 22\r\n                                (  267.11   646.64   -78.42     0.46)  ; 23\r\n                                (  267.11   646.64   -78.53     0.46)  ; 24\r\n\r\n                                (Cross\r\n                                  (Color White)\r\n                                  (Name \"Marker 3\")\r\n                                  (  260.52   638.53   -73.70     0.46)  ; 1\r\n                                  (  255.49   637.95   -70.22     0.46)  ; 2\r\n                                  (  254.06   639.99   -73.25     0.46)  ; 3\r\n                                  (  260.81   643.38   -72.95     0.46)  ; 4\r\n                                  (  264.43   646.01   -72.40     0.46)  ; 5\r\n                                  (  240.63   647.01   -70.00     0.46)  ; 6\r\n                                  (  225.19   644.58   -61.15     0.46)  ; 7\r\n                                  (  233.91   645.35   -34.78     1.83)  ; 8\r\n                                )  ;  End of markers\r\n                                 Normal\r\n                              |\r\n                                (  220.64   649.48   -61.57     1.83)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2\r\n                                (  219.71   653.45   -61.57     1.83)  ; 2\r\n                                (  219.23   657.51   -61.57     1.83)  ; 3\r\n                                (  220.36   660.76   -61.57     1.83)  ; 4\r\n                                (  220.90   664.47   -61.57     1.83)  ; 5\r\n                                (  220.24   667.30   -61.57     1.83)  ; 6\r\n                                (  220.51   672.14   -62.65     1.83)  ; 7\r\n                                (  220.92   676.42   -63.53     1.83)  ; 8\r\n                                (  220.57   679.92   -62.35     1.83)  ; 9\r\n                                (  221.11   683.63   -62.35     1.83)  ; 10\r\n                                (  221.72   689.15   -62.97     1.83)  ; 11\r\n                                (  221.68   693.31   -63.72     1.83)  ; 12\r\n                                (  222.17   695.23   -64.13     1.83)  ; 13\r\n                                (  222.71   698.94   -64.40     1.83)  ; 14\r\n                                (  221.60   701.66   -64.40     1.83)  ; 15\r\n                                (  221.97   704.14   -64.85     1.83)  ; 16\r\n                                (  222.56   709.64   -64.85     1.83)  ; 17\r\n                                (  222.66   713.25   -64.27     1.83)  ; 18\r\n                                (  222.58   715.62   -64.27     1.83)  ; 19\r\n                                (  223.83   718.30   -64.60     1.83)  ; 20\r\n                                (  224.42   723.82   -64.32     1.83)  ; 21\r\n                                (  224.34   726.19   -63.05     1.83)  ; 22\r\n                                (  224.57   729.23   -61.88     1.83)  ; 23\r\n                                (  224.08   733.29   -61.10     1.83)  ; 24\r\n                                (  223.41   736.13   -60.25     1.83)  ; 25\r\n                                (  224.68   738.81   -59.27     1.83)  ; 26\r\n                                (  225.09   743.09   -58.82     1.83)  ; 27\r\n                                (  225.63   746.79   -58.82     1.83)  ; 28\r\n                                (  224.70   750.76   -59.95     1.83)  ; 29\r\n                                (  226.41   753.55   -59.85     1.83)  ; 30\r\n                                (  226.31   755.92   -59.85     1.83)  ; 31\r\n                                (  226.29   760.08   -59.85     1.83)  ; 32\r\n                                (  224.99   761.58   -60.45     1.83)  ; 33\r\n                                (  224.96   765.74   -60.45     1.83)  ; 34\r\n                                (  225.33   768.23   -61.00     1.83)  ; 35\r\n                                (  224.80   770.49   -59.97     1.83)  ; 36\r\n                                (  224.00   773.88   -59.32     1.83)  ; 37\r\n\r\n                                (Cross\r\n                                  (Color White)\r\n                                  (Name \"Marker 3\")\r\n                                  (  217.22   650.48   -59.32     0.46)  ; 1\r\n                                  (  218.60   652.58   -59.32     0.46)  ; 2\r\n                                  (  223.07   653.64   -58.45     0.46)  ; 3\r\n                                  (  222.57   651.73   -58.45     0.46)  ; 4\r\n                                  (  221.69   657.49   -57.28     0.46)  ; 5\r\n                                )  ;  End of markers\r\n\r\n                                (Cross\r\n                                  (Color RGB (128, 255, 128))\r\n                                  (Name \"Marker 3\")\r\n                                  (  217.92   647.06   -62.08     1.83)  ; 1\r\n                                  (  223.31   666.23   -59.80     1.83)  ; 2\r\n                                  (  222.20   668.97   -59.80     1.83)  ; 3\r\n                                  (  218.55   670.49   -59.80     1.83)  ; 4\r\n                                  (  223.01   671.53   -59.80     1.83)  ; 5\r\n                                  (  218.83   675.33   -59.80     1.83)  ; 6\r\n                                  (  220.14   685.80   -62.97     1.83)  ; 7\r\n                                  (  219.92   688.73   -62.97     1.83)  ; 8\r\n                                  (  224.29   686.17   -62.97     1.83)  ; 9\r\n                                  (  223.80   684.26   -63.05     1.83)  ; 10\r\n                                  (  224.76   698.22   -64.40     1.83)  ; 11\r\n                                  (  220.75   697.28   -64.40     1.83)  ; 12\r\n                                  (  224.01   703.42   -64.85     1.83)  ; 13\r\n                                  (  221.30   723.08   -64.32     1.83)  ; 14\r\n                                  (  227.49   722.75   -64.32     1.83)  ; 15\r\n                                  (  221.28   717.11   -64.32     1.83)  ; 16\r\n                                  (  226.50   735.05   -59.27     1.83)  ; 17\r\n                                  (  221.50   736.28   -58.27     1.83)  ; 18\r\n                                  (  227.49   738.87   -58.27     1.83)  ; 19\r\n                                  (  228.62   748.10   -59.95     1.83)  ; 20\r\n                                  (  224.17   753.03   -60.63     1.83)  ; 21\r\n                                  (  224.63   759.10   -61.88     1.83)  ; 22\r\n                                  (  223.79   760.69   -61.88     1.83)  ; 23\r\n                                )  ;  End of markers\r\n                                (\r\n                                  (  223.30   779.28   -59.32     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1\r\n                                  (  223.10   782.21   -59.32     1.38)  ; 2\r\n                                  (  223.64   785.92   -59.22     1.38)  ; 3\r\n                                  (  224.58   787.94   -60.57     1.38)  ; 4\r\n                                  (  224.49   790.31   -60.85     1.38)  ; 5\r\n                                  (  225.04   794.01   -60.85     1.38)  ; 6\r\n                                  (  225.08   795.81   -61.30     1.38)  ; 7\r\n                                  (  225.31   798.85   -60.32     1.38)  ; 8\r\n                                  (  225.54   801.90   -59.20     1.38)  ; 9\r\n                                  (  225.32   804.84   -58.70     1.38)  ; 10\r\n                                  (  225.57   807.87   -59.15     1.38)  ; 11\r\n                                  (  225.21   811.37   -59.65     1.38)  ; 12\r\n                                  (  225.17   815.55   -60.07     1.38)  ; 13\r\n                                  (  224.96   818.49   -60.72     1.38)  ; 14\r\n                                  (  224.03   822.44   -61.60     1.38)  ; 15\r\n                                  (  224.26   825.48   -62.25     1.38)  ; 16\r\n                                  (  223.33   829.44   -62.25     1.38)  ; 17\r\n                                  (  222.40   833.41   -62.95     1.38)  ; 18\r\n                                  (  222.63   836.45   -64.02     1.38)  ; 19\r\n                                  (  223.75   839.70   -64.65     1.38)  ; 20\r\n                                  (  222.50   842.99   -64.65     1.38)  ; 21\r\n                                  (  221.26   846.27   -65.13     1.38)  ; 22\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  223.43   794.83   -61.30     1.38)  ; 1\r\n                                    (  222.96   804.88   -58.70     1.38)  ; 2\r\n                                    (  226.44   802.12   -58.70     1.38)  ; 3\r\n                                    (  227.26   810.67   -59.60     1.38)  ; 4\r\n                                    (  224.22   807.55   -59.60     1.38)  ; 5\r\n                                    (  222.65   820.33   -61.60     1.38)  ; 6\r\n                                    (  226.03   819.93   -61.60     1.38)  ; 7\r\n                                    (  221.99   829.12   -62.25     1.38)  ; 8\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  222.29   850.00   -65.80     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1\r\n                                    (  222.38   853.60   -64.88     1.38)  ; 2\r\n                                    (  222.62   856.64   -64.88     1.38)  ; 3\r\n                                    (  221.55   861.17   -66.20     1.38)  ; 4\r\n                                    (  221.47   863.54   -66.50     1.38)  ; 5\r\n                                    (  220.67   866.94   -66.75     1.38)  ; 6\r\n                                    (  220.67   866.94   -66.77     1.38)  ; 7\r\n                                    (  221.34   870.08   -67.28     1.38)  ; 8\r\n                                    (  221.08   871.21   -67.40     1.38)  ; 9\r\n                                    (  221.05   875.38   -67.40     1.38)  ; 10\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  224.74   847.59   -64.88     1.38)  ; 1\r\n                                      (  220.64   849.02   -64.88     1.38)  ; 2\r\n                                      (  224.26   851.65   -62.00     1.38)  ; 3\r\n                                      (  219.33   866.62   -66.38     1.38)  ; 4\r\n                                      (  220.31   864.46   -66.35     1.38)  ; 5\r\n                                      (  219.12   869.56   -67.30     1.38)  ; 6\r\n                                      (  222.79   874.00   -67.40     1.38)  ; 7\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  219.47   878.56   -67.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1-1\r\n                                      (  218.92   880.84   -67.40     0.92)  ; 2\r\n                                      (  219.30   883.30   -67.40     0.92)  ; 3\r\n                                      (  218.77   885.57   -68.17     0.92)  ; 4\r\n                                      (  218.68   887.94   -69.07     0.92)  ; 5\r\n                                      (  218.34   891.44   -69.70     0.92)  ; 6\r\n                                      (  216.64   894.63   -70.20     0.92)  ; 7\r\n                                      (  217.01   897.10   -70.20     0.92)  ; 8\r\n                                      (  216.48   899.37   -70.20     0.92)  ; 9\r\n                                      (  215.81   902.20   -70.70     0.92)  ; 10\r\n                                      (  215.28   904.47   -71.15     0.92)  ; 11\r\n                                      (  214.62   907.29   -71.22     0.92)  ; 12\r\n                                      (  212.75   909.24   -70.78     0.92)  ; 13\r\n                                      (  212.08   912.06   -70.78     0.92)  ; 14\r\n\r\n                                      (Cross\r\n                                        (Color White)\r\n                                        (Name \"Marker 3\")\r\n                                        (  213.22   910.03   -27.00     1.38)  ; 1\r\n                                      )  ;  End of markers\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  220.67   879.45   -67.40     0.92)  ; 1\r\n                                        (  217.72   879.95   -67.40     0.92)  ; 2\r\n                                        (  220.64   883.62   -67.40     0.92)  ; 3\r\n                                        (  220.55   885.99   -69.70     0.92)  ; 4\r\n                                        (  217.82   905.66   -71.22     0.92)  ; 5\r\n                                        (  214.80   908.52   -71.22     0.92)  ; 6\r\n                                        (  211.41   908.93   -70.78     0.92)  ; 7\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  213.34   914.76   -71.52     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1-1-1\r\n                                        (  214.01   917.90   -72.17     0.92)  ; 2\r\n                                        (  214.69   921.04   -73.00     0.92)  ; 3\r\n                                        (  216.98   923.37   -73.28     0.92)  ; 4\r\n                                        (  218.23   926.05   -74.13     0.92)  ; 5\r\n                                        (  221.09   927.92   -74.13     0.92)  ; 6\r\n                                        (  220.96   928.49   -74.13     0.92)  ; 7\r\n                                        (  223.96   929.78   -74.97     0.92)  ; 8\r\n                                        (  225.78   932.00   -75.90     0.92)  ; 9\r\n                                        (  226.60   934.58   -76.75     0.92)  ; 10\r\n                                        (  229.15   935.77   -77.50     0.92)  ; 11\r\n                                        (  230.13   939.59   -77.50     0.92)  ; 12\r\n                                        (  231.21   941.03   -78.88     0.92)  ; 13\r\n                                        (  231.52   941.70   -78.65     0.92)  ; 14\r\n                                        (  233.03   943.26   -80.50     0.92)  ; 15\r\n                                        (  233.40   945.72   -81.07     0.92)  ; 16\r\n                                        (  234.04   947.07   -81.53     0.92)  ; 17\r\n                                        (  235.51   946.82   -82.45     0.92)  ; 18\r\n                                        (  237.21   949.61   -83.88     0.92)  ; 19\r\n                                        (  238.72   951.15   -83.80     0.92)  ; 20\r\n                                        (  240.26   952.70   -83.82     0.92)  ; 21\r\n                                        (  240.43   953.95   -85.13     0.92)  ; 22\r\n                                        (  241.82   956.06   -86.85     0.92)  ; 23\r\n                                        (  243.34   957.61   -87.75     0.92)  ; 24\r\n                                        (  243.56   960.65   -88.65     0.92)  ; 25\r\n                                        (  242.90   963.48   -88.65     0.92)  ; 26\r\n                                        (  243.27   965.96   -88.65     0.92)  ; 27\r\n                                        (  244.03   966.73   -89.55     0.92)  ; 28\r\n                                        (  245.15   969.98   -90.65     0.92)  ; 29\r\n                                        (  245.15   969.98   -90.68     0.92)  ; 30\r\n                                        (  246.36   970.86   -90.85     0.92)  ; 31\r\n                                        (  248.77   972.63   -90.88     0.92)  ; 32\r\n                                        (  249.00   975.66   -91.62     0.92)  ; 33\r\n                                        (  248.92   978.04   -92.38     0.92)  ; 34\r\n                                        (  250.26   978.34   -92.97     0.92)  ; 35\r\n                                        (  249.81   978.25   -92.97     0.92)  ; 36\r\n                                        (  251.39   981.60   -93.53     0.92)  ; 37\r\n                                        (  251.60   984.62   -94.90     0.92)  ; 38\r\n                                        (  251.60   984.62   -94.92     0.92)  ; 39\r\n                                        (  251.33   985.76   -95.77     0.92)  ; 40\r\n                                        (  253.44   986.84   -96.43     0.92)  ; 41\r\n                                        (  253.92   988.75   -97.38     0.92)  ; 42\r\n                                        (  253.92   988.75   -97.42     0.92)  ; 43\r\n                                        (  255.01   990.20   -98.15     0.92)  ; 44\r\n                                        (  256.67   991.19   -98.47     0.92)  ; 45\r\n                                        (  257.60   993.20   -98.47     0.92)  ; 46\r\n                                        (  258.41   995.77   -99.63     0.92)  ; 47\r\n                                        (  259.99   999.12   -99.63     0.92)  ; 48\r\n                                        (  259.90  1001.50   -99.63     0.92)  ; 49\r\n                                        (  258.65  1004.79  -100.22     0.92)  ; 50\r\n                                        (  257.98  1007.62  -101.13     0.92)  ; 51\r\n                                        (  258.36  1010.09  -102.13     0.92)  ; 52\r\n                                        (  258.41  1011.88  -103.23     0.92)  ; 53\r\n                                        (  258.46  1013.69  -103.60     0.92)  ; 54\r\n                                        (  259.84  1015.81  -104.88     0.92)  ; 55\r\n                                        (  260.91  1017.26  -105.42     0.92)  ; 56\r\n                                        (  262.21  1021.75  -106.13     0.92)  ; 57\r\n                                        (  262.72  1023.66  -106.32     0.92)  ; 58\r\n                                        (  262.72  1023.66  -106.35     0.92)  ; 59\r\n                                        (  262.77  1025.46  -107.10     0.92)  ; 60\r\n                                        (  262.82  1027.26  -108.42     0.92)  ; 61\r\n                                        (  263.75  1029.27  -109.82     0.92)  ; 62\r\n                                        (  264.43  1032.41  -110.63     0.92)  ; 63\r\n                                        (  264.30  1032.98  -112.15     0.92)  ; 64\r\n                                        (  265.41  1036.23  -113.67     0.92)  ; 65\r\n                                        (  266.95  1037.78  -115.37     0.92)  ; 66\r\n                                        (  266.10  1039.37  -117.02     0.92)  ; 67\r\n                                        (  264.48  1040.20  -118.32     0.92)  ; 68\r\n                                        (  264.48  1040.20  -118.38     0.92)  ; 69\r\n                                        (  263.34  1041.11  -120.65     0.92)  ; 70\r\n                                        (  264.28  1043.12  -122.50     0.92)  ; 71\r\n                                        (  263.74  1045.39  -124.52     0.92)  ; 72\r\n                                        (  263.48  1046.52  -126.65     0.92)  ; 73\r\n                                        (  262.18  1048.01  -130.23     0.92)  ; 74\r\n                                        (  263.85  1049.00  -133.30     0.92)  ; 75\r\n                                        (  263.85  1049.00  -133.32     0.92)  ; 76\r\n                                        (  265.06  1049.88  -132.68     0.92)  ; 77\r\n                                        (  265.06  1049.88  -132.77     0.92)  ; 78\r\n                                        (  265.76  1048.86  -135.93     0.92)  ; 79\r\n                                        (  266.84  1050.30  -138.70     0.92)  ; 80\r\n                                        (  266.84  1050.30  -138.72     0.92)  ; 81\r\n                                        (  267.25  1054.57  -140.47     0.92)  ; 82\r\n                                        (  269.67  1056.33  -140.47     0.92)  ; 83\r\n                                        (  271.41  1060.92  -141.35     0.92)  ; 84\r\n                                        (  274.53  1061.65  -141.38     0.92)  ; 85\r\n                                        (  275.09  1065.37  -142.68     0.92)  ; 86\r\n                                        (  278.21  1066.10  -143.20     0.92)  ; 87\r\n                                        (  279.24  1065.74  -145.63     0.92)  ; 88\r\n                                        (  280.26  1065.39  -148.48     0.92)  ; 89\r\n                                        (  280.26  1065.39  -148.52     0.92)  ; 90\r\n                                        (  282.36  1066.47  -149.73     0.92)  ; 91\r\n                                        (  282.36  1066.47  -149.75     0.92)  ; 92\r\n                                        (  283.30  1068.48  -152.05     0.92)  ; 93\r\n                                        (  283.80  1070.39  -153.82     0.46)  ; 94\r\n                                        (  285.76  1072.05  -156.72     0.46)  ; 95\r\n                                        (  285.76  1072.05  -157.27     0.46)  ; 96\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  215.21   912.80   -71.52     0.92)  ; 1\r\n                                          (  214.96   925.89   -73.28     0.92)  ; 2\r\n                                          (  217.21   926.42   -74.13     0.92)  ; 3\r\n                                          (  218.41   927.29   -74.13     0.92)  ; 4\r\n                                          (  220.38   928.94   -74.13     0.92)  ; 5\r\n                                          (  218.32   923.68   -74.13     0.92)  ; 6\r\n                                          (  224.80   928.19   -76.75     0.92)  ; 7\r\n                                          (  231.95   935.83   -77.50     0.92)  ; 8\r\n                                          (  231.57   943.51   -78.65     0.92)  ; 9\r\n                                          (  235.41   943.21   -79.60     0.92)  ; 10\r\n                                          (  235.20   946.15   -79.60     0.92)  ; 11\r\n                                          (  238.77   946.99   -83.70     0.92)  ; 12\r\n                                          (  236.50   950.64   -82.40     0.92)  ; 13\r\n                                          (  242.66   954.47   -86.85     0.92)  ; 14\r\n                                          (  240.82   968.37   -88.97     0.92)  ; 15\r\n                                          (  242.34   969.91   -88.97     0.92)  ; 16\r\n                                          (  244.14   970.34   -89.90     0.92)  ; 17\r\n                                          (  247.52   969.93   -89.90     0.92)  ; 18\r\n                                          (  247.94   980.18   -93.53     0.92)  ; 19\r\n                                          (  251.07   986.88   -93.40     0.92)  ; 20\r\n                                          (  253.57   986.28   -95.88     0.92)  ; 21\r\n                                          (  259.79   991.91   -97.42     0.92)  ; 22\r\n                                          (  258.94   993.50   -99.63     0.92)  ; 23\r\n                                          (  260.71  1004.08   -99.63     0.92)  ; 24\r\n                                          (  258.28  1002.31   -99.63     0.92)  ; 25\r\n                                          (  256.16  1011.37  -101.77     0.92)  ; 26\r\n                                          (  260.10  1014.68  -105.42     0.92)  ; 27\r\n                                          (  260.87  1015.45  -105.42     0.92)  ; 28\r\n                                          (  259.64  1024.72  -105.63     0.92)  ; 29\r\n                                          (  262.94  1020.71  -105.65     0.92)  ; 30\r\n                                          (  263.56  1022.07  -105.65     0.92)  ; 31\r\n                                          (  262.69  1027.82  -105.65     0.92)  ; 32\r\n                                          (  267.14  1028.88  -108.53     0.92)  ; 33\r\n                                          (  260.32  1027.87  -112.15     0.92)  ; 34\r\n                                          (  262.69  1033.80  -112.15     0.92)  ; 35\r\n                                          (  269.12  1036.50  -112.90     0.92)  ; 36\r\n                                          (  263.10  1038.07  -113.15     0.92)  ; 37\r\n                                          (  262.21  1037.86  -118.45     0.92)  ; 38\r\n                                          (  267.18  1040.83  -118.45     0.92)  ; 39\r\n                                          (  260.78  1039.92  -120.65     0.92)  ; 40\r\n                                          (  266.25  1044.79  -124.52     0.92)  ; 41\r\n                                          (  266.16  1047.15  -124.52     0.92)  ; 42\r\n                                          (  264.79  1051.00  -133.05     0.92)  ; 43\r\n                                          (  260.93  1045.33  -129.75     0.92)  ; 44\r\n                                          (  269.72  1058.14  -141.35     0.92)  ; 45\r\n                                          (  272.71  1065.41  -143.20     0.92)  ; 46\r\n                                          (  280.67  1069.67  -145.63     0.92)  ; 47\r\n                                          (  278.48  1064.97  -145.63     0.92)  ; 48\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    |\r\n                                      (  222.75   878.16   -67.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-1-2\r\n                                      (  224.14   880.29   -68.25     0.92)  ; 2\r\n                                      (  225.08   882.30   -68.25     0.92)  ; 3\r\n                                      (  226.20   885.55   -68.25     0.92)  ; 4\r\n                                      (  227.91   888.34   -69.05     0.92)  ; 5\r\n                                      (  228.40   890.24   -68.75     0.92)  ; 6\r\n                                      (  230.06   891.23   -68.67     0.92)  ; 7\r\n                                      (  231.62   894.59   -68.67     0.92)  ; 8\r\n                                      (  234.49   896.45   -69.32     0.92)  ; 9\r\n                                      (  236.90   898.21   -69.32     0.92)  ; 10\r\n                                      (  238.69   898.63   -70.32     0.92)  ; 11\r\n                                      (  240.48   899.04   -71.17     0.92)  ; 12\r\n                                      (  241.55   900.49   -71.85     0.92)  ; 13\r\n                                      (  242.93   902.60   -72.53     0.92)  ; 14\r\n                                      (  245.04   903.70   -72.53     0.92)  ; 15\r\n                                      (  247.01   905.35   -72.28     0.92)  ; 16\r\n                                      (  247.64   906.70   -73.78     0.92)  ; 17\r\n                                      (  251.26   909.33   -74.13     0.92)  ; 18\r\n                                      (  254.57   911.30   -74.13     0.92)  ; 19\r\n                                      (  257.42   913.17   -73.80     0.92)  ; 20\r\n                                      (  259.52   914.26   -73.80     0.92)  ; 21\r\n                                      (  260.34   916.83   -73.80     0.92)  ; 22\r\n                                      (  262.75   918.60   -74.10     0.92)  ; 23\r\n                                      (  264.71   920.25   -74.10     0.92)  ; 24\r\n                                      (  266.56   922.47   -74.10     0.92)  ; 25\r\n                                      (  268.20   923.46   -73.47     0.92)  ; 26\r\n                                      (  270.93   925.89   -73.47     0.92)  ; 27\r\n                                      (  274.24   927.86   -73.47     0.92)  ; 28\r\n                                      (  276.52   930.18   -74.53     0.92)  ; 29\r\n                                      (  278.94   931.94   -74.82     0.92)  ; 30\r\n                                      (  279.88   933.95   -75.10     0.92)  ; 31\r\n                                      (  283.18   935.93   -75.38     0.92)  ; 32\r\n                                      (  285.60   937.69   -75.67     0.92)  ; 33\r\n                                      (  286.81   938.57   -74.67     0.92)  ; 34\r\n                                      (  286.81   938.57   -74.72     0.92)  ; 35\r\n                                      (  289.54   940.99   -73.42     0.92)  ; 36\r\n                                      (  290.03   942.90   -72.20     0.92)  ; 37\r\n                                      (  291.86   945.13   -70.75     0.92)  ; 38\r\n                                      (  292.80   947.14   -69.57     0.92)  ; 39\r\n                                      (  294.64   949.36   -69.57     0.92)  ; 40\r\n                                      (  294.19   949.26   -69.57     0.92)  ; 41\r\n                                      (  296.22   952.71   -68.65     0.92)  ; 42\r\n                                      (  298.04   954.94   -68.20     0.92)  ; 43\r\n                                      (  298.04   954.94   -68.25     0.92)  ; 44\r\n                                      (  298.86   957.52   -67.47     0.92)  ; 45\r\n                                      (  298.86   957.52   -67.50     0.92)  ; 46\r\n                                      (  301.41   958.71   -66.82     0.92)  ; 47\r\n                                      (  303.81   960.47   -66.22     0.46)  ; 48\r\n                                      (  304.71   960.69   -65.52     0.46)  ; 49\r\n                                      (  305.97   963.36   -64.50     0.46)  ; 50\r\n                                      (  308.65   963.99   -63.22     0.46)  ; 51\r\n                                      (  310.30   964.97   -63.17     0.46)  ; 52\r\n                                      (  311.99   967.77   -62.28     0.46)  ; 53\r\n                                      (  314.02   971.23   -61.05     0.46)  ; 54\r\n                                      (  316.88   973.09   -60.15     0.46)  ; 55\r\n                                      (  316.88   973.09   -60.17     0.46)  ; 56\r\n                                      (  319.48   976.08   -59.32     0.46)  ; 57\r\n                                      (  319.48   976.08   -59.30     0.46)  ; 58\r\n                                      (  321.89   977.85   -59.95     0.46)  ; 59\r\n                                      (  322.97   979.29   -59.13     0.46)  ; 60\r\n                                      (  325.12   982.18   -58.58     0.46)  ; 61\r\n                                      (  328.29   984.72   -58.10     0.46)  ; 62\r\n                                      (  328.47   985.95   -57.50     0.46)  ; 63\r\n                                      (  331.06   988.96   -56.82     0.46)  ; 64\r\n                                      (  333.03   990.61   -55.83     0.46)  ; 65\r\n                                      (  335.58   991.80   -54.78     0.46)  ; 66\r\n                                      (  336.78   992.67   -54.45     0.46)  ; 67\r\n                                      (  337.60   995.26   -53.85     0.46)  ; 68\r\n                                      (  338.54   997.27   -54.32     0.46)  ; 69\r\n                                      (  341.26   999.70   -54.85     0.46)  ; 70\r\n                                      (  343.85  1002.70   -55.60     0.46)  ; 71\r\n                                      (  343.85  1002.70   -55.63     0.46)  ; 72\r\n                                      (  344.18  1003.36   -54.40     0.46)  ; 73\r\n                                      (  345.30  1006.61   -52.82     0.46)  ; 74\r\n                                      (  347.40  1007.71   -51.82     0.46)  ; 75\r\n                                      (  349.99  1010.71   -51.52     0.46)  ; 76\r\n                                      (  349.99  1010.71   -51.55     0.46)  ; 77\r\n                                      (  350.62  1012.03   -49.72     0.46)  ; 78\r\n                                      (  350.67  1013.83   -48.65     0.46)  ; 79\r\n                                      (  353.52  1015.70   -48.00     0.46)  ; 80\r\n                                      (  352.99  1017.97   -48.05     0.46)  ; 81\r\n                                      (  355.40  1019.73   -47.33     0.46)  ; 82\r\n                                      (  358.71  1021.69   -47.33     0.46)  ; 83\r\n                                      (  358.55  1026.44   -47.92     0.46)  ; 84\r\n                                      (  359.62  1027.88   -49.13     0.46)  ; 85\r\n                                      (  360.88  1030.56   -49.13     0.46)  ; 86\r\n                                      (  360.67  1033.50   -48.42     0.46)  ; 87\r\n                                      (  360.21  1033.40   -48.42     0.46)  ; 88\r\n                                      (  361.16  1035.40   -47.37     0.46)  ; 89\r\n                                      (  360.95  1038.35   -47.72     0.46)  ; 90\r\n                                      (  360.95  1038.35   -47.75     0.46)  ; 91\r\n                                      (  360.55  1040.04   -47.75     0.46)  ; 92\r\n                                      (  362.64  1041.13   -48.95     0.46)  ; 93\r\n                                      (  365.19  1042.31   -48.58     0.46)  ; 94\r\n                                      (  365.19  1042.31   -48.60     0.46)  ; 95\r\n                                      (  364.80  1044.02   -49.65     0.46)  ; 96\r\n                                      (  364.80  1044.02   -49.67     0.46)  ; 97\r\n                                      (  365.29  1045.92   -49.80     0.46)  ; 98\r\n                                      (  367.39  1047.02   -50.27     0.46)  ; 99\r\n                                      (  369.09  1049.81   -49.95     0.46)  ; 100\r\n                                      (  371.06  1051.46   -51.57     0.46)  ; 101\r\n                                      (  372.72  1052.45   -52.15     0.46)  ; 102\r\n                                      (  373.97  1055.13   -53.65     0.46)  ; 103\r\n                                      (  373.97  1055.13   -53.67     0.46)  ; 104\r\n                                      (  374.91  1057.14   -54.92     0.46)  ; 105\r\n                                      (  374.96  1058.95   -56.77     0.46)  ; 106\r\n                                      (  373.98  1061.10   -59.55     0.46)  ; 107\r\n                                      (  373.98  1061.10   -59.67     0.46)  ; 108\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  223.16   882.44   -67.70     0.92)  ; 1\r\n                                        (  225.62   886.01   -66.38     0.92)  ; 2\r\n                                        (  227.24   891.17   -68.75     0.92)  ; 3\r\n                                        (  227.64   889.47   -71.05     0.92)  ; 4\r\n                                        (  231.04   889.07   -68.67     0.92)  ; 5\r\n                                        (  235.07   895.99   -69.32     0.92)  ; 6\r\n                                        (  242.35   897.10   -71.17     0.92)  ; 7\r\n                                        (  241.60   902.29   -72.53     0.92)  ; 8\r\n                                        (  243.65   901.58   -70.95     0.92)  ; 9\r\n                                        (  249.91   909.02   -71.28     0.92)  ; 10\r\n                                        (  247.56   909.07   -73.53     0.92)  ; 11\r\n                                        (  250.44   906.76   -74.38     0.92)  ; 12\r\n                                        (  257.82   911.46   -72.70     0.92)  ; 13\r\n                                        (  260.82   912.77   -72.55     0.92)  ; 14\r\n                                        (  262.83   916.22   -74.45     0.92)  ; 15\r\n                                        (  274.19   926.06   -71.75     0.92)  ; 16\r\n                                        (  276.16   927.71   -73.35     0.92)  ; 17\r\n                                        (  277.81   928.69   -73.35     0.92)  ; 18\r\n                                        (  277.01   932.09   -73.35     0.92)  ; 19\r\n                                        (  273.53   928.89   -73.90     0.92)  ; 20\r\n                                        (  287.17   941.04   -73.42     0.92)  ; 21\r\n                                        (  298.44   953.24   -68.65     0.92)  ; 22\r\n                                        (  293.85   952.75   -67.15     0.92)  ; 23\r\n                                        (  291.54   944.46   -73.42     0.92)  ; 24\r\n                                        (  300.15   956.03   -68.28     0.92)  ; 25\r\n                                        (  297.89   959.68   -65.05     0.92)  ; 26\r\n                                        (  305.11   958.98   -69.10     0.92)  ; 27\r\n                                        (  320.14   973.26   -58.67     0.46)  ; 28\r\n                                        (  317.37   975.00   -61.67     0.46)  ; 29\r\n                                        (  325.19   979.81   -58.58     0.46)  ; 30\r\n                                        (  329.94   985.70   -59.20     0.46)  ; 31\r\n                                        (  329.46   989.77   -57.88     0.46)  ; 32\r\n                                        (  341.36   997.34   -55.95     0.46)  ; 33\r\n                                        (  339.85  1001.75   -55.50     0.46)  ; 34\r\n                                        (  340.33  1003.67   -53.88     0.46)  ; 35\r\n                                        (  337.43  1000.00   -53.07     0.46)  ; 36\r\n                                        (  343.00   998.31   -53.07     0.46)  ; 37\r\n                                        (  350.20  1007.77   -51.52     0.46)  ; 38\r\n                                        (  349.51  1014.76   -48.00     0.46)  ; 39\r\n                                        (  352.67  1011.33   -48.00     0.46)  ; 40\r\n                                        (  354.32  1012.31   -48.00     0.46)  ; 41\r\n                                        (  354.57  1021.32   -47.08     0.46)  ; 42\r\n                                        (  353.31  1018.63   -49.85     0.46)  ; 43\r\n                                        (  351.93  1016.52   -48.05     0.46)  ; 44\r\n                                        (  354.15  1017.04   -48.05     0.46)  ; 45\r\n                                        (  361.69  1033.14   -46.50     0.46)  ; 46\r\n                                        (  362.09  1031.45   -46.32     0.46)  ; 47\r\n                                        (  362.15  1039.22   -48.45     0.46)  ; 48\r\n                                        (  361.55  1027.73   -49.13     0.46)  ; 49\r\n                                        (  359.72  1031.49   -50.05     0.46)  ; 50\r\n                                        (  365.79  1047.84   -50.27     0.46)  ; 51\r\n                                        (  369.44  1046.30   -50.27     0.46)  ; 52\r\n                                        (  358.49  1040.75   -45.40     0.46)  ; 53\r\n                                        (  358.80  1035.44   -45.42     0.46)  ; 54\r\n                                        (  370.69  1048.99   -51.57     0.46)  ; 55\r\n                                        (  372.36  1049.98   -51.57     0.46)  ; 56\r\n                                        (  369.63  1053.52   -52.35     0.46)  ; 57\r\n                                        (  367.44  1048.82   -49.30     0.46)  ; 58\r\n                                        (  372.41  1051.77   -49.90     0.46)  ; 59\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  218.53   847.92   -63.30     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-1-2\r\n                                    (  218.57   849.72   -62.45     1.38)  ; 2\r\n                                    (  217.65   853.69   -61.28     1.38)  ; 3\r\n                                    (  216.98   856.52   -61.32     1.38)  ; 4\r\n                                    (  215.88   859.24   -60.85     1.38)  ; 5\r\n                                    (  214.46   861.30   -60.07     1.38)  ; 6\r\n                                    (  213.61   862.88   -59.13     1.38)  ; 7\r\n                                    (  213.97   865.37   -58.45     1.38)  ; 8\r\n                                    (  213.26   866.39   -57.35     1.38)  ; 9\r\n                                    (  211.70   869.02   -56.57     1.38)  ; 10\r\n                                    (  210.99   870.04   -55.25     1.38)  ; 11\r\n                                    (  210.86   870.61   -54.38     1.38)  ; 12\r\n                                    (  208.55   872.46   -53.13     1.38)  ; 13\r\n                                    (  207.26   873.94   -52.60     1.38)  ; 14\r\n                                    (  206.54   874.97   -51.38     1.38)  ; 15\r\n                                    (  207.35   877.55   -50.63     1.38)  ; 16\r\n                                    (  207.09   878.68   -49.60     1.38)  ; 17\r\n                                    (  206.24   880.28   -48.70     1.38)  ; 18\r\n                                    (  205.89   883.78   -47.77     1.38)  ; 19\r\n                                    (  206.25   886.24   -47.95     1.38)  ; 20\r\n                                    (  204.83   888.30   -47.57     1.38)  ; 21\r\n                                    (  200.73   889.72   -47.10     1.38)  ; 22\r\n                                    (  200.07   892.55   -45.50     1.38)  ; 23\r\n                                    (  200.16   896.16   -44.30     1.38)  ; 24\r\n                                    (  199.06   898.90   -43.13     1.38)  ; 25\r\n                                    (  197.26   898.48   -41.75     1.38)  ; 26\r\n                                    (  197.34   896.10   -40.80     1.38)  ; 27\r\n                                    (  197.29   894.30   -39.55     1.38)  ; 28\r\n                                    (  197.74   894.41   -37.63     1.38)  ; 29\r\n                                    (  196.86   894.20   -35.38     1.38)  ; 30\r\n                                    (  194.04   894.13   -33.33     1.38)  ; 31\r\n                                    (  197.66   896.77   -32.13     1.38)  ; 32\r\n                                    (  198.69   896.42   -30.02     1.38)  ; 33\r\n                                    (  195.35   898.63   -29.40     1.38)  ; 34\r\n                                    (  194.95   900.32   -28.55     1.38)  ; 35\r\n                                    (  194.77   905.05   -27.35     1.38)  ; 36\r\n                                    (  196.89   906.14   -26.05     1.38)  ; 37\r\n                                    (  197.64   906.93   -24.35     1.38)  ; 38\r\n                                    (  197.95   907.59   -25.05     1.38)  ; 39\r\n                                    (  194.42   908.56   -23.05     1.38)  ; 40\r\n                                    (  191.48   909.05   -21.85     1.38)  ; 41\r\n                                    (  190.06   911.11   -22.08     1.38)  ; 42\r\n                                    (  189.79   912.24   -20.80     1.38)  ; 43\r\n                                    (  187.93   914.20   -19.75     1.38)  ; 44\r\n                                    (  186.64   915.68   -18.85     1.38)  ; 45\r\n                                    (  186.64   915.68   -18.88     1.38)  ; 46\r\n                                    (  187.45   918.27   -17.05     1.38)  ; 47\r\n                                    (  186.29   919.18   -15.95     1.38)  ; 48\r\n                                    (  186.29   919.18   -15.92     1.38)  ; 49\r\n                                    (  185.26   919.54   -14.60     1.38)  ; 50\r\n                                    (  184.73   921.81   -13.42     1.38)  ; 51\r\n                                    (  184.78   923.61   -12.30     1.38)  ; 52\r\n                                    (  184.78   923.61   -12.32     1.38)  ; 53\r\n                                    (  185.72   925.63   -12.00     1.38)  ; 54\r\n                                    (  184.43   927.12   -12.00     1.38)  ; 55\r\n                                    (  182.06   927.15   -10.12     1.38)  ; 56\r\n                                    (  182.24   928.39    -8.95     1.38)  ; 57\r\n                                    (  183.77   929.95    -8.25     1.38)  ; 58\r\n                                    (  183.10   932.77    -8.35     1.38)  ; 59\r\n                                    (  182.26   934.37    -7.67     1.38)  ; 60\r\n                                    (  179.50   936.10    -6.78     1.38)  ; 61\r\n                                    (  179.59   939.71    -6.28     1.38)  ; 62\r\n                                    (  177.98   940.53    -5.82     1.38)  ; 63\r\n                                    (  176.82   941.46    -4.03     1.38)  ; 64\r\n                                    (  176.82   941.46    -4.05     1.38)  ; 65\r\n                                    (  174.46   941.50    -2.45     1.38)  ; 66\r\n                                    (  170.81   943.03    -1.67     1.38)  ; 67\r\n                                    (  171.17   945.50     0.07     1.38)  ; 68\r\n                                    (  172.61   949.42     0.63     1.38)  ; 69\r\n                                    (  174.25   950.41    -0.50     1.38)  ; 70\r\n                                    (  174.25   950.41    -0.55     1.38)  ; 71\r\n                                    (  173.90   953.90     0.28     1.38)  ; 72\r\n                                    (  176.06   956.80     1.00     1.38)  ; 73\r\n                                    (  177.90   959.03     2.05     1.38)  ; 74\r\n                                    (  177.90   959.03     2.17     1.38)  ; 75\r\n                                    (  179.99   960.11     3.82     1.38)  ; 76\r\n                                    (  179.99   960.11     3.80     1.38)  ; 77\r\n                                    (  182.01   963.57     4.40     1.38)  ; 78\r\n                                    (  182.01   963.57     4.38     1.38)  ; 79\r\n                                    (  184.78   967.79     4.85     1.38)  ; 80\r\n                                    (  187.20   969.56     5.35     1.38)  ; 81\r\n                                    (  186.48   970.59     4.47     1.38)  ; 82\r\n                                    (  186.85   973.06     5.72     0.92)  ; 83\r\n                                    (  186.77   975.43     6.82     0.92)  ; 84\r\n                                    (  188.69   975.29     7.47     0.92)  ; 85\r\n                                    (  188.69   975.29     7.45     0.92)  ; 86\r\n                                    (  190.02   975.60     8.05     0.92)  ; 87\r\n                                    (  190.02   975.60     8.02     0.92)  ; 88\r\n                                    (  191.63   974.78     8.97     0.92)  ; 89\r\n                                    (  191.63   974.78     8.95     0.92)  ; 90\r\n                                    (  193.09   974.54    10.90     0.92)  ; 91\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  217.54   844.11   -63.30     1.38)  ; 1\r\n                                      (  216.45   852.81   -61.28     1.38)  ; 2\r\n                                      (  218.54   853.90   -62.00     1.38)  ; 3\r\n                                      (  210.89   866.43   -56.57     1.38)  ; 4\r\n                                      (  214.79   867.94   -56.57     1.38)  ; 5\r\n                                      (  208.00   868.74   -53.13     1.38)  ; 6\r\n                                      (  205.38   875.89   -50.63     1.38)  ; 7\r\n                                      (  207.49   882.95   -47.22     1.38)  ; 8\r\n                                      (  207.77   887.80   -47.22     1.38)  ; 9\r\n                                      (  199.65   888.28   -47.10     1.38)  ; 10\r\n                                      (  202.28   887.11   -47.10     1.38)  ; 11\r\n                                      (  206.03   889.18   -48.55     1.38)  ; 12\r\n                                      (  203.32   892.72   -46.40     1.38)  ; 13\r\n                                      (  202.35   894.88   -44.30     1.38)  ; 14\r\n                                      (  196.23   898.83   -39.55     1.38)  ; 15\r\n                                      (  195.88   896.35   -39.55     1.38)  ; 16\r\n                                      (  198.45   893.37   -37.63     1.38)  ; 17\r\n                                      (  199.54   894.83   -30.02     1.38)  ; 18\r\n                                      (  200.03   896.73   -29.20     1.38)  ; 19\r\n                                      (  197.71   898.58   -28.27     1.38)  ; 20\r\n                                      (  197.55   903.32   -28.27     1.38)  ; 21\r\n                                      (  192.50   902.73   -27.30     1.38)  ; 22\r\n                                      (  197.11   909.18   -25.05     1.38)  ; 23\r\n                                      (  191.98   910.97   -21.85     1.38)  ; 24\r\n                                      (  189.66   906.84   -20.03     1.38)  ; 25\r\n                                      (  188.33   912.49   -18.70     1.38)  ; 26\r\n                                      (  186.01   914.34   -17.27     1.38)  ; 27\r\n                                      (  190.47   915.40   -16.42     1.38)  ; 28\r\n                                      (  185.34   917.17   -16.47     1.38)  ; 29\r\n                                      (  183.47   919.12   -14.60     1.38)  ; 30\r\n                                      (  188.03   923.78   -12.00     1.38)  ; 31\r\n                                      (  185.15   932.06    -8.35     1.38)  ; 32\r\n                                      (  183.07   936.94    -8.35     1.38)  ; 33\r\n                                      (  181.45   931.79    -6.75     1.38)  ; 34\r\n                                      (  180.92   934.05    -6.67     1.38)  ; 35\r\n                                      (  180.88   938.23    -4.95     1.38)  ; 36\r\n                                      (  179.64   941.52    -7.40     1.38)  ; 37\r\n                                      (  178.33   937.04    -8.35     1.38)  ; 38\r\n                                      (  170.31   941.12    -1.67     1.38)  ; 39\r\n                                      (  176.56   942.58    -1.67     1.38)  ; 40\r\n                                      (  172.19   945.15     0.07     1.38)  ; 41\r\n                                      (  172.90   944.12     0.07     1.38)  ; 42\r\n                                      (  171.00   950.24     0.63     1.38)  ; 43\r\n                                      (  176.54   952.73     0.85     1.38)  ; 44\r\n                                      (  178.19   953.72     0.85     1.38)  ; 45\r\n                                      (  174.41   955.81     2.50     1.38)  ; 46\r\n                                      (  180.85   964.49     4.35     1.38)  ; 47\r\n                                      (  175.56   954.89     3.52     1.38)  ; 48\r\n                                      (  187.60   967.86     5.35     1.38)  ; 49\r\n                                      (  184.88   971.40     4.68     1.38)  ; 50\r\n                                      (  188.01   972.14     1.75     1.38)  ; 51\r\n                                      (  188.54   969.88     1.75     1.38)  ; 52\r\n                                    )  ;  End of markers\r\n                                     Normal\r\n                                  )  ;  End of split\r\n                                |\r\n                                  (  225.26   776.47   -57.92     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2\r\n                                  (  225.89   777.81   -56.15     1.38)  ; 2\r\n                                  (  225.23   780.64   -55.70     1.38)  ; 3\r\n                                  (  225.58   783.11   -55.70     1.38)  ; 4\r\n                                  (  227.16   786.47   -55.70     1.38)  ; 5\r\n                                  (  227.38   789.51   -55.15     1.38)  ; 6\r\n                                  (  226.72   792.33   -55.03     1.38)  ; 7\r\n                                  (  227.66   794.35   -55.03     1.38)  ; 8\r\n                                  (  229.06   796.46   -54.55     1.38)  ; 9\r\n                                  (  229.86   799.04   -54.55     1.38)  ; 10\r\n\r\n                                  (Cross\r\n                                    (Color RGB (128, 255, 128))\r\n                                    (Name \"Marker 3\")\r\n                                    (  229.49   790.59   -55.03     1.38)  ; 1\r\n                                  )  ;  End of markers\r\n                                  (\r\n                                    (  229.27   802.04   -54.55     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1\r\n                                    (  229.50   805.07   -55.27     1.38)  ; 2\r\n                                    (  229.73   808.11   -55.90     1.38)  ; 3\r\n                                    (  229.07   810.94   -56.70     1.38)  ; 4\r\n                                    (  228.09   813.10   -57.40     1.38)  ; 5\r\n                                    (  227.74   816.60   -57.97     1.38)  ; 6\r\n                                    (  226.36   820.46   -58.55     1.38)  ; 7\r\n                                    (  225.88   824.52   -58.82     1.38)  ; 8\r\n                                    (  224.69   829.62   -58.82     1.38)  ; 9\r\n                                    (  224.47   832.55   -58.82     1.38)  ; 10\r\n                                    (  222.20   836.20   -58.10     1.38)  ; 11\r\n                                    (  221.94   837.33   -58.10     1.38)  ; 12\r\n                                    (  222.93   841.15   -58.10     1.38)  ; 13\r\n                                    (  223.03   844.76   -58.55     1.38)  ; 14\r\n                                    (  221.52   849.18   -58.78     1.38)  ; 15\r\n                                    (  220.99   851.45   -59.20     1.38)  ; 16\r\n                                    (  220.77   854.37   -60.03     1.38)  ; 17\r\n                                    (  220.77   854.37   -60.05     1.38)  ; 18\r\n                                    (  220.86   857.98   -60.47     1.38)  ; 19\r\n                                    (  219.93   861.95   -59.50     1.38)  ; 20\r\n                                    (  220.04   865.56   -58.75     1.38)  ; 21\r\n                                    (  220.85   868.13   -57.25     1.38)  ; 22\r\n                                    (  220.67   872.87   -56.47     1.38)  ; 23\r\n                                    (  221.35   876.01   -58.00     1.38)  ; 24\r\n                                    (  221.77   880.29   -58.80     1.38)  ; 25\r\n                                    (  221.94   881.53   -59.78     1.38)  ; 26\r\n                                    (  221.59   885.02   -59.78     1.38)  ; 27\r\n                                    (  223.30   887.81   -59.78     1.38)  ; 28\r\n                                    (  223.35   889.61   -58.20     1.38)  ; 29\r\n                                    (  222.38   891.78   -56.17     1.38)  ; 30\r\n                                    (  223.58   892.66   -55.20     1.38)  ; 31\r\n                                    (  223.81   895.70   -54.38     1.38)  ; 32\r\n                                    (  225.46   896.68   -53.40     1.38)  ; 33\r\n                                    (  226.53   898.12   -52.10     1.38)  ; 34\r\n                                    (  227.17   899.47   -50.55     1.38)  ; 35\r\n                                    (  225.15   901.98   -49.00     1.38)  ; 36\r\n                                    (  225.07   904.36   -47.40     1.38)  ; 37\r\n                                    (  227.80   906.78   -45.83     1.38)  ; 38\r\n                                    (  228.88   908.22   -45.07     1.38)  ; 39\r\n                                    (  228.66   911.17   -45.07     1.38)  ; 40\r\n                                    (  229.86   912.04   -43.70     1.38)  ; 41\r\n                                    (  231.58   914.84   -43.70     1.38)  ; 42\r\n                                    (  232.51   916.84   -42.75     1.38)  ; 43\r\n                                    (  233.90   918.97   -42.45     1.38)  ; 44\r\n                                    (  233.69   921.89   -42.45     1.38)  ; 45\r\n                                    (  234.27   925.50   -40.03     1.38)  ; 46\r\n                                    (  237.17   929.17   -41.32     1.38)  ; 47\r\n                                    (  238.60   933.09   -41.75     1.38)  ; 48\r\n                                    (  238.83   936.13   -42.67     1.38)  ; 49\r\n                                    (  241.88   939.23   -43.53     1.38)  ; 50\r\n                                    (  240.64   942.52   -43.90     1.38)  ; 51\r\n                                    (  240.42   945.47   -43.90     1.38)  ; 52\r\n                                    (  241.80   947.57   -42.90     1.38)  ; 53\r\n                                    (  242.35   951.29   -42.90     1.38)  ; 54\r\n                                    (  244.50   954.18   -44.10     1.38)  ; 55\r\n                                    (  243.70   957.57   -42.85     1.38)  ; 56\r\n                                    (  243.70   957.57   -42.90     1.38)  ; 57\r\n                                    (  242.73   959.73   -41.73     1.38)  ; 58\r\n                                    (  242.69   963.91   -40.65     1.38)  ; 59\r\n                                    (  243.59   970.09   -41.80     1.38)  ; 60\r\n                                    (  244.27   973.23   -41.30     1.38)  ; 61\r\n                                    (  244.50   976.27   -42.37     1.38)  ; 62\r\n                                    (  243.08   978.33   -42.37     1.38)  ; 63\r\n                                    (  244.20   981.58   -41.95     1.38)  ; 64\r\n                                    (  244.62   985.86   -41.95     1.38)  ; 65\r\n                                    (  243.24   989.71   -41.95     1.38)  ; 66\r\n                                    (  243.47   992.75   -41.95     1.38)  ; 67\r\n                                    (  243.87   997.03   -42.35     1.38)  ; 68\r\n                                    (  244.24   999.50   -43.00     1.38)  ; 69\r\n                                    (  244.39  1004.90   -43.00     1.38)  ; 70\r\n                                    (  245.78  1007.03   -43.90     1.38)  ; 71\r\n                                    (  246.19  1011.31   -44.70     1.38)  ; 72\r\n                                    (  246.42  1014.35   -45.17     1.38)  ; 73\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  224.30   837.30   -58.10     1.38)  ; 1\r\n                                      (  217.89   862.65   -59.50     1.38)  ; 2\r\n                                      (  218.73   861.07   -59.32     1.38)  ; 3\r\n                                      (  223.87   881.37   -58.80     1.38)  ; 4\r\n                                      (  222.92   879.36   -58.80     1.38)  ; 5\r\n                                      (  220.39   884.15   -59.78     1.38)  ; 6\r\n                                      (  220.44   885.96   -59.78     1.38)  ; 7\r\n                                      (  220.92   881.89   -59.78     1.38)  ; 8\r\n                                      (  220.45   891.92   -57.88     1.38)  ; 9\r\n                                      (  220.48   887.75   -56.82     1.38)  ; 10\r\n                                      (  222.43   893.58   -54.38     1.38)  ; 11\r\n                                      (  227.71   903.17   -45.07     1.38)  ; 12\r\n                                      (  224.49   904.81   -45.07     1.38)  ; 13\r\n                                      (  232.73   913.90   -43.70     1.38)  ; 14\r\n                                      (  230.77   918.24   -42.45     1.38)  ; 15\r\n                                      (  229.25   916.67   -41.75     1.38)  ; 16\r\n                                      (  230.70   920.60   -40.57     1.38)  ; 17\r\n                                      (  236.31   920.72   -42.02     1.38)  ; 18\r\n                                      (  235.33   922.88   -40.77     1.38)  ; 19\r\n                                      (  232.94   927.10   -40.00     1.38)  ; 20\r\n                                      (  238.02   927.58   -41.32     1.38)  ; 21\r\n                                      (  236.32   930.77   -40.63     1.38)  ; 22\r\n                                      (  237.26   932.78   -40.63     1.38)  ; 23\r\n                                      (  238.57   937.26   -43.53     1.38)  ; 24\r\n                                      (  243.26   941.35   -44.72     1.38)  ; 25\r\n                                      (  241.65   936.20   -44.72     1.38)  ; 26\r\n                                      (  243.66   939.65   -44.72     1.38)  ; 27\r\n                                      (  238.23   946.74   -43.90     1.38)  ; 28\r\n                                      (  240.34   947.83   -43.90     1.38)  ; 29\r\n                                      (  244.97   966.23   -43.50     1.38)  ; 30\r\n                                      (  241.35   963.60   -37.97     1.38)  ; 31\r\n                                      (  244.87   962.63   -44.72     1.38)  ; 32\r\n                                      (  246.37   974.32   -42.37     1.38)  ; 33\r\n                                      (  245.91   984.38   -41.95     1.38)  ; 34\r\n                                      (  242.29   987.71   -43.65     1.38)  ; 35\r\n                                      (  245.43   988.44   -41.57     1.38)  ; 36\r\n                                      (  246.26   986.84   -41.57     1.38)  ; 37\r\n                                      (  246.59   993.49   -41.90     1.38)  ; 38\r\n                                      (  241.68   992.34   -41.90     1.38)  ; 39\r\n                                      (  246.29   998.79   -43.30     1.38)  ; 40\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  245.44  1016.50   -43.55     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1\r\n                                      (  245.44  1016.50   -43.53     1.38)  ; 2\r\n                                      (  243.12  1018.35   -42.05     1.38)  ; 3\r\n                                      (  243.12  1018.35   -42.10     1.38)  ; 4\r\n                                      (  243.67  1022.06   -40.85     0.92)  ; 5\r\n                                      (  243.59  1024.43   -39.70     0.92)  ; 6\r\n                                      (  242.66  1028.39   -39.15     0.92)  ; 7\r\n                                      (  244.04  1030.50   -39.15     0.92)  ; 8\r\n                                      (  242.93  1033.23   -38.50     0.92)  ; 9\r\n                                      (  242.89  1037.40   -37.15     0.92)  ; 10\r\n                                      (  243.26  1039.88   -37.15     0.92)  ; 11\r\n                                      (  244.91  1040.86   -37.15     0.92)  ; 12\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  241.50  1029.31   -39.15     0.92)  ; 1\r\n                                        (  245.15  1027.79   -39.15     0.92)  ; 2\r\n                                        (  241.98  1025.24   -39.15     0.92)  ; 3\r\n                                        (  242.03  1027.05   -39.15     0.92)  ; 4\r\n                                        (  245.36  1034.99   -37.15     0.92)  ; 5\r\n                                        (  244.11  1038.28   -37.15     0.92)  ; 6\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  243.22  1044.05   -35.80     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1\r\n                                        (  243.15  1046.42   -35.15     0.46)  ; 2\r\n                                        (  243.95  1049.00   -33.95     0.46)  ; 3\r\n                                        (  243.87  1051.37   -33.95     0.46)  ; 4\r\n                                        (  242.49  1055.22   -33.95     0.46)  ; 5\r\n                                        (  241.83  1058.06   -32.75     0.46)  ; 6\r\n                                        (  241.61  1060.99   -31.92     0.46)  ; 7\r\n                                        (  240.32  1062.48   -34.10     0.46)  ; 8\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  240.54  1043.42   -35.15     0.46)  ; 1\r\n                                          (  241.22  1046.56   -33.42     0.46)  ; 2\r\n                                          (  246.40  1046.59   -33.27     0.46)  ; 3\r\n                                          (  241.69  1052.65   -33.95     0.46)  ; 4\r\n                                          (  239.41  1056.30   -33.95     0.46)  ; 5\r\n                                          (  239.92  1058.21   -33.95     0.46)  ; 6\r\n                                          (  245.13  1060.03   -30.25     0.46)  ; 7\r\n                                          (  239.34  1064.64   -34.08     0.46)  ; 8\r\n                                        )  ;  End of markers\r\n                                        (\r\n                                          (  236.47  1062.77   -34.10     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-1\r\n                                          (  233.35  1062.03   -32.25     0.46)  ; 2\r\n                                          (  231.13  1061.51   -31.48     0.46)  ; 3\r\n                                          (  229.83  1063.00   -31.95     0.46)  ; 4\r\n                                          (  227.20  1064.18   -30.73     0.46)  ; 5\r\n                                          (  222.97  1066.17   -29.25     0.46)  ; 6\r\n                                          (  221.49  1066.42   -27.90     0.46)  ; 7\r\n                                          (  217.98  1067.27   -31.52     0.46)  ; 8\r\n                                          (  217.96  1067.40   -31.55     0.46)  ; 9\r\n                                          (  214.84  1066.66   -32.67     0.46)  ; 10\r\n                                          (  211.77  1067.73   -33.97     0.46)  ; 11\r\n\r\n                                          (Cross\r\n                                            (Color RGB (128, 255, 128))\r\n                                            (Name \"Marker 3\")\r\n                                            (  238.03  1060.15   -34.10     0.46)  ; 1\r\n                                            (  234.46  1059.32   -32.52     0.46)  ; 2\r\n                                            (  230.50  1060.17   -31.60     0.46)  ; 3\r\n                                            (  225.63  1060.83   -33.30     0.46)  ; 4\r\n                                            (  231.26  1066.93   -30.25     0.46)  ; 5\r\n                                          )  ;  End of markers\r\n                                           Normal\r\n                                        |\r\n                                          (  242.34  1065.94   -32.40     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2\r\n                                          (  243.22  1066.15   -32.17     0.46)  ; 2\r\n                                          (  243.54  1066.81   -28.72     0.46)  ; 3\r\n\r\n                                          (Cross\r\n                                            (Color RGB (128, 255, 128))\r\n                                            (Name \"Marker 3\")\r\n                                            (  240.91  1067.99   -33.53     0.46)  ; 1\r\n                                          )  ;  End of markers\r\n                                          (\r\n                                            (  246.85  1068.78   -28.72     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-1\r\n                                            (  248.91  1068.08   -27.60     0.46)  ; 2\r\n\r\n                                            (Cross\r\n                                              (Color RGB (128, 255, 128))\r\n                                              (Name \"Marker 3\")\r\n                                              (  246.90  1070.59   -28.72     0.46)  ; 1\r\n                                              (  253.18  1067.89   -25.10     0.46)  ; 2\r\n                                            )  ;  End of markers\r\n                                            (\r\n                                              (  248.41  1066.17   -26.25     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-1-1\r\n                                              (  247.47  1064.15   -24.08     0.46)  ; 2\r\n                                              (  247.47  1064.15   -24.10     0.46)  ; 3\r\n                                              (  248.63  1063.24   -21.67     0.46)  ; 4\r\n                                              (  248.63  1063.24   -21.70     0.46)  ; 5\r\n                                              (  247.37  1060.54   -19.88     0.46)  ; 6\r\n                                              (  247.05  1059.88   -18.70     0.46)  ; 7\r\n                                               Normal\r\n                                            |\r\n                                              (  251.89  1069.37   -26.20     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-1-2\r\n                                              (  255.47  1070.21   -25.52     0.46)  ; 2\r\n                                              (  257.43  1071.86   -23.60     0.46)  ; 3\r\n                                              (  257.43  1071.86   -23.57     0.46)  ; 4\r\n                                               Normal\r\n                                            )  ;  End of split\r\n                                          |\r\n                                            (  241.98  1069.44   -28.72     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-1-2-2\r\n                                            (  240.30  1072.63   -28.72     0.46)  ; 2\r\n\r\n                                            (Cross\r\n                                              (Color RGB (128, 255, 128))\r\n                                              (Name \"Marker 3\")\r\n                                              (  236.91  1073.03   -28.72     0.46)  ; 1\r\n                                              (  244.41  1077.16   -28.72     0.46)  ; 2\r\n                                            )  ;  End of markers\r\n                                             Normal\r\n                                          )  ;  End of split\r\n                                        )  ;  End of split\r\n                                      |\r\n                                        (  245.99  1042.31   -37.15     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-1-2\r\n                                        (  245.33  1045.14   -37.15     0.46)  ; 2\r\n                                        (  248.63  1047.11   -37.15     0.46)  ; 3\r\n                                        (  249.76  1050.36   -36.15     0.46)  ; 4\r\n                                        (  251.73  1052.01   -35.57     0.46)  ; 5\r\n                                        (  253.87  1054.91   -34.85     0.46)  ; 6\r\n                                        (  255.00  1058.15   -34.25     0.46)  ; 7\r\n                                        (  256.65  1059.13   -36.20     0.46)  ; 8\r\n                                        (  258.48  1061.36   -36.20     0.46)  ; 9\r\n                                        (  259.69  1062.24   -38.15     0.46)  ; 10\r\n                                        (  259.92  1065.29   -39.52     0.46)  ; 11\r\n                                        (  261.12  1066.16   -39.82     0.46)  ; 12\r\n                                        (  263.68  1067.36   -41.38     0.46)  ; 13\r\n                                        (  264.88  1068.23   -42.40     0.46)  ; 14\r\n                                        (  264.88  1068.23   -42.42     0.46)  ; 15\r\n                                        (  266.67  1068.65   -43.00     0.46)  ; 16\r\n                                        (  268.32  1069.63   -43.00     0.46)  ; 17\r\n                                        (  269.93  1068.82   -44.43     0.46)  ; 18\r\n                                        (  271.58  1069.80   -45.05     0.46)  ; 19\r\n                                        (  273.19  1068.99   -46.05     0.46)  ; 20\r\n                                        (  273.19  1068.99   -46.08     0.46)  ; 21\r\n                                        (  274.53  1069.31   -47.13     0.46)  ; 22\r\n                                        (  274.53  1069.31   -47.15     0.46)  ; 23\r\n                                        (  276.94  1071.06   -47.17     0.46)  ; 24\r\n                                        (  278.28  1071.37   -48.95     0.46)  ; 25\r\n                                        (  279.13  1069.78   -50.15     0.46)  ; 26\r\n                                        (  281.05  1069.64   -51.67     0.46)  ; 27\r\n                                        (  282.43  1071.75   -53.62     0.46)  ; 28\r\n                                        (  282.43  1071.75   -53.67     0.46)  ; 29\r\n                                        (  283.72  1070.26   -55.72     0.46)  ; 30\r\n                                        (  284.75  1069.90   -58.47     0.46)  ; 31\r\n                                        (  284.75  1069.90   -58.65     0.46)  ; 32\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  251.83  1055.62   -34.25     0.46)  ; 1\r\n                                          (  256.10  1055.43   -34.25     0.46)  ; 2\r\n                                          (  257.18  1056.88   -37.83     0.46)  ; 3\r\n                                          (  257.02  1061.62   -36.25     0.46)  ; 4\r\n                                          (  259.46  1059.20   -36.25     0.46)  ; 5\r\n                                          (  261.47  1062.66   -39.52     0.46)  ; 6\r\n                                          (  263.63  1065.56   -39.82     0.46)  ; 7\r\n                                          (  263.72  1069.15   -43.00     0.46)  ; 8\r\n                                          (  266.72  1070.46   -43.00     0.46)  ; 9\r\n                                          (  266.75  1066.29   -43.00     0.46)  ; 10\r\n                                          (  273.34  1074.40   -47.17     0.46)  ; 11\r\n                                          (  281.00  1073.81   -50.15     0.46)  ; 12\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    |\r\n                                      (  248.12  1017.13   -43.80     0.46)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-1-2\r\n                                      (  249.24  1020.38   -43.80     0.46)  ; 2\r\n                                      (  251.21  1022.03   -43.88     0.46)  ; 3\r\n                                      (  254.78  1022.87   -44.30     0.46)  ; 4\r\n                                      (  254.78  1022.87   -44.32     0.46)  ; 5\r\n                                      (  257.09  1021.03   -44.70     0.46)  ; 6\r\n                                      (  257.09  1021.03   -44.80     0.46)  ; 7\r\n                                      (  259.47  1020.98   -44.90     0.46)  ; 8\r\n                                      (  259.47  1020.98   -45.15     0.46)  ; 9\r\n                                      (  261.57  1022.08   -47.13     0.46)  ; 10\r\n                                      (  261.57  1022.08   -47.20     0.46)  ; 11\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  248.73  1006.53   -45.17     1.38)  ; 1\r\n                                        (  247.30  1008.58   -45.17     1.38)  ; 2\r\n                                        (  248.42  1011.83   -45.17     1.38)  ; 3\r\n                                        (  242.74  1009.90   -43.80     1.38)  ; 4\r\n                                      )  ;  End of markers\r\n                                       Normal\r\n                                    )  ;  End of split\r\n                                  |\r\n                                    (  231.44   802.40   -54.55     1.38)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2\r\n                                    (  233.80   802.34   -53.30     1.38)  ; 2\r\n                                    (  234.88   803.81   -52.50     1.38)  ; 3\r\n                                    (  234.88   803.81   -52.55     1.38)  ; 4\r\n                                    (  235.10   806.84   -52.55     1.38)  ; 5\r\n                                    (  237.08   808.50   -51.55     1.38)  ; 6\r\n                                    (  238.01   810.51   -50.40     1.38)  ; 7\r\n                                    (  238.19   811.74   -49.60     1.38)  ; 8\r\n                                    (  237.80   813.44   -48.75     1.38)  ; 9\r\n                                    (  241.74   816.75   -48.75     1.38)  ; 10\r\n                                    (  242.94   817.62   -47.67     1.38)  ; 11\r\n                                    (  244.38   821.55   -47.67     1.38)  ; 12\r\n                                    (  246.03   822.53   -47.67     1.38)  ; 13\r\n                                    (  246.84   825.11   -47.67     1.38)  ; 14\r\n                                    (  247.65   827.70   -47.67     1.38)  ; 15\r\n                                    (  250.38   830.12   -47.67     1.38)  ; 16\r\n                                    (  250.34   834.30   -47.67     1.38)  ; 17\r\n                                    (  251.91   837.65   -47.57     1.38)  ; 18\r\n                                    (  252.50   843.16   -47.55     1.38)  ; 19\r\n                                    (  255.10   846.16   -47.15     1.38)  ; 20\r\n                                    (  255.46   848.63   -46.65     1.38)  ; 21\r\n                                    (  255.96   850.53   -46.38     1.38)  ; 22\r\n                                    (  256.45   852.45   -45.65     1.38)  ; 23\r\n                                    (  258.87   854.21   -45.65     1.38)  ; 24\r\n                                    (  259.36   856.12   -44.43     1.38)  ; 25\r\n                                    (  259.36   856.12   -44.45     1.38)  ; 26\r\n                                    (  259.59   859.15   -43.58     1.38)  ; 27\r\n                                    (  262.44   861.02   -43.15     1.38)  ; 28\r\n                                    (  262.44   861.02   -43.18     1.38)  ; 29\r\n                                    (  261.33   863.74   -43.10     1.38)  ; 30\r\n                                    (  263.18   865.97   -42.57     1.38)  ; 31\r\n                                    (  263.18   865.97   -42.60     1.38)  ; 32\r\n                                    (  263.02   870.70   -43.50     1.38)  ; 33\r\n                                    (  265.74   873.13   -42.92     1.38)  ; 34\r\n                                    (  266.09   875.61   -42.05     1.38)  ; 35\r\n                                    (  266.91   878.18   -41.20     1.38)  ; 36\r\n                                    (  269.77   880.06   -40.48     1.38)  ; 37\r\n                                    (  269.77   880.06   -40.50     1.38)  ; 38\r\n                                    (  270.90   883.30   -39.65     1.38)  ; 39\r\n                                    (  273.76   885.17   -39.20     1.38)  ; 40\r\n                                    (  276.35   888.16   -39.02     1.38)  ; 41\r\n                                    (  278.41   893.42   -39.02     1.38)  ; 42\r\n                                    (  278.92   895.33   -37.70     1.38)  ; 43\r\n                                    (  281.64   897.76   -38.03     1.38)  ; 44\r\n                                    (  283.60   899.43   -38.03     1.38)  ; 45\r\n                                    (  284.33   904.37   -38.03     1.38)  ; 46\r\n                                    (  285.77   908.29   -38.03     1.38)  ; 47\r\n                                    (  286.89   911.53   -36.10     1.38)  ; 48\r\n                                    (  286.72   916.27   -36.10     1.38)  ; 49\r\n                                    (  287.22   918.18   -37.20     1.38)  ; 50\r\n                                    (  287.22   918.18   -37.17     1.38)  ; 51\r\n                                    (  287.58   920.65   -38.35     1.38)  ; 52\r\n                                    (  287.58   920.65   -38.38     1.38)  ; 53\r\n                                    (  288.13   924.36   -38.70     1.38)  ; 54\r\n                                    (  288.04   926.73   -38.90     1.38)  ; 55\r\n                                    (  289.17   929.99   -38.90     1.38)  ; 56\r\n                                    (  288.95   932.92   -39.60     1.38)  ; 57\r\n                                    (  291.10   935.81   -40.45     1.38)  ; 58\r\n                                    (  293.12   939.27   -41.00     1.38)  ; 59\r\n                                    (  295.08   940.92   -40.48     1.38)  ; 60\r\n                                    (  295.08   940.92   -40.50     1.38)  ; 61\r\n                                    (  295.31   943.96   -40.50     1.38)  ; 62\r\n                                    (  295.85   947.67   -40.50     1.38)  ; 63\r\n                                    (  298.45   950.67   -40.50     1.38)  ; 64\r\n                                    (  299.70   953.35   -39.72     1.38)  ; 65\r\n                                    (  301.32   958.52   -38.85     1.38)  ; 66\r\n                                    (  302.72   960.62   -38.57     1.38)  ; 67\r\n                                    (  304.86   963.52   -38.57     1.38)  ; 68\r\n                                    (  307.19   967.64   -37.58     1.38)  ; 69\r\n                                    (  308.45   970.33   -36.40     1.38)  ; 70\r\n                                    (  308.10   973.84   -35.15     1.38)  ; 71\r\n                                    (  308.51   978.11   -36.88     1.38)  ; 72\r\n                                    (  309.50   981.93   -35.90     1.38)  ; 73\r\n                                    (  309.42   984.30   -34.70     1.38)  ; 74\r\n                                    (  311.51   985.38   -33.55     1.38)  ; 75\r\n                                    (  313.22   988.17   -32.50     1.38)  ; 76\r\n                                    (  313.85   989.51   -31.80     1.38)  ; 77\r\n                                    (  312.39   995.74   -31.25     1.38)  ; 78\r\n                                    (  313.95   999.09   -31.80     1.38)  ; 79\r\n\r\n                                    (Cross\r\n                                      (Color White)\r\n                                      (Name \"Marker 3\")\r\n                                      (  231.13   801.49   -32.20     1.83)  ; 1\r\n                                      (  230.68   801.39   -32.02     1.83)  ; 2\r\n                                    )  ;  End of markers\r\n\r\n                                    (Cross\r\n                                      (Color RGB (128, 255, 128))\r\n                                      (Name \"Marker 3\")\r\n                                      (  227.32   797.85   -54.55     1.38)  ; 1\r\n                                      (  239.88   808.56   -50.40     1.38)  ; 2\r\n                                      (  235.88   813.59   -48.75     1.38)  ; 3\r\n                                      (  239.72   813.30   -48.75     1.38)  ; 4\r\n                                      (  241.15   817.21   -47.67     1.38)  ; 5\r\n                                      (  252.03   831.11   -46.57     1.38)  ; 6\r\n                                      (  247.76   821.15   -46.57     1.38)  ; 7\r\n                                      (  249.23   837.02   -46.20     1.38)  ; 8\r\n                                      (  254.06   840.54   -46.20     1.38)  ; 9\r\n                                      (  256.88   846.57   -46.20     1.38)  ; 10\r\n                                      (  255.42   852.80   -45.65     1.38)  ; 11\r\n                                      (  256.50   854.25   -45.65     1.38)  ; 12\r\n                                      (  260.50   865.34   -43.95     1.38)  ; 13\r\n                                      (  259.55   863.33   -42.17     1.38)  ; 14\r\n                                      (  262.57   870.60   -45.00     1.38)  ; 15\r\n                                      (  270.63   884.43   -39.65     1.38)  ; 16\r\n                                      (  272.31   881.25   -39.65     1.38)  ; 17\r\n                                      (  267.28   880.66   -39.65     1.38)  ; 18\r\n                                      (  273.45   890.47   -39.02     1.38)  ; 19\r\n                                      (  276.30   886.36   -37.70     1.38)  ; 20\r\n                                      (  282.57   893.80   -39.42     1.38)  ; 21\r\n                                      (  282.92   896.27   -39.90     1.38)  ; 22\r\n                                      (  285.44   901.64   -39.90     1.38)  ; 23\r\n                                      (  286.70   904.33   -40.35     1.38)  ; 24\r\n                                      (  281.96   904.41   -37.38     1.38)  ; 25\r\n                                      (  281.43   906.68   -37.38     1.38)  ; 26\r\n                                      (  289.17   913.86   -36.55     1.38)  ; 27\r\n                                      (  287.18   922.35   -39.50     1.38)  ; 28\r\n                                      (  289.15   924.01   -38.38     1.38)  ; 29\r\n                                      (  290.90   928.59   -38.38     1.38)  ; 30\r\n                                      (  288.11   918.39   -37.47     1.38)  ; 31\r\n                                      (  286.19   918.53   -37.47     1.38)  ; 32\r\n                                      (  291.82   940.76   -41.00     1.38)  ; 33\r\n                                      (  295.03   939.12   -41.00     1.38)  ; 34\r\n                                      (  300.19   949.28   -40.50     1.38)  ; 35\r\n                                      (  297.03   952.72   -40.50     1.38)  ; 36\r\n                                      (  301.63   953.20   -38.85     1.38)  ; 37\r\n                                      (  300.08   961.80   -38.85     1.38)  ; 38\r\n                                      (  302.82   964.23   -38.57     1.38)  ; 39\r\n                                      (  304.33   965.78   -38.57     1.38)  ; 40\r\n                                      (  308.48   966.16   -38.57     1.38)  ; 41\r\n                                      (  308.66   967.40   -38.57     1.38)  ; 42\r\n                                      (  306.39   971.04   -35.15     1.38)  ; 43\r\n                                      (  310.65   975.03   -36.40     1.38)  ; 44\r\n                                      (  315.27   987.46   -31.25     1.38)  ; 45\r\n                                      (  311.31   988.32   -31.25     1.38)  ; 46\r\n                                      (  313.43   985.23   -35.00     1.38)  ; 47\r\n                                      (  311.71   992.60   -35.00     1.38)  ; 48\r\n                                    )  ;  End of markers\r\n                                    (\r\n                                      (  313.70  1000.24   -30.57     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-1\r\n                                      (  313.89  1001.48   -29.17     0.92)  ; 2\r\n                                      (  312.91  1003.64   -27.38     0.92)  ; 3\r\n                                      (  313.90  1007.45   -26.77     0.92)  ; 4\r\n                                      (  315.03  1010.70   -26.15     0.92)  ; 5\r\n                                      (  314.68  1014.20   -25.23     0.92)  ; 6\r\n                                      (  314.91  1017.24   -24.40     0.92)  ; 7\r\n                                      (  315.89  1021.06   -24.40     0.92)  ; 8\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  311.53  1001.53   -27.38     0.92)  ; 1\r\n                                        (  315.01  1004.73   -27.38     0.92)  ; 2\r\n                                        (  312.24  1006.47   -27.38     0.92)  ; 3\r\n                                        (  312.44  1013.67   -24.40     0.92)  ; 4\r\n                                        (  319.04  1011.64   -24.40     0.92)  ; 5\r\n                                        (  317.22  1015.39   -24.40     0.92)  ; 6\r\n                                        (  313.64  1014.56   -24.40     0.92)  ; 7\r\n                                        (  313.67  1020.53   -24.40     0.92)  ; 8\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  318.18  1023.38   -24.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-1-1\r\n                                        (  320.46  1025.71   -24.38     0.92)  ; 2\r\n                                        (  321.00  1029.42   -24.35     0.92)  ; 3\r\n                                        (  322.39  1031.54   -23.67     0.92)  ; 4\r\n                                        (  323.69  1036.02   -22.60     0.92)  ; 5\r\n                                        (  323.61  1038.39   -21.60     0.92)  ; 6\r\n                                        (  326.16  1039.59   -20.92     0.92)  ; 7\r\n                                        (  327.60  1043.50   -20.92     0.92)  ; 8\r\n                                        (  328.35  1044.28   -19.70     0.92)  ; 9\r\n                                        (  330.90  1045.48   -18.50     0.92)  ; 10\r\n                                        (  331.71  1048.05   -17.45     0.92)  ; 11\r\n                                        (  334.00  1050.38   -16.80     0.92)  ; 12\r\n                                        (  335.01  1050.02   -16.73     0.92)  ; 13\r\n                                        (  337.25  1050.55   -16.07     0.92)  ; 14\r\n                                        (  340.43  1053.08   -16.90     0.92)  ; 15\r\n                                        (  340.43  1053.08   -16.88     0.92)  ; 16\r\n                                        (  340.84  1057.36   -15.57     0.92)  ; 17\r\n                                        (  342.62  1057.78   -15.57     0.92)  ; 18\r\n                                        (  341.51  1060.50   -14.75     0.92)  ; 19\r\n                                        (  341.51  1060.50   -14.77     0.92)  ; 20\r\n                                        (  339.91  1061.33   -12.98     0.92)  ; 21\r\n                                        (  339.91  1061.33   -13.00     0.92)  ; 22\r\n                                        (  338.57  1061.01   -11.37     0.92)  ; 23\r\n                                        (  338.57  1061.01   -11.40     0.92)  ; 24\r\n                                        (  336.78  1060.59    -9.93     0.92)  ; 25\r\n                                        (  336.78  1060.59    -9.95     0.92)  ; 26\r\n                                        (  332.94  1060.88    -7.80     0.92)  ; 27\r\n                                        (  332.94  1060.88    -7.72     0.92)  ; 28\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  323.90  1027.12   -24.32     0.92)  ; 1\r\n                                          (  320.62  1020.97   -24.32     0.92)  ; 2\r\n                                          (  325.11  1033.97   -22.60     0.92)  ; 3\r\n                                          (  324.90  1036.90   -22.60     0.92)  ; 4\r\n                                          (  321.11  1039.00   -21.60     0.92)  ; 5\r\n                                          (  323.31  1043.70   -21.60     0.92)  ; 6\r\n                                          (  328.53  1039.54   -20.92     0.92)  ; 7\r\n                                          (  326.70  1043.29   -18.50     0.92)  ; 8\r\n                                          (  328.88  1042.02   -18.50     0.92)  ; 9\r\n                                          (  330.54  1043.00   -17.45     0.92)  ; 10\r\n                                          (  328.00  1047.77   -17.45     0.92)  ; 11\r\n                                          (  338.41  1049.61   -16.88     0.92)  ; 12\r\n                                          (  339.75  1049.93   -16.88     0.92)  ; 13\r\n                                          (  339.77  1055.92   -15.57     0.92)  ; 14\r\n                                          (  335.99  1063.99   -11.40     0.92)  ; 15\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      |\r\n                                        (  314.65  1024.35   -24.40     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-1-2\r\n                                        (  315.96  1028.83   -24.40     0.92)  ; 2\r\n                                        (  316.19  1031.88   -22.88     0.92)  ; 3\r\n                                        (  316.19  1031.88   -22.90     0.92)  ; 4\r\n                                        (  315.83  1035.37   -22.05     0.92)  ; 5\r\n                                        (  316.51  1038.52   -22.02     0.92)  ; 6\r\n                                        (  313.09  1043.09   -22.77     0.92)  ; 7\r\n                                        (  313.09  1043.09   -22.80     0.92)  ; 8\r\n                                        (  311.48  1043.90   -24.17     0.92)  ; 9\r\n                                        (  310.27  1043.03   -26.00     0.92)  ; 10\r\n                                        (  308.62  1042.04   -28.98     0.92)  ; 11\r\n                                        (  308.62  1042.04   -29.00     0.92)  ; 12\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  314.63  1034.49   -22.05     0.92)  ; 1\r\n                                          (  315.30  1037.64   -22.05     0.92)  ; 2\r\n                                          (  319.77  1038.69   -21.10     0.92)  ; 3\r\n                                          (  315.71  1041.92   -22.02     0.92)  ; 4\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    |\r\n                                      (  316.83  1000.97   -30.90     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-2\r\n                                      (  316.83  1000.97   -30.92     0.92)  ; 2\r\n                                      (  316.61  1003.91   -31.35     0.92)  ; 3\r\n                                      (  316.61  1003.91   -31.38     0.92)  ; 4\r\n                                      (  318.19  1007.27   -30.60     0.92)  ; 5\r\n                                      (  318.19  1007.27   -30.63     0.92)  ; 6\r\n                                      (  319.18  1011.07   -29.67     0.92)  ; 7\r\n                                      (  318.96  1014.01   -29.67     0.92)  ; 8\r\n\r\n                                      (Cross\r\n                                        (Color RGB (128, 255, 128))\r\n                                        (Name \"Marker 3\")\r\n                                        (  316.29   997.26   -33.13     0.92)  ; 1\r\n                                        (  319.20  1000.93   -30.50     0.92)  ; 2\r\n                                      )  ;  End of markers\r\n                                      (\r\n                                        (  320.92  1015.66   -31.32     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-2-1\r\n                                        (  320.92  1015.66   -31.35     0.92)  ; 2\r\n                                        (  320.84  1018.04   -32.15     0.92)  ; 3\r\n                                        (  321.79  1020.05   -33.13     0.92)  ; 4\r\n                                        (  322.86  1021.50   -34.42     0.92)  ; 5\r\n                                        (  323.22  1023.96   -34.88     0.92)  ; 6\r\n                                        (  323.09  1024.53   -36.40     0.92)  ; 7\r\n                                        (  325.50  1026.30   -37.32     0.92)  ; 8\r\n                                        (  325.50  1026.30   -37.38     0.92)  ; 9\r\n                                        (  325.81  1026.97   -39.02     0.92)  ; 10\r\n                                        (  326.57  1027.74   -40.77     0.92)  ; 11\r\n                                        (  326.57  1027.74   -40.80     0.92)  ; 12\r\n                                        (  326.36  1030.68   -42.20     0.92)  ; 13\r\n                                        (  328.20  1032.90   -42.98     0.92)  ; 14\r\n                                        (  329.19  1036.72   -43.75     0.92)  ; 15\r\n                                        (  331.02  1038.94   -43.75     0.92)  ; 16\r\n                                        (  334.02  1040.23   -44.35     0.92)  ; 17\r\n                                        (  335.99  1041.89   -44.75     0.92)  ; 18\r\n                                        (  335.99  1041.89   -44.78     0.92)  ; 19\r\n                                        (  337.10  1045.13   -46.10     0.92)  ; 20\r\n                                        (  337.10  1045.13   -46.12     0.92)  ; 21\r\n                                        (  340.86  1047.22   -46.63     0.92)  ; 22\r\n                                        (  343.09  1047.74   -46.63     0.92)  ; 23\r\n                                        (  345.15  1047.02   -47.65     0.92)  ; 24\r\n                                        (  345.99  1045.43   -48.07     0.92)  ; 25\r\n                                        (  345.99  1045.43   -48.10     0.92)  ; 26\r\n                                        (  348.99  1046.72   -47.72     0.92)  ; 27\r\n                                        (  352.43  1048.13   -48.42     0.92)  ; 28\r\n                                        (  352.43  1048.13   -48.45     0.92)  ; 29\r\n                                        (  356.81  1051.55   -49.07     0.92)  ; 30\r\n                                        (  360.51  1051.83   -50.07     0.92)  ; 31\r\n                                        (  360.74  1054.86   -50.92     0.46)  ; 32\r\n                                        (  360.74  1054.86   -50.97     0.46)  ; 33\r\n                                        (  358.11  1056.03   -51.77     0.46)  ; 34\r\n                                        (  358.11  1056.03   -51.80     0.46)  ; 35\r\n                                        (  356.06  1056.75   -50.50     0.46)  ; 36\r\n                                        (  356.06  1056.75   -50.52     0.46)  ; 37\r\n                                        (  352.14  1059.41   -53.18     0.46)  ; 38\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  322.27  1015.98   -31.35     0.92)  ; 1\r\n                                          (  322.35  1013.62   -31.35     0.92)  ; 2\r\n                                          (  322.64  1018.46   -30.27     0.92)  ; 3\r\n                                          (  321.44  1023.55   -34.88     0.92)  ; 4\r\n                                          (  321.93  1025.46   -37.38     0.92)  ; 5\r\n                                          (  325.64  1031.71   -42.98     0.92)  ; 6\r\n                                          (  332.00  1036.78   -43.75     0.92)  ; 7\r\n                                          (  335.68  1047.19   -46.12     0.92)  ; 8\r\n                                          (  341.85  1051.03   -46.63     0.92)  ; 9\r\n                                          (  348.81  1045.49   -47.72     0.92)  ; 10\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      |\r\n                                        (  318.74  1016.95   -29.67     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-1-2-2-2-2-2-2-2-2-2\r\n                                        (  319.73  1020.76   -29.67     0.92)  ; 2\r\n                                        (  319.11  1025.39   -29.17     0.92)  ; 3\r\n                                        (  320.83  1028.18   -28.02     0.92)  ; 4\r\n                                        (  320.34  1032.24   -26.32     0.92)  ; 5\r\n                                        (  320.70  1034.73   -26.32     0.92)  ; 6\r\n                                        (  321.11  1039.00   -26.17     0.92)  ; 7\r\n                                        (  322.81  1041.79   -26.90     0.92)  ; 8\r\n                                        (  321.12  1044.97   -26.50     0.92)  ; 9\r\n                                        (  323.15  1048.44   -25.87     0.92)  ; 10\r\n                                        (  323.50  1050.90   -24.20     0.92)  ; 11\r\n                                        (  322.92  1051.37   -21.50     0.92)  ; 12\r\n                                        (  321.77  1052.30   -18.67     0.92)  ; 13\r\n                                        (  321.77  1052.30   -18.63     0.92)  ; 14\r\n\r\n                                        (Cross\r\n                                          (Color RGB (128, 255, 128))\r\n                                          (Name \"Marker 3\")\r\n                                          (  322.02  1023.09   -29.17     0.92)  ; 1\r\n                                          (  322.38  1025.56   -29.17     0.92)  ; 2\r\n                                          (  319.10  1035.55   -26.15     0.92)  ; 3\r\n                                          (  324.14  1036.13   -26.15     0.92)  ; 4\r\n                                        )  ;  End of markers\r\n                                         Normal\r\n                                      )  ;  End of split\r\n                                    )  ;  End of split\r\n                                  )  ;  End of split\r\n                                )  ;  End of split\r\n                              )  ;  End of split\r\n                            )  ;  End of split\r\n                          )  ;  End of split\r\n                        )  ;  End of split\r\n                      )  ;  End of split\r\n                    |\r\n                      (  255.98   279.41   -19.42     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-2\r\n                      (\r\n                        (  257.70   278.01   -18.00     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-2-1\r\n                        (  258.24   275.74   -16.80     0.92)  ; 2\r\n                        (  259.08   274.15   -15.90     0.92)  ; 3\r\n                        (  260.68   273.34   -14.97     0.92)  ; 4\r\n                        (  260.46   270.30   -13.87     0.92)  ; 5\r\n                        (  259.64   267.72   -12.30     0.92)  ; 6\r\n                        (  259.55   264.11   -10.88     0.92)  ; 7\r\n                        (  260.66   261.38    -9.20     0.92)  ; 8\r\n                        (  261.37   260.36    -8.00     0.92)  ; 9\r\n                        (  262.66   258.87    -7.22     0.92)  ; 10\r\n                        (  263.19   256.61    -5.53     0.92)  ; 11\r\n                        (  265.55   256.57    -4.85     0.92)  ; 12\r\n                        (  267.74   255.28    -4.15     0.92)  ; 13\r\n                        (  268.54   251.88    -3.70     0.92)  ; 14\r\n                        (  270.09   249.26    -3.00     0.92)  ; 15\r\n                        (  272.28   247.98    -1.07     0.92)  ; 16\r\n                        (  274.72   245.58    -0.25     0.92)  ; 17\r\n                        (  277.18   243.16     0.30     0.92)  ; 18\r\n                        (  279.17   240.65     1.85     0.92)  ; 19\r\n                        (  280.33   239.72     3.40     0.92)  ; 20\r\n                        (  282.84   239.12     4.80     0.92)  ; 21\r\n                        (  283.68   237.52     4.52     0.92)  ; 22\r\n                        (  284.08   235.83     3.85     0.92)  ; 23\r\n                        (  285.19   233.09     4.07     0.92)  ; 24\r\n                        (  285.19   233.09     4.05     0.92)  ; 25\r\n                        (  284.39   230.53     5.55     0.92)  ; 26\r\n                        (  284.39   230.53     5.48     0.92)  ; 27\r\n                        (  286.25   228.57     6.95     0.92)  ; 28\r\n                        (  288.65   224.36     7.50     0.92)  ; 29\r\n                        (  290.08   222.30     9.88     0.92)  ; 30\r\n                        (  290.08   222.30     9.85     0.92)  ; 31\r\n                        (  291.54   222.05    11.30     0.92)  ; 32\r\n                        (  294.76   220.41    12.83     0.92)  ; 33\r\n                        (  297.84   219.34    13.20     0.92)  ; 34\r\n                        (  299.57   217.95    14.55     0.92)  ; 35\r\n                        (  299.57   217.95    14.52     0.92)  ; 36\r\n                        (  299.97   216.25    16.07     0.92)  ; 37\r\n                        (  299.04   214.25    17.35     0.92)  ; 38\r\n                        (  300.58   211.62    17.92     0.92)  ; 39\r\n                        (  299.60   207.81    18.42     0.92)  ; 40\r\n                        (  299.19   203.53    20.52     0.92)  ; 41\r\n                        (  299.19   203.53    20.60     0.92)  ; 42\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  262.87   272.05   -15.35     0.92)  ; 1\r\n                          (  261.43   268.14   -12.52     0.92)  ; 2\r\n                          (  257.50   264.82   -10.88     0.92)  ; 3\r\n                          (  261.70   267.00   -10.88     0.92)  ; 4\r\n                          (  260.44   264.32   -10.10     0.92)  ; 5\r\n                          (  259.33   261.07    -7.22     0.92)  ; 6\r\n                          (  262.13   261.13    -7.22     0.92)  ; 7\r\n                          (  263.56   259.07    -8.42     0.92)  ; 8\r\n                          (  261.98   255.72    -6.57     0.92)  ; 9\r\n                          (  268.30   248.85    -3.00     0.92)  ; 10\r\n                          (  266.89   256.88    -3.00     0.92)  ; 11\r\n                          (  269.80   254.57    -1.73     0.92)  ; 12\r\n                          (  270.06   247.46    -2.75     0.92)  ; 13\r\n                          (  270.86   250.04    -2.75     0.92)  ; 14\r\n                          (  271.29   244.16    -2.10     0.92)  ; 15\r\n                          (  271.52   247.21    -2.10     0.92)  ; 16\r\n                          (  276.51   245.99     0.30     0.92)  ; 17\r\n                          (  274.06   248.40     0.30     0.92)  ; 18\r\n                          (  278.24   244.60     0.30     0.92)  ; 19\r\n                          (  275.92   240.48     0.30     0.92)  ; 20\r\n                          (  275.26   243.31     2.42     0.92)  ; 21\r\n                          (  279.54   243.12     0.55     0.92)  ; 22\r\n                          (  285.34   238.50     4.52     0.92)  ; 23\r\n                          (  286.66   232.84     4.52     0.92)  ; 24\r\n                          (  284.84   236.60     2.95     0.92)  ; 25\r\n                          (  283.63   229.74     7.50     0.92)  ; 26\r\n                          (  286.60   225.07     7.50     0.92)  ; 27\r\n                          (  284.28   226.92     8.57     0.92)  ; 28\r\n                          (  287.64   230.68     8.57     0.92)  ; 29\r\n                          (  287.28   228.21     8.57     0.92)  ; 30\r\n                          (  291.01   224.32     9.22     0.92)  ; 31\r\n                          (  293.79   222.57    12.83     0.92)  ; 32\r\n                          (  294.26   218.50    13.45     0.92)  ; 33\r\n                          (  295.57   222.98    13.45     0.92)  ; 34\r\n                          (  298.02   220.57    14.52     0.92)  ; 35\r\n                          (  300.70   221.21    14.75     0.92)  ; 36\r\n                          (  297.61   216.29    17.35     0.92)  ; 37\r\n                          (  301.69   208.90    18.92     0.92)  ; 38\r\n                        )  ;  End of markers\r\n                         Normal\r\n                      |\r\n                        (  258.33   279.34   -19.85     0.92)  ; 1, R-1-1-1-1-1-1-1-1-1-2-2\r\n                        (  260.96   278.18   -20.52     0.92)  ; 2\r\n                        (  263.29   276.33   -20.80     0.92)  ; 3\r\n                        (  264.70   274.27   -20.80     0.92)  ; 4\r\n                        (  265.73   273.93   -20.80     0.92)  ; 5\r\n                        (  267.87   270.84   -21.45     0.92)  ; 6\r\n                        (  270.05   269.55   -21.70     0.92)  ; 7\r\n                        (  271.92   267.61   -20.30     0.92)  ; 8\r\n                        (  273.33   265.55   -19.48     0.92)  ; 9\r\n                        (  274.00   262.72   -19.33     0.92)  ; 10\r\n                        (  274.98   260.56   -18.70     0.92)  ; 11\r\n                        (  277.75   258.82   -18.10     0.92)  ; 12\r\n                        (  281.27   257.85   -20.65     0.92)  ; 13\r\n                        (  284.49   256.21   -20.73     0.92)  ; 14\r\n                        (  284.49   256.21   -20.75     0.92)  ; 15\r\n                        (  286.63   253.14   -19.52     0.92)  ; 16\r\n                        (  288.17   250.52   -19.23     0.92)  ; 17\r\n                        (  289.42   247.22   -18.57     0.92)  ; 18\r\n                        (  289.42   247.22   -18.63     0.92)  ; 19\r\n                        (  291.11   244.04   -18.15     0.92)  ; 20\r\n                        (  293.55   241.63   -18.15     0.92)  ; 21\r\n                        (  296.00   239.22   -19.40     0.92)  ; 22\r\n                        (  298.46   236.80   -18.20     0.92)  ; 23\r\n                        (  299.57   234.08   -16.83     0.92)  ; 24\r\n                        (  299.57   234.08   -16.85     0.92)  ; 25\r\n                        (  300.50   230.12   -17.30     0.92)  ; 26\r\n                        (  302.05   227.50   -17.08     0.92)  ; 27\r\n                        (  302.05   227.50   -17.10     0.92)  ; 28\r\n                        (  305.26   225.86   -15.70     0.92)  ; 29\r\n                        (  307.31   225.14   -15.60     0.92)  ; 30\r\n                        (  309.57   221.50   -14.88     0.92)  ; 31\r\n                        (  311.14   218.87   -14.40     0.92)  ; 32\r\n                        (  313.33   217.60   -14.40     0.92)  ; 33\r\n                        (  314.13   214.20   -15.35     0.92)  ; 34\r\n                        (  315.73   213.40   -16.45     0.92)  ; 35\r\n                        (  317.86   210.30   -17.13     0.92)  ; 36\r\n                        (  319.73   208.37   -17.20     0.92)  ; 37\r\n                        (  321.47   206.98   -17.05     0.92)  ; 38\r\n                        (  324.37   204.67   -18.05     0.92)  ; 39\r\n                        (  327.89   203.71   -18.77     0.92)  ; 40\r\n                        (  329.63   202.32   -17.02     0.92)  ; 41\r\n                        (  331.23   201.50   -15.00     0.92)  ; 42\r\n                        (  333.61   201.46   -13.87     0.92)  ; 43\r\n                        (  334.59   199.30   -12.85     0.92)  ; 44\r\n                        (  337.34   197.55   -12.30     0.92)  ; 45\r\n                        (  339.48   194.47   -11.95     0.92)  ; 46\r\n                        (  340.45   192.32   -13.63     0.92)  ; 47\r\n                        (  342.90   189.90   -13.00     0.92)  ; 48\r\n                        (  342.46   189.80   -13.00     0.92)  ; 49\r\n                        (  345.08   188.63   -11.53     0.92)  ; 50\r\n                        (  346.96   186.67   -11.45     0.46)  ; 51\r\n                        (  349.45   186.06   -10.68     0.46)  ; 52\r\n                        (  350.75   184.57   -10.68     0.46)  ; 53\r\n                        (  352.04   183.09    -8.50     0.46)  ; 54\r\n                        (  352.04   183.09    -8.45     0.46)  ; 55\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  262.91   273.86   -21.70     0.92)  ; 1\r\n                          (  265.33   275.62   -18.40     0.92)  ; 2\r\n                          (  267.11   270.07   -19.15     0.92)  ; 3\r\n                          (  270.54   271.46   -21.35     0.92)  ; 4\r\n                          (  268.71   269.23   -21.35     0.92)  ; 5\r\n                          (  270.39   266.06   -20.35     0.92)  ; 6\r\n                          (  271.82   264.00   -20.35     0.92)  ; 7\r\n                          (  273.31   269.72   -20.55     0.92)  ; 8\r\n                          (  277.26   256.91   -18.10     0.92)  ; 9\r\n                          (  276.05   256.03   -19.42     0.92)  ; 10\r\n                          (  279.75   256.30   -17.77     0.92)  ; 11\r\n                          (  284.54   258.02   -22.22     0.92)  ; 12\r\n                          (  282.12   256.26   -22.22     0.92)  ; 13\r\n                          (  291.92   252.59   -19.23     0.92)  ; 14\r\n                          (  290.94   248.77   -17.02     0.92)  ; 15\r\n                          (  291.34   247.08   -17.02     0.92)  ; 16\r\n                          (  286.26   250.66   -17.02     0.92)  ; 17\r\n                          (  288.09   246.91   -17.02     0.92)  ; 18\r\n                          (  291.72   239.41   -19.40     0.92)  ; 19\r\n                          (  298.69   239.84   -18.30     0.92)  ; 20\r\n                          (  299.53   238.24   -18.20     0.92)  ; 21\r\n                          (  296.67   236.38   -18.20     0.92)  ; 22\r\n                          (  300.11   237.79   -15.30     0.92)  ; 23\r\n                          (  304.33   229.82   -18.35     0.92)  ; 24\r\n                          (  303.84   227.92   -15.55     0.92)  ; 25\r\n                          (  305.89   227.20   -14.95     0.92)  ; 26\r\n                          (  306.01   220.66   -14.72     0.92)  ; 27\r\n                          (  312.00   223.26   -14.45     0.92)  ; 28\r\n                          (  312.52   215.01   -15.35     0.92)  ; 29\r\n                          (  322.83   213.26   -17.20     0.92)  ; 30\r\n                          (  322.85   209.09   -17.57     0.92)  ; 31\r\n                          (  317.69   209.07   -14.15     0.92)  ; 32\r\n                          (  320.98   205.08   -16.50     0.92)  ; 33\r\n                          (  326.95   201.70   -18.77     0.92)  ; 34\r\n                          (  328.69   200.31   -13.87     0.92)  ; 35\r\n                          (  334.81   202.33   -12.40     0.92)  ; 36\r\n                          (  334.27   198.63   -11.58     0.92)  ; 37\r\n                          (  336.27   196.11   -11.58     0.92)  ; 38\r\n                          (  338.99   198.54   -11.42     0.92)  ; 39\r\n                          (  343.09   191.14   -13.00     0.92)  ; 40\r\n                          (  341.71   195.00   -14.95     0.92)  ; 41\r\n                          (  342.99   187.53   -14.40     0.92)  ; 42\r\n                          (  340.05   188.04   -12.15     0.92)  ; 43\r\n                          (  346.46   184.77   -10.68     0.46)  ; 44\r\n                          (  350.65   180.96   -10.68     0.46)  ; 45\r\n                        )  ;  End of markers\r\n                         Normal\r\n                      )  ;  End of split\r\n                    )  ;  End of split\r\n                  |\r\n                    (  244.93   254.13   -18.75     0.92)  ; 1, R-1-1-1-1-1-1-1-1-2\r\n                    (  244.68   255.26   -16.65     0.92)  ; 2\r\n                    (  246.14   255.02   -14.95     0.92)  ; 3\r\n                    (  247.03   255.22   -12.80     0.92)  ; 4\r\n                    (  242.74   255.42   -11.95     0.92)  ; 5\r\n                    (  240.17   258.39   -11.40     0.92)  ; 6\r\n                    (  236.25   261.06   -11.15     0.92)  ; 7\r\n                    (  236.25   261.06   -11.17     0.92)  ; 8\r\n                    (  234.45   260.64   -10.05     0.92)  ; 9\r\n                    (  231.64   260.57    -9.72     0.92)  ; 10\r\n                    (  230.30   260.26    -8.73     0.92)  ; 11\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  242.84   259.02   -11.40     0.92)  ; 1\r\n                      (  240.83   255.56   -12.13     0.92)  ; 2\r\n                      (  238.96   257.51   -12.13     0.92)  ; 3\r\n                      (  236.33   258.69   -12.13     0.92)  ; 4\r\n                      (  237.58   261.37   -11.63     0.92)  ; 5\r\n                      (  233.37   259.19    -9.72     0.92)  ; 6\r\n                      (  233.65   264.03    -9.72     0.92)  ; 7\r\n                      (  230.80   262.17    -9.72     0.92)  ; 8\r\n                      (  230.25   258.46    -7.43     0.92)  ; 9\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  228.08   259.73    -9.43     0.92)  ; 1, R-1-1-1-1-1-1-1-1-2-1\r\n                      (  224.23   260.04    -9.43     0.92)  ; 2\r\n                      (  223.20   260.39   -10.57     0.92)  ; 3\r\n                      (  223.20   260.39   -10.60     0.92)  ; 4\r\n                      (  220.00   262.02   -11.63     0.92)  ; 5\r\n                      (  217.99   264.54   -12.75     0.92)  ; 6\r\n                      (  215.76   264.02   -13.70     0.92)  ; 7\r\n                      (  213.27   264.62   -14.67     0.92)  ; 8\r\n                      (  210.05   266.26   -14.67     0.92)  ; 9\r\n                      (  210.05   266.26   -14.70     0.92)  ; 10\r\n                      (  209.21   267.85   -15.20     0.92)  ; 11\r\n                      (  207.02   269.14   -15.88     0.92)  ; 12\r\n                      (  207.02   269.14   -15.90     0.92)  ; 13\r\n                      (  204.08   269.64   -16.70     0.92)  ; 14\r\n                      (  202.65   271.70   -17.42     0.92)  ; 15\r\n                      (  198.94   271.43   -17.42     0.92)  ; 16\r\n                      (  194.98   272.28   -17.45     0.92)  ; 17\r\n                      (  193.42   274.91   -18.77     0.92)  ; 18\r\n                      (  193.42   274.91   -18.75     0.92)  ; 19\r\n                      (  191.90   273.35   -20.10     0.92)  ; 20\r\n                      (  189.97   273.50   -21.18     0.92)  ; 21\r\n                      (  187.93   274.22   -22.02     0.92)  ; 22\r\n                      (  185.74   275.49   -22.70     0.92)  ; 23\r\n                      (  183.11   276.68   -23.50     0.92)  ; 24\r\n                      (  181.56   279.29   -23.50     0.92)  ; 25\r\n                      (  179.05   279.91   -23.50     0.92)  ; 26\r\n                      (  175.98   280.97   -23.63     0.92)  ; 27\r\n                      (  172.77   282.61   -23.63     0.92)  ; 28\r\n                      (  169.60   286.05   -23.40     0.46)  ; 29\r\n                      (  166.22   286.44   -24.17     0.46)  ; 30\r\n                      (  162.70   287.41   -25.15     0.46)  ; 31\r\n                      (  160.95   288.80   -25.03     0.46)  ; 32\r\n                      (  160.55   290.49   -24.00     0.46)  ; 33\r\n                      (  157.97   293.47   -24.92     0.46)  ; 34\r\n                      (  157.97   293.47   -24.95     0.46)  ; 35\r\n                      (  156.81   294.38   -26.35     0.46)  ; 36\r\n                      (  156.81   294.38   -26.38     0.46)  ; 37\r\n                      (  154.50   296.24   -27.20     0.46)  ; 38\r\n                      (  151.74   297.97   -27.97     0.46)  ; 39\r\n                      (  150.76   300.14   -28.15     0.46)  ; 40\r\n                      (  148.65   299.06   -28.15     0.46)  ; 41\r\n                      (  144.29   301.61   -28.15     0.46)  ; 42\r\n                      (  140.31   302.46   -28.25     0.46)  ; 43\r\n                      (  140.31   302.46   -28.30     0.46)  ; 44\r\n                      (  137.55   304.21   -28.30     0.46)  ; 45\r\n                      (  134.07   306.99   -28.30     0.46)  ; 46\r\n                      (  130.60   309.74   -27.60     0.46)  ; 47\r\n                      (  127.97   310.92   -26.65     0.46)  ; 48\r\n                      (  125.08   313.23   -26.00     0.46)  ; 49\r\n                      (  123.79   314.72   -25.07     0.46)  ; 50\r\n                      (  121.33   317.13   -24.20     0.46)  ; 51\r\n                      (  118.57   318.87   -22.77     0.46)  ; 52\r\n                      (  117.14   320.93   -22.27     0.46)  ; 53\r\n                      (  114.43   324.47   -21.72     0.46)  ; 54\r\n                      (  114.43   324.47   -21.75     0.46)  ; 55\r\n                      (  114.35   326.83   -20.07     0.46)  ; 56\r\n                      (  114.35   326.83   -19.95     0.46)  ; 57\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  222.00   259.51   -11.63     0.92)  ; 1\r\n                        (  216.39   265.36   -13.50     0.92)  ; 2\r\n                        (  218.89   264.75   -14.35     0.92)  ; 3\r\n                        (  207.50   265.07   -14.32     0.92)  ; 4\r\n                        (  212.29   266.79   -14.27     0.92)  ; 5\r\n                        (  208.81   269.56   -14.27     0.92)  ; 6\r\n                        (  205.86   270.06   -14.27     0.92)  ; 7\r\n                        (  201.80   267.32   -17.45     0.92)  ; 8\r\n                        (  201.49   272.62   -17.45     0.92)  ; 9\r\n                        (  187.25   271.07   -21.18     0.92)  ; 10\r\n                        (  185.79   277.30   -22.70     0.92)  ; 11\r\n                        (  190.03   275.30   -22.70     0.92)  ; 12\r\n                        (  181.16   280.99   -23.50     0.92)  ; 13\r\n                        (  179.40   276.40   -23.50     0.92)  ; 14\r\n                        (  181.33   276.26   -23.50     0.92)  ; 15\r\n                        (  182.32   280.07   -23.50     0.92)  ; 16\r\n                        (  176.25   279.85   -23.63     0.92)  ; 17\r\n                        (  176.78   283.55   -24.77     0.92)  ; 18\r\n                        (  170.40   282.65   -23.27     0.92)  ; 19\r\n                        (  169.55   284.24   -23.85     0.92)  ; 20\r\n                        (  165.68   288.71   -25.17     0.92)  ; 21\r\n                        (  165.68   288.71   -25.15     0.46)  ; 22\r\n                        (  159.75   293.89   -25.27     0.46)  ; 23\r\n                        (  152.85   295.26   -27.97     0.46)  ; 24\r\n                        (  155.25   297.01   -27.97     0.46)  ; 25\r\n                        (  155.34   294.64   -27.97     0.46)  ; 26\r\n                        (  148.12   301.31   -28.15     0.46)  ; 27\r\n                        (  145.37   303.06   -30.73     0.46)  ; 28\r\n                        (  142.64   300.63   -31.45     0.46)  ; 29\r\n                        (  137.47   306.58   -29.42     0.46)  ; 30\r\n                        (  140.95   303.81   -29.45     0.46)  ; 31\r\n                        (  131.28   312.89   -26.65     0.46)  ; 32\r\n                        (  127.21   310.15   -26.00     0.46)  ; 33\r\n                        (  122.78   321.05   -24.20     0.46)  ; 34\r\n                        (  121.52   318.37   -23.15     0.46)  ; 35\r\n                        (  123.16   313.38   -23.50     0.46)  ; 36\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  227.15   263.70    -8.00     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2\r\n                      (\r\n                        (  227.20   265.51    -6.93     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2-1\r\n                        (  225.71   265.76    -5.60     0.46)  ; 2\r\n                        (  223.84   267.71    -4.92     0.46)  ; 3\r\n                        (  222.64   266.82    -4.88     0.46)  ; 4\r\n                        (  223.14   268.74    -2.97     0.46)  ; 5\r\n                        (  223.14   268.74    -2.95     0.46)  ; 6\r\n                        (  222.42   269.76    -1.75     0.46)  ; 7\r\n                        (  221.58   271.35    -0.12     0.46)  ; 8\r\n                        (  222.34   272.14     1.75     0.46)  ; 9\r\n                        (  219.97   272.18     3.82     0.46)  ; 10\r\n                        (  217.03   272.68     4.72     0.46)  ; 11\r\n                        (  217.21   273.91     6.18     0.46)  ; 12\r\n                        (  217.98   274.68     8.10     0.46)  ; 13\r\n                        (  217.44   276.95     9.45     0.46)  ; 14\r\n                        (  218.46   276.60     9.85     0.46)  ; 15\r\n                        (  218.06   278.29    11.98     0.46)  ; 16\r\n                        (  216.15   278.44    14.23     0.46)  ; 17\r\n                        (  217.67   279.99    16.15     0.46)  ; 18\r\n                        (  217.32   283.49    17.35     0.46)  ; 19\r\n                        (  217.32   283.49    17.40     0.46)  ; 20\r\n                        (  218.71   285.61    18.75     0.46)  ; 21\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  223.80   265.91    -4.92     0.46)  ; 1\r\n                          (  226.80   267.20    -4.92     0.46)  ; 2\r\n                          (  224.24   266.01    -2.80     0.46)  ; 3\r\n                          (  220.63   269.35     1.75     0.46)  ; 4\r\n                          (  223.09   272.90     2.63     0.46)  ; 5\r\n                          (  215.66   276.53     8.15     0.46)  ; 6\r\n                          (  219.44   274.43     8.85     0.46)  ; 7\r\n                          (  220.20   275.21     9.90     0.46)  ; 8\r\n                          (  218.69   279.63    17.40     0.46)  ; 9\r\n                        )  ;  End of markers\r\n                        (\r\n                          (  219.38   288.76    18.80     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2-1-1\r\n                          (  219.75   291.23    20.27     0.46)  ; 2\r\n                          (  221.54   291.64    21.92     0.46)  ; 3\r\n                          (  222.56   291.29    23.33     0.46)  ; 4\r\n                          (  222.56   291.29    23.30     0.46)  ; 5\r\n                           Normal\r\n                        |\r\n                          (  220.17   285.36    17.50     0.46)  ; 1, R-1-1-1-1-1-1-1-1-2-2-1-2\r\n                          (  221.26   286.80    19.13     0.46)  ; 2\r\n                          (  221.26   286.80    19.20     0.46)  ; 3\r\n                          (  221.76   288.71    21.05     0.46)  ; 4\r\n                          (  220.91   290.31    21.80     0.46)  ; 5\r\n                          (  221.54   291.64    23.10     0.46)  ; 6\r\n                          (  223.32   292.06    23.78     0.46)  ; 7\r\n                          (  223.32   292.06    23.95     0.46)  ; 8\r\n                           Normal\r\n                        )  ;  End of split\r\n                      |\r\n                        (  225.08   264.42    -9.15     0.92)  ; 1, R-1-1-1-1-1-1-1-1-2-2-2\r\n                        (  222.46   265.59    -9.40     0.92)  ; 2\r\n                        (  220.15   267.44    -8.32     0.92)  ; 3\r\n                        (  220.15   267.44    -8.35     0.92)  ; 4\r\n                        (  218.36   267.02    -8.20     0.92)  ; 5\r\n                        (  214.38   267.87    -8.32     0.92)  ; 6\r\n                        (  211.04   270.08    -9.43     0.92)  ; 7\r\n                        (  207.91   269.35    -9.43     0.92)  ; 8\r\n                        (  207.25   272.18   -10.17     0.92)  ; 9\r\n                        (  205.38   274.12   -10.17     0.92)  ; 10\r\n                        (  202.88   274.73    -9.25     0.92)  ; 11\r\n                        (  200.21   274.10    -8.15     0.92)  ; 12\r\n                        (  196.99   275.74    -7.95     0.92)  ; 13\r\n                        (  195.17   279.50    -7.47     0.92)  ; 14\r\n                        (  191.51   281.02    -7.47     0.92)  ; 15\r\n                        (  188.11   281.43    -6.55     0.92)  ; 16\r\n                        (  185.22   283.73    -6.55     0.92)  ; 17\r\n                        (  181.97   283.56    -7.90     0.92)  ; 18\r\n                        (  179.33   284.75    -9.15     0.92)  ; 19\r\n                        (  177.02   286.59    -9.60     0.92)  ; 20\r\n                        (  177.02   286.59    -9.63     0.92)  ; 21\r\n                        (  173.10   289.25    -7.15     0.92)  ; 22\r\n                        (  173.10   289.25    -7.17     0.92)  ; 23\r\n                        (  171.67   291.31    -7.17     0.92)  ; 24\r\n                        (  169.23   293.71    -6.52     0.92)  ; 25\r\n                        (  166.77   296.13    -6.52     0.92)  ; 26\r\n                        (  165.61   297.05    -5.80     0.92)  ; 27\r\n                        (  163.83   296.63    -5.80     0.92)  ; 28\r\n                        (  160.18   298.16    -5.03     0.92)  ; 29\r\n                        (  156.91   297.99    -4.15     0.92)  ; 30\r\n                        (  154.28   299.18    -4.10     0.92)  ; 31\r\n                        (  153.61   302.00    -3.57     0.92)  ; 32\r\n                        (  150.72   304.31    -3.57     0.46)  ; 33\r\n                        (  147.38   306.52    -3.57     0.46)  ; 34\r\n                        (  144.44   307.02    -3.57     0.46)  ; 35\r\n                        (  141.01   311.59    -3.95     0.46)  ; 36\r\n                        (  141.01   311.59    -3.97     0.46)  ; 37\r\n                        (  138.96   312.30    -3.27     0.46)  ; 38\r\n                        (  134.59   314.86    -4.35     0.46)  ; 39\r\n                        (  132.40   316.14    -5.13     0.46)  ; 40\r\n                        (  129.77   317.31    -5.13     0.46)  ; 41\r\n                        (  127.89   319.27    -6.22     0.46)  ; 42\r\n                        (  127.18   320.29    -8.30     0.46)  ; 43\r\n\r\n                        (Cross\r\n                          (Color RGB (128, 255, 128))\r\n                          (Name \"Marker 3\")\r\n                          (  223.88   263.53    -9.15     0.92)  ; 1\r\n                          (  218.41   268.82    -8.32     0.92)  ; 2\r\n                          (  213.49   267.66    -9.27     0.92)  ; 3\r\n                          (  215.54   266.96   -10.60     0.92)  ; 4\r\n                          (  216.04   268.86    -9.05     0.92)  ; 5\r\n                          (  213.14   271.17   -10.70     0.92)  ; 6\r\n                          (  211.23   271.31    -7.67     0.92)  ; 7\r\n                          (  209.65   267.96    -9.13     0.92)  ; 8\r\n                          (  206.04   271.29    -8.15     0.92)  ; 9\r\n                          (  196.76   272.70    -8.15     0.92)  ; 10\r\n                          (  193.48   282.69    -8.25     0.92)  ; 11\r\n                          (  187.94   280.18    -6.55     0.92)  ; 12\r\n                          (  183.84   281.62    -6.55     0.92)  ; 13\r\n                          (  187.72   283.13    -6.55     0.92)  ; 14\r\n                          (  177.10   284.22    -9.63     0.92)  ; 15\r\n                          (  180.86   286.29    -8.57     0.92)  ; 16\r\n                          (  177.64   287.94    -8.40     0.92)  ; 17\r\n                          (  171.27   293.01    -9.38     0.92)  ; 18\r\n                          (  165.65   292.88    -6.95     0.92)  ; 19\r\n                          (  166.14   294.79    -6.95     0.92)  ; 20\r\n                          (  169.15   296.09    -7.27     0.92)  ; 21\r\n                          (  173.73   290.60    -7.57     0.92)  ; 22\r\n                          (  160.81   299.51    -4.15     0.92)  ; 23\r\n                          (  163.30   298.90    -4.15     0.92)  ; 24\r\n                          (  160.44   297.04    -4.15     0.92)  ; 25\r\n                          (  153.13   300.10    -2.88     0.92)  ; 26\r\n                          (  150.67   302.52    -2.88     0.92)  ; 27\r\n                          (  148.58   307.40    -2.88     0.92)  ; 28\r\n                          (  142.84   313.82    -3.27     0.46)  ; 29\r\n                          (  142.48   311.33    -4.30     0.46)  ; 30\r\n                          (  132.50   319.75    -4.65     0.46)  ; 31\r\n                          (  134.64   316.67    -4.72     0.46)  ; 32\r\n                          (  137.48   312.55    -2.63     0.46)  ; 33\r\n                        )  ;  End of markers\r\n                         Normal\r\n                      )  ;  End of split\r\n                    )  ;  End of split\r\n                  )  ;  End of split\r\n                |\r\n                  (  247.52   217.10   -12.10     1.38)  ; 1, R-1-1-1-1-1-1-1-2\r\n                  (  244.97   215.90   -13.25     1.38)  ; 2\r\n                  (  242.46   216.52   -14.40     1.38)  ; 3\r\n                  (  241.62   218.11   -15.60     1.38)  ; 4\r\n                  (  240.82   221.51   -19.42     1.38)  ; 5\r\n\r\n                  (Cross\r\n                    (Color RGB (128, 255, 128))\r\n                    (Name \"Marker 3\")\r\n                    (  249.43   216.94   -12.10     1.38)  ; 1\r\n                    (  246.54   219.26   -12.10     1.38)  ; 2\r\n                    (  245.45   217.82   -12.10     1.38)  ; 3\r\n                    (  240.15   218.36   -15.60     1.38)  ; 4\r\n                    (  243.76   215.03   -15.60     1.38)  ; 5\r\n                    (  243.09   217.86   -17.90     1.38)  ; 6\r\n                    (  243.01   220.23   -14.35     1.38)  ; 7\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  238.64   222.78   -20.75     1.38)  ; 1, R-1-1-1-1-1-1-1-2-1\r\n                    (  234.35   222.98   -22.30     1.38)  ; 2\r\n                    (  232.13   222.46   -23.25     1.38)  ; 3\r\n                    (  232.13   222.46   -23.27     1.38)  ; 4\r\n                    (  230.78   222.14   -25.00     1.38)  ; 5\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  235.02   220.14   -22.30     1.38)  ; 1\r\n                      (  236.67   221.13   -22.30     1.38)  ; 2\r\n                      (  232.18   224.26   -21.50     1.38)  ; 3\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  228.51   219.81   -25.70     0.92)  ; 1, R-1-1-1-1-1-1-1-2-1-1\r\n                      (  226.09   218.05   -27.15     0.92)  ; 2\r\n                      (  224.48   218.87   -28.85     0.92)  ; 3\r\n                      (  223.59   218.66   -30.23     0.92)  ; 4\r\n                      (  222.97   217.32   -31.67     0.92)  ; 5\r\n                      (  221.63   217.01   -33.10     0.92)  ; 6\r\n                      (  219.65   215.35   -34.17     0.92)  ; 7\r\n                      (  218.71   213.33   -35.92     0.92)  ; 8\r\n                      (  220.05   213.65   -38.53     0.92)  ; 9\r\n                      (  218.94   216.38   -40.95     0.92)  ; 10\r\n                      (  216.13   216.31   -43.05     0.92)  ; 11\r\n                      (  212.99   215.58   -44.35     0.92)  ; 12\r\n                      (  210.59   213.83   -44.25     0.92)  ; 13\r\n                      (  207.86   211.39   -44.75     0.92)  ; 14\r\n                      (  203.79   208.65   -45.15     0.92)  ; 15\r\n                      (  202.14   207.66   -45.15     0.46)  ; 16\r\n                      (  197.62   204.81   -45.97     0.46)  ; 17\r\n                      (  195.26   204.86   -46.93     0.46)  ; 18\r\n                      (  195.26   204.86   -46.95     0.46)  ; 19\r\n                      (  193.52   206.24   -47.80     0.46)  ; 20\r\n                      (  190.84   205.61   -48.70     0.46)  ; 21\r\n                      (  190.84   205.61   -48.72     0.46)  ; 22\r\n                      (  187.27   204.78   -49.58     0.46)  ; 23\r\n                      (  183.88   205.17   -49.38     0.46)  ; 24\r\n                      (  181.38   205.79   -49.38     0.46)  ; 25\r\n                      (  177.99   206.19   -49.38     0.46)  ; 26\r\n                      (  175.94   206.89   -50.32     0.46)  ; 27\r\n                      (  175.94   206.89   -50.35     0.46)  ; 28\r\n                      (  173.75   208.18   -51.25     0.46)  ; 29\r\n                      (  172.90   209.77   -52.08     0.46)  ; 30\r\n                      (  172.52   211.46   -53.38     0.46)  ; 31\r\n                      (  172.52   211.46   -53.40     0.46)  ; 32\r\n                      (  172.11   213.16   -54.67     0.46)  ; 33\r\n                      (  172.11   213.16   -54.83     0.46)  ; 34\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  226.76   221.20   -27.15     0.92)  ; 1\r\n                        (  225.91   216.82   -28.85     0.92)  ; 2\r\n                        (  221.86   220.05   -31.67     0.92)  ; 3\r\n                        (  216.57   216.42   -35.92     0.92)  ; 4\r\n                        (  214.88   213.64   -44.35     0.92)  ; 5\r\n                        (  212.33   212.44   -44.25     0.92)  ; 6\r\n                        (  209.56   214.18   -44.25     0.92)  ; 7\r\n                        (  211.08   215.73   -44.25     0.92)  ; 8\r\n                        (  206.87   207.58   -45.15     0.92)  ; 9\r\n                        (  196.38   208.10   -45.97     0.46)  ; 10\r\n                        (  195.79   202.59   -46.12     0.46)  ; 11\r\n                        (  188.43   203.85   -49.58     0.46)  ; 12\r\n                        (  178.65   203.35   -49.38     0.46)  ; 13\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  226.81   222.99   -25.00     0.92)  ; 1, R-1-1-1-1-1-1-1-2-1-2\r\n                      (  225.08   224.38   -27.63     0.92)  ; 2\r\n                      (  224.94   224.95   -27.63     0.92)  ; 3\r\n                      (  223.60   224.63   -29.77     0.92)  ; 4\r\n                      (  221.51   223.54   -30.60     0.92)  ; 5\r\n                      (  219.64   225.50   -32.38     0.92)  ; 6\r\n                      (  217.27   225.54   -33.45     0.92)  ; 7\r\n                      (  213.96   223.57   -34.32     0.92)  ; 8\r\n                      (  211.96   226.09   -35.25     0.92)  ; 9\r\n                      (  212.01   227.89   -35.25     0.92)  ; 10\r\n                      (  208.93   228.96   -35.25     0.92)  ; 11\r\n                      (  207.50   231.01   -34.28     0.92)  ; 12\r\n                      (  205.36   234.09   -34.35     0.92)  ; 13\r\n                      (  203.68   237.29   -33.35     0.92)  ; 14\r\n                      (  202.07   238.10   -32.13     0.92)  ; 15\r\n                      (  199.58   238.71   -31.67     0.92)  ; 16\r\n                      (  199.05   240.97   -30.73     0.92)  ; 17\r\n                      (  198.52   243.24   -29.77     0.92)  ; 18\r\n                      (  196.90   244.06   -30.55     0.92)  ; 19\r\n                      (  192.89   243.12   -30.55     0.92)  ; 20\r\n                      (  190.13   244.86   -29.40     0.92)  ; 21\r\n                      (  188.02   243.76   -28.33     0.92)  ; 22\r\n                      (  186.10   243.91   -26.17     0.92)  ; 23\r\n                      (  183.30   243.85   -24.70     0.92)  ; 24\r\n                      (  181.64   242.87   -24.32     0.92)  ; 25\r\n                      (  179.72   243.01   -24.32     0.92)  ; 26\r\n                      (  177.43   240.69   -24.42     0.92)  ; 27\r\n                      (  173.16   240.88   -24.13     0.92)  ; 28\r\n                      (  169.05   242.31   -23.23     0.92)  ; 29\r\n                      (  164.95   243.73   -22.53     0.92)  ; 30\r\n                      (  160.98   244.59   -22.00     0.92)  ; 31\r\n                      (  158.39   247.58   -21.18     0.92)  ; 32\r\n                      (  155.77   248.75   -19.85     0.92)  ; 33\r\n                      (  153.53   248.22   -19.85     0.92)  ; 34\r\n                      (  151.21   250.07   -18.55     0.92)  ; 35\r\n                      (  146.80   250.82   -18.95     0.92)  ; 36\r\n                      (  141.78   252.03   -18.15     0.92)  ; 37\r\n                      (  139.46   253.86   -17.55     0.92)  ; 38\r\n                      (  137.78   257.05   -16.67     0.92)  ; 39\r\n                      (  135.86   257.19   -16.07     0.92)  ; 40\r\n                      (  133.76   256.11   -15.65     0.92)  ; 41\r\n                      (  131.17   259.09   -14.65     0.92)  ; 42\r\n                      (  127.39   261.18   -14.13     0.92)  ; 43\r\n                      (  124.36   264.05   -14.13     0.92)  ; 44\r\n                      (  122.94   266.12   -13.15     0.92)  ; 45\r\n                      (  121.32   266.93   -12.30     0.92)  ; 46\r\n                      (  119.28   267.64   -11.30     0.92)  ; 47\r\n                      (  116.39   269.95   -10.30     0.46)  ; 48\r\n                      (  114.38   272.47    -9.88     0.46)  ; 49\r\n                      (  110.95   277.04    -9.22     0.46)  ; 50\r\n                      (  107.47   279.80    -8.25     0.46)  ; 51\r\n\r\n                      (Cross\r\n                        (Color RGB (128, 255, 128))\r\n                        (Name \"Marker 3\")\r\n                        (  229.36   224.19   -25.00     0.92)  ; 1\r\n                        (  226.59   225.94   -28.35     0.92)  ; 2\r\n                        (  224.55   226.65   -28.47     0.92)  ; 3\r\n                        (  221.55   225.35   -28.55     0.92)  ; 4\r\n                        (  218.63   221.68   -32.38     0.92)  ; 5\r\n                        (  221.91   221.85   -32.38     0.92)  ; 6\r\n                        (  217.97   224.51   -32.05     0.92)  ; 7\r\n                        (  214.32   226.03   -34.32     0.92)  ; 8\r\n                        (  205.46   237.70   -30.13     0.92)  ; 9\r\n                        (  203.28   238.98   -29.72     0.92)  ; 10\r\n                        (  200.70   241.96   -28.82     0.92)  ; 11\r\n                        (  197.63   243.03   -28.82     0.92)  ; 12\r\n                        (  198.16   240.76   -28.82     0.92)  ; 13\r\n                        (  199.85   243.55   -31.05     0.92)  ; 14\r\n                        (  194.58   245.90   -30.55     0.92)  ; 15\r\n                        (  196.67   241.01   -30.55     0.92)  ; 16\r\n                        (  177.40   244.86   -24.42     0.92)  ; 17\r\n                        (  174.54   243.00   -24.42     0.92)  ; 18\r\n                        (  174.58   238.83   -26.52     0.92)  ; 19\r\n                        (  176.10   240.37   -26.52     0.92)  ; 20\r\n                        (  168.57   246.38   -23.23     0.92)  ; 21\r\n                        (  171.18   239.22   -23.23     0.92)  ; 22\r\n                        (  159.20   250.15   -19.40     0.92)  ; 23\r\n                        (  155.55   251.68   -19.63     0.92)  ; 24\r\n                        (  156.30   246.48   -19.65     0.92)  ; 25\r\n                        (  151.52   250.74   -16.55     0.92)  ; 26\r\n                        (  150.15   254.60   -18.95     0.92)  ; 27\r\n                        (  143.89   253.11   -18.15     0.92)  ; 28\r\n                        (  140.67   254.74   -15.73     0.92)  ; 29\r\n                        (  137.07   258.08   -15.73     0.92)  ; 30\r\n                        (  130.41   258.31   -14.65     0.92)  ; 31\r\n                        (  129.48   262.27   -14.65     0.92)  ; 32\r\n                        (  126.05   260.87   -13.92     0.92)  ; 33\r\n                        (  124.89   261.79   -13.92     0.92)  ; 34\r\n                        (  123.73   262.72   -13.92     0.92)  ; 35\r\n                        (  119.23   265.84   -11.30     0.92)  ; 36\r\n                        (  116.16   266.91   -10.55     0.92)  ; 37\r\n                        (  120.04   268.42   -10.55     0.92)  ; 38\r\n                        (  110.45   275.13    -8.25     0.46)  ; 39\r\n                        (  111.63   280.18    -7.27     0.46)  ; 40\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  |\r\n                    (  242.02   222.35   -22.63     0.92)  ; 1, R-1-1-1-1-1-1-1-2-2\r\n                    (  239.97   223.08   -24.72     0.92)  ; 2\r\n                    (  239.97   223.08   -24.75     0.92)  ; 3\r\n                    (  237.78   224.35   -26.85     0.92)  ; 4\r\n                    (  237.21   224.80   -28.05     0.92)  ; 5\r\n                    (  236.53   221.67   -28.35     0.46)  ; 6\r\n                    (  239.74   220.03   -29.50     0.46)  ; 7\r\n                    (  239.74   220.03   -29.52     0.46)  ; 8\r\n                    (  241.97   220.55   -31.25     0.46)  ; 9\r\n                    (  243.95   222.21   -32.45     0.46)  ; 10\r\n                    (  244.57   223.55   -33.38     0.46)  ; 11\r\n                    (  245.64   224.99   -34.63     0.46)  ; 12\r\n                    (  246.40   225.77   -36.05     0.46)  ; 13\r\n                    (  246.80   224.08   -37.72     0.46)  ; 14\r\n                    (  246.35   223.97   -39.32     0.46)  ; 15\r\n                    (  248.32   225.62   -40.88     0.46)  ; 16\r\n                    (  249.40   227.07   -42.80     0.46)  ; 17\r\n                    (  249.40   227.07   -42.82     0.46)  ; 18\r\n                    (  247.83   229.70   -44.63     0.46)  ; 19\r\n                    (  247.58   230.82   -46.40     0.46)  ; 20\r\n                    (  247.68   234.43   -47.67     0.46)  ; 21\r\n                    (  245.39   232.10   -49.07     0.46)  ; 22\r\n                    (  244.45   230.09   -50.83     0.46)  ; 23\r\n                    (  246.29   232.31   -53.40     0.46)  ; 24\r\n                    (  249.37   231.24   -56.05     0.46)  ; 25\r\n                    (  251.02   232.22   -58.38     0.46)  ; 26\r\n                    (  250.62   233.93   -60.85     0.46)  ; 27\r\n                    (  249.42   233.04   -63.70     0.46)  ; 28\r\n                    (  248.97   232.94   -63.72     0.46)  ; 29\r\n                    (  249.68   231.91   -67.00     0.46)  ; 30\r\n                    (  250.44   232.69   -70.25     0.46)  ; 31\r\n                    (  253.12   233.32   -72.47     0.46)  ; 32\r\n                    (  253.12   233.32   -72.50     0.46)  ; 33\r\n                    (  251.69   235.37   -75.15     0.46)  ; 34\r\n                    (  251.56   235.94   -75.15     0.46)  ; 35\r\n                    (  249.51   236.65   -77.63     0.46)  ; 36\r\n                    (  249.51   236.65   -77.65     0.46)  ; 37\r\n                    (  248.17   236.34   -80.18     0.46)  ; 38\r\n                    (  248.17   236.34   -80.20     0.46)  ; 39\r\n                    (  247.68   234.43   -82.83     0.46)  ; 40\r\n                    (  247.68   234.43   -82.85     0.46)  ; 41\r\n                    (  248.22   238.15   -84.40     0.46)  ; 42\r\n                    (  248.09   238.70   -84.43     0.46)  ; 43\r\n                    (  249.60   240.25   -84.43     0.46)  ; 44\r\n                    (  246.53   241.33   -86.90     0.46)  ; 45\r\n                    (  247.48   243.34   -91.43     0.46)  ; 46\r\n                    (  247.34   243.90   -91.45     0.46)  ; 47\r\n                    (  249.26   243.75   -94.10     0.46)  ; 48\r\n                    (  249.26   243.75   -94.15     0.46)  ; 49\r\n                    (  249.35   247.36   -96.62     0.46)  ; 50\r\n                    (  248.91   247.26   -96.65     0.46)  ; 51\r\n                    (  247.88   247.61  -100.60     0.46)  ; 52\r\n                    (  247.88   247.61  -100.63     0.46)  ; 53\r\n                    (  248.46   247.15  -104.05     0.46)  ; 54\r\n                    (  248.46   247.15  -104.12     0.46)  ; 55\r\n                    (  248.69   250.20  -106.80     0.46)  ; 56\r\n                    (  248.69   250.20  -106.82     0.46)  ; 57\r\n                    (  247.13   252.81  -108.63     0.46)  ; 58\r\n                    (  247.13   252.81  -108.65     0.46)  ; 59\r\n                    (  247.90   253.59  -110.85     0.46)  ; 60\r\n                    (  249.01   256.84  -111.97     0.46)  ; 61\r\n                    (  248.56   256.74  -112.00     0.46)  ; 62\r\n                    (  248.62   258.54  -114.55     0.46)  ; 63\r\n                    (  248.62   258.54  -114.57     0.46)  ; 64\r\n                    (  248.21   260.24  -117.90     0.46)  ; 65\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  247.23   256.42  -112.13     0.46)  ; 1\r\n                      (  247.90   259.57  -113.93     0.46)  ; 2\r\n                      (  250.13   238.00   -79.25     0.46)  ; 3\r\n                      (  253.30   234.56   -70.60     0.46)  ; 4\r\n                      (  252.94   232.08   -61.57     0.46)  ; 5\r\n                      (  246.77   250.34  -102.78     0.46)  ; 6\r\n                    )  ;  End of markers\r\n\r\n                    (Cross\r\n                      (Color RGB (128, 255, 128))\r\n                      (Name \"Marker 3\")\r\n                      (  236.93   219.96   -29.52     0.46)  ; 1\r\n                      (  248.16   262.63   -19.60     2.29)  ; 2\r\n                      (  246.06   257.39   -12.80     0.92)  ; 3\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                )  ;  End of split\r\n              |\r\n                (  251.92   127.92     1.32     0.92)  ; 1, R-1-1-1-1-1-1-2\r\n                (  250.64   129.42     2.38     0.92)  ; 2\r\n                (  250.24   131.12     2.38     0.46)  ; 3\r\n                (  251.57   131.43     3.50     0.46)  ; 4\r\n                (  252.33   132.21     4.78     0.46)  ; 5\r\n                (  252.33   132.21     6.42     0.46)  ; 6\r\n                (  251.94   133.90     7.75     0.46)  ; 7\r\n                (  249.31   135.08     7.15     0.46)  ; 8\r\n                (  247.16   138.16     8.07     0.46)  ; 9\r\n                (  244.14   141.04     8.45     0.46)  ; 10\r\n                (  241.95   142.32     9.22     0.46)  ; 11\r\n                (  241.06   142.11    10.05     0.46)  ; 12\r\n                (  238.57   142.71    10.70     0.46)  ; 13\r\n                (  235.62   143.22    11.75     0.46)  ; 14\r\n                (  235.04   143.69    12.85     0.46)  ; 15\r\n                (  233.83   142.80    14.23     0.46)  ; 16\r\n                (  233.69   143.37    14.20     0.46)  ; 17\r\n                (  235.75   142.64    15.90     0.46)  ; 18\r\n                (  235.75   142.64    17.55     0.46)  ; 19\r\n                (  236.59   141.05    19.27     0.46)  ; 20\r\n                (  236.59   141.05    19.25     0.46)  ; 21\r\n                (  234.09   141.66    20.73     0.46)  ; 22\r\n                (  231.02   142.74    21.38     0.46)  ; 23\r\n                (  228.65   142.78    22.08     0.46)  ; 24\r\n                (  227.72   146.75    22.85     0.46)  ; 25\r\n                (  229.56   148.97    23.40     0.46)  ; 26\r\n                (  229.74   150.20    24.60     0.46)  ; 27\r\n                (  229.74   150.20    24.58     0.46)  ; 28\r\n                (  230.06   150.87    26.58     0.46)  ; 29\r\n                (  231.39   151.19    28.70     0.46)  ; 30\r\n                (  232.28   151.40    29.92     0.46)  ; 31\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  248.57   130.13     2.38     0.46)  ; 1\r\n                  (  248.89   130.81     2.38     0.46)  ; 2\r\n                  (  247.54   140.64     7.57     0.46)  ; 3\r\n                  (  244.84   140.00     8.57     0.46)  ; 4\r\n                  (  242.48   140.04     8.57     0.46)  ; 5\r\n                  (  243.03   143.76     8.95     0.46)  ; 6\r\n                  (  240.12   140.10    10.48     0.46)  ; 7\r\n                  (  241.82   142.88    11.45     0.46)  ; 8\r\n                  (  237.72   144.31    11.45     0.46)  ; 9\r\n                  (  230.52   140.82    21.38     0.46)  ; 10\r\n                  (  229.78   146.02    23.55     0.46)  ; 11\r\n                  (  226.33   144.62    23.40     0.46)  ; 12\r\n                  (  226.26   152.96    23.40     0.46)  ; 13\r\n                  (  250.51   135.96     7.15     0.46)  ; 14\r\n                )  ;  End of markers\r\n                 High\r\n              |\r\n                (  258.12   127.60     1.30     0.92)  ; 1, R-1-1-1-1-1-1-3\r\n                (  261.87   129.67     1.73     0.92)  ; 2\r\n                (  266.15   129.48     1.85     0.92)  ; 3\r\n                (  269.11   128.97     2.55     0.92)  ; 4\r\n                (  271.74   127.80     2.08     0.92)  ; 5\r\n                (  274.10   127.76     0.28     0.92)  ; 6\r\n                (  276.48   127.72    -0.70     0.92)  ; 7\r\n                (  278.39   127.57    -0.05     0.92)  ; 8\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  264.64   127.93     1.85     0.92)  ; 1\r\n                  (  261.74   130.23     2.55     0.92)  ; 2\r\n                  (  263.53   130.65     2.55     0.92)  ; 3\r\n                  (  265.89   130.61     2.55     0.92)  ; 4\r\n                  (  268.40   130.01     2.55     0.92)  ; 5\r\n                  (  270.35   125.68     2.55     0.92)  ; 6\r\n                  (  266.55   127.78     2.55     0.92)  ; 7\r\n                )  ;  End of markers\r\n                (\r\n                  (  282.40   128.51     0.90     0.46)  ; 1, R-1-1-1-1-1-1-3-1\r\n                  (  283.58   127.58     2.50     0.46)  ; 2\r\n                  (  283.58   127.58     2.47     0.46)  ; 3\r\n                  (  283.70   127.02     4.22     0.46)  ; 4\r\n                  (  282.89   124.44     5.50     0.46)  ; 5\r\n                  (  282.89   124.44     5.48     0.46)  ; 6\r\n                  (  284.49   123.62     5.95     0.46)  ; 7\r\n                  (  288.07   124.45     6.47     0.46)  ; 8\r\n                  (  290.75   125.09     7.00     0.46)  ; 9\r\n                  (  291.97   125.96     7.75     0.46)  ; 10\r\n                  (  293.75   126.38     6.90     0.46)  ; 11\r\n                  (  294.77   126.02     6.90     0.46)  ; 12\r\n                  (  297.18   127.79     8.35     0.46)  ; 13\r\n                  (  301.16   126.93     9.80     0.46)  ; 14\r\n                  (  301.11   125.13    11.75     0.46)  ; 15\r\n                  (  301.11   125.13    11.70     0.46)  ; 16\r\n                  (  300.17   129.09    14.10     0.46)  ; 17\r\n                  (  300.17   129.09    15.68     0.46)  ; 18\r\n                  (  300.17   129.09    15.65     0.46)  ; 19\r\n                  (  302.37   127.81    17.30     0.46)  ; 20\r\n                  (  302.37   127.81    17.27     0.46)  ; 21\r\n                  (  303.78   125.76    19.23     0.46)  ; 22\r\n                  (  303.78   125.76    19.20     0.46)  ; 23\r\n                  (  305.21   123.70    20.33     0.46)  ; 24\r\n                  (  309.22   124.64    20.33     0.46)  ; 25\r\n                  (  312.80   125.48    21.60     0.46)  ; 26\r\n                  (  312.85   127.29    21.60     0.46)  ; 27\r\n                  (  314.68   129.51    22.67     0.46)  ; 28\r\n                  (  315.71   129.15    24.05     0.46)  ; 29\r\n                  (  315.71   129.15    24.00     0.46)  ; 30\r\n                  (  315.18   131.41    25.13     0.46)  ; 31\r\n                  (  314.20   133.57    26.70     0.46)  ; 32\r\n                  (  313.17   133.92    27.77     0.46)  ; 33\r\n                  (  312.72   133.82    28.05     0.46)  ; 34\r\n                  (  312.46   134.95    29.15     0.46)  ; 35\r\n                  (  312.01   134.84    29.30     0.46)  ; 36\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  288.25   125.71     8.15     0.46)  ; 1\r\n                    (  286.65   126.52     6.47     0.46)  ; 2\r\n                    (  283.29   122.74     8.13     0.46)  ; 3\r\n                    (  294.42   129.53     9.80     0.46)  ; 4\r\n                    (  317.08   125.29    21.28     0.46)  ; 5\r\n                    (  315.86   134.56    21.55     0.46)  ; 6\r\n                    (  318.34   127.97    23.67     0.46)  ; 7\r\n                    (  307.48   126.03    20.33     0.46)  ; 8\r\n                    (  305.57   126.18    20.33     0.46)  ; 9\r\n                    (  303.16   124.41    19.20     0.46)  ; 10\r\n                    (  301.11   125.13    19.20     0.46)  ; 11\r\n                    (  299.45   124.15     9.80     0.46)  ; 12\r\n                    (  296.90   122.94     8.35     0.46)  ; 13\r\n                    (  289.67   123.64     7.87     0.46)  ; 14\r\n                  )  ;  End of markers\r\n                   High\r\n                |\r\n                  (  280.13   126.18    -1.30     0.92)  ; 1, R-1-1-1-1-1-1-3-2\r\n                  (  281.73   125.37    -2.70     0.92)  ; 2\r\n                  (  283.20   125.12    -4.20     0.92)  ; 3\r\n                  (  283.15   123.31    -5.60     0.92)  ; 4\r\n                  (  284.63   123.06    -7.20     0.92)  ; 5\r\n                  (  285.39   123.82    -9.57     0.92)  ; 6\r\n                  (  286.86   123.58   -11.15     0.92)  ; 7\r\n                  (  288.47   122.76   -11.65     0.92)  ; 8\r\n                  (  289.13   119.93   -10.35     0.92)  ; 9\r\n                  (  290.86   118.55    -9.55     0.92)  ; 10\r\n                  (  293.55   119.18   -10.05     0.92)  ; 11\r\n                  (  295.74   117.89   -10.25     0.92)  ; 12\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  286.91   125.39    -9.63     0.92)  ; 1\r\n                    (  285.75   126.31   -11.65     0.92)  ; 2\r\n                    (  289.09   124.10   -10.12     0.92)  ; 3\r\n                    (  290.78   120.91   -11.77     0.92)  ; 4\r\n                    (  281.81   122.99    -5.60     0.92)  ; 5\r\n                    (  292.02   117.62   -11.77     0.92)  ; 6\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  298.10   117.85   -10.25     0.92)  ; 1, R-1-1-1-1-1-1-3-2-1\r\n                    (  301.35   118.02   -11.73     0.92)  ; 2\r\n                    (  303.55   116.74   -12.85     0.92)  ; 3\r\n                    (  303.55   116.74   -12.88     0.92)  ; 4\r\n                    (  306.44   114.43   -12.13     0.92)  ; 5\r\n                    (  310.54   113.00   -14.07     0.92)  ; 6\r\n                    (  314.20   111.47   -17.77     0.92)  ; 7\r\n                    (  317.28   110.40   -18.15     0.92)  ; 8\r\n                    (  319.32   109.69   -18.15     0.92)  ; 9\r\n                    (  323.17   109.40   -17.10     0.92)  ; 10\r\n                    (  325.53   109.36   -15.70     0.92)  ; 11\r\n                    (  327.26   107.97   -16.30     0.92)  ; 12\r\n                    (  327.26   107.97   -16.32     0.92)  ; 13\r\n                    (  330.70   109.37   -16.75     0.92)  ; 14\r\n                    (  332.49   109.79   -17.77     0.92)  ; 15\r\n                    (  335.61   110.51   -18.65     0.92)  ; 16\r\n                    (  338.56   110.02   -17.20     0.92)  ; 17\r\n                    (  341.45   107.71   -18.13     0.92)  ; 18\r\n                    (  345.92   108.76   -18.57     0.92)  ; 19\r\n                    (  349.31   108.36   -17.25     0.92)  ; 20\r\n                    (  352.97   106.83   -17.25     0.92)  ; 21\r\n                    (  354.34   102.97   -18.38     0.92)  ; 22\r\n                    (  358.00   101.43   -18.82     0.92)  ; 23\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  308.71   110.79   -14.07     0.92)  ; 1\r\n                      (  311.74   113.89   -15.12     0.92)  ; 2\r\n                      (  316.75   112.67   -16.75     0.92)  ; 3\r\n                      (  319.37   111.50   -16.75     0.92)  ; 4\r\n                      (  312.54   110.49   -16.25     0.92)  ; 5\r\n                      (  313.67   113.73   -17.85     0.92)  ; 6\r\n                      (  319.72   108.00   -17.10     0.92)  ; 7\r\n                      (  322.53   108.06   -17.10     0.92)  ; 8\r\n                      (  317.55   109.27   -17.10     0.92)  ; 9\r\n                      (  324.73   106.77   -17.10     0.92)  ; 10\r\n                      (  332.27   112.72   -18.65     0.92)  ; 11\r\n                      (  338.30   111.15   -19.05     0.92)  ; 12\r\n                      (  340.21   111.00   -20.33     0.92)  ; 13\r\n                      (  344.54   106.64   -18.57     0.92)  ; 14\r\n                      (  341.72   106.58   -16.98     0.92)  ; 15\r\n                      (  352.30   109.65   -19.23     0.92)  ; 16\r\n                      (  349.94   109.69   -16.67     0.92)  ; 17\r\n                      (  357.92   103.81   -18.82     0.92)  ; 18\r\n                      (  356.61    99.32   -18.82     0.92)  ; 19\r\n                    )  ;  End of markers\r\n                    (\r\n                      (  359.35   101.75   -20.57     0.92)  ; 1, R-1-1-1-1-1-1-3-2-1-1\r\n                      (  358.98    99.27   -22.25     0.92)  ; 2\r\n                      (  360.14    98.35   -24.00     0.92)  ; 3\r\n                      (  362.05    98.20   -24.88     0.92)  ; 4\r\n                      (  363.75    95.02   -25.87     0.92)  ; 5\r\n                      (  366.06    93.18   -25.20     0.92)  ; 6\r\n                      (  369.77    93.45   -25.97     0.92)  ; 7\r\n                      (  372.22    91.04   -26.58     0.92)  ; 8\r\n                      (  374.71    90.43   -27.97     0.46)  ; 9\r\n                      (  379.40    88.54   -29.20     0.46)  ; 10\r\n                      (  382.92    87.57   -30.00     0.46)  ; 11\r\n                      (  384.66    86.19   -30.35     0.46)  ; 12\r\n                      (  386.71    85.47   -30.40     0.46)  ; 13\r\n                      (  388.69    87.13   -31.32     0.46)  ; 14\r\n                      (  388.24    87.02   -31.35     0.46)  ; 15\r\n                      (  392.74    83.90   -32.40     0.46)  ; 16\r\n                      (  394.20    83.64   -33.85     0.46)  ; 17\r\n                      (  394.20    83.64   -33.88     0.46)  ; 18\r\n                      (  395.64    81.59   -35.42     0.46)  ; 19\r\n                      (  398.01    81.55   -36.95     0.46)  ; 20\r\n                      (  398.18    82.79   -39.08     0.46)  ; 21\r\n                      (  398.18    82.79   -39.10     0.46)  ; 22\r\n                      (  398.62    82.90   -41.35     0.46)  ; 23\r\n                      (  400.99    82.85   -43.47     0.46)  ; 24\r\n                      (  400.99    82.85   -43.50     0.46)  ; 25\r\n                      (  401.39    81.15   -45.85     0.46)  ; 26\r\n                      (  402.24    79.56   -49.07     0.46)  ; 27\r\n                      (  402.24    79.56   -49.13     0.46)  ; 28\r\n                      (  402.94    78.53   -52.40     0.46)  ; 29\r\n                      (  402.94    78.53   -52.45     0.46)  ; 30\r\n\r\n                      (Cross\r\n                        (Color White)\r\n                        (Name \"Marker 3\")\r\n                        (  363.79    96.83   -20.47     0.92)  ; 1\r\n                        (  367.46    95.29   -23.35     0.92)  ; 2\r\n                        (  371.42    94.44   -26.05     0.92)  ; 3\r\n                        (  369.59    92.21   -20.47     0.92)  ; 4\r\n                        (  367.35    91.68   -25.85     0.92)  ; 5\r\n                        (  383.55    88.92   -28.35     0.46)  ; 6\r\n                        (  378.11    90.02   -19.13     0.92)  ; 7\r\n                        (  377.89    86.99   -21.02     0.92)  ; 8\r\n                        (  384.03    84.84   -29.67     0.46)  ; 9\r\n                        (  396.45    84.17   -34.67     0.46)  ; 10\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    |\r\n                      (  358.90   101.65   -17.20     0.92)  ; 1, R-1-1-1-1-1-1-3-2-1-2\r\n                      (  358.90   101.65   -17.23     0.92)  ; 2\r\n                      (  360.01    98.92   -17.17     0.92)  ; 3\r\n                      (  360.41    97.23   -16.10     0.92)  ; 4\r\n                      (  362.14    95.84   -19.13     0.92)  ; 5\r\n                      (  362.14    95.84   -19.15     0.92)  ; 6\r\n                      (  363.88    94.45   -19.73     0.92)  ; 7\r\n                      (  363.88    94.45   -19.75     0.92)  ; 8\r\n                      (  367.22    92.25   -20.47     0.92)  ; 9\r\n                      (  368.65    90.20   -20.47     0.92)  ; 10\r\n                      (  371.28    89.02   -20.47     0.92)  ; 11\r\n                      (  374.36    87.95   -20.60     0.46)  ; 12\r\n                      (  376.35    85.43   -21.00     0.46)  ; 13\r\n                      (  378.63    81.78   -21.00     0.46)  ; 14\r\n                      (  380.76    78.71   -22.43     0.46)  ; 15\r\n                      (  383.21    76.30   -22.57     0.46)  ; 16\r\n                      (  385.22    73.78   -22.63     0.46)  ; 17\r\n                      (  389.00    71.67   -23.35     0.46)  ; 18\r\n                      (  390.16    70.76   -24.88     0.46)  ; 19\r\n                      (  390.55    69.06   -26.88     0.46)  ; 20\r\n                      (  390.55    69.06   -26.90     0.46)  ; 21\r\n                      (  391.99    67.00   -28.33     0.46)  ; 22\r\n                      (  392.97    64.85   -30.00     0.46)  ; 23\r\n                      (  394.26    63.35   -31.50     0.46)  ; 24\r\n                      (  394.26    63.35   -31.52     0.46)  ; 25\r\n                      (  392.99    60.68   -33.57     0.46)  ; 26\r\n                      (  392.95    58.89   -37.38     0.46)  ; 27\r\n                      (  392.95    58.89   -37.42     0.46)  ; 28\r\n                      (  393.93    56.72   -39.17     0.46)  ; 29\r\n                      (  393.93    56.72   -39.20     0.46)  ; 30\r\n                      (  395.08    55.80   -41.80     0.46)  ; 31\r\n                      (  395.08    55.80   -41.93     0.46)  ; 32\r\n                      (  395.30    52.87   -43.42     0.46)  ; 33\r\n                      (  395.30    52.87   -43.45     0.46)  ; 34\r\n                      (  394.67    51.53   -45.72     0.46)  ; 35\r\n\r\n                      (Cross\r\n                        (Color White)\r\n                        (Name \"Marker 3\")\r\n                        (  361.07    94.39   -23.40     0.92)  ; 1\r\n                        (  374.36    87.95   -29.40     0.92)  ; 2\r\n                        (  373.46    87.75   -21.02     0.92)  ; 3\r\n                        (  375.42    83.42   -21.02     0.92)  ; 4\r\n                        (  381.75    82.52   -19.52     0.92)  ; 5\r\n                        (  381.84    80.15   -20.67     0.92)  ; 6\r\n                        (  381.42    75.88   -22.57     0.46)  ; 7\r\n                        (  387.31    74.86   -23.57     0.46)  ; 8\r\n                        (  390.93    71.53   -24.97     0.46)  ; 9\r\n                        (  391.04    64.99   -28.33     0.46)  ; 10\r\n                        (  391.71    62.16   -33.57     0.46)  ; 11\r\n                      )  ;  End of markers\r\n                       Normal\r\n                    )  ;  End of split\r\n                  |\r\n                    (  296.55   116.33   -11.65     0.92)  ; 1, R-1-1-1-1-1-1-3-2-2\r\n                    (  297.08   114.06   -13.57     0.92)  ; 2\r\n                    (  297.08   114.06   -13.60     0.92)  ; 3\r\n                    (  295.35   115.45   -16.02     0.92)  ; 4\r\n                    (  293.69   114.46   -18.05     0.92)  ; 5\r\n                    (  292.05   113.48   -18.72     0.92)  ; 6\r\n                    (  292.05   113.48   -18.75     0.92)  ; 7\r\n                    (  290.44   114.30   -20.10     0.92)  ; 8\r\n                    (  290.06   111.82   -21.45     0.92)  ; 9\r\n                    (  290.91   110.24   -22.97     0.92)  ; 10\r\n                    (  290.24   107.08   -24.52     0.92)  ; 11\r\n                    (  289.70   103.38   -25.10     0.92)  ; 12\r\n                    (  288.75   101.37   -27.75     0.92)  ; 13\r\n                    (  288.22    97.65   -29.95     0.46)  ; 14\r\n                    (  289.06    96.06   -29.95     0.46)  ; 15\r\n                    (  290.08    95.70   -31.17     0.46)  ; 16\r\n                    (  289.41    92.56   -32.60     0.46)  ; 17\r\n                    (  290.52    89.83   -33.53     0.46)  ; 18\r\n                    (  290.60    87.46   -34.75     0.46)  ; 19\r\n                    (  290.60    87.46   -34.78     0.46)  ; 20\r\n                    (  290.95    83.96   -34.47     0.46)  ; 21\r\n                    (  291.48    81.70   -36.52     0.46)  ; 22\r\n                    (  292.41    77.73   -37.58     0.46)  ; 23\r\n                    (  292.25    72.33   -38.05     0.46)  ; 24\r\n                    (  291.80    66.25   -36.63     0.46)  ; 25\r\n                    (  291.12    63.11   -38.25     0.46)  ; 26\r\n                    (  289.56    59.75   -39.55     0.46)  ; 27\r\n                    (  289.56    59.75   -39.60     0.46)  ; 28\r\n                    (  286.96    56.75   -41.05     0.46)  ; 29\r\n                    (  286.96    56.75   -41.08     0.46)  ; 30\r\n                    (  284.99    55.09   -43.00     0.46)  ; 31\r\n                    (  284.24    54.32   -45.68     0.46)  ; 32\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  288.19   107.80   -25.10     0.92)  ; 1\r\n                      (  292.25   104.57   -26.72     0.92)  ; 2\r\n                      (  286.31   103.77   -26.32     0.92)  ; 3\r\n                      (  289.55    97.97   -29.95     0.46)  ; 4\r\n                      (  287.19    98.01   -29.95     0.46)  ; 5\r\n                      (  286.78    93.73   -33.27     0.46)  ; 6\r\n                      (  291.85    90.14   -33.53     0.46)  ; 7\r\n                      (  291.85    84.17   -34.47     0.46)  ; 8\r\n                      (  290.13    81.39   -34.78     0.46)  ; 9\r\n                      (  290.38    68.30   -36.63     0.46)  ; 10\r\n                      (  291.49    62.14   -15.32     0.92)  ; 11\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                )  ;  End of split\r\n              )  ;  End of split\r\n            |\r\n              (  255.22   125.77     2.25     0.92)  ; 1, R-1-1-1-1-1-2\r\n              (  255.60   128.24     3.50     0.92)  ; 2\r\n              (  257.69   129.32     3.57     0.92)  ; 3\r\n              (  259.93   129.85     4.60     0.92)  ; 4\r\n              (  260.28   132.33     5.13     0.92)  ; 5\r\n              (  261.10   134.90     5.37     0.92)  ; 6\r\n              (  261.33   137.94     5.97     0.92)  ; 7\r\n              (  262.40   139.39     6.00     0.92)  ; 8\r\n\r\n              (Cross\r\n                (Color White)\r\n                (Name \"Marker 3\")\r\n                (  259.91   140.00     6.00     0.92)  ; 1\r\n                (  256.98   130.36     3.57     0.92)  ; 2\r\n              )  ;  End of markers\r\n              (\r\n                (  264.82   141.16     6.65     0.92)  ; 1, R-1-1-1-1-1-2-1\r\n                (  265.13   141.82     8.20     0.92)  ; 2\r\n                (  265.13   141.82     8.18     0.92)  ; 3\r\n                (  267.82   142.45     9.07     0.92)  ; 4\r\n                (  269.20   144.56    10.52     0.92)  ; 5\r\n                (  269.83   145.91    10.60     0.92)  ; 6\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  267.08   137.50     9.07     0.92)  ; 1\r\n                )  ;  End of markers\r\n                (\r\n                  (  270.77   147.92    12.07     0.92)  ; 1, R-1-1-1-1-1-2-1-1\r\n                  (  270.81   149.72    13.52     0.92)  ; 2\r\n                  (  269.84   151.88    14.50     0.92)  ; 3\r\n                  (  270.01   153.12    15.85     0.92)  ; 4\r\n                  (  270.38   155.59    16.77     0.92)  ; 5\r\n                  (  268.95   157.64    18.00     0.92)  ; 6\r\n                  (  268.42   159.90    19.42     0.92)  ; 7\r\n                  (  268.34   162.27    20.42     0.92)  ; 8\r\n                  (  270.19   164.50    21.13     0.92)  ; 9\r\n                  (  273.44   164.67    21.70     0.92)  ; 10\r\n                  (  275.44   162.15    21.07     0.92)  ; 11\r\n                  (  278.48   159.27    22.25     0.92)  ; 12\r\n                  (  280.13   160.26    23.47     0.92)  ; 13\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  272.81   163.32    21.07     0.92)  ; 1\r\n                    (  269.05   155.28    16.52     0.92)  ; 2\r\n                    (  271.59   156.48    16.15     0.92)  ; 3\r\n                    (  271.57   150.50    14.92     0.92)  ; 4\r\n                    (  272.24   147.67    14.50     0.92)  ; 5\r\n                    (  270.06   148.94    14.92     0.92)  ; 6\r\n                    (  270.51   149.05     1.25     0.92)  ; 7\r\n                    (  280.21   157.90    22.32     0.92)  ; 8\r\n                  )  ;  End of markers\r\n                  (\r\n                    (  282.40   162.59    23.47     0.92)  ; 1, R-1-1-1-1-1-2-1-1-1\r\n                    (  284.11   165.38    23.57     0.92)  ; 2\r\n                    (  284.11   165.38    23.55     0.92)  ; 3\r\n                    (  284.88   166.15    24.70     0.46)  ; 4\r\n                    (  284.88   166.15    24.80     0.46)  ; 5\r\n                    (  285.77   166.36    27.42     0.46)  ; 6\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  280.27   165.67    21.32     0.92)  ; 1\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  |\r\n                    (  282.00   158.32    23.47     0.46)  ; 1, R-1-1-1-1-1-2-1-1-2\r\n                    (  282.48   154.25    23.47     0.46)  ; 2\r\n                    (  281.50   150.43    24.70     0.46)  ; 3\r\n                    (  280.86   149.09    26.80     0.46)  ; 4\r\n                    (  280.86   149.09    26.75     0.46)  ; 5\r\n                    (  280.68   147.85    28.42     0.46)  ; 6\r\n\r\n                    (Cross\r\n                      (Color White)\r\n                      (Name \"Marker 3\")\r\n                      (  284.18   157.02    23.57     0.92)  ; 1\r\n                    )  ;  End of markers\r\n                     Normal\r\n                  )  ;  End of split\r\n                |\r\n                  (  268.67   146.83    11.77     0.92)  ; 1, R-1-1-1-1-1-2-1-2\r\n                  (  267.07   147.65    13.60     0.92)  ; 2\r\n                  (  264.82   147.12    15.48     0.92)  ; 3\r\n                  (  262.07   148.87    16.30     0.92)  ; 4\r\n                  (  260.19   150.81    17.15     0.92)  ; 5\r\n                  (  257.30   153.12    17.85     0.92)  ; 6\r\n                  (  254.49   153.06    19.27     0.92)  ; 7\r\n                  (  251.63   151.19    20.57     0.92)  ; 8\r\n                  (  249.66   149.53    22.08     0.46)  ; 9\r\n                  (  248.46   148.66    23.67     0.46)  ; 10\r\n                  (  248.53   146.29    25.63     0.46)  ; 11\r\n\r\n                  (Cross\r\n                    (Color White)\r\n                    (Name \"Marker 3\")\r\n                    (  250.39   154.49    20.57     0.92)  ; 1\r\n                    (  257.80   155.02    19.00     0.92)  ; 2\r\n                    (  263.85   149.29    17.42     0.92)  ; 3\r\n                    (  259.84   148.34    17.50     0.92)  ; 4\r\n                    (  259.66   153.08    17.85     0.92)  ; 5\r\n                    (  261.98   151.23    17.85     0.92)  ; 6\r\n                    (  262.55   144.80    15.48     0.92)  ; 7\r\n                    (  256.08   152.24    18.95     0.92)  ; 8\r\n                  )  ;  End of markers\r\n                   Normal\r\n                )  ;  End of split\r\n              |\r\n                (  262.76   141.86     4.85     0.92)  ; 1, R-1-1-1-1-1-2-2\r\n                (  263.97   142.74     3.60     0.92)  ; 2\r\n                (  266.12   145.64     2.88     0.92)  ; 3\r\n                (  267.64   147.18     2.27     0.92)  ; 4\r\n                (  268.58   149.19     1.27     0.92)  ; 5\r\n                (  270.59   152.65     0.12     0.92)  ; 6\r\n                (  270.64   154.45    -0.72     0.92)  ; 7\r\n                (  272.05   156.58    -1.40     0.92)  ; 8\r\n                (  274.27   157.10    -2.85     0.92)  ; 9\r\n                (  274.32   158.90    -4.20     0.46)  ; 10\r\n                (  273.87   158.79    -4.22     0.46)  ; 11\r\n                (  275.26   160.92    -5.63     0.46)  ; 12\r\n                (  275.26   160.92    -5.68     0.46)  ; 13\r\n                (  275.88   162.25    -6.93     0.46)  ; 14\r\n                (  276.56   165.40    -8.73     0.92)  ; 15\r\n                (  277.07   167.30    -9.43     0.92)  ; 16\r\n                (  279.39   171.44   -10.12     0.92)  ; 17\r\n                (  279.48   175.04   -11.53     0.92)  ; 18\r\n                (  279.40   177.40   -13.18     0.92)  ; 19\r\n                (  279.18   180.35   -13.70     0.92)  ; 20\r\n                (  281.34   183.24   -12.93     0.92)  ; 21\r\n                (  284.64   185.21   -13.00     0.92)  ; 22\r\n                (  285.32   188.36   -13.13     0.92)  ; 23\r\n                (  285.86   192.06   -14.00     0.92)  ; 24\r\n                (  285.86   192.06   -14.02     0.92)  ; 25\r\n                (  287.70   194.28   -14.60     0.92)  ; 26\r\n                (  292.35   196.56   -15.30     0.92)  ; 27\r\n                (  294.45   197.66   -15.32     0.92)  ; 28\r\n                (  297.11   198.30   -17.63     0.92)  ; 29\r\n                (  300.05   197.79   -18.45     0.92)  ; 30\r\n                (  303.50   199.20   -19.60     0.92)  ; 31\r\n                (  306.99   202.40   -20.60     0.92)  ; 32\r\n                (  308.68   205.19   -21.07     0.92)  ; 33\r\n                (  311.60   208.86   -21.20     0.92)  ; 34\r\n                (  313.88   211.19   -22.63     0.92)  ; 35\r\n                (  313.88   211.19   -22.65     0.92)  ; 36\r\n                (  316.17   213.50   -24.08     0.92)  ; 37\r\n                (  317.68   215.06   -25.55     0.92)  ; 38\r\n                (  317.68   215.06   -25.57     0.92)  ; 39\r\n                (  317.60   217.43   -26.45     0.92)  ; 40\r\n                (  318.63   217.07   -27.67     0.46)  ; 41\r\n                (  318.63   217.07   -27.70     0.46)  ; 42\r\n                (  318.99   219.55   -29.80     0.46)  ; 43\r\n                (  318.51   223.62   -31.32     0.46)  ; 44\r\n                (  318.51   223.62   -31.35     0.46)  ; 45\r\n                (  317.53   225.77   -32.52     0.46)  ; 46\r\n                (  319.23   228.57   -33.40     0.46)  ; 47\r\n                (  321.69   232.11   -33.92     0.46)  ; 48\r\n                (  322.77   233.58   -35.57     0.46)  ; 49\r\n                (  322.77   233.58   -35.60     0.46)  ; 50\r\n                (  323.89   236.82   -36.78     0.46)  ; 51\r\n                (  323.89   236.82   -36.80     0.46)  ; 52\r\n                (  325.28   238.94   -38.47     0.46)  ; 53\r\n                (  325.28   238.94   -38.50     0.46)  ; 54\r\n                (  325.24   243.11   -39.88     0.46)  ; 55\r\n                (  325.24   243.11   -39.90     0.46)  ; 56\r\n                (  324.71   245.37   -41.47     0.46)  ; 57\r\n                (  324.45   246.51   -43.40     0.46)  ; 58\r\n                (  324.45   246.51   -43.42     0.46)  ; 59\r\n                (  324.23   249.43   -45.25     0.46)  ; 60\r\n\r\n                (Cross\r\n                  (Color White)\r\n                  (Name \"Marker 3\")\r\n                  (  316.64   225.56   -32.47     0.46)  ; 1\r\n                  (  319.76   226.30   -32.77     0.46)  ; 2\r\n                  (  320.45   219.29   -31.35     0.46)  ; 3\r\n                  (  316.00   218.25   -26.45     0.92)  ; 4\r\n                  (  309.29   210.70   -21.07     0.92)  ; 5\r\n                  (  311.82   205.93   -20.13     0.92)  ; 6\r\n                  (  308.92   208.23   -21.07     0.92)  ; 7\r\n                  (  308.11   205.65   -21.07     0.92)  ; 8\r\n                  (  305.87   205.13   -21.07     0.92)  ; 9\r\n                  (  306.41   202.86   -21.07     0.92)  ; 10\r\n                  (  306.62   199.93   -21.07     0.92)  ; 11\r\n                  (  300.14   195.42   -18.45     0.92)  ; 12\r\n                  (  302.42   197.75   -18.45     0.92)  ; 13\r\n                  (  300.10   199.59   -21.07     0.92)  ; 14\r\n                  (  295.74   196.17   -15.32     0.92)  ; 15\r\n                  (  290.51   194.34   -15.32     0.92)  ; 16\r\n                  (  282.46   186.48   -12.88     0.92)  ; 17\r\n                  (  281.52   184.47   -12.88     0.92)  ; 18\r\n                  (  283.13   183.66   -12.88     0.92)  ; 19\r\n                  (  278.28   174.16   -11.53     0.92)  ; 20\r\n                  (  281.94   172.63   -11.00     0.92)  ; 21\r\n                  (  278.02   181.26   -12.77     0.92)  ; 22\r\n                  (  274.07   166.01    -8.18     0.46)  ; 23\r\n                  (  272.89   154.98    -3.50     0.92)  ; 24\r\n                  (  272.39   153.08     2.08     0.92)  ; 25\r\n                  (  271.31   151.63     1.25     0.92)  ; 26\r\n                  (  268.68   152.80    -0.43     0.92)  ; 27\r\n                  (  263.56   138.46     6.00     0.92)  ; 28\r\n                  (  261.43   141.55     6.00     0.92)  ; 29\r\n                  (  274.95   160.24    21.07     0.92)  ; 30\r\n                )  ;  End of markers\r\n                 Normal\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (  259.91   101.78    -7.35     0.92)  ; 1, R-1-1-1-1-2\r\n            (  261.79    99.83    -8.82     0.92)  ; 2\r\n            (  263.04   102.52    -9.43     0.92)  ; 3\r\n            (  263.01   106.69   -10.07     0.92)  ; 4\r\n            (  264.53   108.24   -10.57     0.92)  ; 5\r\n            (  266.76   108.76   -10.85     0.92)  ; 6\r\n            (  267.83   110.21   -11.35     0.92)  ; 7\r\n            (  269.81   111.87   -12.57     0.92)  ; 8\r\n            (  270.57   112.64   -13.72     0.92)  ; 9\r\n            (  273.37   112.71   -14.80     0.92)  ; 10\r\n            (  271.50   114.65   -16.13     0.92)  ; 11\r\n            (  273.29   115.06   -17.95     0.92)  ; 12\r\n            (  275.15   113.13   -18.82     0.92)  ; 13\r\n            (  276.82   114.11   -19.97     0.92)  ; 14\r\n            (  275.84   116.27   -21.28     0.92)  ; 15\r\n            (  275.84   116.27   -21.30     0.92)  ; 16\r\n            (  276.79   118.28   -22.77     0.92)  ; 17\r\n            (  276.79   118.28   -22.80     0.92)  ; 18\r\n            (  277.54   119.05   -23.70     0.92)  ; 19\r\n            (  278.75   119.94   -24.42     0.92)  ; 20\r\n            (  280.49   118.55   -24.42     0.92)  ; 21\r\n            (  282.99   117.95   -26.00     0.92)  ; 22\r\n            (  283.93   119.96   -28.55     0.92)  ; 23\r\n            (  284.11   121.19   -30.67     0.92)  ; 24\r\n            (  285.90   121.61   -32.35     0.92)  ; 25\r\n            (  285.90   121.61   -32.38     0.92)  ; 26\r\n            (  288.70   121.67   -31.60     0.92)  ; 27\r\n            (  288.70   121.67   -31.63     0.92)  ; 28\r\n            (  290.54   123.89   -34.50     0.92)  ; 29\r\n            (  291.22   127.04   -35.42     0.92)  ; 30\r\n            (  291.22   127.04   -35.45     0.92)  ; 31\r\n            (  291.98   127.81   -37.40     0.92)  ; 32\r\n            (  294.03   127.10   -39.47     0.92)  ; 33\r\n            (  294.03   127.10   -39.50     0.92)  ; 34\r\n            (  296.85   127.16   -41.55     0.92)  ; 35\r\n            (  296.85   127.16   -41.57     0.92)  ; 36\r\n            (  298.55   129.95   -44.35     0.92)  ; 37\r\n            (  299.49   131.96   -45.85     0.92)  ; 38\r\n            (  299.49   131.96   -45.88     0.92)  ; 39\r\n            (  301.19   134.74   -47.33     0.92)  ; 40\r\n            (  300.79   136.44   -48.38     0.92)  ; 41\r\n            (  300.79   136.44   -48.40     0.92)  ; 42\r\n            (  303.34   137.63   -49.40     0.92)  ; 43\r\n            (  301.73   138.45   -51.85     0.92)  ; 44\r\n            (  303.70   140.12   -53.70     0.92)  ; 45\r\n            (  306.25   141.31   -55.05     0.92)  ; 46\r\n            (  306.25   141.31   -57.67     0.92)  ; 47\r\n            (  306.25   141.31   -57.70     0.92)  ; 48\r\n            (  306.43   142.55   -59.80     0.92)  ; 49\r\n            (  308.27   144.76   -61.15     0.92)  ; 50\r\n            (  310.24   146.42   -62.85     0.92)  ; 51\r\n            (  311.49   149.10   -64.67     0.92)  ; 52\r\n            (  310.51   151.27   -64.97     0.92)  ; 53\r\n            (  310.51   151.27   -65.00     0.92)  ; 54\r\n            (  309.67   152.88   -66.52     0.46)  ; 55\r\n            (  308.06   153.69   -69.25     0.46)  ; 56\r\n            (  307.08   155.86   -71.17     0.46)  ; 57\r\n            (  307.45   158.33   -73.50     0.46)  ; 58\r\n            (  307.76   159.00   -75.63     0.46)  ; 59\r\n            (  307.76   159.00   -75.65     0.46)  ; 60\r\n            (  308.25   160.91   -77.93     0.46)  ; 61\r\n            (  308.25   160.91   -77.95     0.46)  ; 62\r\n            (  308.03   163.84   -80.02     0.46)  ; 63\r\n            (  308.40   166.31   -82.15     0.46)  ; 64\r\n            (  309.21   168.89   -83.35     0.46)  ; 65\r\n            (  307.34   170.85   -85.05     0.46)  ; 66\r\n            (  308.15   173.42   -86.40     0.46)  ; 67\r\n            (  307.44   174.45   -88.42     0.46)  ; 68\r\n            (  306.59   176.05   -90.80     0.46)  ; 69\r\n            (  306.37   178.97   -91.93     0.46)  ; 70\r\n            (  306.91   182.69   -92.70     0.46)  ; 71\r\n            (  306.51   184.39   -94.07     0.46)  ; 72\r\n            (  306.51   184.39   -94.10     0.46)  ; 73\r\n            (  306.61   188.00   -95.57     0.46)  ; 74\r\n            (  306.61   188.00   -95.63     0.46)  ; 75\r\n            (  306.80   189.23   -98.40     0.46)  ; 76\r\n            (  307.03   192.27  -100.40     0.46)  ; 77\r\n            (  306.10   196.24  -101.55     0.46)  ; 78\r\n            (  305.13   198.38  -103.85     0.46)  ; 79\r\n            (  305.49   200.87  -104.97     0.46)  ; 80\r\n            (  305.49   200.87  -105.00     0.46)  ; 81\r\n            (  305.80   201.54  -106.17     0.46)  ; 82\r\n            (  305.80   201.54  -106.35     0.46)  ; 83\r\n\r\n            (Cross\r\n              (Color White)\r\n              (Name \"Marker 3\")\r\n              (  308.51   197.99  -101.55     0.46)  ; 1\r\n              (  307.91   199.14   -97.07     0.46)  ; 2\r\n              (  308.95   176.00   -90.80     0.46)  ; 3\r\n              (  308.80   177.26   -87.05     0.46)  ; 4\r\n              (  306.26   169.40   -85.05     0.46)  ; 5\r\n              (  305.92   169.42   -81.80     0.46)  ; 6\r\n              (  306.97   146.25   -61.15     0.92)  ; 7\r\n              (  309.39   148.01   -64.67     0.92)  ; 8\r\n              (  312.26   146.42   -65.25     0.46)  ; 9\r\n              (  299.04   131.86   -42.33     0.92)  ; 10\r\n              (  298.65   133.55   -48.20     0.92)  ; 11\r\n              (  260.67   102.56    -7.32     0.92)  ; 12\r\n              (  261.79    99.83    -7.32     0.92)  ; 13\r\n              (  262.02   102.87    -7.32     0.92)  ; 14\r\n              (  274.04   115.85   -16.13     0.92)  ; 15\r\n              (  282.63   115.46   -26.45     0.92)  ; 16\r\n              (  285.17   116.66   -26.45     0.92)  ; 17\r\n              (  287.63   120.23   -31.63     0.92)  ; 18\r\n              (  285.22   122.59     7.53     0.46)  ; 19\r\n              (  290.42   130.44   -34.47     0.92)  ; 20\r\n              (  289.74   127.28   -34.95     0.92)  ; 21\r\n              (  285.36   123.87   -31.63     0.92)  ; 22\r\n            )  ;  End of markers\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (  255.15    73.83     0.70     0.92)  ; 1, R-1-1-1-2\r\n          (  252.75    72.06     0.70     0.92)  ; 2\r\n          (  251.36    69.94     1.80     0.92)  ; 3\r\n          (  251.36    69.94     1.77     0.92)  ; 4\r\n          (  249.81    72.56     3.70     0.92)  ; 5\r\n          (  250.04    75.60     5.25     0.92)  ; 6\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  255.35    75.06     0.70     0.92)  ; 1\r\n            (  254.72    73.72     7.57     0.92)  ; 2\r\n            (  249.44    70.09     3.78     0.92)  ; 3\r\n            (  249.18    71.22     3.78     0.92)  ; 4\r\n            (  248.51    74.05     8.02     0.92)  ; 5\r\n          )  ;  End of markers\r\n          (\r\n            (  252.26    76.12     4.52     1.38)  ; 1, R-1-1-1-2-1\r\n            (  251.87    77.83     6.42     1.38)  ; 2\r\n            (  252.05    79.06     8.70     1.38)  ; 3\r\n            (  252.05    79.06     8.68     1.38)  ; 4\r\n            (  251.34    80.09    10.82     1.38)  ; 5\r\n            (  252.54    80.97    12.07     1.38)  ; 6\r\n            (  253.89    81.29    14.00     1.38)  ; 7\r\n            (  253.44    81.18    13.95     1.38)  ; 8\r\n            (  255.94    80.57    15.65     1.38)  ; 9\r\n            (  259.51    81.40    16.80     1.38)  ; 10\r\n            (  263.22    81.68    17.95     1.38)  ; 11\r\n            (  265.35    78.59    19.15     1.38)  ; 12\r\n\r\n            (Cross\r\n              (Color White)\r\n              (Name \"Marker 3\")\r\n              (  262.42    85.07    16.80     1.38)  ; 1\r\n              (  257.32    82.69    16.83     1.38)  ; 2\r\n              (  255.31    79.22    15.65     1.38)  ; 3\r\n            )  ;  End of markers\r\n            (\r\n              (  263.70    77.61    19.75     1.38)  ; 1, R-1-1-1-2-1-1\r\n              (  263.70    77.61    19.73     1.38)  ; 2\r\n              (  260.62    78.69    21.45     0.92)  ; 3\r\n              (  260.35    79.81    23.33     0.92)  ; 4\r\n              (  261.03    82.96    24.40     0.92)  ; 5\r\n              (  261.21    84.19    26.13     0.92)  ; 6\r\n              (  259.48    85.58    27.02     0.92)  ; 7\r\n              (  258.31    86.51    28.88     0.92)  ; 8\r\n              (  257.60    87.53    30.23     0.92)  ; 9\r\n\r\n              (Cross\r\n                (Color White)\r\n                (Name \"Marker 3\")\r\n                (  261.47    77.09    21.45     0.92)  ; 1\r\n                (  259.15    78.94    24.40     0.92)  ; 2\r\n              )  ;  End of markers\r\n               High\r\n            |\r\n              (  266.46    75.88    18.17     0.92)  ; 1, R-1-1-1-2-1-2\r\n              (  267.17    74.84    18.57     0.92)  ; 2\r\n              (  269.85    75.47    20.65     0.92)  ; 3\r\n              (  271.02    74.54    21.75     0.92)  ; 4\r\n              (  271.91    74.75    23.42     0.92)  ; 5\r\n              (  273.61    77.55    24.77     0.92)  ; 6\r\n              (  273.61    77.55    24.75     0.92)  ; 7\r\n              (  276.86    77.71    25.38     0.92)  ; 8\r\n              (  279.37    77.10    24.95     0.92)  ; 9\r\n              (  281.28    76.95    25.87     0.92)  ; 10\r\n              (  281.28    76.95    25.85     0.92)  ; 11\r\n              (  281.86    76.49    28.10     0.92)  ; 12\r\n\r\n              (Cross\r\n                (Color White)\r\n                (Name \"Marker 3\")\r\n                (  266.42    80.05    19.15     1.38)  ; 1\r\n                (  268.33    73.92    21.75     0.92)  ; 2\r\n              )  ;  End of markers\r\n               Normal\r\n            )  ;  End of split\r\n          |\r\n            (  247.40    76.78     6.15     0.92)  ; 1, R-1-1-1-2-2\r\n            (  244.85    75.58     8.02     0.92)  ; 2\r\n            (  241.33    76.55     8.60     0.92)  ; 3\r\n            (  241.33    76.55     8.57     0.92)  ; 4\r\n            (  236.86    75.50     9.50     0.92)  ; 5\r\n            (  236.86    75.50     9.48     0.92)  ; 6\r\n            (  234.32    74.31    10.85     0.92)  ; 7\r\n            (  234.32    74.31    10.82     0.92)  ; 8\r\n            (  231.59    71.88    10.85     0.92)  ; 9\r\n            (  230.70    71.67    10.85     0.92)  ; 10\r\n            (  230.20    69.76    10.85     0.92)  ; 11\r\n            (  227.12    70.84    12.10     0.92)  ; 12\r\n            (  225.21    70.99    13.07     0.92)  ; 13\r\n            (  225.21    70.99    13.05     0.92)  ; 14\r\n            (  223.10    69.89    12.35     0.92)  ; 15\r\n            (  219.71    70.29    13.40     0.92)  ; 16\r\n            (  217.48    69.76    14.25     0.92)  ; 17\r\n            (  213.83    71.29    14.60     0.92)  ; 18\r\n            (  210.65    68.76    14.10     0.92)  ; 19\r\n            (  209.39    66.07    14.43     0.92)  ; 20\r\n            (  207.55    63.86    15.68     0.92)  ; 21\r\n            (  203.40    63.48    14.97     0.92)  ; 22\r\n            (  201.76    62.49    15.43     0.92)  ; 23\r\n            (  200.50    59.81    17.20     0.92)  ; 24\r\n            (  197.90    56.82    18.45     0.92)  ; 25\r\n            (  194.73    54.28    20.15     0.92)  ; 26\r\n            (  192.94    53.86    21.75     0.92)  ; 27\r\n            (  190.70    53.34    22.95     0.92)  ; 28\r\n            (  190.70    53.34    22.92     0.92)  ; 29\r\n            (  188.03    52.71    24.27     0.92)  ; 30\r\n            (  183.74    52.91    25.20     0.92)  ; 31\r\n            (  182.32    54.95    25.20     0.92)  ; 32\r\n            (  181.79    57.22    24.30     0.92)  ; 33\r\n            (  182.41    58.56    24.67     0.92)  ; 34\r\n            (  182.41    58.56    24.65     0.92)  ; 35\r\n            (  183.50    60.00    25.63     0.92)  ; 36\r\n            (  183.50    60.00    27.35     0.92)  ; 37\r\n            (  183.50    60.00    27.32     0.92)  ; 38\r\n            (  183.94    60.10    29.42     0.92)  ; 39\r\n            (  183.54    61.81    30.60     0.92)  ; 40\r\n            (  183.54    61.81    30.57     0.92)  ; 41\r\n\r\n            (Cross\r\n              (Color White)\r\n              (Name \"Marker 3\")\r\n              (  247.72    77.45     6.82     0.92)  ; 1\r\n              (  241.78    76.66     8.35     0.92)  ; 2\r\n              (  238.26    77.62     8.07     0.92)  ; 3\r\n              (  235.71    76.43     8.30     0.92)  ; 4\r\n              (  227.17    72.64    13.02     0.92)  ; 5\r\n              (  231.51    74.25    14.10     0.92)  ; 6\r\n              (  231.86    70.74    14.20     0.92)  ; 7\r\n              (  230.20    69.76    14.20     0.92)  ; 8\r\n              (  234.71    72.62    11.35     0.92)  ; 9\r\n              (  218.01    67.50    14.60     0.92)  ; 10\r\n              (  213.77    69.49    12.60     0.92)  ; 11\r\n              (  212.39    67.39    15.20     0.92)  ; 12\r\n              (  201.84    60.13    18.45     0.92)  ; 13\r\n              (  201.21    58.78    16.20     0.92)  ; 14\r\n              (  198.58    59.97    16.80     0.92)  ; 15\r\n              (  184.36    54.24    24.65     0.92)  ; 16\r\n              (  192.22    54.90    24.27     0.92)  ; 17\r\n              (  187.89    47.30    25.20     0.92)  ; 18\r\n              (  198.88    54.65    18.45     0.92)  ; 19\r\n            )  ;  End of markers\r\n             High\r\n          )  ;  End of split\r\n        |\r\n          (  259.28    74.90     3.67     0.92)  ; 1, R-1-1-1-3\r\n          (  259.28    74.90     3.65     0.92)  ; 2\r\n          (  260.35    76.34     5.97     0.92)  ; 3\r\n          (  259.95    78.04     7.47     0.92)  ; 4\r\n          (  261.73    78.46     8.38     0.46)  ; 5\r\n          (  263.83    79.56     8.77     0.46)  ; 6\r\n          (  265.68    81.77     8.77     0.46)  ; 7\r\n          (  268.09    83.53     9.57     0.46)  ; 8\r\n          (  267.55    85.80    10.10     0.46)  ; 9\r\n          (  266.44    88.52    11.00     0.46)  ; 10\r\n          (  266.76    89.19    11.90     0.46)  ; 11\r\n          (  268.28    90.74    12.65     0.46)  ; 12\r\n          (  270.96    91.37    13.80     0.46)  ; 13\r\n          (  272.43    91.12    14.75     0.46)  ; 14\r\n          (  273.59    90.19    15.88     0.46)  ; 15\r\n          (  276.00    91.95    16.92     0.46)  ; 16\r\n          (  278.42    93.72    17.95     0.46)  ; 17\r\n          (  279.68    96.40    19.63     0.46)  ; 18\r\n          (  279.68    96.40    19.60     0.46)  ; 19\r\n          (  281.47    96.82    20.90     0.46)  ; 20\r\n          (  281.47    96.82    20.87     0.46)  ; 21\r\n          (  282.04    96.36    22.10     0.46)  ; 22\r\n          (  285.47    97.76    23.47     0.46)  ; 23\r\n          (  287.45    99.41    24.45     0.46)  ; 24\r\n          (  287.01    99.31    24.45     0.46)  ; 25\r\n          (  288.88   103.34    25.30     0.46)  ; 26\r\n          (  289.11   106.38    26.42     0.46)  ; 27\r\n          (  289.16   108.18    24.05     0.46)  ; 28\r\n          (  290.23   109.63    23.17     0.46)  ; 29\r\n          (  289.83   111.32    22.45     0.46)  ; 30\r\n          (  289.83   111.32    22.43     0.46)  ; 31\r\n          (  288.99   112.91    20.55     0.46)  ; 32\r\n          (  288.42   113.38    18.50     0.46)  ; 33\r\n          (  288.42   113.38    18.48     0.46)  ; 34\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  289.62   111.72   -21.60     0.92)  ; 1\r\n            (  286.18    96.74    23.45     0.46)  ; 2\r\n            (  267.12    91.67    12.65     0.46)  ; 3\r\n            (  270.24    92.40    13.80     0.46)  ; 4\r\n            (  270.19    90.59    13.80     0.46)  ; 5\r\n            (  271.01    93.18    14.75     0.46)  ; 6\r\n            (  276.18    93.20    17.95     0.46)  ; 7\r\n            (  277.16    91.04    19.50     0.46)  ; 8\r\n            (  282.00    94.56    22.30     0.46)  ; 9\r\n            (  277.52    93.52    22.32     0.46)  ; 10\r\n            (  279.10    96.87    18.45     0.46)  ; 11\r\n            (  283.64    95.54    23.45     0.46)  ; 12\r\n            (  265.27    80.96    19.15     1.38)  ; 13\r\n            (  266.64    79.62     8.77     0.46)  ; 14\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  251.51    49.77    -3.38     0.92)  ; 1, R-1-1-2\r\n        (  249.56    54.10    -2.95     0.92)  ; 2\r\n        (  246.47    55.17    -2.95     0.92)  ; 3\r\n        (  244.11    55.21    -3.97     0.92)  ; 4\r\n        (  242.82    56.69    -5.65     0.92)  ; 5\r\n        (  241.97    58.29    -6.60     0.92)  ; 6\r\n        (  241.27    59.32    -8.23     0.92)  ; 7\r\n        (  239.67    60.14    -8.95     0.92)  ; 8\r\n        (  236.85    60.07    -9.17     0.92)  ; 9\r\n        (  232.75    61.50    -8.42     0.92)  ; 10\r\n        (  232.75    61.50    -8.45     0.92)  ; 11\r\n        (  231.09    60.51    -9.22     0.92)  ; 12\r\n        (  230.16    58.50   -10.12     0.92)  ; 13\r\n        (  228.81    58.19   -11.25     0.92)  ; 14\r\n        (  225.73    59.26   -12.00     0.92)  ; 15\r\n        (  222.30    57.86   -12.35     0.92)  ; 16\r\n        (  219.17    57.13   -13.00     0.92)  ; 17\r\n        (  215.91    56.96   -13.72     0.92)  ; 18\r\n        (  213.41    57.57   -14.75     0.92)  ; 19\r\n        (  210.81    54.57   -15.22     0.92)  ; 20\r\n        (  207.11    54.30   -15.50     0.92)  ; 21\r\n        (  204.16    54.80   -17.13     0.92)  ; 22\r\n        (  201.61    55.31   -16.57     0.92)  ; 23\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  238.82    61.73    -9.17     0.92)  ; 1\r\n          (  242.87    58.50    -5.70     0.92)  ; 2\r\n          (  240.91    56.84    -7.32     0.92)  ; 3\r\n          (  238.01    59.15    -9.17     0.92)  ; 4\r\n          (  234.60    61.47   -58.22     0.46)  ; 5\r\n          (  228.28    60.45   -11.25     0.92)  ; 6\r\n          (  223.36    59.30   -10.80     0.92)  ; 7\r\n          (  219.79    58.46   -14.65     0.92)  ; 8\r\n          (  221.21    56.41   -10.80     0.92)  ; 9\r\n          (  208.27    53.37   -15.50     0.92)  ; 10\r\n        )  ;  End of markers\r\n        (\r\n          (  200.64    55.77   -16.57     0.92)  ; 1, R-1-1-2-1\r\n          (  198.32    57.62   -15.30     0.92)  ; 2\r\n          (  195.32    56.31   -14.65     0.92)  ; 3\r\n          (  190.34    57.54   -14.27     0.92)  ; 4\r\n          (  187.16    55.00   -13.63     0.92)  ; 5\r\n          (  183.23    51.68   -12.57     0.92)  ; 6\r\n          (  183.31    49.32   -10.82     0.92)  ; 7\r\n          (  180.76    48.13    -8.42     0.92)  ; 8\r\n          (  179.23    46.57    -6.78     0.92)  ; 9\r\n          (  178.48    45.79    -4.63     0.92)  ; 10\r\n          (  174.32    45.41    -2.15     0.46)  ; 11\r\n          (  174.32    45.41    -2.17     0.46)  ; 12\r\n          (  171.87    47.83    -1.67     0.46)  ; 13\r\n          (  171.87    47.83    -1.70     0.46)  ; 14\r\n          (  170.94    51.80    -1.00     0.46)  ; 15\r\n          (  168.63    53.64     0.15     0.46)  ; 16\r\n          (  168.63    53.64     0.12     0.46)  ; 17\r\n          (  167.65    55.80     1.07     0.46)  ; 18\r\n          (  167.65    55.80     1.05     0.46)  ; 19\r\n          (  166.94    56.82     2.45     0.46)  ; 20\r\n          (  166.94    56.82     2.42     0.46)  ; 21\r\n          (  166.36    57.29     4.32     0.46)  ; 22\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  176.11    47.54   -25.90     0.92)  ; 1\r\n            (  178.65    47.02    -4.63     0.92)  ; 2\r\n            (  198.35    55.14   -22.05     0.92)  ; 3\r\n            (  176.16    47.64    -3.47     0.92)  ; 4\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  198.54    56.39   -16.98     0.46)  ; 1, R-1-1-2-2\r\n          (  195.27    56.22   -15.97     0.46)  ; 2\r\n          (  190.86    56.97   -16.45     0.46)  ; 3\r\n          (  188.32    55.78   -15.80     0.46)  ; 4\r\n          (  185.90    54.02   -15.07     0.46)  ; 5\r\n          (  184.51    51.90   -14.63     0.46)  ; 6\r\n          (  183.13    49.78   -13.30     0.46)  ; 7\r\n          (  183.52    48.08   -11.47     0.46)  ; 8\r\n          (  183.52    48.08   -11.50     0.46)  ; 9\r\n          (  182.18    47.77    -9.90     0.46)  ; 10\r\n          (  179.81    47.81    -8.57     0.46)  ; 11\r\n          (  179.81    47.81    -8.55     0.46)  ; 12\r\n          (  179.00    45.23    -7.15     0.46)  ; 13\r\n          (  178.69    44.56    -6.30     0.46)  ; 14\r\n          (  176.77    44.71    -4.70     0.46)  ; 15\r\n          (  172.93    45.01    -3.47     0.46)  ; 16\r\n          (  172.41    47.26    -3.47     0.46)  ; 17\r\n          (  170.71    50.46    -2.95     0.46)  ; 18\r\n          (  170.18    52.72    -1.88     0.46)  ; 19\r\n          (  168.75    54.77    -1.20     0.46)  ; 20\r\n          (  167.47    56.27     0.00     0.46)  ; 21\r\n          (  166.44    56.63     1.63     0.46)  ; 22\r\n          (  166.44    56.63     1.60     0.46)  ; 23\r\n          (  165.42    56.98     2.90     0.46)  ; 24\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  186.92    53.66   -14.63     0.46)  ; 1\r\n            (  172.75    43.77    -4.07     0.46)  ; 2\r\n            (  172.35    43.76    -3.47     0.92)  ; 3\r\n            (  180.92    43.39    -3.47     0.92)  ; 4\r\n            (  179.40    43.54    -4.70     0.46)  ; 5\r\n            (  198.85    55.35   -16.57     0.92)  ; 6\r\n            (  177.44    47.85    -4.70     0.46)  ; 7\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  199.07    54.11   -19.70     0.92)  ; 1, R-1-1-2-3\r\n          (  196.97    53.03   -22.05     0.92)  ; 2\r\n          (  191.92    52.44   -23.00     0.92)  ; 3\r\n          (  189.38    51.24   -23.63     0.92)  ; 4\r\n          (  188.29    49.80   -24.60     0.92)  ; 5\r\n          (  184.46    50.09   -24.60     0.92)  ; 6\r\n          (  181.39    51.17   -25.25     0.92)  ; 7\r\n          (  178.39    49.87   -25.25     0.92)  ; 8\r\n          (  177.89    47.96   -24.82     0.92)  ; 9\r\n          (  177.89    47.96   -24.85     0.92)  ; 10\r\n          (  174.37    48.92   -25.90     0.92)  ; 11\r\n          (  172.27    47.83   -27.67     0.92)  ; 12\r\n          (  171.51    47.06   -29.62     0.92)  ; 13\r\n          (  171.06    46.96   -31.63     0.92)  ; 14\r\n          (  168.20    45.09   -33.10     0.92)  ; 15\r\n          (  165.34    43.23   -34.02     0.92)  ; 16\r\n          (  162.26    44.29   -35.65     0.92)  ; 17\r\n          (  161.55    45.32   -37.77     0.92)  ; 18\r\n          (  160.21    45.01   -39.95     0.92)  ; 19\r\n          (  157.79    43.25   -42.10     0.92)  ; 20\r\n          (  157.79    43.25   -42.13     0.92)  ; 21\r\n          (  154.09    42.98   -43.47     0.92)  ; 22\r\n          (  149.22    43.63   -49.65     0.46)  ; 23\r\n          (  145.38    43.92   -51.22     0.46)  ; 24\r\n          (  145.38    43.92   -51.28     0.46)  ; 25\r\n          (  142.58    43.86   -52.63     0.46)  ; 26\r\n          (  142.58    43.86   -52.65     0.46)  ; 27\r\n          (  141.28    45.36   -54.50     0.46)  ; 28\r\n          (  140.08    44.47   -56.20     0.46)  ; 29\r\n          (  140.08    44.47   -56.22     0.46)  ; 30\r\n          (  137.27    44.42   -57.47     0.46)  ; 31\r\n          (  137.27    44.42   -57.50     0.46)  ; 32\r\n          (  135.61    43.42   -59.47     0.46)  ; 33\r\n          (  135.61    43.42   -59.50     0.46)  ; 34\r\n          (  133.57    44.15   -60.95     0.46)  ; 35\r\n          (  132.54    44.49   -63.05     0.46)  ; 36\r\n          (  132.54    44.49   -63.07     0.46)  ; 37\r\n          (  130.04    45.10   -63.15     0.46)  ; 38\r\n          (  130.08    46.90   -64.70     0.46)  ; 39\r\n          (  127.72    46.96   -66.03     0.46)  ; 40\r\n          (  126.56    47.87   -67.65     0.46)  ; 41\r\n          (  124.46    46.79   -69.70     0.46)  ; 42\r\n          (  121.83    47.96   -71.68     0.46)  ; 43\r\n          (  121.83    47.96   -71.70     0.46)  ; 44\r\n          (  120.49    47.64   -73.78     0.46)  ; 45\r\n          (  120.49    47.64   -73.82     0.46)  ; 46\r\n          (  119.01    47.89   -76.38     0.46)  ; 47\r\n          (  116.97    48.62   -78.07     0.46)  ; 48\r\n          (  116.97    48.62   -78.10     0.46)  ; 49\r\n          (  115.49    48.86   -80.35     0.46)  ; 50\r\n          (  115.49    48.86   -80.40     0.46)  ; 51\r\n          (  114.02    49.11   -82.97     0.46)  ; 52\r\n          (  110.18    49.41   -84.80     0.46)  ; 53\r\n          (  108.89    50.90   -86.67     0.46)  ; 54\r\n          (  108.89    50.90   -86.70     0.46)  ; 55\r\n          (  107.42    51.15   -88.57     0.46)  ; 56\r\n          (  107.42    51.15   -88.60     0.46)  ; 57\r\n          (  104.34    52.22   -90.25     0.46)  ; 58\r\n          (  104.34    52.22   -90.28     0.46)  ; 59\r\n          (  102.46    54.17   -92.15     0.46)  ; 60\r\n          (  101.89    54.63   -93.82     0.46)  ; 61\r\n          (  101.89    54.63   -93.85     0.46)  ; 62\r\n          (   99.13    56.37   -96.10     0.46)  ; 63\r\n          (   97.21    56.53   -98.80     0.46)  ; 64\r\n          (   96.37    58.12  -101.65     0.46)  ; 65\r\n          (   93.55    58.05  -104.25     0.46)  ; 66\r\n          (   91.10    60.46  -106.15     0.46)  ; 67\r\n          (   88.74    60.51  -109.20     0.46)  ; 68\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  192.44    51.95    24.27     0.92)  ; 1\r\n            (  175.89    50.48   -25.90     0.92)  ; 2\r\n            (  175.80    46.87    -3.47     0.46)  ; 3\r\n            (  168.47    43.96   -31.42     0.92)  ; 4\r\n            (  160.29    42.64   -37.77     0.92)  ; 5\r\n            (  155.75    43.96   -39.02     0.46)  ; 6\r\n            (  153.46    41.63   -44.07     0.46)  ; 7\r\n            (  147.97    40.95   -50.55     0.46)  ; 8\r\n            (  165.13    46.16   -32.55     0.92)  ; 9\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  262.09    46.88    -7.07     1.38)  ; 1, R-1-2\r\n      (  266.25    47.26    -7.70     1.38)  ; 2\r\n      (  268.34    48.34    -8.52     1.38)  ; 3\r\n      (  270.17    50.56    -9.20     1.38)  ; 4\r\n      (  271.96    50.98    -9.85     1.38)  ; 5\r\n      (  274.33    50.94    -8.23     1.38)  ; 6\r\n      (  276.25    50.79    -7.32     1.38)  ; 7\r\n      (  278.21    52.45    -7.95     1.38)  ; 8\r\n      (  278.97    53.22    -9.95     0.92)  ; 9\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  274.51    52.17    -9.77     1.38)  ; 1\r\n        (  268.26    50.71    -9.20     1.38)  ; 2\r\n        (  264.76    47.51   -10.57     1.38)  ; 3\r\n      )  ;  End of markers\r\n      (\r\n        (  279.16    54.47   -12.22     0.92)  ; 1, R-1-2-1\r\n        (  280.68    56.01   -13.52     0.92)  ; 2\r\n        (  282.77    57.10   -13.55     0.92)  ; 3\r\n        (  284.12    57.41   -13.35     0.92)  ; 4\r\n        (  288.21    55.99   -13.38     0.92)  ; 5\r\n        (  288.08    56.56   -13.38     0.92)  ; 6\r\n        (  291.13    59.65   -14.00     0.92)  ; 7\r\n        (  290.33    63.05   -13.07     0.92)  ; 8\r\n        (  290.33    63.05   -13.13     0.92)  ; 9\r\n        (  289.67    65.88   -13.52     0.92)  ; 10\r\n        (  287.62    66.60   -14.63     0.92)  ; 11\r\n        (  287.80    67.83   -16.45     0.92)  ; 12\r\n        (  287.80    67.83   -16.47     0.92)  ; 13\r\n        (  288.69    68.04   -18.85     0.92)  ; 14\r\n        (  290.03    68.36   -21.18     0.92)  ; 15\r\n        (  291.42    70.48   -22.45     0.92)  ; 16\r\n        (  291.42    70.48   -22.47     0.92)  ; 17\r\n        (  291.91    72.38   -23.40     0.92)  ; 18\r\n        (  290.93    74.54   -23.33     0.46)  ; 19\r\n        (  291.12    75.78   -24.50     0.46)  ; 20\r\n        (  291.12    75.78   -24.52     0.46)  ; 21\r\n        (  292.47    76.09   -26.00     0.46)  ; 22\r\n        (  295.64    78.63   -27.05     0.46)  ; 23\r\n        (  297.73    79.72   -27.63     0.46)  ; 24\r\n        (  297.12    84.36   -28.90     0.46)  ; 25\r\n        (  297.75    85.69   -30.45     0.46)  ; 26\r\n        (  299.54    86.12   -31.92     0.46)  ; 27\r\n        (  300.75    86.99   -33.80     0.46)  ; 28\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  297.17    86.16   -31.92     0.46)  ; 1\r\n          (  300.30    86.88   -32.25     0.46)  ; 2\r\n          (  301.58    85.40   -31.92     0.46)  ; 3\r\n          (  291.41    73.92   -38.07     0.46)  ; 4\r\n          (  294.15    72.90   -23.40     0.92)  ; 5\r\n          (  288.54    62.63   -11.17     0.92)  ; 6\r\n          (  291.41    64.49   -11.07     0.92)  ; 7\r\n          (  291.13    59.65   -15.32     0.92)  ; 8\r\n          (  277.86    55.95   -13.55     0.92)  ; 9\r\n          (  279.64    56.37   -13.55     0.92)  ; 10\r\n          (  280.86    57.24   -13.55     0.92)  ; 11\r\n        )  ;  End of markers\r\n        (\r\n          (  307.13    87.89   -34.28     0.46)  ; 1, R-1-2-1-1\r\n          (  306.42    88.92   -35.63     0.46)  ; 2\r\n          (  310.39    88.06   -36.63     0.46)  ; 3\r\n          (  312.35    89.72   -37.77     0.46)  ; 4\r\n          (  314.63    92.04   -39.10     0.46)  ; 5\r\n          (  314.63    92.04   -39.13     0.46)  ; 6\r\n          (  316.74    93.14   -40.10     0.46)  ; 7\r\n          (  319.73    94.43   -41.20     0.46)  ; 8\r\n          (  323.80    97.17   -41.52     0.46)  ; 9\r\n          (  326.93    97.90   -41.70     0.46)  ; 10\r\n          (  327.60   101.05   -41.60     0.46)  ; 11\r\n          (  327.60   101.05   -41.63     0.46)  ; 12\r\n          (  328.68   102.49   -43.15     0.46)  ; 13\r\n          (  328.68   102.49   -43.27     0.46)  ; 14\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  303.50    85.25   -33.55     0.46)  ; 1\r\n            (  313.19    88.13   -39.10     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  302.71    88.64   -34.67     0.46)  ; 1, R-1-2-1-2\r\n          (  303.20    90.56   -36.10     0.46)  ; 2\r\n          (  304.28    92.00   -38.83     0.46)  ; 3\r\n          (  303.57    93.02   -39.60     0.46)  ; 4\r\n          (  303.17    94.72   -41.82     0.46)  ; 5\r\n          (  301.43    96.11   -42.37     0.46)  ; 6\r\n          (  301.43    96.11   -42.42     0.46)  ; 7\r\n          (  301.48    97.92   -44.05     0.46)  ; 8\r\n          (  303.27    98.33   -45.72     0.46)  ; 9\r\n          (  300.96   100.17   -46.15     0.46)  ; 10\r\n          (  301.76   102.76   -47.65     0.46)  ; 11\r\n          (  301.68   105.12   -49.47     0.46)  ; 12\r\n          (  301.68   105.12   -49.50     0.46)  ; 13\r\n          (  301.28   106.82   -50.68     0.46)  ; 14\r\n          (  301.82   110.54   -51.15     0.46)  ; 15\r\n          (  302.81   114.35   -51.15     0.46)  ; 16\r\n          (  302.46   117.85   -51.42     0.46)  ; 17\r\n          (  303.27   120.43   -52.80     0.46)  ; 18\r\n          (  303.27   120.43   -52.82     0.46)  ; 19\r\n          (  303.33   122.23   -53.65     0.46)  ; 20\r\n          (  303.33   122.23   -53.67     0.46)  ; 21\r\n          (  302.53   125.62   -54.32     0.46)  ; 22\r\n          (  301.94   126.09   -55.77     0.46)  ; 23\r\n          (  301.94   126.09   -55.80     0.46)  ; 24\r\n          (  302.75   128.66   -57.25     0.46)  ; 25\r\n          (  303.75   132.49   -58.85     0.46)  ; 26\r\n          (  304.38   133.82   -59.75     0.46)  ; 27\r\n          (  304.38   133.82   -59.78     0.46)  ; 28\r\n          (  306.52   136.71   -61.10     0.46)  ; 29\r\n          (  307.33   139.29   -61.38     0.46)  ; 30\r\n          (  307.33   139.29   -61.40     0.46)  ; 31\r\n          (  308.99   140.28   -61.38     0.46)  ; 32\r\n          (  310.15   145.32   -62.95     0.46)  ; 33\r\n          (  311.01   149.71   -63.72     0.46)  ; 34\r\n          (  311.01   149.71   -63.75     0.46)  ; 35\r\n          (  311.82   152.29   -63.05     0.46)  ; 36\r\n          (  313.08   154.97   -61.28     0.46)  ; 37\r\n          (  314.02   156.99   -59.80     0.46)  ; 38\r\n          (  314.02   156.99   -59.83     0.46)  ; 39\r\n          (  317.96   160.29   -59.15     0.46)  ; 40\r\n          (  319.13   165.34   -58.05     0.46)  ; 41\r\n          (  319.04   167.71   -58.62     0.46)  ; 42\r\n          (  317.71   173.37   -60.07     0.46)  ; 43\r\n          (  317.05   176.20   -61.42     0.46)  ; 44\r\n          (  315.63   178.26   -64.27     0.46)  ; 45\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  321.46   169.47   -58.62     0.46)  ; 1\r\n            (  316.51   172.50   -60.07     0.46)  ; 2\r\n            (  305.00   135.16   -58.75     0.46)  ; 3\r\n            (  302.99   129.16    19.20     0.46)  ; 4\r\n            (  301.89   124.29   -53.72     0.46)  ; 5\r\n            (  304.68   112.40   -51.15     0.46)  ; 6\r\n            (  300.88   108.52   -51.15     0.46)  ; 7\r\n            (  304.43    97.41   -45.72     0.46)  ; 8\r\n            (  300.83    90.60   -36.10     0.46)  ; 9\r\n            (  302.18    90.91   -39.10     0.46)  ; 10\r\n            (  301.95    87.87   -33.53     0.46)  ; 11\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (  278.18    52.44   -12.95     0.92)  ; 1, R-1-2-2\r\n        (  278.63    52.55   -14.80     0.92)  ; 2\r\n        (  278.63    52.55   -14.92     0.92)  ; 3\r\n        (  278.76    51.98   -17.47     0.92)  ; 4\r\n        (  281.14    51.94   -18.02     0.92)  ; 5\r\n        (  283.28    54.84   -18.50     0.92)  ; 6\r\n        (  284.93    55.82   -18.88     0.92)  ; 7\r\n        (  286.99    55.10   -19.50     0.92)  ; 8\r\n        (  289.57    52.13   -19.50     0.92)  ; 9\r\n        (  291.63    51.41   -20.00     0.92)  ; 10\r\n        (  292.92    49.93   -20.77     0.92)  ; 11\r\n        (  295.28    49.89   -21.88     0.92)  ; 12\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  293.32    48.24   -20.00     0.92)  ; 1\r\n          (  290.28    51.10   -20.00     0.92)  ; 2\r\n        )  ;  End of markers\r\n        (\r\n          (  296.62    50.20   -23.27     0.46)  ; 1, R-1-2-2-1\r\n          (  294.75    52.14   -24.77     0.46)  ; 2\r\n          (  294.30    52.03   -24.80     0.46)  ; 3\r\n          (  293.85    51.93   -26.32     0.46)  ; 4\r\n          (  295.38    53.50   -27.83     0.46)  ; 5\r\n          (  294.80    53.95   -27.85     0.46)  ; 6\r\n          (  295.56    54.73   -29.42     0.46)  ; 7\r\n          (  295.43    55.30   -29.42     0.46)  ; 8\r\n          (  294.85    55.75   -31.30     0.46)  ; 9\r\n          (  293.32    54.20   -31.13     0.46)  ; 10\r\n          (  292.29    54.56   -33.75     0.46)  ; 11\r\n          (  294.00    57.35   -35.55     0.46)  ; 12\r\n          (  293.34    60.18   -36.52     0.46)  ; 13\r\n          (  292.18    61.10   -40.80     0.46)  ; 14\r\n          (  293.52    61.42   -43.72     0.46)  ; 15\r\n          (  293.70    62.65   -45.80     0.46)  ; 16\r\n          (  293.70    62.65   -45.83     0.46)  ; 17\r\n          (  292.09    63.47   -47.02     0.46)  ; 18\r\n          (  291.96    64.03   -49.25     0.46)  ; 19\r\n          (  293.75    64.45   -51.25     0.46)  ; 20\r\n          (  294.51    65.23   -52.97     0.46)  ; 21\r\n          (  294.51    65.23   -53.00     0.46)  ; 22\r\n          (  295.90    67.34   -55.83     0.46)  ; 23\r\n          (  294.93    69.50   -57.85     0.46)  ; 24\r\n          (  295.68    70.29   -59.83     0.46)  ; 25\r\n          (  296.12    70.38   -59.83     0.46)  ; 26\r\n          (  297.28    69.46   -62.10     0.46)  ; 27\r\n          (  297.65    71.94   -63.82     0.46)  ; 28\r\n          (  300.01    71.90   -65.32     0.46)  ; 29\r\n          (  300.78    72.67   -67.25     0.46)  ; 30\r\n          (  300.78    72.67   -67.28     0.46)  ; 31\r\n          (  299.35    74.72   -69.55     0.46)  ; 32\r\n          (  298.76    75.19   -70.57     0.46)  ; 33\r\n          (  299.21    75.30   -72.77     0.46)  ; 34\r\n          (  301.46    75.81   -74.45     0.46)  ; 35\r\n          (  301.28    74.57   -76.60     0.46)  ; 36\r\n          (  301.28    74.57   -76.63     0.46)  ; 37\r\n          (  302.61    74.89   -78.97     0.46)  ; 38\r\n          (  303.72    72.17   -80.52     0.46)  ; 39\r\n          (  303.72    72.17   -80.60     0.46)  ; 40\r\n          (  305.11    74.28   -83.02     0.46)  ; 41\r\n          (  305.46    76.76   -85.02     0.46)  ; 42\r\n          (  304.50    78.92   -87.07     0.46)  ; 43\r\n          (  304.50    78.92   -87.10     0.46)  ; 44\r\n          (  303.65    80.51   -89.65     0.46)  ; 45\r\n          (  303.65    80.51   -89.70     0.46)  ; 46\r\n          (  303.87    83.55   -91.32     0.46)  ; 47\r\n          (  303.79    85.91   -92.15     0.46)  ; 48\r\n          (  303.66    86.48   -95.30     0.46)  ; 49\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  296.80    73.53   -69.55     0.46)  ; 1\r\n            (  297.70    73.74   -65.67     0.46)  ; 2\r\n            (  297.05    66.43   -57.82     0.46)  ; 3\r\n            (  295.04    62.97   -51.25     0.46)  ; 4\r\n            (  291.81    58.62   -36.52     0.46)  ; 5\r\n            (  293.99    51.37   -21.82     0.92)  ; 6\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  296.12    48.29   -21.10     0.46)  ; 1, R-1-2-2-2\r\n          (  295.58    44.58   -22.67     0.46)  ; 2\r\n          (  295.58    44.58   -22.70     0.46)  ; 3\r\n          (  296.60    44.22   -24.40     0.46)  ; 4\r\n          (  298.21    43.41   -25.90     0.46)  ; 5\r\n          (  299.05    41.82   -27.60     0.46)  ; 6\r\n          (  299.01    40.01   -29.17     0.46)  ; 7\r\n          (  302.09    38.94   -30.13     0.46)  ; 8\r\n          (  303.11    38.59   -31.17     0.46)  ; 9\r\n          (  305.25    35.50   -31.70     0.46)  ; 10\r\n          (  307.70    33.09   -32.88     0.46)  ; 11\r\n          (  308.98    31.60   -34.30     0.46)  ; 12\r\n          (  310.67    28.41   -35.45     0.46)  ; 13\r\n          (  311.60    24.45   -36.33     0.46)  ; 14\r\n          (  314.50    22.14   -36.58     0.46)  ; 15\r\n          (  316.90    17.92   -37.00     0.46)  ; 16\r\n          (  318.41    13.50   -37.00     0.46)  ; 17\r\n          (  320.68     9.85   -37.22     0.46)  ; 18\r\n          (  320.68     9.85   -37.25     0.46)  ; 19\r\n          (  320.77     7.48   -38.40     0.46)  ; 20\r\n          (  320.62     2.08   -39.25     0.46)  ; 21\r\n          (  321.37    -3.12   -40.15     0.46)  ; 22\r\n          (  323.64    -6.77   -40.03     0.46)  ; 23\r\n          (  324.17    -9.04   -42.05     0.46)  ; 24\r\n          (  325.99   -12.79   -43.80     0.46)  ; 25\r\n          (  324.29   -15.58   -45.45     0.46)  ; 26\r\n          (  326.19   -21.70   -46.08     0.46)  ; 27\r\n          (  328.20   -24.21   -46.73     0.46)  ; 28\r\n          (  330.99   -30.13   -47.77     0.46)  ; 29\r\n          (  332.25   -31.60   -47.85     0.46)  ; 30\r\n          (  331.62   -32.95   -49.83     0.46)  ; 31\r\n          (  331.62   -32.95   -49.87     0.46)  ; 32\r\n          (  331.57   -34.76   -51.32     0.46)  ; 33\r\n          (  331.57   -34.76   -51.35     0.46)  ; 34\r\n          (  333.13   -37.37   -52.70     0.46)  ; 35\r\n          (  333.13   -37.37   -52.72     0.46)  ; 36\r\n          (  335.97   -41.48   -53.10     0.46)  ; 37\r\n          (  335.97   -41.48   -53.13     0.46)  ; 38\r\n          (  337.53   -44.10   -52.85     0.46)  ; 39\r\n          (  339.80   -47.75   -52.85     0.46)  ; 40\r\n          (  339.80   -47.75   -52.88     0.46)  ; 41\r\n          (  340.28   -51.82   -52.70     0.46)  ; 42\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  295.00    45.05   -24.42     0.46)  ; 1\r\n            (  311.54    32.79   -35.45     0.46)  ; 2\r\n            (  307.87    28.35   -35.45     0.46)  ; 3\r\n            (  317.98    19.37   -37.67     0.46)  ; 4\r\n            (  316.62    13.08   -37.67     0.46)  ; 5\r\n            (  319.43     7.17   -38.40     0.46)  ; 6\r\n            (  321.42    -1.32   -40.03     0.46)  ; 7\r\n            (  330.96   -25.96   -47.77     0.46)  ; 8\r\n            (  336.65   -38.34   -53.13     0.46)  ; 9\r\n            (  338.33   -47.50   -52.70     0.46)  ; 10\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (  256.57    35.95    -7.82     1.83)  ; 1, R-2\r\n    (  253.05    36.90    -9.30     1.83)  ; 2\r\n    (  250.73    38.75    -9.32     1.83)  ; 3\r\n    (  246.88    39.05    -9.32     1.83)  ; 4\r\n    (  245.14    40.44   -10.50     1.83)  ; 5\r\n    (  243.23    40.58   -11.90     1.83)  ; 6\r\n    (  242.78    40.48   -13.27     1.38)  ; 7\r\n    (  241.30    40.73   -14.92     1.38)  ; 8\r\n    (  240.41    40.52   -15.92     1.38)  ; 9\r\n    (  236.35    37.78   -16.20     1.38)  ; 10\r\n\r\n    (Cross\r\n      (Color White)\r\n      (Name \"Marker 3\")\r\n      (  238.71    37.72   -16.20     1.38)  ; 1\r\n    )  ;  End of markers\r\n    (\r\n      (  235.99    35.30   -17.17     1.38)  ; 1, R-2-1\r\n      (  235.04    33.29   -18.33     1.38)  ; 2\r\n      (  233.20    31.06   -19.33     1.38)  ; 3\r\n      (  232.58    29.73   -21.72     1.38)  ; 4\r\n      (\r\n        (  232.49    26.12   -22.53     0.92)  ; 1, R-2-1-1\r\n        (  231.49    22.30   -23.50     0.92)  ; 2\r\n        (  231.27    19.26   -24.10     0.92)  ; 3\r\n        (  230.90    16.80   -24.85     0.92)  ; 4\r\n        (  231.11    13.85   -25.38     0.92)  ; 5\r\n        (  230.25     9.47   -25.77     0.92)  ; 6\r\n        (  228.82     5.55   -25.80     0.92)  ; 7\r\n        (  227.84     1.75   -26.13     0.92)  ; 8\r\n        (  225.24    -1.26   -24.70     0.92)  ; 9\r\n        (  222.50    -3.68   -23.30     0.92)  ; 10\r\n        (  219.90    -6.69   -22.88     0.92)  ; 11\r\n        (  218.97    -8.69   -23.27     0.92)  ; 12\r\n        (  215.67   -10.67   -23.27     0.92)  ; 13\r\n        (  215.62   -12.47   -23.27     0.46)  ; 14\r\n        (  213.82   -12.89   -23.27     0.46)  ; 15\r\n        (  213.78   -14.69   -23.27     0.46)  ; 16\r\n        (  211.86   -14.54   -23.27     0.46)  ; 17\r\n        (  210.48   -16.66   -23.27     0.46)  ; 18\r\n        (  209.27   -17.54   -24.08     0.46)  ; 19\r\n        (  208.19   -18.99   -24.08     0.46)  ; 20\r\n        (  204.43   -21.05   -24.08     0.46)  ; 21\r\n        (  200.69   -23.14   -24.75     0.46)  ; 22\r\n        (  197.11   -23.98   -24.05     0.46)  ; 23\r\n        (  195.50   -23.15   -24.08     0.46)  ; 24\r\n        (  195.50   -23.15   -24.10     0.46)  ; 25\r\n        (  193.59   -23.00   -24.40     0.46)  ; 26\r\n        (  193.59   -23.00   -24.42     0.46)  ; 27\r\n        (  190.90   -23.63   -25.57     0.46)  ; 28\r\n        (  189.52   -25.76   -27.22     0.46)  ; 29\r\n        (  188.62   -25.97   -29.48     0.46)  ; 30\r\n        (  188.62   -25.97   -29.50     0.46)  ; 31\r\n        (  188.36   -24.82   -31.85     0.46)  ; 32\r\n        (  188.36   -24.82   -31.88     0.46)  ; 33\r\n        (  186.44   -24.67   -34.13     0.46)  ; 34\r\n        (  186.44   -24.67   -34.15     0.46)  ; 35\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  194.65   -27.53   -24.67     0.46)  ; 1\r\n          (  193.23   -25.48   -25.57     0.46)  ; 2\r\n          (  190.98   -26.01   -25.57     0.46)  ; 3\r\n          (  233.18    19.11   -24.10     0.92)  ; 4\r\n          (  216.02    -8.20   -23.27     0.92)  ; 5\r\n          (  229.80     3.40   -26.13     0.92)  ; 6\r\n        )  ;  End of markers\r\n         Normal\r\n      |\r\n        (  229.63    30.23   -21.72     0.92)  ; 1, R-2-1-2\r\n        (  227.27    30.27   -22.88     0.92)  ; 2\r\n        (  226.64    28.93   -24.30     0.92)  ; 3\r\n        (  227.35    27.91   -26.58     0.92)  ; 4\r\n        (  226.59    27.13   -28.30     0.92)  ; 5\r\n        (  226.85    25.99   -27.80     0.92)  ; 6\r\n        (  225.79    24.55   -29.22     0.92)  ; 7\r\n        (  224.39    22.43   -30.45     0.92)  ; 8\r\n        (  222.66    23.81   -31.60     0.92)  ; 9\r\n        (  222.66    23.81   -31.63     0.92)  ; 10\r\n        (  221.95    24.84   -33.00     0.92)  ; 11\r\n        (  220.79    25.77   -34.88     0.92)  ; 12\r\n        (  219.62    26.69   -36.60     0.92)  ; 13\r\n\r\n        (Cross\r\n          (Color White)\r\n          (Name \"Marker 3\")\r\n          (  228.78    25.84   -27.80     0.92)  ; 1\r\n        )  ;  End of markers\r\n        (\r\n          (  217.66    25.04   -38.13     0.92)  ; 1, R-2-1-2-1\r\n          (  216.45    24.15   -41.22     0.46)  ; 2\r\n          (  216.45    24.15   -41.20     0.46)  ; 3\r\n          (  214.14    26.00   -42.37     0.46)  ; 4\r\n          (  211.19    26.51   -43.60     0.46)  ; 5\r\n          (  208.38    26.43   -43.72     0.46)  ; 6\r\n          (  206.06    28.28   -44.58     0.46)  ; 7\r\n          (  206.51    28.39   -46.45     0.46)  ; 8\r\n          (  205.93    28.85   -49.10     0.46)  ; 9\r\n          (  203.44    29.46   -50.77     0.46)  ; 10\r\n          (  202.04    27.35   -52.80     0.46)  ; 11\r\n          (  200.39    26.36   -54.38     0.46)  ; 12\r\n          (  200.39    26.36   -54.40     0.46)  ; 13\r\n          (  196.43    27.22   -55.32     0.46)  ; 14\r\n          (  192.85    26.38   -56.80     0.46)  ; 15\r\n          (  191.64    25.51   -58.15     0.46)  ; 16\r\n          (  191.64    25.51   -58.17     0.46)  ; 17\r\n          (  189.73    25.65   -58.90     0.46)  ; 18\r\n          (  187.21    26.26   -59.80     0.46)  ; 19\r\n          (  184.54    25.64   -60.70     0.46)  ; 20\r\n          (  181.82    23.20   -61.38     0.46)  ; 21\r\n          (  178.92    25.50   -62.88     0.46)  ; 22\r\n          (  176.28    26.68   -63.95     0.46)  ; 23\r\n          (  174.54    28.07   -65.30     0.46)  ; 24\r\n          (  172.05    28.68   -66.88     0.46)  ; 25\r\n          (  172.18    28.11   -66.88     0.46)  ; 26\r\n          (  168.65    29.07   -68.20     0.46)  ; 27\r\n          (  166.03    30.25   -69.40     0.46)  ; 28\r\n          (  164.42    31.07   -71.00     0.46)  ; 29\r\n          (  162.77    30.09   -72.93     0.46)  ; 30\r\n          (  160.40    30.12   -74.55     0.46)  ; 31\r\n          (  160.40    30.12   -74.57     0.46)  ; 32\r\n          (  157.32    31.20   -75.63     0.46)  ; 33\r\n          (  156.48    32.79   -77.28     0.46)  ; 34\r\n          (  154.43    33.50   -79.50     0.46)  ; 35\r\n          (  151.93    34.11   -81.22     0.46)  ; 36\r\n          (  150.32    34.93   -83.02     0.46)  ; 37\r\n          (  149.38    32.92   -85.30     0.46)  ; 38\r\n          (  147.02    32.96   -87.70     0.46)  ; 39\r\n          (  147.37    29.46   -89.92     0.46)  ; 40\r\n          (  147.37    29.46   -89.95     0.46)  ; 41\r\n          (  144.95    27.70   -91.13     0.46)  ; 42\r\n          (  142.71    27.17   -92.90     0.46)  ; 43\r\n          (  142.71    27.17   -92.92     0.46)  ; 44\r\n          (  140.31    25.42   -94.10     0.46)  ; 45\r\n          (  140.31    25.42   -94.15     0.46)  ; 46\r\n          (  138.79    23.87   -93.63     0.46)  ; 47\r\n          (  138.79    23.87   -93.88     0.46)  ; 48\r\n          (  136.36    22.10   -94.17     0.46)  ; 49\r\n          (  135.56    19.53   -95.18     0.46)  ; 50\r\n          (  134.04    17.98   -97.28     0.46)  ; 51\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  134.00    22.15   -93.82     0.46)  ; 1\r\n            (  185.22    28.78   -60.65     0.46)  ; 2\r\n            (  181.02    26.60   -62.95     0.46)  ; 3\r\n            (  190.69    23.49   -58.90     0.46)  ; 4\r\n            (  219.40    23.64   -41.20     0.46)  ; 5\r\n            (  160.30    26.52   -74.57     0.46)  ; 6\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  223.20    27.53   -39.55     0.46)  ; 1, R-2-1-2-2\r\n          (  223.65    27.63   -42.47     0.46)  ; 2\r\n          (  223.65    27.63   -42.53     0.46)  ; 3\r\n          (  223.38    28.76   -45.97     0.46)  ; 4\r\n          (  223.38    28.76   -46.10     0.46)  ; 5\r\n          (  225.12    27.38   -48.70     0.46)  ; 6\r\n          (  225.12    27.38   -48.72     0.46)  ; 7\r\n          (  225.17    29.18   -50.95     0.46)  ; 8\r\n          (  224.72    29.07   -50.97     0.46)  ; 9\r\n          (  223.83    28.86   -53.40     0.46)  ; 10\r\n          (  223.70    29.43   -53.42     0.46)  ; 11\r\n          (  223.43    30.57   -55.67     0.46)  ; 12\r\n          (  223.43    30.57   -55.70     0.46)  ; 13\r\n          (  225.34    30.41   -58.42     0.46)  ; 14\r\n          (  225.34    30.41   -58.47     0.46)  ; 15\r\n          (  224.95    32.11   -61.40     0.46)  ; 16\r\n          (  224.95    32.11   -61.47     0.46)  ; 17\r\n          (  224.06    31.91   -63.88     0.46)  ; 18\r\n          (  224.06    31.91   -63.90     0.46)  ; 19\r\n          (  224.86    34.48   -66.65     0.46)  ; 20\r\n          (  225.84    32.32   -69.35     0.46)  ; 21\r\n          (  226.10    31.20   -72.07     0.46)  ; 22\r\n          (  226.10    31.20   -72.17     0.46)  ; 23\r\n          (  228.04    31.04   -76.22     0.46)  ; 24\r\n          (  228.04    31.04   -76.30     0.46)  ; 25\r\n          (  228.04    31.04   -81.53     0.46)  ; 26\r\n          (  228.04    31.04   -81.58     0.46)  ; 27\r\n          (  227.72    30.38   -85.92     0.46)  ; 28\r\n          (  226.50    29.49   -89.05     0.46)  ; 29\r\n          (  226.50    29.49   -89.08     0.46)  ; 30\r\n          (  224.81    26.72   -92.40     0.46)  ; 31\r\n          (  224.76    24.91   -94.75     0.46)  ; 32\r\n          (  225.16    23.21   -97.13     0.46)  ; 33\r\n          (  225.87    22.17   -99.50     0.46)  ; 34\r\n          (  225.87    22.17   -99.52     0.46)  ; 35\r\n          (  224.79    20.73  -102.68     0.46)  ; 36\r\n          (  224.79    20.73  -102.72     0.46)  ; 37\r\n          (  225.24    20.84  -106.25     0.46)  ; 38\r\n          (  225.37    20.27  -110.32     0.46)  ; 39\r\n          (  225.37    20.27  -110.37     0.46)  ; 40\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  228.06    26.87   -88.72     0.46)  ; 1\r\n            (  223.20    27.53   -92.40     0.46)  ; 2\r\n          )  ;  End of markers\r\n           Normal\r\n        |\r\n          (  219.81    27.93   -40.52     0.46)  ; 1, R-2-1-2-3\r\n          (  218.34    28.18   -43.45     0.46)  ; 2\r\n          (  218.34    28.18   -43.47     0.46)  ; 3\r\n          (  216.78    30.80   -45.70     0.46)  ; 4\r\n          (  217.68    31.01   -48.20     0.46)  ; 5\r\n          (  218.62    33.02   -51.08     0.46)  ; 6\r\n          (  219.25    34.37   -54.20     0.46)  ; 7\r\n          (  219.25    34.37   -54.22     0.46)  ; 8\r\n          (  218.17    32.91   -57.15     0.46)  ; 9\r\n          (  218.17    32.91   -57.20     0.46)  ; 10\r\n          (  216.61    35.53   -59.35     0.46)  ; 11\r\n          (  217.87    38.23   -60.27     0.46)  ; 12\r\n          (  217.39    42.29   -61.38     0.46)  ; 13\r\n          (  217.26    42.86   -63.10     0.46)  ; 14\r\n          (  215.77    43.11   -65.45     0.46)  ; 15\r\n          (  215.25    45.36   -67.50     0.46)  ; 16\r\n          (  216.60    45.68   -69.15     0.46)  ; 17\r\n          (  218.24    46.67   -70.45     0.46)  ; 18\r\n          (  216.06    47.95   -70.27     0.46)  ; 19\r\n          (  214.59    48.20   -72.17     0.46)  ; 20\r\n          (  216.11    49.75   -73.95     0.46)  ; 21\r\n          (  216.29    50.99   -74.45     0.46)  ; 22\r\n          (  214.92    54.85   -74.57     0.46)  ; 23\r\n          (  214.92    54.85   -74.60     0.46)  ; 24\r\n          (  214.21    55.87   -76.38     0.46)  ; 25\r\n          (  213.68    58.14   -78.13     0.46)  ; 26\r\n          (  214.17    60.05   -79.55     0.46)  ; 27\r\n          (  213.69    64.11   -80.57     0.46)  ; 28\r\n          (  214.09    68.39   -81.25     0.46)  ; 29\r\n          (  213.56    70.64   -82.75     0.46)  ; 30\r\n          (  213.56    70.64   -82.78     0.46)  ; 31\r\n          (  212.32    73.94   -84.57     0.46)  ; 32\r\n          (  213.58    76.63   -86.10     0.46)  ; 33\r\n          (  214.65    78.07   -87.70     0.46)  ; 34\r\n          (  214.65    78.07   -87.72     0.46)  ; 35\r\n          (  213.40    81.36   -89.25     0.46)  ; 36\r\n          (  212.17    84.66   -90.40     0.46)  ; 37\r\n          (  213.11    86.67   -92.83     0.46)  ; 38\r\n          (  213.11    86.67   -92.90     0.46)  ; 39\r\n\r\n          (Cross\r\n            (Color White)\r\n            (Name \"Marker 3\")\r\n            (  213.13    54.43   -74.60     0.46)  ; 1\r\n            (  217.89    50.17   -70.25     0.46)  ; 2\r\n            (  218.87    42.03   -61.38     0.46)  ; 3\r\n          )  ;  End of markers\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  234.11    37.26   -15.38     0.92)  ; 1, R-2-2\r\n      (  232.72    35.13   -15.65     0.92)  ; 2\r\n      (  229.47    34.97   -16.52     0.92)  ; 3\r\n      (  227.02    37.38   -17.25     0.92)  ; 4\r\n      (  224.38    38.55   -18.17     0.92)  ; 5\r\n      (  222.42    36.90   -19.38     0.92)  ; 6\r\n      (  220.81    37.71   -20.38     0.92)  ; 7\r\n      (  218.85    36.06   -21.55     0.92)  ; 8\r\n      (  218.85    36.06   -21.58     0.92)  ; 9\r\n      (  216.65    37.34   -22.30     0.92)  ; 10\r\n      (  215.82    38.93   -23.95     0.92)  ; 11\r\n      (  213.00    38.87   -25.33     0.92)  ; 12\r\n      (  212.87    39.44   -27.15     0.92)  ; 13\r\n      (  210.77    38.34   -28.65     0.92)  ; 14\r\n      (  210.77    38.34   -28.67     0.92)  ; 15\r\n      (  208.22    37.15   -29.70     0.46)  ; 16\r\n      (  208.01    40.09   -30.40     0.46)  ; 17\r\n      (  203.91    41.52   -31.10     0.46)  ; 18\r\n      (  200.56    43.72   -32.08     0.46)  ; 19\r\n      (  198.70    45.67   -33.13     0.46)  ; 20\r\n      (  193.83    46.32   -33.67     0.92)  ; 21\r\n      (  191.07    48.05   -35.60     0.92)  ; 22\r\n      (  188.56    48.67   -37.72     0.92)  ; 23\r\n      (  186.51    49.38   -39.15     0.92)  ; 24\r\n      (  183.84    48.76   -41.05     0.92)  ; 25\r\n      (  183.84    48.76   -41.08     0.92)  ; 26\r\n      (  182.68    49.67   -44.17     0.92)  ; 27\r\n      (  182.68    49.67   -44.20     0.92)  ; 28\r\n      (  180.32    49.72   -45.57     0.92)  ; 29\r\n      (  177.54    51.46   -46.82     0.92)  ; 30\r\n      (  173.84    51.19   -48.35     0.92)  ; 31\r\n      (  171.02    51.12   -49.05     0.92)  ; 32\r\n      (  169.28    52.51   -51.22     0.46)  ; 33\r\n      (  165.90    52.91   -53.35     0.46)  ; 34\r\n      (  165.90    52.91   -53.38     0.46)  ; 35\r\n      (  163.72    54.19   -55.40     0.46)  ; 36\r\n      (  163.72    54.19   -55.42     0.46)  ; 37\r\n      (  161.52    55.47   -57.28     0.46)  ; 38\r\n      (  161.52    55.47   -57.30     0.46)  ; 39\r\n      (  159.03    56.08   -58.22     0.46)  ; 40\r\n      (  155.64    56.48   -59.90     0.46)  ; 41\r\n      (  155.11    58.74   -60.97     0.46)  ; 42\r\n      (  151.90    60.37   -62.45     0.46)  ; 43\r\n      (  149.84    61.10   -64.20     0.46)  ; 44\r\n      (  147.40    63.50   -66.00     0.46)  ; 45\r\n      (  145.53    65.45   -67.95     0.46)  ; 46\r\n      (  145.53    65.45   -67.97     0.46)  ; 47\r\n      (  143.70    69.21   -69.72     0.46)  ; 48\r\n      (  140.76    69.71   -71.50     0.46)  ; 49\r\n      (  140.76    69.71   -71.52     0.46)  ; 50\r\n      (  138.62    72.80   -73.40     0.46)  ; 51\r\n      (  136.44    74.07   -74.97     0.46)  ; 52\r\n      (  134.57    76.03   -76.70     0.46)  ; 53\r\n      (  133.28    77.51   -78.65     0.46)  ; 54\r\n      (  130.51    79.26   -79.80     0.46)  ; 55\r\n      (  126.72    81.35   -82.00     0.46)  ; 56\r\n      (  124.10    82.53   -84.08     0.46)  ; 57\r\n      (  124.10    82.53   -84.10     0.46)  ; 58\r\n      (  121.78    84.37   -86.70     0.46)  ; 59\r\n      (  120.67    87.09   -88.97     0.46)  ; 60\r\n      (  120.67    87.09   -89.00     0.46)  ; 61\r\n      (  118.79    89.05   -90.88     0.46)  ; 62\r\n      (  118.79    89.05   -90.90     0.46)  ; 63\r\n      (  117.10    92.24   -93.50     0.46)  ; 64\r\n      (  117.10    92.24   -93.53     0.46)  ; 65\r\n      (  117.28    93.47   -96.20     0.46)  ; 66\r\n      (  116.57    94.49   -99.92     0.46)  ; 67\r\n      (  116.57    94.49  -100.00     0.46)  ; 68\r\n\r\n      (Cross\r\n        (Color White)\r\n        (Name \"Marker 3\")\r\n        (  155.03    61.11   -60.97     0.46)  ; 1\r\n        (  150.87    60.74   -60.97     0.46)  ; 2\r\n        (  160.46    54.02   -58.22     0.46)  ; 3\r\n        (  173.30    47.47   -49.05     0.92)  ; 4\r\n        (  124.28    83.76   -84.10     0.46)  ; 5\r\n        (  208.45    40.19   -30.38     0.46)  ; 6\r\n        (  198.01    42.53   -33.13     0.46)  ; 7\r\n        (  194.22    44.61   -33.67     0.46)  ; 8\r\n      )  ;  End of markers\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\n"
