Repository: BlueBrain/BluePyOpt Branch: master Commit: 87325945d120 Files: 363 Total size: 30.7 MB Directory structure: gitextract_wgjz66lh/ ├── .coveragerc ├── .gitattributes ├── .github/ │ └── workflows/ │ ├── build.yml │ ├── keep-alive.yml │ ├── mirror-ebrains.yml │ └── test.yml ├── .gitignore ├── .readthedocs.yaml ├── .zenodo.json ├── AUTHORS.txt ├── COPYING ├── COPYING.lesser ├── Dockerfile ├── LICENSE.txt ├── MANIFEST.in ├── Makefile ├── README.rst ├── bluepyopt/ │ ├── __init__.py │ ├── api.py │ ├── deapext/ │ │ ├── CMA_MO.py │ │ ├── CMA_SO.py │ │ ├── __init__.py │ │ ├── algorithms.py │ │ ├── hype.py │ │ ├── optimisations.py │ │ ├── optimisationsCMA.py │ │ ├── stoppingCriteria.py │ │ ├── tools/ │ │ │ ├── __init__.py │ │ │ └── selIBEA.py │ │ └── utils.py │ ├── ephys/ │ │ ├── __init__.py │ │ ├── acc.py │ │ ├── base.py │ │ ├── create_acc.py │ │ ├── create_hoc.py │ │ ├── efeatures.py │ │ ├── evaluators.py │ │ ├── examples/ │ │ │ ├── __init__.py │ │ │ └── simplecell/ │ │ │ ├── __init__.py │ │ │ ├── simple.swc │ │ │ └── simplecell.py │ │ ├── extra_features_utils.py │ │ ├── locations.py │ │ ├── mechanisms.py │ │ ├── models.py │ │ ├── morphologies.py │ │ ├── objectives.py │ │ ├── objectivescalculators.py │ │ ├── parameters.py │ │ ├── parameterscalers/ │ │ │ ├── __init__.py │ │ │ ├── acc_iexpr.py │ │ │ └── parameterscalers.py │ │ ├── protocols.py │ │ ├── recordings.py │ │ ├── responses.py │ │ ├── serializer.py │ │ ├── simulators.py │ │ ├── static/ │ │ │ └── arbor_mechanisms.json │ │ ├── stimuli.py │ │ └── templates/ │ │ ├── acc/ │ │ │ ├── _json_template.jinja2 │ │ │ ├── decor_acc_template.jinja2 │ │ │ └── label_dict_acc_template.jinja2 │ │ └── cell_template.jinja2 │ ├── evaluators.py │ ├── ipyp/ │ │ ├── __init__.py │ │ └── bpopt_tasksdb.py │ ├── neuroml/ │ │ ├── NeuroML2_mechanisms/ │ │ │ ├── Ca.channel.nml │ │ │ ├── Ca_HVA.channel.nml │ │ │ ├── Ca_LVAst.channel.nml │ │ │ ├── Ih.channel.nml │ │ │ ├── Im.channel.nml │ │ │ ├── K_Pst.channel.nml │ │ │ ├── K_Tst.channel.nml │ │ │ ├── KdShu2007.channel.nml │ │ │ ├── NaTa_t.channel.nml │ │ │ ├── NaTs2_t.channel.nml │ │ │ ├── Nap_Et2.channel.nml │ │ │ ├── SK_E2.channel.nml │ │ │ ├── SKv3_1.channel.nml │ │ │ ├── StochKv_deterministic.channel.nml │ │ │ ├── baseCaDynamics_E2_NML2.nml │ │ │ └── pas.channel.nml │ │ ├── __init__.py │ │ ├── biophys.py │ │ ├── cell.py │ │ ├── morphology.py │ │ └── simulation.py │ ├── objectives.py │ ├── optimisations.py │ ├── parameters.py │ ├── stoppingCriteria.py │ ├── tests/ │ │ ├── .gitignore │ │ ├── __init__.py │ │ ├── disable_simplecell_scoop.py │ │ ├── expected_results.json │ │ ├── test_bluepyopt.py │ │ ├── test_deapext/ │ │ │ ├── __init__.py │ │ │ ├── deapext_test_utils.py │ │ │ ├── test_algorithms.py │ │ │ ├── test_hype.py │ │ │ ├── test_optimisations.py │ │ │ ├── test_optimisationsCMA.py │ │ │ ├── test_selIBEA.py │ │ │ ├── test_stoppingCriteria.py │ │ │ └── test_utils.py │ │ ├── test_ephys/ │ │ │ ├── __init__.py │ │ │ ├── test_acc.py │ │ │ ├── test_create_acc.py │ │ │ ├── test_create_hoc.py │ │ │ ├── test_evaluators.py │ │ │ ├── test_extra_features_utils.py │ │ │ ├── test_features.py │ │ │ ├── test_init.py │ │ │ ├── test_locations.py │ │ │ ├── test_mechanisms.py │ │ │ ├── test_models.py │ │ │ ├── test_morphologies.py │ │ │ ├── test_objectives.py │ │ │ ├── test_parameters.py │ │ │ ├── test_parameterscalers.py │ │ │ ├── test_protocols.py │ │ │ ├── test_recordings.py │ │ │ ├── test_serializer.py │ │ │ ├── test_simulators.py │ │ │ ├── test_stimuli.py │ │ │ ├── testdata/ │ │ │ │ ├── TimeVoltageResponse.csv │ │ │ │ ├── acc/ │ │ │ │ │ ├── CCell/ │ │ │ │ │ │ ├── CCell.json │ │ │ │ │ │ ├── CCell_decor.acc │ │ │ │ │ │ ├── CCell_label_dict.acc │ │ │ │ │ │ └── simple_axon_replacement.acc │ │ │ │ │ ├── expsyn/ │ │ │ │ │ │ ├── simple.swc │ │ │ │ │ │ ├── simple_cell.json │ │ │ │ │ │ ├── simple_cell_decor.acc │ │ │ │ │ │ └── simple_cell_label_dict.acc │ │ │ │ │ ├── l5pc/ │ │ │ │ │ │ ├── C060114A7.asc │ │ │ │ │ │ ├── C060114A7_axon_replacement.acc │ │ │ │ │ │ ├── C060114A7_modified.acc │ │ │ │ │ │ ├── l5pc.json │ │ │ │ │ │ ├── l5pc_decor.acc │ │ │ │ │ │ └── l5pc_label_dict.acc │ │ │ │ │ ├── l5pc_py37/ │ │ │ │ │ │ └── l5pc_decor.acc │ │ │ │ │ ├── simplecell/ │ │ │ │ │ │ ├── simple.swc │ │ │ │ │ │ ├── simple_axon_replacement.acc │ │ │ │ │ │ ├── simple_cell.json │ │ │ │ │ │ ├── simple_cell_decor.acc │ │ │ │ │ │ ├── simple_cell_label_dict.acc │ │ │ │ │ │ └── simple_modified.acc │ │ │ │ │ └── templates/ │ │ │ │ │ ├── cell_json_template.jinja2 │ │ │ │ │ ├── decor_acc_template.jinja2 │ │ │ │ │ └── label_dict_acc_template.jinja2 │ │ │ │ ├── apic.swc │ │ │ │ ├── lfpy_soma_time.npy │ │ │ │ ├── lfpy_soma_voltage.npy │ │ │ │ ├── lfpy_time.npy │ │ │ │ ├── lfpy_voltage.npy │ │ │ │ ├── simple.swc │ │ │ │ ├── simple.wrong │ │ │ │ ├── simple_ax1.swc │ │ │ │ ├── simple_ax2.asc │ │ │ │ ├── simple_ax2.swc │ │ │ │ └── test.jinja2 │ │ │ ├── testmodels/ │ │ │ │ ├── __init__.py │ │ │ │ └── dummycells.py │ │ │ └── utils.py │ │ ├── test_evaluators.py │ │ ├── test_l5pc.py │ │ ├── test_lfpy.py │ │ ├── test_neuroml_fcts.py │ │ ├── test_parameters.py │ │ ├── test_simplecell.py │ │ ├── test_stochkv.py │ │ ├── test_tools.py │ │ └── testdata/ │ │ └── l5pc_validate_neuron_arbor/ │ │ └── param_values.json │ └── tools.py ├── cloud-config/ │ ├── README.md │ ├── config/ │ │ ├── amazon/ │ │ │ ├── README.md │ │ │ ├── ansible.cfg │ │ │ ├── create_instance.yaml │ │ │ ├── gather_config.py │ │ │ ├── site.yaml │ │ │ └── vars.yaml │ │ ├── cluster-user/ │ │ │ ├── README.md │ │ │ ├── ansible.cfg │ │ │ ├── hosts │ │ │ ├── site.yaml │ │ │ └── vars.yaml │ │ └── vagrant/ │ │ ├── README.md │ │ ├── Vagrantfile │ │ ├── ansible.cfg │ │ ├── hosts │ │ ├── site.yaml │ │ └── vars.yaml │ └── roles/ │ ├── base/ │ │ └── tasks/ │ │ └── main.yaml │ ├── deap/ │ │ └── tasks/ │ │ └── main.yaml │ ├── granule-example/ │ │ └── tasks/ │ │ └── main.yaml │ ├── neuron/ │ │ └── tasks/ │ │ ├── main.yaml │ │ └── python27.yaml │ └── scoop-master/ │ └── tasks/ │ └── main.yaml ├── codecov.yml ├── docs/ │ ├── .gitignore │ ├── Makefile │ └── source/ │ ├── .gitignore │ ├── _templates/ │ │ └── module.rst │ ├── api.rst │ ├── conf.py │ ├── deapext.rst │ ├── ephys.rst │ ├── index.rst │ └── optimisations.rst ├── examples/ │ ├── BluePyOpt-ipyparallel.md │ ├── README.md │ ├── __init__.py │ ├── cma_strategy/ │ │ └── cma.ipynb │ ├── expsyn/ │ │ ├── .gitignore │ │ ├── ExpSyn.ipynb │ │ ├── ExpSyn_arbor.ipynb │ │ ├── expsyn.py │ │ ├── generate_acc.py │ │ └── simple.swc │ ├── graupnerbrunelstdp/ │ │ ├── checkpoints/ │ │ │ └── .gitignore │ │ ├── figures/ │ │ │ └── .gitignore │ │ ├── gbevaluator.py │ │ ├── graupnerbrunelstdp.ipynb │ │ ├── run_fit.py │ │ ├── stdputil.py │ │ └── test_stdputil.py │ ├── l5pc/ │ │ ├── .gitignore │ │ ├── L5PC.ipynb │ │ ├── L5PC_arbor.ipynb │ │ ├── benchmark/ │ │ │ ├── get_stats.py │ │ │ ├── l5pc_benchmark.sbatch │ │ │ ├── logs/ │ │ │ │ └── .gitignore │ │ │ ├── run_benchmark.sh │ │ │ ├── start.sh │ │ │ └── task_stats.py │ │ ├── cADpyr_76.hoc │ │ ├── checkpoints/ │ │ │ └── .gitignore │ │ ├── config/ │ │ │ ├── features.json │ │ │ ├── fixed_params.json │ │ │ ├── mechanisms.json │ │ │ ├── parameters.json │ │ │ ├── params.json │ │ │ └── protocols.json │ │ ├── convert_noise_exp.py │ │ ├── convert_params.py │ │ ├── create_tables.py │ │ ├── exp_data/ │ │ │ ├── .gitignore │ │ │ └── noise_i.txt │ │ ├── figures/ │ │ │ └── .gitignore │ │ ├── generate_acc.py │ │ ├── generate_hoc.py │ │ ├── hocmodel.py │ │ ├── l5pc_analysis.py │ │ ├── l5pc_evaluator.py │ │ ├── l5pc_model.py │ │ ├── l5pc_validate_neuron_arbor.ipynb │ │ ├── l5pc_validate_neuron_arbor_pm.py │ │ ├── mechanisms/ │ │ │ ├── CaDynamics_E2.mod │ │ │ ├── Ca_HVA.mod │ │ │ ├── Ca_LVAst.mod │ │ │ ├── Ih.mod │ │ │ ├── Im.mod │ │ │ ├── K_Pst.mod │ │ │ ├── K_Tst.mod │ │ │ ├── LICENSE │ │ │ ├── NaTa_t.mod │ │ │ ├── NaTs2_t.mod │ │ │ ├── Nap_Et2.mod │ │ │ ├── SK_E2.mod │ │ │ ├── SKv3_1.mod │ │ │ └── dummy.inc │ │ ├── morphology/ │ │ │ ├── C060114A7.asc │ │ │ └── LICENSE │ │ ├── nsg/ │ │ │ ├── .gitignore │ │ │ ├── Makefile │ │ │ └── init.py │ │ ├── opt_l5pc.py │ │ ├── opt_l5pc.sh │ │ ├── tables/ │ │ │ └── .gitignore │ │ └── tasks2dataframe.py │ ├── l5pc_lfpy/ │ │ ├── L5PC_LFPy.ipynb │ │ ├── __init__.py │ │ ├── extra_features.json │ │ ├── generate_extra_features.py │ │ ├── l5pc_lfpy_evaluator.py │ │ └── l5pc_lfpy_model.py │ ├── metaparameters/ │ │ ├── .gitignore │ │ ├── metaparameters.ipynb │ │ └── twocompartment.swc │ ├── neuroml/ │ │ └── neuroml.ipynb │ ├── simplecell/ │ │ ├── .gitignore │ │ ├── checkpoints/ │ │ │ └── .gitignore │ │ ├── figures/ │ │ │ └── .gitignore │ │ ├── generate_acc.py │ │ ├── generate_hoc.py │ │ ├── responses.pkl │ │ ├── simple.swc │ │ ├── simplecell-paperfig.ipynb │ │ ├── simplecell.ipynb │ │ ├── simplecell_arbor.ipynb │ │ └── simplecell_model.py │ ├── stochkv/ │ │ ├── .gitignore │ │ ├── mechanisms/ │ │ │ ├── StochKv.mod │ │ │ ├── StochKv3.mod │ │ │ └── dummy.inc │ │ ├── morphology/ │ │ │ └── simple.swc │ │ ├── stochkv3cell.hoc │ │ ├── stochkv3cell.py │ │ ├── stochkv3cell_det.hoc │ │ ├── stochkvcell.hoc │ │ ├── stochkvcell.py │ │ └── stochkvcell_det.hoc │ ├── thalamocortical-cell/ │ │ ├── CellEvalSetup/ │ │ │ ├── __init__.py │ │ │ ├── evaluator.py │ │ │ ├── protocols.py │ │ │ ├── template.py │ │ │ └── tools.py │ │ ├── LICENSE.txt │ │ ├── checkpoints/ │ │ │ └── checkpoint.pkl │ │ ├── config/ │ │ │ ├── features/ │ │ │ │ ├── cAD_ltb.json │ │ │ │ └── cNAD_ltb.json │ │ │ ├── params/ │ │ │ │ └── TC.json │ │ │ ├── protocols/ │ │ │ │ ├── cAD_ltb.json │ │ │ │ └── cNAD_ltb.json │ │ │ └── recipes.json │ │ ├── mechanisms/ │ │ │ ├── SK_E2.mod │ │ │ ├── TC_HH.mod │ │ │ ├── TC_ITGHK_Des98.mod │ │ │ ├── TC_Ih_Bud97.mod │ │ │ ├── TC_Nap_Et2.mod │ │ │ ├── TC_cadecay.mod │ │ │ ├── TC_iA.mod │ │ │ └── TC_iL.mod │ │ ├── morphologies/ │ │ │ ├── jy160728_A_idA.asc │ │ │ └── jy170517_A_idA.asc │ │ ├── results/ │ │ │ ├── cAD_ltb_params.csv │ │ │ └── cNAD_ltb_params.csv │ │ └── thalamocortical-cell_opt.ipynb │ └── tsodyksmarkramstp/ │ ├── AUTHORS.txt │ ├── README.md │ ├── amps.pkl │ ├── tmevaluator.py │ ├── tmevaluator_multiplefreqs.py │ ├── tmodeint.py │ ├── tmodesolve.py │ ├── trace.pkl │ ├── tsodyksmarkramstp.ipynb │ └── tsodyksmarkramstp_multiplefreqs.ipynb ├── misc/ │ ├── github_wiki/ │ │ ├── bibtex/ │ │ │ ├── mentions_BPO.bib │ │ │ ├── mentions_BPO_extra.bib │ │ │ ├── poster_uses_BPO.bib │ │ │ ├── thesis_mentions_BPO.bib │ │ │ ├── thesis_uses_BPO.bib │ │ │ ├── uses_BPO.bib │ │ │ └── uses_BPO_extra.bib │ │ └── creates_publication_list_markdown.py │ └── pytest_migration/ │ └── convert_pytest.sh ├── package.json ├── pyproject.toml ├── pytest.ini ├── requirements.txt ├── requirements_docs.txt └── tox.ini ================================================ FILE CONTENTS ================================================ ================================================ FILE: .coveragerc ================================================ [run] omit = */tests/*,bluepyopt/_version.py [report] omit=bluepyopt/_version.py ================================================ FILE: .gitattributes ================================================ bluepyopt/_version.py export-subst ================================================ FILE: .github/workflows/build.yml ================================================ name: Build on: push: branches: - master tags: - '[0-9]+.[0-9]+.[0-9]+' jobs: call-test-workflow: uses: BlueBrain/BluePyOpt/.github/workflows/test.yml@master build-tag-n-publish: name: Build, tag and publish on PyPI runs-on: ubuntu-latest needs: call-test-workflow permissions: contents: write steps: - uses: actions/checkout@v3 - name: Set up Python 3.10 uses: actions/setup-python@v4 with: python-version: "3.10" - name: Bump version and push tag uses: anothrNick/github-tag-action@1.64.0 if: ${{ !startsWith(github.ref, 'refs/tags/') }} id: tag env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} WITH_V: false DEFAULT_BUMP: patch - name: Build a source tarball and wheel run: | pip install build python -m build - name: Get and store tag from 'Bump version and push tag' step if: ${{ !startsWith(github.ref, 'refs/tags/') }} run: echo "TAG_NAME=${{ steps.tag.outputs.new_tag }}" >> $GITHUB_ENV - name: Get and store tag from triggered tag push if: ${{ startsWith(github.ref, 'refs/tags/') }} run: echo "TAG_NAME=${{ github.ref_name }}" >> $GITHUB_ENV - name: Release uses: softprops/action-gh-release@v1 with: tag_name: ${{ env.TAG_NAME }} name: ${{ env.TAG_NAME }} generate_release_notes: true - name: Publish package to PyPI uses: pypa/gh-action-pypi-publish@release/v1 with: user: __token__ password: ${{ secrets.PYPI_PASSWORD }} ================================================ FILE: .github/workflows/keep-alive.yml ================================================ name: Keep-alive on: schedule: # Runs every sunday at 3 a.m. - cron: '0 3 * * SUN' jobs: call-test-workflow: uses: BlueBrain/BluePyOpt/.github/workflows/test.yml@master keep-workflow-alive: name: Keep workflow alive runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 with: ref: master - name: Get date from 50 days ago run: | datethen=`date -d "-50 days" --utc +%FT%TZ` echo "datelimit=$datethen" >> $GITHUB_ENV - name: setup git config if: github.event.repository.pushed_at <= env.datelimit run: | # setup the username and email. git config user.name "Github Actions Keepalive Bot" git config user.email "<>" - name: commit IF last commit is older than 50 days if: github.event.repository.pushed_at <= env.datelimit run: | git commit -m "Empty commit to keep the gihub workflows alive" --allow-empty git push origin master ================================================ FILE: .github/workflows/mirror-ebrains.yml ================================================ name: Mirror to Ebrains on: push: branches: [ master ] jobs: to_ebrains: runs-on: ubuntu-latest steps: - name: syncmaster uses: wei/git-sync@v3 with: source_repo: "BlueBrain/BluePyOpt" source_branch: "master" destination_repo: "https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/BlueBrain/bluepyopt.git" destination_branch: "master" - name: synctags uses: wei/git-sync@v3 with: source_repo: "BlueBrain/BluePyOpt" source_branch: "refs/tags/*" destination_repo: "https://ghpusher:${{ secrets.EBRAINS_GITLAB_ACCESS_TOKEN }}@gitlab.ebrains.eu/BlueBrain/bluepyopt.git" destination_branch: "refs/tags/*" ================================================ FILE: .github/workflows/test.yml ================================================ name: Test on: pull_request: # allows this workflow to be reusable (e.g. by the build workflow) workflow_call: jobs: test: name: Test for python ${{ matrix.python-version }} on ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest] python-version: ["3.9", "3.10", "3.11", "3.12"] include: - os: macos-12 python-version: "3.10" steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip setuptools pip install tox tox-gh-actions - name: Run tox run: tox - name: "Upload coverage to Codecov" uses: codecov/codecov-action@v4 with: token: ${{ secrets.CODECOV_TOKEN }} fail_ci_if_error: false ================================================ FILE: .gitignore ================================================ *.pyc *.swp x86_64 /bluepyopt.egg-info/ /build/ /dist/ .DS_Store /.tox .ipynb_checkpoints /.python-version /cov_reports .coverage coverage.xml .idea/ ================================================ FILE: .readthedocs.yaml ================================================ # .readthedocs.yml # Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 sphinx: configuration: docs/source/conf.py fail_on_warning: true python: install: - method: pip path: . - requirements: requirements_docs.txt build: os: ubuntu-22.04 tools: python: "3.10" ================================================ FILE: .zenodo.json ================================================ { "title" : "BluePyOpt", "license": "LGPL-3.0", "upload_type": "software", "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.", "creators": [ { "affiliation": "Blue Brain Project, EPFL", "name": "Van Geit, Werner", "orcid": "0000-0002-2915-720X" }, { "affiliation": "Blue Brain Project, EPFL", "name": "Gevaert, Michael", "orcid": "0000-0002-7547-3297" }, { "affiliation": "Blue Brain Project, EPFL", "name": "Damart, Tanguy", "orcid": "0000-0003-2175-7304" }, { "affiliation": "Blue Brain Project, EPFL", "name": "Rössert, Christian", "orcid": "0000-0002-4839-2424" }, { "affiliation": "Blue Brain Project, EPFL", "name": "Courcol, Jean-Denis", "orcid": "0000-0002-9351-1461" }, { "affiliation": "Blue Brain Project, EPFL", "name": "Chindemi, Guiseppe", "orcid": "0000-0001-6872-2366" }, { "affiliation": "Blue Brain Project, EPFL", "name": "Jaquier, Aurélien", "orcid": "0000-0001-6202-6175" }, { "affiliation": "Blue Brain Project, EPFL", "name": "Muller, Eilif", "orcid": "0000-0003-4309-8266" } ] } ================================================ FILE: AUTHORS.txt ================================================ Werner Van Geit @ BBP Christian Roessert @ BBP Mike Gevaert @ BBP Jean-Denis Courcol @ BBP Giuseppe Chindemi @ BBP Tanguy Damart @ BBP Elisabetta Iavarone @ BBP Anil Tuncel @ BBP Aurelien Jaquier @ BBP ================================================ FILE: COPYING ================================================ GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. 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Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . ================================================ FILE: COPYING.lesser ================================================ GNU LESSER GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. This version of the GNU Lesser General Public License incorporates the terms and conditions of version 3 of the GNU General Public License, supplemented by the additional permissions listed below. 0. Additional Definitions. As used herein, "this License" refers to version 3 of the GNU Lesser General Public License, and the "GNU GPL" refers to version 3 of the GNU General Public License. "The Library" refers to a covered work governed by this License, other than an Application or a Combined Work as defined below. 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If you modify a copy of the Library, and, in your modifications, a facility refers to a function or data to be supplied by an Application that uses the facility (other than as an argument passed when the facility is invoked), then you may convey a copy of the modified version: a) under this License, provided that you make a good faith effort to ensure that, in the event an Application does not supply the function or data, the facility still operates, and performs whatever part of its purpose remains meaningful, or b) under the GNU GPL, with none of the additional permissions of this License applicable to that copy. 3. Object Code Incorporating Material from Library Header Files. The object code form of an Application may incorporate material from a header file that is part of the Library. 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Each version is given a distinguishing version number. If the Library as you received it specifies that a certain numbered version of the GNU Lesser General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that published version or of any later version published by the Free Software Foundation. If the Library as you received it does not specify a version number of the GNU Lesser General Public License, you may choose any version of the GNU Lesser General Public License ever published by the Free Software Foundation. If the Library as you received it specifies that a proxy can decide whether future versions of the GNU Lesser General Public License shall apply, that proxy's public statement of acceptance of any version is permanent authorization for you to choose that version for the Library. ================================================ FILE: Dockerfile ================================================ # Copyright (c) 2016-2022, EPFL/Blue Brain Project # # This file is part of BluePyOpt # # This library is free software; you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License version 3.0 as published # by the Free Software Foundation. # # This library is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more # details. # # You should have received a copy of the GNU Lesser General Public License # along with this library; if not, write to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. FROM andrewosh/binder-base MAINTAINER Werner Van Geit USER root RUN apt-get update RUN apt-get install -y wget libx11-6 python-dev git build-essential libncurses-dev RUN wget https://bootstrap.pypa.io/get-pip.py RUN python get-pip.py RUN wget http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.x86_64.deb RUN dpkg -i nrn-7.4.x86_64.deb RUN rm nrn-7.4.x86_64.deb RUN pip install bluepyopt ENV PYTHONPATH /usr/local/nrn/lib/python:$PYTHONPATH ================================================ FILE: LICENSE.txt ================================================ BluePyOpt - Bluebrain Python Optimisation Library BluePyOpt is licensed under the LGPL, unless noted otherwise, e.g., for external dependencies. See files COPYING and COPYING.lesser for the full license. Examples and test are BSD-licensed. External dependencies are either LGPL or BSD-licensed. See file ACKNOWLEDGEMENTS.txt and AUTHORS.txt for further details. Copyright (c) Blue Brain Project/EPFL 2016-2022. This program is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this program. If not, see . ================================================ FILE: MANIFEST.in ================================================ include versioneer.py include bluepyopt/_version.py include bluepyopt/ephys/static/arbor_mechanisms.json include bluepyopt/ephys/templates/cell_template.jinja2 include bluepyopt/ephys/templates/acc/_json_template.jinja2 include bluepyopt/ephys/templates/acc/decor_acc_template.jinja2 include bluepyopt/ephys/templates/acc/label_dict_acc_template.jinja2 include.txt include AUTHORS.txt include COPYING include COPYING.lesser recursive-include bluepyopt/tests * ================================================ FILE: Makefile ================================================ TEST_REQUIREMENTS=nose coverage mock all: install install: pip install -q . --upgrade doc: install pip install -q sphinx sphinx-autobuild sphinx_rtd_theme cd docs; $(MAKE) clean; $(MAKE) html docopen: doc open docs/build/html/index.html docpdf: install pip install sphinx sphinx-autobuild cd docs; $(MAKE) clean; $(MAKE) latexpdf l5pc_nbconvert: jupyter cd examples/l5pc && \ jupyter nbconvert --to python L5PC.ipynb && \ sed '/get_ipython/d;/plt\./d;/plot_responses/d;/import matplotlib/d;/neurom/d;/axes/d;/fig/d;/for index/d' L5PC.py >L5PC.tmp && \ mv L5PC.tmp L5PC.py && \ python l5pc_validate_neuron_arbor_pm.py --prepare-only --regions somatic --param-values ../../bluepyopt/tests/testdata/l5pc_validate_neuron_arbor/param_values.json && \ jupyter nbconvert --to python l5pc_validate_neuron_arbor_somatic.ipynb && \ sed '/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 && \ mv l5pc_validate_neuron_arbor_somatic.tmp l5pc_validate_neuron_arbor_somatic.py l5pc_nrnivmodl: cd examples/l5pc && nrnivmodl mechanisms l5pc_zip: cd examples/l5pc && \ zip -qr l5_config.zip config/ morphology/ mechanisms/ l5pc_model.py l5pc_evaluator.py checkpoints/checkpoint.pkl l5pc_prepare: l5pc_nbconvert l5pc_nrnivmodl stochkv_prepare: cd examples/stochkv && ls mechanisms && nrnivmodl mechanisms sc_prepare: jupyter cd examples/simplecell && \ jupyter nbconvert --to python simplecell.ipynb && \ sed '/get_ipython/d;/plt\./d;/plot_responses/d;/import matplotlib/d' simplecell.py >simplecell.tmp && \ mv simplecell.tmp simplecell.py && \ jupyter nbconvert --to python simplecell_arbor.ipynb && \ sed '/get_ipython/d;/plt\./d;/plot_responses/d;/import matplotlib/d' simplecell_arbor.py >simplecell_arbor.tmp && \ mv simplecell_arbor.tmp simplecell_arbor.py meta_prepare: jupyter cd examples/metaparameters && \ jupyter nbconvert --to python metaparameters.ipynb && \ sed '/get_ipython/d;/plt\./d;/plot_responses/d;/import matplotlib/d' metaparameters.py >metaparameters.tmp && \ mv metaparameters.tmp metaparameters.py coverage_unit: unit cd bluepyopt/tests; coverage html -d coverage_html; open coverage_html/index.html coverage_test: test cd bluepyopt/tests; coverage html -d coverage_html; open coverage_html/index.html jupyter: pip install jupyter pip install ipython --upgrade pip install papermill pip install scipy install_test_requirements: pip install -q $(TEST_REQUIREMENTS) --upgrade test: clean unit functional unit: install install_test_requirements cd bluepyopt/tests; nosetests -a 'unit' -s -v -x --with-coverage --cover-xml \ --cover-package bluepyopt; functional: install install_test_requirements stochkv_prepare l5pc_prepare sc_prepare cd bluepyopt/tests; nosetests -a '!unit' -s -v -x --with-coverage --cover-xml \ --cover-package bluepyopt; pypi: test pip install twine --upgrade rm -rf dist python setup.py sdist bdist twine upload dist/* example: install cd examples/simplecell && \ python ./opt_simplecell.py clean: rm -rf build rm -rf docs/build rm -rf bluepyopt/tests/.coverage rm -rf bluepyopt/tests/coverage.xml rm -rf bluepyopt/tests/coverage_html rm -rf examples/l5pc/L5PC.py rm -rf examples/l5pc/l5pc_validate_neuron_arbor_somatic.ipynb rm -rf examples/l5pc/l5pc_validate_neuron_arbor_somatic.py rm -rf examples/l5pc/x86_64 rm -rf examples/stochkv/x86_64 rm -rf x86_64 rm -rf .coverage rm -rf coverage.xml rm -rf channels rm -rf LEMS_l5pc.xml rm -rf LEMS_l5pc_nrn.py rm -rf l5pc.Pop_l5pc_0_0.v.dat rm -rf time.dat rm -rf l5pc.hoc rm -rf l5pc.net.nml rm -rf l5pc_0_0.cell.nml rm -rf l5pc_0_0.hoc rm -rf loadcell.hoc rm -rf *.mod find . -name "*.pyc" -exec rm -rf {} \; l5pc_start: install cd examples/l5pc && \ @nrnivmodl mechanisms && \ python ./opt_l5pc.py --start l5pc_cont: install cd examples/l5pc && \ @nrnivmodl mechanisms && \ python ./opt_l5pc.py --continue_cp l5pc_analyse: install cd examples/l5pc && \ @nrnivmodl mechanisms && \ python ./opt_l5pc.py --analyse push: clean test git push git push --tags check_codecov: cat codecov.yml | curl --data-binary @- https://codecov.io/validate toxbinlinks: cd ${TOX_ENVBINDIR}; find $(TOX_NRNBINDIR) -type f -exec ln -sf \{\} . \; ================================================ FILE: README.rst ================================================ .. warning:: The Blue Brain Project concluded in December 2024, so development has ceased under the BlueBrain GitHub organization. Future development will take place at: https://github.com/openbraininstitute/BluePyOpt |banner| BluePyOpt ========= +----------------+------------+ | Latest Release | |pypi| | +----------------+------------+ | Documentation | |docs| | +----------------+------------+ | License | |license| | +----------------+------------+ | Build Status | |build| | +----------------+------------+ | Coverage | |coverage| | +----------------+------------+ | Gitter | |gitter| | +----------------+------------+ | Zenodo | |zenodo| | +----------------+------------+ Introduction ============ 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. Citation ======== When you use the BluePyOpt software or method for your research, we ask you to cite the following publication (**this includes poster presentations**): `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 `_. .. code-block:: @ARTICLE{bluepyopt, AUTHOR={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}, TITLE={BluePyOpt: Leveraging open source software and cloud infrastructure to optimise model parameters in neuroscience}, JOURNAL={Frontiers in Neuroinformatics}, VOLUME={10}, YEAR={2016}, NUMBER={17}, URL={http://www.frontiersin.org/neuroinformatics/10.3389/fninf.2016.00017/abstract}, DOI={10.3389/fninf.2016.00017}, ISSN={1662-5196} } Publications that use or mention BluePyOpt ========================================== The list of publications that use or mention BluePyOpt can be found on `the github wiki page `_. Support ======= We are providing support using a chat channel on `Gitter `_, or the `Github discussion page `_. News ==== - 2023/01: BluePyOpt now supports the Arbor simulator. - 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 - 2022/12: BluePyOpt now has the ability to write out NeuroML files: https://github.com/BlueBrain/BluePyOpt/tree/master/bluepyopt/neuroml - 2021/08/30: BluePyOpt dropped Python 2.7 support. - 2017/01/04: BluePyOpt is now considered compatible with Python 3.6+. - 2016/11/10: BluePyOpt now supports NEURON point processes. This means we can fit parameters of Adex/GIF/Izhikevich models, and also synapse models. - 2016/06/14: Started a wiki: https://github.com/BlueBrain/BluePyOpt/wiki - 2016/06/07: The BluePyOpt paper was published in Frontiers in Neuroinformatics (for link, see above) - 2016/05/03: The API documentation was moved to `ReadTheDocs `_ - 2016/04/20: BluePyOpt now contains the code of the IBEA selector, no need to install a BBP-specific version of DEAP anymore - 2016/03/24: Released version 1.0 Requirements ============ * `Python 3.9+ `_ * `Pip `_ (installed by default in newer versions of Python) * `Neuron 7.4+ `_ (compiled with Python support) * `eFEL eFeature Extraction Library `_ (automatically installed by pip) * `Numpy `_ (automatically installed by pip) * `Pandas `_ (automatically installed by pip) * The instruction below are written assuming you have access to a command shell on Linux / UNIX / MacOSX / Cygwin Installation ============ If you want to use the ephys module of BluePyOpt, you first need to install NEURON with Python support on your machine. And then bluepyopt itself: .. code-block:: bash pip install bluepyopt Support 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. .. code-block:: bash pip install bluepyopt[arbor] Cloud infrastructure ==================== We provide instructions on how to set up an optimisation environment on cloud infrastructure or cluster computers `here `_ Quick Start =========== Single compartmental model -------------------------- An iPython notebook with an introductory optimisation of a one compartmental model with 2 HH channels can be found at https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell.ipynb (NEURON) https://github.com/BlueBrain/BluePyOpt/blob/master/examples/simplecell/simplecell_arbor.ipynb (Arbor) |landscape_example| **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. Neocortical Layer 5 Pyramidal Cell ---------------------------------- Scripts for a more complex neocortical L5PC are in `this directory `__ With a notebook: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC.ipynb (NEURON) https://github.com/BlueBrain/BluePyOpt/blob/master/examples/l5pc/L5PC_arbor.ipynb (Arbor) Thalamocortical Cells --------------------- Scripts for 2 thalamocortical cell types are in `this directory `__ With a notebook: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/thalamocortical-cell/thalamocortical-cell_opt.ipynb Tsodyks-Markram Model of Short-Term Plasticity ---------------------------------------------- Scripts for 2 version of fitting the Tsodyks-Markram model to synaptic traces are in `this directory `__ With 2 notebooks: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb https://github.com/BlueBrain/BluePyOpt/blob/master/examples/tsodyksmarkramstp/tsodyksmarkramstp_multiplefreqs.ipynb Exporting cell in neuroml format -------------------------------- An iPython notebook showing how to export a BluePyOpt cell in the neuroml format, how to create a LEMS simulation, and how to run the LEMS simulation with the neuroml cell can be found at: https://github.com/BlueBrain/BluePyOpt/blob/master/examples/neuroml/neuroml.ipynb API documentation ================= The API documentation can be found on `ReadTheDocs `_. Funding ======= This 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). This 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. Copyright (c) 2016-2024 Blue Brain Project/EPFL .. The following image is also defined in the index.rst file, as the relative path is different, depending from where it is sourced. The following location is used for the github README The index.rst location is used for the docs README; index.rst also defined an end-marker, to skip content after the marker 'substitutions'. .. |pypi| image:: https://img.shields.io/pypi/v/bluepyopt.svg :target: https://pypi.org/project/bluepyopt/ :alt: latest release .. |docs| image:: https://readthedocs.org/projects/bluepyopt/badge/?version=latest :target: https://bluepyopt.readthedocs.io/ :alt: latest documentation .. |license| image:: https://img.shields.io/pypi/l/bluepyopt.svg :target: https://github.com/BlueBrain/bluepyopt/blob/master/LICENSE.txt :alt: license .. |build| image:: https://github.com/BlueBrain/BluePyOpt/workflows/Build/badge.svg?branch=master :target: https://github.com/BlueBrain/BluePyOpt/actions :alt: actions build status .. |coverage| image:: https://codecov.io/github/BlueBrain/BluePyOpt/coverage.svg?branch=master :target: https://codecov.io/gh/BlueBrain/bluepyopt :alt: coverage .. |gitter| image:: https://badges.gitter.im/Join%20Chat.svg :target: https://gitter.im/BlueBrain/blueptopt :alt: Join the chat at https://gitter.im/BlueBrain/BluePyOpt .. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.8135890.svg :target: https://doi.org/10.5281/zenodo.8135890 .. substitutions .. |banner| image:: docs/source/logo/BluePyOptBanner.png .. |landscape_example| image:: examples/simplecell/figures/landscape_example.png ================================================ FILE: bluepyopt/__init__.py ================================================ """Init script""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0611 from importlib.metadata import version __version__ = version("bluepyopt") from . import tools # NOQA from .api import * # NOQA import bluepyopt.optimisations import bluepyopt.deapext.algorithms import bluepyopt.stoppingCriteria import bluepyopt.deapext.optimisations import bluepyopt.deapext.optimisationsCMA # Add some backward compatibility for the time when DEAPoptimisation not in # deapext yet # TODO deprecate this bluepyopt.optimisations.DEAPOptimisation = \ bluepyopt.deapext.optimisations.DEAPOptimisation import bluepyopt.evaluators import bluepyopt.objectives import bluepyopt.parameters # NOQA # TODO let objects read / write themselves using json # TODO create 'Variables' class # TODO use 'locations' instead of 'location' # TODO add island functionality to optimiser # TODO add plotting functionality # TODO show progress bar during optimisation ================================================ FILE: bluepyopt/api.py ================================================ """Common API functionality""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ ''' import logging logger = logging.getLogger(__name__) def set_verboselevel(level): """Set verbose level""" logger.setLevel(level) ''' ================================================ FILE: bluepyopt/deapext/CMA_MO.py ================================================ """Multi Objective CMA-es class""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0912, R0914 import logging import numpy import copy from math import log import deap from deap import base from deap import cma from .stoppingCriteria import MaxNGen, Stagnationv2 from . import utils from . import hype logger = logging.getLogger("__main__") def get_hyped(pop, ubound_score=250., threshold_improvement=240.): """Compute the hypervolume contribution of each individual. The fitness space is first bounded and all dimension who do not show improvement are ignored. """ # Cap the obj at 250 points = numpy.array([ind.fitness.values for ind in pop]) points[points > ubound_score] = ubound_score lbounds = numpy.min(points, axis=0) ubounds = numpy.max(points, axis=0) # Remove the dimensions that do not show any improvement to_remove = [] for i, lb in enumerate(lbounds): if lb >= threshold_improvement: to_remove.append(i) points = numpy.delete(points, to_remove, axis=1) lbounds = numpy.delete(lbounds, to_remove) ubounds = numpy.delete(ubounds, to_remove) if not len(lbounds): logger.warning("No dimension along which to compute the hypervolume.") return [0.] * len(pop) # Rescale the objective space # Note: 2 here is a magic number used to make the hypercube larger than it # really is. It makes sure that the individual always have a non-zero # hyper-volume contribution and improves the results while avoiding an # edge case. points = (points - lbounds) / numpy.max(ubounds.flatten()) ubounds = numpy.max(points, axis=0) + 2.0 hv = hype.hypeIndicatorSampled( points=points, bounds=ubounds, k=5, nrOfSamples=1000000 ) return hv class CMA_MO(cma.StrategyMultiObjective): """Multiple objective covariance matrix adaption""" def __init__( self, centroids, offspring_size, sigma, max_ngen, IndCreator, RandIndCreator, weight_hv=0.5, map_function=None, use_scoop=False, use_stagnation_criterion=True, ): """Constructor Args: centroid (list): initial guess used as the starting point of the CMA-ES offspring_size (int): number of offspring individuals in each generation sigma (float): initial standard deviation of the distribution max_ngen (int): total number of generation to run IndCreator (fcn): function returning an individual of the pop RandIndCreator (fcn): function creating a random individual. weight_hv (float): between 0 and 1. Weight given to the hypervolume contribution when computing the score of an individual in MO-CMA. The weight of the fitness contribution is computed as 1 - weight_hv. map_function (map): function used to map (parallelize) the evaluation function calls use_scoop (bool): use scoop map for parallel computation use_stagnation_criterion (bool): whether to use the stagnation stopping criterion on top of the maximum generation criterion """ if offspring_size is None: lambda_ = int(4 + 3 * log(len(RandIndCreator()))) else: lambda_ = offspring_size if centroids is None: starters = [RandIndCreator() for i in range(lambda_)] else: if len(centroids) != lambda_: from itertools import cycle generator = cycle(centroids) starters = [ copy.deepcopy(next(generator)) for i in range(lambda_) ] else: starters = centroids cma.StrategyMultiObjective.__init__( self, starters, sigma, mu=int(lambda_ * 0.5), lambda_=lambda_ ) self.population = [] self.problem_size = len(starters[0]) self.weight_hv = weight_hv self.map_function = map_function self.use_scoop = use_scoop # Toolbox specific to this CMA-ES self.toolbox = base.Toolbox() self.toolbox.register("generate", self.generate, IndCreator) self.toolbox.register("update", self.update) if self.use_scoop: if self.map_function: raise Exception( "Impossible to use scoop and provide self defined map " "function: %s" % self.map_function ) from scoop import futures self.map_function = futures.map # Set termination conditions self.active = True if max_ngen <= 0: max_ngen = 100 + 50 * (self.problem_size + 3) ** 2 / numpy.sqrt( lambda_ ) self.stopping_conditions = [ MaxNGen(max_ngen), ] if use_stagnation_criterion: self.stopping_conditions.append( Stagnationv2(lambda_, self.problem_size) ) def _select(self, candidates): """Select the best candidates of the population Fill the next population (chosen) with the Pareto fronts until there is not enough space. When an entire front does not fit in the space left we rely on a mixture of hypervolume and fitness. The respective weights of hypervolume and fitness are "hv" and "1-hv". The remaining fronts are explicitly not chosen""" if self.weight_hv == 0.0: fit = [numpy.sum(ind.fitness.values) for ind in candidates] idx_scores = list(numpy.argsort(fit)) elif self.weight_hv == 1.0: hv = get_hyped(candidates) idx_scores = list(numpy.argsort(hv))[::-1] else: hv = get_hyped(candidates) idx_hv = list(numpy.argsort(hv))[::-1] fit = [numpy.sum(ind.fitness.values) for ind in candidates] idx_fit = list(numpy.argsort(fit)) scores = [] for i in range(len(candidates)): score = (self.weight_hv * idx_hv.index(i)) + ( (1.0 - self.weight_hv) * idx_fit.index(i) ) scores.append(score) idx_scores = list(numpy.argsort(scores)) chosen = [candidates[i] for i in idx_scores[: self.mu]] not_chosen = [candidates[i] for i in idx_scores[self.mu:]] return chosen, not_chosen def get_population(self, to_space): """Returns the population in the original parameter space""" pop = copy.deepcopy(self.population) for i, ind in enumerate(pop): for j, v in enumerate(ind): pop[i][j] = to_space[j](v) return pop def get_parents(self, to_space): """Returns the population in the original parameter space""" pop = copy.deepcopy(self.parents) for i, ind in enumerate(pop): for j, v in enumerate(ind): pop[i][j] = to_space[j](v) return pop def generate_new_pop(self, lbounds, ubounds): """Generate a new population bounded in the normalized space""" self.population = self.toolbox.generate() return utils.bound(self.population, lbounds, ubounds) def update_strategy(self): self.toolbox.update(self.population) def set_fitness(self, fitnesses): for f, ind in zip(fitnesses, self.population): ind.fitness.values = f def set_fitness_parents(self, fitnesses): for f, ind in zip(fitnesses, self.parents): ind.fitness.values = f def check_termination(self, gen): stopping_params = { "gen": gen, "population": self.population, } [c.check(stopping_params) for c in self.stopping_conditions] for c in self.stopping_conditions: if c.criteria_met: logger.info( "CMA stopped because of termination criteria: " + "" + " ".join(c.name) ) self.active = False ================================================ FILE: bluepyopt/deapext/CMA_SO.py ================================================ """Single Objective CMA-es class""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0912, R0914 import logging import numpy from math import sqrt, log import copy from deap import base from deap import cma from .stoppingCriteria import ( MaxNGen, Stagnationv2, TolHistFun, EqualFunVals, NoEffectAxis, TolUpSigma, TolX, ConditionCov, NoEffectCoor, ) from . import utils logger = logging.getLogger("__main__") class CMA_SO(cma.Strategy): """Single objective covariance matrix adaption""" def __init__( self, centroids, offspring_size, sigma, max_ngen, IndCreator, RandIndCreator, map_function=None, use_scoop=False, use_stagnation_criterion=True, ): """Constructor Args: centroid (list): initial guess used as the starting point of the CMA-ES offspring_size (int): number of offspring individuals in each generation sigma (float): initial standard deviation of the distribution max_ngen (int): total number of generation to run IndCreator (fcn): function returning an individual of the pop RandIndCreator (fcn): function creating a random individual. map_function (map): function used to map (parallelize) the evaluation function calls use_scoop (bool): use scoop map for parallel computation use_stagnation_criterion (bool): whether to use the stagnation stopping criterion on top of the maximum generation criterion """ if offspring_size is None: lambda_ = int(4 + 3 * log(len(RandIndCreator()))) else: lambda_ = offspring_size if centroids is None: starter = RandIndCreator() else: starter = centroids[0] cma.Strategy.__init__(self, starter, sigma, lambda_=lambda_) self.population = [] self.problem_size = len(starter) self.map_function = map_function self.use_scoop = use_scoop # Toolbox specific to this CMA-ES self.toolbox = base.Toolbox() self.toolbox.register("generate", self.generate, IndCreator) self.toolbox.register("update", self.update) # Set termination conditions self.active = True if max_ngen <= 0: max_ngen = 100 + 50 * (self.problem_size + 3) ** 2 / numpy.sqrt( lambda_ ) self.stopping_conditions = [ MaxNGen(max_ngen), TolHistFun(lambda_, self.problem_size), EqualFunVals(lambda_, self.problem_size), NoEffectAxis(self.problem_size), TolUpSigma(float(self.sigma)), TolX(), ConditionCov(), NoEffectCoor(), ] if use_stagnation_criterion: self.stopping_conditions.append( Stagnationv2(lambda_, self.problem_size) ) def update(self, population): """Update the current covariance matrix strategy from the population""" population.sort(key=lambda ind: ind.fitness.weighted_reduce, reverse=True) old_centroid = self.centroid self.centroid = numpy.dot(self.weights, population[0:self.mu]) c_diff = self.centroid - old_centroid # Cumulation : update evolution path self.ps = (1 - self.cs) * self.ps + sqrt( self.cs * (2 - self.cs) * self.mueff ) / self.sigma * numpy.dot( self.B, (1.0 / self.diagD) * numpy.dot(self.B.T, c_diff) ) # noqa hsig = float( ( numpy.linalg.norm(self.ps) / sqrt(1.0 - (1.0 - self.cs) ** (2.0 * (self.update_count + 1.0))) / self.chiN < (1.4 + 2.0 / (self.dim + 1.0)) ) ) # noqa self.update_count += 1 self.pc = (1 - self.cc) * self.pc + hsig * sqrt( self.cc * (2 - self.cc) * self.mueff ) / self.sigma * c_diff # Update covariance matrix artmp = population[0:self.mu] - old_centroid self.C = ( ( 1 - self.ccov1 - self.ccovmu + (1 - hsig) * self.ccov1 * self.cc * (2 - self.cc) ) * self.C + self.ccov1 * numpy.outer(self.pc, self.pc) + self.ccovmu * numpy.dot((self.weights * artmp.T), artmp) / self.sigma ** 2 ) self.sigma *= numpy.exp( (numpy.linalg.norm(self.ps) / self.chiN - 1.0) * self.cs / self.damps ) self.diagD, self.B = numpy.linalg.eigh(self.C) indx = numpy.argsort(self.diagD) self.cond = self.diagD[indx[-1]] / self.diagD[indx[0]] self.diagD = self.diagD[indx] ** 0.5 self.B = self.B[:, indx] self.BD = self.B * self.diagD def get_population(self, to_space): """Returns the population in the original parameter space""" pop = copy.deepcopy(self.population) for i, ind in enumerate(pop): for j, v in enumerate(ind): pop[i][j] = to_space[j](v) return pop def generate_new_pop(self, lbounds, ubounds): """Generate a new population bounded in the normalized space""" self.population = self.toolbox.generate() return utils.bound(self.population, lbounds, ubounds) def update_strategy(self): self.toolbox.update(self.population) def set_fitness(self, fitnesses): for f, ind in zip(fitnesses, self.population): ind.fitness.values = f def check_termination(self, gen): stopping_params = { "gen": gen, "population": self.population, "centroid": self.centroid, "pc": self.pc, "C": self.C, "B": self.B, "sigma": self.sigma, "diagD": self.diagD, "cond": self.cond, } [c.check(stopping_params) for c in self.stopping_conditions] for c in self.stopping_conditions: if c.criteria_met: logger.info( "CMA stopped because of termination criteria: " + "" + " ".join(c.name) ) self.active = False ================================================ FILE: bluepyopt/deapext/__init__.py ================================================ """Init script""" ================================================ FILE: bluepyopt/deapext/algorithms.py ================================================ """Optimisation class""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0914, R0912 import random import logging import shutil import os import time import deap.algorithms import deap.tools import pickle from .stoppingCriteria import MaxNGen from . import utils logger = logging.getLogger('__main__') def _define_fitness(pop, obj_size): ''' Re-instanciate the fitness of the individuals for it to matches the evaluation function. ''' from .optimisations import WSListIndividual new_pop = [] if pop: for ind in pop: new_pop.append(WSListIndividual(list(ind), obj_size=obj_size)) return new_pop def _evaluate_invalid_fitness(toolbox, population): '''Evaluate the individuals with an invalid fitness Returns the count of individuals with invalid fitness ''' invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit return len(invalid_ind) def _get_offspring(parents, toolbox, cxpb, mutpb): '''return the offspring, use toolbox.variate if possible''' if hasattr(toolbox, 'variate'): return toolbox.variate(parents, toolbox, cxpb, mutpb) return deap.algorithms.varAnd(parents, toolbox, cxpb, mutpb) def _check_stopping_criteria(criteria, params): for c in criteria: c.check(params) if c.criteria_met: logger.info('Run stopped because of stopping criteria: ' + c.name) return True else: return False def eaAlphaMuPlusLambdaCheckpoint( population, toolbox, mu, cxpb, mutpb, ngen, stats=None, halloffame=None, cp_frequency=1, cp_period=None, cp_filename=None, continue_cp=False, terminator=None, param_names=None): r"""This is the :math:`(~\alpha,\mu~,~\lambda)` evolutionary algorithm Args: population(list of deap Individuals) toolbox(deap Toolbox) mu(int): Total parent population size of EA cxpb(float): Crossover probability mutpb(float): Mutation probability ngen(int): Total number of generation to run stats(deap.tools.Statistics): generation of statistics halloffame(deap.tools.HallOfFame): hall of fame cp_frequency(int): generations between checkpoints cp_period(float): minimum time (in s) between checkpoint. None to save checkpoint independently of the time between them cp_filename(string): path to checkpoint filename continue_cp(bool): whether to continue terminator (multiprocessing.Event): exit loop when is set. Not taken into account if None. param_names(list): names of the parameters optimized by the evaluator """ if param_names is None: param_names = [] if cp_filename: cp_filename_tmp = cp_filename + '.tmp' if continue_cp: # A file name has been given, then load the data from the file cp = pickle.load(open(cp_filename, "rb")) population = cp["population"] parents = cp["parents"] start_gen = cp["generation"] halloffame = cp["halloffame"] logbook = cp["logbook"] history = cp["history"] random.setstate(cp["rndstate"]) # Assert that the fitness of the individuals match the evaluator obj_size = len(population[0].fitness.wvalues) population = _define_fitness(population, obj_size) parents = _define_fitness(parents, obj_size) _evaluate_invalid_fitness(toolbox, parents) _evaluate_invalid_fitness(toolbox, population) else: # Start a new evolution start_gen = 1 parents = population[:] logbook = deap.tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) history = deap.tools.History() invalid_count = _evaluate_invalid_fitness(toolbox, population) utils.update_history_and_hof(halloffame, history, population) utils.record_stats( stats, logbook, start_gen, population, invalid_count ) stopping_criteria = [MaxNGen(ngen)] # Begin the generational process gen = start_gen + 1 stopping_params = {"gen": gen} time_last_save = time.time() while utils.run_next_gen( not (_check_stopping_criteria(stopping_criteria, stopping_params)), terminator): offspring = _get_offspring(parents, toolbox, cxpb, mutpb) population = parents + offspring invalid_count = _evaluate_invalid_fitness(toolbox, offspring) utils.update_history_and_hof(halloffame, history, population) utils.record_stats(stats, logbook, gen, population, invalid_count) # Select the next generation parents parents = toolbox.select(population, mu) logger.info(logbook.stream) if (cp_filename and cp_frequency and gen % cp_frequency == 0 and (cp_period is None or time.time() - time_last_save > cp_period)): cp = dict(population=population, generation=gen, parents=parents, halloffame=halloffame, history=history, logbook=logbook, rndstate=random.getstate(), param_names=param_names) pickle.dump(cp, open(cp_filename_tmp, "wb")) if os.path.isfile(cp_filename_tmp): shutil.copy(cp_filename_tmp, cp_filename) logger.debug('Wrote checkpoint to %s', cp_filename) time_last_save = time.time() gen += 1 stopping_params["gen"] = gen return population, halloffame, logbook, history ================================================ FILE: bluepyopt/deapext/hype.py ================================================ """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import numpy def hypesub(la, A, actDim, bounds, pvec, alpha, k): """HypE algorithm sub function""" h = numpy.zeros(la) i = numpy.argsort(A[:, actDim - 1]) S = A[i] pvec = pvec[i] for i in range(1, S.shape[0] + 1): if i < S.shape[0]: extrusion = S[i, actDim - 1] - S[i - 1, actDim - 1] else: extrusion = bounds[actDim - 1] - S[i - 1, actDim - 1] if actDim == 1: if i > k: break if alpha[i - 1] >= 0: h[pvec[0:i]] += extrusion * alpha[i - 1] elif extrusion > 0.0: h += extrusion * hypesub( la, S[0:i, :], actDim - 1, bounds, pvec[0:i], alpha, k ) return h def hypeIndicatorExact(points, bounds, k): """HypE algorithm. Python implementation of the Matlab code available at https://sop.tik.ee.ethz.ch/download/supplementary/hype/ Args: points(array): 2D array containing the objective values of the population bounds(array): 1D array containing the reference point from which to compute the hyper-volume k(int): HypE parameter """ Ps = points.shape[0] if k < 0: k = Ps actDim = points.shape[1] pvec = numpy.arange(points.shape[0]) alpha = [] for i in range(1, k + 1): j = numpy.arange(1, i) alpha.append(numpy.prod((k - j) / (Ps - j) / i)) alpha = numpy.asarray(alpha) return hypesub(points.shape[0], points, actDim, bounds, pvec, alpha, k) def hypeIndicatorSampled(points, bounds, k, nrOfSamples): """Monte-Carlo approximation of the HypE algorithm. Python implementation of the Matlab code available at https://sop.tik.ee.ethz.ch/download/supplementary/hype/ Args: points(array): 2D array containing the objective values of the population bounds(array): 1D array containing the reference point from which to compute the hyper-volume k(int): HypE parameter nrOfSamples(int): number of random samples to use for the Monte-Carlo approximation """ nrP = points.shape[0] dim = points.shape[1] F = numpy.zeros(nrP) BoxL = numpy.min(points, axis=0) alpha = [] for i in range(1, k + 1): j = numpy.arange(1, i) alpha.append(numpy.prod((k - j) / (nrP - j) / i)) alpha = numpy.asarray(alpha + [0.0] * nrP) S = numpy.random.uniform(low=BoxL, high=bounds, size=(nrOfSamples, dim)) dominated = numpy.zeros(nrOfSamples, dtype="uint") for j in range(1, nrP + 1): B = S - points[j - 1] ind = numpy.sum(B >= 0, axis=1) == dim dominated[ind] += 1 for j in range(1, nrP + 1): B = S - points[j - 1] ind = numpy.sum(B >= 0, axis=1) == dim x = dominated[ind] F[j - 1] = numpy.sum(alpha[x - 1]) F = F * numpy.prod(bounds - BoxL) / nrOfSamples return F ================================================ FILE: bluepyopt/deapext/optimisations.py ================================================ """Optimisation class""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0912, R0914 import random import logging import functools import deap import deap.base import deap.algorithms import deap.tools from . import algorithms from . import tools from . import utils import bluepyopt.optimisations logger = logging.getLogger('__main__') # TODO decide which variables go in constructor,which ones go in 'run' function # TODO abstract the algorithm by creating a class for every algorithm, that way # settings of the algorithm can be stored in objects of these classes class WeightedSumFitness(deap.base.Fitness): """Fitness that compares by weighted sum""" def __init__(self, values=(), obj_size=None): self.weights = [-1.0] * obj_size if obj_size is not None else [-1] super(WeightedSumFitness, self).__init__(values) @property def weighted_sum(self): """Weighted sum of wvalues""" return sum(self.wvalues) @property def sum(self): """Weighted sum of values""" return sum(self.values) def __le__(self, other): return self.weighted_sum <= other.weighted_sum def __lt__(self, other): return self.weighted_sum < other.weighted_sum def __deepcopy__(self, _): """Override deepcopy""" cls = self.__class__ result = cls.__new__(cls) result.__dict__.update(self.__dict__) return result class WSListIndividual(list): """Individual consisting of list with weighted sum field""" def __init__(self, *args, **kwargs): """Constructor""" self.fitness = WeightedSumFitness(obj_size=kwargs['obj_size']) del kwargs['obj_size'] super(WSListIndividual, self).__init__(*args, **kwargs) class DEAPOptimisation(bluepyopt.optimisations.Optimisation): """DEAP Optimisation class""" def __init__(self, evaluator=None, use_scoop=False, seed=1, offspring_size=10, eta=10, mutpb=1.0, cxpb=1.0, map_function=None, hof=None, selector_name=None): """Constructor Args: evaluator (Evaluator): Evaluator object use_scoop (bool): use scoop map for parallel computation seed (float): Random number generator seed offspring_size (int): Number of offspring individuals in each generation eta (float): Parameter that controls how far the crossover and mutation operator disturbe the original individuals mutpb (float): Mutation probability cxpb (float): Crossover probability map_function (function): Function used to map (parallelise) the evaluation function calls hof (hof): Hall of Fame object selector_name (str): The selector used in the evolutionary algorithm, possible values are 'IBEA' or 'NSGA2' """ super(DEAPOptimisation, self).__init__(evaluator=evaluator) self.use_scoop = use_scoop self.seed = seed self.offspring_size = offspring_size self.eta = eta self.cxpb = cxpb self.mutpb = mutpb self.map_function = map_function self.selector_name = selector_name if self.selector_name is None: self.selector_name = 'IBEA' self.hof = hof if self.hof is None: self.hof = deap.tools.HallOfFame(10) # Create a DEAP toolbox self.toolbox = deap.base.Toolbox() self.setup_deap() def setup_deap(self): """Set up optimisation""" # Number of objectives OBJ_SIZE = len(self.evaluator.objectives) # Set random seed random.seed(self.seed) # Eta parameter of crossover / mutation parameters # Basically defines how much they 'spread' solution around # The lower this value, the more spread ETA = self.eta # Number of parameters IND_SIZE = len(self.evaluator.params) if IND_SIZE == 0: raise ValueError( "Length of evaluator.params is zero. At least one " "non-fix parameter is needed to run an optimization." ) # Bounds for the parameters LOWER = [] UPPER = [] for parameter in self.evaluator.params: LOWER.append(parameter.lower_bound) UPPER.append(parameter.upper_bound) # Register the 'uniform' function self.toolbox.register("uniformparams", utils.uniform, LOWER, UPPER, IND_SIZE) # Register the individual format # An indiviual is create by WSListIndividual and parameters # are initially # picked by 'uniform' self.toolbox.register( "Individual", deap.tools.initIterate, functools.partial(WSListIndividual, obj_size=OBJ_SIZE), self.toolbox.uniformparams) # Register the population format. It is a list of individuals self.toolbox.register( "population", deap.tools.initRepeat, list, self.toolbox.Individual) # Register the evaluation function for the individuals # import deap_efel_eval1 self.toolbox.register( "evaluate", self.evaluator.init_simulator_and_evaluate_with_lists ) # Register the mate operator self.toolbox.register( "mate", deap.tools.cxSimulatedBinaryBounded, eta=ETA, low=LOWER, up=UPPER) # Register the mutation operator self.toolbox.register( "mutate", deap.tools.mutPolynomialBounded, eta=ETA, low=LOWER, up=UPPER, indpb=0.5) # Register the variate operator self.toolbox.register("variate", deap.algorithms.varAnd) # Register the selector (picks parents from population) if self.selector_name == 'IBEA': self.toolbox.register("select", tools.selIBEA) elif self.selector_name == 'NSGA2': self.toolbox.register("select", deap.tools.emo.selNSGA2) else: raise ValueError('DEAPOptimisation: Constructor selector_name ' 'argument only accepts "IBEA" or "NSGA2"') import copyreg import types copyreg.pickle(types.MethodType, utils.reduce_method) if self.use_scoop: if self.map_function: raise Exception( 'Impossible to use scoop is providing self ' 'defined map function: %s' % self.map_function) from scoop import futures self.toolbox.register("map", futures.map) elif self.map_function: self.toolbox.register("map", self.map_function) def run(self, max_ngen=10, offspring_size=None, continue_cp=False, cp_filename=None, cp_frequency=1, cp_period=None, parent_population=None, terminator=None): """Run optimisation""" # Allow run function to override offspring_size # TODO probably in the future this should not be an object field # anymore # keeping for backward compatibility if offspring_size is None: offspring_size = self.offspring_size # Generate the population object if parent_population is not None: if len(parent_population) != offspring_size: offspring_size = len(parent_population) self.offspring_size = len(parent_population) logger.warning( 'The length of the provided population is different from ' 'the offspring_size. The offspring_size will be ' 'overwritten.' ) OBJ_SIZE = len(self.evaluator.objectives) IND_SIZE = len(self.evaluator.params) pop = [] for ind in parent_population: if len(ind) != IND_SIZE: raise Exception( 'The length of the provided individual is not equal ' 'to the number of parameter in the evaluator ') pop.append(WSListIndividual(ind, obj_size=OBJ_SIZE)) else: pop = self.toolbox.population(n=offspring_size) stats = deap.tools.Statistics(key=lambda ind: ind.fitness.sum) import numpy stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) param_names = [] if hasattr(self.evaluator, "param_names"): param_names = self.evaluator.param_names pop, hof, log, history = algorithms.eaAlphaMuPlusLambdaCheckpoint( pop, self.toolbox, offspring_size, self.cxpb, self.mutpb, max_ngen, stats=stats, halloffame=self.hof, cp_frequency=cp_frequency, cp_period=None, continue_cp=continue_cp, cp_filename=cp_filename, terminator=terminator, param_names=param_names) # Update hall of fame self.hof = hof return pop, self.hof, log, history class IBEADEAPOptimisation(DEAPOptimisation): """IBEA DEAP class""" def __init__(self, *args, **kwargs): """Constructor""" super(IBEADEAPOptimisation, self).__init__(*args, **kwargs) ================================================ FILE: bluepyopt/deapext/optimisationsCMA.py ================================================ """CMA Optimisation class""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import logging import numpy import pickle import random import functools import shutil import os import time import deap.tools from .CMA_SO import CMA_SO from .CMA_MO import CMA_MO from . import utils import bluepyopt.optimisations logger = logging.getLogger("__main__") def _ind_convert_space(ind, convert_fcn): """util function to pass the individual from normalized to real space and inversely""" return [f(x) for f, x in zip(convert_fcn, ind)] class DEAPOptimisationCMA(bluepyopt.optimisations.Optimisation): """Optimisation class for CMA-based evolution strategies""" def __init__( self, evaluator=None, use_scoop=False, seed=1, offspring_size=None, centroids=None, sigma=0.4, map_function=None, hof=None, selector_name="single_objective", weight_hv=0.5, fitness_reduce=numpy.sum, use_stagnation_criterion=True, ): """Constructor Args: evaluator (Evaluator): Evaluator object use_scoop (bool): use scoop map for parallel computation seed (float): Random number generator seed offspring_size (int): Number of offspring individuals in each generation centroids (list): list of initial guesses used as the starting points of the CMA-ES sigma (float): initial standard deviation of the distribution map_function (function): Function used to map (parallelize) the evaluation function calls hof (hof): Hall of Fame object selector_name (str): The selector used in the evolutionary algorithm, possible values are 'single_objective' or 'multi_objective' weight_hv (float): between 0 and 1. Weight given to the hyper-volume contribution when computing the score of an individual in MO-CMA. The weight of the fitness contribution is computed as 1 - weight_hv. fitness_reduce (fcn): function used to reduce the objective values to a single fitness score use_stagnation_criterion (bool): whether to use the stagnation stopping criterion on top of the maximum generation criterion """ super(DEAPOptimisationCMA, self).__init__(evaluator=evaluator) self.use_scoop = use_scoop self.seed = seed self.map_function = map_function self.hof = hof if self.hof is None: self.hof = deap.tools.HallOfFame(10) self.offspring_size = offspring_size self.fitness_reduce = fitness_reduce self.centroids = centroids self.sigma = sigma if weight_hv > 1.0 or weight_hv < 0.0: raise Exception("weight_hv has to be between 0 and 1.") self.weight_hv = weight_hv self.selector_name = selector_name if self.selector_name == "single_objective": self.cma_creator = CMA_SO elif self.selector_name == "multi_objective": self.cma_creator = CMA_MO else: raise Exception( "The selector_name has to be 'single_objective' " "or 'multi_objective'. Not " "{}".format(self.selector_name) ) self.use_stagnation_criterion = use_stagnation_criterion # Number of objective values self.problem_size = len(self.evaluator.params) # Number of parameters self.ind_size = len(self.evaluator.objectives) # Create a DEAP toolbox self.toolbox = deap.base.Toolbox() # Bounds for the parameters self.lbounds = [p.lower_bound for p in self.evaluator.params] self.ubounds = [p.upper_bound for p in self.evaluator.params] # Instantiate functions converting individuals from the original # parameter space to (and from) a normalized space bounded to [-1.;1] self.ubounds = numpy.asarray(self.ubounds) self.lbounds = numpy.asarray(self.lbounds) bounds_radius = (self.ubounds - self.lbounds) / 2.0 bounds_mean = (self.ubounds + self.lbounds) / 2.0 self.to_norm = [] self.to_space = [] for r, m in zip(bounds_radius, bounds_mean): self.to_norm.append( functools.partial( lambda param, bm, br: (param - bm) / br, bm=m, br=r) ) self.to_space.append( functools.partial( lambda param, bm, br: (param * br) + bm, bm=m, br=r ) ) # Overwrite the bounds with -1. and 1. self.lbounds = numpy.full(self.problem_size, -1.0) self.ubounds = numpy.full(self.problem_size, 1.0) self.setup_deap() # In case initial guesses were provided, rescale them to the norm space if self.centroids is not None: self.centroids = [ self.toolbox.Individual(_ind_convert_space(ind, self.to_norm)) for ind in centroids ] def setup_deap(self): """Set up optimisation""" # Set random seed random.seed(self.seed) numpy.random.seed(self.seed) # Register the 'uniform' function self.toolbox.register( "uniformparams", utils.uniform, self.lbounds, self.ubounds, self.ind_size ) # Register the individual format self.toolbox.register( "Individual", functools.partial( utils.WSListIndividual, obj_size=self.ind_size, reduce_fcn=self.fitness_reduce, ), ) # A Random Individual is created by ListIndividual and parameters are # initially picked by 'uniform' self.toolbox.register( "RandomInd", deap.tools.initIterate, self.toolbox.Individual, self.toolbox.uniformparams, ) # Register the population format. It is a list of individuals self.toolbox.register( "population", deap.tools.initRepeat, list, self.toolbox.RandomInd ) # Register the evaluation function for the individuals self.toolbox.register( "evaluate", self.evaluator.init_simulator_and_evaluate_with_lists ) import copyreg import types copyreg.pickle(types.MethodType, utils.reduce_method) if self.use_scoop: if self.map_function: raise Exception( "Impossible to use scoop is providing self defined map " "function: %s" % self.map_function ) from scoop import futures self.toolbox.register("map", futures.map) elif self.map_function: self.toolbox.register("map", self.map_function) def run( self, max_ngen=0, cp_frequency=1, cp_period=None, continue_cp=False, cp_filename=None, terminator=None, ): """ Run the optimizer until a stopping criteria is met. Args: max_ngen(int): Total number of generation to run cp_frequency(int): generations between checkpoints cp_period(float): minimum time (in s) between checkpoint. None to save checkpoint independently of the time between them continue_cp(bool): whether to continue cp_filename(string): path to checkpoint filename terminator (multiprocessing.Event): exit loop when is set. Not taken into account if None. """ if cp_filename: cp_filename_tmp = cp_filename + '.tmp' stats = self.get_stats() if continue_cp: # A file name has been given, then load the data from the file cp = pickle.load(open(cp_filename, "rb")) gen = cp["generation"] self.hof = cp["halloffame"] logbook = cp["logbook"] history = cp["history"] random.setstate(cp["rndstate"]) numpy.random.set_state(cp["np_rndstate"]) CMA_es = cp["CMA_es"] CMA_es.map_function = self.map_function else: history = deap.tools.History() logbook = deap.tools.Logbook() logbook.header = ["gen", "nevals"] + stats.fields # Instantiate the CMA strategy centered on the centroids CMA_es = self.cma_creator( centroids=self.centroids, offspring_size=self.offspring_size, sigma=self.sigma, max_ngen=max_ngen, IndCreator=self.toolbox.Individual, RandIndCreator=self.toolbox.RandomInd, map_function=self.map_function, use_scoop=self.use_scoop, use_stagnation_criterion=self.use_stagnation_criterion, ) if self.selector_name == "multi_objective": CMA_es.weight_hv = self.weight_hv to_evaluate = CMA_es.get_parents(self.to_space) fitness = self.toolbox.map(self.toolbox.evaluate, to_evaluate) fitness = list(map(list, fitness)) CMA_es.set_fitness_parents(fitness) gen = 1 pop = CMA_es.get_population(self.to_space) param_names = [] if hasattr(self.evaluator, "param_names"): param_names = self.evaluator.param_names time_last_save = time.time() # Run until a termination criteria is met while utils.run_next_gen(CMA_es.active, terminator): logger.info("Generation {}".format(gen)) # Generate the new populations n_out = CMA_es.generate_new_pop( lbounds=self.lbounds, ubounds=self.ubounds ) logger.debug( "Number of individuals outside of bounds: {} ({:.2f}%)".format( n_out, 100.0 * n_out / len(CMA_es.population) ) ) # Get all the individuals in the original space for evaluation to_evaluate = CMA_es.get_population(self.to_space) # Compute the fitness fitness = self.toolbox.map(self.toolbox.evaluate, to_evaluate) fitness = list(map(list, fitness)) nevals = len(to_evaluate) CMA_es.set_fitness(fitness) # Update the hall of fame, history and logbook pop = CMA_es.get_population(self.to_space) utils.update_history_and_hof(self.hof, history, pop) record = utils.record_stats(stats, logbook, gen, pop, nevals) logger.info(logbook.stream) # Update the CMA strategy using the new fitness and check if # termination conditions were reached CMA_es.update_strategy() CMA_es.check_termination(gen) if ( cp_filename and cp_frequency and gen % cp_frequency == 0 and (cp_period is None or time.time() - time_last_save > cp_period) ): # Map function shouldn't be pickled temp_mf = CMA_es.map_function CMA_es.map_function = None cp = dict( population=pop, generation=gen, halloffame=self.hof, history=history, logbook=logbook, rndstate=random.getstate(), np_rndstate=numpy.random.get_state(), CMA_es=CMA_es, param_names=param_names, ) pickle.dump(cp, open(cp_filename_tmp, "wb")) if os.path.isfile(cp_filename_tmp): shutil.copy(cp_filename_tmp, cp_filename) logger.debug("Wrote checkpoint to %s", cp_filename) CMA_es.map_function = temp_mf time_last_save = time.time() gen += 1 return pop, self.hof, logbook, history def get_stats(self): """Get the stats that will be saved during optimisation""" stats = deap.tools.Statistics(key=lambda ind: ind.fitness.reduce) stats.register("avg", numpy.mean) stats.register("std", numpy.std) stats.register("min", numpy.min) stats.register("max", numpy.max) return stats ================================================ FILE: bluepyopt/deapext/stoppingCriteria.py ================================================ """StoppingCriteria class""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0912, R0914 import logging import numpy from collections import deque import bluepyopt.stoppingCriteria logger = logging.getLogger("__main__") def isclose(a, b, rel_tol=1e-09, abs_tol=0.0): return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol) class MaxNGen(bluepyopt.stoppingCriteria.StoppingCriteria): """Max ngen stopping criteria class""" name = "Max ngen" def __init__(self, max_ngen): """Constructor""" super(MaxNGen, self).__init__() self.max_ngen = max_ngen def check(self, kwargs): """Check if the maximum number of iteration is reached""" gen = kwargs.get("gen") if gen > self.max_ngen: self.criteria_met = True class Stagnation(bluepyopt.stoppingCriteria.StoppingCriteria): """Stagnation stopping criteria class""" name = "Stagnation" def __init__(self, lambda_, problem_size): """Constructor""" super(Stagnation, self).__init__() self.lambda_ = lambda_ self.problem_size = problem_size self.stagnation_iter = None self.best = [] self.median = [] def check(self, kwargs): """Check if the population stopped improving""" ngen = kwargs.get("gen") population = kwargs.get("population") fitness = [ind.fitness.reduce for ind in population] fitness.sort() # condition to avoid duplicates when re-starting if len(self.best) < ngen: self.best.append(fitness[0]) self.median.append(fitness[int(round(len(fitness) / 2.0))]) self.stagnation_iter = int( numpy.ceil( 0.2 * ngen + 120 + 30.0 * self.problem_size / self.lambda_ ) ) cbest = len(self.best) > self.stagnation_iter cmed = len(self.median) > self.stagnation_iter cbest2 = numpy.median(self.best[-20:]) >= numpy.median( self.best[-self.stagnation_iter:-self.stagnation_iter + 20] ) cmed2 = numpy.median(self.median[-20:]) >= numpy.median( self.median[-self.stagnation_iter:-self.stagnation_iter + 20] ) if cbest and cmed and cbest2 and cmed2: self.criteria_met = True class Stagnationv2(bluepyopt.stoppingCriteria.StoppingCriteria): """Stagnation stopping criteria class""" name = "Stagnationv2" def __init__( self, lambda_, problem_size, threshold=0.01, std_threshold=0.02 ): """Constructor Args: lambda_ (int): offspring size problem_size (int): problem size threshold (float): 1st criterion is triggered if best fitness improves less than this threshold for 100 generations std_threshold (float): 2nd criterion is triggered if standard deviation of the best fitness over the last 20 generations is below the best fitness multiplied by this threshold """ super(Stagnationv2, self).__init__() self.lambda_ = lambda_ self.problem_size = problem_size self.stagnation_iter = None self.threshold = threshold self.std_threshold = std_threshold self.best = [] def check(self, kwargs): """Check if best model fitness does not improve over 1% over 100 gens and is not noisy in the last 20 generations """ ngen = kwargs.get("gen") population = kwargs.get("population") fitness = [ind.fitness.reduce for ind in population] fitness.sort() # condition to avoid duplicates when re-starting if len(self.best) < ngen: self.best.append(fitness[0]) self.stagnation_iter = int( numpy.ceil( 0.2 * ngen + 120 + 30.0 * self.problem_size / self.lambda_ ) ) crit1 = len(self.best) > self.stagnation_iter crit2 = numpy.median(self.best[-20:]) * (1 + self.threshold) \ > numpy.median(self.best[-120:-100]) crit3 = numpy.std(self.best[-20:]) < ( self.std_threshold * self.best[-1] ) if crit1 and crit2 and crit3: self.criteria_met = True class TolHistFun(bluepyopt.stoppingCriteria.StoppingCriteria): """TolHistFun stopping criteria class""" name = "TolHistFun" def __init__(self, lambda_, problem_size): """Constructor""" super(TolHistFun, self).__init__() self.tolhistfun = 10 ** -12 self.mins = deque( maxlen=10 + int(numpy.ceil(30.0 * problem_size / lambda_))) def check(self, kwargs): """Check if the range of the best values is smaller than the threshold""" population = kwargs.get("population") self.mins.append(numpy.min([ind.fitness.reduce for ind in population])) if ( len(self.mins) == self.mins.maxlen and max(self.mins) - min(self.mins) < self.tolhistfun ): self.criteria_met = True class EqualFunVals(bluepyopt.stoppingCriteria.StoppingCriteria): """EqualFunVals stopping criteria class""" name = "EqualFunVals" def __init__(self, lambda_, problem_size): """Constructor""" super(EqualFunVals, self).__init__() self.problem_size = problem_size self.equalvals = float(problem_size) / 3.0 self.equalvals_k = int(numpy.ceil(0.1 + lambda_ / 4.0)) self.equalvalues = [] def check(self, kwargs): """Check if in 1/3rd of the last problem_size iterations the best and k'th best solutions are equal""" ngen = kwargs.get("gen") population = kwargs.get("population") fitness = [ind.fitness.reduce for ind in population] fitness.sort() if isclose(fitness[0], fitness[-self.equalvals_k], rel_tol=1e-6): self.equalvalues.append(1) else: self.equalvalues.append(0) if ( ngen > self.problem_size and sum(self.equalvalues[-self.problem_size:]) > self.equalvals ): self.criteria_met = True class TolX(bluepyopt.stoppingCriteria.StoppingCriteria): """TolX stopping criteria class""" name = "TolX" def __init__(self): """Constructor""" super(TolX, self).__init__() self.tolx = 10 ** -12 def check(self, kwargs): """Check if all components of pc and sqrt(diag(C)) are smaller than a threshold""" pc = kwargs.get("pc") C = kwargs.get("C") if all(pc < self.tolx) and all(numpy.sqrt(numpy.diag(C)) < self.tolx): self.criteria_met = True class TolUpSigma(bluepyopt.stoppingCriteria.StoppingCriteria): """TolUpSigma stopping criteria class""" name = "TolUpSigma" def __init__(self, sigma0): """Constructor""" super(TolUpSigma, self).__init__() self.sigma0 = sigma0 self.tolupsigma = 10 ** 20 def check(self, kwargs): """Check if the sigma/sigma0 ratio is bigger than a threshold""" sigma = kwargs.get("sigma") diagD = kwargs.get("diagD") if sigma / self.sigma0 > float(diagD[-1] ** 2) * self.tolupsigma: self.criteria_met = True class ConditionCov(bluepyopt.stoppingCriteria.StoppingCriteria): """ConditionCov stopping criteria class""" name = "ConditionCov" def __init__(self): """Constructor""" super(ConditionCov, self).__init__() self.conditioncov = 10 ** 14 def check(self, kwargs): """Check if the condition number of the covariance matrix is too large""" cond = kwargs.get("cond") if cond > self.conditioncov: self.criteria_met = True class NoEffectAxis(bluepyopt.stoppingCriteria.StoppingCriteria): """NoEffectAxis stopping criteria class""" name = "NoEffectAxis" def __init__(self, problem_size): """Constructor""" super(NoEffectAxis, self).__init__() self.conditioncov = 10 ** 14 self.problem_size = problem_size def check(self, kwargs): """Check if the coordinate axis std is too low""" ngen = kwargs.get("gen") centroid = kwargs.get("centroid") sigma = kwargs.get("sigma") diagD = kwargs.get("diagD") B = kwargs.get("B") noeffectaxis_index = ngen % self.problem_size if all( centroid == centroid + 0.1 * sigma * diagD[-noeffectaxis_index] * B[-noeffectaxis_index] ): self.criteria_met = True class NoEffectCoor(bluepyopt.stoppingCriteria.StoppingCriteria): """NoEffectCoor stopping criteria class""" name = "NoEffectCoor" def __init__(self): """Constructor""" super(NoEffectCoor, self).__init__() def check(self, kwargs): """Check if main axis std has no effect""" centroid = kwargs.get("centroid") sigma = kwargs.get("sigma") C = kwargs.get("C") if any(centroid == centroid + 0.2 * sigma * numpy.diag(C)): self.criteria_met = True ================================================ FILE: bluepyopt/deapext/tools/__init__.py ================================================ """Init""" from .selIBEA import * # NOQA ================================================ FILE: bluepyopt/deapext/tools/selIBEA.py ================================================ """IBEA selector""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. The code in this file was original written in 2015 at the BlueBrain Project, EPFL, Lausanne The authors were Werner Van Geit, Michael Gevaert and Jean-Denis Courcol It is based on a C implementation of the IBEA algorithm in the PISA optimization framework developed at the ETH, Zurich http://www.tik.ee.ethz.ch/pisa/selectors/ibea/?page=ibea.php """ import numpy import random def selIBEA(population, mu, alpha=None, kappa=.05, tournament_n=4): """IBEA Selector""" if alpha is None: alpha = len(population) # Calculate a matrix with the fitness components of every individual components = _calc_fitness_components(population, kappa=kappa) # Calculate the fitness values _calc_fitnesses(population, components) # Do the environmental selection population[:] = _environmental_selection(population, alpha) # Select the parents in a tournament parents = _mating_selection(population, mu, tournament_n) return parents def _calc_fitness_components(population, kappa): """returns an N * N numpy array of doubles, which is their IBEA fitness """ # DEAP selector are supposed to maximise the objective values # We take the negative objectives because this algorithm will minimise population_matrix = numpy.fromiter( iter(-x for individual in population for x in individual.fitness.wvalues), dtype=numpy.float64) pop_len = len(population) feat_len = len(population[0].fitness.wvalues) population_matrix = population_matrix.reshape((pop_len, feat_len)) # Calculate minimal square bounding box of the objectives box_ranges = (numpy.max(population_matrix, axis=0) - numpy.min(population_matrix, axis=0)) # Replace all possible zeros to avoid division by zero # Basically 0/0 is replaced by 0/1 box_ranges[box_ranges == 0] = 1.0 components_matrix = numpy.zeros((pop_len, pop_len)) for i in range(0, pop_len): diff = population_matrix - population_matrix[i, :] components_matrix[i, :] = numpy.max( numpy.divide(diff, box_ranges), axis=1) # Calculate max of absolute value of all elements in matrix max_absolute_indicator = numpy.max(numpy.abs(components_matrix)) # Normalisation if max_absolute_indicator != 0: components_matrix = numpy.exp( (-1.0 / (kappa * max_absolute_indicator)) * components_matrix.T) return components_matrix def _calc_fitnesses(population, components): """Calculate the IBEA fitness of every individual""" # Calculate sum of every column in the matrix, ignore diagonal elements column_sums = numpy.sum(components, axis=0) - numpy.diagonal(components) # Fill the 'ibea_fitness' field on the individuals with the fitness value for individual, ibea_fitness in zip(population, column_sums): individual.ibea_fitness = ibea_fitness def _choice(seq): """Python 2 implementation of choice""" return seq[int(random.random() * len(seq))] def _mating_selection(population, mu, tournament_n): """Returns the n_of_parents individuals with the best fitness""" parents = [] for _ in range(mu): winner = _choice(population) for _ in range(tournament_n - 1): individual = _choice(population) # Save winner is element with smallest fitness if individual.ibea_fitness < winner.ibea_fitness: winner = individual parents.append(winner) return parents def _environmental_selection(population, selection_size): """Returns the selection_size individuals with the best fitness""" # Sort the individuals based on their fitness population.sort(key=lambda ind: ind.ibea_fitness) # Return the first 'selection_size' elements return population[:selection_size] __all__ = ['selIBEA'] ================================================ FILE: bluepyopt/deapext/utils.py ================================================ """Utils function""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import numpy import random import deap.base # pylint: disable=R0914, R0912 class WeightedReducedFitness(deap.base.Fitness): """Fitness that compares by weighted objective values""" def __init__(self, values=(), obj_size=None, reduce_fcn=numpy.sum): self.weights = [-1.0] * obj_size if obj_size is not None else [-1] self.reduce_fcn = reduce_fcn super(WeightedReducedFitness, self).__init__(values) @property def reduce(self): """Reduce of values""" return self.reduce_fcn(self.values) @property def weighted_reduce(self): """Reduce of weighted values""" return self.reduce_fcn(self.wvalues) def __le__(self, other): return self.weighted_reduce <= other.weighted_reduce def __lt__(self, other): return self.weighted_reduce < other.weighted_reduce def __deepcopy__(self, _): """Override deepcopy""" cls = self.__class__ result = cls.__new__(cls) result.__dict__.update(self.__dict__) return result class WSListIndividual(list): """Individual consisting of a list with weighted fitness""" def __init__(self, *args, **kwargs): """Constructor""" reduce_fcn = kwargs.get("reduce_fcn", numpy.sum) self.fitness = WeightedReducedFitness( obj_size=kwargs["obj_size"], reduce_fcn=reduce_fcn ) # Index of the parent, used by MO-CMA self._ps = "p", 0 del kwargs["obj_size"] if "reduce_fcn" in kwargs: del kwargs["reduce_fcn"] super(WSListIndividual, self).__init__(*args, **kwargs) def update_history_and_hof(halloffame, history, population): """Update the hall of fame with the generated individuals Note: History and Hall-of-Fame behave like dictionaries """ if halloffame is not None: halloffame.update(population) history.update(population) def record_stats(stats, logbook, gen, population, invalid_count): """Update the statistics with the new population""" record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=invalid_count, **record) def closest_feasible(individual, lbounds, ubounds): """Returns the closest individual in the parameter bounds""" # TODO: Fix 1e-9 hack for i, (u, l, el) in enumerate(zip(ubounds, lbounds, individual)): if el >= u: individual[i] = u - 1e-9 elif el <= l: individual[i] = l + 1e-9 return individual def bound(population, lbounds, ubounds): """Bounds the population based on lower and upper parameter bounds.""" n_out = 0 for i, ind in enumerate(population): if numpy.any(numpy.less(ind, lbounds)) or numpy.any( numpy.greater(ind, ubounds) ): population[i] = closest_feasible(ind, lbounds, ubounds) n_out += 1 return n_out def uniform(lower_list, upper_list, dimensions): """Uniformly pick an individual""" if hasattr(lower_list, "__iter__"): return [ random.uniform(lower, upper) for lower, upper in zip(lower_list, upper_list) ] else: return [random.uniform(lower_list, upper_list) for _ in range(dimensions)] def reduce_method(meth): """Overwrite reduce""" return (getattr, (meth.__self__, meth.__func__.__name__)) def run_next_gen(criteria, terminator): """Condition to stay inside the loop.""" if terminator is None: return criteria return criteria and not terminator.is_set() ================================================ FILE: bluepyopt/ephys/__init__.py ================================================ """Init script""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 from . import base # NOQA from . import simulators # NOQA from . import models # NOQA from . import evaluators # NOQA from . import mechanisms # NOQA from . import locations # NOQA from . import parameterscalers # NOQA from . import parameters # NOQA from . import morphologies # NOQA from . import efeatures # NOQA from . import objectives # NOQA from . import protocols # NOQA from . import responses # NOQA from . import recordings # NOQA from . import objectivescalculators # NOQA from . import stimuli # NOQA # TODO create all the necessary abstract methods # TODO check inheritance structure # TODO instantiate using 'simulation env' as parameter, instead of cell ================================================ FILE: bluepyopt/ephys/acc.py ================================================ '''Dependencies of Arbor simulator backend''' try: import arbor except ImportError as e: class arbor: def __getattribute__(self, _): raise ImportError("Exporting cell models to ACC/JSON, loading" " them or optimizing them with the Arbor" " simulator requires missing dependency arbor." " To install BluePyOpt with arbor," " run 'pip install bluepyopt[arbor]'.") class ArbLabel: """Arbor label""" def __init__(self, type, name, s_expr): if type not in ['locset', 'region', 'iexpr']: raise ValueError('Invalid Arbor label type %s' % type) self._type = type self._name = name self._s_expr = s_expr @property def defn(self): """Label definition for label-dict""" return '(%s-def "%s" %s)' % (self._type, self._name, self._s_expr) @property def ref(self): """Reference to label defined in label-dict""" return '(%s "%s")' % (self._type, self._name) @property def name(self): """Name of the label""" return self._name @property def loc(self): """S-expression defining the location of the label""" return self._s_expr def __eq__(self, other): if other is None: return False elif not isinstance(other, ArbLabel): raise TypeError('%s is not an ArbLabel' % str(other)) else: return self._s_expr == other._s_expr def __hash__(self): return hash(self._s_expr) def __repr__(self): return self.defn ================================================ FILE: bluepyopt/ephys/base.py ================================================ '''Base class for ephys classes''' class BaseEPhys(object): '''Base class for ephys classes''' def __init__(self, name='', comment=''): self.name = name self.comment = comment def __str__(self): return '%s: %s (%s)' % (self.__class__.__name__, self.name, self.comment) ================================================ FILE: bluepyopt/ephys/create_acc.py ================================================ """create JSON/ACC files for Arbor from a set of BluePyOpt.ephys parameters""" # pylint: disable=R0914 import io import logging import pathlib from collections import ChainMap, namedtuple, OrderedDict import re import jinja2 import json import shutil from bluepyopt.ephys.acc import arbor from bluepyopt.ephys.morphologies import ArbFileMorphology from bluepyopt.ephys.create_hoc import ( Location, RangeExpr, PointExpr, _get_template_params, format_float, ) logger = logging.getLogger(__name__) # Inhomogeneous expression for scaled parameter in Arbor RangeIExpr = namedtuple("RangeIExpr", "name, value, scale") class ArbVar: """Definition of a Neuron to Arbor parameter conversion""" def __init__(self, name, conv=None): """Constructor Args: name (str): Arbor parameter name conv (): Conversion of parameter value from Neuron units to Arbor (defaults to identity) """ self.name = name self.conv = conv def __repr__(self): return "ArbVar(%s, %s)" % (self.name, self.conv) class Nrn2ArbParamAdapter: """Converts a Neuron parameter to Arbor format (name and value)""" _mapping = dict( v_init=ArbVar(name="membrane-potential"), celsius=ArbVar( name="temperature-kelvin", conv=lambda celsius: celsius + 273.15 ), Ra=ArbVar(name="axial-resistivity"), cm=ArbVar( name="membrane-capacitance", conv=lambda cm: cm / 100.0 ), # NEURON: uF/cm^2, Arbor: F/m^2 **{ species + loc[0]: ArbVar( name='ion-%sternal-concentration "%s"' % (loc, species) ) for species in ["na", "k", "ca"] for loc in ["in", "ex"] }, **{ "e" + species: ArbVar(name='ion-reversal-potential "%s"' % species) for species in ["na", "k", "ca"] }, ) @classmethod def _param_name(cls, name): """Neuron to Arbor parameter renaming Args: name (str): Neuron parameter name """ return cls._mapping[name].name if name in cls._mapping else name @classmethod def _param_value(cls, param): """Neuron to Arbor units conversion for parameter values Args: param (): A Neuron parameter with a value in Neuron units """ if ( param.name in cls._mapping and cls._mapping[param.name].conv is not None ): return format_float( cls._mapping[param.name].conv(float(param.value)) ) else: return ( param.value if isinstance(param.value, str) else format_float(param.value) ) @classmethod def _conv_param(cls, param, name): """Convert a Neuron parameter to Arbor format (name and units) Args: param (): A Neuron parameter name (): Parameter name without mech prefix/suffix """ if isinstance(param, Location): return Location( name=cls._param_name(name), value=cls._param_value(param) ) elif isinstance(param, RangeExpr): return RangeExpr( location=param.location, name=cls._param_name(name), value=cls._param_value(param), value_scaler=param.value_scaler, ) elif isinstance(param, PointExpr): return PointExpr( name=cls._param_name(name), point_loc=param.point_loc, value=cls._param_value(param), ) else: raise CreateAccException( "Unsupported parameter expression type %s." % type(param) ) @classmethod def format(cls, param, mechs): """Find a parameter's mechanism and convert name to Arbor format Args: param (): A parameter in Neuron format mechs (): List of co-located NMODL mechanisms Returns: A tuple of mechanism name (None for a non-mechanism parameter) and parameter in Arbor format """ if not isinstance(param, PointExpr): mech_matches = [ i for i, mech in enumerate(mechs) if param.name.endswith("_" + mech) ] else: param_pprocesses = [loc.pprocess_mech for loc in param.point_loc] mech_matches = [ i for i, mech in enumerate(mechs) if mech in param_pprocesses ] if len(mech_matches) == 0: return None, cls._conv_param(param, name=param.name) elif len(mech_matches) == 1: mech = mechs[mech_matches[0]] if not isinstance(param, PointExpr): name = param.name[: -(len(mech) + 1)] else: name = param.name return mech, cls._conv_param(param, name=name) else: raise CreateAccException( "Parameter name %s matches" % param.name + " multiple mechanisms %s" % [repr(mechs[i]) for i in mech_matches] ) class Nrn2ArbMechGrouper: """Group parameters by mechanism and convert them to Arbor format""" @staticmethod def _is_global_property(loc, param): """Returns if a label-specific variable is a global property in Arbor Args: loc (): An Arbor label describing the location param (): A parameter in Arbor format (name and units) """ return loc == ArbFileMorphology.region_labels["all"] and ( param.name in [ "membrane-potential", "temperature-kelvin", "axial-resistivity", "membrane-capacitance", ] or param.name.split(" ")[0] in [ "ion-internal-concentration", "ion-external-concentration", "ion-reversal-potential", ] ) @classmethod def _separate_global_properties(cls, loc, mechs): """Separates global properties from a label-specific dict of mechanisms Args: loc (): An Arbor label describing the location mechs (): A mapping of mechanism name to list of parameters in Arbor format (None for non-mechanism parameters). Returns: A split of mechs into mechanisms without Arbor global properties (first component) and a dict with Arbor global properties (second component) """ local_mechs = dict() global_properties = [] for mech, params in mechs.items(): if mech is None: local_properties = [] for param in params: if cls._is_global_property(loc, param): global_properties.append(param) else: local_properties.append(param) local_mechs[mech] = local_properties else: local_mechs[mech] = params return local_mechs, {None: global_properties} @staticmethod def _format_params_and_group_by_mech(params, channels): """Group list of parameters by mechanism and turn them to Arbor format Args: params (): List of parameters in Neuron format channels (): List of co-located NMODL mechanisms Returns: Mapping of Arbor mechanism name to list of parameters in Arbor format """ mech_params = [ Nrn2ArbParamAdapter.format(param, channels) for param in params ] mechs = {mech: [] for mech, _ in mech_params} for mech in channels: if mech not in mechs: mechs[mech] = [] for mech, param in mech_params: mechs[mech].append(param) return mechs @classmethod def process_global(cls, params): """Group global BluePyOpt params by mech, convert them to Arbor format Args: params (): List of global parameters in Neuron format Returns: A mapping of mechanism to parameters representing Arbor global properties. The mechanism parameters are in Arbor format (mechanism name is None for non-mechanism parameters). """ return cls._format_params_and_group_by_mech( [ Location(name=name, value=value) for name, value in params.items() ], [], # no default mechanisms ) @classmethod def process_local(cls, params, channels): """Group local BluePyOpt params by mech, convert them to Arbor format Args: params (): List of Arbor label/local parameters pairs in Neuron format channels (): Mapping of Arbor label to co-located NMODL mechanisms Returns: The return value is a tuple. In the first component, a two-level mapping of Arbor label to mechanism to parameters. The mechanism parameters are in Arbor format (mechanism name is None for non-mechanism parameters). In the second component, the Arbor global properties found are returned. """ local_mechs = dict() global_properties = dict() for loc, loc_params in params: mechs = cls._format_params_and_group_by_mech( loc_params, channels[loc] ) # move Arbor global properties to global_params mechs, global_props = cls._separate_global_properties(loc, mechs) if global_props.keys() != {None}: raise CreateAccException( "Support for Arbor default mechanisms not implemented." ) # iterate over global_props items if above exception triggers global_properties[None] = ( global_properties.get(None, []) + global_props[None] ) local_mechs[loc] = mechs return local_mechs, global_properties def _arb_filter_point_proc_locs(pprocess_mechs): """Filter locations from point process parameters Args: pprocess_mechs (): Point process mechanisms with parameters in Arbor format """ result = {loc: dict() for loc in pprocess_mechs} for loc, mechs in pprocess_mechs.items(): for mech, point_exprs in mechs.items(): result[loc][mech.name] = dict( mech=mech.suffix, params=[ Location(point_expr.name, point_expr.value) for point_expr in point_exprs ], ) return result def _arb_append_scaled_mechs(mechs, scaled_mechs): """Append scaled mechanism parameters to constant ones""" for mech, scaled_params in scaled_mechs.items(): if mech is None and len(scaled_params) > 0: raise CreateAccException( "Non-mechanism parameters cannot have inhomogeneous" " expressions in Arbor %s" % scaled_params ) mechs[mech] = mechs.get(mech, []) + [ RangeIExpr( name=p.name, value=p.value, scale=p.value_scaler.acc_scale_iexpr(p.value), ) for p in scaled_params ] # An mechanism's NMODL GLOBAL and RANGE variables in Arbor MechMetaData = namedtuple("MechMetaData", "globals, ranges") class ArbNmodlMechFormatter: """Loads catalogue metadata and reformats mechanism name for ACC""" def __init__(self, ext_catalogues): """Load metadata of external and Arbor's built-in mechanism catalogues Args: ext_catalogues (): Mapping of catalogue name to directory with NMODL files defining the mechanisms. """ self.cats = self._load_mech_catalogue_meta(ext_catalogues) @staticmethod def _load_catalogue_meta(cat_dir): """Load mechanism catalogue metadata from NMODL files Args: cat_dir (): Path to directory with NMODL files of catalogue Returns: Mapping of name to meta data for each mechanism in the directory """ # used to generate arbor_mechanisms.json on NMODL from arbor/mechanisms nmodl_pattern = r"^\s*%s\s+((?:\w+\,\s*)*?\w+)\s*?$" # NOQA suffix_pattern = nmodl_pattern % "SUFFIX" globals_pattern = nmodl_pattern % "GLOBAL" ranges_pattern = nmodl_pattern % "RANGE" def process_nmodl(nmodl_str): """Extract global and range params from Arbor-conforming NMODL""" try: nrn = re.search( r"NEURON\s+{([^}]+)}", nmodl_str, flags=re.MULTILINE ).group(1) suffix_ = re.search(suffix_pattern, nrn, flags=re.MULTILINE) suffix_ = suffix_ if suffix_ is None else suffix_.group(1) globals_ = re.search(globals_pattern, nrn, flags=re.MULTILINE) globals_ = ( globals_ if globals_ is None else re.findall(r"\w+", globals_.group(1)) ) ranges_ = re.search(ranges_pattern, nrn, flags=re.MULTILINE) ranges_ = ( ranges_ if ranges_ is None else re.findall(r"\w+", ranges_.group(1)) ) except Exception as e: raise CreateAccException( "NMODL-inspection for %s failed." % nmodl_file ) from e # skipping suffix_ return MechMetaData(globals=globals_, ranges=ranges_) mechs = dict() cat_dir = pathlib.Path(cat_dir) for nmodl_file in cat_dir.glob("*.mod"): with open(cat_dir.joinpath(nmodl_file)) as f: mechs[nmodl_file.stem] = process_nmodl(f.read()) return mechs @classmethod def _load_mech_catalogue_meta(cls, ext_catalogues): """Load metadata of external and Arbor's built-in mechanism catalogues Args: ext_catalogues (): Mapping of catalogue name to directory with NMODL files defining the mechanisms Returns: Ordered mapping of catalogue name -> mechanism name -> meta data for external and built-in catalogues (external ones taking precedence) """ arb_cats = OrderedDict() if ext_catalogues is not None: for cat, cat_nmodl in ext_catalogues.items(): arb_cats[cat] = cls._load_catalogue_meta( pathlib.Path(cat_nmodl).resolve() ) builtin_catalogues = ( pathlib.Path(__file__) .parent.joinpath("static/arbor_mechanisms.json") .resolve() ) with open(builtin_catalogues) as f: builtin_arb_cats = json.load(f) for cat in ["BBP", "default", "allen"]: if cat not in arb_cats: arb_cats[cat] = { mech: MechMetaData(**meta) for mech, meta in builtin_arb_cats[cat].items() } return arb_cats @staticmethod def _mech_name(name): """Neuron to Arbor mechanism name conversion Args: name (): A Neuron mechanism name """ if name in ["Exp2Syn", "ExpSyn"]: return name.lower() else: return name @classmethod def _translate_mech(cls, mech_name, mech_params, arb_cats): """Translate NMODL mechanism to Arbor ACC format Args: mech_name (): NMODL mechanism name (suffix) mech_params (): Mechanism parameters in Arbor format arb_cats (): Mapping of catalogue names to mechanisms with theirmeta data Returns: Tuple of mechanism name with NMODL GLOBAL parameters integrated and catalogue prefix added as well as the remaining RANGE parameters """ arb_mech = None arb_mech_name = cls._mech_name(mech_name) for cat in arb_cats: # in order of precedence if arb_mech_name in arb_cats[cat]: arb_mech = arb_cats[cat][arb_mech_name] mech_name = cat + "::" + arb_mech_name break if arb_mech is None: # not Arbor built-in mech, no qualifier added if mech_name is not None: logger.warn( "create_acc: Could not find Arbor mech for %s (%s)." % (mech_name, mech_params) ) return (mech_name, mech_params) else: if arb_mech.globals is None: # only Arbor range params for param in mech_params: if param.name not in arb_mech.ranges: raise CreateAccException( "%s not a GLOBAL or RANGE parameter of %s" % (param.name, mech_name) ) return (mech_name, mech_params) else: for param in mech_params: if ( param.name not in arb_mech.globals and param.name not in arb_mech.ranges ): raise CreateAccException( "%s not a GLOBAL or RANGE parameter of %s" % (param.name, mech_name) ) mech_name_suffix = [] remaining_mech_params = [] for mech_param in mech_params: if mech_param.name in arb_mech.globals: mech_name_suffix.append( mech_param.name + "=" + mech_param.value ) if isinstance(mech_param, RangeIExpr): remaining_mech_params.append( RangeIExpr( name=mech_param.name, value=None, scale=mech_param.scale, ) ) else: remaining_mech_params.append(mech_param) if len(mech_name_suffix) > 0: mech_name += "/" + ",".join(mech_name_suffix) return (mech_name, remaining_mech_params) def translate_density(self, mechs): """Translate all density mechanisms in a specific region""" return dict( [ self._translate_mech(mech, params, self.cats) for mech, params in mechs.items() ] ) def translate_points(self, mechs): """Translate all point mechanisms for a specific locset""" result = dict() for synapse_name, mech_desc in mechs.items(): mech, params = self._translate_mech( mech_desc["mech"], mech_desc["params"], self.cats ) result[synapse_name] = dict(mech=mech, params=params) return result def _arb_project_scaled_mechs(mechs): """Returns all (iexpr) parameters of scaled mechanisms in Arbor""" scaled_mechs = dict() for mech, params in mechs.items(): range_iexprs = [p for p in params if isinstance(p, RangeIExpr)] if len(range_iexprs) > 0: scaled_mechs[mech] = range_iexprs return scaled_mechs def _arb_populate_label_dict(local_mechs, local_scaled_mechs, pprocess_mechs): """Creates a dict of labels from label-specific parameters/mechanisms Args: local_mechs (): label-specific parameters/density mechanisms local_scaled_mechs (): label-specific iexpr parameters/density mechs pprocess_mechs (): label-specific point processes Returns: A dict mapping label name to ArbLabel for each label in the input """ label_dict = dict() acc_labels = ChainMap(local_mechs, local_scaled_mechs, pprocess_mechs) for acc_label in acc_labels: if ( acc_label.name in label_dict and acc_label != label_dict[acc_label.name] ): raise CreateAccException( "Label %s already exists in" % acc_label.name + " label_dict with different s-expression: " " %s != %s." % (label_dict[acc_label.name].loc, acc_label.loc) ) elif acc_label.name not in label_dict: label_dict[acc_label.name] = acc_label return label_dict def _read_templates(template_dir, template_filename): """Expand Jinja2 template filepath with glob and return dict of target filename -> parsed template""" if template_dir is None: template_dir = ( pathlib.Path(__file__).parent.joinpath("templates").resolve() ) template_paths = pathlib.Path(template_dir).glob(template_filename) templates = dict() for template_path in template_paths: with open(template_path) as template_file: template = template_file.read() name = template_path.name if name.endswith(".jinja2"): name = name[:-7] if name.endswith("_template"): name = name[:-9] if "_" in name: name = ".".join(name.rsplit("_", 1)) templates[name] = jinja2.Template(template) if templates == {}: raise FileNotFoundError( f"No templates found for JSON/ACC-export in {template_dir}" ) return templates def _arb_loc_desc(location, param_or_mech): """Generate Arbor location description for label dict and decor""" return location.acc_label() def create_acc( mechs, parameters, morphology=None, morphology_dir=None, ext_catalogues=None, ignored_globals=(), replace_axon=None, create_mod_morph=False, template_name="CCell", template_filename="acc/*_template.jinja2", disable_banner=None, template_dir=None, custom_jinja_params=None, ): """return a dict with strings containing the rendered JSON/ACC templates Args: mechs (): All the mechs for the decor template parameters (): All the parameters in the decor/label-dict template morphology (str): Name of morphology morphology_dir (str): Directory of morphology ext_catalogues (): Name to path mapping of non-Arbor built-in NMODL mechanism catalogues compiled with modcc ignored_globals (iterable str): Skipped NrnGlobalParameter in decor replace_axon (): Axon replacement morphology create_mod_morph (): Create ACC morphology with axon replacement template_filename (str): file path of the cell.json , decor.acc and label_dict.acc jinja2 templates (with wildcards expanded by glob) template_dir (str): dir name of the jinja2 templates custom_jinja_params (dict): dict of additional jinja2 params in case of a custom template """ if custom_jinja_params is None: custom_jinja_params = {} if pathlib.Path(morphology).suffix.lower() not in [".swc", ".asc"]: raise CreateAccException( "Morphology file %s not supported in Arbor " " (only supported types are .swc and .asc)." % morphology ) if replace_axon is not None: if not hasattr(arbor.segment_tree, "tag_roots"): raise NotImplementedError( "Need a newer version of Arbor" " for axon replacement." ) logger.debug( "Obtain axon replacement by applying " "ArbFileMorphology.replace_axon after loading " "morphology in Arbor." ) replace_axon_path = ( pathlib.Path(morphology).stem + "_axon_replacement.acc" ) replace_axon_acc = io.StringIO() arbor.write_component(replace_axon, replace_axon_acc) replace_axon_acc.seek(0) if create_mod_morph: modified_morphology_path = ( pathlib.Path(morphology).stem + "_modified.acc" ) modified_morpho = ArbFileMorphology.load( pathlib.Path(morphology_dir).joinpath(morphology), replace_axon_acc, ) replace_axon_acc.seek(0) modified_morphology_acc = io.StringIO() arbor.write_component(modified_morpho, modified_morphology_acc) modified_morphology_acc.seek(0) modified_morphology_acc = modified_morphology_acc.read() else: modified_morphology_path = None modified_morphology_acc = None replace_axon_acc = replace_axon_acc.read() else: replace_axon_path = None modified_morphology_path = None templates = _read_templates(template_dir, template_filename) default_location_order = list(ArbFileMorphology.region_labels.values()) template_params = _get_template_params( mechs, parameters, ignored_globals, disable_banner, default_location_order, _arb_loc_desc, ) filenames = { name: template_name + (name if name.startswith(".") else "_" + name) for name in templates.keys() } # postprocess template parameters for Arbor channels = template_params["channels"] point_channels = template_params["point_channels"] banner = template_params["banner"] # global_mechs refer to default density mechs/params in Arbor # [mech -> param] (params under mech == None) global_mechs = Nrn2ArbMechGrouper.process_global( template_params["global_params"] ) # local_mechs refer to locally painted density mechs/params in Arbor # [label -> mech -> param.name/.value] (params under mech == None) local_mechs, additional_global_mechs = Nrn2ArbMechGrouper.process_local( template_params["section_params"], channels ) for mech, params in additional_global_mechs.items(): global_mechs[mech] = global_mechs.get(mech, []) + params # scaled_mechs refer to iexpr params of scaled density mechs in Arbor # [label -> mech -> param.location/.name/.value/.value_scaler] range_params = {loc: [] for loc in default_location_order} for param in template_params["range_params"]: range_params[param.location].append(param) range_params = list(range_params.items()) local_scaled_mechs, global_scaled_mechs = Nrn2ArbMechGrouper.process_local( range_params, channels ) # join each mech's constant params with inhomogeneous ones on mechanisms _arb_append_scaled_mechs(global_mechs, global_scaled_mechs) for loc in local_scaled_mechs: _arb_append_scaled_mechs(local_mechs[loc], local_scaled_mechs[loc]) # pprocess_mechs refer to locally placed mechs/params in Arbor # [label -> mech -> param.name/.value] pprocess_mechs, global_pprocess_mechs = Nrn2ArbMechGrouper.process_local( template_params["pprocess_params"], point_channels ) if any(len(params) > 0 for params in global_pprocess_mechs.values()): raise CreateAccException( "Point process mechanisms cannot be" " placed globally in Arbor." ) # Evaluate synapse locations # (no new labels introduced, but locations explicitly defined) pprocess_mechs = _arb_filter_point_proc_locs(pprocess_mechs) # NMODL formatter loads metadata of external and Arbor's built-in # mech catalogues nmodl_formatter = ArbNmodlMechFormatter(ext_catalogues) # translate mechs to Arbor's nomenclature global_mechs = nmodl_formatter.translate_density(global_mechs) local_mechs = { loc: nmodl_formatter.translate_density(mechs) for loc, mechs in local_mechs.items() } pprocess_mechs = { loc: nmodl_formatter.translate_points(mechs) for loc, mechs in pprocess_mechs.items() } # get iexpr parameters of scaled density mechs global_scaled_mechs = _arb_project_scaled_mechs(global_mechs) local_scaled_mechs = { loc: _arb_project_scaled_mechs(mechs) for loc, mechs in local_mechs.items() } # populate label dict label_dict = _arb_populate_label_dict( local_mechs, local_scaled_mechs, pprocess_mechs ) ret = { filenames[name]: template.render( template_name=template_name, banner=banner, morphology=morphology, replace_axon=replace_axon_path, modified_morphology=modified_morphology_path, filenames=filenames, label_dict=label_dict, global_mechs=global_mechs, global_scaled_mechs=global_scaled_mechs, local_mechs=local_mechs, local_scaled_mechs=local_scaled_mechs, pprocess_mechs=pprocess_mechs, **custom_jinja_params, ) for name, template in templates.items() } if replace_axon is not None: ret[replace_axon_path] = replace_axon_acc if modified_morphology_path is not None: ret[modified_morphology_path] = modified_morphology_acc return ret def write_acc( output_dir, cell, parameters, template_filename="acc/*_template.jinja2", ext_catalogues=None, create_mod_morph=False, sim=None, ): """Output mixed JSON/ACC format for Arbor cable cell to files Args: output_dir (str): Output directory. If not exists, will be created cell (): Cell model to output parameters (): Values for mechanism parameters, etc. template_filename (str): file path of the cell.json , decor.acc and label_dict.acc jinja2 templates (with wildcards expanded by glob) ext_catalogues (): Name to path mapping of non-Arbor built-in NMODL mechanism catalogues compiled with modcc create_mod_morph (str): Output ACC with axon replacement sim (): Neuron simulator instance (only used used with axon replacement if morphology has not yet been instantiated) """ output = cell.create_acc( parameters, template=template_filename, ext_catalogues=ext_catalogues, create_mod_morph=create_mod_morph, sim=sim, ) cell_json = [ comp_rendered for comp, comp_rendered in output.items() if pathlib.Path(comp).suffix == ".json" ] if len(cell_json) != 1: raise CreateAccException( "JSON file from create_acc is non-unique: %s" % cell_json ) cell_json = json.loads(cell_json[0]) output_dir = pathlib.Path(output_dir) if not output_dir.exists(): output_dir.mkdir() for comp, comp_rendered in output.items(): comp_filename = output_dir.joinpath(comp) if comp_filename.exists(): raise CreateAccException("%s already exists!" % comp_filename) with open(output_dir.joinpath(comp), "w") as f: f.write(comp_rendered) morpho_filename = output_dir.joinpath(cell_json["morphology"]["original"]) if morpho_filename.exists(): raise CreateAccException("%s already exists!" % morpho_filename) shutil.copy2(cell.morphology.morphology_path, morpho_filename) # Read the mixed JSON/ACC-output, to be moved to Arbor in future release def read_acc(cell_json_filename): """Return constituents to build an Arbor cable cell from create_acc-export Args: cell_json_filename (str): The path to the JSON file containing meta-information on morphology, label-dict and decor of exported cell """ with open(cell_json_filename) as cell_json_file: cell_json = json.load(cell_json_file) cell_json_dir = pathlib.Path(cell_json_filename).parent morpho_filename = cell_json_dir.joinpath( cell_json["morphology"]["original"] ) replace_axon = cell_json["morphology"].get("replace_axon", None) if replace_axon is not None: replace_axon = cell_json_dir.joinpath(replace_axon) morpho = ArbFileMorphology.load(morpho_filename, replace_axon) decor = arbor.load_component( cell_json_dir.joinpath(cell_json["decor"]) ).component labels = arbor.load_component( cell_json_dir.joinpath(cell_json["label_dict"]) ).component return cell_json, morpho, decor, labels class CreateAccException(Exception): """Exceptions generated by create_acc module""" def __init__(self, message): """Constructor""" super(CreateAccException, self).__init__(message) ================================================ FILE: bluepyopt/ephys/create_hoc.py ================================================ '''create a hoc file from a set of BluePyOpt.ephys parameters''' # pylint: disable=R0914 import os import re from collections import defaultdict, namedtuple, OrderedDict from datetime import datetime import jinja2 import bluepyopt from bluepyopt.ephys.locations import (NrnSeclistCompLocation, NrnSeclistLocation, NrnSectionCompLocation, NrnSomaDistanceCompLocation, NrnSecSomaDistanceCompLocation, NrnTrunkSomaDistanceCompLocation, ArbLocation) from bluepyopt.ephys.mechanisms import (Mechanism, NrnMODMechanism, NrnMODPointProcessMechanism) from bluepyopt.ephys.parameters import (NrnGlobalParameter, NrnSectionParameter, NrnRangeParameter, NrnPointProcessParameter, MetaParameter) from bluepyopt.ephys.parameterscalers import (NrnSegmentSomaDistanceScaler, NrnSegmentLinearScaler, FLOAT_FORMAT, format_float) PointExpr = namedtuple('PointExpr', 'name, point_loc, value') RangeExpr = namedtuple('RangeExpr', 'location, name, value, value_scaler') # Consider renaming Location as name already used in locations module Location = namedtuple('Location', 'name, value') Range = namedtuple('Range', 'location, param_name, value') DEFAULT_LOCATION_ORDER = [ 'all', 'apical', 'axonal', 'basal', 'somatic', 'myelinated'] def generate_channels_by_location(mechs, location_order): """Create a OrderedDictionary of all channel mechs for hoc template. Args: mechs (list of bluepyopt.ephys.mechanisms.Mechanism): mechanisms location_order (list of str): order of locations Returns: tuple of channels, point_channels and location order """ loc_desc = _loc_desc return _generate_channels_by_location(mechs, location_order, loc_desc) def _generate_channels_by_location(mechs, location_order, loc_desc): """Create a OrderedDictionary of all channel mechs for hoc template.""" channels = OrderedDict((location, []) for location in location_order) point_channels = OrderedDict((location, []) for location in location_order) for mech in mechs: name = mech.suffix for location in mech.locations: if isinstance(mech, NrnMODPointProcessMechanism): point_channels[loc_desc(location, mech)].append(mech) else: channels[loc_desc(location, mech)].append(name) return channels, point_channels def generate_reinitrng(mechs) -> str: """Create re_init_rng function""" for mech in mechs: if isinstance(mech, NrnMODPointProcessMechanism): raise NotImplementedError( 'HOC generation for models with point process mechanisms' ' is not yet supported.') reinitrng_hoc_blocks = '' for mech in mechs: reinitrng_hoc_blocks += mech.generate_reinitrng_hoc_block() reinitrng_content = NrnMODMechanism.hash_hoc_string reinitrng_content += NrnMODMechanism.reinitrng_hoc_string % { 'reinitrng_hoc_blocks': reinitrng_hoc_blocks} return reinitrng_content def range_exprs_to_hoc(range_params): """Process raw range parameters to hoc strings""" ret = [] for param in range_params: value = param.value_scaler.inst_distribution value = re.sub(r'math\.', '', value) value = re.sub(r'\&', '&&', value) value = re.sub('{distance}', FLOAT_FORMAT, value) value = re.sub('{value}', format_float(param.value), value) if hasattr(param.value_scaler, "step_begin"): value = re.sub( '{step_begin}', format_float(param.value_scaler.step_begin), value ) value = re.sub( '{step_end}', format_float(param.value_scaler.step_end), value ) ret.append(Range(param.location, param.name, value)) return ret def _loc_desc(location, param_or_mech): """Generate Neuron location description for HOC template""" if isinstance(param_or_mech, Mechanism): if isinstance(param_or_mech, NrnMODMechanism): if isinstance(location, NrnSeclistLocation): return location.seclist_name else: raise CreateHocException( "%s is currently not supported for mechs." % type(location).__name__) elif isinstance(param_or_mech, NrnMODPointProcessMechanism): raise CreateHocException("%s is currently not supported." % type(param_or_mech).__name__) elif not isinstance(location, (NrnSeclistCompLocation, NrnSectionCompLocation, NrnSomaDistanceCompLocation, NrnSecSomaDistanceCompLocation, NrnTrunkSomaDistanceCompLocation, ArbLocation)) and \ not isinstance(param_or_mech, NrnPointProcessParameter): return location.seclist_name else: raise CreateHocException("%s is currently not supported." % type(param_or_mech).__name__) def generate_parameters(parameters): """Create a list of parameters that need to be added to the hoc template Args: parameters (list of bluepyopt.Parameters): parameters in hoc template Returns: tuple of global, section, range, pprocess and location order """ location_order = DEFAULT_LOCATION_ORDER loc_desc = _loc_desc return _generate_parameters(parameters, location_order, loc_desc) def _generate_parameters(parameters, location_order, loc_desc): """Create a list of parameters that need to be added to the hoc template""" param_locations = defaultdict(list) global_params = {} for param in parameters: if isinstance(param, NrnGlobalParameter): global_params[param.param_name] = param.value elif isinstance(param, MetaParameter): pass else: assert isinstance( param.locations, (tuple, list)), 'Must have locations list' for location in param.locations: locs = loc_desc(location, param) if not isinstance(locs, list): param_locations[locs].append(param) else: for loc in locs: param_locations[loc].append(param) section_params = defaultdict(list) pprocess_params = defaultdict(list) range_params = [] for loc in param_locations: if loc not in location_order: location_order.append(loc) for loc in location_order: if loc not in param_locations: continue for param in param_locations[loc]: if not isinstance(param.param_dependencies, list) or \ len(param.param_dependencies) > 0: raise CreateHocException( # also an ACC exception 'Exporting models with parameters that have' ' param_dependencies is not yet supported.') if isinstance(param, NrnRangeParameter): if isinstance( param.value_scaler, NrnSegmentSomaDistanceScaler): range_params.append( RangeExpr(loc, param.param_name, param.value, param.value_scaler)) elif isinstance(param.value_scaler, NrnSegmentLinearScaler): value = param.value_scale_func(param.value) section_params[loc].append( Location(param.param_name, format_float(value))) elif isinstance(param, NrnSectionParameter): value = param.value_scale_func(param.value) section_params[loc].append( Location(param.param_name, format_float(value))) elif isinstance(param, NrnPointProcessParameter): value = param.value pprocess_params[loc].append( PointExpr(param.param_name, param.locations, format_float(value))) ordered_section_params = [(loc, section_params[loc]) for loc in location_order] ordered_pprocess_params = [(loc, pprocess_params[loc]) for loc in location_order] return global_params, ordered_section_params, range_params, \ ordered_pprocess_params, location_order def _read_template(template_dir, template_filename): """Read Jinja2 hoc template to render""" if template_dir is None: template_dir = os.path.abspath( os.path.join( os.path.dirname(__file__), 'templates')) template_path = os.path.join(template_dir, template_filename) with open(template_path) as template_file: template = template_file.read() template = jinja2.Template(template) return template def _get_template_params( mechs, parameters, ignored_globals, disable_banner, default_location_order, loc_desc): '''return parameters to render Jinja2 templates with simulator descriptions Args: mechs (): All the mechs for the hoc template parameters (): All the parameters in the hoc template ignored_globals (iterable str): HOC coded is added for each NrnGlobalParameter that exists, to test that it matches the values set in the parameters. This iterable contains parameter names that aren't checked default_location_order (): list of ordered simulator-specific locations to use by default loc_desc (): method that extracts simulator-specific location description from pair of locations and mechanisms/parameters ''' global_params, section_params, range_params, \ pprocess_params, location_order = \ _generate_parameters(parameters, default_location_order, loc_desc) channels, point_channels = _generate_channels_by_location( mechs, location_order, loc_desc) ignored_global_params = {} for ignored_global in ignored_globals: if ignored_global in global_params: ignored_global_params[ ignored_global] = global_params[ignored_global] del global_params[ignored_global] if not disable_banner: banner = 'Created by BluePyOpt(%s) at %s' % ( bluepyopt.__version__, datetime.now()) else: banner = None return dict(global_params=global_params, ignored_global_params=ignored_global_params, section_params=section_params, range_params=range_params, pprocess_params=pprocess_params, location_order=location_order, channels=channels, point_channels=point_channels, banner=banner) def create_hoc(mechs, parameters, morphology=None, ignored_globals=(), replace_axon=None, template_name='CCell', template_filename='cell_template.jinja2', disable_banner=None, template_dir=None, custom_jinja_params=None): '''return a string containing the hoc template Args: mechs (): All the mechs for the hoc template parameters (): All the parameters in the hoc template morpholgy (str): Name of morphology ignored_globals (iterable str): HOC coded is added for each NrnGlobalParameter that exists, to test that it matches the values set in the parameters. This iterable contains parameter names that aren't checked replace_axon (str): String replacement for the 'replace_axon' command. Must include 'proc replace_axon(){ ... } template_filename (str): file name of the jinja2 template template_dir (str): dir name of the jinja2 template custom_jinja_params (dict): dict of additional jinja2 params in case of a custom template ''' template = _read_template(template_dir, template_filename) template_params = _get_template_params(mechs, parameters, ignored_globals, disable_banner, DEFAULT_LOCATION_ORDER, _loc_desc) # delete empty dicts to avoid conflict with custom_jinja_params del template_params['pprocess_params'] del template_params['point_channels'] template_params['range_params'] = range_exprs_to_hoc( template_params['range_params'] ) re_init_rng = generate_reinitrng(mechs) if custom_jinja_params is None: custom_jinja_params = {} return template.render(template_name=template_name, morphology=morphology, replace_axon=replace_axon, re_init_rng=re_init_rng, **template_params, **custom_jinja_params) class CreateHocException(Exception): """All exceptions generated by create_hoc module""" def __init__(self, message): """Constructor""" super(CreateHocException, self).__init__(message) ================================================ FILE: bluepyopt/ephys/efeatures.py ================================================ """eFeature classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0914 import logging import numpy as np from bluepyopt.ephys.base import BaseEPhys from bluepyopt.ephys.serializer import DictMixin from .extra_features_utils import * logger = logging.getLogger(__name__) def masked_cosine_distance(exp, model): from scipy.spatial import distance exp_mask = np.isfinite(exp) model_mask = np.isfinite(model) valid_mask = exp_mask & model_mask score = distance.cosine( exp[valid_mask], model[valid_mask] ) score *= sum(exp_mask) / len(valid_mask) return score class EFeature(BaseEPhys): """EPhys feature""" pass class eFELFeature(EFeature, DictMixin): """eFEL feature""" SERIALIZED_FIELDS = ('name', 'efel_feature_name', 'recording_names', 'stim_start', 'stim_end', 'exp_mean', 'exp_std', 'threshold', 'comment') def __init__( self, name, efel_feature_name=None, recording_names=None, stim_start=None, stim_end=None, exp_mean=None, exp_std=None, threshold=None, stimulus_current=None, comment='', interp_step=None, double_settings=None, int_settings=None, string_settings=None, force_max_score=False, max_score=250 ): """Constructor Args: name (str): name of the eFELFeature object efel_feature_name (str): name of the eFeature in the eFEL library (ex: 'AP1_peak') recording_names (dict): eFEL features can accept several recordings as input stim_start (float): stimulation start time (ms) stim_end (float): stimulation end time (ms) exp_mean (float): experimental mean of this eFeature exp_std(float): experimental standard deviation of this eFeature threshold(float): spike detection threshold (mV) comment (str): comment interp_step(float): interpolation step (ms) double_settings(dict): dictionary with efel double settings that should be set before extracting the features int_settings(dict): dictionary with efel int settings that should be set before extracting the features string_settings(dict): dictionary with efel string settings that should be set before extracting the features """ super(eFELFeature, self).__init__(name, comment) self.recording_names = recording_names self.efel_feature_name = efel_feature_name self.exp_mean = exp_mean self.exp_std = exp_std self.stim_start = stim_start self.stim_end = stim_end self.threshold = threshold self.interp_step = interp_step self.stimulus_current = stimulus_current self.double_settings = double_settings self.int_settings = int_settings self.string_settings = string_settings self.force_max_score = force_max_score self.max_score = max_score def _construct_efel_trace(self, responses): """Construct trace that can be passed to eFEL""" trace = {} if '' not in self.recording_names: raise Exception( 'eFELFeature: \'\' needs to be in recording_names') for location_name, recording_name in self.recording_names.items(): if location_name == '': postfix = '' else: postfix = ';%s' % location_name if recording_name not in responses: logger.debug( "Recording named %s not found in responses %s", recording_name, str(responses)) return None if responses[self.recording_names['']] is None or \ responses[recording_name] is None: return None trace['T%s' % postfix] = \ responses[self.recording_names['']]['time'] trace['V%s' % postfix] = responses[recording_name]['voltage'] trace['stim_start%s' % postfix] = [self.stim_start] trace['stim_end%s' % postfix] = [self.stim_end] return trace def _setup_efel(self): """Set up efel before extracting the feature""" import efel efel.reset() if self.threshold is not None: efel.setThreshold(self.threshold) if self.stimulus_current is not None: efel.setDoubleSetting('stimulus_current', self.stimulus_current) if self.interp_step is not None: efel.setDoubleSetting('interp_step', self.interp_step) if self.double_settings is not None: for setting_name, setting_value in self.double_settings.items(): efel.setDoubleSetting(setting_name, setting_value) if self.int_settings is not None: for setting_name, setting_value in self.int_settings.items(): efel.setIntSetting(setting_name, setting_value) if self.string_settings is not None: for setting_name, setting_value in self.string_settings.items(): efel.setStrSetting(setting_name, setting_value) def calculate_feature(self, responses, raise_warnings=False): """Calculate feature value""" efel_trace = self._construct_efel_trace(responses) if efel_trace is None: feature_value = None else: self._setup_efel() import efel values = efel.getMeanFeatureValues( [efel_trace], [self.efel_feature_name], raise_warnings=raise_warnings) feature_value = values[0][self.efel_feature_name] efel.reset() logger.debug( 'Calculated value for %s: %s', self.name, str(feature_value)) return feature_value def calculate_score(self, responses, trace_check=False): """Calculate the score""" efel_trace = self._construct_efel_trace(responses) if efel_trace is None: score = self.max_score else: self._setup_efel() import efel score = efel.getDistance( efel_trace, self.efel_feature_name, self.exp_mean, self.exp_std, trace_check=trace_check, error_dist=self.max_score ) if self.force_max_score: score = min(score, self.max_score) efel.reset() logger.debug('Calculated score for %s: %f', self.name, score) return score def __str__(self): """String representation""" return "%s for %s with stim start %s and end %s, " \ "exp mean %s and std %s and AP threshold override %s" % \ (self.efel_feature_name, self.recording_names, self.stim_start, self.stim_end, self.exp_mean, self.exp_std, self.threshold) class extraFELFeature(EFeature, DictMixin): """extraFEL feature""" SERIALIZED_FIELDS = ('name', 'extrafel_feature_name', 'recording_names', 'somatic_recording_name', 'fcut', 'fs', 'channel_ids', 'stim_start', 'stim_end', 'exp_mean', 'exp_std', 'threshold', 'comment') def __init__( self, name, extrafel_feature_name=None, recording_names=None, somatic_recording_name=None, fcut=None, fs=None, filt_type=None, ms_cut=None, upsample=None, skip_first_spike=True, skip_last_spike=True, channel_ids=None, stim_start=None, stim_end=None, exp_mean=None, exp_std=None, threshold=None, comment='', interp_step=None, double_settings=None, int_settings=None, force_max_score=False, max_score=250, ): """Constructor Args: name (str): name of the extraFELFeature object extrafel_feature_name (str): name of the eFeature in the spikefeatures library (ex: 'halfwidth') recording_names (dict): eFEL features can accept several recordings as input somatic_recording_name (str): intracellualar recording from soma, used to detect spikes. If None, spikes are detected from extracellular trace fcut (float, array, or None): cutoff frequency(ies) for filter. If float, a high-pass filter is used. If array-like a bandpass filter is used. If None, traces are note filtered fs (float): sampling frequency to resample extracellular traces (in kHz) filt_type (str): type of the bandpass filter used (default 'filtfilt') ms_cut (float, list, or None): cut in ms before and after the intra peak. If scalar, the cut is symmetrical upsample (int, or None): upsample factor for average waveform before computing features skip_first_spike (bool): if True, the first spike is skipped before computing the average waveform (to avoid artifacts) skip_last_spike (bool): if True, the last spike is skipped before computing the average waveform (to avoid artifacts) channel_ids (int, np.array, or None): if None, all channels are used to compute the feature and calculate the score (using the cosine_dist). If int, a single channel is used and the score is the normalised deviation form the exp value. If list/array, the cosine distance is computed over a subset of channels stim_start (float): stimulation start time (ms) stim_end (float): stimulation end time (ms) exp_mean (list of floats): experimental mean of this eFeature exp_std (list of floats): experimental standard deviation of this eFeature threshold (float): spike detection threshold (mV) comment (str): comment interp_step (float): interpolation step (ms) double_settings (dict): dictionary with efel double settings that should be set before extracting the features int_settings (dict): dictionary with efel int settings that should be set before extracting the features """ super(extraFELFeature, self).__init__(name, comment) self.recording_names = recording_names self.somatic_recording_name = somatic_recording_name self.extrafel_feature_name = extrafel_feature_name self.fcut = fcut self.fs = fs self.filt_type = filt_type self.ms_cut = ms_cut self.upsample = upsample self.skip_first_spike = skip_first_spike self.skip_last_spike = skip_last_spike self.channel_ids = channel_ids self.exp_mean = exp_mean self.exp_std = exp_std self.stim_start = stim_start self.stim_end = stim_end self.threshold = threshold self.interp_step = interp_step self.double_settings = double_settings self.int_settings = int_settings self.force_max_score = force_max_score self.max_score = max_score def _construct_somatic_efel_trace(self, responses): """Construct trace that can be passed to eFEL""" trace = {} if self.somatic_recording_name not in responses: logger.debug( "Recording named %s not found in responses %s", self.somatic_recording_name, str(responses), ) return None if responses[self.somatic_recording_name] is None: return None response = responses[self.somatic_recording_name] trace["T"] = response["time"] trace["V"] = response["voltage"] trace["stim_start"] = [self.stim_start] trace["stim_end"] = [self.stim_end] return trace def _setup_efel(self): """Set up efel before extracting the feature""" import efel efel.reset() if self.threshold is not None: efel.setThreshold(self.threshold) if self.interp_step is not None: efel.setDoubleSetting("interp_step", self.interp_step) if self.double_settings is not None: for setting_name, setting_value in self.double_settings.items(): efel.setDoubleSetting(setting_name, setting_value) if self.int_settings is not None: for setting_name, setting_value in self.int_settings.items(): efel.setIntSetting(setting_name, setting_value) def _get_peak_times(self, responses, raise_warnings=False): efel_trace = self._construct_somatic_efel_trace(responses) if efel_trace is None: peak_times = None else: self._setup_efel() import efel peaks = efel.getFeatureValues( [efel_trace], ["peak_time"], raise_warnings=raise_warnings ) peak_times = peaks[0]["peak_time"] efel.reset() return peak_times def calculate_feature( self, responses, raise_warnings=False, return_waveforms=False, ): from .extra_features_utils import calculate_features """Calculate feature value""" peak_times = self._get_peak_times( responses, raise_warnings=raise_warnings ) if peak_times is None: if return_waveforms: return None, None else: return None if len(peak_times) > 1 and self.skip_first_spike: peak_times = peak_times[1:] if len(peak_times) > 1 and self.skip_last_spike: peak_times = peak_times[:-1] if responses[self.recording_names[""]] is not None: response = responses[self.recording_names[""]] else: if return_waveforms: return None, None else: return None if np.std(np.diff(response["time"])) > 0.001 * np.mean( np.diff(response["time"]) ): assert self.fs is not None logger.info("extraFELFeature.calculate_feature: interpolate") response_interp = _interpolate_response(response, fs=self.fs) else: response_interp = response if self.fcut is not None: logger.info("extraFELFeature.calculate_feature: enabled") response_filter = _filter_response(response_interp, fcut=self.fcut, filt_type=self.filt_type) else: logger.info("extraFELFeature.calculate_feature: filter disabled") response_filter = response_interp ewf = _get_waveforms(response_filter, peak_times, self.ms_cut) mean_wf = np.mean(ewf, axis=0) values = calculate_features( mean_wf, self.fs * 1000, upsample=self.upsample, feature_names=[self.extrafel_feature_name] ) feature_value = values[self.extrafel_feature_name] if self.channel_ids is not None: feature_value = feature_value[self.channel_ids] logger.debug( "Calculated value for %s: %s", self.name, str(feature_value) ) if return_waveforms: return feature_value, mean_wf else: return feature_value def calculate_score(self, responses, trace_check=False): """Calculate the score""" if ( responses[self.recording_names[""].replace("soma.v", "MEA.LFP")] is None or responses[self.recording_names[""]] is None ): return self.max_score feature_value = self.calculate_feature(responses) if np.isscalar(feature_value): # scalar feature if np.isfinite(feature_value): score = np.abs((feature_value - self.exp_mean)) / self.exp_std else: score = self.max_score if not np.isfinite(score): logger.debug( f"Found score nan value {self.extrafel_feature_name} " f"- std: {self.exp_std} - channel: {self.channel_ids}" ) score = self.max_score else: score = masked_cosine_distance( np.asarray(self.exp_mean), np.asarray(feature_value) ) if np.isnan(score): score = self.max_score if self.force_max_score: score = min(score, self.max_score) logger.debug("Calculated score for %s: %f", self.name, score) return score def __str__(self): """String representation""" return ("%s for %s with stim start %s and end %s, " "exp mean %s and std %s and AP threshold override %s" % (self.extrafel_feature_name, self.recording_names, self.stim_start, self.stim_end, self.exp_mean, self.exp_std, self.threshold) ) def _interpolate_response(response, fs=20.0): from scipy.interpolate import interp1d x = response["time"] y = response["voltage"] f = interp1d(x, y, axis=1) xnew = np.arange(np.min(x), np.max(x), 1.0 / fs) ynew = f(xnew) # use interpolation function returned by `interp1d` response_new = {} response_new["time"] = xnew response_new["voltage"] = ynew return response_new def _filter_response(response, fcut=[0.5, 6000], order=2, filt_type="lfilter"): import scipy.signal as ss fs = 1 / np.mean(np.diff(response["time"])) * 1000 fn = fs / 2.0 trace = response["voltage"] if isinstance(fcut, (float, int, np.floating, np.integer)): btype = "highpass" band = fcut / fn else: assert isinstance(fcut, (list, np.ndarray)) and len(fcut) == 2 btype = "bandpass" band = np.array(fcut) / fn b, a = ss.butter(order, band, btype=btype) if len(trace.shape) == 2: if filt_type == "filtfilt": filtered = ss.filtfilt(b, a, trace, axis=1) else: filtered = ss.lfilter(b, a, trace, axis=1) else: if filt_type == "filtfilt": filtered = ss.filtfilt(b, a, trace) else: filtered = ss.lfilter(b, a, trace) response_new = {} response_new["time"] = response["time"] response_new["voltage"] = filtered return response_new def _get_waveforms(response, peak_times, snippet_len_ms): times = response["time"] traces = response["voltage"] assert np.std(np.diff(times)) < 0.001 * np.mean( np.diff(times) ), "Sampling frequency must be constant" fs = 1.0 / np.mean(np.diff(times)) # kHz reference_frames = (peak_times * fs).astype(int) if isinstance(snippet_len_ms, (tuple, list, np.ndarray)): snippet_len_before = int(snippet_len_ms[0] * fs) snippet_len_after = int(snippet_len_ms[1] * fs) else: snippet_len_before = int((snippet_len_ms + 1) / 2 * fs) snippet_len_after = int((snippet_len_ms - snippet_len_before) * fs) num_snippets = len(peak_times) if len(traces.shape) == 2: num_channels = traces.shape[0] else: num_channels = 1 traces = traces[np.newaxis, :] num_frames = len(times) snippet_len_total = int(snippet_len_before + snippet_len_after) waveforms = np.zeros( (num_snippets, num_channels, snippet_len_total), dtype=traces.dtype ) for i in range(num_snippets): snippet_chunk = np.zeros( (num_channels, snippet_len_total), dtype=traces.dtype ) if 0 <= reference_frames[i] < num_frames: snippet_range = np.array( [ int(reference_frames[i]) - snippet_len_before, int(reference_frames[i]) + snippet_len_after, ] ) snippet_buffer = np.array([0, snippet_len_total], dtype="int") # The following handles the out-of-bounds cases if snippet_range[0] < 0: snippet_buffer[0] -= snippet_range[0] snippet_range[0] -= snippet_range[0] if snippet_range[1] >= num_frames: snippet_buffer[1] -= snippet_range[1] - num_frames snippet_range[1] -= snippet_range[1] - num_frames snippet_chunk[:, snippet_buffer[0]:snippet_buffer[1]] = \ traces[:, snippet_range[0]:snippet_range[1]] waveforms[i] = snippet_chunk return waveforms ================================================ FILE: bluepyopt/ephys/evaluators.py ================================================ """Cell evaluator class""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import logging logger = logging.getLogger(__name__) import bluepyopt as bpopt import bluepyopt.tools class CellEvaluator(bpopt.evaluators.Evaluator): """Simple cell class""" def __init__( self, cell_model=None, param_names=None, fitness_protocols=None, fitness_calculator=None, isolate_protocols=None, sim=None, use_params_for_seed=False, timeout=None): """Constructor Args: cell_model (ephys.models.CellModel): CellModel object to evaluate param_names (list of str): names of the parameters (parameters will be initialised in this order) fitness_protocols (dict of str -> ephys.protocols.Protocol): protocols used during the fitness evaluation fitness_calculator (ObjectivesCalculator): ObjectivesCalculator object used for the transformation of Responses into Objective objects isolate_protocols (bool): whether to use multiprocessing to isolate the simulations (disabling this could lead to unexpected behavior, and might hinder the reproducability of the simulations) sim (ephys.simulators.NrnSimulator): simulator to use for the cell evaluation use_params_for_seed (bool): use a hashed version of the parameter dictionary as a seed for the simulator timeout (int): duration in second after which a Process will be interrupted when using multiprocessing """ super(CellEvaluator, self).__init__( fitness_calculator.objectives, cell_model.params_by_names(param_names)) if sim is None: raise ValueError("CellEvaluator: you have to provide a Simulator " "object to the 'sim' argument of the " "CellEvaluator constructor") self.sim = sim self.cell_model = cell_model self.param_names = param_names # Stimuli used for fitness calculation self.fitness_protocols = fitness_protocols # Fitness value calculator self.fitness_calculator = fitness_calculator self.isolate_protocols = isolate_protocols self.timeout = timeout self.use_params_for_seed = use_params_for_seed def param_dict(self, param_array): """Convert param_array in param_dict""" param_dict = {} for param_name, param_value in \ zip(self.param_names, param_array): param_dict[param_name] = param_value return param_dict def objective_dict(self, objective_array): """Convert objective_array in objective_dict""" objective_dict = {} objective_names = [objective.name for objective in self.fitness_calculator.objectives] if len(objective_names) != len(objective_array): raise Exception( 'CellEvaluator: list given to objective_dict() ' 'has wrong number of objectives') for objective_name, objective_value in \ zip(objective_names, objective_array): objective_dict[objective_name] = objective_value return objective_dict def objective_list(self, objective_dict): """Convert objective_dict in objective_list""" objective_list = [] objective_names = [objective.name for objective in self.fitness_calculator.objectives] for objective_name in objective_names: objective_list.append(objective_dict[objective_name]) return objective_list @staticmethod def seed_from_param_dict(param_dict): """Return a seed value based on a param_dict""" sorted_keys = sorted(param_dict.keys()) string = '' for key in sorted_keys: string += '%s%s' % (key, str(param_dict[key])) return bluepyopt.tools.uint32_seed(string) def run_protocol( self, protocol, param_values, isolate=None, cell_model=None, sim=None, timeout=None): """Run protocol""" sim = self.sim if sim is None else sim if self.use_params_for_seed: sim.random123_globalindex = self.seed_from_param_dict(param_values) # Try/except added for backward compatibility try: return protocol.run( self.cell_model if cell_model is None else cell_model, param_values, sim=sim, isolate=isolate, timeout=timeout) except TypeError as e: if "unexpected keyword" in str(e): return protocol.run( self.cell_model if cell_model is None else cell_model, param_values, sim=sim, isolate=isolate) else: raise def run_protocols(self, protocols, param_values): """Run a set of protocols""" responses = {} for protocol in protocols: responses.update(self.run_protocol( protocol, param_values=param_values, isolate=self.isolate_protocols, timeout=self.timeout)) return responses def evaluate_with_dicts(self, param_dict=None, target='scores'): """Run evaluation with dict as input and output""" if target not in ['scores', 'values']: raise Exception( 'CellEvaluator: target has to be "scores" or "values".') if self.fitness_calculator is None: raise Exception( 'CellEvaluator: need fitness_calculator to evaluate') logger.debug('Evaluating %s', self.cell_model.name) responses = self.run_protocols( self.fitness_protocols.values(), param_dict) if target == 'scores': return self.fitness_calculator.calculate_scores(responses) elif target == 'values': return self.fitness_calculator.calculate_values(responses) def evaluate_with_lists(self, param_list=None, target='scores'): """Run evaluation with lists as input and outputs""" param_dict = self.param_dict(param_list) obj_dict = self.evaluate_with_dicts( param_dict=param_dict, target=target ) return self.objective_list(obj_dict) def init_simulator_and_evaluate_with_lists( self, param_list=None, target='scores' ): """Set NEURON variables and run evaluation with lists. Setting the NEURON variables is necessary when using ipyparallel, since the new subprocesses have pristine NEURON. """ self.sim.initialize() return self.evaluate_with_lists(param_list=param_list, target=target) def evaluate(self, param_list=None, target='scores'): """Run evaluation with lists as input and outputs""" return self.evaluate_with_lists(param_list, target=target) def __str__(self): content = 'cell evaluator:\n' content += ' cell model:\n' if self.cell_model is not None: content += ' %s\n' % str(self.cell_model) content += ' fitness protocols:\n' if self.fitness_protocols is not None: for fitness_protocol in self.fitness_protocols.values(): content += ' %s\n' % str(fitness_protocol) content += ' fitness calculator:\n' if self.fitness_calculator is not None: content += ' %s\n' % str(self.fitness_calculator) return content ================================================ FILE: bluepyopt/ephys/examples/__init__.py ================================================ """Init""" from . import simplecell # NOQA ================================================ FILE: bluepyopt/ephys/examples/simplecell/__init__.py ================================================ """Init""" from .simplecell import * # NOQA ================================================ FILE: bluepyopt/ephys/examples/simplecell/simple.swc ================================================ # Dummy granule cell morphology 1 1 -5.0 0.0 0.0 5.0 -1 2 1 0.0 0.0 0.0 5.0 1 3 1 5.0 0.0 0.0 5.0 2 ================================================ FILE: bluepyopt/ephys/examples/simplecell/simplecell.py ================================================ """Simple cell test model""" import os import bluepyopt.ephys as ephys class SimpleCell: def __init__(self): self.nrn_sim = ephys.simulators.NrnSimulator() self.morph = ephys.morphologies.NrnFileMorphology( os.path.join( os.path.dirname(os.path.abspath(__file__)), 'simple.swc')) self.somatic_loc = ephys.locations.NrnSeclistLocation( 'somatic', seclist_name='somatic') self.somacenter_loc = ephys.locations.NrnSeclistCompLocation( name='somacenter', seclist_name='somatic', sec_index=0, comp_x=0.5) self.hh_mech = ephys.mechanisms.NrnMODMechanism( name='hh', suffix='hh', locations=[self.somatic_loc]) self.cm_param = ephys.parameters.NrnSectionParameter( name='cm', param_name='cm', value=1.0, locations=[self.somatic_loc], frozen=True) self.gnabar_param = ephys.parameters.NrnSectionParameter( name='gnabar_hh', param_name='gnabar_hh', locations=[self.somatic_loc], bounds=[0.05, 0.125], frozen=False) self.gkbar_param = ephys.parameters.NrnSectionParameter( name='gkbar_hh', param_name='gkbar_hh', bounds=[0.01, 0.075], locations=[self.somatic_loc], frozen=False) self.cell_model = ephys.models.CellModel( name='simple_cell', morph=self.morph, mechs=[self.hh_mech], params=[self.cm_param, self.gnabar_param, self.gkbar_param]) self.default_param_values = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03} self.efel_feature_means = { 'step1': { 'Spikecount': 1}, 'step2': { 'Spikecount': 5}} self.objectives = [] self.soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma', seclist_name='somatic', sec_index=0, comp_x=0.5) self.stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.01, step_delay=100, step_duration=50, location=self.soma_loc, total_duration=200) self.rec = ephys.recordings.CompRecording( name='Step1.soma.v', location=self.soma_loc, variable='v') self.protocol = ephys.protocols.SweepProtocol( 'Step1', [self.stim], [self.rec]) self.stim_start = 100 self.stim_end = 150 self.feature_name = 'Step1.Spikecount' self.feature = ephys.efeatures.eFELFeature( self.feature_name, efel_feature_name='Spikecount', recording_names={'': '%s.soma.v' % self.protocol.name}, stim_start=self.stim_start, stim_end=self.stim_end, exp_mean=1.0, exp_std=0.05) self.objective = ephys.objectives.SingletonObjective( self.feature_name, self.feature) self.score_calc = \ ephys.objectivescalculators.ObjectivesCalculator([self.objective]) self.nrn = ephys.simulators.NrnSimulator() self.cell_evaluator = ephys.evaluators.CellEvaluator( cell_model=self.cell_model, param_names=['gnabar_hh', 'gkbar_hh'], fitness_protocols={'Step1': self.protocol}, fitness_calculator=self.score_calc, sim=self.nrn) ================================================ FILE: bluepyopt/ephys/extra_features_utils.py ================================================ """Extra features functions""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import numpy as np all_1D_features = [ "peak_to_valley", "halfwidth", "peak_trough_ratio", "repolarization_slope", "recovery_slope", "neg_peak_relative", "pos_peak_relative", "neg_peak_diff", "pos_peak_diff", "neg_image", "pos_image", ] def calculate_features( waveforms, sampling_frequency, upsample=None, feature_names=None, recovery_slope_window=0.7 ): """Calculate features for all waveforms Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute features for sampling_frequency : float rate at which the waveforms are sampled (Hz) feature_names : list or None (if None, compute all) features to compute recovery_slope_window : float windowlength in ms after peak wherein recovery slope is computed Returns ------- metrics : dict (num_waveforms x num_metrics) Dictionary with computed metrics. Keys are the metric names, values are the computed features """ metrics = dict() if feature_names is None: feature_names = all_1D_features else: for name in feature_names: assert name in all_1D_features, f"{name} not in {all_1D_features}" if upsample is not None: assert upsample > 0 waveforms = _upsample_wf(waveforms, int(upsample)) sampling_frequency = upsample * sampling_frequency if "peak_to_valley" in feature_names: metrics["peak_to_valley"] = peak_to_valley( waveforms=waveforms, sampling_frequency=sampling_frequency ) if "peak_trough_ratio" in feature_names: metrics["peak_trough_ratio"] = peak_trough_ratio(waveforms=waveforms) if "halfwidth" in feature_names: metrics["halfwidth"] = halfwidth( waveforms=waveforms, sampling_frequency=sampling_frequency ) if "repolarization_slope" in feature_names: metrics["repolarization_slope"] = repolarization_slope( waveforms=waveforms, sampling_frequency=sampling_frequency, ) if "recovery_slope" in feature_names: metrics["recovery_slope"] = recovery_slope( waveforms=waveforms, sampling_frequency=sampling_frequency, window=recovery_slope_window, ) if "neg_peak_diff" in feature_names: metrics["neg_peak_diff"] = peak_time_diff( waveforms=waveforms, fs=sampling_frequency, sign="negative" ) if "pos_peak_diff" in feature_names: metrics["pos_peak_diff"] = peak_time_diff( waveforms=waveforms, fs=sampling_frequency, sign="positive" ) if "neg_peak_relative" in feature_names: metrics["neg_peak_relative"] = relative_amplitude( waveforms=waveforms, sign="negative" ) if "pos_peak_relative" in feature_names: metrics["pos_peak_relative"] = relative_amplitude( waveforms=waveforms, sign="positive" ) if "neg_image" in feature_names: metrics["neg_image"] = peak_image(waveforms=waveforms, sign="negative") if "pos_image" in feature_names: metrics["pos_image"] = peak_image(waveforms=waveforms, sign="positive") return metrics def peak_to_valley(waveforms, sampling_frequency): """ Time between trough and peak. If the peak precedes the trough, peak_to_valley is negative. Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute feature for sampling_frequency : float rate at which the waveforms are sampled (Hz) Returns ------- np.ndarray (num_waveforms) peak_to_valley in seconds """ trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms) ptv = (peak_idx - trough_idx) * (1 / sampling_frequency) ptv[ptv == 0] = np.nan return ptv def peak_trough_ratio(waveforms): """ Normalized ratio peak height and trough depth Assumes baseline is 0 Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute feature for Returns ------- np.ndarray (num_waveforms) Peak to trough ratio """ trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms) ptratio = np.empty(trough_idx.shape[0]) ptratio[:] = np.nan for i in range(waveforms.shape[0]): if peak_idx[i] == 0 and trough_idx[i] == 0: continue ptratio[i] = np.abs(waveforms[i, peak_idx[i]] / waveforms[i, trough_idx[i]]) return ptratio def halfwidth(waveforms, sampling_frequency, return_idx=False): """ Width of waveform at its half of amplitude. If the peak precedes the trough, halfwidth is negative. Computes the width of the waveform peak at half it's height Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute features for sampling_frequency : float rate at which the waveforms are sampled (Hz) return_idx : bool if true, also returns index of threshold crossing before and index of threshold crossing after peak Returns ------- np.ndarray or (np.ndarray, np.ndarray, np.ndarray) Halfwidth of the waveforms or (Halfwidth of the waveforms, index_cross_pre_peak, index_cross_post_peak) """ trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms) hw = np.empty(waveforms.shape[0]) hw[:] = np.nan cross_pre_pk = np.empty(waveforms.shape[0], dtype=int) cross_post_pk = np.empty(waveforms.shape[0], dtype=int) for i in range(waveforms.shape[0]): if peak_idx[i] >= trough_idx[i]: trough_val = waveforms[i, trough_idx[i]] threshold = ( 0.5 * trough_val ) # threshold is half of peak heigth (assuming baseline is 0) cpre_idx = np.where(waveforms[i, :trough_idx[i]] < threshold)[0] cpost_idx = np.where(waveforms[i, trough_idx[i]:] < threshold)[0] if len(cpre_idx) == 0 or len(cpost_idx) == 0: continue cross_pre_pk[i] = ( cpre_idx[0] - 1 ) # last occurence of waveform lower than thr, before peak cross_post_pk[i] = ( cpost_idx[-1] + 1 + trough_idx[i] ) # first occurence of waveform lower than peak, after peak hw[i] = (cross_post_pk[i] - cross_pre_pk[i]) * ( 1 / sampling_frequency ) # + peak_idx[i] else: peak_val = waveforms[i, peak_idx[i]] threshold = ( 0.5 * peak_val ) # threshold is half of peak heigth (assuming baseline is 0) cpre_idx = np.where(waveforms[i, :peak_idx[i]] > threshold)[0] cpost_idx = np.where(waveforms[i, peak_idx[i]:] > threshold)[0] if len(cpre_idx) == 0 or len(cpost_idx) == 0: continue cross_pre_pk[i] = ( cpre_idx[0] - 1 ) # last occurence of waveform lower than thr, before peak cross_post_pk[i] = ( cpost_idx[-1] + 1 + trough_idx[i] ) # first occurence of waveform lower than peak, after peak hw[i] = -(cross_post_pk[i] - cross_pre_pk[i]) * ( 1 / sampling_frequency ) # + peak_idx[i] if not return_idx: return hw else: return hw, cross_pre_pk, cross_post_pk def repolarization_slope(waveforms, sampling_frequency, return_idx=False): """ Return slope of repolarization period between trough and baseline After reaching its maxumum polarization, the neuron potential will recover. The repolarization slope is defined as the dV/dT of the action potential between trough and baseline. Optionally the function returns also the indices per waveform where the potential crosses baseline. Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute features for sampling_frequency : float rate at which the waveforms are sampled (Hz) return_idx : bool if true, also returns index of threshold crossing before and index of threshold crossing after peak Returns ------- np.ndarray or (np.ndarray, np.ndarray) Repolarization slope of the waveforms or (Repolarization slope of the waveforms, return to base index) """ trough_idx, peak_idx = _get_trough_and_peak_idx(waveforms) rslope = np.empty(waveforms.shape[0]) rslope[:] = np.nan return_to_base_idx = np.empty(waveforms.shape[0], dtype=np.int_) return_to_base_idx[:] = 0 time = np.arange(0, waveforms.shape[1]) * (1 / sampling_frequency) # in s for i in range(waveforms.shape[0]): if trough_idx[i] == 0: continue rtrn_idx = np.where(waveforms[i, trough_idx[i]:] >= 0)[0] if len(rtrn_idx) == 0: continue return_to_base_idx[i] = ( rtrn_idx[0] + trough_idx[i] ) # first time after trough, where waveform is at baseline if return_to_base_idx[i] - trough_idx[i] < 3: continue slope = _get_slope( time[trough_idx[i]:return_to_base_idx[i]], waveforms[i, trough_idx[i]:return_to_base_idx[i]] ) rslope[i] = slope[0] if not return_idx: return rslope else: return rslope, return_to_base_idx def recovery_slope(waveforms, sampling_frequency, window): """ Return the recovery slope of input waveforms. After repolarization, the neuron hyperpolarizes until it peaks. The recovery slope is the slope of the action potential after the peak, returning to the baseline in dV/dT. The slope is computed within a user-defined window after the peak. Takes a numpy array of waveforms and returns an array with recovery slopes per waveform. Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute features for sampling_frequency : float rate at which the waveforms are sampled (Hz) window : float length after peak wherein to compute recovery slope (ms) Returns ------- np.ndarray Recovery slope of the waveforms """ _, peak_idx = _get_trough_and_peak_idx(waveforms) rslope = np.empty(waveforms.shape[0]) rslope[:] = np.nan time = np.arange(0, waveforms.shape[1]) * (1 / sampling_frequency) # in s for i in range(waveforms.shape[0]): if peak_idx[i] in [0, waveforms.shape[1]]: continue max_idx = int(peak_idx[i] + ((window / 1000) * sampling_frequency)) max_idx = np.min([max_idx, waveforms.shape[1]]) if len(time[peak_idx[i]:max_idx]) < 3: continue slope = _get_slope( time[peak_idx[i]:max_idx], waveforms[i, peak_idx[i]:max_idx] ) rslope[i] = slope[0] return rslope def peak_image(waveforms, sign="negative"): """ Normalized amplitude at the time of minimum or maximum peak. Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute features for sign : str "pos" | "neg" Returns ------- np.ndarray Peak images for the waveforms """ assert len(waveforms) > 1 if sign == "negative": funarg = np.argmin fun = np.min else: funarg = np.argmax fun = np.max peak_channel, peak_time = np.unravel_index( funarg(waveforms), waveforms.shape ) relative_peaks = waveforms[:, peak_time] / fun(waveforms[peak_channel]) return relative_peaks def relative_amplitude(waveforms, sign="negative"): """ Normalized amplitude with respect to channel with largest amplitude. Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute features for fs : float Sampling rate in Hz sign : str "positive" | "negative" Returns ------- np.ndarray Relative amplitudes for the waveforms """ assert len(waveforms) > 1 if sign == "negative": fun = np.min else: fun = np.max peak_amp = np.abs(fun(waveforms)) relative_peaks = np.abs(fun(waveforms, 1)) / peak_amp return relative_peaks def peak_time_diff(waveforms, fs, sign="negative"): """ Peak time differences with respect to channel with largest amplitude. Parameters ---------- waveforms : numpy.ndarray (num_waveforms x num_samples) waveforms to compute features for fs : float Sampling rate in Hz sign : str "positive" | "negative" Returns ------- np.ndarray Peak time differences for the waveforms """ assert len(waveforms) > 1 if sign == "negative": argfun = np.argmin else: argfun = np.argmax peak_chan = np.unravel_index(argfun(waveforms), waveforms.shape)[0] peak_time = argfun(waveforms[peak_chan]) relative_peak_times = (argfun(waveforms, 1) - peak_time) / fs return relative_peak_times def _get_slope(x, y): """ Retrun the slope of x and y data, using scipy.signal.linregress """ from scipy.stats import linregress slope = linregress(x, y) return slope def _get_trough_and_peak_idx(waveform, after_max_trough=False): """ Return the indices into the input waveforms of the detected troughs (minimum of waveform) and peaks (maximum of waveform, after trough). Assumes negative troughs and positive peaks Returns 0 if not detected """ if after_max_trough: max_through_idx = np.unravel_index( np.argmin(waveform), waveform.shape)[1] trough_idx = ( np.argmin(waveform[:, max_through_idx:], axis=1) + max_through_idx ) peak_idx = ( np.argmax(waveform[:, max_through_idx:], axis=1) + max_through_idx ) else: trough_idx = np.argmin(waveform, axis=1) peak_idx = np.argmax(waveform, axis=1) return trough_idx, peak_idx def _upsample_wf(waveforms, upsample): from scipy.signal import resample_poly ndim = len(waveforms.shape) waveforms_up = resample_poly(waveforms, up=upsample, down=1, axis=ndim - 1) return waveforms_up ================================================ FILE: bluepyopt/ephys/locations.py ================================================ """Location classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import itertools from bluepyopt.ephys.base import BaseEPhys from bluepyopt.ephys.serializer import DictMixin from bluepyopt.ephys.parameterscalers import format_float from bluepyopt.ephys.acc import ArbLabel from bluepyopt.ephys.morphologies import ArbFileMorphology import numpy as np import logging logger = logging.getLogger(__name__) class Location(BaseEPhys): """Location""" pass # TODO make all these locations accept a cell name # TODO instantiate should get the entire simulation environment # TODO find better/more general name for this # TODO specify in document abrevation comp=compartment, sec=section, ... def _nth_isectionlist(isectionlist, index): """Get nth element of isectionlist Sectionlists don't support direct indexing """ isection = next( itertools.islice( isectionlist, index, index + 1)) return isection class NrnSeclistCompLocation(Location, DictMixin): """Compartment in a sectionlist""" SERIALIZED_FIELDS = ( 'name', 'comment', 'seclist_name', 'sec_index', 'comp_x', ) def __init__( self, name, seclist_name=None, sec_index=None, comp_x=None, comment=''): """Constructor Args: name (str): name of the object seclist_name (str): name of Neuron section list (ex: 'somatic') sec_index (int): index of the section in the section list comp_x (float): segx (0..1) of segment inside section """ super(NrnSeclistCompLocation, self).__init__(name, comment) self.seclist_name = seclist_name self.sec_index = sec_index self.comp_x = comp_x def instantiate(self, sim=None, icell=None): # pylint: disable=W0613 """Find the instantiate compartment""" iseclist = getattr(icell, self.seclist_name) iseclist_size = len([x for x in iseclist]) if self.sec_index >= iseclist_size: raise Exception( 'NrnSeclistCompLocation: section index %d falls out of ' 'SectionList size of %d' % (self.sec_index, iseclist_size)) isection = _nth_isectionlist(iseclist, self.sec_index) icomp = isection(self.comp_x) # The code above seems to add put a section on the stack # TODO remove line below once we figure out where the section is pushed sim.neuron.h.pop_section() return icomp def acc_label(self): """Arbor label""" raise EPhysLocAccException( '%s not supported in Arbor' % type(self).__name__ + ' (uses branches instead of NEURON sections).' ' Use ArbBranchRelLocation/ArbSegmentRelLocation/' 'ArbLocsetLocation instead (consider using the' ' Arbor GUI to identify the precise branch/segment index' ' and relative position).') def __str__(self): """String representation""" return '%s[%s](%s)' % (self.seclist_name, self.sec_index, self.comp_x) class NrnSectionCompLocation(Location, DictMixin): """Compartment in a section""" SERIALIZED_FIELDS = ( 'name', 'comment', 'seclist_name', 'sec_index', 'comp_x', ) def __init__( self, name, sec_name=None, comp_x=None, comment=''): """Constructor Args: name (str): name of the object sec_name (str): name of Neuron section (ex: 'soma[0]') comp_x (float): segx (0..1) of segment inside section """ super(NrnSectionCompLocation, self).__init__(name, comment) self.sec_name = sec_name self.comp_x = comp_x def instantiate(self, sim=None, icell=None): # pylint: disable=W0613 """Find the instantiate compartment""" # Dont see any other way but to use eval, apart from parsing the # sec_name string which can be complicated isection = eval('icell.%s' % self.sec_name) # pylint: disable=W0123 icomp = isection(self.comp_x) return icomp def acc_label(self): """Arbor label""" raise EPhysLocAccException( '%s not supported in Arbor' % type(self).__name__ + ' (uses branches instead of NEURON sections).' ' Use ArbBranchRelLocation/ArbSegmentRelLocation/' 'ArbLocsetLocation instead (consider using the' ' Arbor GUI to identify the precise branch/segment index' ' and relative position).') def __str__(self): return '%s(%s)' % (self.sec_name, self.comp_x) class NrnPointProcessLocation(Location): """Point process location""" def __init__( self, name, pprocess_mech, comment=''): """Constructor Args: name (str): name of the object pprocess_mech (str): point process mechanism """ super(NrnPointProcessLocation, self).__init__(name, comment) self.pprocess_mech = pprocess_mech def instantiate(self, sim=None, icell=None): # pylint: disable=W0613 """Find the instantiated point processes""" return self.pprocess_mech.pprocesses def acc_label(self): """Arbor label""" return [loc.acc_label() for loc in self.pprocess_mech.locations] def __str__(self): """String representation""" return '%s' % (self.pprocess_mech.name) class NrnSeclistLocation(Location, DictMixin): """Section in a sectionlist""" SERIALIZED_FIELDS = ('name', 'comment', 'seclist_name', ) def __init__( self, name, seclist_name=None, comment=''): """Constructor Args: name (str): name of the object seclist_name (str): name of NEURON section list (ex: 'somatic') """ super(NrnSeclistLocation, self).__init__(name, comment) self.seclist_name = seclist_name def instantiate(self, sim=None, icell=None): # pylint: disable=W0613 """Find the instantiate compartment""" isectionlist = getattr(icell, self.seclist_name) return (isection for isection in isectionlist) def acc_label(self): """Arbor label""" return ArbFileMorphology.region_labels[self.seclist_name] def __str__(self): """String representation""" return '%s' % (self.seclist_name) class NrnSeclistSecLocation(Location, DictMixin): """Section in a sectionlist""" SERIALIZED_FIELDS = ('name', 'comment', 'seclist_name', 'sec_index', ) def __init__( self, name, seclist_name=None, sec_index=None, comment=''): """Constructor Args: name (str): name of this object seclist_name (str): name of Neuron section list (ex: 'somatic') sec_index (int): index of the section """ super(NrnSeclistSecLocation, self).__init__(name, comment) self.seclist_name = seclist_name self.sec_index = sec_index def instantiate(self, sim=None, icell=None): # pylint: disable=W0613 """Find the instantiate compartment""" isectionlist = getattr(icell, self.seclist_name) isection = _nth_isectionlist(isectionlist, self.sec_index) return isection def acc_label(self): """Arbor label""" raise EPhysLocAccException( '%s not supported in Arbor' % type(self).__name__ + ' (uses branches instead of NEURON sections).' ' Use ArbBranchLocation/ArbSegmentLocation/ArbRegionLocation' ' instead (consider using the Arbor GUI to identify the' ' precise branch/segment index).') def __str__(self): """String representation""" return '%s[%s]' % (self.seclist_name, self.sec_index) class NrnSomaDistanceCompLocation(Location, DictMixin): """Compartment at distance from soma""" SERIALIZED_FIELDS = ('name', 'comment', 'soma_distance', 'seclist_name', ) def __init__( self, name, soma_distance=None, seclist_name=None, comment=''): """Constructor Args: name (str): name of this object soma_distance (float): distance from soma to this compartment seclist_name (str): name of Neuron section list (ex: 'apical') """ super(NrnSomaDistanceCompLocation, self).__init__(name, comment) self.soma_distance = soma_distance self.seclist_name = seclist_name # TODO this definitely has to be unit-tested # TODO add ability to specify origin def find_icomp(self, sim, iseclist): """Find the index of the compartment based on a list of isec and a distance""" icomp = None for isec in iseclist: start_distance = sim.neuron.h.distance(1, 0.0, sec=isec) end_distance = sim.neuron.h.distance(1, 1.0, sec=isec) min_distance = min(start_distance, end_distance) max_distance = max(start_distance, end_distance) if min_distance <= self.soma_distance <= max_distance: comp_x = float(self.soma_distance - min_distance) / \ (max_distance - min_distance) comp_diam = isec(comp_x).diam if comp_diam > 0.0: icomp = isec(comp_x) if icomp is None: raise EPhysLocInstantiateException( 'No comp found at %s distance from soma' % self.soma_distance) return icomp def instantiate(self, sim=None, icell=None): """Find the instantiate compartment""" soma = icell.soma[0] sim.neuron.h.distance(0, 0.5, sec=soma) iseclist = getattr(icell, self.seclist_name) return self.find_icomp(sim, iseclist) def acc_label(self): """Arbor label""" # Potentially non-unique location - in that case to be refined in the # Arbor GUI (create ArbLocsetLocation directly). # Alternatives to (on-components 0.5 (region "soma")) are # - '(segment )' # - '(proximal (region %s)))' % self.seclist_name # If outer restrict results in non-unique location (cf. GUI) use # specific branch or similar instead of seclist_name, e.g. # - (proximal-interval (distal (branch ))) # for a branch distally from the desired location acc_label = ArbLabel( 'locset', self.name, '(restrict (distal-translate (on-components 0.5 %s) %s) %s)' % (ArbFileMorphology.region_labels['somatic'].ref, format_float(self.soma_distance), ArbFileMorphology.region_labels[self.seclist_name].ref)) logger.warning( 'Make sure that ACC label %s' % acc_label.loc + ' for NrnSomaDistanceCompLocation (%s) ' % str(self) + ' instantiates to a unique location on the morphology.' ' Use the Arbor GUI to validate/refine the location expression.') return acc_label def __str__(self): """String representation""" return '%f micron from soma in %s' % ( self.soma_distance, self.seclist_name) class NrnSecSomaDistanceCompLocation(NrnSomaDistanceCompLocation): """Compartment on a section defined both by a section index and distance from the soma """ SERIALIZED_FIELDS = ('name', 'comment', 'soma_distance', 'seclist_name', 'sec_index', ) def __init__( self, name, soma_distance=None, sec_index=None, seclist_name=None, comment="" ): """Constructor Args: name (str): name of this object soma_distance (float): distance from soma to this compartment sec_index (int): index of the section to consider seclist_name (str): name of Neuron sections (ex: 'apic') """ super(NrnSecSomaDistanceCompLocation, self).__init__( name, soma_distance=soma_distance, seclist_name=seclist_name, comment=comment, ) self.sec_index = sec_index def instantiate(self, sim=None, icell=None): """Find the instantiate compartment""" if self.sec_index is None: raise EPhysLocInstantiateException( "No apical point was given") sections = getattr(icell, self.seclist_name) section = _nth_isectionlist(sections, self.sec_index) branches = [] while True: name = str(section.name()).split(".")[-1] if name == "soma[0]": break branches.append(section) if sim.neuron.h.SectionRef(sec=section).has_parent(): section = sim.neuron.h.SectionRef(sec=section).parent else: raise EPhysLocInstantiateException( "soma[0] was not reached from isec point " "%f" % self.sec_index ) soma = icell.soma[0] sim.neuron.h.distance(0, 0.5, sec=soma) return self.find_icomp(sim, branches) def acc_label(self): """Arbor label""" raise EPhysLocAccException('%s not supported in Arbor.' % type(self).__name__) class NrnTrunkSomaDistanceCompLocation(NrnSecSomaDistanceCompLocation): """Location at a distance from soma along a main direction. We search for the section that is the furthest away from some along a direction, and pick a location at a given distance from soma along the path to that section. If direction == 'radial', the largest radial direction is used. This is most useful to follow the trunk of an apical dendrite without knowing the apical point, but only that apical trunk goes along y. """ def __init__( self, name, soma_distance=None, sec_index=None, seclist_name=None, direction=None, comment="" ): """Constructor Args: name (str): name of this object soma_distance (float): distance from soma to this compartment sec_index (int): index of the section to consider sec_name (str): name of Neuron sections (ex: 'apic') direction (list of 3 elements): 3d vector representing direction, if None, default is [0, 1, 0] """ super(NrnTrunkSomaDistanceCompLocation, self).__init__( name, soma_distance=soma_distance, sec_index=sec_index, seclist_name=seclist_name, comment=comment ) if direction is None: direction = [0.0, 1.0, 0.0] self.direction = direction def set_sec_index(self, icell=None): """Search for the point furthest away along given direction.""" points = np.array( [ [ section.x3d(section.n3d() - 1), section.y3d(section.n3d() - 1), section.z3d(section.n3d() - 1), ] for section in getattr(icell, self.seclist_name) ] ) if len(points) > 0: if self.direction == 'radial': self.sec_index = int(np.argmax(np.linalg.norm(points, axis=1))) else: self.sec_index = int(np.argmax(points.dot(self.direction))) else: raise EPhysLocInstantiateException( "Empty seclist: %s" % self.seclist_name ) def instantiate(self, sim=None, icell=None): """ """ if self.sec_index is None: self.set_sec_index(icell=icell) return super().instantiate(sim=sim, icell=icell) def acc_label(self): """Arbor label""" raise EPhysLocAccException('%s not supported in Arbor.' % type(self).__name__) class ArbLocation(Location): """Arbor Location""" def instantiate(self, sim=None, icell=None): # pylint: disable=W0613 """Find the instantiate compartment (default implementation)""" raise EPhysLocInstantiateException( '%s not supported in NEURON.' % type(self).__name__) def __str__(self): """String representation""" return '%s \'%s\'' % (type(self).__name__, self.acc_label().defn) class ArbSegmentLocation(ArbLocation): """Segment in an Arbor morphology. """ def __init__(self, name, segment, comment=''): super().__init__(name, comment) self.segment = segment def acc_label(self): """Arbor label""" return ArbLabel('region', self.name, '(segment %s)' % (self.segment)) class ArbBranchLocation(ArbLocation): """Branch in an Arbor morphology. Arbor's counterpart of NrnSeclistSecLocation. """ def __init__(self, name, branch, comment=''): super().__init__(name, comment) self.branch = branch def acc_label(self): """Arbor label""" return ArbLabel('region', self.name, '(branch %s)' % (self.branch)) class ArbSegmentRelLocation(ArbLocation): """Relative position on a segment in an Arbor morphology. """ def __init__(self, name, segment, pos, comment=''): super().__init__(name, comment) self.segment = segment self.pos = pos def acc_label(self): """Arbor label""" return ArbLabel('locset', self.name, '(on-components %s (segment %s))' % (format_float(self.pos), self.segment)) class ArbBranchRelLocation(ArbLocation): """Relative position on a branch in an Arbor morphology. Arbor's counterpart of NrnSeclistCompLocation. """ def __init__(self, name, branch, pos, comment=''): super().__init__(name, comment) self.branch = branch self.pos = pos def acc_label(self): """Arbor label""" return ArbLabel('locset', self.name, '(location %s %s)' % (self.branch, format_float(self.pos))) class ArbLocsetLocation(ArbLocation): """Arbor location set defined by a user-supplied string (S-expression). """ def __init__(self, name, locset, comment=''): super().__init__(name, comment) self.locset = locset def acc_label(self): """Arbor label""" return ArbLabel('locset', self.name, self.locset) class ArbRegionLocation(ArbLocation): """Arbor region defined by a user-supplied string (S-expression). """ def __init__(self, name, region, comment=''): super().__init__(name, comment) self.region = region def acc_label(self): """Arbor label""" return ArbLabel('region', self.name, self.region) class EPhysLocInstantiateException(Exception): """All exceptions generated by location instantiation""" def __init__(self, message): """Constructor""" super(EPhysLocInstantiateException, self).__init__(message) class EPhysLocAccException(Exception): """All exceptions generated by ACC label creation""" def __init__(self, message): """Constructor""" super(EPhysLocAccException, self).__init__(message) ================================================ FILE: bluepyopt/ephys/mechanisms.py ================================================ """ Mechanism classes Theses classes represent mechanisms in the model """ """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import logging from . import base from . import serializer logger = logging.getLogger(__name__) # TODO: use Location class to specify location class Mechanism(base.BaseEPhys): """Base parameter class""" pass class NrnMODMechanism(Mechanism, serializer.DictMixin): """Neuron mechanism""" SERIALIZED_FIELDS = ( 'name', 'comment', 'mod_path', 'suffix', 'locations', 'preloaded', ) def __init__( self, name, mod_path=None, suffix=None, locations=None, preloaded=True, deterministic=True, prefix=None, comment=''): """Constructor Args: name (str): name of this object mod_path (str): path to the MOD file (not used for the moment) suffix (str): suffix of this mechanism in the MOD file locations (list of Locations): a list of Location objects pointing to where this mechanism should be added to. preloaded (bool): should this mechanism be side-loaded by BluePyOpt, or was it already loaded and compiled by the user ? (not used for the moment) prefix (str): Deprecated. Use suffix instead. """ super(NrnMODMechanism, self).__init__(name, comment) self.mod_path = mod_path self.suffix = suffix self.locations = locations self.preloaded = preloaded self.cell_model = None self.deterministic = deterministic if prefix is not None and suffix is not None: raise TypeError('NrnMODMechanism: it is not allowed to set both ' 'prefix and suffix in constructor: %s %s' % (self.prefix, self.suffix)) elif prefix is not None: self.suffix = prefix def instantiate(self, sim=None, icell=None): """Instantiate""" for location in self.locations: isec_list = location.instantiate(sim=sim, icell=icell) for isec in isec_list: try: isec.insert(self.suffix) except ValueError as e: raise ValueError(str(e) + ': ' + self.suffix) self.instantiate_determinism( self.deterministic, icell, isec, sim) logger.debug( 'Inserted %s in %s', self.suffix, [ str(location) for location in self.locations]) def instantiate_determinism(self, deterministic, icell, isec, sim): """Instantiate enable/disable determinism""" if 'Stoch' in self.suffix: setattr( isec, 'deterministic_%s' % (self.suffix), 1 if deterministic else 0) # Set the seeds even when deterministic, # that way neuron's psection does not crash # when encountering a stoch mech var that is not set (e.g. rng) short_secname = sim.neuron.h.secname(sec=isec).split('.')[-1] for iseg in isec: seg_name = '%s.%.19g' % (short_secname, iseg.x) getattr(sim.neuron.h, "setdata_%s" % self.suffix)(iseg.x, sec=isec) seed_id1 = icell.gid seed_id2 = self.hash_py(seg_name) getattr( sim.neuron.h, "setRNG_%s" % self.suffix)(seed_id1, seed_id2) else: if not deterministic: # can't do this for non-Stoch channels raise TypeError( 'Deterministic can only be set to False for ' 'Stoch channel, not %s' % self.suffix) def destroy(self, sim=None): """Destroy mechanism instantiation""" pass def __str__(self): """String representation""" return "%s: %s at %s" % ( self.name, self.suffix, [str(location) for location in self.locations]) @staticmethod def hash_hoc(string, sim): """Calculate hash value of string in Python""" # Load hash function in hoc, only do this once if not hasattr(sim.neuron.h, 'hash_str'): sim.neuron.h(NrnMODMechanism.hash_hoc_string) return sim.neuron.h.hash_str(string) @staticmethod def hash_py(string): """Calculate hash value of string in Python""" hash_value = 0.0 for char in string: # Multiplicative hash function using Mersenne prime close to 2^32 hash_value = (hash_value * 31.0 + ord(char)) % (2.0 ** 31.0 - 1.0) return hash_value def generate_reinitrng_hoc_block(self): """"Create re_init_rng code blocks for this channel""" reinitrng_hoc_block = '' if 'Stoch' in self.suffix: # TODO this is dangerous, implicitely assumes type of location for location in self.locations: if self.deterministic: reinitrng_hoc_block += \ ' forsec %(seclist_name)s { ' \ 'deterministic_%(suffix)s = 1 }\n' % { 'seclist_name': location.seclist_name, 'suffix': self.suffix} else: reinitrng_hoc_block += \ ' forsec %(seclist_name)s {%(mech_reinitrng)s' \ ' }\n' % { 'seclist_name': location.seclist_name, 'mech_reinitrng': self.mech_reinitrng_block_template % { 'suffix': self.suffix}} return reinitrng_hoc_block @property def prefix(self): """Deprecated, prefix is now replaced by suffix""" return self.suffix @prefix.setter def prefix(self, value): """Deprecated, prefix is now replaced by suffix""" self.suffix = value hash_hoc_string = \ """ func hash_str() {localobj sf strdef right sf = new StringFunctions() right = $s1 n_of_c = sf.len(right) hash = 0 char_int = 0 for i = 0, n_of_c - 1 { sscanf(right, "%c", & char_int) hash = (hash * 31 + char_int) % (2 ^ 31 - 1) sf.right(right, 1) } return hash } """ reinitrng_hoc_string = """ proc re_init_rng() {localobj sf strdef full_str, name sf = new StringFunctions() if(numarg() == 1) { // We received a third seed channel_seed = $1 channel_seed_set = 1 } else { channel_seed_set = 0 } %(reinitrng_hoc_blocks)s } """ mech_reinitrng_block_template = """ for (x, 0) { setdata_%(suffix)s(x) sf.tail(secname(), "\\\\.", name) sprint(full_str, "%%s.%%.19g", name, x) if (channel_seed_set) { setRNG_%(suffix)s(gid, hash_str(full_str), channel_seed) } else { setRNG_%(suffix)s(gid, hash_str(full_str)) } } """ class NrnMODPointProcessMechanism(Mechanism): """Neuron mechanism""" def __init__( self, name, mod_path=None, suffix=None, locations=None, preloaded=True, comment=''): """Constructor Args: name (str): name of this object mod_path (str): path to the MOD file (not used for the moment) suffix (str): suffix of this mechanism in the MOD file locations (list of Locations): a list of Location objects pointing to compartments where this mechanism should be added to. preloaded (bool): should this mechanism be side-loaded by BluePyOpt, or was it already loaded and compiled by the user ? (not used for the moment) """ super(NrnMODPointProcessMechanism, self).