  },
  {
    "path": "examples/l5pc/morphology/LICENSE",
    "content": "The CC-BY-NC-SA license applies, as indicated by headers in the\nrespective source files.\n\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/\n\nThe detailed text is available here\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/legalcode\n\nor in this tarball in the seperate file LICENSE_CC-BY-CA-SA-4.0\n\n\nThe HOC code, Python code, synapse MOD code and cell morphology are licensed with the above mentioned CC-BY-NC-SA license.\n\n\nFor models for which the original source is available on ModelDB, any\nspecific licenses on mentioned on ModelDB, or the generic License of ModelDB \napply:\n\n1) Ih model\nAuthor: Stefan Hallermann\nOriginal URL: http://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=144526&file=\\HallermannEtAl2012\\h.mod\n\n2) StochKv model\nAuthors: Zach Mainen Adaptations: Kamran Diba, Mickey London, Peter N. Steinmetz, Werner Van Geit\nOriginal URL: http://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=125385&file=\\Sbpap_code\\mod\\skm.mod\n\n3) D-type K current model\nAuthors: Yuguo Yu\nOriginal URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=135898&file=\\YuEtAlPNAS2007\\kd.mod\n\n4) Internal calcium concentration model\nAuthor: Alain Destexhe\nOriginal URL: http://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=3670&file=\\NTW_NEW\\capump.mod\n\n5) Ca_LVAst, Im, K_Tst, NaTa_t, SK_E2, Ca_HVA, Ih, K_Pst, Nap_Et2, NaTs2_t, SKv3_1            \nAuthor: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit\nOriginal URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=139653&file=\\L5bPCmodelsEH\\mod\\\n"
  },
  {
    "path": "examples/l5pc/nsg/.gitignore",
    "content": "/l5pc_nsg.zip\n/zipcontent\n"
  },
  {
    "path": "examples/l5pc/nsg/Makefile",
    "content": "zip:\n\trm -f l5pc_nsg.zip\n\trm -rf zipcontent\n\tmkdir -p zipcontent\n\tcp init.py zipcontent\n\tcp -r ../config ../morphology ../opt_l5pc.py ../l5pc_model.py ../l5pc_evaluator.py ../checkpoints/checkpoint.pkl zipcontent\n\tcp ../mechanisms/*.mod zipcontent\n\tzip -r l5pc_nsg.zip zipcontent\n"
  },
  {
    "path": "examples/l5pc/nsg/init.py",
    "content": "\"\"\"Run BluePyOpt on Neuroscience Gateway\"\"\"\n\nimport os\n\nos.system('python opt_l5pc.py --start --max_ngen=100 '\n          '--offspring_size=50 --checkpoint checkpoint.pkl')\n"
  },
  {
    "path": "examples/l5pc/opt_l5pc.py",
    "content": "#!/usr/bin/env python\n\"\"\"Run simple cell optimisation\n\nThis optimisation is based on L5PC optimisations developed by Etay Hay in the\ncontext of the BlueBrain project\n\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nimport argparse\nimport logging\nimport os\nimport sys\nimport textwrap\n\nfrom datetime import datetime\n\nimport bluepyopt\n\nimport l5pc_evaluator\n\nlogger = logging.getLogger()\n\n# TODO store definition dicts in json\n# TODO add functionality to read settings of every object from config format\n\n\ndef create_optimizer(args):\n    '''returns configured bluepyopt.optimisations.DEAPOptimisation'''\n    if args.ipyparallel or os.getenv('L5PCBENCHMARK_USEIPYP'):\n        from ipyparallel import Client\n        rc = Client(profile=os.getenv('IPYTHON_PROFILE'))\n\n        logger.debug('Using ipyparallel with %d engines', len(rc))\n\n        lview = rc.load_balanced_view()\n\n        def mapper(func, it):\n            start_time = datetime.now()\n            ret = lview.map_sync(func, it)\n            logger.debug('Generation took %s', datetime.now() - start_time)\n            return ret\n\n        map_function = mapper\n    else:\n        map_function = None\n\n    evaluator = l5pc_evaluator.create(sim=args.sim)\n    seed = os.getenv('BLUEPYOPT_SEED', args.seed)\n    opt = bluepyopt.optimisations.DEAPOptimisation(\n        evaluator=evaluator,\n        map_function=map_function,\n        seed=seed)\n\n    return opt\n\n\ndef get_parser():\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        description='L5PC example',\n        epilog=textwrap.dedent('''\\\nThe folling environment variables are considered:\n    L5PCBENCHMARK_USEIPYP: if set, will use ipyparallel\n    IPYTHON_PROFILE: if set, used as the path to the ipython profile\n    BLUEPYOPT_SEED: The seed used for initial randomization\n        '''))\n    parser.add_argument('--sim', default='nrn', choices=['nrn', 'arb'])\n    parser.add_argument('--start', action=\"store_true\")\n    parser.add_argument('--continu', action=\"store_false\", default=False)\n    parser.add_argument('--checkpoint', required=False, default=None,\n                        help='Checkpoint pickle to avoid recalculation')\n    parser.add_argument('--offspring_size', type=int, required=False, default=2,\n                        help='number of individuals in offspring')\n    parser.add_argument('--max_ngen', type=int, required=False, default=2,\n                        help='maximum number of generations')\n    parser.add_argument('--responses', required=False, default=None,\n                        help='Response pickle file to avoid recalculation')\n    parser.add_argument('--analyse', action=\"store_true\")\n    parser.add_argument('--compile', action=\"store_true\")\n    parser.add_argument('--hocanalyse', action=\"store_true\")\n    parser.add_argument('--seed', type=int, default=42,\n                        help='Seed to use for optimization')\n    parser.add_argument('--ipyparallel', action=\"store_true\", default=False,\n                        help='Use ipyparallel')\n    parser.add_argument(\n        '--diversity',\n        help='plot the diversity of parameters from checkpoint pickle file')\n    parser.add_argument('-v', '--verbose', action='count', dest='verbose',\n                        default=0, help='-v for INFO, -vv for DEBUG')\n\n    return parser\n\n\ndef main():  # pylint: disable=too-many-statements\n    \"\"\"Main\"\"\"\n    args = get_parser().parse_args()\n\n    if args.verbose > 2:\n        sys.exit('cannot be more verbose than -vv')\n    logging.basicConfig(level=(logging.WARNING,\n                               logging.INFO,\n                               logging.DEBUG)[args.verbose],\n                        stream=sys.stdout)\n\n    opt = create_optimizer(args)\n\n    if args.compile:\n        logger.debug('Doing compile')\n        import commands\n        commands.getstatusoutput('cd mechanisms/; nrnivmodl; cd ..')\n\n    if args.hocanalyse:\n        if args.sim != 'nrn':\n            raise argparse.ArgumentError(\n                'Simulator must be \\'nrn\\' with option --hocanalyse.')\n        logger.debug('Doing hocanalyse')\n        try:\n            import bglibpy  # NOQA\n        except ImportError:\n            raise ImportError(\n                'bglibpy not installed, '\n                '--hocanalyse for internal testing only!')\n\n    if args.start or args.continu:\n        logger.debug('Doing start or continue')\n        opt.run(max_ngen=args.max_ngen,\n                offspring_size=args.offspring_size,\n                continue_cp=args.continu,\n                cp_filename=args.checkpoint)\n\n    if args.analyse:\n        logger.debug('Doing analyse')\n        import l5pc_analysis\n        import matplotlib.pyplot as plt\n\n        box = {'left': 0.0,\n               'bottom': 0.0,\n               'width': 1.0,\n               'height': 1.0}\n\n        release_responses_fig = plt.figure(figsize=(10, 10), facecolor='white')\n        release_objectives_fig = plt.figure(figsize=(10, 10), facecolor='white')\n\n        l5pc_analysis.analyse_releasecircuit_model(\n            opt=opt, figs=(\n                (release_responses_fig, box),\n                (release_objectives_fig, box), ), box=box, sim=args.sim)\n        release_objectives_fig.savefig('figures/l5pc_release_objectives.eps')\n        release_responses_fig.savefig('figures/l5pc_release_responses.eps')\n\n        if args.checkpoint is not None and os.path.isfile(args.checkpoint):\n            responses_fig = plt.figure(figsize=(10, 10), facecolor='white')\n            objectives_fig = plt.figure(figsize=(10, 10), facecolor='white')\n            evol_fig = plt.figure(figsize=(10, 10), facecolor='white')\n\n            l5pc_analysis.analyse_cp(opt=opt,\n                                     cp_filename=args.checkpoint,\n                                     responses_filename=args.responses,\n                                     figs=((responses_fig, box),\n                                           (objectives_fig, box),\n                                           (evol_fig, box),),\n                                     sim=args.sim)\n\n            responses_fig.savefig('figures/l5pc_responses.eps')\n            objectives_fig.savefig('figures/l5pc_objectives.eps')\n            evol_fig.savefig('figures/l5pc_evolution.eps')\n\n        else:\n            print('No checkpoint file available run optimization '\n                  'first with --start')\n\n        plt.show()\n\n    elif args.hocanalyse:\n        logger.debug('Continuing hocanalyse')\n\n        import matplotlib.pyplot as plt\n        import l5pc_analysis\n\n        fig_release = plt.figure(figsize=(10, 10), facecolor='white')\n\n        box = {\n            'left': 0.0,\n            'bottom': 0.0,\n            'width': 1.0,\n            'height': 1.0}\n\n        l5pc_analysis.analyse_releasecircuit_hocmodel(\n            opt=opt,\n            fig=fig_release,\n            box=box)\n\n        fig_release.savefig('figures/release_l5pc_hoc.eps')\n\n        plt.show()\n\n    elif args.diversity:\n        logger.debug('Plotting Diversity')\n\n        import matplotlib.pyplot as plt\n        import l5pc_analysis\n\n        if not os.path.exists(args.diversity):\n            raise Exception('Need a pickle file to plot the diversity')\n\n        fig_diversity = plt.figure(figsize=(10, 10), facecolor='white')\n\n        l5pc_analysis.plot_diversity(opt, args.diversity, fig_diversity,\n                                     opt.evaluator.param_names)\n        fig_diversity.savefig('figures/l5pc_diversity.eps')\n        plt.show()\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/l5pc/opt_l5pc.sh",
    "content": "#!/bin/bash\n\nnrnivmodl ./mechanisms\npython opt_l5pc.py --start\n"
  },
  {
    "path": "examples/l5pc/tables/.gitignore",
    "content": "/*.tex\n"
  },
  {
    "path": "examples/l5pc/tasks2dataframe.py",
    "content": "#!/usr/bin/env python\n'''Utility functions to read the tasks.db from ipyparallel when used\nwith BluePyOpt.\n\nThe script is executable, as an example on how to extract the top 10\nlongest running engines for the l5pc model.\n'''\n\nimport sqlite3\n\nimport pandas\nimport numpy as np\n\nfrom ipyparallel.controller import sqlitedb\n\n\ndef _open_db(dbfile):\n    '''open the sqlite file, and set up the converters to serialize data'''\n    sqlite3.register_adapter(dict, sqlitedb._adapt_dict)\n    sqlite3.register_converter('dict', sqlitedb._convert_dict)\n    sqlite3.register_adapter(list, sqlitedb._adapt_bufs)\n    sqlite3.register_converter('bufs', sqlitedb._convert_bufs)\n    db = sqlite3.connect(dbfile,\n                         detect_types=sqlite3.PARSE_DECLTYPES,\n                         cached_statements=64)\n    return db\n\n\ndef _add_buffers(df, arg_names=None, result_names=None, delete_buffers=False):\n    '''add argument and result buffers to the dataframe'''\n    if arg_names is None:\n        row = df['buffers'][0]\n        count = len(sqlitedb._convert_bufs(row[3])[0])\n        arg_names = ['arg_%02d' % i for i in range(count)]\n\n    args = np.empty(shape=(df.shape[0], len(arg_names)))\n    for i, row in enumerate(df['buffers']):\n        # row[0] is pickled ref to __builtin__.map,\n        # row[1] is pickled description of args:\n        #  {'kw_keys': [], 'nargs': 2, 'narg_bufs': 2}\n        # row[2] is pickled function to be mapped\n        # row[3] are the pickled arguments, packed into a list\n        args[i, :] = sqlitedb._convert_bufs(row[3])[0]\n    args_df = pandas.DataFrame(args, columns=arg_names)\n\n    if result_names is None:\n        row = df['result_buffers'][0]\n        count = len(sqlitedb._convert_bufs(row[0])[0])\n        result_names = ['res_%02d' % i for i in range(count)]\n\n    results = np.empty(shape=(df.shape[0], len(result_names)))\n    for i, row in enumerate(df['result_buffers']):\n        if row:\n            # row[0] are the pickled results, in a list\n            results[i, :] = sqlitedb._convert_bufs(row[0])[0]\n        else:\n            # either the task didn't return, or returned an error\n            results[i, :] = float('NaN')\n    results_df = pandas.DataFrame(results, columns=result_names)\n\n    if delete_buffers:\n        df.drop(['buffers', 'result_buffers'], axis=1, inplace=True)\n\n    return pandas.concat(\n        [df, args_df, results_df],\n        axis=1, join_axes=[df.index])\n\n\ndef create_df(db, arg_names=None, result_names=None):\n    '''convert a tasks_db to a pandas dataframe'''\n    df = pandas.read_sql('select * from `ipython-tasks`;', db,\n                         parse_dates=('submitted', 'started', 'completed',))\n    df = _add_buffers(df, arg_names, result_names)\n    return df\n\n\nif __name__ == '__main__':\n    import sys\n\n    from l5pc_model import define_parameters\n    arg_names = [p.name for p in define_parameters() if not p.frozen]\n\n    from l5pc_evaluator import (define_protocols, define_fitness_calculator)\n    fitness_protocols = define_protocols()\n    fitness_calculator = define_fitness_calculator(fitness_protocols)\n    result_names = [o.name for o in fitness_calculator.objectives]\n\n    db_filename = sys.argv[1]\n    db = _open_db(db_filename)\n    df = create_df(db, arg_names, result_names)\n    df['run_time'] = df['completed'] - df['started']\n\n    pandas.set_option('display.expand_frame_repr', False)\n    title = '10 Longest times'\n    print('%s\\n%s' % (title, '*' * len(title)))\n    print(\n        df.nlargest(10, 'run_time')\n        [['run_time', ] + arg_names + result_names])\n"
  },
  {
    "path": "examples/l5pc_lfpy/L5PC_LFPy.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Setup of a cell model with multi electrode simulation for local field potential recording\\n\",\n    \"\\n\",\n    \"This notebook will demonstrate how to instantiate a cell model and evaluator that include local field potential (LFP) computation and its recording using a simulated multi electrode array (MEA).\\n\",\n    \"\\n\",\n    \"First, let's start by installing the modules required to perform LFP simulation and recordings as well as compile the mechanisms:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Requirement already satisfied: LFPy in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (2.2.6)\\n\",\n      \"Requirement already satisfied: scipy>=0.14 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from LFPy) (1.8.0)\\n\",\n      \"Requirement already satisfied: h5py>=2.5 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from LFPy) (3.4.0)\\n\",\n      \"Requirement already satisfied: Cython>=0.20 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from LFPy) (0.29.26)\\n\",\n      \"Requirement already satisfied: LFPykit<0.5,>=0.4 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from LFPy) (0.4)\\n\",\n      \"Requirement already satisfied: numpy>=1.8 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from LFPy) (1.22.3)\\n\",\n      \"Requirement already satisfied: neuron>=7.7.2 in /Applications/NEURON/lib/python (from LFPy) (8.0)\\n\",\n      \"Requirement already satisfied: mpi4py>=1.2 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from LFPy) (3.1.3)\\n\",\n      \"Requirement already satisfied: meautility in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from LFPykit<0.5,>=0.4->LFPy) (1.5.0)\\n\",\n      \"Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from meautility->LFPykit<0.5,>=0.4->LFPy) (3.5.1)\\n\",\n      \"Requirement already satisfied: pyyaml in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from meautility->LFPykit<0.5,>=0.4->LFPy) (6.0)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (4.33.3)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (21.3)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (0.10.0)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (2.8.2)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (8.4.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (2.4.7)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (1.3.2)\\n\",\n      \"Requirement already satisfied: six in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from cycler>=0.10->matplotlib->meautility->LFPykit<0.5,>=0.4->LFPy) (1.16.0)\\n\",\n      \"Requirement already satisfied: MEAutility in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (1.5.0)\\n\",\n      \"Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from MEAutility) (1.22.3)\\n\",\n      \"Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from MEAutility) (3.5.1)\\n\",\n      \"Requirement already satisfied: pyyaml in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from MEAutility) (6.0)\\n\",\n      \"Requirement already satisfied: pillow>=6.2.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->MEAutility) (8.4.0)\\n\",\n      \"Requirement already satisfied: pyparsing>=2.2.1 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->MEAutility) (2.4.7)\\n\",\n      \"Requirement already satisfied: packaging>=20.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->MEAutility) (21.3)\\n\",\n      \"Requirement already satisfied: fonttools>=4.22.0 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->MEAutility) (4.33.3)\\n\",\n      \"Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->MEAutility) (1.3.2)\\n\",\n      \"Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->MEAutility) (0.10.0)\\n\",\n      \"Requirement already satisfied: python-dateutil>=2.7 in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from matplotlib->MEAutility) (2.8.2)\\n\",\n      \"Requirement already satisfied: six in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from cycler>=0.10->matplotlib->MEAutility) (1.16.0)\\n\",\n      \"/usr/bin/xcrun\\n\",\n      \"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy\\n\",\n      \"-n Mod files:\\n\",\n      \"-n  \\\"../l5pc/mechanisms/CaDynamics_E2.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/Ca_HVA.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/Ca_LVAst.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/Ih.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/Im.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/K_Pst.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/K_Tst.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/NaTa_t.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/NaTs2_t.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/Nap_Et2.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/SK_E2.mod\\\"\\n\",\n      \"-n  \\\"../l5pc/mechanisms/SKv3_1.mod\\\"\\n\",\n      \"\\n\",\n      \"\\n\",\n      \"Creating x86_64 directory for .o files.\\n\",\n      \"\\n\",\n      \"COBJS=''\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m mod_func.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c mod_func.c -o mod_func.o\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/CaDynamics_E2.mod\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/Ca_HVA.mod\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/Ca_LVAst.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl Ca_LVAst.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl Ca_HVA.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl CaDynamics_E2.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"Translating CaDynamics_E2.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/CaDynamics_E2.c\\n\",\n      \"Translating Ca_LVAst.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/Ca_LVAst.c\\n\",\n      \"Translating Ca_HVA.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/Ca_HVA.c\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/Ih.mod\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/Im.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl Ih.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/K_Pst.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl Im.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl K_Pst.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"Translating Im.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/Im.c\\n\",\n      \"Translating Ih.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/Ih.c\\n\",\n      \"Translating K_Pst.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/K_Pst.c\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/K_Tst.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl K_Tst.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/NaTa_t.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl NaTa_t.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/NaTs2_t.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl NaTs2_t.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"Translating K_Tst.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/K_Tst.c\\n\",\n      \"Translating NaTs2_t.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/NaTs2_t.c\\n\",\n      \"Translating NaTa_t.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/NaTa_t.c\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/Nap_Et2.mod\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/SK_E2.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl Nap_Et2.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \" -> \\u001b[32mNMODL\\u001b[0m ../../l5pc/mechanisms/SKv3_1.mod\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl SK_E2.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"(cd \\\"../../l5pc/mechanisms\\\"; MODLUNIT=/Applications/NEURON/share/nrn/lib/nrnunits.lib /Applications/NEURON/bin/nocmodl SKv3_1.mod -o \\\"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64\\\")\\n\",\n      \"Translating Nap_Et2.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/Nap_Et2.c\\n\",\n      \"Translating SK_E2.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/SK_E2.c\\n\",\n      \"Translating SKv3_1.mod into /Users/damart/Desktop/BPO_LFPy/BluePyOpt/examples/l5pc_lfpy/x86_64/SKv3_1.c\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \"Thread Safe\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Ca_HVA.c\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m CaDynamics_E2.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c Ca_HVA.c -o Ca_HVA.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Ca_LVAst.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c CaDynamics_E2.c -o CaDynamics_E2.o\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c Ca_LVAst.c -o Ca_LVAst.o\\n\",\n      \"\\u001b[1mCa_HVA.c:264:15: \\u001b[0m\\u001b[0;1;35mwarning: \\u001b[0m\\u001b[1mequality comparison with extraneous parentheses [-Wparentheses-equality]\\u001b[0m\\n\",\n      \"    if ( ( v  == - 27.0 ) ) {\\n\",\n      \"\\u001b[0;1;32m           ~~~^~~~~~~~~\\n\",\n      \"\\u001b[0m\\u001b[1mCa_HVA.c:264:15: \\u001b[0m\\u001b[0;1;30mnote: \\u001b[0mremove extraneous parentheses around the comparison to silence this warning\\u001b[0m\\n\",\n      \"    if ( ( v  == - 27.0 ) ) {\\n\",\n      \"\\u001b[0;1;32m         ~~   ^         ~~\\n\",\n      \"\\u001b[0m\\u001b[1mCa_HVA.c:264:15: \\u001b[0m\\u001b[0;1;30mnote: \\u001b[0muse '=' to turn this equality comparison into an assignment\\u001b[0m\\n\",\n      \"    if ( ( v  == - 27.0 ) ) {\\n\",\n      \"\\u001b[0;1;32m              ^~\\n\",\n      \"\\u001b[0m\\u001b[0;32m              =\\n\",\n      \"\\u001b[0m -> \\u001b[32mCompiling\\u001b[0m Ih.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c Ih.c -o Ih.o\\n\",\n      \"1 warning generated.\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Im.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c Im.c -o Im.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m K_Pst.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c K_Pst.c -o K_Pst.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m K_Tst.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c K_Tst.c -o K_Tst.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m NaTa_t.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c NaTa_t.c -o NaTa_t.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m NaTs2_t.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c NaTs2_t.c -o NaTs2_t.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m Nap_Et2.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c Nap_Et2.c -o Nap_Et2.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m SK_E2.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c SK_E2.c -o SK_E2.o\\n\",\n      \" -> \\u001b[32mCompiling\\u001b[0m SKv3_1.c\\n\",\n      \"/usr/bin/clang -g  -O2   -I\\\"../../l5pc/mechanisms\\\" -I.   -I/Applications/NEURON/include  -I/usr/local/Cellar/open-mpi/4.0.5/include -fPIC -c SKv3_1.c -o SKv3_1.o\\n\",\n      \" => \\u001b[32mLINKING\\u001b[0m shared library ./libnrnmech.dylib\\n\",\n      \"/usr/bin/clang++ -g  -O2  -std=c++11 -dynamiclib -Wl,-headerpad_max_install_names -undefined dynamic_lookup -fPIC  -I /Applications/NEURON/include -o ./libnrnmech.dylib -Wl,-install_name,@rpath/libnrnmech.dylib \\\\\\n\",\n      \"\\t  ./mod_func.o ./CaDynamics_E2.o ./Ca_HVA.o ./Ca_LVAst.o ./Ih.o ./Im.o ./K_Pst.o ./K_Tst.o ./NaTa_t.o ./NaTs2_t.o ./Nap_Et2.o ./SK_E2.o ./SKv3_1.o  -L/Applications/NEURON/lib -lnrniv -Wl,-rpath,/Applications/NEURON/lib    -lreadline\\n\",\n      \"rm -f ./.libs/libnrnmech.so ; mkdir -p ./.libs ; cp ./libnrnmech.dylib ./.libs/libnrnmech.so\\n\",\n      \"Successfully created x86_64/special\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!pip install LFPy\\n\",\n    \"!pip install MEAutility\\n\",\n    \"!nrnivmodl ../l5pc/mechanisms\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Creating the neuron model and electrode\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's plot the MEA that will be used to record LFP signal:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:>\"\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       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import l5pc_lfpy_model\\n\",\n    \"import MEAutility as MEA\\n\",\n    \"\\n\",\n    \"electrode = l5pc_lfpy_model.define_electrode()\\n\",\n    \"\\n\",\n    \"MEA.plot_probe(electrode)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The models used in the present notebook is based on the L5PC cell presented in examples/l5pc.\\n\",\n    \"However, the present cell model will also include an MEA. The resulting object will therefore be of type `LFPyCellModel` instead of `CellModel`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"<class 'bluepyopt.ephys.models.LFPyCellModel'>\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import bluepyopt.ephys as ephys\\n\",\n    \"l5pc_cell = l5pc_lfpy_model.create()\\n\",\n    \"print(type(l5pc_cell))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Creating the LFPy simulator and evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"To be able to compute the LFP, we need a simulator relying on FLPy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/bluepyopt/ephys/simulators.py:88: UserWarning: Unable to find Neuron hoc shared library in /Applications/NEURON/lib/python/neuron, not disabling banner\\n\",\n      \"  warnings.warn('Unable to find Neuron hoc shared library in %s, '\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"lfpy_sim = ephys.simulators.LFPySimulator(cvode_active=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Then, as for the L5PC example, we create and evaluator from a cell model, a set of fitness protocol (here we only selected a single protocol) and a fitness calculator containig the efeature values the model is evaluated on: \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from l5pc_lfpy_evaluator import define_protocols, define_fitness_calculator\\n\",\n    \"\\n\",\n    \"fitness_protocols = define_protocols()\\n\",\n    \"fitness_calculator = define_fitness_calculator(fitness_protocols, feature_file=\\\"extra_features.json\\\")\\n\",\n    \"\\n\",\n    \"param_names = [\\n\",\n    \"    param.name for param in l5pc_cell.params.values() if not param.frozen\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"evaluator = ephys.evaluators.CellEvaluator(\\n\",\n    \"    cell_model=l5pc_cell,\\n\",\n    \"    param_names=param_names,\\n\",\n    \"    fitness_protocols=fitness_protocols,\\n\",\n    \"    fitness_calculator=fitness_calculator,\\n\",\n    \"    sim=lfpy_sim,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can now run the protocol on the our model and plot the voltage recording at the soma.\\n\",\n    \"\\n\",\n    \"Note that computing the LFP at every electrode of the MEA is computationally intensive. Therefore you can expect the evaluation of the model to be longer than usual.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/bluepyopt/ephys/responses.py:61: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\\n\",\n      \"  self.response['voltage'] = pandas.Series(voltage)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from generate_extra_features import release_params\\n\",\n    \"release_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=release_params)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"Text(0.5, 1.0, 'Somatic voltage recording')\"\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       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"plt.plot(release_responses['Step1.soma.v']['time'], \\n\",\n    \"         release_responses['Step1.soma.v']['voltage']\\n\",\n    \"        )\\n\",\n    \"plt.title(\\\"Somatic voltage recording\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can also plot the LFP recording at every one of the electroe of the MEA:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/folders/x4/6vtsd6lx2fs_zpll9fgn6769mhvgb5/T/ipykernel_82221/2015095035.py:14: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\\n\",\n      \"  fig.show()\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 40 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_MEA_recordings(response, mea_dim=[10, 4]):\\n\",\n    \"    fig, axes = plt.subplots(mea_dim[0], mea_dim[1], figsize=(10,10))\\n\",\n    \"    for index, rec in enumerate(response['voltage']):\\n\",\n    \"        n_row = index%10\\n\",\n    \"        n_col = int(index/10)\\n\",\n    \"        axes[n_row][n_col].plot(response['time'], rec)\\n\",\n    \"        axes[n_row][n_col].get_xaxis().set_ticks([])\\n\",\n    \"        axes[n_row][n_col].get_yaxis().set_ticks([])\\n\",\n    \"        axes[n_row][n_col].spines['top'].set_visible(False)\\n\",\n    \"        axes[n_row][n_col].spines['right'].set_visible(False)\\n\",\n    \"        axes[n_row][n_col].spines['bottom'].set_visible(False)\\n\",\n    \"        axes[n_row][n_col].spines['left'].set_visible(False)\\n\",\n    \"    fig.tight_layout()\\n\",\n    \"    fig.show()\\n\",\n    \"\\n\",\n    \"plot_MEA_recordings(release_responses['Step1.MEA.v'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The evaluator can then be used in the context of optimisation as is demonstrated in the notebook examples/l5pc/L5PC.ipynb.\\n\",\n    \"\\n\",\n    \"For demonstration purppose, let's evaluate the model and plot the extracellular features for each of the electrodes:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/damart/Desktop/BPO_LFPy/BluePyOpt/bluepyopt/ephys/responses.py:61: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\\n\",\n      \"  self.response['voltage'] = pandas.Series(voltage)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"efeature_values = evaluator.evaluate_with_dicts(param_dict=release_params, target='values')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/var/folders/x4/6vtsd6lx2fs_zpll9fgn6769mhvgb5/T/ipykernel_82221/639034954.py:26: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\\n\",\n      \"  fig.show()\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 22 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_MEA_features(efeatures):\\n\",\n    \"    \\n\",\n    \"    rows = 4\\n\",\n    \"    cols = 3\\n\",\n    \"\\n\",\n    \"    fig, axes = plt.subplots(rows, cols, figsize=(10,10))\\n\",\n    \"    index = 0\\n\",\n    \"    for name, feat in efeatures.items():\\n\",\n    \"        if \\\"MEA\\\" not in name:\\n\",\n    \"            continue\\n\",\n    \"        index += 1\\n\",\n    \"        n_row = index%rows\\n\",\n    \"        n_col = int(index/rows)\\n\",\n    \"        im = axes[n_row][n_col].imshow(feat.reshape(10, 4))\\n\",\n    \"        if im:\\n\",\n    \"            fig.colorbar(im, ax=axes[n_row][n_col], location='right')\\n\",\n    \"        axes[n_row][n_col].get_xaxis().set_ticks([])\\n\",\n    \"        axes[n_row][n_col].get_yaxis().set_ticks([])\\n\",\n    \"        axes[n_row][n_col].spines['top'].set_visible(False)\\n\",\n    \"        axes[n_row][n_col].spines['right'].set_visible(False)\\n\",\n    \"        axes[n_row][n_col].spines['bottom'].set_visible(False)\\n\",\n    \"        axes[n_row][n_col].spines['left'].set_visible(False)\\n\",\n    \"        axes[n_row][n_col].set_title(name.replace(\\\"Step1.MEA.\\\", \\\"\\\"))\\n\",\n    \"    axes[0][0].remove()\\n\",\n    \"    fig.tight_layout()\\n\",\n    \"    fig.show()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_MEA_features(efeature_values)\"\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\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/l5pc_lfpy/__init__.py",
    "content": ""
  },
  {
    "path": "examples/l5pc_lfpy/extra_features.json",
    "content": "{\n  \"Step1\": {\n    \"soma\": {\n      \"AP_height\": [\n        28.26404333055272,\n        5.652808666110545\n      ],\n      \"AHP_slow_time\": [\n        0.14049247097637657,\n        0.028098494195275315\n      ],\n      \"ISI_CV\": [\n        0.03312177279548569,\n        0.006624354559097139\n      ],\n      \"doublet_ISI\": [\n        67.10000000001526,\n        13.420000000003052\n      ],\n      \"adaptation_index2\": [\n        -0.0038068052826263975,\n        0.0007613610565252796\n      ],\n      \"mean_frequency\": [\n        7.1022727272775334,\n        1.4204545454555069\n      ],\n      \"AHP_depth_abs_slow\": [\n        -62.28566823165539,\n        12.45713364633108\n      ],\n      \"AP_width\": [\n        0.8857142857136913,\n        0.17714285714273825\n      ],\n      \"time_to_first_spike\": [\n        33.30000000009818,\n        6.660000000019636\n      ],\n      \"AHP_depth_abs\": [\n        -62.132607119558145,\n        12.42652142391163\n      ]\n    },\n    \"MEA\": {\n      \"peak_to_valley\": [\n        [\n          -0.0006900000000000001,\n          -0.0006950000000000001,\n          -0.0006950000000000001,\n          -0.00032,\n          -0.0011300000000000001,\n          0.00067,\n          0.000665,\n          0.00067,\n          0.00067,\n          0.00067,\n          -0.0006900000000000001,\n          -0.0006950000000000001,\n          -0.00072,\n          0.00043000000000000004,\n          -0.0008900000000000001,\n          0.0006550000000000001,\n          0.000665,\n          0.00067,\n          0.00067,\n          0.00067,\n          -0.0006850000000000001,\n          -0.0006900000000000001,\n          -0.0007000000000000001,\n          -0.00072,\n          -0.000635,\n          0.000675,\n          0.00067,\n          0.00067,\n          0.000675,\n          0.00067,\n          -0.0006850000000000001,\n          -0.00068,\n          -0.00068,\n          -0.0006500000000000001,\n          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0.015542566265081374,\n          0.018541690152799915,\n          0.0023211447835187553,\n          0.010633388296387205,\n          0.007385177211404566,\n          0.004788172387229991,\n          0.0032497816036278753,\n          0.0023178889605124857\n        ]\n      ]\n    }\n  }\n}"
  },
  {
    "path": "examples/l5pc_lfpy/generate_extra_features.py",
    "content": "\"\"\"Compute the efeatures generated by the released l5pc model\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport json\nimport numpy\n\nimport bluepyopt.ephys as ephys\nfrom bluepyopt.ephys.extra_features_utils import all_1D_features\n\nfrom l5pc_lfpy_evaluator import create, get_recording_names, get_feature_name, extra_kwargs, config_dir\n\nrelease_params = {\n    'gNaTs2_tbar_NaTs2_t.apical': 0.026145,\n    'gSKv3_1bar_SKv3_1.apical': 0.004226,\n    'gImbar_Im.apical': 0.000143,\n    'gNaTa_tbar_NaTa_t.axonal': 3.137968,\n    'gK_Tstbar_K_Tst.axonal': 0.089259,\n    'gamma_CaDynamics_E2.axonal': 0.002910,\n    'gNap_Et2bar_Nap_Et2.axonal': 0.006827,\n    'gSK_E2bar_SK_E2.axonal': 0.007104,\n    'gCa_HVAbar_Ca_HVA.axonal': 0.000990,\n    'gK_Pstbar_K_Pst.axonal': 0.973538,\n    'gSKv3_1bar_SKv3_1.axonal': 1.021945,\n    'decay_CaDynamics_E2.axonal': 287.198731,\n    'gCa_LVAstbar_Ca_LVAst.axonal': 0.008752,\n    'gamma_CaDynamics_E2.somatic': 0.000609,\n    'gSKv3_1bar_SKv3_1.somatic': 0.303472,\n    'gSK_E2bar_SK_E2.somatic': 0.008407,\n    'gCa_HVAbar_Ca_HVA.somatic': 0.000994,\n    'gNaTs2_tbar_NaTs2_t.somatic': 0.983955,\n    'decay_CaDynamics_E2.somatic': 210.485284,\n    'gCa_LVAstbar_Ca_LVAst.somatic': 0.000333\n}\n\n\nclass NumpyEncoder(json.JSONEncoder):\n    def default(self, obj):\n        if isinstance(\n            obj,\n            (\n                numpy.int_,\n                numpy.intc,\n                numpy.intp,\n                numpy.int8,\n                numpy.int16,\n                numpy.int32,\n                numpy.int64,\n                numpy.uint8,\n                numpy.uint16,\n                numpy.uint32,\n                numpy.uint64,\n            ),\n        ):\n            return int(obj)\n        elif isinstance(\n            obj, (numpy.float16, numpy.float32, numpy.float64)\n        ):\n            return float(obj)\n        elif isinstance(obj, numpy.ndarray):\n            return obj.tolist()\n        return json.JSONEncoder.default(self, obj)\n\n\ndef dict_to_json(data, path):\n    \"\"\"Save some data in a json file.\"\"\"\n    with open(path, \"w\") as f:\n        json.dump(data, f, indent=2, cls=NumpyEncoder)\n\n\ndef add_extra_objectives(evaluator):\n\n    threshold = -20\n\n    for protocol_name in evaluator.fitness_protocols:\n\n        location = 'MEA'\n        recording_names = get_recording_names(protocol_name, location)\n        somatic_recording_name = get_recording_names(protocol_name, \"soma\")[\"\"]\n\n        stimulus = evaluator.fitness_protocols[protocol_name].stimuli[0]\n\n        args = {\n            \"stim_start\": stimulus.step_delay, \n            \"stim_end\": stimulus.step_delay + stimulus.step_duration,\n            \"recording_names\": recording_names,\n            \"threshold\": threshold\n        }\n\n        for feature in all_1D_features:\n\n            feature_name = get_feature_name(protocol_name, location, feature)\n\n            feature = ephys.efeatures.extraFELFeature(\n                name=feature_name,\n                extrafel_feature_name=feature,\n                somatic_recording_name=somatic_recording_name,\n                exp_mean=0.,\n                exp_std=0.1,\n                channel_ids=None,\n                **args,\n                **extra_kwargs\n            )\n\n            objective = ephys.objectives.SingletonObjective(\n                feature_name, feature\n            )\n\n            evaluator.fitness_calculator.objectives.append(objective)\n\n\ndef save_extra_efeatures(efeature_values):\n    \n    path = \"./extra_features.json\"\n    data = {}\n    \n    for feature_name in efeature_values:\n        \n        protocol_name, location, feature  = feature_name.split('.')\n\n        if protocol_name not in data:\n            data[protocol_name] = {}\n\n        if location not in data[protocol_name]:\n            data[protocol_name][location] = {}\n\n        mean = efeature_values[feature_name]\n        std = abs(0.2 * mean)\n\n        data[protocol_name][location][feature] = [mean, std]\n\n    dict_to_json(data, path)\n\n\nif __name__ == \"__main__\":\n\n    # Create evaluator\n    evaluator = create(config_dir / \"features.json\")\n\n    # Add extra features to the evaluator\n    add_extra_objectives(evaluator)\n\n    # Compute features value\n    efeature_values = evaluator.evaluate_with_dicts(param_dict=release_params, target='values')\n\n    # Save feature values in a json\n    save_extra_efeatures(efeature_values)\n"
  },
  {
    "path": "examples/l5pc_lfpy/l5pc_lfpy_evaluator.py",
    "content": "\"\"\"Create evaluator with LFP related efeatures\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport os\nimport json\nimport pathlib\n\nimport l5pc_lfpy_model\n\nimport bluepyopt.ephys as ephys\nfrom bluepyopt.ephys.extra_features_utils import all_1D_features\n\nconfig_dir = pathlib.Path(__file__).parents[1] / \"l5pc\" / \"config\"\n\n\nextra_kwargs = dict(\n    fs=20,\n    fcut=[300, 6000],\n    filt_type=\"filtfilt\",\n    ms_cut=[3, 5],\n    upsample=10\n)\n\n\ndef define_protocols():\n    \"\"\"Define protocols\"\"\"\n    \n    protocol_name = \"Step1\"\n\n    protocol_definition = json.load(open(config_dir / \"protocols.json\")\n                                    )[protocol_name]\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name=\"soma\", seclist_name=\"somatic\", sec_index=0, comp_x=0.5\n    )\n\n    # By default include somatic recording\n    somav_recording = ephys.recordings.CompRecording(\n        name=\"%s.soma.v\" % protocol_name, location=soma_loc, variable=\"v\"\n    )\n    mea_recording = ephys.recordings.LFPRecording(\"%s.MEA.v\" % protocol_name)\n    \n    recordings = [somav_recording, mea_recording]\n\n    stimuli = []\n    for stimulus_definition in protocol_definition[\"stimuli\"]:\n        stimuli.append(\n            ephys.stimuli.LFPySquarePulse(\n                step_amplitude=stimulus_definition[\"amp\"],\n                step_delay=stimulus_definition[\"delay\"],\n                step_duration=stimulus_definition[\"duration\"],\n                location=soma_loc,\n                total_duration=stimulus_definition[\"totduration\"],\n            )\n        )\n\n    return {\n        protocol_name: ephys.protocols.SweepProtocol(\n            protocol_name, stimuli, recordings)\n    }\n\n\ndef get_feature_name(protocol_name, location, feature):\n    return \"%s.%s.%s\" % (protocol_name, location, feature)\n\n\ndef get_recording_names(protocol_name, location=None):\n    return {\"\": \"%s.%s.v\" % (protocol_name, location)}\n\n\ndef define_fitness_calculator(protocols, feature_file):\n\n    with open(feature_file, \"r\") as f:\n        feature_definitions = json.load(f)\n\n    objectives = []\n    threshold = -20\n\n    for protocol_name in protocols:\n        for location, features in feature_definitions[protocol_name].items():\n            recording_names = get_recording_names(protocol_name, location)\n            for efel_feature_name, meanstd in features.items():\n                feature_name = get_feature_name(protocol_name, location, efel_feature_name)\n\n                stimulus = protocols[protocol_name].stimuli[0]\n\n                args = {\n                    \"name\": feature_name,\n                    \"stim_start\": stimulus.step_delay, \n                    \"stim_end\": stimulus.step_delay + stimulus.step_duration,\n                    \"exp_mean\": meanstd[0],\n                    \"exp_std\": meanstd[1],\n                    \"recording_names\": recording_names,\n                    \"threshold\": threshold,\n                }\n\n                if \"MEA\" not in location:\n                    feature = ephys.efeatures.eFELFeature(\n                        efel_feature_name=efel_feature_name,\n                        stimulus_current=stimulus.step_amplitude,\n                        **args\n                    )\n                else:\n                    somatic_recording_name = recording_names[\"\"].replace(\"MEA\", \"soma\")\n                    feature = ephys.efeatures.extraFELFeature(\n                        extrafel_feature_name=efel_feature_name,\n                        somatic_recording_name=somatic_recording_name,\n                        channel_ids=None,\n                        **args,\n                        **extra_kwargs\n                    )\n\n                objective = ephys.objectives.SingletonObjective(\n                    feature_name, feature\n                )\n                objectives.append(objective)\n\n    return ephys.objectivescalculators.ObjectivesCalculator(objectives)\n\n\ndef create(feature_file=\"extra_features.json\", cvode_active=True, dt=None):\n    \"\"\"Setup\"\"\"\n\n    l5pc_cell = l5pc_lfpy_model.create()\n\n    fitness_protocols = define_protocols()\n    fitness_calculator = define_fitness_calculator(fitness_protocols, feature_file)\n\n    param_names = [\n        param.name for param in l5pc_cell.params.values() if not param.frozen\n    ]\n\n    lfpy_sim = ephys.simulators.LFPySimulator(cvode_active=cvode_active, dt=dt)\n\n    return ephys.evaluators.CellEvaluator(\n        cell_model=l5pc_cell,\n        param_names=param_names,\n        fitness_protocols=fitness_protocols,\n        fitness_calculator=fitness_calculator,\n        sim=lfpy_sim,\n    )\n"
  },
  {
    "path": "examples/l5pc_lfpy/l5pc_lfpy_model.py",
    "content": "\"\"\"Create l5pc model with MEA\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2022, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport sys\nimport os\nimport MEAutility as mu\n\nimport bluepyopt\nimport bluepyopt.ephys as ephys\n\nL5PC_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '../l5pc'))\nsys.path.insert(0, L5PC_PATH)\n\nimport l5pc_model\n\n\ndef define_electrode(\n    probe_center=[0, 300, 20],\n    mea_dim=[10, 4],\n    mea_pitch=[300, 300],\n):\n    \"\"\"\n    Defines LFPy electrode object\n    Parameters\n    ----------\n    probe_center: 3d array\n        The center of the probe\n    mea_dim: 2d array\n        Dimensions of planar probe (nrows, ncols)\n    mea_pitch: 3d arraay\n        The pitch of the planar probe (row pitch, column pitch)\n    Returns\n    -------\n    electrode: MEAutility.MEA object\n        The MEAutility electrode object\n    \"\"\"\n\n    mea_info = {\n        'dim': mea_dim,\n        'electrode_name': 'hd-mea',\n        'pitch': mea_pitch,\n        'shape': 'square',\n        'size': 5,\n        'type': 'mea',\n        'plane': 'xy'\n    }\n    probe = mu.return_mea(info=mea_info)\n\n    # Move the MEA out of the neuron plane (yz)\n    probe.move(probe_center)\n\n    return probe\n\n\ndef create():\n\n    l5pc_cell = ephys.models.LFPyCellModel(\n        \"l5pc_lfpy\",\n        morph=l5pc_model.define_morphology(do_replace_axon=True),\n        mechs=l5pc_model.define_mechanisms(),\n        params=l5pc_model.define_parameters(),\n        electrode=define_electrode()\n    )\n\n    return l5pc_cell\n"
  },
  {
    "path": "examples/metaparameters/.gitignore",
    "content": "/metaparameters.py\n"
  },
  {
    "path": "examples/metaparameters/metaparameters.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Creating an optimisation with meta parameters\\n\",\n    \"\\n\",\n    \"This notebook will explain how to set up an optimisation that uses metaparameters (parameters that control other parameters)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!pip install -q matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%load_ext autoreload\\n\",\n    \"%autoreload\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First we need to import the module that contains all the functionality to create electrical cell models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import bluepyopt as bpop\\n\",\n    \"import bluepyopt.ephys as ephys\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you want to see a lot of information about the internals, \\n\",\n    \"the verbose level can be set to 'debug' by commenting out\\n\",\n    \"the following lines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# import logging\\n\",\n    \"# logger = logging.getLogger()\\n\",\n    \"# logger.setLevel(logging.DEBUG)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Setting up the cell\\n\",\n    \"---------------------\\n\",\n    \"This is very similar to the simplecell example in the directory above. For a more detailed explanation, look there.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"morph = ephys.morphologies.NrnFileMorphology('twocompartment.swc')\\n\",\n    \"somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic')\\n\",\n    \"axonal_loc = ephys.locations.NrnSeclistLocation('axonal', seclist_name='axonal')\\n\",\n    \"hh_mech = ephys.mechanisms.NrnMODMechanism(\\n\",\n    \"        name='hh',\\n\",\n    \"        suffix='hh',\\n\",\n    \"        locations=[somatic_loc, axonal_loc])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"For this example we will create two separate parameters to store the specific capacitance. One for the soma and one for the soma. We will put a metaparameter on top of these two to keep the value between soma and axon the same.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cm_param_soma = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='cm_soma',\\n\",\n    \"        param_name='cm',\\n\",\n    \"        locations=[somatic_loc])\\n\",\n    \"cm_param_axon = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='cm_axon',\\n\",\n    \"        param_name='cm',\\n\",\n    \"        locations=[axonal_loc])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The metaparameter, the one that will be optimised, will make sure the two parameters above keep always the same value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cm_metaparam = ephys.parameters.NrnMetaListEqualParameter(\\n\",\n    \"        name='cm_meta',\\n\",\n    \"        bounds=[0.5, 1.5],\\n\",\n    \"        sub_parameters=[cm_param_soma, cm_param_axon])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be not be optimised but are frozen.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"gnabar_param = ephys.parameters.NrnSectionParameter(                                    \\n\",\n    \"        name='gnabar_hh',\\n\",\n    \"        param_name='gnabar_hh',\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        value=0.1,\\n\",\n    \"        frozen=True)     \\n\",\n    \"gkbar_param = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='gkbar_hh',\\n\",\n    \"        param_name='gkbar_hh',\\n\",\n    \"        value=0.03,\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        frozen=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating the template\\n\",\n    \"\\n\",\n    \"To create the cell template, we pass all these objects to the constructor of the template.\\n\",\n    \"We *only* put the metaparameter, not its subparameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"simple_cell = ephys.models.CellModel(\\n\",\n    \"        name='simple_cell',\\n\",\n    \"        morph=morph,\\n\",\n    \"        mechs=[hh_mech],\\n\",\n    \"        params=[cm_metaparam, gnabar_param, gkbar_param])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can print out a description of the cell\"\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      \"simple_cell:\\n\",\n      \"  morphology:\\n\",\n      \"    twocompartment.swc\\n\",\n      \"  mechanisms:\\n\",\n      \"    hh: hh at ['somatic', 'axonal']\\n\",\n      \"  params:\\n\",\n      \"    cm_meta (sub_params: cm_soma: ['somatic'] cm = None,cm_axon: ['axonal'] cm = None): value = [0.5, 1.5]\\n\",\n      \"    gnabar_hh: ['somatic'] gnabar_hh = 0.1\\n\",\n      \"    gkbar_hh: ['somatic'] gkbar_hh = 0.03\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(simple_cell)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"With this cell we can build a cell evaluator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Setting up a cell evaluator\\n\",\n    \"\\n\",\n    \"To optimise the parameters of the cell we need to create cell evaluator object. \\n\",\n    \"This object will need to know which protocols to inject, which parameters to optimise, etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating the protocols\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"soma_loc = ephys.locations.NrnSeclistCompLocation(\\n\",\n    \"        name='soma',\\n\",\n    \"        seclist_name='somatic',\\n\",\n    \"        sec_index=0,\\n\",\n    \"        comp_x=0.5)\\n\",\n    \"sweep_protocols = []\\n\",\n    \"for protocol_name, amplitude in [('step1', 0.05)]:\\n\",\n    \"    stim = ephys.stimuli.NrnSquarePulse(\\n\",\n    \"                step_amplitude=amplitude,\\n\",\n    \"                step_delay=100,\\n\",\n    \"                step_duration=50,\\n\",\n    \"                location=soma_loc,\\n\",\n    \"                total_duration=200)\\n\",\n    \"    rec = ephys.recordings.CompRecording(\\n\",\n    \"            name='%s.soma.v' % protocol_name,\\n\",\n    \"            location=soma_loc,\\n\",\n    \"            variable='v')\\n\",\n    \"    protocol = ephys.protocols.SweepProtocol(protocol_name, [stim], [rec])\\n\",\n    \"    sweep_protocols.append(protocol)\\n\",\n    \"twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Running a protocol on a cell\\n\",\n    \"\\n\",\n    \"Now we're at a stage where we can actually run a protocol on the cell. We first need to create a Simulator object.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/werner/src/bluepyopt/bluepyopt/ephys/simulators.py:74: UserWarning: Unable to find Neuron hoc shared library in /Users/werner/local/nrnnogui/lib/python/neuron, not disabling banner\\n\",\n      \"  'not disabling banner' % nrnpy_path)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"nrn = ephys.simulators.NrnSimulator()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The run() method of a protocol accepts a cell model, a set of parameter values and a simulator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"default_params = {'cm_meta': 1.0}\\n\",\n    \"responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=nrn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plotting the response traces is now easy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_responses(responses):\\n\",\n    \"    plt.subplot(1,1,1)\\n\",\n    \"    plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\\n\",\n    \"    plt.legend()\\n\",\n    \"    \\n\",\n    \"    plt.tight_layout()\\n\",\n    \"\\n\",\n    \"plot_responses(responses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Defining eFeatures and objectives\\n\",\n    \"\\n\",\n    \"For every response we need to define a set of eFeatures we will use for the fitness calculation later. We have to combine features together into objectives that will be used by the optimalisation algorithm. In this case we will create one objective per feature:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"efel_feature_means = {'step1': {'Spikecount': 4}}\\n\",\n    \"\\n\",\n    \"objectives = []\\n\",\n    \"\\n\",\n    \"for protocol in sweep_protocols:\\n\",\n    \"    stim_start = protocol.stimuli[0].step_delay\\n\",\n    \"    stim_end = stim_start + protocol.stimuli[0].step_duration\\n\",\n    \"    for efel_feature_name, mean in efel_feature_means[protocol.name].items():\\n\",\n    \"        feature_name = '%s.%s' % (protocol.name, efel_feature_name)\\n\",\n    \"        feature = ephys.efeatures.eFELFeature(\\n\",\n    \"                    feature_name,\\n\",\n    \"                    efel_feature_name=efel_feature_name,\\n\",\n    \"                    recording_names={'': '%s.soma.v' % protocol.name},\\n\",\n    \"                    stim_start=stim_start,\\n\",\n    \"                    stim_end=stim_end,\\n\",\n    \"                    exp_mean=mean,\\n\",\n    \"                    exp_std=0.05 * mean)\\n\",\n    \"        objective = ephys.objectives.SingletonObjective(\\n\",\n    \"            feature_name,\\n\",\n    \"            feature)\\n\",\n    \"        objectives.append(objective)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating the cell evaluator\\n\",\n    \"\\n\",\n    \"We will need an object that can use these objective definitions to calculate the scores from a protocol response. This is called a ScoreCalculator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Combining everything together we have a CellEvaluator. The CellEvaluator constructor has a field 'parameter_names' which contains the (ordered) list of names of the parameters that are used as input (and will be fitted later on).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cell_evaluator = ephys.evaluators.CellEvaluator(\\n\",\n    \"        cell_model=simple_cell,\\n\",\n    \"        param_names=['cm_meta'],\\n\",\n    \"        fitness_protocols={twostep_protocol.name: twostep_protocol},\\n\",\n    \"        fitness_calculator=score_calc,\\n\",\n    \"        sim=nrn)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Evaluating the cell\\n\",\n    \"\\n\",\n    \"The cell can now be evaluate for a certain set of parameter values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step1.Spikecount': 0.0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(cell_evaluator.evaluate_with_dicts(default_params))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Setting up and running an optimisation\\n\",\n    \"\\n\",\n    \"Now that we have a cell template and an evaluator for this cell, we can set up an optimisation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = bpop.optimisations.DEAPOptimisation(\\n\",\n    \"        evaluator=cell_evaluator,\\n\",\n    \"        offspring_size = 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And this optimisation can be run for a certain number of generations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \\n\",\n    \"\\n\",\n    \"The final population contains a list of tuples, with each tuple representing the two parameters of the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"('Final population: ', [[1.131498179416234], [1.054460177424259], [1.1387038392017454], [1.1274290577322232], [1.2128668362121315], [1.054460177424259], [1.2153949563172928], [1.2153949563172928], [1.2153949563172928], [1.2153949563172928], [1.0068218267063997], [1.1133807857168363], [1.1277234274235448], [1.1384094695104239], [1.1560133870673583], [1.0837685752835604], [1.4421613763075767], [1.2153949563172928], [1.0437064815624266], [1.2153949563172928]])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Final population: ', final_pop)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best individual found during the optimisation is the first individual of the hall of fame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"('Best individual: ', [1.2724611068862803])\\n\",\n      \"('Fitness values: ', (0.0,))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind = hall_of_fame[0]\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step1.Spikecount': 0.0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind_dict = cell_evaluator.param_dict(best_ind)\\n\",\n    \"print(cell_evaluator.evaluate_with_dicts(best_ind_dict))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \\n\",\n    \"We can have a look at the responses now.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's have a look at the optimisation statistics.\\n\",\n    \"We can plot the minimal score (sum of all objective scores) found in every optimisation. \\n\",\n    \"The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/werner/.pyenv/versions/2.7.14/lib/python2.7/site-packages/matplotlib/axes/_base.py:3471: UserWarning: Attempting to set identical bottom==top results\\n\",\n      \"in singular transformations; automatically expanding.\\n\",\n      \"bottom=0.0, top=0.0\\n\",\n      \"  'bottom=%s, top=%s') % (bottom, top))\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(-0.001, 0.001)\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy\\n\",\n    \"gen_numbers = logs.select('gen')\\n\",\n    \"min_fitness = logs.select('min')\\n\",\n    \"max_fitness = logs.select('max')\\n\",\n    \"plt.plot(gen_numbers, min_fitness, label='min fitness')\\n\",\n    \"plt.xlabel('generation #')\\n\",\n    \"plt.ylabel('score (# std)')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \\n\",\n    \"plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) \"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.14\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "examples/metaparameters/twocompartment.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n4 2 5.0 0.0 0.0 1.0 3\n5 2 10.0 0.0 0.0 1.0 4\n"
  },
  {
    "path": "examples/neuroml/neuroml.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0eb6d681-8042-48fa-9a7e-d094b5b13eb2\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Exporting a cell in the neuroml format and running it\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5eddcf29-5590-48d5-a936-bef388601471\",\n   \"metadata\": {},\n   \"source\": [\n    \"To run this notebook, it is necessary to have bluepyopt, pyneuroml and libNeuroML installed. This can be achieved with the following pip install:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"ee91d4f4-1bba-4cc1-a172-90aab83c38f7\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"pip install bluepyopt[neuroml]\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"id\": \"63d69fd0-dd92-424b-b38a-be50f03e3dd7\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that the bluepyopt neuroml module cannot yet handle:\\n\",\n    \"- non uniform parameter\\n\",\n    \"- axon replacement\\n\",\n    \"- stochasticity\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8027678b-0701-467f-b90f-dfb6530d2969\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Exporting a cell to neuroml\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"487c93d9-ba25-4b1b-9c69-2b4fb7ecb9e7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"from pyneuroml import pynml\\n\",\n    \"from bluepyopt.neuroml import cell\\n\",\n    \"from bluepyopt.neuroml import simulation\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"id\": \"00917451-185c-4885-ab95-7256f195190e\",\n   \"metadata\": {},\n   \"source\": [\n    \"For simplicity, in this notebook we will use the cell from bluepyopt's l5pc example:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"10394750-fd29-43fa-ac7f-3910ebf2b433\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"L5PC_PATH = os.path.abspath(\\\"../l5pc\\\")\\n\",\n    \"sys.path.insert(0, L5PC_PATH)\\n\",\n    \"\\n\",\n    \"import l5pc_model\\n\",\n    \"\\n\",\n    \"l5pc_cell = l5pc_model.create()\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d9c3323-c1da-4272-b3ab-9eeac3391743\",\n   \"metadata\": {},\n   \"source\": [\n    \"We have to disable replace_axon, since it is not supported by bluepyopt's neuroml module yet:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"ac925939-fd13-480b-ae05-ce744330f51e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"l5pc_cell.morphology.do_replace_axon = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73b4697a-a403-45cf-b0ff-e22564cd14e6\",\n   \"metadata\": {},\n   \"source\": [\n    \"We have to define the cell's optimised parameters:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"218482bc-9a2f-4627-9c4b-3ffaa3b9e595\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"release_params = {\\n\",\n    \"    \\\"gNaTs2_tbar_NaTs2_t.apical\\\": 0.026145,\\n\",\n    \"    \\\"gSKv3_1bar_SKv3_1.apical\\\": 0.004226,\\n\",\n    \"    \\\"gImbar_Im.apical\\\": 0.000143,\\n\",\n    \"    \\\"gNaTa_tbar_NaTa_t.axonal\\\": 3.137968,\\n\",\n    \"    \\\"gK_Tstbar_K_Tst.axonal\\\": 0.089259,\\n\",\n    \"    \\\"gamma_CaDynamics_E2.axonal\\\": 0.002910,\\n\",\n    \"    \\\"gNap_Et2bar_Nap_Et2.axonal\\\": 0.006827,\\n\",\n    \"    \\\"gSK_E2bar_SK_E2.axonal\\\": 0.007104,\\n\",\n    \"    \\\"gCa_HVAbar_Ca_HVA.axonal\\\": 0.000990,\\n\",\n    \"    \\\"gK_Pstbar_K_Pst.axonal\\\": 0.973538,\\n\",\n    \"    \\\"gSKv3_1bar_SKv3_1.axonal\\\": 1.021945,\\n\",\n    \"    \\\"decay_CaDynamics_E2.axonal\\\": 287.198731,\\n\",\n    \"    \\\"gCa_LVAstbar_Ca_LVAst.axonal\\\": 0.008752,\\n\",\n    \"    \\\"gamma_CaDynamics_E2.somatic\\\": 0.000609,\\n\",\n    \"    \\\"gSKv3_1bar_SKv3_1.somatic\\\": 0.303472,\\n\",\n    \"    \\\"gSK_E2bar_SK_E2.somatic\\\": 0.008407,\\n\",\n    \"    \\\"gCa_HVAbar_Ca_HVA.somatic\\\": 0.000994,\\n\",\n    \"    \\\"gNaTs2_tbar_NaTs2_t.somatic\\\": 0.983955,\\n\",\n    \"    \\\"decay_CaDynamics_E2.somatic\\\": 210.485284,\\n\",\n    \"    \\\"gCa_LVAstbar_Ca_LVAst.somatic\\\": 0.000333,\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3bdf4cd0-6ebd-4294-9f95-5accaf76c098\",\n   \"metadata\": {},\n   \"source\": [\n    \"And we have to compile the mechanisms:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f4728e2d-a890-40f3-b8a7-4dad84eb8a2f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"os.system(\\\"nrnivmodl ../l5pc/mechanisms/\\\")\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"id\": \"5a08bdc0-66da-486b-a91a-52be1ec01385\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, we can create the neuroml cell. Using `skip_channels_copy=False` will copy the neuroml mechanisms from `bluepyopt/neuroml/NeuroML2_mechanisms/` to a folder named `channels`.\\n\",\n    \"\\n\",\n    \"This creates:\\n\",\n    \"- a neuroml cell file named after the bluepyopt cell's name: Here, `l5pc_0_0.cell.nml`.\\n\",\n    \"- a neuroml network file containing the neuroml cell, named after the bluepyopt cell's name. Here, `l5pc.net.nml`.\\n\",\n    \"\\n\",\n    \"Skip this step if you want to use custom mechanisms not present in `bluepyopt/neuroml/NeuroML2_mechanisms/`.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"7d2469c6-8931-4da3-9dae-f89c59ee10d6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cell.create_neuroml_cell(\\n\",\n    \"    l5pc_cell, release_params, skip_channels_copy=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"id\": \"a0db4c6b\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you want to create a neuroml cell with custom mechanisms that are not present in `bluepyopt/neuroml/NeuroML2_mechanisms/`, you will have to:\\n\",\n    \"- copy your custom mechanisms in the `.nml` format in the `./channels` directory\\n\",\n    \"- give as argument `custom_channel_ion`, a dict mapping channel name to ion name, e.g. `custom_channel_ion = {\\\"NaCustom\\\": \\\"na\\\"}`\\n\",\n    \"- if one of the ion in `custom_channel_ion` is not in pre-registered ions (na, k, hcn, ca, pas), you'll also have to give as argument `custom_ion_erevs`, a dict mapping ions to their reversal potential\\n\",\n    \"\\n\",\n    \"Below is an example of how to use the `create_neuroml_cell` function with custom mechanisms.\\n\",\n    \"\\n\",\n    \"However, be aware that `create_neuroml_cell` might not be able to deal with any given custom mechanism, and some might break it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"adf01098\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"custom_channel_ion = {\\\"Cl_custom_mech_name\\\": \\\"cl\\\"}\\n\",\n    \"custom_ion_erevs = {\\\"cl\\\": \\\"-60.0 mV\\\"}\\n\",\n    \"\\n\",\n    \"cell.create_neuroml_cell(\\n\",\n    \"    l5pc_cell, release_params, skip_channels_copy=False, custom_channel_ion=custom_channel_ion, custom_ion_erevs=custom_ion_erevs,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"192726fa-6809-4667-8ff7-b786537528b8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Creating a LEMS simulation able to run the neuroml cell\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ec02ca3a-fc6c-4155-83c4-2925d89e013e\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, we have to input the name of the neuroml network created with the cell at the previous step.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"aec6b630-1239-4234-a425-06405e4a1e8e\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"network_filename = f\\\"{l5pc_cell.name}.net.nml\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d1574c6b-9fe1-4d1d-9a55-36bd26bb0c3f\",\n   \"metadata\": {},\n   \"source\": [\n    \"We have to get the protocols. Here, we can get the ones from the bluepyopt l5pc example.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"51c0cb30-4de6-4df7-9519-9a6027e39ea1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import l5pc_evaluator\\n\",\n    \"\\n\",\n    \"protocols = l5pc_evaluator.define_protocols()\\n\",\n    \"protocol_name = \\\"Step3\\\"\\n\",\n    \"bpo_test_protocol = protocols[protocol_name]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"88b8e31f-146b-40cf-8a23-6e7a00531cca\",\n   \"metadata\": {},\n   \"source\": [\n    \"We also have to define a timestep.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"80a4b756-3091-4643-99ea-300603632f78\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"dt = 0.025\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"725c81cf-bc9a-4989-b3e4-68aae28a1cca\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, we have to define the name of the LEMS simulation file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"b8364c84-6438-4b81-9c48-984070baa74e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lems_filename = f\\\"LEMS_{l5pc_cell.name}.xml\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"369f1a56-5a0d-4ce6-a6d4-648d2c68e16a\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can  now create the LEMS simulation file.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"05416e97-7167-48d6-8b1d-14b4fe465d92\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"simulation.create_neuroml_simulation(\\n\",\n    \"    network_filename, bpo_test_protocol, dt, l5pc_cell.name, lems_filename\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bce6ca89-4eb3-41d3-9f2d-ffaad46910c1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Run the simulation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"53225126-af48-4729-8715-d7cd53f0b888\",\n   \"metadata\": {},\n   \"source\": [\n    \"First, we have to remove the compiled mechanisms for the LEMS simulation to run without issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"8a06da11-ef5d-41ce-99ae-c683661ddd4c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"os.system(\\\"rm -rf x86_64/\\\")\"\n   ]\n  },\n  {\n   \"attachments\": {},\n   \"cell_type\": \"markdown\",\n   \"id\": \"8ca18971\",\n   \"metadata\": {},\n   \"source\": [\n    \"Before running the LEMS simulation, you might have to set the env variable NEURON_HOME in your jupyter kernel. Change the following jupyter cell accordingly to set your NEURON_HOME variable. It should link to the parent directory of the nrniv executable, up to but not including bin.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"a6f182b5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%env NEURON_HOME=path/to/neuron/home\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2162fbeb-4eb3-4fac-8bac-c23f47f5faee\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since the default jNeuroML simulator can only simulate single compartment cells, we will run the simulation with the NEURON simulator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"7eec49fb-5dd5-4675-bb23-8bb1c5087f16\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pynml.run_lems_with_jneuroml_neuron(\\n\",\n    \"    lems_filename, nogui=True, plot=False\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"170d1c75-40f8-4415-9244-5688a8d497d3\",\n   \"metadata\": {},\n   \"source\": [\n    \"This will output a trace in the following file: `l5pc.Pop_l5pc_0_0.v.dat`. Note that the data are recorded in volts and seconds.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"myenv-py310\",\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\": \"3.10.8 (main, Oct 13 2022, 10:17:43) [Clang 14.0.0 (clang-1400.0.29.102)]\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"891adc0cb08a4acf2327ce13c187b82b86b16ff1d60877363e9705a5e6a5aa86\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "examples/simplecell/.gitignore",
    "content": "/.ipynb_checkpoints/\n/simplecell.py\n/simplecell_arbor.py\n"
  },
  {
    "path": "examples/simplecell/checkpoints/.gitignore",
    "content": "/*.pkl\n"
  },
  {
    "path": "examples/simplecell/figures/.gitignore",
    "content": "/*.eps\n"
  },
  {
    "path": "examples/simplecell/generate_acc.py",
    "content": "#!/usr/bin/env python\n\n'''Example for generating a mixed JSON/ACC Arbor cable cell description (with optional axon-replacement)\n\n $ python generate_acc.py --output-dir test_acc/ --replace-axon\n\n Will save 'simple_cell.json', 'simple_cell_label_dict.acc' and 'simple_cell_decor.acc'\n into the folder 'test_acc' that can be loaded in Arbor with:\n     'cell_json, morpho, decor, labels = \\\n        ephys.create_acc.read_acc(\"test_acc/simple_cell_cell.json\")'\n An Arbor cable cell is then created with\n     'cell = arbor.cable_cell(morphology=morpho, decor=decor, labels=labels)'\n The resulting cable cell can be output to ACC for visual inspection \n and e.g. validating/deriving custom Arbor locset/region/iexpr \n expressions in the Arbor GUI (File > Cable cell > Load) using\n     'arbor.write_component(cell, \"simple_cell_cable_cell.acc\")'\n'''\nimport argparse\n\nfrom bluepyopt import ephys\n\nimport simplecell_model\nfrom generate_hoc import param_values\n\n\ndef main():\n    '''main'''\n    parser = argparse.ArgumentParser(\n        formatter_class=argparse.RawDescriptionHelpFormatter,\n        description=__doc__)\n    parser.add_argument('-o', '--output-dir', dest='output_dir',\n                        help='Output directory for JSON/ACC files')\n    parser.add_argument('-ra', '--replace-axon', action='store_true',\n                        help='Replace axon with Neuron-dependent policy')\n    args = parser.parse_args()\n\n    cell = simplecell_model.create(do_replace_axon=args.replace_axon)\n    if args.replace_axon:\n        nrn_sim = ephys.simulators.NrnSimulator()\n        cell.instantiate_morphology_3d(nrn_sim)\n\n    # Add modcc-compiled external mechanisms catalogues here\n    # ext_catalogues = {'cat-name': 'path/to/nmodl-dir', ...}\n\n    if args.output_dir is not None:\n        cell.write_acc(args.output_dir,\n                       param_values,\n                       # ext_catalogues=ext_catalogues,\n                       create_mod_morph=True)\n    else:\n        output = cell.create_acc(\n            param_values,\n            template='acc/*_template.jinja2',\n            # ext_catalogues=ext_catalogues,\n            create_mod_morph=True)\n        for el, val in output.items():\n            print(\"%s:\\n%s\\n\" % (el, val))\n\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/simplecell/generate_hoc.py",
    "content": "#!/usr/bin/env python\n\n'''Example for generating a hoc template\n\n $ python generate_hoc.py > test.hoc\n\n Will save 'test.hoc' file, which can be loaded in neuron with:\n     'load_file(\"test.hoc\")'\n Then the hoc template needs to be instantiated with a morphology\n     CCell(\"ignored\", \"path/to/morphology.swc\")\n'''\nimport sys\n\nimport simplecell_model\n\n\nparam_values = {\n    'gnabar_hh': 0.10299326453483033, \n    'gkbar_hh': 0.027124836082684685\n}\n\n\ndef main():\n    '''main'''\n    cell = simplecell_model.create(do_replace_axon=True)\n\n    output = cell.create_hoc(param_values, template='cell_template.jinja2')\n    print(output)\n\n\nif __name__ == '__main__':\n    if '-h' in sys.argv or '--help' in sys.argv:\n        print(__doc__)\n    else:\n        main()\n"
  },
  {
    "path": "examples/simplecell/simple.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n"
  },
  {
    "path": "examples/simplecell/simplecell-paperfig.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Creating a simple cell optimisation\\n\",\n    \"\\n\",\n    \"This notebook will explain how to set up an optimisation of simple single compartmental cell with two free parameters that need to be optimised\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib notebook\\n\",\n    \"import matplotlib\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"plt.rcParams['lines.linewidth'] = 2\\n\",\n    \"%load_ext autoreload\\n\",\n    \"%autoreload\\n\",\n    \"import os\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First we need to import the module that contains all the functionality to create electrical cell models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import bluepyopt as bpop\\n\",\n    \"import bluepyopt.ephys as ephys\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you want to see a lot of information about the internals, \\n\",\n    \"the verbose level can be set to 'debug' by commenting out\\n\",\n    \"the following lines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import logging\\n\",\n    \"logger = logging.getLogger()\\n\",\n    \"logger.setLevel(logging.DEBUG)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Setting up a cell template\\n\",\n    \"-------------------------\\n\",\n    \"First a template that will describe the cell has to be defined. A template consists of:\\n\",\n    \"* a morphology\\n\",\n    \"* model mechanisms\\n\",\n    \"* model parameters\\n\",\n    \"\\n\",\n    \"### Creating a morphology\\n\",\n    \"A morphology can be loaded from a file (SWC or ASC).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"morph = ephys.morphologies.NrnFileMorphology('simple.swc')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By default a Neuron morphology has the following sectionlists: somatic, axonal, apical and basal. Let's create an object that points to the somatic sectionlist. This object will be used later to specify where mechanisms have to be added etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating a mechanism\\n\",\n    \"\\n\",\n    \"Now we can add ion channels to this morphology. Let's add the default Neuron Hodgkin-Huxley mechanism to the soma. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"hh_mech = ephys.mechanisms.NrnMODMechanism(\\n\",\n    \"        name='hh',\\n\",\n    \"        prefix='hh',\\n\",\n    \"        locations=[somatic_loc])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"The 'name' field can be chosen by the user, this name should be unique. The 'prefix' points to the same field in the NMODL file of the channel. 'locations' specifies which sections the mechanism will be added to.\\n\",\n    \"\\n\",\n    \"### Creating parameters\\n\",\n    \"\\n\",\n    \"Next we need to specify the parameters of the model. A parameter can be in two states: frozen and not-frozen. When a parameter is frozen it has an exact value, otherwise it only has some bounds but the exact value is not known yet.\\n\",\n    \"Let's first a parameter that sets the capacitance of the soma to a frozen value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"cm_param = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='cm',\\n\",\n    \"        param_name='cm',\\n\",\n    \"        value=1.0,\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        frozen=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be optimised later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gnabar_param = ephys.parameters.NrnSectionParameter(                                    \\n\",\n    \"        name='gnabar_hh',\\n\",\n    \"        param_name='gnabar_hh',\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        bounds=[0, .2],\\n\",\n    \"        frozen=False)     \\n\",\n    \"gkbar_param = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='gkbar_hh',\\n\",\n    \"        param_name='gkbar_hh',\\n\",\n    \"        bounds=[0, .1],\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        frozen=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating the template\\n\",\n    \"\\n\",\n    \"To create the cell template, we pass all these objects to the constructor of the template\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"simple_cell = ephys.models.CellModel(\\n\",\n    \"        name='simple_cell',\\n\",\n    \"        morph=morph,\\n\",\n    \"        mechs=[hh_mech],\\n\",\n    \"        params=[cm_param, gnabar_param, gkbar_param])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can print out a description of the cell\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"simple_cell:\\n\",\n      \"  morphology:\\n\",\n      \"    simple.swc\\n\",\n      \"  mechanisms:\\n\",\n      \"    hh: ['somatic'] hh\\n\",\n      \"  params:\\n\",\n      \"    cm: ['somatic'] cm = 1.0\\n\",\n      \"    gnabar_hh: ['somatic'] gnabar_hh = [0, 0.2]\\n\",\n      \"    gkbar_hh: ['somatic'] gkbar_hh = [0, 0.1]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(simple_cell)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"With this cell we can build a cell evaluator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Setting up a cell evaluator\\n\",\n    \"\\n\",\n    \"To optimise the parameters of the cell we need to create cell evaluator object. \\n\",\n    \"This object will need to know which protocols to injection, which parameters to optimise, etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating the protocols\\n\",\n    \"\\n\",\n    \"A protocol consists of a set of stimuli, and a set of responses (i.e. recordings). These responses will later be used by a calculate\\n\",\n    \"the score of the parameter values.\\n\",\n    \"Let's create two protocols, two square current pulse at somatic[0](0.5) with different amplitudes.\\n\",\n    \"We first need to create a location object\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"soma_loc = ephys.locations.NrnSeclistCompLocation(\\n\",\n    \"        name='soma',\\n\",\n    \"        seclist_name='somatic',\\n\",\n    \"        sec_index=0,\\n\",\n    \"        comp_x=0.5)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"and then the stimuli, recordings and protocols. For each protocol we add a recording and a stimulus in the soma.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sweep_protocols = []\\n\",\n    \"for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\\n\",\n    \"    stim = ephys.stimuli.NrnSquarePulse(\\n\",\n    \"                step_amplitude=amplitude,\\n\",\n    \"                step_delay=100,\\n\",\n    \"                step_duration=50,\\n\",\n    \"                location=soma_loc,\\n\",\n    \"                total_duration=200)\\n\",\n    \"    rec = ephys.recordings.CompRecording(\\n\",\n    \"            name='%s.soma.v' % protocol_name,\\n\",\n    \"            location=soma_loc,\\n\",\n    \"            variable='v')\\n\",\n    \"    protocol = ephys.protocols.SweepProtocol(protocol_name, [stim], [rec])\\n\",\n    \"    sweep_protocols.append(protocol)\\n\",\n    \"twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Running protocols on a cell\\n\",\n    \"\\n\",\n    \"Now we're at a stage where we can actually run a protocol on the cell. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"nrn = ephys.simulators.NrnSimulator()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The run() method of a protocol accepts a cell model, a set of parameter values and a simulator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\\n\",\n    \"responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=nrn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plotting the response traces is now easy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        this.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width);\\n\",\n       \"        canvas.attr('height', height);\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'];\\n\",\n       \"    var y0 = fig.canvas.height - msg['y0'];\\n\",\n       \"    var x1 = msg['x1'];\\n\",\n       \"    var y1 = fig.canvas.height - msg['y1'];\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x;\\n\",\n       \"    var y = canvas_pos.y;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_responses(responses):\\n\",\n    \"    plt.subplot(2,1,1)\\n\",\n    \"    plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.subplot(2,1,2)\\n\",\n    \"    plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.tight_layout()\\n\",\n    \"\\n\",\n    \"plot_responses(responses)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, when we use different parameter values, the response looks different.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"other_params = {'gnabar_hh': 0.11, 'gkbar_hh': 0.04}\\n\",\n    \"plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=nrn))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Defining eFeatures and objectives\\n\",\n    \"\\n\",\n    \"For every response we need to define a set of eFeatures we will use for the fitness calculation later. We have to combine features together into objectives that will be used by the optimisation algorithm. In this case we will create one objective per feature:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\\n\",\n    \"\\n\",\n    \"objectives = []\\n\",\n    \"\\n\",\n    \"for protocol in sweep_protocols:\\n\",\n    \"    stim_start = protocol.stimuli[0].step_delay\\n\",\n    \"    stim_end = stim_start + protocol.stimuli[0].step_duration\\n\",\n    \"    for efel_feature_name, mean in efel_feature_means[protocol.name].items():\\n\",\n    \"        feature_name = '%s.%s' % (protocol.name, efel_feature_name)\\n\",\n    \"        feature = ephys.efeatures.eFELFeature(\\n\",\n    \"                    feature_name,\\n\",\n    \"                    efel_feature_name=efel_feature_name,\\n\",\n    \"                    recording_names={'': '%s.soma.v' % protocol.name},\\n\",\n    \"                    stim_start=stim_start,\\n\",\n    \"                    stim_end=stim_end,\\n\",\n    \"                    exp_mean=mean,\\n\",\n    \"                    exp_std=0.05 * mean)\\n\",\n    \"        objective = ephys.objectives.SingletonObjective(\\n\",\n    \"            feature_name,\\n\",\n    \"            feature)\\n\",\n    \"        objectives.append(objective)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating the cell evaluator\\n\",\n    \"\\n\",\n    \"We will need an object that can use these objective definitions to calculate the scores from a protocol response. This is called a ScoreCalculator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Combining everything together we have a CellEvaluator. The CellEvaluator constructor has a field 'parameter_names' which contains the (ordered) list of names of the parameters that are used as input (and will be fitted later on).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"cell_evaluator = ephys.evaluators.CellEvaluator(\\n\",\n    \"        cell_model=simple_cell,\\n\",\n    \"        param_names=['gnabar_hh', 'gkbar_hh'],\\n\",\n    \"        fitness_protocols={twostep_protocol.name: twostep_protocol},\\n\",\n    \"        fitness_calculator=score_calc,\\n\",\n    \"        sim=nrn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Evaluating the cell\\n\",\n    \"\\n\",\n    \"The cell can now be evaluate for a certain set of parameter values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\\n\",\n      \"{'step2.Spikecount': 4.0, 'step1.Spikecount': 0.0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(cell_evaluator.evaluate_with_dicts(default_params))\\n\",\n    \"print(cell_evaluator.evaluate_with_dicts(other_params))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Setting up and running an optimisation\\n\",\n    \"\\n\",\n    \"Now that we have a cell template and an evaluator for this cell, we can set up an optimisation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And this optimisation can be run for a certain number of generations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = bpop.optimisations.DEAPOptimisation(\\n\",\n    \"    evaluator=cell_evaluator,\\n\",\n    \"    offspring_size = 100,\\n\",\n    \"    seed=1)\\n\",\n    \"\\n\",\n    \"# results = optimisation.run(max_ngen=10, cp_filename='checkpoints/checkpoint.pkl')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \\n\",\n    \"\\n\",\n    \"The final population contains a list of tuples, with each tuple representing the two parameters of the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"# pickle.dump(results, open('results.pkl', 'w'))\\n\",\n    \"# results = pickle.load(open('results.pkl')\\n\",\n    \"\\n\",\n    \"cp = pickle.load(open('checkpoints/checkpoint.pkl'))\\n\",\n    \"results = (cp['population'],\\n\",\n    \"        cp['halloffame'],\\n\",\n    \"        cp['logbook'],\\n\",\n    \"        cp['history'])\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"pop, hall_of_fame, logs, hist = results\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best individual found during the optimisation is the first individual of the hall of fame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best individual:  [0.05047162045596707, 0.01553579607479454]\\n\",\n      \"Fitness values:  (0.0, 0.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"all_inds = hist.genealogy_history.values()\\n\",\n    \"best_inds = [ind for ind in all_inds if ind.fitness.valid and ind.fitness.sum == 0]\\n\",\n    \"# print([ind.fitness.values for ind in best_inds])\\n\",\n    \"# best_inds = hall_of_fame[:100]\\n\",\n    \"best_ind = best_inds[1]\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.15948084951086058, 0.040876554474167215]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.05047162045596707, 0.01553579607479454]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.0951754875850295, 0.025347861639078685]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.09432136998860223, 0.026756980292376657]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.14284515471059417, 0.03785780405241329]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.05047162045596707, 0.01553579607479454]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.0951754875850295, 0.025347861639078685]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.12853697616017354, 0.03533022234039911]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.09160460586691756, 0.026756980292376657]\\n\",\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.15409363218638306, 0.03995407384574071]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for best_ind in best_inds[:10]:\\n\",\n    \"    best_ind_dict = cell_evaluator.param_dict(best_ind)\\n\",\n    \"    print(cell_evaluator.evaluate_with_dicts(best_ind_dict), best_ind)\\n\",\n    \"    plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \\n\",\n    \"We can have a look at the responses now.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import pickle\\n\",\n    \"RESPONSE_PICKLE = 'responses.pkl'\\n\",\n    \"\\n\",\n    \"if os.path.exists(RESPONSE_PICKLE):\\n\",\n    \"    responses = pickle.load(open(RESPONSE_PICKLE))\\n\",\n    \"else:\\n\",\n    \"    responses = []\\n\",\n    \"    for best_ind in best_inds:\\n\",\n    \"       best_ind_dict = cell_evaluator.param_dict(best_ind)\\n\",\n    \"       responses.append(cell_evaluator.run_protocols(cell_evaluator.fitness_protocols.values(), param_values=best_ind_dict))\\n\",\n    \"    pickle.dump(responses, open(RESPONSE_PICKLE, 'w'), protocol=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's have a look at the optimisation statistics.\\n\",\n    \"We can plot the minimal score (sum of all objective scores) found in every optimisation. \\n\",\n    \"The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        this.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width);\\n\",\n       \"        canvas.attr('height', height);\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'];\\n\",\n       \"    var y0 = fig.canvas.height - msg['y0'];\\n\",\n       \"    var x1 = msg['x1'];\\n\",\n       \"    var y1 = fig.canvas.height - msg['y1'];\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x;\\n\",\n       \"    var y = canvas_pos.y;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img src=\\\"data:image/png;base64,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\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        this.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width);\\n\",\n       \"        canvas.attr('height', height);\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'];\\n\",\n       \"    var y0 = fig.canvas.height - msg['y0'];\\n\",\n       \"    var x1 = msg['x1'];\\n\",\n       \"    var y1 = fig.canvas.height - msg['y1'];\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x;\\n\",\n       \"    var y = canvas_pos.y;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        event.shiftKey = false;\\n\",\n       \"        // Send a \\\"J\\\" for go to next cell\\n\",\n       \"        event.which = 74;\\n\",\n       \"        event.keyCode = 74;\\n\",\n       \"        manager.command_mode();\\n\",\n       \"        manager.handle_keydown(event);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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\\\">\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy\\n\",\n    \"\\n\",\n    \"gen_numbers = logs.select('gen')\\n\",\n    \"min_fitness = numpy.array(logs.select('min'))\\n\",\n    \"max_fitness = logs.select('max')\\n\",\n    \"mean_fitness = numpy.array(logs.select('avg'))\\n\",\n    \"std_fitness = numpy.array(logs.select('std'))\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(3, figsize=(10, 10), facecolor='white')\\n\",\n    \"fig_trip, ax_trip = plt.subplots(1, figsize=(10, 5), facecolor='white')\\n\",\n    \"\\n\",\n    \"plot_count = len(responses)\\n\",\n    \"for index, response in enumerate(responses[:plot_count]):\\n\",\n    \"    color='lightblue'\\n\",\n    \"    if index == plot_count - 1:\\n\",\n    \"        color='blue'\\n\",\n    \"        \\n\",\n    \"    # best_ind_dict = cell_evaluator.param_dict(best_ind)\\n\",\n    \"    # responses = cell_evaluator.run_protocols(cell_evaluator.fitness_protocols.values(), param_values=best_ind_dict)\\n\",\n    \"    ax[0].plot(response['step1.soma.v']['time'], response['step1.soma.v']['voltage'], color=color, linewidth=1)\\n\",\n    \"    axes = ax[1].plot(response['step2.soma.v']['time'], response['step2.soma.v']['voltage'], color=color, linewidth=1)\\n\",\n    \"    # axes[0].set_rasterized(True)\\n\",\n    \"    \\n\",\n    \"ax[0].set_xlabel('Time (ms)')\\n\",\n    \"ax[0].set_ylabel('Voltage (mV)')\\n\",\n    \"ax[0].set_xlim(80, 200)\\n\",\n    \"ax[1].set_xlabel('Time (ms)')\\n\",\n    \"ax[1].set_ylabel('Voltage (mV)')\\n\",\n    \"ax[1].set_xlim(80, 200)\\n\",\n    \"\\n\",\n    \"# ax[0].legend()\\n\",\n    \"\\n\",\n    \"std = std_fitness\\n\",\n    \"mean = mean_fitness\\n\",\n    \"minimum = min_fitness\\n\",\n    \"stdminus = mean - std                                                           \\n\",\n    \"stdplus = mean + std\\n\",\n    \"\\n\",\n    \"ax[2].plot(                                                                      \\n\",\n    \"    gen_numbers,                                                                \\n\",\n    \"    mean,                                                                       \\n\",\n    \"    color='black',                                                              \\n\",\n    \"    linewidth=2,                                                                \\n\",\n    \"    label='population average')                                                 \\n\",\n    \"\\n\",\n    \"ax[2].fill_between(                                                              \\n\",\n    \"    gen_numbers,                                                                \\n\",\n    \"    stdminus,                                                                   \\n\",\n    \"    stdplus,                                                                    \\n\",\n    \"    color='lightgray',                                                          \\n\",\n    \"    linewidth=2,                                                                \\n\",\n    \"    label=r'population standard deviation')                                     \\n\",\n    \"\\n\",\n    \"ax[2].plot(                                                                      \\n\",\n    \"    gen_numbers,                                                                \\n\",\n    \"    minimum,                                                                    \\n\",\n    \"    color='red',                                                                \\n\",\n    \"    linewidth=2,                                                                \\n\",\n    \"    label='population minimum')                                                 \\n\",\n    \"\\n\",\n    \"ax[2].set_xlim(min(gen_numbers) - 1, max(gen_numbers) + 1)                       \\n\",\n    \"ax[2].set_xlabel('Generation #')                                                 \\n\",\n    \"ax[2].set_ylabel('Sum of objectives')                                            \\n\",\n    \"ax[2].set_ylim([0, max(stdplus)])                                                \\n\",\n    \"ax[2].legend()                        \\n\",\n    \"\\n\",\n    \"all_inds = hist.genealogy_history.values()\\n\",\n    \"\\n\",\n    \"gnabars = numpy.array([ind[0] for ind in all_inds])\\n\",\n    \"gkbars = numpy.array([ind[1] for ind in all_inds])\\n\",\n    \"sums = numpy.array([ind.fitness.sum for ind in all_inds])\\n\",\n    \"# psums = zip(gnabars, gkbars, sums)\\n\",\n    \"zero_gnabars = gnabars[numpy.where(sums == 0)]\\n\",\n    \"zero_gkbars = gkbars[numpy.where(sums == 0)]\\n\",\n    \"\\n\",\n    \"# X = numpy.linspace(gnabar_param.bounds[0], gnabar_param.bounds[1], 150)\\n\",\n    \"# Y = numpy.linspace(gkbar_param.bounds[0], gkbar_param.bounds[1], 150)\\n\",\n    \"# X,Y = numpy.meshgrid(X,Y)\\n\",\n    \"# import matplotlib\\n\",\n    \"# import scipy.interpolate\\n\",\n    \"# Z1 = scipy.interpolate.griddata(numpy.vstack((gnabars.flatten(), gkbars.flatten())).T, numpy.vstack(sums.flatten()), (X, Y), method='linear').reshape(X.shape)\\n\",\n    \"# Z = matplotlib.mlab.griddata(gnabars, gkbars, sums, X, Y, interp='linear')\\n\",\n    \"# Z1m = numpy.ma.masked_where(numpy.isnan(Z1),Z1)\\n\",\n    \"# mesh_axes = ax[2].pcolormesh(X,Y,Z1m)\\n\",\n    \"\\n\",\n    \"# plt.plot(gnabars, gkbars, '.', color='k')\\n\",\n    \"\\n\",\n    \"trip_axis = ax_trip.tripcolor(gnabars,gkbars,sums+1,20,norm=matplotlib.colors.LogNorm())\\n\",\n    \"# plt.tricontourf(gnabars,gkbars,sums,20)\\n\",\n    \"# cbar_ax = fig.add_axes([0.85, 0.29, 0.025, 0.2])\\n\",\n    \"fig_trip.colorbar(trip_axis, label='sum of objectives + 1')\\n\",\n    \"ax_trip.set_xlabel('Parameter gnabar')\\n\",\n    \"ax_trip.set_ylabel('Parameter gkbar')\\n\",\n    \"\\n\",\n    \"plot_axis = ax_trip.plot(list(zero_gnabars), list(zero_gkbars), 'o', color='lightblue')\\n\",\n    \"# ax[4].set_xlabel('Parameter gnabar')\\n\",\n    \"# ax[4].set_ylabel('Parameter gkbar')\\n\",\n    \"# ax[4].set_xlim(gnabar_param.bounds)\\n\",\n    \"# ax[4].set_ylim(gkbar_param.bounds)\\n\",\n    \"# fig.colorbar(trip_axis, ax=ax[3])\\n\",\n    \"\\n\",\n    \"fig.tight_layout()\\n\",\n    \"fig_trip.tight_layout()\\n\",\n    \"\\n\",\n    \"# fig_trip.subplots_adjust(right=0.8, hspace=0.4)\\n\",\n    \"\\n\",\n    \"# print(X, Y)\\n\",\n    \"# print(Z)\\n\",\n    \"\\n\",\n    \"fig.savefig('figures/simplecell_traces.eps')\\n\",\n    \"fig_trip.savefig('figures/simplecell_trip.eps')\\n\",\n    \"\\n\",\n    \"fig.show()\\n\",\n    \"fig_trip.show()\\n\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.11\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "examples/simplecell/simplecell.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Creating a simple cell optimisation\\n\",\n    \"\\n\",\n    \"This notebook will explain how to set up an optimisation of simple single compartmental cell with two free parameters that need to be optimised.\\n\",\n    \"As this optimisation is for example purpose only, no real experimental data is used in this notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Install matplotlib if needed\\n\",\n    \"!pip install matplotlib\"\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    \"%load_ext autoreload\\n\",\n    \"%autoreload\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"First we need to import the module that contains all the functionality to create electrical cell models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import bluepyopt as bpop\\n\",\n    \"import bluepyopt.ephys as ephys\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you want to see a lot of information about the internals, \\n\",\n    \"the verbose level can be set to 'debug' by commenting out\\n\",\n    \"the following lines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# import logging\\n\",\n    \"# logger = logging.getLogger()\\n\",\n    \"# logger.setLevel(logging.DEBUG)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Setting up a cell template\\n\",\n    \"-------------------------\\n\",\n    \"First a template that will describe the cell has to be defined. A template consists of:\\n\",\n    \"* a morphology\\n\",\n    \"* model mechanisms\\n\",\n    \"* model parameters\\n\",\n    \"\\n\",\n    \"### Creating a morphology\\n\",\n    \"A morphology can be loaded from a file (SWC or ASC).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"morph = ephys.morphologies.NrnFileMorphology('simple.swc')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"By default a Neuron morphology has the following sectionlists: somatic, axonal, apical and basal. Let's create an object that points to the somatic sectionlist. This object will be used later to specify where mechanisms have to be added etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating a mechanism\\n\",\n    \"\\n\",\n    \"Now we can add ion channels to this morphology. Let's add the default Neuron Hodgkin-Huxley mechanism to the soma. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"hh_mech = ephys.mechanisms.NrnMODMechanism(\\n\",\n    \"        name='hh',\\n\",\n    \"        suffix='hh',\\n\",\n    \"        locations=[somatic_loc])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"The 'name' field can be chosen by the user, this name should be unique. The 'suffix' points to the same field in the NMODL file of the channel. 'locations' specifies which sections the mechanism will be added to.\\n\",\n    \"\\n\",\n    \"### Creating parameters\\n\",\n    \"\\n\",\n    \"Next we need to specify the parameters of the model. A parameter can be in two states: frozen and not-frozen. When a parameter is frozen it has an exact value, otherwise it only has some bounds but the exact value is not known yet.\\n\",\n    \"Let's define first a parameter that sets the capacitance of the soma to a frozen value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cm_param = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='cm',\\n\",\n    \"        param_name='cm',\\n\",\n    \"        value=1.0,\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        frozen=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be optimised later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"gnabar_param = ephys.parameters.NrnSectionParameter(                                    \\n\",\n    \"        name='gnabar_hh',\\n\",\n    \"        param_name='gnabar_hh',\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        bounds=[0.05, 0.125],\\n\",\n    \"        frozen=False)     \\n\",\n    \"gkbar_param = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='gkbar_hh',\\n\",\n    \"        param_name='gkbar_hh',\\n\",\n    \"        bounds=[0.01, 0.075],\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        frozen=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating the template\\n\",\n    \"\\n\",\n    \"To create the cell template, we pass all these objects to the constructor of the template\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"simple_cell = ephys.models.CellModel(\\n\",\n    \"        name='simple_cell',\\n\",\n    \"        morph=morph,\\n\",\n    \"        mechs=[hh_mech],\\n\",\n    \"        params=[cm_param, gnabar_param, gkbar_param])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can print out a description of the cell\"\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      \"simple_cell:\\n\",\n      \"  morphology:\\n\",\n      \"    simple.swc\\n\",\n      \"  mechanisms:\\n\",\n      \"    hh: [<bluepyopt.ephys.locations.NrnSeclistLocation object at 0x109c30090>] hh\\n\",\n      \"  params:\\n\",\n      \"    cm: ['somatic'] cm = 1.0\\n\",\n      \"    gnabar_hh: ['somatic'] gnabar_hh = [0.05, 0.125]\\n\",\n      \"    gkbar_hh: ['somatic'] gkbar_hh = [0.01, 0.075]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(simple_cell)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"With this cell we can build a cell evaluator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Setting up a cell evaluator\\n\",\n    \"\\n\",\n    \"To optimise the parameters of the cell we need to create cell evaluator object. \\n\",\n    \"This object will need to know which protocols to inject, which parameters to optimise, etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Creating the protocols\\n\",\n    \"\\n\",\n    \"A protocol consists of a set of stimuli, and a set of responses (i.e. recordings). These responses will later be used to calculate\\n\",\n    \"the score of the parameter values.\\n\",\n    \"Let's create two protocols, two square current pulses at somatic`[0]`(0.5) with different amplitudes.\\n\",\n    \"We first need to create a location object\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"soma_loc = ephys.locations.NrnSeclistCompLocation(\\n\",\n    \"        name='soma',\\n\",\n    \"        seclist_name='somatic',\\n\",\n    \"        sec_index=0,\\n\",\n    \"        comp_x=0.5)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"and then the stimuli, recordings and protocols. For each protocol we add a recording and a stimulus in the soma.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"sweep_protocols = []\\n\",\n    \"for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\\n\",\n    \"    stim = ephys.stimuli.NrnSquarePulse(\\n\",\n    \"                step_amplitude=amplitude,\\n\",\n    \"                step_delay=100,\\n\",\n    \"                step_duration=50,\\n\",\n    \"                location=soma_loc,\\n\",\n    \"                total_duration=200)\\n\",\n    \"    rec = ephys.recordings.CompRecording(\\n\",\n    \"            name='%s.soma.v' % protocol_name,\\n\",\n    \"            location=soma_loc,\\n\",\n    \"            variable='v')\\n\",\n    \"    protocol = ephys.protocols.SweepProtocol(protocol_name, [stim], [rec])\\n\",\n    \"    sweep_protocols.append(protocol)\\n\",\n    \"twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Running a protocol on a cell\\n\",\n    \"\\n\",\n    \"Now we're at a stage where we can actually run a protocol on the cell. We first need to create a Simulator object.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"nrn = ephys.simulators.NrnSimulator()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The run() method of a protocol accepts a cell model, a set of parameter values and a simulator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\\n\",\n    \"responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=nrn)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Plotting the response traces is now easy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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KFg/Xr40z+Fp59u/8bcTC+9BF/9Kvw2guFFa9bAnDnw4otw3XVw2WUw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\\nClAiIlKSFKBERKQk/X8VJQqrY+AsNwAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109c37d90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_responses(responses):\\n\",\n    \"    plt.subplot(2,1,1)\\n\",\n    \"    plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.subplot(2,1,2)\\n\",\n    \"    plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.tight_layout()\\n\",\n    \"\\n\",\n    \"plot_responses(responses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see, when we use different parameter values, the response looks different.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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Gl6G5G2QNdBxUcBqjjFHqA2b467BCL5oZkk4qMAVZyyGSSRE7lqQdXU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\\niYhIQfr/vuwszbfvtwAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109c92750>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"other_params = {'gnabar_hh': 0.05, 'gkbar_hh': 0.05}\\n\",\n    \"plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=nrn))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"### Defining eFeatures and objectives\\n\",\n    \"\\n\",\n    \"For every response we need to define a set of eFeatures we will use for the fitness calculation later. We have to combine features together into objectives that will be used by the optimalisation algorithm. In this case we will create one objective per feature:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\\n\",\n    \"\\n\",\n    \"objectives = []\\n\",\n    \"\\n\",\n    \"for protocol in sweep_protocols:\\n\",\n    \"    stim_start = protocol.stimuli[0].step_delay\\n\",\n    \"    stim_end = stim_start + protocol.stimuli[0].step_duration\\n\",\n    \"    for efel_feature_name, mean in efel_feature_means[protocol.name].items():\\n\",\n    \"        feature_name = '%s.%s' % (protocol.name, efel_feature_name)\\n\",\n    \"        feature = ephys.efeatures.eFELFeature(\\n\",\n    \"                    feature_name,\\n\",\n    \"                    efel_feature_name=efel_feature_name,\\n\",\n    \"                    recording_names={'': '%s.soma.v' % protocol.name},\\n\",\n    \"                    stim_start=stim_start,\\n\",\n    \"                    stim_end=stim_end,\\n\",\n    \"                    exp_mean=mean,\\n\",\n    \"                    exp_std=0.05 * mean)\\n\",\n    \"        objective = ephys.objectives.SingletonObjective(\\n\",\n    \"            feature_name,\\n\",\n    \"            feature)\\n\",\n    \"        objectives.append(objective)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating the cell evaluator\\n\",\n    \"\\n\",\n    \"We will need an object that can use these objective definitions to calculate the scores from a protocol response. This is called a ScoreCalculator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Combining everything together we have a CellEvaluator. The CellEvaluator constructor has a field 'parameter_names' which contains the (ordered) list of names of the parameters that are used as input (and will be fitted later on).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"cell_evaluator = ephys.evaluators.CellEvaluator(\\n\",\n    \"        cell_model=simple_cell,\\n\",\n    \"        param_names=['gnabar_hh', 'gkbar_hh'],\\n\",\n    \"        fitness_protocols={twostep_protocol.name: twostep_protocol},\\n\",\n    \"        fitness_calculator=score_calc,\\n\",\n    \"        sim=nrn)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Evaluating the cell\\n\",\n    \"\\n\",\n    \"The cell can now be evaluate for a certain set of parameter values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(cell_evaluator.evaluate_with_dicts(default_params))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"## Setting up and running an optimisation\\n\",\n    \"\\n\",\n    \"Now that we have a cell template and an evaluator for this cell, we can set up an optimisation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = bpop.optimisations.DEAPOptimisation(\\n\",\n    \"        evaluator=cell_evaluator,\\n\",\n    \"        offspring_size = 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And this optimisation can be run for a certain number of generations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"gen\\tnevals\\tavg  \\tstd    \\tmin\\tmax\\n\",\n      \"1  \\t10    \\t149.4\\t189.154\\t4  \\t500\\n\",\n      \"2  \\t10    \\t137.9\\t173.863\\t4  \\t500\\n\",\n      \"3  \\t10    \\t25   \\t15.4337\\t4  \\t40 \\n\",\n      \"4  \\t10    \\t10   \\t13.5941\\t0  \\t40 \\n\",\n      \"5  \\t10    \\t7.4  \\t12.7922\\t0  \\t40 \\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \\n\",\n    \"\\n\",\n    \"The final population contains a list of tuples, with each tuple representing the two parameters of the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Final population:  [[0.10250198363407481, 0.027124836082684685], [0.11390738180576473, 0.03287250134852333], [0.10250198363407481, 0.027124836082684685], [0.10250198363407481, 0.027124836082684685], [0.08975403922850962, 0.027124836082684685], [0.10563659695553552, 0.029575522695622657], [0.10101393431903045, 0.026690299820979417], [0.10299326453483033, 0.027124836082684685], [0.10728309642324604, 0.02657948667306241], [0.11010771065262573, 0.02657948667306241], [0.11382709430476985, 0.04098326369171712], [0.11382709430476985, 0.04098326369171712], [0.1170346997494812, 0.036309352642717674], [0.09315627382586422, 0.03338461398216595], [0.09315627382586422, 0.03338461398216595], [0.11382709430476985, 0.04098326369171712], [0.1170346997494812, 0.036309352642717674], [0.09157830984841198, 0.029628023252804073], [0.09315627382586422, 0.03427793299271563], [0.11284691872242712, 0.04421675460560999]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Final population: ', final_pop)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The best individual found during the optimisation is the first individual of the hall of fame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best individual:  [0.10299326453483033, 0.027124836082684685]\\n\",\n      \"Fitness values:  (0.0, 0.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind = hall_of_fame[0]\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind_dict = cell_evaluator.param_dict(best_ind)\\n\",\n    \"print(cell_evaluator.evaluate_with_dicts(best_ind_dict))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \\n\",\n    \"We can have a look at the responses now.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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cMFF4Rz+p57mj+3q6vDgrHV1fF89rJlISC9+mqY8HDxxdC9e+vLueee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\\nRESkLP1/QNTmhvEqndUAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10a23ef90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Let's have a look at the optimisation statistics.\\n\",\n    \"We can plot the minimal score (sum of all objective scores) found in every optimisation. \\n\",\n    \"The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0.0, 4.4000000000000004)\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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5Y0TNIASUskvSHpf/b0jSQ1ANOBZe2eCuAUSc9IekTStJ7u26xaeFzA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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109fe7e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy\\n\",\n    \"gen_numbers = logs.select('gen')\\n\",\n    \"min_fitness = logs.select('min')\\n\",\n    \"max_fitness = logs.select('max')\\n\",\n    \"plt.plot(gen_numbers, min_fitness, label='min fitness')\\n\",\n    \"plt.xlabel('generation #')\\n\",\n    \"plt.ylabel('score (# std)')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \\n\",\n    \"plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\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\": \"3.8.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "examples/simplecell/simplecell_arbor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"# Creating a simple cell optimisation with Arbor\\n\",\n    \"\\n\",\n    \"This notebook will explain how to set up an optimisation of simple single compartmental cell with two free parameters that need to be optimised using Arbor as the simulator.\\n\",\n    \"As this optimisation is for example purpose only, no real experimental data is used in this notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# Install matplotlib if needed\\n\",\n    \"!pip install matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%load_ext autoreload\\n\",\n    \"%autoreload\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"First we need to import the module that contains all the functionality to create electrical cell models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import bluepyopt as bpop\\n\",\n    \"import bluepyopt.ephys as ephys\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"If you want to see a lot of information about the internals, \\n\",\n    \"the verbose level can be set to 'debug' by commenting out\\n\",\n    \"the following lines\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"# import logging\\n\",\n    \"# logger = logging.getLogger()\\n\",\n    \"# logger.setLevel(logging.DEBUG)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Setting up a cell template\\n\",\n    \"-------------------------\\n\",\n    \"First a template that will describe the cell has to be defined. A template consists of:\\n\",\n    \"* a morphology\\n\",\n    \"* model mechanisms\\n\",\n    \"* model parameters\\n\",\n    \"\\n\",\n    \"### Creating a morphology\\n\",\n    \"A morphology can be loaded from a file (SWC or ASC).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"morph = ephys.morphologies.NrnFileMorphology('simple.swc')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"By default a Neuron morphology has the following sectionlists: somatic, axonal, apical and basal. Let's create an object that points to the soma using Arbor's S-expression language. This object will be used later to specify where mechanisms have to be added etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"somatic_loc = ephys.locations.ArbRegionLocation('somatic', region='(intersect (region \\\"all\\\") (region \\\"soma\\\"))')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Creating a mechanism\\n\",\n    \"\\n\",\n    \"Now we can add ion channels to this morphology. Let's add the default Neuron Hodgkin-Huxley mechanism to the soma. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"hh_mech = ephys.mechanisms.NrnMODMechanism(\\n\",\n    \"        name='hh',\\n\",\n    \"        suffix='hh',\\n\",\n    \"        locations=[somatic_loc])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The 'name' field can be chosen by the user, this name should be unique. The 'suffix' points to the same field in the NMODL file of the channel. 'locations' specifies which sections the mechanism will be added to.\\n\",\n    \"\\n\",\n    \"### Creating parameters\\n\",\n    \"\\n\",\n    \"Next we need to specify the parameters of the model. A parameter can be in two states: frozen and not-frozen. When a parameter is frozen it has an exact value, otherwise it only has some bounds but the exact value is not known yet.\\n\",\n    \"Let's define first a parameter that sets the capacitance of the soma to a frozen value\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"cm_param = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='cm',\\n\",\n    \"        param_name='cm',\\n\",\n    \"        value=1.0,\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        frozen=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be optimised later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"gnabar_param = ephys.parameters.NrnSectionParameter(                                    \\n\",\n    \"        name='gnabar_hh',\\n\",\n    \"        param_name='gnabar_hh',\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        bounds=[0.05, 0.125],\\n\",\n    \"        frozen=False)     \\n\",\n    \"gkbar_param = ephys.parameters.NrnSectionParameter(\\n\",\n    \"        name='gkbar_hh',\\n\",\n    \"        param_name='gkbar_hh',\\n\",\n    \"        bounds=[0.01, 0.075],\\n\",\n    \"        locations=[somatic_loc],\\n\",\n    \"        frozen=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Creating the template\\n\",\n    \"\\n\",\n    \"To create the cell template, we pass all these objects to the constructor of the template\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"simple_cell = ephys.models.CellModel(\\n\",\n    \"        name='simple_cell',\\n\",\n    \"        morph=morph,\\n\",\n    \"        mechs=[hh_mech],\\n\",\n    \"        params=[cm_param, gnabar_param, gkbar_param])  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Now we can print out a description of the cell\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"simple_cell:\\n\",\n      \"  morphology:\\n\",\n      \"    simple.swc\\n\",\n      \"  mechanisms:\\n\",\n      \"    hh: hh at ['ArbRegionLocation (region-def \\\"somatic\\\" (intersect (region \\\"all\\\") (region \\\"soma\\\")))']\\n\",\n      \"  params:\\n\",\n      \"    cm: ['ArbRegionLocation (region-def \\\"somatic\\\" (intersect (region \\\"all\\\") (region \\\"soma\\\")))'] cm = 1.0\\n\",\n      \"    gnabar_hh: ['ArbRegionLocation (region-def \\\"somatic\\\" (intersect (region \\\"all\\\") (region \\\"soma\\\")))'] gnabar_hh = [0.05, 0.125]\\n\",\n      \"    gkbar_hh: ['ArbRegionLocation (region-def \\\"somatic\\\" (intersect (region \\\"all\\\") (region \\\"soma\\\")))'] gkbar_hh = [0.01, 0.075]\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(simple_cell)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"With this cell we can build a cell evaluator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Setting up a cell evaluator\\n\",\n    \"\\n\",\n    \"To optimise the parameters of the cell we need to create cell evaluator object. \\n\",\n    \"This object will need to know which protocols to inject, which parameters to optimise, etc.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Creating the protocols\\n\",\n    \"\\n\",\n    \"A protocol consists of a set of stimuli, and a set of responses (i.e. recordings). These responses will later be used to calculate\\n\",\n    \"the score of the parameter values.\\n\",\n    \"Let's create two protocols, two square current pulses at the relative position 0.5 of branch 0 with different amplitudes.\\n\",\n    \"We first need to create a location object\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"soma_loc = ephys.locations.ArbLocsetLocation(\\n\",\n    \"        name='soma_center',\\n\",\n    \"        locset='(location 0 0.5)')\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"and then the stimuli, recordings and protocols. For each protocol we add a recording and a stimulus in the soma.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sweep_protocols = []\\n\",\n    \"for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\\n\",\n    \"    stim = ephys.stimuli.NrnSquarePulse(\\n\",\n    \"                step_amplitude=amplitude,\\n\",\n    \"                step_delay=100,\\n\",\n    \"                step_duration=50,\\n\",\n    \"                location=soma_loc,\\n\",\n    \"                total_duration=200)\\n\",\n    \"    rec = ephys.recordings.CompRecording(\\n\",\n    \"            name='%s.soma.v' % protocol_name,\\n\",\n    \"            location=soma_loc,\\n\",\n    \"            variable='v')\\n\",\n    \"    protocol = ephys.protocols.ArbSweepProtocol(protocol_name, [stim], [rec])\\n\",\n    \"    sweep_protocols.append(protocol)\\n\",\n    \"twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Running a protocol on a cell\\n\",\n    \"\\n\",\n    \"Now we're at a stage where we can actually run a protocol on the cell. We first need to create a Simulator object.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"sim = ephys.simulators.ArbSimulator()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The run() method of a protocol accepts a cell model, a set of parameter values and a simulator\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\\n\",\n    \"responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=sim)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Plotting the response traces is now easy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"def plot_responses(responses):\\n\",\n    \"    plt.subplot(2,1,1)\\n\",\n    \"    plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.subplot(2,1,2)\\n\",\n    \"    plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\\n\",\n    \"    plt.legend()\\n\",\n    \"    plt.tight_layout()\\n\",\n    \"\\n\",\n    \"plot_responses(responses)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"As you can see, when we use different parameter values, the response looks different.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"other_params = {'gnabar_hh': 0.05, 'gkbar_hh': 0.05}\\n\",\n    \"plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=sim))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Defining eFeatures and objectives\\n\",\n    \"\\n\",\n    \"For every response we need to define a set of eFeatures we will use for the fitness calculation later. We have to combine features together into objectives that will be used by the optimisation algorithm. In this case we will create one objective per feature:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\\n\",\n    \"\\n\",\n    \"objectives = []\\n\",\n    \"\\n\",\n    \"for protocol in sweep_protocols:\\n\",\n    \"    stim_start = protocol.stimuli[0].step_delay\\n\",\n    \"    stim_end = stim_start + protocol.stimuli[0].step_duration\\n\",\n    \"    for efel_feature_name, mean in efel_feature_means[protocol.name].items():\\n\",\n    \"        feature_name = '%s.%s' % (protocol.name, efel_feature_name)\\n\",\n    \"        feature = ephys.efeatures.eFELFeature(\\n\",\n    \"                    feature_name,\\n\",\n    \"                    efel_feature_name=efel_feature_name,\\n\",\n    \"                    recording_names={'': '%s.soma.v' % protocol.name},\\n\",\n    \"                    stim_start=stim_start,\\n\",\n    \"                    stim_end=stim_end,\\n\",\n    \"                    exp_mean=mean,\\n\",\n    \"                    exp_std=0.05 * mean)\\n\",\n    \"        objective = ephys.objectives.SingletonObjective(\\n\",\n    \"            feature_name,\\n\",\n    \"            feature)\\n\",\n    \"        objectives.append(objective)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Creating the cell evaluator\\n\",\n    \"\\n\",\n    \"We will need an object that can use these objective definitions to calculate the scores from a protocol response. This is called a ScoreCalculator.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Combining everything together we have a CellEvaluator. The CellEvaluator constructor has a field 'parameter_names' which contains the (ordered) list of names of the parameters that are used as input (and will be fitted later on).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"cell_evaluator = ephys.evaluators.CellEvaluator(\\n\",\n    \"        cell_model=simple_cell,\\n\",\n    \"        param_names=['gnabar_hh', 'gkbar_hh'],\\n\",\n    \"        fitness_protocols={twostep_protocol.name: twostep_protocol},\\n\",\n    \"        fitness_calculator=score_calc,\\n\",\n    \"        sim=sim)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"### Evaluating the cell\\n\",\n    \"\\n\",\n    \"The cell can now be evaluate for a certain set of parameter values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step1.Spikecount': 0.0, 'step2.Spikecount': 0.0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(cell_evaluator.evaluate_with_dicts(default_params))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"## Setting up and running an optimisation\\n\",\n    \"\\n\",\n    \"Now that we have a cell template and an evaluator for this cell, we can set up an optimisation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"optimisation = bpop.optimisations.DEAPOptimisation(\\n\",\n    \"        evaluator=cell_evaluator,\\n\",\n    \"        offspring_size = 10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"And this optimisation can be run for a certain number of generations\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=5)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \\n\",\n    \"\\n\",\n    \"The final population contains a list of tuples, with each tuple representing the two parameters of the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Final population:  [[0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.10724858027620049, 0.030384510486737674], [0.08869789340527853, 0.02498056382291062], [0.09897779748630668, 0.02669588721168879], [0.1057605282594995, 0.030384510486737674], [0.1076294309890433, 0.030384510486737674], [0.09181168257196179, 0.02498056382291062], [0.09319078965060591, 0.03367988191444934], [0.09315627382586422, 0.030727169741078426], [0.09319078965060591, 0.03367988191444934], [0.09319078965060591, 0.03367988191444934], [0.10887316587433475, 0.03367988191444934], [0.09058616618750419, 0.033619209959752636], [0.09450057314084677, 0.030406932859913336], [0.09315627382586422, 0.030704747367902765], [0.09161282398694515, 0.029920395084114818], [0.09319078965060591, 0.03457319907176254]]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Final population: ', final_pop)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"The best individual found during the optimisation is the first individual of the hall of fame\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best individual:  [0.09181168257196179, 0.02498056382291062]\\n\",\n      \"Fitness values:  (0.0, 0.0)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind = hall_of_fame[0]\\n\",\n    \"print('Best individual: ', best_ind)\\n\",\n    \"print('Fitness values: ', best_ind.fitness.values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'step1.Spikecount': 0.0, 'step2.Spikecount': 0.0}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"best_ind_dict = cell_evaluator.param_dict(best_ind)\\n\",\n    \"print(cell_evaluator.evaluate_with_dicts(best_ind_dict))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \\n\",\n    \"We can have a look at the responses now.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=sim))\\n\",\n    \" \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%% md\\n\"\n    }\n   },\n   \"source\": [\n    \"Let's have a look at the optimisation statistics.\\n\",\n    \"We can plot the minimal score (sum of all objective scores) found in every optimisation. \\n\",\n    \"The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    }\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(0.0, 4.4)\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy\\n\",\n    \"gen_numbers = logs.select('gen')\\n\",\n    \"min_fitness = logs.select('min')\\n\",\n    \"max_fitness = logs.select('max')\\n\",\n    \"plt.plot(gen_numbers, min_fitness, label='min fitness')\\n\",\n    \"plt.xlabel('generation #')\\n\",\n    \"plt.ylabel('score (# std)')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \\n\",\n    \"plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"pycharm\": {\n     \"name\": \"#%%\\n\"\n    },\n    \"tags\": []\n   },\n   \"outputs\": [],\n   \"source\": []\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\": \"3.8.10\"\n  },\n  \"vscode\": {\n   \"interpreter\": {\n    \"hash\": \"581988038cf9ce8838e7faf3da7c29f4ff88d898cd43cb17e0086e389d8deda2\"\n   }\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/simplecell/simplecell_model.py",
    "content": "\"\"\"Run simple cell optimisation\"\"\"\n\n\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n# pylint: disable=R0914\n\nimport bluepyopt.ephys as ephys\n\n\ndef define_morphology(do_replace_axon):\n\n    return ephys.morphologies.NrnFileMorphology('simple.swc',\n                                                 do_replace_axon=do_replace_axon)\n\ndef define_mechanisms():\n    somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic')\n\n    hh_mech = ephys.mechanisms.NrnMODMechanism(\n        name='hh',\n        suffix='hh',\n        locations=[somatic_loc])\n    return [hh_mech]\n\n\ndef define_parameters():\n    somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic')\n\n    cm_param = ephys.parameters.NrnSectionParameter(\n        name='cm',\n        param_name='cm',\n        value=1.0,\n        locations=[somatic_loc],\n        frozen=True)\n\n    gnabar_param = ephys.parameters.NrnSectionParameter(                                    \n        name='gnabar_hh',\n        param_name='gnabar_hh',\n        locations=[somatic_loc],\n        bounds=[0.05, 0.125],\n        frozen=False)\n    gkbar_param = ephys.parameters.NrnSectionParameter(\n        name='gkbar_hh',\n        param_name='gkbar_hh',\n        bounds=[0.01, 0.075],\n        locations=[somatic_loc],\n        frozen=False)\n    return [cm_param, gnabar_param, gkbar_param]\n\n\ndef create(do_replace_axon):\n    \"\"\"Create cell model (identical to simplecell.ipynb)\"\"\"\n\n    cell = ephys.models.CellModel(\n        'simple_cell',\n        morph=define_morphology(do_replace_axon),\n        mechs=define_mechanisms(),\n        params=define_parameters())\n\n    return cell\n"
  },
  {
    "path": "examples/stochkv/.gitignore",
    "content": "/x86_64\n"
  },
  {
    "path": "examples/stochkv/mechanisms/StochKv.mod",
    "content": "TITLE skm95.mod\n\nCOMMENT\n----------------------------------------------------------------\nStochastic version of the K channel mechanism kd3h5.mod by\nZ. Mainen in Mainen & Sejnowski 95.\n\nThis represents a potassium channel, with Hodgkin-Huxley like kinetics,\nbased on the gates model, assuming stochastic opening and closing.\n\nKinetic rates based roughly on Sah et al. and Hamill et al. (1991)\nThe main kinetic difference from the standard H-H model (shh.mod) is\nthat the K+ kinetic is different, not n^4, but just n,\nand the activation curves are different.\n\nThe rate functions are adapted directly from the Kd3h5.mod file\nby Zach Mainen.\n\nThe stochastic model is as following:\n\nPotassium\n\n       = alpha_n =>\n   [N0]             [N1]\n      <= beta_n =\n\n\nThe model keeps track on the number of channels in each state, and\nuses a binomial distribution to update these number.\n\nJan 1999, Mickey London, Hebrew University, mikilon@lobster.ls.huji.ac.il\n        Peter N. Steinmetz, Caltech, peter@klab.caltech.edu\n14 Sep 99 PNS. Added deterministic flag.\n19 May 2002 Kamran Diba.  Changed gamma and deterministic from GLOBAL to RANGE.\n23 Nov 2011 Werner Van Geit @ BBP. Changed the file so that it can use the neuron random number generator. Tuned voltage dependence\n16 Mar 2016 James G King @ BBP.  Incorporate modifications suggested by Michael Hines to improve stiching to deterministic mode, thread safety, and using Random123\n\n16 Jan 2017 Christian Roessert @ BBP:\n\nWARNING unit declaration is wrong! modlunit gives errors!\nTo maintain backward compatibility this channel is not corrected but usage is DISCOURAGED!\nStochKv.mod and inactivating version of this channel uses corrected units!\n\n----------------------------------------------------------------\nENDCOMMENT\n\nINDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}\n\nNEURON {\n    SUFFIX StochKv\n    THREADSAFE\n    USEION k READ ek WRITE ik\n    RANGE N, eta, gk, gamma, deterministic, gkbar, ik\n    RANGE N0, N1, n0_n1, n1_n0\n    RANGE ninf, ntau, a, b, P_a, P_b\n    RANGE Ra, Rb, tadj\n    GLOBAL vmin, vmax, q10, temp\n    BBCOREPOINTER rng\n}\n\nUNITS {\n    (mA) = (milliamp)\n    (mV) = (millivolt)\n    (pS) = (picosiemens)\n    (S) = (siemens)\n    (um) = (micron)\n}\n\nPARAMETER {\n    v           (mV)\n    dt      (ms)\n    area    (um2)\n\n    gamma  =  30          (pS)\n    eta              (1/um2)\n    gkbar = .75      (S/cm2)\n\n    tha  = -40   (mV)        : v 1/2 for inf\n    qa   = 9            : inf slope\n    Ra   = 0.02 (/ms)       : max act rate\n    Rb   = 0.002    (/ms)       : max deact rate\n\n    celsius (degC)\n    temp = 23 (degC)   : original temperature for kinetic set\n    q10 = 2.3               : temperature sensitivity\n\n    deterministic = 0   : if non-zero, will use deterministic version\n    vmin = -120 (mV)    : range to construct tables for\n    vmax = 100  (mV)\n}\n\nASSIGNED {\n    a       (/ms)\n    b       (/ms)\n    ik      (mA/cm2)\n    gk      (S/cm2)\n    ek      (mV)\n    ninf        : steady-state value\n    ntau (ms)   : time constant for relaxation\n    tadj\n\n    N\n    scale_dens (pS/um2)\n    P_a     : probability of one channel making alpha transition\n    P_b     : probability of one channel making beta transition\n\n    rng\n\n    n0_n1_new\n    usingR123\n}\n\n\nSTATE {\n    n         : state variable of deterministic description\n}\nASSIGNED {\n    N0 N1     : N states populations (These currently will not be saved via the bbsavestate functionality.  Would need to be STATE again)\n    n0_n1 n1_n0 : number of channels moving from one state to the other\n}\n\nCOMMENT\nThe Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1\nfor comparison with Pr to decide whether to activate the synapse or not\nENDCOMMENT\n\nVERBATIM\n#ifndef NRN_VERSION_GTEQ_8_2_0\n#include \"nrnran123.h\"\nextern int cvode_active_;\n\n#include <stdlib.h>\n#include <stdio.h>\n#include <math.h>\n\ndouble nrn_random_pick(void* r);\nvoid* nrn_random_arg(int argpos);\n#define RANDCAST\n#else\n#define RANDCAST (Rand*)\n#endif\n\nENDVERBATIM\n: ----------------------------------------------------------------\n: initialization\nINITIAL {\n    VERBATIM\n    if (cvode_active_ && !deterministic) {\n        hoc_execerror(\"StochKv with deterministic=0\", \"cannot be used with cvode\");\n    }\n\n    if( usingR123 ) {\n        nrnran123_setseq((nrnran123_State*)_p_rng, 0, 0);\n    }\n    ENDVERBATIM\n\n    eta = (gkbar / gamma) : * (10000) for proper fix\n    trates(v)\n    n = ninf\n    scale_dens = gamma/area\n    N = floor(eta*area + 0.5)\n\n    N1 = n*N\n    if( !deterministic) {\n        N1 = floor(N1 + 0.5)\n    }\n    N0 = N-N1       : any round off into non-conducting state\n\n    n0_n1 = 0\n    n1_n0 = 0\n}\n\n: ----------------------------------------------------------------\n: Breakpoint for each integration step\nBREAKPOINT {\n  SOLVE states METHOD cnexp\n\n  gk = (strap(N1) * scale_dens * tadj) : * (0.0001) for proper fix\n\n  ik = 1e-4 * gk * (v - ek) : remove 1e-4 for proper fix\n}\n\n\n: ----------------------------------------------------------------\n: states - updates number of channels in each state\nDERIVATIVE states {\n\n    trates(v)\n\n    n' = a - (a + b)*n\n    if (deterministic || dt > 1) { : ForwardSkip is also deterministic\n        N1 = n*N\n    }else{\n\n    : ensure that N0 is an integer for when transitioning from deterministic mode to stochastic mode\n    N0 = floor(N0+0.5)\n    N1 = N - N0\n\n    P_a = strap(a*dt)\n    P_b = strap(b*dt)\n\n    : check that will represent probabilities when used\n    ChkProb( P_a)\n    ChkProb( P_b)\n\n    : transitions\n    n0_n1 = BnlDev(P_a, N0)\n    n1_n0 = BnlDev(P_b, N1)\n\n    : move the channels\n    N0    = strap(N0 - n0_n1 + n1_n0)\n    N1    = N - N0\n\n    }\n\n    N0 = N-N1       : any round off into non-conducting state\n}\n\n: ----------------------------------------------------------------\n: trates - compute rates, using table if possible\nPROCEDURE trates(v (mV)) {\n    TABLE ntau, ninf, a, b, tadj\n    DEPEND dt, Ra, Rb, tha, qa, q10, temp, celsius\n    FROM vmin TO vmax WITH 199\n\n    tadj = q10 ^ ((celsius - temp)/(10 (K)))\n    a = SigmoidRate(v, tha, Ra, qa)\n    a = a * tadj\n    b = SigmoidRate(-v, -tha, Rb, qa)\n    b = b * tadj\n    ntau = 1/(a+b)\n    ninf = a*ntau\n}\n\n\n: ----------------------------------------------------------------\n: SigmoidRate - Compute a sigmoid rate function given the\n: 50% point th, the slope q, and the amplitude a.\nFUNCTION SigmoidRate(v (mV),th (mV),a (1/ms),q) (1/ms){\n    UNITSOFF\n    if (fabs(v-th) > 1e-6 ) {\n        SigmoidRate = a * (v - th) / (1 - exp(-(v - th)/q))\n    UNITSON\n\n    } else {\n        SigmoidRate = a * q\n    }\n}\n\n\n: ----------------------------------------------------------------\n: sign trap - trap for negative values and replace with zero\nFUNCTION strap(x) {\n    if (x < 0) {\n        strap = 0\nVERBATIM\n        fprintf (stderr,\"skv.mod:strap: negative state\");\nENDVERBATIM\n    } else {\n        strap = x\n    }\n}\n\n: ----------------------------------------------------------------\n: ChkProb - Check that number represents a probability\nPROCEDURE ChkProb(p) {\n\n  if (p < 0.0 || p > 1.0) {\n    VERBATIM\n    fprintf(stderr, \"StochKv.mod:ChkProb: argument not a probability.\\n\");\n    ENDVERBATIM\n  }\n\n}\n\nPROCEDURE setRNG() {\n\nVERBATIM\n    // For compatibility, allow for either MCellRan4 or Random123.  Distinguish by the arg types\n    // Object => MCellRan4, seeds (double) => Random123\n#ifndef CORENEURON_BUILD\n    usingR123 = 0;\n    if( ifarg(1) && hoc_is_double_arg(1) ) {\n        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);\n        uint32_t a2 = 0;\n        uint32_t a3 = 0;\n\n        if (*pv) {\n            nrnran123_deletestream(*pv);\n            *pv = (nrnran123_State*)0;\n        }\n        if (ifarg(2)) {\n            a2 = (uint32_t)*getarg(2);\n        }\n        if (ifarg(3)) {\n            a3 = (uint32_t)*getarg(3);\n        }\n        *pv = nrnran123_newstream3((uint32_t)*getarg(1), a2, a3);\n        usingR123 = 1;\n    } else if( ifarg(1) ) {\n        void** pv = (void**)(&_p_rng);\n        *pv = nrn_random_arg(1);\n    } else {\n        void** pv = (void**)(&_p_rng);\n        *pv = (void*)0;\n    }\n#endif\nENDVERBATIM\n}\n\nFUNCTION urand() {\n\nVERBATIM\n    double value;\n    if( usingR123 ) {\n        value = nrnran123_dblpick((nrnran123_State*)_p_rng);\n    } else if (_p_rng) {\n#ifndef CORENEURON_BUILD\n        value = nrn_random_pick(RANDCAST _p_rng);\n#endif\n    } else {\n        value = 0.5;\n    }\n    _lurand = value;\nENDVERBATIM\n}\n\nVERBATIM\nstatic void bbcore_write(double* x, int* d, int* xx, int* offset, _threadargsproto_) {\n    if (d) {\n        uint32_t* di = ((uint32_t*)d) + *offset;\n      // temporary just enough to see how much space is being used\n      if (!_p_rng) {\n        di[0] = 0; di[1] = 0, di[2] = 0;\n      }else{\n        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);\n        nrnran123_getids3(*pv, di, di+1, di+2);\n        // write stream sequence\n        char which;\n        nrnran123_getseq(*pv, di+3, &which);\n        di[4] = (int)which;\n      }\n      //printf(\"StochKv.mod %p: bbcore_write offset=%d %d %d\\n\", _p, *offset, d?di[0]:-1, d?di[1]:-1);\n    }\n    *offset += 5;\n}\nstatic void bbcore_read(double* x, int* d, int* xx, int* offset, _threadargsproto_) {\n    assert(!_p_rng);\n    uint32_t* di = ((uint32_t*)d) + *offset;\n        if (di[0] != 0 || di[1] != 0|| di[2] != 0)\n        {\n      nrnran123_State** pv = (nrnran123_State**)(&_p_rng);\n      *pv = nrnran123_newstream3(di[0], di[1], di[2]);\n      // restore stream sequence\n      nrnran123_setseq(*pv, di[3], (char)di[4]);\n        }\n      //printf(\"StochKv.mod %p: bbcore_read offset=%d %d %d\\n\", _p, *offset, di[0], di[1]);\n    *offset += 5;\n}\nENDVERBATIM\n\n: Returns random numbers drawn from a binomial distribution\nFUNCTION brand(P, N) {\n\nVERBATIM\n        /*\n        :Supports separate independent but reproducible streams for\n        : each instance. However, the corresponding hoc Random\n        : distribution MUST be set to Random.uniform(0,1)\n        */\n\n        // Should probably be optimized\n        double value = 0.0;\n        int i;\n        for (i = 0; i < _lN; i++) {\n           if (urand(_threadargs_) < _lP) {\n              value = value + 1;\n           }\n        }\n        return(value);\n\nENDVERBATIM\n\n        brand = value\n}\n\nVERBATIM\n#define        PI 3.141592654\n#define        r_ia     16807\n#define        r_im     2147483647\n#define        r_am     (1.0/r_im)\n#define        r_iq     127773\n#define        r_ir     2836\n#define        r_ntab   32\n#define        r_ndiv   (1+(r_im-1)/r_ntab)\n#define        r_eps    1.2e-7\n#define        r_rnmx   (1.0-r_eps)\nENDVERBATIM\n\nVERBATIM\n/* ---------------------------------------------------------------- */\n/* gammln - compute natural log of gamma function of xx */\nstatic double\ngammln(double xx)\n{\n    double x,tmp,ser;\n    static double cof[6]={76.18009173,-86.50532033,24.01409822,\n        -1.231739516,0.120858003e-2,-0.536382e-5};\n    int j;\n    x=xx-1.0;\n    tmp=x+5.5;\n    tmp -= (x+0.5)*log(tmp);\n    ser=1.0;\n    for (j=0;j<=5;j++) {\n        x += 1.0;\n        ser += cof[j]/x;\n    }\n    return -tmp+log(2.50662827465*ser);\n}\nENDVERBATIM\n\n\n: ----------------------------------------------------------------\n: BnlDev - draw a uniform deviate from the generator\nFUNCTION BnlDev (ppr, nnr) {\n\nVERBATIM\n        int j;\n        double am,em,g,angle,p,bnl,sq,bt,y;\n        double pc,plog,pclog,en,oldg;\n\n        /* prepare to always ignore errors within this routine */\n\n        p=(_lppr <= 0.5 ? _lppr : 1.0-_lppr);\n        am=_lnnr*p;\n        if (_lnnr < 25) {\n            bnl=0.0;\n            for (j=1;j<=_lnnr;j++)\n                if (urand(_threadargs_) < p) bnl += 1.0;\n        }\n        else if (am < 1.0) {\n            g=exp(-am);\n            bt=1.0;\n            for (j=0;j<=_lnnr;j++) {\n                bt *= urand(_threadargs_);\n                if (bt < g) break;\n            }\n            bnl=(j <= _lnnr ? j : _lnnr);\n        }\n        else {\n            {\n                en=_lnnr;\n                oldg=gammln(en+1.0);\n            }\n            {\n                pc=1.0-p;\n                plog=log(p);\n                pclog=log(pc);\n            }\n            sq=sqrt(2.0*am*pc);\n            do {\n                do {\n                    angle=PI*urand(_threadargs_);\n                    angle=PI*urand(_threadargs_);\n                    y=tan(angle);\n                    em=sq*y+am;\n                } while (em < 0.0 || em >= (en+1.0));\n                em=floor(em);\n                    bt=1.2*sq*(1.0+y*y)*exp(oldg-gammln(em+1.0) -\n                    gammln(en-em+1.0)+em*plog+(en-em)*pclog);\n            } while (urand(_threadargs_) > bt);\n            bnl=em;\n        }\n        if (p != _lppr) bnl=_lnnr-bnl;\n\n        /* recover error if changed during this routine, thus ignoring\n            any errors during this routine */\n\n\n        return bnl;\n\n    ENDVERBATIM\n    BnlDev = bnl\n}\n\nFUNCTION bbsavestate() {\n        bbsavestate = 0\nVERBATIM\n #ifndef CORENEURON_BUILD\n        // TODO: since N0,N1 are no longer state variables, they will need to be written using this callback\n        //  provided that it is the version that supports multivalue writing\n        /* first arg is direction (-1 get info, 0 save, 1 restore), second is value*/\n        double *xdir, *xval;\n        #ifndef NRN_VERSION_GTEQ_8_2_0\n        double *hoc_pgetarg();\n        long nrn_get_random_sequence(void* r);\n        void nrn_set_random_sequence(void* r, int val);\n        #endif\n        xdir = hoc_pgetarg(1);\n        xval = hoc_pgetarg(2);\n        int saveCount = 0;\n\n        // N0 always needs to be saved (N1 is computed from N and N0)\n        if( *xdir == -1. ) {\n            saveCount = 1;\n        } else if ( *xdir == 0. ) {\n            xval[0] = N0;\n        } else {\n            N0 = xval[0];\n            N1 = N - N0;\n        }\n\n        // Handle RNG\n        if (_p_rng) {\n            if (*xdir == -1.) {\n                if( usingR123 ) {\n                    saveCount += 2.0;\n                } else {\n                    saveCount += 1.0;\n                }\n            } else if (*xdir == 0.) {\n                if( usingR123 ) {\n                    uint32_t seq;\n                    char which;\n                    nrnran123_getseq( (nrnran123_State*)_p_rng, &seq, &which );\n                    xval[1] = (double) seq;\n                    xval[2] = (double) which;\n                } else {\n                    xval[1] = (double)nrn_get_random_sequence(RANDCAST _p_rng);\n                }\n            } else {\n                if( usingR123 ) {\n                    nrnran123_setseq( (nrnran123_State*)_p_rng, (uint32_t)xval[1], (char)xval[2] );\n                } else {\n                    nrn_set_random_sequence(RANDCAST _p_rng, (long)(xval[1]));\n                }\n            }\n        }\n\n        if( *xdir == -1 ) {\n            *xdir = saveCount;\n        }\n\n        return 0.0;\n#endif\nENDVERBATIM\n}\n"
  },
  {
    "path": "examples/stochkv/mechanisms/StochKv3.mod",
    "content": "TITLE StochKv3.mod\n\nCOMMENT\n----------------------------------------------------------------\nStochastic inactivating channel using values reported in Mendonca et al. 2016.\n(Modified from https://senselab.med.yale.edu/modeldb/showmodel.cshtml?model=125385&file=/Sbpap_code/mod/skaprox.mod)\n\nThe model keeps track on the number of channels in each state, and\nuses a binomial distribution to update these number.\n\nJan 1999, Mickey London, Hebrew University, mikilon@lobster.ls.huji.ac.il\n        Peter N. Steinmetz, Caltech, peter@klab.caltech.edu\n14 Sep 99 PNS. Added deterministic flag.\n19 May 2002 Kamran Diba.  Changed gamma and deterministic from GLOBAL to RANGE.\n23 Nov 2011 Werner Van Geit @ BBP. Changed the file so that it can use the neuron random number generator. Tuned voltage dependence\n16 Mar 2016 James G King @ BBP.  Incorporate modifications suggested by Michael Hines to improve stiching to deterministic mode, thread safety, and using Random123\n26 Sep 2016 Christian Roessert @ BBP. Adding inactivation, changing dynamics to values reported in Mendonca et al. 2016\n: LJP: OK, whole-cell patch, corrected by 10 mV (Mendonca et al. 2016)\n\n----------------------------------------------------------------\nENDCOMMENT\n\nINDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}\n\nNEURON {\n    SUFFIX StochKv3\n    THREADSAFE\n    USEION k READ ek WRITE ik\n    RANGE N, eta, gk, gamma, deterministic, gkbar, ik\n    RANGE N0, N1, n0_n1, n1_n0\n    RANGE ninf, linf, ltau, ntau, an, bn, al, bl\n    RANGE P_an, P_bn, P_al, P_bl\n    GLOBAL vmin, vmax\n    BBCOREPOINTER rng\n    :POINTER rng\n}\n\nUNITS {\n    (mA) = (milliamp)\n    (mV) = (millivolt)\n    (pS) = (picosiemens)\n    (S) = (siemens)\n    (um) = (micron)\n}\n\nPARAMETER {\n    v           (mV)\n    dt      (ms)\n    area    (um2)\n\n    gamma  =  50      (pS)\n    eta              (1/um2)\n    gkbar = .01      (S/cm2)\n\n    deterministic = 0   : if non-zero, will use deterministic version\n    vmin = -120 (mV)    : range to construct tables for\n    vmax = 100  (mV)\n}\n\nASSIGNED {\n    an      (/ms)\n    bn      (/ms)\n    al      (/ms)\n    bl      (/ms)\n    ik      (mA/cm2)\n    gk      (S/cm2)\n    ek      (mV)\n    ninf        : steady-state value\n    ntau (ms)   : time constant for relaxation\n    linf        : steady-state value\n    ltau (ms)   : time constant for relaxation\n\n    N\n    scale_dens (pS/um2)\n    P_an     : probability of one channel making alpha n transition\n    P_bn     : probability of one channel making beta n transition\n    P_al     : probability of one channel making alpha l transition\n    P_bl     : probability of one channel making beta l transition\n\n    rng\n    usingR123\n}\n\n\nSTATE {\n    n l  : state variable of deterministic description\n}\nASSIGNED {\n    N0L0 N1L0 N0L1 N1L1     : N states populations (These currently will not be saved via the bbsavestate functionality.  Would need to be STATE again)\n    n0l0_n1l0 n0l0_n0l1     : number of channels moving from one state to the other\n    n1l0_n1l1 n1l0_n0l0\n    n0l1_n1l1 n0l1_n0l0\n    n1l1_n0l1 n1l1_n1l0\n}\n\nCOMMENT\nThe Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1\nfor comparison with Pr to decide whether to activate the synapse or not\nENDCOMMENT\n\nVERBATIM\n#ifndef NRN_VERSION_GTEQ_8_2_0\n#include \"nrnran123.h\"\nextern int cvode_active_;\n\n#include <stdlib.h>\n#include <stdio.h>\n#include <math.h>\n\n#ifndef CORENEURON_BUILD\ndouble nrn_random_pick(void* r);\nvoid* nrn_random_arg(int argpos);\n#endif\n#define RANDCAST\n#else\n#define RANDCAST (Rand*)\n#endif\n\nENDVERBATIM\n: ----------------------------------------------------------------\n: initialization\nINITIAL {\n    VERBATIM\n    if (cvode_active_ && !deterministic) {\n        hoc_execerror(\"StochKv2 with deterministic=0\", \"cannot be used with cvode\");\n    }\n\n    if( usingR123 ) {\n        nrnran123_setseq((nrnran123_State*)_p_rng, 0, 0);\n    }\n    ENDVERBATIM\n\n    eta = (gkbar / gamma) * (10000)\n    trates(v)\n    n=ninf\n    l=linf\n    scale_dens = gamma/area\n    N = floor(eta*area + 0.5)\n\n    N1L1 = n*l*N\n    N1L0 = n*(1-l)*N\n    N0L1 = (1-n)*l*N\n    if( !deterministic) {\n        N1L1 = floor(N1L1 + 0.5)\n        N1L0 = floor(N1L0 + 0.5)\n        N0L1 = floor(N0L1 + 0.5)\n    }\n    N0L0 = N - N1L1 - N1L0 - N0L1  : put rest into non-conducting state\n\n    n0l0_n1l0 = 0\n    n0l0_n0l1 = 0\n    n1l0_n1l1 = 0\n    n1l0_n0l0 = 0\n    n0l1_n1l1 = 0\n    n0l1_n0l0 = 0\n    n1l1_n0l1 = 0\n    n1l1_n1l0 = 0\n}\n\n: ----------------------------------------------------------------\n: Breakpoint for each integration step\nBREAKPOINT {\n  SOLVE states METHOD cnexp\n\n  gk = (strap(N1L1) * scale_dens) * (0.0001)\n\n  ik = gk * (v - ek)\n}\n\n\n: ----------------------------------------------------------------\n: states - updates number of channels in each state\nDERIVATIVE states {\n\n    trates(v)\n\n    l' = al - (al + bl)*l\n    n' = an - (an + bn)*n\n\n    if (deterministic || dt > 1) { : ForwardSkip is also deterministic\n\n      N1L1 = n*l*N\n      N1L0 = n*(1-l)*N\n      N0L1 = (1-n)*l*N\n\n    }else{\n\n      : ensure that N0 is an integer for when transitioning from deterministic mode to stochastic mode\n      N1L1 = floor(N1L1 + 0.5)\n      N1L0 = floor(N1L0 + 0.5)\n      N0L1 = floor(N0L1 + 0.5)\n      N0L0 = N - N1L1 - N1L0 - N0L1\n\n      P_an = strap(an*dt)\n      P_bn = strap(bn*dt)\n      : check that will represent probabilities when used\n      ChkProb(P_an)\n      ChkProb(P_bn)\n\n      : n gate transitions\n      n0l0_n1l0 = BnlDev(P_an, N0L0)\n      n0l1_n1l1 = BnlDev(P_an, N0L1)\n      n1l1_n0l1 = BnlDev(P_bn, N1L1)\n      n1l0_n0l0 = BnlDev(P_bn, N1L0)\n\n      : move the channels\n      N0L0 = strap(N0L0 - n0l0_n1l0 + n1l0_n0l0)\n      N1L0 = strap(N1L0 - n1l0_n0l0 + n0l0_n1l0)\n      N0L1 = strap(N0L1 - n0l1_n1l1 + n1l1_n0l1)\n      N1L1 = strap(N1L1 - n1l1_n0l1 + n0l1_n1l1)\n\n      : probabilities of making l gate transitions\n      P_al = strap(al*dt)\n      P_bl  = strap(bl*dt)\n      : check that will represent probabilities when used\n      ChkProb(P_al)\n      ChkProb(P_bl)\n\n      : number making l gate transitions\n      n0l0_n0l1 = BnlDev(P_al,N0L0-n0l0_n1l0)\n      n1l0_n1l1 = BnlDev(P_al,N1L0-n1l0_n0l0)\n      n0l1_n0l0 = BnlDev(P_bl,N0L1-n0l1_n1l1)\n      n1l1_n1l0 = BnlDev(P_bl,N1L1-n1l1_n0l1)\n\n      : move the channels\n      N0L0 = strap(N0L0 - n0l0_n0l1  + n0l1_n0l0)\n      N1L0 = strap(N1L0 - n1l0_n1l1  + n1l1_n1l0)\n      N0L1 = strap(N0L1 - n0l1_n0l0  + n0l0_n0l1)\n      N1L1 = strap(N1L1 - n1l1_n1l0  + n1l0_n1l1)\n\n    }\n\n    N0L0 = N - N1L1 - N1L0 - N0L1  : put rest into non-conducting state\n}\n\n: ----------------------------------------------------------------\n: trates - compute rates, using table if possible\nPROCEDURE trates(v (mV)) {\n    TABLE ntau,ltau,ninf,linf,al,bl,an,bn\n    DEPEND dt\n    FROM vmin TO vmax WITH 199\n\n    v = v + 10\n    linf = 1/(1+exp((-30(mV)-v)/10(mV)))\n    ltau = 0.346(ms)*exp(-v/(18.272(mV)))+2.09(ms)\n\n    ninf = 1/(1+exp(0.0878(1/mV)*(v+55.1(mV))))\n    ntau = 2.1(ms)*exp(-v/21.2(mV))+4.627(ms)\n    v = v - 10\n\n    al = linf/ltau\n    bl = 1/ltau - al\n    an = ninf/ntau\n    bn = 1/ntau - an\n}\n\n: ----------------------------------------------------------------\n: sign trap - trap for negative values and replace with zero\nFUNCTION strap(x) {\n    if (x < 0) {\n        strap = 0\nVERBATIM\n        fprintf (stderr,\"skv.mod:strap: negative state\");\nENDVERBATIM\n    } else {\n        strap = x\n    }\n}\n\n: ----------------------------------------------------------------\n: ChkProb - Check that number represents a probability\nPROCEDURE ChkProb(p) {\n\n  if (p < 0.0 || p > 1.0) {\n    VERBATIM\n    fprintf(stderr, \"StochKv2.mod:ChkProb: argument not a probability.\\n\");\n    ENDVERBATIM\n  }\n\n}\n\nPROCEDURE setRNG() {\n\nVERBATIM\n    // For compatibility, allow for either MCellRan4 or Random123.  Distinguish by the arg types\n    // Object => MCellRan4, seeds (double) => Random123\n#ifndef CORENEURON_BUILD\n    usingR123 = 0;\n    if( ifarg(1) && hoc_is_double_arg(1) ) {\n        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);\n        uint32_t a2 = 0;\n        uint32_t a3 = 0;\n\n        if (*pv) {\n            nrnran123_deletestream(*pv);\n            *pv = (nrnran123_State*)0;\n        }\n        if (ifarg(2)) {\n            a2 = (uint32_t)*getarg(2);\n        }\n        if (ifarg(3)) {\n            a3 = (uint32_t)*getarg(3);\n        }\n        *pv = nrnran123_newstream3((uint32_t)*getarg(1), a2, a3);\n        usingR123 = 1;\n    } else if( ifarg(1) ) {\n        void** pv = (void**)(&_p_rng);\n        *pv = nrn_random_arg(1);\n    } else {\n        void** pv = (void**)(&_p_rng);\n        *pv = (void*)0;\n    }\n#endif\nENDVERBATIM\n}\n\nFUNCTION urand() {\n\nVERBATIM\n    double value;\n    if( usingR123 ) {\n        value = nrnran123_dblpick((nrnran123_State*)_p_rng);\n    } else if (_p_rng) {\n#ifndef CORENEURON_BUILD\n        value = nrn_random_pick(RANDCAST _p_rng);\n#endif\n    } else {\n        value = 0.5;\n    }\n    _lurand = value;\nENDVERBATIM\n}\n\nVERBATIM\nstatic void bbcore_write(double* x, int* d, int* xx, int* offset, _threadargsproto_) {\n    if (d) {\n        uint32_t* di = ((uint32_t*)d) + *offset;\n      // temporary just enough to see how much space is being used\n      if (!_p_rng) {\n        di[0] = 0; di[1] = 0, di[2] = 0;\n      }else{\n        nrnran123_State** pv = (nrnran123_State**)(&_p_rng);\n        nrnran123_getids3(*pv, di, di+1, di+2);\n        // write stream sequence\n        char which;\n        nrnran123_getseq(*pv, di+3, &which);\n        di[4] = (int)which;\n      }\n      //printf(\"StochKv3.mod %p: bbcore_write offset=%d %d %d\\n\", _p, *offset, d?di[0]:-1, d?di[1]:-1);\n    }\n    *offset += 5;\n}\nstatic void bbcore_read(double* x, int* d, int* xx, int* offset, _threadargsproto_) {\n    uint32_t* di = ((uint32_t*)d) + *offset;\n        if (di[0] != 0 || di[1] != 0|| di[2] != 0)\n        {\n      nrnran123_State** pv = (nrnran123_State**)(&_p_rng);\n#if !NRNBBCORE\n      if(*pv) {\n          nrnran123_deletestream(*pv);\n      }\n#endif\n      *pv = nrnran123_newstream3(di[0], di[1], di[2]);\n      nrnran123_setseq(*pv, di[3], (char)di[4]);\n        }\n      //printf(\"StochKv3.mod %p: bbcore_read offset=%d %d %d\\n\", _p, *offset, di[0], di[1]);\n    *offset += 5;\n}\nENDVERBATIM\n\n: Returns random numbers drawn from a binomial distribution\nFUNCTION brand(P, N) {\n\nVERBATIM\n        /*\n        :Supports separate independent but reproducible streams for\n        : each instance. However, the corresponding hoc Random\n        : distribution MUST be set to Random.uniform(0,1)\n        */\n\n        // Should probably be optimized\n        double value = 0.0;\n        int i;\n        for (i = 0; i < _lN; i++) {\n           if (urand(_threadargs_) < _lP) {\n              value = value + 1;\n           }\n        }\n        return(value);\n\nENDVERBATIM\n\n        brand = value\n}\n\nVERBATIM\n#define        PI 3.141592654\n#define        r_ia     16807\n#define        r_im     2147483647\n#define        r_am     (1.0/r_im)\n#define        r_iq     127773\n#define        r_ir     2836\n#define        r_ntab   32\n#define        r_ndiv   (1+(r_im-1)/r_ntab)\n#define        r_eps    1.2e-7\n#define        r_rnmx   (1.0-r_eps)\nENDVERBATIM\n\nVERBATIM\n/* ---------------------------------------------------------------- */\n/* gammln - compute natural log of gamma function of xx */\nstatic double\ngammln(double xx)\n{\n    double x,tmp,ser;\n    static double cof[6]={76.18009173,-86.50532033,24.01409822,\n        -1.231739516,0.120858003e-2,-0.536382e-5};\n    int j;\n    x=xx-1.0;\n    tmp=x+5.5;\n    tmp -= (x+0.5)*log(tmp);\n    ser=1.0;\n    for (j=0;j<=5;j++) {\n        x += 1.0;\n        ser += cof[j]/x;\n    }\n    return -tmp+log(2.50662827465*ser);\n}\nENDVERBATIM\n\n\n: ----------------------------------------------------------------\n: BnlDev - draw a uniform deviate from the generator\nFUNCTION BnlDev (ppr, nnr) {\n\nVERBATIM\n        int j;\n        double am,em,g,angle,p,bnl,sq,bt,y;\n        double pc,plog,pclog,en,oldg;\n\n        /* prepare to always ignore errors within this routine */\n\n        p=(_lppr <= 0.5 ? _lppr : 1.0-_lppr);\n        am=_lnnr*p;\n        if (_lnnr < 25) {\n            bnl=0.0;\n            for (j=1;j<=_lnnr;j++)\n                if (urand(_threadargs_) < p) bnl += 1.0;\n        }\n        else if (am < 1.0) {\n            g=exp(-am);\n            bt=1.0;\n            for (j=0;j<=_lnnr;j++) {\n                bt *= urand(_threadargs_);\n                if (bt < g) break;\n            }\n            bnl=(j <= _lnnr ? j : _lnnr);\n        }\n        else {\n            {\n                en=_lnnr;\n                oldg=gammln(en+1.0);\n            }\n            {\n                pc=1.0-p;\n                plog=log(p);\n                pclog=log(pc);\n            }\n            sq=sqrt(2.0*am*pc);\n            do {\n                do {\n                    angle=PI*urand(_threadargs_);\n                    angle=PI*urand(_threadargs_);\n                    y=tan(angle);\n                    em=sq*y+am;\n                } while (em < 0.0 || em >= (en+1.0));\n                em=floor(em);\n                    bt=1.2*sq*(1.0+y*y)*exp(oldg-gammln(em+1.0) -\n                    gammln(en-em+1.0)+em*plog+(en-em)*pclog);\n            } while (urand(_threadargs_) > bt);\n            bnl=em;\n        }\n        if (p != _lppr) bnl=_lnnr-bnl;\n\n        /* recover error if changed during this routine, thus ignoring\n            any errors during this routine */\n\n\n        return bnl;\n\n    ENDVERBATIM\n    BnlDev = bnl\n}\n\nFUNCTION bbsavestate() {\n        bbsavestate = 0\nVERBATIM\n #ifndef CORENEURON_BUILD\n        // TODO: since N0,N1 are no longer state variables, they will need to be written using this callback\n        //  provided that it is the version that supports multivalue writing\n        /* first arg is direction (-1 get info, 0 save, 1 restore), second is value*/\n        double *xdir, *xval;\n        #ifndef NRN_VERSION_GTEQ_8_2_0\n        double *hoc_pgetarg();\n        long nrn_get_random_sequence(void* r);\n        void nrn_set_random_sequence(void* r, int val);\n        #endif\n        xdir = hoc_pgetarg(1);\n        xval = hoc_pgetarg(2);\n        if (_p_rng) {\n                // tell how many items need saving\n                if (*xdir == -1.) {\n                    if( usingR123 ) {\n                        *xdir = 2.0;\n                    } else {\n                        *xdir = 1.0;\n                    }\n                    return 0.0;\n                }\n                else if (*xdir == 0.) {\n                    if( usingR123 ) {\n                        uint32_t seq;\n                        char which;\n                        nrnran123_getseq( (nrnran123_State*)_p_rng, &seq, &which );\n                        xval[0] = (double) seq;\n                        xval[1] = (double) which;\n                    } else {\n                        xval[0] = (double)nrn_get_random_sequence(RANDCAST _p_rng);\n                    }\n                } else{\n                    if( usingR123 ) {\n                        nrnran123_setseq( (nrnran123_State*)_p_rng, (uint32_t)xval[0], (char)xval[1] );\n                    } else {\n                        nrn_set_random_sequence(RANDCAST _p_rng, (long)(xval[0]));\n                    }\n                }\n        }\n\n        // TODO: check for random123 and get the seq values\n#endif\nENDVERBATIM\n}\n"
  },
  {
    "path": "examples/stochkv/mechanisms/dummy.inc",
    "content": ""
  },
  {
    "path": "examples/stochkv/morphology/simple.swc",
    "content": "# Dummy granule cell morphology\n1 1 -5.0 0.0 0.0 5.0 -1\n2 1 0.0 0.0 0.0 5.0 1\n3 1 5.0 0.0 0.0 5.0 2\n"
  },
  {
    "path": "examples/stochkv/stochkv3cell.hoc",
    "content": "/*\n*/\n{load_file(\"stdrun.hoc\")}\n{load_file(\"import3d.hoc\")}\n/*\n * Check that global parameters are the same as with the optimization\n */\nproc check_parameter(/* name, expected_value, value */){\n  strdef error\n  if($2 != $3){\n    sprint(error, \"Parameter %s has different value %f != %f\", $s1, $2, $3)\n    execerror(error)\n  }\n}\nproc check_simulator() {\n  check_parameter(\"celsius\", 34.0, celsius)\n}\n\nbegintemplate stochkv3_cell\n  public init, morphology, geom_nseg_fixed, geom_nsec, gid\n  public channel_seed, channel_seed_set\n  public soma, dend, apic, axon, myelin\n  create soma[1], dend[1], apic[1], axon[1], myelin[1]\n\n  objref this, CellRef, segCounts\n\n  public all, somatic, apical, axonal, basal, myelinated, APC\n  objref all, somatic, apical, axonal, basal, myelinated, APC\n\nobfunc getCell(){\n        return this\n}\n\nproc init(/* args: morphology_dir, morphology_name */) {\n  all = new SectionList()\n  apical = new SectionList()\n  axonal = new SectionList()\n  basal = new SectionList()\n  somatic = new SectionList()\n  myelinated = new SectionList()\n\n  //gid in this case is only used for rng seeding\n  gid = 0\n\n  //For compatibility with BBP CCells\n  CellRef = this\n\n  forall delete_section()\n\n  if(numarg() >= 2) {\n    load_morphology($s1, $s2)\n  } else {\n    load_morphology($s1, \"simple.swc\")\n  }\n\n  geom_nseg()\n  insertChannel()\n  biophys()\n\n  // Initialize channel_seed_set to avoid accidents\n  channel_seed_set = 0\n  // Initialize random number generators\n  re_init_rng()\n}\n\nproc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension\n  strdef morph_path\n  sprint(morph_path, \"%s/%s\", $s1, $s2)\n\n  sf = new StringFunctions()\n  extension = new String()\n\n  sscanf(morph_path, \"%s\", extension.s)\n  sf.right(extension.s, sf.len(extension.s)-4)\n\n  if( strcmp(extension.s, \".asc\") == 0 ) {\n    morph = new Import3d_Neurolucida3()\n  } else if( strcmp(extension.s, \".swc\" ) == 0) {\n    morph = new Import3d_SWC_read()\n  } else {\n    printf(\"Unsupported file format: Morphology file has to end with .asc or .swc\" )\n    quit()\n  }\n\n  morph.quiet = 1\n  morph.input(morph_path)\n\n  import = new Import3d_GUI(morph, 0)\n  import.instantiate(this)\n}\n\n/*\n * Assignment of mechanism values based on distance from the soma\n * Matches the BluePyOpt method\n */\nproc distribute_distance(){local x localobj sl\n  strdef stmp, distfunc, mech\n\n  sl = $o1\n  mech = $s2\n  distfunc = $s3\n  this.soma[0] distance(0, 0.5)\n  sprint(distfunc, \"%%s %s(%%f) = %s\", mech, distfunc)\n  forsec sl for(x, 0) {\n    // use distance(x) twice for the step distribution case, e.g. for calcium hotspot\n    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))\n    execute(stmp)\n  }\n}\n\nproc geom_nseg() {\n  this.geom_nsec() //To count all sections\n  //TODO: geom_nseg_fixed depends on segCounts which is calculated by\n  //  geom_nsec.  Can this be collapsed?\n  this.geom_nseg_fixed(40)\n  this.geom_nsec() //To count all sections\n}\n\nproc insertChannel() {\n  forsec this.all {\n  }\n  forsec this.apical {\n  }\n  forsec this.axonal {\n  }\n  forsec this.basal {\n  }\n  forsec this.somatic {\n    insert pas\n    insert StochKv3\n  }\n  forsec this.myelinated {\n  }\n}\n\nproc biophys() {\n  \n  forsec CellRef.all {\n  }\n  \n  forsec CellRef.apical {\n  }\n  \n  forsec CellRef.axonal {\n  }\n  \n  forsec CellRef.basal {\n  }\n  \n  forsec CellRef.somatic {\n    e_pas = -90\n    gkbar_StochKv3 = 0.5\n  }\n  \n  forsec CellRef.myelinated {\n  }\n  \n}\n\nfunc sec_count(/* SectionList */) { local nSec\n  nSec = 0\n  forsec $o1 {\n      nSec += 1\n  }\n  return nSec\n}\n\n/*\n * Iterate over the section and compute how many segments should be allocate to\n * each.\n */\nproc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize\n  chunkSize = $1\n  soma area(.5) // make sure diam reflects 3d points\n  secIndex = 0\n  forsec all {\n    nseg = 1 + 2*int(L/chunkSize)\n    segCounts.x[secIndex] = nseg\n    secIndex += 1\n  }\n}\n\n/*\n * Count up the number of sections\n */\nproc geom_nsec() { local nSec\n  nSecAll = sec_count(all)\n  nSecSoma = sec_count(somatic)\n  nSecApical = sec_count(apical)\n  nSecBasal = sec_count(basal)\n  nSecMyelinated = sec_count(myelinated)\n  nSecAxonalOrig = nSecAxonal = sec_count(axonal)\n\n  segCounts = new Vector()\n  segCounts.resize(nSecAll)\n  nSec = 0\n  forsec all {\n    segCounts.x[nSec] = nseg\n    nSec += 1\n  }\n}\n\n/*\n * Replace the axon built from the original morphology file with a stub axon\n */\n\n\n\nfunc hash_str() {localobj sf strdef right\n  sf = new StringFunctions()\n\n  right = $s1\n\n  n_of_c = sf.len(right)\n\n  hash = 0\n  char_int = 0\n  for i = 0, n_of_c - 1 {\n     sscanf(right, \"%c\", & char_int)\n     hash = (hash * 31 + char_int) % (2 ^ 31 - 1)\n     sf.right(right, 1)\n  }\n\n  return hash\n}\n\nproc re_init_rng() {localobj sf\n    strdef full_str, name\n\n    sf = new StringFunctions()\n\n    if(numarg() == 1) {\n        // We received a third seed\n        channel_seed = $1\n        channel_seed_set = 1\n    } else {\n        channel_seed_set = 0\n    }\n\n    forsec somatic {\n        for (x, 0) {\n            setdata_StochKv3(x)\n            sf.tail(secname(), \"\\\\.\", name)\n            sprint(full_str, \"%s.%.19g\", name, x)\n            if (channel_seed_set) {\n                setRNG_StochKv3(gid, hash_str(full_str), channel_seed)\n            } else {\n                setRNG_StochKv3(gid, hash_str(full_str))\n            }\n        }\n    }\n\n}\n\n\nendtemplate stochkv3_cell"
  },
  {
    "path": "examples/stochkv/stochkv3cell.py",
    "content": "\"\"\"StochKv3 cell example\"\"\"\n\n# pylint: disable=R0914\n\nimport os\n\nimport bluepyopt.ephys as ephys\n\nscript_dir = os.path.dirname(__file__)\nmorph_dir = os.path.join(script_dir, 'morphology')\n\n\ndef stochkv3_hoc_filename(deterministic=False):\n    \"\"\"Return stochkv3 hoc model filename\"\"\"\n    return os.path.join(\n        script_dir,\n        'stochkv3cell%s.hoc' %\n        ('_det' if deterministic else ''))\n\n\ndef run_stochkv3_model(deterministic=False):\n    \"\"\"Run stochkv3 model\"\"\"\n\n    morph = ephys.morphologies.NrnFileMorphology(\n        os.path.join(\n            morph_dir,\n            'simple.swc'))\n\n    somatic_loc = ephys.locations.NrnSeclistLocation(\n        'somatic',\n        seclist_name='somatic')\n    stochkv3_mech = ephys.mechanisms.NrnMODMechanism(\n        name='StochKv3',\n        suffix='StochKv3',\n        locations=[somatic_loc],\n        deterministic=deterministic)\n    pas_mech = ephys.mechanisms.NrnMODMechanism(\n        name='pas',\n        suffix='pas',\n        locations=[somatic_loc])\n    gkbar_param = ephys.parameters.NrnSectionParameter(\n        name='gkbar_StochKv3',\n        param_name='gkbar_StochKv3',\n        locations=[somatic_loc],\n        bounds=[0.0, 10.0],\n        frozen=False)\n    epas_param = ephys.parameters.NrnSectionParameter(\n        name='e_pas',\n        param_name='e_pas',\n        locations=[somatic_loc],\n        value=-90,\n        frozen=True)\n    celsius_param = ephys.parameters.NrnGlobalParameter(\n        name='celsius',\n        param_name='celsius',\n        value=34.0,\n        frozen=True)\n    params = [epas_param, celsius_param, gkbar_param]\n    stochkv3_cell = ephys.models.CellModel(\n        name='stochkv3_cell',\n        morph=morph,\n        mechs=[pas_mech, stochkv3_mech],\n        params=params)\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=0.5)\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.1,\n        step_delay=50,\n        step_duration=50,\n        location=soma_loc,\n        total_duration=150)\n    hold_stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=-0.025,\n        step_delay=0,\n        step_duration=10000,\n        location=soma_loc,\n        total_duration=150)\n    rec = ephys.recordings.CompRecording(\n        name='Step.soma.v',\n        location=soma_loc,\n        variable='v')\n\n    protocol = ephys.protocols.SweepProtocol('Step', [stim, hold_stim], [rec])\n\n    nrn = ephys.simulators.NrnSimulator(cvode_active=False)\n\n    evaluator = ephys.evaluators.CellEvaluator(\n        cell_model=stochkv3_cell,\n        param_names=[param.name for param in params],\n        fitness_calculator=ephys.objectivescalculators.ObjectivesCalculator(),\n        sim=nrn)\n\n    best_param_values = {'gkbar_StochKv3': 0.5}\n    responses = evaluator.run_protocol(\n        protocol,\n        cell_model=stochkv3_cell,\n        param_values=best_param_values,\n        sim=nrn)\n\n    hoc_string = stochkv3_cell.create_hoc(\n        param_values=best_param_values,\n        disable_banner=True)\n\n    stochkv3_hoc_cell = ephys.models.HocCellModel(\n        'stochkv3_hoc_cell',\n        morphology_path=morph_dir,\n        hoc_string=hoc_string)\n\n    nrn.neuron.h.celsius = 34\n\n    hoc_responses = protocol.run(stochkv3_hoc_cell, best_param_values, sim=nrn)\n\n    evaluator.use_params_for_seed = True\n    different_seed_responses = evaluator.run_protocol(\n        protocol,\n        cell_model=stochkv3_cell,\n        param_values=best_param_values,\n        sim=nrn)\n\n    return responses, hoc_responses, different_seed_responses, hoc_string\n\n\ndef main():\n    \"\"\"Main\"\"\"\n    import matplotlib.pyplot as plt\n\n    for deterministic in [True, False]:\n        stochkv3_responses, stochkv3_hoc_responses, different_seed_responses, \\\n            stochkv3_hoc_string = \\\n            run_stochkv3_model(deterministic=deterministic)\n\n        with open(stochkv3_hoc_filename(deterministic=deterministic), 'w') as \\\n                stochkv3_hoc_file:\n            stochkv3_hoc_file.write(stochkv3_hoc_string)\n\n        time = stochkv3_responses['Step.soma.v']['time']\n        py_voltage = stochkv3_responses['Step.soma.v']['voltage']\n        hoc_voltage = stochkv3_hoc_responses['Step.soma.v']['voltage']\n        different_seed_voltage = \\\n            different_seed_responses['Step.soma.v']['voltage']\n\n        plt.figure()\n        plt.plot(time, py_voltage - hoc_voltage, label='py - hoc diff')\n        plt.xlabel('time (ms)')\n        plt.ylabel('voltage diff(mV)')\n        plt.title('Deterministic' if deterministic else 'Stochastic')\n        plt.legend()\n\n        plt.figure()\n        plt.plot(time, py_voltage, label='py')\n        plt.plot(time, hoc_voltage, label='hoc')\n        plt.xlabel('time (ms)')\n        plt.ylabel('voltage (mV)')\n        plt.title('Deterministic' if deterministic else 'Stochastic')\n        plt.legend()\n\n        plt.figure()\n        plt.plot(time, py_voltage, label='py')\n        plt.plot(time, different_seed_voltage, label='different seed')\n        plt.xlabel('time (ms)')\n        plt.ylabel('voltage (mV)')\n        plt.title('Deterministic' if deterministic else 'Stochastic')\n        plt.legend()\n\n    plt.show()\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/stochkv/stochkv3cell_det.hoc",
    "content": "/*\n*/\n{load_file(\"stdrun.hoc\")}\n{load_file(\"import3d.hoc\")}\n/*\n * Check that global parameters are the same as with the optimization\n */\nproc check_parameter(/* name, expected_value, value */){\n  strdef error\n  if($2 != $3){\n    sprint(error, \"Parameter %s has different value %f != %f\", $s1, $2, $3)\n    execerror(error)\n  }\n}\nproc check_simulator() {\n  check_parameter(\"celsius\", 34.0, celsius)\n}\n\nbegintemplate stochkv3_cell\n  public init, morphology, geom_nseg_fixed, geom_nsec, gid\n  public channel_seed, channel_seed_set\n  public soma, dend, apic, axon, myelin\n  create soma[1], dend[1], apic[1], axon[1], myelin[1]\n\n  objref this, CellRef, segCounts\n\n  public all, somatic, apical, axonal, basal, myelinated, APC\n  objref all, somatic, apical, axonal, basal, myelinated, APC\n\nobfunc getCell(){\n        return this\n}\n\nproc init(/* args: morphology_dir, morphology_name */) {\n  all = new SectionList()\n  apical = new SectionList()\n  axonal = new SectionList()\n  basal = new SectionList()\n  somatic = new SectionList()\n  myelinated = new SectionList()\n\n  //gid in this case is only used for rng seeding\n  gid = 0\n\n  //For compatibility with BBP CCells\n  CellRef = this\n\n  forall delete_section()\n\n  if(numarg() >= 2) {\n    load_morphology($s1, $s2)\n  } else {\n    load_morphology($s1, \"simple.swc\")\n  }\n\n  geom_nseg()\n  insertChannel()\n  biophys()\n\n  // Initialize channel_seed_set to avoid accidents\n  channel_seed_set = 0\n  // Initialize random number generators\n  re_init_rng()\n}\n\nproc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension\n  strdef morph_path\n  sprint(morph_path, \"%s/%s\", $s1, $s2)\n\n  sf = new StringFunctions()\n  extension = new String()\n\n  sscanf(morph_path, \"%s\", extension.s)\n  sf.right(extension.s, sf.len(extension.s)-4)\n\n  if( strcmp(extension.s, \".asc\") == 0 ) {\n    morph = new Import3d_Neurolucida3()\n  } else if( strcmp(extension.s, \".swc\" ) == 0) {\n    morph = new Import3d_SWC_read()\n  } else {\n    printf(\"Unsupported file format: Morphology file has to end with .asc or .swc\" )\n    quit()\n  }\n\n  morph.quiet = 1\n  morph.input(morph_path)\n\n  import = new Import3d_GUI(morph, 0)\n  import.instantiate(this)\n}\n\n/*\n * Assignment of mechanism values based on distance from the soma\n * Matches the BluePyOpt method\n */\nproc distribute_distance(){local x localobj sl\n  strdef stmp, distfunc, mech\n\n  sl = $o1\n  mech = $s2\n  distfunc = $s3\n  this.soma[0] distance(0, 0.5)\n  sprint(distfunc, \"%%s %s(%%f) = %s\", mech, distfunc)\n  forsec sl for(x, 0) {\n    // use distance(x) twice for the step distribution case, e.g. for calcium hotspot\n    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))\n    execute(stmp)\n  }\n}\n\nproc geom_nseg() {\n  this.geom_nsec() //To count all sections\n  //TODO: geom_nseg_fixed depends on segCounts which is calculated by\n  //  geom_nsec.  Can this be collapsed?\n  this.geom_nseg_fixed(40)\n  this.geom_nsec() //To count all sections\n}\n\nproc insertChannel() {\n  forsec this.all {\n  }\n  forsec this.apical {\n  }\n  forsec this.axonal {\n  }\n  forsec this.basal {\n  }\n  forsec this.somatic {\n    insert pas\n    insert StochKv3\n  }\n  forsec this.myelinated {\n  }\n}\n\nproc biophys() {\n  \n  forsec CellRef.all {\n  }\n  \n  forsec CellRef.apical {\n  }\n  \n  forsec CellRef.axonal {\n  }\n  \n  forsec CellRef.basal {\n  }\n  \n  forsec CellRef.somatic {\n    e_pas = -90\n    gkbar_StochKv3 = 0.5\n  }\n  \n  forsec CellRef.myelinated {\n  }\n  \n}\n\nfunc sec_count(/* SectionList */) { local nSec\n  nSec = 0\n  forsec $o1 {\n      nSec += 1\n  }\n  return nSec\n}\n\n/*\n * Iterate over the section and compute how many segments should be allocate to\n * each.\n */\nproc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize\n  chunkSize = $1\n  soma area(.5) // make sure diam reflects 3d points\n  secIndex = 0\n  forsec all {\n    nseg = 1 + 2*int(L/chunkSize)\n    segCounts.x[secIndex] = nseg\n    secIndex += 1\n  }\n}\n\n/*\n * Count up the number of sections\n */\nproc geom_nsec() { local nSec\n  nSecAll = sec_count(all)\n  nSecSoma = sec_count(somatic)\n  nSecApical = sec_count(apical)\n  nSecBasal = sec_count(basal)\n  nSecMyelinated = sec_count(myelinated)\n  nSecAxonalOrig = nSecAxonal = sec_count(axonal)\n\n  segCounts = new Vector()\n  segCounts.resize(nSecAll)\n  nSec = 0\n  forsec all {\n    segCounts.x[nSec] = nseg\n    nSec += 1\n  }\n}\n\n/*\n * Replace the axon built from the original morphology file with a stub axon\n */\n\n\n\nfunc hash_str() {localobj sf strdef right\n  sf = new StringFunctions()\n\n  right = $s1\n\n  n_of_c = sf.len(right)\n\n  hash = 0\n  char_int = 0\n  for i = 0, n_of_c - 1 {\n     sscanf(right, \"%c\", & char_int)\n     hash = (hash * 31 + char_int) % (2 ^ 31 - 1)\n     sf.right(right, 1)\n  }\n\n  return hash\n}\n\nproc re_init_rng() {localobj sf\n    strdef full_str, name\n\n    sf = new StringFunctions()\n\n    if(numarg() == 1) {\n        // We received a third seed\n        channel_seed = $1\n        channel_seed_set = 1\n    } else {\n        channel_seed_set = 0\n    }\n\n    forsec somatic { deterministic_StochKv3 = 1 }\n\n}\n\n\nendtemplate stochkv3_cell"
  },
  {
    "path": "examples/stochkv/stochkvcell.hoc",
    "content": "/*\n*/\n{load_file(\"stdrun.hoc\")}\n{load_file(\"import3d.hoc\")}\n/*\n * Check that global parameters are the same as with the optimization\n */\nproc check_parameter(/* name, expected_value, value */){\n  strdef error\n  if($2 != $3){\n    sprint(error, \"Parameter %s has different value %f != %f\", $s1, $2, $3)\n    execerror(error)\n  }\n}\nproc check_simulator() {\n  check_parameter(\"celsius\", 34.0, celsius)\n}\n\nbegintemplate stochkv_cell\n  public init, morphology, geom_nseg_fixed, geom_nsec, gid\n  public channel_seed, channel_seed_set\n  public soma, dend, apic, axon, myelin\n  create soma[1], dend[1], apic[1], axon[1], myelin[1]\n\n  objref this, CellRef, segCounts\n\n  public all, somatic, apical, axonal, basal, myelinated, APC\n  objref all, somatic, apical, axonal, basal, myelinated, APC\n\nobfunc getCell(){\n        return this\n}\n\nproc init(/* args: morphology_dir, morphology_name */) {\n  all = new SectionList()\n  apical = new SectionList()\n  axonal = new SectionList()\n  basal = new SectionList()\n  somatic = new SectionList()\n  myelinated = new SectionList()\n\n  //gid in this case is only used for rng seeding\n  gid = 0\n\n  //For compatibility with BBP CCells\n  CellRef = this\n\n  forall delete_section()\n\n  if(numarg() >= 2) {\n    load_morphology($s1, $s2)\n  } else {\n    load_morphology($s1, \"simple.swc\")\n  }\n\n  geom_nseg()\n  insertChannel()\n  biophys()\n\n  // Initialize channel_seed_set to avoid accidents\n  channel_seed_set = 0\n  // Initialize random number generators\n  re_init_rng()\n}\n\nproc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension\n  strdef morph_path\n  sprint(morph_path, \"%s/%s\", $s1, $s2)\n\n  sf = new StringFunctions()\n  extension = new String()\n\n  sscanf(morph_path, \"%s\", extension.s)\n  sf.right(extension.s, sf.len(extension.s)-4)\n\n  if( strcmp(extension.s, \".asc\") == 0 ) {\n    morph = new Import3d_Neurolucida3()\n  } else if( strcmp(extension.s, \".swc\" ) == 0) {\n    morph = new Import3d_SWC_read()\n  } else {\n    printf(\"Unsupported file format: Morphology file has to end with .asc or .swc\" )\n    quit()\n  }\n\n  morph.quiet = 1\n  morph.input(morph_path)\n\n  import = new Import3d_GUI(morph, 0)\n  import.instantiate(this)\n}\n\n/*\n * Assignment of mechanism values based on distance from the soma\n * Matches the BluePyOpt method\n */\nproc distribute_distance(){local x localobj sl\n  strdef stmp, distfunc, mech\n\n  sl = $o1\n  mech = $s2\n  distfunc = $s3\n  this.soma[0] distance(0, 0.5)\n  sprint(distfunc, \"%%s %s(%%f) = %s\", mech, distfunc)\n  forsec sl for(x, 0) {\n    // use distance(x) twice for the step distribution case, e.g. for calcium hotspot\n    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))\n    execute(stmp)\n  }\n}\n\nproc geom_nseg() {\n  this.geom_nsec() //To count all sections\n  //TODO: geom_nseg_fixed depends on segCounts which is calculated by\n  //  geom_nsec.  Can this be collapsed?\n  this.geom_nseg_fixed(40)\n  this.geom_nsec() //To count all sections\n}\n\nproc insertChannel() {\n  forsec this.all {\n  }\n  forsec this.apical {\n  }\n  forsec this.axonal {\n  }\n  forsec this.basal {\n  }\n  forsec this.somatic {\n    insert pas\n    insert StochKv\n  }\n  forsec this.myelinated {\n  }\n}\n\nproc biophys() {\n  \n  forsec CellRef.all {\n  }\n  \n  forsec CellRef.apical {\n  }\n  \n  forsec CellRef.axonal {\n  }\n  \n  forsec CellRef.basal {\n  }\n  \n  forsec CellRef.somatic {\n    e_pas = -90\n    gkbar_StochKv = 0.5\n  }\n  \n  forsec CellRef.myelinated {\n  }\n  \n}\n\nfunc sec_count(/* SectionList */) { local nSec\n  nSec = 0\n  forsec $o1 {\n      nSec += 1\n  }\n  return nSec\n}\n\n/*\n * Iterate over the section and compute how many segments should be allocate to\n * each.\n */\nproc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize\n  chunkSize = $1\n  soma area(.5) // make sure diam reflects 3d points\n  secIndex = 0\n  forsec all {\n    nseg = 1 + 2*int(L/chunkSize)\n    segCounts.x[secIndex] = nseg\n    secIndex += 1\n  }\n}\n\n/*\n * Count up the number of sections\n */\nproc geom_nsec() { local nSec\n  nSecAll = sec_count(all)\n  nSecSoma = sec_count(somatic)\n  nSecApical = sec_count(apical)\n  nSecBasal = sec_count(basal)\n  nSecMyelinated = sec_count(myelinated)\n  nSecAxonalOrig = nSecAxonal = sec_count(axonal)\n\n  segCounts = new Vector()\n  segCounts.resize(nSecAll)\n  nSec = 0\n  forsec all {\n    segCounts.x[nSec] = nseg\n    nSec += 1\n  }\n}\n\n/*\n * Replace the axon built from the original morphology file with a stub axon\n */\n\n\n\nfunc hash_str() {localobj sf strdef right\n  sf = new StringFunctions()\n\n  right = $s1\n\n  n_of_c = sf.len(right)\n\n  hash = 0\n  char_int = 0\n  for i = 0, n_of_c - 1 {\n     sscanf(right, \"%c\", & char_int)\n     hash = (hash * 31 + char_int) % (2 ^ 31 - 1)\n     sf.right(right, 1)\n  }\n\n  return hash\n}\n\nproc re_init_rng() {localobj sf\n    strdef full_str, name\n\n    sf = new StringFunctions()\n\n    if(numarg() == 1) {\n        // We received a third seed\n        channel_seed = $1\n        channel_seed_set = 1\n    } else {\n        channel_seed_set = 0\n    }\n\n    forsec somatic {\n        for (x, 0) {\n            setdata_StochKv(x)\n            sf.tail(secname(), \"\\\\.\", name)\n            sprint(full_str, \"%s.%.19g\", name, x)\n            if (channel_seed_set) {\n                setRNG_StochKv(gid, hash_str(full_str), channel_seed)\n            } else {\n                setRNG_StochKv(gid, hash_str(full_str))\n            }\n        }\n    }\n\n}\n\n\nendtemplate stochkv_cell"
  },
  {
    "path": "examples/stochkv/stochkvcell.py",
    "content": "\"\"\"StochKv cell example\"\"\"\n\n# pylint: disable=R0914\n\nimport os\n\nimport bluepyopt.ephys as ephys\n\nscript_dir = os.path.dirname(__file__)\nmorph_dir = os.path.join(script_dir, 'morphology')\n\n\ndef stochkv_hoc_filename(deterministic=False):\n    \"\"\"Return stochkv hoc model filename\"\"\"\n    return os.path.join(\n        script_dir,\n        'stochkvcell%s.hoc' %\n        ('_det' if deterministic else ''))\n\n\ndef run_stochkv_model(deterministic=False):\n    \"\"\"Run stochkv model\"\"\"\n\n    morph = ephys.morphologies.NrnFileMorphology(\n        os.path.join(\n            morph_dir,\n            'simple.swc'))\n\n    somatic_loc = ephys.locations.NrnSeclistLocation(\n        'somatic',\n        seclist_name='somatic')\n    stochkv_mech = ephys.mechanisms.NrnMODMechanism(\n        name='StochKv',\n        suffix='StochKv',\n        locations=[somatic_loc],\n        deterministic=deterministic)\n    pas_mech = ephys.mechanisms.NrnMODMechanism(\n        name='pas',\n        suffix='pas',\n        locations=[somatic_loc])\n    gkbar_param = ephys.parameters.NrnSectionParameter(\n        name='gkbar_StochKv',\n        param_name='gkbar_StochKv',\n        locations=[somatic_loc],\n        bounds=[0.0, 10.0],\n        frozen=False)\n    epas_param = ephys.parameters.NrnSectionParameter(\n        name='e_pas',\n        param_name='e_pas',\n        locations=[somatic_loc],\n        value=-90,\n        frozen=True)\n    celsius_param = ephys.parameters.NrnGlobalParameter(\n        name='celsius',\n        param_name='celsius',\n        value=34.0,\n        frozen=True)\n    params = [epas_param, celsius_param, gkbar_param]\n    stochkv_cell = ephys.models.CellModel(\n        name='stochkv_cell',\n        morph=morph,\n        mechs=[pas_mech, stochkv_mech],\n        params=params)\n\n    soma_loc = ephys.locations.NrnSeclistCompLocation(\n        name='soma',\n        seclist_name='somatic',\n        sec_index=0,\n        comp_x=0.5)\n\n    stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=0.1,\n        step_delay=50,\n        step_duration=50,\n        location=soma_loc,\n        total_duration=150)\n    hold_stim = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=-0.025,\n        step_delay=0,\n        step_duration=10000,\n        location=soma_loc,\n        total_duration=150)\n    rec = ephys.recordings.CompRecording(\n        name='Step.soma.v',\n        location=soma_loc,\n        variable='v')\n\n    protocol = ephys.protocols.SweepProtocol('Step', [stim, hold_stim], [rec])\n\n    nrn = ephys.simulators.NrnSimulator(cvode_active=False)\n\n    evaluator = ephys.evaluators.CellEvaluator(\n        cell_model=stochkv_cell,\n        param_names=[param.name for param in params],\n        fitness_calculator=ephys.objectivescalculators.ObjectivesCalculator(),\n        sim=nrn)\n\n    best_param_values = {'gkbar_StochKv': 0.5}\n    responses = evaluator.run_protocol(\n        protocol,\n        cell_model=stochkv_cell,\n        param_values=best_param_values,\n        sim=nrn)\n\n    hoc_string = stochkv_cell.create_hoc(\n        param_values=best_param_values,\n        disable_banner=True)\n\n    stochkv_hoc_cell = ephys.models.HocCellModel(\n        'stochkv_hoc_cell',\n        morphology_path=morph_dir,\n        hoc_string=hoc_string)\n\n    nrn.neuron.h.celsius = 34\n\n    hoc_responses = protocol.run(stochkv_hoc_cell, best_param_values, sim=nrn)\n\n    evaluator.use_params_for_seed = True\n    different_seed_responses = evaluator.run_protocol(\n        protocol,\n        cell_model=stochkv_cell,\n        param_values=best_param_values,\n        sim=nrn)\n\n    return responses, hoc_responses, different_seed_responses, hoc_string\n\n\ndef main():\n    \"\"\"Main\"\"\"\n    import matplotlib.pyplot as plt\n\n    for deterministic in [True, False]:\n        stochkv_responses, stochkv_hoc_responses, different_seed_responses, \\\n            stochkv_hoc_string = \\\n            run_stochkv_model(deterministic=deterministic)\n\n        with open(stochkv_hoc_filename(deterministic=deterministic), 'w') as \\\n                stochkv_hoc_file:\n            stochkv_hoc_file.write(stochkv_hoc_string)\n\n        time = stochkv_responses['Step.soma.v']['time']\n        py_voltage = stochkv_responses['Step.soma.v']['voltage']\n        hoc_voltage = stochkv_hoc_responses['Step.soma.v']['voltage']\n        different_seed_voltage = \\\n            different_seed_responses['Step.soma.v']['voltage']\n\n        plt.figure()\n        plt.plot(time, py_voltage - hoc_voltage, label='py - hoc diff')\n        plt.xlabel('time (ms)')\n        plt.ylabel('voltage diff(mV)')\n        plt.title('Deterministic' if deterministic else 'Stochastic')\n        plt.legend()\n\n        plt.figure()\n        plt.plot(time, py_voltage, label='py')\n        plt.plot(time, hoc_voltage, label='hoc')\n        plt.xlabel('time (ms)')\n        plt.ylabel('voltage (mV)')\n        plt.title('Deterministic' if deterministic else 'Stochastic')\n        plt.legend()\n\n        plt.figure()\n        plt.plot(time, py_voltage, label='py')\n        plt.plot(time, different_seed_voltage, label='different seed')\n        plt.xlabel('time (ms)')\n        plt.ylabel('voltage (mV)')\n        plt.title('Deterministic' if deterministic else 'Stochastic')\n        plt.legend()\n\n    plt.show()\n\nif __name__ == '__main__':\n    main()\n"
  },
  {
    "path": "examples/stochkv/stochkvcell_det.hoc",
    "content": "/*\n*/\n{load_file(\"stdrun.hoc\")}\n{load_file(\"import3d.hoc\")}\n/*\n * Check that global parameters are the same as with the optimization\n */\nproc check_parameter(/* name, expected_value, value */){\n  strdef error\n  if($2 != $3){\n    sprint(error, \"Parameter %s has different value %f != %f\", $s1, $2, $3)\n    execerror(error)\n  }\n}\nproc check_simulator() {\n  check_parameter(\"celsius\", 34.0, celsius)\n}\n\nbegintemplate stochkv_cell\n  public init, morphology, geom_nseg_fixed, geom_nsec, gid\n  public channel_seed, channel_seed_set\n  public soma, dend, apic, axon, myelin\n  create soma[1], dend[1], apic[1], axon[1], myelin[1]\n\n  objref this, CellRef, segCounts\n\n  public all, somatic, apical, axonal, basal, myelinated, APC\n  objref all, somatic, apical, axonal, basal, myelinated, APC\n\nobfunc getCell(){\n        return this\n}\n\nproc init(/* args: morphology_dir, morphology_name */) {\n  all = new SectionList()\n  apical = new SectionList()\n  axonal = new SectionList()\n  basal = new SectionList()\n  somatic = new SectionList()\n  myelinated = new SectionList()\n\n  //gid in this case is only used for rng seeding\n  gid = 0\n\n  //For compatibility with BBP CCells\n  CellRef = this\n\n  forall delete_section()\n\n  if(numarg() >= 2) {\n    load_morphology($s1, $s2)\n  } else {\n    load_morphology($s1, \"simple.swc\")\n  }\n\n  geom_nseg()\n  insertChannel()\n  biophys()\n\n  // Initialize channel_seed_set to avoid accidents\n  channel_seed_set = 0\n  // Initialize random number generators\n  re_init_rng()\n}\n\nproc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension\n  strdef morph_path\n  sprint(morph_path, \"%s/%s\", $s1, $s2)\n\n  sf = new StringFunctions()\n  extension = new String()\n\n  sscanf(morph_path, \"%s\", extension.s)\n  sf.right(extension.s, sf.len(extension.s)-4)\n\n  if( strcmp(extension.s, \".asc\") == 0 ) {\n    morph = new Import3d_Neurolucida3()\n  } else if( strcmp(extension.s, \".swc\" ) == 0) {\n    morph = new Import3d_SWC_read()\n  } else {\n    printf(\"Unsupported file format: Morphology file has to end with .asc or .swc\" )\n    quit()\n  }\n\n  morph.quiet = 1\n  morph.input(morph_path)\n\n  import = new Import3d_GUI(morph, 0)\n  import.instantiate(this)\n}\n\n/*\n * Assignment of mechanism values based on distance from the soma\n * Matches the BluePyOpt method\n */\nproc distribute_distance(){local x localobj sl\n  strdef stmp, distfunc, mech\n\n  sl = $o1\n  mech = $s2\n  distfunc = $s3\n  this.soma[0] distance(0, 0.5)\n  sprint(distfunc, \"%%s %s(%%f) = %s\", mech, distfunc)\n  forsec sl for(x, 0) {\n    // use distance(x) twice for the step distribution case, e.g. for calcium hotspot\n    sprint(stmp, distfunc, secname(), x, distance(x), distance(x))\n    execute(stmp)\n  }\n}\n\nproc geom_nseg() {\n  this.geom_nsec() //To count all sections\n  //TODO: geom_nseg_fixed depends on segCounts which is calculated by\n  //  geom_nsec.  Can this be collapsed?\n  this.geom_nseg_fixed(40)\n  this.geom_nsec() //To count all sections\n}\n\nproc insertChannel() {\n  forsec this.all {\n  }\n  forsec this.apical {\n  }\n  forsec this.axonal {\n  }\n  forsec this.basal {\n  }\n  forsec this.somatic {\n    insert pas\n    insert StochKv\n  }\n  forsec this.myelinated {\n  }\n}\n\nproc biophys() {\n  \n  forsec CellRef.all {\n  }\n  \n  forsec CellRef.apical {\n  }\n  \n  forsec CellRef.axonal {\n  }\n  \n  forsec CellRef.basal {\n  }\n  \n  forsec CellRef.somatic {\n    e_pas = -90\n    gkbar_StochKv = 0.5\n  }\n  \n  forsec CellRef.myelinated {\n  }\n  \n}\n\nfunc sec_count(/* SectionList */) { local nSec\n  nSec = 0\n  forsec $o1 {\n      nSec += 1\n  }\n  return nSec\n}\n\n/*\n * Iterate over the section and compute how many segments should be allocate to\n * each.\n */\nproc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize\n  chunkSize = $1\n  soma area(.5) // make sure diam reflects 3d points\n  secIndex = 0\n  forsec all {\n    nseg = 1 + 2*int(L/chunkSize)\n    segCounts.x[secIndex] = nseg\n    secIndex += 1\n  }\n}\n\n/*\n * Count up the number of sections\n */\nproc geom_nsec() { local nSec\n  nSecAll = sec_count(all)\n  nSecSoma = sec_count(somatic)\n  nSecApical = sec_count(apical)\n  nSecBasal = sec_count(basal)\n  nSecMyelinated = sec_count(myelinated)\n  nSecAxonalOrig = nSecAxonal = sec_count(axonal)\n\n  segCounts = new Vector()\n  segCounts.resize(nSecAll)\n  nSec = 0\n  forsec all {\n    segCounts.x[nSec] = nseg\n    nSec += 1\n  }\n}\n\n/*\n * Replace the axon built from the original morphology file with a stub axon\n */\n\n\n\nfunc hash_str() {localobj sf strdef right\n  sf = new StringFunctions()\n\n  right = $s1\n\n  n_of_c = sf.len(right)\n\n  hash = 0\n  char_int = 0\n  for i = 0, n_of_c - 1 {\n     sscanf(right, \"%c\", & char_int)\n     hash = (hash * 31 + char_int) % (2 ^ 31 - 1)\n     sf.right(right, 1)\n  }\n\n  return hash\n}\n\nproc re_init_rng() {localobj sf\n    strdef full_str, name\n\n    sf = new StringFunctions()\n\n    if(numarg() == 1) {\n        // We received a third seed\n        channel_seed = $1\n        channel_seed_set = 1\n    } else {\n        channel_seed_set = 0\n    }\n\n    forsec somatic { deterministic_StochKv = 1 }\n\n}\n\n\nendtemplate stochkv_cell"
  },
  {
    "path": "examples/thalamocortical-cell/CellEvalSetup/__init__.py",
    "content": "\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\nimport evaluator\nimport template\nimport tools\n"
  },
  {
    "path": "examples/thalamocortical-cell/CellEvalSetup/evaluator.py",
    "content": "\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n# pylint: disable=R0914, R0912\n\nimport json\n\nimport bluepyopt.ephys as ephys\n\nimport template  # pylint: disable=W0403\nimport protocols  # pylint: disable=W0403\n\n\nimport logging\nlogger = logging.getLogger(__name__)\nimport os\nimport bluepyopt as bpopt\n\nsoma_loc = ephys.locations.NrnSeclistCompLocation(\n    name='soma',\n    seclist_name='somatic',\n    sec_index=0,\n    comp_x=0.5)\n\n\ndef read_step_protocol(protocol_name,\n                    protocol_definition,\n                    recordings,\n                    prefix=\"\"):\n    \"\"\"Read step protocol from definition\"\"\"\n\n    step_definition = protocol_definition['stimuli']['step']\n    step_stimulus = ephys.stimuli.NrnSquarePulse(\n        step_amplitude=step_definition['amp'],\n        step_delay=step_definition['delay'],\n        step_duration=step_definition['duration'],\n        location=soma_loc,\n        total_duration=step_definition['totduration'])\n\n    if 'holding' in protocol_definition['stimuli']:\n        holding_definition = protocol_definition[\n            'stimuli']['holding']\n        holding_stimulus = ephys.stimuli.NrnSquarePulse(\n            step_amplitude=holding_definition['amp'],\n            step_delay=holding_definition['delay'],\n            step_duration=holding_definition['duration'],\n            location=soma_loc,\n            total_duration=holding_definition['totduration'])\n    else:\n        holding_stimulus = None\n\n    return protocols.StepProtocolCustom(\n        name=protocol_name,\n        step_stimulus=step_stimulus,\n        holding_stimulus=holding_stimulus,\n        recordings=recordings)\n\ndef read_ramp_protocol(\n        protocol_name,\n        protocol_definition,\n        recordings):\n    \"\"\"Read step protocol from definition\"\"\"\n\n    ramp_definition = protocol_definition['stimuli']['ramp']\n    ramp_stimulus = ephys.stimuli.NrnRampPulse(\n        ramp_amplitude_start = ramp_definition['ramp_amp_start'],\n        ramp_amplitude_end = ramp_definition['ramp_amp_end'],\n        ramp_delay=ramp_definition['delay'],\n        ramp_duration=ramp_definition['duration'],\n        location=soma_loc,\n        total_duration=ramp_definition['totduration'])\n\n    if 'holding' in protocol_definition['stimuli']:\n        holding_definition = protocol_definition[\n            'stimuli']['holding']\n        holding_stimulus = ephys.stimuli.NrnSquarePulse(\n            step_amplitude=holding_definition['amp'],\n            step_delay=holding_definition['delay'],\n            step_duration=holding_definition['duration'],\n            location=soma_loc,\n            total_duration=holding_definition['totduration'])\n    else:\n        holding_stimulus = None\n\n\n    return protocols.RampProtocol(\n        name=protocol_name,\n        ramp_stimulus=ramp_stimulus,\n        holding_stimulus=holding_stimulus,\n        recordings=recordings)\n\n\ndef define_protocols(protocols_filename, stochkv_det=None,\n                runopt=False, prefix=\"\", apical_sec=None):\n    \"\"\"Define protocols\"\"\"\n    \n    with open(os.path.join(os.path.dirname(__file__), '..', protocols_filename)) as protocol_file:\n        protocol_definitions = json.load(protocol_file)\n\n    if \"__comment\" in protocol_definitions:\n        del protocol_definitions[\"__comment\"]\n\n    protocols_dict = {}\n\n    for protocol_name, protocol_definition in protocol_definitions.items():\n            # By default include somatic recording\n            somav_recording = ephys.recordings.CompRecording(\n                name='%s.%s.soma.v' % (prefix, protocol_name),\n                location=soma_loc,\n                variable='v')\n\n            recordings = [somav_recording]\n      \n            if 'type' in protocol_definition and \\\n                    protocol_definition['type'] == 'StepProtocol':\n                protocols_dict[protocol_name] = read_step_protocol(\n                    protocol_name, protocol_definition, recordings, stochkv_det)\n            elif 'type' in protocol_definition and \\\n                    protocol_definition['type'] == 'RampProtocol':\n                protocols_dict[protocol_name] = read_ramp_protocol(\n                    protocol_name, protocol_definition, recordings)                    \n            else:\n                stimuli = []\n                for stimulus_definition in protocol_definition['stimuli']:\n                    stimuli.append(ephys.stimuli.NrnSquarePulse(\n                        step_amplitude=stimulus_definition['amp'],\n                        step_delay=stimulus_definition['delay'],\n                        step_duration=stimulus_definition['duration'],\n                        location=soma_loc,\n                        total_duration=stimulus_definition['totduration']))\n\n                protocols_dict[protocol_name] = ephys.protocols.SweepProtocol(\n                    name=protocol_name,\n                    stimuli=stimuli,\n                    recordings=recordings)\n\n    return protocols_dict\n\n\nfrom bluepyopt.ephys.efeatures import eFELFeature\n\nclass eFELFeatureExtra(eFELFeature):\n\n    \"\"\"eFEL feature extra\"\"\"\n\n    SERIALIZED_FIELDS = ('name', 'efel_feature_name', 'recording_names',\n                         'stim_start', 'stim_end', 'exp_mean',\n                         'exp_std', 'threshold', 'comment')\n\n    def __init__(\n            self,\n            name,\n            efel_feature_name=None,\n            recording_names=None,\n            stim_start=None,\n            stim_end=None,\n            exp_mean=None,\n            exp_std=None,\n            threshold=None,\n            stimulus_current=None,\n            comment='',\n            interp_step=None,\n            double_settings=None,\n            int_settings=None,\n            force_max_score=False,\n            max_score = 250,\n            prefix=''):\n\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of the eFELFeature object\n            efel_feature_name (str): name of the eFeature in the eFEL library\n                (ex: 'AP1_peak')\n            recording_names (dict): eFEL features can accept several recordings\n                as input\n            stim_start (float): stimulation start time (ms)\n            stim_end (float): stimulation end time (ms)\n            exp_mean (float): experimental mean of this eFeature\n            exp_std(float): experimental standard deviation of this eFeature\n            threshold(float): spike detection threshold (mV)\n            comment (str): comment\n        \"\"\"\n\n        super(eFELFeatureExtra, self).__init__(name,\n            efel_feature_name, recording_names,\n            stim_start, stim_end, exp_mean, exp_std,\n            threshold, stimulus_current, comment,\n            interp_step, double_settings, int_settings, force_max_score, max_score)\n\n        extra_features = ['spikerate_tau_jj_skip', 'spikerate_drop_skip',\n                        'spikerate_tau_log_skip', 'spikerate_tau_fit_skip']\n\n        self.prefix = prefix\n\n    def get_bpo_score(self, responses):\n        \"\"\"Return internal score which is directly passed as a response\"\"\"\n\n        feature_value = self.get_bpo_feature(responses)\n        if feature_value == None:\n            score = 250.\n        else:\n            score = abs(feature_value - self.exp_mean) / self.exp_std\n        return score\n\n    def calculate_feature(self, responses, raise_warnings=False):\n        \"\"\"Calculate feature value\"\"\"\n\n        if self.efel_feature_name.startswith('bpo_'): # check if internal feature\n            feature_value = self.get_bpo_feature(responses)\n        else:\n            efel_trace = self._construct_efel_trace(responses)\n\n            if efel_trace is None:\n                feature_value = None\n            else:\n                self._setup_efel()\n\n                import efel\n                values = efel.getMeanFeatureValues(\n                    [efel_trace],\n                    [self.efel_feature_name],\n                    raise_warnings=raise_warnings)\n                feature_value = values[0][self.efel_feature_name]\n\n                efel.reset()\n\n        logger.debug(\n            'Calculated value for %s: %s',\n            self.name,\n            str(feature_value))\n\n        return feature_value\n\n\n    def calculate_score(self, responses, trace_check=False):\n        \"\"\"Calculate the score\"\"\"\n\n        if self.efel_feature_name.startswith('bpo_'): # check if internal feature\n            score = self.get_bpo_score(responses)\n\n        elif self.exp_mean is None:\n            score = 0\n\n        else:\n            efel_trace = self._construct_efel_trace(responses)\n\n            if efel_trace is None:\n                score = 250.0\n            else:\n                self._setup_efel()\n\n                import efel\n                score = efel.getDistance(\n                    efel_trace,\n                    self.efel_feature_name,\n                    self.exp_mean,\n                    self.exp_std,\n                    trace_check=trace_check,\n                    error_dist = self.max_score)\n\n                if self.force_max_score:\n                    score = min(score, self.max_score)\n\n                efel.reset()\n\n        logger.debug('Calculated score for %s: %f', self.name, score)\n\n        return score\n\n\nfrom bluepyopt.ephys.objectives import SingletonObjective, EFeatureObjective, MaxObjective\n\nclass SingletonWeightObjective(EFeatureObjective):\n\n    \"\"\"Single EPhys feature\"\"\"\n\n    def __init__(self, name, feature, weight):\n        \"\"\"Constructor\n\n        Args:\n            name (str): name of this object\n            features (EFeature): single eFeature inside this objective\n        \"\"\"\n\n        super(SingletonWeightObjective, self).__init__(name, [feature])\n        self.weight = weight\n\n    def calculate_score(self, responses):\n        \"\"\"Objective score\"\"\"\n\n        return self.calculate_feature_scores(responses)[0] * self.weight\n\n    def __str__(self):\n        \"\"\"String representation\"\"\"\n\n        return '( %s ), weight:%f' % (self.features[0], self.weight)\n\n\ndef define_fitness_calculator(main_protocol, features_filename, prefix=\"\"):\n    \"\"\"Define fitness calculator\"\"\"\n\n    with open(os.path.join(os.path.dirname(__file__), '..', features_filename)) as protocol_file:\n        feature_definitions = json.load(protocol_file)\n\n    if \"__comment\" in feature_definitions:\n        del feature_definitions[\"__comment\"]\n\n    objectives = []\n    efeatures = {}\n    features = []\n\n    for protocol_name, locations in feature_definitions.items():\n        for recording_name, feature_configs in locations.items():\n            for feature_config in feature_configs:\n                efel_feature_name = feature_config[\"feature\"]\n                meanstd = feature_config[\"val\"]\n\n                if hasattr(main_protocol, 'subprotocols'):\n                    protocol = main_protocol.subprotocols()[protocol_name]\n                else:\n                    protocol = main_protocol[protocol_name]\n\n                feature_name = '%s.%s.%s.%s' % (\n                    prefix, protocol_name, recording_name, efel_feature_name)\n                recording_names = \\\n                    {'': '%s.%s.%s' % (prefix, protocol_name, recording_name)}\n\n                if 'weight' in feature_config:\n                    weight = feature_config['weight']\n                else:\n                    weight = 1\n\n                if 'strict_stim' in feature_config:\n                    strict_stim = feature_config['strict_stim']\n                else:\n                    strict_stim = True\n\n                if hasattr(protocol, 'step_delay'):\n\n                    stim_start = protocol.step_delay\n\n                    if 'threshold' in feature_config:\n                        threshold = feature_config['threshold']\n                    else:\n                        threshold = -30\n\n                    if 'bAP' in protocol_name:\n                        # bAP response can be after stimulus\n                        stim_end = protocol.total_duration\n                    else:\n                        stim_end = protocol.step_delay + protocol.step_duration\n\n                    try:\n                        stimulus_current=protocol.step_stimulus.step_amplitude\n                    except AttributeError:\n                        print(\"Check stim_amp for RampProtocol\")\n                        stimulus_current = None\n                else:\n                    stim_start = None\n                    stim_end = None\n                    stimulus_current = None\n                    threshold = None\n\n                feature = eFELFeatureExtra(\n                    feature_name,\n                    efel_feature_name=efel_feature_name,\n                    recording_names=recording_names,\n                    stim_start=stim_start,\n                    stim_end=stim_end,\n                    exp_mean=meanstd[0],\n                    exp_std=meanstd[1],\n                    stimulus_current=stimulus_current,\n                    threshold=threshold,\n                    prefix=prefix,\n                    int_settings={'strict_stiminterval': strict_stim},\n                    force_max_score = True,\n                    max_score = 250)\n                efeatures[feature_name] = feature\n                features.append(feature)\n                objective = SingletonWeightObjective(\n                    feature_name,\n                    feature, weight)\n                objectives.append(objective)\n\n    #objectives.append(MaxObjective('global_maximum', features))\n    fitcalc = ephys.objectivescalculators.ObjectivesCalculator(objectives)\n\n    return fitcalc, efeatures\n\ndef create(etype, runopt=False, altmorph=None):\n    \"\"\"Setup\"\"\"\n\n    with open(os.path.join(os.path.dirname(__file__), '..', 'config/recipes.json')) as f:\n        recipe = json.load(f)\n\n    prot_path = recipe[etype]['protocol']\n\n    cell = template.create(recipe, etype, altmorph)\n\n    protocols_dict = define_protocols(prot_path, runopt)\n\n    fitness_calculator, efeatures = define_fitness_calculator(\n        protocols_dict,\n        recipe[etype]['features'])\n\n    fitness_protocols=protocols_dict\n\n    param_names = [param.name\n                   for param in cell.params.values()\n                   if not param.frozen]\n\n    nrn_sim = ephys.simulators.NrnSimulator(cvode_active = True)\n\n    cell_eval = ephys.evaluators.CellEvaluator(\n        cell_model=cell,\n        param_names=param_names,\n        fitness_protocols=fitness_protocols,\n        fitness_calculator=fitness_calculator,\n        sim=nrn_sim,\n        use_params_for_seed=True)\n\n    return cell_eval\n\n"
  },
  {
    "path": "examples/thalamocortical-cell/CellEvalSetup/protocols.py",
    "content": "\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=R0914\n\nimport numpy\nimport warnings\nimport collections\nimport copy\nimport json\nimport bluepyopt.ephys as ephys\n\nimport os\n\nimport argparse\nparser = argparse.ArgumentParser(description='cell')\nparser.add_argument('--live', action=\"store_true\", default=False,\n                  help='plot live')\nargs, unknown = parser.parse_known_args()\n\nlive_plot = False\n\nif live_plot:\n    import matplotlib.pyplot as plt\n\n\nclass StepProtocolCustom(ephys.protocols.StepProtocol):\n\n    \"\"\"Step protocol with custom options to turn stochkv_det on or off\"\"\"\n\n    def __init__(\n            self,\n            name=None,\n            step_stimulus=None,\n            holding_stimulus=None,\n            recordings=None,\n            cvode_active=None):\n        \"\"\"Constructor\"\"\"\n\n        super(StepProtocolCustom, self).__init__(\n            name,\n            step_stimulus=step_stimulus,\n            holding_stimulus=holding_stimulus,\n            recordings=recordings,\n            cvode_active=cvode_active)\n\n\n    def run(self, cell_model, param_values, sim=None, isolate=None):\n        \"\"\"Run protocol\"\"\"\n\n        responses = {}\n\n        responses.update(super(StepProtocolCustom, self).run(\n            cell_model,\n            param_values,\n            sim=sim,\n            isolate=isolate))\n\n        \n        for mechanism in cell_model.mechanisms:\n            mechanism.deterministic = True\n        self.cvode_active = True\n\n        return responses\n\n\nclass RampProtocol(ephys.protocols.SweepProtocol):\n\n    \"\"\"Protocol consisting of ramp and holding current\"\"\"\n\n    def __init__(\n            self,\n            name=None,\n            ramp_stimulus=None,\n            holding_stimulus=None,\n            recordings=None,\n            cvode_active=None):\n        \"\"\"Constructor\n        Args:\n            name (str): name of this object\n            step_stimulus (list of Stimuli): Stimulus objects used in protocol\n            recordings (list of Recordings): Recording objects used in the\n                protocol\n            cvode_active (bool): whether to use variable time step\n        \"\"\"\n\n        super(RampProtocol, self).__init__(\n            name,\n            stimuli=[\n                ramp_stimulus,\n                holding_stimulus]\n            if holding_stimulus is not None else [ramp_stimulus],\n            recordings=recordings,\n            cvode_active=cvode_active)\n\n        self.ramp_stimulus = ramp_stimulus\n        self.holding_stimulus = holding_stimulus\n\n    @property\n    def step_delay(self):\n        \"\"\"Time stimulus starts\"\"\"\n        return self.ramp_stimulus.ramp_delay\n\n    @property\n    def step_duration(self):\n        \"\"\"Time stimulus starts\"\"\"\n        return self.ramp_stimulus.ramp_duration\n"
  },
  {
    "path": "examples/thalamocortical-cell/CellEvalSetup/template.py",
    "content": "\"\"\"\nCopyright (c) 2016-2020, EPFL/Blue Brain Project\n\n This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n\n This library is free software; you can redistribute it and/or modify it under\n the terms of the GNU Lesser General Public License version 3.0 as published\n by the Free Software Foundation.\n\n This library is distributed in the hope that it will be useful, but WITHOUT\n ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n details.\n\n You should have received a copy of the GNU Lesser General Public License\n along with this library; if not, write to the Free Software Foundation, Inc.,\n 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport os\nimport collections\n\ntry:\n    import simplejson as json\nexcept ImportError:\n    import json\n\nimport bluepyopt.ephys as ephys\n\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n\n\nimport random\n\ndef multi_locations(sectionlist):\n    \"\"\"Define mechanisms\"\"\"\n\n    if sectionlist == \"alldend\":\n        seclist_locs = [\n            ephys.locations.NrnSeclistLocation(\"basal\", seclist_name=\"basal\")\n        ]\n    elif sectionlist == \"somadend\":\n        seclist_locs = [\n            ephys.locations.NrnSeclistLocation(\n                \"basal\", seclist_name=\"basal\"),\n            ephys.locations.NrnSeclistLocation(\n                \"somatic\", seclist_name=\"somatic\")\n        ]\n    elif sectionlist == \"somaxon\":\n        seclist_locs = [\n            ephys.locations.NrnSeclistLocation(\n                \"axonal\", seclist_name=\"axonal\"),\n            ephys.locations.NrnSeclistLocation(\n                \"somatic\", seclist_name=\"somatic\")\n        ]\n    elif sectionlist == \"allact\":\n        seclist_locs = [\n            ephys.locations.NrnSeclistLocation(\n                \"basal\", seclist_name=\"basal\"),\n            ephys.locations.NrnSeclistLocation(\n                \"somatic\", seclist_name=\"somatic\"),\n            ephys.locations.NrnSeclistLocation(\n                \"axonal\", seclist_name=\"axonal\")\n        ]\n    else:\n        seclist_locs = [ephys.locations.NrnSeclistLocation(\n            sectionlist,\n            seclist_name=sectionlist)]\n\n    return seclist_locs\n\n\ndef define_mechanisms(params_filename):\n    \"\"\"Define mechanisms\"\"\"\n\n    with open(os.path.join(os.path.dirname(__file__), '..', params_filename)) as params_file:\n        mech_definitions = json.load(\n            params_file,\n            object_pairs_hook=collections.OrderedDict)[\"mechanisms\"]\n\n    mechanisms_list = []\n    for sectionlist, channels in mech_definitions.items():\n\n        seclist_locs = multi_locations(sectionlist)\n\n        for channel in channels[\"mech\"]:\n            mechanisms_list.append(ephys.mechanisms.NrnMODMechanism(\n                name='%s.%s' % (channel, sectionlist),\n                mod_path=None,\n                prefix=channel,\n                locations=seclist_locs,\n                preloaded=True))\n\n    return mechanisms_list\n\n\ndef define_parameters(params_filename):\n    \"\"\"Define parameters\"\"\"\n\n    parameters = []\n\n    with open(os.path.join(os.path.dirname(__file__), '..', params_filename)) as params_file:\n        definitions = json.load(\n            params_file,\n            object_pairs_hook=collections.OrderedDict)\n\n    # set distributions\n    distributions = collections.OrderedDict()\n    distributions[\"uniform\"] = ephys.parameterscalers.NrnSegmentLinearScaler()\n\n    distributions_definitions = definitions[\"distributions\"]\n    for distribution, definition in distributions_definitions.items():\n        distributions[distribution] = \\\n            ephys.parameterscalers.NrnSegmentSomaDistanceScaler(\n                distribution=definition[\"fun\"])\n\n    params_definitions = definitions[\"parameters\"]\n\n    if \"__comment\" in params_definitions:\n        del params_definitions[\"__comment\"]\n\n    for sectionlist, params in params_definitions.items():\n        if sectionlist == 'global':\n            seclist_locs = None\n            is_global = True\n        else:\n            seclist_locs = multi_locations(sectionlist)\n            is_global = False\n\n        bounds = None\n        value = None\n        for param_config in params:\n            param_name = param_config[\"name\"]\n\n            if isinstance(param_config[\"val\"], (list, tuple)):\n                is_frozen = False\n                bounds = param_config[\"val\"]\n                value = None\n            else:\n                is_frozen = True\n                value = param_config[\"val\"]\n                bounds = None\n\n            if is_global:\n                parameters.append(\n                    ephys.parameters.NrnGlobalParameter(\n                        name=param_name,\n                        param_name=param_name,\n                        frozen=is_frozen,\n                        bounds=bounds,\n                        value=value))\n            else:\n                if \"dist\" in param_config:\n                    dist = distributions[param_config[\"dist\"]]\n                    use_range = True\n                else:\n                    dist = distributions[\"uniform\"]\n                    use_range = False\n\n                if use_range:\n                    parameters.append(ephys.parameters.NrnRangeParameter(\n                        name='%s.%s' % (param_name, sectionlist),\n                        param_name=param_name,\n                        value_scaler=dist,\n                        value=value,\n                        bounds=bounds,\n                        frozen=is_frozen,\n                        locations=seclist_locs))\n                else:\n                    parameters.append(ephys.parameters.NrnSectionParameter(\n                        name='%s.%s' % (param_name, sectionlist),\n                        param_name=param_name,\n                        value_scaler=dist,\n                        value=value,\n                        bounds=bounds,\n                        frozen=is_frozen,\n                        locations=seclist_locs))\n\n    return parameters\n\n\nfrom bluepyopt.ephys.morphologies import NrnFileMorphology\n\ndef define_morphology(morphology_filename, do_set_nseg=1e9):\n    \"\"\"Define morphology\"\"\"\n\n    # Use default moprhology class from BluePyOpt\n    return ephys.morphologies.NrnFileMorphology(\n        os.path.join(morphology_filename),\n        do_replace_axon=True,\n        do_set_nseg=do_set_nseg)\n\n\ndef create(recipe, etype, altmorph=None):\n    \"\"\"Create cell template\"\"\"\n\n    if altmorph is None:\n        morph_path = os.path.join(os.path.join(recipe[etype]['morph_path'], recipe[etype]['morphology']))\n    else:\n        morph_path = altmorph\n\n    cell = ephys.models.CellModel(\n        etype,\n        morph=define_morphology(morph_path, do_set_nseg=40.),\n        mechs=define_mechanisms(recipe[etype]['params']),\n        params=define_parameters(recipe[etype]['params']))\n\n    return cell\n"
  },
  {
    "path": "examples/thalamocortical-cell/CellEvalSetup/tools.py",
    "content": "def rename_prot(name):\n    if 'RMP' in name:\n        name = \"(No stim)\"\n    elif 'Rin_dep' in name:\n        #name = name.replace('.Rin','Hyp_-40')\n        name = \"($-$40%)\" #\"R_{input} - tonic\"\n    elif 'Rin_hyp' in name:\n        #name = name.replace('.Rin','Hyp_-40')\n        name = \"($-$40% - b)\"\n    elif 'hyp' in name and \"Step\" in name:\n        name = '(150% - burst)'\n        name = '(225% - burst)'\n        name = '(200% - burst)'\n    elif 'Step_150' in name:\n        name = '(150% - tonic)'\n    elif 'Step_200' in name:\n        name = '(200% - tonic)'\n    elif 'Step_250' in name:\n        name = '(250% - tonic)'\n    elif 'IV_-140' in name:\n        name = \"($-$140%)\"\n    elif 'ThresholdDetection_dep' in name:\n         name = ''\n    elif 'ThresholdDetection_hyp' in name:\n         name = ''\n    elif 'hold_dep' in name:\n        name = \"(I$_{hold}$ - tonic)\"\n    elif 'hold_hyp' in name:\n        name = \"(I$_{hold}$ - burst)\"\n\n    #name = name.replace(\"soma.v.\", \"\")\n    return name\n\ndef rename_featpart(name):\n    import json\n    namemap = {\n        \"steady_state_voltage_stimend\": \"V$_{rest}$\",\n        \"sag_amplitude\": \"Sag amp.\",\n        \"ohmic_input_resistance_vb_ssse\": \"R$_{input}$\",\n        \"voltage_base\": \"Baseline V$_m$\",\n        \"Spikecount\": \"Num. of APs\",\n        \"voltage_after_stim\": \"V$_m$ after stim.\",\n        \"inv_first_ISI\": \"Inv. 1$^{st}$ ISI\",\n        \"inv_last_ISI\": \"Inv. last ISI\",\n        \"inv_second_ISI\": \"Inv. 2$^{nd}$ ISI\",\n        \"time_to_first_spike\": \"Latency 1$^{st}$ AP\",\n        \"AP1_amp\": \"Amp. 1$^{st}$ AP \",\n        \"AP2_amp\": \"Amp. 2$^{nd}$ AP \",\n        \"AHP_depth\": \"AHP depth\",\n        \"AHP_depth_abs\": \"AHP depth\",\n        \"AP_amplitude\": \"AP amp.\",\n        \"AP_width\": \"AP width\",\n        \"AP_duration_half_width\": \"AP half-width\",\n        \"Spikecount_stimint\": \"Num. of APs\",\n        \"bpo_threshold_current_dep\": \"I$_{thr}$ - tonic\",\n        \"bpo_threshold_current_hyp\": \"I$_{thr}$ - burst\",\n        \"time_to_last_spike\": \"Latency last AP\",\n        \"adaptation_index2\": \"Adaptation idx\",\n        \"mean_frequency\": \"Frequency\"\n    }\n    return namemap[name]\n\ndef rename_feat(name, sep = \" \"):\n    prot_name = name.split(\".\")[1]\n    feat_name = name.split(\".\")[-1]\n    new_prot = rename_prot(prot_name)\n    new_feat = rename_featpart(feat_name)\n\n    return new_feat + sep + new_prot\n\n"
  },
  {
    "path": "examples/thalamocortical-cell/LICENSE.txt",
    "content": "If not otherwise specified, the CC-BY-NC-SA license applies.\n\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/\n\nThe detailed text is available here:\nhttps://creativecommons.org/licenses/by-nc-sa/4.0/legalcode\n\nThe Python code are generated from examples available in this repository and are licensed under the GNU Lesser\nGeneral Public License version 3.0 as published by the Free Software Foundation, as stated in the headers of\nthe files.\n\nExperimental features and protocols are extracted from experimental data from\nJane Yi, Laboratory of Neural Microcircuitry (https://www.epfl.ch/labs/markram-lab/).\nThey are part of this example and Supporting Information for the paper:\nhttps://www.biorxiv.org/content/10.1101/512269v3 \n\n\nThe experimental morphologies were provided by Jane Yi, Ying Shi, Laboratory of Neural Microcircuitry (https://www.epfl.ch/labs/markram-lab/).\nThey will be available on NeuroMorpho.org under the\nODC Public Domain Dedication and Licence (PDDL) https://opendatacommons.org/licenses/pddl/1.0/\n\nFor models for which the original source is available on ModelDB, any\nspecific licenses mentioned on ModelDB, or the generic License of ModelDB \napply:\n\n1) TC_Nap_Et2, persistent sodium current\nAuthors: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit, Elisabetta Iavarone\nAccession: 139653\nOriginal URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=139653&file=/L5bPCmodelsEH/mod/Nap_Et2.mod#tabs-2\n\n2) SK_E2, calcium-activated potassium current\nAuthors: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit\nAccession: 139653\nOriginal URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=139653&file=/L5bPCmodelsEH/mod/SK_E2.mod#tabs-2\n\n3) Intracellular calcium dynamics\nAuthors: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit\nAccession: 139653\nOriginal URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=139653&file=/L5bPCmodelsEH/mod/CaDynamics_E2.mod#tabs-2\n\n4) TC_ITGHK_Des98, low-threshold calcium current\nAuthor: Alain Destexhe\nAccession: 279\nOriginal URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=279&file=/dendtc/ITGHK.mod#tabs-2\n\n5) TC_HH, fast Na+ and K+ currents responsible for action potentials\nAuthors: Alain Destexhe, Elisabetta Iavarone\nAccession: 279\nOriginal URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=279&file=/dendtc/hh2.mod#tabs-2\n\n5) TC_iL, high-threshold calcium current\nAuthor: Arthur Houweling\nAccession: 3808\nOriginal url: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=3808&file=/MyFirstNEURON/il.mod#tabs-2\n\n7) TC_iA, fast transient potassium current:\nAuthor: Yimi Amarillo\nPublications: Amarillo et al., J Neurophysiol, 2014, Huguenard and McCormick, J Neurophysiol, 1992\n\n8) TC_Ih_Bud97, Ih current\nAuthor: Elisabetta Iavarone\nPublications: Budde et al., J Physiol, 1997, Huguenard and McCormick, J Neurophysiol, 1992\n\n"
  },
  {
    "path": "examples/thalamocortical-cell/checkpoints/checkpoint.pkl",
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\\x1cE!\\x12\\xc0'\np375\ntp376\nRp377\ng29\n(g33\nS'\\xdc\\xc9B\\x86rZ\\n\\xc0'\np378\ntp379\nRp380\ng29\n(g33\nS'4\\x0b\\x05PR\\xf5\\x02\\xc0'\np381\ntp382\nRp383\ng29\n(g33\nS'\\x98kN\\xac\\xa1\\xd6\\xf3\\xbf'\np384\ntp385\nRp386\ntp387\nsbsbssbsS'parents'\np388\n(lp389\ng2\n(g14\ng15\n(lp390\nF8.459776330097977e-05\naF0.15168906708994337\naF0.336457264664676\naF3.058343996090739e-05\naF5.9022049332218484e-05\naF4.049341374504143e-05\naF0.05486590123243409\naF0.06000995579013797\naF0.09531939083047117\naF0.0005833820394550312\naF0.0046462799281463815\naF8.065615981443464\naF0.28219692547750397\naF7.55804204157224e-05\naF7.4296780638054e-05\naF0.0022203480307705324\naF0.009061603766623298\naF0.005942316155463084\naF0.0007384762374565938\naF0.002615491103308344\naF6.557721287233644\naF0.6243445607373651\natp391\nRp392\n(dp393\ng20\nI5\nsS'ibea_fitness'\np394\ng29\n(g33\nS'\\x00\\x00\\x00\\x80\\xe3%>>'\np395\ntp396\nRp397\nsg21\ng2\n(g22\ng4\nNtp398\nRp399\n(dp400\ng26\ng27\nsg28\ng301\nsbsbag392\nasS'generation'\np401\nI2\nsS'logbook'\np402\ng2\n(cdeap.tools.support\nLogbook\np403\ng15\n(lp404\n(dp405\nS'std'\np406\ng29\n(g33\nS'\\x00\\xfc\\xf4!\\x7f0\\x05@'\np407\ntp408\nRp409\nsS'nevals'\np410\nI2\nsS'min'\np411\ng29\n(g33\nS'o\\xdbA\\xbe\\xb1\\xd4\\xb1@'\np412\ntp413\nRp414\nsS'max'\np415\ng29\n(g33\nS'\\xaeX\\n\\xde\\xfd\\xd9\\xb1@'\np416\ntp417\nRp418\nsS'avg'\np419\ng29\n(g33\nS'\\x0e\\x1a&\\xceW\\xd7\\xb1@'\np420\ntp421\nRp422\nsS'gen'\np423\nI1\nsa(dp424\ng406\ng29\n(g33\nS'\\xa7\\xf8A\\xa8\\xab\\xfe\\x04@'\np425\ntp426\nRp427\nsg410\nI2\nsg411\ng29\n(g33\nS'o\\xdbA\\xbe\\xb1\\xd4\\xb1@'\np428\ntp429\nRp430\nsg415\ng29\n(g33\nS'=\\xe2\\r\\xde\\x10\\xda\\xb1@'\np431\ntp432\nRp433\nsg419\ng29\n(g33\nS'\\xf1\\xa5\\xfb\\xc0g\\xd7\\xb1@'\np434\ntp435\nRp436\nsg423\nI2\nsatp437\nRp438\n(dp439\nS'log_header'\np440\nI01\nsS'header'\np441\n(lp442\ng423\nag410\nag419\nag406\nag411\nag415\nasS'columns_len'\np443\n(lp444\nI3\naI6\naI7\naI7\naI7\naI7\nasS'buffindex'\np445\nI2\nsS'chapters'\np446\nccollections\ndefaultdict\np447\n(g403\ntp448\nRp449\nsbsS'rndstate'\np450\n(I3\n(L1372342863L\nL3221959423L\nL4180954279L\nL3990540705L\nL1021773023L\nL2223090966L\nL3843864895L\nL597749037L\nL4038628808L\nL2184695301L\nL2599863370L\nL1285463076L\nL3619013288L\nL3445797418L\nL662770754L\nL586125062L\nL1346770502L\nL3658746180L\nL3438111236L\nL309182431L\nL3796115977L\nL3396521978L\nL406330169L\nL2387780391L\nL2469455462L\nL2266287431L\nL1699932795L\nL4049251296L\nL2880579946L\nL2212080061L\nL3732465507L\nL2085677040L\nL3780473213L\nL2666506866L\nL2546029306L\nL3062944794L\nL3831291871L\nL2699197260L\nL1538606110L\nL2628704661L\nL2899336459L\nL3451611859L\nL99931860L\nL1959678400L\nL1104806618L\nL222110430L\nL2129932650L\nL956476906L\nL1123944865L\nL4148113835L\nL3692694084L\nL1144181241L\nL329767184L\nL3790223790L\nL2243139411L\nL3664375038L\nL1649616822L\nL2085955322L\nL878670707L\nL427637247L\nL1097949229L\nL3413060003L\nL2868874638L\nL714350532L\nL2486509003L\nL3288554389L\nL1763249054L\nL3065280929L\nL3617934738L\nL630740700L\nL76720155L\nL3508325981L\nL3932676767L\nL1329309665L\nL1100166609L\nL3150737391L\nL3713849116L\nL1027154122L\nL621059281L\nL2649419324L\nL1724153688L\nL787407208L\nL2006574677L\nL2947315466L\nL1182636319L\nL3824689780L\nL1212132099L\nL4044342334L\nL315023297L\nL3553864982L\nL1157225958L\nL1432164329L\nL1160723985L\nL371272026L\nL927525248L\nL1908778666L\nL1373080387L\nL1470677278L\nL801074131L\nL3914200339L\nL3381407135L\nL2878632719L\nL3924891213L\nL4265632409L\nL2538399467L\nL2281243182L\nL3528786809L\nL875255179L\nL900526794L\nL3848503840L\nL1800107094L\nL99435770L\nL2252333719L\nL459600575L\nL3980788272L\nL642015534L\nL3185107417L\nL3358361764L\nL109426005L\nL1526990449L\nL2897953574L\nL735827974L\nL292571356L\nL656909865L\nL1811241718L\nL3852326868L\nL935626136L\nL576421325L\nL428560518L\nL2760997449L\nL2373789786L\nL2483545676L\nL990053668L\nL4260816364L\nL2400672108L\nL931204375L\nL1656208850L\nL1752543171L\nL3393345042L\nL2975883932L\nL3535909646L\nL441369518L\nL1875275912L\nL3788298971L\nL1604151293L\nL3265285628L\nL3389517324L\nL1129673657L\nL158637840L\nL3401368526L\nL3511603269L\nL1365694929L\nL2076135726L\nL144518747L\nL1738447909L\nL3315335210L\nL696514991L\nL2903756646L\nL1692635312L\nL2492835022L\nL793066282L\nL3701291598L\nL1682146840L\nL2833764772L\nL1874922241L\nL1109375682L\nL2157966518L\nL2136578897L\nL2614256798L\nL2861779371L\nL2698885417L\nL160504417L\nL554219202L\nL1430333145L\nL716754647L\nL529846619L\nL3482487583L\nL829954169L\nL2479439816L\nL1934256524L\nL140815401L\nL1125214395L\nL1642506558L\nL1276896160L\nL3983009834L\nL1007760484L\nL3281113000L\nL139221400L\nL361504304L\nL3713284634L\nL2928612637L\nL935745191L\nL4151018351L\nL3848439956L\nL3104510935L\nL615014456L\nL2939062496L\nL1306813042L\nL2930774231L\nL1126465550L\nL39679357L\nL4157006879L\nL3487860998L\nL4211840723L\nL4199318320L\nL3081958281L\nL1231549084L\nL114041891L\nL1456998828L\nL4027354301L\nL7797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p515\nF9.094879779990037e-05\naF0.1366985998811062\naF0.3777141723621707\naF1.007012080683658e-05\naF4.682722002434101e-05\naF6.108869734438017e-05\naF0.06391077372665288\naF0.1873655711711291\naF0.09540195531054341\naF0.0006169669963403399\naF0.001302461551959797\naF10.602709904797184\naF0.6289506896678183\naF1.4041700164018957e-06\naF6.028389833499836e-05\naF0.002730997230902337\naF0.008550495952499656\naF0.003477632527733394\naF1.1428193144282783e-06\naF0.0036857304306474217\naF13.146438856898932\naF0.21611889680663607\natp516\nRp517\n(dp518\ng20\nI6\nsg394\ng29\n(g33\nS'\\xcc\\xb0\\xba\\x1a\\xcb:\\xee?'\np519\ntp520\nRp521\nsg21\ng2\n(g22\ng4\nNtp522\nRp523\n(dp524\ng26\ng122\nsg28\ng387\nsbsbas."