__init__(name, comment) self.mod_path = mod_path self.suffix = suffix self.locations = locations self.preloaded = preloaded self.cell_model = None self.pprocesses = None def instantiate(self, sim=None, icell=None): """Instantiate""" self.pprocesses = [] for location in self.locations: icomp = location.instantiate(sim=sim, icell=icell) try: iclass = getattr(sim.neuron.h, self.suffix) self.pprocesses.append(iclass(icomp.x, sec=icomp.sec)) except AttributeError as e: raise AttributeError(str(e) + ': ' + self.suffix) logger.debug( 'Inserted %s at %s ', self.suffix, [ str(location) for location in self.locations]) def destroy(self, sim=None): """Destroy mechanism instantiation""" self.pprocesses = None def __str__(self): """String representation""" return "%s: %s at %s" % ( self.name, self.suffix, [str(location) for location in self.locations]) ================================================ FILE: bluepyopt/ephys/models.py ================================================ """Cell template class""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 # TODO take into account that one might want to run protocols on different # machines # TODO rename this to 'CellModel' -> definitely import sys import os import collections import string from . import create_hoc, create_acc from . import morphologies import logging logger = logging.getLogger(__name__) class Model(object): """Model""" def __init__(self, name): """Constructor Args: name (str): name of the model """ self.name = name def instantiate(self, sim=None): """Instantiate model in simulator""" pass def destroy(self, sim=None): """Destroy instantiated model in simulator""" pass class CellModel(Model): """Cell model class""" def __init__( self, name, morph=None, mechs=None, params=None, gid=0, seclist_names=None, secarray_names=None ): """Constructor Args: name (str): name of this object should be alphanumeric string, underscores are allowed, first char should be a letter morph (Morphology): underlying Morphology of the cell mechs (list of Mechanisms): Mechanisms associated with the cell params (list of Parameters): Parameters of the cell model seclist_names (list of strings): Names of the lists of sections secarray_names (list of strings): Names of the sections """ super(CellModel, self).__init__(name) self.check_name() self.morphology = morph self.mechanisms = mechs self.params = collections.OrderedDict() if params is not None: for param in params: self.params[param.name] = param # Cell instantiation in simulator self.icell = None self.icell_existing_secs = None self.param_values = None self.gid = gid if seclist_names is None: self.seclist_names = [ 'all', 'somatic', 'basal', 'apical', 'axonal', 'myelinated' ] else: self.seclist_names = seclist_names if secarray_names is None: self.secarray_names = [ 'soma', 'dend', 'apic', 'axon', 'myelin' ] else: self.secarray_names = secarray_names def check_name(self): """Check if name complies with requirements""" allowed_chars = string.ascii_letters + string.digits + '_' if sys.version_info[0] < 3: translate_args = [None, allowed_chars] else: translate_args = [str.maketrans('', '', allowed_chars)] if self.name == '' \ or self.name[0] not in string.ascii_letters \ or not str(self.name).translate(*translate_args) == '': raise TypeError( 'CellModel: name "%s" provided to constructor does not comply ' 'with the rules for Neuron template name: name should be ' 'alphanumeric ' 'non-empty string, underscores are allowed, ' 'first char should be letter' % self.name) def params_by_names(self, param_names): """Get parameter objects by name""" return [self.params[param_name] for param_name in param_names] def freeze(self, param_dict): """Set params""" for param_name, param_value in param_dict.items(): self.params[param_name].freeze(param_value) def unfreeze(self, param_names): """Unset params""" for param_name in param_names: self.params[param_name].unfreeze() @staticmethod def create_empty_template( template_name, seclist_names=None, secarray_names=None): '''create an hoc template named template_name for an empty cell''' objref_str = 'objref this, CellRef' newseclist_str = '' if seclist_names: for seclist_name in seclist_names: objref_str += ', %s' % seclist_name newseclist_str += \ ' %s = new SectionList()\n' % seclist_name create_str = '' if secarray_names: create_str = 'create ' create_str += ', '.join( '%s[1]' % secarray_name for secarray_name in secarray_names) create_str += '\n' template = '''\ begintemplate %(template_name)s %(objref_str)s proc init() {\n%(newseclist_str)s forall delete_section() CellRef = this } gid = 0 proc destroy() {localobj nil CellRef = nil } %(create_str)s endtemplate %(template_name)s ''' % dict(template_name=template_name, objref_str=objref_str, newseclist_str=newseclist_str, create_str=create_str) return template @staticmethod def create_empty_cell( name, sim, seclist_names=None, secarray_names=None): """Create an empty cell in Neuron""" # TODO minize hardcoded definition # E.g. sectionlist can be procedurally generated hoc_template = CellModel.create_empty_template( name, seclist_names, secarray_names) sim.neuron.h(hoc_template) template_function = getattr(sim.neuron.h, name) return template_function() def instantiate_morphology(self, sim=None): """Instantiate morphology in simulator""" # TODO replace this with the real template name if not hasattr(sim.neuron.h, self.name): self.icell = self.create_empty_cell( self.name, sim=sim, seclist_names=self.seclist_names, secarray_names=self.secarray_names) else: self.icell = getattr(sim.neuron.h, self.name)() self.icell.gid = self.gid self.morphology.instantiate(sim=sim, icell=self.icell) self.icell_existing_secs = [ sec for sec in self.secarray_names if sim.neuron.h.section_exists(sec, self.icell)] def instantiate_morphology_3d(self, sim=None): """Instantiate morphology and fill in 3d pts for stylized geometry""" self.instantiate_morphology(sim=sim) sim.neuron.h.define_shape() def instantiate(self, sim=None): """Instantiate model in simulator""" self.instantiate_morphology(sim) if self.mechanisms is not None: for mechanism in self.mechanisms: mechanism.instantiate(sim=sim, icell=self.icell) if self.params is not None: for param in self.params.values(): param.instantiate(sim=sim, icell=self.icell, params=self.params) def destroy(self, sim=None): # pylint: disable=W0613 """Destroy instantiated model in simulator""" # Make sure the icell's destroy() method is called # without it a circular reference exists between CellRef and the object # this prevents the icells from being garbage collected, and # cell objects pile up in the simulator self.icell.destroy() # The line below is some M. Hines magic # DON'T remove it, because it will make sure garbage collection # is called on the icell object sim.neuron.h.Vector().size() self.icell = None self.icell_existing_secs = None self.morphology.destroy(sim=sim) for mechanism in self.mechanisms: mechanism.destroy(sim=sim) for param in self.params.values(): param.destroy(sim=sim) def check_nonfrozen_params(self, param_names): # pylint: disable=W0613 """Check if all nonfrozen params are set""" for param_name, param in self.params.items(): if not param.frozen: raise Exception( 'CellModel: Nonfrozen param %s needs to be ' 'set before simulation' % param_name) def _create_sim_desc(self, param_values, ignored_globals=(), template=None, disable_banner=False, template_dir=None, extra_params=None, sim_desc_creator=None): """Create simulator description for this model""" to_unfreeze = [] for param in self.params.values(): if not param.frozen: param.freeze(param_values[param.name]) to_unfreeze.append(param.name) template_name = self.name morphology = os.path.basename(self.morphology.morphology_path) if sim_desc_creator is create_hoc.create_hoc: if self.morphology.do_replace_axon: replace_axon = self.morphology.replace_axon_hoc else: replace_axon = None if ( self.morphology.morph_modifiers is not None and self.morphology.morph_modifiers_hoc is None ): logger.warning('You have provided custom morphology' ' modifiers, but no corresponding hoc files.') elif ( self.morphology.morph_modifiers is not None and self.morphology.morph_modifiers_hoc is not None ): if replace_axon is None: replace_axon = '' for morph_modifier_hoc in self.morphology.morph_modifiers_hoc: replace_axon += '\n' replace_axon += morph_modifier_hoc elif sim_desc_creator is create_acc.create_acc: if self.morphology.do_replace_axon: replace_axon = morphologies.\ ArbFileMorphology.extract_nrn_seclists( self.icell, [sl for sl in ['axon', 'myelin'] if sl in self.icell_existing_secs]) else: replace_axon = None else: raise ValueError('Unsupported sim_desc_creator %s ' '(choose either create_hoc.create_hoc or ' 'create_acc.create_acc)', str(sim_desc_creator)) if extra_params is None: extra_params = dict() ret = sim_desc_creator(mechs=self.mechanisms, parameters=self.params.values(), morphology=morphology, ignored_globals=ignored_globals, replace_axon=replace_axon, template_name=template_name, template_filename=template, template_dir=template_dir, disable_banner=disable_banner, **extra_params) self.unfreeze(to_unfreeze) return ret def create_hoc(self, param_values, ignored_globals=(), template='cell_template.jinja2', disable_banner=False, template_dir=None): """Create hoc code for this model""" return self._create_sim_desc(param_values, ignored_globals, template, disable_banner, template_dir, sim_desc_creator=create_hoc.create_hoc) def create_acc(self, param_values, ignored_globals=(), template='acc/*_template.jinja2', disable_banner=False, template_dir=None, ext_catalogues=None, create_mod_morph=False, sim=None): """Create JSON/ACC-description for this model""" destroy_cell = False if self.morphology.do_replace_axon: if self.icell is None: if sim is None: raise ValueError('Need an instance of NrnSimulator in sim' ' to instantiate morphology in order to' ' create JSON/ACC-description with' ' axon replacement.') self.instantiate_morphology_3d(sim=sim) destroy_cell = True extra_params = dict( morphology_dir=os.path.dirname(self.morphology.morphology_path), create_mod_morph=create_mod_morph, ext_catalogues=ext_catalogues ) ret = self._create_sim_desc(param_values, ignored_globals, template, disable_banner, template_dir, extra_params=extra_params, sim_desc_creator=create_acc.create_acc) if destroy_cell: self.destroy(sim=sim) return ret def write_acc(self, output_dir, param_values, template_filename='acc/*_template.jinja2', ext_catalogues=None, create_mod_morph=False, sim=None): """Write JSON/ACC-description for this model to output directory""" create_acc.write_acc(output_dir, self, param_values, template_filename=template_filename, ext_catalogues=ext_catalogues, create_mod_morph=create_mod_morph, sim=sim) def __str__(self): """Return string representation""" content = '%s:\n' % self.name content += ' morphology:\n' if self.morphology is not None: content += ' %s\n' % str(self.morphology) content += ' mechanisms:\n' if self.mechanisms is not None: for mechanism in self.mechanisms: content += ' %s\n' % mechanism content += ' params:\n' if self.params is not None: for param in self.params.values(): content += ' %s\n' % param return content class HocMorphology(morphologies.Morphology): '''wrapper for Morphology so that it has a morphology_path''' def __init__(self, morphology_path): super(HocMorphology, self).__init__() if not os.path.exists(morphology_path): raise Exception('HocCellModel: Morphology not found at: %s' % morphology_path) self.morphology_path = morphology_path class HocCellModel(CellModel): '''Wrapper class for a hoc template so it can be used by BluePyOpt''' def __init__(self, name, morphology_path, hoc_path=None, hoc_string=None): """Constructor Args: name(str): name of this object sim(NrnSimulator): simulator in which to instatiate hoc_string hoc_path(str): Path to a hoc file (hoc_path and hoc_string can't be used simultaneously, but one of them has to specified) hoc_string(str): String that of hoc code that defines a template (hoc_path and hoc_string can't be used simultaneously, but one of them has to specified)) morphology_path(str path): path to morphology that can be loaded by Neuron """ super(HocCellModel, self).__init__(name, morph=None, mechs=[], params=[]) if hoc_path is not None and hoc_string is not None: raise TypeError('HocCellModel: cant specify both hoc_string ' 'and hoc_path argument') if hoc_path is not None: with open(hoc_path) as hoc_file: self.hoc_string = hoc_file.read() else: self.hoc_string = hoc_string self.morphology = HocMorphology(morphology_path) self.cell = None self.icell = None def params_by_names(self, param_names): pass def freeze(self, param_dict): pass def unfreeze(self, param_names): pass def instantiate(self, sim=None): sim.neuron.h.load_file('stdrun.hoc') template_name = self.load_hoc_template(sim, self.hoc_string) morph_path = self.morphology.morphology_path assert os.path.exists(morph_path), \ 'Morphology path does not exist: %s' % morph_path if os.path.isdir(morph_path): # will use the built in morphology name, if the init() only # gets one parameter self.cell = getattr(sim.neuron.h, template_name)(morph_path) else: morph_dir = os.path.dirname(morph_path) morph_name = os.path.basename(morph_path) self.cell = getattr(sim.neuron.h, template_name)(morph_dir, morph_name) self.icell = self.cell.CellRef def destroy(self, sim=None): self.cell = None self.icell = None def check_nonfrozen_params(self, param_names): pass def __str__(self): """Return string representation""" return ( '%s: %s of %s(%s)' % (self.__class__, self.name, self.get_template_name(self.hoc_string), self.morphology.morphology_path,)) @staticmethod def get_template_name(hoc_string): """Find the template name from hoc_string Note: this will fail if there is a begintemplate in a `/* */` style comment before the real begintemplate """ for i, line in enumerate(hoc_string.split('\n')): if 'begintemplate' in line: line = line.strip().split() assert line[0] == 'begintemplate', \ 'begintemplate must come first, line %d' % i template_name = line[1] logger.info('Found template %s on line %d', template_name, i) return template_name else: # pylint: disable=W0120 raise Exception('Could not find begintemplate in hoc file') @staticmethod def load_hoc_template(sim, hoc_string): """Have neuron hoc template, and detect what the name template name is The template must have an init that takes two parameters, the second of which is the path to a morphology. It must also have a CellRef member that is the result of `Import3d_GUI(...).instantiate()` """ template_name = HocCellModel.get_template_name(hoc_string) if not hasattr(sim.neuron.h, template_name): sim.neuron.h(hoc_string) assert hasattr(sim.neuron.h, template_name), \ 'NEURON does not have template: ' + template_name return template_name class LFPyCellModel(Model): """LFPy.Cell model class""" def __init__( self, name, electrode=None, morph=None, mechs=None, params=None, dt=0.025, v_init=-65.0, gid=0, seclist_names=None, secarray_names=None, ): """Constructor Args: name (str): name of this object should be alphanumeric string, underscores are allowed, first char should be a letter morph (Morphology): underlying Morphology of the cell mechs (list of Mechanisms): Mechanisms associated with the cell params (list of Parameters): Parameters of the cell model seclist_names (list of strings): Names of the lists of sections secarray_names (list of strings): Names of the sections """ super(LFPyCellModel, self).__init__(name) self.check_name() self.morphology = morph self.mechanisms = mechs self.params = collections.OrderedDict() if params is not None: for param in params: self.params[param.name] = param # Cell instantiation in simulator self.icell = None self.lfpy_cell = None self.electrode = electrode self.lfpy_electrode = None self.dt = dt self.v_init = v_init self.param_values = None self.gid = gid if seclist_names is None: self.seclist_names = [ "all", "somatic", "basal", "apical", "axonal", "myelinated", ] else: self.seclist_names = seclist_names if secarray_names is None: self.secarray_names = ["soma", "dend", "apic", "axon", "myelin"] else: self.secarray_names = secarray_names def check_name(self): """Check if name complies with requirements""" allowed_chars = string.ascii_letters + string.digits + "_" if sys.version_info[0] < 3: translate_args = [None, allowed_chars] else: translate_args = [str.maketrans("", "", allowed_chars)] if ( self.name == "" or self.name[0] not in string.ascii_letters or not str(self.name).translate(*translate_args) == "" ): raise TypeError( 'CellModel: name "%s" provided to constructor does not comply ' "with the rules for Neuron template name: name should be " "alphanumeric " "non-empty string, underscores are allowed, " "first char should be letter" % self.name ) def params_by_names(self, param_names): """Get parameter objects by name""" return [self.params[param_name] for param_name in param_names] def freeze(self, param_dict): """Set params""" for param_name, param_value in param_dict.items(): self.params[param_name].freeze(param_dict[param_name]) def unfreeze(self, param_names): """Unset params""" for param_name in param_names: self.params[param_name].unfreeze() @staticmethod def create_empty_template( template_name, seclist_names=None, secarray_names=None ): """create an hoc template named template_name for an empty cell""" objref_str = "objref this, CellRef" newseclist_str = "" if seclist_names: for seclist_name in seclist_names: objref_str += ", %s" % seclist_name newseclist_str += ( " %s = new SectionList()\n" % seclist_name ) create_str = "" if secarray_names: create_str = "create " create_str += ", ".join( "%s[1]" % secarray_name for secarray_name in secarray_names ) create_str += "\n" template = """\ begintemplate %(template_name)s %(objref_str)s proc init() {\n%(newseclist_str)s forall delete_section() CellRef = this } gid = 0 proc destroy() {localobj nil CellRef = nil } %(create_str)s endtemplate %(template_name)s """ % dict( template_name=template_name, objref_str=objref_str, newseclist_str=newseclist_str, create_str=create_str, ) return template @staticmethod def create_empty_cell(name, sim, seclist_names=None, secarray_names=None): """Create an empty cell in Neuron""" # TODO minize hardcoded definition # E.g. sectionlist can be procedurally generated hoc_template = CellModel.create_empty_template( name, seclist_names, secarray_names ) sim.neuron.h(hoc_template) template_function = getattr(sim.neuron.h, name) return template_function() def instantiate(self, sim=None): """Instantiate model in simulator""" from LFPy import Cell from lfpykit import RecExtElectrode # TODO replace this with the real template name if not hasattr(sim.neuron.h, self.name): self.icell = self.create_empty_cell( self.name, sim=sim, seclist_names=self.seclist_names, secarray_names=self.secarray_names, ) else: self.icell = getattr(sim.neuron.h, self.name)() self.icell.gid = self.gid self.morphology.instantiate(sim=sim, icell=self.icell) self.lfpy_cell = Cell( morphology=sim.neuron.h.allsec(), dt=self.dt, v_init=self.v_init, pt3d=True, delete_sections=False, nsegs_method=None, ) self.lfpy_electrode = RecExtElectrode( self.lfpy_cell, probe=self.electrode ) if self.mechanisms is not None: for mechanism in self.mechanisms: mechanism.instantiate(sim=sim, icell=self.icell) if self.params is not None: for param in self.params.values(): param.instantiate(sim=sim, icell=self.icell, params=self.params) def destroy(self, sim=None): # pylint: disable=W0613 """Destroy instantiated model in simulator""" # Make sure the icell's destroy() method is called # without it a circular reference exists between CellRef and the object # this prevents the icells from being garbage collected, and # cell objects pile up in the simulator self.icell.destroy() # The line below is some M. Hines magic # DON'T remove it, because it will make sure garbage collection # is called on the icell object sim.neuron.h.Vector().size() self.icell = None self.morphology.destroy(sim=sim) for mechanism in self.mechanisms: mechanism.destroy(sim=sim) for param in self.params.values(): param.destroy(sim=sim) def check_nonfrozen_params(self, param_names): # pylint: disable=W0613 """Check if all nonfrozen params are set""" for param_name, param in self.params.items(): if not param.frozen: raise Exception( "CellModel: Nonfrozen param %s needs to be " "set before simulation" % param_name ) def create_hoc( self, param_values, ignored_globals=(), template="cell_template.jinja2", disable_banner=False, template_dir=None, ): """Create hoc code for this model""" to_unfreeze = [] for param in self.params.values(): if not param.frozen: param.freeze(param_values[param.name]) to_unfreeze.append(param.name) template_name = self.name morphology = os.path.basename(self.morphology.morphology_path) if self.morphology.do_replace_axon: replace_axon = self.morphology.replace_axon_hoc else: replace_axon = None ret = create_hoc.create_hoc( mechs=self.mechanisms, parameters=self.params.values(), morphology=morphology, ignored_globals=ignored_globals, replace_axon=replace_axon, template_name=template_name, template_filename=template, template_dir=template_dir, disable_banner=disable_banner, ) self.unfreeze(to_unfreeze) return ret def __str__(self): """Return string representation""" content = "%s:\n" % self.name content += " morphology:\n" if self.morphology is not None: content += " %s\n" % str(self.morphology) content += " mechanisms:\n" if self.mechanisms is not None: for mechanism in self.mechanisms: content += " %s\n" % mechanism content += " params:\n" if self.params is not None: for param in self.params.values(): content += " %s\n" % param return content ================================================ FILE: bluepyopt/ephys/morphologies.py ================================================ """Morphology classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import os import platform import logging import pathlib import bisect import numpy from bluepyopt.ephys.base import BaseEPhys from bluepyopt.ephys.serializer import DictMixin from bluepyopt.ephys.acc import arbor, ArbLabel logger = logging.getLogger(__name__) # TODO define an addressing scheme class Morphology(BaseEPhys): """Morphology class""" pass class NrnFileMorphology(Morphology, DictMixin): """Morphology loaded from a file""" SERIALIZED_FIELDS = ('morphology_path', 'do_replace_axon', 'do_set_nseg', 'replace_axon_hoc', 'nseg_frequency', 'morph_modifiers', 'morph_modifiers_hoc', 'morph_modifiers_kwargs') def __init__( self, morphology_path, do_replace_axon=False, do_set_nseg=True, comment='', replace_axon_hoc=None, axon_stub_length=60, axon_nseg_frequency=40, nseg_frequency=40, morph_modifiers=None, morph_modifiers_hoc=None, morph_modifiers_kwargs=None): """Constructor Args: morphology_path (str or Path): location of the file describing the morphology do_replace_axon (bool): Does the axon need to be replaced by an AIS stub with default function ? replace_axon_hoc (str): Translation in HOC language for the 'replace_axon' method. This code will 'only' be used when calling create_hoc on a cell model. While the model is run in python, replace_axon is used instead. Must include 'proc replace_axon(){ ... } If None,the default replace_axon is used axon_stub_length (float): Length of replacement axon axon_nseg_frequency (int): frequency of nseg, for axon nseg_frequency (float): frequency of nseg do_set_nseg (bool): if True, it will use nseg_frequency morph_modifiers (list): list of functions to modify the icell with (sim, icell) as arguments morph_modifiers_hoc (list): list of hoc strings corresponding to morph_modifiers morph_modifiers_kwargs (dict): kwargs for morph_modifiers functions """ name = os.path.basename(morphology_path) super(NrnFileMorphology, self).__init__(name=name, comment=comment) # TODO speed up loading of morphologies from files # Path to morphology if isinstance(morphology_path, pathlib.Path): morphology_path = str(morphology_path) self.morphology_path = morphology_path self.do_replace_axon = do_replace_axon self.axon_stub_length = axon_stub_length self.axon_nseg_frequency = axon_nseg_frequency self.do_set_nseg = do_set_nseg self.nseg_frequency = nseg_frequency self.morph_modifiers = morph_modifiers self.morph_modifiers_hoc = morph_modifiers_hoc self.morph_modifiers_kwargs = morph_modifiers_kwargs if self.morph_modifiers_kwargs is None: self.morph_modifiers_kwargs = {} if replace_axon_hoc is None: self.replace_axon_hoc = self.default_replace_axon_hoc else: self.replace_axon_hoc = replace_axon_hoc def __str__(self): """Return string representation""" return self.morphology_path def instantiate(self, sim=None, icell=None): """Load morphology""" logger.debug('Loading morphology %s', self.morphology_path) if not os.path.exists(self.morphology_path): raise IOError( 'Morphology not found at \'%s\'' % self.morphology_path) sim.neuron.h.load_file('stdrun.hoc') sim.neuron.h.load_file('import3d.hoc') extension = self.morphology_path.split('.')[-1] if extension.lower() == 'swc': imorphology = sim.neuron.h.Import3d_SWC_read() elif extension.lower() == 'asc': imorphology = sim.neuron.h.Import3d_Neurolucida3() else: raise ValueError("Unknown filetype: %s" % extension) # TODO this is to get rid of stdout print of neuron # probably should be more intelligent here, and filter out the # lines we don't want imorphology.quiet = 1 if platform.system() == 'Windows': sim.neuron.h.hoc_stdout('NUL') else: sim.neuron.h.hoc_stdout('/dev/null') imorphology.input(str(self.morphology_path)) sim.neuron.h.hoc_stdout() morphology_importer = sim.neuron.h.Import3d_GUI(imorphology, 0) morphology_importer.instantiate(icell) # TODO Set nseg should be called after all the parameters have been # set # (in case e.g. Ra was changed) if self.do_set_nseg: self.set_nseg(icell) if self.do_replace_axon: self.replace_axon(sim=sim, icell=icell, axon_stub_length=self.axon_stub_length, axon_nseg_frequency=self.axon_nseg_frequency) if self.morph_modifiers is not None: for morph_modifier in self.morph_modifiers: morph_modifier(sim=sim, icell=icell, **self.morph_modifiers_kwargs) def destroy(self, sim=None): """Destroy morphology instantiation""" pass def set_nseg(self, icell): """Set the nseg of every section""" for section in icell.all: section.nseg = 1 + 2 * int(section.L / self.nseg_frequency) @staticmethod def replace_axon(sim=None, icell=None, axon_stub_length=60, axon_nseg_frequency=40): """Replace axon""" nsec = len([sec for sec in icell.axonal]) if nsec == 0: ais_diams = [1, 1] elif nsec == 1: ais_diams = [icell.axon[0].diam, icell.axon[0].diam] else: ais_diams = [icell.axon[0].diam, icell.axon[0].diam] # Define origin of distance function sim.neuron.h.distance(0, 0.5, sec=icell.soma[0]) for section in icell.axonal: # If distance to soma is larger than # axon_stub_length, store diameter if sim.neuron.h.distance(1, 0.5, sec=section) \ > axon_stub_length: ais_diams[1] = section.diam break for section in icell.axonal: sim.neuron.h.delete_section(sec=section) # Create new axon array sim.neuron.h.execute('create axon[2]', icell) for index, section in enumerate(icell.axon): section.L = axon_stub_length / 2 section.nseg = 1 + 2 * int(section.L / axon_nseg_frequency) section.diam = ais_diams[index] icell.axonal.append(sec=section) icell.all.append(sec=section) icell.axon[0].connect(icell.soma[0], 1.0, 0.0) icell.axon[1].connect(icell.axon[0], 1.0, 0.0) logger.debug(f"Replace axon with AIS, {axon_stub_length=}") default_replace_axon_hoc = \ ''' proc replace_axon(){ local nSec, D1, D2 // preserve the number of original axonal sections nSec = sec_count(axonal) // Try to grab info from original axon if(nSec == 0) { //No axon section present D1 = D2 = 1 } else if(nSec == 1) { axon[0] D1 = D2 = diam } else { axon[0] D1 = D2 = diam soma distance() //to calculate distance from soma forsec axonal{ //if section is longer than 60um then store diam and exit from loop if(distance(0.5) > 60){ D2 = diam break } } } // get rid of the old axon forsec axonal{ delete_section() } create axon[2] axon[0] { L = 30 diam = D1 nseg = 1 + 2*int(L/40) all.append() axonal.append() } axon[1] { L = 30 diam = D2 nseg = 1 + 2*int(L/40) all.append() axonal.append() } nSecAxonal = 2 soma[0] connect axon[0](0), 1 axon[0] connect axon[1](0), 1 } ''' class ArbFileMorphology(Morphology, DictMixin): """Arbor morphology utilities""" # Arbor morphology tags tags = dict( soma=1, axon=2, dend=3, apic=4, myelin=5 ) # Correspondence of BluePyOpt seclists to Arbor region labels # (renaming locations according to SWC convention: using # 'dend' for basal dendrite, 'apic' for apical dendrite) region_labels = dict( all=ArbLabel( type='region', name='all', s_expr='(all)'), somatic=ArbLabel( type='region', name='soma', s_expr='(tag %i)' % tags['soma']), axonal=ArbLabel( type='region', name='axon', s_expr='(tag %i)' % tags['axon']), basal=ArbLabel( type='region', name='dend', s_expr='(tag %i)' % tags['dend']), apical=ArbLabel( type='region', name='apic', s_expr='(tag %i)' % tags['apic']), myelinated=ArbLabel( type='region', name='myelin', s_expr='(tag %i)' % tags['myelin']), ) @staticmethod def load(morpho_filename, replace_axon): '''Load morphology and optionally perform axon replacement Args: morpho_filename (str): Path to file with original morphology. replace_axon (): Path to/ACC string for morphology to replace axon with (if not None). ''' morpho_suffix = pathlib.Path(morpho_filename).suffix if morpho_suffix == '.acc': morpho = arbor.load_component(morpho_filename).component elif morpho_suffix == '.swc': morpho = arbor.load_swc_arbor(morpho_filename) # turn loaded_morphology into morphology type morpho = morpho.morphology elif morpho_suffix == '.asc': morpho = arbor.load_asc(morpho_filename).morphology else: raise RuntimeError( 'Unsupported morphology %s' % morpho_filename + ' (only .swc and .asc supported)') if replace_axon is not None: replacement = arbor.load_component(replace_axon).component morpho = ArbFileMorphology.replace_axon(morpho, replacement) return morpho @staticmethod def extract_nrn_seclists(icell, seclists): '''Extract section lists from an instantiated cell (axon replacement) Args: icell (): Instantiated cell model in the NEURON simulator. seclists (): List of section lists to extract (typically ['axon'] or ['axon', 'myelin']). ''' replace_axon = arbor.segment_tree() nrn_seg_to_dist = dict() nrn_seg_to_arb_seg = dict() for sec in seclists: for section in getattr(icell, sec): if replace_axon.size == 0: # root arb_parent_seg = arbor.mnpos else: parent_seg = section.parentseg() parent_sec = parent_seg.sec.name() parent_x = parent_seg.x parent_seg_id = bisect.bisect_left( nrn_seg_to_dist[parent_sec], parent_x) arb_parent_seg = \ nrn_seg_to_arb_seg[parent_sec][parent_seg_id] pts3d = section.psection()['morphology']['pts3d'] if len(pts3d) == 0: # stylized geometry, must use sim.neuron.h.define_shape() raise ValueError('Before exporting to ACC, embed' ' stylized geometry in 3d' ' by instantiating morphology with' ' cell_model.instantiate_morphology_3d.') pts3d = numpy.array(pts3d) dist_x = numpy.cumsum( numpy.linalg.norm( pts3d[1:, :3] - pts3d[:-1, :3], axis=1)) /\ section.psection()['morphology']['L'] relative_length_err = abs(1. - dist_x[-1]) if relative_length_err > 1e-4: logger.warn('pts3d length does not add up to' ' section length, relative error = %s' % relative_length_err) if relative_length_err > 1e-2: raise ValueError('pts3d length inconsistent' ' with section length, relative' ' error = %s' % relative_length_err) dist_x[-1] = 1. nrn_seg_to_dist[section.name()] = dist_x arb_seg_ids = [] for i in range(1, len(pts3d)): prox = pts3d[i - 1] dist = pts3d[i] arb_parent_seg = replace_axon.append( arb_parent_seg, arbor.mpoint(*prox[:3], 0.5 * prox[3]), arbor.mpoint(*dist[:3], 0.5 * dist[3]), ArbFileMorphology.tags[sec]) arb_seg_ids.append(arb_parent_seg) # dist, arb_seg_id pairs nrn_seg_to_arb_seg[section.name()] = arb_seg_ids replace_axon = arbor.morphology(replace_axon) return replace_axon @staticmethod def replace_axon(morphology, replacement=None): '''return a morphology with the axon replaced by another morphology Args: morphology (arbor.morphology): The original Arbor morphology replacement (): An Arbor morphology to replace the axon with ''' # Check if tag_roots is available if not hasattr(arbor.segment_tree, 'tag_roots'): raise NotImplementedError( "Need a newer version of Arbor for axon replacement.") # Arbor tags axon_tag = ArbFileMorphology.tags['axon'] # prune morphology at axon root st = morphology.to_segment_tree() axon_roots = st.tag_roots(axon_tag) if len(axon_roots) > 1: raise ValueError("Axon replacement is only supported for " "morphologies with a single axon root.") elif len(axon_roots) == 1: axon_root = axon_roots[0] logger.debug('Axon replacement: splitting segment tree' ' at segment %d.', axon_root) pruned_st, axon_st = st.split_at(axon_root) axon_parent = st.parents[axon_root] else: pruned_st = st axon_parent = arbor.mnpos # join pruned segment tree and replacement at axon parent axon_replacement_st = replacement.to_segment_tree() logger.debug('Axon replacement: joining replacement onto' 'pruned tree at parent segment %d.', axon_parent) joined_st = pruned_st.join_at( axon_parent, axon_replacement_st) return arbor.morphology(joined_st) ================================================ FILE: bluepyopt/ephys/objectives.py ================================================ """Objective classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import bluepyopt class EFeatureObjective(bluepyopt.objectives.Objective): """EPhys feature objective""" def __init__(self, name, features=None): """Constructor Args: name (str): name of this object features (list of eFeatures): features used in the Objective """ super(EFeatureObjective, self).__init__(name) self.name = name self.features = features def calculate_feature_scores(self, responses): """Calculate the scores for the individual features""" scores = [] for feature in self.features: scores.append(feature.calculate_score(responses)) return scores def calculate_feature_values(self, responses): """Calculate the value of an individual features""" values = [] for feature in self.features: values.append(feature.calculate_feature(responses)) return values class SingletonObjective(EFeatureObjective): """Single EPhys feature""" def __init__(self, name, feature): """Constructor Args: name (str): name of this object features (EFeature): single eFeature inside this objective """ super(SingletonObjective, self).__init__(name, [feature]) def calculate_score(self, responses): """Objective score""" return self.calculate_feature_scores(responses)[0] def calculate_value(self, responses): """Objective value""" return self.calculate_feature_values(responses)[0] def __str__(self): """String representation""" return '( %s )' % self.features[0] class SingletonWeightObjective(SingletonObjective): """Single EPhys feature""" def __init__(self, name, feature, weight): """Constructor Args: name (str): name of this object features (EFeature): single eFeature inside this objective weight (float): weight to scale to the efeature with """ super(SingletonWeightObjective, self).__init__(name, feature) self.weight = weight def calculate_score(self, responses): """Objective score""" return self.calculate_feature_scores(responses)[0] * self.weight def __str__(self): """String representation""" return '( %s ), weight:%f' % (self.features[0], self.weight) class MaxObjective(EFeatureObjective): """Max of list of EPhys feature""" def calculate_score(self, responses): """Objective score""" return max(self.calculate_feature_scores(responses)) class WeightedSumObjective(EFeatureObjective): """Weighted sum of list of eFeatures""" def __init__(self, name, features, weights): """Constructor Args: name (str): name of this object features (list of EFeatures): eFeatures in the objective weights (list of float): weights of the eFeatures """ super(WeightedSumObjective, self).__init__(name, features) if len(weights) != len(features): raise Exception( 'WeightedSumObjective: number of weights must be equal to ' 'number of features') self.weights = weights def calculate_score(self, responses): """Objective score""" score = 0.0 feature_scores = self.calculate_feature_scores(responses) for feature_score, weight in zip(feature_scores, self.weights): score += weight * feature_score return score ================================================ FILE: bluepyopt/ephys/objectivescalculators.py ================================================ """Score calculator classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ class ObjectivesCalculator(object): """Score calculator""" def __init__( self, objectives=None): """Constructor Args: objectives (list of Objective): objectives over which to calculate """ self.objectives = objectives def calculate_scores(self, responses): """Calculator the score for every objective""" return {objective.name: objective.calculate_score(responses) for objective in self.objectives} def calculate_values(self, responses): """Calculator the value of each objective""" return {objective.name: objective.calculate_value(responses) for objective in self.objectives} def __str__(self): return 'objectives:\n %s' % '\n '.join( [str(obj) for obj in self.objectives]) \ if self.objectives is not None else 'objectives:\n' ================================================ FILE: bluepyopt/ephys/parameters.py ================================================ """Parameter classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import logging import bluepyopt from bluepyopt.ephys.serializer import DictMixin from . import parameterscalers logger = logging.getLogger(__name__) # TODO location and stimulus parameters should also be optimisable class NrnParameter(bluepyopt.parameters.Parameter): """Abstract Parameter class for Neuron object parameters""" def __init__( self, name, value=None, frozen=False, bounds=None, param_dependencies=None): """Constructor""" super(NrnParameter, self).__init__( name, value=value, frozen=frozen, bounds=bounds, param_dependencies=param_dependencies) def instantiate(self, sim=None, icell=None, params=None): """Instantiate the parameter in the simulator""" pass def destroy(self, sim=None): """Remove parameter from the simulator""" pass class MetaParameter(NrnParameter): """Parameter class that controls attributes of other objects""" def __init__( self, name, obj=None, attr_name=None, value=None, frozen=False, bounds=None, param_dependencies=None): """Constructor""" super(MetaParameter, self).__init__( name, value=value, frozen=frozen, bounds=bounds, param_dependencies=param_dependencies) self.obj = obj self.attr_name = attr_name setattr(self.obj, self.attr_name, value) @bluepyopt.parameters.Parameter.value.setter def value(self, _value): """Setter for value""" # Call setter of superclass super(MetaParameter, self.__class__).value.fset(self, _value) setattr(self.obj, self.attr_name, _value) def __str__(self): """String representation""" return '%s: %s.%s = %s' % (self.name, self.obj.name, self.attr_name, self.value) class NrnMetaListEqualParameter(bluepyopt.parameters.MetaListEqualParameter): """Nrn version of MetaListEqualParameter, implements instantiate""" def instantiate(self, sim=None, icell=None, params=None): """Instantiate""" for sub_parameter in self.sub_parameters: sub_parameter.instantiate(sim=sim, icell=icell) logger.debug('Set %s to %s', self.name, str(self.value)) def destroy(self, sim=None): """Remove parameter from the simulator""" for sub_parameter in self.sub_parameters: sub_parameter.destroy(sim=sim) class NrnGlobalParameter(NrnParameter, DictMixin): """Parameter set in the global namespace of neuron""" SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name',) def __init__( self, name, value=None, frozen=False, bounds=None, param_name=None, param_dependencies=None): """Constructor Args: name (str): name of this object value (float): Value for the parameter, required if Frozen=True frozen (bool): Whether the parameter can be varied, or its values is permently set bounds (indexable): two elements; the lower and upper bounds (Optional) param_name (str): name used within NEURON """ super(NrnGlobalParameter, self).__init__( name, value=value, frozen=frozen, bounds=bounds, param_dependencies=param_dependencies) self.param_name = param_name def instantiate(self, sim=None, icell=None, params=None): """Instantiate""" setattr(sim.neuron.h, self.param_name, self.value) logger.debug('Set %s to %s', self.param_name, str(self.value)) def __str__(self): """String representation""" return '%s: %s = %s' % (self.name, self.param_name, self.value if self.frozen else self.bounds) class NrnSectionParameter(NrnParameter, DictMixin): """Parameter of a section""" SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name', 'value_scaler', 'locations', 'param_dependencies') def __init__( self, name, value=None, frozen=False, bounds=None, param_name=None, value_scaler=None, locations=None, param_dependencies=None): """Constructor Args: name (str): name of the Parameter value (float): Value for the parameter, required if Frozen=True frozen (bool): Whether the parameter can be varied, or its values is permently set bounds (indexable): two elements; the lower and upper bounds (Optional) param_name (str): name used within NEURON value_scaler (float): value used to scale the parameter value locations (list of ephys.locations.Location): locations on which to instantiate the parameter param_dependencies (list): dependencies needed to instantiate the parameter """ super(NrnSectionParameter, self).__init__( name, value=value, frozen=frozen, bounds=bounds, param_dependencies=param_dependencies) self.locations = locations self.param_name = param_name # TODO value_scaler has to be made more general self.value_scaler = value_scaler # TODO add a default value for a scaler that is picklable if self.value_scaler is None: self.value_scaler = parameterscalers.NrnSegmentLinearScaler() self.value_scale_func = self.value_scaler.scale def instantiate(self, sim=None, icell=None, params=None): """Instantiate""" if self.value is None: raise Exception( 'NrnSectionParameter: impossible to instantiate parameter "%s"' ' without value' % self.name) _values = {"value": self.value} for param in self.param_dependencies: _values[param] = params[param].value for location in self.locations: iseclist = location.instantiate(sim=sim, icell=icell) for section in iseclist: setattr(section, self.param_name, self.value_scale_func(_values, section, sim=sim)) logger.debug( 'Set %s in %s to %s', self.param_name, location, self.value) def __str__(self): """String representation""" return '%s: %s %s = %s' % (self.name, [str(location) for location in self.locations], self.param_name, self.value if self.frozen else self.bounds) class NrnPointProcessParameter(NrnParameter, DictMixin): """Parameter of a section""" SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name', 'value_scaler', 'locations', 'param_name', 'param_dependencies') def __init__( self, name, value=None, frozen=False, bounds=None, locations=None, param_name=None, param_dependencies=None): """Constructor Args: name (str): name of the Parameter value (float): Value for the parameter, required if Frozen=True frozen (bool): Whether the parameter can be varied, or its values is permently set bounds (indexable): two elements; the lower and upper bounds (Optional) locations: an iterator of the point process locations you want to set the parameters of param_name (str): name of parameter used within the point process param_dependencies (list): dependencies needed to intantiate the parameter """ super(NrnPointProcessParameter, self).__init__( name, value=value, frozen=frozen, bounds=bounds, param_dependencies=param_dependencies) self.locations = locations self.param_name = param_name def instantiate(self, sim=None, icell=None, params=None): """Instantiate""" if self.value is None: raise Exception( 'NrnSectionParameter: impossible to instantiate parameter "%s"' ' without value' % self.name) for location in self.locations: for pprocess in location.instantiate(sim=sim, icell=icell): setattr(pprocess, self.param_name, self.value) logger.debug( 'Set %s to %s for point process', self.param_name, self.value) def __str__(self): """String representation""" return '%s: %s = %s' % (self.name, self.param_name, self.value if self.frozen else self.bounds) # TODO change mech_suffix and mech_param to param_name, and maybe add # NrnRangeMechParameter class NrnRangeParameter(NrnParameter, DictMixin): """Parameter that has a range over a section""" SERIALIZED_FIELDS = ('name', 'value', 'frozen', 'bounds', 'param_name', 'value_scaler', 'locations', 'param_dependencies') def __init__( self, name, value=None, frozen=False, bounds=None, param_name=None, value_scaler=None, locations=None, param_dependencies=None): """Constructor Args: name (str): name of the Parameter value (float): Value for the parameter, required if Frozen=True frozen (bool): Whether the parameter can be varied, or its values is permently set bounds (indexable): two elements; the lower and upper bounds (Optional) param_name (str): name used within NEURON value_scaler (float): value used to scale the parameter value locations (list of ephys.locations.Location): locations on which to instantiate the parameter param_dependencies (list): dependencies needed to intantiate the parameter """ super(NrnRangeParameter, self).__init__( name, value=value, frozen=frozen, bounds=bounds, param_dependencies=param_dependencies) self.locations = locations self.param_name = param_name # TODO value_scaler has to be made more general self.value_scaler = value_scaler if self.value_scaler is None: self.value_scaler = parameterscalers.NrnSegmentLinearScaler() self.value_scale_func = self.value_scaler.scale def instantiate(self, sim=None, icell=None, params=None): """Instantiate""" if self.value is None: raise Exception( 'NrnRangeParameter: impossible to instantiate parameter "%s" ' 'without value' % self.name) _values = {"value": self.value} for param in self.param_dependencies: _values[param] = params[param].value for location in self.locations: for isection in location.instantiate(sim=sim, icell=icell): for seg in isection: setattr(seg, '%s' % self.param_name, self.value_scale_func(_values, seg, sim=sim)) logger.debug( 'Set %s in %s to %s with scaler %s', self.param_name, [str(location) for location in self.locations], self.value, self.value_scaler) def __str__(self): """String representation""" return '%s: %s %s = %s' % (self.name, [str(location) for location in self.locations], self.param_name, self.value if self.frozen else self.bounds) ================================================ FILE: bluepyopt/ephys/parameterscalers/__init__.py ================================================ from .parameterscalers import * ================================================ FILE: bluepyopt/ephys/parameterscalers/acc_iexpr.py ================================================ """Translate spatially varying parameter-scaler expressions to Arbor iexprs""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import ast # Utilities to generate Arbor S-expressions for morphologically # inhomogeneous parameter scalers class ArbIExprValueEliminator(ast.NodeTransformer): """Divide expression (symbolically) by named variable and replace non-linear occurrences by numeric value""" def __init__(self, variable_name, value): self._stack = [] self._nodes_to_remove = [] self._remove_count = 0 self._variable_name = variable_name self._value = value def generic_visit(self, node): self._stack.append(node) # keep track of visitor stack node = super(ArbIExprValueEliminator, self).generic_visit(node) nodes_removed = [] for node_to_remove in self._nodes_to_remove: if node_to_remove in ast.iter_child_nodes(node): # replace this node and remove child node = node.left if node.right == node_to_remove \ else node.right nodes_removed.append(node_to_remove) self._remove_count += 1 if self._remove_count > 1: raise ValueError( 'Unsupported inhomogeneous expression in Arbor' ' - must be linear in the parameter value.') self._nodes_to_remove = [n for n in self._nodes_to_remove if n not in nodes_removed] self._stack.pop() # top-level expression node that is non-linear in the value if len(self._stack) == 2 and self._remove_count == 0: return ast.BinOp(left=node, op=ast.Div(), right=ast.Constant(value=self._value)) else: return node def _is_linear(self, node): """Check if expression is linear in this node""" prev_frame = node for next_frame in reversed(self._stack[2:]): if not isinstance(next_frame, ast.BinOp) or \ not (isinstance(next_frame.op, ast.Mult) or isinstance(next_frame.op, ast.Div) and next_frame.left == prev_frame): return False prev_frame = next_frame return True def visit_Name(self, node): if node.id == self._variable_name: # remove if expression is linear in value, else replace by constant if self._is_linear(node) and \ self._remove_count + len(self._nodes_to_remove) == 0: self._nodes_to_remove.append(node) return node else: return ast.Constant(value=self._value) else: return node class ArbIExprEmitter(ast.NodeVisitor): """Emit Arbor S-expression from parse tree replacing named variables by specified S-expression""" _iexpr_symbols = { ast.Constant: 'scalar', ast.Num: 'scalar', ast.Add: 'add', ast.Sub: 'sub', ast.Mult: 'mul', ast.Div: 'div', 'math.pi': 'pi', 'math.exp': 'exp', 'math.log': 'log', } def __init__(self, var_name_to_sexpr, constant_formatter): self._base_stack = [] self._emitted = [] self._var_name_to_sexpr = var_name_to_sexpr self._constant_formatter = constant_formatter def emit(self): return ' '.join(self._emitted) def _emit(self, expr): return self._emitted.append(expr) def generic_visit(self, node): self._base_stack.append(node) # fail if more than base stack if len(self._base_stack) > 2: raise ValueError('Arbor inhomogeneous expression generation' ' failed: Unsupported node %s' % repr(node)) ret = super(ArbIExprEmitter, self).generic_visit(node) self._base_stack.pop() return ret def visit_Constant(self, node): self._emit( '(%s %s)' % (self._iexpr_symbols[type(node)], self._constant_formatter(node.value)) ) def visit_Num(self, node): self._emit( '(%s %s)' % (self._iexpr_symbols[type(node)], self._constant_formatter(node.n)) ) def visit_Attribute(self, node): if node.value.id == 'math' and node.attr == 'pi': self._emit( '(%s)' % self._iexpr_symbols['math.pi'] ) else: raise ValueError('Unsupported attribute %s in Arbor' % node) def visit_UnaryOp(self, node): if isinstance(node.op, ast.UAdd): self.visit(node.value) elif isinstance(node.op, ast.USub): if isinstance(node.operand, ast.Constant): self.visit(ast.Constant(-node.operand.value)) else: self.visit(ast.BinOp(left=ast.Constant(-1), op=ast.Mult(), right=node.operand)) else: raise ValueError('Unsupported unary operation %s in Arbor' % node.op) def visit_BinOp(self, node): op_type = type(node.op) if op_type not in self._iexpr_symbols: raise ValueError('Unsupported binary operation %s in Arbor' % op_type) self._emit( '(' + self._iexpr_symbols[type(node.op)] ) self.visit(node.left), self.visit(node.right) self._emit( ')' ) def visit_Call(self, node): func = node.func if isinstance(func, ast.Attribute): if isinstance(func.value, ast.Name): if func.value.id == 'math': if len(node.args) > 1: raise ValueError('Arbor iexpr generation failed -' ' math functions can only have a' ' single argument.') func_symbol = func.value.id + '.' + func.attr if func_symbol not in self._iexpr_symbols: raise ValueError('Arbor iexpr generation failed -' ' unknown symbol %s.' % func_symbol) self._emit( '(' + self._iexpr_symbols[func_symbol] ) self.visit(node.args[0]) self._emit( ')' ) else: raise ValueError('Arbor iexpr generation failed -' ' unsupported module %s.' % func.value.id) else: raise ValueError('Arbor iexpr generation failed -' ' unsupported attribute %s.' % func.value.attr) else: raise ValueError('Arbor iexpr generation failed -' ' unsupported function %s.' % func.id) def visit_Name(self, node): if node.id in self._var_name_to_sexpr: self._emit( self._var_name_to_sexpr[node.id] ) else: raise ValueError('Arb iexpr generation failed:' ' No valid substitution for %s.' % node.id) def generate_acc_scale_iexpr(iexpr, variables, constant_formatter): """Translate parameter-scaler python arithmetic expression to Arbor iexpr Args: iexpr (str): Python arithmetic expression (instantiated distribution) variables (): Mapping of variable name (referenced in the iexpr argument) to Arbor iexpr representation Returns: The Arbor iexpr corresponding to the python arithmetic expression with the variables substituted by their value. """ if 'value' not in variables: raise ValueError('Arbor iexpr generation failed for %s:' % iexpr + ' \'value\' not in variables dict: %s' % variables) emit_dict = {'_arb_parse_iexpr_' + k: v for k, v in variables.items()} scaler_expr = iexpr.format( **{k: '_arb_parse_iexpr_' + k for k in variables}) # Parse expression scaler_ast = ast.parse(scaler_expr) # Turn into scaling expression, replacing non-linear occurrences of value value_eliminator = ArbIExprValueEliminator( variable_name='_arb_parse_iexpr_value', value=variables['value']) scaler_ast = value_eliminator.visit(scaler_ast) # Generate S-expression iexpr_emitter = ArbIExprEmitter( var_name_to_sexpr=emit_dict, constant_formatter=constant_formatter) iexpr_emitter.visit(scaler_ast) return iexpr_emitter.emit() ================================================ FILE: bluepyopt/ephys/parameterscalers/parameterscalers.py ================================================ """Parameter scaler classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import string from bluepyopt.ephys.base import BaseEPhys from bluepyopt.ephys.parameterscalers.acc_iexpr import generate_acc_scale_iexpr from bluepyopt.ephys.serializer import DictMixin from bluepyopt.ephys.morphologies import ArbFileMorphology FLOAT_FORMAT = '%.17g' def format_float(value): """Return formatted float string""" return FLOAT_FORMAT % value class MissingFormatDict(dict): """Extend dict for string formatting with missing values""" def __missing__(self, key): # pylint: disable=R0201 """Return string with format key for missing keys""" return '{' + key + '}' class ParameterScaler(BaseEPhys): """Parameter scalers""" pass # TODO get rid of the 'segment' here class NrnSegmentLinearScaler(ParameterScaler, DictMixin): """Linear scaler""" SERIALIZED_FIELDS = ('name', 'comment', 'multiplier', 'offset', ) def __init__( self, name=None, multiplier=1.0, offset=0.0, comment=''): """Constructor Args: name (str): name of this object multiplier (float): slope of the linear scaler offset (float): intercept of the linear scaler """ super(NrnSegmentLinearScaler, self).__init__(name, comment) self.multiplier = multiplier self.offset = offset def scale(self, values, segment=None, sim=None): # pylint: disable=W0613 """Scale a value based on a segment""" if isinstance(values, dict): value = values["value"] else: value = values return self.multiplier * value + self.offset def __str__(self): """String representation""" return '%s * value + %s' % (self.multiplier, self.offset) class NrnSegmentSectionDistanceScaler(ParameterScaler, DictMixin): """Scaler based on distance from soma""" SERIALIZED_FIELDS = ('name', 'comment', 'distribution', "distribution", "dist_param_names", "ref_sec", "ref_location",) def __init__( self, name=None, distribution=None, comment='', dist_param_names=None, ref_section='soma[0]', ref_location=0,): """Constructor Args: name (str): name of this object distribution (str): distribution of parameter dependent on distance from soma. string can contain `distance` and/or `value` as placeholders for the distance to the soma and parameter value respectivily dist_param_names (list): list of names of parameters that parametrise the distribution. These names will become attributes of this object. The distribution string should contain these names, and they will be replaced by values of the corresponding attributes ref_section (str): string with name of reference section to compute distance (e.g. "soma[0]", "dend[2]") ref_location (float): location along the soma used as origin from which to compute the distances. Expressed as a fraction (between 0.0 and 1.0). """ super(NrnSegmentSectionDistanceScaler, self).__init__(name, comment) self.distribution = distribution self.dist_param_names = dist_param_names self.ref_location = ref_location self.ref_section = ref_section if not (0.0 <= self.ref_location <= 1.0): raise ValueError("ref_location must be between 0 and 1.") if self.dist_param_names is not None: for dist_param_name in self.dist_param_names: if dist_param_name not in self.distribution: raise ValueError( 'NrnSegmentSectionDistanceScaler: "{%s}" ' 'missing from distribution string "%s"' % (dist_param_name, distribution)) setattr(self, dist_param_name, None) @property def inst_distribution(self): """The instantiated distribution""" dist_dict = MissingFormatDict() if self.dist_param_names is not None: for dist_param_name in self.dist_param_names: dist_param_value = getattr(self, dist_param_name) if dist_param_value is None: raise ValueError("NrnSegmentSomaDistanceScaler: %s " "was uninitialised" % dist_param_name) dist_dict[dist_param_name] = dist_param_value # Use this special formatting to bypass missing keys return string.Formatter().vformat(self.distribution, (), dist_dict) def scale_dict(self, values, distance): """Create scale dictionary""" scale_dict = {} if isinstance(values, dict): for k, v in values.items(): scale_dict[k] = format_float(v) else: scale_dict["value"] = format_float(values) scale_dict["distance"] = format_float(distance) return scale_dict def eval_dist(self, values, distance): """Create the final dist string""" scale_dict = self.scale_dict(values, distance) return self.inst_distribution.format(**scale_dict) def scale(self, values, segment, sim=None): """Scale a value based on a segment""" # find section target_sec = None for sec in segment.sec.wholetree(): if "." in sec.name(): # deal with templates sec_name = sec.name().split(".")[1] else: sec_name = sec.name() if self.ref_section in sec_name: target_sec = sec break if target_sec is None: raise Exception(f"Could not find section {self.ref_section} " f"in section list") # Initialise origin sim.neuron.h.distance(0, self.ref_location, sec=target_sec) distance = sim.neuron.h.distance(1, segment.x, sec=segment.sec) # Find something to generalise this import math # pylint:disable=W0611 #NOQA # This eval is unsafe (but is it ever dangerous ?) # pylint: disable=W0123 return eval(self.eval_dist(values, distance)) def acc_scale_iexpr(self, value, constant_formatter=format_float): """Generate Arbor scale iexpr for a given value""" raise ValueError( "Parameter scaling based on general Neuron segment/section" " distances not supported in Arbor.") def __str__(self): """String representation""" return self.distribution class NrnSegmentSomaDistanceScaler(NrnSegmentSectionDistanceScaler, ParameterScaler, DictMixin): """Scaler based on distance from soma""" SERIALIZED_FIELDS = ('name', 'comment', 'distribution', ) def __init__( self, name=None, distribution=None, comment='', dist_param_names=None, soma_ref_location=0.5): """Constructor Args: name (str): name of this object distribution (str): distribution of parameter dependent on distance from soma. string can contain `distance` and/or `value` as placeholders for the distance to the soma and parameter value respectivily dist_param_names (list): list of names of parameters that parametrise the distribution. These names will become attributes of this object. The distribution string should contain these names, and they will be replaced by values of the corresponding attributes soma_ref_location (float): location along the soma used as origin from which to compute the distances. Expressed as a fraction (between 0.0 and 1.0). """ super(NrnSegmentSomaDistanceScaler, self).__init__( name, distribution, comment, dist_param_names, ref_section='soma[0]', ref_location=soma_ref_location) def acc_scale_iexpr(self, value, constant_formatter=format_float): """Generate Arbor scale iexpr for a given value""" iexpr = self.inst_distribution variables = dict( value=value, distance='(distance %s)' % # could be a ctor param if required ArbFileMorphology.region_labels['somatic'].ref ) return generate_acc_scale_iexpr(iexpr, variables, constant_formatter) class NrnSegmentSomaDistanceStepScaler(NrnSegmentSomaDistanceScaler, ParameterScaler, DictMixin): """Scaler based on distance from soma with a step function""" SERIALIZED_FIELDS = ('name', 'comment', 'distribution', ) def __init__( self, name=None, distribution=None, comment='', dist_param_names=None, soma_ref_location=0.5, step_begin=None, step_end=None): """Constructor Args: name (str): name of this object distribution (str): distribution of parameter dependent on distance from soma. string can contain `distance` and/or `value` as placeholders for the distance to the soma and parameter value respectively. It can also contain step_begin and step_end. dist_param_names (list): list of names of parameters that parametrise the distribution. These names will become attributes of this object. The distribution string should contain these names, and they will be replaced by values of the corresponding attributes soma_ref_location (float): location along the soma used as origin from which to compute the distances. Expressed as a fraction (between 0.0 and 1.0). step_begin (float): distance at which the step begins step_end (float): distance at which the step ends """ super(NrnSegmentSomaDistanceStepScaler, self).__init__( name, distribution, comment, dist_param_names, soma_ref_location=soma_ref_location) self.step_begin = step_begin self.step_end = step_end def scale_dict(self, values, distance): scale_dict = super().scale_dict(values, distance) scale_dict["step_begin"] = self.step_begin scale_dict["step_end"] = self.step_end return scale_dict ================================================ FILE: bluepyopt/ephys/protocols.py ================================================ """Protocol classes""" from .recordings import LFPRecording from .simulators import LFPySimulator from .stimuli import LFPStimulus """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import os import sys import collections import tempfile # TODO: maybe find a better name ? -> sweep ? import logging logger = logging.getLogger(__name__) from . import models from . import locations from . import simulators from . import stimuli from .responses import TimeVoltageResponse from .acc import arbor from . import create_acc class Protocol(object): """Class representing a protocol (stimulus and recording).""" def __init__(self, name=None): """Constructor Args: name (str): name of the feature """ self.name = name class SequenceProtocol(Protocol): """A protocol consisting of a sequence of other protocols""" def __init__(self, name=None, protocols=None): """Constructor Args: name (str): name of this object protocols (list of Protocols): subprotocols this protocol consists of """ super(SequenceProtocol, self).__init__(name) self.protocols = protocols def run( self, cell_model, param_values, sim=None, isolate=None, timeout=None): """Instantiate protocol""" responses = collections.OrderedDict({}) for protocol in self.protocols: # Try/except added for backward compatibility try: response = protocol.run( cell_model=cell_model, param_values=param_values, sim=sim, isolate=isolate, timeout=timeout) except TypeError as e: if "unexpected keyword" in str(e): response = protocol.run( cell_model=cell_model, param_values=param_values, sim=sim, isolate=isolate) else: raise key_intersect = set( response.keys()).intersection(set(responses.keys())) if len(key_intersect) != 0: raise Exception( 'SequenceProtocol: one of the protocols (%s) is trying to ' 'add already existing keys to the response: %s' % (protocol.name, key_intersect)) responses.update(response) return responses def subprotocols(self): """Return subprotocols""" subprotocols = collections.OrderedDict({self.name: self}) for protocol in self.protocols: subprotocols.update(protocol.subprotocols()) return subprotocols def __str__(self): """String representation""" content = 'Sequence protocol %s:\n' % self.name content += '%d subprotocols:\n' % len(self.protocols) for protocol in self.protocols: content += '%s\n' % str(protocol) return content class SweepProtocol(Protocol): """Sweep protocol""" def __init__( self, name=None, stimuli=None, recordings=None, cvode_active=None, deterministic=False): """Constructor Args: name (str): name of this object stimuli (list of Stimuli): Stimulus objects used in the protocol recordings (list of Recordings): Recording objects used in the protocol cvode_active (bool): whether to use variable time step deterministic (bool): whether to force all mechanism to be deterministic """ super(SweepProtocol, self).__init__(name) self.stimuli = stimuli self.recordings = recordings self.cvode_active = cvode_active self.deterministic = deterministic @property def total_duration(self): """Total duration""" return max([stimulus.total_duration for stimulus in self.stimuli]) def subprotocols(self): """Return subprotocols""" return collections.OrderedDict({self.name: self}) def adjust_stochasticity(func): """Decorator method to adjust the stochasticity of the mechanisms""" def inner(self, cell_model, param_values, **kwargs): """Inner function""" previous_stoch_state = [] if self.deterministic: for mech in cell_model.mechanisms: previous_stoch_state.append(mech.deterministic) mech.deterministic = True responses = func(self, cell_model, param_values, **kwargs) if self.deterministic: for i, mech in enumerate(cell_model.mechanisms): mech.deterministic = previous_stoch_state[i] return responses return inner def _run_func(self, cell_model, param_values, sim=None): """Run protocols""" try: cell_model.freeze(param_values) cell_model.instantiate(sim=sim) self.instantiate(sim=sim, cell_model=cell_model) try: if isinstance(sim, LFPySimulator): sim.run( lfpy_cell=cell_model.lfpy_cell, lfpy_electrode=cell_model.lfpy_electrode, tstop=self.total_duration, cvode_active=self.cvode_active) else: sim.run( self.total_duration, cvode_active=self.cvode_active ) except (RuntimeError, simulators.NrnSimulatorException): logger.debug( 'SweepProtocol: Running of parameter set {%s} generated ' 'an exception, returning None in responses', str(param_values)) responses = {recording.name: None for recording in self.recordings} else: responses = { recording.name: recording.response for recording in self.recordings} self.destroy(sim=sim) cell_model.destroy(sim=sim) cell_model.unfreeze(param_values.keys()) return responses except BaseException as e: raise SweepProtocolException( 'Failed to run Neuron Sweep Protocol') from e @adjust_stochasticity def run( self, cell_model, param_values, sim=None, isolate=None, timeout=None): """Instantiate protocol""" if isolate is None: isolate = True if isolate: def _reduce_method(meth): """Overwrite reduce""" return (getattr, (meth.__self__, meth.__func__.__name__)) import copyreg import types copyreg.pickle(types.MethodType, _reduce_method) import pebble from concurrent.futures import TimeoutError import multiprocessing # Default context for python>=3.8 on macos is spawn. # Spwan context would reset NEURON properties, such as dt. if sys.platform == 'win32': multiprocessing_context = multiprocessing.get_context('spawn') if ( sim is not None and not sim.cvode_active and sim.dt != 0.025 ): logger.warning("On Windows, evaluation might break when" "using non-default fixed time steps.") else: multiprocessing_context = multiprocessing.get_context('fork') if timeout is not None: if timeout < 0: raise ValueError("timeout should be > 0") with pebble.ProcessPool( max_workers=1, max_tasks=1, context=multiprocessing_context ) as pool: tasks = pool.schedule(self._run_func, kwargs={ 'cell_model': cell_model, 'param_values': param_values, 'sim': sim}, timeout=timeout) try: responses = tasks.result() except TimeoutError: logger.debug('SweepProtocol: task took longer than ' 'timeout, will return empty response ' 'for this recording') responses = {recording.name: None for recording in self.recordings} else: responses = self._run_func(cell_model=cell_model, param_values=param_values, sim=sim) return responses def instantiate(self, sim=None, cell_model=None): """Instantiate""" for stimulus in self.stimuli: if isinstance(stimulus, LFPStimulus): stimulus.instantiate(sim=sim, lfpy_cell=cell_model.lfpy_cell) else: stimulus.instantiate(sim=sim, icell=cell_model.icell) for recording in self.recordings: try: if isinstance(recording, LFPRecording): recording.instantiate(sim=sim, lfpy_cell=cell_model.lfpy_cell, electrode=cell_model.lfpy_electrode) else: recording.instantiate(sim=sim, icell=cell_model.icell) except locations.EPhysLocInstantiateException: logger.debug( 'SweepProtocol: Instantiating recording generated ' 'location exception, will return empty response for ' 'this recording') def destroy(self, sim=None): """Destroy protocol""" for stimulus in self.stimuli: stimulus.destroy(sim=sim) for recording in self.recordings: recording.destroy(sim=sim) def __str__(self): """String representation""" content = '%s:\n' % self.name content += ' stimuli:\n' for stimulus in self.stimuli: content += ' %s\n' % str(stimulus) content += ' recordings:\n' for recording in self.recordings: content += ' %s\n' % str(recording) return content class StepProtocol(SweepProtocol): """Protocol consisting of step and holding current""" def __init__( self, name=None, step_stimulus=None, holding_stimulus=None, recordings=None, cvode_active=None, deterministic=False): """Constructor Args: name (str): name of this object step_stimulus (list of Stimuli): Stimulus objects used in protocol recordings (list of Recordings): Recording objects used in the protocol cvode_active (bool): whether to use variable time step deterministic (bool): whether to force all mechanism to be deterministic """ super(StepProtocol, self).__init__( name, stimuli=[ step_stimulus, holding_stimulus] if holding_stimulus is not None else [step_stimulus], recordings=recordings, cvode_active=cvode_active) self.step_stimulus = step_stimulus self.holding_stimulus = holding_stimulus @property def step_delay(self): """Time stimulus starts""" return self.step_stimulus.step_delay @property def step_duration(self): """Time stimulus starts""" return self.step_stimulus.step_duration class ArbSweepProtocol(Protocol): """Arbor Sweep protocol""" def __init__( self, name=None, stimuli=None, recordings=None, use_labels=False): """Constructor Args: name (str): name of this object stimuli (list of Stimuli): Stimulus objects used in the protocol recordings (list of Recordings): Recording objects used in the protocol use_labels (bool): Add stimuli/recording locations to label dict """ super(ArbSweepProtocol, self).__init__(name) self.stimuli = stimuli self.recordings = recordings self.use_labels = use_labels @property def total_duration(self): """Total duration""" return max([stimulus.total_duration for stimulus in self.stimuli]) def subprotocols(self): """Return subprotocols""" return collections.OrderedDict({self.name: self}) def _run_func(self, cell_json, param_values, sim=None): """Run protocols""" try: # Loading cell constituents from ACC cell_json, morph, decor, labels = \ create_acc.read_acc(cell_json) # Locations of stimuli and recordings can be instantiated # as labels (useful for visualization in Arbor GUI) if self.use_labels: labels = self.instantiate_locations(labels) # Adding stimuli to decor (could also be written/loaded from ACC) decor = self.instantiate_iclamp_stimuli( decor, use_labels=self.use_labels) arb_cell_model = sim.instantiate(morph, decor, labels) # Adding synaptic stimuli to cell model (no representation in ACC) arb_cell_model = self.instantiate_synaptic_stimuli( arb_cell_model, use_labels=self.use_labels) # Adding recordings to cell model (no representation in ACC) arb_cell_model = self.instantiate_recordings( arb_cell_model, use_labels=self.use_labels) try: sim.run(arb_cell_model, tstop=self.total_duration) except (RuntimeError, simulators.ArbSimulatorException): logger.debug( 'ArbSweepProtocol: Running of parameter set {%s} ' 'generated an exception, returning None in responses', str(param_values)) responses = {recording.name: None for recording in self.recordings} else: if len(self.recordings) != len(arb_cell_model.traces): raise ValueError('Number of Arbor voltage traces ' '(%d) != number of recordings (%d)' % (len(self.recordings), len(arb_cell_model.traces))) responses = { recording.name: TimeVoltageResponse( recording.name, trace.time, trace.value) for recording, trace in zip(self.recordings, arb_cell_model.traces)} return responses except BaseException as e: raise ArbSweepProtocolException( 'Failed to run Arbor Sweep Protocol') from e def run( self, cell_model, param_values, sim=None, isolate=None, timeout=None): """Instantiate protocol""" # Export cell model to mixed JSON/ACC-format with tempfile.TemporaryDirectory() as acc_dir: cell_model.write_acc(acc_dir, param_values, ext_catalogues=sim.ext_catalogues) cell_json = os.path.join(acc_dir, cell_model.name + '.json') # protocols are directly instantiated on Arbor cell # (serialization would require representation for probes, events) if isolate is None: isolate = True if isolate: def _reduce_method(meth): """Overwrite reduce""" return (getattr, (meth.__self__, meth.__func__.__name__)) import copyreg import types copyreg.pickle(types.MethodType, _reduce_method) import pebble from concurrent.futures import TimeoutError if timeout is not None: if timeout < 0: raise ValueError("timeout should be > 0") with pebble.ProcessPool(max_workers=1, max_tasks=1) as pool: tasks = pool.schedule(self._run_func, kwargs={ 'cell_json': cell_json, 'param_values': param_values, 'sim': sim}, timeout=timeout) try: responses = tasks.result() except TimeoutError: logger.debug('SweepProtocol: task took longer than ' 'timeout, will return empty response ' 'for this recording') responses = {recording.name: None for recording in self.recordings} else: responses = self._run_func(cell_json=cell_json, param_values=param_values, sim=sim) return responses def instantiate_locations(self, label_dict): """Instantiate protocol (stimuli/recordings) locations on label_dict""" stim_rec_labels = [] for stim in self.stimuli: if hasattr(stim, 'location'): arb_loc = stim.location.acc_label() else: arb_loc = [label for loc in stim.locations for label in loc.acc_label()] for loc in (arb_loc if isinstance(arb_loc, list) else [arb_loc]): stim_rec_labels.append((loc.name, loc.loc, stim)) for rec in self.recordings: arb_loc = rec.location.acc_label() if isinstance(arb_loc, list) and len(arb_loc) != 1: raise ValueError('ArbSweepProtocol: ACC label %s' % arb_loc + ' of recording with length != 1.') stim_rec_labels.append((arb_loc.name, arb_loc.loc, rec)) stim_rec_label_dict = dict() for label_name, label_loc, stim_rec in stim_rec_labels: if label_name in label_dict and \ label_loc != label_dict[label_name]: raise ValueError( 'Label %s already exists in' % label_name + ' label_dict with different value: ' ' %s != %s.' % (label_dict[label_name], label_loc) + ' Choose different location name for %s.' % stim_rec) elif label_name in stim_rec_label_dict and \ label_loc != stim_rec_label_dict[label_name]: raise ValueError( 'Label %s defined multiple times' % label_name + ' with different values: ' ' %s != %s.' % (stim_rec_label_dict[label_name], label_loc) + ' Choose different location name for %s.' % stim_rec) elif label_name not in label_dict and \ label_name not in stim_rec_label_dict: stim_rec_label_dict[label_name] = label_loc label_dict.append(arbor.label_dict(stim_rec_label_dict)) return label_dict def instantiate_iclamp_stimuli(self, decor, use_labels=False): """Instantiate iclamp stimuli""" for i, stim in enumerate(self.stimuli): if not isinstance(stim, stimuli.SynapticStimulus): if hasattr(stim, 'envelope'): envelope = stim.envelope() envelope = [ (t * arbor.units.ms, curr * arbor.units.nA) for (t, curr) in envelope ] arb_iclamp = arbor.iclamp(envelope) else: raise ValueError('Stimulus must provide envelope method ' ' or be of type NrnNetStimStimulus to be' ' supported in Arbor.') arb_loc = stim.location.acc_label() for loc in (arb_loc if isinstance(arb_loc, list) else [arb_loc]): decor.place(loc.ref if use_labels else loc.loc, arb_iclamp, '%s.iclamp.%d.%s' % (self.name, i, loc.name)) return decor def instantiate_synaptic_stimuli(self, cell_model, use_labels=False): """Instantiate synaptic stimuli""" for i, stim in enumerate(self.stimuli): if isinstance(stim, stimuli.SynapticStimulus): for acc_events in stim.acc_events(): cell_model.event_generator(acc_events) return cell_model def instantiate_recordings(self, cell_model, use_labels=False): """Instantiate recordings""" # Attach voltage probe sampling at 10 kHz (every 0.1 ms) for i, rec in enumerate(self.recordings): # alternatively arbor.cable_probe_membrane_voltage arb_loc = rec.location.acc_label() if isinstance(arb_loc, list) and len(arb_loc) != 1: raise ValueError('ArbSweepProtocol: ACC label %s' % arb_loc + ' of recording with length != 1.') if hasattr(cell_model, 'cable_cell'): rec_locations = cell_model.cable_cell.locations(arb_loc.loc) if len(rec_locations) != 1: raise ValueError( 'Recording %s\'s' % rec.name + ' location "%s"' % arb_loc.loc + ' is non-unique in Arbor: %s.' % rec_locations) cell_model.probe('voltage', arb_loc.ref if use_labels else arb_loc.loc, f"probe-{i}", # frequency could be a parameter frequency=10 * arbor.units.kHz) return cell_model def __str__(self): """String representation""" content = '%s:\n' % self.name content += ' stimuli:\n' for stimulus in self.stimuli: content += ' %s\n' % str(stimulus) content += ' recordings:\n' for recording in self.recordings: content += ' %s\n' % str(recording) return content class SweepProtocolException(Exception): """All exceptions generated by SweepProtocol""" def __init__(self, message): """Constructor""" super(SweepProtocolException, self).__init__(message) class ArbSweepProtocolException(Exception): """All exceptions generated by ArbSweepProtocol""" def __init__(self, message): """Constructor""" super(ArbSweepProtocolException, self).__init__(message) ================================================ FILE: bluepyopt/ephys/recordings.py ================================================ """Recording classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import logging from . import responses logger = logging.getLogger(__name__) class Recording(object): """Class to represent object that record variables during simulations""" def __init__(self, name=None): """Constructor Args: name (str): name of this object """ self.name = name class CompRecording(Recording): """Response to stimulus""" def __init__( self, name=None, location=None, variable='v'): """Constructor Args: name (str): name of this object location (Location): location in the model of the recording variable (str): which variable to record from (e.g. 'v') """ super(CompRecording, self).__init__( name=name) self.location = location self.variable = variable self.varvector = None self.tvector = None self.time = None self.voltage = None self.instantiated = False @property def response(self): """Return recording response""" if not self.instantiated: return None return responses.TimeVoltageResponse(self.name, self.tvector.to_python(), self.varvector.to_python()) def instantiate(self, sim=None, icell=None): """Instantiate recording""" logger.debug('Adding compartment recording of %s at %s', self.variable, self.location) self.varvector = sim.neuron.h.Vector() seg = self.location.instantiate(sim=sim, icell=icell) self.varvector.record(getattr(seg, '_ref_%s' % self.variable)) self.tvector = sim.neuron.h.Vector() self.tvector.record(sim.neuron.h._ref_t) # pylint: disable=W0212 self.instantiated = True def destroy(self, sim=None): """Destroy recording""" self.varvector = None self.tvector = None self.instantiated = False def __str__(self): """String representation""" return '%s: %s at %s' % (self.name, self.variable, self.location) class LFPRecording(Recording): """Extracellular electrode response to stimulus""" location = "extracellular" variable = "v" def __init__(self, name=None): """Constructor Args: name (str): name of this object """ super(LFPRecording, self).__init__(name=name) self.electrode = None self.cell = None self.tvector = None self.time = None self.sim = None self.instantiated = False @property def response(self): """Return recording response""" if not self.instantiated: return None self.tvector = self.cell.tvec return responses.TimeLFPResponse( self.name, self.tvector, self.electrode.data ) def instantiate(self, sim=None, lfpy_cell=None, electrode=None): import LFPy """Instantiate recording""" logger.debug( "Adding recording of %s at %s", self.variable, self.location ) assert isinstance( lfpy_cell, LFPy.Cell ), "LFPRecording is only available for LFPCellModel" self.cell = lfpy_cell self.tvector = None self.electrode = electrode self.sim = sim self.instantiated = True def destroy(self, sim=None): """Destroy recording""" self.electrode = None self.LFP = None self.tvector = None self.electrode = None self.cell = None self.instantiated = False def __str__(self): """String representation""" return "%s: %s at %s" % (self.name, self.variable, self.location) ================================================ FILE: bluepyopt/ephys/responses.py ================================================ """Responses classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import pandas class Response(object): """Response to stimulus""" def __init__(self, name): """Constructor Args: name (str): name of this object """ self.response = None self.name = name def __str__(self): return '%s: %s' % (self.__class__.__name__, self.name) class TimeVoltageResponse(Response): """Response to stimulus""" def __init__(self, name, time=None, voltage=None): """Constructor Args: name (str): name of this object time (list of floats): time series voltage (list of floats): voltage series """ super(TimeVoltageResponse, self).__init__(name) self.response = pandas.DataFrame() self.response['time'] = pandas.Series(time) self.response['voltage'] = pandas.Series(voltage) def read_csv(self, filename): """Load response from csv file""" self.response = pandas.read_csv(filename) def to_csv(self, filename): """Write response to csv file""" self.response.to_csv(filename) def __getitem__(self, index): """Return item at index""" return self.response.__getitem__(index) # This plot has to be generalised to several subplots def plot(self, axes): """Plot the response""" axes.plot( self.response['time'], self.response['voltage'], label='%s' % self.name) class TimeLFPResponse(TimeVoltageResponse): """Response to stimulus""" def __init__(self, name, time=None, lfp=None): """Constructor Args: name (str): name of this object time (list of floats): time series lfp (list of floats): voltage series """ super(TimeLFPResponse, self).__init__(name, time=time, voltage=None) self.response = {} self.response["time"] = time self.response["voltage"] = lfp def plot(self, axes): raise NotImplementedError ================================================ FILE: bluepyopt/ephys/serializer.py ================================================ '''Mixin class to make dictionaries''' # Disabling lines below, generate error when loading ephys.examples # from future import standard_library # standard_library.install_aliases() SENTINAL = 'class' class DictMixin(object): '''Mixin class to create dictionaries of selected elements''' SERIALIZED_FIELDS = () @staticmethod def _serializer(value): """_serializer""" if hasattr(value, 'to_dict'): return value.to_dict() elif isinstance(value, (list, tuple)) and \ value and hasattr(value[0], 'to_dict'): return [v.to_dict() for v in value] elif (isinstance(value, dict) and value and hasattr( next(iter(list(value.values()))), 'to_dict')): return {k: v.to_dict() for k, v in list(value.items())} return value @staticmethod def _deserializer(value): """_deserializer""" if (isinstance(value, list) and value and isinstance(value[0], dict) and SENTINAL in value[0]): return [instantiator(v) for v in value] elif isinstance(value, dict) and value: if SENTINAL in value: return instantiator(value) model_value = next(iter(list(value.values()))) if isinstance(model_value, dict) and SENTINAL in model_value: return {k: instantiator(v) for k, v in list(value.items())} return value def to_dict(self): '''create dictionary''' ret = {} for field in self.SERIALIZED_FIELDS: ret[field] = DictMixin._serializer(getattr(self, field)) ret['class'] = repr(self.__class__) return ret @classmethod def from_dict(cls, fields): '''create class from serialized values''' klass = fields[SENTINAL] assert klass == repr(cls), 'Class names much match %s != %s' % ( klass, repr(cls)) del fields['class'] for name in list(fields.keys()): fields[name] = DictMixin._deserializer(fields[name]) return cls(**fields) def instantiator(fields): """instantiator""" klass = fields[SENTINAL] for subclass in DictMixin.__subclasses__(): if repr(subclass) == klass: return subclass.from_dict(fields) raise Exception('Could not find class "%s" to instantiate' % klass) ================================================ FILE: bluepyopt/ephys/simulators.py ================================================ """Simulator classes""" # pylint: disable=W0511 import ctypes import importlib.util import logging import os import pathlib import platform import warnings from bluepyopt.ephys.acc import arbor logger = logging.getLogger(__name__) class NrnSimulator(object): """Neuron simulator""" def __init__( self, dt=None, cvode_active=True, cvode_minstep=None, random123_globalindex=None, mechanisms_directory=None, ): """Constructor Args: dt (float): the integration time step used by Neuron. cvode_active (bool): should Neuron use the variable time step integration method cvode_minstep (float): the minimum time step allowed for a cvode step. Default is 0.0. random123_globalindex (int): used to set the global index used by all instances of the Random123 instances of Random mechanisms_directory (str): path to the parent directory of the directory containing the mod files. If the mod files are in "./data/mechanisms", then mechanisms_directory should be "./data/". """ # hoc.so does not exist on NEURON Windows or MacOS # although \\hoc.pyd can work here, it gives an error for # nrn_nobanner_ line self.disable_banner = platform.system() not in ["Windows", "Darwin"] self.banner_disabled = False self.mechanisms_directory = mechanisms_directory self.dt = dt if dt is not None else self.neuron.h.dt self.cvode_minstep_value = cvode_minstep self.cvode_active = cvode_active self.initialize() self.random123_globalindex = random123_globalindex @property def cvode(self): """Return cvode instance""" return self.neuron.h.CVode() @property def cvode_minstep(self): """Return cvode minstep value""" return self.cvode.minstep() @cvode_minstep.setter def cvode_minstep(self, value): """Set cvode minstep value""" self.cvode.minstep(value) @staticmethod def _nrn_disable_banner(): """Disable Neuron banner""" nrnpy_path = pathlib.Path( importlib.util.find_spec("neuron").origin ).parent hoc_so_list = list(nrnpy_path.glob("hoc*.so")) if len(hoc_so_list) != 1: warnings.warn( "Unable to find Neuron hoc shared library in %s, " "not disabling banner" % nrnpy_path ) else: hoc_so = hoc_so_list[0] nrndll = ctypes.cdll[str(hoc_so)] ctypes.c_int.in_dll(nrndll, "nrn_nobanner_").value = 1 # pylint: disable=R0201 @property def neuron(self): """Return Neuron module""" if self.disable_banner and not self.banner_disabled: NrnSimulator._nrn_disable_banner() self.banner_disabled = True import neuron # NOQA if self.mechanisms_directory is not None: neuron.load_mechanisms( self.mechanisms_directory, warn_if_already_loaded=False ) return neuron def initialize(self): """Initialize simulator: Set Neuron variables""" self.neuron.h.load_file("stdrun.hoc") self.neuron.h.dt = self.dt self.neuron.h.cvode_active(1 if self.cvode_active else 0) def run( self, tstop=None, dt=None, cvode_active=None, random123_globalindex=None, ): """Run protocol""" self.neuron.h.tstop = tstop if cvode_active and dt is not None: raise ValueError( "NrnSimulator: Impossible to combine the dt argument when " "cvode_active is True in the NrnSimulator run method" ) if cvode_active is None: cvode_active = self.cvode_active if not cvode_active and dt is None: # use dt of simulator if self.neuron.h.dt != self.dt: raise Exception( "NrnSimulator: Some process has changed the " "time step dt of Neuron since the creation of this " "NrnSimulator object. Not sure this is intended:\n" "current dt: %.6g\n" "init dt: %.6g" % (self.neuron.h.dt, self.dt) ) dt = self.dt self.neuron.h.cvode_active(1 if cvode_active else 0) if self.cvode_minstep_value is not None: save_minstep = self.cvode_minstep self.cvode_minstep = self.cvode_minstep_value if cvode_active: logger.debug("Running Neuron simulator %.6g ms, with cvode", tstop) else: self.neuron.h.dt = dt self.neuron.h.steps_per_ms = 1.0 / dt logger.debug( "Running Neuron simulator %.6g ms, with dt=%r", tstop, dt ) if random123_globalindex is None: random123_globalindex = self.random123_globalindex if random123_globalindex is not None: rng = self.neuron.h.Random() rng.Random123_globalindex(random123_globalindex) try: self.neuron.h.run() except Exception as e: raise NrnSimulatorException("Neuron simulator error", e) if self.cvode_minstep_value is not None: self.cvode_minstep = save_minstep logger.debug("Neuron simulation finished") class NrnSimulatorException(Exception): """All exception generated by Neuron simulator""" def __init__(self, message, original): """Constructor""" super(NrnSimulatorException, self).__init__(message) self.original = original class LFPySimulator(NrnSimulator): """LFPy simulator""" def __init__( self, dt=None, cvode_active=True, cvode_minstep=None, random123_globalindex=None, mechanisms_directory=None, ): """Constructor Args: dt (float): the integration time step used by neuron. cvode_active (bool): should neuron use the variable time step integration method cvode_minstep (float): the minimum time step allowed for a cvode step. Default is 0.0. random123_globalindex (int): used to set the global index used by all instances of the Random123 instances of Random mechanisms_directory (str): path to the parent directory of the directory containing the mod files. If the mod files are in "./data/mechanisms", then mechanisms_directory should be "./data/". """ super(LFPySimulator, self).__init__( dt=dt, cvode_active=cvode_active, cvode_minstep=cvode_minstep, random123_globalindex=random123_globalindex, mechanisms_directory=mechanisms_directory, ) def run( self, lfpy_cell, lfpy_electrode, tstop=None, dt=None, cvode_active=None, random123_globalindex=None, ): """Run protocol""" _ = self.neuron lfpy_cell.tstart = 0.0 lfpy_cell.tstop = tstop if dt is not None: lfpy_cell.dt = dt if cvode_active and dt is not None: raise ValueError( "NrnSimulator: Impossible to combine the dt argument when " "cvode_active is True in the NrnSimulator run method" ) if cvode_active is None: cvode_active = self.cvode_active if cvode_active is not None: self.cvode_active = cvode_active if random123_globalindex is None: random123_globalindex = self.random123_globalindex if random123_globalindex is not None: rng = self.neuron.h.Random() rng.Random123_globalindex(random123_globalindex) probes = [lfpy_electrode] if lfpy_electrode is not None else None sim_params = { "probes": probes, "rec_vmem": False, "rec_imem": False, "rec_ipas": False, "rec_icap": False, "rec_variables": [], "variable_dt": self.cvode_active, "atol": 0.001, "to_memory": True, "to_file": False, "file_name": None, } try: lfpy_cell.simulate(**sim_params) except Exception as e: raise LFPySimulatorException("LFPy simulator error", e) logger.debug("LFPy simulation finished") class LFPySimulatorException(Exception): """All exception generated by LFPy simulator""" def __init__(self, message, original): """Constructor""" super(LFPySimulatorException, self).__init__(message) self.original = original class ArbSimulator(object): """Arbor simulator""" def __init__(self, dt=None, ext_catalogues=None): """Constructor Args: dt (float): the integration time step used by Arbor. ext_catalogues (): Name to path mapping of non-Arbor built-in NMODL mechanism catalogues compiled with modcc """ self.dt = dt self.ext_catalogues = ext_catalogues if ext_catalogues is not None: for cat, cat_path in ext_catalogues.items(): cat_lib = "%s-catalogue.so" % cat cat_path = pathlib.Path(cat_path).resolve() if not os.path.exists(cat_path / cat_lib): raise ArbSimulatorException( "Cannot find %s at %s - first build" % (cat_lib, cat_path) + " mechanism catalogue with modcc:" + " arbor-build-catalogue %s %s" % (cat, cat_path) ) # TODO: add parameters for discretization def initialize(self): """Initialize simulator""" pass def instantiate(self, morph, decor, labels): cable_cell = arbor.cable_cell( morphology=morph, decor=decor, labels=labels ) arb_cell_model = arbor.single_cell_model(cable_cell) # Add catalogues with explicit qualifiers arb_cell_model.properties.catalogue = arbor.catalogue() # User-supplied catalogues take precedence if self.ext_catalogues is not None: for cat, cat_path in self.ext_catalogues.items(): cat_lib = "%s-catalogue.so" % cat cat_path = pathlib.Path(cat_path).resolve() arb_cell_model.properties.catalogue.extend( arbor.load_catalogue(cat_path / cat_lib), cat + "::" ) # Built-in catalogues are always added (could be made optional) if self.ext_catalogues is None or "default" not in self.ext_catalogues: arb_cell_model.properties.catalogue.extend( arbor.default_catalogue(), "default::" ) if self.ext_catalogues is None or "BBP" not in self.ext_catalogues: arb_cell_model.properties.catalogue.extend( arbor.bbp_catalogue(), "BBP::" ) if self.ext_catalogues is None or "allen" not in self.ext_catalogues: arb_cell_model.properties.catalogue.extend( arbor.allen_catalogue(), "allen::" ) return arb_cell_model def run(self, arb_cell_model, tstop=None, dt=None): dt = dt if dt is not None else self.dt if dt is not None: return arb_cell_model.run( tfinal=tstop * arbor.units.ms, dt=dt * arbor.units.ms ) else: return arb_cell_model.run(tfinal=tstop * arbor.units.ms) class ArbSimulatorException(Exception): """All exception generated by Arbor simulator""" def __init__(self, message): """Constructor""" super(ArbSimulatorException, self).__init__(message) ================================================ FILE: bluepyopt/ephys/static/arbor_mechanisms.json ================================================ { "allen": { "CaDynamics": { "globals": [ "F" ], "ranges": [ "decay", "gamma", "minCai", "depth" ] }, "Ca_HVA": { "globals": null, "ranges": [ "gbar" ] }, "Ca_LVA": { "globals": null, "ranges": [ "gbar" ] }, "Ih": { "globals": null, "ranges": [ "gbar" ] }, "Im": { "globals": null, "ranges": [ "gbar", "g", "ik" ] }, "Im_v2": { "globals": null, "ranges": [ "gbar", "ik" ] }, "K_P": { "globals": null, "ranges": [ "gbar", "g", "ik" ] }, "K_T": { "globals": null, "ranges": [ "gbar" ] }, "Kd": { "globals": null, "ranges": [ "gbar", "ik" ] }, "Kv2like": { "globals": null, "ranges": [ "gbar" ] }, "Kv3_1": { "globals": null, "ranges": [ "gbar", "ik" ] }, "NaTa": { "globals": null, "ranges": [ "gbar", "g", "ina" ] }, "NaTs": { "globals": null, "ranges": [ "gbar", "g", "ina" ] }, "NaV": { "globals": null, "ranges": [ "gbar" ] }, "Nap": { "globals": null, "ranges": [ "gbar", "g", "ina" ] }, "SK": { "globals": null, "ranges": [ "gbar", "ik" ] } }, "BBP": { "CaDynamics_E2": { "globals": null, "ranges": [ "decay", "gamma", "minCai", "depth", "initCai" ] }, "Ca_HVA": { "globals": null, "ranges": [ "gCa_HVAbar" ] }, "Ca_LVAst": { "globals": null, "ranges": [ "gCa_LVAstbar" ] }, "Ih": { "globals": null, "ranges": [ "gIhbar" ] }, "Im": { "globals": null, "ranges": [ "gImbar" ] }, "K_Pst": { "globals": null, "ranges": [ "gK_Pstbar" ] }, "K_Tst": { "globals": null, "ranges": [ "gK_Tstbar" ] }, "NaTa_t": { "globals": null, "ranges": [ "gNaTa_tbar" ] }, "NaTs2_t": { "globals": null, "ranges": [ "gNaTs2_tbar" ] }, "Nap_Et2": { "globals": null, "ranges": [ "gNap_Et2bar" ] }, "SK_E2": { "globals": null, "ranges": [ "gSK_E2bar" ] }, "SKv3_1": { "globals": null, "ranges": [ "gSKv3_1bar" ] } }, "default": { "exp2syn": { "globals": null, "ranges": [ "tau1", "tau2", "e" ] }, "expsyn": { "globals": null, "ranges": [ "tau", "e" ] }, "expsyn_stdp": { "globals": null, "ranges": [ "tau", "taupre", "taupost", "e", "Apost", "Apre", "max_weight" ] }, "gj": { "globals": null, "ranges": [ "g" ] }, "hh": { "globals": null, "ranges": [ "gnabar", "gkbar", "gl", "el", "q10" ] }, "kamt": { "globals": [ "minf", "mtau", "hinf", "htau" ], "ranges": [ "gbar", "q10" ] }, "kdrmt": { "globals": [ "minf", "mtau" ], "ranges": [ "gbar", "q10", "vhalfm" ] }, "nax": { "globals": null, "ranges": [ "gbar", "sh" ] }, "nernst": { "globals": [ "R", "F" ], "ranges": [ "coeff" ] }, "pas": { "globals": [ "e" ], "ranges": [ "g" ] } } } ================================================ FILE: bluepyopt/ephys/stimuli.py ================================================ """Stimuli classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=W0511 import numpy as np import logging from bluepyopt.ephys.acc import arbor logger = logging.getLogger(__name__) class Stimulus(object): """Stimulus protocol""" pass class SynapticStimulus(Stimulus): """Synaptic stimulus protocol""" pass class LFPStimulus(Stimulus): """Base class for stimulus supporting LFPy cells.""" def instantiate(self, sim=None, lfpy_cell=None): """Run stimulus""" raise NotImplementedError class NrnCurrentPlayStimulus(Stimulus): """Current stimulus based on current amplitude and time series""" def __init__(self, time_points=None, current_points=None, location=None): """Constructor Args: time_points(): time series (ms) current_points(): current series of injected current amplitudes(nA) location(Location): location of stimulus """ super(NrnCurrentPlayStimulus, self).__init__() self.time_points = time_points self.current_points = current_points self.location = location self.total_duration = max(time_points) self.iclamp = None self.current_vec = None self.time_vec = None def envelope(self): """Stimulus envelope""" envelope = list(zip(self.time_points, self.current_points)) return envelope def instantiate(self, sim=None, icell=None): """Run stimulus""" icomp = self.location.instantiate(sim=sim, icell=icell) logger.debug( 'Adding current play stimulus to %s', str(self.location)) self.iclamp = sim.neuron.h.IClamp( icomp.x, sec=icomp.sec) self.current_vec = sim.neuron.h.Vector(self.current_points) self.time_vec = sim.neuron.h.Vector(self.time_points) self.iclamp.dur = self.total_duration self.iclamp.delay = 0 self.current_vec.play( self.iclamp._ref_amp, # pylint:disable=W0212 self.time_vec, 1, sec=icomp.sec) def destroy(self, sim=None): """Destroy stimulus""" self.iclamp = None self.time_vec = None self.current_vec = None def __str__(self): """String representation""" return "Current play at %s" % (self.location) class NrnNetStimStimulus(SynapticStimulus): """Current stimulus based on current amplitude and time series""" def __init__(self, locations=None, total_duration=None, interval=None, number=None, start=None, noise=0, weight=1): """Constructor Args: location: synapse point process location to connect to interval: time between spikes (ms) number: average number of spikes start: most likely start time of first spike (ms) noise: fractional randomness (0 deterministic, 1 negexp interval distribution) """ super(NrnNetStimStimulus, self).__init__() if total_duration is None: raise ValueError( 'NrnNetStimStimulus: Need to specify a total duration') else: self.total_duration = total_duration self.locations = locations self.interval = interval self.number = number self.start = start self.noise = noise self.weight = weight self.connections = {} def instantiate(self, sim=None, icell=None): """Run stimulus""" for location in self.locations: self.connections[location.name] = [] for synapse in location.instantiate(sim=sim, icell=icell): netstim = sim.neuron.h.NetStim() netstim.interval = self.interval netstim.number = self.number netstim.start = self.start netstim.noise = self.noise netcon = sim.neuron.h.NetCon(netstim, synapse) netcon.weight[0] = self.weight self.connections[location.name].append((netcon, netstim)) def destroy(self, sim=None): """Destroy stimulus""" self.connections = {} def acc_events(self): event_generators = [] for loc in self.locations: if self.noise == 0.: schedule = arbor.explicit_schedule( [self.start + i * self.interval for i in range(self.number)]) elif self.noise == 1.: schedule = arbor.poisson_schedule( tstart=self.start, freq=1. / self.interval, seed=0, tstop=self.start + self.number * self.interval) else: raise ValueError( 'Only noise = 0 or 1 for NrnNetStimStimulus' ' supported in Arbor.') event_generators.append( arbor.event_generator(target=loc.pprocess_mech.name, weight=self.weight, sched=schedule)) return event_generators def __str__(self): """String representation""" return "Netstim at %s" % ','.join( location for location in self.locations) \ if self.locations is not None else "Netstim" # TODO Add 'current' to the name class NrnSquarePulse(Stimulus): """Square pulse current clamp injection""" def __init__(self, step_amplitude=None, step_delay=None, step_duration=None, total_duration=None, location=None): """Constructor Args: step_amplitude (float): amplitude (nA) step_delay (float): delay (ms) step_duration (float): duration (ms) total_duration (float): total duration (ms) location (Location): stimulus Location """ super(NrnSquarePulse, self).__init__() self.step_amplitude = step_amplitude self.step_delay = step_delay self.step_duration = step_duration self.location = location self.total_duration = total_duration self.iclamp = None def envelope(self): """Stimulus envelope""" envelope = [(0., 0.), (self.step_delay, 0.), (self.step_delay, self.step_amplitude), (self.step_delay + self.step_duration, self.step_amplitude), (self.step_delay + self.step_duration, 0.), (self.total_duration, 0.)] return envelope def instantiate(self, sim=None, icell=None): """Run stimulus""" icomp = self.location.instantiate(sim=sim, icell=icell) logger.debug( 'Adding square step stimulus to %s with delay %f, ' 'duration %f, and amplitude %f', str(self.location), self.step_delay, self.step_duration, self.step_amplitude) self.iclamp = sim.neuron.h.IClamp( icomp.x, sec=icomp.sec) self.iclamp.dur = self.step_duration self.iclamp.amp = self.step_amplitude self.iclamp.delay = self.step_delay def destroy(self, sim=None): """Destroy stimulus""" self.iclamp = None def __str__(self): """String representation""" return "Square pulse amp %f delay %f duration %f totdur %f at %s" % ( self.step_amplitude, self.step_delay, self.step_duration, self.total_duration, self.location) class NrnRampPulse(Stimulus): """Ramp current clamp injection""" def __init__(self, ramp_amplitude_start=None, ramp_amplitude_end=None, ramp_delay=None, ramp_duration=None, total_duration=None, location=None): """Constructor Args: ramp_amplitude_start (float): amplitude at start of ramp (nA) ramp_amplitude_start (float): amplitude at end of ramp (nA) ramp_delay (float): delay of ramp (ms) ramp_duration (float): duration oframp (ms) total_duration (float): total duration (ms) location (Location): stimulus Location """ super(NrnRampPulse, self).__init__() self.ramp_amplitude_start = ramp_amplitude_start self.ramp_amplitude_end = ramp_amplitude_end self.ramp_delay = ramp_delay self.ramp_duration = ramp_duration self.location = location self.total_duration = total_duration self.iclamp = None self.persistent = [] # TODO move this into higher abstract classes def envelope(self): """Stimulus envelope""" envelope = [ # at time 0.0, current is 0.0 (0.0, 0.0), # until time ramp_delay, current is 0.0 (self.ramp_delay, 0.0), # at time ramp_delay, current is ramp_amplitude_start (self.ramp_delay, self.ramp_amplitude_start), # at time ramp_delay+ramp_duration, current is ramp_amplitude_end (self.ramp_delay + self.ramp_duration, self.ramp_amplitude_end), # after ramp, current is set 0.0 (self.ramp_delay + self.ramp_duration, 0.0), (self.total_duration, 0.0) ] return envelope def instantiate(self, sim=None, icell=None): """Run stimulus""" icomp = self.location.instantiate(sim=sim, icell=icell) logger.debug( 'Adding ramp stimulus to %s with delay %f, ' 'duration %f, amplitude at start %f and end %f', str(self.location), self.ramp_delay, self.ramp_duration, self.ramp_amplitude_start, self.ramp_amplitude_end ) times, amps = zip(*self.envelope()) # create vector to store the times at which stim amp changes times = sim.neuron.h.Vector(times) # create vector to store to which stim amps over time amps = sim.neuron.h.Vector(amps) # create a current clamp self.iclamp = sim.neuron.h.IClamp( icomp.x, sec=icomp.sec) self.iclamp.dur = self.total_duration # play the above current amplitudes into the current clamp amps.play(self.iclamp._ref_amp, times, 1) # pylint: disable=W0212 # Make sure the following objects survive after instantiation self.persistent.append(times) self.persistent.append(amps) def destroy(self, sim=None): """Destroy stimulus""" # Destroy all persistent objects self.persistent = [] self.iclamp = None def __str__(self): """String representation""" return "Ramp pulse amp_start %f amp_end %f delay %f duration %f " \ "totdur %f at %s" % ( self.ramp_amplitude_start, self.ramp_amplitude_end, self.ramp_delay, self.ramp_duration, self.total_duration, self.location) class LFPySquarePulse(LFPStimulus): """Square pulse current clamp injection""" def __init__(self, step_amplitude=None, step_delay=None, step_duration=None, total_duration=None, location=None): """Constructor Args: step_amplitude (float): amplitude (nA) step_delay (float): delay (ms) step_duration (float): duration (ms) total_duration (float): total duration (ms) location (Location): stimulus Location """ super(LFPySquarePulse, self).__init__() self.step_amplitude = step_amplitude self.step_delay = step_delay self.step_duration = step_duration self.location = location self.total_duration = total_duration self.iclamp = None def instantiate(self, sim=None, lfpy_cell=None): """Run stimulus""" import LFPy from .locations import NrnSomaDistanceCompLocation if hasattr(self.location, "sec_index"): sec_index = self.location.sec_index elif isinstance(self.location, NrnSomaDistanceCompLocation): # compute sec_index closest to soma_distance cell_seg_locs = np.array([lfpy_cell.x, lfpy_cell.y, lfpy_cell.z]).T soma_loc = lfpy_cell.somapos dist_from_soma = np.array( [np.linalg.norm(loc - soma_loc) for loc in cell_seg_locs] ) sec_index = np.argmin( np.abs(dist_from_soma - self.location.soma_distance) ) else: raise NotImplementedError( f"{type(self.location)} is currently not implemented " "with the LFPy backend" ) self.iclamp = LFPy.StimIntElectrode( cell=lfpy_cell, idx=sec_index, pptype="IClamp", amp=self.step_amplitude, delay=self.step_delay, dur=self.step_duration, record_current=True ) logger.debug( "Adding square step stimulus to %s with delay %f, " "duration %f, and amplitude %f", str(self.location), self.step_delay, self.step_duration, self.step_amplitude, ) def destroy(self, sim=None): """Destroy stimulus""" self.iclamp = None def __str__(self): """String representation""" return "Square pulse amp %f delay %f duration %f totdur %f at %s" % ( self.step_amplitude, self.step_delay, self.step_duration, self.total_duration, self.location, ) ================================================ FILE: bluepyopt/ephys/templates/acc/_json_template.jinja2 ================================================ { "cell_model_name": "{{template_name}}", {%- if banner %} "produced_by": "{{banner}}", {%- endif %} {%- if morphology %} {# feed morphology separately as a SWC/ASC file #} {%- if replace_axon is not none %} "morphology": { "original": "{{morphology}}", "replace_axon": "{{replace_axon}}"{%- if modified_morphology is not none %}, "modified": "{{modified_morphology}}"{%- endif %} }, {%- else %} "morphology": { "original": "{{morphology}}" }, {%- endif %} {%- else %} execerror("Template {{template_name}} requires morphology name to instantiate") {%- endif %} "label_dict": "{{filenames['label_dict.acc']}}", "decor": "{{filenames['decor.acc']}}" } ================================================ FILE: bluepyopt/ephys/templates/acc/decor_acc_template.jinja2 ================================================ (arbor-component (meta-data (version "0.9-dev")) (decor {%- for mech, params in global_mechs.items() %} {%- if mech is not none %} {%- if mech in global_scaled_mechs %} (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 %})) {%- else %} (default (density (mechanism "{{ mech }}" {%- for param in params %} ("{{ param.name }}" {{ param.value }}){%- endfor %}))) {%- endif %} {%- else %} {%- for param in params %} (default ({{ param.name }} {{ param.value }} (scalar 1.0))) {%- endfor %} {%- endif %} {%- endfor %} {%- for loc, mech_parameters in local_mechs.items() %}{# paint-to-region instead of default #} {%- for mech, params in mech_parameters.items() %} {%- if mech is not none %} {%- if mech in local_scaled_mechs[loc] %} (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 %})) {%- else %} (paint {{loc.ref}} (density (mechanism "{{ mech }}" {%- for param in params %} ("{{ param.name }}" {{ param.value }}){%- endfor %}))) {%- endif %} {%- else %} {%- for param in params %} (paint {{loc.ref}} ({{ param.name }} {{ param.value }} (scalar 1.0))) {%- endfor %} {%- endif %} {%- endfor %} {%- for synapse_name, mech_params in pprocess_mechs[loc].items() %} (place {{loc.ref}} (synapse (mechanism "{{ mech_params.mech }}" {%- for param in mech_params.params %} ("{{ param.name }}" {{ param.value }} (scalar 1.0)){%- endfor %})) "{{ synapse_name }}") {%- endfor %} {%- endfor %})) ================================================ FILE: bluepyopt/ephys/templates/acc/label_dict_acc_template.jinja2 ================================================ (arbor-component (meta-data (version "0.9-dev")) (label-dict {%- for loc, label in label_dict.items() %} {{ label.defn }} {%- endfor %})) ================================================ FILE: bluepyopt/ephys/templates/cell_template.jinja2 ================================================ /* {%- if banner %} {{banner}} {%- endif %} */ {load_file("stdrun.hoc")} {load_file("import3d.hoc")} {%- if global_params %} /* * Check that global parameters are the same as with the optimization */ proc check_parameter(/* name, expected_value, value */){ strdef error if($2 != $3){ sprint(error, "Parameter %s has different value %f != %f", $s1, $2, $3) execerror(error) } } proc check_simulator() { {%- for param, value in global_params.items() %} check_parameter("{{param}}", {{value}}, {{param}}) {%- endfor %} } {%- endif %} {%- if ignored_global_params %} /* The following global parameters were set in BluePyOpt {%- for param, value in ignored_global_params.items() %} * {{param}} = {{value}} {%- endfor %} */ {%- endif %} begintemplate {{template_name}} public init, morphology, geom_nseg_fixed, geom_nsec, gid public channel_seed, channel_seed_set public soma, dend, apic, axon, myelin create soma[1], dend[1], apic[1], axon[1], myelin[1] objref this, CellRef, segCounts public all, somatic, apical, axonal, basal, myelinated, APC objref all, somatic, apical, axonal, basal, myelinated, APC obfunc getCell(){ return this } proc init(/* args: morphology_dir, morphology_name */) { all = new SectionList() apical = new SectionList() axonal = new SectionList() basal = new SectionList() somatic = new SectionList() myelinated = new SectionList() //gid in this case is only used for rng seeding gid = 0 //For compatibility with BBP CCells CellRef = this forall delete_section() if(numarg() >= 2) { load_morphology($s1, $s2) } else { {%- if morphology %} load_morphology($s1, "{{morphology}}") {%- else %} execerror("Template {{template_name}} requires morphology name to instantiate") {%- endif %} } geom_nseg() {%- if replace_axon %} replace_axon() {%- endif %} insertChannel() biophys() // Initialize channel_seed_set to avoid accidents channel_seed_set = 0 // Initialize random number generators re_init_rng() } proc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension strdef morph_path sprint(morph_path, "%s/%s", $s1, $s2) sf = new StringFunctions() extension = new String() sscanf(morph_path, "%s", extension.s) sf.right(extension.s, sf.len(extension.s)-4) if( strcmp(extension.s, ".asc") == 0 ) { morph = new Import3d_Neurolucida3() } else if( strcmp(extension.s, ".swc" ) == 0) { morph = new Import3d_SWC_read() } else { printf("Unsupported file format: Morphology file has to end with .asc or .swc" ) quit() } morph.quiet = 1 morph.input(morph_path) import = new Import3d_GUI(morph, 0) import.instantiate(this) } /* * Assignment of mechanism values based on distance from the soma * Matches the BluePyOpt method */ proc distribute_distance(){local x localobj sl strdef stmp, distfunc, mech sl = $o1 mech = $s2 distfunc = $s3 this.soma[0] distance(0, 0.5) sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc) forsec sl for(x, 0) { // use distance(x) twice for the step distribution case, e.g. for calcium hotspot sprint(stmp, distfunc, secname(), x, distance(x), distance(x)) execute(stmp) } } proc geom_nseg() { this.geom_nsec() //To count all sections //TODO: geom_nseg_fixed depends on segCounts which is calculated by // geom_nsec. Can this be collapsed? this.geom_nseg_fixed(40) this.geom_nsec() //To count all sections } proc insertChannel() { {%- for location, names in channels.items() %} forsec this.{{location}} { {%- for channel in names %} insert {{channel}} {%- endfor %} } {%- endfor %} } proc biophys() { {% for loc, parameters in section_params %} forsec CellRef.{{ loc }} { {%- for param in parameters %} {{ param.name }} = {{ param.value }} {%- endfor %} } {% endfor %} {%- for location, param_name, value in range_params %} distribute_distance(CellRef.{{location}}, "{{param_name}}", "{{value}}") {%- endfor %} } func sec_count(/* SectionList */) { local nSec nSec = 0 forsec $o1 { nSec += 1 } return nSec } /* * Iterate over the section and compute how many segments should be allocate to * each. */ proc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize chunkSize = $1 soma area(.5) // make sure diam reflects 3d points secIndex = 0 forsec all { nseg = 1 + 2*int(L/chunkSize) segCounts.x[secIndex] = nseg secIndex += 1 } } /* * Count up the number of sections */ proc geom_nsec() { local nSec nSecAll = sec_count(all) nSecSoma = sec_count(somatic) nSecApical = sec_count(apical) nSecBasal = sec_count(basal) nSecMyelinated = sec_count(myelinated) nSecAxonalOrig = nSecAxonal = sec_count(axonal) segCounts = new Vector() segCounts.resize(nSecAll) nSec = 0 forsec all { segCounts.x[nSec] = nseg nSec += 1 } } /* * Replace the axon built from the original morphology file with a stub axon */ {%- if replace_axon %} {{replace_axon}} {%- endif %} {{re_init_rng}} endtemplate {{template_name}} ================================================ FILE: bluepyopt/evaluators.py ================================================ """Cell evaluator class""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ from abc import abstractmethod class Evaluator(object): """Evaluator class An Evaluator maps a set of parameter values to objective values Args: objectives (Objectives): The objectives that will be the output of the evaluator. params (Parameters): The parameters that will be evaluated. Attributes: objectives (Objectives): Objective objects. params (Objectives): Parameter objects. """ def __init__(self, objectives=None, params=None): self.objectives = objectives self.params = params # TODO add evaluate_with_dicts @abstractmethod def evaluate_with_dicts(self, param_dict): """Evaluate parameter a parameter set (abstract). Args: params (dict with values Parameters, and keys parameter names): The parameter values to be evaluated. Returns: objectives (dict with values Parameters, and keys objective names): Dict of Objective with values calculated by the Evaluator. """ @abstractmethod def evaluate_with_lists(self, params): """Evaluate parameter a parameter set (abstract). Args: params (list of Parameters): The parameter values to be evaluated. Returns: objectives (list of Objectives): List of Objectives with values calculated by the Evaluator. """ ================================================ FILE: bluepyopt/ipyp/__init__.py ================================================ ================================================ FILE: bluepyopt/ipyp/bpopt_tasksdb.py ================================================ """Get stats out of ipyparallel's tasks.db""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import sys import os import argparse import sqlite3 import collections import datetime import dateutil.parser import itertools import numpy as np import matplotlib.pyplot as plt def get_engine_data(tasksdb_filename): """Main""" conn = sqlite3.connect(tasksdb_filename) cursor = conn.cursor() tasks = collections.defaultdict(list) SQL = 'SELECT started, completed, engine_uuid FROM "ipython-tasks";' for started, completed, engine_uuid in cursor.execute(SQL).fetchall(): if started and completed: started = dateutil.parser.parse(started) completed = dateutil.parser.parse(completed) duration = ( completed - started).total_seconds() if completed else None task = {'started': started, 'completed': completed, 'duration': duration, 'engine_uuid': engine_uuid} tasks[engine_uuid].append(task) if len(tasks) == 0: raise Exception("No completed tasks found in the db") engine_number_map = dict(zip(tasks.keys(), range(len(tasks.keys())))) return tasks, engine_number_map def plot_usage(tasks, engine_number_map): """Plot usage stats""" plt.figure(figsize=(8, 8)) for engine_uuid, task_list in tasks.items(): engine_number = engine_number_map[engine_uuid] number_list = [engine_number] * len(task_list) start_list = [task['started'] for task in task_list] completed_list = [task['completed'] for task in task_list] plt.plot( [number_list, number_list], [start_list, completed_list], linewidth=10, solid_capstyle="butt") plt.xlim(min(engine_number_map.values()) - 1, max(engine_number_map.values()) + 1) plt.xlabel('Compute engine number') plt.ylabel('Compute time') idle_time, idle_perc = calculate_unused_compute(tasks) plt.title( 'Cumulative idle time: %s, perc: %.2f %%' % (idle_time, idle_perc)) def plot_duration_histogram(tasks): """Plot duration histogram""" plt.figure(figsize=(8, 8)) durations = np.fromiter((t['duration'] for task_list in tasks.values() for t in task_list), dtype=np.float64) plt.hist(durations, 100) plt.xlabel('Duration (s)') plt.ylabel('Count') plt.title('Histogram of task execution') plt.grid(True) def calculate_unused_compute(tasks): """Calculate unused compute time""" all_tasks = list(itertools.chain.from_iterable(tasks.values())) start_time = min(task['started'] for task in all_tasks) end_time = max(task['completed'] for task in all_tasks) total_compute_time = sum( (datetime.timedelta(seconds=task['duration']) for task in all_tasks), datetime.timedelta()) n_of_engines = len(tasks.keys()) idle_time = n_of_engines * (end_time - start_time) - total_compute_time idle_perc = 100 * \ (idle_time.total_seconds() / total_compute_time.total_seconds()) return idle_time, idle_perc def run(arg_list): """Main run""" parser = argparse.ArgumentParser() parser.add_argument('tasksdb_filename') args = parser.parse_args(arg_list) if not os.path.isfile(args.tasksdb_filename): raise IOError('Tasks db file not found at: %s' % args.tasksdb_filename) tasks, engine_number_map = get_engine_data(args.tasksdb_filename) plot_usage(tasks, engine_number_map) plot_duration_histogram(tasks) plt.show() def main(): """Main""" run(sys.argv[1:]) if __name__ == '__main__': main() ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/Ca.channel.nml ================================================ NeuroML file containing a single Channel description NeuroML file containing a single Channel description Note: was called Ca_HVA in Hay et al 2011: http://www.opensourcebrain.org/projects/l5bpyrcellhayetal2011 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 Calcium channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/Ca_HVA.channel.nml ================================================ NeuroML file containing a single Channel description High voltage activated Ca2+ current. Comment from original mod file: Reuveni, Friedman, Amitai, and Gutnick, J.Neurosci. 1993 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 Calcium channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/Ca_LVAst.channel.nml ================================================ NeuroML file containing a single Channel description Low voltage activated Ca2+ current Comment from original mod file: Note: mtau is an approximation from the plots :Reference : : Avery and Johnston 1996, tau from Randall 1997 :Comment: shifted by -10 mv to correct for junction potential :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 Ca channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/Ih.channel.nml ================================================ NeuroML file containing a single Channel description Non-specific cation current Comment from original mod file: Reference : : Kole,Hallermann,and Stuart, J. Neurosci. 2006 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/Im.channel.nml ================================================ NeuroML file containing a single Channel description Muscarinic K+ current Comment from original mod file: :Reference : : Adams et al. 1982 - M-currents and other potassium currents in bullfrog sympathetic neurones :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 K channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/K_Pst.channel.nml ================================================ NeuroML file containing a single Channel description Slow inactivating K+ current Comment from original mod file: :Comment : The persistent component of the K current :Reference : : Voltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000 :Comment : shifted -10 mv to correct for junction potential :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 K channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/K_Tst.channel.nml ================================================ NeuroML file containing a single Channel description Fast inactivating K+ current Comment from original mod file: :Comment : The transient component of the K current :Reference : : Voltage-gated K+ channels in layer 5 neocortical pyramidal neurones from young rats:subtypes and gradients,Korngreen and Sakmann, J. Physiology, 2000 :Comment : shifted -10 mv to correct for junction potential :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 K channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/KdShu2007.channel.nml ================================================ NeuroML file containing a single Channel description K-D current for prefrontal cortical neuron - Yuguo Yu 2007 K channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/NaTa_t.channel.nml ================================================ NeuroML file containing a single Channel description Fast inactivating Na+ current Comment from original mod file: :Reference :Colbert and Pan 2002 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 Na channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/NaTs2_t.channel.nml ================================================ NeuroML file containing a single Channel description Fast inactivating Na+ current. Comment from mod file (NaTs2_t.mod): took the NaTa and shifted both activation/inactivation by 6 mv Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 Na channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/Nap_Et2.channel.nml ================================================ NeuroML file containing a single Channel description Persistent Na+ current Comment from original mod file: :Comment : mtau deduced from text (said to be 6 times faster than for NaTa) :Comment : so I used the equations from NaT and multiplied by 6 :Reference : Modeled according to kinetics derived from Magistretti and Alonso 1999 :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 Na channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/SK_E2.channel.nml ================================================ NeuroML file containing a single Channel description Small-conductance, Ca2+ activated K+ current Comment from original mod file: : SK-type calcium-activated potassium current : Reference : Kohler et al. 1996 Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 K channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/SKv3_1.channel.nml ================================================ NeuroML file containing a single Channel description Fast, non inactivating K+ current Comment from original mod file: :Reference : : Characterization of a Shaw-related potassium channel family in rat brain, The EMBO Journal, vol.11, no.7,2473-2486 (1992) Models of Neocortical Layer 5b Pyramidal Cells Capturing a Wide Range of Dendritic and Perisomatic Active Properties, Etay Hay, Sean Hill, Felix Schürmann, Henry Markram and Idan Segev, PLoS Comp Biol 2011 K channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/StochKv_deterministic.channel.nml ================================================ NeuroML2 file containing a single Channel description Deterministic version of StochKv channel. See https://github.com/OpenSourceBrain/BlueBrainProjectShowcase/tree/master/NMC/NEURON/test K channels ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/baseCaDynamics_E2_NML2.nml ================================================ NeuroML 2 implementation of the Ca Pool mechanism ================================================ FILE: bluepyopt/neuroml/NeuroML2_mechanisms/pas.channel.nml ================================================ NeuroML file containing a single Channel description Simple example of a leak/passive conductance. Note: for GENESIS cells with a single leak conductance, it is better to use the Rm and Em variables for a passive current. ================================================ FILE: bluepyopt/neuroml/__init__.py ================================================ ================================================ FILE: bluepyopt/neuroml/biophys.py ================================================ """Functions to create neuroml biophysiology""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import os import shutil from pathlib import Path import neuroml from bluepyopt import ephys ignore_params = ["cm", "Ra", "ena", "ek"] # Due to the use of 1e-4 in BREAKPOINT in StochKv.mod: # ik = 1e-4 * gk * (v - ek) density_scales = {"StochKv": 1e-4} channel_substitutes = {"StochKv": "StochKv_deterministic"} channel_ions = { "Ih": "hcn", "NaTa_t": "na", "NaTs2_t": "na", "Nap_Et2": "na", "K_Tst": "k", "K_Pst": "k", "SKv3_1": "k", "SK_E2": "k", "StochKv": "k", "KdShu2007": "k", "Im": "k", "Ca": "ca", "Ca_HVA": "ca", "Ca_LVAst": "ca", "pas": "pas", "CaDynamics_E2": "ca", } ion_erevs = { "na": "50.0 mV", "k": "-85.0 mV", "hcn": "-45.0 mV", "ca": "nernst", "pas": "pas", } default_capacitances = { "axonal": "1.0 uF_per_cm2", "somatic": "1.0 uF_per_cm2", "basal": "1.0 uF_per_cm2", "apical": "1.0 uF_per_cm2", } def get_nml_mech_dir(): """Returns path to repo containing neuroml mechanisms.""" return os.path.abspath( os.path.join(os.path.dirname(__file__), "NeuroML2_mechanisms") ) def adapt_CaDynamics_nml( new_concentrations, channel_dir="channels", ): """Write concentration model if not present in CaDynamics nml file. We have to use this non-neuroml2 approved trick until https://github.com/NeuroML/NeuroML2/issues/153 is solved. Arguments: new_concentrations (dict): dict of the form "model_name": {"gamma": value "decay": value} describing the concentrations to append to ./{channel_dir}/CaDynamics_E2_NML2.nml channel_dir (str): repo in which to copy the channel files """ place_after = ( ' \n' ) cadyn_filename = "baseCaDynamics_E2_NML2.nml" new_cadyn_filename = "CaDynamics_E2_NML2.nml" nml_mech_dir = get_nml_mech_dir() with open(Path(nml_mech_dir) / cadyn_filename, "r") as f: lines = f.readlines() for model, value_dict in new_concentrations.items(): new_concentration = ( f' \n' ) if new_concentration not in lines: idx = lines.index(place_after) + 2 # 2 to account for blank line lines.insert(idx, f"{new_concentration}\n") Path(channel_dir).mkdir(exist_ok=True) with open(Path(channel_dir) / new_cadyn_filename, "w") as f: contents = "".join(lines) f.write(contents) def get_channel_from_param_name(param_name): """Return channel name, given parameter name. Arguments: param_name (str): parameter name used within NEURON """ split_name = param_name.split("_") if len(split_name) == 4: channel = "_".join(split_name[2:4]) elif len(split_name) == 3: channel = "_".join(split_name[1:3]) elif len(split_name) == 2: channel = split_name[1] else: raise Exception( f"Could not extract channel from parameter name {param_name}" ) return channel def format_dist_fun(raw_expr, value, dist_param_names): """Format and return the distribution expression. Arguments: raw_expr (str): the function expression to be formatted value (float): the value to be put in the expression dist_param_names (list): list of names of parameters that parametrise the distribution """ if dist_param_names is not None: raise NotImplementedError( "Functions that depend on other parameters, " "like decay function, are not implemented yet." ) new_expr = raw_expr.format(distance="p", value=value) if "math" in new_expr: new_expr = new_expr.replace("math.", "") if "(p)" in new_expr: new_expr = new_expr.replace("(p)", "p") return new_expr def add_nml_channel_to_nml_cell_file( cell_doc, included_channels, channel_name=None, channel_nml2_file=None, channel_dir="channels", skip_channels_copy=False, ): """Add NeuroML channel file to NeuroML cell file. And copy channel in the current directory. Arguments: cell_doc (NeuroMLDocument): nml document of the cell model included_channels (list): list of already included channels channel_name (str): name of the channel channel_nml2_file (str): name of the neuroml channel file channel_dir (str): repo in which to copy the channel files skip_channels_copy (bool): True to skip the copy pasting of the neuroml channel files """ if channel_nml2_file is None: if channel_name is None: raise ValueError( "Plaise provide either a channel name or a channel nml2 file." ) channel_nml2_file = f"{channel_name}.channel.nml" if channel_nml2_file not in included_channels: channel_new_path = Path(channel_dir) / channel_nml2_file cell_doc.includes.append(neuroml.IncludeType(href=channel_new_path)) # for some reason, pynml cannot accept absolute paths in IncludeType, # so copy paste files in current directory for the simulation to work if not skip_channels_copy: Path(channel_dir).mkdir(exist_ok=True) nml_mech_dir = Path(get_nml_mech_dir()) channel_path = nml_mech_dir / channel_nml2_file if channel_path.is_file(): shutil.copy(channel_path, channel_new_path) included_channels.append(channel_nml2_file) def get_channel_ion(channel, custom_channel_ion=None): """Get ion name given channel name. Arguments: channel (str): ion channel (e.g. StochKv) custom_channel_ion (dict): dict mapping channel to ion """ ion = channel_ions.get(channel, None) if ion is None and custom_channel_ion is not None: ion = custom_channel_ion.get(channel, None) if ion is None: raise KeyError( f"Ion not found for channel {channel}." " Please set channel-ion mapping using custom_channel_ion." ) return ion def get_erev(ion, custom_ion_erevs=None): """Get reversal potential as str given ion name. Arguments: ion (str): ion name (e.g. na) custom_ion_erevs (dict): dict mapping ion to erev (reversal potential) """ erev = ion_erevs.get(ion, None) if erev is None and custom_ion_erevs is not None: erev = custom_ion_erevs.get(ion, None) if erev is None: raise KeyError( f"Reversal potential not found for ion {ion}." " Please set ion-erev mapping using custom_ion_erevs." ) return erev def get_arguments( params, parameter_name, section_list, channel, channel_name, variable_parameters, cond_density, release_params, custom_channel_ion=None, custom_ion_erevs=None, ): """Get arguments for channel density function. Arguments: params (dict): contains the cell's parameters parameter_name (str): name of the parameter (e.g. e_pas) section_list (str): name of the location of the parameter (e.g. axonal) channel (str): ion channel (e.g. StochKv) channel_name (str): ion channel name used in the neuroML channel file (e.g. StochKv_deterministic) variable_parameters (list of neuroml.VariableParameter): parameters for non-uniform distributions cond_density (str): conductance density release_params (dict): optimized parameters custom_channel_ion (dict): dict mapping channel to ion custom_ion_erevs (dict): dict mapping ion to erev (reversal potential) """ arguments = {} arguments["ion"] = get_channel_ion(channel, custom_channel_ion) erev = get_erev(arguments["ion"], custom_ion_erevs) channel_class = "ChannelDensity" if erev == "nernst": erev = None channel_class = "ChannelDensityNernst" elif erev == "pas": erev = params[f"e_pas.{section_list}"].value if erev is None: # non frozen parameter erev = release_params[f"e_pas.{section_list}"] erev = f"{erev} mV" arguments["ion"] = "non_specific" if variable_parameters is not None: channel_class += "NonUniform" else: arguments["segment_groups"] = section_list if erev is not None: arguments["erev"] = erev arguments["id"] = f"{section_list}_{parameter_name}" if cond_density is not None: arguments["cond_density"] = cond_density arguments["ion_channel"] = channel_name if variable_parameters is not None: arguments["variable_parameters"] = variable_parameters return arguments, channel_class def extract_parameter_value( parameter, section_list, channel, skip_non_uniform, release_params ): """Returns conductance density and variable parameters. Arguments: parameter (ephys.parameters) section_list (str): location channel (str): ion channel skip_non_uniform (bool): True to skip non uniform distributions release_params (dict): optimized parameters """ cond_density = None variable_parameters = None # uniform if isinstance( parameter.value_scaler, ephys.parameterscalers.NrnSegmentLinearScaler ): value = parameter.value if value is None: # non frozen parameter value = release_params[parameter.name] if channel in density_scales: value = value * density_scales[channel] cond_density = f"{value} S_per_cm2" # non uniform elif not skip_non_uniform: value = parameter.value if value is None: # non frozen parameter value = release_params[parameter.name] # did not mulyiply by 1e4. Is that ok? new_expr = format_dist_fun( raw_expr=parameter.value_scaler.distribution, value=value, dist_param_names=parameter.value_scaler.dist_param_names, ) iv = neuroml.InhomogeneousValue( inhomogeneous_parameters=f"PathLengthOver_{section_list}", value=new_expr, ) variable_parameters = [ neuroml.VariableParameter( segment_groups=section_list, parameter="condDensity", inhomogeneous_value=iv, ) ] else: return None, None return cond_density, variable_parameters def get_density( cell_doc, cell, parameter, section_list, included_channels, skip_non_uniform, release_params, skip_channels_copy, custom_channel_ion=None, custom_ion_erevs=None, ): """Return density. Arguments: cell_doc (NeuroMLDocument): nml document of the cell model cell (ephys.CellModel): bluepyopt cell parameter (ephys.parameters) section_list (str): location included_channels (list): list of channels already included in the nml file skip_non_uniform (bool): True to skip non uniform distributions release_params (dict): optimized parameters skip_channels_copy (bool): True to skip the copy pasting of the neuroml channel files custom_channel_ion (dict): dict mapping channel to ion custom_ion_erevs (dict): dict mapping ion to erev (reversal potential) """ channel = get_channel_from_param_name(parameter.param_name) cond_density, variable_parameters = extract_parameter_value( parameter, section_list, channel, skip_non_uniform, release_params ) if cond_density is None and variable_parameters is None: return None, None channel_name = channel if channel in channel_substitutes: channel_name = channel_substitutes[channel] # add nml channel to nml cell file add_nml_channel_to_nml_cell_file( cell_doc, included_channels, channel_name=channel_name, skip_channels_copy=skip_channels_copy, ) arguments, channel_class = get_arguments( params=cell.params, parameter_name=parameter.param_name, section_list=section_list, channel=channel, channel_name=channel_name, variable_parameters=variable_parameters, cond_density=cond_density, release_params=release_params, custom_channel_ion=custom_channel_ion, custom_ion_erevs=custom_ion_erevs, ) density = getattr(neuroml, channel_class)(**arguments) return density, channel_class def get_specific_capacitance(capacitance_overwrites): """Returns the specific capacitance. Arguments: capacitance_overwrites (dict): capacitance values from parameters to overwrites default ones. """ specific_capacitances = [] for section_list in default_capacitances: if section_list in capacitance_overwrites: capacitance = capacitance_overwrites[section_list] elif "all" in capacitance_overwrites: capacitance = capacitance_overwrites["all"] else: capacitance = default_capacitances[section_list] specific_capacitances.append( neuroml.SpecificCapacitance( value=capacitance, segment_groups=section_list ) ) return specific_capacitances def get_biophys( cell, cell_doc, release_params, skip_non_uniform=False, skip_channels_copy=False, custom_channel_ion=None, custom_ion_erevs=None, ): """Get biophys in neuroml format. Arguments: cell (ephys.CellModel): bluepyopt cell cell_doc (NeuroMLDocument): nml document of the cell model release_params (dict): optimized parameters skip_non_uniform (bool): True to skip non uniform distributions skip_channels_copy (bool): True to skip the copy pasting of the neuroml channel files custom_channel_ion (dict): dict mapping channel to ion custom_ion_erevs (dict): dict mapping ion to erev (reversal potential) """ concentrationModels = {} # Membrane properties included_channels = [] channel_densities = [] channel_density_nernsts = [] channel_density_non_unif_nernsts = [] channel_density_non_uniforms = [] species = [] capacitance_overwrites = {} for parameter in cell.params.values(): if not ( isinstance(parameter, ephys.parameters.NrnGlobalParameter) or isinstance(parameter, ephys.parameters.MetaParameter) ): for location in parameter.locations: section_list = location.seclist_name if ( parameter.param_name != "e_pas" and "CaDynamics_E2" not in parameter.param_name and parameter.param_name not in ignore_params ): density, channel_class = get_density( cell_doc, cell, parameter, section_list, included_channels, skip_non_uniform, release_params, skip_channels_copy, custom_channel_ion, custom_ion_erevs, ) if density is not None: # add density to list of densities if channel_class == "ChannelDensityNernst": channel_density_nernsts.append(density) elif ( channel_class == "ChannelDensityNernstNonUniform" ): channel_density_non_unif_nernsts.append( density ) elif channel_class == "ChannelDensityNonUniform": channel_density_non_uniforms.append(density) else: channel_densities.append(density) elif "gamma_CaDynamics_E2" in parameter.param_name: model = f"CaDynamics_E2_NML2__{cell.name}_{section_list}" value = parameter.value if value is None: # non frozen parameter value = release_params[parameter.name] if model not in concentrationModels: concentrationModels[model] = {} concentrationModels[model]["gamma"] = value elif "decay_CaDynamics_E2" in parameter.param_name: model = f"CaDynamics_E2_NML2__{cell.name}_{section_list}" species.append( neuroml.Species( id="ca", ion="ca", initial_concentration="5.0E-11 mol_per_cm3", initial_ext_concentration="2.0E-6 mol_per_cm3", concentration_model=model, segment_groups=section_list, ) ) channel_nml2_file = "CaDynamics_E2_NML2.nml" add_nml_channel_to_nml_cell_file( cell_doc, included_channels, channel_nml2_file=channel_nml2_file, ) value = parameter.value if value is None: # non frozen parameter value = release_params[parameter.name] if model not in concentrationModels: concentrationModels[model] = {} concentrationModels[model]["decay"] = value elif parameter.param_name == "cm": capacitance_overwrites[ section_list ] = f"{parameter.value} uF_per_cm2" # append new concentrations to the CaDynamics_E2_NML2.nml file if concentrationModels and not skip_channels_copy: adapt_CaDynamics_nml(concentrationModels) specific_capacitances = get_specific_capacitance(capacitance_overwrites) v_init = cell.params["v_init"].value init_memb_potentials = [ neuroml.InitMembPotential(value=f"{v_init} mV", segment_groups="all") ] membrane_properties = neuroml.MembraneProperties( channel_densities=channel_densities, channel_density_nernsts=channel_density_nernsts, channel_density_non_uniform_nernsts=channel_density_non_unif_nernsts, channel_density_non_uniforms=channel_density_non_uniforms, specific_capacitances=specific_capacitances, init_memb_potentials=init_memb_potentials, ) # Intracellular Properties Ra = cell.params["Ra.all"].value resistivities = [ neuroml.Resistivity(value=f"{Ra} ohm_cm", segment_groups="all") ] intracellular_properties = neuroml.IntracellularProperties( resistivities=resistivities, species=species, ) # Biophysical Properties biophysical_properties = neuroml.BiophysicalProperties( id="biophys", intracellular_properties=intracellular_properties, membrane_properties=membrane_properties, ) return biophysical_properties ================================================ FILE: bluepyopt/neuroml/cell.py ================================================ """Functions to create neuroml cell""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import logging from neuroml import NeuroMLDocument from pyneuroml import pynml from .biophys import get_biophys from .morphology import add_segment_groups from .morphology import create_morph_nml logger = logging.getLogger(__name__) def create_neuroml_cell( bpo_cell, release_params, skip_channels_copy=False, custom_channel_ion=None, custom_ion_erevs=None, ): """Create the cell. Arguments: bpo_cell (ephys.CellModel): bluepyopt cell release_params (dict): the optimized parameters skip_channels_copy (bool): True to skip the copy pasting of the neuroml channel files custom_channel_ion (dict): dict mapping channel to ion custom_ion_erevs (dict): dict mapping ion to erev (reversal potential) :returns: name of the cell nml file """ # Create the nml file and add the ion channels cell_doc = NeuroMLDocument(id=bpo_cell.name) # the network name network_filename = f"{bpo_cell.name}.net.nml" # Morphology logger.info( "This will create a cell hoc file in order to create a cell nml file" ) create_morph_nml(bpo_cell, network_filename, release_params) # change the network temperature. # because the pyneurom.export_to_neuroml2 sets it automatically to 6C. network_doc = pynml.read_neuroml2_file(network_filename) network = network_doc.networks[0] network.temperature = f"{bpo_cell.params['celsius'].value} degC" pynml.write_neuroml2_file( nml2_doc=network_doc, nml2_file_name=network_filename, validate=True ) # get the cell nml_cell_loc = f"{bpo_cell.name}_0_0.cell.nml" nml_doc = pynml.read_neuroml2_file(nml_cell_loc) cell = nml_doc.cells[0] # add segment groups (cell) add_segment_groups(cell) # get biophys bio_prop = get_biophys( bpo_cell, cell_doc, release_params, skip_non_uniform=True, skip_channels_copy=skip_channels_copy, custom_channel_ion=custom_channel_ion, custom_ion_erevs=custom_ion_erevs, ) # Append biophys to cell cell.biophysical_properties = bio_prop # Adding notes notes = "This cell was exported from BluePyOpt." cell.notes = notes # write neuroml cell doc cell_doc.cells.append(cell) pynml.write_neuroml2_file( nml2_doc=cell_doc, nml2_file_name=nml_cell_loc, validate=True ) return nml_cell_loc ================================================ FILE: bluepyopt/neuroml/morphology.py ================================================ """Functions to create neuroml morphology""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import logging import os from pathlib import Path import neuroml from pyneuroml.neuron import export_to_neuroml2 logger = logging.getLogger(__name__) def create_loadcell_hoc( loadcell_hoc_filename, hoc_filename, morphology_path, v_init, cell_name ): """Create a hoc file able to load the cell. Arguments: loadcell_hoc_filename (str): path to the loadcell hoc file to output hoc_filename (str): file name of the cell hoc file morphology_path (str): path to the morphology file v_init (float): inital voltage in mV cell_name (str): cell name """ morph_path = Path(morphology_path) morph_dir = morph_path.parent morph_file = morph_path.name cell_cmd = '"cell = new %s(\\"%s\\", \\"%s\\")"' cell = f'{cell_cmd}, "{cell_name}", "{morph_dir}", "{morph_file}"' loadcell_hoc = f""" load_file("nrngui.hoc") load_file("import3d.hoc") load_file("{hoc_filename}") // ================== constants ================== v_init={v_init} // ================== creating cell object ================== objref cell proc create_cell() {{ localobj cellstring cellstring = new String() sprint(cellstring.s, {cell}) execute(cellstring.s) }} create_cell(0) """ with open(loadcell_hoc_filename, "w", encoding="utf-8") as hoc_file: hoc_file.write(loadcell_hoc) def create_morph_nml(bpo_cell, network_filename, release_params): """Create cell hoc file, then cell nml file. Arguments: bpo_cell (ephys.CellModel): bluepyopt cell network_filename (str): name of the neuroml network file release_params (dict): name and values of optimised parameters """ import pebble hoc_filename = f"{bpo_cell.name}.hoc" loadcell_hoc_filename = "loadcell.hoc" # write the cell in a hoc file cell_hoc = bpo_cell.create_hoc(release_params) with open(hoc_filename, "w", encoding="utf-8") as hoc_file: hoc_file.write(cell_hoc) # write a hoc file able to load the cell create_loadcell_hoc( loadcell_hoc_filename, hoc_filename, bpo_cell.morphology.morphology_path, bpo_cell.params["v_init"].value, bpo_cell.name, ) if not os.path.isdir("x86_64"): logger.warning( "It seems you have not compiled the mechanisms. " "This program will likely fail." ) # isolate the export_to_neuroml to a subprocess # so that the cell remain non-instantiated in the main process # that way, using this function will not prevent us to run the cell # with bluepyopt with pebble.ProcessPool(max_workers=1, max_tasks=1) as pool: tasks = pool.schedule( export_to_neuroml2, kwargs={ "hoc_or_python_file": loadcell_hoc_filename, "nml2_file_name": network_filename, "separateCellFiles": True, "includeBiophysicalProperties": False, }, ) tasks.result() def add_segment_groups(cell): """Add the segment groups to be consistent with naming in biophys. Arguments: cell (neuroml.Cell): neuroml cell """ groups = {"somatic": [], "axonal": [], "basal": [], "apical": []} for seg in cell.morphology.segment_groups: if "soma" in seg.id: groups["somatic"].append(seg) elif "axon" in seg.id: groups["axonal"].append(seg) elif "dend" in seg.id: groups["basal"].append(seg) elif "apic" in seg.id: groups["apical"].append(seg) for g, segments in groups.items(): new_seg_group = neuroml.SegmentGroup(id=g) cell.morphology.segment_groups.append(new_seg_group) for sg in segments: new_seg_group.includes.append(neuroml.Include(sg.id)) if g in ["basal", "apical"]: new_seg_group.inhomogeneous_parameters.append( neuroml.InhomogeneousParameter( id=f"PathLengthOver_{g}", variable="p", metric="Path Length from root", proximal=neuroml.ProximalDetails(translation_start="0"), ) ) cell.morphology.segment_groups.append( neuroml.SegmentGroup( id="soma_group", includes=[neuroml.Include("somatic")] ) ) cell.morphology.segment_groups.append( neuroml.SegmentGroup( id="axon_group", includes=[neuroml.Include("axonal")] ) ) cell.morphology.segment_groups.append( neuroml.SegmentGroup( id="dendrite_group", includes=[neuroml.Include("basal"), neuroml.Include("apical")], ) ) ================================================ FILE: bluepyopt/neuroml/simulation.py ================================================ """Functions to create neuroml simulation""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import logging import neuroml from neuroml import ExplicitInput from pyneuroml import pynml from pyneuroml.lems import generate_lems_file_for_neuroml logger = logging.getLogger(__name__) def create_neuroml_simulation( network_filename, protocols, dt, cell_name, lems_filename ): """Append simulation data to a neuroml network. Arguments: network_filename (str): name of the neuroml network file protocols (ephys.protocols.Protocol): protocols dt (float): timestep cell_name (str): name of the cell lems_filename (str): file name under which to register the lems file """ # Get neuroml netowrk file new_net_doc = pynml.read_neuroml2_file(network_filename) new_net = new_net_doc.networks[0] pop_id = new_net.populations[0].id stim_sim_duration = 0 # Create neuroml stimuli # Expects ephys.stimuli.NrnSquarePulse objects for i, stim in enumerate(protocols.stimuli): stim_sim_duration = stim.total_duration stim_ref = f"stimulus_{i}" # create Pulse new_nml_stim = neuroml.PulseGenerator( id=stim_ref, delay=f"{stim.step_delay}ms", duration=f"{stim.step_duration}ms", amplitude=f"{stim.step_amplitude}nA", ) new_net_doc.pulse_generators.append(new_nml_stim) exp_input = ExplicitInput(target=f"{pop_id}[0]", input=stim_ref) new_net.explicit_inputs.append(exp_input) # write updated netowrk file pynml.write_neuroml2_file(new_net_doc, network_filename) local_nml2_cell_dir = "." # target dir generate_lems_file_for_neuroml( cell_name, network_filename, "network", stim_sim_duration, dt, lems_filename, local_nml2_cell_dir, copy_neuroml=False, simulation_seed=1234, ) ================================================ FILE: bluepyopt/objectives.py ================================================ """Objective classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ class Objective(object): """Objective of the optimisation algorithm""" def __init__(self, name, value=None): """Constructor""" self.name = name self.value = value ================================================ FILE: bluepyopt/optimisations.py ================================================ """Optimisation class""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ class Optimisation(object): """Optimisation class""" _instance_counter = 0 def __init__(self, evaluator=None): """Constructor""" self.evaluator = evaluator ================================================ FILE: bluepyopt/parameters.py ================================================ """Parameter classes""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ class Parameter(object): """Base parameter class""" def __init__(self, name, value=None, frozen=False, bounds=None, param_dependencies=None): """Constructor""" self.name = name self.prefix = None self.bounds = bounds self._value = value self.check_bounds() self.frozen = frozen self.param_dependencies = param_dependencies if param_dependencies is None: self.param_dependencies = [] @property def lower_bound(self): """Lower bound""" if self.bounds is not None: return self.bounds[0] else: return None @property def upper_bound(self): """Lower bound""" if self.bounds is not None: return self.bounds[1] else: return None @property def value(self): """Parameter value""" return self._value def freeze(self, value): """Freeze parameter to certain value""" self.value = value self.frozen = True def unfreeze(self): """Unfreeze parameter""" self._value = None self.frozen = False @value.setter def value(self, value): """Set parameter value""" if self.frozen: raise Exception( 'Parameter: parameter %s is frozen, unable to change value' % self.name) else: self._value = value self.check_bounds() def check_bounds(self): """Check if parameter is within bounds""" if self.bounds and self._value is not None: if not self.lower_bound <= self._value <= self.upper_bound: raise ValueError( 'Parameter %s has value %s outside of bounds [%s, %s]' % (self.name, self._value, str(self.lower_bound), str(self.upper_bound))) def __str__(self): """String representation""" return '%s: value = %s' % ( self.name, self.value if self.frozen else self.bounds) class MetaListEqualParameter(Parameter): """Metaparameter that makes sure list of parameter values are all equal""" def __init__(self, name, value=None, frozen=False, bounds=None, sub_parameters=None): """Constructor""" if sub_parameters is None: raise ValueError( 'MetaListEqualParameter: impossible to have None as ' 'sub_parameters attribute') else: self.sub_parameters = sub_parameters super( MetaListEqualParameter, self).__init__( name, value=value, frozen=frozen, bounds=bounds) if value is not None: for sub_parameter in self.sub_parameters: sub_parameter.value = value @Parameter.value.setter def value(self, value): """Set parameter value""" for sub_parameter in self.sub_parameters: sub_parameter.value = value Parameter.value.fset(self, value) def freeze(self, value): """Freeze parameter to certain value""" super(MetaListEqualParameter, self).freeze(value) for sub_parameter in self.sub_parameters: sub_parameter.frozen = True def unfreeze(self): """Unfreeze parameter""" for sub_parameter in self.sub_parameters: sub_parameter.unfreeze() super(MetaListEqualParameter, self).unfreeze() def check_bounds(self): """Check if parameter is within bounds""" for sub_parameter in self.sub_parameters: sub_parameter.check_bounds() super(MetaListEqualParameter, self).check_bounds() def __str__(self): """String representation""" return '%s (sub_params: %s): value = %s' % (self.name, ",".join( str(sub_param) for sub_param in self.sub_parameters), self.value if self.frozen else self.bounds) ================================================ FILE: bluepyopt/stoppingCriteria.py ================================================ """Stopping Criteria class""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ class StoppingCriteria(object): """Stopping Criteria class""" def __init__(self): """Constructor""" self.criteria_met = False pass def check(self, kwargs): """Check if the stopping criteria is met""" pass def reset(self): self.criteria_met = False ================================================ FILE: bluepyopt/tests/.gitignore ================================================ /.coverage /coverage.xml /coverage_html/ ================================================ FILE: bluepyopt/tests/__init__.py ================================================ ================================================ FILE: bluepyopt/tests/disable_simplecell_scoop.py ================================================ ''' Note: this is a bizarre test due to the fact that scoop can't be started from within python: https://github.com/soravux/scoop/issues/29 Therefore, the way this works is for the test_ that's detected by nt then creates a subprocess that runs scoop using the normal command line arguments It then captures the output, and looks for the BEST: magic string which should match the precomputed output ''' import os import subprocess import bluepyopt as nrp import bluepyopt.ephys as nrpel SIMPLE_SWC = os.path.join(os.path.abspath(os.path.dirname(__file__)), '../../examples/simplecell/simple.swc') # Disabled this test. Doesn't work on a mac for some reason # Error message: can't import scoop # TODO Renable once this is fixed def disabled_scoop(): """Simplecell: test scoop""" cmd = ['python', '-m', 'scoop', '-n', '2', __file__] output = subprocess.check_output(cmd) for line in output.split('\n'): if line.startswith('BEST'): break assert line == 'BEST: [0.11268238279399023, 0.038129859413828474]' # The rest defines the optimization we run with scoop morph = nrpel.morphologies.NrnFileMorphology(SIMPLE_SWC) somatic_loc = nrpel.locations.NrnSeclistLocation('somatic', seclist_name='somatic') hh_mech = nrpel.mechanisms.NrnMODMechanism(name='hh', suffix='hh', locations=[somatic_loc]) cm_param = nrpel.parameters.NrnSectionParameter(name='cm', param_name='cm', value=1.0, locations=[somatic_loc], frozen=True) gnabar_param = nrpel.parameters.NrnSectionParameter(name='gnabar_hh', param_name='gnabar_hh', locations=[somatic_loc], bounds=[0.05, 0.125], frozen=False) gkbar_param = nrpel.parameters.NrnSectionParameter(name='gkbar_hh', param_name='gkbar_hh', bounds=[0.01, 0.075], locations=[somatic_loc], frozen=False) simple_cell = nrpel.celltemplate.CellTemplate(name='simple_cell', morph=morph, mechs=[hh_mech], params=[cm_param, gnabar_param, gkbar_param]) soma_loc = nrpel.locations.NrnSeclistCompLocation(name='soma', seclist_name='somatic', sec_index=0, comp_x=0.5) protocols = {} for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]: stim = nrpel.stimuli.NrnSquarePulse(step_amplitude=amplitude, step_delay=100, step_duration=50, location=soma_loc, total_duration=200) rec = nrpel.recordings.CompRecording(name='%s.soma.v' % protocol_name, location=soma_loc, variable='v') protocol = nrpel.protocols.Protocol(protocol_name, [stim], [rec]) protocols[protocol.name] = protocol default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03} responses = simple_cell.run_protocols(protocols, param_values=default_params) efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}} objectives = [] for protocol_name, protocol in protocols.items(): stim_start = protocol.stimuli[0].step_delay stim_end = stim_start + protocol.stimuli[0].step_duration for efel_feature_name, mean in \ efel_feature_means[protocol_name].items(): feature_name = '%s.%s' % (protocol_name, efel_feature_name) feature = nrpel.efeatures.eFELFeature( feature_name, efel_feature_name=efel_feature_name, recording_names={'': '%s.soma.v' % protocol_name}, stim_start=stim_start, stim_end=stim_end, exp_mean=mean, exp_std=0.05 * mean) objective = objective = nrpel.objectives.SingletonObjective( feature_name, feature) objectives.append(objective) score_calc = nrpel.scorecalculators.ObjectivesScoreCalculator(objectives) cell_evaluator = nrpel.cellevaluator.CellEvaluator( cell_template=simple_cell, param_names=[ 'gnabar_hh', 'gkbar_hh'], fitness_protocols=protocols, fitness_calculator=score_calc) optimisation = nrp.Optimisation( evaluator=cell_evaluator, eval_function=cell_evaluator.evaluate_with_lists, offspring_size=10, use_scoop=True) if __name__ == '__main__': final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=2) print('BEST:', hall_of_fame[0]) ================================================ FILE: bluepyopt/tests/expected_results.json ================================================ { "TestL5PCEvaluator.test_eval": { "bAP.soma.AP_width": 1.9999999999995453, "bAP.soma.AP_height": 2.50384474984601, "bAP.soma.Spikecount": 0.0, "bAP.dend1.AP_amplitude_from_voltagebase": 0.8267263765251129, "bAP.dend2.AP_amplitude_from_voltagebase": 0.5795372919702172, "Step3.soma.AP_height": 0.9933359179333321, "Step3.soma.AHP_slow_time": 1.7605122073692951, "Step3.soma.ISI_CV": 0.8718173723988226, "Step3.soma.doublet_ISI": 0.39855072463652236, "Step3.soma.adaptation_index2": 1.1379787461057103, "Step3.soma.mean_frequency": 1.7684352065813025, "Step3.soma.AHP_depth_abs_slow": 2.24354209144176, "Step3.soma.AP_width": 3.044293354575099, "Step3.soma.time_to_first_spike": 0.07352941183310188, "Step3.soma.AHP_depth_abs": 0.8314513271562025, "Step2.soma.AP_height": 0.40331820266634766, "Step2.soma.AHP_slow_time": 0.5635329569575162, "Step2.soma.ISI_CV": 0.4167583130087039, "Step2.soma.doublet_ISI": 0.08411961809835558, "Step2.soma.adaptation_index2": 0.9608331502738571, "Step2.soma.mean_frequency": 0.3526065669686068, "Step2.soma.AHP_depth_abs_slow": 0.3002832227214548, "Step2.soma.AP_width": 2.5539797463199387, "Step2.soma.time_to_first_spike": 1.1294134000889067, "Step2.soma.AHP_depth_abs": 0.47825588583451795, "Step1.soma.AP_height": 1.193221084786481, "Step1.soma.AHP_slow_time": 0.4051335332920494, "Step1.soma.ISI_CV": 0.6241490491551318, "Step1.soma.doublet_ISI": 0.4396536563682781, "Step1.soma.adaptation_index2": 0.1682382448448352, "Step1.soma.mean_frequency": 0.9054156314648848, "Step1.soma.AHP_depth_abs_slow": 0.48627438571478304, "Step1.soma.AP_width": 3.062383612663639, "Step1.soma.time_to_first_spike": 1.0572856593789417, "Step1.soma.AHP_depth_abs": 0.770012863966287 } } ================================================ FILE: bluepyopt/tests/test_bluepyopt.py ================================================ """Tests of the main bluepyopt module""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import pytest import numpy @pytest.mark.unit def test_import(): """bluepyopt: test importing bluepyopt""" import bluepyopt # NOQA ================================================ FILE: bluepyopt/tests/test_deapext/__init__.py ================================================ ================================================ FILE: bluepyopt/tests/test_deapext/deapext_test_utils.py ================================================ import random import numpy as np from deap import base from deap import creator def make_mock_population(features_count=5, population_count=5): """Create pop of inds that we have full control over,creating a DEAP one""" # TODO: Use mock instead class Individual(object): class Fitness(object): def __init__(self, wvalues, valid): self.wvalues = wvalues self.valid = valid def __init__(self, wvalues, valid, ibea_fitness): self.fitness = Individual.Fitness(wvalues, valid) self.ibea_fitness = ibea_fitness MU, SIGMA = 0, 1 np.random.seed(0) random.seed(0) # create individuals w/ a random weight values, and an ibea_fitness # according to their position return [Individual(np.random.normal(MU, SIGMA, features_count), bool( i % 2), i) for i in range(population_count)] def make_population(features_count=5, population_count=5): '''create population w/ DEAP Individuals ''' creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0)) creator.create("Individual", list, fitness=creator.FitnessMin) random.seed(0) population = [creator.Individual(range(i * features_count, (i + 1) * features_count)) for i in range(population_count)] return population ================================================ FILE: bluepyopt/tests/test_deapext/test_algorithms.py ================================================ """bluepyopt.optimisations tests""" import numpy from unittest import mock import deap.creator import deap.benchmarks import bluepyopt.deapext.algorithms import pytest @pytest.mark.unit def test_eaAlphaMuPlusLambdaCheckpoint(): """deapext.algorithms: Testing eaAlphaMuPlusLambdaCheckpoint""" deap.creator.create('fit', deap.base.Fitness, weights=(-1.0,)) deap.creator.create( 'ind', numpy.ndarray, fitness=deap.creator.__dict__['fit']) population = [deap.creator.__dict__['ind'](x) for x in numpy.random.uniform(0, 1, (10, 2))] toolbox = deap.base.Toolbox() toolbox.register("evaluate", deap.benchmarks.sphere) toolbox.register("mate", lambda x, y: (x, y)) toolbox.register("mutate", lambda x: (x,)) toolbox.register("select", lambda pop, mu: pop) toolbox.register("variate", lambda par, toolb, cxpb, mutpb: par) population, hof, logbook, history = \ bluepyopt.deapext.algorithms.eaAlphaMuPlusLambdaCheckpoint( population=population, toolbox=toolbox, mu=1.0, cxpb=1.0, mutpb=1.0, ngen=2, stats=None, halloffame=None, cp_frequency=1, cp_filename=None, continue_cp=False) assert isinstance(population, list) assert len(population) == 20 assert isinstance(logbook, deap.tools.support.Logbook) assert isinstance(history, deap.tools.support.History) @pytest.mark.unit def test_eaAlphaMuPlusLambdaCheckpoint_with_checkpoint(): """deapext.algorithms: Testing eaAlphaMuPlusLambdaCheckpoint""" deap.creator.create('fit', deap.base.Fitness, weights=(-1.0,)) deap.creator.create( 'ind', numpy.ndarray, fitness=deap.creator.__dict__['fit']) population = [deap.creator.__dict__['ind'](x) for x in numpy.random.uniform(0, 1, (10, 2))] toolbox = deap.base.Toolbox() toolbox.register("evaluate", deap.benchmarks.sphere) toolbox.register("mate", lambda x, y: (x, y)) toolbox.register("mutate", lambda x: (x,)) toolbox.register("select", lambda pop, mu: pop) with mock.patch('pickle.dump'): with mock.patch('bluepyopt.deapext.algorithms.open', mock.mock_open()): population, hof, logbook, history = \ bluepyopt.deapext.algorithms.eaAlphaMuPlusLambdaCheckpoint( population=population, toolbox=toolbox, mu=1.0, cxpb=1.0, mutpb=1.0, ngen=2, stats=None, halloffame=None, cp_frequency=1, cp_filename='cp_test', continue_cp=False) import random with mock.patch('pickle.load', return_value={'population': population, 'logbook': logbook, 'history': history, 'parents': None, 'halloffame': None, 'rndstate': random.getstate(), 'generation': 1}): with mock.patch('bluepyopt.deapext.algorithms.open', mock.mock_open()): new_population, hof, logbook, history = \ bluepyopt.deapext.algorithms.eaAlphaMuPlusLambdaCheckpoint( population=population, toolbox=toolbox, mu=1.0, cxpb=1.0, mutpb=1.0, ngen=0, stats=None, halloffame=None, cp_frequency=1, cp_filename='cp_test', continue_cp=True) for ind1, ind2 in zip(new_population, population): assert list(ind1) == list(ind2) ================================================ FILE: bluepyopt/tests/test_deapext/test_hype.py ================================================ """bluepyopt.deapext.hype tests""" import numpy import bluepyopt.deapext.hype import pytest @pytest.mark.unit def test_hypeIndicatorExact(): """deapext.hype: Testing hypeIndicatorExact""" points = numpy.asarray([[250., 250.], [0., 0.], [240., 240.]]) bounds = numpy.asarray([250., 250.]) hv = bluepyopt.deapext.hype.hypeIndicatorExact(points, bounds, k=5) assert hv[0] == 0 assert hv[1] == 62500 assert hv[2] == 100 @pytest.mark.unit def test_hypeIndicatorSampled(): """deapext.hype: Testing hypeIndicatorSampled""" points = numpy.asarray([[250., 250.], [0., 0.], [240., 240.]]) bounds = numpy.asarray([250., 250.]) numpy.random.seed(42) hv = bluepyopt.deapext.hype.hypeIndicatorSampled( points, bounds, nrOfSamples=1000000, k=5 ) assert hv[0] == 0 assert hv[1] == 62500 assert numpy.abs((hv[2] / 100) - 1) < 0.05 ================================================ FILE: bluepyopt/tests/test_deapext/test_optimisations.py ================================================ """bluepyopt.optimisations tests""" import bluepyopt.optimisations import bluepyopt.ephys.examples.simplecell import pytest import numpy import deap.tools @pytest.mark.unit def test_DEAPOptimisation_constructor(): "deapext.optimisation: Testing constructor DEAPOptimisation" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() optimisation = bluepyopt.deapext.optimisations.DEAPOptimisation( simplecell.cell_evaluator, map_function=map) assert isinstance(optimisation, bluepyopt.deapext.optimisations.DEAPOptimisation) assert isinstance(optimisation.evaluator, bluepyopt.evaluators.Evaluator) pytest.raises( ValueError, bluepyopt.deapext.optimisations.DEAPOptimisation, simplecell.cell_evaluator, selector_name='wrong') @pytest.mark.unit def test_IBEADEAPOptimisation_constructor(): "deapext.optimisation: Testing constructor IBEADEAPOptimisation" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() optimisation = bluepyopt.deapext.optimisations.IBEADEAPOptimisation( simplecell.cell_evaluator, map_function=map) assert isinstance(optimisation, bluepyopt.deapext.optimisations.IBEADEAPOptimisation) @pytest.mark.unit def test_DEAPOptimisation_run(): "deapext.optimisation: Testing DEAPOptimisation run" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() optimisation = bluepyopt.optimisations.DEAPOptimisation( simplecell.cell_evaluator, offspring_size=1) pop, hof, log, hist = optimisation.run(max_ngen=1) ind = [0.06007731830843009, 0.06508319290092013] assert len(pop) == 1 numpy.testing.assert_almost_equal(pop[0], ind) numpy.testing.assert_almost_equal(hof[0], ind) assert log[0]['nevals'] == 1 numpy.testing.assert_almost_equal(hist.genealogy_history[1], ind) @pytest.mark.unit def test_DEAPOptimisation_run_from_parents(): "deapext.optimisation: Testing DEAPOptimisation run using prior parents" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() optimisation = bluepyopt.optimisations.DEAPOptimisation( simplecell.cell_evaluator, offspring_size=1) parent_population = [[0.060, 0.065]] pop, hof, log, hist = optimisation.run(max_ngen=0, parent_population=parent_population) assert len(pop) == 1 numpy.testing.assert_almost_equal(pop[0], parent_population[0]) @pytest.mark.unit def test_selectorname(): "deapext.optimisation: Testing selector_name argument" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() # Test default value ibea_optimisation = bluepyopt.optimisations.DEAPOptimisation( simplecell.cell_evaluator) assert ibea_optimisation.selector_name == 'IBEA' simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() # Test NSGA2 selector nsga2_optimisation = bluepyopt.optimisations.DEAPOptimisation( simplecell.cell_evaluator, selector_name='NSGA2') assert ( nsga2_optimisation.toolbox.select.func == deap.tools.emo.selNSGA2) simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() # Test IBEA selector ibea_optimisation = bluepyopt.optimisations.DEAPOptimisation( simplecell.cell_evaluator, selector_name='IBEA') assert ( ibea_optimisation.toolbox.select.func == bluepyopt.deapext.tools.selIBEA) ================================================ FILE: bluepyopt/tests/test_deapext/test_optimisationsCMA.py ================================================ """bluepyopt.optimisationsCMA tests""" import pytest import bluepyopt import bluepyopt.ephys.examples.simplecell @pytest.mark.unit def test_optimisationsCMA_normspace(): """deapext.optimisationsCMA: Testing optimisationsCMA normspace""" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() evaluator = simplecell.cell_evaluator optimisation = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA( evaluator=evaluator) x = [n * 0.1 for n in range(len(evaluator.params))] y = [f2(f1(p)) for p, f1, f2 in zip(x, optimisation.to_norm, optimisation.to_space)] for a, b in zip(x, y): assert b == pytest.approx(a, abs=1e-5) @pytest.mark.unit def test_optimisationsCMA_SO_run(): """deapext.optimisationsCMA: Testing optimisationsCMA run from centroid""" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() evaluator = simplecell.cell_evaluator x = [n * 0.1 for n in range(len(evaluator.params))] optimiser = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA optimisation = optimiser(evaluator=evaluator, centroids=[x]) pop, hof, log, hist = optimisation.run(max_ngen=2) assert log.select("avg")[-1] == pytest.approx(53.3333, abs=1e-4) assert log.select("std")[-1] == pytest.approx(83.7987, abs=1e-4) assert pop[0][0] == pytest.approx(0.10525059698894745, abs=1e-6) assert pop[0][1] == pytest.approx(0.01000000003249999, abs=1e-6) @pytest.mark.unit def test_optimisationsCMA_MO_run(): """deapext.optimisationsCMA: Testing optimisationsCMA run from centroid""" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() evaluator = simplecell.cell_evaluator optimiser = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA optimisation = optimiser( selector_name="multi_objective", offspring_size=3, evaluator=evaluator, seed=42 ) pop, hof, log, hist = optimisation.run(max_ngen=2) assert log.select("avg")[-1] == pytest.approx(40., abs=1e-4) assert log.select("std")[-1] == pytest.approx(16.32993, abs=1e-4) assert pop[0][0] == pytest.approx(0.09601241274168831, abs=1e-6) assert pop[0][1] == pytest.approx(0.024646650865379722, abs=1e-6) ================================================ FILE: bluepyopt/tests/test_deapext/test_selIBEA.py ================================================ """selIBEA tests""" import deap import numpy import bluepyopt.deapext from bluepyopt.deapext.tools.selIBEA \ import (_calc_fitness_components, _mating_selection,) from .deapext_test_utils import make_mock_population import pytest @pytest.mark.unit def test_calc_fitness_components(): """deapext.selIBEA: test calc_fitness_components""" KAPPA = 0.05 population = make_mock_population() components = _calc_fitness_components(population, kappa=KAPPA) expected = numpy.array( [ [1.00000000e+00, 4.30002298e-05, 4.26748513e-09, 2.06115362e-09, 9.71587289e-03], [5.11484499e-09, 1.00000000e+00, 2.02317572e-07, 4.79335491e-05, 3.52720088e-08], [6.75130710e-07, 1.23735078e+00, 1.00000000e+00, 2.77149617e-01, 8.37712763e-06], [2.06115362e-09, 3.04444453e-04, 8.15288827e-08, 1.00000000e+00, 2.06115362e-09], [2.06115362e-09, 6.75565231e-04, 4.39228177e-07, 2.12142918e-07, 1.00000000e+00] ]) assert numpy.allclose(expected, components) @pytest.mark.unit def test_mating_selection(): """deapext.selIBEA: test mating selection""" PARENT_COUNT = 10 population = make_mock_population() parents = _mating_selection(population, PARENT_COUNT, 5) assert len(parents) == PARENT_COUNT expected = [1, 1, 1, 1, 1, 0, 1, 0, 0, 0] assert expected == [ind.ibea_fitness for ind in parents] @pytest.mark.unit def test_selibea_init(): """deapext.selIBEA: test selIBEA init""" deap.creator.create('fit', deap.base.Fitness, weights=(-1.0,)) deap.creator.create( 'ind', numpy.ndarray, fitness=deap.creator.__dict__['fit']) numpy.random.seed(1) population = [deap.creator.