  },
  {
    "path": "examples/thalamocortical-cell/config/features/cAD_ltb.json",
    "content": "{\n  \"Step_150\": {\n    \"soma.v\": [\n      {\"feature\": \"AP_amplitude\", \"val\": [58.1456, 4.2632], \"n\": 11, \"fid\": 553, \"strict_stim\": true},\n      {\"feature\": \"AHP_depth\", \"val\": [7.1542, 2.8024], \"n\": 11, \"fid\": 889, \"strict_stim\": true},\n      {\"feature\": \"AP_duration_half_width\", \"val\": [1.0534, 0.1648], \"n\": 11, \"fid\": 875, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [8.5179, 3.7782], \"n\": 11, \"fid\": 861, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [116.1049, 62.0552], \"n\": 11, \"fid\": 665, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [13.267, 6.7753], \"n\": 11, \"fid\": 749, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [11.4479, 5.9315], \"n\": 11, \"fid\": 763, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [5.3322, 2.3386], \"n\": 11, \"fid\": 819, \"strict_stim\": true},\n      {\"feature\": \"adaptation_index2\", \"val\": [0.0756, 0.0504], \"n\": 11, \"fid\": 609, \"strict_stim\": true}\n    ]\n  }, \n  \"Step_250\": {\n    \"soma.v\": [\n      {\"feature\": \"AP_amplitude\", \"val\": [57.15, 3.9886], \"n\": 11, \"fid\": 558, \"strict_stim\": true},\n      {\"feature\": \"AHP_depth\", \"val\": [10.3652, 3.2496], \"n\": 11, \"fid\": 894, \"strict_stim\": true},\n      {\"feature\": \"AP_duration_half_width\", \"val\": [0.9025, 0.134], \"n\": 11, \"fid\": 880, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [19.2727, 8.8153], \"n\": 11, \"fid\": 866, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [61.2511, 30.0464], \"n\": 11, \"fid\": 670, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [30.9829, 19.9475], \"n\": 11, \"fid\": 754, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [31.8299, 24.4034], \"n\": 11, \"fid\": 768, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [10.3225, 4.8976], \"n\": 11, \"fid\": 824, \"strict_stim\": true},\n      {\"feature\": \"adaptation_index2\", \"val\": [0.035, 0.0145], \"n\": 11, \"fid\": 614, \"strict_stim\": true}\n    ]\n  }, \n  \"Step_200\": {\n    \"soma.v\": [\n      {\"feature\": \"voltage_base\", \"val\": [-65.7569, 1.7149], \"n\": 11, \"fid\": 961, \"strict_stim\": true},\n      {\"feature\": \"AP_amplitude\", \"val\": [57.6566, 4.1184], \"n\": 11, \"fid\": 555, \"strict_stim\": true},\n      {\"feature\": \"AHP_depth\", \"val\": [8.8972, 3.1542], \"n\": 11, \"fid\": 891, \"strict_stim\": true},\n      {\"feature\": \"AP_duration_half_width\", \"val\": [0.9608, 0.1278], \"n\": 11, \"fid\": 877, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [14.2667, 6.2645], \"n\": 11, \"fid\": 863, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [82.5958, 44.1591], \"n\": 11, \"fid\": 667, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [21.8452, 11.7211], \"n\": 11, \"fid\": 751, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [21.8161, 13.2209], \"n\": 11, \"fid\": 765, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [7.9611, 3.9428], \"n\": 11, \"fid\": 821, \"strict_stim\": true},\n      {\"feature\": \"adaptation_index2\", \"val\": [0.0507, 0.0238], \"n\": 11, \"fid\": 611, \"strict_stim\": true}\n    ]\n  }, \n  \"Step_200_hyp\": {\n    \"soma.v\": [\n      {\"feature\": \"voltage_base\", \"val\": [-84.2499, 1.2726], \"n\": 22, \"fid\": 935, \"strict_stim\": true},\n      {\"feature\": \"AP1_amp\", \"val\": [51.4818, 7.514], \"n\": 22, \"fid\": 800, \"strict_stim\": true},\n      {\"feature\": \"AP2_amp\", \"val\": [46.5557, 8.2272], \"n\": 22, \"fid\": 1010, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [49.3602, 12.5966], \"n\": 22, \"fid\": 620, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [5.2045, 1.0221], \"n\": 22, \"fid\": 830, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [291.4432, 46.1345], \"n\": 22, \"fid\": 710, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [241.55, 53.293], \"n\": 22, \"fid\": 725, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [119.366, 36.2356], \"n\": 22, \"fid\": 785, \"strict_stim\": true},\n      {\"feature\": \"voltage_after_stim\", \"val\": [-87.2874, 1.6681], \"n\": 22, \"fid\": 950, \"strict_stim\": true}\n    ]\n  }, \n  \"IV_-140\": {\n    \"soma.v\": [\n      {\"feature\": \"sag_amplitude\", \"val\": [3.205, 2.5646], \"n\": 10, \"fid\": 516, \"strict_stim\": true}\n    ]\n  }, \n  \"RMP\": {\n    \"soma.v\": [\n      {\"feature\": \"steady_state_voltage_stimend\", \"val\": [-78.4467, 4.4534], \"n\": 11, \"fid\": 1457, \"strict_stim\": true},\n      {\"feature\": \"Spikecount_stimint\", \"val\": [0.0, 0.001], \"n\": 11, \"fid\": 1460, \"strict_stim\": true}\n    ]\n  }, \n  \"hold_dep\": {\n    \"soma.v\": [\n      {\"feature\": \"Spikecount_stimint\", \"val\": [0.0, 0.001], \"n\": 11, \"fid\": 1454, \"strict_stim\": true}\n    ]\n  }, \n  \"hold_hyp\": {\n    \"soma.v\": [\n      {\"feature\": \"Spikecount_stimint\", \"val\": [0.0, 0.001], \"n\": 22, \"fid\": 1616, \"strict_stim\": true}\n    ]\n  }, \n  \"Rin_dep\": {\n    \"soma.v\": [\n      {\"feature\": \"voltage_base\", \"val\": [-64.4907, 1.0285], \"n\": 11, \"fid\": 461, \"strict_stim\": true},\n      {\"feature\": \"ohmic_input_resistance_vb_ssse\", \"val\": [131.6508, 52.9132], \"n\": 11, \"fid\": 482, \"strict_stim\": true}\n    ]\n  }\n}"
  },
  {
    "path": "examples/thalamocortical-cell/config/features/cNAD_ltb.json",
    "content": "{\n  \"Step_150\": {\n    \"soma.v\": [\n      {\"feature\": \"AP_amplitude\", \"val\": [58.0318, 7.0345], \"n\": 16, \"fid\": 536, \"strict_stim\": true},\n      {\"feature\": \"AHP_depth\", \"val\": [7.8795, 3.4388], \"n\": 16, \"fid\": 872, \"strict_stim\": true},\n      {\"feature\": \"AP_duration_half_width\", \"val\": [1.0657, 0.0844], \"n\": 16, \"fid\": 858, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [13.0208, 5.5638], \"n\": 16, \"fid\": 844, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [150.3546, 100.0313], \"n\": 16, \"fid\": 648, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [11.6562, 4.5162], \"n\": 16, \"fid\": 732, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [11.8118, 5.2192], \"n\": 16, \"fid\": 746, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [9.6796, 4.1202], \"n\": 16, \"fid\": 802, \"strict_stim\": true},\n      {\"feature\": \"adaptation_index2\", \"val\": [0.0206, 0.0256], \"n\": 16, \"fid\": 592, \"strict_stim\": true}\n    ]\n  }, \n  \"Step_250\": {\n    \"soma.v\": [\n      {\"feature\": \"AP_amplitude\", \"val\": [57.8473, 6.2018], \"n\": 14, \"fid\": 541, \"strict_stim\": true},\n      {\"feature\": \"AHP_depth\", \"val\": [10.9129, 4.0415], \"n\": 14, \"fid\": 877, \"strict_stim\": true},\n      {\"feature\": \"AP_duration_half_width\", \"val\": [1.0532, 0.129], \"n\": 14, \"fid\": 863, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [30.5923, 10.1621], \"n\": 14, \"fid\": 849, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [55.1098, 21.2185], \"n\": 14, \"fid\": 653, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [27.2243, 9.3839], \"n\": 14, \"fid\": 737, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [27.9719, 9.7227], \"n\": 14, \"fid\": 751, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [22.8733, 7.9702], \"n\": 14, \"fid\": 807, \"strict_stim\": true},\n      {\"feature\": \"adaptation_index2\", \"val\": [0.0062, 0.0075], \"n\": 14, \"fid\": 597, \"strict_stim\": true}\n    ]\n  }, \n  \"Step_200\": {\n    \"soma.v\": [\n      {\"feature\": \"voltage_base\", \"val\": [-65.5929, 1.8487], \"n\": 15, \"fid\": 944, \"strict_stim\": true},\n      {\"feature\": \"AP_amplitude\", \"val\": [57.7503, 6.7182], \"n\": 15, \"fid\": 538, \"strict_stim\": true},\n      {\"feature\": \"AHP_depth\", \"val\": [9.2791, 3.6017], \"n\": 15, \"fid\": 874, \"strict_stim\": true},\n      {\"feature\": \"AP_duration_half_width\", \"val\": [1.0665, 0.0959], \"n\": 15, \"fid\": 860, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [22.4306, 8.0686], \"n\": 15, \"fid\": 846, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [75.2681, 30.3746], \"n\": 15, \"fid\": 650, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [19.9615, 6.4584], \"n\": 15, \"fid\": 734, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [20.7575, 8.9678], \"n\": 15, \"fid\": 748, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [16.7102, 6.298], \"n\": 15, \"fid\": 804, \"strict_stim\": true},\n      {\"feature\": \"adaptation_index2\", \"val\": [0.0073, 0.0057], \"n\": 15, \"fid\": 594, \"strict_stim\": true}\n    ]\n  }, \n  \"Step_200_hyp\": {\n    \"soma.v\": [\n      {\"feature\": \"voltage_base\", \"val\": [-84.2499, 1.2726], \"n\": 22, \"fid\": 935, \"strict_stim\": true},\n      {\"feature\": \"AP1_amp\", \"val\": [51.4818, 7.514], \"n\": 22, \"fid\": 800, \"strict_stim\": true},\n      {\"feature\": \"AP2_amp\", \"val\": [46.5557, 8.2272], \"n\": 22, \"fid\": 1010, \"strict_stim\": true},\n      {\"feature\": \"time_to_first_spike\", \"val\": [49.3602, 12.5966], \"n\": 22, \"fid\": 620, \"strict_stim\": true},\n      {\"feature\": \"Spikecount\", \"val\": [5.2045, 1.0221], \"n\": 22, \"fid\": 830, \"strict_stim\": true},\n      {\"feature\": \"inv_first_ISI\", \"val\": [291.4432, 46.1345], \"n\": 22, \"fid\": 710, \"strict_stim\": true},\n      {\"feature\": \"inv_second_ISI\", \"val\": [241.55, 53.293], \"n\": 22, \"fid\": 725, \"strict_stim\": true},\n      {\"feature\": \"inv_last_ISI\", \"val\": [119.366, 36.2356], \"n\": 22, \"fid\": 785, \"strict_stim\": true},\n      {\"feature\": \"voltage_after_stim\", \"val\": [-87.2874, 1.6681], \"n\": 22, \"fid\": 950, \"strict_stim\": true}\n    ]\n  }, \n  \"IV_-140\": {\n    \"soma.v\": [\n      {\"feature\": \"sag_amplitude\", \"val\": [4.2017, 2.6117], \"n\": 13, \"fid\": 497, \"strict_stim\": true}\n    ]\n  }, \n  \"RMP\": {\n    \"soma.v\": [\n      {\"feature\": \"steady_state_voltage_stimend\", \"val\": [-79.8137, 5.0122], \"n\": 16, \"fid\": 1482, \"strict_stim\": true},\n      {\"feature\": \"Spikecount_stimint\", \"val\": [0.0, 0.001], \"n\": 16, \"fid\": 1486, \"strict_stim\": true}\n    ]\n  }, \n  \"hold_dep\": {\n    \"soma.v\": [\n      {\"feature\": \"Spikecount_stimint\", \"val\": [0.0, 0.001], \"n\": 16, \"fid\": 1477, \"strict_stim\": true}\n    ]\n  }, \n  \"hold_hyp\": {\n    \"soma.v\": [\n      {\"feature\": \"Spikecount_stimint\", \"val\": [0.0, 0.001], \"n\": 22, \"fid\": 1616, \"strict_stim\": true}\n    ]\n  }, \n  \"Rin_dep\": {\n    \"soma.v\": [\n      {\"feature\": \"voltage_base\", \"val\": [-64.6893, 0.8318], \"n\": 16, \"fid\": 428, \"strict_stim\": true},\n      {\"feature\": \"ohmic_input_resistance_vb_ssse\", \"val\": [147.7406, 61.0938], \"n\": 16, \"fid\": 455, \"strict_stim\": true}\n    ]\n  }\n}"
  },
  {
    "path": "examples/thalamocortical-cell/config/params/TC.json",
    "content": "{\n    \"mechanisms\": {\n        \"all\":\n            {\"mech\":[\"pas\",\n                    \"TC_cad\"]},\n        \"somatic\":\n            {\"mech\":[\"TC_ih_Bud97\", \"TC_Nap_Et2\", \n\t\t     \"TC_iA\",\"TC_iL\", \"SK_E2\", \"TC_HH\"]},\n        \"alldend\":\n            {\"mech\":[\"TC_ih_Bud97\", \"TC_Nap_Et2\",\n\t\t     \"TC_iA\",\"TC_iL\", \"SK_E2\", \"TC_HH\"]},\n\t\"axonal\":\n            {\"mech\":[\"TC_HH\"]},\n\n\t\"somadend\":\n        {\"mech\":[\"TC_iT_Des98\"]}\n\n    },\n    \"distributions\": {\n    },\n    \"parameters\": {\n        \"__comment\": \"define constants as single values and params to optimize as tuples of bounds: [lower, upper]\",\n        \"global\":   [\n            {\"name\":\"v_init\",   \"val\":-79},\n            {\"name\":\"celsius\",  \"val\":34}\n        ],\n        \"all\": [\n            {\"name\":\"cm\",                     \"val\":1},\n            {\"name\":\"Ra\",                     \"val\":100},\n            {\"name\":\"ena\",                    \"val\":50},\n            {\"name\":\"ek\",                     \"val\":-90},\n\t    {\"name\":\"e_pas\",                  \"val\":-80},\n            {\"name\":\"g_pas\",                  \"val\":[1e-06, 1e-04]}\n        ],\n        \"axonal\": [\n            {\"name\":\"gk_max_TC_HH\",           \"val\":[0, 0.2]},\n            {\"name\":\"gna_max_TC_HH\",          \"val\":[0, 0.8]}\n        ],\n\t\"somadend\": [\n            {\"name\":\"pcabar_TC_iT_Des98\",     \"val\":[0, 1e-4]}\n\t    ],\n\n        \"somatic\": [\n            {\"name\":\"gh_max_TC_ih_Bud97\",     \"val\":[0, 1e-4]},\n\t    {\"name\":\"gNap_Et2bar_TC_Nap_Et2\", \"val\":[0, 0.0001]},\n\t    {\"name\":\"gk_max_TC_iA\",           \"val\":[0, 0.07]},\n            {\"name\":\"gk_max_TC_HH\",           \"val\":[0, 0.2]},\n\t    {\"name\":\"gna_max_TC_HH\",          \"val\":[0, 0.2]},\n\t    {\"name\":\"pcabar_TC_iL\",           \"val\":[0, 0.001]},\n\t    {\"name\":\"gSK_E2bar_SK_E2\",        \"val\":[0, 0.005]},\n\t    {\"name\":\"taur_TC_cad\",            \"val\":[1.0, 15.0]},\n\t    {\"name\":\"gamma_TC_cad\",\t      \"val\":[0.0005, 1]}\n        ],\n        \"alldend\": [\n\t    {\"name\":\"gh_max_TC_ih_Bud97\",     \"val\":[0, 1e-4]},\n            {\"name\":\"gNap_Et2bar_TC_Nap_Et2\", \"val\":[0, 0.0001]},\n\t    {\"name\":\"gk_max_TC_iA\",           \"val\":[0, 0.008]},\n            {\"name\":\"gk_max_TC_HH\",           \"val\":[0, 0.01]},\n\t    {\"name\":\"gna_max_TC_HH\",          \"val\":[0, 0.006]},\n\t    {\"name\":\"pcabar_TC_iL\",           \"val\":[0, 0.001]},\n\t    {\"name\":\"gSK_E2bar_SK_E2\",        \"val\":[0, 0.005]},\n\t    {\"name\":\"taur_TC_cad\",            \"val\":[1.0, 15.0]},\n\t    {\"name\":\"gamma_TC_cad\",\t      \"val\":[0.0005, 1]}\n\n        ]\n    }\n}\n"
  },
  {
    "path": "examples/thalamocortical-cell/config/protocols/cAD_ltb.json",
    "content": "{\n  \"Step_150\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.089041, \"thresh_perc\": 150.0271, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.134445, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"Step_250\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.14832, \"thresh_perc\": 249.961, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.134943, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"Step_200\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.118859, \"thresh_perc\": 200.2936, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.134536, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"Step_200_hyp\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.14673, \"thresh_perc\": 200.2118, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": -0.121695, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"IV_-140\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": -0.07745, \"thresh_perc\": -134.8425, \"duration\": 3000.0, \"totduration\": 4050.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.135615, \"duration\": 4050.0, \"totduration\": 4050.0}\n    }\n  }, \n  \"RMP\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 300, \"amp\": 0, \"duration\": 900.0, \"totduration\": 1200.0}\n    }\n  }, \n  \"hold_dep\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 300.0, \"amp\": 0.0, \"thresh_perc\": 0.0, \"duration\": 900.0, \"totduration\": 1200.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.134051, \"duration\": 1200.0, \"totduration\": 1200.0}\n    }\n  }, \n  \"hold_hyp\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 300.0, \"amp\": 0.0, \"thresh_perc\": 0.0, \"duration\": 900.0, \"totduration\": 1200.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": -0.129995, \"duration\": 1200.0, \"totduration\": 1200.0}\n    }\n  }, \n  \"Rin_dep\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": -0.023425, \"thresh_perc\": -40.1228, \"duration\": 3000.0, \"totduration\": 4050.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.135101, \"duration\": 4050.0, \"totduration\": 4050.0}\n    }\n  }\n}"
  },
  {
    "path": "examples/thalamocortical-cell/config/protocols/cNAD_ltb.json",
    "content": "{\n  \"Step_150\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.106854, \"thresh_perc\": 149.9653, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.150141, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"Step_250\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.18113, \"thresh_perc\": 249.9147, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.154962, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"Step_200\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.140843, \"thresh_perc\": 200.2739, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.153092, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"Step_200_hyp\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": 0.14673, \"thresh_perc\": 200.2118, \"duration\": 1350.0, \"totduration\": 2400.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": -0.121695, \"duration\": 2400.0, \"totduration\": 2400.0}\n    }\n  }, \n  \"IV_-140\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": -0.098644, \"thresh_perc\": -139.1379, \"duration\": 3000.0, \"totduration\": 4050.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.153485, \"duration\": 4050.0, \"totduration\": 4050.0}\n    }\n  }, \n  \"RMP\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 300, \"amp\": 0, \"duration\": 900.0, \"totduration\": 1200.0}\n    }\n  }, \n  \"hold_dep\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 300.0, \"amp\": 0.0, \"thresh_perc\": 0.0, \"duration\": 900.0, \"totduration\": 1200.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.147305, \"duration\": 1200.0, \"totduration\": 1200.0}\n    }\n  }, \n  \"hold_hyp\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 300.0, \"amp\": 0.0, \"thresh_perc\": 0.0, \"duration\": 900.0, \"totduration\": 1200.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": -0.129995, \"duration\": 1200.0, \"totduration\": 1200.0}\n    }\n  }, \n  \"Rin_dep\": {\n    \"type\": \"StepProtocol\", \n    \"stimuli\": {\n      \"step\": {\"delay\": 800.0, \"amp\": -0.028535, \"thresh_perc\": -39.8617, \"duration\": 3000.0, \"totduration\": 4050.0},\n      \"holding\": {\"delay\": 0.0, \"amp\": 0.15064, \"duration\": 4050.0, \"totduration\": 4050.0}\n    }\n  }\n}"
  },
  {
    "path": "examples/thalamocortical-cell/config/recipes.json",
    "content": "{\n    \"cAD_ltb\": {\n        \"morph_path\": \"morphologies/\",  \t\t\t \n        \"morphology\": \"jy160728_A_idA.asc\",\n        \"params\": \"config/params/TC.json\",\n        \"protocol\": \"config/protocols/cAD_ltb.json\",\n        \"features\": \"config/features/cAD_ltb.json\"\n    },\n    \"cNAD_ltb\": {\n        \"morph_path\": \"morphologies/\",  \t\t\t \n        \"morphology\": \"jy170517_A_idA.asc\",\n        \"params\": \"config/params/TC.json\",\n        \"protocol\": \"config/protocols/cNAD_ltb.json\",\n        \"features\": \"config/features/cNAD_ltb.json\"\n    }\n}\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/SK_E2.mod",
    "content": ": SK-type calcium-activated potassium current\n: Reference : Kohler et al. 1996\n\n: From ModelDB, accession no. 139653\n\nNEURON {\n       SUFFIX SK_E2\n       USEION k READ ek WRITE ik\n       USEION ca READ cai\n       RANGE gSK_E2bar, gSK_E2, ik, zTau\n}\n\nUNITS {\n      (mV) = (millivolt)\n      (mA) = (milliamp)\n      (mM) = (milli/liter)\n}\n\nPARAMETER {\n          v            (mV)\n          gSK_E2bar = .000001 (mho/cm2)\n          zTau = 1              (ms)\n          ek           (mV)\n          cai          (mM)\n}\n\nASSIGNED {\n         zInf\n         ik            (mA/cm2)\n         gSK_E2\t       (S/cm2)\n}\n\nSTATE {\n      z   FROM 0 TO 1\n}\n\nBREAKPOINT {\n           SOLVE states METHOD cnexp\n           gSK_E2  = gSK_E2bar * z\n           ik   =  gSK_E2 * (v - ek)\n}\n\nDERIVATIVE states {\n        rates(cai)\n        z' = (zInf - z) / zTau\n}\n\nPROCEDURE rates(ca(mM)) {\n          if(ca < 1e-7){\n\t              ca = ca + 1e-07\n          }\n          zInf = 1/(1 + (0.00043 / ca)^4.8)\n}\n\nINITIAL {\n        rates(cai)\n        z = zInf\n}\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/TC_HH.mod",
    "content": "TITLE Hippocampal HH channels\n: \n:\n: Fast Na+ and K+ currents responsible for action potentials\n: Iterative equations\n:\n: Equations modified by Traub, for Hippocampal Pyramidal cells, in:\n: Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991\n:\n: range variable vtraub adjust threshold\n:\n: Written by Alain Destexhe, Salk Institute, Aug 1992\n:\n\n: Modified from ModelDB, accession no. 279\n\nINDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}\n\nNEURON {\n\tSUFFIX TC_HH\n\tUSEION na READ ena WRITE ina\n\tUSEION k READ ek WRITE ik\n\tRANGE gna_max, gk_max, vtraub, vtraub2, i_rec\n\tRANGE m_inf, h_inf, n_inf\n\tRANGE tau_m, tau_h, tau_n\n\tRANGE m_exp, h_exp, n_exp\n\tRANGE ina, ik\n}\n\n\nUNITS {\n\t(mA) = (milliamp)\n\t(mV) = (millivolt)\n\t(S)  = (siemens)\n}\n\nPARAMETER {\n\tgna_max\t= 1.0e-1 \t(S/cm2) \n\tgk_max\t= 1.0e-1 \t(S/cm2) \n\n\tcelsius         (degC)\n\tdt              (ms)\n\tv               (mV)\n\tvtraub = -55.5   : Average of original value and Amarillo et al., J Neurophysiol 112:393-410, 2014\n\tvtraub2 = -45.5  : Shift for K current\n}\n\nSTATE {\n\tm h n\n}\n\nASSIGNED {\n\tina\t(mA/cm2)\n\tik\t(mA/cm2)\n\tena\t(mV)\n\tek\t(mV)\n\ti_rec\t(mA/cm2)\n\tm_inf\n\th_inf\n\tn_inf\n\ttau_m\n\ttau_h\n\ttau_n\n\tm_exp\n\th_exp\n\tn_exp\n\ttcorr\n}\n\n\nBREAKPOINT {\n\tSOLVE states METHOD cnexp\n\tina   = gna_max * m*m*m*h * (v - ena)\n\tik    = gk_max * n*n*n*n * (v - ek)\n\ti_rec = ina + ik\n}\n\n\nDERIVATIVE states {   : exact Hodgkin-Huxley equations\n\tevaluate_fct(v)\n\tm' = (m_inf - m) / tau_m\n\th' = (h_inf - h) / tau_h\n\tn' = (n_inf - n) / tau_n\n}\n\n:PROCEDURE states() {\t: exact when v held constant\n:\tevaluate_fct(v)\n:\tm = m + m_exp * (m_inf - m)\n:\th = h + h_exp * (h_inf - h)\n:\tn = n + n_exp * (n_inf - n)\n:\tVERBATIM\n:\treturn 0;\n:\tENDVERBATIM\n:}\n\nUNITSOFF\nINITIAL {\n\tm = 0\n\th = 0\n\tn = 0\n:\n:  Q10 was assumed to be 3 for both currents\n:\n: original measurements at roomtemperature?\n\n\ttcorr = 3.0 ^ ((celsius-36)/ 10 )\n}\n\nPROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2, v3\n\n\tv2 = v - vtraub : convert to traub convention\n\tv3 = v - vtraub2 : EI: shift only K\n\n\tif(v2 == 13 || v2 == 40 || v2 == 15 ){\n    \tv = v+0.0001\n    }\n\n\ta = 0.32 * (13-v2) / ( exp((13-v2)/4) - 1)\n\tb = 0.28 * (v2-40) / ( exp((v2-40)/5) - 1)\n\ttau_m = 1 / (a + b) / tcorr\n\tm_inf = a / (a + b)\n\n\ta = 0.128 * exp((17-v2)/18)\n\tb = 4 / ( 1 + exp((40-v2)/5) )\n\ttau_h = 1 / (a + b) / tcorr\n\th_inf = a / (a + b)\n\n\ta = 0.032 * (15-v3) / ( exp((15-v3)/5) - 1)\n\tb = 0.5 * exp((10-v3)/40)\n\ttau_n = 1 / (a + b) / tcorr\n\tn_inf = a / (a + b)\n\n\tm_exp = 1 - exp(-dt/tau_m)\n\th_exp = 1 - exp(-dt/tau_h)\n\tn_exp = 1 - exp(-dt/tau_n)\n\n}\n\nUNITSON\n\n\n\n\n\n\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/TC_ITGHK_Des98.mod",
    "content": "TITLE Low threshold calcium current\n:\n:   Ca++ current responsible for low threshold spikes (LTS)\n:   Differential equations\n:\n:   Model of Huguenard & McCormick, J Neurophysiol 68: 1373-1383, 1992.\n:   The kinetics is described by Goldman-Hodgkin-Katz equations,\n:   using a m2h format, according to the voltage-clamp data\n:   (whole cell patch clamp) of Huguenard & Prince, J. Neurosci. \n:   12: 3804-3817, 1992.\n:\n:   This model is described in detail in:\n:   Destexhe A, Neubig M, Ulrich D and Huguenard JR.  \n:   Dendritic low-threshold calcium currents in thalamic relay cells.  \n:   Journal of Neuroscience 18: 3574-3588, 1998.\n:   (a postscript version of this paper, including figures, is available on\n:   the Internet at http://cns.fmed.ulaval.ca)\n:\n:    - shift parameter for screening charge\n:    - empirical correction for contamination by inactivation (Huguenard)\n:    - GHK equations\n:\n:\n:   Written by Alain Destexhe, Laval University, 1995\n:\n\n: From ModelDB, accession no. 279, modified qm and qh\n\nINDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}\n\nNEURON {\n\tSUFFIX TC_iT_Des98\n\tUSEION ca READ cai,cao WRITE ica\n\tRANGE pcabar, m_inf, tau_m, h_inf, tau_h, shift, actshift, ica\n\tGLOBAL qm, qh\n}\n\nUNITS {\n\t(molar) = (1/liter)\n\t(mV) =\t(millivolt)\n\t(mA) =\t(milliamp)\n\t(mM) =\t(millimolar)\n\n\tFARADAY = (faraday) (coulomb)\n\tR = (k-mole) (joule/degC)\n}\n\nPARAMETER {\n\tv\t\t(mV)\n\t:celsius\t= 36\t(degC)\n\tcelsius \t(degC)  : EI\n\tpcabar\t=.2e-3\t(cm/s)\t: Maximum Permeability\n\tshift\t= 2 \t(mV)\t: corresponds to 2mM ext Ca++\n\tactshift = 0 \t(mV)\t: shift of activation curve (towards hyperpol)\n\tcai\t= 2.4e-4 (mM)\t: adjusted for eca=120 mV\n\tcao\t= 2\t(mM)\n\tqm      = 2.5\t\t: Amarillo et al., J Neurophysiol, 2014\n\tqh      = 2.5           : Amarillo et al., J Neurophysiol, 2014\n}\n\nSTATE {\n\tm h\n}\n\nASSIGNED {\n\tica\t(mA/cm2)\n\tm_inf\n\ttau_m\t(ms)\n\th_inf\n\ttau_h\t(ms)\n\tphi_m\n\tphi_h\n}\n\nBREAKPOINT {\n\tSOLVE castate METHOD cnexp\n\tica = pcabar * m*m*h * ghk(v, cai, cao)\n}\n\nDERIVATIVE castate {\n\tevaluate_fct(v)\n\n\tm' = (m_inf - m) / tau_m\n\th' = (h_inf - h) / tau_h\n}\n\n\nUNITSOFF\nINITIAL {\n\tphi_m = qm ^ ((celsius-24)/10)\n\tphi_h = qh ^ ((celsius-24)/10)\n\n\tevaluate_fct(v)\n\n\tm = m_inf\n\th = h_inf\n}\n\nPROCEDURE evaluate_fct(v(mV)) {\n:\n:   The kinetic functions are taken as described in the model of \n:   Huguenard & McCormick, and corresponds to a temperature of 23-25 deg.\n:   Transformation to 36 deg assuming Q10 of 5 and 3 for m and h\n:   (as in Coulter et al., J Physiol 414: 587, 1989).\n:\n:   The activation functions were estimated by John Huguenard.\n:   The V_1/2 were of -57 and -81 in the vclamp simulations, \n:   and -60 and -84 in the current clamp simulations.\n:\n:   The activation function were empirically corrected in order to account\n:   for the contamination of inactivation.  Therefore the simulations \n:   using these values reproduce more closely the voltage clamp experiments.\n:   (cfr. Huguenard & McCormick, J Neurophysiol, 1992).\n:\n\n\tm_inf = 1.0 / ( 1 + exp(-(v+shift+actshift+57)/6.2) )\n\th_inf = 1.0 / ( 1 + exp((v+shift+81)/4.0) )\n\n\ttau_m = ( 0.612 + 1.0 / ( exp(-(v+shift+actshift+132)/16.7) + exp((v+shift+actshift+16.8)/18.2) ) ) / phi_m\n\tif( (v+shift) < -80) {\n\t\ttau_h = exp((v+shift+467)/66.6) / phi_h\n\t} else {\n\t\ttau_h = ( 28 + exp(-(v+shift+22)/10.5) ) / phi_h\n\t}\n\n\t: EI compare with tau_h on ModelDB, no. 3817\n}\n\nFUNCTION ghk(v(mV), ci(mM), co(mM)) (.001 coul/cm3) {\n\tLOCAL z, eci, eco\n\tz = (1e-3)*2*FARADAY*v/(R*(celsius+273.15))\n\teco = co*efun(z)\n\teci = ci*efun(-z)\n\t:high cao charge moves inward\n\t:negative potential charge moves inward\n\tghk = (.001)*2*FARADAY*(eci - eco)\n}\n\nFUNCTION efun(z) {\n\tif (fabs(z) < 1e-4) {\n\t\tefun = 1 - z/2\n\t}else{\n\t\tefun = z/(exp(z) - 1)\n\t}\n}\nFUNCTION nongat(v,cai,cao) {\t: non gated current\n\tnongat = pcabar * ghk(v, cai, cao)\n}\nUNITSON\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/TC_Ih_Bud97.mod",
    "content": ": Ih current for thalamo-cortical neurons\n: Ref.: Budde et al. (minf), J Physiol, 1997, Huguenard and McCormick, J Neurophysiol, 1992 (taum)\n\nNEURON\t{\n\tSUFFIX TC_ih_Bud97\n\tNONSPECIFIC_CURRENT ih\n\tRANGE gh_max, g_h, i_rec \n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgh_max = 2.2e-5 (S/cm2) \n\te_h =  -43.0 (mV)\n        celsius (degC)\n\tq10 = 4 : Santoro et al., J. Neurosci. 2000\n\t\t     \n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tih\t(mA/cm2)\n\tg_h\t(S/cm2)\n\tmInf\n\tmTau\n\ttcorr\t\t: Add temperature correction\n\ti_rec\n}\n\nSTATE\t{ \n\tm\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tg_h = gh_max*m\n\tih = g_h*(v-e_h)\n\ti_rec = ih\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\ttcorr = q10^((celsius-34)/10)  : EI: Recording temp. 34 C Huguenard et al.\n}\n\nUNITSOFF\nPROCEDURE rates(){\n        mInf = 1/(1+exp((v+86.4)/11.2)) : Budde et al., 1997\n        mTau = (1/(exp(-14.59 - 0.086*v) + exp(-1.87 + 0.0701*v )))/tcorr : Huguenard et al., 1992\n}\nUNITSON\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/TC_Nap_Et2.mod",
    "content": ":Comment : mtau deduced from text (said to be 6 times faster than for NaTa)\n:Comment : so I used the equations from NaT and multiplied by 6\n:Reference : Modeled according to kinetics derived from Magistretti & Alonso 1999\n:Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21\n\n: From ModelDB, accession no. 139653. mInf and hInf for TC models, see equations for references\n\t\t\t\t\t\t\t\t\t  \nNEURON\t{\n\tSUFFIX TC_Nap_Et2\n\tUSEION na READ ena WRITE ina\n\tRANGE gNap_Et2bar, gNap_Et2, ina\n}\n\nUNITS\t{\n\t(S) = (siemens)\n\t(mV) = (millivolt)\n\t(mA) = (milliamp)\n}\n\nPARAMETER\t{\n\tgNap_Et2bar = 0.00001 (S/cm2)\n}\n\nASSIGNED\t{\n\tv\t(mV)\n\tena\t(mV)\n\tina\t(mA/cm2)\n\tgNap_Et2\t(S/cm2)\n\tmInf\n\tmTau\n\tmAlpha\n\tmBeta\n\thInf\n\thTau\n\thAlpha\n\thBeta\n}\n\nSTATE\t{\n\tm\n\th\n}\n\nBREAKPOINT\t{\n\tSOLVE states METHOD cnexp\n\tgNap_Et2 = gNap_Et2bar*m*m*m*h\n\tina = gNap_Et2*(v-ena)\n}\n\nDERIVATIVE states\t{\n\trates()\n\tm' = (mInf-m)/mTau\n\th' = (hInf-h)/hTau\n}\n\nINITIAL{\n\trates()\n\tm = mInf\n\th = hInf\n}\n\nPROCEDURE rates(){\n  LOCAL qt\n  qt = 2.3^((34-21)/10)\n\n\tUNITSOFF\n\t\tmInf = 1.0/(1+exp(-(v+56.93)/9.09)) : Parri and Crunelli, J. Neurosci. 1998\n    if(v == -38){\n    \tv = v+0.0001\n    }\n\t\tmAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6)))\n\t\tmBeta  = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6)))\n\t\tmTau = 6*(1/(mAlpha + mBeta))/qt\n\n  \tif(v == -17){\n   \t\tv = v + 0.0001\n  \t}\n    if(v == -64.4){\n      v = v+0.0001\n    }\n                hInf = 1.0/(1+exp((v+58.7)/14.2)) : Amarillo et al., J Neurophysiol, 2014 \n    hAlpha = -2.88e-6 * (v + 17) / (1 - exp((v + 17)/4.63))\n    hBeta = 6.94e-6 * (v + 64.4) / (1 - exp(-(v + 64.4)/2.63))\n\t\thTau = (1/(hAlpha + hBeta))/qt\n\tUNITSON\n}\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/TC_cadecay.mod",
    "content": ": From ModelDB no. 139653 \n\nNEURON {\n\tSUFFIX TC_cad\n\tUSEION ca READ ica WRITE cai\n\tRANGE depth,kt,kd,cainf,taur, cai_rec, gamma\n}\n\nUNITS {\n\n\t(mM)\t= (milli/liter)\n\t(um)\t= (micron)\n\t(mA)\t= (milliamp)\n\t(msM)\t= (ms mM)\n\tFARADAY = (faraday) (coulombs)\n}\n\nPARAMETER {\n\tdepth\t= .1\t (um)\t\t: depth of shell\n\tgamma   = 0.05 \t (1)\t\t: EI: percent of free calcium (not buffered)\n\ttaur\t= 5\t (ms)\t\t: rate of calcium removal\n\tcainf\t= 5e-5 (mM)\t\t: Value from Amarillo et al., J Neurophysiol, 2014\n}\n\nSTATE {\n\tcai\t\t(mM) \n}\n\nINITIAL {\n\tcai = cainf\n}\n\nASSIGNED {\n\tica\t\t(mA/cm2)\n\tcai_rec\t\t(mM)\n}\n\t\nBREAKPOINT {\n\tSOLVE state METHOD derivimplicit\n\t\n}\n\nDERIVATIVE state { \n\n\tcai' = -(10000)*(ica*gamma/(2*FARADAY*depth)) - (cai - cainf)/taur\n\tcai_rec = cai\n\n}\n\n\n\n\n\n\n\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/TC_iA.mod",
    "content": "TITLE Fast Transient Potassium Current IA\r\n\r\n:   From the model by Huguenard and McCormick, J Neurophysiol, 1992\r\n:   Written by Yimy Amarillo, 2014 (Amarillo et al., J Neurophysiol, 2014)\r\n\r\nUNITS {\r\n    (mV) = (millivolt)\r\n    (mA) = (milliamp)\r\n    (S)  = (siemens)\r\n}\r\n\r\nNEURON {\r\n    SUFFIX TC_iA\r\n    USEION k READ ek WRITE ik\r\n    RANGE gk_max, ik, taom, taoh1, taoh2\r\n}\r\n\r\nPARAMETER {\r\n    gk_max  = 5.5e-3 (S/cm2)\t: Default maximum conductance\r\n    celsius\r\n}\r\n\r\nASSIGNED { \r\n    v     (mV)\r\n    ek    (mV)\r\n    ik    (mA/cm2)\r\n    m1inf \r\n    m2inf\r\n    hinf\r\n    taoh1 (ms)\r\n    taoh2 (ms)\r\n    taom  (ms)\r\n    tadj\r\n}\r\n\r\nSTATE {\r\n    m1 m2 h1 h2\r\n}\r\n\r\nBREAKPOINT {\r\n    SOLVE states METHOD cnexp\r\n    ik = gk_max*(0.6*h1*m1^4+0.4*h2*m2^4)*(v-ek)\r\n}\r\n\r\nINITIAL {\r\n    settables(v)\r\n    tadj = 2.8 ^ ((celsius-23)/10)\r\n    m1 = m1inf\r\n    m2 = m2inf\r\n    h1 = hinf\r\n    h2 = hinf\r\n}\r\n\r\nDERIVATIVE states {  \r\n    settables(v)      \r\n    m1' = (m1inf-m1)/taom\r\n    m2' = (m2inf-m2)/taom\r\n    h1' = (hinf-h1)/taoh1\r\n    h2' = (hinf-h2)/taoh2\r\n}\r\n\r\nUNITSOFF\r\n\r\nPROCEDURE settables(v (mV)) { \r\n    LOCAL taodef\r\n\r\n    m1inf = 1/(1+exp(-(v+60)/8.5))\r\n    m2inf = 1/(1+exp(-(v+36)/20))\r\n    hinf  = 1/(1+exp((v+78)/6))\r\n\r\n    taom  = (0.37 + 1/(exp((v+35.8)/19.7)+exp(-(v+79.7)/12.7))) / tadj\r\n    \r\n    taodef = (1/(exp((v+46)/5)+exp(-(v+238)/37.5))) / tadj\r\n    if (v<(-63)) {taoh1 = taodef} else {taoh1 = (19 / tadj)}\r\n    if (v<(-73)) {taoh2 = taodef} else {taoh2 = (60 / tadj)}\r\n\r\n}\r\n\r\nUNITSON\r\n"
  },
  {
    "path": "examples/thalamocortical-cell/mechanisms/TC_iL.mod",
    "content": "TITLE high threshold calcium current (L-current)\n\n: From ModelDB, accession: 3808\n: Based on the model by McCormick & Huguenard, J Neurophysiol, 1992\n: and errata in https://huguenardlab.stanford.edu/reprints/Errata_thalamic_cell_models.pdf\n\nINDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)}\n\nNEURON {\n\tSUFFIX TC_iL\n\tUSEION ca READ cai,cao WRITE ica\n        RANGE pcabar, m_inf, tau_m, ica, i_rec\n}\n\nUNITS {\n\t(mA)\t= (milliamp)\n\t(mV)\t= (millivolt)\n\t(molar) = (1/liter)\n\t(mM)\t= (millimolar)\n        FARADAY = (faraday) (coulomb)\n        R       = 8.314 (volt-coul/degC)\n}\n\nPARAMETER {\n\tv\t\t\t(mV)\n\tcelsius\t\t\t(degC)\n        dt              \t(ms)\n\tcai  = 0.5E-4    \t(mM)\n\tcao  = 2\t\t(mM)\n\tpcabar= 1e-4\t        (cm/s)\t\t\n}\n\nSTATE {\n\tm\n}\n\nASSIGNED {\n\tica\t\t(mA/cm2)\n\ti_rec\t\t(mA/cm2)\t\n\ttau_m\t\t(ms)\n\tm_inf \n\ttcorr\n}\n\nBREAKPOINT { \n\tSOLVE states METHOD cnexp\n\tica = pcabar * m*m * ghk(v,cai,cao)\n\ti_rec = ica\n}\n\nDERIVATIVE states {\n       rates(v)\n\n       m'= (m_inf-m) / tau_m \n}\n  \nINITIAL {\n\trates(v)\n\ttcorr = 3^((celsius-23.5)/10)\n\tm = 0\n}\n\nUNITSOFF\n\nFUNCTION ghk( v(mV), ci(mM), co(mM))  (millicoul/cm3) {\n        LOCAL z, eci, eco\n        z = v * (.001) * 2 *FARADAY / (R*(celsius+273.15))\n\teco = co*efun(z)\n\teci = ci*efun(-z)\n\t:high cao charge moves inward\n\t:negative potential charge moves inward\n\tghk = (.001)*2*FARADAY*(eci - eco)\n}\n\nFUNCTION efun(z) {\n\t if (fabs(z) < 1e-4) {\n\t    efun = 1 - z/2\n\t }else{\n\t    efun = z/(exp(z) - 1)\n         }\n}\n\nPROCEDURE rates(v(mV)) { LOCAL a,b\n\ta = 1.6 / (1+ exp(-0.072*(v-5)))\n\tb = 0.02 * vtrap( -(v-1.31), 5.36)\n\n\ttau_m = 1/(a+b) / tcorr\n\tm_inf = 1/(1+exp((v+10)/-10))\n}\n\nFUNCTION vtrap(x,c) { \n\t: Traps for 0 in denominator of rate equations\n        if (fabs(x/c) < 1e-6) {\n          vtrap = c + x/2 }\n        else {\n          vtrap = x / (1-exp(-x/c)) }\n}\nUNITSON\n\n\n\n\n\n\n\n\n"
  },
  {
    "path": "examples/thalamocortical-cell/morphologies/jy160728_A_idA.asc",
    "content": ";\tV3 text file written for MicroBrightField products.\r\n(ImageCoords)\r\n\r\n(\"Hippocampus\"\r\n  (Color Magenta)\r\n  (Closed)\r\n  (FillDensity 0)\r\n  (GUID \"FABB7BADECCE4ADF932DFB753057CCB3\")\r\n  (MBFObjectType 5)\r\n  (Resolution 1.846036)\r\n  (  531.54  3438.80     0.00     1.85)  ;  1, 1\r\n  (  644.09  3364.95     0.00     1.85)  ;  1, 2\r\n  (  819.37  3237.58     0.00     1.85)  ;  1, 3\r\n  (  985.42  3064.05     0.00     1.85)  ;  1, 4\r\n  ( 1134.87  2886.83     0.00     1.85)  ;  1, 5\r\n  ( 1245.57  2704.07     0.00     1.85)  ;  1, 6\r\n  ( 1269.36  2598.20     0.19     1.85)  ;  1, 7\r\n  ( 1260.14  2450.52     0.19     1.85)  ;  1, 8\r\n  ( 1274.90  2330.53     0.19     1.85)  ;  1, 9\r\n  ( 1232.46  2264.07     0.19     1.85)  ;  1, 10\r\n  ( 1092.24  2245.61     0.19     1.85)  ;  1, 11\r\n  (  804.41  2245.61     0.19     1.85)  ;  1, 12\r\n  (  675.26  2201.30     0.19     1.85)  ;  1, 13\r\n  (  452.01  2182.84     0.19     1.85)  ;  1, 14\r\n  (  147.58  2219.76     0.19     1.85)  ; 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 2, 373\r\n  (-1827.86  3664.11     0.66     1.85)  ;  2, 374\r\n  (-1803.87  3723.18     0.66     1.85)  ;  2, 375\r\n  (-1792.80  3839.48     0.66     1.85)  ;  2, 376\r\n  (-1790.96  3933.63     0.66     1.85)  ;  2, 377\r\n  (-1794.65  3998.24     0.66     1.85)  ;  2, 378\r\n  (-1783.58  4075.78     0.66     1.85)  ;  2, 379\r\n  (-1778.19  4157.50     0.66     1.85)  ;  2, 380\r\n  (-1754.20  4222.11     0.66     1.85)  ;  2, 381\r\n  (-1741.29  4310.72     0.66     1.85)  ;  2, 382\r\n  (-1748.67  4373.48     0.66     1.85)  ;  2, 383\r\n  (-1757.89  4421.48     0.66     1.85)  ;  2, 384\r\n  (-1754.20  4460.25     0.66     1.85)  ;  2, 385\r\n  (-1754.20  4508.24     0.66     1.85)  ;  2, 386\r\n  (-1732.06  4572.86     0.66     1.85)  ;  2, 387\r\n  (-1722.84  4635.62     0.66     1.85)  ;  2, 388\r\n  (-1704.39  4674.39     0.66     1.85)  ;  2, 389\r\n  (-1689.63  4768.54     0.66     1.85)  ;  2, 390\r\n  (-1665.64  4834.99     0.66     1.85)  ;  2, 391\r\n  (-1625.33  4950.73     0.85     1.85)  ;  2, 392\r\n  (-1601.35  5063.33     0.85     1.85)  ;  2, 393\r\n  (-1573.67  5144.56     0.85     1.85)  ;  2, 394\r\n  (-1510.94  5275.63     0.85     1.85)  ;  2, 395\r\n  (-1424.23  5406.70     0.85     1.85)  ;  2, 396\r\n  (-1368.87  5510.07     0.85     1.85)  ;  2, 397\r\n  (-1309.83  5548.84     0.85     1.85)  ;  2, 398\r\n  (-1217.58  5624.53     0.85     1.85)  ;  2, 399\r\n  (-1167.71  5647.42     0.85     1.85)  ;  2, 400\r\n  (-1154.79  5676.96     0.85     1.85)  ;  2, 401 Closure point\r\n)  ;  End of contour\r\n\r\n(\"NewContour\"\r\n  (Color Magenta)\r\n  (FillDensity 0)\r\n  (GUID \"BA0641A17ACC468FB6B274439F7D2DEA\")\r\n  (MBFObjectType 5)\r\n  (Resolution 1.846036)\r\n  (-1444.46  3573.10     0.85     1.85)  ;  3, 1\r\n  (-1499.82  3766.93     0.85     1.85)  ;  3, 2\r\n  (-1534.87  4038.30     0.85     1.85)  ;  3, 3\r\n  (-1555.17  4389.05     0.85     1.85)  ;  3, 4\r\n  (-1540.41  4636.42     0.85     1.85)  ;  3, 5\r\n  (-1527.49  4905.94     0.85     1.85)  ;  3, 6\r\n  (-1529.34  5031.47     0.85     1.85)  ;  3, 7\r\n  (-1535.26  5146.43     0.95     1.85)  ;  3, 8\r\n)  ;  End of contour\r\n\r\n(\"NewContour\"\r\n  (Color Magenta)\r\n  (FillDensity 0)\r\n  (GUID \"DAB7BDC80AA94212B77DBC0AFFD1108E\")\r\n  (MBFObjectType 5)\r\n  (Resolution 1.846036)\r\n  ( 1140.31  1025.00     0.76     1.85)  ;  4, 1\r\n  ( 1000.08  1061.92     0.76     1.85)  ;  4, 2\r\n  (  922.59  1121.00     0.76     1.85)  ;  4, 3\r\n  (  907.83  1233.60     0.66     1.85)  ;  4, 4\r\n  (  907.83  1329.60     0.66     1.85)  ;  4, 5\r\n  (  894.92  1353.60     0.66     1.85)  ;  4, 6\r\n  (  843.26  1355.44     0.66     1.85)  ;  4, 7\r\n  (  773.14  1298.21     0.66     1.85)  ;  4, 8\r\n  (  732.55  1259.45     0.66     1.85)  ;  4, 9\r\n  (  725.17  1196.68     0.66     1.85)  ;  4, 10\r\n  (  760.23  1144.99     0.66     1.85)  ;  4, 11\r\n  (  852.48  1054.54     0.66     1.85)  ;  4, 12\r\n  (  994.55   893.93     0.66     1.85)  ;  4, 13\r\n  ( 1083.11   823.78     0.66     1.85)  ;  4, 14\r\n  ( 1110.79   720.41     0.66     1.85)  ;  4, 15\r\n  ( 1156.91   607.80     0.66     1.85)  ;  4, 16\r\n  ( 1121.82   414.84     0.66     1.85)  ;  4, 17\r\n  ( 1029.57   121.32     0.66     1.85)  ;  4, 18\r\n  (  957.61   -26.36     0.66     1.85)  ;  4, 19\r\n  (  893.03  -175.89     0.66     1.85)  ;  4, 20\r\n  (  782.47  -343.42     0.66     1.85)  ;  4, 21\r\n  (  609.04  -435.72     0.66     1.85)  ;  4, 22\r\n  (  376.57  -528.02     0.66     1.85)  ;  4, 23\r\n  (  241.88  -592.63     0.66     1.85)  ;  4, 24\r\n  (   49.99  -670.17     0.66     1.85)  ;  4, 25\r\n  ( -104.99  -786.47     0.66     1.85)  ;  4, 26\r\n)  ;  End of contour\r\n\r\n(\"NewContour\"\r\n  (Color Magenta)\r\n  (FillDensity 0)\r\n  (GUID \"F8C3F7E747A74F21B265C67AE2609B18\")\r\n  (MBFObjectType 5)\r\n  (Resolution 1.846036)\r\n  ( -103.14  -297.27     0.66     1.85)  ;  5, 1\r\n  (  118.26  -238.19     0.66     1.85)  ;  5, 2\r\n  (  415.31  -134.82     0.66     1.85)  ;  5, 3\r\n  (  594.28    55.33     0.66     1.85)  ;  5, 4\r\n  (  710.52   308.23     0.66     1.85)  ;  5, 5\r\n  (  738.19   466.99     0.66     1.85)  ;  5, 6\r\n  (  710.52   585.14     0.66     1.85)  ;  5, 7\r\n  (  658.31   796.42     0.66     1.85)  ;  5, 8\r\n  (  595.58   940.41     0.66     1.85)  ;  5, 9\r\n  (  534.70  1060.40     0.66     1.85)  ;  5, 10\r\n  (  486.73  1163.78     0.66     1.85)  ;  5, 11\r\n  (  449.83  1222.85     0.66     1.85)  ;  5, 12\r\n  (  446.13  1339.15     0.66     1.85)  ;  5, 13\r\n  (  529.00  1437.71     0.66     1.85)  ;  5, 14\r\n  (  626.79  1500.48     0.66     1.85)  ;  5, 15\r\n  (  672.91  1524.48     0.66     1.85)  ;  5, 16\r\n  (  861.11  1587.24     0.66     1.85)  ;  5, 17\r\n)  ;  End of contour\r\n\r\n(\"CellBody\"\r\n  (Color Blue)\r\n  (CellBody)\r\n  (    7.73     8.19    -2.13     0.15)  ;  6, 1\r\n  (    7.07     8.26    -1.81     0.15)  ;  6, 2\r\n  (    5.96     8.63    -0.81     0.15)  ;  6, 3\r\n  (    5.01     9.00     0.13     0.15)  ;  6, 4\r\n  (    4.05     9.29    -0.38     0.15)  ;  6, 5\r\n  (    3.02     9.65    -0.44     0.15)  ;  6, 6\r\n  (    1.76    10.09    -0.44     0.15)  ;  6, 7\r\n  (    0.29    10.53    -0.63     0.15)  ;  6, 8\r\n  (   -0.96    10.82     0.06     0.15)  ;  6, 9\r\n  (   -1.85    10.90     0.69     0.15)  ;  6, 10\r\n  (   -4.20    10.38     0.00     0.15)  ;  6, 11\r\n  (   -6.12     9.80    -0.56     0.15)  ;  6, 12\r\n  (   -7.15     8.92    -0.56     0.15)  ;  6, 13\r\n  (   -8.11     8.34    -0.56     0.15)  ;  6, 14\r\n  (   -8.62     7.53    -0.56     0.15)  ;  6, 15\r\n  (   -9.51     6.73     1.00     0.15)  ;  6, 16\r\n  (  -10.32     5.63     0.56     0.15)  ;  6, 17\r\n  (  -11.50     4.54     1.44     0.15)  ;  6, 18\r\n  (  -12.23     3.44     1.88     0.15)  ;  6, 19\r\n  (  -12.97     2.20     2.50     0.15)  ;  6, 20\r\n  (  -13.86     1.10     2.06     0.15)  ;  6, 21\r\n  (  -14.37    -0.21     2.13     0.15)  ;  6, 22\r\n  (  -14.81    -1.46     2.38     0.15)  ;  6, 23\r\n  (  -14.96    -2.99     2.63     0.15)  ;  6, 24\r\n  (  -14.67    -4.16     4.25     0.15)  ;  6, 25\r\n  (  -14.08    -5.70     4.25     0.15)  ;  6, 26\r\n  (  -13.71    -6.72     4.31     0.15)  ;  6, 27\r\n  (  -12.68    -6.94     4.31     0.15)  ;  6, 28\r\n  (  -11.64    -7.52     3.13     0.15)  ;  6, 29\r\n  (  -10.69    -8.26     2.75     0.15)  ;  6, 30\r\n  (   -9.58    -8.55     2.50     0.15)  ;  6, 31\r\n  (   -8.26    -8.77     1.50     0.15)  ;  6, 32\r\n  (   -7.15    -9.06     1.44     0.15)  ;  6, 33\r\n  (   -6.49    -9.21     1.44     0.15)  ;  6, 34\r\n  (   -5.01    -9.50     0.94     0.15)  ;  6, 35\r\n  (   -3.76    -9.64     0.69     0.15)  ;  6, 36\r\n  (   -3.17    -9.79     0.94     0.15)  ;  6, 37\r\n  (   -2.29   -10.16     1.31     0.15)  ;  6, 38\r\n  (   -1.26   -10.67     1.38     0.15)  ;  6, 39\r\n  (   -0.45   -10.89     1.38     0.15)  ;  6, 40\r\n  (    0.29   -11.25     1.38     0.15)  ;  6, 41\r\n  (    1.10   -11.69     0.44     0.15)  ;  6, 42\r\n  (    1.98   -12.42     0.56     0.15)  ;  6, 43\r\n  (    2.57   -13.01     0.00     0.15)  ;  6, 44\r\n  (    3.38   -14.03    -1.13     0.15)  ;  6, 45\r\n  (    3.97   -14.62    -1.25     0.15)  ;  6, 46\r\n  (    5.01   -15.05    -1.31     0.15)  ;  6, 47\r\n  (    6.18   -15.13    -1.38     0.15)  ;  6, 48\r\n  (    6.99   -14.91    -1.31     0.15)  ;  6, 49\r\n  (    7.51   -14.62    -1.31     0.15)  ;  6, 50\r\n  (    8.39   -14.54    -2.44     0.15)  ;  6, 51\r\n  (    9.20   -14.10    -3.00     0.15)  ;  6, 52\r\n  (    9.65   -13.08    -3.31     0.15)  ;  6, 53\r\n  (    9.87   -11.98    -3.56     0.15)  ;  6, 54\r\n  (    9.72   -10.81    -1.69     0.15)  ;  6, 55\r\n  (    9.65    -9.79    -1.06     0.15)  ;  6, 56\r\n  (    9.72    -9.06     0.69     0.15)  ;  6, 57\r\n  (   10.24    -7.96     1.50     0.15)  ;  6, 58\r\n  (   11.05    -6.72     1.81     0.15)  ;  6, 59\r\n  (   12.15    -5.48     1.75     0.15)  ;  6, 60\r\n  (   12.81    -4.31     2.56     0.15)  ;  6, 61\r\n  (   13.92    -2.12     3.06     0.15)  ;  6, 62\r\n  (   15.39    -0.51     1.63     0.15)  ;  6, 63\r\n  (   16.06     1.03     0.75     0.15)  ;  6, 64\r\n  (   16.06     2.71    -0.56     0.15)  ;  6, 65\r\n  (   15.61     3.95    -2.19     0.15)  ;  6, 66\r\n  (   14.80     4.68    -2.75     0.15)  ;  6, 67\r\n  (   13.40     5.49    -2.94     0.15)  ;  6, 68\r\n  (   12.15     6.14    -2.94     0.15)  ;  6, 69\r\n  (   11.12     6.36    -2.94     0.15)  ;  6, 70\r\n  (    9.94     6.95    -2.88     0.15)  ;  6, 71\r\n  (    8.91     7.53    -3.00     0.15)  ;  6, 72\r\n  (    8.25     7.90    -2.94     0.15)  ;  6, 73\r\n)  ;  End of contour\r\n\r\n( (Color Cyan)\r\n  (Axon)\r\n  (  -10.14    -2.94   -12.75     1.33)  ; Root\r\n  (  -11.25    -2.86   -15.19     1.33)  ; 1, R\r\n  (  -11.69    -1.62   -16.75     1.25)  ; 2\r\n  (  -11.17    -0.38   -17.56     1.25)  ; 3\r\n  (  -10.36     1.16   -21.25     1.03)  ; 4\r\n  (  -10.88     1.45   -26.63     0.88)  ; 5\r\n  (  -11.61     2.18   -29.19     0.88)  ; 6\r\n  (  -12.94     2.84   -34.94     1.03)  ; 7\r\n  (  -14.56     3.50   -39.38     1.03)  ; 8\r\n  (  -15.30     4.15   -42.63     0.74)  ; 9\r\n   Incomplete\r\n)  ;  End of tree\r\n\r\n( (Color Yellow)\r\n  (Dendrite)\r\n  (   -6.05     8.56    -6.00     3.46)  ; Root\r\n  (   -6.71     9.22    -5.75     2.58)  ; 1, R\r\n  (   -7.22     9.95    -5.38     1.84)  ; 2\r\n  (   -7.74    10.75    -5.38     1.84)  ; 3\r\n  (   -8.33    11.41    -5.38     1.84)  ; 4\r\n  (   -8.77    11.85    -5.38     1.99)  ; 5\r\n  (   -9.29    12.36    -4.81     1.84)  ; 6\r\n  (   -9.80    12.94    -3.69     1.99)  ; 7\r\n  (  -10.10    13.60    -2.25     2.14)  ; 8\r\n  (  -10.54    14.19    -2.56     2.36)  ; 9\r\n  (  -10.76    15.14    -0.94     2.21)  ; 10\r\n  (  -10.98    16.52     0.63     2.06)  ; 11\r\n  (  -11.28    16.96     2.06     1.62)  ; 12\r\n  (  -11.72    18.06     2.94     1.62)  ; 13\r\n  (  -11.79    19.08     3.81     1.69)  ; 14\r\n  (  -11.42    19.38     4.50     1.92)  ; 15\r\n  (  -11.64    20.33     4.81     2.14)  ; 16\r\n  (  -11.79    21.28     4.94     2.21)  ; 17\r\n  (  -11.79    22.37     5.25     2.73)  ; 18\r\n  (  -11.87    22.96     5.25     3.17)  ; 19\r\n  (\r\n    (  -11.06    23.47     6.50     1.77)  ; 1, R-1\r\n    (  -10.69    23.98     6.56     1.11)  ; 2\r\n    (  -10.47    24.57     6.63     0.96)  ; 3\r\n    (  -10.10    25.08     6.81     0.74)  ; 4\r\n    (   -9.88    25.52     7.06     0.74)  ; 5\r\n    (   -9.51    26.10     6.75     1.03)  ; 6\r\n    (   -8.99    26.98     6.19     1.25)  ; 7\r\n    (   -8.70    27.42     5.81     1.03)  ; 8\r\n    (   -8.18    28.00     5.69     1.11)  ; 9\r\n    (   -7.74    28.73     5.69     1.55)  ; 10\r\n    (   -7.39    29.42     5.88     0.96)  ; 11\r\n    (   -6.87    30.66     6.50     0.74)  ; 12\r\n    (   -6.95    31.76     6.25     0.96)  ; 13\r\n    (   -7.24    32.93     6.00     1.11)  ; 14\r\n    (   -7.54    33.88     6.06     0.81)  ; 15\r\n    (   -7.46    34.97     6.38     0.66)  ; 16\r\n    (   -7.39    36.14     6.06     1.69)  ; 17\r\n    (   -7.39    36.65     6.13     1.92)  ; 18\r\n    (   -7.39    36.95     6.38     1.77)  ; 19\r\n    (\r\n      (   -7.98    37.53     5.94     0.74)  ; 1, R-1-1\r\n      (   -8.27    38.12     6.31     0.44)  ; 2\r\n      (   -8.42    38.33     6.44     0.44)  ; 3\r\n      (   -8.42    38.70     6.06     0.44)  ; 4\r\n      (   -8.20    38.77     6.06     0.44)  ; 5\r\n      (   -7.98    39.07     5.44     0.66)  ; 6\r\n      (   -7.61    39.14     4.13     0.96)  ; 7\r\n      (   -6.87    39.21     2.63     1.18)  ; 8\r\n      (   -6.50    39.21     2.25     1.47)  ; 9\r\n      (   -5.84    39.14     1.88     1.47)  ; 10\r\n      (   -5.25    39.28     1.38     1.11)  ; 11\r\n      (   -4.52    39.87     1.38     0.74)  ; 12\r\n      (   -4.37    40.38     1.88     0.59)  ; 13\r\n      (   -4.37    41.11     1.50     0.59)  ; 14\r\n      (   -4.29    42.21     1.50     0.74)  ; 15\r\n      (   -4.07    43.23     1.69     0.66)  ; 16\r\n      (   -4.07    44.11     1.88     0.66)  ; 17\r\n      (   -3.78    45.42     1.88     1.25)  ; 18\r\n      (   -3.56    46.16     2.13     1.25)  ; 19\r\n      (   -3.56    47.11     1.38     1.25)  ; 20\r\n      (   -3.63    48.13     1.31     0.88)  ; 21\r\n      (   -3.63    48.86     1.06     0.59)  ; 22\r\n      (   -3.26    49.59     0.94     0.44)  ; 23\r\n      (   -2.75    50.18     0.50     0.81)  ; 24\r\n      (   -2.16    51.05     0.38     0.96)  ; 25\r\n      (   -1.79    51.49     0.38     0.96)  ; 26\r\n      (   -1.57    52.08     0.19     0.59)  ; 27\r\n      (   -1.20    52.59     0.19     0.44)  ; 28\r\n      (   -0.76    52.88     0.19     0.44)  ; 29\r\n      (   -0.46    53.25     0.13     0.44)  ; 30\r\n      (   -0.17    53.39     0.13     0.44)  ; 31\r\n      (   -0.17    53.90     0.13     0.44)  ; 32\r\n      (   -0.10    54.56     0.06     0.44)  ; 33\r\n      (   -0.76    55.15    -0.06     0.59)  ; 34\r\n      (   -1.27    55.51    -0.69     0.66)  ; 35\r\n      (   -1.64    55.88    -0.69     0.66)  ; 36\r\n      (   -2.01    56.54    -0.69     0.66)  ; 37\r\n      (   -2.08    57.27    -0.81     0.52)  ; 38\r\n      (   -1.79    57.92    -1.06     0.52)  ; 39\r\n      (   -1.34    58.54    -1.44     1.11)  ; 40\r\n      (   -0.75    58.98    -1.56     1.55)  ; 41\r\n      (   -0.16    59.27    -1.69     1.69)  ; 42\r\n      (    0.28    59.71    -1.81     0.96)  ; 43\r\n      (    1.02    60.37    -1.81     0.66)  ; 44\r\n      (    1.17    61.39    -2.00     0.44)  ; 45\r\n      (    0.80    62.20    -2.25     0.88)  ; 46\r\n      (    0.58    63.00    -2.38     0.74)  ; 47\r\n      (    0.36    63.88    -2.44     0.59)  ; 48\r\n      (    0.21    64.76    -3.19     0.59)  ; 49\r\n      (    0.21    65.56    -3.38     0.59)  ; 50\r\n      (    0.14    66.51    -3.38     0.74)  ; 51\r\n      (   -0.16    67.39    -3.38     0.52)  ; 52\r\n      (   -0.30    67.90    -3.38     0.52)  ; 53\r\n      (   -0.67    68.19    -3.38     0.44)  ; 54\r\n      (   -0.97    67.97    -3.38     0.44)  ; 55\r\n      (   -1.12    68.34    -3.38     0.44)  ; 56\r\n      (   -0.82    68.70    -3.44     0.44)  ; 57\r\n      (   -0.60    69.29    -3.56     0.66)  ; 58\r\n      (   -0.38    69.95    -3.56     0.96)  ; 59\r\n      (   -0.30    70.24    -3.56     0.96)  ; 60\r\n      (   -0.23    70.39    -3.56     0.59)  ; 61\r\n      (   -0.08    70.75    -3.56     0.37)  ; 62\r\n      (   -0.75    71.63    -3.56     0.22)  ; 63\r\n      (   -1.04    72.29    -3.63     0.22)  ; 64\r\n      (   -1.48    73.31    -3.81     0.22)  ; 65\r\n      (   -1.78    74.11    -3.81     0.22)  ; 66\r\n      (   -1.85    74.92    -3.38     0.74)  ; 67\r\n      (   -1.70    75.50    -3.69     1.40)  ; 68\r\n      (   -1.63    75.72    -3.69     2.21)  ; 69\r\n      (   -1.63    76.16    -3.69     2.58)  ; 70\r\n      (   -1.56    76.82    -3.81     1.69)  ; 71\r\n      (   -1.41    77.33    -3.88     0.88)  ; 72\r\n      (   -1.26    77.77    -3.88     0.37)  ; 73\r\n      (   -1.12    78.21    -3.88     0.37)  ; 74\r\n      (   -0.60    78.65    -3.94     0.37)  ; 75\r\n      (   -0.30    79.08    -4.13     0.37)  ; 76\r\n      (   -0.01    79.52    -4.19     0.37)  ; 77\r\n      (    0.28    80.03    -4.19     0.37)  ; 78\r\n      (    0.51    80.62    -4.25     0.81)  ; 79\r\n      (    0.80    81.28    -4.50     0.88)  ; 80\r\n      (    1.09    81.93    -4.50     0.59)  ; 81\r\n      (    1.09    82.59    -4.50     0.37)  ; 82\r\n      (    1.09    83.10    -4.69     0.22)  ; 83\r\n      (    0.95    83.84    -4.69     0.52)  ; 84\r\n      (    0.95    84.13    -4.81     1.33)  ; 85\r\n      (    1.02    84.71    -4.50     1.99)  ; 86\r\n      (    1.02    85.00    -4.44     0.81)  ; 87\r\n      (    1.09    85.37    -4.31     0.52)  ; 88\r\n      (    1.17    85.59    -4.06     0.37)  ; 89\r\n      (    1.39    86.32    -4.00     0.22)  ; 90\r\n      (    1.98    86.61    -3.81     0.22)  ; 91\r\n      (    2.28    87.42    -3.81     0.37)  ; 92\r\n      (    2.36    87.78    -3.88     0.66)  ; 93\r\n      (    2.36    88.22    -3.94     0.66)  ; 94\r\n      (    2.21    89.02    -4.06     0.44)  ; 95\r\n      (    2.14    89.54    -3.31     0.52)  ; 96\r\n      (    1.92    90.34    -2.56     0.66)  ; 97\r\n      (    1.62    91.07    -2.19     0.52)  ; 98\r\n      (    1.40    91.95    -2.19     0.37)  ; 99\r\n      (    1.40    92.61    -2.13     0.59)  ; 100\r\n      (    1.62    93.26    -2.06     0.96)  ; 101\r\n      (    1.70    94.00    -1.81     1.33)  ; 102\r\n      (    1.70    94.65    -1.69     1.92)  ; 103\r\n      (    1.84    95.09    -1.69     2.58)  ; 104\r\n      (    2.14    95.38    -1.25     2.87)  ; 105\r\n      (    2.21    95.82    -1.56     2.14)  ; 106\r\n      (    2.21    96.48    -1.69     1.40)  ; 107\r\n      (    2.36    97.21    -1.81     0.66)  ; 108\r\n      (    2.43    97.50    -1.81     0.37)  ; 109\r\n      (    2.65    98.02    -1.81     0.22)  ; 110\r\n      (    2.73    98.53    -1.81     0.52)  ; 111\r\n      (    2.87    99.19    -1.81     1.11)  ; 112\r\n      (    2.87    99.70    -1.81     1.33)  ; 113\r\n      (    3.02   100.06    -1.81     0.81)  ; 114\r\n      (    3.10   100.35    -1.81     0.52)  ; 115\r\n      (    3.32   101.45    -2.19     0.29)  ; 116\r\n      (    3.10   102.26    -2.50     0.29)  ; 117\r\n      (    2.87   102.84    -1.38     0.52)  ; 118\r\n      (    2.87   103.35    -1.38     1.25)  ; 119\r\n      (    2.95   103.79    -1.19     1.99)  ; 120\r\n      (    2.87   104.59    -1.00     2.73)  ; 121\r\n      (    2.95   104.78    -1.00     2.73)  ; 122\r\n      (\r\n        (    3.10   105.47    -0.81     2.06)  ; 1, R-1-1-1\r\n        (    3.17   106.13    -0.94     1.18)  ; 2\r\n        (    3.24   106.50    -0.88     0.52)  ; 3\r\n        (    3.32   107.15    -0.81     0.22)  ; 4\r\n        (    3.68   108.10    -0.81     0.59)  ; 5\r\n        (    3.76   108.40    -0.81     0.59)  ; 6\r\n        (    3.91   109.13    -0.81     0.44)  ; 7\r\n        (    4.20   109.42    -0.81     0.44)  ; 8\r\n        (    4.42   109.86    -0.81     0.44)  ; 9\r\n        (    4.57   110.22    -0.81     0.29)  ; 10\r\n        (    4.27   110.66    -0.81     0.29)  ; 11\r\n        (    3.98   111.10    -0.69     0.29)  ; 12\r\n        (    4.13   111.69    -0.06     0.81)  ; 13\r\n        (    4.13   112.27     0.00     1.25)  ; 14\r\n        (    4.35   112.85     0.00     1.55)  ; 15\r\n        (    4.27   113.29     0.06     1.25)  ; 16\r\n        (    4.35   113.95     0.06     0.66)  ; 17\r\n        (    4.42   114.76     0.06     0.37)  ; 18\r\n        (    4.64   115.05     0.25     0.22)  ; 19\r\n        (    4.72   115.41     0.06     0.22)  ; 20\r\n        (    4.76   116.19    -0.44     0.22)  ; 21\r\n        (    4.76   116.77    -0.50     0.22)  ; 22\r\n        (    4.98   117.29    -0.81     0.44)  ; 23\r\n        (    4.91   117.51    -0.88     0.44)  ; 24\r\n        (    4.62   117.80    -1.06     0.44)  ; 25\r\n        (    4.32   118.38    -1.13     0.59)  ; 26\r\n        (    4.03   118.67    -1.44     0.59)  ; 27\r\n        (    3.66   119.19    -1.63     0.59)  ; 28\r\n        (    3.36   119.48    -1.81     0.59)  ; 29\r\n        (    3.14   120.14    -2.25     0.59)  ; 30\r\n        (    3.14   121.31    -2.19     0.59)  ; 31\r\n        (    3.51   122.11    -2.06     1.40)  ; 32\r\n        (    3.81   123.13    -2.25     1.92)  ; 33\r\n        (    4.10   123.72    -2.31     1.55)  ; 34\r\n        (    4.10   124.45    -2.31     0.88)  ; 35\r\n        (    4.10   125.03    -2.31     0.59)  ; 36\r\n        (    4.10   125.40    -2.31     0.29)  ; 37\r\n        (    4.25   125.98    -2.31     0.22)  ; 38\r\n        (    3.73   126.50    -2.94     0.22)  ; 39\r\n        (    3.88   127.01    -2.00     1.18)  ; 40\r\n        (    3.88   127.52    -1.81     1.55)  ; 41\r\n        (    4.03   127.96    -1.69     1.33)  ; 42\r\n        (    4.03   128.40    -1.38     0.74)  ; 43\r\n        (    4.03   128.84    -0.81     0.44)  ; 44\r\n        (    4.32   129.71    -0.81     0.22)  ; 45\r\n        (    4.32   130.52    -0.69     0.22)  ; 46\r\n        (    4.03   130.96    -0.31     0.22)  ; 47\r\n        (    3.88   131.32    -0.25     0.22)  ; 48\r\n        (    3.81   132.05    -0.13     0.22)  ; 49\r\n        (    3.81   132.42    -0.06     0.44)  ; 50\r\n        (    3.88   133.15    -0.06     0.66)  ; 51\r\n        (    4.25   134.03     0.56     0.29)  ; 52\r\n        (    4.25   134.54     0.81     0.29)  ; 53\r\n        (    4.62   134.83     2.00     0.52)  ; 54\r\n        (    5.13   135.20     2.25     0.52)  ; 55\r\n        (    5.35   136.22     2.25     0.52)  ; 56\r\n        (    5.50   136.80     2.25     0.52)  ; 57\r\n        (    5.80   137.83     2.63     0.44)  ; 58\r\n        (    5.72   138.63     2.69     0.44)  ; 59\r\n        (    5.57   139.22     2.69     0.44)  ; 60\r\n        (    5.72   140.09     2.69     0.44)  ; 61\r\n        (    5.87   140.60     2.63     0.44)  ; 62\r\n        (    6.09   140.90     2.63     0.52)  ; 63\r\n        (    6.24   141.99     2.63     0.59)  ; 64\r\n        (    6.24   143.38     1.31     0.22)  ; 65\r\n        (    6.24   143.82     1.13     0.22)  ; 66\r\n        (    5.87   144.33     1.06     0.44)  ; 67\r\n        (    5.66   144.82    -0.25     0.44)  ; 68\r\n        (    5.58   145.34    -0.56     0.29)  ; 69\r\n        (    5.58   145.92    -0.88     0.29)  ; 70\r\n        (    5.44   146.29    -0.94     0.29)  ; 71\r\n        (    5.36   146.72    -0.75     0.29)  ; 72\r\n        (    5.22   146.94    -0.44     0.37)  ; 73\r\n         Low\r\n      |\r\n        (    2.14   104.56     4.25     0.52)  ; 1, R-1-1-2\r\n        (    1.77   103.97     6.00     0.44)  ; 2\r\n        (    1.99   103.02     6.25     0.44)  ; 3\r\n        (    1.25   102.51     6.31     0.44)  ; 4\r\n        (    1.03   102.51     6.31     0.44)  ; 5\r\n        (    0.37   102.44     6.31     0.44)  ; 6\r\n        (   -0.44   101.56     6.50     0.44)  ; 7\r\n        (   -0.52   100.98     7.31     0.44)  ; 8\r\n        (   -1.33   100.68     7.44     0.44)  ; 9\r\n        (   -2.28   100.10     8.94     0.44)  ; 10\r\n        (   -2.65    99.51     9.06     0.44)  ; 11\r\n        (   -3.09    98.86     8.69     0.37)  ; 12\r\n        (   -3.68    98.13     8.56     0.37)  ; 13\r\n        (   -4.49    97.39     8.56     0.37)  ; 14\r\n        (   -4.49    96.30     8.19     0.37)  ; 15\r\n        (   -4.27    95.42     7.75     0.37)  ; 16\r\n        (   -4.35    94.69     7.75     0.66)  ; 17\r\n        (   -4.49    93.67     8.13     0.52)  ; 18\r\n        (   -4.79    92.94     8.56     0.81)  ; 19\r\n        (   -5.16    92.50     6.69     0.52)  ; 20\r\n        (   -5.89    91.33     6.50     0.37)  ; 21\r\n        (   -6.85    90.60     6.50     0.37)  ; 22\r\n        (   -7.22    89.43     6.31     0.37)  ; 23\r\n        (   -7.66    88.26     6.25     0.37)  ; 24\r\n        (   -7.66    87.89     6.19     0.66)  ; 25\r\n        (   -7.15    87.16     8.81     0.52)  ; 26\r\n        (   -7.74    85.92     8.94     0.52)  ; 27\r\n        (   -8.10    85.11     9.00     0.44)  ; 28\r\n        (   -8.91    84.68     9.00     0.81)  ; 29\r\n        (   -9.36    84.09     9.69     0.59)  ; 30\r\n        (   -9.43    82.92     9.88     0.59)  ; 31\r\n        (   -8.77    81.90     9.88     0.44)  ; 32\r\n        (   -8.69    81.02    10.00     0.44)  ; 33\r\n        (   -8.18    80.36    10.06     0.74)  ; 34\r\n        (   -7.78    79.18    11.31     0.44)  ; 35\r\n        (   -7.27    78.74    13.56     0.29)  ; 36\r\n         Incomplete\r\n      )  ;  End of split\r\n    |\r\n      (   -6.37    37.81     7.63     0.88)  ; 1, R-1-2\r\n      (   -5.78    38.54     8.56     0.74)  ; 2\r\n      (   -5.78    39.20     9.81     0.88)  ; 3\r\n      (   -6.15    39.71    11.00     0.88)  ; 4\r\n      (   -6.22    40.08    11.06     0.88)  ; 5\r\n      (   -6.44    40.59    11.25     0.52)  ; 6\r\n      (   -6.96    41.25    11.63     0.66)  ; 7\r\n      (   -7.18    41.98    12.56     0.88)  ; 8\r\n      (   -7.55    42.56    13.06     1.11)  ; 9\r\n      (   -7.99    43.15    13.69     0.74)  ; 10\r\n      (   -8.28    43.95    14.94     0.66)  ; 11\r\n      (   -8.14    45.12    15.19     0.37)  ; 12\r\n      (   -7.62    45.56    15.50     0.59)  ; 13\r\n      (   -7.18    46.00    15.44     0.66)  ; 14\r\n      (   -6.74    46.58    15.25     0.66)  ; 15\r\n      (   -6.81    47.17    14.94     0.52)  ; 16\r\n      (   -7.47    47.90    14.94     0.37)  ; 17\r\n      (   -8.06    48.26    14.94     0.88)  ; 18\r\n      (   -8.43    48.70    14.88     1.18)  ; 19\r\n      (   -8.95    49.51    14.25     1.40)  ; 20\r\n      (   -8.95    49.95    14.75     0.81)  ; 21\r\n      (   -8.58    50.82    14.19     0.37)  ; 22\r\n      (   -8.50    51.33    13.94     0.37)  ; 23\r\n      (   -8.65    52.14    13.88     0.37)  ; 24\r\n      (   -8.65    52.36    13.63     0.52)  ; 25\r\n      (   -9.61    52.65    13.50     0.52)  ; 26\r\n      (  -10.42    53.02    13.31     0.44)  ; 27\r\n      (  -11.45    53.60    13.31     0.37)  ; 28\r\n      (  -12.04    53.75    13.31     0.37)  ; 29\r\n      (  -12.33    53.75    13.31     0.37)  ; 30\r\n      (  -12.78    54.33    13.19     0.52)  ; 31\r\n       Low\r\n    )  ;  End of split\r\n  |\r\n    (  -12.42    23.74     4.81     2.14)  ; 1, R-2\r\n    (  -12.78    24.25     4.63     1.40)  ; 2\r\n    (  -13.37    24.69     3.94     1.18)  ; 3\r\n    (  -13.89    24.69     3.25     1.03)  ; 4\r\n    (  -14.26    24.62     2.63     1.03)  ; 5\r\n    (  -14.55    24.18     1.63     1.03)  ; 6\r\n    (  -14.92    23.89     1.00     1.03)  ; 7\r\n    (  -15.14    24.04     0.38     1.03)  ; 8\r\n    (  -15.21    24.40    -0.50     1.25)  ; 9\r\n    (  -15.29    24.55    -1.06     1.62)  ; 10\r\n    (  -15.29    25.13    -1.81     1.62)  ; 11\r\n    (  -15.36    25.86    -2.13     1.47)  ; 12\r\n    (  -15.14    26.59    -2.38     1.62)  ; 13\r\n    (  -15.07    27.11    -2.38     1.69)  ; 14\r\n    (\r\n      (  -14.40    27.62    -2.94     0.88)  ; 1, R-2-1\r\n      (  -13.89    28.20    -3.19     0.88)  ; 2\r\n      (  -13.37    28.86    -3.31     1.03)  ; 3\r\n      (  -13.00    29.81    -3.81     1.11)  ; 4\r\n      (  -12.34    30.83    -4.00     1.11)  ; 5\r\n      (  -11.90    31.86    -4.06     0.88)  ; 6\r\n      (  -11.68    32.73    -4.44     1.03)  ; 7\r\n      (  -12.12    33.83    -3.75     0.96)  ; 8\r\n      (  -12.19    34.56    -3.75     1.11)  ; 9\r\n      (  -12.19    35.15    -3.75     1.11)  ; 10\r\n      (  -12.56    35.95    -3.69     0.96)  ; 11\r\n      (  -12.71    36.83    -3.56     0.96)  ; 12\r\n      (  -12.49    37.56    -3.69     1.47)  ; 13\r\n      (  -12.19    38.44    -3.94     1.33)  ; 14\r\n      (  -12.19    39.31    -4.44     1.11)  ; 15\r\n      (  -12.05    40.63    -5.19     1.18)  ; 16\r\n      (  -11.83    41.80    -5.63     1.11)  ; 17\r\n      (  -11.09    42.60    -6.69     1.03)  ; 18\r\n      (  -10.35    43.48    -7.56     0.96)  ; 19\r\n      (   -9.84    43.92    -8.38     0.81)  ; 20\r\n      (   -9.32    44.36    -8.75     0.74)  ; 21\r\n      (   -9.10    45.67    -9.56     0.88)  ; 22\r\n      (   -9.10    46.48    -9.06     1.18)  ; 23\r\n      (   -9.47    47.57    -9.00     1.03)  ; 24\r\n      (   -9.69    48.45    -9.00     1.11)  ; 25\r\n      (  -10.13    49.84    -9.00     1.18)  ; 26\r\n      (  -10.35    50.72    -9.13     1.18)  ; 27\r\n      (  -10.50    52.18    -9.13     1.18)  ; 28\r\n      (  -10.85    52.57    -9.25     1.25)  ; 29\r\n      (  -11.59    53.37    -9.38     1.25)  ; 30\r\n      (  -12.40    54.18    -9.56     1.25)  ; 31\r\n      (  -12.99    55.13    -9.25     1.11)  ; 32\r\n      (  -13.21    56.52    -8.75     1.18)  ; 33\r\n      (  -13.36    57.61    -8.50     1.18)  ; 34\r\n      (  -13.80    58.64    -8.31     1.11)  ; 35\r\n      (  -14.24    59.00    -8.38     1.11)  ; 36\r\n      (  -14.98    59.73    -8.50     0.96)  ; 37\r\n      (  -15.57    60.17    -8.50     0.96)  ; 38\r\n      (  -16.38    60.68    -8.50     0.74)  ; 39\r\n      (  -16.89    60.76    -8.50     0.74)  ; 40\r\n      (  -17.63    60.83    -8.50     0.96)  ; 41\r\n      (  -18.15    61.34    -8.50     0.81)  ; 42\r\n      (  -18.81    62.37    -8.25     0.74)  ; 43\r\n      (  -18.44    63.32    -8.75     0.88)  ; 44\r\n      (  -17.92    64.41    -9.13     0.96)  ; 45\r\n      (  -17.63    65.36    -9.31     0.81)  ; 46\r\n      (  -17.41    66.09   -10.50     0.66)  ; 47\r\n      (  -17.48    66.82   -11.88     0.88)  ; 48\r\n      (  -17.63    67.04   -12.56     0.88)  ; 49\r\n      (  -18.07    67.19   -12.81     0.88)  ; 50\r\n      (  -18.44    67.34   -12.88     0.88)  ; 51\r\n      (  -18.66    67.34   -13.69     0.88)  ; 52\r\n      (  -19.10    66.90   -14.06     0.88)  ; 53\r\n      (  -19.32    66.75   -14.06     0.88)  ; 54\r\n      (  -19.69    66.39   -15.13     0.96)  ; 55\r\n      (  -20.43    65.36   -15.38     0.88)  ; 56\r\n      (  -20.87    64.92   -15.88     0.66)  ; 57\r\n      (  -21.31    65.00   -16.44     0.66)  ; 58\r\n      (  -21.61    65.44   -17.00     0.66)  ; 59\r\n      (  -21.90    65.80   -18.06     0.66)  ; 60\r\n      (  -21.90    66.46   -18.25     1.11)  ; 61\r\n      (  -22.27    67.12   -18.81     1.11)  ; 62\r\n      (  -22.49    67.85   -19.31     1.33)  ; 63\r\n      (  -22.71    68.58   -20.06     1.33)  ; 64\r\n      (  -22.86    69.24   -20.56     1.33)  ; 65\r\n      (  -23.01    69.68   -21.00     1.11)  ; 66\r\n      (  -23.30    70.33   -21.56     1.03)  ; 67\r\n      (  -23.45    70.63   -22.19     1.03)  ; 68\r\n      (  -23.45    70.92   -22.88     0.96)  ; 69\r\n      (  -23.38    71.14   -23.56     0.96)  ; 70\r\n      (  -22.93    71.21   -23.94     0.96)  ; 71\r\n      (  -22.35    70.99   -24.13     0.96)  ; 72\r\n      (  -21.68    70.92   -24.44     0.96)  ; 73\r\n      (  -22.27    71.14   -25.06     0.88)  ; 74\r\n      (  -22.86    71.28   -25.13     0.88)  ; 75\r\n      (  -23.52    71.72   -25.38     0.96)  ; 76\r\n      (  -23.74    71.94   -25.38     0.96)  ; 77\r\n      (  -24.70    72.09   -26.13     1.18)  ; 78\r\n      (  -25.22    72.45   -26.38     1.18)  ; 79\r\n      (  -25.73    73.33   -26.63     1.03)  ; 80\r\n      (  -25.96    74.06   -27.06     1.33)  ; 81\r\n      (  -25.96    74.87   -27.69     1.33)  ; 82\r\n      (  -26.10    75.96   -28.19     0.81)  ; 83\r\n      (  -26.03    76.55   -28.25     0.59)  ; 84\r\n      (  -25.81    77.72   -28.38     0.44)  ; 85\r\n      (  -25.59    78.15   -28.63     0.44)  ; 86\r\n      (  -25.44    78.96   -28.75     0.88)  ; 87\r\n      (  -25.29    79.47   -29.31     1.55)  ; 88\r\n      (  -25.22    79.84   -29.63     1.92)  ; 89\r\n      (  -24.92    80.57   -29.63     1.55)  ; 90\r\n      (  -24.92    81.15   -29.63     1.69)  ; 91\r\n      (  -24.78    82.24   -30.56     1.18)  ; 92\r\n      (  -24.78    83.41   -31.38     1.11)  ; 93\r\n      (  -24.70    84.22   -32.00     0.96)  ; 94\r\n      (  -24.63    85.09   -32.25     0.74)  ; 95\r\n      (  -24.12    86.04   -32.63     0.66)  ; 96\r\n      (  -24.12    86.41   -33.00     0.66)  ; 97\r\n      (  -24.26    86.85   -33.44     0.88)  ; 98\r\n      (  -24.56    87.36   -34.88     1.11)  ; 99\r\n      (  -24.78    88.02   -35.44     1.40)  ; 100\r\n      (  -24.85    88.46   -36.81     1.77)  ; 101\r\n      (  -24.85    88.97   -37.25     2.21)  ; 102\r\n      (  -24.85    89.70   -37.63     2.58)  ; 103\r\n      (  -24.85    90.06   -38.06     2.36)  ; 104\r\n      (  -24.93    90.50   -38.13     1.92)  ; 105\r\n      (  -25.22    91.31   -38.44     1.25)  ; 106\r\n      (  -25.37    91.67   -38.63     0.66)  ; 107\r\n      (  -25.59    92.40   -38.75     0.44)  ; 108\r\n      (  -25.22    92.92   -39.25     0.44)  ; 109\r\n      (  -24.93    93.21   -39.38     0.44)  ; 110\r\n      (  -24.78    93.87   -39.75     0.44)  ; 111\r\n      (  -25.07    94.60   -39.69     0.74)  ; 112\r\n      (  -25.81    95.40   -40.13     1.11)  ; 113\r\n      (  -26.18    96.21   -40.31     1.18)  ; 114\r\n      (  -26.47    96.94   -39.25     0.96)  ; 115\r\n      (  -26.91    97.45   -39.38     0.81)  ; 116\r\n      (  -27.28    97.74   -39.56     0.52)  ; 117\r\n      (  -27.80    98.25   -39.69     0.44)  ; 118\r\n      (  -28.31    98.98   -39.75     0.44)  ; 119\r\n      (  -29.35   100.01   -38.94     0.37)  ; 120\r\n      (  -30.08   100.66   -38.63     0.96)  ; 121\r\n      (  -30.23   101.18   -38.50     1.69)  ; 122\r\n      (  -30.67   101.40   -38.44     2.06)  ; 123\r\n      (  -31.19   101.76   -38.19     2.36)  ; 124\r\n      (  -32.00   102.27   -38.13     1.77)  ; 125\r\n      (  -32.51   102.64   -38.06     0.81)  ; 126\r\n      (  -33.47   103.22   -37.81     0.29)  ; 127\r\n      (  -33.99   103.51   -37.81     0.29)  ; 128\r\n      (  -34.95   104.25   -37.63     0.29)  ; 129\r\n      (  -36.05   104.90   -37.38     0.29)  ; 130\r\n      (  -37.30   105.78   -37.75     0.52)  ; 131\r\n      (  -38.19   106.44   -37.75     1.11)  ; 132\r\n      (  -38.85   107.02   -38.38     1.47)  ; 133\r\n      (  -39.29   107.32   -38.69     1.92)  ; 134\r\n      (  -39.88   107.39   -39.06     1.55)  ; 135\r\n      (  -40.32   107.68   -39.63     1.18)  ; 136\r\n      (  -40.62   107.97   -39.88     0.52)  ; 137\r\n      (  -41.21   108.27   -40.06     0.37)  ; 138\r\n      (  -42.75   109.51   -40.50     0.29)  ; 139\r\n      (  -43.49   110.31   -41.31     0.29)  ; 140\r\n      (  -44.15   110.90   -41.38     0.96)  ; 141\r\n      (  -44.79   111.51   -42.25     1.62)  ; 142\r\n      (  -45.16   111.87   -43.00     1.33)  ; 143\r\n      (  -45.68   112.39   -43.75     0.88)  ; 144\r\n      (  -46.05   112.82   -44.19     0.59)  ; 145\r\n      (  -46.34   113.26   -44.56     0.29)  ; 146\r\n      (  -46.64   113.63   -44.81     0.29)  ; 147\r\n      (  -47.23   113.92   -45.19     0.29)  ; 148\r\n      (  -47.81   114.43   -45.19     0.74)  ; 149\r\n      (  -47.81   114.65   -45.88     1.18)  ; 150\r\n      (  -48.11   115.09   -46.13     1.62)  ; 151\r\n      (  -48.63   115.53   -46.94     2.43)  ; 152\r\n      (  -48.85   115.68   -48.13     2.87)  ; 153\r\n      (  -48.99   115.82   -49.06     2.06)  ; 154\r\n      (  -49.44   116.04   -49.69     1.69)  ; 155\r\n      (  -49.73   116.19   -50.38     1.25)  ; 156\r\n      (  -50.02   116.48   -51.00     0.88)  ; 157\r\n      (  -50.02   116.48   -53.31     0.66)  ; 158\r\n       Low\r\n    |\r\n      (  -16.06    28.04    -1.06     0.88)  ; 1, R-2-2\r\n      (  -16.50    28.77    -0.56     1.03)  ; 2\r\n      (  -16.43    29.58     0.19     1.03)  ; 3\r\n      (  -16.20    30.09     0.75     0.88)  ; 4\r\n      (  -15.76    30.82     1.25     0.88)  ; 5\r\n      (  -15.54    31.55     1.63     1.03)  ; 6\r\n      (  -15.76    32.72     1.94     0.74)  ; 7\r\n      (  -15.62    33.53     2.19     0.88)  ; 8\r\n      (  -15.54    34.40     2.38     1.18)  ; 9\r\n      (  -15.54    34.84     3.00     1.25)  ; 10\r\n      (  -15.54    35.79     1.25     1.25)  ; 11\r\n      (  -15.76    36.60     0.94     1.25)  ; 12\r\n      (  -16.20    37.69     0.94     1.03)  ; 13\r\n      (  -16.79    38.35     0.50     1.03)  ; 14\r\n      (  -17.68    38.50     0.38     0.74)  ; 15\r\n      (  -18.34    38.64     0.19     0.74)  ; 16\r\n      (  -19.08    38.93     0.06     0.74)  ; 17\r\n      (  -19.74    39.52     0.06     0.52)  ; 18\r\n      (  -20.40    39.30     0.06     0.52)  ; 19\r\n      (  -21.14    39.08     1.50     0.81)  ; 20\r\n      (  -22.02    39.59     2.44     0.88)  ; 21\r\n      (  -22.76    40.54     3.56     0.81)  ; 22\r\n      (  -23.13    41.35     4.19     0.96)  ; 23\r\n      (  -23.35    42.37     4.31     1.18)  ; 24\r\n      (  -23.65    43.25     4.31     1.18)  ; 25\r\n      (  -24.09    43.98     4.31     0.96)  ; 26\r\n      (  -25.05    44.93     4.44     0.81)  ; 27\r\n      (  -26.08    45.73     5.00     1.03)  ; 28\r\n      (  -26.74    46.17     6.00     1.25)  ; 29\r\n      (  -27.48    46.68     6.25     1.55)  ; 30\r\n      (  -27.92    47.49     7.00     0.88)  ; 31\r\n      (  -28.21    48.36     7.88     0.88)  ; 32\r\n      (  -28.14    49.39    10.19     1.18)  ; 33\r\n      (  -27.55    50.19    10.25     0.59)  ; 34\r\n      (  -27.70    51.22    10.75     1.25)  ; 35\r\n      (  -27.70    52.17    11.50     1.03)  ; 36\r\n      (  -27.99    53.26    12.38     0.81)  ; 37\r\n      (  -28.66    54.58    12.75     1.03)  ; 38\r\n      (  -29.17    55.97    13.13     1.03)  ; 39\r\n      (  -29.32    57.43    13.69     0.81)  ; 40\r\n      (  -29.28    59.16    14.88     0.88)  ; 41\r\n      (  -28.98    60.11    15.25     0.88)  ; 42\r\n      (  -28.98    61.13    16.50     0.88)  ; 43\r\n      (  -29.20    62.30    16.19     0.66)  ; 44\r\n      (  -30.02    63.11    16.06     0.74)  ; 45\r\n      (  -30.60    63.47    15.56     1.03)  ; 46\r\n      (  -31.19    63.69    14.25     1.03)  ; 47\r\n      (  -32.30    63.91    13.44     0.96)  ; 48\r\n      (  -32.74    64.20    12.81     0.96)  ; 49\r\n      (  -33.33    64.93    12.63     0.74)  ; 50\r\n      (  -34.51    65.74    13.06     0.96)  ; 51\r\n      (  -35.39    66.25    13.06     1.25)  ; 52\r\n      (  -36.06    66.62    13.13     0.88)  ; 53\r\n      (  -37.31    67.27    12.69     0.59)  ; 54\r\n      (  -38.34    67.71    12.38     0.59)  ; 55\r\n      (  -39.15    68.00    13.44     0.96)  ; 56\r\n      (  -40.03    68.44    14.13     0.74)  ; 57\r\n      (  -41.21    68.30    15.50     0.59)  ; 58\r\n      (  -41.21    68.22    16.13     0.96)  ; 59\r\n      (  -41.95    68.15    17.00     1.40)  ; 60\r\n      (  -41.58    67.86    17.44     1.47)  ; 61\r\n      (  -41.36    66.98    17.50     0.66)  ; 62\r\n      (  -40.99    66.40    17.13     0.44)  ; 63\r\n      (  -41.07    66.10    16.94     0.44)  ; 64\r\n       Low\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color White)\r\n  (Dendrite)\r\n  (    5.62   -13.29    -2.00     3.90)  ; Root\r\n  (    5.62   -14.02    -2.19     3.09)  ; 1, R\r\n  (    5.55   -14.90    -2.19     2.80)  ; 2\r\n  (    5.62   -16.00    -2.44     2.43)  ; 3\r\n  (    5.69   -17.17    -2.44     2.14)  ; 4\r\n  (    6.28   -18.04    -2.69     2.14)  ; 5\r\n  (    7.17   -19.14    -3.19     2.28)  ; 6\r\n  (    7.98   -20.02    -3.56     2.50)  ; 7\r\n  (    8.71   -20.60    -4.13     2.80)  ; 8\r\n  (    9.38   -21.11    -4.31     2.80)  ; 9\r\n  (   10.41   -21.77    -4.31     2.58)  ; 10\r\n  (   11.66   -22.43    -4.69     2.43)  ; 11\r\n  (   12.69   -22.79    -4.94     2.43)  ; 12\r\n  (   13.80   -23.31    -4.06     2.65)  ; 13\r\n  (   15.12   -24.18    -3.63     3.09)  ; 14\r\n  (   16.30   -25.06    -2.69     3.17)  ; 15\r\n  (   17.56   -25.86    -1.63     3.83)  ; 16\r\n  (\r\n    (   17.56   -26.16    -1.50     3.46)  ; 1, R-1\r\n    (   17.70   -27.84    -1.06     2.21)  ; 2\r\n    (   17.76   -28.84    -1.06     2.21)  ; 3\r\n    (\r\n      (   17.85   -29.23     0.00     2.58)  ; 1, R-1-1\r\n      (   17.97   -30.36    -0.38     2.28)  ; 2\r\n      (   17.97   -31.75     0.44     2.28)  ; 3\r\n      (   18.13   -31.84     0.44     2.28)  ; 4\r\n      (\r\n        (   17.83   -32.55     1.50     1.62)  ; 1, R-1-1-1\r\n        (   17.46   -33.35     1.88     1.33)  ; 2\r\n        (   16.57   -34.38     2.69     1.47)  ; 3\r\n        (   15.76   -35.40     3.50     1.55)  ; 4\r\n        (   15.03   -36.42     4.13     1.84)  ; 5\r\n        (   14.44   -37.81     5.13     1.92)  ; 6\r\n        (   13.92   -39.28     6.13     2.95)  ; 7\r\n        (   13.77   -40.59     6.88     3.17)  ; 8\r\n        (\r\n          (   14.14   -41.47     7.19     2.43)  ; 1, R-1-1-1-1\r\n          (   14.22   -42.71     7.31     2.21)  ; 2\r\n          (   14.24   -43.52     7.25     2.06)  ; 3\r\n          (\r\n            (   13.80   -44.62     7.19     1.18)  ; 1, R-1-1-1-1-1\r\n            (   13.43   -45.57     6.94     0.96)  ; 2\r\n            (   13.58   -46.30     6.50     0.96)  ; 3\r\n            (   13.50   -47.03     5.31     1.03)  ; 4\r\n            (   13.58   -48.06     4.69     1.11)  ; 5\r\n            (   14.02   -48.79     3.56     1.33)  ; 6\r\n            (   14.31   -49.44     2.63     1.25)  ; 7\r\n            (   14.39   -50.39     2.50     1.55)  ; 8\r\n            (   14.39   -51.71     1.50     1.84)  ; 9\r\n            (   14.39   -52.59     0.94     1.69)  ; 10\r\n            (   14.54   -53.68     0.56     1.47)  ; 11\r\n            (   14.39   -54.56    -0.50     1.47)  ; 12\r\n            (   14.17   -55.58    -1.63     1.33)  ; 13\r\n            (   14.31   -56.83    -2.44     1.18)  ; 14\r\n            (   14.54   -58.07    -3.56     1.25)  ; 15\r\n            (   14.83   -58.95    -3.94     1.25)  ; 16\r\n            (   15.35   -59.68    -3.56     1.47)  ; 17\r\n            (   15.64   -60.70    -2.81     1.62)  ; 18\r\n            (   16.30   -61.87    -2.88     1.47)  ; 19\r\n            (   16.52   -63.04    -2.88     1.47)  ; 20\r\n            (   16.45   -63.84    -2.94     1.33)  ; 21\r\n            (   16.45   -64.58    -2.94     1.11)  ; 22\r\n            (   16.67   -65.23    -2.94     1.11)  ; 23\r\n            (   16.82   -65.89    -2.94     1.33)  ; 24\r\n            (   17.19   -66.91    -3.19     1.18)  ; 25\r\n            (   17.26   -68.01    -3.19     1.25)  ; 26\r\n            (   17.19   -68.82    -3.31     1.47)  ; 27\r\n            (   17.19   -69.84    -3.31     1.77)  ; 28\r\n            (   16.82   -70.94    -3.50     1.69)  ; 29\r\n            (   16.16   -72.54    -3.81     1.47)  ; 30\r\n            (   15.71   -73.93    -3.94     1.33)  ; 31\r\n            (   15.12   -75.03    -3.94     1.33)  ; 32\r\n            (   14.90   -76.20    -4.06     1.33)  ; 33\r\n            (   14.54   -77.00    -4.44     0.96)  ; 34\r\n            (   14.39   -77.66    -4.50     1.33)  ; 35\r\n            (   14.17   -78.46    -4.50     2.06)  ; 36\r\n            (   14.31   -79.41    -4.50     2.21)  ; 37\r\n            (   14.39   -80.66    -4.50     2.65)  ; 38\r\n            (\r\n              (   13.72   -80.95    -5.06     1.40)  ; 1, R-1-1-1-1-1-1\r\n              (   13.36   -81.61    -5.38     0.96)  ; 2\r\n              (   12.62   -82.41    -5.75     0.74)  ; 3\r\n              (   12.03   -83.14    -6.06     0.66)  ; 4\r\n              (   11.22   -83.80    -6.56     0.66)  ; 5\r\n              (   11.05   -84.65    -7.00     0.59)  ; 6\r\n              (   10.97   -85.46    -7.00     0.59)  ; 7\r\n              (   11.05   -85.97    -7.06     0.59)  ; 8\r\n              (   11.56   -86.33    -7.50     0.59)  ; 9\r\n              (   12.01   -86.92    -7.50     0.81)  ; 10\r\n              (   12.37   -87.28    -7.56     0.96)  ; 11\r\n              (   12.67   -87.65    -8.06     0.81)  ; 12\r\n              (   13.04   -88.45    -8.25     0.96)  ; 13\r\n              (   12.82   -89.48    -8.94     1.03)  ; 14\r\n              (   12.37   -90.28    -9.63     1.25)  ; 15\r\n              (   11.78   -90.79    -9.88     1.69)  ; 16\r\n              (   10.83   -91.23   -10.25     1.69)  ; 17\r\n              (   10.02   -91.67    -9.81     1.18)  ; 18\r\n              (    9.43   -91.82    -9.56     0.81)  ; 19\r\n              (    8.40   -92.18    -9.56     0.52)  ; 20\r\n              (    7.66   -92.84   -10.00     0.81)  ; 21\r\n              (    7.14   -93.64   -10.75     1.18)  ; 22\r\n              (    6.77   -94.38   -11.38     0.88)  ; 23\r\n              (    6.26   -94.96   -12.00     0.74)  ; 24\r\n              (    5.23   -95.69   -12.81     0.81)  ; 25\r\n              (    4.42   -96.13   -12.94     1.11)  ; 26\r\n              (    3.61   -97.08   -12.69     0.88)  ; 27\r\n              (    2.94   -97.88   -12.19     0.88)  ; 28\r\n              (    2.28   -98.83   -11.56     0.66)  ; 29\r\n              (    2.13   -99.86   -11.25     0.52)  ; 30\r\n              (    2.21  -100.37   -11.25     0.52)  ; 31\r\n              (    2.35  -101.39   -11.81     0.74)  ; 32\r\n              (    2.58  -102.42   -12.25     0.74)  ; 33\r\n              (    2.28  -103.44   -13.00     1.18)  ; 34\r\n              (    2.06  -104.39   -13.50     1.18)  ; 35\r\n              (    1.69  -105.63   -13.50     0.81)  ; 36\r\n              (    1.18  -106.58   -14.38     0.81)  ; 37\r\n              (    0.37  -107.02   -14.38     1.11)  ; 38\r\n              (   -0.22  -107.61   -15.56     1.40)  ; 39\r\n              (   -0.74  -108.19   -16.44     0.88)  ; 40\r\n              (   -0.96  -108.92   -17.13     0.74)  ; 41\r\n              (   -1.48  -109.21   -17.81     0.88)  ; 42\r\n              (   -2.14  -109.51   -18.06     0.74)  ; 43\r\n              (   -2.88  -109.87   -18.31     0.74)  ; 44\r\n              (   -4.20  -110.09   -18.56     0.59)  ; 45\r\n              (   -5.23  -110.02   -17.69     0.81)  ; 46\r\n              (   -5.90  -110.02   -17.69     0.96)  ; 47\r\n              (   -6.41  -110.09   -17.69     0.59)  ; 48\r\n              (   -7.15  -110.02   -17.63     0.44)  ; 49\r\n              (   -8.03  -110.60   -17.63     0.44)  ; 50\r\n              (   -8.62  -111.26   -18.06     1.11)  ; 51\r\n              (   -9.21  -111.77   -18.06     1.18)  ; 52\r\n              (   -9.80  -112.28   -19.00     0.66)  ; 53\r\n              (  -10.46  -112.80   -19.38     0.66)  ; 54\r\n              (  -11.35  -113.53   -20.19     0.59)  ; 55\r\n              (  -11.45  -114.38   -20.13     0.52)  ; 56\r\n              (  -11.52  -114.97   -19.63     0.81)  ; 57\r\n              (  -11.89  -115.70   -18.56     0.88)  ; 58\r\n              (  -12.26  -116.43   -18.38     0.88)  ; 59\r\n              (  -12.99  -117.16   -18.19     0.88)  ; 60\r\n              (  -13.58  -117.75   -17.88     0.74)  ; 61\r\n              (  -14.25  -118.33   -17.81     0.74)  ; 62\r\n              (  -14.84  -118.92   -17.81     0.96)  ; 63\r\n              (  -15.28  -119.57   -17.81     1.11)  ; 64\r\n              (  -15.87  -120.45   -17.81     0.81)  ; 65\r\n              (  -16.24  -121.40   -18.06     0.59)  ; 66\r\n              (  -16.82  -121.91   -19.19     0.81)  ; 67\r\n              (  -17.49  -122.35   -19.25     0.81)  ; 68\r\n              (  -18.37  -122.57   -19.38     0.81)  ; 69\r\n              (  -19.48  -122.79   -19.38     0.52)  ; 70\r\n              (  -20.29  -123.30   -19.38     0.52)  ; 71\r\n              (  -20.73  -124.18   -20.00     1.11)  ; 72\r\n              (  -20.80  -124.76   -20.50     1.92)  ; 73\r\n              (  -20.51  -125.20   -20.50     2.06)  ; 74\r\n              (  -19.99  -125.71   -20.88     1.18)  ; 75\r\n              (  -19.77  -126.08   -21.38     0.74)  ; 76\r\n              (  -19.33  -126.45   -21.56     0.37)  ; 77\r\n              (  -18.59  -126.59   -21.69     0.37)  ; 78\r\n              (  -17.71  -126.52   -21.94     0.88)  ; 79\r\n              (  -16.97  -126.74   -22.69     1.25)  ; 80\r\n              (  -16.09  -126.96   -22.81     1.55)  ; 81\r\n              (  -15.50  -127.32   -23.63     1.03)  ; 82\r\n              (  -14.84  -127.62   -24.00     0.66)  ; 83\r\n              (  -14.10  -128.20   -24.31     0.66)  ; 84\r\n              (  -13.73  -128.78   -25.56     1.03)  ; 85\r\n              (  -13.21  -129.30   -26.69     1.25)  ; 86\r\n              (  -12.55  -129.44   -27.50     1.25)  ; 87\r\n              (  -11.96  -129.74   -28.13     0.96)  ; 88\r\n              (  -11.30  -130.03   -29.00     0.59)  ; 89\r\n              (  -10.71  -130.25   -29.88     0.52)  ; 90\r\n              (  -10.42  -130.10   -32.13     0.66)  ; 91\r\n              (  -10.56  -130.10   -33.38     0.66)  ; 92\r\n              (  -10.64  -130.39   -34.63     0.88)  ; 93\r\n              (  -10.71  -130.83   -34.63     0.88)  ; 94\r\n              (  -10.27  -131.05   -35.00     0.66)  ; 95\r\n              (   -9.83  -131.27   -35.25     0.44)  ; 96\r\n              (   -9.24  -131.49   -35.63     0.44)  ; 97\r\n              (   -8.72  -131.49   -36.31     0.59)  ; 98\r\n              (   -8.72  -131.49   -38.00     0.96)  ; 99\r\n              (   -8.43  -131.49   -40.38     1.25)  ; 100\r\n              (   -7.91  -131.42   -40.56     1.69)  ; 101\r\n              (   -7.54  -131.05   -40.88     1.69)  ; 102\r\n              (   -6.73  -130.61   -41.31     1.47)  ; 103\r\n              (   -6.44  -130.10   -43.38     1.47)  ; 104\r\n              (   -5.63  -129.81   -44.00     1.77)  ; 105\r\n              (   -5.18  -129.37   -45.63     0.96)  ; 106\r\n              (   -4.74  -129.22   -45.94     0.59)  ; 107\r\n              (   -4.01  -129.22   -46.31     0.52)  ; 108\r\n              (   -3.42  -129.00   -47.69     0.52)  ; 109\r\n              (   -2.61  -128.93   -48.13     0.52)  ; 110\r\n              (   -2.02  -128.93   -48.63     0.52)  ; 111\r\n              (   -1.35  -128.71   -48.63     0.52)  ; 112\r\n              (   -0.17  -129.00   -49.56     0.52)  ; 113\r\n               Low\r\n            |\r\n              (   14.82   -80.85    -5.25     2.21)  ; 1, R-1-1-1-1-1-2\r\n              (\r\n                (   15.93   -81.44    -6.25     0.88)  ; 1, R-1-1-1-1-1-2-1\r\n                (   16.66   -81.88    -6.75     0.74)  ; 2\r\n                (   17.33   -82.24    -7.06     0.74)  ; 3\r\n                (   17.84   -82.61    -8.06     0.66)  ; 4\r\n                (   18.58   -83.12    -8.56     0.66)  ; 5\r\n                (   19.09   -83.78    -9.81     0.66)  ; 6\r\n                (   19.61   -84.87    -9.88     0.52)  ; 7\r\n                (   20.27   -85.90   -10.63     0.52)  ; 8\r\n                (   20.49   -86.48   -10.81     0.52)  ; 9\r\n                (   20.57   -86.99   -11.88     0.59)  ; 10\r\n                (   20.94   -87.94   -12.94     0.88)  ; 11\r\n                (   21.38   -88.09   -14.44     0.96)  ; 12\r\n                (   22.04   -88.60   -14.50     0.81)  ; 13\r\n                (   22.41   -88.97   -15.13     0.81)  ; 14\r\n                (   22.93   -89.55   -15.63     0.96)  ; 15\r\n                (   23.00   -90.36   -16.63     1.18)  ; 16\r\n                (   23.59   -90.72   -17.19     0.96)  ; 17\r\n                (   24.25   -91.31   -18.13     0.74)  ; 18\r\n                (   24.62   -91.82   -18.81     0.74)  ; 19\r\n                (   24.55   -92.70   -19.63     0.66)  ; 20\r\n                (   23.96   -93.65   -20.13     0.88)  ; 21\r\n                (   23.37   -94.30   -20.63     0.66)  ; 22\r\n                (   22.48   -95.11   -21.13     0.66)  ; 23\r\n                (   21.97   -96.20   -22.06     0.88)  ; 24\r\n                (   21.67   -96.50   -23.25     0.81)  ; 25\r\n                (   22.04   -97.01   -24.75     0.66)  ; 26\r\n                (   22.26   -97.01   -26.81     0.88)  ; 27\r\n                (   22.70   -97.01   -27.06     1.18)  ; 28\r\n                (   23.59   -97.30   -27.06     1.18)  ; 29\r\n                (   24.47   -97.52   -27.25     0.81)  ; 30\r\n                (   25.14   -97.45   -27.50     0.66)  ; 31\r\n                (   25.58   -97.37   -29.06     0.96)  ; 32\r\n                (   26.02   -97.81   -30.00     0.74)  ; 33\r\n                (   26.76   -98.18   -31.00     0.74)  ; 34\r\n                (   27.42   -98.69   -31.38     0.81)  ; 35\r\n                (   28.67   -99.20   -32.19     0.81)  ; 36\r\n                (   29.92   -99.79   -32.94     0.96)  ; 37\r\n                (   30.88  -100.15   -33.31     1.11)  ; 38\r\n                (   31.62  -100.37   -33.56     1.11)  ; 39\r\n                (   32.36  -100.81   -34.00     0.74)  ; 40\r\n                (   33.02  -101.32   -35.25     0.59)  ; 41\r\n                (   34.12  -101.98   -36.88     0.37)  ; 42\r\n                (   34.71  -102.05   -37.69     0.44)  ; 43\r\n                (   35.45  -102.34   -37.75     0.44)  ; 44\r\n                (   35.60  -102.78   -38.00     0.44)  ; 45\r\n                (   35.74  -103.44   -38.00     0.44)  ; 46\r\n                (   36.19  -103.73   -38.25     0.44)  ; 47\r\n                (   36.63  -103.95   -38.63     0.74)  ; 48\r\n                (   37.14  -103.95   -38.81     1.11)  ; 49\r\n                (   37.73  -103.66   -38.81     0.74)  ; 50\r\n                (   38.40  -103.59   -38.81     0.37)  ; 51\r\n                (   38.91  -103.59   -38.81     0.37)  ; 52\r\n                (   39.28  -103.15   -39.31     0.37)  ; 53\r\n                (   40.17  -102.64   -38.63     0.37)  ; 54\r\n                (   41.05  -102.34   -38.38     0.66)  ; 55\r\n                (   41.71  -102.13   -37.88     0.52)  ; 56\r\n                (   42.60  -101.54   -37.88     0.66)  ; 57\r\n                (   43.63  -101.17   -37.81     1.03)  ; 58\r\n                (   44.29  -100.74   -37.25     1.33)  ; 59\r\n                (   45.17  -100.30   -37.13     1.62)  ; 60\r\n                (   45.91   -99.64   -37.00     0.96)  ; 61\r\n                (   46.65   -98.84   -36.50     0.81)  ; 62\r\n                (   47.31   -98.18   -38.88     1.62)  ; 63\r\n                (   48.12   -97.52   -39.31     3.02)  ; 64\r\n                (   48.64   -97.52   -39.31     3.98)  ; 65\r\n                (   49.67   -97.45   -39.31     4.79)  ; 66\r\n                (   50.63   -97.30   -39.81     4.42)  ; 67\r\n                (   51.36   -96.94   -40.94     3.02)  ; 68\r\n                (   52.10   -96.64   -41.56     1.77)  ; 69\r\n                (   52.47   -96.57   -42.38     0.88)  ; 70\r\n                (   52.98   -96.50   -42.69     0.74)  ; 71\r\n                (   53.13   -96.57   -44.31     0.44)  ; 72\r\n                 Low\r\n              |\r\n                (   15.04   -81.66    -4.50     0.66)  ; 1, R-1-1-1-1-1-2-2\r\n                (   15.26   -82.32    -3.81     0.66)  ; 2\r\n                (   15.63   -82.68    -3.50     0.66)  ; 3\r\n                (   16.22   -83.12    -3.44     0.66)  ; 4\r\n                (   16.96   -83.56    -3.38     0.81)  ; 5\r\n                (   17.69   -83.78    -3.38     0.81)  ; 6\r\n                (   18.28   -84.29    -3.19     0.81)  ; 7\r\n                (   18.73   -85.17    -3.13     0.66)  ; 8\r\n                (   19.17   -86.12    -3.88     0.59)  ; 9\r\n                (   19.83   -86.85    -3.94     0.74)  ; 10\r\n                (   20.35   -87.58    -4.25     0.74)  ; 11\r\n                (   20.72   -88.16    -4.38     0.74)  ; 12\r\n                (   21.08   -88.97    -3.44     0.88)  ; 13\r\n                (   21.45   -89.33    -3.31     0.59)  ; 14\r\n                (   22.26   -89.70    -2.75     0.66)  ; 15\r\n                (   23.22   -90.36    -2.75     0.59)  ; 16\r\n                (   23.44   -90.65    -2.19     0.59)  ; 17\r\n                (   23.66   -91.23    -2.13     0.44)  ; 18\r\n                (   23.66   -92.18    -1.44     0.88)  ; 19\r\n                (   23.51   -92.70    -1.13     0.88)  ; 20\r\n                (   23.44   -93.43    -1.06     0.88)  ; 21\r\n                (   23.44   -94.08    -1.00     0.88)  ; 22\r\n                (   23.44   -94.74    -1.00     0.88)  ; 23\r\n                (   23.44   -95.62    -1.00     0.88)  ; 24\r\n                (   23.00   -96.50    -1.00     0.74)  ; 25\r\n                (   22.63   -97.59    -0.94     0.59)  ; 26\r\n                (   22.56   -98.40    -0.81     0.81)  ; 27\r\n                (   22.12   -98.84    -0.31     0.52)  ; 28\r\n                (   21.97   -99.35    -2.19     0.44)  ; 29\r\n                (   21.89   -99.86    -3.94     0.59)  ; 30\r\n                (   22.12  -100.30    -4.75     0.52)  ; 31\r\n                (   22.41  -100.88    -5.50     0.88)  ; 32\r\n                (   22.78  -101.39    -5.50     0.88)  ; 33\r\n                (   23.07  -101.98    -5.50     1.11)  ; 34\r\n                (   23.51  -102.49    -5.50     0.74)  ; 35\r\n                (   23.96  -103.22    -5.75     0.74)  ; 36\r\n                (   24.33  -104.03    -6.31     0.74)  ; 37\r\n                (   24.62  -105.12    -6.69     1.03)  ; 38\r\n                (   24.33  -105.27    -7.25     1.25)  ; 39\r\n                (   24.47  -105.93    -8.00     0.96)  ; 40\r\n                (   24.40  -106.80    -8.50     0.66)  ; 41\r\n                (   24.33  -107.53    -8.50     0.88)  ; 42\r\n                (   24.03  -108.41    -9.00     1.33)  ; 43\r\n                (   23.88  -109.29    -9.50     1.33)  ; 44\r\n                (   23.52  -109.95    -9.69     0.74)  ; 45\r\n                (   23.30  -111.05   -10.38     0.59)  ; 46\r\n                (   23.15  -111.85   -10.88     0.88)  ; 47\r\n                (   23.01  -112.73   -11.31     0.88)  ; 48\r\n                (   23.23  -113.68   -11.50     0.66)  ; 49\r\n                (   23.74  -114.85   -11.69     0.88)  ; 50\r\n                (   24.18  -115.51   -11.44     0.88)  ; 51\r\n                (   24.70  -116.53   -11.94     0.66)  ; 52\r\n                (   24.70  -117.19   -11.94     0.66)  ; 53\r\n                (   24.33  -118.43   -11.94     0.88)  ; 54\r\n                (   23.82  -119.38   -12.25     0.88)  ; 55\r\n                (   23.30  -120.04   -11.63     0.74)  ; 56\r\n                (   23.15  -120.70   -11.56     0.66)  ; 57\r\n                (   22.71  -121.58   -11.56     1.03)  ; 58\r\n                (   22.42  -122.60   -11.38     1.03)  ; 59\r\n                (   22.12  -123.62   -11.56     0.52)  ; 60\r\n                (   22.12  -125.01   -11.88     0.88)  ; 61\r\n                (   21.97  -126.04   -11.81     1.62)  ; 62\r\n                (   21.83  -126.84   -11.81     1.99)  ; 63\r\n                (   21.61  -127.20   -11.81     1.99)  ; 64\r\n                (   21.39  -128.08   -11.81     1.33)  ; 65\r\n                (   20.65  -129.03   -11.81     0.66)  ; 66\r\n                (   20.28  -129.54   -12.00     0.44)  ; 67\r\n                (   19.91  -130.20   -12.25     0.37)  ; 68\r\n                (   20.13  -130.79   -12.00     0.37)  ; 69\r\n                (   20.65  -131.23   -11.56     0.59)  ; 70\r\n                (   21.24  -131.44   -11.19     1.03)  ; 71\r\n                (   21.53  -132.03   -10.31     1.40)  ; 72\r\n                (   22.12  -132.91   -10.31     1.47)  ; 73\r\n                (   22.42  -133.64   -10.25     0.88)  ; 74\r\n                (   23.15  -134.15    -9.88     0.66)  ; 75\r\n                (   23.74  -134.81    -8.94     0.59)  ; 76\r\n                (   24.04  -135.39    -8.00     0.52)  ; 77\r\n                (   23.96  -136.49    -7.00     0.52)  ; 78\r\n                (   24.18  -137.22    -6.94     0.74)  ; 79\r\n                (   24.77  -138.17    -7.31     0.52)  ; 80\r\n                (   25.44  -138.90    -8.38     0.66)  ; 81\r\n                (   26.01  -139.75    -8.38     1.03)  ; 82\r\n                (   26.60  -140.55    -8.56     1.33)  ; 83\r\n                (   27.11  -141.79    -8.56     1.69)  ; 84\r\n                (   27.48  -143.18    -8.75     1.47)  ; 85\r\n                (   27.55  -144.42    -8.75     1.18)  ; 86\r\n                (   27.70  -145.52    -9.63     1.40)  ; 87\r\n                (   27.33  -146.47    -7.19     1.03)  ; 88\r\n                (   26.96  -147.42    -5.94     0.74)  ; 89\r\n                (   26.23  -148.37    -4.75     0.59)  ; 90\r\n                (   25.42  -149.54    -4.75     0.44)  ; 91\r\n                (   24.90  -150.27    -4.75     0.81)  ; 92\r\n                (   24.68  -151.00    -5.69     1.84)  ; 93\r\n                (   24.46  -151.59    -5.81     2.14)  ; 94\r\n                (   24.46  -152.83    -6.69     1.33)  ; 95\r\n                (   24.68  -153.49    -7.19     0.59)  ; 96\r\n                (   24.83  -153.85    -7.81     0.81)  ; 97\r\n                (   25.05  -154.51    -8.63     1.11)  ; 98\r\n                (   25.42  -155.39    -8.81     1.33)  ; 99\r\n                (   25.64  -156.19    -9.19     1.33)  ; 100\r\n                (   25.86  -156.92    -9.56     1.03)  ; 101\r\n                (   26.37  -157.73    -9.56     0.59)  ; 102\r\n                (   26.52  -158.39    -9.88     0.59)  ; 103\r\n                (   27.18  -159.48    -9.88     0.59)  ; 104\r\n                (   27.04  -160.36   -10.25     0.59)  ; 105\r\n                (   26.52  -161.24   -10.50     0.59)  ; 106\r\n                (   26.37  -162.70   -10.50     0.66)  ; 107\r\n                (   26.37  -163.36   -10.50     0.66)  ; 108\r\n                (   26.45  -164.45   -10.50     0.44)  ; 109\r\n                (   26.67  -165.04   -10.50     0.44)  ; 110\r\n                (   26.89  -165.48   -11.25     0.74)  ; 111\r\n                (   27.92  -165.84   -11.88     0.74)  ; 112\r\n                 Low\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (   14.94   -44.13     7.56     0.96)  ; 1, R-1-1-1-1-2\r\n            (   15.82   -45.08     7.13     1.11)  ; 2\r\n            (   16.41   -45.66     6.81     0.96)  ; 3\r\n            (   16.93   -46.32     6.81     0.88)  ; 4\r\n            (   17.52   -47.05     6.56     0.81)  ; 5\r\n            (   17.96   -47.78     6.13     1.03)  ; 6\r\n            (   18.33   -48.22     5.81     1.25)  ; 7\r\n            (   18.84   -48.44     5.38     1.25)  ; 8\r\n            (   19.36   -48.51     5.19     1.33)  ; 9\r\n            (   20.39   -49.61     4.50     1.11)  ; 10\r\n            (   21.05   -50.41     3.25     1.11)  ; 11\r\n            (   21.94   -51.29     3.13     1.11)  ; 12\r\n            (   22.45   -51.80     2.75     1.18)  ; 13\r\n            (   22.82   -52.97     2.25     1.11)  ; 14\r\n            (   22.89   -54.07     2.13     1.25)  ; 15\r\n            (   23.04   -55.02     1.69     1.25)  ; 16\r\n            (   22.75   -55.90     1.56     1.11)  ; 17\r\n            (   22.67   -57.21     1.44     0.96)  ; 18\r\n            (   22.97   -58.38     1.19     0.96)  ; 19\r\n            (   22.97   -59.33     1.19     1.25)  ; 20\r\n            (   23.34   -60.28     0.63     1.40)  ; 21\r\n            (   23.26   -61.38     0.25     1.25)  ; 22\r\n            (   23.11   -62.77    -0.81     1.11)  ; 23\r\n            (   23.11   -63.79    -0.81     1.03)  ; 24\r\n            (   23.41   -64.89    -1.38     1.03)  ; 25\r\n            (   23.78   -65.91    -1.69     1.40)  ; 26\r\n            (   24.15   -66.79    -0.19     1.69)  ; 27\r\n            (\r\n              (   24.88   -67.52     1.00     1.03)  ; 1, R-1-1-1-1-2-1\r\n              (   25.77   -68.10     1.63     0.88)  ; 2\r\n              (   26.36   -69.20     1.94     0.96)  ; 3\r\n              (   26.87   -69.86     2.19     0.96)  ; 4\r\n              (   27.54   -70.52     3.13     1.03)  ; 5\r\n              (   28.20   -70.73     4.00     0.81)  ; 6\r\n              (   28.71   -71.39     4.06     0.59)  ; 7\r\n              (   28.94   -72.12     4.06     0.59)  ; 8\r\n              (   28.79   -72.71     3.50     0.88)  ; 9\r\n              (   28.72   -73.72     2.94     0.96)  ; 10\r\n              (   28.57   -74.45     2.94     0.96)  ; 11\r\n              (   28.57   -75.40     2.69     0.96)  ; 12\r\n              (   28.57   -76.13     2.63     0.96)  ; 13\r\n              (   28.80   -77.30     2.63     1.11)  ; 14\r\n              (   29.09   -78.32     2.63     1.03)  ; 15\r\n              (   29.31   -79.06     2.19     0.81)  ; 16\r\n              (   29.68   -79.93     3.44     0.74)  ; 17\r\n              (   30.20   -80.81     4.06     0.66)  ; 18\r\n              (   30.93   -81.54     4.94     0.81)  ; 19\r\n              (   31.67   -82.49     4.75     0.88)  ; 20\r\n              (   32.18   -83.15     4.31     0.88)  ; 21\r\n              (   32.18   -84.10     3.38     1.11)  ; 22\r\n              (   32.11   -85.20     2.81     1.03)  ; 23\r\n              (   31.74   -86.58     2.81     0.96)  ; 24\r\n              (   31.52   -87.61     2.63     0.88)  ; 25\r\n              (   31.67   -88.78     2.44     0.74)  ; 26\r\n              (   31.89   -89.07     2.19     1.11)  ; 27\r\n              (   32.11   -89.80     2.06     1.11)  ; 28\r\n              (   32.33   -90.39     2.81     0.74)  ; 29\r\n              (   32.85   -91.04     2.94     0.66)  ; 30\r\n              (   33.66   -91.34     3.00     0.81)  ; 31\r\n              (   34.39   -92.14     3.13     0.59)  ; 32\r\n              (   34.69   -92.43     3.19     0.59)  ; 33\r\n              (   35.20   -92.94     3.19     1.33)  ; 34\r\n              (   35.79   -93.60     2.75     1.99)  ; 35\r\n              (   36.46   -94.41     2.25     1.69)  ; 36\r\n              (   36.75   -95.36     2.25     0.88)  ; 37\r\n              (   37.19   -96.01     2.00     0.88)  ; 38\r\n              (   37.64   -96.67     1.94     0.66)  ; 39\r\n              (   38.23   -97.48     1.56     0.59)  ; 40\r\n              (   38.52   -98.13     2.44     0.81)  ; 41\r\n              (   38.00   -99.01     2.94     1.03)  ; 42\r\n              (   37.41   -99.74     4.00     0.74)  ; 43\r\n              (   36.75  -100.55     4.25     0.88)  ; 44\r\n              (   36.31  -100.98     4.31     1.25)  ; 45\r\n              (   36.02  -101.42     3.50     1.62)  ; 46\r\n              (   35.35  -102.45     3.69     1.18)  ; 47\r\n              (   34.87  -103.58     3.00     0.88)  ; 48\r\n              (   34.28  -104.68     2.94     0.74)  ; 49\r\n              (   33.99  -105.19     2.94     0.74)  ; 50\r\n              (   33.84  -106.29     3.00     0.88)  ; 51\r\n              (   33.70  -107.39     2.81     1.03)  ; 52\r\n              (   33.62  -108.56     2.44     1.03)  ; 53\r\n              (   33.47  -109.51     2.13     1.03)  ; 54\r\n              (   33.47  -110.02     2.13     0.74)  ; 55\r\n              (   34.21  -110.67     2.13     0.74)  ; 56\r\n              (   34.87  -111.63     2.13     0.96)  ; 57\r\n              (   35.54  -112.43     1.81     0.74)  ; 58\r\n              (   36.27  -112.94     1.31     0.74)  ; 59\r\n              (   36.57  -113.60     3.19     0.66)  ; 60\r\n              (   36.57  -114.40     4.63     0.88)  ; 61\r\n              (   36.27  -114.84     5.06     1.11)  ; 62\r\n              (   36.57  -115.72     5.19     1.11)  ; 63\r\n              (   36.50  -116.82     5.19     0.81)  ; 64\r\n              (   36.79  -117.47     5.25     0.81)  ; 65\r\n              (   37.31  -117.91     5.50     0.81)  ; 66\r\n              (   38.12  -118.28     5.69     0.96)  ; 67\r\n              (   38.78  -118.79     5.25     0.96)  ; 68\r\n              (   39.74  -119.37     5.25     0.96)  ; 69\r\n              (   40.40  -120.47     4.69     0.96)  ; 70\r\n              (   40.55  -121.27     4.63     1.40)  ; 71\r\n              (   40.62  -121.93     5.13     1.11)  ; 72\r\n              (   40.84  -122.96     5.13     0.81)  ; 73\r\n              (   40.99  -123.61     5.19     0.66)  ; 74\r\n              (   41.21  -124.42     5.19     0.88)  ; 75\r\n              (   41.36  -125.00     5.31     0.59)  ; 76\r\n              (   41.50  -125.73     5.31     0.37)  ; 77\r\n              (   42.09  -126.39     5.31     0.37)  ; 78\r\n              (   42.76  -126.83     5.06     0.74)  ; 79\r\n              (   43.42  -127.05     4.06     0.88)  ; 80\r\n              (   44.30  -127.93     3.56     0.74)  ; 81\r\n              (   45.11  -128.44     3.38     0.59)  ; 82\r\n              (   46.44  -128.36     3.38     0.52)  ; 83\r\n              (   46.96  -128.66     4.13     0.88)  ; 84\r\n              (   47.69  -129.10     4.38     1.40)  ; 85\r\n              (   48.28  -129.61     4.94     1.40)  ; 86\r\n              (   49.17  -130.34     5.06     0.81)  ; 87\r\n              (   49.68  -130.92     5.06     0.52)  ; 88\r\n              (   50.12  -131.58     5.25     0.44)  ; 89\r\n              (   50.39  -132.53     4.13     0.52)  ; 90\r\n              (   50.39  -133.18     3.25     0.52)  ; 91\r\n              (   50.68  -133.92     2.63     0.52)  ; 92\r\n              (   51.05  -134.79     2.63     0.52)  ; 93\r\n              (   51.42  -135.45     2.63     0.74)  ; 94\r\n              (   52.08  -135.89     2.38     0.88)  ; 95\r\n              (   52.45  -136.47     2.38     0.88)  ; 96\r\n              (   52.97  -137.06     2.19     0.74)  ; 97\r\n              (   53.56  -137.94     2.00     0.66)  ; 98\r\n              (   53.48  -138.74     1.88     0.66)  ; 99\r\n              (   53.48  -139.69     1.50     0.66)  ; 100\r\n              (   53.85  -140.49     1.19     0.52)  ; 101\r\n              (   54.66  -140.86     1.19     0.74)  ; 102\r\n              (   55.69  -141.30     0.94     1.03)  ; 103\r\n              (   56.50  -141.30     0.94     0.74)  ; 104\r\n              (   57.46  -141.44     0.94     0.59)  ; 105\r\n              (   58.57  -141.81     0.81     0.59)  ; 106\r\n              (   58.94  -142.76     0.81     0.59)  ; 107\r\n              (   59.08  -143.42     0.81     0.96)  ; 108\r\n              (   59.23  -144.22     0.38     1.62)  ; 109\r\n              (   59.45  -145.03     0.06     1.40)  ; 110\r\n              (   59.82  -145.68     0.06     0.66)  ; 111\r\n              (   60.34  -146.05    -1.06     0.44)  ; 112\r\n              (   60.93  -146.05    -1.31     0.44)  ; 113\r\n              (   61.44  -145.76    -2.25     0.74)  ; 114\r\n              (   61.81  -145.68    -3.50     1.55)  ; 115\r\n              (   62.03  -145.61    -3.94     1.92)  ; 116\r\n              (   62.47  -145.61    -4.31     1.40)  ; 117\r\n              (   62.18  -145.61    -5.38     1.40)  ; 118\r\n              (   62.18  -146.12    -5.75     0.88)  ; 119\r\n              (   62.62  -146.78    -6.25     0.81)  ; 120\r\n              (   62.99  -147.44    -7.06     0.52)  ; 121\r\n               Low\r\n            |\r\n              (   23.78   -67.43     0.13     0.96)  ; 1, R-1-1-1-1-2-2\r\n              (   23.26   -67.87     0.19     0.52)  ; 2\r\n              (   23.19   -68.67     0.19     0.52)  ; 3\r\n              (   23.48   -69.25     0.19     0.52)  ; 4\r\n              (   24.07   -70.13     0.38     0.66)  ; 5\r\n              (   24.52   -71.15    -0.50     0.66)  ; 6\r\n              (   24.96   -72.25    -0.63     0.74)  ; 7\r\n              (   25.18   -72.98    -0.88     0.81)  ; 8\r\n              (   25.03   -73.71    -0.81     0.81)  ; 9\r\n              (   24.66   -74.74    -0.19     0.81)  ; 10\r\n              (   24.44   -75.69     0.31     0.81)  ; 11\r\n              (   24.37   -76.27     0.31     0.81)  ; 12\r\n              (   24.15   -77.00     0.63     0.81)  ; 13\r\n              (   23.78   -77.81     0.69     0.66)  ; 14\r\n              (   23.19   -78.39     1.19     0.88)  ; 15\r\n              (   22.75   -79.05     1.19     0.81)  ; 16\r\n              (   22.38   -79.63     1.50     0.81)  ; 17\r\n              (   21.57   -80.15     1.94     0.66)  ; 18\r\n              (   21.27   -80.80     2.50     0.81)  ; 19\r\n              (   21.20   -81.90     2.50     0.66)  ; 20\r\n              (   21.20   -82.78     2.00     0.66)  ; 21\r\n              (   21.64   -83.44     2.44     0.59)  ; 22\r\n              (   22.31   -84.24     2.50     0.59)  ; 23\r\n              (   23.04   -84.90     2.44     0.74)  ; 24\r\n              (   23.56   -85.41     2.00     0.74)  ; 25\r\n              (   24.00   -85.85     2.00     0.59)  ; 26\r\n              (   24.59   -86.51     2.00     0.59)  ; 27\r\n              (   24.66   -87.24     1.88     0.59)  ; 28\r\n              (   25.03   -88.33     1.75     0.74)  ; 29\r\n              (   25.62   -89.36     1.56     0.74)  ; 30\r\n              (   26.43   -90.67     1.44     0.81)  ; 31\r\n              (   27.24   -91.48     2.31     1.03)  ; 32\r\n              (   27.83   -92.28     2.50     0.88)  ; 33\r\n              (   28.35   -92.79     2.63     0.74)  ; 34\r\n              (   28.72   -92.79     3.19     0.66)  ; 35\r\n              (   29.38   -93.16     3.44     0.66)  ; 36\r\n              (   29.97   -93.82     3.44     0.66)  ; 37\r\n              (   30.41   -94.25     3.44     0.66)  ; 38\r\n              (   31.15   -94.69     3.50     0.44)  ; 39\r\n              (   31.88   -94.98     3.50     0.52)  ; 40\r\n              (   32.25   -95.28     3.50     0.52)  ; 41\r\n              (   32.03   -96.37     3.19     0.59)  ; 42\r\n              (   31.41   -97.78     2.44     0.66)  ; 43\r\n              (   31.04   -98.65     2.44     0.96)  ; 44\r\n              (   30.60   -99.31     1.75     0.96)  ; 45\r\n              (   30.45  -100.04     1.50     0.66)  ; 46\r\n              (   30.23  -100.70     0.63     0.66)  ; 47\r\n              (   30.16  -101.87     0.13     0.66)  ; 48\r\n              (   30.16  -103.33    -0.50     1.03)  ; 49\r\n              (   29.94  -104.06    -0.69     1.62)  ; 50\r\n              (   29.72  -104.57    -0.69     2.28)  ; 51\r\n              (   29.50  -104.94    -1.06     2.50)  ; 52\r\n              (   29.05  -105.67    -1.19     1.69)  ; 53\r\n              (   28.83  -106.33    -1.19     0.81)  ; 54\r\n              (   28.54  -107.35    -1.63     0.52)  ; 55\r\n              (   28.17  -108.08    -1.88     0.52)  ; 56\r\n              (   27.58  -108.52    -1.88     0.52)  ; 57\r\n              (   26.92  -109.25    -2.06     0.74)  ; 58\r\n              (   26.48  -109.84    -3.19     0.96)  ; 59\r\n              (   26.18  -110.57    -4.31     1.18)  ; 60\r\n              (   26.40  -111.01    -5.25     1.92)  ; 61\r\n              (   26.25  -111.52    -5.50     1.55)  ; 62\r\n              (   26.11  -112.32    -5.50     0.74)  ; 63\r\n              (   26.11  -112.84    -6.56     0.37)  ; 64\r\n              (   25.08  -113.27    -9.13     0.37)  ; 65\r\n              (   24.63  -113.42    -9.81     0.37)  ; 66\r\n              (   24.56  -113.93    -9.81     0.37)  ; 67\r\n              (   24.85  -114.66    -9.81     0.52)  ; 68\r\n              (   24.85  -115.69    -9.81     0.29)  ; 69\r\n              (   24.49  -116.05    -9.94     0.52)  ; 70\r\n              (   24.04  -116.42   -10.13     0.74)  ; 71\r\n              (   23.53  -116.86   -10.38     0.44)  ; 72\r\n              (   23.09  -117.81   -10.44     0.29)  ; 73\r\n              (   22.79  -118.46   -10.81     0.29)  ; 74\r\n              (   22.50  -118.83   -10.88     0.66)  ; 75\r\n              (   22.05  -119.19   -10.88     1.03)  ; 76\r\n              (   21.83  -119.71   -10.88     0.66)  ; 77\r\n              (   21.69  -120.00   -10.94     0.44)  ; 78\r\n              (   21.10  -121.17   -11.13     0.29)  ; 79\r\n              (   20.88  -121.46   -11.25     0.96)  ; 80\r\n              (   20.51  -121.75   -11.25     1.62)  ; 81\r\n              (   20.21  -122.19   -11.25     1.62)  ; 82\r\n              (   19.92  -123.00   -11.31     0.96)  ; 83\r\n              (   19.77  -123.36   -11.31     0.59)  ; 84\r\n              (   19.40  -124.02   -11.31     0.37)  ; 85\r\n              (   19.18  -124.60   -11.44     0.37)  ; 86\r\n              (   18.37  -125.55   -11.56     0.37)  ; 87\r\n              (   18.18  -126.04   -11.13     0.37)  ; 88\r\n              (   17.81  -126.63   -11.25     1.03)  ; 89\r\n              (   17.52  -127.14   -11.25     1.92)  ; 90\r\n              (   17.52  -127.50   -11.25     2.28)  ; 91\r\n              (   16.93  -127.80   -11.38     1.92)  ; 92\r\n              (   16.34  -128.23   -11.38     1.03)  ; 93\r\n              (   16.04  -128.75   -11.38     0.59)  ; 94\r\n              (   15.45  -129.04   -11.38     0.37)  ; 95\r\n              (   15.01  -129.55   -11.38     0.74)  ; 96\r\n              (   14.72  -129.92   -11.38     1.11)  ; 97\r\n              (   14.13  -130.35   -11.38     1.47)  ; 98\r\n              (   13.54  -130.94   -11.63     0.96)  ; 99\r\n              (   13.10  -131.60   -11.75     0.66)  ; 100\r\n              (   12.58  -132.04   -11.75     0.37)  ; 101\r\n              (   12.06  -132.55   -11.75     0.37)  ; 102\r\n              (   11.62  -132.91   -11.75     0.37)  ; 103\r\n               Low\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (   12.60   -40.81     7.63     1.25)  ; 1, R-1-1-1-2\r\n          (   11.71   -41.32     8.69     0.74)  ; 2\r\n          (   10.68   -42.13     9.31     0.88)  ; 3\r\n          (   10.02   -42.93    10.63     0.88)  ; 4\r\n          (    9.72   -43.66    11.31     1.25)  ; 5\r\n          (    9.80   -44.47    11.56     0.88)  ; 6\r\n          (    9.72   -45.27    11.63     0.88)  ; 7\r\n          (    9.72   -46.15    11.63     0.88)  ; 8\r\n          (    9.87   -46.88    11.94     1.11)  ; 9\r\n          (   10.16   -47.54    11.19     1.33)  ; 10\r\n          (   10.09   -48.19    10.56     0.96)  ; 11\r\n          (    9.43   -49.22     9.19     1.18)  ; 12\r\n          (    9.06   -49.58     9.19     1.25)  ; 13\r\n          (    8.69   -50.31     9.19     0.88)  ; 14\r\n          (    8.40   -50.90     8.81     0.88)  ; 15\r\n          (    7.73   -51.26     9.88     1.11)  ; 16\r\n          (    7.59   -51.85    11.69     0.88)  ; 17\r\n          (    7.73   -52.43    12.31     0.88)  ; 18\r\n          (    7.88   -53.02    13.00     1.11)  ; 19\r\n          (    8.03   -53.75    14.06     1.40)  ; 20\r\n          (    8.25   -54.26    15.00     1.25)  ; 21\r\n          (    8.25   -55.21    15.13     1.03)  ; 22\r\n          (    8.10   -56.31    15.31     1.03)  ; 23\r\n          (    7.88   -57.26    15.44     1.55)  ; 24\r\n          (    7.44   -57.84    15.69     1.69)  ; 25\r\n          (    6.92   -58.50    15.75     1.84)  ; 26\r\n          (\r\n            (    6.63   -59.57    15.50     0.96)  ; 1, R-1-1-1-2-1\r\n            (    6.70   -60.23    16.13     0.66)  ; 2\r\n            (    6.92   -60.38    16.31     0.59)  ; 3\r\n            (    7.66   -61.03    16.56     0.59)  ; 4\r\n            (    7.88   -61.33    16.75     0.59)  ; 5\r\n            (    8.47   -61.77    16.75     0.59)  ; 6\r\n            (    8.91   -62.13    16.81     0.59)  ; 7\r\n            (    9.65   -62.79    16.88     0.81)  ; 8\r\n            (    9.79   -63.45    16.88     0.81)  ; 9\r\n            (   10.02   -64.03    16.88     0.81)  ; 10\r\n            (   10.16   -64.62    16.88     0.44)  ; 11\r\n             Low\r\n          |\r\n            (    6.04   -59.35    14.50     1.40)  ; 1, R-1-1-1-2-2\r\n            (    5.52   -59.94    13.81     1.03)  ; 2\r\n            (    4.93   -60.60    13.00     1.18)  ; 3\r\n            (    4.42   -61.33    11.50     1.33)  ; 4\r\n            (    4.20   -62.13    10.31     1.11)  ; 5\r\n            (    4.27   -63.23     9.13     1.18)  ; 6\r\n            (    4.42   -64.18     8.94     1.18)  ; 7\r\n            (    4.49   -65.06     8.88     1.18)  ; 8\r\n            (    4.27   -66.01     8.69     1.03)  ; 9\r\n            (    4.12   -67.17     8.13     1.18)  ; 10\r\n            (    3.31   -67.61     7.75     1.18)  ; 11\r\n            (    3.16   -68.27     8.94     1.25)  ; 12\r\n            (    3.09   -69.22     8.69     1.62)  ; 13\r\n            (    3.16   -70.17     9.13     1.92)  ; 14\r\n            (    3.46   -71.12     8.75     2.21)  ; 15\r\n            (\r\n              (    2.87   -72.73     8.63     1.25)  ; 1, R-1-1-1-2-2-1\r\n              (    2.87   -73.53     8.63     0.96)  ; 2\r\n              (    2.57   -74.56     8.63     0.96)  ; 3\r\n              (    1.76   -75.14     9.50     1.18)  ; 4\r\n              (    1.18   -76.09     9.69     1.18)  ; 5\r\n              (    0.14   -77.04    10.63     1.03)  ; 6\r\n              (   -0.22   -77.99    11.63     1.40)  ; 7\r\n              (   -0.52   -78.87    12.19     1.62)  ; 8\r\n              (   -0.74   -79.75    12.56     1.18)  ; 9\r\n              (   -0.96   -81.14    12.63     0.96)  ; 10\r\n              (   -1.18   -82.23    12.94     1.40)  ; 11\r\n              (   -1.77   -83.40    11.50     1.77)  ; 12\r\n              (   -2.14   -84.35    10.19     1.55)  ; 13\r\n              (   -2.88   -84.86     8.75     1.33)  ; 14\r\n              (   -3.25   -85.89     7.00     1.47)  ; 15\r\n              (   -3.02   -86.77     5.50     1.33)  ; 16\r\n              (   -2.66   -87.57     5.06     1.33)  ; 17\r\n              (   -2.36   -88.08     5.13     0.81)  ; 18\r\n              (   -1.88   -88.87     4.50     0.81)  ; 19\r\n              (   -0.85   -89.89     4.19     0.81)  ; 20\r\n              (   -0.26   -90.77     4.00     0.81)  ; 21\r\n              (    0.18   -92.01     3.63     0.81)  ; 22\r\n              (    0.47   -92.96     5.06     1.18)  ; 23\r\n              (    0.25   -94.20     5.88     1.33)  ; 24\r\n              (   -0.19   -95.37     6.94     1.55)  ; 25\r\n              (   -0.78   -96.54     7.06     1.62)  ; 26\r\n              (   -1.52   -97.13     8.44     1.62)  ; 27\r\n              (   -2.10   -97.49     8.75     0.96)  ; 28\r\n              (   -2.99   -98.37     8.63     1.18)  ; 29\r\n              (   -3.50   -99.10     8.63     1.40)  ; 30\r\n              (   -4.39   -99.90     8.94     1.25)  ; 31\r\n              (   -4.90  -100.49     9.00     0.88)  ; 32\r\n              (   -5.35  -101.44     9.69     0.66)  ; 33\r\n              (   -5.20  -102.10    10.44     0.66)  ; 34\r\n              (   -4.90  -102.68    10.56     0.96)  ; 35\r\n              (   -4.61  -103.63    11.13     1.62)  ; 36\r\n              (   -4.39  -104.36    10.63     1.92)  ; 37\r\n              (   -4.54  -105.09    10.44     2.36)  ; 38\r\n              (   -4.68  -106.55    10.75     1.69)  ; 39\r\n              (   -4.46  -107.65    10.75     1.18)  ; 40\r\n              (   -4.39  -108.46    10.75     1.18)  ; 41\r\n              (   -4.02  -109.19    11.06     1.18)  ; 42\r\n              (   -3.50  -110.28    10.81     1.62)  ; 43\r\n              (   -2.84  -111.31    11.94     1.03)  ; 44\r\n              (   -2.55  -112.33    12.25     1.03)  ; 45\r\n              (   -2.92  -113.13    12.63     0.81)  ; 46\r\n              (   -3.43  -113.94    12.94     0.74)  ; 47\r\n              (   -4.24  -114.08    13.25     0.74)  ; 48\r\n              (   -4.83  -114.08    13.56     1.03)  ; 49\r\n              (   -6.01  -114.01    14.19     1.55)  ; 50\r\n              (   -7.11  -114.23    14.38     1.25)  ; 51\r\n              (   -7.92  -114.52    14.63     0.81)  ; 52\r\n              (   -9.32  -115.03    15.19     0.81)  ; 53\r\n              (   -9.91  -115.18    15.94     0.96)  ; 54\r\n              (  -10.58  -115.77    15.94     0.66)  ; 55\r\n              (  -11.46  -116.35    16.25     0.66)  ; 56\r\n              (  -11.61  -117.23    16.31     0.81)  ; 57\r\n              (  -11.79  -118.01    16.38     0.81)  ; 58\r\n              (  -11.72  -119.03    16.44     0.81)  ; 59\r\n              (  -11.43  -119.84    16.69     1.03)  ; 60\r\n              (  -11.28  -120.57    15.69     1.03)  ; 61\r\n              (  -11.06  -121.89    14.56     0.96)  ; 62\r\n              (  -10.84  -123.35    14.38     1.03)  ; 63\r\n              (  -10.62  -124.22    14.38     0.96)  ; 64\r\n              (  -10.32  -124.88    14.38     1.62)  ; 65\r\n              (  -10.10  -125.61    14.06     1.62)  ; 66\r\n              (   -9.80  -126.42    14.63     1.40)  ; 67\r\n              (   -9.36  -127.08    15.06     0.96)  ; 68\r\n              (   -9.14  -127.81    15.19     0.81)  ; 69\r\n              (   -8.92  -128.54    16.00     0.81)  ; 70\r\n              (   -8.99  -129.71    15.75     0.96)  ; 71\r\n              (   -9.73  -131.24    16.13     0.81)  ; 72\r\n              (  -10.47  -132.48    15.69     0.96)  ; 73\r\n              (  -10.98  -133.44    15.69     0.96)  ; 74\r\n              (  -11.20  -134.53    15.50     0.88)  ; 75\r\n              (  -10.98  -135.41    15.00     1.18)  ; 76\r\n              (  -11.35  -136.14    15.00     1.18)  ; 77\r\n              (  -11.79  -137.24    14.88     0.96)  ; 78\r\n              (  -12.68  -138.11    14.88     0.96)  ; 79\r\n              (  -13.27  -138.63    14.88     0.96)  ; 80\r\n              (  -14.00  -138.99    14.88     1.11)  ; 81\r\n              (  -15.04  -139.36    14.94     1.33)  ; 82\r\n              (  -15.99  -139.72    14.94     1.18)  ; 83\r\n              (  -17.17  -139.87    14.69     1.11)  ; 84\r\n              (  -18.20  -140.38    14.69     0.96)  ; 85\r\n              (  -18.94  -140.89    14.63     0.66)  ; 86\r\n              (  -19.60  -141.04    14.50     0.66)  ; 87\r\n              (  -20.34  -141.70    13.94     0.96)  ; 88\r\n              (  -20.63  -142.43    13.88     1.33)  ; 89\r\n              (  -20.86  -143.67    13.19     1.47)  ; 90\r\n              (  -21.15  -144.55    12.81     1.18)  ; 91\r\n              (  -21.52  -145.13    12.00     1.03)  ; 92\r\n              (  -22.18  -146.01    11.13     0.96)  ; 93\r\n              (  -22.20  -147.10    11.00     0.59)  ; 94\r\n              (  -22.57  -147.68    10.13     0.81)  ; 95\r\n              (  -22.57  -148.49     9.50     0.59)  ; 96\r\n              (  -22.65  -149.37     9.19     0.59)  ; 97\r\n               Low\r\n            |\r\n              (    3.98   -71.66     9.44     1.25)  ; 1, R-1-1-1-2-2-2\r\n              (    4.57   -72.32    10.63     0.74)  ; 2\r\n              (    4.57   -73.05    11.06     0.74)  ; 3\r\n              (    4.21   -73.63    11.19     0.74)  ; 4\r\n              (    4.06   -74.07    11.19     0.74)  ; 5\r\n              (    4.06   -74.80    11.63     0.74)  ; 6\r\n              (    4.21   -75.61    12.06     1.11)  ; 7\r\n              (    4.28   -76.27    12.81     1.47)  ; 8\r\n              (    3.98   -76.85    13.56     1.84)  ; 9\r\n              (    3.54   -77.66    14.44     1.84)  ; 10\r\n              (    2.88   -78.68    15.06     1.03)  ; 11\r\n              (    2.51   -79.26    15.88     0.88)  ; 12\r\n              (    2.73   -79.92    16.13     0.74)  ; 13\r\n              (    2.95   -80.58    16.25     0.74)  ; 14\r\n              (    3.54   -81.31    16.44     0.74)  ; 15\r\n              (    4.13   -81.89    16.31     0.96)  ; 16\r\n              (    4.87   -82.26    16.06     0.81)  ; 17\r\n              (    5.53   -82.55    16.00     0.66)  ; 18\r\n              (    6.27   -82.99    15.69     0.66)  ; 19\r\n               Low\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (   18.93   -32.07     1.44     0.96)  ; 1, R-1-1-2\r\n        (   19.38   -32.43     2.06     0.66)  ; 2\r\n        (   19.89   -32.65     3.19     0.66)  ; 3\r\n        (   19.97   -32.65     3.44     0.96)  ; 4\r\n        (   20.19   -33.02     3.56     0.96)  ; 5\r\n        (   19.74   -33.53     4.56     0.96)  ; 6\r\n        (   19.45   -34.48     6.06     0.74)  ; 7\r\n        (   19.89   -35.50     6.50     0.74)  ; 8\r\n        (   20.26   -36.01     6.94     0.74)  ; 9\r\n        (   21.14   -36.23     7.31     0.96)  ; 10\r\n        (   22.32   -36.60     6.56     0.96)  ; 11\r\n        (   23.28   -36.89     6.50     0.96)  ; 12\r\n        (   24.24   -37.18     5.38     0.66)  ; 13\r\n        (   25.20   -37.70     4.81     0.66)  ; 14\r\n        (   26.38   -38.06     4.81     0.81)  ; 15\r\n        (   27.11   -38.65     5.56     0.81)  ; 16\r\n        (   27.63   -39.67     4.88     0.96)  ; 17\r\n        (   27.70   -40.55     5.81     0.81)  ; 18\r\n        (   27.70   -41.64     6.06     0.81)  ; 19\r\n        (   27.78   -42.67     6.31     0.81)  ; 20\r\n        (   27.55   -44.42     5.94     0.74)  ; 21\r\n        (   27.41   -45.66     5.50     0.74)  ; 22\r\n        (   27.55   -47.13     5.50     0.74)  ; 23\r\n        (   27.48   -48.37     5.13     0.74)  ; 24\r\n        (   27.26   -49.90     5.13     0.74)  ; 25\r\n        (   27.19   -51.07     5.13     0.74)  ; 26\r\n        (   27.63   -52.46     5.13     0.66)  ; 27\r\n        (   28.14   -53.78     4.50     0.66)  ; 28\r\n        (   28.59   -54.95     4.50     0.81)  ; 29\r\n        (   29.32   -55.90     3.25     0.81)  ; 30\r\n        (   30.35   -56.63     3.19     0.81)  ; 31\r\n        (   30.43   -57.36     1.81     0.88)  ; 32\r\n        (   30.80   -58.02     1.44     0.88)  ; 33\r\n        (   31.02   -58.75     1.13     0.88)  ; 34\r\n        (   31.16   -59.41     0.38     0.81)  ; 35\r\n        (   31.31   -60.36    -0.75     0.96)  ; 36\r\n        (   31.68   -61.67    -0.75     0.96)  ; 37\r\n        (   31.75   -62.62    -0.75     0.66)  ; 38\r\n        (   31.75   -62.77    -0.75     0.66)  ; 39\r\n        (   32.20   -62.99    -1.00     0.66)  ; 40\r\n        (   33.01   -63.50    -0.13     0.81)  ; 41\r\n        (   33.60   -64.01     1.63     0.96)  ; 42\r\n        (   34.33   -64.38     1.75     1.03)  ; 43\r\n        (   35.14   -64.96     2.31     1.03)  ; 44\r\n        (   35.95   -65.84     2.63     1.18)  ; 45\r\n        (   36.17   -66.64     2.88     1.18)  ; 46\r\n        (   36.47   -67.37     3.00     0.59)  ; 47\r\n        (   36.91   -67.74     3.00     0.59)  ; 48\r\n        (   37.87   -68.98     2.94     0.66)  ; 49\r\n        (   38.24   -69.57     2.88     0.66)  ; 50\r\n        (   38.83   -70.52     2.63     0.66)  ; 51\r\n        (   39.19   -71.25     2.94     0.66)  ; 52\r\n        (   38.83   -72.49     3.88     0.88)  ; 53\r\n        (   38.46   -73.51     5.94     0.96)  ; 54\r\n        (   38.53   -74.39     6.88     0.81)  ; 55\r\n        (   38.02   -74.83     7.25     0.81)  ; 56\r\n        (   37.87   -75.85     7.63     0.66)  ; 57\r\n        (   38.75   -76.88     7.63     0.66)  ; 58\r\n        (   39.78   -77.68     8.06     0.88)  ; 59\r\n        (   40.89   -78.27     7.88     0.88)  ; 60\r\n        (   41.55   -78.92     7.06     0.66)  ; 61\r\n        (   41.70   -79.58     4.75     0.66)  ; 62\r\n        (   41.85   -80.46     4.63     1.03)  ; 63\r\n        (   41.77   -81.41     4.63     1.11)  ; 64\r\n        (   42.44   -81.99     4.38     0.66)  ; 65\r\n        (   43.17   -82.21     2.94     0.96)  ; 66\r\n        (   43.69   -82.58     2.06     1.55)  ; 67\r\n        (   44.13   -82.72     1.31     1.11)  ; 68\r\n        (   44.46   -83.36     1.31     0.74)  ; 69\r\n        (   44.91   -83.87     0.69     0.52)  ; 70\r\n        (   45.35   -84.39     0.69     0.52)  ; 71\r\n        (   45.64   -85.04     0.69     0.52)  ; 72\r\n        (   46.31   -85.92     0.69     0.66)  ; 73\r\n        (   46.67   -87.02     0.69     0.29)  ; 74\r\n        (   47.04   -87.67     0.69     1.18)  ; 75\r\n        (   47.26   -88.19     0.50     1.18)  ; 76\r\n        (   47.63   -88.84     1.63     0.66)  ; 77\r\n        (   48.29   -89.58     2.19     0.66)  ; 78\r\n        (   49.03   -90.82     1.63     0.81)  ; 79\r\n        (   49.33   -91.70     1.63     0.81)  ; 80\r\n        (   49.55   -92.50     1.31     0.44)  ; 81\r\n        (   49.69   -93.30     1.31     0.44)  ; 82\r\n        (   49.47   -94.11     1.31     0.66)  ; 83\r\n        (   49.10   -94.98     1.31     0.66)  ; 84\r\n        (   48.52   -96.23     1.31     0.44)  ; 85\r\n        (   48.15   -96.81    -0.69     0.74)  ; 86\r\n        (   48.22   -97.47    -1.19     0.74)  ; 87\r\n        (   48.44   -98.05    -1.56     0.52)  ; 88\r\n        (   48.74   -99.08    -2.19     0.52)  ; 89\r\n        (   49.18  -100.25    -2.88     0.74)  ; 90\r\n        (   49.33  -101.42    -5.00     1.11)  ; 91\r\n        (   49.55  -102.00    -5.63     0.59)  ; 92\r\n        (   49.92  -103.17    -6.06     0.44)  ; 93\r\n        (   50.28  -104.12    -5.81     0.74)  ; 94\r\n        (   50.50  -104.71    -5.88     1.40)  ; 95\r\n        (   50.73  -105.66    -7.25     1.18)  ; 96\r\n        (   51.09  -106.32    -6.69     0.59)  ; 97\r\n        (   51.02  -107.12    -7.38     0.59)  ; 98\r\n        (   51.02  -107.85    -7.81     1.33)  ; 99\r\n        (   51.09  -108.58    -8.50     1.69)  ; 100\r\n        (   50.95  -109.53    -9.13     0.59)  ; 101\r\n        (   50.95  -110.99    -9.31     0.44)  ; 102\r\n        (   51.02  -111.65    -9.63     0.44)  ; 103\r\n        (   51.39  -112.82    -9.81     0.74)  ; 104\r\n        (   51.42  -113.87    -9.75     1.47)  ; 105\r\n        (   51.64  -114.74   -10.00     1.03)  ; 106\r\n        (   51.56  -115.04   -11.00     0.59)  ; 107\r\n        (   51.64  -115.69   -11.56     0.29)  ; 108\r\n        (   51.93  -116.86   -11.63     0.29)  ; 109\r\n        (   52.23  -117.67   -11.81     1.03)  ; 110\r\n        (   52.45  -118.18   -12.06     1.84)  ; 111\r\n        (   52.82  -118.76   -12.13     2.65)  ; 112\r\n        (   53.11  -119.71   -12.81     0.74)  ; 113\r\n        (   53.26  -120.23   -13.31     0.52)  ; 114\r\n        (   53.48  -120.81   -13.94     0.88)  ; 115\r\n        (   53.99  -120.96   -14.38     1.25)  ; 116\r\n         Low\r\n      )  ;  End of split\r\n    |\r\n      (   17.17   -29.58     0.38     0.52)  ; 1, R-1-2\r\n      (   16.65   -30.09    -1.63     0.52)  ; 2\r\n      (   16.80   -31.04    -3.94     0.52)  ; 3\r\n      (   17.17   -31.85    -6.38     0.52)  ; 4\r\n      (   17.17   -32.58    -6.69     0.52)  ; 5\r\n      (   16.72   -33.68    -7.69     0.66)  ; 6\r\n      (   16.13   -34.77    -9.38     0.74)  ; 7\r\n      (   15.32   -35.58   -10.19     0.74)  ; 8\r\n      (   14.88   -36.53   -10.88     0.96)  ; 9\r\n      (   14.96   -36.96   -11.88     1.03)  ; 10\r\n      (   14.66   -38.21   -13.19     1.03)  ; 11\r\n      (   14.59   -38.94   -13.94     1.40)  ; 12\r\n      (   14.59   -39.89   -15.31     1.03)  ; 13\r\n      (   14.88   -40.84   -15.31     0.81)  ; 14\r\n      (   15.62   -42.01   -15.63     0.74)  ; 15\r\n      (   16.36   -43.10   -16.13     0.66)  ; 16\r\n      (   17.24   -43.91   -16.13     0.66)  ; 17\r\n      (   17.61   -44.57   -16.75     0.74)  ; 18\r\n      (   17.61   -45.30   -18.00     0.74)  ; 19\r\n      (   17.98   -46.10   -18.13     0.59)  ; 20\r\n      (   18.64   -47.34   -18.69     0.74)  ; 21\r\n      (   19.30   -47.42   -19.31     0.66)  ; 22\r\n      (   20.11   -47.71   -20.25     0.66)  ; 23\r\n      (   20.70   -48.51   -21.00     0.88)  ; 24\r\n      (   20.56   -49.03   -22.19     0.96)  ; 25\r\n      (   20.41   -49.76   -23.25     0.74)  ; 26\r\n      (   20.56   -51.15   -23.94     0.74)  ; 27\r\n      (   20.70   -51.80   -24.06     0.74)  ; 28\r\n      (   21.29   -52.32   -24.31     0.74)  ; 29\r\n      (   22.32   -53.27   -24.50     0.52)  ; 30\r\n      (   22.77   -53.70   -25.06     0.52)  ; 31\r\n      (   23.21   -53.70   -26.13     0.81)  ; 32\r\n      (   23.58   -53.92   -26.63     1.18)  ; 33\r\n      (   23.72   -53.92   -28.38     1.55)  ; 34\r\n      (   24.09   -54.36   -28.31     2.28)  ; 35\r\n      (   24.17   -54.58   -28.31     2.65)  ; 36\r\n      (   24.24   -54.65   -30.06     1.69)  ; 37\r\n      (   24.53   -55.17   -32.81     0.96)  ; 38\r\n      (   24.68   -55.60   -34.19     0.74)  ; 39\r\n      (   25.34   -56.04   -36.00     0.74)  ; 40\r\n      (   26.30   -56.26   -35.94     1.03)  ; 41\r\n      (   27.11   -56.26   -36.25     1.25)  ; 42\r\n      (   27.70   -56.56   -36.50     0.81)  ; 43\r\n      (   28.51   -56.63   -38.81     0.59)  ; 44\r\n      (   29.03   -56.99   -41.00     0.81)  ; 45\r\n      (   29.32   -57.29   -41.56     1.18)  ; 46\r\n      (   29.54   -57.51   -42.13     1.99)  ; 47\r\n      (   30.06   -57.87   -42.13     2.36)  ; 48\r\n      (   30.21   -58.02   -43.94     1.84)  ; 49\r\n      (   30.43   -58.46   -46.44     0.96)  ; 50\r\n      (   30.50   -58.82   -49.88     0.74)  ; 51\r\n      (   30.28   -59.70   -50.38     0.74)  ; 52\r\n      (   29.91   -60.14   -51.31     0.74)  ; 53\r\n      (   29.84   -60.72   -51.31     1.18)  ; 54\r\n      (   29.40   -61.01   -51.94     1.84)  ; 55\r\n      (   28.66   -61.01   -53.63     1.40)  ; 56\r\n      (   28.29   -60.58   -54.13     0.96)  ; 57\r\n      (   27.63   -60.65   -56.25     0.59)  ; 58\r\n      (   26.82   -60.72   -57.63     0.96)  ; 59\r\n      (   26.23   -60.58   -58.25     1.33)  ; 60\r\n      (   25.34   -60.72   -59.69     1.77)  ; 61\r\n      (   25.27   -60.36   -63.00     0.81)  ; 62\r\n      (   23.87   -60.28   -64.44     0.44)  ; 63\r\n      (   23.21   -59.99   -65.25     0.44)  ; 64\r\n      (   22.40   -59.77   -66.06     0.44)  ; 65\r\n      (   21.51   -60.21   -66.31     0.74)  ; 66\r\n      (   21.07   -60.06   -67.56     0.74)  ; 67\r\n       Low\r\n    )  ;  End of split\r\n  |\r\n    (   18.38   -25.63    -0.19     1.92)  ; 1, R-2\r\n    (   19.56   -25.85     0.13     1.62)  ; 2\r\n    (   20.82   -26.65     0.56     1.77)  ; 3\r\n    (   21.41   -27.53     0.75     1.77)  ; 4\r\n    (   21.77   -28.85     0.94     1.40)  ; 5\r\n    (   22.36   -29.94     1.06     1.40)  ; 6\r\n    (   22.95   -30.97     0.13     1.40)  ; 7\r\n    (   23.54   -31.99    -0.06     1.18)  ; 8\r\n    (   24.35   -33.45    -0.25     1.33)  ; 9\r\n    (   24.94   -34.40    -0.44     1.33)  ; 10\r\n    (   25.60   -34.91    -0.88     1.18)  ; 11\r\n    (   26.27   -36.16    -1.25     1.11)  ; 12\r\n    (   27.37   -37.84    -2.13     0.96)  ; 13\r\n    (   27.74   -39.37    -3.25     1.11)  ; 14\r\n    (   27.81   -39.96    -3.44     1.33)  ; 15\r\n    (   28.26   -40.98    -3.81     1.11)  ; 16\r\n    (   28.63   -42.00    -4.31     1.33)  ; 17\r\n    (   28.77   -43.03    -4.50     1.69)  ; 18\r\n    (   28.92   -43.54    -4.63     2.06)  ; 19\r\n    (   29.17   -44.17    -4.63     2.06)  ; 20\r\n    (\r\n      (   29.36   -44.42    -4.63     2.50)  ; 1, R-2-1\r\n      (   30.10   -44.85    -3.75     1.69)  ; 2\r\n      (   30.84   -45.73    -4.19     1.33)  ; 3\r\n      (   31.57   -46.68    -4.19     0.96)  ; 4\r\n      (   32.09   -47.41    -4.19     0.96)  ; 5\r\n      (   33.12   -47.78    -4.38     0.96)  ; 6\r\n      (   34.08   -47.93    -4.31     1.11)  ; 7\r\n      (   35.11   -48.14    -4.25     1.33)  ; 8\r\n      (   35.40   -48.44    -4.19     1.33)  ; 9\r\n      (   35.85   -48.66    -4.19     1.03)  ; 10\r\n      (   37.10   -49.02    -4.19     1.03)  ; 11\r\n      (   37.69   -49.24    -4.19     1.18)  ; 12\r\n      (   38.72   -49.68    -4.19     1.18)  ; 13\r\n      (   39.46   -49.53    -3.63     1.33)  ; 14\r\n      (   40.63   -49.83    -3.25     1.69)  ; 15\r\n      (   41.30   -50.19    -3.25     1.69)  ; 16\r\n      (   41.81   -50.63    -3.25     1.33)  ; 17\r\n      (   42.77   -51.51    -3.19     1.18)  ; 18\r\n      (   43.73   -52.16    -3.19     1.18)  ; 19\r\n      (   44.02   -53.19    -2.75     1.33)  ; 20\r\n      (   44.32   -54.50    -3.31     0.88)  ; 21\r\n      (   45.20   -55.38    -3.94     1.40)  ; 22\r\n      (   46.15   -55.93    -4.38     1.69)  ; 23\r\n      (   46.78   -56.22    -4.38     1.69)  ; 24\r\n      (\r\n        (   46.66   -56.30    -4.81     1.69)  ; 1, R-2-1-1\r\n        (   46.88   -57.25    -4.81     1.25)  ; 2\r\n        (   47.55   -57.69    -4.81     0.74)  ; 3\r\n        (   48.06   -58.86    -5.06     0.52)  ; 4\r\n        (   48.36   -59.37    -5.25     0.74)  ; 5\r\n        (   48.73   -60.25    -5.50     0.74)  ; 6\r\n        (   48.80   -61.20    -6.38     0.74)  ; 7\r\n        (   48.87   -62.22    -7.00     0.81)  ; 8\r\n        (   49.17   -63.10    -6.81     0.59)  ; 9\r\n        (   49.61   -64.05    -5.81     0.59)  ; 10\r\n        (   49.68   -65.22    -5.44     0.74)  ; 11\r\n        (   49.32   -66.02    -4.25     0.74)  ; 12\r\n        (   48.87   -67.05    -4.19     0.59)  ; 13\r\n        (   48.21   -67.78    -3.88     0.59)  ; 14\r\n        (   48.36   -68.14    -3.81     0.52)  ; 15\r\n        (   49.32   -68.58    -4.13     0.74)  ; 16\r\n        (   49.83   -68.87    -4.31     0.96)  ; 17\r\n        (   50.49   -69.60    -2.19     1.18)  ; 18\r\n        (   51.38   -70.12    -1.81     1.03)  ; 19\r\n        (   52.63   -70.70    -1.44     0.74)  ; 20\r\n        (   53.15   -71.50    -1.38     0.88)  ; 21\r\n        (   53.59   -72.24    -0.88     1.18)  ; 22\r\n        (   54.25   -73.19    -0.88     1.18)  ; 23\r\n        (   53.81   -74.21    -0.56     0.81)  ; 24\r\n        (   53.66   -75.38     0.06     0.66)  ; 25\r\n        (   54.33   -75.67     0.25     0.66)  ; 26\r\n        (   55.06   -75.74     0.88     0.81)  ; 27\r\n        (   56.17   -75.31     1.13     0.96)  ; 28\r\n        (   57.12   -75.01     1.75     0.81)  ; 29\r\n        (   57.71   -75.09     3.44     0.81)  ; 30\r\n        (   58.23   -75.45     3.75     0.81)  ; 31\r\n        (   58.82   -76.18     4.13     0.81)  ; 32\r\n        (   59.19   -77.79     4.25     1.03)  ; 33\r\n        (   59.34   -78.67     4.25     0.88)  ; 34\r\n        (   59.78   -79.47     4.25     0.74)  ; 35\r\n        (   60.00   -80.20     4.25     0.88)  ; 36\r\n        (   60.44   -80.93     4.25     0.88)  ; 37\r\n        (   60.73   -81.37     4.25     0.88)  ; 38\r\n        (   61.03   -81.59     4.25     0.88)  ; 39\r\n        (   61.62   -82.18     4.94     0.74)  ; 40\r\n        (   62.28   -82.62     5.13     0.52)  ; 41\r\n        (   62.80   -83.13     5.25     0.52)  ; 42\r\n        (   63.31   -83.42     5.69     0.52)  ; 43\r\n        (   63.90   -83.78     5.69     0.52)  ; 44\r\n        (   64.20   -84.00     7.19     0.74)  ; 45\r\n        (   64.20   -84.30     9.19     0.59)  ; 46\r\n        (   63.31   -83.78     9.44     0.74)  ; 47\r\n        (   62.80   -83.49     9.56     0.74)  ; 48\r\n         Low\r\n      |\r\n        (   47.66   -56.66    -5.13     0.81)  ; 1, R-2-1-2\r\n        (   48.40   -57.39    -5.44     0.66)  ; 2\r\n        (   48.91   -57.68    -5.88     0.66)  ; 3\r\n        (   49.50   -58.20    -6.13     0.66)  ; 4\r\n        (   50.31   -58.78    -7.38     0.88)  ; 5\r\n        (   50.76   -59.66    -7.75     1.11)  ; 6\r\n        (   51.20   -60.32    -8.31     0.96)  ; 7\r\n        (   52.23   -61.41    -8.56     1.18)  ; 8\r\n        (   52.97   -62.00    -9.13     1.18)  ; 9\r\n        (   53.48   -62.95    -9.13     1.03)  ; 10\r\n        (   54.22   -63.82    -9.25     0.88)  ; 11\r\n        (   54.96   -64.85    -9.63     0.74)  ; 12\r\n        (   55.91   -65.58    -9.81     0.52)  ; 13\r\n        (   56.80   -66.38   -10.06     0.66)  ; 14\r\n        (   56.80   -67.52   -11.00     0.74)  ; 15\r\n        (   57.09   -68.76   -11.56     0.74)  ; 16\r\n        (   57.02   -70.01   -12.19     0.96)  ; 17\r\n        (   57.24   -70.74   -12.75     0.96)  ; 18\r\n        (   57.38   -71.17   -13.13     1.33)  ; 19\r\n        (   57.46   -72.56   -14.75     1.40)  ; 20\r\n        (   57.61   -73.37   -14.75     1.03)  ; 21\r\n        (   57.61   -74.25   -15.38     1.33)  ; 22\r\n        (   57.61   -74.98   -16.56     1.84)  ; 23\r\n        (   57.83   -75.78   -17.69     1.03)  ; 24\r\n        (   57.46   -76.44   -18.63     0.74)  ; 25\r\n        (   57.53   -77.02   -18.63     0.37)  ; 26\r\n        (   56.65   -77.46   -18.63     0.37)  ; 27\r\n        (   56.21   -78.63   -18.69     0.66)  ; 28\r\n        (   56.28   -79.36   -18.88     0.52)  ; 29\r\n        (   56.80   -80.02   -19.38     1.11)  ; 30\r\n        (   57.68   -80.90   -19.38     1.55)  ; 31\r\n        (   58.27   -81.77   -20.00     1.11)  ; 32\r\n        (   58.71   -82.58   -20.00     1.03)  ; 33\r\n        (   59.15   -83.31   -20.31     1.77)  ; 34\r\n        (   59.45   -84.19   -20.81     1.77)  ; 35\r\n        (   59.67   -85.06   -21.56     0.96)  ; 36\r\n        (   60.41   -85.87   -21.56     0.44)  ; 37\r\n        (   60.77   -86.45   -22.50     0.37)  ; 38\r\n        (   60.92   -86.89   -22.63     0.37)  ; 39\r\n        (   61.22   -87.70   -21.19     0.66)  ; 40\r\n        (   61.58   -88.35   -20.94     1.47)  ; 41\r\n        (   61.88   -89.60   -21.38     1.47)  ; 42\r\n        (   62.03   -90.62   -21.25     0.81)  ; 43\r\n        (   61.88   -91.50   -21.06     0.81)  ; 44\r\n        (   62.47   -92.67   -20.81     0.81)  ; 45\r\n        (   62.54   -93.54   -20.81     0.81)  ; 46\r\n        (   63.06   -94.64   -20.81     1.03)  ; 47\r\n        (   63.94   -95.66   -20.94     0.88)  ; 48\r\n        (   65.27   -96.28   -22.69     0.81)  ; 49\r\n        (   65.72   -96.57   -22.75     0.81)  ; 50\r\n        (   66.60   -97.09   -23.06     0.59)  ; 51\r\n        (   67.19   -97.30   -23.06     0.59)  ; 52\r\n        (   68.15   -97.89   -22.56     1.11)  ; 53\r\n        (   68.44   -98.18   -21.69     1.92)  ; 54\r\n        (   68.66   -98.62   -21.19     2.28)  ; 55\r\n        (   69.33   -98.99   -20.94     2.28)  ; 56\r\n        (   70.21   -99.35   -20.94     1.40)  ; 57\r\n        (   71.09  -100.01   -20.44     1.18)  ; 58\r\n        (   72.05  -100.16   -20.06     1.03)  ; 59\r\n        (   72.71  -100.52   -19.63     1.33)  ; 60\r\n        (   73.38  -101.03   -19.69     1.62)  ; 61\r\n        (   74.48  -101.54   -19.81     1.33)  ; 62\r\n        (   75.51  -102.20   -20.06     0.74)  ; 63\r\n        (   76.10  -102.64   -20.75     0.74)  ; 64\r\n        (   76.55  -103.45   -21.13     0.74)  ; 65\r\n        (   77.21  -103.88   -21.13     1.11)  ; 66\r\n        (   78.31  -104.40   -21.69     1.47)  ; 67\r\n        (   78.98  -104.91   -21.69     1.77)  ; 68\r\n        (   79.79  -105.27   -21.88     1.03)  ; 69\r\n        (   80.52  -105.56   -22.00     0.66)  ; 70\r\n        (   81.11  -105.93   -22.38     0.59)  ; 71\r\n        (   81.56  -105.78   -22.44     0.37)  ; 72\r\n        (   82.07  -105.64   -22.44     0.37)  ; 73\r\n        (   82.81  -106.37   -22.63     0.44)  ; 74\r\n        (   83.40  -107.32   -22.94     0.81)  ; 75\r\n        (   83.84  -107.98   -24.31     1.55)  ; 76\r\n        (   84.43  -108.64   -24.63     1.99)  ; 77\r\n        (   85.31  -109.51   -25.19     1.25)  ; 78\r\n        (   85.98  -110.39   -25.88     1.55)  ; 79\r\n        (   86.27  -111.41   -26.31     0.81)  ; 80\r\n        (   86.64  -112.07   -26.31     0.44)  ; 81\r\n        (   87.30  -112.58   -26.31     0.44)  ; 82\r\n        (   88.26  -113.09   -26.50     0.44)  ; 83\r\n        (   88.92  -113.39   -26.50     0.81)  ; 84\r\n        (   89.36  -113.83   -26.56     0.81)  ; 85\r\n        (   89.59  -113.97   -26.75     0.44)  ; 86\r\n        (   90.25  -114.63   -26.75     0.44)  ; 87\r\n        (   90.76  -115.14   -27.06     1.25)  ; 88\r\n        (   91.58  -115.58   -27.75     1.92)  ; 89\r\n        (   92.39  -115.29   -27.75     1.47)  ; 90\r\n        (   93.12  -115.43   -27.94     0.96)  ; 91\r\n        (   93.49  -115.58   -27.94     0.52)  ; 92\r\n        (   94.15  -116.16   -28.06     0.37)  ; 93\r\n        (   94.74  -116.90   -28.94     1.18)  ; 94\r\n        (   95.19  -117.55   -29.63     1.62)  ; 95\r\n        (   95.77  -117.99   -29.63     0.88)  ; 96\r\n        (   96.36  -118.06   -30.31     0.52)  ; 97\r\n        (   96.95  -118.50   -31.50     0.52)  ; 98\r\n        (   97.40  -118.58   -31.81     0.96)  ; 99\r\n        (   97.62  -118.65   -33.00     0.96)  ; 100\r\n        (   98.43  -118.36   -33.25     0.59)  ; 101\r\n        (   99.02  -118.65   -33.25     0.37)  ; 102\r\n        (   99.61  -118.87   -33.25     1.18)  ; 103\r\n        (  100.34  -119.31   -34.38     1.92)  ; 104\r\n        (  101.08  -119.45   -34.75     1.92)  ; 105\r\n        (  101.74  -119.38   -34.94     1.03)  ; 106\r\n        (  102.40  -119.75   -35.69     0.66)  ; 107\r\n        (  102.70  -120.18   -37.81     0.52)  ; 108\r\n        (  103.58  -120.33   -37.81     0.59)  ; 109\r\n        (  104.10  -120.33   -37.88     0.59)  ; 110\r\n        (  104.98  -119.75   -38.06     0.37)  ; 111\r\n        (  105.72  -119.53   -38.38     1.18)  ; 112\r\n        (  106.31  -119.53   -39.44     1.99)  ; 113\r\n        (  106.97  -118.94   -41.69     1.62)  ; 114\r\n         Low\r\n      )  ;  End of split\r\n    |\r\n      (   28.66   -45.12    -4.44     0.96)  ; 1, R-2-2\r\n      (   28.80   -45.78    -4.81     0.66)  ; 2\r\n      (   29.10   -46.29    -4.94     0.66)  ; 3\r\n      (   29.76   -47.02    -4.94     0.66)  ; 4\r\n      (   30.35   -47.53    -4.94     0.59)  ; 5\r\n      (   31.01   -48.19    -5.13     0.66)  ; 6\r\n      (   31.46   -49.65    -5.13     0.66)  ; 7\r\n      (   31.53   -50.24    -5.69     0.66)  ; 8\r\n      (   32.04   -50.97    -5.69     0.74)  ; 9\r\n      (   32.56   -51.26    -5.75     0.74)  ; 10\r\n      (   33.30   -51.70    -6.25     0.81)  ; 11\r\n      (   34.03   -52.14    -7.25     0.66)  ; 12\r\n      (   34.99   -52.87    -7.25     0.96)  ; 13\r\n      (   35.65   -53.82    -7.69     1.18)  ; 14\r\n      (   36.39   -54.62    -7.75     1.18)  ; 15\r\n      (   36.98   -55.06    -8.69     1.55)  ; 16\r\n      (\r\n        (   37.64   -56.08    -7.00     1.84)  ; 1, R-2-2-1\r\n        (   38.16   -57.11    -5.81     1.40)  ; 2\r\n        (   38.90   -58.06    -4.63     1.18)  ; 3\r\n        (   39.78   -58.64    -2.69     0.96)  ; 4\r\n        (   41.18   -58.86    -2.31     0.66)  ; 5\r\n        (   41.84   -58.94    -1.13     0.66)  ; 6\r\n        (   42.21   -58.79     0.06     0.66)  ; 7\r\n        (   41.70   -58.86    -1.63     0.66)  ; 8\r\n        (   40.89   -58.86    -0.13     0.88)  ; 9\r\n        (   40.30   -58.64     1.38     1.18)  ; 10\r\n        (   39.78   -59.01     1.38     1.18)  ; 11\r\n        (   40.37   -59.30     2.31     0.81)  ; 12\r\n        (   41.03   -59.30     2.81     0.81)  ; 13\r\n        (   41.55   -59.30     3.38     0.81)  ; 14\r\n        (   41.77   -59.81     3.88     0.81)  ; 15\r\n        (   42.14   -60.98     4.38     1.03)  ; 16\r\n        (   42.36   -62.01     4.63     0.81)  ; 17\r\n        (   42.36   -62.81     4.75     0.81)  ; 18\r\n        (   42.65   -63.61     5.25     0.66)  ; 19\r\n        (   43.24   -63.98     5.31     0.66)  ; 20\r\n        (   43.91   -64.35     5.69     0.66)  ; 21\r\n        (   44.57   -64.56     6.13     0.66)  ; 22\r\n        (   44.86   -65.00     6.88     0.96)  ; 23\r\n        (   45.38   -65.81     7.06     1.11)  ; 24\r\n        (   46.26   -66.39     7.44     0.96)  ; 25\r\n        (   46.93   -66.98     7.44     0.74)  ; 26\r\n        (   47.59   -67.78     7.50     0.59)  ; 27\r\n        (   48.69   -68.58     7.56     0.52)  ; 28\r\n        (   49.51   -69.54     7.63     0.81)  ; 29\r\n        (   50.39   -70.19     7.75     0.96)  ; 30\r\n        (   51.49   -70.49     8.13     0.66)  ; 31\r\n        (   52.16   -70.56     8.50     0.66)  ; 32\r\n        (   52.53   -70.41     9.50     0.66)  ; 33\r\n        (   52.30   -70.19    10.00     0.66)  ; 34\r\n        (   52.16   -70.49    10.63     0.81)  ; 35\r\n        (   52.16   -70.78    10.75     1.11)  ; 36\r\n        (   52.60   -71.65    10.75     0.88)  ; 37\r\n        (   52.67   -72.31    10.75     0.88)  ; 38\r\n        (   52.82   -73.12    11.19     0.74)  ; 39\r\n        (   53.04   -73.77    10.19     0.74)  ; 40\r\n        (   53.78   -74.51     9.38     1.11)  ; 41\r\n        (   54.66   -75.31     9.38     1.03)  ; 42\r\n        (   55.47   -76.11     9.19     1.03)  ; 43\r\n        (   56.43   -77.14     7.50     0.81)  ; 44\r\n        (   57.02   -78.01     8.88     0.59)  ; 45\r\n        (   57.54   -78.67     8.88     0.59)  ; 46\r\n        (   58.05   -78.96     8.94     0.59)  ; 47\r\n        (   58.71   -79.48     9.06     0.59)  ; 48\r\n        (   59.08   -79.70     9.56     0.59)  ; 49\r\n        (   59.16   -80.21    10.75     0.59)  ; 50\r\n        (   59.30   -80.50    11.19     0.59)  ; 51\r\n        (   59.89   -81.23    11.25     0.81)  ; 52\r\n        (   59.97   -81.89    11.25     1.55)  ; 53\r\n        (   60.34   -82.62    11.25     1.55)  ; 54\r\n        (   60.70   -83.57    11.81     0.74)  ; 55\r\n        (   61.15   -84.52    11.13     0.59)  ; 56\r\n        (   60.78   -84.74    11.88     0.44)  ; 57\r\n        (   60.63   -85.25    11.88     0.44)  ; 58\r\n        (   60.92   -85.81    12.06     0.81)  ; 59\r\n        (   61.65   -86.32    12.56     1.11)  ; 60\r\n        (   62.09   -86.47    13.19     1.40)  ; 61\r\n        (   61.65   -87.20    13.50     1.11)  ; 62\r\n        (   61.06   -87.56    14.13     0.81)  ; 63\r\n        (   60.25   -88.29    14.94     0.66)  ; 64\r\n        (   59.96   -89.10    15.00     0.96)  ; 65\r\n        (   59.66   -89.61    15.06     1.62)  ; 66\r\n        (   59.15   -90.41    15.06     0.81)  ; 67\r\n        (   58.26   -91.22    15.06     0.52)  ; 68\r\n        (   57.90   -92.02    15.06     0.52)  ; 69\r\n        (   58.04   -93.34    14.75     0.59)  ; 70\r\n        (   58.04   -94.29    15.06     0.44)  ; 71\r\n        (   58.34   -94.87    15.38     0.81)  ; 72\r\n        (   58.41   -95.31    15.69     1.25)  ; 73\r\n        (   58.41   -95.68    16.06     1.62)  ; 74\r\n        (   59.07   -96.19    16.25     1.62)  ; 75\r\n        (   59.59   -96.63    17.00     1.33)  ; 76\r\n        (   59.81   -96.92    17.13     0.66)  ; 77\r\n        (   60.03   -97.94    17.19     0.44)  ; 78\r\n        (   60.33   -98.53    17.19     0.44)  ; 79\r\n        (   60.99   -98.89    17.56     0.66)  ; 80\r\n        (   61.21   -99.04    17.63     1.11)  ; 81\r\n        (   61.95   -99.48    17.63     0.81)  ; 82\r\n        (   62.39   -99.77    17.63     0.44)  ; 83\r\n         Low\r\n      |\r\n        (   36.94   -56.04    -8.25     1.18)  ; 1, R-2-2-2\r\n        (   37.16   -56.91    -8.19     0.88)  ; 2\r\n        (   37.24   -57.50    -8.81     0.59)  ; 3\r\n        (   37.46   -58.45    -9.31     0.59)  ; 4\r\n        (   36.94   -59.25    -9.31     0.59)  ; 5\r\n        (   36.65   -60.49    -9.38     0.66)  ; 6\r\n        (   36.87   -61.96   -10.88     0.81)  ; 7\r\n        (   37.24   -63.20   -11.69     0.66)  ; 8\r\n        (   37.68   -64.37   -12.19     0.88)  ; 9\r\n        (   38.19   -65.68   -14.38     0.96)  ; 10\r\n        (   38.93   -66.78   -14.50     0.96)  ; 11\r\n        (   40.11   -68.17   -14.50     0.96)  ; 12\r\n        (   40.77   -69.34   -14.81     0.96)  ; 13\r\n        (   41.58   -70.65   -15.38     0.81)  ; 14\r\n        (   41.88   -72.12   -16.06     0.66)  ; 15\r\n        (   42.03   -73.58   -16.06     0.66)  ; 16\r\n        (   41.95   -74.75   -16.06     0.81)  ; 17\r\n        (   41.88   -75.77   -16.19     1.11)  ; 18\r\n        (   42.54   -76.43   -16.50     1.18)  ; 19\r\n        (   43.57   -77.31   -16.94     1.11)  ; 20\r\n        (   44.38   -77.75   -17.31     1.40)  ; 21\r\n        (   45.49   -78.40   -17.31     1.03)  ; 22\r\n        (   46.67   -78.84   -17.88     0.88)  ; 23\r\n        (   47.62   -79.50   -18.06     0.66)  ; 24\r\n        (   48.36   -80.60   -18.94     0.96)  ; 25\r\n        (   48.58   -81.40   -20.13     1.40)  ; 26\r\n        (   48.80   -82.35   -20.13     2.21)  ; 27\r\n        (   48.66   -83.74   -20.88     1.55)  ; 28\r\n        (   48.66   -84.18   -22.50     1.11)  ; 29\r\n        (   49.10   -84.98   -22.50     0.74)  ; 30\r\n        (   49.54   -85.90   -22.50     0.74)  ; 31\r\n        (   49.98   -87.07   -23.94     0.52)  ; 32\r\n        (   50.28   -87.87   -24.19     0.52)  ; 33\r\n        (   50.94   -89.34   -24.56     0.74)  ; 34\r\n        (   51.31   -90.29   -24.75     0.96)  ; 35\r\n        (   51.31   -91.16   -24.81     0.96)  ; 36\r\n        (   51.24   -92.11   -25.63     0.74)  ; 37\r\n        (   51.16   -93.58   -26.13     0.59)  ; 38\r\n        (   51.24   -94.82   -26.31     0.96)  ; 39\r\n        (   51.24   -96.13   -26.56     0.96)  ; 40\r\n        (   51.02   -97.45   -26.88     0.59)  ; 41\r\n        (   50.72   -99.06   -27.00     0.44)  ; 42\r\n        (   50.79  -100.89   -27.13     0.44)  ; 43\r\n        (   51.24  -101.98   -27.13     0.44)  ; 44\r\n        (   51.46  -103.08   -27.13     1.11)  ; 45\r\n        (   51.75  -103.81   -27.13     1.84)  ; 46\r\n        (   52.05  -104.61   -27.19     2.28)  ; 47\r\n        (   52.34  -105.64   -27.31     1.92)  ; 48\r\n        (   52.86  -106.44   -27.31     1.11)  ; 49\r\n        (   53.08  -107.32   -27.69     0.74)  ; 50\r\n        (   53.08  -108.49   -27.81     0.74)  ; 51\r\n        (   53.23  -109.44   -27.88     0.52)  ; 52\r\n        (   53.23  -110.10   -28.94     0.52)  ; 53\r\n         Generated\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (    8.64   -12.54    -3.44     3.61)  ; Root\r\n  (   10.40   -13.35    -3.94     3.39)  ; 1, R\r\n  (   11.58   -14.22    -4.06     3.68)  ; 2\r\n  (   12.98   -14.88    -4.19     4.05)  ; 3\r\n  (   13.94   -15.39    -4.19     4.35)  ; 4\r\n  (   15.34   -15.98    -3.69     4.49)  ; 5\r\n  (   16.67   -16.20    -3.63     4.57)  ; 6\r\n  (\r\n    (   18.95   -16.34    -3.94     3.09)  ; 1, R-1\r\n    (   19.98   -16.78    -3.63     2.87)  ; 2\r\n    (   21.01   -17.22    -3.06     2.58)  ; 3\r\n    (   21.82   -17.59    -3.00     2.58)  ; 4\r\n    (   23.08   -18.46    -2.94     2.50)  ; 5\r\n    (   23.11   -18.54    -2.94     2.50)  ; 6\r\n    (\r\n      (   23.96   -19.12    -2.94     2.36)  ; 1, R-1-1\r\n      (   25.14   -19.78    -2.88     2.36)  ; 2\r\n      (   26.46   -20.87    -1.94     2.50)  ; 3\r\n      (   27.42   -21.46    -1.94     2.65)  ; 4\r\n      (   28.31   -21.90    -1.94     2.87)  ; 5\r\n      (\r\n        (   29.19   -21.97    -1.88     2.06)  ; 1, R-1-1-1\r\n        (   29.93   -22.04    -2.31     1.84)  ; 2\r\n        (   30.66   -22.34    -2.88     1.99)  ; 3\r\n        (   30.89   -22.41    -3.13     2.28)  ; 4\r\n        (\r\n          (   31.55   -23.36    -4.31     1.47)  ; 1, R-1-1-1-1\r\n          (   32.06   -24.16    -4.38     1.18)  ; 2\r\n          (   32.65   -24.97    -4.38     1.18)  ; 3\r\n          (   33.17   -25.85    -4.44     1.18)  ; 4\r\n          (   33.98   -26.50    -4.69     1.18)  ; 5\r\n          (   34.94   -27.23    -5.13     1.18)  ; 6\r\n          (   35.75   -27.82    -5.25     1.47)  ; 7\r\n          (   36.24   -28.29    -5.25     1.47)  ; 8\r\n          (\r\n            (   36.48   -28.40    -5.31     1.99)  ; 1, R-1-1-1-1-1\r\n            (   37.07   -28.84    -5.56     1.99)  ; 2\r\n            (   38.03   -29.94    -5.81     1.25)  ; 3\r\n            (   38.75   -30.76    -6.63     1.11)  ; 4\r\n            (   39.64   -31.28    -6.94     1.55)  ; 5\r\n            (   40.45   -32.01    -7.56     1.62)  ; 6\r\n            (\r\n              (   41.18   -32.59    -8.25     1.77)  ; 1, R-1-1-1-1-1-1\r\n              (   42.07   -32.96    -7.75     0.81)  ; 2\r\n              (   43.03   -33.54    -7.56     0.66)  ; 3\r\n              (   43.69   -34.05    -6.50     0.52)  ; 4\r\n              (   43.84   -34.20    -5.94     0.66)  ; 5\r\n              (   44.65   -34.42    -5.25     0.66)  ; 6\r\n              (   45.02   -35.15    -5.25     0.81)  ; 7\r\n              (   45.68   -35.74    -5.06     0.81)  ; 8\r\n              (   46.27   -36.03    -5.00     0.96)  ; 9\r\n              (   47.00   -36.54    -5.50     1.11)  ; 10\r\n              (   47.67   -37.34    -5.88     0.96)  ; 11\r\n              (   48.26   -37.71    -6.44     0.88)  ; 12\r\n              (   48.55   -38.29    -6.88     0.88)  ; 13\r\n              (   48.92   -39.03    -7.31     0.88)  ; 14\r\n              (   49.58   -39.61    -7.44     0.74)  ; 15\r\n              (   50.32   -40.19    -7.88     0.74)  ; 16\r\n              (   50.91   -40.63    -7.88     0.74)  ; 17\r\n              (   51.57   -40.93    -7.88     0.88)  ; 18\r\n              (   52.46   -41.15    -8.00     0.88)  ; 19\r\n              (   53.34   -41.29    -8.50     0.88)  ; 20\r\n              (   54.30   -41.80    -9.00     0.88)  ; 21\r\n              (   55.33   -42.75    -9.00     0.88)  ; 22\r\n              (   55.99   -43.56    -9.44     1.03)  ; 23\r\n              (   56.43   -44.07    -9.75     1.03)  ; 24\r\n              (   57.32   -45.09    -9.88     1.18)  ; 25\r\n              (   57.83   -45.68   -10.19     0.88)  ; 26\r\n              (   57.76   -46.70   -10.50     0.81)  ; 27\r\n              (   57.76   -47.80   -11.00     0.81)  ; 28\r\n              (   58.28   -48.75   -11.94     1.18)  ; 29\r\n              (   58.57   -49.70   -12.31     1.40)  ; 30\r\n              (   59.46   -49.99   -12.31     1.40)  ; 31\r\n              (   59.75   -50.57   -12.63     1.40)  ; 32\r\n              (   59.97   -50.94   -13.00     0.88)  ; 33\r\n              (   60.49   -51.23   -13.00     0.88)  ; 34\r\n              (   61.00   -51.74   -13.06     0.88)  ; 35\r\n              (   61.44   -52.26   -13.25     0.88)  ; 36\r\n              (   62.25   -53.13   -13.25     0.88)  ; 37\r\n              (   62.99   -53.94   -13.25     0.74)  ; 38\r\n              (   63.73   -54.45   -13.25     1.11)  ; 39\r\n              (   64.32   -55.76   -13.69     1.11)  ; 40\r\n              (   64.76   -56.79   -13.69     1.03)  ; 41\r\n              (   65.64   -57.37   -14.31     1.11)  ; 42\r\n              (   66.45   -58.10   -14.56     1.11)  ; 43\r\n              (   67.34   -59.27   -14.69     0.88)  ; 44\r\n              (   67.91   -60.04   -14.69     0.74)  ; 45\r\n              (   68.86   -60.70   -14.94     0.59)  ; 46\r\n              (   69.45   -60.84   -15.13     0.59)  ; 47\r\n              (   70.48   -60.84   -15.50     1.11)  ; 48\r\n              (   71.22   -60.92   -15.88     1.55)  ; 49\r\n              (   72.03   -61.13   -16.06     1.25)  ; 50\r\n              (   73.21   -61.35   -16.19     0.96)  ; 51\r\n              (   74.24   -61.50   -16.56     0.74)  ; 52\r\n              (   75.05   -61.50   -16.69     0.59)  ; 53\r\n              (   76.16   -62.01   -16.88     0.59)  ; 54\r\n              (   76.53   -62.45   -16.88     1.25)  ; 55\r\n              (   76.75   -62.89   -16.88     0.81)  ; 56\r\n              (   77.26   -63.33   -16.88     0.81)  ; 57\r\n              (   77.85   -63.77   -16.88     0.96)  ; 58\r\n              (   78.74   -64.57   -17.44     0.81)  ; 59\r\n              (   79.55   -65.16   -17.50     0.81)  ; 60\r\n              (   80.21   -65.74   -17.75     1.03)  ; 61\r\n              (   81.24   -66.18   -18.44     1.03)  ; 62\r\n              (   82.12   -66.47   -19.00     1.11)  ; 63\r\n              (   82.86   -66.91   -19.81     1.40)  ; 64\r\n              (   83.45   -67.20   -20.13     0.96)  ; 65\r\n              (   84.33   -67.42   -21.06     0.74)  ; 66\r\n              (   85.07   -67.42   -22.38     0.52)  ; 67\r\n              (   86.03   -67.42   -22.88     0.81)  ; 68\r\n              (   86.91   -67.49   -23.19     0.96)  ; 69\r\n              (   87.87   -67.27   -23.94     0.81)  ; 70\r\n              (   88.46   -67.35   -24.94     0.81)  ; 71\r\n              (   89.42   -67.64   -25.56     0.81)  ; 72\r\n              (   90.15   -68.23   -25.56     0.81)  ; 73\r\n              (   91.04   -68.74   -25.81     0.52)  ; 74\r\n              (   91.55   -68.81   -25.13     0.52)  ; 75\r\n              (   92.37   -69.03   -24.56     0.74)  ; 76\r\n              (   93.10   -69.69   -23.88     1.03)  ; 77\r\n              (   93.62   -70.20   -23.50     1.33)  ; 78\r\n              (   94.50   -70.56   -23.13     1.69)  ; 79\r\n              (   95.68   -70.93   -23.13     1.69)  ; 80\r\n              (   96.49   -71.08   -22.00     0.74)  ; 81\r\n              (   97.23   -71.44   -22.00     0.66)  ; 82\r\n              (   97.89   -71.51   -22.00     0.66)  ; 83\r\n              (   98.55   -71.81   -21.94     0.81)  ; 84\r\n              (   99.66   -72.03   -21.94     0.81)  ; 85\r\n              (  100.54   -72.54   -21.94     0.59)  ; 86\r\n              (  100.98   -72.76   -21.94     0.59)  ; 87\r\n              (  101.65   -73.34   -22.06     0.59)  ; 88\r\n              (  102.31   -74.07   -22.31     0.59)  ; 89\r\n              (  102.75   -74.80   -22.25     0.59)  ; 90\r\n               Low\r\n            |\r\n              (   40.72   -33.06    -8.69     0.88)  ; 1, R-1-1-1-1-1-2\r\n              (   41.01   -33.65    -9.25     0.88)  ; 2\r\n              (   41.53   -34.23    -9.69     0.74)  ; 3\r\n              (   41.75   -34.75   -10.31     0.74)  ; 4\r\n              (   42.27   -35.55   -10.56     0.74)  ; 5\r\n              (   42.49   -36.50   -11.31     1.18)  ; 6\r\n              (   42.71   -37.82   -11.88     1.03)  ; 7\r\n              (   42.56   -38.91   -12.81     1.11)  ; 8\r\n              (   42.49   -39.64   -13.56     1.40)  ; 9\r\n              (   42.41   -40.45   -13.88     1.62)  ; 10\r\n              (   42.56   -41.11   -14.63     1.25)  ; 11\r\n              (   42.27   -41.91   -14.69     0.96)  ; 12\r\n              (   42.41   -43.08   -15.31     0.81)  ; 13\r\n              (   42.41   -44.10   -16.19     0.96)  ; 14\r\n              (   42.41   -44.83   -16.50     0.96)  ; 15\r\n              (   43.23   -46.00   -15.63     0.88)  ; 16\r\n              (   43.74   -47.10   -15.50     0.74)  ; 17\r\n              (   44.40   -47.83   -15.56     0.88)  ; 18\r\n              (   45.36   -48.85   -15.63     0.81)  ; 19\r\n              (   46.10   -49.29   -16.50     0.66)  ; 20\r\n              (   46.54   -50.02   -16.69     0.66)  ; 21\r\n              (   47.28   -51.41   -16.88     0.74)  ; 22\r\n              (   47.50   -52.29   -17.19     0.74)  ; 23\r\n              (   47.72   -52.87   -17.88     0.74)  ; 24\r\n              (   48.16   -53.68   -18.50     0.81)  ; 25\r\n              (   48.60   -54.56   -19.63     0.88)  ; 26\r\n              (   49.27   -55.21   -21.50     0.74)  ; 27\r\n              (   49.86   -56.24   -22.44     0.66)  ; 28\r\n              (   51.11   -57.33   -23.63     0.66)  ; 29\r\n              (   52.51   -58.43   -25.31     0.66)  ; 30\r\n              (   53.61   -59.60   -25.50     0.66)  ; 31\r\n              (   54.42   -60.48   -26.00     0.66)  ; 32\r\n              (   54.94   -61.35   -27.06     0.66)  ; 33\r\n              (   55.38   -62.52   -27.25     0.66)  ; 34\r\n              (   55.90   -63.91   -27.25     0.66)  ; 35\r\n              (   56.04   -65.01   -27.25     0.81)  ; 36\r\n              (   56.49   -65.74   -27.44     0.81)  ; 37\r\n              (   57.08   -66.32   -27.63     1.03)  ; 38\r\n              (   58.33   -66.47   -26.75     1.55)  ; 39\r\n              (   59.06   -66.76   -26.50     1.18)  ; 40\r\n              (   59.88   -66.76   -25.38     0.88)  ; 41\r\n              (   60.39   -67.35   -24.38     1.25)  ; 42\r\n              (   60.98   -67.79   -24.25     1.25)  ; 43\r\n              (   61.57   -67.93   -23.94     1.25)  ; 44\r\n              (   62.31   -68.23   -23.69     1.03)  ; 45\r\n              (   63.12   -68.23   -23.69     1.03)  ; 46\r\n              (   64.52   -68.01   -24.06     0.88)  ; 47\r\n              (   65.33   -67.93   -22.88     0.74)  ; 48\r\n              (   65.92   -67.71   -19.75     0.96)  ; 49\r\n              (   66.51   -68.01   -18.56     0.88)  ; 50\r\n              (   66.80   -68.59   -17.56     1.11)  ; 51\r\n              (   67.61   -69.32   -18.31     0.88)  ; 52\r\n              (   68.20   -70.20   -18.25     0.66)  ; 53\r\n              (   68.86   -70.93   -17.81     0.88)  ; 54\r\n              (   69.45   -70.86   -19.06     0.59)  ; 55\r\n              (   70.26   -70.78   -18.50     0.59)  ; 56\r\n              (   70.48   -70.35   -17.94     0.59)  ; 57\r\n              (   71.07   -69.83   -17.63     0.88)  ; 58\r\n              (   71.81   -69.54   -16.94     0.88)  ; 59\r\n              (   72.69   -69.76   -15.00     0.96)  ; 60\r\n              (   73.43   -70.42   -14.94     1.33)  ; 61\r\n              (   74.24   -71.00   -14.56     1.62)  ; 62\r\n              (   75.35   -71.37   -14.69     1.40)  ; 63\r\n              (   76.30   -71.66   -14.69     1.18)  ; 64\r\n              (   77.26   -71.88   -14.69     0.88)  ; 65\r\n              (   78.29   -72.32   -14.69     0.66)  ; 66\r\n              (   79.32   -72.68   -15.25     0.88)  ; 67\r\n              (   79.99   -73.05   -15.75     1.18)  ; 68\r\n              (   80.95   -73.42   -16.69     1.47)  ; 69\r\n              (   82.12   -74.07   -17.06     1.18)  ; 70\r\n              (   83.08   -75.02   -17.31     0.88)  ; 71\r\n              (   84.11   -75.75   -17.75     0.59)  ; 72\r\n              (   85.07   -76.49   -17.81     0.59)  ; 73\r\n              (   85.88   -77.14   -17.81     0.96)  ; 74\r\n              (   87.06   -77.65   -17.81     1.11)  ; 75\r\n              (   88.17   -77.65   -17.81     0.81)  ; 76\r\n              (   89.64   -77.80   -19.13     0.81)  ; 77\r\n              (   90.23   -78.17   -21.69     1.03)  ; 78\r\n              (   90.82   -78.46   -22.81     1.25)  ; 79\r\n              (   91.92   -78.31   -22.81     1.55)  ; 80\r\n              (   92.44   -78.24   -23.00     1.55)  ; 81\r\n              (   92.88   -78.02   -23.19     1.03)  ; 82\r\n              (   93.84   -77.36   -23.50     0.66)  ; 83\r\n              (   94.58   -77.07   -23.94     0.74)  ; 84\r\n              (   95.53   -76.78   -22.31     0.74)  ; 85\r\n              (   96.64   -76.27   -21.19     0.52)  ; 86\r\n              (   97.15   -76.41   -20.69     0.52)  ; 87\r\n              (   97.67   -76.78   -20.69     0.52)  ; 88\r\n              (   98.41   -76.92   -20.69     0.74)  ; 89\r\n              (   99.14   -76.85   -20.19     1.11)  ; 90\r\n              (  100.17   -77.00   -20.94     1.33)  ; 91\r\n              (  101.06   -76.85   -21.00     0.88)  ; 92\r\n              (  101.50   -76.78   -21.56     0.59)  ; 93\r\n              (  101.80   -76.78   -21.94     0.59)  ; 94\r\n              (  102.46   -76.78   -22.25     0.59)  ; 95\r\n              (  103.56   -77.14   -22.50     0.88)  ; 96\r\n              (  104.23   -77.44   -22.94     1.11)  ; 97\r\n              (  105.26   -77.51   -23.13     1.11)  ; 98\r\n              (  106.66   -77.80   -24.88     1.11)  ; 99\r\n              (  107.47   -77.65   -25.25     0.96)  ; 100\r\n              (  108.35   -77.95   -25.25     1.25)  ; 101\r\n              (  109.38   -78.31   -27.19     1.62)  ; 102\r\n              (  109.75   -78.61   -27.31     1.40)  ; 103\r\n              (  110.86   -78.75   -28.19     1.18)  ; 104\r\n              (  111.52   -78.75   -28.75     0.88)  ; 105\r\n              (  112.48   -78.97   -29.06     1.18)  ; 106\r\n              (  112.99   -79.04   -30.44     1.47)  ; 107\r\n              (  113.73   -78.82   -30.63     2.14)  ; 108\r\n              (  114.98   -78.82   -31.31     1.25)  ; 109\r\n              (  115.94   -78.70   -31.81     0.59)  ; 110\r\n              (  116.82   -78.19   -32.13     0.52)  ; 111\r\n              (  117.26   -77.46   -32.56     0.44)  ; 112\r\n              (  117.71   -77.09   -32.00     0.74)  ; 113\r\n              (  118.37   -76.14   -30.88     0.88)  ; 114\r\n              (  118.88   -75.63   -30.56     1.18)  ; 115\r\n              (  119.33   -75.19   -30.50     0.88)  ; 116\r\n              (  119.69   -74.68   -30.38     0.66)  ; 117\r\n              (  119.99   -74.46   -30.13     0.44)  ; 118\r\n              (  120.58   -74.02   -30.13     0.29)  ; 119\r\n              (  121.46   -73.44   -30.06     0.22)  ; 120\r\n              (  121.90   -73.07   -29.63     0.22)  ; 121\r\n               Low\r\n            )  ;  End of split\r\n          |\r\n            (   37.49   -28.72    -7.50     0.74)  ; 1, R-1-1-1-1-2\r\n            (   38.38   -29.02    -7.81     0.81)  ; 2\r\n            (   38.77   -29.24    -8.19     0.59)  ; 3\r\n            (   39.21   -29.54    -9.13     0.59)  ; 4\r\n            (   39.80   -29.83    -9.44     0.66)  ; 5\r\n            (   40.39   -30.12   -10.25     0.74)  ; 6\r\n            (   41.20   -30.34   -10.25     1.03)  ; 7\r\n            (   42.08   -30.78   -11.25     0.88)  ; 8\r\n            (   42.45   -31.07   -12.19     0.74)  ; 9\r\n            (   43.12   -31.29   -12.19     0.59)  ; 10\r\n            (   43.78   -31.58   -12.63     0.81)  ; 11\r\n            (   44.44   -31.07   -13.63     0.96)  ; 12\r\n            (   45.03   -30.85   -14.88     0.96)  ; 13\r\n            (   46.21   -30.78   -15.19     0.81)  ; 14\r\n            (   46.87   -30.56   -15.44     0.81)  ; 15\r\n            (   47.68   -29.90   -15.81     0.81)  ; 16\r\n            (   48.71   -29.83   -16.31     0.74)  ; 17\r\n            (   49.16   -30.05   -17.31     0.74)  ; 18\r\n            (   49.89   -30.70   -17.31     0.74)  ; 19\r\n            (   50.04   -31.36   -18.44     0.96)  ; 20\r\n            (   50.19   -32.24   -19.94     1.18)  ; 21\r\n            (   50.41   -32.90   -20.56     1.47)  ; 22\r\n            (   50.56   -33.77   -20.63     1.18)  ; 23\r\n            (   50.92   -34.43   -22.00     0.96)  ; 24\r\n            (   51.37   -35.24   -22.19     0.96)  ; 25\r\n            (   52.10   -36.26   -22.63     1.03)  ; 26\r\n            (   52.77   -37.06   -23.50     1.03)  ; 27\r\n            (   53.36   -37.58   -24.19     0.74)  ; 28\r\n            (   53.65   -38.09   -24.19     0.52)  ; 29\r\n            (   54.46   -38.60   -24.25     0.52)  ; 30\r\n            (   55.79   -39.33   -24.75     0.44)  ; 31\r\n            (   56.52   -40.13   -25.06     0.66)  ; 32\r\n            (   57.19   -41.30   -25.38     0.96)  ; 33\r\n            (   58.00   -42.55   -26.00     0.88)  ; 34\r\n            (   58.96   -43.20   -26.31     0.88)  ; 35\r\n            (   59.54   -44.08   -26.31     1.25)  ; 36\r\n            (   60.35   -44.89   -25.50     1.33)  ; 37\r\n            (   61.31   -46.06   -25.44     1.11)  ; 38\r\n            (   61.68   -46.42   -27.19     0.81)  ; 39\r\n            (   62.20   -47.08   -27.13     0.59)  ; 40\r\n            (   63.67   -47.37   -27.38     0.81)  ; 41\r\n            (   64.55   -47.52   -28.69     0.66)  ; 42\r\n            (   65.07   -47.52   -30.88     0.66)  ; 43\r\n            (   65.88   -47.74   -31.25     0.52)  ; 44\r\n            (   66.54   -47.59   -31.81     0.52)  ; 45\r\n            (   67.21   -47.66   -31.94     0.52)  ; 46\r\n            (   67.80   -47.81   -31.94     0.81)  ; 47\r\n            (   68.61   -47.37   -32.69     1.11)  ; 48\r\n            (   69.27   -47.01   -33.75     1.11)  ; 49\r\n            (   69.56   -46.79   -34.44     0.88)  ; 50\r\n            (   70.08   -46.79   -35.06     0.66)  ; 51\r\n            (   70.74   -46.86   -35.00     0.44)  ; 52\r\n            (   72.00   -46.71   -35.00     0.74)  ; 53\r\n            (   72.29   -46.71   -35.44     1.47)  ; 54\r\n            (   73.32   -46.64   -35.50     2.21)  ; 55\r\n            (   74.21   -46.27   -35.81     2.95)  ; 56\r\n            (   75.09   -45.98   -35.81     1.62)  ; 57\r\n            (   76.12   -45.84   -36.00     0.81)  ; 58\r\n            (   76.64   -45.62   -36.44     0.52)  ; 59\r\n            (   77.52   -45.54   -37.31     0.37)  ; 60\r\n            (   78.63   -45.11   -37.69     0.44)  ; 61\r\n            (   79.51   -44.89   -37.63     0.52)  ; 62\r\n            (   80.25   -44.74   -37.63     0.88)  ; 63\r\n            (   80.69   -44.45   -38.63     1.25)  ; 64\r\n            (   81.20   -44.30   -38.81     1.55)  ; 65\r\n            (   81.50   -44.01   -39.38     1.25)  ; 66\r\n            (   81.79   -43.94   -40.56     0.81)  ; 67\r\n            (   82.60   -43.64   -43.44     0.66)  ; 68\r\n            (   82.97   -43.35   -45.13     0.88)  ; 69\r\n            (   83.27   -42.91   -46.38     1.18)  ; 70\r\n            (   83.49   -42.33   -46.94     0.96)  ; 71\r\n            (   83.78   -41.74   -47.75     0.74)  ; 72\r\n            (   84.15   -41.60   -50.19     1.03)  ; 73\r\n            (   84.15   -41.52   -52.81     1.33)  ; 74\r\n            (   84.81   -41.67   -53.63     0.96)  ; 75\r\n            (   85.11   -41.67   -54.19     0.74)  ; 76\r\n            (   85.85   -41.74   -54.19     0.96)  ; 77\r\n            (   86.34   -41.58   -55.25     1.62)  ; 78\r\n            (   86.71   -41.58   -56.94     1.62)  ; 79\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (   31.93   -22.13    -2.94     1.18)  ; 1, R-1-1-1-2\r\n          (   32.67   -21.84    -2.94     0.81)  ; 2\r\n          (   33.41   -22.20    -2.63     0.81)  ; 3\r\n          (   34.14   -22.57    -1.88     1.11)  ; 4\r\n          (   34.95   -22.86    -1.19     1.11)  ; 5\r\n          (   35.84   -22.86     0.44     1.11)  ; 6\r\n          (   36.94   -22.79     0.50     0.88)  ; 7\r\n          (   37.83   -22.42     1.06     1.03)  ; 8\r\n          (   38.49   -22.13     1.25     1.03)  ; 9\r\n          (   39.00   -21.55     0.63     0.81)  ; 10\r\n          (   39.59   -20.89     0.25     1.03)  ; 11\r\n          (   39.74   -20.52     0.19     1.40)  ; 12\r\n          (   39.81   -19.50    -0.06     1.40)  ; 13\r\n          (   40.04   -18.77    -0.31     1.25)  ; 14\r\n          (   40.26   -17.89     0.00     1.11)  ; 15\r\n          (   40.48   -17.16     0.13     0.96)  ; 16\r\n          (   40.92   -16.50     0.75     1.18)  ; 17\r\n          (   41.29   -15.55     1.44     1.33)  ; 18\r\n          (   41.73   -14.38     2.44     1.11)  ; 19\r\n          (   42.10   -13.65     2.56     1.11)  ; 20\r\n          (   42.84   -13.14     2.69     1.11)  ; 21\r\n          (   43.57   -12.70     2.75     1.11)  ; 22\r\n          (   44.31   -12.33     2.75     0.81)  ; 23\r\n          (   45.05   -12.12     2.75     1.11)  ; 24\r\n          (   46.15   -11.82     3.19     1.25)  ; 25\r\n          (   47.03   -11.46     2.75     1.11)  ; 26\r\n          (   48.58   -10.87     1.88     1.11)  ; 27\r\n          (   50.94   -10.51     1.88     1.11)  ; 28\r\n          (   52.41   -10.36     1.75     1.11)  ; 29\r\n          (   53.52   -10.00     1.50     1.11)  ; 30\r\n          (   54.62    -9.92     1.50     0.88)  ; 31\r\n          (   55.58    -9.41     1.50     0.88)  ; 32\r\n          (   56.69    -8.68     2.69     1.03)  ; 33\r\n          (   57.72    -7.88     3.31     0.88)  ; 34\r\n          (   58.53    -7.66     3.38     0.88)  ; 35\r\n          (   59.78    -7.22     4.19     0.88)  ; 36\r\n          (   60.44    -7.14     4.69     0.81)  ; 37\r\n          (   61.18    -7.73     4.94     0.81)  ; 38\r\n          (   61.92    -7.88     5.13     0.81)  ; 39\r\n          (   62.65    -7.66     5.38     0.81)  ; 40\r\n          (   63.61    -6.85     5.88     0.96)  ; 41\r\n          (   64.42    -6.41     4.94     1.18)  ; 42\r\n          (   65.53    -6.05     4.69     1.55)  ; 43\r\n          (   66.78    -5.90     4.13     1.25)  ; 44\r\n          (   67.66    -5.61     3.81     1.11)  ; 45\r\n          (   68.33    -5.61     3.50     1.25)  ; 46\r\n          (   69.28    -5.83     3.06     1.03)  ; 47\r\n          (   70.17    -6.12     2.75     1.03)  ; 48\r\n          (   71.79    -6.71     2.75     1.03)  ; 49\r\n          (   73.04    -6.63     2.75     1.03)  ; 50\r\n          (   74.22    -6.12     2.63     1.03)  ; 51\r\n          (   74.81    -5.39     2.38     1.25)  ; 52\r\n          (   75.62    -4.59     2.38     1.84)  ; 53\r\n          (   76.36    -3.56     2.19     1.11)  ; 54\r\n          (   77.02    -2.83     2.56     0.81)  ; 55\r\n          (   77.83    -2.17     2.63     1.11)  ; 56\r\n          (   78.79    -1.59     2.69     0.81)  ; 57\r\n          (   79.84    -1.06     2.88     0.74)  ; 58\r\n          (   80.58    -1.13     2.94     0.74)  ; 59\r\n          (   81.46    -1.50     3.19     0.74)  ; 60\r\n          (   81.76    -2.30     2.50     0.96)  ; 61\r\n          (   82.20    -2.67     1.69     1.18)  ; 62\r\n          (   82.86    -3.10     1.19     0.96)  ; 63\r\n          (   83.53    -3.40     0.81     0.66)  ; 64\r\n          (   84.48    -3.54     0.69     0.66)  ; 65\r\n          (   85.88    -3.76     0.38     0.88)  ; 66\r\n          (   86.91    -3.83    -0.13     1.25)  ; 67\r\n          (   87.95    -3.76    -0.31     1.62)  ; 68\r\n          (   88.83    -3.47    -0.31     1.18)  ; 69\r\n          (   89.79    -3.25    -0.31     0.74)  ; 70\r\n          (   90.30    -3.10    -0.50     0.74)  ; 71\r\n          (   91.26    -2.45    -0.94     1.11)  ; 72\r\n          (   92.22    -1.79    -0.94     0.88)  ; 73\r\n          (   93.10    -1.42    -1.38     0.88)  ; 74\r\n          (   94.43    -1.42     0.31     0.81)  ; 75\r\n          (   95.46    -1.57     1.19     0.88)  ; 76\r\n          (   96.20    -1.71     1.50     1.11)  ; 77\r\n          (   97.08    -1.93     1.63     0.81)  ; 78\r\n          (   98.26    -1.93     2.00     0.81)  ; 79\r\n          (   99.66    -1.28     2.25     1.03)  ; 80\r\n          (  100.62    -0.47     1.56     0.88)  ; 81\r\n          (  101.50    -0.62     1.50     0.88)  ; 82\r\n          (  102.53    -0.76     1.81     1.18)  ; 83\r\n          (  103.34    -1.13     1.81     1.40)  ; 84\r\n          (  104.30    -1.79     1.81     1.40)  ; 85\r\n          (  105.41    -2.30     1.81     1.03)  ; 86\r\n          (  106.29    -3.10     1.81     0.74)  ; 87\r\n          (  107.18    -3.91     2.50     0.66)  ; 88\r\n          (  107.25    -4.42     3.06     0.66)  ; 89\r\n          (  107.54    -4.93     3.56     0.66)  ; 90\r\n          (  108.35    -5.52     3.31     0.52)  ; 91\r\n          (  109.09    -5.81     3.19     0.81)  ; 92\r\n          (  109.90    -6.10     3.69     1.18)  ; 93\r\n          (  110.56    -6.10     2.94     0.96)  ; 94\r\n          (  111.52    -6.17     2.94     0.74)  ; 95\r\n          (  112.11    -6.25     2.50     1.03)  ; 96\r\n          (  112.63    -6.47     2.31     1.03)  ; 97\r\n          (  113.36    -6.39     2.31     0.59)  ; 98\r\n          (  114.17    -6.39     2.19     0.59)  ; 99\r\n          (  115.35    -6.17     1.38     0.44)  ; 100\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (   28.05   -22.27    -1.88     2.65)  ; 1, R-1-1-2\r\n        (   27.91   -23.00    -1.63     1.77)  ; 2\r\n        (   27.98   -24.17    -1.63     1.55)  ; 3\r\n        (   28.05   -24.75    -1.56     1.47)  ; 4\r\n        (   28.79   -25.63    -1.44     1.33)  ; 5\r\n        (   29.53   -26.72    -1.31     1.25)  ; 6\r\n        (   30.56   -27.60    -1.13     1.55)  ; 7\r\n        (   31.42   -28.80    -1.13     1.55)  ; 8\r\n        (\r\n          (   31.52   -29.06    -0.94     1.55)  ; 1, R-1-1-2-1\r\n          (   32.33   -29.72    -1.06     1.55)  ; 2\r\n          (   32.84   -30.38    -1.13     1.69)  ; 3\r\n          (   33.21   -31.55    -1.13     1.33)  ; 4\r\n          (   33.87   -32.35    -1.50     1.25)  ; 5\r\n          (   34.39   -33.67    -1.63     1.25)  ; 6\r\n          (   34.76   -34.40    -1.63     1.55)  ; 7\r\n          (   34.83   -35.06    -1.63     1.77)  ; 8\r\n          (\r\n            (   35.35   -35.72    -2.38     0.96)  ; 1, R-1-1-2-1-1\r\n            (   36.01   -36.45    -3.13     0.88)  ; 2\r\n            (   36.23   -37.47    -3.25     1.03)  ; 3\r\n            (   36.67   -38.35    -3.63     1.03)  ; 4\r\n            (   36.75   -39.15    -3.75     1.03)  ; 5\r\n            (   36.97   -40.17    -3.75     0.88)  ; 6\r\n            (   37.48   -41.05    -4.00     1.03)  ; 7\r\n            (   38.29   -42.07    -4.06     1.33)  ; 8\r\n            (   39.18   -42.73    -4.31     1.33)  ; 9\r\n            (   40.06   -43.46    -4.56     1.18)  ; 10\r\n            (   40.95   -44.41    -4.88     1.18)  ; 11\r\n            (   42.05   -45.00    -5.13     1.47)  ; 12\r\n            (   42.86   -45.58    -5.13     1.69)  ; 13\r\n            (   43.60   -46.02    -5.31     1.69)  ; 14\r\n            (   44.56   -46.90    -5.50     1.69)  ; 15\r\n            (   44.85   -47.48    -5.63     2.06)  ; 16\r\n            (   45.73   -47.78    -5.75     2.50)  ; 17\r\n            (\r\n              (   45.29   -48.87    -6.19     1.69)  ; 1, R-1-1-2-1-1-1\r\n              (   45.29   -49.97    -5.56     1.40)  ; 2\r\n              (   45.37   -51.07    -5.44     1.40)  ; 3\r\n              (   45.44   -52.21    -5.19     1.25)  ; 4\r\n              (   45.36   -53.53    -5.50     1.33)  ; 5\r\n              (   45.29   -54.62    -5.88     1.33)  ; 6\r\n              (   44.63   -55.35    -6.81     1.11)  ; 7\r\n              (   44.55   -56.01    -7.19     0.81)  ; 8\r\n              (\r\n                (   44.70   -57.11    -7.06     0.74)  ; 1, R-1-1-2-1-1-1-1\r\n                (   45.00   -57.84    -7.56     0.66)  ; 2\r\n                (   45.07   -58.72    -8.31     0.66)  ; 3\r\n                (   45.36   -59.52    -8.31     0.66)  ; 4\r\n                (   45.88   -60.47    -9.38     0.66)  ; 5\r\n                (   46.84   -61.35    -9.75     0.96)  ; 6\r\n                (   47.06   -62.52   -10.31     0.88)  ; 7\r\n                (   47.79   -63.91   -10.88     0.74)  ; 8\r\n                (   48.16   -64.56   -11.31     0.74)  ; 9\r\n                (   48.61   -65.15   -11.56     0.74)  ; 10\r\n                (   49.12   -66.47   -12.06     0.88)  ; 11\r\n                (   49.34   -67.05   -12.38     1.25)  ; 12\r\n                (   49.64   -67.93   -12.31     0.96)  ; 13\r\n                (   50.00   -68.66   -12.63     0.74)  ; 14\r\n                (   50.37   -69.75   -12.69     0.74)  ; 15\r\n                (   50.59   -70.49   -12.69     0.96)  ; 16\r\n                (   50.67   -71.51   -13.06     0.66)  ; 17\r\n                (   50.67   -72.24   -13.38     0.81)  ; 18\r\n                (   51.11   -73.26   -13.75     1.11)  ; 19\r\n                (   51.26   -74.14   -13.81     0.88)  ; 20\r\n                (   51.55   -74.87   -13.88     0.66)  ; 21\r\n                (   51.48   -75.82   -14.00     0.66)  ; 22\r\n                (   50.89   -76.70   -14.19     0.66)  ; 23\r\n                (   50.23   -77.72   -14.50     0.66)  ; 24\r\n                (   50.74   -78.82   -14.50     0.66)  ; 25\r\n                (   51.70   -79.77   -14.50     0.81)  ; 26\r\n                (   52.36   -80.43   -14.50     1.40)  ; 27\r\n                (   53.18   -81.78   -15.50     1.62)  ; 28\r\n                (   53.55   -83.31   -12.75     1.33)  ; 29\r\n                (   53.99   -84.04   -12.56     0.96)  ; 30\r\n                (   54.58   -85.29   -12.19     0.66)  ; 31\r\n                (   54.28   -86.31   -11.88     0.52)  ; 32\r\n                (   53.62   -87.48   -11.88     0.66)  ; 33\r\n                (   53.33   -88.36   -12.13     0.81)  ; 34\r\n                (   52.96   -89.31   -13.31     0.59)  ; 35\r\n                (   53.40   -90.26   -13.88     0.52)  ; 36\r\n                (   53.84   -91.06   -14.19     0.52)  ; 37\r\n                (   53.84   -92.01   -14.19     0.52)  ; 38\r\n                (   54.14   -93.33   -14.19     0.59)  ; 39\r\n                (   54.36   -94.35   -14.44     1.25)  ; 40\r\n                (   54.43   -94.93   -14.00     1.55)  ; 41\r\n                (   53.99   -96.18   -13.63     1.55)  ; 42\r\n                (   53.70   -97.27   -13.56     1.18)  ; 43\r\n                (   53.62   -98.00   -13.56     0.88)  ; 44\r\n                (   53.62   -99.10   -13.63     0.74)  ; 45\r\n                (   53.25  -100.78   -13.63     0.74)  ; 46\r\n                (   52.66  -101.59   -13.81     0.59)  ; 47\r\n                (   52.00  -102.83   -13.81     0.59)  ; 48\r\n                (   51.41  -103.93   -14.69     1.03)  ; 49\r\n                (   50.53  -104.73   -16.13     0.88)  ; 50\r\n                (   50.01  -105.68   -16.88     0.88)  ; 51\r\n                (   49.42  -106.19   -18.63     1.18)  ; 52\r\n                (   49.64  -106.92   -20.56     0.96)  ; 53\r\n                (   50.01  -107.87   -22.44     0.88)  ; 54\r\n                (   50.45  -108.90   -22.63     1.18)  ; 55\r\n                (   50.90  -109.48   -23.44     1.47)  ; 56\r\n                (   51.16  -110.81   -23.75     0.81)  ; 57\r\n                (   51.24  -111.32   -24.06     0.59)  ; 58\r\n                (   51.75  -112.27   -24.25     0.52)  ; 59\r\n                (   52.49  -113.08   -24.25     0.74)  ; 60\r\n                (   53.01  -113.81   -24.75     0.74)  ; 61\r\n                (   53.37  -114.69   -24.81     0.74)  ; 62\r\n                (   53.37  -115.34   -23.69     1.33)  ; 63\r\n                (   53.52  -115.93   -23.81     2.14)  ; 64\r\n                (   53.74  -116.51   -23.81     2.36)  ; 65\r\n                (   53.67  -117.97   -23.13     1.47)  ; 66\r\n                (   53.52  -118.78   -23.13     0.81)  ; 67\r\n                (   53.08  -119.88   -23.13     0.44)  ; 68\r\n                (   52.34  -120.83   -23.13     0.66)  ; 69\r\n                (   51.97  -121.63   -23.13     0.66)  ; 70\r\n                (   51.46  -122.51   -23.06     0.66)  ; 71\r\n                (   50.94  -123.38   -23.06     0.52)  ; 72\r\n                (   50.57  -124.12   -22.75     0.52)  ; 73\r\n                (   49.91  -124.04   -22.44     0.44)  ; 74\r\n                (   49.54  -124.33   -22.25     0.44)  ; 75\r\n                (   48.59  -124.77   -22.25     0.74)  ; 76\r\n                (   47.70  -125.28   -22.19     1.11)  ; 77\r\n                (   46.67  -125.80   -22.00     1.40)  ; 78\r\n                (   45.86  -126.16   -21.75     1.69)  ; 79\r\n                (   45.27  -126.16   -21.69     1.69)  ; 80\r\n                (   44.31  -126.60   -21.69     0.96)  ; 81\r\n                (   43.58  -126.67   -21.69     0.52)  ; 82\r\n                (   42.76  -127.19   -21.69     0.29)  ; 83\r\n                (   42.25  -127.77   -21.69     0.29)  ; 84\r\n                (   41.66  -128.50   -21.38     0.44)  ; 85\r\n                (   41.22  -128.94   -21.56     0.74)  ; 86\r\n                (   40.55  -129.09   -21.81     0.74)  ; 87\r\n                (   40.19  -129.45   -21.81     0.44)  ; 88\r\n                (   39.74  -129.45   -21.94     0.44)  ; 89\r\n                (   39.01  -129.89   -22.13     0.44)  ; 90\r\n                 Low\r\n              |\r\n                (   44.53   -57.29    -8.75     0.59)  ; 1, R-1-1-2-1-1-1-2\r\n                (   44.24   -57.87    -8.88     0.88)  ; 2\r\n                (   43.87   -58.46    -9.75     0.88)  ; 3\r\n                (   43.72   -59.12   -10.00     0.66)  ; 4\r\n                (   43.65   -59.99   -10.56     0.44)  ; 5\r\n                (   43.72   -60.36   -11.13     0.44)  ; 6\r\n                (   44.09   -60.65   -11.31     0.66)  ; 7\r\n                (   44.53   -61.31   -12.13     0.88)  ; 8\r\n                (   44.98   -61.60   -12.50     0.74)  ; 9\r\n                (   45.49   -62.41   -13.44     0.59)  ; 10\r\n                (   45.71   -63.28   -14.25     0.81)  ; 11\r\n                (   45.49   -64.38   -14.88     0.81)  ; 12\r\n                (   45.12   -65.40   -14.94     0.81)  ; 13\r\n                (   44.90   -66.35   -15.94     0.81)  ; 14\r\n                (   44.75   -67.67   -16.38     0.88)  ; 15\r\n                (   44.98   -68.55   -16.81     0.88)  ; 16\r\n                (   44.98   -69.35   -17.25     0.88)  ; 17\r\n                (   45.27   -70.01   -18.00     1.03)  ; 18\r\n                (   45.42   -70.59   -18.50     0.96)  ; 19\r\n                (   45.64   -71.32   -19.69     1.18)  ; 20\r\n                (   45.56   -72.57   -20.50     1.03)  ; 21\r\n                (   45.79   -73.15   -20.81     0.74)  ; 22\r\n                (   46.15   -73.96   -21.63     0.59)  ; 23\r\n                (   46.60   -74.10   -22.31     0.44)  ; 24\r\n                (   46.38   -74.91   -22.50     0.81)  ; 25\r\n                (   45.86   -75.78   -23.13     1.03)  ; 26\r\n                (   45.56   -76.73   -24.19     0.74)  ; 27\r\n                (   45.64   -77.46   -24.69     0.74)  ; 28\r\n                (   45.93   -78.56   -24.69     0.81)  ; 29\r\n                (   46.45   -79.66   -25.38     1.03)  ; 30\r\n                (   46.74   -80.24   -25.81     1.03)  ; 31\r\n                (   47.26   -81.27   -26.06     0.81)  ; 32\r\n                (   47.26   -81.92   -26.75     0.66)  ; 33\r\n                (   47.48   -82.87   -26.88     0.81)  ; 34\r\n                (   47.94   -83.98   -27.75     0.88)  ; 35\r\n                (   47.87   -85.29   -27.94     0.88)  ; 36\r\n                (   47.65   -86.32   -28.56     0.74)  ; 37\r\n                (   47.65   -86.97   -28.88     0.59)  ; 38\r\n                (   47.94   -87.56   -29.25     0.59)  ; 39\r\n                (   48.31   -88.29   -30.00     0.81)  ; 40\r\n                (   48.09   -88.95   -30.19     0.59)  ; 41\r\n                (   47.80   -89.75   -30.19     0.59)  ; 42\r\n                (   47.80   -90.56   -30.31     1.25)  ; 43\r\n                (   47.57   -91.21   -30.63     1.84)  ; 44\r\n                (   47.57   -91.80   -30.88     1.84)  ; 45\r\n                (   47.35   -92.38   -30.88     2.06)  ; 46\r\n                (   47.87   -93.04   -31.06     1.18)  ; 47\r\n                (   47.87   -93.63   -31.13     0.59)  ; 48\r\n                (   47.87   -94.28   -31.38     0.59)  ; 49\r\n                (   48.09   -95.53   -30.56     0.52)  ; 50\r\n                (   48.09   -96.40   -30.56     0.52)  ; 51\r\n                (   48.31   -97.72   -30.56     0.74)  ; 52\r\n                (   48.38   -98.89   -30.56     1.03)  ; 53\r\n                (   48.46   -99.91   -30.56     0.96)  ; 54\r\n                (   48.75  -100.57   -30.56     0.66)  ; 55\r\n                (   48.68  -101.23   -30.56     0.44)  ; 56\r\n                (   48.90  -102.11   -30.56     0.44)  ; 57\r\n                (   48.90  -102.84   -30.63     1.03)  ; 58\r\n                (   48.97  -103.79   -30.88     1.33)  ; 59\r\n                (   49.05  -104.74   -30.88     1.33)  ; 60\r\n                (   49.20  -105.61   -31.00     1.18)  ; 61\r\n                (   49.64  -106.13   -31.00     0.88)  ; 62\r\n                (   50.08  -107.00   -31.31     0.59)  ; 63\r\n                (   50.67  -107.51   -31.50     0.44)  ; 64\r\n                (   51.41  -108.54   -32.19     0.74)  ; 65\r\n                (   51.63  -109.27   -32.75     1.55)  ; 66\r\n                (   52.22  -110.22   -31.63     1.55)  ; 67\r\n                (   52.44  -111.10   -31.38     1.33)  ; 68\r\n                (   52.80  -112.19   -33.31     1.33)  ; 69\r\n                (   52.92  -113.33   -34.56     1.25)  ; 70\r\n                (   52.62  -114.57   -31.69     0.96)  ; 71\r\n                (   52.40  -115.52   -32.13     0.74)  ; 72\r\n                (   52.11  -116.18   -32.13     0.59)  ; 73\r\n                (   52.11  -117.28   -32.19     0.44)  ; 74\r\n                (   51.88  -118.37   -32.25     0.44)  ; 75\r\n                (   51.88  -119.40   -32.44     0.74)  ; 76\r\n                (   51.81  -119.98   -32.88     0.52)  ; 77\r\n                (   51.30  -120.93   -33.13     0.37)  ; 78\r\n                (   51.07  -121.44   -33.13     0.37)  ; 79\r\n                (   50.93  -122.61   -33.13     0.37)  ; 80\r\n                (   51.00  -123.71   -33.06     0.88)  ; 81\r\n                (   50.93  -124.51   -33.50     1.47)  ; 82\r\n                (   51.30  -125.32   -34.06     1.25)  ; 83\r\n                (   51.74  -125.98   -34.06     1.03)  ; 84\r\n                (   52.18  -126.49   -34.31     0.66)  ; 85\r\n                (   52.47  -127.22   -34.44     0.59)  ; 86\r\n                (   52.84  -127.88   -34.50     0.81)  ; 87\r\n                (   53.21  -128.46   -35.00     1.11)  ; 88\r\n                (   53.73  -129.12   -35.00     1.40)  ; 89\r\n                (   54.02  -129.85   -35.00     1.18)  ; 90\r\n                (   54.17  -130.22   -35.19     0.74)  ; 91\r\n                (   54.24  -130.87   -35.19     0.44)  ; 92\r\n                (   54.68  -131.75   -35.50     0.44)  ; 93\r\n                (   54.98  -132.70   -35.88     0.37)  ; 94\r\n                (   55.27  -133.58   -36.19     0.37)  ; 95\r\n                (   55.49  -134.24   -36.31     0.37)  ; 96\r\n                (   55.42  -134.82   -36.31     0.74)  ; 97\r\n                (   55.49  -135.33   -36.56     1.47)  ; 98\r\n                (   55.49  -135.77   -36.75     1.47)  ; 99\r\n                (   55.27  -136.28   -36.94     1.03)  ; 100\r\n                (   55.05  -136.94   -37.50     0.44)  ; 101\r\n                (   54.98  -138.11   -39.25     0.29)  ; 102\r\n                (   54.76  -139.21   -39.25     0.29)  ; 103\r\n                (   54.68  -139.87   -39.81     0.29)  ; 104\r\n                (   54.54  -140.89   -40.75     0.59)  ; 105\r\n                (   54.24  -141.62   -41.13     1.18)  ; 106\r\n                (   53.95  -142.13   -41.94     1.69)  ; 107\r\n                (   53.88  -142.57   -42.31     0.74)  ; 108\r\n                (   54.10  -143.23   -42.75     0.44)  ; 109\r\n                (   54.40  -143.74   -43.06     0.29)  ; 110\r\n                (   54.62  -144.47   -43.06     0.29)  ; 111\r\n                (   54.40  -145.28   -43.56     0.29)  ; 112\r\n                (   54.25  -145.94   -43.75     0.88)  ; 113\r\n                (   54.25  -146.37   -43.75     0.88)  ; 114\r\n                (   54.47  -146.89   -44.00     0.52)  ; 115\r\n                (   54.76  -147.47   -44.25     0.22)  ; 116\r\n                (   55.21  -148.20   -45.19     0.22)  ; 117\r\n                 Low\r\n              )  ;  End of split\r\n            |\r\n              (   46.25   -47.80    -5.56     1.55)  ; 1, R-1-1-2-1-1-2\r\n              (   46.77   -47.50    -5.69     0.44)  ; 2\r\n              (   47.14   -47.50    -5.81     0.29)  ; 3\r\n              (   47.73   -47.65    -5.88     0.29)  ; 4\r\n              (   48.09   -47.94    -5.94     0.52)  ; 5\r\n              (   48.46   -48.45    -6.38     0.66)  ; 6\r\n              (   48.83   -48.67    -6.94     0.66)  ; 7\r\n              (   49.35   -48.67    -7.06     0.66)  ; 8\r\n              (   49.79   -48.45    -7.56     0.66)  ; 9\r\n              (   50.67   -48.60    -7.56     0.66)  ; 10\r\n              (   51.63   -48.89    -7.56     0.81)  ; 11\r\n              (   52.37   -49.18    -7.88     0.59)  ; 12\r\n              (   52.96   -49.62    -8.06     0.59)  ; 13\r\n              (   53.91   -50.21    -8.06     0.74)  ; 14\r\n              (   54.87   -50.79    -8.06     0.74)  ; 15\r\n              (   55.39   -51.45    -8.06     0.59)  ; 16\r\n              (   56.05   -52.33    -8.31     0.74)  ; 17\r\n              (   56.42   -53.06    -8.31     1.03)  ; 18\r\n              (   56.49   -53.79    -8.56     1.03)  ; 19\r\n              (   57.01   -54.74    -9.00     0.74)  ; 20\r\n              (   57.30   -55.25    -9.13     0.74)  ; 21\r\n              (   57.82   -55.76    -9.69     0.74)  ; 22\r\n              (   58.41   -56.35    -9.88     0.74)  ; 23\r\n              (   59.00   -57.15   -10.38     0.59)  ; 24\r\n              (   59.51   -57.88   -10.81     0.59)  ; 25\r\n              (   60.10   -58.32   -11.63     0.52)  ; 26\r\n              (   60.62   -58.98    -9.88     0.81)  ; 27\r\n              (   61.28   -59.78    -9.25     0.81)  ; 28\r\n              (   61.95   -60.51    -9.13     0.81)  ; 29\r\n              (   62.39   -61.25    -9.13     1.40)  ; 30\r\n              (   62.76   -61.98    -9.38     1.55)  ; 31\r\n              (   62.98   -62.85    -9.69     1.84)  ; 32\r\n              (   63.27   -63.95   -10.13     1.11)  ; 33\r\n              (   63.27   -64.68   -10.44     0.88)  ; 34\r\n              (   63.64   -65.92   -10.69     1.11)  ; 35\r\n              (   63.71   -66.36   -11.56     1.11)  ; 36\r\n              (   63.86   -67.39   -12.94     0.81)  ; 37\r\n              (   63.93   -68.26   -13.94     0.59)  ; 38\r\n              (   63.64   -68.70   -15.06     0.66)  ; 39\r\n              (   63.57   -70.24   -15.50     0.74)  ; 40\r\n              (   63.79   -71.48   -16.38     1.11)  ; 41\r\n              (   64.16   -72.50   -16.56     1.11)  ; 42\r\n              (   64.89   -73.67   -16.88     0.88)  ; 43\r\n              (   65.78   -74.48   -17.19     0.59)  ; 44\r\n              (   66.73   -75.06   -16.69     0.59)  ; 45\r\n              (   66.73   -75.87   -16.44     0.52)  ; 46\r\n              (   66.44   -76.96   -16.38     0.81)  ; 47\r\n              (   66.27   -77.82   -16.38     1.33)  ; 48\r\n              (   66.20   -78.55   -16.38     1.33)  ; 49\r\n              (   65.83   -79.50   -16.63     0.96)  ; 50\r\n              (   65.75   -80.45   -16.63     0.59)  ; 51\r\n              (   66.12   -81.18   -16.50     0.37)  ; 52\r\n              (   66.34   -81.77   -16.38     0.59)  ; 53\r\n              (   66.71   -82.94   -15.81     0.88)  ; 54\r\n              (   66.86   -83.52   -15.63     0.66)  ; 55\r\n              (   67.23   -84.18   -15.63     0.81)  ; 56\r\n              (   67.37   -84.76   -15.38     0.81)  ; 57\r\n              (   67.30   -85.27   -15.00     0.52)  ; 58\r\n              (   67.96   -85.71   -14.63     0.74)  ; 59\r\n              (   68.55   -86.01   -14.63     0.88)  ; 60\r\n              (   69.36   -86.52   -14.63     0.74)  ; 61\r\n              (   69.88   -86.74   -14.63     1.03)  ; 62\r\n              (   70.69   -87.25   -14.63     1.03)  ; 63\r\n              (   71.20   -87.83   -14.63     0.66)  ; 64\r\n              (   71.57   -88.49   -14.63     0.66)  ; 65\r\n              (   71.65   -89.37   -14.56     0.66)  ; 66\r\n              (   71.65   -90.61   -14.38     0.59)  ; 67\r\n              (   71.50   -91.56   -14.38     0.59)  ; 68\r\n              (   71.50   -92.58   -14.38     0.88)  ; 69\r\n              (   71.57   -93.46   -14.38     1.18)  ; 70\r\n              (   71.79   -94.34   -14.94     2.06)  ; 71\r\n              (   71.87   -95.07   -15.31     2.36)  ; 72\r\n              (   71.65   -96.02   -15.56     1.62)  ; 73\r\n              (   71.65   -96.82   -15.69     1.18)  ; 74\r\n              (   71.28   -97.41   -15.94     0.74)  ; 75\r\n              (   70.91   -97.99   -16.25     0.52)  ; 76\r\n              (   70.98   -98.73   -16.25     0.88)  ; 77\r\n              (   70.84   -99.46   -17.06     1.11)  ; 78\r\n              (   70.91  -100.48   -18.00     0.74)  ; 79\r\n              (   71.35  -101.06   -18.13     0.52)  ; 80\r\n              (   72.09  -101.72   -19.19     0.59)  ; 81\r\n              (   72.53  -103.11   -19.69     0.81)  ; 82\r\n              (   72.53  -103.77   -20.69     1.18)  ; 83\r\n              (   72.75  -104.35   -20.63     2.36)  ; 84\r\n              (   72.83  -104.87   -20.94     2.36)  ; 85\r\n              (   72.75  -105.60   -21.69     1.99)  ; 86\r\n              (   72.90  -106.33   -22.69     0.81)  ; 87\r\n              (   72.62  -107.14   -23.25     0.44)  ; 88\r\n              (   72.62  -107.94   -23.44     0.44)  ; 89\r\n              (   72.70  -108.45   -23.63     0.44)  ; 90\r\n              (   72.99  -109.62   -23.69     0.44)  ; 91\r\n              (   72.70  -110.35   -23.63     0.66)  ; 92\r\n               Low\r\n            )  ;  End of split\r\n          |\r\n            (   34.63   -35.97    -0.94     0.88)  ; 1, R-1-1-2-1-2\r\n            (   34.26   -36.92    -0.81     0.66)  ; 2\r\n            (   33.74   -37.87    -0.75     0.74)  ; 3\r\n            (   33.37   -38.74    -0.75     0.74)  ; 4\r\n            (   33.37   -39.91    -0.44     0.96)  ; 5\r\n            (   33.52   -41.23    -0.44     0.96)  ; 6\r\n            (   33.52   -41.74    -0.25     0.96)  ; 7\r\n            (   33.52   -42.69    -0.19     0.81)  ; 8\r\n            (   33.89   -43.64    -0.19     0.81)  ; 9\r\n            (   34.11   -45.10    -0.19     0.81)  ; 10\r\n            (   34.77   -45.91    -0.19     0.81)  ; 11\r\n            (   35.51   -46.78     0.31     0.74)  ; 12\r\n            (   36.25   -47.30     1.00     0.88)  ; 13\r\n            (   37.06   -47.95     1.81     1.03)  ; 14\r\n            (   38.02   -48.83     3.13     1.03)  ; 15\r\n            (   38.68   -49.42     3.13     0.81)  ; 16\r\n            (   39.42   -50.00     3.25     0.81)  ; 17\r\n            (   39.86   -50.80     3.38     0.66)  ; 18\r\n            (   40.00   -51.90     3.50     0.81)  ; 19\r\n            (   39.49   -53.00     3.88     0.74)  ; 20\r\n            (   38.90   -53.95     3.44     0.81)  ; 21\r\n            (   38.53   -54.68     2.44     0.81)  ; 22\r\n            (   38.16   -55.63     1.00     0.88)  ; 23\r\n            (   38.16   -56.51     0.56     0.88)  ; 24\r\n            (   38.68   -57.82     0.25     1.03)  ; 25\r\n            (   38.75   -58.77    -0.25     1.03)  ; 26\r\n            (   38.68   -60.09     0.75     0.88)  ; 27\r\n            (   39.34   -60.89     0.75     0.66)  ; 28\r\n            (   39.64   -61.92     0.81     0.66)  ; 29\r\n            (   39.93   -63.23     0.81     0.81)  ; 30\r\n            (   40.30   -64.55     0.81     0.81)  ; 31\r\n            (   40.76   -65.50     0.88     0.96)  ; 32\r\n            (   41.27   -66.30     0.88     0.96)  ; 33\r\n            (   41.57   -67.40     0.88     0.88)  ; 34\r\n            (   41.72   -68.20     1.25     0.81)  ; 35\r\n            (   41.86   -70.18     1.56     0.88)  ; 36\r\n            (   41.94   -71.06     1.69     0.88)  ; 37\r\n            (   42.67   -71.86     2.31     0.88)  ; 38\r\n            (   43.34   -72.37     2.94     0.74)  ; 39\r\n            (   43.63   -72.88     3.19     0.52)  ; 40\r\n            (   43.71   -73.54     3.19     0.52)  ; 41\r\n            (   43.48   -74.13     3.31     0.74)  ; 42\r\n            (   43.26   -75.08     3.50     0.74)  ; 43\r\n            (   43.19   -76.03     3.06     1.11)  ; 44\r\n            (   43.41   -76.83     2.06     1.11)  ; 45\r\n            (   43.63   -77.56     0.50     1.11)  ; 46\r\n            (   43.56   -77.93     0.25     1.92)  ; 47\r\n            (   43.56   -78.44    -0.19     1.47)  ; 48\r\n            (   43.85   -78.80    -0.19     0.81)  ; 49\r\n            (   43.93   -79.53    -0.19     0.59)  ; 50\r\n            (   44.15   -80.70    -0.44     0.81)  ; 51\r\n            (   44.66   -81.73    -0.88     0.81)  ; 52\r\n            (   45.47   -82.68    -1.19     0.81)  ; 53\r\n            (   46.14   -83.12    -1.63     0.81)  ; 54\r\n            (   46.65   -84.29    -1.63     0.81)  ; 55\r\n            (   47.17   -84.94    -1.63     0.81)  ; 56\r\n            (   48.05   -85.75    -2.56     0.81)  ; 57\r\n            (   48.35   -86.70    -2.56     0.96)  ; 58\r\n            (   48.86   -87.94    -1.44     0.88)  ; 59\r\n            (   49.08   -88.74    -0.56     0.74)  ; 60\r\n            (   49.38   -88.74     1.13     0.74)  ; 61\r\n            (   49.75   -88.96     1.19     0.74)  ; 62\r\n            (   50.19   -89.70     1.19     0.88)  ; 63\r\n            (   50.63   -90.65     1.38     0.88)  ; 64\r\n            (   50.70   -91.45     1.63     0.88)  ; 65\r\n            (   50.85   -92.18     2.63     1.18)  ; 66\r\n            (   50.85   -92.25     2.63     1.55)  ; 67\r\n            (   50.78   -92.91     2.94     1.11)  ; 68\r\n            (   50.78   -93.57     3.19     0.66)  ; 69\r\n            (   50.14   -94.00     4.44     0.59)  ; 70\r\n            (   50.51   -94.73     4.81     0.44)  ; 71\r\n            (   50.95   -94.95     4.81     0.66)  ; 72\r\n            (   51.17   -95.76     4.81     0.88)  ; 73\r\n            (   51.17   -96.56     4.81     0.96)  ; 74\r\n            (   51.02   -97.37     4.81     0.81)  ; 75\r\n            (   51.32   -98.46     4.88     0.81)  ; 76\r\n            (   51.46   -99.49     4.88     0.74)  ; 77\r\n            (   51.76  -100.29     4.88     0.59)  ; 78\r\n            (   52.05  -100.95     4.88     0.59)  ; 79\r\n            (   52.64  -101.31     4.88     0.88)  ; 80\r\n            (   53.45  -101.82     4.94     1.11)  ; 81\r\n            (   53.89  -101.90     4.94     1.11)  ; 82\r\n            (   54.48  -102.41     5.00     0.96)  ; 83\r\n            (   55.22  -103.07     5.00     0.74)  ; 84\r\n            (   56.03  -103.51     5.00     0.74)  ; 85\r\n            (   56.69  -104.16     5.00     0.74)  ; 86\r\n            (   57.28  -104.75     6.00     0.59)  ; 87\r\n            (   57.87  -105.41     6.38     0.74)  ; 88\r\n            (   58.02  -106.65     7.31     0.59)  ; 89\r\n            (   58.32  -107.16     7.81     0.59)  ; 90\r\n            (   58.39  -107.96     7.94     0.52)  ; 91\r\n            (   58.02  -108.77     7.94     0.52)  ; 92\r\n            (   57.50  -109.28     7.94     0.52)  ; 93\r\n            (   57.06  -110.08     8.06     0.52)  ; 94\r\n            (   56.55  -111.04     8.25     0.52)  ; 95\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (   30.98   -29.75    -0.94     0.59)  ; 1, R-1-1-2-2\r\n          (   30.76   -30.19     1.06     0.59)  ; 2\r\n          (   30.39   -30.56     2.00     0.59)  ; 3\r\n          (   30.02   -31.07     2.88     0.59)  ; 4\r\n          (   30.32   -31.95     3.00     0.59)  ; 5\r\n          (   30.76   -32.39     3.00     0.59)  ; 6\r\n          (   31.06   -33.19     3.00     0.59)  ; 7\r\n          (   31.57   -34.14     3.69     0.59)  ; 8\r\n          (   31.79   -35.24     2.81     1.03)  ; 9\r\n          (   32.23   -36.41     2.25     1.03)  ; 10\r\n          (   32.68   -37.21     2.00     0.52)  ; 11\r\n          (   33.12   -38.38     2.69     0.52)  ; 12\r\n          (   33.86   -39.33     4.44     0.52)  ; 13\r\n          (   34.45   -39.26     5.88     0.81)  ; 14\r\n          (   35.03   -38.96     6.50     0.59)  ; 15\r\n          (   35.84   -39.18     6.38     0.81)  ; 16\r\n          (   36.14   -39.18     5.75     0.81)  ; 17\r\n          (   37.10   -39.55     5.63     0.59)  ; 18\r\n          (   37.24   -40.13     7.75     0.59)  ; 19\r\n          (   36.95   -40.35     8.38     0.59)  ; 20\r\n          (   36.43   -40.57     8.88     0.96)  ; 21\r\n          (   35.92   -41.01     9.81     0.96)  ; 22\r\n          (   35.48   -41.01     9.81     0.96)  ; 23\r\n          (   34.59   -41.08    10.75     0.96)  ; 24\r\n          (   34.67   -41.23    12.13     1.11)  ; 25\r\n          (   35.48   -41.38    12.50     1.11)  ; 26\r\n          (   36.07   -41.52    12.94     0.74)  ; 27\r\n          (   37.54   -41.23    13.00     0.52)  ; 28\r\n          (   38.42   -41.01    13.19     0.52)  ; 29\r\n          (   39.23   -40.65    13.50     0.74)  ; 30\r\n          (   40.04   -40.13    14.25     1.03)  ; 31\r\n          (   40.63   -39.70    14.63     1.11)  ; 32\r\n          (   41.00   -39.62    15.00     0.59)  ; 33\r\n          (   40.78   -40.06    15.63     0.59)  ; 34\r\n          (   40.27   -40.57    15.75     0.59)  ; 35\r\n          (   39.90   -41.30    15.81     0.59)  ; 36\r\n          (   40.19   -41.74    16.50     0.59)  ; 37\r\n          (   40.56   -42.40    16.25     0.66)  ; 38\r\n          (   41.15   -42.91    16.25     0.66)  ; 39\r\n          (   41.67   -43.28    16.25     0.66)  ; 40\r\n          (   42.18   -43.94    16.94     0.66)  ; 41\r\n          (   42.99   -44.23    17.00     0.66)  ; 42\r\n          (   44.10   -44.01    16.63     0.59)  ; 43\r\n          (   44.69   -44.23    16.63     0.74)  ; 44\r\n          (   45.05   -44.74    16.63     0.59)  ; 45\r\n          (   45.72   -44.96    16.63     0.59)  ; 46\r\n          (   46.45   -45.18    15.69     0.59)  ; 47\r\n          (   47.12   -44.96    16.13     0.59)  ; 48\r\n          (   47.63   -44.08    16.69     0.66)  ; 49\r\n          (   48.52   -43.57    17.50     0.59)  ; 50\r\n          (   49.55   -43.86    17.75     0.52)  ; 51\r\n          (   50.43   -44.23    17.63     0.37)  ; 52\r\n          (   50.95   -45.03    17.38     0.29)  ; 53\r\n           Low\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (   23.41   -19.71    -4.56     1.55)  ; 1, R-1-2\r\n      (   23.92   -20.59    -7.56     1.55)  ; 2\r\n      (   24.59   -21.17    -9.50     1.62)  ; 3\r\n      (   25.10   -21.83   -10.44     2.06)  ; 4\r\n      (   25.00   -21.92   -10.44     2.06)  ; 5\r\n      (\r\n        (   25.25   -22.63   -13.44     1.99)  ; 1, R-1-2-1\r\n        (   26.50   -24.02   -13.13     1.84)  ; 2\r\n        (   27.83   -25.92   -12.63     1.69)  ; 3\r\n        (   27.87   -26.22   -12.63     1.69)  ; 4\r\n        (\r\n          (   28.42   -27.82   -12.69     1.69)  ; 1, R-1-2-1-1\r\n          (   28.86   -29.14   -12.81     2.21)  ; 2\r\n          (\r\n            (   29.15   -30.16   -13.13     2.65)  ; 1, R-1-2-1-1-1\r\n            (   29.32   -30.27   -13.13     2.65)  ; 2\r\n            (\r\n              (   29.76   -31.47   -14.69     2.06)  ; 1, R-1-2-1-1-1-1\r\n              (   31.61   -33.23   -15.00     1.92)  ; 2\r\n              (\r\n                (   32.71   -34.32   -13.75     2.21)  ; 1, R-1-2-1-1-1-1-1\r\n                (   32.78   -35.20   -13.81     1.92)  ; 2\r\n                (\r\n                  (   32.56   -36.23   -14.50     0.74)  ; 1, R-1-2-1-1-1-1-1-1\r\n                  (   32.19   -37.03   -14.50     0.44)  ; 2\r\n                  (   32.34   -37.98   -14.50     0.66)  ; 3\r\n                  (   32.56   -38.78   -15.06     0.88)  ; 4\r\n                  (   32.93   -39.44   -15.19     0.88)  ; 5\r\n                  (   33.74   -40.46   -15.56     0.52)  ; 6\r\n                  (   34.40   -41.49   -15.69     0.96)  ; 7\r\n                  (   35.14   -42.51   -16.19     1.33)  ; 8\r\n                  (   35.44   -43.39   -16.50     1.03)  ; 9\r\n                  (   35.88   -44.19   -16.31     0.81)  ; 10\r\n                  (   35.73   -44.78   -16.19     1.03)  ; 11\r\n                  (   35.95   -45.58   -16.56     1.03)  ; 12\r\n                  (   36.25   -46.53   -15.63     0.81)  ; 13\r\n                  (   36.69   -47.26   -15.50     0.59)  ; 14\r\n                  (   36.91   -48.07   -15.56     0.59)  ; 15\r\n                  (   36.76   -49.16   -15.63     0.88)  ; 16\r\n                  (   37.06   -49.97   -15.63     0.88)  ; 17\r\n                  (   37.57   -50.84   -15.63     0.88)  ; 18\r\n                  (   37.72   -51.87   -15.63     0.88)  ; 19\r\n                  (   38.16   -53.70   -17.13     0.88)  ; 20\r\n                  (   38.09   -54.79   -18.06     1.18)  ; 21\r\n                  (   38.01   -56.18   -18.19     0.88)  ; 22\r\n                  (   38.53   -57.20   -18.31     0.88)  ; 23\r\n                  (   39.19   -58.15   -18.31     0.88)  ; 24\r\n                  (   39.49   -58.74   -18.31     0.88)  ; 25\r\n                  (   40.08   -59.62   -18.63     1.77)  ; 26\r\n                  (   40.50   -60.58   -19.25     1.99)  ; 27\r\n                  (   41.23   -61.60   -19.56     1.62)  ; 28\r\n                  (   41.45   -62.19   -19.94     1.18)  ; 29\r\n                  (   41.97   -63.14   -20.13     0.88)  ; 30\r\n                  (   42.56   -64.16   -20.50     0.88)  ; 31\r\n                  (   42.71   -65.55   -21.00     1.03)  ; 32\r\n                  (   42.78   -67.01   -21.19     1.03)  ; 33\r\n                  (   43.07   -68.11   -21.44     0.96)  ; 34\r\n                  (   43.59   -69.06   -22.63     0.88)  ; 35\r\n                  (   44.03   -70.16   -23.38     0.74)  ; 36\r\n                  (   44.11   -71.47   -23.81     0.66)  ; 37\r\n                  (   44.55   -73.01   -24.88     0.66)  ; 38\r\n                  (   45.06   -74.32   -24.88     0.66)  ; 39\r\n                  (   45.80   -76.15   -25.69     0.88)  ; 40\r\n                  (   46.54   -77.76   -26.25     0.88)  ; 41\r\n                  (   47.13   -78.64   -26.63     0.96)  ; 42\r\n                  (   47.64   -79.51   -26.56     0.59)  ; 43\r\n                  (   48.30   -80.17   -26.56     0.59)  ; 44\r\n                  (   48.67   -80.61   -26.75     0.59)  ; 45\r\n                  (   49.41   -81.49   -26.75     0.96)  ; 46\r\n                  (   50.51   -82.51   -27.06     1.40)  ; 47\r\n                  (   51.03   -83.32   -27.06     1.03)  ; 48\r\n                  (   51.55   -84.19   -27.06     0.81)  ; 49\r\n                  (   51.99   -85.29   -27.38     0.88)  ; 50\r\n                  (   52.43   -85.95   -26.44     0.96)  ; 51\r\n                  (   52.72   -86.97   -26.56     0.74)  ; 52\r\n                  (   53.31   -88.07   -26.56     0.52)  ; 53\r\n                  (   54.12   -89.16   -26.56     0.74)  ; 54\r\n                  (   54.84   -90.15   -27.13     1.03)  ; 55\r\n                  (   54.84   -91.18   -27.94     1.40)  ; 56\r\n                  (   54.84   -92.06   -28.63     1.62)  ; 57\r\n                  (   54.54   -93.74   -28.81     1.55)  ; 58\r\n                  (   53.95   -94.98   -29.75     1.84)  ; 59\r\n                  (   53.66   -95.71   -30.75     1.47)  ; 60\r\n                  (   53.14   -96.44   -30.81     0.81)  ; 61\r\n                  (   52.55   -97.39   -31.38     0.66)  ; 62\r\n                  (   52.41   -97.68   -31.44     0.66)  ; 63\r\n                  (   52.63   -98.49   -31.44     0.66)  ; 64\r\n                  (   52.48   -99.29   -32.88     1.25)  ; 65\r\n                  (   52.18  -100.53   -33.25     1.99)  ; 66\r\n                  (   51.96  -101.63   -33.75     1.55)  ; 67\r\n                  (   51.74  -102.73   -33.75     1.55)  ; 68\r\n                  (   51.67  -104.92   -34.25     1.77)  ; 69\r\n                  (   51.89  -106.31   -34.69     1.47)  ; 70\r\n                  (   52.11  -107.55   -35.75     0.88)  ; 71\r\n                  (   52.77  -108.79   -35.75     0.66)  ; 72\r\n                  (   53.44  -109.96   -36.44     0.66)  ; 73\r\n                  (   53.58  -110.70   -36.63     0.52)  ; 74\r\n                  (   53.36  -111.21   -37.13     0.52)  ; 75\r\n                  (   53.14  -111.35   -38.00     0.74)  ; 76\r\n                  (   52.92  -112.52   -38.31     0.81)  ; 77\r\n                  (   52.85  -113.84   -38.94     1.47)  ; 78\r\n                  (   53.00  -114.79   -39.25     1.84)  ; 79\r\n                  (   53.29  -116.10   -39.38     1.84)  ; 80\r\n                  (   53.22  -117.35   -39.38     1.25)  ; 81\r\n                  (   53.51  -118.59   -40.06     0.81)  ; 82\r\n                  (   53.80  -119.82   -40.38     0.52)  ; 83\r\n                  (   53.80  -120.92   -40.69     0.52)  ; 84\r\n                  (   53.36  -121.14   -41.19     0.52)  ; 85\r\n                  (   52.77  -121.28   -41.25     0.52)  ; 86\r\n                  (   52.55  -122.16   -41.25     1.11)  ; 87\r\n                  (   52.18  -123.11   -41.44     1.47)  ; 88\r\n                  (   51.66  -124.13   -41.44     1.84)  ; 89\r\n                  (   51.22  -124.86   -42.13     2.65)  ; 90\r\n                  (   50.56  -125.81   -42.19     1.33)  ; 91\r\n                  (   50.19  -126.62   -42.56     0.88)  ; 92\r\n                  (   50.04  -127.50   -42.75     0.52)  ; 93\r\n                  (   50.04  -128.37   -43.56     0.44)  ; 94\r\n                  (   50.04  -129.10   -44.81     0.44)  ; 95\r\n                  (   49.60  -129.83   -44.81     0.66)  ; 96\r\n                  (   48.86  -130.57   -45.19     0.66)  ; 97\r\n                  (   48.50  -132.39   -45.63     0.44)  ; 98\r\n                  (   48.35  -133.12   -46.00     0.44)  ; 99\r\n                  (   47.83  -134.22   -46.13     1.25)  ; 100\r\n                  (   47.39  -135.10   -46.31     1.99)  ; 101\r\n                  (   46.51  -136.27   -46.63     1.69)  ; 102\r\n                  (   45.62  -137.14   -46.63     0.96)  ; 103\r\n                  (   45.11  -137.73   -46.63     0.52)  ; 104\r\n                  (   44.44  -138.31   -46.63     0.37)  ; 105\r\n                  (   44.30  -138.61   -46.63     0.29)  ; 106\r\n                  (   43.71  -139.12   -47.06     0.66)  ; 107\r\n                  (   43.41  -139.85   -47.56     0.96)  ; 108\r\n                  (   42.97  -140.43   -47.69     0.59)  ; 109\r\n                  (   42.38  -140.73   -48.13     0.37)  ; 110\r\n                  (   41.64  -141.38   -48.00     0.22)  ; 111\r\n                  (   41.20  -142.19   -48.00     0.22)  ; 112\r\n                  (   40.54  -142.85   -47.88     0.22)  ; 113\r\n                  (   39.95  -143.36   -47.50     0.22)  ; 114\r\n                  (   39.58  -143.58   -47.31     0.22)  ; 115\r\n                  (   39.29  -143.94   -47.19     0.96)  ; 116\r\n                  (   38.92  -144.38   -46.94     1.33)  ; 117\r\n                  (   38.40  -144.67   -46.88     0.66)  ; 118\r\n                  (   37.59  -145.11   -46.81     0.29)  ; 119\r\n                  (   37.15  -145.48   -46.75     0.66)  ; 120\r\n                  (   36.56  -145.92   -46.69     1.03)  ; 121\r\n                  (   36.05  -146.43   -46.50     0.66)  ; 122\r\n                  (   35.60  -146.72   -46.44     0.29)  ; 123\r\n                  (   34.94  -147.23   -46.44     0.29)  ; 124\r\n                  (   34.50  -147.74   -46.44     0.96)  ; 125\r\n                  (   34.15  -148.87   -46.31     0.96)  ; 126\r\n                  (   33.63  -149.68   -46.31     0.59)  ; 127\r\n                  (   33.41  -150.34   -46.31     0.44)  ; 128\r\n                   Low\r\n                |\r\n                  (   33.52   -35.82   -14.50     0.66)  ; 1, R-1-2-1-1-1-1-1-2\r\n                  (   34.25   -36.04   -14.50     0.66)  ; 2\r\n                  (   35.36   -36.25   -14.50     0.74)  ; 3\r\n                  (   36.32   -36.62   -14.50     0.74)  ; 4\r\n                  (   37.64   -36.40   -14.69     0.59)  ; 5\r\n                  (   38.60   -36.56   -14.69     0.59)  ; 6\r\n                  (   40.30   -36.49   -15.25     1.18)  ; 7\r\n                  (   41.04   -35.90   -17.56     1.18)  ; 8\r\n                  (   42.44   -35.98   -18.19     0.88)  ; 9\r\n                  (   43.25   -36.34   -19.63     0.88)  ; 10\r\n                  (   44.13   -36.56   -19.75     1.11)  ; 11\r\n                  (   45.31   -36.93   -19.75     0.96)  ; 12\r\n                  (   46.05   -37.66   -20.06     0.81)  ; 13\r\n                  (   46.27   -38.83   -20.31     0.81)  ; 14\r\n                  (   46.86   -40.51   -20.63     0.66)  ; 15\r\n                  (   47.30   -41.38   -21.00     0.88)  ; 16\r\n                  (   47.59   -42.75   -20.50     0.74)  ; 17\r\n                  (   47.59   -43.26   -20.44     0.59)  ; 18\r\n                  (   47.88   -44.65   -19.81     0.59)  ; 19\r\n                  (   48.25   -45.67   -20.63     0.59)  ; 20\r\n                  (   48.84   -46.91   -21.13     0.59)  ; 21\r\n                  (   49.51   -47.79   -22.50     0.59)  ; 22\r\n                  (   50.24   -48.96   -23.94     0.74)  ; 23\r\n                  (   50.76   -49.62   -23.94     0.52)  ; 24\r\n                  (   51.35   -51.01   -24.19     0.74)  ; 25\r\n                  (   51.20   -52.03   -24.75     0.74)  ; 26\r\n                  (   50.54   -52.47   -25.88     0.52)  ; 27\r\n                  (   50.68   -53.13   -26.44     0.52)  ; 28\r\n                  (   51.20   -53.57   -27.81     0.52)  ; 29\r\n                  (   51.64   -54.22   -29.06     0.74)  ; 30\r\n                  (   52.08   -55.25   -29.31     0.74)  ; 31\r\n                  (   52.75   -56.12   -31.25     0.59)  ; 32\r\n                  (   52.60   -57.07   -31.56     0.59)  ; 33\r\n                  (   52.60   -57.59   -31.94     0.88)  ; 34\r\n                  (   53.04   -58.39   -33.25     0.66)  ; 35\r\n                  (   53.26   -59.12   -35.06     0.66)  ; 36\r\n                  (   53.48   -60.29   -36.75     0.44)  ; 37\r\n                  (   53.85   -61.68   -37.88     0.44)  ; 38\r\n                  (   54.37   -62.70   -37.88     0.44)  ; 39\r\n                  (   55.03   -63.29   -38.75     0.74)  ; 40\r\n                  (   55.91   -64.09   -37.38     0.74)  ; 41\r\n                  (   56.50   -64.75   -37.63     0.74)  ; 42\r\n                  (   57.02   -65.70   -37.81     0.44)  ; 43\r\n                  (   57.46   -66.65   -38.00     0.44)  ; 44\r\n                  (   58.20   -66.72   -38.50     0.59)  ; 45\r\n                  (   59.23   -67.16   -38.81     0.59)  ; 46\r\n                  (   60.26   -67.60   -39.50     0.74)  ; 47\r\n                  (   61.15   -68.04   -39.50     1.40)  ; 48\r\n                  (   61.96   -68.40   -40.25     1.77)  ; 49\r\n                  (   63.21   -68.77   -40.25     1.92)  ; 50\r\n                  (   64.31   -69.06   -40.25     1.11)  ; 51\r\n                  (   65.42   -69.28   -40.25     0.74)  ; 52\r\n                  (   66.45   -69.79   -40.69     0.44)  ; 53\r\n                  (   67.19   -70.52   -40.69     0.29)  ; 54\r\n                  (   68.02   -71.33   -42.06     0.59)  ; 55\r\n                  (   68.61   -72.35   -42.38     1.18)  ; 56\r\n                  (   69.12   -73.30   -44.25     1.18)  ; 57\r\n                  (   69.86   -73.45   -45.25     1.18)  ; 58\r\n                  (   70.37   -74.03   -45.38     1.03)  ; 59\r\n                  (   71.63   -74.91   -45.38     0.88)  ; 60\r\n                  (   72.80   -75.86   -46.50     0.74)  ; 61\r\n                  (   73.54   -76.74   -46.56     0.66)  ; 62\r\n                  (   74.13   -77.61   -46.94     0.66)  ; 63\r\n                  (   74.57   -78.35   -47.44     0.81)  ; 64\r\n                  (   75.24   -79.30   -47.81     1.03)  ; 65\r\n                  (   75.97   -79.66   -48.38     0.88)  ; 66\r\n                  (   76.34   -80.54   -48.69     0.88)  ; 67\r\n                  (   76.93   -81.71   -48.69     0.59)  ; 68\r\n                  (   77.52   -82.95   -48.69     0.37)  ; 69\r\n                  (   77.89   -84.05   -48.69     0.29)  ; 70\r\n                  (   78.18   -84.85   -49.00     0.66)  ; 71\r\n                  (   78.48   -86.02   -48.81     0.96)  ; 72\r\n                  (   78.77   -86.97   -47.13     1.62)  ; 73\r\n                  (   79.07   -87.77   -47.38     2.36)  ; 74\r\n                  (   79.07   -88.87   -47.50     1.99)  ; 75\r\n                  (   79.07   -89.75   -47.50     1.18)  ; 76\r\n                  (   78.92   -90.63   -47.50     0.59)  ; 77\r\n                  (   78.77   -91.72   -47.50     0.29)  ; 78\r\n                  (   78.62   -92.75   -47.88     0.29)  ; 79\r\n                  (   78.40   -93.26   -48.00     1.03)  ; 80\r\n                  (   78.48   -94.06   -48.00     2.06)  ; 81\r\n                  (   78.33   -94.79   -48.25     2.06)  ; 82\r\n                  (   77.89   -95.45   -49.19     0.66)  ; 83\r\n                  (   77.74   -96.69   -49.81     0.44)  ; 84\r\n                   Low\r\n                )  ;  End of split\r\n              |\r\n                (   32.75   -33.80   -14.50     1.25)  ; 1, R-1-2-1-1-1-1-2\r\n                (   33.34   -34.02   -14.06     0.81)  ; 2\r\n                (   34.00   -34.17   -13.75     0.52)  ; 3\r\n                (   35.03   -34.24   -12.94     0.59)  ; 4\r\n                (   35.77   -34.32   -12.94     0.74)  ; 5\r\n                (   37.46   -34.97   -13.44     0.74)  ; 6\r\n                (   38.05   -35.41   -13.88     0.74)  ; 7\r\n                (   39.01   -36.14   -13.75     0.96)  ; 8\r\n                (   40.34   -36.44   -14.75     0.96)  ; 9\r\n                (   41.22   -36.80   -14.75     0.96)  ; 10\r\n                (   42.03   -37.39   -14.25     0.81)  ; 11\r\n                (   43.28   -38.19   -15.19     0.66)  ; 12\r\n                (   44.02   -38.77   -15.81     0.66)  ; 13\r\n                (   45.12   -39.65   -16.75     1.03)  ; 14\r\n                (   46.16   -40.60   -16.13     0.96)  ; 15\r\n                (   47.48   -40.75   -15.81     0.96)  ; 16\r\n                (   48.51   -40.89   -15.25     0.74)  ; 17\r\n                (   49.62   -41.48   -15.75     0.74)  ; 18\r\n                (   50.06   -41.99   -15.75     0.81)  ; 19\r\n                (   50.65   -42.65   -15.88     0.81)  ; 20\r\n                (   51.17   -43.16   -16.44     0.81)  ; 21\r\n                (   51.98   -43.82   -17.69     1.18)  ; 22\r\n                (   52.71   -44.92   -17.94     1.18)  ; 23\r\n                (   53.23   -45.43   -18.19     1.18)  ; 24\r\n                (   53.89   -46.23   -18.69     1.33)  ; 25\r\n                (   54.55   -46.89   -19.56     1.33)  ; 26\r\n                (   55.37   -47.55   -19.81     1.11)  ; 27\r\n                (   55.73   -47.91   -21.06     0.96)  ; 28\r\n                (   56.69   -48.50   -21.06     0.74)  ; 29\r\n                (   56.99   -49.67   -21.81     0.81)  ; 30\r\n                (   57.72   -50.76   -23.69     1.03)  ; 31\r\n                (   58.39   -51.64   -24.63     0.88)  ; 32\r\n                (   59.27   -51.86   -27.06     1.11)  ; 33\r\n                (   60.60   -52.15   -27.88     1.11)  ; 34\r\n                (   61.48   -52.44   -27.94     0.88)  ; 35\r\n                (   62.36   -52.96   -27.94     0.88)  ; 36\r\n                (   63.03   -53.03   -28.19     0.88)  ; 37\r\n                (   64.35   -53.03   -29.31     0.88)  ; 38\r\n                (   65.09   -53.10   -31.00     1.18)  ; 39\r\n                (   66.27   -53.61   -31.38     1.47)  ; 40\r\n                (   67.23   -54.05   -32.00     1.47)  ; 41\r\n                (   68.04   -54.34   -32.81     1.18)  ; 42\r\n                (   68.70   -54.20   -33.25     0.88)  ; 43\r\n                (   69.44   -54.56   -34.44     0.59)  ; 44\r\n                (   70.17   -55.00   -35.56     0.52)  ; 45\r\n                (   71.06   -55.22   -36.44     0.81)  ; 46\r\n                (   71.65   -55.22   -37.00     0.81)  ; 47\r\n                (   72.16   -54.78   -37.94     0.66)  ; 48\r\n                (   73.27   -54.71   -38.75     0.59)  ; 49\r\n                (   73.86   -55.00   -40.00     0.59)  ; 50\r\n                (   74.30   -55.30   -40.38     0.59)  ; 51\r\n                (   76.07   -56.03   -41.13     0.88)  ; 52\r\n                (   76.80   -56.39   -41.38     1.25)  ; 53\r\n                (   77.32   -56.61   -42.50     1.47)  ; 54\r\n                (   78.35   -56.98   -42.50     1.47)  ; 55\r\n                (   79.11   -57.40   -42.44     0.44)  ; 56\r\n                (   79.62   -58.13   -42.81     0.37)  ; 57\r\n                (   80.28   -58.57   -43.00     1.03)  ; 58\r\n                (   81.32   -58.71   -43.56     1.33)  ; 59\r\n                (   82.35   -59.00   -43.56     1.33)  ; 60\r\n                (   83.16   -59.00   -43.56     0.66)  ; 61\r\n                (   84.34   -59.08   -43.56     0.44)  ; 62\r\n                (   85.44   -59.22   -43.69     1.11)  ; 63\r\n                (   86.62   -59.44   -44.00     1.62)  ; 64\r\n                (   87.50   -59.52   -43.56     0.88)  ; 65\r\n                (   88.02   -59.66   -42.69     0.52)  ; 66\r\n                (   88.68   -60.03   -42.44     0.29)  ; 67\r\n                (   88.90   -60.25   -41.81     0.29)  ; 68\r\n                (   89.86   -60.69   -41.75     0.88)  ; 69\r\n                (   90.75   -61.20   -41.75     1.11)  ; 70\r\n                (   91.56   -61.56   -42.06     0.81)  ; 71\r\n                (   92.22   -61.78   -41.75     0.52)  ; 72\r\n                (   92.88   -62.29   -41.56     0.44)  ; 73\r\n                (   93.77   -62.95   -41.56     0.81)  ; 74\r\n                (   94.28   -63.10   -41.56     1.18)  ; 75\r\n                (   95.17   -63.39   -41.50     1.18)  ; 76\r\n                (   95.76   -63.39   -41.38     0.59)  ; 77\r\n                (   96.34   -63.61   -41.38     0.37)  ; 78\r\n                (   96.49   -64.27   -41.38     0.37)  ; 79\r\n                (   97.08   -64.93   -40.75     0.37)  ; 80\r\n                (   97.82   -65.29   -40.50     1.11)  ; 81\r\n                (   98.33   -65.73   -39.94     1.99)  ; 82\r\n                (   99.29   -66.02   -39.88     2.28)  ; 83\r\n                (  100.32   -66.68   -39.88     1.40)  ; 84\r\n                (  100.77   -66.83   -39.25     0.66)  ; 85\r\n                (  101.58   -67.05   -39.06     0.37)  ; 86\r\n                (  102.02   -67.12   -37.88     0.22)  ; 87\r\n                (  102.61   -67.48   -36.31     0.66)  ; 88\r\n                (  102.90   -67.56   -35.56     1.47)  ; 89\r\n                (  103.42   -67.70   -34.44     1.92)  ; 90\r\n                (  104.08   -68.07   -34.31     1.55)  ; 91\r\n                (  104.60   -68.51   -32.00     1.11)  ; 92\r\n                (  105.41   -69.02   -30.31     0.59)  ; 93\r\n                (  105.85   -69.53   -28.19     0.37)  ; 94\r\n                 Low\r\n              )  ;  End of split\r\n            |\r\n              (   29.91   -30.27   -15.13     1.03)  ; 1, R-1-2-1-1-1-2\r\n              (   30.57   -30.27   -18.69     1.03)  ; 2\r\n              (   31.23   -30.49   -21.56     1.03)  ; 3\r\n              (   32.19   -30.42   -24.00     1.03)  ; 4\r\n              (   33.00   -30.49   -25.44     0.88)  ; 5\r\n              (   33.81   -30.64   -26.19     0.88)  ; 6\r\n              (   35.51   -31.51   -26.19     0.88)  ; 7\r\n              (   36.83   -31.59   -27.19     0.88)  ; 8\r\n              (   37.42   -31.08   -27.44     1.03)  ; 9\r\n              (   37.72   -30.49   -29.06     1.03)  ; 10\r\n              (   37.64   -30.64   -33.13     1.03)  ; 11\r\n              (   38.16   -31.66   -34.63     0.88)  ; 12\r\n              (   39.71   -31.88   -34.94     0.74)  ; 13\r\n              (   41.18   -32.32   -35.38     0.59)  ; 14\r\n              (   41.84   -32.32   -36.50     0.59)  ; 15\r\n              (   42.21   -31.88   -37.94     0.88)  ; 16\r\n              (   42.87   -31.51   -37.94     1.11)  ; 17\r\n              (   43.98   -31.08   -37.94     1.40)  ; 18\r\n              (   44.64   -30.78   -38.63     1.40)  ; 19\r\n              (   45.16   -30.71   -38.75     1.03)  ; 20\r\n              (   46.11   -30.27   -39.94     0.81)  ; 21\r\n              (   47.37   -29.61   -40.81     0.74)  ; 22\r\n              (   48.69   -29.83   -39.69     0.59)  ; 23\r\n              (   49.58   -29.98   -42.38     0.66)  ; 24\r\n              (   50.76   -30.42   -42.44     0.66)  ; 25\r\n              (   51.94   -30.78   -42.81     0.66)  ; 26\r\n              (   53.33   -30.86   -41.94     0.96)  ; 27\r\n              (   54.37   -30.64   -44.56     0.96)  ; 28\r\n              (   55.03   -30.78   -47.25     0.96)  ; 29\r\n              (   55.99   -31.66   -48.31     0.96)  ; 30\r\n              (   56.87   -32.17   -48.56     1.25)  ; 31\r\n              (   58.05   -32.39   -48.88     1.40)  ; 32\r\n              (   58.93   -32.03   -49.50     0.74)  ; 33\r\n              (   59.97   -31.88   -50.38     0.59)  ; 34\r\n              (   60.55   -31.73   -52.25     0.59)  ; 35\r\n              (   61.00   -30.86   -53.94     0.59)  ; 36\r\n              (   61.95   -29.76   -51.75     0.29)  ; 37\r\n              (   62.18   -29.39   -53.25     0.29)  ; 38\r\n              (   63.13   -28.59   -54.31     0.66)  ; 39\r\n              (   63.50   -28.01   -55.88     0.96)  ; 40\r\n              (   63.87   -27.27   -56.06     1.25)  ; 41\r\n              (   64.24   -26.62   -57.06     1.62)  ; 42\r\n              (   64.53   -26.10   -57.63     1.62)  ; 43\r\n              (   64.68   -25.15   -58.50     0.81)  ; 44\r\n              (   64.53   -24.64   -61.81     0.59)  ; 45\r\n              (   64.09   -23.77   -62.19     0.37)  ; 46\r\n              (   64.09   -22.45   -64.44     0.37)  ; 47\r\n              (   64.16   -21.35   -58.56     0.29)  ; 48\r\n              (   64.53   -21.06   -61.19     0.66)  ; 49\r\n              (   64.98   -20.48   -61.19     0.37)  ; 50\r\n              (   65.79   -19.16   -61.19     0.15)  ; 51\r\n              (   66.23   -18.14   -62.25     0.15)  ; 52\r\n              (   66.74   -17.41   -62.44     0.15)  ; 53\r\n              (   67.48   -16.75   -62.56     0.52)  ; 54\r\n              (   67.55   -16.31   -63.25     1.33)  ; 55\r\n              (   68.00   -15.58   -63.56     2.95)  ; 56\r\n              (   68.00   -14.85   -64.19     2.95)  ; 57\r\n              (   68.36   -14.19   -65.81     1.69)  ; 58\r\n              (   68.51   -13.31   -66.19     0.96)  ; 59\r\n              (   69.03   -12.95   -68.13     0.66)  ; 60\r\n              (   70.21   -11.27   -68.63     0.22)  ; 61\r\n              (   70.94   -10.75   -69.69     0.22)  ; 62\r\n              (   72.42    -9.80   -69.69     0.22)  ; 63\r\n              (   73.30    -9.22   -69.69     0.22)  ; 64\r\n              (   74.41    -9.07   -69.69     0.59)  ; 65\r\n              (   75.22    -8.93   -70.75     0.81)  ; 66\r\n              (   76.03    -8.56   -73.00     0.52)  ; 67\r\n               Low\r\n            )  ;  End of split\r\n          |\r\n            (   28.31   -29.93   -12.38     1.69)  ; 1, R-1-2-1-1-2\r\n            (   27.87   -30.59   -13.13     0.81)  ; 2\r\n            (   27.43   -31.68   -13.25     0.59)  ; 3\r\n            (   27.06   -32.63   -13.31     0.59)  ; 4\r\n            (   26.91   -33.29   -13.69     0.59)  ; 5\r\n            (   26.62   -34.75   -13.88     0.74)  ; 6\r\n            (   26.25   -35.85   -14.81     0.52)  ; 7\r\n            (   25.95   -37.09   -15.88     0.52)  ; 8\r\n            (   25.44   -38.04   -14.88     0.52)  ; 9\r\n            (   25.22   -38.99   -15.63     0.66)  ; 10\r\n            (   25.59   -40.09   -16.25     0.66)  ; 11\r\n            (   26.03   -40.97   -16.44     0.66)  ; 12\r\n            (   26.62   -42.06   -16.94     0.66)  ; 13\r\n            (   27.06   -42.87   -16.94     0.66)  ; 14\r\n            (   27.21   -43.23   -17.94     0.74)  ; 15\r\n            (   27.65   -43.96   -18.44     1.03)  ; 16\r\n            (   27.50   -45.42   -19.44     1.03)  ; 17\r\n            (   27.35   -46.01   -19.44     0.81)  ; 18\r\n            (   27.13   -47.62   -19.69     0.81)  ; 19\r\n            (   27.06   -48.71   -20.25     0.81)  ; 20\r\n            (   26.98   -49.96   -20.25     0.81)  ; 21\r\n            (   26.84   -50.91   -20.63     0.66)  ; 22\r\n            (   27.35   -52.08   -21.75     1.03)  ; 23\r\n            (   27.57   -53.32   -22.50     1.03)  ; 24\r\n            (   28.02   -54.12   -22.50     0.96)  ; 25\r\n            (   27.50   -55.73   -23.13     1.11)  ; 26\r\n            (   27.13   -56.90   -23.13     0.81)  ; 27\r\n            (   26.91   -57.85   -23.25     0.59)  ; 28\r\n            (   26.96   -59.04   -23.25     0.59)  ; 29\r\n            (   27.11   -59.99   -23.38     1.11)  ; 30\r\n            (   27.55   -61.01   -23.38     1.77)  ; 31\r\n            (   27.63   -62.18   -23.56     1.03)  ; 32\r\n            (   27.92   -63.20   -23.88     0.81)  ; 33\r\n            (   28.29   -64.30   -23.88     1.11)  ; 34\r\n            (   29.25   -65.18   -23.88     1.11)  ; 35\r\n            (   29.76   -66.35   -25.06     0.81)  ; 36\r\n            (   30.28   -67.51   -25.38     0.96)  ; 37\r\n            (   30.28   -68.32   -25.38     0.66)  ; 38\r\n            (   30.28   -69.42   -25.38     0.66)  ; 39\r\n            (   30.21   -70.22   -25.38     0.96)  ; 40\r\n            (   30.28   -71.39   -25.50     0.96)  ; 41\r\n            (   30.28   -72.78   -25.50     0.96)  ; 42\r\n            (   30.06   -73.87   -26.25     1.33)  ; 43\r\n            (   29.76   -74.97   -25.19     0.81)  ; 44\r\n            (   29.62   -75.63   -24.88     0.52)  ; 45\r\n            (   29.76   -76.87   -24.44     1.11)  ; 46\r\n            (   29.91   -77.68   -24.88     1.11)  ; 47\r\n            (   29.69   -78.63   -24.88     0.81)  ; 48\r\n            (   29.98   -79.43   -25.19     0.81)  ; 49\r\n            (   29.69   -80.45   -26.44     1.03)  ; 50\r\n            (   29.69   -81.62   -27.06     1.40)  ; 51\r\n            (   29.62   -82.35   -27.06     1.69)  ; 52\r\n            (   29.62   -83.38   -27.06     1.11)  ; 53\r\n            (   29.98   -84.11   -27.63     0.74)  ; 54\r\n            (   29.76   -84.99   -27.31     0.59)  ; 55\r\n            (   29.98   -86.01   -27.69     0.74)  ; 56\r\n            (   30.43   -87.03   -27.50     0.59)  ; 57\r\n            (   30.72   -88.06   -27.88     0.59)  ; 58\r\n            (   30.79   -89.34   -27.81     0.88)  ; 59\r\n            (   30.57   -90.36   -27.88     1.40)  ; 60\r\n            (   29.91   -91.61   -29.25     1.77)  ; 61\r\n            (   29.54   -92.99   -29.25     1.03)  ; 62\r\n            (   29.54   -94.09   -29.25     0.59)  ; 63\r\n            (   29.91   -94.60   -29.25     0.59)  ; 64\r\n            (   30.57   -95.33   -29.75     0.59)  ; 65\r\n            (   31.01   -95.92   -29.75     0.59)  ; 66\r\n            (   31.16   -96.65   -29.75     0.59)  ; 67\r\n            (   30.86   -97.53   -29.88     1.18)  ; 68\r\n            (   30.42   -98.33   -30.19     1.18)  ; 69\r\n            (   29.54   -99.28   -29.13     0.81)  ; 70\r\n            (   29.02  -100.16   -29.06     0.81)  ; 71\r\n            (   28.65  -101.47   -29.19     0.59)  ; 72\r\n            (   28.51  -102.64   -28.63     0.59)  ; 73\r\n            (   28.14  -103.37   -28.25     1.33)  ; 74\r\n            (   27.70  -104.47   -27.94     1.62)  ; 75\r\n            (   27.25  -105.79   -27.88     1.84)  ; 76\r\n            (   27.03  -106.30   -27.88     1.03)  ; 77\r\n            (   26.81  -106.96   -27.88     0.59)  ; 78\r\n            (   26.59  -107.76   -27.88     0.37)  ; 79\r\n            (   26.89  -108.34   -27.94     0.37)  ; 80\r\n            (   27.18  -108.56   -28.00     0.37)  ; 81\r\n            (   27.33  -109.22   -28.00     0.37)  ; 82\r\n            (   26.52  -109.44   -28.00     0.59)  ; 83\r\n            (   25.85  -109.95   -28.00     0.59)  ; 84\r\n            (   24.45  -110.61   -28.00     0.44)  ; 85\r\n            (   24.45  -111.27   -28.00     0.44)  ; 86\r\n            (   23.72  -112.95   -28.00     0.74)  ; 87\r\n            (   23.35  -113.68   -28.19     1.40)  ; 88\r\n            (   23.20  -114.41   -28.25     2.21)  ; 89\r\n            (   23.35  -115.22   -28.38     2.21)  ; 90\r\n            (   22.69  -116.75   -28.44     1.69)  ; 91\r\n            (   22.28  -117.54   -28.44     0.81)  ; 92\r\n            (   21.62  -118.71   -29.06     0.37)  ; 93\r\n            (   20.74  -119.44   -29.31     0.37)  ; 94\r\n            (   20.59  -120.17   -30.31     0.37)  ; 95\r\n            (   20.15  -120.91   -31.25     0.37)  ; 96\r\n            (   19.71  -120.98   -33.06     0.44)  ; 97\r\n            (   19.26  -121.49   -32.88     0.44)  ; 98\r\n            (   19.04  -121.86   -32.88     0.81)  ; 99\r\n            (   18.45  -122.44   -32.75     0.81)  ; 100\r\n            (   18.01  -122.59   -32.56     0.37)  ; 101\r\n            (   16.61  -122.81   -32.25     0.52)  ; 102\r\n            (   16.46  -122.81   -31.38     0.59)  ; 103\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (   27.50   -26.88   -15.06     0.66)  ; 1, R-1-2-1-2\r\n          (   27.28   -27.54   -18.75     0.66)  ; 2\r\n          (   27.87   -28.63   -20.31     0.66)  ; 3\r\n          (   27.87   -29.22   -21.88     0.66)  ; 4\r\n          (   28.24   -30.46   -22.44     0.66)  ; 5\r\n          (   29.05   -31.63   -24.38     0.66)  ; 6\r\n          (   30.82   -33.46   -26.63     0.66)  ; 7\r\n          (   31.70   -34.70   -28.06     0.66)  ; 8\r\n          (   32.36   -37.33   -29.88     0.66)  ; 9\r\n          (   31.77   -38.50   -32.31     0.66)  ; 10\r\n          (   32.44   -39.89   -33.44     0.88)  ; 11\r\n          (   33.03   -41.65   -34.69     1.11)  ; 12\r\n          (   33.91   -42.60   -35.13     0.96)  ; 13\r\n          (   35.16   -43.69   -36.94     0.81)  ; 14\r\n          (   36.71   -44.79   -37.81     0.74)  ; 15\r\n          (   37.96   -46.03   -38.50     0.59)  ; 16\r\n          (   39.44   -46.91   -40.06     0.59)  ; 17\r\n          (   40.76   -47.27   -41.38     0.59)  ; 18\r\n          (   42.09   -48.01   -42.13     0.59)  ; 19\r\n          (   42.90   -48.23   -42.13     0.59)  ; 20\r\n          (   43.34   -48.81   -42.56     0.88)  ; 21\r\n          (   43.56   -49.18   -43.13     1.25)  ; 22\r\n          (   44.30   -49.83   -43.63     1.47)  ; 23\r\n          (   44.67   -50.35   -44.56     1.03)  ; 24\r\n          (   45.11   -51.30   -44.69     0.59)  ; 25\r\n          (   45.92   -52.39   -45.00     0.59)  ; 26\r\n          (   46.51   -53.34   -45.00     0.59)  ; 27\r\n          (   47.54   -54.37   -45.00     0.74)  ; 28\r\n          (   48.33   -55.73   -45.50     0.52)  ; 29\r\n          (   49.06   -56.90   -45.69     0.37)  ; 30\r\n          (   49.58   -57.99   -45.69     0.74)  ; 31\r\n          (   50.39   -59.60   -45.69     0.74)  ; 32\r\n          (   50.76   -60.63   -46.31     0.81)  ; 33\r\n          (   51.27   -62.09   -46.31     0.81)  ; 34\r\n          (   51.49   -63.11   -46.75     0.66)  ; 35\r\n          (   52.01   -64.65   -47.50     0.88)  ; 36\r\n          (   52.08   -65.60   -48.56     0.88)  ; 37\r\n          (   52.53   -66.55   -49.31     0.88)  ; 38\r\n          (   53.04   -67.72   -49.44     0.74)  ; 39\r\n          (   53.19   -68.81   -50.38     0.88)  ; 40\r\n          (   53.70   -70.13   -50.75     0.96)  ; 41\r\n          (   54.37   -71.74   -52.06     0.37)  ; 42\r\n          (   55.10   -72.76   -53.13     0.37)  ; 43\r\n          (   55.33   -73.71   -53.56     0.59)  ; 44\r\n          (   55.99   -74.66   -54.19     0.88)  ; 45\r\n          (   56.95   -75.68   -54.69     1.03)  ; 46\r\n          (   58.05   -76.71   -56.00     0.81)  ; 47\r\n          (   58.71   -77.36   -56.44     1.33)  ; 48\r\n          (   59.16   -78.39   -57.06     1.33)  ; 49\r\n          (   60.04   -79.34   -57.94     0.66)  ; 50\r\n          (   60.63   -80.29   -58.31     0.66)  ; 51\r\n          (   61.07   -81.24   -59.06     0.66)  ; 52\r\n          (   61.37   -81.82   -57.75     0.66)  ; 53\r\n          (   62.18   -83.07   -59.06     0.37)  ; 54\r\n          (   62.62   -84.31   -59.06     0.29)  ; 55\r\n          (   63.11   -85.59   -59.06     0.29)  ; 56\r\n          (   63.33   -86.10   -59.31     0.88)  ; 57\r\n          (   63.92   -87.06   -60.06     1.99)  ; 58\r\n          (   64.58   -87.57   -61.31     1.99)  ; 59\r\n          (   64.81   -88.52   -62.19     1.25)  ; 60\r\n          (   65.32   -89.47   -62.50     0.44)  ; 61\r\n          (   65.84   -90.64   -63.75     0.22)  ; 62\r\n          (   66.72   -92.03   -64.94     0.22)  ; 63\r\n          (   66.72   -92.76   -66.50     0.22)  ; 64\r\n          (   67.24   -93.49   -67.06     1.33)  ; 65\r\n          (   67.02   -94.44   -68.06     1.99)  ; 66\r\n          (   66.79   -95.32   -68.56     1.55)  ; 67\r\n          (   66.79   -96.41   -68.94     1.11)  ; 68\r\n          (   66.79   -97.29   -68.94     0.66)  ; 69\r\n          (   66.79   -98.31   -69.13     0.66)  ; 70\r\n          (   67.02   -99.19   -69.31     1.47)  ; 71\r\n          (   66.57  -100.14   -71.19     1.55)  ; 72\r\n          (   66.35  -100.51   -72.19     1.11)  ; 73\r\n          (   66.13  -101.09   -72.81     0.66)  ; 74\r\n          (   65.98  -101.75   -74.56     0.52)  ; 75\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (   24.64   -22.76   -17.69     0.74)  ; 1, R-1-2-2\r\n        (   25.15   -23.64   -20.00     0.96)  ; 2\r\n        (   25.01   -24.52   -20.44     0.96)  ; 3\r\n        (   24.12   -24.59   -22.56     0.96)  ; 4\r\n        (   23.02   -24.96   -22.56     0.96)  ; 5\r\n        (   22.43   -25.98   -22.56     0.96)  ; 6\r\n        (   21.69   -26.78   -23.50     0.96)  ; 7\r\n        (   20.59   -27.66   -23.81     1.11)  ; 8\r\n        (   19.85   -28.83   -25.50     0.81)  ; 9\r\n        (   19.11   -29.41   -27.25     0.66)  ; 10\r\n        (   19.41   -28.32   -29.19     0.66)  ; 11\r\n        (   18.23   -28.98   -30.06     0.66)  ; 12\r\n        (   17.42   -29.49   -31.63     0.66)  ; 13\r\n        (   16.39   -30.51   -32.69     0.88)  ; 14\r\n        (   15.65   -30.95   -33.63     0.88)  ; 15\r\n        (   14.69   -31.39   -34.81     0.88)  ; 16\r\n        (   14.25   -32.12   -35.69     0.88)  ; 17\r\n        (   13.37   -32.78   -36.81     0.88)  ; 18\r\n        (   12.55   -33.95   -40.75     0.59)  ; 19\r\n        (   11.97   -34.97   -42.00     0.59)  ; 20\r\n        (   11.23   -35.92   -42.19     0.88)  ; 21\r\n        (   10.12   -36.21   -44.00     0.66)  ; 22\r\n        (    9.24   -36.80   -44.63     0.66)  ; 23\r\n        (    8.80   -37.24   -44.63     1.03)  ; 24\r\n        (    7.91   -37.82   -46.25     1.25)  ; 25\r\n        (    7.32   -38.70   -48.00     0.96)  ; 26\r\n        (    7.03   -39.50   -50.44     0.44)  ; 27\r\n        (    6.37   -40.31   -50.94     0.44)  ; 28\r\n        (    5.33   -41.04   -51.31     0.44)  ; 29\r\n        (    5.04   -41.62   -51.50     0.81)  ; 30\r\n        (    4.52   -42.72   -53.06     1.47)  ; 31\r\n        (    3.79   -43.16   -55.13     1.47)  ; 32\r\n        (    3.05   -43.89   -55.38     1.11)  ; 33\r\n        (    2.54   -44.25   -56.75     0.74)  ; 34\r\n        (    1.50   -45.50   -56.75     0.37)  ; 35\r\n        (    1.14   -46.37   -57.13     1.11)  ; 36\r\n        (    0.77   -46.74   -58.19     1.55)  ; 37\r\n        (    0.69   -47.03   -59.06     1.55)  ; 38\r\n        (    0.62   -47.62   -61.81     1.11)  ; 39\r\n        (    0.40   -48.86   -63.44     0.66)  ; 40\r\n        (   -0.12   -50.39   -63.44     0.52)  ; 41\r\n        (   -0.78   -51.27   -63.44     0.88)  ; 42\r\n        (   -1.58   -51.93   -64.88     1.25)  ; 43\r\n        (   -2.46   -52.37   -65.81     1.25)  ; 44\r\n        (   -2.98   -52.51   -69.69     0.88)  ; 45\r\n        (   -3.27   -52.44   -72.19     0.74)  ; 46\r\n        (   -3.86   -51.93   -73.00     0.96)  ; 47\r\n         Low\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (   16.75   -16.73    -2.56     2.87)  ; 1, R-2\r\n    (   17.19   -17.47    -1.69     2.50)  ; 2\r\n    (\r\n      (   17.48   -18.93    -0.88     1.77)  ; 1, R-2-1\r\n      (   17.41   -20.10     0.25     1.11)  ; 2\r\n      (   17.71   -20.46     1.31     1.33)  ; 3\r\n      (   18.00   -21.70     3.69     1.25)  ; 4\r\n      (\r\n        (   18.37   -22.87     4.56     0.81)  ; 1, R-2-1-1\r\n        (   18.66   -23.97     5.13     0.81)  ; 2\r\n        (   18.88   -25.14     4.44     1.03)  ; 3\r\n        (   18.96   -26.60     3.81     1.11)  ; 4\r\n        (   19.03   -27.77     4.56     1.03)  ; 5\r\n        (   18.44   -28.94     4.56     0.96)  ; 6\r\n        (   17.48   -30.18     4.63     0.96)  ; 7\r\n        (   16.90   -30.40     4.75     0.81)  ; 8\r\n        (   15.86   -30.48     4.75     0.81)  ; 9\r\n        (   14.91   -30.92     5.00     0.81)  ; 10\r\n        (   13.73   -31.28     5.63     0.66)  ; 11\r\n        (   13.36   -31.57     6.00     0.66)  ; 12\r\n        (   13.29   -32.30     6.19     0.66)  ; 13\r\n        (   12.92   -32.45     7.00     0.81)  ; 14\r\n        (   12.18   -32.60     7.81     0.81)  ; 15\r\n        (   10.93   -33.11     8.19     0.66)  ; 16\r\n        (    9.97   -33.77     8.31     0.66)  ; 17\r\n        (    8.94   -34.50     8.44     0.52)  ; 18\r\n        (    8.05   -35.23     8.44     0.81)  ; 19\r\n        (    7.32   -36.11     8.56     0.81)  ; 20\r\n        (    6.80   -36.98     9.19     0.74)  ; 21\r\n        (    6.14   -37.86     9.50     0.59)  ; 22\r\n        (    5.84   -38.96    10.00     0.66)  ; 23\r\n        (    5.55   -39.61    10.00     0.81)  ; 24\r\n        (    5.62   -40.56    10.13     0.81)  ; 25\r\n        (    4.89   -41.30    10.56     0.81)  ; 26\r\n        (    3.93   -41.81    11.06     0.66)  ; 27\r\n        (    2.97   -41.59    12.25     0.74)  ; 28\r\n        (    2.23   -40.78    13.56     0.66)  ; 29\r\n        (    2.01   -39.98    13.75     0.88)  ; 30\r\n        (    1.35   -39.25    14.44     0.96)  ; 31\r\n        (    0.47   -38.81    15.31     0.96)  ; 32\r\n        (   -0.34   -38.15    16.06     0.88)  ; 33\r\n        (   -0.93   -38.37    17.19     0.66)  ; 34\r\n        (   -1.52   -39.25    17.75     0.52)  ; 35\r\n        (   -1.60   -39.98    18.00     0.52)  ; 36\r\n        (   -1.67   -40.20    18.19     0.52)  ; 37\r\n        (   -1.45   -40.71    18.44     0.52)  ; 38\r\n         Low\r\n      |\r\n        (   18.74   -22.07     4.38     0.59)  ; 1, R-2-1-2\r\n        (   19.18   -22.29     4.63     0.59)  ; 2\r\n        (   19.92   -22.66     5.00     0.74)  ; 3\r\n        (   20.87   -23.61     5.00     0.88)  ; 4\r\n        (   21.39   -23.90     5.00     1.11)  ; 5\r\n        (   22.20   -24.48     5.19     0.81)  ; 6\r\n        (   22.86   -25.29     5.50     0.81)  ; 7\r\n        (   23.75   -26.09     5.19     0.81)  ; 8\r\n        (   24.26   -27.33     5.44     0.81)  ; 9\r\n        (   24.56   -28.21     4.56     0.81)  ; 10\r\n        (   24.78   -29.23     4.56     0.66)  ; 11\r\n        (   24.85   -30.18     4.38     0.66)  ; 12\r\n        (   25.00   -31.13     5.25     0.66)  ; 13\r\n        (   24.48   -32.08     6.63     0.81)  ; 14\r\n        (   24.48   -33.18     7.00     0.81)  ; 15\r\n        (   24.70   -33.91     7.31     0.81)  ; 16\r\n        (   25.29   -34.94     7.38     0.81)  ; 17\r\n        (   26.25   -35.52     7.19     0.81)  ; 18\r\n        (   26.84   -35.74     6.94     0.81)  ; 19\r\n        (   27.58   -35.81     6.75     0.81)  ; 20\r\n        (   28.31   -36.03     6.75     0.81)  ; 21\r\n        (   28.90   -36.40     6.75     0.81)  ; 22\r\n        (   30.01   -37.13     6.44     0.66)  ; 23\r\n        (   30.30   -37.79     6.38     0.81)  ; 24\r\n        (   30.60   -38.30     7.00     0.96)  ; 25\r\n        (   30.97   -39.25     8.88     1.18)  ; 26\r\n        (   30.45   -40.20    10.44     1.03)  ; 27\r\n        (   30.01   -40.78    12.00     0.88)  ; 28\r\n        (   29.20   -41.37    12.13     0.81)  ; 29\r\n        (   28.61   -41.95    12.31     0.81)  ; 30\r\n        (   27.87   -42.83    12.13     0.66)  ; 31\r\n        (   27.50   -43.20    12.13     0.66)  ; 32\r\n        (   27.43   -43.42    12.25     0.66)  ; 33\r\n        (   27.50   -44.22    12.63     0.81)  ; 34\r\n        (   27.43   -44.66    13.06     0.96)  ; 35\r\n        (   27.14   -45.02    13.31     0.81)  ; 36\r\n        (   26.99   -45.68    13.38     0.66)  ; 37\r\n        (   27.16   -46.35    13.44     0.74)  ; 38\r\n        (   27.75   -47.15    13.81     0.81)  ; 39\r\n        (   28.49   -47.81    12.81     0.88)  ; 40\r\n        (   28.64   -48.10    11.38     0.96)  ; 41\r\n        (   28.64   -48.83    11.13     0.74)  ; 42\r\n        (   29.01   -49.71    11.63     0.74)  ; 43\r\n        (   29.52   -50.22    12.44     0.74)  ; 44\r\n        (   30.18   -50.15    11.13     0.88)  ; 45\r\n        (   30.70   -49.85    13.94     0.96)  ; 46\r\n        (   31.14   -50.07    15.19     0.88)  ; 47\r\n        (   31.36   -50.73    15.88     1.11)  ; 48\r\n        (   31.66   -51.17    16.50     1.11)  ; 49\r\n        (   32.03   -51.24    17.19     1.11)  ; 50\r\n        (   32.47   -50.59    17.88     1.11)  ; 51\r\n        (   33.06   -50.15    18.00     0.96)  ; 52\r\n        (   33.65   -50.00    17.63     0.88)  ; 53\r\n        (   34.46   -50.15    17.56     0.74)  ; 54\r\n        (   35.41   -50.66    17.25     0.66)  ; 55\r\n        (   36.08   -50.66    16.81     0.81)  ; 56\r\n        (   36.52   -50.80    18.25     0.81)  ; 57\r\n        (   37.11   -51.17    18.38     0.66)  ; 58\r\n        (   37.33   -51.54    18.44     0.52)  ; 59\r\n        (   38.07   -52.05    19.06     0.37)  ; 60\r\n        (   38.73   -51.90    18.31     0.37)  ; 61\r\n        (   39.32   -51.90    17.31     0.37)  ; 62\r\n        (   40.06   -51.90    18.25     0.37)  ; 63\r\n        (   40.42   -52.05    18.31     0.37)  ; 64\r\n        (   40.57   -52.78    18.31     0.37)  ; 65\r\n        (   41.01   -53.29    18.31     0.37)  ; 66\r\n        (   41.82   -53.80    18.31     0.37)  ; 67\r\n         Low\r\n      )  ;  End of split\r\n    |\r\n      (   18.47   -18.13     0.31     0.81)  ; 1, R-2-2\r\n      (   19.21   -18.57     2.06     0.88)  ; 2\r\n      (   19.72   -18.93     2.25     1.55)  ; 3\r\n      (   20.14   -19.38     2.25     1.55)  ; 4\r\n      (\r\n        (   20.68   -19.45     2.69     1.25)  ; 1, R-2-2-1\r\n        (   21.64   -19.96     3.06     0.88)  ; 2\r\n        (   21.79   -20.61     3.50     0.66)  ; 3\r\n        (   22.23   -21.64     3.94     0.81)  ; 4\r\n        (   22.89   -22.30     4.31     0.81)  ; 5\r\n        (   23.85   -23.03     4.75     0.66)  ; 6\r\n        (   24.00   -23.61     5.19     0.66)  ; 7\r\n        (   23.92   -24.34     5.94     0.81)  ; 8\r\n        (   23.77   -25.00     8.00     0.81)  ; 9\r\n        (   23.26   -25.66     8.19     0.66)  ; 10\r\n        (   23.26   -26.46     8.94     0.66)  ; 11\r\n        (   23.77   -27.05     9.25     0.66)  ; 12\r\n        (   24.51   -26.76     9.44     0.66)  ; 13\r\n        (   24.51   -26.17    10.56     0.66)  ; 14\r\n        (   24.81   -25.95    11.06     0.66)  ; 15\r\n        (   25.25   -26.10    11.31     0.66)  ; 16\r\n        (   26.13   -26.83    11.69     0.66)  ; 17\r\n        (   27.02   -27.49    10.06     0.66)  ; 18\r\n        (   27.38   -28.07    11.13     0.81)  ; 19\r\n        (   27.75   -28.36    12.50     0.81)  ; 20\r\n        (   28.05   -27.78    13.19     0.74)  ; 21\r\n        (   28.42   -27.34    13.38     0.59)  ; 22\r\n        (   28.42   -26.46    14.00     0.59)  ; 23\r\n        (   28.78   -26.02    14.25     0.59)  ; 24\r\n        (   28.42   -25.07    14.31     0.59)  ; 25\r\n        (   29.01   -24.42    14.31     0.59)  ; 26\r\n        (   29.30   -23.54    14.31     0.81)  ; 27\r\n        (   29.37   -22.44    13.50     0.74)  ; 28\r\n        (   29.37   -21.42    14.75     0.74)  ; 29\r\n        (   29.15   -20.69    14.75     0.74)  ; 30\r\n        (   29.23   -20.40    15.13     0.74)  ; 31\r\n        (   29.74   -20.25    15.38     0.74)  ; 32\r\n        (   30.26   -19.74    15.75     0.74)  ; 33\r\n        (   30.99   -19.23    16.31     0.74)  ; 34\r\n        (   31.07   -18.57    16.75     0.74)  ; 35\r\n        (   30.70   -18.06    18.13     0.74)  ; 36\r\n        (   29.89   -17.18    18.31     0.44)  ; 37\r\n        (   28.86   -16.67    18.38     0.44)  ; 38\r\n        (   28.23   -15.88    18.56     0.37)  ; 39\r\n        (   28.01   -14.63    18.56     0.37)  ; 40\r\n        (   28.23   -13.61    18.50     0.44)  ; 41\r\n        (   27.86   -12.81    18.44     0.44)  ; 42\r\n        (   27.72   -11.56    18.31     0.44)  ; 43\r\n        (   27.05   -11.27    18.25     0.44)  ; 44\r\n        (   26.10   -11.42    18.13     0.37)  ; 45\r\n        (   25.58   -11.20    18.13     0.37)  ; 46\r\n        (   25.36   -10.83    18.13     0.37)  ; 47\r\n         Low\r\n      |\r\n        (   20.14   -20.41     4.31     0.66)  ; 1, R-2-2-2\r\n        (   19.18   -20.77     6.81     0.52)  ; 2\r\n        (   18.67   -20.55     9.63     0.44)  ; 3\r\n        (   18.15   -19.60    10.00     0.44)  ; 4\r\n        (   18.37   -18.65    10.88     0.44)  ; 5\r\n        (   19.77   -18.65    11.50     0.44)  ; 6\r\n        (   20.29   -19.82    14.25     0.74)  ; 7\r\n        (   19.48   -19.75    14.50     0.52)  ; 8\r\n        (   19.55   -18.58    14.94     0.52)  ; 9\r\n        (   19.99   -17.56    15.38     0.44)  ; 10\r\n        (   21.02   -17.34    15.81     0.44)  ; 11\r\n        (   21.24   -16.46    16.44     0.37)  ; 12\r\n        (   21.17   -15.80    17.13     0.37)  ; 13\r\n        (   20.43   -15.36    17.69     0.37)  ; 14\r\n        (   19.84   -15.00    18.00     0.29)  ; 15\r\n        (   19.11   -14.56    18.31     0.44)  ; 16\r\n        (   18.67   -13.97    18.50     0.44)  ; 17\r\n        (   18.74   -12.00    19.06     0.29)  ; 18\r\n        (   18.00   -11.34    19.25     0.44)  ; 19\r\n        (   16.60   -11.05    19.31     0.44)  ; 20\r\n        (   15.57   -11.42    19.38     0.22)  ; 21\r\n        (   14.69   -11.93    19.38     0.22)  ; 22\r\n         High\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Green)\r\n  (Dendrite)\r\n  (  -13.75    -4.63     5.38     3.61)  ; Root\r\n  (  -15.67    -5.36     5.56     2.80)  ; 1, R\r\n  (  -16.62    -6.02     6.56     2.65)  ; 2\r\n  (  -17.80    -6.75     6.63     2.36)  ; 3\r\n  (  -18.98    -7.48     7.38     2.28)  ; 4\r\n  (  -19.86    -8.28     7.94     2.58)  ; 5\r\n  (  -20.68    -8.94     9.13     3.17)  ; 6\r\n  (  -21.71    -9.82     9.44     4.05)  ; 7\r\n  (  -22.00    -9.96     9.94     4.27)  ; 8\r\n  (\r\n    (  -22.30   -10.62    10.31     3.24)  ; 1, R-1\r\n    (  -22.44   -11.94    10.56     2.80)  ; 2\r\n    (  -22.66   -13.03    10.56     2.28)  ; 3\r\n    (\r\n      (  -23.03   -14.28     9.31     1.84)  ; 1, R-1-1\r\n      (  -23.40   -15.52     9.06     1.62)  ; 2\r\n      (  -23.25   -16.62     8.81     1.40)  ; 3\r\n      (  -23.18   -17.71     8.69     1.40)  ; 4\r\n      (  -22.81   -18.52     8.56     1.62)  ; 5\r\n      (  -22.30   -19.32     8.25     1.62)  ; 6\r\n      (  -21.56   -20.27     7.75     1.62)  ; 7\r\n      (  -20.90   -21.51     7.44     1.69)  ; 8\r\n      (  -20.23   -22.76     6.88     1.77)  ; 9\r\n      (  -20.09   -23.78     6.31     1.77)  ; 10\r\n      (  -20.09   -25.10     6.31     1.47)  ; 11\r\n      (  -20.23   -25.97     6.31     1.84)  ; 12\r\n      (  -20.75   -27.00     6.25     1.69)  ; 13\r\n      (  -21.26   -27.95     6.13     1.69)  ; 14\r\n      (  -22.00   -29.19     5.50     1.69)  ; 15\r\n      (  -22.59   -30.43     4.88     1.99)  ; 16\r\n      (  -22.92   -31.87     4.13     2.21)  ; 17\r\n      (  -23.60   -33.09     4.13     2.21)  ; 18\r\n      (\r\n        (  -23.44   -33.18     2.75     2.28)  ; 1, R-1-1-1\r\n        (  -23.58   -33.99     2.44     1.62)  ; 2\r\n        (  -23.80   -34.94     2.38     1.18)  ; 3\r\n        (  -24.32   -35.82     2.38     0.88)  ; 4\r\n        (  -24.98   -36.55     2.25     1.18)  ; 5\r\n        (  -26.16   -36.84     2.19     1.18)  ; 6\r\n        (  -27.41   -37.42     1.94     1.18)  ; 7\r\n        (  -28.15   -38.01     1.75     1.18)  ; 8\r\n        (  -28.45   -39.11     2.06     1.33)  ; 9\r\n        (  -28.74   -40.06     1.69     1.47)  ; 10\r\n        (  -28.67   -41.01     1.50     1.62)  ; 11\r\n        (  -28.89   -42.18     1.50     1.33)  ; 12\r\n        (  -29.04   -43.42     2.19     1.25)  ; 13\r\n        (  -29.18   -44.66     2.69     1.25)  ; 14\r\n        (  -29.70   -45.90     4.31     1.25)  ; 15\r\n        (  -30.66   -47.07     4.81     1.33)  ; 16\r\n        (  -31.39   -48.17     5.69     1.33)  ; 17\r\n        (  -32.50   -49.12     5.69     1.33)  ; 18\r\n        (  -33.46   -49.85     6.38     1.77)  ; 19\r\n        (  -35.15   -50.66     7.31     2.21)  ; 20\r\n        (\r\n          (  -36.11   -50.95     7.31     2.14)  ; 1, R-1-1-1-1\r\n          (  -37.58   -51.09     8.69     1.92)  ; 2\r\n          (  -38.39   -50.73    10.69     1.18)  ; 3\r\n          (  -39.42   -50.66    11.56     1.33)  ; 4\r\n          (  -40.38   -51.75    12.75     0.88)  ; 5\r\n          (  -41.19   -52.56    12.88     0.88)  ; 6\r\n          (  -41.85   -53.29    13.00     0.88)  ; 7\r\n          (  -42.74   -53.73    12.63     1.40)  ; 8\r\n          (  -43.99   -53.80    12.38     1.40)  ; 9\r\n          (  -44.36   -53.94    12.44     1.18)  ; 10\r\n          (  -44.80   -54.46    12.44     0.96)  ; 11\r\n          (  -45.91   -54.60    12.44     0.74)  ; 12\r\n          (  -47.16   -54.68    12.06     0.96)  ; 13\r\n          (  -48.56   -54.38    12.06     0.96)  ; 14\r\n          (  -50.03   -53.87    11.81     0.74)  ; 15\r\n          (  -50.70   -53.43    11.81     0.74)  ; 16\r\n          (  -51.28   -52.99    12.38     0.74)  ; 17\r\n          (  -51.87   -52.63    12.69     0.74)  ; 18\r\n          (  -52.68   -52.48    12.69     0.74)  ; 19\r\n          (  -53.86   -52.63    13.13     0.74)  ; 20\r\n          (  -54.38   -52.78    13.25     0.88)  ; 21\r\n          (  -55.41   -52.85    13.25     0.88)  ; 22\r\n          (  -56.29   -52.78    13.81     1.11)  ; 23\r\n          (  -57.10   -53.07    13.94     0.81)  ; 24\r\n          (  -57.99   -53.29    14.38     0.81)  ; 25\r\n          (  -58.80   -53.65    14.69     1.03)  ; 26\r\n          (  -59.46   -54.31    15.50     1.18)  ; 27\r\n          (  -60.57   -54.97    15.69     0.81)  ; 28\r\n          (  -61.38   -55.55    16.25     0.81)  ; 29\r\n          (  -62.48   -55.70    16.56     0.74)  ; 30\r\n          (  -63.15   -55.63    17.13     0.96)  ; 31\r\n          (  -64.32   -55.26    17.25     0.96)  ; 32\r\n          (  -65.14   -55.04    17.44     0.74)  ; 33\r\n          (  -66.46   -54.90    17.50     0.59)  ; 34\r\n          (  -67.49   -55.92    17.88     0.52)  ; 35\r\n          (  -67.79   -56.72    17.75     0.52)  ; 36\r\n          (  -68.01   -57.75    17.50     0.52)  ; 37\r\n          (  -67.86   -58.26    17.38     0.81)  ; 38\r\n          (  -68.16   -59.21    17.31     0.59)  ; 39\r\n           Low\r\n        |\r\n          (  -36.14   -51.09     6.69     1.25)  ; 1, R-1-1-1-2\r\n          (  -37.25   -51.24     6.69     1.25)  ; 2\r\n          (  -38.35   -51.53     6.75     1.40)  ; 3\r\n          (  -38.79   -51.82     6.75     1.18)  ; 4\r\n          (\r\n            (  -39.82   -51.75     7.38     0.96)  ; 1, R-1-1-1-2-1\r\n            (  -40.78   -51.39     7.31     0.81)  ; 2\r\n            (  -41.67   -51.02     7.31     0.74)  ; 3\r\n            (  -42.99   -50.36     6.75     0.74)  ; 4\r\n            (  -44.17   -50.00     5.88     0.88)  ; 5\r\n            (  -45.13   -49.63     4.75     1.03)  ; 6\r\n            (  -46.38   -49.19     4.50     0.88)  ; 7\r\n            (  -47.49   -48.75     4.00     0.96)  ; 8\r\n            (  -48.08   -48.68     3.38     1.47)  ; 9\r\n            (  -49.03   -48.68     3.06     1.47)  ; 10\r\n            (  -50.58   -48.53     2.75     1.25)  ; 11\r\n            (  -51.47   -48.53     2.75     1.25)  ; 12\r\n            (  -52.50   -48.61     2.75     1.03)  ; 13\r\n            (  -53.68   -48.53     2.75     1.03)  ; 14\r\n            (  -55.37   -48.32     2.75     0.88)  ; 15\r\n            (  -56.70   -48.39     2.13     0.74)  ; 16\r\n            (  -57.95   -48.68     1.69     0.74)  ; 17\r\n            (  -58.91   -48.97     1.38     0.96)  ; 18\r\n            (  -59.64   -49.27     1.00     0.96)  ; 19\r\n            (  -60.53   -49.56     0.88     0.81)  ; 20\r\n            (  -61.04   -49.85     0.69     0.81)  ; 21\r\n            (  -61.93   -50.36     0.31     0.96)  ; 22\r\n            (  -63.03   -50.73     0.00     0.96)  ; 23\r\n            (  -64.21   -50.80    -0.13     0.96)  ; 24\r\n            (  -65.46   -50.95     0.13     0.96)  ; 25\r\n            (  -66.05   -51.09     0.50     1.55)  ; 26\r\n            (  -66.79   -51.09     0.81     1.84)  ; 27\r\n            (  -67.16   -51.09     1.00     2.14)  ; 28\r\n            (  -67.89   -51.02     1.75     1.33)  ; 29\r\n            (  -68.78   -50.87     1.94     0.96)  ; 30\r\n            (  -69.66   -50.36     2.06     0.96)  ; 31\r\n            (  -70.55   -49.85     2.31     0.96)  ; 32\r\n            (  -71.50   -49.05     2.44     0.96)  ; 33\r\n            (  -72.39   -48.17     2.44     0.96)  ; 34\r\n            (  -73.42   -47.51     2.56     0.81)  ; 35\r\n            (  -74.08   -46.78     2.81     0.96)  ; 36\r\n            (  -75.26   -45.98     3.38     0.81)  ; 37\r\n            (  -76.15   -45.54     4.56     0.88)  ; 38\r\n            (  -76.96   -45.17     5.19     0.88)  ; 39\r\n            (  -78.21   -44.81     5.19     0.88)  ; 40\r\n            (  -78.87   -44.73     5.19     1.18)  ; 41\r\n            (  -79.83   -44.59     5.38     1.40)  ; 42\r\n            (  -80.86   -44.29     5.19     1.18)  ; 43\r\n            (  -82.26   -44.37     5.00     1.03)  ; 44\r\n            (  -83.37   -44.29     4.38     0.88)  ; 45\r\n            (  -83.81   -44.00     4.25     0.81)  ; 46\r\n            (  -84.18   -44.22     4.13     0.81)  ; 47\r\n            (  -84.77   -44.29     3.88     0.81)  ; 48\r\n            (  -86.09   -43.86     3.56     0.81)  ; 49\r\n            (  -87.49   -44.15     3.38     0.81)  ; 50\r\n            (  -88.45   -43.93     3.56     0.66)  ; 51\r\n            (  -88.96   -44.08     4.50     0.81)  ; 52\r\n            (  -89.19   -44.59     5.44     1.25)  ; 53\r\n            (  -89.48   -44.81     6.06     1.03)  ; 54\r\n            (  -89.70   -44.81     6.88     0.81)  ; 55\r\n            (  -90.14   -44.59     7.94     0.74)  ; 56\r\n            (  -91.03   -44.15     8.19     0.81)  ; 57\r\n            (  -92.06   -43.56     8.19     0.74)  ; 58\r\n            (  -93.24   -43.71     8.19     0.88)  ; 59\r\n            (  -94.42   -43.49     8.19     1.03)  ; 60\r\n            (  -95.23   -43.71     7.94     0.88)  ; 61\r\n            (  -95.89   -43.86     8.94     0.88)  ; 62\r\n            (  -96.70   -43.86     9.94     0.81)  ; 63\r\n            (  -97.14   -43.13    12.19     0.81)  ; 64\r\n            (  -97.66   -42.39    12.75     0.81)  ; 65\r\n            (  -97.88   -41.81    14.06     1.25)  ; 66\r\n            (  -98.62   -41.15    15.19     1.18)  ; 67\r\n            (  -99.06   -40.93    15.88     1.40)  ; 68\r\n            (  -99.65   -40.42    15.94     1.69)  ; 69\r\n            ( -100.31   -40.13    16.56     1.69)  ; 70\r\n            ( -100.61   -39.98    16.69     1.33)  ; 71\r\n            ( -101.05   -39.91    16.75     1.03)  ; 72\r\n            ( -101.86   -39.69    16.63     0.74)  ; 73\r\n            ( -102.37   -39.54    17.06     0.59)  ; 74\r\n            ( -103.48   -39.69    17.13     0.59)  ; 75\r\n            ( -104.29   -39.76    16.69     0.59)  ; 76\r\n            ( -104.95   -39.40    16.44     0.66)  ; 77\r\n            ( -105.98   -38.89    16.31     0.66)  ; 78\r\n            ( -107.01   -38.59    16.06     0.81)  ; 79\r\n            ( -107.75   -38.45    15.69     0.81)  ; 80\r\n            ( -108.41   -38.52    15.63     1.03)  ; 81\r\n            ( -108.93   -38.81    15.00     1.25)  ; 82\r\n            ( -109.89   -38.96    16.38     0.96)  ; 83\r\n            ( -110.77   -39.10    16.44     0.66)  ; 84\r\n            ( -111.88   -39.18    16.25     0.66)  ; 85\r\n            ( -112.98   -39.54    15.94     1.11)  ; 86\r\n            ( -113.72   -39.76    14.94     1.11)  ; 87\r\n            ( -114.53   -40.20    14.13     0.96)  ; 88\r\n            ( -115.27   -41.01    14.06     0.96)  ; 89\r\n            ( -115.19   -41.66    12.81     0.66)  ; 90\r\n            ( -114.82   -42.03    12.50     0.66)  ; 91\r\n            ( -114.60   -42.69    12.44     0.74)  ; 92\r\n            ( -114.60   -43.64    11.94     0.88)  ; 93\r\n            ( -114.68   -44.37    11.38     1.25)  ; 94\r\n            ( -114.53   -44.95    10.44     1.62)  ; 95\r\n            ( -114.31   -45.32     9.81     2.21)  ; 96\r\n            ( -114.53   -46.05     8.94     1.62)  ; 97\r\n            ( -114.68   -46.78     7.81     0.88)  ; 98\r\n            ( -115.12   -47.29     7.50     0.66)  ; 99\r\n            ( -115.78   -47.44     7.50     0.52)  ; 100\r\n            ( -116.52   -47.31     7.44     0.59)  ; 101\r\n            ( -117.47   -47.10     6.81     0.66)  ; 102\r\n            ( -118.65   -47.10     6.81     0.66)  ; 103\r\n            ( -119.31   -47.53     6.81     0.66)  ; 104\r\n            ( -120.13   -48.41     7.13     0.66)  ; 105\r\n            ( -121.01   -49.22     7.13     0.66)  ; 106\r\n            ( -121.52   -49.73     7.38     0.52)  ; 107\r\n            ( -121.97   -50.60     7.44     0.44)  ; 108\r\n            ( -122.34   -51.26     6.81     0.88)  ; 109\r\n            ( -122.56   -51.48     5.13     1.33)  ; 110\r\n            ( -122.78   -51.99     3.88     1.92)  ; 111\r\n            ( -122.85   -51.99     3.19     2.65)  ; 112\r\n            ( -123.44   -52.36     3.13     2.65)  ; 113\r\n            ( -123.88   -52.50     2.81     1.77)  ; 114\r\n            ( -124.47   -52.50     2.81     1.33)  ; 115\r\n            ( -125.06   -52.72     2.81     0.88)  ; 116\r\n            ( -125.58   -52.50     2.81     0.52)  ; 117\r\n            ( -126.02   -52.36     2.56     0.52)  ; 118\r\n            ( -126.68   -51.99     2.38     0.52)  ; 119\r\n            ( -126.68   -51.63     2.38     0.52)  ; 120\r\n            ( -127.12   -51.26     3.19     0.52)  ; 121\r\n            ( -127.93   -50.82     3.31     0.52)  ; 122\r\n            ( -128.23   -50.82     3.00     0.88)  ; 123\r\n            ( -128.45   -50.97     2.69     1.33)  ; 124\r\n            ( -128.82   -51.12     1.94     2.06)  ; 125\r\n            ( -129.19   -51.19     1.75     2.80)  ; 126\r\n            ( -129.85   -51.41     1.44     2.80)  ; 127\r\n            ( -130.81   -51.55     1.13     1.99)  ; 128\r\n            ( -131.25   -51.63     0.94     1.11)  ; 129\r\n            ( -131.84   -51.63     0.75     0.74)  ; 130\r\n            ( -132.58   -51.77     0.38     0.52)  ; 131\r\n            ( -133.31   -51.77     0.19     0.52)  ; 132\r\n            ( -134.79   -52.29     0.19     0.52)  ; 133\r\n            ( -136.04   -52.65     0.31     0.66)  ; 134\r\n            ( -137.07   -53.16     0.31     0.37)  ; 135\r\n            ( -137.88   -53.53     0.31     0.66)  ; 136\r\n            ( -138.47   -53.82     0.31     0.74)  ; 137\r\n            ( -139.35   -54.19     0.31     0.37)  ; 138\r\n            ( -140.16   -54.62     0.31     0.37)  ; 139\r\n             Low\r\n          |\r\n            (  -39.99   -52.27     8.81     0.74)  ; 1, R-1-1-1-2-2\r\n            (  -40.50   -52.63     8.31     0.52)  ; 2\r\n            (  -40.87   -53.14     6.63     0.52)  ; 3\r\n            (  -41.76   -53.43     6.63     0.59)  ; 4\r\n            (  -42.49   -53.80     6.25     0.52)  ; 5\r\n            (  -42.79   -54.24     5.56     0.52)  ; 6\r\n            (  -42.57   -54.90     5.50     0.52)  ; 7\r\n            (  -42.27   -55.48     5.38     0.52)  ; 8\r\n            (  -42.42   -56.29     4.75     0.59)  ; 9\r\n            (  -42.57   -56.65     5.63     0.44)  ; 10\r\n            (  -43.53   -57.16     4.88     0.44)  ; 11\r\n            (  -44.41   -57.53     4.56     0.66)  ; 12\r\n            (  -45.15   -57.89     4.56     1.18)  ; 13\r\n            (  -45.66   -58.62     4.56     1.18)  ; 14\r\n            (  -46.40   -58.70     4.44     0.88)  ; 15\r\n            (  -47.36   -58.99     4.00     0.66)  ; 16\r\n            (  -47.95   -59.79     4.00     0.52)  ; 17\r\n            (  -48.53   -60.45     3.56     0.88)  ; 18\r\n            (  -49.27   -60.82     3.38     1.11)  ; 19\r\n            (  -50.23   -61.33     1.94     1.47)  ; 20\r\n            (  -51.26   -61.55     1.38     1.69)  ; 21\r\n            (  -52.29   -61.77     1.38     1.47)  ; 22\r\n            (  -53.10   -62.50     0.94     1.47)  ; 23\r\n            (  -53.54   -63.23     1.56     1.33)  ; 24\r\n            (  -54.36   -63.60     3.13     1.03)  ; 25\r\n            (  -54.80   -63.67     3.19     0.74)  ; 26\r\n            (  -55.17   -63.89     3.50     0.66)  ; 27\r\n            (  -55.31   -63.67     3.69     0.66)  ; 28\r\n            (  -56.12   -63.67     4.31     0.52)  ; 29\r\n            (  -56.71   -64.40     4.50     0.52)  ; 30\r\n            (  -57.38   -65.42     4.75     0.52)  ; 31\r\n            (  -57.89   -66.23     4.06     0.81)  ; 32\r\n            (  -58.48   -66.67     3.75     1.11)  ; 33\r\n            (  -59.22   -67.32     3.13     0.88)  ; 34\r\n            (  -59.73   -67.76     3.13     0.59)  ; 35\r\n            (  -60.76   -67.91     3.13     0.44)  ; 36\r\n            (  -61.43   -68.35     3.50     0.81)  ; 37\r\n            (  -61.94   -68.64     3.63     1.40)  ; 38\r\n            (  -62.97   -69.30     3.75     2.06)  ; 39\r\n            (  -63.27   -69.59     3.81     2.21)  ; 40\r\n            (  -63.86   -70.25     3.81     1.18)  ; 41\r\n            (  -64.23   -70.83     4.00     0.59)  ; 42\r\n            (  -64.67   -71.56     4.00     0.59)  ; 43\r\n            (  -65.19   -72.29     4.00     0.59)  ; 44\r\n            (  -65.55   -72.07     4.00     0.37)  ; 45\r\n            (  -66.07   -71.78     4.00     0.37)  ; 46\r\n            (  -66.58   -72.00     4.13     0.88)  ; 47\r\n            (  -67.03   -72.29     4.13     1.62)  ; 48\r\n            (  -67.47   -72.66     4.06     1.62)  ; 49\r\n            (  -67.98   -73.32     4.06     0.74)  ; 50\r\n            (  -68.50   -73.68     3.81     0.37)  ; 51\r\n            (  -68.94   -74.05     3.56     0.37)  ; 52\r\n            (  -69.09   -74.41     3.56     0.37)  ; 53\r\n            (  -68.87   -75.00     3.69     0.37)  ; 54\r\n            (  -68.72   -75.58     4.81     0.37)  ; 55\r\n            (  -68.72   -76.31     5.13     0.52)  ; 56\r\n            (  -69.02   -76.75     5.50     0.52)  ; 57\r\n            (  -69.83   -76.75     6.00     0.44)  ; 58\r\n            (  -70.19   -77.26     6.06     0.44)  ; 59\r\n            (  -70.56   -77.63     5.81     0.74)  ; 60\r\n            (  -70.78   -78.22     5.44     0.74)  ; 61\r\n            (  -71.01   -78.87     5.19     0.59)  ; 62\r\n            (  -71.37   -79.68     5.19     0.52)  ; 63\r\n            (  -71.52   -80.33     5.19     0.88)  ; 64\r\n            (  -71.67   -81.36     4.81     1.47)  ; 65\r\n            (  -71.90   -82.11     5.06     1.92)  ; 66\r\n            (  -72.05   -82.92     5.06     1.25)  ; 67\r\n            (  -72.19   -83.57     5.06     0.88)  ; 68\r\n            (  -72.27   -83.87     5.06     0.59)  ; 69\r\n            (  -72.27   -84.45     5.06     0.37)  ; 70\r\n            (  -72.27   -84.89     5.06     0.29)  ; 71\r\n            (  -71.68   -85.40     5.06     0.29)  ; 72\r\n            (  -71.68   -86.28     4.94     0.81)  ; 73\r\n            (  -72.05   -86.94     4.50     1.47)  ; 74\r\n            (  -72.49   -87.37     4.31     1.11)  ; 75\r\n            (  -73.00   -87.89     3.63     0.44)  ; 76\r\n            (  -73.52   -88.47     3.19     0.29)  ; 77\r\n            (  -73.59   -88.91     2.81     0.52)  ; 78\r\n            (  -73.52   -89.35     2.25     0.81)  ; 79\r\n            (  -73.08   -89.93     1.88     0.66)  ; 80\r\n            (  -72.86   -90.23     1.44     0.37)  ; 81\r\n            (  -72.86   -90.81     1.06     0.37)  ; 82\r\n            (  -72.93   -91.47     0.75     0.59)  ; 83\r\n            (  -72.64   -91.91     0.31     1.40)  ; 84\r\n            (  -72.49   -92.56     0.00     2.65)  ; 85\r\n            (  -72.27   -92.78    -0.13     3.02)  ; 86\r\n            (  -71.83   -93.22    -0.31     1.62)  ; 87\r\n            (  -71.31   -93.88    -0.44     0.74)  ; 88\r\n            (  -71.16   -94.03    -0.44     0.29)  ; 89\r\n            (  -70.57   -94.39    -0.56     0.52)  ; 90\r\n            (  -70.28   -94.68    -0.56     0.88)  ; 91\r\n            (  -69.69   -95.20    -0.56     0.52)  ; 92\r\n            (  -69.39   -95.85    -1.19     0.74)  ; 93\r\n            (  -69.39   -96.44    -1.75     1.11)  ; 94\r\n            (  -69.25   -96.95    -2.13     1.92)  ; 95\r\n            (  -68.88   -97.46    -2.44     1.92)  ; 96\r\n            (  -68.81   -98.19    -2.69     0.66)  ; 97\r\n            (  -68.73   -98.63    -2.69     0.37)  ; 98\r\n            (  -68.88   -99.22    -2.75     0.29)  ; 99\r\n            (  -68.88   -99.95    -3.13     0.29)  ; 100\r\n            (  -68.88  -100.53    -3.13     0.29)  ; 101\r\n            (  -68.66  -101.19    -2.50     0.59)  ; 102\r\n            (  -68.58  -101.34    -2.38     0.96)  ; 103\r\n            (  -68.58  -101.63    -2.38     1.77)  ; 104\r\n            (  -68.51  -101.92    -2.38     1.77)  ; 105\r\n            (  -68.58  -102.43    -2.31     0.81)  ; 106\r\n            (  -68.51  -102.87    -2.31     0.44)  ; 107\r\n            (  -68.66  -104.04    -2.31     0.29)  ; 108\r\n            (  -68.66  -104.55    -2.31     0.29)  ; 109\r\n            (  -68.44  -104.92    -2.31     0.66)  ; 110\r\n            (  -68.36  -104.99    -2.31     0.81)  ; 111\r\n             Low\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (  -24.41   -33.60     0.69     0.96)  ; 1, R-1-1-2\r\n        (  -25.15   -34.26    -1.69     0.81)  ; 2\r\n        (  -25.89   -34.62    -1.69     0.81)  ; 3\r\n        (  -26.92   -35.57    -3.19     0.96)  ; 4\r\n        (  -27.28   -36.67    -4.69     1.40)  ; 5\r\n        (  -27.58   -37.69    -5.44     1.40)  ; 6\r\n        (  -28.32   -39.45    -6.00     1.47)  ; 7\r\n        (  -28.76   -40.55    -6.00     1.25)  ; 8\r\n        (  -28.68   -41.57    -6.25     1.25)  ; 9\r\n        (  -28.61   -42.96    -6.75     1.33)  ; 10\r\n        (  -28.98   -44.27    -6.13     1.25)  ; 11\r\n        (  -29.57   -45.30    -5.81     1.25)  ; 12\r\n        (  -30.31   -46.39    -5.44     1.25)  ; 13\r\n        (  -30.67   -47.56    -4.56     1.47)  ; 14\r\n        (  -31.04   -48.51    -5.75     1.99)  ; 15\r\n        (\r\n          (  -31.85   -49.32    -5.00     3.24)  ; 1, R-1-1-2-1\r\n          (\r\n            (  -32.52   -50.41    -3.63     1.47)  ; 1, R-1-1-2-1-1\r\n            (  -32.81   -51.07    -3.25     0.96)  ; 2\r\n            (  -33.25   -51.80    -3.31     0.81)  ; 3\r\n            (  -33.47   -52.46    -3.44     0.81)  ; 4\r\n            (  -33.55   -53.05    -3.44     0.66)  ; 5\r\n            (  -33.55   -53.78    -3.44     0.96)  ; 6\r\n            (  -33.55   -54.21    -4.25     0.96)  ; 7\r\n            (  -34.06   -55.16    -4.44     0.96)  ; 8\r\n            (  -34.28   -55.09    -8.13     0.88)  ; 9\r\n            (  -33.92   -55.68    -9.06     0.88)  ; 10\r\n            (  -34.50   -55.90   -10.38     0.88)  ; 11\r\n            (  -35.24   -56.26   -10.75     0.74)  ; 12\r\n            (  -35.90   -56.48   -10.94     0.74)  ; 13\r\n            (  -36.42   -57.14   -11.31     0.74)  ; 14\r\n            (  -37.08   -58.16   -12.06     0.81)  ; 15\r\n            (  -37.30   -58.89   -14.06     1.77)  ; 16\r\n            (  -37.67   -59.55   -14.25     1.77)  ; 17\r\n            (  -38.04   -60.50   -15.13     1.47)  ; 18\r\n            (  -38.11   -61.23   -16.81     1.11)  ; 19\r\n            (  -38.34   -62.11   -18.00     0.96)  ; 20\r\n            (  -38.04   -62.69   -20.19     0.96)  ; 21\r\n            (  -37.60   -62.99   -20.19     1.11)  ; 22\r\n            (  -36.94   -63.64   -20.50     1.11)  ; 23\r\n            (  -36.49   -64.30   -21.25     0.88)  ; 24\r\n            (  -36.27   -64.67   -22.44     0.74)  ; 25\r\n            (  -36.13   -65.03   -22.94     0.59)  ; 26\r\n            (  -37.16   -65.47   -23.25     0.37)  ; 27\r\n            (  -37.82   -66.06   -23.38     1.03)  ; 28\r\n            (  -38.11   -67.15   -23.38     1.47)  ; 29\r\n            (  -39.00   -68.40   -23.56     1.40)  ; 30\r\n            (  -39.29   -69.71   -23.81     1.40)  ; 31\r\n            (  -40.25   -71.25   -24.63     1.40)  ; 32\r\n            (  -40.62   -72.34   -25.06     1.11)  ; 33\r\n            (  -41.06   -72.85   -25.19     0.81)  ; 34\r\n            (  -41.58   -73.59   -25.50     0.59)  ; 35\r\n            (  -42.31   -73.73   -26.31     0.59)  ; 36\r\n            (  -42.83   -73.37   -27.56     0.59)  ; 37\r\n            (  -42.98   -73.95   -27.56     0.59)  ; 38\r\n            (  -43.27   -74.46   -28.63     1.33)  ; 39\r\n            (  -43.42   -74.90   -29.44     1.77)  ; 40\r\n            (  -43.64   -75.41   -29.75     2.21)  ; 41\r\n            (  -43.86   -76.44   -30.25     1.25)  ; 42\r\n            (  -44.60   -76.80   -30.25     0.96)  ; 43\r\n            (  -45.04   -77.17   -31.31     0.66)  ; 44\r\n            (  -45.33   -77.75   -32.25     0.44)  ; 45\r\n            (  -45.33   -78.26   -33.00     0.88)  ; 46\r\n            (  -45.56   -79.00   -35.69     1.25)  ; 47\r\n            (  -45.63   -79.29   -37.00     2.14)  ; 48\r\n            (  -45.48   -79.73   -38.00     2.58)  ; 49\r\n            (  -45.48   -80.24   -40.31     1.33)  ; 50\r\n            (  -45.70   -80.90   -41.63     0.96)  ; 51\r\n            (  -46.44   -81.48   -43.00     0.59)  ; 52\r\n            (  -46.51   -82.36   -43.31     0.44)  ; 53\r\n            (  -46.59   -83.16   -44.31     0.29)  ; 54\r\n            (  -46.73   -84.04   -45.31     0.44)  ; 55\r\n            (  -46.62   -85.36   -47.13     0.59)  ; 56\r\n            (  -46.84   -86.23   -48.00     1.18)  ; 57\r\n            (  -46.69   -87.18   -48.69     1.62)  ; 58\r\n            (  -46.55   -88.14   -48.69     2.43)  ; 59\r\n            (  -46.47   -89.16   -48.81     2.80)  ; 60\r\n            (  -45.74   -89.60   -49.06     1.69)  ; 61\r\n            (  -45.29   -90.47   -49.06     0.96)  ; 62\r\n            (  -44.93   -91.21   -49.44     0.59)  ; 63\r\n            (  -44.48   -91.57   -49.44     0.37)  ; 64\r\n            (  -44.19   -93.18   -49.75     0.29)  ; 65\r\n            (  -43.67   -94.42   -49.94     0.29)  ; 66\r\n            (  -43.31   -95.88   -50.19     0.29)  ; 67\r\n            (  -43.31   -96.54   -50.19     0.29)  ; 68\r\n            (  -43.23   -97.49   -50.19     0.66)  ; 69\r\n            (  -43.23   -97.86   -50.19     1.47)  ; 70\r\n            (  -43.23   -98.30   -50.31     1.92)  ; 71\r\n            (  -43.23   -98.73   -50.44     2.36)  ; 72\r\n            (  -42.86   -99.25   -50.56     0.96)  ; 73\r\n            (  -42.86   -99.90   -50.56     0.52)  ; 74\r\n            (  -42.42  -100.78   -51.13     0.52)  ; 75\r\n            (  -42.20  -101.44   -51.13     0.88)  ; 76\r\n            (  -42.05  -102.17   -51.50     1.25)  ; 77\r\n            (  -41.46  -102.90   -51.13     1.55)  ; 78\r\n            (  -41.02  -103.49   -51.13     1.03)  ; 79\r\n            (  -40.51  -104.22   -51.13     0.59)  ; 80\r\n            (  -40.14  -104.73   -51.13     0.29)  ; 81\r\n            (  -39.84  -105.46   -51.25     0.29)  ; 82\r\n            (  -39.62  -106.99   -51.44     0.29)  ; 83\r\n            (  -39.55  -108.24   -50.75     0.29)  ; 84\r\n            (  -39.18  -108.82   -50.69     0.96)  ; 85\r\n            (  -38.74  -109.48   -50.69     1.40)  ; 86\r\n            (  -38.37  -109.63   -50.44     1.40)  ; 87\r\n            (  -37.93  -110.06   -50.63     0.74)  ; 88\r\n            (  -37.41  -110.58   -51.00     0.52)  ; 89\r\n            (  -36.75  -111.38   -51.69     0.52)  ; 90\r\n            (  -36.53  -112.18   -51.75     0.96)  ; 91\r\n            (  -36.38  -112.62   -52.31     1.40)  ; 92\r\n            (  -36.01  -113.43   -52.69     1.40)  ; 93\r\n             Low\r\n          |\r\n            (  -32.64   -49.41    -4.44     0.88)  ; 1, R-1-1-2-1-2\r\n            (  -33.45   -49.48    -3.88     0.59)  ; 2\r\n            (  -33.82   -49.48    -3.44     0.44)  ; 3\r\n            (  -34.48   -49.55    -3.19     0.44)  ; 4\r\n            (  -35.29   -49.99    -3.06     0.44)  ; 5\r\n            (  -36.03   -50.58    -2.19     0.66)  ; 6\r\n            (  -36.33   -51.45    -4.88     0.66)  ; 7\r\n            (  -36.55   -52.11    -6.06     0.66)  ; 8\r\n            (  -37.28   -51.97    -6.50     0.81)  ; 9\r\n            (  -38.02   -51.67    -7.50     0.96)  ; 10\r\n            (  -39.42   -51.16    -7.50     0.74)  ; 11\r\n            (  -40.23   -51.45    -6.19     0.74)  ; 12\r\n            (  -41.56   -51.38    -6.00     0.74)  ; 13\r\n            (  -42.37   -51.16    -6.00     0.74)  ; 14\r\n            (  -43.32   -51.16    -5.94     0.74)  ; 15\r\n            (  -44.58   -51.60    -5.94     0.52)  ; 16\r\n            (  -45.83   -52.26    -5.94     0.52)  ; 17\r\n            (  -46.86   -52.92    -5.88     0.81)  ; 18\r\n            (  -47.60   -53.65    -6.31     1.47)  ; 19\r\n            (  -48.48   -54.16    -6.81     1.47)  ; 20\r\n            (  -49.37   -54.82    -6.81     1.03)  ; 21\r\n            (  -50.54   -55.47    -6.81     0.66)  ; 22\r\n            (  -51.13   -56.21    -7.19     0.66)  ; 23\r\n            (  -51.65   -57.52    -7.44     0.66)  ; 24\r\n            (  -52.02   -58.33    -8.19     0.66)  ; 25\r\n            (  -52.46   -58.91    -7.38     0.66)  ; 26\r\n            (  -53.42   -58.76    -6.56     0.66)  ; 27\r\n            (  -54.15   -58.84    -6.31     0.96)  ; 28\r\n            (  -55.55   -58.84    -6.25     1.18)  ; 29\r\n            (  -56.73   -58.76    -6.06     1.18)  ; 30\r\n            (  -57.40   -58.33    -5.88     0.74)  ; 31\r\n            (  -58.87   -57.74    -6.75     0.96)  ; 32\r\n            (  -59.53   -57.16    -7.44     0.96)  ; 33\r\n            (  -60.27   -57.16    -7.44     0.59)  ; 34\r\n            (  -60.93   -57.37    -8.00     0.88)  ; 35\r\n            (  -61.96   -57.89    -8.44     0.96)  ; 36\r\n            (  -62.77   -58.47    -9.88     0.96)  ; 37\r\n            (  -63.58   -58.62    -9.88     0.96)  ; 38\r\n            (  -64.47   -59.57   -10.44     0.74)  ; 39\r\n            (  -65.65   -60.23   -10.44     0.44)  ; 40\r\n            (  -66.90   -60.59   -10.31     0.74)  ; 41\r\n            (  -67.71   -60.52   -10.31     1.11)  ; 42\r\n            (  -68.67   -60.37    -9.69     1.11)  ; 43\r\n            (  -69.33   -60.45    -9.63     0.74)  ; 44\r\n            (  -70.51   -60.59    -9.63     0.59)  ; 45\r\n            (  -71.54   -60.88    -9.56     0.37)  ; 46\r\n            (  -72.06   -61.25    -9.56     0.37)  ; 47\r\n            (  -72.87   -62.05    -9.56     0.74)  ; 48\r\n            (  -74.49   -62.64    -9.56     1.03)  ; 49\r\n            (  -75.37   -62.64    -9.56     1.03)  ; 50\r\n            (  -76.18   -62.78    -9.75     0.74)  ; 51\r\n            (  -76.99   -63.37    -9.31     0.59)  ; 52\r\n            (  -77.66   -63.95    -9.63     1.03)  ; 53\r\n            (  -78.47   -64.83    -9.38     1.77)  ; 54\r\n            (  -79.28   -65.71    -8.56     1.03)  ; 55\r\n            (  -80.23   -66.76    -7.88     0.66)  ; 56\r\n            (  -81.04   -67.93    -6.94     0.52)  ; 57\r\n            (  -81.48   -69.10    -7.56     0.74)  ; 58\r\n            (  -81.93   -69.91    -7.94     0.74)  ; 59\r\n            (  -82.74   -71.00    -8.88     0.88)  ; 60\r\n            (  -83.40   -71.74    -9.19     0.59)  ; 61\r\n            (  -83.99   -72.32    -9.63     1.25)  ; 62\r\n            (  -84.28   -72.47    -8.19     1.55)  ; 63\r\n            (  -85.09   -72.69    -7.94     1.55)  ; 64\r\n            (  -85.46   -73.20    -7.44     0.88)  ; 65\r\n            (  -86.12   -73.56    -7.25     0.52)  ; 66\r\n            (  -86.49   -73.86    -6.81     0.44)  ; 67\r\n            (  -86.42   -74.59    -6.19     0.44)  ; 68\r\n            (  -86.35   -75.17    -5.75     0.66)  ; 69\r\n            (  -86.27   -75.76    -4.75     1.33)  ; 70\r\n            (  -85.83   -76.56    -4.50     1.03)  ; 71\r\n            (  -85.76   -77.22    -3.94     0.66)  ; 72\r\n            (  -85.61   -77.66    -3.50     0.44)  ; 73\r\n            (  -85.46   -78.17    -3.31     0.44)  ; 74\r\n            (  -85.90   -78.53    -3.13     0.44)  ; 75\r\n            (  -87.01   -78.39    -3.13     0.59)  ; 76\r\n            (  -87.82   -79.05    -3.13     0.37)  ; 77\r\n            (  -87.75   -79.85    -2.63     0.59)  ; 78\r\n            (  -87.52   -80.14    -0.75     0.88)  ; 79\r\n            (  -87.45   -80.51    -0.31     1.33)  ; 80\r\n            (  -87.38   -80.87     1.56     0.88)  ; 81\r\n            (  -87.75   -81.75     2.31     0.74)  ; 82\r\n            (  -87.75   -82.63     3.25     0.74)  ; 83\r\n            (  -87.89   -83.65     3.63     0.37)  ; 84\r\n            (  -87.89   -84.67     4.44     0.37)  ; 85\r\n            (  -87.82   -85.70     4.75     0.37)  ; 86\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (  -30.88   -49.44    -6.31     0.74)  ; 1, R-1-1-2-2\r\n          (  -30.88   -50.61    -6.31     0.59)  ; 2\r\n          (  -30.29   -50.83    -7.13     0.52)  ; 3\r\n          (  -29.93   -50.24    -7.69     0.52)  ; 4\r\n          (  -29.34   -49.73    -8.06     0.52)  ; 5\r\n          (  -28.75   -49.51    -9.38     0.74)  ; 6\r\n          (  -28.16   -48.78   -10.81     0.96)  ; 7\r\n          (  -27.64   -47.98   -11.88     0.74)  ; 8\r\n          (  -27.05   -48.12   -13.31     0.88)  ; 9\r\n          (  -26.54   -48.12   -15.13     0.88)  ; 10\r\n          (  -26.10   -48.41   -15.38     0.74)  ; 11\r\n          (  -25.80   -49.00   -16.88     0.74)  ; 12\r\n          (  -25.51   -49.66   -18.38     0.74)  ; 13\r\n          (  -25.21   -50.24   -20.81     0.74)  ; 14\r\n          (  -24.55   -50.90   -20.81     0.74)  ; 15\r\n          (  -24.03   -51.70   -21.94     0.96)  ; 16\r\n          (  -23.52   -52.65   -22.50     1.25)  ; 17\r\n          (  -23.07   -53.97   -22.75     1.40)  ; 18\r\n          (  -23.15   -55.14   -23.63     1.11)  ; 19\r\n          (  -23.30   -55.94   -25.00     0.81)  ; 20\r\n          (  -23.66   -56.89   -25.75     0.52)  ; 21\r\n          (  -24.18   -57.41   -27.00     0.88)  ; 22\r\n          (  -24.18   -57.70   -28.75     1.33)  ; 23\r\n          (  -24.40   -57.99   -30.25     1.77)  ; 24\r\n          (  -24.33   -58.58   -30.56     2.21)  ; 25\r\n          (  -24.33   -59.45   -31.25     1.25)  ; 26\r\n          (  -24.18   -59.89   -33.00     0.59)  ; 27\r\n          (  -24.25   -60.77   -35.50     0.74)  ; 28\r\n          (  -24.40   -61.28   -36.13     0.37)  ; 29\r\n          (  -24.62   -61.65   -36.94     0.37)  ; 30\r\n          (  -24.99   -63.03   -38.50     0.52)  ; 31\r\n          (  -25.14   -63.69   -38.50     0.88)  ; 32\r\n          (  -24.92   -64.13   -39.31     1.33)  ; 33\r\n          (  -25.14   -64.79   -40.31     0.88)  ; 34\r\n          (  -25.28   -65.30   -41.06     0.44)  ; 35\r\n          (  -25.21   -66.91   -41.13     0.44)  ; 36\r\n          (  -25.14   -67.42   -42.56     1.25)  ; 37\r\n          (  -25.14   -68.15   -43.50     1.99)  ; 38\r\n          (  -25.06   -68.52   -43.50     1.99)  ; 39\r\n          (  -25.14   -69.54   -43.50     1.18)  ; 40\r\n          (  -24.99   -70.56   -43.75     0.52)  ; 41\r\n          (  -24.55   -71.95   -44.50     0.44)  ; 42\r\n          (  -24.40   -72.90   -44.94     1.11)  ; 43\r\n          (  -24.40   -73.63   -44.94     1.92)  ; 44\r\n          (  -24.18   -74.29   -46.19     2.21)  ; 45\r\n          (  -23.88   -75.02   -47.13     0.74)  ; 46\r\n           Low\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (  -22.41   -13.79    12.63     0.88)  ; 1, R-1-2\r\n      (  -22.12   -14.45    13.19     0.74)  ; 2\r\n      (  -21.75   -14.60    14.25     0.74)  ; 3\r\n      (  -20.57   -14.23    13.63     0.74)  ; 4\r\n      (  -19.98   -14.16    13.50     0.74)  ; 5\r\n      (  -19.39   -14.23    13.56     0.74)  ; 6\r\n      (  -18.80   -14.52    13.94     0.88)  ; 7\r\n      (  -18.14   -14.82    13.75     1.18)  ; 8\r\n      (  -17.40   -15.11    13.38     1.18)  ; 9\r\n      (  -16.81   -15.47    13.19     0.81)  ; 10\r\n      (  -16.52   -15.91    12.94     0.52)  ; 11\r\n      (  -16.74   -15.98    15.94     0.66)  ; 12\r\n      (  -17.03   -15.98    17.94     0.52)  ; 13\r\n      (  -17.62   -15.47    17.88     0.37)  ; 14\r\n      (  -18.06   -15.55    17.88     0.37)  ; 15\r\n      (  -18.28   -16.35    17.75     0.52)  ; 16\r\n       Low\r\n    )  ;  End of split\r\n  |\r\n    (  -22.78    -9.63    10.31     2.65)  ; 1, R-2\r\n    (  -23.88    -8.97     9.75     1.55)  ; 2\r\n    (  -24.55    -8.46    11.31     1.11)  ; 3\r\n    (  -25.21    -8.09    11.38     0.96)  ; 4\r\n    (  -25.58    -7.51    11.63     0.81)  ; 5\r\n    (  -26.17    -6.99    12.69     0.96)  ; 6\r\n    (  -26.76    -6.48    13.50     1.03)  ; 7\r\n    (  -27.86    -5.75    14.81     0.88)  ; 8\r\n    (  -28.75    -5.24    14.88     0.96)  ; 9\r\n    (  -29.41    -4.95    15.13     0.96)  ; 10\r\n    (  -30.44    -4.65    15.56     0.96)  ; 11\r\n    (  -31.40    -4.58    17.31     0.96)  ; 12\r\n    (  -32.21    -4.87    17.31     1.18)  ; 13\r\n    (  -32.72    -5.31    17.38     1.40)  ; 14\r\n    (  -33.31    -5.75    17.50     1.11)  ; 15\r\n    (  -33.68    -6.41    17.50     0.74)  ; 16\r\n    (  -33.98    -6.70    17.50     0.59)  ; 17\r\n    (  -34.12    -7.29    17.50     0.59)  ; 18\r\n    (  -34.12    -7.80    17.50     0.59)  ; 19\r\n    (  -34.12    -8.31    17.56     0.59)  ; 20\r\n    (  -34.42    -8.89    17.56     0.59)  ; 21\r\n    (  -35.01    -9.41    17.75     0.59)  ; 22\r\n    (  -35.74    -9.84    17.88     0.59)  ; 23\r\n    (  -36.56   -10.58    17.94     0.59)  ; 24\r\n     Low\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Cyan)\r\n  (Dendrite)\r\n  (   13.32     5.16    -5.56     3.54)  ; Root\r\n  (   13.99     5.89    -5.00     2.87)  ; 1, R\r\n  (   14.65     6.62    -5.06     2.43)  ; 2\r\n  (   15.24     6.98    -5.50     2.36)  ; 3\r\n  (   16.20     7.57    -5.50     2.87)  ; 4\r\n  (   17.01     8.23    -5.50     3.32)  ; 5\r\n  (   17.75     8.81    -5.50     3.32)  ; 6\r\n  (   18.63     9.40    -5.50     3.24)  ; 7\r\n  (   20.32    10.13    -4.50     3.24)  ; 8\r\n  (   21.21    10.78    -4.19     3.09)  ; 9\r\n  (   22.46    11.44    -3.88     3.09)  ; 10\r\n  (   23.93    12.17    -3.56     3.09)  ; 11\r\n  (   25.19    13.20    -3.25     3.61)  ; 12\r\n  (   25.92    13.85    -3.06     4.35)  ; 13\r\n  (\r\n    (   26.73    15.90    -2.25     3.83)  ; 1, R-1\r\n    (\r\n      (   27.54    17.58    -2.81     3.39)  ; 1, R-1-1\r\n      (   28.50    18.83    -2.00     2.73)  ; 2\r\n      (   29.16    19.78    -1.50     2.36)  ; 3\r\n      (   29.53    20.65    -1.31     1.69)  ; 4\r\n      (   29.46    22.19    -0.94     1.62)  ; 5\r\n      (\r\n        (   29.53    24.02    -0.38     1.03)  ; 1, R-1-1-1\r\n        (   29.75    25.40     0.13     0.88)  ; 2\r\n        (   29.83    26.50     0.31     0.88)  ; 3\r\n        (   30.42    27.38     1.13     0.88)  ; 4\r\n        (   30.70    27.81     1.13     0.88)  ; 5\r\n        (\r\n          (   31.23    28.33     0.69     0.66)  ; 1, R-1-1-1-1\r\n          (   31.60    29.35     0.69     0.81)  ; 2\r\n          (   31.60    31.45     0.69     0.88)  ; 3\r\n          (   31.67    33.27     1.13     0.74)  ; 4\r\n          (   32.19    34.44     1.13     0.74)  ; 5\r\n          (   33.00    35.39     1.31     0.74)  ; 6\r\n          (   34.25    36.05     1.06     0.74)  ; 7\r\n          (   35.73    36.71     0.81     0.88)  ; 8\r\n          (   36.98    37.37     0.44     1.11)  ; 9\r\n          (   37.94    37.30     0.00     1.11)  ; 10\r\n          (   39.04    37.59    -0.19     1.33)  ; 11\r\n          (   40.44    38.25    -0.19     1.11)  ; 12\r\n          (   41.69    39.27    -0.19     0.81)  ; 13\r\n          (   43.09    40.29    -0.19     0.81)  ; 14\r\n          (   44.27    41.68    -0.19     0.81)  ; 15\r\n          (   45.16    44.17    -0.94     1.11)  ; 16\r\n          (   45.45    45.70    -1.69     1.33)  ; 17\r\n          (   45.16    46.80    -2.88     1.55)  ; 18\r\n          (   45.08    48.19    -2.94     1.25)  ; 19\r\n          (   44.27    49.36    -3.25     1.03)  ; 20\r\n          (   43.98    50.45    -4.13     1.25)  ; 21\r\n          (   43.90    51.99    -4.13     1.25)  ; 22\r\n          (   43.76    53.23    -4.06     1.25)  ; 23\r\n          (   43.83    54.77    -3.00     1.33)  ; 24\r\n          (   43.54    55.64    -2.69     1.11)  ; 25\r\n          (   43.46    56.45    -2.00     1.92)  ; 26\r\n          (   43.39    57.10    -1.38     2.58)  ; 27\r\n          (   43.24    57.62    -1.31     2.87)  ; 28\r\n          (\r\n            (   42.65    58.27    -0.50     1.18)  ; 1, R-1-1-1-1-1\r\n            (   41.62    59.01    -0.44     0.96)  ; 2\r\n            (   40.98    60.14     0.88     0.81)  ; 3\r\n            (   40.76    61.09     1.13     1.47)  ; 4\r\n            (   40.40    61.74     1.38     1.33)  ; 5\r\n            (   39.88    62.62     1.38     0.96)  ; 6\r\n            (   39.59    63.86     1.38     0.81)  ; 7\r\n            (   39.59    65.18     1.44     0.81)  ; 8\r\n            (   39.81    66.86     1.69     1.03)  ; 9\r\n            (   39.51    68.03     1.38     1.03)  ; 10\r\n            (   39.44    69.27     2.31     0.81)  ; 11\r\n            (   39.59    70.59     2.38     1.03)  ; 12\r\n            (   39.59    71.83     2.56     1.11)  ; 13\r\n            (   39.00    73.44     2.63     0.88)  ; 14\r\n            (   38.55    74.32     3.00     1.55)  ; 15\r\n            (   37.67    76.00     3.13     2.06)  ; 16\r\n            (   37.37    76.88     3.25     1.77)  ; 17\r\n            (   37.08    77.61     3.25     1.40)  ; 18\r\n            (   36.79    78.34     3.25     1.03)  ; 19\r\n            (   36.49    79.14     3.50     0.88)  ; 20\r\n            (   35.61    80.09     3.13     0.74)  ; 21\r\n            (   35.16    81.26     2.94     0.88)  ; 22\r\n            (   34.72    82.43     2.94     1.03)  ; 23\r\n            (   34.43    83.38     2.94     1.03)  ; 24\r\n            (   34.06    85.14     2.25     0.96)  ; 25\r\n            (   34.35    86.38     2.19     1.62)  ; 26\r\n            (   34.50    87.18     2.19     1.25)  ; 27\r\n            (   34.13    88.13     2.13     1.11)  ; 28\r\n            (   33.93    90.04     2.13     0.81)  ; 29\r\n            (   33.86    91.72     2.94     0.59)  ; 30\r\n            (   34.08    92.38     3.88     0.88)  ; 31\r\n            (   34.45    92.82     4.75     1.11)  ; 32\r\n            (   34.82    93.26     4.94     0.88)  ; 33\r\n            (   35.04    94.72     5.25     0.59)  ; 34\r\n            (   35.26    95.30     5.50     0.37)  ; 35\r\n            (   35.77    95.52     5.63     0.37)  ; 36\r\n            (   36.36    95.81     6.06     0.66)  ; 37\r\n            (   37.17    96.33     6.25     0.96)  ; 38\r\n            (   37.76    96.62     6.63     1.11)  ; 39\r\n            (   38.35    96.84     6.94     0.88)  ; 40\r\n            (   38.79    96.91     7.00     0.59)  ; 41\r\n            (   39.53    96.98     7.31     0.59)  ; 42\r\n            (   40.86    97.13     7.44     0.81)  ; 43\r\n            (   41.96    97.57     7.50     0.96)  ; 44\r\n            (   42.77    97.72     7.50     1.25)  ; 45\r\n            (   43.73    98.30     7.56     1.11)  ; 46\r\n            (   44.91    99.03     7.38     0.66)  ; 47\r\n            (   45.87    99.18     7.06     0.59)  ; 48\r\n            (   46.82    99.54     6.63     0.59)  ; 49\r\n            (   47.86   100.05     6.56     0.81)  ; 50\r\n            (   48.37   100.71     7.00     0.59)  ; 51\r\n            (   49.18   101.52     7.13     0.81)  ; 52\r\n            (   50.07   101.88     7.13     1.11)  ; 53\r\n            (   50.43   102.10     7.13     0.81)  ; 54\r\n            (   50.95   102.61     7.13     0.44)  ; 55\r\n            (   51.47   103.27     7.44     0.44)  ; 56\r\n            (   52.06   103.49     6.69     0.81)  ; 57\r\n            (   52.72   103.93     6.00     1.11)  ; 58\r\n            (   53.31   103.93     5.25     0.81)  ; 59\r\n            (   54.27   103.78     3.94     0.59)  ; 60\r\n            (   54.86   103.42     3.69     0.59)  ; 61\r\n            (   55.81   102.61     3.25     0.59)  ; 62\r\n            (   56.26   102.69     3.25     0.81)  ; 63\r\n            (   56.99   102.69     3.25     0.52)  ; 64\r\n            (   57.95   102.61     1.63     0.81)  ; 65\r\n            (   58.69   102.25     0.75     0.66)  ; 66\r\n             Low\r\n          |\r\n            (   43.86    58.67    -0.88     1.25)  ; 1, R-1-1-1-1-2\r\n            (   44.15    59.69    -0.88     0.59)  ; 2\r\n            (   44.52    60.50    -0.25     0.59)  ; 3\r\n            (   44.45    61.45    -0.25     0.88)  ; 4\r\n            (   44.37    62.03    -0.25     1.18)  ; 5\r\n            (   44.52    62.69    -0.25     0.96)  ; 6\r\n            (   44.74    63.42    -0.13     0.66)  ; 7\r\n            (   45.18    64.15     0.00     0.52)  ; 8\r\n            (   45.99    64.96    -0.31     0.52)  ; 9\r\n            (   46.66    65.18    -0.69     0.74)  ; 10\r\n            (   47.62    65.54    -0.69     0.74)  ; 11\r\n            (   48.20    66.27    -0.81     0.44)  ; 12\r\n            (   48.43    67.30    -0.81     0.59)  ; 13\r\n            (   48.35    68.90    -0.81     0.74)  ; 14\r\n            (   48.20    69.85    -0.81     1.03)  ; 15\r\n            (   47.91    70.73    -0.81     0.66)  ; 16\r\n            (   47.47    71.97    -1.06     0.96)  ; 17\r\n            (   47.54    72.78    -0.94     0.74)  ; 18\r\n            (   47.76    73.66    -0.31     0.74)  ; 19\r\n            (   47.54    74.90     0.31     0.88)  ; 20\r\n            (   47.76    76.14     0.38     0.74)  ; 21\r\n            (   48.20    76.80     0.44     0.74)  ; 22\r\n            (   49.09    77.24     0.56     0.88)  ; 23\r\n            (   49.97    77.31     0.56     0.88)  ; 24\r\n            (   50.86    77.46    -0.06     0.88)  ; 25\r\n            (   51.89    77.68    -0.50     0.66)  ; 26\r\n            (   52.85    78.92    -1.38     0.66)  ; 27\r\n            (   53.73    79.72    -1.81     0.96)  ; 28\r\n            (   55.06    80.23    -2.00     1.25)  ; 29\r\n            (   56.31    80.38    -2.31     1.11)  ; 30\r\n            (   57.34    80.75    -2.69     1.25)  ; 31\r\n            (   58.08    81.62    -2.81     1.03)  ; 32\r\n            (   58.81    82.06    -2.94     0.81)  ; 33\r\n            (   59.48    82.43    -3.06     0.66)  ; 34\r\n            (   59.85    82.65    -3.50     0.66)  ; 35\r\n            (   60.07    83.52    -3.94     0.74)  ; 36\r\n            (   59.62    84.47    -3.94     0.88)  ; 37\r\n            (   59.48    85.13    -4.19     0.74)  ; 38\r\n            (   59.26    86.16    -4.31     0.74)  ; 39\r\n            (   59.33    86.89    -4.38     0.96)  ; 40\r\n            (   59.12    88.29    -4.94     0.81)  ; 41\r\n            (   59.93    89.17    -5.56     0.74)  ; 42\r\n            (   60.82    89.09    -7.00     0.74)  ; 43\r\n            (   61.70    89.24    -8.00     0.81)  ; 44\r\n            (   62.44    89.02    -8.75     1.03)  ; 45\r\n            (   63.17    88.65    -9.06     1.33)  ; 46\r\n            (   63.84    88.14   -10.13     1.33)  ; 47\r\n            (   64.57    88.29   -11.81     0.74)  ; 48\r\n            (   65.16    88.73   -12.38     0.59)  ; 49\r\n            (   65.75    89.53   -13.69     0.81)  ; 50\r\n            (   66.12    90.26   -13.69     1.18)  ; 51\r\n            (   66.42    90.99   -13.81     1.84)  ; 52\r\n            (   66.71    91.94   -14.19     1.18)  ; 53\r\n            (   66.86    92.82   -14.13     0.52)  ; 54\r\n            (   66.93    93.77   -14.00     0.52)  ; 55\r\n            (   66.78    95.09   -13.75     0.66)  ; 56\r\n            (   66.56    95.96   -13.75     1.40)  ; 57\r\n            (   66.49    96.33   -13.56     1.99)  ; 58\r\n            (   66.34    97.06   -13.63     1.62)  ; 59\r\n            (   66.34    97.65   -13.75     1.25)  ; 60\r\n            (   66.42    98.60   -13.75     0.88)  ; 61\r\n            (   66.42    99.33   -13.75     0.59)  ; 62\r\n            (   66.64    99.69   -13.75     0.59)  ; 63\r\n            (   66.93    99.69   -13.88     0.59)  ; 64\r\n            (   67.52    99.84   -14.25     0.59)  ; 65\r\n            (   68.04   100.42   -15.44     0.59)  ; 66\r\n            (   68.33   101.37   -15.69     1.11)  ; 67\r\n            (   68.70   102.32   -15.75     1.40)  ; 68\r\n            (   68.99   102.84   -16.13     1.40)  ; 69\r\n            (   69.51   103.64   -16.31     1.03)  ; 70\r\n            (   70.03   104.30   -15.56     0.74)  ; 71\r\n            (   70.39   105.03   -15.44     0.52)  ; 72\r\n            (   70.91   105.54   -15.44     0.37)  ; 73\r\n            (   71.57   105.61   -15.31     0.37)  ; 74\r\n            (   71.65   106.56   -15.19     0.37)  ; 75\r\n            (   72.31   107.08   -15.56     0.59)  ; 76\r\n            (   73.05   107.66   -15.94     0.59)  ; 77\r\n            (   73.64   108.25   -16.69     0.44)  ; 78\r\n            (   74.00   109.05   -18.25     0.44)  ; 79\r\n            (   74.22   109.85   -18.63     0.74)  ; 80\r\n            (   74.45   110.07   -20.00     1.47)  ; 81\r\n            (   74.96   110.07   -20.63     2.14)  ; 82\r\n            (   75.03   110.00   -21.56     2.87)  ; 83\r\n            (   75.33   109.78   -23.06     2.06)  ; 84\r\n            (   75.55   109.71   -24.75     1.62)  ; 85\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (   29.81    28.03     2.56     0.88)  ; 1, R-1-1-1-2\r\n          (   29.37    28.40     3.44     0.59)  ; 2\r\n          (   28.41    28.91     3.75     0.44)  ; 3\r\n          (   27.01    29.28     3.56     0.44)  ; 4\r\n          (   26.72    29.72     3.63     0.74)  ; 5\r\n          (   27.23    30.01     4.25     0.74)  ; 6\r\n          (   27.68    30.01     4.56     0.74)  ; 7\r\n          (   28.19    30.45     4.94     0.59)  ; 8\r\n          (   28.71    30.52     5.63     0.59)  ; 9\r\n          (   29.15    30.88     6.81     0.59)  ; 10\r\n          (   29.74    31.47     8.06     0.59)  ; 11\r\n          (   29.81    32.20     8.06     0.59)  ; 12\r\n          (   29.81    33.22     8.31     0.59)  ; 13\r\n          (   30.18    34.32     9.13     0.81)  ; 14\r\n          (   30.40    34.98     9.56     0.81)  ; 15\r\n          (   30.77    35.86     9.94     0.74)  ; 16\r\n          (   31.06    36.81    10.50     0.59)  ; 17\r\n          (   31.21    37.76    11.19     0.44)  ; 18\r\n          (   30.55    38.34    12.06     0.59)  ; 19\r\n          (   29.81    38.85    12.25     0.74)  ; 20\r\n          (   29.22    39.51    12.69     0.74)  ; 21\r\n          (   28.93    40.61    12.81     0.74)  ; 22\r\n          (   28.85    41.56    12.88     0.74)  ; 23\r\n          (   28.78    42.65    13.31     0.74)  ; 24\r\n          (   28.56    43.60    13.56     0.74)  ; 25\r\n          (   28.49    44.70    14.31     0.88)  ; 26\r\n          (   29.30    46.02    14.69     1.03)  ; 27\r\n          (   29.59    47.62    15.19     0.88)  ; 28\r\n          (   29.66    48.21    15.44     0.66)  ; 29\r\n          (   29.37    49.38    16.00     0.52)  ; 30\r\n          (   28.85    50.33    16.00     0.66)  ; 31\r\n          (   29.00    51.43    16.00     0.52)  ; 32\r\n          (   29.00    51.50    16.25     0.74)  ; 33\r\n          (   28.26    52.01    16.75     0.74)  ; 34\r\n          (   27.82    52.96    17.19     0.81)  ; 35\r\n          (   27.68    54.20    17.69     0.81)  ; 36\r\n          (   28.04    55.08    17.75     0.81)  ; 37\r\n          (   28.26    56.25    17.81     0.66)  ; 38\r\n          (   28.49    57.20    17.63     0.66)  ; 39\r\n          (   28.93    58.30    17.56     0.81)  ; 40\r\n          (   29.30    59.17    17.56     1.03)  ; 41\r\n          (   29.89    59.83    18.06     0.88)  ; 42\r\n          (   30.70    60.05    18.06     0.66)  ; 43\r\n          (   31.29    60.27    18.31     0.66)  ; 44\r\n          (   32.39    61.00    18.94     0.52)  ; 45\r\n          (   32.83    61.95    18.75     0.66)  ; 46\r\n          (   32.98    62.61    18.69     0.66)  ; 47\r\n          (   32.24    63.34    16.94     0.52)  ; 48\r\n          (   31.58    63.71    16.56     0.37)  ; 49\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (   29.72    23.78    -4.06     2.36)  ; 1, R-1-1-2\r\n        (   29.79    24.88    -5.31     2.36)  ; 2\r\n        (   29.79    25.39    -7.38     2.21)  ; 3\r\n        (   30.16    27.44    -9.25     3.09)  ; 4\r\n        (   30.45    28.97    -9.75     3.24)  ; 5\r\n        (   30.55    29.13    -9.75     3.24)  ; 6\r\n        (\r\n          (   30.97    30.21   -10.44     2.80)  ; 1, R-1-1-2-1\r\n          (   31.71    31.53   -11.38     2.80)  ; 2\r\n          (   32.07    32.19   -11.63     3.24)  ; 3\r\n          (   32.57    32.80   -11.63     3.24)  ; 4\r\n          (\r\n            (   32.44    32.99   -12.63     3.02)  ; 1, R-1-1-2-1-1\r\n            (   33.11    33.87   -12.56     3.61)  ; 2\r\n            (   33.77    35.04   -13.00     3.61)  ; 3\r\n            (\r\n              (   33.11    35.77   -14.19     2.36)  ; 1, R-1-1-2-1-1-1\r\n              (   32.52    37.38   -14.81     1.99)  ; 2\r\n              (   32.66    38.55   -16.19     2.50)  ; 3\r\n              (\r\n                (   33.62    39.86   -16.19     1.03)  ; 1, R-1-1-2-1-1-1-1\r\n                (   34.28    40.16   -16.88     0.88)  ; 2\r\n                (   34.80    40.38   -18.50     0.81)  ; 3\r\n                (   35.32    40.08   -18.94     0.81)  ; 4\r\n                (   35.46    39.64   -19.13     1.03)  ; 5\r\n                (   35.83    38.77   -21.06     1.33)  ; 6\r\n                (   36.20    38.33   -22.75     1.33)  ; 7\r\n                (   36.94    38.55   -24.31     1.33)  ; 8\r\n                (   37.38    38.99   -24.94     1.03)  ; 9\r\n                (   37.38    40.45   -26.44     0.96)  ; 10\r\n                (   37.23    41.47   -24.63     0.81)  ; 11\r\n                (   37.53    42.35   -25.25     0.81)  ; 12\r\n                (   37.45    43.23   -25.50     0.88)  ; 13\r\n                (   37.23    43.96   -25.88     0.74)  ; 14\r\n                (   38.04    44.61   -26.06     0.74)  ; 15\r\n                (   38.70    45.27   -26.13     0.74)  ; 16\r\n                (   39.00    46.37   -26.13     0.96)  ; 17\r\n                (   39.07    47.61   -26.31     0.96)  ; 18\r\n                (   39.22    49.22   -26.63     0.96)  ; 19\r\n                (   39.96    50.24   -26.69     0.96)  ; 20\r\n                (   40.62    51.49   -26.63     0.81)  ; 21\r\n                (   41.50    52.32   -26.56     0.81)  ; 22\r\n                (   42.23    52.97   -29.31     0.88)  ; 23\r\n                (   43.04    53.34   -30.44     0.96)  ; 24\r\n                (   43.04    54.22   -30.56     0.59)  ; 25\r\n                (   43.19    54.80   -31.13     0.81)  ; 26\r\n                (   43.93    55.83   -30.00     0.81)  ; 27\r\n                (   44.37    56.56   -30.00     0.59)  ; 28\r\n                (   44.74    56.92   -30.00     0.59)  ; 29\r\n                (   44.88    57.65   -30.00     0.59)  ; 30\r\n                (   45.33    58.75   -30.13     0.74)  ; 31\r\n                (   45.77    59.99   -30.69     1.18)  ; 32\r\n                (   46.14    61.02   -30.75     1.40)  ; 33\r\n                (   46.43    61.82   -31.06     1.40)  ; 34\r\n                (   46.95    62.84   -31.56     1.25)  ; 35\r\n                (   47.02    63.28   -31.56     0.96)  ; 36\r\n                (   47.17    64.16   -32.31     0.74)  ; 37\r\n                (   47.46    65.18   -32.63     0.66)  ; 38\r\n                (   47.83    65.84   -32.69     0.66)  ; 39\r\n                (   48.27    65.84   -32.88     0.66)  ; 40\r\n                (   48.64    66.35   -33.31     0.66)  ; 41\r\n                (   49.82    66.94   -33.63     0.81)  ; 42\r\n                (   51.15    67.45   -34.13     0.74)  ; 43\r\n                (   51.88    68.03   -34.56     1.18)  ; 44\r\n                (   52.47    68.54   -34.81     1.47)  ; 45\r\n                (   52.69    68.98   -34.81     1.11)  ; 46\r\n                (   52.84    69.57   -35.19     0.66)  ; 47\r\n                (   53.14    70.23   -35.19     0.66)  ; 48\r\n                (   53.36    70.81   -35.50     0.66)  ; 49\r\n                (   53.58    71.54   -36.25     0.66)  ; 50\r\n                (   53.36    72.64   -36.38     0.66)  ; 51\r\n                (   53.28    73.44   -36.44     1.40)  ; 52\r\n                (   52.99    74.32   -37.56     1.84)  ; 53\r\n                (   52.77    75.27   -38.25     1.55)  ; 54\r\n                (   52.33    75.85   -39.38     1.33)  ; 55\r\n                (   51.88    76.15   -39.81     1.03)  ; 56\r\n                (   51.37    76.66   -41.69     0.81)  ; 57\r\n                (   50.85    76.73   -42.25     0.81)  ; 58\r\n                (   50.78    77.32   -44.13     0.81)  ; 59\r\n                (   50.41    77.32   -46.00     0.66)  ; 60\r\n                (   49.38    78.12   -47.06     0.88)  ; 61\r\n                (   49.16    78.63   -47.63     1.40)  ; 62\r\n                (   49.01    79.36   -47.81     1.03)  ; 63\r\n                (   48.79    80.09   -48.38     0.66)  ; 64\r\n                (   48.86    81.04   -49.13     0.66)  ; 65\r\n                (   48.57    82.14   -49.81     0.52)  ; 66\r\n                (   48.35    83.17   -50.38     1.11)  ; 67\r\n                (   48.28    84.26   -52.31     1.47)  ; 68\r\n                (   48.50    84.92   -53.69     1.77)  ; 69\r\n                (   48.65    85.94   -55.31     1.99)  ; 70\r\n                (   48.65    86.38   -57.06     1.55)  ; 71\r\n                (   48.57    87.19   -57.56     1.18)  ; 72\r\n                (   48.57    87.84   -57.50     0.88)  ; 73\r\n                (   48.57    88.36   -57.50     0.59)  ; 74\r\n                (   48.87    89.38   -57.69     0.37)  ; 75\r\n                (   49.46    90.62   -58.63     0.37)  ; 76\r\n                (   50.20    91.87   -58.94     0.37)  ; 77\r\n                (   50.64    92.38   -59.50     0.37)  ; 78\r\n                (   51.08    93.18   -59.50     0.66)  ; 79\r\n                (   51.37    93.69   -59.81     1.77)  ; 80\r\n                (   51.82    94.13   -60.63     2.06)  ; 81\r\n                (   52.41    94.79   -60.88     2.06)  ; 82\r\n                (   52.55    95.15   -61.25     1.40)  ; 83\r\n                (   52.77    95.52   -61.44     1.03)  ; 84\r\n                (   53.07    96.18   -61.94     0.66)  ; 85\r\n                (   53.36    96.54   -62.50     0.44)  ; 86\r\n                (   54.84    97.79   -63.13     0.29)  ; 87\r\n                (   55.87    98.44   -64.88     0.29)  ; 88\r\n                (   56.60    99.61   -65.44     0.29)  ; 89\r\n                (   57.64   100.34   -65.50     0.29)  ; 90\r\n                (   58.45   100.93   -65.50     0.29)  ; 91\r\n                (   58.67   101.37   -66.06     0.66)  ; 92\r\n                (   58.89   101.88   -66.19     0.96)  ; 93\r\n                (   59.04   102.17   -66.31     0.66)  ; 94\r\n                (   59.26   102.83   -66.56     0.52)  ; 95\r\n                (   59.63   103.34   -67.06     0.74)  ; 96\r\n                (   59.92   103.71   -67.63     1.03)  ; 97\r\n                (   60.29   104.00   -69.19     1.33)  ; 98\r\n                (   60.58   104.15   -69.31     1.25)  ; 99\r\n                (   60.88   104.73   -69.69     0.59)  ; 100\r\n                (   61.47   105.53   -70.00     0.29)  ; 101\r\n                (   61.91   105.83   -70.56     0.29)  ; 102\r\n                (   62.50   106.34   -70.56     0.59)  ; 103\r\n                (   63.01   107.07   -71.19     1.25)  ; 104\r\n                (   63.53   107.29   -72.06     1.69)  ; 105\r\n                (   64.12   107.80   -72.63     1.11)  ; 106\r\n                (   64.27   108.39   -73.63     0.74)  ; 107\r\n                (   64.49   108.97   -73.75     0.52)  ; 108\r\n                (   64.93   109.70   -74.63     0.37)  ; 109\r\n                (   65.81   110.43   -73.63     0.81)  ; 110\r\n                (   66.56   111.13   -73.31     0.81)  ; 111\r\n                (   67.07   111.94   -73.19     0.52)  ; 112\r\n                (   68.11   112.74   -72.94     0.29)  ; 113\r\n                (   68.99   113.18   -73.25     0.29)  ; 114\r\n                (   70.24   113.98   -72.81     0.29)  ; 115\r\n                (   70.90   114.35   -72.44     0.29)  ; 116\r\n                (   71.79   114.06   -72.44     0.29)  ; 117\r\n                (   72.60   114.06   -72.31     0.66)  ; 118\r\n                (   73.19   114.20   -72.19     1.03)  ; 119\r\n                (   73.93   114.20   -71.88     0.59)  ; 120\r\n                (   74.74   114.27   -71.19     0.37)  ; 121\r\n                (   75.33   114.42   -72.25     0.59)  ; 122\r\n                (   75.55   114.49   -73.63     0.96)  ; 123\r\n                (   76.06   114.57   -74.19     1.33)  ; 124\r\n                (   76.28   114.27   -75.00     1.69)  ; 125\r\n                 Low\r\n              |\r\n                (   32.09    38.65   -16.38     1.11)  ; 1, R-1-1-2-1-1-1-2\r\n                (   30.84    38.35   -16.81     0.66)  ; 2\r\n                (   30.32    37.77   -16.13     0.52)  ; 3\r\n                (   29.66    36.97   -16.13     0.52)  ; 4\r\n                (   28.63    36.75   -17.69     0.59)  ; 5\r\n                (   27.96    36.45   -18.25     0.81)  ; 6\r\n                (   27.23    35.94   -18.25     0.81)  ; 7\r\n                (   26.64    35.50   -19.00     0.81)  ; 8\r\n                (   25.83    34.99   -19.25     0.74)  ; 9\r\n                (   25.53    34.77   -19.88     0.74)  ; 10\r\n                (   25.16    34.26   -20.38     0.74)  ; 11\r\n                (   24.35    33.90   -21.25     0.52)  ; 12\r\n                (   23.84    33.90   -22.31     0.52)  ; 13\r\n                (   23.62    34.26   -22.81     0.74)  ; 14\r\n                (   22.95    34.70   -24.19     0.96)  ; 15\r\n                (   22.37    35.28   -24.19     0.66)  ; 16\r\n                (   21.70    35.80   -24.63     0.66)  ; 17\r\n                (   21.19    35.94   -24.69     0.66)  ; 18\r\n                (   20.16    36.16   -24.69     0.44)  ; 19\r\n                (   19.64    35.87   -24.69     0.44)  ; 20\r\n                (   19.12    35.28   -25.31     0.44)  ; 21\r\n                (   18.76    34.92   -25.25     0.44)  ; 22\r\n                (   18.24    34.85   -25.88     0.44)  ; 23\r\n                (   17.36    35.06   -25.94     0.59)  ; 24\r\n                (   16.84    35.14   -26.38     0.88)  ; 25\r\n                (   16.32    35.14   -26.94     0.88)  ; 26\r\n                (   15.22    35.21   -27.38     0.66)  ; 27\r\n                (   15.07    34.63   -28.69     0.88)  ; 28\r\n                (   14.26    34.48   -28.88     0.88)  ; 29\r\n                (   13.38    34.92   -29.13     0.74)  ; 30\r\n                (   13.01    35.80   -29.75     0.59)  ; 31\r\n                (   12.71    36.45   -30.38     0.59)  ; 32\r\n                (   11.61    37.11   -30.75     0.44)  ; 33\r\n                (   10.06    37.77   -31.13     0.44)  ; 34\r\n                (    9.55    38.28   -31.13     0.52)  ; 35\r\n                (    8.59    38.50   -31.25     0.52)  ; 36\r\n                (    7.85    38.65   -31.50     0.66)  ; 37\r\n                (    7.04    39.30   -32.44     0.66)  ; 38\r\n                (    6.60    40.47   -32.69     0.66)  ; 39\r\n                (    5.86    41.50   -32.69     0.81)  ; 40\r\n                (    5.13    42.01   -33.06     1.11)  ; 41\r\n                (    4.32    42.52   -33.38     1.11)  ; 42\r\n                (    3.73    42.89   -33.50     0.88)  ; 43\r\n                (    3.14    43.47   -33.44     0.88)  ; 44\r\n                (    2.77    44.42   -33.44     0.66)  ; 45\r\n                (    2.11    44.86   -33.69     0.81)  ; 46\r\n                (    1.07    44.86   -34.13     0.81)  ; 47\r\n                (    0.19    45.23   -36.06     0.88)  ; 48\r\n                (   -0.40    46.10   -36.69     0.88)  ; 49\r\n                (   -1.50    46.91   -36.31     0.88)  ; 50\r\n                (   -2.68    47.42   -35.88     0.81)  ; 51\r\n                (   -3.86    48.37   -35.44     0.81)  ; 52\r\n                (   -4.53    49.32   -35.38     1.11)  ; 53\r\n                (   -4.67    50.42   -36.13     1.11)  ; 54\r\n                (   -4.97    51.51   -36.50     0.88)  ; 55\r\n                (   -5.56    52.39   -38.31     0.74)  ; 56\r\n                (   -6.07    52.68   -38.94     0.59)  ; 57\r\n                (   -6.59    52.24   -39.50     0.52)  ; 58\r\n                (   -7.18    52.39   -39.75     0.52)  ; 59\r\n                (   -7.69    52.83   -39.75     0.52)  ; 60\r\n                (   -8.14    53.27   -39.44     0.88)  ; 61\r\n                (   -9.39    54.22   -39.50     0.74)  ; 62\r\n                (  -10.20    54.80   -39.56     0.59)  ; 63\r\n                (  -11.08    55.31   -39.56     0.88)  ; 64\r\n                (  -11.82    55.68   -39.56     1.47)  ; 65\r\n                (  -12.56    56.34   -39.56     1.33)  ; 66\r\n                (  -13.22    57.07   -40.44     0.81)  ; 67\r\n                (  -13.66    57.36   -39.31     0.52)  ; 68\r\n                (  -14.18    57.94   -40.06     0.52)  ; 69\r\n                (  -15.09    57.99   -40.69     0.29)  ; 70\r\n                (  -16.13    58.29   -41.56     0.29)  ; 71\r\n                (  -16.79    58.43   -41.88     0.29)  ; 72\r\n                (  -17.08    58.50   -41.88     0.59)  ; 73\r\n                (  -17.67    58.94   -41.81     0.81)  ; 74\r\n                (  -18.63    59.45   -41.81     0.81)  ; 75\r\n                (  -19.15    59.75   -41.81     0.59)  ; 76\r\n                (  -19.74    59.82   -41.81     0.59)  ; 77\r\n                (  -20.32    60.19   -41.94     1.11)  ; 78\r\n                (  -20.99    60.48   -42.63     1.69)  ; 79\r\n                (  -21.36    60.55   -42.63     2.43)  ; 80\r\n                (  -22.09    61.06   -42.81     1.69)  ; 81\r\n                (  -22.61    61.21   -42.88     1.25)  ; 82\r\n                (  -23.42    61.72   -43.56     0.96)  ; 83\r\n                (  -24.01    61.79   -43.81     0.59)  ; 84\r\n                (  -24.52    62.23   -44.19     0.44)  ; 85\r\n                (  -24.82    62.38   -46.13     0.44)  ; 86\r\n                (  -25.41    62.31   -46.44     0.44)  ; 87\r\n                (  -25.85    62.01   -46.44     0.44)  ; 88\r\n                (  -26.15    61.94   -46.44     0.74)  ; 89\r\n                (  -26.51    62.23   -46.56     1.11)  ; 90\r\n                (  -27.03    62.45   -46.75     1.47)  ; 91\r\n                (  -27.62    62.60   -46.69     0.66)  ; 92\r\n                (  -27.91    62.82   -47.56     0.37)  ; 93\r\n                 Low\r\n              )  ;  End of split\r\n            |\r\n              (   35.06    35.40   -13.25     1.47)  ; 1, R-1-1-2-1-1-2\r\n              (   35.57    35.40   -12.69     1.11)  ; 2\r\n              (   36.16    35.48   -12.63     1.11)  ; 3\r\n              (   37.05    35.62   -11.88     1.40)  ; 4\r\n              (   37.71    35.70   -11.94     1.40)  ; 5\r\n              (   38.74    35.92   -12.44     1.18)  ; 6\r\n              (   39.99    36.28   -12.44     1.03)  ; 7\r\n              (   40.58    36.65   -12.94     1.40)  ; 8\r\n              (   41.69    37.09   -13.25     1.55)  ; 9\r\n              (   42.35    37.52   -13.56     1.84)  ; 10\r\n              (   43.24    38.33   -13.69     1.69)  ; 11\r\n              (   43.97    39.13   -14.56     1.84)  ; 12\r\n              (   44.64    39.64   -16.06     1.62)  ; 13\r\n              (\r\n                (   45.45    40.96   -17.00     1.03)  ; 1, R-1-1-2-1-1-2-1\r\n                (   45.74    41.98   -17.13     0.81)  ; 2\r\n                (   45.89    42.86   -17.50     0.81)  ; 3\r\n                (   45.89    44.32   -17.00     0.81)  ; 4\r\n                (   45.89    44.98   -17.00     0.81)  ; 5\r\n                (   46.26    45.42   -17.00     0.81)  ; 6\r\n                (   46.92    46.15   -17.44     0.81)  ; 7\r\n                (   47.73    46.52   -18.88     0.81)  ; 8\r\n                (   49.06    46.88   -19.38     0.81)  ; 9\r\n                (   49.65    47.54   -19.38     0.81)  ; 10\r\n                (   50.31    48.12   -19.56     1.40)  ; 11\r\n                (   51.12    48.42   -19.63     2.14)  ; 12\r\n                (   51.49    48.93   -20.06     2.36)  ; 13\r\n                (\r\n                  (   52.52    48.56   -19.81     0.74)  ; 1, R-1-1-2-1-1-2-1-1\r\n                  (   53.03    49.00   -19.81     0.59)  ; 2\r\n                  (   53.48    49.15   -19.63     0.59)  ; 3\r\n                  (   54.29    49.73   -19.00     0.74)  ; 4\r\n                  (   55.02    49.81   -18.38     0.81)  ; 5\r\n                  (   55.98    50.46   -18.38     0.96)  ; 6\r\n                  (   56.57    51.27   -17.88     0.81)  ; 7\r\n                  (   57.45    52.29   -17.63     0.66)  ; 8\r\n                  (   58.04    53.02   -17.56     0.81)  ; 9\r\n                  (   58.41    53.75   -17.56     0.66)  ; 10\r\n                  (   58.56    54.34   -17.56     0.44)  ; 11\r\n                  (   58.85    54.70   -17.56     0.44)  ; 12\r\n                  (   59.30    54.63   -17.56     0.44)  ; 13\r\n                  (   59.89    54.48   -17.38     0.44)  ; 14\r\n                  (   60.33    54.92   -17.25     0.44)  ; 15\r\n                  (   60.84    55.58   -17.69     0.44)  ; 16\r\n                  (   61.65    56.09   -17.69     0.74)  ; 17\r\n                  (   62.17    56.53   -18.38     0.81)  ; 18\r\n                  (   63.35    56.82   -18.88     0.66)  ; 19\r\n                  (   63.86    57.19   -19.50     0.66)  ; 20\r\n                  (   64.82    57.26   -19.75     0.59)  ; 21\r\n                  (   65.41    57.70   -20.13     0.59)  ; 22\r\n                  (   66.15    58.43   -20.94     0.81)  ; 23\r\n                  (   66.37    58.72   -20.94     1.62)  ; 24\r\n                  (   66.89    59.02   -20.88     2.36)  ; 25\r\n                  (\r\n                    (   67.62    59.38   -21.69     2.58)  ; 1, R-1-1-2-1-1-2-1-1-1\r\n                    (   68.28    60.04   -21.38     1.33)  ; 2\r\n                    (   68.95    60.48   -20.81     0.74)  ; 3\r\n                    (   69.46    60.84   -20.06     0.52)  ; 4\r\n                    (   70.42    61.50   -19.44     0.52)  ; 5\r\n                    (   71.01    61.94   -19.44     0.66)  ; 6\r\n                    (   71.53    62.45   -19.44     0.52)  ; 7\r\n                    (   72.04    62.74   -19.44     0.44)  ; 8\r\n                    (   73.15    62.82   -19.44     0.44)  ; 9\r\n                    (   73.74    62.60   -19.06     0.44)  ; 10\r\n                    (   74.25    62.52   -18.56     0.44)  ; 11\r\n                    (   74.55    62.60   -18.50     0.44)  ; 12\r\n                    (   74.84    63.26   -18.50     0.74)  ; 13\r\n                    (   75.06    64.06   -18.50     1.11)  ; 14\r\n                    (   75.37    64.99   -18.38     0.88)  ; 15\r\n                    (   75.89    65.65   -18.19     0.59)  ; 16\r\n                    (   76.18    66.09   -17.88     0.44)  ; 17\r\n                    (   76.70    66.45   -17.75     0.74)  ; 18\r\n                    (   77.22    66.74   -17.75     1.40)  ; 19\r\n                    (   77.66    66.96   -17.75     1.40)  ; 20\r\n                    (   78.17    67.18   -17.56     0.59)  ; 21\r\n                    (   78.91    67.55   -17.56     0.37)  ; 22\r\n                    (   79.13    68.28   -17.56     0.59)  ; 23\r\n                    (   79.21    69.30   -17.63     0.74)  ; 24\r\n                    (   79.21    70.40   -17.38     0.52)  ; 25\r\n                    (   79.35    70.84   -16.75     0.88)  ; 26\r\n                    (   79.94    71.35   -16.63     1.62)  ; 27\r\n                    (   80.24    71.57   -16.44     1.92)  ; 28\r\n                    (   80.46    71.93   -16.44     1.92)  ; 29\r\n                    (   80.68    72.15   -16.19     1.03)  ; 30\r\n                    (   81.05    72.45   -16.13     0.66)  ; 31\r\n                    (   81.34    72.88   -15.81     0.44)  ; 32\r\n                    (   81.93    73.54   -15.50     0.96)  ; 33\r\n                    (   82.52    74.05   -15.13     1.47)  ; 34\r\n                    (   82.89    74.78   -14.88     1.11)  ; 35\r\n                    (   83.48    75.73   -14.75     0.88)  ; 36\r\n                    (   84.14    76.32   -14.69     0.66)  ; 37\r\n                    (   84.36    76.69   -14.69     0.29)  ; 38\r\n                    (   85.10    76.47   -14.63     0.29)  ; 39\r\n                    (   85.61    76.39   -14.44     0.29)  ; 40\r\n                    (   86.28    76.90   -14.25     0.88)  ; 41\r\n                    (   86.79    77.42   -14.25     1.18)  ; 42\r\n                    (   87.38    77.78   -14.31     1.03)  ; 43\r\n                    (   87.90    78.37   -14.38     0.81)  ; 44\r\n                    (   88.27    79.02   -14.38     0.59)  ; 45\r\n                    (   88.78    79.83   -14.38     0.44)  ; 46\r\n                    (   88.93    80.19   -14.50     1.47)  ; 47\r\n                    (   89.37    80.63   -14.56     2.14)  ; 48\r\n                    (   89.89    81.14   -14.06     2.43)  ; 49\r\n                    (   90.48    81.58   -13.69     1.11)  ; 50\r\n                    (   90.77    81.80   -13.44     0.74)  ; 51\r\n                    (   90.99    82.02   -13.44     0.44)  ; 52\r\n                    (   91.36    82.61   -13.44     0.29)  ; 53\r\n                    (   91.95    83.48   -13.38     0.59)  ; 54\r\n                    (   92.32    84.00   -13.38     0.96)  ; 55\r\n                    (   92.47    84.21   -13.38     0.66)  ; 56\r\n                    (   92.91    84.87   -13.38     0.37)  ; 57\r\n                    (   93.13    85.24   -13.38     0.22)  ; 58\r\n                    (   94.01    85.97   -12.69     0.74)  ; 59\r\n                    (   94.46    86.33   -11.88     0.96)  ; 60\r\n                    (   94.97    86.77   -11.81     0.96)  ; 61\r\n                    (   95.19    86.99   -11.19     0.66)  ; 62\r\n                    (   95.56    87.36   -11.19     0.37)  ; 63\r\n                    (   96.15    87.36   -11.19     0.29)  ; 64\r\n                    (   96.89    88.31   -11.19     0.29)  ; 65\r\n                    (   97.18    89.26   -10.94     0.29)  ; 66\r\n                    (   97.70    90.43    -9.56     0.29)  ; 67\r\n                    (   97.84    90.94    -9.56     0.29)  ; 68\r\n                    (   97.84    91.45    -9.50     0.52)  ; 69\r\n                     Low\r\n                  |\r\n                    (   66.83    58.12   -23.13     0.81)  ; 1, R-1-1-2-1-1-2-1-1-2\r\n                    (   66.83    57.46   -23.31     0.59)  ; 2\r\n                    (   67.42    57.02   -25.19     0.59)  ; 3\r\n                    (   67.64    56.44   -25.19     0.59)  ; 4\r\n                    (   68.30    56.22   -25.63     0.88)  ; 5\r\n                    (   68.82    56.22   -27.25     0.88)  ; 6\r\n                    (   69.19    56.07   -27.25     0.88)  ; 7\r\n                    (   69.70    56.29   -29.38     0.74)  ; 8\r\n                    (   70.51    56.58   -31.25     0.59)  ; 9\r\n                    (   70.95    56.14   -31.94     0.44)  ; 10\r\n                    (   71.76    55.49   -32.88     0.44)  ; 11\r\n                    (   72.50    55.19   -33.69     0.44)  ; 12\r\n                    (   73.09    55.12   -33.94     0.81)  ; 13\r\n                    (   73.31    55.27   -35.50     1.33)  ; 14\r\n                    (   74.20    54.97   -37.06     1.33)  ; 15\r\n                    (   74.64    54.76   -37.50     1.33)  ; 16\r\n                    (   75.37    54.76   -38.25     1.33)  ; 17\r\n                    (   76.18    54.76   -39.94     1.03)  ; 18\r\n                    (   77.14    54.68   -41.31     0.81)  ; 19\r\n                    (   77.88    54.76   -41.44     0.59)  ; 20\r\n                    (   78.76    54.54   -42.44     0.59)  ; 21\r\n                    (   79.87    54.54   -43.63     0.88)  ; 22\r\n                    (   80.38    54.54   -43.88     0.59)  ; 23\r\n                    (   81.12    54.76   -43.88     0.37)  ; 24\r\n                    (   82.30    55.19   -44.13     0.37)  ; 25\r\n                    (   83.26    55.71   -44.31     0.66)  ; 26\r\n                    (   83.99    56.14   -44.44     1.33)  ; 27\r\n                    (   84.80    56.36   -45.38     1.62)  ; 28\r\n                    (   85.54    56.44   -43.63     1.18)  ; 29\r\n                    (   86.35    56.95   -44.31     0.74)  ; 30\r\n                    (   87.01    57.46   -44.31     0.52)  ; 31\r\n                    (   87.31    58.12   -44.94     0.22)  ; 32\r\n                    (   88.41    59.00   -45.63     0.22)  ; 33\r\n                    (   89.81    59.73   -45.69     0.22)  ; 34\r\n                    (   90.55    60.31   -44.50     0.81)  ; 35\r\n                    (   91.14    60.68   -44.50     1.55)  ; 36\r\n                    (   91.88    61.12   -44.50     1.55)  ; 37\r\n                    (   92.10    61.41   -44.06     1.18)  ; 38\r\n                    (   92.69    61.85   -44.00     0.81)  ; 39\r\n                    (   93.35    62.21   -44.31     0.52)  ; 40\r\n                    (   94.09    62.65   -44.94     1.11)  ; 41\r\n                    (   94.68    62.87   -46.69     1.40)  ; 42\r\n                    (   95.27    63.16   -46.69     1.69)  ; 43\r\n                    (   95.86    63.31   -47.56     0.96)  ; 44\r\n                    (   96.59    63.53   -48.94     0.59)  ; 45\r\n                     Low\r\n                  )  ;  End of split\r\n                |\r\n                  (   51.82    50.46   -21.19     0.66)  ; 1, R-1-1-2-1-1-2-1-2\r\n                  (   52.33    51.27   -20.75     0.52)  ; 2\r\n                  (   52.70    52.00   -20.75     0.52)  ; 3\r\n                  (   52.70    52.95   -20.75     0.52)  ; 4\r\n                  (   52.26    54.19   -20.75     0.74)  ; 5\r\n                  (   51.96    55.43   -20.75     0.74)  ; 6\r\n                  (   52.11    56.60   -20.56     0.74)  ; 7\r\n                  (   52.55    57.99   -20.00     1.03)  ; 8\r\n                  (   52.92    59.38   -19.44     1.18)  ; 9\r\n                  (   53.22    60.19   -18.38     1.47)  ; 10\r\n                  (   53.51    61.21   -18.31     1.92)  ; 11\r\n                  (   53.50    62.51   -17.69     1.40)  ; 12\r\n                  (   53.80    63.32   -16.13     1.11)  ; 13\r\n                  (   54.16    64.85   -16.00     0.81)  ; 14\r\n                  (   54.31    65.73   -15.94     0.81)  ; 15\r\n                  (   54.53    66.90   -15.75     0.66)  ; 16\r\n                  (   54.90    67.85   -15.56     0.96)  ; 17\r\n                  (   55.05    68.87   -15.50     0.96)  ; 18\r\n                  (   55.49    69.89   -15.50     0.81)  ; 19\r\n                  (   56.23    70.26   -15.88     0.81)  ; 20\r\n                  (   56.89    70.77   -15.88     0.74)  ; 21\r\n                  (   57.41    71.06   -15.88     0.74)  ; 22\r\n                  (   57.63    71.72   -15.88     0.74)  ; 23\r\n                  (   57.04    72.82   -16.19     1.03)  ; 24\r\n                  (   56.52    74.06   -15.69     1.47)  ; 25\r\n                  (   56.08    74.72   -15.56     1.47)  ; 26\r\n                  (   55.56    75.45   -15.25     1.33)  ; 27\r\n                  (   55.19    75.96   -14.75     1.11)  ; 28\r\n                  (   54.75    77.06   -14.75     0.88)  ; 29\r\n                  (   54.31    78.67   -14.69     1.11)  ; 30\r\n                  (   54.09    79.98   -14.38     1.25)  ; 31\r\n                  (   54.24    81.37   -14.19     1.18)  ; 32\r\n                  (   54.90    82.47   -13.88     1.18)  ; 33\r\n                  (   55.42    83.42   -14.25     0.96)  ; 34\r\n                  (   55.49    84.30   -14.06     1.55)  ; 35\r\n                  (   55.56    85.90   -14.94     1.40)  ; 36\r\n                  (   55.56    86.78   -15.56     0.96)  ; 37\r\n                  (   55.86    87.80   -15.88     0.81)  ; 38\r\n                  (   55.34    89.05   -15.56     1.11)  ; 39\r\n                  (   55.12    89.56   -15.31     1.47)  ; 40\r\n                  (   54.53    90.58   -15.31     0.74)  ; 41\r\n                  (   54.24    91.88   -15.19     0.59)  ; 42\r\n                  (   54.24    92.76   -15.19     0.88)  ; 43\r\n                  (   54.75    93.35   -14.69     0.88)  ; 44\r\n                  (   55.49    94.15   -17.31     0.81)  ; 45\r\n                  (   56.23    94.66   -19.63     0.81)  ; 46\r\n                  (   56.89    95.76   -19.63     0.81)  ; 47\r\n                  (   58.07    97.66   -19.63     1.03)  ; 48\r\n                  (   58.73    98.39   -19.88     1.03)  ; 49\r\n                  (   59.17    99.19   -20.00     0.81)  ; 50\r\n                  (   59.17    99.93   -20.06     0.81)  ; 51\r\n                  (   58.58   100.66   -19.44     0.81)  ; 52\r\n                  (   57.77   101.61   -18.81     1.11)  ; 53\r\n                  (   56.96   102.92   -18.81     0.81)  ; 54\r\n                  (   56.52   104.97   -18.75     1.47)  ; 55\r\n                  (   57.18   106.94   -19.19     1.47)  ; 56\r\n                  (   57.85   107.97   -18.63     1.47)  ; 57\r\n                  (   58.29   108.92   -17.06     1.18)  ; 58\r\n                  (   58.73   109.94   -16.50     0.96)  ; 59\r\n                  (   59.25   111.33   -16.00     0.74)  ; 60\r\n                  (   59.54   111.77   -15.94     0.52)  ; 61\r\n                  (   59.69   112.43   -15.75     0.52)  ; 62\r\n                  (   59.98   113.38   -15.63     0.74)  ; 63\r\n                  (   60.20   113.81   -15.63     0.74)  ; 64\r\n                  (   59.91   114.62   -16.06     0.59)  ; 65\r\n                  (   59.47   115.13   -16.06     0.59)  ; 66\r\n                  (   58.95   116.37   -16.00     0.44)  ; 67\r\n                  (   58.95   116.81   -15.88     0.44)  ; 68\r\n                  (   58.88   116.96   -15.06     0.74)  ; 69\r\n                  (   58.14   117.83   -13.06     0.81)  ; 70\r\n                  (   57.70   118.42   -13.06     0.44)  ; 71\r\n                  (   56.89   118.93   -12.88     0.44)  ; 72\r\n                   Low\r\n                )  ;  End of split\r\n              |\r\n                (   45.51    40.00   -18.19     0.74)  ; 1, R-1-1-2-1-1-2-2\r\n                (   46.10    40.00   -19.44     0.74)  ; 2\r\n                (   47.06    39.93   -21.00     0.74)  ; 3\r\n                (   47.94    39.79   -21.06     0.74)  ; 4\r\n                (   48.90    39.35   -21.19     0.74)  ; 5\r\n                (   50.15    39.05   -22.25     0.81)  ; 6\r\n                (   51.26    39.71   -23.56     0.74)  ; 7\r\n                (   52.29    40.37   -24.63     0.96)  ; 8\r\n                (   53.03    41.25   -25.50     0.81)  ; 9\r\n                (   53.62    41.69   -26.06     0.81)  ; 10\r\n                (   54.50    41.76   -27.19     0.74)  ; 11\r\n                (   55.46    41.54   -28.00     0.74)  ; 12\r\n                (   56.49    41.10   -28.00     0.66)  ; 13\r\n                (   57.08    40.37   -28.00     0.96)  ; 14\r\n                (   57.96    39.79   -28.06     1.69)  ; 15\r\n                (   58.55    39.49   -28.44     2.06)  ; 16\r\n                (   59.29    39.20   -28.88     2.06)  ; 17\r\n                (   60.47    38.69   -29.00     1.40)  ; 18\r\n                (   61.50    38.25   -29.00     0.96)  ; 19\r\n                (   62.31    38.25   -29.00     0.81)  ; 20\r\n                (   63.12    38.47   -29.56     0.81)  ; 21\r\n                (   64.00    38.69   -29.75     0.81)  ; 22\r\n                (   65.11    38.91   -29.88     1.11)  ; 23\r\n                (   65.70    39.13   -31.06     0.96)  ; 24\r\n                (   66.51    39.42   -32.69     0.96)  ; 25\r\n                (   67.61    39.86   -32.69     0.81)  ; 26\r\n                (   68.13    39.79   -33.63     0.81)  ; 27\r\n                (   69.01    39.49   -34.44     0.74)  ; 28\r\n                (   70.19    40.00   -34.81     0.88)  ; 29\r\n                (   71.37    40.15   -35.00     0.96)  ; 30\r\n                (   72.48    40.37   -35.81     0.74)  ; 31\r\n                (   73.43    40.44   -36.50     0.81)  ; 32\r\n                (   74.39    40.15   -36.50     0.66)  ; 33\r\n                (   75.20    40.44   -37.13     0.66)  ; 34\r\n                (   76.09    40.37   -37.88     0.74)  ; 35\r\n                (   77.26    40.30   -39.13     0.96)  ; 36\r\n                (   78.59    40.37   -39.13     0.74)  ; 37\r\n                (   79.25    40.95   -39.44     1.03)  ; 38\r\n                (   80.06    41.03   -40.00     2.14)  ; 39\r\n                (   80.58    41.17   -40.44     2.50)  ; 40\r\n                (   81.32    40.88   -40.88     1.84)  ; 41\r\n                (   82.20    40.44   -40.88     1.40)  ; 42\r\n                (   83.16    40.08   -41.50     0.96)  ; 43\r\n                (   84.85    40.22   -41.81     0.74)  ; 44\r\n                (   86.25    40.30   -42.81     0.74)  ; 45\r\n                (   87.51    40.66   -43.00     0.74)  ; 46\r\n                (   88.76    41.32   -43.00     1.03)  ; 47\r\n                (   89.94    41.61   -43.00     1.03)  ; 48\r\n                (   90.75    42.12   -43.56     1.03)  ; 49\r\n                (   92.00    43.15   -43.75     0.88)  ; 50\r\n                (   92.74    43.73   -43.75     0.66)  ; 51\r\n                (   93.47    44.03   -44.75     0.66)  ; 52\r\n                (   94.14    44.83   -45.31     0.66)  ; 53\r\n                (   95.54    45.85   -45.31     1.03)  ; 54\r\n                (   96.49    46.22   -45.63     1.33)  ; 55\r\n                (   97.97    46.66   -46.63     1.99)  ; 56\r\n                (   99.00    47.31   -47.00     1.25)  ; 57\r\n                (   99.88    48.07   -48.50     0.52)  ; 58\r\n                (  101.79    49.24   -48.88     0.52)  ; 59\r\n                (  103.27    49.97   -49.75     1.25)  ; 60\r\n                (  104.30    50.48   -50.63     1.55)  ; 61\r\n                (  105.63    50.77   -50.81     1.18)  ; 62\r\n                (  106.95    50.70   -51.25     0.81)  ; 63\r\n                (  108.57    50.63   -51.94     0.59)  ; 64\r\n                (  110.27    50.26   -52.44     0.81)  ; 65\r\n                (  111.15    50.70   -53.38     1.18)  ; 66\r\n                (  111.37    50.99   -55.56     1.47)  ; 67\r\n                (  111.74    51.65   -56.75     1.11)  ; 68\r\n                (  112.40    52.75   -56.75     0.81)  ; 69\r\n                (  112.70    53.04   -58.50     0.81)  ; 70\r\n                (  113.07    53.26   -62.25     1.03)  ; 71\r\n                (  113.95    53.33   -59.56     0.59)  ; 72\r\n                 Low\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (   31.99    33.14   -15.94     0.44)  ; 1, R-1-1-2-1-2\r\n            (   31.77    33.66   -17.31     0.52)  ; 2\r\n            (   32.29    34.17   -19.81     0.66)  ; 3\r\n            (   32.14    34.61   -20.69     0.88)  ; 4\r\n            (   32.06    35.41   -21.06     0.88)  ; 5\r\n            (   32.65    35.92   -21.75     0.88)  ; 6\r\n            (   33.91    36.21   -22.94     1.11)  ; 7\r\n            (   35.45    36.14   -23.94     1.11)  ; 8\r\n            (   35.97    36.14   -24.81     1.11)  ; 9\r\n            (   35.96    36.31   -24.81     1.11)  ; 10\r\n            (\r\n              (   35.90    36.43   -26.50     1.11)  ; 1, R-1-1-2-1-2-1\r\n              (   35.53    36.65   -27.00     1.11)  ; 2\r\n              (   35.08    36.58   -27.50     1.11)  ; 3\r\n              (   34.79    36.58   -29.31     0.96)  ; 4\r\n              (   34.20    36.80   -30.81     0.81)  ; 5\r\n              (   33.61    37.46   -31.25     0.81)  ; 6\r\n              (   33.98    38.19   -31.69     0.81)  ; 7\r\n              (   34.35    39.14   -32.06     0.81)  ; 8\r\n              (   34.86    40.38   -32.63     0.81)  ; 9\r\n              (   35.16    41.55   -34.69     0.96)  ; 10\r\n              (   35.75    42.43   -35.31     0.96)  ; 11\r\n              (   35.60    43.52   -35.44     0.96)  ; 12\r\n              (   35.38    44.40   -35.88     1.33)  ; 13\r\n              (   35.97    45.13   -36.81     1.18)  ; 14\r\n              (   35.97    46.08   -39.06     0.81)  ; 15\r\n              (   36.34    47.11   -39.38     0.44)  ; 16\r\n              (   35.97    48.06   -41.13     0.59)  ; 17\r\n              (   35.97    48.50   -42.63     0.96)  ; 18\r\n              (   35.90    48.79   -44.38     1.40)  ; 19\r\n              (   35.38    49.52   -45.56     1.18)  ; 20\r\n              (   35.23    49.96   -47.50     0.81)  ; 21\r\n              (   34.64    50.77   -49.00     0.44)  ; 22\r\n              (   34.42    51.57   -49.75     0.44)  ; 23\r\n              (   34.72    52.23   -50.19     0.74)  ; 24\r\n              (   34.50    53.33   -51.56     1.11)  ; 25\r\n              (   34.64    53.98   -52.31     0.66)  ; 26\r\n              (   34.79    54.50   -53.38     0.29)  ; 27\r\n              (   34.79    55.74   -53.38     0.29)  ; 28\r\n              (   35.01    57.20   -53.50     0.52)  ; 29\r\n              (   35.23    57.79   -54.69     1.03)  ; 30\r\n              (   35.23    58.52   -55.69     0.59)  ; 31\r\n              (   35.38    59.25   -56.19     0.96)  ; 32\r\n              (   34.79    60.56   -56.81     1.40)  ; 33\r\n              (   34.20    61.37   -58.69     1.92)  ; 34\r\n              (   33.76    62.17   -59.06     1.55)  ; 35\r\n              (   33.46    62.83   -60.13     1.11)  ; 36\r\n              (   33.24    63.78   -61.06     0.74)  ; 37\r\n              (   33.46    64.80   -61.50     0.37)  ; 38\r\n              (   34.35    65.24   -61.75     0.29)  ; 39\r\n              (   34.79    66.70   -62.63     0.29)  ; 40\r\n              (   35.31    67.87   -62.94     0.29)  ; 41\r\n              (   35.53    69.19   -65.06     0.44)  ; 42\r\n              (   35.82    69.77   -67.94     0.74)  ; 43\r\n              (   36.26    70.21   -70.38     1.11)  ; 44\r\n              (   36.63    70.72   -70.56     1.55)  ; 45\r\n              (   36.63    71.31   -72.38     1.55)  ; 46\r\n              (   36.63    72.19   -72.94     0.66)  ; 47\r\n              (   36.93    72.84   -73.31     0.29)  ; 48\r\n              (   37.37    73.57   -73.44     0.52)  ; 49\r\n              (   37.30    74.53   -74.50     0.81)  ; 50\r\n              (   37.37    74.96   -75.38     1.18)  ; 51\r\n              (   37.30    75.62   -75.63     0.74)  ; 52\r\n              (   37.30    76.28   -75.63     0.37)  ; 53\r\n              (   37.22    76.86   -75.63     0.37)  ; 54\r\n              (   37.22    77.89   -75.69     0.37)  ; 55\r\n              (   37.13    79.00   -78.00     0.52)  ; 56\r\n              (   36.98    79.44   -78.06     0.81)  ; 57\r\n              (   36.76    79.81   -78.06     1.69)  ; 58\r\n              (   36.54    80.76   -78.06     0.96)  ; 59\r\n              (   36.69    81.34   -78.19     0.29)  ; 60\r\n              (   36.54    82.07   -78.31     0.29)  ; 61\r\n              (   36.76    82.88   -78.31     0.29)  ; 62\r\n              (   37.21    84.05   -78.31     0.29)  ; 63\r\n              (   37.65    85.07   -80.63     0.66)  ; 64\r\n              (   38.16    85.87   -80.63     1.11)  ; 65\r\n              (   38.31    86.60   -80.63     0.88)  ; 66\r\n              (   38.60    87.12   -80.63     0.22)  ; 67\r\n              (   39.05    88.07   -80.63     0.22)  ; 68\r\n              (   39.56    89.09   -80.63     0.22)  ; 69\r\n              (   40.37    89.82   -81.06     0.22)  ; 70\r\n              (   40.59    90.26   -82.88     0.52)  ; 71\r\n              (   41.18    90.99   -82.44     1.40)  ; 72\r\n              (   41.55    91.65   -82.44     1.40)  ; 73\r\n              (   42.21    92.38   -80.88     0.66)  ; 74\r\n              (   42.51    92.96   -82.00     0.37)  ; 75\r\n              (   42.66    93.99   -82.44     0.37)  ; 76\r\n              (   42.95    94.72   -82.63     0.59)  ; 77\r\n              (   43.25    95.45   -82.88     0.81)  ; 78\r\n               Generated\r\n            |\r\n              (   36.35    36.43   -29.06     0.81)  ; 1, R-1-1-2-1-2-2\r\n              (   37.16    36.72   -29.63     0.81)  ; 2\r\n              (   37.97    36.94   -32.19     0.66)  ; 3\r\n              (   39.08    37.31   -32.81     0.81)  ; 4\r\n              (   40.18    37.60   -32.94     0.96)  ; 5\r\n              (   41.43    37.24   -33.81     0.96)  ; 6\r\n              (   42.24    37.09   -35.56     0.81)  ; 7\r\n              (   43.35    37.31   -36.50     1.03)  ; 8\r\n              (\r\n                (   43.79    37.60   -41.25     1.55)  ; 1, R-1-1-2-1-2-2-1\r\n                (   44.75    37.38   -41.50     1.11)  ; 2\r\n                (   45.41    36.94   -43.63     0.74)  ; 3\r\n                (   46.96    37.16   -43.88     0.74)  ; 4\r\n                (   48.58    37.16   -45.38     0.96)  ; 5\r\n                (   49.91    37.02   -46.00     1.33)  ; 6\r\n                (   50.49    37.09   -47.69     1.33)  ; 7\r\n                (   51.53    37.31   -49.75     1.03)  ; 8\r\n                (   52.41    37.82   -50.31     0.74)  ; 9\r\n                (   53.44    38.11   -51.13     0.74)  ; 10\r\n                (   54.33    38.63   -51.50     0.74)  ; 11\r\n                (   54.99    39.21   -51.56     0.74)  ; 12\r\n                (   55.87    39.14   -52.50     0.44)  ; 13\r\n                (   56.68    39.58   -53.13     0.44)  ; 14\r\n                (   57.42    40.09   -54.00     0.44)  ; 15\r\n                (   58.97    40.31   -55.00     0.52)  ; 16\r\n                (   60.00    40.45   -55.63     0.81)  ; 17\r\n                (   60.81    40.67   -57.25     1.25)  ; 18\r\n                (   61.18    41.11   -58.56     0.96)  ; 19\r\n                (   61.40    41.62   -59.56     0.22)  ; 20\r\n                (   62.28    42.13   -60.06     0.22)  ; 21\r\n                (   63.68    42.06   -60.63     0.37)  ; 22\r\n                (   64.49    42.28   -61.56     0.74)  ; 23\r\n                (   65.52    42.72   -62.81     1.62)  ; 24\r\n                (   65.89    43.16   -63.13     2.14)  ; 25\r\n                (   66.48    43.23   -63.38     2.14)  ; 26\r\n                (   66.78    43.45   -64.13     1.69)  ; 27\r\n                (   67.22    43.67   -64.81     0.96)  ; 28\r\n                (   67.59    43.89   -64.88     0.52)  ; 29\r\n                (   68.47    44.11   -64.88     0.22)  ; 30\r\n                (   69.43    44.33   -65.00     0.22)  ; 31\r\n                (   71.57    44.84   -65.13     0.22)  ; 32\r\n                (   72.89    44.98   -65.44     0.22)  ; 33\r\n                (   75.03    45.06   -65.50     0.37)  ; 34\r\n                (   75.99    45.06   -65.94     0.74)  ; 35\r\n                (   76.43    45.28   -67.25     1.03)  ; 36\r\n                (   76.94    45.50   -67.38     2.43)  ; 37\r\n                (   77.53    45.79   -67.56     2.14)  ; 38\r\n                (   77.68    45.79   -68.13     1.69)  ; 39\r\n                (   78.34    46.23   -68.19     0.81)  ; 40\r\n                (   78.79    46.74   -68.63     0.44)  ; 41\r\n                (   79.82    47.47   -69.44     0.44)  ; 42\r\n                (   81.14    47.98   -69.81     0.66)  ; 43\r\n                (   81.73    48.57   -70.63     0.88)  ; 44\r\n                (   82.98    49.44   -72.75     0.74)  ; 45\r\n                (   83.59    49.97   -74.56     0.66)  ; 46\r\n                (   84.26    50.48   -74.69     0.81)  ; 47\r\n                (   84.92    51.07   -77.44     0.81)  ; 48\r\n                (   85.58    51.50   -79.44     0.81)  ; 49\r\n                (   85.88    51.87   -82.38     1.25)  ; 50\r\n                (   86.61    52.38   -83.75     1.25)  ; 51\r\n                (   86.83    53.04   -84.38     0.96)  ; 52\r\n                (   86.98    53.84   -85.75     0.59)  ; 53\r\n                (   88.01    54.58   -88.56     0.74)  ; 54\r\n                (   88.60    54.79   -93.75     1.11)  ; 55\r\n                (   89.19    55.53   -93.94     0.74)  ; 56\r\n                (   89.63    56.04   -94.44     0.44)  ; 57\r\n                (   90.08    56.26   -94.69     0.44)  ; 58\r\n                (   90.37    57.13   -96.00     0.44)  ; 59\r\n                (   90.81    57.65   -99.19     0.59)  ; 60\r\n                (   91.62    58.45  -100.00     0.88)  ; 61\r\n                 Generated\r\n              |\r\n                (   45.25    37.63   -36.69     0.74)  ; 1, R-1-1-2-1-2-2-2\r\n                (   45.25    38.43   -38.25     0.74)  ; 2\r\n                (   45.98    39.31   -38.88     0.88)  ; 3\r\n                (   46.65    39.67   -39.19     0.66)  ; 4\r\n                (   47.16    39.82   -39.19     0.66)  ; 5\r\n                (   48.19    40.55   -39.63     0.66)  ; 6\r\n                (   48.93    41.28   -40.13     0.88)  ; 7\r\n                (   50.11    42.08   -40.13     0.88)  ; 8\r\n                (   51.07    43.03   -40.13     0.59)  ; 9\r\n                (   51.14    44.06   -40.13     0.59)  ; 10\r\n                (   51.95    44.94   -40.38     0.81)  ; 11\r\n                (   52.47    45.52   -40.50     1.03)  ; 12\r\n                (   53.06    46.32   -40.94     1.33)  ; 13\r\n                (   53.64    47.20   -41.38     1.33)  ; 14\r\n                (   54.01    48.37   -41.81     1.62)  ; 15\r\n                (   54.53    48.88   -42.75     1.62)  ; 16\r\n                (   55.34    49.91   -43.00     1.25)  ; 17\r\n                (   55.86    50.71   -43.00     0.74)  ; 18\r\n                (   56.00    51.22   -43.38     0.52)  ; 19\r\n                (   56.15    52.76   -43.88     0.44)  ; 20\r\n                (   56.74    53.49   -44.06     0.44)  ; 21\r\n                (   57.03    54.58   -45.13     0.74)  ; 22\r\n                (   56.67    55.24   -48.75     0.96)  ; 23\r\n                (   56.52    56.34   -49.94     0.96)  ; 24\r\n                (   56.30    57.29   -55.00     0.96)  ; 25\r\n                (   56.89    57.43   -56.94     1.11)  ; 26\r\n                (   56.67    58.75   -60.94     0.52)  ; 27\r\n                (   56.74    59.99   -61.75     0.52)  ; 28\r\n                (   56.52    60.43   -62.31     0.81)  ; 29\r\n                (   56.37    61.09   -62.50     1.25)  ; 30\r\n                (   56.30    61.67   -64.31     1.62)  ; 31\r\n                (   56.30    62.48   -66.06     1.18)  ; 32\r\n                (   56.30    62.92   -67.63     0.81)  ; 33\r\n                (   56.37    63.79   -66.63     0.59)  ; 34\r\n                (   56.30    64.67   -67.25     0.37)  ; 35\r\n                (   56.00    65.84   -67.25     0.37)  ; 36\r\n                (   55.78    67.34   -67.19     0.29)  ; 37\r\n                (   55.93    68.21   -68.13     0.66)  ; 38\r\n                (   55.93    68.73   -68.38     1.11)  ; 39\r\n                (   56.01    69.82   -69.19     1.47)  ; 40\r\n                (   56.08    70.41   -69.69     1.47)  ; 41\r\n                (   56.30    71.58   -71.31     1.11)  ; 42\r\n                (   56.37    72.09   -72.06     0.81)  ; 43\r\n                (   56.45    73.40   -70.25     0.37)  ; 44\r\n                (   56.23    74.21   -70.81     0.66)  ; 45\r\n                (   56.23    75.23   -71.19     0.96)  ; 46\r\n                (   56.52    75.52   -71.19     0.59)  ; 47\r\n                (   57.04    76.33   -71.19     0.29)  ; 48\r\n                (   57.18    77.13   -71.19     0.29)  ; 49\r\n                (   57.33    77.79   -71.31     0.29)  ; 50\r\n                (   57.41    78.67   -71.69     0.66)  ; 51\r\n                (   57.70    79.18   -72.31     1.11)  ; 52\r\n                (   57.77    79.84   -72.94     1.11)  ; 53\r\n                (   58.07    80.64   -73.31     0.74)  ; 54\r\n                (   58.29    81.15   -73.31     0.44)  ; 55\r\n                (   58.66    81.81   -73.63     0.44)  ; 56\r\n                (   58.95    82.40   -74.13     1.18)  ; 57\r\n                (   59.25    83.05   -74.44     1.40)  ; 58\r\n                (   59.62    83.49   -74.63     1.03)  ; 59\r\n                (   59.69    83.78   -74.69     0.74)  ; 60\r\n                (   60.06    84.59   -74.69     0.37)  ; 61\r\n                (   60.57    85.39   -75.06     0.29)  ; 62\r\n                (   61.02    86.20   -75.81     0.29)  ; 63\r\n                (   61.68    87.37   -77.81     0.29)  ; 64\r\n                (   61.97    87.80   -78.56     0.29)  ; 65\r\n                (   62.12    88.10   -78.56     0.66)  ; 66\r\n                (   62.34    88.61   -78.69     1.40)  ; 67\r\n                (   62.56    89.34   -79.44     1.62)  ; 68\r\n                (   62.93    89.85   -80.25     0.88)  ; 69\r\n                (   63.08    90.14   -81.38     0.52)  ; 70\r\n                (   63.15    90.58   -81.75     0.29)  ; 71\r\n                (   64.18    92.19   -82.25     0.29)  ; 72\r\n                (   64.99    93.87   -82.81     0.29)  ; 73\r\n                (   65.21    94.75   -82.75     0.29)  ; 74\r\n                (   65.72    95.70   -83.25     1.03)  ; 75\r\n                (   66.09    96.51   -84.13     1.03)  ; 76\r\n                (   66.46    97.16   -84.69     0.66)  ; 77\r\n                (   66.61    97.60   -83.13     0.29)  ; 78\r\n                (   66.97    98.55   -83.13     0.29)  ; 79\r\n                (   67.42    99.65   -82.25     0.52)  ; 80\r\n                (   67.49    99.94   -82.00     0.74)  ; 81\r\n                (   67.56   100.38   -81.94     0.44)  ; 82\r\n                (   67.71   101.26   -81.94     0.22)  ; 83\r\n                (   67.78   102.06   -81.94     0.22)  ; 84\r\n                (   68.15   103.01   -81.94     0.59)  ; 85\r\n                (   68.30   103.45   -82.38     0.88)  ; 86\r\n                (   68.45   104.40   -82.38     0.88)  ; 87\r\n                (   68.37   104.84   -81.88     0.59)  ; 88\r\n                (   68.89   105.57   -80.81     0.29)  ; 89\r\n                (   69.04   105.86   -80.56     0.29)  ; 90\r\n                (   69.55   106.89   -80.25     0.59)  ; 91\r\n                (   69.70   107.40   -80.56     0.88)  ; 92\r\n                (   69.99   107.98   -81.00     0.88)  ; 93\r\n                (   69.99   108.57   -81.75     0.52)  ; 94\r\n                 Incomplete\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (   29.81    29.72    -9.06     0.74)  ; 1, R-1-1-2-2\r\n          (   29.07    30.08    -7.31     0.59)  ; 2\r\n          (   28.34    30.37    -6.69     0.59)  ; 3\r\n          (   27.82    31.32    -6.19     0.66)  ; 4\r\n          (   27.53    32.27    -6.00     0.66)  ; 5\r\n          (   27.97    33.74    -5.44     0.66)  ; 6\r\n          (   28.26    35.56    -4.81     0.81)  ; 7\r\n          (   28.93    36.73    -4.69     0.88)  ; 8\r\n          (   29.44    37.76    -4.88     0.96)  ; 9\r\n          (   29.74    39.07    -6.00     0.81)  ; 10\r\n          (   30.33    40.53    -5.38     0.88)  ; 11\r\n          (   30.70    41.19    -5.25     0.74)  ; 12\r\n          (   31.21    41.85    -4.13     0.59)  ; 13\r\n          (   31.65    43.02    -3.75     0.52)  ; 14\r\n          (   31.95    43.60    -3.25     0.52)  ; 15\r\n          (   32.10    43.75    -1.69     0.66)  ; 16\r\n          (   31.95    44.04    -0.81     0.66)  ; 17\r\n          (   32.02    44.70    -0.38     0.66)  ; 18\r\n          (   32.17    45.43     0.44     0.59)  ; 19\r\n          (   32.17    45.87     1.13     0.59)  ; 20\r\n          (   32.02    46.89     1.31     0.81)  ; 21\r\n          (   32.39    47.84     1.31     0.81)  ; 22\r\n          (   32.46    49.45     1.00     0.96)  ; 23\r\n          (   32.32    50.62     1.00     0.96)  ; 24\r\n          (   32.17    51.50     0.81     0.96)  ; 25\r\n          (   32.10    52.52     0.69     0.96)  ; 26\r\n          (   31.65    53.76     0.63     0.74)  ; 27\r\n          (   31.73    54.57     0.63     0.74)  ; 28\r\n          (   31.95    55.81     1.88     1.11)  ; 29\r\n          (   31.87    56.91     1.94     1.25)  ; 30\r\n          (   31.87    57.35     1.63     0.96)  ; 31\r\n          (   31.58    58.30     1.44     0.88)  ; 32\r\n          (   30.99    59.32     0.88     0.88)  ; 33\r\n          (   30.55    60.27     0.63     0.81)  ; 34\r\n          (   30.55    61.29     0.19     0.88)  ; 35\r\n          (   30.55    61.95     0.13     0.81)  ; 36\r\n          (   31.06    62.90     0.00     0.74)  ; 37\r\n          (   31.21    63.78     0.25     0.81)  ; 38\r\n          (   31.21    64.80     0.25     0.81)  ; 39\r\n          (   30.77    65.53     0.25     0.81)  ; 40\r\n          (   30.11    66.56     0.56     0.81)  ; 41\r\n          (   29.30    67.36     0.63     0.81)  ; 42\r\n          (   28.71    68.38     0.63     0.66)  ; 43\r\n          (   28.41    69.19     0.38     0.66)  ; 44\r\n          (   28.63    70.58    -0.13     0.96)  ; 45\r\n          (   28.63    71.09    -0.75     0.96)  ; 46\r\n          (   28.63    71.89    -1.44     0.96)  ; 47\r\n          (   28.93    73.21    -1.56     0.81)  ; 48\r\n          (   29.15    73.94    -1.56     0.81)  ; 49\r\n          (   29.15    74.52    -1.31     0.52)  ; 50\r\n          (   29.37    75.18    -1.13     0.52)  ; 51\r\n          (   29.44    75.69    -1.00     0.81)  ; 52\r\n          (   29.66    76.86    -0.56     0.88)  ; 53\r\n          (   29.44    77.67    -0.13     0.66)  ; 54\r\n          (   29.22    78.47     1.06     0.66)  ; 55\r\n          (   28.56    79.06     1.25     0.81)  ; 56\r\n          (   27.90    79.42     1.56     0.96)  ; 57\r\n          (   27.23    80.52     1.75     0.74)  ; 58\r\n          (   26.79    81.40     1.81     0.74)  ; 59\r\n          (   26.50    82.71     2.19     0.88)  ; 60\r\n          (   26.50    84.10     2.00     1.11)  ; 61\r\n          (   26.87    84.85     1.94     0.59)  ; 62\r\n          (   27.02    85.88     2.94     0.52)  ; 63\r\n          (   27.09    87.12     3.38     0.59)  ; 64\r\n          (   27.16    88.58     3.81     0.74)  ; 65\r\n          (   27.09    89.24     4.13     0.74)  ; 66\r\n          (   27.09    90.34     4.44     1.03)  ; 67\r\n          (   26.87    91.43     4.13     0.74)  ; 68\r\n          (   26.72    92.31     4.06     0.74)  ; 69\r\n          (   27.24    93.19     5.31     0.96)  ; 70\r\n          (   27.46    94.36     6.69     0.88)  ; 71\r\n          (   27.68    95.89     8.38     0.81)  ; 72\r\n          (   27.31    96.99    10.06     1.11)  ; 73\r\n          (   27.24    97.72    10.38     0.81)  ; 74\r\n          (   26.80    98.67    11.13     0.81)  ; 75\r\n          (   26.13    99.40    11.31     0.81)  ; 76\r\n          (   25.76   100.28    10.44     0.81)  ; 77\r\n          (   25.62   101.01    10.38     0.81)  ; 78\r\n          (   26.28   102.03     9.81     0.66)  ; 79\r\n          (   26.94   103.20     9.13     0.66)  ; 80\r\n          (   27.46   103.93     8.50     0.66)  ; 81\r\n          (   28.34   104.74     8.06     0.88)  ; 82\r\n          (   29.23   105.76     6.69     1.03)  ; 83\r\n          (   29.52   106.34     6.38     0.74)  ; 84\r\n          (   29.82   107.22     6.19     0.52)  ; 85\r\n          (   29.74   107.88     6.19     0.52)  ; 86\r\n          (   29.67   108.76     6.19     0.52)  ; 87\r\n          (   30.04   109.49     6.19     0.52)  ; 88\r\n          (   30.18   109.78     6.13     0.52)  ; 89\r\n          (   29.82   110.73     5.56     0.44)  ; 90\r\n          (   29.37   111.53     5.56     0.44)  ; 91\r\n          (   29.08   111.90     6.19     0.44)  ; 92\r\n          (   28.56   112.19     6.31     0.59)  ; 93\r\n           Low\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (   27.41    15.56    -1.19     0.81)  ; 1, R-1-2\r\n      (   28.00    15.85    -0.75     0.66)  ; 2\r\n      (   28.37    16.14    -0.50     0.66)  ; 3\r\n      (   29.03    16.43    -0.19     0.66)  ; 4\r\n      (   29.55    16.65    -0.13     1.03)  ; 5\r\n      (   29.57    16.69    -0.13     1.03)  ; 6\r\n      (\r\n        (   29.99    17.38     0.06     0.74)  ; 1, R-1-2-1\r\n        (   30.51    18.04     0.31     0.52)  ; 2\r\n        (   30.73    18.55     0.63     0.52)  ; 3\r\n        (   31.10    19.58     0.81     0.52)  ; 4\r\n        (   31.32    20.31     1.25     0.66)  ; 5\r\n        (   31.32    21.04     1.31     0.66)  ; 6\r\n        (   31.69    21.55     1.38     0.66)  ; 7\r\n        (   32.27    22.28     1.44     0.66)  ; 8\r\n        (   32.50    23.01     1.50     0.66)  ; 9\r\n        (   32.86    23.16     1.69     0.66)  ; 10\r\n        (   33.60    23.45     1.69     0.66)  ; 11\r\n        (   34.34    23.60     1.81     0.66)  ; 12\r\n        (   35.07    23.82     1.56     0.96)  ; 13\r\n        (   35.66    23.89     1.44     1.47)  ; 14\r\n        (   36.62    24.47     1.44     1.47)  ; 15\r\n        (   37.06    24.69     1.25     1.18)  ; 16\r\n        (   37.51    24.69     1.25     0.81)  ; 17\r\n        (   37.95    25.06     1.25     0.44)  ; 18\r\n        (   39.05    25.72     1.00     0.44)  ; 19\r\n        (   39.94    26.30     0.56     0.44)  ; 20\r\n        (   40.45    27.03     0.50     0.44)  ; 21\r\n        (   41.26    27.18     1.50     0.59)  ; 22\r\n        (   41.48    27.25     2.06     0.81)  ; 23\r\n        (   42.29    27.40     2.56     0.59)  ; 24\r\n        (   42.81    27.91     2.94     0.59)  ; 25\r\n        (   43.47    28.79     3.00     0.59)  ; 26\r\n        (   44.38    29.33     3.13     0.44)  ; 27\r\n        (   45.78    29.70     3.63     0.59)  ; 28\r\n        (   46.29    30.28     3.88     0.37)  ; 29\r\n        (   46.88    31.09     3.94     0.59)  ; 30\r\n        (   47.47    31.60     4.44     0.29)  ; 31\r\n        (   48.36    31.96     4.63     0.29)  ; 32\r\n        (   49.32    32.40     4.75     0.29)  ; 33\r\n        (   49.76    32.99     4.81     0.29)  ; 34\r\n        (   49.98    33.06     5.56     0.96)  ; 35\r\n        (   50.20    33.43     6.19     1.62)  ; 36\r\n        (   50.72    33.64     6.25     2.21)  ; 37\r\n        (   51.53    33.79     6.50     1.11)  ; 38\r\n        (   52.04    34.16     7.25     0.74)  ; 39\r\n        (   52.70    34.59     7.81     0.44)  ; 40\r\n        (   53.29    34.96     9.13     0.29)  ; 41\r\n        (   53.66    35.62     9.69     0.52)  ; 42\r\n        (   53.81    35.84    10.56     0.88)  ; 43\r\n        (   54.03    36.57    10.81     0.74)  ; 44\r\n        (   54.25    37.37    11.13     0.44)  ; 45\r\n        (   54.62    38.03    11.81     0.44)  ; 46\r\n        (   55.28    38.47    12.19     0.29)  ; 47\r\n        (   55.87    38.91    12.50     0.29)  ; 48\r\n        (   56.76    39.93    12.50     0.29)  ; 49\r\n        (   56.98    40.37    13.00     0.66)  ; 50\r\n        (   57.57    40.81    13.75     0.66)  ; 51\r\n        (   57.79    41.83    14.56     0.81)  ; 52\r\n        (   58.38    42.12    15.00     0.59)  ; 53\r\n        (   59.11    42.49    15.25     0.59)  ; 54\r\n        (   59.70    43.00    15.75     1.18)  ; 55\r\n        (   60.51    43.29    15.75     1.84)  ; 56\r\n        (   61.10    43.59    15.88     1.92)  ; 57\r\n        (   61.84    43.95    16.06     0.81)  ; 58\r\n        (   62.72    44.32    16.06     0.52)  ; 59\r\n        (   63.61    44.97    16.06     0.52)  ; 60\r\n        (   64.20    45.41    16.06     0.52)  ; 61\r\n        (   64.57    46.07    16.06     0.52)  ; 62\r\n        (   64.64    46.51    16.06     0.52)  ; 63\r\n         Low\r\n      |\r\n        (   29.20    17.55     1.50     0.52)  ; 1, R-1-2-2\r\n        (   29.12    17.69     1.94     0.59)  ; 2\r\n        (   28.39    18.42     2.13     0.52)  ; 3\r\n        (   28.09    19.16     2.13     0.52)  ; 4\r\n        (   27.58    20.62     2.63     0.66)  ; 5\r\n        (   26.84    21.28     2.94     0.66)  ; 6\r\n        (   26.54    21.64     4.25     0.66)  ; 7\r\n        (   26.54    22.52     4.38     0.52)  ; 8\r\n        (   26.99    23.03     6.19     0.66)  ; 9\r\n        (   27.87    23.91     6.25     0.66)  ; 10\r\n        (   28.90    25.08     6.25     0.59)  ; 11\r\n        (   29.49    25.37     6.44     0.59)  ; 12\r\n        (   31.19    26.25     6.88     0.74)  ; 13\r\n        (   31.63    27.34     7.63     0.88)  ; 14\r\n        (   31.77    28.22     8.13     1.11)  ; 15\r\n        (   31.92    28.95     8.19     1.11)  ; 16\r\n        (   31.98    30.44     8.44     0.52)  ; 17\r\n        (   32.57    31.61     8.69     0.52)  ; 18\r\n        (   32.87    32.34     9.06     0.52)  ; 19\r\n        (   33.09    32.92    10.38     0.81)  ; 20\r\n        (   33.61    33.36    11.38     0.81)  ; 21\r\n        (   33.90    34.17    11.38     0.66)  ; 22\r\n        (   33.90    35.48    11.44     0.52)  ; 23\r\n        (   34.42    36.21    11.75     0.52)  ; 24\r\n        (   34.56    37.31    12.63     0.74)  ; 25\r\n        (   34.56    37.97    13.06     0.74)  ; 26\r\n        (   34.49    39.06    14.31     0.88)  ; 27\r\n        (   34.71    39.58    14.38     0.88)  ; 28\r\n        (   34.56    40.82    14.81     0.66)  ; 29\r\n        (   34.71    41.40    15.06     0.66)  ; 30\r\n        (   34.93    41.70    15.50     0.66)  ; 31\r\n        (   35.45    41.99    16.13     0.66)  ; 32\r\n        (   35.37    43.08    16.56     0.81)  ; 33\r\n        (   35.30    44.11    17.56     0.81)  ; 34\r\n        (   35.01    45.86    18.06     0.59)  ; 35\r\n        (   34.93    47.47    17.69     0.74)  ; 36\r\n        (   35.30    49.08    17.38     0.74)  ; 37\r\n        (   35.89    49.88    17.94     0.59)  ; 38\r\n        (   36.11    50.91    17.88     0.66)  ; 39\r\n        (   35.96    51.93    17.88     0.66)  ; 40\r\n        (   35.82    52.66    18.00     0.66)  ; 41\r\n        (   36.99    53.90    18.25     0.52)  ; 42\r\n        (   37.88    53.90    17.88     0.52)  ; 43\r\n        (   38.98    52.95    17.13     0.52)  ; 44\r\n        (   39.65    51.71    16.56     0.37)  ; 45\r\n        (   40.31    51.27    16.19     0.59)  ; 46\r\n        (   40.68    50.83    14.69     0.59)  ; 47\r\n         Low\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (   26.48    13.03    -3.00     2.80)  ; 1, R-2\r\n    (   27.14    13.10    -3.75     1.47)  ; 2\r\n    (   27.73    12.81    -3.75     1.11)  ; 3\r\n    (   28.54    12.59    -3.94     0.96)  ; 4\r\n    (   28.98    12.81    -4.00     0.81)  ; 5\r\n    (   29.42    13.03    -4.00     0.81)  ; 6\r\n    (   30.23    13.76    -4.31     1.03)  ; 7\r\n    (   31.12    14.57    -5.44     1.03)  ; 8\r\n    (   31.85    14.93    -6.19     1.03)  ; 9\r\n    (   32.81    15.52    -6.38     1.18)  ; 10\r\n    (   33.77    15.81    -6.50     1.18)  ; 11\r\n    (   34.36    15.81    -7.06     1.18)  ; 12\r\n    (   35.61    15.81    -7.19     1.18)  ; 13\r\n    (   36.64    15.44    -7.94     1.25)  ; 14\r\n    (   37.60    15.30    -8.88     1.25)  ; 15\r\n    (   38.63    15.00    -8.94     1.03)  ; 16\r\n    (   39.59    15.22   -10.06     1.18)  ; 17\r\n    (   41.14    15.52   -10.38     1.33)  ; 18\r\n    (   42.32    15.88   -10.38     1.62)  ; 19\r\n    (   43.13    16.25   -10.44     2.28)  ; 20\r\n    (   44.16    16.54   -11.00     2.43)  ; 21\r\n    (\r\n      (   45.26    16.32   -11.50     0.88)  ; 1, R-2-1\r\n      (   46.37    16.17   -11.25     0.74)  ; 2\r\n      (   47.03    16.25   -11.38     0.74)  ; 3\r\n      (   48.14    16.61   -11.38     1.03)  ; 4\r\n      (   48.80    16.69   -11.56     0.81)  ; 5\r\n      (   49.90    16.83   -11.81     0.66)  ; 6\r\n      (   50.79    17.34   -11.94     0.81)  ; 7\r\n      (   51.67    17.56   -12.00     0.81)  ; 8\r\n      (   52.78    17.71   -12.25     0.88)  ; 9\r\n      (   53.81    17.56   -11.06     0.88)  ; 10\r\n      (   54.33    17.56   -11.06     0.88)  ; 11\r\n      (   54.84    17.56   -11.06     0.88)  ; 12\r\n      (   55.95    17.34   -10.69     0.74)  ; 13\r\n      (   58.23    17.20    -7.19     0.66)  ; 14\r\n      (   58.89    18.00   -14.06     0.96)  ; 15\r\n      (   59.41    18.59   -16.00     0.96)  ; 16\r\n      (   60.29    19.68   -16.94     0.96)  ; 17\r\n      (   60.88    20.56   -18.00     0.81)  ; 18\r\n      (   61.10    20.93   -20.00     0.81)  ; 19\r\n      (   62.50    21.22   -17.00     0.81)  ; 20\r\n      (   63.31    21.44   -18.44     1.25)  ; 21\r\n      (   63.98    21.36   -19.63     1.03)  ; 22\r\n      (   64.57    21.14   -19.69     1.03)  ; 23\r\n      (   65.08    20.93   -19.69     0.81)  ; 24\r\n      (   65.67    20.49   -19.94     0.81)  ; 25\r\n      (   66.11    20.12   -20.38     1.03)  ; 26\r\n      (   67.22    19.90   -19.56     0.81)  ; 27\r\n      (   68.54    19.83   -19.75     0.81)  ; 28\r\n      (   69.50    19.61   -19.81     0.81)  ; 29\r\n      (   70.83    19.76   -21.19     1.11)  ; 30\r\n      (   71.49    19.76   -23.00     1.11)  ; 31\r\n      (   72.38    19.90   -23.63     1.11)  ; 32\r\n      (   72.96    19.61   -24.75     1.11)  ; 33\r\n      (   73.11    19.68   -26.25     0.88)  ; 34\r\n      (   73.63    20.34   -27.44     0.88)  ; 35\r\n      (   74.59    20.49   -28.50     0.88)  ; 36\r\n      (   75.69    20.85   -29.25     1.03)  ; 37\r\n      (   76.50    21.00   -29.88     1.03)  ; 38\r\n      (   77.31    21.00   -30.38     1.18)  ; 39\r\n      (   77.97    21.44   -31.63     1.25)  ; 40\r\n      (   78.56    21.66   -32.69     1.25)  ; 41\r\n      (   79.08    22.02   -33.13     0.96)  ; 42\r\n      (   79.37    22.24   -33.13     0.66)  ; 43\r\n      (   79.45    22.90   -33.44     0.52)  ; 44\r\n      (   80.18    23.63   -33.81     0.66)  ; 45\r\n      (   80.99    24.00   -34.19     0.88)  ; 46\r\n      (   81.73    24.14   -35.19     0.88)  ; 47\r\n      (   82.47    24.51   -36.50     0.66)  ; 48\r\n      (   82.91    25.46   -36.69     0.66)  ; 49\r\n      (   83.35    26.04   -37.00     0.96)  ; 50\r\n      (   84.02    26.77   -37.94     1.18)  ; 51\r\n      (   84.60    27.07   -38.13     0.81)  ; 52\r\n      (   85.27    27.50   -38.50     0.59)  ; 53\r\n      (   85.86    27.87   -38.94     0.59)  ; 54\r\n      (   86.15    28.60   -39.81     0.59)  ; 55\r\n      (   86.89    29.33   -40.06     0.74)  ; 56\r\n      (   87.85    29.84   -40.06     0.74)  ; 57\r\n      (   88.44    30.21   -40.38     0.74)  ; 58\r\n      (   89.10    30.43   -40.94     0.74)  ; 59\r\n      (   89.69    30.65   -41.44     0.74)  ; 60\r\n      (   90.57    30.72   -39.81     0.74)  ; 61\r\n      (   92.10    30.25   -39.69     0.44)  ; 62\r\n      (   93.06    29.95   -39.69     0.44)  ; 63\r\n      (   93.94    29.95   -38.25     0.88)  ; 64\r\n      (   94.90    30.25   -38.25     1.18)  ; 65\r\n      (   95.93    30.61   -37.75     1.40)  ; 66\r\n      (   97.11    30.90   -37.50     1.11)  ; 67\r\n      (   97.85    31.42   -38.31     0.59)  ; 68\r\n      (   98.51    31.78   -40.31     0.37)  ; 69\r\n      (   99.84    32.29   -41.50     0.37)  ; 70\r\n      (  101.01    32.37   -41.50     0.37)  ; 71\r\n      (  101.46    32.29   -41.50     1.03)  ; 72\r\n      (  102.19    32.22   -41.69     1.03)  ; 73\r\n      (  102.71    32.07   -42.88     1.03)  ; 74\r\n      (  103.45    32.15   -43.63     0.74)  ; 75\r\n      (  104.48    32.15   -44.44     0.44)  ; 76\r\n      (  105.36    32.51   -45.56     0.29)  ; 77\r\n      (  106.54    32.66   -46.06     0.15)  ; 78\r\n      (  107.28    32.73   -46.63     0.15)  ; 79\r\n      (  108.09    32.37   -46.94     0.15)  ; 80\r\n      (  108.97    32.29   -46.94     0.15)  ; 81\r\n      (  110.00    32.07   -47.69     0.44)  ; 82\r\n      (  110.37    32.15   -48.63     1.11)  ; 83\r\n      (  110.81    32.07   -47.75     1.77)  ; 84\r\n      (  111.55    31.93   -48.25     2.43)  ; 85\r\n      (  112.43    31.56   -48.38     1.62)  ; 86\r\n      (  112.95    31.64   -49.56     0.74)  ; 87\r\n      (  113.39    31.71   -51.19     0.29)  ; 88\r\n      (  114.05    31.93   -53.44     0.29)  ; 89\r\n      (  115.31    31.85   -53.44     0.29)  ; 90\r\n      (  116.34    31.34   -54.38     0.22)  ; 91\r\n      (  117.74    31.05   -55.19     0.22)  ; 92\r\n      (  119.21    31.05   -56.75     0.22)  ; 93\r\n      (  120.54    31.27   -57.50     0.74)  ; 94\r\n      (  121.35    31.49   -58.06     1.33)  ; 95\r\n      (  122.01    31.64   -58.63     0.44)  ; 96\r\n      (  122.60    32.07   -59.00     0.44)  ; 97\r\n      (  123.12    32.37   -60.13     1.11)  ; 98\r\n      (  123.56    32.59   -61.06     1.40)  ; 99\r\n      (  123.71    32.59   -62.94     1.03)  ; 100\r\n      (  124.22    32.95   -63.25     0.66)  ; 101\r\n      (  124.96    33.02   -63.31     0.37)  ; 102\r\n      (  125.99    32.88   -64.06     0.37)  ; 103\r\n      (  126.58    33.32   -64.31     0.59)  ; 104\r\n      (  126.87    33.76   -64.38     0.59)  ; 105\r\n      (  127.61    34.12   -64.94     0.96)  ; 106\r\n      (  127.98    34.34   -65.88     1.33)  ; 107\r\n      (  128.13    34.56   -66.94     1.69)  ; 108\r\n      (  128.64    34.85   -68.13     1.84)  ; 109\r\n      (  129.23    35.44   -67.69     0.66)  ; 110\r\n      (  129.67    35.44   -70.31     0.44)  ; 111\r\n      (  130.63    35.51   -71.56     0.44)  ; 112\r\n      (  131.52    35.88   -71.69     0.44)  ; 113\r\n      (  132.18    36.09   -72.94     0.44)  ; 114\r\n      (  132.62    36.39   -72.94     0.88)  ; 115\r\n      (  133.28    36.31   -73.44     1.62)  ; 116\r\n      (  134.31    36.17   -74.06     1.62)  ; 117\r\n       Low\r\n    |\r\n      (   44.58    17.67   -11.19     0.88)  ; 1, R-2-2\r\n      (   45.10    18.55   -11.19     0.59)  ; 2\r\n      (   45.24    19.65   -11.44     0.59)  ; 3\r\n      (   45.47    20.52   -11.44     0.74)  ; 4\r\n      (   45.69    21.18   -11.44     0.59)  ; 5\r\n      (   46.35    21.33   -11.44     0.59)  ; 6\r\n      (   47.23    21.69   -11.81     0.81)  ; 7\r\n      (   47.75    21.69   -12.00     0.81)  ; 8\r\n      (   48.63    21.47   -12.81     0.81)  ; 9\r\n      (   49.30    21.18   -14.56     0.66)  ; 10\r\n      (   49.74    21.04   -15.81     0.66)  ; 11\r\n      (   50.25    21.47   -15.81     0.66)  ; 12\r\n      (   50.84    21.84   -16.25     0.66)  ; 13\r\n      (   51.29    22.50   -16.31     0.66)  ; 14\r\n      (   51.58    23.01   -16.81     0.96)  ; 15\r\n      (   51.87    23.74   -17.13     1.11)  ; 16\r\n      (   51.87    24.25   -18.63     0.88)  ; 17\r\n      (   52.61    25.06   -18.81     0.74)  ; 18\r\n      (   53.13    26.15   -20.00     0.59)  ; 19\r\n      (   53.94    26.52   -20.13     0.59)  ; 20\r\n      (   54.53    26.52   -20.69     0.74)  ; 21\r\n      (   55.48    26.45   -20.88     0.59)  ; 22\r\n      (   56.30    26.37   -21.31     0.59)  ; 23\r\n      (   57.03    26.45   -21.31     0.59)  ; 24\r\n      (   57.70    26.96   -21.69     0.88)  ; 25\r\n      (   58.21    27.54   -21.94     1.18)  ; 26\r\n      (   58.95    27.54   -22.75     1.18)  ; 27\r\n      (   59.61    27.76   -23.94     0.96)  ; 28\r\n      (   60.49    28.13   -24.13     0.96)  ; 29\r\n      (   61.97    28.86   -24.63     0.81)  ; 30\r\n      (   63.07    29.22   -25.75     0.81)  ; 31\r\n      (   64.10    29.30   -25.75     0.66)  ; 32\r\n      (   64.99    29.00   -26.31     0.66)  ; 33\r\n      (   66.02    28.64   -27.25     0.66)  ; 34\r\n      (   67.35    29.00   -28.19     0.59)  ; 35\r\n      (   68.23    29.00   -28.69     0.74)  ; 36\r\n      (   69.11    29.15   -28.81     0.74)  ; 37\r\n      (   69.85    29.30   -29.00     0.74)  ; 38\r\n      (   70.59    29.37   -29.13     0.74)  ; 39\r\n      (   71.25    29.73   -29.88     0.59)  ; 40\r\n      (   71.99    29.95   -29.88     0.59)  ; 41\r\n      (   73.46    31.20   -30.13     0.66)  ; 42\r\n      (   73.90    31.34   -30.75     1.33)  ; 43\r\n      (   74.49    31.78   -30.94     1.92)  ; 44\r\n      (   75.38    32.07   -31.38     2.06)  ; 45\r\n      (   76.26    32.29   -31.69     1.77)  ; 46\r\n      (   77.00    32.37   -31.69     0.81)  ; 47\r\n      (   77.81    32.37   -32.06     0.59)  ; 48\r\n      (   78.40    32.44   -32.63     0.59)  ; 49\r\n      (   79.21    32.37   -33.06     0.59)  ; 50\r\n      (   79.87    32.22   -33.31     0.59)  ; 51\r\n      (   80.53    32.00   -34.38     0.59)  ; 52\r\n      (   82.38    31.64   -33.00     0.52)  ; 53\r\n      (   83.19    31.42   -34.44     0.29)  ; 54\r\n      (   84.36    30.90   -34.75     0.29)  ; 55\r\n      (   85.62    30.17   -35.00     0.29)  ; 56\r\n      (   86.57    29.52   -35.38     0.52)  ; 57\r\n      (   86.87    29.22   -35.38     0.88)  ; 58\r\n      (   87.68    28.78   -35.56     1.47)  ; 59\r\n      (   88.27    28.20   -35.69     1.84)  ; 60\r\n      (   88.93    27.47   -35.75     1.84)  ; 61\r\n      (   89.52    27.03   -35.75     1.03)  ; 62\r\n      (   89.89    26.59   -35.94     0.52)  ; 63\r\n      (   90.48    26.37   -36.13     0.37)  ; 64\r\n      (   91.22    25.64   -36.31     0.29)  ; 65\r\n      (   91.73    24.98   -36.56     0.29)  ; 66\r\n      (   92.10    24.54   -36.56     1.03)  ; 67\r\n      (   92.62    23.81   -36.38     1.69)  ; 68\r\n      (   93.35    23.01   -36.38     1.40)  ; 69\r\n      (   93.87    22.35   -36.38     1.18)  ; 70\r\n      (   94.46    21.77   -36.38     0.88)  ; 71\r\n      (   95.12    21.26   -36.38     0.52)  ; 72\r\n      (   96.30    20.45   -36.31     0.29)  ; 73\r\n      (   98.14    19.50   -36.38     0.29)  ; 74\r\n      (   99.17    19.14   -36.56     0.29)  ; 75\r\n      (   99.47    19.06   -35.94     0.59)  ; 76\r\n      (   99.98    18.92   -35.88     0.74)  ; 77\r\n      (  100.87    18.70   -35.56     0.74)  ; 78\r\n      (  101.60    18.77   -35.44     0.44)  ; 79\r\n      (  102.86    18.70   -35.38     0.29)  ; 80\r\n      (  104.55    18.48   -35.38     0.29)  ; 81\r\n      (  106.02    18.55   -35.06     0.29)  ; 82\r\n      (  106.83    18.92   -35.31     0.88)  ; 83\r\n      (  107.79    19.35   -35.75     1.11)  ; 84\r\n      (  108.31    19.87   -36.06     1.18)  ; 85\r\n      (  109.41    20.38   -36.19     0.96)  ; 86\r\n      (  110.59    20.74   -36.50     0.44)  ; 87\r\n      (  111.11    21.18   -36.50     0.29)  ; 88\r\n      (  112.14    21.69   -36.50     1.03)  ; 89\r\n      (  112.66    22.21   -36.13     0.81)  ; 90\r\n      (  113.02    22.64   -35.50     0.59)  ; 91\r\n      (  113.69    23.38   -35.94     0.37)  ; 92\r\n      (  115.09    24.25   -37.06     0.22)  ; 93\r\n      (  115.97    24.98   -37.19     0.22)  ; 94\r\n      (  116.63    25.93   -37.38     0.52)  ; 95\r\n      (  117.00    26.66   -37.38     0.66)  ; 96\r\n      (  117.44    27.18   -37.50     0.37)  ; 97\r\n      (  118.11    28.57   -37.63     0.29)  ; 98\r\n      (  118.70    29.37   -37.88     0.29)  ; 99\r\n      (  119.21    29.95   -37.31     0.81)  ; 100\r\n      (  119.58    30.69   -37.13     1.77)  ; 101\r\n      (  120.46    31.05   -36.69     2.06)  ; 102\r\n      (  121.20    30.98   -36.44     1.33)  ; 103\r\n      (  121.79    30.90   -36.38     0.66)  ; 104\r\n      (  122.23    30.83   -36.19     0.37)  ; 105\r\n      (  123.26    30.39   -36.19     0.29)  ; 106\r\n      (  124.22    29.95   -35.88     0.29)  ; 107\r\n      (  125.18    29.44   -35.81     0.29)  ; 108\r\n      (  125.47    29.30   -35.81     0.29)  ; 109\r\n      (  125.99    28.71   -35.81     1.18)  ; 110\r\n      (  126.51    28.27   -35.81     1.18)  ; 111\r\n      (  126.80    27.98   -35.81     0.44)  ; 112\r\n      (  127.09    27.47   -35.81     0.22)  ; 113\r\n      (  127.46    27.18   -35.69     0.29)  ; 114\r\n       Low\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color MoneyGreen)\r\n  (Dendrite)\r\n  (   -0.20     8.59   -12.13     3.90)  ; Root\r\n  (   -0.20    10.27   -12.44     3.54)  ; 1, R\r\n  (    0.16    11.88   -12.94     3.83)  ; 2\r\n  (    0.53    13.27   -13.19     3.98)  ; 3\r\n  (    0.83    14.88   -13.06     4.57)  ; 4\r\n  (    0.75    15.90   -13.06     4.79)  ; 5\r\n  (    0.68    17.00   -13.06     4.35)  ; 6\r\n  (    0.90    18.53   -13.06     4.57)  ; 7\r\n  (    0.97    20.21   -13.44     5.23)  ; 8\r\n  (\r\n    (    1.20    21.31   -13.38     5.16)  ; 1, R-1\r\n    (\r\n      (    2.15    21.82   -14.06     3.76)  ; 1, R-1-1\r\n      (    2.90    22.70   -14.06     3.76)  ; 2\r\n      (\r\n        (    2.89    22.77   -14.06     3.02)  ; 1, R-1-1-1\r\n        (    3.63    24.02   -14.06     3.09)  ; 2\r\n        (    4.66    25.55   -14.06     3.09)  ; 3\r\n        (    5.10    26.43   -14.06     2.87)  ; 4\r\n        (    5.98    27.74   -14.88     2.73)  ; 5\r\n        (    6.50    28.69   -15.25     3.39)  ; 6\r\n        (    7.24    29.72   -15.44     4.13)  ; 7\r\n        (    7.33    29.94   -15.44     4.13)  ; 8\r\n        (\r\n          (    7.74    30.99   -15.88     4.13)  ; 1, R-1-1-1-1\r\n          (    7.88    31.79   -16.50     3.68)  ; 2\r\n          (\r\n            (    8.92    32.16   -16.31     1.84)  ; 1, R-1-1-1-1-1\r\n            (    9.43    32.67   -16.50     1.18)  ; 2\r\n            (   10.17    33.18   -15.94     0.96)  ; 3\r\n            (   10.61    33.40   -15.94     1.11)  ; 4\r\n            (\r\n              (   11.49    33.55   -15.94     1.33)  ; 1, R-1-1-1-1-1-1\r\n              (   11.94    33.55   -16.25     1.33)  ; 2\r\n              (   12.38    33.40   -16.44     1.11)  ; 3\r\n              (   13.41    33.26   -15.88     0.96)  ; 4\r\n              (   14.29    33.62   -15.19     1.11)  ; 5\r\n              (   14.88    34.06   -14.25     0.96)  ; 6\r\n              (   15.47    34.79   -13.31     0.96)  ; 7\r\n              (   16.36    34.94   -12.38     0.96)  ; 8\r\n              (   17.24    35.45   -11.63     0.66)  ; 9\r\n              (   17.90    35.59   -11.06     0.66)  ; 10\r\n              (   18.27    35.89   -10.69     0.66)  ; 11\r\n              (   18.71    35.89    -9.81     0.66)  ; 12\r\n              (   19.23    35.89    -9.81     0.88)  ; 13\r\n              (   19.97    35.89    -9.75     0.88)  ; 14\r\n              (   21.15    35.89    -9.50     0.88)  ; 15\r\n              (   22.55    36.11    -9.38     0.88)  ; 16\r\n              (   23.36    36.40    -9.50     1.03)  ; 17\r\n              (   24.39    36.84   -10.06     0.96)  ; 18\r\n              (   25.42    37.20   -10.38     0.96)  ; 19\r\n              (   26.45    37.64    -9.88     1.03)  ; 20\r\n              (   27.33    38.37    -9.38     1.18)  ; 21\r\n              (   27.63    38.66    -9.25     1.18)  ; 22\r\n              (\r\n                (   28.51    39.18    -9.13     0.52)  ; 1, R-1-1-1-1-1-1-1\r\n                (   28.73    39.83    -9.06     0.52)  ; 2\r\n                (   29.40    40.64    -9.00     0.74)  ; 3\r\n                (   29.84    41.44    -9.00     0.96)  ; 4\r\n                (   30.06    42.10    -8.94     0.74)  ; 5\r\n                (   30.58    42.69    -9.00     0.59)  ; 6\r\n                (   31.39    42.98    -9.00     0.74)  ; 7\r\n                (   32.12    43.27    -9.00     0.96)  ; 8\r\n                (   33.38    43.27    -9.00     0.96)  ; 9\r\n                (   34.26    43.34    -9.50     0.88)  ; 10\r\n                (   35.14    43.34   -10.44     0.88)  ; 11\r\n                (   35.88    43.64   -10.44     1.03)  ; 12\r\n                (   36.99    43.78   -10.75     0.81)  ; 13\r\n                (   37.72    43.71   -11.69     0.81)  ; 14\r\n                (   38.53    43.71   -12.19     0.81)  ; 15\r\n                (   39.49    43.64   -12.19     0.74)  ; 16\r\n                (   40.52    44.15   -12.31     0.74)  ; 17\r\n                (   41.33    45.02   -12.69     0.96)  ; 18\r\n                (   42.00    46.12   -12.19     0.74)  ; 19\r\n                (   42.73    47.29   -11.69     0.81)  ; 20\r\n                (   43.54    48.09   -12.38     0.96)  ; 21\r\n                (   44.50    48.46   -12.94     1.18)  ; 22\r\n                (   45.38    49.48   -13.44     1.40)  ; 23\r\n                (   46.42    50.43   -13.81     1.84)  ; 24\r\n                (   47.08    51.09   -13.81     1.84)  ; 25\r\n                (   48.11    51.46   -14.00     1.33)  ; 26\r\n                (   49.14    51.90   -14.00     1.11)  ; 27\r\n                (   50.17    51.90   -14.63     0.81)  ; 28\r\n                (   51.28    51.90   -14.94     0.66)  ; 29\r\n                (   52.38    51.90   -15.38     0.96)  ; 30\r\n                (   53.41    52.63   -15.75     0.81)  ; 31\r\n                (   54.37    54.09   -16.06     0.74)  ; 32\r\n                (   54.97    55.18   -14.81     0.81)  ; 33\r\n                (   55.26    56.35   -14.81     0.81)  ; 34\r\n                (   55.19    57.45   -14.63     0.81)  ; 35\r\n                (   56.00    58.33   -14.25     0.81)  ; 36\r\n                (   56.74    59.49   -14.06     0.66)  ; 37\r\n                (   57.33    60.15   -14.00     0.59)  ; 38\r\n                (   58.06    60.15   -14.00     0.88)  ; 39\r\n                (   58.73    60.44   -13.31     0.74)  ; 40\r\n                (   59.32    61.25   -13.75     0.59)  ; 41\r\n                (   59.61    62.05   -15.63     1.18)  ; 42\r\n                (   59.90    63.22   -15.38     1.33)  ; 43\r\n                (   59.90    63.88   -15.31     1.03)  ; 44\r\n                (   60.49    65.20   -15.31     0.81)  ; 45\r\n                (   61.01    66.22   -15.31     0.74)  ; 46\r\n                (   61.67    66.95   -15.31     0.59)  ; 47\r\n                (   62.56    67.68   -15.38     0.81)  ; 48\r\n                (   63.22    68.92   -15.63     0.88)  ; 49\r\n                (   63.22    69.80   -15.63     1.03)  ; 50\r\n                (   62.85    70.68   -15.31     0.88)  ; 51\r\n                (   62.78    71.04   -13.25     0.74)  ; 52\r\n                (   62.93    71.26   -13.13     0.59)  ; 53\r\n                (   63.37    70.82   -12.88     0.59)  ; 54\r\n                (   63.74    70.75   -12.56     0.59)  ; 55\r\n                (   64.40    70.90   -12.25     0.59)  ; 56\r\n                (   64.69    71.26   -11.81     0.59)  ; 57\r\n                (   65.06    71.99   -11.19     0.59)  ; 58\r\n                (   65.36    73.24   -11.19     0.88)  ; 59\r\n                (   65.87    74.41   -10.88     1.03)  ; 60\r\n                (   66.54    74.92   -10.75     1.40)  ; 61\r\n                (   66.83    76.01    -9.81     1.11)  ; 62\r\n                (   67.79    76.60    -9.56     0.88)  ; 63\r\n                (   68.23    77.55    -8.63     1.03)  ; 64\r\n                (   68.45    78.13    -7.94     0.74)  ; 65\r\n                (   68.52    79.01    -7.75     0.52)  ; 66\r\n                (   68.60    79.45    -7.25     0.52)  ; 67\r\n                (   68.97    79.74    -7.13     0.52)  ; 68\r\n                (   69.34    80.11    -7.13     0.52)  ; 69\r\n                (   69.56    80.91    -7.38     1.18)  ; 70\r\n                (   69.92    81.57    -7.56     1.55)  ; 71\r\n                (   70.07    81.94    -7.56     1.18)  ; 72\r\n                (   70.22    82.37    -7.56     0.81)  ; 73\r\n                (   70.44    83.25    -7.56     0.52)  ; 74\r\n                (   70.72    84.55    -7.06     0.59)  ; 75\r\n                (   71.23    85.06    -8.50     0.81)  ; 76\r\n                (   71.75    85.13    -8.63     0.81)  ; 77\r\n                (   72.71    85.35    -8.88     0.59)  ; 78\r\n                (   73.22    85.28    -9.06     0.44)  ; 79\r\n                (   74.11    84.77    -9.44     0.44)  ; 80\r\n                (   75.21    84.40    -8.56     0.59)  ; 81\r\n                (   76.02    84.33    -8.94     0.52)  ; 82\r\n                (   77.13    84.91    -9.06     0.59)  ; 83\r\n                (   78.23    85.21    -9.06     0.66)  ; 84\r\n                (   78.89    85.50    -9.81     0.88)  ; 85\r\n                (   79.56    85.72    -8.63     1.18)  ; 86\r\n                (   80.29    85.94    -8.44     1.18)  ; 87\r\n                (   80.74    86.16    -8.31     0.81)  ; 88\r\n                (   81.33    86.52    -8.13     0.59)  ; 89\r\n                (   81.99    86.59    -7.13     0.44)  ; 90\r\n                (   82.58    86.67    -6.38     0.37)  ; 91\r\n                 Low\r\n              |\r\n                (   27.31    40.12    -9.00     0.52)  ; 1, R-1-1-1-1-1-1-2\r\n                (   27.68    40.85    -8.06     0.52)  ; 2\r\n                (   27.68    41.66    -7.19     0.81)  ; 3\r\n                (   27.68    42.39    -6.44     1.03)  ; 4\r\n                (   27.61    43.41    -3.75     1.03)  ; 5\r\n                (   27.61    43.63    -3.25     0.88)  ; 6\r\n                (   27.97    44.51    -3.00     0.66)  ; 7\r\n                (   28.71    44.94    -2.38     0.59)  ; 8\r\n                (   29.01    45.97    -1.88     0.74)  ; 9\r\n                (   29.30    47.06    -1.88     0.96)  ; 10\r\n                (   29.74    48.02    -1.88     1.25)  ; 11\r\n                (   30.11    48.89    -1.56     1.47)  ; 12\r\n                (   30.18    49.55    -2.25     1.18)  ; 13\r\n                (   30.70    50.65    -2.25     0.88)  ; 14\r\n                (   31.07    51.52    -2.63     0.74)  ; 15\r\n                (   31.51    52.40    -2.44     0.81)  ; 16\r\n                (   31.66    53.13    -2.44     0.81)  ; 17\r\n                (   31.88    54.01    -2.44     0.66)  ; 18\r\n                (   31.66    54.81    -2.50     0.66)  ; 19\r\n                (   31.44    55.69    -2.56     0.59)  ; 20\r\n                (   31.29    56.49    -1.56     0.52)  ; 21\r\n                (   31.88    57.15    -1.19     0.66)  ; 22\r\n                (   32.39    57.74    -1.44     0.88)  ; 23\r\n                (   32.98    58.69    -1.63     1.11)  ; 24\r\n                (   33.43    59.56    -2.00     0.88)  ; 25\r\n                (   33.35    60.44    -2.00     0.59)  ; 26\r\n                (   33.35    61.10    -2.19     0.52)  ; 27\r\n                (   33.43    61.54    -2.25     0.52)  ; 28\r\n                (   33.06    62.05    -2.00     0.74)  ; 29\r\n                (   32.54    63.00    -2.38     0.81)  ; 30\r\n                (   32.62    64.02    -2.44     0.59)  ; 31\r\n                (   32.10    65.34    -2.56     0.66)  ; 32\r\n                (   31.73    66.14    -2.56     0.66)  ; 33\r\n                (   31.29    67.31    -2.63     0.59)  ; 34\r\n                (   31.05    68.31    -2.06     0.52)  ; 35\r\n                (   31.34    69.33    -2.06     0.66)  ; 36\r\n                (   31.56    70.50    -2.19     0.81)  ; 37\r\n                (   31.86    71.31    -2.50     0.74)  ; 38\r\n                (   32.08    72.48    -2.50     0.59)  ; 39\r\n                (   32.37    73.06    -2.50     0.59)  ; 40\r\n                (   32.45    74.16    -2.63     0.52)  ; 41\r\n                (   32.60    74.89    -2.19     0.52)  ; 42\r\n                (   32.74    75.25    -1.13     0.52)  ; 43\r\n                (   33.18    75.98    -0.25     0.59)  ; 44\r\n                (   33.85    76.50     0.00     0.59)  ; 45\r\n                (   33.99    77.30     0.13     0.52)  ; 46\r\n                (   33.92    77.88     0.88     0.52)  ; 47\r\n                (   33.77    78.83     1.06     0.74)  ; 48\r\n                (   33.63    80.00     0.94     0.96)  ; 49\r\n                (   33.85    81.32     0.88     0.59)  ; 50\r\n                (   34.07    82.34     0.63     0.66)  ; 51\r\n                (   34.51    83.59    -0.25     0.81)  ; 52\r\n                (   34.36    85.27     1.06     1.18)  ; 53\r\n                (   33.99    86.58     1.38     0.88)  ; 54\r\n                (   34.14    88.41     1.56     0.74)  ; 55\r\n                (   34.14    89.29     1.38     0.59)  ; 56\r\n                (   34.95    90.38     0.75     0.52)  ; 57\r\n                (   35.54    91.33     1.13     0.66)  ; 58\r\n                (   36.20    92.36     1.63     0.52)  ; 59\r\n                (   36.43    93.53     3.00     0.74)  ; 60\r\n                (   36.28    94.33     4.19     0.74)  ; 61\r\n                (   36.20    95.21     5.25     0.74)  ; 62\r\n                (   35.32    95.57     6.81     0.66)  ; 63\r\n                (   34.81    96.74     7.13     0.66)  ; 64\r\n                (   34.26    98.02     8.94     0.66)  ; 65\r\n                (   34.18    99.26     9.00     0.88)  ; 66\r\n                (   34.33    99.77     8.69     1.03)  ; 67\r\n                (   34.70   100.43     8.50     0.74)  ; 68\r\n                (   34.92   101.23     8.06     0.52)  ; 69\r\n                (   34.77   102.18     7.69     0.52)  ; 70\r\n                (   34.70   103.13     8.50     1.03)  ; 71\r\n                (   34.77   103.57     8.69     1.77)  ; 72\r\n                (   34.77   104.38     8.69     2.06)  ; 73\r\n                (   34.85   105.03     8.69     1.03)  ; 74\r\n                (   34.85   106.42     8.69     0.81)  ; 75\r\n                (   34.70   107.30     8.69     1.33)  ; 76\r\n                (   34.77   107.81     8.69     1.92)  ; 77\r\n                (   34.63   108.40     8.69     1.18)  ; 78\r\n                (   34.41   108.98     8.75     0.88)  ; 79\r\n                (   34.11   110.30     8.75     0.66)  ; 80\r\n                (   33.89   111.10     8.75     0.37)  ; 81\r\n                (   34.41   112.05     8.75     0.59)  ; 82\r\n                (   34.70   112.78     8.75     0.59)  ; 83\r\n                (   34.63   113.51     9.31     0.52)  ; 84\r\n                (   34.33   113.88     9.81     0.74)  ; 85\r\n                (   34.04   114.90    10.13     0.96)  ; 86\r\n                (   33.89   115.63    10.19     0.66)  ; 87\r\n                (   33.45   116.44    10.25     0.66)  ; 88\r\n                (   32.79   116.88    10.63     0.66)  ; 89\r\n                (   31.83   117.46    10.69     0.52)  ; 90\r\n                (   31.53   117.97    12.00     0.66)  ; 91\r\n                (   31.83   119.21    12.44     0.59)  ; 92\r\n                (   31.97   120.31    12.88     0.59)  ; 93\r\n                (   32.27   121.04    13.13     0.59)  ; 94\r\n                (   32.79   121.19    13.25     0.81)  ; 95\r\n                (   33.52   121.48    13.44     1.03)  ; 96\r\n                (   34.26   122.14    14.38     1.03)  ; 97\r\n                (   34.63   122.65    15.06     0.81)  ; 98\r\n                (   35.22   123.24    15.63     0.81)  ; 99\r\n                (   35.73   123.89    15.63     0.81)  ; 100\r\n                (   36.17   124.48    16.25     0.66)  ; 101\r\n                (   36.69   124.77    16.25     0.66)  ; 102\r\n                (   37.35   125.65    16.75     0.81)  ; 103\r\n                (   37.72   126.89    17.00     1.11)  ; 104\r\n                (   37.21   127.91    17.56     0.66)  ; 105\r\n                (   36.98   128.57    17.63     0.66)  ; 106\r\n                (   36.69   129.37    18.19     0.59)  ; 107\r\n                (   36.25   129.81    18.44     0.59)  ; 108\r\n                (   35.73   130.98    19.00     0.52)  ; 109\r\n                (   35.73   131.71    19.94     0.52)  ; 110\r\n                (   36.62   132.88    19.88     0.44)  ; 111\r\n                (   37.21   134.05    19.63     0.44)  ; 112\r\n                 Low\r\n              )  ;  End of split\r\n            |\r\n              (   11.78    34.15   -18.06     0.66)  ; 1, R-1-1-1-1-1-2\r\n              (   12.45    34.74   -18.38     0.59)  ; 2\r\n              (   13.18    35.10   -18.38     0.59)  ; 3\r\n              (   13.92    35.69   -18.38     0.81)  ; 4\r\n              (   14.51    36.56   -18.81     0.81)  ; 5\r\n              (   15.47    37.15   -18.13     0.66)  ; 6\r\n              (   16.57    37.22   -17.13     0.59)  ; 7\r\n              (   17.46    37.59   -16.31     0.74)  ; 8\r\n              (   17.97    38.39   -16.25     0.74)  ; 9\r\n              (   18.64    38.68   -16.19     0.96)  ; 10\r\n              (   19.45    38.98   -16.19     1.18)  ; 11\r\n              (   20.55    39.56   -17.31     1.03)  ; 12\r\n              (   21.66    40.00   -18.25     0.96)  ; 13\r\n              (   22.61    40.51   -18.69     1.18)  ; 14\r\n              (   23.35    40.80   -19.44     0.88)  ; 15\r\n              (   24.09    41.24   -19.44     0.66)  ; 16\r\n              (   25.05    41.75   -19.75     0.66)  ; 17\r\n              (   25.56    42.26   -19.38     0.96)  ; 18\r\n              (   26.15    43.00   -18.75     1.11)  ; 19\r\n              (   26.67    43.51   -18.44     1.33)  ; 20\r\n              (   27.26    44.31   -17.94     1.33)  ; 21\r\n              (   27.70    45.33   -17.63     1.11)  ; 22\r\n              (   28.29    46.65   -17.63     0.88)  ; 23\r\n              (   28.73    47.67   -17.63     0.88)  ; 24\r\n              (   29.02    48.77   -17.13     1.40)  ; 25\r\n              (   29.25    49.43   -16.56     1.69)  ; 26\r\n              (\r\n                (   29.02    50.38   -17.13     1.11)  ; 1, R-1-1-1-1-1-2-1\r\n                (   28.95    50.67   -16.38     0.74)  ; 2\r\n                (   28.58    51.40   -16.00     0.59)  ; 3\r\n                (   28.51    52.13   -16.00     0.44)  ; 4\r\n                (   28.88    52.50   -16.00     0.44)  ; 5\r\n                (   29.61    52.64   -16.00     0.44)  ; 6\r\n                (   30.35    52.50   -16.00     0.59)  ; 7\r\n                (   30.94    52.64   -15.69     0.59)  ; 8\r\n                (   31.38    53.01   -15.50     0.59)  ; 9\r\n                (   31.68    54.18   -15.19     0.96)  ; 10\r\n                (   32.12    55.42   -15.06     0.96)  ; 11\r\n                (   32.34    56.15   -15.06     0.74)  ; 12\r\n                (   32.63    57.10   -15.00     0.74)  ; 13\r\n                (   32.71    57.98   -14.81     1.03)  ; 14\r\n                (   33.30    59.00   -15.06     0.74)  ; 15\r\n                (   34.03    59.81   -15.06     0.74)  ; 16\r\n                (   34.62    60.32   -15.06     0.52)  ; 17\r\n                (   35.36    60.90   -13.94     0.66)  ; 18\r\n                (   36.17    62.07   -12.88     0.74)  ; 19\r\n                (   36.91    63.24   -12.44     0.88)  ; 20\r\n                (   37.09    64.73   -12.31     0.59)  ; 21\r\n                (   37.53    65.46   -11.88     0.81)  ; 22\r\n                (   38.34    66.34   -11.19     0.96)  ; 23\r\n                (   39.08    66.92   -10.88     0.81)  ; 24\r\n                (   39.67    67.80   -10.06     0.74)  ; 25\r\n                (   40.04    68.75   -10.00     0.74)  ; 26\r\n                (   40.63    69.48    -9.94     0.59)  ; 27\r\n                (   41.36    69.99    -9.81     0.59)  ; 28\r\n                (   42.03    71.01    -9.25     0.66)  ; 29\r\n                (   42.47    72.26    -8.81     0.81)  ; 30\r\n                (   42.54    73.13    -8.50     0.88)  ; 31\r\n                (   42.54    74.23    -8.06     0.66)  ; 32\r\n                (   41.73    75.99    -7.75     0.44)  ; 33\r\n                (   41.07    76.50    -7.44     0.44)  ; 34\r\n                (   40.77    77.59    -7.81     0.66)  ; 35\r\n                (   40.70    78.47    -9.19     0.59)  ; 36\r\n                (   40.92    79.20   -11.13     1.25)  ; 37\r\n                (   41.36    80.15   -12.13     1.25)  ; 38\r\n                (   41.58    80.81   -12.38     0.88)  ; 39\r\n                (   41.73    81.39   -12.69     0.59)  ; 40\r\n                (   42.25    82.13   -12.94     0.52)  ; 41\r\n                (   43.13    82.13   -13.44     0.74)  ; 42\r\n                (   44.09    82.27   -13.63     0.74)  ; 43\r\n                (   45.12    82.49   -13.69     0.74)  ; 44\r\n                (   46.08    83.30   -14.50     0.96)  ; 45\r\n                (   45.86    84.83   -15.19     0.81)  ; 46\r\n                (   45.71    85.93   -15.69     0.66)  ; 47\r\n                (   45.27    86.95   -15.75     0.88)  ; 48\r\n                (   44.97    87.90   -15.88     0.88)  ; 49\r\n                (   44.97    88.63   -16.06     0.74)  ; 50\r\n                (   45.34    89.29   -17.06     1.03)  ; 51\r\n                (   45.86    89.73   -17.31     1.33)  ; 52\r\n                (   46.59    90.46   -17.81     1.11)  ; 53\r\n                (   47.33    90.90   -17.75     0.88)  ; 54\r\n                (   48.21    91.34   -18.19     0.88)  ; 55\r\n                (   49.10    91.99   -18.56     0.88)  ; 56\r\n                (   49.98    92.58   -18.63     0.74)  ; 57\r\n                (   50.52    93.10   -18.56     0.66)  ; 58\r\n                (   50.82    94.27   -18.88     0.88)  ; 59\r\n                (   51.18    95.44   -19.00     0.74)  ; 60\r\n                (   51.63    95.58   -19.63     0.59)  ; 61\r\n                (   52.44    95.95   -19.94     0.81)  ; 62\r\n                (   53.39    96.31   -20.44     1.03)  ; 63\r\n                (   54.13    96.75   -21.19     0.96)  ; 64\r\n                (   55.24    97.41   -21.31     0.74)  ; 65\r\n                (   56.27    98.36   -21.75     0.74)  ; 66\r\n                (   57.23    99.31   -21.88     0.59)  ; 67\r\n                (   58.11   100.33   -21.88     0.59)  ; 68\r\n                (   58.63   101.50   -22.31     1.03)  ; 69\r\n                (   59.07   102.38   -22.75     1.03)  ; 70\r\n                (   59.51   103.11   -21.50     0.88)  ; 71\r\n                (   60.10   103.40   -21.88     0.59)  ; 72\r\n                (   60.61   103.48   -22.31     0.59)  ; 73\r\n                (   61.57   103.48   -22.31     0.81)  ; 74\r\n                (   62.82   103.84   -22.31     0.96)  ; 75\r\n                (   63.27   103.84   -22.31     0.66)  ; 76\r\n                (   63.93   103.92   -22.31     0.52)  ; 77\r\n                (   64.89   103.99   -22.50     0.66)  ; 78\r\n                (   65.48   104.28   -22.69     0.44)  ; 79\r\n                (   65.70   104.43   -23.25     0.44)  ; 80\r\n                (   66.36   104.87   -24.31     0.74)  ; 81\r\n                (   66.88   105.09   -25.31     1.03)  ; 82\r\n                 Low\r\n              |\r\n                (   29.96    50.47   -17.81     0.88)  ; 1, R-1-1-1-1-1-2-2\r\n                (   30.55    50.54   -18.63     0.74)  ; 2\r\n                (   31.36    50.62   -19.19     0.74)  ; 3\r\n                (   32.09    50.91   -19.19     0.74)  ; 4\r\n                (   32.83    51.27   -19.38     0.74)  ; 5\r\n                (   33.72    51.86   -19.44     0.88)  ; 6\r\n                (   34.16    52.37   -19.19     0.88)  ; 7\r\n                (   34.53    52.74   -19.38     0.66)  ; 8\r\n                (   34.89    53.03   -19.63     0.59)  ; 9\r\n                (   35.41    53.69   -19.94     0.52)  ; 10\r\n                (   35.93    53.54   -21.19     0.66)  ; 11\r\n                (   36.29    53.54   -21.75     0.88)  ; 12\r\n                (   37.03    53.32   -22.25     0.88)  ; 13\r\n                (   37.33    53.32   -22.88     0.88)  ; 14\r\n                (   38.06    53.25   -23.56     0.88)  ; 15\r\n                (   38.73    53.39   -23.81     0.88)  ; 16\r\n                (   39.46    53.69   -24.31     0.88)  ; 17\r\n                (   40.20    53.98   -24.56     0.88)  ; 18\r\n                (   40.79    54.49   -24.69     0.88)  ; 19\r\n                (   41.60    55.22   -25.00     0.74)  ; 20\r\n                (   42.56    55.81   -25.25     0.81)  ; 21\r\n                (   43.37    55.88   -25.44     0.88)  ; 22\r\n                (   44.25    56.32   -26.06     0.88)  ; 23\r\n                (   45.13    56.90   -26.25     0.81)  ; 24\r\n                (   46.09    57.41   -26.56     0.81)  ; 25\r\n                (   47.64    57.71   -26.56     0.66)  ; 26\r\n                (   48.52    57.56   -26.81     0.81)  ; 27\r\n                (   49.19    57.71   -27.00     1.11)  ; 28\r\n                (   49.85    58.07   -27.81     1.33)  ; 29\r\n                (   50.73    58.51   -28.38     1.33)  ; 30\r\n                (   51.25    58.88   -29.13     1.03)  ; 31\r\n                (   51.91    59.32   -29.13     0.66)  ; 32\r\n                (   52.50    59.97   -29.19     0.59)  ; 33\r\n                (   53.46    60.56   -29.50     0.59)  ; 34\r\n                (   54.64    61.14   -29.81     0.59)  ; 35\r\n                (   55.96    61.73   -28.69     0.59)  ; 36\r\n                (   57.29    62.46   -28.69     0.59)  ; 37\r\n                (   58.25    62.90   -28.19     0.59)  ; 38\r\n                (   59.28    63.41   -28.19     0.59)  ; 39\r\n                (   60.46    64.07   -28.88     0.59)  ; 40\r\n                (   61.42    64.58   -28.88     0.81)  ; 41\r\n                (   62.08    65.24   -29.00     1.03)  ; 42\r\n                (   63.18    65.97   -29.31     1.25)  ; 43\r\n                (   64.07    66.62   -29.63     0.96)  ; 44\r\n                (   64.88    67.14   -30.13     0.59)  ; 45\r\n                (   65.47    67.72   -30.44     0.52)  ; 46\r\n                (   66.57    68.53   -31.31     0.37)  ; 47\r\n                (   67.16    69.04   -31.69     0.37)  ; 48\r\n                (   67.97    69.84   -32.06     0.59)  ; 49\r\n                (   68.86    70.43   -32.06     0.88)  ; 50\r\n                (   69.45    70.94   -32.75     0.88)  ; 51\r\n                (   70.04    71.45   -33.00     0.88)  ; 52\r\n                (   70.63    71.96   -33.00     0.59)  ; 53\r\n                (   71.22    72.11   -33.63     0.37)  ; 54\r\n                (   72.32    72.55   -34.13     0.29)  ; 55\r\n                (   72.98    73.20   -34.94     0.88)  ; 56\r\n                (   73.28    73.42   -34.06     1.92)  ; 57\r\n                (   73.94    73.72   -34.00     2.58)  ; 58\r\n                (   74.97    74.01   -34.00     1.55)  ; 59\r\n                (   75.49    74.15   -34.00     0.81)  ; 60\r\n                (   76.22    74.45   -34.00     0.52)  ; 61\r\n                (   77.37    75.30   -34.69     0.29)  ; 62\r\n                (   78.63    76.04   -34.69     0.29)  ; 63\r\n                (   79.29    76.69   -34.69     0.29)  ; 64\r\n                (   80.10    77.28   -34.69     0.81)  ; 65\r\n                (   80.76    77.79   -34.69     1.99)  ; 66\r\n                (   81.57    78.08   -34.69     2.28)  ; 67\r\n                (   82.46    78.45   -34.69     1.40)  ; 68\r\n                (   83.49    78.59   -35.38     1.03)  ; 69\r\n                (   84.23    78.96   -35.69     0.66)  ; 70\r\n                (   84.81    78.81   -36.19     0.37)  ; 71\r\n                (   85.18    78.52   -37.69     0.66)  ; 72\r\n                (   85.48    78.30   -37.88     0.96)  ; 73\r\n                (   86.07    78.16   -37.94     0.96)  ; 74\r\n                (   86.44    78.16   -39.56     0.59)  ; 75\r\n                 Low\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          |\r\n            (    7.58    32.20   -15.94     2.73)  ; 1, R-1-1-1-1-2\r\n            (    7.22    33.23   -15.63     1.33)  ; 2\r\n            (    6.70    34.25   -15.81     1.11)  ; 3\r\n            (    6.77    35.20   -15.19     1.11)  ; 4\r\n            (    6.81    35.21   -15.19     1.11)  ; 5\r\n            (\r\n              (    6.77    36.15   -14.31     1.47)  ; 1, R-1-1-1-1-2-1\r\n              (    6.92    36.88   -13.06     1.69)  ; 2\r\n              (\r\n                (    7.66    37.76   -11.56     1.33)  ; 1, R-1-1-1-1-2-1-1\r\n                (    8.32    38.42   -10.38     1.11)  ; 2\r\n                (    8.62    38.93    -9.94     0.88)  ; 3\r\n                (    8.98    39.51    -9.75     0.66)  ; 4\r\n                (    8.98    40.25    -9.75     0.96)  ; 5\r\n                (    9.21    40.83    -9.75     0.96)  ; 6\r\n                (    9.35    41.78    -9.56     0.96)  ; 7\r\n                (    9.57    42.22    -9.13     0.81)  ; 8\r\n                (    9.35    42.95    -9.13     0.66)  ; 9\r\n                (    9.06    43.68    -9.44     0.66)  ; 10\r\n                (    8.54    44.63    -9.50     0.59)  ; 11\r\n                (    7.95    45.58   -10.06     0.59)  ; 12\r\n                (    7.73    46.31   -10.50     0.66)  ; 13\r\n                (    7.73    47.26   -10.88     0.52)  ; 14\r\n                (    7.44    48.07   -10.75     0.52)  ; 15\r\n                (    7.29    48.87    -9.88     0.81)  ; 16\r\n                (    7.36    49.46    -9.69     0.81)  ; 17\r\n                (    7.07    50.33    -9.81     0.66)  ; 18\r\n                (    6.48    51.50    -9.81     0.74)  ; 19\r\n                (    6.34    52.60   -10.81     0.88)  ; 20\r\n                (    6.04    54.06   -11.50     0.88)  ; 21\r\n                (    6.04    55.23   -11.63     0.81)  ; 22\r\n                (    6.55    56.40   -13.00     0.81)  ; 23\r\n                (    6.85    57.42   -13.25     0.81)  ; 24\r\n                (    7.36    58.23   -13.88     0.81)  ; 25\r\n                (    7.73    59.25   -14.31     1.03)  ; 26\r\n                (    8.03    60.20   -15.19     0.74)  ; 27\r\n                (    7.22    60.49   -16.00     0.52)  ; 28\r\n                (    6.63    60.35   -16.00     0.74)  ; 29\r\n                (    5.67    60.42   -16.44     1.03)  ; 30\r\n                (    4.64    60.71   -16.75     1.03)  ; 31\r\n                (    3.55    60.93   -16.88     0.81)  ; 32\r\n                (    2.59    61.37   -17.31     0.66)  ; 33\r\n                (    1.26    61.59   -17.38     0.59)  ; 34\r\n                (    0.38    62.25   -17.44     0.59)  ; 35\r\n                (   -0.36    64.07   -17.50     0.74)  ; 36\r\n                (    0.01    65.76   -17.88     0.81)  ; 37\r\n                (    0.38    66.85   -18.19     0.66)  ; 38\r\n                (    0.53    67.88   -18.25     0.52)  ; 39\r\n                (    0.38    68.46   -18.31     0.52)  ; 40\r\n                (   -0.21    69.19   -19.69     0.88)  ; 41\r\n                (   -0.80    69.26   -20.69     0.88)  ; 42\r\n                (   -1.17    69.48   -22.50     0.88)  ; 43\r\n                (   -1.68    70.43   -23.44     1.11)  ; 44\r\n                (   -1.90    71.31   -24.00     1.62)  ; 45\r\n                (   -2.12    72.04   -24.19     1.33)  ; 46\r\n                (   -2.27    72.92   -24.25     1.11)  ; 47\r\n                (   -1.90    74.16   -24.25     0.96)  ; 48\r\n                (   -1.90    75.11   -24.25     0.66)  ; 49\r\n                (   -1.83    76.14   -24.50     0.59)  ; 50\r\n                (   -1.68    76.87   -22.81     0.88)  ; 51\r\n                (   -1.46    77.82   -22.44     1.03)  ; 52\r\n                (   -1.46    78.62   -22.38     0.66)  ; 53\r\n                (   -1.31    79.64   -22.06     0.52)  ; 54\r\n                (   -0.72    80.38   -21.63     0.52)  ; 55\r\n                (   -0.65    80.74   -21.25     0.52)  ; 56\r\n                (   -1.02    81.03   -21.19     0.52)  ; 57\r\n                (   -1.31    81.55   -21.19     0.81)  ; 58\r\n                (   -1.90    82.35   -21.13     0.88)  ; 59\r\n                (   -2.12    82.93   -21.00     0.59)  ; 60\r\n                (   -2.35    83.74   -20.88     0.44)  ; 61\r\n                (   -2.42    84.47   -20.75     0.44)  ; 62\r\n                (   -1.46    84.40   -19.75     0.44)  ; 63\r\n                (   -1.31    85.20   -19.44     0.44)  ; 64\r\n                (   -1.02    86.22   -18.63     1.25)  ; 65\r\n                (   -0.72    86.74   -18.50     1.62)  ; 66\r\n                (   -0.36    87.47   -18.13     1.69)  ; 67\r\n                (   -0.50    88.27   -18.13     0.74)  ; 68\r\n                (   -0.28    89.15   -17.88     0.52)  ; 69\r\n                (   -0.21    90.61   -17.50     0.52)  ; 70\r\n                (    0.16    91.79   -16.94     0.81)  ; 71\r\n                (    0.53    92.89   -16.13     1.11)  ; 72\r\n                (    0.90    93.76   -15.69     1.40)  ; 73\r\n                (    1.42    93.98   -15.44     1.77)  ; 74\r\n                (    1.64    94.35   -15.44     1.40)  ; 75\r\n                (    2.01    95.37   -15.44     0.66)  ; 76\r\n                (    2.45    95.81   -15.44     0.52)  ; 77\r\n                (    2.74    96.47   -15.13     0.52)  ; 78\r\n                (    2.74    97.13   -14.75     0.52)  ; 79\r\n                (    2.30    98.00   -14.56     0.52)  ; 80\r\n                (    1.93    98.66   -14.44     0.66)  ; 81\r\n                (    2.01    99.39   -14.25     0.66)  ; 82\r\n                (    2.82   100.42   -13.63     0.88)  ; 83\r\n                (    3.33   101.44   -13.06     0.88)  ; 84\r\n                (    3.92   102.17   -13.06     0.59)  ; 85\r\n                (    5.10   102.97   -12.75     0.59)  ; 86\r\n                (    5.39   103.85   -12.19     0.52)  ; 87\r\n                (    5.25   104.95   -11.50     0.81)  ; 88\r\n                (    5.10   105.61   -10.94     1.11)  ; 89\r\n                (    5.10   106.34   -10.94     0.81)  ; 90\r\n                (    5.25   107.14   -10.81     0.59)  ; 91\r\n                (    5.47   107.87   -10.06     0.52)  ; 92\r\n                (    5.76   108.82   -10.06     0.52)  ; 93\r\n                (    5.98   109.33   -10.06     0.52)  ; 94\r\n                (    6.87   109.99   -10.06     0.66)  ; 95\r\n                (    7.16   110.43   -11.00     0.88)  ; 96\r\n                (    7.02   110.87   -12.44     1.25)  ; 97\r\n                (    6.65   111.09   -13.44     1.55)  ; 98\r\n                (    6.79   111.89   -15.13     0.81)  ; 99\r\n                (    7.31   112.62   -15.25     0.52)  ; 100\r\n                (    7.97   113.35   -15.44     0.74)  ; 101\r\n                (    8.49   113.57   -15.56     1.11)  ; 102\r\n                (    8.93   114.01   -15.63     1.47)  ; 103\r\n                (    9.67   114.30   -15.88     1.47)  ; 104\r\n                (   10.40   114.82   -16.63     0.81)  ; 105\r\n                (   11.36   115.25   -18.81     0.44)  ; 106\r\n                (   11.88   115.77   -18.81     0.44)  ; 107\r\n                (   12.91   115.77   -19.38     0.44)  ; 108\r\n                (   14.09   116.13   -19.81     0.44)  ; 109\r\n                (   14.90   116.72   -21.06     0.44)  ; 110\r\n                (   15.78   117.45   -21.94     0.74)  ; 111\r\n                 Low\r\n              |\r\n                (    7.26    37.84    -9.63     0.88)  ; 1, R-1-1-1-1-2-1-2\r\n                (    6.31    37.92    -8.75     0.74)  ; 2\r\n                (    5.86    37.77    -7.81     0.74)  ; 3\r\n                (    5.05    37.70    -7.63     0.74)  ; 4\r\n                (    4.46    37.40    -7.25     0.74)  ; 5\r\n                (    2.99    37.19    -7.13     0.74)  ; 6\r\n                (    2.18    37.26    -6.88     0.59)  ; 7\r\n                (    1.66    37.84    -6.25     0.66)  ; 8\r\n                (    1.15    38.79    -6.25     0.81)  ; 9\r\n                (    0.93    39.60    -6.25     0.96)  ; 10\r\n                (    0.63    40.26    -6.13     0.74)  ; 11\r\n                (    0.41    41.21    -5.81     0.59)  ; 12\r\n                (    0.04    42.08    -6.56     0.74)  ; 13\r\n                (   -0.18    42.74    -8.13     1.11)  ; 14\r\n                (   -0.77    43.18    -8.50     1.40)  ; 15\r\n                (   -1.06    44.28    -8.56     0.81)  ; 16\r\n                (   -1.28    45.08    -9.00     0.81)  ; 17\r\n                (   -1.43    45.96    -9.50     0.81)  ; 18\r\n                (   -1.72    47.05    -9.75     0.74)  ; 19\r\n                (   -1.72    48.15   -10.00     0.74)  ; 20\r\n                (   -1.65    48.81    -9.06     0.88)  ; 21\r\n                (   -1.21    49.47    -9.06     0.88)  ; 22\r\n                (   -1.14    50.27    -8.94     0.88)  ; 23\r\n                (   -1.14    51.29    -8.56     0.74)  ; 24\r\n                (   -1.14    52.17    -7.75     0.59)  ; 25\r\n                (   -1.14    53.05    -7.63     0.81)  ; 26\r\n                (   -1.14    54.36    -7.56     1.03)  ; 27\r\n                (   -1.65    55.75    -7.56     0.96)  ; 28\r\n                (   -2.31    57.00    -7.56     0.81)  ; 29\r\n                (   -2.90    58.09    -7.56     0.96)  ; 30\r\n                (   -3.05    59.11    -7.06     0.96)  ; 31\r\n                (   -3.05    59.99    -5.88     0.81)  ; 32\r\n                (   -3.05    60.50    -4.00     0.96)  ; 33\r\n                (   -3.42    60.94    -3.13     1.18)  ; 34\r\n                (   -3.86    61.45    -2.81     0.81)  ; 35\r\n                (   -4.52    61.75    -2.19     0.74)  ; 36\r\n                (   -5.41    62.11    -1.50     0.81)  ; 37\r\n                (   -6.37    62.19    -0.56     0.59)  ; 38\r\n                (   -6.96    62.33     0.69     0.66)  ; 39\r\n                (   -7.32    62.55     1.69     0.66)  ; 40\r\n                (   -7.18    63.35     1.81     0.52)  ; 41\r\n                (   -7.54    64.09     1.88     0.66)  ; 42\r\n                (   -8.06    64.74     1.88     0.66)  ; 43\r\n                (   -8.36    65.77     2.06     0.52)  ; 44\r\n                (   -8.21    66.35     2.31     0.52)  ; 45\r\n                (   -7.56    65.97     3.75     0.66)  ; 46\r\n                (   -7.41    66.11     6.06     0.81)  ; 47\r\n                (   -7.41    66.62     6.31     0.81)  ; 48\r\n                (   -7.70    67.21     7.75     0.96)  ; 49\r\n                (   -7.85    67.94     8.00     1.18)  ; 50\r\n                (   -8.07    68.60     8.38     0.88)  ; 51\r\n                (   -8.52    69.18     9.06     0.59)  ; 52\r\n                (   -9.03    69.04     9.75     0.81)  ; 53\r\n                (   -9.91    69.04    10.25     0.81)  ; 54\r\n                (  -11.17    69.26    10.69     0.81)  ; 55\r\n                (  -12.27    69.33    11.19     0.74)  ; 56\r\n                (  -13.08    69.91    12.06     0.59)  ; 57\r\n                (  -12.64    71.30    13.94     0.44)  ; 58\r\n                (  -12.64    71.74    14.63     0.59)  ; 59\r\n                (  -13.60    72.40    15.00     0.74)  ; 60\r\n                (  -13.97    72.62    16.13     1.03)  ; 61\r\n                (  -14.41    72.91    16.13     1.25)  ; 62\r\n                (  -15.29    73.20    16.69     0.66)  ; 63\r\n                (  -16.40    73.06    17.56     0.52)  ; 64\r\n                (  -17.80    73.35    18.13     0.59)  ; 65\r\n                (  -18.39    73.64    19.00     0.59)  ; 66\r\n                (  -18.68    74.23    19.31     0.59)  ; 67\r\n                (  -18.53    74.96    19.38     0.59)  ; 68\r\n                (  -18.09    75.47    19.13     0.59)  ; 69\r\n                (  -17.80    75.91    18.56     0.59)  ; 70\r\n                (  -17.50    75.98    17.94     0.59)  ; 71\r\n                 Low\r\n              )  ;  End of split\r\n            |\r\n              (    6.29    36.45   -15.50     0.74)  ; 1, R-1-1-1-1-2-2\r\n              (    5.93    37.25   -16.44     0.74)  ; 2\r\n              (    5.48    37.54   -16.94     0.74)  ; 3\r\n              (\r\n                (    5.19    37.91   -19.06     0.44)  ; 1, R-1-1-1-1-2-2-1\r\n                (    4.53    37.98   -19.31     0.44)  ; 2\r\n                (    3.86    37.84   -19.63     0.66)  ; 3\r\n                (    3.27    38.06   -19.81     0.88)  ; 4\r\n                (    2.76    38.49   -20.50     0.88)  ; 5\r\n                (    2.24    39.59   -21.06     0.88)  ; 6\r\n                (    1.80    40.54   -21.06     0.88)  ; 7\r\n                (    1.14    41.56   -20.06     0.81)  ; 8\r\n                (    0.99    42.59   -20.06     0.81)  ; 9\r\n                (    0.47    43.54   -20.38     1.03)  ; 10\r\n                (   -0.26    44.12   -20.63     0.74)  ; 11\r\n                (   -0.85    44.78   -20.69     0.74)  ; 12\r\n                (   -1.59    45.29   -20.88     0.74)  ; 13\r\n                (   -2.47    46.24   -20.88     0.52)  ; 14\r\n                (   -2.77    47.05   -21.19     0.52)  ; 15\r\n                (   -2.77    48.44   -21.38     0.59)  ; 16\r\n                (   -2.77    49.09   -20.94     0.59)  ; 17\r\n                (   -2.99    49.90   -19.81     0.81)  ; 18\r\n                (   -3.28    50.56   -18.31     1.03)  ; 19\r\n                (   -3.80    50.92   -17.44     0.88)  ; 20\r\n                (   -4.76    51.07   -17.31     0.88)  ; 21\r\n                (   -5.27    52.02   -17.13     0.96)  ; 22\r\n                (   -6.23    52.38   -17.06     0.59)  ; 23\r\n                (   -6.67    53.04   -17.06     0.44)  ; 24\r\n                (   -7.41    53.85   -17.06     0.66)  ; 25\r\n                (   -8.22    54.58   -17.56     0.66)  ; 26\r\n                (   -9.55    55.53   -17.50     0.66)  ; 27\r\n                (   -9.84    56.18   -16.88     0.88)  ; 28\r\n                (  -10.14    56.48   -15.38     1.25)  ; 29\r\n                (  -10.72    56.77   -15.31     1.47)  ; 30\r\n                (  -11.54    56.55   -15.00     0.74)  ; 31\r\n                (  -12.64    56.77   -13.31     0.74)  ; 32\r\n                (  -13.89    56.77   -13.31     0.52)  ; 33\r\n                (  -14.78    56.40   -13.31     0.81)  ; 34\r\n                (  -15.88    56.55   -13.31     0.81)  ; 35\r\n                (  -17.14    56.62   -13.19     0.66)  ; 36\r\n                (  -18.17    57.13   -12.75     0.52)  ; 37\r\n                (  -19.49    58.22   -11.31     0.52)  ; 38\r\n                (  -20.75    59.32   -11.56     0.66)  ; 39\r\n                (  -21.26    60.12   -11.56     0.66)  ; 40\r\n                (  -21.85    60.93   -12.06     1.18)  ; 41\r\n                (  -22.29    61.66   -12.63     0.96)  ; 42\r\n                (  -22.88    62.39   -13.31     0.66)  ; 43\r\n                (  -22.81    62.90   -13.38     0.44)  ; 44\r\n                (  -22.81    63.41   -13.69     0.74)  ; 45\r\n                (  -23.62    64.66   -13.94     0.96)  ; 46\r\n                (  -24.14    65.31   -13.38     0.81)  ; 47\r\n                (  -25.09    66.26   -13.38     0.66)  ; 48\r\n                (  -26.27    66.78   -12.88     0.52)  ; 49\r\n                (  -26.79    66.85   -12.31     1.18)  ; 50\r\n                (  -27.97    67.07   -11.44     1.18)  ; 51\r\n                (  -28.63    67.07   -11.13     0.81)  ; 52\r\n                (  -30.03    68.31   -10.25     0.44)  ; 53\r\n                (  -30.69    68.82    -9.06     0.44)  ; 54\r\n                (  -31.43    69.41    -8.69     0.44)  ; 55\r\n                (  -32.17    70.72    -8.63     0.74)  ; 56\r\n                (  -32.24    72.26    -7.38     0.59)  ; 57\r\n                (  -32.68    73.28    -8.44     0.44)  ; 58\r\n                (  -32.83    74.45    -8.19     0.44)  ; 59\r\n                (  -32.98    75.04    -7.44     0.81)  ; 60\r\n                (  -33.05    76.13    -7.44     1.11)  ; 61\r\n                (  -33.27    76.79    -7.44     0.81)  ; 62\r\n                (  -33.57    77.81    -7.38     0.59)  ; 63\r\n                (  -33.20    78.40    -6.06     0.59)  ; 64\r\n                (  -33.12    78.84    -5.63     0.44)  ; 65\r\n                (  -33.34    79.35    -5.38     0.44)  ; 66\r\n                (  -33.71    79.86    -5.44     0.44)  ; 67\r\n                (  -34.08    80.45    -6.31     1.18)  ; 68\r\n                (  -34.30    80.74    -6.56     1.99)  ; 69\r\n                (  -34.45    81.10    -6.69     2.73)  ; 70\r\n                (  -33.86    81.83    -7.19     1.47)  ; 71\r\n                (  -33.64    82.13    -7.81     1.11)  ; 72\r\n                 Low\r\n              )  ;  End of split\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (    7.47    29.94   -16.94     2.95)  ; 1, R-1-1-1-2\r\n          (    7.40    30.45   -18.94     2.65)  ; 2\r\n          (    7.40    30.52   -20.63     2.43)  ; 3\r\n          (    8.36    30.15   -23.38     2.43)  ; 4\r\n          (\r\n            (   10.13    30.15   -26.13     3.17)  ; 1, R-1-1-1-2-1\r\n            (   11.30    30.08   -27.56     2.87)  ; 2\r\n            (   12.41    29.64   -29.00     2.21)  ; 3\r\n            (   13.66    29.35   -29.63     2.14)  ; 4\r\n            (   15.43    29.13   -30.19     2.36)  ; 5\r\n            (   16.90    29.06   -31.44     2.80)  ; 6\r\n            (   18.08    29.20   -32.94     3.02)  ; 7\r\n            (\r\n              (   20.00    29.20   -34.06     2.36)  ; 1, R-1-1-1-2-1-1\r\n              (   21.10    29.64   -35.56     1.84)  ; 2\r\n              (   22.50    30.01   -37.50     2.21)  ; 3\r\n              (   23.24    30.37   -38.81     1.92)  ; 4\r\n              (   24.64    30.67   -36.63     1.03)  ; 5\r\n              (   25.52    31.18   -37.19     1.03)  ; 6\r\n              (   27.00    32.13   -37.94     1.25)  ; 7\r\n              (   27.88    32.93   -39.44     1.47)  ; 8\r\n              (   28.69    33.59   -39.69     1.47)  ; 9\r\n              (\r\n                (   30.39    34.32   -39.38     0.74)  ; 1, R-1-1-1-2-1-1-1\r\n                (   31.56    34.98   -39.50     0.74)  ; 2\r\n                (   32.52    35.86   -39.75     0.74)  ; 3\r\n                (   33.41    36.95   -40.06     0.74)  ; 4\r\n                (   34.81    38.20   -40.06     1.03)  ; 5\r\n                (   36.21    39.73   -41.75     1.03)  ; 6\r\n                (   38.05    40.97   -43.38     0.88)  ; 7\r\n                (   39.08    42.29   -43.50     0.88)  ; 8\r\n                (   40.26    43.75   -44.50     0.88)  ; 9\r\n                (   40.70    44.85   -46.06     0.88)  ; 10\r\n                (   42.91    46.24   -46.06     0.81)  ; 11\r\n                (   44.16    48.21   -46.38     1.03)  ; 12\r\n                (   45.12    49.67   -46.50     1.33)  ; 13\r\n                (   45.71    50.48   -46.50     0.74)  ; 14\r\n                (   47.33    51.43   -46.50     0.66)  ; 15\r\n                (   48.51    52.52   -46.50     0.88)  ; 16\r\n                (   49.65    53.58   -47.38     0.88)  ; 17\r\n                (   50.31    54.97   -48.13     0.88)  ; 18\r\n                (   50.83    55.92   -48.50     0.59)  ; 19\r\n                (   51.34    56.51   -48.50     0.59)  ; 20\r\n                (   51.93    58.04   -49.06     1.03)  ; 21\r\n                (   52.08    59.80   -49.13     0.81)  ; 22\r\n                (   52.30    60.97   -49.13     0.59)  ; 23\r\n                (   52.30    61.77   -49.13     0.59)  ; 24\r\n                (   52.23    62.80   -49.13     1.03)  ; 25\r\n                (   52.23    63.45   -49.38     0.66)  ; 26\r\n                (   52.30    64.33   -50.31     0.52)  ; 27\r\n                (   52.30    65.28   -50.88     0.52)  ; 28\r\n                (   52.30    66.08   -52.31     1.18)  ; 29\r\n                (   52.45    67.25   -52.44     1.99)  ; 30\r\n                (   52.82    68.20   -52.88     2.58)  ; 31\r\n                (   53.55    69.01   -53.63     1.62)  ; 32\r\n                (   54.14    69.67   -53.63     0.88)  ; 33\r\n                (   54.73    70.40   -53.63     0.59)  ; 34\r\n                (   55.61    70.98   -53.63     0.37)  ; 35\r\n                (   56.87    72.22   -53.63     0.29)  ; 36\r\n                (   58.19    73.54   -54.00     0.44)  ; 37\r\n                (   59.00    74.49   -53.88     1.03)  ; 38\r\n                (   59.67    75.15   -53.88     1.33)  ; 39\r\n                (   60.85    75.81   -53.88     0.74)  ; 40\r\n                (   61.51    76.10   -53.88     0.37)  ; 41\r\n                (   62.25    76.61   -53.81     0.29)  ; 42\r\n                (   63.35    77.05   -53.81     0.29)  ; 43\r\n                (   64.01    77.27   -52.69     0.66)  ; 44\r\n                (   64.75    77.63   -52.69     1.03)  ; 45\r\n                (   65.63    77.85   -52.38     0.66)  ; 46\r\n                (   67.03    78.22   -52.19     0.52)  ; 47\r\n                (   67.84    78.51   -51.88     1.18)  ; 48\r\n                (   68.66    78.66   -51.88     1.92)  ; 49\r\n                (   69.39    78.73   -51.88     2.21)  ; 50\r\n                (   70.28    78.95   -51.88     0.88)  ; 51\r\n                (   70.79    79.10   -51.88     0.52)  ; 52\r\n                 Low\r\n              |\r\n                (   27.84    34.65   -39.56     0.81)  ; 1, R-1-1-1-2-1-1-2\r\n                (   28.06    35.68   -40.94     0.66)  ; 2\r\n                (   29.39    36.41   -43.63     0.66)  ; 3\r\n                (   30.12    36.48   -43.94     0.66)  ; 4\r\n                (   30.27    36.92   -45.31     0.66)  ; 5\r\n                (   30.42    37.58   -46.00     0.66)  ; 6\r\n                (   30.42    38.16   -47.56     1.03)  ; 7\r\n                (   30.42    38.89   -47.63     1.33)  ; 8\r\n                (   30.64    39.26   -48.94     1.11)  ; 9\r\n                (   30.79    39.40   -50.38     0.96)  ; 10\r\n                (   31.01    39.55   -52.75     0.81)  ; 11\r\n                (   31.82    39.99   -53.69     0.81)  ; 12\r\n                (   32.48    41.38   -55.31     0.52)  ; 13\r\n                (   32.70    42.11   -55.50     0.52)  ; 14\r\n                (   33.29    42.91   -56.50     0.88)  ; 15\r\n                (   34.03    43.13   -57.63     1.18)  ; 16\r\n                (   35.50    43.57   -58.25     0.81)  ; 17\r\n                (   36.02    43.94   -60.81     0.52)  ; 18\r\n                (   37.71    44.37   -62.38     0.37)  ; 19\r\n                (   38.38    44.96   -62.94     0.37)  ; 20\r\n                (   39.55    46.06   -63.75     0.37)  ; 21\r\n                (   39.85    46.57   -63.75     0.74)  ; 22\r\n                (   40.36    47.23   -64.00     1.03)  ; 23\r\n                (   40.95    47.88   -64.44     1.03)  ; 24\r\n                (   41.91    48.83   -67.44     0.74)  ; 25\r\n                (   42.57    50.37   -67.63     0.59)  ; 26\r\n                (   43.68    52.42   -70.00     0.52)  ; 27\r\n                (   43.46    53.29   -70.19     0.52)  ; 28\r\n                (   43.39    53.95   -70.25     0.81)  ; 29\r\n                (   43.39    54.61   -71.00     1.25)  ; 30\r\n                (   43.68    55.19   -71.00     1.62)  ; 31\r\n                (   44.49    56.58   -71.31     1.33)  ; 32\r\n                (   44.56    57.02   -71.31     1.03)  ; 33\r\n                (   44.64    57.82   -69.69     0.52)  ; 34\r\n                (   44.93    59.14   -68.88     0.29)  ; 35\r\n                (   45.37    61.04   -68.44     0.29)  ; 36\r\n                (   45.89    62.87   -69.75     0.29)  ; 37\r\n                (   46.04    63.53   -69.75     0.29)  ; 38\r\n                (   46.04    64.33   -69.75     0.66)  ; 39\r\n                (   45.82    65.50   -69.75     0.88)  ; 40\r\n                (   45.96    66.16   -69.75     0.44)  ; 41\r\n                (   45.89    67.91   -68.31     0.29)  ; 42\r\n                (   45.74    69.15   -68.31     0.29)  ; 43\r\n                (   45.45    69.74   -68.00     0.29)  ; 44\r\n                (   45.45    70.18   -67.63     1.03)  ; 45\r\n                (   45.45    71.20   -67.56     1.77)  ; 46\r\n                (   45.23    72.15   -67.56     1.18)  ; 47\r\n                (   45.15    72.88   -67.56     0.44)  ; 48\r\n                (   44.79    73.83   -67.56     0.22)  ; 49\r\n                (   44.71    74.49   -67.56     0.22)  ; 50\r\n                (   44.34    75.59   -66.56     0.22)  ; 51\r\n                (   44.20    76.46   -65.88     0.59)  ; 52\r\n                (   43.90    77.27   -65.81     1.40)  ; 53\r\n                (   43.61    78.00   -65.81     2.06)  ; 54\r\n                (   43.53    78.88   -65.75     0.96)  ; 55\r\n                (   43.24    79.61   -65.75     0.37)  ; 56\r\n                (   42.87    81.22   -65.69     0.22)  ; 57\r\n                (   42.43    82.02   -65.69     0.22)  ; 58\r\n                (   41.88    83.71   -65.63     0.22)  ; 59\r\n                (   41.30    84.88   -66.38     0.22)  ; 60\r\n                (   41.37    85.83   -64.50     0.59)  ; 61\r\n                (   41.44    86.56   -64.44     0.59)  ; 62\r\n                (   42.47    87.95   -62.25     0.37)  ; 63\r\n                (   43.87    89.26   -62.19     0.22)  ; 64\r\n                (   45.27    90.07   -61.81     0.22)  ; 65\r\n                (   47.12    91.02   -61.50     0.22)  ; 66\r\n                (   49.77    92.26   -61.38     0.22)  ; 67\r\n                (   50.73    93.36   -61.94     0.22)  ; 68\r\n                (   50.95    94.09   -61.94     1.33)  ; 69\r\n                (   51.54    94.67   -61.94     2.36)  ; 70\r\n                (   51.98    95.18   -61.94     2.36)  ; 71\r\n                (   52.20    95.77   -61.94     1.25)  ; 72\r\n                (   52.79    96.28   -61.94     0.96)  ; 73\r\n                 Generated\r\n              )  ;  End of split\r\n            |\r\n              (   18.06    29.97   -32.88     1.92)  ; 1, R-1-1-1-2-1-2\r\n              (   18.28    30.92   -32.06     0.81)  ; 2\r\n              (   18.51    31.43   -32.06     0.37)  ; 3\r\n              (   18.87    32.09   -32.06     0.37)  ; 4\r\n              (   19.39    32.97   -32.06     0.37)  ; 5\r\n              (   20.05    33.48   -32.06     0.37)  ; 6\r\n              (   20.86    34.36   -32.06     0.96)  ; 7\r\n              (   21.60    34.72   -32.06     0.96)  ; 8\r\n              (   22.19    35.01   -32.38     0.96)  ; 9\r\n              (   22.63    35.45   -32.06     0.66)  ; 10\r\n              (   23.07    36.18   -32.69     0.44)  ; 11\r\n              (   24.03    36.69   -32.69     0.29)  ; 12\r\n              (   24.40    37.50   -32.88     0.29)  ; 13\r\n              (   25.21    37.79   -33.56     0.29)  ; 14\r\n              (   26.24    38.23   -33.94     0.29)  ; 15\r\n              (   27.49    38.74   -33.94     0.29)  ; 16\r\n              (   28.97    40.06   -33.94     0.29)  ; 17\r\n              (   29.63    40.79   -33.94     0.52)  ; 18\r\n              (   30.15    41.59   -33.94     0.52)  ; 19\r\n              (   30.66    42.47   -33.94     0.52)  ; 20\r\n              (   31.69    43.35   -33.94     0.81)  ; 21\r\n              (   32.28    43.93   -33.94     0.81)  ; 22\r\n              (   33.24    44.52   -33.94     0.81)  ; 23\r\n              (   33.61    45.03   -33.94     0.81)  ; 24\r\n              (   34.57    46.12   -33.94     0.96)  ; 25\r\n              (   35.74    47.29   -34.19     0.96)  ; 26\r\n              (   36.78    48.10   -34.19     0.96)  ; 27\r\n              (   37.73    49.63   -34.19     0.66)  ; 28\r\n              (   38.69    50.22   -34.19     0.66)  ; 29\r\n              (   39.94    50.95   -34.19     0.96)  ; 30\r\n              (   41.42    52.05   -35.06     0.74)  ; 31\r\n              (   42.01    52.56   -35.69     1.11)  ; 32\r\n              (   42.60    53.21   -35.69     1.33)  ; 33\r\n              (   43.26    53.95   -35.69     0.81)  ; 34\r\n              (   43.55    54.46   -35.69     0.52)  ; 35\r\n              (   44.14    55.12   -36.06     0.44)  ; 36\r\n              (   44.73    55.63   -36.81     0.44)  ; 37\r\n              (   45.62    56.43   -34.25     0.81)  ; 38\r\n              (   46.21    57.09   -33.38     1.11)  ; 39\r\n              (   46.50    58.19   -33.31     0.81)  ; 40\r\n              (   47.04    58.87   -32.38     0.37)  ; 41\r\n              (   47.70    59.45   -31.69     0.37)  ; 42\r\n              (   48.22    60.33   -31.50     0.37)  ; 43\r\n              (   48.58    60.84   -31.50     0.66)  ; 44\r\n              (   48.88    61.64   -30.69     1.47)  ; 45\r\n              (   49.17    62.45   -29.88     1.62)  ; 46\r\n              (   49.25    63.25   -29.75     0.96)  ; 47\r\n              (   49.62    63.84   -28.81     0.59)  ; 48\r\n              (   50.21    64.27   -25.81     0.44)  ; 49\r\n              (   51.09    65.23   -25.00     0.29)  ; 50\r\n              (   51.75    65.52   -24.75     0.29)  ; 51\r\n              (   52.27    65.74   -24.13     0.59)  ; 52\r\n               Low\r\n            )  ;  End of split\r\n          |\r\n            (    9.32    30.80   -24.25     1.25)  ; 1, R-1-1-1-2-2\r\n            (    9.17    31.53   -25.31     1.25)  ; 2\r\n            (    8.73    32.77   -26.00     0.96)  ; 3\r\n            (    8.21    34.01   -26.69     0.96)  ; 4\r\n            (    7.62    35.04   -26.88     1.40)  ; 5\r\n            (\r\n              (    7.03    36.06   -28.13     1.99)  ; 1, R-1-1-1-2-2-1\r\n              (    5.63    36.35   -29.06     2.36)  ; 2\r\n              (    4.31    36.06   -30.88     1.40)  ; 3\r\n              (\r\n                (    3.05    36.06   -30.69     1.03)  ; 1, R-1-1-1-2-2-1-1\r\n                (    2.24    35.77   -31.69     0.81)  ; 2\r\n                (    0.99    35.33   -33.19     0.59)  ; 3\r\n                (    0.25    35.33   -33.56     0.59)  ; 4\r\n                (   -0.56    35.33   -34.56     1.03)  ; 5\r\n                (   -1.06    35.03   -35.06     1.99)  ; 6\r\n                (   -1.58    35.32   -36.25     1.77)  ; 7\r\n                (   -1.65    35.47   -39.31     1.40)  ; 8\r\n                (   -1.65    35.90   -40.00     0.81)  ; 9\r\n                (   -2.17    36.34   -40.44     0.59)  ; 10\r\n                (   -3.42    37.00   -41.19     0.44)  ; 11\r\n                (   -4.75    37.73   -42.06     0.44)  ; 12\r\n                (   -5.11    38.17   -42.06     0.74)  ; 13\r\n                (   -5.93    38.32   -43.81     0.74)  ; 14\r\n                (   -6.00    38.97   -44.63     0.59)  ; 15\r\n                (   -6.44    39.63   -45.19     0.59)  ; 16\r\n                (   -6.37    40.22   -45.19     0.81)  ; 17\r\n                (   -6.74    40.95   -46.38     0.74)  ; 18\r\n                (   -7.25    41.09   -47.88     0.74)  ; 19\r\n                (   -7.40    41.09   -49.25     0.74)  ; 20\r\n                (   -8.06    41.24   -52.00     0.88)  ; 21\r\n                (   -8.80    41.24   -54.06     0.88)  ; 22\r\n                (   -9.09    41.31   -54.38     1.18)  ; 23\r\n                (   -9.98    41.39   -55.19     1.18)  ; 24\r\n                (  -10.27    41.83   -58.81     0.52)  ; 25\r\n                (  -10.64    42.34   -60.25     0.52)  ; 26\r\n                (  -11.16    42.70   -61.94     0.88)  ; 27\r\n                (  -11.60    42.85   -63.63     1.18)  ; 28\r\n                (  -11.89    43.21   -64.25     0.59)  ; 29\r\n                (  -12.19    43.58   -65.06     0.96)  ; 30\r\n                (  -12.70    44.02   -65.63     1.03)  ; 31\r\n                (  -13.15    44.46   -66.75     0.66)  ; 32\r\n                (  -13.66    44.53   -67.50     0.44)  ; 33\r\n                (  -14.40    44.75   -68.00     0.81)  ; 34\r\n                (  -15.50    45.04   -68.25     0.81)  ; 35\r\n                (  -16.31    45.55   -68.25     0.52)  ; 36\r\n                (  -17.12    45.92   -68.81     0.52)  ; 37\r\n                (  -18.38    46.36   -69.94     0.52)  ; 38\r\n                (  -18.89    46.58   -70.06     1.33)  ; 39\r\n                (  -19.48    47.16   -71.44     1.62)  ; 40\r\n                (  -19.48    47.31   -73.06     1.33)  ; 41\r\n                (  -19.92    47.38   -75.75     1.03)  ; 42\r\n                (  -20.81    47.67   -75.75     0.66)  ; 43\r\n                (  -22.13    47.82   -76.38     0.66)  ; 44\r\n                (  -22.50    47.97   -76.63     1.03)  ; 45\r\n                (  -23.53    48.62   -76.63     1.40)  ; 46\r\n                (  -24.05    48.92   -78.38     0.66)  ; 47\r\n                (  -24.34    50.01   -79.19     0.44)  ; 48\r\n                (  -25.15    50.45   -80.06     0.81)  ; 49\r\n                (  -25.23    50.60   -81.75     1.18)  ; 50\r\n                (  -25.52    51.18   -82.25     1.69)  ; 51\r\n                (  -25.60    51.47   -86.56     1.33)  ; 52\r\n                (  -25.82    51.84   -88.50     0.96)  ; 53\r\n                (  -26.41    52.13   -88.50     0.59)  ; 54\r\n                (  -26.70    52.50   -88.50     0.29)  ; 55\r\n                (  -27.66    53.01   -88.50     0.29)  ; 56\r\n                (  -28.40    53.52   -88.44     0.29)  ; 57\r\n                (  -29.35    54.11   -88.81     0.29)  ; 58\r\n                (  -30.09    54.32   -89.13     0.74)  ; 59\r\n                (  -30.97    54.91   -89.13     1.03)  ; 60\r\n                (  -32.01    55.13   -89.19     1.03)  ; 61\r\n                (  -32.01    55.86   -93.69     0.74)  ; 62\r\n                (  -32.45    56.52   -94.25     0.74)  ; 63\r\n                (  -33.04    57.69   -95.19     0.52)  ; 64\r\n                (  -33.33    58.27   -95.19     0.88)  ; 65\r\n                (  -33.70    59.22   -96.13     1.18)  ; 66\r\n                 Low\r\n              |\r\n                (    4.76    36.78   -32.50     0.88)  ; 1, R-1-1-1-2-2-1-2\r\n                (    3.65    36.71   -33.25     0.88)  ; 2\r\n                (    2.25    37.37   -34.13     0.88)  ; 3\r\n                (    0.85    38.17   -34.13     0.66)  ; 4\r\n                (    0.04    38.68   -35.25     0.88)  ; 5\r\n                (   -0.47    39.63   -35.88     1.03)  ; 6\r\n                (   -0.99    40.14   -36.56     1.03)  ; 7\r\n                (   -1.95    40.22   -38.94     0.88)  ; 8\r\n                (   -2.39    40.58   -39.69     0.88)  ; 9\r\n                (   -2.83    41.39   -40.75     0.88)  ; 10\r\n                (   -2.90    42.19   -41.75     0.88)  ; 11\r\n                (   -3.13    42.78   -42.88     0.88)  ; 12\r\n                (   -2.61    42.41   -44.88     0.88)  ; 13\r\n                (   -3.13    43.14   -45.19     0.88)  ; 14\r\n                (   -3.79    43.87   -45.19     0.66)  ; 15\r\n                (   -4.08    44.24   -46.56     1.03)  ; 16\r\n                (   -4.30    44.16   -47.88     1.40)  ; 17\r\n                (   -3.94    43.94   -49.00     2.14)  ; 18\r\n                (   -4.08    43.58   -50.94     1.25)  ; 19\r\n                (   -4.08    43.58   -53.63     0.96)  ; 20\r\n                (   -3.94    43.51   -55.75     0.96)  ; 21\r\n                (   -4.01    43.51   -57.25     0.96)  ; 22\r\n                (   -4.30    43.80   -61.31     0.96)  ; 23\r\n                (   -4.60    44.46   -62.13     0.96)  ; 24\r\n                (   -5.04    45.04   -65.88     0.81)  ; 25\r\n                (   -5.78    45.41   -67.38     1.11)  ; 26\r\n                (   -5.48    45.77   -66.44     0.66)  ; 27\r\n                (   -5.70    47.38   -66.63     0.44)  ; 28\r\n                (   -5.63    48.62   -67.88     0.29)  ; 29\r\n                (   -5.19    49.57   -70.31     0.66)  ; 30\r\n                (   -4.97    49.65   -71.88     1.11)  ; 31\r\n                (   -4.67    50.01   -71.13     2.80)  ; 32\r\n                (   -4.30    50.74   -71.00     3.09)  ; 33\r\n                (   -4.16    51.47   -71.00     3.09)  ; 34\r\n                (   -3.64    51.77   -74.25     1.69)  ; 35\r\n                (   -4.08    52.57   -75.88     0.88)  ; 36\r\n                (   -4.75    53.52   -76.69     0.59)  ; 37\r\n                (   -5.19    53.89   -77.38     0.59)  ; 38\r\n                (   -5.85    54.69   -78.63     1.03)  ; 39\r\n                (   -6.44    55.79   -80.13     1.03)  ; 40\r\n                (   -7.25    56.59   -81.06     1.47)  ; 41\r\n                (   -7.99    56.96   -83.19     1.47)  ; 42\r\n                (   -8.43    57.25   -83.38     0.81)  ; 43\r\n                (   -9.02    57.69   -84.63     0.44)  ; 44\r\n                (   -9.90    58.64   -85.50     0.29)  ; 45\r\n                (  -10.12    58.86   -86.88     0.66)  ; 46\r\n                (  -10.35    59.37   -87.75     1.40)  ; 47\r\n                (  -10.42    59.59   -89.56     2.28)  ; 48\r\n                (  -10.86    60.10   -91.56     0.96)  ; 49\r\n                (  -10.93    61.12   -92.13     0.59)  ; 50\r\n                (  -11.01    62.37   -93.19     0.44)  ; 51\r\n                (  -11.08    62.80   -93.81     0.88)  ; 52\r\n                (  -10.71    63.61   -94.81     1.33)  ; 53\r\n                (  -10.86    64.34   -95.63     0.74)  ; 54\r\n                (  -10.90    65.44   -95.56     0.37)  ; 55\r\n                (  -11.20    66.69   -96.31     0.22)  ; 56\r\n                (  -11.49    67.64   -96.75     0.22)  ; 57\r\n                (  -11.57    68.22   -97.25     0.22)  ; 58\r\n                (  -11.64    69.46   -97.25     0.66)  ; 59\r\n                (  -11.64    69.76   -97.25     1.62)  ; 60\r\n                (  -11.79    70.41   -98.38     2.43)  ; 61\r\n                (  -12.67    70.71  -100.00     1.33)  ; 62\r\n                (  -13.12    71.29  -100.00     0.66)  ; 63\r\n                (  -13.34    71.66  -100.13     0.37)  ; 64\r\n                (  -13.85    72.53  -100.13     0.15)  ; 65\r\n                (  -14.37    73.26  -100.50     0.15)  ; 66\r\n                (  -15.10    74.00  -101.19     0.15)  ; 67\r\n                (  -15.84    74.65  -101.44     0.52)  ; 68\r\n                (  -16.21    75.09  -101.44     1.77)  ; 69\r\n                (  -16.73    75.46  -102.06     2.36)  ; 70\r\n                (  -17.46    75.75  -102.81     1.33)  ; 71\r\n                (  -18.12    75.75  -103.88     0.59)  ; 72\r\n                (  -19.01    76.19  -103.94     0.29)  ; 73\r\n                (  -19.97    76.70  -104.56     0.29)  ; 74\r\n                (  -20.92    77.07  -104.81     0.29)  ; 75\r\n                (  -21.44    77.36  -103.63     1.03)  ; 76\r\n                (  -22.25    78.31  -103.88     1.33)  ; 77\r\n                (  -22.77    78.38  -104.44     0.59)  ; 78\r\n                (  -23.28    78.45  -106.44     0.29)  ; 79\r\n                 Low\r\n              )  ;  End of split\r\n            |\r\n              (    7.37    36.30   -26.38     1.55)  ; 1, R-1-1-1-2-2-2\r\n              (    7.45    37.25   -25.00     1.18)  ; 2\r\n              (    7.01    38.34   -24.63     0.81)  ; 3\r\n              (    6.56    38.71   -24.13     0.81)  ; 4\r\n              (    5.61    39.59   -23.38     1.03)  ; 5\r\n              (    4.80    40.46   -21.06     1.03)  ; 6\r\n              (    4.57    41.41   -19.56     1.03)  ; 7\r\n              (    4.94    42.07   -18.69     1.03)  ; 8\r\n              (    5.16    42.80   -18.69     0.74)  ; 9\r\n              (    5.38    43.46   -18.69     0.44)  ; 10\r\n              (    5.38    44.12   -18.44     0.44)  ; 11\r\n              (    4.87    44.78   -18.06     0.81)  ; 12\r\n              (    4.65    45.80   -18.31     0.81)  ; 13\r\n              (    4.57    46.82   -18.44     0.66)  ; 14\r\n              (    4.43    47.77   -18.44     0.66)  ; 15\r\n              (    4.28    48.58   -18.19     0.88)  ; 16\r\n              (    4.06    49.38   -18.19     0.88)  ; 17\r\n              (    4.21    50.11   -17.88     0.59)  ; 18\r\n              (    4.35    50.55   -17.75     0.59)  ; 19\r\n              (    4.72    51.14   -18.38     0.88)  ; 20\r\n              (    5.24    51.65   -17.94     1.11)  ; 21\r\n              (    5.42    52.95   -20.38     0.52)  ; 22\r\n              (    5.05    54.05   -21.38     0.52)  ; 23\r\n              (    5.20    55.00   -21.81     0.52)  ; 24\r\n              (    5.27    56.82   -22.56     0.52)  ; 25\r\n              (    5.56    58.29   -22.19     0.52)  ; 26\r\n              (    5.56    59.09   -23.44     0.52)  ; 27\r\n              (    6.01    60.26   -24.50     0.81)  ; 28\r\n              (    5.56    60.77   -25.19     0.81)  ; 29\r\n              (    5.27    61.58   -25.75     0.44)  ; 30\r\n              (    5.05    62.60   -25.75     0.22)  ; 31\r\n              (    5.20    62.96   -25.75     0.22)  ; 32\r\n              (    5.93    62.96   -25.88     0.22)  ; 33\r\n              (    6.30    63.84   -26.38     0.22)  ; 34\r\n              (    5.78    64.94   -26.31     0.52)  ; 35\r\n              (    5.93    65.67   -28.13     0.66)  ; 36\r\n              (    6.30    67.13   -28.81     0.81)  ; 37\r\n              (    6.59    68.08   -28.13     1.11)  ; 38\r\n              (    6.82    69.32   -28.06     1.40)  ; 39\r\n              (    6.96    70.35   -28.06     1.03)  ; 40\r\n              (    7.04    70.86   -28.06     0.74)  ; 41\r\n              (    6.89    72.03   -27.25     0.88)  ; 42\r\n              (    6.67    73.20   -26.63     0.59)  ; 43\r\n              (    6.59    74.22   -26.19     0.44)  ; 44\r\n              (    6.74    74.88   -26.19     0.44)  ; 45\r\n              (    7.55    75.76   -26.69     0.52)  ; 46\r\n              (    7.77    77.44   -26.69     0.66)  ; 47\r\n              (    7.48    78.24   -26.56     0.52)  ; 48\r\n              (    6.89    78.90   -26.19     1.11)  ; 49\r\n              (    6.23    79.70   -26.19     1.11)  ; 50\r\n              (    5.49    80.44   -26.19     0.81)  ; 51\r\n              (    4.83    81.17   -26.19     0.96)  ; 52\r\n              (    3.89    81.92   -26.06     0.52)  ; 53\r\n              (    3.89    82.58   -25.50     0.37)  ; 54\r\n              (    3.60    83.17   -25.06     0.37)  ; 55\r\n              (    2.78    83.60   -24.88     0.37)  ; 56\r\n              (    2.71    84.34   -24.81     1.11)  ; 57\r\n              (    2.49    84.77   -24.81     2.06)  ; 58\r\n              (    2.42    84.99   -24.81     2.06)  ; 59\r\n              (    2.05    85.80   -24.75     0.88)  ; 60\r\n              (    1.68    86.31   -24.75     0.37)  ; 61\r\n              (    1.46    86.82   -24.75     0.37)  ; 62\r\n              (    0.87    87.70   -24.75     0.59)  ; 63\r\n              (    0.43    88.14   -24.19     0.88)  ; 64\r\n              (   -0.16    88.36   -25.19     1.03)  ; 65\r\n              (   -1.12    88.72   -26.06     0.66)  ; 66\r\n              (   -1.93    89.16   -26.06     0.37)  ; 67\r\n              (   -2.74    89.38   -26.06     0.37)  ; 68\r\n               Low\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (    2.90    23.14   -10.13     0.81)  ; 1, R-1-1-2\r\n        (    2.75    23.87    -8.94     0.81)  ; 2\r\n        (    2.97    25.04    -7.50     1.33)  ; 3\r\n        (    3.42    26.14    -6.88     1.33)  ; 4\r\n        (    3.93    27.24    -6.25     1.18)  ; 5\r\n        (    4.37    28.04    -5.50     1.18)  ; 6\r\n        (    4.96    28.99    -5.44     1.18)  ; 7\r\n        (    5.40    30.31    -4.75     1.84)  ; 8\r\n        (    5.40    31.55    -4.38     1.84)  ; 9\r\n        (    5.18    33.23    -3.69     1.40)  ; 10\r\n        (    4.74    34.62    -3.44     1.25)  ; 11\r\n        (    4.52    35.64    -2.81     1.84)  ; 12\r\n        (    4.30    37.54    -2.50     1.84)  ; 13\r\n        (    4.30    39.37    -1.63     1.33)  ; 14\r\n        (    4.89    40.54    -1.50     1.55)  ; 15\r\n        (    5.33    41.42    -1.06     1.55)  ; 16\r\n        (    5.33    43.02    -1.63     1.55)  ; 17\r\n        (    5.77    44.12    -1.25     1.99)  ; 18\r\n        (\r\n          (    6.29    45.07    -0.69     2.87)  ; 1, R-1-1-2-1\r\n          (    6.73    45.80    -0.50     2.58)  ; 2\r\n          (    7.25    46.46    -1.50     1.33)  ; 3\r\n          (    7.69    47.04    -2.06     0.88)  ; 4\r\n          (    8.13    47.56    -2.06     0.66)  ; 5\r\n          (    9.09    47.85    -2.38     0.66)  ; 6\r\n          (    9.60    48.29    -2.50     0.88)  ; 7\r\n          (   10.34    48.43    -1.69     0.88)  ; 8\r\n          (   10.78    48.58    -1.44     1.03)  ; 9\r\n          (   11.22    49.24    -0.31     1.03)  ; 10\r\n          (   11.89    49.90    -0.31     1.03)  ; 11\r\n          (   12.40    50.77    -0.25     1.11)  ; 12\r\n          (   12.70    51.65    -0.25     0.88)  ; 13\r\n          (   12.85    52.97    -0.06     1.11)  ; 14\r\n          (   12.77    53.77    -0.25     1.11)  ; 15\r\n          (   12.77    54.50    -0.25     0.81)  ; 16\r\n          (   13.07    55.31    -0.44     0.74)  ; 17\r\n          (   13.80    56.18    -0.94     1.11)  ; 18\r\n          (   14.76    56.91    -0.88     1.18)  ; 19\r\n          (   15.28    57.64    -0.81     1.25)  ; 20\r\n          (   15.94    58.59    -1.56     0.81)  ; 21\r\n          (   16.82    59.40    -1.88     0.74)  ; 22\r\n          (   16.90    60.28    -1.88     0.74)  ; 23\r\n          (   16.97    61.37    -1.88     0.96)  ; 24\r\n          (   16.90    62.03    -2.13     1.47)  ; 25\r\n          (   16.75    63.05    -2.13     1.69)  ; 26\r\n          (   16.46    64.37    -2.13     1.25)  ; 27\r\n          (   16.46    65.39    -2.13     1.11)  ; 28\r\n          (   16.46    66.34    -2.25     0.96)  ; 29\r\n          (   16.90    67.73    -2.25     1.25)  ; 30\r\n          (   17.49    68.61    -2.06     0.88)  ; 31\r\n          (   17.49    69.56    -1.63     0.96)  ; 32\r\n          (   17.49    70.36    -1.56     1.18)  ; 33\r\n          (   17.27    71.39    -1.44     1.03)  ; 34\r\n          (   17.19    72.56    -1.38     1.03)  ; 35\r\n          (   17.12    72.85    -1.38     0.96)  ; 36\r\n          (   17.63    73.73    -1.38     0.96)  ; 37\r\n          (   18.08    74.82    -1.38     1.18)  ; 38\r\n          (   18.44    76.07    -1.56     1.03)  ; 39\r\n          (   18.81    77.67    -1.19     0.88)  ; 40\r\n          (   18.74    78.84    -1.00     0.88)  ; 41\r\n          (   18.00    79.94    -1.00     0.96)  ; 42\r\n          (   17.05    80.93    -0.94     1.11)  ; 43\r\n          (   16.24    81.74    -0.94     0.88)  ; 44\r\n          (   15.50    82.25    -0.94     0.59)  ; 45\r\n          (   14.91    82.90    -0.38     0.74)  ; 46\r\n          (   14.10    83.85    -0.38     0.81)  ; 47\r\n          (   13.66    84.59    -0.38     0.96)  ; 48\r\n          (   13.14    85.32    -0.38     0.96)  ; 49\r\n          (   12.63    86.05    -0.38     1.11)  ; 50\r\n          (   12.48    86.41    -0.38     0.74)  ; 51\r\n          (   12.04    87.44    -0.38     0.74)  ; 52\r\n          (   11.60    88.39     0.50     0.96)  ; 53\r\n          (   11.23    89.41     1.50     0.88)  ; 54\r\n          (   10.86    90.29     1.50     0.66)  ; 55\r\n          (   10.71    90.95     1.56     0.66)  ; 56\r\n          (   10.71    91.90     1.56     0.66)  ; 57\r\n          (   10.86    92.55     1.56     0.66)  ; 58\r\n          (   11.23    93.28     1.56     0.59)  ; 59\r\n          (   11.23    93.80     1.56     0.59)  ; 60\r\n          (   11.23    94.31     1.56     0.88)  ; 61\r\n          (   11.45    94.82     1.56     1.62)  ; 62\r\n          (   11.67    95.33     1.56     1.84)  ; 63\r\n          (   12.33    96.43     1.56     1.25)  ; 64\r\n          (   12.77    97.45     1.63     0.81)  ; 65\r\n          (   13.66    98.26     1.63     0.74)  ; 66\r\n          (   14.40    99.13     1.50     0.74)  ; 67\r\n          (   14.91   100.01     1.50     0.52)  ; 68\r\n          (   14.84   100.81     1.50     0.74)  ; 69\r\n          (   14.76   101.40     2.44     0.96)  ; 70\r\n          (   14.76   102.86     4.31     1.03)  ; 71\r\n          (   14.69   103.45     4.81     1.03)  ; 72\r\n          (   14.47   104.54     5.69     0.66)  ; 73\r\n          (   14.54   105.20     5.75     0.59)  ; 74\r\n          (   14.76   105.86     5.75     0.37)  ; 75\r\n          (   15.21   105.93     5.75     0.37)  ; 76\r\n          (   16.02   105.93     5.19     0.81)  ; 77\r\n          (   16.68   106.08     4.13     0.96)  ; 78\r\n          (   17.49   106.66     3.44     0.66)  ; 79\r\n          (   18.30   107.39     2.63     0.66)  ; 80\r\n          (   18.45   107.83     4.19     0.96)  ; 81\r\n          (   19.04   108.56     3.31     0.96)  ; 82\r\n          (   19.18   109.51     4.19     1.11)  ; 83\r\n          (   19.53   110.53     4.63     1.40)  ; 84\r\n          (   19.90   111.55     5.38     0.88)  ; 85\r\n          (   20.41   112.36     6.13     0.74)  ; 86\r\n          (   20.63   113.31     6.19     1.03)  ; 87\r\n          (   21.37   113.82     6.19     1.03)  ; 88\r\n          (   21.81   114.70     6.19     0.59)  ; 89\r\n          (   22.47   114.99     6.19     0.44)  ; 90\r\n          (   23.51   115.36     5.75     0.66)  ; 91\r\n          (   24.46   115.79     5.31     0.59)  ; 92\r\n          (   25.13   116.31     5.31     0.59)  ; 93\r\n          (   25.27   117.11     5.31     0.81)  ; 94\r\n          (   25.57   117.69     4.88     0.96)  ; 95\r\n          (   26.23   118.72     6.00     0.66)  ; 96\r\n          (   26.67   119.89     6.63     0.96)  ; 97\r\n          (   27.11   121.13     5.13     1.11)  ; 98\r\n          (   27.41   122.15     4.31     0.88)  ; 99\r\n          (   27.85   123.40     4.31     1.11)  ; 100\r\n          (   28.22   124.05     2.69     0.74)  ; 101\r\n          (   28.66   125.08     2.69     0.52)  ; 102\r\n          (   28.88   125.66     3.06     0.81)  ; 103\r\n          (   29.40   126.61     3.19     1.18)  ; 104\r\n          (   29.91   127.49     4.44     0.81)  ; 105\r\n          (   30.36   128.81     4.44     0.59)  ; 106\r\n          (   30.58   129.83     4.44     0.59)  ; 107\r\n          (   30.95   130.56     5.13     0.59)  ; 108\r\n          (   31.83   131.14     3.94     1.40)  ; 109\r\n          (   32.35   131.07     2.75     2.06)  ; 110\r\n          (   32.71   131.22     0.69     1.11)  ; 111\r\n          (   33.52   131.51     0.31     0.74)  ; 112\r\n          (   34.33   131.80    -0.63     0.74)  ; 113\r\n          (   35.44   131.88    -1.69     0.96)  ; 114\r\n          (   36.10   131.80    -2.13     0.88)  ; 115\r\n          (   36.55   131.36    -3.31     0.88)  ; 116\r\n          (   36.69   131.00    -4.63     1.18)  ; 117\r\n          (   36.62   131.00    -6.13     1.03)  ; 118\r\n          (   36.91   131.73    -6.88     1.62)  ; 119\r\n          (   37.13   132.17    -7.38     1.92)  ; 120\r\n          (   37.43   132.90    -7.81     1.40)  ; 121\r\n          (   37.80   133.56    -8.31     1.11)  ; 122\r\n          (   38.02   133.85    -8.81     0.81)  ; 123\r\n          (   38.31   134.14    -9.25     0.59)  ; 124\r\n          (   38.76   134.87   -10.31     0.37)  ; 125\r\n          (   39.42   135.02   -10.31     0.29)  ; 126\r\n          (   39.42   135.60   -10.31     0.29)  ; 127\r\n          (   39.86   136.48   -10.31     0.29)  ; 128\r\n          (   40.30   137.21   -10.31     0.29)  ; 129\r\n          (   40.67   137.50   -10.31     0.88)  ; 130\r\n          (   41.04   137.72   -10.94     0.88)  ; 131\r\n           Low\r\n        |\r\n          (    6.00    45.00    -0.19     2.73)  ; 1, R-1-1-2-2\r\n          (    6.14    46.17     0.50     1.99)  ; 2\r\n          (    6.07    46.69     0.56     1.25)  ; 3\r\n          (    5.63    47.56     1.75     0.96)  ; 4\r\n          (    5.55    48.37     3.00     1.55)  ; 5\r\n          (    5.63    49.24     3.19     2.06)  ; 6\r\n          (\r\n            (    6.07    50.19     3.69     0.96)  ; 1, R-1-1-2-2-1\r\n            (    6.58    50.78     3.69     0.59)  ; 2\r\n            (    7.10    51.36     3.75     0.59)  ; 3\r\n            (    7.17    51.95     3.75     0.59)  ; 4\r\n            (    7.25    52.68     4.00     0.59)  ; 5\r\n            (    7.25    53.12     4.13     0.59)  ; 6\r\n            (    7.25    54.21     4.13     0.96)  ; 7\r\n            (    7.25    54.87     4.13     1.69)  ; 8\r\n            (    6.81    55.75     3.06     1.40)  ; 9\r\n            (    6.58    56.63     2.56     1.11)  ; 10\r\n            (    6.44    57.28     2.56     0.81)  ; 11\r\n            (    6.51    58.67     2.56     0.66)  ; 12\r\n            (    7.10    59.33     1.50     0.66)  ; 13\r\n            (    7.91    59.70     3.75     0.81)  ; 14\r\n            (    8.79    60.14     3.75     0.81)  ; 15\r\n            (    9.68    60.65     4.00     0.81)  ; 16\r\n            (   10.27    61.45     4.25     0.74)  ; 17\r\n            (   10.86    62.69     4.25     0.96)  ; 18\r\n            (   11.08    63.79     4.25     0.81)  ; 19\r\n            (   11.15    65.33     4.25     1.11)  ; 20\r\n            (   10.93    66.64     3.63     1.33)  ; 21\r\n            (   10.78    68.76     2.06     1.25)  ; 22\r\n            (   11.00    70.08     1.56     1.11)  ; 23\r\n            (   11.23    71.83     1.50     1.11)  ; 24\r\n            (   11.23    72.93     1.25     1.11)  ; 25\r\n            (   11.15    74.61     1.19     0.96)  ; 26\r\n            (   10.81    76.45     1.06     1.03)  ; 27\r\n            (   10.66    77.62     0.88     0.88)  ; 28\r\n            (   10.44    79.01     0.44     1.03)  ; 29\r\n            (    9.70    79.96     0.75     1.47)  ; 30\r\n            (    8.74    80.76     1.19     1.11)  ; 31\r\n            (    7.93    81.78     2.44     1.18)  ; 32\r\n            (    7.56    82.15     3.19     1.55)  ; 33\r\n            (    7.20    82.44     4.38     1.33)  ; 34\r\n            (    6.90    82.66     4.94     1.33)  ; 35\r\n            (    6.16    82.81     4.94     1.33)  ; 36\r\n            (    5.72    83.46     6.00     0.81)  ; 37\r\n            (    5.13    84.41     6.63     0.59)  ; 38\r\n            (    4.62    85.58     8.00     1.11)  ; 39\r\n            (    3.95    87.12     8.00     1.18)  ; 40\r\n            (    3.44    87.92     8.06     0.96)  ; 41\r\n            (    3.22    89.24     8.06     0.74)  ; 42\r\n            (    3.73    90.41     8.13     0.96)  ; 43\r\n            (    4.54    91.72     8.25     1.40)  ; 44\r\n            (    4.91    93.04     8.88     1.03)  ; 45\r\n            (    5.06    93.99     8.88     1.11)  ; 46\r\n            (    5.50    94.58     9.94     0.52)  ; 47\r\n            (    5.65    95.45     9.94     0.37)  ; 48\r\n            (    5.43    95.89     9.94     0.37)  ; 49\r\n            (    4.99    95.89    10.94     0.88)  ; 50\r\n            (    4.18    96.40    11.56     1.47)  ; 51\r\n            (    3.66    96.77    11.56     1.18)  ; 52\r\n            (    3.36    97.72    12.06     0.52)  ; 53\r\n            (    2.85    98.67    12.13     0.37)  ; 54\r\n            (    2.85    99.98    13.69     0.96)  ; 55\r\n            (    2.63   101.15    13.75     1.33)  ; 56\r\n            (    2.55   102.18    14.31     1.11)  ; 57\r\n            (    2.41   103.35    14.94     0.88)  ; 58\r\n            (    2.14   104.99    15.25     0.81)  ; 59\r\n            (    2.14   105.79    15.88     1.11)  ; 60\r\n            (    1.55   107.18    16.13     1.62)  ; 61\r\n            (    1.18   108.13    16.38     1.25)  ; 62\r\n            (    0.88   109.01    16.38     0.88)  ; 63\r\n            (    0.66   109.81    16.31     0.66)  ; 64\r\n            (   -0.44   110.91    16.06     0.52)  ; 65\r\n            (   -0.81   111.86    16.25     1.03)  ; 66\r\n            (   -1.03   112.96    15.75     1.33)  ; 67\r\n            (   -1.18   114.27    15.75     0.81)  ; 68\r\n            (   -0.74   115.22    15.75     0.66)  ; 69\r\n            (   -0.88   115.73    15.75     0.66)  ; 70\r\n            (   -1.18   116.68    15.75     0.66)  ; 71\r\n            (   -0.44   117.56    15.50     0.59)  ; 72\r\n            (    0.07   118.80    16.00     0.81)  ; 73\r\n            (    0.30   120.12    16.00     1.11)  ; 74\r\n            (    0.30   121.07    16.00     1.11)  ; 75\r\n            (   -0.07   122.02    16.00     0.96)  ; 76\r\n            (   -0.96   123.26    16.81     0.96)  ; 77\r\n            (   -1.10   124.21    17.56     0.74)  ; 78\r\n            (   -1.84   125.60    17.63     0.59)  ; 79\r\n            (   -2.65   126.11    18.75     0.66)  ; 80\r\n            (   -3.39   126.70    18.75     1.03)  ; 81\r\n            (   -3.68   126.92    18.75     1.11)  ; 82\r\n            (   -4.79   127.87    18.75     0.74)  ; 83\r\n            (   -5.82   128.31    18.75     0.74)  ; 84\r\n            (   -6.63   128.53    18.75     0.74)  ; 85\r\n            (   -6.78   129.62    18.75     0.74)  ; 86\r\n            (   -6.78   130.87    18.75     0.59)  ; 87\r\n            (   -6.41   131.82    18.75     0.59)  ; 88\r\n            (   -6.63   133.35    18.31     0.59)  ; 89\r\n            (   -6.67   134.80    18.25     0.66)  ; 90\r\n            (   -6.67   136.41    18.25     0.44)  ; 91\r\n            (   -6.67   136.77    18.25     0.44)  ; 92\r\n            (   -6.30   137.36    18.25     0.44)  ; 93\r\n            (   -5.35   138.31    18.56     0.44)  ; 94\r\n            (   -5.20   139.33    18.56     0.44)  ; 95\r\n             Low\r\n          |\r\n            (    4.77    50.01     3.13     1.11)  ; 1, R-1-1-2-2-2\r\n            (    3.88    50.01     3.56     0.59)  ; 2\r\n            (    3.44    50.45     3.56     0.59)  ; 3\r\n            (    2.71    50.60     3.50     0.59)  ; 4\r\n            (    1.38    50.45     3.31     0.74)  ; 5\r\n            (    0.05    50.52     2.81     0.88)  ; 6\r\n            (   -1.20    50.89     2.44     0.81)  ; 7\r\n            (   -2.38    50.89     2.13     0.81)  ; 8\r\n            (   -3.56    51.04     2.13     0.81)  ; 9\r\n            (   -4.81    51.11     2.13     0.81)  ; 10\r\n            (   -6.13    51.18     2.13     0.81)  ; 11\r\n            (   -7.09    51.77     1.81     0.88)  ; 12\r\n            (   -7.24    52.35     3.38     0.96)  ; 13\r\n            (   -7.31    53.30     3.44     1.03)  ; 14\r\n            (   -7.24    54.25     3.50     0.74)  ; 15\r\n            (   -7.31    55.35     3.50     0.74)  ; 16\r\n            (   -7.39    55.93     3.50     0.74)  ; 17\r\n            (   -7.61    56.44     3.50     0.74)  ; 18\r\n            (   -7.98    57.18     3.50     0.74)  ; 19\r\n            (   -8.42    57.25     3.06     0.74)  ; 20\r\n            (   -9.30    57.47     1.56     0.81)  ; 21\r\n            (  -10.41    57.91     1.56     1.55)  ; 22\r\n            (  -11.44    58.56     1.56     1.69)  ; 23\r\n            (  -12.47    59.15     0.88     1.69)  ; 24\r\n            (  -13.21    59.66     0.63     1.33)  ; 25\r\n            (  -13.72    60.17     0.38     0.81)  ; 26\r\n            (  -14.61    61.42     0.25     0.88)  ; 27\r\n            (  -14.98    62.58    -0.06     1.18)  ; 28\r\n            (  -15.27    63.46     0.25     0.88)  ; 29\r\n            (  -15.71    64.19     1.00     0.74)  ; 30\r\n            (  -16.38    65.00     1.63     1.11)  ; 31\r\n            (  -16.62    65.05     1.63     1.11)  ; 32\r\n            (\r\n              (  -17.04    65.36     2.13     1.62)  ; 1, R-1-1-2-2-2-1\r\n              (  -17.78    65.14     2.06     1.25)  ; 2\r\n              (  -18.88    65.58     2.00     0.74)  ; 3\r\n              (  -19.84    66.02     1.88     0.74)  ; 4\r\n              (  -20.50    66.68     1.75     0.96)  ; 5\r\n              (  -21.31    67.77     1.50     0.88)  ; 6\r\n              (  -22.20    68.87     1.06     1.11)  ; 7\r\n              (  -22.56    69.75    -0.38     1.03)  ; 8\r\n              (  -23.30    70.04    -0.69     0.66)  ; 9\r\n              (  -24.26    70.55    -1.38     0.59)  ; 10\r\n              (  -25.88    71.06    -1.94     0.52)  ; 11\r\n              (  -26.91    71.72    -1.38     0.52)  ; 12\r\n              (  -27.72    72.38    -0.56     0.59)  ; 13\r\n              (  -28.97    73.33     0.00     0.44)  ; 14\r\n              (  -30.00    73.04    -0.44     0.59)  ; 15\r\n              (  -30.45    72.23    -2.88     0.59)  ; 16\r\n              (  -30.67    71.58    -3.13     0.59)  ; 17\r\n              (  -31.18    70.55    -3.31     0.44)  ; 18\r\n              (  -31.77    70.48    -3.56     0.44)  ; 19\r\n              (  -32.14    70.48    -4.31     1.11)  ; 20\r\n              (  -32.80    70.48    -4.63     1.69)  ; 21\r\n              (  -33.54    70.84    -5.19     1.03)  ; 22\r\n              (  -34.57    71.36    -7.00     0.37)  ; 23\r\n              (  -35.09    72.09    -7.19     0.37)  ; 24\r\n              (  -35.16    72.38    -7.19     1.25)  ; 25\r\n              (  -35.90    73.33    -7.25     1.47)  ; 26\r\n              (  -36.34    73.91    -7.63     1.18)  ; 27\r\n              (  -37.15    75.08    -7.63     0.81)  ; 28\r\n              (  -37.89    76.11    -7.69     0.81)  ; 29\r\n              (  -38.40    76.77    -7.88     0.81)  ; 30\r\n              (  -39.36    77.13    -7.00     0.52)  ; 31\r\n              (  -40.10    77.13    -5.94     0.52)  ; 32\r\n              (  -41.13    77.20    -5.75     0.52)  ; 33\r\n              (  -41.65    77.28    -5.75     0.74)  ; 34\r\n              (  -42.24    77.47    -5.63     0.96)  ; 35\r\n              (  -42.68    77.69    -5.63     0.59)  ; 36\r\n              (  -43.64    78.20    -5.63     0.29)  ; 37\r\n              (  -44.23    78.28    -7.81     0.29)  ; 38\r\n              (  -43.78    78.64    -8.19     0.29)  ; 39\r\n              (  -44.37    79.01    -9.00     0.29)  ; 40\r\n              (  -44.89    79.30    -9.38     0.74)  ; 41\r\n              (  -45.40    79.74    -9.56     1.77)  ; 42\r\n              (  -45.70    80.03   -10.00     2.58)  ; 43\r\n              (  -45.92    80.18   -10.00     2.58)  ; 44\r\n              (  -46.73    80.47   -10.00     1.33)  ; 45\r\n              (  -47.25    80.91   -10.38     0.52)  ; 46\r\n              (  -47.61    80.98   -10.38     0.29)  ; 47\r\n              (  -48.50    81.57   -10.75     0.29)  ; 48\r\n              (  -49.16    82.44   -11.00     0.29)  ; 49\r\n              (  -48.65    82.88   -11.06     1.03)  ; 50\r\n              (  -49.75    83.39   -11.06     1.84)  ; 51\r\n              (  -49.82    83.39   -11.06     2.28)  ; 52\r\n               Low\r\n            |\r\n              (  -16.62    65.56     3.50     0.81)  ; 1, R-1-1-2-2-2-2\r\n              (  -16.77    66.51     3.63     0.81)  ; 2\r\n              (  -17.36    67.61     4.38     0.66)  ; 3\r\n              (  -18.10    68.12     4.56     0.81)  ; 4\r\n              (  -19.13    68.56     4.75     0.81)  ; 5\r\n              (  -19.72    69.07     5.13     0.96)  ; 6\r\n              (  -20.23    69.80     5.19     0.81)  ; 7\r\n              (  -20.82    70.68     5.19     1.18)  ; 8\r\n              (  -21.12    71.63     5.81     1.18)  ; 9\r\n              (  -21.63    72.29     7.13     0.81)  ; 10\r\n              (  -22.37    73.16     8.38     0.74)  ; 11\r\n              (  -23.03    73.53     9.00     0.88)  ; 12\r\n              (  -23.40    73.75     9.25     1.11)  ; 13\r\n              (  -24.06    74.04     9.69     1.33)  ; 14\r\n              (  -25.02    74.19    10.00     1.33)  ; 15\r\n              (  -25.69    74.11    10.25     1.33)  ; 16\r\n              (  -26.79    74.11    10.38     0.66)  ; 17\r\n              (  -27.31    74.48    11.25     0.96)  ; 18\r\n              (  -27.75    75.14    11.75     1.11)  ; 19\r\n              (  -28.93    76.31    11.81     0.81)  ; 20\r\n              (  -29.30    77.40    12.63     0.66)  ; 21\r\n              (  -29.59    78.35    12.75     0.52)  ; 22\r\n              (  -29.96    79.01    12.75     0.52)  ; 23\r\n              (  -30.77    79.45    12.81     0.52)  ; 24\r\n              (  -31.28    79.67    12.94     1.03)  ; 25\r\n              (  -31.95    80.11    13.13     1.62)  ; 26\r\n              (  -32.83    80.25    13.94     1.03)  ; 27\r\n              (  -34.23    80.55    14.06     0.52)  ; 28\r\n              (  -35.34    80.98    14.63     0.37)  ; 29\r\n              (  -36.29    81.13    14.81     0.37)  ; 30\r\n              (  -37.47    81.35    15.44     0.66)  ; 31\r\n              (  -38.21    81.13    15.56     0.66)  ; 32\r\n              (  -39.09    81.06    15.69     0.29)  ; 33\r\n              (  -39.54    80.91    16.25     0.29)  ; 34\r\n              (  -39.90    80.62    16.63     0.52)  ; 35\r\n              (  -40.49    80.47    16.69     0.52)  ; 36\r\n               Normal\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (    0.46    21.78   -12.69     3.39)  ; 1, R-1-2\r\n      (   -0.13    22.87   -12.94     2.28)  ; 2\r\n      (   -0.19    23.40   -12.94     2.28)  ; 3\r\n      (\r\n        (   -0.43    23.39   -13.25     1.92)  ; 1, R-1-2-1\r\n        (   -0.28    24.70   -13.81     2.21)  ; 2\r\n        (   -0.57    25.73   -13.81     2.14)  ; 3\r\n        (   -0.87    26.38   -14.38     2.14)  ; 4\r\n        (\r\n          (   -1.68    26.68   -12.69     1.25)  ; 1, R-1-2-1-1\r\n          (   -2.86    26.75   -11.88     1.11)  ; 2\r\n          (   -3.37    26.68   -11.19     1.33)  ; 3\r\n          (   -4.04    26.60   -10.94     1.62)  ; 4\r\n          (   -4.55    26.46   -10.50     1.92)  ; 5\r\n          (   -5.44    26.82   -10.50     1.77)  ; 6\r\n          (\r\n            (   -6.84    26.24   -10.50     0.96)  ; 1, R-1-2-1-1-1\r\n            (   -7.57    25.87   -10.25     0.88)  ; 2\r\n            (   -8.46    25.51   -10.44     0.74)  ; 3\r\n            (   -9.27    25.51   -10.50     0.74)  ; 4\r\n            (  -10.22    25.51   -10.50     1.03)  ; 5\r\n            (  -11.48    25.94   -10.81     1.03)  ; 6\r\n            (  -12.29    26.53   -11.94     1.03)  ; 7\r\n            (  -13.10    27.41   -13.81     0.88)  ; 8\r\n            (  -14.06    28.36   -14.88     0.88)  ; 9\r\n            (  -13.91    29.01   -15.50     0.88)  ; 10\r\n            (  -13.98    29.38   -16.56     1.03)  ; 11\r\n            (  -14.06    29.89   -18.06     0.81)  ; 12\r\n            (  -14.64    30.18   -18.38     0.74)  ; 13\r\n            (  -15.01    30.04   -19.75     0.81)  ; 14\r\n            (  -15.82    30.04   -20.25     0.81)  ; 15\r\n            (  -16.71    29.75   -20.25     0.81)  ; 16\r\n            (  -17.74    29.67   -20.38     0.66)  ; 17\r\n            (  -18.48    30.26   -20.75     0.66)  ; 18\r\n            (  -19.36    30.99   -20.75     0.81)  ; 19\r\n            (  -19.95    31.87   -20.56     1.03)  ; 20\r\n            (  -20.54    32.23   -20.31     1.03)  ; 21\r\n            (  -21.42    32.67   -20.06     1.03)  ; 22\r\n            (  -22.38    33.03   -19.94     1.03)  ; 23\r\n            (  -23.49    33.18   -19.75     1.18)  ; 24\r\n            (  -24.22    33.40   -19.44     0.88)  ; 25\r\n            (  -25.33    33.11   -19.19     0.88)  ; 26\r\n            (  -26.58    31.94   -19.13     0.96)  ; 27\r\n            (  -27.32    30.84   -19.19     0.96)  ; 28\r\n            (  -27.46    29.75   -19.69     1.11)  ; 29\r\n            (  -27.98    28.65   -20.19     0.96)  ; 30\r\n            (  -28.27    28.21   -20.56     0.96)  ; 31\r\n            (  -28.64    27.11   -20.75     0.96)  ; 32\r\n            (  -29.16    25.73   -21.19     0.81)  ; 33\r\n            (  -30.04    25.21   -22.38     0.66)  ; 34\r\n            (  -30.71    25.29   -22.94     0.96)  ; 35\r\n            (  -31.52    25.36   -23.25     0.96)  ; 36\r\n            (  -32.10    25.07   -24.06     0.96)  ; 37\r\n            (  -33.14    25.07   -24.69     0.96)  ; 38\r\n            (  -34.54    25.29   -24.81     0.96)  ; 39\r\n            (  -35.35    25.58   -25.19     0.96)  ; 40\r\n            (  -36.08    25.21   -25.75     0.81)  ; 41\r\n            (  -36.89    24.77   -26.19     0.81)  ; 42\r\n            (  -37.78    24.12   -26.44     0.88)  ; 43\r\n            (  -38.37    23.75   -26.88     0.66)  ; 44\r\n            (  -38.74    23.68   -28.31     0.66)  ; 45\r\n            (  -38.88    24.04   -29.13     0.66)  ; 46\r\n            (  -38.59    24.34   -29.25     0.66)  ; 47\r\n            (  -38.37    24.70   -30.63     0.66)  ; 48\r\n            (  -38.81    25.29   -31.25     0.66)  ; 49\r\n            (  -39.25    24.92   -31.25     0.88)  ; 50\r\n            (  -40.43    23.75   -31.56     0.66)  ; 51\r\n            (  -41.17    23.09   -31.63     0.66)  ; 52\r\n            (  -42.57    23.17   -31.63     0.66)  ; 53\r\n            (  -43.52    23.31   -31.63     1.03)  ; 54\r\n            (  -44.56    23.31   -31.88     1.25)  ; 55\r\n            (  -45.44    23.31   -32.13     1.25)  ; 56\r\n            (  -46.25    23.39   -32.25     1.11)  ; 57\r\n            (  -46.74    23.63   -32.38     0.52)  ; 58\r\n            (  -47.33    24.36   -32.50     0.37)  ; 59\r\n            (  -47.55    24.87   -32.50     1.03)  ; 60\r\n            (  -47.77    25.17   -32.50     1.62)  ; 61\r\n            (  -47.77    25.61   -32.50     2.43)  ; 62\r\n            (  -47.99    26.34   -32.63     1.62)  ; 63\r\n            (  -48.58    26.56   -32.63     1.03)  ; 64\r\n            (  -50.13    27.51   -32.63     0.81)  ; 65\r\n            (  -50.79    28.31   -32.81     0.88)  ; 66\r\n            (  -51.60    28.75   -32.81     0.88)  ; 67\r\n            (  -52.27    29.70   -32.94     1.11)  ; 68\r\n            (  -52.71    30.43   -32.94     0.81)  ; 69\r\n            (  -52.93    30.94   -32.94     0.52)  ; 70\r\n            (  -53.45    31.31   -33.06     0.37)  ; 71\r\n            (  -53.89    31.31   -32.88     0.66)  ; 72\r\n            (  -54.55    30.94   -32.38     0.88)  ; 73\r\n            (  -55.36    30.87   -34.69     1.18)  ; 74\r\n            (  -56.32    30.87   -35.19     1.18)  ; 75\r\n            (  -57.06    31.09   -35.19     0.74)  ; 76\r\n            (  -57.65    31.38   -35.38     0.44)  ; 77\r\n            (  -58.01    31.82   -35.13     0.44)  ; 78\r\n            (  -57.94    32.33   -34.38     0.44)  ; 79\r\n            (  -58.75    32.84   -34.38     0.44)  ; 80\r\n            (  -60.00    33.35   -34.38     0.44)  ; 81\r\n            (  -60.81    33.50   -34.38     0.74)  ; 82\r\n            (  -62.29    33.50   -34.38     1.03)  ; 83\r\n            (  -63.02    33.43   -34.38     1.03)  ; 84\r\n            (  -63.91    33.35   -34.38     0.74)  ; 85\r\n            (  -64.87    33.28   -34.38     0.44)  ; 86\r\n            (  -65.90    33.13   -34.38     0.44)  ; 87\r\n            (  -66.93    33.13   -34.38     0.66)  ; 88\r\n            (  -67.89    32.77   -34.38     0.96)  ; 89\r\n            (  -68.70    32.40   -34.38     0.96)  ; 90\r\n            (  -69.87    32.62   -34.38     0.74)  ; 91\r\n            (  -70.83    32.92   -34.50     0.52)  ; 92\r\n            (  -71.27    32.84   -35.06     0.37)  ; 93\r\n            (  -72.16    32.99   -35.06     0.37)  ; 94\r\n            (  -72.82    33.28   -35.06     0.37)  ; 95\r\n            (  -73.85    33.43   -35.06     0.37)  ; 96\r\n            (  -75.11    33.50   -35.31     0.37)  ; 97\r\n            (  -75.99    33.50   -35.81     0.37)  ; 98\r\n            (  -76.95    32.92   -35.88     0.37)  ; 99\r\n            (  -77.46    32.48   -35.88     0.37)  ; 100\r\n            (  -78.20    31.96   -36.69     1.03)  ; 101\r\n            (  -78.42    31.75   -37.50     2.06)  ; 102\r\n            (  -78.94    31.60   -37.88     2.87)  ; 103\r\n            (  -79.30    31.45   -37.94     3.24)  ; 104\r\n            (  -79.89    31.01   -38.75     2.80)  ; 105\r\n            (  -80.26    30.50   -39.50     1.40)  ; 106\r\n            (  -80.70    30.43   -39.94     0.59)  ; 107\r\n            (  -81.59    30.36   -40.38     0.37)  ; 108\r\n            (  -82.77    30.36   -40.75     0.29)  ; 109\r\n            (  -83.50    30.36   -41.00     0.29)  ; 110\r\n            (  -84.17    30.14   -41.56     0.66)  ; 111\r\n            (  -84.76    30.14   -41.56     1.03)  ; 112\r\n            (  -85.05    30.06   -41.81     1.84)  ; 113\r\n            (  -85.57    29.92   -42.50     2.21)  ; 114\r\n            (  -86.08    29.55   -42.94     2.21)  ; 115\r\n            (  -86.82    29.55   -43.44     1.18)  ; 116\r\n            (  -87.26    29.26   -42.81     0.44)  ; 117\r\n            (  -88.07    28.82   -42.38     0.22)  ; 118\r\n            (  -88.81    28.24   -42.38     0.22)  ; 119\r\n            (  -89.99    27.73   -42.38     0.22)  ; 120\r\n             Low\r\n          |\r\n            (   -6.59    27.80    -9.06     0.52)  ; 1, R-1-2-1-1-2\r\n            (   -7.18    28.24    -9.56     0.52)  ; 2\r\n            (   -7.92    28.60   -10.38     0.66)  ; 3\r\n            (   -8.58    28.75   -12.19     0.88)  ; 4\r\n            (   -8.80    28.09   -13.31     0.74)  ; 5\r\n            (   -8.95    27.36   -14.75     0.59)  ; 6\r\n            (   -9.32    26.48   -16.44     0.59)  ; 7\r\n            (   -9.68    26.26   -17.88     0.66)  ; 8\r\n            (  -10.27    25.68   -19.00     0.66)  ; 9\r\n            (  -11.01    25.46   -20.13     0.66)  ; 10\r\n            (  -11.75    25.46   -20.69     0.81)  ; 11\r\n            (  -12.85    25.82   -21.06     0.88)  ; 12\r\n            (  -13.51    26.04   -21.69     0.88)  ; 13\r\n            (  -14.18    26.41   -22.38     0.88)  ; 14\r\n            (  -14.55    26.41   -23.19     0.66)  ; 15\r\n            (  -15.06    25.97   -23.56     0.66)  ; 16\r\n            (  -15.58    25.46   -23.56     0.66)  ; 17\r\n            (  -15.95    24.73   -23.88     0.66)  ; 18\r\n            (  -16.54    24.22   -23.25     0.59)  ; 19\r\n            (  -17.64    23.85   -23.06     0.59)  ; 20\r\n            (  -18.45    23.63   -23.06     0.59)  ; 21\r\n            (  -18.97    23.41   -22.94     0.59)  ; 22\r\n            (  -19.04    22.97   -22.75     0.81)  ; 23\r\n            (  -18.97    22.32   -21.88     0.66)  ; 24\r\n            (  -18.45    22.10   -25.94     0.52)  ; 25\r\n            (  -18.52    22.10   -28.81     0.52)  ; 26\r\n            (  -18.89    22.24   -30.25     0.52)  ; 27\r\n            (  -19.34    22.68   -30.19     0.66)  ; 28\r\n            (  -20.29    22.97   -30.19     0.66)  ; 29\r\n            (  -21.18    22.46   -30.19     0.66)  ; 30\r\n            (  -22.06    22.32   -30.56     0.66)  ; 31\r\n            (  -22.65    22.68   -30.75     0.81)  ; 32\r\n            (  -23.39    22.75   -31.00     0.81)  ; 33\r\n            (  -24.42    22.83   -31.25     0.66)  ; 34\r\n            (  -25.08    22.61   -31.38     0.66)  ; 35\r\n            (  -25.23    21.73   -31.75     0.81)  ; 36\r\n            (  -25.52    20.93   -32.44     0.59)  ; 37\r\n            (  -26.19    20.27   -32.44     0.59)  ; 38\r\n            (  -27.07    20.34   -32.88     0.59)  ; 39\r\n            (  -28.32    20.63   -33.81     0.74)  ; 40\r\n            (  -28.40    20.63   -36.56     0.59)  ; 41\r\n            (  -28.47    21.07   -37.56     0.59)  ; 42\r\n            (  -28.62    21.58   -37.81     0.52)  ; 43\r\n            (  -28.91    22.24   -37.81     0.52)  ; 44\r\n            (  -29.43    22.97   -37.88     0.52)  ; 45\r\n            (  -29.80    22.83   -38.81     0.74)  ; 46\r\n            (  -30.09    22.68   -39.75     1.47)  ; 47\r\n            (  -30.46    22.46   -40.56     1.84)  ; 48\r\n            (  -30.90    22.53   -41.31     1.92)  ; 49\r\n            (  -31.64    22.53   -41.50     1.25)  ; 50\r\n            (  -32.15    22.75   -41.56     0.59)  ; 51\r\n            (  -32.60    23.27   -41.81     0.52)  ; 52\r\n            (  -33.11    23.85   -42.13     0.81)  ; 53\r\n            (  -33.85    24.22   -42.63     1.18)  ; 54\r\n            (  -34.22    24.51   -42.81     1.84)  ; 55\r\n            (  -34.81    25.02   -43.19     1.84)  ; 56\r\n            (  -35.10    25.17   -43.44     1.84)  ; 57\r\n            (  -35.40    25.90   -43.44     1.18)  ; 58\r\n            (  -35.54    26.48   -43.75     0.81)  ; 59\r\n            (  -35.76    27.29   -43.13     0.59)  ; 60\r\n            (  -35.69    27.73   -43.50     0.59)  ; 61\r\n            (  -35.47    28.02   -43.56     0.59)  ; 62\r\n            (  -35.76    29.19   -43.75     0.81)  ; 63\r\n            (  -35.99    29.84   -43.75     0.81)  ; 64\r\n            (  -36.21    30.58   -43.81     0.81)  ; 65\r\n            (  -36.57    31.53   -43.88     0.52)  ; 66\r\n            (  -36.80    32.26   -43.94     0.52)  ; 67\r\n            (  -37.02    32.84   -44.13     0.81)  ; 68\r\n            (  -37.31    33.79   -44.31     1.18)  ; 69\r\n            (  -37.83    34.60   -44.44     1.18)  ; 70\r\n            (  -38.12    35.33   -44.63     0.88)  ; 71\r\n            (  -38.93    36.42   -44.69     0.59)  ; 72\r\n            (  -39.23    37.23   -45.69     0.52)  ; 73\r\n            (  -39.74    38.18   -46.38     0.52)  ; 74\r\n            (  -40.55    39.79   -46.50     0.44)  ; 75\r\n            (  -40.92    41.18   -47.31     0.37)  ; 76\r\n            (  -41.58    41.61   -47.38     0.37)  ; 77\r\n            (  -42.25    41.61   -47.38     0.52)  ; 78\r\n            (  -43.21    42.56   -47.31     0.37)  ; 79\r\n            (  -44.09    42.93   -47.31     1.03)  ; 80\r\n            (  -44.46    43.22   -47.31     2.14)  ; 81\r\n            (  -44.97    43.37   -47.31     2.14)  ; 82\r\n            (  -45.34    43.59   -47.75     1.33)  ; 83\r\n            (  -46.00    44.32   -47.81     0.52)  ; 84\r\n            (  -46.52    44.83   -47.81     0.29)  ; 85\r\n            (  -46.89    45.20   -47.81     0.29)  ; 86\r\n            (  -47.33    45.71   -47.81     0.29)  ; 87\r\n            (  -47.85    46.22   -47.81     0.66)  ; 88\r\n            (  -48.51    46.58   -47.81     1.11)  ; 89\r\n            (  -49.03    46.80   -47.81     1.11)  ; 90\r\n            (  -49.61    47.10   -47.81     1.11)  ; 91\r\n            (  -50.06    47.32   -47.81     0.74)  ; 92\r\n            (  -50.72    47.90   -47.81     0.44)  ; 93\r\n            (  -51.16    48.41   -48.13     0.29)  ; 94\r\n            (  -51.53    49.58   -46.88     0.37)  ; 95\r\n            (  -51.82    50.02   -46.44     1.11)  ; 96\r\n            (  -52.12    50.90   -46.44     1.11)  ; 97\r\n            (  -52.34    51.48   -45.69     0.74)  ; 98\r\n            (  -52.64    51.92   -46.75     0.44)  ; 99\r\n            (  -52.93    52.58   -47.19     0.29)  ; 100\r\n            (  -53.64    53.75   -48.44     0.22)  ; 101\r\n            (  -54.15    54.19   -49.50     0.22)  ; 102\r\n            (  -54.81    54.41   -51.00     0.22)  ; 103\r\n            (  -56.58    54.85   -51.56     0.22)  ; 104\r\n            (  -57.39    54.78   -49.00     0.88)  ; 105\r\n            (  -58.06    54.71   -51.00     1.69)  ; 106\r\n            (  -58.42    54.49   -50.94     2.58)  ; 107\r\n            (  -59.16    55.00   -50.94     2.58)  ; 108\r\n            (  -60.12    55.14   -52.50     1.33)  ; 109\r\n            (  -61.22    54.71   -55.44     0.88)  ; 110\r\n            (  -61.96    54.34   -55.94     0.59)  ; 111\r\n            (  -62.62    54.34   -56.63     0.59)  ; 112\r\n            (  -63.51    54.19   -56.75     0.59)  ; 113\r\n            (  -64.17    54.19   -56.75     1.03)  ; 114\r\n            (  -64.98    54.19   -56.75     1.03)  ; 115\r\n            (  -65.20    54.27   -57.56     0.74)  ; 116\r\n            (  -66.01    54.85   -58.19     0.74)  ; 117\r\n            (  -66.75    55.14   -58.38     0.74)  ; 118\r\n            (  -67.19    55.22   -59.13     0.74)  ; 119\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (   -0.46    27.37   -13.81     0.81)  ; 1, R-1-2-1-2\r\n          (   -0.61    28.03   -14.06     0.52)  ; 2\r\n          (   -0.83    28.39   -14.25     0.52)  ; 3\r\n          (   -1.42    28.39   -14.69     0.74)  ; 4\r\n          (   -2.30    28.69   -14.38     1.03)  ; 5\r\n          (   -3.34    28.69   -14.06     1.11)  ; 6\r\n          (   -4.15    28.83   -13.63     0.88)  ; 7\r\n          (   -4.59    28.76   -13.31     0.59)  ; 8\r\n          (   -5.47    28.76   -13.69     0.37)  ; 9\r\n          (   -5.62    28.32   -14.44     0.37)  ; 10\r\n          (   -5.18    28.39   -16.94     0.96)  ; 11\r\n          (   -5.03    28.39   -18.13     0.96)  ; 12\r\n          (   -4.81    28.39   -19.44     0.96)  ; 13\r\n          (   -4.59    28.39   -21.13     0.96)  ; 14\r\n          (   -4.44    28.25   -22.25     1.18)  ; 15\r\n          (   -4.29    28.17   -22.88     1.03)  ; 16\r\n          (   -3.93    28.91   -23.81     0.81)  ; 17\r\n          (   -4.15    29.64   -24.69     0.66)  ; 18\r\n          (   -4.22    30.29   -25.50     0.81)  ; 19\r\n          (   -4.44    30.88   -26.19     0.81)  ; 20\r\n          (   -5.40    31.54   -26.56     0.88)  ; 21\r\n          (   -6.14    32.12   -27.25     1.11)  ; 22\r\n          (   -7.02    32.85   -28.75     0.96)  ; 23\r\n          (   -7.98    33.66   -29.44     0.81)  ; 24\r\n          (   -8.49    34.46   -29.94     0.59)  ; 25\r\n          (   -8.12    34.90   -32.44     0.88)  ; 26\r\n          (   -7.83    35.19   -32.56     0.88)  ; 27\r\n          (   -7.76    35.34   -35.06     1.11)  ; 28\r\n          (   -8.20    35.41   -35.38     0.74)  ; 29\r\n          (   -9.08    36.22   -35.44     0.59)  ; 30\r\n          (   -9.45    36.80   -36.00     0.59)  ; 31\r\n          (   -9.67    37.82   -36.38     0.88)  ; 32\r\n          (   -9.67    38.99   -36.63     1.03)  ; 33\r\n          (   -9.60    39.87   -36.63     0.74)  ; 34\r\n          (   -9.23    40.53   -37.13     0.44)  ; 35\r\n          (   -8.94    41.04   -37.19     0.44)  ; 36\r\n          (   -8.79    41.62   -37.56     0.74)  ; 37\r\n          (   -8.79    42.36   -37.75     1.11)  ; 38\r\n          (   -8.94    43.09   -38.31     1.11)  ; 39\r\n          (   -8.71    44.18   -37.06     0.88)  ; 40\r\n          (   -8.20    45.28   -38.06     1.03)  ; 41\r\n          (   -7.61    46.38   -38.31     0.88)  ; 42\r\n          (   -7.24    47.62   -38.31     0.66)  ; 43\r\n          (   -7.09    48.71   -38.31     0.59)  ; 44\r\n          (   -6.65    49.52   -39.06     0.59)  ; 45\r\n          (   -6.28    50.83   -39.44     0.81)  ; 46\r\n          (   -6.36    51.71   -40.25     0.81)  ; 47\r\n          (   -6.43    53.17   -40.25     0.81)  ; 48\r\n          (   -6.43    54.49   -40.25     0.59)  ; 49\r\n          (   -5.91    55.29   -40.31     0.88)  ; 50\r\n          (   -5.91    55.66   -40.75     1.25)  ; 51\r\n          (   -6.09    56.85   -40.75     0.74)  ; 52\r\n          (   -6.32    57.88   -40.88     0.44)  ; 53\r\n          (   -6.32    58.32   -41.06     0.44)  ; 54\r\n          (   -6.24    59.49   -41.13     0.66)  ; 55\r\n          (   -6.02    60.80   -41.94     0.88)  ; 56\r\n          (   -6.02    61.68   -43.31     1.25)  ; 57\r\n          (   -5.87    62.26   -43.44     1.62)  ; 58\r\n          (   -5.87    62.34   -44.38     1.99)  ; 59\r\n          (   -5.80    63.14   -43.63     2.50)  ; 60\r\n          (   -5.80    63.65   -44.44     1.69)  ; 61\r\n          (   -5.65    64.02   -45.69     1.03)  ; 62\r\n          (   -5.36    64.46   -47.75     0.74)  ; 63\r\n          (   -4.77    64.75   -47.81     0.52)  ; 64\r\n          (   -4.47    65.26   -48.56     0.37)  ; 65\r\n          (   -4.03    65.70   -49.38     0.37)  ; 66\r\n          (   -3.44    66.58   -49.69     0.74)  ; 67\r\n          (   -3.07    67.45   -50.31     0.88)  ; 68\r\n          (   -2.78    68.19   -50.75     0.88)  ; 69\r\n          (   -2.48    68.77   -52.00     0.66)  ; 70\r\n          (   -2.12    69.50   -52.13     0.59)  ; 71\r\n          (   -1.38    70.16   -52.50     0.37)  ; 72\r\n          (   -0.72    70.89   -53.75     0.37)  ; 73\r\n          (   -0.13    71.47   -53.94     0.66)  ; 74\r\n          (    0.54    72.64   -53.00     0.74)  ; 75\r\n          (    1.35    73.67   -53.56     0.59)  ; 76\r\n          (    1.64    74.40   -54.13     0.59)  ; 77\r\n          (    2.16    75.71   -54.38     0.52)  ; 78\r\n          (    2.67    77.25   -55.25     0.44)  ; 79\r\n          (    2.97    78.49   -55.75     0.37)  ; 80\r\n          (    3.19    79.73   -56.13     0.37)  ; 81\r\n          (    3.26    80.90   -56.50     0.37)  ; 82\r\n          (    3.70    82.07   -56.50     0.37)  ; 83\r\n          (    3.93    83.02   -56.50     0.59)  ; 84\r\n          (    4.07    84.19   -56.75     0.81)  ; 85\r\n          (    4.37    85.29   -57.50     1.25)  ; 86\r\n          (    4.54    86.40   -57.88     1.92)  ; 87\r\n          (    4.76    87.13   -58.31     2.50)  ; 88\r\n          (    5.06    87.79   -58.81     2.14)  ; 89\r\n          (    5.13    88.23   -59.06     1.69)  ; 90\r\n          (    5.43    88.81   -59.25     1.25)  ; 91\r\n          (    5.57    89.25   -59.44     0.81)  ; 92\r\n          (    5.72    89.76   -59.63     0.52)  ; 93\r\n          (    5.65    91.08   -60.00     0.29)  ; 94\r\n          (    5.50    92.40   -60.38     0.29)  ; 95\r\n          (    4.91    93.42   -60.50     0.29)  ; 96\r\n          (    4.10    94.37   -60.50     0.29)  ; 97\r\n          (    3.44    94.95   -60.44     0.29)  ; 98\r\n          (    2.85    95.25   -60.00     0.66)  ; 99\r\n          (    2.33    95.98   -60.00     0.96)  ; 100\r\n          (    1.89    96.42   -60.00     0.66)  ; 101\r\n          (    1.52    97.07   -60.06     0.37)  ; 102\r\n          (    0.86    98.10   -60.06     0.29)  ; 103\r\n          (    0.71    98.97   -60.38     0.29)  ; 104\r\n          (    0.56    99.85   -60.63     0.66)  ; 105\r\n          (    0.56   100.44   -61.00     1.47)  ; 106\r\n          (    0.49   101.39   -61.50     2.21)  ; 107\r\n          (    0.49   101.68   -62.13     1.84)  ; 108\r\n          (    0.42   102.19   -62.13     1.11)  ; 109\r\n          (    0.42   102.70   -62.50     0.74)  ; 110\r\n          (    0.34   103.21   -63.06     0.44)  ; 111\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (   -0.42    23.55   -15.94     0.44)  ; 1, R-1-2-2\r\n        (   -0.71    23.69   -18.88     0.44)  ; 2\r\n        (   -1.30    23.55   -19.63     0.44)  ; 3\r\n        (   -1.45    22.96   -19.63     0.44)  ; 4\r\n        (   -2.04    22.16   -21.75     0.52)  ; 5\r\n        (   -2.33    21.28   -24.31     0.59)  ; 6\r\n        (   -2.26    20.55   -27.31     0.59)  ; 7\r\n        (   -1.89    20.18   -29.56     0.59)  ; 8\r\n        (   -1.89    19.75   -30.13     0.59)  ; 9\r\n        (   -2.40    19.09   -31.44     0.59)  ; 10\r\n        (   -3.22    19.02   -31.44     0.59)  ; 11\r\n        (   -4.25    19.02   -32.69     0.74)  ; 12\r\n        (   -4.76    19.09   -34.06     0.96)  ; 13\r\n        (   -5.28    19.09   -36.69     1.18)  ; 14\r\n        (   -5.35    19.09   -38.00     1.40)  ; 15\r\n        (   -5.35    19.09   -40.63     1.11)  ; 16\r\n        (   -5.43    19.02   -41.94     0.81)  ; 17\r\n        (   -5.35    19.45   -44.31     0.81)  ; 18\r\n        (   -5.20    20.26   -45.13     0.81)  ; 19\r\n        (   -5.57    20.48   -45.81     0.81)  ; 20\r\n        (   -6.16    20.62   -46.56     0.81)  ; 21\r\n        (   -6.53    20.62   -48.63     0.81)  ; 22\r\n        (   -7.27    20.18   -50.19     0.81)  ; 23\r\n        (   -7.56    19.97   -51.25     0.81)  ; 24\r\n        (   -8.30    19.53   -54.75     0.81)  ; 25\r\n        (   -8.96    19.60   -56.25     0.81)  ; 26\r\n        (   -9.62    19.23   -56.25     0.81)  ; 27\r\n        (   -9.77    18.72   -56.56     0.81)  ; 28\r\n        (  -10.88    18.21   -58.94     0.81)  ; 29\r\n        (  -10.95    17.70   -60.00     0.81)  ; 30\r\n        (  -11.17    16.24   -60.00     0.81)  ; 31\r\n        (  -10.58    15.65   -60.13     0.81)  ; 32\r\n        (   -9.85    15.43   -60.94     1.11)  ; 33\r\n        (   -9.18    14.92   -62.88     1.33)  ; 34\r\n        (   -8.74    14.34   -62.88     1.33)  ; 35\r\n        (   -8.08    13.53   -64.19     1.33)  ; 36\r\n        (   -8.45    12.87   -65.25     1.33)  ; 37\r\n        (   -7.86    11.92   -66.00     1.33)  ; 38\r\n        (   -7.27    11.27   -66.25     0.96)  ; 39\r\n        (   -6.83    10.39   -67.06     0.66)  ; 40\r\n        (   -6.53     9.88   -65.06     0.44)  ; 41\r\n        (   -6.31     8.64   -65.00     0.29)  ; 42\r\n        (   -6.60     7.03   -65.00     0.29)  ; 43\r\n        (   -6.83     6.00   -65.00     0.29)  ; 44\r\n        (   -6.90     5.27   -65.56     0.66)  ; 45\r\n        (   -7.05     4.61   -66.94     0.96)  ; 46\r\n        (   -7.34     4.03   -67.00     0.96)  ; 47\r\n        (   -7.93     3.37   -68.31     0.96)  ; 48\r\n        (   -8.22     3.01   -68.31     0.59)  ; 49\r\n        (   -7.78     1.84   -68.31     0.59)  ; 50\r\n        (   -8.08     1.25   -68.75     1.33)  ; 51\r\n        (   -8.08     0.74   -69.63     1.77)  ; 52\r\n        (   -8.08     0.74   -70.19     2.21)  ; 53\r\n        (   -8.30     0.59   -70.94     2.65)  ; 54\r\n        (   -8.59    -0.21   -73.63     1.03)  ; 55\r\n        (   -9.04    -0.72   -75.19     0.66)  ; 56\r\n        (   -9.40    -0.79   -78.25     0.44)  ; 57\r\n        (   -9.99    -0.72   -80.63     0.74)  ; 58\r\n        (  -10.21    -0.14   -82.75     1.03)  ; 59\r\n         Incomplete\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (   -0.04    20.53   -13.94     1.69)  ; 1, R-2\r\n    (   -0.99    20.68   -13.94     1.40)  ; 2\r\n    (   -1.44    20.46   -14.38     1.03)  ; 3\r\n    (   -2.39    20.24   -13.81     0.74)  ; 4\r\n    (   -3.13    20.24   -13.69     0.66)  ; 5\r\n    (   -3.79    20.46   -13.56     0.88)  ; 6\r\n    (   -4.31    21.41   -13.44     0.74)  ; 7\r\n    (   -4.60    21.70   -13.38     0.96)  ; 8\r\n    (   -5.12    21.04   -13.63     0.59)  ; 9\r\n    (   -5.19    20.60   -14.75     0.59)  ; 10\r\n    (   -5.56    20.24   -15.50     0.59)  ; 11\r\n    (   -6.37    19.58   -15.56     0.59)  ; 12\r\n    (   -6.89    19.07   -15.56     0.88)  ; 13\r\n    (   -7.48    18.48   -15.75     1.25)  ; 14\r\n    (   -8.07    17.97   -15.94     1.25)  ; 15\r\n    (   -8.80    17.61   -16.31     0.88)  ; 16\r\n    (   -9.91    17.53   -16.63     0.88)  ; 17\r\n    (  -10.50    17.32   -16.69     0.88)  ; 18\r\n    (  -11.60    17.24   -16.69     0.88)  ; 19\r\n    (  -12.27    17.17   -17.88     1.18)  ; 20\r\n    (  -12.78    16.95   -18.13     1.18)  ; 21\r\n    (  -13.89    16.95   -18.50     0.96)  ; 22\r\n    (  -14.84    17.02   -18.88     0.81)  ; 23\r\n    (  -14.99    16.51   -20.00     0.81)  ; 24\r\n    (  -15.29    15.85   -20.50     0.88)  ; 25\r\n    (  -15.95    15.05   -21.38     0.88)  ; 26\r\n    (  -16.39    14.24   -21.94     0.88)  ; 27\r\n    (  -17.28    13.59   -21.88     0.88)  ; 28\r\n    (  -18.16    12.86   -21.88     0.74)  ; 29\r\n    (  -18.68    12.42   -22.50     0.74)  ; 30\r\n    (  -19.26    11.98   -22.44     0.74)  ; 31\r\n    (  -20.15    11.76   -21.75     0.88)  ; 32\r\n    (  -21.25    11.76   -21.75     0.88)  ; 33\r\n    (  -21.92    11.76   -21.75     0.88)  ; 34\r\n    (  -23.10    11.83   -21.81     0.88)  ; 35\r\n    (  -24.20    11.91   -21.44     0.88)  ; 36\r\n    (  -24.94    11.91   -21.50     0.88)  ; 37\r\n    (  -25.60    11.54   -21.94     0.88)  ; 38\r\n    (  -26.04    11.39   -21.94     1.62)  ; 39\r\n    (  -26.85    11.32   -22.00     1.92)  ; 40\r\n    (  -27.44    11.03   -22.06     1.18)  ; 41\r\n    (  -28.33    10.66   -22.06     0.81)  ; 42\r\n    (  -28.99    10.37   -22.44     0.59)  ; 43\r\n    (  -30.17     9.86   -22.75     0.59)  ; 44\r\n    (  -31.20     9.64   -22.75     0.59)  ; 45\r\n    (  -31.86    10.15   -23.13     0.59)  ; 46\r\n    (  -31.72    10.96   -23.25     0.59)  ; 47\r\n    (  -31.72    11.69   -23.00     0.59)  ; 48\r\n    (  -32.16    12.71   -23.00     0.66)  ; 49\r\n    (  -32.67    13.00   -23.19     0.66)  ; 50\r\n    (  -33.56    13.15   -23.50     0.74)  ; 51\r\n    (  -34.52    13.15   -24.69     0.74)  ; 52\r\n    (  -35.25    13.73   -26.06     1.03)  ; 53\r\n    (  -35.91    14.32   -27.56     0.88)  ; 54\r\n    (  -36.43    14.54   -27.63     0.66)  ; 55\r\n    (  -36.87    15.20   -27.88     0.66)  ; 56\r\n    (  -37.02    16.00   -28.25     0.66)  ; 57\r\n    (  -36.80    16.88   -28.81     0.59)  ; 58\r\n    (  -36.65    17.53   -28.88     0.44)  ; 59\r\n    (  -37.24    17.97   -29.19     0.29)  ; 60\r\n    (  -37.76    18.27   -29.31     0.29)  ; 61\r\n    (  -37.76    19.22   -29.50     0.52)  ; 62\r\n    (  -37.76    19.43   -30.25     0.81)  ; 63\r\n    (  -38.13    19.80   -31.13     1.11)  ; 64\r\n    (  -38.57    20.24   -31.31     1.11)  ; 65\r\n    (  -38.86    20.90   -31.69     0.81)  ; 66\r\n    (  -38.94    21.63   -31.69     0.52)  ; 67\r\n    (  -38.79    22.14   -31.69     0.37)  ; 68\r\n    (  -38.57    23.02   -32.06     0.59)  ; 69\r\n    (  -38.64    23.53   -32.44     0.88)  ; 70\r\n    (  -38.79    23.97   -32.44     1.33)  ; 71\r\n    (  -38.86    24.70   -32.75     1.62)  ; 72\r\n    (  -38.86    25.28   -32.94     1.62)  ; 73\r\n    (  -38.94    25.65   -33.25     1.25)  ; 74\r\n    (  -39.08    26.09   -33.25     0.81)  ; 75\r\n    (  -39.08    26.53   -33.44     0.52)  ; 76\r\n    (  -39.23    27.04   -33.50     0.29)  ; 77\r\n    (  -39.01    27.77   -33.94     0.29)  ; 78\r\n    (  -39.08    28.50   -34.06     0.52)  ; 79\r\n    (  -39.16    28.57   -34.69     0.96)  ; 80\r\n    (  -39.45    28.79   -34.94     1.40)  ; 81\r\n    (  -40.04    28.94   -35.31     1.40)  ; 82\r\n    (  -40.92    29.23   -35.88     1.03)  ; 83\r\n    (  -41.37    29.30   -37.25     0.66)  ; 84\r\n    (  -42.25    29.67   -37.31     0.44)  ; 85\r\n    (  -43.36    30.40   -38.06     0.37)  ; 86\r\n    (  -43.80    30.40   -38.38     0.37)  ; 87\r\n    (  -44.68    30.55   -38.69     0.37)  ; 88\r\n    (  -45.12    30.55   -38.69     0.74)  ; 89\r\n    (  -46.16    30.40   -38.75     0.96)  ; 90\r\n    (  -47.08    30.26   -39.25     0.96)  ; 91\r\n    (  -48.04    30.40   -39.25     1.18)  ; 92\r\n    (  -48.92    30.40   -39.63     1.18)  ; 93\r\n    (  -49.80    30.48   -39.63     1.47)  ; 94\r\n    (  -50.54    30.62   -38.81     0.66)  ; 95\r\n    (  -51.35    30.77   -38.50     0.37)  ; 96\r\n    (  -52.60    31.28   -38.50     0.37)  ; 97\r\n    (  -53.34    31.21   -37.63     0.74)  ; 98\r\n    (  -54.22    31.21   -36.69     0.96)  ; 99\r\n    (  -54.74    31.43   -35.75     0.74)  ; 100\r\n    (  -55.55    31.72   -35.38     0.59)  ; 101\r\n    (  -56.36    31.87   -34.56     0.44)  ; 102\r\n    (  -56.88    32.74   -33.75     0.44)  ; 103\r\n    (  -57.47    33.55   -33.50     0.52)  ; 104\r\n    (  -58.06    34.06   -33.06     0.52)  ; 105\r\n    (  -58.94    34.50   -32.81     0.52)  ; 106\r\n    (  -59.75    35.08   -32.25     1.47)  ; 107\r\n    (  -60.49    35.38   -32.19     2.14)  ; 108\r\n    (  -60.86    35.89   -32.19     2.14)  ; 109\r\n    (  -61.59    36.33   -32.13     1.33)  ; 110\r\n    (  -62.11    36.84   -32.13     0.59)  ; 111\r\n    (  -63.07    37.50   -32.19     0.44)  ; 112\r\n    (  -64.10    37.79   -32.69     0.44)  ; 113\r\n    (  -64.91    37.79   -32.81     0.44)  ; 114\r\n    (  -65.72    37.71   -32.81     0.44)  ; 115\r\n    (  -66.45    37.64   -32.94     0.44)  ; 116\r\n    (  -67.26    37.93   -32.94     0.37)  ; 117\r\n    (  -67.78    38.01   -33.19     1.11)  ; 118\r\n    (  -68.30    38.01   -33.75     1.47)  ; 119\r\n    (  -68.89    38.01   -33.75     1.84)  ; 120\r\n    (  -69.55    38.01   -33.88     1.11)  ; 121\r\n    (  -70.06    38.01   -34.06     0.74)  ; 122\r\n    (  -70.51    37.93   -34.19     0.37)  ; 123\r\n    (  -71.32    37.50   -34.63     0.15)  ; 124\r\n     Low\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color DarkYellow)\r\n  (Dendrite)\r\n  (   14.76     2.09    -2.75     4.79)  ; Root\r\n  (   17.05     2.52    -2.94     3.32)  ; 1, R\r\n  (   18.15     2.52    -2.94     3.09)  ; 2\r\n  (   20.29     3.77    -2.56     3.39)  ; 3\r\n  (   21.39     3.99    -2.31     4.20)  ; 4\r\n  (\r\n    (   22.50     4.86    -3.75     3.90)  ; 1, R-1\r\n    (   23.60     4.57    -4.69     3.32)  ; 2\r\n    (   24.78     4.21    -5.06     2.73)  ; 3\r\n    (   26.33     4.13    -3.81     2.73)  ; 4\r\n    (   27.14     3.77    -3.44     3.24)  ; 5\r\n    (\r\n      (   28.83     4.13    -1.88     2.36)  ; 1, R-1-1\r\n      (   30.16     3.91    -1.06     2.36)  ; 2\r\n      (   31.41     3.91     0.25     2.06)  ; 3\r\n      (   32.52     4.06     2.50     1.84)  ; 4\r\n      (\r\n        (   33.55     4.57     3.50     1.33)  ; 1, R-1-1-1\r\n        (   34.21     5.52     3.75     0.96)  ; 2\r\n        (   34.95     6.25     3.75     0.81)  ; 3\r\n        (   35.46     7.35     4.00     0.96)  ; 4\r\n        (   35.76     7.79     4.25     1.25)  ; 5\r\n        (   35.98     8.15     4.00     1.25)  ; 6\r\n        (\r\n          (   36.05     9.47     3.56     0.74)  ; 1, R-1-1-1-1\r\n          (   36.72    10.27     3.25     0.74)  ; 2\r\n          (   37.31    11.30     3.25     0.88)  ; 3\r\n          (   37.67    11.88     3.25     0.88)  ; 4\r\n          (   37.97    13.05     3.31     0.88)  ; 5\r\n          (   38.48    14.07     4.44     0.81)  ; 6\r\n          (   39.00    14.73     4.50     0.81)  ; 7\r\n          (   40.10    15.39     4.75     0.81)  ; 8\r\n          (   41.06    16.19     4.81     0.66)  ; 9\r\n          (   41.58    16.92     5.06     0.66)  ; 10\r\n          (   41.80    18.17     5.56     0.74)  ; 11\r\n          (   41.95    19.26     5.19     0.88)  ; 12\r\n          (   42.54    20.21     4.56     0.88)  ; 13\r\n          (   43.13    21.16     4.44     0.96)  ; 14\r\n          (   43.13    22.55     4.13     1.11)  ; 15\r\n          (   43.42    23.36     4.13     0.96)  ; 16\r\n          (   43.64    24.09     4.06     0.81)  ; 17\r\n          (   43.79    24.82     3.81     0.81)  ; 18\r\n          (   44.23    25.77     4.19     0.81)  ; 19\r\n          (   44.82    26.35     4.13     0.96)  ; 20\r\n          (   45.41    27.23     3.88     0.96)  ; 21\r\n          (   45.48    28.33     3.31     0.81)  ; 22\r\n          (   45.34    29.64     2.69     0.88)  ; 23\r\n          (   45.26    31.29     1.94     0.96)  ; 24\r\n          (   46.07    32.38     1.75     0.81)  ; 25\r\n          (   46.29    33.48     2.88     1.03)  ; 26\r\n          (   46.58    34.28     3.56     1.33)  ; 27\r\n          (   46.29    35.31     4.06     1.11)  ; 28\r\n          (   46.58    36.40     5.31     0.88)  ; 29\r\n          (   46.73    37.13     5.38     0.88)  ; 30\r\n          (   47.03    37.65     5.38     0.88)  ; 31\r\n          (   47.32    38.16     6.00     1.11)  ; 32\r\n          (   47.32    38.96     6.31     0.88)  ; 33\r\n          (   47.40    39.91     6.50     0.81)  ; 34\r\n          (   47.76    40.86     6.81     0.96)  ; 35\r\n          (   48.57    42.18     6.50     0.88)  ; 36\r\n          (   49.53    43.13     6.50     0.81)  ; 37\r\n          (   50.64    43.93     6.44     0.74)  ; 38\r\n          (   51.96    45.25     5.75     0.59)  ; 39\r\n          (   52.99    45.25     5.38     0.52)  ; 40\r\n          (   53.29    45.83     5.13     0.52)  ; 41\r\n          (   53.58    46.78     5.13     0.52)  ; 42\r\n          (   53.73    47.29     5.13     1.33)  ; 43\r\n          (   54.25    47.88     4.75     1.33)  ; 44\r\n          (   54.98    48.17     4.38     0.81)  ; 45\r\n          (   56.01    48.39     4.19     0.59)  ; 46\r\n          (   56.68    47.73     3.94     0.81)  ; 47\r\n          (   57.56    47.29     3.94     0.81)  ; 48\r\n          (   58.15    47.08     3.44     0.81)  ; 49\r\n          (   58.81    46.78     3.44     0.66)  ; 50\r\n          (   59.55    46.71     2.94     0.52)  ; 51\r\n          (   59.55    46.34     1.94     0.37)  ; 52\r\n          (   59.33    45.98     1.25     0.37)  ; 53\r\n          (   59.11    45.32     1.25     0.37)  ; 54\r\n          (   59.04    44.88     0.38     0.37)  ; 55\r\n          (   59.18    44.66    -0.25     0.37)  ; 56\r\n          (   59.62    44.37    -0.44     0.37)  ; 57\r\n          (   60.14    44.59    -0.44     0.37)  ; 58\r\n          (   60.88    44.96    -0.44     0.59)  ; 59\r\n          (   61.76    45.17    -1.00     0.88)  ; 60\r\n          (   61.98    45.25    -1.13     1.25)  ; 61\r\n          (   62.57    45.32    -2.50     1.55)  ; 62\r\n          (   63.16    45.25    -2.50     1.55)  ; 63\r\n          (   63.46    44.81    -3.13     1.33)  ; 64\r\n          (   63.68    44.52    -3.94     1.03)  ; 65\r\n          (   63.97    43.79    -5.56     0.81)  ; 66\r\n          (   64.12    42.91    -6.81     0.66)  ; 67\r\n          (   64.19    42.54    -7.06     0.96)  ; 68\r\n          (   64.34    42.47    -7.69     1.25)  ; 69\r\n          (   65.00    41.89    -8.25     0.59)  ; 70\r\n          (   65.59    41.74    -8.44     0.59)  ; 71\r\n          (   65.96    41.45    -9.31     0.88)  ; 72\r\n          (   66.40    41.01    -9.63     1.25)  ; 73\r\n          (   66.77    40.72   -10.69     1.62)  ; 74\r\n          (   67.73    40.13   -11.44     0.81)  ; 75\r\n          (   68.10    39.69   -11.56     0.52)  ; 76\r\n          (   68.47    38.89   -12.44     0.81)  ; 77\r\n          (   68.83    38.30   -13.31     1.47)  ; 78\r\n          (   68.83    38.01   -14.13     2.28)  ; 79\r\n          (   69.20    37.35   -15.56     1.40)  ; 80\r\n          (   69.20    36.55   -16.06     0.96)  ; 81\r\n          (   69.13    35.53   -16.06     0.52)  ; 82\r\n          (   69.05    34.14   -16.69     0.52)  ; 83\r\n          (   69.28    33.70   -15.63     0.96)  ; 84\r\n          (   69.42    32.89   -16.00     0.66)  ; 85\r\n          (   69.05    32.09   -15.75     0.29)  ; 86\r\n          (   68.76    31.43   -17.19     0.29)  ; 87\r\n          (   68.10    30.63   -18.06     0.29)  ; 88\r\n          (   67.66    30.34   -18.13     0.29)  ; 89\r\n          (   67.14    29.82   -19.00     0.66)  ; 90\r\n          (   67.07    29.60   -20.13     1.40)  ; 91\r\n          (   67.07    29.02   -20.75     1.77)  ; 92\r\n           Low\r\n        |\r\n          (   36.49     8.63     4.38     0.81)  ; 1, R-1-1-1-2\r\n          (   37.23     8.92     4.75     0.81)  ; 2\r\n          (   38.19     8.84     5.25     0.74)  ; 3\r\n          (   39.29     8.70     6.06     0.74)  ; 4\r\n          (   40.03     9.06     7.19     0.88)  ; 5\r\n          (   40.69     9.87     8.13     0.88)  ; 6\r\n          (   40.99    10.89     8.38     0.88)  ; 7\r\n          (   41.28    11.77     8.50     0.66)  ; 8\r\n          (   41.72    12.50     8.69     0.66)  ; 9\r\n          (   42.53    13.38     8.31     0.81)  ; 10\r\n          (   43.27    13.67     7.56     0.81)  ; 11\r\n          (   43.56    14.18     7.06     0.66)  ; 12\r\n          (   44.37    14.11     9.75     0.66)  ; 13\r\n          (   45.55    14.33    10.00     0.66)  ; 14\r\n          (   46.73    14.77    10.63     0.66)  ; 15\r\n          (   47.32    15.28     9.13     0.96)  ; 16\r\n          (   47.62    16.45     8.06     0.81)  ; 17\r\n          (   47.84    17.32    10.63     1.11)  ; 18\r\n          (   48.13    18.05    11.00     0.88)  ; 19\r\n          (   49.16    18.71    11.25     0.74)  ; 20\r\n          (   49.83    18.93    10.13     0.74)  ; 21\r\n          (   50.86    19.37     9.88     0.96)  ; 22\r\n          (   51.67    20.03    10.50     1.03)  ; 23\r\n          (   52.77    20.54     9.81     1.47)  ; 24\r\n          (   53.88    21.13     9.38     1.77)  ; 25\r\n          (   54.69    21.34     9.00     1.03)  ; 26\r\n          (   55.13    21.64     9.44     0.74)  ; 27\r\n          (   55.72    22.08     9.44     0.59)  ; 28\r\n          (   56.75    22.73     9.63     0.81)  ; 29\r\n          (   57.64    22.81     9.63     0.81)  ; 30\r\n          (   58.52    22.59     9.69     0.66)  ; 31\r\n          (   59.70    22.37    11.31     0.81)  ; 32\r\n          (   61.25    22.66    11.38     0.81)  ; 33\r\n          (   61.91    22.88    12.31     1.03)  ; 34\r\n          (   62.65    23.10    12.63     1.03)  ; 35\r\n          (   63.38    23.61    13.00     1.25)  ; 36\r\n          (   64.27    23.76    13.13     1.55)  ; 37\r\n          (   64.86    24.05    13.25     1.55)  ; 38\r\n          (   65.52    24.27    13.63     0.81)  ; 39\r\n          (   66.48    24.41    14.00     0.66)  ; 40\r\n          (   67.36    24.56    14.50     0.88)  ; 41\r\n          (   68.47    24.56    14.00     0.88)  ; 42\r\n          (   69.13    24.85    13.50     1.03)  ; 43\r\n          (   70.31    25.15    14.63     0.81)  ; 44\r\n          (   71.41    25.29    14.69     0.81)  ; 45\r\n          (   72.44    25.29    14.88     0.81)  ; 46\r\n          (   73.25    25.07    15.25     0.74)  ; 47\r\n          (   74.14    25.36    15.25     0.74)  ; 48\r\n          (   75.10    25.66    14.94     0.74)  ; 49\r\n          (   76.20    26.10    16.06     0.81)  ; 50\r\n          (   77.38    25.73    16.63     0.96)  ; 51\r\n          (   78.49    25.51    16.69     0.88)  ; 52\r\n          (   79.22    24.93    17.19     0.81)  ; 53\r\n          (   80.03    23.90    17.69     0.96)  ; 54\r\n          (   80.55    23.46    16.69     1.18)  ; 55\r\n          (   82.02    22.66    16.69     0.81)  ; 56\r\n          (   82.61    22.59    16.06     0.81)  ; 57\r\n          (   83.57    22.22    17.56     1.03)  ; 58\r\n          (   84.31    22.00    17.94     1.33)  ; 59\r\n          (   84.89    21.71    17.38     1.33)  ; 60\r\n          (   85.48    21.56    17.00     0.96)  ; 61\r\n          (   86.07    21.64    17.00     0.66)  ; 62\r\n          (   86.74    21.56    17.00     0.66)  ; 63\r\n          (   87.47    21.34    17.00     0.66)  ; 64\r\n          (   88.14    21.49    16.25     0.66)  ; 65\r\n          (   88.73    21.86    17.19     0.52)  ; 66\r\n          (   88.95    22.44    19.00     0.52)  ; 67\r\n          (   89.02    23.03    19.31     0.52)  ; 68\r\n          (   90.13    22.59    19.63     0.52)  ; 69\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (   32.59     4.24     3.06     0.81)  ; 1, R-1-1-2\r\n        (   32.59     4.46     4.56     0.81)  ; 2\r\n        (   32.51     5.19     5.75     0.66)  ; 3\r\n        (   32.81     5.85     8.06     0.74)  ; 4\r\n        (   33.25     6.65     8.88     0.66)  ; 5\r\n        (   33.54     7.24    10.19     0.66)  ; 6\r\n        (   34.35     7.60    10.88     0.66)  ; 7\r\n        (   35.83     7.60    12.69     1.03)  ; 8\r\n        (   36.79     8.41    13.38     0.88)  ; 9\r\n        (   37.38     9.06    14.25     0.74)  ; 10\r\n        (   37.38     9.94    14.50     0.74)  ; 11\r\n        (   37.38    10.67    15.25     0.74)  ; 12\r\n        (   37.67    11.70    15.38     0.96)  ; 13\r\n        (   38.11    12.50    15.44     0.81)  ; 14\r\n        (   38.70    13.52    15.75     0.66)  ; 15\r\n        (   39.07    14.40    16.00     0.66)  ; 16\r\n        (   39.36    15.72    16.63     0.88)  ; 17\r\n        (   39.95    16.23    17.38     1.11)  ; 18\r\n        (   40.54    16.96    17.44     1.11)  ; 19\r\n        (   41.28    17.18    17.50     0.81)  ; 20\r\n        (   42.68    17.69    17.63     0.59)  ; 21\r\n        (   43.79    18.13    17.88     0.52)  ; 22\r\n        (   44.30    19.08    18.38     0.81)  ; 23\r\n        (\r\n          (   45.41    19.74    19.00     0.29)  ; 1, R-1-1-2-1\r\n          (   46.14    19.96    19.31     0.29)  ; 2\r\n          (   47.10    19.96    19.44     0.29)  ; 3\r\n          (   47.47    19.81    19.44     0.29)  ; 4\r\n          (   47.98    18.86    19.44     0.22)  ; 5\r\n          (   48.72    17.69    19.50     0.22)  ; 6\r\n          (   49.24    17.25    19.50     0.22)  ; 7\r\n          (   50.27    16.59    19.50     0.22)  ; 8\r\n           Low\r\n        |\r\n          (   43.42    20.17    18.31     0.52)  ; 1, R-1-1-2-2\r\n          (   43.34    20.76    18.31     0.44)  ; 2\r\n          (   42.97    20.91    17.88     0.44)  ; 3\r\n          (   42.39    21.78    17.63     0.37)  ; 4\r\n          (   41.87    22.15    17.69     0.22)  ; 5\r\n          (   41.87    22.95    17.50     0.22)  ; 6\r\n          (   41.94    23.61    17.19     0.22)  ; 7\r\n          (   41.57    24.20    16.44     0.22)  ; 8\r\n          (   41.28    24.85    16.13     0.22)  ; 9\r\n          (   40.84    25.36    14.75     0.22)  ; 10\r\n          (   40.03    25.73    13.94     0.22)  ; 11\r\n          (   38.85    25.80    13.00     0.22)  ; 12\r\n          (   38.55    25.51    14.13     0.44)  ; 13\r\n           Low\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (   26.99     3.44    -3.69     2.28)  ; 1, R-1-2\r\n      (   26.99     2.41    -4.88     1.40)  ; 2\r\n      (   27.65     1.75    -6.19     0.88)  ; 3\r\n      (   27.58     0.95    -3.31     1.47)  ; 4\r\n      (   28.17     0.15    -2.00     1.33)  ; 5\r\n      (   28.72    -0.61    -1.31     1.33)  ; 6\r\n      (   29.97    -1.12    -0.44     1.47)  ; 7\r\n      (   30.78    -1.12    -0.31     1.77)  ; 8\r\n      (   31.96    -1.78     0.63     1.69)  ; 9\r\n      (\r\n        (   32.84    -1.85     0.88     1.69)  ; 1, R-1-2-1\r\n        (   33.65    -2.07     1.75     1.47)  ; 2\r\n        (   34.68    -2.43     1.88     1.62)  ; 3\r\n        (   35.86    -3.09     2.31     1.62)  ; 4\r\n        (   37.11    -3.09     2.94     2.21)  ; 5\r\n        (   38.00    -3.38     2.38     2.43)  ; 6\r\n        (   38.44    -3.38     1.94     2.58)  ; 7\r\n        (\r\n          (   38.88    -4.26     1.00     1.47)  ; 1, R-1-2-1-1\r\n          (   39.69    -4.70     0.75     1.11)  ; 2\r\n          (   40.72    -5.36     0.25     0.96)  ; 3\r\n          (   41.68    -5.80    -0.38     0.96)  ; 4\r\n          (   42.42    -6.24    -0.50     1.18)  ; 5\r\n          (   43.45    -6.67    -1.38     1.33)  ; 6\r\n          (   44.19    -7.26    -1.94     1.18)  ; 7\r\n          (   45.07    -7.92    -2.75     1.03)  ; 8\r\n          (   45.59    -8.50    -3.31     1.18)  ; 9\r\n          (   46.62    -9.31    -4.19     1.18)  ; 10\r\n          (   47.13   -10.11    -4.25     1.33)  ; 11\r\n          (   47.94   -11.35    -5.00     1.33)  ; 12\r\n          (   48.24   -12.16    -4.38     1.47)  ; 13\r\n          (   48.61   -13.55    -4.38     1.33)  ; 14\r\n          (   49.27   -14.28    -4.63     1.55)  ; 15\r\n          (   49.42   -15.15    -3.56     1.55)  ; 16\r\n          (   49.79   -15.74    -3.56     1.25)  ; 17\r\n          (   49.71   -16.47    -3.50     1.11)  ; 18\r\n          (   50.01   -16.98    -3.50     1.11)  ; 19\r\n          (   49.86   -18.59    -3.50     1.03)  ; 20\r\n          (   49.71   -19.76    -3.56     1.18)  ; 21\r\n          (   49.49   -20.93    -4.44     1.03)  ; 22\r\n          (   49.27   -21.95    -4.44     0.96)  ; 23\r\n          (   49.57   -22.61    -4.94     0.96)  ; 24\r\n          (   49.86   -23.71    -5.19     1.11)  ; 25\r\n          (   50.38   -24.73    -5.81     1.33)  ; 26\r\n          (   50.82   -25.75    -7.06     1.11)  ; 27\r\n          (   51.41   -26.34    -7.13     1.11)  ; 28\r\n          (   52.22   -27.07    -7.38     1.40)  ; 29\r\n          (   53.40   -27.95    -8.00     1.40)  ; 30\r\n          (   54.50   -28.68    -8.31     1.62)  ; 31\r\n          (   55.83   -29.26    -8.75     1.25)  ; 32\r\n          (   57.04   -30.17    -8.81     0.96)  ; 33\r\n          (   58.00   -31.04    -9.06     1.11)  ; 34\r\n          (   58.81   -32.14    -8.63     1.62)  ; 35\r\n          (   59.25   -33.09    -8.56     2.36)  ; 36\r\n          (   59.84   -34.04    -8.19     2.14)  ; 37\r\n          (   60.35   -34.77    -8.63     1.47)  ; 38\r\n          (   60.79   -35.72    -9.56     1.18)  ; 39\r\n          (   61.31   -36.38    -9.88     1.03)  ; 40\r\n          (   61.97   -37.47   -10.50     0.88)  ; 41\r\n          (   62.42   -38.43   -11.06     0.88)  ; 42\r\n          (   62.71   -39.59   -11.06     0.88)  ; 43\r\n          (   63.15   -40.11   -11.94     1.18)  ; 44\r\n          (   63.59   -40.62   -12.19     1.47)  ; 45\r\n          (\r\n            (   64.40   -41.20   -12.19     0.66)  ; 1, R-1-2-1-1-1\r\n            (   65.29   -42.01   -12.75     0.59)  ; 2\r\n            (   65.58   -42.30   -12.81     0.59)  ; 3\r\n            (   66.03   -42.52   -13.13     0.59)  ; 4\r\n            (   66.47   -43.18   -13.06     0.88)  ; 5\r\n            (   66.47   -44.05   -13.38     1.03)  ; 6\r\n            (   66.84   -45.00   -13.19     1.03)  ; 7\r\n            (   66.98   -45.88   -12.69     1.03)  ; 8\r\n            (   67.35   -46.90   -12.31     1.03)  ; 9\r\n            (   67.28   -47.85   -12.31     0.81)  ; 10\r\n            (   67.57   -48.73   -12.31     0.59)  ; 11\r\n            (   67.94   -49.24   -12.31     0.44)  ; 12\r\n            (   68.90   -49.39   -12.44     0.59)  ; 13\r\n            (   69.93   -49.83   -12.56     0.74)  ; 14\r\n            (   70.81   -49.97   -12.56     0.74)  ; 15\r\n            (   71.33   -50.34   -12.88     0.74)  ; 16\r\n            (   72.36   -50.41   -12.63     1.03)  ; 17\r\n            (   72.88   -50.63   -11.44     0.74)  ; 18\r\n            (   73.54   -51.07   -11.00     0.66)  ; 19\r\n            (   74.87   -51.51   -11.00     0.66)  ; 20\r\n            (   75.53   -51.88   -10.44     0.66)  ; 21\r\n            (   76.19   -52.61    -9.56     1.25)  ; 22\r\n            (   76.93   -53.26    -9.31     1.03)  ; 23\r\n            (   77.52   -53.92    -8.88     0.74)  ; 24\r\n            (   78.18   -54.51    -8.81     0.74)  ; 25\r\n            (   78.62   -55.09    -8.88     0.74)  ; 26\r\n            (   79.14   -55.60    -8.56     0.74)  ; 27\r\n            (   79.73   -55.53    -8.19     0.59)  ; 28\r\n            (   80.61   -55.60    -8.00     0.59)  ; 29\r\n            (   81.20   -55.60    -8.00     0.88)  ; 30\r\n            (   82.09   -55.82    -7.88     0.88)  ; 31\r\n            (   82.38   -55.82    -7.56     0.88)  ; 32\r\n            (   82.97   -56.04    -7.06     0.66)  ; 33\r\n            (   82.90   -56.85    -5.88     0.88)  ; 34\r\n            (   83.26   -57.80    -5.50     0.88)  ; 35\r\n            (   83.71   -59.19    -5.56     0.81)  ; 36\r\n            (   84.12   -60.16    -5.56     0.88)  ; 37\r\n            (   84.71   -61.03    -5.75     0.88)  ; 38\r\n            (   85.15   -61.98    -5.94     1.03)  ; 39\r\n            (   85.52   -62.64    -6.25     1.25)  ; 40\r\n            (   85.60   -63.59    -7.13     0.81)  ; 41\r\n            (   85.52   -64.10    -6.44     0.52)  ; 42\r\n            (   85.15   -64.54    -4.88     0.52)  ; 43\r\n            (   84.78   -64.76    -4.56     0.52)  ; 44\r\n            (   84.86   -65.35    -4.13     0.52)  ; 45\r\n            (   85.52   -66.01    -3.50     0.52)  ; 46\r\n            (   85.45   -67.03    -3.44     0.52)  ; 47\r\n            (   85.52   -68.20    -3.56     1.03)  ; 48\r\n            (   85.45   -69.22    -3.94     0.81)  ; 49\r\n            (   85.67   -70.54    -4.06     0.59)  ; 50\r\n            (   85.60   -71.85    -5.44     0.52)  ; 51\r\n            (   85.74   -72.58    -6.69     0.52)  ; 52\r\n            (   86.11   -72.95    -6.81     0.74)  ; 53\r\n            (   86.99   -73.61    -6.13     0.74)  ; 54\r\n            (   87.51   -74.48    -4.06     0.59)  ; 55\r\n            (   87.88   -75.22    -3.56     1.33)  ; 56\r\n            (   87.95   -75.36    -3.44     1.69)  ; 57\r\n            (   88.25   -76.31    -2.81     1.69)  ; 58\r\n            (   88.25   -77.04    -2.75     1.25)  ; 59\r\n            (   88.32   -77.70    -2.69     0.66)  ; 60\r\n            (   88.62   -78.58    -2.25     0.29)  ; 61\r\n            (   88.76   -79.24    -2.06     0.29)  ; 62\r\n            (   88.39   -80.11    -1.94     0.59)  ; 63\r\n            (   87.88   -81.28    -1.94     0.74)  ; 64\r\n            (   87.95   -82.31    -1.88     0.74)  ; 65\r\n            (   87.95   -83.04    -1.75     0.74)  ; 66\r\n            (   88.10   -84.35    -1.38     0.59)  ; 67\r\n            (   88.62   -85.23    -2.75     0.66)  ; 68\r\n            (   89.13   -86.18    -3.00     0.88)  ; 69\r\n            (   89.50   -87.28    -3.25     0.88)  ; 70\r\n            (   90.16   -88.08    -3.44     0.88)  ; 71\r\n            (   90.59   -88.89    -4.56     1.11)  ; 72\r\n            (   91.47   -89.48    -5.31     1.40)  ; 73\r\n            (   92.14   -89.62    -5.50     0.88)  ; 74\r\n            (   92.65   -89.77    -5.75     0.52)  ; 75\r\n            (   93.46   -90.06    -6.50     0.81)  ; 76\r\n            (   93.91   -90.43    -7.69     0.66)  ; 77\r\n            (   95.23   -91.30    -8.63     0.66)  ; 78\r\n            (   95.82   -92.18   -10.06     0.66)  ; 79\r\n            (   96.26   -92.47   -10.31     0.52)  ; 80\r\n            (   96.78   -93.72   -10.88     0.52)  ; 81\r\n            (   96.85   -95.25   -10.88     0.52)  ; 82\r\n            (   97.07   -96.06   -10.88     0.52)  ; 83\r\n             Low\r\n          |\r\n            (   63.61   -41.69   -14.06     0.66)  ; 1, R-1-2-1-1-2\r\n            (   63.97   -42.35   -14.13     0.59)  ; 2\r\n            (   64.56   -42.86   -14.38     0.74)  ; 3\r\n            (   65.67   -43.23   -14.75     0.59)  ; 4\r\n            (   66.26   -43.59   -15.00     0.59)  ; 5\r\n            (   67.07   -44.03   -14.94     0.74)  ; 6\r\n            (   67.73   -44.62   -15.13     0.74)  ; 7\r\n            (   68.69   -45.42   -15.13     1.25)  ; 8\r\n            (   69.13   -45.64   -15.19     1.92)  ; 9\r\n            (   69.50   -46.00   -15.19     1.92)  ; 10\r\n            (   69.94   -46.81   -15.19     0.88)  ; 11\r\n            (   70.60   -47.17   -15.38     0.74)  ; 12\r\n            (   71.78   -47.83   -15.06     0.59)  ; 13\r\n            (   72.45   -47.91   -14.50     0.52)  ; 14\r\n            (   73.33   -47.39   -13.94     0.44)  ; 15\r\n            (   73.92   -47.39   -13.75     0.44)  ; 16\r\n            (   74.66   -47.76   -13.50     0.66)  ; 17\r\n            (   75.10   -48.20   -13.63     0.88)  ; 18\r\n            (   75.61   -48.93   -13.88     0.88)  ; 19\r\n            (   76.20   -49.66   -13.88     0.74)  ; 20\r\n            (   76.72   -50.10   -13.88     0.81)  ; 21\r\n            (   77.31   -50.90   -13.88     0.66)  ; 22\r\n            (   77.97   -51.63   -13.88     0.66)  ; 23\r\n            (   78.34   -52.36   -13.81     0.59)  ; 24\r\n            (   79.08   -53.39   -13.81     0.74)  ; 25\r\n            (   79.89   -54.19   -13.63     0.81)  ; 26\r\n            (   80.11   -55.00   -14.69     0.96)  ; 27\r\n            (   80.11   -55.65   -14.81     0.59)  ; 28\r\n            (   80.18   -56.31   -14.81     0.59)  ; 29\r\n            (   80.77   -56.82   -15.31     0.59)  ; 30\r\n            (   81.36   -57.41   -14.06     0.59)  ; 31\r\n            (   81.88   -57.92   -14.06     0.81)  ; 32\r\n            (   82.61   -58.50   -13.88     1.03)  ; 33\r\n            (   83.42   -59.16   -13.81     0.81)  ; 34\r\n            (   84.01   -59.82   -13.81     0.66)  ; 35\r\n            (   84.68   -60.55   -14.00     0.59)  ; 36\r\n            (   85.19   -61.50   -14.44     0.74)  ; 37\r\n            (   86.00   -62.09   -14.50     0.81)  ; 38\r\n            (   86.59   -62.60   -14.81     0.81)  ; 39\r\n            (   86.89   -63.26   -15.06     0.81)  ; 40\r\n            (   87.62   -64.35   -15.19     0.81)  ; 41\r\n            (   88.29   -65.23   -15.94     0.66)  ; 42\r\n            (   88.43   -66.25   -16.50     0.96)  ; 43\r\n            (   88.80   -66.69   -17.31     1.33)  ; 44\r\n            (   89.24   -67.42   -17.31     1.03)  ; 45\r\n            (   89.61   -67.72   -17.56     0.59)  ; 46\r\n            (   90.35   -67.93   -17.56     0.44)  ; 47\r\n            (   90.86   -68.01   -17.56     0.44)  ; 48\r\n            (   91.53   -68.37   -17.56     0.44)  ; 49\r\n            (   92.19   -68.88   -17.56     0.66)  ; 50\r\n            (   92.63   -69.54   -18.06     0.88)  ; 51\r\n            (   93.15   -70.13   -19.13     0.81)  ; 52\r\n            (   93.88   -70.69   -19.25     1.33)  ; 53\r\n            (   94.25   -71.72   -20.56     1.62)  ; 54\r\n            (   95.36   -72.59   -20.56     1.84)  ; 55\r\n            (   96.46   -73.03   -20.56     1.11)  ; 56\r\n            (   97.27   -73.40   -20.69     0.74)  ; 57\r\n            (   98.16   -73.91   -20.88     0.44)  ; 58\r\n            (   98.82   -74.05   -20.94     0.44)  ; 59\r\n            (   98.89   -74.79   -21.13     0.44)  ; 60\r\n            (   98.97   -75.08   -21.25     0.81)  ; 61\r\n            (   99.12   -75.74   -21.50     0.59)  ; 62\r\n            (   99.34   -76.17   -21.50     0.37)  ; 63\r\n            (  100.07   -76.83   -21.69     0.37)  ; 64\r\n            (  100.74   -77.56   -22.13     0.37)  ; 65\r\n            (  101.47   -77.93   -22.31     0.59)  ; 66\r\n            (  101.99   -78.44   -22.81     0.88)  ; 67\r\n            (  102.65   -79.17   -22.94     1.18)  ; 68\r\n            (  103.31   -80.05   -23.06     1.18)  ; 69\r\n            (  103.76   -80.85   -23.56     0.81)  ; 70\r\n            (  104.20   -81.73   -24.25     0.52)  ; 71\r\n            (  104.64   -82.31   -24.19     0.52)  ; 72\r\n            (  104.86   -83.41   -25.56     0.88)  ; 73\r\n            (  104.79   -83.92   -26.19     1.77)  ; 74\r\n            (  104.71   -84.51   -26.50     2.65)  ; 75\r\n            (  105.08   -85.31   -26.88     2.65)  ; 76\r\n            (  105.60   -85.90   -27.13     1.84)  ; 77\r\n            (  106.11   -86.85   -27.38     0.96)  ; 78\r\n            (  106.70   -87.29   -27.38     0.59)  ; 79\r\n            (  107.29   -87.72   -27.44     0.59)  ; 80\r\n            (  108.40   -88.16   -27.50     0.88)  ; 81\r\n            (  109.21   -88.75   -27.50     0.88)  ; 82\r\n            (  109.80   -88.75   -28.06     0.44)  ; 83\r\n            (  110.76   -88.75   -28.56     0.44)  ; 84\r\n            (  110.83   -88.53   -31.94     0.81)  ; 85\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (   39.34    -2.61     1.81     1.33)  ; 1, R-1-2-1-2\r\n          (   40.44    -1.95     1.81     0.81)  ; 2\r\n          (   41.03    -1.66     1.63     0.81)  ; 3\r\n          (   42.06    -1.08     2.00     0.88)  ; 4\r\n          (   43.54    -0.64     2.38     0.96)  ; 5\r\n          (   45.30    -0.49     2.44     1.03)  ; 6\r\n          (   46.63    -0.56     2.88     0.96)  ; 7\r\n          (   48.10    -0.34     2.69     0.96)  ; 8\r\n          (   49.36    -0.27     2.31     0.96)  ; 9\r\n          (   50.24    -0.27     1.94     1.18)  ; 10\r\n          (   51.35    -0.49     1.94     1.03)  ; 11\r\n          (   52.45    -0.56     2.13     0.88)  ; 12\r\n          (   53.26    -0.71     2.38     0.88)  ; 13\r\n          (   54.29    -0.34     2.63     0.96)  ; 14\r\n          (   55.18     0.24     3.38     0.81)  ; 15\r\n          (   55.84     0.68     3.69     0.88)  ; 16\r\n          (   56.80     0.83     4.19     0.88)  ; 17\r\n          (   57.90     0.97     4.19     1.11)  ; 18\r\n          (   58.64     0.68     4.19     1.11)  ; 19\r\n          (   59.67     0.24     4.19     1.11)  ; 20\r\n          (   60.92     0.02     4.19     1.11)  ; 21\r\n          (   62.03    -0.13     4.13     1.11)  ; 22\r\n          (   63.72     0.31     3.88     1.25)  ; 23\r\n          (   65.05     1.41     3.88     1.25)  ; 24\r\n          (   66.01     2.29     4.31     0.81)  ; 25\r\n          (   66.96     3.24     5.19     0.74)  ; 26\r\n          (   68.07     3.53     5.44     0.96)  ; 27\r\n          (   68.95     3.97     5.44     1.33)  ; 28\r\n          (   70.06     4.19     5.56     1.11)  ; 29\r\n          (   70.87     4.41     5.75     1.11)  ; 30\r\n          (   71.83     4.55     6.13     1.33)  ; 31\r\n          (   72.93     4.26     6.69     0.96)  ; 32\r\n          (   74.33     4.19     6.75     0.96)  ; 33\r\n          (   75.51     4.26     7.31     0.96)  ; 34\r\n          (   76.54     4.55     7.69     1.18)  ; 35\r\n          (   77.94     5.21     7.94     0.96)  ; 36\r\n          (   78.97     5.50     8.19     0.96)  ; 37\r\n          (   80.45     5.65     8.44     0.96)  ; 38\r\n          (   82.44     5.94     8.44     1.11)  ; 39\r\n          (   84.72     5.72     8.50     1.03)  ; 40\r\n          (   86.27     5.58     8.56     0.88)  ; 41\r\n          (   86.98     5.29     9.31     0.88)  ; 42\r\n          (   88.16     4.63     7.69     1.03)  ; 43\r\n          (   88.90     3.68     7.38     0.81)  ; 44\r\n          (   89.19     2.73     6.81     0.81)  ; 45\r\n          (   89.64     2.00     6.63     0.81)  ; 46\r\n          (   90.08     0.90     6.00     0.81)  ; 47\r\n          (   90.59    -0.56     6.00     0.66)  ; 48\r\n          (   91.18    -1.66     5.69     1.33)  ; 49\r\n          (   91.85    -2.17     5.50     1.99)  ; 50\r\n          (   92.66    -2.83     5.13     1.92)  ; 51\r\n          (   93.47    -3.63     4.75     0.88)  ; 52\r\n          (   94.35    -4.29     4.69     0.59)  ; 53\r\n          (   95.24    -4.80     3.88     0.81)  ; 54\r\n          (   96.27    -5.24     3.81     1.03)  ; 55\r\n          (   97.23    -5.53     3.69     0.66)  ; 56\r\n          (   98.62    -6.12     3.13     0.66)  ; 57\r\n          (   99.44    -6.19     2.63     0.59)  ; 58\r\n          (   99.88    -6.19     1.81     0.44)  ; 59\r\n          (  100.54    -6.63     0.88     0.37)  ; 60\r\n          (  101.50    -6.85     0.44     0.59)  ; 61\r\n          (  101.94    -6.92     0.38     1.40)  ; 62\r\n          (  102.38    -7.00    -0.06     2.21)  ; 63\r\n          (  103.12    -7.29    -0.25     2.21)  ; 64\r\n          (  103.86    -8.16    -0.44     1.99)  ; 65\r\n          (  104.45    -8.68    -0.75     1.25)  ; 66\r\n          (  105.40    -9.04     0.00     0.74)  ; 67\r\n          (  106.21    -9.04     0.00     0.96)  ; 68\r\n          (  107.32    -9.55    -0.13     0.66)  ; 69\r\n          (  108.06    -9.63    -0.56     0.66)  ; 70\r\n          (  108.64    -9.77    -0.56     0.96)  ; 71\r\n          (  109.75   -10.14    -0.56     0.66)  ; 72\r\n          (  110.34   -10.28    -0.69     0.66)  ; 73\r\n          (  110.63   -10.65    -0.69     1.03)  ; 74\r\n          (  111.44   -11.09    -0.69     1.03)  ; 75\r\n          (  111.96   -11.53    -0.69     1.03)  ; 76\r\n          (  112.48   -12.19    -0.69     0.66)  ; 77\r\n          (  113.06   -13.14    -0.75     0.66)  ; 78\r\n          (  113.51   -13.35    -0.31     1.03)  ; 79\r\n          (  114.02   -13.87     0.19     1.33)  ; 80\r\n          (  114.54   -14.67     0.75     1.25)  ; 81\r\n          (  115.35   -15.18     1.44     1.25)  ; 82\r\n          (  116.31   -15.69     1.44     1.25)  ; 83\r\n          (  116.97   -16.13     1.63     1.25)  ; 84\r\n          (  117.41   -16.86     2.19     0.88)  ; 85\r\n          (  118.22   -17.89     2.81     1.03)  ; 86\r\n          (  118.89   -18.69     2.88     0.88)  ; 87\r\n          (  119.11   -19.50     3.00     0.88)  ; 88\r\n          (  119.70   -20.30     3.19     0.66)  ; 89\r\n          (  120.14   -20.81     3.63     0.59)  ; 90\r\n          (  120.51   -20.74     3.81     0.59)  ; 91\r\n          (  120.95   -19.79     4.06     0.44)  ; 92\r\n          (  121.54   -19.13     4.63     0.74)  ; 93\r\n          (  122.57   -18.47     4.63     1.11)  ; 94\r\n          (  123.08   -17.96     4.63     0.66)  ; 95\r\n          (  123.53   -17.52     4.63     0.44)  ; 96\r\n          (  124.19   -17.67     5.31     0.37)  ; 97\r\n          (  124.71   -18.11     5.31     0.37)  ; 98\r\n          (  125.15   -19.13     5.31     0.96)  ; 99\r\n          (  125.59   -20.15     5.25     1.55)  ; 100\r\n          (  125.96   -20.81     5.00     2.21)  ; 101\r\n          (  126.10   -21.03     4.56     2.21)  ; 102\r\n          (  126.47   -22.05     5.63     0.88)  ; 103\r\n          (  126.99   -22.71     5.81     0.66)  ; 104\r\n          (  127.65   -23.30     6.13     0.96)  ; 105\r\n          (  128.83   -23.95     6.13     1.18)  ; 106\r\n          (  130.15   -24.71     6.25     1.18)  ; 107\r\n          (  131.18   -25.07     6.56     0.88)  ; 108\r\n          (  132.36   -25.15     6.38     0.66)  ; 109\r\n          (  133.68   -24.41     5.75     0.59)  ; 110\r\n          (  134.42   -23.61     5.19     0.81)  ; 111\r\n          (  135.30   -23.03     5.56     0.96)  ; 112\r\n          (  135.67   -22.37     6.00     0.96)  ; 113\r\n          (  136.19   -22.15     6.31     0.96)  ; 114\r\n          (  136.26   -22.15     6.56     1.77)  ; 115\r\n          (  136.56   -21.71     7.06     1.03)  ; 116\r\n          (  137.07   -21.42     7.44     0.88)  ; 117\r\n          (  137.44   -20.91     8.50     0.88)  ; 118\r\n          (  137.66   -20.69     8.94     1.33)  ; 119\r\n          (  137.81   -20.54     9.63     1.69)  ; 120\r\n          (  138.10   -20.32    11.06     1.33)  ; 121\r\n          (  138.18   -20.17    12.25     1.11)  ; 122\r\n          (  138.62   -19.74    13.19     0.96)  ; 123\r\n          (  139.06   -19.44    13.31     0.52)  ; 124\r\n          (  139.65   -19.30    13.50     0.52)  ; 125\r\n          (  140.54   -18.93    13.75     0.52)  ; 126\r\n          (  141.27   -18.64    13.44     0.74)  ; 127\r\n          (  141.86   -18.57    13.19     0.74)  ; 128\r\n          (  143.04   -18.42    13.19     0.74)  ; 129\r\n          (  144.07   -18.35    13.19     0.59)  ; 130\r\n          (  144.59   -18.49    12.63     0.59)  ; 131\r\n          (  145.55   -19.01    13.88     0.66)  ; 132\r\n          (  146.13   -19.15    15.56     0.52)  ; 133\r\n          (  147.24   -19.59    15.63     0.81)  ; 134\r\n          (  148.05   -19.81    15.63     0.81)  ; 135\r\n          (  148.86   -20.10    15.63     0.52)  ; 136\r\n          (  149.74   -20.25    15.81     0.52)  ; 137\r\n          (  151.37   -19.96    15.94     0.52)  ; 138\r\n          (  152.32   -19.08    15.94     0.74)  ; 139\r\n          (  152.99   -18.13    15.94     1.25)  ; 140\r\n          (  153.50   -17.54    15.25     1.62)  ; 141\r\n          (  154.24   -16.89    15.19     1.69)  ; 142\r\n          (  154.53   -16.52    15.19     0.88)  ; 143\r\n          (  155.42   -15.86    15.19     0.59)  ; 144\r\n          (  156.15   -15.72    15.19     0.59)  ; 145\r\n          (  157.11   -15.35    14.69     0.59)  ; 146\r\n          (  158.14   -14.91    14.69     0.96)  ; 147\r\n          (  159.10   -14.25    14.69     0.96)  ; 148\r\n          (  159.76   -13.60    14.69     0.59)  ; 149\r\n          (  160.72   -13.01    14.69     0.59)  ; 150\r\n          (  160.94   -11.70    14.06     0.44)  ; 151\r\n          (  160.94   -11.04    15.13     0.44)  ; 152\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (   33.42    -0.89     1.56     0.52)  ; 1, R-1-2-2\r\n        (   34.16    -0.38     2.38     0.37)  ; 2\r\n        (   34.75    -0.08     3.63     0.52)  ; 3\r\n        (   35.41     0.35     4.06     0.74)  ; 4\r\n        (   36.15     0.57     4.38     0.74)  ; 5\r\n        (   36.74     0.87     4.88     0.66)  ; 6\r\n        (   36.74     0.87     6.06     0.88)  ; 7\r\n        (   36.81     0.28     6.31     0.88)  ; 8\r\n        (   37.55    -0.30     7.13     0.66)  ; 9\r\n        (   38.21    -0.74     7.13     0.52)  ; 10\r\n        (   39.32    -0.74     7.63     0.52)  ; 11\r\n        (   39.32    -0.23     8.69     0.74)  ; 12\r\n        (   39.83     0.43     9.69     0.88)  ; 13\r\n        (   40.05     0.79    10.25     0.74)  ; 14\r\n        (   40.49     1.01    11.50     0.74)  ; 15\r\n        (   41.08     0.50    13.81     0.52)  ; 16\r\n        (   41.08     0.35    14.25     0.52)  ; 17\r\n        (   40.72    -0.01    14.63     0.52)  ; 18\r\n         Low\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (   22.01     5.66    -2.75     1.69)  ; 1, R-2\r\n    (   22.89     6.32    -3.13     1.33)  ; 2\r\n    (   23.04     6.61    -4.50     1.03)  ; 3\r\n    (   23.33     7.42    -4.81     0.74)  ; 4\r\n    (   23.92     7.63    -5.38     0.59)  ; 5\r\n    (   24.59     7.78    -6.19     0.59)  ; 6\r\n    (   25.40     7.78    -6.19     0.59)  ; 7\r\n    (   26.80     7.93    -6.19     0.88)  ; 8\r\n    (   27.83     8.00    -6.56     1.11)  ; 9\r\n    (   28.64     8.22    -6.88     1.11)  ; 10\r\n    (   29.00     8.40    -6.88     1.11)  ; 11\r\n    (\r\n      (   29.60     8.37    -6.88     1.11)  ; 1, R-2-1\r\n      (   30.48     8.58    -6.88     0.96)  ; 2\r\n      (   31.07     8.88    -7.00     0.96)  ; 3\r\n      (   32.03     9.24    -7.38     0.81)  ; 4\r\n      (   32.98     9.53    -7.50     0.74)  ; 5\r\n      (   33.57    10.70    -6.44     0.59)  ; 6\r\n      (   34.83    11.44    -5.88     0.74)  ; 7\r\n      (   36.15    12.17    -5.31     0.88)  ; 8\r\n      (   37.40    12.46    -4.56     1.03)  ; 9\r\n      (   38.73    11.95    -3.88     0.88)  ; 10\r\n      (   39.69    11.36    -3.50     0.88)  ; 11\r\n      (   40.72    10.85    -3.31     0.88)  ; 12\r\n      (   42.12    10.92    -2.25     1.11)  ; 13\r\n      (   43.52    11.29    -1.44     0.88)  ; 14\r\n      (   44.70    11.73    -1.81     0.88)  ; 15\r\n      (   45.95    12.68    -2.38     0.88)  ; 16\r\n      (   46.91    13.26    -2.75     0.88)  ; 17\r\n      (   47.65    14.21    -3.44     1.11)  ; 18\r\n      (   48.53    14.94    -3.44     0.96)  ; 19\r\n      (   49.49    15.68    -3.44     0.81)  ; 20\r\n      (   50.15    16.55    -3.44     0.66)  ; 21\r\n      (   50.59    17.21    -3.75     0.88)  ; 22\r\n      (   51.77    18.31    -3.25     0.81)  ; 23\r\n      (   52.88    18.89    -3.25     0.74)  ; 24\r\n      (   54.13    18.67    -2.94     0.66)  ; 25\r\n      (   55.75    18.09    -2.94     0.52)  ; 26\r\n      (   56.85    17.80    -2.75     0.52)  ; 27\r\n      (   57.81    17.58    -2.25     0.74)  ; 28\r\n      (   58.77    17.43    -3.00     1.03)  ; 29\r\n      (   59.80    17.28    -3.44     1.03)  ; 30\r\n      (   60.76    16.99    -4.81     0.74)  ; 31\r\n      (   61.35    16.84    -5.06     0.74)  ; 32\r\n      (   62.16    16.77    -5.94     0.74)  ; 33\r\n      (   63.12    16.55    -6.13     1.03)  ; 34\r\n      (   64.37    16.70    -8.00     1.03)  ; 35\r\n      (   65.25    17.14    -8.13     0.88)  ; 36\r\n      (   66.06    17.58    -8.69     0.88)  ; 37\r\n      (   66.28    18.09    -8.75     0.88)  ; 38\r\n      (   67.46    18.53    -9.00     0.74)  ; 39\r\n      (   68.27    19.26    -9.56     0.74)  ; 40\r\n      (   69.01    20.43    -9.38     0.96)  ; 41\r\n      (   69.31    21.60    -8.81     1.25)  ; 42\r\n      (   69.82    22.40    -8.81     1.03)  ; 43\r\n      (   70.04    22.99    -8.81     0.74)  ; 44\r\n      (   70.63    23.64    -8.81     0.59)  ; 45\r\n      (   71.37    23.86    -8.75     0.88)  ; 46\r\n      (   72.69    24.45    -8.75     1.11)  ; 47\r\n      (   73.87    24.74    -8.75     1.40)  ; 48\r\n      (   74.68    24.96    -8.75     1.40)  ; 49\r\n      (   75.27    25.25    -8.75     0.66)  ; 50\r\n      (   76.53    25.69    -8.75     0.59)  ; 51\r\n      (   77.19    26.71    -9.19     0.88)  ; 52\r\n      (   78.07    27.96    -9.75     0.74)  ; 53\r\n      (   78.55    28.99   -10.38     0.74)  ; 54\r\n      (   79.73    29.79   -10.56     0.74)  ; 55\r\n      (   80.76    30.45   -10.31     0.96)  ; 56\r\n      (   81.57    30.74   -10.25     1.18)  ; 57\r\n      (   82.23    30.82   -10.25     0.88)  ; 58\r\n      (   83.34    31.40   -10.19     0.66)  ; 59\r\n      (   84.00    31.55   -10.19     0.66)  ; 60\r\n      (   85.33    31.99   -10.44     0.66)  ; 61\r\n      (   86.14    32.06   -10.63     0.66)  ; 62\r\n      (   86.73    32.28   -10.75     0.52)  ; 63\r\n      (   86.87    31.99   -11.19     0.52)  ; 64\r\n      (   86.87    31.69   -11.75     0.52)  ; 65\r\n      (   86.80    31.33   -12.31     0.52)  ; 66\r\n      (   87.24    30.82   -12.44     0.52)  ; 67\r\n      (   87.98    31.40   -12.44     0.44)  ; 68\r\n      (   88.57    31.91   -12.75     0.74)  ; 69\r\n      (   89.38    33.08   -12.75     0.88)  ; 70\r\n      (   90.11    33.74   -13.31     0.88)  ; 71\r\n      (   91.29    34.47   -11.19     0.59)  ; 72\r\n      (   92.47    35.42   -12.06     0.52)  ; 73\r\n      (   93.14    36.01   -11.75     0.52)  ; 74\r\n      (   94.09    36.59   -11.19     0.88)  ; 75\r\n      (   94.53    36.66   -11.13     2.28)  ; 76\r\n      (   95.42    37.18   -10.94     3.02)  ; 77\r\n      (   95.93    37.83   -11.69     2.21)  ; 78\r\n      (   96.38    38.35   -11.56     1.47)  ; 79\r\n      (   96.60    38.57   -11.56     0.81)  ; 80\r\n      (   97.33    39.15   -11.56     0.59)  ; 81\r\n      (   97.85    39.88   -11.56     0.59)  ; 82\r\n      (   98.07    40.47   -11.56     0.59)  ; 83\r\n      (   98.51    41.56   -11.56     0.59)  ; 84\r\n      (   98.66    42.22   -12.06     0.88)  ; 85\r\n      (   98.88    42.88   -12.88     1.77)  ; 86\r\n      (   98.59    43.68   -13.44     2.14)  ; 87\r\n      (   98.29    44.56   -13.75     1.25)  ; 88\r\n      (   98.14    45.00   -15.88     0.81)  ; 89\r\n      (   97.56    45.66   -15.25     0.52)  ; 90\r\n      (   97.04    46.53   -15.56     0.52)  ; 91\r\n      (   96.75    47.34   -15.63     0.37)  ; 92\r\n      (   97.11    48.07   -15.63     0.22)  ; 93\r\n      (   97.11    48.80   -15.63     0.22)  ; 94\r\n      (   97.26    49.68   -15.69     0.22)  ; 95\r\n      (   97.11    50.41   -15.69     0.52)  ; 96\r\n      (   97.11    50.55   -15.69     0.88)  ; 97\r\n      (   97.11    50.77   -15.69     1.25)  ; 98\r\n      (   96.89    51.07   -15.69     0.96)  ; 99\r\n      (   96.67    51.50   -15.69     0.59)  ; 100\r\n      (   96.45    51.80   -15.69     0.29)  ; 101\r\n      (   95.93    52.53   -16.25     0.22)  ; 102\r\n      (   95.20    52.97   -16.25     0.74)  ; 103\r\n      (   94.61    53.26   -16.25     0.88)  ; 104\r\n      (   93.95    53.48   -16.25     0.52)  ; 105\r\n      (   93.43    54.21   -16.25     0.22)  ; 106\r\n      (   93.06    54.79   -16.25     0.22)  ; 107\r\n      (   92.62    55.23   -16.25     0.22)  ; 108\r\n       Low\r\n    |\r\n      (   29.96     7.81    -2.25     0.59)  ; 1, R-2-2\r\n      (   30.25     7.67    -0.63     0.59)  ; 2\r\n      (   30.62     7.45     0.44     0.59)  ; 3\r\n      (   30.77     6.94     1.19     0.74)  ; 4\r\n      (   30.77     6.28     1.94     0.96)  ; 5\r\n      (   30.33     5.48     2.13     0.96)  ; 6\r\n      (   30.25     4.89     2.31     0.96)  ; 7\r\n      (   30.25     4.23     2.88     0.96)  ; 8\r\n      (   30.11     3.36     3.69     1.18)  ; 9\r\n      (   29.74     2.55     3.69     1.18)  ; 10\r\n      (   29.52     1.82     4.19     1.18)  ; 11\r\n      (   29.89     1.38     5.56     0.96)  ; 12\r\n      (   30.92     1.24     6.44     0.74)  ; 13\r\n      (   31.87     1.24     6.88     0.66)  ; 14\r\n      (   32.69     1.67     7.75     0.66)  ; 15\r\n      (   33.64     2.11     8.63     0.66)  ; 16\r\n      (   35.19     2.77     9.50     0.52)  ; 17\r\n      (   36.00     2.70    10.94     0.52)  ; 18\r\n      (   36.52     2.55    11.50     0.52)  ; 19\r\n      (   37.33     2.19    11.81     0.74)  ; 20\r\n      (   38.36     1.24    14.13     0.59)  ; 21\r\n      (   39.39     0.80    14.19     0.66)  ; 22\r\n      (   39.68     0.43    15.38     0.66)  ; 23\r\n      (   40.20     0.65    15.88     0.66)  ; 24\r\n      (   40.86     1.38    15.44     0.66)  ; 25\r\n      (   41.30     2.04    15.38     0.66)  ; 26\r\n      (   41.53     2.55    15.38     0.66)  ; 27\r\n      (   42.19     2.84    15.38     0.88)  ; 28\r\n      (   42.63     2.92    15.38     0.88)  ; 29\r\n      (   43.37     2.99    15.06     1.18)  ; 30\r\n      (   44.18     2.99    14.50     1.40)  ; 31\r\n      (   44.69     3.79    15.81     1.25)  ; 32\r\n      (   45.36     4.16    15.44     0.81)  ; 33\r\n      (   46.09     4.82    15.31     0.66)  ; 34\r\n      (   46.61     5.77    16.06     0.66)  ; 35\r\n      (   46.50     6.63    17.06     1.11)  ; 36\r\n      (   47.09     6.92    18.13     0.88)  ; 37\r\n      (\r\n        (   47.68     7.58    18.50     0.88)  ; 1, R-2-2-1\r\n        (   47.46     8.60    18.88     1.18)  ; 2\r\n        (   47.02     9.04    19.00     0.81)  ; 3\r\n        (   46.65     9.63    19.00     0.59)  ; 4\r\n        (   46.65    10.36    19.06     0.37)  ; 5\r\n        (   46.94    10.94    19.06     0.37)  ; 6\r\n        (   47.61    11.38    19.06     0.37)  ; 7\r\n        (   48.34    11.45    19.06     0.37)  ; 8\r\n        (   48.86    11.16    19.06     0.37)  ; 9\r\n        (   49.59    10.72    19.06     0.37)  ; 10\r\n         Low\r\n      |\r\n        (   47.97     7.43    15.00     0.96)  ; 1, R-2-2-2\r\n        (   48.86     8.60    14.31     0.81)  ; 2\r\n        (   49.59     9.26    12.19     0.59)  ; 3\r\n        (   50.41     9.55    13.56     0.66)  ; 4\r\n        (   51.95     9.63    13.56     0.59)  ; 5\r\n        (   52.98     9.63    13.56     0.59)  ; 6\r\n        (   53.79     9.99    14.13     0.66)  ; 7\r\n        (   54.53    10.43    15.38     0.74)  ; 8\r\n        (   55.56    10.94    15.50     0.96)  ; 9\r\n        (   56.81    11.74    15.50     0.96)  ; 10\r\n        (   57.99    12.77    16.31     0.96)  ; 11\r\n        (   59.10    13.86    17.25     0.81)  ; 12\r\n        (   60.06    14.38    17.44     0.66)  ; 13\r\n        (   61.01    15.55    17.94     0.52)  ; 14\r\n        (   61.75    15.69    18.25     0.81)  ; 15\r\n        (   62.41    15.98    18.75     0.59)  ; 16\r\n         Low\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color DarkMagenta)\r\n  (Dendrite)\r\n  (  -13.09    -4.16    -9.69     2.21)  ; Root\r\n  (  -14.34    -3.87   -10.06     1.69)  ; 1, R\r\n  (  -15.23    -3.94   -10.31     1.47)  ; 2\r\n  (  -16.70    -4.53   -10.31     1.40)  ; 3\r\n  (  -16.55    -5.84   -11.25     2.06)  ; 4\r\n  (  -16.26    -6.94   -12.38     2.43)  ; 5\r\n  (  -15.74    -8.69   -13.06     2.50)  ; 6\r\n  (  -15.74    -9.50   -14.50     2.21)  ; 7\r\n  (  -15.16   -10.81   -14.81     1.92)  ; 8\r\n  (  -14.71   -12.20   -15.81     2.06)  ; 9\r\n  (  -14.49   -13.30   -18.38     2.14)  ; 10\r\n  (  -14.79   -13.66   -20.50     1.47)  ; 11\r\n  (  -15.08   -14.54   -21.50     1.33)  ; 12\r\n  (  -16.33   -14.91   -22.13     1.18)  ; 13\r\n  (  -17.00   -14.76   -22.94     1.33)  ; 14\r\n  (  -17.88   -14.62   -22.94     1.33)  ; 15\r\n  (  -18.84   -14.10   -22.88     1.33)  ; 16\r\n  (  -20.16   -14.03   -23.25     1.33)  ; 17\r\n  (  -21.12   -14.03   -22.06     1.25)  ; 18\r\n  (  -22.08   -13.66   -22.06     1.25)  ; 19\r\n  (  -22.74   -13.45   -22.38     1.25)  ; 20\r\n  (  -23.33   -13.01   -23.06     1.47)  ; 21\r\n  (  -23.63   -12.86   -23.50     1.47)  ; 22\r\n  (  -23.70   -12.50   -24.19     1.47)  ; 23\r\n  (  -23.63   -12.06   -25.06     1.47)  ; 24\r\n  (  -23.70   -12.06   -26.19     1.33)  ; 25\r\n  (  -24.00   -12.20   -27.38     1.33)  ; 26\r\n  (  -23.92   -12.93   -28.00     1.33)  ; 27\r\n  (  -24.44   -13.52   -28.81     1.55)  ; 28\r\n  (  -24.73   -14.18   -29.50     1.40)  ; 29\r\n  (  -24.59   -14.98   -30.13     1.62)  ; 30\r\n  (  -24.59   -16.15   -31.13     1.84)  ; 31\r\n  (  -24.59   -17.90   -31.13     2.06)  ; 32\r\n  (  -25.03   -19.00   -31.38     2.21)  ; 33\r\n  (  -25.40   -20.61   -32.19     2.36)  ; 34\r\n  (  -25.99   -21.71   -32.94     2.58)  ; 35\r\n  (  -27.09   -22.44   -39.00     2.36)  ; 36\r\n  (\r\n    (  -27.61   -23.02   -38.13     3.98)  ; 1, R-1\r\n    (  -27.68   -23.31   -37.19     3.76)  ; 2\r\n    (\r\n      (  -28.93   -23.39   -38.31     4.05)  ; 1, R-1-1\r\n      (  -29.74   -23.90   -39.38     3.39)  ; 2\r\n      (\r\n        (  -30.33   -23.90   -40.63     1.77)  ; 1, R-1-1-1\r\n        (  -31.88   -23.97   -40.75     1.18)  ; 2\r\n        (  -32.84   -24.12   -40.81     1.18)  ; 3\r\n        (  -32.91   -24.12   -40.81     1.77)  ; 4\r\n        (  -33.72   -23.83   -41.38     1.69)  ; 5\r\n        (\r\n          (  -34.46   -23.39   -40.38     0.88)  ; 1, R-1-1-1-1\r\n          (  -35.12   -22.80   -40.38     0.66)  ; 2\r\n          (  -35.93   -22.44   -40.38     0.52)  ; 3\r\n          (  -37.04   -22.07   -40.38     0.52)  ; 4\r\n          (  -37.92   -21.49   -40.38     0.74)  ; 5\r\n          (  -38.80   -20.97   -40.38     0.88)  ; 6\r\n          (  -39.47   -20.46   -40.69     0.88)  ; 7\r\n          (  -40.50   -20.02   -40.69     1.11)  ; 8\r\n          (  -41.16   -19.95   -41.06     1.11)  ; 9\r\n          (  -41.82   -19.88   -41.19     1.11)  ; 10\r\n          (  -42.49   -19.88   -41.19     1.11)  ; 11\r\n          (  -43.08   -19.95   -40.94     0.96)  ; 12\r\n          (  -43.96   -19.66   -40.81     0.66)  ; 13\r\n          (  -44.48   -19.00   -41.75     0.44)  ; 14\r\n          (  -44.48   -18.49   -42.13     0.44)  ; 15\r\n          (  -44.92   -18.34   -42.38     0.44)  ; 16\r\n          (  -45.58   -18.42   -42.38     0.44)  ; 17\r\n          (  -46.10   -18.42   -42.38     0.74)  ; 18\r\n          (  -46.76   -17.98   -42.50     1.11)  ; 19\r\n          (  -47.57   -17.54   -42.88     1.18)  ; 20\r\n          (  -48.55   -16.90   -43.50     1.33)  ; 21\r\n          (  -49.51   -16.25   -43.50     1.33)  ; 22\r\n          (  -50.47   -15.59   -43.63     1.18)  ; 23\r\n          (  -50.98   -15.29   -43.63     0.88)  ; 24\r\n          (  -51.79   -14.78   -43.50     0.59)  ; 25\r\n          (  -52.53   -15.22   -43.63     0.44)  ; 26\r\n          (  -52.82   -15.51   -43.38     0.44)  ; 27\r\n          (  -53.78   -15.15   -43.06     0.59)  ; 28\r\n          (  -54.52   -14.93   -43.44     0.88)  ; 29\r\n          (  -55.18   -14.64   -43.69     0.88)  ; 30\r\n          (  -55.55   -13.83   -44.06     1.18)  ; 31\r\n          (  -55.85   -13.10   -45.50     1.18)  ; 32\r\n          (  -56.14   -12.74   -48.44     1.25)  ; 33\r\n          (  -56.73   -12.22   -50.69     0.96)  ; 34\r\n          (  -57.39   -12.30   -51.00     0.96)  ; 35\r\n          (  -58.42   -12.37   -51.50     0.96)  ; 36\r\n          (  -59.68   -12.52   -51.50     0.74)  ; 37\r\n          (  -60.04   -12.37   -52.00     0.74)  ; 38\r\n          (  -60.34   -11.86   -52.56     0.52)  ; 39\r\n          (  -59.97   -11.57   -53.50     0.52)  ; 40\r\n          (  -59.82   -11.13   -54.13     0.52)  ; 41\r\n          (  -60.27   -11.27   -55.88     0.81)  ; 42\r\n          (  -60.71   -11.35   -56.50     0.59)  ; 43\r\n          (  -61.52   -11.20   -57.25     0.74)  ; 44\r\n          (  -62.33   -10.91   -57.38     1.03)  ; 45\r\n          (  -62.92   -11.06   -57.69     1.25)  ; 46\r\n          (  -63.36   -11.27   -57.88     0.66)  ; 47\r\n          (  -64.17   -11.27   -60.00     0.81)  ; 48\r\n          (  -64.76   -10.47   -61.13     0.59)  ; 49\r\n          (  -65.28    -9.74   -61.56     1.03)  ; 50\r\n          (  -65.72    -8.86   -62.25     1.03)  ; 51\r\n          (  -65.87    -8.35   -63.94     0.74)  ; 52\r\n          (  -65.79    -7.98   -65.88     0.59)  ; 53\r\n          (  -65.28    -7.55   -67.13     0.44)  ; 54\r\n          (  -65.28    -6.96   -68.13     0.44)  ; 55\r\n          (  -65.57    -6.08   -68.56     0.74)  ; 56\r\n          (  -65.72    -5.65   -69.25     1.47)  ; 57\r\n          (  -66.09    -4.18   -70.31     1.69)  ; 58\r\n          (  -66.16    -3.38   -71.69     1.92)  ; 59\r\n          (  -66.23    -2.36   -72.50     1.62)  ; 60\r\n          (  -66.23    -1.77   -73.75     1.25)  ; 61\r\n          (  -66.09    -0.97   -74.00     0.96)  ; 62\r\n          (  -66.09    -0.16   -74.19     0.59)  ; 63\r\n          (  -66.16     0.49   -74.63     0.59)  ; 64\r\n          (  -66.01     1.88   -74.81     0.44)  ; 65\r\n          (  -66.01     2.83   -75.69     0.44)  ; 66\r\n          (  -66.01     3.71   -76.38     0.44)  ; 67\r\n          (  -66.16     4.59   -76.81     0.44)  ; 68\r\n          (  -66.38     5.68   -76.88     0.74)  ; 69\r\n          (  -66.45     6.27   -77.44     1.18)  ; 70\r\n          (  -66.90     7.07   -78.94     1.62)  ; 71\r\n          (  -67.12     7.51   -79.19     2.06)  ; 72\r\n          (  -67.34     7.95   -79.94     1.62)  ; 73\r\n          (  -67.34     8.32   -80.31     1.18)  ; 74\r\n          (  -67.63     8.90   -80.31     0.81)  ; 75\r\n          (  -67.71     9.41   -80.56     0.52)  ; 76\r\n          (  -67.71    10.14   -80.81     0.37)  ; 77\r\n          (  -68.08    10.80   -81.63     0.37)  ; 78\r\n          (  -69.18    11.09   -83.31     1.03)  ; 79\r\n          (  -69.99    11.46   -84.38     1.25)  ; 80\r\n          (  -70.92    11.47   -85.50     0.59)  ; 81\r\n          (  -71.28    12.05   -86.81     0.59)  ; 82\r\n          (  -71.65    12.78   -86.75     1.40)  ; 83\r\n          (  -72.02    12.78   -87.38     2.21)  ; 84\r\n          (  -72.46    12.85   -87.69     2.58)  ; 85\r\n          (  -73.13    12.85   -88.44     1.40)  ; 86\r\n          (  -74.16    13.15   -89.13     0.66)  ; 87\r\n          (  -75.48    13.37   -89.88     0.29)  ; 88\r\n          (  -76.15    13.29   -88.75     0.96)  ; 89\r\n          (  -76.88    13.29   -88.75     0.96)  ; 90\r\n          (  -77.32    13.44   -88.75     0.59)  ; 91\r\n           Low\r\n        |\r\n          (  -34.11   -24.48   -41.63     0.59)  ; 1, R-1-1-1-2\r\n          (  -34.85   -25.58   -42.44     0.44)  ; 2\r\n          (  -35.95   -25.73   -42.63     0.66)  ; 3\r\n          (  -36.61   -26.75   -43.06     0.52)  ; 4\r\n          (  -36.47   -27.55   -43.06     0.52)  ; 5\r\n          (  -36.61   -28.36   -43.63     0.81)  ; 6\r\n          (  -36.91   -28.72   -43.94     1.62)  ; 7\r\n          (  -37.20   -28.94   -44.88     1.11)  ; 8\r\n          (  -37.43   -29.16   -46.38     0.74)  ; 9\r\n          (  -38.16   -28.87   -47.25     0.52)  ; 10\r\n          (  -39.05   -29.09   -49.06     0.52)  ; 11\r\n          (  -39.93   -29.02   -49.63     0.52)  ; 12\r\n          (  -40.59   -29.02   -49.75     0.88)  ; 13\r\n          (  -41.11   -28.72   -49.81     1.03)  ; 14\r\n          (  -42.14   -28.87   -50.06     0.66)  ; 15\r\n          (  -43.10   -29.16   -50.44     0.66)  ; 16\r\n          (  -43.91   -29.60   -50.69     0.66)  ; 17\r\n          (  -44.35   -30.33   -50.69     0.66)  ; 18\r\n          (  -45.53   -30.62   -50.88     0.66)  ; 19\r\n          (  -46.41   -30.04   -51.50     0.96)  ; 20\r\n          (  -46.78   -29.67   -52.13     1.33)  ; 21\r\n          (  -47.37   -29.82   -53.25     0.81)  ; 22\r\n          (  -47.59   -30.26   -55.19     0.81)  ; 23\r\n          (  -47.81   -31.06   -57.19     0.81)  ; 24\r\n          (  -48.48   -31.14   -58.56     0.81)  ; 25\r\n          (  -49.21   -31.14   -59.44     0.81)  ; 26\r\n          (  -49.80   -31.21   -59.50     0.81)  ; 27\r\n          (  -50.76   -31.57   -60.56     0.74)  ; 28\r\n          (  -51.28   -32.53   -62.13     0.66)  ; 29\r\n          (  -50.98   -33.18   -62.88     0.66)  ; 30\r\n          (  -50.91   -34.13   -63.50     0.88)  ; 31\r\n          (  -50.69   -34.79   -65.00     0.88)  ; 32\r\n          (  -50.32   -35.81   -65.00     0.88)  ; 33\r\n          (  -50.61   -36.91   -66.69     1.18)  ; 34\r\n          (  -50.54   -38.01   -67.31     1.33)  ; 35\r\n          (  -51.28   -38.74   -69.00     1.03)  ; 36\r\n          (  -51.94   -39.40   -70.06     0.81)  ; 37\r\n          (  -52.97   -40.57   -70.44     0.59)  ; 38\r\n          (  -53.49   -41.30   -70.94     0.59)  ; 39\r\n          (  -54.22   -42.03   -71.69     0.59)  ; 40\r\n          (  -55.25   -42.91   -71.94     0.59)  ; 41\r\n          (  -56.21   -42.76   -72.63     0.59)  ; 42\r\n          (  -57.02   -42.25   -73.50     0.59)  ; 43\r\n          (  -58.35   -42.25   -73.50     0.59)  ; 44\r\n          (  -58.79   -43.05   -74.00     0.59)  ; 45\r\n          (  -59.60   -43.86   -74.56     0.59)  ; 46\r\n          (  -59.90   -44.51   -74.88     0.59)  ; 47\r\n          (  -60.85   -44.81   -74.88     0.74)  ; 48\r\n          (  -61.81   -45.32   -75.13     0.74)  ; 49\r\n          (  -62.62   -46.05   -75.38     0.74)  ; 50\r\n          (  -62.99   -46.63   -74.38     0.52)  ; 51\r\n          (  -64.32   -46.85   -74.06     0.37)  ; 52\r\n          (  -64.76   -47.36   -73.88     0.37)  ; 53\r\n          (  -65.35   -48.17   -73.69     0.37)  ; 54\r\n          (  -66.01   -48.90   -73.63     0.66)  ; 55\r\n          (  -66.60   -49.63   -73.69     0.81)  ; 56\r\n          (  -67.26   -50.43   -73.69     0.52)  ; 57\r\n          (  -67.93   -50.95   -74.06     0.81)  ; 58\r\n          (  -68.44   -51.24   -74.06     1.18)  ; 59\r\n          (  -69.10   -51.53   -74.06     2.06)  ; 60\r\n          (  -69.84   -51.97   -74.81     2.43)  ; 61\r\n          (  -70.73   -52.04   -74.56     1.92)  ; 62\r\n          (  -71.54   -52.33   -74.00     1.11)  ; 63\r\n          (  -72.60   -52.94   -73.25     0.52)  ; 64\r\n          (  -73.49   -53.23   -73.56     0.44)  ; 65\r\n          (  -75.18   -53.52   -75.19     0.29)  ; 66\r\n          (  -76.44   -53.74   -75.88     0.29)  ; 67\r\n          (  -77.02   -54.40   -76.38     0.29)  ; 68\r\n          (  -77.98   -54.47   -76.38     0.59)  ; 69\r\n          (  -78.79   -54.69   -77.06     0.96)  ; 70\r\n          (  -79.16   -54.77   -77.50     1.40)  ; 71\r\n          (  -79.68   -54.62   -78.56     2.21)  ; 72\r\n          (  -80.56   -54.84   -79.31     1.55)  ; 73\r\n          (  -81.37   -55.35   -80.69     0.88)  ; 74\r\n          (  -81.74   -55.42   -82.06     0.52)  ; 75\r\n          (  -82.85   -55.94   -83.06     0.29)  ; 76\r\n          (  -83.73   -56.01   -83.88     0.59)  ; 77\r\n          (  -84.76   -56.52   -84.06     0.37)  ; 78\r\n          (  -85.42   -56.59   -84.44     0.37)  ; 79\r\n          (  -86.16   -56.59   -84.88     0.37)  ; 80\r\n          (  -87.34   -56.45   -85.00     0.74)  ; 81\r\n          (  -88.00   -56.67   -85.69     1.84)  ; 82\r\n          (  -88.44   -56.89   -86.38     2.58)  ; 83\r\n          (  -89.25   -57.03   -86.56     1.33)  ; 84\r\n          (  -89.77   -57.40   -86.56     0.88)  ; 85\r\n          (  -90.29   -57.62   -86.56     0.52)  ; 86\r\n          (  -90.80   -57.98   -86.56     0.52)  ; 87\r\n          (  -91.32   -58.64   -87.63     0.59)  ; 88\r\n          (  -91.76   -59.37   -88.13     0.59)  ; 89\r\n          (  -91.61   -59.96   -88.63     0.59)  ; 90\r\n          (  -92.20   -60.32   -88.63     0.88)  ; 91\r\n          (  -92.86   -61.05   -89.38     1.40)  ; 92\r\n          (  -93.53   -61.42   -90.56     1.55)  ; 93\r\n          (  -94.04   -61.86   -91.50     1.11)  ; 94\r\n          (  -94.34   -62.22   -92.63     0.66)  ; 95\r\n          (  -94.41   -61.86   -94.38     0.66)  ; 96\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (  -29.80   -24.94   -38.56     1.25)  ; 1, R-1-1-2\r\n        (  -29.87   -25.82   -39.13     0.96)  ; 2\r\n        (  -30.17   -26.77   -39.25     0.74)  ; 3\r\n        (  -30.10   -27.50   -40.44     0.74)  ; 4\r\n        (  -30.76   -28.45   -40.69     0.66)  ; 5\r\n        (  -31.13   -28.96   -40.75     0.66)  ; 6\r\n        (  -31.20   -29.47   -41.56     0.66)  ; 7\r\n        (  -31.64   -30.64   -41.56     0.66)  ; 8\r\n        (  -31.64   -31.15   -43.50     0.66)  ; 9\r\n        (  -31.64   -31.81   -43.75     0.66)  ; 10\r\n        (  -31.94   -32.69   -43.81     0.66)  ; 11\r\n        (  -32.45   -33.42   -45.25     0.66)  ; 12\r\n        (  -33.63   -33.86   -45.69     0.66)  ; 13\r\n        (  -34.15   -34.15   -47.06     0.66)  ; 14\r\n        (  -34.81   -34.81   -48.50     0.81)  ; 15\r\n        (  -34.96   -35.10   -51.44     0.96)  ; 16\r\n        (  -35.25   -35.18   -53.81     0.96)  ; 17\r\n        (  -35.33   -35.98   -55.13     0.96)  ; 18\r\n        (  -35.92   -36.34   -57.44     0.96)  ; 19\r\n        (  -36.06   -35.98   -57.44     0.96)  ; 20\r\n        (  -37.39   -36.49   -58.00     0.74)  ; 21\r\n        (  -38.05   -36.49   -58.50     0.74)  ; 22\r\n        (  -39.08   -36.49   -59.31     0.88)  ; 23\r\n        (  -39.97   -36.64   -61.81     0.59)  ; 24\r\n        (  -40.63   -37.44   -62.31     0.59)  ; 25\r\n        (  -41.00   -38.10   -63.00     0.59)  ; 26\r\n        (  -41.44   -38.25   -64.63     0.88)  ; 27\r\n        (  -41.00   -38.46   -66.44     1.18)  ; 28\r\n        (  -41.37   -38.90   -67.94     1.18)  ; 29\r\n        (  -41.66   -39.12   -69.25     1.55)  ; 30\r\n        (  -41.88   -39.78   -69.69     1.99)  ; 31\r\n        (  -42.55   -40.37   -70.31     1.11)  ; 32\r\n        (  -42.84   -40.73   -71.38     0.66)  ; 33\r\n        (  -43.21   -41.90   -71.75     0.37)  ; 34\r\n        (  -43.65   -42.41   -72.56     0.37)  ; 35\r\n        (  -44.09   -43.14   -73.75     0.74)  ; 36\r\n        (  -44.83   -43.65   -73.75     1.18)  ; 37\r\n        (  -45.27   -43.87   -75.50     1.18)  ; 38\r\n        (  -45.57   -44.39   -76.69     1.11)  ; 39\r\n        (  -45.71   -45.19   -77.56     0.74)  ; 40\r\n        (  -45.93   -45.99   -78.31     0.52)  ; 41\r\n        (  -46.38   -46.80   -78.75     0.52)  ; 42\r\n        (  -46.60   -46.87   -79.88     0.52)  ; 43\r\n        (  -47.11   -47.75   -80.88     0.88)  ; 44\r\n        (  -47.63   -48.99   -81.44     1.33)  ; 45\r\n        (  -48.15   -49.79   -82.56     1.33)  ; 46\r\n        (  -49.10   -49.58   -83.06     0.66)  ; 47\r\n        (  -49.77   -49.87   -83.63     0.44)  ; 48\r\n        (  -50.94   -50.16   -83.63     0.37)  ; 49\r\n        (  -51.61   -50.60   -84.19     0.37)  ; 50\r\n        (  -52.27   -50.82   -84.38     0.66)  ; 51\r\n        (  -53.01   -50.89   -83.06     1.03)  ; 52\r\n        (  -53.52   -51.33   -82.75     1.47)  ; 53\r\n        (  -54.41   -51.99   -83.31     1.77)  ; 54\r\n        (  -55.22   -52.43   -83.31     1.03)  ; 55\r\n        (  -56.25   -52.79   -83.81     0.66)  ; 56\r\n        (  -57.06   -52.86   -82.69     0.29)  ; 57\r\n        (  -58.09   -52.86   -82.31     0.29)  ; 58\r\n        (  -58.75   -53.08   -81.63     0.29)  ; 59\r\n        (  -59.86   -53.16   -81.25     0.29)  ; 60\r\n        (  -60.67   -53.30   -79.81     0.59)  ; 61\r\n        (  -61.70   -54.25   -79.69     0.66)  ; 62\r\n        (  -62.07   -54.55   -79.69     0.66)  ; 63\r\n        (  -62.44   -54.98   -79.75     1.03)  ; 64\r\n        (  -62.73   -55.42   -79.81     1.47)  ; 65\r\n        (  -63.54   -56.01   -79.81     1.84)  ; 66\r\n        (  -64.28   -56.23   -79.81     1.62)  ; 67\r\n        (  -65.09   -56.45   -79.81     0.74)  ; 68\r\n        (  -66.05   -56.67   -79.81     0.29)  ; 69\r\n        (  -67.67   -56.45   -77.88     0.22)  ; 70\r\n        (  -69.29   -56.45   -76.50     0.22)  ; 71\r\n        (  -70.84   -56.45   -76.31     0.22)  ; 72\r\n        (  -72.38   -56.15   -76.19     0.88)  ; 73\r\n        (  -73.05   -55.79   -75.50     1.11)  ; 74\r\n        (  -73.71   -55.35   -74.88     0.74)  ; 75\r\n        (  -74.74   -54.84   -74.31     0.37)  ; 76\r\n        (  -75.40   -54.47   -73.25     0.37)  ; 77\r\n        (  -76.07   -53.82   -72.06     0.37)  ; 78\r\n        (  -76.66   -53.23   -71.88     0.59)  ; 79\r\n        (  -77.32   -52.94   -71.69     0.96)  ; 80\r\n        (  -78.06   -52.28   -72.13     0.96)  ; 81\r\n        (  -78.65   -51.55   -72.63     0.52)  ; 82\r\n         Low\r\n      )  ;  End of split\r\n    |\r\n      (  -27.15   -24.28   -39.06     1.40)  ; 1, R-1-2\r\n      (  -27.00   -24.72   -40.38     0.81)  ; 2\r\n      (  -26.86   -25.36   -40.38     0.81)  ; 3\r\n      (  -26.71   -25.38   -41.75     0.74)  ; 4\r\n      (  -26.19   -25.60   -43.38     0.74)  ; 5\r\n      (  -25.60   -25.96   -43.31     0.74)  ; 6\r\n      (  -25.01   -26.33   -44.31     1.03)  ; 7\r\n      (  -24.57   -26.48   -44.31     1.25)  ; 8\r\n      (  -25.36   -26.72   -46.63     0.96)  ; 9\r\n      (  -25.51   -26.94   -48.44     0.96)  ; 10\r\n      (  -25.95   -27.08   -48.94     0.96)  ; 11\r\n      (  -26.32   -27.52   -49.25     1.33)  ; 12\r\n      (  -26.91   -27.60   -50.38     1.33)  ; 13\r\n      (  -27.79   -27.30   -51.50     1.18)  ; 14\r\n      (  -28.38   -27.30   -54.13     0.96)  ; 15\r\n      (  -28.83   -27.30   -54.19     0.96)  ; 16\r\n      (  -29.27   -27.16   -57.00     0.96)  ; 17\r\n      (  -29.49   -26.94   -58.81     0.96)  ; 18\r\n      (  -29.34   -27.67   -59.63     0.96)  ; 19\r\n      (  -29.34   -27.82   -61.31     0.96)  ; 20\r\n      (  -29.12   -28.11   -62.25     0.96)  ; 21\r\n      (  -28.38   -28.40   -63.94     0.81)  ; 22\r\n      (  -27.72   -28.33   -66.63     0.81)  ; 23\r\n      (  -27.20   -28.40   -67.13     1.11)  ; 24\r\n      (  -26.03   -28.55   -69.44     1.11)  ; 25\r\n      (  -24.99   -28.25   -70.25     0.96)  ; 26\r\n      (  -23.74   -28.11   -70.50     0.66)  ; 27\r\n      (  -23.00   -27.96   -71.38     0.66)  ; 28\r\n      (  -22.56   -27.67   -72.06     1.25)  ; 29\r\n      (  -22.05   -27.60   -73.94     1.62)  ; 30\r\n      (  -21.24   -27.74   -74.44     1.62)  ; 31\r\n      (  -21.24   -28.40   -75.63     1.25)  ; 32\r\n      (  -21.02   -29.06   -77.75     0.96)  ; 33\r\n      (  -20.79   -29.42   -78.25     0.96)  ; 34\r\n      (  -21.16   -29.86   -80.56     0.96)  ; 35\r\n      (  -21.02   -29.35   -83.50     0.96)  ; 36\r\n      (  -22.05   -29.79   -84.50     0.96)  ; 37\r\n      (  -22.27   -29.50   -87.31     0.96)  ; 38\r\n      (  -22.56   -29.20   -89.25     0.96)  ; 39\r\n      (  -22.78   -29.57   -89.81     0.96)  ; 40\r\n      (  -22.93   -29.94   -92.06     1.18)  ; 41\r\n      (  -23.52   -30.23   -93.31     1.33)  ; 42\r\n      (  -23.82   -30.52   -96.00     1.69)  ; 43\r\n      (  -24.33   -29.94   -96.56     1.69)  ; 44\r\n      (  -24.33   -29.50   -98.38     1.99)  ; 45\r\n      (  -24.33   -29.50  -100.06     2.36)  ; 46\r\n      (  -24.04   -29.28  -100.81     0.81)  ; 47\r\n      (  -23.45   -29.35  -102.00     0.66)  ; 48\r\n      (  -23.37   -28.69  -104.81     0.66)  ; 49\r\n      (  -23.67   -28.03  -107.50     0.66)  ; 50\r\n      (  -24.04   -26.79  -108.25     0.88)  ; 51\r\n      (  -24.26   -26.21  -110.50     1.18)  ; 52\r\n      (  -24.77   -25.40  -113.00     1.40)  ; 53\r\n      (  -25.29   -25.40  -114.13     1.69)  ; 54\r\n      (  -25.07   -25.26  -116.88     1.69)  ; 55\r\n      (  -26.21   -25.70  -116.88     0.37)  ; 56\r\n      (  -28.13   -25.70  -117.13     0.29)  ; 57\r\n      (  -29.23   -25.77  -117.88     0.29)  ; 58\r\n      (  -30.63   -25.85  -118.75     0.29)  ; 59\r\n      (  -31.66   -26.14  -119.94     0.29)  ; 60\r\n      (  -32.91   -26.07  -120.75     0.29)  ; 61\r\n      (  -34.02   -26.07  -120.69     0.29)  ; 62\r\n      (  -34.76   -26.07  -120.69     0.29)  ; 63\r\n      (  -35.35   -26.21  -120.13     0.29)  ; 64\r\n      (  -35.79   -26.43  -120.06     0.66)  ; 65\r\n      (  -36.52   -26.43  -120.19     1.11)  ; 66\r\n      (  -36.82   -26.43  -120.38     1.11)  ; 67\r\n      (  -37.63   -26.36  -121.50     0.66)  ; 68\r\n      (  -38.37   -26.14  -121.44     0.66)  ; 69\r\n      (  -39.32   -26.14  -121.63     0.66)  ; 70\r\n      (  -40.43   -26.21  -122.13     0.66)  ; 71\r\n      (  -40.80   -26.29  -122.25     0.44)  ; 72\r\n      (  -41.53   -26.80  -122.44     0.74)  ; 73\r\n      (  -41.75   -27.16  -122.56     1.18)  ; 74\r\n      (  -42.34   -27.60  -122.56     1.47)  ; 75\r\n      (  -42.79   -27.82  -122.69     0.59)  ; 76\r\n       Incomplete\r\n    )  ;  End of split\r\n  |\r\n    (  -27.20   -21.60   -40.00     0.88)  ; 1, R-2\r\n    (  -27.50   -21.02   -41.00     0.59)  ; 2\r\n    (  -26.91   -20.43   -41.94     0.37)  ; 3\r\n    (  -26.69   -19.92   -43.06     0.37)  ; 4\r\n    (  -26.54   -19.12   -41.44     0.66)  ; 5\r\n    (  -26.47   -18.53   -42.50     0.96)  ; 6\r\n    (  -26.10   -18.17   -43.44     1.11)  ; 7\r\n    (  -25.66   -17.73   -45.06     0.96)  ; 8\r\n    (  -25.14   -17.58   -46.88     0.81)  ; 9\r\n    (  -24.55   -17.65   -48.31     1.03)  ; 10\r\n    (  -23.59   -17.73   -50.00     1.03)  ; 11\r\n    (  -23.15   -16.85   -53.13     0.88)  ; 12\r\n    (  -22.49   -15.46   -53.69     0.66)  ; 13\r\n    (  -22.64   -14.58   -53.81     0.66)  ; 14\r\n    (  -23.08   -14.15   -57.56     0.96)  ; 15\r\n    (  -22.86   -13.56   -59.56     1.18)  ; 16\r\n    (  -22.49   -13.71   -61.44     1.18)  ; 17\r\n    (  -22.78   -13.49   -63.00     1.18)  ; 18\r\n    (  -23.00   -13.27   -64.50     1.18)  ; 19\r\n    (  -22.93   -13.27   -67.63     1.18)  ; 20\r\n    (  -22.49   -13.20   -69.50     1.18)  ; 21\r\n    (  -22.27   -12.83   -72.50     1.03)  ; 22\r\n    (  -23.23   -12.17   -76.19     0.52)  ; 23\r\n    (  -24.85   -10.93   -76.94     0.52)  ; 24\r\n    (  -26.03   -10.20   -77.06     0.52)  ; 25\r\n    (  -27.28    -9.39   -77.69     0.52)  ; 26\r\n    (  -28.16    -8.30   -78.56     0.66)  ; 27\r\n    (  -28.97    -7.35   -79.94     0.96)  ; 28\r\n    (  -29.78    -6.76   -81.19     0.96)  ; 29\r\n    (  -30.30    -5.89   -81.69     0.59)  ; 30\r\n    (  -30.74    -5.01   -82.63     0.59)  ; 31\r\n    (  -30.96    -4.57   -83.06     0.88)  ; 32\r\n    (  -31.48    -4.13   -83.25     0.88)  ; 33\r\n    (  -31.85    -4.13   -85.81     1.25)  ; 34\r\n    (  -32.44    -4.35   -86.50     0.96)  ; 35\r\n    (  -33.02    -4.13   -88.06     0.96)  ; 36\r\n    (  -33.61    -3.77   -88.19     0.96)  ; 37\r\n    (  -34.06    -3.62   -88.25     0.96)  ; 38\r\n    (  -34.35    -3.55   -89.63     0.59)  ; 39\r\n    (  -35.01    -3.03   -91.31     0.44)  ; 40\r\n    (  -35.09    -2.96   -92.00     0.81)  ; 41\r\n    (  -35.31    -2.30   -93.13     1.18)  ; 42\r\n    (  -35.82    -1.50   -94.50     1.18)  ; 43\r\n    (  -36.12    -0.40   -96.06     0.44)  ; 44\r\n    (  -36.27     0.33   -97.06     0.81)  ; 45\r\n    (  -36.27     0.84   -97.69     1.18)  ; 46\r\n    (  -36.27     1.13   -99.75     0.81)  ; 47\r\n    (  -36.27     1.28  -103.25     0.59)  ; 48\r\n     Low\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color DarkCyan)\r\n  (Dendrite)\r\n  (   14.75    -0.10    -9.25     1.47)  ; Root\r\n  (   16.22     0.12    -9.63     1.33)  ; 1, R\r\n  (   17.47     0.34   -10.25     1.62)  ; 2\r\n  (   19.02     0.34   -10.69     1.62)  ; 3\r\n  (   20.71     0.85   -11.31     1.40)  ; 4\r\n  (   21.89     1.00   -12.69     1.18)  ; 5\r\n  (   22.56     1.07   -14.69     1.03)  ; 6\r\n  (   23.88     1.07   -15.56     1.11)  ; 7\r\n  (   24.69     1.44   -15.94     1.25)  ; 8\r\n  (   26.24     1.29   -17.50     1.25)  ; 9\r\n  (   28.45     1.00   -18.75     1.55)  ; 10\r\n  (\r\n    (   29.78     0.12   -17.38     1.55)  ; 1, R-1\r\n    (   30.88     0.12   -17.44     1.62)  ; 2\r\n    (   32.43     0.12   -17.69     1.84)  ; 3\r\n    (   33.61     0.12   -18.38     2.06)  ; 4\r\n    (   34.86     0.27   -18.69     2.28)  ; 5\r\n    (\r\n      (   37.29     1.36   -21.00     2.36)  ; 1, R-1-1\r\n      (   38.47     1.82   -21.00     2.36)  ; 2\r\n      (\r\n        (   38.84     2.10   -22.38     2.50)  ; 1, R-1-1-1\r\n        (   39.35     2.83   -22.88     1.84)  ; 2\r\n        (   40.39     3.85   -21.75     1.25)  ; 3\r\n        (   40.90     4.44   -21.75     1.25)  ; 4\r\n        (   42.15     5.60   -21.56     1.11)  ; 5\r\n        (   42.74     6.12   -21.50     1.11)  ; 6\r\n        (   43.26     7.80   -21.44     0.96)  ; 7\r\n        (   43.48     8.82   -21.44     0.88)  ; 8\r\n        (   43.92     9.33   -21.44     0.88)  ; 9\r\n        (   44.95    10.14   -22.50     0.88)  ; 10\r\n        (   46.43    10.79   -19.88     0.88)  ; 11\r\n        (   47.31    11.16   -19.81     1.18)  ; 12\r\n        (   48.27    11.67   -19.81     1.18)  ; 13\r\n        (   49.15    12.77   -19.69     1.18)  ; 14\r\n        (   50.33    13.13   -19.31     1.40)  ; 15\r\n        (   51.51    14.01   -19.19     1.18)  ; 16\r\n        (   52.84    14.60   -20.31     0.88)  ; 17\r\n        (   54.31    15.18   -21.88     1.18)  ; 18\r\n        (   55.19    15.33   -22.13     1.18)  ; 19\r\n        (   55.86    15.91   -22.69     1.18)  ; 20\r\n        (   56.37    16.79   -23.31     1.18)  ; 21\r\n        (   57.26    17.67   -23.31     1.18)  ; 22\r\n        (   58.44    18.25   -23.63     1.18)  ; 23\r\n        (   59.69    18.91   -24.06     1.11)  ; 24\r\n        (   60.57    19.57   -24.19     1.11)  ; 25\r\n        (   61.01    20.08   -24.88     1.11)  ; 26\r\n        (   62.41    20.96   -24.94     0.96)  ; 27\r\n        (   63.45    21.54   -25.13     0.88)  ; 28\r\n        (   64.11    21.61   -25.25     0.88)  ; 29\r\n        (   64.99    22.05   -25.38     0.88)  ; 30\r\n        (   65.88    22.64   -25.44     1.03)  ; 31\r\n        (   66.32    23.51   -25.94     1.03)  ; 32\r\n        (   66.91    24.54   -26.44     1.25)  ; 33\r\n        (   67.28    25.05   -25.69     1.47)  ; 34\r\n        (   67.96    26.00   -25.69     1.47)  ; 35\r\n        (\r\n          (   68.09    25.85   -27.50     1.33)  ; 1, R-1-1-1-1\r\n          (   68.82    26.36   -28.31     1.33)  ; 2\r\n          (   69.27    26.29   -30.50     1.33)  ; 3\r\n          (   70.59    26.51   -30.56     1.03)  ; 4\r\n          (   71.55    26.44   -30.94     1.03)  ; 5\r\n          (   72.14    26.66   -30.94     1.18)  ; 6\r\n          (   72.80    26.73   -31.38     1.18)  ; 7\r\n          (\r\n            (   73.17    27.90   -30.56     0.74)  ; 1, R-1-1-1-1-1\r\n            (   73.46    28.56   -30.69     0.74)  ; 2\r\n            (   73.98    29.07   -30.88     0.59)  ; 3\r\n            (   74.72    29.43   -31.25     0.37)  ; 4\r\n            (   75.68    29.58   -31.25     0.37)  ; 5\r\n            (   76.49    29.73   -31.56     0.52)  ; 6\r\n            (   76.78    30.17   -31.69     0.52)  ; 7\r\n            (   77.52    30.02   -32.06     0.52)  ; 8\r\n            (   77.89    30.39   -31.38     0.37)  ; 9\r\n            (   78.18    30.75   -31.38     0.37)  ; 10\r\n            (   78.99    30.60   -31.00     0.37)  ; 11\r\n            (   79.73    30.60   -31.19     0.59)  ; 12\r\n            (   81.35    30.60   -31.13     0.59)  ; 13\r\n            (   82.53    30.60   -31.06     0.74)  ; 14\r\n            (   84.00    30.60   -31.56     0.74)  ; 15\r\n            (   85.18    31.04   -31.13     0.74)  ; 16\r\n            (   86.21    31.19   -30.69     0.74)  ; 17\r\n            (   87.54    31.48   -29.88     0.74)  ; 18\r\n            (   88.94    31.77   -29.81     0.59)  ; 19\r\n            (   90.26    32.21   -29.81     0.52)  ; 20\r\n            (   91.81    32.58   -29.25     0.74)  ; 21\r\n            (   92.55    33.60   -28.94     0.74)  ; 22\r\n            (   93.65    34.70   -28.88     0.96)  ; 23\r\n            (   94.23    35.68   -28.81     0.66)  ; 24\r\n            (   94.60    36.41   -28.75     0.66)  ; 25\r\n            (   95.48    37.07   -28.75     0.88)  ; 26\r\n            (   96.36    37.66   -28.75     0.88)  ; 27\r\n            (   96.81    38.17   -28.31     0.66)  ; 28\r\n            (   97.69    38.83   -28.19     0.52)  ; 29\r\n            (   98.28    39.04   -28.19     0.52)  ; 30\r\n            (   99.09    38.90   -28.13     0.44)  ; 31\r\n            (   99.83    38.68   -28.13     0.44)  ; 32\r\n            (  100.34    38.61   -28.06     0.44)  ; 33\r\n            (  101.08    38.97   -27.63     0.52)  ; 34\r\n            (  101.74    39.78   -26.75     0.66)  ; 35\r\n            (  102.70    41.09   -26.69     0.74)  ; 36\r\n            (  103.07    42.55   -26.31     0.88)  ; 37\r\n            (  103.29    43.50   -26.19     1.77)  ; 38\r\n            (  103.73    44.38   -26.13     1.77)  ; 39\r\n            (  104.25    44.82   -26.00     0.96)  ; 40\r\n            (  104.69    45.11   -26.19     0.66)  ; 41\r\n            (  105.28    45.92   -26.19     0.44)  ; 42\r\n            (  105.57    46.50   -26.19     0.44)  ; 43\r\n            (  105.57    47.45   -26.38     0.66)  ; 44\r\n            (  105.50    48.18   -26.63     0.81)  ; 45\r\n            (  105.79    48.91   -26.63     0.59)  ; 46\r\n            (  106.31    49.79   -26.81     0.52)  ; 47\r\n            (  106.75    50.16   -27.00     0.52)  ; 48\r\n            (  107.78    50.45   -26.81     0.66)  ; 49\r\n            (  108.59    50.81   -26.25     0.88)  ; 50\r\n            (  109.26    51.11   -25.81     0.88)  ; 51\r\n            (  109.85    51.54   -25.38     0.96)  ; 52\r\n            (  110.43    52.28   -25.06     1.11)  ; 53\r\n            (  111.25    52.71   -24.75     0.88)  ; 54\r\n            (  111.61    52.93   -24.56     0.74)  ; 55\r\n            (  112.13    53.30   -24.31     0.52)  ; 56\r\n            (  112.72    53.74   -24.19     0.52)  ; 57\r\n            (  113.31    54.47   -23.81     0.96)  ; 58\r\n            (  113.90    55.13   -23.56     1.25)  ; 59\r\n            (  114.19    55.71   -23.50     1.25)  ; 60\r\n            (  114.12    56.22   -23.38     0.96)  ; 61\r\n            (  114.56    56.73   -23.25     0.66)  ; 62\r\n            (  114.86    57.25   -22.44     0.66)  ; 63\r\n            (  115.30    58.20   -21.44     0.44)  ; 64\r\n            (  115.89    58.71   -20.94     0.44)  ; 65\r\n            (  116.03    59.37   -21.31     0.81)  ; 66\r\n            (  116.18    59.88   -21.50     1.25)  ; 67\r\n            (  116.26    61.05   -22.19     1.62)  ; 68\r\n            (  116.26    61.49   -22.63     1.33)  ; 69\r\n            (  116.33    62.14   -22.81     1.03)  ; 70\r\n            (  116.40    62.73   -23.00     0.74)  ; 71\r\n            (  116.03    64.26   -23.13     0.44)  ; 72\r\n            (  115.74    65.41   -23.69     0.44)  ; 73\r\n            (  115.67    66.14   -24.25     0.22)  ; 74\r\n            (  115.67    66.80   -24.63     0.22)  ; 75\r\n            (  115.60    67.31   -24.94     0.22)  ; 76\r\n             Low\r\n          |\r\n            (   73.40    26.21   -32.50     0.44)  ; 1, R-1-1-1-1-2\r\n            (   74.28    26.29   -33.19     0.66)  ; 2\r\n            (   74.87    26.36   -34.00     0.66)  ; 3\r\n            (   75.31    26.36   -35.38     0.66)  ; 4\r\n            (   75.83    26.50   -35.81     0.66)  ; 5\r\n            (   76.71    26.72   -35.81     0.66)  ; 6\r\n            (   77.15    26.65   -35.88     0.66)  ; 7\r\n            (   77.45    26.43   -37.19     0.66)  ; 8\r\n            (   77.67    26.14   -37.25     0.66)  ; 9\r\n            (   78.11    25.48   -37.94     0.66)  ; 10\r\n            (   78.40    25.19   -38.50     0.66)  ; 11\r\n            (   78.99    24.82   -39.00     0.66)  ; 12\r\n            (   79.44    24.60   -39.31     0.66)  ; 13\r\n            (   80.25    24.31   -39.31     0.52)  ; 14\r\n            (   81.20    24.09   -40.00     0.52)  ; 15\r\n            (   82.31    24.09   -40.06     0.52)  ; 16\r\n            (   83.49    23.95   -40.06     0.81)  ; 17\r\n            (   84.96    23.87   -41.00     1.18)  ; 18\r\n            (   86.36    23.87   -41.00     1.18)  ; 19\r\n            (   87.54    24.09   -41.06     0.88)  ; 20\r\n            (   88.42    24.31   -42.25     0.59)  ; 21\r\n            (   89.60    24.46   -43.06     0.59)  ; 22\r\n            (   90.34    24.97   -43.88     0.59)  ; 23\r\n            (   91.08    25.48   -44.31     0.59)  ; 24\r\n            (   91.74    26.36   -45.44     0.59)  ; 25\r\n            (   92.40    27.24   -45.44     0.59)  ; 26\r\n            (   92.84    27.89   -44.44     0.59)  ; 27\r\n            (   93.95    28.92   -43.75     0.59)  ; 28\r\n            (   94.24    30.31   -43.75     0.59)  ; 29\r\n            (   95.13    30.96   -43.63     0.59)  ; 30\r\n            (   95.87    32.28   -43.50     0.81)  ; 31\r\n            (   96.53    33.08   -43.50     0.81)  ; 32\r\n            (   97.63    34.03   -43.25     0.59)  ; 33\r\n            (   99.25    34.55   -43.19     0.81)  ; 34\r\n            (  100.36    34.98   -43.19     0.81)  ; 35\r\n            (  101.46    35.79   -42.75     0.81)  ; 36\r\n            (  103.01    36.23   -42.31     0.59)  ; 37\r\n            (  104.04    36.96   -44.06     0.44)  ; 38\r\n            (  105.00    37.83   -44.81     0.44)  ; 39\r\n            (  105.96    39.15   -44.94     0.44)  ; 40\r\n            (  107.21    40.69   -44.94     0.44)  ; 41\r\n            (  108.24    41.42   -44.94     0.66)  ; 42\r\n            (  108.68    41.78   -44.94     0.96)  ; 43\r\n            (  109.20    42.15   -45.25     1.25)  ; 44\r\n            (  110.01    42.66   -45.38     1.99)  ; 45\r\n            (  110.75    43.54   -45.75     1.18)  ; 46\r\n            (  110.75    43.68   -46.00     0.81)  ; 47\r\n            (  111.48    44.12   -46.06     0.37)  ; 48\r\n            (  112.15    45.58   -46.69     0.29)  ; 49\r\n            (  112.81    46.24   -47.00     0.29)  ; 50\r\n            (  113.40    46.75   -47.50     0.59)  ; 51\r\n            (  113.92    47.12   -48.13     0.96)  ; 52\r\n            (  114.80    47.63   -48.81     0.96)  ; 53\r\n            (  114.80    48.58   -49.56     1.25)  ; 54\r\n             Low\r\n          )  ;  End of split\r\n        |\r\n          (   68.18    26.95   -27.56     0.81)  ; 1, R-1-1-1-2\r\n          (   68.55    27.39   -32.69     0.96)  ; 2\r\n          (   68.03    27.46   -34.50     0.88)  ; 3\r\n          (   67.52    27.31   -36.06     0.88)  ; 4\r\n          (   67.08    27.10   -36.75     0.88)  ; 5\r\n          (   67.52    27.46   -39.69     0.88)  ; 6\r\n          (   68.11    27.83   -41.06     0.88)  ; 7\r\n          (   68.99    28.92   -41.88     0.74)  ; 8\r\n          (   69.58    29.14   -42.81     0.74)  ; 9\r\n          (   70.25    29.51   -44.00     0.74)  ; 10\r\n          (   71.06    30.38   -45.13     0.74)  ; 11\r\n          (   71.50    30.90   -45.06     1.03)  ; 12\r\n          (   71.87    31.41   -46.44     1.03)  ; 13\r\n          (   72.60    31.70   -46.94     0.66)  ; 14\r\n          (   73.34    31.85   -47.75     0.52)  ; 15\r\n          (   74.59    31.85   -48.81     0.52)  ; 16\r\n          (   75.99    32.14   -49.19     0.74)  ; 17\r\n          (   76.88    32.36   -50.38     0.96)  ; 18\r\n          (   77.91    32.43   -50.56     1.18)  ; 19\r\n          (   79.09    32.72   -52.19     1.03)  ; 20\r\n          (   80.19    32.87   -52.50     0.74)  ; 21\r\n          (   81.59    33.38   -53.63     0.59)  ; 22\r\n          (   82.18    34.11   -55.75     0.88)  ; 23\r\n          (   83.21    34.48   -56.06     0.88)  ; 24\r\n          (   84.39    35.43   -58.50     0.74)  ; 25\r\n          (   85.42    36.60   -59.25     0.74)  ; 26\r\n          (   85.79    37.62   -59.81     0.74)  ; 27\r\n          (   86.45    39.30   -60.25     0.59)  ; 28\r\n          (   86.60    39.96   -60.81     0.88)  ; 29\r\n          (   86.60    40.40   -61.50     1.33)  ; 30\r\n          (   86.82    41.42   -62.13     1.69)  ; 31\r\n          (   86.90    42.08   -63.19     2.14)  ; 32\r\n          (   87.04    42.74   -63.25     1.40)  ; 33\r\n          (   87.12    43.84   -63.25     0.81)  ; 34\r\n          (   86.97    45.00   -63.94     0.59)  ; 35\r\n          (   86.97    46.03   -64.56     0.44)  ; 36\r\n          (   87.48    48.00   -64.63     0.37)  ; 37\r\n          (   88.00    48.88   -65.00     0.37)  ; 38\r\n          (   88.22    49.76   -65.25     0.74)  ; 39\r\n          (   88.44    50.56   -66.19     1.03)  ; 40\r\n          (   88.59    51.51   -66.56     1.03)  ; 41\r\n          (   88.74    52.31   -66.94     0.66)  ; 42\r\n          (   89.11    53.26   -68.06     0.44)  ; 43\r\n          (   89.33    53.92   -68.44     0.44)  ; 44\r\n          (   89.62    54.80   -68.94     0.44)  ; 45\r\n          (   89.92    55.60   -69.06     0.74)  ; 46\r\n          (   90.36    56.55   -70.06     1.03)  ; 47\r\n          (   90.80    57.36   -70.56     0.74)  ; 48\r\n          (   91.32    57.80   -70.56     0.44)  ; 49\r\n          (   91.54    59.04   -71.31     0.29)  ; 50\r\n          (   91.98    59.77   -71.56     0.29)  ; 51\r\n          (   92.35    60.50   -71.56     0.29)  ; 52\r\n          (   92.72    61.45   -72.00     0.96)  ; 53\r\n          (   93.53    61.82   -73.25     1.25)  ; 54\r\n          (   93.67    62.55   -73.94     1.25)  ; 55\r\n          (   94.63    63.13   -75.19     0.96)  ; 56\r\n          (   95.29    63.79   -76.06     0.74)  ; 57\r\n          (   96.18    64.30   -77.75     0.96)  ; 58\r\n          (   96.40    64.74   -78.88     0.96)  ; 59\r\n          (   97.43    65.76   -81.75     0.74)  ; 60\r\n          (   97.58    66.28   -84.38     0.96)  ; 61\r\n          (   98.39    67.67   -86.31     0.96)  ; 62\r\n          (   98.31    68.25   -85.69     1.62)  ; 63\r\n          (   98.46    68.83   -85.69     1.62)  ; 64\r\n          (   98.83    69.49   -86.38     1.25)  ; 65\r\n          (   98.90    69.93   -87.81     0.88)  ; 66\r\n          (   99.35    70.88   -89.19     0.66)  ; 67\r\n          (   99.64    72.05   -85.75     0.44)  ; 68\r\n          (   99.49    73.07   -85.31     0.44)  ; 69\r\n          (   99.12    74.02   -86.44     0.74)  ; 70\r\n          (   99.12    74.68   -87.69     1.03)  ; 71\r\n          (   98.61    74.83   -89.88     0.74)  ; 72\r\n          (   98.09    75.19   -91.06     0.74)  ; 73\r\n          (   97.50    75.71   -92.06     0.74)  ; 74\r\n          (   96.77    75.56   -92.13     1.11)  ; 75\r\n           Low\r\n        )  ;  End of split\r\n      |\r\n        (   39.54     1.28   -27.13     0.52)  ; 1, R-1-1-2\r\n        (   39.54     0.62   -27.19     0.52)  ; 2\r\n        (   39.83    -0.26   -28.25     0.74)  ; 3\r\n        (   39.91    -0.38   -28.25     0.74)  ; 4\r\n        (\r\n          (   39.83    -0.84   -29.63     0.74)  ; 1, R-1-1-2-1\r\n          (   40.13    -1.57   -30.13     0.74)  ; 2\r\n          (   39.91    -2.38   -30.13     0.74)  ; 3\r\n          (   39.61    -3.40   -30.44     0.66)  ; 4\r\n          (   39.46    -4.06   -30.94     0.66)  ; 5\r\n          (   39.32    -5.23   -30.94     0.66)  ; 6\r\n          (   39.54    -5.81   -30.00     0.66)  ; 7\r\n          (   39.98    -6.69   -31.31     0.66)  ; 8\r\n          (   40.57    -7.86   -31.69     0.66)  ; 9\r\n          (   41.16    -8.74   -31.69     0.88)  ; 10\r\n          (   41.60   -10.27   -31.69     0.88)  ; 11\r\n          (   41.82   -11.44   -31.63     0.59)  ; 12\r\n          (   41.97   -12.32   -31.63     1.11)  ; 13\r\n          (   42.12   -13.20   -31.13     1.40)  ; 14\r\n          (   42.70   -14.51   -32.00     1.11)  ; 15\r\n          (   42.70   -15.61   -31.94     0.96)  ; 16\r\n          (   43.22   -17.00   -32.25     0.81)  ; 17\r\n          (   43.44   -17.80   -32.25     1.18)  ; 18\r\n          (   43.88   -18.46   -32.25     1.33)  ; 19\r\n          (   43.81   -19.55   -32.19     0.96)  ; 20\r\n          (   43.52   -20.65   -33.69     0.74)  ; 21\r\n          (   43.96   -21.38   -33.63     0.74)  ; 22\r\n          (   44.18   -22.62   -33.63     1.03)  ; 23\r\n          (   44.62   -23.87   -34.13     0.88)  ; 24\r\n          (   44.92   -24.60   -34.75     0.66)  ; 25\r\n          (   45.28   -25.84   -34.88     0.81)  ; 26\r\n          (   45.58   -27.08   -36.31     0.88)  ; 27\r\n          (   45.86   -28.54   -37.56     0.88)  ; 28\r\n          (   46.45   -29.13   -38.00     0.88)  ; 29\r\n          (   47.04   -29.71   -38.44     0.88)  ; 30\r\n          (   48.44   -30.45   -38.75     0.88)  ; 31\r\n          (   49.03   -30.81   -40.19     1.11)  ; 32\r\n          (   50.14   -31.40   -40.38     1.11)  ; 33\r\n          (   50.87   -32.20   -40.38     1.11)  ; 34\r\n          (   51.90   -32.93   -40.63     1.11)  ; 35\r\n          (   52.71   -34.03   -41.44     0.88)  ; 36\r\n          (   53.53   -34.47   -42.63     0.74)  ; 37\r\n          (   53.97   -35.12   -43.56     0.74)  ; 38\r\n          (   54.11   -35.93   -44.50     0.74)  ; 39\r\n          (   54.41   -37.10   -45.50     0.74)  ; 40\r\n          (   53.89   -37.90   -46.94     0.74)  ; 41\r\n          (   53.38   -38.49   -47.94     0.88)  ; 42\r\n          (   53.01   -39.44   -48.06     0.88)  ; 43\r\n          (   52.35   -40.39   -48.81     0.74)  ; 44\r\n          (   51.54   -40.90   -49.63     0.59)  ; 45\r\n          (   51.24   -42.07   -49.63     0.59)  ; 46\r\n          (   51.09   -43.60   -49.63     0.59)  ; 47\r\n          (   51.02   -44.70   -49.63     0.59)  ; 48\r\n          (   50.43   -45.43   -49.63     0.59)  ; 49\r\n          (   49.77   -46.60   -49.75     0.88)  ; 50\r\n          (   49.10   -47.92   -49.94     1.33)  ; 51\r\n          (   48.81   -48.72   -50.63     1.33)  ; 52\r\n          (   48.66   -50.04   -50.94     0.96)  ; 53\r\n          (   48.96   -50.99   -51.13     0.66)  ; 54\r\n          (   49.03   -51.72   -51.81     0.44)  ; 55\r\n          (   49.03   -52.74   -51.38     0.29)  ; 56\r\n          (   48.81   -54.13   -52.31     0.29)  ; 57\r\n          (   48.59   -54.79   -52.56     0.59)  ; 58\r\n          (   48.44   -55.88   -52.56     0.59)  ; 59\r\n          (   48.94   -57.03   -52.63     0.59)  ; 60\r\n          (   48.64   -57.91   -54.38     0.59)  ; 61\r\n          (   48.57   -58.64   -55.75     0.59)  ; 62\r\n          (   48.64   -59.52   -57.25     0.44)  ; 63\r\n          (   48.13   -60.18   -58.88     0.59)  ; 64\r\n          (   48.05   -60.91   -59.63     0.59)  ; 65\r\n          (   48.05   -61.86   -60.38     0.52)  ; 66\r\n          (   47.76   -62.66   -60.81     0.81)  ; 67\r\n          (   47.68   -63.25   -60.75     1.25)  ; 68\r\n          (   47.39   -63.61   -61.50     1.77)  ; 69\r\n          (   47.17   -64.34   -61.50     1.77)  ; 70\r\n          (   46.80   -64.93   -63.38     1.03)  ; 71\r\n          (   45.99   -65.44   -65.50     0.59)  ; 72\r\n          (   45.47   -65.51   -67.25     0.59)  ; 73\r\n          (   44.74   -65.51   -69.25     0.59)  ; 74\r\n          (   44.59   -65.07   -71.94     0.88)  ; 75\r\n          (   44.52   -64.78   -73.75     0.88)  ; 76\r\n          (   44.15   -64.64   -76.13     0.88)  ; 77\r\n          (   42.90   -64.20   -78.06     0.59)  ; 78\r\n          (   42.01   -63.90   -78.56     0.37)  ; 79\r\n          (   41.35   -64.12   -79.44     0.66)  ; 80\r\n          (   40.69   -64.56   -79.44     0.66)  ; 81\r\n          (   39.95   -64.85   -80.63     0.66)  ; 82\r\n          (   39.73   -65.44   -80.69     0.66)  ; 83\r\n          (   39.43   -66.24   -81.63     0.52)  ; 84\r\n          (   38.77   -66.68   -84.63     0.52)  ; 85\r\n          (   38.70   -66.97   -86.69     0.66)  ; 86\r\n          (   38.25   -67.49   -90.13     0.66)  ; 87\r\n           Incomplete\r\n        |\r\n          (   40.72    -0.68   -28.25     0.66)  ; 1, R-1-1-2-2\r\n          (   41.01    -1.19   -31.63     0.66)  ; 2\r\n          (   41.60    -1.70   -32.81     0.66)  ; 3\r\n          (   43.15    -2.72   -33.69     0.66)  ; 4\r\n          (   43.96    -3.82   -35.44     0.66)  ; 5\r\n          (   44.40    -4.26   -36.19     0.66)  ; 6\r\n          (   46.54    -4.84   -36.50     0.74)  ; 7\r\n          (   47.20    -5.72   -37.88     0.74)  ; 8\r\n          (   48.52    -6.30   -38.50     0.88)  ; 9\r\n          (   49.85    -6.30   -38.56     1.18)  ; 10\r\n          (   50.66    -6.38   -38.56     1.18)  ; 11\r\n          (   51.69    -6.45   -38.81     0.96)  ; 12\r\n          (   52.50    -6.89   -39.00     0.96)  ; 13\r\n          (   53.17    -7.18   -39.25     0.96)  ; 14\r\n          (   54.64    -7.18   -39.25     1.25)  ; 15\r\n          (   55.82    -7.18   -39.88     1.25)  ; 16\r\n          (   57.29    -7.04   -38.69     0.88)  ; 17\r\n          (   58.69    -6.74   -38.38     1.18)  ; 18\r\n          (   59.87    -6.74   -38.69     1.40)  ; 19\r\n          (   60.68    -5.94   -38.69     0.81)  ; 20\r\n          (   61.27    -5.21   -38.88     0.66)  ; 21\r\n          (   62.67    -4.55   -39.06     0.88)  ; 22\r\n          (   63.85    -3.97   -39.06     0.81)  ; 23\r\n          (   64.51    -3.45   -39.25     0.81)  ; 24\r\n          (   64.95    -2.65   -39.56     0.81)  ; 25\r\n          (   65.76    -2.14   -40.44     0.66)  ; 26\r\n          (   67.75    -2.28   -40.81     0.66)  ; 27\r\n          (   69.01    -2.21   -41.56     0.96)  ; 28\r\n          (   69.96    -2.58   -41.56     1.25)  ; 29\r\n          (   71.07    -2.72   -42.69     1.47)  ; 30\r\n          (   71.73    -2.87   -44.56     1.77)  ; 31\r\n          (   72.47    -3.16   -45.31     1.99)  ; 32\r\n          (   73.21    -3.82   -46.06     1.25)  ; 33\r\n          (   73.79    -4.55   -47.50     0.66)  ; 34\r\n          (   74.61    -5.57   -47.94     0.59)  ; 35\r\n          (   75.34    -6.23   -48.00     0.59)  ; 36\r\n          (   75.86    -7.11   -48.81     0.59)  ; 37\r\n          (   76.23    -7.99   -49.69     0.59)  ; 38\r\n          (   76.67    -8.57   -49.69     0.88)  ; 39\r\n          (   77.40    -9.59   -50.31     0.88)  ; 40\r\n          (   78.14   -10.40   -50.13     0.52)  ; 41\r\n          (   78.66   -11.20   -51.31     0.52)  ; 42\r\n          (   78.95   -12.01   -52.38     0.81)  ; 43\r\n          (   79.47   -12.81   -54.31     0.88)  ; 44\r\n          (   80.06   -13.83   -54.25     1.47)  ; 45\r\n          (   80.50   -14.35   -55.13     1.84)  ; 46\r\n          (   81.01   -14.71   -56.19     1.84)  ; 47\r\n          (   81.46   -15.00   -56.56     1.33)  ; 48\r\n          (   81.75   -15.44   -57.50     0.81)  ; 49\r\n          (   82.49   -15.73   -58.69     0.66)  ; 50\r\n          (   83.22   -16.03   -58.63     0.66)  ; 51\r\n          (   84.55   -16.10   -59.25     0.88)  ; 52\r\n          (   85.36   -16.17   -59.25     0.88)  ; 53\r\n          (   86.25   -16.39   -60.00     0.66)  ; 54\r\n          (   87.92   -16.23   -60.31     0.66)  ; 55\r\n          (   88.51   -16.30   -60.75     1.40)  ; 56\r\n          (   89.32   -16.44   -61.94     1.62)  ; 57\r\n          (   89.98   -16.81   -63.13     1.62)  ; 58\r\n          (   90.43   -17.10   -64.06     1.25)  ; 59\r\n          (   91.60   -17.69   -65.00     0.88)  ; 60\r\n          (   92.12   -18.56   -65.00     0.44)  ; 61\r\n          (   92.78   -19.15   -66.31     0.44)  ; 62\r\n          (   93.30   -19.44   -66.44     1.18)  ; 63\r\n          (   93.74   -19.66   -67.44     1.55)  ; 64\r\n          (   94.04   -20.39   -68.63     1.11)  ; 65\r\n          (   94.40   -20.61   -68.63     0.74)  ; 66\r\n          (   94.92   -21.34   -69.25     0.37)  ; 67\r\n          (   95.14   -22.58   -70.44     0.37)  ; 68\r\n          (   95.88   -23.53   -71.06     0.29)  ; 69\r\n          (   96.76   -24.12   -71.63     0.59)  ; 70\r\n          (   98.09   -24.85   -71.63     0.96)  ; 71\r\n          (   98.60   -25.65   -72.06     1.84)  ; 72\r\n          (   98.97   -26.53   -72.38     2.73)  ; 73\r\n          (   99.49   -27.34   -72.38     1.99)  ; 74\r\n          (  100.08   -27.99   -74.00     1.55)  ; 75\r\n          (  100.74   -28.36   -74.25     0.66)  ; 76\r\n          (  101.48   -29.02   -74.50     0.44)  ; 77\r\n          (  102.66   -29.60   -75.44     0.29)  ; 78\r\n          (  103.32   -30.41   -76.00     0.29)  ; 79\r\n          (  105.68   -32.01   -76.63     0.29)  ; 80\r\n          (  106.04   -32.16   -76.63     0.66)  ; 81\r\n          (  106.41   -32.31   -77.44     1.11)  ; 82\r\n          (  107.08   -32.45   -78.44     1.55)  ; 83\r\n          (  108.11   -33.26   -78.38     1.55)  ; 84\r\n          (  108.40   -33.55   -78.44     1.03)  ; 85\r\n          (  109.07   -34.13   -78.44     0.52)  ; 86\r\n          (  109.73   -34.79   -79.13     0.44)  ; 87\r\n          (  110.39   -35.60   -79.13     0.44)  ; 88\r\n          (  111.42   -36.33   -79.13     0.74)  ; 89\r\n          (  112.09   -36.91   -79.94     0.74)  ; 90\r\n          (  112.53   -37.28   -81.38     0.74)  ; 91\r\n          (  113.78   -38.15   -82.56     0.52)  ; 92\r\n          (  115.25   -39.69   -82.56     0.37)  ; 93\r\n          (  115.99   -40.57   -82.94     0.66)  ; 94\r\n          (  116.36   -40.64   -84.19     1.18)  ; 95\r\n          (  116.51   -40.79   -84.56     1.62)  ; 96\r\n          (  116.73   -40.79   -86.13     1.69)  ; 97\r\n          (  117.32   -40.27   -86.50     1.18)  ; 98\r\n          (  117.76   -39.84   -86.50     0.74)  ; 99\r\n          (  118.27   -39.69   -87.13     0.52)  ; 100\r\n          (  118.86   -39.25   -89.13     0.81)  ; 101\r\n          (  119.31   -38.96   -89.94     1.25)  ; 102\r\n          (  119.82   -38.52   -91.00     1.69)  ; 103\r\n          (  120.48   -38.01   -91.00     1.69)  ; 104\r\n          (  120.78   -38.01   -91.75     0.81)  ; 105\r\n          (  121.37   -37.86   -92.81     0.52)  ; 106\r\n          (  122.40   -37.72   -93.56     0.44)  ; 107\r\n          (  122.69   -37.72   -93.56     0.44)  ; 108\r\n          (  123.21   -37.50   -94.75     0.81)  ; 109\r\n          (  124.32   -36.84   -94.75     1.03)  ; 110\r\n          (  124.76   -36.84   -94.75     0.74)  ; 111\r\n          (  125.35   -36.84   -94.75     0.74)  ; 112\r\n          (  126.38   -36.91   -95.56     1.11)  ; 113\r\n          (  127.19   -36.84   -95.56     1.11)  ; 114\r\n          (  127.48   -36.84   -96.38     0.74)  ; 115\r\n          (  128.66   -37.20   -97.31     0.44)  ; 116\r\n           Incomplete\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (   35.45    -0.98   -19.19     0.96)  ; 1, R-1-2\r\n      (   35.90    -1.64   -20.69     0.74)  ; 2\r\n      (   37.15    -1.93   -21.44     0.59)  ; 3\r\n      (   38.92    -2.37   -22.13     0.59)  ; 4\r\n      (   40.02    -2.59   -22.44     0.88)  ; 5\r\n      (   41.27    -2.88   -23.56     1.11)  ; 6\r\n      (   42.17    -3.35   -23.69     0.88)  ; 7\r\n      (   43.50    -4.30   -25.38     0.66)  ; 8\r\n      (   44.16    -4.59   -26.63     0.74)  ; 9\r\n      (   44.82    -5.32   -26.75     0.66)  ; 10\r\n      (   45.41    -5.98   -27.81     0.88)  ; 11\r\n      (   46.07    -6.85   -27.88     0.88)  ; 12\r\n      (   46.81    -7.95   -27.81     1.03)  ; 13\r\n      (   47.69    -8.76   -28.38     0.81)  ; 14\r\n      (   48.36    -9.12   -28.56     0.81)  ; 15\r\n      (   49.39    -9.85   -28.69     0.96)  ; 16\r\n      (   50.20   -10.44   -29.13     1.18)  ; 17\r\n      (   51.45   -11.53   -29.13     0.74)  ; 18\r\n      (   52.70   -12.19   -29.13     0.66)  ; 19\r\n      (   53.81   -12.85   -29.31     0.66)  ; 20\r\n      (   54.10   -13.80   -29.63     0.66)  ; 21\r\n      (   54.47   -14.46   -29.75     0.96)  ; 22\r\n      (   55.21   -15.55   -29.81     0.74)  ; 23\r\n      (   55.87   -16.21   -30.56     0.59)  ; 24\r\n      (   56.46   -17.75   -30.69     0.52)  ; 25\r\n      (   57.27   -18.33   -32.38     0.66)  ; 26\r\n      (   58.16   -19.72   -33.06     0.74)  ; 27\r\n      (   58.52   -20.74   -34.38     0.74)  ; 28\r\n      (   59.19   -22.35   -36.00     0.74)  ; 29\r\n      (   59.63   -23.52   -36.00     0.74)  ; 30\r\n      (   60.22   -25.13   -37.06     1.11)  ; 31\r\n      (   60.59   -26.23   -37.56     1.33)  ; 32\r\n      (   60.73   -27.25   -38.88     1.11)  ; 33\r\n      (   60.88   -28.35   -39.31     0.59)  ; 34\r\n      (   60.96   -29.81   -41.44     0.59)  ; 35\r\n      (   61.40   -31.27   -41.44     0.81)  ; 36\r\n      (   61.77   -32.29   -42.06     1.18)  ; 37\r\n      (   62.00   -33.24   -42.75     0.81)  ; 38\r\n      (   62.58   -34.48   -43.00     0.52)  ; 39\r\n      (   63.25   -35.14   -43.06     0.52)  ; 40\r\n      (   63.98   -35.87   -44.00     0.88)  ; 41\r\n      (   64.87   -36.38   -44.81     0.96)  ; 42\r\n      (   65.90   -36.89   -44.81     0.59)  ; 43\r\n      (   67.37   -37.48   -45.44     0.29)  ; 44\r\n      (   68.77   -38.14   -45.44     0.29)  ; 45\r\n      (   70.17   -39.01   -45.94     0.52)  ; 46\r\n      (   71.72   -39.74   -44.75     0.66)  ; 47\r\n      (   72.75   -40.26   -44.94     0.66)  ; 48\r\n      (   74.00   -41.06   -45.06     0.66)  ; 49\r\n      (   75.04   -41.50   -45.13     1.03)  ; 50\r\n      (   76.36   -42.16   -45.56     1.69)  ; 51\r\n      (   76.88   -42.59   -45.56     1.69)  ; 52\r\n      (   77.32   -43.11   -45.56     1.18)  ; 53\r\n      (   77.69   -43.62   -45.56     0.81)  ; 54\r\n      (   78.35   -43.91   -45.56     0.44)  ; 55\r\n      (   79.38   -45.23   -45.44     0.29)  ; 56\r\n      (   80.34   -45.96   -45.00     0.66)  ; 57\r\n      (   80.93   -46.32   -47.00     1.03)  ; 58\r\n      (   82.18   -47.20   -47.69     1.40)  ; 59\r\n      (   83.14   -47.86   -47.69     1.11)  ; 60\r\n      (   84.17   -48.30   -47.69     0.66)  ; 61\r\n      (   85.05   -49.32   -48.69     0.52)  ; 62\r\n      (   85.64   -50.12   -48.69     0.88)  ; 63\r\n      (   86.53   -50.93   -49.69     0.88)  ; 64\r\n      (   87.27   -51.15   -50.94     1.11)  ; 65\r\n      (   87.78   -51.59   -50.94     0.66)  ; 66\r\n      (   88.37   -52.02   -51.75     0.44)  ; 67\r\n      (   89.40   -52.90   -52.88     0.37)  ; 68\r\n      (   89.70   -53.56   -54.69     0.37)  ; 69\r\n      (   90.21   -54.44   -55.94     0.37)  ; 70\r\n      (   90.73   -55.09   -55.94     0.74)  ; 71\r\n      (   91.54   -55.83   -56.63     1.18)  ; 72\r\n      (   91.98   -55.83   -56.63     1.69)  ; 73\r\n      (   92.50   -56.56   -58.69     1.33)  ; 74\r\n      (   92.64   -57.00   -61.63     0.66)  ; 75\r\n       Low\r\n    )  ;  End of split\r\n  |\r\n    (   28.61     0.15   -14.94     0.96)  ; 1, R-2\r\n    (   28.90    -0.73   -12.94     1.25)  ; 2\r\n    (   28.97    -2.19   -11.50     1.03)  ; 3\r\n    (   29.20    -3.44    -9.19     0.88)  ; 4\r\n    (   29.56    -4.68    -9.00     0.88)  ; 5\r\n    (   30.08    -5.77    -8.19     0.88)  ; 6\r\n    (   30.89    -6.43    -8.13     0.66)  ; 7\r\n    (   31.85    -6.72    -8.13     0.66)  ; 8\r\n    (   32.95    -7.31    -8.31     0.88)  ; 9\r\n    (   33.76    -7.67    -8.69     0.88)  ; 10\r\n    (   35.09    -8.26    -9.13     0.88)  ; 11\r\n    (   35.68    -9.21    -9.56     1.03)  ; 12\r\n    (   36.12    -9.94    -9.56     1.03)  ; 13\r\n    (   37.15   -10.89    -9.81     0.88)  ; 14\r\n    (   38.04   -11.48   -10.13     0.88)  ; 15\r\n    (   38.63   -12.79   -10.19     0.74)  ; 16\r\n    (   38.99   -13.45   -10.25     0.74)  ; 17\r\n    (   39.66   -13.60   -10.75     0.96)  ; 18\r\n    (   40.39   -13.89   -13.00     0.96)  ; 19\r\n    (   40.69   -13.74   -15.00     1.18)  ; 20\r\n    (   41.28   -14.18   -16.19     0.88)  ; 21\r\n    (   42.01   -15.72   -16.19     0.74)  ; 22\r\n    (   42.46   -16.81   -17.69     0.74)  ; 23\r\n    (   42.97   -17.47   -17.69     0.74)  ; 24\r\n    (   43.78   -17.54   -17.88     0.74)  ; 25\r\n    (   44.74   -18.20   -18.19     0.88)  ; 26\r\n    (   45.85   -18.86   -18.88     0.88)  ; 27\r\n    (   46.80   -20.03   -18.94     1.11)  ; 28\r\n    (   47.61   -20.32   -19.38     1.11)  ; 29\r\n    (   48.87   -20.54   -19.63     0.88)  ; 30\r\n    (   49.60   -20.91   -20.81     0.88)  ; 31\r\n    (   50.41   -21.05   -22.13     0.88)  ; 32\r\n    (   51.15   -21.42   -23.00     0.88)  ; 33\r\n    (   51.52   -21.93   -24.06     1.25)  ; 34\r\n    (   51.96   -22.15   -24.44     1.40)  ; 35\r\n    (   52.70   -22.66   -25.56     1.18)  ; 36\r\n    (   53.80   -23.10   -26.38     0.88)  ; 37\r\n    (   54.54   -23.90   -26.63     0.88)  ; 38\r\n    (   55.28   -24.41   -27.31     0.88)  ; 39\r\n    (   55.94   -24.78   -28.88     0.88)  ; 40\r\n    (   56.60   -25.44   -28.88     0.88)  ; 41\r\n    (   57.49   -26.24   -29.75     0.66)  ; 42\r\n    (   58.37   -26.68   -30.06     0.66)  ; 43\r\n    (   58.89   -27.56   -30.25     0.88)  ; 44\r\n    (   59.62   -28.58   -30.44     0.96)  ; 45\r\n    (   60.68   -29.65   -30.81     0.81)  ; 46\r\n    (   61.27   -30.38   -31.13     0.74)  ; 47\r\n    (   62.16   -31.26   -31.13     0.59)  ; 48\r\n    (   63.19   -31.70   -31.19     0.59)  ; 49\r\n    (   64.15   -31.84   -31.94     0.96)  ; 50\r\n    (   65.33   -32.14   -32.19     0.96)  ; 51\r\n    (   66.28   -31.92   -32.38     0.96)  ; 52\r\n    (   67.76   -32.14   -33.00     0.74)  ; 53\r\n    (   69.82   -32.65   -33.31     0.59)  ; 54\r\n    (   70.70   -32.72   -33.44     0.96)  ; 55\r\n    (   71.81   -32.79   -34.69     0.74)  ; 56\r\n    (   72.99   -32.65   -34.94     0.59)  ; 57\r\n    (   73.58   -32.79   -35.06     0.59)  ; 58\r\n    (   74.90   -33.38   -35.31     0.59)  ; 59\r\n    (   75.79   -33.74   -34.56     0.81)  ; 60\r\n    (   77.19   -34.26   -34.25     0.81)  ; 61\r\n    (   78.59   -35.06   -34.13     0.81)  ; 62\r\n    (   79.40   -35.72   -33.81     1.18)  ; 63\r\n    (   80.28   -36.30   -33.88     1.55)  ; 64\r\n    (   80.94   -36.81   -33.88     2.21)  ; 65\r\n    (\r\n      (   82.20   -36.52   -34.00     1.11)  ; 1, R-2-1\r\n      (   83.15   -36.59   -33.94     0.74)  ; 2\r\n      (   83.97   -36.74   -33.94     0.52)  ; 3\r\n      (   84.55   -36.59   -33.94     0.52)  ; 4\r\n      (   85.81   -36.81   -33.69     0.52)  ; 5\r\n      (   86.69   -36.89   -33.25     0.81)  ; 6\r\n      (   87.50   -36.96   -33.19     1.18)  ; 7\r\n      (   88.61   -36.52   -33.19     1.18)  ; 8\r\n      (   89.71   -35.94   -33.56     0.88)  ; 9\r\n      (   90.82   -35.79   -33.88     0.88)  ; 10\r\n      (   91.77   -35.57   -34.25     0.88)  ; 11\r\n      (   92.73   -34.62   -34.38     0.66)  ; 12\r\n      (   93.62   -33.60   -33.69     0.44)  ; 13\r\n      (   94.57   -33.09   -33.44     0.44)  ; 14\r\n      (   95.24   -32.43   -33.50     0.59)  ; 15\r\n      (   95.97   -31.62   -33.56     0.59)  ; 16\r\n      (   96.34   -31.04   -33.56     0.59)  ; 17\r\n      (   97.01   -30.31   -33.69     0.59)  ; 18\r\n      (   97.37   -30.23   -33.81     0.59)  ; 19\r\n      (   98.55   -30.09   -34.06     0.81)  ; 20\r\n      (   99.95   -29.94   -34.31     1.25)  ; 21\r\n      (  101.13   -29.72   -34.63     0.88)  ; 22\r\n      (  101.72   -29.58   -34.88     0.52)  ; 23\r\n      (  103.19   -29.21   -35.06     0.52)  ; 24\r\n      (  104.00   -28.85   -34.50     0.81)  ; 25\r\n      (  104.96   -28.48   -34.31     0.81)  ; 26\r\n      (  105.48   -28.48   -34.88     0.52)  ; 27\r\n      (  106.58   -28.11   -35.06     0.74)  ; 28\r\n      (  107.78   -27.94   -36.25     0.74)  ; 29\r\n      (  109.11   -27.80   -36.63     0.52)  ; 30\r\n      (  110.14   -27.80   -36.63     0.37)  ; 31\r\n      (  112.13   -28.09   -36.94     0.81)  ; 32\r\n      (  112.42   -28.09   -37.13     1.92)  ; 33\r\n      (  113.75   -28.31   -37.50     1.92)  ; 34\r\n      (  114.63   -28.23   -37.56     1.18)  ; 35\r\n      (  115.15   -28.23   -37.88     0.81)  ; 36\r\n      (  115.96   -28.38   -38.06     0.59)  ; 37\r\n      (  117.28   -28.53   -38.88     0.44)  ; 38\r\n       Low\r\n    |\r\n      (   80.89   -38.10   -37.63     0.44)  ; 1, R-2-2\r\n      (   81.26   -38.76   -39.19     0.44)  ; 2\r\n      (   82.07   -39.78   -39.19     0.44)  ; 3\r\n      (   82.51   -40.30   -39.19     0.44)  ; 4\r\n      (   82.95   -41.17   -39.63     0.44)  ; 5\r\n      (   83.47   -42.12   -40.25     0.88)  ; 6\r\n      (   83.84   -42.93   -40.56     1.18)  ; 7\r\n      (   84.79   -43.73   -40.38     1.18)  ; 8\r\n      (   85.31   -44.24   -39.88     0.88)  ; 9\r\n      (   86.12   -44.68   -39.00     0.52)  ; 10\r\n      (   87.08   -45.05   -38.50     0.29)  ; 11\r\n      (   87.45   -45.78   -38.50     0.29)  ; 12\r\n      (   87.96   -46.29   -39.94     0.59)  ; 13\r\n      (   88.48   -46.95   -39.94     0.59)  ; 14\r\n      (   89.14   -47.75   -39.94     0.44)  ; 15\r\n      (   89.66   -48.48   -40.25     0.44)  ; 16\r\n      (   90.10   -49.14   -40.44     0.44)  ; 17\r\n      (   91.13   -50.16   -40.44     0.44)  ; 18\r\n      (   91.72   -50.75   -40.44     0.44)  ; 19\r\n      (   92.38   -51.63   -40.44     0.81)  ; 20\r\n      (   93.04   -52.28   -40.63     1.18)  ; 21\r\n      (   93.56   -52.72   -41.00     0.74)  ; 22\r\n      (   94.30   -53.45   -41.06     0.37)  ; 23\r\n      (   95.11   -54.40   -41.19     0.37)  ; 24\r\n      (   95.62   -55.72   -41.94     0.96)  ; 25\r\n      (   95.99   -56.74   -42.19     1.33)  ; 26\r\n      (   96.21   -57.25   -42.44     1.33)  ; 27\r\n      (   96.17   -58.23   -43.13     1.84)  ; 28\r\n      (   96.62   -59.33   -42.25     1.11)  ; 29\r\n      (   96.69   -60.06   -40.88     0.44)  ; 30\r\n      (   97.50   -61.23   -42.13     0.29)  ; 31\r\n       Low\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color DarkYellow)\r\n  (Dendrite)\r\n  (    0.94     2.22    14.63     0.88)  ; Root\r\n  (    1.67     3.68    14.63     1.18)  ; 1, R\r\n  (    1.97     4.85    14.63     1.18)  ; 2\r\n  (    2.41     6.31    15.25     0.96)  ; 3\r\n  (    2.48     7.33    15.25     0.74)  ; 4\r\n  (    2.41     8.72    15.25     0.66)  ; 5\r\n  (    2.19     9.67    14.75     0.66)  ; 6\r\n  (    2.56    10.77    15.31     0.59)  ; 7\r\n  (    3.15    11.65    15.38     0.74)  ; 8\r\n  (    3.88    12.16    16.00     0.88)  ; 9\r\n  (\r\n    (    5.06    12.16    16.06     0.44)  ; 1, R-1\r\n    (    6.02    12.16    16.06     0.44)  ; 2\r\n    (    6.46    12.74    15.56     0.44)  ; 3\r\n    (    6.98    13.18    16.31     0.44)  ; 4\r\n    (    6.98    13.69    16.38     0.44)  ; 5\r\n    (    6.76    13.99    16.50     0.44)  ; 6\r\n    (    6.24    14.86    16.75     0.44)  ; 7\r\n    (    5.58    15.38    16.81     0.29)  ; 8\r\n    (    4.99    16.11    16.81     0.29)  ; 9\r\n    (    4.69    16.69    16.81     0.59)  ; 10\r\n    (    4.33    16.91    18.25     0.59)  ; 11\r\n    (    3.74    17.42    18.25     0.37)  ; 12\r\n    (    3.22    17.57    18.25     0.37)  ; 13\r\n    (    2.26    17.13    18.31     0.37)  ; 14\r\n     Incomplete\r\n  |\r\n    (    3.52    13.18    16.00     0.37)  ; 1, R-2\r\n    (    3.15    14.13    17.06     0.37)  ; 2\r\n    (    2.41    14.57    17.13     0.37)  ; 3\r\n    (    1.67    14.79    17.94     0.37)  ; 4\r\n    (    1.16    15.08    18.44     0.37)  ; 5\r\n    (    0.20    15.81    18.56     0.37)  ; 6\r\n    (   -0.61    16.84    18.56     0.29)  ; 7\r\n    (   -0.68    17.93    18.56     0.29)  ; 8\r\n    (   -0.39    18.88    18.56     0.29)  ; 9\r\n    (   -0.98    19.62    18.56     0.29)  ; 10\r\n     Incomplete\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n"
  },
  {