__dict__['ind'](x) for x in numpy.random.uniform(0, 1, (10, 2))] for ind in population: ind.fitness.values = (numpy.random.uniform(0, 1), ) mu = 5 parents = bluepyopt.deapext.tools.selIBEA(population, mu) assert len(parents) == mu ================================================ FILE: bluepyopt/tests/test_deapext/test_stoppingCriteria.py ================================================ """bluepyopt.stoppingCriteria tests""" import bluepyopt.stoppingCriteria import pytest @pytest.mark.unit def test_MaxNGen(): """deapext.stoppingCriteria: Testing MaxNGen""" max_gen = 3 criteria = bluepyopt.deapext.stoppingCriteria.MaxNGen(max_gen) assert criteria.criteria_met is False criteria.check({"gen": max_gen + 1}) assert criteria.criteria_met is True criteria.reset() criteria.check({"gen": max_gen}) assert criteria.criteria_met is False ================================================ FILE: bluepyopt/tests/test_deapext/test_utils.py ================================================ """bluepyopt.utils tests""" import multiprocessing import time import bluepyopt.deapext.utils as utils import pytest def flag(event): """Send a multiprocessing event.""" time.sleep(1) event.set() def catch_event(event): """Verify that run_next_gen changes when event is caught.""" # None case assert utils.run_next_gen(True, None) # event is not set case assert utils.run_next_gen(True, event) # event is set by another process case time.sleep(2) assert not (utils.run_next_gen(True, event)) @pytest.mark.unit def test_run_next_gen_condition(): """deapext.utils: Testing run_next_gen.""" event = multiprocessing.Event() p1 = multiprocessing.Process(target=catch_event, args=(event,)) p2 = multiprocessing.Process(target=flag, args=(event,)) p1.start() p2.start() p1.join() p2.join() ================================================ FILE: bluepyopt/tests/test_ephys/__init__.py ================================================ ================================================ FILE: bluepyopt/tests/test_ephys/test_acc.py ================================================ """Unit tests for acc.""" from bluepyopt.ephys.acc import arbor, ArbLabel import pytest @pytest.mark.unit def test_arbor_labels(): """Test Arbor labels.""" region_label = ArbLabel(type='region', name='first_branch', s_expr='(branch 0)') assert region_label.defn == '(region-def "first_branch" (branch 0))' assert region_label.ref == '(region "first_branch")' assert region_label.name == 'first_branch' assert region_label.loc == '(branch 0)' assert region_label == region_label assert region_label is not None locset_label = ArbLabel(type='locset', name='first_branch_center', s_expr='(location 0 0.5)') assert locset_label.defn == \ '(locset-def "first_branch_center" (location 0 0.5))' assert locset_label.ref == '(locset "first_branch_center")' assert locset_label.name == 'first_branch_center' assert locset_label.loc == '(location 0 0.5)' assert locset_label == locset_label assert locset_label is not None assert locset_label != region_label arbor.label_dict({region_label.name: region_label.loc, locset_label.name: locset_label.loc}) ================================================ FILE: bluepyopt/tests/test_ephys/test_create_acc.py ================================================ """Tests for create_acc.py""" # pylint: disable=W0212 import json import os import pathlib import re import sys import tempfile import pytest from bluepyopt import ephys from bluepyopt.ephys import create_acc from bluepyopt.ephys.acc import ArbLabel, arbor from bluepyopt.ephys.create_acc import ( ArbNmodlMechFormatter, Nrn2ArbMechGrouper, Nrn2ArbParamAdapter, ) from bluepyopt.ephys.morphologies import ArbFileMorphology from bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceScaler from . import utils DEFAULT_ARBOR_REGION_ORDER = [ ("soma", 1), ("axon", 2), ("dend", 3), ("apic", 4), ("myelin", 5), ] testdata_dir = pathlib.Path(__file__).parent.joinpath("testdata") @pytest.mark.unit def test_read_templates(): """Unit test for _read_templates function.""" template_dir = testdata_dir / "acc/templates" template_filename = "*_template.jinja2" templates = create_acc._read_templates(template_dir, template_filename) assert templates.keys() == {"label_dict.acc", "cell.json", "decor.acc"} with pytest.raises(FileNotFoundError): create_acc._read_templates("DOES_NOT_EXIST", template_filename) @pytest.mark.unit def test_Nrn2ArbParamAdapter_param_name(): """Test Neuron to Arbor parameter mapping.""" # Identity mech_param_name = "gSKv3_1bar_SKv3_1" assert Nrn2ArbParamAdapter._param_name(mech_param_name) == mech_param_name # Non-trivial transformation global_property_name = "v_init" assert ( Nrn2ArbParamAdapter._param_name(global_property_name) == "membrane-potential" ) @pytest.mark.unit def test_Nrn2ArbParamAdapter_param_value(): """Test Neuron to Arbor parameter units conversion.""" # Identity for region parameter mech_param = create_acc.Location(name="gSKv3_1bar_SKv3_1", value="1.025") assert Nrn2ArbParamAdapter._param_value(mech_param) == "1.025" # Non-trivial name transformation, but identical value/units global_property = create_acc.Location(name="v_init", value=-65) assert Nrn2ArbParamAdapter._param_value(global_property) == "-65" # Non-trivial name and value/units transformation global_property = create_acc.Location(name="celsius", value=34) assert Nrn2ArbParamAdapter._param_value(global_property) == ( "307.14999999999998" ) @pytest.mark.unit def test_Nrn2ArbParamAdapter_format(): """Test Neuron to Arbor parameter reformatting.""" # Constant mechanism parameter mech_param = create_acc.Location(name="gSKv3_1bar_SKv3_1", value="1.025") mech = "SKv3_1" arb_mech_param = create_acc.Location(name="gSKv3_1bar", value="1.025") assert Nrn2ArbParamAdapter.format(mech_param, mechs=[mech]) == ( mech, arb_mech_param, ) # Non-unique mapping to mechanisms with pytest.raises(create_acc.CreateAccException): Nrn2ArbParamAdapter.format(mech_param, mechs=["SKv3_1", "1"]) # Global property with non-trivial transformation global_property = create_acc.Location(name="celsius", value="0") mech = None arb_global_property = create_acc.Location( name="temperature-kelvin", value="273.14999999999998" ) # Non-trivial name and value/units transformation assert Nrn2ArbParamAdapter.format(global_property, []) == ( mech, arb_global_property, ) # Inhomogeneuos mechanism parameter apical_region = ArbLabel("region", "apic", "(tag 4)") param_scaler = NrnSegmentSomaDistanceScaler( name="soma-distance-scaler", distribution="(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}", ) iexpr_param = create_acc.RangeExpr( location=apical_region, name="gkbar_hh", value="0.025", value_scaler=param_scaler, ) mech = "hh" arb_iexpr_param = create_acc.RangeExpr( location=apical_region, name="gkbar", value="0.025", value_scaler=param_scaler, ) assert Nrn2ArbParamAdapter.format(iexpr_param, mechs=[mech]) == ( mech, arb_iexpr_param, ) # Point process mechanism parameter loc = ephys.locations.ArbLocsetLocation( name="somacenter", locset="(location 0 0.5)" ) mech = ephys.mechanisms.NrnMODPointProcessMechanism( name="expsyn", suffix="ExpSyn", locations=[loc] ) mech_loc = ephys.locations.NrnPointProcessLocation( "expsyn_loc", pprocess_mech=mech ) point_expr_param = create_acc.PointExpr( name="tau", value="10", point_loc=[mech_loc] ) arb_point_expr_param = create_acc.PointExpr( name="tau", value="10", point_loc=[mech_loc] ) assert Nrn2ArbParamAdapter.format(point_expr_param, mechs=[mech]) == ( mech, arb_point_expr_param, ) @pytest.mark.unit def test_Nrn2ArbMechGrouper_format_params_and_group_by_mech(): """Test grouping of parameters by mechanism.""" params = [ create_acc.Location(name="gSKv3_1bar_SKv3_1", value="1.025"), create_acc.Location(name="ena", value="-30"), ] mechs = ["SKv3_1"] local_mechs = Nrn2ArbMechGrouper._format_params_and_group_by_mech( params, mechs ) assert local_mechs == { None: [ create_acc.Location( name='ion-reversal-potential "na"', value="-30" ) ], "SKv3_1": [create_acc.Location(name="gSKv3_1bar", value="1.025")], } @pytest.mark.unit def test_Nrn2ArbMechGrouper_process_global(): """Test adapting global parameters from Neuron to Arbor.""" params = {"ki": 3, "v_init": -65} global_mechs = Nrn2ArbMechGrouper.process_global(params) assert global_mechs == { None: [ create_acc.Location( name='ion-internal-concentration "k"', value="3" ), create_acc.Location(name="membrane-potential", value="-65"), ] } @pytest.mark.unit def test_Nrn2ArbMechGrouper_is_global_property(): """Test adapting local parameters from Neuron to Arbor.""" all_regions = ArbLabel("region", "all_regions", "(all)") param = create_acc.Location(name="axial-resistivity", value="1") assert Nrn2ArbMechGrouper._is_global_property(all_regions, param) is True soma_region = ArbLabel("region", "soma", "(tag 1)") assert Nrn2ArbMechGrouper._is_global_property(soma_region, param) is False @pytest.mark.unit def test_separate_global_properties(): """Test separating global properties from label-specific mechs.""" all_regions = ArbLabel("region", "all_regions", "(all)") mechs = { None: [create_acc.Location(name="axial-resistivity", value="1")], "SKv3_1": [create_acc.Location(name="gSKv3_1bar", value="1.025")], } local_mechs, global_properties = ( Nrn2ArbMechGrouper._separate_global_properties(all_regions, mechs) ) assert local_mechs == {None: [], "SKv3_1": mechs["SKv3_1"]} assert global_properties == {None: mechs[None]} @pytest.mark.unit def test_Nrn2ArbMechGrouper_process_local(): """Test adapting local parameters from Neuron to Arbor.""" all_regions = ArbLabel("region", "all_regions", "(all)") soma_region = ArbLabel("region", "soma", "(tag 1)") params = [ (all_regions, [create_acc.Location(name="cm", value="100")]), ( soma_region, [ create_acc.Location(name="v_init", value="-65"), create_acc.Location(name="gSKv3_1bar_SKv3_1", value="1.025"), ], ), ] channels = {all_regions: [], soma_region: ["SKv3_1"]} local_mechs, global_properties = Nrn2ArbMechGrouper.process_local( params, channels ) assert local_mechs.keys() == {all_regions, soma_region} assert local_mechs[all_regions] == {None: []} assert local_mechs[soma_region] == { None: [create_acc.Location(name="membrane-potential", value="-65")], "SKv3_1": [create_acc.Location(name="gSKv3_1bar", value="1.025")], } assert global_properties == { None: [create_acc.Location(name="membrane-capacitance", value="1")] } @pytest.mark.unit def test_ArbNmodlMechFormatter_load_mech_catalogue_meta(): """Test loading Arbor built-in mech catalogue metadata.""" nmodl_formatter = ArbNmodlMechFormatter(None) assert isinstance(nmodl_formatter.cats, dict) assert nmodl_formatter.cats.keys() == {"BBP", "default", "allen"} assert "Ca_HVA" in nmodl_formatter.cats["BBP"] @pytest.mark.unit def test_ArbNmodlMechFormatter_mech_name(): """Test mechanism name translation.""" assert ArbNmodlMechFormatter._mech_name("Ca_HVA") == "Ca_HVA" assert ArbNmodlMechFormatter._mech_name("ExpSyn") == "expsyn" @pytest.mark.unit def test_ArbNmodlMechFormatter_translate_density(): """Test NMODL GLOBAL parameter handling in mechanism translation.""" mechs = { "hh": [ create_acc.Location(name="gnabar", value="0.10000000000000001"), create_acc.RangeIExpr( name="gkbar", value="0.029999999999999999", scale=( "(add (scalar -0.62109375) (mul (scalar 0.546875) " '(log (add (mul (distance (region "soma"))' " (scalar 0.421875) ) (scalar 1.25) ) ) ) )" ), ), ], "pas": [ create_acc.Location(name="e", value="0.25"), create_acc.RangeIExpr( name="g", value="0.029999999999999999", scale=( "(add (scalar -0.62109375) (mul (scalar 0.546875) " '(log (add (mul (distance (region "soma"))' " (scalar 0.421875) ) (scalar 1.25) ) ) ) )" ), ), ], } nmodl_formatter = ArbNmodlMechFormatter(None) translated_mechs = nmodl_formatter.translate_density(mechs) assert translated_mechs.keys() == {"default::hh", "default::pas/e=0.25"} assert translated_mechs["default::hh"] == mechs["hh"] assert translated_mechs["default::pas/e=0.25"] == mechs["pas"][1:] @pytest.mark.unit def test_arb_populate_label_dict(): """Unit test for _populate_label_dict.""" local_mechs = {ArbLabel("region", "all", "(all)"): {}} local_scaled_mechs = {ArbLabel("region", "first_branch", "(branch 0)"): {}} pprocess_mechs = {} label_dict = create_acc._arb_populate_label_dict( local_mechs, local_scaled_mechs, pprocess_mechs ) assert label_dict.keys() == {"all", "first_branch"} with pytest.raises(create_acc.CreateAccException): other_pprocess_mechs = { ArbLabel("region", "first_branch", "(branch 1)"): {} } create_acc._arb_populate_label_dict( local_mechs, local_scaled_mechs, other_pprocess_mechs ) @pytest.mark.unit def test_create_acc(): """ephys.create_acc: Test create_acc""" mech = utils.make_mech() parameters = utils.make_parameters() acc = create_acc.create_acc( [ mech, ], parameters, morphology="CCell.swc", template_name="CCell", ) ref_dir = testdata_dir / "acc/CCell" cell_json = "CCell.json" decor_acc = "CCell_decor.acc" label_dict_acc = "CCell_label_dict.acc" # Testing keys assert cell_json in acc cell_json_dict = json.loads(acc[cell_json]) assert "cell_model_name" in cell_json_dict assert "produced_by" in cell_json_dict assert "morphology" in cell_json_dict assert "label_dict" in cell_json_dict assert "decor" in cell_json_dict # Testing values with open(ref_dir / cell_json) as f: ref_cell_json = json.load(f) for k in ref_cell_json: if k != "produced_by": assert ref_cell_json[k] == cell_json_dict[k] # Testing building blocks assert decor_acc in acc assert acc[decor_acc].startswith("(arbor-component") assert "(decor" in acc[decor_acc] # Testing values with open(ref_dir / decor_acc) as f: ref_decor = f.read() assert ref_decor == acc[decor_acc] # decor data not exposed in Python # Testing building blocks assert label_dict_acc in acc assert acc[label_dict_acc].startswith("(arbor-component") assert "(label-dict" in acc[label_dict_acc] matches = re.findall( r'\(region-def "(?P\w+)" \(tag (?P\d+)\)\)', acc[label_dict_acc], ) for pos, loc_tag in enumerate(DEFAULT_ARBOR_REGION_ORDER): assert matches[pos][0] == loc_tag[0] assert matches[pos][1] == str(loc_tag[1]) # Testing values ref_labels = arbor.load_component(ref_dir / label_dict_acc).component with tempfile.TemporaryDirectory() as test_dir: test_labels_filename = pathlib.Path(test_dir).joinpath(label_dict_acc) with open(test_labels_filename, "w") as f: f.write(acc[label_dict_acc]) test_labels = arbor.load_component(test_labels_filename).component assert dict(ref_labels.items()) == dict(test_labels.items()) @pytest.mark.unit def test_create_acc_filename(): """ephys.create_acc: Test create_acc template_filename""" mech = utils.make_mech() parameters = utils.make_parameters() custom_param_val = str(__file__) acc = create_acc.create_acc( [ mech, ], parameters, morphology="CCell.asc", template_name="CCell", template_filename="acc/templates/*_template.jinja2", template_dir=testdata_dir, custom_jinja_params={"custom_param": custom_param_val}, ) cell_json = "CCell_cell.json" decor_acc = "CCell_decor.acc" label_dict_acc = "CCell_label_dict.acc" assert cell_json in acc cell_json_dict = json.loads(acc[cell_json]) assert "cell_model_name" in cell_json_dict assert "produced_by" in cell_json_dict assert "morphology" in cell_json_dict assert "label_dict" in cell_json_dict assert "decor" in cell_json_dict assert decor_acc in acc assert acc[decor_acc].startswith("(arbor-component") assert "(decor" in acc[decor_acc] assert label_dict_acc in acc assert acc[label_dict_acc].startswith("(arbor-component") assert "(label-dict" in acc[label_dict_acc] matches = re.findall( r'\(region-def "(?P\w+)" \(tag (?P\d+)\)\)', acc[label_dict_acc], ) for pos, loc_tag in enumerate(DEFAULT_ARBOR_REGION_ORDER): assert matches[pos][0] == loc_tag[0] assert matches[pos][1] == str(loc_tag[1]) assert '(meta-data (info "test-decor"))' in acc[decor_acc] assert '(meta-data (info "test-label-dict"))' in acc[label_dict_acc] assert custom_param_val in cell_json_dict["produced_by"] @pytest.mark.unit def test_create_acc_replace_axon(): """ephys.create_acc: Test create_acc with axon replacement""" mech = utils.make_mech() parameters = utils.make_parameters() replace_axon_st = arbor.segment_tree() latest_seg = arbor.mnpos for prox_x, dist_x in [(5, 35), (35, 65)]: latest_seg = replace_axon_st.append( latest_seg, arbor.mpoint(prox_x, 0, 0, 0.5), arbor.mpoint(dist_x, 0, 0, 0.5), ArbFileMorphology.tags["axon"], ) replace_axon = arbor.morphology(replace_axon_st) try: acc = create_acc.create_acc( [ mech, ], parameters, morphology_dir=testdata_dir, morphology="simple.swc", template_name="CCell", replace_axon=replace_axon, ) except Exception as e: # fail with an older Arbor version assert isinstance(e, NotImplementedError) assert ( len(e.args) == 1 and e.args[0] == "Need a newer version of Arbor for axon replacement." ) return cell_json = "CCell.json" cell_json_dict = json.loads(acc[cell_json]) assert "replace_axon" in cell_json_dict["morphology"] with open(testdata_dir / "acc/CCell/simple_axon_replacement.acc") as f: replace_axon_ref = f.read() assert ( acc[cell_json_dict["morphology"]["replace_axon"]] == replace_axon_ref ) def make_cell(replace_axon): morph_filename = testdata_dir / "simple_ax2.swc" morph = ephys.morphologies.NrnFileMorphology( morph_filename, do_replace_axon=replace_axon ) somatic_loc = ephys.locations.NrnSeclistLocation( "somatic", seclist_name="somatic" ) mechs = [ ephys.mechanisms.NrnMODMechanism( name="hh", suffix="hh", locations=[somatic_loc] ) ] gkbar_hh_scaler = ( "(-0.62109375 + 0.546875*math.log(" "({distance})*0.421875 + 1.25))*{value}" ) params = [ ephys.parameters.NrnSectionParameter( name="gnabar_hh", param_name="gnabar_hh", locations=[somatic_loc] ), ephys.parameters.NrnRangeParameter( name="gkbar_hh", param_name="gkbar_hh", value_scaler=ephys.parameterscalers.NrnSegmentSomaDistanceScaler( distribution=gkbar_hh_scaler ), locations=[somatic_loc], ), ] return ephys.models.CellModel( "simple_ax2", morph=morph, mechs=mechs, params=params ) def run_short_sim(cable_cell): # Create cell model arb_cell_model = arbor.single_cell_model(cable_cell) arb_cell_model.properties.catalogue = arbor.catalogue() arb_cell_model.properties.catalogue.extend( arbor.default_catalogue(), "default::" ) arb_cell_model.properties.catalogue.extend(arbor.bbp_catalogue(), "BBP::") # Run a very short simulation to test mechanism instantiation arb_cell_model.run(tfinal=0.1 * arbor.units.ms) @pytest.mark.unit def test_cell_model_write_and_read_acc(): """ephys.create_acc: Test write_acc and read_acc w/o axon replacement""" cell = make_cell(replace_axon=False) param_values = {"gnabar_hh": 0.1, "gkbar_hh": 0.03} with tempfile.TemporaryDirectory() as acc_dir: cell.write_acc(acc_dir, param_values) cell_json, arb_morph, arb_decor, arb_labels = create_acc.read_acc( pathlib.Path(acc_dir).joinpath(cell.name + ".json") ) assert "replace_axon" not in cell_json["morphology"] cable_cell = arbor.cable_cell( morphology=arb_morph, decor=arb_decor, labels=arb_labels ) assert isinstance(cable_cell, arbor.cable_cell) assert len(cable_cell.cables('"soma"')) == 1 assert len(cable_cell.cables('"axon"')) == 1 assert ( len(arb_morph.branch_segments(cable_cell.cables('"soma"')[0].branch)) == 5 ) assert ( len(arb_morph.branch_segments(cable_cell.cables('"axon"')[0].branch)) == 5 ) run_short_sim(cable_cell) @pytest.mark.unit def test_cell_model_write_and_read_acc_replace_axon(): """ephys.create_acc: Test write_acc and read_acc w/ axon replacement""" cell = make_cell(replace_axon=True) param_values = {"gnabar_hh": 0.1, "gkbar_hh": 0.03} with tempfile.TemporaryDirectory() as acc_dir: try: nrn_sim = ephys.simulators.NrnSimulator() cell.write_acc(acc_dir, param_values, sim=nrn_sim) except Exception as e: # fail with an older Arbor version assert isinstance(e, NotImplementedError) assert ( len(e.args) == 1 and e.args[0] == "Need a newer version of Arbor for axon replacement." ) return # Axon replacement implemented in installed Arbor version cell_json, arb_morph, arb_decor, arb_labels = create_acc.read_acc( pathlib.Path(acc_dir).joinpath(cell.name + ".json") ) assert "replace_axon" in cell_json["morphology"] cable_cell = arbor.cable_cell( morphology=arb_morph, decor=arb_decor, labels=arb_labels ) assert isinstance(cable_cell, arbor.cable_cell) assert len(cable_cell.cables('"soma"')) == 1 assert len(cable_cell.cables('"axon"')) == 1 assert ( len(arb_morph.branch_segments(cable_cell.cables('"soma"')[0].branch)) == 6 ) assert ( len(arb_morph.branch_segments(cable_cell.cables('"axon"')[0].branch)) == 6 ) assert cable_cell.cables('"soma"')[0].prox == 0.0 assert ( abs( cable_cell.cables('"soma"')[0].dist - cable_cell.cables('"axon"')[0].prox ) < 1e-6 ) assert cable_cell.cables('"axon"')[0].dist == 1.0 run_short_sim(cable_cell) @pytest.mark.unit def test_cell_model_create_acc_replace_axon_without_instantiate(): """ephys.create_acc: Test write_acc and read_acc w/ axon replacement""" cell = make_cell(replace_axon=True) param_values = {"gnabar_hh": 0.1, "gkbar_hh": 0.03} with pytest.raises( ValueError, match="Need an instance of NrnSimulator in sim" " to instantiate morphology in order to" " create JSON/ACC-description with" " axon replacement.", ): cell.create_acc(param_values) def check_acc_dir(test_dir, ref_dir): assert sorted(os.listdir(ref_dir)) == sorted(os.listdir(test_dir)) ref_dir_ver_suffix = "_py" + "".join(sys.version.split(".")[:2]) ref_dir_ver = ref_dir.parent / (ref_dir.name + ref_dir_ver_suffix) for file in os.listdir(ref_dir): if (ref_dir_ver / file).exists(): ref_dir_file = ref_dir_ver else: ref_dir_file = ref_dir if file.endswith(".json"): with open(os.path.join(test_dir, file)) as f: cell_json_dict = json.load(f) with open(ref_dir_file / file) as f: ref_cell_json = json.load(f) for k in ref_cell_json: if k != "produced_by": assert ref_cell_json[k] == cell_json_dict[k] else: with open(os.path.join(test_dir, file)) as f: test_file = f.read() with open(ref_dir_file / file) as f: ref_file = f.read() assert ref_file == test_file # check that load_component is not raising any error here fpath = pathlib.Path(test_dir) / file if fpath.suffix == "acc": arbor.load_component(fpath).component @pytest.mark.unit def test_write_acc_simple(): SIMPLECELL_PATH = str( ( pathlib.Path(__file__).parent / "../../../examples/simplecell" ).resolve() ) sys.path.insert(0, SIMPLECELL_PATH) ref_dir = (testdata_dir / "acc/simplecell").resolve() old_cwd = os.getcwd() try: os.chdir(SIMPLECELL_PATH) import simplecell_model param_values = { "gnabar_hh": 0.10299326453483033, "gkbar_hh": 0.027124836082684685, } cell = simplecell_model.create(do_replace_axon=True) nrn_sim = ephys.simulators.NrnSimulator() cell.instantiate_morphology_3d(nrn_sim) with tempfile.TemporaryDirectory() as test_dir: cell.write_acc( test_dir, param_values, # ext_catalogues=ext_catalogues, create_mod_morph=True, ) check_acc_dir(test_dir, ref_dir) except NotImplementedError as e: # fail with an older Arbor version assert ( len(e.args) == 1 and e.args[0] == "Need a newer version of Arbor for axon replacement." ) finally: cell.destroy(nrn_sim) os.chdir(old_cwd) sys.path.pop(0) @pytest.mark.unit def test_write_acc_l5pc(): L5PC_PATH = str( (pathlib.Path(__file__).parent / "../../../examples/l5pc").resolve() ) sys.path.insert(0, L5PC_PATH) ref_dir = (testdata_dir / "acc/l5pc").resolve() old_cwd = os.getcwd() try: import l5pc_model param_values = { "gNaTs2_tbar_NaTs2_t.apical": 0.026145, "gSKv3_1bar_SKv3_1.apical": 0.004226, "gImbar_Im.apical": 0.000143, "gNaTa_tbar_NaTa_t.axonal": 3.137968, "gK_Tstbar_K_Tst.axonal": 0.089259, "gamma_CaDynamics_E2.axonal": 0.002910, "gNap_Et2bar_Nap_Et2.axonal": 0.006827, "gSK_E2bar_SK_E2.axonal": 0.007104, "gCa_HVAbar_Ca_HVA.axonal": 0.000990, "gK_Pstbar_K_Pst.axonal": 0.973538, "gSKv3_1bar_SKv3_1.axonal": 1.021945, "decay_CaDynamics_E2.axonal": 287.198731, "gCa_LVAstbar_Ca_LVAst.axonal": 0.008752, "gamma_CaDynamics_E2.somatic": 0.000609, "gSKv3_1bar_SKv3_1.somatic": 0.303472, "gSK_E2bar_SK_E2.somatic": 0.008407, "gCa_HVAbar_Ca_HVA.somatic": 0.000994, "gNaTs2_tbar_NaTs2_t.somatic": 0.983955, "decay_CaDynamics_E2.somatic": 210.485284, "gCa_LVAstbar_Ca_LVAst.somatic": 0.000333, } cell = l5pc_model.create(do_replace_axon=True) nrn_sim = ephys.simulators.NrnSimulator() cell.instantiate_morphology_3d(nrn_sim) with tempfile.TemporaryDirectory() as test_dir: cell.write_acc( test_dir, param_values, # ext_catalogues=ext_catalogues, create_mod_morph=True, ) check_acc_dir(test_dir, ref_dir) except Exception as e: # fail with an older Arbor version assert isinstance(e, NotImplementedError) assert ( len(e.args) == 1 and e.args[0] == "Need a newer version of Arbor for axon replacement." ) finally: cell.destroy(nrn_sim) os.chdir(old_cwd) sys.path.pop(0) @pytest.mark.unit def test_write_acc_expsyn(): EXPSYN_PATH = str( (pathlib.Path(__file__).parent / "../../../examples/expsyn").resolve() ) sys.path.insert(0, EXPSYN_PATH) ref_dir = (testdata_dir / "acc/expsyn").resolve() old_cwd = os.getcwd() try: import expsyn param_values = {"expsyn_tau": 10.0} cell = expsyn.create_model(sim="arb", do_replace_axon=False) with tempfile.TemporaryDirectory() as test_dir: cell.write_acc( test_dir, param_values, # ext_catalogues=ext_catalogues, create_mod_morph=True, ) check_acc_dir(test_dir, ref_dir) finally: os.chdir(old_cwd) sys.path.pop(0) ================================================ FILE: bluepyopt/tests/test_ephys/test_create_hoc.py ================================================ """Tests for create_hoc.py""" # pylint: disable=W0212 import os from bluepyopt.ephys.acc import ArbLabel from bluepyopt.ephys.locations import NrnSomaDistanceCompLocation from bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceScaler from bluepyopt.ephys.parameterscalers import NrnSegmentSomaDistanceStepScaler from . import utils from bluepyopt.ephys import create_acc, create_hoc import pytest DEFAULT_LOCATION_ORDER = [ 'all', 'apical', 'axonal', 'basal', 'somatic', 'myelinated'] @pytest.mark.unit def test_generate_channels_by_location(): """ephys.create_hoc: Test generate_channels_by_location""" mech = utils.make_mech() public_res = create_hoc.generate_channels_by_location( [mech], DEFAULT_LOCATION_ORDER, ) private_res = create_hoc._generate_channels_by_location( [mech], DEFAULT_LOCATION_ORDER, create_hoc._loc_desc ) assert public_res == private_res @pytest.mark.unit def test__generate_channels_by_location(): """ephys.create_hoc: Test _generate_channels_by_location""" mech = utils.make_mech() channels, point_channels = create_hoc._generate_channels_by_location( [mech, ], DEFAULT_LOCATION_ORDER, create_hoc._loc_desc) assert len(channels['apical']) == 1 assert len(channels['basal']) == 1 assert channels['apical'] == ['Ih'] assert channels['basal'] == ['Ih'] for loc in point_channels: assert len(point_channels[loc]) == 0 @pytest.mark.unit def test_generate_parameters(): """ephys.create_hoc: Test generate_parameters""" parameters = utils.make_parameters() assert create_hoc.generate_parameters(parameters) == \ create_hoc._generate_parameters(parameters, DEFAULT_LOCATION_ORDER, create_hoc._loc_desc) @pytest.mark.unit def test__generate_parameters(): """ephys.create_hoc: Test _generate_parameters""" parameters = utils.make_parameters() global_params, section_params, range_params, \ pprocess_params, location_order = \ create_hoc._generate_parameters(parameters, DEFAULT_LOCATION_ORDER, create_hoc._loc_desc) assert global_params == {'gSKv3_1bar_SKv3_1': 65} assert len(section_params[1]) == 2 assert len(section_params[4]) == 2 assert section_params[4][0] == 'somatic' assert len(section_params[4][1]) == 2 assert range_params == [] for loc, pparams in pprocess_params: assert loc in DEFAULT_LOCATION_ORDER assert len(pparams) == 0 assert location_order == DEFAULT_LOCATION_ORDER @pytest.mark.unit def test_create_hoc(): """ephys.create_hoc: Test create_hoc""" mech = utils.make_mech() parameters = utils.make_parameters() hoc = create_hoc.create_hoc([mech, ], parameters, template_name='CCell') assert 'load_file' in hoc assert 'CCell' in hoc assert 'begintemplate' in hoc assert 'endtemplate' in hoc @pytest.mark.unit def test_create_hoc_filename(): """ephys.create_hoc: Test create_hoc template_filename""" mech = utils.make_mech() parameters = utils.make_parameters() custom_param_val = 'printf("Hello world!")' hoc = create_hoc.create_hoc([mech, ], parameters, template_name='CCell', template_filename='test.jinja2', template_dir=os.path.join( os.path.dirname(__file__), 'testdata'), custom_jinja_params={ 'custom_param': custom_param_val}) assert 'load_file' in hoc assert 'CCell' in hoc assert 'begintemplate' in hoc assert 'endtemplate' in hoc assert 'Test template' in hoc assert custom_param_val in hoc @pytest.mark.unit def test_generate_reinitrng(): """ephys.create_hoc: Test generate_reinitrng""" mech = utils.make_mech() re_init_rng = create_hoc.generate_reinitrng([mech]) assert 'func hash_str() {localobj sf strdef right' in re_init_rng assert ' hash = (hash * 31 + char_int) % (2 ^ 31 - 1)' in re_init_rng @pytest.mark.unit def test_range_exprs_to_hoc(): """ephys.create_hoc: Test range_exprs_to_hoc""" apical_region = ArbLabel("region", "apic", "(tag 4)") param_scaler = NrnSegmentSomaDistanceScaler( name='soma-distance-scaler', distribution='(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}' ) range_expr = create_acc.RangeExpr( location=apical_region, name="gkbar_hh", value=0.025, value_scaler=param_scaler ) hoc = create_hoc.range_exprs_to_hoc([range_expr]) assert hoc[0].param_name == 'gkbar_hh' val_gt = '(-0.8696 + 2.087*exp((%.17g)*0.0031))*0.025000000000000001' assert hoc[0].value == val_gt @pytest.mark.unit def test_range_exprs_to_hoc_step_scaler(): """ephys.create_hoc: Test range_exprs_to_hoc with step scaler""" # apical_region = ArbLabel("region", "apic", "(tag 4)") apical_location = NrnSomaDistanceCompLocation( name='apic100', soma_distance=100, seclist_name='apical', ) param_scaler = NrnSegmentSomaDistanceStepScaler( name='soma-distance-step-scaler', distribution='{value} * (0.1 + 0.9 * int(' '({distance} > {step_begin}) & (' '{distance} < {step_end})))', step_begin=300, step_end=500) range_expr = create_hoc.RangeExpr( location=apical_location, name="gCa_LVAstbar_Ca_LVAst", value=1, value_scaler=param_scaler ) hoc = create_hoc.range_exprs_to_hoc([range_expr]) assert hoc[0].param_name == 'gCa_LVAstbar_Ca_LVAst' val_gt = '1 * (0.1 + 0.9 * int((%.17g > 300) && (%.17g < 500)))' assert hoc[0].value == val_gt ================================================ FILE: bluepyopt/tests/test_ephys/test_evaluators.py ================================================ """Test ephys model objects""" # pylint: disable=R0914 import os import pytest import numpy from bluepyopt import ephys TESTDATA_DIR = os.path.join( os.path.dirname( os.path.abspath(__file__)), 'testdata') simple_morphology_path = os.path.join(TESTDATA_DIR, 'simple.swc') @pytest.mark.unit def test_CellEvaluator_init(): """ephys.evaluators: Test CellEvaluator init""" sim = ephys.simulators.NrnSimulator() model = ephys.models.CellModel('test_model', params=[]) fitness_calc = ephys.objectivescalculators.ObjectivesCalculator() evaluator = ephys.evaluators.CellEvaluator( cell_model=model, param_names=[], fitness_calculator=fitness_calc, sim=sim) assert isinstance(evaluator, ephys.evaluators.CellEvaluator) assert ( str(evaluator) == 'cell evaluator:\n cell model:\n test_model:\n morphology:\n ' 'mechanisms:\n params:\n\n fitness protocols:\n ' 'fitness calculator:\n objectives:\n\n') @pytest.mark.unit def test_CellEvaluator_evaluate(): """ephys.evaluators: Test CellEvaluator evaluate""" sim = ephys.simulators.NrnSimulator() simple_morph = ephys.morphologies.NrnFileMorphology( simple_morphology_path, do_replace_axon=True) all_loc = ephys.locations.NrnSeclistLocation('all', 'all') cm = ephys.parameters.NrnRangeParameter( name='cm', param_name='cm', bounds=[.5, 1.5], locations=[all_loc]) cell_model = ephys.models.CellModel('CellModel', morph=simple_morph, mechs=[], params=[cm]) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.1, step_delay=100.0, step_duration=100, total_duration=200, location=soma_loc) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[rec_soma]) mean = -65 efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names={'': 'soma.v'}, stim_start=100.0, stim_end=200.0, exp_mean=-65, exp_std=1) s_obj = ephys.objectives.SingletonObjective( 'singleton', feature=efeature) fitness_calc = ephys.objectivescalculators.ObjectivesCalculator( objectives=[s_obj]) evaluator = ephys.evaluators.CellEvaluator( cell_model=cell_model, param_names=['cm'], fitness_calculator=fitness_calc, fitness_protocols={'sweep': protocol}, sim=sim) responses = protocol.run(cell_model, {'cm': 1.0}, sim=sim) feature_value = efeature.calculate_feature(responses) feature_value_eva = evaluator.evaluate_with_dicts( {'cm': 1.0}, target='values' ) score = evaluator.evaluate([1.0]) expected_score = abs(mean - feature_value) numpy.testing.assert_almost_equal(score, expected_score) score_dict = evaluator.objective_dict(score) numpy.testing.assert_almost_equal(score_dict['singleton'], expected_score) numpy.testing.assert_almost_equal( feature_value, feature_value_eva['singleton'] ) ================================================ FILE: bluepyopt/tests/test_ephys/test_extra_features_utils.py ================================================ """Tests for ephys.extra_features_utils""" import os import numpy import pytest from bluepyopt import ephys testdata_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'testdata' ) waveforms_fpath = os.path.join(testdata_dir, 'mean_waveforms.dat') waveforms = numpy.loadtxt(waveforms_fpath) waveform = numpy.array([waveforms[0]]) sampling_freq = 10000 @pytest.mark.unit def test_peak_to_valley(): """ephys.extra_features_utils: Test peak_to_valley""" ptv = ephys.extra_features_utils.peak_to_valley(waveform, sampling_freq) assert len(ptv) == 1 assert ptv[0] == pytest.approx(0.0013) @pytest.mark.unit def test_peak_trough_ratio(): """ephys.extra_features_utils: Test peak_trough_ratio""" ptratio = ephys.extra_features_utils.peak_trough_ratio(waveform) assert len(ptratio) == 1 print(ptratio) assert ptratio[0] == pytest.approx(0.53804035) @pytest.mark.unit def test_halfwidth(): """ephys.extra_features_utils: Test halfwidth""" ret = ephys.extra_features_utils.halfwidth(waveform, sampling_freq, True) assert len(ret) == 3 hw = ephys.extra_features_utils.halfwidth(waveform, sampling_freq) assert len(hw) == 1 assert hw[0] == pytest.approx(0.0015) @pytest.mark.unit def test_repolarization_slope(): """ephys.extra_features_utils: Test repolarization_slope""" ret = ephys.extra_features_utils.repolarization_slope( waveform, sampling_freq, True ) assert len(ret) == 2 rslope = ephys.extra_features_utils.repolarization_slope( waveform, sampling_freq ) assert len(rslope) == 1 assert rslope[0] == pytest.approx(73.12572131) @pytest.mark.unit def test_recovery_slope(): """ephys.extra_features_utils: Test recovery_slope""" window = 0.7 rslope = ephys.extra_features_utils.recovery_slope( waveform, sampling_freq, window=window ) assert len(rslope) == 1 assert rslope[0] == pytest.approx(-3.63355521) @pytest.mark.unit def test_peak_image(): """ephys.extra_features_utils: Test peak_image""" rel_peaks = ephys.extra_features_utils.peak_image( waveforms, sign="negative" ) assert len(rel_peaks) == 209 assert rel_peaks[0] == pytest.approx(0.06084468) rel_peaks = ephys.extra_features_utils.peak_image( waveforms, sign="positive" ) assert len(rel_peaks) == 209 assert rel_peaks[0] == pytest.approx(0.10850117) @pytest.mark.unit def test_relative_amplitude(): """ephys.extra_features_utils: Test relative_amplitude""" rel_amp = ephys.extra_features_utils.relative_amplitude( waveforms, sign="negative" ) assert len(rel_amp) == 209 assert rel_amp[0] == pytest.approx(0.09513392) rel_amp = ephys.extra_features_utils.relative_amplitude( waveforms, sign="positive" ) assert len(rel_amp) == 209 assert rel_amp[0] == pytest.approx(0.2135929) @pytest.mark.unit def test_peak_time_diff(): """ephys.extra_features_utils: Test peak_time_diff""" peak_t = ephys.extra_features_utils.peak_time_diff( waveforms, sampling_freq, sign="negative" ) assert len(peak_t) == 209 assert peak_t[0] == pytest.approx(0.0009) peak_t = ephys.extra_features_utils.peak_time_diff( waveforms, sampling_freq, sign="positive" ) assert len(peak_t) == 209 assert peak_t[0] == pytest.approx(0.0007) @pytest.mark.unit def test__get_trough_and_peak_idx(): """ephys.extra_features_utils: Test _get_trough_and_peak_idx""" t_idx, p_idx = ephys.extra_features_utils._get_trough_and_peak_idx( waveform ) assert t_idx == 102 assert p_idx == 115 @pytest.mark.unit def test_calculate_features(): """ephys.extra_features_utils: Test calculate_features""" feats = ephys.extra_features_utils.calculate_features( waveforms, sampling_freq ) for feature_name in ephys.extra_features_utils.all_1D_features: assert feature_name in feats ================================================ FILE: bluepyopt/tests/test_ephys/test_features.py ================================================ """Tests for ephys.efeatures""" import os from os.path import join as joinp import pytest import numpy from bluepyopt.ephys import efeatures from bluepyopt.ephys.responses import TimeVoltageResponse, TimeLFPResponse from bluepyopt.ephys.serializer import instantiator @pytest.mark.unit def test_EFeature(): """ephys.efeatures: Testing EFeature creation""" efeature = efeatures.EFeature('name') assert efeature.name == 'name' @pytest.mark.unit def test_eFELFeature(): """ephys.efeatures: Testing eFELFeature creation""" recording_names = {'': 'square_pulse_step1.soma.v'} efeature = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=1, exp_std=1) response = TimeVoltageResponse('mock_response') testdata_dir = joinp( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } ret = efeature.calculate_feature(responses, raise_warnings=True) numpy.testing.assert_almost_equal(ret, -72.05761247316858) score = efeature.calculate_score(responses) numpy.testing.assert_almost_equal(score, 73.05761247316858) assert efeature.name == 'test_eFELFeature' assert 'voltage_base' in str(efeature) @pytest.mark.unit def test_eFELFeature_max_score(): """ephys.efeatures: Testing eFELFeature max_score option""" recording_names = {'': 'square_pulse_step1.soma.v'} response = TimeVoltageResponse('mock_response') testdata_dir = joinp( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } efeature_normal = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='AP_amplitude', recording_names=recording_names, stim_start=600, stim_end=700, exp_mean=1, exp_std=1) score_normal = efeature_normal.calculate_score(responses) numpy.testing.assert_almost_equal(score_normal, 250) efeature_150 = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='AP_amplitude', recording_names=recording_names, stim_start=600, stim_end=700, exp_mean=1, exp_std=1, max_score=150) score_150 = efeature_150.calculate_score(responses) numpy.testing.assert_almost_equal(score_150, 150) @pytest.mark.unit def test_eFELFeature_force_max_score(): """ephys.efeatures: Testing eFELFeature force_max_score option""" recording_names = {'': 'square_pulse_step1.soma.v'} response = TimeVoltageResponse('mock_response') testdata_dir = joinp( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } efeature_normal = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=1, exp_std=.001) score_normal = efeature_normal.calculate_score(responses) assert score_normal > 250 efeature_force = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=1, exp_std=.001, force_max_score=True) score_force = efeature_force.calculate_score(responses) numpy.testing.assert_almost_equal(score_force, 250) @pytest.mark.unit def test_eFELFeature_double_settings(): """ephys.efeatures: Testing eFELFeature double_settings""" recording_names = {'': 'square_pulse_step1.soma.v'} efeature = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=1, exp_std=1) efeature_ds = efeatures.eFELFeature( name='test_eFELFeature_other_perc', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=1, exp_std=1, double_settings={ 'voltage_base_start_perc': 0.01}) response = TimeVoltageResponse('mock_response') testdata_dir = joinp( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } vb_other_perc = efeature_ds.calculate_feature( responses, raise_warnings=True) vb = efeature.calculate_feature(responses, raise_warnings=True) assert vb_other_perc != vb @pytest.mark.unit def test_eFELFeature_int_settings(): """ephys.efeatures: Testing eFELFeature int_settings""" recording_names = {'': 'square_pulse_step1.soma.v'} efeature = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='Spikecount', recording_names=recording_names, stim_start=1200, stim_end=2000, exp_mean=1, exp_std=1) efeature_strict = efeatures.eFELFeature( name='test_eFELFeature_strict', efel_feature_name='Spikecount', recording_names=recording_names, stim_start=1200, stim_end=2000, exp_mean=1, exp_std=1, int_settings={ 'strict_stiminterval': True}) response = TimeVoltageResponse('mock_response') testdata_dir = joinp( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } spikecount = efeature.calculate_feature(responses) spikecount_strict = efeature_strict.calculate_feature(responses) assert spikecount_strict != spikecount @pytest.mark.unit def test_eFELFeature_string_settings(): """ephys.efeatures: Testing eFELFeature string_settings""" recording_names = {'': 'square_pulse_step1.soma.v'} efeature = efeatures.eFELFeature(name='test_eFELFeature_vb_default', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700) efeature_median = efeatures.eFELFeature( name='test_eFELFeature_vb_median', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, string_settings={ 'voltage_base_mode': "median"}) response = TimeVoltageResponse('mock_response') testdata_dir = joinp( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(joinp(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } vb_median = efeature_median.calculate_feature( responses, raise_warnings=True) vb_default = efeature.calculate_feature(responses, raise_warnings=True) assert vb_median != vb_default @pytest.mark.unit def test_eFELFeature_serialize(): """ephys.efeatures: Testing eFELFeature serialization""" recording_names = {'': 'square_pulse_step1.soma.v'} efeature = efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=1, exp_std=1) serialized = efeature.to_dict() deserialized = instantiator(serialized) assert isinstance(deserialized, efeatures.eFELFeature) assert deserialized.stim_start == 700 assert deserialized.recording_names == recording_names @pytest.mark.unit def test_extraFELFeature(): """ephys.efeatures: Testing extraFELFeature calculation""" import pandas as pd somatic_recording_name = 'soma_response' recording_names = {'': 'lfp_response'} channel_ids = 0 extrafel_feature_name = 'halfwidth' name = 'test_extraFELFeature' stim_start = 400 stim_end = 1750 fs = 10 ms_cut = [10, 25] # load responses from file testdata_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'testdata' ) soma_time = numpy.load(os.path.join(testdata_dir, 'lfpy_soma_time.npy')) soma_voltage = numpy.load( os.path.join(testdata_dir, 'lfpy_soma_voltage.npy') ) lfpy_time = numpy.load(os.path.join(testdata_dir, 'lfpy_time.npy')) lfpy_voltage = numpy.load(os.path.join(testdata_dir, 'lfpy_voltage.npy')) soma_response = TimeVoltageResponse( name='soma_response', time=soma_time, voltage=soma_voltage ) lfpy_response = TimeLFPResponse( name="lfpy_response", time=lfpy_time, lfp=lfpy_voltage ) responses = { somatic_recording_name: soma_response, recording_names['']: lfpy_response, } # compute for all electrodes efeature = efeatures.extraFELFeature( name=name, extrafel_feature_name=extrafel_feature_name, somatic_recording_name=somatic_recording_name, recording_names=recording_names, channel_ids=None, exp_mean=0.001, exp_std=0.001, stim_start=stim_start, stim_end=stim_end, fs=fs, ms_cut=ms_cut ) ret = efeature.calculate_feature(responses, raise_warnings=True) assert len(ret) == 209 # compute for 1 electrode efeature = efeatures.extraFELFeature( name=name, extrafel_feature_name=extrafel_feature_name, somatic_recording_name=somatic_recording_name, recording_names=recording_names, channel_ids=channel_ids, exp_mean=0.001, exp_std=0.001, stim_start=stim_start, stim_end=stim_end, fs=fs, ms_cut=ms_cut ) ret = efeature.calculate_feature(responses, raise_warnings=True) numpy.testing.assert_almost_equal(ret, 0.0015) score = efeature.calculate_score(responses) numpy.testing.assert_almost_equal(score, 0.5) assert efeature.name == name assert extrafel_feature_name in str(efeature) @pytest.mark.unit def test_masked_cosine_distance(): """ephys.efeatures: Testing masked_cosine_distance""" from scipy.spatial import distance exp = numpy.array([0.5, 0.5, 0.5]) model = numpy.array([0.7, 0.8, 0.4]) score = efeatures.masked_cosine_distance(exp, model) assert score == distance.cosine(exp, model) # test nan in model feature values model = numpy.array([0.7, numpy.nan, 0.4]) score = efeatures.masked_cosine_distance(exp, model) assert score == distance.cosine([0.5, 0.5], [0.7, 0.4]) # test nan in experimental feature values exp = numpy.array([0.5, 0.5, numpy.nan]) model = numpy.array([0.7, 0.8, 0.4]) score = efeatures.masked_cosine_distance(exp, model) assert score == distance.cosine([0.5, 0.5], [0.7, 0.8]) * 2. / 3. # test na in both exp and model feature values exp = numpy.array([0.5, 0.5, numpy.nan]) model = numpy.array([0.7, numpy.nan, 0.4]) score = efeatures.masked_cosine_distance(exp, model) assert score == distance.cosine([0.5], [0.7]) * 2. / 3. ================================================ FILE: bluepyopt/tests/test_ephys/test_init.py ================================================ """bluepy.ephys test""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import pytest import numpy @pytest.mark.unit def test_import(): """ephys: test importing bluepyopt.ephys""" import bluepyopt.ephys # NOQA @pytest.mark.unit def test_ephys_base(): """ephys: test ephys base class""" import bluepyopt.ephys as ephys base = ephys.base.BaseEPhys(name='test', comment='comm') assert str(base) == 'BaseEPhys: test (comm)' ================================================ FILE: bluepyopt/tests/test_ephys/test_locations.py ================================================ """bluepyopt.ephys.simulators tests""" """ Copyright (c) 2016-2022, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612, W0201 import json import numpy as np import pytest from bluepyopt import ephys from bluepyopt.ephys.serializer import instantiator @pytest.mark.unit def test_location_init(): """ephys.locations: test if Location works""" loc = ephys.locations.Location("test") assert isinstance(loc, ephys.locations.Location) assert loc.name == "test" @pytest.mark.unit class TestNrnSectionCompLocation(object): """Test class for NrnSectionCompLocation""" def setup_method(self): """Setup""" self.loc = ephys.locations.NrnSectionCompLocation( name="test", sec_name="soma[0]", comp_x=0.5 ) self.loc_dend = ephys.locations.NrnSectionCompLocation( name="test", sec_name="dend[1]", comp_x=0.5 ) assert self.loc.name == "test" self.sim = ephys.simulators.NrnSimulator() def test_instantiate(self): """ephys.locations.NrnSomaDistanceCompLocation: test instantiate""" # Create a little test class with a soma and two dendritic sections class Cell(object): """Cell class""" pass cell = Cell() soma = self.sim.neuron.h.Section() dend1 = self.sim.neuron.h.Section(name="dend1") dend2 = self.sim.neuron.h.Section(name="dend2") cell.soma = [soma] cell.dend = [dend1, dend2] soma_comp = self.loc.instantiate(sim=self.sim, icell=cell) assert soma_comp == soma(0.5) dend_comp = self.loc_dend.instantiate(sim=self.sim, icell=cell) assert dend_comp == dend2(0.5) @pytest.mark.unit class TestNrnSeclistCompLocation(object): """Test class for NrnSectionCompLocation""" def setup_method(self): """Setup""" self.loc = ephys.locations.NrnSeclistCompLocation( name="test", seclist_name="somatic", sec_index=0, comp_x=0.5 ) self.loc_dend = ephys.locations.NrnSeclistCompLocation( name="test", seclist_name="basal", sec_index=1, comp_x=0.5 ) assert self.loc.name == "test" self.sim = ephys.simulators.NrnSimulator() def test_instantiate(self): """ephys.locations.NrnSeclistCompLocation: test instantiate""" # Create a little test class with a soma and two dendritic sections class Cell(object): """Cell class""" pass cell = Cell() soma = self.sim.neuron.h.Section() dend1 = self.sim.neuron.h.Section(name="dend1") dend2 = self.sim.neuron.h.Section(name="dend2") cell.somatic = self.sim.neuron.h.SectionList() cell.somatic.append(soma) cell.basal = self.sim.neuron.h.SectionList() cell.basal.append(dend1) cell.basal.append(dend2) soma_comp = self.loc.instantiate(sim=self.sim, icell=cell) assert soma_comp == soma(0.5) dend_comp = self.loc_dend.instantiate(sim=self.sim, icell=cell) assert dend_comp == dend2(0.5) for _ in range(10000): soma_comp = self.loc.instantiate(sim=self.sim, icell=cell) @pytest.mark.unit class TestNrnSeclistSecLocation(object): """Test class for NrnSeclistSecLocation""" def setup_method(self): """Setup""" self.loc = ephys.locations.NrnSeclistSecLocation( name="test", seclist_name="somatic", sec_index=0 ) self.loc_dend = ephys.locations.NrnSeclistSecLocation( name="test", seclist_name="basal", sec_index=1 ) assert self.loc.name == "test" self.sim = ephys.simulators.NrnSimulator() def test_instantiate(self): """ephys.locations.NrnSeclistSecLocation: test instantiate""" # Create a little test class with a soma and two dendritic sections class Cell(object): """Cell class""" pass cell = Cell() soma = self.sim.neuron.h.Section() dend1 = self.sim.neuron.h.Section(name="dend1") dend2 = self.sim.neuron.h.Section(name="dend2") cell.somatic = self.sim.neuron.h.SectionList() cell.somatic.append(soma) cell.basal = self.sim.neuron.h.SectionList() cell.basal.append(dend1) cell.basal.append(dend2) soma_comp = self.loc.instantiate(sim=self.sim, icell=cell) assert soma_comp == soma dend_comp = self.loc_dend.instantiate(sim=self.sim, icell=cell) assert dend_comp == dend2 @pytest.mark.unit class TestNrnSomaDistanceCompLocation(object): """Test class for NrnSomaDistanceCompLocation""" def setup_method(self): """Setup""" self.loc = ephys.locations.NrnSomaDistanceCompLocation( "test", 125, "testdend" ) assert self.loc.name == "test" self.sim = ephys.simulators.NrnSimulator() def test_instantiate(self): """ephys.locations.NrnSomaDistanceCompLocation: test instantiate""" # Create a little test class with a soma and two dendritic sections class Cell(object): """Cell class""" pass cell = Cell() soma = self.sim.neuron.h.Section() cell.soma = [soma] cell.testdend = self.sim.neuron.h.SectionList() dend1 = self.sim.neuron.h.Section(name="dend1") dend2 = self.sim.neuron.h.Section(name="dend2") cell.testdend.append(sec=dend1) cell.testdend.append(sec=dend2) pytest.raises( ephys.locations.EPhysLocInstantiateException, self.loc.instantiate, sim=self.sim, icell=cell, ) dend1.connect(soma(0.5), 0.0) dend2.connect(dend1(1.0), 0.0) comp = self.loc.instantiate(sim=self.sim, icell=cell) assert comp == dend2(0.5) @pytest.mark.unit class TestNrnSecSomaDistanceCompLocation(object): """Test class for NrnSecSomaDistanceCompLocation""" def setup_method(self): """Setup""" self.loc = ephys.locations.NrnSecSomaDistanceCompLocation( "test", 125, 1, "testdend" ) self.loc_other = ephys.locations.NrnSecSomaDistanceCompLocation( "test", 250, 4, "testdend" ) assert self.loc.name == "test" self.sim = ephys.simulators.NrnSimulator() def test_instantiate(self): """ephys.locations.NrnSomaDistanceCompLocation: test instantiate""" # Create a little test class with a soma and two dendritic sections class Cell(object): """Cell class""" pass cell = Cell() soma = self.sim.neuron.h.Section(name="soma[0]") cell.soma = [soma] cell.testdend = self.sim.neuron.h.SectionList() dend1 = self.sim.neuron.h.Section(name="dend[0]") dend2 = self.sim.neuron.h.Section(name="dend[1]") dend3 = self.sim.neuron.h.Section(name="dend[2]") dend4 = self.sim.neuron.h.Section(name="dend[3]") dend5 = self.sim.neuron.h.Section(name="dend[4]") cell.testdend.append(sec=dend1) cell.testdend.append(sec=dend2) cell.testdend.append(sec=dend3) cell.testdend.append(sec=dend4) cell.testdend.append(sec=dend5) dend1.connect(soma(0.5), 0.0) dend2.connect(dend1(1.0), 0.0) dend3.connect(dend1(1.0), 0.0) dend4.connect(dend3(1.0), 0.0) dend5.connect(dend4(1.0), 0.0) comp = self.loc.instantiate(sim=self.sim, icell=cell) assert comp == dend2(0.5) comp = self.loc_other.instantiate(sim=self.sim, icell=cell) assert comp == dend4(0.5) @pytest.mark.unit class TestNrnTrunkSomaDistanceCompLocation(object): """Test class for NrnTrunkSomaDistanceCompLocation""" def setup_method(self): """Setup""" self.loc = ephys.locations.NrnTrunkSomaDistanceCompLocation( "test", soma_distance=150, seclist_name="testdend" ) self.loc_other = ephys.locations.NrnTrunkSomaDistanceCompLocation( "test", soma_distance=350, seclist_name="testdend" ) assert self.loc.name == "test" self.sim = ephys.simulators.NrnSimulator() def test_instantiate(self): """ephys.locations.NrnSomaDistanceCompLocation: test instantiate""" # Create a little test class with a soma and two dendritic sections class Cell(object): """Cell class""" pass cell = Cell() soma = self.sim.neuron.h.Section(name="soma[0]") cell.soma = [soma] cell.testdend = self.sim.neuron.h.SectionList() dend1 = self.sim.neuron.h.Section(name="dend[0]") dend2 = self.sim.neuron.h.Section(name="dend[1]") dend3 = self.sim.neuron.h.Section(name="dend[2]") dend4 = self.sim.neuron.h.Section(name="dend[3]") dend5 = self.sim.neuron.h.Section(name="dend[4]") cell.testdend.append(sec=dend1) cell.testdend.append(sec=dend2) cell.testdend.append(sec=dend3) cell.testdend.append(sec=dend4) cell.testdend.append(sec=dend5) x0 = self.sim.neuron.h.Vector([0] * 10) d = self.sim.neuron.h.Vector([1] * 10) x1 = self.sim.neuron.h.Vector(np.linspace(0, 100, 10)) self.sim.neuron.h.pt3dadd(x0, x1, x0, d, sec=dend1) x2 = self.sim.neuron.h.Vector(np.linspace(100, 200, 10)) self.sim.neuron.h.pt3dadd(x0, x2, x0, d, sec=dend2) x3 = self.sim.neuron.h.Vector(np.linspace(200, 300, 10)) self.sim.neuron.h.pt3dadd(x0, x3, x0, d, sec=dend3) x4 = self.sim.neuron.h.Vector(np.linspace(300, 400, 10)) self.sim.neuron.h.pt3dadd(x0, x4, x0, d, sec=dend4) x5 = self.sim.neuron.h.Vector(np.linspace(400, 500, 10)) self.sim.neuron.h.pt3dadd(x0, x5, x0, d, sec=dend5) dend1.connect(soma(0.5), 0.0) dend2.connect(dend1(1.0), 0.0) dend3.connect(dend1(1.0), 0.0) dend4.connect(dend3(1.0), 0.0) dend5.connect(dend4(1.0), 0.0) comp = self.loc.instantiate(sim=self.sim, icell=cell) assert comp == dend3(0.5) comp = self.loc_other.instantiate(sim=self.sim, icell=cell) assert comp == dend5(0.5) @pytest.mark.unit def test_serialize(): """ephys.locations: Test serialize functionality""" from bluepyopt.ephys.locations import ( NrnSeclistCompLocation, NrnSeclistLocation, NrnSeclistSecLocation, NrnSomaDistanceCompLocation, ) seclist_name, sec_index, comp_x, soma_distance = "somatic", 0, 0.5, 800 locations = ( NrnSeclistCompLocation( "NrnSeclistCompLocation", seclist_name, sec_index, comp_x ), NrnSeclistLocation("NrnSeclistLocation", seclist_name), NrnSeclistSecLocation( "NrnSeclistSecLocation", seclist_name, sec_index ), NrnSomaDistanceCompLocation( "NrnSomaDistanceCompLocation", soma_distance, seclist_name ), ) for loc in locations: serialized = loc.to_dict() assert isinstance(json.dumps(serialized), str) deserialized = instantiator(serialized) assert isinstance(deserialized, loc.__class__) assert deserialized.seclist_name == seclist_name assert deserialized.name == loc.__class__.__name__ ================================================ FILE: bluepyopt/tests/test_ephys/test_mechanisms.py ================================================ """Tests for ephys.mechanisms""" import string import random import json import difflib import pytest from . import utils from bluepyopt import ephys import bluepyopt.ephys.examples.simplecell simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() from bluepyopt.ephys.serializer import instantiator simple_cell = simplecell.cell_model simple_cell.freeze(simplecell.default_param_values) sim = simplecell.nrn_sim @pytest.mark.unit def test_mechanism_serialize(): """ephys.mechanisms: Testing serialize""" mech = utils.make_mech() serialized = mech.to_dict() assert isinstance(json.dumps(serialized), str) deserialized = instantiator(serialized) assert isinstance(deserialized, ephys.mechanisms.NrnMODMechanism) @pytest.mark.unit def test_nrnmod_instantiate(): """ephys.mechanisms: Testing insert mechanism""" test_mech = ephys.mechanisms.NrnMODMechanism( 'test.pas', suffix='pas', locations=[simplecell.somatic_loc]) assert str(test_mech) == "test.pas: pas at ['somatic']" simple_cell.instantiate(sim=sim) test_mech.instantiate(sim=sim, icell=simple_cell.icell) test_mech.destroy(sim=sim) simple_cell.destroy(sim=sim) pytest.raises(TypeError, ephys.mechanisms.NrnMODMechanism, 'test.pas', suffix='pas', prefix='pas', locations=[simplecell.somatic_loc]) test_mech = ephys.mechanisms.NrnMODMechanism( 'test.pas', prefix='pas', locations=[simplecell.somatic_loc]) assert test_mech.suffix == 'pas' test_mech.prefix = 'pas2' assert test_mech.suffix == 'pas2' test_mech = ephys.mechanisms.NrnMODMechanism( 'unknown', suffix='unknown', locations=[simplecell.somatic_loc]) simple_cell.instantiate(sim=sim) pytest.raises( ValueError, test_mech.instantiate, sim=sim, icell=simple_cell.icell) test_mech.destroy(sim=sim) simple_cell.destroy(sim=sim) def compare_strings(s1, s2): """Compare two strings""" diff = list(difflib.unified_diff(s1.splitlines(1), s2.splitlines(1))) if len(diff) > 0: print(''.join(diff)) return False else: return True @pytest.mark.unit def test_nrnmod_reinitrng_block(): """ephys.mechanisms: Testing reinitrng_block""" test_mech = ephys.mechanisms.NrnMODMechanism( 'stoch', suffix='Stoch', locations=[simplecell.somatic_loc]) block = test_mech.generate_reinitrng_hoc_block() expected_block = ' forsec somatic { deterministic_Stoch = 1 }\n' assert compare_strings(block, expected_block) test_mech = ephys.mechanisms.NrnMODMechanism( 'stoch', suffix='Stoch', deterministic=False, locations=[simplecell.somatic_loc]) block = test_mech.generate_reinitrng_hoc_block() expected_block = \ """ forsec somatic { for (x, 0) { setdata_Stoch(x) sf.tail(secname(), "\\\\.", name) sprint(full_str, "%s.%.19g", name, x) if (channel_seed_set) { setRNG_Stoch(gid, hash_str(full_str), channel_seed) } else { setRNG_Stoch(gid, hash_str(full_str)) } } } """ assert compare_strings(block, expected_block) @pytest.mark.unit def test_nrnmod_determinism(): """ephys.mechanisms: Testing determinism""" test_mech = ephys.mechanisms.NrnMODMechanism( 'pas', suffix='pas', deterministic=False, locations=[simplecell.somatic_loc]) simple_cell.instantiate(sim=sim) pytest.raises( TypeError, test_mech.instantiate, sim=sim, icell=simple_cell.icell) test_mech.destroy(sim=sim) simple_cell.destroy(sim=sim) @pytest.mark.unit def test_pprocess_instantiate(): """ephys.mechanisms: Testing insert point process""" test_pprocess = ephys.mechanisms.NrnMODPointProcessMechanism( name='expsyn', suffix='ExpSyn', locations=[simplecell.somacenter_loc]) assert ( str(test_pprocess) == "expsyn: ExpSyn at ['somatic[0](0.5)']") simple_cell.instantiate(sim=sim) assert test_pprocess.pprocesses is None test_pprocess.instantiate(sim=sim, icell=simple_cell.icell) assert len(test_pprocess.pprocesses) == 1 pprocess = test_pprocess.pprocesses[0] assert hasattr(pprocess, 'tau') test_pprocess.destroy(sim=sim) assert test_pprocess.pprocesses is None simple_cell.destroy(sim=sim) test_pprocess = ephys.mechanisms.NrnMODPointProcessMechanism( name='expsyn', suffix='Exp', locations=[simplecell.somacenter_loc]) simple_cell.instantiate(sim=sim) pytest.raises( AttributeError, test_pprocess.instantiate, sim=sim, icell=simple_cell.icell) test_pprocess.destroy(sim=sim) simple_cell.destroy(sim=sim) @pytest.mark.unit def test_string_hash_functions(): """ephys.mechanisms: Testing string hash function""" n_of_strings = 100 max_size = 50 random.seed(1) test_strings = ['', 'a'] test_strings += [''.join (random.choice (string.printable) for _ in range(random.choice(range(max_size)))) for _ in range(n_of_strings)] hashes_py = [ ephys.mechanisms.NrnMODMechanism.hash_py (test_string) for test_string in test_strings] hashes_hoc = [ ephys.mechanisms.NrnMODMechanism.hash_hoc (test_string, simplecell.nrn_sim) for test_string in test_strings] assert hashes_py == hashes_hoc assert hashes_py[:2] == [0.0, 97.0] ================================================ FILE: bluepyopt/tests/test_ephys/test_models.py ================================================ """Test ephys model objects""" import os import tempfile import contextlib import pytest import numpy from bluepyopt import ephys sim = ephys.simulators.NrnSimulator() TESTDATA_DIR = os.path.join( os.path.dirname( os.path.abspath(__file__)), 'testdata') simple_morphology_path = os.path.join(TESTDATA_DIR, 'simple.swc') apic_morphology_path = os.path.join(TESTDATA_DIR, 'apic.swc') @contextlib.contextmanager def yield_blank_hoc(template_name): """Create blank hoc template""" hoc_template = ephys.models.CellModel.create_empty_template(template_name) temp_file = tempfile.NamedTemporaryFile(suffix='test_models') with temp_file as fd: fd.write(hoc_template.encode('utf-8')) fd.flush() yield temp_file.name test_morph = ephys.morphologies.NrnFileMorphology(simple_morphology_path) @pytest.mark.unit def test_create_empty_template(): """ephys.models: Test creation of empty template""" template_name = 'FakeTemplate' hoc_template = ephys.models.CellModel.create_empty_template(template_name) sim.neuron.h(hoc_template) assert hasattr(sim.neuron.h, template_name) @pytest.mark.unit def test_model(): """ephys.models: Test Model class""" model = ephys.models.Model('test_model') model.instantiate(sim=None) model.destroy(sim=None) assert isinstance(model, ephys.models.Model) @pytest.mark.unit def test_cellmodel(): """ephys.models: Test CellModel class""" model = ephys.models.CellModel('test_model', morph=test_morph, mechs=[]) assert ( str(model) == 'test_model:\n morphology:\n %s\n mechanisms:\n params:\n' % simple_morphology_path) model.instantiate(sim=sim) model.destroy(sim=sim) assert isinstance(model, ephys.models.CellModel) @pytest.mark.unit def test_cellmodel_namecheck(): """ephys.models: Test CellModel class name checking""" # Test valid name for name in ['test3', 'test_3']: ephys.models.CellModel(name, morph=test_morph, mechs=[]) # Test invalid names for name in ['3test', '', 'test$', 'test 3']: pytest.raises( TypeError, ephys.models.CellModel, name, morph=test_morph, mechs=[]) @pytest.mark.unit def test_load_hoc_template(): """ephys.models: Test loading of hoc template""" template_name = 'test_load_hoc' hoc_string = ephys.models.CellModel.create_empty_template(template_name) ephys.models.HocCellModel.load_hoc_template(sim, hoc_string) assert hasattr(sim.neuron.h, template_name) @pytest.mark.unit def test_HocCellModel(): """ephys.models: Test HOCCellModel class""" template_name = 'test_HocCellModel' hoc_string = ephys.models.CellModel.create_empty_template(template_name) hoc_cell = ephys.models.HocCellModel( 'test_hoc_model', simple_morphology_path, hoc_string=hoc_string) hoc_cell.instantiate(sim) assert hoc_cell.icell is not None assert hoc_cell.cell is not None assert 'simple.swc' in str(hoc_cell) # these should be callable, but don't do anything hoc_cell.freeze(None) hoc_cell.unfreeze(None) hoc_cell.check_nonfrozen_params(None) hoc_cell.params_by_names(None) hoc_cell.destroy(sim=sim) @pytest.mark.unit def test_CellModel_create_empty_cell(): """ephys.models: Test create_empty_cell""" template_name = 'create_empty_cell' cell = ephys.models.CellModel.create_empty_cell(template_name, sim) assert callable(cell) assert hasattr(sim.neuron.h, template_name) @pytest.mark.unit def test_CellModel_create_hoc(): """ephys.models: Test create_hoc""" morph0 = ephys.morphologies.NrnFileMorphology( simple_morphology_path, do_replace_axon=True) cell_model = ephys.models.CellModel('CellModel', morph=morph0, mechs=[], params=[]) hoc_string = cell_model.create_hoc({}) assert 'begintemplate CellModel' in hoc_string assert 'proc replace_axon()' in hoc_string cell_model_hoc = ephys.models.HocCellModel( 'CellModelHOC', simple_morphology_path, hoc_string=hoc_string) assert isinstance(cell_model_hoc, ephys.models.HocCellModel) @pytest.mark.unit def test_CellModel_destroy(): """ephys.models: Test CellModel destroy""" morph0 = ephys.morphologies.NrnFileMorphology(simple_morphology_path) cell_model0 = ephys.models.CellModel('CellModel_destroy', morph=morph0, mechs=[], params=[]) morph1 = ephys.morphologies.NrnFileMorphology(simple_morphology_path) cell_model1 = ephys.models.CellModel('CellModel_destroy', morph=morph1, mechs=[], params=[]) assert not hasattr(sim.neuron.h, 'CellModel_destroy') cell_model0.instantiate(sim=sim) assert hasattr(sim.neuron.h, 'CellModel_destroy') assert 1 == len(sim.neuron.h.CellModel_destroy) cell_model1.instantiate(sim=sim) assert 2 == len(sim.neuron.h.CellModel_destroy) # make sure cleanup works cell_model0.destroy(sim=sim) assert 1 == len(sim.neuron.h.CellModel_destroy) cell_model1.destroy(sim=sim) assert 0 == len(sim.neuron.h.CellModel_destroy) @pytest.mark.unit def test_lfpy_create_empty_template(): """ephys.models: Test creation of lfpy empty template""" template_name = 'FakeTemplate' hoc_template = ephys.models.LFPyCellModel.create_empty_template( template_name ) sim.neuron.h(hoc_template) assert hasattr(sim.neuron.h, template_name) @pytest.mark.unit def test_lfpycellmodel(): """ephys.models: Test LFPyCellModel class""" model = ephys.models.LFPyCellModel('test_lfpy_model', morph=test_morph, mechs=[], v_init=-80) assert ( str(model) == 'test_lfpy_model:\n morphology:\n %s\n ' 'mechanisms:\n params:\n' % simple_morphology_path) model.instantiate(sim=sim) model.destroy(sim=sim) assert isinstance(model, ephys.models.LFPyCellModel) @pytest.mark.unit def test_lfpycellmodel_namecheck(): """ephys.models: Test LFPyCellModel class name checking""" # Test valid name for name in ['test3', 'test_3']: ephys.models.LFPyCellModel(name, morph=test_morph, mechs=[]) # Test invalid names for name in ['3test', '', 'test$', 'test 3']: with pytest.raises(TypeError): ephys.models.LFPyCellModel(name, morph=test_morph, mechs=[]) @pytest.mark.unit def test_load_lfpy_hoc_template(): """ephys.models: Test loading of hoc template with lfpy cell""" template_name = 'test_load_hoc' hoc_string = ephys.models.LFPyCellModel.create_empty_template( template_name ) ephys.models.HocCellModel.load_hoc_template(sim, hoc_string) assert hasattr(sim.neuron.h, template_name) @pytest.mark.unit def test_LFPyCellModel_create_empty_cell(): """ephys.models: Test create_empty_cell with lfpy cell""" template_name = 'create_empty_cell' cell = ephys.models.LFPyCellModel.create_empty_cell(template_name, sim) assert callable(cell) assert hasattr(sim.neuron.h, template_name) @pytest.mark.unit def test_LFPyCellModel_create_hoc(): """ephys.models: Test create_hoc with lfpy cell""" morph0 = ephys.morphologies.NrnFileMorphology( simple_morphology_path, do_replace_axon=True ) cell_model = ephys.models.LFPyCellModel( 'LFPyCellModel', morph=morph0, mechs=[], params=[] ) hoc_string = cell_model.create_hoc({}) assert 'begintemplate LFPyCellModel' in hoc_string assert 'proc replace_axon()' in hoc_string cell_model_hoc = ephys.models.HocCellModel( 'CellModelHOC', simple_morphology_path, hoc_string=hoc_string) assert isinstance(cell_model_hoc, ephys.models.HocCellModel) @pytest.mark.unit def test_LFPyCellModel_destroy(): """ephys.models: Test LFPyCellModel destroy""" morph0 = ephys.morphologies.NrnFileMorphology(simple_morphology_path) cell_model0 = ephys.models.LFPyCellModel( 'LFPyCellModel_destroy', morph=morph0, mechs=[], params=[] ) morph1 = ephys.morphologies.NrnFileMorphology(simple_morphology_path) cell_model1 = ephys.models.LFPyCellModel( 'LFPyCellModel_destroy', morph=morph1, mechs=[], params=[] ) assert not hasattr(sim.neuron.h, 'LFPyCellModel_destroy') cell_model0.instantiate(sim=sim) assert hasattr(sim.neuron.h, 'LFPyCellModel_destroy') assert 1 == len(sim.neuron.h.LFPyCellModel_destroy) cell_model1.instantiate(sim=sim) assert 2 == len(sim.neuron.h.LFPyCellModel_destroy) # make sure cleanup works cell_model0.destroy(sim=sim) assert 1 == len(sim.neuron.h.LFPyCellModel_destroy) cell_model1.destroy(sim=sim) assert 0 == len(sim.neuron.h.LFPyCellModel_destroy) @pytest.mark.unit def test_metaparameter(): """ephys.models: Test model with MetaParameter""" morph = ephys.morphologies.NrnFileMorphology(apic_morphology_path) dist = "({A} + {B} * math.exp({distance} * {C})) * {value}" scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler( distribution=dist, dist_param_names=['A', 'B', 'C']) all_loc = ephys.locations.NrnSeclistLocation('all', 'all') paramA = ephys.parameters.MetaParameter('ParamA', scaler, 'A', -1) paramB = ephys.parameters.MetaParameter( 'ParamB', scaler, 'B', bounds=[1.0, 3.0]) paramC = ephys.parameters.MetaParameter( 'ParamC', obj=scaler, attr_name='C', value=0.003, frozen=True) cm = ephys.parameters.NrnRangeParameter( name='cm', param_name='cm', bounds=[.5, 1.5], value_scaler=scaler, locations=[all_loc]) test_params = {'ParamA': -1, 'ParamB': 2.0, 'cm': 1.0} cell_model = ephys.models.CellModel('CellModel', morph=morph, mechs=[], params=[cm, paramA, paramB, paramC]) pytest.raises(Exception, cell_model.freeze, {'ParamC': 2.0}) cell_model.freeze(test_params) cell_model.instantiate(sim=sim) assert (scaler.eval_dist(1.0, 1.0) == '(-1 + 2.0 * math.exp(1 * 0.003)) * 1') numpy.testing.assert_almost_equal( scaler.scale( 1.0, cell_model.icell.apic[0](.5), sim=sim), 1.0764239941636502) cell_model.unfreeze(param_names=['ParamA', 'ParamB']) hoc_code = cell_model.create_hoc(param_values=test_params) assert ('distribute_distance(CellRef.all, "cm", "(-1 + 2.0 * ' 'exp(%.17g * 0.003)) * 1")' in hoc_code) cell_model.destroy(sim=sim) hoc_model = ephys.models.HocCellModel( 'hoc_model', '.', hoc_string=hoc_code) hoc_model.instantiate(sim=sim) hoc_model.destroy() ================================================ FILE: bluepyopt/tests/test_ephys/test_morphologies.py ================================================ """ephys/morphologies.py unit tests""" import json import os import pytest import bluepyopt.ephys as ephys from bluepyopt.ephys.serializer import instantiator testdata_dir = os.path.join( os.path.dirname( os.path.abspath(__file__)), 'testdata') simpleswc_morphpath = os.path.join(testdata_dir, 'simple.swc') simpleswc_ax1_morphpath = os.path.join(testdata_dir, 'simple_ax1.swc') simpleswc_ax2_morphpath = os.path.join(testdata_dir, 'simple_ax2.asc') simplewrong_morphpath = os.path.join(testdata_dir, 'simple.wrong') @pytest.mark.unit def test_morphology_init(): """ephys.morphologies: testing Morphology constructor""" morph = ephys.morphologies.Morphology() assert isinstance(morph, ephys.morphologies.Morphology) @pytest.mark.unit def test_nrnfilemorphology_init(): """ephys.morphologies: testing NrnFileMorphology constructor""" sim = ephys.simulators.NrnSimulator() morph = ephys.morphologies.NrnFileMorphology('wrong.swc') pytest.raises(IOError, morph.instantiate, sim=sim) morph = ephys.morphologies.NrnFileMorphology(simplewrong_morphpath) assert str(morph) == simplewrong_morphpath pytest.raises(ValueError, morph.instantiate, sim=sim) morph = ephys.morphologies.NrnFileMorphology( simpleswc_morphpath, do_set_nseg=False) morph.instantiate(sim=sim) morph.destroy(sim=sim) @pytest.mark.unit def test_nrnfilemorphology_replace_axon(): """ephys.morphologies: testing NrnFileMorphology replace_axon""" sim = ephys.simulators.NrnSimulator() morph = ephys.morphologies.NrnFileMorphology( simpleswc_morphpath, do_replace_axon=True) cell = ephys.models.CellModel(name='cell_replace') icell = cell.create_empty_cell( cell.name, sim=sim, seclist_names=cell.seclist_names, secarray_names=cell.secarray_names) morph.instantiate(sim=sim, icell=icell) assert len([sec for sec in icell.axon]) == 2 morph.destroy(sim=sim) icell.destroy() @pytest.mark.unit def test_nrnfilemorphology_replace_axon_ax1(): """ephys.morphologies: testing NrnFileMorphology replace_axon with ax1""" sim = ephys.simulators.NrnSimulator() morph = ephys.morphologies.NrnFileMorphology( simpleswc_ax1_morphpath, do_replace_axon=True) cell = ephys.models.CellModel(name='cell_ax1') icell = cell.create_empty_cell( cell.name, sim=sim, seclist_names=cell.seclist_names, secarray_names=cell.secarray_names) morph.instantiate(sim=sim, icell=icell) assert len([sec for sec in icell.axon]) == 2 morph.destroy(sim=sim) icell.destroy() @pytest.mark.unit def test_nrnfilemorphology_replace_axon_ax2(): """ephys.morphologies: testing NrnFileMorphology replace_axon with ax2""" sim = ephys.simulators.NrnSimulator() morph = ephys.morphologies.NrnFileMorphology( simpleswc_ax2_morphpath, do_replace_axon=True) cell = ephys.models.CellModel(name='cell_ax2') icell = cell.create_empty_cell( cell.name, sim=sim, seclist_names=cell.seclist_names, secarray_names=cell.secarray_names) morph.instantiate(sim=sim, icell=icell) assert len([sec for sec in icell.axon]) == 2 morph.destroy(sim=sim) icell.destroy() @pytest.mark.unit def test_serialize(): """ephys.morphology: testing serialization""" morph = ephys.morphologies.NrnFileMorphology(simpleswc_morphpath) serialized = morph.to_dict() assert isinstance(json.dumps(serialized), str) deserialized = instantiator(serialized) assert isinstance(deserialized, ephys.morphologies.NrnFileMorphology) assert deserialized.morphology_path == simpleswc_morphpath ================================================ FILE: bluepyopt/tests/test_ephys/test_objectives.py ================================================ """Tests for ephys.efeatures""" import os import pytest import numpy import bluepyopt.ephys as ephys @pytest.mark.unit def test_EFeatureObjective(): """ephys.objectives: Testing EFeatureObjective""" recording_names = {'': 'square_pulse_step1.soma.v'} mean = 1 efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=mean, exp_std=1) e_obj = ephys.objectives.EFeatureObjective( 'singleton', features=[efeature]) assert e_obj.name == 'singleton' assert e_obj.features == [efeature] response = ephys.responses.TimeVoltageResponse('mock_response') testdata_dir = os.path.join( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } efeature_value = efeature.calculate_feature(responses) numpy.testing.assert_almost_equal( e_obj.calculate_feature_scores(responses), [abs(efeature_value - mean)]) @pytest.mark.unit def test_SingletonObjective(): """ephys.objectives: Testing SingletonObjective""" recording_names = {'': 'square_pulse_step1.soma.v'} mean = 1 efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=mean, exp_std=1) s_obj = ephys.objectives.SingletonObjective( 'singleton', feature=efeature) assert s_obj.name == 'singleton' assert s_obj.features == [efeature] assert str(s_obj) == '( %s )' % str(efeature) response = ephys.responses.TimeVoltageResponse('mock_response') testdata_dir = os.path.join( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } efeature_value = efeature.calculate_feature(responses) efeature_value_obj = s_obj.calculate_value(responses) numpy.testing.assert_almost_equal( s_obj.calculate_score(responses), abs(efeature_value - mean)) numpy.testing.assert_almost_equal(efeature_value_obj, efeature_value) @pytest.mark.unit def test_SingletonWeightObjective(): """ephys.objectives: Testing SingletonWeightObjective""" recording_names = {'': 'square_pulse_step1.soma.v'} mean = 1 weight = 0.5 efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=mean, exp_std=1) s_obj = ephys.objectives.SingletonWeightObjective( 'singleton', feature=efeature, weight=weight) assert s_obj.name == 'singleton' assert s_obj.features == [efeature] assert s_obj.weight == weight assert str(s_obj) == '( %s ), weight:%f' % (str(efeature), weight) response = ephys.responses.TimeVoltageResponse('mock_response') testdata_dir = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'testdata') response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } efeature_value = efeature.calculate_feature(responses) numpy.testing.assert_almost_equal( s_obj.calculate_score(responses), abs(efeature_value - mean) * weight) @pytest.mark.unit def test_MaxObjective(): """ephys.objectives: Testing MaxObjective""" recording_names = {'': 'square_pulse_step1.soma.v'} mean = 1 efeature1 = ephys.efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=mean, exp_std=1) efeature2 = ephys.efeatures.eFELFeature( name='test_eFELFeature', efel_feature_name='steady_state_voltage', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=mean, exp_std=1) max_obj = ephys.objectives.MaxObjective( 'max', features=[efeature1, efeature2]) assert max_obj.name == 'max' assert max_obj.features == [efeature1, efeature2] response = ephys.responses.TimeVoltageResponse('mock_response') testdata_dir = os.path.join( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } efeature_value1 = efeature1.calculate_feature(responses) efeature_value2 = efeature2.calculate_feature(responses) numpy.testing.assert_almost_equal( max_obj.calculate_score(responses), max(abs(efeature_value1 - mean), abs(efeature_value2 - mean))) @pytest.mark.unit def test_WeightedSumObjective(): """ephys.objectives: Testing WeightedSumObjective""" recording_names = {'': 'square_pulse_step1.soma.v'} mean = 1 std = 1 efeature = ephys.efeatures.eFELFeature(name='test_eFELFeature', efel_feature_name='voltage_base', recording_names=recording_names, stim_start=700, stim_end=2700, exp_mean=mean, exp_std=std) weight = .5 w_obj = ephys.objectives.WeightedSumObjective( 'weighted', features=[efeature], weights=[weight]) assert w_obj.name == 'weighted' assert w_obj.features == [efeature] assert w_obj.weights == [weight] response = ephys.responses.TimeVoltageResponse('mock_response') testdata_dir = os.path.join( os.path.dirname( os.path.abspath(__file__)), 'testdata') response.read_csv(os.path.join(testdata_dir, 'TimeVoltageResponse.csv')) responses = {'square_pulse_step1.soma.v': response, } efeature_value = efeature.calculate_feature(responses) numpy.testing.assert_almost_equal( w_obj.calculate_score(responses), abs(efeature_value - mean) * weight) pytest.raises(Exception, ephys.objectives.WeightedSumObjective, 'weighted', features=[efeature], weights=[1, 2]) ================================================ FILE: bluepyopt/tests/test_ephys/test_parameters.py ================================================ """ephys.parameters tests""" import json import pytest import numpy from . import utils from bluepyopt import ephys from bluepyopt.ephys.serializer import instantiator import bluepyopt.ephys.examples.simplecell @pytest.mark.unit def test_pprocessparam_instantiate(): """ephys.parameters: Testing point process parameter""" simplecell = bluepyopt.ephys.examples.simplecell.SimpleCell() simple_cell = simplecell.cell_model simple_cell.freeze(simplecell.default_param_values) sim = simplecell.nrn_sim expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism( name='expsyn', suffix='ExpSyn', locations=[simplecell.somacenter_loc]) expsyn_loc = ephys.locations.NrnPointProcessLocation( 'expsyn_loc', pprocess_mech=expsyn_mech) expsyn_tau_param = ephys.parameters.NrnPointProcessParameter( name='expsyn_tau', param_name='tau', value=2, locations=[expsyn_loc]) simple_cell.mechanisms.append(expsyn_mech) simple_cell.params[expsyn_tau_param.name] = expsyn_tau_param simple_cell.instantiate(sim=sim) assert expsyn_mech.pprocesses[0].tau == 2 simple_cell.destroy(sim=sim) @pytest.mark.unit def test_serialize(): """ephys.parameters: Test serialize""" parameters = utils.make_parameters() for param in parameters: serialized = param.to_dict() assert isinstance(json.dumps(serialized), str) deserialized = instantiator(serialized) assert isinstance(deserialized, param.__class__) assert deserialized.name == param.__class__.__name__ @pytest.mark.unit def test_metaparameter(): """ephys.parameters: Test MetaParameter""" dist = "({A} + {B} * math.exp({distance} * {C}) * {value}" scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler( distribution=dist, dist_param_names=['A', 'B', 'C']) scaler.A = -0.9 scaler.B = 2 scaler.C = 0.003 meta_param = ephys.parameters.MetaParameter('Param A', scaler, 'A', -1) assert meta_param.attr_name == 'A' assert meta_param.value == -1 assert scaler.A == -1 ================================================ FILE: bluepyopt/tests/test_ephys/test_parameterscalers.py ================================================ """Test ephys.parameterscalers""" import json import pathlib import tempfile import arbor import pytest from bluepyopt.ephys.parameterscalers import (NrnSegmentLinearScaler, NrnSegmentSomaDistanceScaler, ) from bluepyopt.ephys.serializer import instantiator import bluepyopt.ephys as ephys @pytest.mark.unit def test_NrnSegmentSomaDistanceScaler_dist_params(): """ephys.parameterscalers: dist_params of NrnSegmentSomaDistanceScaler""" dist = "({A} + {B} * math.exp({distance} * {C}) * {value}" scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler( distribution=dist, dist_param_names=['A', 'B', 'C']) assert hasattr(scaler, 'A') assert hasattr(scaler, 'B') assert hasattr(scaler, 'C') scaler.A = -0.9 assert scaler.A == -0.9 scaler.B = 2 assert scaler.B == 2 scaler.C = 0.003 assert scaler.C == 0.003 assert (scaler.eval_dist(1.0, 1.0) == '(-0.9 + 2 * math.exp(1 * 0.003) * 1') @pytest.mark.unit def test_NrnSegmentSectionDistanceScaler_eval_dist_with_dict(): """ephys.parameterscalers: eval_dist of NrnSegmentSectionDistanceScaler""" dist = '{param1_somatic} + (1 - (abs({distance} - 8) / 4)) * {value}' scaler = ephys.parameterscalers.NrnSegmentSectionDistanceScaler( distribution=dist) _values = {'value': 1, 'param1_somatic': 0.5} assert (scaler.eval_dist(values=_values, distance=10) == '0.5 + (1 - (abs(10 - 8) / 4)) * 1') @pytest.mark.unit def test_NrnSegmentSomaDistanceStepScaler_eval_dist_with_dict(): """ephys.parameterscalers: eval_dist of NrnSegmentSomaDistanceStepScaler""" dist = '{value} * (0.1 + 0.9 * int(' \ '({distance} > {step_begin}) & ({distance} < {step_end})))' scaler = ephys.parameterscalers.NrnSegmentSomaDistanceStepScaler( distribution=dist, step_begin=300, step_end=500) _values = {'value': 1} assert (scaler.eval_dist(values=_values, distance=10) == '1 * (0.1 + 0.9 * int((10 > 300) & (10 < 500)))') @pytest.mark.unit def test_serialize(): """ephys.parameterscalers: test serialization""" multiplier, offset, distribution = 12.12, 3.58, '1 + {distance}' paramscalers = ( NrnSegmentLinearScaler( 'NrnSegmentLinearScaler', multiplier, offset), NrnSegmentSomaDistanceScaler( 'NrnSegmentSomaDistanceScaler', distribution), ) for ps in paramscalers: serialized = ps.to_dict() assert isinstance(json.dumps(serialized), str) deserialized = instantiator(serialized) assert isinstance(deserialized, ps.__class__) assert deserialized.name == ps.__class__.__name__ @pytest.mark.unit def test_parameterscalers_iexpr_generator(): """ephys.parameterscalers: Test iexpr generation from python expression""" value = 2.125 value_scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler( name='soma_distance_scaler', distribution='(0.62109375 - math.log( math.pi ) * math.exp(' '({distance}) / 0.421875)) * {value}') iexpr = value_scaler.acc_scale_iexpr( value=value, constant_formatter=lambda v: '%.9g' % v) assert iexpr == '(sub (scalar 0.62109375) ' \ '(mul (log (pi) ) ' \ '(exp (div (distance (region "soma")) ' \ '(scalar 0.421875) ) ) ) )' @pytest.mark.unit def test_parameterscalers_iexpr_generator_non_existent_op(): """ephys.parameterscalers: Test iexpr generation from python expression with invalid node""" value = 2.125 value_scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler( name='soma_distance_scaler', distribution='(0.62109375 - math.log( math.pi ) * non_existent_func(' '({distance}) / 0.421875)) * {value}') with pytest.raises(ValueError, match='Arbor iexpr generation failed - ' 'unsupported function non_existent_func.'): iexpr = value_scaler.acc_scale_iexpr( value=value, constant_formatter=lambda v: '%.9g' % v) @pytest.mark.unit def test_parameterscalers_iexpr_generator_unsupported_attr(): """ephys.parameterscalers: Test iexpr generation from python expression with invalid node""" value = 2.125 value_scaler = ephys.parameterscalers.NrnSegmentSomaDistanceScaler( name='soma_distance_scaler', distribution='(0.62109375 - math.log( math.pi )* math.tau.hex(' '({distance}) / 0.421875)) * {value}') with pytest.raises(ValueError, match='Arbor iexpr generation failed - ' 'unsupported attribute tau.'): iexpr = value_scaler.acc_scale_iexpr( value=value, constant_formatter=lambda v: '%.9g' % v) @pytest.mark.unit def test_parameterscalers_iexpr(): """ephys.parameterscalers: Test iexpr""" # iexpr from bluepyopt/tests/test_ephys/test_parameterscalers.py iexpr = '(sub (scalar 0.62109375) ' \ '(mul (log (pi) ) ' \ '(exp (div (distance (region "soma")) ' \ '(scalar 0.421875) ) ) ) )' # modified decor as in # bluepyopt/tests/test_ephys/testdata/acc/simplecell/simple_cell_decor.acc simple_cell_decor_with_iexpr = \ '(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") ' \ '(scaled-mechanism (density (mechanism "default::hh" ' \ '("gnabar" 0.10299326453483033) ("gkbar" 0.027124836082684685))) ' \ f'("gkbar" {iexpr})))))' with tempfile.TemporaryDirectory() as test_dir: decor_filename = pathlib.Path(test_dir).joinpath("decor.acc") with open(decor_filename, "w") as f: f.write(simple_cell_decor_with_iexpr) # check that load_component is not raising any error here arbor.load_component(decor_filename).component ================================================ FILE: bluepyopt/tests/test_ephys/test_protocols.py ================================================ """bluepyopt.ephys.simulators tests""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import pytest import bluepyopt.ephys as ephys from .testmodels import dummycells @pytest.mark.unit def test_distloc_exception(): """ephys.protocols: test if protocol raise dist loc exception""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() # icell = dummy_cell.instantiate(sim=nrn_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) dend_loc = ephys.locations.NrnSomaDistanceCompLocation( name='dend_loc', soma_distance=800, seclist_name='apical') rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') rec_dend = ephys.recordings.CompRecording( name='dend.v', location=dend_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[ rec_soma, rec_dend]) responses = protocol.run( cell_model=dummy_cell, param_values={}, sim=nrn_sim) assert responses['soma.v'] is not None assert responses['dend.v'] is None protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) def run_RuntimeError( self, tstop=None, dt=None, cvode_active=None, random123_globalindex=None): """Mock version of run that throws runtimeerror""" raise RuntimeError() def run_NrnSimulatorException( self, tstop=None, dt=None, cvode_active=None, random123_globalindex=None): """Mock version of run that throws runtimeerror""" raise ephys.simulators.NrnSimulatorException('mock', None) @pytest.mark.unit def test_sweepprotocol_init(): """ephys.protocols: Test SweepProtocol init""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() # icell = dummy_cell.instantiate(sim=nrn_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[rec_soma]) assert isinstance(protocol, ephys.protocols.SweepProtocol) assert protocol.total_duration == 50 assert ( protocol.subprotocols() == {'prot': protocol}) assert 'somatic[0](0.5)' in str(protocol) protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_sequenceprotocol_init(): """ephys.protocols: Test SequenceProtocol init""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() # icell = dummy_cell.instantiate(sim=nrn_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) sweep_protocol = ephys.protocols.SweepProtocol( name='sweep_prot', stimuli=[stim], recordings=[rec_soma]) seq_protocol = ephys.protocols.SequenceProtocol( name='seq_prot', protocols=[sweep_protocol]) assert isinstance(seq_protocol, ephys.protocols.SequenceProtocol) assert ( seq_protocol.subprotocols() == { 'seq_prot': seq_protocol, 'sweep_prot': sweep_protocol}) sweep_protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_sequenceprotocol_run(): """ephys.protocols: Test SequenceProtocol run""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() # icell = dummy_cell.instantiate(sim=nrn_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) sweep_protocol = ephys.protocols.SweepProtocol( name='sweep_prot', stimuli=[stim], recordings=[rec_soma]) seq_protocol = ephys.protocols.SequenceProtocol( name='seq_prot', protocols=[sweep_protocol]) responses = seq_protocol.run( cell_model=dummy_cell, param_values={}, sim=nrn_sim) assert responses is not None sweep_protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_sequenceprotocol_overwrite(): """ephys.protocols: Test SequenceProtocol overwriting keys""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() sweep_protocols = [] for x in [.2, .5]: soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=x) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) sweep_protocols.append(ephys.protocols.SweepProtocol( name='sweep_prot', stimuli=[stim], recordings=[rec_soma])) seq_protocol = ephys.protocols.SequenceProtocol( name='seq_prot', protocols=sweep_protocols) pytest.raises(Exception, seq_protocol.run, cell_model=dummy_cell, param_values={}, sim=nrn_sim) for sweep_protocol in sweep_protocols: sweep_protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_stepprotocol_init(): """ephys.protocols: Test StepProtocol init""" soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=5.0, step_duration=50, total_duration=50, location=soma_loc) hold_stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) step_protocol = ephys.protocols.StepProtocol( name='step_prot', step_stimulus=stim, holding_stimulus=hold_stim, recordings=[rec_soma]) assert step_protocol.step_delay == 5.0 assert step_protocol.step_duration == 50 @pytest.mark.unit def test_sweepprotocol_run_unisolated(): """ephys.protocols: Test SweepProtocol unisolated run""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() # icell = dummy_cell.instantiate(sim=nrn_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) unknown_loc = ephys.locations.NrnSomaDistanceCompLocation( name='unknown_loc', seclist_name='somatic', soma_distance=100) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') rec_unknown = ephys.recordings.CompRecording( name='unknown.v', location=unknown_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[rec_soma, rec_unknown]) responses = protocol.run( cell_model=dummy_cell, param_values={}, sim=nrn_sim, isolate=False) assert 'soma.v' in responses assert 'unknown.v' in responses assert responses['unknown.v'] is None protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_sweepprotocol_run_isolated(): """ephys.protocols: Test SweepProtocol isolated run""" nrn_sim = ephys.simulators.NrnSimulator(dt=0.1) dummy_cell = dummycells.DummyCellModel1() soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) unknown_loc = ephys.locations.NrnSomaDistanceCompLocation( name='unknown_loc', seclist_name='somatic', soma_distance=100) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') rec_unknown = ephys.recordings.CompRecording( name='unknown.v', location=unknown_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[rec_soma, rec_unknown]) responses = protocol.run( cell_model=dummy_cell, param_values={}, sim=nrn_sim, isolate=True) assert 'soma.v' in responses assert 'unknown.v' in responses assert responses['unknown.v'] is None protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_nrnsimulator_exception(): """ephys.protocols: test if protocol raise nrn sim exception""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[rec_soma]) nrn_sim.run = run_RuntimeError responses = protocol.run( cell_model=dummy_cell, param_values={}, sim=nrn_sim, isolate=False) assert responses['soma.v'] is None nrn_sim.run = run_NrnSimulatorException responses = protocol.run( cell_model=dummy_cell, param_values={}, sim=nrn_sim, isolate=False) assert responses['soma.v'] is None protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_sweepprotocol_instantiate_with_LFPyCellModel(): """ephys.protocols: Test SweepProtocol instantiate with LFPyCellModel""" import LFPy dummy_cell = dummycells.DummyLFPyCellModel1() nrn_sim = ephys.simulators.LFPySimulator() soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5 ) rec = ephys.recordings.LFPRecording( name='lfpy_rec' ) stim = ephys.stimuli.LFPySquarePulse( step_amplitude=0.1, step_delay=70, step_duration=100, location=soma_loc, total_duration=300 ) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[rec]) dummy_cell.instantiate(sim=nrn_sim) protocol.instantiate(sim=nrn_sim, cell_model=dummy_cell) # check that recording and stimuli have been instantiated with LFPy assert rec.instantiated assert isinstance(rec.cell, LFPy.Cell) assert isinstance(stim.iclamp, LFPy.StimIntElectrode) protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) ================================================ FILE: bluepyopt/tests/test_ephys/test_recordings.py ================================================ """bluepyopt.ephys.simulators tests""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import os import pytest import bluepyopt.ephys as ephys @pytest.mark.unit def test_comprecording_init(): """ephys.recordings: Test CompRecording init""" recording = ephys.recordings.CompRecording() assert isinstance(recording, ephys.recordings.CompRecording) assert recording.response is None ''' nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() # icell = dummy_cell.instantiate(sim=nrn_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma_loc', seclist_name='somatic', sec_index=0, comp_x=.5) rec_soma = ephys.recordings.CompRecording( name='soma.v', location=soma_loc, variable='v') stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.0, step_delay=0.0, step_duration=50, total_duration=50, location=soma_loc) protocol = ephys.protocols.SweepProtocol( name='prot', stimuli=[stim], recordings=[rec_soma]) assert_true(isinstance(protocol, ephys.protocols.SweepProtocol)) assert_equal(protocol.total_duration, 50) assert_equal( protocol.subprotocols(), {'prot': protocol}) assert_true('somatic[0](0.5)' in str(protocol)) protocol.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) ''' @pytest.mark.unit def test_lfprecording_init(): """ephys.recordings: Test LFPRecording init""" recording = ephys.recordings.LFPRecording(name="rec") assert isinstance(recording, ephys.recordings.LFPRecording) assert recording.response is None assert str(recording) == "rec: v at extracellular" @pytest.mark.unit def test_lfprecording_instantiate(): """ephys.recordings: Test LFPRecording instantiate""" TESTDATA_DIR = os.path.join( os.path.dirname(os.path.abspath(__file__)), 'testdata' ) simple_morphology_path = os.path.join(TESTDATA_DIR, 'simple.swc') test_morph = ephys.morphologies.NrnFileMorphology(simple_morphology_path) recording = ephys.recordings.LFPRecording() lfpy_cell = ephys.models.LFPyCellModel( name="lfpy_cell", morph=test_morph, mechs=[] ) neuron_sim = ephys.simulators.LFPySimulator() lfpy_cell.instantiate(sim=neuron_sim) recording.instantiate( sim=neuron_sim, lfpy_cell=lfpy_cell.lfpy_cell ) assert recording.instantiated lfpy_cell.destroy(sim=neuron_sim) ================================================ FILE: bluepyopt/tests/test_ephys/test_serializer.py ================================================ """Test for ephys.serializer""" import json import pytest import numpy import bluepyopt.ephys as ephys class ClassforTesting(ephys.serializer.DictMixin): """Test class for serializer""" SERIALIZED_FIELDS = ('string', 'boolean', 'float_', 'list_', 'dict_') def __init__(self, string, boolean, float_, list_, dict_): self.string = string self.boolean = boolean self.float_ = float_ self.list_ = list_ self.dict_ = dict_ class NestedClassforTesting(ephys.serializer.DictMixin): """Nested test class for serializer""" SERIALIZED_FIELDS = ('test', 'tuples', 'lists', 'dicts', ) def __init__(self, test, tuples, lists, dicts): self.test = test self.tuples = tuples self.lists = lists self.dicts = dicts @pytest.mark.unit def test_serializer(): """ephys.serializer: test serialization of test class""" tc = ClassforTesting('some string', False, 1.0, [1, 2, 3], {'0': 0}) serialized = tc.to_dict() assert isinstance(serialized, dict) json.dumps(serialized) @pytest.mark.unit def test_roundtrip_serializer(): """ephys.serializer: test round trip of serialization of test class""" tc = ClassforTesting('some string', False, 1.0, [1, 2, 3], {'0': 0}) serialized = tc.to_dict() instantiated = ephys.serializer.instantiator(serialized) assert isinstance(instantiated, ClassforTesting) @pytest.mark.unit def test_nested_serializer(): """ephys.serializer: test a nested serialization of test class""" tc = ClassforTesting('some string', False, 1.0, [1, 2, 3], {'0': 0}) ntc = NestedClassforTesting( test=tc, tuples=(tc,), lists=[tc] * 3, dicts={0: tc}) serialized = ntc.to_dict() json.dumps(serialized, indent=2) instantiated = ephys.serializer.instantiator(serialized) assert isinstance(instantiated, NestedClassforTesting) assert isinstance(instantiated.lists[0], ClassforTesting) assert isinstance(instantiated.dicts[0], ClassforTesting) @pytest.mark.unit def test_non_instantiable(): """ephys.serializer: test non instantiable class""" with pytest.raises(Exception): ephys.serializer.instantiator( {'some': 'fake', 'class': ephys.serializer.SENTINAL, }) ================================================ FILE: bluepyopt/tests/test_ephys/test_simulators.py ================================================ """bluepyopt.ephys.simulators tests""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import os import pathlib import types from unittest import mock import numpy import pytest import bluepyopt.ephys as ephys import bluepyopt.ephys.examples as examples @pytest.mark.unit def test_nrnsimulator_init(): """ephys.simulators: test if NrnSimulator constructor works""" neuron_sim = ephys.simulators.NrnSimulator() assert isinstance(neuron_sim, ephys.simulators.NrnSimulator) @pytest.mark.unit def test_nrnsimulator_init_windows(): """ephys.simulators: test if NrnSimulator constructor works on Windows""" with mock.patch("platform.system", mock.MagicMock(return_value="Windows")): neuron_sim = ephys.simulators.NrnSimulator() assert isinstance(neuron_sim, ephys.simulators.NrnSimulator) assert not neuron_sim.disable_banner assert not neuron_sim.banner_disabled neuron_sim.neuron.h.celsius = 34 assert not neuron_sim.disable_banner assert not neuron_sim.banner_disabled @pytest.mark.unit def test_nrnsimulator_cvode_minstep(): """ephys.simulators: test if NrnSimulator constructor works""" # Check with minstep specified neuron_sim = ephys.simulators.NrnSimulator() assert neuron_sim.cvode.minstep() == 0.0 assert neuron_sim.cvode_minstep == 0.0 # Check default minstep before and after run neuron_sim = ephys.simulators.NrnSimulator(cvode_minstep=0.01) assert neuron_sim.cvode.minstep() == 0.0 neuron_sim.run(tstop=10) assert neuron_sim.cvode.minstep() == 0.0 # Check with that minstep is set back to the original value after run neuron_sim = ephys.simulators.NrnSimulator(cvode_minstep=0.0) neuron_sim.cvode_minstep = 0.05 assert neuron_sim.cvode.minstep() == 0.05 neuron_sim.run(tstop=10) assert neuron_sim.cvode.minstep() == 0.05 # Check that the minstep is effective cvode_minstep = 0.012 params = { "gnabar_hh": 0.10299326453483033, "gkbar_hh": 0.027124836082684685, } simplecell = examples.simplecell.SimpleCell() evaluator = simplecell.cell_evaluator evaluator.cell_model.unfreeze(params.keys()) evaluator.sim = ephys.simulators.NrnSimulator(cvode_minstep=cvode_minstep) responses = evaluator.run_protocols( protocols=evaluator.fitness_protocols.values(), param_values=params ) ton = list(evaluator.fitness_protocols.values())[0].stimuli[0].step_delay toff = ( ton + list(evaluator.fitness_protocols.values())[0] .stimuli[0] .step_duration ) t_series = numpy.array(responses["Step1.soma.v"]["time"]) t_series = t_series[((ton + 1.0) < t_series) & (t_series < (toff - 1.0))] min_dt = numpy.min(numpy.ediff1d(t_series)) assert (min_dt >= cvode_minstep) == 1 evaluator.cell_model.freeze(params) @pytest.mark.unit def test_neuron_import(): """ephys.simulators: test if bluepyopt.neuron import was successful""" from bluepyopt import ephys # NOQA neuron_sim = ephys.simulators.NrnSimulator() assert isinstance(neuron_sim.neuron, types.ModuleType) @pytest.mark.unit def test_nrnsim_run_dt_exception(): """ephys.simulators: test if run return exception when dt was changed""" from bluepyopt import ephys # NOQA neuron_sim = ephys.simulators.NrnSimulator() neuron_sim.neuron.h.dt = 1.0 pytest.raises(Exception, neuron_sim.run, 10, cvode_active=False) @pytest.mark.unit def test_nrnsim_run_cvodeactive_dt_exception(): """ephys.simulators: test if run return exception cvode and dt both used""" from bluepyopt import ephys # NOQA neuron_sim = ephys.simulators.NrnSimulator() neuron_sim.neuron.h.dt = 1.0 pytest.raises(ValueError, neuron_sim.run, 10, dt=0.1, cvode_active=True) @pytest.mark.unit @mock.patch.object(pathlib.Path, "glob") def test_disable_banner_exception(mock_glob): """ephys.simulators: test if disable_banner raises exception""" mock_glob.return_value = [] import warnings with warnings.catch_warnings(record=True) as warnings_record: ephys.simulators.NrnSimulator._nrn_disable_banner() assert len(warnings_record) == 1 @pytest.mark.unit def test_lfpysimulator_init(): """ephys.simulators: test if LFPySimulator constructor works""" empty_cell = ephys.models.LFPyCellModel(name="empty_cell") neuron_sim = ephys.simulators.LFPySimulator() assert isinstance(neuron_sim, ephys.simulators.LFPySimulator) @pytest.mark.unit def test_lfpyimulator_init_windows(): """ephys.simulators: test if LFPySimulator constructor works on Windows""" with mock.patch("platform.system", mock.MagicMock(return_value="Windows")): empty_cell = ephys.models.LFPyCellModel(name="empty_cell") neuron_sim = ephys.simulators.LFPySimulator() assert isinstance(neuron_sim, ephys.simulators.LFPySimulator) assert not neuron_sim.disable_banner assert not neuron_sim.banner_disabled neuron_sim.neuron.h.celsius = 34 assert not neuron_sim.disable_banner assert not neuron_sim.banner_disabled @pytest.mark.unit def test__lfpysimulator_neuron_import(): """ephys.simulators: test neuron import from LFPySimulator""" from bluepyopt import ephys # NOQA empty_cell = ephys.models.LFPyCellModel(name="empty_cell") neuron_sim = ephys.simulators.LFPySimulator() assert isinstance(neuron_sim.neuron, types.ModuleType) @pytest.mark.unit def test_lfpysim_run_cvodeactive_dt_exception(): """ephys.simulators: test if LFPySimulator run returns exception""" from bluepyopt import ephys # NOQA TESTDATA_DIR = os.path.join( os.path.dirname(os.path.abspath(__file__)), "testdata" ) simple_morphology_path = os.path.join(TESTDATA_DIR, "simple.swc") test_morph = ephys.morphologies.NrnFileMorphology(simple_morphology_path) lfpy_cell = ephys.models.LFPyCellModel( name="lfpy_cell", morph=test_morph, mechs=[] ) neuron_sim = ephys.simulators.LFPySimulator() lfpy_cell.instantiate(sim=neuron_sim) with pytest.raises( ValueError, match=( "NrnSimulator: " "Impossible to combine the dt argument when " "cvode_active is True in the NrnSimulator run method" ), ): neuron_sim.run( tstop=10, dt=0.1, cvode_active=True, lfpy_cell=lfpy_cell.lfpy_cell, lfpy_electrode=lfpy_cell.lfpy_electrode, ) lfpy_cell.destroy(sim=neuron_sim) @pytest.mark.unit @mock.patch.object(pathlib.Path, "glob") def test_lfpysimulator_disable_banner_exception(mock_glob): """ephys.simulators: test LFPySimulator disable_banner raises exception""" mock_glob.return_value = [] import warnings with warnings.catch_warnings(record=True) as warnings_record: ephys.simulators.LFPySimulator._nrn_disable_banner() assert len(warnings_record) == 1 ================================================ FILE: bluepyopt/tests/test_ephys/test_stimuli.py ================================================ """bluepyopt.ephys.simulators tests""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import numpy import pytest import numpy import bluepyopt.ephys as ephys from .testmodels import dummycells @pytest.mark.unit def test_stimulus_init(): """ephys.stimuli: test if Stimulus constructor works""" stim = ephys.stimuli.Stimulus() assert isinstance(stim, ephys.stimuli.Stimulus) @pytest.mark.unit def test_NrnNetStimStimulus_init(): """ephys.stimuli: test if NrnNetStimStimulus constructor works""" pytest.raises(ValueError, ephys.stimuli.NrnNetStimStimulus) stim = ephys.stimuli.NrnNetStimStimulus(total_duration=100) assert isinstance(stim, ephys.stimuli.NrnNetStimStimulus) assert str(stim) == 'Netstim' @pytest.mark.unit def test_NrnNetStimStimulus_instantiate(): """ephys.stimuli: test if NrnNetStimStimulus instantiate works""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() icell = dummy_cell.instantiate(sim=nrn_sim) somacenter_loc = ephys.locations.NrnSeclistCompLocation( name=None, seclist_name='somatic', sec_index=0, comp_x=.5) expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism( name='expsyn', suffix='ExpSyn', locations=[somacenter_loc]) expsyn_mech.instantiate(sim=nrn_sim, icell=icell) expsyn_loc = ephys.locations.NrnPointProcessLocation( 'expsyn_loc', pprocess_mech=expsyn_mech) netstim = ephys.stimuli.NrnNetStimStimulus( total_duration=200, number=5, interval=5, start=20, weight=5e-4, locations=[expsyn_loc]) netstim.instantiate(sim=nrn_sim, icell=icell) nrn_sim.run(netstim.total_duration) expsyn_mech.destroy(sim=nrn_sim) netstim.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_NrnCurrentPlayStimulus_instantiate(): """ephys.stimuli: test if NrnNetStimStimulus instantiate works""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() icell = dummy_cell.instantiate(sim=nrn_sim) somacenter_loc = ephys.locations.NrnSeclistCompLocation( name=None, seclist_name='somatic', sec_index=0, comp_x=.5) time_points = [10, 50] current_points = [0.1, 0.2] current_stim = ephys.stimuli.NrnCurrentPlayStimulus( time_points=time_points, current_points=current_points, location=somacenter_loc) assert current_stim.time_points == time_points assert current_stim.current_points == current_points assert str(current_stim) == 'Current play at somatic[0](0.5)' current_stim.instantiate(sim=nrn_sim, icell=icell) nrn_sim.run(100) current_stim.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_NrnRampPulse_init(): """ephys.stimuli: test if NrnRampPulse constructor works""" stim = ephys.stimuli.NrnRampPulse() assert isinstance(stim, ephys.stimuli.NrnRampPulse) @pytest.mark.unit def test_NrnRampPulse_instantiate(): """ephys.stimuli: test if NrnRampPulse injects correct current""" nrn_sim = ephys.simulators.NrnSimulator() dummy_cell = dummycells.DummyCellModel1() icell = dummy_cell.instantiate(sim=nrn_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name=None, seclist_name='somatic', sec_index=0, comp_x=.5) recording = ephys.recordings.CompRecording( location=soma_loc, variable='v') ramp_amplitude_start = 0.1 ramp_amplitude_end = 1.0 ramp_delay = 20.0 ramp_duration = 20.0 total_duration = 50.0 stim = ephys.stimuli.NrnRampPulse( ramp_amplitude_start=ramp_amplitude_start, ramp_amplitude_end=ramp_amplitude_end, ramp_delay=ramp_delay, ramp_duration=ramp_duration, total_duration=total_duration, location=soma_loc) assert ( str(stim) == 'Ramp pulse amp_start 0.100000 amp_end 1.000000 ' 'delay 20.000000 duration 20.000000 totdur 50.000000' ' at somatic[0](0.5)') stim.instantiate(sim=nrn_sim, icell=icell) recording.instantiate(sim=nrn_sim, icell=icell) stim_i_vec = nrn_sim.neuron.h.Vector() stim_i_vec.record(stim.iclamp._ref_i) # pylint: disable=W0212 nrn_sim.run(stim.total_duration) current = numpy.array(stim_i_vec.to_python()) time = numpy.array(recording.response['time']) voltage = numpy.array(recording.response['voltage']) # make sure current is 0 before stimulus assert numpy.max( current[numpy.where((0 <= time) & (time < ramp_delay))]) == 0 # make sure voltage stays at v_init before stimulus assert numpy.max( voltage[ numpy.where((0 <= time) & (time < ramp_delay))]) == nrn_sim.neuron.h.v_init # make sure current is at right amp at end of stimulus assert ( current[numpy.where(time == ramp_delay)][-1] == ramp_amplitude_start) # make sure current is at right amp at end of stimulus assert ( current[numpy.where(time == (ramp_delay + ramp_duration))][0] == ramp_amplitude_end) # make sure current is 0 after stimulus assert numpy.max( current[ numpy.where( (ramp_delay + ramp_duration < time) & (time <= total_duration))]) == 0 # make sure voltage is correct after stimulus numpy.testing.assert_almost_equal(numpy.mean( voltage[ numpy.where( (ramp_delay + ramp_duration < time) & (time <= total_duration))]), -57.994437612124869) recording.destroy(sim=nrn_sim) stim.destroy(sim=nrn_sim) dummy_cell.destroy(sim=nrn_sim) @pytest.mark.unit def test_LFPySquarePulse_init(): """ephys.stimuli: test if LFPySquarePulse constructor works""" soma_loc = ephys.locations.NrnSeclistCompLocation( name=None, seclist_name='somatic', sec_index=0, comp_x=.5) stim = ephys.stimuli.LFPySquarePulse( step_amplitude=0.1, step_delay=70, step_duration=100, location=soma_loc, total_duration=300 ) assert isinstance(stim, ephys.stimuli.LFPySquarePulse) assert stim.step_amplitude == 0.1 assert stim.step_delay == 70 assert stim.step_duration == 100 assert isinstance(stim.location, ephys.locations.NrnSeclistCompLocation) assert stim.total_duration == 300 assert str(stim) == ( "Square pulse amp 0.100000 delay 70.000000 duration 100.000000 " "totdur 300.000000 at somatic[0](0.5)" ) @pytest.mark.unit def test_LFPySquarePulse_instantiate(): """ephys.stimuli: test if LFPySquarePulse instantiate works""" lfpy_sim = ephys.simulators.LFPySimulator() dummy_cell = dummycells.DummyLFPyCellModel1() _, lfpy_cell = dummy_cell.instantiate(sim=lfpy_sim) soma_loc = ephys.locations.NrnSeclistCompLocation( name=None, seclist_name='somatic', sec_index=0, comp_x=.5) stim = ephys.stimuli.LFPySquarePulse( step_amplitude=0.1, step_delay=70, step_duration=100, location=soma_loc, total_duration=300 ) stim.instantiate(sim=lfpy_sim, lfpy_cell=lfpy_cell) lfpy_sim.run( lfpy_cell=lfpy_cell, lfpy_electrode=dummy_cell.lfpy_electrode, tstop=stim.total_duration ) stim.destroy(sim=lfpy_sim) dummy_cell.destroy(sim=lfpy_sim) ================================================ FILE: bluepyopt/tests/test_ephys/testdata/TimeVoltageResponse.csv ================================================ ,time,voltage 0,0.0,-65.0 1,0.000664036278079,-65.0020897598 2,0.00229531725338,-65.0063371218 3,0.00606004223107,-65.0140341127 4,0.0120247735066,-65.0237160215 5,0.0179895047821,-65.0319516314 6,0.0288608614854,-65.0444055596 7,0.0397322181887,-65.0552832155 8,0.0562022809474,-65.0695916981 9,0.0816395739013,-65.0894097754 10,0.107076866855,-65.1070566505 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0.46) ; 115 ( 445.74 368.59 -131.17 0.46) ; 116 ( 447.41 369.57 -134.00 0.46) ; 117 ( 447.41 369.57 -134.02 0.46) ; 118 ( 447.59 370.80 -133.85 0.46) ; 119 ( 449.11 372.36 -137.15 0.46) ; 120 (Dot (Color Yellow) (Name "Marker 1") ( 338.56 200.73 -35.22 0.46) ; 1 ( 379.03 275.32 -62.45 0.46) ; 2 ( 442.74 355.33 -115.40 0.46) ; 3 ( 442.97 364.35 -126.97 0.46) ; 4 ) ; End of markers (OpenCircle (Color Yellow) (Name "Marker 2") ( 329.83 167.64 -39.60 0.46) ; 1 ( 339.74 211.76 -33.40 0.46) ; 2 ( 348.47 222.77 -28.63 0.46) ; 3 ( 355.29 239.90 -27.08 0.46) ; 4 ( 357.63 244.03 -31.85 0.46) ; 5 ( 364.39 253.38 -44.40 0.46) ; 6 ( 375.61 263.77 -56.10 0.46) ; 7 ( 377.81 268.46 -59.35 0.46) ; 8 ( 389.73 287.98 -59.25 0.46) ; 9 ( 394.11 291.39 -61.90 0.46) ; 10 ( 398.81 295.48 -65.22 0.46) ; 11 ( 415.32 315.48 -76.45 0.46) ; 12 ( 420.52 321.46 -80.90 0.46) ; 13 ( 421.46 323.48 -82.22 0.46) ; 14 ( 431.18 338.30 -95.90 0.46) ; 15 ( 442.75 361.31 -121.65 0.46) ; 16 ( 445.08 371.42 -133.85 0.46) ; 17 ) 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0.46) ; 34 ( 372.44 -78.15 -9.60 0.46) ; 35 ( 372.44 -78.15 -9.65 0.46) ; 36 ( 373.07 -76.82 -10.95 0.46) ; 37 ( 372.94 -76.25 -10.97 0.46) ; 38 ( 374.74 -75.84 -10.97 0.46) ; 39 ( 375.60 -74.49 -12.17 0.46) ; 40 ( ( 375.93 -74.94 -11.70 0.46) ; 1, R-2-2-3-2-1 ( 377.99 -75.67 -12.17 0.46) ; 2 ( 382.84 -76.31 -13.00 0.46) ; 3 ( 385.49 -77.48 -14.38 0.46) ; 4 ( 387.45 -75.83 -16.00 0.46) ; 5 ( 387.45 -75.83 -16.02 0.46) ; 6 ( 388.92 -76.08 -18.40 0.46) ; 7 ( 391.73 -76.02 -19.42 0.46) ; 8 (Dot (Color Yellow) (Name "Marker 1") ( 305.65 -86.64 -0.12 0.46) ; 1 ( 314.31 -83.42 -1.73 0.46) ; 2 ( 330.08 -80.32 -4.52 0.46) ; 3 ( 340.61 -79.04 -4.03 0.46) ; 4 ( 381.38 -76.06 -12.98 0.46) ; 5 ( 390.65 -77.47 -19.42 0.46) ; 6 ) ; End of markers (OpenCircle (Color Yellow) (Name "Marker 2") ( 296.63 -86.37 2.70 0.46) ; 1 ( 302.43 -85.01 -0.12 0.46) ; 2 ( 319.48 -83.40 -2.05 0.46) ; 3 ( 334.81 -80.41 -4.03 0.46) ; 4 ( 348.60 -78.96 -4.63 0.46) ; 5 ( 356.60 -78.88 -6.42 0.46) ; 6 ( 364.76 -77.56 -5.97 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0.46) ; 16 ( 172.69 7.93 -10.50 0.46) ; 17 ( 170.95 9.31 -11.27 0.46) ; 18 ( 170.55 11.01 -12.60 0.46) ; 19 ( 170.55 11.01 -12.67 0.46) ; 20 ( 171.67 14.26 -14.18 0.46) ; 21 ( 169.49 15.53 -15.60 0.46) ; 22 ( 166.73 17.28 -16.80 0.46) ; 23 ( 165.30 19.34 -18.20 0.46) ; 24 ( 163.82 19.59 -19.75 0.46) ; 25 ( 162.71 22.31 -20.35 0.46) ; 26 ( 162.71 22.31 -20.38 0.46) ; 27 ( 161.43 23.80 -22.27 0.46) ; 28 ( 159.69 25.19 -23.60 0.46) ; 29 ( 158.08 26.01 -25.50 0.46) ; 30 ( 158.08 26.01 -25.55 0.46) ; 31 ( 155.59 26.62 -26.92 0.46) ; 32 ( 153.40 27.89 -28.70 0.46) ; 33 ( 151.16 27.37 -30.90 0.46) ; 34 ( 150.76 29.07 -32.80 0.46) ; 35 ( 150.19 29.53 -35.40 0.46) ; 36 ( 150.19 29.53 -35.42 0.46) ; 37 ( 148.72 29.79 -37.90 0.46) ; 38 ( 148.72 29.79 -37.92 0.46) ; 39 ( 148.32 31.48 -41.40 0.46) ; 40 ( 147.88 31.38 -43.78 0.46) ; 41 ( 147.88 31.38 -43.80 0.46) ; 42 (Cross (Color Green) (Name "Marker 3") ( 198.67 11.63 4.07 0.92) ; 1 ( 196.31 11.67 2.15 0.92) ; 2 ( 184.91 6.01 -1.63 0.46) ; 3 ( 180.45 4.97 -8.13 0.46) ; 4 ( 176.60 5.27 -7.90 0.46) ; 5 ( 175.63 7.42 -7.32 0.46) ; 6 ( 175.36 8.57 -9.05 0.46) ; 7 ( 174.63 3.61 -10.95 0.46) ; 8 ( 170.14 6.73 -12.42 0.46) ; 9 ( 169.47 9.56 -9.77 0.46) ; 10 ( 169.57 13.17 -14.18 0.46) ; 11 ( 168.06 17.59 -15.02 0.46) ; 12 ( 173.09 12.20 -15.02 0.46) ; 13 ( 173.14 14.01 -15.60 0.46) ; 14 ( 166.81 14.91 -15.60 0.46) ; 15 ( 169.44 13.73 -15.60 0.46) ; 16 ( 159.80 18.64 -20.38 0.46) ; 17 ( 164.64 22.16 -21.45 0.46) ; 18 ( 163.92 23.20 -22.27 0.46) ; 19 ( 156.12 24.35 -27.08 0.46) ; 20 ) ; End of markers Normal ) ; End of split | ( 237.57 0.45 -4.55 1.38) ; 1, R-1-2 ( ( 234.22 2.64 -6.25 0.92) ; 1, R-1-2-1 ( 234.22 2.64 -6.28 0.92) ; 2 ( 231.91 4.49 -7.55 0.92) ; 3 ( 231.91 4.49 -7.57 0.92) ; 4 ( 229.99 4.64 -8.93 0.92) ; 5 ( 228.03 2.98 -10.17 0.92) ; 6 ( 225.21 2.92 -9.88 0.92) ; 7 ( 221.11 4.35 -11.37 0.92) ; 8 ( 221.95 2.76 -13.23 0.92) ; 9 ( 221.95 2.76 -13.25 0.92) ; 10 ( 219.82 5.83 -14.25 0.92) ; 11 ( 219.82 5.83 -14.27 0.92) ; 12 ( 217.82 8.36 -13.82 0.92) ; 13 ( 216.52 9.85 -15.57 0.92) ; 14 ( 215.50 10.20 -17.67 0.92) ; 15 ( 215.50 10.20 -17.70 0.92) ; 16 ( 213.26 9.68 -19.33 0.92) ; 17 ( 211.61 8.69 -19.60 0.92) ; 18 ( 210.85 7.91 -21.10 0.92) ; 19 ( 210.85 7.91 -21.13 0.92) ; 20 ( 209.65 7.03 -22.92 0.46) ; 21 ( 209.11 9.30 -24.90 0.46) ; 22 ( 208.86 10.43 -26.90 0.92) ; 23 ( 205.14 10.16 -28.13 0.92) ; 24 ( 205.14 10.16 -28.15 0.92) ; 25 ( 203.09 10.88 -28.15 0.92) ; 26 ( 199.88 12.51 -28.15 0.92) ; 27 (Cross (Color Green) (Name "Marker 3") ( 224.58 1.58 -9.88 0.92) ; 1 ( 216.74 6.91 -13.82 0.92) ; 2 ( 214.37 6.95 -19.33 0.92) ; 3 ( 213.44 10.91 -19.33 0.92) ; 4 ( 211.70 6.33 -19.60 0.92) ; 5 ( 202.11 13.03 -28.15 0.92) ; 6 ( 205.18 11.96 -27.65 0.92) ; 7 ) ; End of markers ( ( 195.01 13.16 -27.70 0.46) ; 1, R-1-2-1-1 ( 191.49 14.13 -29.27 0.46) ; 2 ( 190.32 15.05 -30.30 0.46) ; 3 ( 188.33 17.57 -30.83 0.46) ; 4 ( 186.06 21.21 -32.00 0.46) ; 5 ( 184.77 22.70 -33.22 0.46) ; 6 ( 183.61 23.63 -34.52 0.46) ; 7 ( 183.61 23.63 -34.55 0.46) ; 8 ( 182.37 26.92 -34.55 0.46) ; 9 ( 179.97 31.13 -34.60 0.46) ; 10 ( 179.97 31.13 -34.63 0.46) ; 11 ( 177.84 34.22 -37.02 0.46) ; 12 ( 177.84 34.22 -37.05 0.46) ; 13 ( 175.64 35.50 -38.03 0.46) ; 14 ( 174.40 38.79 -38.38 0.46) ; 15 ( 171.19 40.42 -38.60 0.46) ; 16 ( 169.64 43.04 -39.67 0.46) ; 17 ( 167.59 43.76 -40.57 0.46) ; 18 ( 167.59 43.76 -40.60 0.46) ; 19 (Cross (Color Green) (Name "Marker 3") ( 183.61 23.63 -33.22 0.46) ; 1 ( 183.17 29.49 -38.35 0.46) ; 2 ) ; End of markers Normal | ( 195.86 11.57 -30.10 0.46) ; 1, R-1-2-1-2 ( 192.74 10.84 -32.25 0.46) ; 2 ( 192.74 10.84 -32.30 0.46) ; 3 ( 190.67 11.54 -33.45 0.46) ; 4 ( 190.67 11.54 -33.47 0.46) ; 5 ( 189.21 11.81 -35.27 0.46) ; 6 ( 189.21 11.81 -35.33 0.46) ; 7 ( 186.71 12.42 -36.92 0.46) ; 8 ( 186.71 12.42 -36.95 0.46) ; 9 ( 185.74 14.56 -37.85 0.46) ; 10 ( 185.74 14.56 -37.88 0.46) ; 11 ( 182.21 15.54 -39.13 0.46) ; 12 ( 178.94 15.37 -40.80 0.46) ; 13 ( 177.13 19.12 -41.82 0.46) ; 14 ( 177.13 19.12 -41.85 0.46) ; 15 ( 174.58 17.93 -44.10 0.46) ; 16 ( 172.53 18.65 -46.25 0.46) ; 17 ( 170.47 19.35 -48.35 0.46) ; 18 ( 170.47 19.35 -48.38 0.46) ; 19 ( 168.56 19.50 -50.35 0.46) ; 20 ( 168.56 19.50 -50.38 0.46) ; 21 ( 167.71 21.10 -52.65 0.46) ; 22 ( 167.71 21.10 -52.70 0.46) ; 23 ( 167.45 22.22 -54.65 0.46) ; 24 ( 167.45 22.22 -54.72 0.46) ; 25 ( 165.71 23.61 -56.42 0.46) ; 26 ( 165.71 23.61 -56.45 0.46) ; 27 ( 163.61 22.52 -58.38 0.46) ; 28 ( 163.61 22.52 -58.40 0.46) ; 29 ( 165.22 21.71 -60.05 0.46) ; 30 ( 165.22 21.71 -60.22 0.46) ; 31 ( 162.84 21.75 -62.70 0.46) ; 32 ( 162.84 21.75 -62.75 0.46) ; 33 ( 161.33 20.20 -66.13 0.46) ; 34 ( 159.99 19.89 -69.00 0.46) ; 35 ( 158.52 20.14 -69.30 0.46) ; 36 ( 158.12 21.83 -71.17 0.46) ; 37 ( 158.12 21.83 -71.20 0.46) ; 38 ( 156.83 23.33 -73.95 0.46) ; 39 ( 156.83 23.33 -73.97 0.46) ; 40 ( 155.77 27.85 -76.28 0.46) ; 41 ( 155.77 27.85 -76.30 0.46) ; 42 ( 155.55 30.79 -78.28 0.46) ; 43 ( 155.55 30.79 -78.32 0.46) ; 44 ( 155.34 33.71 -80.63 0.46) ; 45 ( 155.34 33.71 -80.68 0.46) ; 46 ( 154.62 34.75 -84.12 0.46) ; 47 ( 154.62 34.75 -84.17 0.46) ; 48 ( 154.72 38.36 -86.62 0.46) ; 49 ( 154.72 38.36 -86.65 0.46) ; 50 ( 153.88 39.95 -89.05 0.46) ; 51 ( 152.77 42.68 -92.00 0.46) ; 52 ( 151.61 43.59 -94.80 0.46) ; 53 ( 151.61 43.59 -94.82 0.46) ; 54 ( 149.73 45.55 -96.80 0.46) ; 55 ( 149.92 46.78 -99.05 0.46) ; 56 ( 147.60 48.62 -102.63 0.46) ; 57 ( 147.60 48.62 -102.65 0.46) ; 58 (Cross (Color White) (Name "Marker 3") ( 147.72 48.06 -50.55 0.46) ; 1 ) ; End of markers (Cross (Color Green) (Name "Marker 3") ( 193.45 9.81 -32.30 0.46) ; 1 ( 190.54 12.12 -31.77 0.46) ; 2 ( 183.19 13.37 -39.13 0.46) ; 3 ( 179.21 14.24 -41.85 0.46) ; 4 ( 168.74 20.74 -48.38 0.46) ; 5 ( 158.79 19.00 -67.32 0.46) ; 6 ( 157.53 38.41 -86.65 0.46) ; 7 ) ; End of markers Normal ) ; End of split | ( 234.31 0.28 -6.78 0.92) ; 1, R-1-2-2 ( 232.98 -0.04 -7.43 0.92) ; 2 ( 231.72 -2.72 -8.00 0.92) ; 3 ( 231.72 -2.72 -8.02 0.92) ; 4 ( 230.92 -5.30 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-55.74 -29.02 0.92) ; 35 ( 199.05 -56.36 -30.13 0.92) ; 36 ( 196.81 -56.89 -30.13 0.92) ; 37 ( 194.72 -57.97 -30.30 0.46) ; 38 ( 192.75 -59.63 -30.30 0.46) ; 39 ( 189.63 -60.36 -30.30 0.46) ; 40 ( 187.52 -61.46 -32.28 0.46) ; 41 ( 185.29 -61.97 -33.92 0.46) ; 42 ( 183.63 -62.97 -35.83 0.46) ; 43 ( 183.58 -64.77 -38.00 0.46) ; 44 ( 183.58 -64.77 -38.03 0.46) ; 45 ( 182.52 -66.21 -38.20 0.46) ; 46 ( 180.54 -67.87 -39.78 0.46) ; 47 ( 178.26 -70.20 -40.60 0.46) ; 48 ( 176.74 -71.75 -40.83 0.46) ; 49 ( 175.04 -74.53 -43.18 0.46) ; 50 ( 173.66 -76.65 -44.45 0.46) ; 51 ( 173.28 -79.12 -46.93 0.46) ; 52 ( 171.99 -77.63 -49.52 0.46) ; 53 ( 170.66 -77.94 -52.10 0.46) ; 54 ( 170.66 -77.94 -52.13 0.46) ; 55 ( 169.63 -77.59 -55.03 0.46) ; 56 ( 169.63 -77.59 -55.05 0.46) ; 57 ( 169.05 -77.13 -58.50 0.46) ; 58 ( 167.89 -76.21 -62.47 0.46) ; 59 ( 167.63 -75.08 -67.17 0.46) ; 60 ( 167.63 -75.08 -67.28 0.46) ; 61 (Cross (Color Green) (Name "Marker 3") ( 225.37 -7.79 -8.50 0.92) ; 1 ( 229.07 -7.52 -8.50 0.92) ; 2 ( 226.89 -6.25 -8.50 0.92) ; 3 ( 228.09 -5.36 -8.50 0.92) ; 4 ( 227.97 -20.91 -10.70 0.92) ; 5 ( 225.10 -28.75 -10.30 0.92) ; 6 ( 213.49 -47.60 -13.13 0.92) ; 7 ( 220.98 -33.31 -7.95 0.92) ; 8 ( 223.44 -29.74 -7.95 0.92) ; 9 ( 217.64 -31.10 -7.95 0.92) ; 10 ( 220.16 -35.88 -7.80 0.92) ; 11 ( 220.54 -33.41 -8.82 0.92) ; 12 ( 216.54 -44.49 -9.48 0.92) ; 13 ( 214.01 -55.84 -17.60 0.92) ; 14 ( 203.38 -54.75 -24.15 0.92) ; 15 ( 193.02 -60.77 -30.30 0.46) ; 16 ( 181.95 -59.78 -35.25 0.46) ; 17 ( 179.87 -71.01 -40.83 0.46) ; 18 ( 174.69 -71.03 -42.02 0.46) ; 19 ( 177.46 -72.78 -42.02 0.46) ; 20 ( 175.40 -78.04 -43.80 0.46) ; 21 ) ; End of markers Normal | ( 236.33 -2.03 -4.55 0.92) ; 1, R-1-2-3 ( 235.12 -2.92 -4.35 0.92) ; 2 ( 235.12 -2.92 -4.40 0.92) ; 3 ( 234.19 -4.93 -3.17 0.92) ; 4 ( 234.45 -6.06 -1.70 0.92) ; 5 ( 233.37 -7.50 -0.40 0.92) ; 6 ( ( 233.18 -8.74 0.95 0.92) ; 1, R-1-2-3-1 ( 234.03 -10.34 2.05 0.92) ; 2 ( 235.20 -11.26 3.38 0.92) ; 3 ( 235.20 -11.26 3.35 0.92) ; 4 ( 235.91 -12.28 4.43 0.92) ; 5 ( 236.75 -13.88 5.15 0.92) ; 6 ( 237.28 -16.14 5.85 0.92) ; 7 ( 237.37 -18.51 6.57 0.92) ; 8 ( 235.84 -20.07 8.35 0.92) ; 9 ( 235.08 -20.84 9.20 0.92) ; 10 ( 235.17 -23.21 8.80 0.92) ; 11 ( 235.25 -25.57 10.50 0.92) ; 12 ( 235.47 -28.51 11.35 0.92) ; 13 ( 234.97 -30.41 11.07 0.92) ; 14 ( 235.95 -32.58 12.25 0.92) ; 15 ( 235.95 -32.58 12.22 0.92) ; 16 ( 235.59 -35.06 12.50 0.92) ; 17 ( 234.20 -37.16 12.07 0.92) ; 18 ( 234.20 -37.16 12.05 0.92) ; 19 ( 233.75 -37.27 14.30 0.92) ; 20 ( 234.65 -37.06 16.57 0.92) ; 21 ( 235.36 -38.09 18.67 0.92) ; 22 ( 233.83 -39.65 20.17 0.92) ; 23 ( 233.60 -42.68 20.97 0.92) ; 24 ( 232.35 -45.37 21.88 0.92) ; 25 ( 230.33 -48.83 21.55 0.92) ; 26 ( 230.33 -48.83 21.52 0.92) ; 27 ( 229.79 -52.53 22.08 0.92) ; 28 ( 227.64 -55.43 22.57 0.92) ; 29 ( 226.57 -56.87 23.33 0.92) ; 30 ( 224.92 -57.85 23.80 0.92) ; 31 ( 224.92 -57.85 23.78 0.92) ; 32 ( 224.11 -60.44 24.38 0.92) ; 33 ( 224.11 -60.44 24.35 0.92) ; 34 ( 225.53 -62.48 25.23 0.92) ; 35 ( 223.64 -66.51 26.45 0.92) ; 36 ( 223.41 -69.55 27.25 0.92) ; 37 ( 222.29 -72.80 27.77 0.92) ; 38 ( 220.27 -76.25 29.13 0.92) ; 39 ( 218.93 -76.57 30.65 0.92) ; 40 ( 217.55 -78.69 31.95 0.92) ; 41 ( 216.47 -80.14 33.85 0.92) ; 42 ( 216.02 -80.24 34.40 0.92) ; 43 (Cross (Color White) (Name "Marker 3") ( 239.02 -17.53 5.85 0.92) ; 1 ( 233.48 -20.03 9.20 0.92) ; 2 ( 235.13 -19.04 9.20 0.92) ; 3 ( 240.05 -17.89 10.33 0.92) ; 4 ( 232.19 -34.65 12.50 0.92) ; 5 ( 236.43 -36.65 12.50 0.92) ; 6 ( 236.00 -30.78 12.50 0.92) ; 7 ( 237.11 -33.49 12.50 0.92) ; 8 ( 234.53 -30.52 9.65 0.92) ; 9 ( 232.99 -38.05 18.67 0.92) ; 10 ( 236.83 -38.34 20.97 0.92) ; 11 ( 231.64 -44.34 21.88 0.92) ; 12 ( 231.87 -41.30 21.88 0.92) ; 13 ( 230.58 -55.93 22.57 0.92) ; 14 ( 231.44 -51.55 22.57 0.92) ; 15 ( 225.59 -54.72 26.58 0.92) ; 16 ( 226.20 -59.35 23.07 0.92) ; 17 ( 221.99 -67.50 26.45 0.92) ; 18 ( 219.80 -72.19 27.30 0.92) ; 19 ( 225.59 -70.83 27.30 0.92) ; 20 ( 224.21 -72.95 28.52 0.92) ; 21 ) ; End of markers High | ( 231.22 -10.40 -1.42 0.92) ; 1, R-1-2-3-2 ( 231.89 -13.23 -2.42 0.92) ; 2 ( 231.89 -13.23 -2.45 0.92) ; 3 ( 231.66 -16.27 -1.65 0.92) ; 4 ( 230.85 -18.85 -0.47 0.92) ; 5 ( 229.60 -21.53 0.17 0.92) ; 6 ( 227.44 -24.42 0.95 0.92) ; 7 ( 226.23 -25.31 1.95 0.92) ; 8 ( 224.98 -27.98 3.08 0.92) ; 9 ( 223.72 -30.66 4.35 0.92) ; 10 ( 222.82 -30.87 5.55 0.92) ; 11 ( 221.71 -34.12 4.38 0.92) ; 12 ( 220.14 -37.47 4.38 0.92) ; 13 ( 218.44 -40.27 4.38 0.92) ; 14 ( 219.10 -43.10 5.37 0.92) ; 15 ( 220.34 -46.38 6.07 0.92) ; 16 ( 220.34 -46.38 5.22 0.92) ; 17 ( 220.87 -48.66 4.50 0.92) ; 18 ( 220.87 -48.66 4.22 0.92) ; 19 ( 221.85 -50.82 3.35 0.92) ; 20 ( 220.54 -55.29 2.83 0.92) ; 21 ( 217.81 -57.74 1.67 0.92) ; 22 ( 217.81 -57.74 1.65 0.92) ; 23 ( 215.53 -60.05 1.42 0.92) ; 24 ( 215.56 -64.23 0.45 0.92) ; 25 ( 214.76 -66.81 0.22 0.92) ; 26 ( 214.76 -66.81 0.17 0.92) ; 27 ( 213.77 -70.62 -0.67 0.92) ; 28 ( 213.77 -70.62 -0.72 0.92) ; 29 ( 213.54 -73.66 -1.17 0.92) ; 30 ( 214.51 -75.82 -1.17 0.92) ; 31 ( 213.35 -80.87 -1.47 0.92) ; 32 ( 212.80 -84.59 -1.60 0.92) ; 33 ( 212.07 -89.53 -1.60 0.92) ; 34 ( 212.42 -93.04 -2.75 0.92) ; 35 ( 210.54 -97.06 -3.78 0.92) ; 36 ( 209.86 -100.19 -3.78 0.92) ; 37 ( 209.14 -105.15 -4.78 0.92) ; 38 ( 209.35 -108.08 -3.60 0.92) ; 39 ( 208.94 -112.35 -2.22 0.92) ; 40 ( 208.99 -116.53 -1.32 0.92) ; 41 ( 210.35 -120.38 -0.45 0.92) ; 42 ( 211.15 -123.78 0.32 0.92) ; 43 ( 212.08 -127.75 1.07 0.92) ; 44 ( 209.80 -130.07 1.90 0.92) ; 45 ( 207.43 -130.03 2.92 0.92) ; 46 ( 206.04 -132.15 3.65 0.92) ; 47 ( 206.04 -132.15 3.70 0.92) ; 48 ( 204.57 -131.89 4.63 0.92) ; 49 ( 202.92 -132.88 5.20 0.92) ; 50 ( 202.56 -135.35 4.63 0.92) ; 51 ( 202.56 -135.35 4.60 0.92) ; 52 ( 203.27 -136.37 4.82 0.92) ; 53 ( 204.51 -139.67 6.02 0.92) ; 54 ( 202.18 -143.80 7.10 0.92) ; 55 ( 201.77 -148.07 8.45 0.92) ; 56 ( 202.30 -150.35 9.77 0.92) ; 57 ( 203.01 -151.37 12.00 0.92) ; 58 ( 203.59 -151.83 13.30 0.92) ; 59 (Cross (Color White) (Name "Marker 3") ( 230.69 -8.14 -1.42 0.92) ; 1 ( 233.98 -18.12 -1.42 0.92) ; 2 ( 233.80 -13.38 -2.97 0.92) ; 3 ( 231.20 -22.34 -0.55 0.92) ; 4 ( 227.49 -22.61 1.25 0.92) ; 5 ( 228.59 -25.35 2.88 0.92) ; 6 ( 222.25 -52.51 5.13 0.92) ; 7 ( 219.42 -58.55 5.13 0.92) ; 8 ( 219.24 -59.78 2.57 0.92) ; 9 ( 221.43 -55.08 2.57 0.92) ; 10 ( 217.59 -60.78 2.57 0.92) ; 11 ( 218.12 -63.03 2.57 0.92) ; 12 ( 214.10 -58.00 1.13 0.92) ; 13 ( 217.41 -56.03 1.13 0.92) ; 14 ( 210.58 -79.14 -1.60 0.92) ; 15 ( 213.76 -76.59 -1.60 0.92) ; 16 ( 214.69 -80.56 -1.60 0.92) ; 17 ( 211.46 -84.89 -1.60 0.92) ; 18 ( 210.91 -88.61 -1.60 0.92) ; 19 ( 214.57 -90.13 -1.60 0.92) ; 20 ( 213.10 -67.79 -1.60 0.92) ; 21 ( 216.18 -68.86 -1.60 0.92) ; 22 ( 210.20 -93.55 -3.78 0.92) ; 23 ( 212.42 -99.00 -1.77 0.92) ; 24 ( 207.40 -103.76 -4.70 0.92) ; 25 ( 211.24 -104.05 -5.57 0.92) ; 26 ( 211.18 -111.83 -2.22 0.92) ; 27 ( 208.49 -118.44 -1.32 0.92) ; 28 ( 211.61 -117.71 -1.32 0.92) ; 29 ( 211.88 -118.83 -1.32 0.92) ; 30 ( 202.84 -130.51 4.60 0.92) ; 31 ( 198.85 -135.62 4.60 0.92) ; 32 ( 207.07 -132.50 4.17 0.92) ; 33 ( 203.60 -145.85 9.52 0.92) ; 34 ( 203.24 -148.33 9.52 0.92) ; 35 ( 213.10 -128.10 3.32 0.92) ; 36 ) ; End of markers Normal ) ; End of split ) ; End of split ) ; End of split | ( 250.10 9.55 4.75 0.92) ; 1, R-2 ( 247.61 10.16 6.35 0.92) ; 2 ( 245.28 12.01 6.97 0.92) ; 3 ( 243.42 13.95 8.52 0.92) ; 4 ( 244.49 15.40 9.77 0.92) ; 5 ( 246.99 14.79 10.12 0.92) ; 6 ( 245.92 13.35 11.47 0.92) ; 7 ( 244.26 12.36 14.88 0.92) ; 8 (Cross (Color White) (Name "Marker 3") ( 251.31 10.43 6.35 0.92) ; 1 ( 246.60 16.50 8.85 0.92) ; 2 ( 248.46 14.54 11.47 0.92) ; 3 ) ; End of markers ( ( 245.34 13.80 14.88 0.92) ; 1, R-2-1 ( 241.54 15.91 14.45 0.92) ; 2 ( 240.08 16.16 14.85 0.92) ; 3 ( 240.08 16.16 14.82 0.92) ; 4 ( 239.86 19.09 16.60 0.92) ; 5 ( 239.46 20.79 18.38 0.92) ; 6 ( 238.22 24.08 19.52 0.92) ; 7 ( 236.21 26.60 20.75 0.92) ; 8 ( 234.08 29.69 20.92 0.92) ; 9 ( 231.76 31.53 20.13 0.92) ; 10 (Cross (Color White) (Name "Marker 3") ( 238.20 18.11 14.82 0.92) ; 1 ( 237.23 20.27 18.40 0.92) ; 2 ( 240.13 23.93 18.40 0.92) ; 3 ( 238.44 27.12 19.02 0.92) ; 4 ( 231.53 28.49 18.55 0.92) ; 5 ( 234.26 30.92 18.55 0.92) ; 6 ( 232.58 34.11 18.55 0.92) ; 7 ) ; End of markers ( ( 229.93 35.27 20.85 0.46) ; 1, R-2-1-1 ( 227.31 36.46 21.85 0.46) ; 2 ( 224.86 38.86 22.67 0.46) ; 3 ( 223.84 39.22 24.20 0.46) ; 4 ( 223.39 39.12 24.20 0.46) ; 5 ( 222.63 38.35 26.35 0.46) ; 6 Normal | ( 228.42 33.73 19.23 0.92) ; 1, R-2-1-2 ( 226.23 35.01 19.58 0.92) ; 2 ( 226.23 35.01 19.55 0.92) ; 3 ( 224.63 35.83 21.10 0.92) ; 4 ( 222.00 37.00 23.00 0.92) ; 5 ( 219.99 39.53 24.08 0.92) ; 6 ( 217.41 42.50 25.60 0.92) ; 7 (Cross (Color White) (Name "Marker 3") ( 214.29 41.77 25.05 0.92) ; 1 ( 218.09 45.64 25.05 0.92) ; 2 ) ; End of markers Normal ) ; End of split | ( 242.34 12.51 14.88 0.92) ; 1, R-2-2 ( 242.29 10.71 16.38 0.92) ; 2 ( 242.29 10.71 16.35 0.92) ; 3 ( 239.40 13.01 18.27 0.92) ; 4 ( 239.09 12.34 20.00 0.92) ; 5 ( 237.31 11.93 20.77 0.92) ; 6 ( 235.32 10.27 22.30 0.92) ; 7 ( 233.36 8.61 23.25 0.92) ; 8 ( 233.36 8.61 23.23 0.92) ; 9 ( 230.61 10.35 24.60 0.92) ; 10 ( 228.99 11.18 25.47 0.92) ; 11 ( 227.29 8.38 26.60 0.92) ; 12 ( 224.08 10.02 27.63 0.92) ; 13 ( 221.32 11.76 28.02 0.92) ; 14 ( 218.64 11.14 29.50 0.92) ; 15 ( 218.64 11.14 29.48 0.92) ; 16 ( 217.38 8.45 30.27 0.92) ; 17 ( 217.38 8.45 30.23 0.92) ; 18 ( 219.12 7.07 31.80 0.92) ; 19 ( 221.62 6.46 33.70 0.46) ; 20 (Cross (Color White) (Name "Marker 3") ( 241.49 8.13 18.27 0.92) ; 1 ( 239.04 10.54 20.77 0.92) ; 2 ( 240.61 13.90 20.77 0.92) ; 3 ( 226.45 9.97 27.63 0.92) ; 4 ( 225.73 11.01 29.42 0.92) ; 5 ( 221.64 12.43 30.67 0.92) ; 6 ( 219.30 14.28 30.67 0.92) ; 7 ( 229.42 11.07 -25.80 0.92) ; 8 ) ; End of markers High | ( 245.82 9.74 15.90 0.46) ; 1, R-2-3 ( 247.29 9.49 18.80 0.46) ; 2 ( 245.90 7.37 21.22 0.46) ; 3 ( 244.56 7.06 23.92 0.46) ; 4 ( 244.06 5.15 24.80 0.46) ; 5 ( 243.18 4.94 24.80 0.46) ; 6 ( ( 243.13 3.14 25.77 0.46) ; 1, R-2-3-1 ( 244.64 -1.28 26.52 0.46) ; 2 ( 246.24 -2.10 28.15 0.46) ; 3 ( 246.24 -2.10 28.13 0.46) ; 4 ( 247.13 -1.89 30.63 0.46) ; 5 ( 247.13 -1.89 30.57 0.46) ; 6 ( 248.46 -1.57 33.22 0.46) ; 7 ( 248.46 -1.57 33.27 0.46) ; 8 (Cross (Color Green) (Name "Marker 3") ( 243.40 7.98 24.80 0.46) ; 1 ( 244.34 9.99 24.80 0.46) ; 2 ( 244.86 1.76 26.52 0.46) ; 3 ( 242.19 1.13 26.52 0.46) ; 4 ( 242.72 -1.14 26.52 0.46) ; 5 ) ; End of markers High | ( 246.70 3.98 24.80 0.46) ; 1, R-2-3-2 ( 248.17 3.73 23.27 0.46) ; 2 ( 248.57 2.03 21.77 0.46) ; 3 ( ( 250.71 -1.05 22.50 0.46) ; 1, R-2-3-2-1 ( 253.60 -3.36 22.50 0.46) ; 2 ( 255.92 -5.21 23.75 0.46) ; 3 ( 259.50 -4.37 23.75 0.46) ; 4 ( 260.74 -7.66 23.75 0.46) ; 5 ( 259.80 -9.67 25.35 0.46) ; 6 ( 262.60 -9.61 26.35 0.46) ; 7 ( 264.92 -11.46 27.60 0.46) ; 8 ( 264.92 -11.46 27.57 0.46) ; 9 ( 265.94 -11.82 29.58 0.46) ; 10 ( 266.39 -11.71 32.28 0.46) ; 11 ( 266.39 -11.71 32.30 0.46) ; 12 (Cross (Color White) (Name "Marker 3") ( 252.53 -4.80 22.50 0.46) ; 1 ( 259.09 -8.65 23.75 0.46) ; 2 ( 261.68 -5.65 23.75 0.46) ; 3 ( 259.11 -12.82 23.95 0.46) ; 4 ( 264.53 -9.75 26.65 0.46) ; 5 ( 264.43 -13.36 27.10 0.46) ; 6 ) ; End of markers High | ( 244.55 1.08 21.77 0.46) ; 1, R-2-3-2-2 ( 241.74 1.03 22.25 0.46) ; 2 ( 239.19 -0.17 22.25 0.46) ; 3 ( 238.74 -0.28 22.25 0.46) ; 4 ( 236.51 -0.80 22.25 0.46) ; 5 ( 234.23 -3.13 22.25 0.46) ; 6 ( 231.59 -1.95 22.45 0.46) ; 7 ( 230.35 1.34 22.45 0.46) ; 8 ( 227.94 -0.41 20.73 0.46) ; 9 ( 225.58 -0.37 19.15 0.46) ; 10 ( 222.32 -0.54 17.67 0.46) ; 11 ( 220.35 -2.19 16.17 0.46) ; 12 ( 219.72 -3.54 15.50 0.46) ; 13 ( 216.33 -3.14 15.12 0.46) ; 14 ( 212.88 -4.54 15.12 0.46) ; 15 ( 209.37 -3.58 14.18 0.46) ; 16 ( 207.58 -4.00 12.62 0.46) ; 17 ( 206.23 -4.31 11.07 0.46) ; 18 ( 204.85 -6.42 10.10 0.46) ; 19 ( 202.04 -6.49 9.25 0.46) ; 20 ( 199.62 -8.24 8.85 0.46) ; 21 ( 196.63 -9.55 8.60 0.46) ; 22 ( 193.76 -11.41 8.63 0.46) ; 23 ( 189.56 -13.59 8.63 0.46) ; 24 ( 187.52 -12.88 8.63 0.46) ; 25 ( 184.92 -15.87 7.72 0.46) ; 26 ( 180.27 -18.17 7.72 0.46) ; 27 ( 177.90 -18.11 7.02 0.46) ; 28 ( 176.07 -20.34 6.62 0.46) ; 29 ( 173.08 -21.64 7.55 0.46) ; 30 ( 173.08 -21.64 7.50 0.46) ; 31 ( 171.69 -23.75 7.25 0.46) ; 32 ( 171.69 -23.75 7.22 0.46) ; 33 ( 168.52 -26.29 6.82 0.46) ; 34 ( 166.42 -27.37 6.38 0.46) ; 35 ( 163.73 -28.00 6.38 0.46) ; 36 ( 161.50 -28.53 6.15 0.46) ; 37 ( 161.50 -28.53 6.13 0.46) ; 38 ( 159.67 -30.75 5.75 0.46) ; 39 ( 157.06 -33.75 4.92 0.46) ; 40 ( 157.06 -33.75 4.90 0.46) ; 41 ( 155.10 -35.40 3.45 0.46) ; 42 ( 153.01 -36.49 1.75 0.46) ; 43 ( 151.17 -38.72 -0.28 0.46) ; 44 ( 147.99 -41.25 -1.85 0.46) ; 45 ( 142.90 -43.64 -3.85 0.46) ; 46 ( 142.90 -43.64 -3.95 0.46) ; 47 (Cross (Color White) (Name "Marker 3") ( 247.49 0.58 21.77 0.46) ; 1 ( 231.83 1.09 22.45 0.46) ; 2 ( 229.07 2.84 23.07 0.46) ; 3 ( 228.52 -0.88 22.60 0.46) ; 4 ( 225.50 2.00 18.40 0.46) ; 5 ( 223.91 -1.36 18.33 0.46) ; 6 ( 225.71 -0.94 18.33 0.46) ; 7 ( 223.60 -2.03 14.52 0.46) ; 8 ( 218.65 -4.99 15.12 0.46) ; 9 ( 216.59 -4.27 15.12 0.46) ; 10 ( 214.36 -4.79 15.12 0.46) ; 11 ( 213.28 -6.24 14.40 0.46) ; 12 ( 212.62 -3.41 16.52 0.46) ; 13 ( 207.84 -5.13 12.17 0.46) ; 14 ( 206.32 -6.68 13.15 0.46) ; 15 ( 199.99 -5.78 8.85 0.46) ; 16 ( 201.81 -9.53 10.00 0.46) ; 17 ( 200.46 -9.84 10.00 0.46) ; 18 ( 194.09 -10.75 8.13 0.46) ; 19 ( 191.62 -14.30 9.77 0.46) ; 20 ( 191.58 -10.13 6.70 0.46) ; 21 ( 184.21 -14.85 10.30 0.46) ; 22 ( 184.87 -17.68 8.97 0.46) ; 23 ( 182.33 -18.87 8.93 0.46) ; 24 ( 177.23 -21.26 6.62 0.46) ; 25 ( 173.87 -25.04 7.25 0.46) ; 26 ( 173.13 -19.84 7.25 0.46) ; 27 ( 166.33 -25.01 7.25 0.46) ; 28 ( 164.23 -26.10 7.25 0.46) ; 29 ( 169.49 -28.45 7.82 0.46) ; 30 ( 161.24 -27.40 5.63 0.46) ; 31 ( 161.27 -31.57 5.60 0.46) ; 32 ) ; End of markers Incomplete ) ; End of split ) ; End of split ) ; End of split ) ; End of split ) ; End of tree ( (Color Yellow) (Dendrite) ( 273.70 23.33 0.80 1.38) ; Root ( 273.70 23.33 0.80 1.38) ; 1, R ( 276.57 25.19 0.80 1.38) ; 2 ( 277.82 27.88 0.80 1.38) ; 3 ( 278.77 29.89 0.80 1.38) ; 4 ( 281.58 29.95 -0.35 1.38) ; 5 ( 281.58 29.95 -0.37 1.38) ; 6 ( 282.42 28.35 -2.08 1.38) ; 7 (Cross (Color White) (Name "Marker 3") ( 274.62 25.34 2.42 0.46) ; 1 ) ; End of markers ( ( 284.52 29.45 -3.25 0.92) ; 1, R-1 ( 286.31 29.87 -4.65 0.92) ; 2 ( 289.49 32.40 -6.00 0.92) ; 3 ( 292.74 32.57 -7.00 0.92) ; 4 ( 295.24 31.96 -7.63 0.92) ; 5 ( 299.26 32.91 -7.65 0.92) ; 6 ( 300.47 33.78 -6.02 0.92) ; 7 ( 302.25 34.20 -4.82 0.92) ; 8 ( 302.25 34.20 -4.85 0.92) ; 9 ( 304.67 35.96 -3.92 0.92) ; 10 ( 308.10 37.36 -2.90 0.92) ; 11 ( 308.10 37.36 -2.92 0.92) ; 12 ( 309.77 38.35 -2.27 0.92) ; 13 ( 312.76 39.64 -1.22 0.92) ; 14 ( 316.01 39.81 -3.25 0.92) ; 15 ( 316.01 39.81 -3.27 0.92) ; 16 ( 316.25 42.85 -4.13 0.92) ; 17 ( 316.25 42.85 -4.15 0.92) ; 18 ( 318.66 44.61 -5.15 0.92) ; 19 ( 319.99 44.92 -4.97 0.92) ; 20 ( 322.28 47.26 -5.10 0.92) ; 21 ( 323.35 48.70 -6.20 0.92) ; 22 ( 324.75 50.82 -7.72 0.92) ; 23 ( 329.21 51.86 -8.32 0.92) ; 24 ( 332.38 54.40 -9.22 0.92) ; 25 ( 334.98 57.39 -9.77 0.92) ; 26 ( 336.81 59.62 -10.25 0.92) ; 27 ( 336.81 59.62 -10.28 0.92) ; 28 ( 339.10 61.94 -11.55 0.92) ; 29 ( 339.10 61.94 -11.58 0.92) ; 30 ( 340.93 64.17 -13.45 0.92) ; 31 ( 340.93 64.17 -13.47 0.92) ; 32 ( 341.11 65.40 -15.02 0.92) ; 33 ( 341.47 67.87 -16.35 0.92) ; 34 ( 341.47 67.87 -16.38 0.92) ; 35 ( 342.16 71.02 -16.00 0.92) ; 36 ( 342.67 74.73 -13.85 0.92) ; 37 ( 342.67 74.73 -13.87 0.92) ; 38 ( 343.35 77.87 -12.72 0.92) ; 39 ( 345.94 80.87 -11.98 0.92) ; 40 ( 348.10 83.76 -13.00 0.92) ; 41 ( 350.50 85.52 -14.13 0.92) ; 42 ( 353.67 88.06 -14.75 0.92) ; 43 ( 356.42 90.49 -15.73 0.92) ; 44 ( 359.01 93.49 -16.38 0.92) ; 45 ( 361.29 95.80 -15.05 0.46) ; 46 ( 363.88 98.81 -13.32 0.46) ; 47 ( 363.88 98.81 -13.35 0.46) ; 48 ( 365.00 102.06 -11.32 0.46) ; 49 ( 365.00 102.06 -11.30 0.46) ; 50 ( 365.63 103.40 -9.67 0.46) ; 51 ( 365.87 106.44 -9.67 0.46) ; 52 ( 366.19 107.12 -8.25 0.46) ; 53 ( 367.70 108.66 -7.30 0.46) ; 54 ( 367.70 108.66 -7.32 0.46) ; 55 ( 368.46 109.43 -5.50 0.46) ; 56 ( 369.99 110.99 -3.70 0.46) ; 57 ( 369.99 110.99 -3.72 0.46) ; 58 ( 371.05 112.44 -1.60 0.46) ; 59 ( 371.05 112.44 -1.63 0.46) ; 60 (Cross (Color White) (Name "Marker 3") ( 285.92 31.56 -4.65 0.92) ; 1 ( 297.52 34.28 -7.65 0.92) ; 2 ( 314.05 38.16 -1.22 0.92) ; 3 ( 313.21 45.72 -0.12 0.92) ; 4 ( 323.56 45.76 -5.05 0.92) ; 5 ( 322.69 51.53 -7.75 0.92) ; 6 ( 331.28 57.13 -10.00 0.92) ; 7 ( 334.45 59.66 -9.15 0.92) ; 8 ( 334.67 62.70 -10.95 0.92) ; 9 ( 344.20 70.30 -16.02 0.92) ; 10 ( 341.56 77.45 -11.98 0.92) ; 11 ( 346.21 79.74 -11.98 0.92) ; 12 ( 349.54 87.68 -14.75 0.92) ; 13 ( 355.47 88.48 -14.90 0.92) ; 14 ( 358.33 90.34 -15.05 0.92) ; 15 ( 355.57 92.09 -16.38 0.92) ; 16 ( 366.32 112.52 -5.65 0.46) ; 17 ( 363.90 104.78 -9.67 0.46) ; 18 ) ; End of markers Normal | ( 284.38 30.01 -0.90 0.92) ; 1, R-2 ( 283.99 31.71 -0.25 0.92) ; 2 ( 286.22 32.23 0.17 0.92) ; 3 ( 288.01 32.65 0.17 0.92) ; 4 ( 290.24 33.18 0.45 0.92) ; 5 ( 291.76 34.73 1.38 0.92) ; 6 ( 294.13 34.68 2.63 0.92) ; 7 ( 295.47 35.00 2.38 0.92) ; 8 ( 297.65 33.72 3.08 0.92) ; 9 ( 300.20 34.92 3.08 0.92) ; 10 ( 302.49 37.23 3.08 0.92) ; 11 ( 304.58 38.33 3.82 0.92) ; 12 ( 304.58 38.33 3.80 0.92) ; 13 ( 306.81 38.85 5.22 0.92) ; 14 ( 309.32 38.24 6.80 0.92) ; 15 ( 309.32 38.24 6.78 0.92) ; 16 ( 310.52 39.11 7.57 0.92) ; 17 ( 312.04 40.68 7.55 0.92) ; 18 ( 314.72 41.30 9.82 0.92) ; 19 ( 316.51 41.72 10.07 0.46) ; 20 ( 316.37 42.29 10.07 0.46) ; 21 ( 317.72 42.60 10.82 0.46) ; 22 ( 317.27 42.49 10.82 0.46) ; 23 ( 321.86 42.98 11.53 0.46) ; 24 ( 325.00 43.71 12.10 0.46) ; 25 ( 327.72 46.14 12.32 0.46) ; 26 ( 328.08 48.62 13.52 0.46) ; 27 ( 328.08 48.62 13.47 0.46) ; 28 ( 330.36 50.94 13.60 0.46) ; 29 ( 332.74 50.90 14.30 0.46) ; 30 ( 335.01 53.21 15.55 0.46) ; 31 ( 334.88 53.79 15.55 0.46) ; 32 ( 337.19 51.94 16.90 0.46) ; 33 ( 340.59 51.55 18.45 0.46) ; 34 ( 340.59 51.55 18.40 0.46) ; 35 ( 344.12 50.57 20.65 0.46) ; 36 ( 343.99 51.14 20.65 0.46) ; 37 ( 347.50 50.18 22.50 0.46) ; 38 ( 347.06 50.08 22.50 0.46) ; 39 ( 350.27 48.44 22.05 0.46) ; 40 ( 353.89 51.08 21.42 0.46) ; 41 ( 353.89 51.08 21.40 0.46) ; 42 ( 356.66 49.33 22.50 0.46) ; 43 ( 356.66 49.33 22.47 0.46) ; 44 ( 358.00 49.65 20.33 0.46) ; 45 ( 358.00 49.65 20.30 0.46) ; 46 ( 359.51 51.20 17.38 0.46) ; 47 ( 359.07 51.10 17.30 0.46) ; 48 ( 363.53 52.15 15.82 0.46) ; 49 ( 366.08 53.34 14.48 0.46) ; 50 ( 366.08 53.34 14.45 0.46) ; 51 ( 366.97 53.55 12.20 0.46) ; 52 ( 366.97 53.55 12.15 0.46) ; 53 (Cross (Color White) (Name "Marker 3") ( 297.70 35.52 2.63 0.92) ; 1 ( 300.42 37.96 5.50 0.92) ; 2 ( 304.85 37.19 3.65 0.92) ; 3 ( 307.52 37.82 6.80 0.92) ; 4 ( 315.51 37.90 6.40 0.92) ; 5 ( 313.78 39.28 9.95 0.92) ; 6 ( 316.43 44.09 10.75 0.92) ; 7 ( 322.68 45.55 10.77 0.92) ; 8 ( 349.61 51.27 22.73 0.46) ; 9 ( 334.30 54.26 15.20 0.46) ; 10 ( 359.29 48.17 18.30 0.46) ; 11 ( 354.82 47.11 23.70 0.46) ; 12 ) ; End of markers Normal ) ; End of split ) ; End of tree ( (Color Green) (Dendrite) ( 273.97 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( 260.93 -0.70 -23.60 0.92) ; 1, R-2 ( 260.93 -0.70 -23.65 0.92) ; 2 ( 261.91 -2.86 -25.35 0.92) ; 3 ( 262.49 -3.33 -26.95 0.92) ; 4 ( 262.44 -5.11 -26.65 0.92) ; 5 ( 262.84 -6.82 -26.65 0.92) ; 6 ( ( 262.75 -10.43 -25.55 0.92) ; 1, R-2-1 ( 262.31 -10.53 -25.55 0.92) ; 2 ( 262.40 -12.91 -25.57 0.92) ; 3 ( 258.64 -14.98 -25.83 0.92) ; 4 ( 257.43 -15.86 -24.85 0.92) ; 5 ( 255.73 -18.64 -26.25 0.92) ; 6 ( 255.73 -18.64 -26.28 0.92) ; 7 ( 255.55 -19.88 -27.27 0.92) ; 8 ( 255.55 -19.88 -27.30 0.92) ; 9 ( 254.29 -22.57 -28.02 0.92) ; 10 ( 254.29 -22.57 -28.05 0.92) ; 11 ( 252.82 -22.31 -28.63 0.92) ; 12 ( 252.27 -26.02 -29.42 0.92) ; 13 ( 250.13 -28.91 -30.38 0.92) ; 14 (Cross (Color Yellow) (Name "Marker 3") ( 255.34 -16.94 -24.85 0.92) ; 1 ( 258.46 -16.21 -24.85 0.92) ; 2 ) ; End of markers ( ( 247.00 -29.65 -30.38 0.92) ; 1, R-2-1-1 ( 244.76 -30.18 -31.42 0.92) ; 2 ( 244.76 -30.18 -31.45 0.92) ; 3 ( 243.17 -29.35 -32.83 0.92) ; 4 ( 243.17 -29.35 -32.88 0.92) ; 5 ( 240.80 -29.31 -34.45 0.92) ; 6 ( 240.80 -29.31 -34.47 0.92) ; 7 ( 238.69 -30.41 -36.42 0.92) ; 8 ( 237.44 -33.08 -38.05 0.92) ; 9 ( 237.44 -33.08 -38.07 0.92) ; 10 ( 236.05 -35.21 -39.95 0.92) ; 11 ( 236.05 -35.21 -40.07 0.92) ; 12 ( 234.67 -37.32 -39.38 0.92) ; 13 ( 232.56 -38.40 -41.00 0.92) ; 14 ( 232.56 -38.40 -41.03 0.92) ; 15 ( 232.20 -40.89 -43.07 0.92) ; 16 ( 231.26 -42.89 -47.22 0.92) ; 17 ( 231.26 -42.89 -47.28 0.92) ; 18 (Cross (Color Yellow) (Name "Marker 3") ( 236.71 -38.03 -40.10 0.92) ; 1 ( 233.38 -35.84 -38.10 0.92) ; 2 ) ; End of markers ( ( 233.30 -43.61 -49.10 0.92) ; 1, R-2-1-1-1 ( 233.30 -43.61 -49.13 0.92) ; 2 ( 234.02 -44.63 -51.67 0.92) ; 3 ( 234.02 -44.63 -51.72 0.92) ; 4 ( 233.40 -45.98 -54.20 0.92) ; 5 ( 232.37 -45.62 -59.13 0.92) ; 6 ( 230.27 -46.71 -62.22 0.92) ; 7 ( 230.27 -46.71 -62.33 0.92) ; 8 ( 229.38 -46.92 -66.88 0.92) ; 9 ( 229.91 -49.19 -70.80 0.92) ; 10 ( 229.91 -49.19 -70.82 0.92) ; 11 ( 230.04 -49.75 -73.42 0.92) ; 12 ( 231.11 -48.29 -74.67 0.92) ; 13 ( 232.77 -47.31 -76.82 0.92) ; 14 (Cross (Color Yellow) (Name "Marker 3") ( 234.52 -42.73 -54.20 0.92) ; 1 ) ; End of markers Normal | ( 229.16 -43.98 -47.22 0.92) ; 1, R-2-1-1-2 ( 229.16 -43.98 -47.25 0.92) ; 2 ( 227.74 -41.93 -48.35 0.92) ; 3 ( 226.03 -44.71 -50.77 0.92) ; 4 ( 226.03 -44.71 -50.88 0.92) ; 5 ( 225.27 -45.50 -54.05 0.92) ; 6 ( 223.80 -45.23 -55.60 0.92) ; 7 ( 223.80 -45.23 -55.63 0.92) ; 8 ( 222.02 -45.65 -58.15 0.92) ; 9 ( 220.41 -44.84 -61.38 0.92) ; 10 ( 220.36 -46.64 -63.20 0.92) ; 11 ( 220.36 -46.64 -63.25 0.92) ; 12 ( 218.44 -46.49 -66.77 0.92) ; 13 ( 218.52 -48.86 -70.07 0.92) ; 14 ( 218.52 -48.86 -70.18 0.92) ; 15 ( 216.29 -49.39 -72.72 0.92) ; 16 ( 215.85 -49.49 -72.80 0.92) ; 17 ( 214.37 -49.24 -73.00 0.92) ; 18 ( 214.37 -49.24 -73.02 0.92) ; 19 ( 215.00 -47.91 -76.47 0.92) ; 20 ( 215.00 -47.91 -76.60 0.92) ; 21 ( 217.18 -49.18 -80.68 0.92) ; 22 ( 217.18 -49.18 -80.73 0.92) ; 23 ( 217.00 -50.41 -85.28 0.92) ; 24 ( 217.00 -50.41 -85.37 0.92) ; 25 ( 214.24 -48.67 -89.60 0.92) ; 26 ( 214.24 -48.67 -89.65 0.92) ; 27 ( 213.61 -50.02 -92.30 0.92) ; 28 ( 213.61 -50.02 -92.32 0.92) ; 29 ( 212.50 -47.29 -93.40 0.92) ; 30 ( 210.58 -47.14 -94.67 0.92) ; 31 ( 210.58 -47.14 -94.70 0.92) ; 32 ( 208.66 -46.99 -96.85 0.92) ; 33 ( 208.66 -46.99 -96.90 0.92) ; 34 ( 207.46 -47.87 -99.17 0.92) ; 35 ( 205.80 -48.85 -100.97 0.92) ; 36 ( 204.52 -47.37 -102.20 0.92) ; 37 ( 201.51 -48.67 -104.05 0.92) ; 38 ( 201.51 -48.67 -104.07 0.92) ; 39 ( 199.28 -49.19 -104.90 0.92) ; 40 ( 199.28 -49.19 -104.97 0.92) ; 41 ( 197.14 -52.09 -105.32 0.92) ; 42 ( 194.85 -54.41 -106.62 0.92) ; 43 ( 191.10 -56.48 -108.32 0.92) ; 44 ( 192.53 -58.54 -111.97 0.92) ; 45 ( 192.53 -58.54 -112.02 0.92) ; 46 ( 192.79 -59.67 -113.95 0.92) ; 47 ( 192.79 -59.67 -113.97 0.92) ; 48 (Cross (Color Yellow) (Name "Marker 3") ( 222.77 -44.88 -54.07 0.92) ; 1 ( 216.96 -46.24 -69.75 0.92) ; 2 ( 198.87 -53.47 -105.32 0.92) ; 3 ) ; End of markers Normal ) ; End of split | ( 248.43 -31.71 -30.38 0.92) ; 1, R-2-1-2 ( 247.61 -34.28 -30.75 0.92) ; 2 ( 245.79 -36.51 -30.75 0.92) ; 3 ( 244.66 -39.75 -30.75 0.92) ; 4 ( 243.15 -41.31 -30.75 0.92) ; 5 ( 242.70 -41.41 -30.75 0.92) ; 6 ( 242.14 -45.12 -30.75 0.92) ; 7 ( 239.55 -48.11 -30.75 0.92) ; 8 ( 239.90 -51.62 -30.75 0.92) ; 9 ( 239.93 -55.80 -30.00 0.92) ; 10 ( 239.40 -59.50 -28.88 0.92) ; 11 ( 239.40 -59.50 -28.92 0.92) ; 12 ( 239.93 -61.76 -27.83 0.92) ; 13 ( 239.93 -61.76 -27.77 0.92) ; 14 ( 239.37 -65.48 -27.77 0.92) ; 15 ( 239.27 -69.08 -26.82 0.92) ; 16 ( 239.85 -69.55 -26.82 0.92) ; 17 ( 237.84 -73.00 -26.82 0.92) ; 18 ( 239.09 -76.28 -29.17 0.92) ; 19 ( 239.30 -79.23 -29.17 0.92) ; 20 ( 238.63 -82.37 -29.92 0.92) ; 21 ( 237.90 -87.32 -28.57 0.92) ; 22 ( 235.94 -88.97 -28.57 0.92) ; 23 ( 236.91 -91.14 -28.57 0.92) ; 24 ( 236.63 -95.98 -28.55 0.92) ; 25 ( 236.17 -102.05 -28.55 0.92) ; 26 ( 233.44 -104.49 -29.85 0.92) ; 27 ( 229.38 -107.23 -30.65 0.92) ; 28 ( 228.44 -109.24 -32.70 0.92) ; 29 ( 229.42 -111.40 -32.70 0.92) ; 30 ( 227.75 -112.38 -34.70 0.92) ; 31 ( 227.66 -115.99 -36.07 0.92) ; 32 ( 225.96 -118.78 -38.03 0.92) ; 33 ( 225.96 -118.78 -38.15 0.92) ; 34 ( 226.35 -120.47 -39.72 0.92) ; 35 ( 227.33 -122.64 -40.72 0.92) ; 36 ( 225.19 -125.52 -40.72 0.92) ; 37 ( 224.77 -129.80 -39.63 0.92) ; 38 ( 224.77 -129.80 -39.70 0.92) ; 39 ( 221.46 -131.77 -41.67 0.92) ; 40 ( 219.05 -133.54 -43.22 0.92) ; 41 ( 219.05 -133.54 -43.27 0.92) ; 42 ( 217.27 -133.96 -43.80 0.92) ; 43 ( 214.71 -135.15 -41.47 0.92) ; 44 ( 212.93 -135.57 -40.17 0.92) ; 45 ( 212.93 -135.57 -40.15 0.92) ; 46 ( 210.65 -137.89 -40.13 0.92) ; 47 ( 209.21 -141.81 -39.45 0.92) ; 48 ( 207.25 -143.46 -39.67 0.92) ; 49 ( 204.71 -144.66 -39.67 0.92) ; 50 ( 203.17 -146.22 -37.55 0.92) ; 51 (Cross (Color Yellow) (Name "Marker 3") ( 241.53 -62.58 -27.77 0.92) ; 1 ( 237.94 -69.40 -26.82 0.92) ; 2 ( 239.19 -72.69 -26.82 0.92) ; 3 ( 240.56 -76.54 -29.25 0.92) ; 4 ( 240.46 -80.14 -29.35 0.92) ; 5 ( 238.06 -81.91 -29.35 0.92) ; 6 ( 238.01 -77.74 -29.35 0.92) ; 7 ( 233.17 -103.35 -29.85 0.92) ; 8 ( 226.51 -109.09 -29.65 0.92) ; 9 ( 234.10 -107.31 -29.65 0.92) ; 10 ( 233.35 -108.10 -29.65 0.92) ; 11 ( 233.17 -103.35 -30.65 0.92) ; 12 ( 224.21 -123.38 -40.72 0.92) ; 13 ( 227.60 -123.77 -40.72 0.92) ; 14 ( 225.93 -130.72 -38.75 0.92) ; 15 ( 223.35 -127.75 -41.28 0.92) ; 16 ( 215.97 -132.46 -43.80 0.92) ; 17 ) ; End of markers Normal ) ; End of split | ( 266.05 -8.45 -27.90 0.92) ; 1, R-2-2 ( 267.80 -9.85 -28.92 0.92) ; 2 ( 267.80 -9.85 -28.95 0.92) ; 3 ( 269.35 -12.46 -29.97 0.92) ; 4 ( 269.35 -12.46 -30.00 0.92) ; 5 ( 272.29 -12.97 -31.17 0.92) ; 6 ( 276.13 -13.26 -32.10 0.92) ; 7 ( 277.92 -12.85 -32.92 0.92) ; 8 ( 279.21 -14.33 -32.47 0.92) ; 9 ( 279.21 -14.33 -32.50 0.92) ; 10 ( 281.22 -16.84 -34.17 0.92) ; 11 ( 283.39 -18.12 -33.75 0.92) ; 12 ( 285.62 -17.60 -35.95 0.92) ; 13 ( 287.87 -17.08 -37.55 0.92) ; 14 ( 290.54 -16.44 -39.15 0.92) ; 15 ( 290.54 -16.44 -39.17 0.92) ; 16 ( 294.96 -17.21 -40.15 0.92) ; 17 ( 297.90 -17.71 -41.10 0.92) ; 18 ( 302.14 -19.70 -41.93 0.92) ; 19 ( 304.14 -22.22 -43.47 0.92) ; 20 ( 304.14 -22.22 -43.50 0.92) ; 21 ( 307.36 -23.85 -45.15 0.92) ; 22 ( 310.51 -27.29 -46.70 0.92) ; 23 ( 313.15 -28.48 -48.45 0.92) ; 24 ( 315.38 -27.95 -48.10 0.92) ; 25 ( 315.38 -27.95 -48.13 0.92) ; 26 ( 316.67 -29.44 -49.35 0.92) ; 27 ( 318.40 -30.82 -51.42 0.92) ; 28 ( 318.40 -30.82 -51.50 0.92) ; 29 ( 320.65 -30.30 -52.15 0.92) ; 30 ( 320.65 -30.30 -52.17 0.92) ; 31 ( 323.87 -31.94 -52.95 0.92) ; 32 ( 326.04 -33.22 -52.63 0.92) ; 33 ( 328.94 -35.53 -53.88 0.92) ; 34 ( 328.94 -35.53 -53.90 0.92) ; 35 ( 330.67 -36.91 -55.55 0.92) ; 36 ( 330.67 -36.91 -55.57 0.92) ; 37 ( 331.79 -39.64 -56.47 0.92) ; 38 ( 331.96 -44.37 -57.77 0.92) ; 39 ( 333.91 -48.69 -57.77 0.92) ; 40 ( 334.36 -48.58 -57.77 0.92) ; 41 ( 337.40 -51.46 -57.60 0.92) ; 42 ( 337.40 -51.46 -57.57 0.92) ; 43 ( 339.39 -53.98 -55.52 0.92) ; 44 ( 339.39 -53.98 -55.55 0.92) ; 45 ( 341.71 -55.82 -55.47 0.92) ; 46 ( 342.82 -58.55 -54.80 0.92) ; 47 ( 345.27 -60.97 -55.95 0.92) ; 48 ( 348.03 -62.70 -55.30 0.92) ; 49 ( 348.03 -62.70 -55.35 0.92) ; 50 ( 349.15 -65.43 -55.35 0.92) ; 51 ( 351.20 -66.13 -57.20 0.46) ; 52 ( 351.20 -66.13 -57.22 0.46) ; 53 ( 354.85 -67.67 -58.07 0.46) ; 54 ( 355.30 -67.56 -58.10 0.46) ; 55 ( 358.38 -68.64 -59.22 0.46) ; 56 ( 358.38 -68.64 -59.25 0.46) ; 57 ( 362.48 -70.07 -59.83 0.46) ; 58 ( 362.48 -70.07 -59.85 0.46) ; 59 ( 365.87 -70.47 -61.05 0.46) ; 60 ( 367.67 -70.04 -63.70 0.46) ; 61 ( 367.67 -70.04 -64.00 0.46) ; 62 (Cross (Color Yellow) (Name "Marker 3") ( 276.94 -10.68 -32.92 0.92) ; 1 ( 280.42 -13.45 -32.15 0.92) ; 2 ( 280.01 -17.73 -33.75 0.92) ; 3 ( 314.75 -29.29 -48.15 0.92) ; 4 ( 316.58 -33.05 -51.00 0.92) ; 5 ( 319.99 -27.47 -52.95 0.92) ; 6 ( 333.67 -41.59 -57.77 0.92) ; 7 ( 338.46 -55.99 -55.47 0.92) ; 8 ( 339.76 -51.50 -58.35 0.92) ; 9 ( 339.36 -49.80 -58.35 0.92) ; 10 ( 344.07 -55.86 -54.85 0.92) ; 11 ( 348.08 -60.90 -54.80 0.92) ; 12 ( 359.36 -70.79 -58.38 0.46) ; 13 ( 358.30 -66.27 -59.80 0.46) ; 14 ) ; End of markers Normal ) ; End of split ) ; End of split ) ; End of tree ( (Color Cyan) (Dendrite) ( 265.52 17.95 -15.45 0.92) ; Root ( 265.52 17.95 -15.45 0.92) ; 1, R ( 267.75 18.48 -15.48 0.92) ; 2 ( 267.67 20.84 -18.15 0.92) ; 3 ( 265.57 19.75 -19.20 0.92) ; 4 ( 262.50 20.83 -20.95 0.92) ; 5 ( 264.54 20.11 -22.85 0.92) ; 6 ( 267.48 19.60 -24.72 0.92) ; 7 ( 267.48 19.60 -24.75 0.92) ; 8 ( 270.17 20.24 -26.55 0.92) ; 9 ( 269.73 20.13 -26.55 0.92) ; 10 ( 273.11 19.73 -28.00 0.92) ; 11 ( 274.46 20.05 -27.95 0.92) ; 12 ( ( 276.23 24.52 -27.35 0.46) ; 1, R-1 ( 277.89 25.51 -30.13 0.46) ; 2 ( 282.05 25.89 -31.35 0.46) ; 3 ( 281.60 25.78 -31.35 0.46) ; 4 ( 284.54 25.27 -32.13 0.46) ; 5 ( 285.79 27.95 -33.47 0.46) ; 6 ( 285.79 27.95 -33.53 0.46) ; 7 ( 290.26 29.00 -35.72 0.46) ; 8 ( 293.52 29.17 -36.72 0.46) ; 9 ( 296.83 31.14 -37.88 0.46) ; 10 ( 303.84 33.38 -38.55 0.46) ; 11 ( 303.84 33.38 -38.57 0.46) ; 12 ( 308.99 37.57 -39.70 0.46) ; 13 ( 311.99 38.86 -41.45 0.46) ; 14 ( 311.99 38.86 -41.47 0.46) ; 15 ( 313.95 40.51 -42.15 0.46) ; 16 ( 316.81 42.37 -42.15 0.46) ; 17 ( 319.35 43.58 -42.17 0.46) ; 18 ( 323.55 45.76 -42.22 0.46) ; 19 ( 327.49 49.07 -42.22 0.46) ; 20 ( 327.36 49.64 -42.22 0.46) ; 21 ( 331.43 52.38 -42.22 0.46) ; 22 ( 333.71 54.70 -43.80 0.46) ; 23 ( 335.59 58.73 -45.22 0.46) ; 24 ( 335.59 58.73 -45.25 0.46) ; 25 ( 339.67 61.48 -46.57 0.46) ; 26 ( 341.49 63.69 -48.07 0.46) ; 27 ( 344.58 62.63 -49.58 0.46) ; 28 ( 347.61 65.72 -51.28 0.46) ; 29 ( 347.61 65.72 -51.30 0.46) ; 30 ( 351.46 65.43 -52.75 0.46) ; 31 ( 355.03 66.27 -54.20 0.46) ; 32 ( 355.03 66.27 -54.78 0.46) ; 33 ( 358.79 68.34 -53.38 0.46) ; 34 ( 359.86 69.79 -55.63 0.46) ; 35 ( 359.42 69.68 -55.63 0.46) ; 36 (Cross (Color White) (Name "Marker 3") ( 302.14 30.59 -38.57 0.46) ; 1 ( 303.71 33.94 -38.57 0.46) ; 2 ( 319.17 42.33 -42.17 0.46) ; 3 ( 315.24 39.03 -42.17 0.46) ; 4 ( 315.46 42.07 -42.17 0.46) ; 5 ( 322.71 47.35 -42.17 0.46) ; 6 ( 329.09 48.25 -42.90 0.46) ; 7 ( 330.18 49.69 -42.20 0.46) ; 8 ( 338.85 58.89 -46.57 0.46) ; 9 ( 307.47 36.02 -31.70 0.46) ; 10 ) ; End of markers Normal | ( 271.69 21.78 -29.82 0.46) ; 1, R-2 ( 269.91 21.36 -33.03 0.46) ; 2 ( 269.38 23.64 -32.97 0.46) ; 3 ( 269.38 23.64 -33.05 0.46) ; 4 ( 266.88 24.23 -35.67 0.46) ; 5 ( 266.88 24.23 -35.70 0.46) ; 6 ( 265.00 26.19 -38.05 0.46) ; 7 ( 263.13 28.14 -41.08 0.46) ; 8 ( 261.26 30.09 -43.33 0.46) ; 9 ( 260.20 34.62 -44.90 0.46) ; 10 ( 256.41 36.72 -45.95 0.46) ; 11 ( 255.75 39.55 -47.17 0.46) ; 12 ( 252.53 41.18 -47.17 0.46) ; 13 ( 249.64 43.49 -49.15 0.46) ; 14 ( 249.16 47.56 -50.83 0.46) ; 15 ( 248.18 49.72 -51.75 0.46) ; 16 ( 247.74 49.61 -51.75 0.46) ; 17 ( 247.07 52.44 -52.70 0.46) ; 18 ( 247.07 52.44 -52.72 0.46) ; 19 ( 245.78 53.93 -51.72 0.46) ; 20 ( 244.22 56.55 -54.15 0.46) ; 21 ( 241.46 58.29 -55.65 0.46) ; 22 ( 237.98 61.07 -57.53 0.46) ; 23 ( 237.98 61.07 -57.55 0.46) ; 24 ( 233.49 64.18 -58.20 0.46) ; 25 ( 233.49 64.18 -58.22 0.46) ; 26 ( 230.49 66.94 -57.67 0.46) ; 27 ( 227.73 68.67 -60.22 0.46) ; 28 ( 226.11 69.50 -62.70 0.46) ; 29 ( 223.54 72.48 -64.75 0.46) ; 30 ( 223.54 72.48 -64.78 0.46) ; 31 ( 220.90 73.65 -66.95 0.46) ; 32 ( 220.46 73.55 -66.97 0.46) ; 33 ( 219.92 75.82 -68.85 0.46) ; 34 ( 219.92 75.82 -68.90 0.46) ; 35 ( 217.70 75.29 -71.17 0.46) ; 36 ( 217.70 75.29 -71.20 0.46) ; 37 ( 217.22 79.36 -72.40 0.46) ; 38 ( 216.24 81.51 -75.05 0.46) ; 39 ( 216.11 82.08 -75.05 0.46) ; 40 ( 215.53 82.54 -78.30 0.46) ; 41 ( 215.53 82.54 -78.35 0.46) ; 42 (Cross (Color White) (Name "Marker 3") ( 256.29 43.26 -47.17 0.46) ; 1 ( 245.28 52.02 -51.72 0.46) ; 2 ) ; End of markers Normal ) ; End of split ) ; End of tree ( (Color White) (Dendrite) ( 269.00 15.19 12.42 0.92) ; Root ( 268.88 15.75 12.42 0.92) ; 1, R ( 272.05 18.28 12.42 0.92) ; 2 ( 269.54 18.89 12.42 0.92) ; 3 ( 267.18 18.93 14.05 0.92) ; 4 ( 265.97 18.06 17.32 0.92) ; 5 ( 264.37 18.87 20.73 0.92) ; 6 ( 262.76 19.69 23.02 0.92) ; 7 ( 263.43 16.86 24.60 0.92) ; 8 ( 261.12 18.70 27.02 0.92) ; 9 ( 260.67 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37 ( 237.92 471.44 -18.88 1.83) ; 38 ( 233.23 473.33 -19.30 1.83) ; 39 ( 236.81 474.16 -19.35 1.83) ; 40 ( 234.15 463.39 -17.92 1.83) ; 41 ) ; End of markers ( ( 232.06 615.06 -32.88 1.83) ; 1, R-1-1-1-1-1-1-1-1-1-1-1-1-1-1 ( 231.83 617.99 -32.75 1.83) ; 2 ( 232.52 621.14 -33.15 1.83) ; 3 ( 233.51 624.95 -33.15 1.83) ; 4 ( 232.14 628.81 -32.58 1.83) ; 5 ( 230.71 630.86 -32.65 1.83) ; 6 ( 232.27 634.21 -33.22 1.83) ; 7 ( 233.53 636.90 -34.20 1.83) ; 8 ( 233.67 642.31 -34.78 1.83) ; 9 ( 235.52 644.54 -34.78 1.83) ; 10 ( 237.22 647.31 -34.78 1.83) ; 11 ( 238.29 648.76 -35.83 1.83) ; 12 ( 238.29 648.76 -35.85 1.83) ; 13 ( 238.34 650.57 -37.13 1.83) ; 14 ( 238.21 651.13 -37.15 1.83) ; 15 ( 237.36 652.72 -36.42 1.83) ; 16 ( 237.36 652.72 -36.45 1.83) ; 17 ( 236.70 655.55 -36.42 1.83) ; 18 ( 235.54 656.48 -34.85 1.83) ; 19 ( 235.32 659.41 -34.15 1.83) ; 20 (Cross (Color White) (Name "Marker 3") ( 233.22 658.33 -35.95 1.83) ; 1 ( 237.55 659.94 -35.95 1.83) ; 2 ( 237.82 658.80 -33.05 1.83) ; 3 ( 238.22 657.10 -36.00 1.83) ; 4 ( 236.15 651.84 -34.55 1.83) ; 5 ( 239.22 644.81 -34.78 1.83) ; 6 ( 236.32 641.13 -34.78 1.83) ; 7 ( 230.88 626.12 -34.78 1.83) ; 8 ( 230.33 622.41 -32.75 1.83) ; 9 ( 234.13 620.32 -32.75 1.83) ; 10 ( 234.43 615.00 -31.92 1.83) ; 11 ( 235.72 613.52 -32.88 1.83) ; 12 ( 235.76 637.42 -34.78 1.83) ; 13 ( 228.81 620.86 -32.75 1.83) ; 14 ) ; End of markers ( ( 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 ( 234.36 667.55 -35.13 1.83) ; 2 ( 234.36 667.55 -35.15 1.83) ; 3 ( 233.56 670.95 -35.65 1.83) ; 4 ( 232.32 674.24 -35.65 1.83) ; 5 ( 232.68 676.71 -34.42 1.83) ; 6 ( 232.55 677.27 -34.42 1.83) ; 7 ( 233.68 680.52 -33.13 1.83) ; 8 ( 233.46 683.47 -33.88 1.83) ; 9 ( 233.38 685.82 -33.27 1.83) ; 10 ( 232.58 689.22 -33.05 1.83) ; 11 ( 232.94 691.70 -33.97 1.83) ; 12 ( 232.90 695.87 -35.02 1.83) ; 13 ( 232.90 695.87 -35.05 1.83) ; 14 ( 230.64 699.52 -36.10 1.83) ; 15 ( 231.31 702.66 -37.45 1.83) ; 16 ( 230.78 704.93 -37.77 1.83) ; 17 ( 229.35 706.99 -36.63 1.83) ; 18 ( 229.90 710.69 -35.75 1.83) ; 19 ( 230.45 714.40 -34.95 1.83) ; 20 ( 231.57 717.65 -34.67 1.83) ; 21 ( 230.64 721.62 -35.83 1.83) ; 22 ( 230.64 721.62 -35.85 1.83) ; 23 ( 230.87 724.66 -36.22 1.83) ; 24 ( 230.79 727.03 -37.20 1.83) ; 25 ( 231.91 730.28 -38.15 1.83) ; 26 ( 231.47 736.14 -39.67 1.83) ; 27 ( 229.97 740.57 -39.02 1.83) ; 28 ( 231.22 743.25 -38.88 1.83) ; 29 ( 230.92 748.55 -38.27 1.83) ; 30 ( 231.32 750.94 -35.53 1.83) ; 31 ( 231.32 750.94 -35.55 1.83) ; 32 ( 231.24 753.30 -34.92 1.83) ; 33 ( 229.28 757.62 -34.92 1.83) ; 34 ( 229.43 760.49 -34.63 1.83) ; 35 ( 230.73 764.97 -33.77 1.83) ; 36 ( 231.40 768.12 -34.72 1.83) ; 37 ( 231.91 770.02 -33.97 1.83) ; 38 ( 231.51 771.73 -33.67 1.83) ; 39 ( 229.82 774.90 -33.03 1.83) ; 40 ( 229.74 777.28 -33.00 1.83) ; 41 ( 230.68 779.28 -33.00 1.83) ; 42 ( 231.35 782.44 -32.63 1.83) ; 43 ( 231.53 783.67 -33.13 1.83) ; 44 ( 230.60 787.62 -32.35 1.83) ; 45 ( 231.15 791.34 -31.75 1.83) ; 46 ( 231.07 793.72 -31.35 1.83) ; 47 ( 232.64 797.06 -31.35 1.83) ; 48 ( 233.14 798.98 -31.17 1.83) ; 49 ( 232.21 802.93 -30.80 1.83) ; 50 ( 232.12 805.30 -31.02 1.83) ; 51 ( 232.12 805.30 -31.00 1.83) ; 52 ( 234.08 806.96 -30.57 1.83) ; 53 ( 234.50 811.25 -29.70 1.83) ; 54 ( 233.39 813.96 -29.38 1.83) ; 55 ( 233.17 816.90 -29.38 1.83) ; 56 ( 233.97 819.48 -30.50 1.83) ; 57 ( 233.32 822.30 -31.40 1.83) ; 58 ( 233.32 822.30 -31.42 1.83) ; 59 ( 234.00 825.45 -32.22 1.83) ; 60 ( 234.36 827.93 -31.52 1.83) ; 61 ( 234.28 830.29 -30.38 1.83) ; 62 ( 234.95 833.44 -30.07 1.83) ; 63 ( 235.49 837.15 -30.07 1.83) ; 64 ( 236.17 840.28 -30.07 1.83) ; 65 ( 236.79 841.64 -30.07 1.83) ; 66 ( 235.69 844.36 -29.97 1.83) ; 67 ( 235.21 848.42 -30.75 1.83) ; 68 ( 236.47 851.11 -30.75 1.83) ; 69 ( 237.00 854.82 -29.60 1.83) ; 70 ( 235.89 857.54 -28.63 1.83) ; 71 ( 235.54 861.04 -29.77 1.83) ; 72 ( 236.80 863.73 -30.20 1.83) ; 73 (Cross (Color White) (Name "Marker 3") ( 235.10 860.94 -28.50 1.83) ; 1 ( 237.80 851.43 -30.20 1.83) ; 2 ( 238.54 846.23 -32.38 1.83) ; 3 ( 237.13 848.28 -30.55 1.83) ; 4 ( 233.62 839.10 -30.07 1.83) ; 5 ( 238.34 833.04 -30.07 1.83) ; 6 ( 233.30 832.46 -30.07 1.83) ; 7 ( 232.57 827.51 -30.15 1.83) ; 8 ( 236.41 827.21 -33.10 1.83) ; 9 ( 236.57 822.47 -32.22 1.83) ; 10 ( 235.29 823.97 -32.22 1.83) ; 11 ( 234.83 817.88 -32.22 1.83) ; 12 ( 232.89 812.06 -29.20 1.83) ; 13 ( 230.02 804.22 -30.80 1.83) ; 14 ( 231.41 806.33 -30.30 1.83) ; 15 ( 231.64 809.37 -29.52 1.83) ; 16 ( 231.80 760.45 -33.77 1.83) ; 17 ( 228.63 763.89 -33.77 1.83) ; 18 ( 233.42 765.60 -33.77 1.83) ; 19 ( 233.80 780.02 -32.63 1.83) ; 20 ( 233.87 787.79 -32.25 1.83) ; 21 ( 233.12 792.99 -31.35 1.83) ; 22 ( 234.83 795.79 -31.35 1.83) ; 23 ( 231.12 795.52 -33.30 1.83) ; 24 ( 229.49 790.36 -33.13 1.83) ; 25 ( 236.11 810.41 -29.20 1.83) ; 26 ( 235.56 812.69 -29.20 1.83) ; 27 ( 234.22 744.54 -38.27 1.83) ; 28 ( 228.44 733.04 -39.58 1.83) ; 29 ( 227.49 731.02 -38.85 1.83) ; 30 ( 228.47 728.88 -38.17 1.83) ; 31 ( 234.01 731.36 -39.22 1.83) ; 32 ( 233.63 716.95 -34.67 1.83) ; 33 ( 229.28 715.33 -34.67 1.83) ; 34 ( 232.37 714.25 -33.25 1.83) ; 35 ( 228.61 712.18 -33.25 1.83) ; 36 ( 229.13 703.94 -37.92 1.83) ; 37 ( 233.85 703.86 -37.92 1.83) ; 38 ( 233.58 699.01 -34.92 1.83) ; 39 ( 229.75 699.31 -37.52 1.83) ; 40 ( 233.32 722.25 -33.45 1.83) ; 41 ( 230.74 687.00 -33.05 1.83) ; 42 ( 231.37 688.35 -33.05 1.83) ; 43 ( 231.16 691.28 -33.05 1.83) ; 44 ( 230.05 694.01 -36.88 1.83) ; 45 ( 235.92 687.03 -33.05 1.83) ; 46 ( 231.57 679.44 -33.13 1.83) ; 47 ( 234.79 677.80 -33.77 1.83) ; 48 ( 235.90 681.05 -33.77 1.83) ; 49 ( 235.40 673.17 -36.85 1.83) ; 50 ( 235.16 670.13 -36.03 1.83) ; 51 ( 236.02 668.54 -36.03 1.83) ; 52 ( 232.62 668.94 -36.33 1.83) ; 53 ) ; End of markers ( ( 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 ( 239.13 867.84 -31.58 0.92) ; 2 ( 238.78 871.34 -30.10 0.92) ; 3 ( 239.72 873.35 -30.77 0.92) ; 4 ( 239.59 873.91 -30.77 0.92) ; 5 ( 239.37 876.86 -31.52 0.92) ; 6 ( 239.42 878.66 -33.33 0.92) ; 7 ( 241.13 881.45 -34.45 0.92) ; 8 ( 242.64 882.99 -35.17 0.92) ; 9 ( 242.69 884.79 -36.82 0.92) ; 10 ( 244.71 888.25 -37.55 0.92) ; 11 ( 246.24 889.80 -37.63 0.92) ; 12 ( 247.05 892.39 -38.07 0.92) ; 13 ( 246.82 895.32 -38.90 0.92) ; 14 ( 247.90 896.77 -40.15 0.92) ; 15 ( 249.99 897.85 -40.97 0.92) ; 16 ( 249.64 901.35 -40.38 0.92) ; 17 ( 250.33 904.50 -41.52 0.92) ; 18 (Cross (Color White) (Name "Marker 3") ( 251.76 902.45 -41.52 0.92) ; 1 ( 251.38 899.97 -40.38 0.92) ; 2 ( 248.25 893.26 -39.75 0.92) ; 3 ( 245.57 892.63 -38.88 0.92) ; 4 ( 246.53 884.50 -37.55 0.92) ; 5 ( 241.58 887.52 -32.80 0.92) ; 6 ( 242.66 888.96 -36.90 0.92) ; 7 ( 238.21 877.77 -35.17 0.92) ; 8 ( 240.71 877.16 -31.98 0.92) ; 9 ) ; End of markers ( ( 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 ( 254.31 909.61 -44.17 0.92) ; 2 ( 255.89 912.96 -44.87 0.92) ; 3 (Cross (Color White) (Name "Marker 3") ( 256.04 908.22 -44.17 0.92) ; 1 ( 253.32 905.80 -44.17 0.92) ; 2 ) ; End of markers ( ( 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 ( 258.68 916.60 -42.30 0.46) ; 2 ( 258.68 916.60 -42.28 0.46) ; 3 ( 259.89 917.48 -41.45 0.46) ; 4 ( 263.21 919.45 -40.57 0.46) ; 5 ( 265.12 919.30 -41.52 0.46) ; 6 ( 266.91 919.72 -40.22 0.46) ; 7 ( 268.17 922.41 -39.52 0.46) ; 8 ( 270.54 922.36 -39.52 0.46) ; 9 ( 273.21 922.99 -38.55 0.46) ; 10 ( 275.31 924.08 -37.77 0.46) ; 11 ( 276.92 923.26 -36.78 0.46) ; 12 ( 279.10 921.98 -35.60 0.46) ; 13 ( 281.39 924.30 -34.58 0.46) ; 14 ( 282.45 925.75 -33.33 0.46) ; 15 ( 286.16 926.02 -32.75 0.46) ; 16 ( 287.82 927.02 -32.20 0.46) ; 17 ( 289.97 929.90 -31.60 0.46) ; 18 ( 292.20 930.43 -31.00 0.46) ; 19 ( 296.08 931.93 -30.30 0.46) ; 20 ( 297.88 932.35 -29.38 0.46) ; 21 ( 298.82 934.36 -29.38 0.46) ; 22 ( 300.47 935.35 -28.27 0.46) ; 23 ( 302.38 935.20 -27.27 0.46) ; 24 ( 303.51 938.44 -26.72 0.46) ; 25 ( 303.51 938.44 -26.75 0.46) ; 26 ( 305.61 939.53 -26.75 0.46) ; 27 ( 306.56 941.55 -25.73 0.46) ; 28 ( 308.52 943.20 -24.40 0.46) ; 29 ( 310.30 943.62 -23.60 0.46) ; 30 ( 312.41 944.70 -22.97 0.46) ; 31 ( 314.06 945.69 -22.77 0.46) ; 32 ( 317.18 946.42 -22.67 0.46) ; 33 ( 317.18 946.42 -22.70 0.46) ; 34 ( 318.67 946.18 -20.92 0.46) ; 35 ( 318.85 947.42 -20.00 0.46) ; 36 ( 320.45 946.60 -18.40 0.46) ; 37 ( 323.39 946.09 -17.42 0.46) ; 38 ( 323.39 946.09 -17.45 0.46) ; 39 ( 324.28 946.30 -16.20 0.46) ; 40 ( 324.33 948.10 -14.48 0.46) ; 41 ( 327.29 947.60 -14.38 0.46) ; 42 ( 330.72 949.00 -13.05 0.46) ; 43 ( 331.61 949.21 -13.05 0.46) ; 44 ( 332.10 951.12 -13.05 0.46) ; 45 ( 335.42 953.09 -13.05 0.46) ; 46 ( 337.69 955.42 -14.02 0.46) ; 47 ( 340.10 957.18 -14.88 0.46) ; 48 ( 341.45 957.49 -13.72 0.46) ; 49 ( 342.08 958.84 -12.95 0.46) ; 50 ( 344.36 961.15 -12.32 0.46) ; 51 ( 344.36 961.15 -12.35 0.46) ; 52 ( 345.75 963.28 -12.10 0.46) ; 53 ( 345.75 963.28 -12.13 0.46) ; 54 ( 347.59 965.50 -11.17 0.46) ; 55 ( 350.71 966.22 -10.48 0.46) ; 56 ( 351.52 968.81 -9.65 0.46) ; 57 ( 353.17 969.79 -8.42 0.46) ; 58 ( 353.17 969.79 -8.45 0.46) ; 59 ( 354.70 971.34 -6.65 0.46) ; 60 ( 356.35 972.33 -4.17 0.46) ; 61 ( 355.90 972.22 -4.17 0.46) ; 62 ( 356.80 972.43 -1.60 0.46) ; 63 (Cross (Color White) (Name "Marker 3") ( 261.91 920.94 -41.45 0.46) ; 1 ( 309.33 945.79 -22.20 0.46) ; 2 ( 305.98 942.01 -24.58 0.46) ; 3 ( 307.94 943.66 -22.43 0.46) ; 4 ( 318.34 945.50 -20.00 0.46) ; 5 ( 325.26 944.14 -18.45 0.46) ; 6 ( 319.83 945.25 -17.20 0.46) ; 7 ( 313.79 946.83 -22.85 0.46) ; 8 ( 316.16 946.79 -20.00 0.46) ; 9 ( 324.38 949.91 -14.38 0.46) ; 10 ( 328.09 950.18 -12.62 0.46) ; 11 ( 332.15 952.92 -12.30 0.46) ; 12 ( 335.05 950.61 -12.32 0.46) ; 13 ( 339.02 949.76 -12.32 0.46) ; 14 ( 341.24 960.43 -12.35 0.46) ; 15 ( 346.28 961.00 -12.52 0.46) ; 16 ( 293.01 933.00 -29.38 0.46) ; 17 ( 286.84 929.16 -31.60 0.46) ; 18 ( 284.12 926.74 -32.75 0.46) ; 19 ( 278.03 920.53 -35.63 0.46) ; 20 ( 275.13 922.84 -35.63 0.46) ; 21 ( 271.47 924.37 -41.05 0.46) ; 22 ( 272.00 922.10 -41.67 0.46) ; 23 ( 268.57 920.70 -39.52 0.46) ; 24 ( 343.83 963.42 -12.57 0.46) ; 25 ( 345.66 965.64 -11.02 0.46) ; 26 ( 350.28 972.10 -12.52 0.46) ; 27 ( 275.23 926.45 -36.78 0.46) ; 28 ( 259.35 913.77 -42.28 0.46) ; 29 ) ; End of markers Normal | ( 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 ( 258.40 918.33 -46.67 0.46) ; 2 ( 258.89 920.24 -47.60 0.46) ; 3 ( 258.76 920.81 -48.60 0.46) ; 4 ( 257.28 921.06 -49.05 0.46) ; 5 ( 257.28 921.06 -49.07 0.46) ; 6 ( 257.60 921.72 -49.85 0.46) ; 7 ( 259.43 923.95 -49.85 0.46) ; 8 ( 259.87 924.06 -50.52 0.46) ; 9 ( 258.58 925.54 -52.22 0.46) ; 10 ( 259.43 923.95 -53.60 0.46) ; 11 ( 259.43 923.95 -53.62 0.46) ; 12 ( 259.92 925.86 -55.22 0.46) ; 13 ( 259.92 925.86 -55.30 0.46) ; 14 ( 260.56 927.19 -57.13 0.46) ; 15 ( 262.21 928.18 -58.25 0.46) ; 16 ( 264.18 929.85 -59.38 0.46) ; 17 (Cross (Color White) (Name "Marker 3") ( 255.99 922.55 -49.17 0.46) ; 1 ( 259.08 927.46 -57.13 0.46) ; 2 ( 259.84 928.23 -58.22 0.46) ; 3 ) ; End of markers ( ( 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 ( 264.30 932.84 -61.25 0.46) ; 2 ( 262.56 934.23 -62.67 0.46) ; 3 ( 264.23 935.21 -63.75 0.46) ; 4 ( 262.94 936.70 -64.65 0.46) ; 5 ( 263.57 938.04 -65.52 0.46) ; 6 ( 263.34 940.98 -66.20 0.46) ; 7 ( 264.60 943.66 -67.00 0.46) ; 8 ( 264.65 945.46 -67.55 0.46) ; 9 ( 264.65 945.46 -67.57 0.46) ; 10 ( 263.36 946.95 -69.60 0.46) ; 11 ( 263.09 948.09 -70.70 0.46) ; 12 ( 265.18 949.17 -71.85 0.46) ; 13 ( 265.05 949.74 -72.72 0.46) ; 14 ( 265.10 951.53 -73.38 0.46) ; 15 ( 265.10 951.53 -73.40 0.46) ; 16 ( 263.95 952.47 -74.25 0.46) ; 17 ( 262.92 952.82 -75.52 0.46) ; 18 ( 262.97 954.62 -76.80 0.46) ; 19 ( 262.97 954.62 -76.82 0.46) ; 20 ( 262.89 956.99 -78.38 0.46) ; 21 ( 263.64 957.77 -79.75 0.46) ; 22 ( 262.93 958.79 -81.32 0.46) ; 23 ( 259.72 960.43 -82.97 0.46) ; 24 ( 262.40 961.06 -85.85 0.46) ; 25 ( 261.56 962.65 -88.02 0.46) ; 26 ( 260.85 963.68 -89.32 0.46) ; 27 ( 260.90 965.48 -91.07 0.46) ; 28 ( 260.37 967.75 -92.90 0.46) ; 29 ( 259.34 968.11 -95.02 0.46) ; 30 ( 260.99 969.09 -95.82 0.46) ; 31 ( 260.99 969.09 -95.85 0.46) ; 32 ( 259.71 970.58 -96.65 0.46) ; 33 ( 259.93 973.62 -97.80 0.46) ; 34 ( 260.02 977.22 -98.47 0.46) ; 35 ( 260.02 977.22 -98.53 0.46) ; 36 ( 260.98 979.23 -99.45 0.46) ; 37 ( 261.65 982.38 -100.50 0.46) ; 38 ( 262.09 982.48 -100.50 0.46) ; 39 ( 260.98 985.21 -101.27 0.46) ; 40 ( 261.17 986.45 -102.52 0.46) ; 41 ( 262.24 987.89 -103.90 0.46) ; 42 ( 261.45 991.29 -104.82 0.46) ; 43 ( 260.78 994.12 -105.77 0.46) ; 44 ( 261.15 996.59 -107.35 0.46) ; 45 ( 260.92 999.53 -108.72 0.46) ; 46 ( 260.52 1001.22 -110.02 0.46) ; 47 ( 260.44 1003.59 -111.53 0.46) ; 48 ( 258.08 1003.63 -112.75 0.46) ; 49 ( 256.29 1003.22 -114.65 0.46) ; 50 ( 252.58 1002.95 -115.47 0.46) ; 51 ( 251.38 1002.07 -117.40 0.46) ; 52 ( 249.46 1002.21 -118.10 0.46) ; 53 ( 249.46 1002.21 -118.22 0.46) ; 54 (Cross (Color White) (Name "Marker 3") ( 261.82 999.74 -111.53 0.46) ; 1 ( 263.62 990.02 -104.82 0.46) ; 2 ( 263.71 987.64 -104.48 0.46) ; 3 ( 261.08 988.81 -103.47 0.46) ; 4 ( 264.31 954.93 -78.38 0.46) ; 5 ( 259.41 959.76 -84.72 0.46) ; 6 ( 259.69 964.61 -91.07 0.46) ; 7 ( 259.08 975.20 -99.45 0.46) ; 8 ( 259.54 981.29 -100.50 0.46) ; 9 ( 265.15 943.79 -58.53 0.46) ; 10 ( 265.81 944.54 -65.67 0.46) ; 11 ( 264.68 941.29 -67.35 0.46) ; 12 ( 263.34 979.19 -102.65 0.46) ; 13 ( 259.26 992.56 -105.77 0.46) ; 14 ) ; End of markers Normal | ( 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 ( 266.11 935.65 -57.02 0.46) ; 2 ( 266.21 939.26 -56.95 0.46) ; 3 ( 267.28 940.71 -57.05 0.46) ; 4 ( 267.51 943.75 -57.05 0.46) ; 5 ( 267.47 947.92 -58.17 0.46) ; 6 ( 268.46 951.74 -59.13 0.46) ; 7 ( 271.38 955.39 -59.67 0.46) ; 8 ( 272.84 955.15 -60.15 0.46) ; 9 ( 274.23 957.27 -60.15 0.46) ; 10 ( 278.13 958.78 -60.95 0.46) ; 11 ( 280.09 960.43 -60.95 0.46) ; 12 ( 282.63 961.62 -61.55 0.46) ; 13 ( 284.48 963.85 -62.28 0.46) ; 14 ( 287.02 965.04 -60.77 0.46) ; 15 ( 289.69 965.67 -59.65 0.46) ; 16 ( 291.35 966.65 -59.83 0.46) ; 17 ( 295.19 966.36 -58.95 0.46) ; 18 ( 296.84 967.35 -58.95 0.46) ; 19 ( 299.79 966.83 -60.67 0.46) ; 20 ( 299.66 967.41 -62.13 0.46) ; 21 ( 303.54 968.91 -62.13 0.46) ; 22 ( 304.66 972.16 -62.35 0.46) ; 23 ( 306.77 973.26 -62.28 0.46) ; 24 ( 308.63 974.88 -62.28 0.46) ; 25 ( 311.50 976.74 -60.92 0.46) ; 26 ( 313.46 978.39 -59.78 0.46) ; 27 ( 316.19 980.84 -58.45 0.46) ; 28 ( 317.84 981.82 -57.20 0.46) ; 29 ( 320.13 984.14 -56.57 0.46) ; 30 ( 322.80 984.77 -55.57 0.46) ; 31 ( 324.73 984.62 -53.95 0.46) ; 32 ( 325.31 984.16 -52.77 0.46) ; 33 (Cross (Color White) (Name "Marker 3") ( 265.94 940.39 -57.40 0.46) ; 1 ( 268.85 944.06 -57.40 0.46) ; 2 ( 266.89 948.38 -56.08 0.46) ; 3 ( 314.09 979.74 -58.53 0.46) ; 4 ( 314.62 977.48 -61.00 0.46) ; 5 ( 305.03 974.64 -62.28 0.46) ; 6 ( 289.17 967.93 -60.75 0.46) ; 7 ( 290.54 964.07 -61.20 0.46) ; 8 ( 288.66 960.05 -59.25 0.46) ; 9 ( 268.70 954.77 -59.67 0.46) ; 10 ( 270.25 952.15 -59.67 0.46) ; 11 ) ; End 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250.20 -106.80 0.46) ; 56 ( 248.69 250.20 -106.82 0.46) ; 57 ( 247.13 252.81 -108.63 0.46) ; 58 ( 247.13 252.81 -108.65 0.46) ; 59 ( 247.90 253.59 -110.85 0.46) ; 60 ( 249.01 256.84 -111.97 0.46) ; 61 ( 248.56 256.74 -112.00 0.46) ; 62 ( 248.62 258.54 -114.55 0.46) ; 63 ( 248.62 258.54 -114.57 0.46) ; 64 ( 248.21 260.24 -117.90 0.46) ; 65 (Cross (Color White) (Name "Marker 3") ( 247.23 256.42 -112.13 0.46) ; 1 ( 247.90 259.57 -113.93 0.46) ; 2 ( 250.13 238.00 -79.25 0.46) ; 3 ( 253.30 234.56 -70.60 0.46) ; 4 ( 252.94 232.08 -61.57 0.46) ; 5 ( 246.77 250.34 -102.78 0.46) ; 6 ) ; End of markers (Cross (Color RGB (128, 255, 128)) (Name "Marker 3") ( 236.93 219.96 -29.52 0.46) ; 1 ( 248.16 262.63 -19.60 2.29) ; 2 ( 246.06 257.39 -12.80 0.92) ; 3 ) ; End of markers Normal ) ; End of split ) ; End of split | ( 251.92 127.92 1.32 0.92) ; 1, R-1-1-1-1-1-1-2 ( 250.64 129.42 2.38 0.92) ; 2 ( 250.24 131.12 2.38 0.46) ; 3 ( 251.57 131.43 3.50 0.46) ; 4 ( 252.33 132.21 4.78 0.46) ; 5 ( 252.33 132.21 6.42 0.46) ; 6 ( 251.94 133.90 7.75 0.46) ; 7 ( 249.31 135.08 7.15 0.46) ; 8 ( 247.16 138.16 8.07 0.46) ; 9 ( 244.14 141.04 8.45 0.46) ; 10 ( 241.95 142.32 9.22 0.46) ; 11 ( 241.06 142.11 10.05 0.46) ; 12 ( 238.57 142.71 10.70 0.46) ; 13 ( 235.62 143.22 11.75 0.46) ; 14 ( 235.04 143.69 12.85 0.46) ; 15 ( 233.83 142.80 14.23 0.46) ; 16 ( 233.69 143.37 14.20 0.46) ; 17 ( 235.75 142.64 15.90 0.46) ; 18 ( 235.75 142.64 17.55 0.46) ; 19 ( 236.59 141.05 19.27 0.46) ; 20 ( 236.59 141.05 19.25 0.46) ; 21 ( 234.09 141.66 20.73 0.46) ; 22 ( 231.02 142.74 21.38 0.46) ; 23 ( 228.65 142.78 22.08 0.46) ; 24 ( 227.72 146.75 22.85 0.46) ; 25 ( 229.56 148.97 23.40 0.46) ; 26 ( 229.74 150.20 24.60 0.46) ; 27 ( 229.74 150.20 24.58 0.46) ; 28 ( 230.06 150.87 26.58 0.46) ; 29 ( 231.39 151.19 28.70 0.46) ; 30 ( 232.28 151.40 29.92 0.46) ; 31 (Cross (Color White) (Name "Marker 3") ( 248.57 130.13 2.38 0.46) ; 1 ( 248.89 130.81 2.38 0.46) ; 2 ( 247.54 140.64 7.57 0.46) ; 3 ( 244.84 140.00 8.57 0.46) ; 4 ( 242.48 140.04 8.57 0.46) ; 5 ( 243.03 143.76 8.95 0.46) ; 6 ( 240.12 140.10 10.48 0.46) ; 7 ( 241.82 142.88 11.45 0.46) ; 8 ( 237.72 144.31 11.45 0.46) ; 9 ( 230.52 140.82 21.38 0.46) ; 10 ( 229.78 146.02 23.55 0.46) ; 11 ( 226.33 144.62 23.40 0.46) ; 12 ( 226.26 152.96 23.40 0.46) ; 13 ( 250.51 135.96 7.15 0.46) ; 14 ) ; End of markers High | ( 258.12 127.60 1.30 0.92) ; 1, R-1-1-1-1-1-1-3 ( 261.87 129.67 1.73 0.92) ; 2 ( 266.15 129.48 1.85 0.92) ; 3 ( 269.11 128.97 2.55 0.92) ; 4 ( 271.74 127.80 2.08 0.92) ; 5 ( 274.10 127.76 0.28 0.92) ; 6 ( 276.48 127.72 -0.70 0.92) ; 7 ( 278.39 127.57 -0.05 0.92) ; 8 (Cross (Color White) (Name "Marker 3") ( 264.64 127.93 1.85 0.92) ; 1 ( 261.74 130.23 2.55 0.92) ; 2 ( 263.53 130.65 2.55 0.92) ; 3 ( 265.89 130.61 2.55 0.92) ; 4 ( 268.40 130.01 2.55 0.92) ; 5 ( 270.35 125.68 2.55 0.92) ; 6 ( 266.55 127.78 2.55 0.92) ; 7 ) ; End of markers ( ( 282.40 128.51 0.90 0.46) ; 1, R-1-1-1-1-1-1-3-1 ( 283.58 127.58 2.50 0.46) ; 2 ( 283.58 127.58 2.47 0.46) ; 3 ( 283.70 127.02 4.22 0.46) ; 4 ( 282.89 124.44 5.50 0.46) ; 5 ( 282.89 124.44 5.48 0.46) ; 6 ( 284.49 123.62 5.95 0.46) ; 7 ( 288.07 124.45 6.47 0.46) ; 8 ( 290.75 125.09 7.00 0.46) ; 9 ( 291.97 125.96 7.75 0.46) ; 10 ( 293.75 126.38 6.90 0.46) ; 11 ( 294.77 126.02 6.90 0.46) ; 12 ( 297.18 127.79 8.35 0.46) ; 13 ( 301.16 126.93 9.80 0.46) ; 14 ( 301.11 125.13 11.75 0.46) ; 15 ( 301.11 125.13 11.70 0.46) ; 16 ( 300.17 129.09 14.10 0.46) ; 17 ( 300.17 129.09 15.68 0.46) ; 18 ( 300.17 129.09 15.65 0.46) ; 19 ( 302.37 127.81 17.30 0.46) ; 20 ( 302.37 127.81 17.27 0.46) ; 21 ( 303.78 125.76 19.23 0.46) ; 22 ( 303.78 125.76 19.20 0.46) ; 23 ( 305.21 123.70 20.33 0.46) ; 24 ( 309.22 124.64 20.33 0.46) ; 25 ( 312.80 125.48 21.60 0.46) ; 26 ( 312.85 127.29 21.60 0.46) ; 27 ( 314.68 129.51 22.67 0.46) ; 28 ( 315.71 129.15 24.05 0.46) ; 29 ( 315.71 129.15 24.00 0.46) ; 30 ( 315.18 131.41 25.13 0.46) ; 31 ( 314.20 133.57 26.70 0.46) ; 32 ( 313.17 133.92 27.77 0.46) ; 33 ( 312.72 133.82 28.05 0.46) ; 34 ( 312.46 134.95 29.15 0.46) ; 35 ( 312.01 134.84 29.30 0.46) ; 36 (Cross (Color White) (Name "Marker 3") ( 288.25 125.71 8.15 0.46) ; 1 ( 286.65 126.52 6.47 0.46) ; 2 ( 283.29 122.74 8.13 0.46) ; 3 ( 294.42 129.53 9.80 0.46) ; 4 ( 317.08 125.29 21.28 0.46) ; 5 ( 315.86 134.56 21.55 0.46) ; 6 ( 318.34 127.97 23.67 0.46) ; 7 ( 307.48 126.03 20.33 0.46) ; 8 ( 305.57 126.18 20.33 0.46) ; 9 ( 303.16 124.41 19.20 0.46) ; 10 ( 301.11 125.13 19.20 0.46) ; 11 ( 299.45 124.15 9.80 0.46) ; 12 ( 296.90 122.94 8.35 0.46) ; 13 ( 289.67 123.64 7.87 0.46) ; 14 ) ; End of markers High | ( 280.13 126.18 -1.30 0.92) ; 1, R-1-1-1-1-1-1-3-2 ( 281.73 125.37 -2.70 0.92) ; 2 ( 283.20 125.12 -4.20 0.92) ; 3 ( 283.15 123.31 -5.60 0.92) ; 4 ( 284.63 123.06 -7.20 0.92) ; 5 ( 285.39 123.82 -9.57 0.92) ; 6 ( 286.86 123.58 -11.15 0.92) ; 7 ( 288.47 122.76 -11.65 0.92) ; 8 ( 289.13 119.93 -10.35 0.92) ; 9 ( 290.86 118.55 -9.55 0.92) ; 10 ( 293.55 119.18 -10.05 0.92) ; 11 ( 295.74 117.89 -10.25 0.92) ; 12 (Cross (Color White) (Name "Marker 3") ( 286.91 125.39 -9.63 0.92) ; 1 ( 285.75 126.31 -11.65 0.92) ; 2 ( 289.09 124.10 -10.12 0.92) ; 3 ( 290.78 120.91 -11.77 0.92) ; 4 ( 281.81 122.99 -5.60 0.92) ; 5 ( 292.02 117.62 -11.77 0.92) ; 6 ) ; End of markers ( ( 298.10 117.85 -10.25 0.92) ; 1, R-1-1-1-1-1-1-3-2-1 ( 301.35 118.02 -11.73 0.92) ; 2 ( 303.55 116.74 -12.85 0.92) ; 3 ( 303.55 116.74 -12.88 0.92) ; 4 ( 306.44 114.43 -12.13 0.92) ; 5 ( 310.54 113.00 -14.07 0.92) ; 6 ( 314.20 111.47 -17.77 0.92) ; 7 ( 317.28 110.40 -18.15 0.92) ; 8 ( 319.32 109.69 -18.15 0.92) ; 9 ( 323.17 109.40 -17.10 0.92) ; 10 ( 325.53 109.36 -15.70 0.92) ; 11 ( 327.26 107.97 -16.30 0.92) ; 12 ( 327.26 107.97 -16.32 0.92) ; 13 ( 330.70 109.37 -16.75 0.92) ; 14 ( 332.49 109.79 -17.77 0.92) ; 15 ( 335.61 110.51 -18.65 0.92) ; 16 ( 338.56 110.02 -17.20 0.92) ; 17 ( 341.45 107.71 -18.13 0.92) ; 18 ( 345.92 108.76 -18.57 0.92) ; 19 ( 349.31 108.36 -17.25 0.92) ; 20 ( 352.97 106.83 -17.25 0.92) ; 21 ( 354.34 102.97 -18.38 0.92) ; 22 ( 358.00 101.43 -18.82 0.92) ; 23 (Cross (Color White) (Name "Marker 3") ( 308.71 110.79 -14.07 0.92) ; 1 ( 311.74 113.89 -15.12 0.92) ; 2 ( 316.75 112.67 -16.75 0.92) ; 3 ( 319.37 111.50 -16.75 0.92) ; 4 ( 312.54 110.49 -16.25 0.92) ; 5 ( 313.67 113.73 -17.85 0.92) ; 6 ( 319.72 108.00 -17.10 0.92) ; 7 ( 322.53 108.06 -17.10 0.92) ; 8 ( 317.55 109.27 -17.10 0.92) ; 9 ( 324.73 106.77 -17.10 0.92) ; 10 ( 332.27 112.72 -18.65 0.92) ; 11 ( 338.30 111.15 -19.05 0.92) ; 12 ( 340.21 111.00 -20.33 0.92) ; 13 ( 344.54 106.64 -18.57 0.92) ; 14 ( 341.72 106.58 -16.98 0.92) ; 15 ( 352.30 109.65 -19.23 0.92) ; 16 ( 349.94 109.69 -16.67 0.92) ; 17 ( 357.92 103.81 -18.82 0.92) ; 18 ( 356.61 99.32 -18.82 0.92) ; 19 ) ; End of markers ( ( 359.35 101.75 -20.57 0.92) ; 1, R-1-1-1-1-1-1-3-2-1-1 ( 358.98 99.27 -22.25 0.92) ; 2 ( 360.14 98.35 -24.00 0.92) ; 3 ( 362.05 98.20 -24.88 0.92) ; 4 ( 363.75 95.02 -25.87 0.92) ; 5 ( 366.06 93.18 -25.20 0.92) ; 6 ( 369.77 93.45 -25.97 0.92) ; 7 ( 372.22 91.04 -26.58 0.92) ; 8 ( 374.71 90.43 -27.97 0.46) ; 9 ( 379.40 88.54 -29.20 0.46) ; 10 ( 382.92 87.57 -30.00 0.46) ; 11 ( 384.66 86.19 -30.35 0.46) ; 12 ( 386.71 85.47 -30.40 0.46) ; 13 ( 388.69 87.13 -31.32 0.46) ; 14 ( 388.24 87.02 -31.35 0.46) ; 15 ( 392.74 83.90 -32.40 0.46) ; 16 ( 394.20 83.64 -33.85 0.46) ; 17 ( 394.20 83.64 -33.88 0.46) ; 18 ( 395.64 81.59 -35.42 0.46) ; 19 ( 398.01 81.55 -36.95 0.46) ; 20 ( 398.18 82.79 -39.08 0.46) ; 21 ( 398.18 82.79 -39.10 0.46) ; 22 ( 398.62 82.90 -41.35 0.46) ; 23 ( 400.99 82.85 -43.47 0.46) ; 24 ( 400.99 82.85 -43.50 0.46) ; 25 ( 401.39 81.15 -45.85 0.46) ; 26 ( 402.24 79.56 -49.07 0.46) ; 27 ( 402.24 79.56 -49.13 0.46) ; 28 ( 402.94 78.53 -52.40 0.46) ; 29 ( 402.94 78.53 -52.45 0.46) ; 30 (Cross (Color White) (Name "Marker 3") ( 363.79 96.83 -20.47 0.92) ; 1 ( 367.46 95.29 -23.35 0.92) ; 2 ( 371.42 94.44 -26.05 0.92) ; 3 ( 369.59 92.21 -20.47 0.92) ; 4 ( 367.35 91.68 -25.85 0.92) ; 5 ( 383.55 88.92 -28.35 0.46) ; 6 ( 378.11 90.02 -19.13 0.92) ; 7 ( 377.89 86.99 -21.02 0.92) ; 8 ( 384.03 84.84 -29.67 0.46) ; 9 ( 396.45 84.17 -34.67 0.46) ; 10 ) ; End of markers Normal | ( 358.90 101.65 -17.20 0.92) ; 1, R-1-1-1-1-1-1-3-2-1-2 ( 358.90 101.65 -17.23 0.92) ; 2 ( 360.01 98.92 -17.17 0.92) ; 3 ( 360.41 97.23 -16.10 0.92) ; 4 ( 362.14 95.84 -19.13 0.92) ; 5 ( 362.14 95.84 -19.15 0.92) ; 6 ( 363.88 94.45 -19.73 0.92) ; 7 ( 363.88 94.45 -19.75 0.92) ; 8 ( 367.22 92.25 -20.47 0.92) ; 9 ( 368.65 90.20 -20.47 0.92) ; 10 ( 371.28 89.02 -20.47 0.92) ; 11 ( 374.36 87.95 -20.60 0.46) ; 12 ( 376.35 85.43 -21.00 0.46) ; 13 ( 378.63 81.78 -21.00 0.46) ; 14 ( 380.76 78.71 -22.43 0.46) ; 15 ( 383.21 76.30 -22.57 0.46) ; 16 ( 385.22 73.78 -22.63 0.46) ; 17 ( 389.00 71.67 -23.35 0.46) ; 18 ( 390.16 70.76 -24.88 0.46) ; 19 ( 390.55 69.06 -26.88 0.46) ; 20 ( 390.55 69.06 -26.90 0.46) ; 21 ( 391.99 67.00 -28.33 0.46) ; 22 ( 392.97 64.85 -30.00 0.46) ; 23 ( 394.26 63.35 -31.50 0.46) ; 24 ( 394.26 63.35 -31.52 0.46) ; 25 ( 392.99 60.68 -33.57 0.46) ; 26 ( 392.95 58.89 -37.38 0.46) ; 27 ( 392.95 58.89 -37.42 0.46) ; 28 ( 393.93 56.72 -39.17 0.46) ; 29 ( 393.93 56.72 -39.20 0.46) ; 30 ( 395.08 55.80 -41.80 0.46) ; 31 ( 395.08 55.80 -41.93 0.46) ; 32 ( 395.30 52.87 -43.42 0.46) ; 33 ( 395.30 52.87 -43.45 0.46) ; 34 ( 394.67 51.53 -45.72 0.46) ; 35 (Cross (Color White) (Name "Marker 3") ( 361.07 94.39 -23.40 0.92) ; 1 ( 374.36 87.95 -29.40 0.92) ; 2 ( 373.46 87.75 -21.02 0.92) ; 3 ( 375.42 83.42 -21.02 0.92) ; 4 ( 381.75 82.52 -19.52 0.92) ; 5 ( 381.84 80.15 -20.67 0.92) ; 6 ( 381.42 75.88 -22.57 0.46) ; 7 ( 387.31 74.86 -23.57 0.46) ; 8 ( 390.93 71.53 -24.97 0.46) ; 9 ( 391.04 64.99 -28.33 0.46) ; 10 ( 391.71 62.16 -33.57 0.46) ; 11 ) ; End of markers Normal ) ; End of split | ( 296.55 116.33 -11.65 0.92) ; 1, R-1-1-1-1-1-1-3-2-2 ( 297.08 114.06 -13.57 0.92) ; 2 ( 297.08 114.06 -13.60 0.92) ; 3 ( 295.35 115.45 -16.02 0.92) ; 4 ( 293.69 114.46 -18.05 0.92) ; 5 ( 292.05 113.48 -18.72 0.92) ; 6 ( 292.05 113.48 -18.75 0.92) ; 7 ( 290.44 114.30 -20.10 0.92) ; 8 ( 290.06 111.82 -21.45 0.92) ; 9 ( 290.91 110.24 -22.97 0.92) ; 10 ( 290.24 107.08 -24.52 0.92) ; 11 ( 289.70 103.38 -25.10 0.92) ; 12 ( 288.75 101.37 -27.75 0.92) ; 13 ( 288.22 97.65 -29.95 0.46) ; 14 ( 289.06 96.06 -29.95 0.46) ; 15 ( 290.08 95.70 -31.17 0.46) ; 16 ( 289.41 92.56 -32.60 0.46) ; 17 ( 290.52 89.83 -33.53 0.46) ; 18 ( 290.60 87.46 -34.75 0.46) ; 19 ( 290.60 87.46 -34.78 0.46) ; 20 ( 290.95 83.96 -34.47 0.46) ; 21 ( 291.48 81.70 -36.52 0.46) ; 22 ( 292.41 77.73 -37.58 0.46) ; 23 ( 292.25 72.33 -38.05 0.46) ; 24 ( 291.80 66.25 -36.63 0.46) ; 25 ( 291.12 63.11 -38.25 0.46) ; 26 ( 289.56 59.75 -39.55 0.46) ; 27 ( 289.56 59.75 -39.60 0.46) ; 28 ( 286.96 56.75 -41.05 0.46) ; 29 ( 286.96 56.75 -41.08 0.46) ; 30 ( 284.99 55.09 -43.00 0.46) ; 31 ( 284.24 54.32 -45.68 0.46) ; 32 (Cross (Color White) (Name "Marker 3") ( 288.19 107.80 -25.10 0.92) ; 1 ( 292.25 104.57 -26.72 0.92) ; 2 ( 286.31 103.77 -26.32 0.92) ; 3 ( 289.55 97.97 -29.95 0.46) ; 4 ( 287.19 98.01 -29.95 0.46) ; 5 ( 286.78 93.73 -33.27 0.46) ; 6 ( 291.85 90.14 -33.53 0.46) ; 7 ( 291.85 84.17 -34.47 0.46) ; 8 ( 290.13 81.39 -34.78 0.46) ; 9 ( 290.38 68.30 -36.63 0.46) ; 10 ( 291.49 62.14 -15.32 0.92) ; 11 ) ; End of markers Normal ) ; End of split ) ; End of split ) ; End of split | ( 255.22 125.77 2.25 0.92) ; 1, R-1-1-1-1-1-2 ( 255.60 128.24 3.50 0.92) ; 2 ( 257.69 129.32 3.57 0.92) ; 3 ( 259.93 129.85 4.60 0.92) ; 4 ( 260.28 132.33 5.13 0.92) ; 5 ( 261.10 134.90 5.37 0.92) ; 6 ( 261.33 137.94 5.97 0.92) ; 7 ( 262.40 139.39 6.00 0.92) ; 8 (Cross (Color White) (Name "Marker 3") ( 259.91 140.00 6.00 0.92) ; 1 ( 256.98 130.36 3.57 0.92) ; 2 ) ; End of markers ( ( 264.82 141.16 6.65 0.92) ; 1, R-1-1-1-1-1-2-1 ( 265.13 141.82 8.20 0.92) ; 2 ( 265.13 141.82 8.18 0.92) ; 3 ( 267.82 142.45 9.07 0.92) ; 4 ( 269.20 144.56 10.52 0.92) ; 5 ( 269.83 145.91 10.60 0.92) ; 6 (Cross (Color White) (Name "Marker 3") ( 267.08 137.50 9.07 0.92) ; 1 ) ; End of markers ( ( 270.77 147.92 12.07 0.92) ; 1, R-1-1-1-1-1-2-1-1 ( 270.81 149.72 13.52 0.92) ; 2 ( 269.84 151.88 14.50 0.92) ; 3 ( 270.01 153.12 15.85 0.92) ; 4 ( 270.38 155.59 16.77 0.92) ; 5 ( 268.95 157.64 18.00 0.92) ; 6 ( 268.42 159.90 19.42 0.92) ; 7 ( 268.34 162.27 20.42 0.92) ; 8 ( 270.19 164.50 21.13 0.92) ; 9 ( 273.44 164.67 21.70 0.92) ; 10 ( 275.44 162.15 21.07 0.92) ; 11 ( 278.48 159.27 22.25 0.92) ; 12 ( 280.13 160.26 23.47 0.92) ; 13 (Cross (Color White) (Name "Marker 3") ( 272.81 163.32 21.07 0.92) ; 1 ( 269.05 155.28 16.52 0.92) ; 2 ( 271.59 156.48 16.15 0.92) ; 3 ( 271.57 150.50 14.92 0.92) ; 4 ( 272.24 147.67 14.50 0.92) ; 5 ( 270.06 148.94 14.92 0.92) ; 6 ( 270.51 149.05 1.25 0.92) ; 7 ( 280.21 157.90 22.32 0.92) ; 8 ) ; End of markers ( ( 282.40 162.59 23.47 0.92) ; 1, R-1-1-1-1-1-2-1-1-1 ( 284.11 165.38 23.57 0.92) ; 2 ( 284.11 165.38 23.55 0.92) ; 3 ( 284.88 166.15 24.70 0.46) ; 4 ( 284.88 166.15 24.80 0.46) ; 5 ( 285.77 166.36 27.42 0.46) ; 6 (Cross (Color White) (Name "Marker 3") ( 280.27 165.67 21.32 0.92) ; 1 ) ; End of markers Normal | ( 282.00 158.32 23.47 0.46) ; 1, R-1-1-1-1-1-2-1-1-2 ( 282.48 154.25 23.47 0.46) ; 2 ( 281.50 150.43 24.70 0.46) ; 3 ( 280.86 149.09 26.80 0.46) ; 4 ( 280.86 149.09 26.75 0.46) ; 5 ( 280.68 147.85 28.42 0.46) ; 6 (Cross (Color White) (Name "Marker 3") ( 284.18 157.02 23.57 0.92) ; 1 ) ; End of markers Normal ) ; End of split | ( 268.67 146.83 11.77 0.92) ; 1, R-1-1-1-1-1-2-1-2 ( 267.07 147.65 13.60 0.92) ; 2 ( 264.82 147.12 15.48 0.92) ; 3 ( 262.07 148.87 16.30 0.92) ; 4 ( 260.19 150.81 17.15 0.92) ; 5 ( 257.30 153.12 17.85 0.92) ; 6 ( 254.49 153.06 19.27 0.92) ; 7 ( 251.63 151.19 20.57 0.92) ; 8 ( 249.66 149.53 22.08 0.46) ; 9 ( 248.46 148.66 23.67 0.46) ; 10 ( 248.53 146.29 25.63 0.46) ; 11 (Cross (Color White) (Name "Marker 3") ( 250.39 154.49 20.57 0.92) ; 1 ( 257.80 155.02 19.00 0.92) ; 2 ( 263.85 149.29 17.42 0.92) ; 3 ( 259.84 148.34 17.50 0.92) ; 4 ( 259.66 153.08 17.85 0.92) ; 5 ( 261.98 151.23 17.85 0.92) ; 6 ( 262.55 144.80 15.48 0.92) ; 7 ( 256.08 152.24 18.95 0.92) ; 8 ) ; End of markers Normal ) ; End of split | ( 262.76 141.86 4.85 0.92) ; 1, R-1-1-1-1-1-2-2 ( 263.97 142.74 3.60 0.92) ; 2 ( 266.12 145.64 2.88 0.92) ; 3 ( 267.64 147.18 2.27 0.92) ; 4 ( 268.58 149.19 1.27 0.92) ; 5 ( 270.59 152.65 0.12 0.92) ; 6 ( 270.64 154.45 -0.72 0.92) ; 7 ( 272.05 156.58 -1.40 0.92) ; 8 ( 274.27 157.10 -2.85 0.92) ; 9 ( 274.32 158.90 -4.20 0.46) ; 10 ( 273.87 158.79 -4.22 0.46) ; 11 ( 275.26 160.92 -5.63 0.46) ; 12 ( 275.26 160.92 -5.68 0.46) ; 13 ( 275.88 162.25 -6.93 0.46) ; 14 ( 276.56 165.40 -8.73 0.92) ; 15 ( 277.07 167.30 -9.43 0.92) ; 16 ( 279.39 171.44 -10.12 0.92) ; 17 ( 279.48 175.04 -11.53 0.92) ; 18 ( 279.40 177.40 -13.18 0.92) ; 19 ( 279.18 180.35 -13.70 0.92) ; 20 ( 281.34 183.24 -12.93 0.92) ; 21 ( 284.64 185.21 -13.00 0.92) ; 22 ( 285.32 188.36 -13.13 0.92) ; 23 ( 285.86 192.06 -14.00 0.92) ; 24 ( 285.86 192.06 -14.02 0.92) ; 25 ( 287.70 194.28 -14.60 0.92) ; 26 ( 292.35 196.56 -15.30 0.92) ; 27 ( 294.45 197.66 -15.32 0.92) ; 28 ( 297.11 198.30 -17.63 0.92) ; 29 ( 300.05 197.79 -18.45 0.92) ; 30 ( 303.50 199.20 -19.60 0.92) ; 31 ( 306.99 202.40 -20.60 0.92) ; 32 ( 308.68 205.19 -21.07 0.92) ; 33 ( 311.60 208.86 -21.20 0.92) ; 34 ( 313.88 211.19 -22.63 0.92) ; 35 ( 313.88 211.19 -22.65 0.92) ; 36 ( 316.17 213.50 -24.08 0.92) ; 37 ( 317.68 215.06 -25.55 0.92) ; 38 ( 317.68 215.06 -25.57 0.92) ; 39 ( 317.60 217.43 -26.45 0.92) ; 40 ( 318.63 217.07 -27.67 0.46) ; 41 ( 318.63 217.07 -27.70 0.46) ; 42 ( 318.99 219.55 -29.80 0.46) ; 43 ( 318.51 223.62 -31.32 0.46) ; 44 ( 318.51 223.62 -31.35 0.46) ; 45 ( 317.53 225.77 -32.52 0.46) ; 46 ( 319.23 228.57 -33.40 0.46) ; 47 ( 321.69 232.11 -33.92 0.46) ; 48 ( 322.77 233.58 -35.57 0.46) ; 49 ( 322.77 233.58 -35.60 0.46) ; 50 ( 323.89 236.82 -36.78 0.46) ; 51 ( 323.89 236.82 -36.80 0.46) ; 52 ( 325.28 238.94 -38.47 0.46) ; 53 ( 325.28 238.94 -38.50 0.46) ; 54 ( 325.24 243.11 -39.88 0.46) ; 55 ( 325.24 243.11 -39.90 0.46) ; 56 ( 324.71 245.37 -41.47 0.46) ; 57 ( 324.45 246.51 -43.40 0.46) ; 58 ( 324.45 246.51 -43.42 0.46) ; 59 ( 324.23 249.43 -45.25 0.46) ; 60 (Cross (Color White) (Name "Marker 3") ( 316.64 225.56 -32.47 0.46) ; 1 ( 319.76 226.30 -32.77 0.46) ; 2 ( 320.45 219.29 -31.35 0.46) ; 3 ( 316.00 218.25 -26.45 0.92) ; 4 ( 309.29 210.70 -21.07 0.92) ; 5 ( 311.82 205.93 -20.13 0.92) ; 6 ( 308.92 208.23 -21.07 0.92) ; 7 ( 308.11 205.65 -21.07 0.92) ; 8 ( 305.87 205.13 -21.07 0.92) ; 9 ( 306.41 202.86 -21.07 0.92) ; 10 ( 306.62 199.93 -21.07 0.92) ; 11 ( 300.14 195.42 -18.45 0.92) ; 12 ( 302.42 197.75 -18.45 0.92) ; 13 ( 300.10 199.59 -21.07 0.92) ; 14 ( 295.74 196.17 -15.32 0.92) ; 15 ( 290.51 194.34 -15.32 0.92) ; 16 ( 282.46 186.48 -12.88 0.92) ; 17 ( 281.52 184.47 -12.88 0.92) ; 18 ( 283.13 183.66 -12.88 0.92) ; 19 ( 278.28 174.16 -11.53 0.92) ; 20 ( 281.94 172.63 -11.00 0.92) ; 21 ( 278.02 181.26 -12.77 0.92) ; 22 ( 274.07 166.01 -8.18 0.46) ; 23 ( 272.89 154.98 -3.50 0.92) ; 24 ( 272.39 153.08 2.08 0.92) ; 25 ( 271.31 151.63 1.25 0.92) ; 26 ( 268.68 152.80 -0.43 0.92) ; 27 ( 263.56 138.46 6.00 0.92) ; 28 ( 261.43 141.55 6.00 0.92) ; 29 ( 274.95 160.24 21.07 0.92) ; 30 ) ; End of markers Normal ) ; End of split ) ; End of split | ( 259.91 101.78 -7.35 0.92) ; 1, R-1-1-1-1-2 ( 261.79 99.83 -8.82 0.92) ; 2 ( 263.04 102.52 -9.43 0.92) ; 3 ( 263.01 106.69 -10.07 0.92) ; 4 ( 264.53 108.24 -10.57 0.92) ; 5 ( 266.76 108.76 -10.85 0.92) ; 6 ( 267.83 110.21 -11.35 0.92) ; 7 ( 269.81 111.87 -12.57 0.92) ; 8 ( 270.57 112.64 -13.72 0.92) ; 9 ( 273.37 112.71 -14.80 0.92) ; 10 ( 271.50 114.65 -16.13 0.92) ; 11 ( 273.29 115.06 -17.95 0.92) ; 12 ( 275.15 113.13 -18.82 0.92) ; 13 ( 276.82 114.11 -19.97 0.92) ; 14 ( 275.84 116.27 -21.28 0.92) ; 15 ( 275.84 116.27 -21.30 0.92) ; 16 ( 276.79 118.28 -22.77 0.92) ; 17 ( 276.79 118.28 -22.80 0.92) ; 18 ( 277.54 119.05 -23.70 0.92) ; 19 ( 278.75 119.94 -24.42 0.92) ; 20 ( 280.49 118.55 -24.42 0.92) ; 21 ( 282.99 117.95 -26.00 0.92) ; 22 ( 283.93 119.96 -28.55 0.92) ; 23 ( 284.11 121.19 -30.67 0.92) ; 24 ( 285.90 121.61 -32.35 0.92) ; 25 ( 285.90 121.61 -32.38 0.92) ; 26 ( 288.70 121.67 -31.60 0.92) ; 27 ( 288.70 121.67 -31.63 0.92) ; 28 ( 290.54 123.89 -34.50 0.92) ; 29 ( 291.22 127.04 -35.42 0.92) ; 30 ( 291.22 127.04 -35.45 0.92) ; 31 ( 291.98 127.81 -37.40 0.92) ; 32 ( 294.03 127.10 -39.47 0.92) ; 33 ( 294.03 127.10 -39.50 0.92) ; 34 ( 296.85 127.16 -41.55 0.92) ; 35 ( 296.85 127.16 -41.57 0.92) ; 36 ( 298.55 129.95 -44.35 0.92) ; 37 ( 299.49 131.96 -45.85 0.92) ; 38 ( 299.49 131.96 -45.88 0.92) ; 39 ( 301.19 134.74 -47.33 0.92) ; 40 ( 300.79 136.44 -48.38 0.92) ; 41 ( 300.79 136.44 -48.40 0.92) ; 42 ( 303.34 137.63 -49.40 0.92) ; 43 ( 301.73 138.45 -51.85 0.92) ; 44 ( 303.70 140.12 -53.70 0.92) ; 45 ( 306.25 141.31 -55.05 0.92) ; 46 ( 306.25 141.31 -57.67 0.92) ; 47 ( 306.25 141.31 -57.70 0.92) ; 48 ( 306.43 142.55 -59.80 0.92) ; 49 ( 308.27 144.76 -61.15 0.92) ; 50 ( 310.24 146.42 -62.85 0.92) ; 51 ( 311.49 149.10 -64.67 0.92) ; 52 ( 310.51 151.27 -64.97 0.92) ; 53 ( 310.51 151.27 -65.00 0.92) ; 54 ( 309.67 152.88 -66.52 0.46) ; 55 ( 308.06 153.69 -69.25 0.46) ; 56 ( 307.08 155.86 -71.17 0.46) ; 57 ( 307.45 158.33 -73.50 0.46) ; 58 ( 307.76 159.00 -75.63 0.46) ; 59 ( 307.76 159.00 -75.65 0.46) ; 60 ( 308.25 160.91 -77.93 0.46) ; 61 ( 308.25 160.91 -77.95 0.46) ; 62 ( 308.03 163.84 -80.02 0.46) ; 63 ( 308.40 166.31 -82.15 0.46) ; 64 ( 309.21 168.89 -83.35 0.46) ; 65 ( 307.34 170.85 -85.05 0.46) ; 66 ( 308.15 173.42 -86.40 0.46) ; 67 ( 307.44 174.45 -88.42 0.46) ; 68 ( 306.59 176.05 -90.80 0.46) ; 69 ( 306.37 178.97 -91.93 0.46) ; 70 ( 306.91 182.69 -92.70 0.46) ; 71 ( 306.51 184.39 -94.07 0.46) ; 72 ( 306.51 184.39 -94.10 0.46) ; 73 ( 306.61 188.00 -95.57 0.46) ; 74 ( 306.61 188.00 -95.63 0.46) ; 75 ( 306.80 189.23 -98.40 0.46) ; 76 ( 307.03 192.27 -100.40 0.46) ; 77 ( 306.10 196.24 -101.55 0.46) ; 78 ( 305.13 198.38 -103.85 0.46) ; 79 ( 305.49 200.87 -104.97 0.46) ; 80 ( 305.49 200.87 -105.00 0.46) ; 81 ( 305.80 201.54 -106.17 0.46) ; 82 ( 305.80 201.54 -106.35 0.46) ; 83 (Cross (Color White) (Name "Marker 3") ( 308.51 197.99 -101.55 0.46) ; 1 ( 307.91 199.14 -97.07 0.46) ; 2 ( 308.95 176.00 -90.80 0.46) ; 3 ( 308.80 177.26 -87.05 0.46) ; 4 ( 306.26 169.40 -85.05 0.46) ; 5 ( 305.92 169.42 -81.80 0.46) ; 6 ( 306.97 146.25 -61.15 0.92) ; 7 ( 309.39 148.01 -64.67 0.92) ; 8 ( 312.26 146.42 -65.25 0.46) ; 9 ( 299.04 131.86 -42.33 0.92) ; 10 ( 298.65 133.55 -48.20 0.92) ; 11 ( 260.67 102.56 -7.32 0.92) ; 12 ( 261.79 99.83 -7.32 0.92) ; 13 ( 262.02 102.87 -7.32 0.92) ; 14 ( 274.04 115.85 -16.13 0.92) ; 15 ( 282.63 115.46 -26.45 0.92) ; 16 ( 285.17 116.66 -26.45 0.92) ; 17 ( 287.63 120.23 -31.63 0.92) ; 18 ( 285.22 122.59 7.53 0.46) ; 19 ( 290.42 130.44 -34.47 0.92) ; 20 ( 289.74 127.28 -34.95 0.92) ; 21 ( 285.36 123.87 -31.63 0.92) ; 22 ) ; End of markers Normal ) ; End of split | ( 255.15 73.83 0.70 0.92) ; 1, R-1-1-1-2 ( 252.75 72.06 0.70 0.92) ; 2 ( 251.36 69.94 1.80 0.92) ; 3 ( 251.36 69.94 1.77 0.92) ; 4 ( 249.81 72.56 3.70 0.92) ; 5 ( 250.04 75.60 5.25 0.92) ; 6 (Cross (Color White) (Name "Marker 3") ( 255.35 75.06 0.70 0.92) ; 1 ( 254.72 73.72 7.57 0.92) ; 2 ( 249.44 70.09 3.78 0.92) ; 3 ( 249.18 71.22 3.78 0.92) ; 4 ( 248.51 74.05 8.02 0.92) ; 5 ) ; End of markers ( ( 252.26 76.12 4.52 1.38) ; 1, R-1-1-1-2-1 ( 251.87 77.83 6.42 1.38) ; 2 ( 252.05 79.06 8.70 1.38) ; 3 ( 252.05 79.06 8.68 1.38) ; 4 ( 251.34 80.09 10.82 1.38) ; 5 ( 252.54 80.97 12.07 1.38) ; 6 ( 253.89 81.29 14.00 1.38) ; 7 ( 253.44 81.18 13.95 1.38) ; 8 ( 255.94 80.57 15.65 1.38) ; 9 ( 259.51 81.40 16.80 1.38) ; 10 ( 263.22 81.68 17.95 1.38) ; 11 ( 265.35 78.59 19.15 1.38) ; 12 (Cross (Color White) (Name "Marker 3") ( 262.42 85.07 16.80 1.38) ; 1 ( 257.32 82.69 16.83 1.38) ; 2 ( 255.31 79.22 15.65 1.38) ; 3 ) ; End of markers ( ( 263.70 77.61 19.75 1.38) ; 1, R-1-1-1-2-1-1 ( 263.70 77.61 19.73 1.38) ; 2 ( 260.62 78.69 21.45 0.92) ; 3 ( 260.35 79.81 23.33 0.92) ; 4 ( 261.03 82.96 24.40 0.92) ; 5 ( 261.21 84.19 26.13 0.92) ; 6 ( 259.48 85.58 27.02 0.92) ; 7 ( 258.31 86.51 28.88 0.92) ; 8 ( 257.60 87.53 30.23 0.92) ; 9 (Cross (Color White) (Name "Marker 3") ( 261.47 77.09 21.45 0.92) ; 1 ( 259.15 78.94 24.40 0.92) ; 2 ) ; End of markers High | ( 266.46 75.88 18.17 0.92) ; 1, R-1-1-1-2-1-2 ( 267.17 74.84 18.57 0.92) ; 2 ( 269.85 75.47 20.65 0.92) ; 3 ( 271.02 74.54 21.75 0.92) ; 4 ( 271.91 74.75 23.42 0.92) ; 5 ( 273.61 77.55 24.77 0.92) ; 6 ( 273.61 77.55 24.75 0.92) ; 7 ( 276.86 77.71 25.38 0.92) ; 8 ( 279.37 77.10 24.95 0.92) ; 9 ( 281.28 76.95 25.87 0.92) ; 10 ( 281.28 76.95 25.85 0.92) ; 11 ( 281.86 76.49 28.10 0.92) ; 12 (Cross (Color White) (Name "Marker 3") ( 266.42 80.05 19.15 1.38) ; 1 ( 268.33 73.92 21.75 0.92) ; 2 ) ; End of markers Normal ) ; End of split | ( 247.40 76.78 6.15 0.92) ; 1, R-1-1-1-2-2 ( 244.85 75.58 8.02 0.92) ; 2 ( 241.33 76.55 8.60 0.92) ; 3 ( 241.33 76.55 8.57 0.92) ; 4 ( 236.86 75.50 9.50 0.92) ; 5 ( 236.86 75.50 9.48 0.92) ; 6 ( 234.32 74.31 10.85 0.92) ; 7 ( 234.32 74.31 10.82 0.92) ; 8 ( 231.59 71.88 10.85 0.92) ; 9 ( 230.70 71.67 10.85 0.92) ; 10 ( 230.20 69.76 10.85 0.92) ; 11 ( 227.12 70.84 12.10 0.92) ; 12 ( 225.21 70.99 13.07 0.92) ; 13 ( 225.21 70.99 13.05 0.92) ; 14 ( 223.10 69.89 12.35 0.92) ; 15 ( 219.71 70.29 13.40 0.92) ; 16 ( 217.48 69.76 14.25 0.92) ; 17 ( 213.83 71.29 14.60 0.92) ; 18 ( 210.65 68.76 14.10 0.92) ; 19 ( 209.39 66.07 14.43 0.92) ; 20 ( 207.55 63.86 15.68 0.92) ; 21 ( 203.40 63.48 14.97 0.92) ; 22 ( 201.76 62.49 15.43 0.92) ; 23 ( 200.50 59.81 17.20 0.92) ; 24 ( 197.90 56.82 18.45 0.92) ; 25 ( 194.73 54.28 20.15 0.92) ; 26 ( 192.94 53.86 21.75 0.92) ; 27 ( 190.70 53.34 22.95 0.92) ; 28 ( 190.70 53.34 22.92 0.92) ; 29 ( 188.03 52.71 24.27 0.92) ; 30 ( 183.74 52.91 25.20 0.92) ; 31 ( 182.32 54.95 25.20 0.92) ; 32 ( 181.79 57.22 24.30 0.92) ; 33 ( 182.41 58.56 24.67 0.92) ; 34 ( 182.41 58.56 24.65 0.92) ; 35 ( 183.50 60.00 25.63 0.92) ; 36 ( 183.50 60.00 27.35 0.92) ; 37 ( 183.50 60.00 27.32 0.92) ; 38 ( 183.94 60.10 29.42 0.92) ; 39 ( 183.54 61.81 30.60 0.92) ; 40 ( 183.54 61.81 30.57 0.92) ; 41 (Cross (Color White) (Name "Marker 3") ( 247.72 77.45 6.82 0.92) ; 1 ( 241.78 76.66 8.35 0.92) ; 2 ( 238.26 77.62 8.07 0.92) ; 3 ( 235.71 76.43 8.30 0.92) ; 4 ( 227.17 72.64 13.02 0.92) ; 5 ( 231.51 74.25 14.10 0.92) ; 6 ( 231.86 70.74 14.20 0.92) ; 7 ( 230.20 69.76 14.20 0.92) ; 8 ( 234.71 72.62 11.35 0.92) ; 9 ( 218.01 67.50 14.60 0.92) ; 10 ( 213.77 69.49 12.60 0.92) ; 11 ( 212.39 67.39 15.20 0.92) ; 12 ( 201.84 60.13 18.45 0.92) ; 13 ( 201.21 58.78 16.20 0.92) ; 14 ( 198.58 59.97 16.80 0.92) ; 15 ( 184.36 54.24 24.65 0.92) ; 16 ( 192.22 54.90 24.27 0.92) ; 17 ( 187.89 47.30 25.20 0.92) ; 18 ( 198.88 54.65 18.45 0.92) ; 19 ) ; End of markers High ) ; End of split | ( 259.28 74.90 3.67 0.92) ; 1, R-1-1-1-3 ( 259.28 74.90 3.65 0.92) ; 2 ( 260.35 76.34 5.97 0.92) ; 3 ( 259.95 78.04 7.47 0.92) ; 4 ( 261.73 78.46 8.38 0.46) ; 5 ( 263.83 79.56 8.77 0.46) ; 6 ( 265.68 81.77 8.77 0.46) ; 7 ( 268.09 83.53 9.57 0.46) ; 8 ( 267.55 85.80 10.10 0.46) ; 9 ( 266.44 88.52 11.00 0.46) ; 10 ( 266.76 89.19 11.90 0.46) ; 11 ( 268.28 90.74 12.65 0.46) ; 12 ( 270.96 91.37 13.80 0.46) ; 13 ( 272.43 91.12 14.75 0.46) ; 14 ( 273.59 90.19 15.88 0.46) ; 15 ( 276.00 91.95 16.92 0.46) ; 16 ( 278.42 93.72 17.95 0.46) ; 17 ( 279.68 96.40 19.63 0.46) ; 18 ( 279.68 96.40 19.60 0.46) ; 19 ( 281.47 96.82 20.90 0.46) ; 20 ( 281.47 96.82 20.87 0.46) ; 21 ( 282.04 96.36 22.10 0.46) ; 22 ( 285.47 97.76 23.47 0.46) ; 23 ( 287.45 99.41 24.45 0.46) ; 24 ( 287.01 99.31 24.45 0.46) ; 25 ( 288.88 103.34 25.30 0.46) ; 26 ( 289.11 106.38 26.42 0.46) ; 27 ( 289.16 108.18 24.05 0.46) ; 28 ( 290.23 109.63 23.17 0.46) ; 29 ( 289.83 111.32 22.45 0.46) ; 30 ( 289.83 111.32 22.43 0.46) ; 31 ( 288.99 112.91 20.55 0.46) ; 32 ( 288.42 113.38 18.50 0.46) ; 33 ( 288.42 113.38 18.48 0.46) ; 34 (Cross (Color White) (Name "Marker 3") ( 289.62 111.72 -21.60 0.92) ; 1 ( 286.18 96.74 23.45 0.46) ; 2 ( 267.12 91.67 12.65 0.46) ; 3 ( 270.24 92.40 13.80 0.46) ; 4 ( 270.19 90.59 13.80 0.46) ; 5 ( 271.01 93.18 14.75 0.46) ; 6 ( 276.18 93.20 17.95 0.46) ; 7 ( 277.16 91.04 19.50 0.46) ; 8 ( 282.00 94.56 22.30 0.46) ; 9 ( 277.52 93.52 22.32 0.46) ; 10 ( 279.10 96.87 18.45 0.46) ; 11 ( 283.64 95.54 23.45 0.46) ; 12 ( 265.27 80.96 19.15 1.38) ; 13 ( 266.64 79.62 8.77 0.46) ; 14 ) ; End of markers Normal ) ; End of split | ( 251.51 49.77 -3.38 0.92) ; 1, R-1-1-2 ( 249.56 54.10 -2.95 0.92) ; 2 ( 246.47 55.17 -2.95 0.92) ; 3 ( 244.11 55.21 -3.97 0.92) ; 