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 1, 923\r\n  (-4708.53  2872.52     0.91     1.85)  ;  1, 924\r\n  (-4664.59  2878.64     0.91     1.85)  ;  1, 925\r\n  (-4616.03  2913.94     0.91     1.85)  ;  1, 926\r\n  (-4557.48  2988.78     0.91     1.85)  ;  1, 927\r\n  (-4488.77  3045.18     0.91     1.85)  ;  1, 928\r\n  (-4403.65  3098.99     0.91     1.85)  ;  1, 929\r\n  (-4291.11  3172.74     0.91     1.85)  ;  1, 930\r\n  (-4181.24  3241.32     0.91     1.85)  ;  1, 931\r\n  (-4082.59  3309.80     0.91     1.85)  ;  1, 932\r\n  (-3976.79  3354.22     0.91     1.85)  ;  1, 933\r\n  (-3860.42  3404.94     0.91     1.85)  ;  1, 934\r\n  (-3779.05  3435.03     0.91     1.85)  ;  1, 935\r\n  (-3705.57  3462.65     0.91     1.85)  ;  1, 936\r\n  (-3596.01  3493.90     0.91     1.85)  ;  1, 937\r\n  (-3460.17  3537.80     0.91     1.85)  ;  1, 938\r\n  (-3320.29  3571.71     0.91     1.85)  ;  1, 939\r\n  (-3182.54  3604.09     0.91     1.85)  ;  1, 940\r\n  (-3070.40  3616.24     0.91     1.85)  ;  1, 941\r\n  (-2976.08  3657.37     0.91     1.85)  ;  1, 942\r\n  (-2887.15  3688.15     0.91     1.85)  ;  1, 943\r\n  (-2863.80  3692.59     0.91     1.85)  ;  1, 944\r\n  (-2831.84  3717.43     0.91     1.85)  ;  1, 945\r\n  (-2768.25  3729.78     0.91     1.85)  ;  1, 946\r\n  (-2706.64  3764.89     0.91     1.85)  ;  1, 947\r\n  (-2607.36  3790.28     0.91     1.85)  ;  1, 948\r\n  (-2513.91  3825.94     0.91     1.85)  ;  1, 949\r\n  (-2420.46  3861.60     0.91     1.85)  ;  1, 950\r\n  (-2295.46  3896.01     0.98     1.85)  ;  1, 951\r\n  (-2173.43  3923.40     0.98     1.85)  ;  1, 952\r\n  (-2077.30  3964.26     0.98     1.85)  ;  1, 953\r\n  (-2007.57  3968.16     0.98     1.85)  ;  1, 954\r\n  (-1929.47  3989.43     0.98     1.85)  ;  1, 955\r\n  (-1894.93  3995.18     0.98     1.85)  ;  1, 956\r\n  (-1821.74  4020.97     0.98     1.85)  ;  1, 957\r\n  (-1763.60  4034.19     0.98     1.85)  ;  1, 958\r\n  (-1682.25  4064.29     0.98     1.85)  ;  1, 959\r\n  (-1546.72  4083.24     1.14     1.85)  ;  1, 960\r\n  (-1465.94  4109.69     1.14     1.85)  ;  1, 961\r\n  (-1423.25  4119.77     1.14     1.85)  ;  1, 962\r\n  (-1353.32  4136.72     1.14     1.85)  ;  1, 963\r\n  (-1281.85  4151.57     1.14     1.85)  ;  1, 964\r\n  (-1228.97  4178.70     1.14     1.85)  ;  1, 965\r\n  (-1124.60  4212.63     1.14     1.85)  ;  1, 966\r\n  (-1068.58  4224.32     1.14     1.85)  ;  1, 967\r\n  ( -996.15  4233.40     1.14     1.85)  ;  1, 968\r\n  ( -947.70  4244.42     1.14     1.85)  ;  1, 969\r\n  ( -893.42  4245.17     1.14     1.85)  ;  1, 970\r\n  ( -853.88  4270.68     1.14     1.85)  ;  1, 971\r\n  ( -814.83  4281.32     1.14     1.85)  ;  1, 972\r\n  ( -773.77  4304.72     1.14     1.85)  ;  1, 973\r\n  ( -715.06  4321.59     1.14     1.85)  ;  1, 974\r\n  ( -621.90  4355.43     1.14     1.85)  ;  1, 975\r\n  ( -532.87  4374.97     1.14     1.85)  ;  1, 976\r\n  ( -430.52  4396.14     1.14     1.85)  ;  1, 977\r\n  ( -366.38  4422.47     1.14     1.85)  ;  1, 978 Closure point\r\n)  ;  End of contour\r\n\r\n(\"VPM\"\r\n  (Color Blue)\r\n  (Closed)\r\n  (GUID \"3E26506D133B464E9A7476DAC50FBBE0\")\r\n  (MBFObjectType 5)\r\n  (FillDensity 0)\r\n  (Resolution 1.846036)\r\n  (  207.07   487.89     0.00     7.22)  ;  2, 1\r\n  (  -51.94   440.03     0.00     7.22)  ;  2, 2\r\n  ( -282.10   392.69     0.00     7.22)  ;  2, 3\r\n  ( -418.57   354.19     0.00     7.22)  ;  2, 4\r\n  ( -561.63   279.47     0.00     7.22)  ;  2, 5\r\n  ( -682.78   190.70     0.00     7.22)  ;  2, 6\r\n  ( -760.53    95.46     0.00     7.22)  ;  2, 7\r\n  ( -823.32   -28.42     0.00     7.22)  ;  2, 8\r\n  ( -863.86  -188.01     0.00     7.22)  ;  2, 9\r\n  ( -875.64  -339.88     0.00     7.22)  ;  2, 10\r\n  ( -859.07  -462.36     0.00     7.22)  ;  2, 11\r\n  ( -792.98  -526.21     0.00     7.22)  ;  2, 12\r\n  ( -669.30  -581.83     0.00     7.22)  ;  2, 13\r\n  ( -582.22  -609.19     0.00     7.22)  ;  2, 14\r\n  ( -517.40  -600.84     0.00     7.22)  ;  2, 15\r\n  ( -468.40  -513.32     0.00     7.22)  ;  2, 16\r\n  ( -390.66  -418.07     0.00     7.22)  ;  2, 17\r\n  ( -327.74  -301.42     0.00     7.22)  ;  2, 18\r\n  ( -235.55  -205.93     0.00     7.22)  ;  2, 19\r\n  ( -128.83  -117.40     0.00     7.22)  ;  2, 20\r\n  (   14.22   -42.69     0.00     7.22)  ;  2, 21\r\n  (  208.43    -3.19     0.00     7.22)  ;  2, 22\r\n  (  294.27    41.64     0.00     7.22)  ;  2, 23\r\n  (  408.96    86.99     0.00     7.22)  ;  2, 24\r\n  (  478.73   225.42     0.00     7.22)  ;  2, 25\r\n  (  462.65   319.02     0.00     7.22)  ;  2, 26\r\n  (  446.33   427.07     0.00     7.22)  ;  2, 27\r\n  (  387.97   462.16     0.00     7.22)  ;  2, 28\r\n  (  293.91   474.96     0.00     7.22)  ;  2, 29 Closure point\r\n)  ;  End of contour\r\n\r\n(\"PO\"\r\n  (Color Cyan)\r\n  (Closed)\r\n  (GUID \"B9390BD7923C4FC6998CCE01BBC34ACA\")\r\n  (MBFObjectType 5)\r\n  (FillDensity 0)\r\n  (Resolution 1.846036)\r\n  (  468.10   420.23     0.00     7.22)  ;  3, 1\r\n  (  636.21   300.38     0.00     7.22)  ;  3, 2\r\n  (  718.49   135.71     0.00     7.22)  ;  3, 3\r\n  (  787.46   -94.19     0.00     7.22)  ;  3, 4\r\n  (  857.20  -367.41     0.00     7.22)  ;  3, 5\r\n  (  866.93  -511.68     0.00     7.22)  ;  3, 6\r\n  (  855.90  -706.86     0.00     7.22)  ;  3, 7\r\n  (  687.38  -977.03     0.00     7.22)  ;  3, 8\r\n  (  430.64 -1154.83     0.00     7.22)  ;  3, 9\r\n  (   13.37 -1234.34     0.00     7.22)  ;  3, 10\r\n  ( -217.41 -1245.59     0.00     7.22)  ;  3, 11\r\n  ( -353.76 -1291.30     0.00     7.22)  ;  3, 12\r\n  ( -439.34 -1350.57     0.00     7.22)  ;  3, 13\r\n  ( -519.98 -1279.75     0.00     7.22)  ;  3, 14\r\n  ( -587.32 -1143.71     0.00     7.22)  ;  3, 15\r\n  ( -619.08  -978.16     0.00     7.22)  ;  3, 16\r\n  ( -606.67  -862.39     0.00     7.22)  ;  3, 17\r\n  ( -544.23  -716.87     0.00     7.22)  ;  3, 18\r\n  ( -503.09  -593.37     0.00     7.22)  ;  3, 19\r\n  ( -451.79  -490.66     0.00     7.22)  ;  3, 20\r\n  ( -403.20  -434.52     0.00     7.22)  ;  3, 21\r\n  ( -327.04  -307.31     0.00     7.22)  ;  3, 22\r\n  ( -242.90  -211.09     0.00     7.22)  ;  3, 23\r\n  ( -121.12  -112.35     0.00     7.22)  ;  3, 24\r\n  (   19.94   -40.38     0.00     7.22)  ;  3, 25\r\n  (  128.47   -17.51     0.00     7.22)  ;  3, 26\r\n  (  190.68    -6.34     0.00     7.22)  ;  3, 27\r\n  (  281.96    35.34     0.00     7.22)  ;  3, 28\r\n  (  351.61    64.31     0.00     7.22)  ;  3, 29\r\n  (  409.59    87.64     0.00     7.22)  ;  3, 30\r\n  (  477.53   225.77     0.00     7.22)  ;  3, 31\r\n  (  457.75   345.74     0.00     7.22)  ;  3, 32\r\n  (  452.34   413.59     0.00     7.22)  ;  3, 33 Closure point\r\n)  ;  End of contour\r\n\r\n(\"VPL\"\r\n  (Color Yellow)\r\n  (Closed)\r\n  (GUID \"7743B080667A472E8E569DF2CA8AB466\")\r\n  (MBFObjectType 5)\r\n  (FillDensity 0)\r\n  (Resolution 1.846036)\r\n  (  214.04   502.45     0.00     7.22)  ;  4, 1\r\n  (  -39.02   526.92     0.00     7.22)  ;  4, 2\r\n  ( -155.24   568.23     0.00     7.22)  ;  4, 3\r\n  ( -243.22   646.13     0.00     7.22)  ;  4, 4\r\n  ( -430.71   635.64     0.00     7.22)  ;  4, 5\r\n  ( -639.45   603.12     0.00     7.22)  ;  4, 6\r\n  ( -803.92   513.57     0.00     7.22)  ;  4, 7\r\n  ( -917.23   388.82     0.00     7.22)  ;  4, 8\r\n  (-1129.30   132.35     0.00     7.22)  ;  4, 9\r\n  (-1191.46   -27.62     0.00     7.22)  ;  4, 10\r\n  (-1253.01  -223.69     0.00     7.22)  ;  4, 11\r\n  (-1249.48  -425.85     0.00     7.22)  ;  4, 12\r\n  (-1261.89  -541.61     0.00     7.22)  ;  4, 13\r\n  (-1295.71  -672.20     0.00     7.22)  ;  4, 14\r\n  (-1245.58  -649.67     0.00     7.22)  ;  4, 15\r\n  (-1123.78  -596.98     0.00     7.22)  ;  4, 16\r\n  ( -987.31  -558.50     0.00     7.22)  ;  4, 17\r\n  ( -887.42  -491.75     0.00     7.22)  ;  4, 18\r\n  ( -875.26  -361.54     0.00     7.22)  ;  4, 19\r\n  ( -863.72  -195.23     0.00     7.22)  ;  4, 20\r\n  ( -816.49    -6.62     0.00     7.22)  ;  4, 21\r\n  ( -734.33   129.24     0.00     7.22)  ;  4, 22\r\n  ( -687.55   188.05     0.00     7.22)  ;  4, 23\r\n  ( -600.05   250.35     0.00     7.22)  ;  4, 24\r\n  ( -434.09   345.37     0.00     7.22)  ;  4, 25\r\n  ( -274.50   395.01     0.00     7.22)  ;  4, 26\r\n  ( -109.84   429.99     0.00     7.22)  ;  4, 27\r\n  (  124.82   472.97     0.00     7.22)  ;  4, 28\r\n  (  194.61   485.70     0.00     7.22)  ;  4, 29\r\n  (  232.70   483.58     0.00     7.22)  ;  4, 30 Closure point\r\n)  ;  End of contour\r\n\r\n(\"Cell Body\"\r\n  (Color Magenta)\r\n  (CellBody)\r\n  (  -13.43     1.82    10.63     0.15)  ;  5, 1\r\n  (  -12.72     2.52    10.88     0.15)  ;  5, 2\r\n  (  -11.82     3.49    12.00     0.15)  ;  5, 3\r\n  (  -10.80     4.29    12.31     0.15)  ;  5, 4\r\n  (   -9.33     5.09    12.75     0.15)  ;  5, 5\r\n  (   -7.91     6.05    12.88     0.15)  ;  5, 6\r\n  (   -6.12     7.02    12.94     0.15)  ;  5, 7\r\n  (   -4.06     8.26    13.88     0.15)  ;  5, 8\r\n  (   -2.44     9.03    13.38     0.15)  ;  5, 9\r\n  (   -0.99     9.25    15.13     0.15)  ;  5, 10\r\n  (    0.90     9.39    15.63     0.15)  ;  5, 11\r\n  (    2.04     9.06    16.13     0.15)  ;  5, 12\r\n  (    3.09     8.67    16.63     0.15)  ;  5, 13\r\n  (    4.26     8.05    16.13     0.15)  ;  5, 14\r\n  (    5.44     7.42    15.56     0.15)  ;  5, 15\r\n  (    6.68     6.85    14.69     0.15)  ;  5, 16\r\n  (    8.34     5.55    14.25     0.15)  ;  5, 17\r\n  (    9.09     4.61    13.56     0.15)  ;  5, 18\r\n  (    9.71     3.33    12.06     0.15)  ;  5, 19\r\n  (   10.27     2.14    12.06     0.15)  ;  5, 20\r\n  (   11.27     0.94    12.38     0.15)  ;  5, 21\r\n  (   12.15     0.36    12.88     0.15)  ;  5, 22\r\n  (   12.32    -0.48    12.81     0.15)  ;  5, 23\r\n  (   12.26    -1.28    12.25     0.15)  ;  5, 24\r\n  (   11.78    -1.95    11.63     0.15)  ;  5, 25\r\n  (   10.64    -3.03    11.19     0.15)  ;  5, 26\r\n  (    9.76    -3.41    11.63     0.15)  ;  5, 27\r\n  (    8.92    -4.02    12.00     0.15)  ;  5, 28\r\n  (    8.08    -5.07    11.88     0.15)  ;  5, 29\r\n  (    7.22    -5.82     9.44     0.15)  ;  5, 30\r\n  (    6.42    -6.58     7.88     0.15)  ;  5, 31\r\n  (    5.76    -6.99     8.13     0.15)  ;  5, 32\r\n  (    5.34    -7.74     8.25     0.15)  ;  5, 33\r\n  (    4.96    -8.35     7.56     0.15)  ;  5, 34\r\n  (    4.14    -8.74     6.56     0.15)  ;  5, 35\r\n  (    3.13    -9.02     6.31     0.15)  ;  5, 36\r\n  (    1.17    -9.16     5.81     0.15)  ;  5, 37\r\n  (    0.11    -8.77     6.75     0.15)  ;  5, 38\r\n  (   -0.63    -8.27     8.13     0.15)  ;  5, 39\r\n  (   -2.16    -7.58     7.88     0.15)  ;  5, 40\r\n  (   -3.58    -7.14     8.81     0.15)  ;  5, 41\r\n  (   -5.29    -7.09     9.94     0.15)  ;  5, 42\r\n  (   -7.09    -7.10    10.50     0.15)  ;  5, 43\r\n  (   -8.38    -6.82     9.25     0.15)  ;  5, 44\r\n  (  -10.04    -6.41     9.94     0.15)  ;  5, 45\r\n  (  -10.92    -5.83     8.88     0.15)  ;  5, 46\r\n  (  -12.30    -5.17     8.81     0.15)  ;  5, 47\r\n  (  -13.37    -4.40     8.69     0.15)  ;  5, 48\r\n  (  -13.89    -3.88     7.88     0.15)  ;  5, 49\r\n  (  -14.24    -2.86     8.69     0.15)  ;  5, 50\r\n  (  -14.69    -1.91     8.56     0.15)  ;  5, 51\r\n  (  -14.47    -0.53     8.56     0.15)  ;  5, 52\r\n  (  -14.30     0.55     8.19     0.15)  ;  5, 53\r\n  (  -13.91     1.16     8.63     0.15)  ;  5, 54\r\n)  ;  End of contour\r\n\r\n( (Color RGB (255, 128, 255))\r\n  (Axon)\r\n  (    2.54     8.30    17.38     2.14)  ; Root\r\n  (    3.20     9.16    17.81     1.77)  ; 1, R\r\n  (    4.24    10.10    18.25     1.69)  ; 2\r\n  (    5.04    10.94    18.25     1.55)  ; 3\r\n  (    5.66    11.95    18.31     1.33)  ; 4\r\n  (    5.57    12.78    18.50     1.25)  ; 5\r\n  (    4.67    13.22    19.31     1.11)  ; 6\r\n  (    3.55    13.69    19.56     1.03)  ; 7\r\n  (    3.03    14.67    19.63     1.18)  ; 8\r\n  (    2.76    15.81    19.63     1.03)  ; 9\r\n  (    2.66    16.12    19.63     1.03)  ; 10\r\n  (    2.24    16.79    19.63     0.88)  ; 11\r\n  (    1.83    17.52    19.63     1.03)  ; 12\r\n  (    2.00    18.60    19.69     1.11)  ; 13\r\n  (    2.36    19.36    19.81     1.11)  ; 14\r\n  (    2.45    19.93    20.06     0.96)  ; 15\r\n  (    2.66    20.79    20.50     1.18)  ; 16\r\n  (    2.84    21.50    20.50     1.33)  ; 17\r\n  (    2.87    22.61    20.50     1.25)  ; 18\r\n  (    3.57    24.20    20.56     1.33)  ; 19\r\n  (    3.76    25.42    20.94     1.62)  ; 20\r\n  (    3.58    26.64    20.94     1.33)  ; 21\r\n  (    3.25    28.09    21.00     1.62)  ; 22\r\n  (    2.78    29.35    21.25     1.77)  ; 23\r\n  (    2.47    30.21    21.25     1.55)  ; 24\r\n  (    1.95    31.62    21.56     1.62)  ; 25\r\n  (    1.54    32.79    22.19     2.14)  ; 26\r\n  (    0.96    34.29    22.56     3.09)  ; 27\r\n  (    0.33    35.06    23.06     4.35)  ; 28\r\n  (   -0.22    35.82    23.44     4.94)  ; 29\r\n  (   -1.02    36.90    23.44     5.89)  ; 30\r\n  (   -1.49    37.72    23.50     6.56)  ; 31\r\n  (   -2.25    39.02    23.50     5.45)  ; 32\r\n  (   -2.57    39.81    23.50     4.57)  ; 33\r\n  (   -3.11    41.15    23.50     4.57)  ; 34\r\n  (   -3.78    42.08    23.50     5.08)  ; 35\r\n  (   -4.26    42.81    23.50     4.13)  ; 36\r\n   Normal\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (  -12.55    -4.91     6.63     2.73)  ; Root\r\n  (  -13.15    -5.77     5.88     1.25)  ; 1, R\r\n  (  -13.74    -6.71     5.88     1.03)  ; 2\r\n  (  -14.36    -7.35     5.63     1.18)  ; 3\r\n  (  -15.29    -8.10     6.00     1.18)  ; 4\r\n  (  -16.22    -8.76     6.00     1.18)  ; 5\r\n  (  -16.76    -8.83     6.25     1.25)  ; 6\r\n  (  -17.80    -9.26     6.13     0.96)  ; 7\r\n  (  -18.65    -9.49     5.00     1.11)  ; 8\r\n  (  -19.43    -9.66     4.88     0.96)  ; 9\r\n  (  -20.30   -10.48     4.50     0.88)  ; 10\r\n  (  -20.61   -11.21     4.50     1.11)  ; 11\r\n  (  -21.03   -11.96     4.75     0.88)  ; 12\r\n  (  -21.15   -12.75     4.75     0.59)  ; 13\r\n  (  -21.51   -13.14     4.75     0.37)  ; 14\r\n  (  -22.32   -13.52     4.94     0.88)  ; 15\r\n  (  -22.95   -13.73     5.31     1.55)  ; 16\r\n  (  -23.78   -13.82     5.31     1.11)  ; 17\r\n  (  -24.87   -13.65     5.31     1.03)  ; 18\r\n  (  -25.62   -13.22     5.31     0.96)  ; 19\r\n  (  -26.90   -12.80     5.06     0.81)  ; 20\r\n  (  -27.59   -12.91     5.06     0.81)  ; 21\r\n  (  -28.18   -12.90     5.06     0.66)  ; 22\r\n  (  -28.93   -12.85     5.00     1.03)  ; 23\r\n  (  -29.78   -13.08     4.69     0.88)  ; 24\r\n  (  -30.71   -13.31     4.31     0.59)  ; 25\r\n  (  -31.69   -13.38     4.25     0.74)  ; 26\r\n  (  -32.49   -13.25     3.81     0.81)  ; 27\r\n  (  -33.45   -13.17     3.13     0.88)  ; 28\r\n  (  -34.45   -12.87     2.50     0.88)  ; 29\r\n  (  -35.26   -13.26     1.88     1.11)  ; 30\r\n  (  -36.06   -13.65     1.13     0.81)  ; 31\r\n  (  -36.97   -13.73     0.31     0.59)  ; 32\r\n  (  -37.54   -13.56    -0.88     1.03)  ; 33\r\n  (  -38.14   -13.54    -1.63     1.40)  ; 34\r\n  (  -38.69   -13.23    -2.31     1.55)  ; 35\r\n  (  -39.09   -12.94    -3.00     1.40)  ; 36\r\n  (  -39.74   -12.84    -4.25     1.11)  ; 37\r\n  (  -40.40   -12.81    -5.00     0.96)  ; 38\r\n  (  -41.25   -13.41    -6.00     0.59)  ; 39\r\n  (  -41.82   -13.70    -6.13     0.59)  ; 40\r\n  (  -42.24   -14.08    -6.50     0.81)  ; 41\r\n  (  -43.04   -14.39    -6.50     0.88)  ; 42\r\n  (  -43.62   -14.75    -7.44     0.59)  ; 43\r\n  (  -43.76   -15.16    -7.44     0.59)  ; 44\r\n  (  -44.31   -15.30    -7.81     0.81)  ; 45\r\n  (  -45.05   -15.33    -8.13     0.96)  ; 46\r\n  (  -45.68   -15.53    -8.63     0.66)  ; 47\r\n  (  -46.37   -15.50    -8.94     1.18)  ; 48\r\n  (  -47.41   -15.48    -9.31     1.18)  ; 49\r\n  (  -48.47   -15.16    -9.69     0.88)  ; 50\r\n  (  -49.22   -15.20    -9.69     0.88)  ; 51\r\n  (  -50.10   -15.57   -10.13     0.81)  ; 52\r\n  (  -50.81   -15.84   -10.81     0.81)  ; 53\r\n  (  -51.68   -16.14   -11.75     1.03)  ; 54\r\n  (  -52.41   -16.03   -12.81     1.18)  ; 55\r\n  (  -53.29   -16.41   -13.56     0.88)  ; 56\r\n  (  -53.88   -16.31   -14.81     0.66)  ; 57\r\n  (  -54.62   -16.78   -15.00     0.52)  ; 58\r\n  (  -55.21   -17.20   -15.38     0.52)  ; 59\r\n  (  -56.41   -17.24   -15.63     0.66)  ; 60\r\n  (  -56.91   -17.16   -16.69     0.74)  ; 61\r\n  (  -57.99   -17.29   -17.13     0.74)  ; 62\r\n  (  -59.27   -17.38   -17.13     0.59)  ; 63\r\n  (  -60.00   -17.78   -17.69     0.52)  ; 64\r\n  (  -60.34   -18.02   -17.69     0.52)  ; 65\r\n  (  -60.96   -18.15   -17.69     0.81)  ; 66\r\n  (  -61.79   -18.24   -17.88     1.47)  ; 67\r\n  (  -62.66   -18.55   -17.94     1.47)  ; 68\r\n  (  -63.14   -18.32   -17.94     1.11)  ; 69\r\n  (  -63.67   -18.32   -18.00     0.66)  ; 70\r\n  (  -64.55   -18.25   -18.00     0.44)  ; 71\r\n  (  -65.36   -18.63   -18.25     0.37)  ; 72\r\n  (  -66.24   -19.01   -19.25     0.37)  ; 73\r\n  (  -66.74   -18.93   -19.69     0.52)  ; 74\r\n  (  -67.82   -18.62   -20.19     0.96)  ; 75\r\n  (  -68.87   -18.68   -20.88     2.14)  ; 76\r\n  (  -69.50   -18.87   -21.50     2.87)  ; 77\r\n  (  -70.37   -19.18   -22.06     2.43)  ; 78\r\n  (  -71.24   -19.49   -22.44     1.03)  ; 79\r\n  (  -72.02   -19.73   -22.63     0.59)  ; 80\r\n  (  -72.52   -20.02   -22.75     0.44)  ; 81\r\n  (  -72.95   -20.40   -23.13     0.44)  ; 82\r\n  (  -73.77   -20.93   -23.44     0.59)  ; 83\r\n  (  -74.63   -21.10   -24.50     0.29)  ; 84\r\n  (  -75.61   -21.23   -24.88     0.29)  ; 85\r\n  (  -76.31   -21.86   -25.25     0.96)  ; 86\r\n  (  -76.56   -22.49   -25.63     0.59)  ; 87\r\n  (  -76.89   -23.18   -26.00     0.29)  ; 88\r\n  (  -77.26   -24.08   -26.44     0.52)  ; 89\r\n  (  -77.66   -24.69   -26.44     0.66)  ; 90\r\n  (  -78.16   -25.57   -27.19     0.96)  ; 91\r\n  (  -78.64   -25.71   -27.88     0.96)  ; 92\r\n  (  -79.10   -25.78   -28.44     0.59)  ; 93\r\n  (  -79.66   -26.07   -28.69     0.37)  ; 94\r\n  (  -80.54   -26.45   -29.50     0.96)  ; 95\r\n  (  -81.26   -26.70   -29.88     1.77)  ; 96\r\n  (  -81.90   -26.97   -30.19     0.88)  ; 97\r\n  (  -82.87   -26.97   -30.69     0.66)  ; 98\r\n  (  -83.24   -26.98   -31.25     1.40)  ; 99\r\n  (  -83.99   -27.01   -31.56     2.14)  ; 100\r\n  (  -84.50   -26.93   -32.25     1.77)  ; 101\r\n  (  -85.43   -27.15   -32.56     0.59)  ; 102\r\n  (  -86.56   -27.19   -33.50     0.37)  ; 103\r\n  (  -87.16   -27.25   -33.69     0.66)  ; 104\r\n  (  -87.79   -27.00   -34.38     0.66)  ; 105\r\n  (  -88.71   -26.63   -35.25     0.29)  ; 106\r\n  (  -89.76   -26.69   -35.69     0.37)  ; 107\r\n  (  -90.52   -26.79   -36.00     0.74)  ; 108\r\n  (  -91.28   -26.89   -36.31     1.55)  ; 109\r\n  (  -92.08   -27.21   -37.13     0.66)  ; 110\r\n  (  -92.97   -27.58   -37.44     0.81)  ; 111\r\n  (  -93.17   -27.91   -37.94     1.69)  ; 112\r\n  (  -93.45   -28.24   -38.06     1.25)  ; 113\r\n  (  -93.44   -28.16   -40.50     0.74)  ; 114\r\n   Normal\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (    1.67    -7.23     4.25     3.24)  ; Root\r\n  (    1.98    -8.10     5.50     3.02)  ; 1, R\r\n  (    2.22    -8.95     5.50     3.02)  ; 2\r\n  (\r\n    (    2.30    -8.97     4.00     4.20)  ; 1, R-1\r\n    (    2.59    -9.99     3.63     4.13)  ; 2\r\n    (\r\n      (    2.21   -10.96     3.13     3.32)  ; 1, R-1-1\r\n      (    1.94   -11.73     2.56     2.87)  ; 2\r\n      (\r\n        (    2.34   -12.46     2.38     1.77)  ; 1, R-1-1-1\r\n        (    2.49   -12.93     2.38     1.11)  ; 2\r\n        (    2.79   -13.87     2.38     0.88)  ; 3\r\n        (    3.14   -14.96     2.13     0.96)  ; 4\r\n        (    3.48   -15.60     1.81     1.03)  ; 5\r\n        (    3.89   -16.33     1.81     1.11)  ; 6\r\n        (    4.11   -16.81     1.69     1.33)  ; 7\r\n        (    4.29   -17.58     1.56     0.81)  ; 8\r\n        (    4.70   -18.31     1.56     1.25)  ; 9\r\n        (    4.97   -18.94     1.38     1.25)  ; 10\r\n        (    5.08   -19.63     1.38     1.03)  ; 11\r\n        (    5.42   -20.35     1.31     1.18)  ; 12\r\n        (    5.83   -21.53     1.13     1.62)  ; 13\r\n        (    6.27   -22.56     0.94     1.77)  ; 14\r\n        (    6.54   -23.63     0.88     1.25)  ; 15\r\n        (    6.64   -24.46     0.50     1.18)  ; 16\r\n        (    6.66   -25.28     0.50     0.88)  ; 17\r\n        (    6.99   -26.00     0.19     0.74)  ; 18\r\n        (    7.37   -26.87     0.19     0.88)  ; 19\r\n        (    7.82   -27.76    -0.44     1.11)  ; 20\r\n        (    8.25   -28.42    -0.88     1.11)  ; 21\r\n        (    8.28   -29.27    -1.19     1.25)  ; 22\r\n        (    8.37   -30.17    -1.50     1.84)  ; 23\r\n        (    8.44   -30.63    -1.50     1.84)  ; 24\r\n        (    8.45   -31.52    -1.63     1.40)  ; 25\r\n        (    8.48   -32.33    -1.63     1.62)  ; 26\r\n        (    8.37   -32.99    -1.81     1.25)  ; 27\r\n        (    8.59   -33.98    -1.81     1.11)  ; 28\r\n        (    8.85   -35.14    -1.94     0.96)  ; 29\r\n        (    9.03   -36.35    -2.19     1.18)  ; 30\r\n        (    9.24   -37.35    -2.88     1.33)  ; 31\r\n        (    9.62   -38.29    -2.88     1.18)  ; 32\r\n        (    9.92   -39.22    -3.75     1.18)  ; 33\r\n        (   10.41   -39.89    -3.75     1.03)  ; 34\r\n        (   11.08   -40.82    -4.19     1.11)  ; 35\r\n        (   11.62   -41.71    -4.19     1.33)  ; 36\r\n        (   11.55   -42.59    -4.19     1.11)  ; 37\r\n        (   11.08   -43.63    -4.31     0.96)  ; 38\r\n        (   10.45   -44.79    -4.81     0.88)  ; 39\r\n        (    9.72   -45.63    -5.00     1.11)  ; 40\r\n        (    9.37   -46.47    -5.00     1.11)  ; 41\r\n        (    9.05   -47.52    -5.38     1.03)  ; 42\r\n        (    9.06   -48.41    -4.88     1.25)  ; 43\r\n        (    8.96   -49.51    -4.88     1.77)  ; 44\r\n        (    9.06   -50.34    -5.06     2.06)  ; 45\r\n        (    8.52   -51.81    -5.75     2.28)  ; 46\r\n        (\r\n          (    8.44   -53.28    -6.31     2.36)  ; 1, R-1-1-1-1\r\n          (    8.59   -54.19    -6.44     1.77)  ; 2\r\n          (    8.98   -55.06    -6.38     1.33)  ; 3\r\n          (    9.32   -55.97    -6.81     0.88)  ; 4\r\n          (   10.22   -56.93    -6.81     0.74)  ; 5\r\n          (   11.09   -57.50    -6.94     0.88)  ; 6\r\n          (   11.68   -58.04    -7.13     0.88)  ; 7\r\n          (   12.10   -58.70    -7.13     0.96)  ; 8\r\n          (   12.36   -59.85    -7.31     1.18)  ; 9\r\n          (   12.55   -60.55    -7.44     1.33)  ; 10\r\n          (   12.93   -60.98    -7.31     1.18)  ; 11\r\n          (   13.10   -61.82    -7.13     1.03)  ; 12\r\n          (   13.28   -62.96    -6.63     1.11)  ; 13\r\n          (   13.58   -63.97    -5.94     1.33)  ; 14\r\n          (   13.57   -64.93    -5.19     1.11)  ; 15\r\n          (   13.67   -65.69    -5.19     0.96)  ; 16\r\n          (   13.58   -66.79    -5.19     0.88)  ; 17\r\n          (   13.67   -67.61    -5.25     0.88)  ; 18\r\n          (   13.59   -68.56    -5.25     1.11)  ; 19\r\n          (   13.79   -69.70    -5.63     1.33)  ; 20\r\n          (   13.98   -70.84    -6.06     0.96)  ; 21\r\n          (   14.06   -71.74    -6.50     0.88)  ; 22\r\n          (   14.37   -72.60    -6.50     1.11)  ; 23\r\n          (   14.78   -73.33    -6.94     0.96)  ; 24\r\n          (   15.02   -74.19    -7.00     0.96)  ; 25\r\n          (   15.26   -75.04    -7.44     1.11)  ; 26\r\n          (   15.69   -75.56    -8.00     1.11)  ; 27\r\n          (   15.97   -76.63    -8.06     0.96)  ; 28\r\n          (   16.25   -77.64    -8.63     0.88)  ; 29\r\n          (   16.55   -78.65    -8.63     0.88)  ; 30\r\n          (   16.86   -79.52    -8.63     1.11)  ; 31\r\n          (   17.29   -80.55    -8.88     0.88)  ; 32\r\n          (   17.51   -81.99    -8.94     0.81)  ; 33\r\n          (   18.49   -82.88    -9.13     1.03)  ; 34\r\n          (   19.18   -83.73    -9.13     0.74)  ; 35\r\n          (   19.89   -84.57    -9.31     0.59)  ; 36\r\n          (   20.28   -85.45    -9.56     1.55)  ; 37\r\n          (   20.99   -86.15    -9.56     1.77)  ; 38\r\n          (   21.75   -87.01    -9.94     1.03)  ; 39\r\n          (   22.38   -87.71    -9.50     0.66)  ; 40\r\n          (   23.01   -88.40    -9.63     0.66)  ; 41\r\n          (   23.55   -89.30    -9.63     0.66)  ; 42\r\n          (   24.59   -89.76    -9.63     0.96)  ; 43\r\n          (   25.39   -90.40   -10.25     1.03)  ; 44\r\n          (   26.12   -90.97   -10.38     1.11)  ; 45\r\n          (   26.76   -91.58   -10.75     0.96)  ; 46\r\n          (   27.59   -92.52   -11.31     0.96)  ; 47\r\n          (   28.25   -93.07   -11.31     0.66)  ; 48\r\n          (   29.79   -94.14   -11.50     0.66)  ; 49\r\n          (   30.57   -94.92   -11.50     0.96)  ; 50\r\n          (   31.09   -95.82   -10.94     0.88)  ; 51\r\n          (   31.74   -96.89   -10.88     1.18)  ; 52\r\n          (   31.97   -97.82   -10.88     1.33)  ; 53\r\n          (   32.29   -98.61   -10.50     0.81)  ; 54\r\n          (   32.44  -100.04   -10.56     0.74)  ; 55\r\n          (   32.30  -100.90   -10.56     0.81)  ; 56\r\n          (   32.24  -101.78   -10.50     0.81)  ; 57\r\n          (   32.87  -103.44   -10.38     0.88)  ; 58\r\n          (   33.69  -104.37   -10.19     0.88)  ; 59\r\n          (   33.94  -105.15    -9.75     0.52)  ; 60\r\n          (   33.83  -105.81    -9.69     0.52)  ; 61\r\n          (   34.08  -106.65    -9.69     1.18)  ; 62\r\n          (   34.23  -107.05    -9.63     1.84)  ; 63\r\n          (   34.29  -107.65    -9.63     2.21)  ; 64\r\n          (   34.21  -108.60    -9.56     1.25)  ; 65\r\n          (   34.54  -109.32    -9.81     0.88)  ; 66\r\n          (   34.77  -109.80   -10.00     0.52)  ; 67\r\n          (   35.46  -110.20   -10.00     0.52)  ; 68\r\n          (   35.43  -110.80   -10.00     0.88)  ; 69\r\n          (   35.50  -111.77   -10.19     0.88)  ; 70\r\n          (   35.81  -112.63   -10.63     0.74)  ; 71\r\n          (   36.07  -113.78   -10.63     0.66)  ; 72\r\n          (   36.41  -114.47   -11.19     0.59)  ; 73\r\n          (   36.43  -115.28   -11.38     0.81)  ; 74\r\n          (   36.29  -116.22   -11.38     1.18)  ; 75\r\n          (   36.27  -116.81   -11.38     1.92)  ; 76\r\n          (   36.18  -117.39   -11.50     1.92)  ; 77\r\n          (   36.06  -118.18   -11.63     1.69)  ; 78\r\n          (   35.85  -118.96   -11.63     0.81)  ; 79\r\n          (   35.89  -119.71   -11.63     0.52)  ; 80\r\n          (   36.21  -120.50   -11.75     0.44)  ; 81\r\n          (   36.22  -120.94   -11.19     0.44)  ; 82\r\n          (   36.08  -121.81   -11.13     0.44)  ; 83\r\n          (   35.92  -122.30   -11.19     0.88)  ; 84\r\n          (   35.97  -123.42   -11.19     0.96)  ; 85\r\n          (   35.58  -124.47   -11.19     1.03)  ; 86\r\n          (   35.36  -125.84   -11.19     0.81)  ; 87\r\n          (   35.51  -126.83   -11.25     1.03)  ; 88\r\n          (   35.59  -127.73   -10.81     0.88)  ; 89\r\n          (   35.49  -128.82   -10.81     0.74)  ; 90\r\n          (   35.46  -129.93   -10.75     0.59)  ; 91\r\n          (   35.45  -131.40   -10.69     0.52)  ; 92\r\n          (   35.47  -132.23   -10.88     1.11)  ; 93\r\n          (   35.61  -133.21   -10.88     1.47)  ; 94\r\n          (   35.66  -133.88   -10.88     1.77)  ; 95\r\n          (   35.79  -134.86   -10.88     1.25)  ; 96\r\n          (   35.76  -135.60   -10.94     0.59)  ; 97\r\n          (   35.86  -136.36   -11.19     0.37)  ; 98\r\n          (   35.38  -137.02   -10.69     0.37)  ; 99\r\n          (   35.70  -137.81   -10.63     0.37)  ; 100\r\n          (   35.91  -138.43   -10.63     1.11)  ; 101\r\n          (   36.26  -139.45   -10.25     0.96)  ; 102\r\n          (   36.70  -140.48   -10.06     0.59)  ; 103\r\n          (   36.78  -140.94    -9.69     0.37)  ; 104\r\n          (   37.00  -141.75    -9.44     0.29)  ; 105\r\n          (   36.86  -142.62    -9.44     0.29)  ; 106\r\n          (   37.10  -143.47    -8.31     0.29)  ; 107\r\n          (   37.15  -144.59    -7.81     0.44)  ; 108\r\n          (   37.04  -144.80    -7.81     0.44)  ; 109\r\n           Normal\r\n        |\r\n          (    8.37   -52.62    -7.19     0.59)  ; 1, R-1-1-1-2\r\n          (    8.47   -53.37    -7.56     0.59)  ; 2\r\n          (    8.29   -54.01    -7.94     0.59)  ; 3\r\n          (    8.04   -55.15    -7.94     0.59)  ; 4\r\n          (    7.90   -56.02    -7.81     0.59)  ; 5\r\n          (    7.39   -56.90    -7.81     0.88)  ; 6\r\n          (    7.00   -57.50    -7.81     1.18)  ; 7\r\n          (    6.44   -58.15    -7.81     0.59)  ; 8\r\n          (    5.90   -58.74    -7.81     0.52)  ; 9\r\n          (    5.14   -59.80    -7.81     0.74)  ; 10\r\n          (    4.57   -60.60    -7.81     0.74)  ; 11\r\n          (    4.55   -61.63    -7.81     0.88)  ; 12\r\n          (    4.38   -62.72    -8.44     1.33)  ; 13\r\n          (    3.95   -64.05    -9.00     1.55)  ; 14\r\n          (    3.88   -64.93    -9.31     1.55)  ; 15\r\n          (    3.73   -65.86    -9.50     1.11)  ; 16\r\n          (    3.53   -67.17    -9.50     0.81)  ; 17\r\n          (    2.91   -68.70   -10.63     0.66)  ; 18\r\n          (    2.70   -69.99   -10.75     1.03)  ; 19\r\n          (    2.53   -70.64   -11.19     1.03)  ; 20\r\n          (    1.43   -71.87   -11.25     0.74)  ; 21\r\n          (    0.85   -72.74   -11.25     0.81)  ; 22\r\n          (    0.10   -73.73   -11.25     0.81)  ; 23\r\n          (   -0.74   -74.78   -11.56     1.18)  ; 24\r\n          (   -1.50   -75.84   -11.38     1.92)  ; 25\r\n          (   -1.98   -76.51   -11.38     2.73)  ; 26\r\n          (   -2.47   -77.24   -11.38     1.92)  ; 27\r\n          (   -3.14   -78.03   -11.44     1.03)  ; 28\r\n          (   -3.71   -79.27   -11.81     0.81)  ; 29\r\n          (   -4.72   -80.51   -12.00     0.66)  ; 30\r\n          (   -5.50   -81.64   -12.13     0.88)  ; 31\r\n          (   -6.14   -83.40   -12.25     0.88)  ; 32\r\n          (   -6.42   -84.17   -12.63     0.59)  ; 33\r\n          (   -6.68   -85.31   -12.75     0.37)  ; 34\r\n          (   -7.16   -86.05   -12.75     0.88)  ; 35\r\n          (   -7.91   -86.52   -12.75     1.18)  ; 36\r\n          (   -8.37   -87.04   -12.75     0.74)  ; 37\r\n          (   -8.56   -87.82   -13.31     0.52)  ; 38\r\n          (   -8.17   -88.18   -13.31     0.52)  ; 39\r\n          (   -8.04   -89.24   -13.81     0.66)  ; 40\r\n          (   -8.38   -90.00   -14.50     0.74)  ; 41\r\n          (   -8.29   -90.83   -15.69     1.11)  ; 42\r\n          (   -8.28   -91.64   -16.44     1.77)  ; 43\r\n          (   -8.21   -92.17   -16.69     2.43)  ; 44\r\n          (   -8.10   -92.86   -17.06     1.47)  ; 45\r\n          (   -8.17   -93.81   -17.06     0.81)  ; 46\r\n          (   -8.28   -94.53   -18.00     0.66)  ; 47\r\n          (   -8.39   -95.70   -18.00     0.81)  ; 48\r\n          (   -8.49   -96.28   -18.63     1.03)  ; 49\r\n          (   -8.46   -97.54   -19.31     1.03)  ; 50\r\n          (   -8.58   -98.26   -19.31     0.59)  ; 51\r\n          (   -8.52   -99.30   -20.44     0.59)  ; 52\r\n          (   -8.45  -100.27   -21.25     0.59)  ; 53\r\n          (   -8.47  -100.94   -21.25     0.59)  ; 54\r\n          (   -8.81  -101.11   -21.56     0.59)  ; 55\r\n          (   -9.03  -101.14   -22.06     0.59)  ; 56\r\n          (   -9.49  -102.18   -22.06     0.74)  ; 57\r\n          (   -9.70  -102.96   -22.81     1.33)  ; 58\r\n          (  -10.21  -103.84   -23.25     1.84)  ; 59\r\n          (  -10.30  -104.56   -23.75     1.11)  ; 60\r\n          (  -10.68  -105.08   -24.81     0.52)  ; 61\r\n          (  -10.79  -105.80   -25.38     0.52)  ; 62\r\n          (  -10.40  -106.60   -26.56     0.66)  ; 63\r\n          (  -10.63  -107.09   -27.50     0.59)  ; 64\r\n          (  -10.71  -107.60   -27.88     1.40)  ; 65\r\n          (  -10.95  -108.22   -28.19     2.43)  ; 66\r\n          (  -11.06  -108.87   -28.94     2.14)  ; 67\r\n          (  -11.30  -109.43   -28.94     1.11)  ; 68\r\n          (  -11.24  -110.47   -29.63     0.74)  ; 69\r\n          (  -11.70  -111.51   -30.13     0.74)  ; 70\r\n          (  -12.18  -112.18   -31.06     0.44)  ; 71\r\n          (  -12.43  -112.80   -32.25     0.44)  ; 72\r\n          (  -13.32  -113.25   -32.44     1.18)  ; 73\r\n          (  -13.62  -113.72   -33.00     2.06)  ; 74\r\n          (  -13.88  -114.42   -33.75     1.11)  ; 75\r\n          (  -14.39  -115.30   -33.75     0.59)  ; 76\r\n          (  -14.61  -116.23   -34.50     0.59)  ; 77\r\n          (  -15.19  -117.09   -34.88     0.44)  ; 78\r\n          (  -15.61  -117.33   -36.44     0.44)  ; 79\r\n          (  -16.42  -118.24   -36.56     0.44)  ; 80\r\n          (  -16.43  -119.20   -36.88     0.81)  ; 81\r\n          (  -16.78  -120.03   -38.06     0.52)  ; 82\r\n          (  -17.34  -120.75   -38.25     1.11)  ; 83\r\n          (  -17.98  -121.03   -39.25     1.92)  ; 84\r\n          (  -18.78  -121.34   -39.44     2.36)  ; 85\r\n          (  -19.25  -122.01   -40.44     1.33)  ; 86\r\n          (  -19.37  -122.73   -42.31     0.59)  ; 87\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (    0.57   -12.57     2.88     2.06)  ; 1, R-1-1-2\r\n        (   -0.33   -13.10     2.81     2.14)  ; 2\r\n        (   -1.32   -14.13     2.56     2.14)  ; 3\r\n        (   -2.46   -14.76     2.44     1.99)  ; 4\r\n        (   -3.59   -15.33     2.63     2.06)  ; 5\r\n        (   -4.64   -16.26     1.75     2.28)  ; 6\r\n        (   -5.33   -17.34     0.63     2.65)  ; 7\r\n        (   -6.05   -18.12     0.00     3.39)  ; 8\r\n        (   -6.81   -19.18    -0.56     3.68)  ; 9\r\n        (   -7.56   -19.65    -0.56     3.98)  ; 10\r\n        (\r\n          (   -8.47   -20.69    -0.38     2.87)  ; 1, R-1-1-2-1\r\n          (   -8.79   -21.31    -0.38     2.28)  ; 2\r\n          (\r\n            (   -8.99   -22.53     0.31     1.55)  ; 1, R-1-1-2-1-1\r\n            (   -9.23   -23.61     0.38     1.33)  ; 2\r\n            (   -9.33   -24.26     0.44     1.25)  ; 3\r\n            (   -9.51   -25.41     0.81     1.33)  ; 4\r\n            (\r\n              (   -9.55   -26.56     1.00     1.03)  ; 1, R-1-1-2-1-1-1\r\n              (   -8.91   -27.26     1.31     0.66)  ; 2\r\n              (   -8.85   -28.23     1.31     0.81)  ; 3\r\n              (   -8.89   -29.48     1.94     1.03)  ; 4\r\n              (   -9.10   -30.27     2.44     1.18)  ; 5\r\n              (   -9.23   -31.13     2.69     1.18)  ; 6\r\n              (   -9.43   -31.92     2.69     1.03)  ; 7\r\n              (  -10.13   -32.55     3.00     1.03)  ; 8\r\n              (  -10.29   -33.55     3.06     1.03)  ; 9\r\n              (  -10.26   -34.38     3.06     0.88)  ; 10\r\n              (  -10.18   -35.27     3.06     0.88)  ; 11\r\n              (  -10.25   -36.15     3.06     1.25)  ; 12\r\n              (  -10.54   -37.07     2.94     1.25)  ; 13\r\n              (  -10.61   -37.95     3.06     0.96)  ; 14\r\n              (  -10.84   -38.94     3.44     0.96)  ; 15\r\n              (  -11.28   -40.28     3.63     0.96)  ; 16\r\n              (  -11.56   -41.13     3.63     1.40)  ; 17\r\n              (  -11.85   -42.04     3.63     1.25)  ; 18\r\n              (  -12.09   -42.59     3.88     1.03)  ; 19\r\n              (  -11.94   -43.58     3.63     0.81)  ; 20\r\n              (  -11.86   -44.48     3.63     0.81)  ; 21\r\n              (  -12.02   -45.49     3.38     1.03)  ; 22\r\n              (  -12.25   -46.41     3.38     1.40)  ; 23\r\n              (  -12.15   -47.25     3.38     1.47)  ; 24\r\n              (  -12.05   -48.45     2.81     1.11)  ; 25\r\n              (  -12.13   -49.40     3.00     1.11)  ; 26\r\n              (  -12.25   -50.19     3.00     0.74)  ; 27\r\n              (  -12.81   -51.44     3.00     0.81)  ; 28\r\n              (  -12.98   -52.28     3.00     0.74)  ; 29\r\n              (  -12.45   -53.69     3.00     0.74)  ; 30\r\n              (  -12.60   -54.63     3.00     0.74)  ; 31\r\n              (  -12.86   -55.77     2.88     0.88)  ; 32\r\n              (  -13.24   -56.76     2.88     0.88)  ; 33\r\n              (  -13.64   -57.88     2.88     0.88)  ; 34\r\n              (  -13.81   -58.96     2.88     0.59)  ; 35\r\n              (  -14.12   -59.95     3.13     1.11)  ; 36\r\n              (  -14.28   -60.96     3.13     0.96)  ; 37\r\n              (  -14.19   -61.78     3.13     0.74)  ; 38\r\n              (  -14.44   -62.48     3.13     0.74)  ; 39\r\n              (  -14.83   -63.53     3.13     0.96)  ; 40\r\n              (  -15.12   -64.38     3.13     1.47)  ; 41\r\n              (  -15.35   -65.82     3.13     0.74)  ; 42\r\n              (  -15.46   -66.54     3.13     0.74)  ; 43\r\n              (  -15.73   -68.28     2.81     0.59)  ; 44\r\n              (  -16.02   -69.56     2.81     0.52)  ; 45\r\n              (  -15.23   -70.28     2.63     0.52)  ; 46\r\n              (  -14.36   -71.38     2.63     0.59)  ; 47\r\n              (  -14.12   -72.23     3.19     0.59)  ; 48\r\n              (  -13.40   -72.86     2.75     0.81)  ; 49\r\n              (  -12.68   -73.49     2.38     1.11)  ; 50\r\n              (  -12.07   -74.34     2.06     1.25)  ; 51\r\n              (  -11.46   -75.17     1.63     0.96)  ; 52\r\n              (  -10.87   -76.15     1.13     0.59)  ; 53\r\n              (  -10.56   -77.02     0.88     0.59)  ; 54\r\n              (  -10.14   -77.67     0.25     0.66)  ; 55\r\n              (  -10.15   -78.63     0.06     0.96)  ; 56\r\n              (   -9.73   -79.41    -1.19     1.11)  ; 57\r\n              (   -9.47   -80.12    -1.19     0.66)  ; 58\r\n              (   -9.16   -81.49    -1.19     0.44)  ; 59\r\n              (   -9.17   -82.45    -1.44     0.74)  ; 60\r\n              (   -9.04   -83.51    -1.44     0.74)  ; 61\r\n              (   -9.07   -85.13    -1.56     0.59)  ; 62\r\n              (   -9.53   -86.62    -2.19     0.59)  ; 63\r\n              (   -9.42   -87.82    -2.88     0.59)  ; 64\r\n              (   -9.73   -88.36    -2.88     0.74)  ; 65\r\n              (  -10.46   -89.20    -2.88     0.52)  ; 66\r\n              (  -11.18   -89.98    -3.38     0.52)  ; 67\r\n              (  -11.87   -91.05    -3.38     0.66)  ; 68\r\n              (  -12.09   -91.91    -3.38     0.52)  ; 69\r\n              (  -12.46   -92.44    -3.69     0.88)  ; 70\r\n              (  -12.72   -93.07    -3.50     1.03)  ; 71\r\n              (  -12.60   -93.75    -3.31     0.59)  ; 72\r\n              (  -13.07   -94.35    -3.31     0.37)  ; 73\r\n              (  -13.64   -95.66    -4.13     0.81)  ; 74\r\n              (  -14.17   -96.61    -4.00     0.74)  ; 75\r\n              (  -14.77   -97.63    -4.44     0.44)  ; 76\r\n              (  -15.15   -98.53    -3.50     0.74)  ; 77\r\n              (  -15.15   -99.49    -3.50     0.74)  ; 78\r\n              (  -15.28  -100.28    -3.00     0.37)  ; 79\r\n              (  -14.96  -101.07    -2.75     0.37)  ; 80\r\n              (  -15.30  -102.36    -2.56     0.52)  ; 81\r\n              (  -15.55  -102.98    -2.56     1.18)  ; 82\r\n              (  -15.72  -104.06    -2.56     1.55)  ; 83\r\n              (  -15.85  -105.30    -1.13     1.55)  ; 84\r\n              (  -15.42  -106.25    -1.19     1.62)  ; 85\r\n              (  -15.38  -107.37    -1.44     1.03)  ; 86\r\n              (  -15.45  -108.32    -1.31     0.81)  ; 87\r\n              (  -15.15  -109.17    -1.00     0.66)  ; 88\r\n              (  -15.04  -110.45    -0.75     0.44)  ; 89\r\n              (  -14.57  -111.26    -0.75     0.81)  ; 90\r\n              (  -14.40  -112.03    -0.44     1.47)  ; 91\r\n              (  -13.98  -112.69    -0.44     0.96)  ; 92\r\n              (  -13.72  -113.32    -0.44     0.44)  ; 93\r\n              (  -13.31  -114.05     0.19     0.44)  ; 94\r\n              (  -12.62  -114.90     0.44     0.59)  ; 95\r\n              (  -12.00  -115.74     0.44     0.44)  ; 96\r\n              (  -11.64  -116.69     0.81     0.66)  ; 97\r\n              (  -11.44  -117.38     2.25     0.59)  ; 98\r\n              (  -11.83  -117.91     2.25     0.59)  ; 99\r\n              (  -11.59  -118.32     2.25     0.59)  ; 100\r\n              (  -10.69  -118.76     2.81     0.59)  ; 101\r\n              (  -10.69  -119.21     2.81     0.59)  ; 102\r\n              (  -11.26  -120.01     3.44     1.18)  ; 103\r\n              (  -11.81  -120.66     3.75     1.92)  ; 104\r\n              (  -12.43  -121.23     4.31     2.21)  ; 105\r\n              (  -12.61  -121.94     4.69     2.21)  ; 106\r\n              (  -13.13  -122.37     4.88     1.33)  ; 107\r\n              (  -13.43  -122.84     5.19     0.52)  ; 108\r\n              (  -13.67  -123.47     5.75     0.52)  ; 109\r\n              (  -13.38  -124.41     5.75     0.52)  ; 110\r\n              (  -13.04  -125.12     5.31     0.52)  ; 111\r\n              (  -12.42  -125.89     4.81     0.59)  ; 112\r\n               Normal\r\n            |\r\n              (   -9.82   -26.18     0.25     0.96)  ; 1, R-1-1-2-1-1-2\r\n              (  -10.23   -26.85     0.75     0.88)  ; 2\r\n              (  -10.89   -27.78     1.31     0.88)  ; 3\r\n              (  -11.40   -28.66     2.19     1.40)  ; 4\r\n              (  -12.62   -29.73     2.88     1.11)  ; 5\r\n              (  -13.32   -30.43     3.44     0.88)  ; 6\r\n              (  -13.81   -31.61     3.56     0.59)  ; 7\r\n              (  -14.25   -32.50     4.00     0.66)  ; 8\r\n              (  -14.48   -33.43     4.56     1.25)  ; 9\r\n              (  -14.80   -34.56     4.94     1.11)  ; 10\r\n              (  -14.96   -35.58     5.44     1.11)  ; 11\r\n              (  -14.96   -36.46     5.44     0.81)  ; 12\r\n              (  -15.11   -37.91     5.50     0.81)  ; 13\r\n              (  -15.14   -39.09     5.75     0.96)  ; 14\r\n              (  -15.19   -39.39     6.00     1.33)  ; 15\r\n              (  -15.37   -40.54     5.88     0.88)  ; 16\r\n              (  -15.42   -41.72     5.88     0.88)  ; 17\r\n              (  -15.85   -42.61     5.88     0.66)  ; 18\r\n              (  -16.18   -43.30     5.75     0.52)  ; 19\r\n              (  -16.65   -44.33     5.44     0.96)  ; 20\r\n              (  -17.23   -45.65     5.69     1.11)  ; 21\r\n              (  -17.69   -46.69     6.00     1.11)  ; 22\r\n              (  -17.82   -47.92     6.13     1.40)  ; 23\r\n              (  -18.10   -48.84     5.94     1.40)  ; 24\r\n              (  -18.38   -49.62     6.69     0.96)  ; 25\r\n              (  -18.63   -50.68     6.69     0.81)  ; 26\r\n              (  -19.34   -51.90     6.63     0.96)  ; 27\r\n              (  -19.79   -52.86     6.63     0.96)  ; 28\r\n              (  -20.15   -53.70     6.13     0.96)  ; 29\r\n              (  -20.38   -54.70     5.88     0.96)  ; 30\r\n              (  -20.54   -55.71     5.19     0.96)  ; 31\r\n              (  -20.26   -56.79     4.88     1.03)  ; 32\r\n              (  -20.47   -57.65     4.44     1.03)  ; 33\r\n              (  -20.61   -58.95     3.88     1.11)  ; 34\r\n              (  -21.06   -59.99     3.88     0.96)  ; 35\r\n              (  -21.33   -61.14     3.06     0.74)  ; 36\r\n              (  -21.56   -62.13     3.88     0.88)  ; 37\r\n              (  -21.69   -63.45     4.00     1.03)  ; 38\r\n              (  -21.60   -64.79     4.06     1.33)  ; 39\r\n              (  -21.67   -66.11     4.44     1.11)  ; 40\r\n              (  -21.70   -66.85     4.44     1.11)  ; 41\r\n              (  -21.27   -68.32     4.63     0.96)  ; 42\r\n              (  -21.24   -69.07     4.81     0.96)  ; 43\r\n              (  -21.28   -70.32     4.44     0.81)  ; 44\r\n              (  -21.48   -71.55     4.44     0.81)  ; 45\r\n              (  -21.44   -72.73     3.31     1.03)  ; 46\r\n              (  -21.52   -73.68     3.13     1.11)  ; 47\r\n              (  -21.72   -74.81     2.50     1.11)  ; 48\r\n              (  -22.27   -75.98     2.25     1.47)  ; 49\r\n              (  -22.81   -77.01     2.25     1.18)  ; 50\r\n              (  -22.91   -78.10     1.94     0.74)  ; 51\r\n              (  -22.92   -79.57     1.88     0.66)  ; 52\r\n              (  -22.36   -80.26     1.75     0.66)  ; 53\r\n              (  -22.00   -81.73     1.63     0.74)  ; 54\r\n              (  -21.62   -83.11     1.44     0.88)  ; 55\r\n              (  -21.39   -84.55     1.44     0.88)  ; 56\r\n              (  -21.30   -85.39     1.94     1.03)  ; 57\r\n              (  -21.54   -86.38     2.00     1.18)  ; 58\r\n              (  -21.21   -87.18     2.19     0.81)  ; 59\r\n              (  -20.98   -88.55     2.19     0.59)  ; 60\r\n              (  -20.72   -89.69     2.19     0.59)  ; 61\r\n              (  -20.32   -90.43     2.19     0.88)  ; 62\r\n              (  -19.57   -91.43     2.19     0.74)  ; 63\r\n              (  -18.77   -92.52     3.44     0.74)  ; 64\r\n              (  -18.16   -93.29     3.94     0.88)  ; 65\r\n              (  -17.72   -94.32     4.50     0.96)  ; 66\r\n              (  -17.44   -95.33     5.88     0.96)  ; 67\r\n              (  -17.11   -96.12     6.31     0.74)  ; 68\r\n              (  -16.69   -96.77     6.69     0.59)  ; 69\r\n              (  -16.83   -98.16     6.69     0.59)  ; 70\r\n              (  -16.78   -99.21     6.69     0.96)  ; 71\r\n              (  -16.34  -100.24     6.44     1.47)  ; 72\r\n              (  -16.17  -101.00     8.38     1.47)  ; 73\r\n              (  -16.05  -101.89     8.94     0.96)  ; 74\r\n              (  -15.97  -103.31     9.19     0.96)  ; 75\r\n              (  -15.91  -104.36     9.69     0.81)  ; 76\r\n              (  -16.34  -105.18    10.56     0.74)  ; 77\r\n              (  -17.25  -106.14    10.63     0.52)  ; 78\r\n              (  -17.34  -107.24    11.13     0.66)  ; 79\r\n              (  -17.73  -108.74    11.13     0.81)  ; 80\r\n              (  -18.03  -110.17    11.13     1.03)  ; 81\r\n              (  -18.40  -111.59    11.19     0.74)  ; 82\r\n              (  -18.96  -112.75    11.69     0.96)  ; 83\r\n              (  -19.42  -113.28    11.75     0.96)  ; 84\r\n              (  -19.96  -114.38    11.75     0.59)  ; 85\r\n              (  -20.55  -115.24    12.31     1.11)  ; 86\r\n              (  -21.07  -116.12    12.50     1.69)  ; 87\r\n              (  -21.81  -117.11    12.50     1.33)  ; 88\r\n              (  -22.21  -117.72    12.94     0.88)  ; 89\r\n              (  -23.00  -118.48    13.06     0.88)  ; 90\r\n              (  -24.11  -119.34    13.13     1.18)  ; 91\r\n              (  -25.23  -119.83    13.56     1.18)  ; 92\r\n              (  -26.06  -120.37    14.13     0.66)  ; 93\r\n              (  -26.60  -120.50    14.19     0.66)  ; 94\r\n              (  -27.49  -120.95    14.56     0.88)  ; 95\r\n              (  -28.11  -121.52    14.56     0.66)  ; 96\r\n              (  -28.76  -122.31    14.69     0.66)  ; 97\r\n              (  -29.42  -122.72    14.88     0.59)  ; 98\r\n              (  -30.29  -123.03    14.88     0.81)  ; 99\r\n              (  -30.95  -123.44    14.88     0.81)  ; 100\r\n              (  -31.50  -124.61    15.13     0.59)  ; 101\r\n              (  -32.23  -125.90    15.56     0.74)  ; 102\r\n              (  -32.71  -126.96    15.44     0.59)  ; 103\r\n              (  -32.99  -127.73    15.31     1.03)  ; 104\r\n              (  -32.90  -128.56    14.94     1.03)  ; 105\r\n              (  -32.90  -129.52    15.81     0.59)  ; 106\r\n              (  -32.83  -130.05    15.69     0.59)  ; 107\r\n              (  -32.87  -130.79    15.75     0.74)  ; 108\r\n              (  -33.11  -131.34    15.88     0.59)  ; 109\r\n              (  -33.40  -131.74    15.94     0.88)  ; 110\r\n              (  -33.54  -132.16    15.94     1.18)  ; 111\r\n              (  -33.35  -132.86    16.00     0.59)  ; 112\r\n               Normal\r\n            )  ;  End of split\r\n          |\r\n            (   -8.95   -22.33    -2.38     1.18)  ; 1, R-1-1-2-1-2\r\n            (   -9.18   -22.90    -2.75     1.03)  ; 2\r\n            (   -9.57   -23.42    -2.75     0.74)  ; 3\r\n            (  -10.46   -23.88    -2.75     0.74)  ; 4\r\n            (  -11.15   -24.44    -3.19     0.74)  ; 5\r\n            (  -11.79   -25.22    -3.81     0.81)  ; 6\r\n            (  -12.65   -25.90    -4.75     1.40)  ; 7\r\n            (  -13.93   -26.44    -5.56     1.03)  ; 8\r\n            (  -14.98   -26.94    -6.06     1.33)  ; 9\r\n            (  -16.18   -26.96    -6.38     1.33)  ; 10\r\n            (  -17.34   -26.34    -7.44     1.40)  ; 11\r\n            (  -18.33   -26.41    -7.56     1.25)  ; 12\r\n            (  -18.81   -26.18    -7.88     1.55)  ; 13\r\n            (  -19.76   -26.03    -8.88     1.33)  ; 14\r\n            (  -20.21   -26.11   -10.69     0.52)  ; 15\r\n            (  -21.09   -25.97   -10.75     0.52)  ; 16\r\n            (  -21.52   -26.34   -11.38     1.03)  ; 17\r\n            (  -21.91   -26.94   -12.81     1.03)  ; 18\r\n            (  -22.45   -27.53   -13.81     0.81)  ; 19\r\n            (  -22.94   -27.81   -15.13     1.11)  ; 20\r\n            (  -23.65   -28.97   -15.69     1.33)  ; 21\r\n            (  -24.32   -29.89   -16.19     1.11)  ; 22\r\n            (  -25.00   -30.45   -16.00     0.81)  ; 23\r\n            (  -25.42   -31.20   -16.06     0.44)  ; 24\r\n            (  -25.85   -32.02   -16.63     0.44)  ; 25\r\n            (  -26.14   -32.49   -17.19     1.18)  ; 26\r\n            (  -26.51   -32.88   -17.94     1.99)  ; 27\r\n            (  -27.00   -32.73   -18.25     1.55)  ; 28\r\n            (  -27.72   -32.98   -18.38     0.81)  ; 29\r\n            (  -28.67   -33.35   -19.06     0.59)  ; 30\r\n            (  -30.04   -33.50   -19.19     0.59)  ; 31\r\n            (  -30.90   -34.26   -19.44     0.81)  ; 32\r\n            (  -31.82   -34.85   -20.75     0.81)  ; 33\r\n            (  -32.49   -35.33   -22.38     0.81)  ; 34\r\n            (  -32.89   -35.94   -22.81     0.66)  ; 35\r\n            (  -33.25   -36.40   -23.44     0.96)  ; 36\r\n            (  -33.66   -37.07   -23.69     1.62)  ; 37\r\n            (  -34.28   -37.71   -24.38     2.43)  ; 38\r\n            (  -34.66   -38.18   -25.06     2.50)  ; 39\r\n            (  -35.20   -38.75   -25.25     1.47)  ; 40\r\n            (  -35.68   -39.42   -25.25     0.88)  ; 41\r\n            (  -36.06   -40.02   -25.50     0.52)  ; 42\r\n            (  -36.86   -40.79   -25.75     0.66)  ; 43\r\n            (  -37.69   -41.32   -25.75     0.66)  ; 44\r\n            (  -38.18   -42.06   -25.94     0.66)  ; 45\r\n            (  -38.64   -43.03   -26.69     0.66)  ; 46\r\n            (  -39.19   -43.68   -27.00     0.66)  ; 47\r\n            (  -39.30   -44.40   -27.00     0.66)  ; 48\r\n            (  -39.80   -45.20   -27.50     0.59)  ; 49\r\n            (  -40.30   -46.01   -28.44     0.59)  ; 50\r\n            (  -40.90   -46.51   -28.81     1.55)  ; 51\r\n            (  -41.61   -47.21   -29.31     2.14)  ; 52\r\n            (  -42.04   -48.03   -30.06     1.25)  ; 53\r\n            (  -42.52   -48.77   -30.06     0.59)  ; 54\r\n            (  -43.04   -49.34   -30.06     0.37)  ; 55\r\n            (  -43.54   -50.16   -30.69     0.66)  ; 56\r\n            (  -44.05   -50.52   -30.69     0.59)  ; 57\r\n            (  -44.47   -50.82   -30.94     0.66)  ; 58\r\n            (  -44.94   -51.41   -31.75     1.25)  ; 59\r\n            (  -45.27   -51.58   -32.50     1.99)  ; 60\r\n            (  -45.44   -51.78   -33.19     1.99)  ; 61\r\n            (  -46.14   -51.89   -33.69     1.55)  ; 62\r\n            (  -47.10   -52.33   -35.63     1.55)  ; 63\r\n            (  -47.10   -52.33   -36.75     2.28)  ; 64\r\n            (  -47.76   -52.30   -37.63     2.28)  ; 65\r\n            (  -48.19   -52.23   -38.50     0.74)  ; 66\r\n            (  -49.18   -52.30   -39.06     0.59)  ; 67\r\n            (  -49.90   -52.55   -39.06     0.59)  ; 68\r\n            (  -50.33   -53.00   -40.69     1.18)  ; 69\r\n            (  -50.68   -53.32   -42.13     1.77)  ; 70\r\n            (  -51.34   -53.66   -42.88     0.74)  ; 71\r\n            (  -51.85   -54.09   -43.44     0.59)  ; 72\r\n            (  -52.49   -54.36   -43.56     1.18)  ; 73\r\n            (  -53.35   -54.60   -43.69     1.55)  ; 74\r\n            (  -54.15   -54.47   -45.06     0.74)  ; 75\r\n            (  -54.61   -54.99   -46.31     0.44)  ; 76\r\n            (  -55.57   -55.43   -46.31     0.44)  ; 77\r\n            (  -55.96   -56.04   -47.56     1.18)  ; 78\r\n            (  -56.41   -56.48   -47.94     2.43)  ; 79\r\n            (  -56.48   -56.92   -48.25     2.43)  ; 80\r\n            (  -56.87   -57.52   -48.25     1.18)  ; 81\r\n            (  -56.80   -58.05   -48.88     0.66)  ; 82\r\n            (  -57.12   -58.67   -48.88     0.37)  ; 83\r\n            (  -57.22   -59.31   -48.88     0.37)  ; 84\r\n            (  -57.73   -60.12   -48.88     0.37)  ; 85\r\n            (  -58.19   -60.71   -49.13     1.11)  ; 86\r\n            (  -58.33   -61.58   -49.13     1.40)  ; 87\r\n            (  -58.86   -62.09   -49.44     0.74)  ; 88\r\n            (  -59.03   -62.66   -49.81     0.37)  ; 89\r\n            (  -59.14   -63.38   -49.94     0.37)  ; 90\r\n            (  -59.37   -63.93   -50.56     1.11)  ; 91\r\n            (  -59.84   -64.52   -50.88     1.40)  ; 92\r\n            (  -60.25   -65.20   -51.00     1.40)  ; 93\r\n            (  -60.92   -65.68   -51.63     0.52)  ; 94\r\n            (  -61.31   -66.28   -52.06     0.88)  ; 95\r\n            (  -61.53   -66.70   -52.63     0.88)  ; 96\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (   -9.20   -19.63    -0.25     2.58)  ; 1, R-1-1-2-2\r\n          (  -10.18   -20.15     0.00     1.69)  ; 2\r\n          (  -10.94   -20.25     0.13     1.11)  ; 3\r\n          (  -12.17   -20.43     1.25     0.88)  ; 4\r\n          (  -12.83   -20.84     2.19     1.11)  ; 5\r\n          (  -13.52   -21.47     2.25     0.74)  ; 6\r\n          (  -14.47   -21.83     3.06     1.18)  ; 7\r\n          (  -14.91   -22.21     3.44     1.18)  ; 8\r\n          (  -15.65   -22.68     3.69     1.18)  ; 9\r\n          (  -16.12   -23.27     4.06     1.18)  ; 10\r\n          (  -16.75   -23.91     4.44     0.88)  ; 11\r\n          (  -18.04   -24.53     4.75     0.81)  ; 12\r\n          (  -18.56   -25.03     4.88     0.81)  ; 13\r\n          (  -19.29   -25.81     5.94     0.96)  ; 14\r\n          (  -19.84   -26.53     6.75     0.74)  ; 15\r\n          (  -20.27   -27.36     6.75     0.74)  ; 16\r\n          (  -21.35   -28.08     7.44     1.11)  ; 17\r\n          (  -22.21   -28.75     7.75     1.40)  ; 18\r\n          (  -22.96   -29.23     8.50     0.81)  ; 19\r\n          (  -23.82   -29.97     8.88     0.81)  ; 20\r\n          (  -24.99   -30.75     9.13     0.66)  ; 21\r\n          (  -26.03   -31.18     9.13     0.44)  ; 22\r\n          (  -27.45   -31.70     9.13     0.44)  ; 23\r\n          (  -28.42   -32.13     9.25     0.74)  ; 24\r\n          (  -29.18   -32.76     8.63     0.88)  ; 25\r\n          (  -30.16   -32.82     8.69     0.88)  ; 26\r\n          (  -31.27   -32.79     8.69     0.66)  ; 27\r\n          (  -32.21   -33.02     8.69     1.33)  ; 28\r\n          (  -33.49   -33.11     8.81     1.18)  ; 29\r\n          (  -34.62   -33.15     8.19     0.96)  ; 30\r\n          (  -36.14   -33.36     8.19     0.96)  ; 31\r\n          (  -37.11   -33.86     8.19     0.88)  ; 32\r\n          (  -38.13   -34.15     8.19     0.74)  ; 33\r\n          (  -38.76   -34.35     7.69     0.66)  ; 34\r\n          (  -39.66   -34.42     7.69     0.88)  ; 35\r\n          (  -40.72   -34.47     7.69     1.03)  ; 36\r\n          (  -41.53   -34.87     7.69     1.03)  ; 37\r\n          (  -42.44   -35.47     7.13     0.96)  ; 38\r\n          (  -43.26   -35.92     7.19     0.96)  ; 39\r\n          (  -43.77   -36.29     7.13     0.96)  ; 40\r\n          (  -44.42   -36.63     6.19     0.66)  ; 41\r\n          (  -45.36   -37.38     6.19     0.88)  ; 42\r\n          (  -46.52   -37.63     5.44     0.88)  ; 43\r\n          (  -47.14   -38.28     5.44     0.96)  ; 44\r\n          (  -47.85   -38.98     5.50     1.18)  ; 45\r\n          (  -48.51   -39.39     5.31     0.96)  ; 46\r\n          (  -49.59   -40.11     5.19     1.18)  ; 47\r\n          (  -50.09   -40.73     5.19     1.40)  ; 48\r\n          (  -50.57   -41.40     4.94     1.25)  ; 49\r\n          (  -51.44   -42.22     4.94     0.74)  ; 50\r\n          (  -52.33   -42.67     4.94     0.74)  ; 51\r\n          (  -53.59   -43.50     4.94     0.74)  ; 52\r\n          (  -54.31   -44.73     4.75     0.74)  ; 53\r\n          (  -55.08   -45.41     5.00     0.74)  ; 54\r\n          (  -56.05   -45.93     5.25     1.03)  ; 55\r\n          (  -56.80   -46.33     6.06     1.25)  ; 56\r\n          (  -57.37   -46.68     6.06     0.81)  ; 57\r\n          (  -57.85   -47.79     6.38     0.74)  ; 58\r\n          (  -58.62   -48.92     6.81     0.74)  ; 59\r\n          (  -59.57   -49.73     6.88     0.74)  ; 60\r\n          (  -60.92   -50.27     6.88     0.59)  ; 61\r\n          (  -62.39   -51.07     6.88     0.59)  ; 62\r\n          (  -63.03   -51.33     7.00     0.88)  ; 63\r\n          (  -64.00   -51.33     6.75     1.25)  ; 64\r\n          (  -64.90   -51.34     6.56     1.40)  ; 65\r\n          (  -65.92   -51.69     6.00     1.11)  ; 66\r\n          (  -66.82   -52.22     6.00     0.88)  ; 67\r\n          (  -68.14   -53.42     6.00     0.74)  ; 68\r\n          (  -69.25   -53.90     5.94     0.66)  ; 69\r\n          (  -69.76   -54.71     6.75     1.62)  ; 70\r\n          (  -70.19   -55.09     6.88     1.62)  ; 71\r\n          (  -70.95   -56.08     6.94     0.74)  ; 72\r\n          (  -71.69   -57.00     6.94     0.52)  ; 73\r\n          (  -72.22   -58.03     6.94     0.52)  ; 74\r\n          (  -72.59   -58.48     6.94     0.88)  ; 75\r\n          (  -72.95   -59.31     6.94     0.88)  ; 76\r\n          (  -73.74   -60.07     6.94     0.74)  ; 77\r\n          (  -74.27   -60.14     6.94     0.74)  ; 78\r\n          (  -75.02   -60.61     6.94     1.11)  ; 79\r\n          (  -75.72   -61.32     6.94     0.81)  ; 80\r\n          (  -76.20   -61.98     6.94     0.81)  ; 81\r\n          (  -76.69   -62.71     6.94     0.81)  ; 82\r\n          (  -77.45   -63.25     7.06     0.66)  ; 83\r\n          (  -78.34   -64.14     6.50     0.52)  ; 84\r\n          (  -79.06   -64.48     6.38     0.52)  ; 85\r\n          (  -79.66   -65.42     6.38     0.59)  ; 86\r\n          (  -80.16   -66.23     6.31     0.59)  ; 87\r\n          (  -80.33   -66.87     6.69     0.74)  ; 88\r\n          (  -81.00   -67.80     6.69     0.52)  ; 89\r\n          (  -81.91   -68.32     7.06     0.52)  ; 90\r\n          (  -82.85   -68.68     7.31     0.52)  ; 91\r\n          (  -84.02   -69.91     8.19     0.74)  ; 92\r\n          (  -85.00   -70.49     8.44     0.74)  ; 93\r\n          (  -85.93   -71.09     8.94     1.33)  ; 94\r\n          (  -86.71   -71.78     9.44     1.25)  ; 95\r\n          (  -87.03   -72.40     9.69     1.55)  ; 96\r\n          (  -87.70   -72.88     9.69     1.18)  ; 97\r\n          (  -88.66   -73.32     9.69     0.81)  ; 98\r\n          (  -89.05   -73.85     9.69     0.59)  ; 99\r\n          (  -89.77   -74.18     9.75     0.44)  ; 100\r\n          (  -90.60   -74.27     9.88     0.44)  ; 101\r\n          (  -91.22   -74.84     9.94     0.88)  ; 102\r\n          (  -92.11   -75.29    10.19     0.96)  ; 103\r\n          (  -92.86   -76.21    10.44     0.96)  ; 104\r\n          (  -93.51   -76.54    10.44     0.96)  ; 105\r\n          (  -94.70   -77.02    10.63     0.88)  ; 106\r\n          (  -95.59   -77.48    10.88     0.96)  ; 107\r\n          (  -96.16   -77.75    11.75     0.74)  ; 108\r\n          (  -97.21   -78.77    12.25     0.44)  ; 109\r\n          (  -98.22   -79.43    12.31     1.03)  ; 110\r\n          (  -98.79   -79.78    12.38     1.47)  ; 111\r\n          (  -99.60   -80.18    12.63     1.47)  ; 112\r\n          ( -100.13   -80.75    12.63     1.11)  ; 113\r\n          ( -100.90   -81.38    12.63     0.81)  ; 114\r\n          ( -101.90   -81.96    12.63     0.59)  ; 115\r\n          ( -103.48   -82.59    12.63     0.59)  ; 116\r\n          ( -104.38   -82.60    12.63     0.44)  ; 117\r\n          ( -105.15   -83.22    12.38     0.74)  ; 118\r\n          ( -105.67   -83.65    12.38     0.52)  ; 119\r\n          ( -106.20   -83.79    11.94     0.37)  ; 120\r\n          ( -107.20   -83.92    11.94     0.37)  ; 121\r\n          ( -107.91   -84.12    12.44     0.96)  ; 122\r\n          ( -108.80   -84.12    12.81     0.96)  ; 123\r\n          ( -109.22   -84.65    12.94     0.52)  ; 124\r\n          ( -109.79   -84.93    13.63     0.52)  ; 125\r\n          ( -110.88   -85.27    13.75     0.81)  ; 126\r\n          ( -111.59   -85.09    14.63     0.52)  ; 127\r\n          ( -112.54   -84.94    14.63     0.74)  ; 128\r\n          ( -113.18   -84.69    14.63     0.44)  ; 129\r\n          ( -113.84   -84.21    14.63     0.44)  ; 130\r\n          ( -115.17   -83.12    14.63     0.44)  ; 131\r\n          ( -115.89   -83.00    14.56     0.59)  ; 132\r\n          ( -116.28   -82.64    14.56     0.59)  ; 133\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (    2.99   -10.39     4.13     1.99)  ; 1, R-1-2\r\n      (    3.85   -10.67     5.13     1.99)  ; 2\r\n      (    4.96   -11.21     5.13     1.18)  ; 3\r\n      (    5.64   -11.54     5.13     0.81)  ; 4\r\n      (    6.64   -11.92     5.13     0.81)  ; 5\r\n      (    7.76   -12.84     5.13     0.96)  ; 6\r\n      (    8.56   -13.41     5.13     0.81)  ; 7\r\n      (    9.39   -13.84     5.13     1.03)  ; 8\r\n      (   10.23   -14.19     5.19     1.03)  ; 9\r\n      (   10.86   -14.89     5.19     1.25)  ; 10\r\n      (   11.52   -15.44     5.19     0.96)  ; 11\r\n      (   12.23   -16.14     5.19     0.96)  ; 12\r\n      (   12.73   -16.74     5.19     0.74)  ; 13\r\n      (   13.26   -17.64     4.81     0.88)  ; 14\r\n      (   13.29   -18.38     4.63     1.03)  ; 15\r\n      (   13.24   -19.19     4.25     1.18)  ; 16\r\n      (   13.43   -20.33     3.56     1.18)  ; 17\r\n      (   13.63   -21.40     3.56     1.25)  ; 18\r\n      (   14.13   -21.99     3.44     1.11)  ; 19\r\n      (   14.56   -22.66     2.94     1.40)  ; 20\r\n      (   15.02   -23.47     2.94     1.11)  ; 21\r\n      (   16.06   -23.93     2.69     0.88)  ; 22\r\n      (   17.05   -24.38     2.44     0.88)  ; 23\r\n      (   17.66   -24.70     2.25     0.81)  ; 24\r\n      (   18.30   -24.88     1.63     0.81)  ; 25\r\n      (   18.73   -25.02     1.44     0.81)  ; 26\r\n      (   19.16   -25.60     1.38     1.03)  ; 27\r\n      (   19.30   -26.14     0.63     1.33)  ; 28\r\n      (   19.74   -26.66     0.63     0.96)  ; 29\r\n      (   20.11   -27.16    -0.06     0.81)  ; 30\r\n      (   20.86   -27.57    -0.06     0.81)  ; 31\r\n      (   21.49   -27.82    -0.88     1.03)  ; 32\r\n      (   22.27   -28.09    -1.75     1.25)  ; 33\r\n      (   23.31   -28.63    -1.75     0.96)  ; 34\r\n      (   23.76   -29.06    -2.31     0.96)  ; 35\r\n      (   23.99   -29.92    -2.56     1.33)  ; 36\r\n      (   24.15   -30.84    -3.25     2.14)  ; 37\r\n      (   24.30   -32.08    -4.13     2.14)  ; 38\r\n      (   24.40   -32.83    -4.88     1.55)  ; 39\r\n      (   24.63   -33.24    -5.88     1.18)  ; 40\r\n      (   25.11   -33.54    -6.13     0.96)  ; 41\r\n      (   25.83   -33.73    -6.69     0.81)  ; 42\r\n      (   26.79   -33.74    -8.06     0.96)  ; 43\r\n      (   27.54   -33.77    -8.50     1.25)  ; 44\r\n      (   28.37   -33.68    -9.00     1.55)  ; 45\r\n      (   29.25   -33.75   -10.06     1.62)  ; 46\r\n      (   29.87   -34.59   -11.31     0.96)  ; 47\r\n      (   30.38   -35.56   -12.06     0.81)  ; 48\r\n      (   30.85   -36.37   -12.50     0.88)  ; 49\r\n      (   31.64   -36.57   -13.69     0.74)  ; 50\r\n      (   32.08   -36.64   -14.38     0.74)  ; 51\r\n      (   32.87   -37.28   -14.56     1.03)  ; 52\r\n      (   33.08   -37.84   -14.81     1.03)  ; 53\r\n      (   33.73   -38.46   -15.31     1.18)  ; 54\r\n      (\r\n        (   34.29   -38.69   -15.31     0.66)  ; 1, R-1-2-1\r\n        (   35.11   -38.68   -15.50     0.59)  ; 2\r\n        (   35.91   -38.81   -15.50     0.59)  ; 3\r\n        (   36.49   -38.90   -15.50     0.59)  ; 4\r\n        (   37.96   -39.06   -15.75     0.59)  ; 5\r\n        (   39.27   -39.27   -16.00     0.96)  ; 6\r\n        (   40.13   -39.47   -16.19     1.55)  ; 7\r\n        (   41.08   -39.62   -16.88     1.55)  ; 8\r\n        (   41.93   -39.90   -16.88     0.74)  ; 9\r\n        (   42.77   -39.82   -17.25     0.52)  ; 10\r\n        (   43.29   -39.75   -18.13     0.81)  ; 11\r\n        (   44.03   -39.87   -18.75     1.11)  ; 12\r\n        (   44.95   -40.17   -19.00     0.88)  ; 13\r\n        (   45.82   -40.74   -19.00     0.88)  ; 14\r\n        (   46.38   -40.98   -19.13     0.66)  ; 15\r\n        (   47.15   -41.32   -19.13     0.52)  ; 16\r\n        (   47.92   -41.60   -19.38     0.81)  ; 17\r\n        (   48.71   -41.87   -19.38     0.81)  ; 18\r\n        (   49.58   -42.00   -19.38     0.44)  ; 19\r\n        (   50.28   -42.34   -19.81     0.44)  ; 20\r\n        (   50.63   -42.91   -20.06     0.81)  ; 21\r\n        (   51.34   -43.62   -20.38     0.81)  ; 22\r\n        (   52.18   -44.42   -20.38     0.52)  ; 23\r\n        (   52.85   -44.89   -21.31     0.66)  ; 24\r\n        (   53.43   -45.50   -21.31     0.96)  ; 25\r\n        (   54.02   -45.97   -21.81     1.77)  ; 26\r\n        (   54.40   -46.39   -22.50     2.21)  ; 27\r\n        (   54.99   -46.93   -23.13     1.84)  ; 28\r\n        (   56.04   -47.39   -23.13     0.74)  ; 29\r\n        (   56.87   -47.74   -23.63     0.59)  ; 30\r\n        (   57.45   -48.36   -24.06     0.59)  ; 31\r\n        (   58.00   -49.11   -24.50     0.44)  ; 32\r\n        (   58.19   -49.81   -24.50     0.81)  ; 33\r\n        (   58.58   -50.16   -24.94     1.69)  ; 34\r\n        (   59.04   -50.54   -25.25     2.65)  ; 35\r\n        (   59.50   -50.98   -25.69     2.28)  ; 36\r\n        (   59.83   -51.70   -25.69     0.88)  ; 37\r\n        (   59.90   -52.22   -25.69     0.44)  ; 38\r\n        (   60.48   -52.76   -26.13     0.44)  ; 39\r\n        (   60.39   -53.33   -26.38     0.44)  ; 40\r\n        (   61.03   -54.48   -26.63     0.81)  ; 41\r\n        (   61.30   -55.11   -27.38     1.62)  ; 42\r\n        (   61.75   -55.55   -27.75     1.62)  ; 43\r\n        (   61.96   -55.73   -28.06     0.59)  ; 44\r\n        (   62.56   -56.13   -28.06     0.44)  ; 45\r\n        (   63.26   -56.46   -28.06     0.44)  ; 46\r\n        (   64.00   -56.94   -28.50     0.74)  ; 47\r\n        (   64.81   -57.89   -28.69     0.59)  ; 48\r\n        (   65.40   -58.92   -29.06     0.59)  ; 49\r\n        (   65.73   -59.63   -29.38     0.59)  ; 50\r\n        (   66.45   -60.70   -29.38     0.81)  ; 51\r\n        (   66.99   -61.61   -29.38     0.59)  ; 52\r\n        (   67.11   -62.21   -29.44     0.52)  ; 53\r\n        (   67.58   -63.03   -29.75     0.88)  ; 54\r\n        (   67.96   -63.46   -30.00     1.69)  ; 55\r\n        (   68.37   -64.12   -30.00     2.50)  ; 56\r\n        (   69.02   -64.74   -30.25     1.55)  ; 57\r\n        (   69.86   -65.62   -30.25     0.52)  ; 58\r\n        (   70.55   -65.87   -30.44     0.44)  ; 59\r\n        (   71.20   -66.50   -30.44     0.44)  ; 60\r\n        (   72.09   -67.52   -29.75     0.59)  ; 61\r\n        (   72.78   -68.30   -29.50     1.03)  ; 62\r\n        (   73.46   -68.78   -29.31     0.66)  ; 63\r\n        (   74.12   -69.32   -29.31     0.37)  ; 64\r\n        (   74.85   -69.88   -29.06     0.37)  ; 65\r\n        (   75.45   -70.28   -28.69     0.74)  ; 66\r\n        (   76.19   -70.83   -28.69     1.47)  ; 67\r\n        (   76.62   -71.43   -28.75     1.47)  ; 68\r\n        (   77.12   -72.02   -29.06     0.59)  ; 69\r\n         Normal\r\n      |\r\n        (   33.41   -39.49   -15.19     0.66)  ; 1, R-1-2-2\r\n        (   33.33   -40.44   -17.38     0.59)  ; 2\r\n        (   33.90   -41.12   -18.06     0.81)  ; 3\r\n        (   34.36   -42.00   -17.94     1.92)  ; 4\r\n        (   34.77   -42.67   -18.13     1.92)  ; 5\r\n        (   35.35   -43.79   -18.25     0.96)  ; 6\r\n        (   36.12   -44.51   -18.69     0.74)  ; 7\r\n        (   36.78   -45.06   -19.50     0.44)  ; 8\r\n        (   37.64   -46.23   -19.50     0.44)  ; 9\r\n        (   38.25   -47.13   -20.13     0.74)  ; 10\r\n        (   38.54   -48.07   -20.31     1.47)  ; 11\r\n        (   38.93   -48.95   -20.94     0.96)  ; 12\r\n        (   39.45   -49.92   -20.94     0.52)  ; 13\r\n        (   39.58   -50.90   -21.56     0.37)  ; 14\r\n         Normal\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (    2.66    -8.95     4.13     2.21)  ; 1, R-2\r\n    (    2.73    -8.96     1.75     2.06)  ; 2\r\n    (    3.93    -9.89    -0.13     2.21)  ; 3\r\n    (    5.06   -11.18    -1.31     2.21)  ; 4\r\n    (    6.50   -12.00    -3.13     2.36)  ; 5\r\n    (    7.14   -12.68    -5.56     2.06)  ; 6\r\n    (    7.65   -13.73    -7.06     1.92)  ; 7\r\n    (    8.59   -14.85    -7.75     1.92)  ; 8\r\n    (    9.12   -15.74    -7.63     1.99)  ; 9\r\n    (    9.49   -16.24    -7.63     1.99)  ; 10\r\n    (\r\n      (   10.39   -16.68    -7.63     1.03)  ; 1, R-2-1\r\n      (   11.01   -17.00    -7.63     0.66)  ; 2\r\n      (   12.04   -17.54    -7.63     0.59)  ; 3\r\n      (   12.63   -18.51    -7.94     0.74)  ; 4\r\n      (   13.58   -19.63    -8.63     0.88)  ; 5\r\n      (   14.40   -20.58    -9.25     0.88)  ; 6\r\n      (   15.01   -21.41    -9.94     0.88)  ; 7\r\n      (   15.79   -22.12   -10.00     1.47)  ; 8\r\n      (   16.66   -22.71   -11.00     0.96)  ; 9\r\n      (   17.93   -23.65   -11.00     0.74)  ; 10\r\n      (   18.91   -24.54   -11.31     0.96)  ; 11\r\n      (   19.79   -25.13   -11.31     0.96)  ; 12\r\n      (   19.68   -25.85   -11.81     0.81)  ; 13\r\n      (   19.84   -26.69   -12.50     0.74)  ; 14\r\n      (   20.01   -27.53   -13.13     0.59)  ; 15\r\n      (   20.58   -28.14   -14.25     0.66)  ; 16\r\n      (   21.20   -28.46   -15.63     1.18)  ; 17\r\n      (   21.83   -28.78   -16.44     1.18)  ; 18\r\n      (   22.65   -29.20   -16.94     1.03)  ; 19\r\n      (   24.15   -29.66   -17.31     0.81)  ; 20\r\n      (   25.39   -30.23   -17.50     0.88)  ; 21\r\n      (   26.24   -31.04   -17.88     0.88)  ; 22\r\n      (   27.24   -31.78   -18.69     0.88)  ; 23\r\n      (   27.76   -32.31   -18.69     0.88)  ; 24\r\n      (   28.33   -33.36   -19.56     1.47)  ; 25\r\n      (   28.63   -34.30   -20.75     1.25)  ; 26\r\n      (   29.12   -34.97   -21.63     0.88)  ; 27\r\n      (   29.89   -36.20   -21.63     0.66)  ; 28\r\n      (   30.85   -37.25   -22.75     0.44)  ; 29\r\n      (   31.49   -38.38   -22.88     0.66)  ; 30\r\n      (   31.88   -39.18   -23.31     0.96)  ; 31\r\n      (   32.31   -39.83   -23.69     1.18)  ; 32\r\n      (   33.00   -40.61   -25.25     1.40)  ; 33\r\n      (   33.67   -41.61   -25.63     1.40)  ; 34\r\n      (   34.75   -42.92   -25.88     1.47)  ; 35\r\n      (   35.59   -43.79   -26.69     1.77)  ; 36\r\n      (   36.45   -45.92   -27.38     1.47)  ; 37\r\n      (   36.59   -46.83   -27.88     1.11)  ; 38\r\n      (   37.29   -47.60   -28.25     0.88)  ; 39\r\n      (   38.09   -48.70   -28.50     0.66)  ; 40\r\n      (   38.46   -49.72   -29.00     0.66)  ; 41\r\n      (   39.68   -50.94   -29.25     0.96)  ; 42\r\n      (   40.27   -51.85   -29.25     1.25)  ; 43\r\n      (   41.17   -52.36   -29.25     0.88)  ; 44\r\n      (   42.24   -53.12   -29.56     0.59)  ; 45\r\n      (   42.56   -53.48   -29.69     0.59)  ; 46\r\n      (   43.11   -54.22   -29.69     0.88)  ; 47\r\n      (   44.19   -54.92   -30.13     1.18)  ; 48\r\n      (   44.97   -55.18   -30.13     1.18)  ; 49\r\n      (   45.67   -55.52   -30.63     0.74)  ; 50\r\n      (   46.84   -55.63   -30.69     0.52)  ; 51\r\n      (   47.56   -55.75   -31.13     0.52)  ; 52\r\n      (   47.91   -55.94   -31.13     1.18)  ; 53\r\n      (   49.12   -56.29   -31.19     1.69)  ; 54\r\n      (   50.23   -56.84   -31.00     0.96)  ; 55\r\n      (   51.16   -57.06   -31.00     0.59)  ; 56\r\n      (   52.10   -57.28   -31.00     0.59)  ; 57\r\n      (   53.13   -57.81   -31.00     0.74)  ; 58\r\n      (   54.02   -58.32   -31.06     0.81)  ; 59\r\n      (   55.26   -58.51   -31.56     0.52)  ; 60\r\n      (   56.06   -58.64   -31.81     1.33)  ; 61\r\n      (   56.87   -58.77   -32.19     2.58)  ; 62\r\n      (   57.84   -58.70   -32.44     2.80)  ; 63\r\n      (   58.72   -58.84   -32.44     1.84)  ; 64\r\n      (   59.44   -58.95   -33.00     0.81)  ; 65\r\n      (   59.88   -59.02   -33.00     0.37)  ; 66\r\n      (   60.60   -59.66   -33.00     0.37)  ; 67\r\n      (   61.44   -60.97   -34.06     0.37)  ; 68\r\n      (   61.84   -61.26   -34.56     1.25)  ; 69\r\n      (   62.74   -62.14   -35.38     2.14)  ; 70\r\n      (   63.50   -63.01   -35.56     1.62)  ; 71\r\n      (   64.08   -63.62   -35.56     0.59)  ; 72\r\n      (   64.92   -64.42   -37.06     0.44)  ; 73\r\n      (   65.26   -64.61   -37.06     0.74)  ; 74\r\n      (   65.69   -64.75   -37.38     0.74)  ; 75\r\n      (   66.01   -65.03   -38.00     0.74)  ; 76\r\n      (   66.48   -65.40   -38.00     0.96)  ; 77\r\n      (   67.05   -66.01   -38.50     0.96)  ; 78\r\n      (   67.63   -66.62   -38.50     0.59)  ; 79\r\n      (   68.42   -67.26   -38.75     0.44)  ; 80\r\n      (   68.92   -67.41   -39.56     0.74)  ; 81\r\n      (   69.47   -67.72   -40.31     1.62)  ; 82\r\n      (   70.44   -68.18   -40.31     2.36)  ; 83\r\n      (   70.96   -68.70   -40.31     0.81)  ; 84\r\n      (   71.77   -69.20   -40.56     0.52)  ; 85\r\n      (   72.75   -70.09   -40.81     0.52)  ; 86\r\n      (   73.78   -70.63   -40.81     0.88)  ; 87\r\n      (   74.13   -71.05   -40.81     0.44)  ; 88\r\n      (   74.80   -71.53   -40.81     0.66)  ; 89\r\n      (   75.50   -71.86   -40.81     0.66)  ; 90\r\n      (   75.99   -72.01   -41.19     0.44)  ; 91\r\n      (   77.22   -72.73   -40.69     1.03)  ; 92\r\n      (   77.97   -73.21   -40.69     1.99)  ; 93\r\n      (   78.70   -73.77   -40.63     1.99)  ; 94\r\n      (   79.58   -74.35   -40.63     1.92)  ; 95\r\n      (   80.39   -74.86   -40.75     1.11)  ; 96\r\n      (   80.78   -75.21   -40.75     0.59)  ; 97\r\n      (   81.62   -75.57   -40.75     0.37)  ; 98\r\n      (   82.37   -76.05   -40.75     0.37)  ; 99\r\n      (   83.01   -76.23   -41.06     0.66)  ; 100\r\n      (   83.91   -76.67   -41.06     0.66)  ; 101\r\n      (   84.94   -77.21   -41.06     0.37)  ; 102\r\n      (   85.43   -77.43   -41.06     1.11)  ; 103\r\n      (   86.13   -77.76   -41.06     1.47)  ; 104\r\n      (   86.77   -78.38   -41.06     0.66)  ; 105\r\n      (   87.60   -79.25   -41.56     0.37)  ; 106\r\n      (   87.98   -79.68   -41.88     0.37)  ; 107\r\n       Normal\r\n    |\r\n      (    9.44   -17.24    -7.69     0.81)  ; 1, R-2-2\r\n      (    9.11   -17.93    -7.81     0.44)  ; 2\r\n      (    8.48   -19.01    -8.50     0.52)  ; 3\r\n      (    7.91   -19.80    -9.50     0.88)  ; 4\r\n      (    7.12   -21.01    -9.56     1.03)  ; 5\r\n      (    6.63   -22.26    -9.38     1.40)  ; 6\r\n      (    6.34   -23.55    -9.69     1.18)  ; 7\r\n      (    6.31   -24.73   -10.31     0.81)  ; 8\r\n      (    6.34   -25.92   -10.44     1.33)  ; 9\r\n      (    6.35   -26.81   -11.13     1.47)  ; 10\r\n      (    6.20   -27.75   -11.19     1.47)  ; 11\r\n      (    5.51   -27.94   -11.81     1.11)  ; 12\r\n      (    4.80   -28.12   -12.50     1.18)  ; 13\r\n      (    4.48   -29.25   -13.31     1.18)  ; 14\r\n      (    4.69   -30.25   -14.13     1.25)  ; 15\r\n      (    4.80   -31.00   -15.06     0.88)  ; 16\r\n      (    4.14   -31.41   -15.81     0.81)  ; 17\r\n      (    3.50   -31.69   -16.88     0.81)  ; 18\r\n      (    3.04   -32.20   -18.31     1.11)  ; 19\r\n      (    2.78   -32.91   -19.44     1.11)  ; 20\r\n      (    2.66   -34.08   -20.13     1.11)  ; 21\r\n      (    2.62   -34.88   -20.88     0.81)  ; 22\r\n      (    3.16   -35.63   -21.56     0.81)  ; 23\r\n      (    3.07   -36.21   -22.69     0.81)  ; 24\r\n      (    2.03   -37.16   -22.88     0.59)  ; 25\r\n      (    1.77   -37.85   -23.38     1.40)  ; 26\r\n      (    1.44   -38.54   -24.44     2.28)  ; 27\r\n      (    1.35   -39.56   -25.00     2.58)  ; 28\r\n      (    0.84   -40.89   -25.00     2.06)  ; 29\r\n      (    0.38   -41.93   -25.00     1.03)  ; 30\r\n      (   -0.04   -42.90   -25.31     0.74)  ; 31\r\n      (   -0.29   -43.53   -25.44     0.74)  ; 32\r\n      (   -1.24   -44.86   -25.69     0.74)  ; 33\r\n      (   -1.57   -45.99   -25.69     0.81)  ; 34\r\n      (   -2.29   -46.77   -26.44     0.96)  ; 35\r\n      (   -2.57   -47.53   -26.69     1.18)  ; 36\r\n      (   -2.85   -48.38   -27.19     0.96)  ; 37\r\n      (   -3.79   -49.64   -28.25     1.18)  ; 38\r\n      (   -4.45   -51.01   -29.56     1.40)  ; 39\r\n      (   -5.23   -52.15   -29.56     1.11)  ; 40\r\n      (   -5.95   -53.37   -29.56     0.74)  ; 41\r\n      (   -6.74   -54.13   -30.25     0.52)  ; 42\r\n      (   -7.31   -54.93   -31.00     0.52)  ; 43\r\n      (   -8.02   -56.08   -32.38     1.11)  ; 44\r\n      (   -8.18   -56.64   -32.38     2.36)  ; 45\r\n      (   -8.51   -57.25   -32.38     3.24)  ; 46\r\n      (   -8.62   -58.42   -32.75     1.69)  ; 47\r\n      (   -8.62   -59.38   -32.75     0.59)  ; 48\r\n      (   -8.73   -60.10   -32.75     0.81)  ; 49\r\n      (   -8.82   -61.12   -32.75     0.81)  ; 50\r\n      (   -8.98   -62.14   -32.75     0.29)  ; 51\r\n      (   -8.68   -62.63   -32.75     0.29)  ; 52\r\n      (   -8.93   -63.25   -32.75     0.74)  ; 53\r\n      (   -8.95   -63.78   -32.75     1.03)  ; 54\r\n      (   -9.02   -64.73   -34.00     0.52)  ; 55\r\n      (   -9.11   -65.30   -34.06     0.52)  ; 56\r\n      (   -9.00   -66.05   -34.13     1.18)  ; 57\r\n      (   -8.81   -66.68   -34.13     1.84)  ; 58\r\n      (   -8.75   -67.28   -34.25     0.81)  ; 59\r\n      (   -8.51   -67.83   -35.94     0.59)  ; 60\r\n      (   -8.45   -68.88   -36.19     0.22)  ; 61\r\n      (   -8.20   -69.66   -36.25     0.59)  ; 62\r\n      (   -7.87   -70.38   -36.50     0.59)  ; 63\r\n      (   -7.48   -71.18   -36.63     0.37)  ; 64\r\n      (   -7.32   -72.09   -37.06     1.11)  ; 65\r\n      (   -7.52   -72.87   -37.25     1.33)  ; 66\r\n      (   -7.79   -73.64   -37.75     0.52)  ; 67\r\n      (   -8.01   -74.56   -38.38     0.52)  ; 68\r\n      (   -8.04   -75.23   -38.94     0.88)  ; 69\r\n      (   -8.24   -76.02   -39.63     1.62)  ; 70\r\n      (   -8.54   -76.49   -40.00     2.43)  ; 71\r\n      (   -8.71   -77.13   -40.00     3.32)  ; 72\r\n      (   -9.00   -77.96   -40.44     1.18)  ; 73\r\n      (   -9.02   -79.07   -41.50     0.29)  ; 74\r\n      (   -9.69   -80.00   -42.13     1.03)  ; 75\r\n      (   -9.99   -80.48   -42.50     1.03)  ; 76\r\n      (  -10.38   -81.07   -42.75     0.52)  ; 77\r\n      (  -10.86   -81.74   -43.31     0.15)  ; 78\r\n      (  -11.43   -82.54   -43.31     1.03)  ; 79\r\n      (  -11.59   -83.03   -45.06     2.43)  ; 80\r\n      (  -12.48   -83.48   -45.50     2.43)  ; 81\r\n      (  -12.79   -84.02   -45.69     1.25)  ; 82\r\n      (  -13.17   -84.56   -47.50     0.44)  ; 83\r\n      (  -13.65   -85.22   -48.13     0.66)  ; 84\r\n      (  -14.27   -85.79   -49.69     1.47)  ; 85\r\n      (  -14.85   -85.70   -50.38     2.06)  ; 86\r\n      (  -15.40   -86.35   -50.63     1.03)  ; 87\r\n      (  -15.78   -86.88   -51.56     0.52)  ; 88\r\n      (  -16.37   -87.30   -53.00     0.88)  ; 89\r\n      (  -16.70   -87.99   -53.38     1.25)  ; 90\r\n      (  -16.79   -88.57   -54.88     0.59)  ; 91\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (  -11.25    -4.71     3.50     2.43)  ; Root\r\n  (  -11.40    -5.65     3.50     2.28)  ; 1, R\r\n  (  -11.53    -6.44     3.13     2.06)  ; 2\r\n  (  -11.32    -7.50     3.06     1.92)  ; 3\r\n  (  -11.21    -8.19     1.06     1.99)  ; 4\r\n  (  -10.96    -9.41    -0.94     2.36)  ; 5\r\n  (  -10.94   -10.24    -2.81     3.09)  ; 6\r\n  (  -10.71   -11.15    -4.69     2.28)  ; 7\r\n  (  -10.96   -12.23    -7.06     3.17)  ; 8\r\n  (  -11.30   -12.54    -7.06     3.39)  ; 9\r\n  (\r\n    (  -10.99   -13.41    -7.81     2.28)  ; 1, R-1\r\n    (  -10.18   -14.42    -7.81     1.92)  ; 2\r\n    (   -9.56   -15.71    -7.81     1.92)  ; 3\r\n    (   -9.40   -16.54    -8.69     1.92)  ; 4\r\n    (   -9.26   -17.53    -9.88     1.92)  ; 5\r\n    (   -9.08   -18.81   -10.88     1.47)  ; 6\r\n    (   -8.83   -20.03   -12.25     1.40)  ; 7\r\n    (   -8.59   -20.89   -12.88     1.47)  ; 8\r\n    (   -8.46   -21.88   -14.19     1.55)  ; 9\r\n    (   -8.20   -23.10   -16.19     1.40)  ; 10\r\n    (   -8.22   -23.20   -16.19     1.40)  ; 11\r\n    (\r\n      (   -7.14   -23.49   -16.69     1.03)  ; 1, R-1-1\r\n      (   -6.58   -24.17   -17.06     0.66)  ; 2\r\n      (   -6.48   -25.44   -17.06     0.44)  ; 3\r\n      (   -6.70   -26.81   -17.06     0.44)  ; 4\r\n      (   -6.37   -27.98   -17.06     0.52)  ; 5\r\n      (   -5.89   -28.72   -17.06     0.37)  ; 6\r\n      (   -5.62   -29.87   -17.06     0.66)  ; 7\r\n      (   -5.50   -31.00   -17.25     0.66)  ; 8\r\n      (   -5.61   -31.72   -17.25     0.59)  ; 9\r\n      (   -5.53   -32.63   -17.25     0.52)  ; 10\r\n      (   -5.50   -33.37   -17.63     0.52)  ; 11\r\n      (   -5.42   -34.27   -17.75     0.44)  ; 12\r\n      (   -5.69   -35.29   -17.75     0.37)  ; 13\r\n      (   -5.78   -36.31   -17.75     0.88)  ; 14\r\n      (   -6.06   -37.08   -17.88     1.40)  ; 15\r\n      (   -5.95   -37.83   -18.13     1.55)  ; 16\r\n      (   -5.94   -38.73   -18.13     1.18)  ; 17\r\n      (   -5.92   -39.54   -18.13     0.81)  ; 18\r\n      (   -6.08   -40.56   -18.13     0.59)  ; 19\r\n      (   -6.25   -42.08   -19.38     0.96)  ; 20\r\n      (   -6.24   -43.42   -19.88     1.62)  ; 21\r\n      (   -6.39   -43.91   -19.88     1.77)  ; 22\r\n      (   -6.20   -44.61   -19.81     1.62)  ; 23\r\n      (   -6.10   -45.36   -19.81     0.88)  ; 24\r\n      (   -5.60   -46.48   -19.81     0.74)  ; 25\r\n      (   -5.53   -47.45   -19.31     0.59)  ; 26\r\n      (   -5.37   -48.29   -18.75     0.96)  ; 27\r\n      (   -5.18   -49.95   -18.75     0.81)  ; 28\r\n      (   -4.91   -51.10   -18.75     0.74)  ; 29\r\n      (   -4.56   -52.12   -18.75     0.66)  ; 30\r\n      (   -4.63   -53.51   -18.69     0.44)  ; 31\r\n      (   -4.80   -54.60   -18.38     0.44)  ; 32\r\n      (   -4.99   -55.83   -18.38     1.03)  ; 33\r\n      (   -5.19   -57.05   -18.38     1.69)  ; 34\r\n      (   -5.00   -58.20   -18.75     1.77)  ; 35\r\n      (   -5.21   -59.12   -18.75     2.06)  ; 36\r\n      (   -5.03   -60.27   -18.75     1.18)  ; 37\r\n      (   -5.02   -61.59   -18.75     0.74)  ; 38\r\n      (   -4.88   -62.29   -19.38     0.66)  ; 39\r\n      (   -4.92   -63.39   -19.38     0.66)  ; 40\r\n      (   -4.94   -64.50   -19.38     0.96)  ; 41\r\n      (   -4.91   -65.25   -19.38     1.33)  ; 42\r\n      (   -4.63   -65.89   -19.38     0.96)  ; 43\r\n      (   -4.34   -66.81   -19.38     0.52)  ; 44\r\n      (   -3.46   -67.84   -19.38     0.52)  ; 45\r\n      (   -2.86   -68.31   -19.38     0.44)  ; 46\r\n      (   -2.39   -69.13   -19.38     0.96)  ; 47\r\n      (   -2.20   -70.26   -19.38     0.59)  ; 48\r\n      (   -2.12   -71.68   -20.44     0.59)  ; 49\r\n      (   -2.10   -72.94   -20.44     1.18)  ; 50\r\n      (   -1.90   -74.08   -21.19     0.96)  ; 51\r\n      (   -1.48   -75.18   -21.94     0.81)  ; 52\r\n      (   -1.79   -76.18   -22.19     0.59)  ; 53\r\n      (   -1.80   -77.65   -22.75     0.59)  ; 54\r\n      (   -1.57   -79.03   -22.75     1.33)  ; 55\r\n      (   -1.41   -79.93   -23.25     1.40)  ; 56\r\n      (   -1.31   -80.69   -23.44     1.40)  ; 57\r\n      (   -1.34   -81.80   -23.50     1.18)  ; 58\r\n      (   -1.31   -82.54   -23.75     0.81)  ; 59\r\n      (   -1.38   -83.42   -23.81     0.52)  ; 60\r\n      (   -1.43   -84.74   -23.81     0.52)  ; 61\r\n      (   -1.54   -86.35   -24.19     0.88)  ; 62\r\n      (   -1.65   -87.07   -24.19     1.77)  ; 63\r\n      (   -1.80   -88.01   -24.19     1.25)  ; 64\r\n      (   -2.10   -88.93   -24.75     0.81)  ; 65\r\n      (   -2.54   -89.90   -24.81     0.52)  ; 66\r\n      (   -2.62   -90.93   -25.44     0.74)  ; 67\r\n      (   -2.72   -91.58   -25.56     1.33)  ; 68\r\n      (   -2.94   -92.06   -25.56     1.55)  ; 69\r\n      (   -2.86   -92.96   -25.56     0.74)  ; 70\r\n      (   -3.22   -93.87   -25.56     0.37)  ; 71\r\n      (   -3.44   -94.72   -25.69     1.11)  ; 72\r\n      (   -3.50   -95.60   -25.75     1.92)  ; 73\r\n      (   -3.26   -96.38   -25.69     1.47)  ; 74\r\n      (   -3.22   -97.12   -25.56     0.96)  ; 75\r\n      (   -2.83   -98.44   -25.56     0.66)  ; 76\r\n      (   -2.79  -100.08   -25.81     0.52)  ; 77\r\n      (   -2.40  -101.40   -26.00     0.81)  ; 78\r\n      (   -2.16  -102.18   -26.00     0.52)  ; 79\r\n      (   -1.63  -102.18   -26.00     0.52)  ; 80\r\n      (   -1.83  -102.97   -26.00     0.52)  ; 81\r\n      (   -2.08  -104.04   -26.00     0.52)  ; 82\r\n      (   -2.38  -104.95   -26.13     1.25)  ; 83\r\n      (   -2.51  -105.82   -26.19     1.62)  ; 84\r\n      (   -2.64  -106.62   -26.50     0.81)  ; 85\r\n      (   -2.37  -107.77   -28.19     0.59)  ; 86\r\n      (   -2.51  -108.64   -28.19     0.88)  ; 87\r\n      (   -2.58  -109.14   -29.06     1.47)  ; 88\r\n      (   -2.58  -110.03   -29.50     1.47)  ; 89\r\n      (   -2.64  -110.90   -29.69     0.52)  ; 90\r\n      (   -3.09  -111.80   -29.69     0.52)  ; 91\r\n      (   -3.37  -112.72   -31.63     0.52)  ; 92\r\n      (   -3.85  -113.38   -32.13     1.25)  ; 93\r\n      (   -4.05  -114.09   -32.13     1.25)  ; 94\r\n      (   -4.24  -115.32   -33.88     0.44)  ; 95\r\n      (   -4.33  -115.89   -34.25     1.18)  ; 96\r\n      (   -4.63  -116.37   -35.88     1.62)  ; 97\r\n       Normal\r\n    |\r\n      (   -8.12   -23.85   -16.19     1.40)  ; 1, R-1-2\r\n      (\r\n        (   -8.11   -23.89   -17.00     1.99)  ; 1, R-1-2-1\r\n        (   -8.20   -24.90   -17.00     3.32)  ; 2\r\n        (\r\n          (   -8.40   -25.76   -18.19     1.18)  ; 1, R-1-2-1-1\r\n          (   -9.41   -26.19   -21.00     1.03)  ; 2\r\n          (   -9.67   -27.33   -21.94     0.88)  ; 3\r\n          (   -9.57   -27.38   -21.94     0.88)  ; 4\r\n          (\r\n            (   -9.54   -28.83   -22.38     0.74)  ; 1, R-1-2-1-1-1\r\n            (   -9.38   -30.19   -24.00     1.18)  ; 2\r\n            (   -9.42   -30.48   -24.00     1.40)  ; 3\r\n            (\r\n              (   -9.08   -31.13   -23.94     0.52)  ; 1, R-1-2-1-1-1-1\r\n              (   -8.83   -31.91   -26.19     0.37)  ; 2\r\n              (   -9.30   -32.50   -28.56     0.66)  ; 3\r\n              (   -9.58   -32.90   -29.00     0.66)  ; 4\r\n              (   -9.07   -33.42   -29.31     0.66)  ; 5\r\n              (   -8.76   -33.85   -29.63     0.88)  ; 6\r\n              (   -8.93   -34.41   -30.06     1.03)  ; 7\r\n              (   -9.46   -34.92   -32.38     0.96)  ; 8\r\n              (   -9.89   -35.30   -33.44     1.25)  ; 9\r\n              (   -9.73   -35.69   -35.44     0.96)  ; 10\r\n              (   -9.12   -35.12   -38.06     0.96)  ; 11\r\n              (   -8.64   -34.45   -40.00     0.96)  ; 12\r\n              (   -8.27   -33.99   -41.44     1.11)  ; 13\r\n              (   -8.72   -34.00   -42.06     0.81)  ; 14\r\n              (   -9.01   -34.91   -43.56     0.81)  ; 15\r\n              (   -9.38   -35.81   -44.63     1.18)  ; 16\r\n              (   -9.65   -36.14   -46.13     1.47)  ; 17\r\n              (  -10.57   -36.29   -46.38     1.84)  ; 18\r\n              (  -11.06   -36.59   -46.75     0.96)  ; 19\r\n              (  -11.75   -37.15   -47.44     0.59)  ; 20\r\n              (  -12.49   -38.07   -47.88     0.44)  ; 21\r\n              (  -13.23   -38.98   -48.44     0.44)  ; 22\r\n              (  -13.84   -40.00   -48.88     0.74)  ; 23\r\n              (  -14.21   -40.45   -48.94     1.03)  ; 24\r\n              (  -14.56   -41.21   -49.31     1.33)  ; 25\r\n              (  -14.56   -41.66   -49.69     0.88)  ; 26\r\n              (  -14.53   -42.03   -49.06     0.52)  ; 27\r\n              (  -14.53   -42.48   -49.00     0.52)  ; 28\r\n              (  -14.55   -43.51   -50.06     0.74)  ; 29\r\n              (  -14.43   -44.20   -51.00     0.96)  ; 30\r\n              (  -13.88   -44.95   -51.63     0.52)  ; 31\r\n              (  -13.65   -45.87   -51.88     0.52)  ; 32\r\n              (  -13.75   -46.45   -51.88     0.96)  ; 33\r\n              (  -13.48   -47.16   -51.94     1.40)  ; 34\r\n              (  -13.19   -47.64   -52.19     1.84)  ; 35\r\n              (  -13.11   -48.10   -52.25     2.14)  ; 36\r\n              (  -13.20   -48.68   -52.69     0.81)  ; 37\r\n              (  -13.21   -49.64   -53.44     0.52)  ; 38\r\n              (  -13.27   -50.53   -53.81     0.52)  ; 39\r\n              (  -13.16   -51.72   -53.81     0.52)  ; 40\r\n              (  -12.63   -53.11   -53.94     0.52)  ; 41\r\n              (  -12.33   -54.04   -54.31     0.88)  ; 42\r\n              (  -11.98   -54.69   -54.31     1.69)  ; 43\r\n              (  -11.49   -55.35   -54.44     2.06)  ; 44\r\n              (  -11.10   -56.15   -54.56     1.03)  ; 45\r\n              (  -10.63   -56.97   -55.06     0.52)  ; 46\r\n              (  -10.22   -58.15   -55.81     0.52)  ; 47\r\n              (   -9.88   -58.80   -56.19     0.88)  ; 48\r\n              (   -9.63   -59.58   -57.00     1.40)  ; 49\r\n              (   -9.31   -60.37   -57.31     0.96)  ; 50\r\n              (   -9.24   -60.89   -57.31     0.59)  ; 51\r\n              (   -8.47   -62.20   -57.88     0.29)  ; 52\r\n              (   -7.45   -63.25   -58.44     0.29)  ; 53\r\n              (   -6.44   -63.93   -58.63     0.59)  ; 54\r\n              (   -6.10   -64.13   -58.75     1.03)  ; 55\r\n              (   -5.20   -64.57   -59.06     1.11)  ; 56\r\n              (   -4.57   -64.81   -59.31     0.66)  ; 57\r\n              (   -3.89   -65.30   -59.94     0.44)  ; 58\r\n              (   -3.37   -65.74   -60.13     0.44)  ; 59\r\n              (   -2.63   -66.30   -60.63     0.74)  ; 60\r\n              (   -2.05   -66.84   -61.31     1.33)  ; 61\r\n              (   -1.27   -67.56   -61.88     1.03)  ; 62\r\n              (   -0.52   -68.05   -62.38     0.66)  ; 63\r\n              (    0.35   -68.63   -63.00     0.52)  ; 64\r\n              (    1.12   -69.42   -63.50     0.52)  ; 65\r\n              (    2.09   -70.38   -63.88     0.74)  ; 66\r\n              (    2.44   -70.96   -64.31     1.47)  ; 67\r\n              (    2.80   -71.54   -64.56     1.77)  ; 68\r\n              (    3.07   -72.17   -65.13     0.81)  ; 69\r\n              (    3.55   -72.91   -65.38     0.44)  ; 70\r\n              (    4.12   -73.59   -65.44     0.74)  ; 71\r\n              (    4.59   -74.40   -65.44     0.81)  ; 72\r\n              (    5.14   -75.16   -65.69     0.37)  ; 73\r\n              (    5.77   -75.86   -66.44     0.37)  ; 74\r\n               Normal\r\n            |\r\n              (   -9.69   -31.29   -24.19     0.59)  ; 1, R-1-2-1-1-1-2\r\n              (  -10.27   -32.02   -24.81     0.44)  ; 2\r\n              (  -10.71   -32.53   -25.19     0.44)  ; 3\r\n              (  -11.33   -33.55   -25.44     0.52)  ; 4\r\n              (  -11.56   -34.10   -25.44     0.52)  ; 5\r\n              (  -11.73   -34.67   -25.63     0.37)  ; 6\r\n              (  -12.11   -35.65   -26.00     0.37)  ; 7\r\n              (  -12.58   -36.31   -26.69     0.52)  ; 8\r\n              (  -13.16   -37.62   -26.69     0.44)  ; 9\r\n              (  -13.31   -38.56   -27.31     0.44)  ; 10\r\n              (  -13.57   -39.26   -27.44     0.66)  ; 11\r\n              (  -13.84   -40.48   -27.44     0.52)  ; 12\r\n              (  -13.58   -41.18   -27.44     0.44)  ; 13\r\n              (  -13.42   -42.10   -28.00     0.66)  ; 14\r\n              (  -13.55   -42.37   -28.56     0.96)  ; 15\r\n              (  -13.58   -43.11   -28.94     1.33)  ; 16\r\n              (  -13.48   -44.31   -28.94     0.88)  ; 17\r\n              (  -13.44   -45.50   -28.81     0.74)  ; 18\r\n              (  -13.44   -46.46   -28.81     0.52)  ; 19\r\n              (  -13.28   -47.38   -29.19     0.52)  ; 20\r\n              (  -13.56   -48.59   -29.69     0.81)  ; 21\r\n              (  -13.76   -48.93   -29.81     1.18)  ; 22\r\n              (  -13.94   -49.57   -29.81     1.84)  ; 23\r\n              (  -14.10   -50.13   -30.06     1.84)  ; 24\r\n              (  -14.63   -50.72   -30.06     0.81)  ; 25\r\n              (  -15.04   -51.39   -30.63     0.44)  ; 26\r\n              (  -15.78   -51.79   -31.00     0.44)  ; 27\r\n              (  -16.37   -52.21   -31.00     0.74)  ; 28\r\n              (  -16.79   -52.52   -31.19     0.81)  ; 29\r\n              (  -17.17   -53.05   -31.50     0.52)  ; 30\r\n              (  -17.47   -54.41   -31.94     0.37)  ; 31\r\n              (  -17.76   -54.88   -32.25     0.37)  ; 32\r\n              (  -18.10   -55.57   -32.31     0.74)  ; 33\r\n              (  -18.31   -56.50   -32.31     1.40)  ; 34\r\n              (  -18.68   -57.40   -32.31     2.28)  ; 35\r\n              (  -18.91   -57.88   -32.44     2.65)  ; 36\r\n              (  -19.11   -58.73   -32.44     1.55)  ; 37\r\n              (  -19.15   -59.40   -32.56     0.88)  ; 38\r\n              (  -19.10   -60.07   -33.00     0.59)  ; 39\r\n               Normal\r\n            )  ;  End of split\r\n          |\r\n            (  -10.12   -28.48   -22.00     0.37)  ; 1, R-1-2-1-1-2\r\n            (  -10.76   -29.71   -21.38     0.37)  ; 2\r\n            (  -11.50   -30.63   -21.25     0.96)  ; 3\r\n            (  -12.30   -31.46   -20.81     1.18)  ; 4\r\n            (  -13.11   -32.37   -21.38     0.74)  ; 5\r\n            (  -14.10   -32.88   -22.25     0.81)  ; 6\r\n            (  -15.15   -33.45   -24.06     0.66)  ; 7\r\n            (  -16.38   -34.15   -25.25     0.59)  ; 8\r\n            (  -17.25   -34.90   -26.06     0.81)  ; 9\r\n            (  -17.95   -35.53   -26.06     1.47)  ; 10\r\n            (  -18.64   -36.09   -26.94     1.47)  ; 11\r\n            (  -19.29   -36.50   -26.94     0.81)  ; 12\r\n            (  -20.11   -37.40   -26.94     0.66)  ; 13\r\n            (  -20.89   -38.10   -27.88     0.59)  ; 14\r\n            (  -21.65   -38.72   -27.88     0.44)  ; 15\r\n            (  -22.56   -39.68   -28.44     0.81)  ; 16\r\n            (  -22.99   -40.50   -28.63     0.59)  ; 17\r\n            (  -23.20   -41.36   -28.88     0.37)  ; 18\r\n            (  -23.85   -41.70   -29.31     0.96)  ; 19\r\n            (  -24.30   -42.22   -29.44     2.06)  ; 20\r\n            (  -24.49   -42.48   -30.00     2.36)  ; 21\r\n            (  -25.01   -42.93   -30.00     1.77)  ; 22\r\n            (  -25.54   -43.43   -30.00     0.74)  ; 23\r\n            (  -25.98   -43.88   -30.44     0.52)  ; 24\r\n            (  -26.35   -44.78   -30.69     0.81)  ; 25\r\n            (  -26.26   -45.61   -31.25     1.18)  ; 26\r\n            (  -26.25   -46.50   -32.00     0.96)  ; 27\r\n            (  -26.37   -47.30   -32.00     0.59)  ; 28\r\n            (  -26.65   -48.26   -32.44     0.44)  ; 29\r\n            (  -27.00   -49.08   -32.69     0.44)  ; 30\r\n            (  -26.54   -49.97   -32.69     0.44)  ; 31\r\n            (  -26.51   -50.72   -32.69     0.44)  ; 32\r\n            (  -26.82   -51.70   -33.13     0.44)  ; 33\r\n            (  -27.15   -52.83   -32.75     0.66)  ; 34\r\n            (  -27.48   -53.52   -32.69     1.03)  ; 35\r\n            (  -28.04   -54.69   -32.19     1.69)  ; 36\r\n            (  -28.84   -55.52   -32.19     2.43)  ; 37\r\n            (  -29.69   -56.65   -31.75     1.99)  ; 38\r\n            (  -30.32   -57.29   -32.38     1.03)  ; 39\r\n            (  -31.16   -57.97   -33.56     0.59)  ; 40\r\n            (  -32.21   -58.92   -33.56     0.52)  ; 41\r\n            (  -33.09   -59.81   -33.81     0.52)  ; 42\r\n            (  -33.08   -60.63   -34.13     0.52)  ; 43\r\n            (  -33.05   -61.38   -34.13     1.11)  ; 44\r\n            (  -32.92   -61.98   -35.31     1.11)  ; 45\r\n            (  -33.47   -62.64   -35.69     0.52)  ; 46\r\n            (  -34.36   -63.09   -36.50     0.81)  ; 47\r\n            (  -34.78   -63.83   -37.00     0.44)  ; 48\r\n            (  -34.92   -64.70   -38.06     0.44)  ; 49\r\n            (  -35.09   -65.79   -38.06     1.11)  ; 50\r\n            (  -35.08   -66.67   -38.44     1.77)  ; 51\r\n            (  -35.30   -67.60   -38.44     1.25)  ; 52\r\n            (  -35.32   -68.64   -38.44     0.81)  ; 53\r\n            (  -35.63   -69.69   -38.81     0.37)  ; 54\r\n            (  -36.05   -70.45   -39.56     0.81)  ; 55\r\n            (  -36.30   -71.08   -40.00     1.62)  ; 56\r\n            (  -36.80   -71.88   -41.69     1.25)  ; 57\r\n            (  -37.44   -72.65   -42.38     0.81)  ; 58\r\n            (  -37.78   -73.41   -42.94     0.88)  ; 59\r\n            (  -38.24   -73.93   -43.19     0.88)  ; 60\r\n            (  -38.87   -74.65   -43.19     0.52)  ; 61\r\n            (  -39.37   -74.93   -43.56     0.81)  ; 62\r\n            (  -39.78   -75.24   -43.88     1.40)  ; 63\r\n            (  -40.45   -75.72   -43.88     1.40)  ; 64\r\n            (  -41.12   -76.14   -43.88     0.66)  ; 65\r\n            (  -41.64   -77.09   -44.19     0.52)  ; 66\r\n            (  -41.77   -77.88   -44.44     0.52)  ; 67\r\n            (  -42.02   -78.51   -45.13     0.52)  ; 68\r\n            (  -43.05   -79.38   -45.44     0.52)  ; 69\r\n            (  -44.20   -80.54   -46.56     0.74)  ; 70\r\n            (  -44.97   -80.64   -47.13     1.11)  ; 71\r\n            (  -45.63   -81.05   -48.00     1.84)  ; 72\r\n            (  -46.21   -81.47   -48.31     2.21)  ; 73\r\n            (  -46.87   -82.26   -48.31     0.81)  ; 74\r\n            (  -47.43   -82.98   -48.81     0.74)  ; 75\r\n            (  -47.85   -83.29   -48.94     1.11)  ; 76\r\n            (  -48.20   -83.68   -49.50     0.81)  ; 77\r\n            (  -48.97   -84.23   -50.06     0.74)  ; 78\r\n            (  -49.11   -84.64   -51.75     1.47)  ; 79\r\n            (  -49.56   -85.17   -52.56     2.21)  ; 80\r\n            (  -49.89   -85.85   -53.13     2.21)  ; 81\r\n            (  -50.09   -86.63   -53.13     1.11)  ; 82\r\n            (  -50.31   -87.04   -55.31     0.52)  ; 83\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (   -9.17   -25.38   -22.63     0.66)  ; 1, R-1-2-1-2\r\n          (  -10.02   -25.54   -23.94     0.74)  ; 2\r\n          (  -10.56   -25.67   -25.06     0.96)  ; 3\r\n          (  -11.88   -25.99   -27.13     0.88)  ; 4\r\n          (  -12.82   -25.84   -27.38     0.81)  ; 5\r\n          (  -13.21   -26.37   -29.69     0.81)  ; 6\r\n          (  -13.38   -27.01   -30.81     1.11)  ; 7\r\n          (  -13.72   -27.77   -30.81     1.77)  ; 8\r\n          (  -14.25   -28.20   -31.31     1.33)  ; 9\r\n          (  -14.63   -29.18   -32.13     0.96)  ; 10\r\n          (  -15.25   -29.89   -32.50     0.74)  ; 11\r\n          (  -16.02   -29.99   -33.88     0.66)  ; 12\r\n          (  -16.66   -30.27   -35.38     0.59)  ; 13\r\n          (  -16.82   -31.27   -38.50     0.81)  ; 14\r\n          (  -16.24   -31.37   -39.81     1.03)  ; 15\r\n          (  -15.72   -31.45   -40.81     1.03)  ; 16\r\n          (  -15.14   -31.54   -41.25     0.74)  ; 17\r\n          (  -15.32   -32.18   -41.56     0.96)  ; 18\r\n          (  -15.87   -32.83   -42.06     0.81)  ; 19\r\n          (  -16.16   -33.68   -42.81     0.66)  ; 20\r\n          (  -15.96   -34.37   -44.56     0.66)  ; 21\r\n          (  -15.91   -34.90   -46.38     0.81)  ; 22\r\n          (  -16.19   -35.29   -47.13     1.11)  ; 23\r\n          (  -16.12   -35.83   -47.88     2.14)  ; 24\r\n          (  -16.26   -36.24   -48.44     2.73)  ; 25\r\n          (  -16.36   -36.89   -49.06     1.25)  ; 26\r\n          (  -16.20   -37.74   -49.50     0.81)  ; 27\r\n          (  -16.64   -39.08   -50.63     0.66)  ; 28\r\n          (  -16.92   -39.91   -50.63     0.74)  ; 29\r\n          (  -16.92   -40.88   -51.25     0.59)  ; 30\r\n          (  -16.99   -41.76   -52.63     0.52)  ; 31\r\n          (  -17.60   -42.33   -54.56     0.52)  ; 32\r\n          (  -17.96   -43.23   -56.19     0.74)  ; 33\r\n          (  -18.78   -43.17   -56.63     1.11)  ; 34\r\n          (  -19.26   -43.39   -57.19     0.74)  ; 35\r\n          (  -19.90   -43.67   -57.44     0.74)  ; 36\r\n          (  -20.48   -44.02   -57.94     0.88)  ; 37\r\n          (  -20.98   -44.82   -59.19     0.74)  ; 38\r\n          (  -21.51   -45.33   -60.06     1.40)  ; 39\r\n          (  -21.87   -45.79   -60.75     2.43)  ; 40\r\n          (  -22.11   -46.35   -61.06     1.40)  ; 41\r\n          (  -22.76   -47.13   -61.75     0.74)  ; 42\r\n          (  -23.26   -47.87   -62.25     0.52)  ; 43\r\n          (  -24.60   -48.40   -62.44     0.52)  ; 44\r\n          (  -25.16   -49.12   -63.06     0.52)  ; 45\r\n          (  -25.43   -49.97   -64.25     1.11)  ; 46\r\n          (  -25.66   -50.46   -64.94     1.84)  ; 47\r\n          (  -26.21   -51.11   -65.56     1.99)  ; 48\r\n          (  -26.64   -51.48   -67.31     0.88)  ; 49\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (   -9.13   -23.62   -18.06     0.66)  ; 1, R-1-2-2\r\n        (  -10.44   -23.41   -18.88     0.66)  ; 2\r\n        (  -11.76   -23.21   -19.25     0.52)  ; 3\r\n        (  -12.34   -23.11   -19.94     0.52)  ; 4\r\n        (  -13.42   -22.94   -20.44     0.74)  ; 5\r\n        (  -14.53   -23.35   -20.63     0.74)  ; 6\r\n        (  -15.23   -23.98   -21.31     1.03)  ; 7\r\n        (  -15.88   -24.77   -21.69     1.33)  ; 8\r\n        (  -17.20   -25.76   -22.00     1.33)  ; 9\r\n        (  -18.63   -26.27   -21.75     0.81)  ; 10\r\n        (  -19.99   -26.87   -22.19     0.66)  ; 11\r\n        (  -21.10   -27.73   -22.19     0.66)  ; 12\r\n        (  -21.78   -28.81   -23.38     1.03)  ; 13\r\n        (  -22.98   -30.25   -23.38     1.18)  ; 14\r\n        (  -23.44   -30.76   -23.44     1.18)  ; 15\r\n        (  -24.43   -31.36   -24.38     0.96)  ; 16\r\n        (  -25.43   -32.52   -24.38     1.18)  ; 17\r\n        (  -25.98   -33.62   -24.38     0.88)  ; 18\r\n        (  -26.73   -34.61   -24.38     0.59)  ; 19\r\n        (  -27.37   -35.32   -24.38     0.81)  ; 20\r\n        (  -28.22   -36.44   -24.38     1.11)  ; 21\r\n        (  -28.75   -37.40   -24.56     0.74)  ; 22\r\n        (  -29.56   -38.75   -24.56     0.52)  ; 23\r\n        (  -30.07   -39.63   -24.56     0.52)  ; 24\r\n        (  -30.61   -40.22   -24.56     0.52)  ; 25\r\n        (  -31.34   -41.06   -24.75     0.52)  ; 26\r\n        (  -30.98   -41.64   -24.75     0.52)  ; 27\r\n        (  -31.16   -42.72   -24.75     0.52)  ; 28\r\n        (  -31.40   -43.79   -24.75     1.18)  ; 29\r\n        (  -31.75   -44.62   -24.81     2.28)  ; 30\r\n        (  -32.17   -45.37   -24.81     2.58)  ; 31\r\n        (  -32.24   -46.32   -24.81     1.69)  ; 32\r\n        (  -32.68   -47.14   -24.81     0.81)  ; 33\r\n        (  -33.27   -48.08   -24.81     0.81)  ; 34\r\n        (  -33.48   -48.94   -24.81     0.81)  ; 35\r\n        (  -33.80   -49.55   -24.88     0.59)  ; 36\r\n        (  -34.07   -50.49   -25.06     1.03)  ; 37\r\n        (  -34.64   -51.29   -25.06     1.03)  ; 38\r\n        (  -35.31   -52.22   -25.13     0.59)  ; 39\r\n        (  -35.76   -52.66   -25.25     0.59)  ; 40\r\n        (  -36.47   -53.37   -26.06     0.81)  ; 41\r\n        (  -38.07   -54.08   -26.06     0.66)  ; 42\r\n        (  -39.21   -54.71   -26.88     0.81)  ; 43\r\n        (  -39.49   -55.04   -29.00     1.11)  ; 44\r\n        (  -40.38   -55.49   -29.31     1.47)  ; 45\r\n        (  -41.34   -55.85   -29.31     0.81)  ; 46\r\n        (  -41.87   -55.99   -29.31     0.52)  ; 47\r\n        (  -42.48   -56.04   -29.81     0.52)  ; 48\r\n        (  -43.30   -56.06   -30.00     0.81)  ; 49\r\n        (  -44.13   -56.52   -31.00     0.81)  ; 50\r\n        (  -45.10   -57.04   -32.13     0.66)  ; 51\r\n        (  -45.52   -57.34   -32.19     0.66)  ; 52\r\n        (  -45.91   -57.87   -33.00     0.96)  ; 53\r\n        (  -46.33   -58.17   -33.00     1.77)  ; 54\r\n        (  -46.70   -58.63   -33.44     2.14)  ; 55\r\n        (  -47.35   -59.41   -34.31     0.74)  ; 56\r\n        (  -48.23   -60.31   -35.19     0.52)  ; 57\r\n        (  -49.10   -61.06   -36.75     0.59)  ; 58\r\n        (  -49.76   -61.99   -37.06     0.59)  ; 59\r\n        (  -50.45   -62.55   -37.31     0.88)  ; 60\r\n        (  -50.93   -63.21   -37.31     1.55)  ; 61\r\n        (  -51.90   -63.66   -37.88     1.55)  ; 62\r\n        (  -53.02   -64.15   -39.44     1.11)  ; 63\r\n        (  -53.93   -64.74   -39.44     0.66)  ; 64\r\n        (  -55.54   -65.52   -39.94     0.52)  ; 65\r\n        (  -56.42   -66.34   -40.00     0.81)  ; 66\r\n        (  -57.28   -67.01   -41.31     0.66)  ; 67\r\n        (  -58.56   -67.63   -41.31     0.52)  ; 68\r\n        (  -59.91   -68.09   -41.94     0.81)  ; 69\r\n        (  -61.26   -68.61   -42.31     0.96)  ; 70\r\n        (  -61.85   -69.11   -43.50     1.69)  ; 71\r\n        (  -62.94   -69.31   -44.69     2.06)  ; 72\r\n        (  -63.81   -69.62   -44.69     0.88)  ; 73\r\n        (  -64.73   -69.83   -44.69     0.59)  ; 74\r\n        (  -65.74   -70.05   -45.06     0.59)  ; 75\r\n        (  -66.49   -70.15   -45.13     0.74)  ; 76\r\n        (  -67.35   -70.31   -45.31     0.52)  ; 77\r\n        (  -68.13   -70.55   -45.38     1.25)  ; 78\r\n        (  -69.14   -71.14   -46.25     1.99)  ; 79\r\n        (  -69.65   -71.57   -46.25     0.96)  ; 80\r\n        (  -70.37   -71.84   -46.25     0.44)  ; 81\r\n        (  -71.85   -72.71   -46.69     0.44)  ; 82\r\n        (  -73.25   -73.53   -46.38     0.66)  ; 83\r\n         Normal\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (  -11.76   -12.49    -7.75     1.99)  ; 1, R-2\r\n    (  -12.64   -11.98    -7.75     1.03)  ; 2\r\n    (  -13.54   -11.54    -7.75     0.74)  ; 3\r\n    (  -14.33   -10.83    -8.19     0.88)  ; 4\r\n    (  -14.49    -9.54    -9.50     0.88)  ; 5\r\n    (  -14.87    -8.60   -10.31     0.88)  ; 6\r\n    (  -15.63    -8.26   -10.75     1.18)  ; 7\r\n    (  -16.65    -8.09   -10.06     0.81)  ; 8\r\n    (  -17.38    -7.98   -10.06     0.81)  ; 9\r\n    (  -18.20    -7.99   -10.13     0.81)  ; 10\r\n    (  -19.24    -8.42   -11.19     0.96)  ; 11\r\n    (  -19.71    -8.13   -12.06     1.25)  ; 12\r\n    (  -20.37    -8.47   -12.81     1.03)  ; 13\r\n    (  -19.93    -8.09   -14.25     0.88)  ; 14\r\n    (  -19.80    -7.74   -15.44     0.88)  ; 15\r\n    (  -19.51    -7.71   -16.13     0.88)  ; 16\r\n    (  -19.51    -7.71   -17.44     1.18)  ; 17\r\n    (  -19.27    -8.13   -18.38     1.47)  ; 18\r\n    (  -19.72    -8.20   -19.69     1.18)  ; 19\r\n    (  -20.49    -7.85   -20.69     0.88)  ; 20\r\n    (  -21.31    -7.36   -20.69     0.88)  ; 21\r\n    (  -21.93    -7.04   -21.75     1.03)  ; 22\r\n    (  -22.57    -7.30   -23.50     1.18)  ; 23\r\n    (  -22.67    -7.95   -24.81     1.40)  ; 24\r\n    (  -23.14    -8.10   -25.44     1.03)  ; 25\r\n    (  -23.53    -7.23   -25.81     0.74)  ; 26\r\n    (  -23.13    -6.11   -26.88     0.74)  ; 27\r\n    (  -22.70    -5.28   -28.69     0.88)  ; 28\r\n    (  -22.75    -4.24   -28.81     1.11)  ; 29\r\n    (  -23.32    -3.56   -29.63     0.66)  ; 30\r\n    (  -24.31    -3.69   -30.88     0.66)  ; 31\r\n    (  -25.16    -3.42   -31.38     0.81)  ; 32\r\n    (  -26.11    -2.82   -32.25     1.03)  ; 33\r\n    (  -27.12    -2.14   -33.19     0.59)  ; 34\r\n    (  -27.80    -1.74   -33.69     0.59)  ; 35\r\n    (  -28.46    -1.64   -34.19     0.88)  ; 36\r\n    (  -29.02    -1.84   -34.88     1.03)  ; 37\r\n    (  -29.74    -2.17   -35.19     0.66)  ; 38\r\n    (  -30.91    -2.51   -35.81     0.52)  ; 39\r\n    (  -31.28    -2.00   -36.25     0.52)  ; 40\r\n    (  -32.26    -1.62   -36.25     0.81)  ; 41\r\n    (  -33.08    -1.64   -36.31     0.44)  ; 42\r\n    (  -34.76    -1.89   -36.63     0.44)  ; 43\r\n    (  -35.38    -2.02   -36.69     1.18)  ; 44\r\n    (  -36.25    -2.33   -37.06     1.33)  ; 45\r\n    (  -36.97    -2.58   -37.19     0.44)  ; 46\r\n    (  -37.48    -2.57   -37.75     0.44)  ; 47\r\n    (  -38.15    -2.54   -38.13     0.96)  ; 48\r\n    (  -39.11    -2.46   -38.13     1.25)  ; 49\r\n    (  -39.87    -2.56   -38.56     1.25)  ; 50\r\n    (  -40.60    -2.45   -38.88     0.81)  ; 51\r\n    (  -41.32    -2.33   -39.44     0.66)  ; 52\r\n    (  -42.51    -1.85   -39.88     0.96)  ; 53\r\n    (  -43.39    -1.71   -41.13     1.25)  ; 54\r\n    (  -44.41    -2.00   -41.44     0.88)  ; 55\r\n    (  -45.72    -2.31   -42.69     0.81)  ; 56\r\n    (  -46.17    -2.31   -43.94     1.92)  ; 57\r\n    (  -46.73    -2.08   -44.63     2.28)  ; 58\r\n    (  -47.72    -2.21   -44.81     0.81)  ; 59\r\n    (  -48.36    -2.03   -46.19     0.52)  ; 60\r\n    (  -48.66    -2.51   -46.63     0.52)  ; 61\r\n    (  -48.89    -2.99   -48.44     1.18)  ; 62\r\n    (  -49.60    -3.69   -49.19     0.52)  ; 63\r\n    (  -50.07    -4.28   -49.19     0.52)  ; 64\r\n    (  -50.58    -4.72   -49.81     1.55)  ; 65\r\n    (  -51.06    -5.38   -50.69     2.73)  ; 66\r\n    (  -51.66    -5.88   -50.69     2.21)  ; 67\r\n    (  -52.37    -6.13   -51.75     0.59)  ; 68\r\n    (  -53.25    -6.51   -51.75     0.37)  ; 69\r\n    (  -54.34    -6.78   -52.00     0.29)  ; 70\r\n    (  -54.97    -6.99   -52.00     0.29)  ; 71\r\n    (  -55.77    -7.44   -53.63     0.81)  ; 72\r\n    (  -56.50    -7.84   -53.88     1.18)  ; 73\r\n    (  -57.05    -7.97   -54.00     0.74)  ; 74\r\n    (  -57.61    -8.26   -54.06     0.37)  ; 75\r\n    (  -58.15    -8.76   -54.25     0.59)  ; 76\r\n    (  -58.80    -9.10   -54.50     0.59)  ; 77\r\n    (  -59.90    -9.45   -55.31     0.37)  ; 78\r\n    (  -60.17    -9.78   -55.44     0.37)  ; 79\r\n    (  -61.14   -10.74   -55.94     0.96)  ; 80\r\n    (  -61.66   -11.17   -56.25     1.77)  ; 81\r\n    (  -62.19   -11.76   -56.63     2.50)  ; 82\r\n    (  -62.98   -12.44   -56.94     1.55)  ; 83\r\n    (  -64.05   -13.08   -57.38     0.66)  ; 84\r\n    (  -65.15   -13.50   -57.81     0.44)  ; 85\r\n    (  -66.09   -14.17   -58.25     0.59)  ; 86\r\n    (  -66.82   -14.57   -58.44     0.29)  ; 87\r\n    (  -67.68   -14.80   -58.56     0.29)  ; 88\r\n    (  -68.18   -15.17   -58.69     0.96)  ; 89\r\n    (  -68.71   -16.12   -59.00     1.62)  ; 90\r\n    (  -69.59   -16.50   -59.69     0.66)  ; 91\r\n    (  -70.18   -16.92   -61.69     0.74)  ; 92\r\n    (  -71.12   -17.22   -62.38     1.33)  ; 93\r\n    (  -71.82   -16.88   -63.06     1.33)  ; 94\r\n    (  -72.13   -16.54   -63.81     0.88)  ; 95\r\n    (  -72.25   -16.38   -66.19     0.59)  ; 96\r\n     Normal\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (   11.04    -1.79    10.06     2.36)  ; Root\r\n  (   11.69    -2.41     9.69     1.77)  ; 1, R\r\n  (   12.77    -3.10     9.19     1.99)  ; 2\r\n  (   13.49    -3.74     9.06     2.28)  ; 3\r\n  (   14.14    -3.84     9.06     2.43)  ; 4\r\n  (   14.23    -3.86     9.06     2.43)  ; 5\r\n  (\r\n    (   15.23    -4.53     9.06     1.62)  ; 1, R-1\r\n    (   16.64    -5.04     9.00     1.55)  ; 2\r\n    (   17.71    -5.37     8.31     1.77)  ; 3\r\n    (   18.83    -5.84     7.50     1.69)  ; 4\r\n    (   19.82    -6.14     7.50     1.69)  ; 5\r\n    (   21.23    -6.66     7.50     1.77)  ; 6\r\n    (   22.36    -7.06     7.00     1.99)  ; 7\r\n    (   23.53    -7.25     6.81     1.62)  ; 8\r\n    (   25.14    -7.42     6.81     1.62)  ; 9\r\n    (   26.58    -7.80     7.06     1.69)  ; 10\r\n    (   28.14    -8.27     7.06     1.47)  ; 11\r\n    (   29.21    -8.59     7.06     1.40)  ; 12\r\n    (   30.66    -8.82     6.56     1.69)  ; 13\r\n    (   31.77    -8.92     7.44     2.36)  ; 14\r\n    (\r\n      (   32.59    -9.34     8.00     2.43)  ; 1, R-1-1\r\n      (   33.42    -9.78     8.19     2.43)  ; 2\r\n      (\r\n        (   34.68   -10.27     8.56     1.92)  ; 1, R-1-1-1\r\n        (   35.78   -10.45     8.50     1.55)  ; 2\r\n        (   36.43   -10.55     8.50     1.33)  ; 3\r\n        (\r\n          (   36.92   -10.78     8.81     0.96)  ; 1, R-1-1-1-1\r\n          (   37.67   -11.64     9.50     1.03)  ; 2\r\n          (   38.02   -12.28     9.56     0.81)  ; 3\r\n          (   38.22   -12.91    10.19     0.74)  ; 4\r\n          (\r\n            (   38.95   -13.02    10.00     0.59)  ; 1, R-1-1-1-1-1\r\n            (   39.51   -13.70     9.81     0.59)  ; 2\r\n            (   40.41   -14.65    10.75     0.59)  ; 3\r\n            (   41.07   -15.65    11.69     0.44)  ; 4\r\n            (   41.87   -16.22    11.75     0.81)  ; 5\r\n            (   42.82   -16.46    12.31     1.03)  ; 6\r\n            (   43.87   -16.41    12.81     0.66)  ; 7\r\n            (   44.82   -16.56    12.88     0.59)  ; 8\r\n            (   45.37   -16.87    13.06     0.59)  ; 9\r\n            (   45.98   -17.70    13.25     0.66)  ; 10\r\n            (   46.06   -18.67    13.44     0.66)  ; 11\r\n            (   46.22   -19.06    13.81     0.66)  ; 12\r\n            (   46.72   -19.66    14.19     0.74)  ; 13\r\n            (   47.07   -20.24    14.88     0.74)  ; 14\r\n            (   47.69   -21.00    14.88     0.59)  ; 15\r\n            (   48.51   -21.50    14.88     0.59)  ; 16\r\n            (   48.88   -22.01    14.88     0.59)  ; 17\r\n            (   49.86   -22.90    15.00     0.59)  ; 18\r\n            (   50.46   -23.36    15.31     1.18)  ; 19\r\n            (   51.12   -23.91    15.38     1.92)  ; 20\r\n            (   51.67   -24.22    15.38     1.92)  ; 21\r\n            (   52.08   -24.88    15.38     0.88)  ; 22\r\n            (   52.74   -25.43    15.38     0.52)  ; 23\r\n            (   53.57   -26.30    16.13     0.59)  ; 24\r\n            (   54.37   -26.88    16.63     0.59)  ; 25\r\n            (   54.43   -27.47    16.94     0.59)  ; 26\r\n            (   54.54   -28.23    17.25     0.88)  ; 27\r\n            (   55.00   -29.04    17.63     0.66)  ; 28\r\n            (   55.52   -30.01    17.69     0.66)  ; 29\r\n            (   56.17   -31.53    17.69     0.52)  ; 30\r\n            (   56.70   -31.97    17.69     1.03)  ; 31\r\n            (   57.43   -32.53    17.69     1.40)  ; 32\r\n            (   57.93   -33.13    17.69     0.96)  ; 33\r\n            (   58.33   -33.94    18.25     0.66)  ; 34\r\n            (   59.19   -34.67    18.25     0.66)  ; 35\r\n            (   60.09   -35.55    18.25     0.81)  ; 36\r\n            (   61.40   -36.20    18.50     0.81)  ; 37\r\n            (   62.44   -36.21    19.31     0.74)  ; 38\r\n            (   63.20   -36.19    20.31     1.25)  ; 39\r\n            (   63.86   -35.78    20.69     1.25)  ; 40\r\n            (   64.52   -35.80    21.25     0.88)  ; 41\r\n            (   65.23   -35.55    21.25     0.59)  ; 42\r\n            (   66.04   -36.13    21.75     0.52)  ; 43\r\n            (   66.24   -37.19    21.81     0.74)  ; 44\r\n            (   66.63   -38.06    21.81     0.44)  ; 45\r\n            (   67.19   -39.26    22.50     0.74)  ; 46\r\n            (   67.37   -39.96    22.88     1.33)  ; 47\r\n            (   67.38   -40.84    23.06     0.96)  ; 48\r\n            (   67.37   -41.88    23.13     0.59)  ; 49\r\n            (   67.25   -42.61    23.81     0.81)  ; 50\r\n            (   66.81   -43.50    24.06     0.96)  ; 51\r\n            (   66.32   -44.23    24.19     0.52)  ; 52\r\n            (   66.20   -44.95    24.25     0.52)  ; 53\r\n            (   66.37   -45.80    24.31     0.66)  ; 54\r\n            (   66.64   -46.51    24.31     0.44)  ; 55\r\n            (   66.64   -47.39    24.06     0.52)  ; 56\r\n            (   66.74   -48.21    23.75     0.52)  ; 57\r\n            (   67.42   -48.62    23.75     0.52)  ; 58\r\n            (   67.31   -49.34    24.13     0.52)  ; 59\r\n            (   66.65   -49.75    24.44     0.52)  ; 60\r\n            (   66.18   -49.90    24.63     0.66)  ; 61\r\n            (   65.60   -50.25    24.94     0.66)  ; 62\r\n            (   64.93   -50.66    25.50     0.66)  ; 63\r\n             Normal\r\n          |\r\n            (   38.58   -13.71     9.06     0.59)  ; 1, R-1-1-1-1-2\r\n            (   39.13   -14.47     8.50     0.66)  ; 2\r\n            (   39.86   -15.03     9.88     0.74)  ; 3\r\n            (   40.84   -15.93    10.50     0.66)  ; 4\r\n            (   41.57   -16.93    10.75     0.66)  ; 5\r\n            (   43.10   -17.62    10.88     0.66)  ; 6\r\n            (   43.86   -18.55    10.88     0.88)  ; 7\r\n            (   44.44   -19.09    10.94     1.40)  ; 8\r\n            (   45.22   -19.80    11.44     0.66)  ; 9\r\n            (   45.82   -20.27    11.38     0.52)  ; 10\r\n            (   46.40   -20.87    11.19     0.52)  ; 11\r\n            (   47.26   -21.53    10.94     0.52)  ; 12\r\n            (   47.81   -21.84    11.81     0.66)  ; 13\r\n            (   48.49   -22.25    11.88     0.59)  ; 14\r\n            (   48.83   -22.89    11.88     0.52)  ; 15\r\n            (   49.23   -23.69    11.75     0.74)  ; 16\r\n            (   49.69   -24.59    11.44     0.74)  ; 17\r\n            (   50.03   -25.74    12.13     0.52)  ; 18\r\n            (   50.76   -26.30    12.13     0.52)  ; 19\r\n            (   51.39   -26.56    12.63     0.52)  ; 20\r\n            (   52.44   -26.95    12.75     0.44)  ; 21\r\n            (   53.62   -27.05    12.94     0.44)  ; 22\r\n            (   54.30   -27.02    13.25     0.96)  ; 23\r\n            (   54.97   -27.04    13.44     1.18)  ; 24\r\n            (   55.99   -27.13    13.44     0.66)  ; 25\r\n            (   56.98   -27.51    13.69     0.59)  ; 26\r\n            (   57.75   -27.79    13.94     0.81)  ; 27\r\n            (   58.58   -28.28    14.69     1.03)  ; 28\r\n            (   58.94   -28.79    14.75     0.59)  ; 29\r\n            (   59.78   -29.14    15.00     0.52)  ; 30\r\n            (   60.14   -29.20    15.31     0.52)  ; 31\r\n            (   61.05   -29.63    15.44     0.74)  ; 32\r\n            (   61.85   -29.76    15.75     0.37)  ; 33\r\n            (   62.60   -29.74    16.50     0.59)  ; 34\r\n            (   63.08   -29.96    16.75     1.47)  ; 35\r\n            (   63.67   -29.98    16.94     1.47)  ; 36\r\n            (   64.45   -29.81    16.94     0.88)  ; 37\r\n            (   65.30   -29.64    17.13     0.52)  ; 38\r\n            (   65.73   -29.71    17.56     0.44)  ; 39\r\n            (   66.33   -30.18    17.56     0.44)  ; 40\r\n            (   66.94   -30.57    17.75     0.66)  ; 41\r\n            (   67.66   -30.68    18.25     0.44)  ; 42\r\n            (   68.39   -30.80    18.63     0.44)  ; 43\r\n            (   69.25   -31.01    18.63     0.66)  ; 44\r\n            (   70.34   -31.26    18.81     0.52)  ; 45\r\n            (   71.34   -31.04    19.31     0.66)  ; 46\r\n            (   72.21   -31.18    19.38     1.47)  ; 47\r\n            (   72.90   -31.07    19.44     1.18)  ; 48\r\n            (   74.05   -30.88    19.44     0.96)  ; 49\r\n            (   74.98   -31.17    19.81     0.74)  ; 50\r\n            (   75.63   -31.28    19.81     0.66)  ; 51\r\n            (   76.43   -31.40    19.81     0.74)  ; 52\r\n            (   77.17   -31.45    20.00     0.52)  ; 53\r\n            (   77.86   -32.30    20.13     0.44)  ; 54\r\n            (   78.53   -33.22    20.25     0.59)  ; 55\r\n            (   79.22   -33.56    20.25     0.88)  ; 56\r\n            (   80.22   -34.34    20.38     0.66)  ; 57\r\n            (   80.96   -34.82    20.38     0.66)  ; 58\r\n            (   81.87   -35.19    20.44     0.88)  ; 59\r\n            (   82.47   -35.66    20.50     0.74)  ; 60\r\n            (   82.95   -35.96    19.94     1.11)  ; 61\r\n            (   83.42   -36.26    19.88     1.40)  ; 62\r\n            (   83.87   -36.69    19.88     0.74)  ; 63\r\n            (   84.78   -37.13    19.88     0.74)  ; 64\r\n            (   85.39   -37.45    19.69     0.88)  ; 65\r\n            (   86.16   -37.79    19.69     0.66)  ; 66\r\n            (   87.93   -38.89    19.38     0.52)  ; 67\r\n            (   88.74   -39.39    19.38     0.74)  ; 68\r\n            (   89.92   -40.46    19.38     0.74)  ; 69\r\n            (   90.57   -41.01    19.25     0.59)  ; 70\r\n            (   91.45   -42.04    19.13     0.81)  ; 71\r\n            (   92.30   -42.84    18.63     0.81)  ; 72\r\n            (   93.14   -43.64    17.88     0.96)  ; 73\r\n            (   93.84   -44.42    17.88     1.18)  ; 74\r\n            (   94.39   -45.17    17.44     0.96)  ; 75\r\n            (   95.07   -46.02    17.06     0.59)  ; 76\r\n            (   95.95   -47.12    16.50     0.59)  ; 77\r\n            (   96.73   -48.28    16.50     0.59)  ; 78\r\n            (   97.29   -49.48    16.56     0.81)  ; 79\r\n            (   97.95   -50.03    17.06     1.47)  ; 80\r\n            (   98.32   -50.53    17.06     1.18)  ; 81\r\n            (   99.05   -51.08    16.94     0.74)  ; 82\r\n            (  100.36   -51.22    16.63     0.66)  ; 83\r\n            (  102.21   -51.37    16.63     0.52)  ; 84\r\n            (  103.50   -51.20    16.25     0.59)  ; 85\r\n            (  104.17   -51.24    16.25     0.66)  ; 86\r\n            (  105.92   -51.07    16.69     0.44)  ; 87\r\n            (  106.66   -51.03    16.69     0.74)  ; 88\r\n            (  107.20   -50.98    16.75     0.88)  ; 89\r\n            (  107.82   -50.77    16.81     0.59)  ; 90\r\n            (  108.64   -50.83    16.81     0.37)  ; 91\r\n            (  109.25   -50.71    16.81     0.59)  ; 92\r\n            (  109.87   -50.58    16.81     0.59)  ; 93\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (   37.77   -10.27     9.69     0.74)  ; 1, R-1-1-1-2\r\n          (   39.40    -9.87     9.88     0.81)  ; 2\r\n          (   40.92    -9.66    10.31     0.96)  ; 3\r\n          (   41.38    -9.35    10.31     0.96)  ; 4\r\n          (\r\n            (   41.74    -9.20    10.44     1.25)  ; 1, R-1-1-1-2-1\r\n            (   42.31    -8.92    11.25     0.88)  ; 2\r\n            (   42.39    -8.88    11.25     0.88)  ; 3\r\n            (\r\n              (   43.15    -8.31    11.25     0.52)  ; 1, R-1-1-1-2-1-1\r\n              (   43.38    -7.83    11.50     0.52)  ; 2\r\n              (   44.20    -7.37    11.75     0.59)  ; 3\r\n              (   44.77    -7.46    11.75     0.59)  ; 4\r\n              (   45.31    -7.84    11.75     0.66)  ; 5\r\n              (   45.84    -8.81    11.75     0.59)  ; 6\r\n              (   46.26    -9.39    12.06     0.74)  ; 7\r\n              (   47.25   -10.22    12.19     0.59)  ; 8\r\n              (   47.71   -10.58    12.19     0.59)  ; 9\r\n              (   48.36   -11.21    12.44     0.74)  ; 10\r\n              (   48.57   -11.76    12.25     1.18)  ; 11\r\n              (   48.70   -12.37    11.19     1.18)  ; 12\r\n              (   49.16   -12.74    12.88     0.81)  ; 13\r\n              (   50.08   -13.11    12.94     0.44)  ; 14\r\n              (   51.28   -13.52    12.88     0.44)  ; 15\r\n              (   52.11   -13.95    12.88     0.59)  ; 16\r\n              (   52.97   -14.61    12.88     0.74)  ; 17\r\n              (   54.09   -15.08    12.88     0.59)  ; 18\r\n              (   55.01   -15.38    12.88     0.66)  ; 19\r\n              (   55.74   -15.49    12.75     0.66)  ; 20\r\n              (   56.36   -15.81    12.75     1.33)  ; 21\r\n              (   57.07   -16.07    12.56     1.84)  ; 22\r\n              (   57.76   -15.95    12.44     1.62)  ; 23\r\n              (   58.51   -15.93    12.44     0.74)  ; 24\r\n              (   59.39   -15.99    12.31     0.66)  ; 25\r\n              (   60.18   -15.75    12.31     0.59)  ; 26\r\n              (   61.39   -15.65    12.31     0.74)  ; 27\r\n              (   62.25   -15.41    12.25     0.44)  ; 28\r\n              (   63.17   -15.18    12.25     0.37)  ; 29\r\n              (   64.02   -15.02    12.13     0.59)  ; 30\r\n              (   64.75   -15.14    12.06     0.44)  ; 31\r\n              (   65.75   -14.93    12.50     0.81)  ; 32\r\n              (   66.76   -14.64    12.50     0.66)  ; 33\r\n              (   67.29   -14.65    12.50     0.66)  ; 34\r\n              (   68.17   -14.27    12.69     0.74)  ; 35\r\n              (   69.02   -14.04    12.69     0.74)  ; 36\r\n              (   69.65   -13.84    12.81     1.33)  ; 37\r\n              (   70.43   -13.67    12.94     1.62)  ; 38\r\n              (   70.93   -13.75    13.00     1.62)  ; 39\r\n              (   71.52   -13.39    13.13     0.96)  ; 40\r\n              (   72.20   -13.28    13.13     0.44)  ; 41\r\n              (   72.82   -12.72    13.31     0.37)  ; 42\r\n              (   73.41   -12.73    13.38     0.59)  ; 43\r\n              (   74.43   -12.89    13.38     0.59)  ; 44\r\n              (   75.37   -13.04    13.38     0.74)  ; 45\r\n              (   76.04   -13.08    13.38     0.96)  ; 46\r\n              (   76.79   -13.04    13.38     0.88)  ; 47\r\n              (   77.60   -13.10    13.38     1.18)  ; 48\r\n              (   77.82   -13.14    13.38     1.40)  ; 49\r\n              (   78.41   -13.23    13.38     0.96)  ; 50\r\n              (   79.18   -13.50    13.81     0.59)  ; 51\r\n              (   80.04   -13.71    13.81     0.74)  ; 52\r\n              (   81.15   -13.96    13.94     0.44)  ; 53\r\n              (   81.73   -14.05    13.94     0.44)  ; 54\r\n              (   82.59   -14.25    14.56     0.66)  ; 55\r\n              (   83.15   -14.50    14.81     0.81)  ; 56\r\n              (   83.81   -14.52    14.88     0.59)  ; 57\r\n              (   84.77   -14.60    15.06     0.59)  ; 58\r\n              (   85.65   -14.74    15.38     0.74)  ; 59\r\n              (   86.45   -14.87    15.88     1.18)  ; 60\r\n              (   87.19   -15.36    15.88     1.18)  ; 61\r\n              (   87.98   -15.55    15.88     0.59)  ; 62\r\n              (   88.38   -15.84    15.88     0.59)  ; 63\r\n              (   88.81   -16.36    15.88     0.59)  ; 64\r\n              (   89.43   -16.75    15.88     0.66)  ; 65\r\n              (   90.01   -17.28    15.88     0.44)  ; 66\r\n              (   90.73   -17.91    15.88     0.44)  ; 67\r\n              (   91.28   -18.22    16.69     1.03)  ; 68\r\n              (   91.83   -18.53    17.19     1.62)  ; 69\r\n              (   92.32   -18.68    17.50     1.18)  ; 70\r\n              (   93.04   -18.87    17.56     0.59)  ; 71\r\n              (   93.48   -18.94    17.69     0.44)  ; 72\r\n              (   93.92   -19.45    18.06     0.44)  ; 73\r\n              (   94.39   -20.27    18.13     0.66)  ; 74\r\n              (   94.69   -20.76    18.44     0.81)  ; 75\r\n              (   95.04   -21.33    18.69     0.96)  ; 76\r\n              (   95.39   -21.54    18.75     0.44)  ; 77\r\n              (   95.87   -21.76    19.00     0.37)  ; 78\r\n              (   96.70   -22.19    19.06     0.59)  ; 79\r\n              (   97.56   -22.40    19.06     0.88)  ; 80\r\n              (   98.44   -22.54    19.06     1.03)  ; 81\r\n              (   99.13   -22.80    18.94     0.37)  ; 82\r\n              (   99.86   -22.91    18.81     0.37)  ; 83\r\n              (  100.72   -23.12    19.38     0.74)  ; 84\r\n              (  101.72   -23.43    19.69     0.81)  ; 85\r\n              (  102.60   -23.57    19.94     0.52)  ; 86\r\n              (  103.89   -23.85    19.94     0.59)  ; 87\r\n              (  104.55   -23.95    19.94     1.03)  ; 88\r\n              (  104.83   -24.06    19.94     0.66)  ; 89\r\n              (  105.77   -24.21    19.94     0.74)  ; 90\r\n              (  106.56   -24.42    19.94     1.03)  ; 91\r\n              (  107.29   -24.54    20.75     1.18)  ; 92\r\n              (  108.00   -24.72    21.13     0.88)  ; 93\r\n              (  109.06   -25.11    21.25     0.52)  ; 94\r\n              (  109.71   -25.28    21.94     0.52)  ; 95\r\n              (  110.58   -25.42    22.13     0.66)  ; 96\r\n              (  111.20   -25.74    22.13     1.03)  ; 97\r\n              (  111.65   -26.26    22.13     0.59)  ; 98\r\n              (  112.49   -26.54    22.44     0.37)  ; 99\r\n              (  113.12   -26.79    22.44     0.66)  ; 100\r\n              (  113.60   -27.08    22.44     0.66)  ; 101\r\n              (  114.29   -27.93    22.63     0.52)  ; 102\r\n              (  114.71   -28.52    22.94     0.66)  ; 103\r\n              (  114.94   -29.00    23.31     0.96)  ; 104\r\n              (  115.38   -29.44    23.88     1.03)  ; 105\r\n              (  115.66   -29.63    24.44     0.74)  ; 106\r\n              (  116.24   -30.17    24.44     0.52)  ; 107\r\n              (  116.67   -30.76    24.44     0.37)  ; 108\r\n              (  116.54   -31.62    24.44     0.37)  ; 109\r\n               Normal\r\n            |\r\n              (   43.01    -9.20    10.38     0.52)  ; 1, R-1-1-1-2-1-2\r\n              (   43.47    -9.12     8.81     0.59)  ; 2\r\n              (   44.70    -9.32     8.25     0.44)  ; 3\r\n              (   45.94    -9.07     8.00     0.44)  ; 4\r\n              (   46.88    -8.77     8.00     0.66)  ; 5\r\n              (   47.78    -8.77     8.00     0.88)  ; 6\r\n              (   48.57    -8.97     8.06     0.88)  ; 7\r\n              (   49.72    -9.22     8.31     0.44)  ; 8\r\n              (   50.90    -9.78     8.31     0.81)  ; 9\r\n              (   51.70    -9.91     8.31     0.81)  ; 10\r\n              (   52.58   -10.05     8.06     0.66)  ; 11\r\n              (   53.96   -10.27     7.88     0.44)  ; 12\r\n              (   55.11   -10.52     7.88     0.74)  ; 13\r\n              (   55.88   -10.87     7.31     0.96)  ; 14\r\n              (   57.12   -11.44     7.63     1.40)  ; 15\r\n              (   58.06   -11.66     7.81     1.40)  ; 16\r\n              (   59.02   -11.74     8.38     0.96)  ; 17\r\n              (   60.09   -12.05     8.38     0.66)  ; 18\r\n              (   60.60   -12.13     8.38     0.66)  ; 19\r\n              (   61.04   -12.13     8.38     0.66)  ; 20\r\n              (   62.23   -11.72     8.38     0.52)  ; 21\r\n              (   63.48   -11.40     8.38     0.52)  ; 22\r\n              (   64.73   -11.46     8.38     0.52)  ; 23\r\n              (   65.64   -11.38     8.38     0.74)  ; 24\r\n              (   66.92   -11.28     8.38     0.66)  ; 25\r\n              (   68.81   -11.14     8.38     0.81)  ; 26\r\n              (   69.96   -11.39     8.00     1.33)  ; 27\r\n              (   70.58   -10.82     7.69     1.33)  ; 28\r\n              (   71.89   -10.52     7.31     0.96)  ; 29\r\n              (   73.42   -10.31     7.31     0.96)  ; 30\r\n              (   74.60   -10.43     6.25     0.59)  ; 31\r\n              (   75.94   -10.86     7.38     1.62)  ; 32\r\n              (   77.18   -10.79     8.56     1.11)  ; 33\r\n              (   77.99   -10.40     9.56     0.88)  ; 34\r\n              (   79.17   -10.51     9.88     0.66)  ; 35\r\n              (   80.19   -10.23    10.13     0.66)  ; 36\r\n              (   81.46    -9.69    11.31     0.74)  ; 37\r\n              (   82.30    -9.53    13.19     0.74)  ; 38\r\n              (   84.02    -9.14    13.75     0.44)  ; 39\r\n              (   86.06    -8.86    14.13     0.44)  ; 40\r\n              (   87.59    -8.67    14.31     0.74)  ; 41\r\n              (   89.33    -8.50    14.31     1.11)  ; 42\r\n              (   90.42    -8.23    14.75     1.11)  ; 43\r\n              (   91.56    -8.11    14.56     0.59)  ; 44\r\n              (   92.63    -8.35    14.63     0.81)  ; 45\r\n              (   93.90    -8.40    14.63     0.81)  ; 46\r\n              (   95.07    -8.59    15.31     1.40)  ; 47\r\n              (   95.81    -9.08    15.31     1.69)  ; 48\r\n              (   96.91    -9.17    15.31     0.88)  ; 49\r\n              (   98.22    -9.38    15.31     0.59)  ; 50\r\n              (   99.32    -9.48    15.31     0.81)  ; 51\r\n              (  100.51    -9.53    15.31     0.52)  ; 52\r\n              (  101.64    -9.49    15.38     1.03)  ; 53\r\n              (  102.30    -9.51    15.56     0.88)  ; 54\r\n              (  103.56    -9.57    15.56     0.74)  ; 55\r\n              (  104.67    -9.66    15.56     0.96)  ; 56\r\n              (  105.76    -9.77    15.56     0.66)  ; 57\r\n              (  106.67   -10.20    16.31     0.44)  ; 58\r\n              (  107.58   -10.57    16.31     0.74)  ; 59\r\n              (  108.59   -11.25    16.69     0.74)  ; 60\r\n              (  109.62   -11.34    17.06     1.55)  ; 61\r\n              (  110.46   -11.18    17.13     2.21)  ; 62\r\n              (  111.18   -11.36    17.19     1.18)  ; 63\r\n              (  112.72   -11.98    17.25     0.66)  ; 64\r\n              (  113.27   -12.29    17.25     0.66)  ; 65\r\n              (  113.96   -13.14    17.31     0.96)  ; 66\r\n              (  114.43   -13.95    17.31     1.33)  ; 67\r\n              (  114.67   -14.35    17.31     1.62)  ; 68\r\n              (  115.34   -14.76    17.31     0.59)  ; 69\r\n              (  115.84   -15.36    17.31     0.44)  ; 70\r\n              (  117.04   -15.77    17.31     0.44)  ; 71\r\n              (  118.49   -16.07    17.31     0.44)  ; 72\r\n              (  119.69   -16.49    17.69     0.81)  ; 73\r\n              (  120.21   -16.94    17.81     0.59)  ; 74\r\n              (  121.58   -16.79    17.56     0.52)  ; 75\r\n              (  123.03   -17.02    17.75     0.52)  ; 76\r\n              (  124.48   -17.77    18.00     0.74)  ; 77\r\n              (  125.50   -18.37    18.06     0.74)  ; 78\r\n              (  127.36   -18.89    18.38     0.52)  ; 79\r\n              (  128.27   -18.37    18.38     0.74)  ; 80\r\n              (  129.72   -17.64    18.31     0.74)  ; 81\r\n              (  130.48   -17.09    18.31     1.33)  ; 82\r\n              (  130.87   -16.49    18.31     2.06)  ; 83\r\n              (  131.59   -16.15    18.31     2.06)  ; 84\r\n              (  132.55   -15.79    18.31     0.52)  ; 85\r\n              (  133.95   -15.41    17.94     0.52)  ; 86\r\n              (  135.12   -14.64    17.81     0.52)  ; 87\r\n              (  136.05   -14.42    17.81     0.52)  ; 88\r\n              (  136.87   -14.40    17.81     0.52)  ; 89\r\n              (  138.21   -14.90    17.81     0.74)  ; 90\r\n              (  139.92   -14.88    17.88     0.52)  ; 91\r\n              (  140.59   -14.47    17.88     0.52)  ; 92\r\n              (  141.97   -14.24    17.50     1.11)  ; 93\r\n              (  142.93   -14.33    17.00     1.40)  ; 94\r\n              (  144.64   -14.30    16.75     0.59)  ; 95\r\n              (  145.63   -14.68    16.56     0.44)  ; 96\r\n              (  146.96   -15.19    16.13     0.44)  ; 97\r\n               Normal\r\n            )  ;  End of split\r\n          |\r\n            (   41.56   -10.05     9.94     0.74)  ; 1, R-1-1-1-2-2\r\n            (   41.58   -10.86    10.69     0.59)  ; 2\r\n            (   41.47   -11.58    11.38     0.52)  ; 3\r\n            (   41.81   -12.23    11.63     0.37)  ; 4\r\n            (   42.46   -12.85    11.63     0.59)  ; 5\r\n            (   43.17   -13.03    11.75     0.37)  ; 6\r\n            (   44.12   -13.18    11.81     0.37)  ; 7\r\n            (   44.50   -13.62    11.88     0.44)  ; 8\r\n            (   45.09   -14.15    11.88     0.22)  ; 9\r\n            (   45.55   -14.52    11.88     0.44)  ; 10\r\n            (   46.33   -14.79    11.88     0.88)  ; 11\r\n            (   46.97   -15.04    12.06     1.18)  ; 12\r\n            (   47.75   -15.24    12.31     0.96)  ; 13\r\n            (   48.13   -15.23    12.31     0.52)  ; 14\r\n            (   48.95   -15.65    12.31     0.44)  ; 15\r\n            (   49.44   -16.33    12.31     0.44)  ; 16\r\n            (   49.73   -16.89    12.50     0.59)  ; 17\r\n            (   50.18   -17.32    12.50     0.52)  ; 18\r\n            (   50.54   -17.83    12.50     0.52)  ; 19\r\n            (   50.95   -18.11    12.63     0.66)  ; 20\r\n            (   51.38   -18.25    12.63     0.52)  ; 21\r\n            (   52.22   -18.54    12.69     0.52)  ; 22\r\n            (   52.82   -19.08    12.69     0.88)  ; 23\r\n            (   53.43   -19.40    12.69     0.88)  ; 24\r\n            (   53.82   -19.75    12.94     0.44)  ; 25\r\n            (   54.36   -20.13    12.94     0.44)  ; 26\r\n            (   55.32   -21.17    12.94     0.44)  ; 27\r\n            (   55.86   -21.48    12.94     0.74)  ; 28\r\n            (   56.41   -21.79    13.75     0.81)  ; 29\r\n            (   56.77   -21.85    13.75     0.37)  ; 30\r\n            (   57.47   -22.18    13.75     0.37)  ; 31\r\n            (   58.27   -22.31    14.19     0.66)  ; 32\r\n            (   58.21   -21.70    14.94     0.66)  ; 33\r\n            (   58.74   -21.20    15.38     0.44)  ; 34\r\n            (   59.08   -20.96    15.38     0.44)  ; 35\r\n            (   60.03   -21.99    15.63     0.44)  ; 36\r\n            (   60.38   -22.64    15.63     0.44)  ; 37\r\n            (   60.57   -22.83    15.75     0.44)  ; 38\r\n            (   61.20   -22.62    15.81     0.44)  ; 39\r\n            (   61.54   -21.94    15.88     0.44)  ; 40\r\n            (   62.24   -21.68    15.69     0.44)  ; 41\r\n            (   62.53   -21.28    15.50     0.44)  ; 42\r\n            (   62.94   -21.06    17.13     0.74)  ; 43\r\n            (   62.98   -20.84    17.63     1.03)  ; 44\r\n            (   63.33   -20.00    18.44     1.25)  ; 45\r\n            (   63.61   -19.67    19.19     0.88)  ; 46\r\n            (   64.27   -19.78    20.19     0.66)  ; 47\r\n            (   64.99   -19.89    20.31     0.66)  ; 48\r\n            (   65.68   -19.78    20.31     0.59)  ; 49\r\n            (   66.04   -19.84    20.63     0.59)  ; 50\r\n            (   66.87   -20.71    20.88     0.59)  ; 51\r\n            (   66.88   -21.60    20.94     0.59)  ; 52\r\n             Normal\r\n          )  ;  End of split\r\n        )  ;  End of split\r\n      |\r\n        (   34.00   -10.56     7.13     1.03)  ; 1, R-1-1-2\r\n        (   34.26   -11.27     7.00     0.59)  ; 2\r\n        (   34.48   -12.26     7.00     0.59)  ; 3\r\n        (   34.65   -13.03     6.81     0.59)  ; 4\r\n        (\r\n          (   34.58   -13.98     6.69     0.66)  ; 1, R-1-1-2-1\r\n          (   34.10   -15.09     6.31     0.52)  ; 2\r\n          (   34.04   -15.97     6.19     0.74)  ; 3\r\n          (   34.03   -16.93     6.19     0.74)  ; 4\r\n          (   34.12   -17.84     6.19     0.74)  ; 5\r\n          (   34.17   -19.39     6.19     0.52)  ; 6\r\n          (   34.38   -19.87     6.19     0.52)  ; 7\r\n          (   34.34   -20.69     6.19     0.96)  ; 8\r\n          (   34.49   -21.59     6.19     1.40)  ; 9\r\n          (   34.44   -22.40     6.19     0.66)  ; 10\r\n          (   34.49   -23.51     6.13     0.59)  ; 11\r\n          (   34.56   -24.93     6.13     0.59)  ; 12\r\n          (   34.75   -26.08     6.13     0.59)  ; 13\r\n          (   35.48   -27.08     6.13     0.59)  ; 14\r\n          (   35.69   -28.15     6.06     0.88)  ; 15\r\n          (   36.01   -29.01     5.94     0.52)  ; 16\r\n          (   36.38   -29.89     7.81     0.52)  ; 17\r\n          (   36.79   -31.06     7.81     1.11)  ; 18\r\n          (   37.11   -31.85     8.50     1.11)  ; 19\r\n          (   37.51   -32.66     9.00     0.66)  ; 20\r\n          (   38.04   -33.55     9.81     0.66)  ; 21\r\n          (   37.79   -33.74     9.88     0.66)  ; 22\r\n          (   37.86   -34.71    10.44     0.66)  ; 23\r\n          (   38.50   -35.40    11.00     0.81)  ; 24\r\n          (   38.84   -36.50    11.25     0.66)  ; 25\r\n          (   39.66   -37.44    12.06     0.59)  ; 26\r\n          (   40.66   -38.71    12.06     0.44)  ; 27\r\n          (   41.04   -39.54    12.13     0.74)  ; 28\r\n          (   41.37   -40.25    12.25     0.96)  ; 29\r\n          (   41.85   -40.99    12.50     0.37)  ; 30\r\n          (   42.46   -41.38    12.75     0.37)  ; 31\r\n          (   43.28   -42.33    12.69     0.44)  ; 32\r\n          (   44.16   -43.36    12.50     0.59)  ; 33\r\n          (   44.76   -44.34    12.38     0.66)  ; 34\r\n          (   45.44   -45.63    12.00     0.96)  ; 35\r\n          (   45.94   -46.67    12.00     0.96)  ; 36\r\n          (   46.22   -47.31    12.00     0.66)  ; 37\r\n          (   46.78   -48.06    12.00     0.59)  ; 38\r\n          (   47.11   -48.78    11.94     0.66)  ; 39\r\n          (   47.81   -49.56    11.69     0.66)  ; 40\r\n          (   48.72   -50.37    11.69     0.52)  ; 41\r\n          (   49.41   -51.22    11.56     0.44)  ; 42\r\n          (   50.12   -51.92    11.44     0.66)  ; 43\r\n          (   50.63   -52.89    11.44     0.88)  ; 44\r\n          (   51.20   -53.58    11.50     1.40)  ; 45\r\n          (   51.69   -54.24    11.44     1.62)  ; 46\r\n          (   52.39   -55.02    11.44     0.96)  ; 47\r\n          (   53.06   -55.42    11.19     0.59)  ; 48\r\n          (   53.43   -56.00    11.19     0.44)  ; 49\r\n          (   53.91   -56.67    11.19     0.44)  ; 50\r\n          (   54.95   -57.65    11.13     0.44)  ; 51\r\n          (   55.75   -58.29    10.75     0.96)  ; 52\r\n          (   56.38   -58.54    10.75     1.33)  ; 53\r\n          (   56.63   -58.87    10.69     1.33)  ; 54\r\n          (   57.11   -59.55    10.75     0.52)  ; 55\r\n          (   57.70   -60.09    10.00     0.52)  ; 56\r\n          (   58.09   -60.44     9.69     0.74)  ; 57\r\n          (   58.90   -60.50     9.69     0.59)  ; 58\r\n          (   60.25   -60.93     9.63     0.59)  ; 59\r\n          (   60.91   -61.48     9.06     0.81)  ; 60\r\n          (   61.30   -61.84     8.13     0.81)  ; 61\r\n          (   61.76   -62.21     8.06     0.52)  ; 62\r\n          (   62.10   -62.40     7.13     0.37)  ; 63\r\n          (   62.86   -62.83     5.88     0.37)  ; 64\r\n          (   63.60   -63.82     5.88     0.37)  ; 65\r\n          (   64.52   -64.57     5.88     0.37)  ; 66\r\n          (   65.06   -65.39     5.69     0.96)  ; 67\r\n          (   65.67   -65.78     5.31     1.11)  ; 68\r\n          (   66.19   -66.24     4.88     0.66)  ; 69\r\n          (   66.80   -66.79     3.06     0.29)  ; 70\r\n          (   67.51   -67.49     2.75     0.29)  ; 71\r\n          (   67.95   -68.52     2.44     0.29)  ; 72\r\n          (   68.16   -69.52     4.06     0.29)  ; 73\r\n          (   68.24   -70.87     5.06     0.29)  ; 74\r\n          (   68.30   -71.91     5.06     0.29)  ; 75\r\n          (   68.07   -72.91     5.06     0.52)  ; 76\r\n          (   67.93   -73.33     5.06     0.81)  ; 77\r\n          (   67.79   -73.75     5.00     1.47)  ; 78\r\n          (   67.52   -74.45     4.63     1.11)  ; 79\r\n          (   67.32   -74.86     4.63     0.66)  ; 80\r\n          (   67.29   -75.90     4.50     0.44)  ; 81\r\n          (   67.39   -76.73     4.38     0.44)  ; 82\r\n          (   67.59   -77.79     4.38     0.66)  ; 83\r\n          (   67.48   -78.51     4.69     0.44)  ; 84\r\n          (   67.28   -79.75     5.00     0.44)  ; 85\r\n          (   67.52   -80.60     5.06     0.74)  ; 86\r\n          (   67.92   -81.40     5.06     1.33)  ; 87\r\n          (   68.48   -82.08     5.25     1.47)  ; 88\r\n          (   68.93   -82.59     5.25     0.96)  ; 89\r\n          (   69.27   -83.24     5.25     0.59)  ; 90\r\n          (   69.47   -83.86     5.81     0.37)  ; 91\r\n          (   70.51   -84.84     5.63     0.37)  ; 92\r\n          (   71.01   -85.44     5.63     0.44)  ; 93\r\n          (   71.14   -86.05     5.31     1.18)  ; 94\r\n          (   71.42   -86.62     5.25     1.18)  ; 95\r\n          (   71.33   -87.19     5.13     0.44)  ; 96\r\n          (   71.78   -88.24     4.25     0.29)  ; 97\r\n          (   71.79   -89.13     4.25     0.29)  ; 98\r\n          (   71.95   -89.96     4.25     0.59)  ; 99\r\n          (   71.84   -90.69     4.25     1.40)  ; 100\r\n          (   71.80   -91.43     4.25     1.69)  ; 101\r\n          (   72.00   -92.05     4.00     0.59)  ; 102\r\n          (   71.88   -92.77     3.44     0.44)  ; 103\r\n          (   71.73   -93.71     3.00     0.44)  ; 104\r\n          (   71.61   -94.51     2.44     0.74)  ; 105\r\n          (   71.44   -95.14     2.44     1.11)  ; 106\r\n          (   71.00   -95.96     2.13     1.77)  ; 107\r\n          (   70.88   -96.76     1.81     0.81)  ; 108\r\n          (   70.83   -97.56     1.81     0.44)  ; 109\r\n          (   70.81   -98.59     1.75     0.44)  ; 110\r\n          (   70.75   -98.96     1.63     0.66)  ; 111\r\n          (   70.49   -99.72     1.63     0.44)  ; 112\r\n          (   70.41  -100.67     1.63     0.74)  ; 113\r\n          (   70.03  -101.21     1.44     1.40)  ; 114\r\n          (   69.84  -101.92     1.38     0.88)  ; 115\r\n          (   69.44  -102.60     0.75     0.52)  ; 116\r\n          (   68.81  -103.23    -0.81     0.52)  ; 117\r\n          (   67.78  -104.10    -1.56     0.66)  ; 118\r\n           Normal\r\n        |\r\n          (   35.20   -13.74     6.63     0.59)  ; 1, R-1-1-2-2\r\n          (   35.56   -14.32     6.38     0.52)  ; 2\r\n          (   35.59   -15.06     6.38     0.81)  ; 3\r\n          (   35.76   -15.83     6.13     0.52)  ; 4\r\n          (   36.17   -16.56     6.00     0.52)  ; 5\r\n          (   36.91   -17.56     5.69     0.66)  ; 6\r\n          (   38.30   -18.67     5.56     0.66)  ; 7\r\n          (   39.01   -19.38     5.44     0.66)  ; 8\r\n          (   39.63   -20.22     5.00     0.52)  ; 9\r\n          (   39.96   -20.93     6.50     0.66)  ; 10\r\n          (   40.34   -21.81     6.63     0.66)  ; 11\r\n          (   40.75   -22.54     6.75     0.44)  ; 12\r\n          (   41.26   -23.06     6.81     0.74)  ; 13\r\n          (   42.04   -23.33     6.94     1.18)  ; 14\r\n          (   43.17   -23.22     7.44     0.88)  ; 15\r\n          (   44.37   -23.70     7.44     0.74)  ; 16\r\n          (   45.19   -24.13     7.69     0.59)  ; 17\r\n          (   45.25   -24.73     8.00     0.52)  ; 18\r\n          (   44.38   -25.49     8.69     0.52)  ; 19\r\n          (   44.39   -25.92     8.81     0.59)  ; 20\r\n          (   44.59   -26.55     9.25     0.59)  ; 21\r\n          (   45.15   -27.23     9.50     0.59)  ; 22\r\n          (   46.01   -27.89    10.06     0.44)  ; 23\r\n          (   46.60   -28.42    10.38     0.59)  ; 24\r\n          (   46.93   -28.69    10.75     0.96)  ; 25\r\n          (   47.32   -29.06    11.06     1.33)  ; 26\r\n          (   47.46   -29.60    11.38     1.11)  ; 27\r\n          (   47.59   -30.21    11.69     0.81)  ; 28\r\n          (   47.77   -30.90    12.88     0.52)  ; 29\r\n          (   48.00   -31.83    13.44     0.52)  ; 30\r\n          (   48.66   -32.38    13.69     0.66)  ; 31\r\n          (   49.21   -33.13    13.75     0.66)  ; 32\r\n          (   49.91   -33.46    13.75     0.66)  ; 33\r\n          (   50.32   -34.64    14.69     0.81)  ; 34\r\n          (   50.61   -35.65    14.81     0.52)  ; 35\r\n          (   51.21   -36.04    14.81     0.52)  ; 36\r\n          (   51.75   -36.86    14.88     0.81)  ; 37\r\n          (   52.38   -37.18    15.31     0.52)  ; 38\r\n          (   52.93   -37.94    15.56     1.18)  ; 39\r\n          (   53.36   -38.52    16.00     1.18)  ; 40\r\n          (   53.82   -39.33    16.00     0.59)  ; 41\r\n          (   54.71   -40.37    16.38     0.81)  ; 42\r\n          (   55.21   -40.96    16.56     0.52)  ; 43\r\n          (   55.76   -41.72    16.56     0.44)  ; 44\r\n          (   56.21   -42.68    16.94     0.44)  ; 45\r\n          (   56.65   -43.63    17.31     1.11)  ; 46\r\n          (   56.93   -44.27    17.56     1.11)  ; 47\r\n          (   58.09   -44.90    17.63     0.74)  ; 48\r\n          (   59.66   -44.93    17.63     0.59)  ; 49\r\n          (   60.75   -44.58    17.69     0.44)  ; 50\r\n          (   62.00   -44.70    17.75     0.66)  ; 51\r\n          (   62.64   -44.88    17.88     1.03)  ; 52\r\n          (   63.33   -45.28    18.06     1.40)  ; 53\r\n          (   64.12   -45.93    18.19     0.74)  ; 54\r\n          (   65.20   -46.61    18.25     0.59)  ; 55\r\n          (   66.67   -46.78    18.69     0.59)  ; 56\r\n          (   68.01   -46.77    19.06     0.59)  ; 57\r\n          (   69.22   -46.66    19.81     0.74)  ; 58\r\n          (   69.82   -47.12    20.63     0.74)  ; 59\r\n          (   70.72   -47.57    21.13     0.59)  ; 60\r\n          (   71.75   -48.17    21.25     0.59)  ; 61\r\n          (   72.62   -48.75    21.44     0.74)  ; 62\r\n          (   73.84   -49.10    20.94     0.66)  ; 63\r\n          (   74.89   -49.48    20.69     0.59)  ; 64\r\n          (   75.74   -49.84    21.69     0.81)  ; 65\r\n          (   76.50   -50.19    21.81     0.52)  ; 66\r\n          (   77.49   -51.00    21.63     0.52)  ; 67\r\n          (   78.49   -51.24    21.56     0.52)  ; 68\r\n          (   79.73   -50.99    21.63     0.52)  ; 69\r\n          (   81.13   -51.06    21.63     0.52)  ; 70\r\n          (   82.31   -50.73    21.63     0.74)  ; 71\r\n          (   83.02   -50.48    21.63     0.74)  ; 72\r\n          (   83.96   -50.63    21.63     0.52)  ; 73\r\n          (   84.94   -51.03    21.69     0.52)  ; 74\r\n          (   85.67   -51.59    21.69     0.37)  ; 75\r\n          (   85.69   -51.95    21.69     0.37)  ; 76\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (   32.50    -8.17     7.44     1.25)  ; 1, R-1-2\r\n      (   33.37    -7.87     7.75     0.66)  ; 2\r\n      (   34.23    -7.64     7.81     0.37)  ; 3\r\n      (   35.55    -7.25     7.81     0.37)  ; 4\r\n      (   37.02    -6.97     8.56     0.59)  ; 5\r\n      (   37.86    -6.81     8.69     0.59)  ; 6\r\n      (   38.89    -6.45     8.69     0.59)  ; 7\r\n      (   39.59    -6.26     8.63     0.52)  ; 8\r\n      (   40.64    -5.32     8.69     0.66)  ; 9\r\n      (   41.67    -4.44     8.63     0.74)  ; 10\r\n      (   42.58    -3.41     8.88     0.59)  ; 11\r\n      (   43.73    -2.26     9.13     0.66)  ; 12\r\n      (   44.63    -1.29     9.19     0.59)  ; 13\r\n      (   45.46    -0.23     9.19     0.59)  ; 14\r\n      (   46.32     1.85     9.56     0.52)  ; 15\r\n      (   46.89     2.65     9.63     0.81)  ; 16\r\n      (   47.43     4.11     9.63     0.59)  ; 17\r\n      (   47.73     5.11     9.63     1.18)  ; 18\r\n      (   48.47     6.02     9.88     0.88)  ; 19\r\n      (   49.15     7.46     9.81     0.74)  ; 20\r\n      (   49.98     8.96     9.56     0.66)  ; 21\r\n      (   50.62     9.68     9.44     0.66)  ; 22\r\n      (   51.73    10.54     9.00     0.66)  ; 23\r\n      (   53.02    11.66     8.81     0.59)  ; 24\r\n      (   55.11    13.04     8.56     0.81)  ; 25\r\n      (   57.09    14.28     8.25     0.88)  ; 26\r\n      (   58.38    14.89     8.06     0.66)  ; 27\r\n      (   59.58    15.36     8.06     0.81)  ; 28\r\n      (   61.50    16.17     8.19     0.59)  ; 29\r\n      (   62.49    16.39     8.19     0.88)  ; 30\r\n      (   64.14    17.30     8.13     0.81)  ; 31\r\n      (   65.08    17.99     7.75     0.96)  ; 32\r\n      (   66.51    19.02     7.50     0.96)  ; 33\r\n      (   67.73    19.64     7.25     1.03)  ; 34\r\n      (   68.29    20.37     7.31     0.37)  ; 35\r\n      (   69.03    21.73     8.06     0.37)  ; 36\r\n      (   69.49    22.25     8.56     0.37)  ; 37\r\n      (   69.99    23.06     9.00     0.37)  ; 38\r\n      (   70.06    24.45     9.38     0.59)  ; 39\r\n      (   70.03    25.71     9.69     1.18)  ; 40\r\n      (   70.20    26.73    10.06     1.03)  ; 41\r\n      (   70.92    28.02    10.19     0.81)  ; 42\r\n      (   71.33    28.70    10.31     0.66)  ; 43\r\n      (   71.58    29.84    10.06     0.88)  ; 44\r\n      (   72.50    30.50    10.06     0.88)  ; 45\r\n      (   72.96    31.03    11.44     0.59)  ; 46\r\n      (   73.83    31.77    11.50     0.74)  ; 47\r\n      (   74.63    32.61    11.63     0.74)  ; 48\r\n      (   75.46    33.14    12.13     0.52)  ; 49\r\n      (   76.46    33.81    12.44     0.52)  ; 50\r\n      (   77.12    34.66    12.94     0.59)  ; 51\r\n      (   77.84    35.43    13.56     0.59)  ; 52\r\n      (   78.38    36.53    13.75     0.59)  ; 53\r\n      (   78.60    37.39    13.75     1.18)  ; 54\r\n      (   79.00    38.06    14.31     1.40)  ; 55\r\n      (   79.51    38.94    14.31     0.74)  ; 56\r\n      (   80.22    40.08    14.31     0.59)  ; 57\r\n      (   81.51    40.70    14.50     0.44)  ; 58\r\n      (   82.40    41.15    14.56     0.66)  ; 59\r\n      (   83.13    41.55    14.13     0.96)  ; 60\r\n      (   83.92    41.89    13.38     1.03)  ; 61\r\n      (   85.26    42.34    13.25     0.74)  ; 62\r\n      (   86.11    43.46    13.25     0.59)  ; 63\r\n      (   87.34    44.23    13.06     0.59)  ; 64\r\n      (   88.40    45.18    12.88     0.59)  ; 65\r\n      (   88.97    45.98    12.88     0.81)  ; 66\r\n      (   89.39    46.79    11.50     1.11)  ; 67\r\n      (   89.90    47.67    11.25     0.74)  ; 68\r\n      (   90.66    48.67    11.25     0.44)  ; 69\r\n      (   91.01    49.50    10.25     0.66)  ; 70\r\n      (   91.28    50.20     9.81     0.66)  ; 71\r\n      (   91.64    50.66     9.69     0.66)  ; 72\r\n      (   93.35    51.50     8.44     0.88)  ; 73\r\n      (   93.80    51.57     8.44     0.59)  ; 74\r\n      (   94.91    51.48     7.25     0.88)  ; 75\r\n      (   95.66    51.05     6.75     1.18)  ; 76\r\n      (   96.63    50.09     5.94     0.81)  ; 77\r\n      (   97.37    51.01     5.13     0.44)  ; 78\r\n      (   98.24    51.76     5.06     0.44)  ; 79\r\n      (   99.41    51.64     5.06     1.25)  ; 80\r\n      (  100.24    52.11     4.63     2.50)  ; 81\r\n      (  101.41    52.51     3.13     2.50)  ; 82\r\n      (  101.74    52.68     1.75     2.14)  ; 83\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (   14.85    -4.18     8.00     0.88)  ; 1, R-2\r\n    (   15.41    -4.42     7.19     0.96)  ; 2\r\n    (   16.82    -4.87     6.13     1.25)  ; 3\r\n    (   17.19    -5.37     4.81     1.99)  ; 4\r\n    (   17.90    -6.15     3.06     1.47)  ; 5\r\n    (   18.90    -6.39     2.50     2.06)  ; 6\r\n    (\r\n      (   19.23    -5.25     1.44     2.21)  ; 1, R-2-1\r\n      (   19.96    -4.41     1.06     1.33)  ; 2\r\n      (   20.40    -3.95     1.00     1.11)  ; 3\r\n      (   20.97    -3.68     1.00     1.03)  ; 4\r\n      (   21.79    -3.21     0.81     0.74)  ; 5\r\n      (   22.61    -3.20     0.81     0.96)  ; 6\r\n      (   23.47    -2.96     0.56     1.18)  ; 7\r\n      (   24.15    -2.92     0.25     1.11)  ; 8\r\n      (   25.08    -2.70    -0.44     0.96)  ; 9\r\n      (   26.00    -2.55    -0.63     1.11)  ; 10\r\n      (   26.87    -2.24    -1.19     1.25)  ; 11\r\n      (   28.07    -2.21    -1.44     1.11)  ; 12\r\n      (   29.05    -2.07    -1.81     1.33)  ; 13\r\n      (   30.04    -2.00    -1.81     1.77)  ; 14\r\n      (   31.01    -2.01    -2.94     1.99)  ; 15\r\n      (\r\n        (   31.84    -2.36    -3.44     0.96)  ; 1, R-2-1-1\r\n        (   32.72    -2.50    -3.13     0.66)  ; 2\r\n        (   33.26    -2.36    -2.69     0.74)  ; 3\r\n        (   34.32    -2.24    -2.44     0.81)  ; 4\r\n        (   34.97    -2.41    -2.25     0.88)  ; 5\r\n        (   35.71    -2.46    -2.25     0.88)  ; 6\r\n        (   36.54    -2.37    -2.25     0.81)  ; 7\r\n        (   37.38    -2.72    -2.19     0.81)  ; 8\r\n        (   38.20    -3.22    -1.94     0.88)  ; 9\r\n        (   39.48    -3.64    -1.94     1.11)  ; 10\r\n        (   40.29    -4.14    -2.31     0.96)  ; 11\r\n        (   41.12    -4.57    -3.31     1.11)  ; 12\r\n        (   41.73    -4.96    -3.88     1.18)  ; 13\r\n        (   42.18    -5.41    -3.88     0.96)  ; 14\r\n        (   42.55    -5.91    -4.50     0.81)  ; 15\r\n        (   43.28    -6.47    -4.94     0.88)  ; 16\r\n        (   44.29    -6.99    -5.19     1.18)  ; 17\r\n        (   45.56    -7.05    -4.81     0.88)  ; 18\r\n        (   46.68    -7.01    -3.81     1.03)  ; 19\r\n        (   48.08    -7.08    -3.75     1.03)  ; 20\r\n        (   49.09    -7.31    -3.44     0.81)  ; 21\r\n        (   50.39    -7.60    -2.75     0.74)  ; 22\r\n        (   51.58    -8.08    -2.75     0.59)  ; 23\r\n        (   52.67    -8.32    -1.88     0.59)  ; 24\r\n        (   53.40    -8.37    -1.44     0.74)  ; 25\r\n        (   54.31    -8.29    -1.19     0.74)  ; 26\r\n        (   55.14    -8.27    -1.13     0.96)  ; 27\r\n        (   56.16    -8.36    -0.88     0.96)  ; 28\r\n        (   57.09    -8.66    -0.81     0.96)  ; 29\r\n        (   58.15    -8.97    -0.69     0.96)  ; 30\r\n        (   59.75    -8.79    -0.63     0.96)  ; 31\r\n        (   60.62    -8.92    -0.19     1.11)  ; 32\r\n        (   61.94    -8.60     0.31     1.03)  ; 33\r\n        (   63.01    -8.48     1.06     0.96)  ; 34\r\n        (   63.79    -8.76     1.25     0.74)  ; 35\r\n        (   64.63    -9.03     1.75     0.44)  ; 36\r\n        (   65.81    -9.15     1.75     0.74)  ; 37\r\n        (   66.54    -9.26     1.88     1.25)  ; 38\r\n        (   67.19    -9.37     1.94     1.55)  ; 39\r\n        (   67.71    -9.38     2.38     1.69)  ; 40\r\n        (   68.78    -9.69     2.06     0.88)  ; 41\r\n        (   69.68    -9.61     2.00     0.81)  ; 42\r\n        (   70.76    -9.93     0.88     0.81)  ; 43\r\n        (   71.67   -10.30     0.19     0.88)  ; 44\r\n        (   72.86   -10.34    -0.44     0.88)  ; 45\r\n        (   74.32   -10.50    -0.81     0.88)  ; 46\r\n        (   75.58   -10.54    -0.94     0.96)  ; 47\r\n        (   76.31   -10.66    -1.00     1.11)  ; 48\r\n        (   77.26   -10.81    -1.19     0.96)  ; 49\r\n        (   78.18   -11.11    -0.88     0.66)  ; 50\r\n        (   79.11   -11.33    -0.88     0.88)  ; 51\r\n        (   80.01   -11.77    -0.88     1.25)  ; 52\r\n        (   81.01   -11.63    -0.63     1.40)  ; 53\r\n        (   82.17   -11.81    -0.75     1.03)  ; 54\r\n        (   83.31   -11.70    -1.00     0.81)  ; 55\r\n        (   84.67   -12.06    -1.13     0.81)  ; 56\r\n        (   85.58   -12.43    -1.38     1.03)  ; 57\r\n        (   86.44   -12.64    -1.56     0.96)  ; 58\r\n        (   87.61   -13.05    -1.63     1.03)  ; 59\r\n        (   88.53   -12.90    -1.75     1.25)  ; 60\r\n        (   89.47   -13.12    -1.63     0.74)  ; 61\r\n        (   90.52   -13.06    -1.63     0.74)  ; 62\r\n        (   91.93   -13.14    -1.63     0.88)  ; 63\r\n        (   93.01   -13.31    -1.63     0.88)  ; 64\r\n        (   94.13   -13.79    -1.44     1.03)  ; 65\r\n        (   95.46   -13.41    -1.50     0.81)  ; 66\r\n        (   96.66   -13.38    -1.63     0.96)  ; 67\r\n        (   97.58   -13.66    -1.63     1.47)  ; 68\r\n        (   98.14   -13.90    -1.63     1.47)  ; 69\r\n        (   98.91   -13.80    -1.63     1.18)  ; 70\r\n        (   99.79   -13.87    -2.00     0.88)  ; 71\r\n        (  100.44   -13.97    -2.00     0.52)  ; 72\r\n        (  101.09   -14.15    -2.00     0.52)  ; 73\r\n        (  102.14   -14.54    -2.25     0.74)  ; 74\r\n        (  102.97   -15.03    -2.13     0.74)  ; 75\r\n        (  103.93   -15.56    -2.13     0.37)  ; 76\r\n        (  104.60   -15.96    -2.81     0.59)  ; 77\r\n        (  105.47   -16.18    -3.19     0.59)  ; 78\r\n        (  106.31   -16.45    -2.44     1.03)  ; 79\r\n        (  106.95   -16.71    -2.31     1.33)  ; 80\r\n        (  108.55   -16.96    -2.31     1.47)  ; 81\r\n        (  109.55   -17.26    -2.31     1.33)  ; 82\r\n        (  110.52   -17.72    -2.31     1.11)  ; 83\r\n        (  112.31   -18.59    -2.75     0.81)  ; 84\r\n        (  113.07   -19.00    -3.06     0.66)  ; 85\r\n        (  113.67   -19.47    -3.06     0.66)  ; 86\r\n        (  114.42   -19.95    -2.38     0.74)  ; 87\r\n        (  115.38   -20.48    -2.38     0.37)  ; 88\r\n        (  116.11   -21.04    -2.38     0.59)  ; 89\r\n        (  116.92   -21.54    -2.38     0.66)  ; 90\r\n        (  117.51   -22.07    -2.38     0.66)  ; 91\r\n        (  118.76   -22.65    -2.00     0.59)  ; 92\r\n        (  120.21   -22.95    -2.00     0.52)  ; 93\r\n        (  120.72   -23.47    -1.25     0.52)  ; 94\r\n        (  121.51   -24.12    -0.81     0.74)  ; 95\r\n        (  122.31   -24.69    -0.56     0.96)  ; 96\r\n        (  122.77   -25.13    -0.31     1.03)  ; 97\r\n        (  123.36   -25.15    -0.13     0.37)  ; 98\r\n        (  124.29   -25.45    -0.13     0.37)  ; 99\r\n        (  125.30   -25.68     0.06     0.37)  ; 100\r\n        (  126.24   -25.83     0.19     0.66)  ; 101\r\n        (  127.47   -26.10    -0.25     0.66)  ; 102\r\n        (  128.86   -26.24    -0.13     0.59)  ; 103\r\n        (  129.97   -26.23    -0.06     0.74)  ; 104\r\n        (  130.75   -26.44     0.00     1.33)  ; 105\r\n        (  131.51   -26.41     0.00     1.03)  ; 106\r\n        (  132.62   -26.43     0.06     0.66)  ; 107\r\n        (  133.76   -25.80     0.06     0.52)  ; 108\r\n        (  134.93   -25.54     0.31     0.52)  ; 109\r\n        (  135.87   -24.73     0.31     0.44)  ; 110\r\n        (  136.13   -24.10     0.06     0.66)  ; 111\r\n        (  136.24   -23.38    -0.13     0.81)  ; 112\r\n        (  136.74   -23.01    -0.75     0.59)  ; 113\r\n        (  136.88   -22.16    -1.63     0.44)  ; 114\r\n        (  137.15   -21.38    -2.13     0.44)  ; 115\r\n         Normal\r\n      |\r\n        (   31.59    -1.64    -3.69     0.88)  ; 1, R-2-1-2\r\n        (   32.56    -1.21    -4.50     0.88)  ; 2\r\n        (   33.13    -0.85    -6.44     1.25)  ; 3\r\n        (   33.83    -0.74    -7.38     1.33)  ; 4\r\n        (   33.75    -0.28    -8.69     1.18)  ; 5\r\n        (   33.21    -0.35    -9.75     1.40)  ; 6\r\n        (   32.87    -0.21   -10.38     1.40)  ; 7\r\n        (   32.51    -0.16   -11.69     1.18)  ; 8\r\n        (   32.01    -0.45   -12.63     1.18)  ; 9\r\n        (   32.00    -0.52   -14.44     1.03)  ; 10\r\n        (   32.62    -0.33   -14.88     1.03)  ; 11\r\n        (   32.94     0.21   -15.75     0.74)  ; 12\r\n        (   33.04     0.42   -16.75     0.74)  ; 13\r\n        (   33.25    -0.13   -18.25     0.52)  ; 14\r\n        (   33.77    -0.58   -18.81     0.52)  ; 15\r\n        (   34.31    -0.97   -19.06     0.66)  ; 16\r\n        (   35.05    -1.01   -19.88     1.25)  ; 17\r\n        (   35.26    -0.67   -20.56     1.25)  ; 18\r\n        (   35.58    -0.50   -21.50     0.88)  ; 19\r\n        (   35.60    -0.35   -23.88     0.81)  ; 20\r\n        (   35.97    -0.86   -25.31     0.81)  ; 21\r\n        (   36.81    -0.70   -25.69     0.74)  ; 22\r\n        (   37.73    -0.10   -25.69     0.74)  ; 23\r\n        (   38.57     0.06   -26.13     0.52)  ; 24\r\n        (   39.09    -0.46   -26.44     0.74)  ; 25\r\n        (   39.32    -1.39   -27.25     0.74)  ; 26\r\n        (   39.52    -2.02   -28.19     0.59)  ; 27\r\n        (   39.58    -2.54   -28.94     0.81)  ; 28\r\n        (   39.85    -3.17   -29.94     0.96)  ; 29\r\n        (   39.99    -3.71   -31.13     0.81)  ; 30\r\n        (   40.64    -4.34   -31.63     0.81)  ; 31\r\n        (   40.14    -4.70   -32.81     0.88)  ; 32\r\n        (   39.89    -5.33   -33.88     0.74)  ; 33\r\n        (   39.40    -6.07   -34.94     0.96)  ; 34\r\n        (   39.57    -6.90   -36.25     1.18)  ; 35\r\n        (   39.86    -7.84   -36.81     0.88)  ; 36\r\n        (   40.15    -7.89   -38.31     0.81)  ; 37\r\n        (   41.35    -7.41   -38.19     0.81)  ; 38\r\n        (   41.75    -6.73   -38.50     0.81)  ; 39\r\n        (   42.37    -6.16   -38.94     0.81)  ; 40\r\n        (   42.68    -6.06   -40.44     0.81)  ; 41\r\n        (   42.80    -5.79   -42.63     0.81)  ; 42\r\n        (   42.76    -5.56   -43.69     1.18)  ; 43\r\n        (   42.26    -5.85   -44.25     1.18)  ; 44\r\n        (   42.18    -5.91   -45.75     0.88)  ; 45\r\n        (   41.90    -6.31   -47.06     0.88)  ; 46\r\n        (   41.71    -7.02   -47.50     0.88)  ; 47\r\n        (   41.24    -7.17   -48.69     0.88)  ; 48\r\n        (   40.37    -7.03   -49.19     0.74)  ; 49\r\n        (   40.03    -6.83   -50.38     0.74)  ; 50\r\n        (   39.73    -6.86   -49.56     0.52)  ; 51\r\n        (   38.46    -7.33   -50.00     0.52)  ; 52\r\n        (   37.89    -8.05   -50.25     0.52)  ; 53\r\n        (   37.35    -9.15   -50.25     1.03)  ; 54\r\n        (   36.68    -9.63   -50.31     1.03)  ; 55\r\n        (   36.21   -10.22   -50.88     0.66)  ; 56\r\n        (   34.79   -11.18   -51.25     0.37)  ; 57\r\n        (   33.96   -12.16   -51.25     0.66)  ; 58\r\n        (   33.65   -12.70   -51.75     0.88)  ; 59\r\n        (   32.79   -13.90   -51.75     0.88)  ; 60\r\n        (   32.32   -14.49   -51.75     0.59)  ; 61\r\n        (   31.60   -15.71   -51.69     0.37)  ; 62\r\n        (   31.38   -16.19   -51.69     0.66)  ; 63\r\n        (   30.71   -17.12   -51.69     0.66)  ; 64\r\n        (   29.81   -18.09   -51.75     0.22)  ; 65\r\n        (   28.46   -19.57   -51.88     0.22)  ; 66\r\n        (   27.59   -20.84   -52.75     0.22)  ; 67\r\n        (   26.49   -22.08   -52.75     0.52)  ; 68\r\n        (   25.98   -22.51   -53.69     0.88)  ; 69\r\n        (   25.53   -22.97   -53.88     1.18)  ; 70\r\n        (   25.17   -23.43   -54.00     0.74)  ; 71\r\n        (   23.85   -24.18   -54.00     0.37)  ; 72\r\n        (   23.22   -24.82   -54.81     0.66)  ; 73\r\n        (   22.35   -25.64   -56.00     0.81)  ; 74\r\n        (   22.09   -25.89   -57.50     0.52)  ; 75\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (   18.71    -7.16     2.25     1.40)  ; 1, R-2-2\r\n      (   18.67    -8.85     2.50     0.96)  ; 2\r\n      (   18.60   -10.69     2.88     0.88)  ; 3\r\n      (   18.68   -12.11     2.56     0.96)  ; 4\r\n      (   18.84   -12.95     2.19     0.96)  ; 5\r\n      (   19.21   -13.90     2.00     0.74)  ; 6\r\n      (   19.57   -14.99     2.00     0.59)  ; 7\r\n      (   19.73   -15.39     2.00     0.96)  ; 8\r\n      (   20.12   -16.19     1.75     0.96)  ; 9\r\n      (   20.27   -17.10     1.81     0.59)  ; 10\r\n      (   20.63   -18.12     1.56     0.52)  ; 11\r\n      (   20.86   -19.04     1.25     0.74)  ; 12\r\n      (   21.02   -20.40     1.13     0.88)  ; 13\r\n      (   21.27   -21.18     1.00     0.88)  ; 14\r\n      (   21.47   -22.24     0.88     0.74)  ; 15\r\n      (   21.73   -23.91     0.63     0.88)  ; 16\r\n      (   21.97   -25.21     0.63     0.81)  ; 17\r\n      (   22.67   -26.88     0.63     0.74)  ; 18\r\n      (   23.15   -27.36     1.00     0.74)  ; 19\r\n      (   23.83   -28.21     1.13     0.74)  ; 20\r\n      (   24.35   -28.73     1.25     0.74)  ; 21\r\n      (   24.74   -29.98     1.44     0.96)  ; 22\r\n      (   25.28   -31.25     1.44     1.25)  ; 23\r\n      (   25.57   -31.81     1.44     1.55)  ; 24\r\n      (   25.87   -32.30     1.44     1.18)  ; 25\r\n      (   26.49   -33.07     1.44     0.81)  ; 26\r\n      (   27.38   -34.02     1.75     0.74)  ; 27\r\n      (   27.91   -34.92     2.00     0.66)  ; 28\r\n      (   27.81   -36.53     2.00     0.81)  ; 29\r\n      (   27.78   -38.09     1.81     0.81)  ; 30\r\n      (   27.64   -39.02     1.38     0.81)  ; 31\r\n      (   27.62   -40.05     1.00     1.33)  ; 32\r\n      (   27.75   -41.11     1.25     0.96)  ; 33\r\n      (   28.21   -42.00     1.31     0.66)  ; 34\r\n      (   28.49   -42.56     1.44     0.66)  ; 35\r\n      (   28.84   -43.20     1.44     0.88)  ; 36\r\n      (   29.24   -44.45     1.44     0.74)  ; 37\r\n      (   29.59   -45.55     1.56     0.74)  ; 38\r\n      (   29.93   -46.64     1.50     0.88)  ; 39\r\n      (   30.45   -47.68     1.50     0.88)  ; 40\r\n      (   30.81   -48.63     1.31     0.88)  ; 41\r\n      (   31.34   -49.52     1.19     1.03)  ; 42\r\n      (   32.06   -50.68     0.94     1.03)  ; 43\r\n      (   32.64   -51.73     1.56     1.40)  ; 44\r\n      (   32.99   -52.75     1.25     1.84)  ; 45\r\n      (   33.51   -53.27     2.00     1.40)  ; 46\r\n      (   33.92   -54.00     2.44     0.74)  ; 47\r\n      (   34.58   -54.48     2.44     0.52)  ; 48\r\n      (   35.22   -55.17     2.69     0.74)  ; 49\r\n      (   35.83   -56.09     1.94     0.96)  ; 50\r\n      (   36.33   -57.57     1.94     1.11)  ; 51\r\n      (   36.70   -58.65     1.75     0.74)  ; 52\r\n      (   36.98   -59.67     1.75     0.96)  ; 53\r\n      (   37.27   -60.68     1.56     0.96)  ; 54\r\n      (   37.52   -61.89     1.56     0.59)  ; 55\r\n      (   36.46   -62.99     2.38     1.25)  ; 56\r\n      (   36.26   -63.77     2.63     1.33)  ; 57\r\n      (   35.93   -64.38     3.25     0.59)  ; 58\r\n      (   35.90   -65.12     3.63     0.59)  ; 59\r\n      (   35.94   -65.79     3.75     0.59)  ; 60\r\n      (   36.08   -67.22     4.00     0.74)  ; 61\r\n      (   35.88   -68.00     4.00     0.59)  ; 62\r\n      (   36.11   -68.93     3.06     1.18)  ; 63\r\n      (   36.21   -69.24     2.50     1.69)  ; 64\r\n      (   36.22   -69.68     1.69     1.33)  ; 65\r\n      (   35.91   -70.22     2.44     0.59)  ; 66\r\n      (   36.11   -70.78     0.94     0.44)  ; 67\r\n      (   36.12   -71.66     0.25     0.66)  ; 68\r\n      (   36.24   -72.87    -0.81     0.66)  ; 69\r\n      (   36.10   -74.18    -0.88     0.52)  ; 70\r\n      (   36.41   -75.49    -0.75     0.52)  ; 71\r\n      (   36.48   -76.53    -0.38     1.11)  ; 72\r\n      (   36.43   -77.27    -0.31     1.40)  ; 73\r\n      (   36.52   -78.09     0.06     0.88)  ; 74\r\n      (   36.40   -78.89     0.38     0.59)  ; 75\r\n      (   36.12   -80.18     0.50     0.44)  ; 76\r\n      (   35.93   -80.89     1.31     0.44)  ; 77\r\n      (   35.89   -81.62     1.88     0.52)  ; 78\r\n      (   35.43   -82.66     2.63     0.59)  ; 79\r\n      (   35.16   -83.36     3.13     0.59)  ; 80\r\n      (   34.99   -84.00     3.81     0.88)  ; 81\r\n      (   34.58   -84.74     4.00     1.62)  ; 82\r\n      (   34.41   -85.57     4.25     1.25)  ; 83\r\n      (   33.89   -86.45     4.50     0.66)  ; 84\r\n      (   33.41   -87.11     4.75     0.66)  ; 85\r\n      (   32.87   -88.21     6.25     0.81)  ; 86\r\n      (   33.12   -88.99     7.25     0.74)  ; 87\r\n      (   33.53   -89.72     7.56     0.74)  ; 88\r\n      (   33.88   -90.29     7.63     0.52)  ; 89\r\n      (   34.36   -91.04     7.69     0.66)  ; 90\r\n      (   34.80   -92.07     7.69     0.66)  ; 91\r\n      (   35.54   -92.55     7.69     1.25)  ; 92\r\n      (   36.04   -93.15     7.69     1.25)  ; 93\r\n      (   36.45   -93.81     7.88     0.66)  ; 94\r\n      (   37.00   -94.63     8.00     0.44)  ; 95\r\n      (   36.26   -95.99     7.25     0.66)  ; 96\r\n      (   35.72   -97.02     7.38     0.88)  ; 97\r\n      (   35.25   -97.69     7.31     0.52)  ; 98\r\n      (   35.07   -98.84     7.31     0.52)  ; 99\r\n      (   34.85  -100.22     7.31     0.52)  ; 100\r\n      (   34.50  -100.97     7.31     0.52)  ; 101\r\n      (   34.17  -100.77     7.31     0.52)  ; 102\r\n      (   34.03  -101.12     7.31     0.52)  ; 103\r\n      (   33.75  -101.97     7.56     0.52)  ; 104\r\n      (   33.70  -102.77     8.25     0.96)  ; 105\r\n      (   33.44  -103.46     8.31     1.18)  ; 106\r\n      (   33.44  -103.98     8.81     0.81)  ; 107\r\n      (   34.13  -104.76     9.31     0.52)  ; 108\r\n      (   34.47  -105.41     9.31     0.52)  ; 109\r\n      (   34.62  -106.32    10.19     0.81)  ; 110\r\n      (   34.59  -107.05    11.31     0.66)  ; 111\r\n      (   34.73  -108.03    12.69     0.52)  ; 112\r\n      (   34.83  -108.80    13.19     0.52)  ; 113\r\n      (   35.08  -109.58    13.31     0.81)  ; 114\r\n      (   35.16  -110.47    14.13     1.11)  ; 115\r\n      (   35.32  -111.39    14.13     1.11)  ; 116\r\n      (   35.24  -112.43    14.94     0.52)  ; 117\r\n      (   35.11  -113.23    15.38     0.52)  ; 118\r\n      (   34.94  -114.30    15.44     0.52)  ; 119\r\n      (   35.40  -115.20    15.50     0.44)  ; 120\r\n      (   35.57  -116.48    15.50     0.44)  ; 121\r\n      (   35.73  -117.31    16.06     0.44)  ; 122\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (    6.08     2.23    -2.31     3.39)  ; Root\r\n  (    6.78     2.85    -3.56     3.09)  ; 1, R\r\n  (\r\n    (    8.39     3.13    -2.88     1.92)  ; 1, R-1\r\n    (    9.09     2.79    -3.38     2.14)  ; 2\r\n    (    9.96     2.65    -3.69     2.50)  ; 3\r\n    (\r\n      (   10.84     3.48    -3.69     1.92)  ; 1, R-1-1\r\n      (   11.58     3.95    -4.13     1.55)  ; 2\r\n      (\r\n        (   12.76     3.83    -4.94     0.66)  ; 1, R-1-1-1\r\n        (   13.99     3.64    -5.13     0.59)  ; 2\r\n        (   15.28     3.81    -5.69     0.59)  ; 3\r\n        (   16.55     4.27    -5.88     0.52)  ; 4\r\n        (   17.34     4.96    -6.00     0.52)  ; 5\r\n        (   17.81     5.62    -6.13     0.52)  ; 6\r\n        (   18.77     5.99    -6.19     0.74)  ; 7\r\n        (   19.72     6.36    -6.19     0.81)  ; 8\r\n        (   20.31     6.79    -6.19     1.03)  ; 9\r\n        (   21.02     7.04    -6.38     1.03)  ; 10\r\n        (   21.91     7.49    -6.38     0.74)  ; 11\r\n        (   23.20     8.10    -6.38     0.59)  ; 12\r\n        (   24.10     8.56    -6.63     0.59)  ; 13\r\n        (   25.17     9.20    -6.44     1.11)  ; 14\r\n        (   26.14     9.71    -6.38     1.62)  ; 15\r\n        (   26.87    10.04    -6.19     1.69)  ; 16\r\n        (   27.54    10.52    -6.13     1.25)  ; 17\r\n        (   28.30    11.07    -6.31     0.88)  ; 18\r\n        (   29.03    11.47    -6.31     1.03)  ; 19\r\n        (   29.58    11.61    -6.63     0.81)  ; 20\r\n        (   30.13    11.81    -6.94     0.74)  ; 21\r\n        (   30.44    12.36    -7.38     0.59)  ; 22\r\n        (   30.76    12.98    -7.56     0.74)  ; 23\r\n        (   31.25    13.71    -8.13     0.52)  ; 24\r\n        (   32.05    14.03    -8.44     0.52)  ; 25\r\n        (   33.24    14.51    -8.44     0.66)  ; 26\r\n        (   34.13    14.88    -8.75     0.88)  ; 27\r\n        (   35.25    15.38    -8.75     1.11)  ; 28\r\n        (   36.15    15.89    -9.25     0.88)  ; 29\r\n        (   36.99    16.51    -9.50     1.03)  ; 30\r\n        (   37.85    16.81   -10.44     1.33)  ; 31\r\n        (   39.05    16.84   -10.88     1.47)  ; 32\r\n        (   40.05    17.42   -11.13     1.18)  ; 33\r\n        (   40.77    17.75   -11.56     0.88)  ; 34\r\n        (   41.75    18.33   -11.44     0.74)  ; 35\r\n        (   42.79    18.77   -11.63     0.81)  ; 36\r\n        (   43.59    19.09   -11.56     0.52)  ; 37\r\n        (   44.69    19.43   -11.56     0.52)  ; 38\r\n        (   45.42    19.89   -11.06     1.25)  ; 39\r\n        (   46.20    20.51   -11.06     1.25)  ; 40\r\n        (   46.50    21.04   -11.06     1.25)  ; 41\r\n        (   47.11    21.62   -11.06     0.74)  ; 42\r\n        (   47.57    22.14   -11.06     0.44)  ; 43\r\n        (   47.98    22.82   -11.19     0.44)  ; 44\r\n        (   48.71    23.22   -11.06     0.59)  ; 45\r\n        (   49.77    23.79   -10.88     1.18)  ; 46\r\n        (   51.05    24.32   -11.63     1.33)  ; 47\r\n        (   51.65    24.83   -11.69     1.11)  ; 48\r\n        (   52.68    25.18   -12.31     1.03)  ; 49\r\n        (   53.72    25.60   -12.44     1.55)  ; 50\r\n        (   54.64    25.83   -13.00     1.55)  ; 51\r\n        (   55.16    26.26   -13.00     0.81)  ; 52\r\n        (   55.98    26.72   -13.31     0.52)  ; 53\r\n        (   56.81    27.26   -13.44     0.74)  ; 54\r\n        (   57.35    27.84   -13.63     0.66)  ; 55\r\n        (   57.71    28.68   -13.94     0.44)  ; 56\r\n        (   58.04    29.36   -13.94     0.44)  ; 57\r\n        (   58.77    29.76   -13.94     0.96)  ; 58\r\n        (   59.39    29.89   -14.13     0.96)  ; 59\r\n        (   60.15    29.99   -14.25     0.59)  ; 60\r\n        (   60.64    30.28   -14.56     0.44)  ; 61\r\n        (   61.07    30.58   -14.88     0.44)  ; 62\r\n        (   61.68    31.15   -15.13     0.44)  ; 63\r\n        (   62.60    31.75   -15.13     0.81)  ; 64\r\n        (   62.95    32.14   -15.94     1.40)  ; 65\r\n        (   63.55    32.56   -15.94     2.14)  ; 66\r\n        (   64.17    32.75   -16.63     2.14)  ; 67\r\n        (   64.93    33.30   -16.81     1.03)  ; 68\r\n        (   65.53    33.79   -16.94     0.52)  ; 69\r\n        (   66.16    34.51   -17.38     0.37)  ; 70\r\n        (   66.62    35.03   -17.63     0.37)  ; 71\r\n        (   67.01    35.64   -17.94     1.40)  ; 72\r\n        (   67.49    35.78   -18.38     2.06)  ; 73\r\n        (   67.82    36.02   -18.69     2.28)  ; 74\r\n        (   68.70    36.40   -19.25     0.59)  ; 75\r\n        (   69.30    36.39   -19.19     0.37)  ; 76\r\n        (   69.81    36.31   -19.50     0.37)  ; 77\r\n        (   70.13    36.03   -19.81     0.88)  ; 78\r\n        (   70.83    35.70   -20.69     1.47)  ; 79\r\n        (   71.72    35.63   -22.00     1.77)  ; 80\r\n        (   72.19    35.33   -22.69     1.33)  ; 81\r\n        (   72.68    35.11   -22.94     0.96)  ; 82\r\n        (   73.27    34.64   -23.44     0.66)  ; 83\r\n        (   73.66    34.29   -24.94     0.66)  ; 84\r\n        (   74.03    34.23   -25.31     0.66)  ; 85\r\n        (   74.77    34.18   -25.50     0.88)  ; 86\r\n        (   75.33    34.47   -26.75     0.66)  ; 87\r\n        (   75.96    34.66   -27.19     0.52)  ; 88\r\n        (   76.68    34.92   -27.63     0.52)  ; 89\r\n        (   77.18    35.28   -29.19     0.81)  ; 90\r\n        (   77.90    35.61   -29.69     1.92)  ; 91\r\n        (   78.58    36.10   -30.75     2.14)  ; 92\r\n        (   79.16    36.46   -31.81     0.96)  ; 93\r\n        (   80.19    36.36   -32.63     0.59)  ; 94\r\n        (   81.45    36.39   -33.19     0.44)  ; 95\r\n        (   82.47    36.22   -33.69     0.44)  ; 96\r\n        (   83.44    36.21   -34.13     1.03)  ; 97\r\n        (   84.06    36.34   -34.50     1.77)  ; 98\r\n        (   84.81    36.44   -35.00     2.14)  ; 99\r\n        (   85.43    36.57   -35.00     0.81)  ; 100\r\n        (   86.04    36.62   -35.06     0.52)  ; 101\r\n        (   86.99    36.47   -35.13     0.37)  ; 102\r\n        (   87.91    36.62   -35.31     0.37)  ; 103\r\n        (   88.35    36.55   -35.31     0.74)  ; 104\r\n        (   89.00    36.45   -35.63     0.74)  ; 105\r\n        (   89.58    36.35   -36.19     0.37)  ; 106\r\n        (   90.31    36.24   -36.44     0.37)  ; 107\r\n        (   91.12    36.18   -36.50     0.66)  ; 108\r\n        (   91.83    36.00   -36.69     1.33)  ; 109\r\n        (   92.33    35.60   -36.94     1.55)  ; 110\r\n        (   93.06    35.03   -37.25     0.66)  ; 111\r\n        (   93.72    34.55   -37.44     1.11)  ; 112\r\n        (   94.18    34.12   -37.63     1.33)  ; 113\r\n         Normal\r\n      |\r\n        (   11.87     4.27    -5.94     1.33)  ; 1, R-1-1-2\r\n        (   12.58     4.53    -7.38     0.96)  ; 2\r\n        (\r\n          (   13.76     4.86    -7.56     0.66)  ; 1, R-1-1-2-1\r\n          (   15.25     5.29    -7.56     0.59)  ; 2\r\n          (   16.15     5.82    -7.56     0.66)  ; 3\r\n          (   16.81     6.74    -7.56     0.81)  ; 4\r\n          (   17.52     7.89    -7.63     1.11)  ; 5\r\n          (   18.24     8.66    -7.69     1.69)  ; 6\r\n          (   18.85     9.68    -7.75     2.14)  ; 7\r\n          (   19.41    10.40    -7.75     2.28)  ; 8\r\n          (   19.62    11.78    -8.06     2.21)  ; 9\r\n          (   19.86    12.77    -8.63     2.36)  ; 10\r\n          (\r\n            (   19.66    13.40    -9.69     2.14)  ; 1, R-1-1-2-1-1\r\n            (   19.49    14.17    -9.63     1.40)  ; 2\r\n            (   19.36    14.77    -9.56     0.74)  ; 3\r\n            (   19.03    15.57    -9.56     0.66)  ; 4\r\n            (   18.89    16.11    -9.56     0.66)  ; 5\r\n            (   19.38    17.29    -9.56     0.44)  ; 6\r\n            (   19.73    18.57    -9.56     0.74)  ; 7\r\n            (   19.85    19.36    -9.56     0.96)  ; 8\r\n            (   20.21    20.12    -9.94     0.74)  ; 9\r\n            (   20.51    21.11    -9.94     0.96)  ; 10\r\n            (   20.96    22.07   -10.19     1.18)  ; 11\r\n            (   21.18    23.00   -10.19     1.18)  ; 12\r\n            (   21.59    23.67   -10.19     1.33)  ; 13\r\n            (   22.06    24.34   -10.19     0.74)  ; 14\r\n            (   22.57    25.66   -10.25     0.52)  ; 15\r\n            (   22.92    26.43   -10.44     0.59)  ; 16\r\n            (   23.16    27.50   -10.50     0.74)  ; 17\r\n            (   23.34    28.65   -10.13     0.74)  ; 18\r\n            (   24.02    30.01   -10.06     0.66)  ; 19\r\n            (   24.99    30.96   -10.00     0.74)  ; 20\r\n            (   25.96    31.41   -10.00     0.88)  ; 21\r\n            (   27.06    32.26   -10.00     1.03)  ; 22\r\n            (   28.04    33.22   -10.00     1.11)  ; 23\r\n            (   29.04    33.87    -9.63     1.69)  ; 24\r\n            (   29.91    34.63    -9.63     1.47)  ; 25\r\n            (   30.58    35.12    -9.31     1.18)  ; 26\r\n            (   31.06    35.85    -9.38     0.81)  ; 27\r\n            (   31.56    36.66    -9.38     1.40)  ; 28\r\n            (   32.03    37.25    -9.38     1.69)  ; 29\r\n            (   32.83    38.08   -10.25     0.88)  ; 30\r\n            (   33.54    38.79   -11.13     0.96)  ; 31\r\n            (   34.39    38.95   -11.56     0.81)  ; 32\r\n            (   35.17    39.65   -11.94     0.96)  ; 33\r\n            (   36.17    40.30   -12.69     0.81)  ; 34\r\n            (   36.78    41.32   -12.75     1.11)  ; 35\r\n            (   37.06    42.15   -13.31     0.96)  ; 36\r\n            (   37.23    42.79   -13.31     0.59)  ; 37\r\n            (   37.52    43.64   -13.19     0.37)  ; 38\r\n            (   37.86    44.39   -13.19     0.37)  ; 39\r\n            (   37.93    44.83   -13.19     0.37)  ; 40\r\n            (   37.91    45.64   -13.25     1.03)  ; 41\r\n            (   37.65    46.35   -13.56     1.84)  ; 42\r\n            (   37.62    46.66   -12.69     2.58)  ; 43\r\n            (   37.60    47.40   -12.69     2.28)  ; 44\r\n            (   37.60    48.36   -12.69     1.47)  ; 45\r\n            (   37.60    48.88   -12.69     0.66)  ; 46\r\n            (   37.68    49.83   -12.69     0.52)  ; 47\r\n            (   37.85    50.47   -12.69     0.52)  ; 48\r\n            (   38.16    50.94   -12.75     0.52)  ; 49\r\n            (   38.43    51.79   -13.19     0.88)  ; 50\r\n            (   38.46    52.81   -13.44     1.92)  ; 51\r\n            (   38.40    53.89   -13.94     1.47)  ; 52\r\n            (   38.50    54.98   -14.25     0.59)  ; 53\r\n            (   38.55    55.34   -14.44     0.59)  ; 54\r\n            (   38.27    55.91   -14.75     0.88)  ; 55\r\n            (   38.28    56.43   -15.94     1.11)  ; 56\r\n            (   38.36    56.93   -16.25     0.74)  ; 57\r\n            (   38.56    57.79   -16.94     0.44)  ; 58\r\n            (   38.72    58.80   -17.38     0.52)  ; 59\r\n            (   39.51    59.48   -17.56     0.74)  ; 60\r\n            (   40.01    60.29   -17.69     0.37)  ; 61\r\n            (   40.64    61.01   -17.94     0.37)  ; 62\r\n            (   41.10    61.53   -18.25     0.66)  ; 63\r\n            (   41.83    61.93   -18.31     0.96)  ; 64\r\n            (   42.71    62.31   -18.63     1.33)  ; 65\r\n            (   43.30    62.66   -19.13     0.66)  ; 66\r\n            (   44.15    62.90   -19.50     0.52)  ; 67\r\n            (   44.92    63.52   -19.94     1.03)  ; 68\r\n            (   45.59    64.00   -20.69     1.33)  ; 69\r\n            (   46.31    64.33   -20.88     0.96)  ; 70\r\n            (   47.05    64.73   -21.94     0.59)  ; 71\r\n            (   47.99    65.02   -23.44     0.88)  ; 72\r\n            (   48.84    65.63   -24.44     1.03)  ; 73\r\n            (   49.45    65.75   -25.13     1.40)  ; 74\r\n            (   49.98    66.27   -26.63     1.69)  ; 75\r\n            (   50.04    67.07   -28.25     0.81)  ; 76\r\n            (   50.35    67.62   -29.13     0.59)  ; 77\r\n            (   50.85    67.98   -29.50     1.18)  ; 78\r\n            (   51.27    68.28   -30.13     1.77)  ; 79\r\n            (   51.39    69.00   -31.88     0.74)  ; 80\r\n            (   51.33    70.04   -32.06     0.52)  ; 81\r\n            (   51.11    70.60   -32.63     0.74)  ; 82\r\n            (   51.20    71.62   -33.19     0.96)  ; 83\r\n            (   51.30    72.19   -35.00     1.11)  ; 84\r\n            (   51.31    72.79   -35.38     1.40)  ; 85\r\n            (   51.73    73.53   -35.69     0.74)  ; 86\r\n            (   51.98    74.16   -36.25     0.59)  ; 87\r\n            (   52.31    74.85   -37.38     1.25)  ; 88\r\n            (   52.57    75.55   -38.13     2.14)  ; 89\r\n            (   52.62    76.36   -39.31     1.33)  ; 90\r\n            (   52.93    76.90   -39.75     0.52)  ; 91\r\n            (   53.59    77.75   -40.38     0.37)  ; 92\r\n            (   53.95    78.60   -40.69     0.44)  ; 93\r\n            (   54.27    79.22   -40.81     1.18)  ; 94\r\n            (   54.35    79.72   -41.13     1.84)  ; 95\r\n            (   54.23    80.40   -40.63     1.11)  ; 96\r\n            (   53.94    80.89   -40.19     0.44)  ; 97\r\n            (   53.47    81.70   -39.88     0.66)  ; 98\r\n            (   53.56    82.28   -39.63     0.66)  ; 99\r\n             Normal\r\n          |\r\n            (   20.12    13.57    -9.63     0.96)  ; 1, R-1-1-2-1-2\r\n            (   20.46    14.33   -11.13     0.81)  ; 2\r\n            (   21.13    15.18   -11.38     0.96)  ; 3\r\n            (   22.23    15.60   -12.19     0.96)  ; 4\r\n            (   23.15    15.75   -12.38     1.11)  ; 5\r\n            (   23.98    15.40   -12.69     0.96)  ; 6\r\n            (   24.85    15.63   -12.94     0.81)  ; 7\r\n            (   25.47    15.83   -13.75     1.11)  ; 8\r\n            (   25.86    16.36   -14.25     1.40)  ; 9\r\n            (   26.33    16.95   -15.75     1.11)  ; 10\r\n            (   26.54    16.92   -17.31     0.88)  ; 11\r\n            (   27.08    17.05   -18.44     0.88)  ; 12\r\n            (   27.64    17.26   -19.25     1.18)  ; 13\r\n            (   28.34    17.89   -19.75     1.47)  ; 14\r\n            (   28.69    18.28   -20.88     1.84)  ; 15\r\n            (   29.07    18.73   -21.94     1.69)  ; 16\r\n            (   29.66    19.24   -22.63     1.40)  ; 17\r\n            (   29.86    19.50   -24.25     1.11)  ; 18\r\n            (   29.94    20.00   -25.56     0.81)  ; 19\r\n            (   30.18    21.01   -25.75     0.44)  ; 20\r\n            (   30.85    22.00   -26.19     0.66)  ; 21\r\n            (   31.86    23.18   -26.25     1.62)  ; 22\r\n            (   32.35    23.91   -26.94     1.84)  ; 23\r\n            (   32.77    25.18   -28.69     0.96)  ; 24\r\n            (   33.05    25.95   -29.56     0.66)  ; 25\r\n            (   33.18    26.82   -30.69     0.66)  ; 26\r\n            (   33.71    27.32   -31.13     0.66)  ; 27\r\n            (   34.47    27.42   -31.31     0.66)  ; 28\r\n            (   35.51    27.86   -31.88     0.96)  ; 29\r\n            (   36.24    28.19   -33.81     2.14)  ; 30\r\n            (   36.82    28.10   -33.94     2.95)  ; 31\r\n            (   37.55    28.05   -33.94     2.95)  ; 32\r\n            (   38.31    27.64   -33.94     1.55)  ; 33\r\n            (   39.06    27.22   -35.00     0.88)  ; 34\r\n            (   39.89    26.79   -35.56     0.81)  ; 35\r\n            (   41.04    26.47   -36.25     0.74)  ; 36\r\n            (   41.89    26.25   -36.56     1.03)  ; 37\r\n            (   42.32    26.11   -37.38     1.33)  ; 38\r\n            (   43.22    26.12   -37.94     1.11)  ; 39\r\n            (   44.09    26.43   -39.69     0.74)  ; 40\r\n            (   45.93    26.72   -41.44     0.52)  ; 41\r\n            (   46.72    27.04   -42.50     0.81)  ; 42\r\n            (   47.18    27.11   -43.06     1.62)  ; 43\r\n            (   47.79    27.24   -43.25     2.50)  ; 44\r\n            (   48.67    27.55   -43.56     2.50)  ; 45\r\n            (   49.46    27.35   -43.75     1.11)  ; 46\r\n            (   50.07    27.03   -43.75     0.66)  ; 47\r\n            (   51.02    26.43   -43.75     0.66)  ; 48\r\n            (   52.50    26.35   -44.75     0.66)  ; 49\r\n            (   53.47    26.79   -46.06     0.81)  ; 50\r\n            (   54.14    26.38   -46.56     0.59)  ; 51\r\n            (   55.06    26.02   -46.69     0.81)  ; 52\r\n            (   55.65    25.99   -47.06     1.69)  ; 53\r\n            (   56.69    25.98   -47.50     2.50)  ; 54\r\n            (   57.22    25.97   -47.94     2.50)  ; 55\r\n            (   57.85    25.73   -48.63     2.06)  ; 56\r\n            (   58.48    25.48   -49.00     1.11)  ; 57\r\n            (   59.54    26.05   -49.19     0.52)  ; 58\r\n            (   61.20    26.16   -49.50     0.52)  ; 59\r\n            (   62.41    26.26   -49.75     0.52)  ; 60\r\n            (   62.84    26.64   -50.00     1.25)  ; 61\r\n            (   63.91    26.83   -50.00     1.99)  ; 62\r\n            (   64.84    26.99   -50.19     1.99)  ; 63\r\n            (   66.06    27.68   -50.44     0.81)  ; 64\r\n            (   67.14    28.33   -50.44     0.74)  ; 65\r\n            (   68.27    29.14   -50.88     0.52)  ; 66\r\n            (   69.34    29.78   -51.13     0.44)  ; 67\r\n            (   70.69    30.30   -51.13     0.66)  ; 68\r\n            (   72.00    30.54   -50.94     0.66)  ; 69\r\n            (   72.90    31.00   -50.69     0.96)  ; 70\r\n            (   73.49    31.49   -50.38     1.77)  ; 71\r\n            (   74.20    31.68   -50.19     2.58)  ; 72\r\n            (   74.84    31.95   -50.19     2.21)  ; 73\r\n            (   75.27    32.39   -50.19     0.88)  ; 74\r\n            (   75.87    32.82   -50.19     0.59)  ; 75\r\n            (   77.05    33.67   -50.19     0.29)  ; 76\r\n            (   78.99    34.62   -50.44     0.29)  ; 77\r\n            (   79.54    34.76   -50.44     0.29)  ; 78\r\n            (   80.19    35.17   -50.44     0.66)  ; 79\r\n            (   80.89    35.80   -50.75     1.03)  ; 80\r\n            (   81.49    36.30   -50.75     0.59)  ; 81\r\n            (   82.09    37.68   -50.81     0.52)  ; 82\r\n            (   82.73    38.91   -52.00     0.81)  ; 83\r\n            (   82.59    39.46   -52.00     1.69)  ; 84\r\n            (   82.86    40.22   -52.63     1.69)  ; 85\r\n             Normal\r\n          )  ;  End of split\r\n        |\r\n          (   11.60     4.68    -8.88     0.81)  ; 1, R-1-1-2-2\r\n          (   10.93     4.79   -11.00     0.74)  ; 2\r\n          (   11.20     5.49   -11.63     0.74)  ; 3\r\n          (   11.94     5.96   -11.81     0.74)  ; 4\r\n          (   13.39     7.06   -11.81     0.74)  ; 5\r\n          (   14.58     7.54   -12.00     0.74)  ; 6\r\n          (   15.26     8.54   -12.69     0.59)  ; 7\r\n          (   14.86     9.79   -13.56     0.59)  ; 8\r\n          (   14.91    10.60   -14.06     0.88)  ; 9\r\n          (   15.05    11.90   -15.13     0.88)  ; 10\r\n          (   15.20    12.84   -15.56     1.03)  ; 11\r\n          (   15.88    13.40   -17.13     0.88)  ; 12\r\n          (   16.42    13.98   -18.69     1.18)  ; 13\r\n          (   17.06    14.25   -19.81     0.96)  ; 14\r\n          (   17.76    14.44   -21.38     0.66)  ; 15\r\n          (   18.36    14.93   -22.00     0.66)  ; 16\r\n          (   19.02    15.34   -23.25     1.11)  ; 17\r\n          (   19.44    16.10   -25.00     1.25)  ; 18\r\n          (   19.78    16.34   -26.19     1.62)  ; 19\r\n          (   19.87    16.91   -27.13     1.25)  ; 20\r\n          (   21.09    17.09   -28.06     0.96)  ; 21\r\n          (   22.04    17.46   -29.19     0.66)  ; 22\r\n          (   23.32    17.99   -31.19     0.66)  ; 23\r\n          (   24.26    19.18   -32.00     0.96)  ; 24\r\n          (   24.97    19.43   -33.38     1.69)  ; 25\r\n          (   25.81    19.60   -34.06     2.06)  ; 26\r\n          (   26.59    19.77   -34.38     1.18)  ; 27\r\n          (   27.75    20.04   -34.88     0.81)  ; 28\r\n          (   28.58    20.13   -35.50     0.74)  ; 29\r\n          (   29.38    20.44   -36.31     1.11)  ; 30\r\n          (   29.72    20.76   -37.81     1.47)  ; 31\r\n          (   29.95    21.24   -39.31     1.11)  ; 32\r\n          (   30.24    22.53   -40.50     0.88)  ; 33\r\n          (   30.50    22.78   -42.25     1.18)  ; 34\r\n          (   30.72    23.26   -43.13     1.62)  ; 35\r\n          (   30.84    23.54   -44.06     1.18)  ; 36\r\n          (   31.20    24.37   -46.25     0.81)  ; 37\r\n          (   31.69    25.10   -46.81     1.11)  ; 38\r\n          (   32.36    25.15   -48.19     1.40)  ; 39\r\n          (   31.78    25.68   -49.38     1.40)  ; 40\r\n          (   31.55    25.65   -51.19     1.62)  ; 41\r\n          (   31.28    25.32   -53.88     1.40)  ; 42\r\n          (   31.00    25.51   -56.31     1.25)  ; 43\r\n          (   30.49    25.08   -59.94     1.40)  ; 44\r\n          (   29.00    24.65   -61.75     1.03)  ; 45\r\n          (   27.49    24.07   -62.31     1.03)  ; 46\r\n          (   26.99    23.26   -62.50     0.81)  ; 47\r\n          (   26.36    22.62   -63.56     0.81)  ; 48\r\n          (   26.07    22.15   -65.38     1.11)  ; 49\r\n          (   25.97    21.06   -66.19     1.11)  ; 50\r\n          (   26.41    20.03   -67.88     0.81)  ; 51\r\n          (   26.72    19.16   -69.31     1.18)  ; 52\r\n          (   26.98    18.45   -70.38     1.25)  ; 53\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    |\r\n      (   10.57     1.96    -4.56     0.88)  ; 1, R-1-2\r\n      (   11.41     1.16    -5.75     0.74)  ; 2\r\n      (   12.47     0.89    -5.75     0.74)  ; 3\r\n      (\r\n        (   12.49     0.91    -5.94     0.88)  ; 1, R-1-2-1\r\n        (   13.23     0.43    -6.75     0.88)  ; 2\r\n        (   13.60    -0.08    -7.56     1.03)  ; 3\r\n        (   14.03    -0.66    -9.19     0.96)  ; 4\r\n        (   13.89    -1.53   -10.69     0.81)  ; 5\r\n        (   13.66    -2.01   -13.25     0.81)  ; 6\r\n        (   13.95    -3.02   -13.81     0.66)  ; 7\r\n        (   14.34    -3.89   -15.13     0.66)  ; 8\r\n        (   14.71    -4.77   -16.44     0.66)  ; 9\r\n        (   15.19    -5.51   -17.00     0.66)  ; 10\r\n        (   16.36    -6.66   -17.81     0.88)  ; 11\r\n        (   17.37    -7.85   -17.25     1.18)  ; 12\r\n        (   17.72    -8.43   -18.38     1.47)  ; 13\r\n        (\r\n          (   17.15    -9.23   -19.19     1.03)  ; 1, R-1-2-1-1\r\n          (   16.62   -10.62   -20.38     0.74)  ; 2\r\n          (   16.12   -12.39   -21.06     0.52)  ; 3\r\n          (   15.85   -13.68   -22.44     0.52)  ; 4\r\n          (   15.68   -14.69   -22.75     0.52)  ; 5\r\n          (   15.62   -15.57   -23.69     0.74)  ; 6\r\n          (   15.42   -16.80   -24.13     0.74)  ; 7\r\n          (   15.61   -17.50   -25.75     0.59)  ; 8\r\n          (   16.68   -17.81   -24.19     0.88)  ; 9\r\n          (   17.64   -18.34   -24.75     0.96)  ; 10\r\n          (   18.52   -18.92   -25.38     0.81)  ; 11\r\n          (   19.51   -19.22   -26.88     0.88)  ; 12\r\n          (   20.14   -19.54   -29.38     0.66)  ; 13\r\n          (   21.24   -20.16   -30.69     0.44)  ; 14\r\n          (   22.19   -20.76   -30.69     0.44)  ; 15\r\n          (   23.71   -21.00   -31.50     0.74)  ; 16\r\n          (   24.13   -21.21   -32.38     1.11)  ; 17\r\n          (   25.10   -21.15   -32.94     1.40)  ; 18\r\n          (   26.41   -21.36   -32.94     0.66)  ; 19\r\n          (   27.44   -21.95   -33.56     0.44)  ; 20\r\n          (   28.66   -22.74   -34.25     0.74)  ; 21\r\n          (   29.12   -23.11   -35.38     1.11)  ; 22\r\n          (   29.49   -24.06   -36.19     1.77)  ; 23\r\n          (   30.10   -24.97   -36.81     0.59)  ; 24\r\n          (   30.65   -25.65   -38.06     0.44)  ; 25\r\n          (   31.62   -26.78   -38.69     0.37)  ; 26\r\n          (   32.10   -27.52   -39.63     0.37)  ; 27\r\n          (   32.97   -28.17   -40.44     0.74)  ; 28\r\n          (   34.26   -28.52   -41.31     0.96)  ; 29\r\n          (   34.53   -29.09   -42.19     0.66)  ; 30\r\n          (   34.95   -29.81   -44.19     0.88)  ; 31\r\n          (   35.51   -30.05   -45.44     1.77)  ; 32\r\n          (   36.33   -30.48   -45.75     2.14)  ; 33\r\n          (   36.93   -31.39   -48.00     0.81)  ; 34\r\n          (   37.80   -32.49   -48.75     0.88)  ; 35\r\n          (   38.63   -32.99   -49.44     1.18)  ; 36\r\n          (   39.20   -33.60   -50.63     0.74)  ; 37\r\n          (   41.06   -34.56   -51.25     0.59)  ; 38\r\n          (   42.18   -35.48   -52.13     0.59)  ; 39\r\n          (   42.49   -35.90   -52.25     0.96)  ; 40\r\n          (   43.05   -36.57   -52.69     1.84)  ; 41\r\n          (   43.73   -36.99   -52.69     2.65)  ; 42\r\n          (   44.65   -37.79   -52.69     1.69)  ; 43\r\n          (   44.88   -38.72   -52.69     0.74)  ; 44\r\n          (   45.71   -39.59   -53.06     0.74)  ; 45\r\n          (   46.21   -40.63   -53.50     0.59)  ; 46\r\n          (   47.00   -41.27   -53.69     0.29)  ; 47\r\n          (   48.36   -42.60   -54.13     0.59)  ; 48\r\n          (   48.34   -43.19   -54.81     1.03)  ; 49\r\n          (   48.90   -43.94   -54.88     0.59)  ; 50\r\n          (   49.24   -45.04   -55.06     0.29)  ; 51\r\n          (   49.41   -46.77   -55.38     0.29)  ; 52\r\n          (   49.57   -47.24   -55.56     1.11)  ; 53\r\n          (   49.72   -48.14   -56.06     1.99)  ; 54\r\n          (   50.17   -49.10   -56.44     1.03)  ; 55\r\n          (   50.62   -49.98   -56.88     0.59)  ; 56\r\n          (   52.35   -51.82   -57.25     0.37)  ; 57\r\n          (   52.39   -51.98   -57.25     0.37)  ; 58\r\n          (   52.70   -52.84   -57.25     0.74)  ; 59\r\n          (   52.93   -53.76   -57.88     1.18)  ; 60\r\n          (   53.48   -54.52   -58.38     1.40)  ; 61\r\n          (   54.36   -55.62   -59.00     0.59)  ; 62\r\n          (   54.78   -56.72   -60.00     1.11)  ; 63\r\n          (   55.67   -57.53   -60.44     1.18)  ; 64\r\n          (   56.09   -58.19   -61.25     0.74)  ; 65\r\n          (   56.85   -58.98   -61.31     0.52)  ; 66\r\n          (   57.92   -59.81   -61.38     0.66)  ; 67\r\n          (   58.74   -60.76   -61.63     0.66)  ; 68\r\n          (   59.00   -60.94   -61.75     1.03)  ; 69\r\n          (   59.50   -61.55   -61.88     1.40)  ; 70\r\n          (   60.09   -62.08   -62.25     1.40)  ; 71\r\n          (   60.36   -62.71   -62.44     0.88)  ; 72\r\n          (   60.91   -63.47   -61.81     0.59)  ; 73\r\n          (   61.50   -64.45   -61.81     0.66)  ; 74\r\n          (   62.02   -65.42   -61.81     0.29)  ; 75\r\n           Normal\r\n        |\r\n          (   18.40    -7.68   -18.50     1.18)  ; 1, R-1-2-1-2\r\n          (   19.07    -7.27   -17.31     0.74)  ; 2\r\n          (   21.35    -6.97   -18.75     0.59)  ; 3\r\n          (   23.27    -6.61   -18.94     0.59)  ; 4\r\n          (   24.19    -6.02   -19.94     0.74)  ; 5\r\n          (   26.03    -5.71   -20.38     0.74)  ; 6\r\n          (   28.20    -4.72   -22.94     0.44)  ; 7\r\n          (   29.22    -5.33   -24.13     0.74)  ; 8\r\n          (   29.93    -5.52   -25.75     0.96)  ; 9\r\n          (   30.07    -5.61   -28.44     1.33)  ; 10\r\n          (   30.15    -5.56   -29.25     0.88)  ; 11\r\n          (   30.78    -5.35   -31.50     0.81)  ; 12\r\n          (   31.09    -5.77   -33.44     0.81)  ; 13\r\n          (   31.41    -6.56   -33.81     0.81)  ; 14\r\n          (   31.92    -7.09   -34.31     0.81)  ; 15\r\n          (   33.08    -7.35   -35.44     0.81)  ; 16\r\n          (   33.31    -6.86   -38.25     0.81)  ; 17\r\n          (   33.93    -6.67   -38.88     1.11)  ; 18\r\n          (   35.41    -6.75   -39.94     0.74)  ; 19\r\n          (   37.06    -6.72   -41.31     0.74)  ; 20\r\n          (   38.07    -7.84   -43.50     0.44)  ; 21\r\n          (   38.11    -8.59   -47.25     0.96)  ; 22\r\n          (   38.50    -8.94   -47.81     0.96)  ; 23\r\n          (   39.37    -9.53   -49.50     1.03)  ; 24\r\n           Generated\r\n        )  ;  End of split\r\n      |\r\n        (   12.95     1.04    -4.06     0.66)  ; 1, R-1-2-2\r\n        (   13.26     1.13    -2.75     0.52)  ; 2\r\n        (   13.55     1.08    -0.13     0.52)  ; 3\r\n        (   14.75    -0.28     0.25     0.52)  ; 4\r\n        (   15.78    -0.89     0.25     0.52)  ; 5\r\n        (   17.11    -1.40     0.38     0.52)  ; 6\r\n        (   17.84    -1.96    -2.06     0.66)  ; 7\r\n        (   18.84    -3.23    -2.38     0.88)  ; 8\r\n        (   19.68    -4.47    -5.75     0.88)  ; 9\r\n        (   20.50    -6.38    -6.50     0.74)  ; 10\r\n        (   21.21    -7.97    -7.81     0.59)  ; 11\r\n        (   21.98    -9.27    -9.06     0.59)  ; 12\r\n        (   22.89   -10.60    -9.44     0.59)  ; 13\r\n        (   23.58   -11.90    -9.44     0.59)  ; 14\r\n        (   23.98   -13.14    -9.44     0.59)  ; 15\r\n        (   24.28   -14.08    -9.44     0.59)  ; 16\r\n        (   24.66   -15.40    -9.69     0.59)  ; 17\r\n        (   24.95   -16.92    -9.88     0.81)  ; 18\r\n        (   25.14   -18.00   -10.06     0.81)  ; 19\r\n        (   25.54   -18.80   -10.13     0.44)  ; 20\r\n        (   26.18   -20.38   -10.13     0.44)  ; 21\r\n        (   26.38   -21.52   -10.38     0.74)  ; 22\r\n        (   26.26   -22.24   -10.63     0.74)  ; 23\r\n        (   26.40   -23.30   -10.88     0.44)  ; 24\r\n        (   26.73   -24.46    -9.69     0.44)  ; 25\r\n        (   27.01   -25.61    -9.63     0.59)  ; 26\r\n        (   27.70   -27.28    -9.31     0.74)  ; 27\r\n        (   28.33   -28.49    -8.88     0.88)  ; 28\r\n        (   28.96   -29.70    -8.63     0.52)  ; 29\r\n        (   29.79   -31.08    -8.63     0.52)  ; 30\r\n        (   30.74   -32.28    -7.81     0.37)  ; 31\r\n        (   30.92   -33.42   -10.31     0.66)  ; 32\r\n        (   31.04   -34.62   -10.31     0.66)  ; 33\r\n        (   31.32   -36.14   -10.56     0.52)  ; 34\r\n        (   30.75   -36.86   -11.50     0.52)  ; 35\r\n        (   29.80   -37.68   -11.81     0.59)  ; 36\r\n        (   29.70   -38.33   -11.81     0.59)  ; 37\r\n        (   29.73   -39.07   -12.50     0.74)  ; 38\r\n        (   29.94   -40.15   -13.81     0.59)  ; 39\r\n        (   30.46   -41.04   -14.81     0.88)  ; 40\r\n        (   30.76   -41.53   -15.13     1.11)  ; 41\r\n        (   31.46   -42.31   -17.13     0.96)  ; 42\r\n        (   32.36   -43.19   -18.63     0.96)  ; 43\r\n        (   33.25   -43.70   -18.69     0.59)  ; 44\r\n        (   33.89   -44.84   -19.56     0.59)  ; 45\r\n        (   34.57   -46.21   -19.63     0.88)  ; 46\r\n        (   35.45   -46.35   -20.13     0.88)  ; 47\r\n        (   36.36   -47.16   -21.50     0.59)  ; 48\r\n        (   36.86   -47.75   -21.50     0.59)  ; 49\r\n        (   38.30   -48.57   -22.38     0.59)  ; 50\r\n        (   39.37   -48.89   -22.94     0.81)  ; 51\r\n        (   40.02   -48.99   -23.75     1.03)  ; 52\r\n        (   40.59   -49.15   -24.38     1.03)  ; 53\r\n        (   41.53   -49.38   -24.81     1.03)  ; 54\r\n        (\r\n          (   42.70   -49.94   -25.38     0.59)  ; 1, R-1-2-2-1\r\n          (   44.20   -50.39   -25.56     0.81)  ; 2\r\n          (   44.84   -50.57   -26.63     0.81)  ; 3\r\n          (   45.25   -50.86   -27.25     1.03)  ; 4\r\n          (   45.77   -51.38   -28.13     1.03)  ; 5\r\n          (   46.43   -51.86   -28.13     0.66)  ; 6\r\n          (   46.70   -52.49   -29.44     0.52)  ; 7\r\n          (   47.57   -53.59   -31.19     0.52)  ; 8\r\n           Normal\r\n        |\r\n          (   41.06   -50.49   -24.81     0.59)  ; 1, R-1-2-2-2\r\n          (   40.79   -51.19   -24.94     0.59)  ; 2\r\n          (   40.66   -52.05   -24.25     0.66)  ; 3\r\n          (   40.50   -52.62   -24.44     0.81)  ; 4\r\n          (   40.28   -53.03   -24.63     0.81)  ; 5\r\n          (   40.06   -53.88   -25.19     0.59)  ; 6\r\n          (   39.91   -54.38   -25.50     0.59)  ; 7\r\n          (   39.98   -54.91   -26.69     0.59)  ; 8\r\n          (   40.00   -55.28   -26.75     0.96)  ; 9\r\n          (   39.84   -55.78   -26.81     1.69)  ; 10\r\n          (   39.59   -56.40   -27.06     2.65)  ; 11\r\n          (   39.34   -57.03   -27.25     1.84)  ; 12\r\n          (   39.31   -57.76   -28.44     0.96)  ; 13\r\n          (   39.78   -58.57   -29.25     0.59)  ; 14\r\n          (   39.93   -59.49   -29.25     0.88)  ; 15\r\n          (   39.86   -60.58   -29.88     0.52)  ; 16\r\n          (   39.77   -61.68   -30.38     0.52)  ; 17\r\n          (   39.87   -62.44   -30.81     0.88)  ; 18\r\n          (   40.11   -63.28   -30.81     0.88)  ; 19\r\n          (   40.01   -63.94   -31.38     1.11)  ; 20\r\n          (   39.98   -64.60   -31.94     1.47)  ; 21\r\n          (   39.95   -64.74   -32.06     1.47)  ; 22\r\n          (   39.87   -65.24   -32.44     0.96)  ; 23\r\n          (   39.98   -66.01   -32.44     0.52)  ; 24\r\n          (   39.98   -66.52   -32.88     0.52)  ; 25\r\n          (   39.87   -67.16   -33.13     0.81)  ; 26\r\n          (   39.94   -67.62   -33.19     1.25)  ; 27\r\n          (   39.77   -68.26   -33.88     1.40)  ; 28\r\n          (   39.64   -69.06   -35.19     0.59)  ; 29\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (    6.84     3.96    -2.69     1.47)  ; 1, R-2\r\n    (    7.04     5.19    -1.69     1.47)  ; 2\r\n    (    7.93     6.16    -1.69     1.25)  ; 3\r\n    (    8.64     6.86    -2.13     1.18)  ; 4\r\n    (    9.18     7.88    -3.00     1.25)  ; 5\r\n    (    9.44     9.02    -3.25     1.47)  ; 6\r\n    (    9.68    10.10    -3.63     2.36)  ; 7\r\n    (    9.60    11.52    -4.38     2.43)  ; 8\r\n    (    9.67    12.40    -5.25     2.14)  ; 9\r\n    (    9.73    13.72    -5.44     1.77)  ; 10\r\n    (   10.16    14.54    -5.63     1.77)  ; 11\r\n    (\r\n      (    9.30    15.26    -5.94     0.96)  ; 1, R-2-1\r\n      (    8.40    16.14    -6.13     0.59)  ; 2\r\n      (    7.67    17.15    -6.44     0.74)  ; 3\r\n      (    7.38    18.23    -6.44     0.74)  ; 4\r\n      (    7.12    18.94    -6.50     0.52)  ; 5\r\n      (    6.51    19.77    -6.88     0.66)  ; 6\r\n      (    5.90    20.10    -7.13     1.25)  ; 7\r\n      (    5.31    20.70    -8.44     1.25)  ; 8\r\n      (    5.22    21.09    -9.38     1.11)  ; 9\r\n      (    4.85    21.96   -10.81     0.74)  ; 10\r\n      (    4.65    23.11   -11.63     0.59)  ; 11\r\n      (    4.42    24.03   -12.56     0.52)  ; 12\r\n      (    4.71    24.87   -12.88     0.37)  ; 13\r\n      (    5.12    25.62   -13.06     1.03)  ; 14\r\n      (    4.85    26.18   -14.00     1.62)  ; 15\r\n      (    4.73    27.39   -15.69     1.47)  ; 16\r\n      (    4.82    28.06   -16.00     1.55)  ; 17\r\n      (    4.58    28.85   -17.63     1.69)  ; 18\r\n      (    4.66    29.35   -18.31     0.96)  ; 19\r\n      (    4.56    30.11   -19.25     0.74)  ; 20\r\n      (    4.68    30.90   -19.75     1.03)  ; 21\r\n      (    5.16    31.64   -20.75     1.18)  ; 22\r\n      (    5.31    32.06   -21.81     0.59)  ; 23\r\n      (    5.36    32.87   -22.19     0.59)  ; 24\r\n      (    5.12    33.20   -22.94     0.96)  ; 25\r\n      (    4.85    33.91   -23.38     1.25)  ; 26\r\n      (    4.80    34.50   -26.25     0.66)  ; 27\r\n      (    4.77    34.81   -27.13     0.66)  ; 28\r\n      (    4.43    35.45   -27.25     0.96)  ; 29\r\n      (    4.14    36.46   -28.69     0.66)  ; 30\r\n      (    4.58    37.35   -29.44     0.52)  ; 31\r\n      (    5.19    37.85   -29.81     0.52)  ; 32\r\n      (    5.49    38.39   -30.31     0.59)  ; 33\r\n      (    5.87    38.93   -31.44     0.74)  ; 34\r\n      (    6.41    39.50   -32.44     0.52)  ; 35\r\n      (    6.74    39.67   -33.31     0.52)  ; 36\r\n      (    7.12    40.21   -33.81     0.52)  ; 37\r\n      (    7.64    41.16   -34.31     0.81)  ; 38\r\n      (    7.94    41.63   -35.69     1.55)  ; 39\r\n      (    8.58    42.35   -36.69     1.84)  ; 40\r\n      (    8.81    42.83   -38.50     1.03)  ; 41\r\n      (    9.29    43.56   -39.25     0.66)  ; 42\r\n      (    9.44    43.99   -40.19     0.66)  ; 43\r\n      (    9.76    44.60   -41.00     0.59)  ; 44\r\n      (    9.86    45.70   -41.88     0.74)  ; 45\r\n      (    9.76    46.53   -42.25     0.74)  ; 46\r\n      (    9.57    47.67   -43.19     0.44)  ; 47\r\n      (    9.68    48.31   -43.63     0.44)  ; 48\r\n      (    9.98    49.31   -45.63     0.37)  ; 49\r\n      (   10.56    50.62   -43.44     0.52)  ; 50\r\n      (   10.70    51.48   -43.63     0.52)  ; 51\r\n      (   11.30    51.98   -45.50     0.66)  ; 52\r\n      (   11.82    52.42   -45.50     0.66)  ; 53\r\n      (   12.55    53.26   -46.50     0.81)  ; 54\r\n      (   13.68    53.63   -48.00     0.59)  ; 55\r\n      (   14.32    54.35   -48.75     0.44)  ; 56\r\n      (   14.84    54.85   -49.44     0.66)  ; 57\r\n      (   15.59    55.33   -50.69     1.03)  ; 58\r\n      (   15.91    55.42   -51.50     1.55)  ; 59\r\n      (   16.40    55.72   -51.50     1.55)  ; 60\r\n      (   16.78    56.25   -51.63     0.74)  ; 61\r\n      (   16.99    56.66   -52.19     0.37)  ; 62\r\n      (   17.39    57.26   -52.81     0.66)  ; 63\r\n      (   17.87    58.00   -53.31     0.96)  ; 64\r\n      (   18.20    58.61   -53.50     0.52)  ; 65\r\n      (   18.56    59.00   -54.13     0.37)  ; 66\r\n      (   18.87    59.55   -55.00     0.37)  ; 67\r\n      (   19.17    60.02   -56.00     0.96)  ; 68\r\n      (   18.87    60.95   -56.81     1.84)  ; 69\r\n      (   18.65    61.43   -57.88     1.84)  ; 70\r\n      (   18.36    61.99   -59.00     1.03)  ; 71\r\n      (   18.49    62.79   -61.88     0.74)  ; 72\r\n      (   18.39    63.10   -63.25     1.03)  ; 73\r\n      (   18.36    63.40   -64.88     1.33)  ; 74\r\n      (   18.10    64.11   -66.06     1.62)  ; 75\r\n      (   17.82    64.22   -67.06     1.84)  ; 76\r\n      (   17.55    64.42   -68.75     1.40)  ; 77\r\n      (   18.10    65.07   -68.88     0.59)  ; 78\r\n      (   18.30    65.86   -69.31     0.59)  ; 79\r\n      (   18.58    66.18   -69.75     0.59)  ; 80\r\n      (   18.80    66.66   -70.06     0.81)  ; 81\r\n      (   19.43    67.30   -72.06     0.52)  ; 82\r\n       Normal\r\n    |\r\n      (   10.81    15.39    -5.63     0.81)  ; 1, R-2-2\r\n      (   10.95    16.25    -6.81     0.81)  ; 2\r\n      (   10.90    16.85    -7.50     1.11)  ; 3\r\n      (   10.90    17.37    -8.81     1.11)  ; 4\r\n      (   10.82    18.27    -9.31     0.81)  ; 5\r\n      (   10.61    18.82   -10.44     0.81)  ; 6\r\n      (    9.85    19.68   -10.44     0.74)  ; 7\r\n      (    9.16    20.53   -10.56     0.96)  ; 8\r\n      (    9.13    21.72   -11.19     1.11)  ; 9\r\n      (    8.94    22.86   -11.50     1.18)  ; 10\r\n      (    8.09    23.58   -11.75     0.81)  ; 11\r\n      (    7.51    24.19   -12.56     1.11)  ; 12\r\n      (    8.36    24.88   -13.69     1.40)  ; 13\r\n      (    8.80    25.70   -14.63     1.92)  ; 14\r\n      (    8.40    26.43   -15.25     1.40)  ; 15\r\n      (    8.55    27.37   -15.88     0.96)  ; 16\r\n      (    9.07    28.32   -15.88     0.81)  ; 17\r\n      (    9.50    29.59   -17.63     0.96)  ; 18\r\n      (    9.54    30.39   -17.63     0.74)  ; 19\r\n      (    9.69    31.33   -17.75     0.74)  ; 20\r\n      (    9.65    32.45   -18.94     1.18)  ; 21\r\n      (    9.66    32.52   -19.81     1.55)  ; 22\r\n      (    9.77    33.24   -20.69     1.55)  ; 23\r\n      (   10.24    33.83   -21.19     1.84)  ; 24\r\n      (   10.74    34.65   -22.38     1.11)  ; 25\r\n      (   10.88    35.51   -22.94     0.74)  ; 26\r\n      (   10.56    37.27   -23.19     0.96)  ; 27\r\n      (   10.73    38.34   -23.56     0.96)  ; 28\r\n      (   11.52    39.11   -26.13     0.66)  ; 29\r\n      (   11.62    40.21   -27.13     1.33)  ; 30\r\n      (   11.61    41.09   -28.31     1.69)  ; 31\r\n      (   11.60    41.47   -28.50     1.40)  ; 32\r\n      (\r\n        (   11.20    42.71   -29.44     0.59)  ; 1, R-2-2-1\r\n        (   10.71    43.82   -29.69     0.52)  ; 2\r\n        (   10.55    44.74   -30.31     0.52)  ; 3\r\n        (   10.34    45.29   -30.69     0.52)  ; 4\r\n        (    9.98    45.79   -31.06     0.52)  ; 5\r\n        (    9.06    46.68   -31.75     0.81)  ; 6\r\n        (    8.55    47.20   -32.63     1.18)  ; 7\r\n        (    8.35    47.38   -32.88     1.62)  ; 8\r\n        (    8.10    48.08   -33.94     1.84)  ; 9\r\n        (    7.52    48.69   -33.94     1.03)  ; 10\r\n        (    6.47    49.08   -35.31     0.59)  ; 11\r\n        (    5.36    49.19   -36.31     0.59)  ; 12\r\n        (    4.63    49.23   -37.00     0.96)  ; 13\r\n        (    4.15    49.53   -38.81     1.77)  ; 14\r\n        (    3.24    49.90   -40.75     1.62)  ; 15\r\n        (    2.72    50.86   -41.00     1.11)  ; 16\r\n        (    2.50    51.41   -42.31     0.74)  ; 17\r\n        (    2.01    52.09   -43.88     0.37)  ; 18\r\n        (    1.98    52.76   -43.88     0.37)  ; 19\r\n        (    2.53    54.05   -43.88     0.37)  ; 20\r\n        (    3.44    55.09   -44.63     0.74)  ; 21\r\n        (    4.16    56.30   -44.63     0.52)  ; 22\r\n        (    4.21    57.12   -45.94     0.81)  ; 23\r\n        (    4.22    57.63   -46.63     1.18)  ; 24\r\n        (    4.39    58.27   -47.25     1.62)  ; 25\r\n        (    4.69    58.74   -47.25     1.18)  ; 26\r\n        (    5.09    59.34   -47.25     0.74)  ; 27\r\n        (    5.20    60.07   -48.50     0.37)  ; 28\r\n        (    5.52    60.68   -48.50     0.44)  ; 29\r\n        (    5.96    62.02   -48.50     1.18)  ; 30\r\n        (    6.40    62.98   -50.69     1.92)  ; 31\r\n        (    6.52    63.71   -51.44     1.92)  ; 32\r\n        (    6.38    64.69   -52.63     1.33)  ; 33\r\n        (    6.55    65.33   -52.81     0.81)  ; 34\r\n        (    7.72    66.55   -53.06     0.59)  ; 35\r\n        (    8.33    67.11   -53.06     0.59)  ; 36\r\n        (    9.29    68.00   -54.25     1.33)  ; 37\r\n        (   10.20    68.53   -54.75     0.88)  ; 38\r\n        (   11.68    68.95   -55.13     0.52)  ; 39\r\n        (   12.27    69.39   -55.56     0.88)  ; 40\r\n        (   12.89    70.03   -56.88     1.18)  ; 41\r\n        (   13.22    70.71   -57.31     0.44)  ; 42\r\n        (   13.75    71.66   -57.94     0.22)  ; 43\r\n        (   14.71    72.54   -58.00     0.44)  ; 44\r\n        (   15.82    72.96   -58.44     0.59)  ; 45\r\n        (   16.46    73.23   -58.69     0.37)  ; 46\r\n        (   17.19    73.64   -59.31     0.74)  ; 47\r\n        (   17.69    73.99   -59.38     1.55)  ; 48\r\n        (   18.29    74.43   -59.38     1.84)  ; 49\r\n        (   18.95    74.84   -59.38     0.59)  ; 50\r\n        (   20.07    75.33   -60.13     0.37)  ; 51\r\n        (   20.95    75.70   -60.31     0.37)  ; 52\r\n         Normal\r\n      |\r\n        (   12.23    42.30   -29.56     0.74)  ; 1, R-2-2-2\r\n        (   12.80    42.58   -31.13     0.74)  ; 2\r\n        (   12.92    43.37   -31.63     1.03)  ; 3\r\n        (   12.98    44.25   -33.06     0.59)  ; 4\r\n        (   13.19    45.55   -33.25     0.66)  ; 5\r\n        (   13.31    45.83   -34.63     0.59)  ; 6\r\n        (   13.54    46.31   -36.44     0.59)  ; 7\r\n        (   13.22    47.17   -36.50     0.59)  ; 8\r\n        (   12.78    48.13   -37.56     0.59)  ; 9\r\n        (   12.31    48.95   -38.19     0.96)  ; 10\r\n        (   12.14    49.71   -39.81     1.77)  ; 11\r\n        (   11.78    50.29   -40.25     2.14)  ; 12\r\n        (   11.33    51.18   -41.00     1.69)  ; 13\r\n        (   11.25    51.64   -41.88     1.25)  ; 14\r\n        (   11.33    52.14   -43.50     0.96)  ; 15\r\n        (   10.84    51.91   -43.19     0.74)  ; 16\r\n        (   10.39    51.84   -45.56     0.66)  ; 17\r\n        (    9.73    52.39   -47.38     0.59)  ; 18\r\n        (    9.66    52.92   -47.75     0.96)  ; 19\r\n        (    9.36    53.41   -47.75     1.84)  ; 20\r\n        (    8.86    53.94   -48.50     2.28)  ; 21\r\n        (    8.66    55.07   -48.94     0.81)  ; 22\r\n        (    8.50    56.43   -50.56     0.44)  ; 23\r\n        (    8.65    57.82   -50.81     0.44)  ; 24\r\n        (    8.99    58.57   -51.75     0.81)  ; 25\r\n        (    8.69    59.00   -51.88     1.55)  ; 26\r\n        (    8.62    59.52   -51.88     1.55)  ; 27\r\n        (    8.39    59.93   -52.38     0.74)  ; 28\r\n        (    8.24    60.48   -53.69     0.52)  ; 29\r\n         Normal\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (   12.03    -0.93    12.38     2.14)  ; Root\r\n  (   13.41    -1.15    12.56     1.47)  ; 1, R\r\n  (   14.18    -1.50    12.44     1.40)  ; 2\r\n  (   15.28    -2.05    12.50     1.92)  ; 3\r\n  (   15.89    -2.44    12.56     2.06)  ; 4\r\n  (\r\n    (   16.88    -3.26    12.38     1.62)  ; 1, R-1\r\n    (   17.40    -4.22    12.25     0.88)  ; 2\r\n    (   18.16    -5.08    12.25     0.74)  ; 3\r\n    (   19.36    -6.01    11.94     0.66)  ; 4\r\n    (   20.41    -6.85    11.88     0.88)  ; 5\r\n    (   21.18    -7.71    11.38     1.03)  ; 6\r\n    (   21.91    -8.71    10.94     1.11)  ; 7\r\n    (   22.63    -9.34    10.94     1.18)  ; 8\r\n    (   23.59    -9.87    10.50     0.81)  ; 9\r\n    (   24.88   -10.66    10.13     0.74)  ; 10\r\n    (   26.02   -11.51     9.94     0.74)  ; 11\r\n    (   26.94   -12.26     9.94     0.88)  ; 12\r\n    (   27.84   -13.21     9.94     0.88)  ; 13\r\n    (   28.58   -13.70     9.50     0.74)  ; 14\r\n    (   29.88   -14.42     9.31     0.74)  ; 15\r\n    (   30.20   -15.21     8.81     0.74)  ; 16\r\n    (   30.95   -15.69     8.44     0.74)  ; 17\r\n    (   31.69   -16.19     8.44     0.74)  ; 18\r\n    (   32.99   -16.91     8.44     0.81)  ; 19\r\n    (   34.41   -17.43     7.88     0.74)  ; 20\r\n    (   35.31   -17.79     7.88     0.96)  ; 21\r\n    (   36.16   -18.60     7.50     1.25)  ; 22\r\n    (   37.14   -19.49     7.00     1.25)  ; 23\r\n    (   38.16   -20.10     6.50     0.88)  ; 24\r\n    (   38.90   -20.59     6.50     0.81)  ; 25\r\n    (   40.17   -21.08     5.19     1.03)  ; 26\r\n    (   41.14   -21.53     5.13     0.74)  ; 27\r\n    (   41.86   -21.18     4.50     0.59)  ; 28\r\n    (   42.66   -21.31     3.94     0.59)  ; 29\r\n    (   43.69   -21.84     3.38     0.81)  ; 30\r\n    (   44.81   -22.83     3.25     0.81)  ; 31\r\n    (   45.63   -23.70     3.25     0.59)  ; 32\r\n    (   46.17   -24.08     2.75     0.88)  ; 33\r\n    (   46.46   -24.65     3.38     1.47)  ; 34\r\n    (   47.00   -25.48     3.38     1.11)  ; 35\r\n    (   47.64   -26.09     3.25     0.59)  ; 36\r\n    (   48.22   -26.77     3.81     0.66)  ; 37\r\n    (   48.74   -28.12     3.69     0.66)  ; 38\r\n    (   49.20   -29.00     3.31     0.74)  ; 39\r\n    (   49.95   -29.93     3.31     0.44)  ; 40\r\n    (   50.85   -30.82     3.06     0.44)  ; 41\r\n    (   51.68   -31.69     3.06     1.03)  ; 42\r\n    (   52.55   -32.35     2.94     1.69)  ; 43\r\n    (   53.31   -32.69     2.63     1.69)  ; 44\r\n    (   53.93   -33.08     2.56     0.88)  ; 45\r\n    (   54.73   -33.66     1.94     0.59)  ; 46\r\n    (   55.85   -34.13     1.69     0.59)  ; 47\r\n    (   57.28   -34.50     1.38     0.59)  ; 48\r\n    (   57.90   -34.83     0.88     0.81)  ; 49\r\n    (   58.13   -35.23     0.50     1.11)  ; 50\r\n    (   58.88   -36.17     0.50     1.25)  ; 51\r\n    (   59.89   -36.84    -0.19     0.59)  ; 52\r\n    (   60.97   -37.08    -0.81     0.44)  ; 53\r\n    (   61.53   -37.32    -0.81     0.74)  ; 54\r\n    (   62.29   -37.67    -1.13     1.25)  ; 55\r\n    (   62.69   -38.03    -1.44     1.77)  ; 56\r\n    (   63.16   -38.84    -0.94     0.74)  ; 57\r\n    (   63.55   -39.64    -0.94     0.44)  ; 58\r\n    (   64.64   -40.33    -0.69     0.59)  ; 59\r\n    (   65.02   -40.96    -0.69     0.59)  ; 60\r\n    (\r\n      (   65.06   -40.99    -0.69     0.59)  ; 1, R-1-1\r\n      (   65.42   -41.50    -0.19     1.18)  ; 2\r\n      (   65.77   -42.14    -0.19     1.55)  ; 3\r\n      (   66.20   -42.72    -0.06     1.33)  ; 4\r\n      (   66.61   -42.94     0.38     0.88)  ; 5\r\n      (   67.21   -43.40     0.38     0.59)  ; 6\r\n      (   67.71   -44.00     0.38     0.44)  ; 7\r\n      (   68.46   -44.42     0.38     0.44)  ; 8\r\n      (   69.41   -45.01     0.88     0.74)  ; 9\r\n      (   70.19   -45.79     2.06     0.96)  ; 10\r\n      (   70.93   -46.29     2.19     0.52)  ; 11\r\n      (   72.06   -47.13     2.19     0.44)  ; 12\r\n      (   73.05   -47.96     2.19     0.66)  ; 13\r\n      (   73.41   -48.53     2.19     0.66)  ; 14\r\n      (   73.98   -49.14     2.19     0.59)  ; 15\r\n      (   74.20   -49.62     2.19     1.18)  ; 16\r\n      (   74.49   -50.18     2.19     1.62)  ; 17\r\n      (   75.01   -50.63     2.19     1.11)  ; 18\r\n      (   75.78   -51.50     2.19     0.29)  ; 19\r\n      (   76.98   -52.35     2.19     0.29)  ; 20\r\n      (   77.76   -52.62     2.00     0.74)  ; 21\r\n      (   78.73   -53.07     2.19     0.74)  ; 22\r\n      (   79.17   -53.58     2.19     0.37)  ; 23\r\n      (   79.74   -54.19     2.19     0.88)  ; 24\r\n      (   80.43   -54.59     2.25     1.62)  ; 25\r\n      (   80.94   -55.05     2.00     1.84)  ; 26\r\n      (   81.55   -55.44     1.75     1.40)  ; 27\r\n      (   82.41   -55.73     2.06     0.66)  ; 28\r\n      (   83.49   -55.90     2.06     0.52)  ; 29\r\n      (   84.14   -56.52     2.06     0.81)  ; 30\r\n      (   85.01   -56.66     2.06     0.52)  ; 31\r\n      (   86.35   -56.72     2.06     0.44)  ; 32\r\n      (   86.96   -56.67     2.44     0.66)  ; 33\r\n      (   88.02   -56.55     2.19     0.66)  ; 34\r\n      (   89.10   -56.72     2.00     0.66)  ; 35\r\n      (   89.82   -56.90     2.00     0.66)  ; 36\r\n      (   90.61   -57.10     1.75     1.25)  ; 37\r\n      (   91.30   -57.50     2.06     1.47)  ; 38\r\n      (   91.86   -57.74     2.06     1.03)  ; 39\r\n      (   92.44   -57.83     2.06     0.59)  ; 40\r\n      (   93.16   -58.46     2.06     0.44)  ; 41\r\n      (   94.21   -59.37     2.44     0.44)  ; 42\r\n      (   94.97   -59.79     3.06     0.44)  ; 43\r\n      (   95.81   -60.07     3.25     0.74)  ; 44\r\n      (   96.51   -60.40     3.25     1.03)  ; 45\r\n      (   96.85   -60.61     3.81     0.66)  ; 46\r\n      (   98.01   -61.31     3.81     0.29)  ; 47\r\n      (   98.91   -61.74     3.81     0.59)  ; 48\r\n      (   99.61   -62.01     3.88     0.59)  ; 49\r\n      (  100.04   -62.07     3.88     0.52)  ; 50\r\n      (  100.98   -62.81     3.88     0.74)  ; 51\r\n      (  102.06   -62.98     3.75     0.52)  ; 52\r\n      (  102.98   -63.35     3.63     0.52)  ; 53\r\n      (  103.47   -63.50     3.63     0.81)  ; 54\r\n      (  103.81   -63.70     4.00     1.11)  ; 55\r\n      (  104.46   -63.88     4.00     1.40)  ; 56\r\n      (  105.03   -64.05     4.00     0.59)  ; 57\r\n      (  106.20   -64.15     4.00     0.37)  ; 58\r\n      (  106.93   -64.27     4.19     0.37)  ; 59\r\n      (  107.37   -64.34     4.56     0.44)  ; 60\r\n      (  109.24   -64.79     4.56     0.37)  ; 61\r\n      (  110.90   -65.12     4.56     0.37)  ; 62\r\n      (  111.64   -65.61     4.75     1.03)  ; 63\r\n      (  112.36   -65.80     4.75     1.40)  ; 64\r\n      (  113.23   -65.93     5.13     0.59)  ; 65\r\n      (  114.02   -66.13     5.19     0.44)  ; 66\r\n      (  114.53   -66.21     5.50     0.44)  ; 67\r\n       Normal\r\n    |\r\n      (   65.95   -41.87    -2.44     0.74)  ; 1, R-1-2\r\n      (   67.10   -42.20    -2.75     1.03)  ; 2\r\n      (   67.68   -42.29    -3.00     1.18)  ; 3\r\n      (   68.18   -42.45    -3.00     0.59)  ; 4\r\n      (   68.84   -42.92    -3.31     0.59)  ; 5\r\n      (   69.28   -42.99    -3.75     0.96)  ; 6\r\n      (   69.72   -43.06    -5.00     1.25)  ; 7\r\n      (   70.12   -43.34    -5.63     0.88)  ; 8\r\n      (   70.88   -43.31    -6.94     0.66)  ; 9\r\n      (   72.28   -43.39    -7.31     0.52)  ; 10\r\n      (   73.05   -43.22    -7.81     0.59)  ; 11\r\n      (   74.04   -43.07    -7.88     0.74)  ; 12\r\n      (   74.43   -42.99    -7.88     1.47)  ; 13\r\n      (   75.28   -42.82    -8.25     1.77)  ; 14\r\n      (   75.59   -42.28    -8.88     1.33)  ; 15\r\n      (   76.09   -41.92    -8.63     0.52)  ; 16\r\n      (   77.04   -41.62    -8.38     0.44)  ; 17\r\n      (   78.03   -41.42    -7.94     0.44)  ; 18\r\n      (   79.78   -40.73    -7.56     0.44)  ; 19\r\n      (   80.37   -40.31    -7.88     1.03)  ; 20\r\n      (   81.00   -40.11    -8.50     1.77)  ; 21\r\n      (   81.40   -39.95    -8.75     1.77)  ; 22\r\n      (   81.99   -39.52    -8.75     0.88)  ; 23\r\n      (   82.37   -39.44    -9.19     0.52)  ; 24\r\n      (   83.04   -39.47    -9.19     0.44)  ; 25\r\n      (   83.75   -39.28    -9.44     0.74)  ; 26\r\n      (   84.13   -39.19    -9.81     1.11)  ; 27\r\n      (   84.36   -39.16   -11.25     0.74)  ; 28\r\n       Normal\r\n    )  ;  End of split\r\n  |\r\n    (   17.14    -2.55    11.63     1.55)  ; 1, R-2\r\n    (\r\n      (   18.18    -3.01    11.19     0.66)  ; 1, R-2-1\r\n      (   19.01    -2.99    11.13     0.66)  ; 2\r\n      (   20.38    -2.84    11.13     1.11)  ; 3\r\n      (   21.25    -2.98    11.06     1.69)  ; 4\r\n      (\r\n        (   22.08    -3.85    10.56     0.74)  ; 1, R-2-1-1\r\n        (   22.91    -4.21    10.44     0.66)  ; 2\r\n        (   23.78    -4.86    10.31     0.66)  ; 3\r\n        (   25.53    -4.69    10.00     0.74)  ; 4\r\n        (   26.50    -5.14     9.31     0.59)  ; 5\r\n        (   27.68    -5.70     8.94     0.59)  ; 6\r\n        (   28.91    -5.90     8.06     0.74)  ; 7\r\n        (   29.76    -6.25     7.69     0.96)  ; 8\r\n        (   30.64    -6.84     6.94     0.81)  ; 9\r\n        (   31.55    -7.12     6.69     0.52)  ; 10\r\n        (   32.90    -7.56     6.81     0.52)  ; 11\r\n        (   34.04    -7.89     7.06     0.59)  ; 12\r\n        (   34.82    -8.16     7.81     0.52)  ; 13\r\n        (   36.09    -8.66     7.56     0.52)  ; 14\r\n        (   37.71    -9.21     7.56     0.59)  ; 15\r\n        (   38.70    -9.59     7.75     0.81)  ; 16\r\n        (   39.63    -9.81     8.06     1.03)  ; 17\r\n        (   40.07    -9.88     8.69     1.25)  ; 18\r\n        (   42.12   -10.13     7.88     0.74)  ; 19\r\n        (   43.61   -10.59     8.13     0.66)  ; 20\r\n        (   44.92   -10.80     8.44     0.66)  ; 21\r\n        (   45.71   -10.55     