4 ( 242.82 56.69 -5.65 0.92) ; 5 ( 241.97 58.29 -6.60 0.92) ; 6 ( 241.27 59.32 -8.23 0.92) ; 7 ( 239.67 60.14 -8.95 0.92) ; 8 ( 236.85 60.07 -9.17 0.92) ; 9 ( 232.75 61.50 -8.42 0.92) ; 10 ( 232.75 61.50 -8.45 0.92) ; 11 ( 231.09 60.51 -9.22 0.92) ; 12 ( 230.16 58.50 -10.12 0.92) ; 13 ( 228.81 58.19 -11.25 0.92) ; 14 ( 225.73 59.26 -12.00 0.92) ; 15 ( 222.30 57.86 -12.35 0.92) ; 16 ( 219.17 57.13 -13.00 0.92) ; 17 ( 215.91 56.96 -13.72 0.92) ; 18 ( 213.41 57.57 -14.75 0.92) ; 19 ( 210.81 54.57 -15.22 0.92) ; 20 ( 207.11 54.30 -15.50 0.92) ; 21 ( 204.16 54.80 -17.13 0.92) ; 22 ( 201.61 55.31 -16.57 0.92) ; 23 (Cross (Color White) (Name "Marker 3") ( 238.82 61.73 -9.17 0.92) ; 1 ( 242.87 58.50 -5.70 0.92) ; 2 ( 240.91 56.84 -7.32 0.92) ; 3 ( 238.01 59.15 -9.17 0.92) ; 4 ( 234.60 61.47 -58.22 0.46) ; 5 ( 228.28 60.45 -11.25 0.92) ; 6 ( 223.36 59.30 -10.80 0.92) ; 7 ( 219.79 58.46 -14.65 0.92) ; 8 ( 221.21 56.41 -10.80 0.92) ; 9 ( 208.27 53.37 -15.50 0.92) ; 10 ) ; End of markers ( ( 200.64 55.77 -16.57 0.92) ; 1, R-1-1-2-1 ( 198.32 57.62 -15.30 0.92) ; 2 ( 195.32 56.31 -14.65 0.92) ; 3 ( 190.34 57.54 -14.27 0.92) ; 4 ( 187.16 55.00 -13.63 0.92) ; 5 ( 183.23 51.68 -12.57 0.92) ; 6 ( 183.31 49.32 -10.82 0.92) ; 7 ( 180.76 48.13 -8.42 0.92) ; 8 ( 179.23 46.57 -6.78 0.92) ; 9 ( 178.48 45.79 -4.63 0.92) ; 10 ( 174.32 45.41 -2.15 0.46) ; 11 ( 174.32 45.41 -2.17 0.46) ; 12 ( 171.87 47.83 -1.67 0.46) ; 13 ( 171.87 47.83 -1.70 0.46) ; 14 ( 170.94 51.80 -1.00 0.46) ; 15 ( 168.63 53.64 0.15 0.46) ; 16 ( 168.63 53.64 0.12 0.46) ; 17 ( 167.65 55.80 1.07 0.46) ; 18 ( 167.65 55.80 1.05 0.46) ; 19 ( 166.94 56.82 2.45 0.46) ; 20 ( 166.94 56.82 2.42 0.46) ; 21 ( 166.36 57.29 4.32 0.46) ; 22 (Cross (Color White) (Name "Marker 3") ( 176.11 47.54 -25.90 0.92) ; 1 ( 178.65 47.02 -4.63 0.92) ; 2 ( 198.35 55.14 -22.05 0.92) ; 3 ( 176.16 47.64 -3.47 0.92) ; 4 ) ; End of markers Normal | ( 198.54 56.39 -16.98 0.46) ; 1, R-1-1-2-2 ( 195.27 56.22 -15.97 0.46) ; 2 ( 190.86 56.97 -16.45 0.46) ; 3 ( 188.32 55.78 -15.80 0.46) ; 4 ( 185.90 54.02 -15.07 0.46) ; 5 ( 184.51 51.90 -14.63 0.46) ; 6 ( 183.13 49.78 -13.30 0.46) ; 7 ( 183.52 48.08 -11.47 0.46) ; 8 ( 183.52 48.08 -11.50 0.46) ; 9 ( 182.18 47.77 -9.90 0.46) ; 10 ( 179.81 47.81 -8.57 0.46) ; 11 ( 179.81 47.81 -8.55 0.46) ; 12 ( 179.00 45.23 -7.15 0.46) ; 13 ( 178.69 44.56 -6.30 0.46) ; 14 ( 176.77 44.71 -4.70 0.46) ; 15 ( 172.93 45.01 -3.47 0.46) ; 16 ( 172.41 47.26 -3.47 0.46) ; 17 ( 170.71 50.46 -2.95 0.46) ; 18 ( 170.18 52.72 -1.88 0.46) ; 19 ( 168.75 54.77 -1.20 0.46) ; 20 ( 167.47 56.27 0.00 0.46) ; 21 ( 166.44 56.63 1.63 0.46) ; 22 ( 166.44 56.63 1.60 0.46) ; 23 ( 165.42 56.98 2.90 0.46) ; 24 (Cross (Color White) (Name "Marker 3") ( 186.92 53.66 -14.63 0.46) ; 1 ( 172.75 43.77 -4.07 0.46) ; 2 ( 172.35 43.76 -3.47 0.92) ; 3 ( 180.92 43.39 -3.47 0.92) ; 4 ( 179.40 43.54 -4.70 0.46) ; 5 ( 198.85 55.35 -16.57 0.92) ; 6 ( 177.44 47.85 -4.70 0.46) ; 7 ) ; End of markers Normal | ( 199.07 54.11 -19.70 0.92) ; 1, R-1-1-2-3 ( 196.97 53.03 -22.05 0.92) ; 2 ( 191.92 52.44 -23.00 0.92) ; 3 ( 189.38 51.24 -23.63 0.92) ; 4 ( 188.29 49.80 -24.60 0.92) ; 5 ( 184.46 50.09 -24.60 0.92) ; 6 ( 181.39 51.17 -25.25 0.92) ; 7 ( 178.39 49.87 -25.25 0.92) ; 8 ( 177.89 47.96 -24.82 0.92) ; 9 ( 177.89 47.96 -24.85 0.92) ; 10 ( 174.37 48.92 -25.90 0.92) ; 11 ( 172.27 47.83 -27.67 0.92) ; 12 ( 171.51 47.06 -29.62 0.92) ; 13 ( 171.06 46.96 -31.63 0.92) ; 14 ( 168.20 45.09 -33.10 0.92) ; 15 ( 165.34 43.23 -34.02 0.92) ; 16 ( 162.26 44.29 -35.65 0.92) ; 17 ( 161.55 45.32 -37.77 0.92) ; 18 ( 160.21 45.01 -39.95 0.92) ; 19 ( 157.79 43.25 -42.10 0.92) ; 20 ( 157.79 43.25 -42.13 0.92) ; 21 ( 154.09 42.98 -43.47 0.92) ; 22 ( 149.22 43.63 -49.65 0.46) ; 23 ( 145.38 43.92 -51.22 0.46) ; 24 ( 145.38 43.92 -51.28 0.46) ; 25 ( 142.58 43.86 -52.63 0.46) ; 26 ( 142.58 43.86 -52.65 0.46) ; 27 ( 141.28 45.36 -54.50 0.46) ; 28 ( 140.08 44.47 -56.20 0.46) ; 29 ( 140.08 44.47 -56.22 0.46) ; 30 ( 137.27 44.42 -57.47 0.46) ; 31 ( 137.27 44.42 -57.50 0.46) ; 32 ( 135.61 43.42 -59.47 0.46) ; 33 ( 135.61 43.42 -59.50 0.46) ; 34 ( 133.57 44.15 -60.95 0.46) ; 35 ( 132.54 44.49 -63.05 0.46) ; 36 ( 132.54 44.49 -63.07 0.46) ; 37 ( 130.04 45.10 -63.15 0.46) ; 38 ( 130.08 46.90 -64.70 0.46) ; 39 ( 127.72 46.96 -66.03 0.46) ; 40 ( 126.56 47.87 -67.65 0.46) ; 41 ( 124.46 46.79 -69.70 0.46) ; 42 ( 121.83 47.96 -71.68 0.46) ; 43 ( 121.83 47.96 -71.70 0.46) ; 44 ( 120.49 47.64 -73.78 0.46) ; 45 ( 120.49 47.64 -73.82 0.46) ; 46 ( 119.01 47.89 -76.38 0.46) ; 47 ( 116.97 48.62 -78.07 0.46) ; 48 ( 116.97 48.62 -78.10 0.46) ; 49 ( 115.49 48.86 -80.35 0.46) ; 50 ( 115.49 48.86 -80.40 0.46) ; 51 ( 114.02 49.11 -82.97 0.46) ; 52 ( 110.18 49.41 -84.80 0.46) ; 53 ( 108.89 50.90 -86.67 0.46) ; 54 ( 108.89 50.90 -86.70 0.46) ; 55 ( 107.42 51.15 -88.57 0.46) ; 56 ( 107.42 51.15 -88.60 0.46) ; 57 ( 104.34 52.22 -90.25 0.46) ; 58 ( 104.34 52.22 -90.28 0.46) ; 59 ( 102.46 54.17 -92.15 0.46) ; 60 ( 101.89 54.63 -93.82 0.46) ; 61 ( 101.89 54.63 -93.85 0.46) ; 62 ( 99.13 56.37 -96.10 0.46) ; 63 ( 97.21 56.53 -98.80 0.46) ; 64 ( 96.37 58.12 -101.65 0.46) ; 65 ( 93.55 58.05 -104.25 0.46) ; 66 ( 91.10 60.46 -106.15 0.46) ; 67 ( 88.74 60.51 -109.20 0.46) ; 68 (Cross (Color White) (Name "Marker 3") ( 192.44 51.95 24.27 0.92) ; 1 ( 175.89 50.48 -25.90 0.92) ; 2 ( 175.80 46.87 -3.47 0.46) ; 3 ( 168.47 43.96 -31.42 0.92) ; 4 ( 160.29 42.64 -37.77 0.92) ; 5 ( 155.75 43.96 -39.02 0.46) ; 6 ( 153.46 41.63 -44.07 0.46) ; 7 ( 147.97 40.95 -50.55 0.46) ; 8 ( 165.13 46.16 -32.55 0.92) ; 9 ) ; End of markers Normal ) ; End of split ) ; End of split | ( 262.09 46.88 -7.07 1.38) ; 1, R-1-2 ( 266.25 47.26 -7.70 1.38) ; 2 ( 268.34 48.34 -8.52 1.38) ; 3 ( 270.17 50.56 -9.20 1.38) ; 4 ( 271.96 50.98 -9.85 1.38) ; 5 ( 274.33 50.94 -8.23 1.38) ; 6 ( 276.25 50.79 -7.32 1.38) ; 7 ( 278.21 52.45 -7.95 1.38) ; 8 ( 278.97 53.22 -9.95 0.92) ; 9 (Cross (Color White) (Name "Marker 3") ( 274.51 52.17 -9.77 1.38) ; 1 ( 268.26 50.71 -9.20 1.38) ; 2 ( 264.76 47.51 -10.57 1.38) ; 3 ) ; End of markers ( ( 279.16 54.47 -12.22 0.92) ; 1, R-1-2-1 ( 280.68 56.01 -13.52 0.92) ; 2 ( 282.77 57.10 -13.55 0.92) ; 3 ( 284.12 57.41 -13.35 0.92) ; 4 ( 288.21 55.99 -13.38 0.92) ; 5 ( 288.08 56.56 -13.38 0.92) ; 6 ( 291.13 59.65 -14.00 0.92) ; 7 ( 290.33 63.05 -13.07 0.92) ; 8 ( 290.33 63.05 -13.13 0.92) ; 9 ( 289.67 65.88 -13.52 0.92) ; 10 ( 287.62 66.60 -14.63 0.92) ; 11 ( 287.80 67.83 -16.45 0.92) ; 12 ( 287.80 67.83 -16.47 0.92) ; 13 ( 288.69 68.04 -18.85 0.92) ; 14 ( 290.03 68.36 -21.18 0.92) ; 15 ( 291.42 70.48 -22.45 0.92) ; 16 ( 291.42 70.48 -22.47 0.92) ; 17 ( 291.91 72.38 -23.40 0.92) ; 18 ( 290.93 74.54 -23.33 0.46) ; 19 ( 291.12 75.78 -24.50 0.46) ; 20 ( 291.12 75.78 -24.52 0.46) ; 21 ( 292.47 76.09 -26.00 0.46) ; 22 ( 295.64 78.63 -27.05 0.46) ; 23 ( 297.73 79.72 -27.63 0.46) ; 24 ( 297.12 84.36 -28.90 0.46) ; 25 ( 297.75 85.69 -30.45 0.46) ; 26 ( 299.54 86.12 -31.92 0.46) ; 27 ( 300.75 86.99 -33.80 0.46) ; 28 (Cross (Color White) (Name "Marker 3") ( 297.17 86.16 -31.92 0.46) ; 1 ( 300.30 86.88 -32.25 0.46) ; 2 ( 301.58 85.40 -31.92 0.46) ; 3 ( 291.41 73.92 -38.07 0.46) ; 4 ( 294.15 72.90 -23.40 0.92) ; 5 ( 288.54 62.63 -11.17 0.92) ; 6 ( 291.41 64.49 -11.07 0.92) ; 7 ( 291.13 59.65 -15.32 0.92) ; 8 ( 277.86 55.95 -13.55 0.92) ; 9 ( 279.64 56.37 -13.55 0.92) ; 10 ( 280.86 57.24 -13.55 0.92) ; 11 ) ; End of markers ( ( 307.13 87.89 -34.28 0.46) ; 1, R-1-2-1-1 ( 306.42 88.92 -35.63 0.46) ; 2 ( 310.39 88.06 -36.63 0.46) ; 3 ( 312.35 89.72 -37.77 0.46) ; 4 ( 314.63 92.04 -39.10 0.46) ; 5 ( 314.63 92.04 -39.13 0.46) ; 6 ( 316.74 93.14 -40.10 0.46) ; 7 ( 319.73 94.43 -41.20 0.46) ; 8 ( 323.80 97.17 -41.52 0.46) ; 9 ( 326.93 97.90 -41.70 0.46) ; 10 ( 327.60 101.05 -41.60 0.46) ; 11 ( 327.60 101.05 -41.63 0.46) ; 12 ( 328.68 102.49 -43.15 0.46) ; 13 ( 328.68 102.49 -43.27 0.46) ; 14 (Cross (Color White) (Name "Marker 3") ( 303.50 85.25 -33.55 0.46) ; 1 ( 313.19 88.13 -39.10 0.46) ; 2 ) ; End of markers Normal | ( 302.71 88.64 -34.67 0.46) ; 1, R-1-2-1-2 ( 303.20 90.56 -36.10 0.46) ; 2 ( 304.28 92.00 -38.83 0.46) ; 3 ( 303.57 93.02 -39.60 0.46) ; 4 ( 303.17 94.72 -41.82 0.46) ; 5 ( 301.43 96.11 -42.37 0.46) ; 6 ( 301.43 96.11 -42.42 0.46) ; 7 ( 301.48 97.92 -44.05 0.46) ; 8 ( 303.27 98.33 -45.72 0.46) ; 9 ( 300.96 100.17 -46.15 0.46) ; 10 ( 301.76 102.76 -47.65 0.46) ; 11 ( 301.68 105.12 -49.47 0.46) ; 12 ( 301.68 105.12 -49.50 0.46) ; 13 ( 301.28 106.82 -50.68 0.46) ; 14 ( 301.82 110.54 -51.15 0.46) ; 15 ( 302.81 114.35 -51.15 0.46) ; 16 ( 302.46 117.85 -51.42 0.46) ; 17 ( 303.27 120.43 -52.80 0.46) ; 18 ( 303.27 120.43 -52.82 0.46) ; 19 ( 303.33 122.23 -53.65 0.46) ; 20 ( 303.33 122.23 -53.67 0.46) ; 21 ( 302.53 125.62 -54.32 0.46) ; 22 ( 301.94 126.09 -55.77 0.46) ; 23 ( 301.94 126.09 -55.80 0.46) ; 24 ( 302.75 128.66 -57.25 0.46) ; 25 ( 303.75 132.49 -58.85 0.46) ; 26 ( 304.38 133.82 -59.75 0.46) ; 27 ( 304.38 133.82 -59.78 0.46) ; 28 ( 306.52 136.71 -61.10 0.46) ; 29 ( 307.33 139.29 -61.38 0.46) ; 30 ( 307.33 139.29 -61.40 0.46) ; 31 ( 308.99 140.28 -61.38 0.46) ; 32 ( 310.15 145.32 -62.95 0.46) ; 33 ( 311.01 149.71 -63.72 0.46) ; 34 ( 311.01 149.71 -63.75 0.46) ; 35 ( 311.82 152.29 -63.05 0.46) ; 36 ( 313.08 154.97 -61.28 0.46) ; 37 ( 314.02 156.99 -59.80 0.46) ; 38 ( 314.02 156.99 -59.83 0.46) ; 39 ( 317.96 160.29 -59.15 0.46) ; 40 ( 319.13 165.34 -58.05 0.46) ; 41 ( 319.04 167.71 -58.62 0.46) ; 42 ( 317.71 173.37 -60.07 0.46) ; 43 ( 317.05 176.20 -61.42 0.46) ; 44 ( 315.63 178.26 -64.27 0.46) ; 45 (Cross (Color White) (Name "Marker 3") ( 321.46 169.47 -58.62 0.46) ; 1 ( 316.51 172.50 -60.07 0.46) ; 2 ( 305.00 135.16 -58.75 0.46) ; 3 ( 302.99 129.16 19.20 0.46) ; 4 ( 301.89 124.29 -53.72 0.46) ; 5 ( 304.68 112.40 -51.15 0.46) ; 6 ( 300.88 108.52 -51.15 0.46) ; 7 ( 304.43 97.41 -45.72 0.46) ; 8 ( 300.83 90.60 -36.10 0.46) ; 9 ( 302.18 90.91 -39.10 0.46) ; 10 ( 301.95 87.87 -33.53 0.46) ; 11 ) ; End of markers Normal ) ; End of split | ( 278.18 52.44 -12.95 0.92) ; 1, R-1-2-2 ( 278.63 52.55 -14.80 0.92) ; 2 ( 278.63 52.55 -14.92 0.92) ; 3 ( 278.76 51.98 -17.47 0.92) ; 4 ( 281.14 51.94 -18.02 0.92) ; 5 ( 283.28 54.84 -18.50 0.92) ; 6 ( 284.93 55.82 -18.88 0.92) ; 7 ( 286.99 55.10 -19.50 0.92) ; 8 ( 289.57 52.13 -19.50 0.92) ; 9 ( 291.63 51.41 -20.00 0.92) ; 10 ( 292.92 49.93 -20.77 0.92) ; 11 ( 295.28 49.89 -21.88 0.92) ; 12 (Cross (Color White) (Name "Marker 3") ( 293.32 48.24 -20.00 0.92) ; 1 ( 290.28 51.10 -20.00 0.92) ; 2 ) ; End of markers ( ( 296.62 50.20 -23.27 0.46) ; 1, R-1-2-2-1 ( 294.75 52.14 -24.77 0.46) ; 2 ( 294.30 52.03 -24.80 0.46) ; 3 ( 293.85 51.93 -26.32 0.46) ; 4 ( 295.38 53.50 -27.83 0.46) ; 5 ( 294.80 53.95 -27.85 0.46) ; 6 ( 295.56 54.73 -29.42 0.46) ; 7 ( 295.43 55.30 -29.42 0.46) ; 8 ( 294.85 55.75 -31.30 0.46) ; 9 ( 293.32 54.20 -31.13 0.46) ; 10 ( 292.29 54.56 -33.75 0.46) ; 11 ( 294.00 57.35 -35.55 0.46) ; 12 ( 293.34 60.18 -36.52 0.46) ; 13 ( 292.18 61.10 -40.80 0.46) ; 14 ( 293.52 61.42 -43.72 0.46) ; 15 ( 293.70 62.65 -45.80 0.46) ; 16 ( 293.70 62.65 -45.83 0.46) ; 17 ( 292.09 63.47 -47.02 0.46) ; 18 ( 291.96 64.03 -49.25 0.46) ; 19 ( 293.75 64.45 -51.25 0.46) ; 20 ( 294.51 65.23 -52.97 0.46) ; 21 ( 294.51 65.23 -53.00 0.46) ; 22 ( 295.90 67.34 -55.83 0.46) ; 23 ( 294.93 69.50 -57.85 0.46) ; 24 ( 295.68 70.29 -59.83 0.46) ; 25 ( 296.12 70.38 -59.83 0.46) ; 26 ( 297.28 69.46 -62.10 0.46) ; 27 ( 297.65 71.94 -63.82 0.46) ; 28 ( 300.01 71.90 -65.32 0.46) ; 29 ( 300.78 72.67 -67.25 0.46) ; 30 ( 300.78 72.67 -67.28 0.46) ; 31 ( 299.35 74.72 -69.55 0.46) ; 32 ( 298.76 75.19 -70.57 0.46) ; 33 ( 299.21 75.30 -72.77 0.46) ; 34 ( 301.46 75.81 -74.45 0.46) ; 35 ( 301.28 74.57 -76.60 0.46) ; 36 ( 301.28 74.57 -76.63 0.46) ; 37 ( 302.61 74.89 -78.97 0.46) ; 38 ( 303.72 72.17 -80.52 0.46) ; 39 ( 303.72 72.17 -80.60 0.46) ; 40 ( 305.11 74.28 -83.02 0.46) ; 41 ( 305.46 76.76 -85.02 0.46) ; 42 ( 304.50 78.92 -87.07 0.46) ; 43 ( 304.50 78.92 -87.10 0.46) ; 44 ( 303.65 80.51 -89.65 0.46) ; 45 ( 303.65 80.51 -89.70 0.46) ; 46 ( 303.87 83.55 -91.32 0.46) ; 47 ( 303.79 85.91 -92.15 0.46) ; 48 ( 303.66 86.48 -95.30 0.46) ; 49 (Cross (Color White) (Name "Marker 3") ( 296.80 73.53 -69.55 0.46) ; 1 ( 297.70 73.74 -65.67 0.46) ; 2 ( 297.05 66.43 -57.82 0.46) ; 3 ( 295.04 62.97 -51.25 0.46) ; 4 ( 291.81 58.62 -36.52 0.46) ; 5 ( 293.99 51.37 -21.82 0.92) ; 6 ) ; End of markers Normal | ( 296.12 48.29 -21.10 0.46) ; 1, R-1-2-2-2 ( 295.58 44.58 -22.67 0.46) ; 2 ( 295.58 44.58 -22.70 0.46) ; 3 ( 296.60 44.22 -24.40 0.46) ; 4 ( 298.21 43.41 -25.90 0.46) ; 5 ( 299.05 41.82 -27.60 0.46) ; 6 ( 299.01 40.01 -29.17 0.46) ; 7 ( 302.09 38.94 -30.13 0.46) ; 8 ( 303.11 38.59 -31.17 0.46) ; 9 ( 305.25 35.50 -31.70 0.46) ; 10 ( 307.70 33.09 -32.88 0.46) ; 11 ( 308.98 31.60 -34.30 0.46) ; 12 ( 310.67 28.41 -35.45 0.46) ; 13 ( 311.60 24.45 -36.33 0.46) ; 14 ( 314.50 22.14 -36.58 0.46) ; 15 ( 316.90 17.92 -37.00 0.46) ; 16 ( 318.41 13.50 -37.00 0.46) ; 17 ( 320.68 9.85 -37.22 0.46) ; 18 ( 320.68 9.85 -37.25 0.46) ; 19 ( 320.77 7.48 -38.40 0.46) ; 20 ( 320.62 2.08 -39.25 0.46) ; 21 ( 321.37 -3.12 -40.15 0.46) ; 22 ( 323.64 -6.77 -40.03 0.46) ; 23 ( 324.17 -9.04 -42.05 0.46) ; 24 ( 325.99 -12.79 -43.80 0.46) ; 25 ( 324.29 -15.58 -45.45 0.46) ; 26 ( 326.19 -21.70 -46.08 0.46) ; 27 ( 328.20 -24.21 -46.73 0.46) ; 28 ( 330.99 -30.13 -47.77 0.46) ; 29 ( 332.25 -31.60 -47.85 0.46) ; 30 ( 331.62 -32.95 -49.83 0.46) ; 31 ( 331.62 -32.95 -49.87 0.46) ; 32 ( 331.57 -34.76 -51.32 0.46) ; 33 ( 331.57 -34.76 -51.35 0.46) ; 34 ( 333.13 -37.37 -52.70 0.46) ; 35 ( 333.13 -37.37 -52.72 0.46) ; 36 ( 335.97 -41.48 -53.10 0.46) ; 37 ( 335.97 -41.48 -53.13 0.46) ; 38 ( 337.53 -44.10 -52.85 0.46) ; 39 ( 339.80 -47.75 -52.85 0.46) ; 40 ( 339.80 -47.75 -52.88 0.46) ; 41 ( 340.28 -51.82 -52.70 0.46) ; 42 (Cross (Color White) (Name "Marker 3") ( 295.00 45.05 -24.42 0.46) ; 1 ( 311.54 32.79 -35.45 0.46) ; 2 ( 307.87 28.35 -35.45 0.46) ; 3 ( 317.98 19.37 -37.67 0.46) ; 4 ( 316.62 13.08 -37.67 0.46) ; 5 ( 319.43 7.17 -38.40 0.46) ; 6 ( 321.42 -1.32 -40.03 0.46) ; 7 ( 330.96 -25.96 -47.77 0.46) ; 8 ( 336.65 -38.34 -53.13 0.46) ; 9 ( 338.33 -47.50 -52.70 0.46) ; 10 ) ; End of markers Normal ) ; End of split ) ; End of split ) ; End of split | ( 256.57 35.95 -7.82 1.83) ; 1, R-2 ( 253.05 36.90 -9.30 1.83) ; 2 ( 250.73 38.75 -9.32 1.83) ; 3 ( 246.88 39.05 -9.32 1.83) ; 4 ( 245.14 40.44 -10.50 1.83) ; 5 ( 243.23 40.58 -11.90 1.83) ; 6 ( 242.78 40.48 -13.27 1.38) ; 7 ( 241.30 40.73 -14.92 1.38) ; 8 ( 240.41 40.52 -15.92 1.38) ; 9 ( 236.35 37.78 -16.20 1.38) ; 10 (Cross (Color White) (Name "Marker 3") ( 238.71 37.72 -16.20 1.38) ; 1 ) ; End of markers ( ( 235.99 35.30 -17.17 1.38) ; 1, R-2-1 ( 235.04 33.29 -18.33 1.38) ; 2 ( 233.20 31.06 -19.33 1.38) ; 3 ( 232.58 29.73 -21.72 1.38) ; 4 ( ( 232.49 26.12 -22.53 0.92) ; 1, R-2-1-1 ( 231.49 22.30 -23.50 0.92) ; 2 ( 231.27 19.26 -24.10 0.92) ; 3 ( 230.90 16.80 -24.85 0.92) ; 4 ( 231.11 13.85 -25.38 0.92) ; 5 ( 230.25 9.47 -25.77 0.92) ; 6 ( 228.82 5.55 -25.80 0.92) ; 7 ( 227.84 1.75 -26.13 0.92) ; 8 ( 225.24 -1.26 -24.70 0.92) ; 9 ( 222.50 -3.68 -23.30 0.92) ; 10 ( 219.90 -6.69 -22.88 0.92) ; 11 ( 218.97 -8.69 -23.27 0.92) ; 12 ( 215.67 -10.67 -23.27 0.92) ; 13 ( 215.62 -12.47 -23.27 0.46) ; 14 ( 213.82 -12.89 -23.27 0.46) ; 15 ( 213.78 -14.69 -23.27 0.46) ; 16 ( 211.86 -14.54 -23.27 0.46) ; 17 ( 210.48 -16.66 -23.27 0.46) ; 18 ( 209.27 -17.54 -24.08 0.46) ; 19 ( 208.19 -18.99 -24.08 0.46) ; 20 ( 204.43 -21.05 -24.08 0.46) ; 21 ( 200.69 -23.14 -24.75 0.46) ; 22 ( 197.11 -23.98 -24.05 0.46) ; 23 ( 195.50 -23.15 -24.08 0.46) ; 24 ( 195.50 -23.15 -24.10 0.46) ; 25 ( 193.59 -23.00 -24.40 0.46) ; 26 ( 193.59 -23.00 -24.42 0.46) ; 27 ( 190.90 -23.63 -25.57 0.46) ; 28 ( 189.52 -25.76 -27.22 0.46) ; 29 ( 188.62 -25.97 -29.48 0.46) ; 30 ( 188.62 -25.97 -29.50 0.46) ; 31 ( 188.36 -24.82 -31.85 0.46) ; 32 ( 188.36 -24.82 -31.88 0.46) ; 33 ( 186.44 -24.67 -34.13 0.46) ; 34 ( 186.44 -24.67 -34.15 0.46) ; 35 (Cross (Color White) (Name "Marker 3") ( 194.65 -27.53 -24.67 0.46) ; 1 ( 193.23 -25.48 -25.57 0.46) ; 2 ( 190.98 -26.01 -25.57 0.46) ; 3 ( 233.18 19.11 -24.10 0.92) ; 4 ( 216.02 -8.20 -23.27 0.92) ; 5 ( 229.80 3.40 -26.13 0.92) ; 6 ) ; End of markers Normal | ( 229.63 30.23 -21.72 0.92) ; 1, R-2-1-2 ( 227.27 30.27 -22.88 0.92) ; 2 ( 226.64 28.93 -24.30 0.92) ; 3 ( 227.35 27.91 -26.58 0.92) ; 4 ( 226.59 27.13 -28.30 0.92) ; 5 ( 226.85 25.99 -27.80 0.92) ; 6 ( 225.79 24.55 -29.22 0.92) ; 7 ( 224.39 22.43 -30.45 0.92) ; 8 ( 222.66 23.81 -31.60 0.92) ; 9 ( 222.66 23.81 -31.63 0.92) ; 10 ( 221.95 24.84 -33.00 0.92) ; 11 ( 220.79 25.77 -34.88 0.92) ; 12 ( 219.62 26.69 -36.60 0.92) ; 13 (Cross (Color White) (Name "Marker 3") ( 228.78 25.84 -27.80 0.92) ; 1 ) ; End of markers ( ( 217.66 25.04 -38.13 0.92) ; 1, R-2-1-2-1 ( 216.45 24.15 -41.22 0.46) ; 2 ( 216.45 24.15 -41.20 0.46) ; 3 ( 214.14 26.00 -42.37 0.46) ; 4 ( 211.19 26.51 -43.60 0.46) ; 5 ( 208.38 26.43 -43.72 0.46) ; 6 ( 206.06 28.28 -44.58 0.46) ; 7 ( 206.51 28.39 -46.45 0.46) ; 8 ( 205.93 28.85 -49.10 0.46) ; 9 ( 203.44 29.46 -50.77 0.46) ; 10 ( 202.04 27.35 -52.80 0.46) ; 11 ( 200.39 26.36 -54.38 0.46) ; 12 ( 200.39 26.36 -54.40 0.46) ; 13 ( 196.43 27.22 -55.32 0.46) ; 14 ( 192.85 26.38 -56.80 0.46) ; 15 ( 191.64 25.51 -58.15 0.46) ; 16 ( 191.64 25.51 -58.17 0.46) ; 17 ( 189.73 25.65 -58.90 0.46) ; 18 ( 187.21 26.26 -59.80 0.46) ; 19 ( 184.54 25.64 -60.70 0.46) ; 20 ( 181.82 23.20 -61.38 0.46) ; 21 ( 178.92 25.50 -62.88 0.46) ; 22 ( 176.28 26.68 -63.95 0.46) ; 23 ( 174.54 28.07 -65.30 0.46) ; 24 ( 172.05 28.68 -66.88 0.46) ; 25 ( 172.18 28.11 -66.88 0.46) ; 26 ( 168.65 29.07 -68.20 0.46) ; 27 ( 166.03 30.25 -69.40 0.46) ; 28 ( 164.42 31.07 -71.00 0.46) ; 29 ( 162.77 30.09 -72.93 0.46) ; 30 ( 160.40 30.12 -74.55 0.46) ; 31 ( 160.40 30.12 -74.57 0.46) ; 32 ( 157.32 31.20 -75.63 0.46) ; 33 ( 156.48 32.79 -77.28 0.46) ; 34 ( 154.43 33.50 -79.50 0.46) ; 35 ( 151.93 34.11 -81.22 0.46) ; 36 ( 150.32 34.93 -83.02 0.46) ; 37 ( 149.38 32.92 -85.30 0.46) ; 38 ( 147.02 32.96 -87.70 0.46) ; 39 ( 147.37 29.46 -89.92 0.46) ; 40 ( 147.37 29.46 -89.95 0.46) ; 41 ( 144.95 27.70 -91.13 0.46) ; 42 ( 142.71 27.17 -92.90 0.46) ; 43 ( 142.71 27.17 -92.92 0.46) ; 44 ( 140.31 25.42 -94.10 0.46) ; 45 ( 140.31 25.42 -94.15 0.46) ; 46 ( 138.79 23.87 -93.63 0.46) ; 47 ( 138.79 23.87 -93.88 0.46) ; 48 ( 136.36 22.10 -94.17 0.46) ; 49 ( 135.56 19.53 -95.18 0.46) ; 50 ( 134.04 17.98 -97.28 0.46) ; 51 (Cross (Color White) (Name "Marker 3") ( 134.00 22.15 -93.82 0.46) ; 1 ( 185.22 28.78 -60.65 0.46) ; 2 ( 181.02 26.60 -62.95 0.46) ; 3 ( 190.69 23.49 -58.90 0.46) ; 4 ( 219.40 23.64 -41.20 0.46) ; 5 ( 160.30 26.52 -74.57 0.46) ; 6 ) ; End of markers Normal | ( 223.20 27.53 -39.55 0.46) ; 1, R-2-1-2-2 ( 223.65 27.63 -42.47 0.46) ; 2 ( 223.65 27.63 -42.53 0.46) ; 3 ( 223.38 28.76 -45.97 0.46) ; 4 ( 223.38 28.76 -46.10 0.46) ; 5 ( 225.12 27.38 -48.70 0.46) ; 6 ( 225.12 27.38 -48.72 0.46) ; 7 ( 225.17 29.18 -50.95 0.46) ; 8 ( 224.72 29.07 -50.97 0.46) ; 9 ( 223.83 28.86 -53.40 0.46) ; 10 ( 223.70 29.43 -53.42 0.46) ; 11 ( 223.43 30.57 -55.67 0.46) ; 12 ( 223.43 30.57 -55.70 0.46) ; 13 ( 225.34 30.41 -58.42 0.46) ; 14 ( 225.34 30.41 -58.47 0.46) ; 15 ( 224.95 32.11 -61.40 0.46) ; 16 ( 224.95 32.11 -61.47 0.46) ; 17 ( 224.06 31.91 -63.88 0.46) ; 18 ( 224.06 31.91 -63.90 0.46) ; 19 ( 224.86 34.48 -66.65 0.46) ; 20 ( 225.84 32.32 -69.35 0.46) ; 21 ( 226.10 31.20 -72.07 0.46) ; 22 ( 226.10 31.20 -72.17 0.46) ; 23 ( 228.04 31.04 -76.22 0.46) ; 24 ( 228.04 31.04 -76.30 0.46) ; 25 ( 228.04 31.04 -81.53 0.46) ; 26 ( 228.04 31.04 -81.58 0.46) ; 27 ( 227.72 30.38 -85.92 0.46) ; 28 ( 226.50 29.49 -89.05 0.46) ; 29 ( 226.50 29.49 -89.08 0.46) ; 30 ( 224.81 26.72 -92.40 0.46) ; 31 ( 224.76 24.91 -94.75 0.46) ; 32 ( 225.16 23.21 -97.13 0.46) ; 33 ( 225.87 22.17 -99.50 0.46) ; 34 ( 225.87 22.17 -99.52 0.46) ; 35 ( 224.79 20.73 -102.68 0.46) ; 36 ( 224.79 20.73 -102.72 0.46) ; 37 ( 225.24 20.84 -106.25 0.46) ; 38 ( 225.37 20.27 -110.32 0.46) ; 39 ( 225.37 20.27 -110.37 0.46) ; 40 (Cross (Color White) (Name "Marker 3") ( 228.06 26.87 -88.72 0.46) ; 1 ( 223.20 27.53 -92.40 0.46) ; 2 ) ; End of markers Normal | ( 219.81 27.93 -40.52 0.46) ; 1, R-2-1-2-3 ( 218.34 28.18 -43.45 0.46) ; 2 ( 218.34 28.18 -43.47 0.46) ; 3 ( 216.78 30.80 -45.70 0.46) ; 4 ( 217.68 31.01 -48.20 0.46) ; 5 ( 218.62 33.02 -51.08 0.46) ; 6 ( 219.25 34.37 -54.20 0.46) ; 7 ( 219.25 34.37 -54.22 0.46) ; 8 ( 218.17 32.91 -57.15 0.46) ; 9 ( 218.17 32.91 -57.20 0.46) ; 10 ( 216.61 35.53 -59.35 0.46) ; 11 ( 217.87 38.23 -60.27 0.46) ; 12 ( 217.39 42.29 -61.38 0.46) ; 13 ( 217.26 42.86 -63.10 0.46) ; 14 ( 215.77 43.11 -65.45 0.46) ; 15 ( 215.25 45.36 -67.50 0.46) ; 16 ( 216.60 45.68 -69.15 0.46) ; 17 ( 218.24 46.67 -70.45 0.46) ; 18 ( 216.06 47.95 -70.27 0.46) ; 19 ( 214.59 48.20 -72.17 0.46) ; 20 ( 216.11 49.75 -73.95 0.46) ; 21 ( 216.29 50.99 -74.45 0.46) ; 22 ( 214.92 54.85 -74.57 0.46) ; 23 ( 214.92 54.85 -74.60 0.46) ; 24 ( 214.21 55.87 -76.38 0.46) ; 25 ( 213.68 58.14 -78.13 0.46) ; 26 ( 214.17 60.05 -79.55 0.46) ; 27 ( 213.69 64.11 -80.57 0.46) ; 28 ( 214.09 68.39 -81.25 0.46) ; 29 ( 213.56 70.64 -82.75 0.46) ; 30 ( 213.56 70.64 -82.78 0.46) ; 31 ( 212.32 73.94 -84.57 0.46) ; 32 ( 213.58 76.63 -86.10 0.46) ; 33 ( 214.65 78.07 -87.70 0.46) ; 34 ( 214.65 78.07 -87.72 0.46) ; 35 ( 213.40 81.36 -89.25 0.46) ; 36 ( 212.17 84.66 -90.40 0.46) ; 37 ( 213.11 86.67 -92.83 0.46) ; 38 ( 213.11 86.67 -92.90 0.46) ; 39 (Cross (Color White) (Name "Marker 3") ( 213.13 54.43 -74.60 0.46) ; 1 ( 217.89 50.17 -70.25 0.46) ; 2 ( 218.87 42.03 -61.38 0.46) ; 3 ) ; End of markers Normal ) ; End of split ) ; End of split | ( 234.11 37.26 -15.38 0.92) ; 1, R-2-2 ( 232.72 35.13 -15.65 0.92) ; 2 ( 229.47 34.97 -16.52 0.92) ; 3 ( 227.02 37.38 -17.25 0.92) ; 4 ( 224.38 38.55 -18.17 0.92) ; 5 ( 222.42 36.90 -19.38 0.92) ; 6 ( 220.81 37.71 -20.38 0.92) ; 7 ( 218.85 36.06 -21.55 0.92) ; 8 ( 218.85 36.06 -21.58 0.92) ; 9 ( 216.65 37.34 -22.30 0.92) ; 10 ( 215.82 38.93 -23.95 0.92) ; 11 ( 213.00 38.87 -25.33 0.92) ; 12 ( 212.87 39.44 -27.15 0.92) ; 13 ( 210.77 38.34 -28.65 0.92) ; 14 ( 210.77 38.34 -28.67 0.92) ; 15 ( 208.22 37.15 -29.70 0.46) ; 16 ( 208.01 40.09 -30.40 0.46) ; 17 ( 203.91 41.52 -31.10 0.46) ; 18 ( 200.56 43.72 -32.08 0.46) ; 19 ( 198.70 45.67 -33.13 0.46) ; 20 ( 193.83 46.32 -33.67 0.92) ; 21 ( 191.07 48.05 -35.60 0.92) ; 22 ( 188.56 48.67 -37.72 0.92) ; 23 ( 186.51 49.38 -39.15 0.92) ; 24 ( 183.84 48.76 -41.05 0.92) ; 25 ( 183.84 48.76 -41.08 0.92) ; 26 ( 182.68 49.67 -44.17 0.92) ; 27 ( 182.68 49.67 -44.20 0.92) ; 28 ( 180.32 49.72 -45.57 0.92) ; 29 ( 177.54 51.46 -46.82 0.92) ; 30 ( 173.84 51.19 -48.35 0.92) ; 31 ( 171.02 51.12 -49.05 0.92) ; 32 ( 169.28 52.51 -51.22 0.46) ; 33 ( 165.90 52.91 -53.35 0.46) ; 34 ( 165.90 52.91 -53.38 0.46) ; 35 ( 163.72 54.19 -55.40 0.46) ; 36 ( 163.72 54.19 -55.42 0.46) ; 37 ( 161.52 55.47 -57.28 0.46) ; 38 ( 161.52 55.47 -57.30 0.46) ; 39 ( 159.03 56.08 -58.22 0.46) ; 40 ( 155.64 56.48 -59.90 0.46) ; 41 ( 155.11 58.74 -60.97 0.46) ; 42 ( 151.90 60.37 -62.45 0.46) ; 43 ( 149.84 61.10 -64.20 0.46) ; 44 ( 147.40 63.50 -66.00 0.46) ; 45 ( 145.53 65.45 -67.95 0.46) ; 46 ( 145.53 65.45 -67.97 0.46) ; 47 ( 143.70 69.21 -69.72 0.46) ; 48 ( 140.76 69.71 -71.50 0.46) ; 49 ( 140.76 69.71 -71.52 0.46) ; 50 ( 138.62 72.80 -73.40 0.46) ; 51 ( 136.44 74.07 -74.97 0.46) ; 52 ( 134.57 76.03 -76.70 0.46) ; 53 ( 133.28 77.51 -78.65 0.46) ; 54 ( 130.51 79.26 -79.80 0.46) ; 55 ( 126.72 81.35 -82.00 0.46) ; 56 ( 124.10 82.53 -84.08 0.46) ; 57 ( 124.10 82.53 -84.10 0.46) ; 58 ( 121.78 84.37 -86.70 0.46) ; 59 ( 120.67 87.09 -88.97 0.46) ; 60 ( 120.67 87.09 -89.00 0.46) ; 61 ( 118.79 89.05 -90.88 0.46) ; 62 ( 118.79 89.05 -90.90 0.46) ; 63 ( 117.10 92.24 -93.50 0.46) ; 64 ( 117.10 92.24 -93.53 0.46) ; 65 ( 117.28 93.47 -96.20 0.46) ; 66 ( 116.57 94.49 -99.92 0.46) ; 67 ( 116.57 94.49 -100.00 0.46) ; 68 (Cross (Color White) (Name "Marker 3") ( 155.03 61.11 -60.97 0.46) ; 1 ( 150.87 60.74 -60.97 0.46) ; 2 ( 160.46 54.02 -58.22 0.46) ; 3 ( 173.30 47.47 -49.05 0.92) ; 4 ( 124.28 83.76 -84.10 0.46) ; 5 ( 208.45 40.19 -30.38 0.46) ; 6 ( 198.01 42.53 -33.13 0.46) ; 7 ( 194.22 44.61 -33.67 0.46) ; 8 ) ; End of markers Normal ) ; End of split ) ; End of split ) ; End of tree ================================================ FILE: bluepyopt/tests/test_ephys/testdata/acc/l5pc/C060114A7_axon_replacement.acc ================================================ (arbor-component (meta-data (version "0.9-dev")) (morphology (branch 0 -1 (segment 0 (point 263.248016 5.356219 -3.380000 0.690000) (point 262.996735 -9.641676 -3.380000 0.690000) 2) (segment 1 (point 262.996735 -9.641676 -3.380000 0.690000) (point 262.745453 -24.639572 -3.380000 0.690000) 2) (segment 2 (point 262.745453 -24.639572 -3.380000 0.460000) (point 262.494171 -39.637466 -3.380000 0.460000) 2) (segment 3 (point 262.494171 -39.637466 -3.380000 0.460000) (point 262.242889 -54.635365 -3.380000 0.460000) 2)))) ================================================ FILE: bluepyopt/tests/test_ephys/testdata/acc/l5pc/C060114A7_modified.acc ================================================ (arbor-component (meta-data (version "0.9-dev")) (morphology (branch 0 -1 (segment 0 (point 262.132381 19.373333 -3.380000 11.328448) (point 262.132381 8.044885 -3.380000 11.328448) 1)) (branch 1 -1 (segment 1 (point 262.132381 19.373333 -3.380000 11.328448) (point 262.132381 30.701782 -3.380000 11.328448) 1)) (branch 2 -1 (segment 2 (point 254.220000 19.870000 -2.650000 0.690000) (point 254.220000 19.870000 -2.650000 0.690000) 3) (segment 3 (point 254.220000 19.870000 -2.650000 0.690000) (point 253.510000 20.900000 -4.070000 0.690000) 3) (segment 4 (point 253.510000 20.900000 -4.070000 0.690000) (point 251.450000 21.610000 -5.050000 0.690000) 3) (segment 5 (point 251.450000 21.610000 -5.050000 0.690000) (point 248.960000 22.230000 -5.400000 0.690000) 3) (segment 6 (point 248.960000 22.230000 -5.400000 0.690000) (point 247.220000 23.600000 -6.300000 0.690000) 3) (segment 7 (point 247.220000 23.600000 -6.300000 0.690000) (point 244.140000 24.680000 -6.950000 0.690000) 3) (segment 8 (point 244.140000 24.680000 -6.950000 0.690000) (point 241.460000 24.060000 -7.270000 0.690000) 3) (segment 9 (point 241.460000 24.060000 -7.270000 0.690000) (point 239.230000 23.530000 -8.770000 0.690000) 3) (segment 10 (point 239.230000 23.530000 -8.770000 0.690000) (point 237.490000 24.910000 -9.600000 0.460000) 3) (segment 11 (point 237.490000 24.910000 -9.600000 0.460000) (point 234.900000 27.890000 -10.450000 0.460000) 3) (segment 12 (point 234.900000 27.890000 -10.450000 0.460000) (point 232.590000 29.730000 -11.400000 0.460000) 3) (segment 13 (point 232.590000 29.730000 -11.400000 0.460000) (point 228.620000 30.600000 -12.150000 0.460000) 3) (segment 14 (point 228.620000 30.600000 -12.150000 0.460000) (point 224.920000 30.330000 -12.270000 0.460000) 3) (segment 15 (point 224.920000 30.330000 -12.270000 0.460000) (point 222.330000 33.300000 -11.750000 0.460000) 3) (segment 16 (point 222.330000 33.300000 -11.750000 0.460000) (point 220.780000 35.910000 -11.170000 0.460000) 3) (segment 17 (point 220.780000 35.910000 -11.170000 0.460000) (point 217.560000 37.550000 -11.170000 0.460000) 3) (segment 18 (point 217.560000 37.550000 -11.170000 0.460000) (point 214.980000 40.530000 -11.530000 0.460000) 3) (segment 19 (point 214.980000 40.530000 -11.530000 0.460000) (point 211.730000 40.360000 -12.130000 0.460000) 3) (segment 20 (point 211.730000 40.360000 -12.130000 0.460000) (point 211.730000 40.360000 -12.150000 0.460000) 3) (segment 21 (point 211.730000 40.360000 -12.150000 0.460000) (point 209.720000 42.890000 -12.750000 0.460000) 3)) (branch 3 2 (segment 22 (point 209.720000 42.890000 -12.750000 0.460000) (point 209.190000 45.150000 -13.750000 0.230000) 3) (segment 23 (point 209.190000 45.150000 -13.750000 0.230000) (point 207.860000 44.840000 -15.050000 0.230000) 3) (segment 24 (point 207.860000 44.840000 -15.050000 0.230000) (point 205.980000 46.790000 -16.170000 0.230000) 3) (segment 25 (point 205.980000 46.790000 -16.170000 0.230000) (point 204.680000 48.270000 -17.720000 0.230000) 3) (segment 26 (point 204.680000 48.270000 -17.720000 0.230000) (point 204.240000 48.170000 -17.720000 0.230000) 3) (segment 27 (point 204.240000 48.170000 -17.720000 0.230000) (point 201.790000 50.580000 -19.170000 0.230000) 3) (segment 28 (point 201.790000 50.580000 -19.170000 0.230000) (point 199.740000 51.290000 -19.550000 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196.810000 -56.890000 -30.130000 0.460000) (point 194.720000 -57.970000 -30.300000 0.230000) 3) (segment 373 (point 194.720000 -57.970000 -30.300000 0.230000) (point 192.750000 -59.630000 -30.300000 0.230000) 3) (segment 374 (point 192.750000 -59.630000 -30.300000 0.230000) (point 189.630000 -60.360000 -30.300000 0.230000) 3) (segment 375 (point 189.630000 -60.360000 -30.300000 0.230000) (point 187.520000 -61.460000 -32.280000 0.230000) 3) (segment 376 (point 187.520000 -61.460000 -32.280000 0.230000) (point 185.290000 -61.970000 -33.920000 0.230000) 3) (segment 377 (point 185.290000 -61.970000 -33.920000 0.230000) (point 183.630000 -62.970000 -35.830000 0.230000) 3) (segment 378 (point 183.630000 -62.970000 -35.830000 0.230000) (point 183.580000 -64.770000 -38.000000 0.230000) 3) (segment 379 (point 183.580000 -64.770000 -38.000000 0.230000) (point 183.580000 -64.770000 -38.030000 0.230000) 3) (segment 380 (point 183.580000 -64.770000 -38.030000 0.230000) (point 182.520000 -66.210000 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236.760000 -14.130000 -44.500000 0.230000) (point 236.580000 -15.360000 -42.400000 0.230000) 3) (segment 1154 (point 236.580000 -15.360000 -42.400000 0.230000) (point 235.410000 -14.440000 -48.220000 0.230000) 3) (segment 1155 (point 235.410000 -14.440000 -48.220000 0.230000) (point 235.410000 -14.440000 -48.250000 0.230000) 3) (segment 1156 (point 235.410000 -14.440000 -48.250000 0.230000) (point 234.200000 -15.320000 -51.200000 0.230000) 3) (segment 1157 (point 234.200000 -15.320000 -51.200000 0.230000) (point 234.200000 -15.320000 -51.220000 0.230000) 3) (segment 1158 (point 234.200000 -15.320000 -51.220000 0.230000) (point 235.680000 -15.570000 -54.300000 0.230000) 3) (segment 1159 (point 235.680000 -15.570000 -54.300000 0.230000) (point 235.680000 -15.570000 -54.320000 0.230000) 3) (segment 1160 (point 235.680000 -15.570000 -54.320000 0.230000) (point 236.400000 -16.600000 -57.530000 0.230000) 3) (segment 1161 (point 236.400000 -16.600000 -57.530000 0.230000) (point 236.400000 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235.530000 -20.990000 -71.820000 0.230000) (point 236.110000 -21.440000 -74.400000 0.230000) 3) (segment 1171 (point 236.110000 -21.440000 -74.400000 0.230000) (point 236.110000 -21.440000 -74.420000 0.230000) 3) (segment 1172 (point 236.110000 -21.440000 -74.420000 0.230000) (point 234.460000 -22.430000 -74.420000 0.230000) 3) (segment 1173 (point 234.460000 -22.430000 -74.420000 0.230000) (point 235.610000 -23.350000 -77.250000 0.230000) 3) (segment 1174 (point 235.610000 -23.350000 -77.250000 0.230000) (point 235.610000 -23.350000 -77.280000 0.230000) 3) (segment 1175 (point 235.610000 -23.350000 -77.280000 0.230000) (point 237.090000 -23.600000 -80.820000 0.230000) 3) (segment 1176 (point 237.090000 -23.600000 -80.820000 0.230000) (point 237.090000 -23.600000 -80.900000 0.230000) 3) (segment 1177 (point 237.090000 -23.600000 -80.900000 0.230000) (point 238.250000 -24.520000 -83.700000 0.230000) 3) (segment 1178 (point 238.250000 -24.520000 -83.700000 0.230000) (point 238.250000 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228.640000 -29.760000 -99.250000 0.230000) (point 227.880000 -30.540000 -101.600000 0.230000) 3) (segment 1188 (point 227.880000 -30.540000 -101.600000 0.230000) (point 226.670000 -31.420000 -103.800000 0.230000) 3) (segment 1189 (point 226.670000 -31.420000 -103.800000 0.230000) (point 226.670000 -31.420000 -103.820000 0.230000) 3) (segment 1190 (point 226.670000 -31.420000 -103.820000 0.230000) (point 224.490000 -30.140000 -107.320000 0.230000) 3) (segment 1191 (point 224.490000 -30.140000 -107.320000 0.230000) (point 224.490000 -30.140000 -107.380000 0.230000) 3) (segment 1192 (point 224.490000 -30.140000 -107.380000 0.230000) (point 223.780000 -29.120000 -110.550000 0.230000) 3) (segment 1193 (point 223.780000 -29.120000 -110.550000 0.230000) (point 223.780000 -29.120000 -110.630000 0.230000) 3) (segment 1194 (point 223.780000 -29.120000 -110.630000 0.230000) (point 220.650000 -29.850000 -112.130000 0.230000) 3) (segment 1195 (point 220.650000 -29.850000 -112.130000 0.230000) (point 218.870000 -30.260000 -115.470000 0.230000) 3) (segment 1196 (point 218.870000 -30.260000 -115.470000 0.230000) (point 217.220000 -31.250000 -118.220000 0.230000) 3) (segment 1197 (point 217.220000 -31.250000 -118.220000 0.230000) (point 218.950000 -32.630000 -121.470000 0.230000) 3) (segment 1198 (point 218.950000 -32.630000 -121.470000 0.230000) (point 221.630000 -32.000000 -122.750000 0.230000) 3) (segment 1199 (point 221.630000 -32.000000 -122.750000 0.230000) (point 221.940000 -31.340000 -125.400000 0.230000) 3) (segment 1200 (point 221.940000 -31.340000 -125.400000 0.230000) (point 223.410000 -31.580000 -128.450000 0.230000) 3) (segment 1201 (point 223.410000 -31.580000 -128.450000 0.230000) (point 223.410000 -31.580000 -128.480000 0.230000) 3) (segment 1202 (point 223.410000 -31.580000 -128.480000 0.230000) (point 223.870000 -31.480000 -131.670000 0.230000) 3) (segment 1203 (point 223.870000 -31.480000 -131.670000 0.230000) (point 223.870000 -31.480000 -131.750000 0.230000) 3) (segment 1204 (point 223.870000 -31.480000 -131.750000 0.230000) (point 224.180000 -30.810000 -136.000000 0.230000) 3) (segment 1205 (point 224.180000 -30.810000 -136.000000 0.230000) (point 224.940000 -30.030000 -139.320000 0.230000) 3)) (branch 52 50 (segment 1206 (point 238.010000 -11.440000 -39.900000 0.460000) (point 235.200000 -11.500000 -40.770000 0.460000) 3) (segment 1207 (point 235.200000 -11.500000 -40.770000 0.460000) (point 232.740000 -15.070000 -42.350000 0.460000) 3) (segment 1208 (point 232.740000 -15.070000 -42.350000 0.460000) (point 231.350000 -17.180000 -44.270000 0.460000) 3) (segment 1209 (point 231.350000 -17.180000 -44.270000 0.460000) (point 231.350000 -17.180000 -44.300000 0.460000) 3) (segment 1210 (point 231.350000 -17.180000 -44.300000 0.460000) (point 231.560000 -20.120000 -45.720000 0.460000) 3) (segment 1211 (point 231.560000 -20.120000 -45.720000 0.460000) (point 230.320000 -22.810000 -48.070000 0.460000) 3) (segment 1212 (point 230.320000 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1.590000 -16.700000 0.460000) (point 265.530000 -0.210000 -16.700000 0.460000) 3) (segment 1247 (point 265.530000 -0.210000 -16.700000 0.460000) (point 263.340000 1.060000 -16.700000 0.460000) 3) (segment 1248 (point 263.340000 1.060000 -16.700000 0.460000) (point 263.080000 2.190000 -19.000000 0.460000) 3) (segment 1249 (point 263.080000 2.190000 -19.000000 0.460000) (point 263.080000 2.190000 -19.020000 0.460000) 3) (segment 1250 (point 263.080000 2.190000 -19.020000 0.460000) (point 261.430000 1.210000 -21.300000 0.460000) 3) (segment 1251 (point 261.430000 1.210000 -21.300000 0.460000) (point 261.100000 0.660000 -21.300000 0.460000) 3)) (branch 54 53 (segment 1252 (point 261.100000 0.660000 -21.300000 0.460000) (point 263.640000 1.860000 -26.420000 0.460000) 3) (segment 1253 (point 263.640000 1.860000 -26.420000 0.460000) (point 265.300000 2.840000 -34.000000 0.460000) 3) (segment 1254 (point 265.300000 2.840000 -34.000000 0.460000) (point 264.140000 3.760000 -37.750000 0.460000) 3) (segment 1255 (point 264.140000 3.760000 -37.750000 0.460000) (point 264.140000 3.760000 -37.770000 0.460000) 3) (segment 1256 (point 264.140000 3.760000 -37.770000 0.460000) (point 263.830000 3.100000 -39.900000 0.460000) 3) (segment 1257 (point 263.830000 3.100000 -39.900000 0.460000) (point 263.830000 3.100000 -39.920000 0.460000) 3) (segment 1258 (point 263.830000 3.100000 -39.920000 0.460000) (point 264.770000 5.120000 -42.950000 0.460000) 3) (segment 1259 (point 264.770000 5.120000 -42.950000 0.460000) (point 264.770000 5.120000 -43.000000 0.460000) 3) (segment 1260 (point 264.770000 5.120000 -43.000000 0.460000) (point 263.300000 5.360000 -45.900000 0.460000) 3) (segment 1261 (point 263.300000 5.360000 -45.900000 0.460000) (point 263.300000 5.360000 -46.000000 0.460000) 3) (segment 1262 (point 263.300000 5.360000 -46.000000 0.460000) (point 265.650000 5.310000 -50.630000 0.460000) 3) (segment 1263 (point 265.650000 5.310000 -50.630000 0.460000) (point 265.650000 5.310000 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3) (segment 1324 (point 286.180000 20.280000 -91.150000 0.230000) (point 286.180000 20.280000 -91.200000 0.230000) 3) (segment 1325 (point 286.180000 20.280000 -91.200000 0.230000) (point 288.150000 21.930000 -93.600000 0.230000) 3) (segment 1326 (point 288.150000 21.930000 -93.600000 0.230000) (point 288.150000 21.930000 -93.650000 0.230000) 3) (segment 1327 (point 288.150000 21.930000 -93.650000 0.230000) (point 289.940000 22.350000 -95.920000 0.230000) 3) (segment 1328 (point 289.940000 22.350000 -95.920000 0.230000) (point 289.940000 22.350000 -95.950000 0.230000) 3) (segment 1329 (point 289.940000 22.350000 -95.950000 0.230000) (point 290.880000 24.370000 -97.750000 0.230000) 3) (segment 1330 (point 290.880000 24.370000 -97.750000 0.230000) (point 290.880000 24.370000 -97.780000 0.230000) 3) (segment 1331 (point 290.880000 24.370000 -97.780000 0.230000) (point 292.220000 24.680000 -101.400000 0.230000) 3) (segment 1332 (point 292.220000 24.680000 -101.400000 0.230000) (point 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1.145000) (point 252.920000 312.710000 -20.850000 1.145000) 4) (segment 1751 (point 252.920000 312.710000 -20.850000 1.145000) (point 252.440000 316.780000 -21.670000 1.145000) 4) (segment 1752 (point 252.440000 316.780000 -21.670000 1.145000) (point 251.650000 320.170000 -22.500000 1.145000) 4) (segment 1753 (point 251.650000 320.170000 -22.500000 1.145000) (point 249.820000 323.930000 -23.330000 1.145000) 4) (segment 1754 (point 249.820000 323.930000 -23.330000 1.145000) (point 247.240000 326.910000 -23.900000 1.145000) 4) (segment 1755 (point 247.240000 326.910000 -23.900000 1.145000) (point 246.390000 328.510000 -24.920000 1.145000) 4) (segment 1756 (point 246.390000 328.510000 -24.920000 1.145000) (point 246.610000 331.550000 -25.830000 1.145000) 4) (segment 1757 (point 246.610000 331.550000 -25.830000 1.145000) (point 249.090000 335.110000 -26.500000 1.145000) 4) (segment 1758 (point 249.090000 335.110000 -26.500000 1.145000) (point 251.370000 337.440000 -25.850000 1.145000) 4) (segment 1759 (point 251.370000 337.440000 -25.850000 1.145000) (point 251.770000 341.720000 -25.230000 1.145000) 4) (segment 1760 (point 251.770000 341.720000 -25.230000 1.145000) (point 248.480000 345.720000 -25.230000 1.145000) 4) (segment 1761 (point 248.480000 345.720000 -25.230000 1.145000) (point 245.010000 348.490000 -26.380000 1.145000) 4) (segment 1762 (point 245.010000 348.490000 -26.380000 1.145000) (point 243.770000 351.770000 -26.850000 1.145000) 4) (segment 1763 (point 243.770000 351.770000 -26.850000 1.145000) (point 244.180000 356.050000 -27.350000 1.145000) 4) (segment 1764 (point 244.180000 356.050000 -27.350000 1.145000) (point 244.400000 359.090000 -26.950000 1.145000) 4)) (branch 79 78 (segment 1765 (point 244.400000 359.090000 -26.950000 1.145000) (point 243.050000 361.090000 -26.020000 0.915000) 4) (segment 1766 (point 243.050000 361.090000 -26.020000 0.915000) (point 242.090000 363.250000 -24.320000 0.915000) 4) (segment 1767 (point 242.090000 363.250000 -24.320000 0.915000) (point 241.680000 364.950000 -22.800000 0.915000) 4) (segment 1768 (point 241.680000 364.950000 -22.800000 0.915000) (point 241.730000 366.750000 -21.320000 0.915000) 4) (segment 1769 (point 241.730000 366.750000 -21.320000 0.915000) (point 242.220000 368.670000 -19.650000 0.915000) 4) (segment 1770 (point 242.220000 368.670000 -19.650000 0.915000) (point 243.040000 371.250000 -18.720000 0.915000) 4) (segment 1771 (point 243.040000 371.250000 -18.720000 0.915000) (point 244.280000 373.930000 -18.800000 0.915000) 4) (segment 1772 (point 244.280000 373.930000 -18.800000 0.915000) (point 245.100000 376.500000 -17.600000 0.915000) 4) (segment 1773 (point 245.100000 376.500000 -17.600000 0.915000) (point 245.010000 378.870000 -16.670000 0.915000) 4) (segment 1774 (point 245.010000 378.870000 -16.670000 0.915000) (point 244.750000 380.000000 -15.900000 0.915000) 4) (segment 1775 (point 244.750000 380.000000 -15.900000 0.915000) (point 242.740000 382.520000 -15.400000 0.915000) 4) (segment 1776 (point 242.740000 382.520000 -15.400000 0.915000) (point 242.520000 385.460000 -15.100000 0.915000) 4) (segment 1777 (point 242.520000 385.460000 -15.100000 0.915000) (point 240.250000 389.110000 -15.100000 0.915000) 4) (segment 1778 (point 240.250000 389.110000 -15.100000 0.915000) (point 238.390000 391.060000 -15.100000 0.915000) 4) (segment 1779 (point 238.390000 391.060000 -15.100000 0.915000) (point 237.730000 393.890000 -14.130000 0.915000) 4) (segment 1780 (point 237.730000 393.890000 -14.130000 0.915000) (point 238.660000 395.900000 -13.420000 0.915000) 4) (segment 1781 (point 238.660000 395.900000 -13.420000 0.915000) (point 239.600000 397.920000 -12.620000 0.915000) 4) (segment 1782 (point 239.600000 397.920000 -12.620000 0.915000) (point 238.540000 402.440000 -12.400000 0.915000) 4) (segment 1783 (point 238.540000 402.440000 -12.400000 0.915000) (point 237.930000 407.070000 -12.400000 0.915000) 4) (segment 1784 (point 237.930000 407.070000 -12.400000 0.915000) (point 236.190000 408.450000 -12.400000 0.915000) 4) (segment 1785 (point 236.190000 408.450000 -12.400000 0.915000) (point 236.690000 410.360000 -13.320000 0.915000) 4) (segment 1786 (point 236.690000 410.360000 -13.320000 0.915000) (point 237.230000 414.080000 -14.520000 0.915000) 4) (segment 1787 (point 237.230000 414.080000 -14.520000 0.915000) (point 238.040000 416.650000 -15.480000 0.915000) 4) (segment 1788 (point 238.040000 416.650000 -15.480000 0.915000) (point 236.530000 421.070000 -16.380000 0.915000) 4) (segment 1789 (point 236.530000 421.070000 -16.380000 0.915000) (point 235.040000 425.500000 -14.650000 0.915000) 4) (segment 1790 (point 235.040000 425.500000 -14.650000 0.915000) (point 234.060000 427.660000 -15.400000 0.915000) 4) (segment 1791 (point 234.060000 427.660000 -15.400000 0.915000) (point 232.630000 429.710000 -16.350000 0.915000) 4) (segment 1792 (point 232.630000 429.710000 -16.350000 0.915000) (point 233.480000 434.090000 -16.770000 0.915000) 4) (segment 1793 (point 233.480000 434.090000 -16.770000 0.915000) (point 234.210000 439.050000 -17.250000 0.915000) 4) (segment 1794 (point 234.210000 439.050000 -17.250000 0.915000) (point 236.040000 441.270000 -17.300000 0.915000) 4) (segment 1795 (point 236.040000 441.270000 -17.300000 0.915000) (point 236.540000 443.170000 -18.100000 0.915000) 4) (segment 1796 (point 236.540000 443.170000 -18.100000 0.915000) (point 235.160000 447.020000 -18.920000 0.915000) 4) (segment 1797 (point 235.160000 447.020000 -18.920000 0.915000) (point 233.300000 448.980000 -18.920000 0.915000) 4) (segment 1798 (point 233.300000 448.980000 -18.920000 0.915000) (point 236.180000 450.720000 -19.730000 0.915000) 4) (segment 1799 (point 236.180000 450.720000 -19.730000 0.915000) (point 237.250000 452.160000 -18.720000 0.915000) 4) (segment 1800 (point 237.250000 452.160000 -18.720000 0.915000) (point 237.290000 453.970000 -17.270000 0.915000) 4) (segment 1801 (point 237.290000 453.970000 -17.270000 0.915000) (point 237.290000 453.970000 -17.300000 0.915000) 4)) (branch 80 79 (segment 1802 (point 237.290000 453.970000 -17.300000 0.915000) (point 238.430000 457.220000 -16.170000 0.915000) 4) (segment 1803 (point 238.430000 457.220000 -16.170000 0.915000) (point 238.340000 459.590000 -16.170000 0.915000) 4) (segment 1804 (point 238.340000 459.590000 -16.170000 0.915000) (point 236.790000 462.210000 -16.770000 0.915000) 4) (segment 1805 (point 236.790000 462.210000 -16.770000 0.915000) (point 235.810000 464.370000 -16.650000 0.915000) 4)) (branch 81 80 (segment 1806 (point 235.810000 464.370000 -16.650000 0.915000) (point 234.880000 468.330000 -16.650000 0.915000) 4) (segment 1807 (point 234.880000 468.330000 -16.650000 0.915000) (point 234.220000 471.170000 -17.570000 0.915000) 4) (segment 1808 (point 234.220000 471.170000 -17.570000 0.915000) (point 235.210000 474.980000 -18.880000 0.915000) 4) (segment 1809 (point 235.210000 474.980000 -18.880000 0.915000) (point 235.610000 479.260000 -18.880000 0.915000) 4) (segment 1810 (point 235.610000 479.260000 -18.880000 0.915000) (point 234.820000 482.650000 -18.720000 0.915000) 4) (segment 1811 (point 234.820000 482.650000 -18.720000 0.915000) (point 234.150000 485.470000 -19.300000 0.915000) 4) (segment 1812 (point 234.150000 485.470000 -19.300000 0.915000) (point 236.040000 489.510000 -19.330000 0.915000) 4) (segment 1813 (point 236.040000 489.510000 -19.330000 0.915000) (point 237.170000 492.760000 -19.330000 0.915000) 4) (segment 1814 (point 237.170000 492.760000 -19.330000 0.915000) (point 238.550000 494.870000 -19.900000 0.915000) 4) (segment 1815 (point 238.550000 494.870000 -19.900000 0.915000) (point 239.040000 496.780000 -19.800000 0.915000) 4) (segment 1816 (point 239.040000 496.780000 -19.800000 0.915000) (point 236.920000 499.870000 -19.800000 0.915000) 4) (segment 1817 (point 236.920000 499.870000 -19.800000 0.915000) (point 235.230000 503.050000 -20.100000 0.915000) 4) (segment 1818 (point 235.230000 503.050000 -20.100000 0.915000) (point 233.140000 507.940000 -20.470000 0.915000) 4) (segment 1819 (point 233.140000 507.940000 -20.470000 0.915000) (point 233.050000 510.300000 -19.580000 0.915000) 4) (segment 1820 (point 233.050000 510.300000 -19.580000 0.915000) (point 233.600000 514.010000 -20.380000 0.915000) 4) (segment 1821 (point 233.600000 514.010000 -20.380000 0.915000) (point 231.150000 516.430000 -20.900000 0.915000) 4) (segment 1822 (point 231.150000 516.430000 -20.900000 0.915000) (point 230.800000 519.930000 -20.900000 0.915000) 4) (segment 1823 (point 230.800000 519.930000 -20.900000 0.915000) (point 231.160000 522.400000 -20.630000 0.915000) 4) (segment 1824 (point 231.160000 522.400000 -20.630000 0.915000) (point 232.140000 526.210000 -21.070000 0.915000) 4) (segment 1825 (point 232.140000 526.210000 -21.070000 0.915000) (point 231.790000 529.720000 -21.720000 0.915000) 4) (segment 1826 (point 231.790000 529.720000 -21.720000 0.915000) (point 230.870000 533.680000 -21.720000 0.915000) 4) (segment 1827 (point 230.870000 533.680000 -21.720000 0.915000) (point 229.040000 537.430000 -22.550000 0.915000) 4) (segment 1828 (point 229.040000 537.430000 -22.550000 0.915000) (point 229.040000 537.430000 -22.570000 0.915000) 4) (segment 1829 (point 229.040000 537.430000 -22.570000 0.915000) (point 228.250000 540.830000 -23.920000 0.915000) 4) (segment 1830 (point 228.250000 540.830000 -23.920000 0.915000) (point 228.250000 540.830000 -23.980000 0.915000) 4) (segment 1831 (point 228.250000 540.830000 -23.980000 0.915000) (point 228.610000 543.300000 -24.850000 0.915000) 4) (segment 1832 (point 228.610000 543.300000 -24.850000 0.915000) (point 230.230000 548.460000 -25.420000 0.915000) 4) (segment 1833 (point 230.230000 548.460000 -25.420000 0.915000) (point 230.330000 552.060000 -26.070000 0.915000) 4) (segment 1834 (point 230.330000 552.060000 -26.070000 0.915000) (point 232.360000 555.420000 -24.850000 0.915000) 4) (segment 1835 (point 232.360000 555.420000 -24.850000 0.915000) (point 232.470000 559.020000 -24.850000 0.915000) 4) (segment 1836 (point 232.470000 559.020000 -24.850000 0.915000) (point 231.100000 562.870000 -25.520000 0.915000) 4) (segment 1837 (point 231.100000 562.870000 -25.520000 0.915000) (point 231.100000 562.870000 -25.550000 0.915000) 4) (segment 1838 (point 231.100000 562.870000 -25.550000 0.915000) (point 230.170000 566.840000 -26.320000 0.915000) 4) (segment 1839 (point 230.170000 566.840000 -26.320000 0.915000) (point 230.270000 570.440000 -27.500000 0.915000) 4) (segment 1840 (point 230.270000 570.440000 -27.500000 0.915000) (point 231.250000 574.260000 -27.500000 0.915000) 4) (segment 1841 (point 231.250000 574.260000 -27.500000 0.915000) (point 231.480000 577.300000 -28.470000 0.915000) 4) (segment 1842 (point 231.480000 577.300000 -28.470000 0.915000) (point 232.030000 581.020000 -29.020000 0.915000) 4) (segment 1843 (point 232.030000 581.020000 -29.020000 0.915000) (point 231.680000 584.500000 -29.270000 0.915000) 4) (segment 1844 (point 231.680000 584.500000 -29.270000 0.915000) (point 231.320000 588.000000 -29.600000 0.915000) 4) (segment 1845 (point 231.320000 588.000000 -29.600000 0.915000) (point 230.920000 589.710000 -30.550000 0.915000) 4) (segment 1846 (point 230.920000 589.710000 -30.550000 0.915000) (point 232.500000 593.050000 -31.380000 0.915000) 4) (segment 1847 (point 232.500000 593.050000 -31.380000 0.915000) (point 234.780000 595.390000 -32.630000 0.915000) 4) (segment 1848 (point 234.780000 595.390000 -32.630000 0.915000) (point 233.400000 599.250000 -32.650000 0.915000) 4) (segment 1849 (point 233.400000 599.250000 -32.650000 0.915000) (point 232.660000 604.450000 -31.450000 0.915000) 4) (segment 1850 (point 232.660000 604.450000 -31.450000 0.915000) (point 233.520000 608.830000 -32.250000 0.915000) 4) (segment 1851 (point 233.520000 608.830000 -32.250000 0.915000) (point 232.540000 610.990000 -32.880000 0.915000) 4)) (branch 82 81 (segment 1852 (point 232.540000 610.990000 -32.880000 0.915000) (point 232.060000 615.060000 -32.880000 0.915000) 4) (segment 1853 (point 232.060000 615.060000 -32.880000 0.915000) (point 231.830000 617.990000 -32.750000 0.915000) 4) (segment 1854 (point 231.830000 617.990000 -32.750000 0.915000) (point 232.520000 621.140000 -33.150000 0.915000) 4) (segment 1855 (point 232.520000 621.140000 -33.150000 0.915000) (point 233.510000 624.950000 -33.150000 0.915000) 4) (segment 1856 (point 233.510000 624.950000 -33.150000 0.915000) (point 232.140000 628.810000 -32.580000 0.915000) 4) (segment 1857 (point 232.140000 628.810000 -32.580000 0.915000) (point 230.710000 630.860000 -32.650000 0.915000) 4) (segment 1858 (point 230.710000 630.860000 -32.650000 0.915000) (point 232.270000 634.210000 -33.220000 0.915000) 4) (segment 1859 (point 232.270000 634.210000 -33.220000 0.915000) (point 233.530000 636.900000 -34.200000 0.915000) 4) (segment 1860 (point 233.530000 636.900000 -34.200000 0.915000) (point 233.670000 642.310000 -34.780000 0.915000) 4) (segment 1861 (point 233.670000 642.310000 -34.780000 0.915000) (point 235.520000 644.540000 -34.780000 0.915000) 4) (segment 1862 (point 235.520000 644.540000 -34.780000 0.915000) (point 237.220000 647.310000 -34.780000 0.915000) 4) (segment 1863 (point 237.220000 647.310000 -34.780000 0.915000) (point 238.290000 648.760000 -35.830000 0.915000) 4) (segment 1864 (point 238.290000 648.760000 -35.830000 0.915000) (point 238.290000 648.760000 -35.850000 0.915000) 4) (segment 1865 (point 238.290000 648.760000 -35.850000 0.915000) (point 238.340000 650.570000 -37.130000 0.915000) 4) (segment 1866 (point 238.340000 650.570000 -37.130000 0.915000) (point 238.210000 651.130000 -37.150000 0.915000) 4) (segment 1867 (point 238.210000 651.130000 -37.150000 0.915000) (point 237.360000 652.720000 -36.420000 0.915000) 4) (segment 1868 (point 237.360000 652.720000 -36.420000 0.915000) (point 237.360000 652.720000 -36.450000 0.915000) 4) (segment 1869 (point 237.360000 652.720000 -36.450000 0.915000) (point 236.700000 655.550000 -36.420000 0.915000) 4) (segment 1870 (point 236.700000 655.550000 -36.420000 0.915000) (point 235.540000 656.480000 -34.850000 0.915000) 4) (segment 1871 (point 235.540000 656.480000 -34.850000 0.915000) (point 235.320000 659.410000 -34.150000 0.915000) 4)) (branch 83 82 (segment 1872 (point 235.320000 659.410000 -34.150000 0.915000) (point 233.230000 664.300000 -35.470000 0.915000) 4) (segment 1873 (point 233.230000 664.300000 -35.470000 0.915000) (point 234.360000 667.550000 -35.130000 0.915000) 4) (segment 1874 (point 234.360000 667.550000 -35.130000 0.915000) (point 234.360000 667.550000 -35.150000 0.915000) 4) (segment 1875 (point 234.360000 667.550000 -35.150000 0.915000) (point 233.560000 670.950000 -35.650000 0.915000) 4) (segment 1876 (point 233.560000 670.950000 -35.650000 0.915000) (point 232.320000 674.240000 -35.650000 0.915000) 4) (segment 1877 (point 232.320000 674.240000 -35.650000 0.915000) (point 232.680000 676.710000 -34.420000 0.915000) 4) (segment 1878 (point 232.680000 676.710000 -34.420000 0.915000) (point 232.550000 677.270000 -34.420000 0.915000) 4) (segment 1879 (point 232.550000 677.270000 -34.420000 0.915000) (point 233.680000 680.520000 -33.130000 0.915000) 4) (segment 1880 (point 233.680000 680.520000 -33.130000 0.915000) (point 233.460000 683.470000 -33.880000 0.915000) 4) (segment 1881 (point 233.460000 683.470000 -33.880000 0.915000) (point 233.380000 685.820000 -33.270000 0.915000) 4) (segment 1882 (point 233.380000 685.820000 -33.270000 0.915000) (point 232.580000 689.220000 -33.050000 0.915000) 4) (segment 1883 (point 232.580000 689.220000 -33.050000 0.915000) (point 232.940000 691.700000 -33.970000 0.915000) 4) (segment 1884 (point 232.940000 691.700000 -33.970000 0.915000) (point 232.900000 695.870000 -35.020000 0.915000) 4) (segment 1885 (point 232.900000 695.870000 -35.020000 0.915000) (point 232.900000 695.870000 -35.050000 0.915000) 4) (segment 1886 (point 232.900000 695.870000 -35.050000 0.915000) (point 230.640000 699.520000 -36.100000 0.915000) 4) (segment 1887 (point 230.640000 699.520000 -36.100000 0.915000) (point 231.310000 702.660000 -37.450000 0.915000) 4) (segment 1888 (point 231.310000 702.660000 -37.450000 0.915000) (point 230.780000 704.930000 -37.770000 0.915000) 4) (segment 1889 (point 230.780000 704.930000 -37.770000 0.915000) (point 229.350000 706.990000 -36.630000 0.915000) 4) (segment 1890 (point 229.350000 706.990000 -36.630000 0.915000) (point 229.900000 710.690000 -35.750000 0.915000) 4) (segment 1891 (point 229.900000 710.690000 -35.750000 0.915000) (point 230.450000 714.400000 -34.950000 0.915000) 4) (segment 1892 (point 230.450000 714.400000 -34.950000 0.915000) (point 231.570000 717.650000 -34.670000 0.915000) 4) (segment 1893 (point 231.570000 717.650000 -34.670000 0.915000) (point 230.640000 721.620000 -35.830000 0.915000) 4) (segment 1894 (point 230.640000 721.620000 -35.830000 0.915000) (point 230.640000 721.620000 -35.850000 0.915000) 4) (segment 1895 (point 230.640000 721.620000 -35.850000 0.915000) (point 230.870000 724.660000 -36.220000 0.915000) 4) (segment 1896 (point 230.870000 724.660000 -36.220000 0.915000) (point 230.790000 727.030000 -37.200000 0.915000) 4) (segment 1897 (point 230.790000 727.030000 -37.200000 0.915000) (point 231.910000 730.280000 -38.150000 0.915000) 4) (segment 1898 (point 231.910000 730.280000 -38.150000 0.915000) (point 231.470000 736.140000 -39.670000 0.915000) 4) (segment 1899 (point 231.470000 736.140000 -39.670000 0.915000) (point 229.970000 740.570000 -39.020000 0.915000) 4) (segment 1900 (point 229.970000 740.570000 -39.020000 0.915000) (point 231.220000 743.250000 -38.880000 0.915000) 4) (segment 1901 (point 231.220000 743.250000 -38.880000 0.915000) (point 230.920000 748.550000 -38.270000 0.915000) 4) (segment 1902 (point 230.920000 748.550000 -38.270000 0.915000) (point 231.320000 750.940000 -35.530000 0.915000) 4) (segment 1903 (point 231.320000 750.940000 -35.530000 0.915000) (point 231.320000 750.940000 -35.550000 0.915000) 4) (segment 1904 (point 231.320000 750.940000 -35.550000 0.915000) (point 231.240000 753.300000 -34.920000 0.915000) 4) (segment 1905 (point 231.240000 753.300000 -34.920000 0.915000) (point 229.280000 757.620000 -34.920000 0.915000) 4) (segment 1906 (point 229.280000 757.620000 -34.920000 0.915000) (point 229.430000 760.490000 -34.630000 0.915000) 4) (segment 1907 (point 229.430000 760.490000 -34.630000 0.915000) (point 230.730000 764.970000 -33.770000 0.915000) 4) (segment 1908 (point 230.730000 764.970000 -33.770000 0.915000) (point 231.400000 768.120000 -34.720000 0.915000) 4) (segment 1909 (point 231.400000 768.120000 -34.720000 0.915000) (point 231.910000 770.020000 -33.970000 0.915000) 4) (segment 1910 (point 231.910000 770.020000 -33.970000 0.915000) (point 231.510000 771.730000 -33.670000 0.915000) 4) (segment 1911 (point 231.510000 771.730000 -33.670000 0.915000) (point 229.820000 774.900000 -33.030000 0.915000) 4) (segment 1912 (point 229.820000 774.900000 -33.030000 0.915000) (point 229.740000 777.280000 -33.000000 0.915000) 4) (segment 1913 (point 229.740000 777.280000 -33.000000 0.915000) (point 230.680000 779.280000 -33.000000 0.915000) 4) (segment 1914 (point 230.680000 779.280000 -33.000000 0.915000) (point 231.350000 782.440000 -32.630000 0.915000) 4) (segment 1915 (point 231.350000 782.440000 -32.630000 0.915000) (point 231.530000 783.670000 -33.130000 0.915000) 4) (segment 1916 (point 231.530000 783.670000 -33.130000 0.915000) (point 230.600000 787.620000 -32.350000 0.915000) 4) (segment 1917 (point 230.600000 787.620000 -32.350000 0.915000) (point 231.150000 791.340000 -31.750000 0.915000) 4) (segment 1918 (point 231.150000 791.340000 -31.750000 0.915000) (point 231.070000 793.720000 -31.350000 0.915000) 4) (segment 1919 (point 231.070000 793.720000 -31.350000 0.915000) (point 232.640000 797.060000 -31.350000 0.915000) 4) (segment 1920 (point 232.640000 797.060000 -31.350000 0.915000) (point 233.140000 798.980000 -31.170000 0.915000) 4) (segment 1921 (point 233.140000 798.980000 -31.170000 0.915000) (point 232.210000 802.930000 -30.800000 0.915000) 4) (segment 1922 (point 232.210000 802.930000 -30.800000 0.915000) (point 232.120000 805.300000 -31.020000 0.915000) 4) (segment 1923 (point 232.120000 805.300000 -31.020000 0.915000) (point 232.120000 805.300000 -31.000000 0.915000) 4) (segment 1924 (point 232.120000 805.300000 -31.000000 0.915000) (point 234.080000 806.960000 -30.570000 0.915000) 4) (segment 1925 (point 234.080000 806.960000 -30.570000 0.915000) (point 234.500000 811.250000 -29.700000 0.915000) 4) (segment 1926 (point 234.500000 811.250000 -29.700000 0.915000) (point 233.390000 813.960000 -29.380000 0.915000) 4) (segment 1927 (point 233.390000 813.960000 -29.380000 0.915000) (point 233.170000 816.900000 -29.380000 0.915000) 4) (segment 1928 (point 233.170000 816.900000 -29.380000 0.915000) (point 233.970000 819.480000 -30.500000 0.915000) 4) (segment 1929 (point 233.970000 819.480000 -30.500000 0.915000) (point 233.320000 822.300000 -31.400000 0.915000) 4) (segment 1930 (point 233.320000 822.300000 -31.400000 0.915000) (point 233.320000 822.300000 -31.420000 0.915000) 4) (segment 1931 (point 233.320000 822.300000 -31.420000 0.915000) (point 234.000000 825.450000 -32.220000 0.915000) 4) (segment 1932 (point 234.000000 825.450000 -32.220000 0.915000) (point 234.360000 827.930000 -31.520000 0.915000) 4) (segment 1933 (point 234.360000 827.930000 -31.520000 0.915000) (point 234.280000 830.290000 -30.380000 0.915000) 4) (segment 1934 (point 234.280000 830.290000 -30.380000 0.915000) (point 234.950000 833.440000 -30.070000 0.915000) 4) (segment 1935 (point 234.950000 833.440000 -30.070000 0.915000) (point 235.490000 837.150000 -30.070000 0.915000) 4) (segment 1936 (point 235.490000 837.150000 -30.070000 0.915000) (point 236.170000 840.280000 -30.070000 0.915000) 4) (segment 1937 (point 236.170000 840.280000 -30.070000 0.915000) (point 236.790000 841.640000 -30.070000 0.915000) 4) (segment 1938 (point 236.790000 841.640000 -30.070000 0.915000) (point 235.690000 844.360000 -29.970000 0.915000) 4) (segment 1939 (point 235.690000 844.360000 -29.970000 0.915000) (point 235.210000 848.420000 -30.750000 0.915000) 4) (segment 1940 (point 235.210000 848.420000 -30.750000 0.915000) (point 236.470000 851.110000 -30.750000 0.915000) 4) (segment 1941 (point 236.470000 851.110000 -30.750000 0.915000) (point 237.000000 854.820000 -29.600000 0.915000) 4) (segment 1942 (point 237.000000 854.820000 -29.600000 0.915000) (point 235.890000 857.540000 -28.630000 0.915000) 4) (segment 1943 (point 235.890000 857.540000 -28.630000 0.915000) (point 235.540000 861.040000 -29.770000 0.915000) 4) (segment 1944 (point 235.540000 861.040000 -29.770000 0.915000) (point 236.800000 863.730000 -30.200000 0.915000) 4)) (branch 84 83 (segment 1945 (point 236.800000 863.730000 -30.200000 0.915000) (point 238.510000 866.500000 -30.200000 0.460000) 4) (segment 1946 (point 238.510000 866.500000 -30.200000 0.460000) (point 239.130000 867.840000 -31.580000 0.460000) 4) (segment 1947 (point 239.130000 867.840000 -31.580000 0.460000) (point 238.780000 871.340000 -30.100000 0.460000) 4) (segment 1948 (point 238.780000 871.340000 -30.100000 0.460000) (point 239.720000 873.350000 -30.770000 0.460000) 4) (segment 1949 (point 239.720000 873.350000 -30.770000 0.460000) (point 239.590000 873.910000 -30.770000 0.460000) 4) (segment 1950 (point 239.590000 873.910000 -30.770000 0.460000) (point 239.370000 876.860000 -31.520000 0.460000) 4) (segment 1951 (point 239.370000 876.860000 -31.520000 0.460000) (point 239.420000 878.660000 -33.330000 0.460000) 4) (segment 1952 (point 239.420000 878.660000 -33.330000 0.460000) (point 241.130000 881.450000 -34.450000 0.460000) 4) (segment 1953 (point 241.130000 881.450000 -34.450000 0.460000) (point 242.640000 882.990000 -35.170000 0.460000) 4) (segment 1954 (point 242.640000 882.990000 -35.170000 0.460000) (point 242.690000 884.790000 -36.820000 0.460000) 4) (segment 1955 (point 242.690000 884.790000 -36.820000 0.460000) (point 244.710000 888.250000 -37.550000 0.460000) 4) (segment 1956 (point 244.710000 888.250000 -37.550000 0.460000) (point 246.240000 889.800000 -37.630000 0.460000) 4) (segment 1957 (point 246.240000 889.800000 -37.630000 0.460000) (point 247.050000 892.390000 -38.070000 0.460000) 4) (segment 1958 (point 247.050000 892.390000 -38.070000 0.460000) (point 246.820000 895.320000 -38.900000 0.460000) 4) (segment 1959 (point 246.820000 895.320000 -38.900000 0.460000) (point 247.900000 896.770000 -40.150000 0.460000) 4) (segment 1960 (point 247.900000 896.770000 -40.150000 0.460000) (point 249.990000 897.850000 -40.970000 0.460000) 4) (segment 1961 (point 249.990000 897.850000 -40.970000 0.460000) (point 249.640000 901.350000 -40.380000 0.460000) 4) (segment 1962 (point 249.640000 901.350000 -40.380000 0.460000) (point 250.330000 904.500000 -41.520000 0.460000) 4)) (branch 85 84 (segment 1963 (point 250.330000 904.500000 -41.520000 0.460000) (point 252.480000 907.390000 -42.550000 0.460000) 4) (segment 1964 (point 252.480000 907.390000 -42.550000 0.460000) (point 254.310000 909.610000 -44.170000 0.460000) 4) (segment 1965 (point 254.310000 909.610000 -44.170000 0.460000) (point 255.890000 912.960000 -44.870000 0.460000) 4)) (branch 86 85 (segment 1966 (point 255.890000 912.960000 -44.870000 0.460000) (point 256.860000 914.370000 -44.100000 0.230000) 4) (segment 1967 (point 256.860000 914.370000 -44.100000 0.230000) (point 258.680000 916.600000 -42.300000 0.230000) 4) (segment 1968 (point 258.680000 916.600000 -42.300000 0.230000) (point 258.680000 916.600000 -42.280000 0.230000) 4) (segment 1969 (point 258.680000 916.600000 -42.280000 0.230000) (point 259.890000 917.480000 -41.450000 0.230000) 4) (segment 1970 (point 259.890000 917.480000 -41.450000 0.230000) (point 263.210000 919.450000 -40.570000 0.230000) 4) (segment 1971 (point 263.210000 919.450000 -40.570000 0.230000) (point 265.120000 919.300000 -41.520000 0.230000) 4) (segment 1972 (point 265.120000 919.300000 -41.520000 0.230000) (point 266.910000 919.720000 -40.220000 0.230000) 4) (segment 1973 (point 266.910000 919.720000 -40.220000 0.230000) (point 268.170000 922.410000 -39.520000 0.230000) 4) (segment 1974 (point 268.170000 922.410000 -39.520000 0.230000) (point 270.540000 922.360000 -39.520000 0.230000) 4) (segment 1975 (point 270.540000 922.360000 -39.520000 0.230000) (point 273.210000 922.990000 -38.550000 0.230000) 4) (segment 1976 (point 273.210000 922.990000 -38.550000 0.230000) (point 275.310000 924.080000 -37.770000 0.230000) 4) (segment 1977 (point 275.310000 924.080000 -37.770000 0.230000) (point 276.920000 923.260000 -36.780000 0.230000) 4) (segment 1978 (point 276.920000 923.260000 -36.780000 0.230000) (point 279.100000 921.980000 -35.600000 0.230000) 4) (segment 1979 (point 279.100000 921.980000 -35.600000 0.230000) (point 281.390000 924.300000 -34.580000 0.230000) 4) (segment 1980 (point 281.390000 924.300000 -34.580000 0.230000) (point 282.450000 925.750000 -33.330000 0.230000) 4) (segment 1981 (point 282.450000 925.750000 -33.330000 0.230000) (point 286.160000 926.020000 -32.750000 0.230000) 4) (segment 1982 (point 286.160000 926.020000 -32.750000 0.230000) (point 287.820000 927.020000 -32.200000 0.230000) 4) (segment 1983 (point 287.820000 927.020000 -32.200000 0.230000) (point 289.970000 929.900000 -31.600000 0.230000) 4) (segment 1984 (point 289.970000 929.900000 -31.600000 0.230000) (point 292.200000 930.430000 -31.000000 0.230000) 4) (segment 1985 (point 292.200000 930.430000 -31.000000 0.230000) (point 296.080000 931.930000 -30.300000 0.230000) 4) (segment 1986 (point 296.080000 931.930000 -30.300000 0.230000) (point 297.880000 932.350000 -29.380000 0.230000) 4) (segment 1987 (point 297.880000 932.350000 -29.380000 0.230000) (point 298.820000 934.360000 -29.380000 0.230000) 4) (segment 1988 (point 298.820000 934.360000 -29.380000 0.230000) (point 300.470000 935.350000 -28.270000 0.230000) 4) (segment 1989 (point 300.470000 935.350000 -28.270000 0.230000) (point 302.380000 935.200000 -27.270000 0.230000) 4) (segment 1990 (point 302.380000 935.200000 -27.270000 0.230000) (point 303.510000 938.440000 -26.720000 0.230000) 4) (segment 1991 (point 303.510000 938.440000 -26.720000 0.230000) (point 303.510000 938.440000 -26.750000 0.230000) 4) (segment 1992 (point 303.510000 938.440000 -26.750000 0.230000) (point 305.610000 939.530000 -26.750000 0.230000) 4) (segment 1993 (point 305.610000 939.530000 -26.750000 0.230000) (point 306.560000 941.550000 -25.730000 0.230000) 4) (segment 1994 (point 306.560000 941.550000 -25.730000 0.230000) (point 308.520000 943.200000 -24.400000 0.230000) 4) (segment 1995 (point 308.520000 943.200000 -24.400000 0.230000) (point 310.300000 943.620000 -23.600000 0.230000) 4) (segment 1996 (point 310.300000 943.620000 -23.600000 0.230000) (point 312.410000 944.700000 -22.970000 0.230000) 4) (segment 1997 (point 312.410000 944.700000 -22.970000 0.230000) (point 314.060000 945.690000 -22.770000 0.230000) 4) (segment 1998 (point 314.060000 945.690000 -22.770000 0.230000) (point 317.180000 946.420000 -22.670000 0.230000) 4) (segment 1999 (point 317.180000 946.420000 -22.670000 0.230000) (point 317.180000 946.420000 -22.700000 0.230000) 4) (segment 2000 (point 317.180000 946.420000 -22.700000 0.230000) (point 318.670000 946.180000 -20.920000 0.230000) 4) (segment 2001 (point 318.670000 946.180000 -20.920000 0.230000) (point 318.850000 947.420000 -20.000000 0.230000) 4) (segment 2002 (point 318.850000 947.420000 -20.000000 0.230000) (point 320.450000 946.600000 -18.400000 0.230000) 4) (segment 2003 (point 320.450000 946.600000 -18.400000 0.230000) (point 323.390000 946.090000 -17.420000 0.230000) 4) (segment 2004 (point 323.390000 946.090000 -17.420000 0.230000) (point 323.390000 946.090000 -17.450000 0.230000) 4) (segment 2005 (point 323.390000 946.090000 -17.450000 0.230000) (point 324.280000 946.300000 -16.200000 0.230000) 4) (segment 2006 (point 324.280000 946.300000 -16.200000 0.230000) (point 324.330000 948.100000 -14.480000 0.230000) 4) (segment 2007 (point 324.330000 948.100000 -14.480000 0.230000) (point 327.290000 947.600000 -14.380000 0.230000) 4) (segment 2008 (point 327.290000 947.600000 -14.380000 0.230000) (point 330.720000 949.000000 -13.050000 0.230000) 4) (segment 2009 (point 330.720000 949.000000 -13.050000 0.230000) (point 331.610000 949.210000 -13.050000 0.230000) 4) (segment 2010 (point 331.610000 949.210000 -13.050000 0.230000) (point 332.100000 951.120000 -13.050000 0.230000) 4) (segment 2011 (point 332.100000 951.120000 -13.050000 0.230000) (point 335.420000 953.090000 -13.050000 0.230000) 4) (segment 2012 (point 335.420000 953.090000 -13.050000 0.230000) (point 337.690000 955.420000 -14.020000 0.230000) 4) (segment 2013 (point 337.690000 955.420000 -14.020000 0.230000) (point 340.100000 957.180000 -14.880000 0.230000) 4) (segment 2014 (point 340.100000 957.180000 -14.880000 0.230000) (point 341.450000 957.490000 -13.720000 0.230000) 4) (segment 2015 (point 341.450000 957.490000 -13.720000 0.230000) (point 342.080000 958.840000 -12.950000 0.230000) 4) (segment 2016 (point 342.080000 958.840000 -12.950000 0.230000) (point 344.360000 961.150000 -12.320000 0.230000) 4) (segment 2017 (point 344.360000 961.150000 -12.320000 0.230000) (point 344.360000 961.150000 -12.350000 0.230000) 4) (segment 2018 (point 344.360000 961.150000 -12.350000 0.230000) (point 345.750000 963.280000 -12.100000 0.230000) 4) (segment 2019 (point 345.750000 963.280000 -12.100000 0.230000) (point 345.750000 963.280000 -12.130000 0.230000) 4) (segment 2020 (point 345.750000 963.280000 -12.130000 0.230000) (point 347.590000 965.500000 -11.170000 0.230000) 4) (segment 2021 (point 347.590000 965.500000 -11.170000 0.230000) (point 350.710000 966.220000 -10.480000 0.230000) 4) (segment 2022 (point 350.710000 966.220000 -10.480000 0.230000) (point 351.520000 968.810000 -9.650000 0.230000) 4) (segment 2023 (point 351.520000 968.810000 -9.650000 0.230000) (point 353.170000 969.790000 -8.420000 0.230000) 4) (segment 2024 (point 353.170000 969.790000 -8.420000 0.230000) (point 353.170000 969.790000 -8.450000 0.230000) 4) (segment 2025 (point 353.170000 969.790000 -8.450000 0.230000) (point 354.700000 971.340000 -6.650000 0.230000) 4) (segment 2026 (point 354.700000 971.340000 -6.650000 0.230000) (point 356.350000 972.330000 -4.170000 0.230000) 4) (segment 2027 (point 356.350000 972.330000 -4.170000 0.230000) (point 355.900000 972.220000 -4.170000 0.230000) 4) (segment 2028 (point 355.900000 972.220000 -4.170000 0.230000) (point 356.800000 972.430000 -1.600000 0.230000) 4)) (branch 87 85 (segment 2029 (point 255.890000 912.960000 -44.870000 0.460000) (point 255.990000 916.570000 -45.750000 0.230000) 4) (segment 2030 (point 255.990000 916.570000 -45.750000 0.230000) (point 258.400000 918.330000 -46.670000 0.230000) 4) (segment 2031 (point 258.400000 918.330000 -46.670000 0.230000) (point 258.890000 920.240000 -47.600000 0.230000) 4) (segment 2032 (point 258.890000 920.240000 -47.600000 0.230000) (point 258.760000 920.810000 -48.600000 0.230000) 4) (segment 2033 (point 258.760000 920.810000 -48.600000 0.230000) (point 257.280000 921.060000 -49.050000 0.230000) 4) (segment 2034 (point 257.280000 921.060000 -49.050000 0.230000) (point 257.280000 921.060000 -49.070000 0.230000) 4) (segment 2035 (point 257.280000 921.060000 -49.070000 0.230000) (point 257.600000 921.720000 -49.850000 0.230000) 4) (segment 2036 (point 257.600000 921.720000 -49.850000 0.230000) (point 259.430000 923.950000 -49.850000 0.230000) 4) (segment 2037 (point 259.430000 923.950000 -49.850000 0.230000) (point 259.870000 924.060000 -50.520000 0.230000) 4) (segment 2038 (point 259.870000 924.060000 -50.520000 0.230000) (point 258.580000 925.540000 -52.220000 0.230000) 4) (segment 2039 (point 258.580000 925.540000 -52.220000 0.230000) (point 259.430000 923.950000 -53.600000 0.230000) 4) (segment 2040 (point 259.430000 923.950000 -53.600000 0.230000) (point 259.430000 923.950000 -53.620000 0.230000) 4) (segment 2041 (point 259.430000 923.950000 -53.620000 0.230000) (point 259.920000 925.860000 -55.220000 0.230000) 4) (segment 2042 (point 259.920000 925.860000 -55.220000 0.230000) (point 259.920000 925.860000 -55.300000 0.230000) 4) (segment 2043 (point 259.920000 925.860000 -55.300000 0.230000) (point 260.560000 927.190000 -57.130000 0.230000) 4) (segment 2044 (point 260.560000 927.190000 -57.130000 0.230000) (point 262.210000 928.180000 -58.250000 0.230000) 4) (segment 2045 (point 262.210000 928.180000 -58.250000 0.230000) (point 264.180000 929.850000 -59.380000 0.230000) 4)) (branch 88 87 (segment 2046 (point 264.180000 929.850000 -59.380000 0.230000) (point 263.490000 930.270000 -60.570000 0.230000) 4) (segment 2047 (point 263.490000 930.270000 -60.570000 0.230000) (point 264.300000 932.840000 -61.250000 0.230000) 4) (segment 2048 (point 264.300000 932.840000 -61.250000 0.230000) (point 262.560000 934.230000 -62.670000 0.230000) 4) (segment 2049 (point 262.560000 934.230000 -62.670000 0.230000) (point 264.230000 935.210000 -63.750000 0.230000) 4) (segment 2050 (point 264.230000 935.210000 -63.750000 0.230000) (point 262.940000 936.700000 -64.650000 0.230000) 4) (segment 2051 (point 262.940000 936.700000 -64.650000 0.230000) (point 263.570000 938.040000 -65.520000 0.230000) 4) (segment 2052 (point 263.570000 938.040000 -65.520000 0.230000) (point 263.340000 940.980000 -66.200000 0.230000) 4) (segment 2053 (point 263.340000 940.980000 -66.200000 0.230000) (point 264.600000 943.660000 -67.000000 0.230000) 4) (segment 2054 (point 264.600000 943.660000 -67.000000 0.230000) (point 264.650000 945.460000 -67.550000 0.230000) 4) (segment 2055 (point 264.650000 945.460000 -67.550000 0.230000) (point 264.650000 945.460000 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995.610000 -32.400000 0.460000) (point 191.350000 997.460000 -31.050000 0.460000) 4) (segment 2385 (point 191.350000 997.460000 -31.050000 0.460000) (point 188.710000 998.640000 -31.220000 0.460000) 4) (segment 2386 (point 188.710000 998.640000 -31.220000 0.460000) (point 188.310000 1000.340000 -32.170000 0.460000) 4) (segment 2387 (point 188.310000 1000.340000 -32.170000 0.460000) (point 188.670000 1002.810000 -33.250000 0.460000) 4) (segment 2388 (point 188.670000 1002.810000 -33.250000 0.460000) (point 188.670000 1002.810000 -33.270000 0.460000) 4) (segment 2389 (point 188.670000 1002.810000 -33.270000 0.460000) (point 189.580000 1003.020000 -34.880000 0.460000) 4) (segment 2390 (point 189.580000 1003.020000 -34.880000 0.460000) (point 189.580000 1003.020000 -34.900000 0.460000) 4) (segment 2391 (point 189.580000 1003.020000 -34.900000 0.460000) (point 190.960000 1005.140000 -35.750000 0.460000) 4) (segment 2392 (point 190.960000 1005.140000 -35.750000 0.460000) (point 190.960000 1005.140000 -35.770000 0.460000) 4) (segment 2393 (point 190.960000 1005.140000 -35.770000 0.460000) (point 192.530000 1008.490000 -35.750000 0.460000) 4) (segment 2394 (point 192.530000 1008.490000 -35.750000 0.460000) (point 192.750000 1011.530000 -36.200000 0.460000) 4) (segment 2395 (point 192.750000 1011.530000 -36.200000 0.460000) (point 193.120000 1014.000000 -35.530000 0.460000) 4) (segment 2396 (point 193.120000 1014.000000 -35.530000 0.460000) (point 194.190000 1015.460000 -34.250000 0.460000) 4) (segment 2397 (point 194.190000 1015.460000 -34.250000 0.460000) (point 193.990000 1018.390000 -33.000000 0.460000) 4) (segment 2398 (point 193.990000 1018.390000 -33.000000 0.460000) (point 194.210000 1021.420000 -32.300000 0.460000) 4) (segment 2399 (point 194.210000 1021.420000 -32.300000 0.460000) (point 194.210000 1021.420000 -32.320000 0.460000) 4) (segment 2400 (point 194.210000 1021.420000 -32.320000 0.460000) (point 195.730000 1022.980000 -31.670000 0.460000) 4) (segment 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1089.180000 -11.270000 0.460000) 4)) (branch 100 82 (segment 2443 (point 235.320000 659.410000 -34.150000 0.915000) (point 235.390000 662.780000 -32.520000 0.690000) 4) (segment 2444 (point 235.390000 662.780000 -32.520000 0.690000) (point 236.450000 664.220000 -31.200000 0.690000) 4) (segment 2445 (point 236.450000 664.220000 -31.200000 0.690000) (point 237.140000 667.360000 -30.450000 0.690000) 4) (segment 2446 (point 237.140000 667.360000 -30.450000 0.690000) (point 237.820000 670.520000 -29.850000 0.690000) 4) (segment 2447 (point 237.820000 670.520000 -29.850000 0.690000) (point 238.040000 673.550000 -29.250000 0.690000) 4) (segment 2448 (point 238.040000 673.550000 -29.250000 0.690000) (point 238.670000 674.900000 -27.880000 0.690000) 4) (segment 2449 (point 238.670000 674.900000 -27.880000 0.690000) (point 237.830000 676.490000 -26.700000 0.690000) 4) (segment 2450 (point 237.830000 676.490000 -26.700000 0.690000) (point 237.030000 679.880000 -26.000000 0.690000) 4) (segment 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236.740000 707.280000 -27.650000 0.690000) 4) (segment 2460 (point 236.740000 707.280000 -27.650000 0.690000) (point 236.080000 710.110000 -27.800000 0.690000) 4) (segment 2461 (point 236.080000 710.110000 -27.800000 0.690000) (point 235.980000 712.480000 -27.150000 0.690000) 4) (segment 2462 (point 235.980000 712.480000 -27.150000 0.690000) (point 237.380000 714.600000 -26.720000 0.690000) 4) (segment 2463 (point 237.380000 714.600000 -26.720000 0.690000) (point 238.180000 717.180000 -26.300000 0.690000) 4) (segment 2464 (point 238.180000 717.180000 -26.300000 0.690000) (point 237.250000 721.140000 -26.250000 0.690000) 4) (segment 2465 (point 237.250000 721.140000 -26.250000 0.690000) (point 236.050000 726.230000 -26.250000 0.690000) 4) (segment 2466 (point 236.050000 726.230000 -26.250000 0.690000) (point 236.290000 729.270000 -27.250000 0.690000) 4) (segment 2467 (point 236.290000 729.270000 -27.250000 0.690000) (point 236.970000 732.420000 -27.900000 0.690000) 4) (segment 2468 (point 236.970000 732.420000 -27.900000 0.690000) (point 237.340000 734.890000 -27.900000 0.690000) 4) (segment 2469 (point 237.340000 734.890000 -27.900000 0.690000) (point 237.560000 737.930000 -28.170000 0.690000) 4) (segment 2470 (point 237.560000 737.930000 -28.170000 0.690000) (point 238.820000 740.620000 -26.800000 0.690000) 4) (segment 2471 (point 238.820000 740.620000 -26.800000 0.690000) (point 238.820000 740.620000 -26.820000 0.690000) 4) (segment 2472 (point 238.820000 740.620000 -26.820000 0.690000) (point 238.470000 744.120000 -26.380000 0.690000) 4) (segment 2473 (point 238.470000 744.120000 -26.380000 0.690000) (point 238.390000 746.480000 -26.380000 0.690000) 4) (segment 2474 (point 238.390000 746.480000 -26.380000 0.690000) (point 238.390000 746.480000 -26.350000 0.690000) 4) (segment 2475 (point 238.390000 746.480000 -26.350000 0.690000) (point 239.730000 746.790000 -26.300000 0.690000) 4) (segment 2476 (point 239.730000 746.790000 -26.300000 0.690000) (point 239.580000 747.370000 -26.300000 0.690000) 4) (segment 2477 (point 239.580000 747.370000 -26.300000 0.690000) (point 237.320000 751.020000 -25.630000 0.690000) 4) (segment 2478 (point 237.320000 751.020000 -25.630000 0.690000) (point 236.790000 753.270000 -25.420000 0.690000) 4) (segment 2479 (point 236.790000 753.270000 -25.420000 0.690000) (point 237.340000 756.990000 -25.420000 0.690000) 4) (segment 2480 (point 237.340000 756.990000 -25.420000 0.690000) (point 238.330000 760.810000 -25.420000 0.690000) 4) (segment 2481 (point 238.330000 760.810000 -25.420000 0.690000) (point 239.000000 763.950000 -26.000000 0.690000) 4) (segment 2482 (point 239.000000 763.950000 -26.000000 0.690000) (point 238.070000 767.910000 -26.000000 0.690000) 4) (segment 2483 (point 238.070000 767.910000 -26.000000 0.690000) (point 238.070000 767.910000 -26.020000 0.690000) 4) (segment 2484 (point 238.070000 767.910000 -26.020000 0.690000) (point 236.830000 771.200000 -26.020000 0.690000) 4) (segment 2485 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237.800000 795.200000 -24.200000 0.690000) 4) (segment 2494 (point 237.800000 795.200000 -24.200000 0.690000) (point 237.800000 795.200000 -24.170000 0.690000) 4) (segment 2495 (point 237.800000 795.200000 -24.170000 0.690000) (point 237.140000 798.030000 -23.420000 0.690000) 4) (segment 2496 (point 237.140000 798.030000 -23.420000 0.690000) (point 237.630000 799.930000 -22.470000 0.690000) 4) (segment 2497 (point 237.630000 799.930000 -22.470000 0.690000) (point 238.000000 802.410000 -21.320000 0.690000) 4) (segment 2498 (point 238.000000 802.410000 -21.320000 0.690000) (point 237.840000 807.140000 -20.900000 0.690000) 4) (segment 2499 (point 237.840000 807.140000 -20.900000 0.690000) (point 238.070000 810.190000 -21.950000 0.690000) 4) (segment 2500 (point 238.070000 810.190000 -21.950000 0.690000) (point 239.630000 813.540000 -21.950000 0.690000) 4) (segment 2501 (point 239.630000 813.540000 -21.950000 0.690000) (point 239.150000 817.610000 -22.450000 0.690000) 4) (segment 2502 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244.380000 841.520000 -18.950000 0.690000) 4) (segment 2511 (point 244.380000 841.520000 -18.950000 0.690000) (point 244.380000 841.520000 -18.970000 0.690000) 4) (segment 2512 (point 244.380000 841.520000 -18.970000 0.690000) (point 245.010000 842.860000 -18.050000 0.690000) 4) (segment 2513 (point 245.010000 842.860000 -18.050000 0.690000) (point 244.350000 845.690000 -18.170000 0.690000) 4) (segment 2514 (point 244.350000 845.690000 -18.170000 0.690000) (point 244.350000 845.690000 -18.150000 0.690000) 4) (segment 2515 (point 244.350000 845.690000 -18.150000 0.690000) (point 244.120000 848.640000 -16.630000 0.690000) 4) (segment 2516 (point 244.120000 848.640000 -16.630000 0.690000) (point 243.200000 852.590000 -16.050000 0.690000) 4)) (branch 101 100 (segment 2517 (point 243.200000 852.590000 -16.050000 0.690000) (point 243.430000 855.630000 -16.050000 0.690000) 4) (segment 2518 (point 243.430000 855.630000 -16.050000 0.690000) (point 244.370000 857.640000 -16.050000 0.690000) 4) (segment 2519 (point 244.370000 857.640000 -16.050000 0.690000) (point 244.870000 859.550000 -15.770000 0.690000) 4) (segment 2520 (point 244.870000 859.550000 -15.770000 0.690000) (point 244.960000 863.160000 -15.770000 0.690000) 4) (segment 2521 (point 244.960000 863.160000 -15.770000 0.690000) (point 246.410000 867.080000 -15.770000 0.690000) 4) (segment 2522 (point 246.410000 867.080000 -15.770000 0.690000) (point 244.260000 870.160000 -16.950000 0.690000) 4) (segment 2523 (point 244.260000 870.160000 -16.950000 0.690000) (point 242.570000 873.350000 -17.300000 0.690000) 4) (segment 2524 (point 242.570000 873.350000 -17.300000 0.690000) (point 242.090000 877.420000 -16.850000 0.690000) 4) (segment 2525 (point 242.090000 877.420000 -16.850000 0.690000) (point 242.090000 877.420000 -16.880000 0.690000) 4) (segment 2526 (point 242.090000 877.420000 -16.880000 0.690000) (point 241.600000 881.480000 -15.170000 0.690000) 4) (segment 2527 (point 241.600000 881.480000 -15.170000 0.690000) (point 239.470000 884.560000 -14.570000 0.690000) 4) (segment 2528 (point 239.470000 884.560000 -14.570000 0.690000) (point 238.630000 886.160000 -14.100000 0.690000) 4) (segment 2529 (point 238.630000 886.160000 -14.100000 0.690000) (point 237.830000 889.560000 -14.100000 0.690000) 4) (segment 2530 (point 237.830000 889.560000 -14.100000 0.690000) (point 237.620000 892.480000 -15.400000 0.690000) 4) (segment 2531 (point 237.620000 892.480000 -15.400000 0.690000) (point 237.660000 894.290000 -16.520000 0.690000) 4) (segment 2532 (point 237.660000 894.290000 -16.520000 0.690000) (point 237.660000 894.290000 -16.550000 0.690000) 4) (segment 2533 (point 237.660000 894.290000 -16.550000 0.690000) (point 237.770000 897.900000 -17.300000 0.690000) 4) (segment 2534 (point 237.770000 897.900000 -17.300000 0.690000) (point 237.770000 897.900000 -17.320000 0.690000) 4) (segment 2535 (point 237.770000 897.900000 -17.320000 0.690000) (point 238.630000 902.290000 -17.570000 0.690000) 4) (segment 2536 (point 238.630000 902.290000 -17.570000 0.690000) (point 240.510000 906.300000 -17.570000 0.690000) 4) (segment 2537 (point 240.510000 906.300000 -17.570000 0.690000) (point 239.260000 909.590000 -17.750000 0.690000) 4) (segment 2538 (point 239.260000 909.590000 -17.750000 0.690000) (point 239.440000 910.830000 -18.600000 0.690000) 4) (segment 2539 (point 239.440000 910.830000 -18.600000 0.690000) (point 239.440000 910.830000 -18.630000 0.690000) 4) (segment 2540 (point 239.440000 910.830000 -18.630000 0.690000) (point 241.460000 914.290000 -19.020000 0.690000) 4) (segment 2541 (point 241.460000 914.290000 -19.020000 0.690000) (point 243.290000 916.510000 -17.650000 0.690000) 4) (segment 2542 (point 243.290000 916.510000 -17.650000 0.690000) (point 243.970000 919.650000 -18.200000 0.690000) 4) (segment 2543 (point 243.970000 919.650000 -18.200000 0.690000) (point 245.090000 922.910000 -17.630000 0.690000) 4) (segment 2544 (point 245.090000 922.910000 -17.630000 0.690000) (point 245.850000 923.680000 -17.750000 0.690000) 4) (segment 2545 (point 245.850000 923.680000 -17.750000 0.690000) (point 245.900000 925.490000 -16.170000 0.690000) 4) (segment 2546 (point 245.900000 925.490000 -16.170000 0.690000) (point 245.900000 925.490000 -16.150000 0.690000) 4) (segment 2547 (point 245.900000 925.490000 -16.150000 0.690000) (point 245.080000 927.070000 -14.230000 0.690000) 4) (segment 2548 (point 245.080000 927.070000 -14.230000 0.690000) (point 243.330000 928.450000 -12.100000 0.690000) 4) (segment 2549 (point 243.330000 928.450000 -12.100000 0.690000) (point 240.000000 930.660000 -10.880000 0.690000) 4) (segment 2550 (point 240.000000 930.660000 -10.880000 0.690000) (point 238.000000 933.180000 -10.630000 0.690000) 4) (segment 2551 (point 238.000000 933.180000 -10.630000 0.690000) (point 237.200000 936.580000 -10.330000 0.690000) 4) (segment 2552 (point 237.200000 936.580000 -10.330000 0.690000) (point 236.410000 939.980000 -10.330000 0.690000) 4) (segment 2553 (point 236.410000 939.980000 -10.330000 0.690000) (point 236.140000 941.100000 -11.370000 0.690000) 4) (segment 2554 (point 236.140000 941.100000 -11.370000 0.690000) (point 234.900000 944.400000 -12.420000 0.690000) 4) (segment 2555 (point 234.900000 944.400000 -12.420000 0.690000) (point 233.470000 946.450000 -13.670000 0.690000) 4) (segment 2556 (point 233.470000 946.450000 -13.670000 0.690000) (point 233.470000 946.450000 -13.700000 0.690000) 4) (segment 2557 (point 233.470000 946.450000 -13.700000 0.690000) (point 231.650000 950.190000 -13.950000 0.690000) 4) (segment 2558 (point 231.650000 950.190000 -13.950000 0.690000) (point 229.920000 951.580000 -13.180000 0.690000) 4) (segment 2559 (point 229.920000 951.580000 -13.180000 0.690000) (point 228.530000 955.440000 -12.950000 0.690000) 4) (segment 2560 (point 228.530000 955.440000 -12.950000 0.690000) (point 228.950000 959.730000 -12.070000 0.690000) 4) (segment 2561 (point 228.950000 959.730000 -12.070000 0.690000) (point 228.280000 962.540000 -12.420000 0.690000) 4) (segment 2562 (point 228.280000 962.540000 -12.420000 0.690000) (point 226.340000 966.860000 -12.420000 0.690000) 4) (segment 2563 (point 226.340000 966.860000 -12.420000 0.690000) (point 224.650000 970.050000 -14.100000 0.690000) 4) (segment 2564 (point 224.650000 970.050000 -14.100000 0.690000) (point 224.560000 972.420000 -13.630000 0.690000) 4) (segment 2565 (point 224.560000 972.420000 -13.630000 0.690000) (point 224.920000 974.890000 -13.320000 0.690000) 4) (segment 2566 (point 224.920000 974.890000 -13.320000 0.690000) (point 224.390000 977.160000 -11.930000 0.690000) 4) (segment 2567 (point 224.390000 977.160000 -11.930000 0.690000) (point 223.990000 978.850000 -10.450000 0.690000) 4) (segment 2568 (point 223.990000 978.850000 -10.450000 0.690000) (point 223.190000 982.250000 -9.170000 0.690000) 4) (segment 2569 (point 223.190000 982.250000 -9.170000 0.690000) (point 221.780000 984.310000 -7.320000 0.690000) 4) (segment 2570 (point 221.780000 984.310000 -7.320000 0.690000) (point 222.710000 986.320000 -6.470000 0.690000) 4) (segment 2571 (point 222.710000 986.320000 -6.470000 0.690000) (point 226.640000 989.640000 -6.130000 0.690000) 4) (segment 2572 (point 226.640000 989.640000 -6.130000 0.690000) (point 227.010000 992.100000 -6.180000 0.690000) 4) (segment 2573 (point 227.010000 992.100000 -6.180000 0.690000) (point 224.860000 995.180000 -5.070000 0.690000) 4) (segment 2574 (point 224.860000 995.180000 -5.070000 0.690000) (point 222.860000 997.700000 -3.400000 0.690000) 4) (segment 2575 (point 222.860000 997.700000 -3.400000 0.690000) (point 223.090000 1000.740000 -2.500000 0.690000) 4) (segment 2576 (point 223.090000 1000.740000 -2.500000 0.690000) (point 222.600000 1004.810000 -2.250000 0.690000) 4) (segment 2577 (point 222.600000 1004.810000 -2.250000 0.690000) (point 220.910000 1008.000000 -1.820000 0.690000) 4) (segment 2578 (point 220.910000 1008.000000 -1.820000 0.690000) (point 219.800000 1010.720000 -1.600000 0.690000) 4) (segment 2579 (point 219.800000 1010.720000 -1.600000 0.690000) (point 219.950000 1016.130000 -1.020000 0.690000) 4) (segment 2580 (point 219.950000 1016.130000 -1.020000 0.690000) (point 218.710000 1019.420000 -0.630000 0.690000) 4) (segment 2581 (point 218.710000 1019.420000 -0.630000 0.690000) (point 217.150000 1022.030000 -0.630000 0.690000) 4) (segment 2582 (point 217.150000 1022.030000 -0.630000 0.690000) (point 216.710000 1021.930000 -0.630000 0.690000) 4) (segment 2583 (point 216.710000 1021.930000 -0.630000 0.690000) (point 215.600000 1024.660000 -0.630000 0.690000) 4) (segment 2584 (point 215.600000 1024.660000 -0.630000 0.690000) (point 215.560000 1028.830000 -0.070000 0.690000) 4) (segment 2585 (point 215.560000 1028.830000 -0.070000 0.690000) (point 214.760000 1032.230000 0.970000 0.690000) 4)) (branch 102 101 (segment 2586 (point 214.760000 1032.230000 0.970000 0.690000) (point 213.350000 1034.280000 -0.470000 0.690000) 4) (segment 2587 (point 213.350000 1034.280000 -0.470000 0.690000) (point 212.900000 1034.170000 -0.470000 0.690000) 4) (segment 2588 (point 212.900000 1034.170000 -0.470000 0.690000) (point 212.370000 1036.450000 2.130000 0.460000) 4) (segment 2589 (point 212.370000 1036.450000 2.130000 0.460000) (point 212.150000 1039.380000 3.650000 0.460000) 4) (segment 2590 (point 212.150000 1039.380000 3.650000 0.460000) (point 212.150000 1039.380000 3.600000 0.460000) 4) (segment 2591 (point 212.150000 1039.380000 3.600000 0.460000) (point 209.960000 1040.650000 4.320000 0.230000) 4) (segment 2592 (point 209.960000 1040.650000 4.320000 0.230000) (point 209.570000 1042.350000 5.450000 0.230000) 4) (segment 2593 (point 209.570000 1042.350000 5.450000 0.230000) (point 209.880000 1043.030000 5.550000 0.230000) 4) (segment 2594 (point 209.880000 1043.030000 5.550000 0.230000) (point 208.010000 1044.970000 7.550000 0.230000) 4) (segment 2595 (point 208.010000 1044.970000 7.550000 0.230000) (point 208.010000 1044.970000 7.570000 0.230000) 4)) (branch 103 101 (segment 2596 (point 214.760000 1032.230000 0.970000 0.690000) (point 216.660000 1036.260000 2.080000 0.460000) 4) (segment 2597 (point 216.660000 1036.260000 2.080000 0.460000) (point 215.550000 1038.970000 2.970000 0.460000) 4) (segment 2598 (point 215.550000 1038.970000 2.970000 0.460000) (point 217.770000 1039.500000 3.500000 0.460000) 4) (segment 2599 (point 217.770000 1039.500000 3.500000 0.460000) (point 219.250000 1039.250000 4.170000 0.460000) 4) (segment 2600 (point 219.250000 1039.250000 4.170000 0.460000) (point 221.080000 1041.480000 5.070000 0.460000) 4) (segment 2601 (point 221.080000 1041.480000 5.070000 0.460000) (point 221.580000 1043.380000 5.700000 0.460000) 4) (segment 2602 (point 221.580000 1043.380000 5.700000 0.460000) (point 222.520000 1045.380000 6.350000 0.460000) 4) (segment 2603 (point 222.520000 1045.380000 6.350000 0.460000) (point 222.170000 1048.880000 6.800000 0.460000) 4) (segment 2604 (point 222.170000 1048.880000 6.800000 0.460000) (point 222.170000 1048.880000 6.780000 0.460000) 4) (segment 2605 (point 222.170000 1048.880000 6.780000 0.460000) (point 224.060000 1052.910000 5.420000 0.460000) 4) (segment 2606 (point 224.060000 1052.910000 5.420000 0.460000) (point 223.830000 1055.850000 5.170000 0.460000) 4) (segment 2607 (point 223.830000 1055.850000 5.170000 0.460000) (point 223.750000 1058.220000 6.350000 0.460000) 4) (segment 2608 (point 223.750000 1058.220000 6.350000 0.460000) (point 223.750000 1058.220000 6.280000 0.460000) 4) (segment 2609 (point 223.750000 1058.220000 6.280000 0.460000) (point 222.330000 1060.280000 7.670000 0.460000) 4) (segment 2610 (point 222.330000 1060.280000 7.670000 0.460000) (point 222.690000 1062.740000 8.550000 0.460000) 4) (segment 2611 (point 222.690000 1062.740000 8.550000 0.460000) (point 223.950000 1065.430000 8.550000 0.460000) 4) (segment 2612 (point 223.950000 1065.430000 8.550000 0.460000) (point 222.840000 1068.160000 8.550000 0.230000) 4) (segment 2613 (point 222.840000 1068.160000 8.550000 0.230000) (point 223.960000 1071.400000 8.550000 0.230000) 4) (segment 2614 (point 223.960000 1071.400000 8.550000 0.230000) (point 224.640000 1074.550000 8.550000 0.230000) 4) (segment 2615 (point 224.640000 1074.550000 8.550000 0.230000) (point 224.190000 1074.440000 8.550000 0.230000) 4) (segment 2616 (point 224.190000 1074.440000 8.550000 0.230000) (point 228.080000 1075.950000 8.550000 0.230000) 4) (segment 2617 (point 228.080000 1075.950000 8.550000 0.230000) (point 228.980000 1076.160000 9.550000 0.230000) 4)) (branch 104 100 (segment 2618 (point 243.200000 852.590000 -16.050000 0.690000) (point 241.310000 854.660000 -17.080000 0.690000) 4) (segment 2619 (point 241.310000 854.660000 -17.080000 0.690000) (point 241.490000 855.890000 -18.670000 0.690000) 4) (segment 2620 (point 241.490000 855.890000 -18.670000 0.690000) (point 240.910000 856.360000 -19.800000 0.690000) 4) (segment 2621 (point 240.910000 856.360000 -19.800000 0.690000) (point 241.090000 857.590000 -21.630000 0.690000) 4) (segment 2622 (point 241.090000 857.590000 -21.630000 0.690000) (point 241.990000 857.810000 -23.270000 0.690000) 4) (segment 2623 (point 241.990000 857.810000 -23.270000 0.690000) (point 241.590000 859.500000 -25.050000 0.690000) 4) (segment 2624 (point 241.590000 859.500000 -25.050000 0.690000) (point 239.980000 860.330000 -26.220000 0.690000) 4) (segment 2625 (point 239.980000 860.330000 -26.220000 0.690000) (point 238.070000 860.480000 -27.730000 0.690000) 4) (segment 2626 (point 238.070000 860.480000 -27.730000 0.690000) (point 240.480000 862.230000 -29.700000 0.460000) 4) (segment 2627 (point 240.480000 862.230000 -29.700000 0.460000) (point 239.630000 863.820000 -31.700000 0.460000) 4) (segment 2628 (point 239.630000 863.820000 -31.700000 0.460000) (point 238.210000 865.870000 -32.970000 0.460000) 4) (segment 2629 (point 238.210000 865.870000 -32.970000 0.460000) (point 239.020000 868.450000 -34.170000 0.460000) 4) (segment 2630 (point 239.020000 868.450000 -34.170000 0.460000) (point 239.380000 870.940000 -34.450000 0.460000) 4) (segment 2631 (point 239.380000 870.940000 -34.450000 0.460000) (point 237.510000 872.880000 -35.400000 0.460000) 4) (segment 2632 (point 237.510000 872.880000 -35.400000 0.460000) (point 235.600000 873.030000 -36.750000 0.460000) 4) (segment 2633 (point 235.600000 873.030000 -36.750000 0.460000) (point 233.680000 873.180000 -38.380000 0.460000) 4) (segment 2634 (point 233.680000 873.180000 -38.380000 0.460000) (point 233.590000 875.540000 -39.400000 0.460000) 4) (segment 2635 (point 233.590000 875.540000 -39.400000 0.460000) (point 234.090000 877.460000 -40.750000 0.460000) 4) (segment 2636 (point 234.090000 877.460000 -40.750000 0.460000) (point 234.140000 879.260000 -41.930000 0.460000) 4) (segment 2637 (point 234.140000 879.260000 -41.930000 0.460000) (point 231.640000 879.870000 -43.070000 0.460000) 4) (segment 2638 (point 231.640000 879.870000 -43.070000 0.460000) (point 230.840000 883.260000 -43.470000 0.460000) 4) (segment 2639 (point 230.840000 883.260000 -43.470000 0.460000) (point 229.110000 884.650000 -44.050000 0.460000) 4) (segment 2640 (point 229.110000 884.650000 -44.050000 0.460000) (point 229.110000 884.650000 -44.070000 0.460000) 4) (segment 2641 (point 229.110000 884.650000 -44.070000 0.460000) (point 228.450000 887.480000 -44.600000 0.460000) 4) (segment 2642 (point 228.450000 887.480000 -44.600000 0.460000) (point 227.160000 888.970000 -45.950000 0.460000) 4) (segment 2643 (point 227.160000 888.970000 -45.950000 0.460000) (point 227.020000 889.530000 -47.220000 0.460000) 4) (segment 2644 (point 227.020000 889.530000 -47.220000 0.460000) (point 227.250000 892.570000 -48.450000 0.460000) 4) (segment 2645 (point 227.250000 892.570000 -48.450000 0.460000) (point 226.410000 894.160000 -48.620000 0.460000) 4) (segment 2646 (point 226.410000 894.160000 -48.620000 0.460000) (point 226.940000 897.870000 -49.520000 0.460000) 4) (segment 2647 (point 226.940000 897.870000 -49.520000 0.460000) (point 226.940000 897.870000 -49.550000 0.460000) 4) (segment 2648 (point 226.940000 897.870000 -49.550000 0.460000) (point 226.410000 900.140000 -50.000000 0.460000) 4) (segment 2649 (point 226.410000 900.140000 -50.000000 0.460000) (point 226.410000 900.140000 -50.030000 0.460000) 4) (segment 2650 (point 226.410000 900.140000 -50.030000 0.460000) (point 225.130000 901.630000 -51.280000 0.460000) 4) (segment 2651 (point 225.130000 901.630000 -51.280000 0.460000) (point 224.870000 902.750000 -52.650000 0.460000) 4) (segment 2652 (point 224.870000 902.750000 -52.650000 0.460000) (point 224.950000 906.360000 -53.450000 0.460000) 4) (segment 2653 (point 224.950000 906.360000 -53.450000 0.460000) (point 224.870000 908.740000 -54.170000 0.460000) 4) (segment 2654 (point 224.870000 908.740000 -54.170000 0.460000) (point 223.760000 911.450000 -54.580000 0.460000) 4) (segment 2655 (point 223.760000 911.450000 -54.580000 0.460000) (point 222.700000 915.980000 -54.720000 0.460000) 4) (segment 2656 (point 222.700000 915.980000 -54.720000 0.460000) (point 220.970000 917.380000 -55.520000 0.460000) 4) (segment 2657 (point 220.970000 917.380000 -55.520000 0.460000) (point 222.800000 919.590000 -56.200000 0.460000) 4) (segment 2658 (point 222.800000 919.590000 -56.200000 0.460000) (point 222.760000 923.760000 -56.750000 0.460000) 4) (segment 2659 (point 222.760000 923.760000 -56.750000 0.460000) (point 222.050000 924.790000 -58.550000 0.230000) 4) (segment 2660 (point 222.050000 924.790000 -58.550000 0.230000) (point 221.340000 925.830000 -60.350000 0.230000) 4) (segment 2661 (point 221.340000 925.830000 -60.350000 0.230000) (point 221.340000 925.830000 -60.380000 0.230000) 4) (segment 2662 (point 221.340000 925.830000 -60.380000 0.230000) (point 221.390000 927.620000 -62.130000 0.230000) 4) (segment 2663 (point 221.390000 927.620000 -62.130000 0.230000) (point 220.720000 930.450000 -63.420000 0.230000) 4) (segment 2664 (point 220.720000 930.450000 -63.420000 0.230000) (point 218.110000 931.490000 -64.270000 0.230000) 4) (segment 2665 (point 218.110000 931.490000 -64.270000 0.230000) (point 220.080000 933.150000 -65.000000 0.230000) 4) (segment 2666 (point 220.080000 933.150000 -65.000000 0.230000) (point 219.820000 934.270000 -66.170000 0.230000) 4) (segment 2667 (point 219.820000 934.270000 -66.170000 0.230000) (point 218.080000 935.660000 -67.200000 0.230000) 4) (segment 2668 (point 218.080000 935.660000 -67.200000 0.230000) (point 216.020000 936.380000 -68.130000 0.230000) 4) (segment 2669 (point 216.020000 936.380000 -68.130000 0.230000) (point 215.640000 938.070000 -69.180000 0.230000) 4) (segment 2670 (point 215.640000 938.070000 -69.180000 0.230000) (point 215.640000 938.070000 -69.200000 0.230000) 4) (segment 2671 (point 215.640000 938.070000 -69.200000 0.230000) (point 214.150000 938.320000 -70.450000 0.230000) 4) (segment 2672 (point 214.150000 938.320000 -70.450000 0.230000) (point 214.070000 940.690000 -71.430000 0.230000) 4) (segment 2673 (point 214.070000 940.690000 -71.430000 0.230000) (point 214.070000 940.690000 -71.450000 0.230000) 4) (segment 2674 (point 214.070000 940.690000 -71.450000 0.230000) (point 212.460000 941.510000 -72.320000 0.230000) 4) (segment 2675 (point 212.460000 941.510000 -72.320000 0.230000) (point 212.460000 941.510000 -72.350000 0.230000) 4) (segment 2676 (point 212.460000 941.510000 -72.350000 0.230000) (point 210.860000 942.330000 -72.950000 0.230000) 4) (segment 2677 (point 210.860000 942.330000 -72.950000 0.230000) (point 209.890000 944.490000 -74.150000 0.230000) 4) (segment 2678 (point 209.890000 944.490000 -74.150000 0.230000) (point 209.570000 943.820000 -76.000000 0.230000) 4) (segment 2679 (point 209.570000 943.820000 -76.000000 0.230000) (point 209.570000 943.820000 -76.030000 0.230000) 4) (segment 2680 (point 209.570000 943.820000 -76.030000 0.230000) (point 208.280000 945.310000 -77.470000 0.230000) 4) (segment 2681 (point 208.280000 945.310000 -77.470000 0.230000) (point 207.000000 946.800000 -78.570000 0.230000) 4) (segment 2682 (point 207.000000 946.800000 -78.570000 0.230000) (point 206.770000 949.740000 -79.550000 0.230000) 4) (segment 2683 (point 206.770000 949.740000 -79.550000 0.230000) (point 204.530000 949.210000 -80.700000 0.230000) 4) (segment 2684 (point 204.530000 949.210000 -80.700000 0.230000) (point 201.780000 950.950000 -81.750000 0.230000) 4) (segment 2685 (point 201.780000 950.950000 -81.750000 0.230000) (point 200.930000 952.550000 -82.450000 0.230000) 4) (segment 2686 (point 200.930000 952.550000 -82.450000 0.230000) (point 199.500000 954.600000 -83.000000 0.230000) 4) (segment 2687 (point 199.500000 954.600000 -83.000000 0.230000) (point 197.330000 955.870000 -83.000000 0.230000) 4) (segment 2688 (point 197.330000 955.870000 -83.000000 0.230000) (point 196.930000 957.580000 -84.720000 0.230000) 4) (segment 2689 (point 196.930000 957.580000 -84.720000 0.230000) (point 197.100000 958.810000 -86.320000 0.230000) 4) (segment 2690 (point 197.100000 958.810000 -86.320000 0.230000) (point 197.020000 961.190000 -87.000000 0.230000) 4) (segment 2691 (point 197.020000 961.190000 -87.000000 0.230000) (point 194.970000 961.900000 -87.520000 0.230000) 4) (segment 2692 (point 194.970000 961.900000 -87.520000 0.230000) (point 193.500000 962.140000 -88.420000 0.230000) 4) (segment 2693 (point 193.500000 962.140000 -88.420000 0.230000) (point 192.340000 963.070000 -89.520000 0.230000) 4) (segment 2694 (point 192.340000 963.070000 -89.520000 0.230000) (point 191.810000 965.330000 -90.770000 0.230000) 4) (segment 2695 (point 191.810000 965.330000 -90.770000 0.230000) (point 191.810000 965.330000 -90.800000 0.230000) 4) (segment 2696 (point 191.810000 965.330000 -90.800000 0.230000) (point 190.520000 966.820000 -91.720000 0.230000) 4) (segment 2697 (point 190.520000 966.820000 -91.720000 0.230000) (point 189.400000 969.550000 -92.420000 0.230000) 4) (segment 2698 (point 189.400000 969.550000 -92.420000 0.230000) (point 187.350000 970.250000 -93.470000 0.230000) 4) (segment 2699 (point 187.350000 970.250000 -93.470000 0.230000) (point 184.990000 970.310000 -94.700000 0.230000) 4) (segment 2700 (point 184.990000 970.310000 -94.700000 0.230000) (point 183.510000 970.560000 -95.750000 0.230000) 4) (segment 2701 (point 183.510000 970.560000 -95.750000 0.230000) (point 182.230000 972.040000 -96.800000 0.230000) 4) (segment 2702 (point 182.230000 972.040000 -96.800000 0.230000) (point 180.930000 973.540000 -97.650000 0.230000) 4) (segment 2703 (point 180.930000 973.540000 -97.650000 0.230000) (point 180.930000 973.540000 -97.680000 0.230000) 4) (segment 2704 (point 180.930000 973.540000 -97.680000 0.230000) (point 179.640000 975.020000 -98.530000 0.230000) 4) (segment 2705 (point 179.640000 975.020000 -98.530000 0.230000) (point 177.010000 976.200000 -99.300000 0.230000) 4) (segment 2706 (point 177.010000 976.200000 -99.300000 0.230000) (point 173.810000 977.840000 -100.370000 0.230000) 4) (segment 2707 (point 173.810000 977.840000 -100.370000 0.230000) (point 172.070000 979.220000 -100.370000 0.230000) 4) (segment 2708 (point 172.070000 979.220000 -100.370000 0.230000) (point 169.700000 979.260000 -101.320000 0.230000) 4) (segment 2709 (point 169.700000 979.260000 -101.320000 0.230000) (point 168.540000 980.190000 -102.400000 0.230000) 4) (segment 2710 (point 168.540000 980.190000 -102.400000 0.230000) (point 166.400000 983.270000 -103.600000 0.230000) 4) (segment 2711 (point 166.400000 983.270000 -103.600000 0.230000) (point 163.830000 986.240000 -103.970000 0.230000) 4) (segment 2712 (point 163.830000 986.240000 -103.970000 0.230000) (point 162.760000 990.780000 -103.970000 0.230000) 4) (segment 2713 (point 162.760000 990.780000 -103.970000 0.230000) (point 161.390000 994.630000 -104.850000 0.230000) 4) (segment 2714 (point 161.390000 994.630000 -104.850000 0.230000) (point 161.300000 997.000000 -106.100000 0.230000) 4) (segment 2715 (point 161.300000 997.000000 -106.100000 0.230000) (point 159.880000 999.060000 -107.420000 0.230000) 4) (segment 2716 (point 159.880000 999.060000 -107.420000 0.230000) (point 158.680000 1004.150000 -108.350000 0.230000) 4) (segment 2717 (point 158.680000 1004.150000 -108.350000 0.230000) (point 156.150000 1008.930000 -107.780000 0.230000) 4) (segment 2718 (point 156.150000 1008.930000 -107.780000 0.230000) (point 155.310000 1010.530000 -106.600000 0.230000) 4) (segment 2719 (point 155.310000 1010.530000 -106.600000 0.230000) (point 155.350000 1012.330000 -105.900000 0.230000) 4) (segment 2720 (point 155.350000 1012.330000 -105.900000 0.230000) (point 155.270000 1014.700000 -104.100000 0.230000) 4) (segment 2721 (point 155.270000 1014.700000 -104.100000 0.230000) (point 154.740000 1016.960000 -102.700000 0.230000) 4) (segment 2722 (point 154.740000 1016.960000 -102.700000 0.230000) (point 154.290000 1016.850000 -102.700000 0.230000) 4) (segment 2723 (point 154.290000 1016.850000 -102.700000 0.230000) (point 154.920000 1018.200000 -101.880000 0.230000) 4) (segment 2724 (point 154.920000 1018.200000 -101.880000 0.230000) (point 155.280000 1020.670000 -100.370000 0.230000) 4) (segment 2725 (point 155.280000 1020.670000 -100.370000 0.230000) (point 154.750000 1022.930000 -99.780000 0.230000) 4) (segment 2726 (point 154.750000 1022.930000 -99.780000 0.230000) (point 153.770000 1025.090000 -99.030000 0.230000) 4) (segment 2727 (point 153.770000 1025.090000 -99.030000 0.230000) (point 154.400000 1026.440000 -98.000000 0.230000) 4) (segment 2728 (point 154.400000 1026.440000 -98.000000 0.230000) (point 153.560000 1028.030000 -97.250000 0.230000) 4) (segment 2729 (point 153.560000 1028.030000 -97.250000 0.230000) (point 153.470000 1030.400000 -96.070000 0.230000) 4) (segment 2730 (point 153.470000 1030.400000 -96.070000 0.230000) (point 151.520000 1034.720000 -95.250000 0.230000) 4) (segment 2731 (point 151.520000 1034.720000 -95.250000 0.230000) (point 149.830000 1037.910000 -93.530000 0.230000) 4) (segment 2732 (point 149.830000 1037.910000 -93.530000 0.230000) (point 148.580000 1041.190000 -93.150000 0.230000) 4) (segment 2733 (point 148.580000 1041.190000 -93.150000 0.230000) (point 147.480000 1043.920000 -92.720000 0.230000) 4) (segment 2734 (point 147.480000 1043.920000 -92.720000 0.230000) (point 146.950000 1046.200000 -92.200000 0.230000) 4) (segment 2735 (point 146.950000 1046.200000 -92.200000 0.230000) (point 144.800000 1049.280000 -90.420000 0.230000) 4)) (branch 105 81 (segment 2736 (point 232.540000 610.990000 -32.880000 0.915000) (point 231.420000 611.320000 -31.420000 0.460000) 4) (segment 2737 (point 231.420000 611.320000 -31.420000 0.460000) (point 231.210000 611.360000 -31.420000 0.460000) 4) (segment 2738 (point 231.210000 611.360000 -31.420000 0.460000) (point 230.580000 612.920000 -30.000000 0.460000) 4) (segment 2739 (point 230.580000 612.920000 -30.000000 0.460000) (point 230.310000 614.050000 -28.570000 0.460000) 4) (segment 2740 (point 230.310000 614.050000 -28.570000 0.460000) (point 228.850000 614.300000 -27.150000 0.460000) 4) (segment 2741 (point 228.850000 614.300000 -27.150000 0.460000) (point 229.480000 615.640000 -26.320000 0.460000) 4) (segment 2742 (point 229.480000 615.640000 -26.320000 0.460000) (point 229.960000 617.550000 -25.070000 0.230000) 4) (segment 2743 (point 229.960000 617.550000 -25.070000 0.230000) (point 228.410000 620.170000 -23.600000 0.230000) 4) (segment 2744 (point 228.410000 620.170000 -23.600000 0.230000) (point 228.770000 622.640000 -22.570000 0.230000) 4) (segment 2745 (point 228.770000 622.640000 -22.570000 0.230000) (point 228.770000 622.640000 -22.600000 0.230000) 4) (segment 2746 (point 228.770000 622.640000 -22.600000 0.230000) (point 228.060000 623.660000 -21.750000 0.230000) 4) (segment 2747 (point 228.060000 623.660000 -21.750000 0.230000) (point 228.370000 624.350000 -21.750000 0.230000) 4) (segment 2748 (point 228.370000 624.350000 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468.540000 -14.900000 0.230000) 4) (segment 2757 (point 240.860000 468.540000 -14.900000 0.230000) (point 240.600000 469.660000 -14.150000 0.230000) 4) (segment 2758 (point 240.600000 469.660000 -14.150000 0.230000) (point 242.240000 470.640000 -12.620000 0.230000) 4) (segment 2759 (point 242.240000 470.640000 -12.620000 0.230000) (point 245.230000 471.940000 -11.630000 0.230000) 4) (segment 2760 (point 245.230000 471.940000 -11.630000 0.230000) (point 248.370000 472.670000 -10.700000 0.230000) 4) (segment 2761 (point 248.370000 472.670000 -10.700000 0.230000) (point 248.150000 475.610000 -9.770000 0.230000) 4) (segment 2762 (point 248.150000 475.610000 -9.770000 0.230000) (point 248.690000 479.320000 -9.130000 0.230000) 4) (segment 2763 (point 248.690000 479.320000 -9.130000 0.230000) (point 250.970000 481.650000 -10.070000 0.230000) 4) (segment 2764 (point 250.970000 481.650000 -10.070000 0.230000) (point 252.950000 483.300000 -9.220000 0.230000) 4) (segment 2765 (point 252.950000 483.300000 -9.220000 0.230000) (point 254.460000 484.850000 -7.870000 0.230000) 4) (segment 2766 (point 254.460000 484.850000 -7.870000 0.230000) (point 255.090000 486.190000 -6.670000 0.230000) 4) (segment 2767 (point 255.090000 486.190000 -6.670000 0.230000) (point 254.250000 487.780000 -5.130000 0.230000) 4) (segment 2768 (point 254.250000 487.780000 -5.130000 0.230000) (point 254.250000 487.780000 -5.150000 0.230000) 4) (segment 2769 (point 254.250000 487.780000 -5.150000 0.230000) (point 256.420000 486.510000 -3.570000 0.230000) 4) (segment 2770 (point 256.420000 486.510000 -3.570000 0.230000) (point 256.420000 486.510000 -3.600000 0.230000) 4) (segment 2771 (point 256.420000 486.510000 -3.600000 0.230000) (point 257.900000 486.250000 -2.450000 0.230000) 4) (segment 2772 (point 257.900000 486.250000 -2.450000 0.230000) (point 257.630000 487.390000 -0.630000 0.230000) 4) (segment 2773 (point 257.630000 487.390000 -0.630000 0.230000) (point 254.830000 487.330000 0.930000 0.230000) 4) (segment 2774 (point 254.830000 487.330000 0.930000 0.230000) (point 254.560000 488.460000 2.450000 0.230000) 4) (segment 2775 (point 254.560000 488.460000 2.450000 0.230000) (point 256.790000 488.990000 3.850000 0.230000) 4) (segment 2776 (point 256.790000 488.990000 3.850000 0.230000) (point 256.750000 493.160000 3.450000 0.230000) 4) (segment 2777 (point 256.750000 493.160000 3.450000 0.230000) (point 255.960000 496.550000 1.220000 0.230000) 4) (segment 2778 (point 255.960000 496.550000 1.220000 0.230000) (point 255.960000 496.550000 0.880000 0.230000) 4)) (branch 107 79 (segment 2779 (point 237.290000 453.970000 -17.300000 0.915000) (point 235.280000 454.090000 -16.500000 0.460000) 4) (segment 2780 (point 235.280000 454.090000 -16.500000 0.460000) (point 233.820000 454.340000 -15.320000 0.460000) 4) (segment 2781 (point 233.820000 454.340000 -15.320000 0.460000) (point 232.080000 455.720000 -14.050000 0.460000) 4) (segment 2782 (point 232.080000 455.720000 -14.050000 0.460000) (point 231.940000 456.290000 -12.620000 0.460000) 4) (segment 2783 (point 231.940000 456.290000 -12.620000 0.460000) (point 231.580000 453.820000 -11.550000 0.460000) 4) (segment 2784 (point 231.580000 453.820000 -11.550000 0.460000) (point 230.510000 452.360000 -10.200000 0.460000) 4) (segment 2785 (point 230.510000 452.360000 -10.200000 0.460000) (point 229.440000 450.920000 -9.670000 0.460000) 4) (segment 2786 (point 229.440000 450.920000 -9.670000 0.460000) (point 228.680000 450.140000 -8.230000 0.460000) 4) (segment 2787 (point 228.680000 450.140000 -8.230000 0.460000) (point 227.470000 449.270000 -8.230000 0.460000) 4) (segment 2788 (point 227.470000 449.270000 -8.230000 0.460000) (point 226.520000 447.250000 -8.230000 0.460000) 4) (segment 2789 (point 226.520000 447.250000 -8.230000 0.460000) (point 223.580000 447.760000 -6.970000 0.460000) 4) (segment 2790 (point 223.580000 447.760000 -6.970000 0.460000) (point 220.900000 447.130000 -6.550000 0.460000) 4) (segment 2791 (point 220.900000 447.130000 -6.550000 0.460000) (point 217.640000 446.970000 -6.850000 0.460000) 4) (segment 2792 (point 217.640000 446.970000 -6.850000 0.460000) (point 214.830000 446.900000 -6.020000 0.460000) 4) (segment 2793 (point 214.830000 446.900000 -6.020000 0.460000) (point 211.830000 445.610000 -4.920000 0.460000) 4) (segment 2794 (point 211.830000 445.610000 -4.920000 0.460000) (point 209.970000 447.550000 -3.650000 0.460000) 4) (segment 2795 (point 209.970000 447.550000 -3.650000 0.460000) (point 210.450000 449.460000 -3.200000 0.460000) 4) (segment 2796 (point 210.450000 449.460000 -3.200000 0.460000) (point 210.190000 450.590000 -1.700000 0.460000) 4) (segment 2797 (point 210.190000 450.590000 -1.700000 0.460000) (point 207.510000 449.960000 -0.970000 0.460000) 4) (segment 2798 (point 207.510000 449.960000 -0.970000 0.460000) (point 205.200000 451.820000 0.350000 0.460000) 4) (segment 2799 (point 205.200000 451.820000 0.350000 0.460000) (point 205.200000 451.820000 0.320000 0.460000) 4) (segment 2800 (point 205.200000 451.820000 0.320000 0.460000) (point 203.330000 453.760000 0.670000 0.460000) 4) (segment 2801 (point 203.330000 453.760000 0.670000 0.460000) (point 200.380000 454.270000 0.670000 0.460000) 4) (segment 2802 (point 200.380000 454.270000 0.670000 0.460000) (point 196.730000 455.790000 -0.020000 0.460000) 4) (segment 2803 (point 196.730000 455.790000 -0.020000 0.460000) (point 193.960000 457.540000 -1.250000 0.460000) 4) (segment 2804 (point 193.960000 457.540000 -1.250000 0.460000) (point 191.640000 459.380000 -0.300000 0.460000) 4) (segment 2805 (point 191.640000 459.380000 -0.300000 0.460000) (point 190.570000 457.940000 0.320000 0.460000) 4) (segment 2806 (point 190.570000 457.940000 0.320000 0.460000) (point 187.050000 458.900000 1.220000 0.460000) 4) (segment 2807 (point 187.050000 458.900000 1.220000 0.460000) (point 184.540000 459.520000 0.750000 0.460000) 4) (segment 2808 (point 184.540000 459.520000 0.750000 0.460000) (point 181.020000 460.470000 -0.550000 0.460000) 4) (segment 2809 (point 181.020000 460.470000 -0.550000 0.460000) (point 178.840000 461.750000 -0.900000 0.460000) 4) (segment 2810 (point 178.840000 461.750000 -0.900000 0.460000) (point 175.850000 460.450000 -2.150000 0.460000) 4) (segment 2811 (point 175.850000 460.450000 -2.150000 0.460000) (point 174.590000 457.780000 -2.800000 0.460000) 4) (segment 2812 (point 174.590000 457.780000 -2.800000 0.460000) (point 171.460000 457.040000 -3.100000 0.460000) 4) (segment 2813 (point 171.460000 457.040000 -3.100000 0.460000) (point 167.180000 457.230000 -3.970000 0.460000) 4) (segment 2814 (point 167.180000 457.230000 -3.970000 0.460000) (point 167.180000 457.230000 -4.000000 0.460000) 4) (segment 2815 (point 167.180000 457.230000 -4.000000 0.460000) (point 164.810000 457.270000 -3.600000 0.460000) 4) (segment 2816 (point 164.810000 457.270000 -3.600000 0.460000) (point 164.810000 457.270000 -3.620000 0.460000) 4) (segment 2817 (point 164.810000 457.270000 -3.620000 0.460000) (point 163.160000 456.290000 -1.770000 0.460000) 4) (segment 2818 (point 163.160000 456.290000 -1.770000 0.460000) (point 160.480000 455.660000 -1.020000 0.460000) 4) (segment 2819 (point 160.480000 455.660000 -1.020000 0.460000) (point 158.020000 458.070000 0.850000 0.460000) 4) (segment 2820 (point 158.020000 458.070000 0.850000 0.460000) (point 155.850000 459.350000 2.850000 0.460000) 4) (segment 2821 (point 155.850000 459.350000 2.850000 0.460000) (point 155.850000 459.350000 2.830000 0.460000) 4) (segment 2822 (point 155.850000 459.350000 2.830000 0.460000) (point 152.500000 461.560000 4.500000 0.460000) 4)) (branch 108 78 (segment 2823 (point 244.400000 359.090000 -26.950000 1.145000) (point 247.390000 360.390000 -28.370000 0.915000) 4) (segment 2824 (point 247.390000 360.390000 -28.370000 0.915000) (point 247.170000 363.330000 -29.500000 0.915000) 4) (segment 2825 (point 247.170000 363.330000 -29.500000 0.915000) (point 245.930000 366.620000 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391.360000 -35.350000 0.915000) (point 236.110000 394.400000 -36.380000 0.915000) 4) (segment 2835 (point 236.110000 394.400000 -36.380000 0.915000) (point 235.870000 396.360000 -36.400000 0.915000) 4) (segment 2836 (point 235.870000 396.360000 -36.400000 0.915000) (point 235.780000 398.720000 -36.380000 0.915000) 4) (segment 2837 (point 235.780000 398.720000 -36.380000 0.915000) (point 235.840000 403.660000 -36.720000 0.915000) 4) (segment 2838 (point 235.840000 403.660000 -36.720000 0.915000) (point 232.990000 407.770000 -37.020000 0.915000) 4) (segment 2839 (point 232.990000 407.770000 -37.020000 0.915000) (point 233.040000 409.570000 -38.150000 0.915000) 4) (segment 2840 (point 233.040000 409.570000 -38.150000 0.915000) (point 231.660000 413.430000 -39.200000 0.915000) 4) (segment 2841 (point 231.660000 413.430000 -39.200000 0.915000) (point 229.720000 417.760000 -40.170000 0.915000) 4) (segment 2842 (point 229.720000 417.760000 -40.170000 0.915000) (point 229.720000 423.720000 -41.170000 0.915000) 4) (segment 2843 (point 229.720000 423.720000 -41.170000 0.915000) (point 229.550000 426.580000 -41.770000 0.915000) 4) (segment 2844 (point 229.550000 426.580000 -41.770000 0.915000) (point 230.280000 431.520000 -41.850000 0.915000) 4) (segment 2845 (point 230.280000 431.520000 -41.850000 0.915000) (point 229.930000 435.020000 -40.600000 0.915000) 4) (segment 2846 (point 229.930000 435.020000 -40.600000 0.915000) (point 228.870000 439.550000 -41.030000 0.915000) 4) (segment 2847 (point 228.870000 439.550000 -41.030000 0.915000) (point 228.870000 439.550000 -41.050000 0.915000) 4) (segment 2848 (point 228.870000 439.550000 -41.050000 0.915000) (point 226.340000 444.330000 -41.730000 0.915000) 4) (segment 2849 (point 226.340000 444.330000 -41.730000 0.915000) (point 224.380000 448.650000 -42.100000 0.915000) 4) (segment 2850 (point 224.380000 448.650000 -42.100000 0.915000) (point 224.930000 452.360000 -43.100000 0.915000) 4) (segment 2851 (point 224.930000 452.360000 -43.100000 0.915000) (point 225.730000 454.930000 -43.920000 0.915000) 4) (segment 2852 (point 225.730000 454.930000 -43.920000 0.915000) (point 225.730000 454.930000 -43.950000 0.915000) 4) (segment 2853 (point 225.730000 454.930000 -43.950000 0.915000) (point 224.800000 458.900000 -44.970000 0.915000) 4) (segment 2854 (point 224.800000 458.900000 -44.970000 0.915000) (point 223.070000 460.290000 -45.750000 0.915000) 4) (segment 2855 (point 223.070000 460.290000 -45.750000 0.915000) (point 223.070000 460.290000 -45.770000 0.915000) 4) (segment 2856 (point 223.070000 460.290000 -45.770000 0.915000) (point 221.650000 462.340000 -47.020000 0.915000) 4) (segment 2857 (point 221.650000 462.340000 -47.020000 0.915000) (point 222.180000 466.040000 -47.880000 0.915000) 4) (segment 2858 (point 222.180000 466.040000 -47.880000 0.915000) (point 221.650000 468.310000 -47.880000 0.915000) 4)) (branch 109 108 (segment 2859 (point 221.650000 468.310000 -47.880000 0.915000) (point 223.540000 470.570000 -46.930000 0.460000) 4) (segment 2860 (point 223.540000 470.570000 -46.930000 0.460000) (point 225.940000 472.340000 -46.450000 0.460000) 4) (segment 2861 (point 225.940000 472.340000 -46.450000 0.460000) (point 228.220000 474.660000 -46.250000 0.460000) 4) (segment 2862 (point 228.220000 474.660000 -46.250000 0.460000) (point 230.460000 475.190000 -46.250000 0.460000) 4) (segment 2863 (point 230.460000 475.190000 -46.250000 0.460000) (point 231.580000 478.440000 -46.880000 0.460000) 4) (segment 2864 (point 231.580000 478.440000 -46.880000 0.460000) (point 232.530000 480.450000 -47.600000 0.460000) 4) (segment 2865 (point 232.530000 480.450000 -47.600000 0.460000) (point 235.390000 482.300000 -47.920000 0.460000) 4) (segment 2866 (point 235.390000 482.300000 -47.920000 0.460000) (point 236.860000 482.060000 -47.330000 0.460000) 4) (segment 2867 (point 236.860000 482.060000 -47.330000 0.460000) (point 238.780000 481.910000 -48.880000 0.460000) 4) (segment 2868 (point 238.780000 481.910000 -48.880000 0.460000) (point 238.700000 484.280000 -50.130000 0.460000) 4) (segment 2869 (point 238.700000 484.280000 -50.130000 0.460000) (point 238.700000 484.280000 -50.150000 0.460000) 4) (segment 2870 (point 238.700000 484.280000 -50.150000 0.460000) (point 238.700000 484.280000 -51.500000 0.460000) 4) (segment 2871 (point 238.700000 484.280000 -51.500000 0.460000) (point 238.700000 484.280000 -51.520000 0.460000) 4) (segment 2872 (point 238.700000 484.280000 -51.520000 0.460000) (point 237.400000 485.760000 -52.200000 0.460000) 4) (segment 2873 (point 237.400000 485.760000 -52.200000 0.460000) (point 237.590000 487.010000 -53.380000 0.460000) 4) (segment 2874 (point 237.590000 487.010000 -53.380000 0.460000) (point 239.360000 487.410000 -54.350000 0.460000) 4) (segment 2875 (point 239.360000 487.410000 -54.350000 0.460000) (point 241.480000 488.510000 -55.350000 0.460000) 4) (segment 2876 (point 241.480000 488.510000 -55.350000 0.460000) (point 241.480000 488.510000 -55.380000 0.460000) 4) (segment 2877 (point 241.480000 488.510000 -55.380000 0.460000) (point 241.780000 489.190000 -55.720000 0.460000) 4) (segment 2878 (point 241.780000 489.190000 -55.720000 0.460000) (point 241.000000 492.570000 -55.720000 0.460000) 4) (segment 2879 (point 241.000000 492.570000 -55.720000 0.460000) (point 242.510000 494.140000 -56.420000 0.460000) 4) (segment 2880 (point 242.510000 494.140000 -56.420000 0.460000) (point 243.590000 495.580000 -57.500000 0.460000) 4) (segment 2881 (point 243.590000 495.580000 -57.500000 0.460000) (point 244.650000 497.020000 -58.300000 0.460000) 4) (segment 2882 (point 244.650000 497.020000 -58.300000 0.460000) (point 245.230000 496.560000 -58.900000 0.460000) 4) (segment 2883 (point 245.230000 496.560000 -58.900000 0.460000) (point 246.130000 496.770000 -58.880000 0.460000) 4) (segment 2884 (point 246.130000 496.770000 -58.880000 0.460000) (point 245.470000 499.600000 -59.020000 0.460000) 4) (segment 2885 (point 245.470000 499.600000 -59.020000 0.460000) (point 245.910000 499.700000 -59.020000 0.460000) 4) (segment 2886 (point 245.910000 499.700000 -59.020000 0.460000) (point 248.020000 500.800000 -59.630000 0.460000) 4) (segment 2887 (point 248.020000 500.800000 -59.630000 0.460000) (point 248.830000 503.380000 -59.630000 0.460000) 4) (segment 2888 (point 248.830000 503.380000 -59.630000 0.460000) (point 249.270000 503.480000 -60.670000 0.460000) 4) (segment 2889 (point 249.270000 503.480000 -60.670000 0.460000) (point 248.160000 506.210000 -61.520000 0.460000) 4) (segment 2890 (point 248.160000 506.210000 -61.520000 0.460000) (point 251.150000 507.500000 -62.600000 0.460000) 4) (segment 2891 (point 251.150000 507.500000 -62.600000 0.460000) (point 253.260000 508.590000 -63.170000 0.460000) 4) (segment 2892 (point 253.260000 508.590000 -63.170000 0.460000) (point 255.090000 510.820000 -63.950000 0.460000) 4) (segment 2893 (point 255.090000 510.820000 -63.950000 0.460000) (point 255.320000 513.860000 -65.000000 0.460000) 4) (segment 2894 (point 255.320000 513.860000 -65.000000 0.460000) (point 256.970000 514.840000 -65.750000 0.460000) 4) (segment 2895 (point 256.970000 514.840000 -65.750000 0.460000) (point 259.520000 516.030000 -66.600000 0.460000) 4) (segment 2896 (point 259.520000 516.030000 -66.600000 0.460000) (point 262.380000 517.900000 -64.570000 0.460000) 4) (segment 2897 (point 262.380000 517.900000 -64.570000 0.460000) (point 264.170000 518.320000 -65.300000 0.460000) 4) (segment 2898 (point 264.170000 518.320000 -65.300000 0.460000) (point 264.170000 518.320000 -65.320000 0.460000) 4) (segment 2899 (point 264.170000 518.320000 -65.320000 0.460000) (point 266.590000 520.080000 -65.970000 0.460000) 4) (segment 2900 (point 266.590000 520.080000 -65.970000 0.460000) (point 268.190000 519.250000 -66.650000 0.460000) 4) (segment 2901 (point 268.190000 519.250000 -66.650000 0.460000) (point 268.190000 519.250000 -66.680000 0.460000) 4) (segment 2902 (point 268.190000 519.250000 -66.680000 0.460000) (point 270.420000 519.780000 -67.900000 0.460000) 4) (segment 2903 (point 270.420000 519.780000 -67.900000 0.460000) (point 270.420000 519.780000 -67.930000 0.460000) 4) (segment 2904 (point 270.420000 519.780000 -67.930000 0.460000) (point 272.070000 520.760000 -69.420000 0.460000) 4) (segment 2905 (point 272.070000 520.760000 -69.420000 0.460000) (point 272.070000 520.760000 -69.450000 0.460000) 4) (segment 2906 (point 272.070000 520.760000 -69.450000 0.460000) (point 273.150000 522.210000 -70.270000 0.460000) 4) (segment 2907 (point 273.150000 522.210000 -70.270000 0.460000) (point 272.760000 523.910000 -71.720000 0.460000) 4) (segment 2908 (point 272.760000 523.910000 -71.720000 0.460000) (point 272.760000 523.910000 -71.750000 0.460000) 4) (segment 2909 (point 272.760000 523.910000 -71.750000 0.460000) (point 276.330000 524.750000 -72.200000 0.460000) 4) (segment 2910 (point 276.330000 524.750000 -72.200000 0.460000) (point 280.670000 526.360000 -73.570000 0.460000) 4) (segment 2911 (point 280.670000 526.360000 -73.570000 0.460000) (point 283.390000 528.800000 -74.300000 0.460000) 4) (segment 2912 (point 283.390000 528.800000 -74.300000 0.460000) (point 286.690000 530.770000 -74.780000 0.460000) 4) (segment 2913 (point 286.690000 530.770000 -74.780000 0.460000) (point 289.240000 531.950000 -75.250000 0.460000) 4) (segment 2914 (point 289.240000 531.950000 -75.250000 0.460000) (point 293.310000 534.690000 -75.880000 0.460000) 4) (segment 2915 (point 293.310000 534.690000 -75.880000 0.460000) (point 295.550000 535.220000 -76.030000 0.460000) 4) (segment 2916 (point 295.550000 535.220000 -76.030000 0.460000) (point 298.270000 537.660000 -75.720000 0.460000) 4) (segment 2917 (point 298.270000 537.660000 -75.720000 0.460000) (point 301.790000 536.680000 -76.030000 0.460000) 4) (segment 2918 (point 301.790000 536.680000 -76.030000 0.460000) (point 305.100000 538.660000 -76.130000 0.460000) 4) (segment 2919 (point 305.100000 538.660000 -76.130000 0.460000) (point 308.090000 539.960000 -77.350000 0.460000) 4) (segment 2920 (point 308.090000 539.960000 -77.350000 0.460000) (point 309.610000 541.510000 -78.200000 0.460000) 4) (segment 2921 (point 309.610000 541.510000 -78.200000 0.460000) (point 313.190000 542.340000 -79.070000 0.460000) 4) (segment 2922 (point 313.190000 542.340000 -79.070000 0.460000) (point 316.050000 544.200000 -80.370000 0.460000) 4) (segment 2923 (point 316.050000 544.200000 -80.370000 0.460000) (point 318.420000 544.160000 -82.220000 0.460000) 4) (segment 2924 (point 318.420000 544.160000 -82.220000 0.460000) (point 318.420000 544.160000 -82.520000 0.460000) 4)) (branch 110 108 (segment 2925 (point 221.650000 468.310000 -47.880000 0.915000) (point 220.100000 470.930000 -47.880000 0.915000) 4) (segment 2926 (point 220.100000 470.930000 -47.880000 0.915000) (point 219.930000 475.680000 -47.880000 0.915000) 4) (segment 2927 (point 219.930000 475.680000 -47.880000 0.915000) (point 219.000000 479.630000 -47.130000 0.915000) 4) (segment 2928 (point 219.000000 479.630000 -47.130000 0.915000) (point 217.940000 484.160000 -47.670000 0.915000) 4) (segment 2929 (point 217.940000 484.160000 -47.670000 0.915000) (point 216.570000 488.020000 -47.550000 0.915000) 4) (segment 2930 (point 216.570000 488.020000 -47.550000 0.915000) (point 215.460000 490.750000 -48.750000 0.915000) 4) (segment 2931 (point 215.460000 490.750000 -48.750000 0.915000) (point 216.440000 494.550000 -50.050000 0.915000) 4) (segment 2932 (point 216.440000 494.550000 -50.050000 0.915000) (point 215.020000 496.620000 -50.050000 0.915000) 4) (segment 2933 (point 215.020000 496.620000 -50.050000 0.915000) (point 216.140000 499.860000 -48.620000 0.915000) 4) (segment 2934 (point 216.140000 499.860000 -48.620000 0.915000) (point 215.660000 503.930000 -48.620000 0.915000) 4) (segment 2935 (point 215.660000 503.930000 -48.620000 0.915000) (point 215.630000 508.100000 -48.620000 0.915000) 4) (segment 2936 (point 215.630000 508.100000 -48.620000 0.915000) (point 214.960000 510.920000 -48.620000 0.915000) 4)) (branch 111 110 (segment 2937 (point 214.960000 510.920000 -48.620000 0.915000) (point 212.130000 513.270000 -48.620000 0.460000) 4) (segment 2938 (point 212.130000 513.270000 -48.620000 0.460000) (point 210.840000 514.760000 -50.420000 0.460000) 4) (segment 2939 (point 210.840000 514.760000 -50.420000 0.460000) (point 210.030000 512.170000 -51.880000 0.460000) 4) (segment 2940 (point 210.030000 512.170000 -51.880000 0.460000) (point 210.030000 512.170000 -51.900000 0.460000) 4) (segment 2941 (point 210.030000 512.170000 -51.900000 0.460000) (point 209.810000 515.120000 -53.350000 0.460000) 4) (segment 2942 (point 209.810000 515.120000 -53.350000 0.460000) (point 210.570000 515.890000 -54.320000 0.460000) 4) (segment 2943 (point 210.570000 515.890000 -54.320000 0.460000) (point 208.700000 517.830000 -55.050000 0.460000) 4) (segment 2944 (point 208.700000 517.830000 -55.050000 0.460000) (point 207.720000 520.000000 -55.970000 0.460000) 4) (segment 2945 (point 207.720000 520.000000 -55.970000 0.460000) (point 205.990000 521.380000 -56.950000 0.460000) 4) (segment 2946 (point 205.990000 521.380000 -56.950000 0.460000) (point 205.540000 521.280000 -58.130000 0.460000) 4) (segment 2947 (point 205.540000 521.280000 -58.130000 0.460000) (point 205.450000 523.650000 -57.630000 0.460000) 4) (segment 2948 (point 205.450000 523.650000 -57.630000 0.460000) (point 205.640000 524.880000 -58.880000 0.460000) 4) (segment 2949 (point 205.640000 524.880000 -58.880000 0.460000) (point 205.060000 525.340000 -60.270000 0.460000) 4) (segment 2950 (point 205.060000 525.340000 -60.270000 0.460000) (point 202.680000 525.390000 -61.920000 0.460000) 4) (segment 2951 (point 202.680000 525.390000 -61.920000 0.460000) (point 202.290000 527.080000 -63.400000 0.460000) 4) (segment 2952 (point 202.290000 527.080000 -63.400000 0.460000) (point 203.050000 527.860000 -64.900000 0.460000) 4) (segment 2953 (point 203.050000 527.860000 -64.900000 0.460000) (point 202.070000 530.030000 -65.470000 0.460000) 4) (segment 2954 (point 202.070000 530.030000 -65.470000 0.460000) (point 201.670000 531.720000 -64.750000 0.460000) 4) (segment 2955 (point 201.670000 531.720000 -64.750000 0.460000) (point 200.390000 533.210000 -65.630000 0.460000) 4) (segment 2956 (point 200.390000 533.210000 -65.630000 0.460000) (point 199.230000 534.130000 -66.220000 0.460000) 4) (segment 2957 (point 199.230000 534.130000 -66.220000 0.460000) (point 199.410000 535.360000 -67.100000 0.460000) 4) (segment 2958 (point 199.410000 535.360000 -67.100000 0.460000) (point 198.430000 537.530000 -67.570000 0.460000) 4) (segment 2959 (point 198.430000 537.530000 -67.570000 0.460000) (point 197.270000 538.450000 -68.350000 0.460000) 4) (segment 2960 (point 197.270000 538.450000 -68.350000 0.460000) (point 195.360000 538.590000 -69.320000 0.460000) 4) (segment 2961 (point 195.360000 538.590000 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0.460000) 4) (segment 2970 (point 181.730000 554.510000 -78.600000 0.460000) (point 179.540000 555.790000 -80.020000 0.460000) 4) (segment 2971 (point 179.540000 555.790000 -80.020000 0.460000) (point 178.700000 557.380000 -80.920000 0.460000) 4) (segment 2972 (point 178.700000 557.380000 -80.920000 0.460000) (point 178.170000 559.660000 -82.570000 0.460000) 4) (segment 2973 (point 178.170000 559.660000 -82.570000 0.460000) (point 176.610000 562.270000 -83.530000 0.460000) 4) (segment 2974 (point 176.610000 562.270000 -83.530000 0.460000) (point 176.660000 564.070000 -84.880000 0.460000) 4) (segment 2975 (point 176.660000 564.070000 -84.880000 0.460000) (point 174.170000 564.690000 -85.630000 0.460000) 4) (segment 2976 (point 174.170000 564.690000 -85.630000 0.460000) (point 173.320000 566.280000 -86.720000 0.460000) 4) (segment 2977 (point 173.320000 566.280000 -86.720000 0.460000) (point 173.320000 566.280000 -86.750000 0.460000) 4) (segment 2978 (point 173.320000 566.280000 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0.460000) 4) (segment 2987 (point 163.970000 576.030000 -89.480000 0.460000) (point 162.940000 576.380000 -89.550000 0.460000) 4) (segment 2988 (point 162.940000 576.380000 -89.550000 0.460000) (point 161.520000 578.440000 -91.220000 0.460000) 4) (segment 2989 (point 161.520000 578.440000 -91.220000 0.460000) (point 158.760000 580.180000 -92.380000 0.460000) 4) (segment 2990 (point 158.760000 580.180000 -92.380000 0.460000) (point 158.760000 580.180000 -92.400000 0.460000) 4) (segment 2991 (point 158.760000 580.180000 -92.400000 0.460000) (point 157.780000 582.340000 -93.650000 0.460000) 4) (segment 2992 (point 157.780000 582.340000 -93.650000 0.460000) (point 155.910000 584.290000 -95.200000 0.460000) 4) (segment 2993 (point 155.910000 584.290000 -95.200000 0.460000) (point 155.910000 584.290000 -95.220000 0.460000) 4) (segment 2994 (point 155.910000 584.290000 -95.220000 0.460000) (point 155.060000 585.880000 -95.880000 0.460000) 4) (segment 2995 (point 155.060000 585.880000 -95.880000 0.460000) (point 154.660000 587.590000 -96.930000 0.460000) 4) (segment 2996 (point 154.660000 587.590000 -96.930000 0.460000) (point 154.660000 587.590000 -96.950000 0.460000) 4) (segment 2997 (point 154.660000 587.590000 -96.950000 0.460000) (point 154.450000 590.510000 -97.750000 0.460000) 4) (segment 2998 (point 154.450000 590.510000 -97.750000 0.460000) (point 154.450000 590.510000 -97.780000 0.460000) 4) (segment 2999 (point 154.450000 590.510000 -97.780000 0.460000) (point 151.990000 592.920000 -98.380000 0.460000) 4) (segment 3000 (point 151.990000 592.920000 -98.380000 0.460000) (point 150.840000 593.850000 -99.630000 0.460000) 4) (segment 3001 (point 150.840000 593.850000 -99.630000 0.460000) (point 150.260000 594.300000 -101.250000 0.460000) 4) (segment 3002 (point 150.260000 594.300000 -101.250000 0.460000) (point 150.260000 594.300000 -101.270000 0.460000) 4)) (branch 112 110 (segment 3003 (point 214.960000 510.920000 -48.620000 0.915000) (point 214.350000 515.570000 -49.250000 0.915000) 4) (segment 3004 (point 214.350000 515.570000 -49.250000 0.915000) (point 214.850000 517.480000 -49.470000 0.915000) 4) (segment 3005 (point 214.850000 517.480000 -49.470000 0.915000) (point 215.340000 519.380000 -49.470000 0.915000) 4) (segment 3006 (point 215.340000 519.380000 -49.470000 0.915000) (point 213.780000 522.000000 -49.470000 0.915000) 4) (segment 3007 (point 213.780000 522.000000 -49.470000 0.915000) (point 214.200000 526.280000 -49.470000 0.915000) 4) (segment 3008 (point 214.200000 526.280000 -49.470000 0.915000) (point 213.720000 530.350000 -49.470000 0.915000) 4) (segment 3009 (point 213.720000 530.350000 -49.470000 0.915000) (point 213.190000 532.610000 -49.470000 0.915000) 4) (segment 3010 (point 213.190000 532.610000 -49.470000 0.915000) (point 214.170000 536.420000 -50.200000 0.915000) 4) (segment 3011 (point 214.170000 536.420000 -50.200000 0.915000) (point 214.080000 538.800000 -49.320000 0.915000) 4) (segment 3012 (point 214.080000 538.800000 -49.320000 0.915000) (point 212.390000 541.980000 -49.320000 0.915000) 4) (segment 3013 (point 212.390000 541.980000 -49.320000 0.915000) (point 212.050000 545.480000 -49.320000 0.915000) 4)) (branch 113 112 (segment 3014 (point 212.050000 545.480000 -49.320000 0.915000) (point 209.610000 546.120000 -47.820000 0.460000) 4) (segment 3015 (point 209.610000 546.120000 -47.820000 0.460000) (point 209.030000 546.590000 -46.820000 0.460000) 4) (segment 3016 (point 209.030000 546.590000 -46.820000 0.460000) (point 208.580000 546.480000 -46.820000 0.460000) 4) (segment 3017 (point 208.580000 546.480000 -46.820000 0.460000) (point 208.320000 547.610000 -46.820000 0.460000) 4) (segment 3018 (point 208.320000 547.610000 -46.820000 0.460000) (point 205.050000 547.440000 -45.770000 0.460000) 4) (segment 3019 (point 205.050000 547.440000 -45.770000 0.460000) (point 205.550000 549.350000 -45.070000 0.460000) 4) (segment 3020 (point 205.550000 549.350000 -45.070000 0.460000) (point 207.350000 549.770000 -43.670000 0.460000) 4) (segment 3021 (point 207.350000 549.770000 -43.670000 0.460000) (point 205.600000 551.150000 -42.170000 0.460000) 4) (segment 3022 (point 205.600000 551.150000 -42.170000 0.460000) (point 204.620000 553.310000 -41.170000 0.460000) 4) (segment 3023 (point 204.620000 553.310000 -41.170000 0.460000) (point 203.200000 555.370000 -40.570000 0.460000) 4) (segment 3024 (point 203.200000 555.370000 -40.570000 0.460000) (point 201.330000 557.310000 -41.350000 0.460000) 4) (segment 3025 (point 201.330000 557.310000 -41.350000 0.460000) (point 199.460000 559.270000 -41.500000 0.460000) 4) (segment 3026 (point 199.460000 559.270000 -41.500000 0.460000) (point 197.730000 560.650000 -40.630000 0.460000) 4) (segment 3027 (point 197.730000 560.650000 -40.630000 0.460000) (point 196.700000 561.010000 -39.550000 0.460000) 4) (segment 3028 (point 196.700000 561.010000 -39.550000 0.460000) (point 194.870000 564.770000 -39.050000 0.460000) 4) (segment 3029 (point 194.870000 564.770000 -39.050000 0.460000) (point 194.220000 567.590000 -38.770000 0.460000) 4) (segment 3030 (point 194.220000 567.590000 -38.770000 0.460000) (point 192.160000 568.310000 -38.770000 0.460000) 4) (segment 3031 (point 192.160000 568.310000 -38.770000 0.460000) (point 189.970000 569.590000 -37.900000 0.460000) 4) (segment 3032 (point 189.970000 569.590000 -37.900000 0.460000) (point 187.800000 570.860000 -37.300000 0.460000) 4) (segment 3033 (point 187.800000 570.860000 -37.300000 0.460000) (point 185.430000 570.900000 -35.830000 0.460000) 4) (segment 3034 (point 185.430000 570.900000 -35.830000 0.460000) (point 183.110000 572.760000 -35.830000 0.460000) 4) (segment 3035 (point 183.110000 572.760000 -35.830000 0.460000) (point 180.870000 572.230000 -35.830000 0.460000) 4) (segment 3036 (point 180.870000 572.230000 -35.830000 0.460000) (point 178.780000 571.140000 -37.470000 0.460000) 4) (segment 3037 (point 178.780000 571.140000 -37.470000 0.460000) (point 178.780000 571.140000 -37.500000 0.460000) 4) (segment 3038 (point 178.780000 571.140000 -37.500000 0.460000) (point 176.180000 568.150000 -38.250000 0.460000) 4) (segment 3039 (point 176.180000 568.150000 -38.250000 0.460000) (point 173.820000 568.180000 -36.500000 0.460000) 4) (segment 3040 (point 173.820000 568.180000 -36.500000 0.460000) (point 172.660000 569.110000 -36.500000 0.460000) 4) (segment 3041 (point 172.660000 569.110000 -36.500000 0.460000) (point 170.550000 568.020000 -36.780000 0.460000) 4) (segment 3042 (point 170.550000 568.020000 -36.780000 0.460000) (point 167.920000 569.200000 -37.700000 0.460000) 4) (segment 3043 (point 167.920000 569.200000 -37.700000 0.460000) (point 164.090000 569.480000 -38.350000 0.460000) 4) (segment 3044 (point 164.090000 569.480000 -38.350000 0.460000) (point 160.720000 565.720000 -38.630000 0.460000) 4) (segment 3045 (point 160.720000 565.720000 -38.630000 0.460000) (point 159.880000 567.310000 -39.600000 0.460000) 4) (segment 3046 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137.480000 564.450000 -39.800000 0.460000) 4) (segment 3055 (point 137.480000 564.450000 -39.800000 0.460000) (point 134.080000 564.850000 -38.880000 0.460000) 4) (segment 3056 (point 134.080000 564.850000 -38.880000 0.460000) (point 131.090000 563.550000 -37.580000 0.460000) 4) (segment 3057 (point 131.090000 563.550000 -37.580000 0.460000) (point 127.120000 564.410000 -36.970000 0.460000) 4) (segment 3058 (point 127.120000 564.410000 -36.970000 0.460000) (point 125.640000 564.660000 -35.700000 0.460000) 4) (segment 3059 (point 125.640000 564.660000 -35.700000 0.460000) (point 126.040000 562.970000 -34.900000 0.460000) 4) (segment 3060 (point 126.040000 562.970000 -34.900000 0.460000) (point 127.020000 560.800000 -34.700000 0.460000) 4)) (branch 114 112 (segment 3061 (point 212.050000 545.480000 -49.320000 0.915000) (point 212.780000 550.420000 -49.920000 0.915000) 4) (segment 3062 (point 212.780000 550.420000 -49.920000 0.915000) (point 214.300000 551.980000 -49.830000 0.915000) 4) (segment 3063 (point 214.300000 551.980000 -49.830000 0.915000) (point 214.660000 554.450000 -49.830000 0.915000) 4) (segment 3064 (point 214.660000 554.450000 -49.830000 0.915000) (point 213.680000 556.610000 -51.220000 0.915000) 4) (segment 3065 (point 213.680000 556.610000 -51.220000 0.915000) (point 213.330000 560.110000 -52.020000 0.915000) 4) (segment 3066 (point 213.330000 560.110000 -52.020000 0.915000) (point 214.450000 563.350000 -52.770000 0.915000) 4) (segment 3067 (point 214.450000 563.350000 -52.770000 0.915000) (point 214.280000 568.110000 -53.620000 0.915000) 4) (segment 3068 (point 214.280000 568.110000 -53.620000 0.915000) (point 214.840000 571.800000 -55.270000 0.915000) 4) (segment 3069 (point 214.840000 571.800000 -55.270000 0.915000) (point 215.690000 576.190000 -55.270000 0.915000) 4) (segment 3070 (point 215.690000 576.190000 -55.270000 0.915000) (point 216.240000 579.910000 -56.200000 0.915000) 4) (segment 3071 (point 216.240000 579.910000 -56.200000 0.915000) 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220.450000 632.240000 -63.880000 0.915000) 4) (segment 3089 (point 220.450000 632.240000 -63.880000 0.915000) (point 221.460000 635.940000 -64.050000 0.915000) 4) (segment 3090 (point 221.460000 635.940000 -64.050000 0.915000) (point 220.350000 638.660000 -63.050000 0.915000) 4) (segment 3091 (point 220.350000 638.660000 -63.050000 0.915000) (point 221.650000 643.160000 -62.450000 0.915000) 4) (segment 3092 (point 221.650000 643.160000 -62.450000 0.915000) (point 220.990000 645.980000 -62.080000 0.915000) 4)) (branch 115 114 (segment 3093 (point 220.990000 645.980000 -62.080000 0.915000) (point 223.320000 646.540000 -62.080000 0.230000) 4) (segment 3094 (point 223.320000 646.540000 -62.080000 0.230000) (point 227.430000 645.100000 -61.150000 0.230000) 4) (segment 3095 (point 227.430000 645.100000 -61.150000 0.230000) (point 229.210000 645.530000 -62.350000 0.230000) 4) (segment 3096 (point 229.210000 645.530000 -62.350000 0.230000) (point 230.810000 644.700000 -63.530000 0.230000) 4) (segment 3097 (point 230.810000 644.700000 -63.530000 0.230000) (point 231.840000 644.350000 -64.850000 0.230000) 4) (segment 3098 (point 231.840000 644.350000 -64.850000 0.230000) (point 232.600000 645.130000 -66.820000 0.230000) 4) (segment 3099 (point 232.600000 645.130000 -66.820000 0.230000) (point 233.230000 645.890000 -65.950000 0.230000) 4) (segment 3100 (point 233.230000 645.890000 -65.950000 0.230000) (point 234.030000 644.980000 -66.570000 0.230000) 4) (segment 3101 (point 234.030000 644.980000 -66.570000 0.230000) (point 235.280000 645.760000 -68.200000 0.230000) 4) (segment 3102 (point 235.280000 645.760000 -68.200000 0.230000) (point 237.780000 645.150000 -68.850000 0.230000) 4) (segment 3103 (point 237.780000 645.150000 -68.850000 0.230000) (point 239.570000 645.570000 -69.600000 0.230000) 4) (segment 3104 (point 239.570000 645.570000 -69.600000 0.230000) (point 241.160000 644.740000 -70.000000 0.230000) 4) (segment 3105 (point 241.160000 644.740000 -70.000000 0.230000) (point 245.270000 643.320000 -70.780000 0.230000) 4) (segment 3106 (point 245.270000 643.320000 -70.780000 0.230000) (point 246.960000 640.130000 -71.630000 0.230000) 4) (segment 3107 (point 246.960000 640.130000 -71.630000 0.230000) (point 249.460000 639.530000 -70.720000 0.230000) 4) (segment 3108 (point 249.460000 639.530000 -70.720000 0.230000) (point 252.730000 639.690000 -71.750000 0.230000) 4) (segment 3109 (point 252.730000 639.690000 -71.750000 0.230000) (point 255.800000 638.610000 -72.530000 0.230000) 4) (segment 3110 (point 255.800000 638.610000 -72.530000 0.230000) (point 257.270000 638.360000 -73.470000 0.230000) 4) (segment 3111 (point 257.270000 638.360000 -73.470000 0.230000) (point 260.000000 640.790000 -72.850000 0.230000) 4) (segment 3112 (point 260.000000 640.790000 -72.850000 0.230000) (point 262.600000 643.790000 -72.950000 0.230000) 4) (segment 3113 (point 262.600000 643.790000 -72.950000 0.230000) (point 264.250000 644.780000 -74.050000 0.230000) 4) (segment 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0.690000) 4) (segment 3426 (point 200.730000 889.720000 -47.100000 0.690000) (point 200.070000 892.550000 -45.500000 0.690000) 4) (segment 3427 (point 200.070000 892.550000 -45.500000 0.690000) (point 200.160000 896.160000 -44.300000 0.690000) 4) (segment 3428 (point 200.160000 896.160000 -44.300000 0.690000) (point 199.060000 898.900000 -43.130000 0.690000) 4) (segment 3429 (point 199.060000 898.900000 -43.130000 0.690000) (point 197.260000 898.480000 -41.750000 0.690000) 4) (segment 3430 (point 197.260000 898.480000 -41.750000 0.690000) (point 197.340000 896.100000 -40.800000 0.690000) 4) (segment 3431 (point 197.340000 896.100000 -40.800000 0.690000) (point 197.290000 894.300000 -39.550000 0.690000) 4) (segment 3432 (point 197.290000 894.300000 -39.550000 0.690000) (point 197.740000 894.410000 -37.630000 0.690000) 4) (segment 3433 (point 197.740000 894.410000 -37.630000 0.690000) (point 196.860000 894.200000 -35.380000 0.690000) 4) (segment 3434 (point 196.860000 894.200000 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0.690000) 4) (segment 3443 (point 197.950000 907.590000 -25.050000 0.690000) (point 194.420000 908.560000 -23.050000 0.690000) 4) (segment 3444 (point 194.420000 908.560000 -23.050000 0.690000) (point 191.480000 909.050000 -21.850000 0.690000) 4) (segment 3445 (point 191.480000 909.050000 -21.850000 0.690000) (point 190.060000 911.110000 -22.080000 0.690000) 4) (segment 3446 (point 190.060000 911.110000 -22.080000 0.690000) (point 189.790000 912.240000 -20.800000 0.690000) 4) (segment 3447 (point 189.790000 912.240000 -20.800000 0.690000) (point 187.930000 914.200000 -19.750000 0.690000) 4) (segment 3448 (point 187.930000 914.200000 -19.750000 0.690000) (point 186.640000 915.680000 -18.850000 0.690000) 4) (segment 3449 (point 186.640000 915.680000 -18.850000 0.690000) (point 186.640000 915.680000 -18.880000 0.690000) 4) (segment 3450 (point 186.640000 915.680000 -18.880000 0.690000) (point 187.450000 918.270000 -17.050000 0.690000) 4) (segment 3451 (point 187.450000 918.270000 -17.050000 0.690000) (point 186.290000 919.180000 -15.950000 0.690000) 4) (segment 3452 (point 186.290000 919.180000 -15.950000 0.690000) (point 186.290000 919.180000 -15.920000 0.690000) 4) (segment 3453 (point 186.290000 919.180000 -15.920000 0.690000) (point 185.260000 919.540000 -14.600000 0.690000) 4) (segment 3454 (point 185.260000 919.540000 -14.600000 0.690000) (point 184.730000 921.810000 -13.420000 0.690000) 4) (segment 3455 (point 184.730000 921.810000 -13.420000 0.690000) (point 184.780000 923.610000 -12.300000 0.690000) 4) (segment 3456 (point 184.780000 923.610000 -12.300000 0.690000) (point 184.780000 923.610000 -12.320000 0.690000) 4) (segment 3457 (point 184.780000 923.610000 -12.320000 0.690000) (point 185.720000 925.630000 -12.000000 0.690000) 4) (segment 3458 (point 185.720000 925.630000 -12.000000 0.690000) (point 184.430000 927.120000 -12.000000 0.690000) 4) (segment 3459 (point 184.430000 927.120000 -12.000000 0.690000) (point 182.060000 927.150000 -10.120000 0.690000) 4) (segment 3460 (point 182.060000 927.150000 -10.120000 0.690000) (point 182.240000 928.390000 -8.950000 0.690000) 4) (segment 3461 (point 182.240000 928.390000 -8.950000 0.690000) (point 183.770000 929.950000 -8.250000 0.690000) 4) (segment 3462 (point 183.770000 929.950000 -8.250000 0.690000) (point 183.100000 932.770000 -8.350000 0.690000) 4) (segment 3463 (point 183.100000 932.770000 -8.350000 0.690000) (point 182.260000 934.370000 -7.670000 0.690000) 4) (segment 3464 (point 182.260000 934.370000 -7.670000 0.690000) (point 179.500000 936.100000 -6.780000 0.690000) 4) (segment 3465 (point 179.500000 936.100000 -6.780000 0.690000) (point 179.590000 939.710000 -6.280000 0.690000) 4) (segment 3466 (point 179.590000 939.710000 -6.280000 0.690000) (point 177.980000 940.530000 -5.820000 0.690000) 4) (segment 3467 (point 177.980000 940.530000 -5.820000 0.690000) (point 176.820000 941.460000 -4.030000 0.690000) 4) (segment 3468 (point 176.820000 941.460000 -4.030000 0.690000) (point 176.820000 941.460000 -4.050000 0.690000) 4) (segment 3469 (point 176.820000 941.460000 -4.050000 0.690000) (point 174.460000 941.500000 -2.450000 0.690000) 4) (segment 3470 (point 174.460000 941.500000 -2.450000 0.690000) (point 170.810000 943.030000 -1.670000 0.690000) 4) (segment 3471 (point 170.810000 943.030000 -1.670000 0.690000) (point 171.170000 945.500000 0.070000 0.690000) 4) (segment 3472 (point 171.170000 945.500000 0.070000 0.690000) (point 172.610000 949.420000 0.630000 0.690000) 4) (segment 3473 (point 172.610000 949.420000 0.630000 0.690000) (point 174.250000 950.410000 -0.500000 0.690000) 4) (segment 3474 (point 174.250000 950.410000 -0.500000 0.690000) (point 174.250000 950.410000 -0.550000 0.690000) 4) (segment 3475 (point 174.250000 950.410000 -0.550000 0.690000) (point 173.900000 953.900000 0.280000 0.690000) 4) (segment 3476 (point 173.900000 953.900000 0.280000 0.690000) (point 176.060000 956.800000 1.000000 0.690000) 4) (segment 3477 (point 176.060000 956.800000 1.000000 0.690000) (point 177.900000 959.030000 2.050000 0.690000) 4) (segment 3478 (point 177.900000 959.030000 2.050000 0.690000) (point 177.900000 959.030000 2.170000 0.690000) 4) (segment 3479 (point 177.900000 959.030000 2.170000 0.690000) (point 179.990000 960.110000 3.820000 0.690000) 4) (segment 3480 (point 179.990000 960.110000 3.820000 0.690000) (point 179.990000 960.110000 3.800000 0.690000) 4) (segment 3481 (point 179.990000 960.110000 3.800000 0.690000) (point 182.010000 963.570000 4.400000 0.690000) 4) (segment 3482 (point 182.010000 963.570000 4.400000 0.690000) (point 182.010000 963.570000 4.380000 0.690000) 4) (segment 3483 (point 182.010000 963.570000 4.380000 0.690000) (point 184.780000 967.790000 4.850000 0.690000) 4) (segment 3484 (point 184.780000 967.790000 4.850000 0.690000) (point 187.200000 969.560000 5.350000 0.690000) 4) (segment 3485 (point 187.200000 969.560000 5.350000 0.690000) (point 186.480000 970.590000 4.470000 0.690000) 4) (segment 3486 (point 186.480000 970.590000 4.470000 0.690000) (point 186.850000 973.060000 5.720000 0.460000) 4) (segment 3487 (point 186.850000 973.060000 5.720000 0.460000) (point 186.770000 975.430000 6.820000 0.460000) 4) (segment 3488 (point 186.770000 975.430000 6.820000 0.460000) (point 188.690000 975.290000 7.470000 0.460000) 4) (segment 3489 (point 188.690000 975.290000 7.470000 0.460000) (point 188.690000 975.290000 7.450000 0.460000) 4) (segment 3490 (point 188.690000 975.290000 7.450000 0.460000) (point 190.020000 975.600000 8.050000 0.460000) 4) (segment 3491 (point 190.020000 975.600000 8.050000 0.460000) (point 190.020000 975.600000 8.020000 0.460000) 4) (segment 3492 (point 190.020000 975.600000 8.020000 0.460000) (point 191.630000 974.780000 8.970000 0.460000) 4) (segment 3493 (point 191.630000 974.780000 8.970000 0.460000) (point 191.630000 974.780000 8.950000 0.460000) 4) (segment 3494 (point 191.630000 974.780000 8.950000 0.460000) (point 193.090000 974.540000 10.900000 0.460000) 4)) (branch 122 116 (segment 3495 (point 224.000000 773.880000 -59.320000 0.915000) (point 225.260000 776.470000 -57.920000 0.690000) 4) (segment 3496 (point 225.260000 776.470000 -57.920000 0.690000) (point 225.890000 777.810000 -56.150000 0.690000) 4) (segment 3497 (point 225.890000 777.810000 -56.150000 0.690000) (point 225.230000 780.640000 -55.700000 0.690000) 4) (segment 3498 (point 225.230000 780.640000 -55.700000 0.690000) (point 225.580000 783.110000 -55.700000 0.690000) 4) (segment 3499 (point 225.580000 783.110000 -55.700000 0.690000) (point 227.160000 786.470000 -55.700000 0.690000) 4) (segment 3500 (point 227.160000 786.470000 -55.700000 0.690000) (point 227.380000 789.510000 -55.150000 0.690000) 4) (segment 3501 (point 227.380000 789.510000 -55.150000 0.690000) (point 226.720000 792.330000 -55.030000 0.690000) 4) (segment 3502 (point 226.720000 792.330000 -55.030000 0.690000) (point 227.660000 794.350000 -55.030000 0.690000) 4) (segment 3503 (point 227.660000 794.350000 -55.030000 0.690000) (point 229.060000 796.460000 -54.550000 0.690000) 4) (segment 3504 (point 229.060000 796.460000 -54.550000 0.690000) (point 229.860000 799.040000 -54.550000 0.690000) 4)) (branch 123 122 (segment 3505 (point 229.860000 799.040000 -54.550000 0.690000) (point 229.270000 802.040000 -54.550000 0.690000) 4) (segment 3506 (point 229.270000 802.040000 -54.550000 0.690000) (point 229.500000 805.070000 -55.270000 0.690000) 4) (segment 3507 (point 229.500000 805.070000 -55.270000 0.690000) (point 229.730000 808.110000 -55.900000 0.690000) 4) (segment 3508 (point 229.730000 808.110000 -55.900000 0.690000) (point 229.070000 810.940000 -56.700000 0.690000) 4) (segment 3509 (point 229.070000 810.940000 -56.700000 0.690000) (point 228.090000 813.100000 -57.400000 0.690000) 4) (segment 3510 (point 228.090000 813.100000 -57.400000 0.690000) (point 227.740000 816.600000 -57.970000 0.690000) 4) (segment 3511 (point 227.740000 816.600000 -57.970000 0.690000) (point 226.360000 820.460000 -58.550000 0.690000) 4) (segment 3512 (point 226.360000 820.460000 -58.550000 0.690000) (point 225.880000 824.520000 -58.820000 0.690000) 4) (segment 3513 (point 225.880000 824.520000 -58.820000 0.690000) (point 224.690000 829.620000 -58.820000 0.690000) 4) (segment 3514 (point 224.690000 829.620000 -58.820000 0.690000) (point 224.470000 832.550000 -58.820000 0.690000) 4) (segment 3515 (point 224.470000 832.550000 -58.820000 0.690000) (point 222.200000 836.200000 -58.100000 0.690000) 4) (segment 3516 (point 222.200000 836.200000 -58.100000 0.690000) (point 221.940000 837.330000 -58.100000 0.690000) 4) (segment 3517 (point 221.940000 837.330000 -58.100000 0.690000) (point 222.930000 841.150000 -58.100000 0.690000) 4) (segment 3518 (point 222.930000 841.150000 -58.100000 0.690000) (point 223.030000 844.760000 -58.550000 0.690000) 4) (segment 3519 (point 223.030000 844.760000 -58.550000 0.690000) (point 221.520000 849.180000 -58.780000 0.690000) 4) (segment 3520 (point 221.520000 849.180000 -58.780000 0.690000) (point 220.990000 851.450000 -59.200000 0.690000) 4) (segment 3521 (point 220.990000 851.450000 -59.200000 0.690000) (point 220.770000 854.370000 -60.030000 0.690000) 4) (segment 3522 (point 220.770000 854.370000 -60.030000 0.690000) (point 220.770000 854.370000 -60.050000 0.690000) 4) (segment 3523 (point 220.770000 854.370000 -60.050000 0.690000) (point 220.860000 857.980000 -60.470000 0.690000) 4) (segment 3524 (point 220.860000 857.980000 -60.470000 0.690000) (point 219.930000 861.950000 -59.500000 0.690000) 4) (segment 3525 (point 219.930000 861.950000 -59.500000 0.690000) (point 220.040000 865.560000 -58.750000 0.690000) 4) (segment 3526 (point 220.040000 865.560000 -58.750000 0.690000) (point 220.850000 868.130000 -57.250000 0.690000) 4) (segment 3527 (point 220.850000 868.130000 -57.250000 0.690000) (point 220.670000 872.870000 -56.470000 0.690000) 4) (segment 3528 (point 220.670000 872.870000 -56.470000 0.690000) (point 221.350000 876.010000 -58.000000 0.690000) 4) (segment 3529 (point 221.350000 876.010000 -58.000000 0.690000) (point 221.770000 880.290000 -58.800000 0.690000) 4) (segment 3530 (point 221.770000 880.290000 -58.800000 0.690000) (point 221.940000 881.530000 -59.780000 0.690000) 4) (segment 3531 (point 221.940000 881.530000 -59.780000 0.690000) (point 221.590000 885.020000 -59.780000 0.690000) 4) (segment 3532 (point 221.590000 885.020000 -59.780000 0.690000) (point 223.300000 887.810000 -59.780000 0.690000) 4) (segment 3533 (point 223.300000 887.810000 -59.780000 0.690000) (point 223.350000 889.610000 -58.200000 0.690000) 4) (segment 3534 (point 223.350000 889.610000 -58.200000 0.690000) (point 222.380000 891.780000 -56.170000 0.690000) 4) (segment 3535 (point 222.380000 891.780000 -56.170000 0.690000) (point 223.580000 892.660000 -55.200000 0.690000) 4) (segment 3536 (point 223.580000 892.660000 -55.200000 0.690000) (point 223.810000 895.700000 -54.380000 0.690000) 4) (segment 3537 (point 223.810000 895.700000 -54.380000 0.690000) (point 225.460000 896.680000 -53.400000 0.690000) 4) (segment 3538 (point 225.460000 896.680000 -53.400000 0.690000) (point 226.530000 898.120000 -52.100000 0.690000) 4) (segment 3539 (point 226.530000 898.120000 -52.100000 0.690000) (point 227.170000 899.470000 -50.550000 0.690000) 4) (segment 3540 (point 227.170000 899.470000 -50.550000 0.690000) (point 225.150000 901.980000 -49.000000 0.690000) 4) (segment 3541 (point 225.150000 901.980000 -49.000000 0.690000) (point 225.070000 904.360000 -47.400000 0.690000) 4) (segment 3542 (point 225.070000 904.360000 -47.400000 0.690000) (point 227.800000 906.780000 -45.830000 0.690000) 4) (segment 3543 (point 227.800000 906.780000 -45.830000 0.690000) (point 228.880000 908.220000 -45.070000 0.690000) 4) (segment 3544 (point 228.880000 908.220000 -45.070000 0.690000) (point 228.660000 911.170000 -45.070000 0.690000) 4) (segment 3545 (point 228.660000 911.170000 -45.070000 0.690000) (point 229.860000 912.040000 -43.700000 0.690000) 4) (segment 3546 (point 229.860000 912.040000 -43.700000 0.690000) (point 231.580000 914.840000 -43.700000 0.690000) 4) (segment 3547 (point 231.580000 914.840000 -43.700000 0.690000) (point 232.510000 916.840000 -42.750000 0.690000) 4) (segment 3548 (point 232.510000 916.840000 -42.750000 0.690000) (point 233.900000 918.970000 -42.450000 0.690000) 4) (segment 3549 (point 233.900000 918.970000 -42.450000 0.690000) (point 233.690000 921.890000 -42.450000 0.690000) 4) (segment 3550 (point 233.690000 921.890000 -42.450000 0.690000) (point 234.270000 925.500000 -40.030000 0.690000) 4) (segment 3551 (point 234.270000 925.500000 -40.030000 0.690000) (point 237.170000 929.170000 -41.320000 0.690000) 4) (segment 3552 (point 237.170000 929.170000 -41.320000 0.690000) (point 238.600000 933.090000 -41.750000 0.690000) 4) (segment 3553 (point 238.600000 933.090000 -41.750000 0.690000) (point 238.830000 936.130000 -42.670000 0.690000) 4) (segment 3554 (point 238.830000 936.130000 -42.670000 0.690000) (point 241.880000 939.230000 -43.530000 0.690000) 4) (segment 3555 (point 241.880000 939.230000 -43.530000 0.690000) (point 240.640000 942.520000 -43.900000 0.690000) 4) (segment 3556 (point 240.640000 942.520000 -43.900000 0.690000) (point 240.420000 945.470000 -43.900000 0.690000) 4) (segment 3557 (point 240.420000 945.470000 -43.900000 0.690000) (point 241.800000 947.570000 -42.900000 0.690000) 4) (segment 3558 (point 241.800000 947.570000 -42.900000 0.690000) (point 242.350000 951.290000 -42.900000 0.690000) 4) (segment 3559 (point 242.350000 951.290000 -42.900000 0.690000) (point 244.500000 954.180000 -44.100000 0.690000) 4) (segment 3560 (point 244.500000 954.180000 -44.100000 0.690000) (point 243.700000 957.570000 -42.850000 0.690000) 4) (segment 3561 (point 243.700000 957.570000 -42.850000 0.690000) (point 243.700000 957.570000 -42.900000 0.690000) 4) (segment 3562 (point 243.700000 957.570000 -42.900000 0.690000) (point 242.730000 959.730000 -41.730000 0.690000) 4) (segment 3563 (point 242.730000 959.730000 -41.730000 0.690000) (point 242.690000 963.910000 -40.650000 0.690000) 4) (segment 3564 (point 242.690000 963.910000 -40.650000 0.690000) (point 243.590000 970.090000 -41.800000 0.690000) 4) (segment 3565 (point 243.590000 970.090000 -41.800000 0.690000) (point 244.270000 973.230000 -41.300000 0.690000) 4) (segment 3566 (point 244.270000 973.230000 -41.300000 0.690000) (point 244.500000 976.270000 -42.370000 0.690000) 4) (segment 3567 (point 244.500000 976.270000 -42.370000 0.690000) (point 243.080000 978.330000 -42.370000 0.690000) 4) (segment 3568 (point 243.080000 978.330000 -42.370000 0.690000) (point 244.200000 981.580000 -41.950000 0.690000) 4) (segment 3569 (point 244.200000 981.580000 -41.950000 0.690000) (point 244.620000 985.860000 -41.950000 0.690000) 4) (segment 3570 (point 244.620000 985.860000 -41.950000 0.690000) (point 243.240000 989.710000 -41.950000 0.690000) 4) (segment 3571 (point 243.240000 989.710000 -41.950000 0.690000) (point 243.470000 992.750000 -41.950000 0.690000) 4) (segment 3572 (point 243.470000 992.750000 -41.950000 0.690000) (point 243.870000 997.030000 -42.350000 0.690000) 4) (segment 3573 (point 243.870000 997.030000 -42.350000 0.690000) (point 244.240000 999.500000 -43.000000 0.690000) 4) (segment 3574 (point 244.240000 999.500000 -43.000000 0.690000) (point 244.390000 1004.900000 -43.000000 0.690000) 4) (segment 3575 (point 244.390000 1004.900000 -43.000000 0.690000) (point 245.780000 1007.030000 -43.900000 0.690000) 4) (segment 3576 (point 245.780000 1007.030000 -43.900000 0.690000) (point 246.190000 1011.310000 -44.700000 0.690000) 4) (segment 3577 (point 246.190000 1011.310000 -44.700000 0.690000) (point 246.420000 1014.350000 -45.170000 0.690000) 4)) (branch 124 123 (segment 3578 (point 246.420000 1014.350000 -45.170000 0.690000) (point 245.440000 1016.500000 -43.550000 0.690000) 4) (segment 3579 (point 245.440000 1016.500000 -43.550000 0.690000) (point 245.440000 1016.500000 -43.530000 0.690000) 4) (segment 3580 (point 245.440000 1016.500000 -43.530000 0.690000) (point 243.120000 1018.350000 -42.050000 0.690000) 4) (segment 3581 (point 243.120000 1018.350000 -42.050000 0.690000) (point 243.120000 1018.350000 -42.100000 0.690000) 4) (segment 3582 (point 243.120000 1018.350000 -42.100000 0.690000) (point 243.670000 1022.060000 -40.850000 0.460000) 4) (segment 3583 (point 243.670000 1022.060000 -40.850000 0.460000) (point 243.590000 1024.430000 -39.700000 0.460000) 4) (segment 3584 (point 243.590000 1024.430000 -39.700000 0.460000) (point 242.660000 1028.390000 -39.150000 0.460000) 4) (segment 3585 (point 242.660000 1028.390000 -39.150000 0.460000) (point 244.040000 1030.500000 -39.150000 0.460000) 4) (segment 3586 (point 244.040000 1030.500000 -39.150000 0.460000) (point 242.930000 1033.230000 -38.500000 0.460000) 4) (segment 3587 (point 242.930000 1033.230000 -38.500000 0.460000) (point 242.890000 1037.400000 -37.150000 0.460000) 4) (segment 3588 (point 242.890000 1037.400000 -37.150000 0.460000) (point 243.260000 1039.880000 -37.150000 0.460000) 4) (segment 3589 (point 243.260000 1039.880000 -37.150000 0.460000) (point 244.910000 1040.860000 -37.150000 0.460000) 4)) (branch 125 124 (segment 3590 (point 244.910000 1040.860000 -37.150000 0.460000) (point 243.220000 1044.050000 -35.800000 0.230000) 4) (segment 3591 (point 243.220000 1044.050000 -35.800000 0.230000) (point 243.150000 1046.420000 -35.150000 0.230000) 4) (segment 3592 (point 243.150000 1046.420000 -35.150000 0.230000) (point 243.950000 1049.000000 -33.950000 0.230000) 4) (segment 3593 (point 243.950000 1049.000000 -33.950000 0.230000) (point 243.870000 1051.370000 -33.950000 0.230000) 4) (segment 3594 (point 243.870000 1051.370000 -33.950000 0.230000) (point 242.490000 1055.220000 -33.950000 0.230000) 4) (segment 3595 (point 242.490000 1055.220000 -33.950000 0.230000) (point 241.830000 1058.060000 -32.750000 0.230000) 4) (segment 3596 (point 241.830000 1058.060000 -32.750000 0.230000) (point 241.610000 1060.990000 -31.920000 0.230000) 4) (segment 3597 (point 241.610000 1060.990000 -31.920000 0.230000) (point 240.320000 1062.480000 -34.100000 0.230000) 4)) (branch 126 125 (segment 3598 (point 240.320000 1062.480000 -34.100000 0.230000) (point 236.470000 1062.770000 -34.100000 0.230000) 4) (segment 3599 (point 236.470000 1062.770000 -34.100000 0.230000) (point 233.350000 1062.030000 -32.250000 0.230000) 4) (segment 3600 (point 233.350000 1062.030000 -32.250000 0.230000) (point 231.130000 1061.510000 -31.480000 0.230000) 4) (segment 3601 (point 231.130000 1061.510000 -31.480000 0.230000) (point 229.830000 1063.000000 -31.950000 0.230000) 4) (segment 3602 (point 229.830000 1063.000000 -31.950000 0.230000) (point 227.200000 1064.180000 -30.730000 0.230000) 4) (segment 3603 (point 227.200000 1064.180000 -30.730000 0.230000) (point 222.970000 1066.170000 -29.250000 0.230000) 4) (segment 3604 (point 222.970000 1066.170000 -29.250000 0.230000) (point 221.490000 1066.420000 -27.900000 0.230000) 4) (segment 3605 (point 221.490000 1066.420000 -27.900000 0.230000) (point 217.980000 1067.270000 -31.520000 0.230000) 4) (segment 3606 (point 217.980000 1067.270000 -31.520000 0.230000) (point 217.960000 1067.400000 -31.550000 0.230000) 4) (segment 3607 (point 217.960000 1067.400000 -31.550000 0.230000) (point 214.840000 1066.660000 -32.670000 0.230000) 4) (segment 3608 (point 214.840000 1066.660000 -32.670000 0.230000) (point 211.770000 1067.730000 -33.970000 0.230000) 4)) (branch 127 125 (segment 3609 (point 240.320000 1062.480000 -34.100000 0.230000) (point 242.340000 1065.940000 -32.400000 0.230000) 4) (segment 3610 (point 242.340000 1065.940000 -32.400000 0.230000) (point 243.220000 1066.150000 -32.170000 0.230000) 4) (segment 3611 (point 243.220000 1066.150000 -32.170000 0.230000) (point 243.540000 1066.810000 -28.720000 0.230000) 4)) (branch 128 127 (segment 3612 (point 243.540000 1066.810000 -28.720000 0.230000) (point 246.850000 1068.780000 -28.720000 0.230000) 4) (segment 3613 (point 246.850000 1068.780000 -28.720000 0.230000) (point 248.910000 1068.080000 -27.600000 0.230000) 4)) (branch 129 128 (segment 3614 (point 248.910000 1068.080000 -27.600000 0.230000) (point 248.410000 1066.170000 -26.250000 0.230000) 4) (segment 3615 (point 248.410000 1066.170000 -26.250000 0.230000) (point 247.470000 1064.150000 -24.080000 0.230000) 4) (segment 3616 (point 247.470000 1064.150000 -24.080000 0.230000) (point 247.470000 1064.150000 -24.100000 0.230000) 4) (segment 3617 (point 247.470000 1064.150000 -24.100000 0.230000) (point 248.630000 1063.240000 -21.670000 0.230000) 4) (segment 3618 (point 248.630000 1063.240000 -21.670000 0.230000) (point 248.630000 1063.240000 -21.700000 0.230000) 4) (segment 3619 (point 248.630000 1063.240000 -21.700000 0.230000) (point 247.370000 1060.540000 -19.880000 0.230000) 4) (segment 3620 (point 247.370000 1060.540000 -19.880000 0.230000) (point 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0.230000) 4) (segment 3637 (point 259.690000 1062.240000 -38.150000 0.230000) (point 259.920000 1065.290000 -39.520000 0.230000) 4) (segment 3638 (point 259.920000 1065.290000 -39.520000 0.230000) (point 261.120000 1066.160000 -39.820000 0.230000) 4) (segment 3639 (point 261.120000 1066.160000 -39.820000 0.230000) (point 263.680000 1067.360000 -41.380000 0.230000) 4) (segment 3640 (point 263.680000 1067.360000 -41.380000 0.230000) (point 264.880000 1068.230000 -42.400000 0.230000) 4) (segment 3641 (point 264.880000 1068.230000 -42.400000 0.230000) (point 264.880000 1068.230000 -42.420000 0.230000) 4) (segment 3642 (point 264.880000 1068.230000 -42.420000 0.230000) (point 266.670000 1068.650000 -43.000000 0.230000) 4) (segment 3643 (point 266.670000 1068.650000 -43.000000 0.230000) (point 268.320000 1069.630000 -43.000000 0.230000) 4) (segment 3644 (point 268.320000 1069.630000 -43.000000 0.230000) (point 269.930000 1068.820000 -44.430000 0.230000) 4) (segment 3645 (point 269.930000 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281.050000 1069.640000 -51.670000 0.230000) 4) (segment 3654 (point 281.050000 1069.640000 -51.670000 0.230000) (point 282.430000 1071.750000 -53.620000 0.230000) 4) (segment 3655 (point 282.430000 1071.750000 -53.620000 0.230000) (point 282.430000 1071.750000 -53.670000 0.230000) 4) (segment 3656 (point 282.430000 1071.750000 -53.670000 0.230000) (point 283.720000 1070.260000 -55.720000 0.230000) 4) (segment 3657 (point 283.720000 1070.260000 -55.720000 0.230000) (point 284.750000 1069.900000 -58.470000 0.230000) 4) (segment 3658 (point 284.750000 1069.900000 -58.470000 0.230000) (point 284.750000 1069.900000 -58.650000 0.230000) 4)) (branch 133 123 (segment 3659 (point 246.420000 1014.350000 -45.170000 0.690000) (point 248.120000 1017.130000 -43.800000 0.230000) 4) (segment 3660 (point 248.120000 1017.130000 -43.800000 0.230000) (point 249.240000 1020.380000 -43.800000 0.230000) 4) (segment 3661 (point 249.240000 1020.380000 -43.800000 0.230000) (point 251.210000 1022.030000 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(segment 3670 (point 229.860000 799.040000 -54.550000 0.690000) (point 231.440000 802.400000 -54.550000 0.690000) 4) (segment 3671 (point 231.440000 802.400000 -54.550000 0.690000) (point 233.800000 802.340000 -53.300000 0.690000) 4) (segment 3672 (point 233.800000 802.340000 -53.300000 0.690000) (point 234.880000 803.810000 -52.500000 0.690000) 4) (segment 3673 (point 234.880000 803.810000 -52.500000 0.690000) (point 234.880000 803.810000 -52.550000 0.690000) 4) (segment 3674 (point 234.880000 803.810000 -52.550000 0.690000) (point 235.100000 806.840000 -52.550000 0.690000) 4) (segment 3675 (point 235.100000 806.840000 -52.550000 0.690000) (point 237.080000 808.500000 -51.550000 0.690000) 4) (segment 3676 (point 237.080000 808.500000 -51.550000 0.690000) (point 238.010000 810.510000 -50.400000 0.690000) 4) (segment 3677 (point 238.010000 810.510000 -50.400000 0.690000) (point 238.190000 811.740000 -49.600000 0.690000) 4) (segment 3678 (point 238.190000 811.740000 -49.600000 0.690000) (point 237.800000 813.440000 -48.750000 0.690000) 4) (segment 3679 (point 237.800000 813.440000 -48.750000 0.690000) (point 241.740000 816.750000 -48.750000 0.690000) 4) (segment 3680 (point 241.740000 816.750000 -48.750000 0.690000) (point 242.940000 817.620000 -47.670000 0.690000) 4) (segment 3681 (point 242.940000 817.620000 -47.670000 0.690000) (point 244.380000 821.550000 -47.670000 0.690000) 4) (segment 3682 (point 244.380000 821.550000 -47.670000 0.690000) (point 246.030000 822.530000 -47.670000 0.690000) 4) (segment 3683 (point 246.030000 822.530000 -47.670000 0.690000) (point 246.840000 825.110000 -47.670000 0.690000) 4) (segment 3684 (point 246.840000 825.110000 -47.670000 0.690000) (point 247.650000 827.700000 -47.670000 0.690000) 4) (segment 3685 (point 247.650000 827.700000 -47.670000 0.690000) (point 250.380000 830.120000 -47.670000 0.690000) 4) (segment 3686 (point 250.380000 830.120000 -47.670000 0.690000) (point 250.340000 834.300000 -47.670000 0.690000) 4) (segment 3687 (point 250.340000 834.300000 -47.670000 0.690000) (point 251.910000 837.650000 -47.570000 0.690000) 4) (segment 3688 (point 251.910000 837.650000 -47.570000 0.690000) (point 252.500000 843.160000 -47.550000 0.690000) 4) (segment 3689 (point 252.500000 843.160000 -47.550000 0.690000) (point 255.100000 846.160000 -47.150000 0.690000) 4) (segment 3690 (point 255.100000 846.160000 -47.150000 0.690000) (point 255.460000 848.630000 -46.650000 0.690000) 4) (segment 3691 (point 255.460000 848.630000 -46.650000 0.690000) (point 255.960000 850.530000 -46.380000 0.690000) 4) (segment 3692 (point 255.960000 850.530000 -46.380000 0.690000) (point 256.450000 852.450000 -45.650000 0.690000) 4) (segment 3693 (point 256.450000 852.450000 -45.650000 0.690000) (point 258.870000 854.210000 -45.650000 0.690000) 4) (segment 3694 (point 258.870000 854.210000 -45.650000 0.690000) (point 259.360000 856.120000 -44.430000 0.690000) 4) (segment 3695 (point 259.360000 856.120000 -44.430000 0.690000) (point 259.360000 856.120000 -44.450000 0.690000) 4) (segment 3696 (point 259.360000 856.120000 -44.450000 0.690000) (point 259.590000 859.150000 -43.580000 0.690000) 4) (segment 3697 (point 259.590000 859.150000 -43.580000 0.690000) (point 262.440000 861.020000 -43.150000 0.690000) 4) (segment 3698 (point 262.440000 861.020000 -43.150000 0.690000) (point 262.440000 861.020000 -43.180000 0.690000) 4) (segment 3699 (point 262.440000 861.020000 -43.180000 0.690000) (point 261.330000 863.740000 -43.100000 0.690000) 4) (segment 3700 (point 261.330000 863.740000 -43.100000 0.690000) (point 263.180000 865.970000 -42.570000 0.690000) 4) (segment 3701 (point 263.180000 865.970000 -42.570000 0.690000) (point 263.180000 865.970000 -42.600000 0.690000) 4) (segment 3702 (point 263.180000 865.970000 -42.600000 0.690000) (point 263.020000 870.700000 -43.500000 0.690000) 4) (segment 3703 (point 263.020000 870.700000 -43.500000 0.690000) (point 265.740000 873.130000 -42.920000 0.690000) 4) (segment 3704 (point 265.740000 873.130000 -42.920000 0.690000) (point 266.090000 875.610000 -42.050000 0.690000) 4) (segment 3705 (point 266.090000 875.610000 -42.050000 0.690000) (point 266.910000 878.180000 -41.200000 0.690000) 4) (segment 3706 (point 266.910000 878.180000 -41.200000 0.690000) (point 269.770000 880.060000 -40.480000 0.690000) 4) (segment 3707 (point 269.770000 880.060000 -40.480000 0.690000) (point 269.770000 880.060000 -40.500000 0.690000) 4) (segment 3708 (point 269.770000 880.060000 -40.500000 0.690000) (point 270.900000 883.300000 -39.650000 0.690000) 4) (segment 3709 (point 270.900000 883.300000 -39.650000 0.690000) (point 273.760000 885.170000 -39.200000 0.690000) 4) (segment 3710 (point 273.760000 885.170000 -39.200000 0.690000) (point 276.350000 888.160000 -39.020000 0.690000) 4) (segment 3711 (point 276.350000 888.160000 -39.020000 0.690000) (point 278.410000 893.420000 -39.020000 0.690000) 4) (segment 3712 (point 278.410000 893.420000 -39.020000 0.690000) (point 278.920000 895.330000 -37.700000 0.690000) 4) (segment 3713 (point 278.920000 895.330000 -37.700000 0.690000) (point 281.640000 897.760000 -38.030000 0.690000) 4) (segment 3714 (point 281.640000 897.760000 -38.030000 0.690000) (point 283.600000 899.430000 -38.030000 0.690000) 4) (segment 3715 (point 283.600000 899.430000 -38.030000 0.690000) (point 284.330000 904.370000 -38.030000 0.690000) 4) (segment 3716 (point 284.330000 904.370000 -38.030000 0.690000) (point 285.770000 908.290000 -38.030000 0.690000) 4) (segment 3717 (point 285.770000 908.290000 -38.030000 0.690000) (point 286.890000 911.530000 -36.100000 0.690000) 4) (segment 3718 (point 286.890000 911.530000 -36.100000 0.690000) (point 286.720000 916.270000 -36.100000 0.690000) 4) (segment 3719 (point 286.720000 916.270000 -36.100000 0.690000) (point 287.220000 918.180000 -37.200000 0.690000) 4) (segment 3720 (point 287.220000 918.180000 -37.200000 0.690000) (point 287.220000 918.180000 -37.170000 0.690000) 4) (segment 3721 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295.080000 940.920000 -40.480000 0.690000) 4) (segment 3730 (point 295.080000 940.920000 -40.480000 0.690000) (point 295.080000 940.920000 -40.500000 0.690000) 4) (segment 3731 (point 295.080000 940.920000 -40.500000 0.690000) (point 295.310000 943.960000 -40.500000 0.690000) 4) (segment 3732 (point 295.310000 943.960000 -40.500000 0.690000) (point 295.850000 947.670000 -40.500000 0.690000) 4) (segment 3733 (point 295.850000 947.670000 -40.500000 0.690000) (point 298.450000 950.670000 -40.500000 0.690000) 4) (segment 3734 (point 298.450000 950.670000 -40.500000 0.690000) (point 299.700000 953.350000 -39.720000 0.690000) 4) (segment 3735 (point 299.700000 953.350000 -39.720000 0.690000) (point 301.320000 958.520000 -38.850000 0.690000) 4) (segment 3736 (point 301.320000 958.520000 -38.850000 0.690000) (point 302.720000 960.620000 -38.570000 0.690000) 4) (segment 3737 (point 302.720000 960.620000 -38.570000 0.690000) (point 304.860000 963.520000 -38.570000 0.690000) 4) (segment 3738 (point 304.860000 963.520000 -38.570000 0.690000) (point 307.190000 967.640000 -37.580000 0.690000) 4) (segment 3739 (point 307.190000 967.640000 -37.580000 0.690000) (point 308.450000 970.330000 -36.400000 0.690000) 4) (segment 3740 (point 308.450000 970.330000 -36.400000 0.690000) (point 308.100000 973.840000 -35.150000 0.690000) 4) (segment 3741 (point 308.100000 973.840000 -35.150000 0.690000) (point 308.510000 978.110000 -36.880000 0.690000) 4) (segment 3742 (point 308.510000 978.110000 -36.880000 0.690000) (point 309.500000 981.930000 -35.900000 0.690000) 4) (segment 3743 (point 309.500000 981.930000 -35.900000 0.690000) (point 309.420000 984.300000 -34.700000 0.690000) 4) (segment 3744 (point 309.420000 984.300000 -34.700000 0.690000) (point 311.510000 985.380000 -33.550000 0.690000) 4) (segment 3745 (point 311.510000 985.380000 -33.550000 0.690000) (point 313.220000 988.170000 -32.500000 0.690000) 4) (segment 3746 (point 313.220000 988.170000 -32.500000 0.690000) (point 313.850000 989.510000 -31.800000 0.690000) 4) (segment 3747 (point 313.850000 989.510000 -31.800000 0.690000) (point 312.390000 995.740000 -31.250000 0.690000) 4) (segment 3748 (point 312.390000 995.740000 -31.250000 0.690000) (point 313.950000 999.090000 -31.800000 0.690000) 4)) (branch 135 134 (segment 3749 (point 313.950000 999.090000 -31.800000 0.690000) (point 313.700000 1000.240000 -30.570000 0.460000) 4) (segment 3750 (point 313.700000 1000.240000 -30.570000 0.460000) (point 313.890000 1001.480000 -29.170000 0.460000) 4) (segment 3751 (point 313.890000 1001.480000 -29.170000 0.460000) (point 312.910000 1003.640000 -27.380000 0.460000) 4) (segment 3752 (point 312.910000 1003.640000 -27.380000 0.460000) (point 313.900000 1007.450000 -26.770000 0.460000) 4) (segment 3753 (point 313.900000 1007.450000 -26.770000 0.460000) (point 315.030000 1010.700000 -26.150000 0.460000) 4) (segment 3754 (point 315.030000 1010.700000 -26.150000 0.460000) (point 314.680000 1014.200000 -25.230000 0.460000) 4) (segment 3755 (point 314.680000 1014.200000 -25.230000 0.460000) (point 314.910000 1017.240000 -24.400000 0.460000) 4) (segment 3756 (point 314.910000 1017.240000 -24.400000 0.460000) (point 315.890000 1021.060000 -24.400000 0.460000) 4)) (branch 136 135 (segment 3757 (point 315.890000 1021.060000 -24.400000 0.460000) (point 318.180000 1023.380000 -24.400000 0.460000) 4) (segment 3758 (point 318.180000 1023.380000 -24.400000 0.460000) (point 320.460000 1025.710000 -24.380000 0.460000) 4) (segment 3759 (point 320.460000 1025.710000 -24.380000 0.460000) (point 321.000000 1029.420000 -24.350000 0.460000) 4) (segment 3760 (point 321.000000 1029.420000 -24.350000 0.460000) (point 322.390000 1031.540000 -23.670000 0.460000) 4) (segment 3761 (point 322.390000 1031.540000 -23.670000 0.460000) (point 323.690000 1036.020000 -22.600000 0.460000) 4) (segment 3762 (point 323.690000 1036.020000 -22.600000 0.460000) (point 323.610000 1038.390000 -21.600000 0.460000) 4) (segment 3763 (point 323.610000 1038.390000 -21.600000 0.460000) (point 326.160000 1039.590000 -20.920000 0.460000) 4) (segment 3764 (point 326.160000 1039.590000 -20.920000 0.460000) (point 327.600000 1043.500000 -20.920000 0.460000) 4) (segment 3765 (point 327.600000 1043.500000 -20.920000 0.460000) (point 328.350000 1044.280000 -19.700000 0.460000) 4) (segment 3766 (point 328.350000 1044.280000 -19.700000 0.460000) (point 330.900000 1045.480000 -18.500000 0.460000) 4) (segment 3767 (point 330.900000 1045.480000 -18.500000 0.460000) (point 331.710000 1048.050000 -17.450000 0.460000) 4) (segment 3768 (point 331.710000 1048.050000 -17.450000 0.460000) (point 334.000000 1050.380000 -16.800000 0.460000) 4) (segment 3769 (point 334.000000 1050.380000 -16.800000 0.460000) (point 335.010000 1050.020000 -16.730000 0.460000) 4) (segment 3770 (point 335.010000 1050.020000 -16.730000 0.460000) (point 337.250000 1050.550000 -16.070000 0.460000) 4) (segment 3771 (point 337.250000 1050.550000 -16.070000 0.460000) (point 340.430000 1053.080000 -16.900000 0.460000) 4) (segment 3772 (point 340.430000 1053.080000 -16.900000 0.460000) (point 340.430000 1053.080000 -16.880000 0.460000) 4) (segment 3773 (point 340.430000 1053.080000 -16.880000 0.460000) (point 340.840000 1057.360000 -15.570000 0.460000) 4) (segment 3774 (point 340.840000 1057.360000 -15.570000 0.460000) (point 342.620000 1057.780000 -15.570000 0.460000) 4) (segment 3775 (point 342.620000 1057.780000 -15.570000 0.460000) (point 341.510000 1060.500000 -14.750000 0.460000) 4) (segment 3776 (point 341.510000 1060.500000 -14.750000 0.460000) (point 341.510000 1060.500000 -14.770000 0.460000) 4) (segment 3777 (point 341.510000 1060.500000 -14.770000 0.460000) (point 339.910000 1061.330000 -12.980000 0.460000) 4) (segment 3778 (point 339.910000 1061.330000 -12.980000 0.460000) (point 339.910000 1061.330000 -13.000000 0.460000) 4) (segment 3779 (point 339.910000 1061.330000 -13.000000 0.460000) (point 338.570000 1061.010000 -11.370000 0.460000) 4) (segment 3780 (point 338.570000 1061.010000 -11.370000 0.460000) (point 338.570000 1061.010000 -11.400000 0.460000) 4) (segment 3781 (point 338.570000 1061.010000 -11.400000 0.460000) (point 336.780000 1060.590000 -9.930000 0.460000) 4) (segment 3782 (point 336.780000 1060.590000 -9.930000 0.460000) (point 336.780000 1060.590000 -9.950000 0.460000) 4) (segment 3783 (point 336.780000 1060.590000 -9.950000 0.460000) (point 332.940000 1060.880000 -7.800000 0.460000) 4) (segment 3784 (point 332.940000 1060.880000 -7.800000 0.460000) (point 332.940000 1060.880000 -7.720000 0.460000) 4)) (branch 137 135 (segment 3785 (point 315.890000 1021.060000 -24.400000 0.460000) (point 314.650000 1024.350000 -24.400000 0.460000) 4) (segment 3786 (point 314.650000 1024.350000 -24.400000 0.460000) (point 315.960000 1028.830000 -24.400000 0.460000) 4) (segment 3787 (point 315.960000 1028.830000 -24.400000 0.460000) (point 316.190000 1031.880000 -22.880000 0.460000) 4) (segment 3788 (point 316.190000 1031.880000 -22.880000 0.460000) (point 316.190000 1031.880000 -22.900000 0.460000) 4) (segment 3789 (point 316.190000 1031.880000 -22.900000 0.460000) (point 315.830000 1035.370000 -22.050000 0.460000) 4) (segment 3790 (point 315.830000 1035.370000 -22.050000 0.460000) (point 316.510000 1038.520000 -22.020000 0.460000) 4) (segment 3791 (point 316.510000 1038.520000 -22.020000 0.460000) (point 313.090000 1043.090000 -22.770000 0.460000) 4) (segment 3792 (point 313.090000 1043.090000 -22.770000 0.460000) (point 313.090000 1043.090000 -22.800000 0.460000) 4) (segment 3793 (point 313.090000 1043.090000 -22.800000 0.460000) (point 311.480000 1043.900000 -24.170000 0.460000) 4) (segment 3794 (point 311.480000 1043.900000 -24.170000 0.460000) (point 310.270000 1043.030000 -26.000000 0.460000) 4) (segment 3795 (point 310.270000 1043.030000 -26.000000 0.460000) (point 308.620000 1042.040000 -28.980000 0.460000) 4) (segment 3796 (point 308.620000 1042.040000 -28.980000 0.460000) (point 308.620000 1042.040000 -29.000000 0.460000) 4)) (branch 138 134 (segment 3797 (point 313.950000 999.090000 -31.800000 0.690000) (point 316.830000 1000.970000 -30.900000 0.460000) 4) (segment 3798 (point 316.830000 1000.970000 -30.900000 0.460000) (point 316.830000 1000.970000 -30.920000 0.460000) 4) (segment 3799 (point 316.830000 1000.970000 -30.920000 0.460000) (point 316.610000 1003.910000 -31.350000 0.460000) 4) (segment 3800 (point 316.610000 1003.910000 -31.350000 0.460000) (point 316.610000 1003.910000 -31.380000 0.460000) 4) (segment 3801 (point 316.610000 1003.910000 -31.380000 0.460000) (point 318.190000 1007.270000 -30.600000 0.460000) 4) (segment 3802 (point 318.190000 1007.270000 -30.600000 0.460000) (point 318.190000 1007.270000 -30.630000 0.460000) 4) (segment 3803 (point 318.190000 1007.270000 -30.630000 0.460000) (point 319.180000 1011.070000 -29.670000 0.460000) 4) (segment 3804 (point 319.180000 1011.070000 -29.670000 0.460000) (point 318.960000 1014.010000 -29.670000 0.460000) 4)) (branch 139 138 (segment 3805 (point 318.960000 1014.010000 -29.670000 0.460000) (point 320.920000 1015.660000 -31.320000 0.460000) 4) (segment 3806 (point 320.920000 1015.660000 -31.320000 0.460000) (point 320.920000 1015.660000 -31.350000 0.460000) 4) (segment 3807 (point 320.920000 1015.660000 -31.350000 0.460000) (point 320.840000 1018.040000 -32.150000 0.460000) 4) (segment 3808 (point 320.840000 1018.040000 -32.150000 0.460000) (point 321.790000 1020.050000 -33.130000 0.460000) 4) (segment 3809 (point 321.790000 1020.050000 -33.130000 0.460000) (point 322.860000 1021.500000 -34.420000 0.460000) 4) (segment 3810 (point 322.860000 1021.500000 -34.420000 0.460000) (point 323.220000 1023.960000 -34.880000 0.460000) 4) (segment 3811 (point 323.220000 1023.960000 -34.880000 0.460000) (point 323.090000 1024.530000 -36.400000 0.460000) 4) (segment 3812 (point 323.090000 1024.530000 -36.400000 0.460000) (point 325.500000 1026.300000 -37.320000 0.460000) 4) (segment 3813 (point 325.500000 1026.300000 -37.320000 0.460000) (point 325.500000 1026.300000 -37.380000 0.460000) 4) (segment 3814 (point 325.500000 1026.300000 -37.380000 0.460000) (point 325.810000 1026.970000 -39.020000 0.460000) 4) (segment 3815 (point 325.810000 1026.970000 -39.020000 0.460000) (point 326.570000 1027.740000 -40.770000 0.460000) 4) (segment 3816 (point 326.570000 1027.740000 -40.770000 0.460000) (point 326.570000 1027.740000 -40.800000 0.460000) 4) (segment 3817 (point 326.570000 1027.740000 -40.800000 0.460000) (point 326.360000 1030.680000 -42.200000 0.460000) 4) (segment 3818 (point 326.360000 1030.680000 -42.200000 0.460000) (point 328.200000 1032.900000 -42.980000 0.460000) 4) (segment 3819 (point 328.200000 1032.900000 -42.980000 0.460000) (point 329.190000 1036.720000 -43.750000 0.460000) 4) (segment 3820 (point 329.190000 1036.720000 -43.750000 0.460000) (point 331.020000 1038.940000 -43.750000 0.460000) 4) (segment 3821 (point 331.020000 1038.940000 -43.750000 0.460000) (point 334.020000 1040.230000 -44.350000 0.460000) 4) (segment 3822 (point 334.020000 1040.230000 -44.350000 0.460000) (point 335.990000 1041.890000 -44.750000 0.460000) 4) (segment 3823 (point 335.990000 1041.890000 -44.750000 0.460000) (point 335.990000 1041.890000 -44.780000 0.460000) 4) (segment 3824 (point 335.990000 1041.890000 -44.780000 0.460000) (point 337.100000 1045.130000 -46.100000 0.460000) 4) (segment 3825 (point 337.100000 1045.130000 -46.100000 0.460000) (point 337.100000 1045.130000 -46.120000 0.460000) 4) (segment 3826 (point 337.100000 1045.130000 -46.120000 0.460000) (point 340.860000 1047.220000 -46.630000 0.460000) 4) (segment 3827 (point 340.860000 1047.220000 -46.630000 0.460000) (point 343.090000 1047.740000 -46.630000 0.460000) 4) (segment 3828 (point 343.090000 1047.740000 -46.630000 0.460000) (point 345.150000 1047.020000 -47.650000 0.460000) 4) (segment 3829 (point 345.150000 1047.020000 -47.650000 0.460000) (point 345.990000 1045.430000 -48.070000 0.460000) 4) (segment 3830 (point 345.990000 1045.430000 -48.070000 0.460000) (point 345.990000 1045.430000 -48.100000 0.460000) 4) (segment 3831 (point 345.990000 1045.430000 -48.100000 0.460000) (point 348.990000 1046.720000 -47.720000 0.460000) 4) (segment 3832 (point 348.990000 1046.720000 -47.720000 0.460000) (point 352.430000 1048.130000 -48.420000 0.460000) 4) (segment 3833 (point 352.430000 1048.130000 -48.420000 0.460000) (point 352.430000 1048.130000 -48.450000 0.460000) 4) (segment 3834 (point 352.430000 1048.130000 -48.450000 0.460000) (point 356.810000 1051.550000 -49.070000 0.460000) 4) (segment 3835 (point 356.810000 1051.550000 -49.070000 0.460000) (point 360.510000 1051.830000 -50.070000 0.460000) 4) (segment 3836 (point 360.510000 1051.830000 -50.070000 0.460000) (point 360.740000 1054.860000 -50.920000 0.230000) 4) (segment 3837 (point 360.740000 1054.860000 -50.920000 0.230000) (point 360.740000 1054.860000 -50.970000 0.230000) 4) (segment 3838 (point 360.740000 1054.860000 -50.970000 0.230000) (point 358.110000 1056.030000 -51.770000 0.230000) 4) (segment 3839 (point 358.110000 1056.030000 -51.770000 0.230000) (point 358.110000 1056.030000 -51.800000 0.230000) 4) (segment 3840 (point 358.110000 1056.030000 -51.800000 0.230000) (point 356.060000 1056.750000 -50.500000 0.230000) 4) (segment 3841 (point 356.060000 1056.750000 -50.500000 0.230000) (point 356.060000 1056.750000 -50.520000 0.230000) 4) (segment 3842 (point 356.060000 1056.750000 -50.520000 0.230000) (point 352.140000 1059.410000 -53.180000 0.230000) 4)) (branch 140 138 (segment 3843 (point 318.960000 1014.010000 -29.670000 0.460000) (point 318.740000 1016.950000 -29.670000 0.460000) 4) (segment 3844 (point 318.740000 1016.950000 -29.670000 0.460000) (point 319.730000 1020.760000 -29.670000 0.460000) 4) (segment 3845 (point 319.730000 1020.760000 -29.670000 0.460000) (point 319.110000 1025.390000 -29.170000 0.460000) 4) (segment 3846 (point 319.110000 1025.390000 -29.170000 0.460000) (point 320.830000 1028.180000 -28.020000 0.460000) 4) (segment 3847 (point 320.830000 1028.180000 -28.020000 0.460000) (point 320.340000 1032.240000 -26.320000 0.460000) 4) (segment 3848 (point 320.340000 1032.240000 -26.320000 0.460000) (point 320.700000 1034.730000 -26.320000 0.460000) 4) (segment 3849 (point 320.700000 1034.730000 -26.320000 0.460000) (point 321.110000 1039.000000 -26.170000 0.460000) 4) (segment 3850 (point 321.110000 1039.000000 -26.170000 0.460000) (point 322.810000 1041.790000 -26.900000 0.460000) 4) (segment 3851 (point 322.810000 1041.790000 -26.900000 0.460000) (point 321.120000 1044.970000 -26.500000 0.460000) 4) (segment 3852 (point 321.120000 1044.970000 -26.500000 0.460000) (point 323.150000 1048.440000 -25.870000 0.460000) 4) (segment 3853 (point 323.150000 1048.440000 -25.870000 0.460000) (point 323.500000 1050.900000 -24.200000 0.460000) 4) (segment 3854 (point 323.500000 1050.900000 -24.200000 0.460000) (point 322.920000 1051.370000 -21.500000 0.460000) 4) (segment 3855 (point 322.920000 1051.370000 -21.500000 0.460000) (point 321.770000 1052.300000 -18.670000 0.460000) 4) (segment 3856 (point 321.770000 1052.300000 -18.670000 0.460000) (point 321.770000 1052.300000 -18.630000 0.460000) 4)) (branch 141 77 (segment 3857 (point 253.330000 278.770000 -19.420000 1.145000) (point 255.980000 279.410000 -19.420000 0.460000) 4)) (branch 142 141 (segment 3858 (point 255.980000 279.410000 -19.420000 0.460000) (point 257.700000 278.010000 -18.000000 0.460000) 4) (segment 3859 (point 257.700000 278.010000 -18.000000 0.460000) (point 258.240000 275.740000 -16.800000 0.460000) 4) (segment 3860 (point 258.240000 275.740000 -16.800000 0.460000) (point 259.080000 274.150000 -15.900000 0.460000) 4) (segment 3861 (point 259.080000 274.150000 -15.900000 0.460000) (point 260.680000 273.340000 -14.970000 0.460000) 4) (segment 3862 (point 260.680000 273.340000 -14.970000 0.460000) (point 260.460000 270.300000 -13.870000 0.460000) 4) (segment 3863 (point 260.460000 270.300000 -13.870000 0.460000) (point 259.640000 267.720000 -12.300000 0.460000) 4) (segment 3864 (point 259.640000 267.720000 -12.300000 0.460000) (point 259.550000 264.110000 -10.880000 0.460000) 4) (segment 3865 (point 259.550000 264.110000 -10.880000 0.460000) (point 260.660000 261.380000 -9.200000 0.460000) 4) (segment 3866 (point 260.660000 261.380000 -9.200000 0.460000) (point 261.370000 260.360000 -8.000000 0.460000) 4) (segment 3867 (point 261.370000 260.360000 -8.000000 0.460000) (point 262.660000 258.870000 -7.220000 0.460000) 4) (segment 3868 (point 262.660000 258.870000 -7.220000 0.460000) (point 263.190000 256.610000 -5.530000 0.460000) 4) (segment 3869 (point 263.190000 256.610000 -5.530000 0.460000) (point 265.550000 256.570000 -4.850000 0.460000) 4) (segment 3870 (point 265.550000 256.570000 -4.850000 0.460000) (point 267.740000 255.280000 -4.150000 0.460000) 4) (segment 3871 (point 267.740000 255.280000 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(point 283.680000 237.520000 4.520000 0.460000) (point 284.080000 235.830000 3.850000 0.460000) 4) (segment 3881 (point 284.080000 235.830000 3.850000 0.460000) (point 285.190000 233.090000 4.070000 0.460000) 4) (segment 3882 (point 285.190000 233.090000 4.070000 0.460000) (point 285.190000 233.090000 4.050000 0.460000) 4) (segment 3883 (point 285.190000 233.090000 4.050000 0.460000) (point 284.390000 230.530000 5.550000 0.460000) 4) (segment 3884 (point 284.390000 230.530000 5.550000 0.460000) (point 284.390000 230.530000 5.480000 0.460000) 4) (segment 3885 (point 284.390000 230.530000 5.480000 0.460000) (point 286.250000 228.570000 6.950000 0.460000) 4) (segment 3886 (point 286.250000 228.570000 6.950000 0.460000) (point 288.650000 224.360000 7.500000 0.460000) 4) (segment 3887 (point 288.650000 224.360000 7.500000 0.460000) (point 290.080000 222.300000 9.880000 0.460000) 4) (segment 3888 (point 290.080000 222.300000 9.880000 0.460000) (point 290.080000 222.300000 9.850000 0.460000) 4) (segment 3889 (point 290.080000 222.300000 9.850000 0.460000) (point 291.540000 222.050000 11.300000 0.460000) 4) (segment 3890 (point 291.540000 222.050000 11.300000 0.460000) (point 294.760000 220.410000 12.830000 0.460000) 4) (segment 3891 (point 294.760000 220.410000 12.830000 0.460000) (point 297.840000 219.340000 13.200000 0.460000) 4) (segment 3892 (point 297.840000 219.340000 13.200000 0.460000) (point 299.570000 217.950000 14.550000 0.460000) 4) (segment 3893 (point 299.570000 217.950000 14.550000 0.460000) (point 299.570000 217.950000 14.520000 0.460000) 4) (segment 3894 (point 299.570000 217.950000 14.520000 0.460000) (point 299.970000 216.250000 16.070000 0.460000) 4) (segment 3895 (point 299.970000 216.250000 16.070000 0.460000) (point 299.040000 214.250000 17.350000 0.460000) 4) (segment 3896 (point 299.040000 214.250000 17.350000 0.460000) (point 300.580000 211.620000 17.920000 0.460000) 4) (segment 3897 (point 300.580000 211.620000 17.920000 0.460000) (point 299.600000 207.810000 18.420000 0.460000) 4) (segment 3898 (point 299.600000 207.810000 18.420000 0.460000) (point 299.190000 203.530000 20.520000 0.460000) 4) (segment 3899 (point 299.190000 203.530000 20.520000 0.460000) (point 299.190000 203.530000 20.600000 0.460000) 4)) (branch 143 141 (segment 3900 (point 255.980000 279.410000 -19.420000 0.460000) (point 258.330000 279.340000 -19.850000 0.460000) 4) (segment 3901 (point 258.330000 279.340000 -19.850000 0.460000) (point 260.960000 278.180000 -20.520000 0.460000) 4) (segment 3902 (point 260.960000 278.180000 -20.520000 0.460000) (point 263.290000 276.330000 -20.800000 0.460000) 4) (segment 3903 (point 263.290000 276.330000 -20.800000 0.460000) (point 264.700000 274.270000 -20.800000 0.460000) 4) (segment 3904 (point 264.700000 274.270000 -20.800000 0.460000) (point 265.730000 273.930000 -20.800000 0.460000) 4) (segment 3905 (point 265.730000 273.930000 -20.800000 0.460000) (point 267.870000 270.840000 -21.450000 0.460000) 4) (segment 3906 (point 267.870000 270.840000 -21.450000 0.460000) (point 270.050000 269.550000 -21.700000 0.460000) 4) (segment 3907 (point 270.050000 269.550000 -21.700000 0.460000) (point 271.920000 267.610000 -20.300000 0.460000) 4) (segment 3908 (point 271.920000 267.610000 -20.300000 0.460000) (point 273.330000 265.550000 -19.480000 0.460000) 4) (segment 3909 (point 273.330000 265.550000 -19.480000 0.460000) (point 274.000000 262.720000 -19.330000 0.460000) 4) (segment 3910 (point 274.000000 262.720000 -19.330000 0.460000) (point 274.980000 260.560000 -18.700000 0.460000) 4) (segment 3911 (point 274.980000 260.560000 -18.700000 0.460000) (point 277.750000 258.820000 -18.100000 0.460000) 4) (segment 3912 (point 277.750000 258.820000 -18.100000 0.460000) (point 281.270000 257.850000 -20.650000 0.460000) 4) (segment 3913 (point 281.270000 257.850000 -20.650000 0.460000) (point 284.490000 256.210000 -20.730000 0.460000) 4) (segment 3914 (point 284.490000 256.210000 -20.730000 0.460000) (point 284.490000 256.210000 -20.750000 0.460000) 4) (segment 3915 (point 284.490000 256.210000 -20.750000 0.460000) (point 286.630000 253.140000 -19.520000 0.460000) 4) (segment 3916 (point 286.630000 253.140000 -19.520000 0.460000) (point 288.170000 250.520000 -19.230000 0.460000) 4) (segment 3917 (point 288.170000 250.520000 -19.230000 0.460000) (point 289.420000 247.220000 -18.570000 0.460000) 4) (segment 3918 (point 289.420000 247.220000 -18.570000 0.460000) (point 289.420000 247.220000 -18.630000 0.460000) 4) (segment 3919 (point 289.420000 247.220000 -18.630000 0.460000) (point 291.110000 244.040000 -18.150000 0.460000) 4) (segment 3920 (point 291.110000 244.040000 -18.150000 0.460000) (point 293.550000 241.630000 -18.150000 0.460000) 4) (segment 3921 (point 293.550000 241.630000 -18.150000 0.460000) (point 296.000000 239.220000 -19.400000 0.460000) 4) (segment 3922 (point 296.000000 239.220000 -19.400000 0.460000) (point 298.460000 236.800000 -18.200000 0.460000) 4) (segment 3923 (point 298.460000 236.800000 -18.200000 0.460000) (point 299.570000 234.080000 -16.830000 0.460000) 4) (segment 3924 (point 299.570000 234.080000 -16.830000 0.460000) (point 299.570000 234.080000 -16.850000 0.460000) 4) (segment 3925 (point 299.570000 234.080000 -16.850000 0.460000) (point 300.500000 230.120000 -17.300000 0.460000) 4) (segment 3926 (point 300.500000 230.120000 -17.300000 0.460000) (point 302.050000 227.500000 -17.080000 0.460000) 4) (segment 3927 (point 302.050000 227.500000 -17.080000 0.460000) (point 302.050000 227.500000 -17.100000 0.460000) 4) (segment 3928 (point 302.050000 227.500000 -17.100000 0.460000) (point 305.260000 225.860000 -15.700000 0.460000) 4) (segment 3929 (point 305.260000 225.860000 -15.700000 0.460000) (point 307.310000 225.140000 -15.600000 0.460000) 4) (segment 3930 (point 307.310000 225.140000 -15.600000 0.460000) (point 309.570000 221.500000 -14.880000 0.460000) 4) (segment 3931 (point 309.570000 221.500000 -14.880000 0.460000) (point 311.140000 218.870000 -14.400000 0.460000) 4) (segment 3932 (point 311.140000 218.870000 -14.400000 0.460000) (point 313.330000 217.600000 -14.400000 0.460000) 4) (segment 3933 (point 313.330000 217.600000 -14.400000 0.460000) (point 314.130000 214.200000 -15.350000 0.460000) 4) (segment 3934 (point 314.130000 214.200000 -15.350000 0.460000) (point 315.730000 213.400000 -16.450000 0.460000) 4) (segment 3935 (point 315.730000 213.400000 -16.450000 0.460000) (point 317.860000 210.300000 -17.130000 0.460000) 4) (segment 3936 (point 317.860000 210.300000 -17.130000 0.460000) (point 319.730000 208.370000 -17.200000 0.460000) 4) (segment 3937 (point 319.730000 208.370000 -17.200000 0.460000) (point 321.470000 206.980000 -17.050000 0.460000) 4) (segment 3938 (point 321.470000 206.980000 -17.050000 0.460000) (point 324.370000 204.670000 -18.050000 0.460000) 4) (segment 3939 (point 324.370000 204.670000 -18.050000 0.460000) (point 327.890000 203.710000 -18.770000 0.460000) 4) (segment 3940 (point 327.890000 203.710000 -18.770000 0.460000) (point 329.630000 202.320000 -17.020000 0.460000) 4) (segment 3941 (point 329.630000 202.320000 -17.020000 0.460000) (point 331.230000 201.500000 -15.000000 0.460000) 4) (segment 3942 (point 331.230000 201.500000 -15.000000 0.460000) (point 333.610000 201.460000 -13.870000 0.460000) 4) (segment 3943 (point 333.610000 201.460000 -13.870000 0.460000) (point 334.590000 199.300000 -12.850000 0.460000) 4) (segment 3944 (point 334.590000 199.300000 -12.850000 0.460000) (point 337.340000 197.550000 -12.300000 0.460000) 4) (segment 3945 (point 337.340000 197.550000 -12.300000 0.460000) (point 339.480000 194.470000 -11.950000 0.460000) 4) (segment 3946 (point 339.480000 194.470000 -11.950000 0.460000) (point 340.450000 192.320000 -13.630000 0.460000) 4) (segment 3947 (point 340.450000 192.320000 -13.630000 0.460000) (point 342.900000 189.900000 -13.000000 0.460000) 4) (segment 3948 (point 342.900000 189.900000 -13.000000 0.460000) (point 342.460000 189.800000 -13.000000 0.460000) 4) (segment 3949 (point 342.460000 189.800000 -13.000000 0.460000) (point 345.080000 188.630000 -11.530000 0.460000) 4) (segment 3950 (point 345.080000 188.630000 -11.530000 0.460000) (point 346.960000 186.670000 -11.450000 0.230000) 4) (segment 3951 (point 346.960000 186.670000 -11.450000 0.230000) (point 349.450000 186.060000 -10.680000 0.230000) 4) (segment 3952 (point 349.450000 186.060000 -10.680000 0.230000) (point 350.750000 184.570000 -10.680000 0.230000) 4) (segment 3953 (point 350.750000 184.570000 -10.680000 0.230000) (point 352.040000 183.090000 -8.500000 0.230000) 4) (segment 3954 (point 352.040000 183.090000 -8.500000 0.230000) (point 352.040000 183.090000 -8.450000 0.230000) 4)) (branch 144 76 (segment 3955 (point 246.580000 253.310000 -19.880000 1.375000) (point 244.930000 254.130000 -18.750000 0.460000) 4) (segment 3956 (point 244.930000 254.130000 -18.750000 0.460000) (point 244.680000 255.260000 -16.650000 0.460000) 4) (segment 3957 (point 244.680000 255.260000 -16.650000 0.460000) (point 246.140000 255.020000 -14.950000 0.460000) 4) (segment 3958 (point 246.140000 255.020000 -14.950000 0.460000) (point 247.030000 255.220000 -12.800000 0.460000) 4) (segment 3959 (point 247.030000 255.220000 -12.800000 0.460000) (point 242.740000 255.420000 -11.950000 0.460000) 4) (segment 3960 (point 242.740000 255.420000 -11.950000 0.460000) (point 240.170000 258.390000 -11.400000 0.460000) 4) (segment 3961 (point 240.170000 258.390000 -11.400000 0.460000) (point 236.250000 261.060000 -11.150000 0.460000) 4) (segment 3962 (point 236.250000 261.060000 -11.150000 0.460000) (point 236.250000 261.060000 -11.170000 0.460000) 4) (segment 3963 (point 236.250000 261.060000 -11.170000 0.460000) (point 234.450000 260.640000 -10.050000 0.460000) 4) (segment 3964 (point 234.450000 260.640000 -10.050000 0.460000) (point 231.640000 260.570000 -9.720000 0.460000) 4) (segment 3965 (point 231.640000 260.570000 -9.720000 0.460000) (point 230.300000 260.260000 -8.730000 0.460000) 4)) (branch 145 144 (segment 3966 (point 230.300000 260.260000 -8.730000 0.460000) (point 228.080000 259.730000 -9.430000 0.460000) 4) (segment 3967 (point 228.080000 259.730000 -9.430000 0.460000) (point 224.230000 260.040000 -9.430000 0.460000) 4) (segment 3968 (point 224.230000 260.040000 -9.430000 0.460000) (point 223.200000 260.390000 -10.570000 0.460000) 4) (segment 3969 (point 223.200000 260.390000 -10.570000 0.460000) (point 223.200000 260.390000 -10.600000 0.460000) 4) (segment 3970 (point 223.200000 260.390000 -10.600000 0.460000) (point 220.000000 262.020000 -11.630000 0.460000) 4) (segment 3971 (point 220.000000 262.020000 -11.630000 0.460000) (point 217.990000 264.540000 -12.750000 0.460000) 4) (segment 3972 (point 217.990000 264.540000 -12.750000 0.460000) (point 215.760000 264.020000 -13.700000 0.460000) 4) (segment 3973 (point 215.760000 264.020000 -13.700000 0.460000) (point 213.270000 264.620000 -14.670000 0.460000) 4) (segment 3974 (point 213.270000 264.620000 -14.670000 0.460000) (point 210.050000 266.260000 -14.670000 0.460000) 4) (segment 3975 (point 210.050000 266.260000 -14.670000 0.460000) (point 210.050000 266.260000 -14.700000 0.460000) 4) (segment 3976 (point 210.050000 266.260000 -14.700000 0.460000) (point 209.210000 267.850000 -15.200000 0.460000) 4) (segment 3977 (point 209.210000 267.850000 -15.200000 0.460000) (point 207.020000 269.140000 -15.880000 0.460000) 4) (segment 3978 (point 207.020000 269.140000 -15.880000 0.460000) (point 207.020000 269.140000 -15.900000 0.460000) 4) (segment 3979 (point 207.020000 269.140000 -15.900000 0.460000) (point 204.080000 269.640000 -16.700000 0.460000) 4) (segment 3980 (point 204.080000 269.640000 -16.700000 0.460000) (point 202.650000 271.700000 -17.420000 0.460000) 4) (segment 3981 (point 202.650000 271.700000 -17.420000 0.460000) (point 198.940000 271.430000 -17.420000 0.460000) 4) (segment 3982 (point 198.940000 271.430000 -17.420000 0.460000) (point 194.980000 272.280000 -17.450000 0.460000) 4) (segment 3983 (point 194.980000 272.280000 -17.450000 0.460000) (point 193.420000 274.910000 -18.770000 0.460000) 4) (segment 3984 (point 193.420000 274.910000 -18.770000 0.460000) (point 193.420000 274.910000 -18.750000 0.460000) 4) (segment 3985 (point 193.420000 274.910000 -18.750000 0.460000) (point 191.900000 273.350000 -20.100000 0.460000) 4) (segment 3986 (point 191.900000 273.350000 -20.100000 0.460000) (point 189.970000 273.500000 -21.180000 0.460000) 4) (segment 3987 (point 189.970000 273.500000 -21.180000 0.460000) (point 187.930000 274.220000 -22.020000 0.460000) 4) (segment 3988 (point 187.930000 274.220000 -22.020000 0.460000) (point 185.740000 275.490000 -22.700000 0.460000) 4) (segment 3989 (point 185.740000 275.490000 -22.700000 0.460000) (point 183.110000 276.680000 -23.500000 0.460000) 4) (segment 3990 (point 183.110000 276.680000 -23.500000 0.460000) (point 181.560000 279.290000 -23.500000 0.460000) 4) (segment 3991 (point 181.560000 279.290000 -23.500000 0.460000) (point 179.050000 279.910000 -23.500000 0.460000) 4) (segment 3992 (point 179.050000 279.910000 -23.500000 0.460000) (point 175.980000 280.970000 -23.630000 0.460000) 4) (segment 3993 (point 175.980000 280.970000 -23.630000 0.460000) (point 172.770000 282.610000 -23.630000 0.460000) 4) (segment 3994 (point 172.770000 282.610000 -23.630000 0.460000) (point 169.600000 286.050000 -23.400000 0.230000) 4) (segment 3995 (point 169.600000 286.050000 -23.400000 0.230000) (point 166.220000 286.440000 -24.170000 0.230000) 4) (segment 3996 (point 166.220000 286.440000 -24.170000 0.230000) (point 162.700000 287.410000 -25.150000 0.230000) 4) (segment 3997 (point 162.700000 287.410000 -25.150000 0.230000) (point 160.950000 288.800000 -25.030000 0.230000) 4) (segment 3998 (point 160.950000 288.800000 -25.030000 0.230000) (point 160.550000 290.490000 -24.000000 0.230000) 4) (segment 3999 (point 160.550000 290.490000 -24.000000 0.230000) (point 157.970000 293.470000 -24.920000 0.230000) 4) (segment 4000 (point 157.970000 293.470000 -24.920000 0.230000) (point 157.970000 293.470000 -24.950000 0.230000) 4) (segment 4001 (point 157.970000 293.470000 -24.950000 0.230000) (point 156.810000 294.380000 -26.350000 0.230000) 4) (segment 4002 (point 156.810000 294.380000 -26.350000 0.230000) (point 156.810000 294.380000 -26.380000 0.230000) 4) (segment 4003 (point 156.810000 294.380000 -26.380000 0.230000) (point 154.500000 296.240000 -27.200000 0.230000) 4) (segment 4004 (point 154.500000 296.240000 -27.200000 0.230000) (point 151.740000 297.970000 -27.970000 0.230000) 4) (segment 4005 (point 151.740000 297.970000 -27.970000 0.230000) (point 150.760000 300.140000 -28.150000 0.230000) 4) (segment 4006 (point 150.760000 300.140000 -28.150000 0.230000) (point 148.650000 299.060000 -28.150000 0.230000) 4) (segment 4007 (point 148.650000 299.060000 -28.150000 0.230000) (point 144.290000 301.610000 -28.150000 0.230000) 4) (segment 4008 (point 144.290000 301.610000 -28.150000 0.230000) (point 140.310000 302.460000 -28.250000 0.230000) 4) (segment 4009 (point 140.310000 302.460000 -28.250000 0.230000) (point 140.310000 302.460000 -28.300000 0.230000) 4) (segment 4010 (point 140.310000 302.460000 -28.300000 0.230000) (point 137.550000 304.210000 -28.300000 0.230000) 4) (segment 4011 (point 137.550000 304.210000 -28.300000 0.230000) (point 134.070000 306.990000 -28.300000 0.230000) 4) (segment 4012 (point 134.070000 306.990000 -28.300000 0.230000) (point 130.600000 309.740000 -27.600000 0.230000) 4) (segment 4013 (point 130.600000 309.740000 -27.600000 0.230000) (point 127.970000 310.920000 -26.650000 0.230000) 4) (segment 4014 (point 127.970000 310.920000 -26.650000 0.230000) (point 125.080000 313.230000 -26.000000 0.230000) 4) (segment 4015 (point 125.080000 313.230000 -26.000000 0.230000) (point 123.790000 314.720000 -25.070000 0.230000) 4) (segment 4016 (point 123.790000 314.720000 -25.070000 0.230000) (point 121.330000 317.130000 -24.200000 0.230000) 4) (segment 4017 (point 121.330000 317.130000 -24.200000 0.230000) (point 118.570000 318.870000 -22.770000 0.230000) 4) (segment 4018 (point 118.570000 318.870000 -22.770000 0.230000) (point 117.140000 320.930000 -22.270000 0.230000) 4) (segment 4019 (point 117.140000 320.930000 -22.270000 0.230000) (point 114.430000 324.470000 -21.720000 0.230000) 4) (segment 4020 (point 114.430000 324.470000 -21.720000 0.230000) (point 114.430000 324.470000 -21.750000 0.230000) 4) (segment 4021 (point 114.430000 324.470000 -21.750000 0.230000) (point 114.350000 326.830000 -20.070000 0.230000) 4) (segment 4022 (point 114.350000 326.830000 -20.070000 0.230000) (point 114.350000 326.830000 -19.950000 0.230000) 4)) (branch 146 144 (segment 4023 (point 230.300000 260.260000 -8.730000 0.460000) (point 227.150000 263.700000 -8.000000 0.230000) 4)) (branch 147 146 (segment 4024 (point 227.150000 263.700000 -8.000000 0.230000) (point 227.200000 265.510000 -6.930000 0.230000) 4) (segment 4025 (point 227.200000 265.510000 -6.930000 0.230000) (point 225.710000 265.760000 -5.600000 0.230000) 4) (segment 4026 (point 225.710000 265.760000 -5.600000 0.230000) (point 223.840000 267.710000 -4.920000 0.230000) 4) (segment 4027 (point 223.840000 267.710000 -4.920000 0.230000) (point 222.640000 266.820000 -4.880000 0.230000) 4) (segment 4028 (point 222.640000 266.820000 -4.880000 0.230000) (point 223.140000 268.740000 -2.970000 0.230000) 4) (segment 4029 (point 223.140000 268.740000 -2.970000 0.230000) (point 223.140000 268.740000 -2.950000 0.230000) 4) (segment 4030 (point 223.140000 268.740000 -2.950000 0.230000) (point 222.420000 269.760000 -1.750000 0.230000) 4) (segment 4031 (point 222.420000 269.760000 -1.750000 0.230000) (point 221.580000 271.350000 -0.120000 0.230000) 4) (segment 4032 (point 221.580000 271.350000 -0.120000 0.230000) (point 222.340000 272.140000 1.750000 0.230000) 4) (segment 4033 (point 222.340000 272.140000 1.750000 0.230000) (point 219.970000 272.180000 3.820000 0.230000) 4) (segment 4034 (point 219.970000 272.180000 3.820000 0.230000) (point 217.030000 272.680000 4.720000 0.230000) 4) (segment 4035 (point 217.030000 272.680000 4.720000 0.230000) (point 217.210000 273.910000 6.180000 0.230000) 4) (segment 4036 (point 217.210000 273.910000 6.180000 0.230000) (point 217.980000 274.680000 8.100000 0.230000) 4) (segment 4037 (point 217.980000 274.680000 8.100000 0.230000) (point 217.440000 276.950000 9.450000 0.230000) 4) (segment 4038 (point 217.440000 276.950000 9.450000 0.230000) (point 218.460000 276.600000 9.850000 0.230000) 4) (segment 4039 (point 218.460000 276.600000 9.850000 0.230000) (point 218.060000 278.290000 11.980000 0.230000) 4) (segment 4040 (point 218.060000 278.290000 11.980000 0.230000) (point 216.150000 278.440000 14.230000 0.230000) 4) (segment 4041 (point 216.150000 278.440000 14.230000 0.230000) (point 217.670000 279.990000 16.150000 0.230000) 4) (segment 4042 (point 217.670000 279.990000 16.150000 0.230000) (point 217.320000 283.490000 17.350000 0.230000) 4) (segment 4043 (point 217.320000 283.490000 17.350000 0.230000) (point 217.320000 283.490000 17.400000 0.230000) 4) (segment 4044 (point 217.320000 283.490000 17.400000 0.230000) (point 218.710000 285.610000 18.750000 0.230000) 4)) (branch 148 147 (segment 4045 (point 218.710000 285.610000 18.750000 0.230000) (point 219.380000 288.760000 18.800000 0.230000) 4) (segment 4046 (point 219.380000 288.760000 18.800000 0.230000) (point 219.750000 291.230000 20.270000 0.230000) 4) (segment 4047 (point 219.750000 291.230000 20.270000 0.230000) (point 221.540000 291.640000 21.920000 0.230000) 4) (segment 4048 (point 221.540000 291.640000 21.920000 0.230000) (point 222.560000 291.290000 23.330000 0.230000) 4) (segment 4049 (point 222.560000 291.290000 23.330000 0.230000) (point 222.560000 291.290000 23.300000 0.230000) 4)) (branch 149 147 (segment 4050 (point 218.710000 285.610000 18.750000 0.230000) (point 220.170000 285.360000 17.500000 0.230000) 4) (segment 4051 (point 220.170000 285.360000 17.500000 0.230000) (point 221.260000 286.800000 19.130000 0.230000) 4) (segment 4052 (point 221.260000 286.800000 19.130000 0.230000) (point 221.260000 286.800000 19.200000 0.230000) 4) (segment 4053 (point 221.260000 286.800000 19.200000 0.230000) (point 221.760000 288.710000 21.050000 0.230000) 4) (segment 4054 (point 221.760000 288.710000 21.050000 0.230000) (point 220.910000 290.310000 21.800000 0.230000) 4) (segment 4055 (point 220.910000 290.310000 21.800000 0.230000) (point 221.540000 291.640000 23.100000 0.230000) 4) (segment 4056 (point 221.540000 291.640000 23.100000 0.230000) (point 223.320000 292.060000 23.780000 0.230000) 4) (segment 4057 (point 223.320000 292.060000 23.780000 0.230000) (point 223.320000 292.060000 23.950000 0.230000) 4)) (branch 150 146 (segment 4058 (point 227.150000 263.700000 -8.000000 0.230000) (point 225.080000 264.420000 -9.150000 0.460000) 4) (segment 4059 (point 225.080000 264.420000 -9.150000 0.460000) (point 222.460000 265.590000 -9.400000 0.460000) 4) (segment 4060 (point 222.460000 265.590000 -9.400000 0.460000) (point 220.150000 267.440000 -8.320000 0.460000) 4) (segment 4061 (point 220.150000 267.440000 -8.320000 0.460000) (point 220.150000 267.440000 -8.350000 0.460000) 4) (segment 4062 (point 220.150000 267.440000 -8.350000 0.460000) (point 218.360000 267.020000 -8.200000 0.460000) 4) (segment 4063 (point 218.360000 267.020000 -8.200000 0.460000) (point 214.380000 267.870000 -8.320000 0.460000) 4) (segment 4064 (point 214.380000 267.870000 -8.320000 0.460000) (point 211.040000 270.080000 -9.430000 0.460000) 4) (segment 4065 (point 211.040000 270.080000 -9.430000 0.460000) (point 207.910000 269.350000 -9.430000 0.460000) 4) (segment 4066 (point 207.910000 269.350000 -9.430000 0.460000) (point 207.250000 272.180000 -10.170000 0.460000) 4) (segment 4067 (point 207.250000 272.180000 -10.170000 0.460000) (point 205.380000 274.120000 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217.100000 -12.100000 0.690000) (point 244.970000 215.900000 -13.250000 0.690000) 4) (segment 4103 (point 244.970000 215.900000 -13.250000 0.690000) (point 242.460000 216.520000 -14.400000 0.690000) 4) (segment 4104 (point 242.460000 216.520000 -14.400000 0.690000) (point 241.620000 218.110000 -15.600000 0.690000) 4) (segment 4105 (point 241.620000 218.110000 -15.600000 0.690000) (point 240.820000 221.510000 -19.420000 0.690000) 4)) (branch 152 151 (segment 4106 (point 240.820000 221.510000 -19.420000 0.690000) (point 238.640000 222.780000 -20.750000 0.690000) 4) (segment 4107 (point 238.640000 222.780000 -20.750000 0.690000) (point 234.350000 222.980000 -22.300000 0.690000) 4) (segment 4108 (point 234.350000 222.980000 -22.300000 0.690000) (point 232.130000 222.460000 -23.250000 0.690000) 4) (segment 4109 (point 232.130000 222.460000 -23.250000 0.690000) (point 232.130000 222.460000 -23.270000 0.690000) 4) (segment 4110 (point 232.130000 222.460000 -23.270000 0.690000) (point 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(point 197.620000 204.810000 -45.970000 0.230000) 4) (segment 4128 (point 197.620000 204.810000 -45.970000 0.230000) (point 195.260000 204.860000 -46.930000 0.230000) 4) (segment 4129 (point 195.260000 204.860000 -46.930000 0.230000) (point 195.260000 204.860000 -46.950000 0.230000) 4) (segment 4130 (point 195.260000 204.860000 -46.950000 0.230000) (point 193.520000 206.240000 -47.800000 0.230000) 4) (segment 4131 (point 193.520000 206.240000 -47.800000 0.230000) (point 190.840000 205.610000 -48.700000 0.230000) 4) (segment 4132 (point 190.840000 205.610000 -48.700000 0.230000) (point 190.840000 205.610000 -48.720000 0.230000) 4) (segment 4133 (point 190.840000 205.610000 -48.720000 0.230000) (point 187.270000 204.780000 -49.580000 0.230000) 4) (segment 4134 (point 187.270000 204.780000 -49.580000 0.230000) (point 183.880000 205.170000 -49.380000 0.230000) 4) (segment 4135 (point 183.880000 205.170000 -49.380000 0.230000) (point 181.380000 205.790000 -49.380000 0.230000) 4) (segment 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172.110000 213.160000 -54.830000 0.230000) 4)) (branch 154 152 (segment 4145 (point 230.780000 222.140000 -25.000000 0.690000) (point 226.810000 222.990000 -25.000000 0.460000) 4) (segment 4146 (point 226.810000 222.990000 -25.000000 0.460000) (point 225.080000 224.380000 -27.630000 0.460000) 4) (segment 4147 (point 225.080000 224.380000 -27.630000 0.460000) (point 224.940000 224.950000 -27.630000 0.460000) 4) (segment 4148 (point 224.940000 224.950000 -27.630000 0.460000) (point 223.600000 224.630000 -29.770000 0.460000) 4) (segment 4149 (point 223.600000 224.630000 -29.770000 0.460000) (point 221.510000 223.540000 -30.600000 0.460000) 4) (segment 4150 (point 221.510000 223.540000 -30.600000 0.460000) (point 219.640000 225.500000 -32.380000 0.460000) 4) (segment 4151 (point 219.640000 225.500000 -32.380000 0.460000) (point 217.270000 225.540000 -33.450000 0.460000) 4) (segment 4152 (point 217.270000 225.540000 -33.450000 0.460000) (point 213.960000 223.570000 -34.320000 0.460000) 4) 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(point 199.050000 240.970000 -30.730000 0.460000) 4) (segment 4162 (point 199.050000 240.970000 -30.730000 0.460000) (point 198.520000 243.240000 -29.770000 0.460000) 4) (segment 4163 (point 198.520000 243.240000 -29.770000 0.460000) (point 196.900000 244.060000 -30.550000 0.460000) 4) (segment 4164 (point 196.900000 244.060000 -30.550000 0.460000) (point 192.890000 243.120000 -30.550000 0.460000) 4) (segment 4165 (point 192.890000 243.120000 -30.550000 0.460000) (point 190.130000 244.860000 -29.400000 0.460000) 4) (segment 4166 (point 190.130000 244.860000 -29.400000 0.460000) (point 188.020000 243.760000 -28.330000 0.460000) 4) (segment 4167 (point 188.020000 243.760000 -28.330000 0.460000) (point 186.100000 243.910000 -26.170000 0.460000) 4) (segment 4168 (point 186.100000 243.910000 -26.170000 0.460000) (point 183.300000 243.850000 -24.700000 0.460000) 4) (segment 4169 (point 183.300000 243.850000 -24.700000 0.460000) (point 181.640000 242.870000 -24.320000 0.460000) 4) (segment 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56.270000 0.000000 0.230000) (point 166.440000 56.630000 1.630000 0.230000) 4) (segment 4842 (point 166.440000 56.630000 1.630000 0.230000) (point 166.440000 56.630000 1.600000 0.230000) 4) (segment 4843 (point 166.440000 56.630000 1.600000 0.230000) (point 165.420000 56.980000 2.900000 0.230000) 4)) (branch 181 178 (segment 4844 (point 201.610000 55.310000 -16.570000 0.460000) (point 199.070000 54.110000 -19.700000 0.460000) 4) (segment 4845 (point 199.070000 54.110000 -19.700000 0.460000) (point 196.970000 53.030000 -22.050000 0.460000) 4) (segment 4846 (point 196.970000 53.030000 -22.050000 0.460000) (point 191.920000 52.440000 -23.000000 0.460000) 4) (segment 4847 (point 191.920000 52.440000 -23.000000 0.460000) (point 189.380000 51.240000 -23.630000 0.460000) 4) (segment 4848 (point 189.380000 51.240000 -23.630000 0.460000) (point 188.290000 49.800000 -24.600000 0.460000) 4) (segment 4849 (point 188.290000 49.800000 -24.600000 0.460000) (point 184.460000 50.090000 -24.600000 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(point 168.200000 45.090000 -33.100000 0.460000) 4) (segment 4859 (point 168.200000 45.090000 -33.100000 0.460000) (point 165.340000 43.230000 -34.020000 0.460000) 4) (segment 4860 (point 165.340000 43.230000 -34.020000 0.460000) (point 162.260000 44.290000 -35.650000 0.460000) 4) (segment 4861 (point 162.260000 44.290000 -35.650000 0.460000) (point 161.550000 45.320000 -37.770000 0.460000) 4) (segment 4862 (point 161.550000 45.320000 -37.770000 0.460000) (point 160.210000 45.010000 -39.950000 0.460000) 4) (segment 4863 (point 160.210000 45.010000 -39.950000 0.460000) (point 157.790000 43.250000 -42.100000 0.460000) 4) (segment 4864 (point 157.790000 43.250000 -42.100000 0.460000) (point 157.790000 43.250000 -42.130000 0.460000) 4) (segment 4865 (point 157.790000 43.250000 -42.130000 0.460000) (point 154.090000 42.980000 -43.470000 0.460000) 4) (segment 4866 (point 154.090000 42.980000 -43.470000 0.460000) (point 149.220000 43.630000 -49.650000 0.230000) 4) (segment 4867 (point 149.220000 43.630000 -49.650000 0.230000) (point 145.380000 43.920000 -51.220000 0.230000) 4) (segment 4868 (point 145.380000 43.920000 -51.220000 0.230000) (point 145.380000 43.920000 -51.280000 0.230000) 4) (segment 4869 (point 145.380000 43.920000 -51.280000 0.230000) (point 142.580000 43.860000 -52.630000 0.230000) 4) (segment 4870 (point 142.580000 43.860000 -52.630000 0.230000) (point 142.580000 43.860000 -52.650000 0.230000) 4) (segment 4871 (point 142.580000 43.860000 -52.650000 0.230000) (point 141.280000 45.360000 -54.500000 0.230000) 4) (segment 4872 (point 141.280000 45.360000 -54.500000 0.230000) (point 140.080000 44.470000 -56.200000 0.230000) 4) (segment 4873 (point 140.080000 44.470000 -56.200000 0.230000) (point 140.080000 44.470000 -56.220000 0.230000) 4) (segment 4874 (point 140.080000 44.470000 -56.220000 0.230000) (point 137.270000 44.420000 -57.470000 0.230000) 4) (segment 4875 (point 137.270000 44.420000 -57.470000 0.230000) (point 137.270000 44.420000 -57.500000 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116.970000 48.620000 -78.100000 0.230000) (point 115.490000 48.860000 -80.350000 0.230000) 4) (segment 4894 (point 115.490000 48.860000 -80.350000 0.230000) (point 115.490000 48.860000 -80.400000 0.230000) 4) (segment 4895 (point 115.490000 48.860000 -80.400000 0.230000) (point 114.020000 49.110000 -82.970000 0.230000) 4) (segment 4896 (point 114.020000 49.110000 -82.970000 0.230000) (point 110.180000 49.410000 -84.800000 0.230000) 4) (segment 4897 (point 110.180000 49.410000 -84.800000 0.230000) (point 108.890000 50.900000 -86.670000 0.230000) 4) (segment 4898 (point 108.890000 50.900000 -86.670000 0.230000) (point 108.890000 50.900000 -86.700000 0.230000) 4) (segment 4899 (point 108.890000 50.900000 -86.700000 0.230000) (point 107.420000 51.150000 -88.570000 0.230000) 4) (segment 4900 (point 107.420000 51.150000 -88.570000 0.230000) (point 107.420000 51.150000 -88.600000 0.230000) 4) (segment 4901 (point 107.420000 51.150000 -88.600000 0.230000) (point 104.340000 52.220000 -90.250000 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4) (segment 5185 (point 200.390000 26.360000 -54.380000 0.230000) (point 200.390000 26.360000 -54.400000 0.230000) 4) (segment 5186 (point 200.390000 26.360000 -54.400000 0.230000) (point 196.430000 27.220000 -55.320000 0.230000) 4) (segment 5187 (point 196.430000 27.220000 -55.320000 0.230000) (point 192.850000 26.380000 -56.800000 0.230000) 4) (segment 5188 (point 192.850000 26.380000 -56.800000 0.230000) (point 191.640000 25.510000 -58.150000 0.230000) 4) (segment 5189 (point 191.640000 25.510000 -58.150000 0.230000) (point 191.640000 25.510000 -58.170000 0.230000) 4) (segment 5190 (point 191.640000 25.510000 -58.170000 0.230000) (point 189.730000 25.650000 -58.900000 0.230000) 4) (segment 5191 (point 189.730000 25.650000 -58.900000 0.230000) (point 187.210000 26.260000 -59.800000 0.230000) 4) (segment 5192 (point 187.210000 26.260000 -59.800000 0.230000) (point 184.540000 25.640000 -60.700000 0.230000) 4) (segment 5193 (point 184.540000 25.640000 -60.700000 0.230000) (point 181.820000 23.200000 -61.380000 0.230000) 4) (segment 5194 (point 181.820000 23.200000 -61.380000 0.230000) (point 178.920000 25.500000 -62.880000 0.230000) 4) (segment 5195 (point 178.920000 25.500000 -62.880000 0.230000) (point 176.280000 26.680000 -63.950000 0.230000) 4) (segment 5196 (point 176.280000 26.680000 -63.950000 0.230000) (point 174.540000 28.070000 -65.300000 0.230000) 4) (segment 5197 (point 174.540000 28.070000 -65.300000 0.230000) (point 172.050000 28.680000 -66.880000 0.230000) 4) (segment 5198 (point 172.050000 28.680000 -66.880000 0.230000) (point 172.180000 28.110000 -66.880000 0.230000) 4) (segment 5199 (point 172.180000 28.110000 -66.880000 0.230000) (point 168.650000 29.070000 -68.200000 0.230000) 4) (segment 5200 (point 168.650000 29.070000 -68.200000 0.230000) (point 166.030000 30.250000 -69.400000 0.230000) 4) (segment 5201 (point 166.030000 30.250000 -69.400000 0.230000) (point 164.420000 31.070000 -71.000000 0.230000) 4) (segment 5202 (point 164.420000 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138.790000 23.870000 -93.630000 0.230000) 4) (segment 5220 (point 138.790000 23.870000 -93.630000 0.230000) (point 138.790000 23.870000 -93.880000 0.230000) 4) (segment 5221 (point 138.790000 23.870000 -93.880000 0.230000) (point 136.360000 22.100000 -94.170000 0.230000) 4) (segment 5222 (point 136.360000 22.100000 -94.170000 0.230000) (point 135.560000 19.530000 -95.180000 0.230000) 4) (segment 5223 (point 135.560000 19.530000 -95.180000 0.230000) (point 134.040000 17.980000 -97.280000 0.230000) 4)) (branch 194 192 (segment 5224 (point 219.620000 26.690000 -36.600000 0.460000) (point 223.200000 27.530000 -39.550000 0.230000) 4) (segment 5225 (point 223.200000 27.530000 -39.550000 0.230000) (point 223.650000 27.630000 -42.470000 0.230000) 4) (segment 5226 (point 223.650000 27.630000 -42.470000 0.230000) (point 223.650000 27.630000 -42.530000 0.230000) 4) (segment 5227 (point 223.650000 27.630000 -42.530000 0.230000) (point 223.380000 28.760000 -45.970000 0.230000) 4) (segment 5228 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================================================ (arbor-component (meta-data (version "0.9-dev")) (morphology (branch 0 -1 (segment 0 (point -5.000000 0.000000 0.000000 5.000000) (point 0.000000 0.000000 0.000000 5.000000) 1) (segment 1 (point 0.000000 0.000000 0.000000 5.000000) (point 5.000000 0.000000 0.000000 5.000000) 1)) (branch 1 -1 (segment 2 (point 5.000000 0.000000 0.000000 0.500000) (point 20.000000 0.000000 0.000000 0.500000) 2) (segment 3 (point 20.000000 0.000000 0.000000 0.500000) (point 35.000000 0.000000 0.000000 0.500000) 2) (segment 4 (point 35.000000 0.000000 0.000000 0.500000) (point 50.000000 0.000000 0.000000 0.500000) 2) (segment 5 (point 50.000000 0.000000 0.000000 0.500000) (point 65.000000 0.000000 0.000000 0.500000) 2)))) ================================================ FILE: bluepyopt/tests/test_ephys/testdata/acc/templates/cell_json_template.jinja2 ================================================ { "cell_model_name": "{{template_name}}", {%- if banner %} "produced_by": "{{banner}} (from {{ custom_param }})", {%- endif %} {%- if morphology %} {# feed morphology separately as a SWC/ASC file #} {%- if replace_axon is not none %} "morphology": { "original": "{{morphology}}", "replace_axon": {{replace_axon}}, "modified": "{{modified_morphology}}" }, {%- else %} "morphology": { "original": "{{morphology}}" }, {%- endif %} {%- else %} execerror("Template {{template_name}} requires morphology name to instantiate") {%- endif %} "label_dict": "{{filenames['label_dict.acc']}}", "decor": "{{filenames['decor.acc']}}" } ================================================ FILE: bluepyopt/tests/test_ephys/testdata/acc/templates/decor_acc_template.jinja2 ================================================ (arbor-component (meta-data (version "0.9-dev")) (meta-data (info "test-decor")) (decor {%- for mech, params in global_mechs.items() %} {%- if mech is not none %} {%- if mech in global_scaled_mechs %} (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 %})) {%- else %} (default (density (mechanism "{{ mech }}" {%- for param in params %} ("{{ param.name }}" {{ param.value }}){%- endfor %}))) {%- endif %} {%- else %} {%- for param in params %} (default ({{ param.name }} {{ param.value }} (scalar 1.0))) {%- endfor %} {%- endif %} {%- endfor %} {%- for loc, mech_parameters in local_mechs.items() %}{# paint-to-region instead of default #} {%- for mech, params in mech_parameters.items() %} {%- if mech is not none %} {%- if mech in local_scaled_mechs[loc] %} (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 %})) {%- else %} (paint {{loc.ref}} (density (mechanism "{{ mech }}" {%- for param in params %} ("{{ param.name }}" {{ param.value }}){%- endfor %}))) {%- endif %} {%- else %} {%- for param in params %} (paint {{loc.ref}} ({{ param.name }} {{ param.value }} (scalar 1.0))) {%- endfor %} {%- endif %} {%- endfor %} {%- endfor %})) ================================================ FILE: bluepyopt/tests/test_ephys/testdata/acc/templates/label_dict_acc_template.jinja2 ================================================ (arbor-component (meta-data (version "0.9-dev")) (meta-data (info "test-label-dict")) (label-dict {%- for loc, label in label_dict.items() %}{# this is a comment #} {{ label.defn }} {%- endfor %})) ================================================ FILE: bluepyopt/tests/test_ephys/testdata/apic.swc ================================================ # Dummy cell morphology 1 1 -5.0 0.0 0.0 5.0 -1 2 1 0.0 0.0 0.0 5.0 1 3 1 5.0 0.0 0.0 5.0 2 4 4 5.0 0.0 0.0 1.0 3 5 4 20.0 0.0 0.0 1.0 4 ================================================ FILE: bluepyopt/tests/test_ephys/testdata/lfpy_voltage.npy ================================================ [File too large to display: 19.6 MB] ================================================ FILE: bluepyopt/tests/test_ephys/testdata/simple.swc ================================================ # Dummy granule cell morphology 1 1 -5.0 0.0 0.0 5.0 -1 2 1 0.0 0.0 0.0 5.0 1 3 1 5.0 0.0 0.0 5.0 2 ================================================ FILE: bluepyopt/tests/test_ephys/testdata/simple.wrong ================================================ ================================================ FILE: bluepyopt/tests/test_ephys/testdata/simple_ax1.swc ================================================ # Dummy granule cell morphology 1 1 -5.0 0.0 0.0 5.0 -1 2 1 0.0 0.0 0.0 5.0 1 3 1 5.0 0.0 0.0 5.0 2 4 2 5.0 0.0 0.0 1.0 3 5 2 10.0 0.0 0.0 1.0 4 ================================================ FILE: bluepyopt/tests/test_ephys/testdata/simple_ax2.asc ================================================ ; V3 text file written for MicroBrightField products. ("CellBody" (CellBody) ( -5 0 0 0) ; 1, 1 ( 0 -5 0 0) ; 1, 2 ( 5 0 0 0) ; 1, 3 ( 0 5 0 0) ; 1, 4 ) ( (Axon) ( 5 0 0 .5) ; Root ( 10 0 0 .5) ; 1, R ( ( 20 0 0 .5) ; 1, R-1 ( 1000 0 0 .5) ; 2 ) ) ================================================ FILE: bluepyopt/tests/test_ephys/testdata/simple_ax2.swc ================================================ # Dummy granule cell morphology 1 1 -5.0 0.0 0.0 5.0 -1 2 1 0.0 0.0 0.0 5.0 1 3 1 5.0 0.0 0.0 5.0 2 4 2 5.0 0.0 0.0 1.0 3 5 2 10.0 0.0 0.0 1.0 4 6 2 100.0 0.0 0.0 1.0 5 ================================================ FILE: bluepyopt/tests/test_ephys/testdata/test.jinja2 ================================================ /* Test template {%- if banner %} {{banner}} {%- endif %} */ {load_file("stdrun.hoc")} {load_file("import3d.hoc")} {%- if global_params %} /* * Check that global parameters are the same as with the optimization */ proc check_parameter(/* name, expected_value, value */){ strdef error if($2 != $3){ sprint(error, "Parameter %s has different value %f != %f", $s1, $2, $3) execerror(error) } } proc check_simulator() { {%- for param, value in global_params.items() %} check_parameter("{{param}}", {{value}}, {{param}}) {%- endfor %} } {%- endif %} {%- if ignored_global_params %} /* The following global parameters were set in BluePyOpt {%- for param, value in ignored_global_params.items() %} * {{param}} = {{value}} {%- endfor %} */ {%- endif %} begintemplate {{template_name}} public init, morphology, geom_nseg_fixed, geom_nsec public soma, dend, apic, axon, myelin create soma[1], dend[1], apic[1], axon[1], myelin[1] objref this, CellRef, segCounts public all, somatic, apical, axonal, basal, myelinated, APC objref all, somatic, apical, axonal, basal, myelinated, APC proc init(/* args: morphology_dir, morphology_name */) { all = new SectionList() apical = new SectionList() axonal = new SectionList() basal = new SectionList() somatic = new SectionList() myelinated = new SectionList() //For compatibility with BBP CCells CellRef = this forall delete_section() if(numarg() >= 2) { load_morphology($s1, $s2) } else { {%- if morphology %} load_morphology($s1, "{{morphology}}") {%- else %} execerror("Template {{template_name}} requires morphology name to instantiate") {%- endif %} } geom_nseg() {%- if replace_axon %} replace_axon() {%- endif %} insertChannel() biophys() re_init_rng() } proc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension strdef morph_path sprint(morph_path, "%s/%s", $s1, $s2) sf = new StringFunctions() extension = new String() sscanf(morph_path, "%s", extension.s) sf.right(extension.s, sf.len(extension.s)-4) if( strcmp(extension.s, ".asc") == 0 ) { morph = new Import3d_Neurolucida3() } else if( strcmp(extension.s, ".swc" ) == 0) { morph = new Import3d_SWC_read() } else { printf("Unsupported file format: Morphology file has to end with .asc or .swc" ) quit() } morph.quiet = 1 morph.input(morph_path) import = new Import3d_GUI(morph, 0) import.instantiate(this) } /* * Assignment of mechanism values based on distance from the soma * Matches the BluePyOpt method */ proc distribute_distance(){local x localobj sl strdef stmp, distfunc, mech sl = $o1 mech = $s2 distfunc = $s3 this.soma[0] distance(0, 0.5) sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc) forsec sl for(x, 0) { sprint(stmp, distfunc, secname(), x, distance(x)) execute(stmp) } } proc geom_nseg() { this.geom_nsec() //To count all sections //TODO: geom_nseg_fixed depends on segCounts which is calculated by // geom_nsec. Can this be collapsed? this.geom_nseg_fixed(40) this.geom_nsec() //To count all sections } proc insertChannel() { {%- for location, names in channels.items() %} forsec this.{{location}} { {%- for channel in names %} insert {{channel}} {%- endfor %} } {%- endfor %} } proc biophys() { {% for loc, parameters in section_params %} forsec CellRef.{{ loc }} { {%- for param in parameters %} {{ param.name }} = {{ param.value }} {%- endfor %} } {% endfor %} {%- for location, param_name, value in range_params %} distribute_distance(CellRef.{{location}}, "{{param_name}}", "{{value}}") {%- endfor %} } func sec_count(/* SectionList */) { local nSec nSec = 0 forsec $o1 { nSec += 1 } return nSec } /* * Iterate over the section and compute how many segments should be allocate to * each. */ proc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize chunkSize = $1 soma area(.5) // make sure diam reflects 3d points secIndex = 0 forsec all { nseg = 1 + 2*int(L/chunkSize) segCounts.x[secIndex] = nseg secIndex += 1 } } /* * Count up the number of sections */ proc geom_nsec() { local nSec nSecAll = sec_count(all) nSecSoma = sec_count(somatic) nSecApical = sec_count(apical) nSecBasal = sec_count(basal) nSecMyelinated = sec_count(myelinated) nSecAxonalOrig = nSecAxonal = sec_count(axonal) segCounts = new Vector() segCounts.resize(nSecAll) nSec = 0 forsec all { segCounts.x[nSec] = nseg nSec += 1 } } /* * Replace the axon built from the original morphology file with a stub axon */ {%- if replace_axon %} {{replace_axon}} {%- endif %} {%- if custom_param %} {{custom_param}} {%- endif %} {{re_init_rng}} endtemplate {{template_name}} ================================================ FILE: bluepyopt/tests/test_ephys/testmodels/__init__.py ================================================ ================================================ FILE: bluepyopt/tests/test_ephys/testmodels/dummycells.py ================================================ """Dummy cell model used for testing""" import bluepyopt.ephys as ephys class DummyCellModel1(ephys.models.Model): """Dummy cell model 1""" def __init__(self, name=None): """Constructor""" super(DummyCellModel1, self).__init__(name) self.persistent = [] self.icell = None def freeze(self, param_values): """Freeze model""" pass def unfreeze(self, param_names): """Freeze model""" pass def instantiate(self, sim=None): """Instantiate cell in simulator""" class Cell(object): """Empty cell class""" def __init__(self): """Constructor""" self.soma = None self.somatic = None self.icell = Cell() self.icell.soma = [sim.neuron.h.Section(name='soma', cell=self.icell)] self.icell.apic = [ sim.neuron.h.Section( name='apic1', cell=self.icell)] self.icell.somatic = sim.neuron.h.SectionList( ) # pylint: disable = W0201 self.icell.somatic.append(sec=self.icell.soma[0]) self.icell.apical = sim.neuron.h.SectionList() self.icell.apical.append(sec=self.icell.apic[0]) self.persistent.append(self.icell) self.persistent.append(self.icell.soma[0]) return self.icell def destroy(self, sim=None): """Destroy cell from simulator""" self.persistent = [] class DummyLFPyCellModel1(ephys.models.Model): """Dummy LFPy cell model 1""" def __init__(self, name=None): """Constructor""" super(DummyLFPyCellModel1, self).__init__(name) self.persistent = [] self.icell = None self.lfpy_cell = None self.electrode = None self.lfpy_electrode = None def freeze(self, param_values): """Freeze model""" pass def unfreeze(self, param_names): """Freeze model""" pass def instantiate(self, sim=None): """Instantiate cell in simulator""" import LFPy class Cell(object): """Empty cell class""" def __init__(self): """Constructor""" self.soma = None self.somatic = None self.icell = Cell() self.icell.soma = [sim.neuron.h.Section(name='soma', cell=self.icell)] self.icell.apic = [ sim.neuron.h.Section( name='apic1', cell=self.icell)] self.icell.somatic = sim.neuron.h.SectionList( ) # pylint: disable = W0201 self.icell.somatic.append(sec=self.icell.soma[0]) self.icell.apical = sim.neuron.h.SectionList() self.icell.apical.append(sec=self.icell.apic[0]) self.persistent.append(self.icell) self.persistent.append(self.icell.soma[0]) self.lfpy_cell = LFPy.Cell( morphology=sim.neuron.h.allsec(), dt=0.025, v_init=-65, pt3d=True, delete_sections=False, nsegs_method=None, ) return self.icell, self.lfpy_cell def destroy(self, sim=None): """Destroy cell from simulator""" self.persistent = [] ================================================ FILE: bluepyopt/tests/test_ephys/utils.py ================================================ """EPhys test utils""" from bluepyopt import ephys from bluepyopt.ephys.parameters import ( NrnGlobalParameter, NrnSectionParameter, NrnRangeParameter,) from bluepyopt.ephys.locations import NrnSeclistLocation def make_mech(): """Create mechanism""" basal = ephys.locations.NrnSeclistLocation('basal', seclist_name='basal') apical = ephys.locations.NrnSeclistLocation( 'apical', seclist_name='apical') return ephys.mechanisms.NrnMODMechanism( 'Ih', suffix='Ih', locations=[ basal, apical, ]) def make_parameters(): """Create parameters""" value, frozen, bounds, param_name = 65, False, [ 0, 100.0], 'gSKv3_1bar_SKv3_1' value_scaler = ephys.parameterscalers.NrnSegmentLinearScaler() locations = (NrnSeclistLocation('Location0', 'somatic'), NrnSeclistLocation('Location1', 'apical'),) parameters = ( NrnGlobalParameter( 'NrnGlobalParameter', value, frozen, bounds, param_name), NrnSectionParameter( 'NrnSectionParameter', value, frozen, bounds, param_name, value_scaler, locations), NrnRangeParameter( 'NrnRangeParameter', value, frozen, bounds, param_name, value_scaler, locations), ) return parameters ================================================ FILE: bluepyopt/tests/test_evaluators.py ================================================ """bluepyopt.evaluators tests""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import pytest import numpy import bluepyopt @pytest.mark.unit def test_evaluator_init(): """bluepyopt.evaluators: test Evaluator init""" evaluator = bluepyopt.evaluators.Evaluator() assert isinstance(evaluator, bluepyopt.evaluators.Evaluator) ================================================ FILE: bluepyopt/tests/test_l5pc.py ================================================ """Test l5pc example""" import json import os import sys from contextlib import contextmanager import bluepyopt from bluepyopt import ephys if sys.version_info[0] < 3: from StringIO import StringIO else: from io import StringIO import pytest L5PC_PATH = os.path.abspath( os.path.join(os.path.dirname(__file__), "../../examples/l5pc") ) SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, L5PC_PATH) neuron_sim = ephys.simulators.NrnSimulator() neuron_sim.neuron.h.nrn_load_dll( os.path.join(L5PC_PATH, "x86_64/.libs/libnrnmech.so") ) # Parameters in release circuit model release_parameters = { "gNaTs2_tbar_NaTs2_t.apical": 0.026145, "gSKv3_1bar_SKv3_1.apical": 0.004226, "gImbar_Im.apical": 0.000143, "gNaTa_tbar_NaTa_t.axonal": 3.137968, "gK_Tstbar_K_Tst.axonal": 0.089259, "gamma_CaDynamics_E2.axonal": 0.002910, "gNap_Et2bar_Nap_Et2.axonal": 0.006827, "gSK_E2bar_SK_E2.axonal": 0.007104, "gCa_HVAbar_Ca_HVA.axonal": 0.000990, "gK_Pstbar_K_Pst.axonal": 0.973538, "gSKv3_1bar_SKv3_1.axonal": 1.021945, "decay_CaDynamics_E2.axonal": 287.198731, "gCa_LVAstbar_Ca_LVAst.axonal": 0.008752, "gamma_CaDynamics_E2.somatic": 0.000609, "gSKv3_1bar_SKv3_1.somatic": 0.303472, "gSK_E2bar_SK_E2.somatic": 0.008407, "gCa_HVAbar_Ca_HVA.somatic": 0.000994, "gNaTs2_tbar_NaTs2_t.somatic": 0.983955, "decay_CaDynamics_E2.somatic": 210.485284, "gCa_LVAstbar_Ca_LVAst.somatic": 0.000333, } def load_from_json(filename): """Load structure from json""" with open(filename) as json_file: return json.load(json_file) def dump_to_json(content, filename): """Dump structure to json""" with open(filename, "w") as json_file: return json.dump(content, json_file, indent=4, separators=(",", ": ")) def test_import(): """L5PC: test import""" import l5pc_model # NOQA import l5pc_evaluator # NOQA import opt_l5pc # NOQA class TestL5PCModel(object): """Test L5PC model""" def setup_method(self): """Set up class""" sys.path.insert(0, L5PC_PATH) import l5pc_model # NOQA self.l5pc_cell = l5pc_model.create() assert isinstance(self.l5pc_cell, bluepyopt.ephys.models.CellModel) self.nrn = ephys.simulators.NrnSimulator() def test_instantiate(self): """L5PC: test instantiation of l5pc cell model""" self.l5pc_cell.freeze(release_parameters) self.l5pc_cell.instantiate(sim=self.nrn) def teardown_method(self): """Teardown""" self.l5pc_cell.destroy(sim=self.nrn) class TestL5PCEvaluator(object): """Test L5PC evaluator""" def setup_method(self): """Set up class""" import l5pc_evaluator # NOQA self.l5pc_evaluator = l5pc_evaluator.create() assert isinstance( self.l5pc_evaluator, bluepyopt.ephys.evaluators.CellEvaluator ) @pytest.mark.slow def test_eval(self): """L5PC: test evaluation of l5pc evaluator""" result = self.l5pc_evaluator.evaluate_with_dicts( param_dict=release_parameters ) expected_results = load_from_json( os.path.join(SCRIPT_DIR, "expected_results.json") ) # Use two lines below to update expected result # expected_results['TestL5PCEvaluator.test_eval'] = result # dump_to_json(expected_results, 'expected_results.json') assert set(result.keys()) == set( expected_results["TestL5PCEvaluator.test_eval"].keys() ) def teardown_method(self): """Teardown""" pass # backport from python 3.4 @contextmanager def stdout_redirector(stream): """Stdout redirector""" old_stdout = sys.stdout sys.stdout = stream try: yield finally: sys.stdout = old_stdout @pytest.mark.slow def test_exec(): """L5PC Notebook: test execution""" import numpy numpy.seterr(all="raise") old_cwd = os.getcwd() output = StringIO() try: os.chdir(L5PC_PATH) with stdout_redirector(output): # When using import instead of execfile this doesn't work # Probably because multiprocessing doesn't work correctly during # import if sys.version_info[0] < 3: execfile("L5PC.py") # NOQA else: with open("L5PC.py") as l5pc_file: exec(compile(l5pc_file.read(), "L5PC.py", "exec")) # NOQA stdout = output.getvalue() # first and last values of optimal individual assert "0.001017834439738432" in stdout assert "202.18814057682334" in stdout assert "'gamma_CaDynamics_E2.somatic': 0.03229357096515606" in stdout finally: os.chdir(old_cwd) output.close() @pytest.mark.slow def test_l5pc_validate_neuron_arbor(): """L5PC Neuron/Arbor validation Notebook: test execution""" import numpy numpy.seterr(all="raise") old_cwd = os.getcwd() output = StringIO() try: os.chdir(L5PC_PATH) with stdout_redirector(output): # When using import instead of execfile this doesn't work # Probably because multiprocessing doesn't work correctly during # import if sys.version_info[0] < 3: execfile("l5pc_validate_neuron_arbor_somatic.py") # NOQA else: with open( "l5pc_validate_neuron_arbor_somatic.py" ) as l5pc_file: l5pc_globals = {} exec( compile( l5pc_file.read(), "l5pc_validate_neuron_arbor_somatic.py", "exec", ), l5pc_globals, l5pc_globals, ) # NOQA stdout = output.getvalue() # mean relative L1-deviation between Arbor and Neuron below tolerance assert ( "Default dt ({:,.3g}): test_l5pc OK!".format(0.025) + " The mean relative Arbor-Neuron L1-deviation and error" in stdout ) finally: os.chdir(old_cwd) output.close() ================================================ FILE: bluepyopt/tests/test_lfpy.py ================================================ """Functional LFPy test""" import os import sys import pytest L5PC_LFPY_PATH = os.path.abspath( os.path.join(os.path.dirname(__file__), "../../examples/l5pc_lfpy") ) SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) sys.path.insert(0, L5PC_LFPY_PATH) import l5pc_lfpy_evaluator from generate_extra_features import release_params @pytest.mark.slow def test_lfpy_evaluator(): """Test CellEvaluator with an LFPy cell and LFPy simulator""" evaluator = l5pc_lfpy_evaluator.create( feature_file=L5PC_LFPY_PATH + "/extra_features.json", cvode_active=False, dt=0.025, ) responses = evaluator.run_protocols( protocols=evaluator.fitness_protocols.values(), param_values=release_params, ) values = evaluator.fitness_calculator.calculate_values(responses) assert len(values) == 21 assert abs(values["Step1.soma.AP_height"] - 27.85963902931001) < 1e-5 assert len(responses["Step1.MEA.v"]["voltage"]) == 40 ================================================ FILE: bluepyopt/tests/test_neuroml_fcts.py ================================================ """Test neuroml functions""" import os import sys import efel import neuroml import numpy import pytest from pyneuroml import pynml from bluepyopt import ephys from bluepyopt.neuroml import biophys from bluepyopt.neuroml import cell from bluepyopt.neuroml import morphology from bluepyopt.neuroml import simulation L5PC_PATH = os.path.abspath( os.path.join(os.path.dirname(__file__), "../../examples/l5pc") ) sys.path.insert(0, L5PC_PATH) import l5pc_evaluator import l5pc_model # NOQA l5pc_cell = l5pc_model.create() protocols = l5pc_evaluator.define_protocols() release_params = { "gNaTs2_tbar_NaTs2_t.apical": 0.026145, "gSKv3_1bar_SKv3_1.apical": 0.004226, "gImbar_Im.apical": 0.000143, "gNaTa_tbar_NaTa_t.axonal": 3.137968, "gK_Tstbar_K_Tst.axonal": 0.089259, "gamma_CaDynamics_E2.axonal": 0.002910, "gNap_Et2bar_Nap_Et2.axonal": 0.006827, "gSK_E2bar_SK_E2.axonal": 0.007104, "gCa_HVAbar_Ca_HVA.axonal": 0.000990, "gK_Pstbar_K_Pst.axonal": 0.973538, "gSKv3_1bar_SKv3_1.axonal": 1.021945, "decay_CaDynamics_E2.axonal": 287.198731, "gCa_LVAstbar_Ca_LVAst.axonal": 0.008752, "gamma_CaDynamics_E2.somatic": 0.000609, "gSKv3_1bar_SKv3_1.somatic": 0.303472, "gSK_E2bar_SK_E2.somatic": 0.008407, "gCa_HVAbar_Ca_HVA.somatic": 0.000994, "gNaTs2_tbar_NaTs2_t.somatic": 0.983955, "decay_CaDynamics_E2.somatic": 210.485284, "gCa_LVAstbar_Ca_LVAst.somatic": 0.000333, } @pytest.mark.unit def test_get_nml_mech_dir(): """biophys.get_nml_mech_dir: Test get_nml_mech_dir""" channels = [ "Ih", "NaTa_t", "NaTs2_t", "Nap_Et2", "K_Tst", "K_Pst", "SKv3_1", "SK_E2", "StochKv_deterministic", "KdShu2007", "Im", "Ca", "Ca_HVA", "Ca_LVAst", "pas", ] for channel in channels: assert os.path.isfile( os.path.join(biophys.get_nml_mech_dir(), f"{channel}.channel.nml") ) assert os.path.isfile( os.path.join(biophys.get_nml_mech_dir(), "baseCaDynamics_E2_NML2.nml") ) @pytest.mark.unit def test_get_channel_from_param_name(): """biophys.get_channel_from_param_name: Test get_channel_from_param_name """ # 3 underscores case assert biophys.get_channel_from_param_name( "gNaTs2_tbar_NaTs2_t" ) == "NaTs2_t" # 2 underscores case assert biophys.get_channel_from_param_name( "gamma_CaDynamics_E2" ) == "CaDynamics_E2" # 1 underscore case assert biophys.get_channel_from_param_name("gIhbar_Ih") == "Ih" @pytest.mark.unit def test_format_dist_fun(): """biophys.format_dist_fun: Test format_dist_fun""" raw_expr = "(-0.8696 + 2.087*math.exp(({distance})*0.0031))*{value}" formatted_expr = "(-0.8696 + 2.087*exp(p*0.0031))*8e-05" assert biophys.format_dist_fun(raw_expr, 8e-05, None) == formatted_expr @pytest.mark.unit def test_add_nml_channel_to_nml_cell_file(): """biophys.add_nml_channel_to_nml_cell_file: Test add_nml_channel_to_nml_cell_file """ empty_cell_doc = neuroml.NeuroMLDocument(id="test_nml_cell") included_channels = [] pytest.raises( ValueError, biophys.add_nml_channel_to_nml_cell_file, empty_cell_doc, included_channels, ) channel_name = "K_Pst" biophys.add_nml_channel_to_nml_cell_file( empty_cell_doc, included_channels, channel_name=channel_name, skip_channels_copy=True, ) assert len(empty_cell_doc.includes) == 1 assert channel_name in str(empty_cell_doc.includes[0].href) assert len(included_channels) == 1 assert f"{channel_name}.channel.nml" in included_channels # test that we cannot add the same channel twice biophys.add_nml_channel_to_nml_cell_file( empty_cell_doc, included_channels, channel_name=channel_name, skip_channels_copy=True, ) assert len(empty_cell_doc.includes) == 1 assert len(included_channels) == 1 @pytest.mark.unit def test_get_arguments(): """biophys.get_arguments: Test get_arguments""" parameter_name = "gSKv3_1bar_SKv3_1" section_list = "somatic" channel = "SKv3_1" variable_parameters = None cond_density = "0.303472 S_per_cm2" arguments, channel_class = biophys.get_arguments( l5pc_cell.params, parameter_name, section_list, channel, channel, variable_parameters, cond_density, release_params, ) assert arguments["ion"] == "k" assert arguments["segment_groups"] == section_list assert arguments["erev"] == "-85.0 mV" assert arguments["id"] == "somatic_gSKv3_1bar_SKv3_1" assert arguments["ion_channel"] == channel assert arguments["cond_density"] == cond_density assert "variable_parameters" not in arguments assert channel_class == "ChannelDensity" # pas case parameter_name = "g_pas" section_list = "all" channel = "pas" cond_density = "3e-05 S_per_cm2" arguments, channel_class = biophys.get_arguments( l5pc_cell.params, parameter_name, section_list, channel, channel, variable_parameters, cond_density, release_params, ) assert arguments["ion"] == "non_specific" assert arguments["segment_groups"] == section_list assert arguments["erev"] == "-75 mV" assert arguments["id"] == "all_g_pas" assert arguments["ion_channel"] == channel assert arguments["cond_density"] == cond_density assert "variable_parameters" not in arguments assert channel_class == "ChannelDensity" # nernst case parameter_name = "gCa_HVAbar_Ca_HVA" section_list = "axonal" channel = "Ca_HVA" cond_density = "0.00099 S_per_cm2" arguments, channel_class = biophys.get_arguments( l5pc_cell.params, parameter_name, section_list, channel, channel, variable_parameters, cond_density, release_params, ) assert arguments["ion"] == "ca" assert arguments["segment_groups"] == section_list assert "erev" not in arguments assert arguments["id"] == "axonal_gCa_HVAbar_Ca_HVA" assert arguments["ion_channel"] == channel assert arguments["cond_density"] == cond_density assert "variable_parameters" not in arguments assert channel_class == "ChannelDensityNernst" @pytest.mark.unit def test_extract_parameter_value(): """biophys.extract_parameter_value: Test extract_parameter_value""" # uniform parameter case param_name = "gSKv3_1bar_SKv3_1" section_list = "apical" channel = "SKv3_1" cond_density, variable_parameters = biophys.extract_parameter_value( l5pc_cell.params[".".join((param_name, section_list))], section_list, channel, True, release_params, ) assert cond_density == "0.004226 S_per_cm2" assert variable_parameters is None # skipped non uniform parameter case param_name = "gIhbar_Ih" section_list = "apical" channel = "Ih" cond_density, variable_parameters = biophys.extract_parameter_value( l5pc_cell.params[".".join((param_name, section_list))], section_list, channel, True, release_params, ) assert cond_density is None assert variable_parameters is None # non uniform parameter case (unskipped) param_name = "gIhbar_Ih" section_list = "apical" channel = "Ih" cond_density, variable_parameters = biophys.extract_parameter_value( l5pc_cell.params[".".join((param_name, section_list))], section_list, channel, False, release_params, ) assert cond_density is None assert variable_parameters[0].segment_groups == section_list assert variable_parameters[0].parameter == "condDensity" assert ( variable_parameters[0].inhomogeneous_value.inhomogeneous_parameters == "PathLengthOver_apical" ) assert ( variable_parameters[0].inhomogeneous_value.value == "(-0.8696 + 2.087*exp(p*0.0031))*8e-05" ) @pytest.mark.unit def test_get_density(): """biophys.get_density: Test get_density""" empty_cell_doc = neuroml.NeuroMLDocument(id="test_nml_cell") param_name = "gSK_E2bar_SK_E2" section_list = "axonal" density, channel_class = biophys.get_density( empty_cell_doc, l5pc_cell, l5pc_cell.params[".".join((param_name, section_list))], section_list, included_channels=[], skip_non_uniform=True, release_params=release_params, skip_channels_copy=True, ) assert channel_class == "ChannelDensity" assert density.id == "axonal_gSK_E2bar_SK_E2" assert density.ion_channel == "SK_E2" assert density.cond_density == "0.007104 S_per_cm2" assert density.erev == "-85.0 mV" assert density.segment_groups == "axonal" assert density.ion == "k" @pytest.mark.unit def test_get_specific_capacitance(): """biophys.get_specific_capacitance: Test get_specific_capacitance""" # case: default specific_capacitances = biophys.get_specific_capacitance({}) assert neuroml.SpecificCapacitance( value="1.0 uF_per_cm2", segment_groups="axonal" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="1.0 uF_per_cm2", segment_groups="somatic" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="1.0 uF_per_cm2", segment_groups="basal" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="1.0 uF_per_cm2", segment_groups="apical" ) in specific_capacitances # case: all specific_capacitances = biophys.get_specific_capacitance( {"all": "2.0 uF_per_cm2"} ) assert neuroml.SpecificCapacitance( value="2.0 uF_per_cm2", segment_groups="axonal" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="2.0 uF_per_cm2", segment_groups="somatic" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="2.0 uF_per_cm2", segment_groups="basal" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="2.0 uF_per_cm2", segment_groups="apical" ) in specific_capacitances # case: specific section(s) specific_capacitances = biophys.get_specific_capacitance( {"somatic": "2.0 uF_per_cm2", "axonal": "3.0 uF_per_cm2"} ) assert neuroml.SpecificCapacitance( value="3.0 uF_per_cm2", segment_groups="axonal" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="2.0 uF_per_cm2", segment_groups="somatic" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="1.0 uF_per_cm2", segment_groups="basal" ) in specific_capacitances assert neuroml.SpecificCapacitance( value="1.0 uF_per_cm2", segment_groups="apical" ) in specific_capacitances @pytest.mark.unit def test_get_biophys(): """biophys.get_biophys: Test get_biophys""" empty_cell_doc = neuroml.NeuroMLDocument(id="test_nml_cell") bio_prop = biophys.get_biophys( l5pc_cell, empty_cell_doc, release_params, skip_non_uniform=True, skip_channels_copy=True, ) membrane_props = bio_prop.membrane_properties intracell_props = bio_prop.intracellular_properties assert bio_prop.id == "biophys" assert membrane_props.init_memb_potentials[0].value == "-65 mV" assert len(membrane_props.specific_capacitances) == 4 assert len(membrane_props.channel_density_nernsts) == 4 assert len(membrane_props.channel_densities) == 15 assert intracell_props.resistivities[0].value == "100 ohm_cm" assert len(intracell_props.species) == 2 @pytest.mark.unit def test_add_segment_groups(): """morphology.add_segment_groups: Test add_segment_groups""" # creates a neuroml cell with a morphology cell = neuroml.Cell(id="nml_cell") morph = neuroml.Morphology(id="test_nml_cell_morph") for loc in ["soma", "axon", "dend", "apic"]: seg = neuroml.Segment(id=0, name=loc) morph.segments.append(seg) cell.morphology = morph morphology.add_segment_groups(cell) segment_group_names = [ group.id for group in cell.morphology.segment_groups ] assert "somatic" in segment_group_names assert "axonal" in segment_group_names assert "basal" in segment_group_names assert "apical" in segment_group_names assert "soma_group" in segment_group_names assert "axon_group" in segment_group_names assert "dendrite_group" in segment_group_names @pytest.mark.slow @pytest.mark.neuroml def test_neuroml_run(): """Test neuroml conversion and simulation""" # replace axon is not supported yet l5pc_cell.morphology.do_replace_axon = False dt = 0.025 protocol_name = "Step3" bpo_test_protocol = protocols[protocol_name] os.system("nrnivmodl examples/l5pc/mechanisms/") # create neuroml cell cell.create_neuroml_cell( l5pc_cell, release_params, skip_channels_copy=False ) # create LEMS simulation network_filename = f"{l5pc_cell.name}.net.nml" lems_filename = f"LEMS_{l5pc_cell.name}.xml" simulation.create_neuroml_simulation( network_filename, bpo_test_protocol, dt, l5pc_cell.name, lems_filename ) # remove compiled mechanisms if any before running LEMS simulation os.system("rm -rf x86_64/") # run the simulation with the NEURON simulator, # since the default jNeuroML simulator can only simulate single # compartment cells pynml.run_lems_with_jneuroml_neuron( lems_filename, nogui=True, plot=False) # re-compile the mechanisms before running with bluepyopt os.system("rm -rf x86_64/") os.system("nrnivmodl examples/l5pc/mechanisms/") lems_output = numpy.loadtxt("l5pc.Pop_l5pc_0_0.v.dat") lems_voltage = lems_output[:, 1] * 1000 # *1000 -> mV lems_time = lems_output[:, 0] * 1000 # *1000 -> ms # remove non uniform parameter to be coherent with LEMS simulation to_remove = [] for key, param in l5pc_cell.params.items(): if hasattr(param, "value_scaler"): if isinstance( param.value_scaler, ephys.parameterscalers.NrnSegmentSomaDistanceScaler, ): to_remove.append(key) for param_name in to_remove: l5pc_cell.params.pop(param_name) # run with regular bluepyopt sim = ephys.simulators.NrnSimulator(dt=dt, cvode_active=False) bpo_output = bpo_test_protocol.run( cell_model=l5pc_cell, param_values=release_params, sim=sim ) response_name = f"{protocol_name}.soma.v" bpo_voltage = bpo_output[response_name]["voltage"] bpo_time = bpo_output[response_name]["time"] # we don't expect the two traces to be exactly the same, # but to be pretty similar # we consider it satisfactory if they fire within 3 ms respectively trace_lems = { "T": lems_time, "V": lems_voltage, "stim_start": [700], "stim_end": [2700] } trace_bpo = { "T": bpo_time, "V": bpo_voltage, "stim_start": [700], "stim_end": [2700] } traces = [trace_lems, trace_bpo] features = ["peak_time"] feature_values = efel.getFeatureValues( traces, features, raise_warnings=False ) lems_peak_time = feature_values[0]["peak_time"] bpo_peak_time = feature_values[1]["peak_time"] numpy.testing.assert_allclose(lems_peak_time, bpo_peak_time, atol=3) ================================================ FILE: bluepyopt/tests/test_parameters.py ================================================ """bluepyopt.parameters tests""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint:disable=W0612 import pytest import bluepyopt @pytest.mark.unit def test_parameters_init(): """bluepyopt.parameters: test Parameter init""" param = bluepyopt.parameters.Parameter(name='test') assert isinstance(param, bluepyopt.parameters.Parameter) assert param.name == 'test' @pytest.mark.unit def test_parameters_fields(): """bluepyopt.parameters: test Parameter fields""" param = bluepyopt.parameters.Parameter(name='test') assert param.lower_bound is None assert param.upper_bound is None param.freeze(5) pytest.raises(Exception, setattr, param, "value", 5) param = bluepyopt.parameters.Parameter(name='test', bounds=[2, 5]) pytest.raises(ValueError, param.freeze, 1) @pytest.mark.unit def test_parameters_str(): """bluepyopt.parameters: test Parameter str conversion""" param = bluepyopt.parameters.Parameter(name='test') assert str(param) == 'test: value = None' param.freeze(5.5) assert str(param) == 'test: value = 5.5' @pytest.mark.unit def test_MetaListEqualParameter_init(): """bluepyopt.parameters: test MetaListEqualParameter init""" sub_params = [ bluepyopt.parameters.Parameter( name='sub1', value=1), bluepyopt.parameters.Parameter( name='sub2', value=2)] assert sub_params[0].value == 1 assert sub_params[1].value == 2 param = bluepyopt.parameters.MetaListEqualParameter( name='param', value=0, frozen=True, sub_parameters=sub_params) assert isinstance(param, bluepyopt.parameters.Parameter) assert isinstance(param, bluepyopt.parameters.MetaListEqualParameter) assert param.name == 'param' assert param.sub_parameters[0].name == 'sub1' assert param.sub_parameters[1].name == 'sub2' assert param.value == 0 assert sub_params[0].value == 0 assert sub_params[1].value == 0 @pytest.mark.unit def test_MetaListEqualParameter_freeze_unfreeze(): """bluepyopt.parameters: test MetaListEqualParameter freeze and unfreeze""" sub_params = [ bluepyopt.parameters.Parameter( name='sub1', value=1), bluepyopt.parameters.Parameter( name='sub2', value=2)] param = bluepyopt.parameters.MetaListEqualParameter( name='param', sub_parameters=sub_params) assert param.value is None assert sub_params[0].value == 1 assert sub_params[1].value == 2 param.freeze(0) assert param.value == 0 assert sub_params[0].value == 0 assert sub_params[1].value == 0 param.unfreeze() sub_params[0].freeze(1) pytest.raises(Exception, param.freeze, 0) @pytest.mark.unit def test_MetaListEqualParamete_str(): """bluepyopt.parameters: test MetaListEqualParamete str conversion""" sub_params = [ bluepyopt.parameters.Parameter( name='sub1', value=1), bluepyopt.parameters.Parameter( name='sub2', value=2)] param = bluepyopt.parameters.MetaListEqualParameter( name='param', sub_parameters=sub_params) assert ( str(param) == 'param (sub_params: sub1: value = None,sub2: value = None): ' 'value = None') param.freeze(5.5) assert ( str(param) == 'param (sub_params: sub1: value = 5.5,sub2: value = 5.5): ' 'value = 5.5') ================================================ FILE: bluepyopt/tests/test_simplecell.py ================================================ """Simple cell example test class""" import os import sys SIMPLECELL_PATH = os.path.abspath( os.path.join(os.path.dirname(__file__), "../../examples/simplecell") ) # sys.path.insert(0, SIMPLECELL_PATH) class TestSimpleCellClass(object): """Simple cell example test class for NEURON""" def setup_method(self): """Setup""" self.old_cwd = os.getcwd() self.old_stdout = sys.stdout os.chdir(SIMPLECELL_PATH) sys.stdout = open(os.devnull, "w") def test_exec(self): """Simplecell NEURON: test execution""" # When using import instead of execfile this doesn't work # Probably because multiprocessing doesn't work correctly during # import if sys.version_info[0] < 3: execfile("simplecell.py") # NOQA else: with open("simplecell.py") as sc_file: exec(compile(sc_file.read(), "simplecell.py", "exec")) # NOQA def teardown_method(self): """Tear down""" sys.stdout = self.old_stdout os.chdir(self.old_cwd) class TestSimpleCellArborClass(object): """Simple cell example test class for Arbor""" def setup_method(self): """Setup""" self.old_cwd = os.getcwd() self.old_stdout = sys.stdout os.chdir(SIMPLECELL_PATH) sys.stdout = open(os.devnull, "w") def test_exec(self): """Simplecell Arbor: test execution""" # When using import instead of execfile this doesn't work # Probably because multiprocessing doesn't work correctly during # import if sys.version_info[0] < 3: execfile("simplecell_arbor.py") # NOQA else: with open("simplecell_arbor.py") as sc_file: exec(compile(sc_file.read(), "simplecell_arbor.py", "exec")) # NOQA def teardown_method(self): """Tear down""" sys.stdout = self.old_stdout os.chdir(self.old_cwd) ================================================ FILE: bluepyopt/tests/test_stochkv.py ================================================ """Test l5pc example""" import os import sys import difflib STOCHKV_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../examples/stochkv')) sys.path.insert(0, STOCHKV_PATH) from bluepyopt import ephys neuron_sim = ephys.simulators.NrnSimulator() neuron_sim.neuron.h.nrn_load_dll( os.path.join( STOCHKV_PATH, 'x86_64/.libs/libnrnmech.so')) def compare_strings(s1, s2): """Compare two strings""" diff = list(difflib.unified_diff(s1.splitlines(1), s2.splitlines(1))) if len(diff) > 0: print(''.join(diff)) return False else: return True def test_import(): """StochKv example: test import""" import stochkvcell # NOQA def test_run(): """StochKv example: test run""" import stochkvcell # NOQA for deterministic in [True, False]: py_response, hoc_response, different_seed_response, hoc_string = \ stochkvcell.run_stochkv_model(deterministic=deterministic) assert py_response['Step.soma.v']['time'].equals( hoc_response['Step.soma.v']['time']) assert py_response['Step.soma.v']['voltage'].equals( hoc_response['Step.soma.v']['voltage']) if deterministic: assert py_response['Step.soma.v']['voltage'].equals( different_seed_response['Step.soma.v']['voltage']) else: assert not py_response['Step.soma.v']['voltage'].equals( different_seed_response['Step.soma.v']['voltage']) expected_hoc_filename = os.path.join( STOCHKV_PATH, stochkvcell.stochkv_hoc_filename(deterministic=deterministic)) # with open(expected_hoc_filename, 'w') as expected_hoc_file: # expected_hoc_file.write(hoc_string) with open(expected_hoc_filename) as expected_hoc_file: expected_hoc_string = expected_hoc_file.read() assert compare_strings(expected_hoc_string, hoc_string) def test_run_stochkv3(): """StochKv3 example: test run""" import stochkv3cell # NOQA for deterministic in [True, False]: py_response, hoc_response, different_seed_response, hoc_string = \ stochkv3cell.run_stochkv3_model(deterministic=deterministic) assert py_response['Step.soma.v']['time'].equals( hoc_response['Step.soma.v']['time']) assert py_response['Step.soma.v']['voltage'].equals( hoc_response['Step.soma.v']['voltage']) if deterministic: assert py_response['Step.soma.v']['voltage'].equals( different_seed_response['Step.soma.v']['voltage']) else: assert not py_response['Step.soma.v']['voltage'].equals( different_seed_response['Step.soma.v']['voltage']) expected_hoc_filename = os.path.join( STOCHKV_PATH, stochkv3cell.stochkv3_hoc_filename(deterministic=deterministic)) # with open(expected_hoc_filename, 'w') as expected_hoc_file: # expected_hoc_file.write(hoc_string) with open(expected_hoc_filename) as expected_hoc_file: expected_hoc_string = expected_hoc_file.read() assert compare_strings(expected_hoc_string, hoc_string) ================================================ FILE: bluepyopt/tests/test_tools.py ================================================ """Test bluepyopt.tools""" import pytest @pytest.mark.unit def test_load(): """bluepyopt.tools: test import""" import bluepyopt.tools # NOQA @pytest.mark.unit def test_uint32_seed(): """bluepyopt.tools: test uint32_seed""" import bluepyopt.tools as bpoptools assert bpoptools.uint32_seed("test") == 640136438 import random random.seed(1) hashes = [] strings = [] for _ in range(1000): string = ''.join( (chr(random.randint(0, 127)) for x in range(random.randint(10, 255)))) strings.append(string) hashes.append(bpoptools.uint32_seed(string)) assert len(strings) == len(set(strings)) assert len(hashes) == len(set(hashes)) import numpy for hash_value in hashes: assert hash_value == numpy.uint32(hash_value) ================================================ FILE: bluepyopt/tests/testdata/l5pc_validate_neuron_arbor/param_values.json ================================================ [ { "gNaTs2_tbar_NaTs2_t.apical": 0.024728378969164945, "gSKv3_1bar_SKv3_1.apical": 0.03798941330025154, "gImbar_Im.apical": 0.0002539253963547741, "gNaTa_tbar_NaTa_t.axonal": 1.4077868264399775, "gNap_Et2bar_Nap_Et2.axonal": 0.4883696674077984, "gK_Pstbar_K_Pst.axonal": 0.2094093687043327, "gK_Tstbar_K_Tst.axonal": 0.014394661257078424, "gSK_E2bar_SK_E2.axonal": 0.08531938445280127, "gSKv3_1bar_SKv3_1.axonal": 1.9458756462007896, "gCa_HVAbar_Ca_HVA.axonal": 0.00047482720978403063, "gCa_LVAstbar_Ca_LVAst.axonal": 0.003298061450166594, "gamma_CaDynamics_E2.axonal": 0.0016672175965771645, "decay_CaDynamics_E2.axonal": 107.99372196248892, "gNaTs2_tbar_NaTs2_t.somatic": 0.016894566815433776, "gSKv3_1bar_SKv3_1.somatic": 0.5504181382385894, "gSK_E2bar_SK_E2.somatic": 0.0014684200840784367, "gCa_HVAbar_Ca_HVA.somatic": 0.0009900677505308665, "gCa_LVAstbar_Ca_LVAst.somatic": 0.004837611369100268, "gamma_CaDynamics_E2.somatic": 0.04468460763916868, "decay_CaDynamics_E2.somatic": 700.3244890996936 }, { "gNaTs2_tbar_NaTs2_t.apical": 0.012563715900342314, "gSKv3_1bar_SKv3_1.apical": 0.030532088135424608, "gImbar_Im.apical": 0.0003896322863148641, "gNaTa_tbar_NaTa_t.axonal": 0.8703921802368879, "gNap_Et2bar_Nap_Et2.axonal": 2.709684677548464, "gK_Pstbar_K_Pst.axonal": 0.5684212625540379, "gK_Tstbar_K_Tst.axonal": 0.046099219765027045, "gSK_E2bar_SK_E2.axonal": 0.043249366076373946, "gSKv3_1bar_SKv3_1.axonal": 0.9098265241750978, "gCa_HVAbar_Ca_HVA.axonal": 0.0008372326923436864, "gCa_LVAstbar_Ca_LVAst.axonal": 0.004471958629720602, "gamma_CaDynamics_E2.axonal": 0.028712619127496453, "decay_CaDynamics_E2.axonal": 545.1278177178857, "gNaTs2_tbar_NaTs2_t.somatic": 0.871765930712021, "gSKv3_1bar_SKv3_1.somatic": 0.3138873150781861, "gSK_E2bar_SK_E2.somatic": 0.030636708881805065, "gCa_HVAbar_Ca_HVA.somatic": 0.000529125383577793, "gCa_LVAstbar_Ca_LVAst.somatic": 0.00962925219116365, "gamma_CaDynamics_E2.somatic": 0.033127331525490814, "decay_CaDynamics_E2.somatic": 167.9228279662214 }, { "gNaTs2_tbar_NaTs2_t.apical": 0.023754244090072044, "gSKv3_1bar_SKv3_1.apical": 0.006212933071327114, "gImbar_Im.apical": 0.0009715296912019645, "gNaTa_tbar_NaTa_t.axonal": 0.7373787531734117, "gNap_Et2bar_Nap_Et2.axonal": 0.14550976345268296, "gK_Pstbar_K_Pst.axonal": 0.1500413898237769, "gK_Tstbar_K_Tst.axonal": 0.0019137698820653637, "gSK_E2bar_SK_E2.axonal": 0.06696554369807915, "gSKv3_1bar_SKv3_1.axonal": 0.511825279299768, "gCa_HVAbar_Ca_HVA.axonal": 0.0007806372330552697, "gCa_LVAstbar_Ca_LVAst.axonal": 0.007394490284020838, "gamma_CaDynamics_E2.axonal": 0.04003636754376934, "decay_CaDynamics_E2.axonal": 254.82663985773573, "gNaTs2_tbar_NaTs2_t.somatic": 0.9463622816113636, "gSKv3_1bar_SKv3_1.somatic": 0.2409150965622644, "gSK_E2bar_SK_E2.somatic": 0.027392704914052657, "gCa_HVAbar_Ca_HVA.somatic": 0.0003139343269991607, "gCa_LVAstbar_Ca_LVAst.somatic": 0.001884976561409778, "gamma_CaDynamics_E2.somatic": 0.02683739540253667, "decay_CaDynamics_E2.somatic": 422.10139260028944 }, { "gNaTs2_tbar_NaTs2_t.apical": 0.00028168114836296, "gSKv3_1bar_SKv3_1.apical": 0.0002791900939042247, "gImbar_Im.apical": 3.061981691214699e-05, "gNaTa_tbar_NaTa_t.axonal": 2.408780313553162, "gNap_Et2bar_Nap_Et2.axonal": 1.4061498137665431, "gK_Pstbar_K_Pst.axonal": 0.3943872359829874, "gK_Tstbar_K_Tst.axonal": 0.035561350976474206, "gSK_E2bar_SK_E2.axonal": 0.02171178703228345, "gSKv3_1bar_SKv3_1.axonal": 1.7696510942499797, "gCa_HVAbar_Ca_HVA.axonal": 0.0006580710297293586, "gCa_LVAstbar_Ca_LVAst.axonal": 0.0063160804680408205, "gamma_CaDynamics_E2.axonal": 0.023213952061165458, "decay_CaDynamics_E2.axonal": 967.0051783210414, "gNaTs2_tbar_NaTs2_t.somatic": 0.6021516164385508, "gSKv3_1bar_SKv3_1.somatic": 0.6231231198067643, "gSK_E2bar_SK_E2.somatic": 0.031180502899348185, "gCa_HVAbar_Ca_HVA.somatic": 1.8482399998351218e-05, "gCa_LVAstbar_Ca_LVAst.somatic": 0.0006089857296922108, "gamma_CaDynamics_E2.somatic": 0.014921182004896194, "decay_CaDynamics_E2.somatic": 220.06396960215915 }, { "gNaTs2_tbar_NaTs2_t.apical": 0.03414645624289801, "gSKv3_1bar_SKv3_1.apical": 0.03716027643455195, "gImbar_Im.apical": 0.0006141579238080173, "gNaTa_tbar_NaTa_t.axonal": 1.3903172305839067, "gNap_Et2bar_Nap_Et2.axonal": 0.8426141730398107, "gK_Pstbar_K_Pst.axonal": 0.6391552213781772, "gK_Tstbar_K_Tst.axonal": 0.09532353467899872, "gSK_E2bar_SK_E2.axonal": 0.0003580425070283666, "gSKv3_1bar_SKv3_1.axonal": 1.6453573616166768, "gCa_HVAbar_Ca_HVA.axonal": 0.0008843062363314521, "gCa_LVAstbar_Ca_LVAst.axonal": 0.004350888951090779, "gamma_CaDynamics_E2.axonal": 0.013015501138335075, "decay_CaDynamics_E2.axonal": 317.84821369565793, "gNaTs2_tbar_NaTs2_t.somatic": 0.5411660208241308, "gSKv3_1bar_SKv3_1.somatic": 0.16267060224572238, "gSK_E2bar_SK_E2.somatic": 0.019600545141054505, "gCa_HVAbar_Ca_HVA.somatic": 0.0001971878789850715, "gCa_LVAstbar_Ca_LVAst.somatic": 0.003305523187140309, "gamma_CaDynamics_E2.somatic": 0.03067667559799945, "decay_CaDynamics_E2.somatic": 984.7729355155103 } ] ================================================ FILE: bluepyopt/tools.py ================================================ """BluePyOpt tools""" import hashlib def uint32_seed(string): """Get unsigned int seed of a string""" hex_value = hashlib.md5(string.encode('utf-8')).hexdigest() return int(hex_value, 16) & 0xFFFFFFFF ================================================ FILE: cloud-config/README.md ================================================ # Single Cell Optimization ## Introduction This documentation outlines how to setup distributed single cell optimization using [eFEL](https://github.com/BlueBrain/eFEL), [DEAP](http://deap.readthedocs.org/en/master/) and [SCOOP](scoop.readthedocs.org) along with [NEURON](www.neuron.yale.edu). The scope of this documentation is on setup, not on the process of optimization. ## Setup The three sorts of distributed systems that this documentation targets are: 1. [Vagrant](https://www.vagrantup.com/) machines, usually used for testing as all the machines likely run on one local machine. Detailed information [here](config/vagrant/README.md). 2. A cluster with a shared filesystem. The optimization is configured within the home directory of the user such that it can be launched by [SLURM](https://computing.llnl.gov/linux/slurm/) or another cluster resource manager. Detailed information [here](config/cluster-user/README.md). 3. An [Amazon Web Services](https://aws.amazon.com/) Detailed information [here](config/amazon/README.md). ## Ansible Throughout this guide, [Ansible](http://www.ansible.com/) is used for the automatic installation and configuration of the required software. It is recommended to install it into a [Virtualenv](https://virtualenv.readthedocs.org/en/latest/) so as to separate it from the rest of the system. ``` $ virtualenv venv-ansible $ ./venv-ansible/bin/pip install ansible ``` From then on, one can activate the environment, and have access to Ansible: ``` $ source ./venv-ansible/bin/activate $ ansible-playbook --version ``` Note: Ansible is not required, and all the commands can be run manually by examining the `roles/*/tasks/main.yaml` files. ## Ansible Information The three sorts of configurations share [Ansible Roles](http://docs.ansible.com/ansible/playbooks_roles.html#roles) but have different configuration information and parameters. The easiest way to setup one of the three systems is to: 1. Change to the correct directory (`config/vagrant`, `config/cluster-user/`, or `config/amazon/`) and create a symbolic link to the `roles` directory: `$ ln -s ../../roles .` 2. Change any config information in `ansible.cfg` and `vars.yaml` 3. Edit the `hosts` file to include the correct hosts (in the Amazon case, this is handled by the `gather_config.py` script. ## Ansible configuration options The following options are defined in the `vars.yaml` file, which should contain the only options that could potentially need configuration. They are: - *user_name*: the name of the user under who's name the install will be run - *workspace*: the base directory of the installation - *venv*: the location of the virtualenvironment - *build_dir*: the location where software is built - *install_dir*: the base location where software is installed - *add_bin_path*: Boolean on if the path to neuron and the python environment should be added to the users' `.bashrc` - *using_headnode*: Set to true when there is a headnode that will control everything, used for when an SSH has to be distributed to the workers - *neuron_build*: location to build NEURON - *neuron_url*: URL to download the NEURON source - *neuron_version*: NEURON version - *neuron_config*: command to configure neuron - *python27_build*: false - whether a local python version should be compiled - *python27_url*: URL to get Python source - *python27_version*: Python version Versions for various python software that is installed - *pip_version*: 7.1.2 - *numpy_version*: 1.10.4 - *efel_version*: 2.10.7 - *scoop_version*: 0.7.1.1 - *jupyter_version*: 1.0.0 - *matplotlib_version*: 1.5.1 ## Installation Information The installation is composed of several components: 1. Compiled version of NEURON in `~/workspace/install/nrnpython`. This has the python bindings built in, and should also allow for dynamic loading of compiled `.modl` files 2. A python virtual environment with eFEL as well as DEAP and SCOOP. ## Ansible Tips 1. Test that your system is working and you can reach the hosts with `ansible -m ping all` 2. Use the 'Dry-run' command `ansible-playbook site.yml --check` to see what would happen if ansible was to run, this can be augmented with `--diff` to show differences 3. Use `-vv` and `-vvvv` to increase verbosity ## Running an Optimization Example Note: Every optimization is different, this is an example on how to launch one that already exists. ### eFEL Example If there are multiple hosts, this has to be done on each of them. If there is a shared file system, it only needs to be done once. ``` # Get latest eFEL $ git clone https://github.com/BlueBrain/eFEL.git # Compile the modl files with the nrnivmodl located in ~/workspace/install/. (for instance here ~/workspace/install/nrnpython/x86_64/bin/nrnivmodl) $ cd ~/eFEL/examples/deap/GranuleCell1 $ ~/workspace/install/nrnpython/x86_64/bin/nrnivmodl mechanisms ``` #### Launch it: Then, on the head node: ``` $ /home/neuron/workspace/venv/bin/python -m scoop -vvv --hostfile ~/scoop_workers GranuleCellDeap1-scoop.py ``` ### DEAP Example Get 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 have it output data by editing the call to main with: ``` islands = main() with open('output.dat', 'w') as fd: fd.write(str(islands)) fd.write('\n') ``` Then, distribute it to all the worker nodes, if the node are not sharing their file system: (as the 'neuron' user: 'sudo su neuron') ``` $ cd workspace $ for host in `cat ~/scoop_workers`; do scp onemax_island_scoop.py $i:workspace; done ``` #### Launch it: ``` $ cd workspace $ venv/bin/python -m scoop --hostfile ~/scoop_workers onemax_island_scoop.py $ look at the output in 'output.dat' ``` ================================================ FILE: cloud-config/config/amazon/README.md ================================================ # Amazon Setup This documents how an Amazon Web Services cluster can be created. Note: It is quite easy to run up a large bill on AWS, so the following is only guidance for setting it up. Make sure that the costs for setting up a cluster are fully understood. It is recommended to often check the AWS console's `Estimated Billing` to keep track of theses costs. ## Installation It is assumed that a `virtualenv` exists for `Ansible`. It is worth reading the description of `Ansible` and the initial setup in [here](../../README.md). To access the AWS and `Elastic Computing` components (EC2), the following extra components should be installed: ``` $ source ./venv-ansible/activate $ pip install boto boto3 awscli ``` [Boto](https://pypi.python.org/pypi/boto) is the older generation of AWS/EC2 management utilities, however it is need for Ansible [boto3](https://github.com/boto/boto3) and [awscli](https://pypi.python.org/pypi/awscli) are useful for interfacing with AWS. ## Creating AWS Cloud First, one should configure the following in the create_instance.yaml Playbook: ``` region: us-west-2 ami_image: ami-187c9978 # http://cloud-images.ubuntu.com/trusty/current/ instance_type: t2.nano worker_instances: 2 ``` Where: - *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 - *ami_image*: the image that is used as the base for the configuration. Generally, a Ubuntu LTS image will work, and the correct one for the chosen 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. - *instance_type*: A description of the different instance types is [available here](https://aws.amazon.com/ec2/instance-types/). Usually, the costs grows with the computing power. For the example, the cheapest instance type is created, but in a real optimization scenario, the computing resources are likely too small to be effective. - *worker_instances*: How many worker instances to launch, in addition to the head node. To run the creation scrips, the following must exist in the terminal environment so that the AWS infrastructure can authenticate the client. ``` export AWS_ACCESS_KEY_ID= replace with access id export AWS_SECRET_ACCESS_KEY= replace with access key export AWS_REGION= replace with default AWS region ``` It's also a good idea to create the credentials in the ~/.aws/ directory as described [here.](http://boto3.readthedocs.org/en/latest/guide/quickstart.html#configuration) Now the simple cloud can be created and launched with: ``` $ source ./venv-ansible/activate $ ansible-playbook create_instance.yaml ``` ### Cloud Details The above cloud has the following details: - 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. - An [AWS Virtual Private Cloud](https://aws.amazon.com/vpc/) (VPC) is created with the resource tag `single_cell_opt`. By default, a 10.0.0.0/24 block is 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. - 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. ## Configuring AWS Cloud Once the instances are created, then it's a matter of configuring them using the normal `Ansible` methods. However, a couple configuration steps must first be performed: 1. The `hosts` and `amazon_ssh_config` files must be created. This can be accomplished by running the following script: ``` # Note: this can be run with the --dry-run argument to see the output first $ python gather_config.py ``` 2. Then, ansible will take care of the rest of the configuration, as described [here.](../../README.md) ``` $ ansible-playbook site.yaml ``` # AWS Tips From the command line, one can check the running instances: ``` $ aws ec2 describe-instances ``` Or list the current IP addresses ``` $ aws ec2 describe-addresses ``` ================================================ FILE: cloud-config/config/amazon/ansible.cfg ================================================ [defaults] inventory = ./hosts private_key_file = single_cell_opt.pem remote_user = ubuntu host_key_checking = False [ssh_connection] ssh_args = -F amazon_ssh_config ================================================ FILE: cloud-config/config/amazon/create_instance.yaml ================================================ - name: Setup Ubuntu machines vars: region: us-west-2 ami_image: ami-187c9978 # http://cloud-images.ubuntu.com/trusty/current/ instance_type: t2.nano worker_instances: 2 hosts: localhost tasks: - name: Create Keypair ec2_key: name: single_cell_opt region: "{{ region }}" register: keypair - name: Write key to file copy: dest: ./single_cell_opt.pem content: "{{ keypair.key.private_key }}" mode: 0600 when: keypair.changed - name: Setup VPC ec2_vpc: state: present cidr_block: 10.0.0.0/24 region: "{{ region }}" resource_tags: Environment: single_cell_opt subnets: - cidr: 10.0.0.0/24 internet_gateway: yes route_tables: - subnets: - 10.0.0.0/24 routes: - dest: 0.0.0.0/0 gw: igw wait: yes register: vpc - name: Single Cell Optimization Security group ec2_group: name: single_cell_opt description: Allow SSH, all exit and all intra-vpc vpc_id: "{{ vpc.vpc_id }}" region: "{{ region }}" rules: - proto: tcp from_port: 22 to_port: 22 cidr_ip: 0.0.0.0/0 - proto: all cidr_ip: 10.0.0.0/24 rules_egress: - proto: all cidr_ip: 0.0.0.0/0 - name: Start the head instance ec2: image: "{{ ami_image }}" region: "{{ region }}" instance_type: "{{ instance_type }}" key_name: "{{ keypair.key.name }}" vpc_subnet_id: "{{ vpc.subnets[0].id }}" group: [single_cell_opt, ] assign_public_ip: yes instance_tags: { type: single_cell_opt_head } exact_count: 1 count_tag: { type: single_cell_opt_head } wait: yes register: ec2head - name: Wait for SSH server wait_for: host={{ item.public_dns_name }} port=22 search_regex=OpenSSH with_items: ec2head.instances - name: Start the worker instances ec2: image: "{{ ami_image }}" region: "{{ region }}" instance_type: "{{ instance_type }}" key_name: "{{ keypair.key.name }}" vpc_subnet_id: "{{ vpc.subnets[0].id }}" group: [single_cell_opt, ] assign_public_ip: yes instance_tags: { type: single_cell_opt_worker } exact_count: "{{ worker_instances }}" count_tag: { type: single_cell_opt_worker } wait: yes register: ec2workers - name: Wait for SSH server wait_for: host={{ item.public_dns_name }} port=22 search_regex=OpenSSH with_items: ec2workers.instances ================================================ FILE: cloud-config/config/amazon/gather_config.py ================================================ #!/usr/bin/env python import argparse import boto3 from jinja2 import Environment KEY_NAME = 'single_cell_opt.pem' HEAD_INSTANCE_NAME = 'single_cell_opt_head' WORKER_INSTANCE_NAME = 'single_cell_opt_worker' hosts_template = ''' [neuron-optimizer-head] {{ head_public_ip }} [neuron-optimizer-worker] {% for ip in worker_private_ips %} {{ ip }}{% endfor %} ''' ssh_config_template = ''' ControlMaster auto ControlPersist 60s Host 10.0.0.* ProxyCommand ssh -i {{ private_key }} -W %h:%p %r@{{ head_public_ip }} ''' def _get_instances_by_tag(ec2, tag): '''filter on all instances, and get the ones tagged with 'type: tag' ''' filters = [{'Name': 'tag:type', 'Values': [tag]}, {'Name': 'instance-state-name', 'Values': ['running']}, ] return list(ec2.instances.filter(Filters=filters)) def get_head_public_ip(ec2): '''returns string value of the public IP of the head instance''' instance = _get_instances_by_tag(ec2, HEAD_INSTANCE_NAME) assert len(instance) == 1, 'Only expect one head node' instance = instance[0] return instance.public_ip_address def get_work_private_ips(ec2, tag=WORKER_INSTANCE_NAME, include_head=True): '''get the internal IPs of the workers, including the head by default''' instances = _get_instances_by_tag(ec2, tag) if include_head: head_instance = _get_instances_by_tag(ec2, HEAD_INSTANCE_NAME) assert len(head_instance) == 1, 'Only expect one head node' head_ip = head_instance[0].private_ip_address return [head_ip] + [i.private_ip_address for i in instances] def get_parser(): '''return the argument parser''' parser = argparse.ArgumentParser() parser.add_argument( '--dry-run', action='store_true', help='Output the results of the templates without writing them') return parser def main(): '''main function''' args = get_parser().parse_args() ec2 = boto3.resource('ec2') print('Instances:', list(i.id for i in ec2.instances.all())) env = Environment() hosts = env.from_string(hosts_template) ssh_config = env.from_string(ssh_config_template) head_public_ip = get_head_public_ip(ec2) worker_private_ips = get_work_private_ips(ec2) hosts_rendered = hosts.render(head_public_ip=head_public_ip, worker_private_ips=worker_private_ips) ssh_config_rendered = ssh_config.render(head_public_ip=head_public_ip, private_key=KEY_NAME) if args.dry_run: print('{:*^30}'.format(' Hosts ')) print(hosts_rendered) print('{:*^30}'.format(' ssh_config ')) print(ssh_config_rendered) else: with open('hosts', 'w') as fd: fd.write(hosts_rendered) with open('amazon_ssh_config', 'w') as fd: fd.write(ssh_config_rendered) if __name__ == '__main__': main() ================================================ FILE: cloud-config/config/amazon/site.yaml ================================================ --- - name: Install Neuron Optimizer Framework Head hosts: neuron-optimizer-head sudo: true vars_files: - vars.yaml roles: - scoop-master - name: Install Neuron Optimizer Framework Worker hosts: neuron-optimizer-worker sudo: true vars_files: - vars.yaml roles: - base - name: Install Neuron Optimizer Framework Worker hosts: neuron-optimizer-worker become: yes become_user: "{{ user_name }}" vars_files: - vars.yaml roles: - neuron - deap #- name: Granule Example # hosts: neuron-optimizer-worker-granule # sudo: true # # vars_files: # - vars.yaml # # roles: # - granule-example ================================================ FILE: cloud-config/config/amazon/vars.yaml ================================================ user_name: neuron workspace: ~/workspace venv: "{{workspace}}/venv" build_dir: "{{workspace}}/build" install_dir: "{{workspace}}/install" add_bin_path: true using_headnode: true neuron_build: "{{workspace}}/build/neuron" neuron_url: http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.tar.gz neuron_version: 7.4 neuron_config: >- ./configure --prefix=`readlink -f {{ install_dir }}`/nrnpython --with-nrnpython --without-paranrn --without-x --without-iv have_cython=no BUILD_RX3D=0 # if only an old version of python is available, you may need to set this true: python27_build: false python27_url: https://www.python.org/ftp/python/2.7.11/Python-2.7.11.tgz python27_version: 2.7.11 pip_version: 7.1.2 numpy_version: 1.10.4 efel_version: 2.10.7 scoop_version: 0.7.1.1 jupyter_version: 1.0.0 matplotlib_version: 1.5.1 ================================================ FILE: cloud-config/config/cluster-user/README.md ================================================ # Cluster User If you are not the administrator of a cluster, but have access to one, this document outlines how to setup a working environment that can be used for optimization. Note: It is assumed that on this cluster, there is a shared file-system where the software can be installed once and then used by all allocation of resources. ## Basic Configuration 1. Make sure that there is a virtual environment from which you will run `Ansible`, read the documentation [here](../../README.md) to set one up. 2. In the cluster-user directory, modify the `hosts` file to include the cluster head node, the contents should be a single stanza with a single line: ``` [neuron-optimizer-worker] dns.name.of.head.node ``` 3. If the username is different for the cluster from on the host it's being configured from, the `user_name` in `vars.yaml` will have to be changed to this new name. One can also set an absolute path in for `workspace` in `vars.yaml` to modify the installation path. 4. If the python version available on the cluster isn't at least 2.7, then it is recommended to modify `python27_build` in `vars.yaml` to be 'true' such that a local version of Python will be compiled. ## Installation Information Run the installation by issuing: ``` $ source ./venv-ansible/bin/activate $ ansible-playbook site.yaml ``` Once it has run, there should be a `~/workspace` directory on the cluster with all the required software as described [here](../README.md) under 'Installation Information'. ================================================ FILE: cloud-config/config/cluster-user/ansible.cfg ================================================ [defaults] inventory = ./hosts ================================================ FILE: cloud-config/config/cluster-user/hosts ================================================ [neuron-optimizer-worker] viz1 ================================================ FILE: cloud-config/config/cluster-user/site.yaml ================================================ --- - name: Install Neuron Optimizer Framework Worker hosts: neuron-optimizer-worker vars_files: - vars.yaml roles: - neuron - deap #- name: Granule Example # hosts: neuron-optimizer-worker-granule # sudo: true # # vars_files: # - vars.yaml # # roles: # - granule-example ================================================ FILE: cloud-config/config/cluster-user/vars.yaml ================================================ #Note: you may need to change this user_name: "{{ ansible_user_id }}" workspace: ~/workspace venv: "{{workspace}}/venv" build_dir: "{{workspace}}/build" install_dir: "{{workspace}}/install" add_bin_path: false using_headnode: false neuron_build: "{{workspace}}/build/neuron" neuron_url: http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.tar.gz neuron_version: 7.4 neuron_config: >- ./configure --prefix=`readlink -f {{ install_dir }}`/nrnpython --with-nrnpython --without-paranrn --without-x --without-iv have_cython=no BUILD_RX3D=0 # if only an old version of python is available, you may need to set this true: python27_build: false python27_url: https://www.python.org/ftp/python/2.7.11/Python-2.7.11.tgz python27_version: 2.7.11 pip_version: 7.1.2 numpy_version: 1.10.4 efel_version: 2.10.7 scoop_version: 0.7.1.1 jupyter_version: 1.0.0 matplotlib_version: 1.5.1 ================================================ FILE: cloud-config/config/vagrant/README.md ================================================ # Vagrant Setup 1. Install vagrant 2. Get the trusty64 box: `$ vagrant box add ubuntu/trusty64` 3. Use the included `Vagrantfile` to bring up 4 machines: This includes: - *head*: 192.168.61.10 - *worker0*: 192.168.61.20 - *worker1*: 192.168.61.21 - *worker2*: 192.168.61.22 4. Boot the vagrant instances: `$ vagrant up` ## Configure with Ansible Note: Make sure that there is a virtual environment from which you will run `Ansible`, read the documentation [here](../../README.md) to set one up. ``` $ source ./venv-ansible/activate $ ansible-playbook site.yaml ``` Notes: - `hosts.example` defines the host groups, and uses the running Vagrant instances. - `ansible.cfg` defines some defaults (like `hosts.example` and which ssh key to use) - One can `vagrant destroy` and then `vagrant up` to start from a clean slate ================================================ FILE: cloud-config/config/vagrant/Vagrantfile ================================================ # -*- mode: ruby -*- # vi: set ft=ruby : # Vagrantfile API/syntax version. Don't touch unless you know what you're doing! VAGRANTFILE_API_VERSION = "2" Vagrant.configure(VAGRANTFILE_API_VERSION) do |config| config.vm.define "head" do |inventory| inventory.vm.box = "ubuntu/trusty64" inventory.vm.network "private_network", ip: "192.168.61.10" end config.vm.define "worker0" do |inventory| inventory.vm.box = "ubuntu/trusty64" inventory.vm.network "private_network", ip: "192.168.61.20" end config.vm.define "worker1" do |inventory| inventory.vm.box = "ubuntu/trusty64" inventory.vm.network "private_network", ip: "192.168.61.21" end config.vm.define "worker2" do |inventory| inventory.vm.box = "ubuntu/trusty64" inventory.vm.network "private_network", ip: "192.168.61.22" end end ================================================ FILE: cloud-config/config/vagrant/ansible.cfg ================================================ [defaults] inventory = ./hosts private_key_file = ~/.vagrant.d/insecure_private_key remote_user = vagrant host_key_checking = False ================================================ FILE: cloud-config/config/vagrant/hosts ================================================ [neuron-optimizer-head] 192.168.61.10 [neuron-optimizer-worker] 192.168.61.10 192.168.61.20 192.168.61.21 192.168.61.22 [neuron-optimizer-worker-granule] 192.168.61.10 192.168.61.20 192.168.61.21 192.168.61.22 ================================================ FILE: cloud-config/config/vagrant/site.yaml ================================================ --- - name: Install Neuron Optimizer Framework Head hosts: neuron-optimizer-head sudo: true vars_files: - vars.yaml roles: - scoop-master - name: Install Neuron Optimizer Framework Worker hosts: neuron-optimizer-worker sudo: true vars_files: - vars.yaml roles: - base - name: Install Neuron Optimizer Framework Worker hosts: neuron-optimizer-worker become: yes become_user: "{{ user_name }}" vars_files: - vars.yaml roles: - neuron - deap #- name: Granule Example # hosts: neuron-optimizer-worker-granule # sudo: true # # vars_files: # - vars.yaml # # roles: # - granule-example ================================================ FILE: cloud-config/config/vagrant/vars.yaml ================================================ user_name: neuron workspace: ~/workspace venv: "{{workspace}}/venv" build_dir: "{{workspace}}/build" install_dir: "{{workspace}}/install" add_bin_path: true using_headnode: true neuron_build: "{{workspace}}/build/neuron" neuron_url: http://www.neuron.yale.edu/ftp/neuron/versions/v7.4/nrn-7.4.tar.gz neuron_version: 7.4 neuron_config: >- ./configure --prefix=`readlink -f {{ install_dir }}`/nrnpython --with-nrnpython --without-paranrn --without-x --without-iv have_cython=no BUILD_RX3D=0 # if only an old version of python is available, you may need to set this true: python27_build: true python27_url: https://www.python.org/ftp/python/2.7.11/Python-2.7.11.tgz python27_version: 2.7.11 pip_version: 7.1.2 numpy_version: 1.10.4 efel_version: 2.10.7 scoop_version: 0.7.1.1 jupyter_version: 1.0.0 matplotlib_version: 1.5.1 ================================================ FILE: cloud-config/roles/base/tasks/main.yaml ================================================ --- #note: this is for debian and ubuntu distributions - name: update apt cache apt: update_cache=yes - name: Install base packages apt: name={{ item }} force=yes state=installed with_items: - build-essential - git - htop - libreadline-dev - libzmq3-dev - ntp - python-dev - python-pip - python-virtualenv - unzip #for matplotlib - pkg-config - libfreetype6-dev tags: packages - name: Configure User user: name={{ user_name }} ================================================ FILE: cloud-config/roles/deap/tasks/main.yaml ================================================ --- - name: Upgrade pip pip: name=pip version="{{ pip_version }}" virtualenv={{ venv }} - name: Install numpy pip: name=numpy version="{{ numpy_version }}" virtualenv={{ venv }} - name: Install Jupyter pip: name=jupyter version="{{ jupyter_version }}" virtualenv={{ venv }} - name: Install matplotlib pip: name=matplotlib version="{{ matplotlib_version }}" virtualenv={{ venv }} #Note: using the BBP version of DEAP, as it includes the updated IBEA tools - name: Install deap pip: name='git+https://github.com/BlueBrain/deap#egg=deap' virtualenv={{ venv }} - name: Install efel pip: name=efel version="{{ efel_version }}" virtualenv={{ venv }} - name: Install scoop pip: name=scoop version="{{ scoop_version }}" virtualenv={{ venv }} #Use the latest version - name: Install BluePyOpt pip: name=bluepyopt virtualenv={{ venv }} - name: Add virtualenv to setup.sh file lineinfile: dest="{{ workspace }}/setup.sh" create=yes line="source {{ venv }}/bin/activate" - name: Install ssh key authorized_key: user: "{{ user_name }}" key: "{{ lookup('file', 'id_rsa.tmp') }}" when: "{{ using_headnode }}" ================================================ FILE: cloud-config/roles/granule-example/tasks/main.yaml ================================================ - name: Get eFEL Source git: repo=https://github.com/BlueBrain/eFEL.git dest=~/workspace/eFEL - name: Compile models shell: "{{ install_dir }}/nrnpython/x86_64/bin/nrnivmodl mechanisms" args: chdir: "{{ workspace }}/eFEL/examples/deap/GranuleCell1" creates: "{{ workspace }}/eFEL/examples/deap/GranuleCell1/x86_64" ================================================ FILE: cloud-config/roles/neuron/tasks/main.yaml ================================================ --- - include: python27.yaml when: "{{ python27_build }}" - set_fact: extra_path="{{ pythonbin.stdout + ':' }}" when: python27_build - set_fact: extra_path="" when: not python27_build - name: Create directories file: path={{ item }} state=directory with_items: - "{{ workspace }}" - "{{ neuron_build }}" - name: Get Source get_url: url={{ neuron_url }} dest={{ neuron_build }}/nrn-{{ neuron_version }}.tar.gz - name: Untar source unarchive: copy=no src={{ neuron_build }}/nrn-{{ neuron_version }}.tar.gz dest={{ neuron_build }} args: creates: "{{ neuron_build }}/nrn-{{ neuron_version }}/configure" - name: Configure Neuron shell: "{{ neuron_config }}" environment: PATH: "{{ extra_path }}{{ ansible_env.PATH }}" args: chdir: "{{ neuron_build }}/nrn-{{ neuron_version }}" creates: "{{ neuron_build }}/nrn-{{ neuron_version }}/config.log" - name: Build Neuron #Note: could use -j here, but this gets oom-killed often shell: make install args: chdir: "{{ neuron_build }}/nrn-{{ neuron_version }}" creates: "{{ install_dir }}/nrnpython" - name: Install numpy pip: name=numpy version="{{ numpy_version }}" virtualenv="{{ venv }}" - name: Install nrnpython shell: "{{ venv }}/bin/python setup.py install" args: chdir: "{{ neuron_build }}/nrn-{{ neuron_version }}/src/nrnpython" creates: "{{ venv }}/lib/python2.7/site-packages/neuron" - name: Add neuron and python virtualenv to path lineinfile: > dest=~/.bashrc state=present line="export PATH={{ venv }}/bin:{{ install_dir }}/nrnpython/x86_64/bin/:$PATH" when: "{{ add_bin_path }}" - name: Add paths to setup.sh file lineinfile: dest="{{ workspace }}/setup.sh" create=yes line="export PATH={{ venv }}/bin:{{ install_dir }}/nrnpython/x86_64/bin/:$PATH" ================================================ FILE: cloud-config/roles/neuron/tasks/python27.yaml ================================================ --- #NOTE: need zlib1g-dev, libssl-dev package on ubuntu/debian - name: Create directories file: path={{ item }} state=directory with_items: - "{{ workspace }}" - "{{ build_dir }}/python27" - name: Get Source get_url: url={{ python27_url }} dest="{{ build_dir }}/python27/python-{{ python27_version }}.tar.gz" - name: Untar source unarchive: copy=no src="{{ build_dir }}/python27/python-{{ python27_version }}.tar.gz" dest="{{ build_dir }}/python27" args: creates: "{{ build_dir }}/python27/Python-{{ python27_version }}/configure" - name: Configure Python shell: ./configure --prefix=`readlink -f {{ install_dir }}` args: chdir: "{{ build_dir }}/python27/Python-{{ python27_version }}" creates: "{{ build_dir }}/python27/Python-{{ python27_version }}/config.log" - name: Build & Install Python #Note: could use -j here, but this gets oom-killed often shell: make install args: chdir: "{{ build_dir }}/python27/Python-{{ python27_version }}" creates: "{{ install_dir }}/bin/python2.7" - name: Create virtualenv shell: "/usr/bin/virtualenv -p {{ install_dir }}/bin/python {{ venv }}" args: creates: "{{ venv }}/bin/python2.7" - name: Register pythonbin shell: readlink -f {{ install_dir }}/bin/ register: pythonbin ================================================ FILE: cloud-config/roles/scoop-master/tasks/main.yaml ================================================ --- - name: Configure User user: name={{ user_name }} generate_ssh_key=yes - name: Downloading pub key fetch: src=/home/{{ user_name }}/.ssh/id_rsa.pub dest=id_rsa.tmp flat=yes - name: Save SCOOP hostlist become: yes become_user: "{{ user_name }}" copy: content="{{ groups['neuron-optimizer-worker'] | join('\n') }}" dest=/home/{{ user_name }}/scoop_workers ================================================ FILE: codecov.yml ================================================ coverage: range: "90...100" status: project: default: target: "90%" threshold: "5%" patch: false ================================================ FILE: docs/.gitignore ================================================ /build ================================================ FILE: docs/Makefile ================================================ # Makefile for Sphinx documentation # # You can set these variables from the command line. SPHINXOPTS = SPHINXBUILD = sphinx-build PAPER = BUILDDIR = build # User-friendly check for sphinx-build ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) $(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/) endif # Internal variables. PAPEROPT_a4 = -D latex_paper_size=a4 PAPEROPT_letter = -D latex_paper_size=letter ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest coverage gettext help: @echo "Please use \`make ' where is one of" @echo " html to make standalone HTML files" @echo " dirhtml to make HTML files named index.html in directories" @echo " singlehtml to make a single large HTML file" @echo " pickle to make pickle files" @echo " json to make JSON files" @echo " htmlhelp to make HTML files and a HTML help project" @echo " qthelp to make HTML files and a qthelp project" @echo " applehelp to make an Apple Help Book" @echo " devhelp to make HTML files and a Devhelp project" @echo " epub to make an epub" @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" @echo " latexpdf to make LaTeX files and run them through pdflatex" @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" @echo " text to make text files" @echo " man to make manual pages" @echo " texinfo to make Texinfo files" @echo " info to make Texinfo files and run them through makeinfo" @echo " gettext to make PO message catalogs" @echo " changes to make an overview of all changed/added/deprecated items" @echo " xml to make Docutils-native XML files" @echo " pseudoxml to make pseudoxml-XML files for display purposes" @echo " linkcheck to check all external links for integrity" @echo " doctest to run all doctests embedded in the documentation (if enabled)" @echo " coverage to run coverage check of the documentation (if enabled)" clean: rm -rf $(BUILDDIR)/* html: $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." dirhtml: $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml @echo @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." singlehtml: $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml @echo @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." pickle: $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle @echo @echo "Build finished; now you can process the pickle files." json: $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json @echo @echo "Build finished; now you can process the JSON files." htmlhelp: $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp @echo @echo "Build finished; now you can run HTML Help Workshop with the" \ ".hhp project file in $(BUILDDIR)/htmlhelp." qthelp: $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp @echo @echo "Build finished; now you can run "qcollectiongenerator" with the" \ ".qhcp project file in $(BUILDDIR)/qthelp, like this:" @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/eFEL.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/eFEL.qhc" applehelp: $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp @echo @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." @echo "N.B. You won't be able to view it unless you put it in" \ "~/Library/Documentation/Help or install it in your application" \ "bundle." devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/eFEL" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/eFEL" @echo "# devhelp" epub: $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub @echo @echo "Build finished. The epub file is in $(BUILDDIR)/epub." latex: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." @echo "Run \`make' in that directory to run these through (pdf)latex" \ "(use \`make latexpdf' here to do that automatically)." latexpdf: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through pdflatex..." $(MAKE) -C $(BUILDDIR)/latex all-pdf @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." latexpdfja: $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex @echo "Running LaTeX files through platex and dvipdfmx..." $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." text: $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text @echo @echo "Build finished. The text files are in $(BUILDDIR)/text." man: $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man @echo @echo "Build finished. The manual pages are in $(BUILDDIR)/man." texinfo: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." @echo "Run \`make' in that directory to run these through makeinfo" \ "(use \`make info' here to do that automatically)." info: $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo @echo "Running Texinfo files through makeinfo..." make -C $(BUILDDIR)/texinfo info @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." gettext: $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale @echo @echo "Build finished. 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The pseudo-XML files are in $(BUILDDIR)/pseudoxml." ================================================ FILE: docs/source/.gitignore ================================================ /ephys/ /optimisations/ /deapext/ ================================================ FILE: docs/source/_templates/module.rst ================================================ {{ fullname }} {{ underline }} .. automodule:: {{ fullname }} :members: ================================================ FILE: docs/source/api.rst ================================================ .. BluePyOpt documentation master file, created by sphinx-quickstart on Mon May 11 14:40:15 2015. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. Python API ========== .. toctree:: :maxdepth: 3 optimisations ephys deapext ================================================ FILE: docs/source/conf.py ================================================ # -*- coding: utf-8 -*- # # BluePyOpt documentation build configuration file, created by # sphinx-quickstart on Mon May 11 14:40:15 2015. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import bluepyopt import bluepyopt.ephys # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('.')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. needs_sphinx = '1.3' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinx.ext.napoleon'] # napoleon_numpy_docstring = True napoleon_google_docstring = True # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'bluepyopt' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = bluepyopt.__version__ # The full version, including alpha/beta/rc tags. release = bluepyopt.__version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] autosummary_generate = True autodoc_default_flags = ['show-inheritance'] autoclass_content = 'both' tolerate_sphinx_warnings = True # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'sphinx-bluebrain-theme' html_title = 'BluepyOpt' html_show_sourcelink = False html_theme_options = { "repo_url": "https://github.com/BlueBrain/BluePyOpt/", "repo_name": "BlueBrain/BluePyOpt" } # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = ['./'] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "_static/bbp.jpg" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. html_last_updated_fmt = '%b %d, %Y' #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'bluepyoptdoc' # -- Options for LaTeX output -------------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'bluepyopt.tex', u'BluePyOpt Documentation', u'BBP, EPFL', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'bluepyopt', u'BluePyOpt Documentation', [u'BBP, EPFL'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------------ # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'bluepyopt', u'BluePyOpt Documentation', u'BBP, EPFL', 'bluepyopt', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # -- Options for Epub output --------------------------------------------------- # Bibliographic Dublin Core info. epub_title = u'bluepyopt' epub_author = u'BBP, EPFL' epub_publisher = u'BBP, EPFL' epub_copyright = u'2015, BBP, EPFL' # The language of the text. It defaults to the language option # or en if the language is not set. #epub_language = '' # The scheme of the identifier. Typical schemes are ISBN or URL. #epub_scheme = '' # The unique identifier of the text. This can be a ISBN number # or the project homepage. #epub_identifier = '' # A unique identification for the text. #epub_uid = '' # A tuple containing the cover image and cover page html template filenames. #epub_cover = () # HTML files that should be inserted before the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_pre_files = [] # HTML files shat should be inserted after the pages created by sphinx. # The format is a list of tuples containing the path and title. #epub_post_files = [] # A list of files that should not be packed into the epub file. #epub_exclude_files = [] # The depth of the table of contents in toc.ncx. #epub_tocdepth = 3 # Allow duplicate toc entries. #epub_tocdup = True ================================================ FILE: docs/source/deapext.rst ================================================ Deap extension API ================== .. autosummary:: :toctree: deapext :template: module.rst bluepyopt.deapext.optimisations ================================================ FILE: docs/source/ephys.rst ================================================ EPhys model API =============== .. autosummary:: :toctree: ephys :template: module.rst bluepyopt.ephys.evaluators bluepyopt.ephys.models bluepyopt.ephys.efeatures bluepyopt.ephys.locations bluepyopt.ephys.mechanisms bluepyopt.ephys.morphologies bluepyopt.ephys.objectives bluepyopt.ephys.parameters bluepyopt.ephys.parameterscalers bluepyopt.ephys.protocols bluepyopt.ephys.recordings bluepyopt.ephys.responses bluepyopt.ephys.objectivescalculators bluepyopt.ephys.stimuli ================================================ FILE: docs/source/index.rst ================================================ .. BluePyOpt documentation master file, created by sphinx-quickstart on Mon May 11 14:40:15 2015. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. .. include:: ../../README.rst :end-before: .. substitutions .. toctree:: :maxdepth: 3 Home api.rst Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` .. |banner| image:: /logo/BluePyOptBanner.png .. |landscape_example| image:: ../../examples/simplecell/figures/landscape_example.png ================================================ FILE: docs/source/optimisations.rst ================================================ General optimisation API ======================== .. autosummary:: :toctree: optimisations :template: module.rst bluepyopt.optimisations bluepyopt.parameters bluepyopt.objectives bluepyopt.evaluators ================================================ FILE: examples/BluePyOpt-ipyparallel.md ================================================ # BluePyOpt Parallelization with ipyparallel By default, when the optimization is being run, it only uses a single core. If 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/). # Quick Introduction to ipyparallel The `ipyparallel` project is uses the IPython/Jupyter protocol for simplifying distributing work over many cores. In the simplest terms, a worker (called an `ipengine`) is started per core. It is directed by a master process (called an `ipcontroller`) to perform work. The `ipcontroller` gets its work from clients that connect to it. ## Installation `ipyparallel` can be installed using pip: # create and activate virtualenv $ venv venv-ipyparellel; source venv-ipyparellel/bin/activate # install ipyparallel (venv-ipyparellel)$ pip install ipyparallel #check that it installed correctly (venv-ipyparellel)$ ipcontroller -V [returns a version number] With `ipyparallel` installed, let's perform a simple parallel operation. To fully understand what is happening, it is probably best to open three windows. One for the `ipcontroller`, one for the `ipengines`, and lastly one for the client. In the `ipcontroller` window: # activate the virtualenv $ source venv-ipyparellel/bin/activate # run the ipcontroller (venv-ipyparellel)$ ipcontroller In the `ipengine` window: # activate the virtualenv $ source venv-ipyparellel/bin/activate # start two ipengines (venv-ipyparellel)$ ipengine &; ipengine & In the client window: # activate the virtualenv $ source venv-ipyparellel/bin/activate #start python (venv-ipyparellel)$ python >>> from ipyparallel import Client >>> c = Client() # creates a connection to the server >>> view = c[:] # creates a 'view' of all the workers >>> import socket >>> view.apply_sync(socket.gethostname) # run the function gethostname on all ipengines ['your_hostname', 'your_hostname'] If 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. Several things should be noted here: * `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 * 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: # start an ipyparellel cluster with 1 head node, and as many processors as the current machine has (venv-ipyparellel)$ ipcluster start * 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 # Running the L5_PC example With some experience with `ipyparallel`, it's time to try and run an optimization using it. ## Installation If you already have a working environment, with `BluePyOpt`, [NEURON](http://www.neuron.yale.edu/neuron/) and `ipyparallel` installed, you can skip this step. There 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`. Follow [these instructions](https://conda.io/docs/install/quick.html) to install `conda`. Make sure that the `conda` command runs (make sure that the install location is included on your path.) Install the `anaconda` environment manager: $ conda install anaconda-client Install the BluePyOpt suite: $ conda env create bluepyopt/gecco2017 Activate it, and check that it works: $ source activate gecco2017 $ python >>> import neuron If that works, you should have a properly working environment. ## Running Start a cluster of `ipengines` (in one window): (gecco2017)$ ipcluster start In the `BluePyOpt` [git](https://github.com/BlueBrain/BluePyOpt/) repository, there is an `examples/l5pc` directory. From there, one can launch the optimization like so (in another window): (gecco2017)$ ./opt_l5pc.py \ -vv \ --checkpoint check.pkl \ --offspring_size=50 \ --max_ngen=2 \ --ipyparallel \ --start This 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. One should get output something along the lines of: DEBUG:root:Using ipyparallel with 8 engines DEBUG:root:Doing start or continue DEBUG:traitlets:Importing canning map DEBUG:root:Generation took 0:01:10.813599 DEBUG:root:Generation took 0:01:33.446949 INFO:__main__:gen nevals avg std min max 1 2 4862.45 123.749 4738.7 4986.2 2 2 4068.58 1604.07 1307.39 5242.02 ================================================ FILE: examples/README.md ================================================ # BluePyOpt Examples This directory contains examples of optimizations that can be performed with `BluePyOpt`. They can be used to learn the concepts behind the package, and also as a starting point for other optimizations * expsyn: Example optimization of a synapse (a point process) in NEURON * graupnerbrunelstdp: Graupner-Brunel STDP model fitting * l5pc: Layer 5 pyramidal neuron parameter optimization * simplecell: optimisation of simple single compartmental cell with two free parameters * stochkv: simple cell optimization with stochastic channels * tsodyksmarkramstp: optimizing parameters of the Tsodyks-Markram model of short-term synaptic plasticity The expsyn, l5pc and simplecell examples contain an implementation for [Arbor](https://arbor-sim.org/) as an alternative simulator backend to NEURON. # Documentation [Parallelization with ipyparallel](BluePyOpt-ipyparallel.md) ================================================ FILE: examples/__init__.py ================================================ """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ ================================================ FILE: examples/cma_strategy/cma.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimisation using the CMA evolutionary strategy\n", "\n", "This notebook will explain how to optimise a model using the covariance matrix adaptation (CMA) optimisation strategy. \n", "BluePyOpt includes two flavors of CMA: a single objective one and a hybrid single/multi objective one.\n", "\n", "For a tutorial on the theory and algorithm behind CMA, please refer to https://arxiv.org/abs/1604.00772.\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." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: matplotlib in /gpfs/bbp.cscs.ch/ssd/apps/tools/jupyter/venvs/python37/lib/python3.7/site-packages (3.4.3)\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", "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", "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", "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", "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", "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", "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", "\u001b[33mWARNING: You are using pip version 20.1.1; however, version 22.0.3 is available.\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" ] } ], "source": [ "# Install matplotlib if needed\n", "!pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy\n", "\n", "%load_ext autoreload\n", "%autoreload" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def plot_fitness(logbook):\n", " gen_numbers = logbook.select('gen')\n", " min_fitness = logbook.select('min')\n", " max_fitness = logbook.select('max')\n", " plt.plot(gen_numbers, min_fitness, label='min fitness')\n", " plt.xlabel('generation #')\n", " plt.ylabel('score (# std)')\n", " plt.legend()\n", " plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \n", " plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def plot_responses(responses):\n", " plt.subplot(2,1,1)\n", " plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\n", " plt.legend()\n", " plt.subplot(2,1,2)\n", " plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\n", " plt.legend()\n", " plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up the cell template and evaluator\n", "-------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we instantiate the cell template and evaluator as defined in the simplecell example:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import bluepyopt.ephys.examples.simplecell\n", "\n", "simple_cell = bluepyopt.ephys.examples.simplecell.SimpleCell()\n", "evaluator = simple_cell.cell_evaluator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optimisation using single objective CMA (SO-CMA)\n", "-------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will present the single objective version of the CMA strategy.\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", "Note that in CMA, informing the offspring_size is optional as by default, it is automatically set to\n", "int(4 + 3 * log(dimension_parameter_space))." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "optimiser = bluepyopt.deapext.optimisationsCMA.DEAPOptimisationCMA\n", "optimisation = optimiser(evaluator=evaluator, seed=1)\n", "pop, hof, log, hist = optimisation.run(max_ngen=10)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [0.11513442416272959, 0.038816802452611765]\n", "Fitness values: (0.0,)\n" ] } ], "source": [ "best_ind = hof[0]\n", "print('Best individual: ', best_ind)\n", "print('Fitness values: ', best_ind.fitness.values)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_fitness(log)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "For example here,we can restart from the final results of the previous optimisation:" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "optimisation = optimiser(centroids=[list(hof[0])], sigma=0.01, evaluator=evaluator, seed=2)\n", "pop, hof, log, hist = optimisation.run(max_ngen=10)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [0.11485248564327624, 0.039145905479049836]\n", "Fitness values: (0.0,)\n" ] } ], "source": [ "best_ind = hof[0]\n", "print('Best individual: ', best_ind)\n", "print('Fitness values: ', best_ind.fitness.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optimisation using multi objective CMA (MO-CMA)\n", "-------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second is the hybrid single/multi objective CMA strategy. It is tasked with both:\n", "- minimizing the fitness computed as the sum of the scores \n", "- maximizing the hyper-volume of the Pareto front formed by the current population of models.\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", "rank_mixed = w_hv * rank_hv. + ((1 - w_hv) * rank_fitness).\n", "\n", "Following this ranking, the best models are selected to update the CMA kernel for the next generation.\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)." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "optimisation = optimiser(weight_hv=0.5, offspring_size=3, selector_name=\"multi_objective\", evaluator=evaluator, seed=2)\n", "pop, hof, log, hist = optimisation.run(max_ngen=10)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [0.11309639259118459, 0.034266363909519156]\n", "Fitness values: (0.0,)\n" ] } ], "source": [ "best_ind = hof[0]\n", "print('Best individual: ', best_ind)\n", "print('Fitness values: ', best_ind.fitness.values)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_fitness(log)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "CMA versus IBEA \n", "-------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As the present package proposes both the IBEA and CMA evolutionary strategies, one might ask: Which one is the best?\n", "\n", "Unfortunately, there is no definitive answer to this question.\n", "\n", "From a theoretical point of view, the advantages of the CMA strategy seem strong:\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", "- 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", "- 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", "However, CMA is not without drawbacks:\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", "- 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", "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." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "lfpyenv_new", "language": "python", "name": "lfpyenv_new" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/expsyn/.gitignore ================================================ .ipynb_checkpoints/ ================================================ FILE: examples/expsyn/ExpSyn.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimising synaptic parameters " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how the parameters of a NEURON point process (in this case a synapse), can be optimised using BluePyOpt." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First some initial setup:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "%reload_ext autoreload\n", "%autoreload\n", "\n", "import os\n", "\n", "import bluepyopt as bpopt\n", "import bluepyopt.ephys as ephys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need a Simulator (NEURON), Morphology (one compartment) and two Location objects (the 'somatic' sectionlist and the center of the soma)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# NEURON simulator\n", "nrn_sim = ephys.simulators.NrnSimulator()\n", "\n", "# Single compartment\n", "morph = ephys.morphologies.NrnFileMorphology('simple.swc')\n", "\n", "# Object that points to sectionlist somatic\n", "somatic_loc = ephys.locations.NrnSeclistLocation('somatic',seclist_name='somatic')\n", "\n", "# Object that points to the center of the soma\n", "somacenter_loc = ephys.locations.NrnSeclistCompLocation(\n", " name='somacenter',\n", " seclist_name='somatic',\n", " sec_index=0,\n", " comp_x=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also add a leak channel:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pas_mech = ephys.mechanisms.NrnMODMechanism( \n", " name='pas', \n", " suffix='pas', \n", " locations=[somatic_loc]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Add ExpSyn synapse pointprocess at the center of the soma\n", "expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism( \n", " name='expsyn', \n", " suffix='ExpSyn', \n", " locations=[somacenter_loc]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have defined a point process, we can create a Location object that points to it" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "expsyn_loc = ephys.locations.NrnPointProcessLocation( \n", " 'expsyn_loc', \n", " pprocess_mech=expsyn_mech) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this location, we can specify the parameters of the synapse. Let's fit the decay time constant:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "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])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's put all these concepts together in a cell model:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cell = ephys.models.CellModel( \n", " name='simple_cell', \n", " morph=morph, \n", " mechs=[pas_mech, expsyn_mech], \n", " params=[expsyn_tau_param]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "We first create a stimulus that injects the presynaptic events:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "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=[expsyn_loc])\n", "\n", "stim_end = stim_start + interval * number\n", "\n", "rec = ephys.recordings.CompRecording(\n", " name='soma.v', \n", " location=somacenter_loc,\n", " variable='v')\n", "\n", "protocol = ephys.protocols.SweepProtocol('netstim_protocol', [netstim], [rec])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we define an eFELFeature that will target the maximum voltage and we put everything in an evaluator" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "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", "\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=nrn_sim) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try out the evaluator with a decay time constant of 10.0" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'maximum_voltage': 29.716831912606736}\n" ] } ], "source": [ "default_param_values = {'expsyn_tau': 10.0} \n", "\n", "print(cell_evaluator.evaluate_with_dicts(default_param_values)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run the optimisation:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "optimisation = bpopt.optimisations.DEAPOptimisation( \n", " evaluator=cell_evaluator, \n", " offspring_size=10) \n", "\n", "_, hall_of_fame, _, _ = optimisation.run(max_ngen=5) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we can plot the best individual:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [14.03202650928813]\n", "Fitness values: (1.181341617527849,)\n" ] }, { "data": { "image/png": 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BEkL4LtOLifCyivK6m7mUQv3iPugY0nL76tEHPvsIZ34u0LzxvoQQvk2KSXtTfqnGDYuXs0yY0qK7UjF90IDj26PQq1+LvrcQwreY3gEvvKyeMxOP6NEbqComQog2TYpJe1Nebsw54gUqqCNERFFx/Buv7E8IYR4pJu2NN89MMCbfqsiUYiJEWyfFpL2puAS22jctekyvOJzZWejiC97bpxDC66SYtDfl3j8zAeC7TK/tUwjhfW4VE4fDwcmTJzl8+DAnT57E4XB4OpfwAK210czlpT4TwDUisT4hnfBCtGUNXhr8xRdfsGnTJvbv34+fnx+BgYFcvHiRyspKBg0axIQJE7juOhl3qdWodBh3uHuxmKigYPy69aDypJyZCNGW1VtMFi5cSMeOHRk1ahSzZ8/Gbv/hDum8vDwOHjzIRx99xIYNG1i0aJFXwopmqqw0HltiAqxGsMb1p/LgV17dpxDCu+r9VJk1axY9e/as8zW73c6oUaMYNWoUJ0+e9Fg40XT60iWcr6WiesVj+Q9jiH+qmyf9/LyaxRbXn0ufpaGLClEhYV7dtxDCO+rtM+nZsydffPGFa+TehtYTvkdvT4M929Fvr0JnHjYWVlYXkxaY570RbPH9jSffHfPqfoUQ3tNge8df//pXlFKMGjWKsWPHSuFoRfThfcY87JfK0NvSUHH9TTszscZeZWQ6cQw1SPrYhGiLGiwmL7/8Mnv37iU9PZ2nnnqK6OhobrzxRkaPHk1YmDRX+LQTR1EDhoDFD717G/qOOT+cmXi5z8QS1BEiu6Pl8mAh2qwGP1UsFgvXXXcd1113HaWlpWzfvp1PP/2UN998k8GDBzN27Nh6h4kX5tHllyAvF7r1QHWLQe/8GE4cheCqEYH9vD++p+oVjz56wOv7FUJ4h9ufKkFBQa451I8cOcKLL75Iamoqa9eubVaAdevWsXnzZtd0u9OnT2fYsGF8+umnvPvuu671Tp48ydKlS+ndu7db27dreVVzO9u7wlWDQSn0oa9Q1xqFX3n5zAQw7jfZtRV9IR8V2tn7+xdCeJTbnyoVFRXs2rWL9PR0vv76a6666ipuv/32FgkxadIkJk+eXGPZ6NGjGT16NGAUkmXLltUqJA1t364VFQKgwjqjgkMhJhZ9+CvUNVVTGnu5zwRA9Y5Hg3En/OAEr+9fCOFZP1pMDh48SHp6Ojt37iQsLIwbb7yR+++/n4iICG/kA+Czzz5jxIgRXttfq1dUYDxWXYarBlyDTvs3lBYby718NRcAPWONM6Rvj6KkmAjR5jRYTObNm0dpaSk33HADTz75JFdddZVHQmzcuJH09HRiY2OZOXOma673ajt27OCxxx5r8vbtjb5gnJkQWlVM+l+D3rgefWS/sdyEZi4VEATdYmRYFSHaqAY/Ve68804SExOx2Zr3l+yiRYsoKCiotTw5OZmJEycybZpxU93atWtZtWoVc+fOda1z9OhR/P39670s+ce2v1xaWhppaWkApKSkEB4e3qTvx2q1NnlbT6rOVex0UAKE94pF2Ww4E0eSoxTW44epAMLsdvy9mL86V+GAa7i061O6dOmCUuZP4+vrP0dfI7kax1dzgWeyNVhMLm9aysnJ4bvvvqOsrKzGOqNGjfrRnSxcuNCtMOPHj2fp0qU1lm3bto2RI0fWu02nTp0a3P5y1RcQVMvNzXUr15XCw8ObvK0nVedynj0DQR05X1j4w4vde1NRNaRJYXEJyov5Xbmie6KLCsk9tB/VtZvX9v9juXyN5GocydV4zckWHR1d53K32jvWr1/Pv/71L3r06IH/ZcOXV9/Q2Bz5+fl07mxc3bNr1y5iYmJcrzmdTnbs2MEf/vCHJm3fbl0srTXPu4ofgD79rfGFGVdzAarPVWhAf3vEJ4qJEKLluPWp8t5775GSkkKPHj1aPMDq1as5ceIESikiIiKYPXu267VDhw4RHh5OZGRkjW1WrlzJhAkTiIuLa3D79kqXlUJgUM2F8QPgk/eN5ybcZwJAdE/w7wDfHoGfjDEngxDCI9z6VAkODvbY1Vvz58+v97Wrr76axYsX11o+Z84ct7Zvty6WQkDNYqLiBxqX5oJpxUT5+UHveLTMCS9Em+PW5Fh33303L7/8MpmZmeTm5tb4J3xQ2cVaZyaqy2V/DJjUzAWg+vSDU8fRFeWmZRBCtDy3PlUcDgf79u1j27ZttV5r7h3wwgMuljbcJ2HCTYvVVNwA9Mb1xgjC8QNNyyGEaFluFZNXX32V6dOnM3LkyBod8MJH1dVnAqgJU9AfvQMdAk0IVSXOGI5eZx5GSTERos1wq5g4nU5uuukmLBa3WsWE2couQoeAWovV7feiJv4c1dG8mzpVaCfo2g197BD81LQYQogW5lZ1uOWWW9iwYQNa6x9fWZhKaw0V5WDrUOs1pRSqk72OrbxLxQ2AzMPy+yREG+LWmckHH3xAQUEB69evrzVUyV//+lePBBNNVOkArcGXmyPj+8OOLZCdBZF13wAlhGhd3ComcvltK1JedZWUzXeLiYozLlPWmYdQUkyEaBPcKiYDB0pHaavh8P1iQrceENQRjh2CEePNTiOEaAH19pm8//77VFRUNLhxRUUF77//fouHEs1QfWbiw81cymKB2P7ozMNmRxFCtJB6z0wKCgp46KGHuPbaaxk4cCDR0dEEBARQVlbGmTNnOHjwIHv37mXMGBkWw6dUtIIzE0DF9Ufv34MuKTb16jIhRMuot5jccccd3HzzzXzyySds2bKFkydPUlJSQnBwMD179uTaa69l+vTphISEeDOv+DFVxUT5ejGJH2AM73L8Gxh8ndlxhBDN1GCfSWhoKJMnT5YpcVuTVtABD0CffmCxoI8dQkkxEaLVk7sQ25oK3+8zAVAdAoy56Y8dMDuKEKIFSDFpa1pJnwmA6nc1HD8igz4K0QZIMWlrWlMxuWowOCrg+BGzowghmkmKSRujW8GlwS59B4JS6CP7zU4ihGgmtye2KCoqYu/eveTn5zNlyhTy8vLQWtOlS5cWCfLBBx+wceNGLBYLw4YN46677gKMKYO3bNmCxWLhnnvuYejQobW2zc7O5oUXXqCoqIjY2Fjmz5+P1cQ5O0xVfWZi9f1iooKCoUdvKSZCtAFunZkcPHiQhx9+mE8//ZR//etfAJw9e5b//u//bpEQ+/fvZ/fu3Sxbtoznn3+eW265BYDTp0+zfft2nn/+eZ5++mn+9re/4XQ6a22/evVqJk2axEsvvUTHjh3ZsmVLi+RqlVpJB3w11W8QHD+MdjR8g6wQwre5VUz+/ve/8/DDD/P000/jVzWxUnx8PJmZmS0SYtOmTUyZMgWbzQZAWFgYABkZGYwYMQKbzUbXrl2Jiori2LFjNbbVWnPgwAGGDx8OwNixY8nIyGiRXK1Sa7k0uIrqN8jIfOKo2VGEEM3gVltQTk4OgwcPrrmh1UplZWWLhMjKyuLw4cOsWbMGm83GjBkziI+PJy8vj759+7rWs9vt5OXl1di2qKiIoKAgV5Gra51qaWlppKWlAZCSkkJ4eHiT8lqt1iZv60lWq5UgfxslQHhUN2POdR/Q0PFyDh9Nzl+XEHT6WzoOv9FncplJcjWO5Go8T2Rzq5j06NGDL7/8skZ/xddff03Pnj3d3tGiRYsoKCiotTw5ORmn00lxcTGLFy8mMzOT1NRUli9f7vZ7uyspKYmkpCTX102dwz48PLzJ23pSeHg4pRcugLJwPj/f7DguP3q8uvei+IudXBw7yXuh8O2fo+Ryn+RqvOZki46ue6Rvt4rJjBkzWLp0Kddeey3l5eW88sor7Nmzh8cee8ztAAsXLqz3tU2bNnH99dejlCI+Ph6LxUJRURF2u53z58+71svLy8Nurzm5U0hICKWlpVRWVuLn51fnOu1KpcPUOd6bQg0Ygt76Ibqi3OeHgRFC1M2tPpN+/fqxbNkyYmJiuOmmm+jatSt/+tOfiI+Pb5EQiYmJHDhg3Al95swZHA4HISEhJCQksH37dioqKsjOziYrK6vWPpVSXH311ezcuROATz75hISEhBbJ1SpVVoJf67qSTfUfYlw4cOyQ2VGEEE3k9qeO3W5nypQpHgkxbtw4VqxYwaOPPorVamXevHkopYiJieGGG27gkUcewWKxcN9997nmoV+yZAkPPPAAdrudO++8kxdeeIE1a9bQp08fxo0b55GcrUJlBbS2y6KvutoYp+vQV6gBQ8xOI4RoArc+dV566SWUUrU3tlrp0qULiYmJ9O7du+khrFYeeuihOl+bOnUqU6dOrbX8ySefdD2PjIxkyZIlTd5/m1JZ2fqauQKCoE8/9OF9ZkcRQjSRW81cQUFBZGRkoLXGbrejtWb37t1YLBa+//57fvvb37J161ZPZxXuqHS0umYuMPpNOHEMXVpsdhQhRBO49amTlZXFk08+Sf/+/V3Ljhw5wtq1a1m4cCFffvklf//732WiLF/QCs9MoKoT/r218M1+uHa42XGEEI3k1pnJ0aNHa9zvARAbG+u6gXDIkCE1rroSJnK0zjMTYq8C/w7oQ1+anUQI0QRuFZPevXvz5ptvUl51d3V5eTlr16519ZNkZ2cTHCxTr/oCXelofR3wgLLa4KrB6AN7zY4ihGgCtz515s2bx5///Gf+8z//k+DgYIqLi4mLi3N1mhcXF3P//fd7NKhwUytt5gJQVw9Df70bnX0G1bXuG6OEEL7JrWLStWtX/vjHP5Kbm0t+fj6dO3eucSt+XFycxwKKRmqlHfAAavAw9BrQB/ZKMRGilWnUfCbh4eHEx8djt9txOp11juArTNaaz0y6RkNEFHr/F2ZHEUI0klt/wubl5fG3v/2NQ4cOUVJSUuO1tWvXeiSYaKLW2gFfRQ0aht62GV1RgaoaRVoI4fvcOjN55ZVXsFqtPPPMMwQEBLB06VISEhKYNWuWp/OJxmrFzVwA6urroPwSHDtodhQhRCO4VUyOHDnCr371K3r37o1Sit69e/OrX/2K9957z9P5RGO14mYuAK4aBFYrev8es5MIIRrBrWJisVhc84V07NiRCxcu0KFDh3rnDREmau1nJgGB0G8Qel87nuBMiFbIrWISHx/P3r3G9f9DhgwhNTWVZ599Vq7i8kWVDp+ZFKup1DXXw9nv0We/NzuKEMJNbhWT+fPnM3DgQADuvvtuBg0aRExMDL/+9a89Gk78OOenm3C+/hL6QtVkWJWVrfKmxcupIYkA6H27TE4ihHCXW8Vk3759rjvc/f39ue2227jrrrs4fPiwR8OJhunvT6JXLUd/9hHOV55Fa93qm7kAVHgkdO+F/kqauoRoLdwqJitXrqxz+csvv9yiYUTjVP/lrib9Ar75mvKv97T+Dvgqasj1cOwgukRGERaiNWiwmJw7d45z587hdDrJzs52fX3u3Dn27duHv79MsWom/c3X0C0GNemX0KkLpW+93ibOTADUNYngdMpVXUK0Eg1+6lw+YdX8+fNrvNapUyduv/32FgnxwQcfsHHjRiwWC8OGDeOuu+5i3759vPHGGzgcDqxWKzNmzGDQoEG1tl23bh2bN28mNDQUgOnTpzNs2LAWyeXzTn2LGpyAstlQY/4/yt95AyyWVt9nAkCffhASBnt3wk9kagMhfF2DnzrVd7f/7ne/47/+6788EmD//v3s3r2bZcuWYbPZKCwsBCAkJIQnnngCu93OyZMnWbx4cb3NapMmTWLy5MkeyeerdPkluFAAEVEAqBHj0O/+DzidbaOZy2JBXTsc/flWdPkllH8HsyMJIRrgVp+JpwoJwKZNm5gyZQq2qqEzwsLCAOjTpw92ux2AmJgYysvLqaio8FiOVud8tvHYpSsAyh6B/zUJxrI20MwFoIaNgEtlcFCGpRfC19X7qfPMM8/UOe/7lZpbaLKysjh8+DBr1qzBZrMxY8YM4uPja6zz+eefExsb6yo4V9q4cSPp6enExsYyc+bMeudWSUtLIy0tDYCUlJQaIx83htVqbfK2LeXSd0cpADrF9cO/Kkv5hMmUf5VBUEgIwSbnu1xTj5ceeRM5rz6L/4EvCEu62WdyeZrkahzJ1XieyFZvMRk3blyL7WTRokUUFBTUWp6cnIzT6aS4uJjFixeTmZlJamoqy3aISbQAACAASURBVJcvdxWyU6dO8cYbb/D000/X+d4TJ05k2rRpgNEst2rVKubOnVvnuklJSSQlJbm+zs3NbdL3Ex4e3uRtW4rzW2OWy0I/f1RVli4Jo6BHHy527kqZyfku16zjNTiRsl2fUn42y5hAy1dyeZDkahzJ1XjNyRYdXff0EPUWk7FjxzZpR3VZuHBhva9t2rSJ66+/HqUU8fHxWCwWioqKCA0N5fz58zz77LPMmzePqKioOrfv1KmT6/n48eNZunRpi+X2acVG3xKhYa5FqkMH/H73okmBPENdNwK9Ywsc/hoGtZMLK4RohdxuXP/4449JT08nLy8Pu93OjTfeyE033dTsAImJiRw4cIBBgwZx5swZHA4HISEhlJSUkJKSwh133EH//v3r3b56si6AXbt2ERMT0+xMrUJJMXQIbPG/1n3OwKEQEIje/RlKiokQPsutYvL222+zdetWbrnlFtfp0bvvvkt+fj5Tp05tVoBx48axYsUKHn30UaxWK/PmzUMpxYcffsjZs2d56623eOuttwD47W9/S1hYGCtXrmTChAnExcWxevVqTpw4gVKKiIgIZs+e3aw8rUZJEQSHmJ3C45TN37iq64sd6DvnoGxyb5MQvsitYrJ582Z+//vfExER4Vo2ZMgQfve73zW7mFit1hr3s1S77bbbuO222+rcZs6cOa7nV97/0l7okmLoWPeFBm2N+slY9I6P4evdMGyE2XGEEHVw69LgS5cuuW4KrBYSEkJ5eblHQgk3lBRBx7Z/ZgJA/2sgtBPOz7eanUQIUQ+3isnQoUP585//zJkzZygvL+f7779n+fLlDBkyxNP5RH1KilFB7eTMxM8PlTga9mWgS2WsLiF8kVvF5N577yUwMJDf/OY3zJgxg8cff5yAgADuvfdeT+cT9WknfSbV1E/GgsOB3rPd7ChCiDo02GfidDqxWCwEBQXx4IMPMnfuXIqKiggJCcFicasOCU8pLYagjman8J7e8dA1Gv35Vhg90ew0QogrNFgR5syZw+rVqzl58qSxssVCWFiYFBKTaUeFMdR8h0Czo3iNUgr1kzFwZD86/7zZcYQQV2iwKsyaNYvs7GyefPJJnnjiCd5//30uXLjgrWyiPpcuGY8d2tfgh2r4GNAavSvd7ChCiCs02MyVmJhIYmIiJSUlbN++nfT0dFavXs2QIUMYM2YMCQkJWNvCcOetzaWLxqN/gLk5vEx1jYY+/dDbN6Mn3urW2HFCCO9wq72qY8eOTJgwgUWLFpGamkpcXByvv/46DzzwgKfzibq4zkzaVzEBUKMnwpmTkHnI7ChCiMs0qvPD4XCQmZnJ0aNHKSwspGfPnp7KJRpSXgaAao/FJHG0MbxK+kazowghLuNWG9Xhw4fZunUrO3fuJDQ0lNGjR3P//ffXuCNeeNElo5i0yzOTgEDU8LHobZvRv5yFaiejAAjh6xosJuvWrePTTz+luLiY4cOH88QTTzQ46KLwkupmrnY6+6Aa/VP0Jx+gd36MGn+L2XGEEPxIMTl27BjJyckkJibi7y8D7PmM8vZ7ZgKgesZC777orR+ix90sHfFC+IAG+0yeeuopRo4cKYXEx+h23MxVTd34U8g6JR3xQvgIufuwNWqn95lcztURv1U64oXwBVJMWqPqZq52dp/J5VRAIOqGm9C7P0UX5JkdR4h2T4pJa1TdzNVOO+CrqaTJUFmJ/vh/zY4iRLvnE7evf/DBB2zcuBGLxcKwYcO46667yM7OZsGCBa7J6/v27VvnLIrFxcWkpqaSk5NDREQECxYsIDi4jV8uWn4JbP6odj5GmuoaDdcON67s+o9pqID2M1aZEL7G9GKyf/9+du/ezbJly7DZbBQWFrpei4qKYtmyZQ1uv2HDBgYPHsytt97Khg0b2LBhA3fddZenY5vL4YC2Pve7mywTf47zix3obZtR4282O44Q7Zbpf9pu2rSJKVOmYLMZH45hYWGN2j4jI4MxY8YAMGbMGDIyMlo84+X0F9spfvNVj+7jR1VUgIyJBoCK6w9x/dFp76CdlWbHEaLdMv0TKSsri8OHD7NmzRpsNhszZswgPj4egOzsbB5//HECAwNJTk5mwIABtbYvLCykc+fOAHTq1KnGmc2V0tLSSEtLAyAlJYXw8PBG573w3TFK0zfRdfr9jd62pRRa/Sj371Arv9VqbdL35GmezlV220wK/+9ThBw7QMCIcT6Tq6kkV+NIrsbzRDavFJNFixZRUFBQa3lycjJOp5Pi4mIWL15MZmYmqampLF++nM6dO7NixQpCQkI4fvw4y5Yt47nnniMoKKje/SilGryBLSkpiaSkJNfXubm5jf5enJVO9KWyJm3bUpzFxWiLpVaG8PBwU3PVx9O5dNwAiIii8K1VFPUd7PZNjO31eDWV5GocX80FzctW3Y99Ja8Uk4ULF9b72qZNm7j++utRShEfH4/FYqGoqIjQ0FBX01dsbCyRkZFkZWURFxdXY/uwsDDy8/Pp3Lkz+fn5hIaGevR7wd8fKsrRTqdpHeDaUSF9JpdRFj/UT6eiV6+AA1/AoOvMjiREu2N6n0liYiIHDhwA4MyZMzgcDkJCQrhw4QJOpxOAc+fOkZWVRWRkZK3tExIS2Lp1KwBbt24lMTHRs4GrL8etKPfsfhriqACbFJPLqZHjoUtXnBveQGttdhwh2h3T+0zGjRvHihUrePTRR7FarcybNw+lFAcPHmTdunX4+flhsViYNWuW65LflStXMmHCBOLi4rj11ltJTU1ly5YtrkuDPcpWVUzKy80bzsRRAX6m/+h8irLaUDf/Ev36S/DV5zB0uNmRhGhXTP9EslqtPPTQQ7WWDx8+nOHD6/5AmDNnjut5SEgIzzzzjMfy1VI9Tln5Je/t80oOh5yZ1EHdMA79wVs43/kfLNdc3+7vwxHCm+R/W2O5mrnMLCbSZ1IX5eeHuiUZTp+AL7abHUeIdkWKSSOp6mJi6pmJFJP6qOtvhG4xON99U+47EcKLpJg0li8UkwopJvVRFj8sU+6ErFPo9E1mxxGi3ZBi0li26j4Tc6/mUnIHfP2G3QD9BqHfWY0uKTY7jRDtghSTxurgA2cmlTI2V0OUUlimz4KSEvS7/2N2HCHaBSkmjVV1ZqLNvM9Emrl+lOrRBzXmp+hP3kd//53ZcYRo86SYNJYv9JnIpcFuUVPuhIAgnGtflRsZhfAwKSaN5RP3mciowe5QwaGoKXfAoa/QO7aYHUeINk2KSWP5X3YHvAm01lV3wMuZiTvU2P+AvgPRb76Czjlrdhwh2iwpJo1lM7mZy+kErcEmZybuUBY/LPcuAKVw/v+pcu+JEB4ixaSRlJ+f0cRk1h3wjgrjUTrg3abCI1F3PADHDqE/+JfZcYRok6SYNIHqEAhlZebsXIpJk6ifjEUljkb/+030iaNmxxGizZFi0gQqKAjKSs3ZucNhPEoHfKMopVB3/gpCO+N89Xn0JZP+GBCijZJi0gSWoGD0xYvm7LyyqpjIEPSNpjoGY7nn13Due/Rbr5kdR4g2RYpJE6jAjnCxxJydV1Z1IPv5mbP/Vk4NGIKaeCv6kw+4tHub2XGEaDOkmDSB6tgRysw6M6kqJhYpJk2lbp0B3XtxYfmf0PnnzY4jRJvgE20lH3zwARs3bsRisTBs2DDuuusuPv30U959913XOidPnmTp0qX07t27xrbr1q1j8+bNrrnfp0+fzrBhwzya1zgzMWmIjupLW6WZq8mUzYZl1mPolMfRL/4ey2NLUB2DzY4lRKtm+ifS/v372b17N8uWLcNms1FYWAjA6NGjGT16NGAUkmXLltUqJNUmTZrE5MmTvRUZS1Cwec1cVcVE+clJZXOo7j0JezKF/EWP4Fy+CMvDf0BVD+IphGg00z+RNm3axJQpU7BVjTUVFhZWa53PPvuMESNGeDtavVSQNHO1Bf7XJGC5/1HIPIzz5aXo6ivlhBCNZvqZSVZWFocPH2bNmjXYbDZmzJhBfHx8jXV27NjBY489Vu97bNy4kfT0dGJjY5k5cybBwXU3WaSlpZGWlgZASkoK4eHhTcp8MTgEKsrpEhaG8vKAixV558gDQjvb6XBFfqvV2uTvyZN8OVfET6dQqp0UvbwM/7X/Tej8p02fO96Xj5fkcp+v5gLPZPNKMVm0aBEFBQW1licnJ+N0OikuLmbx4sVkZmaSmprK8uXLUUoBcPToUfz9/enZs2ed7z1x4kSmTZsGwNq1a1m1ahVz586tc92kpCSSkpJcX+fm5jbp+wkKCDS2P30KFRLapPdoKp1ndBhfKClGXZE/PDy8yd+TJ/l8roTRqLPfU/bO/3DJPwDL7ff4Ri4fI7kax1dzQfOyRUdH17ncK8Vk4cKF9b62adMmrr/+epRSxMfHY7FYKCoqcnWob9u2jZEjR9a7fadOnVzPx48fz9KlS1sueD1UYEfjycUS8HIxkWYuz1CTfgkXCtGb1uMMDcPy06lmRxKiVTG9zyQxMZEDBw4AcObMGRwOByEhIQA4nU527NjRYDHJz893Pd+1axcxMTGeDQxYqguIGVPCVsrVXJ6glEIlzzKGXHnr7zj/d53MgSJEI5j+iTRu3DhWrFjBo48+itVqZd68ea4mrkOHDhEeHk5kZGSNbVauXMmECROIi4tj9erVnDhxAqUUERERzJ492+OZLWGdjSfFhR7fVy3Vlwab3K7fFimLBe59GJQFvWE1nP0eZj7o9X4xIVoj04uJ1WrloYceqvO1q6++msWLF9daPmfOHNfz+fPneyxbfVSo0bSmLxSivL3zSqfxKHfAe4Sy2uD+R6BbD/Q7b6BzsrDMfcr1MxdC1E3+vG0CU89MZGwuj1NKYbn5l6jZj8PJ4zj/9BuZR16IHyHFpAlUQCDY/OGCmc1ccmbiaZbEUVgeWwIOB86Ux9Ff7zY7khA+S4pJEyilICQMimpf7uxp2tUBLz86b1B9+mJ56lmIiML50h9x/ut1tFmzbArhw+QTqalCwtBFZjRzydVc3qbs4VieWIoaOR794b9w/uFh9NGDZscSwqdIMWmqTnYoyPP+fqWZyxSqQwCW/5yPZcEfwFGBc9mTOP/nZbRZw+oI4WOkmDSRskfA+Wzv79gpzVxmUgOHYvn9S6hxN6M/eR/n7+ejD+w1O5YQppNPpKYK7woXS9GlXr5xUZq5TKcCArEkz8Ly+BKw2XC+8Dsq//wH9Lcyt7xov6SYNJHqUnUjZa6Xz05kOBWfoeIHYnnmRdStd8Hxb3D+6VEqX/wv9PFvzI4mhNdJMWmq8K7GY+457+5Xmrl8irL5Y5n0Cywp/436+Qw4cQTnkseofPH36MzDZscTwmukraSpIo2RM3XWKRQ3eG+/rjvg5UfnS1RAEOpnt6PHTUJ//IExYGTK49CnH2pkEipxtDEPjhBtlHwiNZEKCIKIKDj1rXd3XH0HvDRz+SQVEIT6j9vQN/0M/dlHxr/VK9DrXkUNG4n6yRjoP9gYtkWINkSKSXP06I0+fcK7+5SBHlsFFRCISpqMHn8LnDiG3vYRelc6eufHENgRNSQRNWwEDByK6hBgdlwhmk2KSTOo3n3Re3eiLxR4byDASif4+blGVha+TSkFffqi+vRF//J+OPgVeu929Je70Ds/AasV4gagBgxBDRwKveLMjixEk0gxaQY1YCh6/T/Qh74ymi+8odIhTVytlLL5w5BE1JBEY1icI/vRB75AH/oKvWG1Mex9QCD5/a7G2SMWFXsVxPZDhYSZHV2IHyXFpDl6xRrDquz+DLxVTJyVUkzaAOXnBwOGoAYMAUAXFaIPfQVHD+I8mYn+8C20s+piizA79OiF6tEbuvdGde1m9NeFhMkZqvAZUkyaQVn8UKMmoD98G51zFhUR5fmdOp0yl0kbpELCUNffCNffSJfwcHK+P230tXx3FE6fQJ8+gd78b3A4cM3/2CHQKCpdo4zfvYhuxmMnO4R1hqBgKTbCa3yimKSmpnLmzBkASktLCQoKYtmyZQCsX7+eLVu2YLFYuOeeexg6dGit7bOzs3nhhRcoKioiNjaW+fPnY7V651tTY3+G3vIezr//GcuCP6A8vd/KSikm7YDqEABXDUJdNci1TDsckJMF2WfROVmQcxadcxbOnETvy6hZaMC4fDysE4R0grDOqLDOENoJgkOMQhPUEQKDoWPVY1BHCAiUAiSaxCeKyYIFC1zPV61aRVBQEACnT59m+/btPP/88+Tn57No0SJefPFFLFdcybR69WomTZrEyJEjeeWVV9iyZQsTJ070SnZlD0fd8QD6tRdxPvsUlv+YBrH9UdXzxLc06TNpt5TVCt1ioFtMrRk+tbMS8vMg9yy6IM+YHqGwAArz0RfyIT8X/d0xYw4ebTSf1TnDvbKAfwfo0MF4rP7XIYD84GAqUSj/gMteDzAerVbjjxy/mo/Kaq21rN5Hi8XYv8UCSlU9Vz88r2uZXNXoM3yimFTTWrNjxw6eeeYZADIyMhgxYgQ2m42uXbsSFRXFsWPH6NevX41tDhw4wK9//WsAxo4dyz//+U+vFRMAy4jxOP2s6LWv4lz+R2OhzR8Cg4zH6l/46v8IVP1naIqCPAgMbJHcou1QFj/oEgFdIhqcSlo7K+FiKZSWVP0rhtISY4y56mXll6r+lRlzt1T9cxbmQ0lJjWWUXwJdZ1ky9tfy32ot56DhwoPCOChVR6b6/57rQF2x3LX48uU/sk0dy3MsFpxO5xXvU8f7uzv5t7ufGW6sVj7/aejaw733c5NPFZNDhw4RFhZGt27dAMjLy6Nv376u1+12O3l5NYd9LyoqIigoCL+qpp+61qmWlpZGWloaACkpKYSHhzcpp9Vqrb3tpNvQE2+h4tA+Kr49irMwD11SbPzH09r1T2snOKu+bmJB8b96KEF1ZK8zlw+QXI3jy7kcDkeNZVprKC9HOyqg0mE0xTkqjKvVHA50peOH5a7X61mGNoqdUxtnT1qD02nsw1n1tXa6nmunE7QTCwpnpeOHdV3/xy5br7qsaVfwmo/1vY6uta6rQP7IeyjFDxdR1Fr3yv00TLu5nrvl2xoc2uK/Y14rJosWLaKgoPbMhMnJySQmJgKwbds2Ro4c6bEMSUlJJCUlub7Ozc1t0vuEh4fXv210b+OfBzmA0jr232AuE0muxmn1uSw245+XbvJv9cfLBJZmZIuOjq5zudeKycKFCxt8vbKykl27dpGSkuJaZrfbOX/+vOvrvLw87HZ7je1CQkIoLS2lsrISPz+/OtcRQgjhWT7Te/X1118THR1Nly5dXMsSEhLYvn07FRUVZGdnk5WVRXx8fI3tlFJcffXV7Ny5E4BPPvmEhIQEr2YXQoj2zmeKSV1NXDExMdxwww088sgjLF68mPvuu891JdeSJUtcfSN33nkn7733HvPnz6e4uJhx48Z5Pb8QQrRnSrvfs9PmVN/b0li+2hYquRpHcjWO5GocX80FzctWX5+Jz5yZCCGEaL2kmAghhGg2KSZCCCGaTYqJEEKIZmvXHfBCCCFahpyZNMH/+T//x+wIdZJcjSO5GkdyNY6v5gLPZJNiIoQQotmkmAghhGg2v9///ve/NztEaxQbG2t2hDpJrsaRXI0juRrHV3NBy2eTDnghhBDNJs1cQgghmk2KiRBCiGbzqZkWW4Mvv/yS1157DafTyfjx47n11ltNyZGbm8tf/vIXCgoKUEqRlJTEz372M9atW8fmzZsJDTXmoJ8+fTrDhg3zarZ58+YREBCAxWLBz8+PlJQUiouLSU1NJScnh4iICBYsWEBwcLDXMp05c4bU1FTX19nZ2fziF7+gpKTE68drxYoVfPHFF4SFhfHcc88B1Ht8tNa89tpr7N27lw4dOjB37lyPtcPXlesf//gHe/bswWq1EhkZydy5c+nYsSPZ2dksWLDANehf3759mT17ttdyNfR7vn79erZs2YLFYuGee+5h6NChXsuVmprqGkC2tLSUoKAgli1b5tXjVd9ng8d/x7RwW2VlpX7wwQf12bNndUVFhf7Nb36jT506ZUqWvLw8nZmZqbXWurS0VD/00EP61KlTeu3atfqdd94xJVO1uXPn6sLCwhrL/vGPf+j169drrbVev369/sc//mFGNK218XO8//77dXZ2tinH68CBAzozM1M/8sgjrmX1HZ89e/boxYsXa6fTqb/55hv95JNPejXXl19+qR0Ohytjda5z587VWM+T6spV38/t1KlT+je/+Y0uLy/X586d0w8++KCurKz0Wq7Lvf766/qf//yn1tq7x6u+zwZP/45JM1cjHDt2jKioKCIjI7FarYwYMYKMjAxTsnTu3Nn110NgYCDdu3d3ze/iizIyMhgzZgwAY8aMMe24gTERW1RUFBEREabsf+DAgbXOyuo7Prt37+bGG29EKUW/fv0oKSkhPz/fa7mGDBmCn58fAP369TPld6yuXPXJyMhgxIgR2Gw2unbtSlRUFMeOHfN6Lq01O3bs8Og05PWp77PB079j0szVCHl5eTVmguzSpQtHjx41MZEhOzubb7/9lvj4eA4fPszGjRtJT08nNjaWmTNnerU5qdrixYsBmDBhAklJSRQWFtK5c2cAOnXqRGFhodczVbtyIjZfOF71HZ+8vDzCw8Nd63Xp0oW8vDzXut60ZcsWRowY4fo6Ozubxx9/nMDAQJKTkxkwYIBX89T1c8vLy6Nv376udex2uykF8NChQ4SFhdGtWzfXMjOO1+WfDZ7+HZNi0sqVlZXx3HPPcffddxMUFMTEiROZNm0aAGvXrmXVqlXMnTvXq5kWLVqE3W6nsLCQP/7xj7Um01FKoZTyaqZqDoeDPXv2cMcddwD4xPG6kpnHpz5vv/02fn5+jB49GjD++l2xYgUhISEcP36cZcuW8dxzzxEUFOSVPL74c7vclX+wmHG8rvxsuJwnfsekmasR7HY758+fd319/vx57Ha7aXkcDgfPPfcco0eP5ic/+Qlg/MVhsViwWCyMHz+ezMxMr+eqPiZhYWEkJiZy7NgxwsLCXKfO+fn5ro5Tb9u7dy99+vShU6dOgG8cL6De42O322vMiGfG79wnn3zCnj17eOihh1wfQDabjZCQEMC4+S0yMpKsrCyvZarv53bl/9G8vDyvH6/Kykp27dpV4yzO28errs8GT/+OSTFphLi4OLKyssjOzsbhcLB9+3YSEhJMyaK1ZuXKlXTv3p2bb77Ztfzyts5du3YRExPj1VxlZWVcvHjR9Xzfvn307NmThIQEtm7dCsDWrVtJTEz0aq5qV/7FaPbxqlbf8UlISCA9PR2tNUeOHCEoKMirTVxffvkl77zzDk888QQdOnRwLb9w4QJOpxOAc+fOkZWVRWRkpNdy1fdzS0hIYPv27VRUVJCdnU1WVhbx8fFeywVGn1x0dHSNJnFvHq/6Phs8/Tsmd8A30hdffMHrr7+O0+nkpptuYurUqabkOHz4MM888ww9e/Z0/bU4ffp0tm3bxokTJ1BKERERwezZs7364XPu3DmeffZZwPgLbdSoUUydOpWioiJSU1PJzc015dJgMIrb3LlzWb58ueu0/6WXXvL68XrhhRc4ePAgRUVFhIWF8Ytf/ILExMQ6j4/Wmr/97W989dVX+Pv7M3fuXOLi4ryWa/369TgcDtfPqvqS1p07d7Ju3Tr8/PywWCzcfvvtHvvDqq5cBw4cqPfn9vbbb/Pxxx9jsVi4++67ufbaa72Wa9y4cfzlL3+hb9++TJw40bWuN49XfZ8Nffv29ejvmBQTIYQQzSbNXEIIIZpNiokQQohmk2IihBCi2aSYCCGEaDYpJkIIIZpNiokQTfT222+zcuVKr+1v4cKFfPvtty36nrt3764xmrIQTSXDqQhRjxkzZriel5eXY7VasViMv79mz57t1XuMdu/eTUBAAH369GnR901ISODNN9/ku+++o1evXi363qJ9kftMhHDDvHnzeOCBB7jmmmtM2f+SJUsYNWqUa2yslvT222+Tn5/Pfffd1+LvLdoPOTMRoonWrVvH2bNneeihh8jOzubBBx/kV7/6FevWraOsrIzp06cTGxvLypUryc3NZfTo0TU+sLds2cK///1vCgoKiI+PZ/bs2XUOi+9wONi/f3+NyZTWrVvH6dOnsVqt7N69m4iICB599FE+//xz/vd//xebzcacOXMYMmQIYIyv9dZbb3HhwgVCQkJITk52FaaBAwfy0ksvSTERzSJ9JkK0oKNHj/Liiy/y8MMP8/rrr/P222+zcOFCnn/+eXbs2MHBgwcBY96N9evX8+ijj/Lqq6/Sv39/XnzxxTrfMysrC4vFUmOsJ4A9e/Zw44038tprr9GnTx8WL17sGpfptttu45VXXgGMYWRee+01nnrqKVatWsUf//hHevfu7XqfHj16kJOTQ2lpqWcOimgXpJgI0YKmTZuGv78/Q4YMoUOHDowaNYqwsDDsdjv9+/d3daB/9NFH/PznP6dHjx74+fnx85//nBMnTpCTk1PrPUtKSggICKi1vH///gwdOhQ/Pz+GDx/OhQsXuPXWW7FarYwcOZKcnBxKSkoAY8jxkydPUl5eTufOnWsMaFn93lJMRHNIM5cQLSgsLMz13N/fv9bXZWVlAOTk5PDaa6+xatUq1+taa/Ly8mo1dQUHB7u2a2hfoaGhrgsE/P39AeOspEuXLjz88MP8+9//ZuXKlVx11VXMnDmT7t27u9YBvDYXiWibpJgIYYLw8HCmTp3qVod6VFSUq9A0dW6OoUOHMnToUMrLy1mzZg0vv/wyf/jDHwA4ffo0ERERUkxEs0gzlxAmmDBhAhs2bODUqVOA0cS0Y8eOOte1Wq0MHjzY1d/SWAUFBWRkZFBWVobVaiUgIKDGLHsHDx702DDtov2QMxMhTHD99ddTVlbGCy+8QG5uLkFBQQwePJgbbrihzvUnTJjAhx9+yKhRoxq9L6017733HsuXL0cpePW3xgAAAHNJREFURe/evZk1a5br9W3btjF//vwmfy9CgNxnIkSrsXDhQu69994WvXFx9+7dpKen88gjj7TYe4r2SYqJEEKIZpM+EyGEEM0mxUQIIUSzSTERQgjRbFJMhBBCNJsUEyGEEM0mxUQIIUSzSTERQgjRbP8PEcaoAXDvuuMAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "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=nrn_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 (mV)') \n", "plt.show() " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/expsyn/ExpSyn_arbor.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimising synaptic parameters in Arbor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how the parameters of an Arbor point process (in this case a synapse), can be optimised using BluePyOpt." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First some initial setup:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "%reload_ext autoreload\n", "%autoreload\n", "\n", "import os\n", "\n", "import bluepyopt as bpopt\n", "import bluepyopt.ephys as ephys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need a Simulator (Arbor), Morphology (one compartment) and two Location objects (the 'somatic' sectionlist and the center of the soma)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Arbor simulator\n", "arb_sim = ephys.simulators.ArbSimulator()\n", "\n", "# Single compartment\n", "morph = ephys.morphologies.NrnFileMorphology('simple.swc')\n", "\n", "# Object that points to sectionlist somatic\n", "somatic_loc = ephys.locations.NrnSeclistLocation('somatic',seclist_name='somatic')\n", "\n", "# Object that points to the center of the soma\n", "somacenter_loc = ephys.locations.ArbLocsetLocation(\n", " name='somacenter',\n", " locset='(location 0 0.5)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also add a leak channel:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "pas_mech = ephys.mechanisms.NrnMODMechanism( \n", " name='pas', \n", " suffix='pas', \n", " locations=[somatic_loc]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Add ExpSyn synapse pointprocess at the center of the soma\n", "expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism( \n", " name='expsyn', \n", " suffix='ExpSyn', \n", " locations=[somacenter_loc]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have defined a point process, we can create a Location object that points to it" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "expsyn_loc = ephys.locations.NrnPointProcessLocation( \n", " 'expsyn_loc', \n", " pprocess_mech=expsyn_mech) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this location, we can specify the parameters of the synapse. Let's fit the decay time constant:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "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])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's put all these concepts together in a cell model:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cell = ephys.models.CellModel( \n", " name='simple_cell', \n", " morph=morph, \n", " mechs=[pas_mech, expsyn_mech], \n", " params=[expsyn_tau_param]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "We first create a stimulus that injects the presynaptic events:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "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=[expsyn_loc])\n", "\n", "stim_end = stim_start + interval * number\n", "\n", "rec = ephys.recordings.CompRecording(\n", " name='soma.v', \n", " location=somacenter_loc,\n", " variable='v')\n", "\n", "protocol = ephys.protocols.ArbSweepProtocol('netstim_protocol', [netstim], [rec])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we define an eFELFeature that will target the maximum voltage and we put everything in an evaluator" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "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", "\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=arb_sim) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's try out the evaluator with a decay time constant of 10.0" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'maximum_voltage': 29.690040424145465}\n" ] } ], "source": [ "default_param_values = {'expsyn_tau': 10.0} \n", "\n", "print(cell_evaluator.evaluate_with_dicts(default_param_values)) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can run the optimisation:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "optimisation = bpopt.optimisations.DEAPOptimisation( \n", " evaluator=cell_evaluator, \n", " offspring_size=10) \n", "\n", "_, hall_of_fame, _, _ = optimisation.run(max_ngen=5) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we can plot the best individual:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [14.03202650928813]\n", "Fitness values: (1.1134864832433067,)\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "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=arb_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 (mV)') \n", "plt.show() " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "vscode": { "interpreter": { "hash": "581988038cf9ce8838e7faf3da7c29f4ff88d898cd43cb17e0086e389d8deda2" } } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/expsyn/expsyn.py ================================================ """Expsyn synapse parameter fitting""" # pylint: disable=R0914 import os import argparse import bluepyopt as bpopt import bluepyopt.ephys as ephys def create_model(sim, do_replace_axon, return_locations=False): """Create model and optionally return locations dict""" if sim not in ['nrn', 'arb']: raise ValueError("Invalid simulator %s." % sim) locations = dict() morph = ephys.morphologies.NrnFileMorphology( os.path.join( os.path.dirname(os.path.abspath(__file__)), 'simple.swc'), do_replace_axon=do_replace_axon) somatic_loc = ephys.locations.NrnSeclistLocation( 'somatic', seclist_name='somatic') locations['somatic_loc'] = somatic_loc if sim == 'nrn': somacenter_loc = ephys.locations.NrnSeclistCompLocation( name='somacenter', seclist_name='somatic', sec_index=0, comp_x=0.5) else: somacenter_loc = ephys.locations.ArbLocsetLocation( name='somacenter', locset='(location 0 0.5)') locations['somacenter_loc'] = somacenter_loc pas_mech = ephys.mechanisms.NrnMODMechanism( name='pas', suffix='pas', locations=[somatic_loc]) expsyn_mech = ephys.mechanisms.NrnMODPointProcessMechanism( name='expsyn', suffix='ExpSyn', locations=[somacenter_loc]) expsyn_loc = ephys.locations.NrnPointProcessLocation( 'expsyn_loc', pprocess_mech=expsyn_mech) locations['expsyn_loc'] = expsyn_loc expsyn_tau_param = ephys.parameters.NrnPointProcessParameter( name='expsyn_tau', param_name='tau', value=2, bounds=[0, 50], locations=[expsyn_loc]) cm_param = ephys.parameters.NrnSectionParameter( name='cm', param_name='cm', value=1.0, locations=[somatic_loc], frozen=True) cell = ephys.models.CellModel( name='simple_cell', morph=morph, mechs=[pas_mech, expsyn_mech], params=[cm_param, expsyn_tau_param]) if return_locations is True: return cell, locations else: return cell def main(args): """Main""" if args.sim == 'nrn': sim = ephys.simulators.NrnSimulator() else: sim = ephys.simulators.ArbSimulator() cell, locations = create_model(sim=args.sim, do_replace_axon=False, return_locations=True) stim_start = 20 number = 5 interval = 5 netstim = ephys.stimuli.NrnNetStimStimulus( total_duration=200, number=5, interval=5, start=stim_start, weight=5e-4, locations=[locations['expsyn_loc']]) stim_end = stim_start + interval * number rec = ephys.recordings.CompRecording( name='soma.v', location=locations['somacenter_loc'], variable='v') if args.sim == 'nrn': protocol = ephys.protocols.SweepProtocol( 'netstim_protocol', [netstim], [rec]) else: protocol = ephys.protocols.ArbSweepProtocol( 'netstim_protocol', [netstim], [rec]) max_volt_feature = ephys.efeatures.eFELFeature( 'maximum_voltage', efel_feature_name='maximum_voltage', recording_names={'': 'soma.v'}, stim_start=stim_start, stim_end=stim_end, exp_mean=-50, exp_std=.1) max_volt_objective = ephys.objectives.SingletonObjective( max_volt_feature.name, max_volt_feature) score_calc = ephys.objectivescalculators.ObjectivesCalculator( [max_volt_objective]) cell_evaluator = ephys.evaluators.CellEvaluator( cell_model=cell, param_names=['expsyn_tau'], fitness_protocols={protocol.name: protocol}, fitness_calculator=score_calc, sim=sim) default_param_values = {'expsyn_tau': 10.0} print(cell_evaluator.evaluate_with_dicts(default_param_values)) optimisation = bpopt.optimisations.DEAPOptimisation( evaluator=cell_evaluator, offspring_size=10) _, hall_of_fame, _, _ = optimisation.run(max_ngen=5) best_ind = hall_of_fame[0] print('Best individual: ', best_ind) print('Fitness values: ', best_ind.fitness.values) best_ind_dict = cell_evaluator.param_dict(best_ind) responses = protocol.run( cell_model=cell, param_values=best_ind_dict, sim=sim) time = responses['soma.v']['time'] voltage = responses['soma.v']['voltage'] import matplotlib.pyplot as plt plt.style.use('ggplot') plt.plot(time, voltage) plt.xlabel('Time (ms)') plt.ylabel('Voltage (ms)') if args.output is not None: if not os.path.exists(args.output): output_dir = os.path.dirname(args.output) if len(output_dir) > 0: os.makedirs(output_dir, exist_ok=True) plt.savefig(args.output) plt.show() if __name__ == '__main__': parser = argparse.ArgumentParser(description='expsyn') parser.add_argument('--sim', default='nrn', choices=['nrn', 'arb'], help='Simulator (choose either nrn or arb)') parser.add_argument('-o', '--output', help='Path to store voltage trace plot to') args = parser.parse_args() if args.sim not in ['nrn', 'arb']: raise argparse.ArgumentError('Simulator must be either nrn or arb') main(args) ================================================ FILE: examples/expsyn/generate_acc.py ================================================ #!/usr/bin/env python '''Example for generating a mixed JSON/ACC Arbor cable cell description (with optional axon-replacement) $ python generate_acc.py --output-dir test_acc/ --replace-axon Will save 'simple_cell.json', 'simple_cell_label_dict.acc' and 'simple_cell_decor.acc' into the folder 'test_acc' that can be loaded in Arbor with: 'cell_json, morpho, decor, labels = \ ephys.create_acc.read_acc("test_acc/simple_cell_cell.json")' An Arbor cable cell can then be created with 'cell = arbor.cable_cell(morphology=morpho, decor=decor, labels=labels)' The resulting cable cell can be output to ACC for visual inspection and e.g. validating/deriving custom Arbor locset/region/iexpr expressions in the Arbor GUI (File > Cable cell > Load) using 'arbor.write_component(cell, "simple_cell_cable_cell.acc")' ''' import argparse from bluepyopt import ephys import expsyn def main(): '''main''' parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, description=__doc__) parser.add_argument('-o', '--output-dir', dest='output_dir', help='Output directory for JSON/ACC files') parser.add_argument('-ra', '--replace-axon', action='store_true', help='Replace axon with Neuron-dependent policy') args = parser.parse_args() cell = expsyn.create_model(sim='arb', do_replace_axon=args.replace_axon) if args.replace_axon: nrn_sim = ephys.simulators.NrnSimulator() cell.instantiate_morphology_3d(nrn_sim) param_values = {'expsyn_tau': 10.0} # Add modcc-compiled external mechanisms catalogues here # ext_catalogues = {'cat-name': 'path/to/nmodl-dir', ...} if args.output_dir is not None: cell.write_acc(args.output_dir, param_values, # ext_catalogues=ext_catalogues, create_mod_morph=True) else: output = cell.create_acc( param_values, template='acc/*_template.jinja2', # ext_catalogues=ext_catalogues, create_mod_morph=True) for el, val in output.items(): print("%s:\n%s\n" % (el, val)) if __name__ == '__main__': main() ================================================ FILE: examples/expsyn/simple.swc ================================================ # Dummy granule cell morphology 1 1 -5.0 0.0 0.0 5.0 -1 2 1 0.0 0.0 0.0 5.0 1 3 1 5.0 0.0 0.0 5.0 2 ================================================ FILE: examples/graupnerbrunelstdp/checkpoints/.gitignore ================================================ /*.pkl /checkpoint.* ================================================ FILE: examples/graupnerbrunelstdp/figures/.gitignore ================================================ /*.eps ================================================ FILE: examples/graupnerbrunelstdp/gbevaluator.py ================================================ """Main Graupner-Brunel STDP example script""" import numpy import bluepyopt as bpop import stdputil def gbParam(params): """Create the parameter set for Graupner-Brunel model from an *individual*. :param individual: iterable :rtype : dict """ gbparam = dict( theta_d=1.0, theta_p=1.3, rho_star=0.5, beta=0.75) # Fixed params for param_name, param_value in params: gbparam[param_name] = param_value return gbparam class GraupnerBrunelEvaluator(bpop.evaluators.Evaluator): """Graupner-Brunel Evaluator""" def __init__(self): """Constructor""" super(GraupnerBrunelEvaluator, self).__init__() # Graupner-Brunel model parameters and boundaries, # from (Graupner and Brunel, 2012) self.graup_params = [('tau_ca', 1e-3, 100e-3), ('C_pre', 0.1, 20.0), ('C_post', 0.1, 50.0), ('gamma_d', 5.0, 5000.0), ('gamma_p', 5.0, 2500.0), ('sigma', 0.35, 70.7), ('tau', 2.5, 2500.0), ('D', 0.0, 50e-3), ('b', 1.0, 100.0)] self.params = [bpop.parameters.Parameter (param_name, bounds=(min_bound, max_bound)) for param_name, min_bound, max_bound in self. graup_params] self.param_names = [param.name for param in self.params] self.protocols, self.sg, self.stdev, self.stderr = \ stdputil.load_neviansakmann() self.objectives = [bpop.objectives.Objective(protocol.prot_id) for protocol in self.protocols] def get_param_dict(self, param_values): """Build dictionary of parameters for the Graupner-Brunel model from an ordered list of values (i.e. an individual). :param param_values: iterable Parameters list """ return gbParam(zip(self.param_names, param_values)) def compute_synaptic_gain_with_lists(self, param_values): """Compute synaptic gain for all protocols. :param param_values: iterable Parameters list """ param_dict = self.get_param_dict(param_values) syn_gain = [stdputil.protocol_outcome(protocol, param_dict) for protocol in self.protocols] return syn_gain def evaluate_with_lists(self, param_values): """Evaluate individual :param param_values: iterable Parameters list """ param_dict = self.get_param_dict(param_values) err = [] for protocol, sg, stderr in \ zip(self.protocols, self.sg, self.stderr): res = stdputil.protocol_outcome(protocol, param_dict) err.append(numpy.abs(sg - res) / stderr) return err ================================================ FILE: examples/graupnerbrunelstdp/graupnerbrunelstdp.