8.56     0.66)  ; 22\r\n        (   47.30   -10.88     8.56     0.74)  ; 23\r\n        (   48.79   -11.33     8.56     0.59)  ; 24\r\n        (   49.85   -11.72     8.50     0.52)  ; 25\r\n        (   50.71   -11.94     8.50     0.81)  ; 26\r\n        (   51.35   -12.18     8.50     1.03)  ; 27\r\n        (   52.45   -12.73     8.50     0.52)  ; 28\r\n        (   54.01   -13.27     7.94     0.44)  ; 29\r\n        (   55.62   -13.90     7.94     0.66)  ; 30\r\n        (   56.65   -14.51     7.94     1.40)  ; 31\r\n        (   57.69   -14.97     8.63     0.66)  ; 32\r\n        (   58.82   -15.82     6.06     0.44)  ; 33\r\n        (   60.70   -17.31     6.06     0.44)  ; 34\r\n        (   61.91   -18.17     5.88     0.44)  ; 35\r\n        (   63.28   -19.42     5.88     0.52)  ; 36\r\n        (   64.21   -19.72     5.88     0.74)  ; 37\r\n        (   65.21   -20.39     5.63     0.44)  ; 38\r\n        (   66.49   -20.82     5.44     0.66)  ; 39\r\n        (   68.10   -21.44     5.44     0.66)  ; 40\r\n        (   68.97   -21.58     5.44     0.66)  ; 41\r\n        (   70.25   -21.56     5.00     0.66)  ; 42\r\n        (   71.41   -21.82     6.38     0.96)  ; 43\r\n        (   71.88   -22.12     6.38     1.69)  ; 44\r\n        (   72.94   -21.98     6.44     0.74)  ; 45\r\n        (   73.81   -21.67     7.06     0.66)  ; 46\r\n        (   75.15   -21.67     7.56     0.37)  ; 47\r\n        (   76.37   -21.56     8.13     0.37)  ; 48\r\n        (   77.68   -21.25     8.88     0.37)  ; 49\r\n        (   78.33   -20.91     9.63     0.44)  ; 50\r\n        (   79.29   -20.48     9.81     0.96)  ; 51\r\n        (   79.89   -20.50     9.81     1.69)  ; 52\r\n        (   80.33   -20.04     9.81     1.33)  ; 53\r\n        (   80.92   -19.69    10.06     0.52)  ; 54\r\n        (   81.79   -18.87    10.44     0.44)  ; 55\r\n        (   82.92   -17.86    10.56     0.59)  ; 56\r\n        (   84.08   -16.65    10.19     0.44)  ; 57\r\n        (   84.88   -15.81     9.69     0.44)  ; 58\r\n        (   85.19   -15.26     9.69     0.74)  ; 59\r\n        (   85.83   -14.56     8.94     0.88)  ; 60\r\n        (   86.06   -14.08     8.25     0.59)  ; 61\r\n        (   87.11   -13.49     8.25     0.52)  ; 62\r\n        (   87.84   -13.17     8.06     0.74)  ; 63\r\n        (   89.28   -13.03     8.00     0.74)  ; 64\r\n        (   90.21   -12.80     7.88     0.52)  ; 65\r\n        (   91.09   -12.94     7.44     0.88)  ; 66\r\n        (   91.38   -12.91     7.69     2.28)  ; 67\r\n        (   92.48   -13.09     7.81     1.33)  ; 68\r\n        (   93.52   -13.55     8.06     0.52)  ; 69\r\n        (   94.39   -14.21     8.31     0.37)  ; 70\r\n        (   94.98   -14.67     8.31     0.37)  ; 71\r\n        (   95.72   -15.23     8.44     0.66)  ; 72\r\n        (   96.80   -15.92     8.44     0.66)  ; 73\r\n        (   97.64   -16.79     8.50     0.66)  ; 74\r\n        (   98.36   -17.42     9.00     0.96)  ; 75\r\n        (   99.47   -17.90     9.38     1.25)  ; 76\r\n        (  100.57   -18.52     9.94     1.40)  ; 77\r\n        (  101.38   -19.01    10.06     1.77)  ; 78\r\n        (  102.33   -19.61    10.13     1.92)  ; 79\r\n        (  103.04   -20.31    10.13     0.74)  ; 80\r\n        (  103.61   -21.26     9.69     0.44)  ; 81\r\n        (  104.79   -22.78     9.06     0.37)  ; 82\r\n        (  105.55   -23.64     9.25     0.37)  ; 83\r\n        (  106.32   -24.43     9.25     0.52)  ; 84\r\n         Normal\r\n      |\r\n        (   22.01    -2.40    10.81     0.74)  ; 1, R-2-1-2\r\n        (   22.88    -2.02    10.81     0.44)  ; 2\r\n        (   24.16    -1.93    10.69     0.44)  ; 3\r\n        (   25.02    -1.70    10.44     0.44)  ; 4\r\n        (   25.97    -1.40    10.50     0.74)  ; 5\r\n        (   26.82    -1.24    11.63     1.25)  ; 6\r\n        (   27.50    -1.13    11.69     1.25)  ; 7\r\n        (   28.42    -1.05    12.44     1.03)  ; 8\r\n        (   29.82    -1.13    13.00     0.66)  ; 9\r\n        (   30.85    -1.21    13.88     0.66)  ; 10\r\n        (   31.91    -1.53    14.31     0.59)  ; 11\r\n        (   32.85    -1.75    14.81     0.88)  ; 12\r\n        (   33.79    -1.98    15.13     0.74)  ; 13\r\n        (   34.22    -2.05    15.44     0.74)  ; 14\r\n        (   35.46    -2.24    16.00     0.88)  ; 15\r\n        (   36.77    -2.90    16.38     0.74)  ; 16\r\n        (   37.52    -3.31    16.75     0.81)  ; 17\r\n        (   38.43    -4.19    17.00     0.81)  ; 18\r\n        (   39.30    -4.85    17.06     0.52)  ; 19\r\n        (   40.16    -5.50    17.44     0.52)  ; 20\r\n        (   41.05    -6.46    17.44     0.66)  ; 21\r\n        (   41.89    -7.33    17.44     0.66)  ; 22\r\n        (   42.75    -8.06    17.63     1.11)  ; 23\r\n        (   43.11    -8.56    17.88     1.77)  ; 24\r\n        (   43.88    -8.90    17.88     1.77)  ; 25\r\n        (   44.84    -9.43    17.94     0.59)  ; 26\r\n        (   46.05   -10.28    18.38     0.52)  ; 27\r\n        (   47.09   -10.75    18.44     0.52)  ; 28\r\n        (   47.99   -11.19    18.44     1.11)  ; 29\r\n        (   48.69   -11.52    18.44     1.25)  ; 30\r\n        (   49.75   -12.35    18.44     0.81)  ; 31\r\n        (   50.94   -12.84    18.81     0.66)  ; 32\r\n        (   52.17   -13.48    18.44     0.59)  ; 33\r\n        (   52.78   -13.94    18.44     0.59)  ; 34\r\n        (   53.37   -14.85    18.94     1.18)  ; 35\r\n        (   54.11   -15.40    18.94     1.18)  ; 36\r\n        (   54.79   -16.26    19.00     0.52)  ; 37\r\n        (   55.61   -16.76    19.00     0.52)  ; 38\r\n        (   56.61   -17.51    19.00     0.66)  ; 39\r\n        (   57.77   -18.21    19.00     0.66)  ; 40\r\n        (   58.76   -18.07    19.00     0.66)  ; 41\r\n        (   59.89   -18.03    19.56     0.96)  ; 42\r\n        (   61.00   -18.05    19.56     1.40)  ; 43\r\n        (   62.06   -18.00    19.56     1.47)  ; 44\r\n        (   62.90   -17.84    19.63     0.66)  ; 45\r\n        (   63.93   -17.92    19.69     0.74)  ; 46\r\n        (   64.79   -17.49    19.75     0.52)  ; 47\r\n        (   65.75   -17.13    19.56     0.52)  ; 48\r\n        (   66.59   -16.51    20.13     0.59)  ; 49\r\n        (   66.75   -16.02    20.75     0.88)  ; 50\r\n        (   67.40   -15.68    21.06     1.33)  ; 51\r\n        (   67.82   -15.31    21.75     0.88)  ; 52\r\n        (   68.03   -15.93    22.94     0.74)  ; 53\r\n        (   68.13   -16.68    23.19     0.59)  ; 54\r\n        (   67.84   -17.53    23.63     0.44)  ; 55\r\n        (   68.06   -18.08    23.81     0.44)  ; 56\r\n        (   68.17   -18.32    24.00     0.44)  ; 57\r\n        (   68.67   -18.47    24.00     0.74)  ; 58\r\n        (   69.16   -18.18    24.44     1.55)  ; 59\r\n        (   69.49   -17.50    24.81     1.55)  ; 60\r\n        (   70.14   -17.15    25.25     0.66)  ; 61\r\n        (   71.24   -17.77    25.31     0.52)  ; 62\r\n        (   72.34   -17.42    25.56     0.52)  ; 63\r\n        (   73.53   -16.95    26.00     0.52)  ; 64\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (   17.68    -1.91    14.13     0.59)  ; 1, R-2-2\r\n      (   18.11    -1.98    14.25     0.59)  ; 2\r\n      (   18.73    -2.30    14.50     0.59)  ; 3\r\n      (   19.54    -2.35    14.63     0.59)  ; 4\r\n      (   20.15    -1.85    14.94     0.59)  ; 5\r\n      (   20.53    -1.32    15.50     0.74)  ; 6\r\n      (   21.31    -2.04    15.88     0.59)  ; 7\r\n      (   22.13    -2.53    15.88     0.59)  ; 8\r\n      (   22.32    -3.68    15.94     0.66)  ; 9\r\n      (   23.55    -5.28    16.00     0.88)  ; 10\r\n      (   24.80    -6.37    16.00     1.11)  ; 11\r\n      (   25.53    -7.00    16.25     1.03)  ; 12\r\n      (   26.47    -7.15    16.19     0.74)  ; 13\r\n      (   27.43    -7.23    16.13     0.59)  ; 14\r\n      (   28.32    -8.18    16.13     0.52)  ; 15\r\n      (   28.46    -8.72    16.00     0.52)  ; 16\r\n      (   28.81    -9.29    16.00     0.74)  ; 17\r\n      (   29.14   -10.08    16.00     0.96)  ; 18\r\n      (   29.92   -11.25    15.94     0.66)  ; 19\r\n      (   30.57   -11.42    15.69     0.59)  ; 20\r\n      (   31.51   -12.53    15.69     0.59)  ; 21\r\n      (   32.08   -13.22    16.13     0.66)  ; 22\r\n      (   32.95   -13.79    16.81     0.59)  ; 23\r\n      (   33.75   -13.92    17.38     0.74)  ; 24\r\n      (   34.57   -13.91    18.31     0.96)  ; 25\r\n      (   35.30   -14.02    18.44     1.69)  ; 26\r\n      (   36.40   -14.20    18.44     1.11)  ; 27\r\n      (   37.13   -14.76    19.88     0.74)  ; 28\r\n      (   38.15   -14.92    19.94     0.59)  ; 29\r\n      (   38.96   -14.97    20.81     0.81)  ; 30\r\n      (   40.15   -15.01    21.00     0.96)  ; 31\r\n      (   41.29   -15.34    21.50     0.74)  ; 32\r\n      (   42.85   -15.81    21.75     0.66)  ; 33\r\n      (   44.01   -16.51    23.19     0.81)  ; 34\r\n      (   44.13   -16.68    24.38     0.74)  ; 35\r\n      (   44.75   -17.00    25.06     0.59)  ; 36\r\n      (   45.48   -16.60    25.31     0.59)  ; 37\r\n      (   46.59   -16.18    25.44     0.59)  ; 38\r\n      (   47.48   -16.25    25.69     0.66)  ; 39\r\n      (   48.00   -16.26    25.69     0.66)  ; 40\r\n      (   48.73   -17.34    25.88     0.66)  ; 41\r\n      (   49.23   -17.93    25.94     0.66)  ; 42\r\n      (   49.86   -18.62    26.19     0.44)  ; 43\r\n      (   50.27   -19.35    26.44     1.03)  ; 44\r\n      (   50.68   -20.09    25.88     1.33)  ; 45\r\n      (   51.40   -21.16    25.88     0.88)  ; 46\r\n      (   52.54   -21.94    25.63     0.52)  ; 47\r\n      (   53.57   -22.54    25.38     0.52)  ; 48\r\n      (   53.56   -23.50    25.31     0.52)  ; 49\r\n      (   53.32   -24.13    26.00     0.44)  ; 50\r\n      (   53.32   -25.02    26.06     0.44)  ; 51\r\n      (   53.59   -26.18    26.19     0.44)  ; 52\r\n      (   53.61   -26.99    26.19     0.44)  ; 53\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (    6.19    -3.59     0.00     3.17)  ; Root\r\n  (    7.49    -5.35    -0.19     2.65)  ; 1, R\r\n  (    8.94    -6.98    -0.88     2.80)  ; 2\r\n  (   10.22    -7.85    -1.13     3.02)  ; 3\r\n  (\r\n    (   10.57    -9.65    -1.13     3.02)  ; 1, R-1\r\n    (\r\n      (   10.51    -9.75    -1.75     3.83)  ; 1, R-1-1\r\n      (   11.36   -10.55    -2.13     3.09)  ; 2\r\n      (   12.21   -10.83    -2.31     2.36)  ; 3\r\n      (   13.15   -11.94    -2.63     2.14)  ; 4\r\n      (   14.07   -13.20    -2.38     2.43)  ; 5\r\n      (\r\n        (   14.25   -14.42    -2.44     1.55)  ; 1, R-1-1-1\r\n        (   14.77   -15.38    -2.88     1.69)  ; 2\r\n        (   15.66   -16.34    -2.19     2.28)  ; 3\r\n        (\r\n          (   16.67   -17.46    -1.81     2.58)  ; 1, R-1-1-1-1\r\n          (   18.06   -18.20    -1.81     1.99)  ; 2\r\n          (   19.00   -18.79    -2.00     1.47)  ; 3\r\n          (   19.63   -19.56    -2.31     1.11)  ; 4\r\n          (   20.35   -20.64    -1.63     1.18)  ; 5\r\n          (\r\n            (   21.32   -22.49    -0.88     1.03)  ; 1, R-1-1-1-1-1\r\n            (   22.42   -23.55    -1.25     0.88)  ; 2\r\n            (   23.30   -24.13    -1.31     0.74)  ; 3\r\n            (   24.25   -24.73    -1.13     0.74)  ; 4\r\n            (   25.44   -25.29    -1.25     0.96)  ; 5\r\n            (   26.33   -25.73    -1.38     1.18)  ; 6\r\n            (   27.09   -26.59    -1.38     1.03)  ; 7\r\n            (   27.48   -27.39    -1.50     1.11)  ; 8\r\n            (   28.21   -28.46    -1.63     1.25)  ; 9\r\n            (   28.89   -28.86    -1.69     1.40)  ; 10\r\n            (   29.62   -29.94    -1.69     1.03)  ; 11\r\n            (   30.66   -30.93    -1.75     0.88)  ; 12\r\n            (   31.44   -32.08    -2.06     1.18)  ; 13\r\n            (   32.19   -32.91    -1.31     1.84)  ; 14\r\n            (   32.71   -33.80    -0.81     1.69)  ; 15\r\n            (   32.92   -34.43    -0.44     1.33)  ; 16\r\n            (   33.34   -35.53     0.13     1.11)  ; 17\r\n            (   33.39   -36.65     0.13     0.81)  ; 18\r\n            (   33.64   -37.88     0.19     0.81)  ; 19\r\n            (   33.19   -39.29     0.56     0.96)  ; 20\r\n            (   33.32   -40.34     0.94     1.18)  ; 21\r\n            (   33.08   -41.41     1.50     1.03)  ; 22\r\n            (   32.52   -42.58     1.88     0.81)  ; 23\r\n            (   32.11   -43.77     2.06     1.18)  ; 24\r\n            (   31.43   -44.71     2.31     0.96)  ; 25\r\n            (   30.70   -45.55     2.38     0.96)  ; 26\r\n            (   29.81   -46.52     2.94     0.96)  ; 27\r\n            (   28.97   -47.57     3.00     1.18)  ; 28\r\n            (   27.78   -48.49     3.00     1.03)  ; 29\r\n            (   27.03   -49.41     3.56     1.18)  ; 30\r\n            (   26.59   -50.82     3.88     1.33)  ; 31\r\n            (   26.38   -52.12     4.13     1.11)  ; 32\r\n            (   26.26   -52.91     4.38     0.96)  ; 33\r\n            (   26.39   -53.97     4.44     1.47)  ; 34\r\n            (   26.60   -55.04     4.31     0.96)  ; 35\r\n            (   26.39   -55.83     4.06     0.66)  ; 36\r\n            (   26.53   -57.33     3.44     1.25)  ; 37\r\n            (   26.56   -58.52     2.50     0.96)  ; 38\r\n            (   26.42   -59.70     2.00     1.11)  ; 39\r\n            (   26.68   -60.40     1.69     0.88)  ; 40\r\n            (   26.88   -61.92     1.69     0.81)  ; 41\r\n            (   26.99   -63.12     1.44     0.81)  ; 42\r\n            (   26.95   -64.29     2.13     0.74)  ; 43\r\n            (   26.91   -65.55     1.81     1.03)  ; 44\r\n            (   26.85   -66.35     0.81     1.33)  ; 45\r\n            (   26.84   -66.42     0.31     1.84)  ; 46\r\n            (   26.89   -67.09    -0.88     1.84)  ; 47\r\n            (   27.08   -68.24    -1.81     1.33)  ; 48\r\n            (   27.34   -68.95    -3.13     1.18)  ; 49\r\n            (   27.44   -70.15    -3.31     0.88)  ; 50\r\n            (   27.81   -71.17    -3.88     0.88)  ; 51\r\n            (   27.83   -72.43    -4.00     0.66)  ; 52\r\n            (   28.28   -73.39    -4.44     1.03)  ; 53\r\n            (   28.86   -74.36    -4.94     1.33)  ; 54\r\n            (   29.74   -75.39    -5.88     1.62)  ; 55\r\n            (   30.19   -76.35    -6.38     1.40)  ; 56\r\n            (   31.01   -77.29    -6.38     0.96)  ; 57\r\n            (   31.34   -78.53    -6.69     0.81)  ; 58\r\n            (   31.95   -79.88    -6.69     0.59)  ; 59\r\n            (   32.85   -81.73    -6.69     0.59)  ; 60\r\n            (   33.83   -82.99    -6.75     0.52)  ; 61\r\n            (   33.81   -84.10    -6.94     0.52)  ; 62\r\n            (   34.21   -85.35    -6.94     0.52)  ; 63\r\n            (   34.64   -86.38    -6.56     1.84)  ; 64\r\n            (   34.83   -87.53    -6.44     1.92)  ; 65\r\n            (   35.17   -88.17    -6.44     0.96)  ; 66\r\n            (   35.58   -88.83    -6.44     0.37)  ; 67\r\n            (   36.19   -89.74    -5.88     0.44)  ; 68\r\n            (   36.76   -90.87    -5.88     0.52)  ; 69\r\n            (   37.20   -91.82    -5.94     0.74)  ; 70\r\n            (   37.32   -92.51    -5.25     0.52)  ; 71\r\n            (   37.17   -93.45    -5.25     0.66)  ; 72\r\n            (   36.86   -94.43    -5.19     0.88)  ; 73\r\n            (   36.48   -95.41    -5.38     1.40)  ; 74\r\n            (   36.10   -95.94    -6.56     1.77)  ; 75\r\n            (   35.71   -96.99    -6.69     0.96)  ; 76\r\n            (   35.44   -97.76    -6.69     0.96)  ; 77\r\n            (   34.77   -98.25    -7.31     1.11)  ; 78\r\n            (   34.29   -99.36    -7.38     0.66)  ; 79\r\n            (   34.18  -100.08    -7.38     0.66)  ; 80\r\n            (   34.44  -101.23    -7.06     0.81)  ; 81\r\n            (   34.51  -102.21    -6.75     0.81)  ; 82\r\n            (   34.72  -103.20    -6.56     0.81)  ; 83\r\n            (   35.08  -104.29    -5.75     1.11)  ; 84\r\n            (   35.42  -105.38    -6.13     1.33)  ; 85\r\n            (   35.92  -106.50    -6.00     1.03)  ; 86\r\n            (   36.27  -107.59    -6.50     1.11)  ; 87\r\n            (   36.65  -108.90    -6.50     1.11)  ; 88\r\n            (   37.24  -109.96    -6.75     0.74)  ; 89\r\n            (   38.07  -110.83    -7.19     0.59)  ; 90\r\n            (   38.57  -111.44    -7.19     0.59)  ; 91\r\n            (   38.48  -112.45    -7.19     0.59)  ; 92\r\n            (   38.81  -113.18    -6.94     1.18)  ; 93\r\n            (   39.19  -114.12    -8.44     0.88)  ; 94\r\n            (   39.54  -115.41    -9.25     0.52)  ; 95\r\n            (   40.27  -115.97    -9.31     0.52)  ; 96\r\n            (   40.77  -116.63    -9.31     0.52)  ; 97\r\n            (   40.63  -117.95    -9.31     0.52)  ; 98\r\n            (   40.71  -118.85    -9.31     1.25)  ; 99\r\n            (   41.02  -119.71    -9.25     2.06)  ; 100\r\n            (   41.23  -120.71    -9.25     1.55)  ; 101\r\n            (   41.86  -121.47    -9.25     0.74)  ; 102\r\n            (   42.49  -122.68    -9.94     0.37)  ; 103\r\n            (   43.02  -123.14   -10.13     0.37)  ; 104\r\n            (   43.02  -123.58   -10.56     0.37)  ; 105\r\n            (   43.30  -124.59   -10.75     1.11)  ; 106\r\n            (   43.44  -125.13   -10.94     1.84)  ; 107\r\n            (   44.10  -126.12   -11.50     1.84)  ; 108\r\n            (   44.24  -127.18   -11.50     1.33)  ; 109\r\n            (   44.43  -128.32   -11.63     0.88)  ; 110\r\n            (   44.36  -129.72   -11.81     0.52)  ; 111\r\n            (   44.31  -130.52   -11.81     0.52)  ; 112\r\n            (   44.43  -131.57   -11.81     0.44)  ; 113\r\n            (   44.30  -132.44   -11.81     0.44)  ; 114\r\n            (   43.93  -133.34   -11.94     0.59)  ; 115\r\n            (   43.34  -134.28   -12.13     0.88)  ; 116\r\n            (   43.19  -135.22   -12.19     0.96)  ; 117\r\n            (   43.26  -136.19   -13.13     0.74)  ; 118\r\n            (   43.61  -136.78   -13.88     0.59)  ; 119\r\n             Normal\r\n          |\r\n            (   21.26   -20.53    -1.75     0.52)  ; 1, R-1-1-1-1-2\r\n            (   22.49   -20.72    -1.63     0.52)  ; 2\r\n            (   23.50   -20.96    -1.31     0.59)  ; 3\r\n            (   24.12   -21.27    -0.50     0.81)  ; 4\r\n            (   25.08   -21.80     0.06     0.96)  ; 5\r\n            (   26.07   -22.18     0.50     1.11)  ; 6\r\n            (   27.09   -22.34     0.81     1.11)  ; 7\r\n            (   28.11   -22.06     1.50     1.03)  ; 8\r\n            (   29.24   -21.94     1.81     0.88)  ; 9\r\n            (   30.22   -22.32     1.94     0.66)  ; 10\r\n            (   30.92   -22.66     1.94     0.59)  ; 11\r\n            (   31.89   -23.55     1.94     0.74)  ; 12\r\n            (   32.57   -23.95     2.44     0.88)  ; 13\r\n            (   33.04   -23.80     3.00     0.81)  ; 14\r\n            (   33.92   -23.94     4.00     1.03)  ; 15\r\n            (   34.27   -24.07     4.00     1.40)  ; 16\r\n            (   34.87   -24.02     4.31     1.40)  ; 17\r\n            (   35.68   -24.07     4.69     0.96)  ; 18\r\n            (   36.64   -24.15     4.75     0.81)  ; 19\r\n            (   37.53   -24.22     5.69     0.74)  ; 20\r\n            (   38.81   -24.13     5.81     0.66)  ; 21\r\n            (   39.74   -24.42     6.06     0.66)  ; 22\r\n            (   40.73   -24.73     6.31     0.81)  ; 23\r\n            (   42.11   -25.46     6.38     1.11)  ; 24\r\n            (   43.27   -26.61     6.88     0.74)  ; 25\r\n            (   44.38   -26.63     7.06     0.96)  ; 26\r\n            (   44.83   -26.63     6.94     1.25)  ; 27\r\n            (   45.83   -26.94     8.19     0.96)  ; 28\r\n            (   47.13   -27.15     6.81     0.66)  ; 29\r\n            (   47.91   -27.41     6.81     0.66)  ; 30\r\n            (   48.98   -27.73     7.25     0.81)  ; 31\r\n            (   50.12   -28.14     7.50     0.81)  ; 32\r\n            (   51.17   -28.53     7.31     0.74)  ; 33\r\n            (   52.01   -28.88     7.13     1.03)  ; 34\r\n            (   52.38   -28.86     7.25     1.40)  ; 35\r\n            (\r\n              (   53.40   -28.58     7.31     0.81)  ; 1, R-1-1-1-1-2-1\r\n              (   54.25   -28.86     7.69     0.66)  ; 2\r\n              (   55.45   -29.28     7.69     0.66)  ; 3\r\n              (   56.84   -29.49     8.00     0.52)  ; 4\r\n              (   57.44   -29.88     8.00     1.18)  ; 5\r\n              (   58.05   -30.28     8.19     1.47)  ; 6\r\n              (   58.56   -30.80     9.06     0.59)  ; 7\r\n              (   59.44   -31.39     9.38     0.44)  ; 8\r\n              (   60.20   -31.81    10.00     0.59)  ; 9\r\n              (   60.58   -32.16    10.63     0.81)  ; 10\r\n              (   61.09   -32.76    11.38     0.52)  ; 11\r\n              (   61.82   -33.32    11.56     0.66)  ; 12\r\n              (   63.17   -34.27    11.94     0.81)  ; 13\r\n              (   63.74   -34.68    13.06     0.59)  ; 14\r\n              (   64.12   -35.63    13.69     0.59)  ; 15\r\n              (   64.35   -36.48    14.19     0.88)  ; 16\r\n              (   64.47   -36.65    14.31     1.33)  ; 17\r\n              (   65.16   -37.49    14.63     0.59)  ; 18\r\n              (   65.75   -37.96    14.63     0.59)  ; 19\r\n              (   66.39   -38.65    15.31     0.96)  ; 20\r\n              (   67.50   -39.20    16.13     0.66)  ; 21\r\n              (   67.83   -39.92    16.75     0.66)  ; 22\r\n              (   67.64   -41.15    17.25     0.88)  ; 23\r\n              (   67.70   -42.12    18.00     0.66)  ; 24\r\n              (   67.23   -42.78    18.38     0.52)  ; 25\r\n              (   65.77   -42.55    18.44     0.52)  ; 26\r\n              (   64.24   -42.83    18.25     0.52)  ; 27\r\n               Normal\r\n            |\r\n              (   53.27   -30.13     7.19     0.59)  ; 1, R-1-1-1-1-2-2\r\n              (   53.75   -30.88     6.94     0.59)  ; 2\r\n              (   54.84   -32.01     6.81     0.88)  ; 3\r\n              (   55.71   -32.67     6.75     0.59)  ; 4\r\n              (   56.68   -33.11     6.81     0.88)  ; 5\r\n              (   57.27   -33.58     7.00     1.25)  ; 6\r\n              (   58.30   -34.19     6.69     0.81)  ; 7\r\n              (   59.35   -34.58     6.69     0.74)  ; 8\r\n              (   60.58   -34.85     6.69     0.66)  ; 9\r\n              (   61.35   -35.19     6.44     0.52)  ; 10\r\n              (   62.40   -35.58     6.44     0.88)  ; 11\r\n              (   63.04   -35.75     6.06     1.25)  ; 12\r\n              (   63.93   -36.27     5.81     1.25)  ; 13\r\n              (   64.38   -36.78     5.56     0.52)  ; 14\r\n              (   65.18   -37.36     5.19     0.74)  ; 15\r\n              (   65.65   -37.72     5.13     0.74)  ; 16\r\n              (   66.32   -38.12     4.94     0.52)  ; 17\r\n              (   67.16   -38.55     4.94     0.52)  ; 18\r\n              (   67.76   -38.94     4.69     0.81)  ; 19\r\n              (   68.64   -39.08     4.50     0.96)  ; 20\r\n              (   69.67   -39.62     4.50     0.59)  ; 21\r\n              (   70.73   -39.49     4.50     0.52)  ; 22\r\n              (   71.27   -39.42     4.50     1.25)  ; 23\r\n              (   72.08   -39.48     4.50     1.62)  ; 24\r\n              (   72.85   -39.31     4.50     1.03)  ; 25\r\n              (   73.73   -39.00     4.50     0.66)  ; 26\r\n              (   74.93   -38.90     5.88     0.52)  ; 27\r\n              (   75.70   -38.72     5.88     0.52)  ; 28\r\n              (   76.39   -38.61     5.88     0.52)  ; 29\r\n              (   77.64   -38.29     6.00     0.81)  ; 30\r\n              (   78.26   -38.09     6.81     1.11)  ; 31\r\n              (   78.69   -38.23     7.00     2.06)  ; 32\r\n              (   79.60   -38.15     7.00     2.06)  ; 33\r\n              (   80.26   -37.74     7.44     1.03)  ; 34\r\n              (   81.00   -37.79     7.44     0.66)  ; 35\r\n              (   81.73   -37.90     7.94     0.52)  ; 36\r\n              (   82.45   -38.09     8.81     0.74)  ; 37\r\n              (   83.31   -38.29     9.38     0.74)  ; 38\r\n              (   84.05   -38.34     9.69     0.52)  ; 39\r\n              (   85.31   -38.83    10.19     0.59)  ; 40\r\n              (   85.92   -39.23     9.94     0.96)  ; 41\r\n              (   86.82   -39.67     9.88     1.62)  ; 42\r\n              (   87.62   -39.80    10.06     0.74)  ; 43\r\n              (   88.71   -39.97    10.06     0.44)  ; 44\r\n              (   90.39   -40.24    10.06     0.66)  ; 45\r\n              (   91.26   -40.37    10.06     0.59)  ; 46\r\n              (   91.94   -40.78    10.13     0.81)  ; 47\r\n              (   93.10   -40.96    10.13     0.44)  ; 48\r\n              (   94.18   -40.77    10.13     0.44)  ; 49\r\n              (   95.25   -40.63    10.31     0.44)  ; 50\r\n              (   95.75   -40.71    10.31     1.03)  ; 51\r\n              (   96.49   -40.76    10.56     1.40)  ; 52\r\n              (   97.12   -40.56    10.56     0.88)  ; 53\r\n              (   97.63   -40.64    11.00     0.44)  ; 54\r\n              (   99.26   -40.23    11.56     0.29)  ; 55\r\n              (  100.71   -40.02    11.56     0.59)  ; 56\r\n              (  101.54   -39.93    11.56     0.59)  ; 57\r\n              (  103.30   -39.69    11.56     0.44)  ; 58\r\n              (  103.99   -40.02    12.19     1.03)  ; 59\r\n              (  104.55   -40.26    12.25     1.40)  ; 60\r\n              (  105.04   -40.93    12.38     0.59)  ; 61\r\n              (  105.31   -41.57    12.38     0.59)  ; 62\r\n              (  105.67   -42.14    12.56     0.88)  ; 63\r\n              (  105.84   -42.98    12.69     0.52)  ; 64\r\n              (  106.19   -43.52    12.75     0.74)  ; 65\r\n              (  106.50   -44.82    12.75     0.29)  ; 66\r\n              (  106.56   -45.43    12.75     0.66)  ; 67\r\n              (  106.76   -46.49    12.75     0.88)  ; 68\r\n              (  106.79   -47.24    12.75     0.44)  ; 69\r\n              (  106.83   -47.91    13.69     0.74)  ; 70\r\n               Normal\r\n            )  ;  End of split\r\n          )  ;  End of split\r\n        |\r\n          (   16.17   -17.32    -2.31     1.11)  ; 1, R-1-1-1-2\r\n          (   15.70   -18.36    -2.38     0.74)  ; 2\r\n          (   15.40   -19.35    -3.06     0.74)  ; 3\r\n          (   15.08   -20.41    -4.00     0.74)  ; 4\r\n          (   15.20   -21.54    -4.00     0.59)  ; 5\r\n          (   15.33   -23.04    -4.06     0.81)  ; 6\r\n          (   14.53   -24.32    -4.19     1.18)  ; 7\r\n          (   14.26   -25.09    -4.19     1.55)  ; 8\r\n          (   14.25   -26.12    -4.19     1.40)  ; 9\r\n          (   14.92   -27.05    -4.50     0.96)  ; 10\r\n          (   15.10   -28.26    -4.50     0.74)  ; 11\r\n          (   14.95   -29.71    -4.50     0.88)  ; 12\r\n          (   14.98   -30.80    -4.50     1.03)  ; 13\r\n          (   15.64   -31.35    -4.94     1.11)  ; 14\r\n          (   15.66   -32.61    -4.94     0.96)  ; 15\r\n          (   15.71   -33.73    -4.94     0.81)  ; 16\r\n          (   15.83   -34.86    -4.56     0.81)  ; 17\r\n          (   16.26   -35.88    -4.56     1.03)  ; 18\r\n          (   16.80   -37.16    -4.56     0.81)  ; 19\r\n          (   17.24   -38.19    -4.63     0.81)  ; 20\r\n          (   17.18   -39.00    -4.63     0.74)  ; 21\r\n          (   17.14   -40.25    -4.75     0.88)  ; 22\r\n          (   17.51   -41.20    -4.94     0.74)  ; 23\r\n          (   17.44   -42.08    -5.13     0.74)  ; 24\r\n          (   17.60   -43.43    -4.56     1.03)  ; 25\r\n          (   17.88   -45.03    -4.63     0.96)  ; 26\r\n          (   17.59   -46.83    -4.63     1.03)  ; 27\r\n          (   17.63   -48.02    -4.63     0.66)  ; 28\r\n          (   18.02   -49.27    -4.69     0.74)  ; 29\r\n          (   18.23   -50.26    -4.69     1.03)  ; 30\r\n          (   18.76   -51.23    -4.69     0.81)  ; 31\r\n          (   19.13   -52.63    -4.69     0.52)  ; 32\r\n          (   19.28   -53.54    -4.69     0.66)  ; 33\r\n          (   19.65   -54.56    -4.69     0.52)  ; 34\r\n          (   20.15   -55.60    -4.13     1.25)  ; 35\r\n          (   20.42   -56.68    -4.63     0.96)  ; 36\r\n          (   20.66   -57.53    -4.69     0.44)  ; 37\r\n          (   20.44   -58.48    -4.81     0.44)  ; 38\r\n          (   19.89   -59.13    -4.94     0.74)  ; 39\r\n          (   19.49   -60.25    -5.56     0.59)  ; 40\r\n          (   19.92   -61.35    -5.94     0.81)  ; 41\r\n          (   20.19   -62.44    -6.81     1.11)  ; 42\r\n          (   19.96   -63.43    -6.88     0.74)  ; 43\r\n          (   19.57   -63.97    -7.44     0.59)  ; 44\r\n          (   19.32   -64.67    -7.88     0.59)  ; 45\r\n          (   18.71   -65.67    -8.13     0.81)  ; 46\r\n          (   18.42   -66.59    -8.63     0.81)  ; 47\r\n          (   18.16   -67.73    -9.13     1.11)  ; 48\r\n          (   17.78   -68.72    -9.50     1.55)  ; 49\r\n          (   17.62   -69.72    -9.56     1.92)  ; 50\r\n          (   17.37   -71.31   -10.44     1.40)  ; 51\r\n          (   17.12   -72.91   -10.44     1.03)  ; 52\r\n          (   16.81   -74.41   -10.81     0.74)  ; 53\r\n          (   16.59   -75.78   -11.88     0.59)  ; 54\r\n          (   16.89   -77.16   -12.44     0.52)  ; 55\r\n          (   16.96   -78.65   -12.81     0.52)  ; 56\r\n          (   17.39   -81.09   -13.06     0.74)  ; 57\r\n          (   17.52   -82.14   -13.63     0.74)  ; 58\r\n          (   17.80   -83.66   -14.25     1.03)  ; 59\r\n          (   17.85   -84.46   -12.00     1.92)  ; 60\r\n          (   18.21   -85.04   -12.00     0.81)  ; 61\r\n          (   18.68   -85.77   -12.00     0.52)  ; 62\r\n          (   19.19   -86.82   -12.00     0.66)  ; 63\r\n          (   20.05   -87.99   -10.81     0.52)  ; 64\r\n          (   20.38   -89.23   -10.50     0.52)  ; 65\r\n          (   21.05   -90.22   -10.19     0.81)  ; 66\r\n          (   21.54   -91.33   -10.06     0.81)  ; 67\r\n          (   22.12   -92.39    -9.81     0.44)  ; 68\r\n          (   22.75   -93.07    -9.75     0.44)  ; 69\r\n          (   23.13   -94.03    -9.31     1.03)  ; 70\r\n          (   23.45   -94.82    -8.56     0.81)  ; 71\r\n          (   23.90   -96.14    -8.00     0.81)  ; 72\r\n          (   24.46   -97.35    -7.06     0.59)  ; 73\r\n          (   24.69   -98.72    -6.94     0.59)  ; 74\r\n          (   25.11   -99.37    -6.63     0.96)  ; 75\r\n          (   25.65  -100.20    -6.63     0.59)  ; 76\r\n          (   26.35  -101.42    -6.63     0.52)  ; 77\r\n          (   26.97  -102.26    -6.63     0.81)  ; 78\r\n          (   27.70  -103.78    -6.63     0.96)  ; 79\r\n          (   28.34  -104.91    -6.63     0.59)  ; 80\r\n          (   28.94  -105.83    -6.00     0.52)  ; 81\r\n          (   29.37  -106.86    -6.00     0.52)  ; 82\r\n          (   29.43  -107.90    -6.00     1.03)  ; 83\r\n          (   29.57  -108.89    -6.00     1.40)  ; 84\r\n          (   29.23  -109.64    -6.00     0.81)  ; 85\r\n          (   29.03  -110.43    -6.00     0.44)  ; 86\r\n          (   29.11  -111.33    -6.00     0.44)  ; 87\r\n          (   28.62  -112.07    -6.00     0.66)  ; 88\r\n          (   28.26  -112.97    -6.00     0.52)  ; 89\r\n          (   28.62  -114.44    -6.44     1.11)  ; 90\r\n          (   29.32  -115.49    -7.44     0.74)  ; 91\r\n          (   29.50  -116.33    -8.38     0.52)  ; 92\r\n          (   29.68  -117.54    -8.75     0.52)  ; 93\r\n          (   29.69  -118.36    -8.75     0.88)  ; 94\r\n          (   29.67  -119.03    -9.00     0.88)  ; 95\r\n          (   30.09  -119.61    -9.56     0.59)  ; 96\r\n          (   30.40  -120.02   -10.00     0.59)  ; 97\r\n          (   30.75  -120.60   -10.50     1.33)  ; 98\r\n          (   30.95  -121.29   -11.50     2.14)  ; 99\r\n          (   31.03  -122.20   -12.31     1.62)  ; 100\r\n           Normal\r\n        )  ;  End of split\r\n      |\r\n        (   15.31   -12.94    -2.38     0.96)  ; 1, R-1-1-2\r\n        (   16.55   -13.21    -2.81     0.88)  ; 2\r\n        (   18.43   -14.03    -2.81     0.88)  ; 3\r\n        (   20.11   -14.22    -3.00     1.11)  ; 4\r\n        (   22.07   -15.05    -3.94     0.66)  ; 5\r\n        (   23.83   -15.26    -4.31     0.88)  ; 6\r\n        (   24.61   -15.52    -4.81     0.88)  ; 7\r\n        (   25.52   -15.89    -5.56     0.88)  ; 8\r\n        (   27.29   -16.91    -5.63     1.03)  ; 9\r\n        (   28.66   -18.17    -6.25     0.81)  ; 10\r\n        (   29.71   -18.63    -7.69     1.33)  ; 11\r\n        (   30.52   -19.12    -8.00     1.03)  ; 12\r\n        (   33.05   -20.12    -8.25     0.74)  ; 13\r\n        (   34.15   -20.29    -8.44     0.74)  ; 14\r\n        (   36.23   -19.88    -8.50     0.66)  ; 15\r\n        (   37.80   -19.76    -8.75     0.96)  ; 16\r\n        (   39.64   -19.46    -8.81     0.81)  ; 17\r\n        (   41.25   -19.20    -8.88     1.03)  ; 18\r\n        (   43.71   -19.21    -8.88     1.18)  ; 19\r\n        (   45.03   -18.46    -9.25     0.74)  ; 20\r\n        (   46.52   -17.96    -9.44     0.88)  ; 21\r\n        (   47.48   -17.51    -9.50     0.88)  ; 22\r\n        (   48.64   -16.37    -9.69     0.74)  ; 23\r\n        (   48.90   -16.12    -9.94     0.74)  ; 24\r\n        (   50.44   -15.85   -10.06     0.74)  ; 25\r\n        (   51.79   -15.31   -10.31     1.03)  ; 26\r\n        (   53.04   -14.48   -10.31     1.25)  ; 27\r\n        (   54.03   -13.38   -10.31     0.96)  ; 28\r\n        (   54.51   -12.71   -11.31     0.74)  ; 29\r\n        (   55.13   -12.59   -11.31     0.74)  ; 30\r\n        (   56.11   -12.97   -11.31     1.03)  ; 31\r\n        (   57.71   -13.22   -11.38     0.66)  ; 32\r\n        (   58.34   -12.58   -12.06     1.18)  ; 33\r\n        (   59.43   -11.86   -13.44     1.40)  ; 34\r\n        (   60.42   -11.02   -13.50     2.06)  ; 35\r\n        (   61.54   -10.53   -14.44     1.18)  ; 36\r\n        (   63.00   -10.24   -14.88     0.59)  ; 37\r\n        (   63.68   -10.27   -15.13     0.59)  ; 38\r\n        (   64.25   -10.36   -15.94     0.66)  ; 39\r\n        (   65.92   -11.14   -16.81     0.96)  ; 40\r\n        (   66.67   -11.56   -16.81     1.25)  ; 41\r\n        (   68.31   -11.60   -17.56     0.88)  ; 42\r\n        (   69.57   -11.65   -17.94     0.52)  ; 43\r\n        (   70.75   -12.21   -18.06     0.81)  ; 44\r\n        (   71.63   -12.27   -18.06     1.18)  ; 45\r\n        (   72.67   -12.81   -19.19     0.66)  ; 46\r\n        (   73.54   -12.94   -19.81     0.59)  ; 47\r\n        (   74.39   -12.78   -20.06     0.96)  ; 48\r\n        (   74.72   -12.61   -20.50     1.55)  ; 49\r\n        (   75.40   -12.95   -21.00     1.11)  ; 50\r\n        (   75.93   -13.39   -21.13     0.66)  ; 51\r\n        (   76.87   -14.06   -21.44     0.52)  ; 52\r\n        (   78.58   -15.07   -22.25     0.52)  ; 53\r\n        (   79.93   -15.50   -23.56     0.81)  ; 54\r\n        (   80.59   -15.54   -23.94     1.62)  ; 55\r\n        (   81.20   -15.93   -24.38     2.50)  ; 56\r\n        (   81.57   -16.36   -24.69     2.50)  ; 57\r\n        (   82.06   -16.59   -24.69     1.55)  ; 58\r\n        (   82.61   -16.89   -24.94     1.03)  ; 59\r\n        (   83.64   -17.95   -24.94     0.59)  ; 60\r\n        (   85.39   -19.11   -25.25     0.59)  ; 61\r\n        (   85.61   -19.66   -25.38     0.59)  ; 62\r\n        (   86.24   -20.43   -25.38     0.96)  ; 63\r\n        (   86.84   -20.82   -25.69     1.33)  ; 64\r\n        (   87.39   -21.58   -25.69     0.96)  ; 65\r\n        (   87.90   -22.10   -25.69     0.52)  ; 66\r\n        (   88.73   -23.04   -25.94     0.29)  ; 67\r\n        (   89.36   -23.81   -25.94     0.59)  ; 68\r\n        (   89.61   -24.52   -25.94     1.47)  ; 69\r\n        (   90.18   -25.20   -25.94     1.92)  ; 70\r\n        (   90.69   -26.17   -25.94     1.18)  ; 71\r\n        (   91.18   -26.83   -26.25     0.66)  ; 72\r\n        (   91.40   -27.83   -26.63     0.52)  ; 73\r\n        (   91.90   -28.44   -26.56     0.52)  ; 74\r\n        (   92.05   -29.86   -26.63     1.03)  ; 75\r\n        (   92.27   -30.78   -26.63     1.40)  ; 76\r\n        (   92.49   -32.30   -26.63     0.88)  ; 77\r\n        (   92.61   -32.91   -27.56     0.66)  ; 78\r\n        (   93.01   -34.16   -29.44     0.66)  ; 79\r\n        (   93.71   -35.37   -29.63     0.66)  ; 80\r\n        (   94.14   -36.42   -29.88     1.03)  ; 81\r\n        (   94.62   -36.70   -30.31     1.47)  ; 82\r\n        (   94.73   -36.94   -31.38     1.99)  ; 83\r\n        (   95.20   -37.76   -32.25     2.28)  ; 84\r\n        (   96.38   -38.25   -33.31     1.40)  ; 85\r\n        (   97.02   -38.94   -33.94     0.88)  ; 86\r\n        (   97.23   -40.01   -34.56     0.66)  ; 87\r\n        (   97.96   -41.01   -34.56     0.66)  ; 88\r\n        (   98.16   -41.64   -34.56     1.25)  ; 89\r\n        (   98.51   -42.28   -35.75     1.69)  ; 90\r\n        (   99.02   -42.91   -35.81     0.88)  ; 91\r\n        (   99.15   -43.46   -36.75     0.44)  ; 92\r\n        (   99.46   -44.31   -36.75     0.44)  ; 93\r\n        (   99.83   -44.89   -36.75     0.81)  ; 94\r\n        (   99.99   -45.29   -36.75     0.81)  ; 95\r\n        (  100.38   -45.64   -36.75     0.44)  ; 96\r\n        (  100.83   -46.09   -36.75     0.44)  ; 97\r\n        (  102.07   -46.72   -36.75     0.44)  ; 98\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (   10.08   -10.26    -3.06     0.81)  ; 1, R-1-2\r\n      (    9.00   -10.47    -4.38     0.81)  ; 2\r\n      (    8.37   -11.18    -7.19     0.81)  ; 3\r\n      (    8.97   -11.65    -9.44     0.81)  ; 4\r\n      (    8.97   -13.05    -9.75     0.66)  ; 5\r\n      (    8.80   -14.13   -10.94     0.74)  ; 6\r\n      (    8.43   -15.48   -11.38     0.88)  ; 7\r\n      (    8.44   -16.37   -11.50     0.88)  ; 8\r\n      (    7.67   -17.06   -12.06     0.88)  ; 9\r\n      (    7.67   -17.12   -12.06     0.88)  ; 10\r\n      (\r\n        (    6.80   -17.37   -12.38     0.88)  ; 1, R-1-2-1\r\n        (    6.16   -17.64   -12.38     0.88)  ; 2\r\n        (    5.78   -18.10   -12.38     0.88)  ; 3\r\n        (    4.96   -19.52   -12.81     0.81)  ; 4\r\n        (    4.31   -20.37   -12.81     1.11)  ; 5\r\n        (    3.23   -21.99   -12.81     1.11)  ; 6\r\n        (    2.29   -23.16   -12.81     0.74)  ; 7\r\n        (    2.08   -23.58   -12.81     0.74)  ; 8\r\n        (    1.63   -24.55   -13.75     0.88)  ; 9\r\n        (    1.72   -25.37   -13.94     0.88)  ; 10\r\n        (    1.44   -25.77   -13.94     0.88)  ; 11\r\n        (    0.38   -26.27   -14.88     0.66)  ; 12\r\n        (   -0.28   -26.69   -15.94     1.40)  ; 13\r\n        (   -0.92   -27.58   -16.81     1.47)  ; 14\r\n        (   -1.55   -28.73   -18.63     1.25)  ; 15\r\n        (   -2.01   -29.33   -19.19     0.96)  ; 16\r\n        (   -2.32   -29.86   -20.63     0.88)  ; 17\r\n        (   -2.75   -30.24   -22.00     1.03)  ; 18\r\n        (   -3.65   -31.14   -22.31     0.81)  ; 19\r\n        (   -3.68   -31.36   -24.38     1.11)  ; 20\r\n        (   -3.89   -31.69   -25.63     0.81)  ; 21\r\n        (   -4.21   -32.31   -26.88     0.81)  ; 22\r\n        (   -4.85   -33.54   -27.56     1.03)  ; 23\r\n        (   -5.83   -34.13   -27.94     1.03)  ; 24\r\n        (   -6.18   -34.88   -30.81     0.81)  ; 25\r\n        (   -6.99   -35.79   -31.06     0.81)  ; 26\r\n        (   -7.64   -36.58   -31.31     0.59)  ; 27\r\n        (   -8.44   -37.34   -32.31     0.52)  ; 28\r\n        (   -9.09   -38.20   -33.06     0.59)  ; 29\r\n        (   -9.65   -38.85   -33.38     0.81)  ; 30\r\n        (  -10.24   -39.79   -33.38     0.81)  ; 31\r\n        (  -10.53   -40.70   -34.06     1.11)  ; 32\r\n        (  -11.17   -40.98   -34.94     1.62)  ; 33\r\n        (  -11.89   -41.23   -34.94     0.66)  ; 34\r\n        (  -12.56   -42.23   -35.19     0.44)  ; 35\r\n        (  -12.66   -42.89   -35.63     0.44)  ; 36\r\n        (  -12.49   -43.65   -35.81     0.81)  ; 37\r\n        (  -12.47   -44.47   -36.50     1.03)  ; 38\r\n        (  -12.36   -45.60   -36.94     0.52)  ; 39\r\n        (  -12.80   -46.56   -37.88     0.52)  ; 40\r\n        (  -13.24   -46.94   -38.63     1.33)  ; 41\r\n        (  -13.67   -47.31   -39.13     1.69)  ; 42\r\n        (  -14.28   -48.33   -39.75     0.37)  ; 43\r\n        (  -14.45   -48.97   -39.75     0.37)  ; 44\r\n        (  -15.14   -50.04   -40.31     1.18)  ; 45\r\n        (  -15.43   -50.44   -40.31     1.77)  ; 46\r\n        (  -16.33   -50.96   -40.75     0.81)  ; 47\r\n        (  -17.22   -51.45   -41.19     0.44)  ; 48\r\n        (  -17.66   -51.91   -41.69     0.66)  ; 49\r\n        (  -18.08   -52.20   -41.69     0.88)  ; 50\r\n        (  -18.62   -52.72   -41.69     0.52)  ; 51\r\n        (  -19.13   -53.15   -42.19     0.29)  ; 52\r\n        (  -19.58   -53.60   -42.88     0.66)  ; 53\r\n        (  -19.99   -54.34   -42.94     1.40)  ; 54\r\n        (  -20.42   -54.72   -42.94     1.77)  ; 55\r\n        (  -21.09   -55.66   -43.44     1.03)  ; 56\r\n        (  -21.61   -56.09   -43.25     0.52)  ; 57\r\n        (  -21.69   -56.59   -42.69     0.52)  ; 58\r\n        (  -22.26   -57.39   -43.63     1.03)  ; 59\r\n        (  -22.67   -58.14   -44.00     1.25)  ; 60\r\n        (  -23.65   -59.10   -45.00     0.44)  ; 61\r\n        (  -24.22   -59.37   -45.06     0.44)  ; 62\r\n        (  -24.80   -59.73   -45.63     0.81)  ; 63\r\n        (  -25.68   -60.10   -45.63     1.55)  ; 64\r\n        (  -26.62   -60.40   -46.69     0.96)  ; 65\r\n        (  -27.54   -61.07   -47.88     0.52)  ; 66\r\n        (  -28.15   -62.08   -48.69     0.74)  ; 67\r\n        (  -28.15   -63.04   -49.38     1.11)  ; 68\r\n        (  -28.02   -64.11   -50.94     0.59)  ; 69\r\n        (  -28.26   -65.17   -52.38     0.44)  ; 70\r\n        (  -28.49   -66.10   -52.56     0.44)  ; 71\r\n        (  -28.58   -67.20   -52.94     0.44)  ; 72\r\n        (  -28.48   -67.95   -53.31     1.11)  ; 73\r\n        (  -28.51   -69.06   -53.81     1.84)  ; 74\r\n        (  -28.35   -69.52   -53.81     2.28)  ; 75\r\n        (  -28.64   -70.37   -53.81     1.25)  ; 76\r\n        (  -28.81   -71.45   -54.50     0.74)  ; 77\r\n        (  -29.03   -72.31   -54.50     0.59)  ; 78\r\n        (  -29.17   -73.17   -54.50     0.96)  ; 79\r\n        (  -29.22   -74.05   -55.06     0.59)  ; 80\r\n        (  -29.54   -75.11   -55.63     0.44)  ; 81\r\n        (  -29.63   -75.69   -55.69     0.88)  ; 82\r\n        (  -29.82   -76.71   -55.75     0.88)  ; 83\r\n         Normal\r\n      |\r\n        (    7.77   -17.89   -14.19     0.66)  ; 1, R-1-2-2\r\n        (    7.29   -18.10   -16.00     0.81)  ; 2\r\n        (    7.07   -18.51   -17.44     1.11)  ; 3\r\n        (    7.28   -19.06   -19.38     1.11)  ; 4\r\n        (    7.47   -19.24   -21.94     0.96)  ; 5\r\n        (    8.40   -19.53   -23.19     1.11)  ; 6\r\n        (    9.01   -19.41   -25.44     1.11)  ; 7\r\n        (    9.83   -19.47   -26.81     1.11)  ; 8\r\n        (   10.67   -19.82   -27.13     0.96)  ; 9\r\n        (   12.07   -19.96   -29.06     0.81)  ; 10\r\n        (   13.08   -20.12   -29.06     0.66)  ; 11\r\n        (   13.74   -20.67   -29.69     0.96)  ; 12\r\n        (   14.59   -20.96   -30.56     1.25)  ; 13\r\n        (   15.69   -21.58   -30.56     1.25)  ; 14\r\n        (   16.45   -21.92   -30.94     0.96)  ; 15\r\n        (   17.24   -22.12   -31.50     0.59)  ; 16\r\n        (   18.29   -23.03   -31.75     0.59)  ; 17\r\n        (   20.40   -24.32   -31.75     0.59)  ; 18\r\n        (   21.07   -24.80   -33.50     0.81)  ; 19\r\n        (   22.24   -25.43   -33.81     0.81)  ; 20\r\n        (   22.91   -25.90   -34.38     0.81)  ; 21\r\n        (   23.57   -26.38   -35.13     0.96)  ; 22\r\n        (   24.33   -25.90   -36.94     1.18)  ; 23\r\n        (   25.39   -25.26   -36.94     0.88)  ; 24\r\n        (   26.58   -25.82   -41.13     0.74)  ; 25\r\n        (   26.93   -26.39   -42.00     1.25)  ; 26\r\n        (   26.97   -27.14   -42.19     0.88)  ; 27\r\n        (   27.39   -27.73   -42.19     0.52)  ; 28\r\n        (   27.52   -28.33   -42.63     0.52)  ; 29\r\n        (   27.60   -28.79   -43.25     0.88)  ; 30\r\n        (   27.78   -29.04   -43.44     1.25)  ; 31\r\n        (   28.27   -29.72   -44.50     0.88)  ; 32\r\n        (   28.54   -29.83   -46.38     0.88)  ; 33\r\n        (   28.51   -30.57   -48.19     1.11)  ; 34\r\n        (   29.33   -31.51   -51.44     0.81)  ; 35\r\n        (   30.60   -32.45   -52.25     1.03)  ; 36\r\n        (   31.24   -32.33   -55.44     1.47)  ; 37\r\n        (   32.66   -32.27   -58.69     0.81)  ; 38\r\n        (   33.83   -32.01   -63.50     0.66)  ; 39\r\n        (   34.86   -32.09   -66.69     0.66)  ; 40\r\n        (   35.66   -31.70   -65.56     1.33)  ; 41\r\n        (\r\n          (   35.95   -32.26   -66.56     0.74)  ; 1, R-1-2-2-1\r\n          (   36.34   -32.63   -67.94     0.74)  ; 2\r\n          (   36.71   -32.60   -68.19     1.03)  ; 3\r\n          (   37.36   -32.27   -69.00     1.03)  ; 4\r\n          (   37.63   -32.01   -69.88     1.03)  ; 5\r\n          (   37.98   -31.70   -72.06     1.18)  ; 6\r\n          (   38.47   -30.96   -73.25     1.11)  ; 7\r\n          (   38.94   -30.81   -73.69     0.74)  ; 8\r\n          (   39.87   -30.59   -74.31     0.59)  ; 9\r\n          (   40.42   -29.94   -75.25     0.59)  ; 10\r\n          (   41.01   -29.52   -76.19     0.59)  ; 11\r\n          (   41.61   -29.02   -76.19     0.88)  ; 12\r\n          (   42.49   -28.64   -76.19     0.88)  ; 13\r\n          (   43.36   -28.33   -76.75     0.52)  ; 14\r\n          (   44.28   -27.74   -77.88     0.74)  ; 15\r\n          (   44.88   -27.17   -79.25     0.96)  ; 16\r\n          (   45.39   -27.32   -79.31     1.33)  ; 17\r\n          (   46.37   -27.69   -81.69     0.96)  ; 18\r\n          (   47.32   -27.33   -81.69     0.66)  ; 19\r\n          (   48.12   -27.01   -81.69     0.66)  ; 20\r\n          (   48.53   -26.34   -82.00     0.66)  ; 21\r\n          (   49.12   -24.88   -82.94     0.81)  ; 22\r\n          (   49.61   -24.14   -83.25     0.81)  ; 23\r\n          (   50.33   -23.37   -83.56     0.52)  ; 24\r\n          (   50.85   -22.93   -83.56     0.74)  ; 25\r\n          (   51.50   -22.59   -84.38     0.88)  ; 26\r\n           Normal\r\n        |\r\n          (   35.92   -31.00   -65.75     0.88)  ; 1, R-1-2-2-2\r\n          (   36.43   -30.19   -66.56     0.52)  ; 2\r\n          (   36.73   -29.65   -66.50     0.88)  ; 3\r\n          (   37.21   -28.54   -65.00     1.18)  ; 4\r\n          (   37.21   -28.03   -63.44     0.96)  ; 5\r\n          (   36.62   -27.56   -61.19     0.59)  ; 6\r\n          (   36.54   -27.10   -61.13     1.18)  ; 7\r\n           Normal\r\n        )  ;  End of split\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (   11.38    -8.22    -2.13     1.25)  ; 1, R-2\r\n    (   13.19    -8.48    -2.13     1.25)  ; 2\r\n    (\r\n      (   13.20    -8.51    -2.69     1.25)  ; 1, R-2-1\r\n      (   15.04    -8.66    -3.69     1.03)  ; 2\r\n      (   16.28    -8.86    -5.06     1.18)  ; 3\r\n      (   17.55    -9.28    -6.75     1.40)  ; 4\r\n      (   18.52    -9.35    -8.56     1.18)  ; 5\r\n      (\r\n        (   19.60    -9.09    -8.69     0.74)  ; 1, R-2-1-1\r\n        (   21.18    -8.52    -9.38     0.74)  ; 2\r\n        (   21.76    -8.61    -9.75     1.03)  ; 3\r\n        (   22.49    -8.73   -10.13     1.25)  ; 4\r\n        (   23.21    -8.84   -11.19     1.11)  ; 5\r\n        (   24.57    -8.76   -11.56     0.81)  ; 6\r\n        (   25.53    -8.84   -11.88     0.81)  ; 7\r\n        (   26.14    -8.79   -12.50     0.81)  ; 8\r\n        (   28.56    -8.06   -13.00     0.88)  ; 9\r\n        (   29.30    -7.66   -13.38     1.18)  ; 10\r\n        (   30.51    -7.04   -14.31     0.81)  ; 11\r\n        (   31.18    -7.06   -16.88     0.88)  ; 12\r\n        (   32.88    -7.19   -17.56     0.96)  ; 13\r\n        (   34.49    -6.85   -18.13     0.96)  ; 14\r\n        (   35.67    -6.52   -18.56     0.66)  ; 15\r\n        (   36.74    -6.33   -18.81     0.66)  ; 16\r\n        (   37.83    -6.05   -19.06     0.96)  ; 17\r\n        (   39.29    -5.77   -19.94     1.11)  ; 18\r\n        (   40.65    -5.68   -20.88     1.11)  ; 19\r\n        (   42.39    -5.52   -21.06     1.11)  ; 20\r\n        (   42.98    -5.53   -22.38     1.11)  ; 21\r\n        (   45.43    -6.14   -24.19     0.96)  ; 22\r\n        (   46.47    -7.13   -25.13     0.96)  ; 23\r\n        (   47.48    -7.29   -25.44     0.96)  ; 24\r\n        (   49.06    -7.16   -26.81     0.66)  ; 25\r\n        (   49.51    -6.27   -27.69     1.03)  ; 26\r\n        (   49.98    -5.68   -28.19     1.03)  ; 27\r\n        (   50.69    -5.43   -28.19     1.11)  ; 28\r\n        (   51.10    -5.19   -28.19     0.66)  ; 29\r\n        (   51.51    -4.96   -28.19     0.66)  ; 30\r\n        (   52.29    -4.72   -28.63     0.81)  ; 31\r\n        (   53.04    -4.68   -29.00     0.81)  ; 32\r\n        (   53.85    -4.74   -29.94     0.66)  ; 33\r\n        (   54.47    -4.62   -30.94     0.96)  ; 34\r\n        (   55.04    -4.33   -31.31     2.14)  ; 35\r\n        (   55.70    -3.92   -32.06     2.65)  ; 36\r\n        (   56.66    -3.45   -32.50     1.33)  ; 37\r\n        (   57.31    -3.11   -32.50     0.66)  ; 38\r\n        (   57.80    -2.82   -32.50     0.66)  ; 39\r\n        (   58.60    -2.94   -32.75     0.96)  ; 40\r\n        (   59.26    -3.05   -33.38     1.33)  ; 41\r\n        (   60.41    -2.85   -33.81     0.88)  ; 42\r\n        (   61.09    -2.75   -35.75     0.59)  ; 43\r\n        (   62.32    -2.57   -36.13     0.88)  ; 44\r\n        (   62.94    -1.93   -38.00     1.62)  ; 45\r\n        (   63.45    -1.12   -39.25     0.74)  ; 46\r\n        (   63.73    -0.20   -39.25     0.44)  ; 47\r\n        (   64.02     0.64   -40.56     0.81)  ; 48\r\n        (   64.00     0.94   -42.13     1.25)  ; 49\r\n        (   64.42     1.24   -43.38     2.14)  ; 50\r\n        (   64.75     1.49   -44.19     2.50)  ; 51\r\n        (   65.72     1.48   -44.19     0.81)  ; 52\r\n        (   66.59     1.34   -44.50     0.59)  ; 53\r\n        (   67.14     1.48   -44.69     0.88)  ; 54\r\n        (   67.95     1.42   -44.75     1.03)  ; 55\r\n        (   69.17     1.60   -45.38     0.66)  ; 56\r\n        (   70.05     1.53   -45.75     0.96)  ; 57\r\n        (   70.98     1.69   -46.56     1.69)  ; 58\r\n        (   71.60     1.37   -46.88     1.69)  ; 59\r\n        (   72.31     0.73   -47.13     0.96)  ; 60\r\n        (   73.58     0.23   -47.69     0.66)  ; 61\r\n        (   74.67     0.06   -48.31     0.52)  ; 62\r\n        (   75.52    -0.22   -49.38     0.88)  ; 63\r\n        (   76.37    -0.50   -49.56     1.69)  ; 64\r\n        (   77.09    -0.69   -49.75     1.69)  ; 65\r\n        (   77.79    -0.95   -49.81     0.81)  ; 66\r\n        (   78.57    -1.23   -49.81     0.29)  ; 67\r\n        (   79.91    -1.66   -50.06     0.29)  ; 68\r\n        (   80.85    -1.88   -50.06     0.96)  ; 69\r\n        (   81.76    -2.25   -50.06     0.96)  ; 70\r\n        (   82.73    -2.77   -50.06     0.52)  ; 71\r\n        (   83.41    -3.18   -50.69     1.18)  ; 72\r\n        (   83.90    -3.33   -51.13     1.84)  ; 73\r\n        (   84.83    -3.61   -51.56     1.84)  ; 74\r\n        (   85.95    -4.54   -51.81     0.96)  ; 75\r\n        (   86.71    -4.89   -51.81     0.52)  ; 76\r\n        (   87.15    -4.95   -52.31     0.88)  ; 77\r\n        (   88.26    -5.05   -52.56     0.88)  ; 78\r\n        (   88.90    -5.23   -52.75     0.52)  ; 79\r\n        (   89.88    -5.60   -52.81     0.81)  ; 80\r\n        (   90.60    -5.79   -53.56     1.18)  ; 81\r\n        (   91.32    -5.98   -53.88     1.18)  ; 82\r\n        (   92.21    -5.97   -53.88     0.74)  ; 83\r\n        (   93.55    -6.04   -54.06     0.59)  ; 84\r\n        (   93.97    -6.18   -54.63     0.96)  ; 85\r\n        (   94.93    -6.26   -54.69     1.40)  ; 86\r\n        (   96.09    -5.99   -54.69     0.66)  ; 87\r\n        (   97.03    -5.70   -54.69     0.52)  ; 88\r\n        (   97.90    -5.39   -55.25     0.66)  ; 89\r\n         Normal\r\n      |\r\n        (   19.42   -10.13    -9.31     0.74)  ; 1, R-2-1-2\r\n        (   20.67   -10.78    -9.75     0.59)  ; 2\r\n        (   21.68   -11.01   -10.63     0.81)  ; 3\r\n        (   22.76   -11.18   -11.25     0.81)  ; 4\r\n        (   23.93   -11.36   -11.94     0.74)  ; 5\r\n        (   24.61   -11.77   -13.19     0.74)  ; 6\r\n        (   25.54   -12.06   -13.25     0.66)  ; 7\r\n        (   26.02   -12.29   -14.06     0.66)  ; 8\r\n        (   27.10   -12.53   -15.13     0.81)  ; 9\r\n        (   28.18   -12.78   -16.81     0.66)  ; 10\r\n        (   29.62   -12.64   -17.81     0.74)  ; 11\r\n        (   30.62   -12.42   -18.63     0.74)  ; 12\r\n        (   31.37   -12.47   -19.44     0.66)  ; 13\r\n        (   32.22   -12.68   -21.38     0.88)  ; 14\r\n        (   32.90   -12.20   -20.81     0.96)  ; 15\r\n        (   32.85   -12.93   -22.25     0.88)  ; 16\r\n        (   32.68   -13.57   -23.81     0.88)  ; 17\r\n        (   32.36   -14.62   -25.38     0.81)  ; 18\r\n        (   32.60   -15.48   -27.06     1.03)  ; 19\r\n        (   33.10   -16.08   -29.19     1.03)  ; 20\r\n        (   33.63   -16.52   -30.38     1.33)  ; 21\r\n        (   32.73   -17.05   -31.75     1.03)  ; 22\r\n        (   32.65   -18.00   -32.44     0.81)  ; 23\r\n        (   33.07   -18.66   -33.19     0.81)  ; 24\r\n        (   33.25   -18.91   -34.44     0.66)  ; 25\r\n        (   33.38   -19.08   -37.50     0.66)  ; 26\r\n        (   33.58   -19.19   -39.13     0.66)  ; 27\r\n        (   35.33   -21.31   -39.31     0.66)  ; 28\r\n        (   35.86   -22.21   -39.94     0.66)  ; 29\r\n        (   35.94   -23.12   -40.88     0.66)  ; 30\r\n        (   35.97   -23.86   -42.25     0.96)  ; 31\r\n        (   36.16   -24.55   -43.44     0.81)  ; 32\r\n        (   36.37   -25.11   -47.75     1.03)  ; 33\r\n        (   36.70   -26.35   -50.44     0.96)  ; 34\r\n        (   36.04   -26.24   -54.00     0.81)  ; 35\r\n        (   35.34   -25.98   -54.50     1.03)  ; 36\r\n        (   35.18   -26.99   -56.75     0.74)  ; 37\r\n        (   34.90   -27.83   -56.81     0.66)  ; 38\r\n        (   35.07   -28.92   -58.25     0.81)  ; 39\r\n        (   34.90   -30.01   -58.69     0.81)  ; 40\r\n        (   34.11   -30.76   -59.00     0.81)  ; 41\r\n        (   33.60   -31.06   -58.25     1.25)  ; 42\r\n        (   33.28   -31.23   -58.13     1.69)  ; 43\r\n        (   32.70   -31.58   -58.06     1.69)  ; 44\r\n        (   32.05   -31.93   -58.06     1.69)  ; 45\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (   13.67    -8.34    -4.44     0.74)  ; 1, R-2-2\r\n      (   13.70    -8.19    -6.06     0.74)  ; 2\r\n      (   13.69    -7.82    -7.31     0.88)  ; 3\r\n      (   13.22    -7.45   -10.13     0.96)  ; 4\r\n      (   12.71    -7.30   -12.56     1.18)  ; 5\r\n      (   12.52    -7.12   -15.19     1.33)  ; 6\r\n      (   11.45    -6.88   -18.00     1.03)  ; 7\r\n      (   11.18    -5.72   -19.00     1.03)  ; 8\r\n      (   10.99    -5.55   -22.63     1.25)  ; 9\r\n      (   10.66    -5.27   -24.50     1.40)  ; 10\r\n      (   10.02    -5.98   -26.06     1.18)  ; 11\r\n      (    9.63    -6.59   -26.56     1.18)  ; 12\r\n      (    9.01    -7.60   -27.19     0.96)  ; 13\r\n      (    8.12    -7.60   -27.94     0.96)  ; 14\r\n      (    7.22    -7.68   -28.81     0.96)  ; 15\r\n      (    6.12    -7.51   -29.81     0.96)  ; 16\r\n      (    4.68    -7.21   -32.25     0.81)  ; 17\r\n      (    3.10    -6.81   -33.31     0.74)  ; 18\r\n      (    2.12    -6.43   -34.88     0.74)  ; 19\r\n      (    0.65    -5.83   -35.63     0.88)  ; 20\r\n      (   -0.18    -5.84   -35.69     1.25)  ; 21\r\n      (   -1.24    -5.98   -36.06     1.40)  ; 22\r\n      (   -1.46    -6.39   -39.50     1.03)  ; 23\r\n      (   -1.98    -6.89   -40.25     0.74)  ; 24\r\n      (   -1.92    -7.42   -42.00     0.74)  ; 25\r\n      (   -2.51    -7.84   -43.69     0.74)  ; 26\r\n      (   -3.29    -8.54   -44.63     0.74)  ; 27\r\n      (   -3.73    -8.03   -45.50     1.03)  ; 28\r\n      (   -3.73    -8.03   -48.81     0.88)  ; 29\r\n      (   -3.72    -8.39   -49.81     0.88)  ; 30\r\n      (   -4.08    -9.30   -50.94     1.11)  ; 31\r\n      (   -4.36   -10.14   -51.38     1.40)  ; 32\r\n      (   -4.44   -10.65   -52.00     1.69)  ; 33\r\n      (   -4.81   -11.03   -53.00     1.69)  ; 34\r\n      (   -5.10   -11.50   -53.56     0.81)  ; 35\r\n      (   -5.41   -12.50   -54.50     0.81)  ; 36\r\n      (   -5.95   -13.08   -55.63     0.81)  ; 37\r\n      (   -6.13   -14.23   -53.63     0.44)  ; 38\r\n      (   -7.01   -15.57   -54.56     0.44)  ; 39\r\n      (   -7.19   -17.17   -54.94     0.44)  ; 40\r\n      (   -6.97   -18.09   -54.94     0.74)  ; 41\r\n      (   -7.02   -18.90   -54.94     1.18)  ; 42\r\n      (   -7.29   -19.67   -55.25     1.62)  ; 43\r\n      (   -7.54   -20.30   -55.50     1.62)  ; 44\r\n      (   -7.34   -20.92   -56.25     0.74)  ; 45\r\n      (   -7.34   -21.88   -57.94     0.29)  ; 46\r\n       Normal\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n\r\n( (Color Magenta)\r\n  (Dendrite)\r\n  (    4.01     0.59    -4.81     1.62)  ; Root\r\n  (    3.07     0.81    -6.56     1.77)  ; 1, R\r\n  (\r\n    (    3.01     1.78    -8.31     1.47)  ; 1, R-1\r\n    (    3.06     2.47    -8.31     1.47)  ; 2\r\n    (\r\n      (    2.92     3.12    -9.63     1.03)  ; 1, R-1-1\r\n      (    3.17     3.61    -9.63     1.03)  ; 2\r\n      (\r\n        (    3.23     3.67   -10.88     0.81)  ; 1, R-1-1-1\r\n        (    3.10     4.72   -11.13     0.52)  ; 2\r\n        (    3.04     5.33   -11.31     0.52)  ; 3\r\n        (    3.46     6.07   -11.31     0.52)  ; 4\r\n        (    3.90     6.97   -11.88     0.52)  ; 5\r\n        (    4.27     7.42   -12.50     0.74)  ; 6\r\n        (    4.53     7.61   -13.69     1.03)  ; 7\r\n        (    4.85     7.78   -14.38     1.33)  ; 8\r\n        (    4.93     8.28   -15.38     0.96)  ; 9\r\n        (    4.51     8.94   -15.88     0.74)  ; 10\r\n        (    3.59     9.68   -17.38     0.52)  ; 11\r\n        (    3.61    10.34   -18.69     0.81)  ; 12\r\n        (    3.96    11.11   -18.75     1.11)  ; 13\r\n        (    3.75    11.21   -20.00     1.03)  ; 14\r\n        (    3.13    11.98   -21.38     0.81)  ; 15\r\n        (    3.29    12.98   -22.13     0.59)  ; 16\r\n        (    3.52    13.98   -23.44     0.81)  ; 17\r\n        (    3.54    14.13   -23.81     1.11)  ; 18\r\n        (    3.05    14.79   -23.94     1.25)  ; 19\r\n        (    2.50    15.55   -24.44     0.74)  ; 20\r\n        (    2.06    16.07   -24.88     0.59)  ; 21\r\n        (    1.28    16.34   -25.44     0.74)  ; 22\r\n        (    0.29    17.09   -25.69     0.74)  ; 23\r\n        (   -0.90    17.65   -26.25     0.74)  ; 24\r\n        (   -1.23    18.37   -27.63     0.44)  ; 25\r\n        (   -1.76    18.82   -28.25     0.44)  ; 26\r\n        (   -2.34    18.91   -28.25     0.74)  ; 27\r\n        (   -2.92    19.00   -28.75     1.03)  ; 28\r\n        (   -3.89    19.00   -29.44     0.59)  ; 29\r\n        (   -4.92    19.10   -30.25     0.59)  ; 30\r\n        (   -5.38    19.03   -31.63     1.33)  ; 31\r\n        (   -6.04    18.60   -32.56     2.14)  ; 32\r\n        (   -6.53    18.32   -33.88     2.50)  ; 33\r\n        (   -7.04    17.95   -34.19     2.06)  ; 34\r\n        (   -7.20    17.38   -34.69     1.55)  ; 35\r\n        (   -7.16    17.16   -35.94     0.88)  ; 36\r\n        (   -6.99    18.24   -37.38     0.81)  ; 37\r\n        (   -6.31    17.84   -38.44     1.03)  ; 38\r\n        (   -5.67    17.66   -39.06     1.03)  ; 39\r\n        (   -4.87    17.98   -39.63     1.03)  ; 40\r\n        (   -4.27    18.48   -41.94     0.88)  ; 41\r\n        (   -3.54    19.32   -41.00     0.66)  ; 42\r\n        (   -3.02    19.83   -42.13     0.44)  ; 43\r\n        (   -2.34    20.32   -42.56     0.44)  ; 44\r\n        (   -1.31    21.12   -42.81     0.66)  ; 45\r\n        (   -1.04    21.45   -43.50     0.96)  ; 46\r\n        (   -0.76    21.77   -43.69     1.33)  ; 47\r\n        (   -0.33    22.14   -43.69     1.69)  ; 48\r\n        (   -0.05    22.48   -44.50     0.81)  ; 49\r\n        (    0.95    23.20   -45.94     0.52)  ; 50\r\n        (    1.31    24.03   -46.69     0.52)  ; 51\r\n        (    1.39    24.54   -48.31     0.74)  ; 52\r\n        (    0.84    25.74   -49.13     0.81)  ; 53\r\n        (    0.20    26.87   -50.94     1.11)  ; 54\r\n        (   -0.08    27.58   -51.94     1.92)  ; 55\r\n        (   -0.74    28.05   -53.25     1.92)  ; 56\r\n        (   -1.21    28.43   -54.19     1.40)  ; 57\r\n        (   -1.40    28.60   -57.19     0.66)  ; 58\r\n         Normal\r\n      |\r\n        (    3.06     4.28   -11.06     0.52)  ; 1, R-1-1-2\r\n        (    2.70     5.30   -11.50     0.52)  ; 2\r\n        (    2.39     6.17   -11.56     0.44)  ; 3\r\n        (    2.42     7.80   -12.00     0.37)  ; 4\r\n        (    2.54     8.07   -12.19     0.37)  ; 5\r\n        (    3.32     8.76   -13.88     0.59)  ; 6\r\n        (    4.01     9.32   -14.25     0.59)  ; 7\r\n        (    4.79    10.01   -14.44     0.59)  ; 8\r\n        (    6.01    10.63   -14.44     0.81)  ; 9\r\n        (    6.62    11.27   -15.31     0.88)  ; 10\r\n        (    6.92    12.19   -15.19     1.25)  ; 11\r\n        (    6.82    13.39   -15.25     0.96)  ; 12\r\n        (    6.92    14.04   -15.44     0.81)  ; 13\r\n        (    7.04    14.76   -16.50     0.66)  ; 14\r\n        (    7.17    15.63   -16.94     0.37)  ; 15\r\n        (    7.97    16.47   -17.56     0.37)  ; 16\r\n        (    8.16    17.18   -18.44     0.52)  ; 17\r\n        (    8.18    17.76   -19.31     0.81)  ; 18\r\n        (    7.91    18.40   -19.81     1.11)  ; 19\r\n        (    7.92    18.91   -19.88     0.74)  ; 20\r\n        (    8.13    19.84   -20.56     0.52)  ; 21\r\n        (    8.74    20.86   -21.63     0.44)  ; 22\r\n        (    8.89    21.27   -21.63     1.18)  ; 23\r\n        (    9.36    21.87   -21.44     1.99)  ; 24\r\n        (    9.50    22.36   -21.44     1.62)  ; 25\r\n        (    9.65    23.30   -21.44     1.03)  ; 26\r\n        (    9.77    24.03   -21.94     0.66)  ; 27\r\n        (    9.69    24.92   -22.63     0.44)  ; 28\r\n        (    9.54    25.91   -22.63     0.37)  ; 29\r\n        (    8.98    27.18   -24.00     0.37)  ; 30\r\n        (    9.01    28.29   -24.31     0.37)  ; 31\r\n        (    8.74    29.00   -24.63     0.37)  ; 32\r\n        (    8.57    29.77   -24.63     0.66)  ; 33\r\n        (    8.37    30.39   -25.06     1.03)  ; 34\r\n        (    8.05    31.18   -25.38     1.40)  ; 35\r\n        (    7.77    31.82   -25.38     1.40)  ; 36\r\n        (    7.67    32.13   -25.94     0.88)  ; 37\r\n        (    7.41    32.76   -25.94     0.44)  ; 38\r\n        (    7.13    33.40   -25.94     0.37)  ; 39\r\n        (    6.77    33.90   -26.38     0.74)  ; 40\r\n        (    6.52    34.23   -26.63     1.03)  ; 41\r\n        (    6.33    34.48   -26.63     1.03)  ; 42\r\n        (    5.84    35.15   -26.63     0.59)  ; 43\r\n        (    5.50    35.36   -26.88     0.59)  ; 44\r\n        (    5.28    35.84   -27.06     1.33)  ; 45\r\n        (    4.87    36.05   -28.19     2.06)  ; 46\r\n        (    4.61    36.75   -28.19     1.25)  ; 47\r\n        (    3.72    36.31   -28.69     1.03)  ; 48\r\n        (    3.39    35.69   -29.69     1.11)  ; 49\r\n        (    3.47    36.19   -30.13     1.47)  ; 50\r\n        (    3.32    36.66   -30.38     1.47)  ; 51\r\n        (    2.13    37.14   -30.44     1.03)  ; 52\r\n        (    1.82    37.57   -30.25     0.59)  ; 53\r\n         Normal\r\n      )  ;  End of split\r\n    |\r\n      (    3.81     2.87   -10.56     0.74)  ; 1, R-1-2\r\n      (    4.30     3.17   -11.81     0.74)  ; 2\r\n      (    4.78     3.38   -13.06     0.74)  ; 3\r\n      (    5.42     3.66   -14.81     0.88)  ; 4\r\n      (    5.97     3.79   -15.06     1.03)  ; 5\r\n      (    6.47     3.27   -15.63     1.03)  ; 6\r\n      (    6.45     2.60   -16.88     0.88)  ; 7\r\n      (    6.30     2.18   -17.31     0.88)  ; 8\r\n      (    5.97     1.49   -18.44     0.88)  ; 9\r\n      (    5.30     0.57   -19.69     0.96)  ; 10\r\n      (    5.12     0.37   -22.69     0.96)  ; 11\r\n      (    4.72     0.21   -23.44     0.96)  ; 12\r\n      (    4.49     0.17   -24.31     0.96)  ; 13\r\n      (    4.49     0.17   -25.13     0.81)  ; 14\r\n      (    4.66     0.81   -27.06     0.66)  ; 15\r\n      (    5.52     1.49   -28.13     0.59)  ; 16\r\n      (    6.22     2.12   -28.38     0.59)  ; 17\r\n      (    6.97     2.15   -29.81     0.81)  ; 18\r\n      (    7.10     2.05   -30.88     0.81)  ; 19\r\n      (    7.29     1.36   -32.44     0.81)  ; 20\r\n      (    7.56     0.66   -33.00     0.81)  ; 21\r\n      (    8.31     1.19   -34.94     0.66)  ; 22\r\n      (    9.41     1.54   -35.69     0.66)  ; 23\r\n      (    9.99     1.89   -36.50     0.66)  ; 24\r\n      (   11.74     3.02   -37.25     0.59)  ; 25\r\n      (   12.84     3.89   -37.25     0.59)  ; 26\r\n      (   13.44     4.90   -37.38     0.59)  ; 27\r\n      (   13.97     5.86   -38.19     0.59)  ; 28\r\n      (   14.67     6.49   -38.75     0.59)  ; 29\r\n      (   15.42     6.51   -39.38     0.88)  ; 30\r\n      (   15.90     6.73   -40.69     1.18)  ; 31\r\n      (   16.68     6.91   -41.13     1.25)  ; 32\r\n      (   17.03     7.73   -42.13     1.11)  ; 33\r\n      (\r\n        (   16.92     8.42   -42.81     0.74)  ; 1, R-1-2-1\r\n        (   16.50     8.63   -44.81     0.74)  ; 2\r\n        (   16.73     9.63   -46.56     0.96)  ; 3\r\n        (   17.18    10.08   -47.81     0.81)  ; 4\r\n        (   17.74    10.36   -48.06     0.52)  ; 5\r\n        (   18.10    10.75   -48.44     0.44)  ; 6\r\n        (   18.60    11.04   -49.06     0.66)  ; 7\r\n        (   19.04    11.48   -49.25     1.33)  ; 8\r\n        (   19.83    11.73   -49.81     1.84)  ; 9\r\n        (   20.44    12.30   -50.13     1.18)  ; 10\r\n        (   20.94    12.67   -50.69     0.59)  ; 11\r\n        (   21.21    13.88   -51.25     0.44)  ; 12\r\n        (   21.57    14.72   -52.44     0.66)  ; 13\r\n        (   21.64    15.15   -52.81     0.96)  ; 14\r\n        (   21.91    15.92   -54.13     0.66)  ; 15\r\n        (   22.29    16.89   -54.69     0.52)  ; 16\r\n        (   22.86    17.69   -54.88     0.52)  ; 17\r\n        (   23.17    18.23   -56.63     1.18)  ; 18\r\n        (   23.16    18.61   -56.94     1.33)  ; 19\r\n        (   22.70    19.49   -57.31     0.88)  ; 20\r\n        (   22.53    19.82   -58.38     0.74)  ; 21\r\n        (   22.25    20.45   -59.75     0.96)  ; 22\r\n        (   21.69    21.21   -61.75     0.66)  ; 23\r\n        (   20.72    21.65   -62.75     0.96)  ; 24\r\n        (   20.14    22.19   -63.00     1.25)  ; 25\r\n        (   19.40    23.12   -64.13     0.81)  ; 26\r\n        (   18.91    23.80   -64.94     0.96)  ; 27\r\n        (   18.51    24.15   -65.81     1.11)  ; 28\r\n        (   18.26    24.94   -67.31     0.81)  ; 29\r\n        (   18.06    25.64   -68.50     1.40)  ; 30\r\n        (   17.39    25.60   -70.06     1.03)  ; 31\r\n        (   16.54    25.88   -71.19     0.81)  ; 32\r\n        (   16.03    26.41   -72.25     0.96)  ; 33\r\n        (   15.46    27.10   -74.00     1.18)  ; 34\r\n        (   15.01    27.53   -75.38     1.03)  ; 35\r\n        (   15.04    28.64   -76.63     0.81)  ; 36\r\n        (   14.57    29.01   -78.56     1.55)  ; 37\r\n        (   13.76    29.51   -80.13     1.92)  ; 38\r\n        (   14.07    30.12   -81.13     1.55)  ; 39\r\n        (   14.42    30.88   -81.69     0.74)  ; 40\r\n        (   14.77    32.16   -81.69     0.59)  ; 41\r\n        (   15.36    33.02   -81.94     0.74)  ; 42\r\n        (   16.34    34.50   -81.94     0.59)  ; 43\r\n        (   16.70    35.40   -82.31     0.59)  ; 44\r\n        (   16.69    35.78   -82.44     1.11)  ; 45\r\n        (   16.59    36.61   -82.88     1.40)  ; 46\r\n        (   16.76    37.18   -83.31     1.47)  ; 47\r\n        (   17.21    37.62   -83.63     0.66)  ; 48\r\n        (   17.40    37.96   -84.25     0.52)  ; 49\r\n         Normal\r\n      |\r\n        (   17.03     6.86   -41.00     0.66)  ; 1, R-1-2-2\r\n        (   16.13     6.33   -42.56     0.66)  ; 2\r\n        (   15.47     5.48   -43.50     0.66)  ; 3\r\n        (   14.78     4.92   -44.13     0.66)  ; 4\r\n        (   14.11     3.92   -45.00     0.66)  ; 5\r\n        (   13.83     3.14   -45.50     0.81)  ; 6\r\n        (   13.46     2.62   -46.56     0.81)  ; 7\r\n        (   13.34     1.45   -47.31     0.59)  ; 8\r\n        (   13.04     0.54   -47.75     0.59)  ; 9\r\n        (   13.44    -0.71   -49.00     0.74)  ; 10\r\n        (   12.87    -1.51   -50.06     1.03)  ; 11\r\n        (   12.20    -1.99   -50.31     0.59)  ; 12\r\n        (   11.60    -2.64   -51.81     0.44)  ; 13\r\n        (   11.34    -2.89   -52.13     0.74)  ; 14\r\n        (   10.66    -2.93   -52.31     1.11)  ; 15\r\n        (    9.89    -3.10   -52.69     1.47)  ; 16\r\n        (    9.09    -3.49   -52.88     0.74)  ; 17\r\n        (    8.01    -4.13   -53.25     0.59)  ; 18\r\n        (    6.95    -4.27   -53.56     0.59)  ; 19\r\n        (    5.69    -5.10   -54.19     0.44)  ; 20\r\n        (    4.50    -5.58   -54.19     0.44)  ; 21\r\n        (    3.27    -6.27   -54.19     0.74)  ; 22\r\n        (    2.38    -6.28   -54.63     0.96)  ; 23\r\n        (    1.48    -6.74   -54.75     0.59)  ; 24\r\n        (    0.49    -6.87   -55.31     0.44)  ; 25\r\n        (   -0.91    -6.79   -55.50     0.44)  ; 26\r\n        (   -1.71    -7.11   -56.00     1.11)  ; 27\r\n        (   -2.95    -7.43   -56.44     1.25)  ; 28\r\n        (   -3.67    -7.69   -58.13     0.81)  ; 29\r\n        (   -4.46    -8.01   -58.56     0.52)  ; 30\r\n        (   -5.21    -8.48   -58.75     0.52)  ; 31\r\n        (   -6.35    -9.56   -59.00     0.81)  ; 32\r\n        (   -7.11   -10.10   -59.25     1.18)  ; 33\r\n        (   -8.37   -11.46   -59.44     0.66)  ; 34\r\n        (   -9.14   -12.15   -59.69     0.66)  ; 35\r\n        (   -9.24   -13.18   -60.13     1.47)  ; 36\r\n        (   -9.44   -14.03   -60.44     2.14)  ; 37\r\n        (   -9.72   -14.80   -60.44     2.14)  ; 38\r\n        (   -9.72   -15.76   -60.44     0.52)  ; 39\r\n        (   -9.95   -16.76   -61.00     0.81)  ; 40\r\n        (  -10.16   -18.06   -61.38     1.69)  ; 41\r\n        (  -10.17   -19.09   -61.69     3.02)  ; 42\r\n        (  -10.20   -20.20   -62.31     2.43)  ; 43\r\n        (  -10.36   -21.21   -63.50     0.88)  ; 44\r\n        (  -10.48   -22.45   -63.75     1.25)  ; 45\r\n        (  -11.09   -22.50   -64.75     1.55)  ; 46\r\n        (  -12.37   -22.59   -67.00     0.74)  ; 47\r\n        (  -13.14   -22.76   -68.00     1.18)  ; 48\r\n        (  -14.07   -22.55   -70.88     0.81)  ; 49\r\n        (  -14.48   -21.81   -72.00     1.18)  ; 50\r\n        (  -15.14   -21.26   -72.69     1.18)  ; 51\r\n        (  -15.41   -20.12   -71.06     0.52)  ; 52\r\n         Normal\r\n      )  ;  End of split\r\n    )  ;  End of split\r\n  |\r\n    (    2.68    -0.24    -8.63     0.96)  ; 1, R-2\r\n    (    2.36    -1.06    -9.00     0.74)  ; 2\r\n    (    2.03    -1.68   -10.06     0.59)  ; 3\r\n    (    1.24    -2.00   -10.25     0.52)  ; 4\r\n    (   -0.12    -2.08   -10.75     0.52)  ; 5\r\n    (   -1.35    -3.29   -11.06     0.74)  ; 6\r\n    (   -2.16    -4.19   -11.56     0.74)  ; 7\r\n    (   -3.13    -5.15   -11.88     0.88)  ; 8\r\n    (   -3.69    -6.32   -12.75     0.88)  ; 9\r\n    (   -4.18    -7.50   -13.88     0.88)  ; 10\r\n    (   -3.93    -8.28   -14.94     0.88)  ; 11\r\n    (   -4.83    -9.25   -14.38     0.74)  ; 12\r\n    (   -5.24    -9.92   -16.06     0.74)  ; 13\r\n    (   -4.66   -10.53   -17.63     0.96)  ; 14\r\n    (   -4.70   -10.75   -18.94     1.18)  ; 15\r\n    (   -4.59   -10.99   -20.13     1.47)  ; 16\r\n    (   -5.66   -11.19   -22.94     0.74)  ; 17\r\n    (   -6.94   -11.28   -23.75     0.66)  ; 18\r\n    (   -7.74   -11.60   -24.00     0.81)  ; 19\r\n    (   -9.42   -11.85   -24.44     0.96)  ; 20\r\n    (  -11.20   -12.31   -25.13     0.96)  ; 21\r\n    (  -13.04   -12.61   -25.88     0.96)  ; 22\r\n    (  -14.78   -14.11   -26.19     0.74)  ; 23\r\n    (  -15.49   -14.81   -27.69     1.03)  ; 24\r\n    (  -16.16   -15.37   -29.06     1.33)  ; 25\r\n    (  -17.13   -16.26   -30.50     0.96)  ; 26\r\n    (  -18.09   -17.14   -31.75     0.66)  ; 27\r\n    (  -19.41   -17.96   -32.88     0.88)  ; 28\r\n    (  -20.45   -18.40   -34.56     0.74)  ; 29\r\n    (  -21.72   -19.30   -35.75     0.59)  ; 30\r\n    (  -22.21   -20.56   -36.63     0.88)  ; 31\r\n    (  -22.72   -20.99   -38.00     2.21)  ; 32\r\n    (  -23.15   -21.30   -38.63     2.65)  ; 33\r\n    (  -23.95   -22.06   -38.88     1.77)  ; 34\r\n    (  -24.00   -22.94   -39.88     0.81)  ; 35\r\n    (  -24.31   -24.27   -40.94     0.59)  ; 36\r\n    (  -25.30   -25.36   -41.56     0.59)  ; 37\r\n    (  -25.47   -26.90   -43.44     0.44)  ; 38\r\n    (  -25.73   -28.56   -43.44     0.44)  ; 39\r\n    (  -25.91   -29.64   -43.56     0.59)  ; 40\r\n    (  -26.33   -30.90   -43.75     0.37)  ; 41\r\n    (  -26.54   -31.76   -43.81     0.66)  ; 42\r\n    (  -26.91   -32.73   -43.88     0.66)  ; 43\r\n    (  -27.20   -33.45   -43.88     0.66)  ; 44\r\n    (\r\n      (  -27.29   -34.16   -43.94     0.37)  ; 1, R-2-1\r\n      (  -27.45   -34.72   -44.00     0.37)  ; 2\r\n      (  -27.84   -35.78   -44.38     1.03)  ; 3\r\n      (  -28.10   -36.47   -44.56     2.28)  ; 4\r\n      (  -28.16   -37.27   -44.88     1.84)  ; 5\r\n      (  -28.15   -38.17   -45.38     0.81)  ; 6\r\n      (  -28.26   -38.89   -45.06     0.29)  ; 7\r\n      (  -27.98   -39.89   -45.19     0.29)  ; 8\r\n      (  -27.88   -40.72   -45.19     0.29)  ; 9\r\n      (  -27.92   -41.46   -45.25     1.11)  ; 10\r\n      (  -27.86   -41.99   -45.25     1.92)  ; 11\r\n      (  -27.61   -42.77   -45.50     2.14)  ; 12\r\n      (  -27.30   -43.63   -45.69     0.66)  ; 13\r\n      (  -26.64   -45.14   -46.13     0.52)  ; 14\r\n      (  -26.54   -45.89   -46.56     0.52)  ; 15\r\n      (  -26.45   -47.17   -46.69     1.25)  ; 16\r\n      (  -26.35   -48.07   -47.13     2.06)  ; 17\r\n      (  -26.32   -48.74   -47.38     2.06)  ; 18\r\n      (  -26.75   -49.64   -47.81     0.52)  ; 19\r\n      (  -26.90   -50.87   -48.44     0.44)  ; 20\r\n      (  -26.97   -52.25   -49.06     0.44)  ; 21\r\n      (  -26.95   -53.08   -49.38     0.44)  ; 22\r\n      (  -27.18   -54.07   -50.00     0.44)  ; 23\r\n      (  -27.31   -54.87   -50.81     0.52)  ; 24\r\n      (  -28.35   -55.81   -51.25     0.74)  ; 25\r\n      (  -28.75   -56.42   -51.63     1.47)  ; 26\r\n      (  -28.93   -56.68   -52.06     2.21)  ; 27\r\n      (  -29.27   -57.37   -52.31     1.69)  ; 28\r\n      (  -30.06   -58.13   -52.81     0.66)  ; 29\r\n      (  -30.89   -59.12   -53.63     0.52)  ; 30\r\n      (  -31.64   -60.10   -53.81     0.74)  ; 31\r\n      (  -32.18   -61.13   -53.81     0.74)  ; 32\r\n      (  -32.66   -61.79   -54.13     0.44)  ; 33\r\n      (  -33.09   -62.68   -54.44     0.81)  ; 34\r\n      (  -33.49   -63.29   -54.75     1.11)  ; 35\r\n      (  -33.87   -63.82   -54.38     0.66)  ; 36\r\n      (  -34.44   -65.06   -54.00     0.44)  ; 37\r\n      (  -34.68   -65.61   -54.00     1.18)  ; 38\r\n      (  -34.92   -66.24   -54.00     1.18)  ; 39\r\n       Normal\r\n    |\r\n      (  -26.40   -33.13   -45.19     0.59)  ; 1, R-2-2\r\n      (  -25.78   -32.57   -44.88     0.74)  ; 2\r\n      (  -25.44   -32.25   -43.06     0.88)  ; 3\r\n      (  -24.84   -31.31   -41.69     1.11)  ; 4\r\n      (  -24.76   -30.80   -43.75     1.11)  ; 5\r\n      (  -24.52   -30.69   -45.88     1.25)  ; 6\r\n      (  -24.51   -30.62   -46.69     1.62)  ; 7\r\n      (  -24.83   -30.27   -47.75     1.11)  ; 8\r\n      (  -24.97   -29.28   -49.81     0.88)  ; 9\r\n      (  -25.29   -28.49   -51.88     1.18)  ; 10\r\n      (  -25.54   -28.16   -52.25     0.96)  ; 11\r\n      (  -25.84   -27.67   -53.56     0.81)  ; 12\r\n      (  -26.44   -26.83   -53.56     1.18)  ; 13\r\n      (  -27.38   -26.61   -53.56     1.03)  ; 14\r\n      (  -28.07   -26.73   -53.69     0.66)  ; 15\r\n      (  -28.36   -26.68   -56.19     0.66)  ; 16\r\n      (  -29.13   -26.85   -56.31     1.03)  ; 17\r\n      (  -29.86   -26.29   -58.00     0.88)  ; 18\r\n       Generated\r\n    )  ;  End of split\r\n  )  ;  End of split\r\n)  ;  End of tree\r\n"
  },
  {
    "path": "examples/thalamocortical-cell/results/cAD_ltb_params.csv",
    "content": 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  },
  {
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    "content": 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  },
  {
    "path": "examples/thalamocortical-cell/thalamocortical-cell_opt.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Optimization of burst and tonic firing in thalamo-cortical neurons\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"____\\n\",\n    \"\\n\",\n    \"This notebook illustrates how to **setup** and **configure optimisations** presented in the following paper:\\n\",\n    \"\\n\",\n    \"Iavarone, Elisabetta, Jane Yi, Ying Shi, Bas-Jan Zandt, Christian O'Reilly, Werner Van Geit, Christian Rössert, Henry Markram, and Sean L. Hill. [\\\"Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons.\\\"](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006753).\\n\",\n    \"\\n\",\n    \"Author of this script: Elisabetta Iavarone @ Blue Brain Project\\n\",\n    \"___\\n\",\n    \"\\n\",\n    \"The models were constrained by using the **experimental data** from Jane Yi, Ying Shi and Henry Markram at the [LNMC, EPFL](https://www.epfl.ch/labs/markram-lab/).\\n\",\n    \"\\n\",\n    \"The morphologies will be available on NeuroMorpho.org under the\\n\",\n    \"ODC Public Domain Dedication and Licence (PDDL) https://opendatacommons.org/licenses/pddl/1.0/\\n\",\n    \"_____\\n\",\n    \"\\n\",\n    \"This notebook makes use of scripts to automatically setup the optimisation, stored in the *config* and *setup* subfolders. To learn more about concepts such as *mechanisms*, *cell template*, *cell evaluator*, we suggest to go through the [L5PC example](https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb).\\n\",\n    \"_____\\n\",\n    \"\\n\",\n    \"**If you use methods or data presented in this notebook we ask to cite the following publications:**\\n\",\n    \"\\n\",\n    \"Iavarone, Elisabetta, Jane Yi, Ying Shi, Bas-Jan Zandt, Christian O'reilly, Werner Van Geit, Christian Rössert, Henry Markram, and Sean L. Hill. \\\"Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons.\\\" [BioRxiv (2019): 512269](https://www.biorxiv.org/content/10.1101/512269v3).\\n\",\n    \"\\n\",\n    \"Van Geit, W., Gevaert, M., Chindemi, G., Rössert, C., Courcol, J. D., Muller, E. B., ... & Markram, H. (2016). BluePyOpt: leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience. [Frontiers in neuroinformatics, 10, 17](https://www.frontiersin.org/articles/10.3389/fninf.2016.00017/full).\\n\",\n    \"\\n\",\n    \"___\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**If you re-use any file from the *mechanisms* folder you should also cite the associated publication.**\\n\",\n    \"\\n\",\n    \"See license file for details.\\n\",\n    \"\\n\",\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    \"\\n\",\n    \"import bluepyopt\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"import pprint\\n\",\n    \"pp = pprint.PrettyPrinter(indent=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Set up the cell model and the cell evaluator\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A cell evaluator can be easily created by specifying the desired electrical type (e-type).\\n\",\n    \"\\n\",\n    \"A cell model is part of the cell evaluator and it is built by specifying a **morphology**, **mechanisms**, i.e. the ion channel models and the **bounds for the parameter values** (i.e. the densities of the ion channels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/iavarone/.local/lib/python2.7/site-packages/neurom/io/neurolucida.py:264: UserWarning: This is an experimental reader. There are no guarantees regarding ability to parse Neurolucida .asc files or correctness of output.\\n\",\n      \"  warnings.warn(msg)\\n\",\n      \"No handlers could be found for logger \\\"neurom.io.neurolucida\\\"\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"cAD_ltb:\\n\",\n      \"  morphology:\\n\",\n      \"    morphologies/jy160728_A_idA.asc\\n\",\n      \"  mechanisms:\\n\",\n      \"    pas.all: pas at ['all']\\n\",\n      \"    TC_cad.all: TC_cad at ['all']\\n\",\n      \"    TC_ih_Bud97.somatic: TC_ih_Bud97 at ['somatic']\\n\",\n      \"    TC_Nap_Et2.somatic: TC_Nap_Et2 at ['somatic']\\n\",\n      \"    TC_iA.somatic: TC_iA at ['somatic']\\n\",\n      \"    TC_iL.somatic: TC_iL at ['somatic']\\n\",\n      \"    SK_E2.somatic: SK_E2 at ['somatic']\\n\",\n      \"    TC_HH.somatic: TC_HH at ['somatic']\\n\",\n      \"    TC_ih_Bud97.alldend: TC_ih_Bud97 at ['basal']\\n\",\n      \"    TC_Nap_Et2.alldend: TC_Nap_Et2 at ['basal']\\n\",\n      \"    TC_iA.alldend: TC_iA at ['basal']\\n\",\n      \"    TC_iL.alldend: TC_iL at ['basal']\\n\",\n      \"    SK_E2.alldend: SK_E2 at ['basal']\\n\",\n      \"    TC_HH.alldend: TC_HH at ['basal']\\n\",\n      \"    TC_HH.axonal: TC_HH at ['axonal']\\n\",\n      \"    TC_iT_Des98.somadend: TC_iT_Des98 at ['basal', 'somatic']\\n\",\n      \"  params:\\n\",\n      \"    v_init: v_init = -79\\n\",\n      \"    celsius: celsius = 34\\n\",\n      \"    cm.all: ['all'] cm = 1\\n\",\n      \"    Ra.all: ['all'] Ra = 100\\n\",\n      \"    ena.all: ['all'] ena = 50\\n\",\n      \"    ek.all: ['all'] ek = -90\\n\",\n      \"    e_pas.all: ['all'] e_pas = -80\\n\",\n      \"    g_pas.all: ['all'] g_pas = [1e-06, 0.0001]\\n\",\n      \"    gk_max_TC_HH.axonal: ['axonal'] gk_max_TC_HH = [0, 0.2]\\n\",\n      \"    gna_max_TC_HH.axonal: ['axonal'] gna_max_TC_HH = [0, 0.8]\\n\",\n      \"    pcabar_TC_iT_Des98.somadend: ['basal', 'somatic'] pcabar_TC_iT_Des98 = [0, 0.0001]\\n\",\n      \"    gh_max_TC_ih_Bud97.somatic: ['somatic'] gh_max_TC_ih_Bud97 = [0, 0.0001]\\n\",\n      \"    gNap_Et2bar_TC_Nap_Et2.somatic: ['somatic'] gNap_Et2bar_TC_Nap_Et2 = [0, 0.0001]\\n\",\n      \"    gk_max_TC_iA.somatic: ['somatic'] gk_max_TC_iA = [0, 0.07]\\n\",\n      \"    gk_max_TC_HH.somatic: ['somatic'] gk_max_TC_HH = [0, 0.2]\\n\",\n      \"    gna_max_TC_HH.somatic: ['somatic'] gna_max_TC_HH = [0, 0.2]\\n\",\n      \"    pcabar_TC_iL.somatic: ['somatic'] pcabar_TC_iL = [0, 0.001]\\n\",\n      \"    gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.005]\\n\",\n      \"    taur_TC_cad.somatic: ['somatic'] taur_TC_cad = [1.0, 15.0]\\n\",\n      \"    gamma_TC_cad.somatic: ['somatic'] gamma_TC_cad = [0.0005, 1]\\n\",\n      \"    gh_max_TC_ih_Bud97.alldend: ['basal'] gh_max_TC_ih_Bud97 = [0, 0.0001]\\n\",\n      \"    gNap_Et2bar_TC_Nap_Et2.alldend: ['basal'] gNap_Et2bar_TC_Nap_Et2 = [0, 0.0001]\\n\",\n      \"    gk_max_TC_iA.alldend: ['basal'] gk_max_TC_iA = [0, 0.008]\\n\",\n      \"    gk_max_TC_HH.alldend: ['basal'] gk_max_TC_HH = [0, 0.01]\\n\",\n      \"    gna_max_TC_HH.alldend: ['basal'] gna_max_TC_HH = [0, 0.006]\\n\",\n      \"    pcabar_TC_iL.alldend: ['basal'] pcabar_TC_iL = [0, 0.001]\\n\",\n      \"    gSK_E2bar_SK_E2.alldend: ['basal'] gSK_E2bar_SK_E2 = [0, 0.005]\\n\",\n      \"    taur_TC_cad.alldend: ['basal'] taur_TC_cad = [1.0, 15.0]\\n\",\n      \"    gamma_TC_cad.alldend: ['basal'] gamma_TC_cad = [0.0005, 1]\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Import scripts for setting up the cell model and cell evaluator\\n\",\n    \"import CellEvalSetup \\n\",\n    \"\\n\",\n    \"# Library to visualize and analyse morphologies \\n\",\n    \"import neurom # https://github.com/BlueBrain/NeuroM\\n\",\n    \"import neurom.viewer\\n\",\n    \"\\n\",\n    \"etype = \\\"cAD_ltb\\\" # or cNAD_ltb \\n\",\n    \"\\n\",\n    \"evaluator = CellEvalSetup.evaluator.create(etype)\\n\",\n    \"\\n\",\n    \"neurom.viewer.draw(neurom.load_neuron(evaluator.cell_model.morphology.morphology_path))\\n\",\n    \"print(evaluator.cell_model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Run an optimisation\\n\",\n    \"\\n\",\n    \"Once we have created the cell evaluator, we can run an optimisation. During the optimisation different parameter values will be evaluated, by running different **stimulation protocols** and recording the **voltage responses** of the models. \\n\",\n    \"\\n\",\n    \"The algorithm will try minimise the difference between the **electrical features** measured from the voltage responses in the model and the features extracted from the experimental data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"seed = 0 # Number to initialize the pseudorandom number generator\\n\",\n    \"\\n\",\n    \"opt = bluepyopt.optimisations.DEAPOptimisation(\\n\",\n    \"    evaluator=evaluator,\\n\",\n    \"    map_function=map, # The map function can be used to parallelize the optimisation\\n\",\n    \"    seed=seed,\\n\",\n    \"    eta=10., mutpb=1.0, cxpb=1.0)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As a proof of concept, we run an optimisation with a small number of individuals (n = 2) and generations (n = 2); this step will require some minutes. Typically this optimisation was run with 100 individual for 100 generations. At the end we obtain the \\\"Hall of Fame\\\", where the first individual is the best model.\\n\",\n    \"\\n\",\n    \"Before we create a folder to save the results.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!nrnivmodl mechanisms # Compile NEURON .mod files stored in the \\\"mechanisms\\\" folder\\n\",\n    \"\\n\",\n    \"if not os.path.exists('checkpoints'):\\n\",\n    \"    os.mkdir('checkpoints')\\n\",\n    \"\\n\",\n    \"final_pop, halloffame, log, hist, = opt.run(max_ngen=2,\\n\",\n    \"        offspring_size=2,\\n\",\n    \"        cp_filename='checkpoints/checkpoint.pkl');\"\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      \"\\n\",\n      \"Example of one individual resulting from an optimisation run:\\n\",\n      \"\\n\",\n      \"[8.998499050883135e-05, 0.13679678638308826, 0.3777141723621707, 1.007012080683658e-05, 4.3417183545378374e-05, 6.108869734438017e-05, 0.06391077372665288, 0.19332127355415177, 0.09540195531054341, 0.0008653099277716401, 0.001302461551959797, 12.270389578182312, 0.5489249541836715, 1.4041700164018957e-06, 7.197046864039541e-05, 0.00319058833779415, 0.00824844977148233, 0.004008919207391105, 1.1428193144282783e-06, 0.0024678893323266233, 13.146438856898932, 0.2442889214486884]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(\\\"\\\\nExample of one individual resulting from an optimisation run:\\\\n\\\")\\n\",\n    \"print(halloffame[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Analyse optimisation results\\n\",\n    \"\\n\",\n    \"In this section you will see how to run simulations with models obtained after running a full optimisation.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import csv\\n\",\n    \"\\n\",\n    \"with open('results/{}_params.csv'.format(etype)) as csvfile:\\n\",\n    \"    rows = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)\\n\",\n    \"    params = list(rows)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Select one of the models and create the dictionary of parameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{ 'gNap_Et2bar_TC_Nap_Et2.alldend': 7.465371215899886e-05,\\n\",\n      \"  'gNap_Et2bar_TC_Nap_Et2.somatic': 1.3389527019193626e-05,\\n\",\n      \"  'gSK_E2bar_SK_E2.alldend': 0.0002179960911576617,\\n\",\n      \"  'gSK_E2bar_SK_E2.somatic': 0.0012832790164235054,\\n\",\n      \"  'g_pas.all': 3.250567914095199e-05,\\n\",\n      \"  'gamma_TC_cad.alldend': 0.000539506186561177,\\n\",\n      \"  'gamma_TC_cad.somatic': 0.006740122627193884,\\n\",\n      \"  'gh_max_TC_ih_Bud97.alldend': 8.422890384914114e-06,\\n\",\n      \"  'gh_max_TC_ih_Bud97.somatic': 4.7504774088700974e-05,\\n\",\n      \"  'gk_max_TC_HH.alldend': 0.009924383323607554,\\n\",\n      \"  'gk_max_TC_HH.axonal': 0.11490723847692205,\\n\",\n      \"  'gk_max_TC_HH.somatic': 0.11616929624507591,\\n\",\n      \"  'gk_max_TC_iA.alldend': 0.00418186621417919,\\n\",\n      \"  'gk_max_TC_iA.somatic': 0.06374589309943703,\\n\",\n      \"  'gna_max_TC_HH.alldend': 0.005250846661623297,\\n\",\n      \"  'gna_max_TC_HH.axonal': 0.21874510090978222,\\n\",\n      \"  'gna_max_TC_HH.somatic': 0.09113695987409176,\\n\",\n      \"  'pcabar_TC_iL.alldend': 1.1024317581168538e-05,\\n\",\n      \"  'pcabar_TC_iL.somatic': 0.0004981128655248998,\\n\",\n      \"  'pcabar_TC_iT_Des98.somadend': 8.948755951390262e-05,\\n\",\n      \"  'taur_TC_cad.alldend': 9.551240612869854,\\n\",\n      \"  'taur_TC_cad.somatic': 11.005585762426819}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"modid = 62 # Or e.g. 78 for cNAD_ltb model shown in the paper, Fig. 4\\n\",\n    \"param_dict = evaluator.param_dict(params[modid])\\n\",\n    \"pp.pprint(param_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can run a simulation with the parameters above and the current protocols which are part of the evaluator.\"\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      \"Simulation took 0:00:46.785285.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from datetime import datetime\\n\",\n    \"\\n\",\n    \"t0 = datetime.now()\\n\",\n    \"responses = evaluator.run_protocols(protocols = evaluator.fitness_protocols.values(), param_values=param_dict)\\n\",\n    \"\\n\",\n    \"print(\\\"Simulation took {}.\\\".format(datetime.now()-t0))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can plot the model responses.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 360x576 with 6 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import collections\\n\",\n    \"\\n\",\n    \"def plot_responses(responses):\\n\",\n    \"    # Select and sort reponses\\n\",\n    \"    stim_names = [name for name in sorted(evaluator.fitness_protocols.keys()) \\n\",\n    \"                      if \\\"hold\\\" not in name and \\\"RMP\\\" not in name]\\n\",\n    \"    sel_resp = collections.OrderedDict()\\n\",\n    \"    for name in stim_names:\\n\",\n    \"        sel_resp[name] =  responses[\\\".\\\"+name+\\\".soma.v\\\"]\\n\",\n    \"        \\n\",\n    \"    fig, axes = plt.subplots(len(sel_resp), figsize=(5, 8), sharey = True)\\n\",\n    \"    for index, (resp_name, response) in enumerate(sorted(sel_resp.items())):\\n\",\n    \"        \\n\",\n    \"        startid = 550 if \\\"Step\\\" in resp_name or \\\"IV\\\" or \\\"Rin\\\" in resp_name else 0 # Remove initial transient\\n\",\n    \"        indices = response['time'] >= startid\\n\",\n    \"        \\n\",\n    \"        axes[index].plot(response['time'][indices]-startid, response['voltage'][indices],\\n\",\n    \"                        color = \\\"blue\\\", lw = 0.75, alpha = 0.8)\\n\",\n    \"        \\n\",\n    \"        axes[index].set_ylabel('V$_m$ (mV)', fontsize = 'small')\\n\",\n    \"        axes[-1].set_xlabel('Time (ms)', fontsize = 'small')\\n\",\n    \"    fig.tight_layout()\\n\",\n    \"    fig.show()\\n\",\n    \"plot_responses(responses)\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can evaluate the fitness of the model by computing its errors. Each error quantify how much the model deviates from the experimental features.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 388.8x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"objectives = evaluator.fitness_calculator.calculate_scores(responses)\\n\",\n    \"\\n\",\n    \"def plot_objectives(objectives): \\n\",\n    \"    \\n\",\n    \"    # Names for all the stimuli   \\n\",\n    \"    stim_name = ['RMP', u'IV_-140', u'Rin_dep',  'hold_hyp', u'Step_200_hyp',  u'Step_150', \\n\",\n    \"             u'Step_200', u'Step_250', 'hold_dep']\\n\",\n    \"    \\n\",\n    \"    # Sort objectives\\n\",\n    \"    obj_keys = [[key for key in objectives.keys() if key.split(\\\".\\\")[1] == stim] for stim in stim_name]\\n\",\n    \"    obj_keys = [item for sublist in obj_keys for item in sublist][::-1] \\n\",\n    \"    obj_val = []\\n\",\n    \"    for key in obj_keys:\\n\",\n    \"        obj_val.append(objectives[key])\\n\",\n    \" \\n\",\n    \"    ytick_pos = [x + 0.5 for x in range(len(obj_keys))]\\n\",\n    \"    fig, ax = plt.subplots(figsize = (5.4,9), facecolor = 'white')\\n\",\n    \"  \\n\",\n    \"    ax.barh(ytick_pos,\\n\",\n    \"              obj_val,\\n\",\n    \"              height=0.5,\\n\",\n    \"              align='center',\\n\",\n    \"              color='blue',\\n\",\n    \"              alpha=0.5)\\n\",\n    \"    \\n\",\n    \"    obj_keys = [CellEvalSetup.tools.rename_feat(name) for name in obj_keys]     \\n\",\n    \"        \\n\",\n    \"    ax.set_yticks(ytick_pos)\\n\",\n    \"    ax.set_yticklabels(obj_keys, size='medium')\\n\",\n    \"    ax.set_ylim(-0.5, len(obj_keys) + 0.5)\\n\",\n    \"    ax.set_xlim([0,3])\\n\",\n    \"      \\n\",\n    \"    ax.set_xlabel(\\\"Distance from exp. mean (# STD)\\\")\\n\",\n    \"    ax.set_ylabel(\\\"Feature name\\\")\\n\",\n    \"    ax.xaxis.grid(True)\\n\",\n    \"    fig.tight_layout()\\n\",\n    \"\\n\",\n    \"plot_objectives(objectives)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 2\",\n   \"language\": \"python\",\n   \"name\": \"python2\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 2\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython2\",\n   \"version\": \"2.7.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/AUTHORS.txt",
    "content": "Rodrigo Perin @ LNMC\nGiuseppe Chindemi @ BBP\nAndras Ecker @ BBP\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/README.md",
    "content": "# Tsodyks-Markram model examples\n\nThe Tsodyks-Markram model of short-term plasticity underwent many changes in the last twenty years.\nIn this folder we provide 2 examples to fit 2 different versions using BluePyOpt.\n\n`tsodyksmarkramstp.ipynb` numerically integrates the \"full version\" of the TM model and fits a postsynaptic voltage trace.\n\n\n`tsodyksmarkramstp_multiplefreqs.ipynb` implements the event-based solution of the (reduced, but) more common version of the TM model and fits amplitudes from multiple stimulation frequencies for better generalization.\n\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/tmevaluator.py",
    "content": "\n\"\"\"Tsodyks-Markram model Evaluator.\nThis module contains an evaluator for the Tsodyks-Markram model.\n\n@author: Giuseppe Chindemi\n@remark: Copyright (c) 2017, EPFL/Blue Brain Project\n         This file is part of BluePyOpt\n         <https://github.com/BlueBrain/BluePyOpt>\n         This library is free software; you can redistribute it and/or modify\n         it under the terms of the GNU Lesser General Public License version\n         3.0 as published by the Free Software Foundation.\n         This library is distributed in the hope that it will be useful, but\n         WITHOUT ANY WARRANTY; without even the implied warranty of\n         MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n         See the GNU Lesser General Public License for more details.\n         You should have received a copy of the GNU Lesser General Public\n         License along with this library; if not, write to the Free Software\n         Foundation, Inc.,\n         51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\nimport bluepyopt as bpop\nimport numpy as np\nimport tmodeint\n\ntry:\n    xrange\nexcept NameError:\n    xrange = range\n\n\nclass TsodyksMarkramEvaluator(bpop.evaluators.Evaluator):\n    def __init__(self, t, v, tstim, params):\n        \"\"\"\n        Parameters\n        ----------\n        t : numpy.ndarray\n            Time vector (sec).\n        v : numpy.ndarray\n            Voltage vector (V), must have same dimension of t.\n        tstim : numpy.ndarray\n            Time of the stimuli.\n        params : list\n            List of parameters to fit. Every entry must be a tuple\n            (name, lower bound, upper bound).\n        \"\"\"\n        super(TsodyksMarkramEvaluator, self).__init__()\n        self.v = v\n        self.t = t\n        self.stimidx = np.searchsorted(t, tstim)\n        self.dx = t[1] - t[0]\n        self.nsamples = len(v)\n        # Find voltage baseline\n        bs_stop = np.searchsorted(t, tstim[0])\n        self.vrest = np.mean(v[:bs_stop])\n        # Compute time windows where to compare model and data\n        offset = 0.005  # s\n        window = 0.04  # s\n        window_samples = int(np.round(window / self.dx))\n        psp_start = np.searchsorted(t, tstim + offset)\n        psp_stop = psp_start + window_samples\n        psp_stop[-1] += 2 * window_samples  # Extend last psp window (RTR case)\n        self.split_idx = list(zip(psp_start, psp_stop))\n        # Parameters to be optimized\n        self.params = [bpop.parameters.Parameter(name, bounds=(minval, maxval))\n                       for name, minval, maxval in params]\n        # Objectives\n        self.objectives = [bpop.objectives.Objective('interval_%d' % (i,))\n                           for i in xrange(len(self.split_idx))]\n\n    def generate_model(self, individual):\n        \"\"\"Calls numerical integrator `tmodeint.py` and returns voltage trace\n        based on the input parameters\"\"\"\n\n        v, _ = tmodeint.integrate(self.stimidx, self.nsamples, self.dx,\n                                  self.vrest, *individual)\n        return v\n\n    def evaluate_with_lists(self, individual):\n        \"\"\"Errors used by BluePyOpt for the optimization\"\"\"\n\n        candidate_v = self.generate_model(individual)\n        errors = [np.linalg.norm(self.v[t0:t1] - candidate_v[t0:t1])\n                  for t0, t1 in self.split_idx]\n        return errors\n\n    def init_simulator_and_evaluate_with_lists(self, individual):\n        \"\"\"Calls evaluate_with_lists. Is called during IBEA optimisation.\"\"\"\n        return self.evaluate_with_lists(individual)\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/tmevaluator_multiplefreqs.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"Tsodyks-Markram model Evaluator used to fit data from multiple frequency\nstimulations. This module contains an evaluator for the Tsodyks-Markram model.\n\n@authors: Andras Ecker and Giuseppe Chindemi\n@remark: Copyright (c) 2017, EPFL/Blue Brain Project\n         This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n         This library is free software; you can redistribute it and/or modify it\n         under the terms of the GNU Lesser General Public License version 3.0 as\n         published by the Free Software Foundation.\n         This library is distributed in the hope that it will be useful, but\n         WITHOUT ANY WARRANTY; without even the implied warranty of\n         MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n         See the GNU Lesser General Public License for more details.\n         You should have received a copy of the GNU Lesser General Public\n         License along with this library; if not, write to the Free Software\n         Foundation, Inc.,\n         51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\nimport numpy as np\nimport bluepyopt as bpop\nimport tmodesolve\n\ntry:\n    xrange\nexcept NameError:\n    xrange = range\n\n\nclass TsodyksMarkramEvaluator(bpop.evaluators.Evaluator):\n    def __init__(self, data, params):\n        \"\"\"\n        BluePyOpt evulator class.\n\n        Parameters\n        ----------\n        data : OrderedDict (or a Python 3 dict)\n            Frequecies as keys and {t_stims, amps} numpy.ndarray with stim times (ms) and normalized amplitudes\n        params : list\n            List of parameters to fit. Every entry must be a tuple\n            (name, lower bound, upper bound).\n        \"\"\"\n\n        super(TsodyksMarkramEvaluator, self).__init__()\n        self.t_stims = {freq:vals[\"t_spikes\"] for freq, vals in data.items()}\n        self.amplitudes = {freq:vals[\"amps\"] for freq, vals in data.items()}\n        self.params = params\n        self.params = [bpop.parameters.Parameter(name, bounds=(minval, maxval))\n                       for name, minval, maxval in self.params]\n\n        # Bpop Objectives\n        self.objectives = []\n        for freq, _ in data.items():\n            self.objectives.extend([bpop.objectives.Objective(\"%s_amplitude_%i\"%(freq, i))\n                                    for i in xrange(len(self.t_stims[freq]))])\n\n    def generate_model(self, freq, individual):\n        \"\"\"Calls numerical solver `tmodesolve.py` and returns amplitudes based on the input parameters\"\"\"\n\n        amps, tm_statevars = tmodesolve.solve_TM(self.t_stims[freq], *individual)\n        return amps, tm_statevars\n\n    def evaluate_with_lists(self, individual):\n        \"\"\"Errors used by BluePyOpt for the optimization\"\"\"\n\n        errors = []\n        for freq, _ in self.t_stims.items():\n            candidate_amps, _ = self.generate_model(freq, individual)\n            errors.extend(np.power(self.amplitudes[freq] - candidate_amps, 2).tolist())\n        return errors\n\n    def init_simulator_and_evaluate_with_lists(self, individual):\n        \"\"\"Calls evaluate_with_lists. Is called during IBEA optimisation.\"\"\"\n        return self.evaluate_with_lists(individual)\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/tmodeint.py",
    "content": "\"\"\"Tsodyks-Markram model ODEINT.\nThis module contains functions to numerically integrate the Tsodyks-Markram\nmodel. The current implementation is a port of an Igor Pro (WaveMetrics)\nroutine, written by Rodrigo Perin with the help of Raphael Holzer. Many thanks\nto Misha Tsodyks and Henry Markram for their support during all development\nstages.\n\n@author: Giuseppe Chindemi, Rodrigo Perin\n@remark: Copyright (c) 2017, EPFL/Blue Brain Project\n        This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n        This library is free software; you can redistribute it and/or modify it\n        under the terms of the GNU Lesser General Public License version 3.0 as\n        published by the Free Software Foundation.\n        This library is distributed in the hope that it will be useful, but\n        WITHOUT ANY WARRANTY; without even the implied warranty of\n        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n        See the GNU Lesser General Public License for more details.\n        You should have received a copy of the GNU Lesser General Public\n        License along with this library; if not, write to the Free Software\n        Foundation, Inc.,\n        51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n# pylint: disable=E741\n\n\nimport numpy as np\n\ntry:\n    xrange\nexcept NameError:\n    xrange = range\n\n\ndef integrate(sampstim, nsamples, dt, vRest, Trec, Tfac,\n              ASE, USE, Rinput, Tmem, Tinac, latency):\n    \"\"\"Integrate Tsodyks-Markram model and produce corresponding voltage trace.\n\n    Parameters\n    ----------\n    sampstim : numpy.ndarray\n        Array of sample indices where stimulation occurs.\n    nsamples : int\n        Number of samples in the trace.\n    dt : float\n        Time step of the trace.\n    vRest : float\n        Membrane resting potential (V).\n    Trec : float\n        Recovery time constant (ms).\n    Tfac : float\n        Facilitation time constant (ms).\n    ASE : float\n        Absolute Synaptic Efficacy.\n    USE : float\n        Utilization of Synaptic Efficacy. Has to be in the interval [0, 1].\n    Rinput : float\n        Input resistance (MOhm).\n    Tmem : float\n        Membrane time constant (ms).\n    Tinac : float\n        Inactivation time constant (ms).\n    latency : float\n        PSP latency (ms).\n\n    Returns\n    -------\n    vtrace : numpy.ndarray\n        Voltage trace corresponding to the input parameters.\n    tm_statevar : dictionary\n        Integral of all state variables.\n    \"\"\"\n    AP = np.zeros(nsamples)\n    I = np.zeros(nsamples)  # NOQA\n    R = np.zeros(nsamples)\n    E = np.zeros(nsamples)\n    U = np.zeros(nsamples)\n    P = np.zeros(nsamples)\n    # Set stimuli in AP vector\n    psp_offset = int(np.round(1e-3 * latency / dt))\n    AP[sampstim + psp_offset] = 1\n    # Initialize state vectors\n    R[0] = 1\n    E[0] = 0\n    P[0] = 0\n    U[0] = USE\n    # Integrate TM model ODE\n    for i in xrange(1, nsamples):\n        R[i] = R[i - 1] + dt * (1 - R[i - 1] - E[i - 1]) * \\\n            1e3 / Trec - U[i - 1] * R[i - 1] * AP[i - 1]\n        E[i] = E[i - 1] - dt * E[i - 1] * 1e3 / Tinac + U[i - 1] * \\\n            R[i - 1] * AP[i - 1]\n        U[i] = U[i - 1] - dt * (U[i - 1] - USE) * 1e3 / \\\n            Tfac + USE * (1 - U[i - 1]) * AP[i - 1]\n        P[i] = P[i - 1] + dt * (Rinput * ASE / 10 **\n                                6 * E[i - 1] - P[i - 1]) * 1e3 / Tmem\n    # Update state\n    P = P + vRest\n    E = E * ASE / 10**12\n    I = 1 - R - E  # NOQA\n    tm_statevar = {'recovered': R, 'effective': E, 'used': U, 'inactive': I}\n    return P, tm_statevar\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/tmodesolve.py",
    "content": "# -*- coding: utf-8 -*-\n\"\"\"Tsodyks-Markram model ODE solver\nThis module contains functions to solve the Tsodyks-Markram\nmodel. (same equations as in Maass and Markram 2002)\n\n@author: Andras Ecker\n@remark: Copyright (c) 2017, EPFL/Blue Brain Project\n        This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n        This library is free software; you can redistribute it and/or modify it\n        under the terms of the GNU Lesser General Public License version 3.0 as\n        published by the Free Software Foundation.\n        This library is distributed in the hope that it will be useful, but\n        WITHOUT ANY WARRANTY; without even the implied warranty of\n        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n        See the GNU Lesser General Public License for more details.\n        You should have received a copy of the GNU Lesser General Public\n        License along with this library; if not, write to the Free Software\n        Foundation, Inc.,\n        51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\"\"\"\n\n\nimport numpy as np\n\n\ndef solve_TM(t_stims, USE, Trec, Tfac, ASE):\n    \"\"\"Solve Tsodyks-Markram model and produce corresponding amplitudes.\n\n    Parameters\n    ----------\n    t_stims : numpy.ndarray\n        Array of stimulation times (ms)\n    USE : float\n        Utilization of Synaptic Efficacy. Has to be in the interval [0, 1].\n    Trec : float\n        Recovery time constant (ms).\n    Tfac : float\n        Facilitation time constant (ms).\n    ASE : float\n        Absolute Synaptic Efficacy.\n\n    Returns\n    -------\n    Ampls : numpy.ndarray\n        Normalized amplitudes corresponding to input parameters\n    tm_statevars : dictionary\n        Value of state variables at stimulation times\n    \"\"\"\n\n    # Initialize state vectors\n    U = np.zeros_like(t_stims)\n    R = np.zeros_like(t_stims)\n    Ampls = np.zeros_like(t_stims)\n    R[0] = 1\n    U[0] = USE\n    Ampls[0] = ASE*U[0]*R[0]\n    R[0] = R[0] - R[0]*U[0]\n    U[0] = U[0] + USE*(1-U[0])\n    last_stim = t_stims[0]\n\n    for i in range(1, len(t_stims)):\n        delta_t = t_stims[i] - last_stim\n        R[i] = 1 + (R[i-1] - 1)*np.exp(-delta_t/Trec)\n        U[i] = USE + (U[i-1] - USE)*np.exp(-delta_t/Tfac)\n        Ampls[i] = ASE*U[i]*R[i]\n        R[i] = R[i] - R[i]*U[i]\n        U[i] = U[i] + USE*(1-U[i])\n        last_stim = t_stims[i]\n\n    tm_statevars = {\"R\": R, \"U\": U}\n\n    return Ampls, tm_statevars\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tsodyks-Markram model of short-term synaptic plasticity\\n\",\n    \"In this notebook we demonstrate how to fit the parameters of the Tsodyks-Markram model to a given in vitro somatic recording. The in vitro trace used here shows a typical L5TTPC-L5TTPC depressing connection, kindly provided by Rodrigo Perin (EPFL).\\n\",\n    \"\\n\",\n    \"`tmodeint.py` numerically integrates the \\\"full version\\\" of the TM model and fits a postsynaptic voltage trace:\\n\",\n    \"\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"\\\\frac{dR(t)}{dt} = \\\\frac{1-R(t)-E(t)}{D} - U(t)R(t)\\\\delta(t-t_{spike})\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"\\\\frac{dU(t)}{dt} = \\\\frac{U_{SE}-U(t)}{F} + U_{SE}(1-U(t))\\\\delta(t-t_{spike})\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"\\\\frac{dE(t)}{dt} = \\\\frac{-E}{\\\\tau_{inac}} + U(t)R(t)\\\\delta(t-t_{spike})\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"\\\\tau_{mem} \\\\frac{dV(t)}{dt} = -V + R_{inp}I_{syn}(t)\\n\",\n    \"\\\\end{equation}\\n\",\n    \"where $I_{syn}(t)=A_{SE}E(t)$\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        this.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width);\\n\",\n       \"        canvas.attr('height', height);\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'];\\n\",\n       \"    var y0 = fig.canvas.height - msg['y0'];\\n\",\n       \"    var x1 = msg['x1'];\\n\",\n       \"    var y1 = fig.canvas.height - msg['y1'];\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x;\\n\",\n       \"    var y = canvas_pos.y;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        // select the cell after this one\\n\",\n       \"        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n\",\n       \"        IPython.notebook.select(index + 1);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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ze4zekPSYmc1nIWxbyloW85SFzWchbFgoQ2EY2FKB73loaUlQmLd7VIeN6G26EjPvmmHXpj4nJXBTyloW8ZSFvechcFvKWhQIEtmH1AtTWFnqhArdH59Fzt3XI2Je910PGva/XsrTHxGQuC3nLQt6ykLc8ZC4LectCAQLbsHoBagm7UIHbo/PQGVs6ZOzaK40RY6cLk7ks5C0LectC3vKQuSzkLQsFCGzD6gUo/EIFbo/Og6Zu7pCxa+quoQBZHPKWhbxlIW95yFwW8paFAgS2YfUCFH6hArdH53cmbeyQsatrGyLuA9TW1pbWmJjMZSFvWchbFvKWh8xlIW9ZKEBgG1YvQOEXKnB7dO71cUmHjH3mgjdi7BtNLentLyZzUchbFvKWhbzlIXNZyFsWChDYhtULUPiFCtwenV8fXdwhY5+sro8Y+8rVG2mNiclcFvKWhbxlIW95yFwW8paFAgS2YfUCFO1CBS8OL+qQsY+fvRIxdk3dtbTGxGQuC3nLQt6ykLc8ZC4LectCAQLbsHoBOl/XEFFSnhm8ukPGPnL6csTYp2vSm4QxmctC3rKQtyzkLQ+Zy0LeslCAwDasXoDCL1Tg9uj8xICCDhn70Mm6iLGPnkkvJ0zmspC3LOQtC3nLQ+aykLcsFCCwDasXoHMXr5rl5N63lrLbo/NDffM6ZOzK47URBajyeG1aY2Iyl4W8ZSFvWchbHjKXhbxloQCBbVi9AJ29EChAj7yzgt0ene/pgMtVMzPvO3oxogDtPFST1piYzGUhb1nIWxbylofMZSFvWShAYBtWL0BnagKXqn7qvZXmz03N6V2umpl5d9WFiAK0dd+5tMbEZC4LectC3rKQtzxkLgt5y0IBAtuwegE6dT5wqeoXhq0xf/Y2pHe5ambmnQdrIgrQxp1n0hoTk7ks5C0LectC3vKQuSzkLQsFCGzD6gUo+F49b4wuNn++eDm9y1UzM2+rPB9RgIrKT6Y1JiZzWchbFvKWhbzlIXNZyFsWChDYhtUL0IlzgXv19B6/wfz5TJqXq2Zm3rrvXEQBKig9ltaYmMxlIW9ZyFsW8paHzGUhb1koQGAbVi9AwTcrHTR1s/nzkdPpv57Ne85GFKCl66vSGhOTuSzkLQt5y0Le8pC5LOQtCwUIbMPqBehYUAEaPqvc/Hn/0fQuV83MvGnXmYgCtGDNwbTGxGQuC3nLQt6ykLc8ZC4LectCAQLbsHoBOnrmsllOxs3fbv684+D5tMfeGKUAzS7Yn9aYmMxlIW9ZyFsW8paHzGUhb1koQGAbVi9AVacumeXk0yW7zZ/L9pxNe+yNOyML0GfL9qY1JiZzWchbFvKWhbzlIXNZyFsWChDYhtUL0OGgAjRzxT7z5/XbT6U9dsmO0xEFaNLiXWmNiclcFvKWhbxlIW95yFwW8paFAgS2YfUCdOhknVlOviw8aP68uux42mOXbI8sQOPmb09rTEzmspC3LOQtC3nLQ+aykLcsFCCwDasXoIMnAgVoaUmV+XPehiNpj71++6mIAjRidnlaY2Iyl4W8ZSFvWchbHjKXhbxloQCBbVi9AB04Xhvyro/x88KiQ2mPvW5bZAH64LOytMbEZC4LectC3rKQtzxkLgt5y0IBAtuwegGqPBYoQMHf2fmioDLtsYujFCC3R09rTEzmspC3LOQtC3nLQ+aykLcsFCCwDasXoH1HL4Zc+e2enh1ztTZm5uKKkyhAFoe8ZSFvWchbHjKXhbxloQCBbVi9AO09EihAW/ed44f65rHbo/PERTvTHnstCpDlIW9ZyFsW8paHzGUhb1koQGAbVi9Ae6oumMWkfH81PzGggN0encfM25b22EXlgQI0aOpmdnt0vrunzm1tbSmPiclcFvKWhbxlIW95yFwW8paFAgS2YfUCtPtwoABVVFbzM4NXs9uj84czt6Y9dlH5iZCrvxk/X7venPKYmMxlIW9ZyFsW8paHzGUhb1koQGAbVi9Auw7XmMVkW+V5fnF4Ebs9Or83bXPaYxduCRSgCQt3mj/XXWlMeUxM5rKQtyzkLQt5y0PmspC3LBQgsA2rF6CdBwMFaPuB8/zGmHXs9ujcb+LGtMcu3BK4rPbny/aaP5+pSX0ixmQuC3nLQt6ykLc8ZC4LectCAQLbsHoB2n7gvFlMdh6s4d7jN7Dbo3PPcevTHjv4vkLzVx8wf646dSnlMTGZy0LespC3LOQtD5nLQt6yUIDANqxegLYFF6BDNTxgSim7PTq/MnJt2mOvCipAS9dXmT/vqbqQ8piYzGUhb1nIWxbylofMZSFvWShAYBtWL0AVldVmMdl9+AIPmb6F3R6dXxhWmPbYKzcHClDwx+HK91enPCYmc1nIWxbyloW85SFzWchbFgoQ2IbVC1D5/uqQd2aMq7X9/oNVaY+9cvMxc+yNO8+YP5dsP53ymJjMZSFvWchbFvKWh8xlIW9ZKEBgG1YvQFv3nTOLyd4jF3nsvO3s9uj8+ID8tMcuKA0UoOCP2q0qO57ymJjMZSFvWchbFvKWh8xlIW9ZKEBgG1YvQFv2BgrQvqMXeeIi3+WqH+q7PO2x84MKUOWxWvNnfX1VymNiMpeFvGUhb1nIWx4yl4W8ZaEAgW1YvQBt3nM2pKRMW7qH3R6d731radpj5286ao59/NyVkCvCpQqTuSzkLQt5y0Le8pC5LOQtCwUIbMPqBah0d1ABOl7Ls/L3m7+3tLSmNfbyjYECdL62ge/u6ft5et7elMfEZC4LectC3rKQtzxkLgt5y0IBAtuwegHatCtwcYKDJ+pC7tfTeL05rbGXbzhijlVTd40f6pvHbo/OExbtTHlMTOaykLcs5C0LectD5rKQtywUILANqxeg4KuzHTpZx4vXHjZ/v+y9ntbYeUEF6MKla/zkoAJ2e3T+aE5FymNiMpeFvGUhb1nIWx4yl4W8ZaEAgW1YvQBt2HnaLCmHT14KedfmwqVraY29rCQw1sXL1/gPQwvZ7dF58OdlKY+JyVwW8paFvGUhb3nIXBbyloUCBLZh9QJUsj1QgKpOXeJVZYEblh4/dyWtsZeWVIUUoNdHF7Pbo3P/SZtSHhOTuSzkLQt5y0Le8pC5LOQtCwUIbMPqBWjdtlNmSTl65jLPXRX4DtDHX+5Ia+yl6wMFqPZKI/eZsIHdHp17jl2f8piYzGUhb1nIWxbylofMZSFvWShAYBtWL0DFYQUo+L5AU5bsTmtsPagA1V1p5EFTN7Pbo/NLI4pSHhOTuSzkLQt5y0Le8pC5LOQtCwUIbMPqBWhtxcnAR97OXuG6+kbz9yXrDqc19pJ1QQWovpFHzCpnt0fnp99flfKYmMxlIW9ZyFsW8paHzGUhb1koQGAbVi9AReUnQr7z09DYFLhhaWHqNyxlZl6yLvSKcp8u2c1uj8739VrKra1tKY2JyVwW8paFvGUhb3nIXBbyloUCBLZh9QK0ZmugAJ2srueWllbz95kr9qU1dvgltYMLUe2VxpTGxGQuC3nLQt6ykLc8ZC4LectCAQLbsHoBKtwSuOrbqfP1zMx8X6+l7PboPFXfk9bYi9ceMse+cvVGyBXmztSkNhljMpeFvGUhb1nIWx4yl4W8ZaEAgW1YvQAFl5LT/lLycL/l7PboPH7hzrTGXlQUKED1DTcivm+UCkzmspC3LOQtC3nLQ+aykLcsFCCwDasXoJWbg96VueCbIH83sIDdHp1Hz92W1tgLgwqQt+FGxE1XU4HJXBbyloW8ZSFvechcFvKWhQIEtmH1AlRQeswsJecuXmVm5mcHr2a3R+dhM7emNfaCNQcDBehaE5ftOWv+XnmsNqUxMZnLQt6ykLcs5C0PmctC3rJQgMA2rF6A8jcdjShAL40oYrdH50FTN6c1dnABunqtibdVnjd/3334QkpjYjKXhbxlIW9ZyFseMpeFvGWhAIFtWL0ArQgqQOdrG5iZ+Y0x69jt0bnfxI1pjf1lYaAANTQ28a7DNebv2yrPpzQmJnNZyFsW8paFvOUhc1nIWxYKENiG1QvQ8g1HzFJSU3eNmZl7j9/Abo/OPcetT2vs+YUHQgrQ/qO15u9le86mNCYmc1nIWxbyloW85SFzWchbFgoQ2IbVC9CykkABunDJV4AGfFrKbo/Or45am9bY81cHClDj9WY+fPKS+fuGnadTGhOTuSzkLQt5y0Le8pC5LOQtCwUIbMPqBWhpSZVZSi5e9hWgIdO3sNuj8x+GFqY19txVQQXoRjMfP3vF/L244mRKY2Iyl4W8ZSFvWchbHjKXhbxloQCBbVi9AOnrAwWo7kojMzOPnF3Bbo/OT7+3Mq2x566sNMe+3tTCZ2q85u+ry46nNCYmc1nIWxbyloW85SFzWchbFgoQ2IbVC9CSdYcDBajeV4A+/nIHuz06P9p/RVpjzwkqQDeaWvh8bYP5+4pNR1MaE5O5LOQtC3nLQt7ykLks5C0LBQhsw+oF6KviQAG6VH+dmZknf7WL3R6dH+idl9bYXxQEClBTcwvXXWk0f9fXV6U0JiZzWchbFvKWhbzlIXNZyFsWChDYRjYVoMteXwGanrfXXNfW1pby2LML9gcVoFb2Ntwwf19YdCilMTGZy0LespC3LOQtD5nLQt6yUIAgKiJyK6XKlVL1RLSfiF6Ptb2maT8nohIiukxEVZqmDQka6x0iaiSia0FLIxG1uVyu7xvbuVyuXxLRESLaH2V/2pRSN4L/XilVmcxrsnoBWrw2sgCFf3cnVbPzAwWouaWVG280m7/PXXUgpTExmctC3rKQtyzkLQ+Zy0LeslCAIIKmabcT0XUieszhcNxERD8joksul+uRaNt37dq1i1Kqjoh6devW7ds5OTk9lFJHNU3rHeM53iCi9cbvRPS6UuoYEeXFKEB3pfO6rF+ADkUUoOB1V67eSHnsWUEFqKWllVta28zfZ+XvT2lMTOaykLcs5C0LectD5rKQtywUIIhARGOIaF3YupFEVBJte03T3lBKHQtb9zIRnYy2vcvl+r5SqpaIfhA0/mvdunX7H5qmDWyvABHR/07pBfllUwEyys7yjUfNdefrGlIee+aKfYEC1Or7KN29by1lt0fnqfqelMbEZC4LectC3rKQtzxkLgt5y0IBgghKqY2apo0KXqdp2kNEdM3hcHwjfHsi+oKIFgWvczqddxJRa/fu3f8myvZziGhctOeOU4AWEtEhpZSXiFY7nU5K5nVZvQAtKoosQOu2nTLXHTmd+usKLkDGd4meGFDAbo/Oo+duS2lMTOaykLcs5C0LectD5rKQtywUIIiglDqolOoXvM7pdP4zEbU6nc7vhW9PRKs0TZsSvE7TNJf/Y2s/CVt/u1LqarRi5H+8vQJUSkS9unbt+h3/R+4WKKWO5+TkfCvR12X1ArQwqADVN0QWoFS/q8McejEFwx8/XMNuj87vTytLaUxM5rKQtyzkLQt5y0PmspC3LBQgG9I07d/876i0Bi3G7yVKqQNKqb7Bf6OU+hciatU0LeJAIaJVRPRp8DqXy6URUVtubu6Pw7adTkSfx9i3qAUonNPp/J5SqlnTtHvjv2IfowBVV1ez1+u13DInf49ZUs7V1LHX6+XdB8+Y66Z8tSPlsT9dvMMcx1jXc2wxuz06e8YUpzRmTU0NV1RUcE1NTcazs8OCvJF3Ni/IG5ln+4K8ZZfq6moUIAjlL0EfBa9TSv2WiBqiba+UmkVEi4PXaZr2D0TUmpub+1dBq28iostKqf9o77kTLUD+/azWNO2lRLZ1OAIFyKrLmJlrzZKyoXQrV1RU8LoNW8x1n3xRnPLYQ6euMccx1nk+Wsluj85PDVqe8deOBQsWLFiwYMHS0QsKEJg0TRsVfIU2h8PhIKJxSqmiaNsrpV5RSh0PG+NNIjoSNsZ/+r9HdFOM544oQJqm/S0RfRy27mb/O1K/TuxVWf8doNkrAu8AVfvfAaq5eClwtbblu1Mee/Ki7ez26Hx3z8A7QCNm+crVY++uSGlM/GuW7IK8kXc2L8gbmWf7grxlF7wDBBGcTjHaXZAAACAASURBVCcR0TVN0x53OBw3KaXu8t/fx21so5SqdLlcv3E4HI5bb731L5RSFzRN692tW7dvO53OHxHRSaXUK8Hj+svNzljPrZQaFF6AlFK3ENEVpdSHXbt2/Y7L5fpLpZRORHscDsc3E31dVv8O0JeFB82yc/VaEzMzt7S0pn25ambmaUt95eqet5aa66bqvnX3vrU0pZuser34PLMk5C0LectC3vKQuSzkLQvfAYKoNE37tVJql/+GpVWapj0b/Lj/3ZeHjd9dLtcd/gsVXCOiU+EXUfD/zWSl1Jpoz2fcKFUp1URELcbvTqfzH/2P/4yIionokv9jdPPbu5BCe7KpADU0Npnr7+uV3uWqmQMF6N6gAjS7IHBvoKbm1qTHxGQuC3nLQt6ykLc8ZC4LectCAQLbsHoBml94IGoBeqTfcnZ7dJ6wcGfKYxvv9tzXK1CAFqyJXrgShclcFvKWhbxlIW95yFwW8paFAgS2YfkCtDp6AfrdwPTu18PMPEXf7S9Ay8x1S9ZVmc93qf560mNiMpeFvGUhb1nIWx4yl4W8ZaEAgW1kUwG6dr3ZXP/ckNXs9ug8bMbWlMeessRXgB54O1CAVmw6aj7f+bqGpMfEZC4LectC3rKQtzxkLgt5y0IBAtuwegGa104BemlEEbs9Og+aujnlsSd/tctXgHrnmesKtxw3n+/U+fqkx8RkLgt5y0LespC3PGQuC3nLQgEC27B6AZq7KlCAGoMK0Jtj1rHbo3O/iRtTHnvyYl8BerBPoACt337KfL6jZ5LPDJO5LOQtC3nLQt7ykLks5C0LBQhsI6sK0I1AAeo9fgO7PTr3HLc+5bEn+QvQfwUVoNLdZ83nO3C8NukxMZnLQt6ykLcs5C0PmctC3rJQgMA2LF+AVlZGLUADppSy26PzKyPXpjz2xEU72e3R+aG+gQJUUVltPt/uqgtJj4nJXBbyloW8ZSFvechcFvKWhQIEtmH1AjQnqABdb2ox1w+ZvoXdHp2fH1qY8tgTzAK03Fy3+/AF8/kqKquTHhOTuSzkLQt5y0Le8pC5LOQtCwUIbCObCtCNoAI06osKdnt0fuq9lSmPPWGhrwA93C9QgA4crzWfr3T32aTHxGQuC3nLQt6ykLc8ZC4LectCAQLbsHoB+qIgUICamgMF6JMFO9jt0fm376xIeezx/gL0SFABOnrmsvl867efSnpMTOaykLcs5C0LecsLzjx/01F+tH8+ry47nundylo4xmWhAIFtWL0AzS7YH7UAfRrlHj7JMkrUI0El6tT5evP5CrecSHpMTOaykLcs5C0LecsLztw4F7g9eqZ3K2vhGJeFAgS2YfkClB9cgFrN9TOW7zPXt7a2pTS2UYAe7R8oQOfrGsxxl288mvSYmMxlIW9ZyFsW8paHAiQLx7gsFCCwjWwqQM0tgQLU3v2BkjFu/nZ/Aco313kbbpjjLlhzMOkxMZnLQt6ykLcs5C3PyPz0udqQAhT8D3DQcXCMy0IBAtuwegGa1U4Bmrki8A7QZe/1lMY2CtBj7wYKUEtrmznujOX7kh4Tk7ks5C0LectC3vKMzD+YVhpSgLwNNzK9a1kJx7gsFCCwDasXoOCi0xJUgDxj15nry/cnf7lqZuax83wF6PEB+SHrH+qbx26PzhMW7Ux6TEzmspC3LOQtC3nLMzJ/Z+KGkAJ04dK1TO9aVsIxLgsFCGwjqwpQ0Hd9CkqPmesrj9emNPaYedvY7dH5iQEFIeuNcZ8clPwltjGZy0LespC3LOQtz8h80Zr9IQXo1Pn6TO9aVsIxLgsFCGwjmwpQ8MUOth04b67feagmpbFHz/UXoIHRC1AqX3zFZC4LectC3rKQtzwj8wWr94WcCw6k+A9tEBuOcVkoQGAbVi9A7V3tbe+Ri+b6LXvPpTS2UYB+hwJkWchbFvKWhbzlGZnPLdgbci6oqEzto9YQG45xWShAYBvZVIDa2gIF6PCpS+b6ku2nUxr7ozkV/o+6FURd/0gKN1nFZC4LectC3rKQtzwj81nLd4cUoDVbk78vHMSHY1wWChDYhtUL0PS8vVHfjQm9YWlqd+keZRag0O/6TF68i90enR/sk5f0mJjMZSFvWchbFvKWZ2T++dKdIQVoaUlVpnctK+EYl4UCBLaRrQWopu6auT5vw5GUxh71ha8APf1eaAEKfs5kb7KKyVwW8paFvGUhb3lG5lO+2hFSgOavPpDpXctKOMZloQCBbVi9AH2+LHoBqg+6YemiokMpjT1idrmvAL2/KmR9OjdZxWQuC3nLQt6ykLc8I/OJC7eFFKDPl+3N9K5lJRzjslCAwDaypQDd3TO0AN1oajFPTF8UVKY09ohZvgL0+w9CC9CSdYfNsevqG5MaE5O5LOQtC3nLQt7yjMw/nl8RUoDGL0z+vnAQH45xWShAYBtWL0CftVOA2trazBPTo/3z2/nr2Ib7C9AzYQVo5ebAPYbOXEhuUsZkLgt5y0LespC3PCPz0XO2hhSgkbMrMr1rWQnHuCwUILANqxegaUv3sNuj8z09Iy9Jnc7lqpmZP5zpO8E9M3h1yPribafMcY+eSS43TOaykLcs5C0LecszMh8xa0vIOWbQ1M2Z3rWshGNcFgoQ2EbWFKC3lkY8ZpyYXhy+JqWxh/kL0LNhBah091lz7Mokb36HyVwW8paFvGUhb3lG5kOnbw4pQG9/UpLpXctKOMZloQCBbWRLAbo3SgHq9XEJuz069/o4tRPTsBm+AvTckNACtK3yvHnS23W4JqkxMZnLQt6ykLcs5C3PyPyDaaUhBeiVkWszvWtZCce4LBQgsA2rF6CpevsFqP+kTez26PzaR8UpjT10hu8jDs8PKQxZv6fqgnnSW7DmYFJjYjKXhbxlIW9ZyFuekfn7UzeFFKDwfyiDjoFjXBYKENhGthSg+3pFFqAPPitjt0fnP36Y2kfghkz3F6ChoQWodPeZlL9fhMlcFvKWhbxlIW95RuaDpoQWoCcGFGR617ISjnFZKEBgG1YvQFP03f4CtCziseHtXMY6UUYBemFYaAG6eDlwk9WXRhQlNSYmc1nIWxbyloW85RmZD/x0Y0gBerBPXqZ3LSvhGJeFAgS2YfkCtKT9AjR23nZ2e3R+fEBql8E23kF6YVjkO0jGSe+zJG9+h8lcFvKWhbxlIW95RuYDJocWILdH55bWtkzvXtbBMS4LBQhsw+oF6FN/Abr/7cgCNGnxLnZ7dP6vFP9lLtZH6B7sk8duj86Tv9qV1JiYzGUhb1nIWxbylmdk3n/ShogC1NDYlOndyzo4xmWhAIFtZEsBeiBKATJuknpPT53b2pL/l7n3p5W1exnt376zgt0enT9ZsCOpMTGZy0LespC3LOQtL1YBqr3SmOndyzo4xmWhAIFtWL0ATf5qV7sF6IuCSvPE1NTckvTY703b7C9Akd/zeXJQAbs9Oo+euy2pMTGZy0LespC3LOQtz8i838SSiAJ0pgb/HzoajnFZKEBgG5YvQP6PuT3QO/JjbgvWHDRPTN6GG0mPPWjq5nYvdGCM++aYdUmNiclcFvKWhbxlIW95RuZ9J0QWoKpTlzK9e1kHx7gsFCCwjWwpQNGuwLO0pMo8MV28fC3psY0C9HKMAoTLYHduyFsW8paFvOUZmfcZvz6iAO09cjHTu5d1cIzLQgEC27B6AYp1oYOVm48FPppwIfnJc+CU0nbv8I0CZA3IWxbyloW85RmZv/1JZAEq31+d6d3LOjjGZaEAgW1YvQBNXLSz3QJUXHHSPDEdOZ386xsQowCNnF3Bbo/OD/dbntSYmMxlIW9ZyFsW8pZnZN7r43XmTbiN88yGnaczvXtZB8e4LBQgsI1sKUAP9Y0sQDsP1qT1L3MDPvUVoNdGFUc8NmP5PnPs5pbWhMfEZC4LectC3rKQtzwj87fG+QrQY+/mm+eCwi3HM717WQfHuCwUILANqxegCWYBinwn5nSN1zwxDZ2xJemx+0/e5CtAHxVHPKavT+37RZjMZSFvWchbFvKWZ2Tec2wxuz06PzdktXkuWFZyJNO7l3VwjMtCAQLbsHwBWth+AWpqbjFPTM8PKUx67P6TfAXo9dHFEY+t337KHPtwElf+wWQuC3nLQt6ykLc8I/M3x/gK0Kuj1prngi8LD2Z697IOjnFZKEBgG1YvQOP9Bai97+IYJ6YPZ25Neux3Jm1kt0fnN6IUoF2HU/t4HSZzWchbFvKWhbzlGZm/MdpXfHp9HLgc9qz8/ZnevayDY1wWChDYRrYUoEfaKUB//HANuz06vz+tLOmx+030F6Ao9/o5fvaKedIrKj+R8JiYzGUhb1nIWxbylmdk/vpHvgJknCfa+74opAfHuCwUILANqxegTxbsiFmAeo71Xaq09/gNSY9tnNii3ez0fF2DedJbviHxz31jMpeFvGUhb1nIW56R+Wujitjt0c1PCqRyWwSID8e4LBQgsI2sKUDvrIj6+Lv+Cxm8OiryUtbx9J3gO7F5xkYWIG/DDfOEt2BN4p/7xmQuC3nLQt6ykLc8I/NXR/oKkHGxHBSgrweOcVkoQGAb2VKAfttOARo2cyu7PTo/O3h10mP3mbCB3R6de45dH/FYS0urecKbuWJfwmNiMpeFvGUhb1nIW56R+csjfB+vNm6XYFwRDjoWjnFZKEBgG1YvQB9/6StAj/aPXoDifUQult7j/QVoXGQBYmZ+oHceuz06T168K+ExMZnLQt6ykLcs5C3PyPyl4b4CNHBKacwrhkJ6cIzLQgEC27B6ARo3f3vMAjRzReCGpU3NLUmNbRSgXh+XRH38iQEF7PboPHrutoTHxGQuC3nLQt6ykLc8I/MXPyxkt0fnQVM38weflbHbo/MfP1yT6d3LOjjGZaEAgW1kTwHKj/r4knWHzQJUd6UxqbGNy5u2V4CeH+I7AQ6ZnvhNVjGZy0LespC3LOQtz8j8hWGF5hVGR8wuZ7dH599/sCrTu5d1cIzLQgEC28iWAvTYu9EL0Oqy42YBOlldn9TYRgF6+5PoBei1j3w3wus/aVPCY2Iyl4W8ZSFvWchbnpH5H4auZrdH5w8+K4v7D3GQOhzjslCAwDasXoDGzotdgOasrEzpam3MzG+Ni30JbfM7QlEuktAeTOaykLcs5C0LecszMn9+iK8ADf68jD9dspvdHp0f6J2X6d3LOjjGZaEAWdM3c3Jy/tzhcHwz0ztiJVYvQGPmbWO3R+fHB0QvQLsO15gFaGHRoaTG7hmnAA2aupndHp1fHF6U8JiYzGUhb1nIWxbylmdk/tyQVeZHoGcsD3zXtLW1LdO7mFVwjMtCAbKG/6Zp2uNKqS+VUueIqIWIWv3/rVZKLdA07XGHw/HfMr2jnVm2FKAnBhREfTzV+/UwB26i2mdC9AJkfO776fcT/9w3JnNZyFsW8paFvOUZmT872FeAhs3YyvNWHzDPM403mjO9i1kFx7gsFKBOTtO0e4moSinVTERblFIfEdFbmqY9R0Rv+X8v8z9+xOVy3Zfpfe6srF6ARs/1F6CB0QtQW1sb39drKbs9Ok/Rdyc1tmfsOnZ7dO47YWPUx1O5xDYmc1nIWxbyloW85RmZP/PBSnZ7dP5w5lb+qjhwsZ3L3uuZ3sWsgmNcFgpQJ+YvN1eUUh9qmnZzrG27du3aRSk1jIiuaJo2SmofrSTbCxAz81Pv+U5UI2aXJzX2m2N8BajfxOgFaNrSPez26HxPT53b2hL72AMmc1nIWxbyloW85RmZP/2+/7wyq5zzNx01C9D52oZM72JWwTEuCwWoEyOispycnJxk/iYnJyeHiDZ/TbtkadlSgH4XowAZV2t7Z1L0ItOeN+IUoLmrkv/YAyZzWchbFvKWhbzlGZk/9Z7vPnAjZ1dwUfmJlK82CrHhGJeFAtSJEVGveO/8tOP/6/CdyQJWL0Afzalgt0fnJwe1X4CMu3S/Nqo4qbHfGB37MtdL1lUlfY8hTOaykLcs5C0LecszMn9ykK8AjZpTwRt3njHPBYdO1mV6F7MKjnFZKECdmFKqWSnVRER5RPSgw+G4KdP7ZGVWL0CjEihAxl26XxiW3F26zfv8TI5egFZuDtxj6ExNYpMzJnNZyFsW8paFvOUZmT8xMJ/dHp1Hz93G5furzXPBnqoLmd7FrIJjXBYKUCeWm5v7V5qmvamUqiCiNqVUrVJqglLq7zO9b1aUPQVoZbvbjJiV2l26XxvlK0DvtlOASnacNk96h09eSmhMTOaykLcs5C0LecszMn98gK8AjZm3jXdXXTDPBRWV1ZnexayCY1wWCpBF5OTk9CCiwUR0hIhalVIHNE3ro2lat0zvm1VYvgB94StAT73XfgEy7tLd3s1S2/PqqLXs9ug84NPSqI9XVAb+1W/X4ZqExsRkLgt5y0LespC3PCPzx95dwW6PzmPnbeeDJ+rMc8GmXWcyvYtZBce4LBQgC3K5XL8koolKqQv+ewEVapr2RKb3q7OzegEaOTt+AZq0eBe7PTr/V5/k7tL9ykh/AZoSvQDtP1prnvQ27zmb0JiYzGUhb1nIWxbylmdk/mh/XwEaN387Hz93xTwXFJWfzPQuZhUc47JQgKzNuEHqWSJqzfTOdHZWL0DmzUhjFKDPl+1lt0fnu5O4XDVzoAANbKcAHT+b/EkPk7ks5C0LectC3vKMzH/7znJ2e3T+ZMEOrq5tMM8F+aXHMr2LWQXHuCwUIAtyuVx/SUSvE1GZ/x2gU0qpYZner84uawrQ++1/v2fOykrz5NTU3JLw2C+PKGK3R+dBUzdHffx8XeCkt3zDkYTGxGQuC3nLQt6ykLc8I/NH+vkK0PiFO/lS/XXzXLBkXVWmdzGr4BiXhQJkEV27dv2O/92eAv+V4RqI6Asi+leHw/GNTO+fFVi+ACVwgYOZK/aldJful+IUoKvXmsxxF6w5mNCYmMxlIW9ZyFsW8pZnZP5Q3zx2e3SesGgnX7vebJ4L5q8+kOldzCo4xmWhAHVu39Q07d+UUrOVUl4iaiWi9ZqmPdOlS5fvZnrnrMbqBWh4AgXoTf8NTd0enVeXHU947BeH+wrQe9OiF6CW1jZz3Ol5exMaE5O5LOQtC3nLQt7yjMz/q4+vAE1avItbg84FM1fsy/QuZhUc47JQgDoxIqr2l54qTdMGdu/ePTfT+2Rl2VKAnolRgIq3nTJPTsNmbE147BeHr2G3R+cPPitrd5sHevtOgpO/2pXQmJjMZSFvWchbFvKWZ2T+YO9lIXP/A2/7fp+yZHeG9zC74BiXhQLUiSmlpiml7sr0fmQLqxegD2du9RWgwavb3ebCpWtmAXJ79ITHfmFY/AL0aP9880pAicBkLgt5y0LespC3PCPz+8MKj3FVuE8W7MjwHmYXHOOyUIA6MSL6omvXrt9J5m+6du36HaXUrK9rn6zM6gVoWAIFiJnN8vPRnIqEx35hWCG7PToP/rz9AvT0eyvZ7dF5+KzyhMbEZC4LectC3rKQtzwj8/t6LWW3R+ep+h5mDpwLRs5O/BwD8eEYl4UC1IkppfKJ6Iimab9zOBzfjLP5NzVNe4KIDhPRcon9s5psKUDPxilATw4qYLdH51FJFKA/DPUVoCHTt7S7TbLvLGEyl4W8ZSFvWchbnpH5vW/5CtC0pb4C9NyQ1ez26PzkoIIM72F2wTEuCwWokyOi/v4rvp1WSk0joqddLtdvXC7XL1wu12+UUk8ppaYS0Sn/du86cFW4qCxfgGb4CtBzQ2IXIOPdnFgfZwv3fJIFqPF6c9wxgyfzltY2Plldn9S9iSA5OHnKQt6ykLc8I/O7e+ohBSj4XNDS0prhvcweOMZloQBZQE5Ozl8T0QilVCURtfmXVuNnpdQBIhqek5Pz15ne187M6gVo6IwtCRWgN0YXs9ujc7+JGxMe+/khvgI0dEZiBaiuvjHumMGT+bj529nt0fmN0cUJ7xMkBydPWchbFvKWF16APl/muwJo8LkgmdstQGw4xmWhAFlM165du7hcrh/63wH6oaZpN2d6n6wiWwrQ80MKY27Xd8JGdnt0fnPMuoTHNj7SEOvKccZFENwenc9dvBp3zODJPPiEuXXfuYT3CxKHk6cs5C0LecszMg+/BcLT768y152vbcjwXmYPHOOyUIDANqxegIZM9xegobEL0PvTytjt0fmPH65JeOxnB/sL0Mz2C1BR+UnzpHf87JW4Y7ZXgPpP2pTwfkHicPKUhbxlIW957RWgNVtPmOtOnIt/LoDE4BiXhQIEtpEtBegPcQrQyNm+E9ZT761MeOxn/AXowxgFaNOuM+ZJ7+CJurhjtleAEr2RKiQHJ09ZyFsW8pYXXoBmLPfd+HTjzuTOBZAYHOOyUIDANqxegAZ/XpZQARq/cCe7PTo/3G95wmM/84HvIw0jYlziuqKy2jzp7TpcE3dMYzK/cqUeBUgATp6ykLcs5C3P6/VyeXl5RAEq3x84F+w+fCHDe5k9cIzLQgEC28iWAvTCsNgF6LNle9nt0fmennrCV10zPtM9Ynb7BWh31QXzpFe+vzrumMZkfqH2ckgBGr9wZ0L7BMnByVMW8paFvOV5vV7eGlSAZq7wFaA9QecCfKez4+AYl4UCBLZh9QL0wWdGAYr93Z65qw4kdblq5sRubHfwRJ057sZdZ+KOaUzmp85dDClAsd5lgtTh5CkLectC3vK8Xi9v3RpZgA6fvGSuK9lxOsN7mT1wjMtCAbKWm4joMf8lsWe5XK6/dDgc31BK/X2md8wK7FKAlqyrMk9OFy9fS2jsp/wFaNQX7Reg42evmOMWlZ+MO6YxmR8/fSGkAA2aujmhfYLk4OQpC3nLQt7yvF4vbwkqQLPy9zMz88nqwMeaC7ccz/BeZg8c47JQgCyie/fuuUR0RCnVrJSq9P/XmZube5tS6obL5fpNpvexs8uWAhTv6m7Tlu4xT06z/SeseJ4c5C9Ac9ovQOcuXjXHLSg9FndMYzI/diq0AL06am1C+wTJwclTFvKWhbzlhRcg43xSU3fNXJe34UiG9zJ74BiXhQJkEUS0jIiKlVK3OBwOh1LKq5Ry+n/uR0Rlmd3Dzs/qBci4vPWLw2MXoNn5+82T02ujihMa+8lBBez26PxRjAJUd6XRHFdfXxV3TGMyP3qyJqQAPTko8avTQeJw8pSFvGUhb3ler5fLtgQVoAJfAapvuGGuW1h0KMN7mT1wjMtCAbIIpVS9pml/G/S7WYA0TXMRUWPm9s4arF6A3pu22V+AimJu13ij2Tw5jZu/PaGxfzfQV4BGz93W7jZXrzWZ4w6ZviXumMZkXnXifEgBeqhv4leng8Th5CkLectC3vLCC9AXBZXMzNzU3BJRiiB9OMZloQBZBBHVKKV+YvweVoD+VilVn7m9swa7FCBm5ueG+G9sOqP9+/oEeyKBAtTc0hpSZOIxJvNDx0MLkNujc2trYleng8Th5CkLectC3vLCC9Cclb4C1NbWZq4bjovadBgc47JQgCyCiFYQUWFOTs6fOxy+AtS9e/fcnJycbxHRYiJakel97OysXoAGTfUVoJdGxC9Ar4xcy26PzgM+LU1o7CcG+ArQmHntFyBmDikxzS2tMbc1JvODx6ojClBDY1NC+wWJw8lTFvKWhbzleb1e3hylADFzUv8YBonBMS4LBcgiNE37X0RUQ0TXiKjMfxGErUqpC0RUo2na7Znex84uWwrQywkUoN7jN7Dbo3PPsesTGvvxAfkJfWQu+KR39sLVmNsak/mBo5EFKNGr00HicPKUhbxlIW95Xq+XN5cFCtBcFKCvFY5xWShAFtK1a9cumqb19r/jU0hEi4jobU3Tbs70vlmB1QvQwCmlCRegwCWzY9801fBo/8QKkHEhBrdH531HL8bc1pjM9x85F1GATlbXJ7RfkDicPGUhb1nIW57X6+XSsq2BArTqgPkYClDHwzEuCwUIbCNbCtArI+NfRnrMvG3s9uj8aP8VCY39aP8V7Pbo/PGXO2Jut/9orXnS27znbMxtjcl87+GzEQXo4Im6hPYLEoeTpyzkLQt5ywsvQPNWowB9nXCMy0IBsggi+jzOMpGIejmdzlszva+dldUL0IAkCtDcVQfMk5P3Wvzv2/z2HV8B+mRB7AJ0+FTidwA3JvM9hyIL0M6DNXH3CZKDk6cs5C0Lecvzer28aXOgAM0PKkAvDCs01zfeaM7gXmYPHOOyUIAsgog2K6XOEFGbUqqWiA4ppS76fz+rlDqolLpKRA1Op/MfM72/nZHlC9CniReg/E1HzZNTdW1D3O0fSbAAnTh3xRy3qPxEzG2NyXz3wTMRBah095m4+wTJwclTFvKWhbzlRRSgwkAByttwxFxfU4fvdHYEHOOyUIAsQtO0fyOibU6n887g9S6X65dEVKpp2s8dDsefKKWmElFJhnazU8uWAvTqqPgFqKj8hHlyOnHuStztH+m3nN0enccv3Blzu3MXr5rjFpQei7mtMZnvPBBZgOKVJ0geTp6ykLcs5C0vvAB9WXjQfKxwS+Acg+90dgwc47JQgCyCiPa6XK5fRnvM6XT+o1Kq3OFwOHJzc29TStXJ7p01WL0AvTt5E7s9Or82qjjutht3BkrHoZPxv2/zsL8ATYhTgC5evmaOu3jt4ZjbGpP5jsrTEQWo74SNcfcJkoOTpyzkLQt5y/N6vbyxnQIUfI7Bdzo7Bo5xWShAFkFEjZqm/W20x5xO54+I6JrD4XC4XK4fogBFZ/UC1N8oQB8Vx912857A92627D0Xd/uH+voL0KLYBeiy97o57uMD8mNua0zm2/dHFqB3J2+Ku0+QHJw8ZSFvWchbntfr5Y2lgQK0YE2gAJXvD9zeYPfhCxncy+yBY1wWCpBFKKXKlVLlTqfz73Jycr7lX/1Nl8t1BxEVE9G+nJycbymlCpRSBRnd2U7KTgWoZEegdMT7Xg8z80N989jt0XlinALU1Nya8NV/jMm8Yt+piAIUVdMGGwAAIABJREFU73LbkDycPGUhb1nIW57X6+UN7RSgPVUXzPVb98X/RzaID8e4LBQgi9A07f8notNE1EpErUqpG0TU4v+5TtO0X2ma9mdKqTMul+uHmd7fzsjyBWiSrwC9Pro47raNN5rNk1MiF034rz6+AjR58a642xrjjvqiIuZ2xmRevvdkRAEaNnNr3OeB5ODkKQt5y0Le8sIL0MKiQ+Zjh08mfkVQSAyOcVkoQBaiadp/d7lc/65p2otE9A4RvaWUur9Lly7fDdrsTzK2g52c1QvQO5M2stuj8xsJFCBm5gfeXsZuj859JmyIv21vfwH6KvEC9PR7K2NuZ0zmW/ZEFqCBU0oTeg2QOJw8ZSFvWchbntfr5ZJNgQK0KKgAnayuN9cXbjmewb3MHjjGZaEAZYHu3bv/jVKqKNP70dlZvQD1m5hcAXpuyGp2e3Tu9XFJ3G2NsvTpkt1xt032I3Blu09EFKC3P4m/T5AcnDxlIW9ZyFuerwBtCbrwTaAA1dQFLoiTt+FIBvcye+AYl4UCZCGapv0fIhrsv+mpsUxSSm1UStVnev86u6wpQGPWJbR9MjdOvd9fgKZ8DQVo865AAbrnraUJX8obkoOTpyzkLQt5y/N6vbx+Y3ABClz5s77hRtSPxkHqcIzLQgGyCE3TnvHf9PSc/7s/p4iowb9uk6Zpj2d6Hzu7bClAbyZYgD6c6fvowu8/WBV32/t6+QuQHr8ATVy00zzxXbl6o93tjMl8087j5va/9d9w9fkhhQm9BkgcTp6ykLcs5C0vvAB9VRwoQMEXxJldsD+De5k9cIzLQgGyCCLaT0R/dDgcDqWUVynldDgc39A07SGl1EaXy/X9DO9ip2f1AtR3QnIF6JMFO9jt0fmRfsvjbntfL987M1P1PXG3ffuTEvPEF6swGZP5hu2BAvTMYN/H8p4YUJDQa4DE4eQpC3nLQt7yYhUg5sBHpxP57ijEh2NcFgqQRRBRo9PpvNXhcDiUUvVOp5OMx5RSv8Wlr+PLlgLkGZtYAfps2V52e3S+u6fObW1tMbe91//RtGlL4xeg0t2BG+AFXxY1nDGZl2w7Zm7/6qi17Pbo/GCfvIReAyQOJ09ZyFsW8pbn9Xp5XVABWrIutAAl+nFoSAyOcVkoQBahlKrLzc39sf/noy6X6zfGYzk5OTn4DlB8Vi9AfSZsYLdH555j1ye0/fzVB8yTU0NjU8xt70miANVdaQw6IVa1u50xma+vCBSg3uM3mD+3tMYuZZAcnDxlIW9ZyFue1+vldRu2tDvfowB1LBzjslCALEIptUAptdXlcv2lUmq2UmqXy+W6IycnJ4eIPiaiE5nex87O6gXIKA89xyVWgJaWVJknp5PV9TG3vaenb7vPlu2NO+6NphZz3DkrK9vdzpjMi8uPmtu/N21zwqUMkoOTpyzkLQt5y/N6vVwcVID09aEF6IVhhShAHQjHuCwUIItwOp23ElGp0+m81el0Kv/FEIybojYrpf6Q6X3s7OxWgKbn7Q3ceHRG7BuP3u0vQNPz4hcg5qCLJsS4apwxma/dGihAo76oMH+urm1I6LkgMTh5ykLespC3vPACtDSsAE1Y6LsgzkN943/PFOLDMS4LBciibr755j8lIjcRPZCTk5OT6f2xgmwpQG8lWIA27zkb8s5LLMZ2iRagJwYUsNuj85h529rdxpjMi7YcMccfM2+b+XPlsdqEngsSg5OnLOQtC3nL83q9vLYkqACVhBagmSv2BT7S3NKaob3MHjjGZaEAWYRSam1OTs6fR3tM07T/pZQql94nq7F6ATKuvpbIjU2ZmVtb28yTU6zv9rS1BbabsXxfQmMbN1mN9c6SMZkXlgU+irdk3WHz5y17zyX0XJAYnDxlIW9ZyFteeAFaVhJ6w9NFRYcSuiUCJAbHuCwUoE6OiP63f2nTNO3eoN+Dlw+IqLGDn9etlCpXStX7L8H9eqztNU37ORGVENFlIqrSNG1I0FjvEFEjEV0LWhqJqM24fHdOTk4PIspTSl0kovNEtNi46p3DYb7jNd1//6MaIsrr3r17bjKvyeoFqNfHyRUg5sA7NaPntv9OTXBRmrkisQL0ykjf1dwGTiltdxtjMl+9OVCAisoDN0UtrjiZ8OuA+HDylIW8ZSFveeEFKG9DaAEqKA1c4ObshasZ2svsgWNcFgpQJ0dEe/03Pm0lorb2FqXU/I56Tk3Tbiei60T0mMPhuImIfkZEl1wu1yPRtu/atWsXpVQdEfXq1q3bt3NycnoopY5qmtY7xnO8QUTrHQ6HIycn51tEdEIpNb5bt27f9o9XoJTaGJTDHCIqUUrd0qVLl+9qmjaFiPY5HI5vJPq67FiAXhxexG6PzoOmtv8RuJYUClDPcevNq7q1x5jMV5YG3vUJvoT2ys3HEn4dEB9OnrKQtyzkLc/r9XJRSVm7Bahkx2nzscMnL2VoL7MHjnFZKEAWoGnan/nfLfl3fxkJWXJzc2/ryOcjojFEtC5s3UgiKmln/95QSh0LW/cyEZ2Mtr3L5fq+UqqWiH7gcPgu401En3ft2vU7Qc/3n0TUkpOT861bbrnlfyqlmjVN+z/G406n83tKqSaXy/VPib6ubClAb3+SeAFK5MIJLS2BO3rPyk/sjt79JvruSfTG6OJ2tzEm84JNgQK0/cD5dr9QC+nByVMW8paFvOV5vV4uWh8oQMvDClDwfL7zYE2G9jJ74BiXhQJkEbm5ud0dSbzbkQ6l1EZN00YFr9M07SEiuhZtH4joCyJaFLzO6XTeSUSt3bt3/5so288honFx9uEVIjrv3/5fiaj15ptv/tOwcfYppfom+rqsXoDe8r/rkkwBGjLd9/GF54cUtrtNc1ABmp1gAXp5RJH5N+3dZNWYzPM3Bj4nXnms1vx55OyKhF8HxIeTpyzkLQt5y/N6vbwmuABtPBry+METdeZjG3eeydBeZg8c47JQgDoxIpqY6KKUmtBRz6uUOqiU6he8zul0/jMRtTqdzu9F2c9VmqZNCV6naZrL/9G8n4Stv10pdTVaMTLk5ubeppSqMy7tTUSPKqWaouznBiIak+jrypYCFOtjZ+GCr7rWXlFpag4qQAWJFaDgG+Cdrok+WRuT+fKSg+a2x89eMX8eOmNLwq8D4sPJUxbyloW85YUXoBWbQgvQmRpv0Eeaj2doL7MHjnFZKECdmFLqWBLL0UTH1TTt3/zfHWoNWozfS5RSB8LfWVFK/QsRtWqaFnGgENEqIvo0eJ3L5dKIqC03N/fHYdtOJ6LP29s3/8f6zoddROFRpdSNKPlsVEqNTvR1GwWourqavV6v5ZY3R6/1XwZ7XcJ/8/7UTeYJ6kx1bdRt6i4FSsnnS3clNG5wAdpz6GzUbWpqariiooIXF+4xtz10LPCRieEzyzKeaTYtRt41NTUZ3xc7LMgbeWf7UlNTw4XrAgXoq7WVIY+fqQ68oz9v5d6M76/VFxzjskt1dTUKEITyl6CPgtcppX5LRA3RtldKzSKixcHrNE37ByJqzc3N/aug1TcR0WWl1H+087wPEtEVInoheL3L5fonImrt0qXLd8Oe90CsCy2EMwqQVZcXhuSz26Pzyx/mJ/w3478oNk9Qa9aXRd1m85bywI1Kp69NaNzH380LnBQLNsXc9tP5681tVxeX8WP9fX/bZ9yqjGeKBQsWLFjaX4IL0OR560Ie27I1cO4Y/llRxvcVC5ZUFhQgi3A6nbdqmnY3ET2mlPqPnJycv+7o59A0bZRxhTYDEY1TShVF214p9YpS6njYGG8S0ZGwMf7T/z2im8LH8F/g4bJS6l/CH7v11lv/Qil1w+Vy/V9jXdeuXbsQUYvT6fzHRF+X1d8BesP/DlCvjxN/Byj442eVR85F3eZi7eXAfYDyEnsHaP22wOVPS7Ydi7qN8a9ZC1bvNrc9crKG/zDUdw+hgZ9uzHim2bTgXw+RdzYvyDszma8OKkB68YGIbR7s4/sHrY/nV2R8f62+4BiXXfAOkEV06dLlu0S0LMrlsFuI6AtN0/57Rz2X0+kkIrqmadrjDofjJqXUXf77+7iNbZRSlS6X6zcOh1lQLmia1rtbt27fdjqdPyKik0qpV4LH1TRtIBHtjPbalFJn/c8XFRFN93/k7ZacnJw/1zRtplJqazKvy+rfAfKMXcduj859J2xM+G9KtgcuUzr5q11Rt2m83hz4GMPqAwmNe/RMoDQVbzsVdRuv1/d55q/WVprbnq9t4NdH+96V6j9pU8KvA+Iz8vZ68flxCchbFvKW5/V6QwpQtFsXPPXeSlzUpoPgGJeF7wBZBBF9SkQnNU17PDc397bvf//7XYnoB5qmPauUOhP+kbV0aZr2a6XULv8NS6s0TXs2bH9aNU172Pjd5XLdQUSl/pucngq/iIL/byYrpdZEea7H/cXOuEGq+V/jHZ5u3bp9W9O0Kf6LI9QrpfRYF1KIxuoF6M0xvgLUb2LiBahwS+DGo26PHnWba0EFaH6CBai6tsH8myHTo1/MwJjMFxcFClBN3TXz0txvxbg0NyQPJ09ZyFsW8pbn9Xp5dXHsAvTSiPj3moPE4BiXhQJkEURUTUT/Gu0xTdPuVkqdkd4nq7F6AXojhQK098jFuAWoobEpUIAKEytAwaXpdwMLom5jTOYLC/eb2168fI0HTCllt0fnV0auTfh1QHw4ecpC3rKQtzyv18urQgpQ5JXejJtiP/XeygzsYXbBMS4LBcgilFI3unfvnhvtse7du+dGu0oahLJjAWprazNPXj3HRn/HJbgAfVl4MOGxE30HaMHqfea2tVcaeeiM+PcmguTh5CkLectC3vK8Xi+vWrvZnL9XlUUWoOB/YGtpac3AXmYPHOOyUIAsQil1kIh+H+0xTdNeVkodlN4nq7F8AfJ/d+adSYkXIGbmdyf7LoX96qjo77hcvRYoQAvWJF+A2ntnyZjMv1wVKECX6q/zR3MqYr5zBKnByVMW8paFvOV5vV5eGVSAVscpQHuPXMzAXmYPHOOyUIAsgoheVUrdIKLPlFJ/8F+W+gWl1CylVFP4BQcgktULUKoXD4j3jou3AwpQtJusGpP5vJV7ze0ue6/zhIU72e3R+aG+y5N6HRAbTp6ykLcs5C0vvAAVboksQBMW7TQf33HwfAb2MnvgGJeFAmQhmqa9RESHg68C539n6I+Z3jcrsGsBGj13G7s9Oj8xoJ3v6jTcME9gC4sOJTxucAHadiDyxGdM5nMLAgWovuEGT1vquzHqPT2jFydIDU6espC3LOQtz+v1ckFRcAE6EbHNoZN15uMbdp7OwF5mDxzjslCArONPjB/8l42+JfzGoBCb1QvQax/5C9Dk5ArQ5MW72O3R+YHeeVEfrw8qQIvXJl6A3psWODFOz9sb8bgxmc/J32Nu573WxF8UBK4K19DYlNRrgfbh5CkLectC3vLCC9CarZEF6NzFq+bj+aXH5Hcyi+AYl4UCZBFKqTpN06b4bwb6jUzvjxVZvgCN8hWgd5MsQDOWB76Dc7omcmK97L0eVIAOJzxu8BXmop0Yjcl89oo9IYXHeAfI7dF5zsrKpF4LtA8nT1nIWxbylhdegIrKo8zzKX6EGiLhGJeFAmQRRPQ+Ee31f+ztDBGNcTqdd2Z6v6zErgXoy8KDMS9YEFyAvipOvACdrwvcC2j5xqMRjxuT+czlu83tGq83m99Jcnt0fqQfvgfUUXDylIW8ZSFveV6vl/PjFKC2tja+562l7PboPG3pngzsZfbAMS4LBchicnJyehDRO0S0zf89oCoi+kDTtNszvW+dndUL0Kuj1rLbo/OAT0uT+rt5qw/ELECX6gMFaMm6xAvQlauxPzpnTOYz8naZ211vauHJXwV+f3bw6qReC7QPJ09ZyFsW8pbn9Xo5f01wAToZdbvHB+Sz26Pz6LnbhPcwu+AYl4UCZGE5OTk5SqmeSqndRNSS6f3p7KxegF4Z6S9AU5IrQPMLYxeguvrGoAJUlfC4N5paYn6UzZjMpy8LFJ6m5hauuxJ4vnHztyf1WqB9OHnKQt6ykLc8r9fLK4IK0NqK6AXoob55MW+JAInBMS4LBciinE7n3xHRYCLaSUStRLQ90/vU2dm1ADU1t8YuQEGFRF+feAFqa2vju3vGvwjC50sDl0lt9t8o77F38S+GHQ0nT1nIWxbylhdegIrbKUDGuQkFKD04xmWhAFnHN10u1z9pmvYJEZ30f/xtDxH1dzqdlOmds4JsKUADkyxAzMxj521nt0fnxwfkRzxWG1SAliZRgJiZH3h7Gbs9Ok9ZsjviMWMyn7YkUIBaWn2XvX5+aCG7PToPmb4l6dcC0eHkKQt5y0Le8rxeLy8vLA0UoG2nom73YJ+8kHf5ITU4xmWhAFmEUqrW/07PfqXUICL6Qab3yWqsXoBeHlHEbo/Og6ZuTvpvJ/kvhf1Q38hLYV+8fC1QgEqSK0CP9FvObo/O4xfujHjMmMynfLXDHN+47495Se8k72kE7cPJUxbyloW85YUXoHXtFKDgTxjgUtipwzEuCwXIIohosNPp/FGm98PKrF6AXkqjAH22bG+7Nx8NLkDLSo4kNW6sj9YZk/mni30F6O6egW16j9/Abo/OPceuT/q1QHQ4ecpC3rKQtzyv18t5QQVo/fboBWj4rHJzm/L91cJ7mT1wjMtCAQLbsHMBml2wP+gjCq0hj124FChAyzekXoCMj7cZjMl88iLfx+/ueWup+digqb7Plb84vCjp1wLR4eQpC3nLQt7ywgtQyfbTUbc7fPKSuc2GndG3gfhwjMtCAQLbsHoBenG4rwC9Ny35ArRgTeBeQN5rTSGPxbufTyzBBej42SshjxmT+cSFvgJ0X69AARrh/xfDp99flfRrgehw8pSFvGUhb3ler5fzVscvQGcueM1tVm4+LryX2QPHuCwUILAN6xegNez26P+PvTcPjtvK8zw5G7EdXb07MbMR1dPbHleZJhOY3t6YntiYnZiZjZ2d3ZiO3tnptMtll8t22WW7fJTKt52SrNM6LFGyJEqy7tM6rcM6k5J4iBRFiaQoUrxJ8RJvUrxvgjcz87d/ZAJ4QCIzASTyZSLx+0QgRGUCPzz8+Pje++K99/vBt8eLNV+blt8idFDtMqEyMCIKoPQH2gTQ9XvNwrXN3WOS7/jG/MClcrA7nPDrr28I3+275F0W99qadM3PgiiDnSdd0N90QX/Th+M4uEEKoEplAaQ3lxwiBes4XVAAIZbBygLofFaD0EGt2F8g+a6fEEAZGgVQaX2/cG1N85DkO74x3/dTGdgdTnh5hRiA4XharbAsTr4nCdEHdp50QX/TBf1NH7kACrS8bXZ+UTjnvEJOOEQdWMfpggIoDnjuuef+mmXZ3GiXI9YxuwD603deAbTpB+0C6G5Zl9BB/WGTdNlZ3/CU7gg+dW3DwrXFtb2S7/jGfO9FrwD6zUpRAJ0jBNncAoZNNQLsPOmC/qYL+ps+HMdB2m1RABVW9Sie53Z7hHOUcsIh6sA6ThcUQCbCZrP9Z1/y04PEcYhl2UKWZSejXb5Yx+wCaMlW/QJo0eUO2EGRAihTowBq750QrpVnCecb8+8vePf7/Hb1LeG7a3ni0rmiml65WUQH2HnSBf1NF/Q3ffwEULWyAAIA+LUvJ5yeID2IF6zjdEEBZBJsNtt7DMN4WJbtYxjGxTBMN8Mw077PHthstjejXcZYx/wCKEe3AAIQAxbIr+8dEgVQ1sN2TTb7g+wf4hvz3edL/fb7XM59EjSENqId7Dzpgv6mC/qbPhzHgTNLFEAPggggsj2XRxpF1IF1nC4ogEwCwzD1DMP8KSEhIYFlWY5l2aSEhIR/YrPZXmVZtjA5OfkXUS5izBMvAmjzifAEkFxwhBPBZ3J6Xrj2cu4TyXd8Y77r3COwO5zwxlpRAF29iwLIaLDzpAv6my7ob/p4BdADYrZenQDqH5mmWMr4Aes4XVAAmQSGYWaTkpJ+mZCQkMCy7GRSUhLDf8ey7Ossy2ZGr3TmwOwC6I9bvAIo5WSJrusDCqBBUQDdLtYmgMildafT6yTf8Y35zh+9Auh332QI36Xdb0EBZDDYedIF/U0X9Dd9/AVQ4OXKZHvePTBJsZTxA9ZxuqAAMgksy44+//zzf+f7uS05Ofm/8t8lJiYm4h6g0JhdAH0YpgDir7c7nJLIa08JAZStUQABALy03Lv2++j1GsnnfGO+40wJ2B1OeGtdpvgdMXO0dM99Xc+DSMHOky7ob7qgv+nDcRxc1yGAnnSNUixl/IB1nC4ogEwCy7KXWJZ9lJyc/C9Ylj3Lsmx1cnLyv01MTExkGGYvwzCd0S5jrGN1AXT+dqPQQc3OLQqfd/VPCp/nlHRqtvva6ltgdzhh36VKyed8Y77tdDHYHU54e0Om5Pt3NmbhplkDwc6TLuhvuqC/6cNxHFzPFAXQw9rAAujHTDGypzwlAqIOrON0QQFkEpKTk3/BMExRUlLSL5OSklhfMAQ3wzBulmUXWZb9MNpljHVML4BSvAJoyyl9AshJLDsbHp8RPicFUG6pdgH09oZMsDucsPNcmeRzvjH/7pRXAL2zMUvy/dI99xXzEiH6wM6TLuhvuqC/6cNxHFwjBJA81QHJ41YxJUJJXR/FUsYPWMfpggLIpPzlX/7l/2iz2V5gGOblxMTExGiXxwyYXQB9kJINdocTtp56pOv6nJJOoYPq6J0QPu/omyAEUFcQC8HLJRdmfGO+9eRDsDuc8O630vxDaw95O9YvduXpeh5ECnaedEF/0wX9TR+5ACp5HFjYtD4dF867X9FNsZTxA9ZxuqAAQiyD1QVQWUO/4lK3DiKXT16ZdgEUKJgB35innPAKoPdkCVg3n/DODC3ZmqPreRAp2HnSBf1NF/Q3fTiOg6sZD1TN7IQTTRTxgnWcLiiATMLzzz//dyzL3mVZdpRf+iY/ol3GWMfsAuj9zT4BdFqfAGroGBE6qCVb7wift/WIb+7yyrW/uSMFUO/QlPA535hv/sGbR+L9zdmS61LPlfn2BmXJTSI6wM6TLuhvuqC/6SMXQI+CCKDRiVnhPOf9FoqljB+wjtMFBZBJYBimnGGYKpvN5mAY5l2WZd+RH9EuY6xjdgH0nk8AfadTAC0suhVna0gBpGfpAmmzqVOM/sM35t8e93agH6ZIZ3oOXK4Cu8MJr666pet5ECnYedIF/U0X9Dd95AKotL4/4Lkzc4vCeReyGymWMn7AOk4XFEAmgWXZKZvN9rfRLoeZiRcBtO1MqW4bSgKopXtM+Cy/4qlmm1dyxaSm1c2Dwud8Y77x2APFpW4nbz4WrnO5PXKziEaw86QL+psu6G/6cBwHV1QKILfbI5x38uZjiqWMH7CO0wUFkElgWfZBcnLy/xHtcpgZ0wugTbcjIoCaSQFUqV0ANXWOCtcXVouZwvnGfMPRQrA7nPCn7+5IrrtMCKfJ6XndzzS34IIzGfVBc1RYAew86YL+pgv6mz4cx8GVdHUCCADglZU3vTnf1mcGPQ9RBus4XVAAmQSbzfa/MgyTnZyc/FpiYuLf+MJhS45olzHWMbsA+oNPAG0PQwDxIatJAfSkixAwVT1Brlamd2hKuD6zqF34nG/M1x3xCqCPtuVKrrtV2CZcNzAyrfuZDl2tBrvDCS8tvwGz84uhL4hTsPOkC/qbLuhv+nAcB5fTC4V2uqwhuAAiX7B1D0xSKmX8gHWcLiiATALDMHaWZXsDBEDwYBCE0JhdAL37rU8AndUvgPhcQnaHExo7RgAg8AyOWrjpeeH694hAB3xj/s3hArA7nPDJdqkAyi3tUgzLrZVAQRisBnaedEF/0wX9TR+5ACpvGAh6PtkW39URUdTqYB2nCwogk8CybBvDMLcZhnnZZrP93zab7T/Lj2iXMdZBAQRwOr3ObxlcIxEdrqhGuwAi136TM0t8Y77moFcAfbrjruS6h7W9wjV1bcO6n+k3vmUXdocTGtpHdNsxO9h50gX9TRf0N304joNLtwgB1KheAIVaLof4g3WcLiiATIIvCEJytMthZkwvgDZmgd3hhB1ny3TbKG8Y8BMrDRIBpG8fTTABtPpAPtgdTvg8NU9yjSRzeJAEe6F4a31m2OWPB7DzpAv6my7ob/rIBVBFCAFUTLzUeqBjNYHVwTpOFxRAJoFlWSfDMK9EuxxmxuwC6B2fAEr9Ub8AIkUHL1bq20QBVFyrT0DwZSOXufGN+cr9XgH05a48yTVd/ZPCfcnErFrhE8TaHU44k1Gv247Zwc6TLuhvuqC/6cNxHPxECKDKpuACyKg23apgHacLCiCTYLPZfsUwTCXLsjtZln3HZrP9Vn5Eu4yxDgog/1xAiy431LWFPxOz4dhDsDuc8LGCAFqx7z7YHU74avc9yTXD4zPCfVcdKNT9TJ/uuKs4A2U1sPOkC/qbLuhv+sgFUFXTYNDz+0emFfeDIurAOk4XFEAmwRfoINiBQRBCYHYB9PYGnwA6p18AAQDsOl8uifpGzgoFy/QdjG1nSsHucMI7G7OEz/jG/Ou998DucMLS7+9LrpmeXTBEuHyWigIIADtP2qC/6YL+pg/HcfDTTfUCaJZIhmrltlgvWMfpggLIJDz//PPPhTqiXcZYx/wCyLvXZWeYAogMP213OKG2ZSjsjaup58oEG+PcHACIjfmyPV4BtGyPVAB5PMrBE7Ty4RYxst2HKTmhL4hTsPOkC/qbLuhv+nAcBxdJAfQkuAACEPeDLpW190hosI7TBQWQCfn5z3/+T3/xi18881d/9Vf/Q7TLYibiRQDtOl8elp27ZV0S4VHTHL4AIu2lnCwBALExX/p9HtgdTli+N9/vulUH+BxBd/y+UwuZ20iebNVKYOdJF/Q3XdDf9OE4Di7eEAVQdbN6AYQzQNrBOk4XFEAmwmazORiGaSVzALEs22Cz2T6IdtnMgNkF0O/XGyOACqt7JJ1UdfOgGOY0RJ5k19xnAAAgAElEQVSHQOy+IC6r4xO18o35V7u9AmjF/gK/67b7ls79YdNt3c/z2upbkufxeDy6bZkZ7Dzpgv6mC/qbPhzHwQVCANU0D4W8hmyLZ+asm5haD1jH6YICyCQwDPM1wzBzLMseYxhmCcMwv7PZbB+xLHuWZdl5hmGWRLuMsY7ZBdBbBgmg3NJOSSdV1UQIoBBhTgPBzYj7eX7KafJ+5mvMv9x1N2Cgg70/VYLd4YQ31qbrfp6XV9yUPI9VM5Bj50kX9Ddd0N/08QqgAlEAtWgTQFZOS6AHrON0QQFkEhiGaQk008MwzGcsyzbQLpPZML0AWucVQLsvhCeAWp+OSzqpyqYByc96cLs98MJSr40TNx4DgNiYf7HTK4BWH/QXQEedNWB3OOGl5Td0P8+Ly9L89jRZEew86YL+pgv6mz4cx8H5tAJNbWtGUbtw/vG0WgqljB+wjtMFBZBJYFl2PjExMTHAd0kMw8xRLpLpQAEkQu6bKanrUx3lJxivr0kHu8MJ+y5VAoDYmH+Wmgt2hxPWHnrgd83ZzHrh3guLbs33dLulgRTCiWRndrDzpAv6my7ob/rIBdDj1uHQ10zPC+dfutNEoZTxA9ZxuqAAMgksy/ayLPv3Ab77byzL9tIuk9kwuwB6c10G2B1O+P5CRdi2ruU1C50UGRRBzSbXQLy/2ZuQdOvpRwAgNuaf7vAKoG8O+wugK7lPhHtz0/Oa77mw6PITQPfKu3U/g5nBzpMu6G+6oL/po0cAuYiXUidvPqZQyvgB6zhdUACZBJvNto9hmE6bzfb7pKQkNjEx8X9+/vnn/5XNZnuPZdlem822L9pljHXMLoB+941XAO25GL4AynooLlO4VdCqaZNrID7f6Q12sNYndPjG/ONtd8DucMK6o0V+15AhuQdHZzTfc0aWd8LucEJmUbvuZzAz2HnSBf1NF/Q3fTiOg3MaBRAAwG99gWn2X66KcAnjC6zjdEEBZBKeffbZn7Ese4mMAEdEgruYmJj459EuY6yDAkgkv+Kp0Kl9ukNMJBrO/pnVB73Rgr7afQ8AxMb8o++8eXo2HHvod839im7h3k+6RjXfc5JYbsEf1/KadT+DmcHOky7ob7qgv+nDcRycc4oCqK5NnQDCUNj6wDpOFxRAJsNmsz1rs9lesNlsbzIMY//FL37xTLTLZBbMLoDeWGucACqoeuonHLS84VPiI99MD9/p8Y35kq1eAfTt8WK/a8gIdDcLWjXfc3Ry1u8Zfsxs0P0MZgY7T7qgv+mC/qYPx3HwIyGA6ttGVF2HaQn0gXWcLiiATMSzzz77s4SEhP+e/39iYuLfsCz7a5Zl/2UUi2UazC+AvEEG9v5UGbatksd9igJI7Rs+JUg7brdHaMz/uMW7N2jTD/4CqLl7LKy3hYOjM37PcMxpzchD2HnSBf1NF/Q3feQCqKFduwCqeqJ/X6nVwDpOFxRAJsFms/1HlmVHbTbbf/D9/zcsyy4yDONhGGY6OTn5/4l2GWOdeBFAfJS1cHC53IoCSO0bPiXWHSkS7ExMzQuN+YcpXgGUcrLE75qeIS4sAdQ3POX3DEYIRJKFRRf0Dk0ZajMSYOdJF/Q3XdDf9OE4Ds5eJwRQh7r+gVwNkFfWFeFSxg9Yx+mCAsgkMAxzn2GYIwm+GSCWZdt9e3/+Ocuy3zEMcy+6JYx9zC6A5GGmw2Xt4Qd+4kHtGz4lckrEBKtd/ZNCY/7B5ttgdzhhyyl/AeTxiBGDXl+jPRlqV/+k3zN854tCZxTL9tw3RXQ57Dzpgv6mC/qbPnIB1KhSALX3TgjX5JaiAFIL1nG6oAAyCQzDTCQlJbEJCQkJLMv+G4ZhPElJSf/a9/8klmVHo1vC2MfsAug1gwUQmf9HawenRGl9vySaHN+Yv7cpC+wOMTy2nHBmgMiONhIbb6dmFiK2odflckN+5VNo6zGmPmLnSRf0N13Q3/ThOA7OEAKoqVNdoJqhMXFp8tmM+giXMn7AOk4XFEAmgWGYieeee+5538/LGYbp5L977rnnnmdZdip6pTMHphdABocWVVoGp7aDU4Lcz3O/oltozN/91iuAtp8pVbyOvL/WzrK5a0xRABm18VY+S2YkZP6lmbnFsO1h50kX9Ddd0N/00SuAZmXpCUYnZyNc0vgA6zhdUACZBJZlHzAMsyMpKel/Zximk2GY3fx3NpvtE5Zlq6NZPjOAAsgfMgS23aEvFDXP8Lj41u/6vRahMX9nYybYHU7YcbZM8brjabW6RUZDx4iiAJqaWdD9HCSRmlkCEBPHallbHwzsPOmC/qYL+ps+HMfBmWv5mgUQubTZ7nDCqVt1ES5pfIB1nC4ogExCUlLSf2FZlvPl/Wl77rnn/johISGBYZh/ZFl2gWGYd6NcxJjH7AKITy53wEABlF8pDYfd3DWm25bL5YYXlooZwPnG/O0NXgGUek5ZABVW9+gWGbUtQ8J1pJjrGzYmaIFcAC263IbYBQBYvlccWDyo7gnbHnaedEF/0wX9TR+O4+A0IYC0vCAj280fbjyOYCnjB6zjdEEBZCISExP/eVJS0r975pln/oL/zGazJdtstn+IZrnMgtkF0KurfALoinECqLi2V9JRhbsf5a11XrGz81yZ0Ji/td6bv2jX+XLFa9xuj24BROYR2nep0hAhRyIXQJxBM0sAAJt+KBbsOu+3hG0PO0+6oL/pgv6mj1wAaWlXyXbzYk5jBEsZP2AdpwsKIMQyoADyRz4D1NE3EZY9fhZm3ZEioTH/3Tfe4A27LygLIACAlQe868w/SMnWdD8y8MLZzHrh5/LGgbCeg+flFTcl/hkenzHELgDArvPlgt2jzpqw7WHnSRf0N13Q3/ThOA5OXSUEULc+AXQtrzmCpYwfsI7TBQUQYhnML4C8g/GDBgqggZFpSUfV1T8Zlr2vdt8TbE1OTkJZWZmQv2jPxYqA16WeKwO7wwlvb8jSdD9yButWYZskCIMRvPz1DUP9Q/Ld6UeG7i/CzpMu6G+6oL/pIxdALRoEUOvTceE6I/useAbrOF1QACGWwewC6DcrvQLo0NVqQ+2Sg/CeofAaXtJWYWUHlJWVwetrbglL1ALBL197TWMuoMIqcf9QUY3484381rCeg+el5WmSZ3p/s7YZqmBsPP4QBZCJQX/TBf1NH47j4OQVUQC1PlXfd8qXNs/Ohx/pMt7BOk4XFECIZYgXAXQ4ggKof2TaMFvbThdDWVmZquh1R6/XgN3hhF9/fUPT/e6Vdwv3a+ocFX7WKqQC8eLSyOUYWnOoUGJ3nJsLyx52nnRBf9MF/U0fuQDSukeUbN/yK55GqJTxA9ZxuqAAQiyD2QXQKxQE0OBoeHtcSFtHrlZCWVmZsHQv2N6lU7fqhOvcbvU5fMhcOh2ypKjhIg/larQAWrbnvsRuQVV4AwTsPOmC/qYL+ps+Rgqg4treCJUyfsA6ThcUQIhlMLsA4jfkH74WOQE0MTUflq0viT1AZ9NroaysTNXSvdPpdbrKkFvaKdmfQz6LS4OQUkK+hMPucMKSrXfCsknyeWqexHZRTXihsLHzpAv6my7ob/pwHAcnDBJAeeXG7MuMZ7CO0wUFEGIZTC+AfBvyj1wPP2IYCSk+whUN5DK0w1cqoKysDF5ecSOkcFuy9Y5w3el09Unzcko6hOu6B6QCqK5tOKxnWXS5/QTQ777JCMsmCfnMOANkPtDfdEF/04fjODhxWRRA7b3aooSS7ZtR+zLjGazjdEEBhFgGFEDKzM4vwo38VtVZvoPhImZN/rglG8rKyuDXvnIHC/XM5w+yO5ywfG++6vvdLhYFUM8QZ6gAml9w+QmgF5eGLxJ5/rDptsR2TklHWPZodp61LUNwLa/Z0hubcbBCF/Q3fTiOgx8ui0t1OzQKoPLGAeHac1kNESpl/IB1nC4ogBDLYHYBJAgJgwWQ0ZCD+rKyMviVL5La8bTagNc8JMJZ/3b1LdX3ynrYLlzXNzwFafdbhP+Hmwtodm5RsMXvYzJimSDPm+syJL5Kyw8vGSqtznNhUZwZO3VL/WxdvIGDFbqgv+njJ4B05InjX9wZvXc1HsE6ThcUQIhlMLsAeml56JmUWIAc1N/MLoIXl3kF0A83Hge8hgw4oGWfTcYDMfdP/8i0ZAleYVV4e2qmZxcEW+9tzta9DCQQfHQ8/vgppykse7Q6TzK/h5FBIcwGDlbogv6mD8dxcPySKIA6dQggvp0zcv9kvIJ1nC4ogBDLEC8C6Jgz8ExKLEAOjrefyIUXfKGkT94MLIAAxKAAaw89UH0vMvnp4OiMJBJcbmlXWM/BzSxInoU/ggk5LfBR/fTsfVIsL6XOc/XBQhRAgIMV2qC/6SMXQHoSQZNthcvljkAp4wes43RBAYRYBvMLoDTTCaBdp+6qXi7F58X5Ylee6nvdyG8V7A+Pz0Df8JTw/8yi9rCeY2JqXrC19fQj4eeMB21h2eWRJ1kNN7ofrc7zs9S7KIAAByu0QX/Th+M4OGagALp+rzkCpYwfsI7TBQUQYhnMLoB+tSz0XppYoJ2Yhdl5Uhwsh5rh4EXG+5uzVd+L3PMzMjELIxOz4p6a++HtqRmbnBNsnbjxWPjZGaZdAOUcQ2+tzwzLJq3O81xWg6Tc3MxCRO8Xq+BghS7ob/rIBVD3QHgCyMovTNSAdZwuKIAQy4ACiA5k/pyUo2Ko57OZ9UGv23epUnMQhOv3RAE0NjknWbZ2MacxrOcgxdSV3Ceqn0MNLoUQ23aHE2bn9EdVo9V5kmHT7Q4nXLoT3t4ls4KDFbqgv+nDcRwc+yk8AVRU04MCSCVYx+mCAgixDGYXQC+aRAABgBA17ZsDYvCAHzODh0HlZ1leWOoEj0ddqOmrd5sF++PcnCRCWbid7dDYjGDnZkGrodGM5hRCbOvdZMxDq/M8er0GBRDgYIU26G/6cBwHRwkB9HRQu+/J1AivrlL/csuKYB2nCwogxDKYXgAtNXYTfiR5e0MW2B1OWL5bzHVz/nbwGZkL2Y2aZ0LImZnJaW94anJwrlZIKTEwOi3YuVXYJjxT6o9lum3ykBHmyEPPGnseWp0nP1PHH5dzn0T0frEKDlbogv6mj1wA9egQQAAA648Wgd3hhE+25xpcwvgC6zhdUAAhlsHsAoiPpnbCBALoT995l759tk1McBpqSRq5nG10YlbVfS7daRKumfLtRSEH53MLLt3PQAZUyChqh4+354Ld4YQNxx7qtslDBlggj+auMd02aXWeqefKJGU2Q32MBDhYoQv6mz4cx8GRi4QAGtLn+++IIDJjk3MGlzJ+wDpOFxRAiGVAAUSPr3bf8xvch1oqdS1PXM52I79V1X0u5oizRtOz/gJIrZBSomeIE+xkPeyAr/flg93hhKV77uu2yTNK7C9ae/iB8HNty5Bum7Q6z5STJRIff3+hIqL3i1VwsEIX9Dd9vAJIbMt7h6Z02flom7gX9BAmRA0I1nG6oABCLIPZBRDfgYTKpxML8Dl9yONKiKVSl4nlbGr370iWzc17l83dLu4Ie8kGAED3wKRgJ6ekA749Xgx2hzEJ/QZHxf1FW0+Jb0fLGvp126TVefLLWfjj7Q3hRa8zKzhYoQv6mz4cx8FhQgD1DesTQGQi6dUHCw0uZfyAdZwuKIAQy4ACiB5Ky7uu3g2eA2Kcm9MsgMiQzPO+5W75lU/FJWXd+peUdfaRSVU7Ydf5crA7nPDWuvAH/OTyuu8vVAg/hxPgglbnufJAgd/vlt9/ZSVwsEIX9Dd9OI6DwxfCF0Cf7hDTIby66qbBpYwfsI7TBQUQYhniRQCFSigaCygJoOv3QufPIc9Xs9zibGa9cP7CojfLeGl9v/BZTRhLyjqIfEZ3y7rgqNMb/eyl5WlhBVcAkM4ubT9bqln4KUGr83R877+8sa5tOKL3jEVwsEIX9Dd95AKof2Ral53q5kFJe1HVNGhwSeMDrON0QQGEWAYzCyAycaYZBNDha9V+g+S0fG0CqPVp6N/TmQxRALncXlHyuHVY+Kykrk/3M7T1jAt27pV3w/nb4nK70Un9e4sApOIqt7TTVALoE18wCPKoaByI6D1jERys0AX9TR+O4+CQAQIIQNq2/2pZmoGljB+wjtMFBRBiGcwsgMjkoqfTY18AjU7O+g2SbxaEDmzwgy8XkN2hLiLaqVtiUk5+Vqb1qVS46KW5e0ywk1/5FK7eFfcobT5RrNsuAEALabviqakE0Acp3vX8720SQ5xfzA4v6awZwcEKXdDf9JELoAGDBFA47Vw8g3WcLiiAEMuAAogun2y/I+nw0h+0hbzmUV2fcP7j1tDLqk7efOzXofYOiftrMovadZe/qXNUsFNY3SMJuR1uB07aflDdA6+tSRf+ryfZIAC9zvPtDd7Q5l/KIv1ZDRys0AX9TR+O4+DgeUIAjeoXQPsvVwl23libbmAp4wes43RBAYRYBjMLIJcJBdDWU8WSAXKGCjFCrhV/cWnoQfXxtFrvucSSCjKYQqjAC8Fo6BgR7BTV9BoqgOraiGV6j/sksyn3K/TNWtHqPN9YmwF2hzeaE+mPcJcFmg0crNAF/U0fuQAaHJ0Jy55RM+jxCtZxuqAAQiyDqQWQyy10Hmcy6qNdHFXs/0maMDPrYUfIa0qIGSA1IuOY0yuAXlouCqCFRdFXZ8PwVX2bKICKa40VQDXNQ4KdsoZ+SeS6Rzr3LdHqPH+7+hbYHU44cKVK4o/zWQ0RvW+sgYMVuqC/6cNxHBw4JwqgobHwBNBnqWI0OD5tASKCdZwuKIAQy2BmAbToMmZQT5Ntp6UzQNnFoQXQ3IJLk8g4et0bme3XX9+QfG6EUCGDKTyq64Pi2l7DBFBF44Bgp6pp0G9JnB7knefs3CJ0D0yGVU4lXl5xE+wOJxzx+Z4/zqEAQiII+ps+cgE0PG7MDJB3dj54XjgrgnWcLiiAEMuAAoguaw9J88XcedSp6ro1h8SlVXxkt0AcvuqNNvfKSmluCfK+ekNWy2dpyEh8S/fc12WThwzVXdsy5BdxTg9k50mW1Xk/dPQ9LfxqWRrYHd6cRWSgiGt5+pcbmhEcrNAF/U2fSAqgXefLDSpl/IB1nC4ogBDLYGYBJFnWlWkOAXTcKV0idbesS9V11/KaVe8rOehbhiVPrkfed2pmQVf5q5rE/Uh8mOd1R4rA7nDCZ6l3ddnkeUjMJjW0j0jyAuWUqBOKcsjOk4xYF+5sFYk8HPvs/KK4BO62tSLB4WCFLuhv+nAcB/vP5UVEAB111hhUyvgB6zhdUAAhlsHcAkhcGvZjpjmWGg0Nj0k6vDyVMxun08XQ1oevVgc9l48s9NvVtySfL9maI9ho7BjRVf5ycpnaE2/ivq2nHoHd4YQPUrJ12eQprOoRbD/pGoX+kWnh/xkqouUpQXaepN+NFECSmcjMevB4PPDCUu//d54rM+w+ZgAHK3RBf9OH4zjY/6MogEYmwgt0svbwA8HWwStVBpUyfsA6ThcUQIhliBcBZJa9FvKBeH7FU1XXkQIo1OB936VKsDuc8PoaaVhVMpjCpTtNuspf1iAuU6tpHgIAgN0XysHucMJb6zJ12eQhc/+0Ph2H0Qkxb5LeJWuBBNCfvrsTVllJyBmfC77cP+S9Fhbdht0r1sHBCl3Q3/ThOA72EQJoNEwB1DcspijYeuqRQaWMH7CO0wUFEGIZzCyA5hfML4AKqtQJIDLn0aEQM0B7LlaA3eGEN9ZmSD4n99TonQFRyklERj5bdOkf7N8r7xbstPWMw/TsgvD/izn6lpIFEkArDxToLqecqRmxnLywJO/V1W980IVYBQcrdEF/08dPABkQ6v6T7bmCvf4wEqvGI1jH6YICCLEMZhZAZHQ0s4Qb5jgO3tlwSyi3luhm/DVvrQ8+0xJoRqarfzJsAURGfatv8y6jSzlZInx28uZjXXYBAPLKugQ7Hb0TkjxPejcHk50nn6yUP/QGgpBD5ljigx6Q92l9ar6/Lb3gYIUu6G/6cBwHe8+KAmhsci5sm2R7se5IkQGljB+wjtMFBRBiGeJGAJlks7nfErhKdTNAAOrDWO867xVAv5cJJXIW6cOUHF3lL6oR9+nw+4iM2luTWyoKoM6+CT/beiA7z1dX3ZTYm50zJufGCLFULy2/xa/cNS1DhtzHDOBghS7ob/rIBdA4Z6wACqcNjUewjtMFBRBiGcwsgMwYbUsugHJL1UWBA1AvBlLPeZOtvr0hy++7T3d4k+59c/iB5rID+AcqAAB4bfUtQzrvO486/ZaNkXa56XnNNvnOc2Ji0m+QYcTSFQCAgVExWMOtQm+whm+Pi/meHtb2GnIfM4CDFbqgv+nDcRzsMVgAHfKlLkAB5A/WcbqgAEIsg6kF0Jz/5vNYh+M4+NEp5gJyadgzs/5okXhdkFxA28+Wgt3hhHe/ve33HS+A9Hay+ZVioILm7jEAAOgZ4gzpvHNKOgQbTwe9nR1pt294SrNNvvMcGR33E0A9Q8Z0qL1D4ibmrIftAADQ0TdBiFx9IbzNCA5W6IL+po9cAE1MaX8xowQKIGWwjtMFBRBiGcwsgGYIAXTRRAKorKwM6lv7NM9oHHXWCM8bLDHotjNeAfTeJn8BRHayetauywMV8Kw6UBhQdKkl62GHnzghc/fo2UvD+1seftzucMJXu+/pLisJubeKz1c0PD4jfHYjv9WQ+5gBHKzQBf1NH47jYM+ZyAogo2zGA1jH6YICCLEMcSOAdEYJo004jXnGgzZVbwn5vDzvb/bPy0NeX/K4T3MZ7soCFfAcCJB8VQuZRe1+sz2l9UTYbR17aXh/Dwz5CyCj3rS294qzPXm+xLYzJpydNAIcrNAF/U0fjuPge0IATepYmqsE2S69s9F/+bJVwTpOFxRAiGUwswAyIkwybcJpzCuIJKTBBu98VLYPt/gHOnjcOqxr/xGP0j4dAIBTt8Q8RXqDC5ACb8AXCrauTSxvsY69NLy/+wZGIyaAWrpFccXndfJ4PPDisjSwO5xwPK3WkPuYARys0AX9TR+vABKXEuvZm6gEn74Al8FJwTpOFxRAiGWIFwH0U46+xJ60Cacxb2gfUdVBbj7h3YC/ZKu/ABqdFCOWrT5YqLkM2cX++3QApPuT9A74bxWKAmhwdAYAADp6yb002gUb7++nfSMRE0DNXaIAIvM6/WblTcsNZnCwQhf0N304joPdpwkBNLNgiF2yP/toW64hNuMBrON0QQGEWAYzCyClBJSxTjiNeVPnqKrBOx+B7E/f3fH7zuVyhyUAyH06vUNiUIIdZ8vCFhY3C1qF64fHvQKIjLB2s0D7Xhre31294kzSb4modQuL+hO38pC/FzKvE+mPmmZrhMLGwQpd0N/0kQugKYMEEID48spKL01CgXWcLiiAEMuAAogu4TTmasXLhmMPwe5wwsfbld8ikja0JgPNUNinAwAwODoTtgBKy28Rrh+Z8IaoJn/Hemb5eH93PB0S7Ly7McvQt7cNHeLsUlGNuEyP9IdZ6me44GCFLuhv+nAcB7sIATQ9a5wAItuM0vp+w+yaGazjdEEBhFgGMwsgzmICCABg70+VwjPPL7gUz1nnW4726Y67it+TS7O0dt7kMrWB0WnJd+EKIOd9UQDxOXrcbg+8sNT72cmbjzXb5P3d1j0o2H5jbYbwM5/LKBzq20QBRAaWIP2hFJEvHsHBCl3Q3/ThOA52nYq8AMJZIC9Yx+mCAgixDKYWQNPzQkdxOfdJtIujinAbczLYwOepeYrnfHP4AdgdTvgsVVkAHbkuhtOu1RhZ7Ua+/zI1HnIfkNaZJQCA6/eahevJ5ILhDAh4f7d0igLo1VWiANx3qVKzTTlkYIlHdaIAIp/HKoMZHKzQBf1NH7kAmtEZ9EWJNYcKLddmhALrOF1QACGWwcwCaJIQQFcsIoAuZDeG7CDXHvIKoM935il+/2Nmg3A9n7hTLZJZGt8yNZ7zt8Wy6YkER+b8IfNgkM+rdb097+8nHWIEPfL5jcgfVdMiLq8raxCXrcwtuCRlb2gfCftesQ4OVuiC/qYPx3GwM0ICSBoIwX8PpxXBOk4XFECIZTCzAJqYEgXQ1bvWEECkSAgkgPi3iF/uylP8nty0n13coen+gWZpAKRBDDr7JgJYCMzlXPHZyL055POu2F+gySbv78Y2MZ9QTokYyOGVlfrzFvFUPRFnl8obByTfWW1JCw5W6IL+pg/HcbDzpCiA9Ib9DwQ/g293OKF7YDL0BXEO1nG6oABCLEP8CKDmaBdHFeE25gd9CUeDDahXH/QKoK9231P8vrNvQmJDSyS0QLM0AAB55d3Cd0v33Ff/UD4u3WlSXFdPfq5VRPD+rm/tE67PLe0yVJRUNomzS1VNg5LvUAAhkQT9TR+O4yCVFEDzkRNAgV5iWQms43RBAYQowjCMnWXZUpZlJxmGqWcY5otg59tstv/IMEw+wzDjDMO02Gy2FMLWGoZhZhmGmSGOWYZhPMnJyb9ISEhISExM/BuGYW6yLDvMMMwAwzBXk5KSfknY8LAsO09ez7Jsg5ZnMrMAGufmLCeA+OVtwd4+rjxQ4BUh3yuLkP6RaYmNx63Dqu9PihF5BLXS+v6wBvsXc8QldOSykq7+ybAF0OPmXuH6vPJuIRDE2xvCz7he3iAKoOpmqQAqqhHve+BKVdj3inVwsEIX9Dd95AJoLkAwGr2sOyLupXxxWZqhts0I1nG6oABC/LDZbH/LMMwcwzC/S0hI+DOGYf49wzBjycnJrymd/8wzz/ycZdlRhmGWP/vssz9LTEz8G5Zl22w224og9/iSYZj7CQkJCYmJiX/OMEwny7L7n3322Z/57GWyLPuAP98ngP5TOM8VLwLoWp41BFB7r3T2ZmBk2u+cFfu9AmhZgFkYj8cjsVHeMKB4nhKkSJFHP6puHgxLAJH7m8i3qguL0vDfbrf6AAu8v2ueiEIkv+IpbDlVAnaHEz5IydZcTrDsSAQAACAASURBVDmk8FMKKvHW+kywO5yw/mhR2PeKdXCwQhf0N304joMdJ3KFv/lA0Tj1Qgqgl5bfMNS2GcE6ThcUQIgfDMPsZhjmnuyzHQzD5Cudb7PZvmRZtl322ScMw3QpnZ+cnPwLlmVHGIb5XxISEhISExMTGYY58cwzz/wFcb9/ZBjGlZiY+Oe+/3sYhvm/wnkuMwugsUlRAF2/Zw0BBCDdh6MUxnn53nywO5ywfG9+QBukoCghIpeFQhLoQLb0Y2RiVmK3qVNbiOnzWQ0BBxWk3fO31Qcu4P1d1dgjXF9Y1QN7LlaA3eGEN9amayqjEiV14vI6pdk0veLNjOBghS7ob/rIBdDCosECiIimaXc4obl7zFD7ZgPrOF1QACF+sCxbaLPZUsnPbDbbqwzDzCQkJPwT+fkMw/zIMMwV8rOkpKR/xzCM+7nnnvtrhfPPMQyzJ0QZPmUYZoC4xsMwzGWGYZ6wLMsxDJOdlJTEaHmu+BFALdEujiqMaMzJTfdnMur9vl+25z7YHcEDBpAd7IPqHtX3JiOoKXX84ex5kdqW7ksibWrJB8T7u6L+qXB9UU0vHE+r9S4xWaovZDdJca04u6QU6Y0suzxwRLyBgxW6oL/pw3EcbJcIIPV7KNWQcrLErx29kd9q6D3MBNZxuqAAQvxgWbaJZdnV5GdJSUn/hWEYd1JS0j+Tn88wzG2bzXaU/MxmsyX7lq39G9nnf8uy7JSSMOJ5/vnn/xXLsqMsy/6RuEcRwzDLn3nmmb/wLZG7xLJsBz9DpAYzC6DRSXHGwUoCiAy7rCQylmoUQLsvlKu+99mMeuG6RZd/xy/vvLVA2nbJbJPi6Kcc9UlveX+X1YkBGkoe98FFYrlduKKkqEacXWrsCC6A7pV3h3WvWAcHK3RBf9NHLoCU2sFwIPOKhbOkOF7AOk4XFEAWxGaz/b++GRU3cfD/z2dZtpFl2VXkNSzL/j3DMG6bzeZXURiGuc0wzBHys+TkZBvDMJ7nn3/+72TnnmQY5kSgsvn2Gw2QQRSUSEpK+mcsyy7abLZfqXtqUQD19/cDx3GmOrp7xY7ip+y6qJdHzTE4OAhlZWUwODio28bYmHQfkPz7L3Z6N+h+vfdeQBvyzrW9e0jVvY9drxSumZiY9Ps+r7QtaNn02p6YEAMhHLxcodnfBWXissH88na4ekcUW9vPlIT1O71TIob/rmrs8fv+fnm78H2w30k8HEbUbzzQ37F8DA4OwvYfRAE0Nj5hqP3JyUm/9llrWxpPB9Zxukd/fz8KIESKTwTtJD9jWfZ1hmGmlc5nWfYMwzBXyc9sNtt/YBjG/fzzz/8V8fGfMQwzzrLsfwtw31cYhplgGGaJynL222y2j9Wcm5AgCiAzHnkF4mzD/h/zol4emgfZMZY8KpV898fN6WB3OOGzbZkBr//DxlvSZWVX8lXdd+uxO8I1St+XlpZK7JaWlqqyW1ZWBluP5wa1TdrV6q/L6WKG9Ys3CuGHy/fDskcep67mi0tVsov8vi8uKTXsXnjggUf0j22EAJK3v0YcSgIo2s+Mh7UOFECIgM1mS+UjtPEwDLOHZdlcpfNZlv2UZdkOmY2vGIZpldn4R98+oj+T20hOTv7/fOLo7xXK878xDLNX9tlf+mak/kHtc5l5BqiLmAG6nFMf9fKoOYx6m7VinziA7x0YlXz32Q5v57xyf37A61s6pRHb7j5qVXXfQ1e8wQNeXJYW8Jy31mcIdkdGx1U/0+GrXtsvLFV+20mWd3LSf/YpmL/zHon5i4qqOiG9QJpQNpzfxe0icXbpcXNvyLIPDY9FvR5G6sC3tejveD8GBwclAkhpJjzc4+0NmTgDRPgb6zi9A2eAED+SkpIYhmFmbDbbmwkJCX/Gsux/8uX3sfPnsCzbkJyc/F8TEhISfvnLX/5PLMsO2Wy2Fc8+++zPkpKS/jXDMF0sy35K2rXZbOsZhqmS3+/nP//5P2VZttd3Pz9Ylv2XDMNMsCz73TPPPPMXycnJ/4JlWSfDMLUJCQn/ndrnMvMeoOHxGaFzSMu3zh4gAICMInFZVd/wlOS7z1PzwO5wwjeHHwS1QXauRTXqAiEcddaA3RE8POvVu6Ig0LK/hg9M8KsAuS/I8sqTsAaC93dhZYdwbXXzIDwkAheEu74+r0xMrNreOxGy7Gn3zVFX9WBU/UbUgf6mD8dx8B0xWx2JyI4dsnQH4bZRZgbrOF1wDxCiiM1m+weWZat9CUtbbDbb++T3vtmX3/L/T05O/re+QAUzDMN0y4Mo+K45zLLsHYV7venbf8QnSBX+TUpK+j991/57hmHyGIYZ880UXQwWSEGJeBFAZomSY1Rjfr9C3NTfIguT+ukO7x6gdSHyzpCda25pl6r7HrnuFUAvfx1YAJHi7GKO+pDVx5y1QcXVl7vyBLun0+tU2eT9nU/sw6ltGYL5BZfk+becKlFdTjm5paIA6uwLLYC0RLEzGzhYoQv6mz5yARRuFMlAoADygnWcLiiAEMtgZgE0NCYKoJsF1hJAZQ1i8k15IlNeAG049jCojYGRacHGrcI2Vfc9dLUa7A4n/GblzYDnZBd36Oq4j4YQV61PxyV2lZLAyuH9fa9MDM5Q1+bN1bPvUqUhA4yckk7BRlf/pOI55H3OKoQujxdwsEIX9Dd9OI6T7FeMFCiAvGAdpwsKIMQymFkADY6KAuiWxQRQU+dowM7x4+25qgTQ5PS8cP2V3Ceq7nvgchXYHU747epbAc85efOxro778DWvuHolgLhadLmlS8lULHvk/X33kSiAGnyhqo0SQKTg6xlU/r3uOCvd2Gx06NxYAQcrdEF/04fjOEkwmEix81wZCiDAOk4bFECIZYgbAaRyBiPaGNWY9w5NSTpHF7EO/aNt3s752+PFQW0sLIqC4uUVgWd0SHjR8Pqa9IDntHSP6eq4+dmlV1cFLoteAUSGqm7qHAUAgF8tSzNkgJH1UFxe1zs0pXiOfE3/4avVuu8Xy+BghS7ob/qQAuiFpZETJhzxgirYjHu8g3WcLiiAEMtgZgE0MKp9CVe0Maox52YWAi6RWLLV2zlv+iG4AAKQCgqXis28ey56I7X97puMoOe9svIm2B1O+DAlJ/TD+FAzu6RXAGU/bBGu4/dM7f3JmBkgcs9Tf5BleeS9Pku9q/t+sQwOVuiC/qYPx3GwhYIAAgD44YY4mx6pvUaxDtZxuqAAQiyDqQUQsYcl/YG1BJDH4/ETQAuLLgAA+OOWHLA7nJByMvTGfvJ6p4roZLsvlIPd4YS31mUGPY+fKVI7s0ReE2x2iSzv9XvqBVDWAzEyXVuPt67Pzi8KnwWLaheKW4Xi8rqBUXUCSG0QB7OBgxW6oL/pQwqgFyMsgM5nNQhtxqU7TRG9V6yCdZwuKIAQy2BmAdRPCKAMiwkgAHGWhT/40NAfpngFkJrIZuT1S7aGnq3h16W/vSG4ADqTUS/YzSvvVvU8/IzMG2sDzy59f6FCsKsmmhrv74xCMe9PBxGpjSxnoOVrobhZIC6vGx6fCXge6evjabW67hXr4GCFLuhv+nAcBylHfQIoQMh+o0h/0CZpN6wI1nG6oABCLEPcCKCi9mgXRxVGNuZd/ZOSzpGPivZBSjbYHU7YevpRSBs38sXB+7ojwcNmA4ib+d/dmBX0vLOEsFDbcfPL695cF1gAzcwtSuxyMwtBbfL+vpXfJFxDRmrbfKJY+HzlgQJV5ZSTdl9cXjcyMRvwvJeW3xDO23epUte9Yh0crNAF/U0fUgAFyllmFPmVT1EAYR2nCgogxDKYWQD1DU9ZWgABSGcV+JmN9zZ7BdC2M6Uhr3e5xaV0apbMbTtTCnaHE97bdDvoeVfvPpGUjV+eFwxhed364LNLpN1QSx95f9+4JwogMlIbKYCCCa9gXL8nCqCxycCJX/PKuyVlj8c1/ThYoQv6mz4cx8HmI3d8S2cjK4BKHvehAMI6ThUUQIhliBcBlGlRAVRU0yMuNSvzJjP9w6bbYHc4YbsKAQQA8HmqN8Ho2sMPQp679dQjsDuc8EFKdtDzugeks1NFNT0hbe86X65qeR1pN1QgBN7fzrxG4Zq+YXGpmxGhsK/eFfcXjXOBBZC87MGWy5kVHKzQBf1NH6kA0r93UA3ydrRZlvTaCmAdpwsKIMQymFkAkaGgsx62R7s4qjC6MS9vHPAbwL+7MQvsDifsOFumysaK/QVgdzjh8515Ic9NOVkCdocTPtwSer8QWS414iLVt7/onRDL60iboTYG8/6+lituJiYTqJKRBPUKoCu54mwXNz2vuux8OO54AgcrdEF/04fjONjkE0C/DpC02UjINiPSgisWwTpOFxRAiGUwswDqGeIIAdQR7eKowujGvKpp0G8A//YGrwBKPadOAH2yXcxqPhViT82mH7xLxpZsvRPSLrm8TI1tYX/Rt8GX141Ozgo2j1yvCXou7+8rd8Q9SUNj0pkX6VI97QlKL91pUv2Mt4iACdvPqpuhMxM4WKEL+ps+pAB6mbIAsuIyOKzjdEEBhFiGeBFAt4utKYCUZoB+vz4T7A4n7DpfrsqGlj01G48/BLvDCR9tyw1p90nXqMS2XHjIUbu/CADgrXXeZ3x/c/CleLy/L2XXCeWQByogE6JWNQ2GvLecizni8rqZucWg55JBJ+JxMIODFbqgv+nDcRxsOkxPAJGJluOxzQgF1nG6oABCLIOpBdAgCqCJqXlJ5+jxeOAtnwDafUG7AFq+Nz/o5vz1R4vA7nDCpztCJ/Ik92jZHWL+nUBsPa1uf5G8zHMLgQMs8P6+eFsUQPJABfcrusMaYJy/LQqg2fngAohccvfRttCzaGYDByt0QX/Th+M4+JYXQBrynOlFnvPtSVf8LZ0NBtZxuqAAQiyDmQXQU0IAZVtUAAGIy9LsDidMzy7Am+sywO5wwp6LFaquJxN52h1OqGgcCHjuN4cfePcLpeaFtDs9uyCxGyos95ZT+vYXkWGt5fD+PpcpZlTn8yXxyCMtuVzalsGdI5IVqol2R97L7Y6vSHA4WKEL+ps+XgHkzbX2ysrICyAAgA3HHgpthprlx/EE1nG6oABCLIOZBRAZISenxLoCiNxXMjA6DW+s9QqgvT+pyzXDTUtnkdYfDZwPaO0hrwD6YleeKttrfYJJzewKH2BBTULWkzdFQfOori/geby/z6bXBtyn09A+IinjrYJWVc/GQ+Y8WlQhnniBaqZ6qxYcrNAF/U0fjuNg4yGvAPoNJQH09b58oc2IdPLVWAPrOF1QACGWIX4EUGe0i6OKSDTmZH6Ztp5xeH1NOtgd2pJtkgJgxf7ACUFXHywEu8MJX+2+p8pue++EagHEz2T96bvQbzgPXK4SbAYL9837+/StmqD7dMgy2h1OuJbXHPrhfJxOF5fXqZnRkd9rejZ44AQzgYMVuqC/6UMKoFdX0RFAX+2+J7QXkU6+GmtgHacLCiDEMphZAHX1owACACit7xf88Mn2XHht9S2wO5yw/3KVahvkgDxY8AQ+ZPbSPfd12Q7Gt8eLfXtjQgdYIEOgX8xpDHge7++TN6qD7hm6fq/ZT5iohZyNUpPcVH6fH248Vn2vWAcHK3RBf9OH4zjYIAigW1Tuyedq07tP0cxgHacLCiDEMsSLALrzyLoCqLNvwm9QbXc44cAV9QKInMUItndo+V7vUozle/NV2+ZFk93hhMaOkYDn8evcP94eWgC53R540Re97XhabcDzeH+fSBNnjJRCXd8r79YtgE7c8AqgF5equ0bpdxUv4GCFLuhv+nAcBxsOegXQb1fTEUAfbbsTl+2FGrCO0wUFEGIZzCyAyIF/bql1BdCiy604qD50tVq3jUCBAJbuuR9ymZwctYP9dRoizAGAEO1uy6mSgOfw/j5+XRRALoVlaoOjM37lDBW1jud4Wq2mpSny+4QK5W0mcLBCF/Q3fUgB9BolAcSnH+CPUPnG4gms43RBAYRYhvgRQF3RLo4qItWYKwmgw9fUCyC5jeLaXsVz+LXoqw4UqrZLzi4FE0BChLmdearsvrc5O6RY4f199FplyGVq727MkpTzjbXpqspx1OndX6Q2K3xJXZ/f70rN0jkzgIMVuqC/6cNxHKynLIDkKQUOa3i5ZXawjtMFBRBiGcwsgDpQAAnIQ07bHU44er1Gkw3y2uv3WhTP+WKXdy362sMPVNt93DqsSgDxEea+VBlhTv68zV1jfufw/j58pSLkMrXzRDhrLUtNDl/z7i/SkhOksLpHcp/S+n7V18YyOFihC/qbPhIBtEbdSxIj4JcfW20ZHNZxuqAAQiyDqQUQEWHsbpm1BRAAQEXjgKSDPOYMvDdGCfLaQP78dMddsDuCh8qW0/p0XNXSMq0R5uRiRSl4Au/vg5fLwe5wwkvLAy9TO5tZr0sAHbrqFUBaI0KR93lnY5ama2MVHKzQBf1NH47jYN0BrwB6naIAWnWgUNJmaA3Xb1awjtMFBRBiGcwsgMgQy3kogGBgZFrSQQYLDqBEXlmXcO1nqcr7cD7algt2hxO+PV6s2i4ZrCLYXpmVB3wR5r5XF2HuyPUaiV2lnBy8v/f/VOadpQmyTE1pH5CarOt8SG6tG6L1iK1YBwcrdEF/04cUQGqXyRrBwOh0XLYZocA6ThcUQIhlMLMAausRZxbyyrujXRxVRLIxn5qRLoPTGl55nJsL2bku2ert+DefUC+A5IlWA9nmo8WpjTA3MRXaLu/vvRfLAookEjKktdpBxr5LlbreBh++Wi25zzg3p+n6WAQHK3RBf9OH4zj45kC2TwBlUL23vH2Kl72DwcA6ThcUQIhliBcBdA8FELjdHknnePKm9vwy5PVKCTo/TPEKoK2nHmmy+3tfxDb+mFfIxcOvcf96n/oQ2/LNwdXNg5LveX/vPl+qas1+1ZNBPwE0MTUf9Jo9Fyt0DYZm5hYl9/lS5dK/WAYHK3RBf9OH4zj4Zr9XAP3uG7oCSJ5YelYhqXO8gXWcLiiAEMuAAogukW7MP9meK/jkXFaD5uvlg3950tD3Nt0Gu8MJ28+UarK763y5xO7AyLTfOct0hNiWl/lsZr3kO97fO398pGrA4vF4/N6ydvVPBr3m+wteAfTWukxN5ZaXPR6WtOBghS7ob/pwHAdrfQLozXV0BRAAaGqb4gGs43RBAYRYBjMLIHJz/f0KFEAA4mDc7nDCldwnmq+XD8jly9He8YWKTj1XpsluTkmHxK6SyHF8rz3EtrzMZzOUBdD2MyVekbJenUghbRbVKIcE5+HF3e9V2g50H7tDOUeRmcDBCl3Q3/QhBZCelx7hsu1MqdBeBNvTGC9gHacLCiDEMphZALV0jwkdQX7F02gXRxWRbsz55Vh2hxOu3m3WfL18QC6fleCTj+46X67Jrsvlhi99OYT4Y2FRmmyV/37NIf0C6OWvb4CbEBG8v7eeKga7Q320NTIgRKiZmdRzZZpsk7z77W3JfT5PzdNsI5bAwQpd0N/0ibYAynrYHlezxqHAOk4XFECIZYgbAVSJAghA3JBvdzjhso4ZoNfXpEs6V3nenDfWZoDd4YQ9Fys02ybzNtkdTsgsapd8ryfHEIC/aCPDw/L+TjnhzaT+3qbbqmxWN0v3AgXbbLz9rPeN7LvfqrNNIg88YfYBDQ5W6IL+pg/HcbBmX7amGWUjke/1jHewjtMFBRBiGcwsgJpRAPmRcrJE8InWPEAAAI0dI0EH5K/5BNL+y1Wabc8tuPxsZz1sF77/LNWbY2jdEfU5hgAA6tukZV6yNUf4jvf3puNFYHc44cOUnCCWRIbHpSGx912qDHguvyTlvc3ZmsrNs+5IUdwMaHCwQhf0N31IAaRn2asRBJtJjzewjtMFBRBiGUwtgLpEAVRQhQIIQLoH6Opd7TNAAABf75NmHK9pHhK+e3XVTbA7nHDoarUu20pL7Phoc3qSrAayW9viLTPv7w1HH/jE0Z2wyqrE1lPeAAsfpOgTQBuOPUQBhOgC/U0fjuNg9V6vAHp7Q3QE0ItL1b2ciQewjtMFBRBiGcwsgJ50jQqdQGFVT7SLo4pIN+ZzCy54+esb8PaGLMVQ02qQJy61O8Sw1S9/fQPsDiccvV6jy3Zxba+f7YFRb0S4j30R7DYef6jZrtwmH+2N9/e6w94s6h9vz1Vts7C6x8/u5LR/SGx+1u2PW9TNLsmRJ2A9er0GFl3mfKuLgxW6oL/pIxVA2vf9GcHZjPq4eWkSCqzjdEEBhFgGMwugpk5CAFWjADKStPstkg6W30/0q2VpYHc44Xia9uV1AACTCklR+f1EH227A3aHEzb9oD7JKk+g2Rre32sOepOsfpZ6V7XNx63Dfjb7hqf8ztv0Q7Hm2aVQ5b+Y06jbVjQxS/2OF9Df9OE4Dlb5BJCewCdG8KiuDwUQEhFQACGWAQUQXczSmHs8Hr9Bec8QJ/ysJ8kqAMCiyx1QrCzZ6k2yuvmEdgF01FkTVACt3O9d1vflrjzVNnuHpvxs3nnU6XfexuPeJWwfbVM/uyTnT9/dUbXcLtYxS/2OF9Df9OE4Dlbt8UZvfDdKAqi2Zcj0bYVasI7TBQUQYhniRQA9QAFkOEpChT9Op9cZahcA4MMtXgG05VSJZpuz84uw42yZxObM3KLg76/3ekNsO76/p9rmwqKyWOsdmpLk61l/1BvE4NMd6meX5IxOzPrd53HrsG570cJM9TseQH/TRyKAdER+NIK+YenLmZK6vqiUgwZYx+mCAgixDGYWQGTEsqIaFEBGE0wA/ZjZoNvu4avVikvLPkjxLivZevqRLrvyWaushx2Cv5d+7w2xLU/sGgo+x4/8cHx/TwiN/c1hb4CFcHP4yO+hN6hCNDFT/Y4H0N/04TgOVkZZAAH4txfxCtZxuqAAQiyDmQVQg0QA9Ua7OKowU2P+8oqbAQXQ+dvh7VEhI/jZHU5o7hqD9zZ7BdC2M6W67ZI2j1yvEfz91S5vhLkV+ws027x694miDwZHZwAAYM2hQs3L65RYeaDA9IMaM9XveAD9TR9SAP1BZV6xSPDRtlxTtxVqwTpOFxRAiGWIFwH0sBYFkNGczawPKIB+ymkKy3bPICexdzGnEX6z0iu4tp/VL4BySjoEm/suVQr+/mKnVwCtPlio2WagfUt85MFVBwo1L69TYvXBQhRAiCbQ3/ThOA5WfO8VQGoTK0cCPm2AWdsKtWAdpwsKIMQymFoAtaMAiiTj3FxAAXQlV1+OIZ7ZucWAtlPPlem2K18G91N2HZSVlcGnO7xvS785/ECX3UBlBRDzJmldXidn36VKFECIJtDf9JEIIJ3Jj43gyHVp4JeOvomolSWSYB2nCwogxDKYWQDVt4kCqBgFUEQINPC/fq85bNtp+S2KtnedLze0zDdzioQQ2xuOac8xBOCfd4M/PB4PLNtzX/fyOhJ5ZCe7wwn1bSNh2aSN2eq32UF/04fjOPjaJ4Dej6IAkqcUiOZsVCTBOk4XFECIZTCzAKprE/O0lDw2RxQcszXmgQRQ2v2WsG2TYbXJY/cFYwXQJ99lwJKt3v1FenIMAQC43B54Y216QH/YHU5YdUD78jo5l3P99xuZCbPVb7OD/qYPx3Hw9W6vAIp2oJITNx6btq1QC9ZxuqAAQixD3Aggk4QBNVtjHmiwn/6gLWzbIwqhn+0OMTGqXl5ddcvP5mtrvJ+lnNQeYptndn4RPkzJCeiTNYfCF0AAYl4hMw5qzFa/zQ76mz4cx8FynwD6MCUnqmUpqumVtBVuIjx/vIB1nC4ogBDLYGYB9LgVBVCkaegYgReW+g/2s4s7wrbt8Xj8NvIaIYCCzdSEE2EOAGAmyN4lvfuL5MhzfJgJs9Vvs4P+po9EAG2JrgCS73kcmZiNankiAdZxuqAAQixDvAigRyiAIsrxtFpJRxtOHiCSRZcbOvsmJLMe28MUKX/cEniWJtz9RQAA2cUdirbXHy0K2zYPGRGuunnQMLuRxqz126ygv+kTSwIIAOBiTqPQVuSWdgn5yeIFrON0QQGEWAYzCyBy03hpfX+0i6MKMzfmzd1i7p7RSWPfNKY/aBNs6w1UwHPpTlNAAbTvUmXYZR2dVF66p3d/kRLO+9IAEWbBzPXbjKC/6cNxHCzblQV2hxP+GAMC6GFtr19bFE9gHacLCiDEMphZANWgAKKO2+0BVwTWmeeVdQm/y8935oVla5ybEyKzyY/D16rDLuvCokvRdjj7i+Scz2qQ2N5/ucow25HE7PXbbKC/6UMKoCVboy+AqpsH/dqihUVXtItlGFjH6YICCLEM8SKAyhpQAJmZYuIt5kvLbxhiU0mk/HDjsSG2U38sg9+uvgU7z5UJtt9Ym26IbQBpgA/+mJ5dMMx+pMD6TRf0N304joOlggC6E+3iQHPXmF9bwc3EfluhFqzjdEEBhFgGUwugZhRA8cL07IJhM0A8AyPTfgODMxn1htgGAHC53JKle0YuPfF4PPDt8WKJ7XCXBtIA6zdd0N/04TgOlu70CqA/fRd9ATQ26Z+wejSOgiFgHacLCiDEMphZAJFT/+UNA9EujiqwMQ/M4WvVsGRrDnQamNG8q1c6k/LRNmMHLI0dYjJeo2aXSMy2th/rN13Q3/ThOA4cPgFkdHuiF3k78dAkicHVgHWcLiiAEMtgZgFU9YQQQI0ogBB/OM4/2arRXMxphCPXa8Dlchtu++0NmSiAkICgv+nDcRw4UnkBlBvt4gAAQP/INKw5VGiqtkItWMfpggIIsQzxIoAqUAAhCnAcBwfO3RPqiVnCpfN8vjNPMqgZGpuJdpGCgvWbLuhv+sSiAAIAyK98KmkrJqbmo10kQ8A6ThcUQIhlMLUAakIBhASH9/eTjgFoaB8xXY6MpbJodqsPFka7SEHB+k0X9Dd9OI6Dr3wC6OPtsSOACqt6/Ga7c0rCT1gdbbCO0wUFEGIZzCyAKpsGhIa+sgkFEOKP2f1NJvs1w9IWs/vblBuGkwAAIABJREFUbKC/6UMKoE9iSADllCgnaI7E0lyaYB2nCwogxDKYWQBVNIoCqKppMNrFUQU25nSJB39/vS9fMqA5cr0GACAmw2LHg7/NBPqbPhzHwZc7vALo0x13o10cgUd1fYoCqLrZHH1jILCO0wUFEGIZ4kYAPTFHI4+NOV3iwd9bTpX4DWr4/EOX7jRFu3gS4sHfZgL9TR+vAMqMOQHkdnvghaX+AijWZ41DgXWcLiiAEMtgZgFUjgIICUE8+HvPxQrFQU0sDm7iwd9mAv1NH47j4AufAPosNXYEEIA3f5hSG2G2vY8kWMfpggIIsQxmFkBlDf1CA1/TMhTt4qgCG3O6xIO/B0b9E7qiAEIA0N/RgOM4+GK7VwB9npoX7eIoIg+fzwdEiPUokkpgHacLCiDEMphZAJXWiwLocetwtIujCmzM6RIv/l5YdKEAQvxAf9NHIoB25kW7OIqMTswGbC++Ofwg2sXTBNZxuqAAQiyDmQVQCbHps64NBRDiTzz5e2Ak8EzQOxuz4HR6XbSLGFf+NgPob/pwHAef+wTQF7vyol2cgNx51Bl05nhh0RzR4bCO0wUFEGIZzCyAimt7hca8oX0k2sVRBTbmdIk3fwcb0NgdzqhHhos3f8c66G/6cBwHn37nFUCO7+9FuzgBIYMEKR0HrlRFu4iqwDpOFxRAiGUwswAqqhEFUGMHCiDEn3jzdygBlHKyJKrlizd/xzrob/pwHAcfb80Au8MJy/bcj3ZxAjISZBlcLC6fDQTWcbqgAEIsg5kF0INqMfN1U+dotIujCmzM6RJv/r6Q3RhyUDO/4Ipa1Kd483esg/6mD8dx8NEWrwBasb8g2sUJiMutHBGOPMyQJBXrOF1QACGWwcwCqLBKFEDNXWPRLo4qsDGnS7z5u7NvIuSghj+ikSMo3vwd6yj5m5tZgHVHi4SEuYixcBwHS1LSwe5wwqoDhdEuTlBCtRGfp+bFfIhsbFPoggIIsQxmFkD5lU+FhrylGwUQ4k88+vvOo044m1EPN/JbVQkhmpng49HfsYySvw9eqRJ+961Pzdeuxzocx8GHm70CaM2h2BZAtS1Dkrbg1K06v/ZhnJuLdjGDYsY2ZX7BFe0i6AYFEGIZzCyA7ld0C414W485ym/GxtzMxLO/OzTMBrnd4lvekYlZ+DGzISKD43j2dyzC+3tsfAJuFrTCyZuPJb/3yqaBaBcx7uA4Dj7Y5BVAZggpffXuE1h5oABuFbTC7PyiX9swMDId7SIGxUxtyvD4jMS3iyZYYigHBRBiGcwsgPLKRQHU3jsR7eKowkyNeTwQz/7uG55SLYBGJ2aF677elx+xTdDx7O9YhPf31dwGU290NwNutwfKGwfAmdcIr6xIA7vDCRuOPYx2sTTz6qqbppoFMkObsuhywzFnrZ9fl34fu0EyAoECCLEMkRBA7b0TcPBKVcQb1btlXUJD09GHAgjxJ5797XJ7YMnWO6pF0JmMeknoeBRA5sbl9kB5/VPIKygJ+ntfWDTvcpxYQmnJ6bfHi6NdLM0ovTiJdvTIYMRSm9KvkIutoWMELt1pipsXECiAEMtgtAAa5+Ykf/yPW4clb595GjpG4HR6nWTmpmeIgy9334NjzlrweDwwNbMAs3OLAe+VWyomeuvqnzSk/JEmlhpzKxDv/p6dW4S+4Sm4R8yGajmMJt79HUso7ecIdOz9qTLaxTU18wsuRb++uS4j2kXTzMKi2+85Vh+M3b1MfJsyODwGk9PzUVtWFiwR9Xubs1EAIYjZMFoAfXu8WLERWHS5hTeRBVVi8IIXl6UBAEBX/6Tk/MqmAXh11U343TcZMDWjnNwxp0QUQN0DKIAQf6zk718t8y7L2fSD8t+g0mH0Zl0r+TvaaBW7M0FeJiGB6eqflASWMPsAF0C57sRqSGyO4+BWTpFkzDDna7fcbo8kit3w+AxUNg3AwqJxz7Kw6IbS+n74eHtuTLxkijQogBDLoFUAhVpOEagROJ0e+G3la2vS4ZWV0nXJn6XeFX4+n9WgeK/s4g7hnJ5Bcwy4cIBIF6v6W23nbPSLg2j5+2J2I+y5WCEMjKyA1oFYVdMg3K/ohpK6vmgX3TA8Ho8kwIfRIZ2v32uOuwEuAMDQ2Izfc6w7WhTtYinS8XQoqP+X7rkPLt8LVvLzlu4xyK98GvaM0U85gZe3hTq2nSk1yAv0QAGEWAYtAij1XBnYHU64mN0IAABnM+rhwJUqePfb297Pc0InadR7uNz+HVvWQ1EA9Q5NGd4QRAKrDsijhVX9/VC21yfQkfWww9D7RsPfbT3jwvMcT6uldt9oE057apYlw8EYHp+BD7fkwAcp2TA5PQ/bzpSC3eGE0vp+w+6hxpdmpXtg0u9ZnnTFVkJxl8sNf9wSeHkZeaScVN4Ll5bfAtz0PIwoLMUnWXS5YXrWu9qEHFuEc3ABVq/EMiiAEMugVgCpySodyWN4fEZSnoHRaVj6/X3h+75hFECIP1b2t8vtgcLqnqB/V2YVQB6PB24WtMK1POkb+o+23ZGcd/LmY1i+Nx8GRqZhdn4RaluGTBealpueh2POWjh/u1FYxlZUE/z3GuqtdUZRe3QfKkzk+zH4l3OB+otQDI3NwPV7LX7XheqXfuVbwm1W5M/z6Y670S4SAHjFyPazpYaPI5SEv9vtkQie36z0j5Kn54gVX2oFBRBiGdQKoMFR/ylzmkeHLMy1/PtYz2XAY+UBeTRAf4cexBkJDX8vLLqDRr/jGZucU/z+wJWqiJWNx+PxwPmsBjiTUR/2sqwNxx5Kyh5odu/djVnwYUoOHL5WHXDTPn9sPmGe6GUulxt2XyiHg1eqBF8eddYEfb53N2bBhmMPobFjBIbGQosh/rqXV9yEho4R+Cz1Lqw/WhT0HnaHeRJwB2J0Ytbvmfop96WLLjdkFrXD9XvN0Dc8JVn+bvQhj9qXW9oFv/76RkTuFWrGKVZBAYRYBjUCyOOJ7uyP3eGEqidiNvuyhn6/70cnzdHY4ICcLuhvgDMZ9UH/ttwKy0v1Eml/y6NMKh0nbz6GHWfLgp7T0D4SkfLxPKrrE+4VLAIbvzfy/c3ZsLDogompeegemBT2LrT3qkt2u/ZQgd9ym8etw0GvMQsZRe1CmYtqeqBnkIPPU/M09R8NHeLv+3R6HWw5VQKT0/OwsOiCs5nB/z7kx9DwWFy1KfLne9w6HPF7dvRNwOn0OugemIStpx5RHUuMTMxCzxAHdx516rbRPTAJJ2489vv87Q2ZEfddpEEBhFgGNQJocnpeVyMxzs3B2sMPDGm01h56AG63B0YU3ljZHU5h7W6sgwNyuqC/vctFQ/195ZZ2AgDA3IILDlyuUr1EyuPxwOjELHg8Hpicno+ovxs6RgwbBL2/Odvw8vF0D0z6ReK7V94N1/KaYWHRDSknS+DL3fdCirlle+6repYTV/ID+tvt9gRsg3f6lo2lnCyBdzZmQUHV05DPxotll8sN1c2D0NgxomuGa2JqHgC89Se/8im09Uj7n+nZBTiX1QBVTYOw52KFUObfr8/U/TufmVv0Wyqn54i3NoXm3waA93dr1N9xNI77Fd0AoJxP6alJgjEFAwUQYhlCCaCtp/W9neH35AQatAQLK6rnMPItdiSJt84z1kF/e+noUzeTQB6BkgtXPRmE1HNl0NU/KVmK9MJSJ1zOqTfU33MLLnhnY1ZEBjKR2DdY0xI8YhV5pP4Y/mDc7nDClfQHIf396Q51y4rkv/Nxbg7q20bgO6IfeNI16nfd62vS4VZhmyofrTsiLi27kC0GzuGXqsmTTb6w1Pjfvd7jYnZj3LUphVX++8mMoqZ5CD7engsZRe0wPbsAm08Uw5vrMqL+e9R77LtUKRH8J28+lnwXD6AAQixDMAE0O7eoq5H403fiRmQyQpPd4Y3S1Dc8BQuLbnh11S2wO5yShKbfX6iAY85aTff7cEsOzfYhLOKt84x10N8igaIkBTryyrsV7fDf/2HTbcXrjv103zB/k7m+InF8sStPcr/B0Rk4er0GCqt6gpZreHwGZueleXVGJ5VnpyN9pN0uCunvgdFp2HepMqSt3NJOX0hhN3g8HkXhxOebUjpySjqDlmNiKvBqgne/vQ3Ftb3wic58K5E8Wp+OQ3ZxB8wvuOKuTVHaB8TPcoQLafONtelh/Q4mJiZh7eEH8OJSb6S/NYcKqf3+V+wvgLzybsUXrePcHJTW9xuaeyiaoABCLEMgAeTxeGCdik2g5NHWM+63JEIeapPMO/J0kIOiml4hhn9bz7iQ16GxY0T1VLnRuR8iSbx1nrEO+lvE4/HAiv0F8NLyNCh53Kfqb6u0vh9cbg+k3W+Bo84acKuMBlnf2gf7L1dBc1d4m8TVzBS/tDzwgFzNcfVuM4xzcwAAsOqAOKg6nlYLK/YXwOn0OkmZnnSNwotLvdnfF11ucLu9bda7EZqpCvk7Ki1VVb/V7OW8kd8q/EzuvdFyZD3s8MvHNDw+AwevVEVsw3kkjzWHCiXPEm9tylyAgBlnM+o1D+rP3/bO6C3fm++XlyecIzP3IXAcB263R1g+2dAuXV1y6U4TnJXtdyTrs9rjpeVpcDajHu6Vd/v+f8M0S+yNAAUQYhkCCSD5zA1/HL5aLfzc1jMuhIwMFA3H4/EIy+jKGwY0/zGGaqxCJWaNNeKt84x10N9S3G6P0JnrGYj8oLDxN9Shl+FxdZEnAQBO3QqcaFnN8fqa9KCh/k+n10FD+wi4XG74fKe4Af/A5Sr4ICVbyIVG+9h47IGm+q13SbPWY9MPxfB0kIM9FyugtmUIvjFoL6jS0dU/GVHxKe/b4q1NCSaM7wWYBdZqJ5zj+t2GkP7mZ2Y8Ho+Qj/CHG48BACCvrCuo/ZUHCiT/J1+odvVPag6pbnZQACGWIZAAKq33j7Rmd3gDG/QMcoLwmJpZiKgIeWtd4E2vRmexp0G8dZ6xDvo7MDQGwnaHvv15C4tuVbZ/ymkCgMBvse0O75theVACpeNWgfa3xbQOcr8Qmf8sr7RNU/0O9GIrEkek9m7xx+4L5VDbMiQ8W3vvRMBlmWqPopoe+N034h6V1HNlfj6MxzaFm56H7GL/5J/fnX6k2obeJfPygwz+8fH2XJiYmAzb38FEEDc9D59sz4UXl2rPHxWPoABCLAMpgBYW3eByBR54bDtTSv2PMVjCPzMSj51nLIP+DgytgXD6A3Wb40nS7reosu0ixNXw+AxsPf0Icko64MOUHOGc3iFvsAM9s1excLy41Om3FKmkrg8Kq3s01++hsejmczPqKK3vD/msoZLFyo9RX96WhUU3rD5YCCv2F/gt5QOI7zZF7pP1R4tUXXezoBVeW31L8+9x6+lHUNbQD7Nzi/D2hiz4zcqbMDA6DZPT89DcPQYej8cQf3s8HmhoH5FEEST/rjweD7hMliA5UqAAQiwDL4BeXxM8+zE3PR+1P0hyyQl5mJF47jxjEfR3YPZfrhL+llJ/9L7pJvfAGHloSQrY3D0W1Fbv0BTcKmwT9u0owScLXbL1jiCSFhZdYe8XUntsPlHsF+Vt9UHtvl15oADmFQbhPHrqN29bTVCEWDzeXJeh6jk9Hg/svlAu+P7q3Wa/Wb4fMxtgYdGtaa9LPLcp8qAXv1l5U/hu0eWGmblFxev0/i5J5hZcinttjPb39OwCXLrTBDXNQ6FPtiAogBDLwAugV74OPjCIJosuN2QXd0iWsdS3RTaRYaSI584zFkF/B4abnoeDV6rg8LVqmPIl0VQKcRzqyC0NvsaePx7W9kru3zc8BSknS/wihwULe5yhYTapo3fCLzno7NwilaVuPORnY5PB8/68uS4DuJkF2HOxAm7kt6paOhhu/eYTsYZzvLLypuqcReRxQGUqhC925UHf8BRMzSxAbmmn4qxMMFwyP/YOefO3/PrrG0HFZSDiuU1RevkwMDoNC4tu+OMW76wqmSh1dn4Rjl6v0fy7/zw1DwZH1S03i2d/xyIogBDLoEYAZT1sj/bfJAB43+j1DU+ZKuqbHGzM6YL+1o7Wwczk9HzAWVr5QWaAJwdON/JbAQCgvHFAcYC9sOiCjr4Jw/72W5+OR0z8vLFWnKF4WNsLqw4UCoPGwdEZSH/QBm0947DmUCH8mNkA07MLkPWwA/pHpjU/R7j1m29Ta5qH4KXlyhHayJmiTT8Uw/yCC+YXXPCorg9qW4bA5XLDpTtNmv3Ew0fb4g+X2wPnbzfCnosVugSKGmbnFmFR55InK7Qp5O/js1T/UOhtPeOqBaz8GJsMPHOrhBX8HUugAEIsgxoBpPWNGxIYbMzpgv7WzgHf0rjX1oTO2/HG2gxwuz3wdJCDL3blwSfb70DK0Tshr/twS47fZ5+nKosorQMmNRgZold+VDcPGl7eQBhdvxddbnh1lbgcms/pxs0sQH7l04BLoLRugF+2575wrdvtgdQfy2DVgcKgyxpjBSu0KaF+f0u2+v/9qjnS8ls0l8UK/o4lUAAhlkGNAEKMAxtzuqC/9cEvGwrUJnT2TUBRTY/frAXv7/5B7UvplI5NPxRH5vmCBHshj67+ScXPg+VIo0kk6ndH7wQs2ZoDX+6+B209/gmyA9E3PAXrg/jlZkErvL85G15c6oQnXaOGlZc2VmhTwv273Xr6kRBUYmhsBl5ddRM+SMnWtZfYCv6OJVAAIZYhkACanl2AWwWtAfP7IPrAxpwu6O/w4HNq2B1OyCvrgtzSzqDJTUl/77lYEfZAqvWp+gG4FoIldP0gJRse1fXBg+oeAADIr3wqfHc6vQ7yK57COCfu5/l4+//f3t1HyVXXdxy/QZoCJgHKrCmzm93Ze7+fj4pYT1W0VnzC5yqnR3tsUdpa1OpROfVZ1Noix6KnPiC2cMTQI2oFRUSo+ASHgAQMD0ZReQgiAgYJURQ3GDYkZHf6R+5whskmmbvZ/SUz836d8/tjf/ubmd98zt3fne/eufeuaH7x2zc3X/HebzQvX712Xua7I3vj9r15y9bmWz62ovkPJ36nedL/XP1wTtfcsK65afNDlS6IsTfaGzOfa+889Xuz/pu9+zfb57Lloa2zuhx+szkYee9NKIAwMGYqgN700Uv39N9g32IxT4u8d8/WqenmZavXNu9ct6Gr8e157+joSZU2n3b0mp03bJ6amm7+91evb55yzg8fcanc/zr3+uZxH764eec927KZ7Tklu2Nv3b6nprZdVnjLQ1ubJ591bfNjX/zBdhcj6FV7a+Zz6de/e2DWf7MbJ7e/ktvuGIS89yYUQBgY7QXQ37zvoubXL/95T3wPu1exmKdF3ml15n373RO7df+d+dT+Ossv+Glz+YU/bf68x454s32nNyiZV/1b/cSXVjevKo+azqVByXtvQQGEgdFeAN2/B+/1MyhYzNMi77R2lHfnlb66aVf++FfzOtfW6xz34Yvn9XXmE9t3eoOS+Uc+f+1e8c+KQcl7b0EBhIHRKoDefeplPX156V7BYp4Weae1o7x3dXPTznb0uy6c9TkD3brwituaJ5x2ZXPt+vvn9XXmE9t3eoOS+YNbtjZvuO3e5g233dvc9OBDzWtvvKd5yjk/nPHvdT7vyzcoee8tKIAwMFoF0H339dZXP3oVi3la5J3WjvKenp5++PLKHzzj+w9/cDr1yz9qnlZedvvod2270el5K26dl0tf9yO27/QGPfP2c/u+tuLWGS96MJcGPe/UKIAwMFoF0MTE/FxtCY/EYp4WeafVbd5X/Oiu5hnn/6T5wKa5PWF60LB9p0fmzeZvJyZndUnr2SDvtCiAMDAogNJiMU+LvNMi77TIOz0yT4u806IAwsCgAEqLxTwt8k6LvNMi7/TIPC3yTosCCAODAigtFvO0yDst8k6LvNMj87TIOy0KIAwMCqC0WMzTIu+0yDst8k6PzNMi77QogDAwKIDSYjFPi7zTIu+0yDs9Mk+LvNOiAMLAoABKi8U8LfJOi7zTIu/0yDwt8k6LAggDgwIoLRbztMg7LfJOi7zTI/O0yDstCiAMDAqgtFjM0yLvtMg7LfJOj8zTIu+0KIAwMCiA0mIxT4u80yLvtMg7PTJPi7zTogDCwKAASovFPC3yTou80yLv9Mg8LfJOiwIIA4MCKC0W87TIOy3yTou80yPztMg7LQogDAwKoLRYzNMi77TIOy3yTo/M0yLvtCiAMCNJL7f9A9v3S7pZ0tt2Nj4iniFppaQJSbdFxMltz/WvkjZJmmxrmyRNF0WxLMuyzPbTbK+QNGH7ftvflvT41nMMDQ0tknSWpLsk/UbSRWNjY+NV3hMFUFos5mmRd1rknRZ5p0fmaZF3WhRA2E5EHCbpQUmvybJsoaSnS/p9URR/N9P4er1es32fpPeMjIzs32g0Hmf79og4YSev8XZJV2RZlo2Ojh4saYOk92ZZ9kcRscT2uZJubY2XdLaklbaHa7Xa4ohYLummLMsWdPu+KIDSYjFPi7zTIu+0yDs9Mk+LvNOiAMJ2JH1K0vc6+j4uaeVM4yPi7bbv6Oh7q6S1M40vimKZ7d+1jvCMjY0dKum4jscfLWlqZGRk/+Hh4UNsPxQRz2n9Ps/zA21vKYriqG7fFwVQWizmaZF3WuSdFnmnR+ZpkXdaFEDYju2rIuIT7X0R8SpJk9kMR1wkfUnS19r78jw/QtLU2NjYoTOMP1vSp3f0+o1GoyHpYknnleNfKGlqaGhoUcfz3GT7/d2+LwqgtFjM0yLvtMg7LfJOj8zTIu+0KICwHds/s/2B9r48z58vaSrP8wM7x0u6OCKWt/dFRCFp2vaTOvoPs71xpsIoz/NR25slTdk+t16vH1A+/6ttb5lhnldK+lS374sCKC0W87TIOy3yTou80yPztMg7LQqgARQRL5Y0LWmqrbV+Xmn7ls4jK7ZfIGkqIrbbUMqjNZ9t7yuKIiRNj4+P/1nH2LMkfW5n88vzfFTSeZKuzbJsn7IA2tw5zvZVtk/p9n23CqB169Y1JyYmaPPc1q9f31y9enVz/fr1e3wug9DIm7z7uZE3mfd7I++0bd26dRRAeKSyCPpke5/tYyQ9MNN421+UdH57X0T8haSp8fHxpW3dC8urvP3VrubQaDQOkjQdEc8tiuIoSVO1Wm1xx+vesrMLLXRatWrV8OrVq5s0Go1Go9FoNNqqVauGu/0ciT4XEZ9oXaGtRdKnba+Yabzt423f2fEc75D0i47neFl5HtHCjscfY/sn7X2NRuNPy8tkP3N0dPTg8oIHz2v9vl6v1yRtzfP8yApvbcGqVauGr7nmmiU0Go1Go9FotMFtZfHT9dWE0efyPJekyYg4NsuyhbafVd7f5+WtMbbXFEXxkizbdhlr2/dGxAkjIyP753n+RElrbR/f/rwRcaKkH3e+nu3h8vk/XK/XD2g0GgdFxBds39loNPbLsm1fnSu/8jbc9vvr5jkKAAAAAIMgIl5k+yflDUtvi4jXt/++PB/ob1s/F0XxFEmrypuc3tV5EYXyMWfYvnSm18vz/KmSrrC9sbzR6Tfbb4TaaDT2i4jltu8rb5R64UwXUgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALM3NDS0SNJZku4q7yd00djY2PgOhj/K9idt317eX+iyiPjzpBPucVXyjoglkj5Tjp2wfZWkZ6eecy+ruH0/zPYrJE3b/scU8+wXVfO2fYykmyVtsr1G0mtSzrfXVcl7bGzsUEnn2L5H0u8lrYqIF6eec6+LiMMkXS9pchdD2V/OgW7zZn85Nyps3w9jf4meJOlsSSttD9dqtcURsVzSTVmWLegcGxEn216T57nLG63+u6T1tVpt8R6Yek+qkrftr9q+yvZwRPyxpP+QNDE8PHzIHph6T6qSd0utVlts+87yBsIs6BVUyVvSC8sPhi/IsmyhpJdLurFer9fSz7w3VVxPLpP0zUajcVCWZftGxFslbSqKYln6mfemiHiV7XW2v9LFB3L2l7upSt7sL3dflbxb2F+iJw0PDx9i+6GIeE6rL8/zA21vKYriqI7hC2z/1vZr2/r2sX1PRLwuzYx7W8W8M9un5Xl+ROvnpUuXPlrSdES8KNWce1nVvFtsn277tPI/tyzoXZrF9n1pRJycdpb9YxZ5b2xfv8sPidNFUbw01Zx7naTj8jwftf3aXXxAZH85Byrkzf5yDlTJu4X9JXqS7RdImhoaGlrU3i/pJtvvb+8riiLKneXhHWO/JemzKebb66rkPZOiKA6XNBURh83fLP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     ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x7f555b793450>\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib nbagg\\n\",\n    \"import seaborn as sns\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import multiprocessing\\n\",\n    \"import pickle\\n\",\n    \"import tmevaluator\\n\",\n    \"import bluepyopt as bpop\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# Set plotting context and style\\n\",\n    \"sns.set_context('talk')\\n\",\n    \"sns.set_style('whitegrid')\\n\",\n    \"\\n\",\n    \"# Load and display in vitro trace\\n\",\n    \"with open('trace.pkl', 'rb') as f:\\n\",\n    \"    u = pickle._Unpickler(f)\\n\",\n    \"    u.encoding = 'latin1'\\n\",\n    \"    trace = u.load()\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"ax.plot(trace['t'], trace['v'], label='in vitro')\\n\",\n    \"ax.legend(loc=0)\\n\",\n    \"ax.set_xlabel('time (s)')\\n\",\n    \"ax.set_ylabel('soma voltage (V)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We now estimate the parameters of the Tsodyks-Markram model using BluePyOpt.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"trec = 415.56\\n\",\n      \"tfac = 163.12\\n\",\n      \"ase = 1877.15\\n\",\n      \"use = 0.52\\n\",\n      \"rinput = 51.82\\n\",\n      \"tmem = 32.79\\n\",\n      \"tinac = 1.27\\n\",\n      \"latency = 4.21\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Parameters to be fitted as a list of: (name, lower bound, upper bound)\\n\",\n    \"optconf = [('trec', 1.0, 1000.0),\\n\",\n    \"           ('tfac', 1.0, 1000.0),\\n\",\n    \"           ('ase', 100.0, 10000.0),\\n\",\n    \"           ('use', 0.01, 1.0),\\n\",\n    \"           ('rinput', 10.0, 300.0),\\n\",\n    \"           ('tmem', 10.0, 100.0),\\n\",\n    \"           ('tinac', 0.1, 30.0),\\n\",\n    \"           ('latency', 0.0, 10.0)]\\n\",\n    \"pnames = [name for name, _, _ in optconf]\\n\",\n    \"\\n\",\n    \"# Create multiprocessing pool for parallel evaluation of fitness function\\n\",\n    \"pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\\n\",\n    \"\\n\",\n    \"# Create BluePyOpt optimization and run \\n\",\n    \"evaluator = tmevaluator.TsodyksMarkramEvaluator(trace['t'], trace['v'], trace['tstim'], optconf)\\n\",\n    \"opt = bpop.optimisations.DEAPOptimisation(evaluator, offspring_size=500, map_function=pool.map,\\n\",\n    \"                                          eta=20, mutpb=0.3, cxpb=0.7)\\n\",\n    \"pop, hof, log, history = opt.run(max_ngen=50)\\n\",\n    \"\\n\",\n    \"# Get best individual\\n\",\n    \"best = hof[0]\\n\",\n    \"for pname, value in zip(pnames, best):\\n\",\n    \"    print('%s = %.2f' % (pname, value))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally we compare the original in vitro trace with the one generated by the best model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"/* Put everything inside the global mpl namespace */\\n\",\n       \"window.mpl = {};\\n\",\n       \"\\n\",\n       \"mpl.get_websocket_type = function() {\\n\",\n       \"    if (typeof(WebSocket) !== 'undefined') {\\n\",\n       \"        return WebSocket;\\n\",\n       \"    } else if (typeof(MozWebSocket) !== 'undefined') {\\n\",\n       \"        return MozWebSocket;\\n\",\n       \"    } else {\\n\",\n       \"        alert('Your browser does not have WebSocket support.' +\\n\",\n       \"              'Please try Chrome, Safari or Firefox ≥ 6. ' +\\n\",\n       \"              'Firefox 4 and 5 are also supported but you ' +\\n\",\n       \"              'have to enable WebSockets in about:config.');\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\\n\",\n       \"    this.id = figure_id;\\n\",\n       \"\\n\",\n       \"    this.ws = websocket;\\n\",\n       \"\\n\",\n       \"    this.supports_binary = (this.ws.binaryType != undefined);\\n\",\n       \"\\n\",\n       \"    if (!this.supports_binary) {\\n\",\n       \"        var warnings = document.getElementById(\\\"mpl-warnings\\\");\\n\",\n       \"        if (warnings) {\\n\",\n       \"            warnings.style.display = 'block';\\n\",\n       \"            warnings.textContent = (\\n\",\n       \"                \\\"This browser does not support binary websocket messages. \\\" +\\n\",\n       \"                    \\\"Performance may be slow.\\\");\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.imageObj = new Image();\\n\",\n       \"\\n\",\n       \"    this.context = undefined;\\n\",\n       \"    this.message = undefined;\\n\",\n       \"    this.canvas = undefined;\\n\",\n       \"    this.rubberband_canvas = undefined;\\n\",\n       \"    this.rubberband_context = undefined;\\n\",\n       \"    this.format_dropdown = undefined;\\n\",\n       \"\\n\",\n       \"    this.image_mode = 'full';\\n\",\n       \"\\n\",\n       \"    this.root = $('<div/>');\\n\",\n       \"    this._root_extra_style(this.root)\\n\",\n       \"    this.root.attr('style', 'display: inline-block');\\n\",\n       \"\\n\",\n       \"    $(parent_element).append(this.root);\\n\",\n       \"\\n\",\n       \"    this._init_header(this);\\n\",\n       \"    this._init_canvas(this);\\n\",\n       \"    this._init_toolbar(this);\\n\",\n       \"\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    this.waiting = false;\\n\",\n       \"\\n\",\n       \"    this.ws.onopen =  function () {\\n\",\n       \"            fig.send_message(\\\"supports_binary\\\", {value: fig.supports_binary});\\n\",\n       \"            fig.send_message(\\\"send_image_mode\\\", {});\\n\",\n       \"            fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"    this.imageObj.onload = function() {\\n\",\n       \"            if (fig.image_mode == 'full') {\\n\",\n       \"                // Full images could contain transparency (where diff images\\n\",\n       \"                // almost always do), so we need to clear the canvas so that\\n\",\n       \"                // there is no ghosting.\\n\",\n       \"                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"            }\\n\",\n       \"            fig.context.drawImage(fig.imageObj, 0, 0);\\n\",\n       \"        };\\n\",\n       \"\\n\",\n       \"    this.imageObj.onunload = function() {\\n\",\n       \"        this.ws.close();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    this.ws.onmessage = this._make_on_message_function(this);\\n\",\n       \"\\n\",\n       \"    this.ondownload = ondownload;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_header = function() {\\n\",\n       \"    var titlebar = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\\n\",\n       \"        'ui-helper-clearfix\\\"/>');\\n\",\n       \"    var titletext = $(\\n\",\n       \"        '<div class=\\\"ui-dialog-title\\\" style=\\\"width: 100%; ' +\\n\",\n       \"        'text-align: center; padding: 3px;\\\"/>');\\n\",\n       \"    titlebar.append(titletext)\\n\",\n       \"    this.root.append(titlebar);\\n\",\n       \"    this.header = titletext[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(canvas_div) {\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_canvas = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var canvas_div = $('<div/>');\\n\",\n       \"\\n\",\n       \"    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\\n\",\n       \"\\n\",\n       \"    function canvas_keyboard_event(event) {\\n\",\n       \"        return fig.key_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    canvas_div.keydown('key_press', canvas_keyboard_event);\\n\",\n       \"    canvas_div.keyup('key_release', canvas_keyboard_event);\\n\",\n       \"    this.canvas_div = canvas_div\\n\",\n       \"    this._canvas_extra_style(canvas_div)\\n\",\n       \"    this.root.append(canvas_div);\\n\",\n       \"\\n\",\n       \"    var canvas = $('<canvas/>');\\n\",\n       \"    canvas.addClass('mpl-canvas');\\n\",\n       \"    canvas.attr('style', \\\"left: 0; top: 0; z-index: 0; outline: 0\\\")\\n\",\n       \"\\n\",\n       \"    this.canvas = canvas[0];\\n\",\n       \"    this.context = canvas[0].getContext(\\\"2d\\\");\\n\",\n       \"\\n\",\n       \"    var rubberband = $('<canvas/>');\\n\",\n       \"    rubberband.attr('style', \\\"position: absolute; left: 0; top: 0; z-index: 1;\\\")\\n\",\n       \"\\n\",\n       \"    var pass_mouse_events = true;\\n\",\n       \"\\n\",\n       \"    canvas_div.resizable({\\n\",\n       \"        start: function(event, ui) {\\n\",\n       \"            pass_mouse_events = false;\\n\",\n       \"        },\\n\",\n       \"        resize: function(event, ui) {\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"        stop: function(event, ui) {\\n\",\n       \"            pass_mouse_events = true;\\n\",\n       \"            fig.request_resize(ui.size.width, ui.size.height);\\n\",\n       \"        },\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function mouse_event_fn(event) {\\n\",\n       \"        if (pass_mouse_events)\\n\",\n       \"            return fig.mouse_event(event, event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    rubberband.mousedown('button_press', mouse_event_fn);\\n\",\n       \"    rubberband.mouseup('button_release', mouse_event_fn);\\n\",\n       \"    // Throttle sequential mouse events to 1 every 20ms.\\n\",\n       \"    rubberband.mousemove('motion_notify', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    rubberband.mouseenter('figure_enter', mouse_event_fn);\\n\",\n       \"    rubberband.mouseleave('figure_leave', mouse_event_fn);\\n\",\n       \"\\n\",\n       \"    canvas_div.on(\\\"wheel\\\", function (event) {\\n\",\n       \"        event = event.originalEvent;\\n\",\n       \"        event['data'] = 'scroll'\\n\",\n       \"        if (event.deltaY < 0) {\\n\",\n       \"            event.step = 1;\\n\",\n       \"        } else {\\n\",\n       \"            event.step = -1;\\n\",\n       \"        }\\n\",\n       \"        mouse_event_fn(event);\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    canvas_div.append(canvas);\\n\",\n       \"    canvas_div.append(rubberband);\\n\",\n       \"\\n\",\n       \"    this.rubberband = rubberband;\\n\",\n       \"    this.rubberband_canvas = rubberband[0];\\n\",\n       \"    this.rubberband_context = rubberband[0].getContext(\\\"2d\\\");\\n\",\n       \"    this.rubberband_context.strokeStyle = \\\"#000000\\\";\\n\",\n       \"\\n\",\n       \"    this._resize_canvas = function(width, height) {\\n\",\n       \"        // Keep the size of the canvas, canvas container, and rubber band\\n\",\n       \"        // canvas in synch.\\n\",\n       \"        canvas_div.css('width', width)\\n\",\n       \"        canvas_div.css('height', height)\\n\",\n       \"\\n\",\n       \"        canvas.attr('width', width);\\n\",\n       \"        canvas.attr('height', height);\\n\",\n       \"\\n\",\n       \"        rubberband.attr('width', width);\\n\",\n       \"        rubberband.attr('height', height);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Set the figure to an initial 600x600px, this will subsequently be updated\\n\",\n       \"    // upon first draw.\\n\",\n       \"    this._resize_canvas(600, 600);\\n\",\n       \"\\n\",\n       \"    // Disable right mouse context menu.\\n\",\n       \"    $(this.rubberband_canvas).bind(\\\"contextmenu\\\",function(e){\\n\",\n       \"        return false;\\n\",\n       \"    });\\n\",\n       \"\\n\",\n       \"    function set_focus () {\\n\",\n       \"        canvas.focus();\\n\",\n       \"        canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    window.setTimeout(set_focus, 100);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items) {\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) {\\n\",\n       \"            // put a spacer in here.\\n\",\n       \"            continue;\\n\",\n       \"        }\\n\",\n       \"        var button = $('<button/>');\\n\",\n       \"        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\\n\",\n       \"                        'ui-button-icon-only');\\n\",\n       \"        button.attr('role', 'button');\\n\",\n       \"        button.attr('aria-disabled', 'false');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"\\n\",\n       \"        var icon_img = $('<span/>');\\n\",\n       \"        icon_img.addClass('ui-button-icon-primary ui-icon');\\n\",\n       \"        icon_img.addClass(image);\\n\",\n       \"        icon_img.addClass('ui-corner-all');\\n\",\n       \"\\n\",\n       \"        var tooltip_span = $('<span/>');\\n\",\n       \"        tooltip_span.addClass('ui-button-text');\\n\",\n       \"        tooltip_span.html(tooltip);\\n\",\n       \"\\n\",\n       \"        button.append(icon_img);\\n\",\n       \"        button.append(tooltip_span);\\n\",\n       \"\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fmt_picker_span = $('<span/>');\\n\",\n       \"\\n\",\n       \"    var fmt_picker = $('<select/>');\\n\",\n       \"    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\\n\",\n       \"    fmt_picker_span.append(fmt_picker);\\n\",\n       \"    nav_element.append(fmt_picker_span);\\n\",\n       \"    this.format_dropdown = fmt_picker[0];\\n\",\n       \"\\n\",\n       \"    for (var ind in mpl.extensions) {\\n\",\n       \"        var fmt = mpl.extensions[ind];\\n\",\n       \"        var option = $(\\n\",\n       \"            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\\n\",\n       \"        fmt_picker.append(option)\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add hover states to the ui-buttons\\n\",\n       \"    $( \\\".ui-button\\\" ).hover(\\n\",\n       \"        function() { $(this).addClass(\\\"ui-state-hover\\\");},\\n\",\n       \"        function() { $(this).removeClass(\\\"ui-state-hover\\\");}\\n\",\n       \"    );\\n\",\n       \"\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\\n\",\n       \"    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\\n\",\n       \"    // which will in turn request a refresh of the image.\\n\",\n       \"    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_message = function(type, properties) {\\n\",\n       \"    properties['type'] = type;\\n\",\n       \"    properties['figure_id'] = this.id;\\n\",\n       \"    this.ws.send(JSON.stringify(properties));\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.send_draw_message = function() {\\n\",\n       \"    if (!this.waiting) {\\n\",\n       \"        this.waiting = true;\\n\",\n       \"        this.ws.send(JSON.stringify({type: \\\"draw\\\", figure_id: this.id}));\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    var format_dropdown = fig.format_dropdown;\\n\",\n       \"    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\\n\",\n       \"    fig.ondownload(fig, format);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_resize = function(fig, msg) {\\n\",\n       \"    var size = msg['size'];\\n\",\n       \"    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\\n\",\n       \"        fig._resize_canvas(size[0], size[1]);\\n\",\n       \"        fig.send_message(\\\"refresh\\\", {});\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\\n\",\n       \"    var x0 = msg['x0'];\\n\",\n       \"    var y0 = fig.canvas.height - msg['y0'];\\n\",\n       \"    var x1 = msg['x1'];\\n\",\n       \"    var y1 = fig.canvas.height - msg['y1'];\\n\",\n       \"    x0 = Math.floor(x0) + 0.5;\\n\",\n       \"    y0 = Math.floor(y0) + 0.5;\\n\",\n       \"    x1 = Math.floor(x1) + 0.5;\\n\",\n       \"    y1 = Math.floor(y1) + 0.5;\\n\",\n       \"    var min_x = Math.min(x0, x1);\\n\",\n       \"    var min_y = Math.min(y0, y1);\\n\",\n       \"    var width = Math.abs(x1 - x0);\\n\",\n       \"    var height = Math.abs(y1 - y0);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.clearRect(\\n\",\n       \"        0, 0, fig.canvas.width, fig.canvas.height);\\n\",\n       \"\\n\",\n       \"    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\\n\",\n       \"    // Updates the figure title.\\n\",\n       \"    fig.header.textContent = msg['label'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_cursor = function(fig, msg) {\\n\",\n       \"    var cursor = msg['cursor'];\\n\",\n       \"    switch(cursor)\\n\",\n       \"    {\\n\",\n       \"    case 0:\\n\",\n       \"        cursor = 'pointer';\\n\",\n       \"        break;\\n\",\n       \"    case 1:\\n\",\n       \"        cursor = 'default';\\n\",\n       \"        break;\\n\",\n       \"    case 2:\\n\",\n       \"        cursor = 'crosshair';\\n\",\n       \"        break;\\n\",\n       \"    case 3:\\n\",\n       \"        cursor = 'move';\\n\",\n       \"        break;\\n\",\n       \"    }\\n\",\n       \"    fig.rubberband_canvas.style.cursor = cursor;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_message = function(fig, msg) {\\n\",\n       \"    fig.message.textContent = msg['message'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_draw = function(fig, msg) {\\n\",\n       \"    // Request the server to send over a new figure.\\n\",\n       \"    fig.send_draw_message();\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\\n\",\n       \"    fig.image_mode = msg['mode'];\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Called whenever the canvas gets updated.\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// A function to construct a web socket function for onmessage handling.\\n\",\n       \"// Called in the figure constructor.\\n\",\n       \"mpl.figure.prototype._make_on_message_function = function(fig) {\\n\",\n       \"    return function socket_on_message(evt) {\\n\",\n       \"        if (evt.data instanceof Blob) {\\n\",\n       \"            /* FIXME: We get \\\"Resource interpreted as Image but\\n\",\n       \"             * transferred with MIME type text/plain:\\\" errors on\\n\",\n       \"             * Chrome.  But how to set the MIME type?  It doesn't seem\\n\",\n       \"             * to be part of the websocket stream */\\n\",\n       \"            evt.data.type = \\\"image/png\\\";\\n\",\n       \"\\n\",\n       \"            /* Free the memory for the previous frames */\\n\",\n       \"            if (fig.imageObj.src) {\\n\",\n       \"                (window.URL || window.webkitURL).revokeObjectURL(\\n\",\n       \"                    fig.imageObj.src);\\n\",\n       \"            }\\n\",\n       \"\\n\",\n       \"            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\\n\",\n       \"                evt.data);\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \\\"data:image/png;base64\\\") {\\n\",\n       \"            fig.imageObj.src = evt.data;\\n\",\n       \"            fig.updated_canvas_event();\\n\",\n       \"            fig.waiting = false;\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        var msg = JSON.parse(evt.data);\\n\",\n       \"        var msg_type = msg['type'];\\n\",\n       \"\\n\",\n       \"        // Call the  \\\"handle_{type}\\\" callback, which takes\\n\",\n       \"        // the figure and JSON message as its only arguments.\\n\",\n       \"        try {\\n\",\n       \"            var callback = fig[\\\"handle_\\\" + msg_type];\\n\",\n       \"        } catch (e) {\\n\",\n       \"            console.log(\\\"No handler for the '\\\" + msg_type + \\\"' message type: \\\", msg);\\n\",\n       \"            return;\\n\",\n       \"        }\\n\",\n       \"\\n\",\n       \"        if (callback) {\\n\",\n       \"            try {\\n\",\n       \"                // console.log(\\\"Handling '\\\" + msg_type + \\\"' message: \\\", msg);\\n\",\n       \"                callback(fig, msg);\\n\",\n       \"            } catch (e) {\\n\",\n       \"                console.log(\\\"Exception inside the 'handler_\\\" + msg_type + \\\"' callback:\\\", e, e.stack, msg);\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    };\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\\n\",\n       \"mpl.findpos = function(e) {\\n\",\n       \"    //this section is from http://www.quirksmode.org/js/events_properties.html\\n\",\n       \"    var targ;\\n\",\n       \"    if (!e)\\n\",\n       \"        e = window.event;\\n\",\n       \"    if (e.target)\\n\",\n       \"        targ = e.target;\\n\",\n       \"    else if (e.srcElement)\\n\",\n       \"        targ = e.srcElement;\\n\",\n       \"    if (targ.nodeType == 3) // defeat Safari bug\\n\",\n       \"        targ = targ.parentNode;\\n\",\n       \"\\n\",\n       \"    // jQuery normalizes the pageX and pageY\\n\",\n       \"    // pageX,Y are the mouse positions relative to the document\\n\",\n       \"    // offset() returns the position of the element relative to the document\\n\",\n       \"    var x = e.pageX - $(targ).offset().left;\\n\",\n       \"    var y = e.pageY - $(targ).offset().top;\\n\",\n       \"\\n\",\n       \"    return {\\\"x\\\": x, \\\"y\\\": y};\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"/*\\n\",\n       \" * return a copy of an object with only non-object keys\\n\",\n       \" * we need this to avoid circular references\\n\",\n       \" * http://stackoverflow.com/a/24161582/3208463\\n\",\n       \" */\\n\",\n       \"function simpleKeys (original) {\\n\",\n       \"  return Object.keys(original).reduce(function (obj, key) {\\n\",\n       \"    if (typeof original[key] !== 'object')\\n\",\n       \"        obj[key] = original[key]\\n\",\n       \"    return obj;\\n\",\n       \"  }, {});\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.mouse_event = function(event, name) {\\n\",\n       \"    var canvas_pos = mpl.findpos(event)\\n\",\n       \"\\n\",\n       \"    if (name === 'button_press')\\n\",\n       \"    {\\n\",\n       \"        this.canvas.focus();\\n\",\n       \"        this.canvas_div.focus();\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var x = canvas_pos.x;\\n\",\n       \"    var y = canvas_pos.y;\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {x: x, y: y, button: event.button,\\n\",\n       \"                             step: event.step,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"\\n\",\n       \"    /* This prevents the web browser from automatically changing to\\n\",\n       \"     * the text insertion cursor when the button is pressed.  We want\\n\",\n       \"     * to control all of the cursor setting manually through the\\n\",\n       \"     * 'cursor' event from matplotlib */\\n\",\n       \"    event.preventDefault();\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    // Handle any extra behaviour associated with a key event\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.key_event = function(event, name) {\\n\",\n       \"\\n\",\n       \"    // Prevent repeat events\\n\",\n       \"    if (name == 'key_press')\\n\",\n       \"    {\\n\",\n       \"        if (event.which === this._key)\\n\",\n       \"            return;\\n\",\n       \"        else\\n\",\n       \"            this._key = event.which;\\n\",\n       \"    }\\n\",\n       \"    if (name == 'key_release')\\n\",\n       \"        this._key = null;\\n\",\n       \"\\n\",\n       \"    var value = '';\\n\",\n       \"    if (event.ctrlKey && event.which != 17)\\n\",\n       \"        value += \\\"ctrl+\\\";\\n\",\n       \"    if (event.altKey && event.which != 18)\\n\",\n       \"        value += \\\"alt+\\\";\\n\",\n       \"    if (event.shiftKey && event.which != 16)\\n\",\n       \"        value += \\\"shift+\\\";\\n\",\n       \"\\n\",\n       \"    value += 'k';\\n\",\n       \"    value += event.which.toString();\\n\",\n       \"\\n\",\n       \"    this._key_event_extra(event, name);\\n\",\n       \"\\n\",\n       \"    this.send_message(name, {key: value,\\n\",\n       \"                             guiEvent: simpleKeys(event)});\\n\",\n       \"    return false;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onclick = function(name) {\\n\",\n       \"    if (name == 'download') {\\n\",\n       \"        this.handle_save(this, null);\\n\",\n       \"    } else {\\n\",\n       \"        this.send_message(\\\"toolbar_button\\\", {name: name});\\n\",\n       \"    }\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\\n\",\n       \"    this.message.textContent = tooltip;\\n\",\n       \"};\\n\",\n       \"mpl.toolbar_items = [[\\\"Home\\\", \\\"Reset original view\\\", \\\"fa fa-home icon-home\\\", \\\"home\\\"], [\\\"Back\\\", \\\"Back to  previous view\\\", \\\"fa fa-arrow-left icon-arrow-left\\\", \\\"back\\\"], [\\\"Forward\\\", \\\"Forward to next view\\\", \\\"fa fa-arrow-right icon-arrow-right\\\", \\\"forward\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Pan\\\", \\\"Pan axes with left mouse, zoom with right\\\", \\\"fa fa-arrows icon-move\\\", \\\"pan\\\"], [\\\"Zoom\\\", \\\"Zoom to rectangle\\\", \\\"fa fa-square-o icon-check-empty\\\", \\\"zoom\\\"], [\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\"], [\\\"Download\\\", \\\"Download plot\\\", \\\"fa fa-floppy-o icon-save\\\", \\\"download\\\"]];\\n\",\n       \"\\n\",\n       \"mpl.extensions = [\\\"eps\\\", \\\"pdf\\\", \\\"png\\\", \\\"ps\\\", \\\"raw\\\", \\\"svg\\\"];\\n\",\n       \"\\n\",\n       \"mpl.default_extension = \\\"png\\\";var comm_websocket_adapter = function(comm) {\\n\",\n       \"    // Create a \\\"websocket\\\"-like object which calls the given IPython comm\\n\",\n       \"    // object with the appropriate methods. Currently this is a non binary\\n\",\n       \"    // socket, so there is still some room for performance tuning.\\n\",\n       \"    var ws = {};\\n\",\n       \"\\n\",\n       \"    ws.close = function() {\\n\",\n       \"        comm.close()\\n\",\n       \"    };\\n\",\n       \"    ws.send = function(m) {\\n\",\n       \"        //console.log('sending', m);\\n\",\n       \"        comm.send(m);\\n\",\n       \"    };\\n\",\n       \"    // Register the callback with on_msg.\\n\",\n       \"    comm.on_msg(function(msg) {\\n\",\n       \"        //console.log('receiving', msg['content']['data'], msg);\\n\",\n       \"        // Pass the mpl event to the overriden (by mpl) onmessage function.\\n\",\n       \"        ws.onmessage(msg['content']['data'])\\n\",\n       \"    });\\n\",\n       \"    return ws;\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.mpl_figure_comm = function(comm, msg) {\\n\",\n       \"    // This is the function which gets called when the mpl process\\n\",\n       \"    // starts-up an IPython Comm through the \\\"matplotlib\\\" channel.\\n\",\n       \"\\n\",\n       \"    var id = msg.content.data.id;\\n\",\n       \"    // Get hold of the div created by the display call when the Comm\\n\",\n       \"    // socket was opened in Python.\\n\",\n       \"    var element = $(\\\"#\\\" + id);\\n\",\n       \"    var ws_proxy = comm_websocket_adapter(comm)\\n\",\n       \"\\n\",\n       \"    function ondownload(figure, format) {\\n\",\n       \"        window.open(figure.imageObj.src);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var fig = new mpl.figure(id, ws_proxy,\\n\",\n       \"                           ondownload,\\n\",\n       \"                           element.get(0));\\n\",\n       \"\\n\",\n       \"    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\\n\",\n       \"    // web socket which is closed, not our websocket->open comm proxy.\\n\",\n       \"    ws_proxy.onopen();\\n\",\n       \"\\n\",\n       \"    fig.parent_element = element.get(0);\\n\",\n       \"    fig.cell_info = mpl.find_output_cell(\\\"<div id='\\\" + id + \\\"'></div>\\\");\\n\",\n       \"    if (!fig.cell_info) {\\n\",\n       \"        console.error(\\\"Failed to find cell for figure\\\", id, fig);\\n\",\n       \"        return;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    var output_index = fig.cell_info[2]\\n\",\n       \"    var cell = fig.cell_info[0];\\n\",\n       \"\\n\",\n       \"};\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_close = function(fig, msg) {\\n\",\n       \"    fig.root.unbind('remove')\\n\",\n       \"\\n\",\n       \"    // Update the output cell to use the data from the current canvas.\\n\",\n       \"    fig.push_to_output();\\n\",\n       \"    var dataURL = fig.canvas.toDataURL();\\n\",\n       \"    // Re-enable the keyboard manager in IPython - without this line, in FF,\\n\",\n       \"    // the notebook keyboard shortcuts fail.\\n\",\n       \"    IPython.keyboard_manager.enable()\\n\",\n       \"    $(fig.parent_element).html('<img src=\\\"' + dataURL + '\\\">');\\n\",\n       \"    fig.close_ws(fig, msg);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.close_ws = function(fig, msg){\\n\",\n       \"    fig.send_message('closing', msg);\\n\",\n       \"    // fig.ws.close()\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.push_to_output = function(remove_interactive) {\\n\",\n       \"    // Turn the data on the canvas into data in the output cell.\\n\",\n       \"    var dataURL = this.canvas.toDataURL();\\n\",\n       \"    this.cell_info[1]['text/html'] = '<img src=\\\"' + dataURL + '\\\">';\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.updated_canvas_event = function() {\\n\",\n       \"    // Tell IPython that the notebook contents must change.\\n\",\n       \"    IPython.notebook.set_dirty(true);\\n\",\n       \"    this.send_message(\\\"ack\\\", {});\\n\",\n       \"    var fig = this;\\n\",\n       \"    // Wait a second, then push the new image to the DOM so\\n\",\n       \"    // that it is saved nicely (might be nice to debounce this).\\n\",\n       \"    setTimeout(function () { fig.push_to_output() }, 1000);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._init_toolbar = function() {\\n\",\n       \"    var fig = this;\\n\",\n       \"\\n\",\n       \"    var nav_element = $('<div/>')\\n\",\n       \"    nav_element.attr('style', 'width: 100%');\\n\",\n       \"    this.root.append(nav_element);\\n\",\n       \"\\n\",\n       \"    // Define a callback function for later on.\\n\",\n       \"    function toolbar_event(event) {\\n\",\n       \"        return fig.toolbar_button_onclick(event['data']);\\n\",\n       \"    }\\n\",\n       \"    function toolbar_mouse_event(event) {\\n\",\n       \"        return fig.toolbar_button_onmouseover(event['data']);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    for(var toolbar_ind in mpl.toolbar_items){\\n\",\n       \"        var name = mpl.toolbar_items[toolbar_ind][0];\\n\",\n       \"        var tooltip = mpl.toolbar_items[toolbar_ind][1];\\n\",\n       \"        var image = mpl.toolbar_items[toolbar_ind][2];\\n\",\n       \"        var method_name = mpl.toolbar_items[toolbar_ind][3];\\n\",\n       \"\\n\",\n       \"        if (!name) { continue; };\\n\",\n       \"\\n\",\n       \"        var button = $('<button class=\\\"btn btn-default\\\" href=\\\"#\\\" title=\\\"' + name + '\\\"><i class=\\\"fa ' + image + ' fa-lg\\\"></i></button>');\\n\",\n       \"        button.click(method_name, toolbar_event);\\n\",\n       \"        button.mouseover(tooltip, toolbar_mouse_event);\\n\",\n       \"        nav_element.append(button);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    // Add the status bar.\\n\",\n       \"    var status_bar = $('<span class=\\\"mpl-message\\\" style=\\\"text-align:right; float: right;\\\"/>');\\n\",\n       \"    nav_element.append(status_bar);\\n\",\n       \"    this.message = status_bar[0];\\n\",\n       \"\\n\",\n       \"    // Add the close button to the window.\\n\",\n       \"    var buttongrp = $('<div class=\\\"btn-group inline pull-right\\\"></div>');\\n\",\n       \"    var button = $('<button class=\\\"btn btn-mini btn-primary\\\" href=\\\"#\\\" title=\\\"Stop Interaction\\\"><i class=\\\"fa fa-power-off icon-remove icon-large\\\"></i></button>');\\n\",\n       \"    button.click(function (evt) { fig.handle_close(fig, {}); } );\\n\",\n       \"    button.mouseover('Stop Interaction', toolbar_mouse_event);\\n\",\n       \"    buttongrp.append(button);\\n\",\n       \"    var titlebar = this.root.find($('.ui-dialog-titlebar'));\\n\",\n       \"    titlebar.prepend(buttongrp);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._root_extra_style = function(el){\\n\",\n       \"    var fig = this\\n\",\n       \"    el.on(\\\"remove\\\", function(){\\n\",\n       \"\\tfig.close_ws(fig, {});\\n\",\n       \"    });\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._canvas_extra_style = function(el){\\n\",\n       \"    // this is important to make the div 'focusable\\n\",\n       \"    el.attr('tabindex', 0)\\n\",\n       \"    // reach out to IPython and tell the keyboard manager to turn it's self\\n\",\n       \"    // off when our div gets focus\\n\",\n       \"\\n\",\n       \"    // location in version 3\\n\",\n       \"    if (IPython.notebook.keyboard_manager) {\\n\",\n       \"        IPython.notebook.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"    else {\\n\",\n       \"        // location in version 2\\n\",\n       \"        IPython.keyboard_manager.register_events(el);\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype._key_event_extra = function(event, name) {\\n\",\n       \"    var manager = IPython.notebook.keyboard_manager;\\n\",\n       \"    if (!manager)\\n\",\n       \"        manager = IPython.keyboard_manager;\\n\",\n       \"\\n\",\n       \"    // Check for shift+enter\\n\",\n       \"    if (event.shiftKey && event.which == 13) {\\n\",\n       \"        this.canvas_div.blur();\\n\",\n       \"        // select the cell after this one\\n\",\n       \"        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\\n\",\n       \"        IPython.notebook.select(index + 1);\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"mpl.figure.prototype.handle_save = function(fig, msg) {\\n\",\n       \"    fig.ondownload(fig, null);\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"\\n\",\n       \"mpl.find_output_cell = function(html_output) {\\n\",\n       \"    // Return the cell and output element which can be found *uniquely* in the notebook.\\n\",\n       \"    // Note - this is a bit hacky, but it is done because the \\\"notebook_saving.Notebook\\\"\\n\",\n       \"    // IPython event is triggered only after the cells have been serialised, which for\\n\",\n       \"    // our purposes (turning an active figure into a static one), is too late.\\n\",\n       \"    var cells = IPython.notebook.get_cells();\\n\",\n       \"    var ncells = cells.length;\\n\",\n       \"    for (var i=0; i<ncells; i++) {\\n\",\n       \"        var cell = cells[i];\\n\",\n       \"        if (cell.cell_type === 'code'){\\n\",\n       \"            for (var j=0; j<cell.output_area.outputs.length; j++) {\\n\",\n       \"                var data = cell.output_area.outputs[j];\\n\",\n       \"                if (data.data) {\\n\",\n       \"                    // IPython >= 3 moved mimebundle to data attribute of output\\n\",\n       \"                    data = data.data;\\n\",\n       \"                }\\n\",\n       \"                if (data['text/html'] == html_output) {\\n\",\n       \"                    return [cell, data, j];\\n\",\n       \"                }\\n\",\n       \"            }\\n\",\n       \"        }\\n\",\n       \"    }\\n\",\n       \"}\\n\",\n       \"\\n\",\n       \"// Register the function which deals with the matplotlib target/channel.\\n\",\n       \"// The kernel may be null if the page has been refreshed.\\n\",\n       \"if (IPython.notebook.kernel != null) {\\n\",\n       \"    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\\n\",\n       \"}\\n\"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<img 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     ],\n      \"text/plain\": [\n       \"<IPython.core.display.HTML object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.text.Text at 0x7f558ff9b750>\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# Get v trace for best model\\n\",\n    \"vmodel = evaluator.generate_model(best)\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots()\\n\",\n    \"ax.plot(trace['t'], trace['v'], label='in vitro')\\n\",\n    \"ax.plot(trace['t'], vmodel, label='model')\\n\",\n    \"ax.legend(loc=0)\\n\",\n    \"ax.set_xlabel('time (s)')\\n\",\n    \"ax.set_ylabel('soma voltage (V)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true,\n    \"jupyter\": {\n     \"outputs_hidden\": true\n    }\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"unstable\",\n   \"language\": \"python\",\n   \"name\": \"unstable\"\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\": \"3.8.3\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "examples/tsodyksmarkramstp/tsodyksmarkramstp_multiplefreqs.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Tsodyks-Markram model of short-term synaptic plasticity\\n\",\n    \"In this notebook we demonstrate how to fit the parameters of the Tsodyks-Markram model to normalized PSC amplitudes from in vitro somatic recording at different stimulation frequencies (10, 20, and 40 Hz). The in vitro amplitudes used here show a typical depressing PVBC-PVBC connection, extracted from raw traces open-sourced by Zsolt Kohus (KOKI). The original dataset is publicly available at the CRCNS site under [doi: 10.6080/K0MK69T5](https://crcns.org/data-sets/hc/hc-7)\\n\",\n    \"\\n\",\n    \"`tmodesolve.py` implements the event-based solution of the (reduced, but) more common version of the TM model:\\n\",\n    \"\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"R_{n+1} = 1 + (R_n - R_nU_n -1)e^{-\\\\Delta_t/D}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"\\\\begin{equation}\\n\",\n    \"U_{n+1} = U_{SE} + U_n(1-U_{SE})e^{-\\\\Delta_t/F}\\n\",\n    \"\\\\end{equation}\\n\",\n    \"where the $n^{th}$ amplitude is $A_{SE}U_nR_n$. This implementation fits response amplitudes recorded at different stimulation frequencies (10, 20 and 40 Hz) for better generalization.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1008x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pickle\\n\",\n    \"from collections import OrderedDict  # not needed in Python 3\\n\",\n    \"import numpy as np\\n\",\n    \"import multiprocessing\\n\",\n    \"import bluepyopt as bpop\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\",\n    \"import seaborn as sns\\n\",\n    \"import tmevaluator_multiplefreqs as tmevaluator\\n\",\n    \"\\n\",\n    \"# Set plotting context and style\\n\",\n    \"sns.set_context(\\\"talk\\\")\\n\",\n    \"sns.set_style(\\\"whitegrid\\\")\\n\",\n    \"\\n\",\n    \"# Load and display extracted amplitudes\\n\",\n    \"with open(\\\"amps.pkl\\\", \\\"rb\\\") as f:\\n\",\n    \"    data = pickle.load(f)\\n\",\n    \"fig = plt.figure(figsize=(14, 4))\\n\",\n    \"for i, (freq, tmp) in enumerate(data.items()):\\n\",\n    \"    ax = fig.add_subplot(1, 3, i+1)\\n\",\n    \"    ax.scatter(tmp[\\\"t_spikes\\\"], tmp[\\\"amps\\\"], c=\\\"red\\\")\\n\",\n    \"    if i == 0:\\n\",\n    \"        ax.set_ylabel(\\\"Normalized (PSC) amplitudes\\\")\\n\",\n    \"    ax.set_xlabel(\\\"Time (ms)\\\")\\n\",\n    \"    ax.set_title(freq)\\n\",\n    \"fig.tight_layout()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We now estimate the parameters of the Tsodyks-Markram model using BluePyOpt.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"use = 0.13\\n\",\n      \"trec = 1112.32\\n\",\n      \"tfac = 1.21\\n\",\n      \"ase = 7.04\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Parameters to be fitted as a list of: (name, lower bound, upper bound)\\n\",\n    \"optconf = [(\\\"use\\\", 0.01, 1.0),\\n\",\n    \"           (\\\"trec\\\", 1.0, 2500.0),\\n\",\n    \"           (\\\"tfac\\\", 1.0, 2500.0),\\n\",\n    \"           (\\\"ase\\\", 1.0, 100.0)]\\n\",\n    \"pnames = [name for name, _, _ in optconf]\\n\",\n    \"\\n\",\n    \"# Create multiprocessing pool for parallel evaluation of fitness function\\n\",\n    \"pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\\n\",\n    \"\\n\",\n    \"# Create BluePyOpt optimization and run \\n\",\n    \"evaluator = tmevaluator.TsodyksMarkramEvaluator(data, optconf)\\n\",\n    \"opt = bpop.optimisations.DEAPOptimisation(evaluator, offspring_size=500, map_function=pool.map,\\n\",\n    \"                                          eta=20, mutpb=0.3, cxpb=0.7)\\n\",\n    \"pop, hof, log, history = opt.run(max_ngen=50)\\n\",\n    \"\\n\",\n    \"# Get best individual\\n\",\n    \"best = hof[0]\\n\",\n    \"for pname, value in zip(pnames, best):\\n\",\n    \"    print(\\\"%s = %.2f\\\" % (pname, value))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally we compare the original in vitro amplitudes with the one generated by the best model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1008x288 with 3 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# Get amps (and optionally U, R state vars) for best model at every freqs.\\n\",\n    \"model_amps = OrderedDict()\\n\",\n    \"for freq, vals in data.items():\\n\",\n    \"    amps, _ = evaluator.generate_model(freq, best)\\n\",\n    \"    model_amps[freq] = amps\\n\",\n    \"    \\n\",\n    \"fig = plt.figure(figsize=(14, 4))\\n\",\n    \"for i, (freq, tmp) in enumerate(data.items()):\\n\",\n    \"    ax = fig.add_subplot(1, 3, i+1)\\n\",\n    \"    ax.scatter(tmp[\\\"t_spikes\\\"], tmp[\\\"amps\\\"], c=\\\"red\\\", label=\\\"in vitro\\\")\\n\",\n    \"    ax.scatter(tmp[\\\"t_spikes\\\"], model_amps[freq], c=\\\"blue\\\", label=\\\"in silico\\\")\\n\",\n    \"    if i == 0:\\n\",\n    \"        ax.set_ylabel(\\\"Normalized (PSC) amplitudes\\\")\\n\",\n    \"    ax.set_xlabel(\\\"Time (ms)\\\")\\n\",\n    \"    ax.set_title(freq)\\n\",\n    \"    ax.legend(loc=0)\\n\",\n    \"fig.tight_layout()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\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\": \"3.7.0\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "misc/github_wiki/bibtex/mentions_BPO.bib",