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import bluepyopt as bpop\n", "import gbevaluator\n", "import stdputil\n", "import numpy as np\n", "import run_fit" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "evaluator = gbevaluator.GraupnerBrunelEvaluator()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "opt = bpop.optimisations.DEAPOptimisation(evaluator, offspring_size=100, \n", " eta=20, mutpb=0.3, cxpb=0.7)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_, hof, log, hst = opt.run(max_ngen=200) " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'C_post': 0.48314077418341633,\n", " 'C_pre': 0.7897761163017487,\n", " 'D': 0.043495200524733116,\n", " 'b': 21.765640344877976,\n", " 'beta': 0.75,\n", " 'gamma_d': 1178.419751433852,\n", " 'gamma_p': 2302.4928211408605,\n", " 'rho_star': 0.5,\n", " 'sigma': 14.567793393352874,\n", " 'tau': 2499.1107653632594,\n", " 'tau_ca': 0.06578285231852728,\n", " 'theta_d': 1.0,\n", " 'theta_p': 1.3}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "best_ind_dict = evaluator.get_param_dict(hof[0])\n", "best_ind_dict" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "good_solutions = [evaluator.get_param_dict(ind) for ind in hst.genealogy_history.itervalues() \n", " if np.all(np.array(ind.fitness.values) < 1)]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "protocols, sg, _, stderr = stdputil.load_neviansakmann()\n", "dt = np.array([float(p.prot_id[:3]) for p in protocols])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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T5IoF72+YdryjsRRv9A1yfrmPcwK+Uc8bhsHSqlK8Dht7uyMksnlWLyjHaZv6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\n6U8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JW6dQDyg3WvNwVsRDgm4hhBBiiXhjvETGMTlbqKIRZYRbcSOllOLP/+AA/atSvPtn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\nIAkASh1W/mJtJZcCEf44werBU2s55uXv3n0bXW1vwtBr6S/UdmBfE0cP7eK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SRKTEBTwu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5qqOBNJ8PypAWwbbm6s5KqactLpNP/Y0U1sbAa3AdzSXEXAbfHciT4GEmn8TgeJdI6RbI7m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\noRBf+tKX6O7upqqqis2bN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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run_fit.plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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mYLo//MF0RSZglV2oBW8Hm82MdfDuu6anxJNPSi1bOrdnZZkpya6/3vQaefZZ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XAAAg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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run_fit.plot_calcium_transients(protocols, best_ind_dict)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+vNuxdLoBAAAAoOoZPHiwBg8ebLdt//796tChgynz+/2a7sTERH3++ed6/vnn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\nNmzYoDfffFPVq1e3uyeBM96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+Q+j2kbtg7esSczrdAAAAAFC5ELpd8LXTLRG6AQAAAABX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rqB1cVddaCa4lJUTIaXmyCpvSbDy0snuq+++mqMjo7i05/+NIaHhzMPBc0CbyLBdd22DiOt6PZX\ndv/X//pfCxWD84iqPB5E1un2h5cfd9xxrf/7C88Fne41a9agv7+/rSCch39ptDhHPm/iHlgiToif\nLASFSiEboFg53UA+a3Xb4nSnLcaTRJ6i23anu9FoJP6WrH5D8F5pNpuhNSNM5HQDyYP5rJ3u4Gsi\nrmLW4eVJmBTdMs+rLAup6XC6ve8Gw/fDnnNRE2JFCC9XEd0q100nim4bhFZaZCao0nxfF6KRRDoo\nvej+v//3/+KJJ57A2WefbaI9VuDPWa7VaqGiWzS8fOvWrejq6mq7EF73utfhQx/6kFSbRMPL/ZEG\nGzdujNxGmOjesWMHfvWrXwEId7rXrl3bGowdddRRoaLbXySuqKJb9sa01el2nOSlRzxMh5eLkocj\nmrYjrtfrWkLo0i5zl0SYSKPTvYKIgM1LdEede1PHVJfoFhXLSZ+RdbpFXGybRHdashAgaUV3Wqc7\n6bWoe1PkXMvuW9T9jns97nOik2kq5zIv0a2bThPdRXO6k865znNStPBy6aowO3futFpM6cA/K+fv\nzFUKqfX29rZVKN+wYQM+97nPSc/8iTrdzz//fOvv448/PnEb/vDy4447rvV3mNPtD3H3fr9fdLuu\n2ya649qZN3E3puxDwqacbn/bZdrVyaJbx2/XIcjirjtTTnfanG7R80XRLU7Ssm5Jr6dFJTTQpOgW\nnRgSFcoyOd2qbY7anux7IsgKEJX9yVaLDvaJItEFUYiIXFOrPuThdCfVlpDddtrvpCVv0V308HKZ\ne8eW3yo6qWliX6YjBtMiLbo/+9nP4o//+I/xox/9CFNTU5ibm2v7Vwb8oXz+B4dKeDmAtoJzt956\nK7Zv3y7dJtGc7meffbb19xlnnJG4jSjRHeZ0R4lu76KfnZ1tm5CxeXJG542p46HSaDQwOzsbui2Z\n8EGVgizePtKgqzMrotMN6An1Ne10mwgvF22X7eHlIgNd25zuvCoDmxTdIoImyc0RGfDZ5HRn3ffK\n7s9Ls5Npj86+RuSaMCW6s3a6Xdctneg2sU+ZcYINQisNOiYYGF6+gg3XgnR4+Tve8Q4AwIUXXtj2\nuhdemUdOpm784eX+h4dfiMqI7uuvvx533303/uk//aerjpsoIk634zj49a9/DQA49thjV4WXr1mz\nBn19fVhaWorM6fbwTzDEiW7HcbC8vIy+vr42lzuunTYQd/Pl4XSPjIygUqlgw4YNOOGEE9rek+ko\nVJ1uE4MTFbwl77wlmo466ihs2rRJy7aj0NH2ubk5DAwMpNpG3HWno18VCS+XhU63Xlx3dTG9KJFr\nagChEhpoU3h5Ep0uusNypHVvPyqqxr9fmZzuIDLh5WnI2un2UpVEMB2xoAsT+5SdBCoyOkLpGV6e\n/F5WSIvuRx991EQ7rEK30/26170ucWm0JERyuj3hBqykAURtxy+6/U73jh07Wn974eWu64aK7mAF\n876+Phw4cKBtXzY73TrDy9M+VBzHaZ2PsGOm2ulmGV6uqxNdWlpqOwazs7Po7+9vW6bOj3edDg4O\nKu1Pl3iZmZnB8PBwqsrrJsPLHccJvR5EXcQoRNeUtV102+J0N5vNVW2Oin4xRSeEl+sU3aL3gClU\n7lmZ9DbZezcq3aaITnfWolvU5ZbZdtrvpIWiOx10uuMpvdP9tre9zUQ7rCJYSM3D7woHl+MyjUh4\nuT+0/PTTTw/9TH9/P6amplY53QMDA23OuOfq12q11oXqb4Pf1VtcXMSWLVvaKpfHtdMGdIaXNxqN\nVqSHCv4BQ9jDJAun25ac7jCq1Wqo6F5cXGwtUectYSiLrnY7joPZ2dlUrrzJ8PIowajjvCc5Z3kJ\nbkDs97muK+R0exEYJpe0E5kYAcw6VraFl3si2etfdYSXiw4Ky+p0yyB7rcU53X7SON1lDS+n6Na/\nTZV0imq1irVr10pFhJiijKJb53VYetG9Z8+e2Pf/yT/5J8qNsYUop9svuoOh26YRCS8XFd3+bXi/\naePGjW1C2hPdweXCPPxOvxcBEAwvt9np1hle7m1PdTDuH/A7jrNKxKiK7iLmdIcRNaAK1g/IU3QD\nK8v1mRLdQeEhi2j4pWrIYpFFt5fKIEK9Xo+9z6vVKmq1GgYGBpTOlajoNnm/2eZ0e9vyjmfUOfXn\nxcbB8PJ8RLdOpzv4WtGdbm+bJkW3yHVvgrxFt+xxmpycxPj4ONavX49TTjkl92WRixhenqXTLTOG\nKaTofvvb377qNf9FWbac7iinO2vR7Re8UWJWRnTXajXU6/U20d3d3d1af9sL240S3cHwcgCrwstt\ndrp1hpcDKw99VdEdHKAERYzqA6aIOd1hRP2OpAgBEXR2wl5ovP8+kSHpN6RZ71TU6VYV3WHrSCft\nOwtEfo/MQLder0emOjQaDbzwwgtwHAfbt2/H5s2bhbfr336QIjjdYegU3f6JyLROt+PYtWRY2r5T\nRRSb3H5UeLnqBJ+IyPUmzpLW85ZFxukOmxQ16XTLXjd5jc1179d15Qv7yeAZTsvLy2g0GrHPtiwo\no9PdyeHl0rET09PTbf8OHTqEhx9+GG984xvxyCOPmGhj5og43Rs2bMi0TUk53a7rtoqobd68uW2Z\nsqjtjI+Pty5CL1zec7uTnO5geDmAVeHlRXW6VW7MNA+WYGhrcFuyna73+SLmdIchIrpV96+73dPT\n08rfNVl8JCp8Wpfo9hgbG8Nzzz3XtuSg7U63TOX5uAmEpaWl1jny1/+QwXanO8otMxleHvxu2pxu\nkbYUSXSbdrpl71/HcYSKNqYJLw/7rui9I0PY9S4jbGwKL89LcOgW3SqTQDKoGhemKKPTzfByCcIc\n3osvvhhr167FzTffjCeffFJLw/LE7yb5Hx7ekmh9fX1tbngWJOV0j42NtYqi7dy5MzIkxi+6/eHg\n3nkdHBzEoUOHtISX2+x06w4vT9M5hzndflQermvWrFEqgKMaSmWyU486HzpEt+52z8zMYNu2bUq5\nYEnnK80Dw3R4ObDiDExOTgIARkdHcdppp2HNmjXWO90y93vcb/E/K1R/c9g1kLbOgywqE5JZhJeL\nfsY20W06rNe0c513TreomG00GqvGZTruk2DqWNzEkKrT7jgrhS5NOridKrrTHKeiie480gfCsDW8\n3Ibjo61KwPDwMJ577jldm8sVf8ftv+n8odhZk5TTHbc+d9R2/OHgQad7aWkJjUajTXT7vxsML6/V\naq0Bt4fNTreJ8HJVdItu7/umZ4T932N4+ZHt+SNiZCi60+1fCcFbd977Oy9Erk2Z4yoqulXXbbe9\nkJqMy9cpTnfSuRANZVfFtpzuRqMh5E6ncbrDXhNNzZBF9DpOE5ESFR0g0y6RfeQBRXc6dIjurM89\nne5opJ3uZ555pu3/ruvi4MGDuO2223D22Wdra1iehIWXu66bq+ju7e1Fb28v6vV6qJj1i+6o5cIA\ncdENrIhp/778uYzBzwVDy4HiOt1FDi/3vu+68ss0pRHdJjEZXm6i7YcPH8bQ0JB0O0yKbtM53f7+\n0WNychJHH320FQOXOHSJbn9oaFSOqcr2bQovj7pGs3S6dQwsRYUyRbf8sy0qRFpk4iQM0XQGE+Hl\nYduQuf5kRLdMaLnMtj3yyunW3Vep/A6ZKL4ii24bRCWQrdPtbS9pdQuRdmWBtOh+3eteh66urlUH\n7c1vfjO+9rWvaWtYnoQVUlteXm79nYfoBlYE8+zsbKLTHVVEzduGh190e7/JL6bn5+eFw8uDoeWA\n3U63LeHlYUVngvtXmdn1CrvIfk+lGJzpjizs2AYnFWxxugG1gmoi7Vf9jVGFjbz34v4vQrPZRKVS\nWbWParWKhYWFXMPLgeQBl8xvFnW6vc+uW7dOeNtAuZ1ur95E3ESEiIuYVXi5aARP3qLbtPMn+2zT\nLbpFxawp0S3aR6adHFteXpZqV5HCy9OkroVtTxaZsU2RRbdM/2ySLJ1ub3ve+S2d07137962/69Z\nswbHHHNMZEXXIhLmdOdZudyjr68vUXQPDg7iuOOOi9xGVE53mNO9sLAgXL08THTX63XU6/Xcqz+G\nofvGnJ+fx7Zt26QfLGGDeB1Ot8rDQrVjNt2ReZMIcRXdVdtgqu3T09NSolvkfKm2NU4o6srp9oeW\n+5mcnMx94KIzvLzRaIQOIF139VrftVrNmOg2ec/FDWrT5HRHvSbyvrffOCGs250WvRfyFt22Od0i\nqSxpnO6o74o8S1UIHi8T4eWAvElRFNHt7Vt1dZcgKs8T0f0HzYq8n12AHqfbNtFtwukW2bbXr+e5\nDJy06H7Vq15loh1WEeZ051m53CO4xrbH1NQUDh06BCC+iJp/G0B0ITWPOKc7GF7u35Y/EmJpaclK\n0a3b6a7ValhcXGw7LiKYEN2O4yiHYMVx+PBhTExM4JhjjmlbjzqLDj1pGbU0LrAJ5ubmcOyxxwp/\nXqT9JkS3Dqe70Wi0Ci8GUa3irROdotubAAoO4MKEhqzDH7VeeFkKqXnfjxv8phk05lX8LMnBN90/\nmhbRsp8XKdqYxumO+r2d5nTLXld5LufbbDa1iW5Vp1vlc0UT3UVxuvMS3YDeCSAVhBPOnnjiCXzv\ne99re+1b3/oWTjrpJGzduhV/9Ed/JJ2TYithTrdXuRzIN7wcWBGy/gvLWyoMiM/n9m8DSHa6gznd\nouHlO3bsaP1ta4i5btENrIhSWYridLuui/HxcdTr9dYEj8j3dBH8PcH/2xReDkQXE4qiyKJ7fn7e\nirCtKHQ7jWHHM0x0yxZGirpnbQovlwk9VxEhSYJGZP8yAj3ufZnrIu58dJrTHYUupztr0Z2V0236\nPObZR+vsr0wYCh4U3XrII7zcw3R/mxZh0X3rrbfiV7/6Vev/f//3f49rr70WF110EW655Rb81V/9\nFT7zmc8YaWTW+EW3N3Dyh0/mGV4OrFw0/llR0XxuoF10+wePsjndceHlp5xySutvW4upxXVOqjfl\n3Nyc9EA77PNpXVxV0R3XEdZqtbaq6P5jlMXDPEl0q+Swe98zhcy1IHK+VAcuce3QEV6e90MsCZ1O\nNyAuumWd7qhrIKxPysvplhGXUU63yr794eVJ+9dxPco+B+Lu3yKLbi+yQwe6nG6Z8HITotuU0y0L\nRbc4neJ02xReLtJX60LW6c4TYdH99NNP48ILL2z9//7778dv//Zv4ytf+Qpuvvlm3Hnnnfj2t79t\npJFZk5TT7bnCWRO1bJiq6Pbj/SZ/eHlcTnfQ6faql/f392N4eLj1XtGc7rQPh+npaanPmwov1+10\n+yd5vHBK1fapkCS6Vdth8mEkE/lTZKfbdrIQ3WHnWlZ0y+Te21ZILey9sM+qVuj3tiXiwuchusvq\ndOu8zkw73cHJ4LjPyiC6zTBxbrI/pegWR1V0h11TWVNEpztpn7qvxVKK7unp6TYx9dhjj+HSSy9t\n/f+Nb3wjXn75Zb2tywlbC6klie7169fjxBNPFN6GR09PT8vh9ovpONHd09PTKp63sLDQcrq3b9+e\nuKa4DZgS3YcPH5a6qcMcsjAXV4Zms6k9BCt4Hv3btyG8HFDrTIvkdKu21bTTbTu6RU9Y7qXJ8HJA\nPMRVB6rh5UFE87xF3rfd6U4jutNisnq5TrFk2ukG0j870+w/y3s0rh1R5J3Tnee2VMPLgfzd7iI6\n3YD65G3afZVGdA8PD7cql9dqNTz11FN485vf3Hp/fn7eyoJZKvgLqYWJ7rwLqQFHHOT5+fnWZMdp\np52Gnp742nhhonvDhg2t4muihdT82xodHW05Pdu2bQttp23IhIjJ0Gw22/L/kyiK0x08j1mL7uAx\nCft9Jh/GKhTR6S4juh/CYX1a1OSZzLbj7lld1fpFiBOcMgMpneHlIoJa57VcFKc7GHUkgkw/WSSn\nG1jd15VZdBfJ6ZYtEheH7tS5pM8VSXTb9DzPUnSX0ul+5zvfiVtuuQU//vGP8dGPfhT9/f1461vf\n2nr/mWeeacvlLTK2FlLzi95HH30Uruu2FVFLCi0HwkW3//fELRkW/K73Wf9ntgZe7HsAACAASURB\nVG/f3tbOTnO6AfGCaq4bvn5yMB8m75zuYA2BYJtsDy+fm5vD1NRU5jl3NuR0R11j/vfj/l8GdD+E\nl5eXV82sR51rmRBzW8LLw/aX9DqgJ7w8SdCI7L9oTnda0S1L0Z3uuPabcLpVw8spuo8wMzOj7VrK\nMrwcKIfots3p7uTwcuElwz75yU/i93//9/G2t70NAwMD+OY3v9nmCH/ta1/DJZdcYqSRWZO0ZFhe\novv4449v/X333XfjmWeewWmnndZ6TVV0+3PURXO6gfZQdI9jjz22EE63SdFdqVSwtLSUuE6zt+Zv\nVDu8qAUVJ0NnQaxqtRo76LclvDzs3FWrVYyMjAAA1qxZg6Ghobb3bRHdppzuuGsMoOgWeT/s88vL\ny61+Lu481+t14bW6bQkvB6KX+DHpdMe5traHl+dVSE01pcZ1xdaq1Sk4dDndouHlunKqVZ1u0xNj\n3u8TXXM4z/Byx3Fw+PBhHHPMMam2oxLZ4X1PhKKL7iKEl5toS5HCy4VF95YtW7Bnzx7Mzs5iYGBg\n1QP5O9/5jvQaxbZia073VVddhZGREfz5n/85AGDPnj3Ys2dP6/2k5cKAdE53VHi5n23btrX931an\n21R4ucfhw4fblk4LI87Z8kS3ysDBcfSu0x02ceJ/ENnsdPsd+rAQN5MPo0ajIbw+qamc7iSnNThw\n60TRrXJcl5aWhES3rmgHfxuDkTAmMOV0py3SlpXodhy5qt15Od2qfa+oWLPB6VYNL9f1XFJ1urMQ\nuTKiO2+xcfjwYWzZskW4vWGoHtM0ojvPyQog3b2isg1dqDxDVPH/vqTt5z3GEQ4v99i4cWPoIHLT\npk1tDnGRCVsyzBPd69ataxUQy5qenh7ccsstuOeee7Bp06ZV75166qmJ2whzX/1Od5To7u7uXpWz\nHzbJEiyk1olONyAWTpUkugG1Dkp3IbWwc5i30x32+5Jeyzq8HBAXXaacbpHwZlUHqiiYEt0ecbn7\nMuHlMqLbNLaJbpHwchE3XJQsw8vToHotiH7Phpxu1fByXQ6lqtOdhUOqwwXNinq9LlXvJgzV6zHN\n9Z6n0y3r7Ef1WzaJbhNtKVJ4ubTo7gTiCqnl5XL7eetb34pdu3bhd37nd1qv7dy5U2jSo7u7e5Xw\n9ovu3t7eVjikv5BaX1/fqhnKMKc7mNNtq+iO6px0DTJc100spBUnyNKKbhWi9pUkurPoxPwh81Eu\nVNIsta2iW9RVUw0vT6KTRbfq7/VH8CSFl4uQFJ2S9f2Wh+hOm6/diTndqteC6DPClNMtg2p4+eLi\notL+VPdP0Z3M5ORkqus9D6c7b9Gd9jt5PdOzdLp1hpfX63XMzs4au1+Ew8s7ibhCanlVLg+yZcsW\n3HPPPdi1axcef/xx/Mt/+S+Fv9vX19cmpIITCQMDA6hWq1hYWGi9FubuB3O616xZg61bt2JiYqL1\nmq3h5UB4aJbOGy2psxZxurMMbYqaIQ0Ly87a6fZmfLu7uyOPa5LTHfa+6baLVDAXPcemJmC84wp0\nnuhWvd9rtVordUBHeHlSX+FvZxZ9gm2iW8Tp7kTRnYfzp0rWTndaV1V2/3mFl4t+zgbRvbS01Jaa\nI4uqAC5qITUd+eu2iW6bnW7XdfHSSy+hWq1iaGgoMUVUBTrdIQRFd7VabYlUG5xuj66uLlx55ZW4\n4447cNZZZwl/L9jh+Z1u4EgxNX94eVhYejC8/JhjjkFvb28hnG7AfP5OkstlyulWJayzWl5eTowI\nyKqN3sMv6iFYVKdb9JpTGTiJfL6Tne4059/r23SEl8uI7rI63SLnyVanu0iF1GS+p1Nw6Mrpjvuu\nd785jqPN6RZtd/A9m5xuGwS3x+TkpPJ3O83p1lGpnaK7naRnkPc817nMnR+K7hCCotuG5cJ0EhTd\nYU43sBKe5TnVYaI7uJ3t27ev+qzNTrdpEZY04DaV061KWGcVNWkSrBKbBUmi20anW0R0yxTbkr0e\nRH5fJ4vuNL+3UqnAcZzY+7herwvtI6mv6ATRrSu8XAcqTrdo2HHYvlTphJzu4LlIcrpd18XCwoK2\n60F038FjRdEdztzcnNQzz0+niW6Gl8vvK43oziI6haI7hGAhNRsql+skyen2RLfrHlnjN0x0B8PL\nPdFdhEJqQPjNqfOmS3I/iiC6o2b78nC6vX3KON1JVdZNt10kvFxmRlW2vbIF2jpNdKd1ukWcbJHP\nMLy82OHlQLbOTtI+dX3Phurlwc8ntb3ZbGoLLQ/uT2bSJ8/7VPVzWTE1NaX0PdPpFFFjCNuEq+7v\nmKCITncWx46iO4RgIbWZmZnW/8souoO/yb9Wt4dIeHkap7tSqeCHP/xh2wSHafIML282m0Izblnm\ndIe1J+r8ZZ3TDaR3uvMQ3SKV5EWEuYfJ8PIyCm7ArOgWOXc6RHcnFFIrcng5EN1X25jTLfI913Wt\ncLoBOcFer9cxPz8v1zjBfcsM2LNwSEWPY97LXgWZnp5WcrvzcLrT7DctZczpNvH8kulb4t6n050T\n3d3dWLNm5dB0otMddLABufByf9E1Uaf7s5/9LG644Qb8/u//Pg4dOiT0nbTkGV6eNBC3wel2HCdS\nVASriWeBN4iJ6hiTJlHCQkCzeCAlDS5kRLfsQ0Hk3IgImiJjKiS50WgI5Y2KDC6LEF4uW1PARHi5\nyPd1Xce6okqKmtPtOHrXg/dPZJh0uufm5nKZLAj+PoaXR+M4Dg4cOCB9Hage07SiO68Q8zKKbhPt\n0RVeTqc7Rzy3uxNyuqPCy/3IhJd3d3e3hLeI0+26Lh599FEAwKFDh3DDDTcYK2IQ3G8Q3eHlUR1A\n0kDc6+TzFN3ValXIfcpadKs63cDqAVQWbY8T1f7CHSLQ6ZbHlNMNQCgypyzh5bLiMWun23s/L9Ed\ndQ6LKrpNXGeqfY3MoHp6elq+YZr27X8mZtGfFlV0AyuFev1RpCLkEV4OFEt0B7/TCaJbV3g5ne4c\n8fK6vTXbPMomuvv6+lat7502vNy/DxGne2xsrK3z/Yd/+Ad84hOfMN5ZhHVOunPYom7wIjjdSRMm\n3oPIlvDy4LEKG/jkERaftI6zTDtMiu6yYlJ0iwzGRJxu26qXh/WDSfs1mdPtiWmbRbeq052Goolu\n1WgEGadbt0CS2bf3flbhyHnk5uvk4MGDwis8APmFlxdJdNvudOcdXk6n21I6RXQHXW5A3OmOCi/3\nf15EdD/77LOrXvve976Hb37zm4nfTUMWM4JRDxQbRXdwX0nnzmujLaI7+EBOEg5ZtTvOyZZxuQE6\n3SqYCi8XpSxOty7RHXXMRbYv8pmiie5OcrqzEN26UXG6sxJpacVk3siGmVN0J2O76DYdXi46MRYG\nne4c6RTRHfZ7RJ1uf3j5wMBA2/dURffv/d7vtf7+/Oc/jx//+MeJ31clzgXVRdSAO8n98vKPs5yh\nDh6PpBD/rCcGkqqXBwfcSaI7q3bHnWsbRHcn53RncQ0kiW6R/E8bCqnpEN0qr/v3X0anOw/RLfJc\nMSE0sggv142K052VSCtyeLnH/Py8cAFdk+Hlcf1LkUS37eHldLpJKJ7oLnshtbDfE+Z0B13t4Oe2\nbdsW+vmlpaXEC9kvuq+77jp88IMfBLBy83zkIx/Byy+/HPt9VYLtMiFwozprEfcrLjzdBMHBhW2i\n28uRjztPSety2xZeLlu7wEQhNTrdZklKIRAZ0JXF6Y7bjg4XW2dfVGbRTadbHL8Ys83pLoPoBlbC\nzEWifVR/h8j1FrftYNuq1SpefPFFHDhwQKk9otDpFkNGdMe9T6c7R/yF1MosutOElw8ODmJ4eBgA\ncPbZZ0d+PklYeKJ7cHAQO3bswAc+8AFceOGFAFaKbXznO9+J/b4qYTnAulF1uoHkZcV04++MRMRg\n1uHlzWZTqsqzLU53s9mMHFCYdLpFC8V1sujO4hpIcrJFBuh+wVkUp1tWXIsImizDy2UHYHkUUjPp\n/NlaSC0PASkrurOKUDMdlp0VzWYzce3uNL9Bt+ie+P/s3XmUHGW9N/BvdfesyWSyTzJJIEyWyUJA\nlsANWzAhQIIbR9QjLhFBUUQF7ovKPUf06uX1eEVRUS+yCIYbBF69suYGIUgwEAJkMSBIyJ4Mk0yW\nycxkpmemt/ePpp55qrqqu9au6u7v5xwOPdM91ZVe61u/3/M8hw6hr68PR48etf0dbkc5Vrr9bi8P\ne6U75vs9lCi50q3OXh6LxQzDZ6nxKnRHo1HcfffdeO2113DppZea3r6vr8+wUg4Ahw8fFkuEzZ49\nG4qiQFEU3HzzzVizZg0AYM+ePRb+VfYF1V5uNUwXO3TL92VlWECxJ1IDCodU+Tk0OggudpuuanBw\nELGY9uM2k7E3czlgP3Tb2SZDt38GBwfFd4qe1YmE0uk0otFoyYTuICrdpdhe7kYlVrqD+JyyesKL\nlW7nrAy5c8rOMCsj+udTnmTWzxMa5Vjp9rO93Mq/Vf2OUBQl57pinJxi6DYhV7rVmbUbGxsNn6hS\nI4+9HjVqVM71RqFbXntb1tLSgpaWlpzfy6E7X4D75z//KS7Pnj1bXJ4wYQJisRiSyST2799v+vdu\nFKPSbfTla/UgO5VKBTam22olHijuF3qhkGqn0l3skwX6E0/JZNKXMdp2b1vJle5i/ZvzveetHqCr\nobtS28uLXemu9PbysI7pDiJAqu89tpf7x+rksk54WelOpVKa46Owhe6wPNfqsAx9ZvKzvdxOoSEa\njRr+3m9sLzchVyXUtpdyaC0HgPnz52PWrFkYP348li1blnO91YnU8pEDRr6lp+Tx3HLojkajaG5u\nBgDs37/f95YUoHiVbiuBFgi2vdxq6PbyINcKq+PM9ZeNflfsSreek7Y0O69Rhu6ssFS6zVg9QC/m\nSa4gQjcnUrPP6WvByucIK91D1P22WunmkmH2hT10q9vQH8+GrZMlLJVus/v24/vL7ueK2T6w0h0g\nOXSrB0zlErpramrw6KOPmp7tsdpeno/VSrdZ6AaAKVOmYO/evejr60NnZydGjx5tax8KCaq93E6l\nuxRCdzEVCt1hrXR7Fbr9qHSzvdx/+d7zVj931NBZrHHo6XQakcjQefmgK91hby8v9phuN//WUh3T\nXeyTvKqwTqTmNmSEiRpszbpJ3TymbtvLgez7IRaL5RzPhq3SHabQrf8OAcJT6bbzey+x0m3CaPxd\nuYRuAFAUxTBwA9lQrh9/6lel+6233hLbP/HEEzXXTZ48WVz2YwbzYrSXGwVnq2ErqDHdmUwmtKHb\nzpjusEykBhjvd1hCdyVXusPQXm4ndAc1x4PRz4UE0V5upRpulZOhH0b37VfodvNasPI4hbHSHVR4\ntLrfbC93LpOxvjKJ0+3nU+gxUp9Tfej287H1or086NCtF4bQbXY7zl4eIKPQPWLEiAD2pPgURclp\nMfej0t3V1YW2tjYAQGtra85JADl0+zGuuxjt5UDuF7DVZaLklqZiUO/Laph2MibZLTtfnGFrL9fv\nu93lwgBWup0IQ6U730mscgndVluA/W4vD5LRc2l1ch8v7suOIEK3F5XuINiZSK1QePRSOYVuwJuT\nk2a8CN2ZTIbt5TY4Gabk5n5Y6S5h6kRqsnKqdBcybNgwzc9+VLrfeecdcVnfWg74H7qL0V4OaL9I\nMhnrM1ZbHfvtFfXxsDPmPOiDXD07le5i7ns6nc7ZH1a6iyMMoTvfWt12Qncxx2YWO3SHrb3cCaeh\n2wm3r918f+9XcKyESreX3RaFlNOYbsDf0G11PL6ZZDKJRCKRsx9sLzcX1kq32X4xdAeo3NvLCylG\npTvfeG6g+JVuv95wcqU7mUxa/pC2OvbbK+oBq9XQXezKmxV2xnQXe9/lkG3ndSCz8zdWb1vJobtY\n/+ZMxnytbjvVqkqvdHsRzP1UzEq329eCn628Ziqh0l2s1nLA+msrbN/TZoKsdBfafjKZNCwgsb3c\nXLEq3YC913kx90uPoduEUaXbaE3rcqWfTM1snW0z8u3NQrc6nhsA5syZk3P9pEmTxOVSbi+Xv0js\ntBQXO3Sr7FTYg9pHM2GdvRzQhm4nVW7A3phVVrqzwlDpBszHeVr93Alqjgezn/X8rnRbbS8P8nVs\n9Bxbec6CCN35/t6v78JKqHQH2Y1ipJQ+18PeXm50LOvX4+v0ZEnYQ7efnT9ujo2K9b5l6DZR6ZVu\nfeg2W6fbjFzpNmsvVyvdVVVVhmt9NzQ0iBMdxWgv9+sL3WnoDqIlLJ1Ol3ToVp9Ds6pYUO3lQHbp\nQfU+nYZuwP1kIXpBV5H8FpbQbfZ+Znu5te1bDRilWOl2ohRDt9tKdymE7mJWuq0EjVKpcgP5J6Bz\n+7h60V5udCzr93vFrjA938UM3XY++1npDqFKnkgN0LaX19bW5kz7X0ih9vK+vj7s3r0bADBjxgzD\nxxsYajE/ePCg52Oci1Xplr8s3IStYshkMraCdDEPMKxQn0MrASeISnd3d7e47JTX4/gqudJdzH+z\nWSBje3nhvwGsvZ5LNXQHMZFavufTr891t5VutpfnCmJCPL+EudKdSCQMiyZ+fSY73W6xhk1aUez2\ncla6Sxgr3UOVbrvjuYHCE6lt27ZNvEGMxnOrpkyZAiD7hnrvvfds70c+YW8vD4KdMd1AeCvdZs9l\nkGO6AaCjowOZTMbV68BOZdSKcg3bqjBXuu3cfzmHbisnH6y87os5iZURjunOz+lzU0rt5WEL3WGq\nfBYS5tDd19dnuI2whe4wfZ+Htb28mM+jHkO3iUoP3fLs5U5Cd6FKd6Hx3Co/J1PTHyT63V6eyVif\nuTwo6XTaVpAOW+guVOmWn+cgvpwGBgbQ1dVVlEq33duF6cvaS2EO3XYOJMu5vdzKa4+Vbi23r918\n4TBsY7qD/oyy2t5e7Peoep9urg8Ts1UevDiZ5vZxMrv/sIXuMI3pdnui2Q5WuktcpS8ZJreXu610\nG4XuQjOXq/wM3fIHuZ9fTOr6jgMDA6EPNnbDYNjaywtVuuXbBHUwcvDgQVePm9ehO+jWTb+Z/buK\nHdDchu5iT6Sm37dSCd1BMnpfhzV0e7F2vF1OP2uCrnRb3e8gKt1edIiERSZjvFSdF/8GvzoCwjZn\nQ5hCt9F9c0w3GdJXuqPRaM7kYuVM/rfanUQNKDyRmhq6o9EoZsyYYbodOXTv27fP9n4Uor5J/fxi\nUr9Iwt5aDtgP3WGsdJt9cauCrpq4fcwYuu3JF7qLiZVu8+1Yea1abS8Pkn4f/XyNuf235vusD2ul\nm+3lucqpvRwwPnEV5tDt93vF7d8F+b1eShOpsdIdMH2le8SIEVAUJaC9KT4/K90DAwPYsWMHAOCk\nk07KG+r9Xqu7WGfQE4lE6FvLAfuhO4xn0QuFbvW6UjsYUbG93J5itwWa8SJ0BzWm28rwmzBUuoN+\nTzsN3WGrdHMiNeP7t/IeCFulO+j3hF1GJ6W9eEz9epzC1l6uD59hai/3s7vMzhAEVrpDSF/prqTW\ncsD9RGpVVVWIxWIAcivd27dvFx+i+cZzA0BTU5PYjp9rdfsdHs1mvgybUjgxUEgqlcr7JR101cQt\nvyZSq7TQHYZKd6lMpOY0JBXabr7f+XUbP5VS6E4kEqbbCFt7edCf2XZOFhS7+6sSQneYK91hay/X\n/22YKt1+7oudQO/XEAYrGLpN6EN3JS0XBrgP3cBQtVtf6Var3ADQ2tqadxvRaBTNzc0AsqHb6zdt\nsUJ3Mplk6C6SQm24pV7ZZXu5PWZfxqVY6Q6qvdzKY+V3e7kVQQeMYoZuL14LZgExbKE7LGO6w3ji\np5zGdAOlF7q9mOTNbLte/G2YQref7w07oduoKMNKd8AqvdI9ZswYcXnUqFGOtqGGdX2lu6OjQ1xW\nA3U+aot5X18fOjs7He2LmWJ9mQ8MDIRu/LORoA9avZBKpdhebuN26pdVuYZuM6UWuos9kZqfoVt/\nvVevvaDf0/puhDCP6QbMW8zDOqY76NnLw/gZWW5juv0K3fkeB7fB2Y/HuBxDt9+fh25CNyvdAav0\n0D1lyhR89rOfxbx58/CpT33K0TbMKt1y6B43blzB7fg9gzng/xvu+PHjvm6fhrDSbe92QPBLLfmt\nmLOomnEbugvNVeA1v0K30fXlUukGtM9pmNvLAfPQHdYx3aXQXl5s5dZeHsREam4fozBXuoNU7Eq3\n1e2rE+7KivWYxYpyLyVIP5FapYVuAPj2t7/t6u/lSncmkxET0R06dEjcZvz48QW3M2XKFHF5//79\nOOWUU1ztl6xYobsc2rZLBSvd9m4HVGboDqINVP4cBOx/7hRzkiY3odtKEIhEIpqfvRCG93QqlRIn\n7UsxdPvVLgs4/5wJuhsn6PvPpxLay7343PMzdKdSKTH3kFdY6bZ/X3Y+b9PpNKLRqPgdK90Bq/RK\ntxfU0J1OpzVf7nKle+zYsQW342elO+gz6OQ9q5XuUn3Ora5XbOcLzs8D7TAIQ6UbsL/2tV4x99nP\nSrdfB2NheE+XeqXbz4NPN58zdipZXgvz52Oh5ysM7wk7gmov92vbQWyzEkO33WMe/YkcjukOGCvd\n7snLhsnjutVK9+jRo3NObhjxM3T39vYWvW2T/FWo0h3mAygr8i31o3IS5kr5MSkkDJVuIPfgMcyf\nO/K++R26y63SrQp76DbqwPL7NelmwqogQ3cYXltGyi10G500L5f28s7OTuzcuRPd3d0Fb+tmn8Ky\nZFhYJ1IDckM3K90BY6XbPXnWc3VcdyaTEaHbynhuAJg0aZK47HXoPnz4MNrb20N98Ev2FJpwqtgT\nUnktHo8XnJSPoVuLods++eQUK93WyQdzfrZpe/FvTSQSOfvo9xAGp/sdhvbyMCq3Md2AP4HI7/by\nQo4ePYq2tjb09fWhra3N1wnwwlLp1r9vwtJeDrjvOnOKodsEQ7d7cqVbDd3Hjh0TH6hWQ3dDQwNG\njhwJANi3b5/He5n9MLRy5pFKQ6FKcKlXugGgp6cn7/V2v0BK8cDMjrC2l4c5dAP2ht+EodIdhvd1\nMSrdXj5e+hN4Vjpp3GCl21uFPkPC/hljRP+aLPX28mPHjuG9994TP6dSKfT29rraptW/Dfoz0e4w\nJafcVLqLeUzI0G2C7eXuyZVutb3c7szlKrXF/ODBg74fFFBpK1QFLvVKN+B96A5zJccLrHQ7Y2ei\nyTCE7jCw25YP2D8w9rIarf8+1S/x6TWnr3lWuo2VW3s5oP0O92r4n5+V7nx/393dbdihWajQU46h\nO6xjuov5HmHoNsFKt3tGlW555nInoTuTyWjOGBLpWal0l+KBiOz48eMFW+jtCPNBpRfCGLpLoeNC\nfYysLnmo/nsK/bv0r8+wPw52FKPS7WUwLqXQHdTndpD3XUg5tpfLodurz8kgQvfx48dNuzO7u7t9\n26ewhu6wVrqLefKbodsEQ7d7RpVuu8uFqfycTI3KS6EqUCmEnUIymUze9jQn7eWl/pjkE8b28lI4\nGE6n00gmkwVbIfUKPbb660vhsbCqGKHb7vORjxy6k8kk28tNhLUrpRwnDZW/w706CeRne7nRYzww\nMIA9e/aYPv7JZDLvv60cQ3dYx3Sz0h0CcuhWFAXDhw8PcG9Kk1Gl2217OcDQTe6UQ3s5kL/FnO3l\nWmGsdIf1IF6WTqcLDmWQWa10l3N7uduJ1DKZDPbs2YN3333XcHZxwNvQLd+H31VuoDTby4Hwvl/9\nDJNBkSvddj5/8sn32nH73Br9faFKtnobM16F7qCVQns5K90hII/pbmho0CyiTtZ4WemeMmWKuMzQ\nTW4EXTHxSr4vdYZuLYZuZ9LptK1JJtle7r7SHY/H0dPTg4GBAc1JatXg4GDBeSvskCvb6slxP7HS\n7a1Cle5SpL6+M5mMZ5PcFnsiNSvzLnR1dXn2HW72t0F/thazvdzO9jmmO2TkSjdby51hpZvCKOiD\nN68kk0n09/cbXsf2cq0wtpeH9SBelkgkLI/nBljpBtyHbvnve3p6crbhZZUbyIZu9T5Y6TYX1vdr\nvrBRqu8rNXTH43HPJg30c0y30batvF6SyaThiS63cwiENXSHqb2cle6QYeh2L1+lW1EUjBkzxvK2\nmpqaEIvFADB0k3thPYCyy6z1jpVurTBWukvhgDhfJcaI1dCtv76cXnupVMry46CSb6evDOlDttfB\nOJPJIJlMIpPJFCV0O33dB/2ZHfT952P2mIZ5n/NRh4B5uZRrsdfptrrNrq6unN+5/TwMa+gO00Rq\n8uc0K90hUFtbi1NPPRUAsGDBgoD3pjTJoVtf6R4zZowI0VZEo1E0NzcDyIbuoD9IqLR52Z4ZJLOD\nEoZurTCG7lI4ILZT5Qact5eXwgkIq+QqldtKN5D7Hve60g1kq939/f1F+Qxg6Pae2b6V8vsqmUx6\nNp4bKH57udXXi9EwMS/3J+jv9bBWuoGh56iY723rqacC3Xfffdi+fTtmz54d9K6UJLm9vK+vD6lU\nCkeOHAFgr7VcNXnyZOzduxe9vb04ePAgJkyY4Nm+UmUJ8wGUHf39/UgkEjmrLbC9XIvt5cXB9vKs\nVCqFaDTq6DWmfyzUFnNFUZBIJHyZXXxwcLBoz4HT133Q75eg7z+fcmsvB7In/MwmEnSi2EuGWX29\nJBIJxONxzfGyl+3uQX+vh3UiNSB7YicWi7HSHRY1NTWYO3cuIhE+TE7oK92dnZ3ig8jOJGqqU045\nRVx+9tln3e8gVaxyqXQDxi3mrHRrhanSre5LmA/inXIausvttac+t27by4HsZ5U6d4MfVW4gG7qL\n0VoOOH/feTW216kwv1/N9i3M+1zI0aNHPd1eWEM3kNvNUq6V7jC1lwNDnykc001lQT+RmjxzuZNK\n99KlS8XlVatWuds5qmhBH8B5iaG7sLCEbvlxLuUqlBlWurO8DN3A0EG5X8F4YGCgKDOXA6x0+6Ec\n28vNJgl1KsyhWz93RrmG7rC2l7PSTWVBP5Ga0+XCVC0tLaLV/80338Se9T27UwAAIABJREFUPXvc\n7yRVpHIK3cePH3cdYtheXjxBjCMrFobuLDXAejGmGxg6seZXpTsej/vStm6EY7q9V47t5V4r5phu\nu7OPy90sXu5PGL7TWenWYugm3+gr3U6XC5Nddtll4jKr3eRUOYXuTCaTU6VipVsrLJVugKEbKP/2\ncnUCOi/GdAPZql88Hvd0jKusmMNtWOn2XjlWuv3gx5rYRn/vZHvyCbVyDd1hHNMNsNJNZaKmpgaK\nogDwptINAJdcconY5qpVq0LxoUKlp9xeN/qDZiehu5wxdBcHK91Zvb29mvH7hVhpLW1vb/dk34JW\nqpXuMH9GluOYbj8YvfYymYzrx0m/XSfbkztNvJhILSyv11II3ax0U1lQFEVUu72qdE+YMAFnnnkm\nAGD37t1466233O8oUYnTV+7ZXq7F9vLiKvT6C9OYQ78cP37cs/ZywL/x3MXm9Pku5/eLW2wvt8av\n7wF94HPyWpW7WLx43sLynV6s9nIn/15WuqnsqOO69ZVup6EbAJYtWyYus8WcyJvQXc70X8ZBVgLK\nOXRbrXSX+4RygPPQXa6Ph1thCBBhxfZya/zseJK3E3SlW91GGN4zxTzBWgrrdDN0k6/U0C1XuqPR\nKEaNGuV4m0uWLEEsll1ifvXq1WV58EpkB0N3fvov4yD/vWrbcTk+5lZDNzD0HJTj4wBkJz+z+m9j\n6CY3zF4zPDbSMnqcvHq/uX0PJxIJT09EhjF0h+2zLZlMFv27mKGbfKW2l8uV7jFjxiAajTreZmNj\nI8477zwAQEdHBzZu3Oh+R4lKmDym28mXSNi+DL1mVOkOivpFH4YDIq85Cd3l+DgA2efZyTJc5f5e\nJO+x0m2Nn5Vu+TlwerJDrXaXa+gOw/7Ikslk0d8jDN3kK7XSnUgkcOTIEQDOJ1GTcRZzoiFypdvJ\nF1u5V0TCVOlOp9Nl+3iz0q1ldVZwt+NBqbIxdFsT5vZywNvQHZYTu2EO3el0uugr2TB0k6/kZcPU\nN5yb8dyqhQsXikD/l7/8pWhrjBKFkfzF4eQLu9wPzsJW6S7XYMVKtzPy48bHg+ziRGrWFKu9PAyh\nO4yV7jC+HoudHRi6yVdqMJZ5Uemuq6vD4sWLAWTHzq1bt871NolKVSqVEl9oTr7ww/hl6KUwVbrl\n56rcsNLtTLlPKkf+4pJh1rC9vPjkE4lh2B89hm4qK3KlW+VFpRvQtpg///zznmyTqFS5Wf6i3A/2\nwxa6y/VgmKHbGfXxKtfXBfnLbP3pMIacIBWrvdzpNr0O3WGhngAI4+ux2KE7VtR7o4rjV6UbAObN\nmycuy8uREVWiZDKJ6urqUH3ZhkWY2svLPXRbfWzZXp6L711yQp08MxIZqqPxtZTLz/bysI3pDkul\nGwB27NhheX6LYpPXRy8GVrrJV35WuhsaGqAoCgCgu7vbk20SlSo3le5yx0p3cTgJ3Xy9sr2c3NN/\nppTrZ4wbRp9NXj1OXoVur5awClPoVv9dYcT2ciorRpVur0J3JBLB8OHDAWTHdRNVMoZuc2EK3eU+\nezlDt30M3eSW/rXD11KusM9enslkPFvGKkyhO8wYuqmsGFW6vWovB4ARI0YAYKWbSG3f4sFWrjC1\nlwPF/6IvFraXO8Mx3eSW/rXD74FcYW8vB7LfDQzd5Yuhm3ylr3THYjGMHDnSs+3LoZsfMFTJWOk2\nF6ZKN2B9/eZSxEq3c3wsyCn9a4cncHKFfSI1gKG73DF0k6/0oXv8+PFiHLYX1NCdSqXQ19fn2XaJ\nSg1Dt7mwhW5WurMnSgcGBgJ/LsKA7eXklj5kq98HNKQYodvtmGyG7vLG0E2+0reXezWeW9XQ0CAu\nc1w3VTKGbnNhay8v10q3ndA9MDCAHTt2oLe31+e9Cj+2l5NbDN2FFaO93O172KvZtBm6w4mhm3yl\nr3R7HbrVSjcAdHV1ebptolLCMd3mwlbpLld212JNp9PsUAIr3eSe/rVTrif23ChGpdtt6O7v7/di\nd0K7LnalY+gmX/ld6ZZDNydTo0qWSqU8W26k3DB0FwcP9Jxh6Ca3WOkurBRCt1dDj1jpDieGbvKV\n0ZhuL8mhm+3lVMkymQxSqRQP3A2Erb28XDF0u8P3LjnFSndhxWgvD8t7OCz7QVoM3eSrYo7pZqWb\nKp1Xa3yWG1a6i4Oh2xmO6Sa3WOkurBQq3V5hpTucGLrJV8Uc083QTZWOodsYQ3dxcHiDM2wvJ7fk\nsJfJZBi6DRiFUK9CMkM3WcHQTb7SV7r9bC9n6KZKl0gkeOBugO3lxcFKtzMM3eSW/NpR5/cgLf37\ny8uThAzdZAVDN/mqtrZW8zOXDCPyDyvdxljpLg6Gbnf4uiSn5LDHKrcxP0++MnSTFQzd5KtoNCqC\nd01NjaYy7QVWuomGMHQb04dBPkb+YOh2Rn3cwnLATqVHfu1wEjVjfp58Vd/DYflu4WdxODF0k+/G\njBkDAJg8eTIURfF02wzdREMYuq3hwYh/+Ng6x/cuOSW/dljpNqZ/f3n9fkun06E6cRamfaEshm7y\n3b/+67/izDPPxE033eT5trlkGNEQjuk2x0q3/1hdcS5MVTIqPfI4bla6jfk9zIihmwqJBb0DVP6W\nLFmCJUuW+LLtqqoq1NXVIR6Ps9JNFS+RSDD0mGDo9h9Dt3M8QCa3MpkMFEVhpdtEpYVufs+FDyvd\nVPLUydTMQvddd92FxYsX45lnninmbhEVHSsc5uQDLgZDfzB0Oxemg3UqTepriKHbGNvLKWgM3VTy\n1BZzo9CdTCbx29/+Fh0dHVixYkWxd42IQkINg2zj9Q9Dt3M8QCa31NcQT74a0382ef2eY+imQhi6\nqeSpoXtgYAADAwOa644dOybO+h47dqzo+0ZE4SCHbvIHQ7dzPEAmt9STiax0GytG6A7TCV1+poQP\nQzeVvHyTqR0+fFhcPn78eNH2iYjChaHbfwzdzvEAmdxSJ1Nj6DamD8Rev+fkyezCgJ8p4cPQTSUv\n37JhR44cEZc5uzlR5VIPhsJUiSg3DN3O8QCZ3FLbm/keNKb/fPL6PRe2tn6+DsKHoZtKntXQnUgk\nctrPiagysNLtP4Zu5xi6ya1UKhW64Bc2lRS6KXwYuqnkqbOXA/lDN8AWc6JKpVa4Wen2D0O3cwzd\n5FYqlWJreQEM3RQkhm4qefkq3fKYboChm6hS9ff3A2Do9htDtzMM3eRWOp1m8CvAz9DNEx5USCzo\nHSByy2p7OcDQTVSp4vE4AIZCP7HS7RwP2MmtVCoFRVGC3o1Qk0+6stJNxcbQTSUv3+zl+tDNydSI\nKpMaulnp9g9Dt3OsdJNbfA0Vxko3BYmhm0qenTHdvb29RdknIgqXgYEBpFIphm6f8fF1hoGJ3Arb\nOtFh5GfoJiqEoZtKnp32cla6iSpXPB5nJdZnPOh3hgGA3OJyYYXJq1jws4qKjROpUckzC93JZBKd\nnZ2a23JMN1HlisfjPNDyGR9fZ/i4kVucSK0w9X3Gk1wUBIZuKnlye7lcyT527FjOWV+GbqLKxdDt\nPz6+RMHgkmGFqceEDN0UBIZuKnl1dXWIxbIjJeRKt761HGDoJqpkfX19bL/0GUM3UTBY5S6MlW4K\nEkM3lTxFUUSLuRy69Wt0AwzdRJUsmUxiYGAg6N0oawzdRBRWrHRTkBi6qSyooVtuLzeqdHMiNaLK\n1tfXF/QulDWGbiIKKzV083OKgsDQTWVBDd3Hjx8XZzCNQjeXDCOqbIODg0HvQllj+z4RhZUatjn2\nnYLA0E1lwWgyNVa6iYiIiAhgezkFi6GbyoLRsmEc001EREREANvLKVgM3VQW5NBtVOmur68HwNBN\nREREVInYXk5BYuimsiCH7q6uLgBDobuqqgoTJ04EwNBNREREVIlY6aYgMXRTWTBqL1dD95gxYzB8\n+HAA2ZmLOZaHiIiIqLJwTDcFiaGbyoK+vTyZTKKzsxMAMHbsWBG6AVa7iYiIiCqNWuFm6KYgMHRT\nWZBnL+/u7saxY8fEGU250g0wdBMRERFVGla6KUixoHeAyAv69nJ5ErUxY8ZAURTxM0M3ERERUWVh\n6KYgMXRTWSgUugcHB8XPDN1ERERElSWdTiOTyXAiNQoE28upLOjHdMtrdLO9nIiIiKiyZTIZVrkp\nMKx0U1nQj+nWV7pl6jreRERERFQZGLopSIFXun/0ox/hrLPOwogRI9DU1ITLL78c27Zt09xmYGAA\nX/va1zB27Fg0NDTgiiuuQEdHR0B7TGE0fPhwMW7bKHTLle7e3t6i7x8RERERBSedTjN0U2ACD91/\n+9vf8PWvfx0bNmzAc889h0QigYsvvhjxeFzc5oYbbsDTTz+NP/3pT3jxxRfx3nvv4eMf/3iAe01h\nE4lERLVbH7r1S4ax0k1ERERUWVjppiAF3l6+atUqzc8PPPAAxo8fj40bN+K8885Dd3c3fve73+Hh\nhx/GwoULAQD3338/Zs+ejVdffRVnnXVWELtNITRixAh0d3cbjumWOyM4ppuIiIiosjB0U5ACr3Tr\nHTt2DIqiYPTo0QCAjRs3IplMYvHixeI2ra2tOOGEE7B+/fqgdpNCSK10y6G7qqoKDQ0NnEiNiIiI\nqIKxvZyCFKrQnclkcMMNN+C8887DnDlzAAAHDhxAdXW1ZnZqAGhqasKBAweC2E0KKfU1kkql0NbW\nBmBojW55ojWGbiIiIqLKwko3BSnw9nLZddddh7feegvr1q0reNtMJiMmziICtMuGqXMCjB07FgAw\nbNgwcR1DNxEREVFlYeimIIUmdF9//fVYtWoV/va3v6G5uVn8fsKECRgcHER3d7cmVHV0dKCpqSnv\nNm+88UY0NjYiHo8jmUwCAJYuXYply5b584+gQOm7IYCh5cLkSjcnUiMiIiKqLGwvp3xWrVqF//3f\n/0UkEhHFuq6uLs+2H4rQff311+Pxxx/H2rVrccIJJ2iuO+OMMxCLxbBmzRpcfvnlAIBt27Zh7969\nWLBgQd7t3nHHHTj99NOxZ88eBq0KkC90V1dXo7q6GoODg6x0ExEREVUYVropn2XLlmHZsmWoq6vD\ntGnTAACbNm3CGWec4cn2Aw/d1113Hf7whz/giSeewLBhw3Dw4EEAQGNjI2prazFixAhcffXVuOmm\nmzBq1Cg0NDTgG9/4Bs4991zOXE4a+UI3kF3L++jRo1ynm4iIiKjCMHRTkAIP3XfddRcURcGFF16o\n+f3999+Pz3/+8wCyFetoNIorrrgCAwMDuPTSS/HrX/86gL2lMLMautn1QERERFR51OGmRMUWeOhO\np9MFb1NTU4M777wTd955ZxH2iEqVPG5bpQ/dANDb28uJ+IiIiIgqDEM3BSVUS4YRuVGo0q2G8lQq\nJWY3JyIiIqLKYKXYR+QHhm4qG4VCN5cNIyIiIiKiYmPoprJhFLrVdbqBofZygKGbiIiIiIiKg6Gb\nyoZ+THdVVZXmd1yrm4iIiIiIio2hm8qGPnSPGTNGM1kaK91ERERERFRsDN1UNqqqqlBfXy9+lsdz\nA+5Dd1tbG/7jP/4D69atc76TRERERERUUQJfMozISw0NDejr6wOgHc8NuAvdmUwG3/rWt7B161Y8\n/fTTWLt2Laqrq93vMBERERERlTVWuqmsyJOpeVnp3rx5M7Zu3Sr+tr293cVeEhERERFRpWDoprKS\nL3TLY77thu77779f8/N7773nYO+IiIiIiKjSMHRTWckXup2u071z50688MILmt8xdBMRERERkRUM\n3VRW5Gp2vkq3nSXDVqxYkfO7trY2B3tHRERERESVhqGbyooctJuamjTXORnTffjwYTz55JMAgEhk\n6O3CSjcREREREVnB0E1l5WMf+xhaWlrwwQ9+EKeeeqrmOieh+w9/+AMGBwcBAJ/4xCfE7zmRGhER\nERERWcElw6istLS04PHHHze8rr6+HpFIBOl02lLo7uvrwyOPPAIAiMViuOaaa/Dss8/i6NGjbC8n\nIiIiIiJLWOmmiqEoiphMzUro/vOf/4yuri4AwNKlSzFhwgQ0NzcDADo6OpBIJPzbWSIiIiIiKgsM\n3VRR1MnUCk2klkwm8eCDD4qfly9fDgCYOHEiACCTyeDAgQM+7SUREREREZULhm6qKGqlu7e3N+/t\nXn31VdFCfu6556K1tRUAMGnSJHEbTqZGRERERESFMHR7QFEUxGIcHl8K1Er3wMBA3vbwrVu3issf\n+tCHxGW1vRxg6CYiIiIiosIYuiXyklB2/666utrjvSE/yDOY52sx/8c//iEuz5s3T1yWK92cTI2I\niIiIiAph6JbU1tY6+rtoNMrQXSKsLhv21ltvAchWxqdMmSJ+r47pBrhsGBERERERFcbQLWHoLn9W\nQndHRwc6OjoAAHPmzNF0QMjt5ax0ExERERFRIQzdkrq6Okd/x/by0mEldKtVbiAbumXDhg3DyJEj\nAXBMNxERERERFcbQ/T43wZmV7tKhTqQGmI/plsdzz507N+d6tcW8o6MDyWTS4z0kIiIiIqJywtD9\nvurqakSjUUd/y9BdOqxUuguFbnUytVQqhYMHD3q8h0REREREVE4Yut9XVVXlKnRHo1HHs59T8ajr\ndAPGoTuTyYjQ3djYqJmtXMVlw4iIiIiIyCqmxPe5Cd2RSASKorDaXQLk9nKj0H3w4EEcPXoUQLbK\nrShKzm04mRoREREREVnF0P2+6upqRCIRR9VqNaz7EboZ5L1VqL28UGs5oA3dXDaMiIiIiIjyYeh+\nX1VVFQA4qnb7Fbqrqqowbdo0x0uZUS45dBtNpGY3dLPSTURERERE+TB0v89N6Far416H7qamJkSj\nUUyePNmwzZnsk0N3b29vzvXycmFWQjfHdBMRERERUT4M3e9TA3NYKt11dXVobGwEANTW1oplqsid\nfEuGyZOojR49Gk1NTabbULfD0E1ERERERPkwdANQFEUE57CE7gkTJmiq26NGjdIERnIm3+zl7733\nHo4dOwbAfBI1lTqr+cGDB7lWNxERERERmWLoRjYsqwErFovZ/nu1vVxtUXeroaFBEw6B7ImB+vp6\nT7ZfyWKxGOrq6gDkhm4r47lVaot5MpnEoUOHPN5LIiIiIiIqFwzd0IZlN5Vur5YNmzBhQt77IXfU\ncd1ehG6ALeZERERERGSOoRvuQ7e8zJjb0F1VVYWamhrD6xi6vWEldM+ZMyfvNhi6iYiIiIjICoZu\nuAvdkUhEM/bXbehWW5/N7ito5RD81bHxvb29SKVSALKTqKkzl48fPx7jx4/Puw0uG0ZERERERFYE\nn+JCQA7KdkOl/vZ+hu4wBF6vl0ULgjxeXl02bN++fWI280JVbmBoIjWAlW4iIiIiIjLH0A13le5K\nC91eTRYXJKO1uu2M5wagWcKNoZuIiIiIiMwwdEMblO3OXq5v+XYbSsPeXl4OodtorW67oXvEiBEi\nvDN0ExERERGRGfvrY5UZRVE0QTvISnd1dXXe+2el2xtypfv555/HihUr8Nxzz4nfWWkvVxQFzc3N\n2LZtG9rb25FOp0NxUsSqZDKJaDSady1yIiIiIiJyr3RSgk9isZgmeLgN3dFo1HE4zlflBnInbQtC\nuYXuX//613j88cdFm/nUqVMxZswYS9sp1bW6t27dioULF+Lzn/88kslk0LtDRERERFTWKiJ056s+\n669TFMVWxdIoYDsNpoVCN+CsxdzLCmw5hO4RI0bk/K6+vh5LlizB7bffbnk7Xi4btm3bNtx5553Y\nt2+fq+1Y8eijj6K7uxtbtmzB5s2bfb8/IiIiIqJKVhHt5U1NTaiqqkJPTw/6+vqQyWTEdUZrYkej\nUaTTaUvbNgq01dXV6O/vt72fVkJ3NBoVy1xZoSgKJk+ejL1799reHyN2x7yH0aJFi7By5UrE43Es\nXLgQixYtwtlnn226ProZ/bJhp512mqP96evrw5e//GUcOXIEmzdvxu9+9ztH27Fq27Zt4vL+/fsx\nf/58X++PiIiIiKiSlX6CsiASiWDs2LEYO3Ys0uk0+vr6cPz4caTTaYwbNy7n9tFoFIlEwtK2i13p\nttu6Pm7cODQ0NEBRFM3JBqei0SgikYjlkxJhNHHiRKxatcr1duTQfccdd+DBBx9ELBZDLBbDxRdf\njM985jOWtvPf//3fOHLkCADgnXfecb1f+SSTSezcuVP8XIzKOhERERFRJauI0C2LRCIYPny4Zlyv\nnp1qrlEIdjKZWk1NjaU2cDut4jU1NRg7diwURUF1dTUGBgZs75fR/Zd66PbKCSecIC53dHSgo6ND\n/Lxp0yacc845OOmkk/Juo6urCw888ID4ubu7G319faivr/d8fwFg7969mtfB/v37fbkfIiIiIiLK\nqogx3XbZqSabtZfbZaXKDdjbt3Hjxon9s9s6bXbfiqKEYhb1MJg5cyYuv/xyDB8+HDU1NTkna157\n7bWC23jggQfEsmWqgwcPerqfMrm1HMi2xRMRERERkX8Yug3YCZVetZdbrWza2Tc5BHoVuoFwrBce\nBoqi4Ac/+AHWr1+P119/HZs3b8ZDDz0krt+4cWPevz98+DBWrlyZ83s/Q/e7776r+ZmVbiIiIiIi\nfzE9GXAbuv2sdNsJvAzdxTdr1izxXG7cuDHvOPp7770X8XgcADBq1Cjx+wMHDvi2f/pK99GjR8Vy\naURERERE5D2mJwNu28sjkYitbSiKYjkUOz0hwNBdHFVVVTjllFMAZCvWZkuJtbe349FHHwWQPeFy\n/fXXi+uKWekGWO0mIiIiIvIT05MBt5VuwF61u7a21nKQDUPo5pju/M444wxxedOmTYa3+e1vfytm\nyL/yyisxd+5ccZ1fle7jx48bjuFm6CYiIiIi8g9DtwG3s5cD9kK31dZywHqVWVEUzW0jkYjjpcz0\n981Kd35y6DYa171nzx489thjAIDhw4fjqquuQlNTk7jer0r39u3bxeWGhgZxmaGbiIiIiMg/TE8G\nrFZyFUWBoiiG19kJuHZCt9V9Mzpx4LbazfZya+bNmycef6PQvXLlSqRSKQDAF77wBTQ2NmL06NHi\nNeNXpVsez33BBReIy1yrm4iIiIjIP0xPBqwG23y386vS7WbfGLqLo66uDnPmzAEA7N69G0eOHBHX\nDQ4OYtWqVQCyz8eVV14JIPuYjh8/HoB/lW45dC9atEhcZqWbiIiIiMg/TE8GvAjdVivddiZRA6wH\nXiehu1D455hu68zGda9duxZdXV0AgMWLF2vavNUW8+7ubvT19Xm+T/IkameffbZ4vhm6iYiIiIj8\nw9BtwGqozBeArVa66+rqTFvUjbg5IVBon+QAmG+brHQXZha61bHcAPCxj31M8zcTJkwQl72udmcy\nGRG6m5qa0NjYiClTpgAA2traRLs7ERERERF5i+nJgKIolsKtF5VuO63lhe5TZndMd21tbcFKOEO3\ndR/4wAfEyRR1XPehQ4fw0ksvAcgG7LPOOkvzN/Jkal6P6z5w4AB6enoAADNnzgQATJ48GQCQTCbR\n0dHh6f0REREREVEW05MJt6E7EolYmgV92LBhtvbLTXt5LBYz/fva2tqCJwqshm6GcqCxsRHTp08H\nALzzzjs4fvw4nn76aVFR/vCHP5zzHPlZ6ZZby2fMmAFgKHQDxW8x7+/vRyaTKep9EhEREREFgenI\nhNvQDVhrMbcbuvVLgZkx2rd848fr6uoK7q96v178uyvB6aefDgBIp9PYsmULHn/8cXHdRz/60Zzb\n+7lsmDyJmr7SDRR3BvO//vWvWLBgAb7whS8gnU4X7X6JiIiIiIJgfUHqCmMldBcKv9XV1XknxKqr\nq3M0KVk0Gi0YVsy2W1NTg3g8broviqKYViCtVrqrqqowMDBQ8ZXMM888E4888ggA4MEHHxTrZJ9+\n+uk48cQTc24vV7qN2stff/11/OY3v0EikcCwYcMwfPhwDB8+HFOnTsVnPvOZvJ0KhUK3V5XuRCKB\nzZs3o7W1FY2NjYa3uffee5FMJrFp0ya88847mD17tif3TUREREQURgzdJryodBdq17Zb5VZZqXSb\ntbabVbpra2uhKAqqq6sxMDBgeBuroTsajSIajSKZTBbcz3KmVroB4OWXXxaXjarcQOFK93/+53/i\n7bffNvzbZDKJa665xnRf1PbyWCyGqVOnAoCYSA3wLnT/9Kc/xcqVKzF9+nT88Y9/zHmPtLe3Y+vW\nreLnXbt2MXQTERERUVlje7kJK+Ox3bZZDx8+3NY+Wb3ffLcxCt21tbUiSJudKIhEImJiMKuhu9KN\nHz9eU00Gso/1xRdfbHj70aNHi8dfX+keGBjQVKv1HnvsMdPOgsHBQezatQsA0NLSIu6jublZPKde\nhe7nn38eALB9+3a88sorOdc/99xzmp/V/SIiIiIiKlcM3Sa8aC/PV+lWFAX19fW29wvwPnTLM6ib\nnSiQt8fQbZ1c7QaAiy66yPRkSyQSwfjx4wHkVrq3b98uJmG77LLLsG7dOqxevVpsf8+ePXjzzTcN\nt7tr1y7xt+okakD2uVar616E7s7OTrS3t4uf5THsqmeffTZn34iIiIiIyhlDtwm/J1Krq6tzPMu3\n04nU1H3Srwsuh26zEwX60J1vbXGG7iFnnnmm5mf92tx66rju7u5uzXwA//znP8XlOXPmoLGxEZMm\nTdJs76mnnjLcptF4bpVaie/s7MTx48fz7lshb731lubnNWvWoLu7W/zc0dGBLVu2aG7D0E1ERERE\n5Y6h24TfY7qdtpZbud98t1HHbctqa2vFZSuVbiB/8GfoHiJXuidOnIj58+fnvb3ZuG45dM+aNUtc\nvuiii8Rztnr1aiQSiZxtysuFmYVuwH21Wx+6BwcHsXr1avHzmjVrclrg9+zZI6rwRERERETliKHb\nhBft5YqimAZvp5OoAYX3TZ2F3IzcYq4oiiZ0W6l0AwzdVp1wwgm49NJLUV1dja9//esFXzNmM5i/\n88474nJra6u43NDQgIULFwIAjh49ajiOWq50y+3lgLeTqRlN8ia83eiAAAAgAElEQVS3mMut5ers\n7QMDA5qWdCIiIiKicsPQbcKLSjdgXDlWFEXT0m2XlTHV+cihu6amRrM9s0q3/j4Zuq1RFAU/+clP\nsGHDBnz4wx8ueHujSnc6nRahu7m5OWcprssuu0xcfvrpp3O2qYbuxsZGMWZc5Uelu66uToT7rVu3\nYufOnThy5Ag2btwIIBu4Fy9eLP6OLeZEREREVM4Yuk14MXs5YFw5HjZsmOPx3Fbu185Yc334N6uS\n67eZ7z4YunNZeT0B2tCtVrr3798vxnfLVW7V+eefjxEjRgDIzh4ujwXfunUrDh06BCBb5dY/t3Lo\n3rdvn6V9NHLs2DG0tbUByLa/y2PNn3jiCTz//PNibfklS5agpaVFXM/QTURERETljKHbRKHQqChK\n3hZulVHl2E1rOVB43woFvOHDh4vQr6+aGo35NrrPfCcNYrGYq5MKlUxuL1cr3WbjuVXV1dViGbJ4\nPC6W7Tp8+DBuvPFGcbtzzz0352+9qnTL47lnz56Nyy67TLwOn3zySTzzzDPi+iVLluCkk04SPzN0\nExEREVE5YzIyEYlE8obXQjN4q4wCrJtJ1NT7zsfKBG8zZ87EjBkzDPfFqDpvJ3QXeuzInFGlWw7d\nRpVuAPjQhz4kLj/11FNIJBK48cYb0dHRASA7odvy5ctz/m7UqFFi6To3oVsezz1nzhyMGTMG5513\nHoDsrOUbNmwAAEyaNAmzZ8/G1KlTxe0ZuomIiIionDF0m1AUBaNHjza93mr7tD7ARiIRzcRlTrht\nLwey1WijNbsB4xMFVkO32p7O9nJnRo8eLV4zaqVbnkTNqNINAKeddhomTpwIAHjllVfwb//2b2J5\nrqamJvz0pz81PJmiKIqYTO29995zPJO4XOmeM2cOAOPl0ZYsWQJFUTBixAiMHTsWAEM3EREREZU3\nhu48Ro8ebVrNthoq9QF22LBhlirk+XgRuvOxUuk2uw/192wvdyYSiYjJzvSV7oaGBjQ3N5v+nTqh\nWiqVEkt11dTU4Be/+IUIuEbUFvNkMqlZpswONXTX1taK1vELLrgAI0eO1NxuyZIl4rJ6u6NHj6Kr\nq8vR/RIRERERhR2TUR6xWAyjRo0yvM5qqIzFYpqQ7ba13Mp9u23tdlvpNro9WaeO6+7p6UFbW5to\nEW9tbc17wkaexVz1ve99D3Pnzs17f24nU+vq6hKt6TNnzhSvv6qqKs0+NTU14eSTTxY/FxrXnclk\nctb1JiIiIiIqNQzdBYwZM8bw91ZDpX6tbreTqFm5bz8q3VaXDGPodk8e1/3iiy+Ky2at5arp06dr\nxnx/7nOfs7RMmdvJ1OQx52prueryyy8XJwqWLl2qed3kC907duzARRddhM997nMYGBiwvU9ERERE\nRGHB0F1ATU0NGhoacn5vJ1Sq1e3a2lrTcdR2FJrEzW3g9aLSbXWiOcolz2C+du1acblQ6AaAm2++\nGRMnTsTll1+Om266ydL92Qnd8nJkKqPx3KrW1lb85Cc/wbXXXouvfOUrmuvyhe777rsPHR0d+Pvf\n/45169YV/kcQERERkaceffRR3HXXXSyAeIBTTFswZswY9PT0aH5nZ8zyxIkTMWLECNTW1noWRCOR\niOmkV25DtzoZmtzaazd0q5eTyaSrfalEcqX71VdfFZethO6zzz4bf/nLX2zdnzqRGgBs2bIF8Xg8\nZ/32nTt34gc/+AE2btyIK6+8Erfccou4Ll/oBoBLLrkEl1xySc7vzUJ3IpHACy+8IH7etm0bFi9e\nbOvfRERERETOvfHGG/jhD38IIHtsevnllwe8R6WNlW4Lhg0bljPjuJ1gqygKhg8f7ukyWvnu3+39\n6NfqVhQlJ2QXmkit0D6SObnSnUgkAGSf05aWFl/ur7m5WQwpeP311/HRj34Uzz33HDKZDBKJBP7r\nv/4LV1xxBTZu3AgAeOihh7B+/Xrx92rorq6utrWPEyZMEOF+9+7d4vcbNmzQnOSSZ28PUm9vL954\n4w2k0+mgd4WIiIjIV9u3bxeX9+zZE+CelAeGbgsURckZ2x10oMx3/17smzyu22h7VivdZJ9c6VZN\nnz7dcKy9F6qqqvDNb35TnKxpb2/HjTfeiC9/+cv45Cc/id/85jci/Kt+/OMfI5FIoKenB3v37gWQ\nbSW3s4+RSESs171v3z5xH88995zmdmEI3el0GldddRWuvPJK/PznPw96d4iIiIh8pU7kCwDHjx8P\ncE/KA0O3RY2NjZoKctBLYpndv1FV2gm50s3QXVxypVslT5Dmh+XLl+NPf/oTFixYIH73yiuviLOc\n0WgUX/ziFzFv3jwA2YnOHn300byTqFmhhu5UKoV9+/YhmUzi+eef19xm//79gX/Yv/HGG3j77bcB\nAM8++2yg+0JERETkt0OHDonL+mG2ZB9Dt0WRSERT7TaabKyYrLR3u+G00i2fmGDodmbUqFE5FWMr\n47ndamlpwW9/+1v8/Oc/16wHPmfOHDz88MO48cYbNWO5f/3rX2smOXMSuvXjujdt2oTOzs6c2737\n7ru2t+2lNWvWiMv79+/nuuJERERU1uRKd29vb4B7Uh44kZoNY8eORSaTQTQaRX19faD7YhZovRo3\nXqjSzTHd/olEIhg/fjza2trE7/yudKsURcHixYtxzjnn4IknnkBdXR2WLVsmXlfz5s3DRz/6UTz+\n+OPo6enBAw88IP7Wi9B98OBB8fPpp5+OTZs2Aci2mJ922mkO/1XuZDKZnOr7W2+9pekKICIiIion\nrHR7i5VuGxRFwfjx4zFmzJjAl8Oy0t7thlxpNbovtpf7S99iXoxKt6yurg6f+tSn8JGPfCTnRM4N\nN9wg1ptXJxWrqqrCtGnTbN+PHLp37NghKspVVVW45pprxHV+j+t+4okn8MMf/hBHjx7NuW7Xrl05\nE4jIM7YTERERlRuO6fYWQ3eJ8ru9vLa2VlS7R4wYkXM9Q7e/5MnUJk2aZLhWfFDGjh2La6+9VvO7\nmTNnOpro7cQTTxQnsP7617+Ks6rnnHOOprK9bds2F3uc36FDh/Dd734Xjz76KH7wgx/kXK+vcgMM\n3URERFS+UqkUjhw5In5m6HaPobtE+d1eHolEMH36dLS2tqKxsTHnekVRDKv9DN3ekCvdxa5yW/HZ\nz35WTIIGOGstB7Ind9Tx4/J4oYsuugjDhw/HpEmTAGTHdJutS+/W7t27RcX++eef1yxfpv5Opb6/\nGLqJiIioXHV2dmqOu9he7h5Dd4nyu71cvY981Uv9fUUiEU0QZ+h2Tq50F2s8tx1VVVX4zne+I16H\nF154oeNtyS3mQPZ188EPfhDA0L89Ho9j3759ju8jnwMHDojLmUwGK1asED8fPHgQb7zxhtiXk08+\nGUBpT6bW19eHv/3tb5wUhYiIiAzJ47mBbGEkk8kEtDflgaHbiZ4e4H//F/jWt4D584G6OqC1Ffj4\nx4Hvfx/4058AaRIsP/jdXm6FPvjr75uh27kLLrgAtbW1qKqqwiWXXBL07hg699xz8eCDD+Kee+7B\nBRdc4Hg7+tA9f/580V0hn3Dwa1y3HLqB7PhutaXqhRdeEL9ftGiRpqJfqtXuW2+9Fddddx2++c1v\nBr0rREREFEL60J1Op9HX1xfQ3pSHypi9/KtfBdauzf19dTUwZQpw4onZ/6ZMAfr6gD17hv7r6ADk\nMzvpNLBjB5BMare1bVv2v//5n+zP0Sjwr/8KfO97gA8znTsO3bt3Ay+8ALz4IlBbC/zoR4BB+7gV\nDN3+mTx5Mp577jkAMGzvD4tTTjnF9Tb0oXvJkiXisj50+3ECQh+6BwYG8PDDD+NrX/uaprV80aJF\nmuBfijOYp9NpvPjiiwCATZs2IZPJBD4pJADxRR70qhBERESknURN1dPTIybSJfsqI3Tv2we8/bbx\ndX//u/vtn3QS0N4O9PcP/S6VAv7zP7NV73vuAd5vl3WlvR347W+BAwdQk0yiubs75ybDGhoAo5bw\nnh7gpZeyJxJknZ3AH/7gaHcYuv0V5rDtJTl0K4qCRYsWiZ9nzpwpLvs1mZocuhVFQSaTwcMPP4xP\nfvKTePXVVwEAzc3NaG1t1cyZ8I9//MOX/VH5EYjb2toQj8cBAIlEAn19fYF/gba1teETn/gEkskk\nVq5ciRkzZgS6P0RERJVOX+kGOJmaW5URuuvrAYMZuBGPA4lE/r8dNixbtZZNmgRceCGwaBGwcCEw\nblw2ZG/fDrz5JvDyy8CvfgUMDmar4osWAVdfDXz3u8AJJwB2D6S7uoCf/AS4445sJR7ZJ260va0Y\ne/hh4DOfAT70Idt/Wihkm407J5JNmzYNkUgE6XQap512GsaOHSuumzRpEoYNG4be3l7f28tjsRgu\nvvhirFq1CseOHcO3vvUtJN/vaFm0aBEURcFJJ52Euro6xONxX9vL169fj29/+9s466yz8JOf/MSz\n8K0/cdHZ2Rl46H7yySfFBC0rV67E97///UD3h4iIqNIZVboZut2pjND96KPGv0+ngQMHhlrJ9+3L\nBnS13fzEE43DupFoNDuuWx3bfc01wJe+lK0uA8B992X/a2oCzjor+9/JJwMNDdn7rK/Pjg2XZx/P\nZICnngJ++ENAmrbfsbo64JxzsicKAODWW7P//+pXgQsusP5vfZ8+VOtnTlcnVuPEC5TPqFGjcNNN\nN2Ht2rW4+eabNddFIhHMnDkTmzdvxoEDB9DV1eV5B4AaupuamvDFL34Rq1atAgC8/vrr4jZq9T0a\njaK1tRVbtmxBW1ubL/sDAA899BA6OzvxzDPP4KqrrsLcuXM92a4+dB87dgyTJ0/2ZNtOvfzyy+Ly\nX/7yF9xyyy2oqakJcI+IiIgqm1GlmzOYu1MZodtMJAI0N2f/83ps5uzZ2XHTd90FfPvbgHp26OBB\n4Mkns//ZVVWVDcif/zwy0Sh27NiRc5OpU6caLxsWjQIzZgDqwWwmkz0h8MwzwP79wC23AL/+ta3d\nKdRerv4uqR//TqSzfPlyLF++3PA6NXQD2dA4f/58z+63r68P3e8P02hqakJraysWLFiA9evXi9s0\nNjZq1gyfO3cutmzZAiDbYn7OOed4tj+qd999V1x+/fXXPQvd8naBbKU7SD09Pdi6davm5xdeeCG0\nkwdSZcpkMti8eTOam5s1yzkSEZUrVrq9x/5fP0UiwHXXZceT33orcPHFwMiRzrZ15ZXAP/8J/OIX\nwBlnQPnABzA4ezb6W1s1/0VPPx049dTc/04+eShwA9kW97vuyrbPA8BvfjNUlbf8z7MWup3imHAC\n/J3BXB7PrR5Mf+ELX9DcZuHChZoTWX7PYN7X14f33ntP/CxX3N0KW+h+9dVXc9Zff+qppwLaGyJj\njz32GJYvX44rrriClR4iqgisdHuvsivdxTJ5MvDv/569nMlkx36/+mp2JvF4PDtOu68P6O3NtrzL\nGhuzreqnn56z2Wg0irR0+2g0am/s59SpwG23ATfckP35mmuAzZuzs5rLUingH/8A1q8H3ngj257+\niU/4HrrHjh2LgwcPOv57Kg9moXtwcBArVqyAoii46qqrHM0hYBS6FyxYgFmzZuGf//wnAGgmdgP8\nD927d+/WDMnYuHEjUqmU65NQ/f392Lt3r+Z3QYduubVcHde/bt06dHZ2YtSoUQHuWelJJpO48cYb\n0dbWhl/84heYMmWK4e1+9rOf4bXXXsP3vvc9zJo1q8h7WZo2btwIAOjq6sLbb7+Ns846K+A9IiLy\nTzKZFEunyljpdoehu9gUJdvm7cEMvVZCb0HXX5+dvXzDhmwlfenS7NJpqvb27HXy2a3/+i/g1FMR\nHa2dys3L0F1fX4+GhgaGbsL06dPF3ABq6M5kMvj3f/93PPHEEwCyE65deumltrctv77U0K0oCm65\n5RZ84xvfwKxZs3D++edr/sbvydT0w0Z6enrw7rvvug5IO3bs0JykA7JjuoOSyWTw0vvdNbFYDB//\n+MfxyCOPIJlMYvXq1fj0pz8d2L6Vog0bNoh15R966CF8+9vfzrnNzp07cf/99wMA7r77bvzsZz8r\n5i6WrN7eXnFZ7kIhIipHR44cESf/6+vrxbKeDN3usL28hHmyRFc0Ctx779AyYy+8ADz44NB/zz2n\nDdxAthq/apWvle5Ro0ahurra0d9Seamvr8eJJ54IIBsck8kkHnjgARG4AWDdunWOtm1U6QaA008/\nHevWrcO9996b8zqMRqMiALe1tXkeXI3mavCixVzfWg4EW+net28f2traAGQf709+8pPiOraY27d9\n+3Zx2exk0Jtvvikuy2PpKT+5pbK9vT3APSEi8p/cWi4v68r2cncYukuYPtAaTqBmxcknA//3/5pf\n39wMXHFFdrI11TPP+Ba6I5EIGhsbEYlEUGW05jhVHHW9brWl/I477tBc/9prr5n+7caNG7F69eqc\nscOAeeguxM8W82KGbjsnDB5++GHceuuthpOrOCG3lp9zzjmYOXOmeJ63bt2K3bt3e3I/lWLXrl3i\n8ttvv234epdfqwcPHsThw4eLsm+ljpVuIqok8vd8S0uLuMxKtzsM3SXMk/Zy1f/5P9nl07Zt0/7X\n1pad3fz//b/s+O9Jk7K3X7sWkcHBgvfvZJ/UwA2ASwcRAO247jvuuEO0PdXV1QHIHgirVVPZu+++\ni6uvvho333wznjRYMcBp6JZnE/crdNfV1WHE+8v4vf766zmt4Xa5qXS//PLLuO222/DnP/8Z9957\nr6v9UL0kTdyozgD/4Q9/WPzu6aef9uR+KsXOnTvF5Xg8jj179uTcRv9alSvfZE4+0GSlm4jKnVzp\nZuj2DkN3CfOkvVzW1DQ03lz9r7k5Ow4dyP7/4ouzl+NxVL36asH7d7JP8gRKbDEnQBu6VZdccgmu\nuuoq8bNRtVuucL+qe70CQ6G7pqYGI22sLOBXpTsej2P//v0AgGnTpuH09ydQ7Orq0rQPO6GG7pEj\nR4qTFVZCdzqd1nQWvPHGG672AwASiYR4PkaPHi2e32XLlokTbk899ZRmQjkyl8lkNKEbyC5nJ0ul\nUmJyQBVDtzVsLyeiSiJXutle7h2G7hLmeei2Qg3dAGLPPy8uK4piOHt0vn1SFCXn+mHDholAALDS\nTVn60D1nzhz88Ic/1MwibBS61YmlgNxKbyaTEaF7woQJtmb+nzp1qnidehm65ZnLW1pacOaZZ4rr\n3LSYHz16VLQSz5w5E6PfnwTRSnv5008/rQlr7777LpLJpON9AYC///3vYmKWBQsWiM+O8ePH4+yz\nzwYA7N+/X6yHTvkdPXoUXV1dmt/pX5e7d+9GPB7X/I6h2xq5vby9vd1V18nmzZvx8ssv84QShcKd\nd96J5cuXG3ZCUeWSK92TJ08Ww1dZ6XaHobuE6UOu4zHddlx0kah8R9esEb82C9f5QvfEiRMxe/Zs\nzJkzBzNnzsT06dMxdepUTfhhpZsAoKmpCZMnTwYAjBs3Dr/85S9RV1eHefPmiRMzr732muZAtq2t\nDdu2bRM/79y5UxMWe3p6RPCz01oOZF/Xs2fPFvfj1WRq8nju6dOnY/78+eJnN6FbPqCaMWOGqOp3\ndXUZjv1VDQwM4Fe/+lXO7/RLj9klj+c+99xzNdd96EMfEpeNhgRQLn2VG8gN3frKN5AN3Qx/2feB\n2eOQSCTQ39+v+fno0aOO7mfbtm34/Oc/j2uvvRYbNmxwtI18jh49ikHdsC8iM+3t7bj77ruxadMm\n/P73vw96dyhE5NA9fvx4NDQ0AGDodouhu4QFUukeOxY44wwAQGTrVsTer56ZBX6zfaqrqxNt5JFI\nBNXV1aitrc2pNjJ0E5Dtirjjjjtw7bXXYsWKFWhqagKQfX184AMfAJA9gJDHdctVbiA7CZscFuXx\n3Or27JBbzL///e978mUkh+5p06ahtbVVfNlt3LjRcUAyC93pdDpvu9jDDz8sJo6S38v6NmW75NC9\nYMECzXUXXXSR6CJYvXo129kskCdRU+knU5NDuPqa6urqEsMZKtWf//xnXHDBBfjqV79qeL1c5VY5\nnUxNnjHe6CSIG6+++ioWL16MpUuXGu6z1x544AHcdNNNbLcvYfL3ZaV/DpCWGrpjsRhGjhyJ4cOH\nAyid9vItW7bgiiuuwC9/+cugd0WDobuEBRK6AU2L+fD16wHYD93Nzc2W2nkZukk1a9YsXH/99aLi\nrZKrwXKLuT50A9rw6XQSNdWyZcvEa3jNmjX49Kc/bTjzuB3y37e0tCAajeK0004DkK1iGVU0rdCH\nbrW9HDAf193V1YW7774bQPakx3XXXSeuU9dLd6Kzs1MEwNbWVowdO1ZzfX19vVhzvaenBytXrnR8\nX5VCfl2oz61+MjU5dMsT1nkd/krNU089hXQ6jZdeesmwgm10kOk0dMsdMV4H4zVr1iCZTKKjoyPv\nag5e2LdvH37605/i2WefxYoVK3y9L/KPXM30alUKKg/qa2P8+PFQFEWE7t7e3pLojnrwwQfxzjvv\n4J577sGRI0eC3h2BobuEeTp7uR1S6B67aRNqa2tzDpzz7dOoUaM047bzURSFwZvyMmrB7unpMWzH\n9jJ0z5s3D3feeaeoGu7evRuf/vSnsXr1atvbUskzlzc3NwMw/vfZJbfZT58+XTNpnFnovu+++9Dd\n3Q0gG9I+8pGPiOvchO4NGzaIL2111nK9L33pS+KzY8WKFSVzdj0ocuheunSpuKwGankStUmTJmla\n+st5XPeaNWvw8Y9/HH/84x9NbyMfkKmvd5lROJY/O+yQx917/ZqW/x1GnQ9e2rdvn+FlJ44cOYKn\nn346Z04Cv+zfv58tsu/Th+5SCFPkP3kIzbhx4wAMdUelUqmcuUHCSP48PHjwYIB7osXQXcICq3Qv\nWAC8f9ar9m9/w/SWFnEWTM9oH+228nIyNcrn5JNPRm1tLYBsi2Umk8FLL70kxm9fcMEF4rZehm4A\nWLhwIR555BEx0Vs8HsfNN9+M++67z/a2+vv7RYvfSSedJE6qyZOpOalgpdNpEeanTJmC+vp6zQoB\nRqG7vb1dVJirq6tx/fXXo6mpCY2NjQDchW6jpcL0pkyZIqqxrHYXpoashoYGnH/++eL3anV7165d\n4kBpzpw5OPnkk8Vt3MxG79VcBn751a9+hW3btmlm39eTD86MgnCpVLrlKr3foVsOa/JlJ2666SZ8\n5zvfwXe/+123u1XQCy+8gKVLl+Kyyy7jiTxon7uBgYGinfigcFMnXQWGQrd8jF8K7x3581X+9wSN\nobuE6QNtUSZSA4DqauCDH8xePngQkMap6elnNW9qarK9n6x0Uz7V1dU49dRTAWSD9P79+zWt5Z/5\nzGdEKJdDt3z202noBrIB8cEHH9S07N55552a6rIVu3fvFrMiT5s2Tfx+1qxZGDZsGIBspdtuNWL/\n/v0icM2YMQMANJVuo+D0u9/9TkzIdOWVV2LixIlQFEWcXDh8+LCjL7J0Oi3Gc9fW1orWeSNf/vKX\nNdVuoyokAX19feIEUktLi+FydnJr+Zw5czB69GjRSaEf+23V7bffjvPPPx+33367m933lfoe7+7u\nFpMmypLJpOb1b7XS7XQcs3xfXh+4BhW63RzQptNpMc5dPWHqp7Vr1wLIPlYbN2709b5Kgb6lPEwV\nQQqO/LowCt3FmDPCLfnzlaGbPCGHWbMlu3wjtZjjL3/Je1O1lVxfYbOKlW4qRG7BXr9+PV588UUA\n2crf/PnzRYjdv3+/OPj2otKtqqurw2233Yarr74aQLYF67bbbrN1EKmfRE0Vi8XEZHFHjhzB7t27\nbe2bfjw3gIKVbrX6qSgKrrnmGvF7eek2uycVgGz4U7/QzzrrrLzvbVa7rZEDVktLC0aNGoWJEycC\nyE54l06nc0I3AFHtjsfjjuYKeOyxxwBkl5QLo0QioTnwMhrXp3/tW610exG6/ax079y509cAK4fu\nI0eOOF5Crbu7W3Qk9fb2+j72Uj74djv/RjnQhxGO6yYgd+ZyYKi9HCiNSrc8hIShmzwhV7qL1lqu\nuuSSocvPPJP3pieccAKmTJmCE0880dZayCpWuqkQeb3u+++/X3wpnHfeeaiqqhJhM5PJiIMtNXQP\nGzZM84XilDrZ2IknnggA2LRpE5544gnLf28WugHtSYU//vGPaG9vt3xQLYdjq5VuNVSMGzdOtJQD\n2tDtpMX8+eefF5cXLVpU8PZytfvBBx9ktduAHJhbWloADAXrvr4+7N69O2/oBuyP6+7v7xetqGGt\neugDtVGg00+cZvT6Mhr/60V7uZfjio0q9mZzNXhBPihPpVKO70v/nNg9oWiX/HwzdLPSTcYKVbrD\nPidCJpPR7KPbITBeYuguYXJ1u6qqqrh3Pn06MHVq9vK6dUCeA69oNIrGxkbHJwYYuqkQeVy3vPTJ\nhRdeCGAobALZym8mkxEHGG6r3LLq6mrccsst4uef/exnlsfJ5Qvd8rjuFStW4OKLL8ZFF12Em266\nCWvWrMm7XaNKd77Zy/v7+8XBqVoxVc2aNUtcdrJs2F//+lcA2c+uhQsXFrz9lClTxARurHYbyxe6\ngWyglidRU0+4uAnd8kFMPB4X1cow0Qdqo9Ct/53V9vKenh5HB57yZ4GXB67Hjh3LOQnnZ4DVH8Q6\nrSTpH395tn0/yPfH0M1KNxmT39/6idSA8Fe64/G4ZsgUZy8nTyiKggkTJqCurk60gBTxzoeq3YOD\nwPvtvH6oqqpyVCGnylFVVSVasFWxWEzM0qwP3Z2dnRgYGADgbegGgHPPPRdLliwBkD3wv/POOy39\nnRqeamtrxXhb1dy5czVVZiB7gPTss8/ihhtuyDuGUw3dNTU1OOGEEwAg7+zlcrVDH7pbWlrEnAx2\n28v37duH7du3AwBOOeUU0xUP9L70pS+J+2S1O5ccuk866SQA2tC9atUqzSRqqjlz5ojPVbuhW39w\nHsZqtz50GwVDK6FbPsCU3w92W8zT6bRvodvooNLPcd2lGEGNIoQAACAASURBVLozmUzODO9O2+LL\nQV9fX85r0E3obm9vx0MPPeR4Zn8KD/l1oGaLUqp06/ePlW7yzOjRozFt2jRP2mNtk8d1P/64b3fD\nZcPICrkFGwDOOOMM0RqtD91ejuc28q1vfUvMZfDoo48WXAt5YGAAe/fuBZANTkaTJK5cuRK/+c1v\ncO211+Jf/uVfUF9fL643C039/f1iu3JgHjFihAhc+vZyOUzoQ3dVVZWopu7atUucuLBCbi3/oDoR\nowX6avf//M//WP7bSqCG7pqaGnGyRg7X6sR1+t8PGzZMhPRt27aJifOsKJfQrb+NUQVH/rfJnyN2\nQ3dPT48m5Hl54Gq0vrhfoTuTyeQcxDo9qC1me3lfXx/6+/vFz/F43PEwgXJg9H5wE5hvvPFG/OhH\nP8J3vvMdN7tFIWBU6S7l0M0x3VQeFi0C1Lb2u+8G3p9Yxw+cTI0Kkcd1A0Ot5QAwduxY0VKtD912\nl7CzYsKECfjKV74CIHuQetttt+WtqpjNXC6rqanB+eefj+uvvx733HMPfvzjH4vrzA6wd+zYIbYr\nB4ZYLIYRI0YAyK105wvdwFCLeSqVEpVrK9TWcsDaeG7Z5z73OXGZsw4PSSQSYp3kqVOnipM1o0eP\nFieT5LZjOXQDQy3myWTS1hh9fegO40GYk/byQhOpye8hu4FNf3IrkUjYOmmVTzEr3d3d3TknaEqh\n0m20j04mECwXRlVtp5XuvXv3ihPLb775Jtf7LnFq6K6pqRHHCaXUXq7fvyNHjoTmNcnQTc6NHAl8\n//vZy5kM8NnPAlu2+HJXrHRTIXPnzhXVZQA5Y4bVA+ajR49qJpbyo9INZIOiWhV+44038s7ynG88\nt5mp6pwKMK8QyeO5Z86cqblOncFcHwbkExJGoVvejtWg1tnZic2bN4v9ViusVrW0tIgv/XxdA5s3\nb8bvf//7imlB37dvnxi7pn9M9QHb6HdOx3WXS6Xb7phuN5VuowkLvTpZUcxKt1FV26vQLb+evWZ0\nYqKSx3UbPWdOQ7e8ROfAwECoxtCSferrYNy4caIjrpQr3fF4PDTfUQzd5M4ttwCf/nT2cm8v8JGP\nAD6M6WHopkKqqqpw6aWXAgD+5V/+BVOmTNFcLx8wr1u3Tlz2K3RXVVVpJlW7//77Tc+2OgndkyZN\nEu3iZqFbrkRPnz5dc506rvv48eNIJBLi91Yr3YD10L127VpRcbfTWq6KRCIiMB46dMhwlt2uri58\n5Stfwe2334577rnH9n2UIqNJ1FT6gC1PoqbyKnSH8SDMSqXbSnu5WaXbbug2mlDRj9CtHiS3tbXZ\nGjJglVHo9qq9PJFIOF6Oze59AZUduo0CdldXl6YF3yp1/XNVEOO677vvPlx88cVYtWpV0e+7nPT3\n94uTj/JcUaUUuo0+x8PSYs7QTe4oCnDffcDZZ2d/3rcP+NjHAAcf3PmwvZysuPXWW/GHP/zBcPIy\nOXTK1VK/QjcAnH322TjllFMAZKvO69evN7ydHJ6shu6qqipMnjwZQLa9z6h9PV/oNlurWz7oNXps\nnCwb5qa1XFUoIL7++utiDXYna4iXIqNJ1FT60G1U+W5tbRUnbsqt0m1lyTA7le7q6moxESHgvr0c\n8Cd0qycG0um0mM/BS0YHr15VugH/WswZurXk50xezcJutbu7uxubNm3S/K7YY+X7+vrwq1/9Cu3t\n7bj33nuLet/lRj6BJk92Wkrt5Uafq2GZTI2hm9yrq8uO51Yrixs2AFdfnW059wgr3WRFLBbTLB8m\nk6tUMj9Dt6IoWL58ufj597//veHt1HBcU1ODSZMmWd6+2mI+MDBgWCFSt9vQ0JCzwoFZ6FarFPX1\n9WI8l2zkyJFiHPy2bdsKjpWKx+NiMq8xY8aIkxB2yaHbqMVcPvDzc43iMLFT6TYK3dXV1eIkyq5d\nuyyH51KtdOtfq1Yq3eq/bfjw4aitrRUBxW5F1ug16dXjJgfKM844Q1z2o8XcKJQ5PaA1Cut+TaZm\nFrrDMtaz2OTnUf5stbtW90svvZSzZKBf3Qpm3nzzTbEPbW1tFfucekF+L5dqpdto/8Iy5IGhm7wx\nYQLwxBOAOqPyQw8B0kRPbsViMbEmOZET+kovADQ2NmrGgfth8eLFIki//PLLOdXhwcFBMRmW0czl\n+cjVTf3Bak9PjziAmj59es6ye3KrsVqFy2Qy4oBp4sSJpkv1qUGtp6enYFXjlVdeES2LF154oeP3\ncaFKtzzBWqWF7kgkohnjD2RPcMgnlIxCt/z7TCajmQPATCaTKYlKt/4gq7+/X7OfmUzGMHTrD9jl\n0A1AzBB/6NAhzbCMQorRXq4oCk477TTxez9C9/9n78zDo6jy9f9WL0m6s69ACDuyIwTFKCqIgCzj\nuKHjgsK4IoMKzjibF51xRq8OOFdHXEYG9RHGce5PvIKoKALuKAoOoBIWCSBbSEgCCVk7Sf/+aM/J\nqepTW2/phO/neXio9FJdVV1Vfd7zfpdIOd3i8RfvM9FyusVtZGKivr7+tG1xJR4P8d5q1+nWhpYD\nsXe6xQnXurq606amRzSQVS4HAq1MWVRURxTdFF5OdD5GjgReeaXt7/vvB9asiciqqW0YES5er5eH\nYzOi6XIznE4nZs6cyf9etmyZ6vl3332XFw/SupVmGBVTM8sTlzndVvuXiyHmO3fuNNxGMbQ8lHxu\nRpcuXZCdnQ0g4HSL4qiurk61HbJQ3s5Ga2sr/84LCgqk98dRo0YBCDjaQ4cOla5H7AlvZcAtq15t\nNAhrr8qxsuJiohCvqakJEs3Nzc28pzkQEIZs35KTkwG01Tnw+/22BFsswsszMzNV13q0nW42mRhK\noaLq6mp+/MX7SSzCy8VOF3Y6MHQm2Pfo8XhUk7d2nO7m5mZ88sknAMAFGRB7p5sV6WR09omU5uZm\nbNy4MSoh03qiW1EUPvFI4eWhQ6KbiCxXXAE89FBg2e8PFFmLUH6lLGSYIOygDTGPhegGgCuuuIKH\nar/zzjt8YLNp0yb84Q9/4K8bO3asrfUaiW6jfG5A7nSbFVFjiINko/zplpYW7oR4PB4UsdoPIaAo\nCndkqqureXQAAGzdulVV9bihoUElnuzg9/vh8/lsuZjtQWlpKd9Hvcma+fPn49prr8XChQt5z3ot\nYss8K6JbNijXE4/Lli3DRRddhPnz55uuN5LU19dLv3/R7dALNxRdsvr6en5esZxG8bqIB9EtOsZZ\nWVno1asXd46jEaotHkOxqKLdQa14/M844wx+fGMhukePHs2XY9k2zO/3Y9euXYaipaGhAc899xxW\nrFhh2GYyXNj3mJeXZ/sewNi6dSu/XsaPH8+FdyxFd0tLC7Zt26Z6LNaiP9YsXboUs2fPxrXXXhvx\nYoni969NSWOiO96dbiqkRpxeLFgAXHllYPnkyYAQj0C4D3O5CCJU2kt0e71eXHvttQACs9T/+te/\nsHv3bsyfP5/nok2fPh3Tpk2ztV6rTrdMdMucbquiWxxs79y5Ey0tLfj000/xy1/+EhMnTsSECRMw\nYcIEXHLJJVwQnH/++WFPnIlurRhiri3kA1gPMT9x4gRmzZqFc889F2eddRZGjBiBUaNG4ZxzzsFz\nzz0X1vZGE6Miaoxu3bphwYIFmDBhgu56xIGVlQG3TFzpuZzvvvsuAGDDhg22nNCSkhKsWrWKF8az\ni953LwovmRMOqEW3uM3M6RYjA+yE0UZLdNfV1fH0jezsbFVdiH379kU8yoCdI2lpaar6E3YHteJ3\nkZ2djV69egEIHNNI9S+XfV56erpq0jCWxdRef/11XH311bjyyit193HlypV49tln8dBDD+HJJ5+M\nynbU19dzYZKTk2P7HsAQQ8vHjx/PxXssw8v37NkTdG/p7KJ79erVAAL34kOHDkV03XpON9A28Xjq\n1Km4zpun8HLi9MLhAF5+GWAD5OLiQA/vMGdtPR6PqsomQdilvUQ3ANxwww1wu90AgNdeew1z5szh\nPw7jxo3DggULdHOo9cjMzOQOupHTbTW83Kro7tGjB8+F/+qrrzBlyhTMmTMH77//Po4dO4aysjL+\njxFOaDlDr5haOKL7nXfewddff43a2lo0NTXxwURzczOWLl1qS/ht3LgRL7/8cshi0Q5WRLcVRJfL\nSmipbFCuJx7FPGarg/nGxkbceuutWLBgQciTHqKg9rI6I9B3usU6CqJLIi7LnO5wRXckcuHFfWW/\nj2wyrra2NqJhlX6/X+WQioPycEU3qwzv9/tVUSyRQIwGyMnJUV0vWtHt9/uxatUqvPvuuxEXFhs2\nbAAQuM70wtrFugovvfQSli9fHtFtAILz20XRbSe8nPXndjgcuPDCC/nvaXV1dczqPMju/Z1ZdB88\neFDVlSDSYlKvkBrQ5nQ3NzeH1FouVoi/R2xMRaKb6NykpgKrVgFsYL96NXD22cDcucBLLwHbtwf6\netfVtf2zMLudl5dnq9AUQYhoRbcoOKJNTk4OLr30UgCBwTwTIcOHD8fChQtVOXFWURSFD7BLS0tV\nYo8NJjMyMqRRIqLothte7nA4MGDAAL4vYphtamoqunTpwgdzubm5mDRpEqZOnWp7/7TIiqn5fD5s\n37496LVWRbc4yOzVqxeGDx/OB49NTU346quvLK2ntLQUc+fOxeOPP65bpd4qhw4dwv/+7/8aDhTE\nfF27tQBE7LpcskG53gBbFK1WRfcPP/zA91v2vTI2btyI3//+99ixY0fQc6IQFaM89Jxu0bEVt1nc\nLzbgDDW8XFZILRK5keI+MdEtispI5nXX1NTwwXZOTo7qvhJOeHl2drYqaifSIeZ1dXU83SA7Oxte\nr5d/59oK5itXrsSCBQvw61//OihsOVzE/dI7XtrrZOHChTxiJFKIn5GTkwO3283PHaui+8CBA3yi\nd+TIkcjIyFBFgcRK+GrzuWP52e0B6wTCiHSuMvv+PR4Pj+5hdJQK5uy+6nQ6+RiPRDfR+enXD/j3\nvwPONwD85z/As88Ct9wCjBgBpKQAyclt/zwe4K67DFfpcrmCZt8Iwio9e/bkbjMQW6cbgKqgGhBw\njBcvXqxy4+wiDlbZDPjJkyf5j7GscjmgzulmAlUUEUaiGwBGjBjBlx0OB8aOHYsnn3wSH330Edat\nW4f169dj/fr12LBhA/7nf/5HddxDJTMzkw+Wi4uL0dLSgu+++46HaoqV0a2KblF8PfHEE/jXv/6F\n+++/nz/GCgWZsXnzZp4qYFWo6zFv3jw8/PDDeIjVx5Bg1C7MDl6vl7u4VgbcskGebADm9/tVodpW\nRffhw4cNP4uxYMECvPXWW3jssceCnhO/UzY5BKiFnrgsXkPiNouiOJzwcr/fzye2xIFspJ1uJoKj\nJbq1DmkknW4WXg5EXnRrPwtou2Zqa2v5ee/3+1WFLo3qVdjF5/Opzm2960F2zt9///3YtGlTxLZF\n5maK4kSsj6EHc7mBQKQWEHoUSKj4/X7udItdSDqz6P70009Vf0dSTLa2tvLvTda9pKP06haLX7J7\nVFVVVVBru/aARDcRXS65BHj1VUAY+Oji9wPPPANI2gGJZGVlUVE1IiTcbrdKoMRadPfv3x+TJk0C\nEDiP//73v4ddq0DWNswstBwIzFozd13rdCuKYjq5ddttt+Gmm27CvHnzsHbtWjzzzDOYMGFCRMS1\nESyvu76+HiUlJarwQrFHsVXRLXMKi4qK+H58+umnlsJMxRzznTt3hhyaWl5ezgf7sn7kDCYUMjIy\nVIOhUGDfdVlZmel2y8SCTDyKRcgA6w6aKEyOHz8u3R4xbHrPnj2G/bfF6BZxgCq+RhTdek43O8Zp\naWl8kszq4L6+vp4XPBJd9Ui4RbF0urUOaTiiWztZYFd0+/1+y9eYTHSLERAsKmjz5s2qe2cke/se\nPHhQdT3oiW72eF5eHqZPnw4gINjnzZsX1G4yVETRnZOTwz8PCBQm09Y7+O///m8UFRXhnnvuwfr1\n6+Hz+fDxxx/z5y+66CIAatEdC+F75MgRfrxGjhzJv9vOKrp9Ph++/PJL1WORPEfLy8v5fapHjx5B\nz3cUp5ttW2pqKj+/ZS0i2wMS3UT0+dnPgF27gKoqYMMGYOFC4NprgYsvbvsnuGZ4+mnD1SmKYurC\nEYQeF198MYDAQFt0rWLFww8/jEWLFuH111/neYzhIAoGNsA2K6IGBK4j5nZrc7pzc3NNxXNmZiZ+\n85vf4LbbbotpmL62mJoousWCYVbbhrEfYofDwY+H1+vlAv7w4cOWhMs333zDl630L9dDFO+VlZVS\n10nMrWWDinBgA+7GxkbTHrdMPDudTj7IlQ3AtOsJxenWa0UlruvUqVNBnyUOrvr168cjIOw63eJ+\nMYda/P05evSoJeEnnouRFt1GOd1AZCuYa4ssiedeuOHloug222afz4dbbrkFkyZNstRb3sjpBtru\nl6+++qrqfXYG6R9++CEuvfRS3ToE2okE2fFqbm7m25qXl4cFCxZwF7m2thaLFi2yvD1GyIpl6dV2\nOH78OF599VXU1dXhgw8+wPz58zFhwgRs2bIFQECcsUmeWIeXi6HlhYWF/LosLy+P++4TobB169ag\neiGRdLrFomza9qpAdER3XV1dRAsn+v1+PnGakpIS1j0qGpDoJmJHRgYwfjzw618Hws7Xr2/79/HH\ngXBzAFi+HDAZMCcnJ+u2wCEII2bPno2XX34Zr7zySrvUB/B6vZgyZUpExBIgH2CLA1E9pxtoy+tm\n/bnZgC+eJ7XEvO5vvvmGD7wyMzN5X2rAvtOdkZGhOh8uvPBCvmwWYu7z+YL6lZv1L9dDFN0tLS26\nVa/ZQCUSXR3sFFJiA5fs7GxexE8mjCMhusXPM1qX9j2iWMrNzeWTKVacbj3RLUYTsOujqanJktMk\nfoe5ubk8Uitaojs7O5tvbySdbq1YS0tL4/3hQw0vZ5N/4gDZzOneunUrNm/ejGPHjuGll16y/FlA\n2/Ui3hdLSkpQWlrKC50xrIru0tJS/Pa3v8WBAwfw3HPPSfP3tfsku84qKyt5m7Dc3Fy4XC4sWrSI\nH5fi4mJL22OGTHTr3QP0ilSyycBx48bxMORYh5friW6/32+rCntHQZvPDURWdIsFDGWiO9Lh5Tt2\n7MCFF16IadOmRSyKo7GxkYeRp6SkqKJxIhkVECokuon4IC0NmDUrsFxXFyi2ZkKsQ4OJzoHT6cSo\nUaO4YOjo9OjRI6gvrxWnG2gT3U1NTarBeTyL7iFDhvD9fe+997hIKiwslFZkN0Lb41jEjujevXt3\nUL/UUEW3NqRcNqgSH4uk0w0Yi2Ofz6dy4pjzUVtbG9RTWCu6rboMkRbdWVlZ/BhVVFRwZ5rtR2pq\nqmriwszpBuw7eqLoTk9P5+uKdHg52w9FUbj7ePTo0YhV09eKNUVR+LENVXRnZmbyNBfmdldUVBgO\n6kVR+Nlnn5n2sxa3TeZ0f//991ixYkVQVIkV0e33+/HII4/wY+z3+6Xuu9a9l53Xslxrj8fDv8vq\n6uqInDNmolu8vpijDQQmrKdOnconWpxOJy8OCsQ+vJxNCDidTpx55pmqMWFnDDH/7LPP+DKbILZz\n3a1fvx6//OUvddOWRKc7FuHlK1euRFNTE8rKynDnnXdGpGuBdqJUvLfHQzE1Et1E/DB3btvyM8+Y\nthhzu92q4hkEcTqSmJjIRcD+/fvh9/u56M7OzlYJUS1iMTXRRYln0Z2SksKdSVEgjRo1yrboZm3C\ngGDHuHfv3ny2f8uWLYZFr76V1KEIRXT7/f6gdclm58XHIiG6xdBSI9Etita8vDwuHv1+P68OzdCK\nJis53X6/35Lo1j6mJ7qdTifS0tL4d+vz+fg5w14jusLa7Za1DAPsiwvR+RRz8KPldAPqvG7msra0\ntOCjjz7CF198EdJnycQaO/+qqqosh/SK6RHiNsuKQsrQVqGXVbDXez07F5KTk/n3uHfvXqxYsQJA\n4JxhkwBWRPe6detURcUAeQE2K6JbvPZEhy7U3vB6sM/2eDxcSOmFlzPRrSgKZs2ahYULF+KDDz7A\nokWL8OKLL6rSfZKSkvj3GW3Re/LkSZ5/P2jQIHi93piL/lhSUVHBf6MHDRrEvy+r7m1rayseeOAB\nvP/++1i4cKH0NWZOd6RFt+huHz9+HHfccYclYfzmm29i3Lhx0lQO8Z5N4eUEYcTgwcDEiYHlvXuB\nNWtM36JtaUAQpyNssFpXV4fdu3fzwaKRyw2o24aJA9d4jyIRQ8wZZ511FhITE3mRKyuiW1aEiqEo\nCi644AIAgVxLI6Ei5nMzQhHdhw4dCgpNlQ2qoul0G4ljURSITjcQPAjTOt1WqiLLnDzZIEw7eBId\nGqBNLGVmZsLhcKiOUUVFBRobG/nnZGVlqaJe9Aqpib81dgf34rmYkZHB11VbWxt2L2i2rx6PR9UF\nQVvrYdOmTfjZz36Gu+66C7fffrthOzY9jEQ3YF0AnDp1SjrZZTWvW/s5ZpEoMtENtLndp06d4q+Z\nMGECFzRmoru6uhqPPvpo0OMy0a0NL6+qqgqKjtHrkRwt0Z2Tk8OjhmQTb9XV1XxfBg0apComOGXK\nFFU6DyNWedViO7fCwkLVZwOdT3R//vnnfHnMmDG2J7vKy8v5va24uFh63xHvo2LtCYZ4vw83vLy1\ntTXoN/LQoUO48847Ddft9/uxePFiVFZWYunSpUFRLto2j+EUe4wGJLqJ+OLuu9uWTQqqASS6CQJQ\nu1rr1q3jy2aiuyM63YC6mBoQEByDBg0C0DaRYKWQmqzdkojVEHPmTrtcLgwfPhxAIM/TajE3hizs\nTzZQ0BMRoWI1vFwruo3aX2kHTrKqyFq0jjVgP7xcTBlg54J4jCoqKoK+98TERB4yq9cyTHS67Yog\nrdPNBq+tra1BEQJ20UuPEO8Jjz/+OG677TaVGJT1NzaDfRepqak8Lz2UQa3e+Wu1grn2c7RtlKx+\nnuz+eN111/HXnDhxwrDN0JNPPsmPybnnnssFrFZ0nzp1ytLkkRWnW3aN2KG+vp6f1+JnyO4BW7du\n5eJMJrBlsG1tbW213LEgFMRc80iK7i+//BKXX345Fi9eHN4GRhgxtPz888/n56jf77c0wSzep+rr\n61XtQRlMdOfl5Uk7BEXS6T548CBPyRgxYgT/7nbt2oW7774bDQ0Nuu9j297U1BT0O6O9Z4uTgiS6\nCULLT34CsBn6d98FTPpkJicnS3sQE8TphOhqiaLbqIgaoHa6xVCveBfdWqd7xIgRPCRUFN1mzqqZ\neB09ejQSExMBBES3zB04deoU75k9cOBAnHnmmfw5u263VdEty1ENB6vh5eIg2q7TbbZuwLroNgov\nr62t5c4PE6JaN1YW4cDcbrtOtxXRLU6+iKJb+3l2aW5u5uvWngei6JYdQ210gBliSLgo1kIJ37Qi\nuo2cbu018c033xgKD/Z56enpqq4M2vtj//79cfbZZ6smMPQmzr7++mu89tprAALFMf/85z/zPNg9\ne/ao7j16Ewja4yWLJADUrmO4Trd47MTPSElJ4ZES7DrfvHkzf15sx2hErPKqtUXUgMiI7hdeeAEl\nJSX4xz/+IS2I1x60trbyImoejweFhYW2xaT23sp+sxi1tbV8Ak8WWg6oJx7DFd3ib+OYMWPw/PPP\n89/uLVu24LHHHpO+Txtxpo16EbcrJSWFcroJwhCnE/jFL9r+fuYZw5c7HA7K6yZOe0TRLfaZtRNe\nLs4sx7voHjRoEBfZgNqFYe693+83bX9l5nR7PB6MHj0aQEAwysJGd+zYwcX4sGHDMHDgQP6cXdEt\nyw2PRXh5VlYWP55Gwlgb/ioKUe0gTCYmQxHdsoGSdj1Hjhzh34Esx1kUUcePH5d+72xAaaWQGqss\nrbfNWoxEt1GtACvrZfutdbp79OjB3Xsg8H3dd999/G+7jumpU6e4K68nukNxusX3i0UhreZ0A4Fr\nXVbZWft67TUuFlMDgOuvvx6KoqiOpez68/l8eOihh/jf99xzD7p27YoBAwYACNxLxfxYcQJBjC7S\nnsfaSS1GJMPL9YS9oij8M48dOwa/369yk+063UD0RHdTUxO/V/bo0YPvR2ZmJp8klTm5VmATJH6/\nPyKFvSLBrl27+D2rqKgIbrfb9nWn/S7EYquAebswILJOt/jbOGjQIPTp0wfPPfccH8+/9dZb0gKQ\nZn3KtaI7ISGBdzoi0U0QMm69FWBC+qWXgK1bgcpKQCf3jULMidMdUXSLmDnd4gCQ4fV6476ye2Ji\nIs444wz+t+jCiANms7A7o5xuBsvrBuQh5mI+9/DhwzF48GD+tx3R3dLSwvPqxXaIZuHlkRDdYt6z\nnfByI/EYitMtc1+1bqDf7w96rLGxkR8nmaC24nQz0V1bW8tDitkAzuv1qtrJOZ1OPjA9dOiQaV62\ntnp5pJxuo/PX7XZj3rx56Nu3L+68806sXr0aM2fO5KLEyOneuHEj3n77bdV+6Ym1SIaXJyQkcNF2\n4MAB3eMq+xy99I+6ujo+WaAV3eL9MSUlhVfiFo+lLCXiiy++4E7hmWeeieuuuw4AuOgG1CHmotPN\nJvGA4OuBHWO32626N3fp0oWff+GGl+uFsLPPAQLhx8ePH+eRN3369LEcUROLtmE7duzg+fDM5QYC\nEwfs848ePWq7XkJzc7NKrNuNBokWYmj5mDFjANivpWDmdFsR3dFyutlv5tChQ/GTn/wEQOCeLuax\nAwHH/6uvvlI9pr0+tYXUAKg6LIRbQyNcSHQT8UdWFjBjRmC5pgYoLASyswNCvF8/QJhhBkh0E0Re\nXp6qiBJ7zEw8yyqbd+vWrUOkbEyYMAFAIJxRDOkWB6uREN1med2iOz18+HD069ePu6B2RPf+/fv5\nzL4Y1m7kdDudTunESSgwl6uysjKowBPDKKfbSni5WY6nOEhnAyatwKqurpZuHxtUyr5TbYihTPSJ\n1wqbQGD7JIpkBgslZgLFCCa6HQ4HUlNTDXPh7WAWnqolpwAAIABJREFUqTFz5kysWrUKc+fOhdfr\nhaIoPFT5yJEj0lZbe/bswezZs/G73/0OK1eu5I/rie5IhpcDbROIYnEzkebmZn5ds6rVQGCiQJZO\nYvRZKSkpuOKKK+BwOPgxAsxFtyhQrr76ai6IRdEtpuuITrcV0Z2Xl6e6B7tcLi6Iw3WP9cLLAXWa\nyfvvv88nn6y63EBsnG4xtFy7bSy8va6uzjTSScuxY8dU51C4ExyRQoziOP/88wHYjzDRToAYiW5Z\nuzAgEPnFzvVwC6mx38a0tDRVSsL48eP58gcffKB6z549e4KuR+3fWqcbaDtWDQ0NYd1vIwGJbiI+\nmTcvEGou0tgIlJQAf/wjsGkTf5gNJgjidEVRFFU+JGAeWg7IRXe8Vy5n3HrrrVi6dCleffVVVdEX\ncZ/MCpmZiRYA6NmzJxcC27ZtCxrIMac7OTkZvXv3htvt5sd+3759ugVhtIjifejQoYb9j5mQyMrK\ngsMRmZ9xK3nd7HHWaijSTjcb5CYmJnIBU1NTozqGesKdDRrFiRZ2LthxusVttyK6AZiGobLc0PT0\ndC68GdFyuvVgLpYYHSAiCpq33nqLL1sR3eE63YB5MbWqqiruVnXt2hXnnXcef1xWE8GsbsOf//xn\nfPnll7jxxhv5Y2bh5eI5KN4vzZxuh8OhEoniMW1qauLnrlYMA21i9sSJE2EJByOnWwxpXyN0j7Ga\nzw3EpoL4vn37+PKQIUN0P99uiLlWZMeD6K6rq+PXZEFBAXr27AkgfNG9d+9eletr1i4MCIwz2IRh\nOE738ePH+TYPHjxYNX4vKiriIeYff/yxahJEG1oOBF+fsuKX8dQ2jEQ3EZ8MGwZs2ADcfz9w883A\n5MmAEE6KJ5/kiw6HI8jlEzF6jiA6C9oQcyuiWwxjZsR7PjfD5XKhqKgoKLxaFN1m1bKtihYWYs76\nHDPKysr4AHzYsGFcALNK6q2trdizZ4+V3VGJ7mHDhqkqKIstYVpbW/l2RyK0nGGlgjl7vEuXLlAU\nxTDHjw1+xAGVkej2+/18YJifn68SBOJASVwWc3LZAFmW052RkcEdGr2cbtHprq6uRktLC488kIlu\nNvgFjPOPgbbJHxaVEA2n26roFotyyUSFKGi2bNnCt11PdGdnZ/PvOBaiW1vPQEz/kFUxt5KKwaJK\nZNsku4eIolucrOrevTv/bpno9vv9fD/y8/NVgka8HowcaPZeRjhh2+LniNe89u+tW7fy5bPPPtvy\n+tPT07loipboFgWi1pUNR/THo+jeunUrjzhgoeWAPSHZ2toadM5UV1errg0r4eVAm5ANR3SLEWBi\nDRQgcC0yN7+qqkp1HspEtxWnO57ahsWF6P7kk09w2WWXoXv37nA4HHjzzTeDXvPggw8iPz8fXq8X\nkyZNUhULIjopY8cCjzwCvPhioJL59u0Au3heew0QbrxGIeYZGRnkhBOdHrFaMWBNdHs8nqBChB1F\ndOsRitOdnJwsbZHCEEPeFi9ezMWYVigzmOgGrIeYi33ShwwZohpUiQMLsSp7JCqXM8xEd21tLReI\nbBAj3ne1RW+YW8xacumtl1FRUcEd7e7du+uKbnEdYj6nkeh2OBx82ax6Odt2UQyLzjRDHOwbiW6f\nz8fXxUR3pJzuUES3OKCW5ayKodDiJJOe6Ha5XPya0w7+Dx48iLfffjvo3BCPvzbaRtw+mbjUCnaz\nmguhtNczCy/XE90Oh4PXmjhy5Ahqampw/Phx/v336tULycnJ3AgQz2Vt6oaWSIlu8XO0kxDivoif\na+c3QVEUvq2h5FVbgZ23mZmZQRNi4Yhu7fUQD6JbjN4YOXIkX9a2QTTi+PHj0l7eYog523ePx2N4\nnbDjHc59S1tETYssxLy5uZlX0xcLRBqJbpnTbSX/PZrEheiura3FyJEj8cwzz0jF0V/+8hc8/fTT\neP755/Hll18iOTkZkydP1s07IzopSUnAnDmB5ZYWVWVzI9GdkpKiahNCEJ0RrdNtVkSNoR30dibR\nbdXpNhMso0eP5mGsR48exTM/3nu0+dwMu6Lb5/Px1/Xq1QtpaWm6rU4iXUSNIQ64ZSHcMlFgpWVY\nWloaf72R6BYHvN27d9d1csTlESNG8GWZ6BaPIVuurKzkxzAhIYHvgyi6a2pqdCuXM0TRbVRwSVtE\nTbu+aOZ0yxCdbjPRDQDr168HYOyQygoV1dfXY9asWfjd736Hxx9/XPV6dvwzMjKCfpvNRJPW6e7a\ntSsXut99913QNR9Kez0z0c3O45SUlKBzQwwx37Nnj+p4solRdvz0zmszpzscMciOR1JSUtBkkkx0\n28nnZrDvsLGxMeIip6mpid+fZI5sJJ1uvboHsUScjB06dChfTkxM5N+fmXsrTtKIk+xMdLe0tPB9\nLygoMDSp2P3S5/OhsbHR6m6okBVRExk7diyPTPrggw/g9/uxY8cOfk8W66wYhZezbRWvewovBzBl\nyhT86U9/whVXXCGdFfvb3/6GBx54AD/96U8xbNgwLFu2DEeOHFEV+SBOE+bMAdgs15IlwI8DFo/H\nI71RJCYmIiEhgUQ30ekJVXRrC3F1lJxuPcT9MXK6fT4f/4E2G4wrioIHHniAO7b//Oc/sWPHDlXl\nctHptts2bM+ePXwSmQ2s9GbnI90ujGHmdMtEt14hNZ/PxytGi6K7pqaGP65FHBhqnW5xn8Xt6N+/\nPx94GjndQNt33NzczMN9s7Ky+O+G1n2W5QaKdO/e3VJ7K227MNlnhUo4Od1AsOhuamoKclE///xz\n1NfXGzqk7Ltqbm7m+evr1q3jA9x169Zx8eL3+3VbeAHmObmy858Nwv1+v6rSMxCa0y1Gx2lFt9/v\n56JPJlLFa3/Xrl2qEHkWOs+uBzF6xMzpjlSvbvY5OTk5QWMm2efayedmhJNXbcbhw4e5Toi26Pb5\nfKZ1KKINc7q9Xm/Qb7xR3Q8R8XwpKiriy6xt2LFjx3gIu14RNUYk7l3sNzExMVHaeSUjI4NHMf3w\nww8oKSlRhZZfcMEFfDv0nG5FUXhECYWX22Dfvn0oLS3llWqBwI94UVFRUDl54jSga1fg+usDy1VV\nwLJlAAJhXTI3gl2YJLqJzo6YC5mfn2+5qn9nc7qttgyzK1h69OiBO++8E0AgR+6hhx7iA6K8vDzV\nADwlJYUPXvbs2SOtqiwiC1O3IrojGV5uVkjNzOkWHVutYLWSLy4OeI3Cy7WOIBMjpaWlaG5u5oOw\nhIQEVT0P8VixUEvxMW0hNXF/ZNdSQkICv1aMCqnJRHeknW6Hw2G5ir0oVLQi44cffghy9hoaGrBx\n40Z+3qWmpgalpMiiElatWsUfq6qq4rUN6urqeBqB7PxNSUnh34VMNMkiPYxCzEMR3S6Xix9P7aC+\nurqab79MpGqLqclEt3hus+vBqMAZEJnw8oaGBn5tyrY9KyuLd15ghCK6I9lXXItZwS/xPhau6NZ7\nLFZUVFTwfRgyZEhQ0Ux2/tfV1Ul7WjPE70B0iVn9Bqv53ED4vbpra2v5JOUZZ5wRdL4xtCHmm4Ti\nyUVFRarIJe362Xay4xVKscdoEfeiu7S0FIqiBM0odunSJeIzaEQHYd68tuUnnwR+HCTIBkbsBiHm\ngBBEZ4TVuwCAyy67zPL7xMG67F7b0UhNTeWhaUaiO5R82FmzZvFc+R07dvABrOhyM1iIeX19vbQg\nlIiYt8fWFevw8kg63aLoFp1uvXUD9kW3oijIycnhorulpQWlpaX8exVdbEB+rMTv3Si8XFZIDWhz\nhaqrq7nDq0V8XOZ0h1OQiO2rWCjOjOTkZD7RpnW6xVBosQ3fhg0bVA6pFu2g9tChQ6pBMtBWBMmK\nCGbRNqWlpUGTALJJp5EjR/LvSNs6LJRoAPG12kG9OO6U3SvPEAq+7t69W3VMmasnXg/sfBbPcZkg\n7tKlCxcRoQpZ8TNk36PT6VQ9npWVJXUizYhmBXOz1laJiYn8vLDz2Q0NDdLQ4/YU3Xqh5Qyrucri\nPgwdOpSf28zpttIujBGu6N69ezePVJDlczNE0b127Vpewb1bt24oKCjg+3Dq1ClVmDv77RG3k0R3\nBPD7/VQc63SlsBAYNy6wvHt3oMgagkW3WNWcnG7idOCvf/0r1q9fj7lz51p+j+h05+bmdvhrxeFw\n8NxZq063VQfM7XbjwQcfDHpczOdm2MnrZk63w+Hg79MT3dFyupOSkrjwtJrT7Xa7eci96NiK7cK0\nTrdZyy8g4LaYhZdnZ2fD5XKpwm4PHjzIv3OtwJIdKyOn247oZp8tQzwHZU63bOD6zDPP4Fe/+pXh\nANHv96smGOzAjllZWZmqNo4oEK+99lq+nevXr+dpATJBqP2uRJebwUS4leuOOaU+ny9ITMje73a7\nec2FkydPqtqesdenpaXZmnxnx7S+vl7lIorXgUx0Jycnc7dwz5493E1MSkrir5dNQpk53W63m78/\nVNFtlJcve/yss84KaZwdK9Gt58qyzy8vL5cWEJMhHlPxejeq1xBtxMlYmejW+43QIu5bfn4+7/pw\n/PhxnDx50lK7MEa44eVmRdQYPXr04BPcxcXFXFgXFRVBURTduguyNo9paWl8XEOi24SuXbuqcmgY\nZWVlpo7Mvffei8suu0z179VXX43m5hKx4t5725Z/bB/m8XhU4TfJycn8744uJAjCCoqi6A6m9BBF\nd0fP52awfTLK6Q7F6QYCFbOvueYa1WNGTjdgLLrr6+u549CvXz8euqs3Ox+tnG5AXeBJW19Fz4mT\n9W01crr1CtkwNyYlJQVpaWkqkcTe09rayvefCRNRdBcXF3NnVPud2nG6QxHdenndMqfbSHTv2rUL\nf//737F27Vos+zF9SoZZmLYRbGAttmkD1O3CBgwYwENRxQkVM6f72LFjXHSLPck3b96M5uZmW043\nECza2Pfv9XpV6QMXX3wxX2bF38TX2z1GeoN6cSyqd69lIeZilEvPnj35eEQWXs7OcY/Ho3u+MTFZ\nVVVlGE6sh1FePkMcV4cSWg7oh5cfOXIEf/vb31RtoOxi1C6MwY6T3++3nJMtusFii7T2dLrNRLdV\nB5d9Bx6PBxkZGapWi/v27YtpeLlV0Q2o3W7GOeecA0De1k8s7iZuJ4uKAsxF9zvvvIO7774bd9xx\nB9eN94p6I0ziXnT36dMHXbt2Vd1Eq6ursWnTJlXPOhlPPPEE3nzzTdW/61k+MNGxufRSgN043n8f\n+PZbKIqiGsyIM3IkuglCjhhe3tHzuRlMdNfX1+sW7gql8jNj3rx5/D0ulwtDhgwJeo1V0b1r1y4e\nDiuKd72WMNEKLwfaBtxNTU1BExai2BBFA7vn6jndaWlppvniLDQcCAzYFUWRDpQqKyv5sWKCRxwk\nioXt7DrdoYhusVe3ntMtq14u1iDRDlxF4SuGl2oJddII0O/VLTrdPXv2VAlZhpnTvWbNGi6Ux4wZ\nw3vu1tbWYseOHZZEt5FTys4F7bk/duxYnh+6bt06+P1+1NXV8es/GqJbb5JSzOtmiDU3jMLL8/Ly\ndN3lcIupWXG6xVaT5557ru3PAALnA/su2PdXWlqKm266CUuXLsUvfvGLkCYNgDbn2e126+6D0aSN\nHuJ1wISd+HntAbv+U1NTpRMMVkS33+/nx4DdW0XRvXfvXr6PiqKozjEZMtHd2tqKRx55BDNmzOAT\nyHoUFxcDCNwDZdeJiEx0s0Jw4vXJ7imyyuUMdo+qqqoyjH6YNm0aFi9ejCVLlnDd+MQTTxhupx3i\nQnTX1tZi27ZtfParpKQE27Zt4z9i8+fPx8MPP4zVq1fjm2++wcyZM1FQUIDLL7+8PTebaE+cTnVu\n97RpwJo1XEA4HA7VIIpEN0HIEZ1u0aHoyFjp1R1qricQEE9PPfUUzjvvPCxYsEB1r2Hk5uby9e7c\nuVO3X61er2/R8ZIVUhPbXUUKo9xr9ndWVpbqfsq24dSpU3wftaJbFGV6oeuseq446GODSjZQkoXg\niq83Et2yCQpRiInH0m5ON6DvdMsKqYnr1BZSE3OGd+3apXvehJIewZBVMPf7/Vx0d+3aFV6vFxde\neGFQoSMzp3v37t18+corr1RVS960aZNt0S0ej8bGRj6w1m4HK7DL3mNV4OthRXTrRVuKFcwZYm60\n1umuq6vj+yULLWeEW6DMitM9Y8YMzJo1Cw8//LDlDhhanE6nKhS+uroac+bM4Z9fU1OjMtKs4vf7\nuTju3r17UGExhnicQhHd/fv3599/ezndZWVl/HgNGTJEOhFjRXRXVFRw95cdF1F0l5SUcK3VtWtX\n07GyLLz8ww8/xL///W9s374dS5Ys0X2vz+fD999/DyAwCaUtyKhl6NChquuhT58+/DdKJrqN7tns\n+hfTctqDuBDdmzdvRmFhIc8f+dWvfoVRo0bhD3/4AwDgN7/5De6++27Mnj0bRUVFqK+vx5o1a6g4\n1unOzTcDbMB18CAwbRrS7roL/dLTg6oiOhwO3SqJBHE6c/bZZyMlJQWKouCiiy5q782JCKLo1svr\nDsfpBgKFppYsWYLp06dLn1cUhbvdVVVVurnMYqilNkydbZeskJqs5U+46OVei2HdWneJObbNzc08\nP9gop1vmdGt7dDPEAVdFRYU0xF0cYIvbbMXpFl/jdDr5QC0U0a3niMnCy8V1avMiRZFw4sQJ3cF0\npJ3uqqoq/r0xgZiSkqISzYDcIZUJuPT0dFx00UVhi27xeJi9d+LEiXx53bp1YUWFyMJXAfOcbkDu\ndIuiW3s9iN+xkejWi1CwihWnOz09Hffdd1/YphZzm6urqzF37lwuthhvvvmm7XVWVFTwyAWjgl+h\n5JSLx7OgoEC37kGsEKNcZJFUgLVCauJ+sXulOJmybds2fo8yK6IGyJ3u5cuX88c2bdqkO1FYUlLC\nXWaz0HIgMG4XxyRiBILs+jRq8xgvbcPiQnSPGzcOra2taGlpUf178cUX+Wv++Mc/4siRI6irq8N7\n772nCoEhTlNSU4FPPgGEdnLKP/8Jz1lnwb16ddDLye0miGAyMzPx3nvvYe3atRg1alR7b05EEMWN\nnugOxwWzipiHJxZ3Yvj9fv641+sNGqyz7Tp16hQaGhrg8/n4/kQ6tBzQd7orKyu5E60drMsGYdqc\n7oSEBD4RIhPd2gEvQ9uKSuZ0ezweU0HNtkM78ap9HxuoWQ0v93q9fBv1nG7x/GPh5eI66+vrVdW2\ntSKBtdrSEk6khszpllXZBqBq1wrIzzuv1xtUyPTSSy9FQkICCgoKuAjaunWrav8iLbrHjx/P3U+t\n6A7H6RbXwyZ2EhISVN+nSEFBQZCLJ4aXJyQk8HtUeXm5pTxxIDQHV6SkpIQvR7tLhbitbGIxKyuL\ni/FNmzbZ7kBkteBXKOHl7DpwOBzo2rUrF91ieLYdysvLMXPmTMybNy8k0W6Wzw1YK6QmRkSwfcrN\nzeX3n+3bt/PnzfK5gWCn+7vvvsPmzZv5YxUVFboh5rt27eLLVkQ3EOjEwiaXJ0+ezB+XRaIY3bOt\n5r8/+uijWLRoEd544w1L22eXuBDdBBEyffoEcrpfeAFgP4Dl5cDVVwOaC59EN0HISUtL6zRF1ABr\nvbrZD7XL5ZKGh0cCsSCPODBhHD58mAvJESNGGIbyVlRUhO3OmyEOxEUhIKtczpD1nNaGl4vvKy8v\nD2oDpW0XxtCG4eoVc5PlIWqFqMPhCHpMJsyBwGDSKD9QhLlDx48fl+apMhcpJSVFGpYPqAeLWiEi\nhmuLhON0d+3alYtTM9E9fvx4VUSFnijUivErr7wSQCDig7ndjY2NqlZieudwbm4ub4EmCh6zIoLZ\n2dl84nD//v2WPksPPaebXRddunTRjTRxOByq1mEAglpvsXNbDCMWH5chClm7TnddXR3Pp+3Xr1/E\nU1O0aOuDeDwePP3007jqqqsABMTsW2+9ZWudVltbheN0d+nSBW63O+yoglWrVuE///kPNmzYgHd/\n7LBjByuiOzMzk18nekJS5nSLed2iK21FdGvvW6LLzdC2C2Sw8w+wLrpHjhyJV155BcuXL8fo0aP5\n42aiW+t0W5mgaGlpwRtvvIFly5bh6aeftrR9diHRTXR8FAW45RZgxw6AFX7x+4Gvv1a9jNIRCOL0\nwI7Tre3nHElGjhzJB0Uy0S2634WFhUHPa2fno1lEDdB3uo16CJs53Wzww97X3Nwc9J1oW9rIPuv4\n8eO64sSK6AbUx0xRFFUagritTU1NKqFlRXQD8mJqLKdb64qK6xTzurUiwYrotiso3W43n2STie4+\nffrw5ZycHIwcORJA4DdUTxSKx3bw4MGqvGYxLJS5fmIbHy1iTrAd0Q2oQ8xXCxFvkXC6xdxrM6dY\njFrJzMwM+v7Zue3z+VTRDEZOtzhZYjen+5tvvuERFbJ7TaQRha/D4cCiRYswfPhw/PSnP+WPr169\nWjcUWYZVpzszM5O3MrTiptfU1PCJQnYvEe8p2tSR1tZWbNy40bDImvjcJ598YroNIn6/n4vu9PR0\n3eJmTqeTn6d6XSFkTjegzutm2A0vLykpwXvvvQdAPb7+4osvpO+1U7lcZPjw4fwexJAVGrVSSA3Q\nF92HDh3i6QuyugyRgEQ30XnIzwfmzGn7WzNYIaebIE4PzHK6W1tbdfs5RxKv18vz8UpKSoLy7sxE\nt3Z2PprtwgD9nG5xABmu0w0Eh5jrtazRhpeLA0txECUbgMu+V/F4ii4RQ3RH2GDV6XQiKSkpaF0M\nI9Hd0tLCj4VW4GsLtwEBUact/BcNpxtoO2ZMcIhV07Wu7H/9139h3LhxWLBggW7xI/H7uOKKK1TP\nafPCAXMRzCYFTpw4wSMIrPSoF8PhRecrEoXUrBRRY4iiWwwtZ4jXg+hqGjndbrebP29XdH8tmBCx\nSCMaM2YMEhIS4HA48MADD2DcuHEAAucda0VWUlKi2nczrDrdiqJw0X/06FFTYS8TpkZO96uvvorZ\ns2fjmmuuUd3r9Na5ceNGnp5jhWPHjvFzbujQoYaTwuweWVlZGRRBpN0OcSJEViTPbnj59u3b+X7N\nmjWL3+NYe0CR1tZWHl6el5cX9u9uamoqjwyzG16uN0EhTn6ZVVYPFRLdROdCvFBIdBPEaYmZ6K6p\nqeGDgmjlczPEEPOvNdE3THQ7nU6ceeaZQe/VhpdHOw89MzOTD2SYMG5tbcWKFSv4a7Rhs0ZOt9jG\n0Uh0s0FtZmamqvey1p1ggyWXy6UatMmcIK3IBdTHUzboE9MMmMBjRQb1MGobVlNTwwfCVpxumSsn\nFh8SCSenGwgWFczpTkpKCko1GThwIJ5++mkeMi6DtXDNy8vDT37yE9VzeXl5KvccMD9/ZRXMrUR6\ndO3aNaggodHr9UhOTubunUx0GznSgNrJ005iAOpzWxSeZutl31tlZaWttltmE3yRJj8/H++99x7e\neustXH311arnLrvsMr5sp6CaeH2ZtbZi53BdXZ2uMGbICjmKAlQrutk2nzp1ShUyLSJGaFRXV6s6\nK5hhJbScwa6j5uZmVdFGBhPdSUlJqvtEqE631+sNqhrvcrlw/fXX8/Bv2XHZvHkz/10w2ycrKIrC\n98dKITVxkkwvQkEU3XaceDuQ6CY6F2KBPRLdBHFaYtYyLFzBYgdRdG/ZsoUvnzx5klf0HTRokEps\nMrSiO9pOt8Ph4IN+Jow/+ugjvp0jRozA8OHDVe8RnW4mutkgNzU1lQ/Q9Fz0pqYm/lnagbTWnWDv\ny8nJUQ38tO9LTk6WutOi0JOJPu1ADTAOLQeM24bptQtj28hgx00mun0+Hw4cOBD0OBtoejwe6blj\nhigqDhw4wAeiPXv21G3FZMSVV16JFStW4LXXXpMWGBNDzIHoiW4guPgbYP86lw3q7TjdI0aMwPjx\n49GtWzdcd911Qc+L7xfFgpHTDYRWTK25uRnbtm0DELgOzQRrpMjJyZEKuUmTJvHw7zVr1hj2TRZh\n52hOTo5puynxOJmFmMsKOXbr1o1Ptokirby8XFVZXJbv7ff7gz7TToi5HdFtVCDM7/dz0c16dDO0\nojs1NdVSbRNxIpUxbdo05ObmBnUqEPm///s/1esjAbuHMJdfTNORhZeze3BxcbE0+kGMKqLwcoKw\ngtcLsMGERnRTTjdBnB6Y5XTHonI5Y+TIkXywI+Z1s0EwoO88GYWXR2u7mTg+efIkGhoa8I9//IM/\nd/vttwe5vjLHlokIUcTqOd1i+KdWDGRlZakKajHxo3UDZe+TYcfpZpiJbiOn20h0i8eGiW5RRImi\nUxZizo5FqJNG4jH74osveOSHzJW1ysCBA3W359xzz1X9bUd0s+Minv9G+y3mdQOBYx3K7z/7jKqq\nKrS2tqpEt1nhSYfDgaeeegrvvfeeVDjJxHVqaqotMWk1xHz37t3cFS8sLIxaDQurpKam4uIf6++c\nOHECH3/8sel76urq+PdvxZG1U8FcVsjR7XbziRHx+U8//VT3vYyqqio0NDSoHtO+z4hIiW5Zj25G\nfn6+amKyoKDA8nmhnZy86aabAKivcVF0nzx5EuvWrQMQiPhh3324sOuTpfEYhZcrisJTvSorK6Vd\nNJjTnZSUpLqvRxIS3UTng4WYV1YCwuDa4XCENINPEETHIikpiQ9ezUR3tJ3utLQ0Hqq2e/duHgIo\nhprrie5YF1ID1IJ29erVPCxywIABGDt2bNDrtY6t3+/nTrcoYkVnT8ypk1XXZTgcDi7OxJxjrWAR\nC0wB8tBywNzpDkV0p6en80GoVnSL4Z5a91fmdIviQDzW2rZhzc3NXNCHOvkiOt2fffYZXw5HdBtx\n9tlnqwb1dkQ3E5dMVKSnpxuK6N69e6vayoZ6jNi9obW1FSdPnrTUo1uLnpCRhZGbhZYD6skSq6I7\n1qHlVhBDzFdLWrxq0WsrqId4nGSRInrrFt/Hlk+cOMEnFLWOtUx0y0R+cXGxbi6xiFhELSsry/Q8\nMxLdekXUgMC9VUz5sHJMGeI98ZxzzuG/bz2aTynnAAAgAElEQVR69OCTHf/5z3+44H/nnXf4Mmsl\nGAm0HQbMOk4MHjyYL2vD3+vr63mkUt++fYPqfUQKUiBE50PM6xYGK4qiUIg5QZwmsAGzLLw82q23\ntLDCQWJfbisDYXFCINZONwAsXryYL8tcbiDY6Rb7TosiVhTKomNoNphmg0qxMI9WdIuuFKA/kTJ0\n6FAuzmV5v6GIbqDN7S4tLVX15BXPPb1K6YC56NY63YcPH+bRAZFwusVQ2GiJ7oyMDFWeZDjh5VbO\nfdHtDvVa0Q7q7eR0myF7v1loORBa27BYF1Gzwrnnnsuv7Y8++givvPIKHn/8ccyfPx8zZsxQRdgA\n6gktK063KCjFCTsZ7Dhqq/Nr+9n7fD58/vnn0veKiNexOLlmxe0+fPgwn6wzK6IGyKt4M/S6QjDE\nEHM7olu8d82cOZMva9sDsmgusee1UV0Iu2g7DBg53YBadIspAgCwd+9efk+NVhE1gEQ30RmhYmoE\ncdrDwnlPnDgRVNU1lk43ENyvu6mpCd9++y2AwABSb7Dtdrv5foiF1JKTk03DUENFDMtkUQK9e/fG\npEmTpK/XFlKTVS4HAqKT3X9Fx1DPZWLIjo1MsIjv1RNZPXr0wEsvvYTHH38cU6ZMCXo+lJxutl4g\n4IiK+xNOTndhYSH/bK3ofv/99/nyiBEjTLdPRnZ2tvQc0hY8iyRi+KlZeLY2PLiuro6387ES5SGK\nbm3PaKtoK5gz0e10OsOe9MrKygqKvIuG0y1O9CUnJwcVQmwvXC4XL7jX3NyMxx57DC+//DLWr1+P\n7du346mnnlKFWet1ONBDFJQlJSW6r/P7/fyazc/P160VcfjwYWzdulUl7AD5dyA+dumll/JlK6Jb\nFINWCo4ZOd1GUUSA+hhZmchgTJ48GUDA5b7wwgtVz2nzuouLi7mrPGzYsIjmSmsnHMxENwsvB4Kd\nbjGaKJrXCIluovNBopsgTnuYs9ja2hpUvTbcdkt2Ed2lLVu2YMeOHdwRNQv3ZIOqiooKHp4YrdBy\nQD7wv+WWW3TD7bQtw8RjLYpYRVGCirSdOHFCFVoqy6OTiW7ZY+IA2eg7HTVqFCZPnizdn3BFN6B2\n5ETRrQ0vN3K6U1NTkZqayh2X0tJSVaj62rVr+TIbANtFURTpJEe0nG4gkPs5YsQIXHzxxdI2YiIp\nKSn8GB09etR2lMfAgQMxe/ZsFBYW4uc//3lI26sd1DPRnZubG3b4qdPpDLqOrTjdXbt25e6nFdF9\n6NAhft8YMWIE704QD1x55ZWGx3HVqlV82a7TnZyczKNfRBdTS2VlJZ/M0V4P2grmsmJoZWVlPHSa\nITrdkydP5tf+559/bto6zE4+N2AsusXtkF3r06ZNQ3p6OnJycmzlWV9//fX46KOPsGTJkqCJI63o\nFguoRdLlBoInxVh4udfrlZ5XBQUF/J6idbrFiU0S3QRhBwPRTcXUCOL0QAznFUW29u9YhJdnZmby\nHNPi4mKV42Emutn2NTQ08LzCaG6zVnR37dpV5dZosep0i+tmRdoeeOABLsCLioqkDlYoTneoEymh\nhpdbEd1mTndrayt3upnLKw7+WAX5AwcOqJwjOw6VFu3xzsnJsbS/oZKbm4t//vOf+Nvf/mZJ/DGH\nurS0VBUdYXXS6a677sKyZctCdtfE80jsnRxuaDlDux4rolvs1W0lvDwe87kZ/fr1w7PPPos777wT\nf/zjH7F06VK88cYb0srmdp1utn4g0E1BG3rNMIq00TrdTHQriqKK2tBOfohit6CggLfTq6mpURXQ\nlLF161a+LDqzelh1umXRHgUFBdiwYQPWrl0bUh97mbAV2wN+++23ePvttwEE6qxMnTrV1mdY2QZG\nZWUln7zUu4cpisJTXMrKylTHKxY9ugES3URnpHdvgN0MNAVoyOkmiNMDUeRo87pjHV4OtOV1t7S0\n4N///jd/3CzHUjYYiqXTffPNNxveN6063dp1L168GB9++CGAwHf1yCOPSPMXZfsqEyficbQyWJUR\nqtOtV8FcdKfNqpdXVlZygcEGyOLgjzkx7777Ln9MFiJvB63IiKbLHQrsODQ3N2PXrl388VhMlAHq\ne4MYjmq1iJoZ2vPYqpgXe3Uzl1YPMZ+b3YPiiTFjxmDu3LmYPn06ioqK0L9/f2llcya6PR6P5e/f\nSoi5VdH91Vdf8Ymv4cOHq2pCaEU3+9vpdCI3N1cVgm3UOmz//v38++rdu7el80Fsj6iX052YmKh7\nzBISEiI+LmbtAVtaWrj7fMkll0jvr+GgF15u9Dnib8POnTv5MhPdWVlZUb2/kOgmOh9uN8Butrt3\nA0JYEYlugjg9EAfM2grmzLFKS0uL2T1BzOtmYiw9Pd1U6MhEZ7RFN3Mhs7KycNVVVxm+PikpiTse\np06dUlWQ1TrHoshYtmwZX3744Yd1hYxVp/ucc87BU089hcWLF4dcLCoSTrfYq1s878ycblm7MJno\nfu+99/hjl1xyiem2GaF1DONVdAPgNRCA6J7/ItEW3aE43YA6P3fv3r04ePAgtm7dik8//VTVqxho\nc7pdLpe0eGA8cvnll/PlVatWoaWlhYvj7t27W25tJYruvXv3Sl9jJLpzc3N5dKQYfjx27NggF1yE\nRayweylzugHjvO4VK1bw5enTp+u+TkRRFC4SRefWqEd3tNG2BwRg+jsSCuL1WVZWxiegjO7Zsgrm\nx48f52OCaNc8iJ/kDoKIJAMGBFzuujrgyBFA6L1IEETnx6hXN3MEYuVyA3KXqbCw0LSNoUxgRHMm\nPjExEXfeeSdWrVqF++67T9XLVYaiKEhOTkZ1dbWp0y0TKzNmzMC4ceN0168VIomJiVJxrCgKxo8f\nb7itZng8HrhcLlXepSiOjbYxKSkJDQ0N3Olubm7mg+DExMSgomVJSUn8s7Sim4WXi22v9uzZg5KS\nEu7IjBgxIuQCYYyO4nQD7S+69+/fz5fNisBZRSu6rTrdoui+/vrrVc/17t0bL774InJzc1FVVcUd\n3iFDhkSt+GKkOffcc5GXl4eysjJ88skn2LVrF48CsZNOYcXpNgpbdzgcyM/PV333AHDhhReqoqfE\nddTV1fHfG3b+ZmdnY9iwYfj222+xa9cuHDt2LOhe2NjYiJUrVwIIjFPFiQczcnNzcfjwYZw4cQI+\nnw9utxuVlZW8V7isiFo0GT16NBRF4Xn0vXv3jkrVfPH6FCc7rYpultcdq9BygJxuorOik9dNopsg\nTg/EnG5xgFRfX4+6ujoAsQtTBQIDo169eqkes5JjGWunGwBmz56Nd955x3JxHTbIMXO6taJi0KBB\n+OUvf2m4blmxqWi5NoqiBE0UWAmJVBSFD9gPHTqElStX4oorruCDda3Lzd7DBL2e052SksKF8Z49\ne7BmzRr+mlALqIloRUY0K5eHgii6ReHTHqJb7IAQqZxu7YSSVafbaHJk//79uPXWW3H8+HFVfnC8\n5XMb4XQ6VZXNlyxZwp+z09qK5XQDoYWXyx7LycnBoEGDVNshhpeLHQjE8/eCCy7gy5999lnQ57z/\n/vs8AuqSSy4JajFohHg9sAllox7d0SY9PV3VHvCKK66Iyj3b7Xbz+7M48WF0z+7VqxeffGJOd6yK\nqAEkuonOio7opl7dBHF6oFdILdaVy0XEEHPA2kA41jndocDEo52cbo/Hg4ULF5oWt8zOzlYN2CIl\nePTQThRYcbqBtrzu5uZmPPDAAzhw4AB/Tq8QHZusqK2t1R2ss0FgXV0drwWgKErYoeVAx3K6RWI1\nWSYO6kWiEV6elZVleWwyefJkTJkyBf3790dRURGmTp2KGTNm8O9z3759uP3227Fhwwb+no4kugF1\niPn69ev5sh3RnZGRwe/xZqI7OTk5qMMAEHyNXHDBBXA4HOjWrRu/L4nCXTZ5BkCV1y1+L4zXXnuN\nL//sZz/T3ykJ4vXAomvMenRHG1ZvIjU11ZZrbxe272J0kpHT7XQ6+YQA64keS6ebwsuJzok4WyVp\nG8ZClRgpKSlB/RcJgui46Dnd7Sm6zzrrLLz++usAAgVsrLSEkQmMWDr0VmDCtKGhQXV8tQJ2xIgR\n6N+/Pw4dOoQ//elPlpxVt9uNzMxMvl6rbmCohOJ0A/J2Z0VFRZg9ezZGjx4tfQ8bHNbU1OgO1gcM\nGMALzrHzuLCwMCLCz+v1IisrC5WVlXC5XO0yODdCFsbtcDhsuYDhkp2drYreAKJTSM3OeZ2YmIhF\nixYFPX7jjTfi5ptvRmlpKb7//nte+AvoeKK7X79+PCRbxG61/r59+6KyshLHjx/HyZMnVcK6paWF\nX3d6ueJa0T127FgAgftSXl4ejh07Zkl0Dx06lF9rH330EVavXo2f/vSnAAJRLKyAWr9+/Wx/V9oK\n5i0tLfj888919yEWzJw5E3379kWvXr2iOkmclZUVFP5vds8ePHgwr3VQXFzMRbfD4VClJEQDcrqJ\nzok4W2VSwdzr9YadG0cQRHxhxemOtXgVne5hw4ZZamHYHuHldhHdYNbLGAgW3W63G6+//jo2bNhg\nq/J2qOIkFEJ1us877zy+PGbMGLz88stYunSpruAG2kS3z+fjzrjD4VDtoyzcMRKh5Yxp06bx/+Op\nhzMQ+K6125SZmRl2j2w7yCbmIhVt0a1bN75/dhxcPQoKCvDCCy8EbV/v3r1jPsEYCS677LKgx+we\nJ1FE7du3T/VcWVkZd0j1hKn4uMvlUhUJY89VVVXxlCXRYRbHlU6nE/PmzeN//+lPf+IV+UWX+5pr\nrrEdii3+HnzzzTe47bbb+OQu0D4RLC6XCxdddFHUU1Zkv+FmxS/FCubffvstL7LXs2fPqNc9INFN\ndE66dwfYxWPQq1tRFHTv3h0JCQkxre5IEER0SUtL40XKRKdbbKsSa9HdrVs33H777ejTpw9+8Ytf\nWHpPRkZGkMiItwG0OMgRnR5ZwTOHw2G7dYw4qIx2eHmoTveYMWPw//7f/8Mbb7yB559/3lLhIPG4\nMbdGrB4PBIc7OhyOiISWM37zm99g7dq1ePjhhyO2zkjhdDqDXOVYTzhp7xF2wsDNSE1NxX333Yei\noiLccccdEVlnz549sXTpUtVxikYRq1gwdepU1bXAxmt2MKpgLhbf0luvKPILCwtV9wPxOeZ2i2ki\n2siRq666ilfxbmhowPz581FaWoq33noLQKC4InO/7SB+10uWLMHmzZsBBI7X7NmzQ+5T3xGQ/Raa\nTZSKxdTWrl2LxsZGANHP5wZIdBOdFYejLcR8715AyPcQZ7K6dOmCxMREKIpiyXUiCKJj4HA4eAEr\nsXp5ezrdAHDPPffgzTffRFFRkaXXO51OlWufkZERd3UpRPFYVlYGIDCAjNQ9NZZOt1ZkW3W6gcBg\nTqw4boZ43Jjjpo266tmzJxITE/nfZ599dkSFp6IoqvzUeEMbYh5r0a0d1EcqtJwxY8YMLF26NOTe\n8jL69OmDpUuXoqCgAB6Px3aOcLyQkZGBiy66iP/dpUsX2/cUowrmX3zxBV/WE6aDBg3CmWeeCZfL\nhZ///Oeq50RRzUS3ntPNuP/++3la0aFDh3DDDTfw9IWpU6dKJyrNkF0T3bp1wwsvvIC77rorbq/t\nSCD7DTebKO3bty+/p4qtAEl0E0Q4MIeguRkQcj5SU1ORn5+P7t27qy5YEt0E0blgYlUU3aLTHW+O\nsR7ioCreQssBtTBtaWkBYN0htoKYLy3LnY4k4qA3KSkpqhMcsjBIrch0uVwq4RDJ0PKOgFa4xHqi\nLNqiO1r069cPq1evxieffGKpdkS8IoaY283nBtQVzLVO98cffwwgMPEkVhcXcTqdWL58OTZu3Mjz\nuRmyXt3M6U5PT4fX6w1aX2JiIp544gn+21ReXs6fu+aaayzvl4j2njFt2jSsWLHCMLWlsyD7DTcL\nL3e5XNKCadEuogaQ6CY6MwYVzLOyspCZmamaARTdBIIgOj7M6a6vr+c9S9szvDxUxO2Mx22WDXJC\ncWz0uPrqqzF16lTcdtttGD58eMTWK0OcLDAbvIWLbP0yd+ycc84BEKg/MnHixKhuU7xBojt0XC5X\nhx/XXHDBBTwcOJQJp9zcXH6diTndR48e5a2ihg0bZjiZ6XA4pLm+WtHd0tLCa1oY1Qnq1q0bFi5c\nyNOfgECUzLBhwyzulZqcnBzcfvvtGDp0KB577DH85S9/iej9N56RiW4rE75iiDkjFk53fFXNIIhI\noq1g/mPBGD3I6SaIzoUYll1RUYHu3bu3a/XyUOlITjcjkk53ZmYmFi5cGLH1GSEOVuNFdM+ZMwcF\nBQUYNmxYhzlnI0VnDy8njHG73XjllVdQUVEhrWZvhqIo6Nu3L7Zv344jR46grq4OXq+Xu9wAghxs\nq2hFd3l5uW6aiJZzzz0X8+bNwxNPPAEAuOGGG8IKA7/nnntwzz33hPz+jkoohdQABKVzeDyeiBQz\nNINEN9F5MahgLoNEN0F0LkTRfc0112DixIm8YFViYqKtfN325HR3umNJLEW37PyTCYvk5GRcd911\nUd2WeEUrXkh0n3643e6QBDeDiW4g4HYPHTpUJbrHjRsX0nq7dOkCl8uF5uZmHD58WFVE0kr7vZtv\nvhn5+flobGyUVmonzAklvBwIdrr79++vijyIFhReTnRedMLL9ejoYVgEQagRq/bW1NTgjTfe4OF/\nWVlZHabAzOnudMcScbujPSlj1ek+ndGKFwovJ+wi5nWXlJSgvr4emzZtAhDoFjBo0KCQ1ut0Ovlk\nwJEjR0yLqGlRFAVTpkzB5Zdf3mF+i+KNUMPL+/fvr6qMH4vQcoBEN9GZyc4GmNNlQXS7XC668RFE\nJ+LSSy/F888/j0svvTSoqE20W09FksLCQr48cuTIdtwSOZ3V6Y72xIGVQmqnO+0dXq4V+R3pvkEE\n0LYN+/LLL3mbqAsvvDCscR8LMa+pqeF9twGaPIsVqampQcUurUyWJiQkqIR2rEQ3hZcTnRdFCbjd\nmzYBP/wA1Ne39e6WvlxBYmIiL7hEEETHZ8yYMRgzZgwaGhrwySefYM2aNdi/f3/E+uLGgmHDhuGl\nl16C3+9XCfB4oTM53V27doXD4UBra2vUB85a0e31ejvsZEW0SE5ORlpaGqqrqwHEXnSnpaXxEGKA\nnO6OiLZtGGvRBYQeWs4Q87q3bNnCl0l0xwZWGJlFsHk8HssdJ4YMGcJbhsWicjlAopvo7DDRDQDf\nfw+YVL5NSEgg0U0QnZCkpCRMmjQJkyZNau9NCYmzzz67vTdBl87kdOfm5uLBBx/Etm3bMGvWrKh+\nlva4xXO/7PakZ8+e+Pbbb7kAjyVsUF9WVoa0tDRpGygivsnPz0dSUhIaGhqwd+9e7NixA0BgvFdU\nVBTWukXR/d133/FlEt2xQxTddupw3HDDDfjyyy/Rv39/nHXWWdHaPBUkuonOjbaCuYnoprxugiAI\ne3Qm0Q0A06dPx/Tp06P+OdrjRqHlcu6++248++yzmD59ertMStxwww149tlnceONN8b8s4nwcTgc\n6NOnD4qLi/HDDz/wx0ePHh32JIpYc6ClpQVAQMyfbl0G2hMxBcSO6B4wYADeeeedaGySLiS6ic4N\nVTAnCIKIKrLw8o4sumOFzOkmgmEpIu3FrbfeipkzZ1oOWyXiDya6RcINLQcgbTPVrVu3mFTCJgKI\nExzR7jgRLiS6ic6NKLpffhnYubPt7/x84JJLgPPPB378MSWnmyAIwh4yt6ij5nTHEhLdHQcS3B0b\nsYI5I9T+3CJieDmDIlZiS6hOd3tAopvo3Ijh5Tt3qkU3ADz6KJCaCkycCFxyCRIzM5F2+DB/uql7\ndzQMHRqjjSUIguh4OJ1OeL1e1NXV8cfI6TbH7XYjMTGRV1Im0U0Q0UEspgYEWkbJBLNdsrOzkZCQ\ngKamJv6YlR7dROQgp5sg4oWUFOCGG4B//Uv/NTU1wBtvAG+8ASeAnpqnf3jiCVRPnBjNrSQIgujQ\npKSkqEQ3Od3WSElJ4aKbHDKCiA5a0R0JlxsI5Ivn5+dj//79/DGaPIstotMd7787lHRAdH7++U9g\n3742p3vnTmDHDmD58oAg1/Th1JL31FPAjwUyCIIgiGC0ed3kdFtDdGZosE4Q0aFHjx5wudp8xkjk\nczO0jjldx7GlI4lucrqJzo+iAL17Bz8+eDBw440BQb1lC/DFF4DPhxMnTqC+oQEZb70Fz86dSNq3\nD2nr1qF68uSYbzpBEERHQBSPiqJIi6sRwWRkZODAgQNwuVzUA5ogooTb7cbAgQPx3XffISsrC2ee\neWbE1k2iu30ZNWoUevfujbKyMkyO83E6iW6CcDqBc84J/APQVFaGirIyNJ5xBnrPng0AyF2yBNWT\nJgFUkZIgCCIIUWSnpqZS9V6L/PznP8fChQsxffp0KtZFEFHkwQcfxKuvvorLLrtM5XqHi1Z0U053\nbElKSsLKlSvR2NgYdgu4aEOimyA0sLZhp847D3XDh8P7zTfw7N6N1A8/RM3FF7fz1hEEQcQfotMd\n7yF+8cTEiRMxkWqGEETUGTJkCP785z9HfL1a0U0RK7GHFfOMd2gqmiA08LZhioLyH51uAMh7/nnA\n72+nrSIIgohfxAEP5XMTBHG6IIru3NxcbtwQhBYS3QShQbxh1owdi/pBgwAAnh07kPLpp+21WQRB\nEHGL6HST6CYI4nRBFN2Uz00YQaKbIDQ4nc62fB9FQfkdd/DnyO0mCIIIRszpJtFNEMTpQmZmJqZO\nnQqXy4Wrr766vTeHiGMop5sgJCQkJKC5uRkAUD1hAhr690fS99/Du20bkr/8ErVFRe28hQRBEPED\nOd0EQZyuLFy4EA0NDUhKSmrvTSHiGBLdBCEhISEBdXV1gT8cDpTffjt6/Pa3AIDed9wBv9PJX+tP\nSkLDgAGoHzwYDYMGoX7QIDT27x+oih4Ora1ULZ0giA4BFVIjCOJ0hgQ3YQaJboKQwIup/cjJyZOR\n99xzSNy/H0prK5TW1rYnfT4kb9mC5C1b+EP1gwejZNky+EO8CbsPH0afW2+Fo6YGFTfeiIqZM9FK\nfW8JgohTtC3DCIIgCIJog2w0gpCgFd1wOnHokUdwavRo1A8erPrnk7SH8BQXI/P110P+/C6LFyPh\n8GG4qqvR5dlnMWDqVGQvXw6lsTHkdRIEQUSLc845B0lJSXA4HDj//PPbe3MIgiAIIq4gp5sgJCQn\nJ0NRFPiFomn1Z56J/S++KH29s6oKScXF8G7fji7PPAMAyH3hBVRdfTX8WgFvQkJJCdLXrFE95qqq\nQreFC5G9fDmO/td/oWbcOOl73YcOIeell1A7ejSqp0yx9bkEQRChkpubi/fffx9NTU3Iy8tr780h\nCIIgiLiCRDdBSHA6nUhPT8eJEycsvb4lMxO1Y8agdswYeIqLkbZhA9zl5chcsQKVM2bY+uy8JUt4\n+PrxGTPgqqxExo8iPOHoUfScPx8HnnkGp8aMUb3PVVaGPrfcgoSjR5H12mvY16UL6goLg9afVFyM\ngt//Hq6KCvU+pKXh6O9/j1MXXGBrewmCIAAgIyOjvTeBIAiCIOISCi8nCB0yMzNDel/ZnXfy5dwX\nX7QVEp6wbx93uZszMlB2zz04tHAhvn/tNZw67zwAgNLcjB733oukHTv4+xw1Neg1Zw4Sjh4NvMbv\nR/cHHwz6bEdNDXreey+S9u6F68QJ1b/EH35A3tNPh7TPBEEQBEEQBEHIIdFNEDp4vV4kJCTYfl/D\n4MGovvhiAIC7rMxWbrfK5Z41C61eb2CdgwZh/3PP4eSECQAAZ10dev3iF3AfPAjF50PPe++FZ/du\n1boS9+9H7vPPtz3g9yP/z39GwuHDAIDm9HQ09uyJxp490frjfibt2QP82CqNIAiCIAiCIIjwIdFN\nEDooihIZt/uFFyy53Qn79iH9nXcABFzuyuuvV7/A6cShxx5D7Y8h4+6KCvSeMwcFv/0tUjZt4u/7\n4Ykn0OoKZI7kvvgikoqLAQAZK1fyMPWW1FTs/d//xZ6338aet99GzUUXAQAcTU1I/OGHkPaZIAiC\nIAiCIIhgSHQThAGh5ig2DB6M6vHjAfzodv/f/5m+J8jllrQI8ycl4YfFi9HQpw8AIPHAAaS//z4A\noDUpCQeefhrVEyei/I47AABKSwu6P/ggEvfsQf6jj/L1HP7DH+Dr3r1tewcO5MtJu3bZ3V2CIAiC\nIAiCIHQg0U0QBrjd7pB7zpbNmcOXc5cuhdLUpPvahP3721zu9PRgl1ugJT0dB/7+d/hyc/ljfocD\nBxcuRP2IEQCA47fdhob+/QEAnp070ffGG+GorwcAVE6fjurJk1XrbBgwgC8nacLUCYIgCIIgCIII\nHapeThAmZGVloaamxvb7GgYPRvVFFyHtww/hLivD4PPPh98hn+dSfD7uclfouNwivvx8HHjuOfS+\n7TY4a2pwZMEC1PzorAOA3+3G4T/9CX1vvBFKayucdXWBberbF0d/+9vgbRVFNzndBEEQBEEQBBEx\nSHQThAkpKSlwuVxoDqHAWNmcOUj78EMAgKOhwfT1zenpqDBwuUUaBg7E7rffhqO+Hs1dugQ9Xz98\nOCpuugk5L78MAGhNSMDBRYvg93iCXuvr1g0tqalw1tSQ000QBEEQBEEQEYREN0GYwAqqlZeX235v\nw5AhOHrffchYvRpKS4vha1uTklB+551oTUmxvP7WtDS0pqXpPn9s7lx4//MfJO3YgSN/+AMaBUdb\nhaKg4YwzkPz113AfOwbnyZNoSU+3vB0EQRAEQRAEQcgh0U0QFghVdAOBcPGKWbMivEXW8Hs8KFm+\nHEpTE/xJSYavbRgwAMlffw0gkNddO3p0LDaRIAiCIAiCIDo1VEiNICyQkJCAZJM867jF4TAV3IC6\ngnkihZgTBEEQBEEQREQg0U0QFgm1inlHgSqYEwRBEARBEETkIdFNEBZJsZFr3RFp6N8ffkUBQBXM\nCYIgCIIgCCJSkOgmCIskJibC5bJfBhQT6sQAACAASURBVEFRFPTq1Qt9+vRBdnZ2SOuIBX6vF009\newIAkr7/HjAp/EYQBEEQBEEQhDkkugnCIoqihOR29+jRA6mpqUhOTka3bt3Qu3fvyG9chGAh5o7G\nRiQcONDOW0MQBEEQBEEQHR8S3QRhA7uiu0ePHkjTtPRKTEyE0+mM5GZFDMrrJgiCIAiCIIjIQqKb\nIGxgR3R3794d6ZJe14qiwOv1RnKzIgaJboIgCIIgCIKILCS6CcIGLpcLSRbab6WkpCAzM1P3eRLd\nBEEQBEEQBHF6QKKbIGxixe3Ozs42fD5eRbeve3e0/NiPnEQ3QRAEQRAEQYQPiW6CsImZ6E5ISDB9\njcfjgfJje664QlG4251w9Cgc1dXtvEEEQRAEQRAE0bEh0U0QNvF6vYaCOSsry1RQOxwOS2Hq7QGF\nmBMEQRAEQRBE5CDRTRA2cTgcSP4xBFv2nFEut0i8hpiT6CYIgiAIgiCIyEGimyBCQC98PCMjw3I7\nMD3h3t6Q6CYIgiAIgiCIyEGimyBCQE90Z2VlWV5HvDrdjWecwZdJdBMEQRAEQRBEeJDoJogQSExM\nhMvlUj2WnJxsK0/b5XIhISEh0psWNq3JyWjs0QMAkPT990BLSztvEUEQBEEQBEF0XEh0E0QIKIoS\n5HbbcbkZ8ep2sxBzR309Uj77DPD723mLCIIgCIIgCKJjQqKbIEJEFN1utxtpaWm21xG3onvgQL7c\ne+5c9L/qKmQvXw7niRPtuFUEQRAEQRAE0fFwmb+EIAgZqampcDqdaGlpQU5OTkh9t+NVdJ+cNg3Z\n//43XJWVAAJh5t0WLkTXv/4VrZptbuzdG0fvvx/1w4a1x6YSBEEQBEEQRFxDTjdBhIjT6cQZZ5yB\nvn37hhRaDgRyw61WO48lTb16Yde77+LQww+jtrCQP660tMBZU6P65/3mG/SZNQsZq1e34xYTBEEQ\nBEEQRHxCTjdBhIHL5QoqqGYHRVHg9XpRU1MTwa2KDH6PBycuvxwnLr8ciSUlyHz9dSR/8QUUn4+/\nxllbC3dZGRxNTSi4/34k7dqF0vnzgTCOCUEQBEEQBEF0JmhkTBDtTLyKbpHGvn1R+utfBz2u+Hzo\n9uijyHrtNQBAzssvI3HPHhx65BG05OREdBuUxka4jx1DU7dugNsd0XUTBEEQBEEQRLQg0U0Q7Uy8\n5nVbwe9248iDD6J+0CDkP/oolOZmpG7ciMHjx6OhTx/UFRaibuTIQDV0kzB6f0ICmgoK4Ne0UUv8\n/ntkvv46Mt58E67qajRnZODkJZfg/7d35+FRVOn+wL9V1Xt39kASAlnYZF/DjriARhZxgYsoOAoy\ngg4i/lD0qiOi91FxHBkXRphxEOcyDjAOCgwRr8YBAkpAQEEIwxbC2tnXTie9nd8fIW06+9bpTvx+\nnidP0lWnznkrUqbeOqfOKZwyBaVDhwIy35IhIiIiIv/FpJvIx/R6PSRJgmjHy3Llz5qF8u7dEfP/\n/h9U+fkAAF16OnTp6QjdurXR9QhFQXlsLMp79kR5bCyM338P49GjHmVUBQUI27IFYVu2wBYZicIp\nU5A3cybs19cWJyIiIiLyJ0y6iXxMlmXo9XqUlpb6OpQWKU1IwNktWxD2t7/BePgw9GlpkByOJtUh\nOZ3QnT8P3fnzNfa5tFqUDhkCw48/Qi4rAwBozGZ0Wr8e4R99hOIbb0Te7NkoGTeOvd9ERERE5DeY\ndBP5AZPJ1O6TbgBwREYic9kyAIBUVgb9Tz/B8MMP0Fy92uCxckkJtOfPQ3v+POQqk7WV9eyJvJkz\nUTBtGlxBQZBLSxHwzTcI+uILBHz7LSSHA5IQCNy7F4F798LWtSsyn3gChVOmeO08iYiIiIgai0k3\nkR8wmUzIysrydRitSuh0KE1IQGlCQtMOdDiguXQJ2vR02CMiUNavH1BlDXSXwYDCadNQOG0alNxc\nhHz2GUK3bIHm2jUAgObyZXR79lkYjh6FeflyCE66RkREREQ+xDGYRH5Ar9dD5pDoCioVbPHxKL71\nVpT17++RcFfnDAtDzoIFOP3FF8h45x2UjB7t3he2aRPi5s+HqoM9zCAiIiKi9oV3+UR+QJIkmEwm\nX4fRfikKim+9FRf+/GdceflluK73bht/+AE97rsPxkOHAJer8fU5HFBlZwNVhrkTERERETUHh5cT\n+QmTyYSioiJfh9Hu5c+YgbIbbkC3p56CxmyGOicH8fPnQ6hUsHfuDHtkJOwRERA6ncdxktUKtdlc\n8ZWdDcnpRHlsLNI/+giOTp18dDZERERE1N4x6SbyE+zpbj3WAQNwbvNmdFu+HKbUVACA5HBAc/Vq\noyZ1q6TNyEDM0qVIX78eQqv1VrhERERE1IFxeDmRn9BoNNBoNL4Oo8Nwhobiwtq1uPbssyi66SZY\nb7gBjqCgBo9zhITA2rcvHKGhAADDsWOIfvlloB2vo05EREREvsOebiI/YjKZkJeX5+swOg6VCrlz\n5yJ37lz3Jqm0tGL4eLU1xIVaDXvnzu5h57q0NHR/6CHIViuC//UvlPXsiZxHHvFerLWtaa7i/6KJ\niIiI2jve0RH5EaPRyKTby4TBAFtsbIPlyvr2xeXXXkPMU08BACLeeQfl3buj+JZbWiUOyW6H4fvv\nEbh7NwL27IHmypUaZYpvvBEXV6/m0HYiIiKidoxJN5Ef4Xvd/qVo0iRkLl6MiPffhyQEuj73HIom\nTWpxvUpJCYwHD0IpKam3XEBKCrr8z//gyiuv1Lt0GhERERH5LybdRH5EURTo9XpYrVZfh0LXZT/6\nKLTnziH4iy+glJYiZPv2Vm/DpVKhrE8fiOtLnQGA/uRJyOXlCPn8c1j79EHenDmt3i4REREReR+T\nbiI/YzKZmHT7E0nClVdegdpshvHo0Var1hEYiJIJE1B0000oGTcOroAAj/2Bu3Yh5plnAABRv/sd\nynv1gmXkyFZrn4iIiIjaBpNuIj9jMpmQnZ3t6zCoCqHTIf2jjyqWG6ttwrOmUhTYunSpd6K0ojvu\nQHZaGjqtXw/J6US3ZctwbtMm2KOjW94+EREREbUZJt1EfsZgMECWZbhcrjZtV1EUuFwuCC6NVTtF\nga1btzZtMnPJEuhOn0bAvn1QFRQg5sknkfvgg/UeI9ntUOXkQJWdDXV2NlQ5ObBHRSH7179GWZ8+\nbRQ5EREREVVi0k3kZyRJgtFoRHFxcZu2qygKFEWBzWZr03apHoqCS6tWoccDD0CbkQH9f/6Dri++\n2PR6jh9H4FdfoeCuu5D5xBNwdO5csd3lgv7kSQTs3Qv11as/l5ckCEVB4R13wDJ6dOucCxEREdEv\nFJNuIj/ki6RblmUm3X7IFRiIi+++i+5z5jQ423l9JCEQ8vnnCPryS+TOng1Vfj5MKSlQ5+bWeUzw\njh04s32794e0CwE4ndUClgBF8W67RERERG2ASTeRH9L6YF1mWZahrjJ7NvmP8u7dcW7zZhhTUysS\n1PrIMhxhYXB06gR7p05wBgQg9B//QOd166AUF0O2WtHpo48a1a5ssyHivfdw+Y03WuEsaqe+dg1x\njz4K7YULHtuFLMMyciRy5s1DyZgxXDKNiIiI2i0m3UR+yBfJL5Nu/2aLiYEtJqZZx+Y+9BAK7roL\nndauRdjmzZCuTwbn0utRMno0im+6CaWDBkEoCiQAks2GuF//GqqCAgTv3ImcBx9EWf/+rXg21zmd\n6Pr886iecAOA5HLBdOAATAcOwHrDDciZNw+Ft98O8N8oERERtTNMuon8EJNuam3O4GCYn3sOefff\nD2NqKuxdusAyYgREHaMqshYtQpfrPdyRb7+NCx9+2Oq9zeEbNsD4/fcAAEdwMMq7d3fvU5vNFbPF\nA9D/5z/o9txziFy9GkUTJ6L4pptgSUiA0GhaNR4iIiIib2DSTeSHFEVp8xnMmXT/MthiY2GLjW2w\nXP6sWQj729+gvXQJpoMHYUpJQcmECa0Wh+7kSXR+/30AgJAkXFy9GqUJCT8XcDoR+M03CF+/Hoaf\nfgIAqDMzEfbJJwj75BM4DQaUjB2LnEcegXXAgFaLi4iIiKi1yb4OgIhq19YJsKIoTLrJTajVyHzy\nSffnyNWrW2eNcgCS1Yquzz0H+Xp9OfPneybcAKAoKLrtNpz/5BOcX78eRTffDFeVdc2V0lIEff01\n4h96CJqMjFaJi4iIiMgbmHQT+am2ToDZ003VFd1+O0oHDQIA6M6eRcj27a1Sb+Tq1dClpwMArH37\nIus3v6m7sCShdMQIXHzvPZzauxcX33oL+XfeCUdwMICKyd4i33qrVeIiIiIi8gYOLyfyUypV216e\nlUuGtfWwdvJjkgTzsmXo/tBDAIDO778PR0hI/e92O51QSkshWyzuL6nKcmByaSnCNm0CALi0Wlx+\n4w2IRj7scQUEoCgxEUWJiZBLS9HrzjuhzspC4O7dMH73HSxjxjT/XImIiIi8hEk3kZ/yRU93Zbvl\n5eVt2jb5r9Jhw1B0660I/OYbqLOzEbtkSavVbV62zGPytKZwGQzIfPJJdH3hBQBA1Jtv4uw//gG0\n8cMqfycXF0MpLq6/kCTB3rkz10UnIiLyEt6dEPmp9pB0azQa2Gw2b4ZFfsC8dClMKSmQ7fZWq7Po\nlluQN3t2i+oomDYNoZs2wXD8OHRnzyL0009bXGdHIdntiHj7bYT9/e8eIw3qYu/UCZlPPIGC6dNr\nJt9CQHv6NFxGI+xdu3opYiIioo6LSTeRn/Jl0t1YTLp/GWzx8Uj/+GMYU1MhCVFvWSFJcBkMcBmN\ncBmNcBoMNYaPC50O1j59Wr4EmSzj2rPPosfcuQCAzmvWoGDyZLiCglpWbzunyspCt6efhvHo0UYf\no87ORteXXkLYxo0wP/00LGPGQJWVheAdOxC8bRt06ekQioLsBQuQtXAh10snIiJqAibdRH6qPSTd\nWq0WJSUl3gqJ/Ih14EBYBw70dRg1WAcPRsHUqQjeuROqggJ0XrsW5mef9XVYPmP4/nt0e/ppqHNz\nAQAutRrFN98MUc/QcVVeHkwHDwIA9KdPI/7RR1HWqxe0585BqjK/g+R0ovO6dQjYuxeXX38d5T16\nNDouyWpF0FdfIXj7dqiysmAdPBglo0bBMno0HOHhtR9js0GXlgbDsWPQHz8OpbAQkt1e40uu8jMa\neChUe0MSyuPiYBk1CiUjRsA6eDCERgP1lSswHjwI08GDMBw9CtlqhZAkQJYBSYIzKAi599+P/Jkz\nW30N+/ZALi2F7sQJGH76Cfrjx6E9exalw4fj6osv8lUFIqJqmHQT+an2kHRrNBpvhUPUaOalSxH4\nzTeQrVaEbdoExWKBkJu2OIczOBjWPn1Q1qcPbDExFYlVY7hcUJvN0F64AE16OlR5eU0/gVoIrRZF\nt96K8p49a92vO3ECQV9/DamszL1NLi1FyLZt7uHktshIXFq9ulHrmBsOHULUW29Bf/JkRf1nznjs\nLx0wAPpTpyA5HNCnpaHHrFnIXLIExTffXG+9Sn5+RW95UhKUKg/odOnpCPn8cwBAWc+esEdF1ThO\nd+qUe1k5b1Pl5cF45Ag6f/ABXFotHCEh0JjN9R6jzspC9CuvIOirr3Bl5coa59Ae6U6eROTbb0N3\n5gxcen3Fl9EIl14PyWaDXFpa8WWxQJWf7/FQBqj472oZNgyFd97pozMgIvJPkhDNeSzs344cOYLh\nw4fj8OHDGDZsmK/DIWoWIQTS0tLabCbxnj17QqfToaSkBBcuXGjUMXFxcY0uS+RNnT74ABF//GOr\n1OU0GFDesydcWm1FUuFyub9X/Vmy2aC5ehWyFyceLLr5ZmQvWADr4MEAAMPRo+i0bh0C9u+v97iS\nUaNw6c034QwNbXxjLheCd+5ExB/+AHVWFmxRUSiYPh0Fd90FW7du0J04ga7PPw/d+fMtOSUIRWnU\ne+b11iFJEBoNhFrt+dXEhy0AIJeXQ52VVW8Zl14PR1hYRU+6ywXJ6fQ4xmk0wvz008ifMaNd9npL\nVis6//GPCP/rX2sk0k1li47GmR07Gr0qARGRP9Hr9ehxfSRXa+aU7Okm8lOSJEGlUrXZO9Ps6ab2\nLOfhhxH01Vc1emibQyktheHYsVaIquUCd+9G4O7dKBkxAgBgOnSo3vJCkpDzyCPIXLy46UN8ZRkF\nd96JwsREaC5fRnlcnEePf1n//ji3eTMi3nsPYf/7vw2+31+VS69H4R13IO/ee1HWty8MP/wAY2oq\nTAcOQH/iRK2JXll8PKyDBqF00CBYBw+GLSrKnWi39vBltdkM48G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Pz8/HnDlzEBQUhJCQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "run_fit.plot_log(log)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: examples/graupnerbrunelstdp/run_fit.py ================================================ """Main Graupner-Brunel STDP example script""" # pylint: disable=R0914 import pickle import bluepyopt as bpop import matplotlib.pyplot as plt import numpy as np import gbevaluator import stdputil cp_filename = 'checkpoints/checkpoint.pkl' evaluator = gbevaluator.GraupnerBrunelEvaluator() opt = bpop.optimisations.DEAPOptimisation(evaluator, offspring_size=100, eta=20, mutpb=0.3, cxpb=0.7) def run_model(): """Run model""" _, _, _, _ = opt.run( max_ngen=200, cp_filename=cp_filename, cp_frequency=100) def plot_log(log): """Plot logbook""" fig, axes = plt.subplots(figsize=(10, 10), facecolor='white') gen_numbers = log.select('gen') mean = np.array(log.select('avg')) std = np.array(log.select('std')) minimum = log.select('min') # maximum = log.select('max') stdminus = mean - std stdplus = mean + std axes.plot( gen_numbers, mean, color='black', linewidth=2, label='population average') axes.fill_between( gen_numbers, stdminus, stdplus, color='lightgray', linewidth=2, label=r'population standard deviation') axes.plot( gen_numbers, minimum, color='red', linewidth=2, label='population minimum') axes.set_xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) axes.set_xlabel('Generation #') axes.set_ylabel('Sum of objectives') axes.set_ylim([0, max(stdplus)]) axes.legend() fig.tight_layout() fig.savefig('figures/graupner_evolution.eps') def plot_epspamp_discrete(dt, model_sg, sg, stderr): """Plot EPSP amplitude change for discrete points""" # Plot result summary fig1, ax1 = plt.subplots(figsize=(10, 10), facecolor='white') ax1.errorbar(dt, model_sg, marker='o', label='Model') ax1.errorbar(dt, sg, yerr=stderr, marker='o', label='In vitro') ax1.axhline(y=1, color='k', linestyle='--') ax1.axvline(color='k', linestyle='--') ax1.set_xlabel(r'$\Delta t$ (ms)') ax1.set_ylabel('change in EPSP amplitude') ax1.legend() fig1.savefig('figures/graupner_fit.eps') def plot_calcium_transients(protocols, best_ind_dict): """Plot calcium transients""" # Plot calcium transients for each protocol fig2, axarr2 = plt.subplots( len(protocols), 1, sharex=True, figsize=( 10, 10), facecolor='white') for i, protocol in enumerate(protocols): calcium = stdputil.CalciumTrace(protocol, best_ind_dict) time, ca = calcium.materializetrace() axarr2[i].plot(time, ca) axarr2[i].axhline(y=best_ind_dict['theta_d'], color='g', linestyle='--') axarr2[i].annotate( r'$\theta_d$', xy=( 0.3, best_ind_dict['theta_d'] - 0.15)) axarr2[i].axhline(y=best_ind_dict['theta_p'], color='r', linestyle='--') axarr2[i].annotate( r'$\theta_p$', xy=( 0.3, best_ind_dict['theta_p'] + 0.05)) axarr2[i].set_title(protocol.prot_id) axarr2[i].set_xlim(-0.012, 0.6) axarr2[i].set_ylim(-0.1, 2.2) axarr2[i].set_ylabel('calcium') yloc = plt.MaxNLocator(3) axarr2[i].yaxis.set_major_locator(yloc) axarr2[i].set_xlabel('Time (s)') fig2.tight_layout() fig2.savefig('figures/graupner_ca_traces.eps') def plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr): """Plot dt scan""" dt_vec = np.linspace(-90e-3, 50e-3, 100) sg_vec = [] for model_dt in dt_vec: protocol = stdputil.Protocol( ['pre', 'post', 'post', 'post'], [model_dt, 20e-3, 20e-3], 0.1, 60.0, prot_id='%.2fms' % model_dt) model_sg = stdputil.protocol_outcome(protocol, best_ind_dict) sg_vec.append(model_sg) try: sg_good_sol_vec = pickle.load(open("sg_good_sol_vec.pkl", "rb")) except IOError: sg_good_sol_vec = [] for _, good_sol in enumerate(good_solutions): sg_ind = [] for model_dt in dt_vec: protocol = stdputil.Protocol( ['pre', 'post', 'post', 'post'], [model_dt, 20e-3, 20e-3], 0.1, 60.0, prot_id='%.2fms' % model_dt) model_sg = stdputil.protocol_outcome(protocol, good_sol) sg_ind.append(model_sg) sg_good_sol_vec.append(sg_ind) pickle.dump(sg_good_sol_vec, open("sg_good_sol_vec.pkl", "wb")) fig3, ax3 = plt.subplots(figsize=(10, 10), facecolor='white') ax3.set_rasterization_zorder(1) for sg_ind in sg_good_sol_vec: ax3.plot(dt_vec * 1000.0, sg_ind, lw=1, color='lightblue', zorder=0) ax3.plot(dt_vec * 1000.0, sg_vec, marker='o', lw=1, color='darkblue', label='Best model') ax3.errorbar(dt, sg, yerr=stderr, fmt='o', color='red', ms=10, ecolor='red', elinewidth=3, capsize=5, capthick=3, zorder=10000, label='In vitro') ax3.axhline(y=1, color='k', linestyle='--') ax3.axvline(color='k', linestyle='--') ax3.set_xlabel(r'$\Delta t$ (ms)') ax3.set_ylabel('EPSP amplitude change') ax3.legend() fig3.tight_layout() fig3.savefig('figures/graupner_dtscan.eps', rasterized=True, dpi=72) def analyse(): """Generate plot""" cp = pickle.load(open(cp_filename, "r")) results = ( cp['population'], cp['halloffame'], cp['history'], cp['logbook']) _, hof, hst, log = results best_ind = hof[0] best_ind_dict = evaluator.get_param_dict(best_ind) print('Best Individual') for attribute, value in best_ind_dict.items(): print('\t{} : {}'.format(attribute, value)) good_solutions = [ evaluator.get_param_dict (ind) for ind in hst.genealogy_history.itervalues () if np.all(np.array(ind.fitness.values) < 1)] # model_sg = evaluator.compute_synaptic_gain_with_lists(best_ind) # Load data protocols, sg, _, stderr = stdputil.load_neviansakmann() dt = np.array([float(p.prot_id[:3]) for p in protocols]) plt.rcParams['lines.linewidth'] = 2 # plot_epspamp_discrete(dt, model_sg, sg, stderr) plot_dt_scan(best_ind_dict, good_solutions, dt, sg, stderr) plot_calcium_transients(protocols, best_ind_dict) plot_log(log) plt.show() def main(): """Main""" import argparse parser = argparse.ArgumentParser(description='Graupner-Brunel STDP') parser.add_argument('--start', action="store_true") parser.add_argument('--continue_cp', action="store_true") parser.add_argument('--analyse', action="store_true") args = parser.parse_args() if args.analyse: analyse() elif args.start: run_model() if __name__ == '__main__': main() ================================================ FILE: examples/graupnerbrunelstdp/stdputil.py ================================================ # -*- coding: utf-8 -*- """ Created on Wed Jun 12 17:33:41 2013. Utility functions for calcium-based STDP using simplified calcium model as in (Graupner and Brunel, 2012). @author: Giuseppe Chindemi @remark: Copyright © BBP/EPFL 2005-2016; All rights reserved. Do not distribute without further notice. """ # pylint: disable=R0914, R0912 import logging import numpy as np from scipy.special import erf # NOQA try: xrange except NameError: xrange = range logging.basicConfig(level=logging.WARN) # Note: having debug logging statements increases the run time by ~ 25%, # because they exist in tight loops, and expand their outputs, even when # debug is off, so we disable logging if possible. Set this to true if # verbose output is needed LOGGING_DEBUG = False def logging_debug_vec(fmt, vec): '''log to debug a vector''' if LOGGING_DEBUG: logging.debug(fmt, ', '.join(map(str, vec))) def logging_debug(*args): '''wrapper to log to debug a vector''' if LOGGING_DEBUG: logging.debug(*args) # Parameters for cortical slices (Sjostrom et al., 2001) # From SI, Graupner and Brunel (2012) param_cortical = { 'tau_ca': 22.6936e-3, # [s] 'C_pre': 0.5617539, 'C_post': 1.23964, 'theta_d': 1.0, 'theta_p': 1.3, 'gamma_d': 331.909, 'gamma_p': 725.085, 'sigma': 3.3501, 'tau': 346.3615, # [s] 'rho_star': 0.5, 'D': 4.6098e-3, # [s] 'beta': 0.5, 'b': 5.40988} # Parameters for hippocampal slices (Wittenberg and Wang, 2006) # From SI, Graupner and Brunel (2012) param_hippocampal = { 'tau_ca': 48.8373e-3, # [s] 'C_pre': 1.0, 'C_post': 0.275865, 'theta_d': 1.0, 'theta_p': 1.3, 'gamma_d': 313.0965, 'gamma_p': 1645.59, 'sigma': 9.1844, 'tau': 688.355, # [s] 'rho_star': 0.5, 'D': 18.8008e-3, # [s] 'beta': 0.7, 'b': 5.28145} class Protocol(object): """Protocol""" def __init__(self, stim_vec, delta_vec, f, n, prot_id=None): """A stimulation protocol. :param stim_vec: list List of stimuli ['pre'|'post'|'burst'] :param delta_vec: list List of time deltas between stimuli :param f: float Frequency of the protocol in Hz :param n: int Number of repetitions of the protocol :param prot_id: string ID of the protocol """ # Check stim strings valid_stim = set(['pre', 'post']) for s in stim_vec: assert s in valid_stim, \ '\'{0}\' is not a recognised stimulus'.format(s) self.stim_vec = np.array(stim_vec, dtype='a10') self.delta_vec = np.array(delta_vec) self.f = f self.n = n self.prot_id = prot_id # Compute time of stimuli self.stim_t = np.zeros(len(self.stim_vec)) for i in xrange(len(self.delta_vec)): self.stim_t[i + 1] = self.stim_t[i] + self.delta_vec[i] def sort(self): """Sort stimuli in place.""" logging_debug('Protocol.sort()') # Check if the stimuli are already sorted if np.all(self.delta_vec >= 0.0): logging_debug('Protocol is already sorted.') else: logging_debug('Before sorting:') logging_debug_vec('stim_vec = [%s]', self.stim_vec) logging_debug_vec('delta_vec = [%s]', self.delta_vec) # Sort stimuli index_vec = np.argsort(self.stim_t) self.stim_vec = self.stim_vec[index_vec] self.delta_vec = np.diff(self.stim_t[index_vec]) self.stim_t = self.stim_t[index_vec] logging_debug('After sorting:') logging_debug_vec('stim_vec = [%s]', self.stim_vec) logging_debug_vec('delta_vec = [%s]', self.delta_vec) class CalciumTrace(object): """CalciumTrace""" def __init__(self, protocol, model): """Calcium trace produced by **model** when stimulated by **protocol**. :param protocol: stdputil.Protocol The stimulation protocol. :param model: dict Parameters of the Graupner-Brunel model """ self.protocol = protocol self.model = model # Generate the events corresponding to the configuration n_stim = len(protocol.stim_vec) curr_time = 0.0 event = [] time = [] amplitude = [] for i in xrange(n_stim): if protocol.stim_vec[i] == 'pre': event.append('Cpre') time.append(curr_time + model['D']) amplitude.append(model['C_pre']) elif protocol.stim_vec[i] == 'post': event.append('Cpost') time.append(curr_time) amplitude.append(model['C_post']) if i < n_stim - 1: # Delta vector is shorter than Stimulus vector curr_time += protocol.delta_vec[i] # Convert into numpy arrays for convenience event = np.array(event, dtype='a10') time = np.array(time) amplitude = np.array(amplitude) # Sort calcium events index_vec = np.argsort(time) logging_debug_vec('Sorted indices = [%s]', index_vec) logging_debug_vec('Calcium event = [%s]', event[index_vec]) logging_debug_vec('Calcium time = [%s]', time[index_vec]) logging_debug_vec('Calcium amplitude = [%s]', amplitude[index_vec]) self.__evnt = event[index_vec] self.__t = time[index_vec] self.__amp = amplitude[index_vec] def materializetrace(self): """Materialize trace""" # Create exemplary traces for plotting period = 1.0 / self.protocol.f tstart = -0.01 tstop = tstart + period dt = 0.0001 n = int((tstop - tstart) / dt) tvec = np.linspace(tstart, tstop, n) trace = np.zeros(n) for j in xrange(len(self.__evnt)): offset = int((self.time[j] - tstart) / dt) component = self.amplitude[ j] * np.exp(-(tvec[:n - offset] / self.model['tau_ca'])) trace[offset:] += component return tvec, trace @property def event(self): """Event""" return self.__evnt @property def time(self): """Time""" return self.__t @property def amplitude(self): """Amplitude""" return self.__amp def load_neviansakmann(): """Load in vitro data, from figure 2B in (Nevian and Sakmann, 2006).""" protocols = [ Protocol(['post', 'post', 'post', 'pre'], [20e-3, 20e-3, 50e-3], 0.1, 60.0, prot_id='-90ms'), Protocol(['post', 'post', 'post', 'pre'], [20e-3, 20e-3, 10e-3], 0.1, 60.0, prot_id='-50ms'), Protocol(['post', 'post', 'pre', 'post'], [20e-3, 10e-3, 10e-3], 0.1, 60.0, prot_id='-30ms'), Protocol(['post', 'pre', 'post', 'post'], [10e-3, 10e-3, 20e-3], 0.1, 60.0, prot_id='-10ms'), Protocol(['pre', 'post', 'post', 'post'], [10e-3, 20e-3, 20e-3], 0.1, 60.0, prot_id='+10ms'), Protocol(['pre', 'post', 'post', 'post'], [50e-3, 20e-3, 20e-3], 0.1, 60.0, prot_id='+50ms')] sg = [1.0, 0.68, 0.98, 1.42, 2.01, 0.92] stdev = None stderr = [0.09, 0.05, 0.12, 0.19, 0.22, 0.11] return protocols, sg, stdev, stderr def time_above_threshold(protocol, param): """Compute time spent by calcium above the potentiation and depression thresholds. :param protocol: stdputil.Protocol The stimulation protocol. :param model: dict Parameters of the Graupner-Brunel model """ # Generate calcium trace calcium_trace = CalciumTrace(protocol, param) ca_event_t = calcium_trace.time ca_event_amp = calcium_trace.amplitude # Sort the protocol if not already sorted protocol.sort() # Compute calcium rise time/delta ca_event_delta = np.diff(np.append(ca_event_t, 1.0 / protocol.f)) # Compute calcium baseline A_f = (1.0 / (1.0 - np.exp(-1.0 / (protocol.f * param['tau_ca'])))) if A_f != 1.0: baseline = (A_f - 1.0) * (ca_event_amp[0]) for i in xrange(len(ca_event_amp) - 1): baseline += (A_f - 1.0) * \ (ca_event_amp[i + 1] * np.exp( np.sum(np.abs(ca_event_delta[:i + 1])) / param['tau_ca'])) else: baseline = 0.0 # Calcium amplitudes logging_debug('Calcium amplitudes') n_events = len(ca_event_amp) C_amp = np.zeros(2 * n_events) for i in xrange(n_events - 1): C_amp[i] = baseline * \ np.exp(-np.sum(np.abs(ca_event_delta[:i + 1])) / param['tau_ca']) logging_debug('C_amp[%d] = 0.0', i) for j in xrange(i + 1): C_amp[i] += \ ca_event_amp[j] * \ np.exp(-np.sum( np.abs(ca_event_delta[j:i + 1])) / param['tau_ca']) logging_debug('C_amp[%d] += %s * exp(-sum(abs(deltas[%d:%d])))', i, protocol.stim_vec[j], j, i + 1) logging_debug('C_amp[%d] = %f', i, C_amp[i]) C_amp[n_events - 1] = baseline logging_debug('C_amp[%d] = 0.0', n_events - 1) C_amp[n_events] = baseline + ca_event_amp[0] # For convenience logging_debug('C_amp[%d] = %f', n_events, ca_event_amp[0]) for i in xrange(n_events + 1, 2 * n_events): C_amp[i] = C_amp[i - n_events - 1] + ca_event_amp[i - n_events] logging_debug('C_amp[%d] = C_amp[%d] + %s', i, i - n_events - 1, ca_event_amp[i - n_events]) # Time spent above depression threshold t_d = np.zeros(n_events) for i in xrange(n_events): if C_amp[i] > param['theta_d']: t_d[i] = ca_event_delta[i] elif C_amp[i] <= param['theta_d'] < C_amp[i + n_events]: t_d[i] = param['tau_ca'] * \ np.log(C_amp[i + n_events] / param['theta_d']) else: t_d[i] = 0.0 t_d_tot = np.sum(t_d) logging_debug('Time above depression threshold = %f', t_d_tot) # Time spent above potentiation threshold t_p = np.zeros(n_events) for i in xrange(n_events): if C_amp[i] > param['theta_p']: t_p[i] = ca_event_delta[i] elif C_amp[i] <= param['theta_p'] < C_amp[i + n_events]: t_p[i] = param['tau_ca'] * \ np.log(C_amp[i + n_events] / param['theta_p']) else: t_p[i] = 0.0 t_p_tot = np.sum(t_p) logging_debug('Time above potentiation threshold = %f', t_p_tot) return t_d_tot, t_p_tot def transition_prob(protocol, param=None): """Compute transition probabilities for the given protocol and model parameters. :param protocol: stdputil.Protocol The stimulation protocol. :param model: dict Parameters of the Graupner-Brunel model """ if param is None: param = param_cortical # Sort the protocol if not already sorted protocol.sort() t_d_tot, t_p_tot = time_above_threshold(protocol, param) # TODO Ask Michael if t_d_tot == 0.0 and t_p_tot == 0.0: up = 0.0 down = 0.0 return up, down # Define aliases for convenience f = protocol.f n = protocol.n # Compute alpha depression and alpha potentiation alpha_d = t_d_tot * f alpha_p = t_p_tot * f # Compute Gamma depression and potentiation big_gamma_d = alpha_d * param['gamma_d'] big_gamma_p = alpha_p * param['gamma_p'] # Compute rho bar rho_bar = big_gamma_p / (big_gamma_p + big_gamma_d) sigma_rho_sq = param['sigma'] ** 2 * \ (alpha_p + alpha_d) / (big_gamma_p + big_gamma_d) tau_eff = param['tau'] / (big_gamma_p + big_gamma_d) # Up transition rho_0 = 0.0 erf_arg = -((param['rho_star'] - rho_bar + (rho_bar - rho_0) * np.exp(-n / (tau_eff * f))) / (np.sqrt(sigma_rho_sq * (1.0 - np.exp(-2.0 * n / (tau_eff * f)))))) up = 0.5 * (1.0 + erf(erf_arg)) # Down transition rho_0 = 1.0 erf_arg = -((param['rho_star'] - rho_bar + (rho_bar - rho_0) * np.exp(-n / (tau_eff * f))) / (np.sqrt(sigma_rho_sq * (1.0 - np.exp(-2.0 * n / (tau_eff * f)))))) down = 0.5 * (1.0 - erf(erf_arg)) return up, down def protocol_outcome(protocol, param=None): """Compute the average synaptic gain for a given stimulation protocol and model parameters. :param protocol: stdputil.Protocol The stimulation protocol. :param model: dict Parameters of the Graupner-Brunel model """ if param is None: param = param_cortical # Compute Up and Down transition probabilities up, down = transition_prob(protocol, param) # Compute synaptic gain sg = ((1.0 - up) * param['beta'] + down * (1.0 - param['beta']) + param['b'] * (up * param['beta'] + (1.0 - down) * (1.0 - param['beta']))) / \ (param['beta'] + (1.0 - param['beta']) * param['b']) return sg ================================================ FILE: examples/graupnerbrunelstdp/test_stdputil.py ================================================ import stdputil import numpy as np import numpy.testing as npt try: xrange except NameError: xrange = range def test_protocol_outcome(): """ Test against Fig. 3 of Graupner and Brunel (2012). TODO, Ask permission to Michael to add testing files. Notes ----- Data used for the test were kindly provprot_ided by Dr. M. Graupner. Fig. 3F cannot be reproduced because generated using an approximate solution. """ # Case 1: Fig. 3B mgspike = np.loadtxt( '/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/post_spike.dat') dt = mgspike[:, 0] outcome = np.zeros(len(dt)) for i in xrange(len(dt)): p = stdputil.Protocol(['pre', 'post'], [dt[i] * 1e-3], 5.0, 200.0) outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal) npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05) # Case 2: Fig. 3D mgspike = np.loadtxt( '/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/' 'post_burst_100.dat') dt = mgspike[:, 0] outcome = np.zeros(len(dt)) for i in xrange(len(dt)): p = stdputil.Protocol( ['post', 'post', 'pre'], [11.5e-3, -dt[i] * 1e-3], 5.0, 100.0) outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal) npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05) # Case 3: Fig. 3E mgspike = np.loadtxt( '/gpfs/bbp.cscs.ch/home/chindemi/proj32/graupner/data/' 'post_burst_30.dat') dt = mgspike[:, 0] outcome = np.zeros(len(dt)) for i in xrange(len(dt)): p = stdputil.Protocol( ['post', 'post', 'pre'], [11.5e-3, -dt[i] * 1e-3], 5.0, 30.0) outcome[i] = stdputil.protocol_outcome(p, stdputil.param_hippocampal) npt.assert_allclose(outcome, mgspike[:, 1], rtol=1e-05) ================================================ FILE: examples/l5pc/.gitignore ================================================ /*.pkl /.ipynb_checkpoints/ /L5PC.py /.ipython ================================================ FILE: examples/l5pc/L5PC.ipynb ================================================ { "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Optimisation of a Neocortical Layer 5 Pyramidal Cell" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "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", "Author of this script: Werner Van Geit @ Blue Brain Project\n", "\n", "Choice of parameters, protocols and other settings was done by Etay Hay @ HUJI\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", "**If you use the methods in this notebook, we ask you to cite the following publications when publishing your research:**\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", "http://arxiv.org/abs/1603.00500\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", "http://www.cell.com/abstract/S0092-8674%2815%2901191-5\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 " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We first load the bluepyopt python module, the ephys submodule and some helper functionality" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/werner/src/bpopt/examples/l5pc\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", "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", "\"/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", "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", "\"/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", "libtool: link: rm -fr .libs/libnrnmech.0.so .libs/libnrnmech.0.so.dSYM .libs/libnrnmech.la .libs/libnrnmech.lai .libs/libnrnmech.so\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", "libtool: link: dsymutil .libs/libnrnmech.0.so || :\n", "libtool: link: (cd \".libs\" && rm -f \"libnrnmech.so\" && ln -s \"libnrnmech.0.so\" \"libnrnmech.so\")\n", "libtool: link: ( cd \".libs\" && rm -f \"libnrnmech.la\" && ln -s \"../libnrnmech.la\" \"libnrnmech.la\" )\n", "Successfully created x86_64/special\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload\n", "\n", "!nrnivmodl mechanisms\n", "import bluepyopt as bpopt\n", "import bluepyopt.ephys as ephys\n", "\n", "import pprint\n", "pp = pprint.PrettyPrinter(indent=2)\n", "\n", "%matplotlib notebook\n", "import matplotlib.pyplot as plt" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Enable the code below to enable debug level logging" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import logging \n", "# logging.basicConfig() \n", "# logger = logging.getLogger() \n", "# logger.setLevel(logging.DEBUG) " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Model description" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Morphology" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We're using a complex reconstructed morphology of an L5PC cell. Let's visualise this with the BlueBrain NeuroM software:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already up-to-date: neurom in /usr/local/lib/python2.7/site-packages\n", "Requirement already up-to-date: pyyaml>=3.10 in /usr/local/lib/python2.7/site-packages (from neurom)\n", "Requirement already up-to-date: enum34>=1.0.4 in /usr/local/lib/python2.7/site-packages (from neurom)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.segments is deprecated. \n", " _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n", "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.sections is deprecated. \n", " _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n", "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.bifurcations is deprecated. \n", " _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n", "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.points is deprecated. \n", " _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\n", "/usr/local/lib/python2.7/site-packages/neurom/utils.py:81: DeprecationWarning: Module neurom.ezy is deprecated. \n", " _warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))\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", " warnings.warn(msg)\n" ] }, { "data": { "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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');\n var 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= 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", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:52: DeprecationWarning: Comm._comm_id_default is deprecated: use @default decorator instead.\n", " def _comm_id_default(self):\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", " def _iopub_socket_default(self):\n", "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n", " def _kernel_default(self):\n", "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n", " def _session_default(self):\n", "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n", " def _topic_default(self):\n", "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:24: DeprecationWarning: Comm._kernel_default is deprecated: use @default decorator instead.\n", " def _kernel_default(self):\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", " def _comm_id_default(self):\n", "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:32: DeprecationWarning: Comm._session_default is deprecated: use @default decorator instead.\n", " def _session_default(self):\n", "/usr/local/lib/python2.7/site-packages/ipykernel/comm/comm.py:41: DeprecationWarning: Comm._topic_default is deprecated: use @default decorator instead.\n", " def _topic_default(self):\n" ] } ], "source": [ "def plot_responses(responses):\n", " fig, axes = plt.subplots(len(responses), figsize=(10,10))\n", " for index, (resp_name, response) in enumerate(sorted(responses.items())):\n", " axes[index].plot(response['time'], response['voltage'], label=resp_name)\n", " axes[index].set_title(resp_name)\n", " fig.tight_layout()\n", " fig.show()\n", "plot_responses(release_responses)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Running an optimisation of the parameters now has become very easy. \n", "Of course running the L5PC optimisation will require quite some computing resources. \n", "\n", "To show a proof-of-concept, we will only run 2 generations, with 2 offspring individuals per generations.\n", "If you want to run all full optimisation, you should run for 100 generations with an offspring size of 100 individuals. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "opt = bpopt.optimisations.DEAPOptimisation( \n", " evaluator=evaluator, \n", " offspring_size=2) \n", "final_pop, halloffame, log, hist = opt.run(max_ngen=2, cp_filename='checkpoints/checkpoint.pkl')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The first individual in the hall of fame will contain the best solution found." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[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" ] } ], "source": [ "print(halloffame[0])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "These are the raw parameter values. \n", "The evaluator object can convert this in a dictionary, so that we can see the parameter names corresponding to these values." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{ u'decay_CaDynamics_E2.axonal': 234.40541659093483,\n", " u'decay_CaDynamics_E2.somatic': 150.32985893616308,\n", " u'gCa_HVAbar_Ca_HVA.axonal': 0.00048095122076782077,\n", " u'gCa_HVAbar_Ca_HVA.somatic': 0.00022936710285097395,\n", " u'gCa_LVAstbar_Ca_LVAst.axonal': 0.008079463408928685,\n", " u'gCa_LVAstbar_Ca_LVAst.somatic': 0.007891413026508394,\n", " u'gImbar_Im.apical': 0.0009391491627785106,\n", " u'gK_Pstbar_K_Pst.axonal': 0.3599561642296192,\n", " u'gK_Tstbar_K_Tst.axonal': 0.0029040787574867947,\n", " u'gNaTa_tbar_NaTa_t.axonal': 1.5248169507528497,\n", " u'gNaTs2_tbar_NaTs2_t.apical': 0.0007314357233762349,\n", " u'gNaTs2_tbar_NaTs2_t.somatic': 0.19334802048394228,\n", " u'gNap_Et2bar_Nap_Et2.axonal': 1.1187461922146316,\n", " u'gSK_E2bar_SK_E2.axonal': 0.022169166627303505,\n", " u'gSK_E2bar_SK_E2.somatic': 0.0005121897764114314,\n", " u'gSKv3_1bar_SKv3_1.apical': 0.03367469445915072,\n", " u'gSKv3_1bar_SKv3_1.axonal': 0.8757751873011441,\n", " u'gSKv3_1bar_SKv3_1.somatic': 0.3527777415382436,\n", " u'gamma_CaDynamics_E2.axonal': 0.016843185951228554,\n", " u'gamma_CaDynamics_E2.somatic': 0.03254436076094874}\n", "None\n" ] } ], "source": [ "best_params = evaluator.param_dict(halloffame[0])\n", "print(pp.pprint(best_params))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Then we can run the fitness protocols on the model with these parameter values" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "best_responses = evaluator.run_protocols(protocols=fitness_protocols.values(), param_values=best_params)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "And then we can also plot these responses. \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." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "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 = $('
');\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 '
');\n var titletext = $(\n '
');\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 = $('
');\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 = $('');\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 = $('');\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 = $('
')\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 = $('');\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 = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var 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", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.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", "\n", "mpl.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", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.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= 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.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_responses(responses):\n", " plt.subplot(2,1,1)\n", " plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\n", " plt.legend()\n", " plt.subplot(2,1,2)\n", " plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\n", " plt.legend()\n", " plt.tight_layout()\n", "\n", "plot_responses(responses)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, when we use different parameter values, the response looks different." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "other_params = {'gnabar_hh': 0.11, 'gkbar_hh': 0.04}\n", "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=nrn))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Defining eFeatures and objectives\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:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\n", "\n", "objectives = []\n", "\n", "for protocol in sweep_protocols:\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 efel_feature_means[protocol.name].items():\n", " feature_name = '%s.%s' % (protocol.name, efel_feature_name)\n", " feature = ephys.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 = ephys.objectives.SingletonObjective(\n", " feature_name,\n", " feature)\n", " objectives.append(objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating the cell evaluator\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." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cell_evaluator = ephys.evaluators.CellEvaluator(\n", " cell_model=simple_cell,\n", " param_names=['gnabar_hh', 'gkbar_hh'],\n", " fitness_protocols={twostep_protocol.name: twostep_protocol},\n", " fitness_calculator=score_calc,\n", " sim=nrn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluating the cell\n", "\n", "The cell can now be evaluate for a certain set of parameter values." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\n", "{'step2.Spikecount': 4.0, 'step1.Spikecount': 0.0}\n" ] } ], "source": [ "print(cell_evaluator.evaluate_with_dicts(default_params))\n", "print(cell_evaluator.evaluate_with_dicts(other_params))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Setting up and running an optimisation\n", "\n", "Now that we have a cell template and an evaluator for this cell, we can set up an optimisation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And this optimisation can be run for a certain number of generations" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "optimisation = bpop.optimisations.DEAPOptimisation(\n", " evaluator=cell_evaluator,\n", " offspring_size = 100,\n", " seed=1)\n", "\n", "# results = optimisation.run(max_ngen=10, cp_filename='checkpoints/checkpoint.pkl')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \n", "\n", "The final population contains a list of tuples, with each tuple representing the two parameters of the model" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pickle\n", "# pickle.dump(results, open('results.pkl', 'w'))\n", "# results = pickle.load(open('results.pkl')\n", "\n", "cp = pickle.load(open('checkpoints/checkpoint.pkl'))\n", "results = (cp['population'],\n", " cp['halloffame'],\n", " cp['logbook'],\n", " cp['history'])\n", "\n", " \n", "pop, hall_of_fame, logs, hist = results\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best individual found during the optimisation is the first individual of the hall of fame" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [0.05047162045596707, 0.01553579607479454]\n", "Fitness values: (0.0, 0.0)\n" ] } ], "source": [ "all_inds = hist.genealogy_history.values()\n", "best_inds = [ind for ind in all_inds if ind.fitness.valid and ind.fitness.sum == 0]\n", "# print([ind.fitness.values for ind in best_inds])\n", "# best_inds = hall_of_fame[:100]\n", "best_ind = best_inds[1]\n", "print('Best individual: ', best_ind)\n", "print('Fitness values: ', best_ind.fitness.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.15948084951086058, 0.040876554474167215]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.05047162045596707, 0.01553579607479454]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.0951754875850295, 0.025347861639078685]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.09432136998860223, 0.026756980292376657]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.14284515471059417, 0.03785780405241329]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.05047162045596707, 0.01553579607479454]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.0951754875850295, 0.025347861639078685]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.12853697616017354, 0.03533022234039911]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.09160460586691756, 0.026756980292376657]\n", "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0} [0.15409363218638306, 0.03995407384574071]\n" ] } ], "source": [ "for best_ind in best_inds[:10]:\n", " best_ind_dict = cell_evaluator.param_dict(best_ind)\n", " print(cell_evaluator.evaluate_with_dicts(best_ind_dict), best_ind)\n", " plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \n", "We can have a look at the responses now." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pickle\n", "RESPONSE_PICKLE = 'responses.pkl'\n", "\n", "if os.path.exists(RESPONSE_PICKLE):\n", " responses = pickle.load(open(RESPONSE_PICKLE))\n", "else:\n", " responses = []\n", " for best_ind in best_inds:\n", " best_ind_dict = cell_evaluator.param_dict(best_ind)\n", " responses.append(cell_evaluator.run_protocols(cell_evaluator.fitness_protocols.values(), param_values=best_ind_dict))\n", " pickle.dump(responses, open(RESPONSE_PICKLE, 'w'), protocol=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look at the optimisation statistics.\n", "We can plot the minimal score (sum of all objective scores) found in every optimisation. \n", "The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.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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')\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 = $('');\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 = $('');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('
');\n", " var 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", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.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", "\n", "mpl.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", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.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= 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.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "\n", "gen_numbers = logs.select('gen')\n", "min_fitness = numpy.array(logs.select('min'))\n", "max_fitness = logs.select('max')\n", "mean_fitness = numpy.array(logs.select('avg'))\n", "std_fitness = numpy.array(logs.select('std'))\n", "\n", "fig, ax = plt.subplots(3, figsize=(10, 10), facecolor='white')\n", "fig_trip, ax_trip = plt.subplots(1, figsize=(10, 5), facecolor='white')\n", "\n", "plot_count = len(responses)\n", "for index, response in enumerate(responses[:plot_count]):\n", " color='lightblue'\n", " if index == plot_count - 1:\n", " color='blue'\n", " \n", " # best_ind_dict = cell_evaluator.param_dict(best_ind)\n", " # responses = cell_evaluator.run_protocols(cell_evaluator.fitness_protocols.values(), param_values=best_ind_dict)\n", " ax[0].plot(response['step1.soma.v']['time'], response['step1.soma.v']['voltage'], color=color, linewidth=1)\n", " axes = ax[1].plot(response['step2.soma.v']['time'], response['step2.soma.v']['voltage'], color=color, linewidth=1)\n", " # axes[0].set_rasterized(True)\n", " \n", "ax[0].set_xlabel('Time (ms)')\n", "ax[0].set_ylabel('Voltage (mV)')\n", "ax[0].set_xlim(80, 200)\n", "ax[1].set_xlabel('Time (ms)')\n", "ax[1].set_ylabel('Voltage (mV)')\n", "ax[1].set_xlim(80, 200)\n", "\n", "# ax[0].legend()\n", "\n", "std = std_fitness\n", "mean = mean_fitness\n", "minimum = min_fitness\n", "stdminus = mean - std \n", "stdplus = mean + std\n", "\n", "ax[2].plot( \n", " gen_numbers, \n", " mean, \n", " color='black', \n", " linewidth=2, \n", " label='population average') \n", "\n", "ax[2].fill_between( \n", " gen_numbers, \n", " stdminus, \n", " stdplus, \n", " color='lightgray', \n", " linewidth=2, \n", " label=r'population standard deviation') \n", "\n", "ax[2].plot( \n", " gen_numbers, \n", " minimum, \n", " color='red', \n", " linewidth=2, \n", " label='population minimum') \n", "\n", "ax[2].set_xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \n", "ax[2].set_xlabel('Generation #') \n", "ax[2].set_ylabel('Sum of objectives') \n", "ax[2].set_ylim([0, max(stdplus)]) \n", "ax[2].legend() \n", "\n", "all_inds = hist.genealogy_history.values()\n", "\n", "gnabars = numpy.array([ind[0] for ind in all_inds])\n", "gkbars = numpy.array([ind[1] for ind in all_inds])\n", "sums = numpy.array([ind.fitness.sum for ind in all_inds])\n", "# psums = zip(gnabars, gkbars, sums)\n", "zero_gnabars = gnabars[numpy.where(sums == 0)]\n", "zero_gkbars = gkbars[numpy.where(sums == 0)]\n", "\n", "# X = numpy.linspace(gnabar_param.bounds[0], gnabar_param.bounds[1], 150)\n", "# Y = numpy.linspace(gkbar_param.bounds[0], gkbar_param.bounds[1], 150)\n", "# X,Y = numpy.meshgrid(X,Y)\n", "# import matplotlib\n", "# import scipy.interpolate\n", "# Z1 = scipy.interpolate.griddata(numpy.vstack((gnabars.flatten(), gkbars.flatten())).T, numpy.vstack(sums.flatten()), (X, Y), method='linear').reshape(X.shape)\n", "# Z = matplotlib.mlab.griddata(gnabars, gkbars, sums, X, Y, interp='linear')\n", "# Z1m = numpy.ma.masked_where(numpy.isnan(Z1),Z1)\n", "# mesh_axes = ax[2].pcolormesh(X,Y,Z1m)\n", "\n", "# plt.plot(gnabars, gkbars, '.', color='k')\n", "\n", "trip_axis = ax_trip.tripcolor(gnabars,gkbars,sums+1,20,norm=matplotlib.colors.LogNorm())\n", "# plt.tricontourf(gnabars,gkbars,sums,20)\n", "# cbar_ax = fig.add_axes([0.85, 0.29, 0.025, 0.2])\n", "fig_trip.colorbar(trip_axis, label='sum of objectives + 1')\n", "ax_trip.set_xlabel('Parameter gnabar')\n", "ax_trip.set_ylabel('Parameter gkbar')\n", "\n", "plot_axis = ax_trip.plot(list(zero_gnabars), list(zero_gkbars), 'o', color='lightblue')\n", "# ax[4].set_xlabel('Parameter gnabar')\n", "# ax[4].set_ylabel('Parameter gkbar')\n", "# ax[4].set_xlim(gnabar_param.bounds)\n", "# ax[4].set_ylim(gkbar_param.bounds)\n", "# fig.colorbar(trip_axis, ax=ax[3])\n", "\n", "fig.tight_layout()\n", "fig_trip.tight_layout()\n", "\n", "# fig_trip.subplots_adjust(right=0.8, hspace=0.4)\n", "\n", "# print(X, Y)\n", "# print(Z)\n", "\n", "fig.savefig('figures/simplecell_traces.eps')\n", "fig_trip.savefig('figures/simplecell_trip.eps')\n", "\n", "fig.show()\n", "fig_trip.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: examples/simplecell/simplecell.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating a simple cell optimisation\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", "As this optimisation is for example purpose only, no real experimental data is used in this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Install matplotlib if needed\n", "!pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "%load_ext autoreload\n", "%autoreload" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we need to import the module that contains all the functionality to create electrical cell models" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import bluepyopt as bpop\n", "import bluepyopt.ephys as ephys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to see a lot of information about the internals, \n", "the verbose level can be set to 'debug' by commenting out\n", "the following lines" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import logging\n", "# logger = logging.getLogger()\n", "# logger.setLevel(logging.DEBUG)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setting up a cell template\n", "-------------------------\n", "First a template that will describe the cell has to be defined. A template consists of:\n", "* a morphology\n", "* model mechanisms\n", "* model parameters\n", "\n", "### Creating a morphology\n", "A morphology can be loaded from a file (SWC or ASC)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "morph = ephys.morphologies.NrnFileMorphology('simple.swc')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Creating a mechanism\n", "\n", "Now we can add ion channels to this morphology. Let's add the default Neuron Hodgkin-Huxley mechanism to the soma. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "hh_mech = ephys.mechanisms.NrnMODMechanism(\n", " name='hh',\n", " suffix='hh',\n", " locations=[somatic_loc])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "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", "### Creating parameters\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", "Let's define first a parameter that sets the capacitance of the soma to a frozen value" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "cm_param = ephys.parameters.NrnSectionParameter(\n", " name='cm',\n", " param_name='cm',\n", " value=1.0,\n", " locations=[somatic_loc],\n", " frozen=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be optimised later." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "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)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Creating the template\n", "\n", "To create the cell template, we pass all these objects to the constructor of the template" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "simple_cell = ephys.models.CellModel(\n", " name='simple_cell',\n", " morph=morph,\n", " mechs=[hh_mech],\n", " params=[cm_param, gnabar_param, gkbar_param]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can print out a description of the cell" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "simple_cell:\n", " morphology:\n", " simple.swc\n", " mechanisms:\n", " hh: [] hh\n", " params:\n", " cm: ['somatic'] cm = 1.0\n", " gnabar_hh: ['somatic'] gnabar_hh = [0.05, 0.125]\n", " gkbar_hh: ['somatic'] gkbar_hh = [0.01, 0.075]\n", "\n" ] } ], "source": [ "print(simple_cell)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "With this cell we can build a cell evaluator." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Setting up a cell evaluator\n", "\n", "To optimise the parameters of the cell we need to create cell evaluator object. \n", "This object will need to know which protocols to inject, which parameters to optimise, etc." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Creating the protocols\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", "the score of the parameter values.\n", "Let's create two protocols, two square current pulses at somatic`[0]`(0.5) with different amplitudes.\n", "We first need to create a location object" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "soma_loc = ephys.locations.NrnSeclistCompLocation(\n", " name='soma',\n", " seclist_name='somatic',\n", " sec_index=0,\n", " comp_x=0.5)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and then the stimuli, recordings and protocols. For each protocol we add a recording and a stimulus in the soma." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "sweep_protocols = []\n", "for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\n", " stim = ephys.stimuli.NrnSquarePulse(\n", " step_amplitude=amplitude,\n", " step_delay=100,\n", " step_duration=50,\n", " location=soma_loc,\n", " total_duration=200)\n", " rec = ephys.recordings.CompRecording(\n", " name='%s.soma.v' % protocol_name,\n", " location=soma_loc,\n", " variable='v')\n", " protocol = ephys.protocols.SweepProtocol(protocol_name, [stim], [rec])\n", " sweep_protocols.append(protocol)\n", "twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Running a protocol on a cell\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." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nrn = ephys.simulators.NrnSimulator()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The run() method of a protocol accepts a cell model, a set of parameter values and a simulator" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\n", "responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=nrn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plotting the response traces is now easy:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_responses(responses):\n", " plt.subplot(2,1,1)\n", " plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\n", " plt.legend()\n", " plt.subplot(2,1,2)\n", " plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\n", " plt.legend()\n", " plt.tight_layout()\n", "\n", "plot_responses(responses)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, when we use different parameter values, the response looks different." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "other_params = {'gnabar_hh': 0.05, 'gkbar_hh': 0.05}\n", "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=nrn))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Defining eFeatures and objectives\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:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\n", "\n", "objectives = []\n", "\n", "for protocol in sweep_protocols:\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 efel_feature_means[protocol.name].items():\n", " feature_name = '%s.%s' % (protocol.name, efel_feature_name)\n", " feature = ephys.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 = ephys.objectives.SingletonObjective(\n", " feature_name,\n", " feature)\n", " objectives.append(objective)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating the cell evaluator\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." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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)." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "cell_evaluator = ephys.evaluators.CellEvaluator(\n", " cell_model=simple_cell,\n", " param_names=['gnabar_hh', 'gkbar_hh'],\n", " fitness_protocols={twostep_protocol.name: twostep_protocol},\n", " fitness_calculator=score_calc,\n", " sim=nrn)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluating the cell\n", "\n", "The cell can now be evaluate for a certain set of parameter values." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\n" ] } ], "source": [ "print(cell_evaluator.evaluate_with_dicts(default_params))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Setting up and running an optimisation\n", "\n", "Now that we have a cell template and an evaluator for this cell, we can set up an optimisation." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "optimisation = bpop.optimisations.DEAPOptimisation(\n", " evaluator=cell_evaluator,\n", " offspring_size = 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And this optimisation can be run for a certain number of generations" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gen\tnevals\tavg \tstd \tmin\tmax\n", "1 \t10 \t149.4\t189.154\t4 \t500\n", "2 \t10 \t137.9\t173.863\t4 \t500\n", "3 \t10 \t25 \t15.4337\t4 \t40 \n", "4 \t10 \t10 \t13.5941\t0 \t40 \n", "5 \t10 \t7.4 \t12.7922\t0 \t40 \n" ] } ], "source": [ "final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=5)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \n", "\n", "The final population contains a list of tuples, with each tuple representing the two parameters of the model" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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" ] } ], "source": [ "print('Final population: ', final_pop)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best individual found during the optimisation is the first individual of the hall of fame" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [0.10299326453483033, 0.027124836082684685]\n", "Fitness values: (0.0, 0.0)\n" ] } ], "source": [ "best_ind = hall_of_fame[0]\n", "print('Best individual: ', best_ind)\n", "print('Fitness values: ', best_ind.fitness.values)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'step2.Spikecount': 0.0, 'step1.Spikecount': 0.0}\n" ] } ], "source": [ "best_ind_dict = cell_evaluator.param_dict(best_ind)\n", "print(cell_evaluator.evaluate_with_dicts(best_ind_dict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \n", "We can have a look at the responses now." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=nrn))\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look at the optimisation statistics.\n", "We can plot the minimal score (sum of all objective scores) found in every optimisation. \n", "The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 4.4000000000000004)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy\n", "gen_numbers = logs.select('gen')\n", "min_fitness = logs.select('min')\n", "max_fitness = logs.select('max')\n", "plt.plot(gen_numbers, min_fitness, label='min fitness')\n", "plt.xlabel('generation #')\n", "plt.ylabel('score (# std)')\n", "plt.legend()\n", "plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \n", "plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: examples/simplecell/simplecell_arbor.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "# Creating a simple cell optimisation with Arbor\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", "As this optimisation is for example purpose only, no real experimental data is used in this notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# Install matplotlib if needed\n", "!pip install matplotlib" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "%load_ext autoreload\n", "%autoreload" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "First we need to import the module that contains all the functionality to create electrical cell models" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "import bluepyopt as bpop\n", "import bluepyopt.ephys as ephys" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "If you want to see a lot of information about the internals, \n", "the verbose level can be set to 'debug' by commenting out\n", "the following lines" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "# import logging\n", "# logger = logging.getLogger()\n", "# logger.setLevel(logging.DEBUG)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Setting up a cell template\n", "-------------------------\n", "First a template that will describe the cell has to be defined. A template consists of:\n", "* a morphology\n", "* model mechanisms\n", "* model parameters\n", "\n", "### Creating a morphology\n", "A morphology can be loaded from a file (SWC or ASC)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "morph = ephys.morphologies.NrnFileMorphology('simple.swc')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "somatic_loc = ephys.locations.ArbRegionLocation('somatic', region='(intersect (region \"all\") (region \"soma\"))')" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Creating a mechanism\n", "\n", "Now we can add ion channels to this morphology. Let's add the default Neuron Hodgkin-Huxley mechanism to the soma. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "hh_mech = ephys.mechanisms.NrnMODMechanism(\n", " name='hh',\n", " suffix='hh',\n", " locations=[somatic_loc])" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "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", "### Creating parameters\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", "Let's define first a parameter that sets the capacitance of the soma to a frozen value" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "cm_param = ephys.parameters.NrnSectionParameter(\n", " name='cm',\n", " param_name='cm',\n", " value=1.0,\n", " locations=[somatic_loc],\n", " frozen=True)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "And parameters that represent the maximal conductance of the sodium and potassium channels. These two parameters will be optimised later." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "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)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Creating the template\n", "\n", "To create the cell template, we pass all these objects to the constructor of the template" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "simple_cell = ephys.models.CellModel(\n", " name='simple_cell',\n", " morph=morph,\n", " mechs=[hh_mech],\n", " params=[cm_param, gnabar_param, gkbar_param]) " ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Now we can print out a description of the cell" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "simple_cell:\n", " morphology:\n", " simple.swc\n", " mechanisms:\n", " hh: hh at ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))']\n", " params:\n", " cm: ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))'] cm = 1.0\n", " gnabar_hh: ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))'] gnabar_hh = [0.05, 0.125]\n", " gkbar_hh: ['ArbRegionLocation (region-def \"somatic\" (intersect (region \"all\") (region \"soma\")))'] gkbar_hh = [0.01, 0.075]\n", "\n" ] } ], "source": [ "print(simple_cell)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "With this cell we can build a cell evaluator." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Setting up a cell evaluator\n", "\n", "To optimise the parameters of the cell we need to create cell evaluator object. \n", "This object will need to know which protocols to inject, which parameters to optimise, etc." ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Creating the protocols\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", "the score of the parameter values.\n", "Let's create two protocols, two square current pulses at the relative position 0.5 of branch 0 with different amplitudes.\n", "We first need to create a location object" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "soma_loc = ephys.locations.ArbLocsetLocation(\n", " name='soma_center',\n", " locset='(location 0 0.5)')\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "and then the stimuli, recordings and protocols. For each protocol we add a recording and a stimulus in the soma." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "sweep_protocols = []\n", "for protocol_name, amplitude in [('step1', 0.01), ('step2', 0.05)]:\n", " stim = ephys.stimuli.NrnSquarePulse(\n", " step_amplitude=amplitude,\n", " step_delay=100,\n", " step_duration=50,\n", " location=soma_loc,\n", " total_duration=200)\n", " rec = ephys.recordings.CompRecording(\n", " name='%s.soma.v' % protocol_name,\n", " location=soma_loc,\n", " variable='v')\n", " protocol = ephys.protocols.ArbSweepProtocol(protocol_name, [stim], [rec])\n", " sweep_protocols.append(protocol)\n", "twostep_protocol = ephys.protocols.SequenceProtocol('twostep', protocols=sweep_protocols)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Running a protocol on a cell\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." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "sim = ephys.simulators.ArbSimulator()" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The run() method of a protocol accepts a cell model, a set of parameter values and a simulator" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "default_params = {'gnabar_hh': 0.1, 'gkbar_hh': 0.03}\n", "responses = twostep_protocol.run(cell_model=simple_cell, param_values=default_params, sim=sim)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Plotting the response traces is now easy:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_responses(responses):\n", " plt.subplot(2,1,1)\n", " plt.plot(responses['step1.soma.v']['time'], responses['step1.soma.v']['voltage'], label='step1')\n", " plt.legend()\n", " plt.subplot(2,1,2)\n", " plt.plot(responses['step2.soma.v']['time'], responses['step2.soma.v']['voltage'], label='step2')\n", " plt.legend()\n", " plt.tight_layout()\n", "\n", "plot_responses(responses)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "As you can see, when we use different parameter values, the response looks different." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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HKSsJEgkFtJ6PHLXuA6fGoEZO6TCtqhtPODbuPMjWxlYaW7twzhEKGNXlYarLwlSXlVBdFqIyEqYiEqSiJEQgoGPCsSrIBDWqNMzB9mNvQe1t6eCXL77HU2/sYseB9iE/LhgwysNBSkuChAJGwAwzjvh9zDI4SyyTE8wyXYk0s9fO6KXzQpe3kF6ZlnsfMWXhIB0ZdPE1t0f55YtbefiV99l3qOuoHlsZCVEaDhIMQNCMQMCSxwfvGNGfdJ+5tB+HNBvS3f9onz/d5zAcNP78jY+miypjhZmgykIc7IiRSLij+vbinOPXa+r53rINdMYSLDlxPDd+dCYzx1UwuryEcNCIJRytHTFaOmO0dsRo74pzqCtGW1ecNu93e1echHMkHCScw6X8jjtHJjkqkxZaZq+bwYMzfu3C/wZaVhLk1OPH5DqMglWeQQvqtff385VH1rKzuYOPzZnAZfOOY85xoxhXFSEYMKJxR3N7lANtXTS3R2npiNHqHR96jhPeKt9x50gkHDHvtnMOS/fpOLritJ+T9PfP/PmDI9w6LMgENbYiQjyR3GlGV5QM6THOOe56bhP/5y+bWTxjDD+8ah7TaitGOFIRyYbScPCYri6zats+rr/vVcZWlvDb/3Ymp0wd3e/9xlSUADpeDLeCTFC1VREAmg51DjlB/eLFrfyfv2zmmtOm8IMr5474NwMRyZ7qsjD1+4feXQ+wu7mDG/9tDRNHlfLYDYsZPyorKwZJioLs9K71klJDy9D6il99bx93PvMOl86dyD8qOYkUnOqyMM1HMS7tnOPvH3uNjmicX1y/SMkpRwoyQY2tPNyCGkxHNM43f/U6U0aXceen5mnGjUgB6k5QQ53w8+S6Hbzy3j5u/cQcZo6rHOHoJJ0CTVDJFlRT6+AtqP/7n1t4f18b/3jVXKpKtWCcSCGqKQ8TTzgODWEmX3tXnH96+m3m11Vz9alTshCdpFOQCWp0eQkBg8bWgVtQTa2d/Pz5LVw+fxJnzqzNUnQikm3dqxUfaBv8S+tjq95nb0snt1w2Rz0qOVaQCSoYMMZUlNA4SAvqoZe30xlL8Hfnz8pSZCKSC90JarBxqK5Ygl+8sJXTpo3m9Oma9p9rBZmgIDnVfKAWVHtXnIde3sYFHx7PCePVxyxSyKrLkt3+gyWoP23Yzc7mDr780ZnZCEsGUbAJ6riaUnYOcBWIX6+tZ39blBvO1Y4oUui6W1CDXWHmydd2cFx1KR+dPT4bYckgCjZBTR1Tzvv72vrdFk84/vXFrcyfUsNp0/o/8U5ECkd1efcYVPoE1dTayfObGrh8wSSNPflEQSeolo4Yzf3skM9t3M32pja+dO6MoriMjkixq+meJDFAC+qpN3YRSziuPGVytsKSQRRsgqobXQ5wRCvKOcfPX9jK1DHlXHTSxFyEJiJZVl4SpLwkSENL+nHp3762gw9NrOJDE0dlMTIZSMEmqKlj+k9Qa7bv57X3D/C350zXFSNEioSZMWFUKXsOdvS7/b3GQ6z74ABXLVTryU8KNkFNqy3HDDbtaelV/vMXtlJTHubTp9blKDIRyYXxVZG0CerJ13ZgBpfPV4Lyk4JNUOUlIWaOq+TNHc09ZVsaWln+1h4+t/h4yksK8jq5IpLGxOpS9hw8sovPOceT63Zw5syxTKzWNff8pGATFMDcydW8Xt/cc/2tn67YTCQU4HNnTsttYCKSdRNGlbL7YMcR1+N77YMDbG9q45ML1Hrym4JOUGefUEtjayfrdzSzpaGV363byXUfOZ5a72KyIlI8JowqpSuWOOJk3Sdf20EkFODikzVpym8Kup/rvA+NpyQY4KcrNtPQ0klFSZAv/ZVOzBUpRhO9JTN2HGinpjx5ZYloPMHvX9/JhXMm6GLRPlTQLajRFSX87TnT+dOGPaz74AD/eNVcxlWp9SRSjGaMS654u6XhUE/ZC5sa2N8W1blPPlXQLSiAb37sRBZMqeG46jLm1lXnOhwRyZHptRUEDDbvbe0p+/WaesZWlHDu7HE5jEzSKfgEFQgYH9MJuSJFrzQcZMqYct71Tj1pau1Mzuo9YxrhYEF3JuUtvSsiUjROmVLDqm37cc7xxNodROOOzyzSooR+pQQlIkXjzJnJmb1rtu/nFy9uZfGMMZw4sSrXYUkaSlAiUjQ+dtIEKkqCfPpnL7O3pZN/uPDEXIckA1CCEpGiUVNewvevOJkZtRXc+vE5WjXX5wp+koSISKpPnVrHp3QtzrygFpSIiPiSEpSIiPiSEpSIiPiS9b2yby6ZWQOwfRieqhZoHIbnyYZ8ihXyK958ihXyK958ihXyK95ijPV459wRl/PwVYIaLma22jm3KNdxDEU+xQr5FW8+xQr5FW8+xQr5Fa9iPUxdfCIi4ktKUCIi4kuFmqDuzXUARyGfYoX8ijefYoX8ijefYoX8ilexegpyDEpERPJfobagREQkzylBiYiILxVUgjKzi83sHTPbbGbfznU8fZnZFDNbYWYbzWyDmf29V36bme0ws3Xez6W5jhXAzLaZ2XovptVe2Rgze87M3vV+j851nABmdmJK/a0zs4NmdpNf6tbM7jOzvWb2ZkpZv3VpSf/b24/fMLOFPon3n83sbS+m35pZjVc+zczaU+r4Zz6INe37bmbf8er2HTO7KJuxDhDv4ymxbjOzdV55rus23TErO/uuc64gfoAgsAWYAZQArwNzch1XnxiPAxZ6t6uATcAc4Dbgm7mOr594twG1fcp+BHzbu/1t4M5cx5lmX9gNHO+XugXOBRYCbw5Wl8ClwB8BAxYDr/gk3o8BIe/2nSnxTku9n09i7fd99z5vrwMRYLp3zAjmOt4+238M3OqTuk13zMrKvltILajTgc3Oua3OuS7gMeCKHMfUi3Nul3NurXe7BXgLmJzbqI7aFcCD3u0HgU/mLpS0zge2OOeG46okw8I59wKwr09xurq8AnjIJa0EaszsuKwE6ukvXufcs865mPfnSsAXlwRPU7fpXAE85pzrdM69B2wmeezImoHiNTMDPgM8ms2Y0hngmJWVfbeQEtRk4IOUv+vx8cHfzKYBpwCveEVf9ZrE9/ml2wxwwLNmtsbMbvDKJjjndnm3dwMTchPagK6h9wfcj3UL6esyH/blL5D8ptxtupm9ZmbPm9k5uQqqj/7ed7/X7TnAHufcuyllvqjbPsesrOy7hZSg8oaZVQK/AW5yzh0E7gFmAguAXSSb+H5wtnNuIXAJ8BUzOzd1o0u26X11noKZlQCXA7/yivxat734sS7TMbNbgBjwiFe0C5jqnDsF+Afg381sVK7i8+TF+96Pa+n95coXddvPMavHSO67hZSgdgBTUv6u88p8xczCJN/oR5xzTwA45/Y45+LOuQTwC7Lc5ZCOc26H93sv8FuSce3pbrJ7v/fmLsJ+XQKsdc7tAf/WrSddXfp2XzazzwMfB/7GOzDhdZc1ebfXkBzXmZ2zIBnwffdz3YaAq4DHu8v8ULf9HbPI0r5bSAlqFTDLzKZ736KvAZblOKZevP7lXwJvOefuSilP7aO9Eniz72OzzcwqzKyq+zbJAfI3Sdbp9d7drgd+l5sI0+r1DdSPdZsiXV0uAz7nzYhaDDSndKfkjJldDNwMXO6ca0spH2dmQe/2DGAWsDU3UfbElO59XwZcY2YRM5tOMtZXsx1fGhcAbzvn6rsLcl236Y5ZZGvfzdXskJH4ITmDZBPJbxm35DqefuI7m2RT+A1gnfdzKfBvwHqvfBlwnA9inUFyttPrwIbu+gTGAn8G3gWWA2NyHWtKzBVAE1CdUuaLuiWZNHcBUZL98l9MV5ckZ0D91NuP1wOLfBLvZpLjC9377s+8+37K20fWAWuBT/gg1rTvO3CLV7fvAJf4oW698geAG/vcN9d1m+6YlZV9V5c6EhERXyqkLj4RESkgSlAiIuJLSlAiIuJLSlAiIuJLSlAiIuJLSlAiIuJLSlAiIuJLSlAiIuJLSlAiIuJLSlAiIuJLSlAiIuJLoVwHkKq2ttZNmzYt12GIiEgWrVmzptE5N65vua8S1LRp01i9enWuwxARkSwys+39lauLT0REfEkJSkREfEkJSsSHHnjpPV7Y1JDrMERyyldjUCKSdNvvNwKw7YeX5TgSGW7RaJT6+no6OjpyHUrWlZaWUldXRzgcHtL9laBERLKovr6eqqoqpk2bhpnlOpyscc7R1NREfX0906dPH9Jj1MUn4jPOuVyHICOoo6ODsWPHFlVyAjAzxo4de1QtRyUoEZ/pjCVyHYKMsGJLTt2O9v9WghLxma64EpQIKEGJ+E48ri4+ya67776btra2Y378XXfdxZw5c5g3bx7nn38+27f3e97tUVOCEvGZuMagJMsyTVCnnHIKq1ev5o033uDTn/40N99887DEpQQl4jOJhBKUjJxDhw5x2WWXMX/+fE4++WRuv/12du7cyZIlS1iyZAkAzz77LGeccQYLFy7k6quvprW1FUheju7mm29m7ty5nH766WzevBmAJUuWUF5eDsDixYupr68fllg1zVzEZ2JKUEXj9t9vYOPOg8P6nHMmjeJ7nzgp7fZnnnmGSZMm8Yc//AGA5uZm7r//flasWEFtbS2NjY3ccccdLF++nIqKCu68807uuusubr31VgCqq6tZv349Dz30EDfddBNPPfVUr+f/5S9/ySWXXDIs/4sSlIjPxFMSVCLhCASKc8aXjIy5c+fyjW98g29961t8/OMf55xzzum1feXKlWzcuJGzzjoLgK6uLs4444ye7ddee23P769//eu9Hvvwww+zevVqnn/++WGJVQlKxGcSKWNQXfEEpYFgDqORkTRQS2ekzJ49m7Vr1/L000/z3e9+l/PPP7/XduccF154IY8++mi/j0+dKp56e/ny5fzgBz/g+eefJxKJDEusGoMS8ZnULj6dEyXDbefOnZSXl3PdddexdOlS1q5dS1VVFS0tLUByDOmll17qGV86dOgQmzZt6nn8448/3vO7u2X12muv8aUvfYlly5Yxfvz4YYtVLSgRn0mdJNGlBCXDbP369SxdupRAIEA4HOaee+7h5Zdf5uKLL2bSpEmsWLGCBx54gGuvvZbOzk4A7rjjDmbPng3A/v37mTdvHpFIpKeVtXTpUlpbW7n66qsBmDp1KsuWLcs4VvPTZVUWLVrktGChFLu3dx/k4rtfBOClb5/H5JqyHEckw+mtt97iwx/+cK7DOCbdi8rW1tYe83P09/+b2Rrn3KK+91UXn4jPxOJqQYmAuvhEfCd1kkRnLJ7DSER627ZtW1ZfTy0oEZ+Jawyq4PlpaCWbjvb/VoIS8RklqMJWWlpKU1NT0SWp7vWgSktLh/yYEe/iM7OLgf8FBIF/dc79cKRfUySfKUEVtrq6Ourr62loaMh1KFnXvaLuUI1ogjKzIPBT4EKgHlhlZsuccxtH8nVF8lnqxWI7tfRGwQmHw0NeUbbYjXQX3+nAZufcVudcF/AYcMUIv6ZIXlMLSiRppBPUZOCDlL/rvbIeZnaDma02s9XF2OQV6UsJSiQp55MknHP3OucWOecWjRs3LtfhiORc72nmSlBSvEY6Qe0ApqT8XeeViUgaOlFXJGmkE9QqYJaZTTezEuAaIPMLNIkUsF5XM9eJulLERnQWn3MuZmZfBf5Ecpr5fc65DSP5miL5LnXiXpdm8UkRG/HzoJxzTwNPj/TriBSKWOJwUlIXnxSznE+SEJHeenfxKUFJ8VKCEvGZ1F49nagrxUwJSsRn4ildfJ1RJSgpXkpQIj6jSRIiSUpQIj7TfS2+kmBAY1BS1JSgRHwm7rWaykqCSlBS1JSgRHwm5l2Lr1wJSoqcEpSIz3RfLLasJKgxKClqSlAiPqMWlEiSEpSIz3S3oMrDITp1LT4pYkpQIj7T3YIqVQtKipwSlIjPJBKOYMCIhAJaD0qKmhKUiM/EvARVEgpokoQUNSUoEZ+JJxKEAkZEJ+pKkVOCEvGZXi0oJSgpYkpQIj4TTzhCXoLSGJQUMyUoEZ+JpUySUAtKipkSlIjPxOOaJCECGSYoM/tnM3vbzN4ws9+aWY1XPs3M2s1snffzs2GJVqQIxJ0jFAhQEgwST7ieE3dFik2mLajngJOdc/OATcB3UrZtcc4t8H5uzPB1RIpGPGWSBGjZdyleGSUo59yzzrmY9+dKoC7zkESKWyxlkgQoQUnxGs4xqC8Af0z5e7qZvWZmz5vZOekeZGY3mNlqM1vd0NAwjOGI5Kd4ItGrBdUZ1/X4pDiFBruDmS0HJvaz6Rbn3O+8+9wCxIBHvG27gKnOuSYzOxV40sxOcs4d7Pskzrl7gXsBFi1apM52KXoxb5JEJOglqKhaUFKcBk1QzrkLBtpuZp8HPg6c71xyrWrnXCfQ6d1eY2ZbgNnA6kwDFil03WNQkbDXxaeZfFKkMp3FdzFwM3C5c64tpXycmQW92zOAWcDWTF5LpFj0jEEFNQYlxW3QFtQgfgJEgOfMDGClN2PvXOD7ZhYFEsCNzrl9Gb6WSFFIOM3iE4EME5Rz7oQ05b8BfpPJc4sUq1i8T4JSF58UKV1JQsRnuuIJSkIBdfFJ0VOCEvGZzlicSCioLj4pekpQIj7TGU0QCQUOnwcV03lQUpyUoER8piueTFCRUBBAS25I0VKCEvGZZAsqSKSnBaUEJcVJCUrEZzpjcSLhQE+C0hiUFCslKBGf6Yypi08ElKBEfCeZoIKHL3WkBCVFSglKxEdi8QTxhEvO4gtqFp8UNyUoER/p7s4rCQUIeNfjUxefFCslKBEf6U5G3RMkIqGAltuQoqUEJeIjbV3JBarLSpITJCLhgLr4pGgpQYn4SEtHMkFVlYYBiISC6uKToqUEJeIjhxNUcqGBkpDGoKR4KUGJ+EhLRxRIbUEF6Iyqi0+KkxKUiI/0bUFF1IKSIqYEJeIjh1tQ3QkqqBN1pWgpQYn4yP62ZIKqKSsBNItPiltGCcrMbjOzHWa2zvu5NGXbd8xss5m9Y2YXZR6qSOFraOmkpjzcsxaUuvikmIWG4Tn+p3PuX1ILzGwOcA1wEjAJWG5ms51z+iooMoCGlk7GVUZ6/tY0cylmI9XFdwXwmHOu0zn3HrAZOH2EXkukYOxt6aC2V4JSF58Ur+FIUF81szfM7D4zG+2VTQY+SLlPvVd2BDO7wcxWm9nqhoaGYQhHJH/V72+nbnRZz9+RsC51JMVr0ARlZsvN7M1+fq4A7gFmAguAXcCPjzYA59y9zrlFzrlF48aNO9qHixSM9q44e1s6mTqmvKdMXXxSzAYdg3LOXTCUJzKzXwBPeX/uAKakbK7zykQkjff3tQEwdezhBFWiLj4pYpnO4jsu5c8rgTe928uAa8wsYmbTgVnAq5m8lkih605Qx4+t6CmLhAJ0xRI453IVlkjOZDqL70dmtgBwwDbgSwDOuQ1m9h/ARiAGfEUz+EQGtr3pEADH9+riC5BwEEs4wkHLVWgiOZFRgnLOfXaAbT8AfpDJ84sUk/f3tVEVCVFTHu4pi4SSy250xhKEgzqvXoqL9ngRn9je1MbUseWYHW4pRcLesu+6YKwUISUoEZ/4YF8bx6dMkIDDK+tqJp8UIyUoER+IJxwf7G9jypi+CepwF59IsVGCEvGBXc3tROOO48dU9Co/3IJSF58Un+G4Fp+IZOjwFPM+LaieMaijb0F1xuI8u2EPL2xqYNPeVhpbOgEIBY1IKEBpOEhpKEgknLxdFg4SGmym4BBmuw9lQvxQps0P7XkGv09laYjvXvZhykt0uMs3esdEfOD9Ju8k3TRdfF3xoSco5xxPrN3BD595m4aWTqrLwsydXM3M2grMjFgiQWc0QUcsTkc0TktHjIaWTjqicaJxhw2SowbbDmAMfqehPc8Q7jPAE3XFEuw40M7H5kzgoyeOH8KziZ8oQYn4wPZ9bYQCxnHVpb3Ku5fdGGoLqr0rztcfX8czG3azcGoN/3L1fM4+oZZgoDjPoWpq7eTUO5azpeEQHz0x19HI0VKCEvGB9/e1UTe6jFCfc52OZgyquT3K9fe9yhv1B/jvl36IL549o2gTU7cxFSXUlIfZ0tCa61DkGChBifjA+01tTB1bcUT5UGfxdUTj/NcHV7NhZzP3XHcqF500cUTizDdmxsxxlWxVgspLBTWLr7Uzxt89+hrr65tzHYrIUdnedIipY8qOKB9KC8o5xzf+43VWbd/HXZ9ZoOTUx4zaCrY0HMp1GHIMCqoF1RmNs2b7flZtW82vbjyDutHlgz8oDecc0bgjnhiZi3QOZYA4n15Hjl1ze5SDHbEjppjD0Gbx3f/SNv6wfhffvuRDfGL+pBGLM1/NHF/Jr9bU09wepbosPPgDhiAaT9AZS9DltWyN5GfNMLDu28kWXN+PYN/PZH8TSoZjokq2dLfyR0JBJaixlRHu/dypXHPvSq74yUvcdMEsLpwzkfFVERLOcbAjxs4D7ew80M6OA+3sau6gsbWTptYuGls72X+oi/ZonM5Ygo5onBHKTSL96jvFHAbv4luzfT//+PRbXDhnAl86d8aIxpevZo6rBGDz3lZOPX70IPdO6ooleKP+ABt2HuStXQd5f18be1s62XOwg7au+Ih9cc03JcEAm35wyYg9f0ElKICTJlXz2/92Jkt//Qb/43cb+B+/24BZ/+dLlIQCjKuMUFtZwoRRpXxo4ijKS4KUhgNEQsnffQeth8OxrJzghnRWSOavI7lRFg5y7uwjF+wcqItv36EuvvrvazmuppR/uXr+gNOti9mCKTUArNzaNGCCauuK8cf1u/nTht28tLmRQ13JOh9dHmZ6bQWzxldy1syxVJaGes4fK/GOD47k5y35O/nBS/7d+0PY9zPZ30f0yPsM/By5NNKTcAouQQGcML6KJ758Jht2HmTN9v00tnYSDgaojISYVFPG5JoyJtWUMqaiRB9q8bXuBNXVpwWVSDi+/vg6mlq7+M2Xzxy2rqtCNK4qwpzjRvHcxj18ZckJR2x/a9dBHnp5O79/fSetnTEm15TxyVMmc86scSyYUsOEUREdJ3KkIBMUJPt+T55czcmTq3MdisgxCwUDBAN2RBffPc9v4flNDdzxyZOZW6d9fDCfPrWO7z+1kf/3biNnz6olGk/wl7f3cv9L77Fy6z5KwwEumzuJzyyq4/TpY5SQfKJgE5RIoSgJBnolqJVbm/jxs+/wifmT+JuPTM1hZPnjmtOn8PAr2/nig6uYX1fDO3taaG6PMrmmjO9c8iGuOW0q1eVqhfqNEpSIz5WGA7R1xYDk+VJfeWQt08ZW8E9XzdU3/SEqLwnx6H9dzN3L32XL3lYunDOBi06ayJITx43IOLMMDyUoEZ+bMKqUXQc62Heoi88/8Cpx5/jX6xdRGdHH92hMGFXKP101N9dhyFHIaA83s8eB7itc1QAHnHMLzGwa8BbwjrdtpXPuxkxeS6RYTRlTzsotTVxz78vU72/n4S9+hBne1GmRQpZRgnLO/Zfu22b2YyD1Eg5bnHMLMnl+EYHZEyp5buMe4vvbeeDzp3H69DG5DkkkK4alj8CSHeGfAc4bjucTkcO+suQEJo4q5awTatVykqIyXKOD5wB7nHPvppRNN7PXzOx5Mzsn3QPN7AYzW21mqxsaGoYpHJHCUV4S4rNnTFNykqIzaAvKzJYD/V198hbn3O+829cCj6Zs2wVMdc41mdmpwJNmdpJz7mDfJ3HO3QvcC7Bo0SIfnSMtIiK5NGiCcs5dMNB2MwsBVwGnpjymE+j0bq8xsy3AbGB1RtGKiEjRGI4uvguAt51z9d0FZjbOzILe7RnALGDrMLyWiIgUieGYJHENvbv3AM4Fvm9mUSAB3Oic2zfYE61Zs6bRzLYPQ0y1QOMwPE825FOskF/x5lOskF/x5lOskF/xFmOsx/dXaM5Pl8YdJma22jm3KNdxDEU+xQr5FW8+xQr5FW8+xQr5Fa9iPUzX+BAREV9SghIREV8q1AR1b64DOAr5FCvkV7z5FCvkV7z5FCvkV7yK1VOQY1AiIpL/CrUFJSIieU4JSkREfKmgEpSZXWxm75jZZjP7dq7j6cvMppjZCjPbaGYbzOzvvfLbzGyHma3zfi7NdawAZrbNzNZ7Ma32ysaY2XNm9q73e3Su4wQwsxNT6m+dmR00s5v8Urdmdp+Z7TWzN1PK+q1LS/rf3n78hpkt9Em8/2xmb3sx/dbMarzyaWbWnlLHP/NBrGnfdzP7jle375jZRdmMdYB4H0+JdZuZrfPKc1236Y5Z2dl3nXMF8QMEgS3ADKAEeB2Yk+u4+sR4HLDQu10FbALmALcB38x1fP3Euw2o7VP2I+Db3u1vA3fmOs40+8Jukif/+aJuSZ68vhB4c7C6BC4F/ggYsBh4xSfxfgwIebfvTIl3Wur9fBJrv++793l7HYgA071jRjDX8fbZ/mPgVp/UbbpjVlb23UJqQZ0ObHbObXXOdQGPAVfkOKZenHO7nHNrvdstJBd1nJzbqI7aFcCD3u0HgU/mLpS0zie5HtlwXJVkWDjnXgD6Xk0lXV1eATzkklYCNWZ2XFYC9fQXr3PuWedczPtzJVCXzZjSSVO36VwBPOac63TOvQdsJnnsyJqB4jXrWbqo79V5cmKAY1ZW9t1CSlCTgQ9S/q7Hxwd/S646fArwilf0Va9JfJ9fus0ABzxrZmvM7AavbIJzbpd3ezcwITehDajv5bf8WLeQvi7zYV/+Aslvyt2m2xCW18my/t53v9ftMS9dNNL6HLOysu8WUoLKG2ZWCfwGuMkllyC5B5gJLCC5VMmPcxddL2c75xYClwBfMbNzUze6ZJveV+cpmFkJcDnwK6/Ir3Xbix/rMh0zuwWIAY94Rd3L65wC/APw72Y2KlfxefLife9HuqWLclq3/RyzeozkvltICWoHMCXl7zqvzFfMLEzyjX7EOfcEgHNuj3Mu7pxLAL8gy10O6Tjndni/9wK/JRnXnu4mu/d7b+4i7NclwFrn3B7wb9160tWlb/dlM/s88HHgb7wDE153WZN3ew3JcZ3ZOQuSAd93P9dt99JFj3eX+aFu+ztmkaV9t5AS1CpglplN975FXwMsy3FMvXj9y78E3nLO3ZVSntpHeyXwZt/HZpuZVZhZVfdtkgPkb5Ks0+u9u10P/K7/Z8iZXt9A/Vi3KdLV5TLgc96MqMVAc0p3Ss6Y2cXAzcDlzrm2lHLfLa8zwPu+DLjGzCJmNp1krK9mO740fLd0UbpjFtnad3M1O2QkfkjOINlE8lvGLbmOp5/4zibZFH4DWOf9XAr8G7DeK18GHOeDWGeQnO30OrChuz6BscCfgXeB5cCYXMeaEnMF0ARUp5T5om5JJs1dQJRkv/wX09UlyRlQP/X24/XAIp/Eu5nk+EL3vvsz776f8vaRdcBa4BM+iDXt+w7c4tXtO8Alfqhbr/wBkksTpd4313Wb7piVlX1XlzoSERFfKqQuPhERKSBKUCIi4ktKUCIi4ktKUCIi4ktKUCIi4ktKUCIi4ktKUCIi4kv/HyC2f5DKY+bwAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "other_params = {'gnabar_hh': 0.05, 'gkbar_hh': 0.05}\n", "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=other_params, sim=sim))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Defining eFeatures and objectives\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:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "efel_feature_means = {'step1': {'Spikecount': 1}, 'step2': {'Spikecount': 5}}\n", "\n", "objectives = []\n", "\n", "for protocol in sweep_protocols:\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 efel_feature_means[protocol.name].items():\n", " feature_name = '%s.%s' % (protocol.name, efel_feature_name)\n", " feature = ephys.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 = ephys.objectives.SingletonObjective(\n", " feature_name,\n", " feature)\n", " objectives.append(objective)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Creating the cell evaluator\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." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "score_calc = ephys.objectivescalculators.ObjectivesCalculator(objectives) " ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "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)." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "cell_evaluator = ephys.evaluators.CellEvaluator(\n", " cell_model=simple_cell,\n", " param_names=['gnabar_hh', 'gkbar_hh'],\n", " fitness_protocols={twostep_protocol.name: twostep_protocol},\n", " fitness_calculator=score_calc,\n", " sim=sim)\n" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "### Evaluating the cell\n", "\n", "The cell can now be evaluate for a certain set of parameter values." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'step1.Spikecount': 0.0, 'step2.Spikecount': 0.0}\n" ] } ], "source": [ "print(cell_evaluator.evaluate_with_dicts(default_params))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "## Setting up and running an optimisation\n", "\n", "Now that we have a cell template and an evaluator for this cell, we can set up an optimisation." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "optimisation = bpop.optimisations.DEAPOptimisation(\n", " evaluator=cell_evaluator,\n", " offspring_size = 10)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "And this optimisation can be run for a certain number of generations" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "final_pop, hall_of_fame, logs, hist = optimisation.run(max_ngen=5)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The optimisation has return us 4 objects: final population, hall of fame, statistical logs and history. \n", "\n", "The final population contains a list of tuples, with each tuple representing the two parameters of the model" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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" ] } ], "source": [ "print('Final population: ', final_pop)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "The best individual found during the optimisation is the first individual of the hall of fame" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best individual: [0.09181168257196179, 0.02498056382291062]\n", "Fitness values: (0.0, 0.0)\n" ] } ], "source": [ "best_ind = hall_of_fame[0]\n", "print('Best individual: ', best_ind)\n", "print('Fitness values: ', best_ind.fitness.values)" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "We can evaluate this individual and make use of a convenience function of the cell evaluator to return us a dict of the parameters" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'step1.Spikecount': 0.0, 'step2.Spikecount': 0.0}\n" ] } ], "source": [ "best_ind_dict = cell_evaluator.param_dict(best_ind)\n", "print(cell_evaluator.evaluate_with_dicts(best_ind_dict))" ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "As you can see the evaluation returns the same values as the fitness values provided by the optimisation output. \n", "We can have a look at the responses now." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_responses(twostep_protocol.run(cell_model=simple_cell, param_values=best_ind_dict, sim=sim))\n", " " ] }, { "cell_type": "markdown", "metadata": { "pycharm": { "name": "#%% md\n" } }, "source": [ "Let's have a look at the optimisation statistics.\n", "We can plot the minimal score (sum of all objective scores) found in every optimisation. \n", "The optimisation algorithm uses negative fitness scores, so we actually have to look at the maximum values log." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [ { "data": { "text/plain": [ "(0.0, 4.4)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy\n", "gen_numbers = logs.select('gen')\n", "min_fitness = logs.select('min')\n", "max_fitness = logs.select('max')\n", "plt.plot(gen_numbers, min_fitness, label='min fitness')\n", "plt.xlabel('generation #')\n", "plt.ylabel('score (# std)')\n", "plt.legend()\n", "plt.xlim(min(gen_numbers) - 1, max(gen_numbers) + 1) \n", "plt.ylim(0.9*min(min_fitness), 1.1 * max(min_fitness)) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" }, "tags": [] }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "vscode": { "interpreter": { "hash": "581988038cf9ce8838e7faf3da7c29f4ff88d898cd43cb17e0086e389d8deda2" } } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/simplecell/simplecell_model.py ================================================ """Run simple cell optimisation""" """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0914 import bluepyopt.ephys as ephys def define_morphology(do_replace_axon): return ephys.morphologies.NrnFileMorphology('simple.swc', do_replace_axon=do_replace_axon) def define_mechanisms(): somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic') hh_mech = ephys.mechanisms.NrnMODMechanism( name='hh', suffix='hh', locations=[somatic_loc]) return [hh_mech] def define_parameters(): somatic_loc = ephys.locations.NrnSeclistLocation('somatic', seclist_name='somatic') cm_param = ephys.parameters.NrnSectionParameter( name='cm', param_name='cm', value=1.0, locations=[somatic_loc], frozen=True) gnabar_param = ephys.parameters.NrnSectionParameter( name='gnabar_hh', param_name='gnabar_hh', locations=[somatic_loc], bounds=[0.05, 0.125], frozen=False) gkbar_param = ephys.parameters.NrnSectionParameter( name='gkbar_hh', param_name='gkbar_hh', bounds=[0.01, 0.075], locations=[somatic_loc], frozen=False) return [cm_param, gnabar_param, gkbar_param] def create(do_replace_axon): """Create cell model (identical to simplecell.ipynb)""" cell = ephys.models.CellModel( 'simple_cell', morph=define_morphology(do_replace_axon), mechs=define_mechanisms(), params=define_parameters()) return cell ================================================ FILE: examples/stochkv/.gitignore ================================================ /x86_64 ================================================ FILE: examples/stochkv/mechanisms/StochKv.mod ================================================ TITLE skm95.mod COMMENT ---------------------------------------------------------------- Stochastic version of the K channel mechanism kd3h5.mod by Z. Mainen in Mainen & Sejnowski 95. This represents a potassium channel, with Hodgkin-Huxley like kinetics, based on the gates model, assuming stochastic opening and closing. Kinetic rates based roughly on Sah et al. and Hamill et al. (1991) The main kinetic difference from the standard H-H model (shh.mod) is that the K+ kinetic is different, not n^4, but just n, and the activation curves are different. The rate functions are adapted directly from the Kd3h5.mod file by Zach Mainen. The stochastic model is as following: Potassium = alpha_n => [N0] [N1] <= beta_n = The model keeps track on the number of channels in each state, and uses a binomial distribution to update these number. Jan 1999, Mickey London, Hebrew University, mikilon@lobster.ls.huji.ac.il Peter N. Steinmetz, Caltech, peter@klab.caltech.edu 14 Sep 99 PNS. Added deterministic flag. 19 May 2002 Kamran Diba. Changed gamma and deterministic from GLOBAL to RANGE. 23 Nov 2011 Werner Van Geit @ BBP. Changed the file so that it can use the neuron random number generator. Tuned voltage dependence 16 Mar 2016 James G King @ BBP. Incorporate modifications suggested by Michael Hines to improve stiching to deterministic mode, thread safety, and using Random123 16 Jan 2017 Christian Roessert @ BBP: WARNING unit declaration is wrong! modlunit gives errors! To maintain backward compatibility this channel is not corrected but usage is DISCOURAGED! StochKv.mod and inactivating version of this channel uses corrected units! ---------------------------------------------------------------- ENDCOMMENT INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} NEURON { SUFFIX StochKv THREADSAFE USEION k READ ek WRITE ik RANGE N, eta, gk, gamma, deterministic, gkbar, ik RANGE N0, N1, n0_n1, n1_n0 RANGE ninf, ntau, a, b, P_a, P_b RANGE Ra, Rb, tadj GLOBAL vmin, vmax, q10, temp BBCOREPOINTER rng } UNITS { (mA) = (milliamp) (mV) = (millivolt) (pS) = (picosiemens) (S) = (siemens) (um) = (micron) } PARAMETER { v (mV) dt (ms) area (um2) gamma = 30 (pS) eta (1/um2) gkbar = .75 (S/cm2) tha = -40 (mV) : v 1/2 for inf qa = 9 : inf slope Ra = 0.02 (/ms) : max act rate Rb = 0.002 (/ms) : max deact rate celsius (degC) temp = 23 (degC) : original temperature for kinetic set q10 = 2.3 : temperature sensitivity deterministic = 0 : if non-zero, will use deterministic version vmin = -120 (mV) : range to construct tables for vmax = 100 (mV) } ASSIGNED { a (/ms) b (/ms) ik (mA/cm2) gk (S/cm2) ek (mV) ninf : steady-state value ntau (ms) : time constant for relaxation tadj N scale_dens (pS/um2) P_a : probability of one channel making alpha transition P_b : probability of one channel making beta transition rng n0_n1_new usingR123 } STATE { n : state variable of deterministic description } ASSIGNED { N0 N1 : N states populations (These currently will not be saved via the bbsavestate functionality. Would need to be STATE again) n0_n1 n1_n0 : number of channels moving from one state to the other } COMMENT The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1 for comparison with Pr to decide whether to activate the synapse or not ENDCOMMENT VERBATIM #ifndef NRN_VERSION_GTEQ_8_2_0 #include "nrnran123.h" extern int cvode_active_; #include #include #include double nrn_random_pick(void* r); void* nrn_random_arg(int argpos); #define RANDCAST #else #define RANDCAST (Rand*) #endif ENDVERBATIM : ---------------------------------------------------------------- : initialization INITIAL { VERBATIM if (cvode_active_ && !deterministic) { hoc_execerror("StochKv with deterministic=0", "cannot be used with cvode"); } if( usingR123 ) { nrnran123_setseq((nrnran123_State*)_p_rng, 0, 0); } ENDVERBATIM eta = (gkbar / gamma) : * (10000) for proper fix trates(v) n = ninf scale_dens = gamma/area N = floor(eta*area + 0.5) N1 = n*N if( !deterministic) { N1 = floor(N1 + 0.5) } N0 = N-N1 : any round off into non-conducting state n0_n1 = 0 n1_n0 = 0 } : ---------------------------------------------------------------- : Breakpoint for each integration step BREAKPOINT { SOLVE states METHOD cnexp gk = (strap(N1) * scale_dens * tadj) : * (0.0001) for proper fix ik = 1e-4 * gk * (v - ek) : remove 1e-4 for proper fix } : ---------------------------------------------------------------- : states - updates number of channels in each state DERIVATIVE states { trates(v) n' = a - (a + b)*n if (deterministic || dt > 1) { : ForwardSkip is also deterministic N1 = n*N }else{ : ensure that N0 is an integer for when transitioning from deterministic mode to stochastic mode N0 = floor(N0+0.5) N1 = N - N0 P_a = strap(a*dt) P_b = strap(b*dt) : check that will represent probabilities when used ChkProb( P_a) ChkProb( P_b) : transitions n0_n1 = BnlDev(P_a, N0) n1_n0 = BnlDev(P_b, N1) : move the channels N0 = strap(N0 - n0_n1 + n1_n0) N1 = N - N0 } N0 = N-N1 : any round off into non-conducting state } : ---------------------------------------------------------------- : trates - compute rates, using table if possible PROCEDURE trates(v (mV)) { TABLE ntau, ninf, a, b, tadj DEPEND dt, Ra, Rb, tha, qa, q10, temp, celsius FROM vmin TO vmax WITH 199 tadj = q10 ^ ((celsius - temp)/(10 (K))) a = SigmoidRate(v, tha, Ra, qa) a = a * tadj b = SigmoidRate(-v, -tha, Rb, qa) b = b * tadj ntau = 1/(a+b) ninf = a*ntau } : ---------------------------------------------------------------- : SigmoidRate - Compute a sigmoid rate function given the : 50% point th, the slope q, and the amplitude a. FUNCTION SigmoidRate(v (mV),th (mV),a (1/ms),q) (1/ms){ UNITSOFF if (fabs(v-th) > 1e-6 ) { SigmoidRate = a * (v - th) / (1 - exp(-(v - th)/q)) UNITSON } else { SigmoidRate = a * q } } : ---------------------------------------------------------------- : sign trap - trap for negative values and replace with zero FUNCTION strap(x) { if (x < 0) { strap = 0 VERBATIM fprintf (stderr,"skv.mod:strap: negative state"); ENDVERBATIM } else { strap = x } } : ---------------------------------------------------------------- : ChkProb - Check that number represents a probability PROCEDURE ChkProb(p) { if (p < 0.0 || p > 1.0) { VERBATIM fprintf(stderr, "StochKv.mod:ChkProb: argument not a probability.\n"); ENDVERBATIM } } PROCEDURE setRNG() { VERBATIM // For compatibility, allow for either MCellRan4 or Random123. Distinguish by the arg types // Object => MCellRan4, seeds (double) => Random123 #ifndef CORENEURON_BUILD usingR123 = 0; if( ifarg(1) && hoc_is_double_arg(1) ) { nrnran123_State** pv = (nrnran123_State**)(&_p_rng); uint32_t a2 = 0; uint32_t a3 = 0; if (*pv) { nrnran123_deletestream(*pv); *pv = (nrnran123_State*)0; } if (ifarg(2)) { a2 = (uint32_t)*getarg(2); } if (ifarg(3)) { a3 = (uint32_t)*getarg(3); } *pv = nrnran123_newstream3((uint32_t)*getarg(1), a2, a3); usingR123 = 1; } else if( ifarg(1) ) { void** pv = (void**)(&_p_rng); *pv = nrn_random_arg(1); } else { void** pv = (void**)(&_p_rng); *pv = (void*)0; } #endif ENDVERBATIM } FUNCTION urand() { VERBATIM double value; if( usingR123 ) { value = nrnran123_dblpick((nrnran123_State*)_p_rng); } else if (_p_rng) { #ifndef CORENEURON_BUILD value = nrn_random_pick(RANDCAST _p_rng); #endif } else { value = 0.5; } _lurand = value; ENDVERBATIM } VERBATIM static void bbcore_write(double* x, int* d, int* xx, int* offset, _threadargsproto_) { if (d) { uint32_t* di = ((uint32_t*)d) + *offset; // temporary just enough to see how much space is being used if (!_p_rng) { di[0] = 0; di[1] = 0, di[2] = 0; }else{ nrnran123_State** pv = (nrnran123_State**)(&_p_rng); nrnran123_getids3(*pv, di, di+1, di+2); // write stream sequence char which; nrnran123_getseq(*pv, di+3, &which); di[4] = (int)which; } //printf("StochKv.mod %p: bbcore_write offset=%d %d %d\n", _p, *offset, d?di[0]:-1, d?di[1]:-1); } *offset += 5; } static void bbcore_read(double* x, int* d, int* xx, int* offset, _threadargsproto_) { assert(!_p_rng); uint32_t* di = ((uint32_t*)d) + *offset; if (di[0] != 0 || di[1] != 0|| di[2] != 0) { nrnran123_State** pv = (nrnran123_State**)(&_p_rng); *pv = nrnran123_newstream3(di[0], di[1], di[2]); // restore stream sequence nrnran123_setseq(*pv, di[3], (char)di[4]); } //printf("StochKv.mod %p: bbcore_read offset=%d %d %d\n", _p, *offset, di[0], di[1]); *offset += 5; } ENDVERBATIM : Returns random numbers drawn from a binomial distribution FUNCTION brand(P, N) { VERBATIM /* :Supports separate independent but reproducible streams for : each instance. However, the corresponding hoc Random : distribution MUST be set to Random.uniform(0,1) */ // Should probably be optimized double value = 0.0; int i; for (i = 0; i < _lN; i++) { if (urand(_threadargs_) < _lP) { value = value + 1; } } return(value); ENDVERBATIM brand = value } VERBATIM #define PI 3.141592654 #define r_ia 16807 #define r_im 2147483647 #define r_am (1.0/r_im) #define r_iq 127773 #define r_ir 2836 #define r_ntab 32 #define r_ndiv (1+(r_im-1)/r_ntab) #define r_eps 1.2e-7 #define r_rnmx (1.0-r_eps) ENDVERBATIM VERBATIM /* ---------------------------------------------------------------- */ /* gammln - compute natural log of gamma function of xx */ static double gammln(double xx) { double x,tmp,ser; static double cof[6]={76.18009173,-86.50532033,24.01409822, -1.231739516,0.120858003e-2,-0.536382e-5}; int j; x=xx-1.0; tmp=x+5.5; tmp -= (x+0.5)*log(tmp); ser=1.0; for (j=0;j<=5;j++) { x += 1.0; ser += cof[j]/x; } return -tmp+log(2.50662827465*ser); } ENDVERBATIM : ---------------------------------------------------------------- : BnlDev - draw a uniform deviate from the generator FUNCTION BnlDev (ppr, nnr) { VERBATIM int j; double am,em,g,angle,p,bnl,sq,bt,y; double pc,plog,pclog,en,oldg; /* prepare to always ignore errors within this routine */ p=(_lppr <= 0.5 ? _lppr : 1.0-_lppr); am=_lnnr*p; if (_lnnr < 25) { bnl=0.0; for (j=1;j<=_lnnr;j++) if (urand(_threadargs_) < p) bnl += 1.0; } else if (am < 1.0) { g=exp(-am); bt=1.0; for (j=0;j<=_lnnr;j++) { bt *= urand(_threadargs_); if (bt < g) break; } bnl=(j <= _lnnr ? j : _lnnr); } else { { en=_lnnr; oldg=gammln(en+1.0); } { pc=1.0-p; plog=log(p); pclog=log(pc); } sq=sqrt(2.0*am*pc); do { do { angle=PI*urand(_threadargs_); angle=PI*urand(_threadargs_); y=tan(angle); em=sq*y+am; } while (em < 0.0 || em >= (en+1.0)); em=floor(em); bt=1.2*sq*(1.0+y*y)*exp(oldg-gammln(em+1.0) - gammln(en-em+1.0)+em*plog+(en-em)*pclog); } while (urand(_threadargs_) > bt); bnl=em; } if (p != _lppr) bnl=_lnnr-bnl; /* recover error if changed during this routine, thus ignoring any errors during this routine */ return bnl; ENDVERBATIM BnlDev = bnl } FUNCTION bbsavestate() { bbsavestate = 0 VERBATIM #ifndef CORENEURON_BUILD // TODO: since N0,N1 are no longer state variables, they will need to be written using this callback // provided that it is the version that supports multivalue writing /* first arg is direction (-1 get info, 0 save, 1 restore), second is value*/ double *xdir, *xval; #ifndef NRN_VERSION_GTEQ_8_2_0 double *hoc_pgetarg(); long nrn_get_random_sequence(void* r); void nrn_set_random_sequence(void* r, int val); #endif xdir = hoc_pgetarg(1); xval = hoc_pgetarg(2); int saveCount = 0; // N0 always needs to be saved (N1 is computed from N and N0) if( *xdir == -1. ) { saveCount = 1; } else if ( *xdir == 0. ) { xval[0] = N0; } else { N0 = xval[0]; N1 = N - N0; } // Handle RNG if (_p_rng) { if (*xdir == -1.) { if( usingR123 ) { saveCount += 2.0; } else { saveCount += 1.0; } } else if (*xdir == 0.) { if( usingR123 ) { uint32_t seq; char which; nrnran123_getseq( (nrnran123_State*)_p_rng, &seq, &which ); xval[1] = (double) seq; xval[2] = (double) which; } else { xval[1] = (double)nrn_get_random_sequence(RANDCAST _p_rng); } } else { if( usingR123 ) { nrnran123_setseq( (nrnran123_State*)_p_rng, (uint32_t)xval[1], (char)xval[2] ); } else { nrn_set_random_sequence(RANDCAST _p_rng, (long)(xval[1])); } } } if( *xdir == -1 ) { *xdir = saveCount; } return 0.0; #endif ENDVERBATIM } ================================================ FILE: examples/stochkv/mechanisms/StochKv3.mod ================================================ TITLE StochKv3.mod COMMENT ---------------------------------------------------------------- Stochastic inactivating channel using values reported in Mendonca et al. 2016. (Modified from https://senselab.med.yale.edu/modeldb/showmodel.cshtml?model=125385&file=/Sbpap_code/mod/skaprox.mod) The model keeps track on the number of channels in each state, and uses a binomial distribution to update these number. Jan 1999, Mickey London, Hebrew University, mikilon@lobster.ls.huji.ac.il Peter N. Steinmetz, Caltech, peter@klab.caltech.edu 14 Sep 99 PNS. Added deterministic flag. 19 May 2002 Kamran Diba. Changed gamma and deterministic from GLOBAL to RANGE. 23 Nov 2011 Werner Van Geit @ BBP. Changed the file so that it can use the neuron random number generator. Tuned voltage dependence 16 Mar 2016 James G King @ BBP. Incorporate modifications suggested by Michael Hines to improve stiching to deterministic mode, thread safety, and using Random123 26 Sep 2016 Christian Roessert @ BBP. Adding inactivation, changing dynamics to values reported in Mendonca et al. 2016 : LJP: OK, whole-cell patch, corrected by 10 mV (Mendonca et al. 2016) ---------------------------------------------------------------- ENDCOMMENT INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} NEURON { SUFFIX StochKv3 THREADSAFE USEION k READ ek WRITE ik RANGE N, eta, gk, gamma, deterministic, gkbar, ik RANGE N0, N1, n0_n1, n1_n0 RANGE ninf, linf, ltau, ntau, an, bn, al, bl RANGE P_an, P_bn, P_al, P_bl GLOBAL vmin, vmax BBCOREPOINTER rng :POINTER rng } UNITS { (mA) = (milliamp) (mV) = (millivolt) (pS) = (picosiemens) (S) = (siemens) (um) = (micron) } PARAMETER { v (mV) dt (ms) area (um2) gamma = 50 (pS) eta (1/um2) gkbar = .01 (S/cm2) deterministic = 0 : if non-zero, will use deterministic version vmin = -120 (mV) : range to construct tables for vmax = 100 (mV) } ASSIGNED { an (/ms) bn (/ms) al (/ms) bl (/ms) ik (mA/cm2) gk (S/cm2) ek (mV) ninf : steady-state value ntau (ms) : time constant for relaxation linf : steady-state value ltau (ms) : time constant for relaxation N scale_dens (pS/um2) P_an : probability of one channel making alpha n transition P_bn : probability of one channel making beta n transition P_al : probability of one channel making alpha l transition P_bl : probability of one channel making beta l transition rng usingR123 } STATE { n l : state variable of deterministic description } ASSIGNED { N0L0 N1L0 N0L1 N1L1 : N states populations (These currently will not be saved via the bbsavestate functionality. Would need to be STATE again) n0l0_n1l0 n0l0_n0l1 : number of channels moving from one state to the other n1l0_n1l1 n1l0_n0l0 n0l1_n1l1 n0l1_n0l0 n1l1_n0l1 n1l1_n1l0 } COMMENT The Verbatim block is needed to generate random nos. from a uniform distribution between 0 and 1 for comparison with Pr to decide whether to activate the synapse or not ENDCOMMENT VERBATIM #ifndef NRN_VERSION_GTEQ_8_2_0 #include "nrnran123.h" extern int cvode_active_; #include #include #include #ifndef CORENEURON_BUILD double nrn_random_pick(void* r); void* nrn_random_arg(int argpos); #endif #define RANDCAST #else #define RANDCAST (Rand*) #endif ENDVERBATIM : ---------------------------------------------------------------- : initialization INITIAL { VERBATIM if (cvode_active_ && !deterministic) { hoc_execerror("StochKv2 with deterministic=0", "cannot be used with cvode"); } if( usingR123 ) { nrnran123_setseq((nrnran123_State*)_p_rng, 0, 0); } ENDVERBATIM eta = (gkbar / gamma) * (10000) trates(v) n=ninf l=linf scale_dens = gamma/area N = floor(eta*area + 0.5) N1L1 = n*l*N N1L0 = n*(1-l)*N N0L1 = (1-n)*l*N if( !deterministic) { N1L1 = floor(N1L1 + 0.5) N1L0 = floor(N1L0 + 0.5) N0L1 = floor(N0L1 + 0.5) } N0L0 = N - N1L1 - N1L0 - N0L1 : put rest into non-conducting state n0l0_n1l0 = 0 n0l0_n0l1 = 0 n1l0_n1l1 = 0 n1l0_n0l0 = 0 n0l1_n1l1 = 0 n0l1_n0l0 = 0 n1l1_n0l1 = 0 n1l1_n1l0 = 0 } : ---------------------------------------------------------------- : Breakpoint for each integration step BREAKPOINT { SOLVE states METHOD cnexp gk = (strap(N1L1) * scale_dens) * (0.0001) ik = gk * (v - ek) } : ---------------------------------------------------------------- : states - updates number of channels in each state DERIVATIVE states { trates(v) l' = al - (al + bl)*l n' = an - (an + bn)*n if (deterministic || dt > 1) { : ForwardSkip is also deterministic N1L1 = n*l*N N1L0 = n*(1-l)*N N0L1 = (1-n)*l*N }else{ : ensure that N0 is an integer for when transitioning from deterministic mode to stochastic mode N1L1 = floor(N1L1 + 0.5) N1L0 = floor(N1L0 + 0.5) N0L1 = floor(N0L1 + 0.5) N0L0 = N - N1L1 - N1L0 - N0L1 P_an = strap(an*dt) P_bn = strap(bn*dt) : check that will represent probabilities when used ChkProb(P_an) ChkProb(P_bn) : n gate transitions n0l0_n1l0 = BnlDev(P_an, N0L0) n0l1_n1l1 = BnlDev(P_an, N0L1) n1l1_n0l1 = BnlDev(P_bn, N1L1) n1l0_n0l0 = BnlDev(P_bn, N1L0) : move the channels N0L0 = strap(N0L0 - n0l0_n1l0 + n1l0_n0l0) N1L0 = strap(N1L0 - n1l0_n0l0 + n0l0_n1l0) N0L1 = strap(N0L1 - n0l1_n1l1 + n1l1_n0l1) N1L1 = strap(N1L1 - n1l1_n0l1 + n0l1_n1l1) : probabilities of making l gate transitions P_al = strap(al*dt) P_bl = strap(bl*dt) : check that will represent probabilities when used ChkProb(P_al) ChkProb(P_bl) : number making l gate transitions n0l0_n0l1 = BnlDev(P_al,N0L0-n0l0_n1l0) n1l0_n1l1 = BnlDev(P_al,N1L0-n1l0_n0l0) n0l1_n0l0 = BnlDev(P_bl,N0L1-n0l1_n1l1) n1l1_n1l0 = BnlDev(P_bl,N1L1-n1l1_n0l1) : move the channels N0L0 = strap(N0L0 - n0l0_n0l1 + n0l1_n0l0) N1L0 = strap(N1L0 - n1l0_n1l1 + n1l1_n1l0) N0L1 = strap(N0L1 - n0l1_n0l0 + n0l0_n0l1) N1L1 = strap(N1L1 - n1l1_n1l0 + n1l0_n1l1) } N0L0 = N - N1L1 - N1L0 - N0L1 : put rest into non-conducting state } : ---------------------------------------------------------------- : trates - compute rates, using table if possible PROCEDURE trates(v (mV)) { TABLE ntau,ltau,ninf,linf,al,bl,an,bn DEPEND dt FROM vmin TO vmax WITH 199 v = v + 10 linf = 1/(1+exp((-30(mV)-v)/10(mV))) ltau = 0.346(ms)*exp(-v/(18.272(mV)))+2.09(ms) ninf = 1/(1+exp(0.0878(1/mV)*(v+55.1(mV)))) ntau = 2.1(ms)*exp(-v/21.2(mV))+4.627(ms) v = v - 10 al = linf/ltau bl = 1/ltau - al an = ninf/ntau bn = 1/ntau - an } : ---------------------------------------------------------------- : sign trap - trap for negative values and replace with zero FUNCTION strap(x) { if (x < 0) { strap = 0 VERBATIM fprintf (stderr,"skv.mod:strap: negative state"); ENDVERBATIM } else { strap = x } } : ---------------------------------------------------------------- : ChkProb - Check that number represents a probability PROCEDURE ChkProb(p) { if (p < 0.0 || p > 1.0) { VERBATIM fprintf(stderr, "StochKv2.mod:ChkProb: argument not a probability.\n"); ENDVERBATIM } } PROCEDURE setRNG() { VERBATIM // For compatibility, allow for either MCellRan4 or Random123. Distinguish by the arg types // Object => MCellRan4, seeds (double) => Random123 #ifndef CORENEURON_BUILD usingR123 = 0; if( ifarg(1) && hoc_is_double_arg(1) ) { nrnran123_State** pv = (nrnran123_State**)(&_p_rng); uint32_t a2 = 0; uint32_t a3 = 0; if (*pv) { nrnran123_deletestream(*pv); *pv = (nrnran123_State*)0; } if (ifarg(2)) { a2 = (uint32_t)*getarg(2); } if (ifarg(3)) { a3 = (uint32_t)*getarg(3); } *pv = nrnran123_newstream3((uint32_t)*getarg(1), a2, a3); usingR123 = 1; } else if( ifarg(1) ) { void** pv = (void**)(&_p_rng); *pv = nrn_random_arg(1); } else { void** pv = (void**)(&_p_rng); *pv = (void*)0; } #endif ENDVERBATIM } FUNCTION urand() { VERBATIM double value; if( usingR123 ) { value = nrnran123_dblpick((nrnran123_State*)_p_rng); } else if (_p_rng) { #ifndef CORENEURON_BUILD value = nrn_random_pick(RANDCAST _p_rng); #endif } else { value = 0.5; } _lurand = value; ENDVERBATIM } VERBATIM static void bbcore_write(double* x, int* d, int* xx, int* offset, _threadargsproto_) { if (d) { uint32_t* di = ((uint32_t*)d) + *offset; // temporary just enough to see how much space is being used if (!_p_rng) { di[0] = 0; di[1] = 0, di[2] = 0; }else{ nrnran123_State** pv = (nrnran123_State**)(&_p_rng); nrnran123_getids3(*pv, di, di+1, di+2); // write stream sequence char which; nrnran123_getseq(*pv, di+3, &which); di[4] = (int)which; } //printf("StochKv3.mod %p: bbcore_write offset=%d %d %d\n", _p, *offset, d?di[0]:-1, d?di[1]:-1); } *offset += 5; } static void bbcore_read(double* x, int* d, int* xx, int* offset, _threadargsproto_) { uint32_t* di = ((uint32_t*)d) + *offset; if (di[0] != 0 || di[1] != 0|| di[2] != 0) { nrnran123_State** pv = (nrnran123_State**)(&_p_rng); #if !NRNBBCORE if(*pv) { nrnran123_deletestream(*pv); } #endif *pv = nrnran123_newstream3(di[0], di[1], di[2]); nrnran123_setseq(*pv, di[3], (char)di[4]); } //printf("StochKv3.mod %p: bbcore_read offset=%d %d %d\n", _p, *offset, di[0], di[1]); *offset += 5; } ENDVERBATIM : Returns random numbers drawn from a binomial distribution FUNCTION brand(P, N) { VERBATIM /* :Supports separate independent but reproducible streams for : each instance. However, the corresponding hoc Random : distribution MUST be set to Random.uniform(0,1) */ // Should probably be optimized double value = 0.0; int i; for (i = 0; i < _lN; i++) { if (urand(_threadargs_) < _lP) { value = value + 1; } } return(value); ENDVERBATIM brand = value } VERBATIM #define PI 3.141592654 #define r_ia 16807 #define r_im 2147483647 #define r_am (1.0/r_im) #define r_iq 127773 #define r_ir 2836 #define r_ntab 32 #define r_ndiv (1+(r_im-1)/r_ntab) #define r_eps 1.2e-7 #define r_rnmx (1.0-r_eps) ENDVERBATIM VERBATIM /* ---------------------------------------------------------------- */ /* gammln - compute natural log of gamma function of xx */ static double gammln(double xx) { double x,tmp,ser; static double cof[6]={76.18009173,-86.50532033,24.01409822, -1.231739516,0.120858003e-2,-0.536382e-5}; int j; x=xx-1.0; tmp=x+5.5; tmp -= (x+0.5)*log(tmp); ser=1.0; for (j=0;j<=5;j++) { x += 1.0; ser += cof[j]/x; } return -tmp+log(2.50662827465*ser); } ENDVERBATIM : ---------------------------------------------------------------- : BnlDev - draw a uniform deviate from the generator FUNCTION BnlDev (ppr, nnr) { VERBATIM int j; double am,em,g,angle,p,bnl,sq,bt,y; double pc,plog,pclog,en,oldg; /* prepare to always ignore errors within this routine */ p=(_lppr <= 0.5 ? _lppr : 1.0-_lppr); am=_lnnr*p; if (_lnnr < 25) { bnl=0.0; for (j=1;j<=_lnnr;j++) if (urand(_threadargs_) < p) bnl += 1.0; } else if (am < 1.0) { g=exp(-am); bt=1.0; for (j=0;j<=_lnnr;j++) { bt *= urand(_threadargs_); if (bt < g) break; } bnl=(j <= _lnnr ? j : _lnnr); } else { { en=_lnnr; oldg=gammln(en+1.0); } { pc=1.0-p; plog=log(p); pclog=log(pc); } sq=sqrt(2.0*am*pc); do { do { angle=PI*urand(_threadargs_); angle=PI*urand(_threadargs_); y=tan(angle); em=sq*y+am; } while (em < 0.0 || em >= (en+1.0)); em=floor(em); bt=1.2*sq*(1.0+y*y)*exp(oldg-gammln(em+1.0) - gammln(en-em+1.0)+em*plog+(en-em)*pclog); } while (urand(_threadargs_) > bt); bnl=em; } if (p != _lppr) bnl=_lnnr-bnl; /* recover error if changed during this routine, thus ignoring any errors during this routine */ return bnl; ENDVERBATIM BnlDev = bnl } FUNCTION bbsavestate() { bbsavestate = 0 VERBATIM #ifndef CORENEURON_BUILD // TODO: since N0,N1 are no longer state variables, they will need to be written using this callback // provided that it is the version that supports multivalue writing /* first arg is direction (-1 get info, 0 save, 1 restore), second is value*/ double *xdir, *xval; #ifndef NRN_VERSION_GTEQ_8_2_0 double *hoc_pgetarg(); long nrn_get_random_sequence(void* r); void nrn_set_random_sequence(void* r, int val); #endif xdir = hoc_pgetarg(1); xval = hoc_pgetarg(2); if (_p_rng) { // tell how many items need saving if (*xdir == -1.) { if( usingR123 ) { *xdir = 2.0; } else { *xdir = 1.0; } return 0.0; } else if (*xdir == 0.) { if( usingR123 ) { uint32_t seq; char which; nrnran123_getseq( (nrnran123_State*)_p_rng, &seq, &which ); xval[0] = (double) seq; xval[1] = (double) which; } else { xval[0] = (double)nrn_get_random_sequence(RANDCAST _p_rng); } } else{ if( usingR123 ) { nrnran123_setseq( (nrnran123_State*)_p_rng, (uint32_t)xval[0], (char)xval[1] ); } else { nrn_set_random_sequence(RANDCAST _p_rng, (long)(xval[0])); } } } // TODO: check for random123 and get the seq values #endif ENDVERBATIM } ================================================ FILE: examples/stochkv/mechanisms/dummy.inc ================================================ ================================================ FILE: examples/stochkv/morphology/simple.swc ================================================ # Dummy granule cell morphology 1 1 -5.0 0.0 0.0 5.0 -1 2 1 0.0 0.0 0.0 5.0 1 3 1 5.0 0.0 0.0 5.0 2 ================================================ FILE: examples/stochkv/stochkv3cell.hoc ================================================ /* */ {load_file("stdrun.hoc")} {load_file("import3d.hoc")} /* * Check that global parameters are the same as with the optimization */ proc check_parameter(/* name, expected_value, value */){ strdef error if($2 != $3){ sprint(error, "Parameter %s has different value %f != %f", $s1, $2, $3) execerror(error) } } proc check_simulator() { check_parameter("celsius", 34.0, celsius) } begintemplate stochkv3_cell public init, morphology, geom_nseg_fixed, geom_nsec, gid public channel_seed, channel_seed_set public soma, dend, apic, axon, myelin create soma[1], dend[1], apic[1], axon[1], myelin[1] objref this, CellRef, segCounts public all, somatic, apical, axonal, basal, myelinated, APC objref all, somatic, apical, axonal, basal, myelinated, APC obfunc getCell(){ return this } proc init(/* args: morphology_dir, morphology_name */) { all = new SectionList() apical = new SectionList() axonal = new SectionList() basal = new SectionList() somatic = new SectionList() myelinated = new SectionList() //gid in this case is only used for rng seeding gid = 0 //For compatibility with BBP CCells CellRef = this forall delete_section() if(numarg() >= 2) { load_morphology($s1, $s2) } else { load_morphology($s1, "simple.swc") } geom_nseg() insertChannel() biophys() // Initialize channel_seed_set to avoid accidents channel_seed_set = 0 // Initialize random number generators re_init_rng() } proc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension strdef morph_path sprint(morph_path, "%s/%s", $s1, $s2) sf = new StringFunctions() extension = new String() sscanf(morph_path, "%s", extension.s) sf.right(extension.s, sf.len(extension.s)-4) if( strcmp(extension.s, ".asc") == 0 ) { morph = new Import3d_Neurolucida3() } else if( strcmp(extension.s, ".swc" ) == 0) { morph = new Import3d_SWC_read() } else { printf("Unsupported file format: Morphology file has to end with .asc or .swc" ) quit() } morph.quiet = 1 morph.input(morph_path) import = new Import3d_GUI(morph, 0) import.instantiate(this) } /* * Assignment of mechanism values based on distance from the soma * Matches the BluePyOpt method */ proc distribute_distance(){local x localobj sl strdef stmp, distfunc, mech sl = $o1 mech = $s2 distfunc = $s3 this.soma[0] distance(0, 0.5) sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc) forsec sl for(x, 0) { // use distance(x) twice for the step distribution case, e.g. for calcium hotspot sprint(stmp, distfunc, secname(), x, distance(x), distance(x)) execute(stmp) } } proc geom_nseg() { this.geom_nsec() //To count all sections //TODO: geom_nseg_fixed depends on segCounts which is calculated by // geom_nsec. Can this be collapsed? this.geom_nseg_fixed(40) this.geom_nsec() //To count all sections } proc insertChannel() { forsec this.all { } forsec this.apical { } forsec this.axonal { } forsec this.basal { } forsec this.somatic { insert pas insert StochKv3 } forsec this.myelinated { } } proc biophys() { forsec CellRef.all { } forsec CellRef.apical { } forsec CellRef.axonal { } forsec CellRef.basal { } forsec CellRef.somatic { e_pas = -90 gkbar_StochKv3 = 0.5 } forsec CellRef.myelinated { } } func sec_count(/* SectionList */) { local nSec nSec = 0 forsec $o1 { nSec += 1 } return nSec } /* * Iterate over the section and compute how many segments should be allocate to * each. */ proc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize chunkSize = $1 soma area(.5) // make sure diam reflects 3d points secIndex = 0 forsec all { nseg = 1 + 2*int(L/chunkSize) segCounts.x[secIndex] = nseg secIndex += 1 } } /* * Count up the number of sections */ proc geom_nsec() { local nSec nSecAll = sec_count(all) nSecSoma = sec_count(somatic) nSecApical = sec_count(apical) nSecBasal = sec_count(basal) nSecMyelinated = sec_count(myelinated) nSecAxonalOrig = nSecAxonal = sec_count(axonal) segCounts = new Vector() segCounts.resize(nSecAll) nSec = 0 forsec all { segCounts.x[nSec] = nseg nSec += 1 } } /* * Replace the axon built from the original morphology file with a stub axon */ func hash_str() {localobj sf strdef right sf = new StringFunctions() right = $s1 n_of_c = sf.len(right) hash = 0 char_int = 0 for i = 0, n_of_c - 1 { sscanf(right, "%c", & char_int) hash = (hash * 31 + char_int) % (2 ^ 31 - 1) sf.right(right, 1) } return hash } proc re_init_rng() {localobj sf strdef full_str, name sf = new StringFunctions() if(numarg() == 1) { // We received a third seed channel_seed = $1 channel_seed_set = 1 } else { channel_seed_set = 0 } forsec somatic { for (x, 0) { setdata_StochKv3(x) sf.tail(secname(), "\\.", name) sprint(full_str, "%s.