    "content": "@techreport{amsalemDenseComputerReplica2020,\n  type = {Preprint},\n  title = {Dense {{Computer Replica}} of {{Cortical Microcircuits Unravels Cellular Underpinnings}} of {{Auditory Surprise Response}}},\n  author = {Amsalem, Oren and King, James and Reimann, Michael and Ramaswamy, Srikanth and Muller, Eilif and Markram, Henry and Nelken, Israel and Segev, Idan},\n  year = {2020},\n  month = jun,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2020.05.31.126466},\n  urldate = {2022-06-08},\n  abstract = {The nervous system is notorious for its strong response evoked by a surprising sensory input, but the biophysical and anatomical underpinnings of this phenomenon are only partially understood. Here we utilized in-silico experiments of a biologicallydetailed model of a neocortical microcircuit to study stimulus specific adaptation (SSA) in the auditory cortex, whereby the neuronal response adapts significantly for a repeated (``expected'') tone but not for a rare (``surprise'') tone. SSA experiments were mimicked by stimulating tonotopically-mapped thalamo-cortical afferents projecting to the microcircuit; the activity of these afferents was modeled based on our in-vivo recordings from individual thalamic neurons. The modeled microcircuit expressed naturally many experimentally-observed properties of SSA, suggesting that SSA is a general property of neocortical microcircuits. By systematically modulating circuit parameters, we found that key features of SSA depended on synergistic effects of synaptic depression, spike frequency adaptation and recurrent network connectivity. The relative contribution of each of these mechanisms in shaping SSA was explored, additional SSA-related experimental results were explained and new experiments for further studying SSA were suggested.},\n  langid = {english}\n}\n\n@article{amsalemEfficientAnalyticalReduction2020a,\n  title = {An Efficient Analytical Reduction of Detailed Nonlinear Neuron Models},\n  author = {Amsalem, Oren and Eyal, Guy and Rogozinski, Noa and Gevaert, Michael and Kumbhar, Pramod and Sch{\\\"u}rmann, Felix and Segev, Idan},\n  year = {2020},\n  month = jan,\n  journal = {Nature Communications},\n  volume = {11},\n  number = {1},\n  pages = {288},\n  issn = {2041-1723},\n  doi = {10.1038/s41467-019-13932-6},\n  urldate = {2023-02-28},\n  abstract = {Abstract                            Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility.               Neuron\\_Reduce               is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times.               Neuron\\_Reduce               accelerates the simulations by 40\\textendash 250 folds for a variety of cell types and realistic number (10,000\\textendash 100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired ``deep networks''.               Neuron\\_Reduce               is publicly available and is straightforward to implement.},\n  langid = {english}\n}\n\n@article{awileModernizingNEURONSimulator2022a,\n  title = {Modernizing the {{NEURON Simulator}} for {{Sustainability}}, {{Portability}}, and {{Performance}}},\n  author = {Awile, Omar and Kumbhar, Pramod and Cornu, Nicolas and {Dura-Bernal}, Salvador and King, James Gonzalo and Lupton, Olli and Magkanaris, Ioannis and McDougal, Robert A. and Newton, Adam J. H. and Pereira, Fernando and S{\\u a}vulescu, Alexandru and Carnevale, Nicholas T. and Lytton, William W. and Hines, Michael L. and Sch{\\\"u}rmann, Felix},\n  year = {2022},\n  month = jun,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {16},\n  pages = {884046},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2022.884046},\n  urldate = {2023-02-28},\n  abstract = {The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.},\n  langid = {english}\n}\n\n@article{beiningT2NNewTool2017,\n  title = {{{T2N}} as a New Tool for Robust Electrophysiological Modeling Demonstrated for Mature and Adult-Born Dentate Granule Cells},\n  author = {Beining, Marcel and Mongiat, Lucas Alberto and Schwarzacher, Stephan Wolfgang and Cuntz, Hermann and Jedlicka, Peter},\n  year = {2017},\n  month = nov,\n  journal = {eLife},\n  volume = {6},\n  pages = {e26517},\n  issn = {2050-084X},\n  doi = {10.7554/eLife.26517},\n  urldate = {2022-06-08},\n  abstract = {Compartmental models are the theoretical tool of choice for understanding single neuron computations. However, many models are incomplete, built ad hoc and require tuning for each novel condition rendering them of limited usability. Here, we present T2N, a powerful interface to control NEURON with Matlab and TREES toolbox, which supports generating models stable over a broad range of reconstructed and synthetic morphologies. We illustrate this for a novel, highly detailed active model of dentate granule cells (GCs) replicating a wide palette of experiments from various labs. By implementing known differences in ion channel composition and morphology, our model reproduces data from mouse or rat, mature or adult-born GCs as well as pharmacological interventions and epileptic conditions. This work sets a new benchmark for detailed compartmental modeling. T2N is suitable for creating robust models useful for large-scale networks that could lead to novel predictions. We discuss possible T2N application in degeneracy studies.},\n  langid = {english}\n}\n\n@techreport{ben-shalomInferringNeuronalIonic2019,\n  type = {Preprint},\n  title = {Inferring Neuronal Ionic Conductances from Membrane Potentials Using {{CNNs}}},\n  author = {{Ben-Shalom}, Roy and Balewski, Jan and Siththaranjan, Anand and Baratham, Vyassa and Kyoung, Henry and Kim, Kyung Geun and Bender, Kevin J. and Bouchard, Kristofer E.},\n  year = {2019},\n  month = aug,\n  institution = {{Neuroscience}},\n  doi = {10.1101/727974},\n  urldate = {2022-06-08},\n  abstract = {Abstract           The neuron is the fundamental unit of computation in the nervous system, and different neuron types produce different temporal patterns of voltage fluctuations in response to input currents. Understanding the mechanism of single neuron firing patterns requires accurate knowledge of the spatial densities of diverse ion channels along the membrane. However, direct measurements of these microscopic variables are difficult to obtain experimentally. Alternatively, one can attempt to infer those microscopic variables from the membrane potential (a mesoscopic variable), or features thereof, which are more experimentally tractable. One approach in this direction is to infer the ionic densities as parameters of a neuronal model. Traditionally this is done using a Multi-Objective Optimization (MOO) method to minimize the differences between features extracted from a simulated neuron's membrane potential and the same features extracted from target data. Here, we use Convolutional Neural Networks (CNNs) to directly regress generative parameters (e.g., ionic conductances, membrane resistance, etc.,) from simulated time-varying membrane potentials in response to an input stimulus. We simulated diverse neuron models of increasing complexity (Izikivich: 4 parameters; Hodgkin-Huxley: 7 parameters; Mainen-Sejnowski: 10 parameters) with a large range of variation in the underlying parameter values. We show that hyperparameter optimized CNNs can accurately infer the values of generative variables for these neuron models, and that these results far surpass the previous state-of-the-art method (MOO). We discuss the benefits of optimizing the CNN architecture, improvements in accuracy with additional training data, and some observed limitations. Based on these results, we propose that CNNs may be able to infer the spatial distribution of diverse ionic densities from spatially resolved measurements of neuronal membrane potentials (e.g. voltage imaging).},\n  langid = {english}\n}\n\n@article{bolognaEBRAINSNeuroFeatureExtractOnline2021,\n  title = {The {{EBRAINS NeuroFeatureExtract}}: {{An Online Resource}} for the {{Extraction}} of {{Neural Activity Features From Electrophysiological Data}}},\n  shorttitle = {The {{EBRAINS NeuroFeatureExtract}}},\n  author = {Bologna, Luca L. and Smiriglia, Roberto and Curreri, Dario and Migliore, Michele},\n  year = {2021},\n  month = aug,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {15},\n  pages = {713899},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2021.713899},\n  urldate = {2022-06-08},\n  abstract = {The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by experimentalists but are increasingly being used by computational neuroscientists for the construction of biophysically detailed datadriven models. Nonetheless, online resources enabling users to perform such feature extraction operation are lacking. To address this problem, in the framework of the Human Brain Project and the EBRAINS research infrastructure, we have developed and made available to the scientific community the NeuroFeatureExtract, an open-access online resource for the extraction of electrophysiological features from neural activity data. This tool allows to select electrophysiological traces of interest, fetched from public repositories or from users' own data, and provides ad hoc functionalities to extract relevant features. The output files are properly formatted for further analysis, including data-driven neural model optimization.},\n  langid = {english}\n}\n\n@article{carrilloMetricEvaluatingNeural2018,\n  title = {A {{Metric}} for {{Evaluating Neural Input Representation}} in {{Supervised Learning Networks}}},\n  author = {Carrillo, Richard R. and Naveros, Francisco and Ros, Eduardo and Luque, Niceto R.},\n  year = {2018},\n  month = dec,\n  journal = {Frontiers in Neuroscience},\n  volume = {12},\n  pages = {913},\n  issn = {1662-453X},\n  doi = {10.3389/fnins.2018.00913},\n  urldate = {2022-06-08},\n  abstract = {Supervised learning has long been attributed to several feed-forward neural circuits within the brain, with particular attention being paid to the cerebellar granular layer. The focus of this study is to evaluate the input activity representation of these feed-forward neural networks. The activity of cerebellar granule cells is conveyed by parallel fibers and translated into Purkinje cell activity, which constitutes the sole output of the cerebellar cortex. The learning process at this parallel-fiber-to-Purkinje-cell connection makes each Purkinje cell sensitive to a set of specific cerebellar states, which are roughly determined by the granule-cell activity during a certain time window. A Purkinje cell becomes sensitive to each neural input state and, consequently, the network operates as a function able to generate a desired output for each provided input by means of supervised learning. However, not all sets of Purkinje cell responses can be assigned to any set of input states due to the network's own limitations (inherent to the network neurobiological substrate), that is, not all input-output mapping can be learned. A key limiting factor is the representation of the input states through granule-cell activity. The quality of this representation (e.g., in terms of heterogeneity) will determine the capacity of the network to learn a varied set of outputs. Assessing the quality of this representation is interesting when developing and studying models of these networks to identify those neuron or network characteristics that enhance this representation. In this study we present an algorithm for evaluating quantitatively the level of compatibility/interference amongst a set of given cerebellar states according to their representation (granule-cell activation patterns) without the need for actually conducting simulations and network training. The algorithm input consists of a real-number matrix that codifies the activity level of every considered granule-cell in each state. The capability of this representation to generate a varied set of outputs is evaluated geometrically, thus resulting in a real number that assesses the goodness of the representation.},\n  langid = {english}\n}\n\n@misc{deistlerTruncatedProposalsScalable2022,\n  title = {Truncated Proposals for Scalable and Hassle-Free Simulation-Based Inference},\n  author = {Deistler, Michael and Goncalves, Pedro J. and Macke, Jakob H.},\n  year = {2022},\n  month = nov,\n  number = {arXiv:2210.04815},\n  eprint = {arXiv:2210.04815},\n  publisher = {{arXiv}},\n  urldate = {2023-02-28},\n  abstract = {Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a stochastic simulator and inferring posterior distributions from modelsimulations. To improve simulation efficiency, several inference methods take a sequential approach and iteratively adapt the proposal distributions from which model simulations are generated. However, many of these sequential methods are difficult to use in practice, both because the resulting optimisation problems can be challenging and efficient diagnostic tools are lacking. To overcome these issues, we present Truncated Sequential Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with truncated proposals, sidestepping the optimisation issues of alternative approaches. In addition, TSNPE allows to efficiently perform coverage tests that can scale to complex models with many parameters. We demonstrate that TSNPE performs on par with previous methods on established benchmark tasks. We then apply TSNPE to two challenging problems from neuroscience and show that TSNPE can successfully obtain the posterior distributions, whereas previous methods fail. Overall, our results demonstrate that TSNPE is an efficient, accurate, and robust inference method that can scale to challenging scientific models.},\n  archiveprefix = {arxiv},\n  langid = {english},\n  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning}\n}\n\n@article{diaz-parraStructuralFunctionalEmpirical2017,\n  title = {Structural and Functional, Empirical and Modeled Connectivity in the Cerebral Cortex of the Rat},\n  author = {{D{\\'i}az-Parra}, Antonio and Osborn, Zachary and Canals, Santiago and Moratal, David and Sporns, Olaf},\n  year = {2017},\n  month = oct,\n  journal = {NeuroImage},\n  volume = {159},\n  pages = {170--184},\n  issn = {10538119},\n  doi = {10.1016/j.neuroimage.2017.07.046},\n  urldate = {2022-06-08},\n  abstract = {Connectomics data from animal models provide an invaluable opportunity to reveal the complex interplay between structure and function in the mammalian brain. In this work, we investigate the relationship between structural and functional connectivity in the rat brain cortex using a directed anatomical network generated from a carefully curated meta-analysis of published tracing data, along with resting-state functional MRI data obtained from a group of 14 anesthetized Wistar rats. We found a high correspondence between the strength of functional connections, measured as blood oxygen level dependent (BOLD) signal correlations between cortical regions, and the weight of the corresponding anatomical links in the connectome graph (maximum Spearman rank-order correlation {$\\rho$} {$\\frac{1}{4}$} 0:48). At the network-level, regions belonging to the same functionally defined community tend to form more mutual weighted connections between each other compared to regions located in different communities. We further found that functional communities in resting-state networks are enriched in densely connected anatomical motifs. Importantly, these higher-order structural subgraphs cannot be explained by lowerorder topological properties, suggesting that dense structural patterns support functional associations in the resting brain. Simulations of brain-wide resting-state activity based on neural mass models implemented on the empirical rat anatomical connectome demonstrated high correlation between the simulated and the measured functional connectivity (maximum Pearson correlation {$\\rho$} {$\\frac{1}{4}$} 0:53), further suggesting that the topology of structural connections plays an important role in shaping functional cortical networks.},\n  langid = {english}\n}\n\n@article{dura-bernalNetPyNEToolDatadriven2019,\n  title = {{{NetPyNE}}, a Tool for Data-Driven Multiscale Modeling of Brain Circuits},\n  author = {{Dura-Bernal}, Salvador and Suter, Benjamin A and Gleeson, Padraig and Cantarelli, Matteo and Quintana, Adrian and Rodriguez, Facundo and Kedziora, David J and Chadderdon, George L and Kerr, Cliff C and Neymotin, Samuel A and McDougal, Robert A and Hines, Michael and Shepherd, Gordon MG and Lytton, William W},\n  year = {2019},\n  month = apr,\n  journal = {eLife},\n  volume = {8},\n  pages = {e44494},\n  issn = {2050-084X},\n  doi = {10.7554/eLife.44494},\n  urldate = {2022-06-08},\n  abstract = {Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis \\textendash{} connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.           ,              The approximately 100 billion neurons in our brain are responsible for everything we do and experience. Experiments aimed at discovering how these cells encode and process information generate vast amounts of data. These data span multiple scales, from interactions between individual molecules to coordinated waves of electrical activity that spread across the entire brain surface. To understand how the brain works, we must combine and make sense of these diverse types of information.             Computational modeling provides one way of doing this. Using equations, we can calculate the chemical and electrical changes that take place in neurons. We can then build models of neurons and neural circuits that reproduce the patterns of activity seen in experiments. Exploring these models can provide insights into how the brain itself works. Several software tools are available to simulate neural circuits, but none provide an easy way of incorporating data that span different scales, from molecules to cells to networks. Moreover, most of the models require familiarity with computer programming.             Dura-Bernal et al. have now developed a new software tool called NetPyNE, which allows users without programming expertise to build sophisticated models of brain circuits. It features a user-friendly interface for defining the properties of the model at molecular, cellular and circuit scales. It also provides an easy and automated method to identify the properties of the model that enable it to reproduce experimental data. Finally, NetPyNE makes it possible to run the model on supercomputers and offers a variety of ways to visualize and analyze the resulting output. Users can save the model and output in standardized formats, making them accessible to as many people as possible.             Researchers in labs across the world have used NetPyNE to study different brain regions, phenomena and diseases. The software also features in courses that introduce students to neurobiology and computational modeling. NetPyNE can help to interpret isolated experimental findings, and also makes it easier to explore interactions between brain activity at different scales. This will enable researchers to decipher how the brain encodes and processes information, and ultimately could make it easier to understand and treat brain disorders.},\n  langid = {english}\n}\n\n@article{economidesBiocytinRecovery3D2018,\n  title = {Biocytin {{Recovery}} and {{3D Reconstructions}} of {{Filled Hippocampal CA2 Interneurons}}},\n  author = {Economides, Georgia and Falk, Svenja and Mercer, Audrey},\n  year = {2018},\n  month = nov,\n  journal = {Journal of Visualized Experiments},\n  number = {141},\n  pages = {58592},\n  issn = {1940-087X},\n  doi = {10.3791/58592},\n  urldate = {2022-06-08},\n  abstract = {How cortical network activity processes information is of importance to a large number of basic and clinical scientific questions. The protocol described here identifies the basic building blocks of this circuitry. The in-depth studies of cortical regions will ultimately provide other scientists with the circuit components needed for an understanding of how the brain acquires, processes and stores information and what goes wrong in disease, while the electrophysiological and morphological data are widely used by computational neuroscientists in the construction of model networks that explore information processing. The protocol outlined here describes how biocytin-filled cells recorded in the CA2 region of the hippocampus are recovered and then reconstructed in 3D. Additionally, the protocol describes the demonstration of calcium binding protein or peptide content in recorded interneurons.},\n  langid = {english}\n}\n\n@article{erikssonCombiningHypothesisDatadriven2022a,\n  title = {Combining Hypothesis- and Data-Driven Neuroscience Modeling in {{FAIR}} Workflows},\n  author = {Eriksson, Olivia and Bhalla, Upinder Singh and Blackwell, Kim T and Crook, Sharon M and Keller, Daniel and Kramer, Andrei and Linne, Marja-Leena and Saudargien{\\.e}, Ausra and Wade, Rebecca C and Hellgren Kotaleski, Jeanette},\n  year = {2022},\n  month = jul,\n  journal = {eLife},\n  volume = {11},\n  pages = {e69013},\n  issn = {2050-084X},\n  doi = {10.7554/eLife.69013},\n  urldate = {2022-07-20},\n  abstract = {Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-\\-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-d\\- riven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data \\textendash{} such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles \\textendash{} also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock\\textendash Cooper\\textendash Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.},\n  langid = {english}\n}\n\n@article{ezra-tsurRealisticRetinalModeling2021,\n  title = {Realistic Retinal Modeling Unravels the Differential Role of Excitation and Inhibition to Starburst Amacrine Cells in Direction Selectivity},\n  author = {{Ezra-Tsur}, Elishai and Amsalem, Oren and Ankri, Lea and Patil, Pritish and Segev, Idan and {Rivlin-Etzion}, Michal},\n  editor = {Macke, Jakob H.},\n  year = {2021},\n  month = dec,\n  journal = {PLOS Computational Biology},\n  volume = {17},\n  number = {12},\n  pages = {e1009754},\n  issn = {1553-7358},\n  doi = {10.1371/journal.pcbi.1009754},\n  urldate = {2022-06-13},\n  abstract = {Retinal direction-selectivity originates in starburst amacrine cells (SACs), which display a centrifugal preference, responding with greater depolarization to a stimulus expanding from soma to dendrites than to a collapsing stimulus. Various mechanisms were hypothesized to underlie SAC centrifugal preference, but dissociating them is experimentally challenging and the mechanisms remain debatable. To address this issue, we developed the Retinal Stimulation Modeling Environment (RSME), a multifaceted data-driven retinal model that encompasses detailed neuronal morphology and biophysical properties, retina-tailored connectivity scheme and visual input. Using a genetic algorithm, we demonstrated that spatiotemporally diverse excitatory inputs\\textendash sustained in the proximal and transient in the distal processes\\textendash are sufficient to generate experimentally validated centrifugal preference in a single SAC. Reversing these input kinetics did not produce any centrifugal-preferring SAC. We then explored the contribution of SAC-SAC inhibitory connections in establishing the centrifugal preference. SAC inhibitory network enhanced the centrifugal preference, but failed to generate it in its absence. Embedding a direction selective ganglion cell (DSGC) in a SAC network showed that the known SAC-DSGC asymmetric connectivity by itself produces direction selectivity. Still, this selectivity is sharpened in a centrifugal-preferring SAC network. Finally, we use RSME to demonstrate the contribution of SAC-SAC inhibitory connections in mediating direction selectivity and recapitulate recent experimental findings. Thus, using RSME, we obtained a mechanistic understanding of SACs' centrifugal preference and its contribution to direction selectivity.},\n  langid = {english}\n}\n\n@inproceedings{farnerEvolvingSpikingNeuron2021,\n  title = {Evolving Spiking Neuron Cellular Automata and Networks to Emulate in Vitro Neuronal Activity},\n  booktitle = {2021 {{IEEE Symposium Series}} on {{Computational Intelligence}} ({{SSCI}})},\n  author = {Farner, Jorgen Jensen and Weydahl, Hakon and Jahren, Ruben and Ramstad, Ola Huse and Nichele, Stefano and Heiney, Kristine},\n  year = {2021},\n  month = dec,\n  pages = {1--10},\n  publisher = {{IEEE}},\n  address = {{Orlando, FL, USA}},\n  doi = {10.1109/SSCI50451.2021.9660185},\n  urldate = {2022-06-08},\n  abstract = {Neuro-inspired models and systems have great potential for applications in unconventional computing. Often, the mechanisms of biological neurons are modeled or mimicked in simulated or physical systems in an attempt to harness some of the computational power of the brain. However, the biological mechanisms at play in neural systems are complicated and challenging to capture and engineer; thus, it can be simpler to turn to a data-driven approach to transfer features of neural behavior to artificial substrates. In the present study, we used an evolutionary algorithm (EA) to produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro. The aim of this approach was to develop a method of producing models capable of exhibiting complex behavior that may be suitable for use as computational substrates. Our models were able to produce a level of network-wide synchrony and showed a range of behaviors depending on the target data used for their evolution, which was from a range of neuronal culture densities and maturities. The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.},\n  isbn = {978-1-72819-048-8},\n  langid = {english}\n}\n\n@article{frostnylenDopaminergicCholinergicModulation2021,\n  title = {Dopaminergic and {{Cholinergic Modulation}} of {{Large Scale Networks}} in Silico {{Using Snudda}}},\n  author = {Frost Nylen, Johanna and Hjorth, Jarl Jacob Johannes and Grillner, Sten and Hellgren Kotaleski, Jeanette},\n  year = {2021},\n  month = oct,\n  journal = {Frontiers in Neural Circuits},\n  volume = {15},\n  pages = {748989},\n  issn = {1662-5110},\n  doi = {10.3389/fncir.2021.748989},\n  urldate = {2022-06-08},\n  abstract = {Neuromodulation is present throughout the nervous system and serves a critical role for circuit function and dynamics. The computational investigations of neuromodulation in large scale networks require supportive software platforms. Snudda is a software for the creation and simulation of large scale networks of detailed microcircuits consisting of multicompartmental neuron models. We have developed an extension to Snudda to incorporate neuromodulation in large scale simulations. The extended Snudda framework implements neuromodulation at the level of single cells incorporated into large-scale microcircuits. We also developed Neuromodcell, a software for optimizing neuromodulation in detailed multicompartmental neuron models. The software adds parameters within the models modulating the conductances of ion channels and ionotropic receptors. Bath application of neuromodulators is simulated and models which reproduce the experimentally measured effects are selected. In Snudda, we developed an extension to accommodate large scale simulations of neuromodulation. The simulator has two modes of simulation \\textendash{} denoted replay and adaptive. In the replay mode, transient levels of neuromodulators can be defined as a time-varying function which modulates the receptors and ion channels within the network in a cell-type specific manner. In the adaptive mode, spiking neuromodulatory neurons are connected via integrative modulating mechanisms to ion channels and receptors. Both modes of simulating neuromodulation allow for simultaneous modulation by several neuromodulators that can interact dynamically with each other. Here, we used the Neuromodcell software to simulate dopaminergic and muscarinic modulation of neurons from the striatum. We also demonstrate how to simulate different neuromodulatory states with dopamine and acetylcholine using Snudda. All software is freely available on Github, including tutorials on Neuromodcell and Snudda-neuromodulation.},\n  langid = {english}\n}\n\n@article{galindoSimulationVisualizationAnalysis2020,\n  title = {Simulation, Visualization and Analysis Tools for Pattern Recognition Assessment with Spiking Neuronal Networks},\n  author = {Galindo, Sergio E. and Toharia, Pablo and Robles, {\\'O}scar D. and Ros, Eduardo and Pastor, Luis and Garrido, Jes{\\'u}s A.},\n  year = {2020},\n  month = aug,\n  journal = {Neurocomputing},\n  volume = {400},\n  pages = {309--321},\n  issn = {09252312},\n  doi = {10.1016/j.neucom.2020.02.114},\n  urldate = {2022-06-08},\n  langid = {english}\n}\n\n@article{galRoleHubNeurons2021,\n  title = {The {{Role}} of {{Hub Neurons}} in {{Modulating Cortical Dynamics}}},\n  author = {Gal, Eyal and Amsalem, Oren and Schindel, Alon and London, Michael and Sch{\\\"u}rmann, Felix and Markram, Henry and Segev, Idan},\n  year = {2021},\n  month = sep,\n  journal = {Frontiers in Neural Circuits},\n  volume = {15},\n  pages = {718270},\n  issn = {1662-5110},\n  doi = {10.3389/fncir.2021.718270},\n  urldate = {2022-06-08},\n  abstract = {Many neurodegenerative diseases are associated with the death of specific neuron types in particular brain regions. What makes the death of specific neuron types particularly harmful for the integrity and dynamics of the respective network is not well understood. To start addressing this question we used the most up-to-date biologically realistic dense neocortical microcircuit (NMC) of the rodent, which has reconstructed a volume of 0.3 mm3 and containing 31,000 neurons, {$\\sim$}37 million synapses, and 55 morphological cell types arranged in six cortical layers. Using modern network science tools, we identified hub neurons in the NMC, that are connected synaptically to a large number of their neighbors and systematically examined the impact of abolishing these cells. In general, the structural integrity of the network is robust to cells' attack; yet, attacking hub neurons strongly impacted the small-world topology of the network, whereas similar attacks on random neurons have a negligible effect. Such hub-specific attacks are also impactful on the network dynamics, both when the network is at its spontaneous synchronous state and when it was presented with synchronized thalamocortical visual-like input. We found that attacking layer 5 hub neurons is most harmful to the structural and functional integrity of the NMC. The significance of our results for understanding the role of specific neuron types and cortical layers for disease manifestation is discussed.},\n  langid = {english}\n}\n\n@article{gouwensSystematicGenerationBiophysically2018a,\n  title = {Systematic Generation of Biophysically Detailed Models for Diverse Cortical Neuron Types},\n  author = {Gouwens, Nathan W. and Berg, Jim and Feng, David and Sorensen, Staci A. and Zeng, Hongkui and Hawrylycz, Michael J. and Koch, Christof and Arkhipov, Anton},\n  year = {2018},\n  month = feb,\n  journal = {Nature Communications},\n  volume = {9},\n  number = {1},\n  pages = {710},\n  issn = {2041-1723},\n  doi = {10.1038/s41467-017-02718-3},\n  urldate = {2023-02-28},\n  abstract = {Abstract             The cellular components of mammalian neocortical circuits are diverse, and capturing this diversity in computational models is challenging. Here we report an approach for generating biophysically detailed models of 170 individual neurons in the Allen Cell Types Database to link the systematic experimental characterization of cell types to the construction of cortical models. We build models from 3D morphologies and somatic electrophysiological responses measured in the same cells. Densities of active somatic conductances and additional parameters are optimized with a genetic algorithm to match electrophysiological features. We evaluate the models by applying additional stimuli and comparing model responses to experimental data. Applying this technique across a diverse set of neurons from adult mouse primary visual~cortex, we verify that models preserve the distinctiveness of intrinsic properties between subsets of cells observed in experiments. The optimized models are accessible online alongside the experimental data. Code for optimization and simulation is also openly distributed.},\n  langid = {english}\n}\n\n@article{gutzenReproducibleNeuralNetwork2018,\n  title = {Reproducible {{Neural Network Simulations}}: {{Statistical Methods}} for {{Model Validation}} on the {{Level}} of {{Network Activity Data}}},\n  shorttitle = {Reproducible {{Neural Network Simulations}}},\n  author = {Gutzen, Robin and {von Papen}, Michael and Trensch, Guido and Quaglio, Pietro and Gr{\\\"u}n, Sonja and Denker, Michael},\n  year = {2018},\n  month = dec,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {12},\n  pages = {90},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2018.00090},\n  urldate = {2022-06-08},\n  abstract = {Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence. Despite rapid advances in the formalized description of models, data, and analysis workflows, there is no accepted consensus regarding the terminology and practical implementation of validation workflows in the context of neural simulations. This situation prevents the generic, unbiased comparison between published models, which is a key element of enhancing reproducibility of computational research in neuroscience. In this study, we argue for the establishment of standardized statistical test metrics that enable the quantitative validation of network models on the level of the population dynamics. Despite the importance of validating the elementary components of a simulation, such as single cell dynamics, building networks from validated building blocks does not entail the validity of the simulation on the network scale. Therefore, we introduce a corresponding set of validation tests and present an example workflow that practically demonstrates the iterative model validation of a spiking neural network model against its reproduction on the SpiNNaker neuromorphic hardware system. We formally implement the workflow using a generic Python library that we introduce for validation tests on neural network activity data. Together with the companion study (Trensch et al., 2018), the work presents a consistent definition, formalization, and implementation of the verification and validation process for neural network simulations.},\n  langid = {english}\n}\n\n@article{hauflerSimulationsCorticalNetworks2023,\n  title = {Simulations of Cortical Networks Using Spatially Extended Conductance-based Neuronal Models},\n  author = {Haufler, Darrell and Ito, Shinya and Koch, Christof and Arkhipov, Anton},\n  year = {2023},\n  month = jan,\n  journal = {The Journal of Physiology},\n  pages = {JP284030},\n  issn = {0022-3751, 1469-7793},\n  doi = {10.1113/JP284030},\n  urldate = {2023-02-28},\n  langid = {english}\n}\n\n@article{iyengarCuratedModelDevelopment2019,\n  title = {Curated {{Model Development Using NEUROiD}}: {{A Web-Based NEUROmotor Integration}} and {{Design Platform}}},\n  shorttitle = {Curated {{Model Development Using NEUROiD}}},\n  author = {Iyengar, Raghu Sesha and Pithapuram, Madhav Vinodh and Singh, Avinash Kumar and Raghavan, Mohan},\n  year = {2019},\n  month = aug,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {13},\n  pages = {56},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2019.00056},\n  urldate = {2022-06-08},\n  abstract = {Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.},\n  langid = {english}\n}\n\n@article{jedrzejewski-szmekParameterOptimizationUsing2018,\n  title = {Parameter {{Optimization Using Covariance Matrix Adaptation}}\\textemdash{{Evolutionary Strategy}} ({{CMA-ES}}), an {{Approach}} to {{Investigate Differences}} in {{Channel Properties Between Neuron Subtypes}}},\n  author = {{J{\\c e}drzejewski-Szmek}, Zbigniew and Abrahao, Karina P. and {J{\\c e}drzejewska-Szmek}, Joanna and Lovinger, David M. and Blackwell, Kim T.},\n  year = {2018},\n  month = jul,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {12},\n  pages = {47},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2018.00047},\n  urldate = {2022-06-08},\n  abstract = {Computational models in neuroscience can be used to predict causal relationships between biological mechanisms in neurons and networks, such as the effect of blocking an ion channel or synaptic connection on neuron activity. Since developing a biophysically realistic, single neuron model is exceedingly difficult, software has been developed for automatically adjusting parameters of computational neuronal models. The ideal optimization software should work with commonly used neural simulation software; thus, we present software which works with models specified in declarative format for the MOOSE simulator. Experimental data can be specified using one of two different file formats. The fitness function is customizable as a weighted combination of feature differences. The optimization itself uses the covariance matrix adaptation-evolutionary strategy, because it is robust in the face of local fluctuations of the fitness function, and deals well with a high-dimensional and discontinuous fitness landscape. We demonstrate the versatility of the software by creating several model examples of each of four types of neurons (two subtypes of spiny projection neurons and two subtypes of globus pallidus neurons) by tuning to current clamp data. Optimizations reached convergence within 1,600\\textendash 4,000 model evaluations (200\\textendash 500 generations \\texttimes{} population size of 8). Analysis of the parameters of the best fitting models revealed differences between neuron subtypes, which are consistent with prior experimental results. Overall our results suggest that this easy-to-use, automatic approach for finding neuron channel parameters may be applied to current clamp recordings from neurons exhibiting different biochemical markers to help characterize ionic differences between other neuron subtypes.},\n  langid = {english}\n}\n\n@techreport{jinBayesianInferenceSpectral2023,\n  type = {Preprint},\n  title = {Bayesian {{Inference}} of a {{Spectral Graph Model}} for {{Brain Oscillations}}},\n  author = {Jin, Huaqing and Verma, Parul and Jiang, Fei and Nagarajan, Srikantan and Raj, Ashish},\n  year = {2023},\n  month = mar,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.03.01.530704},\n  urldate = {2023-05-24},\n  abstract = {The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is nontrivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which can not be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.},\n  langid = {english}\n}\n\n@article{jungDynamicCausalModeling2019,\n  title = {Dynamic Causal Modeling for Calcium Imaging: {{Exploration}} of Differential Effective Connectivity for Sensory Processing in a Barrel Cortical Column},\n  shorttitle = {Dynamic Causal Modeling for Calcium Imaging},\n  author = {Jung, Kyesam and Kang, Jiyoung and Chung, Seungsoo and Park, Hae-Jeong},\n  year = {2019},\n  month = nov,\n  journal = {NeuroImage},\n  volume = {201},\n  pages = {116008},\n  issn = {10538119},\n  doi = {10.1016/j.neuroimage.2019.116008},\n  urldate = {2022-06-08},\n  abstract = {Multi-photon calcium imaging (CaI) is an important tool to assess activities of neural populations within a column in the sensory cortex. However, the complex asymmetrical interactions among neural populations, termed effective connectivity, cannot be directly assessed by measuring the activity of each neuron or neural population using CaI but calls for computational modeling. To estimate effective connectivity among neural populations, we proposed a dynamic causal model (DCM) for CaI by combining a convolution-based dynamic neural state model and a dynamic calcium ion concentration model for CaI signals. After conducting a simulation study to evaluate DCM for CaI, we applied it to an experimental CaI signals measured at the layer 2/3 of a barrel cortical column that differentially responds to hit and error whisking trials in mice. We first identified neural populations and constructed computational models with intrinsic connectivity of neural populations within the layer 2/3 of the barrel cortex and extrinsic connectivity with latent external modes. Bayesian model inversion and comparison shows that interactions with latent inhibitory and excitatory external modes explain the observed CaI signals within the barrel cortical column better than any other tested models, with a single external mode or without any latent modes. The best model also showed differential intrinsic and extrinsic effective connectivity between hit and error trials in the functional hierarchy. Both simulation and experimental results suggest the usefulness of DCM for CaI in terms of exploration of hierarchical interactions among neural populations observed in CaI.},\n  langid = {english}\n}\n\n@article{kanariComputationalSynthesisCortical2022,\n  title = {Computational Synthesis of Cortical Dendritic Morphologies},\n  author = {Kanari, Lida and Dictus, Hugo and Chalimourda, Athanassia and Arnaudon, Alexis and Van Geit, Werner and Coste, Benoit and Shillcock, Julian and Hess, Kathryn and Markram, Henry},\n  year = {2022},\n  month = apr,\n  journal = {Cell Reports},\n  volume = {39},\n  number = {1},\n  pages = {110586},\n  issn = {22111247},\n  doi = {10.1016/j.celrep.2022.110586},\n  urldate = {2022-06-08},\n  abstract = {Neuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles, and investigating brain-disease-related dendritic alterations. However, a lack of understanding of the principles underlying neuron morphologies has hindered attempts to computationally synthesize morphologies for decades. We introduce a synthesis algorithm based on a topological descriptor of neurons, which enables the rapid digital reconstruction of entire brain regions from few reference cells. This topology-guided synthesis generates dendrites that are statistically similar to biological reconstructions in terms of morpho-electrical and connectivity properties and offers a significant opportunity to investigate the links between neuronal morphology and brain function across different spatiotemporal scales. Synthesized cortical networks based on structurally altered dendrites associated with diverse brain pathologies revealed principles linking branching properties to the structure of large-scale networks.},\n  langid = {english}\n}\n\n@article{kanekoDevelopmentallyRegulatedImpairment2022,\n  title = {Developmentally Regulated Impairment of Parvalbumin Interneuron Synaptic Transmission in an Experimental Model of {{Dravet}} Syndrome},\n  author = {Kaneko, Keisuke and Currin, Christopher B. and Goff, Kevin M. and Wengert, Eric R. and Somarowthu, Ala and Vogels, Tim P. and Goldberg, Ethan M.},\n  year = {2022},\n  month = mar,\n  journal = {Cell Reports},\n  volume = {38},\n  number = {13},\n  pages = {110580},\n  issn = {22111247},\n  doi = {10.1016/j.celrep.2022.110580},\n  urldate = {2023-02-28},\n  abstract = {Dravet syndrome is a neurodevelopmental disorder characterized by epilepsy, intellectual disability, and sudden death due to pathogenic variants in SCN1A with loss of function of the sodium channel subunit Nav1.1. Nav1.1-expressing parvalbumin GABAergic interneurons (PV-INs) from young Scn1a+/\\`A mice show impaired action potential generation. An approach assessing PV-IN function in the same mice at two time points shows impaired spike generation in all Scn1a+/\\`A mice at postnatal days (P) 16\\textendash 21, whether deceased prior or surviving to P35, with normalization by P35 in surviving mice. However, PV-IN synaptic transmission is dysfunctional in young Scn1a+/\\`A mice that did not survive and in Scn1a+/\\`A mice R P35. Modeling confirms that PV-IN axonal propagation is more sensitive to decreased sodium conductance than spike generation. These results demonstrate dynamic dysfunction in Dravet syndrome: combined abnormalities of PV-IN spike generation and propagation drives early disease severity, while ongoing dysfunction of synaptic transmission contributes to chronic pathology.},\n  langid = {english}\n}\n\n@article{linneNeuroinformaticsComputationalModelling2018,\n  title = {Neuroinformatics and {{Computational Modelling}} as {{Complementary Tools}} for {{Neurotoxicology Studies}}},\n  author = {Linne, Marja-Leena},\n  year = {2018},\n  month = sep,\n  journal = {Basic \\& Clinical Pharmacology \\& Toxicology},\n  volume = {123},\n  pages = {56--61},\n  issn = {17427835},\n  doi = {10.1111/bcpt.13075},\n  urldate = {2022-06-08},\n  abstract = {Neuroinformatics is an area of science that aims to integrate neuroscience data and develop modern computational tools to increase our understanding of the functions of the nervous system in health and disease. Neuroinformatics tools include, among others, databases for storing and sharing data, repositories for managing documents and source code, and software tools for analysing, modelling and simulating signals and images. This MiniReview aims to present the state of the art in neuroinformatics and computational in silico modelling of neurobiological processes and neuroscientific phenomena as well as to discuss the use of in silico models in neurotoxicology research. In silico modelling can be considered a new, complementary tool in chemical design to predict potential neurotoxicity and in neurotoxicity testing to help clarify initial hypothesis obtained in in vitro and in vivo. Validated in silico models can be used to identify pharmacological targets, to help bridge in vitro and in vivo studies and, ultimately, to develop safer chemicals and efficient therapeutic strategies.},\n  langid = {english}\n}\n\n@article{maki-marttunenStepwiseNeuronModel2018,\n  title = {A Stepwise Neuron Model Fitting Procedure Designed for Recordings with High Spatial Resolution: {{Application}} to Layer 5 Pyramidal Cells},\n  shorttitle = {A Stepwise Neuron Model Fitting Procedure Designed for Recordings with High Spatial Resolution},\n  author = {{M{\\\"a}ki-Marttunen}, Tuomo and Halnes, Geir and Devor, Anna and Metzner, Christoph and Dale, Anders M. and Andreassen, Ole A. and Einevoll, Gaute T.},\n  year = {2018},\n  month = jan,\n  journal = {Journal of Neuroscience Methods},\n  volume = {293},\n  pages = {264--283},\n  issn = {01650270},\n  doi = {10.1016/j.jneumeth.2017.10.007},\n  urldate = {2022-06-08},\n  abstract = {Background: Recent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. New method: In this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca2+ imaging. Result: We apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. Comparison with existing methods: Our approach combines features from several previously applied model-fitting strategies. The reduced-morphology neuron model obtained using our approach reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model. Conclusions: The network models produced using our method are cost-efficient and predict that interconnected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies, largely due to the medium after hyperpolarization mediated by the Ca2+-activated SK current.},\n  langid = {english}\n}\n\n@article{marinUseMultimodalOptimizer2021,\n  title = {On the {{Use}} of a {{Multimodal Optimizer}} for {{Fitting Neuron Models}}. {{Application}} to the {{Cerebellar Granule Cell}}},\n  author = {Mar{\\'i}n, Milagros and Cruz, Nicol{\\'a}s C. and Ortigosa, Eva M. and {S{\\'a}ez-Lara}, Mar{\\'i}a J. and Garrido, Jes{\\'u}s A. and Carrillo, Richard R.},\n  year = {2021},\n  month = jun,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {15},\n  pages = {663797},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2021.663797},\n  urldate = {2022-06-08},\n  abstract = {This article extends a recent methodological workflow for creating realistic and computationally efficient neuron models whilst capturing essential aspects of singleneuron dynamics. We overcome the intrinsic limitations of the extant optimization methods by proposing an alternative optimization component based on multimodal algorithms. This approach can natively explore a diverse population of neuron model configurations. In contrast to methods that focus on a single global optimum, the multimodal method allows directly obtaining a set of promising solutions for a single but complex multi-feature objective function. The final sparse population of candidate solutions has to be analyzed and evaluated according to the biological plausibility and their objective to the target features by the expert. In order to illustrate the value of this approach, we base our proposal on the optimization of cerebellar granule cell (GrC) models that replicate the essential properties of the biological cell. Our results show the emerging variability of plausible sets of values that this type of neuron can adopt underlying complex spiking characteristics. Also, the set of selected cerebellar GrC models captured spiking dynamics closer to the reference model than the single model obtained with off-the-shelf parameter optimization algorithms used in our previous article. The method hereby proposed represents a valuable strategy for adjusting a varied population of realistic and simplified neuron models. It can be applied to other kinds of neuron models and biological contexts.},\n  langid = {english}\n}\n\n@article{masoliComputationalModelsNeurotransmission2022,\n  title = {Computational Models of Neurotransmission at Cerebellar Synapses Unveil the Impact on Network Computation},\n  author = {Masoli, Stefano and Rizza, Martina Francesca and Tognolina, Marialuisa and Prestori, Francesca and D'Angelo, Egidio},\n  year = {2022},\n  month = oct,\n  journal = {Frontiers in Computational Neuroscience},\n  volume = {16},\n  pages = {1006989},\n  issn = {1662-5188},\n  doi = {10.3389/fncom.2022.1006989},\n  urldate = {2023-02-28},\n  abstract = {The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.},\n  langid = {english}\n}\n\n@article{meyerPypetPythonToolkit2016,\n  title = {Pypet: {{A Python Toolkit}} for {{Data Management}} of {{Parameter Explorations}}},\n  shorttitle = {Pypet},\n  author = {Meyer, Robert and Obermayer, Klaus},\n  year = {2016},\n  month = aug,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {10},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2016.00038},\n  urldate = {2022-06-08},\n  langid = {english}\n}\n\n@article{neymotinOptimizingComputerModels2017,\n  title = {Optimizing Computer Models of Corticospinal Neurons to Replicate in Vitro Dynamics},\n  author = {Neymotin, Samuel A. and Suter, Benjamin A. and {Dura-Bernal}, Salvador and Shepherd, Gordon M. G. and Migliore, Michele and Lytton, William W.},\n  year = {2017},\n  month = jan,\n  journal = {Journal of Neurophysiology},\n  volume = {117},\n  number = {1},\n  pages = {148--162},\n  issn = {0022-3077, 1522-1598},\n  doi = {10.1152/jn.00570.2016},\n  urldate = {2022-06-08},\n  abstract = {Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor cortex layer 5B that project caudally via the medullary pyramids, display distinct class-specific electrophysiological properties in vitro: strong sag with hyperpolarization, lack of adaptation, and a nearly linear frequency-current ( F\\textendash{} I) relationship. We used our electrophysiological data to produce a pair of large archives of SPI neuron computer models in two model classes: 1) detailed models with full reconstruction; and 2) simplified models with six compartments. We used a PRAXIS and an evolutionary multiobjective optimization (EMO) in sequence to determine ion channel conductances. EMO selected good models from each of the two model classes to form the two model archives. Archived models showed tradeoffs across fitness functions. For example, parameters that produced excellent F\\textendash{} I fit produced a less-optimal fit for interspike voltage trajectory. Because of these tradeoffs, there was no single best model but rather models that would be best for particular usages for either single neuron or network explorations. Further exploration of exemplar models with strong F\\textendash{} I fit demonstrated that both the detailed and simple models produced excellent matches to the experimental data. Although dendritic ion identities and densities cannot yet be fully determined experimentally, we explored the consequences of a demonstrated proximal to distal density gradient of I               h               , demonstrating that this would lead to a gradient of resonance properties with increased resonant frequencies more distally. We suggest that this dynamical feature could serve to make the cell particularly responsive to major frequency bands that differ by cortical layer.                          NEW \\& NOTEWORTHY We developed models of motor cortex corticospinal neurons that replicate in vitro dynamics, including hyperpolarization-induced sag and realistic firing patterns. Models demonstrated resonance in response to synaptic stimulation, with resonance frequency increasing in apical dendrites with increasing distance from soma, matching the increasing oscillation frequencies spanning deep to superficial cortical layers. This gradient may enable specific corticospinal neuron dendrites to entrain to relevant oscillations in different cortical layers, contributing to appropriate motor output commands.},\n  langid = {english}\n}\n\n@article{nolteCorticalReliabilityNoise2019a,\n  title = {Cortical Reliability amid Noise and Chaos},\n  author = {Nolte, Max and Reimann, Michael W. and King, James G. and Markram, Henry and Muller, Eilif B.},\n  year = {2019},\n  month = aug,\n  journal = {Nature Communications},\n  volume = {10},\n  number = {1},\n  pages = {3792},\n  issn = {2041-1723},\n  doi = {10.1038/s41467-019-11633-8},\n  urldate = {2023-02-28},\n  abstract = {Abstract             Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is directly limited by the internally generated variability of cortical circuits, which remains largely unexplored. Here, we quantify this internally generated variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We find that stochastic neurotransmitter release is a critical component of internally generated variability, causing rapidly diverging, chaotic recurrent network dynamics. Surprisingly, the same nonlinear recurrent network dynamics can transiently overcome the chaos in response to weak feed-forward thalamocortical inputs, and support reliable spike times with millisecond precision. Our model shows that the noisy and chaotic network dynamics of recurrent cortical microcircuitry are compatible with stimulus-evoked, millisecond spike-time reliability, resolving a long-standing debate.},\n  langid = {english}\n}\n\n@article{pagkalosIntroducingDendrifyFramework2023,\n  title = {Introducing the {{Dendrify}} Framework for Incorporating Dendrites to Spiking Neural Networks},\n  author = {Pagkalos, Michalis and Chavlis, Spyridon and Poirazi, Panayiota},\n  year = {2023},\n  month = jan,\n  journal = {Nature Communications},\n  volume = {14},\n  number = {1},\n  pages = {131},\n  issn = {2041-1723},\n  doi = {10.1038/s41467-022-35747-8},\n  urldate = {2023-02-28},\n  abstract = {Abstract             Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.},\n  langid = {english}\n}\n\n@article{ramaswamyDataDrivenModelingCholinergic2018,\n  title = {Data-{{Driven Modeling}} of {{Cholinergic Modulation}} of {{Neural Microcircuits}}: {{Bridging Neurons}}, {{Synapses}} and {{Network Activity}}},\n  shorttitle = {Data-{{Driven Modeling}} of {{Cholinergic Modulation}} of {{Neural Microcircuits}}},\n  author = {Ramaswamy, Srikanth and Colangelo, Cristina and Markram, Henry},\n  year = {2018},\n  month = oct,\n  journal = {Frontiers in Neural Circuits},\n  volume = {12},\n  pages = {77},\n  issn = {1662-5110},\n  doi = {10.3389/fncir.2018.00077},\n  urldate = {2022-06-08},\n  abstract = {Neuromodulators, such as acetylcholine (ACh), control information processing in neural microcircuits by regulating neuronal and synaptic physiology. Computational models and simulations enable predictions on the potential role of ACh in reconfiguring network activity. As a prelude into investigating how the cellular and synaptic effects of ACh collectively influence emergent network dynamics, we developed a data-driven framework incorporating phenomenological models of the physiology of cholinergic modulation of neocortical cells and synapses. The first-draft models were integrated into a biologically detailed tissue model of neocortical microcircuitry to investigate the effects of levels of ACh on diverse neuron types and synapses, and consequently on emergent network activity. Preliminary simulations from the framework, which was not tuned to reproduce any specific ACh-induced network effects, not only corroborate the long-standing notion that ACh desynchronizes spontaneous network activity, but also predict that a dose-dependent activation of ACh gives rise to a spectrum of neocortical network activity. We show that low levels of ACh, such as during non-rapid eye movement (nREM) sleep, drive microcircuit activity into slow oscillations and network synchrony, whereas high ACh concentrations, such as during wakefulness and REM sleep, govern fast oscillations and network asynchrony. In addition, spontaneous network activity modulated by ACh levels shape spike-time cross-correlations across distinct neuronal populations in strikingly different ways. These effects are likely due to the regulation of neurons and synapses caused by increasing levels of ACh, which enhances cellular excitability and decreases the efficacy of local synaptic transmission. We conclude by discussing future directions to refine the biological accuracy of the framework, which will extend its utility and foster the development of hypotheses to investigate the role of neuromodulators in neural information processing.},\n  langid = {english}\n}\n\n@article{reyes-sanchezAutomatizedOfflineOnline2023,\n  title = {Automatized Offline and Online Exploration to Achieve a Target Dynamics in Biohybrid Neural Circuits Built with Living and Model Neurons},\n  author = {{Reyes-Sanchez}, Manuel and Amaducci, Rodrigo and {Sanchez-Martin}, Pablo and Elices, Irene and Rodriguez, Francisco B. and Varona, Pablo},\n  year = {2023},\n  month = jul,\n  journal = {Neural Networks},\n  volume = {164},\n  pages = {464--475},\n  issn = {08936080},\n  doi = {10.1016/j.neunet.2023.04.034},\n  urldate = {2023-05-24},\n  abstract = {Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goal.},\n  langid = {english}\n}\n\n@article{royIonChannelDegeneracy2022,\n  title = {Ion-channel Degeneracy and Heterogeneities in the Emergence of Complex Spike Bursts in {{CA3}} Pyramidal Neurons},\n  author = {Roy, Rituparna and Narayanan, Rishikesh},\n  year = {2022},\n  month = oct,\n  journal = {The Journal of Physiology},\n  pages = {JP283539},\n  issn = {0022-3751, 1469-7793},\n  doi = {10.1113/JP283539},\n  urldate = {2023-02-28},\n  langid = {english}\n}\n\n@techreport{sanabriaCellTypeSpecificConnectivity2023,\n  type = {Preprint},\n  title = {Cell-{{Type Specific Connectivity}} of {{Whisker-Related Sensory}} and {{Motor Cortical Input}} to {{Dorsal Striatum}}},\n  author = {Sanabria, Branden D. and Baskar, Sindhuja S. and Yonk, Alex J. and Lee, Christian R. and Margolis, David J.},\n  year = {2023},\n  month = mar,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.03.06.531405},\n  urldate = {2023-05-24},\n  abstract = {Abstract           The anterior dorsolateral striatum (DLS) is heavily innervated by convergent excitatory projections from the primary motor (M1) and sensory cortex (S1) and is considered an important site of sensorimotor integration. M1 and S1 corticostriatal synapses have functional differences in the strength of their connections with striatal spiny projection neurons (SPNs) and fast-spiking interneurons (FSIs) in the DLS, and as a result exert an opposing influence on sensory-guided behaviors. In the present study, we tested whether M1 and S1 inputs exhibit differences in the subcellular anatomical distribution onto striatal neurons. We injected adeno-associated viral vectors encoding spaghetti monster fluorescent proteins (sm.FPs) into M1 and S1, and used confocal microscopy to generate 3D reconstructions of corticostriatal inputs to single identified SPNs and FSIs obtained through ex-vivo patch-clamp electrophysiology. We found that SPNs are less innervated by S1 compared to M1, but FSIs receive a similar number of inputs from both M1 and S1. In addition, M1 and S1 inputs were distributed similarly across the proximal, medial, and distal regions of SPNs and FSIs. Notably, clusters of inputs were prevalent in SPNs but not FSIs. Our results suggest that SPNs have stronger functional connectivity to M1 compared to S1 due to a higher density of synaptic inputs. The clustering of M1 and S1 inputs onto SPNs but not FSIs suggest that cortical inputs are integrated through cell-type specific mechanisms and more generally have implications for how sensorimotor integration is performed in the striatum.                        Significance Statement             The dorsolateral striatum (DLS) is a key brain area involved in sensorimotor integration due to its dense innervation by the primary motor (M1) and sensory cortex (S1). However, the quantity and anatomical distribution of these inputs to the striatal cell population has not been well characterized. In this study we demonstrate that corticostriatal projections from M1 and S1 differentially innervate spiny projection neurons (SPNs) and fast-spiking interneurons (FSIs) in the DLS. S1 inputs innervate SPNs less than M1 and are likely to form synaptic clusters in SPNs but not in FSIs. These findings suggest that sensorimotor integration is partly achieved by differences in the synaptic organization of corticostriatal inputs to local striatal microcircuits.},\n  langid = {english}\n}\n\n@misc{sarmaIntegrativeBiologicalSimulation2019,\n  title = {Integrative {{Biological Simulation}}, {{Neuropsychology}}, and {{AI Safety}}},\n  author = {Sarma, Gopal P. and Safron, Adam and Hay, Nick J.},\n  year = {2019},\n  month = jan,\n  number = {arXiv:1811.03493},\n  eprint = {arXiv:1811.03493},\n  publisher = {{arXiv}},\n  urldate = {2022-06-08},\n  abstract = {We describe a biologically-inspired research agenda with parallel tracks aimed at AI and AI safety. The bottomup component consists of building a sequence of biophysically realistic simulations of simple organisms such as the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the zebrafish Danio rerio to serve as platforms for research into AI algorithms and system architectures. The top-down component consists of an approach to value alignment that grounds AI goal structures in neuropsychology, broadly considered. Our belief is that parallel pursuit of these tracks will inform the development of value-aligned AI systems that have been inspired by embodied organisms with sensorimotor integration. An important set of side benefits is that the research trajectories we describe here are grounded in long-standing intellectual traditions within existing research communities and funding structures. In addition, these research programs overlap with significant contemporary themes in the biological and psychological sciences such as data/model integration and reproducibility.},\n  archiveprefix = {arxiv},\n  langid = {english},\n  keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing,Quantitative Biology - Neurons and Cognition}\n}\n\n@inproceedings{shenAutomaticFittingNeuron2018,\n  title = {Automatic Fitting of Neuron Parameters},\n  booktitle = {2018 {{IEEE}} 3rd {{International Conference}} on {{Cloud Computing}} and {{Big Data Analysis}} ({{ICCCBDA}})},\n  author = {Shen, Jiamin and Wang, Ye and Cao, Lihong},\n  year = {2018},\n  month = apr,\n  pages = {558--562},\n  publisher = {{IEEE}},\n  address = {{Chengdu}},\n  doi = {10.1109/ICCCBDA.2018.8386578},\n  urldate = {2022-06-08},\n  abstract = {Artificial neural networks are inspired by biological neural networks formed by many real neurons with spiking activities. It is important to simulate the spiking activities under different conditions. It is well known that the HodgkinHuxley (HH) equations can be used for simulation. However, we usually don't know the conductance of ion channels in the equation, which is required for simulation. In this paper, we develop a parallel genetic algorithm to estimate the conductance with a visual software tool. By fitting the experimental data, it is shown that when the number of individuals in the genetic algorithm is above 2000, the 5th generation can yield a near optimal solution and achieve a good fitting result.},\n  isbn = {978-1-5386-4301-3},\n  langid = {english}\n}\n\n@article{sinhaActiveDendritesLocal2022,\n  title = {Active {{Dendrites}} and {{Local Field Potentials}}: {{Biophysical Mechanisms}} and {{Computational Explorations}}},\n  shorttitle = {Active {{Dendrites}} and {{Local Field Potentials}}},\n  author = {Sinha, Manisha and Narayanan, Rishikesh},\n  year = {2022},\n  month = may,\n  journal = {Neuroscience},\n  volume = {489},\n  pages = {111--142},\n  issn = {03064522},\n  doi = {10.1016/j.neuroscience.2021.08.035},\n  urldate = {2022-06-08},\n  abstract = {Neurons and glial cells are endowed with membranes that express a rich repertoire of ion channels, transporters, and receptors. The constant flux of ions across the neuronal and glial membranes results in voltage fluctuations that can be recorded from the extracellular matrix. The high frequency components of this voltage signal contain information about the spiking activity, reflecting the output from the neurons surrounding the recording location. The low frequency components of the signal, referred to as the local field potential (LFP), have been traditionally thought to provide information about the synaptic inputs that impinge on the large dendritic trees of various neurons. In this review, we discuss recent computational and experimental studies pointing to a critical role of several active dendritic mechanisms that can influence the genesis and the locationdependent spectro-temporal dynamics of LFPs, spanning different brain regions. We strongly emphasize the need to account for the several fast and slow dendritic events and associated active mechanisms \\textemdash{} including gradients in their expression profiles, inter- and intra-cellular spatio-temporal interactions spanning neurons and glia, heterogeneities and degeneracy across scales, neuromodulatory influences, and activitydependent plasticity \\textemdash{} towards gaining important insights about the origins of LFP under different behavioral states in health and disease. We provide simple but essential guidelines on how to model LFPs taking into account these dendritic mechanisms, with detailed methodology on how to account for various heterogeneities and electrophysiological properties of neurons and synapses while studying LFPs.},\n  langid = {english}\n}\n\n@inproceedings{sivagnanamNeuroscienceGatewayEnabling2018,\n  title = {The {{Neuroscience Gateway}}: {{Enabling Large Scale Modeling}} and {{Data Processing}} in {{Neuroscience}}},\n  shorttitle = {The {{Neuroscience Gateway}}},\n  booktitle = {Proceedings of the {{Practice}} and {{Experience}} on {{Advanced Research Computing}}},\n  author = {Sivagnanam, Subhashini and Yoshimoto, Kenneth and Carnevale, Nicholas T. and Majumdar, Amit},\n  year = {2018},\n  month = jul,\n  pages = {1--7},\n  publisher = {{ACM}},\n  address = {{Pittsburgh PA USA}},\n  doi = {10.1145/3219104.3219139},\n  urldate = {2022-06-08},\n  abstract = {The NSF funded Neuroscience Gateway (NSG) has been in operation since the early 2013. We originally designed NSG to reduce technical and administrative barriers that exist to using high performance computing resources for computational neuroscientists. In the last two years, in addition to computational neuroscientists, cognitive and experimental neuroscientists are also using NSG. Currently NSG has over 600 registered users and it is steadily growing. Users can access NSG via a web portal and via RESTful programmatic access. A particular usage mode of programmatic access to NSG enables users of community neuroscience projects such as the Open Source Brain, research projects within the European Human Brain Project and others to access HPC resources via NSG without having to obtain their own accounts on NSG. Based on demand and usage, over the last five years we have successfully acquired increasingly larger allocations (millions to \\textasciitilde ten million core hours) on resources of the Extreme Science and Engineering Discovery Environment (XSEDE) program via the competitive peer review process. We will discuss the overall NSG architecture. We implemented NSG from the generic CIPRES science gateway software to create the NSG specifically for the neuroscience community. We will describe the front end user interface, based on web portal and RESTful programmatic access, and the backend architecture. We will discuss how NSG is evolving over time in response to the interests and needs of the neuroscience community, adapting itself to become a dissemination platform for new tools and pipelines, and becoming an environment for modelers and experimentalists to jointly develop models.},\n  isbn = {978-1-4503-6446-1},\n  langid = {english}\n}\n\n@article{stocktonIntegratingAllenBrain2017,\n  title = {Integrating the {{Allen Brain Institute Cell Types Database}} into {{Automated Neuroscience Workflow}}},\n  author = {Stockton, David B. and Santamaria, Fidel},\n  year = {2017},\n  month = oct,\n  journal = {Neuroinformatics},\n  volume = {15},\n  number = {4},\n  pages = {333--342},\n  issn = {1539-2791, 1559-0089},\n  doi = {10.1007/s12021-017-9337-x},\n  urldate = {2022-06-08},\n  abstract = {We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.},\n  langid = {english}\n}\n\n@article{yegenogluExploringParameterHyperParameter2022,\n  title = {Exploring {{Parameter}} and {{Hyper-Parameter Spaces}} of {{Neuroscience Models}} on {{High Performance Computers With Learning}} to {{Learn}}},\n  author = {Yegenoglu, Alper and Subramoney, Anand and Hater, Thorsten and {Jimenez-Romero}, Cristian and Klijn, Wouter and P{\\'e}rez Mart{\\'i}n, Aar{\\'o}n and {van der Vlag}, Michiel and Herty, Michael and Morrison, Abigail and {Diaz-Pier}, Sandra},\n  year = {2022},\n  month = may,\n  journal = {Frontiers in Computational Neuroscience},\n  volume = {16},\n  pages = {885207},\n  issn = {1662-5188},\n  doi = {10.3389/fncom.2022.885207},\n  urldate = {2023-02-28},\n  abstract = {Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.},\n  langid = {english}\n}\n"
  },
  {
    "path": "misc/github_wiki/bibtex/mentions_BPO_extra.bib",
    "content": "\n@InProceedings{pmlr-v96-lueckmann19a,\n  title = \t {Likelihood-free inference with emulator networks},\n  author =       {Lueckmann, Jan-Matthis and Bassetto, Giacomo and Karaletsos, Theofanis and Macke, Jakob H.},\n  booktitle = \t {Proceedings of The 1st Symposium on Advances in Approximate Bayesian Inference},\n  pages = \t {32--53},\n  year = \t {2019},\n  editor = \t {Ruiz, Francisco and Zhang, Cheng and Liang, Dawen and Bui, Thang},\n  volume = \t {96},\n  series = \t {Proceedings of Machine Learning Research},\n  month = \t {02 Dec},\n  publisher =    {PMLR},\n  pdf = \t {http://proceedings.mlr.press/v96/lueckmann19a/lueckmann19a.pdf},\n  url = \t {https://proceedings.mlr.press/v96/lueckmann19a.html},\n  abstract = \t {Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based models which do not permit tractable likelihoods. We present a new ABC method which uses probabilistic neural emulator networks to learn synthetic likelihoods on simulated data - both ’local’ emulators which approximate the likelihood for specific observed data, as well as ’global’ ones which are applicable to a range of data. Simulations are chosen adaptively using an acquisition function which takes into account uncertainty about either the posterior distribution of interest, or the parameters of the emulator. Our approach does not rely on user-defined rejection thresholds or distance functions. We illustrate inference with emulator networks on synthetic examples and on a biophysical neuron model, and show that emulators allow accurate and efficient inference even on problems which are challenging for conventional ABC approaches.}\n}\n\n@unpublished{appukuttan:hal-03586825,\n  TITLE = {{A Software Framework for Validating Neuroscience Models}},\n  AUTHOR = {Appukuttan, Shailesh and Sharma, Lungsi and Garcia-Rodriguez, Pedro and Davison, Andrew},\n  URL = {https://hal.archives-ouvertes.fr/hal-03586825},\n  NOTE = {working paper or preprint},\n  YEAR = {2022},\n  MONTH = Feb,\n  PDF = {https://hal.archives-ouvertes.fr/hal-03586825/file/Validation__Methods__Paper___Draft___v1__Reduced_.pdf},\n  HAL_ID = {hal-03586825},\n  HAL_VERSION = {v1},\n}\n\n@article{doi:10.1098/rsob.220073,\n  author = {Jedlicka, Peter  and Bird, Alexander D.  and Cuntz, Hermann },\n  title = {Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons},\n  journal = {Open Biology},\n  volume = {12},\n  number = {7},\n  pages = {220073},\n  year = {2022},\n  doi = {10.1098/rsob.220073},\n  URL = {https://royalsocietypublishing.org/doi/abs/10.1098/rsob.220073},\n  eprint = {https://royalsocietypublishing.org/doi/pdf/10.1098/rsob.220073},\n  abstract = { Neurons encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible while effectively fulfilling their functions. Cells displaying the best performance for such multi-task trade-offs are said to be Pareto optimal, with their ion channel configurations underpinning their functionality. Ion channel degeneracy, however, implies that multiple ion channel configurations can lead to functionally similar behaviour. Therefore, instead of a single model, neuroscientists often use populations of models with distinct combinations of ionic conductances. This approach is called population (database or ensemble) modelling. It remains unclear, which ion channel parameters in the vast population of functional models are more likely to be found in the brain. Here we argue that Pareto optimality can serve as a guiding principle for addressing this issue by helping to identify the subpopulations of conductance-based models that perform best for the trade-off between economy and functionality. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds, potentially explaining experimentally observed ion channel correlations. Conversely, Pareto inference might also help deduce neuronal functions from high-dimensional Patch-seq data. In summary, Pareto optimality is a promising framework for improving population modelling of neurons and their circuits. }\n}\n"
  },
  {
    "path": "misc/github_wiki/bibtex/poster_uses_BPO.bib",
    "content": "@inproceedings{damartDataDrivenBuilding2020,\n  title = {Data Driven Building of Realistic Neuron Model Using {{IBEA}} and {{CMA}} Evolution Strategies},\n  booktitle = {Proceedings of the 2020 {{Genetic}} and {{Evolutionary Computation Conference Companion}}},\n  author = {Damart, Tanguy and Van Geit, Werner and Markram, Henry},\n  year = {2020},\n  month = jul,\n  pages = {35--36},\n  publisher = {{ACM}},\n  address = {{Canc\\'un Mexico}},\n  doi = {10.1145/3377929.3398161},\n  isbn = {978-1-4503-7127-8},\n  langid = {english}\n}\n\n@misc{rizzaRealisticModel,\n  title = {A Realistic Model of Cerebellar Stellate Neurons Predicts Intrinsic Excitability and the Impact of Synaptic Inputs},\n  author = {Rizza, Martina Francesca and Locatelli, Francesca and Masoli, Stefano and Prestori, Francesca and Sanchez-Ponce, Diana and Munoz, Alberto and D‘Angelo, Egidio},\n  year = {2018},\n  howpublished = {https://www.researchgate.net/profile/Joseph-Davids/publication/336990052_Artificial_nano-intelligence_Using_deep_learning_models_to_study_the_formation_of_gold_nanoparticles/links/5dbdd4194585151435e24dab/Artificial-nano-intelligence-Using-deep-learning-models-to-study-the-formation-of-gold-nanoparticles.pdf#page=98}\n},\n\n@misc{nylenReconstructingStriatal,\n  title = {Reconstructing the striatal microcircuit in silico},\n  author = {Nylén, Johanna Frost and Hjorth, Johannes and Kozlov, Alexander and Lindroos, Robert and Carannante, Ilaria and Suryanarayana, Shreyas M. and Silberberg, Gilad and Kotaleski, Jeanette Hellgren and Grillner, Sten},\n  year = {2018},\n  howpublished = {https://www.researchgate.net/profile/Joseph-Davids/publication/336990052_Artificial_nano-intelligence_Using_deep_learning_models_to_study_the_formation_of_gold_nanoparticles/links/5dbdd4194585151435e24dab/Artificial-nano-intelligence-Using-deep-learning-models-to-study-the-formation-of-gold-nanoparticles.pdf#page=72}\n},\n\n@misc{tognolinaModeling,\n  title = {Modeling optimization procedures predict specific filtering channels in the cerebellar granule cell layer},\n  author = {Tognolina, Marialuisa and Masoli, Stefano and Moccia, Francesco and D‘Angelo, Egidio},\n  year = {2018},\n  howpublished = {https://www.researchgate.net/profile/Joseph-Davids/publication/336990052_Artificial_nano-intelligence_Using_deep_learning_models_to_study_the_formation_of_gold_nanoparticles/links/5dbdd4194585151435e24dab/Artificial-nano-intelligence-Using-deep-learning-models-to-study-the-formation-of-gold-nanoparticles.pdf#page=86}\n},\n\n@misc{shieldsOptimizedFitting,\n  title = {Optimized Fitting of a Stochastic Auditory Nerve Fiber Model to Patch-Clamp Data},\n  author = {Shields, Daniel and Rutherford, Mark A. and Bruce, Ian C.},\n  howpublished = {https://vepimg.b8cdn.com/uploads/vjfnew/content/files/16258518561401-poster-pdf1625851856.pdf}\n}\n"
  },
  {
    "path": "misc/github_wiki/bibtex/thesis_mentions_BPO.bib",
    "content": "\n@phdthesis{Cremonesi:271927,\n      title = {Computational characteristics and hardware implications of  brain tissue simulations},\n      author = {Cremonesi, Francesco},\n      publisher = {EPFL},\n      school = {École Polytechnique Fédérale de Lausanne},\n      address = {Lausanne},\n      pages = {196},\n      year = {2019},\n      abstract = {Understanding the link between the brain's anatomy and its  function through computer\nsimulations of neural tissue  models is a widely used approach in computational  neuroscience.\nThis technique enables rapid prototyping and  testing of hypotheses, allowing researchers to\nbridge the  scales of biological phenomena. Until recently, the  constant trend of improvement\nin computational power has  supported an exponential growth in the scale and level of  detail\nof in silico experiments. However, a systematic  characterization of the performance landscape\nhas not yet  been carried out.\nIn this work we intend to capture  intrinsic computational properties of the existing  mod-\nelling abstractions and answer questions about the  intricate relationship between simulation\nalgorithms and  modern hardware architecture. Our first contribution is a  novel set of hardware-\nagnostic metrics that enables us to  bring focus to the heterogeneous landscape of brain  tissue\nmodels. We develop a methodology able to capture  subtle differences between cell-based\nmodels and quantify  their impact on performance based on hardware features. We  show that\nlumping simulation experiments together by  referring to numbers of neurons and synapses\nwithout  further detail hides fundamental differences in  computational and hardware require-\nments across models. In  addition to analysing different neuron representations, we  investigate\nthe impact of biological heterogeneity on the  performance of a cortical microcircuit model.\nOur analysis  indicates that while general-purpose computers have until  now sustained high-\nperformance simulations of all brain  tissue models, the next generation of in silico models\nwill  require hardware tailored to the underlying abstraction. We  find that all formalisms\nsaturate the memory bandwidth with  a fairly small number of shared memory threads, but\nthe  reasons behind this are quite different: conductance-based  models are dominated by the\nlarge memory traffic of  clock-driven kernels, while current-based models are most  affected\nby event-driven execution and memory latency. In  distributed simulations the latency of the\ninterconnect  fabric is the root cause for a significant degradation in  performance.\nWe argue that performance analyses such as  ours are required to enable the next generation\nof brain  tissue simulations - or else scientific progress risks  being hindered by the presence\nof severe hardware  bottlenecks. Our methodology provides a common tool to  facilitate the\ncommunication between modellers, developers  and hardware designers in order to sustain\nthe larger  memory and performance requirements of future brain tissue  simulations.},\n      url = {http://infoscience.epfl.ch/record/271927},\n      doi = {10.5075/epfl-thesis-9767},\n}\n\n@mastersthesis{Johansson1291310,\n   author = {Johansson, Oscar},\n   institution = {Linköping University, Department of Computer and Information Science},\n   pages = {60},\n   school = {Linköping University, Department of Computer and Information Science},\n   title = {Weight Estimation and Evaluation of User Suggestions in Mobile Browsing},\n   abstract = {This study investigates the suggestion system of a mobile browser. The goal of a suggestion system is to assist the user by presenting relevant suggestions in an ordered list. By weighting the different types of suggestions presented to the user, such as history, bookmarks etc., it is investigated how this affects the performance of the suggestion sys- tem. The performance is measured using the position, error and Mean Reciprocal Rank of the chosen suggestion as well as the number of written characters. It is also measured if the user chose to not use the suggestion system, by searching or entering the entire URL. The weights were estimated using a Genetic Algorithm. The evaluation was done by performing an A/B test, were the control group used an unweighted system and the test group used the weights estimated by the genetic algorithm. The results from the A/B test were statistically analyzed using BEST and Bootstrap. The results showed an improvement of position, number of written characters, MMR and the error. There was no change in how much the user used the suggestion system. The thesis concluded that there is a correlation between the position of the desired suggestion and when the user stops typing, and that weighting types is a way to improve said position. The thesis also concludes that there is a need for future work in regards to evaluation of the optimization algorithm and error measurement. },\n   year = {2019}\n}\n\n@phdthesis{Silverstein1187505,\n   author = {Silverstein, David N.},\n   institution = {KTH, Computational Science and Technology (CST)},\n   note = {QC 20180305},\n   school = {KTH, Computational Science and Technology (CST)},\n   title = {Investigations of neural attractor dynamics in human visual awareness},\n   series = {TRITA-EECS-AVL},\n   number = {2018:18},\n   keywords = {attractor dynamics, visual awareness, visual perception, visual attention, attentional blink, backward masking, gliomas, brain tumor, white matter, cell assemblies, neocortical model, threat response, fear signaling, amygdala},\n   abstract = {What we see, how we see it and what emotions may arise from stimuli has long been studied by philosophers, psychologists, medical doctors and neuroscientists. This thesis work investigates a particular view on the possible dynamics, utilizing computational models of spiking neural attractor networks. From neurological studies on humans and other primates, we know visual perception and recognition of objects occur partly along the visual ventral stream, from V1 to V2, V4, IT and downstream to other areas. This visual awareness can be both conscious and unconscious and may also trigger an emotional response. As seen from many psychophysical experiments in backward masking (BM) and attentional blink (AB), some spatial and temporal dynamics can determine what becomes visually conscious and what does not. To explore this computationally, biophysical models of BM and AB were implemented and simulated to mimic human experiments, with the assumption that neural assemblies as attractor networks activate and propagate along the ventral stream and beyond. It was observed that attractor interference between percepts in sensory and associative cortex can occur during this activity. During typical human AB experimental trials in which two expected target symbols amongst distractors are presented less than 500 ms apart, the second target is often not reported as seen. When simulating this paradigm as two expected target neural attractors amongst distractors, it was observed in the present work that an initial attractor in associative cortex can impede the activation and propagation of a following attractor, which mimics missing conscious perception of the second target. It was also observed that simulating the presence of benzodiazepines (GABA agonists) will slow cortical dynamics and increase the AB, as previously shown in human experiments. During typical human BM experimental trials in which a brief target stimulus is followed by a masking stimulus after a short interval of less than 100 ms, recognition of the target can be impaired when in close spatial proximity. When simulating this paradigm using a biophysical model of V1 and V2 with feedforward and feedback connections, attractor targets were activated in V1 before imposition of a proximal metacontrast mask. If an activating target attractor in V1 is quiesced enough with lateral inhibition from a mask, or not reinforced by recurrent feedback from feedforward activation in V2, it is more likely to burn out before becoming fully active and progressing through V2 and beyond. BM was also simulated with an increasing stimulus interval and with the presence and absence of feedback activity. This showed that recurrent feedback diminishes BM effects and can make conscious perception more likely. To better understand possible emotional components of visual perception and early regulation, visual signaling pathways to the amygdala were investigated and proposed for emotional salience and the possible onset of fear. While one subcortical and likely unconscious pathway (before amydala efferent signaling) was affirmed via the superior colliculus and pulvinar, four others traversed through the ventral stream. One traversed though IT on recognition, another via the OFC on conditioning, and two other possibly conscious pathways traversed though the parietal and then prefrontal cortex, one excitatory pathway via the ventral-medial area and one regulatory pathway via the ventral-lateral area. Predicted latencies were determined for these signaling pathways, which can be experimentally testable. The conscious feeling of fear itself may not occur until after interoceptive inspection. A pathology of attractor dynamics was also investigated, which can occur from the presence of a brain tumor in white matter. Due to degradation from tumor invasion of white matter projections between two simulated neocortical patches, information transfer between separate neural attractors degraded, leading first to recall errors and later to epileptic-like activity. Neural plasticity could partially compensate up to a point, before transmission failure. This suggests that once epileptic seizures start in glioma patients, compensatory plasticity may already be exhausted. Interestingly, the presence of additional noise could also partially compensate for white matter loss. },\n   ISBN = {978-91-7729-706-2},\n   year = {2018}\n}\n\n@misc{10481/70162,\nyear = {2021},\nurl = {http://hdl.handle.net/10481/70162},\nabstract = {The cerebellum is a critical brain area for sensorimotor and also non-motor functions\nsuch as cognitive and emotional processes. Cerebellar lesions contribute to pathological\nsyndromes such as autism or schizophrenia. However, the primitives under which the\ncerebellum, and the whole brain, operate at a functional and dysfunctional level are still\nunclear.\nTo address the complexity of the \"diseased\" brain system, it is necessary to extract the\nrelevant underlying molecular mechanisms. The availability of large volumes of biomedical\ndata usually makes it difficult to extract this relevant information and interpret it\ncomprehensively. In this thesis, we have made a preliminary experimentation to analyze\ngenetic correlations between diseases with different clinical symptomatologies and/or clinical\nprognosis (and still based on similar molecular mechanisms). For this purpose, we have\ndeveloped a methodology for the identification and functional annotation of the most relevant\ngenes in disease. This methodology integrates current systems biology methods, such as\nprotein-protein interaction (PPI) networks, together with multidimensional data sets from\ndifferent biological levels. The objectives of this first part of the thesis are: the identification of\npotential diagnostic biomarkers (corresponding to key nodes in the biological and molecular\nprocesses of the interactome); the deductive analysis of multidimensional data as an\nalternative to other search systems; and the extraction of connections between disorders\n(comorbidities) that are a priori unrelated and that usually escape these traditional systems.\nAlthough the methodology is of general purpose, we have applied it to a set of diseases called\nchannelopathies, where ion channels are altered and which generate a wide phenotypic\nvariability. We conclude that our methodology is flexible, fast and easy to apply. Furthermore,\nit is able to find more correlations between relevant genes than other two traditional methods.\nUnderstanding the cerebellar operation in information processing requires decoding\nthe intrinsic functional dynamics of healthy neurons. The tools provided by computational\nneuroscience allow developing large-scale computational models for the study of these\ninformation processing primitives. The most abundant and smallest neurons not only in the\ncerebellar input layer, but also in the whole brain, are the cerebellar granule cells (GrCs). These\nneurons play a crucial role in the creation of somatosensory information representations. Their\nfiring characteristics are related to synchronization, rhythmicity and learning in the cerebellum.\nOne of these features is the frequency of enhanced bursting (i.e., spiking resonance). This\ncomplex firing pattern has been proposed to facilitate input signal transmission in the thetafrequency\nband (4-12Hz). However, the functional role of this feature in the operation of the\ngranular layer (the input layer of the cerebellar cortex) is still unclear. Moreover, inherent complex dynamics such as resonance are usually ignored in most efficient computational\nmodels. The main goal of this thesis is the creation of different mathematical models of\ncerebellar GrCs that meet two requirements: to be efficient enough to allow the simulation of\nlarge-scale neuron networks, and to be biologically plausible enough to enable the evaluation\nof the functional impact of these nonlinear dynamics on the information transmission. Indeed,\na high degree of biological realism in efficient models allows research at levels where in vivo\nor in vitro experimental biology is limited.\nMethodologically, in this thesis we have chosen the \"adaptive exponential integrateand-\nfire\" (AdEx) type of model as the simplified neuron model (it has only two differential\nequations and few parameters) that meets both realism and low computational cost. This\nmodel fits quite well the firing characteristics of real cells, but some of its parameters cannot\nbe directly fitted with measurable experimental values. Therefore, an optimization method is\nnecessary to best fit the parameters to the biological data. We have focused on addressing\nthis challenging optimization problem.\nFirst, we have developed a parametric optimization methodology based on genetic\nalgorithms (GA) applied to the case of GrC. We have presented the obtained AdEx neuron\nmodels and demonstrated their suitability to reproduce not only the main firing properties of\nreal GrCs (including resonance), but also emergent features not defined in the GA (within the\ncost function to be optimized).\nSecond, we evaluated four alternative algorithms, which are the most widely used and\nsuccessful in other fields such as engineering.\nFinally, in the last part of this thesis we have presented an advanced optimization\nmethodology based on multimodal algorithms. The advantage of this approach is that, after a\nsingle optimization process, instead of obtaining an only one candidate numerically\noutperforming the other candidates, as in the previous cases (a single solution), we obtain a\nsparse population of different neuron models. That is, a heterogeneous population of neurons\nof the same type with intrinsic variations in their properties. From this set of promising neuron\nmodels, the researcher can choose and filter based on the desired biological plausibility (and\nneuronal parameter configuration). Thus, we also studied how the target properties of the\nneuron could be obtained with diverse internal parameter configurations. We explored the\nparameter space and its impact on the subset of neuronal properties that we aim to reproduce.},\norganization = {Tesis Univ. Granada.,\n“Human Brain Project” [HBP, Specific Grant Agreement 2 (SGA2 H2020-RIA 785907) and 3 (SGA3 H2020-RIA 945539)] financiado por EU (H2020),\n“Integración sensorimotora para control adaptativo mediante aprendizaje en cerebelo y centros nerviosos relacionados. Aplicación en robótica” [INTSENSO (MICINN-FEDER-PID2019-109991GBI00)] financiado Ministerio de Ciencia e Innovación (MICINN),\n“Cerebelo y oliva inferior en tareas de adaptación sensorimotora” [CEREBIO (J.A. P18-FR-2378)] financiado por la Junta de Andalucía},\npublisher = {Universidad de Granada},\nkeywords = {Dinámica Neuronal, Procesamiento de información, Capa granular del cerebelo, Neuronal Dynamics, Information processing, Granular layer of the cerebellum},\ntitle = {Estudio del Impacto de la Dinámica Neuronal en el Procesamiento de Información en la Capa Granular del Cerebelo},\nauthor = {Marín Alejo, Milagros},\n}\n\n@phdthesis{AlqahtaniMultiDomain,\n   author = {Alqahtani,  Abdulrahman},\n   institution = {School of Biomedical Engineering, UNSW},\n   school = {School of Biomedical Engineering, UNSW},\n   publisher = {UNSW, Sydney},\n   title = {A multi-domain continuum model of electrical stimulation of healthy and degenerate retina},\n   keywords = {Discrete model, Continuum model, Retinal electrical stimulation, Retinal implant, Retinal ganglion cells activation},\n   abstract = {Visual neuroprostheses aim to restore vision to patients suffering from degenerative retinal diseases such as retinitis pigmentosa and age-related macular degeneration. Development of visual implants faces a great number of challenges in both device design and stimulation strategy. Computational modelling is a powerful tool for exploring and testing new visual prostheses design and stimulation strategies. In this thesis, we have proposed and validated a new version of the classical cable equation valid for any fibre morphology, electrode configuration, or non-uniformity in ion channel expression, implemented using a finite element approach. Moreover, we developed the first continuum multi-domain model of retinal electrical stimulation to represent all main retinal ganglion cell (RGC) compartments. The continuum model was validated against discrete morphologically-realistic OFF and ON RGC models as well as RGC excitation thresholds reported in recently published in vitro experimental studies using intra- and extra-cellular electrical stimulation. The continuum model reproduced the same results as that of the discrete model and in vitro experimental studies. Furthermore, the first degenerate model of retinal electrical stimulation accounting for observed changes occurring in the whole retina was developed, using a detailed model of electrical stimulation of OFF and ON RGCs. Interestingly, the model predicted that suprachoroidal stimulation of the degenerate retina exhibited increased current thresholds, mainly due to the presence of the glial scar layer. In contrast, epiretinal stimulation thresholds were almost similar for both healthy and degenerate models, implying epiretinal prostheses can bypass the influence of the glial scar layer. Various stimulation strategies were examined for both healthy and degenerate retinal models. No significant difference among the three return electrode configurations (monopolar, quasi-monopolar and hexapolar) was found when the distance between electrodes and RGCs was less than the electrode diameter. Electrode spacing was the significant factor underlying increased current thresholds, where electrode size had a marginal impact among all three return electrode configurations. Stimulus pulse polarities and durations were found to have a significant impact on the localisation of evoked phosphenes. Moreover, virtual electrodes could be elicited by using an appropriate time shift between two stimulus waveforms applied to the active electrodes.},\n   year = {2019},\n}\n"
  },
  {
    "path": "misc/github_wiki/bibtex/thesis_uses_BPO.bib",
    "content": "\n@phdthesis{rizzaBiophysicallyDetailedCerebellar,\n  title = {A {{Biophysically Detailed Cerebellar Stellate Neuron Model Optimized}} with {{Particle Swarm Optimization Algorithm}}},\n  author = {Rizza, Martina Francesca},\n  pages = {137},\n  langid = {english},\n  school = {Universit{\\`a} Degli Studi Di Milano-Bicocca},\n  year = {2017}\n}\n\n@phdthesis{saraySystematicValidationDetailed,\n  title = {Systematic Validation of Detailed Models of Hippocampal Neurons Based on Electrophysiological Data},\n  author = {S{\\'a}ray, S{\\'a}ra},\n  pages = {112},\n  langid = {english},\n  school = {P{\\'a}zm{\\'a}ny P{\\'e}ter Catholic University},\n  year = {2021}\n}\n\n@phdthesis{deerasooriyaDynamicClampAnalysis,\n  title = {Dynamic Clamp Analysis of Ion Channel Function},\n  author = {Yadeesha Deerasooriya},\n  pages = {162},\n  langid = {english},\n  school = {The University Of Melbourne},\n  year = {2019}\n}\n\n@phdthesis{luckmannSimulationBasedInferenceforNeuroscienceandBeyond,\n  title = {Simulation-Based Inference for Neuroscience and Beyond},\n  author = {L{\\\"u}ckmann, Jan-Matthis},\n  langid = {english},\n  school = {Eberhard Karls Universit{\\\"a}t T{\\\"u}bingen},\n  year = {2022}\n}\n\n@phdthesis{nylenStriatumSilicoThesis,\n  title = {On Striatum in Silico {{Thesis}} for {{Doctoral Degree}} ({{Ph}}.{{D}}.)},\n  author = {Nyl{\\'e}n, Johanna Frost},\n  langid = {english},\n  school = {Karolinska Institutet},\n  year = {2023}\n}\n\n"
  },
  {
    "path": "misc/github_wiki/bibtex/uses_BPO.bib",
    "content": "@article{allamNeuronalPopulationModels2021a,\n  title = {Neuronal Population Models Reveal Specific Linear Conductance Controllers Sufficient to Rescue Preclinical Disease Phenotypes},\n  author = {Allam, Sushmita L. and Rumbell, Timothy H. and {Hoang-Trong}, Tuan and Parikh, Jaimit and Kozloski, James R.},\n  year = {2021},\n  month = nov,\n  journal = {iScience},\n  volume = {24},\n  number = {11},\n  pages = {103279},\n  issn = {25890042},\n  doi = {10.1016/j.isci.2021.103279},\n  urldate = {2023-02-28},\n  abstract = {Preclinical drug candidates are screened for their ability to ameliorate in vitro neuronal electrophysiology, and go/no-go decisions progress drugs to clinical trials based on population means across cells and animals. However, these measures do not mitigate clinical endpoint risk. Population-based modeling captures variability across multiple electrophysiological measures from healthy, disease, and drug phenotypes. We pursued optimizing therapeutic targets by identifying coherent sets of ion channel target modulations for recovering heterogeneous wild-type (WT) population excitability profiles from a heterogeneous Huntington's disease (HD) population. Our approach combines mechanistic simulations with population modeling of striatal neurons using evolutionary optimization algorithms to design `virtual drugs'. We introduce efficacy metrics to score populations and rank virtual drug candidates. We found virtual drugs using heuristic approaches that performed better than single target modulators and standard classification methods. We compare a real drug to virtual candidates and demonstrate a novel in silico triaging method.},\n  langid = {english}\n}\n\n@techreport{arnaudonControllingMorphoelectrophysiologicalVariability2023,\n  type = {Preprint},\n  title = {Controlling Morpho-Electrophysiological Variability of Neurons with Detailed Biophysical Models},\n  author = {Arnaudon, Alexis and Reva, Maria and Zbili, Mickael and Markram, Henry and Van Geit, Werner and Kanari, Lida},\n  year = {2023},\n  month = apr,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.04.06.535923},\n  urldate = {2023-05-24},\n  abstract = {Variability is a universal feature among biological units such as neuronal cells as they enable a robust encoding of a high volume of information in neuronal circuits and prevent hyper synchronizations such as epileptic seizures. While most computational studies on electrophysiological variability in neuronal circuits were done with simplified neuron models, we instead focus on the variability of detailed biophysical models of neurons. With measures of experimental variability, we leverage a Markov chain Monte Carlo method to generate populations of electrical models able to reproduce the variability from sets of experimental recordings. By matching input resistances of soma and axon initial segments with the one of dendrites, we produce a compatible set of morphologies and electrical models that faithfully represent a given morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells with continuous adapting firing type and show that morphological variability is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.},\n  langid = {english}\n}\n\n@article{ben-shalomNeuroGPUAcceleratingMulticompartment2022a,\n  title = {{{NeuroGPU}}: {{Accelerating}} Multi-Compartment, Biophysically Detailed Neuron Simulations on {{GPUs}}},\n  shorttitle = {{{NeuroGPU}}},\n  author = {{Ben-Shalom}, Roy and Ladd, Alexander and Artherya, Nikhil S. and Cross, Christopher and Kim, Kyung Geun and Sanghevi, Hersh and Korngreen, Alon and Bouchard, Kristofer E. and Bender, Kevin J.},\n  year = {2022},\n  month = jan,\n  journal = {Journal of Neuroscience Methods},\n  volume = {366},\n  pages = {109400},\n  issn = {01650270},\n  doi = {10.1016/j.jneumeth.2021.109400},\n  urldate = {2023-02-28},\n  abstract = {Background: The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utili\\- zation of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical opti\\- mization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. New Method: Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation. Results \\& comparison with existing methods: NeuroGPU can simulate most biologically detailed models 10\\textendash 200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (Cor\\- eNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple ({$>$} 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through largescale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. Conclusions: Thus, NeuroGPU is the fastest available platform that enables rapid simulation of multicompartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.},\n  langid = {english}\n}\n\n@article{bereckiSCN1AGainFunction2019a,\n  title = {{{{\\emph{SCN1A}}}} Gain of Function in Early Infantile Encephalopathy},\n  author = {Berecki, G{\\'e}za and Bryson, Alexander and Terhag, Jan and Maljevic, Snezana and Gazina, Elena V. and Hill, Sean L. and Petrou, Steven},\n  year = {2019},\n  month = apr,\n  journal = {Annals of Neurology},\n  volume = {85},\n  number = {4},\n  pages = {514--525},\n  issn = {0364-5134, 1531-8249},\n  doi = {10.1002/ana.25438},\n  urldate = {2023-02-28},\n  langid = {english}\n}\n\n@article{bolognaEBRAINSHodgkinHuxleyNeuron2022,\n  title = {The {{EBRAINS Hodgkin-Huxley Neuron Builder}}: {{An}} Online Resource for Building Data-Driven Neuron Models},\n  shorttitle = {The {{EBRAINS Hodgkin-Huxley Neuron Builder}}},\n  author = {Bologna, Luca Leonardo and Smiriglia, Roberto and Lupascu, Carmen Alina and Appukuttan, Shailesh and Davison, Andrew P. and Ivaska, Genrich and Courcol, Jean-Denis and Migliore, Michele},\n  year = {2022},\n  month = sep,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {16},\n  pages = {991609},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2022.991609},\n  urldate = {2023-02-28},\n  abstract = {In the last decades, brain modeling has been established as a fundamental tool for understanding neural mechanisms and information processing in individual cells and circuits at different scales of observation. Building data-driven brain models requires the availability of experimental data and analysis tools as well as neural simulation environments and, often, large scale computing facilities. All these components are rarely found in a comprehensive framework and usually require               ad hoc               programming. To address this, we developed the EBRAINS Hodgkin-Huxley Neuron Builder (HHNB), a web resource for building single cell neural models via the extraction of activity features from electrophysiological traces, the optimization of the model parameters via a genetic algorithm executed on high performance computing facilities and the simulation of the optimized model in an interactive framework. Thanks to its inherent characteristics, the HHNB facilitates the data-driven model building workflow and its reproducibility, hence fostering a collaborative approach to brain modeling.},\n  langid = {english}\n}\n\n@article{brysonGABAmediatedTonicInhibition2020a,\n  title = {{{GABA-mediated}} Tonic Inhibition Differentially Modulates Gain in Functional Subtypes of Cortical Interneurons},\n  author = {Bryson, Alexander and Hatch, Robert John and Zandt, Bas-Jan and Rossert, Christian and Berkovic, Samuel F. and Reid, Christopher A. and Grayden, David B. and Hill, Sean L. and Petrou, Steven},\n  year = {2020},\n  month = feb,\n  journal = {Proceedings of the National Academy of Sciences},\n  volume = {117},\n  number = {6},\n  pages = {3192--3202},\n  issn = {0027-8424, 1091-6490},\n  doi = {10.1073/pnas.1906369117},\n  urldate = {2023-02-28},\n  abstract = {Significance                            GABA ({$\\gamma$}-aminobutyric acid) is the brain's predominant inhibitory neurotransmitter and exerts a strong inhibitory influence through extrasynaptic GABA               A               receptors. This form of neurotransmission is known as tonic inhibition. Tonic inhibition is usually thought to reduce the excitability of all neurons, but here we show that it can selectively modulate the excitability of different types of neurons. Surprisingly, tonic inhibition can increase excitability in a common subtype of interneuron, and modeling results suggest this is achieved through the neuron's electrophysiological, or functional, properties. These results provide insight into the impact of tonic inhibition upon neural activity and suggest a mechanism through which GABA may modulate the excitability of neurons in a selective manner.                        ,                             The binding of GABA ({$\\gamma$}-aminobutyric acid) to extrasynaptic GABA               A               receptors generates tonic inhibition that acts as a powerful modulator of cortical network activity. Despite GABA being present throughout the extracellular space of the brain, previous work has shown that GABA may differentially modulate the excitability of neuron subtypes according to variation in chloride gradient. Here, using biophysically detailed neuron models, we predict that tonic inhibition can differentially modulate the excitability of neuron subtypes according to variation in electrophysiological properties. Surprisingly, tonic inhibition increased the responsiveness (or gain) in models with features typical for somatostatin interneurons but decreased gain in models with features typical for parvalbumin interneurons. Patch-clamp recordings from cortical interneurons supported these predictions, and further in silico analysis was then performed to seek a putative mechanism underlying gain modulation. We found that gain modulation in models was dependent upon the magnitude of tonic current generated at depolarized membrane potential\\textemdash a property associated with outward rectifying GABA               A               receptors. Furthermore, tonic inhibition produced two biophysical changes in models of relevance to neuronal excitability: 1) enhanced action potential repolarization via increased current flow into the dendritic compartment, and 2) reduced activation of voltage-dependent potassium channels. Finally, we show theoretically that reduced potassium channel activation selectively increases gain in models possessing action potential dynamics typical for somatostatin interneurons. Potassium channels in parvalbumin-type models deactivate rapidly and are unavailable for further modulation. These findings show that GABA can differentially modulate interneuron excitability and suggest a mechanism through which this occurs in silico via differences of intrinsic electrophysiological properties.},\n  langid = {english}\n}\n\n@techreport{buccinoMultimodalFittingApproach2022,\n  type = {Preprint},\n  title = {A Multi-Modal Fitting Approach to Construct Single-Neuron Models with Patch Clamp and High-Density Microelectrode Arrays},\n  author = {Buccino, Alessio Paolo and Damart, Tanguy and Bartram, Julian and Mandge, Darshan and Xue, Xiaohan and Zbili, Mickael and G{\\\"a}nswein, Tobias and Jaquier, Aur{\\'e}lien and Emmenegger, Vishalini and Markram, Henry and Hierlemann, Andreas and Van Geit, Werner},\n  year = {2022},\n  month = aug,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2022.08.03.502468},\n  urldate = {2023-02-28},\n  abstract = {In computational neuroscience, multicompartment models are among the most biophysically realistic representations of single neurons. Constructing such models usually involves the use of the patch-clamp technique to record somatic voltage signals under different experimental conditions. The experimental data are then used to fit the many parameters of the model. While patching of the soma is currently the gold-standard approach to build multicompartment models, several studies have also evidenced a richness of dynamics in dendritic and axonal sections. Recording from the soma alone makes it hard to observe and correctly parameterize the activity of non-somatic compartments.},\n  langid = {english}\n}\n\n@techreport{buchinMultimodalCharacterizationSimulation2020,\n  type = {Preprint},\n  title = {Multi-Modal Characterization and Simulation of Human Epileptic Circuitry},\n  author = {Buchin, Anatoly and {de Frates}, Rebecca and Nandi, Anirban and Mann, Rusty and Chong, Peter and Ng, Lindsay and Miller, Jeremy and Hodge, Rebecca and Kalmbach, Brian and Bose, Soumita and Rutishauser, Ueli and McConoughey, Stephen and Lein, Ed and Berg, Jim and Sorensen, Staci and Gwinn, Ryder and Koch, Christof and Ting, Jonathan and Anastassiou, Costas A.},\n  year = {2020},\n  month = apr,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2020.04.24.060178},\n  urldate = {2022-06-07},\n  abstract = {Temporal lobe epilepsy is the fourth most common neurological disorder with about 40\\% of patients not responding to pharmacological treatment. Increased cellular loss in the hippocampus is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions such as temporal lobe resection, the impact of the disease at the cellular level remains unclear in humans. Here we show that properties of hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from epilepsy patients. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume, surface area and spine density. Single-cell RNA sequencing combined with simulations ascribe the observed electrophysiological changes to gradual modification in three key ion channel conductances: BK, Cav2.2 and Kir2.1. In a bio-realistic computational network model, we show that the changes related to disease progression bring the circuit into a more excitable state. In turn, we observe that by reversing these changes in the three key conductances produces a less excitable, ``early disease-like'' state. These results provide mechanistic understanding of epilepsy in humans and will inform future therapies such as viral gene delivery to reverse the course of the disorder.},\n  langid = {english}\n}\n\n@techreport{cavarrettaModelingSynapticIntegration2023,\n  type = {Preprint},\n  title = {Modeling Synaptic Integration of Bursty and Beta Oscillatory Inputs in Ventromedial Motor Thalamic Neurons in Normal and Parkinsonian States},\n  author = {Cavarretta, Francesco and Jaeger, Dieter},\n  year = {2023},\n  month = apr,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.04.14.536959},\n  urldate = {2023-05-24},\n  abstract = {Abstract           The Ventromedial Motor Thalamus (VM) is implicated in multiple motor functions and occupies a central position in the cortico-basal ganglia-thalamocortical loop. It integrates glutamatergic inputs from motor cortex (MC) and motor-related subcortical areas, and it is a major recipient of inhibition from basal ganglia. Previous experiments in vitro showed that dopamine depletion enhances the excitability of thalamocortical cells (TC) in VM due to reduced M-type potassium currents. To understand how these excitability changes impact synaptic integration in vivo, we constructed biophysically detailed VM TC models fit to normal and dopamine-depleted conditions, using the NEURON simulator. These models allowed us to assess the influence of excitability changes with dopamine depletion on the integration of synaptic inputs expected in vivo. We found that VM TCs in the dopamine-depleted state showed increased firing rates with the same synaptic inputs. Synchronous bursting in inhibitory input from the substantia nigra pars reticulata (SNR), as observed in parkinsonian conditions, evoked a post-inhibitory firing rate increase with a longer duration in dopamine-depleted than control conditions, due to different M-type potassium channel densities. With beta oscillations in the inhibitory inputs from SNR and the excitatory inputs from drivers and modulators, we observed spike-phase locking in the activity of the models in normal and dopamine-depleted states, which relayed and amplified the oscillations of the inputs, suggesting that the increased beta oscillations observed in VM of parkinsonian animals are predominantly a consequence of changes in the presynaptic activity rather than changes in intrinsic properties.                        Significance Statement             The Ventromedial Motor Thalamus is implicated in multiple motor functions. Experiments in vitro showed this area undergoes homeostatic changes following dopamine depletion (parkinsonian state). Here we studied the impact of these changes in vivo, using biophysically detailed modeling. We found that dopamine depletion increased firing rate in the ventromedial thalamocortical neurons and changed their responses to synchronous inhibitory inputs from substantia nigra reticulata. All thalamocortical neuron models relayed and amplified beta oscillations from substantia nigra reticulata and cortical/subcortical inputs, suggesting that increased beta oscillations observed in parkinsonian animals predominantly reflect changes in presynaptic activity.},\n  langid = {english}\n}\n\n@article{chindemiCalciumbasedPlasticityModel2022b,\n  title = {A Calcium-Based Plasticity Model for Predicting Long-Term Potentiation and Depression in the Neocortex},\n  author = {Chindemi, Giuseppe and Abdellah, Marwan and Amsalem, Oren and {Benavides-Piccione}, Ruth and Delattre, Vincent and Doron, Michael and Ecker, Andr{\\'a}s and Jaquier, Aur{\\'e}lien T. and King, James and Kumbhar, Pramod and Monney, Caitlin and Perin, Rodrigo and R{\\\"o}ssert, Christian and Tuncel, Anil M. and Van Geit, Werner and DeFelipe, Javier and Graupner, Michael and Segev, Idan and Markram, Henry and Muller, Eilif B.},\n  year = {2022},\n  month = jun,\n  journal = {Nature Communications},\n  volume = {13},\n  number = {1},\n  pages = {3038},\n  issn = {2041-1723},\n  doi = {10.1038/s41467-022-30214-w},\n  urldate = {2023-02-28},\n  abstract = {Abstract             Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.},\n  langid = {english}\n}\n\n@inproceedings{damartDataDrivenBuilding2020,\n  title = {Data Driven Building of Realistic Neuron Model Using {{IBEA}} and {{CMA}} Evolution Strategies},\n  booktitle = {Proceedings of the 2020 {{Genetic}} and {{Evolutionary Computation Conference Companion}}},\n  author = {Damart, Tanguy and Van Geit, Werner and Markram, Henry},\n  year = {2020},\n  month = jul,\n  pages = {35--36},\n  publisher = {{ACM}},\n  address = {{Canc\\'un Mexico}},\n  doi = {10.1145/3377929.3398161},\n  urldate = {2023-02-28},\n  isbn = {978-1-4503-7127-8},\n  langid = {english}\n}\n\n@inproceedings{doronDiscoveringUnexpectedLocal2019a,\n  title = {Discovering {{Unexpected Local Nonlinear Interactions}} in {{Scientific Black-box Models}}},\n  booktitle = {Proceedings of the 25th {{ACM SIGKDD International Conference}} on {{Knowledge Discovery}} \\& {{Data Mining}}},\n  author = {Doron, Michael and Segev, Idan and Shahaf, Dafna},\n  year = {2019},\n  month = jul,\n  pages = {425--435},\n  publisher = {{ACM}},\n  address = {{Anchorage AK USA}},\n  doi = {10.1145/3292500.3330886},\n  urldate = {2023-02-28},\n  abstract = {Scientific computational models are crucial for analyzing and understanding complex real-life systems that are otherwise difficult for experimentation. However, the complex behavior and the vast inputoutput space of these models often make them opaque, slowing the discovery of novel phenomena. In this work, we present Hint (Hessian INTerestingness) \\textendash{} a new algorithm that can automatically and systematically explore black-box models and highlight local nonlinear interactions in the input-output space of the model. This tool aims to facilitate the discovery of interesting model behaviors that are unknown to the researchers. Using this simple yet powerful tool, we were able to correctly rank all pairwise interactions in known benchmark models and do so faster and with greater accuracy than state-of-the-art methods. We further applied Hint to existing computational neuroscience models, and were able to reproduce important scientific discoveries that were published years after the creation of those models. Finally, we ran Hint on two real-world models (in neuroscience and earth science) and found new behaviors of the model that were of value to domain experts.},\n  isbn = {978-1-4503-6201-6},\n  langid = {english}\n}\n\n@article{eckerDataDrivenIntegration2020,\n  title = {Data-driven Integration of Hippocampal {{{\\textsc{CA1}}}} Synaptic Physiology {\\emph{in Silico}}},\n  shorttitle = {Data-driven Integration of Hippocampal},\n  author = {Ecker, Andr{\\'a}s and Romani, Armando and S{\\'a}ray, S{\\'a}ra and K{\\'a}li, Szabolcs and Migliore, Michele and Falck, Joanne and Lange, Sigrun and Mercer, Audrey and Thomson, Alex M. and Muller, Eilif and Reimann, Michael W. and Ramaswamy, Srikanth},\n  year = {2020},\n  month = nov,\n  journal = {Hippocampus},\n  volume = {30},\n  number = {11},\n  pages = {1129--1145},\n  issn = {1050-9631, 1098-1063},\n  doi = {10.1002/hipo.23220},\n  urldate = {2023-02-28},\n  abstract = {The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data-driven approach to integrate the current state-of-the-art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo-dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short-term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.},\n  langid = {english}\n}\n\n@article{eckerHippocampalSharpWaveripples2022a,\n  title = {Hippocampal Sharp Wave-Ripples and the Associated Sequence Replay Emerge from Structured Synaptic Interactions in a Network Model of Area {{CA3}}},\n  author = {Ecker, Andr{\\'a}s and Bagi, Bence and V{\\'e}rtes, Eszter and {Steinbach-N{\\'e}meth}, Orsolya and Karl{\\'o}cai, M{\\'a}ria R and Papp, Orsolya I and Mikl{\\'o}s, Istv{\\'a}n and H{\\'a}jos, Norbert and Freund, Tam{\\'a}s F and Guly{\\'a}s, Attila I and K{\\'a}li, Szabolcs},\n  year = {2022},\n  month = jan,\n  journal = {eLife},\n  volume = {11},\n  pages = {e71850},\n  issn = {2050-084X},\n  doi = {10.7554/eLife.71850},\n  urldate = {2023-02-28},\n  abstract = {Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated (`replayed'), either in the same or reversed order, during bursts of activity (sharp wave-r\\-ipples [SWRs]) that occur in sleep and awake rest. SWR-\\- associated replay is thought to be critical for the creation and maintenance of long-\\-term memory. In order to identify the cellular and network mechanisms of SWRs and replay, we constructed and simulated a data-\\-driven model of area CA3 of the hippocampus. Our results show that the chain-\\-like structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics, and suggests that the structured neural codes induced by learning may have greater influence over cortical network states than previously appreciated.},\n  langid = {english}\n}\n\n@article{frostnylenReciprocalInteractionStriatal2021,\n  title = {Reciprocal Interaction between Striatal Cholinergic and Low-threshold Spiking Interneurons \\textemdash{} {{A}} Computational Study},\n  author = {Frost Nyl{\\'e}n, Johanna and Carannante, Ilaria and Grillner, Sten and Hellgren Kotaleski, Jeanette},\n  year = {2021},\n  month = apr,\n  journal = {European Journal of Neuroscience},\n  volume = {53},\n  number = {7},\n  pages = {2135--2148},\n  issn = {0953-816X, 1460-9568},\n  doi = {10.1111/ejn.14854},\n  urldate = {2022-06-07},\n  abstract = {The striatum is the main input stage of the basal ganglia receiving extrinsic input from cortex and thalamus. The striatal projection neurons (SPN) constitute 95\\% of the neurons in the striatum in mice while the remaining 5\\% are cholinergic and GABAergic interneurons. The cholinergic (ChIN) and low-threshold spiking interneurons (LTS) are spontaneously active and form a striatal subnetwork involved in salience detection and goal-directed learning. Activation of ChINs has been shown to inhibit LTS via muscarinic receptor type 4 (M4R) and LTS in turn can modulate ChINs via nitric oxide (NO) causing a prolonged depolarization. Thalamic input prefentially excites ChINs, whereas input from motor cortex favours LTS, but can also excite ChINs. This varying extrinsic input with intrinsic reciprocal, yet opposing, effects raises the possibility of a slow input-dependent modulatory subnetwork. Here, we simulate this subnetwork using multicompartmental neuron models that incorporate data regarding known ion channels and detailed morphological reconstructions. The modelled connections replicate the experimental data on muscarinic (M4R) and nitric oxide modulation onto LTS and ChIN, respectively, and capture their physiological interaction. Finally, we show that the cortical and thalamic inputs triggering the opposing modulation within the network induce periods of increased and decreased spiking activity in ChINs and LTS. This could provide different temporal windows for selective modulation by acetylcholine and nitric oxide, and the possibility of interaction with the wider striatal microcircuit.},\n  langid = {english}\n}\n\n@techreport{gerkinNeuronUnitPackageDatadriven2019a,\n  type = {Preprint},\n  title = {{{NeuronUnit}}: {{A}} Package for Data-Driven Validation of Neuron Models Using {{SciUnit}}},\n  shorttitle = {{{NeuronUnit}}},\n  author = {Gerkin, Richard C. and Birgiolas, Justas and Jarvis, Russell J. and Omar, Cyrus and Crook, Sharon M.},\n  year = {2019},\n  month = jun,\n  institution = {{Neuroscience}},\n  doi = {10.1101/665331},\n  urldate = {2023-02-28},\n  abstract = {Validating a quantitative scientific model requires comparing its predictions against many experimental observations, ideally from many labs, using transparent, robust, statistical comparisons. Unfortunately, in rapidly-growing fields like neuroscience, this is becoming increasingly untenable, even for the most conscientious scientists. Thus the merits and limitations of existing models, or whether a new model is an improvement on the state-of-the-art, is often unclear.},\n  langid = {english}\n}\n\n@article{goncalvesTrainingDeepNeural2020a,\n  title = {Training Deep Neural Density Estimators to Identify Mechanistic Models of Neural Dynamics},\n  author = {Gon{\\c c}alves, Pedro J and Lueckmann, Jan-Matthis and Deistler, Michael and Nonnenmacher, Marcel and {\\\"O}cal, Kaan and Bassetto, Giacomo and Chintaluri, Chaitanya and Podlaski, William F and Haddad, Sara A and Vogels, Tim P and Greenberg, David S and Macke, Jakob H},\n  year = {2020},\n  month = sep,\n  journal = {eLife},\n  volume = {9},\n  pages = {e56261},\n  issn = {2050-084X},\n  doi = {10.7554/eLife.56261},\n  urldate = {2022-06-07},\n  abstract = {Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators\\textemdash trained using model simulations\\textemdash to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin\\textendash Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.},\n  langid = {english}\n}\n\n@article{guet-mccreightAgedependentIncreasedSag2023,\n  title = {Age-Dependent Increased Sag Amplitude in Human Pyramidal Neurons Dampens Baseline Cortical Activity},\n  author = {{Guet-McCreight}, Alexandre and Chameh, Homeira Moradi and Mahallati, Sara and Wishart, Margaret and Tripathy, Shreejoy J and Valiante, Taufik A and Hay, Etay},\n  year = {2023},\n  month = apr,\n  journal = {Cerebral Cortex},\n  volume = {33},\n  number = {8},\n  pages = {4360--4373},\n  issn = {1047-3211, 1460-2199},\n  doi = {10.1093/cercor/bhac348},\n  urldate = {2023-05-24},\n  abstract = {Abstract             Aging involves various neurobiological changes, although their effect on brain function in humans remains poorly understood. The growing availability of human neuronal and circuit data provides opportunities for uncovering age-dependent changes of brain networks and for constraining models to predict consequences on brain activity. Here we found increased sag voltage amplitude in human middle temporal gyrus layer 5 pyramidal neurons from older subjects and captured this effect in biophysical models of younger and older pyramidal neurons. We used these models to simulate detailed layer 5 microcircuits and found lower baseline firing in older pyramidal neuron microcircuits, with minimal effect on response. We then validated the predicted reduced baseline firing using extracellular multielectrode recordings from human brain slices of different ages. Our results thus report changes in human pyramidal neuron input integration properties and provide fundamental insights into the neuronal mechanisms of altered cortical excitability and resting-state activity in human aging.},\n  langid = {english}\n}\n\n@techreport{guet-mccreightInsilicoTestingNew2023,\n  type = {Preprint},\n  title = {In-Silico Testing of New Pharmacology for Restoring Inhibition and Human Cortical Function in Depression},\n  author = {{Guet-McCreight}, Alexandre and Chameh, Homeira Moradi and Mazza, Frank and Prevot, Thomas D. and Valiante, Taufik A. and Sibille, Etienne and Hay, Etay},\n  year = {2023},\n  month = feb,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.02.22.529541},\n  urldate = {2023-02-28},\n  abstract = {Reduced inhibition by somatostatin-expressing interneurons is associated with depression. Administration of positive allosteric modulators of {$\\alpha$}5 subunit-containing GABAA receptor ({$\\alpha$}5PAM) that selectively target this lost inhibition exhibit antidepressant and pro-cognitive effects in rodent models of chronic stress. However, the functional effects of {$\\alpha$}5-PAM on the human brain in vivo are unknown, and currently cannot be assessed experimentally. We modeled the effects of {$\\alpha$}5-PAM on tonic inhibition as measured in human neurons, and tested in silico {$\\alpha$}5-PAM effects on detailed models of human cortical microcircuits in health and depression. We found that {$\\alpha$}5PAM effectively recovered impaired cortical processing as quantified by stimulus detection metrics, and also recovered the power spectral density profile of the microcircuit EEG signals. We performed an {$\\alpha$}5-PAM dose response and identified simulated EEG biomarkers. Our results serve to de-risk and facilitate {$\\alpha$}5-PAM translation and provide biomarkers in non-invasive brain signals for monitoring target engagement and drug efficacy.},\n  langid = {english}\n}\n\n@article{hjorthPredictingSynapticConnectivity2021,\n  title = {Predicting {{Synaptic Connectivity}} for {{Large-Scale Microcircuit Simulations Using Snudda}}},\n  author = {Hjorth, J. J. Johannes and Hellgren Kotaleski, Jeanette and Kozlov, Alexander},\n  year = {2021},\n  month = oct,\n  journal = {Neuroinformatics},\n  volume = {19},\n  number = {4},\n  pages = {685--701},\n  issn = {1539-2791, 1559-0089},\n  doi = {10.1007/s12021-021-09531-w},\n  urldate = {2022-06-07},\n  abstract = {Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a `bottomup' manner. Simulated data from these models can be compared with experiments and `top-down' modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further `curate' data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.},\n  langid = {english}\n}\n\n@article{huntStrongReliableSynaptic2022a,\n  title = {Strong and Reliable Synaptic Communication between Pyramidal Neurons in Adult Human Cerebral Cortex},\n  author = {Hunt, Sarah and Leibner, Yoni and Mertens, Eline J and {Barros-Zulaica}, Natal{\\'i} and Kanari, Lida and Heistek, Tim S and Karnani, Mahesh M and Aardse, Romy and Wilbers, Ren{\\'e} and Heyer, Djai B and Goriounova, Natalia A and Verhoog, Matthijs B and {Testa-Silva}, Guilherme and Obermayer, Joshua and Versluis, Tamara and {Benavides-Piccione}, Ruth and {de Witt-Hamer}, Philip and Idema, Sander and Noske, David P and Baayen, Johannes C and Lein, Ed S and DeFelipe, Javier and Markram, Henry and Mansvelder, Huibert D and Sch{\\\"u}rmann, Felix and Segev, Idan and {de Kock}, Christiaan P J},\n  year = {2022},\n  month = jul,\n  journal = {Cerebral Cortex},\n  pages = {bhac246},\n  issn = {1047-3211, 1460-2199},\n  doi = {10.1093/cercor/bhac246},\n  urldate = {2023-02-28},\n  abstract = {Abstract             Synaptic transmission constitutes the primary mode of communication between neurons. It is extensively studied in rodent but not human neocortex. We characterized synaptic transmission between pyramidal neurons in layers 2 and 3 using neurosurgically resected human middle temporal gyrus (MTG, Brodmann area 21), which is part of the distributed language circuitry. We find that local connectivity is comparable with mouse layer 2/3 connections in the anatomical homologue (temporal association area), but synaptic connections in human are 3-fold stronger and more reliable (0\\% vs 25\\% failure rates, respectively). We developed a theoretical approach to quantify properties of spinous synapses showing that synaptic conductance and voltage change in human dendritic spines are 3\\textendash 4-folds larger compared with mouse, leading to significant NMDA receptor activation in human unitary connections. This model prediction was validated experimentally by showing that NMDA receptor activation increases the amplitude and prolongs decay of unitary excitatory postsynaptic potentials in human but not in mouse connections. Since NMDA-dependent recurrent excitation facilitates persistent activity (supporting working memory), our data uncovers cortical microcircuit properties in human that may contribute to language processing in MTG.},\n  langid = {english}\n}\n\n@article{iavaroneExperimentallyconstrainedBiophysicalModels2019a,\n  title = {Experimentally-Constrained Biophysical Models of Tonic and Burst Firing Modes in Thalamocortical Neurons},\n  author = {Iavarone, Elisabetta and Yi, Jane and Shi, Ying and Zandt, Bas-Jan and O'Reilly, Christian and Van Geit, Werner and R{\\\"o}ssert, Christian and Markram, Henry and Hill, Sean L.},\n  editor = {Lytton, William W.},\n  year = {2019},\n  month = may,\n  journal = {PLOS Computational Biology},\n  volume = {15},\n  number = {5},\n  pages = {e1006753},\n  issn = {1553-7358},\n  doi = {10.1371/journal.pcbi.1006753},\n  urldate = {2023-02-28},\n  langid = {english}\n}\n\n@techreport{iavaroneThalamicControlSensory2022,\n  type = {Preprint},\n  title = {Thalamic Control of Sensory Enhancement and Sleep Spindle Properties in a Biophysical Model of Thalamoreticular Microcircuitry},\n  author = {Iavarone, Elisabetta and Simko, Jane and Shi, Ying and Bertschy, Marine and {Garc{\\'i}a-Amado}, Mar{\\'i}a and Litvak, Polina and Kaufmann, Anna-Kristin and O'Reilly, Christian and Amsalem, Oren and Abdellah, Marwan and Chevtchenko, Grigori and Coste, Beno{\\^i}t and Courcol, Jean-Denis and Ecker, Andr{\\'a}s and Favreau, Cyrille and Fleury, Adrien Christian and Geit, Werner Van and Gevaert, Michael and Guerrero, Nadir Rom{\\'a}n and Herttuainen, Joni and Ivaska, Genrich and Kerrien, Samuel and King, James G. and Kumbhar, Pramod and Lurie, Patrycja and Magkanaris, Ioannis and Muddapu, Vignayanandam Ravindernath and Nair, Jayakrishnan and Pereira, Fernando L. and Perin, Rodrigo and Petitjean, Fabien and Ranjan, Rajnish and Reimann, Michael and Soltuzu, Liviu and Sy, Mohameth Fran{\\c c}ois and Tuncel, M. An{\\i}l and Ulbrich, Alexander and Wolf, Matthias and Clasc{\\'a}, Francisco and Markram, Henry and Hill, Sean L.},\n  year = {2022},\n  month = mar,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2022.02.28.482273},\n  urldate = {2022-06-07},\n  abstract = {Thalamoreticular circuitry is known to play a key role in attention, cognition and the generation of sleep spindles, and is implicated in numerous brain disorders, but the cellular and synaptic mechanisms remain intractable. Therefore, we developed the first detailed computational model of mouse thalamus and thalamic reticular nucleus microcircuitry that captures morphological and biophysical properties of \\textasciitilde 14,000 neurons connected via \\textasciitilde 6M synapses, and recreates biological synaptic and gap junction connectivity. Simulations recapitulate multiple independent network-level experimental findings across different brain states, providing a novel unifying cellular and synaptic account of spontaneous and evoked activity in both wakefulness and sleep. Furthermore, we found that: 1.) inhibitory rebound produces frequency-selective enhancement of thalamic responses during wakefulness, in addition to its role in spindle generation; 2.) thalamic interactions generate the characteristic waxing and waning of spindle oscillations; and 3.) changes in thalamic excitability (e.g. due to neuromodulation) control spindle frequency and occurrence. The model is openly available and provides a new tool to interpret spindle oscillations and test hypotheses of thalamoreticular circuit function and dysfunction across different network states in health and disease.},\n  langid = {english}\n}\n\n@techreport{isbisterModelingSimulationNeocortical2023,\n  type = {Preprint},\n  title = {Modeling and {{Simulation}} of {{Neocortical Micro-}} and {{Mesocircuitry}}. {{Part II}}: {{Physiology}} and {{Experimentation}}},\n  shorttitle = {Modeling and {{Simulation}} of {{Neocortical Micro-}} and {{Mesocircuitry}}. {{Part II}}},\n  author = {Isbister, James B and Ecker, Andr{\\'a}s and Pokorny, Christoph and {Bola{\\~n}os-Puchet}, Sirio and Egas Santander, Daniela and Arnaudon, Alexis and Awile, Omar and {Barros-Zulaica}, Natali and Blanco Alonso, Jorge and Boci, Elvis and Chindemi, Giuseppe and Courcol, Jean-Denis and Damart, Tanguy and Delemontex, Thomas and Dietz, Alexander and Ficarelli, Gianluca and Gevaert, Mike and Herttuainen, Joni and Ivaska, Genrich and Ji, Weina and Keller, Daniel and King, James and Kumbhar, Pramod and Lapere, Samuel and Litvak, Polina and Mandge, Darshan and Muller, Eilif B. and Pereira, Fernando and Planas, Judit and Ranjan, Rajnish and Reva, Maria and Romani, Armando and R{\\\"o}ssert, Christian and Sch{\\\"u}rmann, Felix and Sood, Vishal and Teska, Aleksandra and Tuncel, Anil and Van Geit, Werner and Wolf, Matthias and Markram, Henry and Ramaswamy, Srikanth and Reimann, Michael W.},\n  year = {2023},\n  month = may,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.05.17.541168},\n  urldate = {2023-05-24},\n  abstract = {In recent years, large-scale computational models of the cortex have emerged as a powerful way to study the multi-scale mechanisms of neural processing. However, due to computational costs and difficulty of parameterization, detailed biophysical reconstructions have so far been restricted to small volumes of tissue, where the study of macro- and meso-scale interactions that are central to cortical function is not possible. We describe here, and in a companion paper, an approach to address the scaling challenges and provide a model of multiple interacting cortical regions at a subcellular level of detail. The model consists of 4.2 million morphologically detailed neurons in 8 sub-regions and connected with 13.2 billion synapses through local and long-range connectivity. Its anatomical aspects are described in the companion paper; here, we introduce physiological models of neuronal activity and synaptic transmission that integrate a large number of literature sources and were built using previously published algorithms. Biological neuronal diversity was captured in 208 morpho-electrical neuron types, five types of synaptic short-term dynamics, and pathway-specificity of synaptic parameters. A representation of synaptic input from cortical regions not present in the model was added and efficiently calibrated to reference firing rates. The model exhibits a spectrum of dynamical states differing in the degree to which they are internally versus externally driven. We characterized which parts of the spectrum are compatible with available experimental data on layer-specific delays and amplitudes of responses to simple stimuli, and found an in vivo-like regime at the edge of a transition from asynchronous to synchronous spontaneous activity. We developed a rich set of simulation tools to recreate a diverse set of laboratory experiments in silico, providing further validation and demonstrating the utility of the model in a variety of paradigms. Finally, we found that the large spatial scale of the model, that incorporates multiple cortical regions, led to the emergence of multiple independent computational units interacting through long-range synaptic pathways. The model provides a framework for the continued integration of experimental findings, for challenging hypotheses and making testable predictions, and provides a foundation for further simulation-based studies of cortical processing and learning.},\n  langid = {english}\n}\n\n@article{kalmbachHChannelsContributeDivergent2018c,\n  title = {H-{{Channels Contribute}} to {{Divergent Intrinsic Membrane Properties}} of {{Supragranular Pyramidal Neurons}} in {{Human}} versus {{Mouse Cerebral Cortex}}},\n  author = {Kalmbach, Brian E. and Buchin, Anatoly and Long, Brian and Close, Jennie and Nandi, Anirban and Miller, Jeremy A. and Bakken, Trygve E. and Hodge, Rebecca D. and Chong, Peter and {de Frates}, Rebecca and Dai, Kael and Maltzer, Zoe and Nicovich, Philip R. and Keene, C. Dirk and Silbergeld, Daniel L. and Gwinn, Ryder P. and Cobbs, Charles and Ko, Andrew L. and Ojemann, Jeffrey G. and Koch, Christof and Anastassiou, Costas A. and Lein, Ed S. and Ting, Jonathan T.},\n  year = {2018},\n  month = dec,\n  journal = {Neuron},\n  volume = {100},\n  number = {5},\n  pages = {1194-1208.e5},\n  issn = {08966273},\n  doi = {10.1016/j.neuron.2018.10.012},\n  urldate = {2023-02-28},\n  abstract = {Gene expression studies suggest that differential ion channel expression contributes to differences in rodent versus human neuronal physiology. We tested whether h-channels more prominently contribute to the physiological properties of human compared to mouse supragranular pyramidal neurons. Singlecell/nucleus RNA sequencing revealed ubiquitous HCN1-subunit expression in excitatory neurons in human, but not mouse, supragranular layers. Using patch-clamp recordings, we found stronger h-channel-related membrane properties in supragranular pyramidal neurons in human temporal cortex, compared to mouse supragranular pyramidal neurons in temporal association area. The magnitude of these differences depended upon cortical depth and was largest in pyramidal neurons in deep L3. Additionally, pharmacologically blocking h-channels produced a larger change in membrane properties in human compared to mouse neurons. Finally, using biophysical modeling, we provide evidence that h-channels promote the transfer of theta frequencies from dendrite-to-soma in human L3 pyramidal neurons. Thus, h-channels contribute to between-species differences in a fundamental neuronal property.},\n  langid = {english}\n}\n\n@article{laddScalingBenchmarkingEvolutionary2022,\n  title = {Scaling and {{Benchmarking}} an {{Evolutionary Algorithm}} for {{Constructing Biophysical Neuronal Models}}},\n  author = {Ladd, Alexander and Kim, Kyung Geun and Balewski, Jan and Bouchard, Kristofer and {Ben-Shalom}, Roy},\n  year = {2022},\n  month = jun,\n  journal = {Frontiers in Neuroinformatics},\n  volume = {16},\n  pages = {882552},\n  issn = {1662-5196},\n  doi = {10.3389/fninf.2022.882552},\n  urldate = {2023-02-28},\n  abstract = {Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.},\n  langid = {english}\n}\n\n@article{linaroCellTypespecificMechanisms2022a,\n  title = {Cell Type-Specific Mechanisms of Information Transfer in Data-Driven Biophysical Models of Hippocampal {{CA3}} Principal Neurons},\n  author = {Linaro, Daniele and Levy, Matthew J. and Hunt, David L.},\n  editor = {Migliore, Michele},\n  year = {2022},\n  month = apr,\n  journal = {PLOS Computational Biology},\n  volume = {18},\n  number = {4},\n  pages = {e1010071},\n  issn = {1553-7358},\n  doi = {10.1371/journal.pcbi.1010071},\n  urldate = {2023-02-28},\n  abstract = {The transformation of synaptic input into action potential output is a fundamental single-cell computation resulting from the complex interaction of distinct cellular morphology and the unique expression profile of ion channels that define the cellular phenotype. Experimental studies aimed at uncovering the mechanisms of the transfer function have led to important insights, yet are limited in scope by technical feasibility, making biophysical simulations an attractive complementary approach to push the boundaries in our understanding of cellular computation. Here we take a data-driven approach by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm to build two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. We evaluated the performance of these models and find that our approach quantitatively matches the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability in the data. Moreover, we confirm that the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. We then use these models to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input through an information-theoretic treatment of their respective transfer functions. Our simulations identify a host of cell type-specific biophysical mechanisms that define the morpho-functional phenotype to shape the cellular transfer function and place these findings in the context of a role for bursting in CA3 recurrent network synchronization dynamics.},\n  langid = {english}\n}\n\n@inproceedings{linaroModellingEffectsEarly2020a,\n  title = {Modelling the {{Effects}} of {{Early Exposure}} to {{Alcohol}} on the {{Excitability}} of {{Cortical Neurons}}},\n  booktitle = {2020 {{IEEE International Symposium}} on {{Circuits}} and {{Systems}} ({{ISCAS}})},\n  author = {Linaro, Daniele and Bizzarri, Federico and Brambilla, Angelo and Granato, Alberto and Giugliano, Michele},\n  year = {2020},\n  month = oct,\n  pages = {1--5},\n  publisher = {{IEEE}},\n  address = {{Seville, Spain}},\n  doi = {10.1109/ISCAS45731.2020.9180633},\n  urldate = {2023-02-28},\n  abstract = {In recent years, a novel approach based on multiobjective optimization has been developed to automatically tune biophysically realistic, multi-compartmental neuron models starting from electrophysiological recordings. Here, we apply this methodology to the optimization of model neurons capable of reproducing the reduced excitability observed in experiments carried out in cortical pyramidal cells in a rodent model of fetal alcohol spectrum disorder. We find that both control and ethanol-exposed model cells present an excellent match with the experiments in terms of membrane voltage dynamics, with the latter group displaying a small but significant rightward shift of their current-frequency relationship. We identify a possible interplay between model parameters and cellular morphology and suggest future improvements to better capture the features of dendritic voltage dynamics.},\n  isbn = {978-1-72813-320-1},\n  langid = {english}\n}\n\n@article{martimengualEfficientLowPassDendroSomatic2020a,\n  title = {Efficient {{Low-Pass Dendro-Somatic Coupling}} in the {{Apical Dendrite}} of {{Layer}} 5 {{Pyramidal Neurons}} in the {{Anterior Cingulate Cortex}}},\n  author = {Marti Mengual, Ulisses and Wybo, Willem A.M. and Spierenburg, Lotte J.E. and Santello, Mirko and Senn, Walter and Nevian, Thomas},\n  year = {2020},\n  month = nov,\n  journal = {The Journal of Neuroscience},\n  volume = {40},\n  number = {46},\n  pages = {8799--8815},\n  issn = {0270-6474, 1529-2401},\n  doi = {10.1523/JNEUROSCI.3028-19.2020},\n  urldate = {2023-02-28},\n  abstract = {Signal propagation in the dendrites of many neurons, including cortical pyramidal neurons in sensory cortex, is characterized by strong attenuation toward the soma. In contrast, using dual whole-cell recordings from the apical dendrite and soma of layer 5 (L5) pyramidal neurons in the anterior cingulate cortex (ACC) of adult male mice we found good coupling, particularly of slow subthreshold potentials like NMDA spikes or trains of EPSPs from dendrite to soma. Only the fastest EPSPs in the ACC were reduced to a similar degree as in primary somatosensory cortex, revealing differential low-pass filtering capabilities. Furthermore, L5 pyramidal neurons in the ACC did not exhibit dendritic Ca               2+               spikes as prominently found in the apical dendrite of S1 (somatosensory cortex) pyramidal neurons. Fitting the experimental data to a NEURON model revealed that the specific distribution of               I               leak               ,               I               ir               ,                                I                 m                              , and               I               h               was sufficient to explain the electrotonic dendritic structure causing a leaky distal dendritic compartment with correspondingly low input resistance and a compact perisomatic region, resulting in a decoupling of distal tuft branches from each other while at the same time efficiently connecting them to the soma. Our results give a biophysically plausible explanation of how a class of prefrontal cortical pyramidal neurons achieve efficient integration of subthreshold distal synaptic inputs compared with the same cell type in sensory cortices.                                         SIGNIFICANCE STATEMENT               Understanding cortical computation requires the understanding of its fundamental computational subunits. Layer 5 pyramidal neurons are the main output neurons of the cortex, integrating synaptic inputs across different cortical layers. Their elaborate dendritic tree receives, propagates, and transforms synaptic inputs into action potential output. We found good coupling of slow subthreshold potentials like NMDA spikes or trains of EPSPs from the distal apical dendrite to the soma in pyramidal neurons in the ACC, which was significantly better compared with S1. This suggests that frontal pyramidal neurons use a different integration scheme compared with the same cell type in somatosensory cortex, which has important implications for our understanding of information processing across different parts of the neocortex.},\n  langid = {english}\n}\n\n@article{masoliCerebellarGolgiCell2020a,\n  title = {Cerebellar {{Golgi}} Cell Models Predict Dendritic Processing and Mechanisms of Synaptic Plasticity},\n  author = {Masoli, Stefano and Ottaviani, Alessandra and Casali, Stefano and D'Angelo, Egidio},\n  editor = {Cuntz, Hermann},\n  year = {2020},\n  month = dec,\n  journal = {PLOS Computational Biology},\n  volume = {16},\n  number = {12},\n  pages = {e1007937},\n  issn = {1553-7358},\n  doi = {10.1371/journal.pcbi.1007937},\n  urldate = {2023-02-28},\n  abstract = {The Golgi cells are the main inhibitory interneurons of the cerebellar granular layer. Although recent works have highlighted the complexity of their dendritic organization and synaptic inputs, the mechanisms through which these neurons integrate complex input patterns remained unknown. Here we have used 8 detailed morphological reconstructions to develop multicompartmental models of Golgi cells, in which Na, Ca, and K channels were distributed along dendrites, soma, axonal initial segment and axon. The models faithfully reproduced a rich pattern of electrophysiological and pharmacological properties and predicted the operating mechanisms of these neurons. Basal dendrites turned out to be more tightly electrically coupled to the axon initial segment than apical dendrites. During synaptic transmission, parallel fibers caused slow Ca-dependent depolarizations in apical dendrites that boosted the axon initial segment encoder and Na-spike backpropagation into basal dendrites, while inhibitory synapses effectively shunted backpropagating currents. This oriented dendritic processing set up a coincidence detector controlling voltage-dependent NMDA receptor unblock in basal dendrites, which, by regulating local calcium influx, may provide the basis for spike-timing dependent plasticity anticipated by theory.},\n  langid = {english}\n}\n\n@techreport{masoliHumanOutperformMouse2023,\n  type = {Preprint},\n  title = {Human Outperform Mouse {{Purkinje}} Cells in Dendritic Complexity and Computational Capacity},\n  author = {Masoli, Stefano and {Sanchez-Ponce}, Diana and Vrieler, Nora and {Abu-Haya}, Karin and Lerner, Vitaly and Shahar, Tal and Nedelescu, Hermina and Rizza, Martina Francesca and {Benavides-Piccione}, Ruth and DeFelipe, Javier and Yarom, Yosef and Munoz, Alberto and D'Angelo, Egidio},\n  year = {2023},\n  month = mar,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.03.08.531672},\n  urldate = {2023-03-14},\n  abstract = {Purkinje cells (PC) of the cerebellum are amongst the largest neurons of the brain and have been extensively investigated in rodents. However, their morphological and physiological properties in humans are still poorly understood. Here, we have taken advantage of high-resolution morphological reconstructions and of unique electrophysiological recordings of human PCs ex vivo to generate computational models and estimate computational capacity. An inter-species comparison showed that human PCs had similar fractal structure but were bigger than mouse PCs. Consequently, given a similar spine density (2/micrometer), human PCs hosted about 5 times more dendritic spines. Moreover, human had higher dendritic complexity than mouse PCs and usually emitted 2-3 main dendritic trunks instead than 1. Intrinsic electroresponsiveness was similar in the two species but model simulations revealed that the dendrites generated \\textasciitilde 6.5 times (n=51 vs. n=8) more combinations of independent input patterns in human than mouse PCs leading to an exponential 2n increase in Shannon information. Thus, while during evolution human PCs maintained similar patterns of spike discharge as in rodents, they developed more complex dendrites enhancing computational capacity up to the limit of 10 billion times.},\n  langid = {english}\n}\n\n@article{masoliParameterTuningDifferentiates2020a,\n  title = {Parameter Tuning Differentiates Granule Cell Subtypes Enriching Transmission Properties at the Cerebellum Input Stage},\n  author = {Masoli, Stefano and Tognolina, Marialuisa and Laforenza, Umberto and Moccia, Francesco and D'Angelo, Egidio},\n  year = {2020},\n  month = may,\n  journal = {Communications Biology},\n  volume = {3},\n  number = {1},\n  pages = {222},\n  issn = {2399-3642},\n  doi = {10.1038/s42003-020-0953-x},\n  urldate = {2023-02-28},\n  abstract = {Abstract             The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage.},\n  langid = {english}\n}\n\n@article{masoliSingleNeuronOptimization2017a,\n  title = {Single {{Neuron Optimization}} as a {{Basis}} for {{Accurate Biophysical Modeling}}: {{The Case}} of {{Cerebellar Granule Cells}}},\n  shorttitle = {Single {{Neuron Optimization}} as a {{Basis}} for {{Accurate Biophysical Modeling}}},\n  author = {Masoli, Stefano and Rizza, Martina F. and Sgritta, Martina and Van Geit, Werner and Sch{\\\"u}rmann, Felix and D'Angelo, Egidio},\n  year = {2017},\n  month = mar,\n  journal = {Frontiers in Cellular Neuroscience},\n  volume = {11},\n  issn = {1662-5102},\n  doi = {10.3389/fncel.2017.00071},\n  urldate = {2022-06-07},\n  abstract = {In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (Gi-max) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of Gi-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of Gi-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental Gi-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.},\n  langid = {english}\n}\n\n@techreport{michielsElectrophysiologyPredictionSingle2020a,\n  type = {Preprint},\n  title = {Electrophysiology Prediction of Single Neurons Based on Their Morphology},\n  author = {Michiels, Mario},\n  year = {2020},\n  month = feb,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2020.02.04.933697},\n  urldate = {2023-02-28},\n  abstract = {Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon).},\n  langid = {english}\n}\n\n@article{migliorePhysiologicalVariabilityChannel2018a,\n  title = {The Physiological Variability of Channel Density in Hippocampal {{CA1}} Pyramidal Cells and Interneurons Explored Using a Unified Data-Driven Modeling Workflow},\n  author = {Migliore, Rosanna and Lupascu, Carmen A. and Bologna, Luca L. and Romani, Armando and Courcol, Jean-Denis and Antonel, Stefano and Van Geit, Werner A. H. and Thomson, Alex M. and Mercer, Audrey and Lange, Sigrun and Falck, Joanne and R{\\\"o}ssert, Christian A. and Shi, Ying and Hagens, Olivier and Pezzoli, Maurizio and Freund, Tamas F. and Kali, Szabolcs and Muller, Eilif B. and Sch{\\\"u}rmann, Felix and Markram, Henry and Migliore, Michele},\n  editor = {Lytton, William W},\n  year = {2018},\n  month = sep,\n  journal = {PLOS Computational Biology},\n  volume = {14},\n  number = {9},\n  pages = {e1006423},\n  issn = {1553-7358},\n  doi = {10.1371/journal.pcbi.1006423},\n  urldate = {2022-06-07},\n  abstract = {Every neuron is part of a network, exerting its function by transforming multiple spatiotemporal synaptic input patterns into a single spiking output. This function is specified by the particular shape and passive electrical properties of the neuronal membrane, and the composition and spatial distribution of ion channels across its processes. For a variety of physiological or pathological reasons, the intrinsic input/output function may change during a neuron's lifetime. This process results in high variability in the peak specific conductance of ion channels in individual neurons. The mechanisms responsible for this variability are not well understood, although there are clear indications from experiments and modeling that degeneracy and correlation among multiple channels may be involved. Here, we studied this issue in biophysical models of hippocampal CA1 pyramidal neurons and interneurons. Using a unified data-driven simulation workflow and starting from a set of experimental recordings and morphological reconstructions obtained from rats, we built and analyzed several ensembles of morphologically and biophysically accurate single cell models with intrinsic electrophysiological properties consistent with experimental findings. The results suggest that the set of conductances expressed in any given hippocampal neuron may be considered as belonging to two groups: one subset is responsible for the major characteristics of the firing behavior in each population and the other is responsible for a robust degeneracy. Analysis of the model neurons suggests several experimentally testable predictions related to the combination and relative proportion of the different conductances that should be expressed on the membrane of different types of neurons for them to fulfill their role in the hippocampus circuitry.},\n  langid = {english}\n}\n\n@inproceedings{mohacsiUnifiedFrameworkApplication2020a,\n  title = {A Unified Framework for the Application and Evaluation of Different Methods for Neural Parameter Optimization},\n  booktitle = {2020 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})},\n  author = {Mohacsi, Mate and Torok, Mark Patrik and Saray, Sara and Kali, Szabolcs},\n  year = {2020},\n  month = jul,\n  pages = {1--7},\n  publisher = {{IEEE}},\n  address = {{Glasgow, United Kingdom}},\n  doi = {10.1109/IJCNN48605.2020.9206692},\n  urldate = {2023-02-28},\n  abstract = {Automated parameter search has become a standard method in the modeling of neural systems. These studies could potentially take advantage of recent developments in nonlinear optimization, and the availability of software packages containing high-quality implementations of algorithms that proved useful in other domains. However, a systematic comparison of the available algorithms for problems that are typical in neuroscience has not been performed.},\n  isbn = {978-1-72816-926-2},\n  langid = {english}\n}\n\n@article{mosherCellularClassesHuman2020a,\n  title = {Cellular {{Classes}} in the {{Human Brain Revealed In Vivo}} by {{Heartbeat-Related Modulation}} of the {{Extracellular Action Potential Waveform}}},\n  author = {Mosher, Clayton P. and Wei, Yina and Kami{\\'n}ski, Jan and Nandi, Anirban and Mamelak, Adam N. and Anastassiou, Costas A. and Rutishauser, Ueli},\n  year = {2020},\n  month = mar,\n  journal = {Cell Reports},\n  volume = {30},\n  number = {10},\n  pages = {3536-3551.e6},\n  issn = {22111247},\n  doi = {10.1016/j.celrep.2020.02.027},\n  urldate = {2023-02-28},\n  abstract = {Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human wholecell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species.},\n  langid = {english}\n}\n\n@article{octeauTransientConsequentialIncreases2019a,\n  title = {Transient, {{Consequential Increases}} in {{Extracellular Potassium Ions Accompany Channelrhodopsin2 Excitation}}},\n  author = {Octeau, J. Christopher and Gangwani, Mohitkumar R. and Allam, Sushmita L. and Tran, Duy and Huang, Shuhan and {Hoang-Trong}, Tuan M. and Golshani, Peyman and Rumbell, Timothy H. and Kozloski, James R. and Khakh, Baljit S.},\n  year = {2019},\n  month = may,\n  journal = {Cell Reports},\n  volume = {27},\n  number = {8},\n  pages = {2249-2261.e7},\n  issn = {22111247},\n  doi = {10.1016/j.celrep.2019.04.078},\n  urldate = {2022-06-07},\n  abstract = {Channelrhodopsin2 (ChR2) optogenetic excitation is widely used to study neurons, astrocytes, and circuits. Using complementary approaches in situ and in vivo, we found that ChR2 stimulation leads to significant transient elevation of extracellular potassium ions by \\$5 mM. Such elevations were detected in ChR2-expressing mice, following local in vivo expression of ChR2(H134R) with adeno-associated viruses (AAVs), in different brain areas and when ChR2 was expressed in neurons or astrocytes. In particular, ChR2-mediated excitation of striatal astrocytes was sufficient to increase medium spiny neuron (MSN) excitability and immediate early gene expression. The effects on MSN excitability were recapitulated in silico with a computational MSN model and detected in vivo as increased action potential firing in awake, behaving mice. We show that transient, physiologically consequential increases in extracellular potassium ions accompany ChR2 optogenetic excitation. This coincidental effect may be important to consider during astrocyte studies employing ChR2 to interrogate neural circuits and animal behavior.},\n  langid = {english}\n}\n\n@techreport{revaUniversalWorkflowCreation2022,\n  type = {Preprint},\n  title = {A Universal Workflow for Creation, Validation and Generalization of Detailed Neuronal Models},\n  author = {Reva, Maria and R{\\\"o}ssert, Christian and Arnaudon, Alexis and Damart, Tanguy and Mandge, Darshan and Tuncel, An{\\i}l and Ramaswamy, Srikanth and Markram, Henry and Van Geit, Werner},\n  year = {2022},\n  month = dec,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2022.12.13.520234},\n  urldate = {2023-02-28},\n  abstract = {Detailed single neuron modeling is widely used to study neuronal functions. While cellular and functional diversity across the mammalian cortex is vast, most of the available computational tools are dedicated to the reproduction of a small set of specific features characteristic of a single neuron. Here, we present a generalized automated workflow for the creation of robust electrical models and illustrate its performance by building cell models for the rat somatosensory cortex (SSCx). Each model is based on a 3D morphological reconstruction and a set of ionic mechanisms specific to the cell type. We use an evolutionary algorithm to optimize passive and active ionic parameters to match the electrophysiological features extracted from whole-cell patch-clamp recordings. To shed light on which parameters are constrained by experimental data and which could be degenerate, we perform a parameter sensitivity analysis. We also validate the optimized models against additional experimental stimuli and assess their generalizability on a population of morphologies with the same morphological type. With this workflow, we generate SSCx neuronal models producing the variability of neuronal responses. Due to its versatility, our workflow can be used to build robust biophysical models of any neuronal type.},\n  langid = {english}\n}\n\n@article{rizzaStellateCellComputational2021a,\n  title = {Stellate Cell Computational Modeling Predicts Signal Filtering in the Molecular Layer Circuit of Cerebellum},\n  author = {Rizza, Martina Francesca and Locatelli, Francesca and Masoli, Stefano and {S{\\'a}nchez-Ponce}, Diana and Mu{\\~n}oz, Alberto and Prestori, Francesca and D'Angelo, Egidio},\n  year = {2021},\n  month = feb,\n  journal = {Scientific Reports},\n  volume = {11},\n  number = {1},\n  pages = {3873},\n  issn = {2045-2322},\n  doi = {10.1038/s41598-021-83209-w},\n  urldate = {2023-02-28},\n  abstract = {Abstract             The functional properties of cerebellar stellate cells and the way they regulate molecular layer activity are still unclear. We have measured stellate cells electroresponsiveness and their activation by parallel fiber bursts. Stellate cells showed intrinsic pacemaking, along with characteristic responses to depolarization and hyperpolarization, and showed a marked short-term facilitation during repetitive parallel fiber transmission. Spikes were emitted after a lag and only at high frequency, making stellate cells to operate as delay-high-pass filters. A detailed computational model summarizing these physiological properties allowed to explore different functional configurations of the parallel fiber\\textemdash stellate cell\\textemdash Purkinje cell circuit. Simulations showed that, following parallel fiber stimulation, Purkinje cells almost linearly increased their response with input frequency, but such an increase was inhibited by stellate cells, which leveled the Purkinje cell gain curve to its 4~Hz value. When reciprocal inhibitory connections between stellate cells were activated, the control of stellate cells over Purkinje cell discharge was maintained only at very high frequencies. These simulations thus predict a new role for stellate cells, which could endow the molecular layer with low-pass and band-pass filtering properties regulating Purkinje cell gain and, along with this, also burst delay and the burst-pause responses pattern.},\n  langid = {english}\n}\n\n@techreport{romaniCommunitybasedReconstructionSimulation2023,\n  type = {Preprint},\n  title = {Community-Based {{Reconstruction}} and {{Simulation}} of a {{Full-scale Model}} of {{Region CA1}} of {{Rat Hippocampus}}},\n  author = {Romani, Armando and Antonietti, Alberto and Bella, Davide and Budd, Julian and Giacalone, Elisabetta and Kurban, Kerem and S{\\'a}ray, S{\\'a}ra and Abdellah, Marwan and Arnaudon, Alexis and Boci, Elvis and Colangelo, Cristina and Courcol, Jean-Denis and Delemontex, Thomas and Ecker, Andr{\\'a}s and Falck, Joanne and Favreau, Cyrille and Gevaert, Michael and Hernando, Juan B. and Herttuainen, Joni and Ivaska, Genrich and Kanari, Lida and Kaufmann, Anna-Kristin and King, James Gonzalo and Kumbhar, Pramod and Lange, Sigrun and Lu, Huanxiang and Lupascu, Carmen Alina and Migliore, Rosanna and Petitjean, Fabien and Planas, Judit and Rai, Pranav and Ramaswamy, Srikanth and Reimann, Michael W. and Riquelme, Juan Luis and Guerrero, Nadir Rom{\\'a}n and Shi, Ying and Sood, Vishal and Sy, Mohameth Fran{\\c c}ois and Geit, Werner Van and Vanherpe, Liesbeth and Freund, Tam{\\'a}s F. and Mercer, Audrey and Muller, Eilif and Sch{\\\"u}rmann, Felix and Thomson, Alex M. and Migliore, Michele and K{\\'a}li, Szabolcs and Markram, Henry},\n  year = {2023},\n  month = may,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2023.05.17.541167},\n  urldate = {2023-05-24},\n  abstract = {Abstract                        The CA1 region of the hippocampus is one of the most studied regions of the rodent brain, thought to play an important role in cognitive functions such as memory and spatial navigation. Despite a wealth of experimental data on its structure and function, it can be challenging to reconcile information obtained from diverse experimental approaches. To address this challenge, we present a community-driven, full-scale             in silico             model of the rat CA1 that integrates a broad range of experimental data, from synapse to network, including the reconstruction of its principal afferents, the Schaffer collaterals, and a model of the effects that acetylcholine has on the system. We have tested and validated each model component and the final network model, and made input data, assumptions, and strategies explicit and transparent. The flexibility of the model allows scientists to address a range of scientific questions. In this article, we describe the methods used to set up simulations that reproduce and extend             in vitro             and             in vivo             experiments. Among several applications in the article, we focus on theta rhythm, a prominent hippocampal oscillation associated with various behavioral correlates and use our computer model to reproduce and reconcile experimental findings. Finally, we make data, code and model available through the hippocampushub.eu portal, which also provides an extensive set of analyses of the model and a user-friendly interface to facilitate adoption and usage. This neuroscience community-driven model represents a valuable tool for integrating diverse experimental data and provides a foundation for further research into the complex workings of the hippocampal CA1 region.},\n  langid = {english}\n}\n\n@incollection{romaniReconstructionHippocampus2022,\n  title = {Reconstruction of the {{Hippocampus}}},\n  booktitle = {Computational {{Modelling}} of the {{Brain}}},\n  author = {Romani, Armando and Sch{\\\"u}rmann, Felix and Markram, Henry and Migliore, Michele},\n  editor = {Giugliano, Michele and Negrello, Mario and Linaro, Daniele},\n  year = {2022},\n  volume = {1359},\n  pages = {261--283},\n  publisher = {{Springer International Publishing}},\n  address = {{Cham}},\n  doi = {10.1007/978-3-030-89439-9_11},\n  urldate = {2023-02-28},\n  abstract = {The hippocampus is a widely studied brain region thought to play an important role in higher cognitive functions such as learning, memory, and navigation. The amount of data on this region increases every day and delineates a complex and fragmented picture, but an integrated understanding of hippocampal function remains elusive. Computational methods can help to move the research forward, and reconstructing a full-scale model of the hippocampus is a challenging yet feasible task that the research community should undertake.},\n  isbn = {978-3-030-89438-2 978-3-030-89439-9},\n  langid = {english}\n}\n\n@article{rumbellDimensionsControlSubthreshold2019a,\n  title = {Dimensions of Control for Subthreshold Oscillations and Spontaneous Firing in Dopamine Neurons},\n  author = {Rumbell, Timothy and Kozloski, James},\n  editor = {Cuntz, Hermann},\n  year = {2019},\n  month = sep,\n  journal = {PLOS Computational Biology},\n  volume = {15},\n  number = {9},\n  pages = {e1007375},\n  issn = {1553-7358},\n  doi = {10.1371/journal.pcbi.1007375},\n  urldate = {2023-02-28},\n  abstract = {Dopaminergic neurons (DAs) of the rodent substantia nigra pars compacta (SNc) display varied electrophysiological properties in vitro. Despite this, projection patterns and functional inputs from DAs to other structures are conserved, so in vivo delivery of consistent, well-timed dopamine modulation to downstream circuits must be coordinated. Here we show robust coordination by linear parameter controllers, discovered through powerful mathematical analyses of data and models, and from which consistent control of DA subthreshold oscillations (STOs) and spontaneous firing emerges. These units of control represent coordinated intracellular variables, sufficient to regulate complex cellular properties with radical simplicity. Using an evolutionary algorithm and dimensionality reduction, we discovered metaparameters, which when regressed against STO features, revealed a 2dimensional control plane for the neuron's 22-dimensional parameter space that fully maps the natural range of DA subthreshold electrophysiology. This plane provided a basis for spiking currents to reproduce a large range of the naturally occurring spontaneous firing characteristics of SNc DAs. From it we easily produced a unique population of models, derived using unbiased parameter search, that show good generalization to channel blockade and compensatory intracellular mechanisms. From this population of models, we then discovered low-dimensional controllers for regulating spontaneous firing properties, and gain insight into how currents active in different voltage regimes interact to produce the emergent activity of SNc DAs. Our methods therefore reveal simple regulators of neuronal function lurking in the complexity of combined ion channel dynamics.},\n  langid = {english}\n}\n\n@article{sarayHippoUnitSoftwareTool2021a,\n  title = {{{HippoUnit}}: {{A}} Software Tool for the Automated Testing and Systematic Comparison of Detailed Models of Hippocampal Neurons Based on Electrophysiological Data},\n  shorttitle = {{{HippoUnit}}},\n  author = {S{\\'a}ray, S{\\'a}ra and R{\\\"o}ssert, Christian A. and Appukuttan, Shailesh and Migliore, Rosanna and Vitale, Paola and Lupascu, Carmen A. and Bologna, Luca L. and Van Geit, Werner and Romani, Armando and Davison, Andrew P. and Muller, Eilif and Freund, Tam{\\'a}s F. and K{\\'a}li, Szabolcs},\n  editor = {Lytton, William W.},\n  year = {2021},\n  month = jan,\n  journal = {PLOS Computational Biology},\n  volume = {17},\n  number = {1},\n  pages = {e1008114},\n  issn = {1553-7358},\n  doi = {10.1371/journal.pcbi.1008114},\n  urldate = {2023-02-28},\n  abstract = {Anatomically and biophysically detailed data-driven neuronal models have become widely used tools for understanding and predicting the behavior and function of neurons. Due to the increasing availability of experimental data from anatomical and electrophysiological measurements as well as the growing number of computational and software tools that enable accurate neuronal modeling, there are now a large number of different models of many cell types available in the literature. These models were usually built to capture a few important or interesting properties of the given neuron type, and it is often unknown how they would behave outside their original context. In addition, there is currently no simple way of quantitatively comparing different models regarding how closely they match specific experimental observations. This limits the evaluation, re-use and further development of the existing models. Further, the development of new models could also be significantly facilitated by the ability to rapidly test the behavior of model candidates against the relevant collection of experimental data. We address these problems for the representative case of the CA1 pyramidal cell of the rat hippocampus by developing an open-source Python test suite, which makes it possible to automatically and systematically test multiple properties of models by making quantitative comparisons between the models and electrophysiological data. The tests cover various aspects of somatic behavior, and signal propagation and integration in apical dendrites. To demonstrate the utility of our approach, we applied our tests to compare the behavior of several different rat hippocampal CA1 pyramidal cell models from the ModelDB database against electrophysiological data available in the literature, and evaluated how well these models match experimental observations in different domains. We also show how we employed the test suite to aid the development of models within the European Human Brain Project (HBP), and describe the integration of the tests into the validation framework developed in the HBP, with the aim of facilitating more reproducible and transparent model building in the neuroscience community.},\n  langid = {english}\n}\n\n@techreport{schneider-mizellChandelierCellAnatomy2020b,\n  type = {Preprint},\n  title = {Chandelier Cell Anatomy and Function Reveal a Variably Distributed but Common Signal},\n  author = {{Schneider-Mizell}, Casey M. and Bodor, Agnes L. and Collman, Forrest and Brittain, Derrick and Bleckert, Adam A. and Dorkenwald, Sven and Turner, Nicholas L. and Macrina, Thomas and Lee, Kisuk and Lu, Ran and Wu, Jingpeng and Zhuang, Jun and Nandi, Anirban and Hu, Brian and Buchanan, JoAnn and Takeno, Marc M. and Torres, Russel and Mahalingam, Gayathri and Bumbarger, Daniel J. and Li, Yang and Chartrand, Tom and Kemnitz, Nico and Silversmith, William M. and Ih, Dodam and Zung, Jonathan and Zlateski, Aleksandar and Tartavull, Ignacio and Popovych, Sergiy and Wong, William and Castro, Manuel and Jordan, Chris S. and Froudarakis, Emmanouil and Becker, Lynne and Suckow, Shelby and Reimer, Jacob and Tolias, Andreas S. and Anastassiou, Costas and Seung, H. Sebastian and Reid, R. Clay and {Ma{\\c c}arico da Costa}, Nuno},\n  year = {2020},\n  month = apr,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2020.03.31.018952},\n  urldate = {2023-02-28},\n  abstract = {The activity and connectivity of inhibitory cells has a profound impact on the operation of neuronal networks. While the average connectivity of many inhibitory cell types has been characterized, we still lack an understanding of how individual interneurons distribute their synapses onto their targets and how heterogeneous the inhibition is onto different individual excitatory neurons. Here, we use large-scale volumetric electron microscopy (EM) and functional imaging to address this question for chandelier cells in layer 2/3 of mouse visual cortex. Using dense morphological reconstructions from EM, we mapped the complete chandelier input onto 153 pyramidal neurons. We find that the number of input synapses is highly variable across the population, but the variability is correlated with structural features of the target neuron: soma depth, soma size, and the number of perisomatic synapses received. Functionally, we found that chandelier cell activity in vivo was highly correlated and tracks pupil diameter, a proxy for arousal state. We propose that chandelier cells provide a global signal whose strength is individually adjusted for each target neuron. This approach, combining comprehensive structural analysis with functional recordings of identified cell types, will be a powerful tool to uncover the wiring rules across the diversity of cortical cell types.},\n  langid = {english}\n}\n\n@article{schneider-mizellStructureFunctionAxoaxonic2021,\n  title = {Structure and Function of Axo-Axonic Inhibition},\n  author = {{Schneider-Mizell}, Casey M and Bodor, Agnes L and Collman, Forrest and Brittain, Derrick and Bleckert, Adam and Dorkenwald, Sven and Turner, Nicholas L and Macrina, Thomas and Lee, Kisuk and Lu, Ran and Wu, Jingpeng and Zhuang, Jun and Nandi, Anirban and Hu, Brian and Buchanan, JoAnn and Takeno, Marc M and Torres, Russel and Mahalingam, Gayathri and Bumbarger, Daniel J and Li, Yang and Chartrand, Thomas and Kemnitz, Nico and Silversmith, William M and Ih, Dodam and Zung, Jonathan and Zlateski, Aleksandar and Tartavull, Ignacio and Popovych, Sergiy and Wong, William and Castro, Manuel and Jordan, Chris S and Froudarakis, Emmanouil and Becker, Lynne and Suckow, Shelby and Reimer, Jacob and Tolias, Andreas S and Anastassiou, Costas A and Seung, H Sebastian and Reid, R Clay and da Costa, Nuno Ma{\\c c}arico},\n  year = {2021},\n  month = dec,\n  journal = {eLife},\n  volume = {10},\n  pages = {e73783},\n  issn = {2050-084X},\n  doi = {10.7554/eLife.73783},\n  urldate = {2022-06-07},\n  abstract = {Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-\\-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-a\\- xonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-a\\- xonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-\\-active. Finally, we measured chandelier cell activity in awake animals using a cell-t\\-ype-s\\- pecific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-\\-wide signal whose strength is adjusted relative to the properties of target neurons.},\n  langid = {english}\n}\n\n@incollection{schurmannComputationalConceptsReconstructing2022,\n  title = {Computational {{Concepts}} for {{Reconstructing}} and {{Simulating Brain Tissue}}},\n  booktitle = {Computational {{Modelling}} of the {{Brain}}},\n  author = {Sch{\\\"u}rmann, Felix and Courcol, Jean-Denis and Ramaswamy, Srikanth},\n  editor = {Giugliano, Michele and Negrello, Mario and Linaro, Daniele},\n  year = {2022},\n  volume = {1359},\n  pages = {237--259},\n  publisher = {{Springer International Publishing}},\n  address = {{Cham}},\n  doi = {10.1007/978-3-030-89439-9_10},\n  urldate = {2023-02-28},\n  abstract = {Abstract             It has previously been shown that it is possible to derive a new class of biophysically detailed brain tissue models when one computationally analyzes and exploits the interdependencies or the multi-modal and multi-scale organization of the brain. These reconstructions, sometimes referred to as digital twins, enable a spectrum of scientific investigations. Building such models has become possible because of increase in quantitative data but also advances in computational capabilities, algorithmic and methodological innovations. This chapter presents the computational science concepts that provide the foundation to the data-driven approach to reconstructing and simulating brain tissue as developed by the EPFL Blue Brain Project, which was originally applied to neocortical microcircuitry and extended to other brain regions. Accordingly, the chapter covers aspects such as a knowledge graph-based data organization and the importance of the concept of a dataset release. We illustrate algorithmic advances in finding suitable parameters for electrical models of neurons or how spatial constraints can be exploited for predicting synaptic connections. Furthermore, we explain how in silico experimentation with such models necessitates specific addressing schemes or requires strategies for an efficient simulation. The entire data-driven approach relies on the systematic validation of the model. We conclude by discussing complementary strategies that not only enable judging the fidelity of the model but also form the basis for its systematic refinements.},\n  isbn = {978-3-030-89438-2 978-3-030-89439-9},\n  langid = {english}\n}\n\n@article{sekulicIntegrationWithinCellExperimental2020a,\n  title = {Integration of {{Within-Cell Experimental Data With Multi-Compartmental Modeling Predicts H-Channel Densities}} and {{Distributions}} in {{Hippocampal OLM Cells}}},\n  author = {Sekuli{\\'c}, Vladislav and Yi, Feng and Garrett, Tavita and {Guet-McCreight}, Alexandre and Lawrence, J. Josh and Skinner, Frances K.},\n  year = {2020},\n  month = sep,\n  journal = {Frontiers in Cellular Neuroscience},\n  volume = {14},\n  pages = {277},\n  issn = {1662-5102},\n  doi = {10.3389/fncel.2020.00277},\n  urldate = {2023-02-28},\n  langid = {english}\n}\n\n@article{shapiraStatisticalEmulationNeural2022,\n  title = {Statistical {{Emulation}} of {{Neural Simulators}}: {{Application}} to {{Neocortical L2}}/3 {{Large Basket Cells}}},\n  shorttitle = {Statistical {{Emulation}} of {{Neural Simulators}}},\n  author = {Shapira, Gilad and {Marcus-Kalish}, Mira and Amsalem, Oren and Van Geit, Werner and Segev, Idan and Steinberg, David M.},\n  year = {2022},\n  month = mar,\n  journal = {Frontiers in Big Data},\n  volume = {5},\n  pages = {789962},\n  issn = {2624-909X},\n  doi = {10.3389/fdata.2022.789962},\n  urldate = {2022-06-07},\n  abstract = {Many scientific systems are studied using computer codes that simulate the phenomena of interest. Computer simulation enables scientists to study a broad range of possible conditions, generating large quantities of data at a faster rate than the laboratory. Computer models are widespread in neuroscience, where they are used to mimic brain function at different levels. These models offer a variety of new possibilities for the neuroscientist, but also numerous challenges, such as: where to sample the input space for the simulator, how to make sense of the data that is generated, and how to estimate unknown parameters in the model. Statistical emulation can be a valuable complement to simulator-based research. Emulators are able to mimic the simulator, often with a much smaller computational burden and they are especially valuable for parameter estimation, which may require many simulator evaluations. This work compares different statistical models that address these challenges, and applies them to simulations of neocortical L2/3 large basket cells, created and run with the NEURON simulator in the context of the European Human Brain Project. The novelty of our approach is the use of fast empirical emulators, which have the ability to accelerate the optimization process for the simulator and to identify which inputs (in this case, different membrane ion channels) are most influential in affecting simulated features. These contributions are complementary, as knowledge of the important features can further improve the optimization process. Subsequent research, conducted after the process is completed, will gain efficiency by focusing on these inputs.},\n  langid = {english}\n}\n\n@techreport{sunReducedOrienslacunosumMoleculare2022,\n  type = {Preprint},\n  title = {Reduced Oriens-Lacunosum/Moleculare ({{OLM}}) Cell Model Identifies Biophysical Current Balances for {\\emph{in Vivo}} Theta Frequency Spiking Resonance},\n  author = {Sun, Zhenyang and Crompton, David and Lankarany, Milad and Skinner, Frances K},\n  year = {2022},\n  month = oct,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2022.10.20.513073},\n  urldate = {2023-02-28},\n  abstract = {ABSTRACT                        Conductance-based models have played an important role in the development of modern neuroscience. These mathematical models are powerful ``tools'' that enable theoretical explorations in experimentally untenable situations, and can lead to the development of novel hypotheses and predictions. With advances in cell imaging and computational power, multi-compartment models with morphological accuracy are becoming common practice. However, as more biological details are added, they make extensive explorations and analyses more challenging largely due to their huge computational expense. Here, we focus on oriens-lacunosum/moleculare (OLM) cell models. OLM cells can contribute to functionally relevant theta rhythms in the hippocampus by virtue of their ability to express spiking resonance at theta frequencies, but what characteristics underlie this is far from clear. We converted a previously developed detailed multi-compartment OLM cell model into a reduced single compartment model that retained biophysical fidelity with its underlying ion currents. We showed that the reduced OLM cell model can capture complex output that includes spiking resonance in             in vivo             -like scenarios as previously obtained with the multi-compartment model. Using the reduced model, we were able to greatly expand our             in vivo             -like scenarios. Applying spike-triggered average analyses, we were thus able to to determine that it is a combination of hyperpolarization-activated cation and muscarinic type potassium currents that specifically allow OLM cells to exhibit spiking resonance at theta frequencies. Further, we developed a robust Kalman Filtering (KF) method to estimate parameters of the reduced model in real-time. We showed that it may be possible to directly estimate conductance parameters from experiments since this KF method can reliably extract parameter values from model voltage recordings. Overall, our work showcases how the contribution of cellular biophysical current details could be determined and assessed for spiking resonance. As well, our work shows that it may be possible to directly extract these parameters from current clamp voltage recordings.},\n  langid = {english}\n}\n\n@techreport{wilbersStructuralFunctionalSpecializations2022,\n  type = {Preprint},\n  title = {Structural and Functional Specializations of Human Fast Spiking Neurons Support Fast Cortical Signaling},\n  author = {Wilbers, Ren{\\'e} and Galakhova, Anna A. and Heistek, Tim S. and Metodieva, Verjinia D. and Hagemann, Jim and Heyer, Djai B. and Mertens, Eline J. and Deng, Suixin and Idema, Sander and {de Witt Hamer}, Philip C. and Noske, David P. and {van Schie}, Paul and Kommers, Ivar and Luan, Guoming and Li, Tianfu and Shu, Yousheng and {de Kock}, Christiaan P.J. and Mansvelder, Huibert D. and Goriounova, Natalia A.},\n  year = {2022},\n  month = nov,\n  institution = {{Neuroscience}},\n  doi = {10.1101/2022.11.29.518193},\n  urldate = {2023-02-28},\n  abstract = {Word count 150) In rodent cortical networks, fast spiking interneurons (FSINs) provide fast inhibition that synchronizes neuronal activity and is critical for cognitive function. Fast synchronization frequencies are evolutionary conserved in the expanded human neocortex, despite larger neuron-to-neuron distances that challenge fast input-output transfer functions of FSINs. Here, we test which mechanistic specializations of large human FSINs explain their fast-signaling properties in human cortex. With morphological reconstructions, multi-patch recordings, and biophysical modeling we find that despite three-fold longer dendritic path lengths, human FSINs maintain fast inhibition between connected pyramidal neurons through several mechanisms: stronger synapse strength of excitatory inputs, larger dendrite diameter with reduced complexity, faster AP initiation, and faster and larger inhibitory output, while Na+ current activation /inactivation properties are similar. These adaptations underlie short input-output delays in fast inhibition of human pyramidal neurons through FSINs, explaining how cortical synchronization frequencies are conserved despite expanded and sparse network topology of human cortex.},\n  langid = {english}\n}\n\n@article{wyboDatadrivenReductionDendritic2021a,\n  title = {Data-Driven Reduction of Dendritic Morphologies with Preserved Dendro-Somatic Responses},\n  author = {Wybo, Willem AM and Jordan, Jakob and Ellenberger, Benjamin and Marti Mengual, Ulisses and Nevian, Thomas and Senn, Walter},\n  year = {2021},\n  month = jan,\n  journal = {eLife},\n  volume = {10},\n  pages = {e60936},\n  issn = {2050-084X},\n  doi = {10.7554/eLife.60936},\n  urldate = {2023-02-28},\n  abstract = {Dendrites shape information flow in neurons. Yet, there is little consensus on the level of spatial complexity at which they operate. Through carefully chosen parameter fits, solvable in the least-squares sense, we obtain accurate reduced compartmental models at any level of complexity. We show that (back-propagating) action potentials, Ca2+ spikes, and N-methyl-Daspartate spikes can all be reproduced with few compartments. We also investigate whether afferent spatial connectivity motifs admit simplification by ablating targeted branches and grouping affected synapses onto the next proximal dendrite. We find that voltage in the remaining branches is reproduced if temporal conductance fluctuations stay below a limit that depends on the average difference in input resistance between the ablated branches and the next proximal dendrite.},\n  langid = {english}\n}\n\n@article{yaoReducedInhibitionDepression2022,\n  title = {Reduced Inhibition in Depression Impairs Stimulus Processing in Human Cortical Microcircuits},\n  author = {Yao, Heng Kang and {Guet-McCreight}, Alexandre and Mazza, Frank and Moradi Chameh, Homeira and Prevot, Thomas D. and Griffiths, John D. and Tripathy, Shreejoy J. and Valiante, Taufik A. and Sibille, Etienne and Hay, Etay},\n  year = {2022},\n  month = jan,\n  journal = {Cell Reports},\n  volume = {38},\n  number = {2},\n  pages = {110232},\n  issn = {22111247},\n  doi = {10.1016/j.celrep.2021.110232},\n  urldate = {2022-06-07},\n  abstract = {Cortical processing depends on finely tuned excitatory and inhibitory connections in neuronal microcircuits. Reduced inhibition by somatostatin-expressing interneurons is a key component of altered inhibition associated with treatment-resistant major depressive disorder (depression), which is implicated in cognitive deficits and rumination, but the link remains to be better established mechanistically in humans. Here we test the effect of reduced somatostatin interneuron-mediated inhibition on cortical processing in human neuronal microcircuits using a data-driven computational approach. We integrate human cellular, circuit, and gene expression data to generate detailed models of human cortical microcircuits in health and depression. We simulate microcircuit baseline and response activity and find a reduced signal-to-noise ratio and increased false/failed detection of stimuli due to a higher baseline activity in depression. We thus apply models of human cortical microcircuits to demonstrate mechanistically how reduced inhibition impairs cortical processing in depression, providing quantitative links between altered inhibition and cognitive deficits.},\n  langid = {english}\n}\n"
  },
  {
    "path": "misc/github_wiki/bibtex/uses_BPO_extra.bib",
    "content": "@inproceedings{10.5555/3294771.3294894,\nauthor = {Lueckmann, Jan-Matthis and Gon\\c{c}alves, Pedro J. and Bassetto, Giacomo and \\\"{O}cal, Kaan and Nonnenmacher, Marcel and Macke, Jakob H.},\ntitle = {Flexible Statistical Inference for Mechanistic Models of Neural Dynamics},\nyear = {2017},\nisbn = {9781510860964},\npublisher = {Curran Associates Inc.},\naddress = {Red Hook, NY, USA},\nabstract = {Mechanistic models of single-neuron dynamics have been extensively studied in computational neuroscience. However, identifying which models can quantitatively reproduce empirically measured data has been challenging. We propose to overcome this limitation by using likelihood-free inference approaches (also known as Approximate Bayesian Computation, ABC) to perform full Bayesian inference on single-neuron models. Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters. We learn a Bayesian mixture-density network approximating the posterior over multiple rounds of adaptively chosen simulations. Furthermore, we propose an efficient approach for handling missing features and parameter settings for which the simulator fails, as well as a strategy for automatically learning relevant features using recurrent neural networks. On synthetic data, our approach efficiently estimates posterior distributions and recovers ground-truth parameters. On in-vitro recordings of membrane voltages, we recover multivariate posteriors over biophysical parameters, which yield model-predicted voltage traces that accurately match empirical data. Our approach will enable neuroscientists to perform Bayesian inference on complex neuron models without having to design model-specific algorithms, closing the gap between mechanistic and statistical approaches to single-neuron modelling.},\nbooktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems},\npages = {1289–1299},\nnumpages = {11},\nlocation = {Long Beach, California, USA},\nseries = {NIPS'17}\n}\n\n@article{nandiSingleneuronModelsLinking,\n  title = {Single-Neuron Models Linking Electrophysiology, Morphology and Transcriptomics across Cortical Cell Types},\n  author = {Nandi, Anirban and Chartrand, Tom and Geit, Werner Van and Buchin, Anatoly and Yao, Zizhen and Lee, Soo Yeun and Wei, Yina and Kalmbach, Brian and Lee, Brian and Lein, Ed and Berg, Jim and S{\\\"u}mb{\\\"u}l, Uygar and Koch, Christof and Anastassiou, Costas A},\n  abstract = {Identifying the cell types constituting brain circuits is a fundamental question in neuroscience and motivates the generation of taxonomies based on electrophysiological, morphological and molecular single cell properties. Establishing the correspondence across data modalities and understanding the underlying principles has proven challenging. Bio-realistic computational models offer the ability to probe cause-and-effect and have historically been used to explore phenomena at the single-neuron level. Here we introduce a computational optimization workflow used for the generation and evaluation of more than 130 million single neuron models with active conductances. These models were based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that distinct ion channel conductance vectors exist that distinguish between major cortical classes with passive and h-channel conductances emerging as particularly important for classification. Next, using models of genetically defined classes, we show that differences in specific conductances predicted from the models reflect differences in gene expression in excitatory and inhibitory cell types as experimentally validated by single-cell RNA-sequencing. The differences in these conductances, in turn, explain many of the electrophysiological differences observed between cell types. Finally, we show the robustness of the herein generated single-cell models as representations and realizations of specific cell types in face of biological variability and optimization complexity. Our computational effort generated models that reconcile major single-cell data modalities that define cell types allowing for causal relationships to be examined.},\n  langid = {english},\n  year = {2022},\n  doi = {10.1016/j.celrep.2022.111176},\n  journal = {Cell Reports},\n  volume = {40},\n  number = {6}\n}\n\n@article{https://doi.org/10.1002/glia.24317,\nauthor = {Rosenberg, Nadia and Reva, Maria and Binda, Francesca and Restivo, Leonardo and Depierre, Pauline and Puyal, Julien and Briquet, Marc and Bernardinelli, Yann and Rocher, Anne-Bérengère and Markram, Henry and Chatton, Jean-Yves},\ntitle = {Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer's disease},\njournal = {Glia},\nvolume = {71},\nnumber = {4},\npages = {957-973},\nkeywords = {astrocytes, mitochondrial uncoupling proteins, neurodegenerative diseases, neuronal excitability, spatial memory},\ndoi = {https://doi.org/10.1002/glia.24317},\nurl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/glia.24317},\neprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/glia.24317},\nabstract = {Abstract Alzheimer's disease (AD) is becoming increasingly prevalent worldwide. It represents one of the greatest medical challenges as no pharmacologic treatments are available to prevent disease progression. Astrocytes play crucial functions within neuronal circuits by providing metabolic and functional support, regulating interstitial solute composition, and modulating synaptic transmission. In addition to these physiological functions, growing evidence points to an essential role of astrocytes in neurodegenerative diseases like AD. Early-stage AD is associated with hypometabolism and oxidative stress. Contrary to neurons that are vulnerable to oxidative stress, astrocytes are particularly resistant to mitochondrial dysfunction and are therefore more resilient cells. In our study, we leveraged astrocytic mitochondrial uncoupling and examined neuronal function in the 3xTg AD mouse model. We overexpressed the mitochondrial uncoupling protein 4 (UCP4), which has been shown to improve neuronal survival in vitro. We found that this treatment efficiently prevented alterations of hippocampal metabolite levels observed in AD mice, along with hippocampal atrophy and reduction of basal dendrite arborization of subicular neurons. This approach also averted aberrant neuronal excitability observed in AD subicular neurons and preserved episodic-like memory in AD mice assessed in a spatial recognition task. These findings show that targeting astrocytes and their mitochondria is an effective strategy to prevent the decline of neurons facing AD-related stress at the early stages of the disease.},\nyear = {2023}\n}\n"
  },
  {
    "path": "misc/github_wiki/creates_publication_list_markdown.py",
    "content": "\"\"\"Creates markdown github wiki from bibtex files.\n\nUse this version of pybtex for this code to work as expected: https://bitbucket.org/aurelienjaquier/pybtex/src/custom-style/\n\"\"\"\n\nimport re\nfrom pathlib import Path\n\nfrom pybtex import PybtexEngine\n\nfrom pybtex.style.formatting.unsrt import Style as OriginalStyle\nfrom pybtex.style.template import field, sentence, tag\n\n\nclass Style(OriginalStyle):\n    \"\"\"Style similar to unsrt, but with bold titles and sorting by date.\"\"\"\n    default_sorting_style = 'year_month' # must have custom pybtex to use this\n\n    def format_title(self, e, which_field, as_sentence=True):\n        formatted_title = field(\n            which_field, apply_func=lambda text: text.capitalize()\n        )\n        formatted_title = tag('b') [ formatted_title ]\n        if as_sentence:\n            return sentence [ formatted_title ]\n        else:\n            return formatted_title\n\n\ndef put_bullet_points(input):\n    \"\"\"Replace references by bullet points.\"\"\"\n    to_replace = \"\\[[0-9]+\\]\"  # any numbers in braquets\n    return re.sub(to_replace, \"*\", input)\n\n\nworking_directory = Path(\"./\")\nbibtex_folder = working_directory / \"bibtex\"\noutput_path = working_directory / \"output\" / \"gh_wiki.md\"\n\nuses_BPO = bibtex_folder / \"uses_BPO.bib\" # from zotero\nuses_BPO_extra = bibtex_folder / \"uses_BPO_extra.bib\" # extra custom\nmentions_BPO = bibtex_folder / \"mentions_BPO.bib\"\nmentions_BPO_extra = bibtex_folder / \"mentions_BPO_extra.bib\"\nthesis_uses_BPO = bibtex_folder / \"thesis_uses_BPO.bib\"\nthesis_mentions_BPO = bibtex_folder / \"thesis_mentions_BPO.bib\"\nposter_uses_BPO = bibtex_folder / \"poster_uses_BPO.bib\"\n\n\n# style should have number references for them to be replaced later by regex\n# e.g. \"unsrt\"\nstyle = Style\n\n# -- turn bibtex files into markdown -- #\nengine = PybtexEngine()\n\nmd_uses_bpo = engine.format_from_files(\n    [uses_BPO, uses_BPO_extra], style=style, output_backend=\"markdown\"\n)\nmd_mentions_bpo = engine.format_from_files(\n    [mentions_BPO, mentions_BPO_extra], style=style, output_backend=\"markdown\"\n)\nmd_thesis_uses_BPO = engine.format_from_file(\n    thesis_uses_BPO, style=style, output_backend=\"markdown\"\n)\nmd_thesis_mentions_BPO = engine.format_from_file(\n    thesis_mentions_BPO, style=style, output_backend=\"markdown\"\n)\nmd_poster_uses_BPO = engine.format_from_file(\n    poster_uses_BPO, style=style, output_backend=\"markdown\"\n)\n\n# -- replace references by bullet points -- #\nmd_uses_bpo = put_bullet_points(md_uses_bpo)\nmd_mentions_bpo = put_bullet_points(md_mentions_bpo)\nmd_thesis_uses_BPO = put_bullet_points(md_thesis_uses_BPO)\nmd_thesis_mentions_BPO = put_bullet_points(md_thesis_mentions_BPO)\nmd_poster_uses_BPO = put_bullet_points(md_poster_uses_BPO)\n\n# -- assemble markdown parts into one markdown wiki -- #\noutput = f\"\"\"# Publications that use or mention BluePyOpt\n\n\n## Scientific papers that use BluePyOpt\n{md_uses_bpo}\n\n## Scientific papers that mention BluePyOpt\n{md_mentions_bpo}\n\n## Theses that use BluePyOpt\n{md_thesis_uses_BPO}\n\n## Theses that mention BluePyOpt\n{md_thesis_mentions_BPO}\n\n## Posters that use BluePyOpt\n{md_poster_uses_BPO}\n\"\"\"\n\n\n# -- write down markdown wiki -- #\noutput_path.parent.mkdir(parents=True, exist_ok=True)\nwith open(output_path, \"w\") as f:\n    f.write(output)\n"
  },
  {
    "path": "misc/pytest_migration/convert_pytest.sh",
    "content": "#!/bin/bash\n\ncd bluepyopt/tests\n\ndeclare -a StringArray=(\"./\" \"test_ephys/\" \"test_deapext/\" )\n\nfor dir in ${StringArray[@]}\ndo\n    touch ${dir}__init__.py\n    sed -i'' 's/^import utils/from . import utils/g' $dir*.py\n    sed -i'' 's/import testmodels.dummycells/from .testmodels import dummycells/g' $dir*.py\n    sed -i'' 's/testmodels.dummycells/dummycells/g' $dir*.py\n    sed -i'' 's/from deapext_test_utils import make_mock_population/from .deapext_test_utils import make_mock_population/g' $dir*.py\n    sed -i'' 's/nt.assert_raises/pytest.raises/g' $dir*.py\n    sed -i'' 's/nt.ok_/nt.assert_true/g' $dir*.py\n    sed -i'' 's/nt.eq_/nt.assert_equal/g' $dir*.py\n    sed -i'' 's/nt.assert/assert/g' $dir*.py\n    sed -i'' 's/assert_almost_equal/numpy.testing.assert_almost_equal/g' $dir*.py\n    sed -i'' 's/@nt.raises(Exception)/@pytest.mark.xfail(raises=Exception)/g' $dir*.py\n    # sed -i'' 's/import nose.tools as nt/from . import assert_helpers/g' $dir*.py\n    sed -i'' 's/import nose.tools as nt//g' $dir*.py\n    sed -i'' 's/from nose.plugins.attrib import attr/import pytest\\nimport numpy/g' $dir*.py\n    sed -i'' \"s/@attr('unit')/@pytest.mark.unit/g\" $dir*.py\n    sed -i'' \"s/@attr('slow')/@pytest.mark.slow/g\" $dir*.py\n    # cp ../../assert_helpers.py $dir    \ndone\n\nnose2pytest -v .\n"
  },
  {
    "path": "package.json",
    "content": "{\n  \"name\": \"BluePyOpt\",\n  \"description\": \"The Blue Brain Python Optimisation Library (BluePyOpt) is an extensible framework for data-driven model parameter optimisation that wraps and standardises several existing open-source tools.\",\n  \"version\": \"1.9.50\",\n  \"scripts\": {\n    \"build_doc\": \"echo 'bluepyopt.readthedocs.io'\"\n  },\n  \"repository\": {\n    \"type\": \"git\",\n    \"url\": \"https://github.com/BlueBrain/BluePyOpt/releases\",\n    \"issuesurl\": \"https://github.com/BlueBrain/BluePyOpt/issues\"\n  },\n  \"author\": \"Werner Van Geit (werner.vangeit@epfl.ch)\",\n  \"contributors\": [\"Guiseppe Chindemi\", \"Jean-Denis Courcol\", \"Tanguy Damart\", \"Michael Gevaert\", \"Elisabetta Iavarone\", \"Christian Roessert\", \"Anil Tuncel\", \"Werner Van Geit\"],\n  \"license\": \"LGPL/BSD\"\n}\n"
  },
  {
    "path": "pyproject.toml",
    "content": "[build-system]\nrequires = [\"setuptools >= 64\", \"setuptools-scm>=8.0\"]\nbuild-backend = \"setuptools.build_meta\"\n\n[project]\nname = \"bluepyopt\"\nauthors = [\n    {name = \"Blue Brain Project, EPFL\", email = \"werner.vangeit@epfl.ch\"},\n]\ndescription=\"Blue Brain Python Optimisation Library (bluepyopt)\"\nreadme = \"README.rst\"\nlicense = {file = \"LICENSE.txt\"}\nrequires-python = \">= 3.9\"\ndynamic = [\"version\"]\ndependencies = [\n    \"numpy>=1.6\",\n    \"pandas>=0.18\",\n    \"deap>=1.3.3\",\n    \"efel>=2.13\",\n    \"ipyparallel\",\n    \"pickleshare>=0.7.3\",\n    \"Jinja2>=2.8\",\n    \"Pebble>=4.6.0\",\n    \"NEURON>=7.8\",\n]\nclassifiers = [\n    \"Development Status :: 5 - Production/Stable\",\n    \"Environment :: Console\",\n    \"License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)\",\n    \"Programming Language :: Python :: 3 :: Only\",\n    \"Operating System :: POSIX\",\n    \"Topic :: Scientific/Engineering\",\n    \"Topic :: Utilities\",\n]\nkeywords = [\n    \"optimisation\",\n    \"neuroscience\",\n    \"BlueBrainProject\",\n]\n\n[project.optional-dependencies]\nall = [\"scoop>=0.7\", \"pyneuroml>=0.5.20\", \"libNeuroML>=0.3.1\", \"LFPy>=2.3\", \"arbor>=0.10\"]\ntests = [\"pyneuroml>=0.5.20\", \"libNeuroML>=0.3.1\", \"LFPy>=2.3\", \"arbor>=0.10\"]\nscoop = [\"scoop>=0.7\"]\nneuroml = [\"pyneuroml>=0.5.20\", \"libNeuroML>=0.3.1\"]\nlfpy = [\"LFPy>=2.3\"]\narbor = [\"arbor>=0.10\"]\n\n[project.urls]\nHomepage = \"https://github.com/BlueBrain/BluePyOpt\"\nSource = \"https://github.com/BlueBrain/BluePyOpt\"\nRepository = \"https://github.com/BlueBrain/BluePyOpt.git\"\nTracker = \"https://github.com/BlueBrain/BluePyOpt/issues\"\nDocumentation = \"https://bluepyopt.readthedocs.io/en/latest\"\n\n[project.scripts]\nbpopt_tasksdb = \"bluepyopt:ipyp.bpopt_tasksdb.main\"\n\n[tool.setuptools]\ninclude-package-data = true\n\n[tool.setuptools.package-data]\nbluepyopt = [\n    \"ephys/static/arbor_mechanisms.json\",\n    \"ephys/templates/cell_template.jinja2\",\n    \"ephys/templates/acc/_json_template.jinja2\",\n    \"ephys/templates/acc/decor_acc_template.jinja2\",\n    \"ephys/templates/acc/label_dict_acc_template.jinja2\",\n    \"ephys/examples/simplecell/simple.swc\",\n    \"neuroml/NeuroML2_mechanisms/*.nml\"\n]\n\n[tool.setuptools.packages.find]\nexclude = [\"examples*\"]\n\n[tool.setuptools_scm]\nversion_scheme = \"python-simplified-semver\"\nlocal_scheme = \"no-local-version\"\n"
  },
  {
    "path": "pytest.ini",
    "content": "[pytest]\nmarkers =\n    unit\n    slow\n    neuroml\nfilterwarnings =\n    ignore::RuntimeWarning:deap.*\n    ignore::DeprecationWarning\n"
  },
  {
    "path": "requirements.txt",
    "content": "-e .\n"
  },
  {
    "path": "requirements_docs.txt",
    "content": "# Copyright (c) 2016-2022, EPFL/Blue Brain Project\n#\n# This file is part of BluePyOpt <https://github.com/BlueBrain/BluePyOpt>\n# This library is free software; you can redistribute it and/or modify it under\n# the terms of the GNU Lesser General Public License version 3.0 as published\n# by the Free Software Foundation.\n# This library is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public License for more\n# details.\n# You should have received a copy of the GNU Lesser General Public License\n# along with this library; if not, write to the Free Software Foundation, Inc.,\n# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.\n\nsphinx>=2.0.0\nsphinx-bluebrain-theme\nsphinx-autorun"
  },
  {
    "path": "tox.ini",
    "content": "[tox]\nenvlist = py{3}-unit-functional-style\nminversion = 4\n\n[gh-actions]\npython =\n    3.9: py3\n    3.10: py3\n    3.11: py3\n    3.12: py3\n\n[testenv]\nenvdir =\n    py3{9,10,11,12,}{-unit,-functional,-style,-syntax}: {toxworkdir}/py3\n    docs: {toxworkdir}/docs\nextras = tests\ndeps =\n    coverage\n    flake8\n    neuron-nightly\n    sh\n    pytest-cov\ndownload = true\nallowlist_externals =\n    make\n    find\n    cd\n    pwd\npassenv = https_proxy\ncoverage_options = --cov-append --cov-report=xml --cov-config=.coveragerc\nsetenv = \n    ; for neuroml tests\n    NEURON_HOME={envdir}\ncommands =\n    make clean\n\n    style: pycodestyle --ignore=E402,W503,W504 bluepyopt\n    syntax: flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics\n\n    unit: pytest --cov=bluepyopt {[testenv]coverage_options} bluepyopt/tests -k unit\n    functional: make stochkv_prepare l5pc_prepare sc_prepare meta_prepare\n    functional: pytest --cov=bluepyopt {[testenv]coverage_options} bluepyopt/tests -k 'not unit and not neuroml'\n    ; separate neuroml test from the other ones\n    ; because it redefines l5pc template which makes neuron crash and tests fail\n    functional: pytest --cov=bluepyopt {[testenv]coverage_options} bluepyopt/tests -m neuroml\n\n[testenv:docs]\nbasepython = python3.9\nchangedir = docs\ndeps =\n    sphinx\n    sphinx-bluebrain-theme\ncommands = make html SPHINXOPTS=-W\nallowlist_externals = make\n"
  }
]