%.19g", name, x) if (channel_seed_set) { setRNG_StochKv3(gid, hash_str(full_str), channel_seed) } else { setRNG_StochKv3(gid, hash_str(full_str)) } } } } endtemplate stochkv3_cell ================================================ FILE: examples/stochkv/stochkv3cell.py ================================================ """StochKv3 cell example""" # pylint: disable=R0914 import os import bluepyopt.ephys as ephys script_dir = os.path.dirname(__file__) morph_dir = os.path.join(script_dir, 'morphology') def stochkv3_hoc_filename(deterministic=False): """Return stochkv3 hoc model filename""" return os.path.join( script_dir, 'stochkv3cell%s.hoc' % ('_det' if deterministic else '')) def run_stochkv3_model(deterministic=False): """Run stochkv3 model""" morph = ephys.morphologies.NrnFileMorphology( os.path.join( morph_dir, 'simple.swc')) somatic_loc = ephys.locations.NrnSeclistLocation( 'somatic', seclist_name='somatic') stochkv3_mech = ephys.mechanisms.NrnMODMechanism( name='StochKv3', suffix='StochKv3', locations=[somatic_loc], deterministic=deterministic) pas_mech = ephys.mechanisms.NrnMODMechanism( name='pas', suffix='pas', locations=[somatic_loc]) gkbar_param = ephys.parameters.NrnSectionParameter( name='gkbar_StochKv3', param_name='gkbar_StochKv3', locations=[somatic_loc], bounds=[0.0, 10.0], frozen=False) epas_param = ephys.parameters.NrnSectionParameter( name='e_pas', param_name='e_pas', locations=[somatic_loc], value=-90, frozen=True) celsius_param = ephys.parameters.NrnGlobalParameter( name='celsius', param_name='celsius', value=34.0, frozen=True) params = [epas_param, celsius_param, gkbar_param] stochkv3_cell = ephys.models.CellModel( name='stochkv3_cell', morph=morph, mechs=[pas_mech, stochkv3_mech], params=params) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma', seclist_name='somatic', sec_index=0, comp_x=0.5) stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.1, step_delay=50, step_duration=50, location=soma_loc, total_duration=150) hold_stim = ephys.stimuli.NrnSquarePulse( step_amplitude=-0.025, step_delay=0, step_duration=10000, location=soma_loc, total_duration=150) rec = ephys.recordings.CompRecording( name='Step.soma.v', location=soma_loc, variable='v') protocol = ephys.protocols.SweepProtocol('Step', [stim, hold_stim], [rec]) nrn = ephys.simulators.NrnSimulator(cvode_active=False) evaluator = ephys.evaluators.CellEvaluator( cell_model=stochkv3_cell, param_names=[param.name for param in params], fitness_calculator=ephys.objectivescalculators.ObjectivesCalculator(), sim=nrn) best_param_values = {'gkbar_StochKv3': 0.5} responses = evaluator.run_protocol( protocol, cell_model=stochkv3_cell, param_values=best_param_values, sim=nrn) hoc_string = stochkv3_cell.create_hoc( param_values=best_param_values, disable_banner=True) stochkv3_hoc_cell = ephys.models.HocCellModel( 'stochkv3_hoc_cell', morphology_path=morph_dir, hoc_string=hoc_string) nrn.neuron.h.celsius = 34 hoc_responses = protocol.run(stochkv3_hoc_cell, best_param_values, sim=nrn) evaluator.use_params_for_seed = True different_seed_responses = evaluator.run_protocol( protocol, cell_model=stochkv3_cell, param_values=best_param_values, sim=nrn) return responses, hoc_responses, different_seed_responses, hoc_string def main(): """Main""" import matplotlib.pyplot as plt for deterministic in [True, False]: stochkv3_responses, stochkv3_hoc_responses, different_seed_responses, \ stochkv3_hoc_string = \ run_stochkv3_model(deterministic=deterministic) with open(stochkv3_hoc_filename(deterministic=deterministic), 'w') as \ stochkv3_hoc_file: stochkv3_hoc_file.write(stochkv3_hoc_string) time = stochkv3_responses['Step.soma.v']['time'] py_voltage = stochkv3_responses['Step.soma.v']['voltage'] hoc_voltage = stochkv3_hoc_responses['Step.soma.v']['voltage'] different_seed_voltage = \ different_seed_responses['Step.soma.v']['voltage'] plt.figure() plt.plot(time, py_voltage - hoc_voltage, label='py - hoc diff') plt.xlabel('time (ms)') plt.ylabel('voltage diff(mV)') plt.title('Deterministic' if deterministic else 'Stochastic') plt.legend() plt.figure() plt.plot(time, py_voltage, label='py') plt.plot(time, hoc_voltage, label='hoc') plt.xlabel('time (ms)') plt.ylabel('voltage (mV)') plt.title('Deterministic' if deterministic else 'Stochastic') plt.legend() plt.figure() plt.plot(time, py_voltage, label='py') plt.plot(time, different_seed_voltage, label='different seed') plt.xlabel('time (ms)') plt.ylabel('voltage (mV)') plt.title('Deterministic' if deterministic else 'Stochastic') plt.legend() plt.show() if __name__ == '__main__': main() ================================================ FILE: examples/stochkv/stochkv3cell_det.hoc ================================================ /* */ {load_file("stdrun.hoc")} {load_file("import3d.hoc")} /* * Check that global parameters are the same as with the optimization */ proc check_parameter(/* name, expected_value, value */){ strdef error if($2 != $3){ sprint(error, "Parameter %s has different value %f != %f", $s1, $2, $3) execerror(error) } } proc check_simulator() { check_parameter("celsius", 34.0, celsius) } begintemplate stochkv3_cell public init, morphology, geom_nseg_fixed, geom_nsec, gid public channel_seed, channel_seed_set public soma, dend, apic, axon, myelin create soma[1], dend[1], apic[1], axon[1], myelin[1] objref this, CellRef, segCounts public all, somatic, apical, axonal, basal, myelinated, APC objref all, somatic, apical, axonal, basal, myelinated, APC obfunc getCell(){ return this } proc init(/* args: morphology_dir, morphology_name */) { all = new SectionList() apical = new SectionList() axonal = new SectionList() basal = new SectionList() somatic = new SectionList() myelinated = new SectionList() //gid in this case is only used for rng seeding gid = 0 //For compatibility with BBP CCells CellRef = this forall delete_section() if(numarg() >= 2) { load_morphology($s1, $s2) } else { load_morphology($s1, "simple.swc") } geom_nseg() insertChannel() biophys() // Initialize channel_seed_set to avoid accidents channel_seed_set = 0 // Initialize random number generators re_init_rng() } proc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension strdef morph_path sprint(morph_path, "%s/%s", $s1, $s2) sf = new StringFunctions() extension = new String() sscanf(morph_path, "%s", extension.s) sf.right(extension.s, sf.len(extension.s)-4) if( strcmp(extension.s, ".asc") == 0 ) { morph = new Import3d_Neurolucida3() } else if( strcmp(extension.s, ".swc" ) == 0) { morph = new Import3d_SWC_read() } else { printf("Unsupported file format: Morphology file has to end with .asc or .swc" ) quit() } morph.quiet = 1 morph.input(morph_path) import = new Import3d_GUI(morph, 0) import.instantiate(this) } /* * Assignment of mechanism values based on distance from the soma * Matches the BluePyOpt method */ proc distribute_distance(){local x localobj sl strdef stmp, distfunc, mech sl = $o1 mech = $s2 distfunc = $s3 this.soma[0] distance(0, 0.5) sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc) forsec sl for(x, 0) { // use distance(x) twice for the step distribution case, e.g. for calcium hotspot sprint(stmp, distfunc, secname(), x, distance(x), distance(x)) execute(stmp) } } proc geom_nseg() { this.geom_nsec() //To count all sections //TODO: geom_nseg_fixed depends on segCounts which is calculated by // geom_nsec. Can this be collapsed? this.geom_nseg_fixed(40) this.geom_nsec() //To count all sections } proc insertChannel() { forsec this.all { } forsec this.apical { } forsec this.axonal { } forsec this.basal { } forsec this.somatic { insert pas insert StochKv3 } forsec this.myelinated { } } proc biophys() { forsec CellRef.all { } forsec CellRef.apical { } forsec CellRef.axonal { } forsec CellRef.basal { } forsec CellRef.somatic { e_pas = -90 gkbar_StochKv3 = 0.5 } forsec CellRef.myelinated { } } func sec_count(/* SectionList */) { local nSec nSec = 0 forsec $o1 { nSec += 1 } return nSec } /* * Iterate over the section and compute how many segments should be allocate to * each. */ proc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize chunkSize = $1 soma area(.5) // make sure diam reflects 3d points secIndex = 0 forsec all { nseg = 1 + 2*int(L/chunkSize) segCounts.x[secIndex] = nseg secIndex += 1 } } /* * Count up the number of sections */ proc geom_nsec() { local nSec nSecAll = sec_count(all) nSecSoma = sec_count(somatic) nSecApical = sec_count(apical) nSecBasal = sec_count(basal) nSecMyelinated = sec_count(myelinated) nSecAxonalOrig = nSecAxonal = sec_count(axonal) segCounts = new Vector() segCounts.resize(nSecAll) nSec = 0 forsec all { segCounts.x[nSec] = nseg nSec += 1 } } /* * Replace the axon built from the original morphology file with a stub axon */ func hash_str() {localobj sf strdef right sf = new StringFunctions() right = $s1 n_of_c = sf.len(right) hash = 0 char_int = 0 for i = 0, n_of_c - 1 { sscanf(right, "%c", & char_int) hash = (hash * 31 + char_int) % (2 ^ 31 - 1) sf.right(right, 1) } return hash } proc re_init_rng() {localobj sf strdef full_str, name sf = new StringFunctions() if(numarg() == 1) { // We received a third seed channel_seed = $1 channel_seed_set = 1 } else { channel_seed_set = 0 } forsec somatic { deterministic_StochKv3 = 1 } } endtemplate stochkv3_cell ================================================ FILE: examples/stochkv/stochkvcell.hoc ================================================ /* */ {load_file("stdrun.hoc")} {load_file("import3d.hoc")} /* * Check that global parameters are the same as with the optimization */ proc check_parameter(/* name, expected_value, value */){ strdef error if($2 != $3){ sprint(error, "Parameter %s has different value %f != %f", $s1, $2, $3) execerror(error) } } proc check_simulator() { check_parameter("celsius", 34.0, celsius) } begintemplate stochkv_cell public init, morphology, geom_nseg_fixed, geom_nsec, gid public channel_seed, channel_seed_set public soma, dend, apic, axon, myelin create soma[1], dend[1], apic[1], axon[1], myelin[1] objref this, CellRef, segCounts public all, somatic, apical, axonal, basal, myelinated, APC objref all, somatic, apical, axonal, basal, myelinated, APC obfunc getCell(){ return this } proc init(/* args: morphology_dir, morphology_name */) { all = new SectionList() apical = new SectionList() axonal = new SectionList() basal = new SectionList() somatic = new SectionList() myelinated = new SectionList() //gid in this case is only used for rng seeding gid = 0 //For compatibility with BBP CCells CellRef = this forall delete_section() if(numarg() >= 2) { load_morphology($s1, $s2) } else { load_morphology($s1, "simple.swc") } geom_nseg() insertChannel() biophys() // Initialize channel_seed_set to avoid accidents channel_seed_set = 0 // Initialize random number generators re_init_rng() } proc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension strdef morph_path sprint(morph_path, "%s/%s", $s1, $s2) sf = new StringFunctions() extension = new String() sscanf(morph_path, "%s", extension.s) sf.right(extension.s, sf.len(extension.s)-4) if( strcmp(extension.s, ".asc") == 0 ) { morph = new Import3d_Neurolucida3() } else if( strcmp(extension.s, ".swc" ) == 0) { morph = new Import3d_SWC_read() } else { printf("Unsupported file format: Morphology file has to end with .asc or .swc" ) quit() } morph.quiet = 1 morph.input(morph_path) import = new Import3d_GUI(morph, 0) import.instantiate(this) } /* * Assignment of mechanism values based on distance from the soma * Matches the BluePyOpt method */ proc distribute_distance(){local x localobj sl strdef stmp, distfunc, mech sl = $o1 mech = $s2 distfunc = $s3 this.soma[0] distance(0, 0.5) sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc) forsec sl for(x, 0) { // use distance(x) twice for the step distribution case, e.g. for calcium hotspot sprint(stmp, distfunc, secname(), x, distance(x), distance(x)) execute(stmp) } } proc geom_nseg() { this.geom_nsec() //To count all sections //TODO: geom_nseg_fixed depends on segCounts which is calculated by // geom_nsec. Can this be collapsed? this.geom_nseg_fixed(40) this.geom_nsec() //To count all sections } proc insertChannel() { forsec this.all { } forsec this.apical { } forsec this.axonal { } forsec this.basal { } forsec this.somatic { insert pas insert StochKv } forsec this.myelinated { } } proc biophys() { forsec CellRef.all { } forsec CellRef.apical { } forsec CellRef.axonal { } forsec CellRef.basal { } forsec CellRef.somatic { e_pas = -90 gkbar_StochKv = 0.5 } forsec CellRef.myelinated { } } func sec_count(/* SectionList */) { local nSec nSec = 0 forsec $o1 { nSec += 1 } return nSec } /* * Iterate over the section and compute how many segments should be allocate to * each. */ proc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize chunkSize = $1 soma area(.5) // make sure diam reflects 3d points secIndex = 0 forsec all { nseg = 1 + 2*int(L/chunkSize) segCounts.x[secIndex] = nseg secIndex += 1 } } /* * Count up the number of sections */ proc geom_nsec() { local nSec nSecAll = sec_count(all) nSecSoma = sec_count(somatic) nSecApical = sec_count(apical) nSecBasal = sec_count(basal) nSecMyelinated = sec_count(myelinated) nSecAxonalOrig = nSecAxonal = sec_count(axonal) segCounts = new Vector() segCounts.resize(nSecAll) nSec = 0 forsec all { segCounts.x[nSec] = nseg nSec += 1 } } /* * Replace the axon built from the original morphology file with a stub axon */ func hash_str() {localobj sf strdef right sf = new StringFunctions() right = $s1 n_of_c = sf.len(right) hash = 0 char_int = 0 for i = 0, n_of_c - 1 { sscanf(right, "%c", & char_int) hash = (hash * 31 + char_int) % (2 ^ 31 - 1) sf.right(right, 1) } return hash } proc re_init_rng() {localobj sf strdef full_str, name sf = new StringFunctions() if(numarg() == 1) { // We received a third seed channel_seed = $1 channel_seed_set = 1 } else { channel_seed_set = 0 } forsec somatic { for (x, 0) { setdata_StochKv(x) sf.tail(secname(), "\\.", name) sprint(full_str, "%s.%.19g", name, x) if (channel_seed_set) { setRNG_StochKv(gid, hash_str(full_str), channel_seed) } else { setRNG_StochKv(gid, hash_str(full_str)) } } } } endtemplate stochkv_cell ================================================ FILE: examples/stochkv/stochkvcell.py ================================================ """StochKv cell example""" # pylint: disable=R0914 import os import bluepyopt.ephys as ephys script_dir = os.path.dirname(__file__) morph_dir = os.path.join(script_dir, 'morphology') def stochkv_hoc_filename(deterministic=False): """Return stochkv hoc model filename""" return os.path.join( script_dir, 'stochkvcell%s.hoc' % ('_det' if deterministic else '')) def run_stochkv_model(deterministic=False): """Run stochkv model""" morph = ephys.morphologies.NrnFileMorphology( os.path.join( morph_dir, 'simple.swc')) somatic_loc = ephys.locations.NrnSeclistLocation( 'somatic', seclist_name='somatic') stochkv_mech = ephys.mechanisms.NrnMODMechanism( name='StochKv', suffix='StochKv', locations=[somatic_loc], deterministic=deterministic) pas_mech = ephys.mechanisms.NrnMODMechanism( name='pas', suffix='pas', locations=[somatic_loc]) gkbar_param = ephys.parameters.NrnSectionParameter( name='gkbar_StochKv', param_name='gkbar_StochKv', locations=[somatic_loc], bounds=[0.0, 10.0], frozen=False) epas_param = ephys.parameters.NrnSectionParameter( name='e_pas', param_name='e_pas', locations=[somatic_loc], value=-90, frozen=True) celsius_param = ephys.parameters.NrnGlobalParameter( name='celsius', param_name='celsius', value=34.0, frozen=True) params = [epas_param, celsius_param, gkbar_param] stochkv_cell = ephys.models.CellModel( name='stochkv_cell', morph=morph, mechs=[pas_mech, stochkv_mech], params=params) soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma', seclist_name='somatic', sec_index=0, comp_x=0.5) stim = ephys.stimuli.NrnSquarePulse( step_amplitude=0.1, step_delay=50, step_duration=50, location=soma_loc, total_duration=150) hold_stim = ephys.stimuli.NrnSquarePulse( step_amplitude=-0.025, step_delay=0, step_duration=10000, location=soma_loc, total_duration=150) rec = ephys.recordings.CompRecording( name='Step.soma.v', location=soma_loc, variable='v') protocol = ephys.protocols.SweepProtocol('Step', [stim, hold_stim], [rec]) nrn = ephys.simulators.NrnSimulator(cvode_active=False) evaluator = ephys.evaluators.CellEvaluator( cell_model=stochkv_cell, param_names=[param.name for param in params], fitness_calculator=ephys.objectivescalculators.ObjectivesCalculator(), sim=nrn) best_param_values = {'gkbar_StochKv': 0.5} responses = evaluator.run_protocol( protocol, cell_model=stochkv_cell, param_values=best_param_values, sim=nrn) hoc_string = stochkv_cell.create_hoc( param_values=best_param_values, disable_banner=True) stochkv_hoc_cell = ephys.models.HocCellModel( 'stochkv_hoc_cell', morphology_path=morph_dir, hoc_string=hoc_string) nrn.neuron.h.celsius = 34 hoc_responses = protocol.run(stochkv_hoc_cell, best_param_values, sim=nrn) evaluator.use_params_for_seed = True different_seed_responses = evaluator.run_protocol( protocol, cell_model=stochkv_cell, param_values=best_param_values, sim=nrn) return responses, hoc_responses, different_seed_responses, hoc_string def main(): """Main""" import matplotlib.pyplot as plt for deterministic in [True, False]: stochkv_responses, stochkv_hoc_responses, different_seed_responses, \ stochkv_hoc_string = \ run_stochkv_model(deterministic=deterministic) with open(stochkv_hoc_filename(deterministic=deterministic), 'w') as \ stochkv_hoc_file: stochkv_hoc_file.write(stochkv_hoc_string) time = stochkv_responses['Step.soma.v']['time'] py_voltage = stochkv_responses['Step.soma.v']['voltage'] hoc_voltage = stochkv_hoc_responses['Step.soma.v']['voltage'] different_seed_voltage = \ different_seed_responses['Step.soma.v']['voltage'] plt.figure() plt.plot(time, py_voltage - hoc_voltage, label='py - hoc diff') plt.xlabel('time (ms)') plt.ylabel('voltage diff(mV)') plt.title('Deterministic' if deterministic else 'Stochastic') plt.legend() plt.figure() plt.plot(time, py_voltage, label='py') plt.plot(time, hoc_voltage, label='hoc') plt.xlabel('time (ms)') plt.ylabel('voltage (mV)') plt.title('Deterministic' if deterministic else 'Stochastic') plt.legend() plt.figure() plt.plot(time, py_voltage, label='py') plt.plot(time, different_seed_voltage, label='different seed') plt.xlabel('time (ms)') plt.ylabel('voltage (mV)') plt.title('Deterministic' if deterministic else 'Stochastic') plt.legend() plt.show() if __name__ == '__main__': main() ================================================ FILE: examples/stochkv/stochkvcell_det.hoc ================================================ /* */ {load_file("stdrun.hoc")} {load_file("import3d.hoc")} /* * Check that global parameters are the same as with the optimization */ proc check_parameter(/* name, expected_value, value */){ strdef error if($2 != $3){ sprint(error, "Parameter %s has different value %f != %f", $s1, $2, $3) execerror(error) } } proc check_simulator() { check_parameter("celsius", 34.0, celsius) } begintemplate stochkv_cell public init, morphology, geom_nseg_fixed, geom_nsec, gid public channel_seed, channel_seed_set public soma, dend, apic, axon, myelin create soma[1], dend[1], apic[1], axon[1], myelin[1] objref this, CellRef, segCounts public all, somatic, apical, axonal, basal, myelinated, APC objref all, somatic, apical, axonal, basal, myelinated, APC obfunc getCell(){ return this } proc init(/* args: morphology_dir, morphology_name */) { all = new SectionList() apical = new SectionList() axonal = new SectionList() basal = new SectionList() somatic = new SectionList() myelinated = new SectionList() //gid in this case is only used for rng seeding gid = 0 //For compatibility with BBP CCells CellRef = this forall delete_section() if(numarg() >= 2) { load_morphology($s1, $s2) } else { load_morphology($s1, "simple.swc") } geom_nseg() insertChannel() biophys() // Initialize channel_seed_set to avoid accidents channel_seed_set = 0 // Initialize random number generators re_init_rng() } proc load_morphology(/* morphology_dir, morphology_name */) {localobj morph, import, sf, extension strdef morph_path sprint(morph_path, "%s/%s", $s1, $s2) sf = new StringFunctions() extension = new String() sscanf(morph_path, "%s", extension.s) sf.right(extension.s, sf.len(extension.s)-4) if( strcmp(extension.s, ".asc") == 0 ) { morph = new Import3d_Neurolucida3() } else if( strcmp(extension.s, ".swc" ) == 0) { morph = new Import3d_SWC_read() } else { printf("Unsupported file format: Morphology file has to end with .asc or .swc" ) quit() } morph.quiet = 1 morph.input(morph_path) import = new Import3d_GUI(morph, 0) import.instantiate(this) } /* * Assignment of mechanism values based on distance from the soma * Matches the BluePyOpt method */ proc distribute_distance(){local x localobj sl strdef stmp, distfunc, mech sl = $o1 mech = $s2 distfunc = $s3 this.soma[0] distance(0, 0.5) sprint(distfunc, "%%s %s(%%f) = %s", mech, distfunc) forsec sl for(x, 0) { // use distance(x) twice for the step distribution case, e.g. for calcium hotspot sprint(stmp, distfunc, secname(), x, distance(x), distance(x)) execute(stmp) } } proc geom_nseg() { this.geom_nsec() //To count all sections //TODO: geom_nseg_fixed depends on segCounts which is calculated by // geom_nsec. Can this be collapsed? this.geom_nseg_fixed(40) this.geom_nsec() //To count all sections } proc insertChannel() { forsec this.all { } forsec this.apical { } forsec this.axonal { } forsec this.basal { } forsec this.somatic { insert pas insert StochKv } forsec this.myelinated { } } proc biophys() { forsec CellRef.all { } forsec CellRef.apical { } forsec CellRef.axonal { } forsec CellRef.basal { } forsec CellRef.somatic { e_pas = -90 gkbar_StochKv = 0.5 } forsec CellRef.myelinated { } } func sec_count(/* SectionList */) { local nSec nSec = 0 forsec $o1 { nSec += 1 } return nSec } /* * Iterate over the section and compute how many segments should be allocate to * each. */ proc geom_nseg_fixed(/* chunkSize */) { local secIndex, chunkSize chunkSize = $1 soma area(.5) // make sure diam reflects 3d points secIndex = 0 forsec all { nseg = 1 + 2*int(L/chunkSize) segCounts.x[secIndex] = nseg secIndex += 1 } } /* * Count up the number of sections */ proc geom_nsec() { local nSec nSecAll = sec_count(all) nSecSoma = sec_count(somatic) nSecApical = sec_count(apical) nSecBasal = sec_count(basal) nSecMyelinated = sec_count(myelinated) nSecAxonalOrig = nSecAxonal = sec_count(axonal) segCounts = new Vector() segCounts.resize(nSecAll) nSec = 0 forsec all { segCounts.x[nSec] = nseg nSec += 1 } } /* * Replace the axon built from the original morphology file with a stub axon */ func hash_str() {localobj sf strdef right sf = new StringFunctions() right = $s1 n_of_c = sf.len(right) hash = 0 char_int = 0 for i = 0, n_of_c - 1 { sscanf(right, "%c", & char_int) hash = (hash * 31 + char_int) % (2 ^ 31 - 1) sf.right(right, 1) } return hash } proc re_init_rng() {localobj sf strdef full_str, name sf = new StringFunctions() if(numarg() == 1) { // We received a third seed channel_seed = $1 channel_seed_set = 1 } else { channel_seed_set = 0 } forsec somatic { deterministic_StochKv = 1 } } endtemplate stochkv_cell ================================================ FILE: examples/thalamocortical-cell/CellEvalSetup/__init__.py ================================================ """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import evaluator import template import tools ================================================ FILE: examples/thalamocortical-cell/CellEvalSetup/evaluator.py ================================================ """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0914, R0912 import json import bluepyopt.ephys as ephys import template # pylint: disable=W0403 import protocols # pylint: disable=W0403 import logging logger = logging.getLogger(__name__) import os import bluepyopt as bpopt soma_loc = ephys.locations.NrnSeclistCompLocation( name='soma', seclist_name='somatic', sec_index=0, comp_x=0.5) def read_step_protocol(protocol_name, protocol_definition, recordings, prefix=""): """Read step protocol from definition""" step_definition = protocol_definition['stimuli']['step'] step_stimulus = ephys.stimuli.NrnSquarePulse( step_amplitude=step_definition['amp'], step_delay=step_definition['delay'], step_duration=step_definition['duration'], location=soma_loc, total_duration=step_definition['totduration']) if 'holding' in protocol_definition['stimuli']: holding_definition = protocol_definition[ 'stimuli']['holding'] holding_stimulus = ephys.stimuli.NrnSquarePulse( step_amplitude=holding_definition['amp'], step_delay=holding_definition['delay'], step_duration=holding_definition['duration'], location=soma_loc, total_duration=holding_definition['totduration']) else: holding_stimulus = None return protocols.StepProtocolCustom( name=protocol_name, step_stimulus=step_stimulus, holding_stimulus=holding_stimulus, recordings=recordings) def read_ramp_protocol( protocol_name, protocol_definition, recordings): """Read step protocol from definition""" ramp_definition = protocol_definition['stimuli']['ramp'] ramp_stimulus = ephys.stimuli.NrnRampPulse( ramp_amplitude_start = ramp_definition['ramp_amp_start'], ramp_amplitude_end = ramp_definition['ramp_amp_end'], ramp_delay=ramp_definition['delay'], ramp_duration=ramp_definition['duration'], location=soma_loc, total_duration=ramp_definition['totduration']) if 'holding' in protocol_definition['stimuli']: holding_definition = protocol_definition[ 'stimuli']['holding'] holding_stimulus = ephys.stimuli.NrnSquarePulse( step_amplitude=holding_definition['amp'], step_delay=holding_definition['delay'], step_duration=holding_definition['duration'], location=soma_loc, total_duration=holding_definition['totduration']) else: holding_stimulus = None return protocols.RampProtocol( name=protocol_name, ramp_stimulus=ramp_stimulus, holding_stimulus=holding_stimulus, recordings=recordings) def define_protocols(protocols_filename, stochkv_det=None, runopt=False, prefix="", apical_sec=None): """Define protocols""" with open(os.path.join(os.path.dirname(__file__), '..', protocols_filename)) as protocol_file: protocol_definitions = json.load(protocol_file) if "__comment" in protocol_definitions: del protocol_definitions["__comment"] protocols_dict = {} for protocol_name, protocol_definition in protocol_definitions.items(): # By default include somatic recording somav_recording = ephys.recordings.CompRecording( name='%s.%s.soma.v' % (prefix, protocol_name), location=soma_loc, variable='v') recordings = [somav_recording] if 'type' in protocol_definition and \ protocol_definition['type'] == 'StepProtocol': protocols_dict[protocol_name] = read_step_protocol( protocol_name, protocol_definition, recordings, stochkv_det) elif 'type' in protocol_definition and \ protocol_definition['type'] == 'RampProtocol': protocols_dict[protocol_name] = read_ramp_protocol( protocol_name, protocol_definition, recordings) else: stimuli = [] for stimulus_definition in protocol_definition['stimuli']: stimuli.append(ephys.stimuli.NrnSquarePulse( step_amplitude=stimulus_definition['amp'], step_delay=stimulus_definition['delay'], step_duration=stimulus_definition['duration'], location=soma_loc, total_duration=stimulus_definition['totduration'])) protocols_dict[protocol_name] = ephys.protocols.SweepProtocol( name=protocol_name, stimuli=stimuli, recordings=recordings) return protocols_dict from bluepyopt.ephys.efeatures import eFELFeature class eFELFeatureExtra(eFELFeature): """eFEL feature extra""" SERIALIZED_FIELDS = ('name', 'efel_feature_name', 'recording_names', 'stim_start', 'stim_end', 'exp_mean', 'exp_std', 'threshold', 'comment') def __init__( self, name, efel_feature_name=None, recording_names=None, stim_start=None, stim_end=None, exp_mean=None, exp_std=None, threshold=None, stimulus_current=None, comment='', interp_step=None, double_settings=None, int_settings=None, force_max_score=False, max_score = 250, prefix=''): """Constructor Args: name (str): name of the eFELFeature object efel_feature_name (str): name of the eFeature in the eFEL library (ex: 'AP1_peak') recording_names (dict): eFEL features can accept several recordings as input stim_start (float): stimulation start time (ms) stim_end (float): stimulation end time (ms) exp_mean (float): experimental mean of this eFeature exp_std(float): experimental standard deviation of this eFeature threshold(float): spike detection threshold (mV) comment (str): comment """ super(eFELFeatureExtra, self).__init__(name, efel_feature_name, recording_names, stim_start, stim_end, exp_mean, exp_std, threshold, stimulus_current, comment, interp_step, double_settings, int_settings, force_max_score, max_score) extra_features = ['spikerate_tau_jj_skip', 'spikerate_drop_skip', 'spikerate_tau_log_skip', 'spikerate_tau_fit_skip'] self.prefix = prefix def get_bpo_score(self, responses): """Return internal score which is directly passed as a response""" feature_value = self.get_bpo_feature(responses) if feature_value == None: score = 250. else: score = abs(feature_value - self.exp_mean) / self.exp_std return score def calculate_feature(self, responses, raise_warnings=False): """Calculate feature value""" if self.efel_feature_name.startswith('bpo_'): # check if internal feature feature_value = self.get_bpo_feature(responses) else: efel_trace = self._construct_efel_trace(responses) if efel_trace is None: feature_value = None else: self._setup_efel() import efel values = efel.getMeanFeatureValues( [efel_trace], [self.efel_feature_name], raise_warnings=raise_warnings) feature_value = values[0][self.efel_feature_name] efel.reset() logger.debug( 'Calculated value for %s: %s', self.name, str(feature_value)) return feature_value def calculate_score(self, responses, trace_check=False): """Calculate the score""" if self.efel_feature_name.startswith('bpo_'): # check if internal feature score = self.get_bpo_score(responses) elif self.exp_mean is None: score = 0 else: efel_trace = self._construct_efel_trace(responses) if efel_trace is None: score = 250.0 else: self._setup_efel() import efel score = efel.getDistance( efel_trace, self.efel_feature_name, self.exp_mean, self.exp_std, trace_check=trace_check, error_dist = self.max_score) if self.force_max_score: score = min(score, self.max_score) efel.reset() logger.debug('Calculated score for %s: %f', self.name, score) return score from bluepyopt.ephys.objectives import SingletonObjective, EFeatureObjective, MaxObjective class SingletonWeightObjective(EFeatureObjective): """Single EPhys feature""" def __init__(self, name, feature, weight): """Constructor Args: name (str): name of this object features (EFeature): single eFeature inside this objective """ super(SingletonWeightObjective, self).__init__(name, [feature]) self.weight = weight def calculate_score(self, responses): """Objective score""" return self.calculate_feature_scores(responses)[0] * self.weight def __str__(self): """String representation""" return '( %s ), weight:%f' % (self.features[0], self.weight) def define_fitness_calculator(main_protocol, features_filename, prefix=""): """Define fitness calculator""" with open(os.path.join(os.path.dirname(__file__), '..', features_filename)) as protocol_file: feature_definitions = json.load(protocol_file) if "__comment" in feature_definitions: del feature_definitions["__comment"] objectives = [] efeatures = {} features = [] for protocol_name, locations in feature_definitions.items(): for recording_name, feature_configs in locations.items(): for feature_config in feature_configs: efel_feature_name = feature_config["feature"] meanstd = feature_config["val"] if hasattr(main_protocol, 'subprotocols'): protocol = main_protocol.subprotocols()[protocol_name] else: protocol = main_protocol[protocol_name] feature_name = '%s.%s.%s.%s' % ( prefix, protocol_name, recording_name, efel_feature_name) recording_names = \ {'': '%s.%s.%s' % (prefix, protocol_name, recording_name)} if 'weight' in feature_config: weight = feature_config['weight'] else: weight = 1 if 'strict_stim' in feature_config: strict_stim = feature_config['strict_stim'] else: strict_stim = True if hasattr(protocol, 'step_delay'): stim_start = protocol.step_delay if 'threshold' in feature_config: threshold = feature_config['threshold'] else: threshold = -30 if 'bAP' in protocol_name: # bAP response can be after stimulus stim_end = protocol.total_duration else: stim_end = protocol.step_delay + protocol.step_duration try: stimulus_current=protocol.step_stimulus.step_amplitude except AttributeError: print("Check stim_amp for RampProtocol") stimulus_current = None else: stim_start = None stim_end = None stimulus_current = None threshold = None feature = eFELFeatureExtra( feature_name, efel_feature_name=efel_feature_name, recording_names=recording_names, stim_start=stim_start, stim_end=stim_end, exp_mean=meanstd[0], exp_std=meanstd[1], stimulus_current=stimulus_current, threshold=threshold, prefix=prefix, int_settings={'strict_stiminterval': strict_stim}, force_max_score = True, max_score = 250) efeatures[feature_name] = feature features.append(feature) objective = SingletonWeightObjective( feature_name, feature, weight) objectives.append(objective) #objectives.append(MaxObjective('global_maximum', features)) fitcalc = ephys.objectivescalculators.ObjectivesCalculator(objectives) return fitcalc, efeatures def create(etype, runopt=False, altmorph=None): """Setup""" with open(os.path.join(os.path.dirname(__file__), '..', 'config/recipes.json')) as f: recipe = json.load(f) prot_path = recipe[etype]['protocol'] cell = template.create(recipe, etype, altmorph) protocols_dict = define_protocols(prot_path, runopt) fitness_calculator, efeatures = define_fitness_calculator( protocols_dict, recipe[etype]['features']) fitness_protocols=protocols_dict param_names = [param.name for param in cell.params.values() if not param.frozen] nrn_sim = ephys.simulators.NrnSimulator(cvode_active = True) cell_eval = ephys.evaluators.CellEvaluator( cell_model=cell, param_names=param_names, fitness_protocols=fitness_protocols, fitness_calculator=fitness_calculator, sim=nrn_sim, use_params_for_seed=True) return cell_eval ================================================ FILE: examples/thalamocortical-cell/CellEvalSetup/protocols.py ================================================ """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=R0914 import numpy import warnings import collections import copy import json import bluepyopt.ephys as ephys import os import argparse parser = argparse.ArgumentParser(description='cell') parser.add_argument('--live', action="store_true", default=False, help='plot live') args, unknown = parser.parse_known_args() live_plot = False if live_plot: import matplotlib.pyplot as plt class StepProtocolCustom(ephys.protocols.StepProtocol): """Step protocol with custom options to turn stochkv_det on or off""" def __init__( self, name=None, step_stimulus=None, holding_stimulus=None, recordings=None, cvode_active=None): """Constructor""" super(StepProtocolCustom, self).__init__( name, step_stimulus=step_stimulus, holding_stimulus=holding_stimulus, recordings=recordings, cvode_active=cvode_active) def run(self, cell_model, param_values, sim=None, isolate=None): """Run protocol""" responses = {} responses.update(super(StepProtocolCustom, self).run( cell_model, param_values, sim=sim, isolate=isolate)) for mechanism in cell_model.mechanisms: mechanism.deterministic = True self.cvode_active = True return responses class RampProtocol(ephys.protocols.SweepProtocol): """Protocol consisting of ramp and holding current""" def __init__( self, name=None, ramp_stimulus=None, holding_stimulus=None, recordings=None, cvode_active=None): """Constructor Args: name (str): name of this object step_stimulus (list of Stimuli): Stimulus objects used in protocol recordings (list of Recordings): Recording objects used in the protocol cvode_active (bool): whether to use variable time step """ super(RampProtocol, self).__init__( name, stimuli=[ ramp_stimulus, holding_stimulus] if holding_stimulus is not None else [ramp_stimulus], recordings=recordings, cvode_active=cvode_active) self.ramp_stimulus = ramp_stimulus self.holding_stimulus = holding_stimulus @property def step_delay(self): """Time stimulus starts""" return self.ramp_stimulus.ramp_delay @property def step_duration(self): """Time stimulus starts""" return self.ramp_stimulus.ramp_duration ================================================ FILE: examples/thalamocortical-cell/CellEvalSetup/template.py ================================================ """ Copyright (c) 2016-2020, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import os import collections try: import simplejson as json except ImportError: import json import bluepyopt.ephys as ephys import logging logger = logging.getLogger(__name__) import random def multi_locations(sectionlist): """Define mechanisms""" if sectionlist == "alldend": seclist_locs = [ ephys.locations.NrnSeclistLocation("basal", seclist_name="basal") ] elif sectionlist == "somadend": seclist_locs = [ ephys.locations.NrnSeclistLocation( "basal", seclist_name="basal"), ephys.locations.NrnSeclistLocation( "somatic", seclist_name="somatic") ] elif sectionlist == "somaxon": seclist_locs = [ ephys.locations.NrnSeclistLocation( "axonal", seclist_name="axonal"), ephys.locations.NrnSeclistLocation( "somatic", seclist_name="somatic") ] elif sectionlist == "allact": seclist_locs = [ ephys.locations.NrnSeclistLocation( "basal", seclist_name="basal"), ephys.locations.NrnSeclistLocation( "somatic", seclist_name="somatic"), ephys.locations.NrnSeclistLocation( "axonal", seclist_name="axonal") ] else: seclist_locs = [ephys.locations.NrnSeclistLocation( sectionlist, seclist_name=sectionlist)] return seclist_locs def define_mechanisms(params_filename): """Define mechanisms""" with open(os.path.join(os.path.dirname(__file__), '..', params_filename)) as params_file: mech_definitions = json.load( params_file, object_pairs_hook=collections.OrderedDict)["mechanisms"] mechanisms_list = [] for sectionlist, channels in mech_definitions.items(): seclist_locs = multi_locations(sectionlist) for channel in channels["mech"]: mechanisms_list.append(ephys.mechanisms.NrnMODMechanism( name='%s.%s' % (channel, sectionlist), mod_path=None, prefix=channel, locations=seclist_locs, preloaded=True)) return mechanisms_list def define_parameters(params_filename): """Define parameters""" parameters = [] with open(os.path.join(os.path.dirname(__file__), '..', params_filename)) as params_file: definitions = json.load( params_file, object_pairs_hook=collections.OrderedDict) # set distributions distributions = collections.OrderedDict() distributions["uniform"] = ephys.parameterscalers.NrnSegmentLinearScaler() distributions_definitions = definitions["distributions"] for distribution, definition in distributions_definitions.items(): distributions[distribution] = \ ephys.parameterscalers.NrnSegmentSomaDistanceScaler( distribution=definition["fun"]) params_definitions = definitions["parameters"] if "__comment" in params_definitions: del params_definitions["__comment"] for sectionlist, params in params_definitions.items(): if sectionlist == 'global': seclist_locs = None is_global = True else: seclist_locs = multi_locations(sectionlist) is_global = False bounds = None value = None for param_config in params: param_name = param_config["name"] if isinstance(param_config["val"], (list, tuple)): is_frozen = False bounds = param_config["val"] value = None else: is_frozen = True value = param_config["val"] bounds = None if is_global: parameters.append( ephys.parameters.NrnGlobalParameter( name=param_name, param_name=param_name, frozen=is_frozen, bounds=bounds, value=value)) else: if "dist" in param_config: dist = distributions[param_config["dist"]] use_range = True else: dist = distributions["uniform"] use_range = False if use_range: parameters.append(ephys.parameters.NrnRangeParameter( name='%s.%s' % (param_name, sectionlist), param_name=param_name, value_scaler=dist, value=value, bounds=bounds, frozen=is_frozen, locations=seclist_locs)) else: parameters.append(ephys.parameters.NrnSectionParameter( name='%s.%s' % (param_name, sectionlist), param_name=param_name, value_scaler=dist, value=value, bounds=bounds, frozen=is_frozen, locations=seclist_locs)) return parameters from bluepyopt.ephys.morphologies import NrnFileMorphology def define_morphology(morphology_filename, do_set_nseg=1e9): """Define morphology""" # Use default moprhology class from BluePyOpt return ephys.morphologies.NrnFileMorphology( os.path.join(morphology_filename), do_replace_axon=True, do_set_nseg=do_set_nseg) def create(recipe, etype, altmorph=None): """Create cell template""" if altmorph is None: morph_path = os.path.join(os.path.join(recipe[etype]['morph_path'], recipe[etype]['morphology'])) else: morph_path = altmorph cell = ephys.models.CellModel( etype, morph=define_morphology(morph_path, do_set_nseg=40.), mechs=define_mechanisms(recipe[etype]['params']), params=define_parameters(recipe[etype]['params'])) return cell ================================================ FILE: examples/thalamocortical-cell/CellEvalSetup/tools.py ================================================ def rename_prot(name): if 'RMP' in name: name = "(No stim)" elif 'Rin_dep' in name: #name = name.replace('.Rin','Hyp_-40') name = "($-$40%)" #"R_{input} - tonic" elif 'Rin_hyp' in name: #name = name.replace('.Rin','Hyp_-40') name = "($-$40% - b)" elif 'hyp' in name and "Step" in name: name = '(150% - burst)' name = '(225% - burst)' name = '(200% - burst)' elif 'Step_150' in name: name = '(150% - tonic)' elif 'Step_200' in name: name = '(200% - tonic)' elif 'Step_250' in name: name = '(250% - tonic)' elif 'IV_-140' in name: name = "($-$140%)" elif 'ThresholdDetection_dep' in name: name = '' elif 'ThresholdDetection_hyp' in name: name = '' elif 'hold_dep' in name: name = "(I$_{hold}$ - tonic)" elif 'hold_hyp' in name: name = "(I$_{hold}$ - burst)" #name = name.replace("soma.v.", "") return name def rename_featpart(name): import json namemap = { "steady_state_voltage_stimend": "V$_{rest}$", "sag_amplitude": "Sag amp.", "ohmic_input_resistance_vb_ssse": "R$_{input}$", "voltage_base": "Baseline V$_m$", "Spikecount": "Num. of APs", "voltage_after_stim": "V$_m$ after stim.", "inv_first_ISI": "Inv. 1$^{st}$ ISI", "inv_last_ISI": "Inv. last ISI", "inv_second_ISI": "Inv. 2$^{nd}$ ISI", "time_to_first_spike": "Latency 1$^{st}$ AP", "AP1_amp": "Amp. 1$^{st}$ AP ", "AP2_amp": "Amp. 2$^{nd}$ AP ", "AHP_depth": "AHP depth", "AHP_depth_abs": "AHP depth", "AP_amplitude": "AP amp.", "AP_width": "AP width", "AP_duration_half_width": "AP half-width", "Spikecount_stimint": "Num. of APs", "bpo_threshold_current_dep": "I$_{thr}$ - tonic", "bpo_threshold_current_hyp": "I$_{thr}$ - burst", "time_to_last_spike": "Latency last AP", "adaptation_index2": "Adaptation idx", "mean_frequency": "Frequency" } return namemap[name] def rename_feat(name, sep = " "): prot_name = name.split(".")[1] feat_name = name.split(".")[-1] new_prot = rename_prot(prot_name) new_feat = rename_featpart(feat_name) return new_feat + sep + new_prot ================================================ FILE: examples/thalamocortical-cell/LICENSE.txt ================================================ If not otherwise specified, the CC-BY-NC-SA license applies. https://creativecommons.org/licenses/by-nc-sa/4.0/ The detailed text is available here: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode The Python code are generated from examples available in this repository and are licensed under the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation, as stated in the headers of the files. Experimental features and protocols are extracted from experimental data from Jane Yi, Laboratory of Neural Microcircuitry (https://www.epfl.ch/labs/markram-lab/). They are part of this example and Supporting Information for the paper: https://www.biorxiv.org/content/10.1101/512269v3 The experimental morphologies were provided by Jane Yi, Ying Shi, Laboratory of Neural Microcircuitry (https://www.epfl.ch/labs/markram-lab/). They will be available on NeuroMorpho.org under the ODC Public Domain Dedication and Licence (PDDL) https://opendatacommons.org/licenses/pddl/1.0/ For models for which the original source is available on ModelDB, any specific licenses mentioned on ModelDB, or the generic License of ModelDB apply: 1) TC_Nap_Et2, persistent sodium current Authors: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit, Elisabetta Iavarone Accession: 139653 Original URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=139653&file=/L5bPCmodelsEH/mod/Nap_Et2.mod#tabs-2 2) SK_E2, calcium-activated potassium current Authors: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit Accession: 139653 Original URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=139653&file=/L5bPCmodelsEH/mod/SK_E2.mod#tabs-2 3) Intracellular calcium dynamics Authors: Etay Hay, Shaul Druckmann, Srikanth Ramaswamy, James King, Werner Van Geit Accession: 139653 Original URL: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=139653&file=/L5bPCmodelsEH/mod/CaDynamics_E2.mod#tabs-2 4) TC_ITGHK_Des98, low-threshold calcium current Author: Alain Destexhe Accession: 279 Original URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=279&file=/dendtc/ITGHK.mod#tabs-2 5) TC_HH, fast Na+ and K+ currents responsible for action potentials Authors: Alain Destexhe, Elisabetta Iavarone Accession: 279 Original URL: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=279&file=/dendtc/hh2.mod#tabs-2 5) TC_iL, high-threshold calcium current Author: Arthur Houweling Accession: 3808 Original url: https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=3808&file=/MyFirstNEURON/il.mod#tabs-2 7) TC_iA, fast transient potassium current: Author: Yimi Amarillo Publications: Amarillo et al., J Neurophysiol, 2014, Huguenard and McCormick, J Neurophysiol, 1992 8) TC_Ih_Bud97, Ih current Author: Elisabetta Iavarone Publications: Budde et al., J Physiol, 1997, Huguenard and McCormick, J Neurophysiol, 1992 ================================================ FILE: examples/thalamocortical-cell/checkpoints/checkpoint.pkl ================================================ (dp0 S'history' p1 ccopy_reg _reconstructor p2 (cdeap.tools.support History p3 c__builtin__ object p4 Ntp5 Rp6 (dp7 S'genealogy_tree' p8 (dp9 I1 (tsI2 (tsI3 (I1 I2 I1 I2 tp10 sI4 g10 sI5 g10 sI6 g10 ssS'genealogy_index' p11 I6 sS'genealogy_history' p12 (dp13 I1 g2 (cbluepyopt.deapext.optimisations WSListIndividual p14 c__builtin__ list p15 (lp16 F8.459776330097977e-05 aF0.1515908805880605 aF0.336457264664676 aF2.5891675029296335e-05 aF5.112747213686086e-05 aF4.049341374504143e-05 aF0.05486590123243409 aF0.06066254521578549 aF0.09531939083047117 aF0.0005833820394550312 aF0.004540564425976676 aF8.065615981443464 aF0.28219692547750397 aF7.55804204157224e-05 aF6.183689966753316e-05 aF0.0020040507308995243 aF0.009097462559682402 aF0.005896712856225918 aF0.0008102172359965896 aF0.004510829752197913 aF5.342065970470657 aF0.7299668323859986 atp17 Rp18 (dp19 S'history_index' p20 I1 sS'fitness' p21 g2 (cbluepyopt.deapext.optimisations WeightedSumFitness p22 g4 Ntp23 Rp24 (dp25 S'weights' p26 (lp27 F-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 asS'wvalues' p28 (cnumpy.core._multiarray_umath scalar p29 (cnumpy dtype p30 (S'f8' p31 I0 I1 tp32 Rp33 (I3 S'<' p34 NNNI-1 I-1 I0 tp35 bS'\x00\x00\x00\x00\x00\x00\x00\x80' p36 tp37 Rp38 g29 (g33 S'\nw\x81\x99?\x0c\xed\xbf' p39 tp40 Rp41 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p42 tp43 Rp44 g29 (g33 S'\x9a5n\x14R\x84\r\xc0' p45 tp46 Rp47 F-250.0 F-250.0 F-250.0 g29 (g33 S'q\xd5\xb5p\x178\x02\xc0' p48 tp49 Rp50 F-250.0 g29 (g33 S'\xf5\xdbA\x82\xeb\xd1\xfd\xbf' p51 tp52 Rp53 g29 (g33 S"\xba\x12'}\xe6f\xfa\xbf" p54 tp55 Rp56 g29 (g33 S"\x85\x838\x807'\x00\xc0" p57 tp58 Rp59 F-250.0 g29 (g33 S'\x8eJW\xec\xc8\x80\x1d\xc0' p60 tp61 Rp62 g29 (g33 S'\xfd\xe2K\\\xedv\xfc\xbf' p63 tp64 Rp65 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xc7<\x11\x180\t\x02\xc0' p66 tp67 Rp68 F-250.0 g29 (g33 S'\xe4)\xc9\xc8\x8cT\xff\xbf' p69 tp70 Rp71 g29 (g33 S'8u\xf3=Z\xe1\xfe\xbf' p72 tp73 Rp74 g29 (g33 S'\xf4^+\xaf\x9b=\x02\xc0' p75 tp76 Rp77 F-250.0 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p78 tp79 Rp80 F-250.0 F-250.0 F-250.0 g29 (g33 S'so\xef\x87\x7f}\x01\xc0' p81 tp82 Rp83 F-250.0 g29 (g33 S'\x99\xe3\xc0\x87\xff\xd9\xf8\xbf' p84 tp85 Rp86 g29 (g33 S'\x05\xb9e\x1c\x81\xde\xf4\xbf' p87 tp88 Rp89 g29 (g33 S'\xf4\xc3\xd5t\x7f\xdc\x00\xc0' p90 tp91 Rp92 F-250.0 g29 (g33 S'\xad\xb3.\x08\x8e\xce\x18\xc0' p93 tp94 Rp95 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xd2\xee\xa5\xbb,^\x14\xc0' p96 tp97 Rp98 g29 (g33 S'd\xac\x8fZ\xddD\x19\xc0' p99 tp100 Rp101 g29 (g33 S'\xae\x11 \x1cE!\x12\xc0' p102 tp103 Rp104 g29 (g33 S'\xdc\xc9B\x86rZ\n\xc0' p105 tp106 Rp107 g29 (g33 S'\x1e7D\xdd\xca\xaf\x18\xc0' p108 tp109 Rp110 g29 (g33 S'\xdcAp"\x19\xee\xf3\xbf' p111 tp112 Rp113 tp114 sbsbsI2 g2 (g14 g15 (lp115 F8.998499050883135e-05 aF0.13679678638308826 aF0.3777141723621707 aF1.007012080683658e-05 aF4.3417183545378374e-05 aF6.108869734438017e-05 aF0.06391077372665288 aF0.19332127355415177 aF0.09540195531054341 aF0.0008653099277716401 aF0.001302461551959797 aF12.270389578182312 aF0.5489249541836715 aF1.4041700164018957e-06 aF7.197046864039541e-05 aF0.00319058833779415 aF0.00824844977148233 aF0.004008919207391105 aF1.1428193144282783e-06 aF0.0024678893323266233 aF13.146438856898932 aF0.2442889214486884 atp116 Rp117 (dp118 g20 I2 sg21 g2 (g22 g4 Ntp119 Rp120 (dp121 g26 (lp122 F-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 aF-1.0 asg28 (g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p123 tp124 Rp125 g29 (g33 S'L#\x1d@\xf6D\xcb\xbf' p126 tp127 Rp128 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p129 tp130 Rp131 g29 (g33 S'\x01$G\xe0:\xbd\x15\xc0' p132 tp133 Rp134 F-250.0 F-250.0 F-250.0 g29 (g33 S'q\xd5\xb5p\x178\x02\xc0' p135 tp136 Rp137 F-250.0 g29 (g33 S'\xf5\xdbA\x82\xeb\xd1\xfd\xbf' p138 tp139 Rp140 g29 (g33 S"\xba\x12'}\xe6f\xfa\xbf" p141 tp142 Rp143 g29 (g33 S"\x85\x838\x807'\x00\xc0" p144 tp145 Rp146 F-250.0 g29 (g33 S'\x0e\x94\x0b{e\x8c$\xc0' p147 tp148 Rp149 g29 (g33 S'\xc6\x17\xd4\xf5H\xab\xfd\xbf' p150 tp151 Rp152 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xc7<\x11\x180\t\x02\xc0' p153 tp154 Rp155 F-250.0 g29 (g33 S'\xe4)\xc9\xc8\x8cT\xff\xbf' p156 tp157 Rp158 g29 (g33 S'8u\xf3=Z\xe1\xfe\xbf' p159 tp160 Rp161 g29 (g33 S'\xf4^+\xaf\x9b=\x02\xc0' p162 tp163 Rp164 F-250.0 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p165 tp166 Rp167 F-250.0 F-250.0 F-250.0 g29 (g33 S'so\xef\x87\x7f}\x01\xc0' p168 tp169 Rp170 F-250.0 g29 (g33 S'\x99\xe3\xc0\x87\xff\xd9\xf8\xbf' p171 tp172 Rp173 g29 (g33 S'\x05\xb9e\x1c\x81\xde\xf4\xbf' p174 tp175 Rp176 g29 (g33 S'\xf4\xc3\xd5t\x7f\xdc\x00\xc0' p177 tp178 Rp179 F-250.0 g29 (g33 S'A\x0b\xd8\xa9\xf6+\xe7\xbf' p180 tp181 Rp182 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xd2\xee\xa5\xbb,^\x14\xc0' p183 tp184 Rp185 g29 (g33 S'd\xac\x8fZ\xddD\x19\xc0' p186 tp187 Rp188 g29 (g33 S'\xae\x11 \x1cE!\x12\xc0' p189 tp190 Rp191 g29 (g33 S'\xdc\xc9B\x86rZ\n\xc0' p192 tp193 Rp194 g29 (g33 S'fD\x88\x13\xad\xab\x02\xc0' p195 tp196 Rp197 g29 (g33 S'\\\xb8`\xa0\x00\xd5\xf3\xbf' p198 tp199 Rp200 tp201 sbsbsI3 g2 (g14 g15 (lp202 F8.459776330097977e-05 aF0.1515908805880605 aF0.336457264664676 aF2.5891675029296335e-05 aF5.112747213686086e-05 aF4.049341374504143e-05 aF0.05486590123243409 aF0.06066254521578549 aF0.09531939083047117 aF0.0005833820394550312 aF0.004540564425976676 aF8.065615981443464 aF0.28219692547750397 aF7.55804204157224e-05 aF6.183689966753316e-05 aF0.0020040507308995243 aF0.009097462559682402 aF0.005896712856225918 aF0.0008102172359965896 aF0.004510829752197913 aF5.342065970470657 aF0.7299668323859986 atp203 Rp204 (dp205 g20 I3 sg21 g2 (g22 g4 Ntp206 Rp207 (dp208 g26 g27 sg28 g114 sbsbsI4 g2 (g14 g15 (lp209 F8.998499050883135e-05 aF0.13679678638308826 aF0.3777141723621707 aF1.007012080683658e-05 aF4.3417183545378374e-05 aF6.108869734438017e-05 aF0.06391077372665288 aF0.19332127355415177 aF0.09540195531054341 aF0.0008653099277716401 aF0.001302461551959797 aF12.270389578182312 aF0.5489249541836715 aF1.4041700164018957e-06 aF7.197046864039541e-05 aF0.00319058833779415 aF0.00824844977148233 aF0.004008919207391105 aF1.1428193144282783e-06 aF0.0024678893323266233 aF13.146438856898932 aF0.2442889214486884 atp210 Rp211 (dp212 g20 I4 sg21 g2 (g22 g4 Ntp213 Rp214 (dp215 g26 g122 sg28 g201 sbsbsI5 g2 (g14 g15 (lp216 F8.459776330097977e-05 aF0.15168906708994337 aF0.336457264664676 aF3.058343996090739e-05 aF5.9022049332218484e-05 aF4.049341374504143e-05 aF0.05486590123243409 aF0.06000995579013797 aF0.09531939083047117 aF0.0005833820394550312 aF0.0046462799281463815 aF8.065615981443464 aF0.28219692547750397 aF7.55804204157224e-05 aF7.4296780638054e-05 aF0.0022203480307705324 aF0.009061603766623298 aF0.005942316155463084 aF0.0007384762374565938 aF0.002615491103308344 aF6.557721287233644 aF0.6243445607373651 atp217 Rp218 (dp219 g20 I5 sg21 g2 (g22 g4 Ntp220 Rp221 (dp222 g26 g27 sg28 (g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p223 tp224 Rp225 g29 (g33 S'\xa7\xaa\x02*\x1e<\xed\xbf' p226 tp227 Rp228 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p229 tp230 Rp231 g29 (g33 S'\x946e\xdf9\x7f\r\xc0' p232 tp233 Rp234 F-250.0 F-250.0 F-250.0 g29 (g33 S'q\xd5\xb5p\x178\x02\xc0' p235 tp236 Rp237 F-250.0 g29 (g33 S'\xf5\xdbA\x82\xeb\xd1\xfd\xbf' p238 tp239 Rp240 g29 (g33 S"\xba\x12'}\xe6f\xfa\xbf" p241 tp242 Rp243 g29 (g33 S"\x85\x838\x807'\x00\xc0" p244 tp245 Rp246 F-250.0 g29 (g33 S'@\x1b\x94\x91~{\x1d\xc0' p247 tp248 Rp249 g29 (g33 S'\xc7\xf5\xdemVg\xfc\xbf' p250 tp251 Rp252 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xc7<\x11\x180\t\x02\xc0' p253 tp254 Rp255 F-250.0 g29 (g33 S'\xe4)\xc9\xc8\x8cT\xff\xbf' p256 tp257 Rp258 g29 (g33 S'8u\xf3=Z\xe1\xfe\xbf' p259 tp260 Rp261 g29 (g33 S'\xf4^+\xaf\x9b=\x02\xc0' p262 tp263 Rp264 F-250.0 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p265 tp266 Rp267 F-250.0 F-250.0 F-250.0 g29 (g33 S'so\xef\x87\x7f}\x01\xc0' p268 tp269 Rp270 F-250.0 g29 (g33 S'\x99\xe3\xc0\x87\xff\xd9\xf8\xbf' p271 tp272 Rp273 g29 (g33 S'\x05\xb9e\x1c\x81\xde\xf4\xbf' p274 tp275 Rp276 g29 (g33 S'\xf4\xc3\xd5t\x7f\xdc\x00\xc0' p277 tp278 Rp279 F-250.0 g29 (g33 S'\x1fN\xa1\xfa\xba\xf9\x18\xc0' p280 tp281 Rp282 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xd2\xee\xa5\xbb,^\x14\xc0' p283 tp284 Rp285 g29 (g33 S'd\xac\x8fZ\xddD\x19\xc0' p286 tp287 Rp288 g29 (g33 S'\xae\x11 \x1cE!\x12\xc0' p289 tp290 Rp291 g29 (g33 S'\xdc\xc9B\x86rZ\n\xc0' p292 tp293 Rp294 g29 (g33 S'q\xd2`\xc1\x9b\xde\x18\xc0' p295 tp296 Rp297 g29 (g33 S'\xcbt\x17|#\xcd\xf3\xbf' p298 tp299 Rp300 tp301 sbsbsI6 g2 (g14 g15 (lp302 F9.094879779990037e-05 aF0.1366985998811062 aF0.3777141723621707 aF1.007012080683658e-05 aF4.682722002434101e-05 aF6.108869734438017e-05 aF0.06391077372665288 aF0.1873655711711291 aF0.09540195531054341 aF0.0006169669963403399 aF0.001302461551959797 aF10.602709904797184 aF0.6289506896678183 aF1.4041700164018957e-06 aF6.028389833499836e-05 aF0.002730997230902337 aF0.008550495952499656 aF0.003477632527733394 aF1.1428193144282783e-06 aF0.0036857304306474217 aF13.146438856898932 aF0.21611889680663607 atp303 Rp304 (dp305 g20 I6 sg21 g2 (g22 g4 Ntp306 Rp307 (dp308 g26 g122 sg28 (g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p309 tp310 Rp311 g29 (g33 S'\x81\xbd\xd3\xf5s\x03\xcb\xbf' p312 tp313 Rp314 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p315 tp316 Rp317 g29 (g33 S'b\xd4\xe4H8\xdc\x15\xc0' p318 tp319 Rp320 F-250.0 F-250.0 F-250.0 g29 (g33 S'q\xd5\xb5p\x178\x02\xc0' p321 tp322 Rp323 F-250.0 g29 (g33 S'\xf5\xdbA\x82\xeb\xd1\xfd\xbf' p324 tp325 Rp326 g29 (g33 S"\xba\x12'}\xe6f\xfa\xbf" p327 tp328 Rp329 g29 (g33 S"\x85\x838\x807'\x00\xc0" p330 tp331 Rp332 F-250.0 g29 (g33 S'\xc1`\x9b\x10\x02\xa7$\xc0' p333 tp334 Rp335 g29 (g33 S'\x14;\xc5|\xef\xd1\xfd\xbf' p336 tp337 Rp338 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xc7<\x11\x180\t\x02\xc0' p339 tp340 Rp341 F-250.0 g29 (g33 S'\xe4)\xc9\xc8\x8cT\xff\xbf' p342 tp343 Rp344 g29 (g33 S'8u\xf3=Z\xe1\xfe\xbf' p345 tp346 Rp347 g29 (g33 S'\xf4^+\xaf\x9b=\x02\xc0' p348 tp349 Rp350 F-250.0 g29 (g33 S'\x00\x00\x00\x00\x00\x00\x00\x80' p351 tp352 Rp353 F-250.0 F-250.0 F-250.0 g29 (g33 S'so\xef\x87\x7f}\x01\xc0' p354 tp355 Rp356 F-250.0 g29 (g33 S'\x99\xe3\xc0\x87\xff\xd9\xf8\xbf' p357 tp358 Rp359 g29 (g33 S'\x05\xb9e\x1c\x81\xde\xf4\xbf' p360 tp361 Rp362 g29 (g33 S'\xf4\xc3\xd5t\x7f\xdc\x00\xc0' p363 tp364 Rp365 F-250.0 g29 (g33 S'\x10\xe2\x8e\xdb\xe8\xbc\xe8\xbf' p366 tp367 Rp368 F-250.0 F-250.0 F-250.0 g29 (g33 S'\xd2\xee\xa5\xbb,^\x14\xc0' p369 tp370 Rp371 g29 (g33 S'd\xac\x8fZ\xddD\x19\xc0' p372 tp373 Rp374 g29 (g33 S'\xae\x11 \x1cE!\x12\xc0' p375 tp376 Rp377 g29 (g33 S'\xdc\xc9B\x86rZ\n\xc0' p378 tp379 Rp380 g29 (g33 S'4\x0b\x05PR\xf5\x02\xc0' p381 tp382 Rp383 g29 (g33 S'\x98kN\xac\xa1\xd6\xf3\xbf' p384 tp385 Rp386 tp387 sbsbssbsS'parents' p388 (lp389 g2 (g14 g15 (lp390 F8.459776330097977e-05 aF0.15168906708994337 aF0.336457264664676 aF3.058343996090739e-05 aF5.9022049332218484e-05 aF4.049341374504143e-05 aF0.05486590123243409 aF0.06000995579013797 aF0.09531939083047117 aF0.0005833820394550312 aF0.0046462799281463815 aF8.065615981443464 aF0.28219692547750397 aF7.55804204157224e-05 aF7.4296780638054e-05 aF0.0022203480307705324 aF0.009061603766623298 aF0.005942316155463084 aF0.0007384762374565938 aF0.002615491103308344 aF6.557721287233644 aF0.6243445607373651 atp391 Rp392 (dp393 g20 I5 sS'ibea_fitness' p394 g29 (g33 S'\x00\x00\x00\x80\xe3%>>' p395 tp396 Rp397 sg21 g2 (g22 g4 Ntp398 Rp399 (dp400 g26 g27 sg28 g301 sbsbag392 asS'generation' p401 I2 sS'logbook' p402 g2 (cdeap.tools.support Logbook p403 g15 (lp404 (dp405 S'std' p406 g29 (g33 S'\x00\xfc\xf4!\x7f0\x05@' p407 tp408 Rp409 sS'nevals' p410 I2 sS'min' p411 g29 (g33 S'o\xdbA\xbe\xb1\xd4\xb1@' p412 tp413 Rp414 sS'max' p415 g29 (g33 S'\xaeX\n\xde\xfd\xd9\xb1@' p416 tp417 Rp418 sS'avg' p419 g29 (g33 S'\x0e\x1a&\xceW\xd7\xb1@' p420 tp421 Rp422 sS'gen' p423 I1 sa(dp424 g406 g29 (g33 S'\xa7\xf8A\xa8\xab\xfe\x04@' p425 tp426 Rp427 sg410 I2 sg411 g29 (g33 S'o\xdbA\xbe\xb1\xd4\xb1@' p428 tp429 Rp430 sg415 g29 (g33 S'=\xe2\r\xde\x10\xda\xb1@' p431 tp432 Rp433 sg419 g29 (g33 S'\xf1\xa5\xfb\xc0g\xd7\xb1@' p434 tp435 Rp436 sg423 I2 satp437 Rp438 (dp439 S'log_header' p440 I01 sS'header' p441 (lp442 g423 ag410 ag419 ag406 ag411 ag415 asS'columns_len' p443 (lp444 I3 aI6 aI7 aI7 aI7 aI7 asS'buffindex' p445 I2 sS'chapters' p446 ccollections defaultdict p447 (g403 tp448 Rp449 sbsS'rndstate' p450 (I3 (L1372342863L L3221959423L L4180954279L L3990540705L L1021773023L L2223090966L L3843864895L L597749037L L4038628808L L2184695301L L2599863370L L1285463076L L3619013288L L3445797418L L662770754L L586125062L L1346770502L L3658746180L L3438111236L L309182431L L3796115977L L3396521978L L406330169L L2387780391L L2469455462L L2266287431L L1699932795L L4049251296L L2880579946L L2212080061L L3732465507L L2085677040L L3780473213L L2666506866L L2546029306L L3062944794L L3831291871L L2699197260L L1538606110L L2628704661L L2899336459L L3451611859L L99931860L L1959678400L L1104806618L L222110430L L2129932650L L956476906L L1123944865L L4148113835L L3692694084L L1144181241L L329767184L L3790223790L L2243139411L L3664375038L L1649616822L L2085955322L L878670707L L427637247L L1097949229L L3413060003L L2868874638L L714350532L L2486509003L L3288554389L L1763249054L L3065280929L L3617934738L L630740700L L76720155L L3508325981L L3932676767L L1329309665L L1100166609L L3150737391L L3713849116L L1027154122L L621059281L L2649419324L L1724153688L L787407208L L2006574677L L2947315466L L1182636319L L3824689780L L1212132099L L4044342334L L315023297L L3553864982L L1157225958L L1432164329L L1160723985L L371272026L L927525248L L1908778666L L1373080387L L1470677278L L801074131L L3914200339L L3381407135L L2878632719L L3924891213L L4265632409L L2538399467L L2281243182L L3528786809L L875255179L L900526794L L3848503840L L1800107094L L99435770L L2252333719L L459600575L L3980788272L L642015534L L3185107417L L3358361764L L109426005L L1526990449L L2897953574L L735827974L L292571356L L656909865L L1811241718L L3852326868L L935626136L L576421325L L428560518L L2760997449L L2373789786L L2483545676L L990053668L L4260816364L L2400672108L L931204375L L1656208850L L1752543171L L3393345042L L2975883932L L3535909646L L441369518L L1875275912L L3788298971L L1604151293L L3265285628L L3389517324L L1129673657L L158637840L L3401368526L L3511603269L L1365694929L L2076135726L L144518747L L1738447909L L3315335210L L696514991L L2903756646L L1692635312L L2492835022L L793066282L L3701291598L L1682146840L L2833764772L L1874922241L L1109375682L L2157966518L L2136578897L L2614256798L L2861779371L L2698885417L L160504417L L554219202L L1430333145L L716754647L L529846619L L3482487583L L829954169L L2479439816L L1934256524L L140815401L L1125214395L L1642506558L L1276896160L L3983009834L L1007760484L L3281113000L L139221400L L361504304L L3713284634L L2928612637L L935745191L L4151018351L L3848439956L L3104510935L L615014456L L2939062496L L1306813042L L2930774231L L1126465550L L39679357L L4157006879L L3487860998L L4211840723L L4199318320L L3081958281L L1231549084L L114041891L L1456998828L L4027354301L L779738931L L2010884098L L3007041435L L1711812332L L1173077090L L2341061349L L2446045442L L3860139934L L3135005041L L1937407922L L4130038091L L4004503321L L3899652703L L1806610961L L3173634158L L3201508203L L1971746461L L2278546448L L2657844960L L2678611962L L2241832425L L1883633391L L3181942897L L2792225916L L1475682425L L1169520747L L176579167L L3554068333L L4262273005L L2352440083L L1253626969L L572672438L L1524846517L L1147813165L L3942527944L L3167059431L L1688983706L L463078297L L3955475903L L894345047L L3894065346L L1892851780L L1869067318L L3416414633L L2408185539L L2584178019L L1536598083L L1738798172L L212705107L L3295820959L L2164521947L L1063447507L L4125226161L L1611652661L L2186239661L L43480999L L3233893377L L1157189060L L3865800845L L2580347146L L3522458126L L3833272017L L2577087932L L3480966526L L3731431004L L1721158498L L1011307417L L3952810466L L2262537757L L642429441L L971181236L L362223105L L1255663603L L146673027L L2069157663L L1273202481L L120747504L L3207942888L L3721730895L L3643503245L L3816306763L L3137048487L L2860458877L L1819115946L L230113293L L3614309992L L916806549L L923982787L L2460427649L L467621168L L934475584L L1085334794L L3075553476L L2827600963L L614120395L L763452512L L3876411276L L42356791L L149721240L L3427418988L L3168682449L L3386540377L L3369227246L L3430314618L L585185811L L3994790629L L1295417371L L3112055284L L3311144040L L1059910922L L3540388763L L455829500L L3061532685L L2182265378L L607235460L L3248562707L L3580560719L L3868768753L L740125315L L4068402071L L4048707696L L3779755980L L2863645708L L3854638905L L3401978190L L2702849333L L2769927328L L1519450755L L84928381L L1224162609L L1520368433L L480115764L L37675693L L657908362L L3808994824L L1171659743L L2699029399L L2015754554L L2753802761L L2050370709L L1273170742L L2140991184L L4266961092L L1786701666L L3880429141L L759425704L L45926880L L836647469L L1573125967L L1163571132L L203220878L L861333837L L4060383179L L2628819199L L3536629941L L2457334432L L1867577917L L3536511998L L3697335810L L3133380389L L1404482804L L881246958L L4135024728L L534752881L L595543441L L33441746L L4269857928L L1314335199L L2505095316L L1578860014L L1875057095L L509159986L L1944155236L L70374715L L2945217752L L4041521547L L2966052890L L145655727L L714593746L L1002319513L L519269594L L2926922359L L944143625L L3916879502L L2378371484L L912744193L L3910705684L L2276700770L L4142358138L L3964824208L L2780717615L L4081508385L L236938235L L3840831868L L647425534L L2210040950L L2279462069L L168097603L L1461160443L L1580267216L L2324105764L L2690677560L L1690421599L L3577506112L L738462960L L2274273062L L2504137643L L3265350434L L1366106822L L2974016022L L1988817242L L797755685L L2403760280L L641540828L L3788701957L L2779856144L L2104598935L L3745352634L L1577129078L L1373804927L L3903547821L L2872229328L L3056383831L L3072291562L L2275982065L L1522674624L L3582423212L L3669935104L L3633938225L L629521463L L1238893987L L3075431842L L813637316L L3510510638L L2917340917L L2768681237L L1942077161L L3758057471L L1933876081L L2244433730L L1480577031L L1351970442L L396252158L L4175666695L L1357894203L L234330270L L3181220453L L1331961817L L784329267L L779668731L L2328048439L L1103091157L L3004928118L L1162468270L L3007103493L L2725514842L L274062491L L3149489948L L2987599126L L3292515534L L282168320L L959822509L L2291575880L L3673412248L L162691783L L1230667779L L2485346968L L2008336037L L3320353719L L3822671307L L3089220377L L3503681079L L1302975933L L3327766732L L3674135615L L1073069922L L2772807178L L3196662328L L1713328812L L1475744247L L1685385181L L1643753643L L3794162811L L2190568646L L2689827007L L3126829736L L2761526147L L2511266684L L1782816864L L2926893516L L3512983057L L2661970973L L2689095779L L1199154380L L382426272L L643849114L L807267160L L1518924385L L1695080083L L2934795830L L4181065090L L3342589537L L2406648073L L1186040699L L2721983068L L2970957157L L3556085447L L3070386458L L3046088386L L1804573955L L2003863284L L4166932229L L2182095175L L2134701977L L911628328L L2867572258L L1276531023L L518250163L L2617826095L L21746525L L2080648289L L1970004495L L2621101460L L3726800574L L155341573L L2577500508L L1723761283L L3633471938L L222521000L L2717308343L L4013182446L L2258756213L L771120786L L815922213L L3754576141L L2998487214L L3115409480L L3002705922L L1074978656L L3006531599L L823178279L L416424292L L43442668L L1726938224L L3934805529L L161873912L L2803348707L L1043589181L L102006005L L1730373835L L1210399249L L2434080783L L2191863758L L3057527658L L3614195070L L3229819907L L334710481L L2664502177L L1325604787L L3988671798L L2576445889L L2959706600L L2017397749L L1080624230L L903812120L L1214058725L L1799750730L L3417289034L L807297012L L1437878312L L2645448696L L2089144691L L1348214076L L2839011040L L648098687L L4294223352L L3774352159L L968459174L L396311303L L1631056285L L3504308190L L1279074970L L1878080429L L2991953531L L3355603528L L1614988643L L388314152L L964861631L L3836107078L L126268069L L1350108355L L1730114235L L926577527L L430314525L L756676482L L2364788864L L3573266150L L2877606665L L2554592887L L2297285047L L4155839037L L1342607531L L1587367306L L1366061151L L1625602653L L1519619151L L150082289L L2420444017L L2103580590L L1875045396L L3514756929L L3527524385L L3119378202L L418789356L L308L tp451 Ntp452 sS'halloffame' p453 g2 (cdeap.tools.support HallOfFame p454 g4 Ntp455 Rp456 (dp457 S'keys' p458 (lp459 g2 (g22 g4 Ntp460 Rp461 (dp462 g26 g27 sg28 g301 sbag2 (g22 g4 Ntp463 Rp464 (dp465 g26 g27 sg28 g114 sbag2 (g22 g4 Ntp466 Rp467 (dp468 g26 g122 sg28 g387 sbag2 (g22 g4 Ntp469 Rp470 (dp471 g26 g122 sg28 g201 sbasS'items' p472 (lp473 g2 (g14 g15 (lp474 F8.998499050883135e-05 aF0.13679678638308826 aF0.3777141723621707 aF1.007012080683658e-05 aF4.3417183545378374e-05 aF6.108869734438017e-05 aF0.06391077372665288 aF0.19332127355415177 aF0.09540195531054341 aF0.0008653099277716401 aF0.001302461551959797 aF12.270389578182312 aF0.5489249541836715 aF1.4041700164018957e-06 aF7.197046864039541e-05 aF0.00319058833779415 aF0.00824844977148233 aF0.004008919207391105 aF1.1428193144282783e-06 aF0.0024678893323266233 aF13.146438856898932 aF0.2442889214486884 atp475 Rp476 (dp477 g21 g470 sbag2 (g14 g15 (lp478 F9.094879779990037e-05 aF0.1366985998811062 aF0.3777141723621707 aF1.007012080683658e-05 aF4.682722002434101e-05 aF6.108869734438017e-05 aF0.06391077372665288 aF0.1873655711711291 aF0.09540195531054341 aF0.0006169669963403399 aF0.001302461551959797 aF10.602709904797184 aF0.6289506896678183 aF1.4041700164018957e-06 aF6.028389833499836e-05 aF0.002730997230902337 aF0.008550495952499656 aF0.003477632527733394 aF1.1428193144282783e-06 aF0.0036857304306474217 aF13.146438856898932 aF0.21611889680663607 atp479 Rp480 (dp481 g20 I2 sg21 g467 sbag2 (g14 g15 (lp482 F8.459776330097977e-05 aF0.1515908805880605 aF0.336457264664676 aF2.5891675029296335e-05 aF5.112747213686086e-05 aF4.049341374504143e-05 aF0.05486590123243409 aF0.06066254521578549 aF0.09531939083047117 aF0.0005833820394550312 aF0.004540564425976676 aF8.065615981443464 aF0.28219692547750397 aF7.55804204157224e-05 aF6.183689966753316e-05 aF0.0020040507308995243 aF0.009097462559682402 aF0.005896712856225918 aF0.0008102172359965896 aF0.004510829752197913 aF5.342065970470657 aF0.7299668323859986 atp483 Rp484 (dp485 g21 g464 sbag2 (g14 g15 (lp486 F8.459776330097977e-05 aF0.15168906708994337 aF0.336457264664676 aF3.058343996090739e-05 aF5.9022049332218484e-05 aF4.049341374504143e-05 aF0.05486590123243409 aF0.06000995579013797 aF0.09531939083047117 aF0.0005833820394550312 aF0.0046462799281463815 aF8.065615981443464 aF0.28219692547750397 aF7.55804204157224e-05 aF7.4296780638054e-05 aF0.0022203480307705324 aF0.009061603766623298 aF0.005942316155463084 aF0.0007384762374565938 aF0.002615491103308344 aF6.557721287233644 aF0.6243445607373651 atp487 Rp488 (dp489 g20 I1 sg21 g461 sbasS'maxsize' p490 I10 sS'similar' p491 coperator eq p492 sbsS'population' p493 (lp494 g392 ag2 (g14 g15 (lp495 F8.998499050883135e-05 aF0.13679678638308826 aF0.3777141723621707 aF1.007012080683658e-05 aF4.3417183545378374e-05 aF6.108869734438017e-05 aF0.06391077372665288 aF0.19332127355415177 aF0.09540195531054341 aF0.0008653099277716401 aF0.001302461551959797 aF12.270389578182312 aF0.5489249541836715 aF1.4041700164018957e-06 aF7.197046864039541e-05 aF0.00319058833779415 aF0.00824844977148233 aF0.004008919207391105 aF1.1428193144282783e-06 aF0.0024678893323266233 aF13.146438856898932 aF0.2442889214486884 atp496 Rp497 (dp498 g20 I4 sg394 g29 (g33 S'\xc0\xc54\x10\x9b]\xbe?' p499 tp500 Rp501 sg21 g2 (g22 g4 Ntp502 Rp503 (dp504 g26 g122 sg28 g201 sbsbag2 (g14 g15 (lp505 F8.459776330097977e-05 aF0.1515908805880605 aF0.336457264664676 aF2.5891675029296335e-05 aF5.112747213686086e-05 aF4.049341374504143e-05 aF0.05486590123243409 aF0.06066254521578549 aF0.09531939083047117 aF0.0005833820394550312 aF0.004540564425976676 aF8.065615981443464 aF0.28219692547750397 aF7.55804204157224e-05 aF6.183689966753316e-05 aF0.0020040507308995243 aF0.009097462559682402 aF0.005896712856225918 aF0.0008102172359965896 aF0.004510829752197913 aF5.342065970470657 aF0.7299668323859986 atp506 Rp507 (dp508 g20 I3 sg394 g29 (g33 S'\xa0F\xb2\xe9uI\xe9?' p509 tp510 Rp511 sg21 g2 (g22 g4 Ntp512 Rp513 (dp514 g26 g27 sg28 g114 sbsbag2 (g14 g15 (lp515 F9.094879779990037e-05 aF0.1366985998811062 aF0.3777141723621707 aF1.007012080683658e-05 aF4.682722002434101e-05 aF6.108869734438017e-05 aF0.06391077372665288 aF0.1873655711711291 aF0.09540195531054341 aF0.0006169669963403399 aF0.001302461551959797 aF10.602709904797184 aF0.6289506896678183 aF1.4041700164018957e-06 aF6.028389833499836e-05 aF0.002730997230902337 aF0.008550495952499656 aF0.003477632527733394 aF1.1428193144282783e-06 aF0.0036857304306474217 aF13.146438856898932 aF0.21611889680663607 atp516 Rp517 (dp518 g20 I6 sg394 g29 (g33 S'\xcc\xb0\xba\x1a\xcb:\xee?' p519 tp520 Rp521 sg21 g2 (g22 g4 Ntp522 Rp523 (dp524 g26 g122 sg28 g387 sbsbas. ================================================ FILE: examples/thalamocortical-cell/config/features/cAD_ltb.json ================================================ { "Step_150": { "soma.v": [ {"feature": "AP_amplitude", "val": [58.1456, 4.2632], "n": 11, "fid": 553, "strict_stim": true}, {"feature": "AHP_depth", "val": [7.1542, 2.8024], "n": 11, "fid": 889, "strict_stim": true}, {"feature": "AP_duration_half_width", "val": [1.0534, 0.1648], "n": 11, "fid": 875, "strict_stim": true}, {"feature": "Spikecount", "val": [8.5179, 3.7782], "n": 11, "fid": 861, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [116.1049, 62.0552], "n": 11, "fid": 665, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [13.267, 6.7753], "n": 11, "fid": 749, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [11.4479, 5.9315], "n": 11, "fid": 763, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [5.3322, 2.3386], "n": 11, "fid": 819, "strict_stim": true}, {"feature": "adaptation_index2", "val": [0.0756, 0.0504], "n": 11, "fid": 609, "strict_stim": true} ] }, "Step_250": { "soma.v": [ {"feature": "AP_amplitude", "val": [57.15, 3.9886], "n": 11, "fid": 558, "strict_stim": true}, {"feature": "AHP_depth", "val": [10.3652, 3.2496], "n": 11, "fid": 894, "strict_stim": true}, {"feature": "AP_duration_half_width", "val": [0.9025, 0.134], "n": 11, "fid": 880, "strict_stim": true}, {"feature": "Spikecount", "val": [19.2727, 8.8153], "n": 11, "fid": 866, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [61.2511, 30.0464], "n": 11, "fid": 670, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [30.9829, 19.9475], "n": 11, "fid": 754, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [31.8299, 24.4034], "n": 11, "fid": 768, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [10.3225, 4.8976], "n": 11, "fid": 824, "strict_stim": true}, {"feature": "adaptation_index2", "val": [0.035, 0.0145], "n": 11, "fid": 614, "strict_stim": true} ] }, "Step_200": { "soma.v": [ {"feature": "voltage_base", "val": [-65.7569, 1.7149], "n": 11, "fid": 961, "strict_stim": true}, {"feature": "AP_amplitude", "val": [57.6566, 4.1184], "n": 11, "fid": 555, "strict_stim": true}, {"feature": "AHP_depth", "val": [8.8972, 3.1542], "n": 11, "fid": 891, "strict_stim": true}, {"feature": "AP_duration_half_width", "val": [0.9608, 0.1278], "n": 11, "fid": 877, "strict_stim": true}, {"feature": "Spikecount", "val": [14.2667, 6.2645], "n": 11, "fid": 863, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [82.5958, 44.1591], "n": 11, "fid": 667, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [21.8452, 11.7211], "n": 11, "fid": 751, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [21.8161, 13.2209], "n": 11, "fid": 765, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [7.9611, 3.9428], "n": 11, "fid": 821, "strict_stim": true}, {"feature": "adaptation_index2", "val": [0.0507, 0.0238], "n": 11, "fid": 611, "strict_stim": true} ] }, "Step_200_hyp": { "soma.v": [ {"feature": "voltage_base", "val": [-84.2499, 1.2726], "n": 22, "fid": 935, "strict_stim": true}, {"feature": "AP1_amp", "val": [51.4818, 7.514], "n": 22, "fid": 800, "strict_stim": true}, {"feature": "AP2_amp", "val": [46.5557, 8.2272], "n": 22, "fid": 1010, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [49.3602, 12.5966], "n": 22, "fid": 620, "strict_stim": true}, {"feature": "Spikecount", "val": [5.2045, 1.0221], "n": 22, "fid": 830, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [291.4432, 46.1345], "n": 22, "fid": 710, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [241.55, 53.293], "n": 22, "fid": 725, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [119.366, 36.2356], "n": 22, "fid": 785, "strict_stim": true}, {"feature": "voltage_after_stim", "val": [-87.2874, 1.6681], "n": 22, "fid": 950, "strict_stim": true} ] }, "IV_-140": { "soma.v": [ {"feature": "sag_amplitude", "val": [3.205, 2.5646], "n": 10, "fid": 516, "strict_stim": true} ] }, "RMP": { "soma.v": [ {"feature": "steady_state_voltage_stimend", "val": [-78.4467, 4.4534], "n": 11, "fid": 1457, "strict_stim": true}, {"feature": "Spikecount_stimint", "val": [0.0, 0.001], "n": 11, "fid": 1460, "strict_stim": true} ] }, "hold_dep": { "soma.v": [ {"feature": "Spikecount_stimint", "val": [0.0, 0.001], "n": 11, "fid": 1454, "strict_stim": true} ] }, "hold_hyp": { "soma.v": [ {"feature": "Spikecount_stimint", "val": [0.0, 0.001], "n": 22, "fid": 1616, "strict_stim": true} ] }, "Rin_dep": { "soma.v": [ {"feature": "voltage_base", "val": [-64.4907, 1.0285], "n": 11, "fid": 461, "strict_stim": true}, {"feature": "ohmic_input_resistance_vb_ssse", "val": [131.6508, 52.9132], "n": 11, "fid": 482, "strict_stim": true} ] } } ================================================ FILE: examples/thalamocortical-cell/config/features/cNAD_ltb.json ================================================ { "Step_150": { "soma.v": [ {"feature": "AP_amplitude", "val": [58.0318, 7.0345], "n": 16, "fid": 536, "strict_stim": true}, {"feature": "AHP_depth", "val": [7.8795, 3.4388], "n": 16, "fid": 872, "strict_stim": true}, {"feature": "AP_duration_half_width", "val": [1.0657, 0.0844], "n": 16, "fid": 858, "strict_stim": true}, {"feature": "Spikecount", "val": [13.0208, 5.5638], "n": 16, "fid": 844, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [150.3546, 100.0313], "n": 16, "fid": 648, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [11.6562, 4.5162], "n": 16, "fid": 732, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [11.8118, 5.2192], "n": 16, "fid": 746, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [9.6796, 4.1202], "n": 16, "fid": 802, "strict_stim": true}, {"feature": "adaptation_index2", "val": [0.0206, 0.0256], "n": 16, "fid": 592, "strict_stim": true} ] }, "Step_250": { "soma.v": [ {"feature": "AP_amplitude", "val": [57.8473, 6.2018], "n": 14, "fid": 541, "strict_stim": true}, {"feature": "AHP_depth", "val": [10.9129, 4.0415], "n": 14, "fid": 877, "strict_stim": true}, {"feature": "AP_duration_half_width", "val": [1.0532, 0.129], "n": 14, "fid": 863, "strict_stim": true}, {"feature": "Spikecount", "val": [30.5923, 10.1621], "n": 14, "fid": 849, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [55.1098, 21.2185], "n": 14, "fid": 653, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [27.2243, 9.3839], "n": 14, "fid": 737, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [27.9719, 9.7227], "n": 14, "fid": 751, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [22.8733, 7.9702], "n": 14, "fid": 807, "strict_stim": true}, {"feature": "adaptation_index2", "val": [0.0062, 0.0075], "n": 14, "fid": 597, "strict_stim": true} ] }, "Step_200": { "soma.v": [ {"feature": "voltage_base", "val": [-65.5929, 1.8487], "n": 15, "fid": 944, "strict_stim": true}, {"feature": "AP_amplitude", "val": [57.7503, 6.7182], "n": 15, "fid": 538, "strict_stim": true}, {"feature": "AHP_depth", "val": [9.2791, 3.6017], "n": 15, "fid": 874, "strict_stim": true}, {"feature": "AP_duration_half_width", "val": [1.0665, 0.0959], "n": 15, "fid": 860, "strict_stim": true}, {"feature": "Spikecount", "val": [22.4306, 8.0686], "n": 15, "fid": 846, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [75.2681, 30.3746], "n": 15, "fid": 650, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [19.9615, 6.4584], "n": 15, "fid": 734, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [20.7575, 8.9678], "n": 15, "fid": 748, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [16.7102, 6.298], "n": 15, "fid": 804, "strict_stim": true}, {"feature": "adaptation_index2", "val": [0.0073, 0.0057], "n": 15, "fid": 594, "strict_stim": true} ] }, "Step_200_hyp": { "soma.v": [ {"feature": "voltage_base", "val": [-84.2499, 1.2726], "n": 22, "fid": 935, "strict_stim": true}, {"feature": "AP1_amp", "val": [51.4818, 7.514], "n": 22, "fid": 800, "strict_stim": true}, {"feature": "AP2_amp", "val": [46.5557, 8.2272], "n": 22, "fid": 1010, "strict_stim": true}, {"feature": "time_to_first_spike", "val": [49.3602, 12.5966], "n": 22, "fid": 620, "strict_stim": true}, {"feature": "Spikecount", "val": [5.2045, 1.0221], "n": 22, "fid": 830, "strict_stim": true}, {"feature": "inv_first_ISI", "val": [291.4432, 46.1345], "n": 22, "fid": 710, "strict_stim": true}, {"feature": "inv_second_ISI", "val": [241.55, 53.293], "n": 22, "fid": 725, "strict_stim": true}, {"feature": "inv_last_ISI", "val": [119.366, 36.2356], "n": 22, "fid": 785, "strict_stim": true}, {"feature": "voltage_after_stim", "val": [-87.2874, 1.6681], "n": 22, "fid": 950, "strict_stim": true} ] }, "IV_-140": { "soma.v": [ {"feature": "sag_amplitude", "val": [4.2017, 2.6117], "n": 13, "fid": 497, "strict_stim": true} ] }, "RMP": { "soma.v": [ {"feature": "steady_state_voltage_stimend", "val": [-79.8137, 5.0122], "n": 16, "fid": 1482, "strict_stim": true}, {"feature": "Spikecount_stimint", "val": [0.0, 0.001], "n": 16, "fid": 1486, "strict_stim": true} ] }, "hold_dep": { "soma.v": [ {"feature": "Spikecount_stimint", "val": [0.0, 0.001], "n": 16, "fid": 1477, "strict_stim": true} ] }, "hold_hyp": { "soma.v": [ {"feature": "Spikecount_stimint", "val": [0.0, 0.001], "n": 22, "fid": 1616, "strict_stim": true} ] }, "Rin_dep": { "soma.v": [ {"feature": "voltage_base", "val": [-64.6893, 0.8318], "n": 16, "fid": 428, "strict_stim": true}, {"feature": "ohmic_input_resistance_vb_ssse", "val": [147.7406, 61.0938], "n": 16, "fid": 455, "strict_stim": true} ] } } ================================================ FILE: examples/thalamocortical-cell/config/params/TC.json ================================================ { "mechanisms": { "all": {"mech":["pas", "TC_cad"]}, "somatic": {"mech":["TC_ih_Bud97", "TC_Nap_Et2", "TC_iA","TC_iL", "SK_E2", "TC_HH"]}, "alldend": {"mech":["TC_ih_Bud97", "TC_Nap_Et2", "TC_iA","TC_iL", "SK_E2", "TC_HH"]}, "axonal": {"mech":["TC_HH"]}, "somadend": {"mech":["TC_iT_Des98"]} }, "distributions": { }, "parameters": { "__comment": "define constants as single values and params to optimize as tuples of bounds: [lower, upper]", "global": [ {"name":"v_init", "val":-79}, {"name":"celsius", "val":34} ], "all": [ {"name":"cm", "val":1}, {"name":"Ra", "val":100}, {"name":"ena", "val":50}, {"name":"ek", "val":-90}, {"name":"e_pas", "val":-80}, {"name":"g_pas", "val":[1e-06, 1e-04]} ], "axonal": [ {"name":"gk_max_TC_HH", "val":[0, 0.2]}, {"name":"gna_max_TC_HH", "val":[0, 0.8]} ], "somadend": [ {"name":"pcabar_TC_iT_Des98", "val":[0, 1e-4]} ], "somatic": [ {"name":"gh_max_TC_ih_Bud97", "val":[0, 1e-4]}, {"name":"gNap_Et2bar_TC_Nap_Et2", "val":[0, 0.0001]}, {"name":"gk_max_TC_iA", "val":[0, 0.07]}, {"name":"gk_max_TC_HH", "val":[0, 0.2]}, {"name":"gna_max_TC_HH", "val":[0, 0.2]}, {"name":"pcabar_TC_iL", "val":[0, 0.001]}, {"name":"gSK_E2bar_SK_E2", "val":[0, 0.005]}, {"name":"taur_TC_cad", "val":[1.0, 15.0]}, {"name":"gamma_TC_cad", "val":[0.0005, 1]} ], "alldend": [ {"name":"gh_max_TC_ih_Bud97", "val":[0, 1e-4]}, {"name":"gNap_Et2bar_TC_Nap_Et2", "val":[0, 0.0001]}, {"name":"gk_max_TC_iA", "val":[0, 0.008]}, {"name":"gk_max_TC_HH", "val":[0, 0.01]}, {"name":"gna_max_TC_HH", "val":[0, 0.006]}, {"name":"pcabar_TC_iL", "val":[0, 0.001]}, {"name":"gSK_E2bar_SK_E2", "val":[0, 0.005]}, {"name":"taur_TC_cad", "val":[1.0, 15.0]}, {"name":"gamma_TC_cad", "val":[0.0005, 1]} ] } } ================================================ FILE: examples/thalamocortical-cell/config/protocols/cAD_ltb.json ================================================ { "Step_150": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.089041, "thresh_perc": 150.0271, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": 0.134445, "duration": 2400.0, "totduration": 2400.0} } }, "Step_250": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.14832, "thresh_perc": 249.961, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": 0.134943, "duration": 2400.0, "totduration": 2400.0} } }, "Step_200": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.118859, "thresh_perc": 200.2936, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": 0.134536, "duration": 2400.0, "totduration": 2400.0} } }, "Step_200_hyp": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.14673, "thresh_perc": 200.2118, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": -0.121695, "duration": 2400.0, "totduration": 2400.0} } }, "IV_-140": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": -0.07745, "thresh_perc": -134.8425, "duration": 3000.0, "totduration": 4050.0}, "holding": {"delay": 0.0, "amp": 0.135615, "duration": 4050.0, "totduration": 4050.0} } }, "RMP": { "type": "StepProtocol", "stimuli": { "step": {"delay": 300, "amp": 0, "duration": 900.0, "totduration": 1200.0} } }, "hold_dep": { "type": "StepProtocol", "stimuli": { "step": {"delay": 300.0, "amp": 0.0, "thresh_perc": 0.0, "duration": 900.0, "totduration": 1200.0}, "holding": {"delay": 0.0, "amp": 0.134051, "duration": 1200.0, "totduration": 1200.0} } }, "hold_hyp": { "type": "StepProtocol", "stimuli": { "step": {"delay": 300.0, "amp": 0.0, "thresh_perc": 0.0, "duration": 900.0, "totduration": 1200.0}, "holding": {"delay": 0.0, "amp": -0.129995, "duration": 1200.0, "totduration": 1200.0} } }, "Rin_dep": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": -0.023425, "thresh_perc": -40.1228, "duration": 3000.0, "totduration": 4050.0}, "holding": {"delay": 0.0, "amp": 0.135101, "duration": 4050.0, "totduration": 4050.0} } } } ================================================ FILE: examples/thalamocortical-cell/config/protocols/cNAD_ltb.json ================================================ { "Step_150": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.106854, "thresh_perc": 149.9653, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": 0.150141, "duration": 2400.0, "totduration": 2400.0} } }, "Step_250": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.18113, "thresh_perc": 249.9147, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": 0.154962, "duration": 2400.0, "totduration": 2400.0} } }, "Step_200": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.140843, "thresh_perc": 200.2739, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": 0.153092, "duration": 2400.0, "totduration": 2400.0} } }, "Step_200_hyp": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": 0.14673, "thresh_perc": 200.2118, "duration": 1350.0, "totduration": 2400.0}, "holding": {"delay": 0.0, "amp": -0.121695, "duration": 2400.0, "totduration": 2400.0} } }, "IV_-140": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": -0.098644, "thresh_perc": -139.1379, "duration": 3000.0, "totduration": 4050.0}, "holding": {"delay": 0.0, "amp": 0.153485, "duration": 4050.0, "totduration": 4050.0} } }, "RMP": { "type": "StepProtocol", "stimuli": { "step": {"delay": 300, "amp": 0, "duration": 900.0, "totduration": 1200.0} } }, "hold_dep": { "type": "StepProtocol", "stimuli": { "step": {"delay": 300.0, "amp": 0.0, "thresh_perc": 0.0, "duration": 900.0, "totduration": 1200.0}, "holding": {"delay": 0.0, "amp": 0.147305, "duration": 1200.0, "totduration": 1200.0} } }, "hold_hyp": { "type": "StepProtocol", "stimuli": { "step": {"delay": 300.0, "amp": 0.0, "thresh_perc": 0.0, "duration": 900.0, "totduration": 1200.0}, "holding": {"delay": 0.0, "amp": -0.129995, "duration": 1200.0, "totduration": 1200.0} } }, "Rin_dep": { "type": "StepProtocol", "stimuli": { "step": {"delay": 800.0, "amp": -0.028535, "thresh_perc": -39.8617, "duration": 3000.0, "totduration": 4050.0}, "holding": {"delay": 0.0, "amp": 0.15064, "duration": 4050.0, "totduration": 4050.0} } } } ================================================ FILE: examples/thalamocortical-cell/config/recipes.json ================================================ { "cAD_ltb": { "morph_path": "morphologies/", "morphology": "jy160728_A_idA.asc", "params": "config/params/TC.json", "protocol": "config/protocols/cAD_ltb.json", "features": "config/features/cAD_ltb.json" }, "cNAD_ltb": { "morph_path": "morphologies/", "morphology": "jy170517_A_idA.asc", "params": "config/params/TC.json", "protocol": "config/protocols/cNAD_ltb.json", "features": "config/features/cNAD_ltb.json" } } ================================================ FILE: examples/thalamocortical-cell/mechanisms/SK_E2.mod ================================================ : SK-type calcium-activated potassium current : Reference : Kohler et al. 1996 : From ModelDB, accession no. 139653 NEURON { SUFFIX SK_E2 USEION k READ ek WRITE ik USEION ca READ cai RANGE gSK_E2bar, gSK_E2, ik, zTau } UNITS { (mV) = (millivolt) (mA) = (milliamp) (mM) = (milli/liter) } PARAMETER { v (mV) gSK_E2bar = .000001 (mho/cm2) zTau = 1 (ms) ek (mV) cai (mM) } ASSIGNED { zInf ik (mA/cm2) gSK_E2 (S/cm2) } STATE { z FROM 0 TO 1 } BREAKPOINT { SOLVE states METHOD cnexp gSK_E2 = gSK_E2bar * z ik = gSK_E2 * (v - ek) } DERIVATIVE states { rates(cai) z' = (zInf - z) / zTau } PROCEDURE rates(ca(mM)) { if(ca < 1e-7){ ca = ca + 1e-07 } zInf = 1/(1 + (0.00043 / ca)^4.8) } INITIAL { rates(cai) z = zInf } ================================================ FILE: examples/thalamocortical-cell/mechanisms/TC_HH.mod ================================================ TITLE Hippocampal HH channels : : : Fast Na+ and K+ currents responsible for action potentials : Iterative equations : : Equations modified by Traub, for Hippocampal Pyramidal cells, in: : Traub & Miles, Neuronal Networks of the Hippocampus, Cambridge, 1991 : : range variable vtraub adjust threshold : : Written by Alain Destexhe, Salk Institute, Aug 1992 : : Modified from ModelDB, accession no. 279 INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} NEURON { SUFFIX TC_HH USEION na READ ena WRITE ina USEION k READ ek WRITE ik RANGE gna_max, gk_max, vtraub, vtraub2, i_rec RANGE m_inf, h_inf, n_inf RANGE tau_m, tau_h, tau_n RANGE m_exp, h_exp, n_exp RANGE ina, ik } UNITS { (mA) = (milliamp) (mV) = (millivolt) (S) = (siemens) } PARAMETER { gna_max = 1.0e-1 (S/cm2) gk_max = 1.0e-1 (S/cm2) celsius (degC) dt (ms) v (mV) vtraub = -55.5 : Average of original value and Amarillo et al., J Neurophysiol 112:393-410, 2014 vtraub2 = -45.5 : Shift for K current } STATE { m h n } ASSIGNED { ina (mA/cm2) ik (mA/cm2) ena (mV) ek (mV) i_rec (mA/cm2) m_inf h_inf n_inf tau_m tau_h tau_n m_exp h_exp n_exp tcorr } BREAKPOINT { SOLVE states METHOD cnexp ina = gna_max * m*m*m*h * (v - ena) ik = gk_max * n*n*n*n * (v - ek) i_rec = ina + ik } DERIVATIVE states { : exact Hodgkin-Huxley equations evaluate_fct(v) m' = (m_inf - m) / tau_m h' = (h_inf - h) / tau_h n' = (n_inf - n) / tau_n } :PROCEDURE states() { : exact when v held constant : evaluate_fct(v) : m = m + m_exp * (m_inf - m) : h = h + h_exp * (h_inf - h) : n = n + n_exp * (n_inf - n) : VERBATIM : return 0; : ENDVERBATIM :} UNITSOFF INITIAL { m = 0 h = 0 n = 0 : : Q10 was assumed to be 3 for both currents : : original measurements at roomtemperature? tcorr = 3.0 ^ ((celsius-36)/ 10 ) } PROCEDURE evaluate_fct(v(mV)) { LOCAL a,b,v2, v3 v2 = v - vtraub : convert to traub convention v3 = v - vtraub2 : EI: shift only K if(v2 == 13 || v2 == 40 || v2 == 15 ){ v = v+0.0001 } a = 0.32 * (13-v2) / ( exp((13-v2)/4) - 1) b = 0.28 * (v2-40) / ( exp((v2-40)/5) - 1) tau_m = 1 / (a + b) / tcorr m_inf = a / (a + b) a = 0.128 * exp((17-v2)/18) b = 4 / ( 1 + exp((40-v2)/5) ) tau_h = 1 / (a + b) / tcorr h_inf = a / (a + b) a = 0.032 * (15-v3) / ( exp((15-v3)/5) - 1) b = 0.5 * exp((10-v3)/40) tau_n = 1 / (a + b) / tcorr n_inf = a / (a + b) m_exp = 1 - exp(-dt/tau_m) h_exp = 1 - exp(-dt/tau_h) n_exp = 1 - exp(-dt/tau_n) } UNITSON ================================================ FILE: examples/thalamocortical-cell/mechanisms/TC_ITGHK_Des98.mod ================================================ TITLE Low threshold calcium current : : Ca++ current responsible for low threshold spikes (LTS) : Differential equations : : Model of Huguenard & McCormick, J Neurophysiol 68: 1373-1383, 1992. : The kinetics is described by Goldman-Hodgkin-Katz equations, : using a m2h format, according to the voltage-clamp data : (whole cell patch clamp) of Huguenard & Prince, J. Neurosci. : 12: 3804-3817, 1992. : : This model is described in detail in: : Destexhe A, Neubig M, Ulrich D and Huguenard JR. : Dendritic low-threshold calcium currents in thalamic relay cells. : Journal of Neuroscience 18: 3574-3588, 1998. : (a postscript version of this paper, including figures, is available on : the Internet at http://cns.fmed.ulaval.ca) : : - shift parameter for screening charge : - empirical correction for contamination by inactivation (Huguenard) : - GHK equations : : : Written by Alain Destexhe, Laval University, 1995 : : From ModelDB, accession no. 279, modified qm and qh INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} NEURON { SUFFIX TC_iT_Des98 USEION ca READ cai,cao WRITE ica RANGE pcabar, m_inf, tau_m, h_inf, tau_h, shift, actshift, ica GLOBAL qm, qh } UNITS { (molar) = (1/liter) (mV) = (millivolt) (mA) = (milliamp) (mM) = (millimolar) FARADAY = (faraday) (coulomb) R = (k-mole) (joule/degC) } PARAMETER { v (mV) :celsius = 36 (degC) celsius (degC) : EI pcabar =.2e-3 (cm/s) : Maximum Permeability shift = 2 (mV) : corresponds to 2mM ext Ca++ actshift = 0 (mV) : shift of activation curve (towards hyperpol) cai = 2.4e-4 (mM) : adjusted for eca=120 mV cao = 2 (mM) qm = 2.5 : Amarillo et al., J Neurophysiol, 2014 qh = 2.5 : Amarillo et al., J Neurophysiol, 2014 } STATE { m h } ASSIGNED { ica (mA/cm2) m_inf tau_m (ms) h_inf tau_h (ms) phi_m phi_h } BREAKPOINT { SOLVE castate METHOD cnexp ica = pcabar * m*m*h * ghk(v, cai, cao) } DERIVATIVE castate { evaluate_fct(v) m' = (m_inf - m) / tau_m h' = (h_inf - h) / tau_h } UNITSOFF INITIAL { phi_m = qm ^ ((celsius-24)/10) phi_h = qh ^ ((celsius-24)/10) evaluate_fct(v) m = m_inf h = h_inf } PROCEDURE evaluate_fct(v(mV)) { : : The kinetic functions are taken as described in the model of : Huguenard & McCormick, and corresponds to a temperature of 23-25 deg. : Transformation to 36 deg assuming Q10 of 5 and 3 for m and h : (as in Coulter et al., J Physiol 414: 587, 1989). : : The activation functions were estimated by John Huguenard. : The V_1/2 were of -57 and -81 in the vclamp simulations, : and -60 and -84 in the current clamp simulations. : : The activation function were empirically corrected in order to account : for the contamination of inactivation. Therefore the simulations : using these values reproduce more closely the voltage clamp experiments. : (cfr. Huguenard & McCormick, J Neurophysiol, 1992). : m_inf = 1.0 / ( 1 + exp(-(v+shift+actshift+57)/6.2) ) h_inf = 1.0 / ( 1 + exp((v+shift+81)/4.0) ) tau_m = ( 0.612 + 1.0 / ( exp(-(v+shift+actshift+132)/16.7) + exp((v+shift+actshift+16.8)/18.2) ) ) / phi_m if( (v+shift) < -80) { tau_h = exp((v+shift+467)/66.6) / phi_h } else { tau_h = ( 28 + exp(-(v+shift+22)/10.5) ) / phi_h } : EI compare with tau_h on ModelDB, no. 3817 } FUNCTION ghk(v(mV), ci(mM), co(mM)) (.001 coul/cm3) { LOCAL z, eci, eco z = (1e-3)*2*FARADAY*v/(R*(celsius+273.15)) eco = co*efun(z) eci = ci*efun(-z) :high cao charge moves inward :negative potential charge moves inward ghk = (.001)*2*FARADAY*(eci - eco) } FUNCTION efun(z) { if (fabs(z) < 1e-4) { efun = 1 - z/2 }else{ efun = z/(exp(z) - 1) } } FUNCTION nongat(v,cai,cao) { : non gated current nongat = pcabar * ghk(v, cai, cao) } UNITSON ================================================ FILE: examples/thalamocortical-cell/mechanisms/TC_Ih_Bud97.mod ================================================ : Ih current for thalamo-cortical neurons : Ref.: Budde et al. (minf), J Physiol, 1997, Huguenard and McCormick, J Neurophysiol, 1992 (taum) NEURON { SUFFIX TC_ih_Bud97 NONSPECIFIC_CURRENT ih RANGE gh_max, g_h, i_rec } UNITS { (S) = (siemens) (mV) = (millivolt) (mA) = (milliamp) } PARAMETER { gh_max = 2.2e-5 (S/cm2) e_h = -43.0 (mV) celsius (degC) q10 = 4 : Santoro et al., J. Neurosci. 2000 } ASSIGNED { v (mV) ih (mA/cm2) g_h (S/cm2) mInf mTau tcorr : Add temperature correction i_rec } STATE { m } BREAKPOINT { SOLVE states METHOD cnexp g_h = gh_max*m ih = g_h*(v-e_h) i_rec = ih } DERIVATIVE states { rates() m' = (mInf-m)/mTau } INITIAL{ rates() m = mInf tcorr = q10^((celsius-34)/10) : EI: Recording temp. 34 C Huguenard et al. } UNITSOFF PROCEDURE rates(){ mInf = 1/(1+exp((v+86.4)/11.2)) : Budde et al., 1997 mTau = (1/(exp(-14.59 - 0.086*v) + exp(-1.87 + 0.0701*v )))/tcorr : Huguenard et al., 1992 } UNITSON ================================================ FILE: examples/thalamocortical-cell/mechanisms/TC_Nap_Et2.mod ================================================ :Comment : mtau deduced from text (said to be 6 times faster than for NaTa) :Comment : so I used the equations from NaT and multiplied by 6 :Reference : Modeled according to kinetics derived from Magistretti & Alonso 1999 :Comment: corrected rates using q10 = 2.3, target temperature 34, orginal 21 : From ModelDB, accession no. 139653. mInf and hInf for TC models, see equations for references NEURON { SUFFIX TC_Nap_Et2 USEION na READ ena WRITE ina RANGE gNap_Et2bar, gNap_Et2, ina } UNITS { (S) = (siemens) (mV) = (millivolt) (mA) = (milliamp) } PARAMETER { gNap_Et2bar = 0.00001 (S/cm2) } ASSIGNED { v (mV) ena (mV) ina (mA/cm2) gNap_Et2 (S/cm2) mInf mTau mAlpha mBeta hInf hTau hAlpha hBeta } STATE { m h } BREAKPOINT { SOLVE states METHOD cnexp gNap_Et2 = gNap_Et2bar*m*m*m*h ina = gNap_Et2*(v-ena) } DERIVATIVE states { rates() m' = (mInf-m)/mTau h' = (hInf-h)/hTau } INITIAL{ rates() m = mInf h = hInf } PROCEDURE rates(){ LOCAL qt qt = 2.3^((34-21)/10) UNITSOFF mInf = 1.0/(1+exp(-(v+56.93)/9.09)) : Parri and Crunelli, J. Neurosci. 1998 if(v == -38){ v = v+0.0001 } mAlpha = (0.182 * (v- -38))/(1-(exp(-(v- -38)/6))) mBeta = (0.124 * (-v -38))/(1-(exp(-(-v -38)/6))) mTau = 6*(1/(mAlpha + mBeta))/qt if(v == -17){ v = v + 0.0001 } if(v == -64.4){ v = v+0.0001 } hInf = 1.0/(1+exp((v+58.7)/14.2)) : Amarillo et al., J Neurophysiol, 2014 hAlpha = -2.88e-6 * (v + 17) / (1 - exp((v + 17)/4.63)) hBeta = 6.94e-6 * (v + 64.4) / (1 - exp(-(v + 64.4)/2.63)) hTau = (1/(hAlpha + hBeta))/qt UNITSON } ================================================ FILE: examples/thalamocortical-cell/mechanisms/TC_cadecay.mod ================================================ : From ModelDB no. 139653 NEURON { SUFFIX TC_cad USEION ca READ ica WRITE cai RANGE depth,kt,kd,cainf,taur, cai_rec, gamma } UNITS { (mM) = (milli/liter) (um) = (micron) (mA) = (milliamp) (msM) = (ms mM) FARADAY = (faraday) (coulombs) } PARAMETER { depth = .1 (um) : depth of shell gamma = 0.05 (1) : EI: percent of free calcium (not buffered) taur = 5 (ms) : rate of calcium removal cainf = 5e-5 (mM) : Value from Amarillo et al., J Neurophysiol, 2014 } STATE { cai (mM) } INITIAL { cai = cainf } ASSIGNED { ica (mA/cm2) cai_rec (mM) } BREAKPOINT { SOLVE state METHOD derivimplicit } DERIVATIVE state { cai' = -(10000)*(ica*gamma/(2*FARADAY*depth)) - (cai - cainf)/taur cai_rec = cai } ================================================ FILE: examples/thalamocortical-cell/mechanisms/TC_iA.mod ================================================ TITLE Fast Transient Potassium Current IA : From the model by Huguenard and McCormick, J Neurophysiol, 1992 : Written by Yimy Amarillo, 2014 (Amarillo et al., J Neurophysiol, 2014) UNITS { (mV) = (millivolt) (mA) = (milliamp) (S) = (siemens) } NEURON { SUFFIX TC_iA USEION k READ ek WRITE ik RANGE gk_max, ik, taom, taoh1, taoh2 } PARAMETER { gk_max = 5.5e-3 (S/cm2) : Default maximum conductance celsius } ASSIGNED { v (mV) ek (mV) ik (mA/cm2) m1inf m2inf hinf taoh1 (ms) taoh2 (ms) taom (ms) tadj } STATE { m1 m2 h1 h2 } BREAKPOINT { SOLVE states METHOD cnexp ik = gk_max*(0.6*h1*m1^4+0.4*h2*m2^4)*(v-ek) } INITIAL { settables(v) tadj = 2.8 ^ ((celsius-23)/10) m1 = m1inf m2 = m2inf h1 = hinf h2 = hinf } DERIVATIVE states { settables(v) m1' = (m1inf-m1)/taom m2' = (m2inf-m2)/taom h1' = (hinf-h1)/taoh1 h2' = (hinf-h2)/taoh2 } UNITSOFF PROCEDURE settables(v (mV)) { LOCAL taodef m1inf = 1/(1+exp(-(v+60)/8.5)) m2inf = 1/(1+exp(-(v+36)/20)) hinf = 1/(1+exp((v+78)/6)) taom = (0.37 + 1/(exp((v+35.8)/19.7)+exp(-(v+79.7)/12.7))) / tadj taodef = (1/(exp((v+46)/5)+exp(-(v+238)/37.5))) / tadj if (v<(-63)) {taoh1 = taodef} else {taoh1 = (19 / tadj)} if (v<(-73)) {taoh2 = taodef} else {taoh2 = (60 / tadj)} } UNITSON ================================================ FILE: examples/thalamocortical-cell/mechanisms/TC_iL.mod ================================================ TITLE high threshold calcium current (L-current) : From ModelDB, accession: 3808 : Based on the model by McCormick & Huguenard, J Neurophysiol, 1992 : and errata in https://huguenardlab.stanford.edu/reprints/Errata_thalamic_cell_models.pdf INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} NEURON { SUFFIX TC_iL USEION ca READ cai,cao WRITE ica RANGE pcabar, m_inf, tau_m, ica, i_rec } UNITS { (mA) = (milliamp) (mV) = (millivolt) (molar) = (1/liter) (mM) = (millimolar) FARADAY = (faraday) (coulomb) R = 8.314 (volt-coul/degC) } PARAMETER { v (mV) celsius (degC) dt (ms) cai = 0.5E-4 (mM) cao = 2 (mM) pcabar= 1e-4 (cm/s) } STATE { m } ASSIGNED { ica (mA/cm2) i_rec (mA/cm2) tau_m (ms) m_inf tcorr } BREAKPOINT { SOLVE states METHOD cnexp ica = pcabar * m*m * ghk(v,cai,cao) i_rec = ica } DERIVATIVE states { rates(v) m'= (m_inf-m) / tau_m } INITIAL { rates(v) tcorr = 3^((celsius-23.5)/10) m = 0 } UNITSOFF FUNCTION ghk( v(mV), ci(mM), co(mM)) (millicoul/cm3) { LOCAL z, eci, eco z = v * (.001) * 2 *FARADAY / (R*(celsius+273.15)) eco = co*efun(z) eci = ci*efun(-z) :high cao charge moves inward :negative potential charge moves inward ghk = (.001)*2*FARADAY*(eci - eco) } FUNCTION efun(z) { if (fabs(z) < 1e-4) { efun = 1 - z/2 }else{ efun = z/(exp(z) - 1) } } PROCEDURE rates(v(mV)) { LOCAL a,b a = 1.6 / (1+ exp(-0.072*(v-5))) b = 0.02 * vtrap( -(v-1.31), 5.36) tau_m = 1/(a+b) / tcorr m_inf = 1/(1+exp((v+10)/-10)) } FUNCTION vtrap(x,c) { : Traps for 0 in denominator of rate equations if (fabs(x/c) < 1e-6) { vtrap = c + x/2 } else { vtrap = x / (1-exp(-x/c)) } } UNITSON ================================================ FILE: examples/thalamocortical-cell/morphologies/jy160728_A_idA.asc ================================================ ; V3 text file written for MicroBrightField products. 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97 ( 1.62 91.07 -2.19 0.52) ; 98 ( 1.40 91.95 -2.19 0.37) ; 99 ( 1.40 92.61 -2.13 0.59) ; 100 ( 1.62 93.26 -2.06 0.96) ; 101 ( 1.70 94.00 -1.81 1.33) ; 102 ( 1.70 94.65 -1.69 1.92) ; 103 ( 1.84 95.09 -1.69 2.58) ; 104 ( 2.14 95.38 -1.25 2.87) ; 105 ( 2.21 95.82 -1.56 2.14) ; 106 ( 2.21 96.48 -1.69 1.40) ; 107 ( 2.36 97.21 -1.81 0.66) ; 108 ( 2.43 97.50 -1.81 0.37) ; 109 ( 2.65 98.02 -1.81 0.22) ; 110 ( 2.73 98.53 -1.81 0.52) ; 111 ( 2.87 99.19 -1.81 1.11) ; 112 ( 2.87 99.70 -1.81 1.33) ; 113 ( 3.02 100.06 -1.81 0.81) ; 114 ( 3.10 100.35 -1.81 0.52) ; 115 ( 3.32 101.45 -2.19 0.29) ; 116 ( 3.10 102.26 -2.50 0.29) ; 117 ( 2.87 102.84 -1.38 0.52) ; 118 ( 2.87 103.35 -1.38 1.25) ; 119 ( 2.95 103.79 -1.19 1.99) ; 120 ( 2.87 104.59 -1.00 2.73) ; 121 ( 2.95 104.78 -1.00 2.73) ; 122 ( ( 3.10 105.47 -0.81 2.06) ; 1, R-1-1-1 ( 3.17 106.13 -0.94 1.18) ; 2 ( 3.24 106.50 -0.88 0.52) ; 3 ( 3.32 107.15 -0.81 0.22) ; 4 ( 3.68 108.10 -0.81 0.59) ; 5 ( 3.76 108.40 -0.81 0.59) ; 6 ( 3.91 109.13 -0.81 0.44) ; 7 ( 4.20 109.42 -0.81 0.44) ; 8 ( 4.42 109.86 -0.81 0.44) ; 9 ( 4.57 110.22 -0.81 0.29) ; 10 ( 4.27 110.66 -0.81 0.29) ; 11 ( 3.98 111.10 -0.69 0.29) ; 12 ( 4.13 111.69 -0.06 0.81) ; 13 ( 4.13 112.27 0.00 1.25) ; 14 ( 4.35 112.85 0.00 1.55) ; 15 ( 4.27 113.29 0.06 1.25) ; 16 ( 4.35 113.95 0.06 0.66) ; 17 ( 4.42 114.76 0.06 0.37) ; 18 ( 4.64 115.05 0.25 0.22) ; 19 ( 4.72 115.41 0.06 0.22) ; 20 ( 4.76 116.19 -0.44 0.22) ; 21 ( 4.76 116.77 -0.50 0.22) ; 22 ( 4.98 117.29 -0.81 0.44) ; 23 ( 4.91 117.51 -0.88 0.44) ; 24 ( 4.62 117.80 -1.06 0.44) ; 25 ( 4.32 118.38 -1.13 0.59) ; 26 ( 4.03 118.67 -1.44 0.59) ; 27 ( 3.66 119.19 -1.63 0.59) ; 28 ( 3.36 119.48 -1.81 0.59) ; 29 ( 3.14 120.14 -2.25 0.59) ; 30 ( 3.14 121.31 -2.19 0.59) ; 31 ( 3.51 122.11 -2.06 1.40) ; 32 ( 3.81 123.13 -2.25 1.92) ; 33 ( 4.10 123.72 -2.31 1.55) ; 34 ( 4.10 124.45 -2.31 0.88) ; 35 ( 4.10 125.03 -2.31 0.59) ; 36 ( 4.10 125.40 -2.31 0.29) ; 37 ( 4.25 125.98 -2.31 0.22) ; 38 ( 3.73 126.50 -2.94 0.22) ; 39 ( 3.88 127.01 -2.00 1.18) ; 40 ( 3.88 127.52 -1.81 1.55) ; 41 ( 4.03 127.96 -1.69 1.33) ; 42 ( 4.03 128.40 -1.38 0.74) ; 43 ( 4.03 128.84 -0.81 0.44) ; 44 ( 4.32 129.71 -0.81 0.22) ; 45 ( 4.32 130.52 -0.69 0.22) ; 46 ( 4.03 130.96 -0.31 0.22) ; 47 ( 3.88 131.32 -0.25 0.22) ; 48 ( 3.81 132.05 -0.13 0.22) ; 49 ( 3.81 132.42 -0.06 0.44) ; 50 ( 3.88 133.15 -0.06 0.66) ; 51 ( 4.25 134.03 0.56 0.29) ; 52 ( 4.25 134.54 0.81 0.29) ; 53 ( 4.62 134.83 2.00 0.52) ; 54 ( 5.13 135.20 2.25 0.52) ; 55 ( 5.35 136.22 2.25 0.52) ; 56 ( 5.50 136.80 2.25 0.52) ; 57 ( 5.80 137.83 2.63 0.44) ; 58 ( 5.72 138.63 2.69 0.44) ; 59 ( 5.57 139.22 2.69 0.44) ; 60 ( 5.72 140.09 2.69 0.44) ; 61 ( 5.87 140.60 2.63 0.44) ; 62 ( 6.09 140.90 2.63 0.52) ; 63 ( 6.24 141.99 2.63 0.59) ; 64 ( 6.24 143.38 1.31 0.22) ; 65 ( 6.24 143.82 1.13 0.22) ; 66 ( 5.87 144.33 1.06 0.44) ; 67 ( 5.66 144.82 -0.25 0.44) ; 68 ( 5.58 145.34 -0.56 0.29) ; 69 ( 5.58 145.92 -0.88 0.29) ; 70 ( 5.44 146.29 -0.94 0.29) ; 71 ( 5.36 146.72 -0.75 0.29) ; 72 ( 5.22 146.94 -0.44 0.37) ; 73 Low | ( 2.14 104.56 4.25 0.52) ; 1, R-1-1-2 ( 1.77 103.97 6.00 0.44) ; 2 ( 1.99 103.02 6.25 0.44) ; 3 ( 1.25 102.51 6.31 0.44) ; 4 ( 1.03 102.51 6.31 0.44) ; 5 ( 0.37 102.44 6.31 0.44) ; 6 ( -0.44 101.56 6.50 0.44) ; 7 ( -0.52 100.98 7.31 0.44) ; 8 ( -1.33 100.68 7.44 0.44) ; 9 ( -2.28 100.10 8.94 0.44) ; 10 ( -2.65 99.51 9.06 0.44) ; 11 ( -3.09 98.86 8.69 0.37) ; 12 ( -3.68 98.13 8.56 0.37) ; 13 ( -4.49 97.39 8.56 0.37) ; 14 ( -4.49 96.30 8.19 0.37) ; 15 ( -4.27 95.42 7.75 0.37) ; 16 ( -4.35 94.69 7.75 0.66) ; 17 ( -4.49 93.67 8.13 0.52) ; 18 ( -4.79 92.94 8.56 0.81) ; 19 ( -5.16 92.50 6.69 0.52) ; 20 ( -5.89 91.33 6.50 0.37) ; 21 ( -6.85 90.60 6.50 0.37) ; 22 ( -7.22 89.43 6.31 0.37) ; 23 ( -7.66 88.26 6.25 0.37) ; 24 ( -7.66 87.89 6.19 0.66) ; 25 ( -7.15 87.16 8.81 0.52) ; 26 ( -7.74 85.92 8.94 0.52) ; 27 ( -8.10 85.11 9.00 0.44) ; 28 ( -8.91 84.68 9.00 0.81) ; 29 ( -9.36 84.09 9.69 0.59) ; 30 ( -9.43 82.92 9.88 0.59) ; 31 ( -8.77 81.90 9.88 0.44) ; 32 ( -8.69 81.02 10.00 0.44) ; 33 ( -8.18 80.36 10.06 0.74) ; 34 ( -7.78 79.18 11.31 0.44) ; 35 ( -7.27 78.74 13.56 0.29) ; 36 Incomplete ) ; End of split | ( -6.37 37.81 7.63 0.88) ; 1, R-1-2 ( -5.78 38.54 8.56 0.74) ; 2 ( -5.78 39.20 9.81 0.88) ; 3 ( -6.15 39.71 11.00 0.88) ; 4 ( -6.22 40.08 11.06 0.88) ; 5 ( -6.44 40.59 11.25 0.52) ; 6 ( -6.96 41.25 11.63 0.66) ; 7 ( -7.18 41.98 12.56 0.88) ; 8 ( -7.55 42.56 13.06 1.11) ; 9 ( -7.99 43.15 13.69 0.74) ; 10 ( -8.28 43.95 14.94 0.66) ; 11 ( -8.14 45.12 15.19 0.37) ; 12 ( -7.62 45.56 15.50 0.59) ; 13 ( -7.18 46.00 15.44 0.66) ; 14 ( -6.74 46.58 15.25 0.66) ; 15 ( -6.81 47.17 14.94 0.52) ; 16 ( -7.47 47.90 14.94 0.37) ; 17 ( -8.06 48.26 14.94 0.88) ; 18 ( -8.43 48.70 14.88 1.18) ; 19 ( -8.95 49.51 14.25 1.40) ; 20 ( -8.95 49.95 14.75 0.81) ; 21 ( -8.58 50.82 14.19 0.37) ; 22 ( -8.50 51.33 13.94 0.37) ; 23 ( -8.65 52.14 13.88 0.37) ; 24 ( -8.65 52.36 13.63 0.52) ; 25 ( -9.61 52.65 13.50 0.52) ; 26 ( -10.42 53.02 13.31 0.44) ; 27 ( -11.45 53.60 13.31 0.37) ; 28 ( -12.04 53.75 13.31 0.37) ; 29 ( -12.33 53.75 13.31 0.37) ; 30 ( -12.78 54.33 13.19 0.52) ; 31 Low ) ; End of split | ( -12.42 23.74 4.81 2.14) ; 1, R-2 ( -12.78 24.25 4.63 1.40) ; 2 ( -13.37 24.69 3.94 1.18) ; 3 ( -13.89 24.69 3.25 1.03) ; 4 ( -14.26 24.62 2.63 1.03) ; 5 ( -14.55 24.18 1.63 1.03) ; 6 ( -14.92 23.89 1.00 1.03) ; 7 ( -15.14 24.04 0.38 1.03) ; 8 ( -15.21 24.40 -0.50 1.25) ; 9 ( -15.29 24.55 -1.06 1.62) ; 10 ( -15.29 25.13 -1.81 1.62) ; 11 ( -15.36 25.86 -2.13 1.47) ; 12 ( -15.14 26.59 -2.38 1.62) ; 13 ( -15.07 27.11 -2.38 1.69) ; 14 ( ( -14.40 27.62 -2.94 0.88) ; 1, R-2-1 ( -13.89 28.20 -3.19 0.88) ; 2 ( -13.37 28.86 -3.31 1.03) ; 3 ( -13.00 29.81 -3.81 1.11) ; 4 ( -12.34 30.83 -4.00 1.11) ; 5 ( -11.90 31.86 -4.06 0.88) ; 6 ( -11.68 32.73 -4.44 1.03) ; 7 ( -12.12 33.83 -3.75 0.96) ; 8 ( -12.19 34.56 -3.75 1.11) ; 9 ( -12.19 35.15 -3.75 1.11) ; 10 ( -12.56 35.95 -3.69 0.96) ; 11 ( -12.71 36.83 -3.56 0.96) ; 12 ( -12.49 37.56 -3.69 1.47) ; 13 ( -12.19 38.44 -3.94 1.33) ; 14 ( -12.19 39.31 -4.44 1.11) ; 15 ( -12.05 40.63 -5.19 1.18) ; 16 ( -11.83 41.80 -5.63 1.11) ; 17 ( -11.09 42.60 -6.69 1.03) ; 18 ( -10.35 43.48 -7.56 0.96) ; 19 ( -9.84 43.92 -8.38 0.81) ; 20 ( -9.32 44.36 -8.75 0.74) ; 21 ( -9.10 45.67 -9.56 0.88) ; 22 ( -9.10 46.48 -9.06 1.18) ; 23 ( -9.47 47.57 -9.00 1.03) ; 24 ( -9.69 48.45 -9.00 1.11) ; 25 ( -10.13 49.84 -9.00 1.18) ; 26 ( -10.35 50.72 -9.13 1.18) ; 27 ( -10.50 52.18 -9.13 1.18) ; 28 ( -10.85 52.57 -9.25 1.25) ; 29 ( -11.59 53.37 -9.38 1.25) ; 30 ( -12.40 54.18 -9.56 1.25) ; 31 ( -12.99 55.13 -9.25 1.11) ; 32 ( -13.21 56.52 -8.75 1.18) ; 33 ( -13.36 57.61 -8.50 1.18) ; 34 ( -13.80 58.64 -8.31 1.11) ; 35 ( -14.24 59.00 -8.38 1.11) ; 36 ( -14.98 59.73 -8.50 0.96) ; 37 ( -15.57 60.17 -8.50 0.96) ; 38 ( -16.38 60.68 -8.50 0.74) ; 39 ( -16.89 60.76 -8.50 0.74) ; 40 ( -17.63 60.83 -8.50 0.96) ; 41 ( -18.15 61.34 -8.50 0.81) ; 42 ( -18.81 62.37 -8.25 0.74) ; 43 ( -18.44 63.32 -8.75 0.88) ; 44 ( -17.92 64.41 -9.13 0.96) ; 45 ( -17.63 65.36 -9.31 0.81) ; 46 ( -17.41 66.09 -10.50 0.66) ; 47 ( -17.48 66.82 -11.88 0.88) ; 48 ( -17.63 67.04 -12.56 0.88) ; 49 ( -18.07 67.19 -12.81 0.88) ; 50 ( -18.44 67.34 -12.88 0.88) ; 51 ( -18.66 67.34 -13.69 0.88) ; 52 ( -19.10 66.90 -14.06 0.88) ; 53 ( -19.32 66.75 -14.06 0.88) ; 54 ( -19.69 66.39 -15.13 0.96) ; 55 ( -20.43 65.36 -15.38 0.88) ; 56 ( -20.87 64.92 -15.88 0.66) ; 57 ( -21.31 65.00 -16.44 0.66) ; 58 ( -21.61 65.44 -17.00 0.66) ; 59 ( -21.90 65.80 -18.06 0.66) ; 60 ( -21.90 66.46 -18.25 1.11) ; 61 ( -22.27 67.12 -18.81 1.11) ; 62 ( -22.49 67.85 -19.31 1.33) ; 63 ( -22.71 68.58 -20.06 1.33) ; 64 ( -22.86 69.24 -20.56 1.33) ; 65 ( -23.01 69.68 -21.00 1.11) ; 66 ( -23.30 70.33 -21.56 1.03) ; 67 ( -23.45 70.63 -22.19 1.03) ; 68 ( -23.45 70.92 -22.88 0.96) ; 69 ( -23.38 71.14 -23.56 0.96) ; 70 ( -22.93 71.21 -23.94 0.96) ; 71 ( -22.35 70.99 -24.13 0.96) ; 72 ( -21.68 70.92 -24.44 0.96) ; 73 ( -22.27 71.14 -25.06 0.88) ; 74 ( -22.86 71.28 -25.13 0.88) ; 75 ( -23.52 71.72 -25.38 0.96) ; 76 ( -23.74 71.94 -25.38 0.96) ; 77 ( -24.70 72.09 -26.13 1.18) ; 78 ( -25.22 72.45 -26.38 1.18) ; 79 ( -25.73 73.33 -26.63 1.03) ; 80 ( -25.96 74.06 -27.06 1.33) ; 81 ( -25.96 74.87 -27.69 1.33) ; 82 ( -26.10 75.96 -28.19 0.81) ; 83 ( -26.03 76.55 -28.25 0.59) ; 84 ( -25.81 77.72 -28.38 0.44) ; 85 ( -25.59 78.15 -28.63 0.44) ; 86 ( -25.44 78.96 -28.75 0.88) ; 87 ( -25.29 79.47 -29.31 1.55) ; 88 ( -25.22 79.84 -29.63 1.92) ; 89 ( -24.92 80.57 -29.63 1.55) ; 90 ( -24.92 81.15 -29.63 1.69) ; 91 ( -24.78 82.24 -30.56 1.18) ; 92 ( -24.78 83.41 -31.38 1.11) ; 93 ( -24.70 84.22 -32.00 0.96) ; 94 ( -24.63 85.09 -32.25 0.74) ; 95 ( -24.12 86.04 -32.63 0.66) ; 96 ( -24.12 86.41 -33.00 0.66) ; 97 ( -24.26 86.85 -33.44 0.88) ; 98 ( -24.56 87.36 -34.88 1.11) ; 99 ( -24.78 88.02 -35.44 1.40) ; 100 ( -24.85 88.46 -36.81 1.77) ; 101 ( -24.85 88.97 -37.25 2.21) ; 102 ( -24.85 89.70 -37.63 2.58) ; 103 ( -24.85 90.06 -38.06 2.36) ; 104 ( -24.93 90.50 -38.13 1.92) ; 105 ( -25.22 91.31 -38.44 1.25) ; 106 ( -25.37 91.67 -38.63 0.66) ; 107 ( -25.59 92.40 -38.75 0.44) ; 108 ( -25.22 92.92 -39.25 0.44) ; 109 ( -24.93 93.21 -39.38 0.44) ; 110 ( -24.78 93.87 -39.75 0.44) ; 111 ( -25.07 94.60 -39.69 0.74) ; 112 ( -25.81 95.40 -40.13 1.11) ; 113 ( -26.18 96.21 -40.31 1.18) ; 114 ( -26.47 96.94 -39.25 0.96) ; 115 ( -26.91 97.45 -39.38 0.81) ; 116 ( -27.28 97.74 -39.56 0.52) ; 117 ( -27.80 98.25 -39.69 0.44) ; 118 ( -28.31 98.98 -39.75 0.44) ; 119 ( -29.35 100.01 -38.94 0.37) ; 120 ( -30.08 100.66 -38.63 0.96) ; 121 ( -30.23 101.18 -38.50 1.69) ; 122 ( -30.67 101.40 -38.44 2.06) ; 123 ( -31.19 101.76 -38.19 2.36) ; 124 ( -32.00 102.27 -38.13 1.77) ; 125 ( -32.51 102.64 -38.06 0.81) ; 126 ( -33.47 103.22 -37.81 0.29) ; 127 ( -33.99 103.51 -37.81 0.29) ; 128 ( -34.95 104.25 -37.63 0.29) ; 129 ( -36.05 104.90 -37.38 0.29) ; 130 ( -37.30 105.78 -37.75 0.52) ; 131 ( -38.19 106.44 -37.75 1.11) ; 132 ( -38.85 107.02 -38.38 1.47) ; 133 ( -39.29 107.32 -38.69 1.92) ; 134 ( -39.88 107.39 -39.06 1.55) ; 135 ( -40.32 107.68 -39.63 1.18) ; 136 ( -40.62 107.97 -39.88 0.52) ; 137 ( -41.21 108.27 -40.06 0.37) ; 138 ( -42.75 109.51 -40.50 0.29) ; 139 ( -43.49 110.31 -41.31 0.29) ; 140 ( -44.15 110.90 -41.38 0.96) ; 141 ( -44.79 111.51 -42.25 1.62) ; 142 ( -45.16 111.87 -43.00 1.33) ; 143 ( -45.68 112.39 -43.75 0.88) ; 144 ( -46.05 112.82 -44.19 0.59) ; 145 ( -46.34 113.26 -44.56 0.29) ; 146 ( -46.64 113.63 -44.81 0.29) ; 147 ( -47.23 113.92 -45.19 0.29) ; 148 ( -47.81 114.43 -45.19 0.74) ; 149 ( -47.81 114.65 -45.88 1.18) ; 150 ( -48.11 115.09 -46.13 1.62) ; 151 ( -48.63 115.53 -46.94 2.43) ; 152 ( -48.85 115.68 -48.13 2.87) ; 153 ( -48.99 115.82 -49.06 2.06) ; 154 ( -49.44 116.04 -49.69 1.69) ; 155 ( -49.73 116.19 -50.38 1.25) ; 156 ( -50.02 116.48 -51.00 0.88) ; 157 ( -50.02 116.48 -53.31 0.66) ; 158 Low | ( -16.06 28.04 -1.06 0.88) ; 1, R-2-2 ( -16.50 28.77 -0.56 1.03) ; 2 ( -16.43 29.58 0.19 1.03) ; 3 ( -16.20 30.09 0.75 0.88) ; 4 ( -15.76 30.82 1.25 0.88) ; 5 ( -15.54 31.55 1.63 1.03) ; 6 ( -15.76 32.72 1.94 0.74) ; 7 ( -15.62 33.53 2.19 0.88) ; 8 ( -15.54 34.40 2.38 1.18) ; 9 ( -15.54 34.84 3.00 1.25) ; 10 ( -15.54 35.79 1.25 1.25) ; 11 ( -15.76 36.60 0.94 1.25) ; 12 ( -16.20 37.69 0.94 1.03) ; 13 ( -16.79 38.35 0.50 1.03) ; 14 ( -17.68 38.50 0.38 0.74) ; 15 ( -18.34 38.64 0.19 0.74) ; 16 ( -19.08 38.93 0.06 0.74) ; 17 ( -19.74 39.52 0.06 0.52) ; 18 ( -20.40 39.30 0.06 0.52) ; 19 ( -21.14 39.08 1.50 0.81) ; 20 ( -22.02 39.59 2.44 0.88) ; 21 ( -22.76 40.54 3.56 0.81) ; 22 ( -23.13 41.35 4.19 0.96) ; 23 ( -23.35 42.37 4.31 1.18) ; 24 ( -23.65 43.25 4.31 1.18) ; 25 ( -24.09 43.98 4.31 0.96) ; 26 ( -25.05 44.93 4.44 0.81) ; 27 ( -26.08 45.73 5.00 1.03) ; 28 ( -26.74 46.17 6.00 1.25) ; 29 ( -27.48 46.68 6.25 1.55) ; 30 ( -27.92 47.49 7.00 0.88) ; 31 ( -28.21 48.36 7.88 0.88) ; 32 ( -28.14 49.39 10.19 1.18) ; 33 ( -27.55 50.19 10.25 0.59) ; 34 ( -27.70 51.22 10.75 1.25) ; 35 ( -27.70 52.17 11.50 1.03) ; 36 ( -27.99 53.26 12.38 0.81) ; 37 ( -28.66 54.58 12.75 1.03) ; 38 ( -29.17 55.97 13.13 1.03) ; 39 ( -29.32 57.43 13.69 0.81) ; 40 ( -29.28 59.16 14.88 0.88) ; 41 ( -28.98 60.11 15.25 0.88) ; 42 ( -28.98 61.13 16.50 0.88) ; 43 ( -29.20 62.30 16.19 0.66) ; 44 ( -30.02 63.11 16.06 0.74) ; 45 ( -30.60 63.47 15.56 1.03) ; 46 ( -31.19 63.69 14.25 1.03) ; 47 ( -32.30 63.91 13.44 0.96) ; 48 ( -32.74 64.20 12.81 0.96) ; 49 ( -33.33 64.93 12.63 0.74) ; 50 ( -34.51 65.74 13.06 0.96) ; 51 ( -35.39 66.25 13.06 1.25) ; 52 ( -36.06 66.62 13.13 0.88) ; 53 ( -37.31 67.27 12.69 0.59) ; 54 ( -38.34 67.71 12.38 0.59) ; 55 ( -39.15 68.00 13.44 0.96) ; 56 ( -40.03 68.44 14.13 0.74) ; 57 ( -41.21 68.30 15.50 0.59) ; 58 ( -41.21 68.22 16.13 0.96) ; 59 ( -41.95 68.15 17.00 1.40) ; 60 ( -41.58 67.86 17.44 1.47) ; 61 ( -41.36 66.98 17.50 0.66) ; 62 ( -40.99 66.40 17.13 0.44) ; 63 ( -41.07 66.10 16.94 0.44) ; 64 Low ) ; End of split ) ; End of split ) ; End of tree ( (Color White) (Dendrite) ( 5.62 -13.29 -2.00 3.90) ; Root ( 5.62 -14.02 -2.19 3.09) ; 1, R ( 5.55 -14.90 -2.19 2.80) ; 2 ( 5.62 -16.00 -2.44 2.43) ; 3 ( 5.69 -17.17 -2.44 2.14) ; 4 ( 6.28 -18.04 -2.69 2.14) ; 5 ( 7.17 -19.14 -3.19 2.28) ; 6 ( 7.98 -20.02 -3.56 2.50) ; 7 ( 8.71 -20.60 -4.13 2.80) ; 8 ( 9.38 -21.11 -4.31 2.80) ; 9 ( 10.41 -21.77 -4.31 2.58) ; 10 ( 11.66 -22.43 -4.69 2.43) ; 11 ( 12.69 -22.79 -4.94 2.43) ; 12 ( 13.80 -23.31 -4.06 2.65) ; 13 ( 15.12 -24.18 -3.63 3.09) ; 14 ( 16.30 -25.06 -2.69 3.17) ; 15 ( 17.56 -25.86 -1.63 3.83) ; 16 ( ( 17.56 -26.16 -1.50 3.46) ; 1, R-1 ( 17.70 -27.84 -1.06 2.21) ; 2 ( 17.76 -28.84 -1.06 2.21) ; 3 ( ( 17.85 -29.23 0.00 2.58) ; 1, R-1-1 ( 17.97 -30.36 -0.38 2.28) ; 2 ( 17.97 -31.75 0.44 2.28) ; 3 ( 18.13 -31.84 0.44 2.28) ; 4 ( ( 17.83 -32.55 1.50 1.62) ; 1, R-1-1-1 ( 17.46 -33.35 1.88 1.33) ; 2 ( 16.57 -34.38 2.69 1.47) ; 3 ( 15.76 -35.40 3.50 1.55) ; 4 ( 15.03 -36.42 4.13 1.84) ; 5 ( 14.44 -37.81 5.13 1.92) ; 6 ( 13.92 -39.28 6.13 2.95) ; 7 ( 13.77 -40.59 6.88 3.17) ; 8 ( ( 14.14 -41.47 7.19 2.43) ; 1, R-1-1-1-1 ( 14.22 -42.71 7.31 2.21) ; 2 ( 14.24 -43.52 7.25 2.06) ; 3 ( ( 13.80 -44.62 7.19 1.18) ; 1, R-1-1-1-1-1 ( 13.43 -45.57 6.94 0.96) ; 2 ( 13.58 -46.30 6.50 0.96) ; 3 ( 13.50 -47.03 5.31 1.03) ; 4 ( 13.58 -48.06 4.69 1.11) ; 5 ( 14.02 -48.79 3.56 1.33) ; 6 ( 14.31 -49.44 2.63 1.25) ; 7 ( 14.39 -50.39 2.50 1.55) ; 8 ( 14.39 -51.71 1.50 1.84) ; 9 ( 14.39 -52.59 0.94 1.69) ; 10 ( 14.54 -53.68 0.56 1.47) ; 11 ( 14.39 -54.56 -0.50 1.47) ; 12 ( 14.17 -55.58 -1.63 1.33) ; 13 ( 14.31 -56.83 -2.44 1.18) ; 14 ( 14.54 -58.07 -3.56 1.25) ; 15 ( 14.83 -58.95 -3.94 1.25) ; 16 ( 15.35 -59.68 -3.56 1.47) ; 17 ( 15.64 -60.70 -2.81 1.62) ; 18 ( 16.30 -61.87 -2.88 1.47) ; 19 ( 16.52 -63.04 -2.88 1.47) ; 20 ( 16.45 -63.84 -2.94 1.33) ; 21 ( 16.45 -64.58 -2.94 1.11) ; 22 ( 16.67 -65.23 -2.94 1.11) ; 23 ( 16.82 -65.89 -2.94 1.33) ; 24 ( 17.19 -66.91 -3.19 1.18) ; 25 ( 17.26 -68.01 -3.19 1.25) ; 26 ( 17.19 -68.82 -3.31 1.47) ; 27 ( 17.19 -69.84 -3.31 1.77) ; 28 ( 16.82 -70.94 -3.50 1.69) ; 29 ( 16.16 -72.54 -3.81 1.47) ; 30 ( 15.71 -73.93 -3.94 1.33) ; 31 ( 15.12 -75.03 -3.94 1.33) ; 32 ( 14.90 -76.20 -4.06 1.33) ; 33 ( 14.54 -77.00 -4.44 0.96) ; 34 ( 14.39 -77.66 -4.50 1.33) ; 35 ( 14.17 -78.46 -4.50 2.06) ; 36 ( 14.31 -79.41 -4.50 2.21) ; 37 ( 14.39 -80.66 -4.50 2.65) ; 38 ( ( 13.72 -80.95 -5.06 1.40) ; 1, R-1-1-1-1-1-1 ( 13.36 -81.61 -5.38 0.96) ; 2 ( 12.62 -82.41 -5.75 0.74) ; 3 ( 12.03 -83.14 -6.06 0.66) ; 4 ( 11.22 -83.80 -6.56 0.66) ; 5 ( 11.05 -84.65 -7.00 0.59) ; 6 ( 10.97 -85.46 -7.00 0.59) ; 7 ( 11.05 -85.97 -7.06 0.59) ; 8 ( 11.56 -86.33 -7.50 0.59) ; 9 ( 12.01 -86.92 -7.50 0.81) ; 10 ( 12.37 -87.28 -7.56 0.96) ; 11 ( 12.67 -87.65 -8.06 0.81) ; 12 ( 13.04 -88.45 -8.25 0.96) ; 13 ( 12.82 -89.48 -8.94 1.03) ; 14 ( 12.37 -90.28 -9.63 1.25) ; 15 ( 11.78 -90.79 -9.88 1.69) ; 16 ( 10.83 -91.23 -10.25 1.69) ; 17 ( 10.02 -91.67 -9.81 1.18) ; 18 ( 9.43 -91.82 -9.56 0.81) ; 19 ( 8.40 -92.18 -9.56 0.52) ; 20 ( 7.66 -92.84 -10.00 0.81) ; 21 ( 7.14 -93.64 -10.75 1.18) ; 22 ( 6.77 -94.38 -11.38 0.88) ; 23 ( 6.26 -94.96 -12.00 0.74) ; 24 ( 5.23 -95.69 -12.81 0.81) ; 25 ( 4.42 -96.13 -12.94 1.11) ; 26 ( 3.61 -97.08 -12.69 0.88) ; 27 ( 2.94 -97.88 -12.19 0.88) ; 28 ( 2.28 -98.83 -11.56 0.66) ; 29 ( 2.13 -99.86 -11.25 0.52) ; 30 ( 2.21 -100.37 -11.25 0.52) ; 31 ( 2.35 -101.39 -11.81 0.74) ; 32 ( 2.58 -102.42 -12.25 0.74) ; 33 ( 2.28 -103.44 -13.00 1.18) ; 34 ( 2.06 -104.39 -13.50 1.18) ; 35 ( 1.69 -105.63 -13.50 0.81) ; 36 ( 1.18 -106.58 -14.38 0.81) ; 37 ( 0.37 -107.02 -14.38 1.11) ; 38 ( -0.22 -107.61 -15.56 1.40) ; 39 ( -0.74 -108.19 -16.44 0.88) ; 40 ( -0.96 -108.92 -17.13 0.74) ; 41 ( -1.48 -109.21 -17.81 0.88) ; 42 ( -2.14 -109.51 -18.06 0.74) ; 43 ( -2.88 -109.87 -18.31 0.74) ; 44 ( -4.20 -110.09 -18.56 0.59) ; 45 ( -5.23 -110.02 -17.69 0.81) ; 46 ( -5.90 -110.02 -17.69 0.96) ; 47 ( -6.41 -110.09 -17.69 0.59) ; 48 ( -7.15 -110.02 -17.63 0.44) ; 49 ( -8.03 -110.60 -17.63 0.44) ; 50 ( -8.62 -111.26 -18.06 1.11) ; 51 ( -9.21 -111.77 -18.06 1.18) ; 52 ( -9.80 -112.28 -19.00 0.66) ; 53 ( -10.46 -112.80 -19.38 0.66) ; 54 ( -11.35 -113.53 -20.19 0.59) ; 55 ( -11.45 -114.38 -20.13 0.52) ; 56 ( -11.52 -114.97 -19.63 0.81) ; 57 ( -11.89 -115.70 -18.56 0.88) ; 58 ( -12.26 -116.43 -18.38 0.88) ; 59 ( -12.99 -117.16 -18.19 0.88) ; 60 ( -13.58 -117.75 -17.88 0.74) ; 61 ( -14.25 -118.33 -17.81 0.74) ; 62 ( -14.84 -118.92 -17.81 0.96) ; 63 ( -15.28 -119.57 -17.81 1.11) ; 64 ( -15.87 -120.45 -17.81 0.81) ; 65 ( -16.24 -121.40 -18.06 0.59) ; 66 ( -16.82 -121.91 -19.19 0.81) ; 67 ( -17.49 -122.35 -19.25 0.81) ; 68 ( -18.37 -122.57 -19.38 0.81) ; 69 ( -19.48 -122.79 -19.38 0.52) ; 70 ( -20.29 -123.30 -19.38 0.52) ; 71 ( -20.73 -124.18 -20.00 1.11) ; 72 ( -20.80 -124.76 -20.50 1.92) ; 73 ( -20.51 -125.20 -20.50 2.06) ; 74 ( -19.99 -125.71 -20.88 1.18) ; 75 ( -19.77 -126.08 -21.38 0.74) ; 76 ( -19.33 -126.45 -21.56 0.37) ; 77 ( -18.59 -126.59 -21.69 0.37) ; 78 ( -17.71 -126.52 -21.94 0.88) ; 79 ( -16.97 -126.74 -22.69 1.25) ; 80 ( -16.09 -126.96 -22.81 1.55) ; 81 ( -15.50 -127.32 -23.63 1.03) ; 82 ( -14.84 -127.62 -24.00 0.66) ; 83 ( -14.10 -128.20 -24.31 0.66) ; 84 ( -13.73 -128.78 -25.56 1.03) ; 85 ( -13.21 -129.30 -26.69 1.25) ; 86 ( -12.55 -129.44 -27.50 1.25) ; 87 ( -11.96 -129.74 -28.13 0.96) ; 88 ( -11.30 -130.03 -29.00 0.59) ; 89 ( -10.71 -130.25 -29.88 0.52) ; 90 ( -10.42 -130.10 -32.13 0.66) ; 91 ( -10.56 -130.10 -33.38 0.66) ; 92 ( -10.64 -130.39 -34.63 0.88) ; 93 ( -10.71 -130.83 -34.63 0.88) ; 94 ( -10.27 -131.05 -35.00 0.66) ; 95 ( -9.83 -131.27 -35.25 0.44) ; 96 ( -9.24 -131.49 -35.63 0.44) ; 97 ( -8.72 -131.49 -36.31 0.59) ; 98 ( -8.72 -131.49 -38.00 0.96) ; 99 ( -8.43 -131.49 -40.38 1.25) ; 100 ( -7.91 -131.42 -40.56 1.69) ; 101 ( -7.54 -131.05 -40.88 1.69) ; 102 ( -6.73 -130.61 -41.31 1.47) ; 103 ( -6.44 -130.10 -43.38 1.47) ; 104 ( -5.63 -129.81 -44.00 1.77) ; 105 ( -5.18 -129.37 -45.63 0.96) ; 106 ( -4.74 -129.22 -45.94 0.59) ; 107 ( -4.01 -129.22 -46.31 0.52) ; 108 ( -3.42 -129.00 -47.69 0.52) ; 109 ( -2.61 -128.93 -48.13 0.52) ; 110 ( -2.02 -128.93 -48.63 0.52) ; 111 ( -1.35 -128.71 -48.63 0.52) ; 112 ( -0.17 -129.00 -49.56 0.52) ; 113 Low | ( 14.82 -80.85 -5.25 2.21) ; 1, R-1-1-1-1-1-2 ( ( 15.93 -81.44 -6.25 0.88) ; 1, R-1-1-1-1-1-2-1 ( 16.66 -81.88 -6.75 0.74) ; 2 ( 17.33 -82.24 -7.06 0.74) ; 3 ( 17.84 -82.61 -8.06 0.66) ; 4 ( 18.58 -83.12 -8.56 0.66) ; 5 ( 19.09 -83.78 -9.81 0.66) ; 6 ( 19.61 -84.87 -9.88 0.52) ; 7 ( 20.27 -85.90 -10.63 0.52) ; 8 ( 20.49 -86.48 -10.81 0.52) ; 9 ( 20.57 -86.99 -11.88 0.59) ; 10 ( 20.94 -87.94 -12.94 0.88) ; 11 ( 21.38 -88.09 -14.44 0.96) ; 12 ( 22.04 -88.60 -14.50 0.81) ; 13 ( 22.41 -88.97 -15.13 0.81) ; 14 ( 22.93 -89.55 -15.63 0.96) ; 15 ( 23.00 -90.36 -16.63 1.18) ; 16 ( 23.59 -90.72 -17.19 0.96) ; 17 ( 24.25 -91.31 -18.13 0.74) ; 18 ( 24.62 -91.82 -18.81 0.74) ; 19 ( 24.55 -92.70 -19.63 0.66) ; 20 ( 23.96 -93.65 -20.13 0.88) ; 21 ( 23.37 -94.30 -20.63 0.66) ; 22 ( 22.48 -95.11 -21.13 0.66) ; 23 ( 21.97 -96.20 -22.06 0.88) ; 24 ( 21.67 -96.50 -23.25 0.81) ; 25 ( 22.04 -97.01 -24.75 0.66) ; 26 ( 22.26 -97.01 -26.81 0.88) ; 27 ( 22.70 -97.01 -27.06 1.18) ; 28 ( 23.59 -97.30 -27.06 1.18) ; 29 ( 24.47 -97.52 -27.25 0.81) ; 30 ( 25.14 -97.45 -27.50 0.66) ; 31 ( 25.58 -97.37 -29.06 0.96) ; 32 ( 26.02 -97.81 -30.00 0.74) ; 33 ( 26.76 -98.18 -31.00 0.74) ; 34 ( 27.42 -98.69 -31.38 0.81) ; 35 ( 28.67 -99.20 -32.19 0.81) ; 36 ( 29.92 -99.79 -32.94 0.96) ; 37 ( 30.88 -100.15 -33.31 1.11) ; 38 ( 31.62 -100.37 -33.56 1.11) ; 39 ( 32.36 -100.81 -34.00 0.74) ; 40 ( 33.02 -101.32 -35.25 0.59) ; 41 ( 34.12 -101.98 -36.88 0.37) ; 42 ( 34.71 -102.05 -37.69 0.44) ; 43 ( 35.45 -102.34 -37.75 0.44) ; 44 ( 35.60 -102.78 -38.00 0.44) ; 45 ( 35.74 -103.44 -38.00 0.44) ; 46 ( 36.19 -103.73 -38.25 0.44) ; 47 ( 36.63 -103.95 -38.63 0.74) ; 48 ( 37.14 -103.95 -38.81 1.11) ; 49 ( 37.73 -103.66 -38.81 0.74) ; 50 ( 38.40 -103.59 -38.81 0.37) ; 51 ( 38.91 -103.59 -38.81 0.37) ; 52 ( 39.28 -103.15 -39.31 0.37) ; 53 ( 40.17 -102.64 -38.63 0.37) ; 54 ( 41.05 -102.34 -38.38 0.66) ; 55 ( 41.71 -102.13 -37.88 0.52) ; 56 ( 42.60 -101.54 -37.88 0.66) ; 57 ( 43.63 -101.17 -37.81 1.03) ; 58 ( 44.29 -100.74 -37.25 1.33) ; 59 ( 45.17 -100.30 -37.13 1.62) ; 60 ( 45.91 -99.64 -37.00 0.96) ; 61 ( 46.65 -98.84 -36.50 0.81) ; 62 ( 47.31 -98.18 -38.88 1.62) ; 63 ( 48.12 -97.52 -39.31 3.02) ; 64 ( 48.64 -97.52 -39.31 3.98) ; 65 ( 49.67 -97.45 -39.31 4.79) ; 66 ( 50.63 -97.30 -39.81 4.42) ; 67 ( 51.36 -96.94 -40.94 3.02) ; 68 ( 52.10 -96.64 -41.56 1.77) ; 69 ( 52.47 -96.57 -42.38 0.88) ; 70 ( 52.98 -96.50 -42.69 0.74) ; 71 ( 53.13 -96.57 -44.31 0.44) ; 72 Low | ( 15.04 -81.66 -4.50 0.66) ; 1, R-1-1-1-1-1-2-2 ( 15.26 -82.32 -3.81 0.66) ; 2 ( 15.63 -82.68 -3.50 0.66) ; 3 ( 16.22 -83.12 -3.44 0.66) ; 4 ( 16.96 -83.56 -3.38 0.81) ; 5 ( 17.69 -83.78 -3.38 0.81) ; 6 ( 18.28 -84.29 -3.19 0.81) ; 7 ( 18.73 -85.17 -3.13 0.66) ; 8 ( 19.17 -86.12 -3.88 0.59) ; 9 ( 19.83 -86.85 -3.94 0.74) ; 10 ( 20.35 -87.58 -4.25 0.74) ; 11 ( 20.72 -88.16 -4.38 0.74) ; 12 ( 21.08 -88.97 -3.44 0.88) ; 13 ( 21.45 -89.33 -3.31 0.59) ; 14 ( 22.26 -89.70 -2.75 0.66) ; 15 ( 23.22 -90.36 -2.75 0.59) ; 16 ( 23.44 -90.65 -2.19 0.59) ; 17 ( 23.66 -91.23 -2.13 0.44) ; 18 ( 23.66 -92.18 -1.44 0.88) ; 19 ( 23.51 -92.70 -1.13 0.88) ; 20 ( 23.44 -93.43 -1.06 0.88) ; 21 ( 23.44 -94.08 -1.00 0.88) ; 22 ( 23.44 -94.74 -1.00 0.88) ; 23 ( 23.44 -95.62 -1.00 0.88) ; 24 ( 23.00 -96.50 -1.00 0.74) ; 25 ( 22.63 -97.59 -0.94 0.59) ; 26 ( 22.56 -98.40 -0.81 0.81) ; 27 ( 22.12 -98.84 -0.31 0.52) ; 28 ( 21.97 -99.35 -2.19 0.44) ; 29 ( 21.89 -99.86 -3.94 0.59) ; 30 ( 22.12 -100.30 -4.75 0.52) ; 31 ( 22.41 -100.88 -5.50 0.88) ; 32 ( 22.78 -101.39 -5.50 0.88) ; 33 ( 23.07 -101.98 -5.50 1.11) ; 34 ( 23.51 -102.49 -5.50 0.74) ; 35 ( 23.96 -103.22 -5.75 0.74) ; 36 ( 24.33 -104.03 -6.31 0.74) ; 37 ( 24.62 -105.12 -6.69 1.03) ; 38 ( 24.33 -105.27 -7.25 1.25) ; 39 ( 24.47 -105.93 -8.00 0.96) ; 40 ( 24.40 -106.80 -8.50 0.66) ; 41 ( 24.33 -107.53 -8.50 0.88) ; 42 ( 24.03 -108.41 -9.00 1.33) ; 43 ( 23.88 -109.29 -9.50 1.33) ; 44 ( 23.52 -109.95 -9.69 0.74) ; 45 ( 23.30 -111.05 -10.38 0.59) ; 46 ( 23.15 -111.85 -10.88 0.88) ; 47 ( 23.01 -112.73 -11.31 0.88) ; 48 ( 23.23 -113.68 -11.50 0.66) ; 49 ( 23.74 -114.85 -11.69 0.88) ; 50 ( 24.18 -115.51 -11.44 0.88) ; 51 ( 24.70 -116.53 -11.94 0.66) ; 52 ( 24.70 -117.19 -11.94 0.66) ; 53 ( 24.33 -118.43 -11.94 0.88) ; 54 ( 23.82 -119.38 -12.25 0.88) ; 55 ( 23.30 -120.04 -11.63 0.74) ; 56 ( 23.15 -120.70 -11.56 0.66) ; 57 ( 22.71 -121.58 -11.56 1.03) ; 58 ( 22.42 -122.60 -11.38 1.03) ; 59 ( 22.12 -123.62 -11.56 0.52) ; 60 ( 22.12 -125.01 -11.88 0.88) ; 61 ( 21.97 -126.04 -11.81 1.62) ; 62 ( 21.83 -126.84 -11.81 1.99) ; 63 ( 21.61 -127.20 -11.81 1.99) ; 64 ( 21.39 -128.08 -11.81 1.33) ; 65 ( 20.65 -129.03 -11.81 0.66) ; 66 ( 20.28 -129.54 -12.00 0.44) ; 67 ( 19.91 -130.20 -12.25 0.37) ; 68 ( 20.13 -130.79 -12.00 0.37) ; 69 ( 20.65 -131.23 -11.56 0.59) ; 70 ( 21.24 -131.44 -11.19 1.03) ; 71 ( 21.53 -132.03 -10.31 1.40) ; 72 ( 22.12 -132.91 -10.31 1.47) ; 73 ( 22.42 -133.64 -10.25 0.88) ; 74 ( 23.15 -134.15 -9.88 0.66) ; 75 ( 23.74 -134.81 -8.94 0.59) ; 76 ( 24.04 -135.39 -8.00 0.52) ; 77 ( 23.96 -136.49 -7.00 0.52) ; 78 ( 24.18 -137.22 -6.94 0.74) ; 79 ( 24.77 -138.17 -7.31 0.52) ; 80 ( 25.44 -138.90 -8.38 0.66) ; 81 ( 26.01 -139.75 -8.38 1.03) ; 82 ( 26.60 -140.55 -8.56 1.33) ; 83 ( 27.11 -141.79 -8.56 1.69) ; 84 ( 27.48 -143.18 -8.75 1.47) ; 85 ( 27.55 -144.42 -8.75 1.18) ; 86 ( 27.70 -145.52 -9.63 1.40) ; 87 ( 27.33 -146.47 -7.19 1.03) ; 88 ( 26.96 -147.42 -5.94 0.74) ; 89 ( 26.23 -148.37 -4.75 0.59) ; 90 ( 25.42 -149.54 -4.75 0.44) ; 91 ( 24.90 -150.27 -4.75 0.81) ; 92 ( 24.68 -151.00 -5.69 1.84) ; 93 ( 24.46 -151.59 -5.81 2.14) ; 94 ( 24.46 -152.83 -6.69 1.33) ; 95 ( 24.68 -153.49 -7.19 0.59) ; 96 ( 24.83 -153.85 -7.81 0.81) ; 97 ( 25.05 -154.51 -8.63 1.11) ; 98 ( 25.42 -155.39 -8.81 1.33) ; 99 ( 25.64 -156.19 -9.19 1.33) ; 100 ( 25.86 -156.92 -9.56 1.03) ; 101 ( 26.37 -157.73 -9.56 0.59) ; 102 ( 26.52 -158.39 -9.88 0.59) ; 103 ( 27.18 -159.48 -9.88 0.59) ; 104 ( 27.04 -160.36 -10.25 0.59) ; 105 ( 26.52 -161.24 -10.50 0.59) ; 106 ( 26.37 -162.70 -10.50 0.66) ; 107 ( 26.37 -163.36 -10.50 0.66) ; 108 ( 26.45 -164.45 -10.50 0.44) ; 109 ( 26.67 -165.04 -10.50 0.44) ; 110 ( 26.89 -165.48 -11.25 0.74) ; 111 ( 27.92 -165.84 -11.88 0.74) ; 112 Low ) ; End of split ) ; End of split | ( 14.94 -44.13 7.56 0.96) ; 1, R-1-1-1-1-2 ( 15.82 -45.08 7.13 1.11) ; 2 ( 16.41 -45.66 6.81 0.96) ; 3 ( 16.93 -46.32 6.81 0.88) ; 4 ( 17.52 -47.05 6.56 0.81) ; 5 ( 17.96 -47.78 6.13 1.03) ; 6 ( 18.33 -48.22 5.81 1.25) ; 7 ( 18.84 -48.44 5.38 1.25) ; 8 ( 19.36 -48.51 5.19 1.33) ; 9 ( 20.39 -49.61 4.50 1.11) ; 10 ( 21.05 -50.41 3.25 1.11) ; 11 ( 21.94 -51.29 3.13 1.11) ; 12 ( 22.45 -51.80 2.75 1.18) ; 13 ( 22.82 -52.97 2.25 1.11) ; 14 ( 22.89 -54.07 2.13 1.25) ; 15 ( 23.04 -55.02 1.69 1.25) ; 16 ( 22.75 -55.90 1.56 1.11) ; 17 ( 22.67 -57.21 1.44 0.96) ; 18 ( 22.97 -58.38 1.19 0.96) ; 19 ( 22.97 -59.33 1.19 1.25) ; 20 ( 23.34 -60.28 0.63 1.40) ; 21 ( 23.26 -61.38 0.25 1.25) ; 22 ( 23.11 -62.77 -0.81 1.11) ; 23 ( 23.11 -63.79 -0.81 1.03) ; 24 ( 23.41 -64.89 -1.38 1.03) ; 25 ( 23.78 -65.91 -1.69 1.40) ; 26 ( 24.15 -66.79 -0.19 1.69) ; 27 ( ( 24.88 -67.52 1.00 1.03) ; 1, R-1-1-1-1-2-1 ( 25.77 -68.10 1.63 0.88) ; 2 ( 26.36 -69.20 1.94 0.96) ; 3 ( 26.87 -69.86 2.19 0.96) ; 4 ( 27.54 -70.52 3.13 1.03) ; 5 ( 28.20 -70.73 4.00 0.81) ; 6 ( 28.71 -71.39 4.06 0.59) ; 7 ( 28.94 -72.12 4.06 0.59) ; 8 ( 28.79 -72.71 3.50 0.88) ; 9 ( 28.72 -73.72 2.94 0.96) ; 10 ( 28.57 -74.45 2.94 0.96) ; 11 ( 28.57 -75.40 2.69 0.96) ; 12 ( 28.57 -76.13 2.63 0.96) ; 13 ( 28.80 -77.30 2.63 1.11) ; 14 ( 29.09 -78.32 2.63 1.03) ; 15 ( 29.31 -79.06 2.19 0.81) ; 16 ( 29.68 -79.93 3.44 0.74) ; 17 ( 30.20 -80.81 4.06 0.66) ; 18 ( 30.93 -81.54 4.94 0.81) ; 19 ( 31.67 -82.49 4.75 0.88) ; 20 ( 32.18 -83.15 4.31 0.88) ; 21 ( 32.18 -84.10 3.38 1.11) ; 22 ( 32.11 -85.20 2.81 1.03) ; 23 ( 31.74 -86.58 2.81 0.96) ; 24 ( 31.52 -87.61 2.63 0.88) ; 25 ( 31.67 -88.78 2.44 0.74) ; 26 ( 31.89 -89.07 2.19 1.11) ; 27 ( 32.11 -89.80 2.06 1.11) ; 28 ( 32.33 -90.39 2.81 0.74) ; 29 ( 32.85 -91.04 2.94 0.66) ; 30 ( 33.66 -91.34 3.00 0.81) ; 31 ( 34.39 -92.14 3.13 0.59) ; 32 ( 34.69 -92.43 3.19 0.59) ; 33 ( 35.20 -92.94 3.19 1.33) ; 34 ( 35.79 -93.60 2.75 1.99) ; 35 ( 36.46 -94.41 2.25 1.69) ; 36 ( 36.75 -95.36 2.25 0.88) ; 37 ( 37.19 -96.01 2.00 0.88) ; 38 ( 37.64 -96.67 1.94 0.66) ; 39 ( 38.23 -97.48 1.56 0.59) ; 40 ( 38.52 -98.13 2.44 0.81) ; 41 ( 38.00 -99.01 2.94 1.03) ; 42 ( 37.41 -99.74 4.00 0.74) ; 43 ( 36.75 -100.55 4.25 0.88) ; 44 ( 36.31 -100.98 4.31 1.25) ; 45 ( 36.02 -101.42 3.50 1.62) ; 46 ( 35.35 -102.45 3.69 1.18) ; 47 ( 34.87 -103.58 3.00 0.88) ; 48 ( 34.28 -104.68 2.94 0.74) ; 49 ( 33.99 -105.19 2.94 0.74) ; 50 ( 33.84 -106.29 3.00 0.88) ; 51 ( 33.70 -107.39 2.81 1.03) ; 52 ( 33.62 -108.56 2.44 1.03) ; 53 ( 33.47 -109.51 2.13 1.03) ; 54 ( 33.47 -110.02 2.13 0.74) ; 55 ( 34.21 -110.67 2.13 0.74) ; 56 ( 34.87 -111.63 2.13 0.96) ; 57 ( 35.54 -112.43 1.81 0.74) ; 58 ( 36.27 -112.94 1.31 0.74) ; 59 ( 36.57 -113.60 3.19 0.66) ; 60 ( 36.57 -114.40 4.63 0.88) ; 61 ( 36.27 -114.84 5.06 1.11) ; 62 ( 36.57 -115.72 5.19 1.11) ; 63 ( 36.50 -116.82 5.19 0.81) ; 64 ( 36.79 -117.47 5.25 0.81) ; 65 ( 37.31 -117.91 5.50 0.81) ; 66 ( 38.12 -118.28 5.69 0.96) ; 67 ( 38.78 -118.79 5.25 0.96) ; 68 ( 39.74 -119.37 5.25 0.96) ; 69 ( 40.40 -120.47 4.69 0.96) ; 70 ( 40.55 -121.27 4.63 1.40) ; 71 ( 40.62 -121.93 5.13 1.11) ; 72 ( 40.84 -122.96 5.13 0.81) ; 73 ( 40.99 -123.61 5.19 0.66) ; 74 ( 41.21 -124.42 5.19 0.88) ; 75 ( 41.36 -125.00 5.31 0.59) ; 76 ( 41.50 -125.73 5.31 0.37) ; 77 ( 42.09 -126.39 5.31 0.37) ; 78 ( 42.76 -126.83 5.06 0.74) ; 79 ( 43.42 -127.05 4.06 0.88) ; 80 ( 44.30 -127.93 3.56 0.74) ; 81 ( 45.11 -128.44 3.38 0.59) ; 82 ( 46.44 -128.36 3.38 0.52) ; 83 ( 46.96 -128.66 4.13 0.88) ; 84 ( 47.69 -129.10 4.38 1.40) ; 85 ( 48.28 -129.61 4.94 1.40) ; 86 ( 49.17 -130.34 5.06 0.81) ; 87 ( 49.68 -130.92 5.06 0.52) ; 88 ( 50.12 -131.58 5.25 0.44) ; 89 ( 50.39 -132.53 4.13 0.52) ; 90 ( 50.39 -133.18 3.25 0.52) ; 91 ( 50.68 -133.92 2.63 0.52) ; 92 ( 51.05 -134.79 2.63 0.52) ; 93 ( 51.42 -135.45 2.63 0.74) ; 94 ( 52.08 -135.89 2.38 0.88) ; 95 ( 52.45 -136.47 2.38 0.88) ; 96 ( 52.97 -137.06 2.19 0.74) ; 97 ( 53.56 -137.94 2.00 0.66) ; 98 ( 53.48 -138.74 1.88 0.66) ; 99 ( 53.48 -139.69 1.50 0.66) ; 100 ( 53.85 -140.49 1.19 0.52) ; 101 ( 54.66 -140.86 1.19 0.74) ; 102 ( 55.69 -141.30 0.94 1.03) ; 103 ( 56.50 -141.30 0.94 0.74) ; 104 ( 57.46 -141.44 0.94 0.59) ; 105 ( 58.57 -141.81 0.81 0.59) ; 106 ( 58.94 -142.76 0.81 0.59) ; 107 ( 59.08 -143.42 0.81 0.96) ; 108 ( 59.23 -144.22 0.38 1.62) ; 109 ( 59.45 -145.03 0.06 1.40) ; 110 ( 59.82 -145.68 0.06 0.66) ; 111 ( 60.34 -146.05 -1.06 0.44) ; 112 ( 60.93 -146.05 -1.31 0.44) ; 113 ( 61.44 -145.76 -2.25 0.74) ; 114 ( 61.81 -145.68 -3.50 1.55) ; 115 ( 62.03 -145.61 -3.94 1.92) ; 116 ( 62.47 -145.61 -4.31 1.40) ; 117 ( 62.18 -145.61 -5.38 1.40) ; 118 ( 62.18 -146.12 -5.75 0.88) ; 119 ( 62.62 -146.78 -6.25 0.81) ; 120 ( 62.99 -147.44 -7.06 0.52) ; 121 Low | ( 23.78 -67.43 0.13 0.96) ; 1, R-1-1-1-1-2-2 ( 23.26 -67.87 0.19 0.52) ; 2 ( 23.19 -68.67 0.19 0.52) ; 3 ( 23.48 -69.25 0.19 0.52) ; 4 ( 24.07 -70.13 0.38 0.66) ; 5 ( 24.52 -71.15 -0.50 0.66) ; 6 ( 24.96 -72.25 -0.63 0.74) ; 7 ( 25.18 -72.98 -0.88 0.81) ; 8 ( 25.03 -73.71 -0.81 0.81) ; 9 ( 24.66 -74.74 -0.19 0.81) ; 10 ( 24.44 -75.69 0.31 0.81) ; 11 ( 24.37 -76.27 0.31 0.81) ; 12 ( 24.15 -77.00 0.63 0.81) ; 13 ( 23.78 -77.81 0.69 0.66) ; 14 ( 23.19 -78.39 1.19 0.88) ; 15 ( 22.75 -79.05 1.19 0.81) ; 16 ( 22.38 -79.63 1.50 0.81) ; 17 ( 21.57 -80.15 1.94 0.66) ; 18 ( 21.27 -80.80 2.50 0.81) ; 19 ( 21.20 -81.90 2.50 0.66) ; 20 ( 21.20 -82.78 2.00 0.66) ; 21 ( 21.64 -83.44 2.44 0.59) ; 22 ( 22.31 -84.24 2.50 0.59) ; 23 ( 23.04 -84.90 2.44 0.74) ; 24 ( 23.56 -85.41 2.00 0.74) ; 25 ( 24.00 -85.85 2.00 0.59) ; 26 ( 24.59 -86.51 2.00 0.59) ; 27 ( 24.66 -87.24 1.88 0.59) ; 28 ( 25.03 -88.33 1.75 0.74) ; 29 ( 25.62 -89.36 1.56 0.74) ; 30 ( 26.43 -90.67 1.44 0.81) ; 31 ( 27.24 -91.48 2.31 1.03) ; 32 ( 27.83 -92.28 2.50 0.88) ; 33 ( 28.35 -92.79 2.63 0.74) ; 34 ( 28.72 -92.79 3.19 0.66) ; 35 ( 29.38 -93.16 3.44 0.66) ; 36 ( 29.97 -93.82 3.44 0.66) ; 37 ( 30.41 -94.25 3.44 0.66) ; 38 ( 31.15 -94.69 3.50 0.44) ; 39 ( 31.88 -94.98 3.50 0.52) ; 40 ( 32.25 -95.28 3.50 0.52) ; 41 ( 32.03 -96.37 3.19 0.59) ; 42 ( 31.41 -97.78 2.44 0.66) ; 43 ( 31.04 -98.65 2.44 0.96) ; 44 ( 30.60 -99.31 1.75 0.96) ; 45 ( 30.45 -100.04 1.50 0.66) ; 46 ( 30.23 -100.70 0.63 0.66) ; 47 ( 30.16 -101.87 0.13 0.66) ; 48 ( 30.16 -103.33 -0.50 1.03) ; 49 ( 29.94 -104.06 -0.69 1.62) ; 50 ( 29.72 -104.57 -0.69 2.28) ; 51 ( 29.50 -104.94 -1.06 2.50) ; 52 ( 29.05 -105.67 -1.19 1.69) ; 53 ( 28.83 -106.33 -1.19 0.81) ; 54 ( 28.54 -107.35 -1.63 0.52) ; 55 ( 28.17 -108.08 -1.88 0.52) ; 56 ( 27.58 -108.52 -1.88 0.52) ; 57 ( 26.92 -109.25 -2.06 0.74) ; 58 ( 26.48 -109.84 -3.19 0.96) ; 59 ( 26.18 -110.57 -4.31 1.18) ; 60 ( 26.40 -111.01 -5.25 1.92) ; 61 ( 26.25 -111.52 -5.50 1.55) ; 62 ( 26.11 -112.32 -5.50 0.74) ; 63 ( 26.11 -112.84 -6.56 0.37) ; 64 ( 25.08 -113.27 -9.13 0.37) ; 65 ( 24.63 -113.42 -9.81 0.37) ; 66 ( 24.56 -113.93 -9.81 0.37) ; 67 ( 24.85 -114.66 -9.81 0.52) ; 68 ( 24.85 -115.69 -9.81 0.29) ; 69 ( 24.49 -116.05 -9.94 0.52) ; 70 ( 24.04 -116.42 -10.13 0.74) ; 71 ( 23.53 -116.86 -10.38 0.44) ; 72 ( 23.09 -117.81 -10.44 0.29) ; 73 ( 22.79 -118.46 -10.81 0.29) ; 74 ( 22.50 -118.83 -10.88 0.66) ; 75 ( 22.05 -119.19 -10.88 1.03) ; 76 ( 21.83 -119.71 -10.88 0.66) ; 77 ( 21.69 -120.00 -10.94 0.44) ; 78 ( 21.10 -121.17 -11.13 0.29) ; 79 ( 20.88 -121.46 -11.25 0.96) ; 80 ( 20.51 -121.75 -11.25 1.62) ; 81 ( 20.21 -122.19 -11.25 1.62) ; 82 ( 19.92 -123.00 -11.31 0.96) ; 83 ( 19.77 -123.36 -11.31 0.59) ; 84 ( 19.40 -124.02 -11.31 0.37) ; 85 ( 19.18 -124.60 -11.44 0.37) ; 86 ( 18.37 -125.55 -11.56 0.37) ; 87 ( 18.18 -126.04 -11.13 0.37) ; 88 ( 17.81 -126.63 -11.25 1.03) ; 89 ( 17.52 -127.14 -11.25 1.92) ; 90 ( 17.52 -127.50 -11.25 2.28) ; 91 ( 16.93 -127.80 -11.38 1.92) ; 92 ( 16.34 -128.23 -11.38 1.03) ; 93 ( 16.04 -128.75 -11.38 0.59) ; 94 ( 15.45 -129.04 -11.38 0.37) ; 95 ( 15.01 -129.55 -11.38 0.74) ; 96 ( 14.72 -129.92 -11.38 1.11) ; 97 ( 14.13 -130.35 -11.38 1.47) ; 98 ( 13.54 -130.94 -11.63 0.96) ; 99 ( 13.10 -131.60 -11.75 0.66) ; 100 ( 12.58 -132.04 -11.75 0.37) ; 101 ( 12.06 -132.55 -11.75 0.37) ; 102 ( 11.62 -132.91 -11.75 0.37) ; 103 Low ) ; End of split ) ; End of split | ( 12.60 -40.81 7.63 1.25) ; 1, R-1-1-1-2 ( 11.71 -41.32 8.69 0.74) ; 2 ( 10.68 -42.13 9.31 0.88) ; 3 ( 10.02 -42.93 10.63 0.88) ; 4 ( 9.72 -43.66 11.31 1.25) ; 5 ( 9.80 -44.47 11.56 0.88) ; 6 ( 9.72 -45.27 11.63 0.88) ; 7 ( 9.72 -46.15 11.63 0.88) ; 8 ( 9.87 -46.88 11.94 1.11) ; 9 ( 10.16 -47.54 11.19 1.33) ; 10 ( 10.09 -48.19 10.56 0.96) ; 11 ( 9.43 -49.22 9.19 1.18) ; 12 ( 9.06 -49.58 9.19 1.25) ; 13 ( 8.69 -50.31 9.19 0.88) ; 14 ( 8.40 -50.90 8.81 0.88) ; 15 ( 7.73 -51.26 9.88 1.11) ; 16 ( 7.59 -51.85 11.69 0.88) ; 17 ( 7.73 -52.43 12.31 0.88) ; 18 ( 7.88 -53.02 13.00 1.11) ; 19 ( 8.03 -53.75 14.06 1.40) ; 20 ( 8.25 -54.26 15.00 1.25) ; 21 ( 8.25 -55.21 15.13 1.03) ; 22 ( 8.10 -56.31 15.31 1.03) ; 23 ( 7.88 -57.26 15.44 1.55) ; 24 ( 7.44 -57.84 15.69 1.69) ; 25 ( 6.92 -58.50 15.75 1.84) ; 26 ( ( 6.63 -59.57 15.50 0.96) ; 1, R-1-1-1-2-1 ( 6.70 -60.23 16.13 0.66) ; 2 ( 6.92 -60.38 16.31 0.59) ; 3 ( 7.66 -61.03 16.56 0.59) ; 4 ( 7.88 -61.33 16.75 0.59) ; 5 ( 8.47 -61.77 16.75 0.59) ; 6 ( 8.91 -62.13 16.81 0.59) ; 7 ( 9.65 -62.79 16.88 0.81) ; 8 ( 9.79 -63.45 16.88 0.81) ; 9 ( 10.02 -64.03 16.88 0.81) ; 10 ( 10.16 -64.62 16.88 0.44) ; 11 Low | ( 6.04 -59.35 14.50 1.40) ; 1, R-1-1-1-2-2 ( 5.52 -59.94 13.81 1.03) ; 2 ( 4.93 -60.60 13.00 1.18) ; 3 ( 4.42 -61.33 11.50 1.33) ; 4 ( 4.20 -62.13 10.31 1.11) ; 5 ( 4.27 -63.23 9.13 1.18) ; 6 ( 4.42 -64.18 8.94 1.18) ; 7 ( 4.49 -65.06 8.88 1.18) ; 8 ( 4.27 -66.01 8.69 1.03) ; 9 ( 4.12 -67.17 8.13 1.18) ; 10 ( 3.31 -67.61 7.75 1.18) ; 11 ( 3.16 -68.27 8.94 1.25) ; 12 ( 3.09 -69.22 8.69 1.62) ; 13 ( 3.16 -70.17 9.13 1.92) ; 14 ( 3.46 -71.12 8.75 2.21) ; 15 ( ( 2.87 -72.73 8.63 1.25) ; 1, R-1-1-1-2-2-1 ( 2.87 -73.53 8.63 0.96) ; 2 ( 2.57 -74.56 8.63 0.96) ; 3 ( 1.76 -75.14 9.50 1.18) ; 4 ( 1.18 -76.09 9.69 1.18) ; 5 ( 0.14 -77.04 10.63 1.03) ; 6 ( -0.22 -77.99 11.63 1.40) ; 7 ( -0.52 -78.87 12.19 1.62) ; 8 ( -0.74 -79.75 12.56 1.18) ; 9 ( -0.96 -81.14 12.63 0.96) ; 10 ( -1.18 -82.23 12.94 1.40) ; 11 ( -1.77 -83.40 11.50 1.77) ; 12 ( -2.14 -84.35 10.19 1.55) ; 13 ( -2.88 -84.86 8.75 1.33) ; 14 ( -3.25 -85.89 7.00 1.47) ; 15 ( -3.02 -86.77 5.50 1.33) ; 16 ( -2.66 -87.57 5.06 1.33) ; 17 ( -2.36 -88.08 5.13 0.81) ; 18 ( -1.88 -88.87 4.50 0.81) ; 19 ( -0.85 -89.89 4.19 0.81) ; 20 ( -0.26 -90.77 4.00 0.81) ; 21 ( 0.18 -92.01 3.63 0.81) ; 22 ( 0.47 -92.96 5.06 1.18) ; 23 ( 0.25 -94.20 5.88 1.33) ; 24 ( -0.19 -95.37 6.94 1.55) ; 25 ( -0.78 -96.54 7.06 1.62) ; 26 ( -1.52 -97.13 8.44 1.62) ; 27 ( -2.10 -97.49 8.75 0.96) ; 28 ( -2.99 -98.37 8.63 1.18) ; 29 ( -3.50 -99.10 8.63 1.40) ; 30 ( -4.39 -99.90 8.94 1.25) ; 31 ( -4.90 -100.49 9.00 0.88) ; 32 ( -5.35 -101.44 9.69 0.66) ; 33 ( -5.20 -102.10 10.44 0.66) ; 34 ( -4.90 -102.68 10.56 0.96) ; 35 ( -4.61 -103.63 11.13 1.62) ; 36 ( -4.39 -104.36 10.63 1.92) ; 37 ( -4.54 -105.09 10.44 2.36) ; 38 ( -4.68 -106.55 10.75 1.69) ; 39 ( -4.46 -107.65 10.75 1.18) ; 40 ( -4.39 -108.46 10.75 1.18) ; 41 ( -4.02 -109.19 11.06 1.18) ; 42 ( -3.50 -110.28 10.81 1.62) ; 43 ( -2.84 -111.31 11.94 1.03) ; 44 ( -2.55 -112.33 12.25 1.03) ; 45 ( -2.92 -113.13 12.63 0.81) ; 46 ( -3.43 -113.94 12.94 0.74) ; 47 ( -4.24 -114.08 13.25 0.74) ; 48 ( -4.83 -114.08 13.56 1.03) ; 49 ( -6.01 -114.01 14.19 1.55) ; 50 ( -7.11 -114.23 14.38 1.25) ; 51 ( -7.92 -114.52 14.63 0.81) ; 52 ( -9.32 -115.03 15.19 0.81) ; 53 ( -9.91 -115.18 15.94 0.96) ; 54 ( -10.58 -115.77 15.94 0.66) ; 55 ( -11.46 -116.35 16.25 0.66) ; 56 ( -11.61 -117.23 16.31 0.81) ; 57 ( -11.79 -118.01 16.38 0.81) ; 58 ( -11.72 -119.03 16.44 0.81) ; 59 ( -11.43 -119.84 16.69 1.03) ; 60 ( -11.28 -120.57 15.69 1.03) ; 61 ( -11.06 -121.89 14.56 0.96) ; 62 ( -10.84 -123.35 14.38 1.03) ; 63 ( -10.62 -124.22 14.38 0.96) ; 64 ( -10.32 -124.88 14.38 1.62) ; 65 ( -10.10 -125.61 14.06 1.62) ; 66 ( -9.80 -126.42 14.63 1.40) ; 67 ( -9.36 -127.08 15.06 0.96) ; 68 ( -9.14 -127.81 15.19 0.81) ; 69 ( -8.92 -128.54 16.00 0.81) ; 70 ( -8.99 -129.71 15.75 0.96) ; 71 ( -9.73 -131.24 16.13 0.81) ; 72 ( -10.47 -132.48 15.69 0.96) ; 73 ( -10.98 -133.44 15.69 0.96) ; 74 ( -11.20 -134.53 15.50 0.88) ; 75 ( -10.98 -135.41 15.00 1.18) ; 76 ( -11.35 -136.14 15.00 1.18) ; 77 ( -11.79 -137.24 14.88 0.96) ; 78 ( -12.68 -138.11 14.88 0.96) ; 79 ( -13.27 -138.63 14.88 0.96) ; 80 ( -14.00 -138.99 14.88 1.11) ; 81 ( -15.04 -139.36 14.94 1.33) ; 82 ( -15.99 -139.72 14.94 1.18) ; 83 ( -17.17 -139.87 14.69 1.11) ; 84 ( -18.20 -140.38 14.69 0.96) ; 85 ( -18.94 -140.89 14.63 0.66) ; 86 ( -19.60 -141.04 14.50 0.66) ; 87 ( -20.34 -141.70 13.94 0.96) ; 88 ( -20.63 -142.43 13.88 1.33) ; 89 ( -20.86 -143.67 13.19 1.47) ; 90 ( -21.15 -144.55 12.81 1.18) ; 91 ( -21.52 -145.13 12.00 1.03) ; 92 ( -22.18 -146.01 11.13 0.96) ; 93 ( -22.20 -147.10 11.00 0.59) ; 94 ( -22.57 -147.68 10.13 0.81) ; 95 ( -22.57 -148.49 9.50 0.59) ; 96 ( -22.65 -149.37 9.19 0.59) ; 97 Low | ( 3.98 -71.66 9.44 1.25) ; 1, R-1-1-1-2-2-2 ( 4.57 -72.32 10.63 0.74) ; 2 ( 4.57 -73.05 11.06 0.74) ; 3 ( 4.21 -73.63 11.19 0.74) ; 4 ( 4.06 -74.07 11.19 0.74) ; 5 ( 4.06 -74.80 11.63 0.74) ; 6 ( 4.21 -75.61 12.06 1.11) ; 7 ( 4.28 -76.27 12.81 1.47) ; 8 ( 3.98 -76.85 13.56 1.84) ; 9 ( 3.54 -77.66 14.44 1.84) ; 10 ( 2.88 -78.68 15.06 1.03) ; 11 ( 2.51 -79.26 15.88 0.88) ; 12 ( 2.73 -79.92 16.13 0.74) ; 13 ( 2.95 -80.58 16.25 0.74) ; 14 ( 3.54 -81.31 16.44 0.74) ; 15 ( 4.13 -81.89 16.31 0.96) ; 16 ( 4.87 -82.26 16.06 0.81) ; 17 ( 5.53 -82.55 16.00 0.66) ; 18 ( 6.27 -82.99 15.69 0.66) ; 19 Low ) ; End of split ) ; End of split ) ; End of split | ( 18.93 -32.07 1.44 0.96) ; 1, R-1-1-2 ( 19.38 -32.43 2.06 0.66) ; 2 ( 19.89 -32.65 3.19 0.66) ; 3 ( 19.97 -32.65 3.44 0.96) ; 4 ( 20.19 -33.02 3.56 0.96) ; 5 ( 19.74 -33.53 4.56 0.96) ; 6 ( 19.45 -34.48 6.06 0.74) ; 7 ( 19.89 -35.50 6.50 0.74) ; 8 ( 20.26 -36.01 6.94 0.74) ; 9 ( 21.14 -36.23 7.31 0.96) ; 10 ( 22.32 -36.60 6.56 0.96) ; 11 ( 23.28 -36.89 6.50 0.96) ; 12 ( 24.24 -37.18 5.38 0.66) ; 13 ( 25.20 -37.70 4.81 0.66) ; 14 ( 26.38 -38.06 4.81 0.81) ; 15 ( 27.11 -38.65 5.56 0.81) ; 16 ( 27.63 -39.67 4.88 0.96) ; 17 ( 27.70 -40.55 5.81 0.81) ; 18 ( 27.70 -41.64 6.06 0.81) ; 19 ( 27.78 -42.67 6.31 0.81) ; 20 ( 27.55 -44.42 5.94 0.74) ; 21 ( 27.41 -45.66 5.50 0.74) ; 22 ( 27.55 -47.13 5.50 0.74) ; 23 ( 27.48 -48.37 5.13 0.74) ; 24 ( 27.26 -49.90 5.13 0.74) ; 25 ( 27.19 -51.07 5.13 0.74) ; 26 ( 27.63 -52.46 5.13 0.66) ; 27 ( 28.14 -53.78 4.50 0.66) ; 28 ( 28.59 -54.95 4.50 0.81) ; 29 ( 29.32 -55.90 3.25 0.81) ; 30 ( 30.35 -56.63 3.19 0.81) ; 31 ( 30.43 -57.36 1.81 0.88) ; 32 ( 30.80 -58.02 1.44 0.88) ; 33 ( 31.02 -58.75 1.13 0.88) ; 34 ( 31.16 -59.41 0.38 0.81) ; 35 ( 31.31 -60.36 -0.75 0.96) ; 36 ( 31.68 -61.67 -0.75 0.96) ; 37 ( 31.75 -62.62 -0.75 0.66) ; 38 ( 31.75 -62.77 -0.75 0.66) ; 39 ( 32.20 -62.99 -1.00 0.66) ; 40 ( 33.01 -63.50 -0.13 0.81) ; 41 ( 33.60 -64.01 1.63 0.96) ; 42 ( 34.33 -64.38 1.75 1.03) ; 43 ( 35.14 -64.96 2.31 1.03) ; 44 ( 35.95 -65.84 2.63 1.18) ; 45 ( 36.17 -66.64 2.88 1.18) ; 46 ( 36.47 -67.37 3.00 0.59) ; 47 ( 36.91 -67.74 3.00 0.59) ; 48 ( 37.87 -68.98 2.94 0.66) ; 49 ( 38.24 -69.57 2.88 0.66) ; 50 ( 38.83 -70.52 2.63 0.66) ; 51 ( 39.19 -71.25 2.94 0.66) ; 52 ( 38.83 -72.49 3.88 0.88) ; 53 ( 38.46 -73.51 5.94 0.96) ; 54 ( 38.53 -74.39 6.88 0.81) ; 55 ( 38.02 -74.83 7.25 0.81) ; 56 ( 37.87 -75.85 7.63 0.66) ; 57 ( 38.75 -76.88 7.63 0.66) ; 58 ( 39.78 -77.68 8.06 0.88) ; 59 ( 40.89 -78.27 7.88 0.88) ; 60 ( 41.55 -78.92 7.06 0.66) ; 61 ( 41.70 -79.58 4.75 0.66) ; 62 ( 41.85 -80.46 4.63 1.03) ; 63 ( 41.77 -81.41 4.63 1.11) ; 64 ( 42.44 -81.99 4.38 0.66) ; 65 ( 43.17 -82.21 2.94 0.96) ; 66 ( 43.69 -82.58 2.06 1.55) ; 67 ( 44.13 -82.72 1.31 1.11) ; 68 ( 44.46 -83.36 1.31 0.74) ; 69 ( 44.91 -83.87 0.69 0.52) ; 70 ( 45.35 -84.39 0.69 0.52) ; 71 ( 45.64 -85.04 0.69 0.52) ; 72 ( 46.31 -85.92 0.69 0.66) ; 73 ( 46.67 -87.02 0.69 0.29) ; 74 ( 47.04 -87.67 0.69 1.18) ; 75 ( 47.26 -88.19 0.50 1.18) ; 76 ( 47.63 -88.84 1.63 0.66) ; 77 ( 48.29 -89.58 2.19 0.66) ; 78 ( 49.03 -90.82 1.63 0.81) ; 79 ( 49.33 -91.70 1.63 0.81) ; 80 ( 49.55 -92.50 1.31 0.44) ; 81 ( 49.69 -93.30 1.31 0.44) ; 82 ( 49.47 -94.11 1.31 0.66) ; 83 ( 49.10 -94.98 1.31 0.66) ; 84 ( 48.52 -96.23 1.31 0.44) ; 85 ( 48.15 -96.81 -0.69 0.74) ; 86 ( 48.22 -97.47 -1.19 0.74) ; 87 ( 48.44 -98.05 -1.56 0.52) ; 88 ( 48.74 -99.08 -2.19 0.52) ; 89 ( 49.18 -100.25 -2.88 0.74) ; 90 ( 49.33 -101.42 -5.00 1.11) ; 91 ( 49.55 -102.00 -5.63 0.59) ; 92 ( 49.92 -103.17 -6.06 0.44) ; 93 ( 50.28 -104.12 -5.81 0.74) ; 94 ( 50.50 -104.71 -5.88 1.40) ; 95 ( 50.73 -105.66 -7.25 1.18) ; 96 ( 51.09 -106.32 -6.69 0.59) ; 97 ( 51.02 -107.12 -7.38 0.59) ; 98 ( 51.02 -107.85 -7.81 1.33) ; 99 ( 51.09 -108.58 -8.50 1.69) ; 100 ( 50.95 -109.53 -9.13 0.59) ; 101 ( 50.95 -110.99 -9.31 0.44) ; 102 ( 51.02 -111.65 -9.63 0.44) ; 103 ( 51.39 -112.82 -9.81 0.74) ; 104 ( 51.42 -113.87 -9.75 1.47) ; 105 ( 51.64 -114.74 -10.00 1.03) ; 106 ( 51.56 -115.04 -11.00 0.59) ; 107 ( 51.64 -115.69 -11.56 0.29) ; 108 ( 51.93 -116.86 -11.63 0.29) ; 109 ( 52.23 -117.67 -11.81 1.03) ; 110 ( 52.45 -118.18 -12.06 1.84) ; 111 ( 52.82 -118.76 -12.13 2.65) ; 112 ( 53.11 -119.71 -12.81 0.74) ; 113 ( 53.26 -120.23 -13.31 0.52) ; 114 ( 53.48 -120.81 -13.94 0.88) ; 115 ( 53.99 -120.96 -14.38 1.25) ; 116 Low ) ; End of split | ( 17.17 -29.58 0.38 0.52) ; 1, R-1-2 ( 16.65 -30.09 -1.63 0.52) ; 2 ( 16.80 -31.04 -3.94 0.52) ; 3 ( 17.17 -31.85 -6.38 0.52) ; 4 ( 17.17 -32.58 -6.69 0.52) ; 5 ( 16.72 -33.68 -7.69 0.66) ; 6 ( 16.13 -34.77 -9.38 0.74) ; 7 ( 15.32 -35.58 -10.19 0.74) ; 8 ( 14.88 -36.53 -10.88 0.96) ; 9 ( 14.96 -36.96 -11.88 1.03) ; 10 ( 14.66 -38.21 -13.19 1.03) ; 11 ( 14.59 -38.94 -13.94 1.40) ; 12 ( 14.59 -39.89 -15.31 1.03) ; 13 ( 14.88 -40.84 -15.31 0.81) ; 14 ( 15.62 -42.01 -15.63 0.74) ; 15 ( 16.36 -43.10 -16.13 0.66) ; 16 ( 17.24 -43.91 -16.13 0.66) ; 17 ( 17.61 -44.57 -16.75 0.74) ; 18 ( 17.61 -45.30 -18.00 0.74) ; 19 ( 17.98 -46.10 -18.13 0.59) ; 20 ( 18.64 -47.34 -18.69 0.74) ; 21 ( 19.30 -47.42 -19.31 0.66) ; 22 ( 20.11 -47.71 -20.25 0.66) ; 23 ( 20.70 -48.51 -21.00 0.88) ; 24 ( 20.56 -49.03 -22.19 0.96) ; 25 ( 20.41 -49.76 -23.25 0.74) ; 26 ( 20.56 -51.15 -23.94 0.74) ; 27 ( 20.70 -51.80 -24.06 0.74) ; 28 ( 21.29 -52.32 -24.31 0.74) ; 29 ( 22.32 -53.27 -24.50 0.52) ; 30 ( 22.77 -53.70 -25.06 0.52) ; 31 ( 23.21 -53.70 -26.13 0.81) ; 32 ( 23.58 -53.92 -26.63 1.18) ; 33 ( 23.72 -53.92 -28.38 1.55) ; 34 ( 24.09 -54.36 -28.31 2.28) ; 35 ( 24.17 -54.58 -28.31 2.65) ; 36 ( 24.24 -54.65 -30.06 1.69) ; 37 ( 24.53 -55.17 -32.81 0.96) ; 38 ( 24.68 -55.60 -34.19 0.74) ; 39 ( 25.34 -56.04 -36.00 0.74) ; 40 ( 26.30 -56.26 -35.94 1.03) ; 41 ( 27.11 -56.26 -36.25 1.25) ; 42 ( 27.70 -56.56 -36.50 0.81) ; 43 ( 28.51 -56.63 -38.81 0.59) ; 44 ( 29.03 -56.99 -41.00 0.81) ; 45 ( 29.32 -57.29 -41.56 1.18) ; 46 ( 29.54 -57.51 -42.13 1.99) ; 47 ( 30.06 -57.87 -42.13 2.36) ; 48 ( 30.21 -58.02 -43.94 1.84) ; 49 ( 30.43 -58.46 -46.44 0.96) ; 50 ( 30.50 -58.82 -49.88 0.74) ; 51 ( 30.28 -59.70 -50.38 0.74) ; 52 ( 29.91 -60.14 -51.31 0.74) ; 53 ( 29.84 -60.72 -51.31 1.18) ; 54 ( 29.40 -61.01 -51.94 1.84) ; 55 ( 28.66 -61.01 -53.63 1.40) ; 56 ( 28.29 -60.58 -54.13 0.96) ; 57 ( 27.63 -60.65 -56.25 0.59) ; 58 ( 26.82 -60.72 -57.63 0.96) ; 59 ( 26.23 -60.58 -58.25 1.33) ; 60 ( 25.34 -60.72 -59.69 1.77) ; 61 ( 25.27 -60.36 -63.00 0.81) ; 62 ( 23.87 -60.28 -64.44 0.44) ; 63 ( 23.21 -59.99 -65.25 0.44) ; 64 ( 22.40 -59.77 -66.06 0.44) ; 65 ( 21.51 -60.21 -66.31 0.74) ; 66 ( 21.07 -60.06 -67.56 0.74) ; 67 Low ) ; End of split | ( 18.38 -25.63 -0.19 1.92) ; 1, R-2 ( 19.56 -25.85 0.13 1.62) ; 2 ( 20.82 -26.65 0.56 1.77) ; 3 ( 21.41 -27.53 0.75 1.77) ; 4 ( 21.77 -28.85 0.94 1.40) ; 5 ( 22.36 -29.94 1.06 1.40) ; 6 ( 22.95 -30.97 0.13 1.40) ; 7 ( 23.54 -31.99 -0.06 1.18) ; 8 ( 24.35 -33.45 -0.25 1.33) ; 9 ( 24.94 -34.40 -0.44 1.33) ; 10 ( 25.60 -34.91 -0.88 1.18) ; 11 ( 26.27 -36.16 -1.25 1.11) ; 12 ( 27.37 -37.84 -2.13 0.96) ; 13 ( 27.74 -39.37 -3.25 1.11) ; 14 ( 27.81 -39.96 -3.44 1.33) ; 15 ( 28.26 -40.98 -3.81 1.11) ; 16 ( 28.63 -42.00 -4.31 1.33) ; 17 ( 28.77 -43.03 -4.50 1.69) ; 18 ( 28.92 -43.54 -4.63 2.06) ; 19 ( 29.17 -44.17 -4.63 2.06) ; 20 ( ( 29.36 -44.42 -4.63 2.50) ; 1, R-2-1 ( 30.10 -44.85 -3.75 1.69) ; 2 ( 30.84 -45.73 -4.19 1.33) ; 3 ( 31.57 -46.68 -4.19 0.96) ; 4 ( 32.09 -47.41 -4.19 0.96) ; 5 ( 33.12 -47.78 -4.38 0.96) ; 6 ( 34.08 -47.93 -4.31 1.11) ; 7 ( 35.11 -48.14 -4.25 1.33) ; 8 ( 35.40 -48.44 -4.19 1.33) ; 9 ( 35.85 -48.66 -4.19 1.03) ; 10 ( 37.10 -49.02 -4.19 1.03) ; 11 ( 37.69 -49.24 -4.19 1.18) ; 12 ( 38.72 -49.68 -4.19 1.18) ; 13 ( 39.46 -49.53 -3.63 1.33) ; 14 ( 40.63 -49.83 -3.25 1.69) ; 15 ( 41.30 -50.19 -3.25 1.69) ; 16 ( 41.81 -50.63 -3.25 1.33) ; 17 ( 42.77 -51.51 -3.19 1.18) ; 18 ( 43.73 -52.16 -3.19 1.18) ; 19 ( 44.02 -53.19 -2.75 1.33) ; 20 ( 44.32 -54.50 -3.31 0.88) ; 21 ( 45.20 -55.38 -3.94 1.40) ; 22 ( 46.15 -55.93 -4.38 1.69) ; 23 ( 46.78 -56.22 -4.38 1.69) ; 24 ( ( 46.66 -56.30 -4.81 1.69) ; 1, R-2-1-1 ( 46.88 -57.25 -4.81 1.25) ; 2 ( 47.55 -57.69 -4.81 0.74) ; 3 ( 48.06 -58.86 -5.06 0.52) ; 4 ( 48.36 -59.37 -5.25 0.74) ; 5 ( 48.73 -60.25 -5.50 0.74) ; 6 ( 48.80 -61.20 -6.38 0.74) ; 7 ( 48.87 -62.22 -7.00 0.81) ; 8 ( 49.17 -63.10 -6.81 0.59) ; 9 ( 49.61 -64.05 -5.81 0.59) ; 10 ( 49.68 -65.22 -5.44 0.74) ; 11 ( 49.32 -66.02 -4.25 0.74) ; 12 ( 48.87 -67.05 -4.19 0.59) ; 13 ( 48.21 -67.78 -3.88 0.59) ; 14 ( 48.36 -68.14 -3.81 0.52) ; 15 ( 49.32 -68.58 -4.13 0.74) ; 16 ( 49.83 -68.87 -4.31 0.96) ; 17 ( 50.49 -69.60 -2.19 1.18) ; 18 ( 51.38 -70.12 -1.81 1.03) ; 19 ( 52.63 -70.70 -1.44 0.74) ; 20 ( 53.15 -71.50 -1.38 0.88) ; 21 ( 53.59 -72.24 -0.88 1.18) ; 22 ( 54.25 -73.19 -0.88 1.18) ; 23 ( 53.81 -74.21 -0.56 0.81) ; 24 ( 53.66 -75.38 0.06 0.66) ; 25 ( 54.33 -75.67 0.25 0.66) ; 26 ( 55.06 -75.74 0.88 0.81) ; 27 ( 56.17 -75.31 1.13 0.96) ; 28 ( 57.12 -75.01 1.75 0.81) ; 29 ( 57.71 -75.09 3.44 0.81) ; 30 ( 58.23 -75.45 3.75 0.81) ; 31 ( 58.82 -76.18 4.13 0.81) ; 32 ( 59.19 -77.79 4.25 1.03) ; 33 ( 59.34 -78.67 4.25 0.88) ; 34 ( 59.78 -79.47 4.25 0.74) ; 35 ( 60.00 -80.20 4.25 0.88) ; 36 ( 60.44 -80.93 4.25 0.88) ; 37 ( 60.73 -81.37 4.25 0.88) ; 38 ( 61.03 -81.59 4.25 0.88) ; 39 ( 61.62 -82.18 4.94 0.74) ; 40 ( 62.28 -82.62 5.13 0.52) ; 41 ( 62.80 -83.13 5.25 0.52) ; 42 ( 63.31 -83.42 5.69 0.52) ; 43 ( 63.90 -83.78 5.69 0.52) ; 44 ( 64.20 -84.00 7.19 0.74) ; 45 ( 64.20 -84.30 9.19 0.59) ; 46 ( 63.31 -83.78 9.44 0.74) ; 47 ( 62.80 -83.49 9.56 0.74) ; 48 Low | ( 47.66 -56.66 -5.13 0.81) ; 1, R-2-1-2 ( 48.40 -57.39 -5.44 0.66) ; 2 ( 48.91 -57.68 -5.88 0.66) ; 3 ( 49.50 -58.20 -6.13 0.66) ; 4 ( 50.31 -58.78 -7.38 0.88) ; 5 ( 50.76 -59.66 -7.75 1.11) ; 6 ( 51.20 -60.32 -8.31 0.96) ; 7 ( 52.23 -61.41 -8.56 1.18) ; 8 ( 52.97 -62.00 -9.13 1.18) ; 9 ( 53.48 -62.95 -9.13 1.03) ; 10 ( 54.22 -63.82 -9.25 0.88) ; 11 ( 54.96 -64.85 -9.63 0.74) ; 12 ( 55.91 -65.58 -9.81 0.52) ; 13 ( 56.80 -66.38 -10.06 0.66) ; 14 ( 56.80 -67.52 -11.00 0.74) ; 15 ( 57.09 -68.76 -11.56 0.74) ; 16 ( 57.02 -70.01 -12.19 0.96) ; 17 ( 57.24 -70.74 -12.75 0.96) ; 18 ( 57.38 -71.17 -13.13 1.33) ; 19 ( 57.46 -72.56 -14.75 1.40) ; 20 ( 57.61 -73.37 -14.75 1.03) ; 21 ( 57.61 -74.25 -15.38 1.33) ; 22 ( 57.61 -74.98 -16.56 1.84) ; 23 ( 57.83 -75.78 -17.69 1.03) ; 24 ( 57.46 -76.44 -18.63 0.74) ; 25 ( 57.53 -77.02 -18.63 0.37) ; 26 ( 56.65 -77.46 -18.63 0.37) ; 27 ( 56.21 -78.63 -18.69 0.66) ; 28 ( 56.28 -79.36 -18.88 0.52) ; 29 ( 56.80 -80.02 -19.38 1.11) ; 30 ( 57.68 -80.90 -19.38 1.55) ; 31 ( 58.27 -81.77 -20.00 1.11) ; 32 ( 58.71 -82.58 -20.00 1.03) ; 33 ( 59.15 -83.31 -20.31 1.77) ; 34 ( 59.45 -84.19 -20.81 1.77) ; 35 ( 59.67 -85.06 -21.56 0.96) ; 36 ( 60.41 -85.87 -21.56 0.44) ; 37 ( 60.77 -86.45 -22.50 0.37) ; 38 ( 60.92 -86.89 -22.63 0.37) ; 39 ( 61.22 -87.70 -21.19 0.66) ; 40 ( 61.58 -88.35 -20.94 1.47) ; 41 ( 61.88 -89.60 -21.38 1.47) ; 42 ( 62.03 -90.62 -21.25 0.81) ; 43 ( 61.88 -91.50 -21.06 0.81) ; 44 ( 62.47 -92.67 -20.81 0.81) ; 45 ( 62.54 -93.54 -20.81 0.81) ; 46 ( 63.06 -94.64 -20.81 1.03) ; 47 ( 63.94 -95.66 -20.94 0.88) ; 48 ( 65.27 -96.28 -22.69 0.81) ; 49 ( 65.72 -96.57 -22.75 0.81) ; 50 ( 66.60 -97.09 -23.06 0.59) ; 51 ( 67.19 -97.30 -23.06 0.59) ; 52 ( 68.15 -97.89 -22.56 1.11) ; 53 ( 68.44 -98.18 -21.69 1.92) ; 54 ( 68.66 -98.62 -21.19 2.28) ; 55 ( 69.33 -98.99 -20.94 2.28) ; 56 ( 70.21 -99.35 -20.94 1.40) ; 57 ( 71.09 -100.01 -20.44 1.18) ; 58 ( 72.05 -100.16 -20.06 1.03) ; 59 ( 72.71 -100.52 -19.63 1.33) ; 60 ( 73.38 -101.03 -19.69 1.62) ; 61 ( 74.48 -101.54 -19.81 1.33) ; 62 ( 75.51 -102.20 -20.06 0.74) ; 63 ( 76.10 -102.64 -20.75 0.74) ; 64 ( 76.55 -103.45 -21.13 0.74) ; 65 ( 77.21 -103.88 -21.13 1.11) ; 66 ( 78.31 -104.40 -21.69 1.47) ; 67 ( 78.98 -104.91 -21.69 1.77) ; 68 ( 79.79 -105.27 -21.88 1.03) ; 69 ( 80.52 -105.56 -22.00 0.66) ; 70 ( 81.11 -105.93 -22.38 0.59) ; 71 ( 81.56 -105.78 -22.44 0.37) ; 72 ( 82.07 -105.64 -22.44 0.37) ; 73 ( 82.81 -106.37 -22.63 0.44) ; 74 ( 83.40 -107.32 -22.94 0.81) ; 75 ( 83.84 -107.98 -24.31 1.55) ; 76 ( 84.43 -108.64 -24.63 1.99) ; 77 ( 85.31 -109.51 -25.19 1.25) ; 78 ( 85.98 -110.39 -25.88 1.55) ; 79 ( 86.27 -111.41 -26.31 0.81) ; 80 ( 86.64 -112.07 -26.31 0.44) ; 81 ( 87.30 -112.58 -26.31 0.44) ; 82 ( 88.26 -113.09 -26.50 0.44) ; 83 ( 88.92 -113.39 -26.50 0.81) ; 84 ( 89.36 -113.83 -26.56 0.81) ; 85 ( 89.59 -113.97 -26.75 0.44) ; 86 ( 90.25 -114.63 -26.75 0.44) ; 87 ( 90.76 -115.14 -27.06 1.25) ; 88 ( 91.58 -115.58 -27.75 1.92) ; 89 ( 92.39 -115.29 -27.75 1.47) ; 90 ( 93.12 -115.43 -27.94 0.96) ; 91 ( 93.49 -115.58 -27.94 0.52) ; 92 ( 94.15 -116.16 -28.06 0.37) ; 93 ( 94.74 -116.90 -28.94 1.18) ; 94 ( 95.19 -117.55 -29.63 1.62) ; 95 ( 95.77 -117.99 -29.63 0.88) ; 96 ( 96.36 -118.06 -30.31 0.52) ; 97 ( 96.95 -118.50 -31.50 0.52) ; 98 ( 97.40 -118.58 -31.81 0.96) ; 99 ( 97.62 -118.65 -33.00 0.96) ; 100 ( 98.43 -118.36 -33.25 0.59) ; 101 ( 99.02 -118.65 -33.25 0.37) ; 102 ( 99.61 -118.87 -33.25 1.18) ; 103 ( 100.34 -119.31 -34.38 1.92) ; 104 ( 101.08 -119.45 -34.75 1.92) ; 105 ( 101.74 -119.38 -34.94 1.03) ; 106 ( 102.40 -119.75 -35.69 0.66) ; 107 ( 102.70 -120.18 -37.81 0.52) ; 108 ( 103.58 -120.33 -37.81 0.59) ; 109 ( 104.10 -120.33 -37.88 0.59) ; 110 ( 104.98 -119.75 -38.06 0.37) ; 111 ( 105.72 -119.53 -38.38 1.18) ; 112 ( 106.31 -119.53 -39.44 1.99) ; 113 ( 106.97 -118.94 -41.69 1.62) ; 114 Low ) ; End of split | ( 28.66 -45.12 -4.44 0.96) ; 1, R-2-2 ( 28.80 -45.78 -4.81 0.66) ; 2 ( 29.10 -46.29 -4.94 0.66) ; 3 ( 29.76 -47.02 -4.94 0.66) ; 4 ( 30.35 -47.53 -4.94 0.59) ; 5 ( 31.01 -48.19 -5.13 0.66) ; 6 ( 31.46 -49.65 -5.13 0.66) ; 7 ( 31.53 -50.24 -5.69 0.66) ; 8 ( 32.04 -50.97 -5.69 0.74) ; 9 ( 32.56 -51.26 -5.75 0.74) ; 10 ( 33.30 -51.70 -6.25 0.81) ; 11 ( 34.03 -52.14 -7.25 0.66) ; 12 ( 34.99 -52.87 -7.25 0.96) ; 13 ( 35.65 -53.82 -7.69 1.18) ; 14 ( 36.39 -54.62 -7.75 1.18) ; 15 ( 36.98 -55.06 -8.69 1.55) ; 16 ( ( 37.64 -56.08 -7.00 1.84) ; 1, R-2-2-1 ( 38.16 -57.11 -5.81 1.40) ; 2 ( 38.90 -58.06 -4.63 1.18) ; 3 ( 39.78 -58.64 -2.69 0.96) ; 4 ( 41.18 -58.86 -2.31 0.66) ; 5 ( 41.84 -58.94 -1.13 0.66) ; 6 ( 42.21 -58.79 0.06 0.66) ; 7 ( 41.70 -58.86 -1.63 0.66) ; 8 ( 40.89 -58.86 -0.13 0.88) ; 9 ( 40.30 -58.64 1.38 1.18) ; 10 ( 39.78 -59.01 1.38 1.18) ; 11 ( 40.37 -59.30 2.31 0.81) ; 12 ( 41.03 -59.30 2.81 0.81) ; 13 ( 41.55 -59.30 3.38 0.81) ; 14 ( 41.77 -59.81 3.88 0.81) ; 15 ( 42.14 -60.98 4.38 1.03) ; 16 ( 42.36 -62.01 4.63 0.81) ; 17 ( 42.36 -62.81 4.75 0.81) ; 18 ( 42.65 -63.61 5.25 0.66) ; 19 ( 43.24 -63.98 5.31 0.66) ; 20 ( 43.91 -64.35 5.69 0.66) ; 21 ( 44.57 -64.56 6.13 0.66) ; 22 ( 44.86 -65.00 6.88 0.96) ; 23 ( 45.38 -65.81 7.06 1.11) ; 24 ( 46.26 -66.39 7.44 0.96) ; 25 ( 46.93 -66.98 7.44 0.74) ; 26 ( 47.59 -67.78 7.50 0.59) ; 27 ( 48.69 -68.58 7.56 0.52) ; 28 ( 49.51 -69.54 7.63 0.81) ; 29 ( 50.39 -70.19 7.75 0.96) ; 30 ( 51.49 -70.49 8.13 0.66) ; 31 ( 52.16 -70.56 8.50 0.66) ; 32 ( 52.53 -70.41 9.50 0.66) ; 33 ( 52.30 -70.19 10.00 0.66) ; 34 ( 52.16 -70.49 10.63 0.81) ; 35 ( 52.16 -70.78 10.75 1.11) ; 36 ( 52.60 -71.65 10.75 0.88) ; 37 ( 52.67 -72.31 10.75 0.88) ; 38 ( 52.82 -73.12 11.19 0.74) ; 39 ( 53.04 -73.77 10.19 0.74) ; 40 ( 53.78 -74.51 9.38 1.11) ; 41 ( 54.66 -75.31 9.38 1.03) ; 42 ( 55.47 -76.11 9.19 1.03) ; 43 ( 56.43 -77.14 7.50 0.81) ; 44 ( 57.02 -78.01 8.88 0.59) ; 45 ( 57.54 -78.67 8.88 0.59) ; 46 ( 58.05 -78.96 8.94 0.59) ; 47 ( 58.71 -79.48 9.06 0.59) ; 48 ( 59.08 -79.70 9.56 0.59) ; 49 ( 59.16 -80.21 10.75 0.59) ; 50 ( 59.30 -80.50 11.19 0.59) ; 51 ( 59.89 -81.23 11.25 0.81) ; 52 ( 59.97 -81.89 11.25 1.55) ; 53 ( 60.34 -82.62 11.25 1.55) ; 54 ( 60.70 -83.57 11.81 0.74) ; 55 ( 61.15 -84.52 11.13 0.59) ; 56 ( 60.78 -84.74 11.88 0.44) ; 57 ( 60.63 -85.25 11.88 0.44) ; 58 ( 60.92 -85.81 12.06 0.81) ; 59 ( 61.65 -86.32 12.56 1.11) ; 60 ( 62.09 -86.47 13.19 1.40) ; 61 ( 61.65 -87.20 13.50 1.11) ; 62 ( 61.06 -87.56 14.13 0.81) ; 63 ( 60.25 -88.29 14.94 0.66) ; 64 ( 59.96 -89.10 15.00 0.96) ; 65 ( 59.66 -89.61 15.06 1.62) ; 66 ( 59.15 -90.41 15.06 0.81) ; 67 ( 58.26 -91.22 15.06 0.52) ; 68 ( 57.90 -92.02 15.06 0.52) ; 69 ( 58.04 -93.34 14.75 0.59) ; 70 ( 58.04 -94.29 15.06 0.44) ; 71 ( 58.34 -94.87 15.38 0.81) ; 72 ( 58.41 -95.31 15.69 1.25) ; 73 ( 58.41 -95.68 16.06 1.62) ; 74 ( 59.07 -96.19 16.25 1.62) ; 75 ( 59.59 -96.63 17.00 1.33) ; 76 ( 59.81 -96.92 17.13 0.66) ; 77 ( 60.03 -97.94 17.19 0.44) ; 78 ( 60.33 -98.53 17.19 0.44) ; 79 ( 60.99 -98.89 17.56 0.66) ; 80 ( 61.21 -99.04 17.63 1.11) ; 81 ( 61.95 -99.48 17.63 0.81) ; 82 ( 62.39 -99.77 17.63 0.44) ; 83 Low | ( 36.94 -56.04 -8.25 1.18) ; 1, R-2-2-2 ( 37.16 -56.91 -8.19 0.88) ; 2 ( 37.24 -57.50 -8.81 0.59) ; 3 ( 37.46 -58.45 -9.31 0.59) ; 4 ( 36.94 -59.25 -9.31 0.59) ; 5 ( 36.65 -60.49 -9.38 0.66) ; 6 ( 36.87 -61.96 -10.88 0.81) ; 7 ( 37.24 -63.20 -11.69 0.66) ; 8 ( 37.68 -64.37 -12.19 0.88) ; 9 ( 38.19 -65.68 -14.38 0.96) ; 10 ( 38.93 -66.78 -14.50 0.96) ; 11 ( 40.11 -68.17 -14.50 0.96) ; 12 ( 40.77 -69.34 -14.81 0.96) ; 13 ( 41.58 -70.65 -15.38 0.81) ; 14 ( 41.88 -72.12 -16.06 0.66) ; 15 ( 42.03 -73.58 -16.06 0.66) ; 16 ( 41.95 -74.75 -16.06 0.81) ; 17 ( 41.88 -75.77 -16.19 1.11) ; 18 ( 42.54 -76.43 -16.50 1.18) ; 19 ( 43.57 -77.31 -16.94 1.11) ; 20 ( 44.38 -77.75 -17.31 1.40) ; 21 ( 45.49 -78.40 -17.31 1.03) ; 22 ( 46.67 -78.84 -17.88 0.88) ; 23 ( 47.62 -79.50 -18.06 0.66) ; 24 ( 48.36 -80.60 -18.94 0.96) ; 25 ( 48.58 -81.40 -20.13 1.40) ; 26 ( 48.80 -82.35 -20.13 2.21) ; 27 ( 48.66 -83.74 -20.88 1.55) ; 28 ( 48.66 -84.18 -22.50 1.11) ; 29 ( 49.10 -84.98 -22.50 0.74) ; 30 ( 49.54 -85.90 -22.50 0.74) ; 31 ( 49.98 -87.07 -23.94 0.52) ; 32 ( 50.28 -87.87 -24.19 0.52) ; 33 ( 50.94 -89.34 -24.56 0.74) ; 34 ( 51.31 -90.29 -24.75 0.96) ; 35 ( 51.31 -91.16 -24.81 0.96) ; 36 ( 51.24 -92.11 -25.63 0.74) ; 37 ( 51.16 -93.58 -26.13 0.59) ; 38 ( 51.24 -94.82 -26.31 0.96) ; 39 ( 51.24 -96.13 -26.56 0.96) ; 40 ( 51.02 -97.45 -26.88 0.59) ; 41 ( 50.72 -99.06 -27.00 0.44) ; 42 ( 50.79 -100.89 -27.13 0.44) ; 43 ( 51.24 -101.98 -27.13 0.44) ; 44 ( 51.46 -103.08 -27.13 1.11) ; 45 ( 51.75 -103.81 -27.13 1.84) ; 46 ( 52.05 -104.61 -27.19 2.28) ; 47 ( 52.34 -105.64 -27.31 1.92) ; 48 ( 52.86 -106.44 -27.31 1.11) ; 49 ( 53.08 -107.32 -27.69 0.74) ; 50 ( 53.08 -108.49 -27.81 0.74) ; 51 ( 53.23 -109.44 -27.88 0.52) ; 52 ( 53.23 -110.10 -28.94 0.52) ; 53 Generated ) ; End of split ) ; End of split ) ; End of split ) ; End of tree ( (Color Magenta) (Dendrite) ( 8.64 -12.54 -3.44 3.61) ; Root ( 10.40 -13.35 -3.94 3.39) ; 1, R ( 11.58 -14.22 -4.06 3.68) ; 2 ( 12.98 -14.88 -4.19 4.05) ; 3 ( 13.94 -15.39 -4.19 4.35) ; 4 ( 15.34 -15.98 -3.69 4.49) ; 5 ( 16.67 -16.20 -3.63 4.57) ; 6 ( ( 18.95 -16.34 -3.94 3.09) ; 1, R-1 ( 19.98 -16.78 -3.63 2.87) ; 2 ( 21.01 -17.22 -3.06 2.58) ; 3 ( 21.82 -17.59 -3.00 2.58) ; 4 ( 23.08 -18.46 -2.94 2.50) ; 5 ( 23.11 -18.54 -2.94 2.50) ; 6 ( ( 23.96 -19.12 -2.94 2.36) ; 1, R-1-1 ( 25.14 -19.78 -2.88 2.36) ; 2 ( 26.46 -20.87 -1.94 2.50) ; 3 ( 27.42 -21.46 -1.94 2.65) ; 4 ( 28.31 -21.90 -1.94 2.87) ; 5 ( ( 29.19 -21.97 -1.88 2.06) ; 1, R-1-1-1 ( 29.93 -22.04 -2.31 1.84) ; 2 ( 30.66 -22.34 -2.88 1.99) ; 3 ( 30.89 -22.41 -3.13 2.28) ; 4 ( ( 31.55 -23.36 -4.31 1.47) ; 1, R-1-1-1-1 ( 32.06 -24.16 -4.38 1.18) ; 2 ( 32.65 -24.97 -4.38 1.18) ; 3 ( 33.17 -25.85 -4.44 1.18) ; 4 ( 33.98 -26.50 -4.69 1.18) ; 5 ( 34.94 -27.23 -5.13 1.18) ; 6 ( 35.75 -27.82 -5.25 1.47) ; 7 ( 36.24 -28.29 -5.25 1.47) ; 8 ( ( 36.48 -28.40 -5.31 1.99) ; 1, R-1-1-1-1-1 ( 37.07 -28.84 -5.56 1.99) ; 2 ( 38.03 -29.94 -5.81 1.25) ; 3 ( 38.75 -30.76 -6.63 1.11) ; 4 ( 39.64 -31.28 -6.94 1.55) ; 5 ( 40.45 -32.01 -7.56 1.62) ; 6 ( ( 41.18 -32.59 -8.25 1.77) ; 1, R-1-1-1-1-1-1 ( 42.07 -32.96 -7.75 0.81) ; 2 ( 43.03 -33.54 -7.56 0.66) ; 3 ( 43.69 -34.05 -6.50 0.52) ; 4 ( 43.84 -34.20 -5.94 0.66) ; 5 ( 44.65 -34.42 -5.25 0.66) ; 6 ( 45.02 -35.15 -5.25 0.81) ; 7 ( 45.68 -35.74 -5.06 0.81) ; 8 ( 46.27 -36.03 -5.00 0.96) ; 9 ( 47.00 -36.54 -5.50 1.11) ; 10 ( 47.67 -37.34 -5.88 0.96) ; 11 ( 48.26 -37.71 -6.44 0.88) ; 12 ( 48.55 -38.29 -6.88 0.88) ; 13 ( 48.92 -39.03 -7.31 0.88) ; 14 ( 49.58 -39.61 -7.44 0.74) ; 15 ( 50.32 -40.19 -7.88 0.74) ; 16 ( 50.91 -40.63 -7.88 0.74) ; 17 ( 51.57 -40.93 -7.88 0.88) ; 18 ( 52.46 -41.15 -8.00 0.88) ; 19 ( 53.34 -41.29 -8.50 0.88) ; 20 ( 54.30 -41.80 -9.00 0.88) ; 21 ( 55.33 -42.75 -9.00 0.88) ; 22 ( 55.99 -43.56 -9.44 1.03) ; 23 ( 56.43 -44.07 -9.75 1.03) ; 24 ( 57.32 -45.09 -9.88 1.18) ; 25 ( 57.83 -45.68 -10.19 0.88) ; 26 ( 57.76 -46.70 -10.50 0.81) ; 27 ( 57.76 -47.80 -11.00 0.81) ; 28 ( 58.28 -48.75 -11.94 1.18) ; 29 ( 58.57 -49.70 -12.31 1.40) ; 30 ( 59.46 -49.99 -12.31 1.40) ; 31 ( 59.75 -50.57 -12.63 1.40) ; 32 ( 59.97 -50.94 -13.00 0.88) ; 33 ( 60.49 -51.23 -13.00 0.88) ; 34 ( 61.00 -51.74 -13.06 0.88) ; 35 ( 61.44 -52.26 -13.25 0.88) ; 36 ( 62.25 -53.13 -13.25 0.88) ; 37 ( 62.99 -53.94 -13.25 0.74) ; 38 ( 63.73 -54.45 -13.25 1.11) ; 39 ( 64.32 -55.76 -13.69 1.11) ; 40 ( 64.76 -56.79 -13.69 1.03) ; 41 ( 65.64 -57.37 -14.31 1.11) ; 42 ( 66.45 -58.10 -14.56 1.11) ; 43 ( 67.34 -59.27 -14.69 0.88) ; 44 ( 67.91 -60.04 -14.69 0.74) ; 45 ( 68.86 -60.70 -14.94 0.59) ; 46 ( 69.45 -60.84 -15.13 0.59) ; 47 ( 70.48 -60.84 -15.50 1.11) ; 48 ( 71.22 -60.92 -15.88 1.55) ; 49 ( 72.03 -61.13 -16.06 1.25) ; 50 ( 73.21 -61.35 -16.19 0.96) ; 51 ( 74.24 -61.50 -16.56 0.74) ; 52 ( 75.05 -61.50 -16.69 0.59) ; 53 ( 76.16 -62.01 -16.88 0.59) ; 54 ( 76.53 -62.45 -16.88 1.25) ; 55 ( 76.75 -62.89 -16.88 0.81) ; 56 ( 77.26 -63.33 -16.88 0.81) ; 57 ( 77.85 -63.77 -16.88 0.96) ; 58 ( 78.74 -64.57 -17.44 0.81) ; 59 ( 79.55 -65.16 -17.50 0.81) ; 60 ( 80.21 -65.74 -17.75 1.03) ; 61 ( 81.24 -66.18 -18.44 1.03) ; 62 ( 82.12 -66.47 -19.00 1.11) ; 63 ( 82.86 -66.91 -19.81 1.40) ; 64 ( 83.45 -67.20 -20.13 0.96) ; 65 ( 84.33 -67.42 -21.06 0.74) ; 66 ( 85.07 -67.42 -22.38 0.52) ; 67 ( 86.03 -67.42 -22.88 0.81) ; 68 ( 86.91 -67.49 -23.19 0.96) ; 69 ( 87.87 -67.27 -23.94 0.81) ; 70 ( 88.46 -67.35 -24.94 0.81) ; 71 ( 89.42 -67.64 -25.56 0.81) ; 72 ( 90.15 -68.23 -25.56 0.81) ; 73 ( 91.04 -68.74 -25.81 0.52) ; 74 ( 91.55 -68.81 -25.13 0.52) ; 75 ( 92.37 -69.03 -24.56 0.74) ; 76 ( 93.10 -69.69 -23.88 1.03) ; 77 ( 93.62 -70.20 -23.50 1.33) ; 78 ( 94.50 -70.56 -23.13 1.69) ; 79 ( 95.68 -70.93 -23.13 1.69) ; 80 ( 96.49 -71.08 -22.00 0.74) ; 81 ( 97.23 -71.44 -22.00 0.66) ; 82 ( 97.89 -71.51 -22.00 0.66) ; 83 ( 98.55 -71.81 -21.94 0.81) ; 84 ( 99.66 -72.03 -21.94 0.81) ; 85 ( 100.54 -72.54 -21.94 0.59) ; 86 ( 100.98 -72.76 -21.94 0.59) ; 87 ( 101.65 -73.34 -22.06 0.59) ; 88 ( 102.31 -74.07 -22.31 0.59) ; 89 ( 102.75 -74.80 -22.25 0.59) ; 90 Low | ( 40.72 -33.06 -8.69 0.88) ; 1, R-1-1-1-1-1-2 ( 41.01 -33.65 -9.25 0.88) ; 2 ( 41.53 -34.23 -9.69 0.74) ; 3 ( 41.75 -34.75 -10.31 0.74) ; 4 ( 42.27 -35.55 -10.56 0.74) ; 5 ( 42.49 -36.50 -11.31 1.18) ; 6 ( 42.71 -37.82 -11.88 1.03) ; 7 ( 42.56 -38.91 -12.81 1.11) ; 8 ( 42.49 -39.64 -13.56 1.40) ; 9 ( 42.41 -40.45 -13.88 1.62) ; 10 ( 42.56 -41.11 -14.63 1.25) ; 11 ( 42.27 -41.91 -14.69 0.96) ; 12 ( 42.41 -43.08 -15.31 0.81) ; 13 ( 42.41 -44.10 -16.19 0.96) ; 14 ( 42.41 -44.83 -16.50 0.96) ; 15 ( 43.23 -46.00 -15.63 0.88) ; 16 ( 43.74 -47.10 -15.50 0.74) ; 17 ( 44.40 -47.83 -15.56 0.88) ; 18 ( 45.36 -48.85 -15.63 0.81) ; 19 ( 46.10 -49.29 -16.50 0.66) ; 20 ( 46.54 -50.02 -16.69 0.66) ; 21 ( 47.28 -51.41 -16.88 0.74) ; 22 ( 47.50 -52.29 -17.19 0.74) ; 23 ( 47.72 -52.87 -17.88 0.74) ; 24 ( 48.16 -53.68 -18.50 0.81) ; 25 ( 48.60 -54.56 -19.63 0.88) ; 26 ( 49.27 -55.21 -21.50 0.74) ; 27 ( 49.86 -56.24 -22.44 0.66) ; 28 ( 51.11 -57.33 -23.63 0.66) ; 29 ( 52.51 -58.43 -25.31 0.66) ; 30 ( 53.61 -59.60 -25.50 0.66) ; 31 ( 54.42 -60.48 -26.00 0.66) ; 32 ( 54.94 -61.35 -27.06 0.66) ; 33 ( 55.38 -62.52 -27.25 0.66) ; 34 ( 55.90 -63.91 -27.25 0.66) ; 35 ( 56.04 -65.01 -27.25 0.81) ; 36 ( 56.49 -65.74 -27.44 0.81) ; 37 ( 57.08 -66.32 -27.63 1.03) ; 38 ( 58.33 -66.47 -26.75 1.55) ; 39 ( 59.06 -66.76 -26.50 1.18) ; 40 ( 59.88 -66.76 -25.38 0.88) ; 41 ( 60.39 -67.35 -24.38 1.25) ; 42 ( 60.98 -67.79 -24.25 1.25) ; 43 ( 61.57 -67.93 -23.94 1.25) ; 44 ( 62.31 -68.23 -23.69 1.03) ; 45 ( 63.12 -68.23 -23.69 1.03) ; 46 ( 64.52 -68.01 -24.06 0.88) ; 47 ( 65.33 -67.93 -22.88 0.74) ; 48 ( 65.92 -67.71 -19.75 0.96) ; 49 ( 66.51 -68.01 -18.56 0.88) ; 50 ( 66.80 -68.59 -17.56 1.11) ; 51 ( 67.61 -69.32 -18.31 0.88) ; 52 ( 68.20 -70.20 -18.25 0.66) ; 53 ( 68.86 -70.93 -17.81 0.88) ; 54 ( 69.45 -70.86 -19.06 0.59) ; 55 ( 70.26 -70.78 -18.50 0.59) ; 56 ( 70.48 -70.35 -17.94 0.59) ; 57 ( 71.07 -69.83 -17.63 0.88) ; 58 ( 71.81 -69.54 -16.94 0.88) ; 59 ( 72.69 -69.76 -15.00 0.96) ; 60 ( 73.43 -70.42 -14.94 1.33) ; 61 ( 74.24 -71.00 -14.56 1.62) ; 62 ( 75.35 -71.37 -14.69 1.40) ; 63 ( 76.30 -71.66 -14.69 1.18) ; 64 ( 77.26 -71.88 -14.69 0.88) ; 65 ( 78.29 -72.32 -14.69 0.66) ; 66 ( 79.32 -72.68 -15.25 0.88) ; 67 ( 79.99 -73.05 -15.75 1.18) ; 68 ( 80.95 -73.42 -16.69 1.47) ; 69 ( 82.12 -74.07 -17.06 1.18) ; 70 ( 83.08 -75.02 -17.31 0.88) ; 71 ( 84.11 -75.75 -17.75 0.59) ; 72 ( 85.07 -76.49 -17.81 0.59) ; 73 ( 85.88 -77.14 -17.81 0.96) ; 74 ( 87.06 -77.65 -17.81 1.11) ; 75 ( 88.17 -77.65 -17.81 0.81) ; 76 ( 89.64 -77.80 -19.13 0.81) ; 77 ( 90.23 -78.17 -21.69 1.03) ; 78 ( 90.82 -78.46 -22.81 1.25) ; 79 ( 91.92 -78.31 -22.81 1.55) ; 80 ( 92.44 -78.24 -23.00 1.55) ; 81 ( 92.88 -78.02 -23.19 1.03) ; 82 ( 93.84 -77.36 -23.50 0.66) ; 83 ( 94.58 -77.07 -23.94 0.74) ; 84 ( 95.53 -76.78 -22.31 0.74) ; 85 ( 96.64 -76.27 -21.19 0.52) ; 86 ( 97.15 -76.41 -20.69 0.52) ; 87 ( 97.67 -76.78 -20.69 0.52) ; 88 ( 98.41 -76.92 -20.69 0.74) ; 89 ( 99.14 -76.85 -20.19 1.11) ; 90 ( 100.17 -77.00 -20.94 1.33) ; 91 ( 101.06 -76.85 -21.00 0.88) ; 92 ( 101.50 -76.78 -21.56 0.59) ; 93 ( 101.80 -76.78 -21.94 0.59) ; 94 ( 102.46 -76.78 -22.25 0.59) ; 95 ( 103.56 -77.14 -22.50 0.88) ; 96 ( 104.23 -77.44 -22.94 1.11) ; 97 ( 105.26 -77.51 -23.13 1.11) ; 98 ( 106.66 -77.80 -24.88 1.11) ; 99 ( 107.47 -77.65 -25.25 0.96) ; 100 ( 108.35 -77.95 -25.25 1.25) ; 101 ( 109.38 -78.31 -27.19 1.62) ; 102 ( 109.75 -78.61 -27.31 1.40) ; 103 ( 110.86 -78.75 -28.19 1.18) ; 104 ( 111.52 -78.75 -28.75 0.88) ; 105 ( 112.48 -78.97 -29.06 1.18) ; 106 ( 112.99 -79.04 -30.44 1.47) ; 107 ( 113.73 -78.82 -30.63 2.14) ; 108 ( 114.98 -78.82 -31.31 1.25) ; 109 ( 115.94 -78.70 -31.81 0.59) ; 110 ( 116.82 -78.19 -32.13 0.52) ; 111 ( 117.26 -77.46 -32.56 0.44) ; 112 ( 117.71 -77.09 -32.00 0.74) ; 113 ( 118.37 -76.14 -30.88 0.88) ; 114 ( 118.88 -75.63 -30.56 1.18) ; 115 ( 119.33 -75.19 -30.50 0.88) ; 116 ( 119.69 -74.68 -30.38 0.66) ; 117 ( 119.99 -74.46 -30.13 0.44) ; 118 ( 120.58 -74.02 -30.13 0.29) ; 119 ( 121.46 -73.44 -30.06 0.22) ; 120 ( 121.90 -73.07 -29.63 0.22) ; 121 Low ) ; End of split | ( 37.49 -28.72 -7.50 0.74) ; 1, R-1-1-1-1-2 ( 38.38 -29.02 -7.81 0.81) ; 2 ( 38.77 -29.24 -8.19 0.59) ; 3 ( 39.21 -29.54 -9.13 0.59) ; 4 ( 39.80 -29.83 -9.44 0.66) ; 5 ( 40.39 -30.12 -10.25 0.74) ; 6 ( 41.20 -30.34 -10.25 1.03) ; 7 ( 42.08 -30.78 -11.25 0.88) ; 8 ( 42.45 -31.07 -12.19 0.74) ; 9 ( 43.12 -31.29 -12.19 0.59) ; 10 ( 43.78 -31.58 -12.63 0.81) ; 11 ( 44.44 -31.07 -13.63 0.96) ; 12 ( 45.03 -30.85 -14.88 0.96) ; 13 ( 46.21 -30.78 -15.19 0.81) ; 14 ( 46.87 -30.56 -15.44 0.81) ; 15 ( 47.68 -29.90 -15.81 0.81) ; 16 ( 48.71 -29.83 -16.31 0.74) ; 17 ( 49.16 -30.05 -17.31 0.74) ; 18 ( 49.89 -30.70 -17.31 0.74) ; 19 ( 50.04 -31.36 -18.44 0.96) ; 20 ( 50.19 -32.24 -19.94 1.18) ; 21 ( 50.41 -32.90 -20.56 1.47) ; 22 ( 50.56 -33.77 -20.63 1.18) ; 23 ( 50.92 -34.43 -22.00 0.96) ; 24 ( 51.37 -35.24 -22.19 0.96) ; 25 ( 52.10 -36.26 -22.63 1.03) ; 26 ( 52.77 -37.06 -23.50 1.03) ; 27 ( 53.36 -37.58 -24.19 0.74) ; 28 ( 53.65 -38.09 -24.19 0.52) ; 29 ( 54.46 -38.60 -24.25 0.52) ; 30 ( 55.79 -39.33 -24.75 0.44) ; 31 ( 56.52 -40.13 -25.06 0.66) ; 32 ( 57.19 -41.30 -25.38 0.96) ; 33 ( 58.00 -42.55 -26.00 0.88) ; 34 ( 58.96 -43.20 -26.31 0.88) ; 35 ( 59.54 -44.08 -26.31 1.25) ; 36 ( 60.35 -44.89 -25.50 1.33) ; 37 ( 61.31 -46.06 -25.44 1.11) ; 38 ( 61.68 -46.42 -27.19 0.81) ; 39 ( 62.20 -47.08 -27.13 0.59) ; 40 ( 63.67 -47.37 -27.38 0.81) ; 41 ( 64.55 -47.52 -28.69 0.66) ; 42 ( 65.07 -47.52 -30.88 0.66) ; 43 ( 65.88 -47.74 -31.25 0.52) ; 44 ( 66.54 -47.59 -31.81 0.52) ; 45 ( 67.21 -47.66 -31.94 0.52) ; 46 ( 67.80 -47.81 -31.94 0.81) ; 47 ( 68.61 -47.37 -32.69 1.11) ; 48 ( 69.27 -47.01 -33.75 1.11) ; 49 ( 69.56 -46.79 -34.44 0.88) ; 50 ( 70.08 -46.79 -35.06 0.66) ; 51 ( 70.74 -46.86 -35.00 0.44) ; 52 ( 72.00 -46.71 -35.00 0.74) ; 53 ( 72.29 -46.71 -35.44 1.47) ; 54 ( 73.32 -46.64 -35.50 2.21) ; 55 ( 74.21 -46.27 -35.81 2.95) ; 56 ( 75.09 -45.98 -35.81 1.62) ; 57 ( 76.12 -45.84 -36.00 0.81) ; 58 ( 76.64 -45.62 -36.44 0.52) ; 59 ( 77.52 -45.54 -37.31 0.37) ; 60 ( 78.63 -45.11 -37.69 0.44) ; 61 ( 79.51 -44.89 -37.63 0.52) ; 62 ( 80.25 -44.74 -37.63 0.88) ; 63 ( 80.69 -44.45 -38.63 1.25) ; 64 ( 81.20 -44.30 -38.81 1.55) ; 65 ( 81.50 -44.01 -39.38 1.25) ; 66 ( 81.79 -43.94 -40.56 0.81) ; 67 ( 82.60 -43.64 -43.44 0.66) ; 68 ( 82.97 -43.35 -45.13 0.88) ; 69 ( 83.27 -42.91 -46.38 1.18) ; 70 ( 83.49 -42.33 -46.94 0.96) ; 71 ( 83.78 -41.74 -47.75 0.74) ; 72 ( 84.15 -41.60 -50.19 1.03) ; 73 ( 84.15 -41.52 -52.81 1.33) ; 74 ( 84.81 -41.67 -53.63 0.96) ; 75 ( 85.11 -41.67 -54.19 0.74) ; 76 ( 85.85 -41.74 -54.19 0.96) ; 77 ( 86.34 -41.58 -55.25 1.62) ; 78 ( 86.71 -41.58 -56.94 1.62) ; 79 Low ) ; End of split | ( 31.93 -22.13 -2.94 1.18) ; 1, R-1-1-1-2 ( 32.67 -21.84 -2.94 0.81) ; 2 ( 33.41 -22.20 -2.63 0.81) ; 3 ( 34.14 -22.57 -1.88 1.11) ; 4 ( 34.95 -22.86 -1.19 1.11) ; 5 ( 35.84 -22.86 0.44 1.11) ; 6 ( 36.94 -22.79 0.50 0.88) ; 7 ( 37.83 -22.42 1.06 1.03) ; 8 ( 38.49 -22.13 1.25 1.03) ; 9 ( 39.00 -21.55 0.63 0.81) ; 10 ( 39.59 -20.89 0.25 1.03) ; 11 ( 39.74 -20.52 0.19 1.40) ; 12 ( 39.81 -19.50 -0.06 1.40) ; 13 ( 40.04 -18.77 -0.31 1.25) ; 14 ( 40.26 -17.89 0.00 1.11) ; 15 ( 40.48 -17.16 0.13 0.96) ; 16 ( 40.92 -16.50 0.75 1.18) ; 17 ( 41.29 -15.55 1.44 1.33) ; 18 ( 41.73 -14.38 2.44 1.11) ; 19 ( 42.10 -13.65 2.56 1.11) ; 20 ( 42.84 -13.14 2.69 1.11) ; 21 ( 43.57 -12.70 2.75 1.11) ; 22 ( 44.31 -12.33 2.75 0.81) ; 23 ( 45.05 -12.12 2.75 1.11) ; 24 ( 46.15 -11.82 3.19 1.25) ; 25 ( 47.03 -11.46 2.75 1.11) ; 26 ( 48.58 -10.87 1.88 1.11) ; 27 ( 50.94 -10.51 1.88 1.11) ; 28 ( 52.41 -10.36 1.75 1.11) ; 29 ( 53.52 -10.00 1.50 1.11) ; 30 ( 54.62 -9.92 1.50 0.88) ; 31 ( 55.58 -9.41 1.50 0.88) ; 32 ( 56.69 -8.68 2.69 1.03) ; 33 ( 57.72 -7.88 3.31 0.88) ; 34 ( 58.53 -7.66 3.38 0.88) ; 35 ( 59.78 -7.22 4.19 0.88) ; 36 ( 60.44 -7.14 4.69 0.81) ; 37 ( 61.18 -7.73 4.94 0.81) ; 38 ( 61.92 -7.88 5.13 0.81) ; 39 ( 62.65 -7.66 5.38 0.81) ; 40 ( 63.61 -6.85 5.88 0.96) ; 41 ( 64.42 -6.41 4.94 1.18) ; 42 ( 65.53 -6.05 4.69 1.55) ; 43 ( 66.78 -5.90 4.13 1.25) ; 44 ( 67.66 -5.61 3.81 1.11) ; 45 ( 68.33 -5.61 3.50 1.25) ; 46 ( 69.28 -5.83 3.06 1.03) ; 47 ( 70.17 -6.12 2.75 1.03) ; 48 ( 71.79 -6.71 2.75 1.03) ; 49 ( 73.04 -6.63 2.75 1.03) ; 50 ( 74.22 -6.12 2.63 1.03) ; 51 ( 74.81 -5.39 2.38 1.25) ; 52 ( 75.62 -4.59 2.38 1.84) ; 53 ( 76.36 -3.56 2.19 1.11) ; 54 ( 77.02 -2.83 2.56 0.81) ; 55 ( 77.83 -2.17 2.63 1.11) ; 56 ( 78.79 -1.59 2.69 0.81) ; 57 ( 79.84 -1.06 2.88 0.74) ; 58 ( 80.58 -1.13 2.94 0.74) ; 59 ( 81.46 -1.50 3.19 0.74) ; 60 ( 81.76 -2.30 2.50 0.96) ; 61 ( 82.20 -2.67 1.69 1.18) ; 62 ( 82.86 -3.10 1.19 0.96) ; 63 ( 83.53 -3.40 0.81 0.66) ; 64 ( 84.48 -3.54 0.69 0.66) ; 65 ( 85.88 -3.76 0.38 0.88) ; 66 ( 86.91 -3.83 -0.13 1.25) ; 67 ( 87.95 -3.76 -0.31 1.62) ; 68 ( 88.83 -3.47 -0.31 1.18) ; 69 ( 89.79 -3.25 -0.31 0.74) ; 70 ( 90.30 -3.10 -0.50 0.74) ; 71 ( 91.26 -2.45 -0.94 1.11) ; 72 ( 92.22 -1.79 -0.94 0.88) ; 73 ( 93.10 -1.42 -1.38 0.88) ; 74 ( 94.43 -1.42 0.31 0.81) ; 75 ( 95.46 -1.57 1.19 0.88) ; 76 ( 96.20 -1.71 1.50 1.11) ; 77 ( 97.08 -1.93 1.63 0.81) ; 78 ( 98.26 -1.93 2.00 0.81) ; 79 ( 99.66 -1.28 2.25 1.03) ; 80 ( 100.62 -0.47 1.56 0.88) ; 81 ( 101.50 -0.62 1.50 0.88) ; 82 ( 102.53 -0.76 1.81 1.18) ; 83 ( 103.34 -1.13 1.81 1.40) ; 84 ( 104.30 -1.79 1.81 1.40) ; 85 ( 105.41 -2.30 1.81 1.03) ; 86 ( 106.29 -3.10 1.81 0.74) ; 87 ( 107.18 -3.91 2.50 0.66) ; 88 ( 107.25 -4.42 3.06 0.66) ; 89 ( 107.54 -4.93 3.56 0.66) ; 90 ( 108.35 -5.52 3.31 0.52) ; 91 ( 109.09 -5.81 3.19 0.81) ; 92 ( 109.90 -6.10 3.69 1.18) ; 93 ( 110.56 -6.10 2.94 0.96) ; 94 ( 111.52 -6.17 2.94 0.74) ; 95 ( 112.11 -6.25 2.50 1.03) ; 96 ( 112.63 -6.47 2.31 1.03) ; 97 ( 113.36 -6.39 2.31 0.59) ; 98 ( 114.17 -6.39 2.19 0.59) ; 99 ( 115.35 -6.17 1.38 0.44) ; 100 Low ) ; End of split | ( 28.05 -22.27 -1.88 2.65) ; 1, R-1-1-2 ( 27.91 -23.00 -1.63 1.77) ; 2 ( 27.98 -24.17 -1.63 1.55) ; 3 ( 28.05 -24.75 -1.56 1.47) ; 4 ( 28.79 -25.63 -1.44 1.33) ; 5 ( 29.53 -26.72 -1.31 1.25) ; 6 ( 30.56 -27.60 -1.13 1.55) ; 7 ( 31.42 -28.80 -1.13 1.55) ; 8 ( ( 31.52 -29.06 -0.94 1.55) ; 1, R-1-1-2-1 ( 32.33 -29.72 -1.06 1.55) ; 2 ( 32.84 -30.38 -1.13 1.69) ; 3 ( 33.21 -31.55 -1.13 1.33) ; 4 ( 33.87 -32.35 -1.50 1.25) ; 5 ( 34.39 -33.67 -1.63 1.25) ; 6 ( 34.76 -34.40 -1.63 1.55) ; 7 ( 34.83 -35.06 -1.63 1.77) ; 8 ( ( 35.35 -35.72 -2.38 0.96) ; 1, R-1-1-2-1-1 ( 36.01 -36.45 -3.13 0.88) ; 2 ( 36.23 -37.47 -3.25 1.03) ; 3 ( 36.67 -38.35 -3.63 1.03) ; 4 ( 36.75 -39.15 -3.75 1.03) ; 5 ( 36.97 -40.17 -3.75 0.88) ; 6 ( 37.48 -41.05 -4.00 1.03) ; 7 ( 38.29 -42.07 -4.06 1.33) ; 8 ( 39.18 -42.73 -4.31 1.33) ; 9 ( 40.06 -43.46 -4.56 1.18) ; 10 ( 40.95 -44.41 -4.88 1.18) ; 11 ( 42.05 -45.00 -5.13 1.47) ; 12 ( 42.86 -45.58 -5.13 1.69) ; 13 ( 43.60 -46.02 -5.31 1.69) ; 14 ( 44.56 -46.90 -5.50 1.69) ; 15 ( 44.85 -47.48 -5.63 2.06) ; 16 ( 45.73 -47.78 -5.75 2.50) ; 17 ( ( 45.29 -48.87 -6.19 1.69) ; 1, R-1-1-2-1-1-1 ( 45.29 -49.97 -5.56 1.40) ; 2 ( 45.37 -51.07 -5.44 1.40) ; 3 ( 45.44 -52.21 -5.19 1.25) ; 4 ( 45.36 -53.53 -5.50 1.33) ; 5 ( 45.29 -54.62 -5.88 1.33) ; 6 ( 44.63 -55.35 -6.81 1.11) ; 7 ( 44.55 -56.01 -7.19 0.81) ; 8 ( ( 44.70 -57.11 -7.06 0.74) ; 1, R-1-1-2-1-1-1-1 ( 45.00 -57.84 -7.56 0.66) ; 2 ( 45.07 -58.72 -8.31 0.66) ; 3 ( 45.36 -59.52 -8.31 0.66) ; 4 ( 45.88 -60.47 -9.38 0.66) ; 5 ( 46.84 -61.35 -9.75 0.96) ; 6 ( 47.06 -62.52 -10.31 0.88) ; 7 ( 47.79 -63.91 -10.88 0.74) ; 8 ( 48.16 -64.56 -11.31 0.74) ; 9 ( 48.61 -65.15 -11.56 0.74) ; 10 ( 49.12 -66.47 -12.06 0.88) ; 11 ( 49.34 -67.05 -12.38 1.25) ; 12 ( 49.64 -67.93 -12.31 0.96) ; 13 ( 50.00 -68.66 -12.63 0.74) ; 14 ( 50.37 -69.75 -12.69 0.74) ; 15 ( 50.59 -70.49 -12.69 0.96) ; 16 ( 50.67 -71.51 -13.06 0.66) ; 17 ( 50.67 -72.24 -13.38 0.81) ; 18 ( 51.11 -73.26 -13.75 1.11) ; 19 ( 51.26 -74.14 -13.81 0.88) ; 20 ( 51.55 -74.87 -13.88 0.66) ; 21 ( 51.48 -75.82 -14.00 0.66) ; 22 ( 50.89 -76.70 -14.19 0.66) ; 23 ( 50.23 -77.72 -14.50 0.66) ; 24 ( 50.74 -78.82 -14.50 0.66) ; 25 ( 51.70 -79.77 -14.50 0.81) ; 26 ( 52.36 -80.43 -14.50 1.40) ; 27 ( 53.18 -81.78 -15.50 1.62) ; 28 ( 53.55 -83.31 -12.75 1.33) ; 29 ( 53.99 -84.04 -12.56 0.96) ; 30 ( 54.58 -85.29 -12.19 0.66) ; 31 ( 54.28 -86.31 -11.88 0.52) ; 32 ( 53.62 -87.48 -11.88 0.66) ; 33 ( 53.33 -88.36 -12.13 0.81) ; 34 ( 52.96 -89.31 -13.31 0.59) ; 35 ( 53.40 -90.26 -13.88 0.52) ; 36 ( 53.84 -91.06 -14.19 0.52) ; 37 ( 53.84 -92.01 -14.19 0.52) ; 38 ( 54.14 -93.33 -14.19 0.59) ; 39 ( 54.36 -94.35 -14.44 1.25) ; 40 ( 54.43 -94.93 -14.00 1.55) ; 41 ( 53.99 -96.18 -13.63 1.55) ; 42 ( 53.70 -97.27 -13.56 1.18) ; 43 ( 53.62 -98.00 -13.56 0.88) ; 44 ( 53.62 -99.10 -13.63 0.74) ; 45 ( 53.25 -100.78 -13.63 0.74) ; 46 ( 52.66 -101.59 -13.81 0.59) ; 47 ( 52.00 -102.83 -13.81 0.59) ; 48 ( 51.41 -103.93 -14.69 1.03) ; 49 ( 50.53 -104.73 -16.13 0.88) ; 50 ( 50.01 -105.68 -16.88 0.88) ; 51 ( 49.42 -106.19 -18.63 1.18) ; 52 ( 49.64 -106.92 -20.56 0.96) ; 53 ( 50.01 -107.87 -22.44 0.88) ; 54 ( 50.45 -108.90 -22.63 1.18) ; 55 ( 50.90 -109.48 -23.44 1.47) ; 56 ( 51.16 -110.81 -23.75 0.81) ; 57 ( 51.24 -111.32 -24.06 0.59) ; 58 ( 51.75 -112.27 -24.25 0.52) ; 59 ( 52.49 -113.08 -24.25 0.74) ; 60 ( 53.01 -113.81 -24.75 0.74) ; 61 ( 53.37 -114.69 -24.81 0.74) ; 62 ( 53.37 -115.34 -23.69 1.33) ; 63 ( 53.52 -115.93 -23.81 2.14) ; 64 ( 53.74 -116.51 -23.81 2.36) ; 65 ( 53.67 -117.97 -23.13 1.47) ; 66 ( 53.52 -118.78 -23.13 0.81) ; 67 ( 53.08 -119.88 -23.13 0.44) ; 68 ( 52.34 -120.83 -23.13 0.66) ; 69 ( 51.97 -121.63 -23.13 0.66) ; 70 ( 51.46 -122.51 -23.06 0.66) ; 71 ( 50.94 -123.38 -23.06 0.52) ; 72 ( 50.57 -124.12 -22.75 0.52) ; 73 ( 49.91 -124.04 -22.44 0.44) ; 74 ( 49.54 -124.33 -22.25 0.44) ; 75 ( 48.59 -124.77 -22.25 0.74) ; 76 ( 47.70 -125.28 -22.19 1.11) ; 77 ( 46.67 -125.80 -22.00 1.40) ; 78 ( 45.86 -126.16 -21.75 1.69) ; 79 ( 45.27 -126.16 -21.69 1.69) ; 80 ( 44.31 -126.60 -21.69 0.96) ; 81 ( 43.58 -126.67 -21.69 0.52) ; 82 ( 42.76 -127.19 -21.69 0.29) ; 83 ( 42.25 -127.77 -21.69 0.29) ; 84 ( 41.66 -128.50 -21.38 0.44) ; 85 ( 41.22 -128.94 -21.56 0.74) ; 86 ( 40.55 -129.09 -21.81 0.74) ; 87 ( 40.19 -129.45 -21.81 0.44) ; 88 ( 39.74 -129.45 -21.94 0.44) ; 89 ( 39.01 -129.89 -22.13 0.44) ; 90 Low | ( 44.53 -57.29 -8.75 0.59) ; 1, R-1-1-2-1-1-1-2 ( 44.24 -57.87 -8.88 0.88) ; 2 ( 43.87 -58.46 -9.75 0.88) ; 3 ( 43.72 -59.12 -10.00 0.66) ; 4 ( 43.65 -59.99 -10.56 0.44) ; 5 ( 43.72 -60.36 -11.13 0.44) ; 6 ( 44.09 -60.65 -11.31 0.66) ; 7 ( 44.53 -61.31 -12.13 0.88) ; 8 ( 44.98 -61.60 -12.50 0.74) ; 9 ( 45.49 -62.41 -13.44 0.59) ; 10 ( 45.71 -63.28 -14.25 0.81) ; 11 ( 45.49 -64.38 -14.88 0.81) ; 12 ( 45.12 -65.40 -14.94 0.81) ; 13 ( 44.90 -66.35 -15.94 0.81) ; 14 ( 44.75 -67.67 -16.38 0.88) ; 15 ( 44.98 -68.55 -16.81 0.88) ; 16 ( 44.98 -69.35 -17.25 0.88) ; 17 ( 45.27 -70.01 -18.00 1.03) ; 18 ( 45.42 -70.59 -18.50 0.96) ; 19 ( 45.64 -71.32 -19.69 1.18) ; 20 ( 45.56 -72.57 -20.50 1.03) ; 21 ( 45.79 -73.15 -20.81 0.74) ; 22 ( 46.15 -73.96 -21.63 0.59) ; 23 ( 46.60 -74.10 -22.31 0.44) ; 24 ( 46.38 -74.91 -22.50 0.81) ; 25 ( 45.86 -75.78 -23.13 1.03) ; 26 ( 45.56 -76.73 -24.19 0.74) ; 27 ( 45.64 -77.46 -24.69 0.74) ; 28 ( 45.93 -78.56 -24.69 0.81) ; 29 ( 46.45 -79.66 -25.38 1.03) ; 30 ( 46.74 -80.24 -25.81 1.03) ; 31 ( 47.26 -81.27 -26.06 0.81) ; 32 ( 47.26 -81.92 -26.75 0.66) ; 33 ( 47.48 -82.87 -26.88 0.81) ; 34 ( 47.94 -83.98 -27.75 0.88) ; 35 ( 47.87 -85.29 -27.94 0.88) ; 36 ( 47.65 -86.32 -28.56 0.74) ; 37 ( 47.65 -86.97 -28.88 0.59) ; 38 ( 47.94 -87.56 -29.25 0.59) ; 39 ( 48.31 -88.29 -30.00 0.81) ; 40 ( 48.09 -88.95 -30.19 0.59) ; 41 ( 47.80 -89.75 -30.19 0.59) ; 42 ( 47.80 -90.56 -30.31 1.25) ; 43 ( 47.57 -91.21 -30.63 1.84) ; 44 ( 47.57 -91.80 -30.88 1.84) ; 45 ( 47.35 -92.38 -30.88 2.06) ; 46 ( 47.87 -93.04 -31.06 1.18) ; 47 ( 47.87 -93.63 -31.13 0.59) ; 48 ( 47.87 -94.28 -31.38 0.59) ; 49 ( 48.09 -95.53 -30.56 0.52) ; 50 ( 48.09 -96.40 -30.56 0.52) ; 51 ( 48.31 -97.72 -30.56 0.74) ; 52 ( 48.38 -98.89 -30.56 1.03) ; 53 ( 48.46 -99.91 -30.56 0.96) ; 54 ( 48.75 -100.57 -30.56 0.66) ; 55 ( 48.68 -101.23 -30.56 0.44) ; 56 ( 48.90 -102.11 -30.56 0.44) ; 57 ( 48.90 -102.84 -30.63 1.03) ; 58 ( 48.97 -103.79 -30.88 1.33) ; 59 ( 49.05 -104.74 -30.88 1.33) ; 60 ( 49.20 -105.61 -31.00 1.18) ; 61 ( 49.64 -106.13 -31.00 0.88) ; 62 ( 50.08 -107.00 -31.31 0.59) ; 63 ( 50.67 -107.51 -31.50 0.44) ; 64 ( 51.41 -108.54 -32.19 0.74) ; 65 ( 51.63 -109.27 -32.75 1.55) ; 66 ( 52.22 -110.22 -31.63 1.55) ; 67 ( 52.44 -111.10 -31.38 1.33) ; 68 ( 52.80 -112.19 -33.31 1.33) ; 69 ( 52.92 -113.33 -34.56 1.25) ; 70 ( 52.62 -114.57 -31.69 0.96) ; 71 ( 52.40 -115.52 -32.13 0.74) ; 72 ( 52.11 -116.18 -32.13 0.59) ; 73 ( 52.11 -117.28 -32.19 0.44) ; 74 ( 51.88 -118.37 -32.25 0.44) ; 75 ( 51.88 -119.40 -32.44 0.74) ; 76 ( 51.81 -119.98 -32.88 0.52) ; 77 ( 51.30 -120.93 -33.13 0.37) ; 78 ( 51.07 -121.44 -33.13 0.37) ; 79 ( 50.93 -122.61 -33.13 0.37) ; 80 ( 51.00 -123.71 -33.06 0.88) ; 81 ( 50.93 -124.51 -33.50 1.47) ; 82 ( 51.30 -125.32 -34.06 1.25) ; 83 ( 51.74 -125.98 -34.06 1.03) ; 84 ( 52.18 -126.49 -34.31 0.66) ; 85 ( 52.47 -127.22 -34.44 0.59) ; 86 ( 52.84 -127.88 -34.50 0.81) ; 87 ( 53.21 -128.46 -35.00 1.11) ; 88 ( 53.73 -129.12 -35.00 1.40) ; 89 ( 54.02 -129.85 -35.00 1.18) ; 90 ( 54.17 -130.22 -35.19 0.74) ; 91 ( 54.24 -130.87 -35.19 0.44) ; 92 ( 54.68 -131.75 -35.50 0.44) ; 93 ( 54.98 -132.70 -35.88 0.37) ; 94 ( 55.27 -133.58 -36.19 0.37) ; 95 ( 55.49 -134.24 -36.31 0.37) ; 96 ( 55.42 -134.82 -36.31 0.74) ; 97 ( 55.49 -135.33 -36.56 1.47) ; 98 ( 55.49 -135.77 -36.75 1.47) ; 99 ( 55.27 -136.28 -36.94 1.03) ; 100 ( 55.05 -136.94 -37.50 0.44) ; 101 ( 54.98 -138.11 -39.25 0.29) ; 102 ( 54.76 -139.21 -39.25 0.29) ; 103 ( 54.68 -139.87 -39.81 0.29) ; 104 ( 54.54 -140.89 -40.75 0.59) ; 105 ( 54.24 -141.62 -41.13 1.18) ; 106 ( 53.95 -142.13 -41.94 1.69) ; 107 ( 53.88 -142.57 -42.31 0.74) ; 108 ( 54.10 -143.23 -42.75 0.44) ; 109 ( 54.40 -143.74 -43.06 0.29) ; 110 ( 54.62 -144.47 -43.06 0.29) ; 111 ( 54.40 -145.28 -43.56 0.29) ; 112 ( 54.25 -145.94 -43.75 0.88) ; 113 ( 54.25 -146.37 -43.75 0.88) ; 114 ( 54.47 -146.89 -44.00 0.52) ; 115 ( 54.76 -147.47 -44.25 0.22) ; 116 ( 55.21 -148.20 -45.19 0.22) ; 117 Low ) ; End of split | ( 46.25 -47.80 -5.56 1.55) ; 1, R-1-1-2-1-1-2 ( 46.77 -47.50 -5.69 0.44) ; 2 ( 47.14 -47.50 -5.81 0.29) ; 3 ( 47.73 -47.65 -5.88 0.29) ; 4 ( 48.09 -47.94 -5.94 0.52) ; 5 ( 48.46 -48.45 -6.38 0.66) ; 6 ( 48.83 -48.67 -6.94 0.66) ; 7 ( 49.35 -48.67 -7.06 0.66) ; 8 ( 49.79 -48.45 -7.56 0.66) ; 9 ( 50.67 -48.60 -7.56 0.66) ; 10 ( 51.63 -48.89 -7.56 0.81) ; 11 ( 52.37 -49.18 -7.88 0.59) ; 12 ( 52.96 -49.62 -8.06 0.59) ; 13 ( 53.91 -50.21 -8.06 0.74) ; 14 ( 54.87 -50.79 -8.06 0.74) ; 15 ( 55.39 -51.45 -8.06 0.59) ; 16 ( 56.05 -52.33 -8.31 0.74) ; 17 ( 56.42 -53.06 -8.31 1.03) ; 18 ( 56.49 -53.79 -8.56 1.03) ; 19 ( 57.01 -54.74 -9.00 0.74) ; 20 ( 57.30 -55.25 -9.13 0.74) ; 21 ( 57.82 -55.76 -9.69 0.74) ; 22 ( 58.41 -56.35 -9.88 0.74) ; 23 ( 59.00 -57.15 -10.38 0.59) ; 24 ( 59.51 -57.88 -10.81 0.59) ; 25 ( 60.10 -58.32 -11.63 0.52) ; 26 ( 60.62 -58.98 -9.88 0.81) ; 27 ( 61.28 -59.78 -9.25 0.81) ; 28 ( 61.95 -60.51 -9.13 0.81) ; 29 ( 62.39 -61.25 -9.13 1.40) ; 30 ( 62.76 -61.98 -9.38 1.55) ; 31 ( 62.98 -62.85 -9.69 1.84) ; 32 ( 63.27 -63.95 -10.13 1.11) ; 33 ( 63.27 -64.68 -10.44 0.88) ; 34 ( 63.64 -65.92 -10.69 1.11) ; 35 ( 63.71 -66.36 -11.56 1.11) ; 36 ( 63.86 -67.39 -12.94 0.81) ; 37 ( 63.93 -68.26 -13.94 0.59) ; 38 ( 63.64 -68.70 -15.06 0.66) ; 39 ( 63.57 -70.24 -15.50 0.74) ; 40 ( 63.79 -71.48 -16.38 1.11) ; 41 ( 64.16 -72.50 -16.56 1.11) ; 42 ( 64.89 -73.67 -16.88 0.88) ; 43 ( 65.78 -74.48 -17.19 0.59) ; 44 ( 66.73 -75.06 -16.69 0.59) ; 45 ( 66.73 -75.87 -16.44 0.52) ; 46 ( 66.44 -76.96 -16.38 0.81) ; 47 ( 66.27 -77.82 -16.38 1.33) ; 48 ( 66.20 -78.55 -16.38 1.33) ; 49 ( 65.83 -79.50 -16.63 0.96) ; 50 ( 65.75 -80.45 -16.63 0.59) ; 51 ( 66.12 -81.18 -16.50 0.37) ; 52 ( 66.34 -81.77 -16.38 0.59) ; 53 ( 66.71 -82.94 -15.81 0.88) ; 54 ( 66.86 -83.52 -15.63 0.66) ; 55 ( 67.23 -84.18 -15.63 0.81) ; 56 ( 67.37 -84.76 -15.38 0.81) ; 57 ( 67.30 -85.27 -15.00 0.52) ; 58 ( 67.96 -85.71 -14.63 0.74) ; 59 ( 68.55 -86.01 -14.63 0.88) ; 60 ( 69.36 -86.52 -14.63 0.74) ; 61 ( 69.88 -86.74 -14.63 1.03) ; 62 ( 70.69 -87.25 -14.63 1.03) ; 63 ( 71.20 -87.83 -14.63 0.66) ; 64 ( 71.57 -88.49 -14.63 0.66) ; 65 ( 71.65 -89.37 -14.56 0.66) ; 66 ( 71.65 -90.61 -14.38 0.59) ; 67 ( 71.50 -91.56 -14.38 0.59) ; 68 ( 71.50 -92.58 -14.38 0.88) ; 69 ( 71.57 -93.46 -14.38 1.18) ; 70 ( 71.79 -94.34 -14.94 2.06) ; 71 ( 71.87 -95.07 -15.31 2.36) ; 72 ( 71.65 -96.02 -15.56 1.62) ; 73 ( 71.65 -96.82 -15.69 1.18) ; 74 ( 71.28 -97.41 -15.94 0.74) ; 75 ( 70.91 -97.99 -16.25 0.52) ; 76 ( 70.98 -98.73 -16.25 0.88) ; 77 ( 70.84 -99.46 -17.06 1.11) ; 78 ( 70.91 -100.48 -18.00 0.74) ; 79 ( 71.35 -101.06 -18.13 0.52) ; 80 ( 72.09 -101.72 -19.19 0.59) ; 81 ( 72.53 -103.11 -19.69 0.81) ; 82 ( 72.53 -103.77 -20.69 1.18) ; 83 ( 72.75 -104.35 -20.63 2.36) ; 84 ( 72.83 -104.87 -20.94 2.36) ; 85 ( 72.75 -105.60 -21.69 1.99) ; 86 ( 72.90 -106.33 -22.69 0.81) ; 87 ( 72.62 -107.14 -23.25 0.44) ; 88 ( 72.62 -107.94 -23.44 0.44) ; 89 ( 72.70 -108.45 -23.63 0.44) ; 90 ( 72.99 -109.62 -23.69 0.44) ; 91 ( 72.70 -110.35 -23.63 0.66) ; 92 Low ) ; End of split | ( 34.63 -35.97 -0.94 0.88) ; 1, R-1-1-2-1-2 ( 34.26 -36.92 -0.81 0.66) ; 2 ( 33.74 -37.87 -0.75 0.74) ; 3 ( 33.37 -38.74 -0.75 0.74) ; 4 ( 33.37 -39.91 -0.44 0.96) ; 5 ( 33.52 -41.23 -0.44 0.96) ; 6 ( 33.52 -41.74 -0.25 0.96) ; 7 ( 33.52 -42.69 -0.19 0.81) ; 8 ( 33.89 -43.64 -0.19 0.81) ; 9 ( 34.11 -45.10 -0.19 0.81) ; 10 ( 34.77 -45.91 -0.19 0.81) ; 11 ( 35.51 -46.78 0.31 0.74) ; 12 ( 36.25 -47.30 1.00 0.88) ; 13 ( 37.06 -47.95 1.81 1.03) ; 14 ( 38.02 -48.83 3.13 1.03) ; 15 ( 38.68 -49.42 3.13 0.81) ; 16 ( 39.42 -50.00 3.25 0.81) ; 17 ( 39.86 -50.80 3.38 0.66) ; 18 ( 40.00 -51.90 3.50 0.81) ; 19 ( 39.49 -53.00 3.88 0.74) ; 20 ( 38.90 -53.95 3.44 0.81) ; 21 ( 38.53 -54.68 2.44 0.81) ; 22 ( 38.16 -55.63 1.00 0.88) ; 23 ( 38.16 -56.51 0.56 0.88) ; 24 ( 38.68 -57.82 0.25 1.03) ; 25 ( 38.75 -58.77 -0.25 1.03) ; 26 ( 38.68 -60.09 0.75 0.88) ; 27 ( 39.34 -60.89 0.75 0.66) ; 28 ( 39.64 -61.92 0.81 0.66) ; 29 ( 39.93 -63.23 0.81 0.81) ; 30 ( 40.30 -64.55 0.81 0.81) ; 31 ( 40.76 -65.50 0.88 0.96) ; 32 ( 41.27 -66.30 0.88 0.96) ; 33 ( 41.57 -67.40 0.88 0.88) ; 34 ( 41.72 -68.20 1.25 0.81) ; 35 ( 41.86 -70.18 1.56 0.88) ; 36 ( 41.94 -71.06 1.69 0.88) ; 37 ( 42.67 -71.86 2.31 0.88) ; 38 ( 43.34 -72.37 2.94 0.74) ; 39 ( 43.63 -72.88 3.19 0.52) ; 40 ( 43.71 -73.54 3.19 0.52) ; 41 ( 43.48 -74.13 3.31 0.74) ; 42 ( 43.26 -75.08 3.50 0.74) ; 43 ( 43.19 -76.03 3.06 1.11) ; 44 ( 43.41 -76.83 2.06 1.11) ; 45 ( 43.63 -77.56 0.50 1.11) ; 46 ( 43.56 -77.93 0.25 1.92) ; 47 ( 43.56 -78.44 -0.19 1.47) ; 48 ( 43.85 -78.80 -0.19 0.81) ; 49 ( 43.93 -79.53 -0.19 0.59) ; 50 ( 44.15 -80.70 -0.44 0.81) ; 51 ( 44.66 -81.73 -0.88 0.81) ; 52 ( 45.47 -82.68 -1.19 0.81) ; 53 ( 46.14 -83.12 -1.63 0.81) ; 54 ( 46.65 -84.29 -1.63 0.81) ; 55 ( 47.17 -84.94 -1.63 0.81) ; 56 ( 48.05 -85.75 -2.56 0.81) ; 57 ( 48.35 -86.70 -2.56 0.96) ; 58 ( 48.86 -87.94 -1.44 0.88) ; 59 ( 49.08 -88.74 -0.56 0.74) ; 60 ( 49.38 -88.74 1.13 0.74) ; 61 ( 49.75 -88.96 1.19 0.74) ; 62 ( 50.19 -89.70 1.19 0.88) ; 63 ( 50.63 -90.65 1.38 0.88) ; 64 ( 50.70 -91.45 1.63 0.88) ; 65 ( 50.85 -92.18 2.63 1.18) ; 66 ( 50.85 -92.25 2.63 1.55) ; 67 ( 50.78 -92.91 2.94 1.11) ; 68 ( 50.78 -93.57 3.19 0.66) ; 69 ( 50.14 -94.00 4.44 0.59) ; 70 ( 50.51 -94.73 4.81 0.44) ; 71 ( 50.95 -94.95 4.81 0.66) ; 72 ( 51.17 -95.76 4.81 0.88) ; 73 ( 51.17 -96.56 4.81 0.96) ; 74 ( 51.02 -97.37 4.81 0.81) ; 75 ( 51.32 -98.46 4.88 0.81) ; 76 ( 51.46 -99.49 4.88 0.74) ; 77 ( 51.76 -100.29 4.88 0.59) ; 78 ( 52.05 -100.95 4.88 0.59) ; 79 ( 52.64 -101.31 4.88 0.88) ; 80 ( 53.45 -101.82 4.94 1.11) ; 81 ( 53.89 -101.90 4.94 1.11) ; 82 ( 54.48 -102.41 5.00 0.96) ; 83 ( 55.22 -103.07 5.00 0.74) ; 84 ( 56.03 -103.51 5.00 0.74) ; 85 ( 56.69 -104.16 5.00 0.74) ; 86 ( 57.28 -104.75 6.00 0.59) ; 87 ( 57.87 -105.41 6.38 0.74) ; 88 ( 58.02 -106.65 7.31 0.59) ; 89 ( 58.32 -107.16 7.81 0.59) ; 90 ( 58.39 -107.96 7.94 0.52) ; 91 ( 58.02 -108.77 7.94 0.52) ; 92 ( 57.50 -109.28 7.94 0.52) ; 93 ( 57.06 -110.08 8.06 0.52) ; 94 ( 56.55 -111.04 8.25 0.52) ; 95 Low ) ; End of split | ( 30.98 -29.75 -0.94 0.59) ; 1, R-1-1-2-2 ( 30.76 -30.19 1.06 0.59) ; 2 ( 30.39 -30.56 2.00 0.59) ; 3 ( 30.02 -31.07 2.88 0.59) ; 4 ( 30.32 -31.95 3.00 0.59) ; 5 ( 30.76 -32.39 3.00 0.59) ; 6 ( 31.06 -33.19 3.00 0.59) ; 7 ( 31.57 -34.14 3.69 0.59) ; 8 ( 31.79 -35.24 2.81 1.03) ; 9 ( 32.23 -36.41 2.25 1.03) ; 10 ( 32.68 -37.21 2.00 0.52) ; 11 ( 33.12 -38.38 2.69 0.52) ; 12 ( 33.86 -39.33 4.44 0.52) ; 13 ( 34.45 -39.26 5.88 0.81) ; 14 ( 35.03 -38.96 6.50 0.59) ; 15 ( 35.84 -39.18 6.38 0.81) ; 16 ( 36.14 -39.18 5.75 0.81) ; 17 ( 37.10 -39.55 5.63 0.59) ; 18 ( 37.24 -40.13 7.75 0.59) ; 19 ( 36.95 -40.35 8.38 0.59) ; 20 ( 36.43 -40.57 8.88 0.96) ; 21 ( 35.92 -41.01 9.81 0.96) ; 22 ( 35.48 -41.01 9.81 0.96) ; 23 ( 34.59 -41.08 10.75 0.96) ; 24 ( 34.67 -41.23 12.13 1.11) ; 25 ( 35.48 -41.38 12.50 1.11) ; 26 ( 36.07 -41.52 12.94 0.74) ; 27 ( 37.54 -41.23 13.00 0.52) ; 28 ( 38.42 -41.01 13.19 0.52) ; 29 ( 39.23 -40.65 13.50 0.74) ; 30 ( 40.04 -40.13 14.25 1.03) ; 31 ( 40.63 -39.70 14.63 1.11) ; 32 ( 41.00 -39.62 15.00 0.59) ; 33 ( 40.78 -40.06 15.63 0.59) ; 34 ( 40.27 -40.57 15.75 0.59) ; 35 ( 39.90 -41.30 15.81 0.59) ; 36 ( 40.19 -41.74 16.50 0.59) ; 37 ( 40.56 -42.40 16.25 0.66) ; 38 ( 41.15 -42.91 16.25 0.66) ; 39 ( 41.67 -43.28 16.25 0.66) ; 40 ( 42.18 -43.94 16.94 0.66) ; 41 ( 42.99 -44.23 17.00 0.66) ; 42 ( 44.10 -44.01 16.63 0.59) ; 43 ( 44.69 -44.23 16.63 0.74) ; 44 ( 45.05 -44.74 16.63 0.59) ; 45 ( 45.72 -44.96 16.63 0.59) ; 46 ( 46.45 -45.18 15.69 0.59) ; 47 ( 47.12 -44.96 16.13 0.59) ; 48 ( 47.63 -44.08 16.69 0.66) ; 49 ( 48.52 -43.57 17.50 0.59) ; 50 ( 49.55 -43.86 17.75 0.52) ; 51 ( 50.43 -44.23 17.63 0.37) ; 52 ( 50.95 -45.03 17.38 0.29) ; 53 Low ) ; End of split ) ; End of split | ( 23.41 -19.71 -4.56 1.55) ; 1, R-1-2 ( 23.92 -20.59 -7.56 1.55) ; 2 ( 24.59 -21.17 -9.50 1.62) ; 3 ( 25.10 -21.83 -10.44 2.06) ; 4 ( 25.00 -21.92 -10.44 2.06) ; 5 ( ( 25.25 -22.63 -13.44 1.99) ; 1, R-1-2-1 ( 26.50 -24.02 -13.13 1.84) ; 2 ( 27.83 -25.92 -12.63 1.69) ; 3 ( 27.87 -26.22 -12.63 1.69) ; 4 ( ( 28.42 -27.82 -12.69 1.69) ; 1, R-1-2-1-1 ( 28.86 -29.14 -12.81 2.21) ; 2 ( ( 29.15 -30.16 -13.13 2.65) ; 1, R-1-2-1-1-1 ( 29.32 -30.27 -13.13 2.65) ; 2 ( ( 29.76 -31.47 -14.69 2.06) ; 1, R-1-2-1-1-1-1 ( 31.61 -33.23 -15.00 1.92) ; 2 ( ( 32.71 -34.32 -13.75 2.21) ; 1, R-1-2-1-1-1-1-1 ( 32.78 -35.20 -13.81 1.92) ; 2 ( ( 32.56 -36.23 -14.50 0.74) ; 1, R-1-2-1-1-1-1-1-1 ( 32.19 -37.03 -14.50 0.44) ; 2 ( 32.34 -37.98 -14.50 0.66) ; 3 ( 32.56 -38.78 -15.06 0.88) ; 4 ( 32.93 -39.44 -15.19 0.88) ; 5 ( 33.74 -40.46 -15.56 0.52) ; 6 ( 34.40 -41.49 -15.69 0.96) ; 7 ( 35.14 -42.51 -16.19 1.33) ; 8 ( 35.44 -43.39 -16.50 1.03) ; 9 ( 35.88 -44.19 -16.31 0.81) ; 10 ( 35.73 -44.78 -16.19 1.03) ; 11 ( 35.95 -45.58 -16.56 1.03) ; 12 ( 36.25 -46.53 -15.63 0.81) ; 13 ( 36.69 -47.26 -15.50 0.59) ; 14 ( 36.91 -48.07 -15.56 0.59) ; 15 ( 36.76 -49.16 -15.63 0.88) ; 16 ( 37.06 -49.97 -15.63 0.88) ; 17 ( 37.57 -50.84 -15.63 0.88) ; 18 ( 37.72 -51.87 -15.63 0.88) ; 19 ( 38.16 -53.70 -17.13 0.88) ; 20 ( 38.09 -54.79 -18.06 1.18) ; 21 ( 38.01 -56.18 -18.19 0.88) ; 22 ( 38.53 -57.20 -18.31 0.88) ; 23 ( 39.19 -58.15 -18.31 0.88) ; 24 ( 39.49 -58.74 -18.31 0.88) ; 25 ( 40.08 -59.62 -18.63 1.77) ; 26 ( 40.50 -60.58 -19.25 1.99) ; 27 ( 41.23 -61.60 -19.56 1.62) ; 28 ( 41.45 -62.19 -19.94 1.18) ; 29 ( 41.97 -63.14 -20.13 0.88) ; 30 ( 42.56 -64.16 -20.50 0.88) ; 31 ( 42.71 -65.55 -21.00 1.03) ; 32 ( 42.78 -67.01 -21.19 1.03) ; 33 ( 43.07 -68.11 -21.44 0.96) ; 34 ( 43.59 -69.06 -22.63 0.88) ; 35 ( 44.03 -70.16 -23.38 0.74) ; 36 ( 44.11 -71.47 -23.81 0.66) ; 37 ( 44.55 -73.01 -24.88 0.66) ; 38 ( 45.06 -74.32 -24.88 0.66) ; 39 ( 45.80 -76.15 -25.69 0.88) ; 40 ( 46.54 -77.76 -26.25 0.88) ; 41 ( 47.13 -78.64 -26.63 0.96) ; 42 ( 47.64 -79.51 -26.56 0.59) ; 43 ( 48.30 -80.17 -26.56 0.59) ; 44 ( 48.67 -80.61 -26.75 0.59) ; 45 ( 49.41 -81.49 -26.75 0.96) ; 46 ( 50.51 -82.51 -27.06 1.40) ; 47 ( 51.03 -83.32 -27.06 1.03) ; 48 ( 51.55 -84.19 -27.06 0.81) ; 49 ( 51.99 -85.29 -27.38 0.88) ; 50 ( 52.43 -85.95 -26.44 0.96) ; 51 ( 52.72 -86.97 -26.56 0.74) ; 52 ( 53.31 -88.07 -26.56 0.52) ; 53 ( 54.12 -89.16 -26.56 0.74) ; 54 ( 54.84 -90.15 -27.13 1.03) ; 55 ( 54.84 -91.18 -27.94 1.40) ; 56 ( 54.84 -92.06 -28.63 1.62) ; 57 ( 54.54 -93.74 -28.81 1.55) ; 58 ( 53.95 -94.98 -29.75 1.84) ; 59 ( 53.66 -95.71 -30.75 1.47) ; 60 ( 53.14 -96.44 -30.81 0.81) ; 61 ( 52.55 -97.39 -31.38 0.66) ; 62 ( 52.41 -97.68 -31.44 0.66) ; 63 ( 52.63 -98.49 -31.44 0.66) ; 64 ( 52.48 -99.29 -32.88 1.25) ; 65 ( 52.18 -100.53 -33.25 1.99) ; 66 ( 51.96 -101.63 -33.75 1.55) ; 67 ( 51.74 -102.73 -33.75 1.55) ; 68 ( 51.67 -104.92 -34.25 1.77) ; 69 ( 51.89 -106.31 -34.69 1.47) ; 70 ( 52.11 -107.55 -35.75 0.88) ; 71 ( 52.77 -108.79 -35.75 0.66) ; 72 ( 53.44 -109.96 -36.44 0.66) ; 73 ( 53.58 -110.70 -36.63 0.52) ; 74 ( 53.36 -111.21 -37.13 0.52) ; 75 ( 53.14 -111.35 -38.00 0.74) ; 76 ( 52.92 -112.52 -38.31 0.81) ; 77 ( 52.85 -113.84 -38.94 1.47) ; 78 ( 53.00 -114.79 -39.25 1.84) ; 79 ( 53.29 -116.10 -39.38 1.84) ; 80 ( 53.22 -117.35 -39.38 1.25) ; 81 ( 53.51 -118.59 -40.06 0.81) ; 82 ( 53.80 -119.82 -40.38 0.52) ; 83 ( 53.80 -120.92 -40.69 0.52) ; 84 ( 53.36 -121.14 -41.19 0.52) ; 85 ( 52.77 -121.28 -41.25 0.52) ; 86 ( 52.55 -122.16 -41.25 1.11) ; 87 ( 52.18 -123.11 -41.44 1.47) ; 88 ( 51.66 -124.13 -41.44 1.84) ; 89 ( 51.22 -124.86 -42.13 2.65) ; 90 ( 50.56 -125.81 -42.19 1.33) ; 91 ( 50.19 -126.62 -42.56 0.88) ; 92 ( 50.04 -127.50 -42.75 0.52) ; 93 ( 50.04 -128.37 -43.56 0.44) ; 94 ( 50.04 -129.10 -44.81 0.44) ; 95 ( 49.60 -129.83 -44.81 0.66) ; 96 ( 48.86 -130.57 -45.19 0.66) ; 97 ( 48.50 -132.39 -45.63 0.44) ; 98 ( 48.35 -133.12 -46.00 0.44) ; 99 ( 47.83 -134.22 -46.13 1.25) ; 100 ( 47.39 -135.10 -46.31 1.99) ; 101 ( 46.51 -136.27 -46.63 1.69) ; 102 ( 45.62 -137.14 -46.63 0.96) ; 103 ( 45.11 -137.73 -46.63 0.52) ; 104 ( 44.44 -138.31 -46.63 0.37) ; 105 ( 44.30 -138.61 -46.63 0.29) ; 106 ( 43.71 -139.12 -47.06 0.66) ; 107 ( 43.41 -139.85 -47.56 0.96) ; 108 ( 42.97 -140.43 -47.69 0.59) ; 109 ( 42.38 -140.73 -48.13 0.37) ; 110 ( 41.64 -141.38 -48.00 0.22) ; 111 ( 41.20 -142.19 -48.00 0.22) ; 112 ( 40.54 -142.85 -47.88 0.22) ; 113 ( 39.95 -143.36 -47.50 0.22) ; 114 ( 39.58 -143.58 -47.31 0.22) ; 115 ( 39.29 -143.94 -47.19 0.96) ; 116 ( 38.92 -144.38 -46.94 1.33) ; 117 ( 38.40 -144.67 -46.88 0.66) ; 118 ( 37.59 -145.11 -46.81 0.29) ; 119 ( 37.15 -145.48 -46.75 0.66) ; 120 ( 36.56 -145.92 -46.69 1.03) ; 121 ( 36.05 -146.43 -46.50 0.66) ; 122 ( 35.60 -146.72 -46.44 0.29) ; 123 ( 34.94 -147.23 -46.44 0.29) ; 124 ( 34.50 -147.74 -46.44 0.96) ; 125 ( 34.15 -148.87 -46.31 0.96) ; 126 ( 33.63 -149.68 -46.31 0.59) ; 127 ( 33.41 -150.34 -46.31 0.44) ; 128 Low | ( 33.52 -35.82 -14.50 0.66) ; 1, R-1-2-1-1-1-1-1-2 ( 34.25 -36.04 -14.50 0.66) ; 2 ( 35.36 -36.25 -14.50 0.74) ; 3 ( 36.32 -36.62 -14.50 0.74) ; 4 ( 37.64 -36.40 -14.69 0.59) ; 5 ( 38.60 -36.56 -14.69 0.59) ; 6 ( 40.30 -36.49 -15.25 1.18) ; 7 ( 41.04 -35.90 -17.56 1.18) ; 8 ( 42.44 -35.98 -18.19 0.88) ; 9 ( 43.25 -36.34 -19.63 0.88) ; 10 ( 44.13 -36.56 -19.75 1.11) ; 11 ( 45.31 -36.93 -19.75 0.96) ; 12 ( 46.05 -37.66 -20.06 0.81) ; 13 ( 46.27 -38.83 -20.31 0.81) ; 14 ( 46.86 -40.51 -20.63 0.66) ; 15 ( 47.30 -41.38 -21.00 0.88) ; 16 ( 47.59 -42.75 -20.50 0.74) ; 17 ( 47.59 -43.26 -20.44 0.59) ; 18 ( 47.88 -44.65 -19.81 0.59) ; 19 ( 48.25 -45.67 -20.63 0.59) ; 20 ( 48.84 -46.91 -21.13 0.59) ; 21 ( 49.51 -47.79 -22.50 0.59) ; 22 ( 50.24 -48.96 -23.94 0.74) ; 23 ( 50.76 -49.62 -23.94 0.52) ; 24 ( 51.35 -51.01 -24.19 0.74) ; 25 ( 51.20 -52.03 -24.75 0.74) ; 26 ( 50.54 -52.47 -25.88 0.52) ; 27 ( 50.68 -53.13 -26.44 0.52) ; 28 ( 51.20 -53.57 -27.81 0.52) ; 29 ( 51.64 -54.22 -29.06 0.74) ; 30 ( 52.08 -55.25 -29.31 0.74) ; 31 ( 52.75 -56.12 -31.25 0.59) ; 32 ( 52.60 -57.07 -31.56 0.59) ; 33 ( 52.60 -57.59 -31.94 0.88) ; 34 ( 53.04 -58.39 -33.25 0.66) ; 35 ( 53.26 -59.12 -35.06 0.66) ; 36 ( 53.48 -60.29 -36.75 0.44) ; 37 ( 53.85 -61.68 -37.88 0.44) ; 38 ( 54.37 -62.70 -37.88 0.44) ; 39 ( 55.03 -63.29 -38.75 0.74) ; 40 ( 55.91 -64.09 -37.38 0.74) ; 41 ( 56.50 -64.75 -37.63 0.74) ; 42 ( 57.02 -65.70 -37.81 0.44) ; 43 ( 57.46 -66.65 -38.00 0.44) ; 44 ( 58.20 -66.72 -38.50 0.59) ; 45 ( 59.23 -67.16 -38.81 0.59) ; 46 ( 60.26 -67.60 -39.50 0.74) ; 47 ( 61.15 -68.04 -39.50 1.40) ; 48 ( 61.96 -68.40 -40.25 1.77) ; 49 ( 63.21 -68.77 -40.25 1.92) ; 50 ( 64.31 -69.06 -40.25 1.11) ; 51 ( 65.42 -69.28 -40.25 0.74) ; 52 ( 66.45 -69.79 -40.69 0.44) ; 53 ( 67.19 -70.52 -40.69 0.29) ; 54 ( 68.02 -71.33 -42.06 0.59) ; 55 ( 68.61 -72.35 -42.38 1.18) ; 56 ( 69.12 -73.30 -44.25 1.18) ; 57 ( 69.86 -73.45 -45.25 1.18) ; 58 ( 70.37 -74.03 -45.38 1.03) ; 59 ( 71.63 -74.91 -45.38 0.88) ; 60 ( 72.80 -75.86 -46.50 0.74) ; 61 ( 73.54 -76.74 -46.56 0.66) ; 62 ( 74.13 -77.61 -46.94 0.66) ; 63 ( 74.57 -78.35 -47.44 0.81) ; 64 ( 75.24 -79.30 -47.81 1.03) ; 65 ( 75.97 -79.66 -48.38 0.88) ; 66 ( 76.34 -80.54 -48.69 0.88) ; 67 ( 76.93 -81.71 -48.69 0.59) ; 68 ( 77.52 -82.95 -48.69 0.37) ; 69 ( 77.89 -84.05 -48.69 0.29) ; 70 ( 78.18 -84.85 -49.00 0.66) ; 71 ( 78.48 -86.02 -48.81 0.96) ; 72 ( 78.77 -86.97 -47.13 1.62) ; 73 ( 79.07 -87.77 -47.38 2.36) ; 74 ( 79.07 -88.87 -47.50 1.99) ; 75 ( 79.07 -89.75 -47.50 1.18) ; 76 ( 78.92 -90.63 -47.50 0.59) ; 77 ( 78.77 -91.72 -47.50 0.29) ; 78 ( 78.62 -92.75 -47.88 0.29) ; 79 ( 78.40 -93.26 -48.00 1.03) ; 80 ( 78.48 -94.06 -48.00 2.06) ; 81 ( 78.33 -94.79 -48.25 2.06) ; 82 ( 77.89 -95.45 -49.19 0.66) ; 83 ( 77.74 -96.69 -49.81 0.44) ; 84 Low ) ; End of split | ( 32.75 -33.80 -14.50 1.25) ; 1, R-1-2-1-1-1-1-2 ( 33.34 -34.02 -14.06 0.81) ; 2 ( 34.00 -34.17 -13.75 0.52) ; 3 ( 35.03 -34.24 -12.94 0.59) ; 4 ( 35.77 -34.32 -12.94 0.74) ; 5 ( 37.46 -34.97 -13.44 0.74) ; 6 ( 38.05 -35.41 -13.88 0.74) ; 7 ( 39.01 -36.14 -13.75 0.96) ; 8 ( 40.34 -36.44 -14.75 0.96) ; 9 ( 41.22 -36.80 -14.75 0.96) ; 10 ( 42.03 -37.39 -14.25 0.81) ; 11 ( 43.28 -38.19 -15.19 0.66) ; 12 ( 44.02 -38.77 -15.81 0.66) ; 13 ( 45.12 -39.65 -16.75 1.03) ; 14 ( 46.16 -40.60 -16.13 0.96) ; 15 ( 47.48 -40.75 -15.81 0.96) ; 16 ( 48.51 -40.89 -15.25 0.74) ; 17 ( 49.62 -41.48 -15.75 0.74) ; 18 ( 50.06 -41.99 -15.75 0.81) ; 19 ( 50.65 -42.65 -15.88 0.81) ; 20 ( 51.17 -43.16 -16.44 0.81) ; 21 ( 51.98 -43.82 -17.69 1.18) ; 22 ( 52.71 -44.92 -17.94 1.18) ; 23 ( 53.23 -45.43 -18.19 1.18) ; 24 ( 53.89 -46.23 -18.69 1.33) ; 25 ( 54.55 -46.89 -19.56 1.33) ; 26 ( 55.37 -47.55 -19.81 1.11) ; 27 ( 55.73 -47.91 -21.06 0.96) ; 28 ( 56.69 -48.50 -21.06 0.74) ; 29 ( 56.99 -49.67 -21.81 0.81) ; 30 ( 57.72 -50.76 -23.69 1.03) ; 31 ( 58.39 -51.64 -24.63 0.88) ; 32 ( 59.27 -51.86 -27.06 1.11) ; 33 ( 60.60 -52.15 -27.88 1.11) ; 34 ( 61.48 -52.44 -27.94 0.88) ; 35 ( 62.36 -52.96 -27.94 0.88) ; 36 ( 63.03 -53.03 -28.19 0.88) ; 37 ( 64.35 -53.03 -29.31 0.88) ; 38 ( 65.09 -53.10 -31.00 1.18) ; 39 ( 66.27 -53.61 -31.38 1.47) ; 40 ( 67.23 -54.05 -32.00 1.47) ; 41 ( 68.04 -54.34 -32.81 1.18) ; 42 ( 68.70 -54.20 -33.25 0.88) ; 43 ( 69.44 -54.56 -34.44 0.59) ; 44 ( 70.17 -55.00 -35.56 0.52) ; 45 ( 71.06 -55.22 -36.44 0.81) ; 46 ( 71.65 -55.22 -37.00 0.81) ; 47 ( 72.16 -54.78 -37.94 0.66) ; 48 ( 73.27 -54.71 -38.75 0.59) ; 49 ( 73.86 -55.00 -40.00 0.59) ; 50 ( 74.30 -55.30 -40.38 0.59) ; 51 ( 76.07 -56.03 -41.13 0.88) ; 52 ( 76.80 -56.39 -41.38 1.25) ; 53 ( 77.32 -56.61 -42.50 1.47) ; 54 ( 78.35 -56.98 -42.50 1.47) ; 55 ( 79.11 -57.40 -42.44 0.44) ; 56 ( 79.62 -58.13 -42.81 0.37) ; 57 ( 80.28 -58.57 -43.00 1.03) ; 58 ( 81.32 -58.71 -43.56 1.33) ; 59 ( 82.35 -59.00 -43.56 1.33) ; 60 ( 83.16 -59.00 -43.56 0.66) ; 61 ( 84.34 -59.08 -43.56 0.44) ; 62 ( 85.44 -59.22 -43.69 1.11) ; 63 ( 86.62 -59.44 -44.00 1.62) ; 64 ( 87.50 -59.52 -43.56 0.88) ; 65 ( 88.02 -59.66 -42.69 0.52) ; 66 ( 88.68 -60.03 -42.44 0.29) ; 67 ( 88.90 -60.25 -41.81 0.29) ; 68 ( 89.86 -60.69 -41.75 0.88) ; 69 ( 90.75 -61.20 -41.75 1.11) ; 70 ( 91.56 -61.56 -42.06 0.81) ; 71 ( 92.22 -61.78 -41.75 0.52) ; 72 ( 92.88 -62.29 -41.56 0.44) ; 73 ( 93.77 -62.95 -41.56 0.81) ; 74 ( 94.28 -63.10 -41.56 1.18) ; 75 ( 95.17 -63.39 -41.50 1.18) ; 76 ( 95.76 -63.39 -41.38 0.59) ; 77 ( 96.34 -63.61 -41.38 0.37) ; 78 ( 96.49 -64.27 -41.38 0.37) ; 79 ( 97.08 -64.93 -40.75 0.37) ; 80 ( 97.82 -65.29 -40.50 1.11) ; 81 ( 98.33 -65.73 -39.94 1.99) ; 82 ( 99.29 -66.02 -39.88 2.28) ; 83 ( 100.32 -66.68 -39.88 1.40) ; 84 ( 100.77 -66.83 -39.25 0.66) ; 85 ( 101.58 -67.05 -39.06 0.37) ; 86 ( 102.02 -67.12 -37.88 0.22) ; 87 ( 102.61 -67.48 -36.31 0.66) ; 88 ( 102.90 -67.56 -35.56 1.47) ; 89 ( 103.42 -67.70 -34.44 1.92) ; 90 ( 104.08 -68.07 -34.31 1.55) ; 91 ( 104.60 -68.51 -32.00 1.11) ; 92 ( 105.41 -69.02 -30.31 0.59) ; 93 ( 105.85 -69.53 -28.19 0.37) ; 94 Low ) ; End of split | ( 29.91 -30.27 -15.13 1.03) ; 1, R-1-2-1-1-1-2 ( 30.57 -30.27 -18.69 1.03) ; 2 ( 31.23 -30.49 -21.56 1.03) ; 3 ( 32.19 -30.42 -24.00 1.03) ; 4 ( 33.00 -30.49 -25.44 0.88) ; 5 ( 33.81 -30.64 -26.19 0.88) ; 6 ( 35.51 -31.51 -26.19 0.88) ; 7 ( 36.83 -31.59 -27.19 0.88) ; 8 ( 37.42 -31.08 -27.44 1.03) ; 9 ( 37.72 -30.49 -29.06 1.03) ; 10 ( 37.64 -30.64 -33.13 1.03) ; 11 ( 38.16 -31.66 -34.63 0.88) ; 12 ( 39.71 -31.88 -34.94 0.74) ; 13 ( 41.18 -32.32 -35.38 0.59) ; 14 ( 41.84 -32.32 -36.50 0.59) ; 15 ( 42.21 -31.88 -37.94 0.88) ; 16 ( 42.87 -31.51 -37.94 1.11) ; 17 ( 43.98 -31.08 -37.94 1.40) ; 18 ( 44.64 -30.78 -38.63 1.40) ; 19 ( 45.16 -30.71 -38.75 1.03) ; 20 ( 46.11 -30.27 -39.94 0.81) ; 21 ( 47.37 -29.61 -40.81 0.74) ; 22 ( 48.69 -29.83 -39.69 0.59) ; 23 ( 49.58 -29.98 -42.38 0.66) ; 24 ( 50.76 -30.42 -42.44 0.66) ; 25 ( 51.94 -30.78 -42.81 0.66) ; 26 ( 53.33 -30.86 -41.94 0.96) ; 27 ( 54.37 -30.64 -44.56 0.96) ; 28 ( 55.03 -30.78 -47.25 0.96) ; 29 ( 55.99 -31.66 -48.31 0.96) ; 30 ( 56.87 -32.17 -48.56 1.25) ; 31 ( 58.05 -32.39 -48.88 1.40) ; 32 ( 58.93 -32.03 -49.50 0.74) ; 33 ( 59.97 -31.88 -50.38 0.59) ; 34 ( 60.55 -31.73 -52.25 0.59) ; 35 ( 61.00 -30.86 -53.94 0.59) ; 36 ( 61.95 -29.76 -51.75 0.29) ; 37 ( 62.18 -29.39 -53.25 0.29) ; 38 ( 63.13 -28.59 -54.31 0.66) ; 39 ( 63.50 -28.01 -55.88 0.96) ; 40 ( 63.87 -27.27 -56.06 1.25) ; 41 ( 64.24 -26.62 -57.06 1.62) ; 42 ( 64.53 -26.10 -57.63 1.62) ; 43 ( 64.68 -25.15 -58.50 0.81) ; 44 ( 64.53 -24.64 -61.81 0.59) ; 45 ( 64.09 -23.77 -62.19 0.37) ; 46 ( 64.09 -22.45 -64.44 0.37) ; 47 ( 64.16 -21.35 -58.56 0.29) ; 48 ( 64.53 -21.06 -61.19 0.66) ; 49 ( 64.98 -20.48 -61.19 0.37) ; 50 ( 65.79 -19.16 -61.19 0.15) ; 51 ( 66.23 -18.14 -62.25 0.15) ; 52 ( 66.74 -17.41 -62.44 0.15) ; 53 ( 67.48 -16.75 -62.56 0.52) ; 54 ( 67.55 -16.31 -63.25 1.33) ; 55 ( 68.00 -15.58 -63.56 2.95) ; 56 ( 68.00 -14.85 -64.19 2.95) ; 57 ( 68.36 -14.19 -65.81 1.69) ; 58 ( 68.51 -13.31 -66.19 0.96) ; 59 ( 69.03 -12.95 -68.13 0.66) ; 60 ( 70.21 -11.27 -68.63 0.22) ; 61 ( 70.94 -10.75 -69.69 0.22) ; 62 ( 72.42 -9.80 -69.69 0.22) ; 63 ( 73.30 -9.22 -69.69 0.22) ; 64 ( 74.41 -9.07 -69.69 0.59) ; 65 ( 75.22 -8.93 -70.75 0.81) ; 66 ( 76.03 -8.56 -73.00 0.52) ; 67 Low ) ; End of split | ( 28.31 -29.93 -12.38 1.69) ; 1, R-1-2-1-1-2 ( 27.87 -30.59 -13.13 0.81) ; 2 ( 27.43 -31.68 -13.25 0.59) ; 3 ( 27.06 -32.63 -13.31 0.59) ; 4 ( 26.91 -33.29 -13.69 0.59) ; 5 ( 26.62 -34.75 -13.88 0.74) ; 6 ( 26.25 -35.85 -14.81 0.52) ; 7 ( 25.95 -37.09 -15.88 0.52) ; 8 ( 25.44 -38.04 -14.88 0.52) ; 9 ( 25.22 -38.99 -15.63 0.66) ; 10 ( 25.59 -40.09 -16.25 0.66) ; 11 ( 26.03 -40.97 -16.44 0.66) ; 12 ( 26.62 -42.06 -16.94 0.66) ; 13 ( 27.06 -42.87 -16.94 0.66) ; 14 ( 27.21 -43.23 -17.94 0.74) ; 15 ( 27.65 -43.96 -18.44 1.03) ; 16 ( 27.50 -45.42 -19.44 1.03) ; 17 ( 27.35 -46.01 -19.44 0.81) ; 18 ( 27.13 -47.62 -19.69 0.81) ; 19 ( 27.06 -48.71 -20.25 0.81) ; 20 ( 26.98 -49.96 -20.25 0.81) ; 21 ( 26.84 -50.91 -20.63 0.66) ; 22 ( 27.35 -52.08 -21.75 1.03) ; 23 ( 27.57 -53.32 -22.50 1.03) ; 24 ( 28.02 -54.12 -22.50 0.96) ; 25 ( 27.50 -55.73 -23.13 1.11) ; 26 ( 27.13 -56.90 -23.13 0.81) ; 27 ( 26.91 -57.85 -23.25 0.59) ; 28 ( 26.96 -59.04 -23.25 0.59) ; 29 ( 27.11 -59.99 -23.38 1.11) ; 30 ( 27.55 -61.01 -23.38 1.77) ; 31 ( 27.63 -62.18 -23.56 1.03) ; 32 ( 27.92 -63.20 -23.88 0.81) ; 33 ( 28.29 -64.30 -23.88 1.11) ; 34 ( 29.25 -65.18 -23.88 1.11) ; 35 ( 29.76 -66.35 -25.06 0.81) ; 36 ( 30.28 -67.51 -25.38 0.96) ; 37 ( 30.28 -68.32 -25.38 0.66) ; 38 ( 30.28 -69.42 -25.38 0.66) ; 39 ( 30.21 -70.22 -25.38 0.96) ; 40 ( 30.28 -71.39 -25.50 0.96) ; 41 ( 30.28 -72.78 -25.50 0.96) ; 42 ( 30.06 -73.87 -26.25 1.33) ; 43 ( 29.76 -74.97 -25.19 0.81) ; 44 ( 29.62 -75.63 -24.88 0.52) ; 45 ( 29.76 -76.87 -24.44 1.11) ; 46 ( 29.91 -77.68 -24.88 1.11) ; 47 ( 29.69 -78.63 -24.88 0.81) ; 48 ( 29.98 -79.43 -25.19 0.81) ; 49 ( 29.69 -80.45 -26.44 1.03) ; 50 ( 29.69 -81.62 -27.06 1.40) ; 51 ( 29.62 -82.35 -27.06 1.69) ; 52 ( 29.62 -83.38 -27.06 1.11) ; 53 ( 29.98 -84.11 -27.63 0.74) ; 54 ( 29.76 -84.99 -27.31 0.59) ; 55 ( 29.98 -86.01 -27.69 0.74) ; 56 ( 30.43 -87.03 -27.50 0.59) ; 57 ( 30.72 -88.06 -27.88 0.59) ; 58 ( 30.79 -89.34 -27.81 0.88) ; 59 ( 30.57 -90.36 -27.88 1.40) ; 60 ( 29.91 -91.61 -29.25 1.77) ; 61 ( 29.54 -92.99 -29.25 1.03) ; 62 ( 29.54 -94.09 -29.25 0.59) ; 63 ( 29.91 -94.60 -29.25 0.59) ; 64 ( 30.57 -95.33 -29.75 0.59) ; 65 ( 31.01 -95.92 -29.75 0.59) ; 66 ( 31.16 -96.65 -29.75 0.59) ; 67 ( 30.86 -97.53 -29.88 1.18) ; 68 ( 30.42 -98.33 -30.19 1.18) ; 69 ( 29.54 -99.28 -29.13 0.81) ; 70 ( 29.02 -100.16 -29.06 0.81) ; 71 ( 28.65 -101.47 -29.19 0.59) ; 72 ( 28.51 -102.64 -28.63 0.59) ; 73 ( 28.14 -103.37 -28.25 1.33) ; 74 ( 27.70 -104.47 -27.94 1.62) ; 75 ( 27.25 -105.79 -27.88 1.84) ; 76 ( 27.03 -106.30 -27.88 1.03) ; 77 ( 26.81 -106.96 -27.88 0.59) ; 78 ( 26.59 -107.76 -27.88 0.37) ; 79 ( 26.89 -108.34 -27.94 0.37) ; 80 ( 27.18 -108.56 -28.00 0.37) ; 81 ( 27.33 -109.22 -28.00 0.37) ; 82 ( 26.52 -109.44 -28.00 0.59) ; 83 ( 25.85 -109.95 -28.00 0.59) ; 84 ( 24.45 -110.61 -28.00 0.44) ; 85 ( 24.45 -111.27 -28.00 0.44) ; 86 ( 23.72 -112.95 -28.00 0.74) ; 87 ( 23.35 -113.68 -28.19 1.40) ; 88 ( 23.20 -114.41 -28.25 2.21) ; 89 ( 23.35 -115.22 -28.38 2.21) ; 90 ( 22.69 -116.75 -28.44 1.69) ; 91 ( 22.28 -117.54 -28.44 0.81) ; 92 ( 21.62 -118.71 -29.06 0.37) ; 93 ( 20.74 -119.44 -29.31 0.37) ; 94 ( 20.59 -120.17 -30.31 0.37) ; 95 ( 20.15 -120.91 -31.25 0.37) ; 96 ( 19.71 -120.98 -33.06 0.44) ; 97 ( 19.26 -121.49 -32.88 0.44) ; 98 ( 19.04 -121.86 -32.88 0.81) ; 99 ( 18.45 -122.44 -32.75 0.81) ; 100 ( 18.01 -122.59 -32.56 0.37) ; 101 ( 16.61 -122.81 -32.25 0.52) ; 102 ( 16.46 -122.81 -31.38 0.59) ; 103 Low ) ; End of split | ( 27.50 -26.88 -15.06 0.66) ; 1, R-1-2-1-2 ( 27.28 -27.54 -18.75 0.66) ; 2 ( 27.87 -28.63 -20.31 0.66) ; 3 ( 27.87 -29.22 -21.88 0.66) ; 4 ( 28.24 -30.46 -22.44 0.66) ; 5 ( 29.05 -31.63 -24.38 0.66) ; 6 ( 30.82 -33.46 -26.63 0.66) ; 7 ( 31.70 -34.70 -28.06 0.66) ; 8 ( 32.36 -37.33 -29.88 0.66) ; 9 ( 31.77 -38.50 -32.31 0.66) ; 10 ( 32.44 -39.89 -33.44 0.88) ; 11 ( 33.03 -41.65 -34.69 1.11) ; 12 ( 33.91 -42.60 -35.13 0.96) ; 13 ( 35.16 -43.69 -36.94 0.81) ; 14 ( 36.71 -44.79 -37.81 0.74) ; 15 ( 37.96 -46.03 -38.50 0.59) ; 16 ( 39.44 -46.91 -40.06 0.59) ; 17 ( 40.76 -47.27 -41.38 0.59) ; 18 ( 42.09 -48.01 -42.13 0.59) ; 19 ( 42.90 -48.23 -42.13 0.59) ; 20 ( 43.34 -48.81 -42.56 0.88) ; 21 ( 43.56 -49.18 -43.13 1.25) ; 22 ( 44.30 -49.83 -43.63 1.47) ; 23 ( 44.67 -50.35 -44.56 1.03) ; 24 ( 45.11 -51.30 -44.69 0.59) ; 25 ( 45.92 -52.39 -45.00 0.59) ; 26 ( 46.51 -53.34 -45.00 0.59) ; 27 ( 47.54 -54.37 -45.00 0.74) ; 28 ( 48.33 -55.73 -45.50 0.52) ; 29 ( 49.06 -56.90 -45.69 0.37) ; 30 ( 49.58 -57.99 -45.69 0.74) ; 31 ( 50.39 -59.60 -45.69 0.74) ; 32 ( 50.76 -60.63 -46.31 0.81) ; 33 ( 51.27 -62.09 -46.31 0.81) ; 34 ( 51.49 -63.11 -46.75 0.66) ; 35 ( 52.01 -64.65 -47.50 0.88) ; 36 ( 52.08 -65.60 -48.56 0.88) ; 37 ( 52.53 -66.55 -49.31 0.88) ; 38 ( 53.04 -67.72 -49.44 0.74) ; 39 ( 53.19 -68.81 -50.38 0.88) ; 40 ( 53.70 -70.13 -50.75 0.96) ; 41 ( 54.37 -71.74 -52.06 0.37) ; 42 ( 55.10 -72.76 -53.13 0.37) ; 43 ( 55.33 -73.71 -53.56 0.59) ; 44 ( 55.99 -74.66 -54.19 0.88) ; 45 ( 56.95 -75.68 -54.69 1.03) ; 46 ( 58.05 -76.71 -56.00 0.81) ; 47 ( 58.71 -77.36 -56.44 1.33) ; 48 ( 59.16 -78.39 -57.06 1.33) ; 49 ( 60.04 -79.34 -57.94 0.66) ; 50 ( 60.63 -80.29 -58.31 0.66) ; 51 ( 61.07 -81.24 -59.06 0.66) ; 52 ( 61.37 -81.82 -57.75 0.66) ; 53 ( 62.18 -83.07 -59.06 0.37) ; 54 ( 62.62 -84.31 -59.06 0.29) ; 55 ( 63.11 -85.59 -59.06 0.29) ; 56 ( 63.33 -86.10 -59.31 0.88) ; 57 ( 63.92 -87.06 -60.06 1.99) ; 58 ( 64.58 -87.57 -61.31 1.99) ; 59 ( 64.81 -88.52 -62.19 1.25) ; 60 ( 65.32 -89.47 -62.50 0.44) ; 61 ( 65.84 -90.64 -63.75 0.22) ; 62 ( 66.72 -92.03 -64.94 0.22) ; 63 ( 66.72 -92.76 -66.50 0.22) ; 64 ( 67.24 -93.49 -67.06 1.33) ; 65 ( 67.02 -94.44 -68.06 1.99) ; 66 ( 66.79 -95.32 -68.56 1.55) ; 67 ( 66.79 -96.41 -68.94 1.11) ; 68 ( 66.79 -97.29 -68.94 0.66) ; 69 ( 66.79 -98.31 -69.13 0.66) ; 70 ( 67.02 -99.19 -69.31 1.47) ; 71 ( 66.57 -100.14 -71.19 1.55) ; 72 ( 66.35 -100.51 -72.19 1.11) ; 73 ( 66.13 -101.09 -72.81 0.66) ; 74 ( 65.98 -101.75 -74.56 0.52) ; 75 Low ) ; End of split | ( 24.64 -22.76 -17.69 0.74) ; 1, R-1-2-2 ( 25.15 -23.64 -20.00 0.96) ; 2 ( 25.01 -24.52 -20.44 0.96) ; 3 ( 24.12 -24.59 -22.56 0.96) ; 4 ( 23.02 -24.96 -22.56 0.96) ; 5 ( 22.43 -25.98 -22.56 0.96) ; 6 ( 21.69 -26.78 -23.50 0.96) ; 7 ( 20.59 -27.66 -23.81 1.11) ; 8 ( 19.85 -28.83 -25.50 0.81) ; 9 ( 19.11 -29.41 -27.25 0.66) ; 10 ( 19.41 -28.32 -29.19 0.66) ; 11 ( 18.23 -28.98 -30.06 0.66) ; 12 ( 17.42 -29.49 -31.63 0.66) ; 13 ( 16.39 -30.51 -32.69 0.88) ; 14 ( 15.65 -30.95 -33.63 0.88) ; 15 ( 14.69 -31.39 -34.81 0.88) ; 16 ( 14.25 -32.12 -35.69 0.88) ; 17 ( 13.37 -32.78 -36.81 0.88) ; 18 ( 12.55 -33.95 -40.75 0.59) ; 19 ( 11.97 -34.97 -42.00 0.59) ; 20 ( 11.23 -35.92 -42.19 0.88) ; 21 ( 10.12 -36.21 -44.00 0.66) ; 22 ( 9.24 -36.80 -44.63 0.66) ; 23 ( 8.80 -37.24 -44.63 1.03) ; 24 ( 7.91 -37.82 -46.25 1.25) ; 25 ( 7.32 -38.70 -48.00 0.96) ; 26 ( 7.03 -39.50 -50.44 0.44) ; 27 ( 6.37 -40.31 -50.94 0.44) ; 28 ( 5.33 -41.04 -51.31 0.44) ; 29 ( 5.04 -41.62 -51.50 0.81) ; 30 ( 4.52 -42.72 -53.06 1.47) ; 31 ( 3.79 -43.16 -55.13 1.47) ; 32 ( 3.05 -43.89 -55.38 1.11) ; 33 ( 2.54 -44.25 -56.75 0.74) ; 34 ( 1.50 -45.50 -56.75 0.37) ; 35 ( 1.14 -46.37 -57.13 1.11) ; 36 ( 0.77 -46.74 -58.19 1.55) ; 37 ( 0.69 -47.03 -59.06 1.55) ; 38 ( 0.62 -47.62 -61.81 1.11) ; 39 ( 0.40 -48.86 -63.44 0.66) ; 40 ( -0.12 -50.39 -63.44 0.52) ; 41 ( -0.78 -51.27 -63.44 0.88) ; 42 ( -1.58 -51.93 -64.88 1.25) ; 43 ( -2.46 -52.37 -65.81 1.25) ; 44 ( -2.98 -52.51 -69.69 0.88) ; 45 ( -3.27 -52.44 -72.19 0.74) ; 46 ( -3.86 -51.93 -73.00 0.96) ; 47 Low ) ; End of split ) ; End of split | ( 16.75 -16.73 -2.56 2.87) ; 1, R-2 ( 17.19 -17.47 -1.69 2.50) ; 2 ( ( 17.48 -18.93 -0.88 1.77) ; 1, R-2-1 ( 17.41 -20.10 0.25 1.11) ; 2 ( 17.71 -20.46 1.31 1.33) ; 3 ( 18.00 -21.70 3.69 1.25) ; 4 ( ( 18.37 -22.87 4.56 0.81) ; 1, R-2-1-1 ( 18.66 -23.97 5.13 0.81) ; 2 ( 18.88 -25.14 4.44 1.03) ; 3 ( 18.96 -26.60 3.81 1.11) ; 4 ( 19.03 -27.77 4.56 1.03) ; 5 ( 18.44 -28.94 4.56 0.96) ; 6 ( 17.48 -30.18 4.63 0.96) ; 7 ( 16.90 -30.40 4.75 0.81) ; 8 ( 15.86 -30.48 4.75 0.81) ; 9 ( 14.91 -30.92 5.00 0.81) ; 10 ( 13.73 -31.28 5.63 0.66) ; 11 ( 13.36 -31.57 6.00 0.66) ; 12 ( 13.29 -32.30 6.19 0.66) ; 13 ( 12.92 -32.45 7.00 0.81) ; 14 ( 12.18 -32.60 7.81 0.81) ; 15 ( 10.93 -33.11 8.19 0.66) ; 16 ( 9.97 -33.77 8.31 0.66) ; 17 ( 8.94 -34.50 8.44 0.52) ; 18 ( 8.05 -35.23 8.44 0.81) ; 19 ( 7.32 -36.11 8.56 0.81) ; 20 ( 6.80 -36.98 9.19 0.74) ; 21 ( 6.14 -37.86 9.50 0.59) ; 22 ( 5.84 -38.96 10.00 0.66) ; 23 ( 5.55 -39.61 10.00 0.81) ; 24 ( 5.62 -40.56 10.13 0.81) ; 25 ( 4.89 -41.30 10.56 0.81) ; 26 ( 3.93 -41.81 11.06 0.66) ; 27 ( 2.97 -41.59 12.25 0.74) ; 28 ( 2.23 -40.78 13.56 0.66) ; 29 ( 2.01 -39.98 13.75 0.88) ; 30 ( 1.35 -39.25 14.44 0.96) ; 31 ( 0.47 -38.81 15.31 0.96) ; 32 ( -0.34 -38.15 16.06 0.88) ; 33 ( -0.93 -38.37 17.19 0.66) ; 34 ( -1.52 -39.25 17.75 0.52) ; 35 ( -1.60 -39.98 18.00 0.52) ; 36 ( -1.67 -40.20 18.19 0.52) ; 37 ( -1.45 -40.71 18.44 0.52) ; 38 Low | ( 18.74 -22.07 4.38 0.59) ; 1, R-2-1-2 ( 19.18 -22.29 4.63 0.59) ; 2 ( 19.92 -22.66 5.00 0.74) ; 3 ( 20.87 -23.61 5.00 0.88) ; 4 ( 21.39 -23.90 5.00 1.11) ; 5 ( 22.20 -24.48 5.19 0.81) ; 6 ( 22.86 -25.29 5.50 0.81) ; 7 ( 23.75 -26.09 5.19 0.81) ; 8 ( 24.26 -27.33 5.44 0.81) ; 9 ( 24.56 -28.21 4.56 0.81) ; 10 ( 24.78 -29.23 4.56 0.66) ; 11 ( 24.85 -30.18 4.38 0.66) ; 12 ( 25.00 -31.13 5.25 0.66) ; 13 ( 24.48 -32.08 6.63 0.81) ; 14 ( 24.48 -33.18 7.00 0.81) ; 15 ( 24.70 -33.91 7.31 0.81) ; 16 ( 25.29 -34.94 7.38 0.81) ; 17 ( 26.25 -35.52 7.19 0.81) ; 18 ( 26.84 -35.74 6.94 0.81) ; 19 ( 27.58 -35.81 6.75 0.81) ; 20 ( 28.31 -36.03 6.75 0.81) ; 21 ( 28.90 -36.40 6.75 0.81) ; 22 ( 30.01 -37.13 6.44 0.66) ; 23 ( 30.30 -37.79 6.38 0.81) ; 24 ( 30.60 -38.30 7.00 0.96) ; 25 ( 30.97 -39.25 8.88 1.18) ; 26 ( 30.45 -40.20 10.44 1.03) ; 27 ( 30.01 -40.78 12.00 0.88) ; 28 ( 29.20 -41.37 12.13 0.81) ; 29 ( 28.61 -41.95 12.31 0.81) ; 30 ( 27.87 -42.83 12.13 0.66) ; 31 ( 27.50 -43.20 12.13 0.66) ; 32 ( 27.43 -43.42 12.25 0.66) ; 33 ( 27.50 -44.22 12.63 0.81) ; 34 ( 27.43 -44.66 13.06 0.96) ; 35 ( 27.14 -45.02 13.31 0.81) ; 36 ( 26.99 -45.68 13.38 0.66) ; 37 ( 27.16 -46.35 13.44 0.74) ; 38 ( 27.75 -47.15 13.81 0.81) ; 39 ( 28.49 -47.81 12.81 0.88) ; 40 ( 28.64 -48.10 11.38 0.96) ; 41 ( 28.64 -48.83 11.13 0.74) ; 42 ( 29.01 -49.71 11.63 0.74) ; 43 ( 29.52 -50.22 12.44 0.74) ; 44 ( 30.18 -50.15 11.13 0.88) ; 45 ( 30.70 -49.85 13.94 0.96) ; 46 ( 31.14 -50.07 15.19 0.88) ; 47 ( 31.36 -50.73 15.88 1.11) ; 48 ( 31.66 -51.17 16.50 1.11) ; 49 ( 32.03 -51.24 17.19 1.11) ; 50 ( 32.47 -50.59 17.88 1.11) ; 51 ( 33.06 -50.15 18.00 0.96) ; 52 ( 33.65 -50.00 17.63 0.88) ; 53 ( 34.46 -50.15 17.56 0.74) ; 54 ( 35.41 -50.66 17.25 0.66) ; 55 ( 36.08 -50.66 16.81 0.81) ; 56 ( 36.52 -50.80 18.25 0.81) ; 57 ( 37.11 -51.17 18.38 0.66) ; 58 ( 37.33 -51.54 18.44 0.52) ; 59 ( 38.07 -52.05 19.06 0.37) ; 60 ( 38.73 -51.90 18.31 0.37) ; 61 ( 39.32 -51.90 17.31 0.37) ; 62 ( 40.06 -51.90 18.25 0.37) ; 63 ( 40.42 -52.05 18.31 0.37) ; 64 ( 40.57 -52.78 18.31 0.37) ; 65 ( 41.01 -53.29 18.31 0.37) ; 66 ( 41.82 -53.80 18.31 0.37) ; 67 Low ) ; End of split | ( 18.47 -18.13 0.31 0.81) ; 1, R-2-2 ( 19.21 -18.57 2.06 0.88) ; 2 ( 19.72 -18.93 2.25 1.55) ; 3 ( 20.14 -19.38 2.25 1.55) ; 4 ( ( 20.68 -19.45 2.69 1.25) ; 1, R-2-2-1 ( 21.64 -19.96 3.06 0.88) ; 2 ( 21.79 -20.61 3.50 0.66) ; 3 ( 22.23 -21.64 3.94 0.81) ; 4 ( 22.89 -22.30 4.31 0.81) ; 5 ( 23.85 -23.03 4.75 0.66) ; 6 ( 24.00 -23.61 5.19 0.66) ; 7 ( 23.92 -24.34 5.94 0.81) ; 8 ( 23.77 -25.00 8.00 0.81) ; 9 ( 23.26 -25.66 8.19 0.66) ; 10 ( 23.26 -26.46 8.94 0.66) ; 11 ( 23.77 -27.05 9.25 0.66) ; 12 ( 24.51 -26.76 9.44 0.66) ; 13 ( 24.51 -26.17 10.56 0.66) ; 14 ( 24.81 -25.95 11.06 0.66) ; 15 ( 25.25 -26.10 11.31 0.66) ; 16 ( 26.13 -26.83 11.69 0.66) ; 17 ( 27.02 -27.49 10.06 0.66) ; 18 ( 27.38 -28.07 11.13 0.81) ; 19 ( 27.75 -28.36 12.50 0.81) ; 20 ( 28.05 -27.78 13.19 0.74) ; 21 ( 28.42 -27.34 13.38 0.59) ; 22 ( 28.42 -26.46 14.00 0.59) ; 23 ( 28.78 -26.02 14.25 0.59) ; 24 ( 28.42 -25.07 14.31 0.59) ; 25 ( 29.01 -24.42 14.31 0.59) ; 26 ( 29.30 -23.54 14.31 0.81) ; 27 ( 29.37 -22.44 13.50 0.74) ; 28 ( 29.37 -21.42 14.75 0.74) ; 29 ( 29.15 -20.69 14.75 0.74) ; 30 ( 29.23 -20.40 15.13 0.74) ; 31 ( 29.74 -20.25 15.38 0.74) ; 32 ( 30.26 -19.74 15.75 0.74) ; 33 ( 30.99 -19.23 16.31 0.74) ; 34 ( 31.07 -18.57 16.75 0.74) ; 35 ( 30.70 -18.06 18.13 0.74) ; 36 ( 29.89 -17.18 18.31 0.44) ; 37 ( 28.86 -16.67 18.38 0.44) ; 38 ( 28.23 -15.88 18.56 0.37) ; 39 ( 28.01 -14.63 18.56 0.37) ; 40 ( 28.23 -13.61 18.50 0.44) ; 41 ( 27.86 -12.81 18.44 0.44) ; 42 ( 27.72 -11.56 18.31 0.44) ; 43 ( 27.05 -11.27 18.25 0.44) ; 44 ( 26.10 -11.42 18.13 0.37) ; 45 ( 25.58 -11.20 18.13 0.37) ; 46 ( 25.36 -10.83 18.13 0.37) ; 47 Low | ( 20.14 -20.41 4.31 0.66) ; 1, R-2-2-2 ( 19.18 -20.77 6.81 0.52) ; 2 ( 18.67 -20.55 9.63 0.44) ; 3 ( 18.15 -19.60 10.00 0.44) ; 4 ( 18.37 -18.65 10.88 0.44) ; 5 ( 19.77 -18.65 11.50 0.44) ; 6 ( 20.29 -19.82 14.25 0.74) ; 7 ( 19.48 -19.75 14.50 0.52) ; 8 ( 19.55 -18.58 14.94 0.52) ; 9 ( 19.99 -17.56 15.38 0.44) ; 10 ( 21.02 -17.34 15.81 0.44) ; 11 ( 21.24 -16.46 16.44 0.37) ; 12 ( 21.17 -15.80 17.13 0.37) ; 13 ( 20.43 -15.36 17.69 0.37) ; 14 ( 19.84 -15.00 18.00 0.29) ; 15 ( 19.11 -14.56 18.31 0.44) ; 16 ( 18.67 -13.97 18.50 0.44) ; 17 ( 18.74 -12.00 19.06 0.29) ; 18 ( 18.00 -11.34 19.25 0.44) ; 19 ( 16.60 -11.05 19.31 0.44) ; 20 ( 15.57 -11.42 19.38 0.22) ; 21 ( 14.69 -11.93 19.38 0.22) ; 22 High ) ; End of split ) ; End of split ) ; End of split ) ; End of tree ( (Color Green) (Dendrite) ( -13.75 -4.63 5.38 3.61) ; Root ( -15.67 -5.36 5.56 2.80) ; 1, R ( -16.62 -6.02 6.56 2.65) ; 2 ( -17.80 -6.75 6.63 2.36) ; 3 ( -18.98 -7.48 7.38 2.28) ; 4 ( -19.86 -8.28 7.94 2.58) ; 5 ( -20.68 -8.94 9.13 3.17) ; 6 ( -21.71 -9.82 9.44 4.05) ; 7 ( -22.00 -9.96 9.94 4.27) ; 8 ( ( -22.30 -10.62 10.31 3.24) ; 1, R-1 ( -22.44 -11.94 10.56 2.80) ; 2 ( -22.66 -13.03 10.56 2.28) ; 3 ( ( -23.03 -14.28 9.31 1.84) ; 1, R-1-1 ( -23.40 -15.52 9.06 1.62) ; 2 ( -23.25 -16.62 8.81 1.40) ; 3 ( -23.18 -17.71 8.69 1.40) ; 4 ( -22.81 -18.52 8.56 1.62) ; 5 ( -22.30 -19.32 8.25 1.62) ; 6 ( -21.56 -20.27 7.75 1.62) ; 7 ( -20.90 -21.51 7.44 1.69) ; 8 ( -20.23 -22.76 6.88 1.77) ; 9 ( -20.09 -23.78 6.31 1.77) ; 10 ( -20.09 -25.10 6.31 1.47) ; 11 ( -20.23 -25.97 6.31 1.84) ; 12 ( -20.75 -27.00 6.25 1.69) ; 13 ( -21.26 -27.95 6.13 1.69) ; 14 ( -22.00 -29.19 5.50 1.69) ; 15 ( -22.59 -30.43 4.88 1.99) ; 16 ( -22.92 -31.87 4.13 2.21) ; 17 ( -23.60 -33.09 4.13 2.21) ; 18 ( ( -23.44 -33.18 2.75 2.28) ; 1, R-1-1-1 ( -23.58 -33.99 2.44 1.62) ; 2 ( -23.80 -34.94 2.38 1.18) ; 3 ( -24.32 -35.82 2.38 0.88) ; 4 ( -24.98 -36.55 2.25 1.18) ; 5 ( -26.16 -36.84 2.19 1.18) ; 6 ( -27.41 -37.42 1.94 1.18) ; 7 ( -28.15 -38.01 1.75 1.18) ; 8 ( -28.45 -39.11 2.06 1.33) ; 9 ( -28.74 -40.06 1.69 1.47) ; 10 ( -28.67 -41.01 1.50 1.62) ; 11 ( -28.89 -42.18 1.50 1.33) ; 12 ( -29.04 -43.42 2.19 1.25) ; 13 ( -29.18 -44.66 2.69 1.25) ; 14 ( -29.70 -45.90 4.31 1.25) ; 15 ( -30.66 -47.07 4.81 1.33) ; 16 ( -31.39 -48.17 5.69 1.33) ; 17 ( -32.50 -49.12 5.69 1.33) ; 18 ( -33.46 -49.85 6.38 1.77) ; 19 ( -35.15 -50.66 7.31 2.21) ; 20 ( ( -36.11 -50.95 7.31 2.14) ; 1, R-1-1-1-1 ( -37.58 -51.09 8.69 1.92) ; 2 ( -38.39 -50.73 10.69 1.18) ; 3 ( -39.42 -50.66 11.56 1.33) ; 4 ( -40.38 -51.75 12.75 0.88) ; 5 ( -41.19 -52.56 12.88 0.88) ; 6 ( -41.85 -53.29 13.00 0.88) ; 7 ( -42.74 -53.73 12.63 1.40) ; 8 ( -43.99 -53.80 12.38 1.40) ; 9 ( -44.36 -53.94 12.44 1.18) ; 10 ( -44.80 -54.46 12.44 0.96) ; 11 ( -45.91 -54.60 12.44 0.74) ; 12 ( -47.16 -54.68 12.06 0.96) ; 13 ( -48.56 -54.38 12.06 0.96) ; 14 ( -50.03 -53.87 11.81 0.74) ; 15 ( -50.70 -53.43 11.81 0.74) ; 16 ( -51.28 -52.99 12.38 0.74) ; 17 ( -51.87 -52.63 12.69 0.74) ; 18 ( -52.68 -52.48 12.69 0.74) ; 19 ( -53.86 -52.63 13.13 0.74) ; 20 ( -54.38 -52.78 13.25 0.88) ; 21 ( -55.41 -52.85 13.25 0.88) ; 22 ( -56.29 -52.78 13.81 1.11) ; 23 ( -57.10 -53.07 13.94 0.81) ; 24 ( -57.99 -53.29 14.38 0.81) ; 25 ( -58.80 -53.65 14.69 1.03) ; 26 ( -59.46 -54.31 15.50 1.18) ; 27 ( -60.57 -54.97 15.69 0.81) ; 28 ( -61.38 -55.55 16.25 0.81) ; 29 ( -62.48 -55.70 16.56 0.74) ; 30 ( -63.15 -55.63 17.13 0.96) ; 31 ( -64.32 -55.26 17.25 0.96) ; 32 ( -65.14 -55.04 17.44 0.74) ; 33 ( -66.46 -54.90 17.50 0.59) ; 34 ( -67.49 -55.92 17.88 0.52) ; 35 ( -67.79 -56.72 17.75 0.52) ; 36 ( -68.01 -57.75 17.50 0.52) ; 37 ( -67.86 -58.26 17.38 0.81) ; 38 ( -68.16 -59.21 17.31 0.59) ; 39 Low | ( -36.14 -51.09 6.69 1.25) ; 1, R-1-1-1-2 ( -37.25 -51.24 6.69 1.25) ; 2 ( -38.35 -51.53 6.75 1.40) ; 3 ( -38.79 -51.82 6.75 1.18) ; 4 ( ( -39.82 -51.75 7.38 0.96) ; 1, R-1-1-1-2-1 ( -40.78 -51.39 7.31 0.81) ; 2 ( -41.67 -51.02 7.31 0.74) ; 3 ( -42.99 -50.36 6.75 0.74) ; 4 ( -44.17 -50.00 5.88 0.88) ; 5 ( -45.13 -49.63 4.75 1.03) ; 6 ( -46.38 -49.19 4.50 0.88) ; 7 ( -47.49 -48.75 4.00 0.96) ; 8 ( -48.08 -48.68 3.38 1.47) ; 9 ( -49.03 -48.68 3.06 1.47) ; 10 ( -50.58 -48.53 2.75 1.25) ; 11 ( -51.47 -48.53 2.75 1.25) ; 12 ( -52.50 -48.61 2.75 1.03) ; 13 ( -53.68 -48.53 2.75 1.03) ; 14 ( -55.37 -48.32 2.75 0.88) ; 15 ( -56.70 -48.39 2.13 0.74) ; 16 ( -57.95 -48.68 1.69 0.74) ; 17 ( -58.91 -48.97 1.38 0.96) ; 18 ( -59.64 -49.27 1.00 0.96) ; 19 ( -60.53 -49.56 0.88 0.81) ; 20 ( -61.04 -49.85 0.69 0.81) ; 21 ( -61.93 -50.36 0.31 0.96) ; 22 ( -63.03 -50.73 0.00 0.96) ; 23 ( -64.21 -50.80 -0.13 0.96) ; 24 ( -65.46 -50.95 0.13 0.96) ; 25 ( -66.05 -51.09 0.50 1.55) ; 26 ( -66.79 -51.09 0.81 1.84) ; 27 ( -67.16 -51.09 1.00 2.14) ; 28 ( -67.89 -51.02 1.75 1.33) ; 29 ( -68.78 -50.87 1.94 0.96) ; 30 ( -69.66 -50.36 2.06 0.96) ; 31 ( -70.55 -49.85 2.31 0.96) ; 32 ( -71.50 -49.05 2.44 0.96) ; 33 ( -72.39 -48.17 2.44 0.96) ; 34 ( -73.42 -47.51 2.56 0.81) ; 35 ( -74.08 -46.78 2.81 0.96) ; 36 ( -75.26 -45.98 3.38 0.81) ; 37 ( -76.15 -45.54 4.56 0.88) ; 38 ( -76.96 -45.17 5.19 0.88) ; 39 ( -78.21 -44.81 5.19 0.88) ; 40 ( -78.87 -44.73 5.19 1.18) ; 41 ( -79.83 -44.59 5.38 1.40) ; 42 ( -80.86 -44.29 5.19 1.18) ; 43 ( -82.26 -44.37 5.00 1.03) ; 44 ( -83.37 -44.29 4.38 0.88) ; 45 ( -83.81 -44.00 4.25 0.81) ; 46 ( -84.18 -44.22 4.13 0.81) ; 47 ( -84.77 -44.29 3.88 0.81) ; 48 ( -86.09 -43.86 3.56 0.81) ; 49 ( -87.49 -44.15 3.38 0.81) ; 50 ( -88.45 -43.93 3.56 0.66) ; 51 ( -88.96 -44.08 4.50 0.81) ; 52 ( -89.19 -44.59 5.44 1.25) ; 53 ( -89.48 -44.81 6.06 1.03) ; 54 ( -89.70 -44.81 6.88 0.81) ; 55 ( -90.14 -44.59 7.94 0.74) ; 56 ( -91.03 -44.15 8.19 0.81) ; 57 ( -92.06 -43.56 8.19 0.74) ; 58 ( -93.24 -43.71 8.19 0.88) ; 59 ( -94.42 -43.49 8.19 1.03) ; 60 ( -95.23 -43.71 7.94 0.88) ; 61 ( -95.89 -43.86 8.94 0.88) ; 62 ( -96.70 -43.86 9.94 0.81) ; 63 ( -97.14 -43.13 12.19 0.81) ; 64 ( -97.66 -42.39 12.75 0.81) ; 65 ( -97.88 -41.81 14.06 1.25) ; 66 ( -98.62 -41.15 15.19 1.18) ; 67 ( -99.06 -40.93 15.88 1.40) ; 68 ( -99.65 -40.42 15.94 1.69) ; 69 ( -100.31 -40.13 16.56 1.69) ; 70 ( -100.61 -39.98 16.69 1.33) ; 71 ( -101.05 -39.91 16.75 1.03) ; 72 ( -101.86 -39.69 16.63 0.74) ; 73 ( -102.37 -39.54 17.06 0.59) ; 74 ( -103.48 -39.69 17.13 0.59) ; 75 ( -104.29 -39.76 16.69 0.59) ; 76 ( -104.95 -39.40 16.44 0.66) ; 77 ( -105.98 -38.89 16.31 0.66) ; 78 ( -107.01 -38.59 16.06 0.81) ; 79 ( -107.75 -38.45 15.69 0.81) ; 80 ( -108.41 -38.52 15.63 1.03) ; 81 ( -108.93 -38.81 15.00 1.25) ; 82 ( -109.89 -38.96 16.38 0.96) ; 83 ( -110.77 -39.10 16.44 0.66) ; 84 ( -111.88 -39.18 16.25 0.66) ; 85 ( -112.98 -39.54 15.94 1.11) ; 86 ( -113.72 -39.76 14.94 1.11) ; 87 ( -114.53 -40.20 14.13 0.96) ; 88 ( -115.27 -41.01 14.06 0.96) ; 89 ( -115.19 -41.66 12.81 0.66) ; 90 ( -114.82 -42.03 12.50 0.66) ; 91 ( -114.60 -42.69 12.44 0.74) ; 92 ( -114.60 -43.64 11.94 0.88) ; 93 ( -114.68 -44.37 11.38 1.25) ; 94 ( -114.53 -44.95 10.44 1.62) ; 95 ( -114.31 -45.32 9.81 2.21) ; 96 ( -114.53 -46.05 8.94 1.62) ; 97 ( -114.68 -46.78 7.81 0.88) ; 98 ( -115.12 -47.29 7.50 0.66) ; 99 ( -115.78 -47.44 7.50 0.52) ; 100 ( -116.52 -47.31 7.44 0.59) ; 101 ( -117.47 -47.10 6.81 0.66) ; 102 ( -118.65 -47.10 6.81 0.66) ; 103 ( -119.31 -47.53 6.81 0.66) ; 104 ( -120.13 -48.41 7.13 0.66) ; 105 ( -121.01 -49.22 7.13 0.66) ; 106 ( -121.52 -49.73 7.38 0.52) ; 107 ( -121.97 -50.60 7.44 0.44) ; 108 ( -122.34 -51.26 6.81 0.88) ; 109 ( -122.56 -51.48 5.13 1.33) ; 110 ( -122.78 -51.99 3.88 1.92) ; 111 ( -122.85 -51.99 3.19 2.65) ; 112 ( -123.44 -52.36 3.13 2.65) ; 113 ( -123.88 -52.50 2.81 1.77) ; 114 ( -124.47 -52.50 2.81 1.33) ; 115 ( -125.06 -52.72 2.81 0.88) ; 116 ( -125.58 -52.50 2.81 0.52) ; 117 ( -126.02 -52.36 2.56 0.52) ; 118 ( -126.68 -51.99 2.38 0.52) ; 119 ( -126.68 -51.63 2.38 0.52) ; 120 ( -127.12 -51.26 3.19 0.52) ; 121 ( -127.93 -50.82 3.31 0.52) ; 122 ( -128.23 -50.82 3.00 0.88) ; 123 ( -128.45 -50.97 2.69 1.33) ; 124 ( -128.82 -51.12 1.94 2.06) ; 125 ( -129.19 -51.19 1.75 2.80) ; 126 ( -129.85 -51.41 1.44 2.80) ; 127 ( -130.81 -51.55 1.13 1.99) ; 128 ( -131.25 -51.63 0.94 1.11) ; 129 ( -131.84 -51.63 0.75 0.74) ; 130 ( -132.58 -51.77 0.38 0.52) ; 131 ( -133.31 -51.77 0.19 0.52) ; 132 ( -134.79 -52.29 0.19 0.52) ; 133 ( -136.04 -52.65 0.31 0.66) ; 134 ( -137.07 -53.16 0.31 0.37) ; 135 ( -137.88 -53.53 0.31 0.66) ; 136 ( -138.47 -53.82 0.31 0.74) ; 137 ( -139.35 -54.19 0.31 0.37) ; 138 ( -140.16 -54.62 0.31 0.37) ; 139 Low | ( -39.99 -52.27 8.81 0.74) ; 1, R-1-1-1-2-2 ( -40.50 -52.63 8.31 0.52) ; 2 ( -40.87 -53.14 6.63 0.52) ; 3 ( -41.76 -53.43 6.63 0.59) ; 4 ( -42.49 -53.80 6.25 0.52) ; 5 ( -42.79 -54.24 5.56 0.52) ; 6 ( -42.57 -54.90 5.50 0.52) ; 7 ( -42.27 -55.48 5.38 0.52) ; 8 ( -42.42 -56.29 4.75 0.59) ; 9 ( -42.57 -56.65 5.63 0.44) ; 10 ( -43.53 -57.16 4.88 0.44) ; 11 ( -44.41 -57.53 4.56 0.66) ; 12 ( -45.15 -57.89 4.56 1.18) ; 13 ( -45.66 -58.62 4.56 1.18) ; 14 ( -46.40 -58.70 4.44 0.88) ; 15 ( -47.36 -58.99 4.00 0.66) ; 16 ( -47.95 -59.79 4.00 0.52) ; 17 ( -48.53 -60.45 3.56 0.88) ; 18 ( -49.27 -60.82 3.38 1.11) ; 19 ( -50.23 -61.33 1.94 1.47) ; 20 ( -51.26 -61.55 1.38 1.69) ; 21 ( -52.29 -61.77 1.38 1.47) ; 22 ( -53.10 -62.50 0.94 1.47) ; 23 ( -53.54 -63.23 1.56 1.33) ; 24 ( -54.36 -63.60 3.13 1.03) ; 25 ( -54.80 -63.67 3.19 0.74) ; 26 ( -55.17 -63.89 3.50 0.66) ; 27 ( -55.31 -63.67 3.69 0.66) ; 28 ( -56.12 -63.67 4.31 0.52) ; 29 ( -56.71 -64.40 4.50 0.52) ; 30 ( -57.38 -65.42 4.75 0.52) ; 31 ( -57.89 -66.23 4.06 0.81) ; 32 ( -58.48 -66.67 3.75 1.11) ; 33 ( -59.22 -67.32 3.13 0.88) ; 34 ( -59.73 -67.76 3.13 0.59) ; 35 ( -60.76 -67.91 3.13 0.44) ; 36 ( -61.43 -68.35 3.50 0.81) ; 37 ( -61.94 -68.64 3.63 1.40) ; 38 ( -62.97 -69.30 3.75 2.06) ; 39 ( -63.27 -69.59 3.81 2.21) ; 40 ( -63.86 -70.25 3.81 1.18) ; 41 ( -64.23 -70.83 4.00 0.59) ; 42 ( -64.67 -71.56 4.00 0.59) ; 43 ( -65.19 -72.29 4.00 0.59) ; 44 ( -65.55 -72.07 4.00 0.37) ; 45 ( -66.07 -71.78 4.00 0.37) ; 46 ( -66.58 -72.00 4.13 0.88) ; 47 ( -67.03 -72.29 4.13 1.62) ; 48 ( -67.47 -72.66 4.06 1.62) ; 49 ( -67.98 -73.32 4.06 0.74) ; 50 ( -68.50 -73.68 3.81 0.37) ; 51 ( -68.94 -74.05 3.56 0.37) ; 52 ( -69.09 -74.41 3.56 0.37) ; 53 ( -68.87 -75.00 3.69 0.37) ; 54 ( -68.72 -75.58 4.81 0.37) ; 55 ( -68.72 -76.31 5.13 0.52) ; 56 ( -69.02 -76.75 5.50 0.52) ; 57 ( -69.83 -76.75 6.00 0.44) ; 58 ( -70.19 -77.26 6.06 0.44) ; 59 ( -70.56 -77.63 5.81 0.74) ; 60 ( -70.78 -78.22 5.44 0.74) ; 61 ( -71.01 -78.87 5.19 0.59) ; 62 ( -71.37 -79.68 5.19 0.52) ; 63 ( -71.52 -80.33 5.19 0.88) ; 64 ( -71.67 -81.36 4.81 1.47) ; 65 ( -71.90 -82.11 5.06 1.92) ; 66 ( -72.05 -82.92 5.06 1.25) ; 67 ( -72.19 -83.57 5.06 0.88) ; 68 ( -72.27 -83.87 5.06 0.59) ; 69 ( -72.27 -84.45 5.06 0.37) ; 70 ( -72.27 -84.89 5.06 0.29) ; 71 ( -71.68 -85.40 5.06 0.29) ; 72 ( -71.68 -86.28 4.94 0.81) ; 73 ( -72.05 -86.94 4.50 1.47) ; 74 ( -72.49 -87.37 4.31 1.11) ; 75 ( -73.00 -87.89 3.63 0.44) ; 76 ( -73.52 -88.47 3.19 0.29) ; 77 ( -73.59 -88.91 2.81 0.52) ; 78 ( -73.52 -89.35 2.25 0.81) ; 79 ( -73.08 -89.93 1.88 0.66) ; 80 ( -72.86 -90.23 1.44 0.37) ; 81 ( -72.86 -90.81 1.06 0.37) ; 82 ( -72.93 -91.47 0.75 0.59) ; 83 ( -72.64 -91.91 0.31 1.40) ; 84 ( -72.49 -92.56 0.00 2.65) ; 85 ( -72.27 -92.78 -0.13 3.02) ; 86 ( -71.83 -93.22 -0.31 1.62) ; 87 ( -71.31 -93.88 -0.44 0.74) ; 88 ( -71.16 -94.03 -0.44 0.29) ; 89 ( -70.57 -94.39 -0.56 0.52) ; 90 ( -70.28 -94.68 -0.56 0.88) ; 91 ( -69.69 -95.20 -0.56 0.52) ; 92 ( -69.39 -95.85 -1.19 0.74) ; 93 ( -69.39 -96.44 -1.75 1.11) ; 94 ( -69.25 -96.95 -2.13 1.92) ; 95 ( -68.88 -97.46 -2.44 1.92) ; 96 ( -68.81 -98.19 -2.69 0.66) ; 97 ( -68.73 -98.63 -2.69 0.37) ; 98 ( -68.88 -99.22 -2.75 0.29) ; 99 ( -68.88 -99.95 -3.13 0.29) ; 100 ( -68.88 -100.53 -3.13 0.29) ; 101 ( -68.66 -101.19 -2.50 0.59) ; 102 ( -68.58 -101.34 -2.38 0.96) ; 103 ( -68.58 -101.63 -2.38 1.77) ; 104 ( -68.51 -101.92 -2.38 1.77) ; 105 ( -68.58 -102.43 -2.31 0.81) ; 106 ( -68.51 -102.87 -2.31 0.44) ; 107 ( -68.66 -104.04 -2.31 0.29) ; 108 ( -68.66 -104.55 -2.31 0.29) ; 109 ( -68.44 -104.92 -2.31 0.66) ; 110 ( -68.36 -104.99 -2.31 0.81) ; 111 Low ) ; End of split ) ; End of split | ( -24.41 -33.60 0.69 0.96) ; 1, R-1-1-2 ( -25.15 -34.26 -1.69 0.81) ; 2 ( -25.89 -34.62 -1.69 0.81) ; 3 ( -26.92 -35.57 -3.19 0.96) ; 4 ( -27.28 -36.67 -4.69 1.40) ; 5 ( -27.58 -37.69 -5.44 1.40) ; 6 ( -28.32 -39.45 -6.00 1.47) ; 7 ( -28.76 -40.55 -6.00 1.25) ; 8 ( -28.68 -41.57 -6.25 1.25) ; 9 ( -28.61 -42.96 -6.75 1.33) ; 10 ( -28.98 -44.27 -6.13 1.25) ; 11 ( -29.57 -45.30 -5.81 1.25) ; 12 ( -30.31 -46.39 -5.44 1.25) ; 13 ( -30.67 -47.56 -4.56 1.47) ; 14 ( -31.04 -48.51 -5.75 1.99) ; 15 ( ( -31.85 -49.32 -5.00 3.24) ; 1, R-1-1-2-1 ( ( -32.52 -50.41 -3.63 1.47) ; 1, R-1-1-2-1-1 ( -32.81 -51.07 -3.25 0.96) ; 2 ( -33.25 -51.80 -3.31 0.81) ; 3 ( -33.47 -52.46 -3.44 0.81) ; 4 ( -33.55 -53.05 -3.44 0.66) ; 5 ( -33.55 -53.78 -3.44 0.96) ; 6 ( -33.55 -54.21 -4.25 0.96) ; 7 ( -34.06 -55.16 -4.44 0.96) ; 8 ( -34.28 -55.09 -8.13 0.88) ; 9 ( -33.92 -55.68 -9.06 0.88) ; 10 ( -34.50 -55.90 -10.38 0.88) ; 11 ( -35.24 -56.26 -10.75 0.74) ; 12 ( -35.90 -56.48 -10.94 0.74) ; 13 ( -36.42 -57.14 -11.31 0.74) ; 14 ( -37.08 -58.16 -12.06 0.81) ; 15 ( -37.30 -58.89 -14.06 1.77) ; 16 ( -37.67 -59.55 -14.25 1.77) ; 17 ( -38.04 -60.50 -15.13 1.47) ; 18 ( -38.11 -61.23 -16.81 1.11) ; 19 ( -38.34 -62.11 -18.00 0.96) ; 20 ( -38.04 -62.69 -20.19 0.96) ; 21 ( -37.60 -62.99 -20.19 1.11) ; 22 ( -36.94 -63.64 -20.50 1.11) ; 23 ( -36.49 -64.30 -21.25 0.88) ; 24 ( -36.27 -64.67 -22.44 0.74) ; 25 ( -36.13 -65.03 -22.94 0.59) ; 26 ( -37.16 -65.47 -23.25 0.37) ; 27 ( -37.82 -66.06 -23.38 1.03) ; 28 ( -38.11 -67.15 -23.38 1.47) ; 29 ( -39.00 -68.40 -23.56 1.40) ; 30 ( -39.29 -69.71 -23.81 1.40) ; 31 ( -40.25 -71.25 -24.63 1.40) ; 32 ( -40.62 -72.34 -25.06 1.11) ; 33 ( -41.06 -72.85 -25.19 0.81) ; 34 ( -41.58 -73.59 -25.50 0.59) ; 35 ( -42.31 -73.73 -26.31 0.59) ; 36 ( -42.83 -73.37 -27.56 0.59) ; 37 ( -42.98 -73.95 -27.56 0.59) ; 38 ( -43.27 -74.46 -28.63 1.33) ; 39 ( -43.42 -74.90 -29.44 1.77) ; 40 ( -43.64 -75.41 -29.75 2.21) ; 41 ( -43.86 -76.44 -30.25 1.25) ; 42 ( -44.60 -76.80 -30.25 0.96) ; 43 ( -45.04 -77.17 -31.31 0.66) ; 44 ( -45.33 -77.75 -32.25 0.44) ; 45 ( -45.33 -78.26 -33.00 0.88) ; 46 ( -45.56 -79.00 -35.69 1.25) ; 47 ( -45.63 -79.29 -37.00 2.14) ; 48 ( -45.48 -79.73 -38.00 2.58) ; 49 ( -45.48 -80.24 -40.31 1.33) ; 50 ( -45.70 -80.90 -41.63 0.96) ; 51 ( -46.44 -81.48 -43.00 0.59) ; 52 ( -46.51 -82.36 -43.31 0.44) ; 53 ( -46.59 -83.16 -44.31 0.29) ; 54 ( -46.73 -84.04 -45.31 0.44) ; 55 ( -46.62 -85.36 -47.13 0.59) ; 56 ( -46.84 -86.23 -48.00 1.18) ; 57 ( -46.69 -87.18 -48.69 1.62) ; 58 ( -46.55 -88.14 -48.69 2.43) ; 59 ( -46.47 -89.16 -48.81 2.80) ; 60 ( -45.74 -89.60 -49.06 1.69) ; 61 ( -45.29 -90.47 -49.06 0.96) ; 62 ( -44.93 -91.21 -49.44 0.59) ; 63 ( -44.48 -91.57 -49.44 0.37) ; 64 ( -44.19 -93.18 -49.75 0.29) ; 65 ( -43.67 -94.42 -49.94 0.29) ; 66 ( -43.31 -95.88 -50.19 0.29) ; 67 ( -43.31 -96.54 -50.19 0.29) ; 68 ( -43.23 -97.49 -50.19 0.66) ; 69 ( -43.23 -97.86 -50.19 1.47) ; 70 ( -43.23 -98.30 -50.31 1.92) ; 71 ( -43.23 -98.73 -50.44 2.36) ; 72 ( -42.86 -99.25 -50.56 0.96) ; 73 ( -42.86 -99.90 -50.56 0.52) ; 74 ( -42.42 -100.78 -51.13 0.52) ; 75 ( -42.20 -101.44 -51.13 0.88) ; 76 ( -42.05 -102.17 -51.50 1.25) ; 77 ( -41.46 -102.90 -51.13 1.55) ; 78 ( -41.02 -103.49 -51.13 1.03) ; 79 ( -40.51 -104.22 -51.13 0.59) ; 80 ( -40.14 -104.73 -51.13 0.29) ; 81 ( -39.84 -105.46 -51.25 0.29) ; 82 ( -39.62 -106.99 -51.44 0.29) ; 83 ( -39.55 -108.24 -50.75 0.29) ; 84 ( -39.18 -108.82 -50.69 0.96) ; 85 ( -38.74 -109.48 -50.69 1.40) ; 86 ( -38.37 -109.63 -50.44 1.40) ; 87 ( -37.93 -110.06 -50.63 0.74) ; 88 ( -37.41 -110.58 -51.00 0.52) ; 89 ( -36.75 -111.38 -51.69 0.52) ; 90 ( -36.53 -112.18 -51.75 0.96) ; 91 ( -36.38 -112.62 -52.31 1.40) ; 92 ( -36.01 -113.43 -52.69 1.40) ; 93 Low | ( -32.64 -49.41 -4.44 0.88) ; 1, R-1-1-2-1-2 ( -33.45 -49.48 -3.88 0.59) ; 2 ( -33.82 -49.48 -3.44 0.44) ; 3 ( -34.48 -49.55 -3.19 0.44) ; 4 ( -35.29 -49.99 -3.06 0.44) ; 5 ( -36.03 -50.58 -2.19 0.66) ; 6 ( -36.33 -51.45 -4.88 0.66) ; 7 ( -36.55 -52.11 -6.06 0.66) ; 8 ( -37.28 -51.97 -6.50 0.81) ; 9 ( -38.02 -51.67 -7.50 0.96) ; 10 ( -39.42 -51.16 -7.50 0.74) ; 11 ( -40.23 -51.45 -6.19 0.74) ; 12 ( -41.56 -51.38 -6.00 0.74) ; 13 ( -42.37 -51.16 -6.00 0.74) ; 14 ( -43.32 -51.16 -5.94 0.74) ; 15 ( -44.58 -51.60 -5.94 0.52) ; 16 ( -45.83 -52.26 -5.94 0.52) ; 17 ( -46.86 -52.92 -5.88 0.81) ; 18 ( -47.60 -53.65 -6.31 1.47) ; 19 ( -48.48 -54.16 -6.81 1.47) ; 20 ( -49.37 -54.82 -6.81 1.03) ; 21 ( -50.54 -55.47 -6.81 0.66) ; 22 ( -51.13 -56.21 -7.19 0.66) ; 23 ( -51.65 -57.52 -7.44 0.66) ; 24 ( -52.02 -58.33 -8.19 0.66) ; 25 ( -52.46 -58.91 -7.38 0.66) ; 26 ( -53.42 -58.76 -6.56 0.66) ; 27 ( -54.15 -58.84 -6.31 0.96) ; 28 ( -55.55 -58.84 -6.25 1.18) ; 29 ( -56.73 -58.76 -6.06 1.18) ; 30 ( -57.40 -58.33 -5.88 0.74) ; 31 ( -58.87 -57.74 -6.75 0.96) ; 32 ( -59.53 -57.16 -7.44 0.96) ; 33 ( -60.27 -57.16 -7.44 0.59) ; 34 ( -60.93 -57.37 -8.00 0.88) ; 35 ( -61.96 -57.89 -8.44 0.96) ; 36 ( -62.77 -58.47 -9.88 0.96) ; 37 ( -63.58 -58.62 -9.88 0.96) ; 38 ( -64.47 -59.57 -10.44 0.74) ; 39 ( -65.65 -60.23 -10.44 0.44) ; 40 ( -66.90 -60.59 -10.31 0.74) ; 41 ( -67.71 -60.52 -10.31 1.11) ; 42 ( -68.67 -60.37 -9.69 1.11) ; 43 ( -69.33 -60.45 -9.63 0.74) ; 44 ( -70.51 -60.59 -9.63 0.59) ; 45 ( -71.54 -60.88 -9.56 0.37) ; 46 ( -72.06 -61.25 -9.56 0.37) ; 47 ( -72.87 -62.05 -9.56 0.74) ; 48 ( -74.49 -62.64 -9.56 1.03) ; 49 ( -75.37 -62.64 -9.56 1.03) ; 50 ( -76.18 -62.78 -9.75 0.74) ; 51 ( -76.99 -63.37 -9.31 0.59) ; 52 ( -77.66 -63.95 -9.63 1.03) ; 53 ( -78.47 -64.83 -9.38 1.77) ; 54 ( -79.28 -65.71 -8.56 1.03) ; 55 ( -80.23 -66.76 -7.88 0.66) ; 56 ( -81.04 -67.93 -6.94 0.52) ; 57 ( -81.48 -69.10 -7.56 0.74) ; 58 ( -81.93 -69.91 -7.94 0.74) ; 59 ( -82.74 -71.00 -8.88 0.88) ; 60 ( -83.40 -71.74 -9.19 0.59) ; 61 ( -83.99 -72.32 -9.63 1.25) ; 62 ( -84.28 -72.47 -8.19 1.55) ; 63 ( -85.09 -72.69 -7.94 1.55) ; 64 ( -85.46 -73.20 -7.44 0.88) ; 65 ( -86.12 -73.56 -7.25 0.52) ; 66 ( -86.49 -73.86 -6.81 0.44) ; 67 ( -86.42 -74.59 -6.19 0.44) ; 68 ( -86.35 -75.17 -5.75 0.66) ; 69 ( -86.27 -75.76 -4.75 1.33) ; 70 ( -85.83 -76.56 -4.50 1.03) ; 71 ( -85.76 -77.22 -3.94 0.66) ; 72 ( -85.61 -77.66 -3.50 0.44) ; 73 ( -85.46 -78.17 -3.31 0.44) ; 74 ( -85.90 -78.53 -3.13 0.44) ; 75 ( -87.01 -78.39 -3.13 0.59) ; 76 ( -87.82 -79.05 -3.13 0.37) ; 77 ( -87.75 -79.85 -2.63 0.59) ; 78 ( -87.52 -80.14 -0.75 0.88) ; 79 ( -87.45 -80.51 -0.31 1.33) ; 80 ( -87.38 -80.87 1.56 0.88) ; 81 ( -87.75 -81.75 2.31 0.74) ; 82 ( -87.75 -82.63 3.25 0.74) ; 83 ( -87.89 -83.65 3.63 0.37) ; 84 ( -87.89 -84.67 4.44 0.37) ; 85 ( -87.82 -85.70 4.75 0.37) ; 86 Low ) ; End of split | ( -30.88 -49.44 -6.31 0.74) ; 1, R-1-1-2-2 ( -30.88 -50.61 -6.31 0.59) ; 2 ( -30.29 -50.83 -7.13 0.52) ; 3 ( -29.93 -50.24 -7.69 0.52) ; 4 ( -29.34 -49.73 -8.06 0.52) ; 5 ( -28.75 -49.51 -9.38 0.74) ; 6 ( -28.16 -48.78 -10.81 0.96) ; 7 ( -27.64 -47.98 -11.88 0.74) ; 8 ( -27.05 -48.12 -13.31 0.88) ; 9 ( -26.54 -48.12 -15.13 0.88) ; 10 ( -26.10 -48.41 -15.38 0.74) ; 11 ( -25.80 -49.00 -16.88 0.74) ; 12 ( -25.51 -49.66 -18.38 0.74) ; 13 ( -25.21 -50.24 -20.81 0.74) ; 14 ( -24.55 -50.90 -20.81 0.74) ; 15 ( -24.03 -51.70 -21.94 0.96) ; 16 ( -23.52 -52.65 -22.50 1.25) ; 17 ( -23.07 -53.97 -22.75 1.40) ; 18 ( -23.15 -55.14 -23.63 1.11) ; 19 ( -23.30 -55.94 -25.00 0.81) ; 20 ( -23.66 -56.89 -25.75 0.52) ; 21 ( -24.18 -57.41 -27.00 0.88) ; 22 ( -24.18 -57.70 -28.75 1.33) ; 23 ( -24.40 -57.99 -30.25 1.77) ; 24 ( -24.33 -58.58 -30.56 2.21) ; 25 ( -24.33 -59.45 -31.25 1.25) ; 26 ( -24.18 -59.89 -33.00 0.59) ; 27 ( -24.25 -60.77 -35.50 0.74) ; 28 ( -24.40 -61.28 -36.13 0.37) ; 29 ( -24.62 -61.65 -36.94 0.37) ; 30 ( -24.99 -63.03 -38.50 0.52) ; 31 ( -25.14 -63.69 -38.50 0.88) ; 32 ( -24.92 -64.13 -39.31 1.33) ; 33 ( -25.14 -64.79 -40.31 0.88) ; 34 ( -25.28 -65.30 -41.06 0.44) ; 35 ( -25.21 -66.91 -41.13 0.44) ; 36 ( -25.14 -67.42 -42.56 1.25) ; 37 ( -25.14 -68.15 -43.50 1.99) ; 38 ( -25.06 -68.52 -43.50 1.99) ; 39 ( -25.14 -69.54 -43.50 1.18) ; 40 ( -24.99 -70.56 -43.75 0.52) ; 41 ( -24.55 -71.95 -44.50 0.44) ; 42 ( -24.40 -72.90 -44.94 1.11) ; 43 ( -24.40 -73.63 -44.94 1.92) ; 44 ( -24.18 -74.29 -46.19 2.21) ; 45 ( -23.88 -75.02 -47.13 0.74) ; 46 Low ) ; End of split ) ; End of split | ( -22.41 -13.79 12.63 0.88) ; 1, R-1-2 ( -22.12 -14.45 13.19 0.74) ; 2 ( -21.75 -14.60 14.25 0.74) ; 3 ( -20.57 -14.23 13.63 0.74) ; 4 ( -19.98 -14.16 13.50 0.74) ; 5 ( -19.39 -14.23 13.56 0.74) ; 6 ( -18.80 -14.52 13.94 0.88) ; 7 ( -18.14 -14.82 13.75 1.18) ; 8 ( -17.40 -15.11 13.38 1.18) ; 9 ( -16.81 -15.47 13.19 0.81) ; 10 ( -16.52 -15.91 12.94 0.52) ; 11 ( -16.74 -15.98 15.94 0.66) ; 12 ( -17.03 -15.98 17.94 0.52) ; 13 ( -17.62 -15.47 17.88 0.37) ; 14 ( -18.06 -15.55 17.88 0.37) ; 15 ( -18.28 -16.35 17.75 0.52) ; 16 Low ) ; End of split | ( -22.78 -9.63 10.31 2.65) ; 1, R-2 ( -23.88 -8.97 9.75 1.55) ; 2 ( -24.55 -8.46 11.31 1.11) ; 3 ( -25.21 -8.09 11.38 0.96) ; 4 ( -25.58 -7.51 11.63 0.81) ; 5 ( -26.17 -6.99 12.69 0.96) ; 6 ( -26.76 -6.48 13.50 1.03) ; 7 ( -27.86 -5.75 14.81 0.88) ; 8 ( -28.75 -5.24 14.88 0.96) ; 9 ( -29.41 -4.95 15.13 0.96) ; 10 ( -30.44 -4.65 15.56 0.96) ; 11 ( -31.40 -4.58 17.31 0.96) ; 12 ( -32.21 -4.87 17.31 1.18) ; 13 ( -32.72 -5.31 17.38 1.40) ; 14 ( -33.31 -5.75 17.50 1.11) ; 15 ( -33.68 -6.41 17.50 0.74) ; 16 ( -33.98 -6.70 17.50 0.59) ; 17 ( -34.12 -7.29 17.50 0.59) ; 18 ( -34.12 -7.80 17.50 0.59) ; 19 ( -34.12 -8.31 17.56 0.59) ; 20 ( -34.42 -8.89 17.56 0.59) ; 21 ( -35.01 -9.41 17.75 0.59) ; 22 ( -35.74 -9.84 17.88 0.59) ; 23 ( -36.56 -10.58 17.94 0.59) ; 24 Low ) ; End of split ) ; End of tree ( (Color Cyan) (Dendrite) ( 13.32 5.16 -5.56 3.54) ; Root ( 13.99 5.89 -5.00 2.87) ; 1, R ( 14.65 6.62 -5.06 2.43) ; 2 ( 15.24 6.98 -5.50 2.36) ; 3 ( 16.20 7.57 -5.50 2.87) ; 4 ( 17.01 8.23 -5.50 3.32) ; 5 ( 17.75 8.81 -5.50 3.32) ; 6 ( 18.63 9.40 -5.50 3.24) ; 7 ( 20.32 10.13 -4.50 3.24) ; 8 ( 21.21 10.78 -4.19 3.09) ; 9 ( 22.46 11.44 -3.88 3.09) ; 10 ( 23.93 12.17 -3.56 3.09) ; 11 ( 25.19 13.20 -3.25 3.61) ; 12 ( 25.92 13.85 -3.06 4.35) ; 13 ( ( 26.73 15.90 -2.25 3.83) ; 1, R-1 ( ( 27.54 17.58 -2.81 3.39) ; 1, R-1-1 ( 28.50 18.83 -2.00 2.73) ; 2 ( 29.16 19.78 -1.50 2.36) ; 3 ( 29.53 20.65 -1.31 1.69) ; 4 ( 29.46 22.19 -0.94 1.62) ; 5 ( ( 29.53 24.02 -0.38 1.03) ; 1, R-1-1-1 ( 29.75 25.40 0.13 0.88) ; 2 ( 29.83 26.50 0.31 0.88) ; 3 ( 30.42 27.38 1.13 0.88) ; 4 ( 30.70 27.81 1.13 0.88) ; 5 ( ( 31.23 28.33 0.69 0.66) ; 1, R-1-1-1-1 ( 31.60 29.35 0.69 0.81) ; 2 ( 31.60 31.45 0.69 0.88) ; 3 ( 31.67 33.27 1.13 0.74) ; 4 ( 32.19 34.44 1.13 0.74) ; 5 ( 33.00 35.39 1.31 0.74) ; 6 ( 34.25 36.05 1.06 0.74) ; 7 ( 35.73 36.71 0.81 0.88) ; 8 ( 36.98 37.37 0.44 1.11) ; 9 ( 37.94 37.30 0.00 1.11) ; 10 ( 39.04 37.59 -0.19 1.33) ; 11 ( 40.44 38.25 -0.19 1.11) ; 12 ( 41.69 39.27 -0.19 0.81) ; 13 ( 43.09 40.29 -0.19 0.81) ; 14 ( 44.27 41.68 -0.19 0.81) ; 15 ( 45.16 44.17 -0.94 1.11) ; 16 ( 45.45 45.70 -1.69 1.33) ; 17 ( 45.16 46.80 -2.88 1.55) ; 18 ( 45.08 48.19 -2.94 1.25) ; 19 ( 44.27 49.36 -3.25 1.03) ; 20 ( 43.98 50.45 -4.13 1.25) ; 21 ( 43.90 51.99 -4.13 1.25) ; 22 ( 43.76 53.23 -4.06 1.25) ; 23 ( 43.83 54.77 -3.00 1.33) ; 24 ( 43.54 55.64 -2.69 1.11) ; 25 ( 43.46 56.45 -2.00 1.92) ; 26 ( 43.39 57.10 -1.38 2.58) ; 27 ( 43.24 57.62 -1.31 2.87) ; 28 ( ( 42.65 58.27 -0.50 1.18) ; 1, R-1-1-1-1-1 ( 41.62 59.01 -0.44 0.96) ; 2 ( 40.98 60.14 0.88 0.81) ; 3 ( 40.76 61.09 1.13 1.47) ; 4 ( 40.40 61.74 1.38 1.33) ; 5 ( 39.88 62.62 1.38 0.96) ; 6 ( 39.59 63.86 1.38 0.81) ; 7 ( 39.59 65.18 1.44 0.81) ; 8 ( 39.81 66.86 1.69 1.03) ; 9 ( 39.51 68.03 1.38 1.03) ; 10 ( 39.44 69.27 2.31 0.81) ; 11 ( 39.59 70.59 2.38 1.03) ; 12 ( 39.59 71.83 2.56 1.11) ; 13 ( 39.00 73.44 2.63 0.88) ; 14 ( 38.55 74.32 3.00 1.55) ; 15 ( 37.67 76.00 3.13 2.06) ; 16 ( 37.37 76.88 3.25 1.77) ; 17 ( 37.08 77.61 3.25 1.40) ; 18 ( 36.79 78.34 3.25 1.03) ; 19 ( 36.49 79.14 3.50 0.88) ; 20 ( 35.61 80.09 3.13 0.74) ; 21 ( 35.16 81.26 2.94 0.88) ; 22 ( 34.72 82.43 2.94 1.03) ; 23 ( 34.43 83.38 2.94 1.03) ; 24 ( 34.06 85.14 2.25 0.96) ; 25 ( 34.35 86.38 2.19 1.62) ; 26 ( 34.50 87.18 2.19 1.25) ; 27 ( 34.13 88.13 2.13 1.11) ; 28 ( 33.93 90.04 2.13 0.81) ; 29 ( 33.86 91.72 2.94 0.59) ; 30 ( 34.08 92.38 3.88 0.88) ; 31 ( 34.45 92.82 4.75 1.11) ; 32 ( 34.82 93.26 4.94 0.88) ; 33 ( 35.04 94.72 5.25 0.59) ; 34 ( 35.26 95.30 5.50 0.37) ; 35 ( 35.77 95.52 5.63 0.37) ; 36 ( 36.36 95.81 6.06 0.66) ; 37 ( 37.17 96.33 6.25 0.96) ; 38 ( 37.76 96.62 6.63 1.11) ; 39 ( 38.35 96.84 6.94 0.88) ; 40 ( 38.79 96.91 7.00 0.59) ; 41 ( 39.53 96.98 7.31 0.59) ; 42 ( 40.86 97.13 7.44 0.81) ; 43 ( 41.96 97.57 7.50 0.96) ; 44 ( 42.77 97.72 7.50 1.25) ; 45 ( 43.73 98.30 7.56 1.11) ; 46 ( 44.91 99.03 7.38 0.66) ; 47 ( 45.87 99.18 7.06 0.59) ; 48 ( 46.82 99.54 6.63 0.59) ; 49 ( 47.86 100.05 6.56 0.81) ; 50 ( 48.37 100.71 7.00 0.59) ; 51 ( 49.18 101.52 7.13 0.81) ; 52 ( 50.07 101.88 7.13 1.11) ; 53 ( 50.43 102.10 7.13 0.81) ; 54 ( 50.95 102.61 7.13 0.44) ; 55 ( 51.47 103.27 7.44 0.44) ; 56 ( 52.06 103.49 6.69 0.81) ; 57 ( 52.72 103.93 6.00 1.11) ; 58 ( 53.31 103.93 5.25 0.81) ; 59 ( 54.27 103.78 3.94 0.59) ; 60 ( 54.86 103.42 3.69 0.59) ; 61 ( 55.81 102.61 3.25 0.59) ; 62 ( 56.26 102.69 3.25 0.81) ; 63 ( 56.99 102.69 3.25 0.52) ; 64 ( 57.95 102.61 1.63 0.81) ; 65 ( 58.69 102.25 0.75 0.66) ; 66 Low | ( 43.86 58.67 -0.88 1.25) ; 1, R-1-1-1-1-2 ( 44.15 59.69 -0.88 0.59) ; 2 ( 44.52 60.50 -0.25 0.59) ; 3 ( 44.45 61.45 -0.25 0.88) ; 4 ( 44.37 62.03 -0.25 1.18) ; 5 ( 44.52 62.69 -0.25 0.96) ; 6 ( 44.74 63.42 -0.13 0.66) ; 7 ( 45.18 64.15 0.00 0.52) ; 8 ( 45.99 64.96 -0.31 0.52) ; 9 ( 46.66 65.18 -0.69 0.74) ; 10 ( 47.62 65.54 -0.69 0.74) ; 11 ( 48.20 66.27 -0.81 0.44) ; 12 ( 48.43 67.30 -0.81 0.59) ; 13 ( 48.35 68.90 -0.81 0.74) ; 14 ( 48.20 69.85 -0.81 1.03) ; 15 ( 47.91 70.73 -0.81 0.66) ; 16 ( 47.47 71.97 -1.06 0.96) ; 17 ( 47.54 72.78 -0.94 0.74) ; 18 ( 47.76 73.66 -0.31 0.74) ; 19 ( 47.54 74.90 0.31 0.88) ; 20 ( 47.76 76.14 0.38 0.74) ; 21 ( 48.20 76.80 0.44 0.74) ; 22 ( 49.09 77.24 0.56 0.88) ; 23 ( 49.97 77.31 0.56 0.88) ; 24 ( 50.86 77.46 -0.06 0.88) ; 25 ( 51.89 77.68 -0.50 0.66) ; 26 ( 52.85 78.92 -1.38 0.66) ; 27 ( 53.73 79.72 -1.81 0.96) ; 28 ( 55.06 80.23 -2.00 1.25) ; 29 ( 56.31 80.38 -2.31 1.11) ; 30 ( 57.34 80.75 -2.69 1.25) ; 31 ( 58.08 81.62 -2.81 1.03) ; 32 ( 58.81 82.06 -2.94 0.81) ; 33 ( 59.48 82.43 -3.06 0.66) ; 34 ( 59.85 82.65 -3.50 0.66) ; 35 ( 60.07 83.52 -3.94 0.74) ; 36 ( 59.62 84.47 -3.94 0.88) ; 37 ( 59.48 85.13 -4.19 0.74) ; 38 ( 59.26 86.16 -4.31 0.74) ; 39 ( 59.33 86.89 -4.38 0.96) ; 40 ( 59.12 88.29 -4.94 0.81) ; 41 ( 59.93 89.17 -5.56 0.74) ; 42 ( 60.82 89.09 -7.00 0.74) ; 43 ( 61.70 89.24 -8.00 0.81) ; 44 ( 62.44 89.02 -8.75 1.03) ; 45 ( 63.17 88.65 -9.06 1.33) ; 46 ( 63.84 88.14 -10.13 1.33) ; 47 ( 64.57 88.29 -11.81 0.74) ; 48 ( 65.16 88.73 -12.38 0.59) ; 49 ( 65.75 89.53 -13.69 0.81) ; 50 ( 66.12 90.26 -13.69 1.18) ; 51 ( 66.42 90.99 -13.81 1.84) ; 52 ( 66.71 91.94 -14.19 1.18) ; 53 ( 66.86 92.82 -14.13 0.52) ; 54 ( 66.93 93.77 -14.00 0.52) ; 55 ( 66.78 95.09 -13.75 0.66) ; 56 ( 66.56 95.96 -13.75 1.40) ; 57 ( 66.49 96.33 -13.56 1.99) ; 58 ( 66.34 97.06 -13.63 1.62) ; 59 ( 66.34 97.65 -13.75 1.25) ; 60 ( 66.42 98.60 -13.75 0.88) ; 61 ( 66.42 99.33 -13.75 0.59) ; 62 ( 66.64 99.69 -13.75 0.59) ; 63 ( 66.93 99.69 -13.88 0.59) ; 64 ( 67.52 99.84 -14.25 0.59) ; 65 ( 68.04 100.42 -15.44 0.59) ; 66 ( 68.33 101.37 -15.69 1.11) ; 67 ( 68.70 102.32 -15.75 1.40) ; 68 ( 68.99 102.84 -16.13 1.40) ; 69 ( 69.51 103.64 -16.31 1.03) ; 70 ( 70.03 104.30 -15.56 0.74) ; 71 ( 70.39 105.03 -15.44 0.52) ; 72 ( 70.91 105.54 -15.44 0.37) ; 73 ( 71.57 105.61 -15.31 0.37) ; 74 ( 71.65 106.56 -15.19 0.37) ; 75 ( 72.31 107.08 -15.56 0.59) ; 76 ( 73.05 107.66 -15.94 0.59) ; 77 ( 73.64 108.25 -16.69 0.44) ; 78 ( 74.00 109.05 -18.25 0.44) ; 79 ( 74.22 109.85 -18.63 0.74) ; 80 ( 74.45 110.07 -20.00 1.47) ; 81 ( 74.96 110.07 -20.63 2.14) ; 82 ( 75.03 110.00 -21.56 2.87) ; 83 ( 75.33 109.78 -23.06 2.06) ; 84 ( 75.55 109.71 -24.75 1.62) ; 85 Low ) ; End of split | ( 29.81 28.03 2.56 0.88) ; 1, R-1-1-1-2 ( 29.37 28.40 3.44 0.59) ; 2 ( 28.41 28.91 3.75 0.44) ; 3 ( 27.01 29.28 3.56 0.44) ; 4 ( 26.72 29.72 3.63 0.74) ; 5 ( 27.23 30.01 4.25 0.74) ; 6 ( 27.68 30.01 4.56 0.74) ; 7 ( 28.19 30.45 4.94 0.59) ; 8 ( 28.71 30.52 5.63 0.59) ; 9 ( 29.15 30.88 6.81 0.59) ; 10 ( 29.74 31.47 8.06 0.59) ; 11 ( 29.81 32.20 8.06 0.59) ; 12 ( 29.81 33.22 8.31 0.59) ; 13 ( 30.18 34.32 9.13 0.81) ; 14 ( 30.40 34.98 9.56 0.81) ; 15 ( 30.77 35.86 9.94 0.74) ; 16 ( 31.06 36.81 10.50 0.59) ; 17 ( 31.21 37.76 11.19 0.44) ; 18 ( 30.55 38.34 12.06 0.59) ; 19 ( 29.81 38.85 12.25 0.74) ; 20 ( 29.22 39.51 12.69 0.74) ; 21 ( 28.93 40.61 12.81 0.74) ; 22 ( 28.85 41.56 12.88 0.74) ; 23 ( 28.78 42.65 13.31 0.74) ; 24 ( 28.56 43.60 13.56 0.74) ; 25 ( 28.49 44.70 14.31 0.88) ; 26 ( 29.30 46.02 14.69 1.03) ; 27 ( 29.59 47.62 15.19 0.88) ; 28 ( 29.66 48.21 15.44 0.66) ; 29 ( 29.37 49.38 16.00 0.52) ; 30 ( 28.85 50.33 16.00 0.66) ; 31 ( 29.00 51.43 16.00 0.52) ; 32 ( 29.00 51.50 16.25 0.74) ; 33 ( 28.26 52.01 16.75 0.74) ; 34 ( 27.82 52.96 17.19 0.81) ; 35 ( 27.68 54.20 17.69 0.81) ; 36 ( 28.04 55.08 17.75 0.81) ; 37 ( 28.26 56.25 17.81 0.66) ; 38 ( 28.49 57.20 17.63 0.66) ; 39 ( 28.93 58.30 17.56 0.81) ; 40 ( 29.30 59.17 17.56 1.03) ; 41 ( 29.89 59.83 18.06 0.88) ; 42 ( 30.70 60.05 18.06 0.66) ; 43 ( 31.29 60.27 18.31 0.66) ; 44 ( 32.39 61.00 18.94 0.52) ; 45 ( 32.83 61.95 18.75 0.66) ; 46 ( 32.98 62.61 18.69 0.66) ; 47 ( 32.24 63.34 16.94 0.52) ; 48 ( 31.58 63.71 16.56 0.37) ; 49 Low ) ; End of split | ( 29.72 23.78 -4.06 2.36) ; 1, R-1-1-2 ( 29.79 24.88 -5.31 2.36) ; 2 ( 29.79 25.39 -7.38 2.21) ; 3 ( 30.16 27.44 -9.25 3.09) ; 4 ( 30.45 28.97 -9.75 3.24) ; 5 ( 30.55 29.13 -9.75 3.24) ; 6 ( ( 30.97 30.21 -10.44 2.80) ; 1, R-1-1-2-1 ( 31.71 31.53 -11.38 2.80) ; 2 ( 32.07 32.19 -11.63 3.24) ; 3 ( 32.57 32.80 -11.63 3.24) ; 4 ( ( 32.44 32.99 -12.63 3.02) ; 1, R-1-1-2-1-1 ( 33.11 33.87 -12.56 3.61) ; 2 ( 33.77 35.04 -13.00 3.61) ; 3 ( ( 33.11 35.77 -14.19 2.36) ; 1, R-1-1-2-1-1-1 ( 32.52 37.38 -14.81 1.99) ; 2 ( 32.66 38.55 -16.19 2.50) ; 3 ( ( 33.62 39.86 -16.19 1.03) ; 1, R-1-1-2-1-1-1-1 ( 34.28 40.16 -16.88 0.88) ; 2 ( 34.80 40.38 -18.50 0.81) ; 3 ( 35.32 40.08 -18.94 0.81) ; 4 ( 35.46 39.64 -19.13 1.03) ; 5 ( 35.83 38.77 -21.06 1.33) ; 6 ( 36.20 38.33 -22.75 1.33) ; 7 ( 36.94 38.55 -24.31 1.33) ; 8 ( 37.38 38.99 -24.94 1.03) ; 9 ( 37.38 40.45 -26.44 0.96) ; 10 ( 37.23 41.47 -24.63 0.81) ; 11 ( 37.53 42.35 -25.25 0.81) ; 12 ( 37.45 43.23 -25.50 0.88) ; 13 ( 37.23 43.96 -25.88 0.74) ; 14 ( 38.04 44.61 -26.06 0.74) ; 15 ( 38.70 45.27 -26.13 0.74) ; 16 ( 39.00 46.37 -26.13 0.96) ; 17 ( 39.07 47.61 -26.31 0.96) ; 18 ( 39.22 49.22 -26.63 0.96) ; 19 ( 39.96 50.24 -26.69 0.96) ; 20 ( 40.62 51.49 -26.63 0.81) ; 21 ( 41.50 52.32 -26.56 0.81) ; 22 ( 42.23 52.97 -29.31 0.88) ; 23 ( 43.04 53.34 -30.44 0.96) ; 24 ( 43.04 54.22 -30.56 0.59) ; 25 ( 43.19 54.80 -31.13 0.81) ; 26 ( 43.93 55.83 -30.00 0.81) ; 27 ( 44.37 56.56 -30.00 0.59) ; 28 ( 44.74 56.92 -30.00 0.59) ; 29 ( 44.88 57.65 -30.00 0.59) ; 30 ( 45.33 58.75 -30.13 0.74) ; 31 ( 45.77 59.99 -30.69 1.18) ; 32 ( 46.14 61.02 -30.75 1.40) ; 33 ( 46.43 61.82 -31.06 1.40) ; 34 ( 46.95 62.84 -31.56 1.25) ; 35 ( 47.02 63.28 -31.56 0.96) ; 36 ( 47.17 64.16 -32.31 0.74) ; 37 ( 47.46 65.18 -32.63 0.66) ; 38 ( 47.83 65.84 -32.69 0.66) ; 39 ( 48.27 65.84 -32.88 0.66) ; 40 ( 48.64 66.35 -33.31 0.66) ; 41 ( 49.82 66.94 -33.63 0.81) ; 42 ( 51.15 67.45 -34.13 0.74) ; 43 ( 51.88 68.03 -34.56 1.18) ; 44 ( 52.47 68.54 -34.81 1.47) ; 45 ( 52.69 68.98 -34.81 1.11) ; 46 ( 52.84 69.57 -35.19 0.66) ; 47 ( 53.14 70.23 -35.19 0.66) ; 48 ( 53.36 70.81 -35.50 0.66) ; 49 ( 53.58 71.54 -36.25 0.66) ; 50 ( 53.36 72.64 -36.38 0.66) ; 51 ( 53.28 73.44 -36.44 1.40) ; 52 ( 52.99 74.32 -37.56 1.84) ; 53 ( 52.77 75.27 -38.25 1.55) ; 54 ( 52.33 75.85 -39.38 1.33) ; 55 ( 51.88 76.15 -39.81 1.03) ; 56 ( 51.37 76.66 -41.69 0.81) ; 57 ( 50.85 76.73 -42.25 0.81) ; 58 ( 50.78 77.32 -44.13 0.81) ; 59 ( 50.41 77.32 -46.00 0.66) ; 60 ( 49.38 78.12 -47.06 0.88) ; 61 ( 49.16 78.63 -47.63 1.40) ; 62 ( 49.01 79.36 -47.81 1.03) ; 63 ( 48.79 80.09 -48.38 0.66) ; 64 ( 48.86 81.04 -49.13 0.66) ; 65 ( 48.57 82.14 -49.81 0.52) ; 66 ( 48.35 83.17 -50.38 1.11) ; 67 ( 48.28 84.26 -52.31 1.47) ; 68 ( 48.50 84.92 -53.69 1.77) ; 69 ( 48.65 85.94 -55.31 1.99) ; 70 ( 48.65 86.38 -57.06 1.55) ; 71 ( 48.57 87.19 -57.56 1.18) ; 72 ( 48.57 87.84 -57.50 0.88) ; 73 ( 48.57 88.36 -57.50 0.59) ; 74 ( 48.87 89.38 -57.69 0.37) ; 75 ( 49.46 90.62 -58.63 0.37) ; 76 ( 50.20 91.87 -58.94 0.37) ; 77 ( 50.64 92.38 -59.50 0.37) ; 78 ( 51.08 93.18 -59.50 0.66) ; 79 ( 51.37 93.69 -59.81 1.77) ; 80 ( 51.82 94.13 -60.63 2.06) ; 81 ( 52.41 94.79 -60.88 2.06) ; 82 ( 52.55 95.15 -61.25 1.40) ; 83 ( 52.77 95.52 -61.44 1.03) ; 84 ( 53.07 96.18 -61.94 0.66) ; 85 ( 53.36 96.54 -62.50 0.44) ; 86 ( 54.84 97.79 -63.13 0.29) ; 87 ( 55.87 98.44 -64.88 0.29) ; 88 ( 56.60 99.61 -65.44 0.29) ; 89 ( 57.64 100.34 -65.50 0.29) ; 90 ( 58.45 100.93 -65.50 0.29) ; 91 ( 58.67 101.37 -66.06 0.66) ; 92 ( 58.89 101.88 -66.19 0.96) ; 93 ( 59.04 102.17 -66.31 0.66) ; 94 ( 59.26 102.83 -66.56 0.52) ; 95 ( 59.63 103.34 -67.06 0.74) ; 96 ( 59.92 103.71 -67.63 1.03) ; 97 ( 60.29 104.00 -69.19 1.33) ; 98 ( 60.58 104.15 -69.31 1.25) ; 99 ( 60.88 104.73 -69.69 0.59) ; 100 ( 61.47 105.53 -70.00 0.29) ; 101 ( 61.91 105.83 -70.56 0.29) ; 102 ( 62.50 106.34 -70.56 0.59) ; 103 ( 63.01 107.07 -71.19 1.25) ; 104 ( 63.53 107.29 -72.06 1.69) ; 105 ( 64.12 107.80 -72.63 1.11) ; 106 ( 64.27 108.39 -73.63 0.74) ; 107 ( 64.49 108.97 -73.75 0.52) ; 108 ( 64.93 109.70 -74.63 0.37) ; 109 ( 65.81 110.43 -73.63 0.81) ; 110 ( 66.56 111.13 -73.31 0.81) ; 111 ( 67.07 111.94 -73.19 0.52) ; 112 ( 68.11 112.74 -72.94 0.29) ; 113 ( 68.99 113.18 -73.25 0.29) ; 114 ( 70.24 113.98 -72.81 0.29) ; 115 ( 70.90 114.35 -72.44 0.29) ; 116 ( 71.79 114.06 -72.44 0.29) ; 117 ( 72.60 114.06 -72.31 0.66) ; 118 ( 73.19 114.20 -72.19 1.03) ; 119 ( 73.93 114.20 -71.88 0.59) ; 120 ( 74.74 114.27 -71.19 0.37) ; 121 ( 75.33 114.42 -72.25 0.59) ; 122 ( 75.55 114.49 -73.63 0.96) ; 123 ( 76.06 114.57 -74.19 1.33) ; 124 ( 76.28 114.27 -75.00 1.69) ; 125 Low | ( 32.09 38.65 -16.38 1.11) ; 1, R-1-1-2-1-1-1-2 ( 30.84 38.35 -16.81 0.66) ; 2 ( 30.32 37.77 -16.13 0.52) ; 3 ( 29.66 36.97 -16.13 0.52) ; 4 ( 28.63 36.75 -17.69 0.59) ; 5 ( 27.96 36.45 -18.25 0.81) ; 6 ( 27.23 35.94 -18.25 0.81) ; 7 ( 26.64 35.50 -19.00 0.81) ; 8 ( 25.83 34.99 -19.25 0.74) ; 9 ( 25.53 34.77 -19.88 0.74) ; 10 ( 25.16 34.26 -20.38 0.74) ; 11 ( 24.35 33.90 -21.25 0.52) ; 12 ( 23.84 33.90 -22.31 0.52) ; 13 ( 23.62 34.26 -22.81 0.74) ; 14 ( 22.95 34.70 -24.19 0.96) ; 15 ( 22.37 35.28 -24.19 0.66) ; 16 ( 21.70 35.80 -24.63 0.66) ; 17 ( 21.19 35.94 -24.69 0.66) ; 18 ( 20.16 36.16 -24.69 0.44) ; 19 ( 19.64 35.87 -24.69 0.44) ; 20 ( 19.12 35.28 -25.31 0.44) ; 21 ( 18.76 34.92 -25.25 0.44) ; 22 ( 18.24 34.85 -25.88 0.44) ; 23 ( 17.36 35.06 -25.94 0.59) ; 24 ( 16.84 35.14 -26.38 0.88) ; 25 ( 16.32 35.14 -26.94 0.88) ; 26 ( 15.22 35.21 -27.38 0.66) ; 27 ( 15.07 34.63 -28.69 0.88) ; 28 ( 14.26 34.48 -28.88 0.88) ; 29 ( 13.38 34.92 -29.13 0.74) ; 30 ( 13.01 35.80 -29.75 0.59) ; 31 ( 12.71 36.45 -30.38 0.59) ; 32 ( 11.61 37.11 -30.75 0.44) ; 33 ( 10.06 37.77 -31.13 0.44) ; 34 ( 9.55 38.28 -31.13 0.52) ; 35 ( 8.59 38.50 -31.25 0.52) ; 36 ( 7.85 38.65 -31.50 0.66) ; 37 ( 7.04 39.30 -32.44 0.66) ; 38 ( 6.60 40.47 -32.69 0.66) ; 39 ( 5.86 41.50 -32.69 0.81) ; 40 ( 5.13 42.01 -33.06 1.11) ; 41 ( 4.32 42.52 -33.38 1.11) ; 42 ( 3.73 42.89 -33.50 0.88) ; 43 ( 3.14 43.47 -33.44 0.88) ; 44 ( 2.77 44.42 -33.44 0.66) ; 45 ( 2.11 44.86 -33.69 0.81) ; 46 ( 1.07 44.86 -34.13 0.81) ; 47 ( 0.19 45.23 -36.06 0.88) ; 48 ( -0.40 46.10 -36.69 0.88) ; 49 ( -1.50 46.91 -36.31 0.88) ; 50 ( -2.68 47.42 -35.88 0.81) ; 51 ( -3.86 48.37 -35.44 0.81) ; 52 ( -4.53 49.32 -35.38 1.11) ; 53 ( -4.67 50.42 -36.13 1.11) ; 54 ( -4.97 51.51 -36.50 0.88) ; 55 ( -5.56 52.39 -38.31 0.74) ; 56 ( -6.07 52.68 -38.94 0.59) ; 57 ( -6.59 52.24 -39.50 0.52) ; 58 ( -7.18 52.39 -39.75 0.52) ; 59 ( -7.69 52.83 -39.75 0.52) ; 60 ( -8.14 53.27 -39.44 0.88) ; 61 ( -9.39 54.22 -39.50 0.74) ; 62 ( -10.20 54.80 -39.56 0.59) ; 63 ( -11.08 55.31 -39.56 0.88) ; 64 ( -11.82 55.68 -39.56 1.47) ; 65 ( -12.56 56.34 -39.56 1.33) ; 66 ( -13.22 57.07 -40.44 0.81) ; 67 ( -13.66 57.36 -39.31 0.52) ; 68 ( -14.18 57.94 -40.06 0.52) ; 69 ( -15.09 57.99 -40.69 0.29) ; 70 ( -16.13 58.29 -41.56 0.29) ; 71 ( -16.79 58.43 -41.88 0.29) ; 72 ( -17.08 58.50 -41.88 0.59) ; 73 ( -17.67 58.94 -41.81 0.81) ; 74 ( -18.63 59.45 -41.81 0.81) ; 75 ( -19.15 59.75 -41.81 0.59) ; 76 ( -19.74 59.82 -41.81 0.59) ; 77 ( -20.32 60.19 -41.94 1.11) ; 78 ( -20.99 60.48 -42.63 1.69) ; 79 ( -21.36 60.55 -42.63 2.43) ; 80 ( -22.09 61.06 -42.81 1.69) ; 81 ( -22.61 61.21 -42.88 1.25) ; 82 ( -23.42 61.72 -43.56 0.96) ; 83 ( -24.01 61.79 -43.81 0.59) ; 84 ( -24.52 62.23 -44.19 0.44) ; 85 ( -24.82 62.38 -46.13 0.44) ; 86 ( -25.41 62.31 -46.44 0.44) ; 87 ( -25.85 62.01 -46.44 0.44) ; 88 ( -26.15 61.94 -46.44 0.74) ; 89 ( -26.51 62.23 -46.56 1.11) ; 90 ( -27.03 62.45 -46.75 1.47) ; 91 ( -27.62 62.60 -46.69 0.66) ; 92 ( -27.91 62.82 -47.56 0.37) ; 93 Low ) ; End of split | ( 35.06 35.40 -13.25 1.47) ; 1, R-1-1-2-1-1-2 ( 35.57 35.40 -12.69 1.11) ; 2 ( 36.16 35.48 -12.63 1.11) ; 3 ( 37.05 35.62 -11.88 1.40) ; 4 ( 37.71 35.70 -11.94 1.40) ; 5 ( 38.74 35.92 -12.44 1.18) ; 6 ( 39.99 36.28 -12.44 1.03) ; 7 ( 40.58 36.65 -12.94 1.40) ; 8 ( 41.69 37.09 -13.25 1.55) ; 9 ( 42.35 37.52 -13.56 1.84) ; 10 ( 43.24 38.33 -13.69 1.69) ; 11 ( 43.97 39.13 -14.56 1.84) ; 12 ( 44.64 39.64 -16.06 1.62) ; 13 ( ( 45.45 40.96 -17.00 1.03) ; 1, R-1-1-2-1-1-2-1 ( 45.74 41.98 -17.13 0.81) ; 2 ( 45.89 42.86 -17.50 0.81) ; 3 ( 45.89 44.32 -17.00 0.81) ; 4 ( 45.89 44.98 -17.00 0.81) ; 5 ( 46.26 45.42 -17.00 0.81) ; 6 ( 46.92 46.15 -17.44 0.81) ; 7 ( 47.73 46.52 -18.88 0.81) ; 8 ( 49.06 46.88 -19.38 0.81) ; 9 ( 49.65 47.54 -19.38 0.81) ; 10 ( 50.31 48.12 -19.56 1.40) ; 11 ( 51.12 48.42 -19.63 2.14) ; 12 ( 51.49 48.93 -20.06 2.36) ; 13 ( ( 52.52 48.56 -19.81 0.74) ; 1, R-1-1-2-1-1-2-1-1 ( 53.03 49.00 -19.81 0.59) ; 2 ( 53.48 49.15 -19.63 0.59) ; 3 ( 54.29 49.73 -19.00 0.74) ; 4 ( 55.02 49.81 -18.38 0.81) ; 5 ( 55.98 50.46 -18.38 0.96) ; 6 ( 56.57 51.27 -17.88 0.81) ; 7 ( 57.45 52.29 -17.63 0.66) ; 8 ( 58.04 53.02 -17.56 0.81) ; 9 ( 58.41 53.75 -17.56 0.66) ; 10 ( 58.56 54.34 -17.56 0.44) ; 11 ( 58.85 54.70 -17.56 0.44) ; 12 ( 59.30 54.63 -17.56 0.44) ; 13 ( 59.89 54.48 -17.38 0.44) ; 14 ( 60.33 54.92 -17.25 0.44) ; 15 ( 60.84 55.58 -17.69 0.44) ; 16 ( 61.65 56.09 -17.69 0.74) ; 17 ( 62.17 56.53 -18.38 0.81) ; 18 ( 63.35 56.82 -18.88 0.66) ; 19 ( 63.86 57.19 -19.50 0.66) ; 20 ( 64.82 57.26 -19.75 0.59) ; 21 ( 65.41 57.70 -20.13 0.59) ; 22 ( 66.15 58.43 -20.94 0.81) ; 23 ( 66.37 58.72 -20.94 1.62) ; 24 ( 66.89 59.02 -20.88 2.36) ; 25 ( ( 67.62 59.38 -21.69 2.58) ; 1, R-1-1-2-1-1-2-1-1-1 ( 68.28 60.04 -21.38 1.33) ; 2 ( 68.95 60.48 -20.81 0.74) ; 3 ( 69.46 60.84 -20.06 0.52) ; 4 ( 70.42 61.50 -19.44 0.52) ; 5 ( 71.01 61.94 -19.44 0.66) ; 6 ( 71.53 62.45 -19.44 0.52) ; 7 ( 72.04 62.74 -19.44 0.44) ; 8 ( 73.15 62.82 -19.44 0.44) ; 9 ( 73.74 62.60 -19.06 0.44) ; 10 ( 74.25 62.52 -18.56 0.44) ; 11 ( 74.55 62.60 -18.50 0.44) ; 12 ( 74.84 63.26 -18.50 0.74) ; 13 ( 75.06 64.06 -18.50 1.11) ; 14 ( 75.37 64.99 -18.38 0.88) ; 15 ( 75.89 65.65 -18.19 0.59) ; 16 ( 76.18 66.09 -17.88 0.44) ; 17 ( 76.70 66.45 -17.75 0.74) ; 18 ( 77.22 66.74 -17.75 1.40) ; 19 ( 77.66 66.96 -17.75 1.40) ; 20 ( 78.17 67.18 -17.56 0.59) ; 21 ( 78.91 67.55 -17.56 0.37) ; 22 ( 79.13 68.28 -17.56 0.59) ; 23 ( 79.21 69.30 -17.63 0.74) ; 24 ( 79.21 70.40 -17.38 0.52) ; 25 ( 79.35 70.84 -16.75 0.88) ; 26 ( 79.94 71.35 -16.63 1.62) ; 27 ( 80.24 71.57 -16.44 1.92) ; 28 ( 80.46 71.93 -16.44 1.92) ; 29 ( 80.68 72.15 -16.19 1.03) ; 30 ( 81.05 72.45 -16.13 0.66) ; 31 ( 81.34 72.88 -15.81 0.44) ; 32 ( 81.93 73.54 -15.50 0.96) ; 33 ( 82.52 74.05 -15.13 1.47) ; 34 ( 82.89 74.78 -14.88 1.11) ; 35 ( 83.48 75.73 -14.75 0.88) ; 36 ( 84.14 76.32 -14.69 0.66) ; 37 ( 84.36 76.69 -14.69 0.29) ; 38 ( 85.10 76.47 -14.63 0.29) ; 39 ( 85.61 76.39 -14.44 0.29) ; 40 ( 86.28 76.90 -14.25 0.88) ; 41 ( 86.79 77.42 -14.25 1.18) ; 42 ( 87.38 77.78 -14.31 1.03) ; 43 ( 87.90 78.37 -14.38 0.81) ; 44 ( 88.27 79.02 -14.38 0.59) ; 45 ( 88.78 79.83 -14.38 0.44) ; 46 ( 88.93 80.19 -14.50 1.47) ; 47 ( 89.37 80.63 -14.56 2.14) ; 48 ( 89.89 81.14 -14.06 2.43) ; 49 ( 90.48 81.58 -13.69 1.11) ; 50 ( 90.77 81.80 -13.44 0.74) ; 51 ( 90.99 82.02 -13.44 0.44) ; 52 ( 91.36 82.61 -13.44 0.29) ; 53 ( 91.95 83.48 -13.38 0.59) ; 54 ( 92.32 84.00 -13.38 0.96) ; 55 ( 92.47 84.21 -13.38 0.66) ; 56 ( 92.91 84.87 -13.38 0.37) ; 57 ( 93.13 85.24 -13.38 0.22) ; 58 ( 94.01 85.97 -12.69 0.74) ; 59 ( 94.46 86.33 -11.88 0.96) ; 60 ( 94.97 86.77 -11.81 0.96) ; 61 ( 95.19 86.99 -11.19 0.66) ; 62 ( 95.56 87.36 -11.19 0.37) ; 63 ( 96.15 87.36 -11.19 0.29) ; 64 ( 96.89 88.31 -11.19 0.29) ; 65 ( 97.18 89.26 -10.94 0.29) ; 66 ( 97.70 90.43 -9.56 0.29) ; 67 ( 97.84 90.94 -9.56 0.29) ; 68 ( 97.84 91.45 -9.50 0.52) ; 69 Low | ( 66.83 58.12 -23.13 0.81) ; 1, R-1-1-2-1-1-2-1-1-2 ( 66.83 57.46 -23.31 0.59) ; 2 ( 67.42 57.02 -25.19 0.59) ; 3 ( 67.64 56.44 -25.19 0.59) ; 4 ( 68.30 56.22 -25.63 0.88) ; 5 ( 68.82 56.22 -27.25 0.88) ; 6 ( 69.19 56.07 -27.25 0.88) ; 7 ( 69.70 56.29 -29.38 0.74) ; 8 ( 70.51 56.58 -31.25 0.59) ; 9 ( 70.95 56.14 -31.94 0.44) ; 10 ( 71.76 55.49 -32.88 0.44) ; 11 ( 72.50 55.19 -33.69 0.44) ; 12 ( 73.09 55.12 -33.94 0.81) ; 13 ( 73.31 55.27 -35.50 1.33) ; 14 ( 74.20 54.97 -37.06 1.33) ; 15 ( 74.64 54.76 -37.50 1.33) ; 16 ( 75.37 54.76 -38.25 1.33) ; 17 ( 76.18 54.76 -39.94 1.03) ; 18 ( 77.14 54.68 -41.31 0.81) ; 19 ( 77.88 54.76 -41.44 0.59) ; 20 ( 78.76 54.54 -42.44 0.59) ; 21 ( 79.87 54.54 -43.63 0.88) ; 22 ( 80.38 54.54 -43.88 0.59) ; 23 ( 81.12 54.76 -43.88 0.37) ; 24 ( 82.30 55.19 -44.13 0.37) ; 25 ( 83.26 55.71 -44.31 0.66) ; 26 ( 83.99 56.14 -44.44 1.33) ; 27 ( 84.80 56.36 -45.38 1.62) ; 28 ( 85.54 56.44 -43.63 1.18) ; 29 ( 86.35 56.95 -44.31 0.74) ; 30 ( 87.01 57.46 -44.31 0.52) ; 31 ( 87.31 58.12 -44.94 0.22) ; 32 ( 88.41 59.00 -45.63 0.22) ; 33 ( 89.81 59.73 -45.69 0.22) ; 34 ( 90.55 60.31 -44.50 0.81) ; 35 ( 91.14 60.68 -44.50 1.55) ; 36 ( 91.88 61.12 -44.50 1.55) ; 37 ( 92.10 61.41 -44.06 1.18) ; 38 ( 92.69 61.85 -44.00 0.81) ; 39 ( 93.35 62.21 -44.31 0.52) ; 40 ( 94.09 62.65 -44.94 1.11) ; 41 ( 94.68 62.87 -46.69 1.40) ; 42 ( 95.27 63.16 -46.69 1.69) ; 43 ( 95.86 63.31 -47.56 0.96) ; 44 ( 96.59 63.53 -48.94 0.59) ; 45 Low ) ; End of split | ( 51.82 50.46 -21.19 0.66) ; 1, R-1-1-2-1-1-2-1-2 ( 52.33 51.27 -20.75 0.52) ; 2 ( 52.70 52.00 -20.75 0.52) ; 3 ( 52.70 52.95 -20.75 0.52) ; 4 ( 52.26 54.19 -20.75 0.74) ; 5 ( 51.96 55.43 -20.75 0.74) ; 6 ( 52.11 56.60 -20.56 0.74) ; 7 ( 52.55 57.99 -20.00 1.03) ; 8 ( 52.92 59.38 -19.44 1.18) ; 9 ( 53.22 60.19 -18.38 1.47) ; 10 ( 53.51 61.21 -18.31 1.92) ; 11 ( 53.50 62.51 -17.69 1.40) ; 12 ( 53.80 63.32 -16.13 1.11) ; 13 ( 54.16 64.85 -16.00 0.81) ; 14 ( 54.31 65.73 -15.94 0.81) ; 15 ( 54.53 66.90 -15.75 0.66) ; 16 ( 54.90 67.85 -15.56 0.96) ; 17 ( 55.05 68.87 -15.50 0.96) ; 18 ( 55.49 69.89 -15.50 0.81) ; 19 ( 56.23 70.26 -15.88 0.81) ; 20 ( 56.89 70.77 -15.88 0.74) ; 21 ( 57.41 71.06 -15.88 0.74) ; 22 ( 57.63 71.72 -15.88 0.74) ; 23 ( 57.04 72.82 -16.19 1.03) ; 24 ( 56.52 74.06 -15.69 1.47) ; 25 ( 56.08 74.72 -15.56 1.47) ; 26 ( 55.56 75.45 -15.25 1.33) ; 27 ( 55.19 75.96 -14.75 1.11) ; 28 ( 54.75 77.06 -14.75 0.88) ; 29 ( 54.31 78.67 -14.69 1.11) ; 30 ( 54.09 79.98 -14.38 1.25) ; 31 ( 54.24 81.37 -14.19 1.18) ; 32 ( 54.90 82.47 -13.88 1.18) ; 33 ( 55.42 83.42 -14.25 0.96) ; 34 ( 55.49 84.30 -14.06 1.55) ; 35 ( 55.56 85.90 -14.94 1.40) ; 36 ( 55.56 86.78 -15.56 0.96) ; 37 ( 55.86 87.80 -15.88 0.81) ; 38 ( 55.34 89.05 -15.56 1.11) ; 39 ( 55.12 89.56 -15.31 1.47) ; 40 ( 54.53 90.58 -15.31 0.74) ; 41 ( 54.24 91.88 -15.19 0.59) ; 42 ( 54.24 92.76 -15.19 0.88) ; 43 ( 54.75 93.35 -14.69 0.88) ; 44 ( 55.49 94.15 -17.31 0.81) ; 45 ( 56.23 94.66 -19.63 0.81) ; 46 ( 56.89 95.76 -19.63 0.81) ; 47 ( 58.07 97.66 -19.63 1.03) ; 48 ( 58.73 98.39 -19.88 1.03) ; 49 ( 59.17 99.19 -20.00 0.81) ; 50 ( 59.17 99.93 -20.06 0.81) ; 51 ( 58.58 100.66 -19.44 0.81) ; 52 ( 57.77 101.61 -18.81 1.11) ; 53 ( 56.96 102.92 -18.81 0.81) ; 54 ( 56.52 104.97 -18.75 1.47) ; 55 ( 57.18 106.94 -19.19 1.47) ; 56 ( 57.85 107.97 -18.63 1.47) ; 57 ( 58.29 108.92 -17.06 1.18) ; 58 ( 58.73 109.94 -16.50 0.96) ; 59 ( 59.25 111.33 -16.00 0.74) ; 60 ( 59.54 111.77 -15.94 0.52) ; 61 ( 59.69 112.43 -15.75 0.52) ; 62 ( 59.98 113.38 -15.63 0.74) ; 63 ( 60.20 113.81 -15.63 0.74) ; 64 ( 59.91 114.62 -16.06 0.59) ; 65 ( 59.47 115.13 -16.06 0.59) ; 66 ( 58.95 116.37 -16.00 0.44) ; 67 ( 58.95 116.81 -15.88 0.44) ; 68 ( 58.88 116.96 -15.06 0.74) ; 69 ( 58.14 117.83 -13.06 0.81) ; 70 ( 57.70 118.42 -13.06 0.44) ; 71 ( 56.89 118.93 -12.88 0.44) ; 72 Low ) ; End of split | ( 45.51 40.00 -18.19 0.74) ; 1, R-1-1-2-1-1-2-2 ( 46.10 40.00 -19.44 0.74) ; 2 ( 47.06 39.93 -21.00 0.74) ; 3 ( 47.94 39.79 -21.06 0.74) ; 4 ( 48.90 39.35 -21.19 0.74) ; 5 ( 50.15 39.05 -22.25 0.81) ; 6 ( 51.26 39.71 -23.56 0.74) ; 7 ( 52.29 40.37 -24.63 0.96) ; 8 ( 53.03 41.25 -25.50 0.81) ; 9 ( 53.62 41.69 -26.06 0.81) ; 10 ( 54.50 41.76 -27.19 0.74) ; 11 ( 55.46 41.54 -28.00 0.74) ; 12 ( 56.49 41.10 -28.00 0.66) ; 13 ( 57.08 40.37 -28.00 0.96) ; 14 ( 57.96 39.79 -28.06 1.69) ; 15 ( 58.55 39.49 -28.44 2.06) ; 16 ( 59.29 39.20 -28.88 2.06) ; 17 ( 60.47 38.69 -29.00 1.40) ; 18 ( 61.50 38.25 -29.00 0.96) ; 19 ( 62.31 38.25 -29.00 0.81) ; 20 ( 63.12 38.47 -29.56 0.81) ; 21 ( 64.00 38.69 -29.75 0.81) ; 22 ( 65.11 38.91 -29.88 1.11) ; 23 ( 65.70 39.13 -31.06 0.96) ; 24 ( 66.51 39.42 -32.69 0.96) ; 25 ( 67.61 39.86 -32.69 0.81) ; 26 ( 68.13 39.79 -33.63 0.81) ; 27 ( 69.01 39.49 -34.44 0.74) ; 28 ( 70.19 40.00 -34.81 0.88) ; 29 ( 71.37 40.15 -35.00 0.96) ; 30 ( 72.48 40.37 -35.81 0.74) ; 31 ( 73.43 40.44 -36.50 0.81) ; 32 ( 74.39 40.15 -36.50 0.66) ; 33 ( 75.20 40.44 -37.13 0.66) ; 34 ( 76.09 40.37 -37.88 0.74) ; 35 ( 77.26 40.30 -39.13 0.96) ; 36 ( 78.59 40.37 -39.13 0.74) ; 37 ( 79.25 40.95 -39.44 1.03) ; 38 ( 80.06 41.03 -40.00 2.14) ; 39 ( 80.58 41.17 -40.44 2.50) ; 40 ( 81.32 40.88 -40.88 1.84) ; 41 ( 82.20 40.44 -40.88 1.40) ; 42 ( 83.16 40.08 -41.50 0.96) ; 43 ( 84.85 40.22 -41.81 0.74) ; 44 ( 86.25 40.30 -42.81 0.74) ; 45 ( 87.51 40.66 -43.00 0.74) ; 46 ( 88.76 41.32 -43.00 1.03) ; 47 ( 89.94 41.61 -43.00 1.03) ; 48 ( 90.75 42.12 -43.56 1.03) ; 49 ( 92.00 43.15 -43.75 0.88) ; 50 ( 92.74 43.73 -43.75 0.66) ; 51 ( 93.47 44.03 -44.75 0.66) ; 52 ( 94.14 44.83 -45.31 0.66) ; 53 ( 95.54 45.85 -45.31 1.03) ; 54 ( 96.49 46.22 -45.63 1.33) ; 55 ( 97.97 46.66 -46.63 1.99) ; 56 ( 99.00 47.31 -47.00 1.25) ; 57 ( 99.88 48.07 -48.50 0.52) ; 58 ( 101.79 49.24 -48.88 0.52) ; 59 ( 103.27 49.97 -49.75 1.25) ; 60 ( 104.30 50.48 -50.63 1.55) ; 61 ( 105.63 50.77 -50.81 1.18) ; 62 ( 106.95 50.70 -51.25 0.81) ; 63 ( 108.57 50.63 -51.94 0.59) ; 64 ( 110.27 50.26 -52.44 0.81) ; 65 ( 111.15 50.70 -53.38 1.18) ; 66 ( 111.37 50.99 -55.56 1.47) ; 67 ( 111.74 51.65 -56.75 1.11) ; 68 ( 112.40 52.75 -56.75 0.81) ; 69 ( 112.70 53.04 -58.50 0.81) ; 70 ( 113.07 53.26 -62.25 1.03) ; 71 ( 113.95 53.33 -59.56 0.59) ; 72 Low ) ; End of split ) ; End of split | ( 31.99 33.14 -15.94 0.44) ; 1, R-1-1-2-1-2 ( 31.77 33.66 -17.31 0.52) ; 2 ( 32.29 34.17 -19.81 0.66) ; 3 ( 32.14 34.61 -20.69 0.88) ; 4 ( 32.06 35.41 -21.06 0.88) ; 5 ( 32.65 35.92 -21.75 0.88) ; 6 ( 33.91 36.21 -22.94 1.11) ; 7 ( 35.45 36.14 -23.94 1.11) ; 8 ( 35.97 36.14 -24.81 1.11) ; 9 ( 35.96 36.31 -24.81 1.11) ; 10 ( ( 35.90 36.43 -26.50 1.11) ; 1, R-1-1-2-1-2-1 ( 35.53 36.65 -27.00 1.11) ; 2 ( 35.08 36.58 -27.50 1.11) ; 3 ( 34.79 36.58 -29.31 0.96) ; 4 ( 34.20 36.80 -30.81 0.81) ; 5 ( 33.61 37.46 -31.25 0.81) ; 6 ( 33.98 38.19 -31.69 0.81) ; 7 ( 34.35 39.14 -32.06 0.81) ; 8 ( 34.86 40.38 -32.63 0.81) ; 9 ( 35.16 41.55 -34.69 0.96) ; 10 ( 35.75 42.43 -35.31 0.96) ; 11 ( 35.60 43.52 -35.44 0.96) ; 12 ( 35.38 44.40 -35.88 1.33) ; 13 ( 35.97 45.13 -36.81 1.18) ; 14 ( 35.97 46.08 -39.06 0.81) ; 15 ( 36.34 47.11 -39.38 0.44) ; 16 ( 35.97 48.06 -41.13 0.59) ; 17 ( 35.97 48.50 -42.63 0.96) ; 18 ( 35.90 48.79 -44.38 1.40) ; 19 ( 35.38 49.52 -45.56 1.18) ; 20 ( 35.23 49.96 -47.50 0.81) ; 21 ( 34.64 50.77 -49.00 0.44) ; 22 ( 34.42 51.57 -49.75 0.44) ; 23 ( 34.72 52.23 -50.19 0.74) ; 24 ( 34.50 53.33 -51.56 1.11) ; 25 ( 34.64 53.98 -52.31 0.66) ; 26 ( 34.79 54.50 -53.38 0.29) ; 27 ( 34.79 55.74 -53.38 0.29) ; 28 ( 35.01 57.20 -53.50 0.52) ; 29 ( 35.23 57.79 -54.69 1.03) ; 30 ( 35.23 58.52 -55.69 0.59) ; 31 ( 35.38 59.25 -56.19 0.96) ; 32 ( 34.79 60.56 -56.81 1.40) ; 33 ( 34.20 61.37 -58.69 1.92) ; 34 ( 33.76 62.17 -59.06 1.55) ; 35 ( 33.46 62.83 -60.13 1.11) ; 36 ( 33.24 63.78 -61.06 0.74) ; 37 ( 33.46 64.80 -61.50 0.37) ; 38 ( 34.35 65.24 -61.75 0.29) ; 39 ( 34.79 66.70 -62.63 0.29) ; 40 ( 35.31 67.87 -62.94 0.29) ; 41 ( 35.53 69.19 -65.06 0.44) ; 42 ( 35.82 69.77 -67.94 0.74) ; 43 ( 36.26 70.21 -70.38 1.11) ; 44 ( 36.63 70.72 -70.56 1.55) ; 45 ( 36.63 71.31 -72.38 1.55) ; 46 ( 36.63 72.19 -72.94 0.66) ; 47 ( 36.93 72.84 -73.31 0.29) ; 48 ( 37.37 73.57 -73.44 0.52) ; 49 ( 37.30 74.53 -74.50 0.81) ; 50 ( 37.37 74.96 -75.38 1.18) ; 51 ( 37.30 75.62 -75.63 0.74) ; 52 ( 37.30 76.28 -75.63 0.37) ; 53 ( 37.22 76.86 -75.63 0.37) ; 54 ( 37.22 77.89 -75.69 0.37) ; 55 ( 37.13 79.00 -78.00 0.52) ; 56 ( 36.98 79.44 -78.06 0.81) ; 57 ( 36.76 79.81 -78.06 1.69) ; 58 ( 36.54 80.76 -78.06 0.96) ; 59 ( 36.69 81.34 -78.19 0.29) ; 60 ( 36.54 82.07 -78.31 0.29) ; 61 ( 36.76 82.88 -78.31 0.29) ; 62 ( 37.21 84.05 -78.31 0.29) ; 63 ( 37.65 85.07 -80.63 0.66) ; 64 ( 38.16 85.87 -80.63 1.11) ; 65 ( 38.31 86.60 -80.63 0.88) ; 66 ( 38.60 87.12 -80.63 0.22) ; 67 ( 39.05 88.07 -80.63 0.22) ; 68 ( 39.56 89.09 -80.63 0.22) ; 69 ( 40.37 89.82 -81.06 0.22) ; 70 ( 40.59 90.26 -82.88 0.52) ; 71 ( 41.18 90.99 -82.44 1.40) ; 72 ( 41.55 91.65 -82.44 1.40) ; 73 ( 42.21 92.38 -80.88 0.66) ; 74 ( 42.51 92.96 -82.00 0.37) ; 75 ( 42.66 93.99 -82.44 0.37) ; 76 ( 42.95 94.72 -82.63 0.59) ; 77 ( 43.25 95.45 -82.88 0.81) ; 78 Generated | ( 36.35 36.43 -29.06 0.81) ; 1, R-1-1-2-1-2-2 ( 37.16 36.72 -29.63 0.81) ; 2 ( 37.97 36.94 -32.19 0.66) ; 3 ( 39.08 37.31 -32.81 0.81) ; 4 ( 40.18 37.60 -32.94 0.96) ; 5 ( 41.43 37.24 -33.81 0.96) ; 6 ( 42.24 37.09 -35.56 0.81) ; 7 ( 43.35 37.31 -36.50 1.03) ; 8 ( ( 43.79 37.60 -41.25 1.55) ; 1, R-1-1-2-1-2-2-1 ( 44.75 37.38 -41.50 1.11) ; 2 ( 45.41 36.94 -43.63 0.74) ; 3 ( 46.96 37.16 -43.88 0.74) ; 4 ( 48.58 37.16 -45.38 0.96) ; 5 ( 49.91 37.02 -46.00 1.33) ; 6 ( 50.49 37.09 -47.69 1.33) ; 7 ( 51.53 37.31 -49.75 1.03) ; 8 ( 52.41 37.82 -50.31 0.74) ; 9 ( 53.44 38.11 -51.13 0.74) ; 10 ( 54.33 38.63 -51.50 0.74) ; 11 ( 54.99 39.21 -51.56 0.74) ; 12 ( 55.87 39.14 -52.50 0.44) ; 13 ( 56.68 39.58 -53.13 0.44) ; 14 ( 57.42 40.09 -54.00 0.44) ; 15 ( 58.97 40.31 -55.00 0.52) ; 16 ( 60.00 40.45 -55.63 0.81) ; 17 ( 60.81 40.67 -57.25 1.25) ; 18 ( 61.18 41.11 -58.56 0.96) ; 19 ( 61.40 41.62 -59.56 0.22) ; 20 ( 62.28 42.13 -60.06 0.22) ; 21 ( 63.68 42.06 -60.63 0.37) ; 22 ( 64.49 42.28 -61.56 0.74) ; 23 ( 65.52 42.72 -62.81 1.62) ; 24 ( 65.89 43.16 -63.13 2.14) ; 25 ( 66.48 43.23 -63.38 2.14) ; 26 ( 66.78 43.45 -64.13 1.69) ; 27 ( 67.22 43.67 -64.81 0.96) ; 28 ( 67.59 43.89 -64.88 0.52) ; 29 ( 68.47 44.11 -64.88 0.22) ; 30 ( 69.43 44.33 -65.00 0.22) ; 31 ( 71.57 44.84 -65.13 0.22) ; 32 ( 72.89 44.98 -65.44 0.22) ; 33 ( 75.03 45.06 -65.50 0.37) ; 34 ( 75.99 45.06 -65.94 0.74) ; 35 ( 76.43 45.28 -67.25 1.03) ; 36 ( 76.94 45.50 -67.38 2.43) ; 37 ( 77.53 45.79 -67.56 2.14) ; 38 ( 77.68 45.79 -68.13 1.69) ; 39 ( 78.34 46.23 -68.19 0.81) ; 40 ( 78.79 46.74 -68.63 0.44) ; 41 ( 79.82 47.47 -69.44 0.44) ; 42 ( 81.14 47.98 -69.81 0.66) ; 43 ( 81.73 48.57 -70.63 0.88) ; 44 ( 82.98 49.44 -72.75 0.74) ; 45 ( 83.59 49.97 -74.56 0.66) ; 46 ( 84.26 50.48 -74.69 0.81) ; 47 ( 84.92 51.07 -77.44 0.81) ; 48 ( 85.58 51.50 -79.44 0.81) ; 49 ( 85.88 51.87 -82.38 1.25) ; 50 ( 86.61 52.38 -83.75 1.25) ; 51 ( 86.83 53.04 -84.38 0.96) ; 52 ( 86.98 53.84 -85.75 0.59) ; 53 ( 88.01 54.58 -88.56 0.74) ; 54 ( 88.60 54.79 -93.75 1.11) ; 55 ( 89.19 55.53 -93.94 0.74) ; 56 ( 89.63 56.04 -94.44 0.44) ; 57 ( 90.08 56.26 -94.69 0.44) ; 58 ( 90.37 57.13 -96.00 0.44) ; 59 ( 90.81 57.65 -99.19 0.59) ; 60 ( 91.62 58.45 -100.00 0.88) ; 61 Generated | ( 45.25 37.63 -36.69 0.74) ; 1, R-1-1-2-1-2-2-2 ( 45.25 38.43 -38.25 0.74) ; 2 ( 45.98 39.31 -38.88 0.88) ; 3 ( 46.65 39.67 -39.19 0.66) ; 4 ( 47.16 39.82 -39.19 0.66) ; 5 ( 48.19 40.55 -39.63 0.66) ; 6 ( 48.93 41.28 -40.13 0.88) ; 7 ( 50.11 42.08 -40.13 0.88) ; 8 ( 51.07 43.03 -40.13 0.59) ; 9 ( 51.14 44.06 -40.13 0.59) ; 10 ( 51.95 44.94 -40.38 0.81) ; 11 ( 52.47 45.52 -40.50 1.03) ; 12 ( 53.06 46.32 -40.94 1.33) ; 13 ( 53.64 47.20 -41.38 1.33) ; 14 ( 54.01 48.37 -41.81 1.62) ; 15 ( 54.53 48.88 -42.75 1.62) ; 16 ( 55.34 49.91 -43.00 1.25) ; 17 ( 55.86 50.71 -43.00 0.74) ; 18 ( 56.00 51.22 -43.38 0.52) ; 19 ( 56.15 52.76 -43.88 0.44) ; 20 ( 56.74 53.49 -44.06 0.44) ; 21 ( 57.03 54.58 -45.13 0.74) ; 22 ( 56.67 55.24 -48.75 0.96) ; 23 ( 56.52 56.34 -49.94 0.96) ; 24 ( 56.30 57.29 -55.00 0.96) ; 25 ( 56.89 57.43 -56.94 1.11) ; 26 ( 56.67 58.75 -60.94 0.52) ; 27 ( 56.74 59.99 -61.75 0.52) ; 28 ( 56.52 60.43 -62.31 0.81) ; 29 ( 56.37 61.09 -62.50 1.25) ; 30 ( 56.30 61.67 -64.31 1.62) ; 31 ( 56.30 62.48 -66.06 1.18) ; 32 ( 56.30 62.92 -67.63 0.81) ; 33 ( 56.37 63.79 -66.63 0.59) ; 34 ( 56.30 64.67 -67.25 0.37) ; 35 ( 56.00 65.84 -67.25 0.37) ; 36 ( 55.78 67.34 -67.19 0.29) ; 37 ( 55.93 68.21 -68.13 0.66) ; 38 ( 55.93 68.73 -68.38 1.11) ; 39 ( 56.01 69.82 -69.19 1.47) ; 40 ( 56.08 70.41 -69.69 1.47) ; 41 ( 56.30 71.58 -71.31 1.11) ; 42 ( 56.37 72.09 -72.06 0.81) ; 43 ( 56.45 73.40 -70.25 0.37) ; 44 ( 56.23 74.21 -70.81 0.66) ; 45 ( 56.23 75.23 -71.19 0.96) ; 46 ( 56.52 75.52 -71.19 0.59) ; 47 ( 57.04 76.33 -71.19 0.29) ; 48 ( 57.18 77.13 -71.19 0.29) ; 49 ( 57.33 77.79 -71.31 0.29) ; 50 ( 57.41 78.67 -71.69 0.66) ; 51 ( 57.70 79.18 -72.31 1.11) ; 52 ( 57.77 79.84 -72.94 1.11) ; 53 ( 58.07 80.64 -73.31 0.74) ; 54 ( 58.29 81.15 -73.31 0.44) ; 55 ( 58.66 81.81 -73.63 0.44) ; 56 ( 58.95 82.40 -74.13 1.18) ; 57 ( 59.25 83.05 -74.44 1.40) ; 58 ( 59.62 83.49 -74.63 1.03) ; 59 ( 59.69 83.78 -74.69 0.74) ; 60 ( 60.06 84.59 -74.69 0.37) ; 61 ( 60.57 85.39 -75.06 0.29) ; 62 ( 61.02 86.20 -75.81 0.29) ; 63 ( 61.68 87.37 -77.81 0.29) ; 64 ( 61.97 87.80 -78.56 0.29) ; 65 ( 62.12 88.10 -78.56 0.66) ; 66 ( 62.34 88.61 -78.69 1.40) ; 67 ( 62.56 89.34 -79.44 1.62) ; 68 ( 62.93 89.85 -80.25 0.88) ; 69 ( 63.08 90.14 -81.38 0.52) ; 70 ( 63.15 90.58 -81.75 0.29) ; 71 ( 64.18 92.19 -82.25 0.29) ; 72 ( 64.99 93.87 -82.81 0.29) ; 73 ( 65.21 94.75 -82.75 0.29) ; 74 ( 65.72 95.70 -83.25 1.03) ; 75 ( 66.09 96.51 -84.13 1.03) ; 76 ( 66.46 97.16 -84.69 0.66) ; 77 ( 66.61 97.60 -83.13 0.29) ; 78 ( 66.97 98.55 -83.13 0.29) ; 79 ( 67.42 99.65 -82.25 0.52) ; 80 ( 67.49 99.94 -82.00 0.74) ; 81 ( 67.56 100.38 -81.94 0.44) ; 82 ( 67.71 101.26 -81.94 0.22) ; 83 ( 67.78 102.06 -81.94 0.22) ; 84 ( 68.15 103.01 -81.94 0.59) ; 85 ( 68.30 103.45 -82.38 0.88) ; 86 ( 68.45 104.40 -82.38 0.88) ; 87 ( 68.37 104.84 -81.88 0.59) ; 88 ( 68.89 105.57 -80.81 0.29) ; 89 ( 69.04 105.86 -80.56 0.29) ; 90 ( 69.55 106.89 -80.25 0.59) ; 91 ( 69.70 107.40 -80.56 0.88) ; 92 ( 69.99 107.98 -81.00 0.88) ; 93 ( 69.99 108.57 -81.75 0.52) ; 94 Incomplete ) ; End of split ) ; End of split ) ; End of split | ( 29.81 29.72 -9.06 0.74) ; 1, R-1-1-2-2 ( 29.07 30.08 -7.31 0.59) ; 2 ( 28.34 30.37 -6.69 0.59) ; 3 ( 27.82 31.32 -6.19 0.66) ; 4 ( 27.53 32.27 -6.00 0.66) ; 5 ( 27.97 33.74 -5.44 0.66) ; 6 ( 28.26 35.56 -4.81 0.81) ; 7 ( 28.93 36.73 -4.69 0.88) ; 8 ( 29.44 37.76 -4.88 0.96) ; 9 ( 29.74 39.07 -6.00 0.81) ; 10 ( 30.33 40.53 -5.38 0.88) ; 11 ( 30.70 41.19 -5.25 0.74) ; 12 ( 31.21 41.85 -4.13 0.59) ; 13 ( 31.65 43.02 -3.75 0.52) ; 14 ( 31.95 43.60 -3.25 0.52) ; 15 ( 32.10 43.75 -1.69 0.66) ; 16 ( 31.95 44.04 -0.81 0.66) ; 17 ( 32.02 44.70 -0.38 0.66) ; 18 ( 32.17 45.43 0.44 0.59) ; 19 ( 32.17 45.87 1.13 0.59) ; 20 ( 32.02 46.89 1.31 0.81) ; 21 ( 32.39 47.84 1.31 0.81) ; 22 ( 32.46 49.45 1.00 0.96) ; 23 ( 32.32 50.62 1.00 0.96) ; 24 ( 32.17 51.50 0.81 0.96) ; 25 ( 32.10 52.52 0.69 0.96) ; 26 ( 31.65 53.76 0.63 0.74) ; 27 ( 31.73 54.57 0.63 0.74) ; 28 ( 31.95 55.81 1.88 1.11) ; 29 ( 31.87 56.91 1.94 1.25) ; 30 ( 31.87 57.35 1.63 0.96) ; 31 ( 31.58 58.30 1.44 0.88) ; 32 ( 30.99 59.32 0.88 0.88) ; 33 ( 30.55 60.27 0.63 0.81) ; 34 ( 30.55 61.29 0.19 0.88) ; 35 ( 30.55 61.95 0.13 0.81) ; 36 ( 31.06 62.90 0.00 0.74) ; 37 ( 31.21 63.78 0.25 0.81) ; 38 ( 31.21 64.80 0.25 0.81) ; 39 ( 30.77 65.53 0.25 0.81) ; 40 ( 30.11 66.56 0.56 0.81) ; 41 ( 29.30 67.36 0.63 0.81) ; 42 ( 28.71 68.38 0.63 0.66) ; 43 ( 28.41 69.19 0.38 0.66) ; 44 ( 28.63 70.58 -0.13 0.96) ; 45 ( 28.63 71.09 -0.75 0.96) ; 46 ( 28.63 71.89 -1.44 0.96) ; 47 ( 28.93 73.21 -1.56 0.81) ; 48 ( 29.15 73.94 -1.56 0.81) ; 49 ( 29.15 74.52 -1.31 0.52) ; 50 ( 29.37 75.18 -1.13 0.52) ; 51 ( 29.44 75.69 -1.00 0.81) ; 52 ( 29.66 76.86 -0.56 0.88) ; 53 ( 29.44 77.67 -0.13 0.66) ; 54 ( 29.22 78.47 1.06 0.66) ; 55 ( 28.56 79.06 1.25 0.81) ; 56 ( 27.90 79.42 1.56 0.96) ; 57 ( 27.23 80.52 1.75 0.74) ; 58 ( 26.79 81.40 1.81 0.74) ; 59 ( 26.50 82.71 2.19 0.88) ; 60 ( 26.50 84.10 2.00 1.11) ; 61 ( 26.87 84.85 1.94 0.59) ; 62 ( 27.02 85.88 2.94 0.52) ; 63 ( 27.09 87.12 3.38 0.59) ; 64 ( 27.16 88.58 3.81 0.74) ; 65 ( 27.09 89.24 4.13 0.74) ; 66 ( 27.09 90.34 4.44 1.03) ; 67 ( 26.87 91.43 4.13 0.74) ; 68 ( 26.72 92.31 4.06 0.74) ; 69 ( 27.24 93.19 5.31 0.96) ; 70 ( 27.46 94.36 6.69 0.88) ; 71 ( 27.68 95.89 8.38 0.81) ; 72 ( 27.31 96.99 10.06 1.11) ; 73 ( 27.24 97.72 10.38 0.81) ; 74 ( 26.80 98.67 11.13 0.81) ; 75 ( 26.13 99.40 11.31 0.81) ; 76 ( 25.76 100.28 10.44 0.81) ; 77 ( 25.62 101.01 10.38 0.81) ; 78 ( 26.28 102.03 9.81 0.66) ; 79 ( 26.94 103.20 9.13 0.66) ; 80 ( 27.46 103.93 8.50 0.66) ; 81 ( 28.34 104.74 8.06 0.88) ; 82 ( 29.23 105.76 6.69 1.03) ; 83 ( 29.52 106.34 6.38 0.74) ; 84 ( 29.82 107.22 6.19 0.52) ; 85 ( 29.74 107.88 6.19 0.52) ; 86 ( 29.67 108.76 6.19 0.52) ; 87 ( 30.04 109.49 6.19 0.52) ; 88 ( 30.18 109.78 6.13 0.52) ; 89 ( 29.82 110.73 5.56 0.44) ; 90 ( 29.37 111.53 5.56 0.44) ; 91 ( 29.08 111.90 6.19 0.44) ; 92 ( 28.56 112.19 6.31 0.59) ; 93 Low ) ; End of split ) ; End of split | ( 27.41 15.56 -1.19 0.81) ; 1, R-1-2 ( 28.00 15.85 -0.75 0.66) ; 2 ( 28.37 16.14 -0.50 0.66) ; 3 ( 29.03 16.43 -0.19 0.66) ; 4 ( 29.55 16.65 -0.13 1.03) ; 5 ( 29.57 16.69 -0.13 1.03) ; 6 ( ( 29.99 17.38 0.06 0.74) ; 1, R-1-2-1 ( 30.51 18.04 0.31 0.52) ; 2 ( 30.73 18.55 0.63 0.52) ; 3 ( 31.10 19.58 0.81 0.52) ; 4 ( 31.32 20.31 1.25 0.66) ; 5 ( 31.32 21.04 1.31 0.66) ; 6 ( 31.69 21.55 1.38 0.66) ; 7 ( 32.27 22.28 1.44 0.66) ; 8 ( 32.50 23.01 1.50 0.66) ; 9 ( 32.86 23.16 1.69 0.66) ; 10 ( 33.60 23.45 1.69 0.66) ; 11 ( 34.34 23.60 1.81 0.66) ; 12 ( 35.07 23.82 1.56 0.96) ; 13 ( 35.66 23.89 1.44 1.47) ; 14 ( 36.62 24.47 1.44 1.47) ; 15 ( 37.06 24.69 1.25 1.18) ; 16 ( 37.51 24.69 1.25 0.81) ; 17 ( 37.95 25.06 1.25 0.44) ; 18 ( 39.05 25.72 1.00 0.44) ; 19 ( 39.94 26.30 0.56 0.44) ; 20 ( 40.45 27.03 0.50 0.44) ; 21 ( 41.26 27.18 1.50 0.59) ; 22 ( 41.48 27.25 2.06 0.81) ; 23 ( 42.29 27.40 2.56 0.59) ; 24 ( 42.81 27.91 2.94 0.59) ; 25 ( 43.47 28.79 3.00 0.59) ; 26 ( 44.38 29.33 3.13 0.44) ; 27 ( 45.78 29.70 3.63 0.59) ; 28 ( 46.29 30.28 3.88 0.37) ; 29 ( 46.88 31.09 3.94 0.59) ; 30 ( 47.47 31.60 4.44 0.29) ; 31 ( 48.36 31.96 4.63 0.29) ; 32 ( 49.32 32.40 4.75 0.29) ; 33 ( 49.76 32.99 4.81 0.29) ; 34 ( 49.98 33.06 5.56 0.96) ; 35 ( 50.20 33.43 6.19 1.62) ; 36 ( 50.72 33.64 6.25 2.21) ; 37 ( 51.53 33.79 6.50 1.11) ; 38 ( 52.04 34.16 7.25 0.74) ; 39 ( 52.70 34.59 7.81 0.44) ; 40 ( 53.29 34.96 9.13 0.29) ; 41 ( 53.66 35.62 9.69 0.52) ; 42 ( 53.81 35.84 10.56 0.88) ; 43 ( 54.03 36.57 10.81 0.74) ; 44 ( 54.25 37.37 11.13 0.44) ; 45 ( 54.62 38.03 11.81 0.44) ; 46 ( 55.28 38.47 12.19 0.29) ; 47 ( 55.87 38.91 12.50 0.29) ; 48 ( 56.76 39.93 12.50 0.29) ; 49 ( 56.98 40.37 13.00 0.66) ; 50 ( 57.57 40.81 13.75 0.66) ; 51 ( 57.79 41.83 14.56 0.81) ; 52 ( 58.38 42.12 15.00 0.59) ; 53 ( 59.11 42.49 15.25 0.59) ; 54 ( 59.70 43.00 15.75 1.18) ; 55 ( 60.51 43.29 15.75 1.84) ; 56 ( 61.10 43.59 15.88 1.92) ; 57 ( 61.84 43.95 16.06 0.81) ; 58 ( 62.72 44.32 16.06 0.52) ; 59 ( 63.61 44.97 16.06 0.52) ; 60 ( 64.20 45.41 16.06 0.52) ; 61 ( 64.57 46.07 16.06 0.52) ; 62 ( 64.64 46.51 16.06 0.52) ; 63 Low | ( 29.20 17.55 1.50 0.52) ; 1, R-1-2-2 ( 29.12 17.69 1.94 0.59) ; 2 ( 28.39 18.42 2.13 0.52) ; 3 ( 28.09 19.16 2.13 0.52) ; 4 ( 27.58 20.62 2.63 0.66) ; 5 ( 26.84 21.28 2.94 0.66) ; 6 ( 26.54 21.64 4.25 0.66) ; 7 ( 26.54 22.52 4.38 0.52) ; 8 ( 26.99 23.03 6.19 0.66) ; 9 ( 27.87 23.91 6.25 0.66) ; 10 ( 28.90 25.08 6.25 0.59) ; 11 ( 29.49 25.37 6.44 0.59) ; 12 ( 31.19 26.25 6.88 0.74) ; 13 ( 31.63 27.34 7.63 0.88) ; 14 ( 31.77 28.22 8.13 1.11) ; 15 ( 31.92 28.95 8.19 1.11) ; 16 ( 31.98 30.44 8.44 0.52) ; 17 ( 32.57 31.61 8.69 0.52) ; 18 ( 32.87 32.34 9.06 0.52) ; 19 ( 33.09 32.92 10.38 0.81) ; 20 ( 33.61 33.36 11.38 0.81) ; 21 ( 33.90 34.17 11.38 0.66) ; 22 ( 33.90 35.48 11.44 0.52) ; 23 ( 34.42 36.21 11.75 0.52) ; 24 ( 34.56 37.31 12.63 0.74) ; 25 ( 34.56 37.97 13.06 0.74) ; 26 ( 34.49 39.06 14.31 0.88) ; 27 ( 34.71 39.58 14.38 0.88) ; 28 ( 34.56 40.82 14.81 0.66) ; 29 ( 34.71 41.40 15.06 0.66) ; 30 ( 34.93 41.70 15.50 0.66) ; 31 ( 35.45 41.99 16.13 0.66) ; 32 ( 35.37 43.08 16.56 0.81) ; 33 ( 35.30 44.11 17.56 0.81) ; 34 ( 35.01 45.86 18.06 0.59) ; 35 ( 34.93 47.47 17.69 0.74) ; 36 ( 35.30 49.08 17.38 0.74) ; 37 ( 35.89 49.88 17.94 0.59) ; 38 ( 36.11 50.91 17.88 0.66) ; 39 ( 35.96 51.93 17.88 0.66) ; 40 ( 35.82 52.66 18.00 0.66) ; 41 ( 36.99 53.90 18.25 0.52) ; 42 ( 37.88 53.90 17.88 0.52) ; 43 ( 38.98 52.95 17.13 0.52) ; 44 ( 39.65 51.71 16.56 0.37) ; 45 ( 40.31 51.27 16.19 0.59) ; 46 ( 40.68 50.83 14.69 0.59) ; 47 Low ) ; End of split ) ; End of split | ( 26.48 13.03 -3.00 2.80) ; 1, R-2 ( 27.14 13.10 -3.75 1.47) ; 2 ( 27.73 12.81 -3.75 1.11) ; 3 ( 28.54 12.59 -3.94 0.96) ; 4 ( 28.98 12.81 -4.00 0.81) ; 5 ( 29.42 13.03 -4.00 0.81) ; 6 ( 30.23 13.76 -4.31 1.03) ; 7 ( 31.12 14.57 -5.44 1.03) ; 8 ( 31.85 14.93 -6.19 1.03) ; 9 ( 32.81 15.52 -6.38 1.18) ; 10 ( 33.77 15.81 -6.50 1.18) ; 11 ( 34.36 15.81 -7.06 1.18) ; 12 ( 35.61 15.81 -7.19 1.18) ; 13 ( 36.64 15.44 -7.94 1.25) ; 14 ( 37.60 15.30 -8.88 1.25) ; 15 ( 38.63 15.00 -8.94 1.03) ; 16 ( 39.59 15.22 -10.06 1.18) ; 17 ( 41.14 15.52 -10.38 1.33) ; 18 ( 42.32 15.88 -10.38 1.62) ; 19 ( 43.13 16.25 -10.44 2.28) ; 20 ( 44.16 16.54 -11.00 2.43) ; 21 ( ( 45.26 16.32 -11.50 0.88) ; 1, R-2-1 ( 46.37 16.17 -11.25 0.74) ; 2 ( 47.03 16.25 -11.38 0.74) ; 3 ( 48.14 16.61 -11.38 1.03) ; 4 ( 48.80 16.69 -11.56 0.81) ; 5 ( 49.90 16.83 -11.81 0.66) ; 6 ( 50.79 17.34 -11.94 0.81) ; 7 ( 51.67 17.56 -12.00 0.81) ; 8 ( 52.78 17.71 -12.25 0.88) ; 9 ( 53.81 17.56 -11.06 0.88) ; 10 ( 54.33 17.56 -11.06 0.88) ; 11 ( 54.84 17.56 -11.06 0.88) ; 12 ( 55.95 17.34 -10.69 0.74) ; 13 ( 58.23 17.20 -7.19 0.66) ; 14 ( 58.89 18.00 -14.06 0.96) ; 15 ( 59.41 18.59 -16.00 0.96) ; 16 ( 60.29 19.68 -16.94 0.96) ; 17 ( 60.88 20.56 -18.00 0.81) ; 18 ( 61.10 20.93 -20.00 0.81) ; 19 ( 62.50 21.22 -17.00 0.81) ; 20 ( 63.31 21.44 -18.44 1.25) ; 21 ( 63.98 21.36 -19.63 1.03) ; 22 ( 64.57 21.14 -19.69 1.03) ; 23 ( 65.08 20.93 -19.69 0.81) ; 24 ( 65.67 20.49 -19.94 0.81) ; 25 ( 66.11 20.12 -20.38 1.03) ; 26 ( 67.22 19.90 -19.56 0.81) ; 27 ( 68.54 19.83 -19.75 0.81) ; 28 ( 69.50 19.61 -19.81 0.81) ; 29 ( 70.83 19.76 -21.19 1.11) ; 30 ( 71.49 19.76 -23.00 1.11) ; 31 ( 72.38 19.90 -23.63 1.11) ; 32 ( 72.96 19.61 -24.75 1.11) ; 33 ( 73.11 19.68 -26.25 0.88) ; 34 ( 73.63 20.34 -27.44 0.88) ; 35 ( 74.59 20.49 -28.50 0.88) ; 36 ( 75.69 20.85 -29.25 1.03) ; 37 ( 76.50 21.00 -29.88 1.03) ; 38 ( 77.31 21.00 -30.38 1.18) ; 39 ( 77.97 21.44 -31.63 1.25) ; 40 ( 78.56 21.66 -32.69 1.25) ; 41 ( 79.08 22.02 -33.13 0.96) ; 42 ( 79.37 22.24 -33.13 0.66) ; 43 ( 79.45 22.90 -33.44 0.52) ; 44 ( 80.18 23.63 -33.81 0.66) ; 45 ( 80.99 24.00 -34.19 0.88) ; 46 ( 81.73 24.14 -35.19 0.88) ; 47 ( 82.47 24.51 -36.50 0.66) ; 48 ( 82.91 25.46 -36.69 0.66) ; 49 ( 83.35 26.04 -37.00 0.96) ; 50 ( 84.02 26.77 -37.94 1.18) ; 51 ( 84.60 27.07 -38.13 0.81) ; 52 ( 85.27 27.50 -38.50 0.59) ; 53 ( 85.86 27.87 -38.94 0.59) ; 54 ( 86.15 28.60 -39.81 0.59) ; 55 ( 86.89 29.33 -40.06 0.74) ; 56 ( 87.85 29.84 -40.06 0.74) ; 57 ( 88.44 30.21 -40.38 0.74) ; 58 ( 89.10 30.43 -40.94 0.74) ; 59 ( 89.69 30.65 -41.44 0.74) ; 60 ( 90.57 30.72 -39.81 0.74) ; 61 ( 92.10 30.25 -39.69 0.44) ; 62 ( 93.06 29.95 -39.69 0.44) ; 63 ( 93.94 29.95 -38.25 0.88) ; 64 ( 94.90 30.25 -38.25 1.18) ; 65 ( 95.93 30.61 -37.75 1.40) ; 66 ( 97.11 30.90 -37.50 1.11) ; 67 ( 97.85 31.42 -38.31 0.59) ; 68 ( 98.51 31.78 -40.31 0.37) ; 69 ( 99.84 32.29 -41.50 0.37) ; 70 ( 101.01 32.37 -41.50 0.37) ; 71 ( 101.46 32.29 -41.50 1.03) ; 72 ( 102.19 32.22 -41.69 1.03) ; 73 ( 102.71 32.07 -42.88 1.03) ; 74 ( 103.45 32.15 -43.63 0.74) ; 75 ( 104.48 32.15 -44.44 0.44) ; 76 ( 105.36 32.51 -45.56 0.29) ; 77 ( 106.54 32.66 -46.06 0.15) ; 78 ( 107.28 32.73 -46.63 0.15) ; 79 ( 108.09 32.37 -46.94 0.15) ; 80 ( 108.97 32.29 -46.94 0.15) ; 81 ( 110.00 32.07 -47.69 0.44) ; 82 ( 110.37 32.15 -48.63 1.11) ; 83 ( 110.81 32.07 -47.75 1.77) ; 84 ( 111.55 31.93 -48.25 2.43) ; 85 ( 112.43 31.56 -48.38 1.62) ; 86 ( 112.95 31.64 -49.56 0.74) ; 87 ( 113.39 31.71 -51.19 0.29) ; 88 ( 114.05 31.93 -53.44 0.29) ; 89 ( 115.31 31.85 -53.44 0.29) ; 90 ( 116.34 31.34 -54.38 0.22) ; 91 ( 117.74 31.05 -55.19 0.22) ; 92 ( 119.21 31.05 -56.75 0.22) ; 93 ( 120.54 31.27 -57.50 0.74) ; 94 ( 121.35 31.49 -58.06 1.33) ; 95 ( 122.01 31.64 -58.63 0.44) ; 96 ( 122.60 32.07 -59.00 0.44) ; 97 ( 123.12 32.37 -60.13 1.11) ; 98 ( 123.56 32.59 -61.06 1.40) ; 99 ( 123.71 32.59 -62.94 1.03) ; 100 ( 124.22 32.95 -63.25 0.66) ; 101 ( 124.96 33.02 -63.31 0.37) ; 102 ( 125.99 32.88 -64.06 0.37) ; 103 ( 126.58 33.32 -64.31 0.59) ; 104 ( 126.87 33.76 -64.38 0.59) ; 105 ( 127.61 34.12 -64.94 0.96) ; 106 ( 127.98 34.34 -65.88 1.33) ; 107 ( 128.13 34.56 -66.94 1.69) ; 108 ( 128.64 34.85 -68.13 1.84) ; 109 ( 129.23 35.44 -67.69 0.66) ; 110 ( 129.67 35.44 -70.31 0.44) ; 111 ( 130.63 35.51 -71.56 0.44) ; 112 ( 131.52 35.88 -71.69 0.44) ; 113 ( 132.18 36.09 -72.94 0.44) ; 114 ( 132.62 36.39 -72.94 0.88) ; 115 ( 133.28 36.31 -73.44 1.62) ; 116 ( 134.31 36.17 -74.06 1.62) ; 117 Low | ( 44.58 17.67 -11.19 0.88) ; 1, R-2-2 ( 45.10 18.55 -11.19 0.59) ; 2 ( 45.24 19.65 -11.44 0.59) ; 3 ( 45.47 20.52 -11.44 0.74) ; 4 ( 45.69 21.18 -11.44 0.59) ; 5 ( 46.35 21.33 -11.44 0.59) ; 6 ( 47.23 21.69 -11.81 0.81) ; 7 ( 47.75 21.69 -12.00 0.81) ; 8 ( 48.63 21.47 -12.81 0.81) ; 9 ( 49.30 21.18 -14.56 0.66) ; 10 ( 49.74 21.04 -15.81 0.66) ; 11 ( 50.25 21.47 -15.81 0.66) ; 12 ( 50.84 21.84 -16.25 0.66) ; 13 ( 51.29 22.50 -16.31 0.66) ; 14 ( 51.58 23.01 -16.81 0.96) ; 15 ( 51.87 23.74 -17.13 1.11) ; 16 ( 51.87 24.25 -18.63 0.88) ; 17 ( 52.61 25.06 -18.81 0.74) ; 18 ( 53.13 26.15 -20.00 0.59) ; 19 ( 53.94 26.52 -20.13 0.59) ; 20 ( 54.53 26.52 -20.69 0.74) ; 21 ( 55.48 26.45 -20.88 0.59) ; 22 ( 56.30 26.37 -21.31 0.59) ; 23 ( 57.03 26.45 -21.31 0.59) ; 24 ( 57.70 26.96 -21.69 0.88) ; 25 ( 58.21 27.54 -21.94 1.18) ; 26 ( 58.95 27.54 -22.75 1.18) ; 27 ( 59.61 27.76 -23.94 0.96) ; 28 ( 60.49 28.13 -24.13 0.96) ; 29 ( 61.97 28.86 -24.63 0.81) ; 30 ( 63.07 29.22 -25.75 0.81) ; 31 ( 64.10 29.30 -25.75 0.66) ; 32 ( 64.99 29.00 -26.31 0.66) ; 33 ( 66.02 28.64 -27.25 0.66) ; 34 ( 67.35 29.00 -28.19 0.59) ; 35 ( 68.23 29.00 -28.69 0.74) ; 36 ( 69.11 29.15 -28.81 0.74) ; 37 ( 69.85 29.30 -29.00 0.74) ; 38 ( 70.59 29.37 -29.13 0.74) ; 39 ( 71.25 29.73 -29.88 0.59) ; 40 ( 71.99 29.95 -29.88 0.59) ; 41 ( 73.46 31.20 -30.13 0.66) ; 42 ( 73.90 31.34 -30.75 1.33) ; 43 ( 74.49 31.78 -30.94 1.92) ; 44 ( 75.38 32.07 -31.38 2.06) ; 45 ( 76.26 32.29 -31.69 1.77) ; 46 ( 77.00 32.37 -31.69 0.81) ; 47 ( 77.81 32.37 -32.06 0.59) ; 48 ( 78.40 32.44 -32.63 0.59) ; 49 ( 79.21 32.37 -33.06 0.59) ; 50 ( 79.87 32.22 -33.31 0.59) ; 51 ( 80.53 32.00 -34.38 0.59) ; 52 ( 82.38 31.64 -33.00 0.52) ; 53 ( 83.19 31.42 -34.44 0.29) ; 54 ( 84.36 30.90 -34.75 0.29) ; 55 ( 85.62 30.17 -35.00 0.29) ; 56 ( 86.57 29.52 -35.38 0.52) ; 57 ( 86.87 29.22 -35.38 0.88) ; 58 ( 87.68 28.78 -35.56 1.47) ; 59 ( 88.27 28.20 -35.69 1.84) ; 60 ( 88.93 27.47 -35.75 1.84) ; 61 ( 89.52 27.03 -35.75 1.03) ; 62 ( 89.89 26.59 -35.94 0.52) ; 63 ( 90.48 26.37 -36.13 0.37) ; 64 ( 91.22 25.64 -36.31 0.29) ; 65 ( 91.73 24.98 -36.56 0.29) ; 66 ( 92.10 24.54 -36.56 1.03) ; 67 ( 92.62 23.81 -36.38 1.69) ; 68 ( 93.35 23.01 -36.38 1.40) ; 69 ( 93.87 22.35 -36.38 1.18) ; 70 ( 94.46 21.77 -36.38 0.88) ; 71 ( 95.12 21.26 -36.38 0.52) ; 72 ( 96.30 20.45 -36.31 0.29) ; 73 ( 98.14 19.50 -36.38 0.29) ; 74 ( 99.17 19.14 -36.56 0.29) ; 75 ( 99.47 19.06 -35.94 0.59) ; 76 ( 99.98 18.92 -35.88 0.74) ; 77 ( 100.87 18.70 -35.56 0.74) ; 78 ( 101.60 18.77 -35.44 0.44) ; 79 ( 102.86 18.70 -35.38 0.29) ; 80 ( 104.55 18.48 -35.38 0.29) ; 81 ( 106.02 18.55 -35.06 0.29) ; 82 ( 106.83 18.92 -35.31 0.88) ; 83 ( 107.79 19.35 -35.75 1.11) ; 84 ( 108.31 19.87 -36.06 1.18) ; 85 ( 109.41 20.38 -36.19 0.96) ; 86 ( 110.59 20.74 -36.50 0.44) ; 87 ( 111.11 21.18 -36.50 0.29) ; 88 ( 112.14 21.69 -36.50 1.03) ; 89 ( 112.66 22.21 -36.13 0.81) ; 90 ( 113.02 22.64 -35.50 0.59) ; 91 ( 113.69 23.38 -35.94 0.37) ; 92 ( 115.09 24.25 -37.06 0.22) ; 93 ( 115.97 24.98 -37.19 0.22) ; 94 ( 116.63 25.93 -37.38 0.52) ; 95 ( 117.00 26.66 -37.38 0.66) ; 96 ( 117.44 27.18 -37.50 0.37) ; 97 ( 118.11 28.57 -37.63 0.29) ; 98 ( 118.70 29.37 -37.88 0.29) ; 99 ( 119.21 29.95 -37.31 0.81) ; 100 ( 119.58 30.69 -37.13 1.77) ; 101 ( 120.46 31.05 -36.69 2.06) ; 102 ( 121.20 30.98 -36.44 1.33) ; 103 ( 121.79 30.90 -36.38 0.66) ; 104 ( 122.23 30.83 -36.19 0.37) ; 105 ( 123.26 30.39 -36.19 0.29) ; 106 ( 124.22 29.95 -35.88 0.29) ; 107 ( 125.18 29.44 -35.81 0.29) ; 108 ( 125.47 29.30 -35.81 0.29) ; 109 ( 125.99 28.71 -35.81 1.18) ; 110 ( 126.51 28.27 -35.81 1.18) ; 111 ( 126.80 27.98 -35.81 0.44) ; 112 ( 127.09 27.47 -35.81 0.22) ; 113 ( 127.46 27.18 -35.69 0.29) ; 114 Low ) ; End of split ) ; End of split ) ; End of tree ( (Color MoneyGreen) (Dendrite) ( -0.20 8.59 -12.13 3.90) ; Root ( -0.20 10.27 -12.44 3.54) ; 1, R ( 0.16 11.88 -12.94 3.83) ; 2 ( 0.53 13.27 -13.19 3.98) ; 3 ( 0.83 14.88 -13.06 4.57) ; 4 ( 0.75 15.90 -13.06 4.79) ; 5 ( 0.68 17.00 -13.06 4.35) ; 6 ( 0.90 18.53 -13.06 4.57) ; 7 ( 0.97 20.21 -13.44 5.23) ; 8 ( ( 1.20 21.31 -13.38 5.16) ; 1, R-1 ( ( 2.15 21.82 -14.06 3.76) ; 1, R-1-1 ( 2.90 22.70 -14.06 3.76) ; 2 ( ( 2.89 22.77 -14.06 3.02) ; 1, R-1-1-1 ( 3.63 24.02 -14.06 3.09) ; 2 ( 4.66 25.55 -14.06 3.09) ; 3 ( 5.10 26.43 -14.06 2.87) ; 4 ( 5.98 27.74 -14.88 2.73) ; 5 ( 6.50 28.69 -15.25 3.39) ; 6 ( 7.24 29.72 -15.44 4.13) ; 7 ( 7.33 29.94 -15.44 4.13) ; 8 ( ( 7.74 30.99 -15.88 4.13) ; 1, R-1-1-1-1 ( 7.88 31.79 -16.50 3.68) ; 2 ( ( 8.92 32.16 -16.31 1.84) ; 1, R-1-1-1-1-1 ( 9.43 32.67 -16.50 1.18) ; 2 ( 10.17 33.18 -15.94 0.96) ; 3 ( 10.61 33.40 -15.94 1.11) ; 4 ( ( 11.49 33.55 -15.94 1.33) ; 1, R-1-1-1-1-1-1 ( 11.94 33.55 -16.25 1.33) ; 2 ( 12.38 33.40 -16.44 1.11) ; 3 ( 13.41 33.26 -15.88 0.96) ; 4 ( 14.29 33.62 -15.19 1.11) ; 5 ( 14.88 34.06 -14.25 0.96) ; 6 ( 15.47 34.79 -13.31 0.96) ; 7 ( 16.36 34.94 -12.38 0.96) ; 8 ( 17.24 35.45 -11.63 0.66) ; 9 ( 17.90 35.59 -11.06 0.66) ; 10 ( 18.27 35.89 -10.69 0.66) ; 11 ( 18.71 35.89 -9.81 0.66) ; 12 ( 19.23 35.89 -9.81 0.88) ; 13 ( 19.97 35.89 -9.75 0.88) ; 14 ( 21.15 35.89 -9.50 0.88) ; 15 ( 22.55 36.11 -9.38 0.88) ; 16 ( 23.36 36.40 -9.50 1.03) ; 17 ( 24.39 36.84 -10.06 0.96) ; 18 ( 25.42 37.20 -10.38 0.96) ; 19 ( 26.45 37.64 -9.88 1.03) ; 20 ( 27.33 38.37 -9.38 1.18) ; 21 ( 27.63 38.66 -9.25 1.18) ; 22 ( ( 28.51 39.18 -9.13 0.52) ; 1, R-1-1-1-1-1-1-1 ( 28.73 39.83 -9.06 0.52) ; 2 ( 29.40 40.64 -9.00 0.74) ; 3 ( 29.84 41.44 -9.00 0.96) ; 4 ( 30.06 42.10 -8.94 0.74) ; 5 ( 30.58 42.69 -9.00 0.59) ; 6 ( 31.39 42.98 -9.00 0.74) ; 7 ( 32.12 43.27 -9.00 0.96) ; 8 ( 33.38 43.27 -9.00 0.96) ; 9 ( 34.26 43.34 -9.50 0.88) ; 10 ( 35.14 43.34 -10.44 0.88) ; 11 ( 35.88 43.64 -10.44 1.03) ; 12 ( 36.99 43.78 -10.75 0.81) ; 13 ( 37.72 43.71 -11.69 0.81) ; 14 ( 38.53 43.71 -12.19 0.81) ; 15 ( 39.49 43.64 -12.19 0.74) ; 16 ( 40.52 44.15 -12.31 0.74) ; 17 ( 41.33 45.02 -12.69 0.96) ; 18 ( 42.00 46.12 -12.19 0.74) ; 19 ( 42.73 47.29 -11.69 0.81) ; 20 ( 43.54 48.09 -12.38 0.96) ; 21 ( 44.50 48.46 -12.94 1.18) ; 22 ( 45.38 49.48 -13.44 1.40) ; 23 ( 46.42 50.43 -13.81 1.84) ; 24 ( 47.08 51.09 -13.81 1.84) ; 25 ( 48.11 51.46 -14.00 1.33) ; 26 ( 49.14 51.90 -14.00 1.11) ; 27 ( 50.17 51.90 -14.63 0.81) ; 28 ( 51.28 51.90 -14.94 0.66) ; 29 ( 52.38 51.90 -15.38 0.96) ; 30 ( 53.41 52.63 -15.75 0.81) ; 31 ( 54.37 54.09 -16.06 0.74) ; 32 ( 54.97 55.18 -14.81 0.81) ; 33 ( 55.26 56.35 -14.81 0.81) ; 34 ( 55.19 57.45 -14.63 0.81) ; 35 ( 56.00 58.33 -14.25 0.81) ; 36 ( 56.74 59.49 -14.06 0.66) ; 37 ( 57.33 60.15 -14.00 0.59) ; 38 ( 58.06 60.15 -14.00 0.88) ; 39 ( 58.73 60.44 -13.31 0.74) ; 40 ( 59.32 61.25 -13.75 0.59) ; 41 ( 59.61 62.05 -15.63 1.18) ; 42 ( 59.90 63.22 -15.38 1.33) ; 43 ( 59.90 63.88 -15.31 1.03) ; 44 ( 60.49 65.20 -15.31 0.81) ; 45 ( 61.01 66.22 -15.31 0.74) ; 46 ( 61.67 66.95 -15.31 0.59) ; 47 ( 62.56 67.68 -15.38 0.81) ; 48 ( 63.22 68.92 -15.63 0.88) ; 49 ( 63.22 69.80 -15.63 1.03) ; 50 ( 62.85 70.68 -15.31 0.88) ; 51 ( 62.78 71.04 -13.25 0.74) ; 52 ( 62.93 71.26 -13.13 0.59) ; 53 ( 63.37 70.82 -12.88 0.59) ; 54 ( 63.74 70.75 -12.56 0.59) ; 55 ( 64.40 70.90 -12.25 0.59) ; 56 ( 64.69 71.26 -11.81 0.59) ; 57 ( 65.06 71.99 -11.19 0.59) ; 58 ( 65.36 73.24 -11.19 0.88) ; 59 ( 65.87 74.41 -10.88 1.03) ; 60 ( 66.54 74.92 -10.75 1.40) ; 61 ( 66.83 76.01 -9.81 1.11) ; 62 ( 67.79 76.60 -9.56 0.88) ; 63 ( 68.23 77.55 -8.63 1.03) ; 64 ( 68.45 78.13 -7.94 0.74) ; 65 ( 68.52 79.01 -7.75 0.52) ; 66 ( 68.60 79.45 -7.25 0.52) ; 67 ( 68.97 79.74 -7.13 0.52) ; 68 ( 69.34 80.11 -7.13 0.52) ; 69 ( 69.56 80.91 -7.38 1.18) ; 70 ( 69.92 81.57 -7.56 1.55) ; 71 ( 70.07 81.94 -7.56 1.18) ; 72 ( 70.22 82.37 -7.56 0.81) ; 73 ( 70.44 83.25 -7.56 0.52) ; 74 ( 70.72 84.55 -7.06 0.59) ; 75 ( 71.23 85.06 -8.50 0.81) ; 76 ( 71.75 85.13 -8.63 0.81) ; 77 ( 72.71 85.35 -8.88 0.59) ; 78 ( 73.22 85.28 -9.06 0.44) ; 79 ( 74.11 84.77 -9.44 0.44) ; 80 ( 75.21 84.40 -8.56 0.59) ; 81 ( 76.02 84.33 -8.94 0.52) ; 82 ( 77.13 84.91 -9.06 0.59) ; 83 ( 78.23 85.21 -9.06 0.66) ; 84 ( 78.89 85.50 -9.81 0.88) ; 85 ( 79.56 85.72 -8.63 1.18) ; 86 ( 80.29 85.94 -8.44 1.18) ; 87 ( 80.74 86.16 -8.31 0.81) ; 88 ( 81.33 86.52 -8.13 0.59) ; 89 ( 81.99 86.59 -7.13 0.44) ; 90 ( 82.58 86.67 -6.38 0.37) ; 91 Low | ( 27.31 40.12 -9.00 0.52) ; 1, R-1-1-1-1-1-1-2 ( 27.68 40.85 -8.06 0.52) ; 2 ( 27.68 41.66 -7.19 0.81) ; 3 ( 27.68 42.39 -6.44 1.03) ; 4 ( 27.61 43.41 -3.75 1.03) ; 5 ( 27.61 43.63 -3.25 0.88) ; 6 ( 27.97 44.51 -3.00 0.66) ; 7 ( 28.71 44.94 -2.38 0.59) ; 8 ( 29.01 45.97 -1.88 0.74) ; 9 ( 29.30 47.06 -1.88 0.96) ; 10 ( 29.74 48.02 -1.88 1.25) ; 11 ( 30.11 48.89 -1.56 1.47) ; 12 ( 30.18 49.55 -2.25 1.18) ; 13 ( 30.70 50.65 -2.25 0.88) ; 14 ( 31.07 51.52 -2.63 0.74) ; 15 ( 31.51 52.40 -2.44 0.81) ; 16 ( 31.66 53.13 -2.44 0.81) ; 17 ( 31.88 54.01 -2.44 0.66) ; 18 ( 31.66 54.81 -2.50 0.66) ; 19 ( 31.44 55.69 -2.56 0.59) ; 20 ( 31.29 56.49 -1.56 0.52) ; 21 ( 31.88 57.15 -1.19 0.66) ; 22 ( 32.39 57.74 -1.44 0.88) ; 23 ( 32.98 58.69 -1.63 1.11) ; 24 ( 33.43 59.56 -2.00 0.88) ; 25 ( 33.35 60.44 -2.00 0.59) ; 26 ( 33.35 61.10 -2.19 0.52) ; 27 ( 33.43 61.54 -2.25 0.52) ; 28 ( 33.06 62.05 -2.00 0.74) ; 29 ( 32.54 63.00 -2.38 0.81) ; 30 ( 32.62 64.02 -2.44 0.59) ; 31 ( 32.10 65.34 -2.56 0.66) ; 32 ( 31.73 66.14 -2.56 0.66) ; 33 ( 31.29 67.31 -2.63 0.59) ; 34 ( 31.05 68.31 -2.06 0.52) ; 35 ( 31.34 69.33 -2.06 0.66) ; 36 ( 31.56 70.50 -2.19 0.81) ; 37 ( 31.86 71.31 -2.50 0.74) ; 38 ( 32.08 72.48 -2.50 0.59) ; 39 ( 32.37 73.06 -2.50 0.59) ; 40 ( 32.45 74.16 -2.63 0.52) ; 41 ( 32.60 74.89 -2.19 0.52) ; 42 ( 32.74 75.25 -1.13 0.52) ; 43 ( 33.18 75.98 -0.25 0.59) ; 44 ( 33.85 76.50 0.00 0.59) ; 45 ( 33.99 77.30 0.13 0.52) ; 46 ( 33.92 77.88 0.88 0.52) ; 47 ( 33.77 78.83 1.06 0.74) ; 48 ( 33.63 80.00 0.94 0.96) ; 49 ( 33.85 81.32 0.88 0.59) ; 50 ( 34.07 82.34 0.63 0.66) ; 51 ( 34.51 83.59 -0.25 0.81) ; 52 ( 34.36 85.27 1.06 1.18) ; 53 ( 33.99 86.58 1.38 0.88) ; 54 ( 34.14 88.41 1.56 0.74) ; 55 ( 34.14 89.29 1.38 0.59) ; 56 ( 34.95 90.38 0.75 0.52) ; 57 ( 35.54 91.33 1.13 0.66) ; 58 ( 36.20 92.36 1.63 0.52) ; 59 ( 36.43 93.53 3.00 0.74) ; 60 ( 36.28 94.33 4.19 0.74) ; 61 ( 36.20 95.21 5.25 0.74) ; 62 ( 35.32 95.57 6.81 0.66) ; 63 ( 34.81 96.74 7.13 0.66) ; 64 ( 34.26 98.02 8.94 0.66) ; 65 ( 34.18 99.26 9.00 0.88) ; 66 ( 34.33 99.77 8.69 1.03) ; 67 ( 34.70 100.43 8.50 0.74) ; 68 ( 34.92 101.23 8.06 0.52) ; 69 ( 34.77 102.18 7.69 0.52) ; 70 ( 34.70 103.13 8.50 1.03) ; 71 ( 34.77 103.57 8.69 1.77) ; 72 ( 34.77 104.38 8.69 2.06) ; 73 ( 34.85 105.03 8.69 1.03) ; 74 ( 34.85 106.42 8.69 0.81) ; 75 ( 34.70 107.30 8.69 1.33) ; 76 ( 34.77 107.81 8.69 1.92) ; 77 ( 34.63 108.40 8.69 1.18) ; 78 ( 34.41 108.98 8.75 0.88) ; 79 ( 34.11 110.30 8.75 0.66) ; 80 ( 33.89 111.10 8.75 0.37) ; 81 ( 34.41 112.05 8.75 0.59) ; 82 ( 34.70 112.78 8.75 0.59) ; 83 ( 34.63 113.51 9.31 0.52) ; 84 ( 34.33 113.88 9.81 0.74) ; 85 ( 34.04 114.90 10.13 0.96) ; 86 ( 33.89 115.63 10.19 0.66) ; 87 ( 33.45 116.44 10.25 0.66) ; 88 ( 32.79 116.88 10.63 0.66) ; 89 ( 31.83 117.46 10.69 0.52) ; 90 ( 31.53 117.97 12.00 0.66) ; 91 ( 31.83 119.21 12.44 0.59) ; 92 ( 31.97 120.31 12.88 0.59) ; 93 ( 32.27 121.04 13.13 0.59) ; 94 ( 32.79 121.19 13.25 0.81) ; 95 ( 33.52 121.48 13.44 1.03) ; 96 ( 34.26 122.14 14.38 1.03) ; 97 ( 34.63 122.65 15.06 0.81) ; 98 ( 35.22 123.24 15.63 0.81) ; 99 ( 35.73 123.89 15.63 0.81) ; 100 ( 36.17 124.48 16.25 0.66) ; 101 ( 36.69 124.77 16.25 0.66) ; 102 ( 37.35 125.65 16.75 0.81) ; 103 ( 37.72 126.89 17.00 1.11) ; 104 ( 37.21 127.91 17.56 0.66) ; 105 ( 36.98 128.57 17.63 0.66) ; 106 ( 36.69 129.37 18.19 0.59) ; 107 ( 36.25 129.81 18.44 0.59) ; 108 ( 35.73 130.98 19.00 0.52) ; 109 ( 35.73 131.71 19.94 0.52) ; 110 ( 36.62 132.88 19.88 0.44) ; 111 ( 37.21 134.05 19.63 0.44) ; 112 Low ) ; End of split | ( 11.78 34.15 -18.06 0.66) ; 1, R-1-1-1-1-1-2 ( 12.45 34.74 -18.38 0.59) ; 2 ( 13.18 35.10 -18.38 0.59) ; 3 ( 13.92 35.69 -18.38 0.81) ; 4 ( 14.51 36.56 -18.81 0.81) ; 5 ( 15.47 37.15 -18.13 0.66) ; 6 ( 16.57 37.22 -17.13 0.59) ; 7 ( 17.46 37.59 -16.31 0.74) ; 8 ( 17.97 38.39 -16.25 0.74) ; 9 ( 18.64 38.68 -16.19 0.96) ; 10 ( 19.45 38.98 -16.19 1.18) ; 11 ( 20.55 39.56 -17.31 1.03) ; 12 ( 21.66 40.00 -18.25 0.96) ; 13 ( 22.61 40.51 -18.69 1.18) ; 14 ( 23.35 40.80 -19.44 0.88) ; 15 ( 24.09 41.24 -19.44 0.66) ; 16 ( 25.05 41.75 -19.75 0.66) ; 17 ( 25.56 42.26 -19.38 0.96) ; 18 ( 26.15 43.00 -18.75 1.11) ; 19 ( 26.67 43.51 -18.44 1.33) ; 20 ( 27.26 44.31 -17.94 1.33) ; 21 ( 27.70 45.33 -17.63 1.11) ; 22 ( 28.29 46.65 -17.63 0.88) ; 23 ( 28.73 47.67 -17.63 0.88) ; 24 ( 29.02 48.77 -17.13 1.40) ; 25 ( 29.25 49.43 -16.56 1.69) ; 26 ( ( 29.02 50.38 -17.13 1.11) ; 1, R-1-1-1-1-1-2-1 ( 28.95 50.67 -16.38 0.74) ; 2 ( 28.58 51.40 -16.00 0.59) ; 3 ( 28.51 52.13 -16.00 0.44) ; 4 ( 28.88 52.50 -16.00 0.44) ; 5 ( 29.61 52.64 -16.00 0.44) ; 6 ( 30.35 52.50 -16.00 0.59) ; 7 ( 30.94 52.64 -15.69 0.59) ; 8 ( 31.38 53.01 -15.50 0.59) ; 9 ( 31.68 54.18 -15.19 0.96) ; 10 ( 32.12 55.42 -15.06 0.96) ; 11 ( 32.34 56.15 -15.06 0.74) ; 12 ( 32.63 57.10 -15.00 0.74) ; 13 ( 32.71 57.98 -14.81 1.03) ; 14 ( 33.30 59.00 -15.06 0.74) ; 15 ( 34.03 59.81 -15.06 0.74) ; 16 ( 34.62 60.32 -15.06 0.52) ; 17 ( 35.36 60.90 -13.94 0.66) ; 18 ( 36.17 62.07 -12.88 0.74) ; 19 ( 36.91 63.24 -12.44 0.88) ; 20 ( 37.09 64.73 -12.31 0.59) ; 21 ( 37.53 65.46 -11.88 0.81) ; 22 ( 38.34 66.34 -11.19 0.96) ; 23 ( 39.08 66.92 -10.88 0.81) ; 24 ( 39.67 67.80 -10.06 0.74) ; 25 ( 40.04 68.75 -10.00 0.74) ; 26 ( 40.63 69.48 -9.94 0.59) ; 27 ( 41.36 69.99 -9.81 0.59) ; 28 ( 42.03 71.01 -9.25 0.66) ; 29 ( 42.47 72.26 -8.81 0.81) ; 30 ( 42.54 73.13 -8.50 0.88) ; 31 ( 42.54 74.23 -8.06 0.66) ; 32 ( 41.73 75.99 -7.75 0.44) ; 33 ( 41.07 76.50 -7.44 0.44) ; 34 ( 40.77 77.59 -7.81 0.66) ; 35 ( 40.70 78.47 -9.19 0.59) ; 36 ( 40.92 79.20 -11.13 1.25) ; 37 ( 41.36 80.15 -12.13 1.25) ; 38 ( 41.58 80.81 -12.38 0.88) ; 39 ( 41.73 81.39 -12.69 0.59) ; 40 ( 42.25 82.13 -12.94 0.52) ; 41 ( 43.13 82.13 -13.44 0.74) ; 42 ( 44.09 82.27 -13.63 0.74) ; 43 ( 45.12 82.49 -13.69 0.74) ; 44 ( 46.08 83.30 -14.50 0.96) ; 45 ( 45.86 84.83 -15.19 0.81) ; 46 ( 45.71 85.93 -15.69 0.66) ; 47 ( 45.27 86.95 -15.75 0.88) ; 48 ( 44.97 87.90 -15.88 0.88) ; 49 ( 44.97 88.63 -16.06 0.74) ; 50 ( 45.34 89.29 -17.06 1.03) ; 51 ( 45.86 89.73 -17.31 1.33) ; 52 ( 46.59 90.46 -17.81 1.11) ; 53 ( 47.33 90.90 -17.75 0.88) ; 54 ( 48.21 91.34 -18.19 0.88) ; 55 ( 49.10 91.99 -18.56 0.88) ; 56 ( 49.98 92.58 -18.63 0.74) ; 57 ( 50.52 93.10 -18.56 0.66) ; 58 ( 50.82 94.27 -18.88 0.88) ; 59 ( 51.18 95.44 -19.00 0.74) ; 60 ( 51.63 95.58 -19.63 0.59) ; 61 ( 52.44 95.95 -19.94 0.81) ; 62 ( 53.39 96.31 -20.44 1.03) ; 63 ( 54.13 96.75 -21.19 0.96) ; 64 ( 55.24 97.41 -21.31 0.74) ; 65 ( 56.27 98.36 -21.75 0.74) ; 66 ( 57.23 99.31 -21.88 0.59) ; 67 ( 58.11 100.33 -21.88 0.59) ; 68 ( 58.63 101.50 -22.31 1.03) ; 69 ( 59.07 102.38 -22.75 1.03) ; 70 ( 59.51 103.11 -21.50 0.88) ; 71 ( 60.10 103.40 -21.88 0.59) ; 72 ( 60.61 103.48 -22.31 0.59) ; 73 ( 61.57 103.48 -22.31 0.81) ; 74 ( 62.82 103.84 -22.31 0.96) ; 75 ( 63.27 103.84 -22.31 0.66) ; 76 ( 63.93 103.92 -22.31 0.52) ; 77 ( 64.89 103.99 -22.50 0.66) ; 78 ( 65.48 104.28 -22.69 0.44) ; 79 ( 65.70 104.43 -23.25 0.44) ; 80 ( 66.36 104.87 -24.31 0.74) ; 81 ( 66.88 105.09 -25.31 1.03) ; 82 Low | ( 29.96 50.47 -17.81 0.88) ; 1, R-1-1-1-1-1-2-2 ( 30.55 50.54 -18.63 0.74) ; 2 ( 31.36 50.62 -19.19 0.74) ; 3 ( 32.09 50.91 -19.19 0.74) ; 4 ( 32.83 51.27 -19.38 0.74) ; 5 ( 33.72 51.86 -19.44 0.88) ; 6 ( 34.16 52.37 -19.19 0.88) ; 7 ( 34.53 52.74 -19.38 0.66) ; 8 ( 34.89 53.03 -19.63 0.59) ; 9 ( 35.41 53.69 -19.94 0.52) ; 10 ( 35.93 53.54 -21.19 0.66) ; 11 ( 36.29 53.54 -21.75 0.88) ; 12 ( 37.03 53.32 -22.25 0.88) ; 13 ( 37.33 53.32 -22.88 0.88) ; 14 ( 38.06 53.25 -23.56 0.88) ; 15 ( 38.73 53.39 -23.81 0.88) ; 16 ( 39.46 53.69 -24.31 0.88) ; 17 ( 40.20 53.98 -24.56 0.88) ; 18 ( 40.79 54.49 -24.69 0.88) ; 19 ( 41.60 55.22 -25.00 0.74) ; 20 ( 42.56 55.81 -25.25 0.81) ; 21 ( 43.37 55.88 -25.44 0.88) ; 22 ( 44.25 56.32 -26.06 0.88) ; 23 ( 45.13 56.90 -26.25 0.81) ; 24 ( 46.09 57.41 -26.56 0.81) ; 25 ( 47.64 57.71 -26.56 0.66) ; 26 ( 48.52 57.56 -26.81 0.81) ; 27 ( 49.19 57.71 -27.00 1.11) ; 28 ( 49.85 58.07 -27.81 1.33) ; 29 ( 50.73 58.51 -28.38 1.33) ; 30 ( 51.25 58.88 -29.13 1.03) ; 31 ( 51.91 59.32 -29.13 0.66) ; 32 ( 52.50 59.97 -29.19 0.59) ; 33 ( 53.46 60.56 -29.50 0.59) ; 34 ( 54.64 61.14 -29.81 0.59) ; 35 ( 55.96 61.73 -28.69 0.59) ; 36 ( 57.29 62.46 -28.69 0.59) ; 37 ( 58.25 62.90 -28.19 0.59) ; 38 ( 59.28 63.41 -28.19 0.59) ; 39 ( 60.46 64.07 -28.88 0.59) ; 40 ( 61.42 64.58 -28.88 0.81) ; 41 ( 62.08 65.24 -29.00 1.03) ; 42 ( 63.18 65.97 -29.31 1.25) ; 43 ( 64.07 66.62 -29.63 0.96) ; 44 ( 64.88 67.14 -30.13 0.59) ; 45 ( 65.47 67.72 -30.44 0.52) ; 46 ( 66.57 68.53 -31.31 0.37) ; 47 ( 67.16 69.04 -31.69 0.37) ; 48 ( 67.97 69.84 -32.06 0.59) ; 49 ( 68.86 70.43 -32.06 0.88) ; 50 ( 69.45 70.94 -32.75 0.88) ; 51 ( 70.04 71.45 -33.00 0.88) ; 52 ( 70.63 71.96 -33.00 0.59) ; 53 ( 71.22 72.11 -33.63 0.37) ; 54 ( 72.32 72.55 -34.13 0.29) ; 55 ( 72.98 73.20 -34.94 0.88) ; 56 ( 73.28 73.42 -34.06 1.92) ; 57 ( 73.94 73.72 -34.00 2.58) ; 58 ( 74.97 74.01 -34.00 1.55) ; 59 ( 75.49 74.15 -34.00 0.81) ; 60 ( 76.22 74.45 -34.00 0.52) ; 61 ( 77.37 75.30 -34.69 0.29) ; 62 ( 78.63 76.04 -34.69 0.29) ; 63 ( 79.29 76.69 -34.69 0.29) ; 64 ( 80.10 77.28 -34.69 0.81) ; 65 ( 80.76 77.79 -34.69 1.99) ; 66 ( 81.57 78.08 -34.69 2.28) ; 67 ( 82.46 78.45 -34.69 1.40) ; 68 ( 83.49 78.59 -35.38 1.03) ; 69 ( 84.23 78.96 -35.69 0.66) ; 70 ( 84.81 78.81 -36.19 0.37) ; 71 ( 85.18 78.52 -37.69 0.66) ; 72 ( 85.48 78.30 -37.88 0.96) ; 73 ( 86.07 78.16 -37.94 0.96) ; 74 ( 86.44 78.16 -39.56 0.59) ; 75 Low ) ; End of split ) ; End of split | ( 7.58 32.20 -15.94 2.73) ; 1, R-1-1-1-1-2 ( 7.22 33.23 -15.63 1.33) ; 2 ( 6.70 34.25 -15.81 1.11) ; 3 ( 6.77 35.20 -15.19 1.11) ; 4 ( 6.81 35.21 -15.19 1.11) ; 5 ( ( 6.77 36.15 -14.31 1.47) ; 1, R-1-1-1-1-2-1 ( 6.92 36.88 -13.06 1.69) ; 2 ( ( 7.66 37.76 -11.56 1.33) ; 1, R-1-1-1-1-2-1-1 ( 8.32 38.42 -10.38 1.11) ; 2 ( 8.62 38.93 -9.94 0.88) ; 3 ( 8.98 39.51 -9.75 0.66) ; 4 ( 8.98 40.25 -9.75 0.96) ; 5 ( 9.21 40.83 -9.75 0.96) ; 6 ( 9.35 41.78 -9.56 0.96) ; 7 ( 9.57 42.22 -9.13 0.81) ; 8 ( 9.35 42.95 -9.13 0.66) ; 9 ( 9.06 43.68 -9.44 0.66) ; 10 ( 8.54 44.63 -9.50 0.59) ; 11 ( 7.95 45.58 -10.06 0.59) ; 12 ( 7.73 46.31 -10.50 0.66) ; 13 ( 7.73 47.26 -10.88 0.52) ; 14 ( 7.44 48.07 -10.75 0.52) ; 15 ( 7.29 48.87 -9.88 0.81) ; 16 ( 7.36 49.46 -9.69 0.81) ; 17 ( 7.07 50.33 -9.81 0.66) ; 18 ( 6.48 51.50 -9.81 0.74) ; 19 ( 6.34 52.60 -10.81 0.88) ; 20 ( 6.04 54.06 -11.50 0.88) ; 21 ( 6.04 55.23 -11.63 0.81) ; 22 ( 6.55 56.40 -13.00 0.81) ; 23 ( 6.85 57.42 -13.25 0.81) ; 24 ( 7.36 58.23 -13.88 0.81) ; 25 ( 7.73 59.25 -14.31 1.03) ; 26 ( 8.03 60.20 -15.19 0.74) ; 27 ( 7.22 60.49 -16.00 0.52) ; 28 ( 6.63 60.35 -16.00 0.74) ; 29 ( 5.67 60.42 -16.44 1.03) ; 30 ( 4.64 60.71 -16.75 1.03) ; 31 ( 3.55 60.93 -16.88 0.81) ; 32 ( 2.59 61.37 -17.31 0.66) ; 33 ( 1.26 61.59 -17.38 0.59) ; 34 ( 0.38 62.25 -17.44 0.59) ; 35 ( -0.36 64.07 -17.50 0.74) ; 36 ( 0.01 65.76 -17.88 0.81) ; 37 ( 0.38 66.85 -18.19 0.66) ; 38 ( 0.53 67.88 -18.25 0.52) ; 39 ( 0.38 68.46 -18.31 0.52) ; 40 ( -0.21 69.19 -19.69 0.88) ; 41 ( -0.80 69.26 -20.69 0.88) ; 42 ( -1.17 69.48 -22.50 0.88) ; 43 ( -1.68 70.43 -23.44 1.11) ; 44 ( -1.90 71.31 -24.00 1.62) ; 45 ( -2.12 72.04 -24.19 1.33) ; 46 ( -2.27 72.92 -24.25 1.11) ; 47 ( -1.90 74.16 -24.25 0.96) ; 48 ( -1.90 75.11 -24.25 0.66) ; 49 ( -1.83 76.14 -24.50 0.59) ; 50 ( -1.68 76.87 -22.81 0.88) ; 51 ( -1.46 77.82 -22.44 1.03) ; 52 ( -1.46 78.62 -22.38 0.66) ; 53 ( -1.31 79.64 -22.06 0.52) ; 54 ( -0.72 80.38 -21.63 0.52) ; 55 ( -0.65 80.74 -21.25 0.52) ; 56 ( -1.02 81.03 -21.19 0.52) ; 57 ( -1.31 81.55 -21.19 0.81) ; 58 ( -1.90 82.35 -21.13 0.88) ; 59 ( -2.12 82.93 -21.00 0.59) ; 60 ( -2.35 83.74 -20.88 0.44) ; 61 ( -2.42 84.47 -20.75 0.44) ; 62 ( -1.46 84.40 -19.75 0.44) ; 63 ( -1.31 85.20 -19.44 0.44) ; 64 ( -1.02 86.22 -18.63 1.25) ; 65 ( -0.72 86.74 -18.50 1.62) ; 66 ( -0.36 87.47 -18.13 1.69) ; 67 ( -0.50 88.27 -18.13 0.74) ; 68 ( -0.28 89.15 -17.88 0.52) ; 69 ( -0.21 90.61 -17.50 0.52) ; 70 ( 0.16 91.79 -16.94 0.81) ; 71 ( 0.53 92.89 -16.13 1.11) ; 72 ( 0.90 93.76 -15.69 1.40) ; 73 ( 1.42 93.98 -15.44 1.77) ; 74 ( 1.64 94.35 -15.44 1.40) ; 75 ( 2.01 95.37 -15.44 0.66) ; 76 ( 2.45 95.81 -15.44 0.52) ; 77 ( 2.74 96.47 -15.13 0.52) ; 78 ( 2.74 97.13 -14.75 0.52) ; 79 ( 2.30 98.00 -14.56 0.52) ; 80 ( 1.93 98.66 -14.44 0.66) ; 81 ( 2.01 99.39 -14.25 0.66) ; 82 ( 2.82 100.42 -13.63 0.88) ; 83 ( 3.33 101.44 -13.06 0.88) ; 84 ( 3.92 102.17 -13.06 0.59) ; 85 ( 5.10 102.97 -12.75 0.59) ; 86 ( 5.39 103.85 -12.19 0.52) ; 87 ( 5.25 104.95 -11.50 0.81) ; 88 ( 5.10 105.61 -10.94 1.11) ; 89 ( 5.10 106.34 -10.94 0.81) ; 90 ( 5.25 107.14 -10.81 0.59) ; 91 ( 5.47 107.87 -10.06 0.52) ; 92 ( 5.76 108.82 -10.06 0.52) ; 93 ( 5.98 109.33 -10.06 0.52) ; 94 ( 6.87 109.99 -10.06 0.66) ; 95 ( 7.16 110.43 -11.00 0.88) ; 96 ( 7.02 110.87 -12.44 1.25) ; 97 ( 6.65 111.09 -13.44 1.55) ; 98 ( 6.79 111.89 -15.13 0.81) ; 99 ( 7.31 112.62 -15.25 0.52) ; 100 ( 7.97 113.35 -15.44 0.74) ; 101 ( 8.49 113.57 -15.56 1.11) ; 102 ( 8.93 114.01 -15.63 1.47) ; 103 ( 9.67 114.30 -15.88 1.47) ; 104 ( 10.40 114.82 -16.63 0.81) ; 105 ( 11.36 115.25 -18.81 0.44) ; 106 ( 11.88 115.77 -18.81 0.44) ; 107 ( 12.91 115.77 -19.38 0.44) ; 108 ( 14.09 116.13 -19.81 0.44) ; 109 ( 14.90 116.72 -21.06 0.44) ; 110 ( 15.78 117.45 -21.94 0.74) ; 111 Low | ( 7.26 37.84 -9.63 0.88) ; 1, R-1-1-1-1-2-1-2 ( 6.31 37.92 -8.75 0.74) ; 2 ( 5.86 37.77 -7.81 0.74) ; 3 ( 5.05 37.70 -7.63 0.74) ; 4 ( 4.46 37.40 -7.25 0.74) ; 5 ( 2.99 37.19 -7.13 0.74) ; 6 ( 2.18 37.26 -6.88 0.59) ; 7 ( 1.66 37.84 -6.25 0.66) ; 8 ( 1.15 38.79 -6.25 0.81) ; 9 ( 0.93 39.60 -6.25 0.96) ; 10 ( 0.63 40.26 -6.13 0.74) ; 11 ( 0.41 41.21 -5.81 0.59) ; 12 ( 0.04 42.08 -6.56 0.74) ; 13 ( -0.18 42.74 -8.13 1.11) ; 14 ( -0.77 43.18 -8.50 1.40) ; 15 ( -1.06 44.28 -8.56 0.81) ; 16 ( -1.28 45.08 -9.00 0.81) ; 17 ( -1.43 45.96 -9.50 0.81) ; 18 ( -1.72 47.05 -9.75 0.74) ; 19 ( -1.72 48.15 -10.00 0.74) ; 20 ( -1.65 48.81 -9.06 0.88) ; 21 ( -1.21 49.47 -9.06 0.88) ; 22 ( -1.14 50.27 -8.94 0.88) ; 23 ( -1.14 51.29 -8.56 0.74) ; 24 ( -1.14 52.17 -7.75 0.59) ; 25 ( -1.14 53.05 -7.63 0.81) ; 26 ( -1.14 54.36 -7.56 1.03) ; 27 ( -1.65 55.75 -7.56 0.96) ; 28 ( -2.31 57.00 -7.56 0.81) ; 29 ( -2.90 58.09 -7.56 0.96) ; 30 ( -3.05 59.11 -7.06 0.96) ; 31 ( -3.05 59.99 -5.88 0.81) ; 32 ( -3.05 60.50 -4.00 0.96) ; 33 ( -3.42 60.94 -3.13 1.18) ; 34 ( -3.86 61.45 -2.81 0.81) ; 35 ( -4.52 61.75 -2.19 0.74) ; 36 ( -5.41 62.11 -1.50 0.81) ; 37 ( -6.37 62.19 -0.56 0.59) ; 38 ( -6.96 62.33 0.69 0.66) ; 39 ( -7.32 62.55 1.69 0.66) ; 40 ( -7.18 63.35 1.81 0.52) ; 41 ( -7.54 64.09 1.88 0.66) ; 42 ( -8.06 64.74 1.88 0.66) ; 43 ( -8.36 65.77 2.06 0.52) ; 44 ( -8.21 66.35 2.31 0.52) ; 45 ( -7.56 65.97 3.75 0.66) ; 46 ( -7.41 66.11 6.06 0.81) ; 47 ( -7.41 66.62 6.31 0.81) ; 48 ( -7.70 67.21 7.75 0.96) ; 49 ( -7.85 67.94 8.00 1.18) ; 50 ( -8.07 68.60 8.38 0.88) ; 51 ( -8.52 69.18 9.06 0.59) ; 52 ( -9.03 69.04 9.75 0.81) ; 53 ( -9.91 69.04 10.25 0.81) ; 54 ( -11.17 69.26 10.69 0.81) ; 55 ( -12.27 69.33 11.19 0.74) ; 56 ( -13.08 69.91 12.06 0.59) ; 57 ( -12.64 71.30 13.94 0.44) ; 58 ( -12.64 71.74 14.63 0.59) ; 59 ( -13.60 72.40 15.00 0.74) ; 60 ( -13.97 72.62 16.13 1.03) ; 61 ( -14.41 72.91 16.13 1.25) ; 62 ( -15.29 73.20 16.69 0.66) ; 63 ( -16.40 73.06 17.56 0.52) ; 64 ( -17.80 73.35 18.13 0.59) ; 65 ( -18.39 73.64 19.00 0.59) ; 66 ( -18.68 74.23 19.31 0.59) ; 67 ( -18.53 74.96 19.38 0.59) ; 68 ( -18.09 75.47 19.13 0.59) ; 69 ( -17.80 75.91 18.56 0.59) ; 70 ( -17.50 75.98 17.94 0.59) ; 71 Low ) ; End of split | ( 6.29 36.45 -15.50 0.74) ; 1, R-1-1-1-1-2-2 ( 5.93 37.25 -16.44 0.74) ; 2 ( 5.48 37.54 -16.94 0.74) ; 3 ( ( 5.19 37.91 -19.06 0.44) ; 1, R-1-1-1-1-2-2-1 ( 4.53 37.98 -19.31 0.44) ; 2 ( 3.86 37.84 -19.63 0.66) ; 3 ( 3.27 38.06 -19.81 0.88) ; 4 ( 2.76 38.49 -20.50 0.88) ; 5 ( 2.24 39.59 -21.06 0.88) ; 6 ( 1.80 40.54 -21.06 0.88) ; 7 ( 1.14 41.56 -20.06 0.81) ; 8 ( 0.99 42.59 -20.06 0.81) ; 9 ( 0.47 43.54 -20.38 1.03) ; 10 ( -0.26 44.12 -20.63 0.74) ; 11 ( -0.85 44.78 -20.69 0.74) ; 12 ( -1.59 45.29 -20.88 0.74) ; 13 ( -2.47 46.24 -20.88 0.52) ; 14 ( -2.77 47.05 -21.19 0.52) ; 15 ( -2.77 48.44 -21.38 0.59) ; 16 ( -2.77 49.09 -20.94 0.59) ; 17 ( -2.99 49.90 -19.81 0.81) ; 18 ( -3.28 50.56 -18.31 1.03) ; 19 ( -3.80 50.92 -17.44 0.88) ; 20 ( -4.76 51.07 -17.31 0.88) ; 21 ( -5.27 52.02 -17.13 0.96) ; 22 ( -6.23 52.38 -17.06 0.59) ; 23 ( -6.67 53.04 -17.06 0.44) ; 24 ( -7.41 53.85 -17.06 0.66) ; 25 ( -8.22 54.58 -17.56 0.66) ; 26 ( -9.55 55.53 -17.50 0.66) ; 27 ( -9.84 56.18 -16.88 0.88) ; 28 ( -10.14 56.48 -15.38 1.25) ; 29 ( -10.72 56.77 -15.31 1.47) ; 30 ( -11.54 56.55 -15.00 0.74) ; 31 ( -12.64 56.77 -13.31 0.74) ; 32 ( -13.89 56.77 -13.31 0.52) ; 33 ( -14.78 56.40 -13.31 0.81) ; 34 ( -15.88 56.55 -13.31 0.81) ; 35 ( -17.14 56.62 -13.19 0.66) ; 36 ( -18.17 57.13 -12.75 0.52) ; 37 ( -19.49 58.22 -11.31 0.52) ; 38 ( -20.75 59.32 -11.56 0.66) ; 39 ( -21.26 60.12 -11.56 0.66) ; 40 ( -21.85 60.93 -12.06 1.18) ; 41 ( -22.29 61.66 -12.63 0.96) ; 42 ( -22.88 62.39 -13.31 0.66) ; 43 ( -22.81 62.90 -13.38 0.44) ; 44 ( -22.81 63.41 -13.69 0.74) ; 45 ( -23.62 64.66 -13.94 0.96) ; 46 ( -24.14 65.31 -13.38 0.81) ; 47 ( -25.09 66.26 -13.38 0.66) ; 48 ( -26.27 66.78 -12.88 0.52) ; 49 ( -26.79 66.85 -12.31 1.18) ; 50 ( -27.97 67.07 -11.44 1.18) ; 51 ( -28.63 67.07 -11.13 0.81) ; 52 ( -30.03 68.31 -10.25 0.44) ; 53 ( -30.69 68.82 -9.06 0.44) ; 54 ( -31.43 69.41 -8.69 0.44) ; 55 ( -32.17 70.72 -8.63 0.74) ; 56 ( -32.24 72.26 -7.38 0.59) ; 57 ( -32.68 73.28 -8.44 0.44) ; 58 ( -32.83 74.45 -8.19 0.44) ; 59 ( -32.98 75.04 -7.44 0.81) ; 60 ( -33.05 76.13 -7.44 1.11) ; 61 ( -33.27 76.79 -7.44 0.81) ; 62 ( -33.57 77.81 -7.38 0.59) ; 63 ( -33.20 78.40 -6.06 0.59) ; 64 ( -33.12 78.84 -5.63 0.44) ; 65 ( -33.34 79.35 -5.38 0.44) ; 66 ( -33.71 79.86 -5.44 0.44) ; 67 ( -34.08 80.45 -6.31 1.18) ; 68 ( -34.30 80.74 -6.56 1.99) ; 69 ( -34.45 81.10 -6.69 2.73) ; 70 ( -33.86 81.83 -7.19 1.47) ; 71 ( -33.64 82.13 -7.81 1.11) ; 72 Low ) ; End of split ) ; End of split ) ; End of split | ( 7.47 29.94 -16.94 2.95) ; 1, R-1-1-1-2 ( 7.40 30.45 -18.94 2.65) ; 2 ( 7.40 30.52 -20.63 2.43) ; 3 ( 8.36 30.15 -23.38 2.43) ; 4 ( ( 10.13 30.15 -26.13 3.17) ; 1, R-1-1-1-2-1 ( 11.30 30.08 -27.56 2.87) ; 2 ( 12.41 29.64 -29.00 2.21) ; 3 ( 13.66 29.35 -29.63 2.14) ; 4 ( 15.43 29.13 -30.19 2.36) ; 5 ( 16.90 29.06 -31.44 2.80) ; 6 ( 18.08 29.20 -32.94 3.02) ; 7 ( ( 20.00 29.20 -34.06 2.36) ; 1, R-1-1-1-2-1-1 ( 21.10 29.64 -35.56 1.84) ; 2 ( 22.50 30.01 -37.50 2.21) ; 3 ( 23.24 30.37 -38.81 1.92) ; 4 ( 24.64 30.67 -36.63 1.03) ; 5 ( 25.52 31.18 -37.19 1.03) ; 6 ( 27.00 32.13 -37.94 1.25) ; 7 ( 27.88 32.93 -39.44 1.47) ; 8 ( 28.69 33.59 -39.69 1.47) ; 9 ( ( 30.39 34.32 -39.38 0.74) ; 1, R-1-1-1-2-1-1-1 ( 31.56 34.98 -39.50 0.74) ; 2 ( 32.52 35.86 -39.75 0.74) ; 3 ( 33.41 36.95 -40.06 0.74) ; 4 ( 34.81 38.20 -40.06 1.03) ; 5 ( 36.21 39.73 -41.75 1.03) ; 6 ( 38.05 40.97 -43.38 0.88) ; 7 ( 39.08 42.29 -43.50 0.88) ; 8 ( 40.26 43.75 -44.50 0.88) ; 9 ( 40.70 44.85 -46.06 0.88) ; 10 ( 42.91 46.24 -46.06 0.81) ; 11 ( 44.16 48.21 -46.38 1.03) ; 12 ( 45.12 49.67 -46.50 1.33) ; 13 ( 45.71 50.48 -46.50 0.74) ; 14 ( 47.33 51.43 -46.50 0.66) ; 15 ( 48.51 52.52 -46.50 0.88) ; 16 ( 49.65 53.58 -47.38 0.88) ; 17 ( 50.31 54.97 -48.13 0.88) ; 18 ( 50.83 55.92 -48.50 0.59) ; 19 ( 51.34 56.51 -48.50 0.59) ; 20 ( 51.93 58.04 -49.06 1.03) ; 21 ( 52.08 59.80 -49.13 0.81) ; 22 ( 52.30 60.97 -49.13 0.59) ; 23 ( 52.30 61.77 -49.13 0.59) ; 24 ( 52.23 62.80 -49.13 1.03) ; 25 ( 52.23 63.45 -49.38 0.66) ; 26 ( 52.30 64.33 -50.31 0.52) ; 27 ( 52.30 65.28 -50.88 0.52) ; 28 ( 52.30 66.08 -52.31 1.18) ; 29 ( 52.45 67.25 -52.44 1.99) ; 30 ( 52.82 68.20 -52.88 2.58) ; 31 ( 53.55 69.01 -53.63 1.62) ; 32 ( 54.14 69.67 -53.63 0.88) ; 33 ( 54.73 70.40 -53.63 0.59) ; 34 ( 55.61 70.98 -53.63 0.37) ; 35 ( 56.87 72.22 -53.63 0.29) ; 36 ( 58.19 73.54 -54.00 0.44) ; 37 ( 59.00 74.49 -53.88 1.03) ; 38 ( 59.67 75.15 -53.88 1.33) ; 39 ( 60.85 75.81 -53.88 0.74) ; 40 ( 61.51 76.10 -53.88 0.37) ; 41 ( 62.25 76.61 -53.81 0.29) ; 42 ( 63.35 77.05 -53.81 0.29) ; 43 ( 64.01 77.27 -52.69 0.66) ; 44 ( 64.75 77.63 -52.69 1.03) ; 45 ( 65.63 77.85 -52.38 0.66) ; 46 ( 67.03 78.22 -52.19 0.52) ; 47 ( 67.84 78.51 -51.88 1.18) ; 48 ( 68.66 78.66 -51.88 1.92) ; 49 ( 69.39 78.73 -51.88 2.21) ; 50 ( 70.28 78.95 -51.88 0.88) ; 51 ( 70.79 79.10 -51.88 0.52) ; 52 Low | ( 27.84 34.65 -39.56 0.81) ; 1, R-1-1-1-2-1-1-2 ( 28.06 35.68 -40.94 0.66) ; 2 ( 29.39 36.41 -43.63 0.66) ; 3 ( 30.12 36.48 -43.94 0.66) ; 4 ( 30.27 36.92 -45.31 0.66) ; 5 ( 30.42 37.58 -46.00 0.66) ; 6 ( 30.42 38.16 -47.56 1.03) ; 7 ( 30.42 38.89 -47.63 1.33) ; 8 ( 30.64 39.26 -48.94 1.11) ; 9 ( 30.79 39.40 -50.38 0.96) ; 10 ( 31.01 39.55 -52.75 0.81) ; 11 ( 31.82 39.99 -53.69 0.81) ; 12 ( 32.48 41.38 -55.31 0.52) ; 13 ( 32.70 42.11 -55.50 0.52) ; 14 ( 33.29 42.91 -56.50 0.88) ; 15 ( 34.03 43.13 -57.63 1.18) ; 16 ( 35.50 43.57 -58.25 0.81) ; 17 ( 36.02 43.94 -60.81 0.52) ; 18 ( 37.71 44.37 -62.38 0.37) ; 19 ( 38.38 44.96 -62.94 0.37) ; 20 ( 39.55 46.06 -63.75 0.37) ; 21 ( 39.85 46.57 -63.75 0.74) ; 22 ( 40.36 47.23 -64.00 1.03) ; 23 ( 40.95 47.88 -64.44 1.03) ; 24 ( 41.91 48.83 -67.44 0.74) ; 25 ( 42.57 50.37 -67.63 0.59) ; 26 ( 43.68 52.42 -70.00 0.52) ; 27 ( 43.46 53.29 -70.19 0.52) ; 28 ( 43.39 53.95 -70.25 0.81) ; 29 ( 43.39 54.61 -71.00 1.25) ; 30 ( 43.68 55.19 -71.00 1.62) ; 31 ( 44.49 56.58 -71.31 1.33) ; 32 ( 44.56 57.02 -71.31 1.03) ; 33 ( 44.64 57.82 -69.69 0.52) ; 34 ( 44.93 59.14 -68.88 0.29) ; 35 ( 45.37 61.04 -68.44 0.29) ; 36 ( 45.89 62.87 -69.75 0.29) ; 37 ( 46.04 63.53 -69.75 0.29) ; 38 ( 46.04 64.33 -69.75 0.66) ; 39 ( 45.82 65.50 -69.75 0.88) ; 40 ( 45.96 66.16 -69.75 0.44) ; 41 ( 45.89 67.91 -68.31 0.29) ; 42 ( 45.74 69.15 -68.31 0.29) ; 43 ( 45.45 69.74 -68.00 0.29) ; 44 ( 45.45 70.18 -67.63 1.03) ; 45 ( 45.45 71.20 -67.56 1.77) ; 46 ( 45.23 72.15 -67.56 1.18) ; 47 ( 45.15 72.88 -67.56 0.44) ; 48 ( 44.79 73.83 -67.56 0.22) ; 49 ( 44.71 74.49 -67.56 0.22) ; 50 ( 44.34 75.59 -66.56 0.22) ; 51 ( 44.20 76.46 -65.88 0.59) ; 52 ( 43.90 77.27 -65.81 1.40) ; 53 ( 43.61 78.00 -65.81 2.06) ; 54 ( 43.53 78.88 -65.75 0.96) ; 55 ( 43.24 79.61 -65.75 0.37) ; 56 ( 42.87 81.22 -65.69 0.22) ; 57 ( 42.43 82.02 -65.69 0.22) ; 58 ( 41.88 83.71 -65.63 0.22) ; 59 ( 41.30 84.88 -66.38 0.22) ; 60 ( 41.37 85.83 -64.50 0.59) ; 61 ( 41.44 86.56 -64.44 0.59) ; 62 ( 42.47 87.95 -62.25 0.37) ; 63 ( 43.87 89.26 -62.19 0.22) ; 64 ( 45.27 90.07 -61.81 0.22) ; 65 ( 47.12 91.02 -61.50 0.22) ; 66 ( 49.77 92.26 -61.38 0.22) ; 67 ( 50.73 93.36 -61.94 0.22) ; 68 ( 50.95 94.09 -61.94 1.33) ; 69 ( 51.54 94.67 -61.94 2.36) ; 70 ( 51.98 95.18 -61.94 2.36) ; 71 ( 52.20 95.77 -61.94 1.25) ; 72 ( 52.79 96.28 -61.94 0.96) ; 73 Generated ) ; End of split | ( 18.06 29.97 -32.88 1.92) ; 1, R-1-1-1-2-1-2 ( 18.28 30.92 -32.06 0.81) ; 2 ( 18.51 31.43 -32.06 0.37) ; 3 ( 18.87 32.09 -32.06 0.37) ; 4 ( 19.39 32.97 -32.06 0.37) ; 5 ( 20.05 33.48 -32.06 0.37) ; 6 ( 20.86 34.36 -32.06 0.96) ; 7 ( 21.60 34.72 -32.06 0.96) ; 8 ( 22.19 35.01 -32.38 0.96) ; 9 ( 22.63 35.45 -32.06 0.66) ; 10 ( 23.07 36.18 -32.69 0.44) ; 11 ( 24.03 36.69 -32.69 0.29) ; 12 ( 24.40 37.50 -32.88 0.29) ; 13 ( 25.21 37.79 -33.56 0.29) ; 14 ( 26.24 38.23 -33.94 0.29) ; 15 ( 27.49 38.74 -33.94 0.29) ; 16 ( 28.97 40.06 -33.94 0.29) ; 17 ( 29.63 40.79 -33.94 0.52) ; 18 ( 30.15 41.59 -33.94 0.52) ; 19 ( 30.66 42.47 -33.94 0.52) ; 20 ( 31.69 43.35 -33.94 0.81) ; 21 ( 32.28 43.93 -33.94 0.81) ; 22 ( 33.24 44.52 -33.94 0.81) ; 23 ( 33.61 45.03 -33.94 0.81) ; 24 ( 34.57 46.12 -33.94 0.96) ; 25 ( 35.74 47.29 -34.19 0.96) ; 26 ( 36.78 48.10 -34.19 0.96) ; 27 ( 37.73 49.63 -34.19 0.66) ; 28 ( 38.69 50.22 -34.19 0.66) ; 29 ( 39.94 50.95 -34.19 0.96) ; 30 ( 41.42 52.05 -35.06 0.74) ; 31 ( 42.01 52.56 -35.69 1.11) ; 32 ( 42.60 53.21 -35.69 1.33) ; 33 ( 43.26 53.95 -35.69 0.81) ; 34 ( 43.55 54.46 -35.69 0.52) ; 35 ( 44.14 55.12 -36.06 0.44) ; 36 ( 44.73 55.63 -36.81 0.44) ; 37 ( 45.62 56.43 -34.25 0.81) ; 38 ( 46.21 57.09 -33.38 1.11) ; 39 ( 46.50 58.19 -33.31 0.81) ; 40 ( 47.04 58.87 -32.38 0.37) ; 41 ( 47.70 59.45 -31.69 0.37) ; 42 ( 48.22 60.33 -31.50 0.37) ; 43 ( 48.58 60.84 -31.50 0.66) ; 44 ( 48.88 61.64 -30.69 1.47) ; 45 ( 49.17 62.45 -29.88 1.62) ; 46 ( 49.25 63.25 -29.75 0.96) ; 47 ( 49.62 63.84 -28.81 0.59) ; 48 ( 50.21 64.27 -25.81 0.44) ; 49 ( 51.09 65.23 -25.00 0.29) ; 50 ( 51.75 65.52 -24.75 0.29) ; 51 ( 52.27 65.74 -24.13 0.59) ; 52 Low ) ; End of split | ( 9.32 30.80 -24.25 1.25) ; 1, R-1-1-1-2-2 ( 9.17 31.53 -25.31 1.25) ; 2 ( 8.73 32.77 -26.00 0.96) ; 3 ( 8.21 34.01 -26.69 0.96) ; 4 ( 7.62 35.04 -26.88 1.40) ; 5 ( ( 7.03 36.06 -28.13 1.99) ; 1, R-1-1-1-2-2-1 ( 5.63 36.35 -29.06 2.36) ; 2 ( 4.31 36.06 -30.88 1.40) ; 3 ( ( 3.05 36.06 -30.69 1.03) ; 1, R-1-1-1-2-2-1-1 ( 2.24 35.77 -31.69 0.81) ; 2 ( 0.99 35.33 -33.19 0.59) ; 3 ( 0.25 35.33 -33.56 0.59) ; 4 ( -0.56 35.33 -34.56 1.03) ; 5 ( -1.06 35.03 -35.06 1.99) ; 6 ( -1.58 35.32 -36.25 1.77) ; 7 ( -1.65 35.47 -39.31 1.40) ; 8 ( -1.65 35.90 -40.00 0.81) ; 9 ( -2.17 36.34 -40.44 0.59) ; 10 ( -3.42 37.00 -41.19 0.44) ; 11 ( -4.75 37.73 -42.06 0.44) ; 12 ( -5.11 38.17 -42.06 0.74) ; 13 ( -5.93 38.32 -43.81 0.74) ; 14 ( -6.00 38.97 -44.63 0.59) ; 15 ( -6.44 39.63 -45.19 0.59) ; 16 ( -6.37 40.22 -45.19 0.81) ; 17 ( -6.74 40.95 -46.38 0.74) ; 18 ( -7.25 41.09 -47.88 0.74) ; 19 ( -7.40 41.09 -49.25 0.74) ; 20 ( -8.06 41.24 -52.00 0.88) ; 21 ( -8.80 41.24 -54.06 0.88) ; 22 ( -9.09 41.31 -54.38 1.18) ; 23 ( -9.98 41.39 -55.19 1.18) ; 24 ( -10.27 41.83 -58.81 0.52) ; 25 ( -10.64 42.34 -60.25 0.52) ; 26 ( -11.16 42.70 -61.94 0.88) ; 27 ( -11.60 42.85 -63.63 1.18) ; 28 ( -11.89 43.21 -64.25 0.59) ; 29 ( -12.19 43.58 -65.06 0.96) ; 30 ( -12.70 44.02 -65.63 1.03) ; 31 ( -13.15 44.46 -66.75 0.66) ; 32 ( -13.66 44.53 -67.50 0.44) ; 33 ( -14.40 44.75 -68.00 0.81) ; 34 ( -15.50 45.04 -68.25 0.81) ; 35 ( -16.31 45.55 -68.25 0.52) ; 36 ( -17.12 45.92 -68.81 0.52) ; 37 ( -18.38 46.36 -69.94 0.52) ; 38 ( -18.89 46.58 -70.06 1.33) ; 39 ( -19.48 47.16 -71.44 1.62) ; 40 ( -19.48 47.31 -73.06 1.33) ; 41 ( -19.92 47.38 -75.75 1.03) ; 42 ( -20.81 47.67 -75.75 0.66) ; 43 ( -22.13 47.82 -76.38 0.66) ; 44 ( -22.50 47.97 -76.63 1.03) ; 45 ( -23.53 48.62 -76.63 1.40) ; 46 ( -24.05 48.92 -78.38 0.66) ; 47 ( -24.34 50.01 -79.19 0.44) ; 48 ( -25.15 50.45 -80.06 0.81) ; 49 ( -25.23 50.60 -81.75 1.18) ; 50 ( -25.52 51.18 -82.25 1.69) ; 51 ( -25.60 51.47 -86.56 1.33) ; 52 ( -25.82 51.84 -88.50 0.96) ; 53 ( -26.41 52.13 -88.50 0.59) ; 54 ( -26.70 52.50 -88.50 0.29) ; 55 ( -27.66 53.01 -88.50 0.29) ; 56 ( -28.40 53.52 -88.44 0.29) ; 57 ( -29.35 54.11 -88.81 0.29) ; 58 ( -30.09 54.32 -89.13 0.74) ; 59 ( -30.97 54.91 -89.13 1.03) ; 60 ( -32.01 55.13 -89.19 1.03) ; 61 ( -32.01 55.86 -93.69 0.74) ; 62 ( -32.45 56.52 -94.25 0.74) ; 63 ( -33.04 57.69 -95.19 0.52) ; 64 ( -33.33 58.27 -95.19 0.88) ; 65 ( -33.70 59.22 -96.13 1.18) ; 66 Low | ( 4.76 36.78 -32.50 0.88) ; 1, R-1-1-1-2-2-1-2 ( 3.65 36.71 -33.25 0.88) ; 2 ( 2.25 37.37 -34.13 0.88) ; 3 ( 0.85 38.17 -34.13 0.66) ; 4 ( 0.04 38.68 -35.25 0.88) ; 5 ( -0.47 39.63 -35.88 1.03) ; 6 ( -0.99 40.14 -36.56 1.03) ; 7 ( -1.95 40.22 -38.94 0.88) ; 8 ( -2.39 40.58 -39.69 0.88) ; 9 ( -2.83 41.39 -40.75 0.88) ; 10 ( -2.90 42.19 -41.75 0.88) ; 11 ( -3.13 42.78 -42.88 0.88) ; 12 ( -2.61 42.41 -44.88 0.88) ; 13 ( -3.13 43.14 -45.19 0.88) ; 14 ( -3.79 43.87 -45.19 0.66) ; 15 ( -4.08 44.24 -46.56 1.03) ; 16 ( -4.30 44.16 -47.88 1.40) ; 17 ( -3.94 43.94 -49.00 2.14) ; 18 ( -4.08 43.58 -50.94 1.25) ; 19 ( -4.08 43.58 -53.63 0.96) ; 20 ( -3.94 43.51 -55.75 0.96) ; 21 ( -4.01 43.51 -57.25 0.96) ; 22 ( -4.30 43.80 -61.31 0.96) ; 23 ( -4.60 44.46 -62.13 0.96) ; 24 ( -5.04 45.04 -65.88 0.81) ; 25 ( -5.78 45.41 -67.38 1.11) ; 26 ( -5.48 45.77 -66.44 0.66) ; 27 ( -5.70 47.38 -66.63 0.44) ; 28 ( -5.63 48.62 -67.88 0.29) ; 29 ( -5.19 49.57 -70.31 0.66) ; 30 ( -4.97 49.65 -71.88 1.11) ; 31 ( -4.67 50.01 -71.13 2.80) ; 32 ( -4.30 50.74 -71.00 3.09) ; 33 ( -4.16 51.47 -71.00 3.09) ; 34 ( -3.64 51.77 -74.25 1.69) ; 35 ( -4.08 52.57 -75.88 0.88) ; 36 ( -4.75 53.52 -76.69 0.59) ; 37 ( -5.19 53.89 -77.38 0.59) ; 38 ( -5.85 54.69 -78.63 1.03) ; 39 ( -6.44 55.79 -80.13 1.03) ; 40 ( -7.25 56.59 -81.06 1.47) ; 41 ( -7.99 56.96 -83.19 1.47) ; 42 ( -8.43 57.25 -83.38 0.81) ; 43 ( -9.02 57.69 -84.63 0.44) ; 44 ( -9.90 58.64 -85.50 0.29) ; 45 ( -10.12 58.86 -86.88 0.66) ; 46 ( -10.35 59.37 -87.75 1.40) ; 47 ( -10.42 59.59 -89.56 2.28) ; 48 ( -10.86 60.10 -91.56 0.96) ; 49 ( -10.93 61.12 -92.13 0.59) ; 50 ( -11.01 62.37 -93.19 0.44) ; 51 ( -11.08 62.80 -93.81 0.88) ; 52 ( -10.71 63.61 -94.81 1.33) ; 53 ( -10.86 64.34 -95.63 0.74) ; 54 ( -10.90 65.44 -95.56 0.37) ; 55 ( -11.20 66.69 -96.31 0.22) ; 56 ( -11.49 67.64 -96.75 0.22) ; 57 ( -11.57 68.22 -97.25 0.22) ; 58 ( -11.64 69.46 -97.25 0.66) ; 59 ( -11.64 69.76 -97.25 1.62) ; 60 ( -11.79 70.41 -98.38 2.43) ; 61 ( -12.67 70.71 -100.00 1.33) ; 62 ( -13.12 71.29 -100.00 0.66) ; 63 ( -13.34 71.66 -100.13 0.37) ; 64 ( -13.85 72.53 -100.13 0.15) ; 65 ( -14.37 73.26 -100.50 0.15) ; 66 ( -15.10 74.00 -101.19 0.15) ; 67 ( -15.84 74.65 -101.44 0.52) ; 68 ( -16.21 75.09 -101.44 1.77) ; 69 ( -16.73 75.46 -102.06 2.36) ; 70 ( -17.46 75.75 -102.81 1.33) ; 71 ( -18.12 75.75 -103.88 0.59) ; 72 ( -19.01 76.19 -103.94 0.29) ; 73 ( -19.97 76.70 -104.56 0.29) ; 74 ( -20.92 77.07 -104.81 0.29) ; 75 ( -21.44 77.36 -103.63 1.03) ; 76 ( -22.25 78.31 -103.88 1.33) ; 77 ( -22.77 78.38 -104.44 0.59) ; 78 ( -23.28 78.45 -106.44 0.29) ; 79 Low ) ; End of split | ( 7.37 36.30 -26.38 1.55) ; 1, R-1-1-1-2-2-2 ( 7.45 37.25 -25.00 1.18) ; 2 ( 7.01 38.34 -24.63 0.81) ; 3 ( 6.56 38.71 -24.13 0.81) ; 4 ( 5.61 39.59 -23.38 1.03) ; 5 ( 4.80 40.46 -21.06 1.03) ; 6 ( 4.57 41.41 -19.56 1.03) ; 7 ( 4.94 42.07 -18.69 1.03) ; 8 ( 5.16 42.80 -18.69 0.74) ; 9 ( 5.38 43.46 -18.69 0.44) ; 10 ( 5.38 44.12 -18.44 0.44) ; 11 ( 4.87 44.78 -18.06 0.81) ; 12 ( 4.65 45.80 -18.31 0.81) ; 13 ( 4.57 46.82 -18.44 0.66) ; 14 ( 4.43 47.77 -18.44 0.66) ; 15 ( 4.28 48.58 -18.19 0.88) ; 16 ( 4.06 49.38 -18.19 0.88) ; 17 ( 4.21 50.11 -17.88 0.59) ; 18 ( 4.35 50.55 -17.75 0.59) ; 19 ( 4.72 51.14 -18.38 0.88) ; 20 ( 5.24 51.65 -17.94 1.11) ; 21 ( 5.42 52.95 -20.38 0.52) ; 22 ( 5.05 54.05 -21.38 0.52) ; 23 ( 5.20 55.00 -21.81 0.52) ; 24 ( 5.27 56.82 -22.56 0.52) ; 25 ( 5.56 58.29 -22.19 0.52) ; 26 ( 5.56 59.09 -23.44 0.52) ; 27 ( 6.01 60.26 -24.50 0.81) ; 28 ( 5.56 60.77 -25.19 0.81) ; 29 ( 5.27 61.58 -25.75 0.44) ; 30 ( 5.05 62.60 -25.75 0.22) ; 31 ( 5.20 62.96 -25.75 0.22) ; 32 ( 5.93 62.96 -25.88 0.22) ; 33 ( 6.30 63.84 -26.38 0.22) ; 34 ( 5.78 64.94 -26.31 0.52) ; 35 ( 5.93 65.67 -28.13 0.66) ; 36 ( 6.30 67.13 -28.81 0.81) ; 37 ( 6.59 68.08 -28.13 1.11) ; 38 ( 6.82 69.32 -28.06 1.40) ; 39 ( 6.96 70.35 -28.06 1.03) ; 40 ( 7.04 70.86 -28.06 0.74) ; 41 ( 6.89 72.03 -27.25 0.88) ; 42 ( 6.67 73.20 -26.63 0.59) ; 43 ( 6.59 74.22 -26.19 0.44) ; 44 ( 6.74 74.88 -26.19 0.44) ; 45 ( 7.55 75.76 -26.69 0.52) ; 46 ( 7.77 77.44 -26.69 0.66) ; 47 ( 7.48 78.24 -26.56 0.52) ; 48 ( 6.89 78.90 -26.19 1.11) ; 49 ( 6.23 79.70 -26.19 1.11) ; 50 ( 5.49 80.44 -26.19 0.81) ; 51 ( 4.83 81.17 -26.19 0.96) ; 52 ( 3.89 81.92 -26.06 0.52) ; 53 ( 3.89 82.58 -25.50 0.37) ; 54 ( 3.60 83.17 -25.06 0.37) ; 55 ( 2.78 83.60 -24.88 0.37) ; 56 ( 2.71 84.34 -24.81 1.11) ; 57 ( 2.49 84.77 -24.81 2.06) ; 58 ( 2.42 84.99 -24.81 2.06) ; 59 ( 2.05 85.80 -24.75 0.88) ; 60 ( 1.68 86.31 -24.75 0.37) ; 61 ( 1.46 86.82 -24.75 0.37) ; 62 ( 0.87 87.70 -24.75 0.59) ; 63 ( 0.43 88.14 -24.19 0.88) ; 64 ( -0.16 88.36 -25.19 1.03) ; 65 ( -1.12 88.72 -26.06 0.66) ; 66 ( -1.93 89.16 -26.06 0.37) ; 67 ( -2.74 89.38 -26.06 0.37) ; 68 Low ) ; End of split ) ; End of split ) ; End of split | ( 2.90 23.14 -10.13 0.81) ; 1, R-1-1-2 ( 2.75 23.87 -8.94 0.81) ; 2 ( 2.97 25.04 -7.50 1.33) ; 3 ( 3.42 26.14 -6.88 1.33) ; 4 ( 3.93 27.24 -6.25 1.18) ; 5 ( 4.37 28.04 -5.50 1.18) ; 6 ( 4.96 28.99 -5.44 1.18) ; 7 ( 5.40 30.31 -4.75 1.84) ; 8 ( 5.40 31.55 -4.38 1.84) ; 9 ( 5.18 33.23 -3.69 1.40) ; 10 ( 4.74 34.62 -3.44 1.25) ; 11 ( 4.52 35.64 -2.81 1.84) ; 12 ( 4.30 37.54 -2.50 1.84) ; 13 ( 4.30 39.37 -1.63 1.33) ; 14 ( 4.89 40.54 -1.50 1.55) ; 15 ( 5.33 41.42 -1.06 1.55) ; 16 ( 5.33 43.02 -1.63 1.55) ; 17 ( 5.77 44.12 -1.25 1.99) ; 18 ( ( 6.29 45.07 -0.69 2.87) ; 1, R-1-1-2-1 ( 6.73 45.80 -0.50 2.58) ; 2 ( 7.25 46.46 -1.50 1.33) ; 3 ( 7.69 47.04 -2.06 0.88) ; 4 ( 8.13 47.56 -2.06 0.66) ; 5 ( 9.09 47.85 -2.38 0.66) ; 6 ( 9.60 48.29 -2.50 0.88) ; 7 ( 10.34 48.43 -1.69 0.88) ; 8 ( 10.78 48.58 -1.44 1.03) ; 9 ( 11.22 49.24 -0.31 1.03) ; 10 ( 11.89 49.90 -0.31 1.03) ; 11 ( 12.40 50.77 -0.25 1.11) ; 12 ( 12.70 51.65 -0.25 0.88) ; 13 ( 12.85 52.97 -0.06 1.11) ; 14 ( 12.77 53.77 -0.25 1.11) ; 15 ( 12.77 54.50 -0.25 0.81) ; 16 ( 13.07 55.31 -0.44 0.74) ; 17 ( 13.80 56.18 -0.94 1.11) ; 18 ( 14.76 56.91 -0.88 1.18) ; 19 ( 15.28 57.64 -0.81 1.25) ; 20 ( 15.94 58.59 -1.56 0.81) ; 21 ( 16.82 59.40 -1.88 0.74) ; 22 ( 16.90 60.28 -1.88 0.74) ; 23 ( 16.97 61.37 -1.88 0.96) ; 24 ( 16.90 62.03 -2.13 1.47) ; 25 ( 16.75 63.05 -2.13 1.69) ; 26 ( 16.46 64.37 -2.13 1.25) ; 27 ( 16.46 65.39 -2.13 1.11) ; 28 ( 16.46 66.34 -2.25 0.96) ; 29 ( 16.90 67.73 -2.25 1.25) ; 30 ( 17.49 68.61 -2.06 0.88) ; 31 ( 17.49 69.56 -1.63 0.96) ; 32 ( 17.49 70.36 -1.56 1.18) ; 33 ( 17.27 71.39 -1.44 1.03) ; 34 ( 17.19 72.56 -1.38 1.03) ; 35 ( 17.12 72.85 -1.38 0.96) ; 36 ( 17.63 73.73 -1.38 0.96) ; 37 ( 18.08 74.82 -1.38 1.18) ; 38 ( 18.44 76.07 -1.56 1.03) ; 39 ( 18.81 77.67 -1.19 0.88) ; 40 ( 18.74 78.84 -1.00 0.88) ; 41 ( 18.00 79.94 -1.00 0.96) ; 42 ( 17.05 80.93 -0.94 1.11) ; 43 ( 16.24 81.74 -0.94 0.88) ; 44 ( 15.50 82.25 -0.94 0.59) ; 45 ( 14.91 82.90 -0.38 0.74) ; 46 ( 14.10 83.85 -0.38 0.81) ; 47 ( 13.66 84.59 -0.38 0.96) ; 48 ( 13.14 85.32 -0.38 0.96) ; 49 ( 12.63 86.05 -0.38 1.11) ; 50 ( 12.48 86.41 -0.38 0.74) ; 51 ( 12.04 87.44 -0.38 0.74) ; 52 ( 11.60 88.39 0.50 0.96) ; 53 ( 11.23 89.41 1.50 0.88) ; 54 ( 10.86 90.29 1.50 0.66) ; 55 ( 10.71 90.95 1.56 0.66) ; 56 ( 10.71 91.90 1.56 0.66) ; 57 ( 10.86 92.55 1.56 0.66) ; 58 ( 11.23 93.28 1.56 0.59) ; 59 ( 11.23 93.80 1.56 0.59) ; 60 ( 11.23 94.31 1.56 0.88) ; 61 ( 11.45 94.82 1.56 1.62) ; 62 ( 11.67 95.33 1.56 1.84) ; 63 ( 12.33 96.43 1.56 1.25) ; 64 ( 12.77 97.45 1.63 0.81) ; 65 ( 13.66 98.26 1.63 0.74) ; 66 ( 14.40 99.13 1.50 0.74) ; 67 ( 14.91 100.01 1.50 0.52) ; 68 ( 14.84 100.81 1.50 0.74) ; 69 ( 14.76 101.40 2.44 0.96) ; 70 ( 14.76 102.86 4.31 1.03) ; 71 ( 14.69 103.45 4.81 1.03) ; 72 ( 14.47 104.54 5.69 0.66) ; 73 ( 14.54 105.20 5.75 0.59) ; 74 ( 14.76 105.86 5.75 0.37) ; 75 ( 15.21 105.93 5.75 0.37) ; 76 ( 16.02 105.93 5.19 0.81) ; 77 ( 16.68 106.08 4.13 0.96) ; 78 ( 17.49 106.66 3.44 0.66) ; 79 ( 18.30 107.39 2.63 0.66) ; 80 ( 18.45 107.83 4.19 0.96) ; 81 ( 19.04 108.56 3.31 0.96) ; 82 ( 19.18 109.51 4.19 1.11) ; 83 ( 19.53 110.53 4.63 1.40) ; 84 ( 19.90 111.55 5.38 0.88) ; 85 ( 20.41 112.36 6.13 0.74) ; 86 ( 20.63 113.31 6.19 1.03) ; 87 ( 21.37 113.82 6.19 1.03) ; 88 ( 21.81 114.70 6.19 0.59) ; 89 ( 22.47 114.99 6.19 0.44) ; 90 ( 23.51 115.36 5.75 0.66) ; 91 ( 24.46 115.79 5.31 0.59) ; 92 ( 25.13 116.31 5.31 0.59) ; 93 ( 25.27 117.11 5.31 0.81) ; 94 ( 25.57 117.69 4.88 0.96) ; 95 ( 26.23 118.72 6.00 0.66) ; 96 ( 26.67 119.89 6.63 0.96) ; 97 ( 27.11 121.13 5.13 1.11) ; 98 ( 27.41 122.15 4.31 0.88) ; 99 ( 27.85 123.40 4.31 1.11) ; 100 ( 28.22 124.05 2.69 0.74) ; 101 ( 28.66 125.08 2.69 0.52) ; 102 ( 28.88 125.66 3.06 0.81) ; 103 ( 29.40 126.61 3.19 1.18) ; 104 ( 29.91 127.49 4.44 0.81) ; 105 ( 30.36 128.81 4.44 0.59) ; 106 ( 30.58 129.83 4.44 0.59) ; 107 ( 30.95 130.56 5.13 0.59) ; 108 ( 31.83 131.14 3.94 1.40) ; 109 ( 32.35 131.07 2.75 2.06) ; 110 ( 32.71 131.22 0.69 1.11) ; 111 ( 33.52 131.51 0.31 0.74) ; 112 ( 34.33 131.80 -0.63 0.74) ; 113 ( 35.44 131.88 -1.69 0.96) ; 114 ( 36.10 131.80 -2.13 0.88) ; 115 ( 36.55 131.36 -3.31 0.88) ; 116 ( 36.69 131.00 -4.63 1.18) ; 117 ( 36.62 131.00 -6.13 1.03) ; 118 ( 36.91 131.73 -6.88 1.62) ; 119 ( 37.13 132.17 -7.38 1.92) ; 120 ( 37.43 132.90 -7.81 1.40) ; 121 ( 37.80 133.56 -8.31 1.11) ; 122 ( 38.02 133.85 -8.81 0.81) ; 123 ( 38.31 134.14 -9.25 0.59) ; 124 ( 38.76 134.87 -10.31 0.37) ; 125 ( 39.42 135.02 -10.31 0.29) ; 126 ( 39.42 135.60 -10.31 0.29) ; 127 ( 39.86 136.48 -10.31 0.29) ; 128 ( 40.30 137.21 -10.31 0.29) ; 129 ( 40.67 137.50 -10.31 0.88) ; 130 ( 41.04 137.72 -10.94 0.88) ; 131 Low | ( 6.00 45.00 -0.19 2.73) ; 1, R-1-1-2-2 ( 6.14 46.17 0.50 1.99) ; 2 ( 6.07 46.69 0.56 1.25) ; 3 ( 5.63 47.56 1.75 0.96) ; 4 ( 5.55 48.37 3.00 1.55) ; 5 ( 5.63 49.24 3.19 2.06) ; 6 ( ( 6.07 50.19 3.69 0.96) ; 1, R-1-1-2-2-1 ( 6.58 50.78 3.69 0.59) ; 2 ( 7.10 51.36 3.75 0.59) ; 3 ( 7.17 51.95 3.75 0.59) ; 4 ( 7.25 52.68 4.00 0.59) ; 5 ( 7.25 53.12 4.13 0.59) ; 6 ( 7.25 54.21 4.13 0.96) ; 7 ( 7.25 54.87 4.13 1.69) ; 8 ( 6.81 55.75 3.06 1.40) ; 9 ( 6.58 56.63 2.56 1.11) ; 10 ( 6.44 57.28 2.56 0.81) ; 11 ( 6.51 58.67 2.56 0.66) ; 12 ( 7.10 59.33 1.50 0.66) ; 13 ( 7.91 59.70 3.75 0.81) ; 14 ( 8.79 60.14 3.75 0.81) ; 15 ( 9.68 60.65 4.00 0.81) ; 16 ( 10.27 61.45 4.25 0.74) ; 17 ( 10.86 62.69 4.25 0.96) ; 18 ( 11.08 63.79 4.25 0.81) ; 19 ( 11.15 65.33 4.25 1.11) ; 20 ( 10.93 66.64 3.63 1.33) ; 21 ( 10.78 68.76 2.06 1.25) ; 22 ( 11.00 70.08 1.56 1.11) ; 23 ( 11.23 71.83 1.50 1.11) ; 24 ( 11.23 72.93 1.25 1.11) ; 25 ( 11.15 74.61 1.19 0.96) ; 26 ( 10.81 76.45 1.06 1.03) ; 27 ( 10.66 77.62 0.88 0.88) ; 28 ( 10.44 79.01 0.44 1.03) ; 29 ( 9.70 79.96 0.75 1.47) ; 30 ( 8.74 80.76 1.19 1.11) ; 31 ( 7.93 81.78 2.44 1.18) ; 32 ( 7.56 82.15 3.19 1.55) ; 33 ( 7.20 82.44 4.38 1.33) ; 34 ( 6.90 82.66 4.94 1.33) ; 35 ( 6.16 82.81 4.94 1.33) ; 36 ( 5.72 83.46 6.00 0.81) ; 37 ( 5.13 84.41 6.63 0.59) ; 38 ( 4.62 85.58 8.00 1.11) ; 39 ( 3.95 87.12 8.00 1.18) ; 40 ( 3.44 87.92 8.06 0.96) ; 41 ( 3.22 89.24 8.06 0.74) ; 42 ( 3.73 90.41 8.13 0.96) ; 43 ( 4.54 91.72 8.25 1.40) ; 44 ( 4.91 93.04 8.88 1.03) ; 45 ( 5.06 93.99 8.88 1.11) ; 46 ( 5.50 94.58 9.94 0.52) ; 47 ( 5.65 95.45 9.94 0.37) ; 48 ( 5.43 95.89 9.94 0.37) ; 49 ( 4.99 95.89 10.94 0.88) ; 50 ( 4.18 96.40 11.56 1.47) ; 51 ( 3.66 96.77 11.56 1.18) ; 52 ( 3.36 97.72 12.06 0.52) ; 53 ( 2.85 98.67 12.13 0.37) ; 54 ( 2.85 99.98 13.69 0.96) ; 55 ( 2.63 101.15 13.75 1.33) ; 56 ( 2.55 102.18 14.31 1.11) ; 57 ( 2.41 103.35 14.94 0.88) ; 58 ( 2.14 104.99 15.25 0.81) ; 59 ( 2.14 105.79 15.88 1.11) ; 60 ( 1.55 107.18 16.13 1.62) ; 61 ( 1.18 108.13 16.38 1.25) ; 62 ( 0.88 109.01 16.38 0.88) ; 63 ( 0.66 109.81 16.31 0.66) ; 64 ( -0.44 110.91 16.06 0.52) ; 65 ( -0.81 111.86 16.25 1.03) ; 66 ( -1.03 112.96 15.75 1.33) ; 67 ( -1.18 114.27 15.75 0.81) ; 68 ( -0.74 115.22 15.75 0.66) ; 69 ( -0.88 115.73 15.75 0.66) ; 70 ( -1.18 116.68 15.75 0.66) ; 71 ( -0.44 117.56 15.50 0.59) ; 72 ( 0.07 118.80 16.00 0.81) ; 73 ( 0.30 120.12 16.00 1.11) ; 74 ( 0.30 121.07 16.00 1.11) ; 75 ( -0.07 122.02 16.00 0.96) ; 76 ( -0.96 123.26 16.81 0.96) ; 77 ( -1.10 124.21 17.56 0.74) ; 78 ( -1.84 125.60 17.63 0.59) ; 79 ( -2.65 126.11 18.75 0.66) ; 80 ( -3.39 126.70 18.75 1.03) ; 81 ( -3.68 126.92 18.75 1.11) ; 82 ( -4.79 127.87 18.75 0.74) ; 83 ( -5.82 128.31 18.75 0.74) ; 84 ( -6.63 128.53 18.75 0.74) ; 85 ( -6.78 129.62 18.75 0.74) ; 86 ( -6.78 130.87 18.75 0.59) ; 87 ( -6.41 131.82 18.75 0.59) ; 88 ( -6.63 133.35 18.31 0.59) ; 89 ( -6.67 134.80 18.25 0.66) ; 90 ( -6.67 136.41 18.25 0.44) ; 91 ( -6.67 136.77 18.25 0.44) ; 92 ( -6.30 137.36 18.25 0.44) ; 93 ( -5.35 138.31 18.56 0.44) ; 94 ( -5.20 139.33 18.56 0.44) ; 95 Low | ( 4.77 50.01 3.13 1.11) ; 1, R-1-1-2-2-2 ( 3.88 50.01 3.56 0.59) ; 2 ( 3.44 50.45 3.56 0.59) ; 3 ( 2.71 50.60 3.50 0.59) ; 4 ( 1.38 50.45 3.31 0.74) ; 5 ( 0.05 50.52 2.81 0.88) ; 6 ( -1.20 50.89 2.44 0.81) ; 7 ( -2.38 50.89 2.13 0.81) ; 8 ( -3.56 51.04 2.13 0.81) ; 9 ( -4.81 51.11 2.13 0.81) ; 10 ( -6.13 51.18 2.13 0.81) ; 11 ( -7.09 51.77 1.81 0.88) ; 12 ( -7.24 52.35 3.38 0.96) ; 13 ( -7.31 53.30 3.44 1.03) ; 14 ( -7.24 54.25 3.50 0.74) ; 15 ( -7.31 55.35 3.50 0.74) ; 16 ( -7.39 55.93 3.50 0.74) ; 17 ( -7.61 56.44 3.50 0.74) ; 18 ( -7.98 57.18 3.50 0.74) ; 19 ( -8.42 57.25 3.06 0.74) ; 20 ( -9.30 57.47 1.56 0.81) ; 21 ( -10.41 57.91 1.56 1.55) ; 22 ( -11.44 58.56 1.56 1.69) ; 23 ( -12.47 59.15 0.88 1.69) ; 24 ( -13.21 59.66 0.63 1.33) ; 25 ( -13.72 60.17 0.38 0.81) ; 26 ( -14.61 61.42 0.25 0.88) ; 27 ( -14.98 62.58 -0.06 1.18) ; 28 ( -15.27 63.46 0.25 0.88) ; 29 ( -15.71 64.19 1.00 0.74) ; 30 ( -16.38 65.00 1.63 1.11) ; 31 ( -16.62 65.05 1.63 1.11) ; 32 ( ( -17.04 65.36 2.13 1.62) ; 1, R-1-1-2-2-2-1 ( -17.78 65.14 2.06 1.25) ; 2 ( -18.88 65.58 2.00 0.74) ; 3 ( -19.84 66.02 1.88 0.74) ; 4 ( -20.50 66.68 1.75 0.96) ; 5 ( -21.31 67.77 1.50 0.88) ; 6 ( -22.20 68.87 1.06 1.11) ; 7 ( -22.56 69.75 -0.38 1.03) ; 8 ( -23.30 70.04 -0.69 0.66) ; 9 ( -24.26 70.55 -1.38 0.59) ; 10 ( -25.88 71.06 -1.94 0.52) ; 11 ( -26.91 71.72 -1.38 0.52) ; 12 ( -27.72 72.38 -0.56 0.59) ; 13 ( -28.97 73.33 0.00 0.44) ; 14 ( -30.00 73.04 -0.44 0.59) ; 15 ( -30.45 72.23 -2.88 0.59) ; 16 ( -30.67 71.58 -3.13 0.59) ; 17 ( -31.18 70.55 -3.31 0.44) ; 18 ( -31.77 70.48 -3.56 0.44) ; 19 ( -32.14 70.48 -4.31 1.11) ; 20 ( -32.80 70.48 -4.63 1.69) ; 21 ( -33.54 70.84 -5.19 1.03) ; 22 ( -34.57 71.36 -7.00 0.37) ; 23 ( -35.09 72.09 -7.19 0.37) ; 24 ( -35.16 72.38 -7.19 1.25) ; 25 ( -35.90 73.33 -7.25 1.47) ; 26 ( -36.34 73.91 -7.63 1.18) ; 27 ( -37.15 75.08 -7.63 0.81) ; 28 ( -37.89 76.11 -7.69 0.81) ; 29 ( -38.40 76.77 -7.88 0.81) ; 30 ( -39.36 77.13 -7.00 0.52) ; 31 ( -40.10 77.13 -5.94 0.52) ; 32 ( -41.13 77.20 -5.75 0.52) ; 33 ( -41.65 77.28 -5.75 0.74) ; 34 ( -42.24 77.47 -5.63 0.96) ; 35 ( -42.68 77.69 -5.63 0.59) ; 36 ( -43.64 78.20 -5.63 0.29) ; 37 ( -44.23 78.28 -7.81 0.29) ; 38 ( -43.78 78.64 -8.19 0.29) ; 39 ( -44.37 79.01 -9.00 0.29) ; 40 ( -44.89 79.30 -9.38 0.74) ; 41 ( -45.40 79.74 -9.56 1.77) ; 42 ( -45.70 80.03 -10.00 2.58) ; 43 ( -45.92 80.18 -10.00 2.58) ; 44 ( -46.73 80.47 -10.00 1.33) ; 45 ( -47.25 80.91 -10.38 0.52) ; 46 ( -47.61 80.98 -10.38 0.29) ; 47 ( -48.50 81.57 -10.75 0.29) ; 48 ( -49.16 82.44 -11.00 0.29) ; 49 ( -48.65 82.88 -11.06 1.03) ; 50 ( -49.75 83.39 -11.06 1.84) ; 51 ( -49.82 83.39 -11.06 2.28) ; 52 Low | ( -16.62 65.56 3.50 0.81) ; 1, R-1-1-2-2-2-2 ( -16.77 66.51 3.63 0.81) ; 2 ( -17.36 67.61 4.38 0.66) ; 3 ( -18.10 68.12 4.56 0.81) ; 4 ( -19.13 68.56 4.75 0.81) ; 5 ( -19.72 69.07 5.13 0.96) ; 6 ( -20.23 69.80 5.19 0.81) ; 7 ( -20.82 70.68 5.19 1.18) ; 8 ( -21.12 71.63 5.81 1.18) ; 9 ( -21.63 72.29 7.13 0.81) ; 10 ( -22.37 73.16 8.38 0.74) ; 11 ( -23.03 73.53 9.00 0.88) ; 12 ( -23.40 73.75 9.25 1.11) ; 13 ( -24.06 74.04 9.69 1.33) ; 14 ( -25.02 74.19 10.00 1.33) ; 15 ( -25.69 74.11 10.25 1.33) ; 16 ( -26.79 74.11 10.38 0.66) ; 17 ( -27.31 74.48 11.25 0.96) ; 18 ( -27.75 75.14 11.75 1.11) ; 19 ( -28.93 76.31 11.81 0.81) ; 20 ( -29.30 77.40 12.63 0.66) ; 21 ( -29.59 78.35 12.75 0.52) ; 22 ( -29.96 79.01 12.75 0.52) ; 23 ( -30.77 79.45 12.81 0.52) ; 24 ( -31.28 79.67 12.94 1.03) ; 25 ( -31.95 80.11 13.13 1.62) ; 26 ( -32.83 80.25 13.94 1.03) ; 27 ( -34.23 80.55 14.06 0.52) ; 28 ( -35.34 80.98 14.63 0.37) ; 29 ( -36.29 81.13 14.81 0.37) ; 30 ( -37.47 81.35 15.44 0.66) ; 31 ( -38.21 81.13 15.56 0.66) ; 32 ( -39.09 81.06 15.69 0.29) ; 33 ( -39.54 80.91 16.25 0.29) ; 34 ( -39.90 80.62 16.63 0.52) ; 35 ( -40.49 80.47 16.69 0.52) ; 36 Normal ) ; End of split ) ; End of split ) ; End of split ) ; End of split | ( 0.46 21.78 -12.69 3.39) ; 1, R-1-2 ( -0.13 22.87 -12.94 2.28) ; 2 ( -0.19 23.40 -12.94 2.28) ; 3 ( ( -0.43 23.39 -13.25 1.92) ; 1, R-1-2-1 ( -0.28 24.70 -13.81 2.21) ; 2 ( -0.57 25.73 -13.81 2.14) ; 3 ( -0.87 26.38 -14.38 2.14) ; 4 ( ( -1.68 26.68 -12.69 1.25) ; 1, R-1-2-1-1 ( -2.86 26.75 -11.88 1.11) ; 2 ( -3.37 26.68 -11.19 1.33) ; 3 ( -4.04 26.60 -10.94 1.62) ; 4 ( -4.55 26.46 -10.50 1.92) ; 5 ( -5.44 26.82 -10.50 1.77) ; 6 ( ( -6.84 26.24 -10.50 0.96) ; 1, R-1-2-1-1-1 ( -7.57 25.87 -10.25 0.88) ; 2 ( -8.46 25.51 -10.44 0.74) ; 3 ( -9.27 25.51 -10.50 0.74) ; 4 ( -10.22 25.51 -10.50 1.03) ; 5 ( -11.48 25.94 -10.81 1.03) ; 6 ( -12.29 26.53 -11.94 1.03) ; 7 ( -13.10 27.41 -13.81 0.88) ; 8 ( -14.06 28.36 -14.88 0.88) ; 9 ( -13.91 29.01 -15.50 0.88) ; 10 ( -13.98 29.38 -16.56 1.03) ; 11 ( -14.06 29.89 -18.06 0.81) ; 12 ( -14.64 30.18 -18.38 0.74) ; 13 ( -15.01 30.04 -19.75 0.81) ; 14 ( -15.82 30.04 -20.25 0.81) ; 15 ( -16.71 29.75 -20.25 0.81) ; 16 ( -17.74 29.67 -20.38 0.66) ; 17 ( -18.48 30.26 -20.75 0.66) ; 18 ( -19.36 30.99 -20.75 0.81) ; 19 ( -19.95 31.87 -20.56 1.03) ; 20 ( -20.54 32.23 -20.31 1.03) ; 21 ( -21.42 32.67 -20.06 1.03) ; 22 ( -22.38 33.03 -19.94 1.03) ; 23 ( -23.49 33.18 -19.75 1.18) ; 24 ( -24.22 33.40 -19.44 0.88) ; 25 ( -25.33 33.11 -19.19 0.88) ; 26 ( -26.58 31.94 -19.13 0.96) ; 27 ( -27.32 30.84 -19.19 0.96) ; 28 ( -27.46 29.75 -19.69 1.11) ; 29 ( -27.98 28.65 -20.19 0.96) ; 30 ( -28.27 28.21 -20.56 0.96) ; 31 ( -28.64 27.11 -20.75 0.96) ; 32 ( -29.16 25.73 -21.19 0.81) ; 33 ( -30.04 25.21 -22.38 0.66) ; 34 ( -30.71 25.29 -22.94 0.96) ; 35 ( -31.52 25.36 -23.25 0.96) ; 36 ( -32.10 25.07 -24.06 0.96) ; 37 ( -33.14 25.07 -24.69 0.96) ; 38 ( -34.54 25.29 -24.81 0.96) ; 39 ( -35.35 25.58 -25.19 0.96) ; 40 ( -36.08 25.21 -25.75 0.81) ; 41 ( -36.89 24.77 -26.19 0.81) ; 42 ( -37.78 24.12 -26.44 0.88) ; 43 ( -38.37 23.75 -26.88 0.66) ; 44 ( -38.74 23.68 -28.31 0.66) ; 45 ( -38.88 24.04 -29.13 0.66) ; 46 ( -38.59 24.34 -29.25 0.66) ; 47 ( -38.37 24.70 -30.63 0.66) ; 48 ( -38.81 25.29 -31.25 0.66) ; 49 ( -39.25 24.92 -31.25 0.88) ; 50 ( -40.43 23.75 -31.56 0.66) ; 51 ( -41.17 23.09 -31.63 0.66) ; 52 ( -42.57 23.17 -31.63 0.66) ; 53 ( -43.52 23.31 -31.63 1.03) ; 54 ( -44.56 23.31 -31.88 1.25) ; 55 ( -45.44 23.31 -32.13 1.25) ; 56 ( -46.25 23.39 -32.25 1.11) ; 57 ( -46.74 23.63 -32.38 0.52) ; 58 ( -47.33 24.36 -32.50 0.37) ; 59 ( -47.55 24.87 -32.50 1.03) ; 60 ( -47.77 25.17 -32.50 1.62) ; 61 ( -47.77 25.61 -32.50 2.43) ; 62 ( -47.99 26.34 -32.63 1.62) ; 63 ( -48.58 26.56 -32.63 1.03) ; 64 ( -50.13 27.51 -32.63 0.81) ; 65 ( -50.79 28.31 -32.81 0.88) ; 66 ( -51.60 28.75 -32.81 0.88) ; 67 ( -52.27 29.70 -32.94 1.11) ; 68 ( -52.71 30.43 -32.94 0.81) ; 69 ( -52.93 30.94 -32.94 0.52) ; 70 ( -53.45 31.31 -33.06 0.37) ; 71 ( -53.89 31.31 -32.88 0.66) ; 72 ( -54.55 30.94 -32.38 0.88) ; 73 ( -55.36 30.87 -34.69 1.18) ; 74 ( -56.32 30.87 -35.19 1.18) ; 75 ( -57.06 31.09 -35.19 0.74) ; 76 ( -57.65 31.38 -35.38 0.44) ; 77 ( -58.01 31.82 -35.13 0.44) ; 78 ( -57.94 32.33 -34.38 0.44) ; 79 ( -58.75 32.84 -34.38 0.44) ; 80 ( -60.00 33.35 -34.38 0.44) ; 81 ( -60.81 33.50 -34.38 0.74) ; 82 ( -62.29 33.50 -34.38 1.03) ; 83 ( -63.02 33.43 -34.38 1.03) ; 84 ( -63.91 33.35 -34.38 0.74) ; 85 ( -64.87 33.28 -34.38 0.44) ; 86 ( -65.90 33.13 -34.38 0.44) ; 87 ( -66.93 33.13 -34.38 0.66) ; 88 ( -67.89 32.77 -34.38 0.96) ; 89 ( -68.70 32.40 -34.38 0.96) ; 90 ( -69.87 32.62 -34.38 0.74) ; 91 ( -70.83 32.92 -34.50 0.52) ; 92 ( -71.27 32.84 -35.06 0.37) ; 93 ( -72.16 32.99 -35.06 0.37) ; 94 ( -72.82 33.28 -35.06 0.37) ; 95 ( -73.85 33.43 -35.06 0.37) ; 96 ( -75.11 33.50 -35.31 0.37) ; 97 ( -75.99 33.50 -35.81 0.37) ; 98 ( -76.95 32.92 -35.88 0.37) ; 99 ( -77.46 32.48 -35.88 0.37) ; 100 ( -78.20 31.96 -36.69 1.03) ; 101 ( -78.42 31.75 -37.50 2.06) ; 102 ( -78.94 31.60 -37.88 2.87) ; 103 ( -79.30 31.45 -37.94 3.24) ; 104 ( -79.89 31.01 -38.75 2.80) ; 105 ( -80.26 30.50 -39.50 1.40) ; 106 ( -80.70 30.43 -39.94 0.59) ; 107 ( -81.59 30.36 -40.38 0.37) ; 108 ( -82.77 30.36 -40.75 0.29) ; 109 ( -83.50 30.36 -41.00 0.29) ; 110 ( -84.17 30.14 -41.56 0.66) ; 111 ( -84.76 30.14 -41.56 1.03) ; 112 ( -85.05 30.06 -41.81 1.84) ; 113 ( -85.57 29.92 -42.50 2.21) ; 114 ( -86.08 29.55 -42.94 2.21) ; 115 ( -86.82 29.55 -43.44 1.18) ; 116 ( -87.26 29.26 -42.81 0.44) ; 117 ( -88.07 28.82 -42.38 0.22) ; 118 ( -88.81 28.24 -42.38 0.22) ; 119 ( -89.99 27.73 -42.38 0.22) ; 120 Low | ( -6.59 27.80 -9.06 0.52) ; 1, R-1-2-1-1-2 ( -7.18 28.24 -9.56 0.52) ; 2 ( -7.92 28.60 -10.38 0.66) ; 3 ( -8.58 28.75 -12.19 0.88) ; 4 ( -8.80 28.09 -13.31 0.74) ; 5 ( -8.95 27.36 -14.75 0.59) ; 6 ( -9.32 26.48 -16.44 0.59) ; 7 ( -9.68 26.26 -17.88 0.66) ; 8 ( -10.27 25.68 -19.00 0.66) ; 9 ( -11.01 25.46 -20.13 0.66) ; 10 ( -11.75 25.46 -20.69 0.81) ; 11 ( -12.85 25.82 -21.06 0.88) ; 12 ( -13.51 26.04 -21.69 0.88) ; 13 ( -14.18 26.41 -22.38 0.88) ; 14 ( -14.55 26.41 -23.19 0.66) ; 15 ( -15.06 25.97 -23.56 0.66) ; 16 ( -15.58 25.46 -23.56 0.66) ; 17 ( -15.95 24.73 -23.88 0.66) ; 18 ( -16.54 24.22 -23.25 0.59) ; 19 ( -17.64 23.85 -23.06 0.59) ; 20 ( -18.45 23.63 -23.06 0.59) ; 21 ( -18.97 23.41 -22.94 0.59) ; 22 ( -19.04 22.97 -22.75 0.81) ; 23 ( -18.97 22.32 -21.88 0.66) ; 24 ( -18.45 22.10 -25.94 0.52) ; 25 ( -18.52 22.10 -28.81 0.52) ; 26 ( -18.89 22.24 -30.25 0.52) ; 27 ( -19.34 22.68 -30.19 0.66) ; 28 ( -20.29 22.97 -30.19 0.66) ; 29 ( -21.18 22.46 -30.19 0.66) ; 30 ( -22.06 22.32 -30.56 0.66) ; 31 ( -22.65 22.68 -30.75 0.81) ; 32 ( -23.39 22.75 -31.00 0.81) ; 33 ( -24.42 22.83 -31.25 0.66) ; 34 ( -25.08 22.61 -31.38 0.66) ; 35 ( -25.23 21.73 -31.75 0.81) ; 36 ( -25.52 20.93 -32.44 0.59) ; 37 ( -26.19 20.27 -32.44 0.59) ; 38 ( -27.07 20.34 -32.88 0.59) ; 39 ( -28.32 20.63 -33.81 0.74) ; 40 ( -28.40 20.63 -36.56 0.59) ; 41 ( -28.47 21.07 -37.56 0.59) ; 42 ( -28.62 21.58 -37.81 0.52) ; 43 ( -28.91 22.24 -37.81 0.52) ; 44 ( -29.43 22.97 -37.88 0.52) ; 45 ( -29.80 22.83 -38.81 0.74) ; 46 ( -30.09 22.68 -39.75 1.47) ; 47 ( -30.46 22.46 -40.56 1.84) ; 48 ( -30.90 22.53 -41.31 1.92) ; 49 ( -31.64 22.53 -41.50 1.25) ; 50 ( -32.15 22.75 -41.56 0.59) ; 51 ( -32.60 23.27 -41.81 0.52) ; 52 ( -33.11 23.85 -42.13 0.81) ; 53 ( -33.85 24.22 -42.63 1.18) ; 54 ( -34.22 24.51 -42.81 1.84) ; 55 ( -34.81 25.02 -43.19 1.84) ; 56 ( -35.10 25.17 -43.44 1.84) ; 57 ( -35.40 25.90 -43.44 1.18) ; 58 ( -35.54 26.48 -43.75 0.81) ; 59 ( -35.76 27.29 -43.13 0.59) ; 60 ( -35.69 27.73 -43.50 0.59) ; 61 ( -35.47 28.02 -43.56 0.59) ; 62 ( -35.76 29.19 -43.75 0.81) ; 63 ( -35.99 29.84 -43.75 0.81) ; 64 ( -36.21 30.58 -43.81 0.81) ; 65 ( -36.57 31.53 -43.88 0.52) ; 66 ( -36.80 32.26 -43.94 0.52) ; 67 ( -37.02 32.84 -44.13 0.81) ; 68 ( -37.31 33.79 -44.31 1.18) ; 69 ( -37.83 34.60 -44.44 1.18) ; 70 ( -38.12 35.33 -44.63 0.88) ; 71 ( -38.93 36.42 -44.69 0.59) ; 72 ( -39.23 37.23 -45.69 0.52) ; 73 ( -39.74 38.18 -46.38 0.52) ; 74 ( -40.55 39.79 -46.50 0.44) ; 75 ( -40.92 41.18 -47.31 0.37) ; 76 ( -41.58 41.61 -47.38 0.37) ; 77 ( -42.25 41.61 -47.38 0.52) ; 78 ( -43.21 42.56 -47.31 0.37) ; 79 ( -44.09 42.93 -47.31 1.03) ; 80 ( -44.46 43.22 -47.31 2.14) ; 81 ( -44.97 43.37 -47.31 2.14) ; 82 ( -45.34 43.59 -47.75 1.33) ; 83 ( -46.00 44.32 -47.81 0.52) ; 84 ( -46.52 44.83 -47.81 0.29) ; 85 ( -46.89 45.20 -47.81 0.29) ; 86 ( -47.33 45.71 -47.81 0.29) ; 87 ( -47.85 46.22 -47.81 0.66) ; 88 ( -48.51 46.58 -47.81 1.11) ; 89 ( -49.03 46.80 -47.81 1.11) ; 90 ( -49.61 47.10 -47.81 1.11) ; 91 ( -50.06 47.32 -47.81 0.74) ; 92 ( -50.72 47.90 -47.81 0.44) ; 93 ( -51.16 48.41 -48.13 0.29) ; 94 ( -51.53 49.58 -46.88 0.37) ; 95 ( -51.82 50.02 -46.44 1.11) ; 96 ( -52.12 50.90 -46.44 1.11) ; 97 ( -52.34 51.48 -45.69 0.74) ; 98 ( -52.64 51.92 -46.75 0.44) ; 99 ( -52.93 52.58 -47.19 0.29) ; 100 ( -53.64 53.75 -48.44 0.22) ; 101 ( -54.15 54.19 -49.50 0.22) ; 102 ( -54.81 54.41 -51.00 0.22) ; 103 ( -56.58 54.85 -51.56 0.22) ; 104 ( -57.39 54.78 -49.00 0.88) ; 105 ( -58.06 54.71 -51.00 1.69) ; 106 ( -58.42 54.49 -50.94 2.58) ; 107 ( -59.16 55.00 -50.94 2.58) ; 108 ( -60.12 55.14 -52.50 1.33) ; 109 ( -61.22 54.71 -55.44 0.88) ; 110 ( -61.96 54.34 -55.94 0.59) ; 111 ( -62.62 54.34 -56.63 0.59) ; 112 ( -63.51 54.19 -56.75 0.59) ; 113 ( -64.17 54.19 -56.75 1.03) ; 114 ( -64.98 54.19 -56.75 1.03) ; 115 ( -65.20 54.27 -57.56 0.74) ; 116 ( -66.01 54.85 -58.19 0.74) ; 117 ( -66.75 55.14 -58.38 0.74) ; 118 ( -67.19 55.22 -59.13 0.74) ; 119 Low ) ; End of split | ( -0.46 27.37 -13.81 0.81) ; 1, R-1-2-1-2 ( -0.61 28.03 -14.06 0.52) ; 2 ( -0.83 28.39 -14.25 0.52) ; 3 ( -1.42 28.39 -14.69 0.74) ; 4 ( -2.30 28.69 -14.38 1.03) ; 5 ( -3.34 28.69 -14.06 1.11) ; 6 ( -4.15 28.83 -13.63 0.88) ; 7 ( -4.59 28.76 -13.31 0.59) ; 8 ( -5.47 28.76 -13.69 0.37) ; 9 ( -5.62 28.32 -14.44 0.37) ; 10 ( -5.18 28.39 -16.94 0.96) ; 11 ( -5.03 28.39 -18.13 0.96) ; 12 ( -4.81 28.39 -19.44 0.96) ; 13 ( -4.59 28.39 -21.13 0.96) ; 14 ( -4.44 28.25 -22.25 1.18) ; 15 ( -4.29 28.17 -22.88 1.03) ; 16 ( -3.93 28.91 -23.81 0.81) ; 17 ( -4.15 29.64 -24.69 0.66) ; 18 ( -4.22 30.29 -25.50 0.81) ; 19 ( -4.44 30.88 -26.19 0.81) ; 20 ( -5.40 31.54 -26.56 0.88) ; 21 ( -6.14 32.12 -27.25 1.11) ; 22 ( -7.02 32.85 -28.75 0.96) ; 23 ( -7.98 33.66 -29.44 0.81) ; 24 ( -8.49 34.46 -29.94 0.59) ; 25 ( -8.12 34.90 -32.44 0.88) ; 26 ( -7.83 35.19 -32.56 0.88) ; 27 ( -7.76 35.34 -35.06 1.11) ; 28 ( -8.20 35.41 -35.38 0.74) ; 29 ( -9.08 36.22 -35.44 0.59) ; 30 ( -9.45 36.80 -36.00 0.59) ; 31 ( -9.67 37.82 -36.38 0.88) ; 32 ( -9.67 38.99 -36.63 1.03) ; 33 ( -9.60 39.87 -36.63 0.74) ; 34 ( -9.23 40.53 -37.13 0.44) ; 35 ( -8.94 41.04 -37.19 0.44) ; 36 ( -8.79 41.62 -37.56 0.74) ; 37 ( -8.79 42.36 -37.75 1.11) ; 38 ( -8.94 43.09 -38.31 1.11) ; 39 ( -8.71 44.18 -37.06 0.88) ; 40 ( -8.20 45.28 -38.06 1.03) ; 41 ( -7.61 46.38 -38.31 0.88) ; 42 ( -7.24 47.62 -38.31 0.66) ; 43 ( -7.09 48.71 -38.31 0.59) ; 44 ( -6.65 49.52 -39.06 0.59) ; 45 ( -6.28 50.83 -39.44 0.81) ; 46 ( -6.36 51.71 -40.25 0.81) ; 47 ( -6.43 53.17 -40.25 0.81) ; 48 ( -6.43 54.49 -40.25 0.59) ; 49 ( -5.91 55.29 -40.31 0.88) ; 50 ( -5.91 55.66 -40.75 1.25) ; 51 ( -6.09 56.85 -40.75 0.74) ; 52 ( -6.32 57.88 -40.88 0.44) ; 53 ( -6.32 58.32 -41.06 0.44) ; 54 ( -6.24 59.49 -41.13 0.66) ; 55 ( -6.02 60.80 -41.94 0.88) ; 56 ( -6.02 61.68 -43.31 1.25) ; 57 ( -5.87 62.26 -43.44 1.62) ; 58 ( -5.87 62.34 -44.38 1.99) ; 59 ( -5.80 63.14 -43.63 2.50) ; 60 ( -5.80 63.65 -44.44 1.69) ; 61 ( -5.65 64.02 -45.69 1.03) ; 62 ( -5.36 64.46 -47.75 0.74) ; 63 ( -4.77 64.75 -47.81 0.52) ; 64 ( -4.47 65.26 -48.56 0.37) ; 65 ( -4.03 65.70 -49.38 0.37) ; 66 ( -3.44 66.58 -49.69 0.74) ; 67 ( -3.07 67.45 -50.31 0.88) ; 68 ( -2.78 68.19 -50.75 0.88) ; 69 ( -2.48 68.77 -52.00 0.66) ; 70 ( -2.12 69.50 -52.13 0.59) ; 71 ( -1.38 70.16 -52.50 0.37) ; 72 ( -0.72 70.89 -53.75 0.37) ; 73 ( -0.13 71.47 -53.94 0.66) ; 74 ( 0.54 72.64 -53.00 0.74) ; 75 ( 1.35 73.67 -53.56 0.59) ; 76 ( 1.64 74.40 -54.13 0.59) ; 77 ( 2.16 75.71 -54.38 0.52) ; 78 ( 2.67 77.25 -55.25 0.44) ; 79 ( 2.97 78.49 -55.75 0.37) ; 80 ( 3.19 79.73 -56.13 0.37) ; 81 ( 3.26 80.90 -56.50 0.37) ; 82 ( 3.70 82.07 -56.50 0.37) ; 83 ( 3.93 83.02 -56.50 0.59) ; 84 ( 4.07 84.19 -56.75 0.81) ; 85 ( 4.37 85.29 -57.50 1.25) ; 86 ( 4.54 86.40 -57.88 1.92) ; 87 ( 4.76 87.13 -58.31 2.50) ; 88 ( 5.06 87.79 -58.81 2.14) ; 89 ( 5.13 88.23 -59.06 1.69) ; 90 ( 5.43 88.81 -59.25 1.25) ; 91 ( 5.57 89.25 -59.44 0.81) ; 92 ( 5.72 89.76 -59.63 0.52) ; 93 ( 5.65 91.08 -60.00 0.29) ; 94 ( 5.50 92.40 -60.38 0.29) ; 95 ( 4.91 93.42 -60.50 0.29) ; 96 ( 4.10 94.37 -60.50 0.29) ; 97 ( 3.44 94.95 -60.44 0.29) ; 98 ( 2.85 95.25 -60.00 0.66) ; 99 ( 2.33 95.98 -60.00 0.96) ; 100 ( 1.89 96.42 -60.00 0.66) ; 101 ( 1.52 97.07 -60.06 0.37) ; 102 ( 0.86 98.10 -60.06 0.29) ; 103 ( 0.71 98.97 -60.38 0.29) ; 104 ( 0.56 99.85 -60.63 0.66) ; 105 ( 0.56 100.44 -61.00 1.47) ; 106 ( 0.49 101.39 -61.50 2.21) ; 107 ( 0.49 101.68 -62.13 1.84) ; 108 ( 0.42 102.19 -62.13 1.11) ; 109 ( 0.42 102.70 -62.50 0.74) ; 110 ( 0.34 103.21 -63.06 0.44) ; 111 Low ) ; End of split | ( -0.42 23.55 -15.94 0.44) ; 1, R-1-2-2 ( -0.71 23.69 -18.88 0.44) ; 2 ( -1.30 23.55 -19.63 0.44) ; 3 ( -1.45 22.96 -19.63 0.44) ; 4 ( -2.04 22.16 -21.75 0.52) ; 5 ( -2.33 21.28 -24.31 0.59) ; 6 ( -2.26 20.55 -27.31 0.59) ; 7 ( -1.89 20.18 -29.56 0.59) ; 8 ( -1.89 19.75 -30.13 0.59) ; 9 ( -2.40 19.09 -31.44 0.59) ; 10 ( -3.22 19.02 -31.44 0.59) ; 11 ( -4.25 19.02 -32.69 0.74) ; 12 ( -4.76 19.09 -34.06 0.96) ; 13 ( -5.28 19.09 -36.69 1.18) ; 14 ( -5.35 19.09 -38.00 1.40) ; 15 ( -5.35 19.09 -40.63 1.11) ; 16 ( -5.43 19.02 -41.94 0.81) ; 17 ( -5.35 19.45 -44.31 0.81) ; 18 ( -5.20 20.26 -45.13 0.81) ; 19 ( -5.57 20.48 -45.81 0.81) ; 20 ( -6.16 20.62 -46.56 0.81) ; 21 ( -6.53 20.62 -48.63 0.81) ; 22 ( -7.27 20.18 -50.19 0.81) ; 23 ( -7.56 19.97 -51.25 0.81) ; 24 ( -8.30 19.53 -54.75 0.81) ; 25 ( -8.96 19.60 -56.25 0.81) ; 26 ( -9.62 19.23 -56.25 0.81) ; 27 ( -9.77 18.72 -56.56 0.81) ; 28 ( -10.88 18.21 -58.94 0.81) ; 29 ( -10.95 17.70 -60.00 0.81) ; 30 ( -11.17 16.24 -60.00 0.81) ; 31 ( -10.58 15.65 -60.13 0.81) ; 32 ( -9.85 15.43 -60.94 1.11) ; 33 ( -9.18 14.92 -62.88 1.33) ; 34 ( -8.74 14.34 -62.88 1.33) ; 35 ( -8.08 13.53 -64.19 1.33) ; 36 ( -8.45 12.87 -65.25 1.33) ; 37 ( -7.86 11.92 -66.00 1.33) ; 38 ( -7.27 11.27 -66.25 0.96) ; 39 ( -6.83 10.39 -67.06 0.66) ; 40 ( -6.53 9.88 -65.06 0.44) ; 41 ( -6.31 8.64 -65.00 0.29) ; 42 ( -6.60 7.03 -65.00 0.29) ; 43 ( -6.83 6.00 -65.00 0.29) ; 44 ( -6.90 5.27 -65.56 0.66) ; 45 ( -7.05 4.61 -66.94 0.96) ; 46 ( -7.34 4.03 -67.00 0.96) ; 47 ( -7.93 3.37 -68.31 0.96) ; 48 ( -8.22 3.01 -68.31 0.59) ; 49 ( -7.78 1.84 -68.31 0.59) ; 50 ( -8.08 1.25 -68.75 1.33) ; 51 ( -8.08 0.74 -69.63 1.77) ; 52 ( -8.08 0.74 -70.19 2.21) ; 53 ( -8.30 0.59 -70.94 2.65) ; 54 ( -8.59 -0.21 -73.63 1.03) ; 55 ( -9.04 -0.72 -75.19 0.66) ; 56 ( -9.40 -0.79 -78.25 0.44) ; 57 ( -9.99 -0.72 -80.63 0.74) ; 58 ( -10.21 -0.14 -82.75 1.03) ; 59 Incomplete ) ; End of split ) ; End of split | ( -0.04 20.53 -13.94 1.69) ; 1, R-2 ( -0.99 20.68 -13.94 1.40) ; 2 ( -1.44 20.46 -14.38 1.03) ; 3 ( -2.39 20.24 -13.81 0.74) ; 4 ( -3.13 20.24 -13.69 0.66) ; 5 ( -3.79 20.46 -13.56 0.88) ; 6 ( -4.31 21.41 -13.44 0.74) ; 7 ( -4.60 21.70 -13.38 0.96) ; 8 ( -5.12 21.04 -13.63 0.59) ; 9 ( -5.19 20.60 -14.75 0.59) ; 10 ( -5.56 20.24 -15.50 0.59) ; 11 ( -6.37 19.58 -15.56 0.59) ; 12 ( -6.89 19.07 -15.56 0.88) ; 13 ( -7.48 18.48 -15.75 1.25) ; 14 ( -8.07 17.97 -15.94 1.25) ; 15 ( -8.80 17.61 -16.31 0.88) ; 16 ( -9.91 17.53 -16.63 0.88) ; 17 ( -10.50 17.32 -16.69 0.88) ; 18 ( -11.60 17.24 -16.69 0.88) ; 19 ( -12.27 17.17 -17.88 1.18) ; 20 ( -12.78 16.95 -18.13 1.18) ; 21 ( -13.89 16.95 -18.50 0.96) ; 22 ( -14.84 17.02 -18.88 0.81) ; 23 ( -14.99 16.51 -20.00 0.81) ; 24 ( -15.29 15.85 -20.50 0.88) ; 25 ( -15.95 15.05 -21.38 0.88) ; 26 ( -16.39 14.24 -21.94 0.88) ; 27 ( -17.28 13.59 -21.88 0.88) ; 28 ( -18.16 12.86 -21.88 0.74) ; 29 ( -18.68 12.42 -22.50 0.74) ; 30 ( -19.26 11.98 -22.44 0.74) ; 31 ( -20.15 11.76 -21.75 0.88) ; 32 ( -21.25 11.76 -21.75 0.88) ; 33 ( -21.92 11.76 -21.75 0.88) ; 34 ( -23.10 11.83 -21.81 0.88) ; 35 ( -24.20 11.91 -21.44 0.88) ; 36 ( -24.94 11.91 -21.50 0.88) ; 37 ( -25.60 11.54 -21.94 0.88) ; 38 ( -26.04 11.39 -21.94 1.62) ; 39 ( -26.85 11.32 -22.00 1.92) ; 40 ( -27.44 11.03 -22.06 1.18) ; 41 ( -28.33 10.66 -22.06 0.81) ; 42 ( -28.99 10.37 -22.44 0.59) ; 43 ( -30.17 9.86 -22.75 0.59) ; 44 ( -31.20 9.64 -22.75 0.59) ; 45 ( -31.86 10.15 -23.13 0.59) ; 46 ( -31.72 10.96 -23.25 0.59) ; 47 ( -31.72 11.69 -23.00 0.59) ; 48 ( -32.16 12.71 -23.00 0.66) ; 49 ( -32.67 13.00 -23.19 0.66) ; 50 ( -33.56 13.15 -23.50 0.74) ; 51 ( -34.52 13.15 -24.69 0.74) ; 52 ( -35.25 13.73 -26.06 1.03) ; 53 ( -35.91 14.32 -27.56 0.88) ; 54 ( -36.43 14.54 -27.63 0.66) ; 55 ( -36.87 15.20 -27.88 0.66) ; 56 ( -37.02 16.00 -28.25 0.66) ; 57 ( -36.80 16.88 -28.81 0.59) ; 58 ( -36.65 17.53 -28.88 0.44) ; 59 ( -37.24 17.97 -29.19 0.29) ; 60 ( -37.76 18.27 -29.31 0.29) ; 61 ( -37.76 19.22 -29.50 0.52) ; 62 ( -37.76 19.43 -30.25 0.81) ; 63 ( -38.13 19.80 -31.13 1.11) ; 64 ( -38.57 20.24 -31.31 1.11) ; 65 ( -38.86 20.90 -31.69 0.81) ; 66 ( -38.94 21.63 -31.69 0.52) ; 67 ( -38.79 22.14 -31.69 0.37) ; 68 ( -38.57 23.02 -32.06 0.59) ; 69 ( -38.64 23.53 -32.44 0.88) ; 70 ( -38.79 23.97 -32.44 1.33) ; 71 ( -38.86 24.70 -32.75 1.62) ; 72 ( -38.86 25.28 -32.94 1.62) ; 73 ( -38.94 25.65 -33.25 1.25) ; 74 ( -39.08 26.09 -33.25 0.81) ; 75 ( -39.08 26.53 -33.44 0.52) ; 76 ( -39.23 27.04 -33.50 0.29) ; 77 ( -39.01 27.77 -33.94 0.29) ; 78 ( -39.08 28.50 -34.06 0.52) ; 79 ( -39.16 28.57 -34.69 0.96) ; 80 ( -39.45 28.79 -34.94 1.40) ; 81 ( -40.04 28.94 -35.31 1.40) ; 82 ( -40.92 29.23 -35.88 1.03) ; 83 ( -41.37 29.30 -37.25 0.66) ; 84 ( -42.25 29.67 -37.31 0.44) ; 85 ( -43.36 30.40 -38.06 0.37) ; 86 ( -43.80 30.40 -38.38 0.37) ; 87 ( -44.68 30.55 -38.69 0.37) ; 88 ( -45.12 30.55 -38.69 0.74) ; 89 ( -46.16 30.40 -38.75 0.96) ; 90 ( -47.08 30.26 -39.25 0.96) ; 91 ( -48.04 30.40 -39.25 1.18) ; 92 ( -48.92 30.40 -39.63 1.18) ; 93 ( -49.80 30.48 -39.63 1.47) ; 94 ( -50.54 30.62 -38.81 0.66) ; 95 ( -51.35 30.77 -38.50 0.37) ; 96 ( -52.60 31.28 -38.50 0.37) ; 97 ( -53.34 31.21 -37.63 0.74) ; 98 ( -54.22 31.21 -36.69 0.96) ; 99 ( -54.74 31.43 -35.75 0.74) ; 100 ( -55.55 31.72 -35.38 0.59) ; 101 ( -56.36 31.87 -34.56 0.44) ; 102 ( -56.88 32.74 -33.75 0.44) ; 103 ( -57.47 33.55 -33.50 0.52) ; 104 ( -58.06 34.06 -33.06 0.52) ; 105 ( -58.94 34.50 -32.81 0.52) ; 106 ( -59.75 35.08 -32.25 1.47) ; 107 ( -60.49 35.38 -32.19 2.14) ; 108 ( -60.86 35.89 -32.19 2.14) ; 109 ( -61.59 36.33 -32.13 1.33) ; 110 ( -62.11 36.84 -32.13 0.59) ; 111 ( -63.07 37.50 -32.19 0.44) ; 112 ( -64.10 37.79 -32.69 0.44) ; 113 ( -64.91 37.79 -32.81 0.44) ; 114 ( -65.72 37.71 -32.81 0.44) ; 115 ( -66.45 37.64 -32.94 0.44) ; 116 ( -67.26 37.93 -32.94 0.37) ; 117 ( -67.78 38.01 -33.19 1.11) ; 118 ( -68.30 38.01 -33.75 1.47) ; 119 ( -68.89 38.01 -33.75 1.84) ; 120 ( -69.55 38.01 -33.88 1.11) ; 121 ( -70.06 38.01 -34.06 0.74) ; 122 ( -70.51 37.93 -34.19 0.37) ; 123 ( -71.32 37.50 -34.63 0.15) ; 124 Low ) ; End of split ) ; End of tree ( (Color DarkYellow) (Dendrite) ( 14.76 2.09 -2.75 4.79) ; Root ( 17.05 2.52 -2.94 3.32) ; 1, R ( 18.15 2.52 -2.94 3.09) ; 2 ( 20.29 3.77 -2.56 3.39) ; 3 ( 21.39 3.99 -2.31 4.20) ; 4 ( ( 22.50 4.86 -3.75 3.90) ; 1, R-1 ( 23.60 4.57 -4.69 3.32) ; 2 ( 24.78 4.21 -5.06 2.73) ; 3 ( 26.33 4.13 -3.81 2.73) ; 4 ( 27.14 3.77 -3.44 3.24) ; 5 ( ( 28.83 4.13 -1.88 2.36) ; 1, R-1-1 ( 30.16 3.91 -1.06 2.36) ; 2 ( 31.41 3.91 0.25 2.06) ; 3 ( 32.52 4.06 2.50 1.84) ; 4 ( ( 33.55 4.57 3.50 1.33) ; 1, R-1-1-1 ( 34.21 5.52 3.75 0.96) ; 2 ( 34.95 6.25 3.75 0.81) ; 3 ( 35.46 7.35 4.00 0.96) ; 4 ( 35.76 7.79 4.25 1.25) ; 5 ( 35.98 8.15 4.00 1.25) ; 6 ( ( 36.05 9.47 3.56 0.74) ; 1, R-1-1-1-1 ( 36.72 10.27 3.25 0.74) ; 2 ( 37.31 11.30 3.25 0.88) ; 3 ( 37.67 11.88 3.25 0.88) ; 4 ( 37.97 13.05 3.31 0.88) ; 5 ( 38.48 14.07 4.44 0.81) ; 6 ( 39.00 14.73 4.50 0.81) ; 7 ( 40.10 15.39 4.75 0.81) ; 8 ( 41.06 16.19 4.81 0.66) ; 9 ( 41.58 16.92 5.06 0.66) ; 10 ( 41.80 18.17 5.56 0.74) ; 11 ( 41.95 19.26 5.19 0.88) ; 12 ( 42.54 20.21 4.56 0.88) ; 13 ( 43.13 21.16 4.44 0.96) ; 14 ( 43.13 22.55 4.13 1.11) ; 15 ( 43.42 23.36 4.13 0.96) ; 16 ( 43.64 24.09 4.06 0.81) ; 17 ( 43.79 24.82 3.81 0.81) ; 18 ( 44.23 25.77 4.19 0.81) ; 19 ( 44.82 26.35 4.13 0.96) ; 20 ( 45.41 27.23 3.88 0.96) ; 21 ( 45.48 28.33 3.31 0.81) ; 22 ( 45.34 29.64 2.69 0.88) ; 23 ( 45.26 31.29 1.94 0.96) ; 24 ( 46.07 32.38 1.75 0.81) ; 25 ( 46.29 33.48 2.88 1.03) ; 26 ( 46.58 34.28 3.56 1.33) ; 27 ( 46.29 35.31 4.06 1.11) ; 28 ( 46.58 36.40 5.31 0.88) ; 29 ( 46.73 37.13 5.38 0.88) ; 30 ( 47.03 37.65 5.38 0.88) ; 31 ( 47.32 38.16 6.00 1.11) ; 32 ( 47.32 38.96 6.31 0.88) ; 33 ( 47.40 39.91 6.50 0.81) ; 34 ( 47.76 40.86 6.81 0.96) ; 35 ( 48.57 42.18 6.50 0.88) ; 36 ( 49.53 43.13 6.50 0.81) ; 37 ( 50.64 43.93 6.44 0.74) ; 38 ( 51.96 45.25 5.75 0.59) ; 39 ( 52.99 45.25 5.38 0.52) ; 40 ( 53.29 45.83 5.13 0.52) ; 41 ( 53.58 46.78 5.13 0.52) ; 42 ( 53.73 47.29 5.13 1.33) ; 43 ( 54.25 47.88 4.75 1.33) ; 44 ( 54.98 48.17 4.38 0.81) ; 45 ( 56.01 48.39 4.19 0.59) ; 46 ( 56.68 47.73 3.94 0.81) ; 47 ( 57.56 47.29 3.94 0.81) ; 48 ( 58.15 47.08 3.44 0.81) ; 49 ( 58.81 46.78 3.44 0.66) ; 50 ( 59.55 46.71 2.94 0.52) ; 51 ( 59.55 46.34 1.94 0.37) ; 52 ( 59.33 45.98 1.25 0.37) ; 53 ( 59.11 45.32 1.25 0.37) ; 54 ( 59.04 44.88 0.38 0.37) ; 55 ( 59.18 44.66 -0.25 0.37) ; 56 ( 59.62 44.37 -0.44 0.37) ; 57 ( 60.14 44.59 -0.44 0.37) ; 58 ( 60.88 44.96 -0.44 0.59) ; 59 ( 61.76 45.17 -1.00 0.88) ; 60 ( 61.98 45.25 -1.13 1.25) ; 61 ( 62.57 45.32 -2.50 1.55) ; 62 ( 63.16 45.25 -2.50 1.55) ; 63 ( 63.46 44.81 -3.13 1.33) ; 64 ( 63.68 44.52 -3.94 1.03) ; 65 ( 63.97 43.79 -5.56 0.81) ; 66 ( 64.12 42.91 -6.81 0.66) ; 67 ( 64.19 42.54 -7.06 0.96) ; 68 ( 64.34 42.47 -7.69 1.25) ; 69 ( 65.00 41.89 -8.25 0.59) ; 70 ( 65.59 41.74 -8.44 0.59) ; 71 ( 65.96 41.45 -9.31 0.88) ; 72 ( 66.40 41.01 -9.63 1.25) ; 73 ( 66.77 40.72 -10.69 1.62) ; 74 ( 67.73 40.13 -11.44 0.81) ; 75 ( 68.10 39.69 -11.56 0.52) ; 76 ( 68.47 38.89 -12.44 0.81) ; 77 ( 68.83 38.30 -13.31 1.47) ; 78 ( 68.83 38.01 -14.13 2.28) ; 79 ( 69.20 37.35 -15.56 1.40) ; 80 ( 69.20 36.55 -16.06 0.96) ; 81 ( 69.13 35.53 -16.06 0.52) ; 82 ( 69.05 34.14 -16.69 0.52) ; 83 ( 69.28 33.70 -15.63 0.96) ; 84 ( 69.42 32.89 -16.00 0.66) ; 85 ( 69.05 32.09 -15.75 0.29) ; 86 ( 68.76 31.43 -17.19 0.29) ; 87 ( 68.10 30.63 -18.06 0.29) ; 88 ( 67.66 30.34 -18.13 0.29) ; 89 ( 67.14 29.82 -19.00 0.66) ; 90 ( 67.07 29.60 -20.13 1.40) ; 91 ( 67.07 29.02 -20.75 1.77) ; 92 Low | ( 36.49 8.63 4.38 0.81) ; 1, R-1-1-1-2 ( 37.23 8.92 4.75 0.81) ; 2 ( 38.19 8.84 5.25 0.74) ; 3 ( 39.29 8.70 6.06 0.74) ; 4 ( 40.03 9.06 7.19 0.88) ; 5 ( 40.69 9.87 8.13 0.88) ; 6 ( 40.99 10.89 8.38 0.88) ; 7 ( 41.28 11.77 8.50 0.66) ; 8 ( 41.72 12.50 8.69 0.66) ; 9 ( 42.53 13.38 8.31 0.81) ; 10 ( 43.27 13.67 7.56 0.81) ; 11 ( 43.56 14.18 7.06 0.66) ; 12 ( 44.37 14.11 9.75 0.66) ; 13 ( 45.55 14.33 10.00 0.66) ; 14 ( 46.73 14.77 10.63 0.66) ; 15 ( 47.32 15.28 9.13 0.96) ; 16 ( 47.62 16.45 8.06 0.81) ; 17 ( 47.84 17.32 10.63 1.11) ; 18 ( 48.13 18.05 11.00 0.88) ; 19 ( 49.16 18.71 11.25 0.74) ; 20 ( 49.83 18.93 10.13 0.74) ; 21 ( 50.86 19.37 9.88 0.96) ; 22 ( 51.67 20.03 10.50 1.03) ; 23 ( 52.77 20.54 9.81 1.47) ; 24 ( 53.88 21.13 9.38 1.77) ; 25 ( 54.69 21.34 9.00 1.03) ; 26 ( 55.13 21.64 9.44 0.74) ; 27 ( 55.72 22.08 9.44 0.59) ; 28 ( 56.75 22.73 9.63 0.81) ; 29 ( 57.64 22.81 9.63 0.81) ; 30 ( 58.52 22.59 9.69 0.66) ; 31 ( 59.70 22.37 11.31 0.81) ; 32 ( 61.25 22.66 11.38 0.81) ; 33 ( 61.91 22.88 12.31 1.03) ; 34 ( 62.65 23.10 12.63 1.03) ; 35 ( 63.38 23.61 13.00 1.25) ; 36 ( 64.27 23.76 13.13 1.55) ; 37 ( 64.86 24.05 13.25 1.55) ; 38 ( 65.52 24.27 13.63 0.81) ; 39 ( 66.48 24.41 14.00 0.66) ; 40 ( 67.36 24.56 14.50 0.88) ; 41 ( 68.47 24.56 14.00 0.88) ; 42 ( 69.13 24.85 13.50 1.03) ; 43 ( 70.31 25.15 14.63 0.81) ; 44 ( 71.41 25.29 14.69 0.81) ; 45 ( 72.44 25.29 14.88 0.81) ; 46 ( 73.25 25.07 15.25 0.74) ; 47 ( 74.14 25.36 15.25 0.74) ; 48 ( 75.10 25.66 14.94 0.74) ; 49 ( 76.20 26.10 16.06 0.81) ; 50 ( 77.38 25.73 16.63 0.96) ; 51 ( 78.49 25.51 16.69 0.88) ; 52 ( 79.22 24.93 17.19 0.81) ; 53 ( 80.03 23.90 17.69 0.96) ; 54 ( 80.55 23.46 16.69 1.18) ; 55 ( 82.02 22.66 16.69 0.81) ; 56 ( 82.61 22.59 16.06 0.81) ; 57 ( 83.57 22.22 17.56 1.03) ; 58 ( 84.31 22.00 17.94 1.33) ; 59 ( 84.89 21.71 17.38 1.33) ; 60 ( 85.48 21.56 17.00 0.96) ; 61 ( 86.07 21.64 17.00 0.66) ; 62 ( 86.74 21.56 17.00 0.66) ; 63 ( 87.47 21.34 17.00 0.66) ; 64 ( 88.14 21.49 16.25 0.66) ; 65 ( 88.73 21.86 17.19 0.52) ; 66 ( 88.95 22.44 19.00 0.52) ; 67 ( 89.02 23.03 19.31 0.52) ; 68 ( 90.13 22.59 19.63 0.52) ; 69 Low ) ; End of split | ( 32.59 4.24 3.06 0.81) ; 1, R-1-1-2 ( 32.59 4.46 4.56 0.81) ; 2 ( 32.51 5.19 5.75 0.66) ; 3 ( 32.81 5.85 8.06 0.74) ; 4 ( 33.25 6.65 8.88 0.66) ; 5 ( 33.54 7.24 10.19 0.66) ; 6 ( 34.35 7.60 10.88 0.66) ; 7 ( 35.83 7.60 12.69 1.03) ; 8 ( 36.79 8.41 13.38 0.88) ; 9 ( 37.38 9.06 14.25 0.74) ; 10 ( 37.38 9.94 14.50 0.74) ; 11 ( 37.38 10.67 15.25 0.74) ; 12 ( 37.67 11.70 15.38 0.96) ; 13 ( 38.11 12.50 15.44 0.81) ; 14 ( 38.70 13.52 15.75 0.66) ; 15 ( 39.07 14.40 16.00 0.66) ; 16 ( 39.36 15.72 16.63 0.88) ; 17 ( 39.95 16.23 17.38 1.11) ; 18 ( 40.54 16.96 17.44 1.11) ; 19 ( 41.28 17.18 17.50 0.81) ; 20 ( 42.68 17.69 17.63 0.59) ; 21 ( 43.79 18.13 17.88 0.52) ; 22 ( 44.30 19.08 18.38 0.81) ; 23 ( ( 45.41 19.74 19.00 0.29) ; 1, R-1-1-2-1 ( 46.14 19.96 19.31 0.29) ; 2 ( 47.10 19.96 19.44 0.29) ; 3 ( 47.47 19.81 19.44 0.29) ; 4 ( 47.98 18.86 19.44 0.22) ; 5 ( 48.72 17.69 19.50 0.22) ; 6 ( 49.24 17.25 19.50 0.22) ; 7 ( 50.27 16.59 19.50 0.22) ; 8 Low | ( 43.42 20.17 18.31 0.52) ; 1, R-1-1-2-2 ( 43.34 20.76 18.31 0.44) ; 2 ( 42.97 20.91 17.88 0.44) ; 3 ( 42.39 21.78 17.63 0.37) ; 4 ( 41.87 22.15 17.69 0.22) ; 5 ( 41.87 22.95 17.50 0.22) ; 6 ( 41.94 23.61 17.19 0.22) ; 7 ( 41.57 24.20 16.44 0.22) ; 8 ( 41.28 24.85 16.13 0.22) ; 9 ( 40.84 25.36 14.75 0.22) ; 10 ( 40.03 25.73 13.94 0.22) ; 11 ( 38.85 25.80 13.00 0.22) ; 12 ( 38.55 25.51 14.13 0.44) ; 13 Low ) ; End of split ) ; End of split | ( 26.99 3.44 -3.69 2.28) ; 1, R-1-2 ( 26.99 2.41 -4.88 1.40) ; 2 ( 27.65 1.75 -6.19 0.88) ; 3 ( 27.58 0.95 -3.31 1.47) ; 4 ( 28.17 0.15 -2.00 1.33) ; 5 ( 28.72 -0.61 -1.31 1.33) ; 6 ( 29.97 -1.12 -0.44 1.47) ; 7 ( 30.78 -1.12 -0.31 1.77) ; 8 ( 31.96 -1.78 0.63 1.69) ; 9 ( ( 32.84 -1.85 0.88 1.69) ; 1, R-1-2-1 ( 33.65 -2.07 1.75 1.47) ; 2 ( 34.68 -2.43 1.88 1.62) ; 3 ( 35.86 -3.09 2.31 1.62) ; 4 ( 37.11 -3.09 2.94 2.21) ; 5 ( 38.00 -3.38 2.38 2.43) ; 6 ( 38.44 -3.38 1.94 2.58) ; 7 ( ( 38.88 -4.26 1.00 1.47) ; 1, R-1-2-1-1 ( 39.69 -4.70 0.75 1.11) ; 2 ( 40.72 -5.36 0.25 0.96) ; 3 ( 41.68 -5.80 -0.38 0.96) ; 4 ( 42.42 -6.24 -0.50 1.18) ; 5 ( 43.45 -6.67 -1.38 1.33) ; 6 ( 44.19 -7.26 -1.94 1.18) ; 7 ( 45.07 -7.92 -2.75 1.03) ; 8 ( 45.59 -8.50 -3.31 1.18) ; 9 ( 46.62 -9.31 -4.19 1.18) ; 10 ( 47.13 -10.11 -4.25 1.33) ; 11 ( 47.94 -11.35 -5.00 1.33) ; 12 ( 48.24 -12.16 -4.38 1.47) ; 13 ( 48.61 -13.55 -4.38 1.33) ; 14 ( 49.27 -14.28 -4.63 1.55) ; 15 ( 49.42 -15.15 -3.56 1.55) ; 16 ( 49.79 -15.74 -3.56 1.25) ; 17 ( 49.71 -16.47 -3.50 1.11) ; 18 ( 50.01 -16.98 -3.50 1.11) ; 19 ( 49.86 -18.59 -3.50 1.03) ; 20 ( 49.71 -19.76 -3.56 1.18) ; 21 ( 49.49 -20.93 -4.44 1.03) ; 22 ( 49.27 -21.95 -4.44 0.96) ; 23 ( 49.57 -22.61 -4.94 0.96) ; 24 ( 49.86 -23.71 -5.19 1.11) ; 25 ( 50.38 -24.73 -5.81 1.33) ; 26 ( 50.82 -25.75 -7.06 1.11) ; 27 ( 51.41 -26.34 -7.13 1.11) ; 28 ( 52.22 -27.07 -7.38 1.40) ; 29 ( 53.40 -27.95 -8.00 1.40) ; 30 ( 54.50 -28.68 -8.31 1.62) ; 31 ( 55.83 -29.26 -8.75 1.25) ; 32 ( 57.04 -30.17 -8.81 0.96) ; 33 ( 58.00 -31.04 -9.06 1.11) ; 34 ( 58.81 -32.14 -8.63 1.62) ; 35 ( 59.25 -33.09 -8.56 2.36) ; 36 ( 59.84 -34.04 -8.19 2.14) ; 37 ( 60.35 -34.77 -8.63 1.47) ; 38 ( 60.79 -35.72 -9.56 1.18) ; 39 ( 61.31 -36.38 -9.88 1.03) ; 40 ( 61.97 -37.47 -10.50 0.88) ; 41 ( 62.42 -38.43 -11.06 0.88) ; 42 ( 62.71 -39.59 -11.06 0.88) ; 43 ( 63.15 -40.11 -11.94 1.18) ; 44 ( 63.59 -40.62 -12.19 1.47) ; 45 ( ( 64.40 -41.20 -12.19 0.66) ; 1, R-1-2-1-1-1 ( 65.29 -42.01 -12.75 0.59) ; 2 ( 65.58 -42.30 -12.81 0.59) ; 3 ( 66.03 -42.52 -13.13 0.59) ; 4 ( 66.47 -43.18 -13.06 0.88) ; 5 ( 66.47 -44.05 -13.38 1.03) ; 6 ( 66.84 -45.00 -13.19 1.03) ; 7 ( 66.98 -45.88 -12.69 1.03) ; 8 ( 67.35 -46.90 -12.31 1.03) ; 9 ( 67.28 -47.85 -12.31 0.81) ; 10 ( 67.57 -48.73 -12.31 0.59) ; 11 ( 67.94 -49.24 -12.31 0.44) ; 12 ( 68.90 -49.39 -12.44 0.59) ; 13 ( 69.93 -49.83 -12.56 0.74) ; 14 ( 70.81 -49.97 -12.56 0.74) ; 15 ( 71.33 -50.34 -12.88 0.74) ; 16 ( 72.36 -50.41 -12.63 1.03) ; 17 ( 72.88 -50.63 -11.44 0.74) ; 18 ( 73.54 -51.07 -11.00 0.66) ; 19 ( 74.87 -51.51 -11.00 0.66) ; 20 ( 75.53 -51.88 -10.44 0.66) ; 21 ( 76.19 -52.61 -9.56 1.25) ; 22 ( 76.93 -53.26 -9.31 1.03) ; 23 ( 77.52 -53.92 -8.88 0.74) ; 24 ( 78.18 -54.51 -8.81 0.74) ; 25 ( 78.62 -55.09 -8.88 0.74) ; 26 ( 79.14 -55.60 -8.56 0.74) ; 27 ( 79.73 -55.53 -8.19 0.59) ; 28 ( 80.61 -55.60 -8.00 0.59) ; 29 ( 81.20 -55.60 -8.00 0.88) ; 30 ( 82.09 -55.82 -7.88 0.88) ; 31 ( 82.38 -55.82 -7.56 0.88) ; 32 ( 82.97 -56.04 -7.06 0.66) ; 33 ( 82.90 -56.85 -5.88 0.88) ; 34 ( 83.26 -57.80 -5.50 0.88) ; 35 ( 83.71 -59.19 -5.56 0.81) ; 36 ( 84.12 -60.16 -5.56 0.88) ; 37 ( 84.71 -61.03 -5.75 0.88) ; 38 ( 85.15 -61.98 -5.94 1.03) ; 39 ( 85.52 -62.64 -6.25 1.25) ; 40 ( 85.60 -63.59 -7.13 0.81) ; 41 ( 85.52 -64.10 -6.44 0.52) ; 42 ( 85.15 -64.54 -4.88 0.52) ; 43 ( 84.78 -64.76 -4.56 0.52) ; 44 ( 84.86 -65.35 -4.13 0.52) ; 45 ( 85.52 -66.01 -3.50 0.52) ; 46 ( 85.45 -67.03 -3.44 0.52) ; 47 ( 85.52 -68.20 -3.56 1.03) ; 48 ( 85.45 -69.22 -3.94 0.81) ; 49 ( 85.67 -70.54 -4.06 0.59) ; 50 ( 85.60 -71.85 -5.44 0.52) ; 51 ( 85.74 -72.58 -6.69 0.52) ; 52 ( 86.11 -72.95 -6.81 0.74) ; 53 ( 86.99 -73.61 -6.13 0.74) ; 54 ( 87.51 -74.48 -4.06 0.59) ; 55 ( 87.88 -75.22 -3.56 1.33) ; 56 ( 87.95 -75.36 -3.44 1.69) ; 57 ( 88.25 -76.31 -2.81 1.69) ; 58 ( 88.25 -77.04 -2.75 1.25) ; 59 ( 88.32 -77.70 -2.69 0.66) ; 60 ( 88.62 -78.58 -2.25 0.29) ; 61 ( 88.76 -79.24 -2.06 0.29) ; 62 ( 88.39 -80.11 -1.94 0.59) ; 63 ( 87.88 -81.28 -1.94 0.74) ; 64 ( 87.95 -82.31 -1.88 0.74) ; 65 ( 87.95 -83.04 -1.75 0.74) ; 66 ( 88.10 -84.35 -1.38 0.59) ; 67 ( 88.62 -85.23 -2.75 0.66) ; 68 ( 89.13 -86.18 -3.00 0.88) ; 69 ( 89.50 -87.28 -3.25 0.88) ; 70 ( 90.16 -88.08 -3.44 0.88) ; 71 ( 90.59 -88.89 -4.56 1.11) ; 72 ( 91.47 -89.48 -5.31 1.40) ; 73 ( 92.14 -89.62 -5.50 0.88) ; 74 ( 92.65 -89.77 -5.75 0.52) ; 75 ( 93.46 -90.06 -6.50 0.81) ; 76 ( 93.91 -90.43 -7.69 0.66) ; 77 ( 95.23 -91.30 -8.63 0.66) ; 78 ( 95.82 -92.18 -10.06 0.66) ; 79 ( 96.26 -92.47 -10.31 0.52) ; 80 ( 96.78 -93.72 -10.88 0.52) ; 81 ( 96.85 -95.25 -10.88 0.52) ; 82 ( 97.07 -96.06 -10.88 0.52) ; 83 Low | ( 63.61 -41.69 -14.06 0.66) ; 1, R-1-2-1-1-2 ( 63.97 -42.35 -14.13 0.59) ; 2 ( 64.56 -42.86 -14.38 0.74) ; 3 ( 65.67 -43.23 -14.75 0.59) ; 4 ( 66.26 -43.59 -15.00 0.59) ; 5 ( 67.07 -44.03 -14.94 0.74) ; 6 ( 67.73 -44.62 -15.13 0.74) ; 7 ( 68.69 -45.42 -15.13 1.25) ; 8 ( 69.13 -45.64 -15.19 1.92) ; 9 ( 69.50 -46.00 -15.19 1.92) ; 10 ( 69.94 -46.81 -15.19 0.88) ; 11 ( 70.60 -47.17 -15.38 0.74) ; 12 ( 71.78 -47.83 -15.06 0.59) ; 13 ( 72.45 -47.91 -14.50 0.52) ; 14 ( 73.33 -47.39 -13.94 0.44) ; 15 ( 73.92 -47.39 -13.75 0.44) ; 16 ( 74.66 -47.76 -13.50 0.66) ; 17 ( 75.10 -48.20 -13.63 0.88) ; 18 ( 75.61 -48.93 -13.88 0.88) ; 19 ( 76.20 -49.66 -13.88 0.74) ; 20 ( 76.72 -50.10 -13.88 0.81) ; 21 ( 77.31 -50.90 -13.88 0.66) ; 22 ( 77.97 -51.63 -13.88 0.66) ; 23 ( 78.34 -52.36 -13.81 0.59) ; 24 ( 79.08 -53.39 -13.81 0.74) ; 25 ( 79.89 -54.19 -13.63 0.81) ; 26 ( 80.11 -55.00 -14.69 0.96) ; 27 ( 80.11 -55.65 -14.81 0.59) ; 28 ( 80.18 -56.31 -14.81 0.59) ; 29 ( 80.77 -56.82 -15.31 0.59) ; 30 ( 81.36 -57.41 -14.06 0.59) ; 31 ( 81.88 -57.92 -14.06 0.81) ; 32 ( 82.61 -58.50 -13.88 1.03) ; 33 ( 83.42 -59.16 -13.81 0.81) ; 34 ( 84.01 -59.82 -13.81 0.66) ; 35 ( 84.68 -60.55 -14.00 0.59) ; 36 ( 85.19 -61.50 -14.44 0.74) ; 37 ( 86.00 -62.09 -14.50 0.81) ; 38 ( 86.59 -62.60 -14.81 0.81) ; 39 ( 86.89 -63.26 -15.06 0.81) ; 40 ( 87.62 -64.35 -15.19 0.81) ; 41 ( 88.29 -65.23 -15.94 0.66) ; 42 ( 88.43 -66.25 -16.50 0.96) ; 43 ( 88.80 -66.69 -17.31 1.33) ; 44 ( 89.24 -67.42 -17.31 1.03) ; 45 ( 89.61 -67.72 -17.56 0.59) ; 46 ( 90.35 -67.93 -17.56 0.44) ; 47 ( 90.86 -68.01 -17.56 0.44) ; 48 ( 91.53 -68.37 -17.56 0.44) ; 49 ( 92.19 -68.88 -17.56 0.66) ; 50 ( 92.63 -69.54 -18.06 0.88) ; 51 ( 93.15 -70.13 -19.13 0.81) ; 52 ( 93.88 -70.69 -19.25 1.33) ; 53 ( 94.25 -71.72 -20.56 1.62) ; 54 ( 95.36 -72.59 -20.56 1.84) ; 55 ( 96.46 -73.03 -20.56 1.11) ; 56 ( 97.27 -73.40 -20.69 0.74) ; 57 ( 98.16 -73.91 -20.88 0.44) ; 58 ( 98.82 -74.05 -20.94 0.44) ; 59 ( 98.89 -74.79 -21.13 0.44) ; 60 ( 98.97 -75.08 -21.25 0.81) ; 61 ( 99.12 -75.74 -21.50 0.59) ; 62 ( 99.34 -76.17 -21.50 0.37) ; 63 ( 100.07 -76.83 -21.69 0.37) ; 64 ( 100.74 -77.56 -22.13 0.37) ; 65 ( 101.47 -77.93 -22.31 0.59) ; 66 ( 101.99 -78.44 -22.81 0.88) ; 67 ( 102.65 -79.17 -22.94 1.18) ; 68 ( 103.31 -80.05 -23.06 1.18) ; 69 ( 103.76 -80.85 -23.56 0.81) ; 70 ( 104.20 -81.73 -24.25 0.52) ; 71 ( 104.64 -82.31 -24.19 0.52) ; 72 ( 104.86 -83.41 -25.56 0.88) ; 73 ( 104.79 -83.92 -26.19 1.77) ; 74 ( 104.71 -84.51 -26.50 2.65) ; 75 ( 105.08 -85.31 -26.88 2.65) ; 76 ( 105.60 -85.90 -27.13 1.84) ; 77 ( 106.11 -86.85 -27.38 0.96) ; 78 ( 106.70 -87.29 -27.38 0.59) ; 79 ( 107.29 -87.72 -27.44 0.59) ; 80 ( 108.40 -88.16 -27.50 0.88) ; 81 ( 109.21 -88.75 -27.50 0.88) ; 82 ( 109.80 -88.75 -28.06 0.44) ; 83 ( 110.76 -88.75 -28.56 0.44) ; 84 ( 110.83 -88.53 -31.94 0.81) ; 85 Low ) ; End of split | ( 39.34 -2.61 1.81 1.33) ; 1, R-1-2-1-2 ( 40.44 -1.95 1.81 0.81) ; 2 ( 41.03 -1.66 1.63 0.81) ; 3 ( 42.06 -1.08 2.00 0.88) ; 4 ( 43.54 -0.64 2.38 0.96) ; 5 ( 45.30 -0.49 2.44 1.03) ; 6 ( 46.63 -0.56 2.88 0.96) ; 7 ( 48.10 -0.34 2.69 0.96) ; 8 ( 49.36 -0.27 2.31 0.96) ; 9 ( 50.24 -0.27 1.94 1.18) ; 10 ( 51.35 -0.49 1.94 1.03) ; 11 ( 52.45 -0.56 2.13 0.88) ; 12 ( 53.26 -0.71 2.38 0.88) ; 13 ( 54.29 -0.34 2.63 0.96) ; 14 ( 55.18 0.24 3.38 0.81) ; 15 ( 55.84 0.68 3.69 0.88) ; 16 ( 56.80 0.83 4.19 0.88) ; 17 ( 57.90 0.97 4.19 1.11) ; 18 ( 58.64 0.68 4.19 1.11) ; 19 ( 59.67 0.24 4.19 1.11) ; 20 ( 60.92 0.02 4.19 1.11) ; 21 ( 62.03 -0.13 4.13 1.11) ; 22 ( 63.72 0.31 3.88 1.25) ; 23 ( 65.05 1.41 3.88 1.25) ; 24 ( 66.01 2.29 4.31 0.81) ; 25 ( 66.96 3.24 5.19 0.74) ; 26 ( 68.07 3.53 5.44 0.96) ; 27 ( 68.95 3.97 5.44 1.33) ; 28 ( 70.06 4.19 5.56 1.11) ; 29 ( 70.87 4.41 5.75 1.11) ; 30 ( 71.83 4.55 6.13 1.33) ; 31 ( 72.93 4.26 6.69 0.96) ; 32 ( 74.33 4.19 6.75 0.96) ; 33 ( 75.51 4.26 7.31 0.96) ; 34 ( 76.54 4.55 7.69 1.18) ; 35 ( 77.94 5.21 7.94 0.96) ; 36 ( 78.97 5.50 8.19 0.96) ; 37 ( 80.45 5.65 8.44 0.96) ; 38 ( 82.44 5.94 8.44 1.11) ; 39 ( 84.72 5.72 8.50 1.03) ; 40 ( 86.27 5.58 8.56 0.88) ; 41 ( 86.98 5.29 9.31 0.88) ; 42 ( 88.16 4.63 7.69 1.03) ; 43 ( 88.90 3.68 7.38 0.81) ; 44 ( 89.19 2.73 6.81 0.81) ; 45 ( 89.64 2.00 6.63 0.81) ; 46 ( 90.08 0.90 6.00 0.81) ; 47 ( 90.59 -0.56 6.00 0.66) ; 48 ( 91.18 -1.66 5.69 1.33) ; 49 ( 91.85 -2.17 5.50 1.99) ; 50 ( 92.66 -2.83 5.13 1.92) ; 51 ( 93.47 -3.63 4.75 0.88) ; 52 ( 94.35 -4.29 4.69 0.59) ; 53 ( 95.24 -4.80 3.88 0.81) ; 54 ( 96.27 -5.24 3.81 1.03) ; 55 ( 97.23 -5.53 3.69 0.66) ; 56 ( 98.62 -6.12 3.13 0.66) ; 57 ( 99.44 -6.19 2.63 0.59) ; 58 ( 99.88 -6.19 1.81 0.44) ; 59 ( 100.54 -6.63 0.88 0.37) ; 60 ( 101.50 -6.85 0.44 0.59) ; 61 ( 101.94 -6.92 0.38 1.40) ; 62 ( 102.38 -7.00 -0.06 2.21) ; 63 ( 103.12 -7.29 -0.25 2.21) ; 64 ( 103.86 -8.16 -0.44 1.99) ; 65 ( 104.45 -8.68 -0.75 1.25) ; 66 ( 105.40 -9.04 0.00 0.74) ; 67 ( 106.21 -9.04 0.00 0.96) ; 68 ( 107.32 -9.55 -0.13 0.66) ; 69 ( 108.06 -9.63 -0.56 0.66) ; 70 ( 108.64 -9.77 -0.56 0.96) ; 71 ( 109.75 -10.14 -0.56 0.66) ; 72 ( 110.34 -10.28 -0.69 0.66) ; 73 ( 110.63 -10.65 -0.69 1.03) ; 74 ( 111.44 -11.09 -0.69 1.03) ; 75 ( 111.96 -11.53 -0.69 1.03) ; 76 ( 112.48 -12.19 -0.69 0.66) ; 77 ( 113.06 -13.14 -0.75 0.66) ; 78 ( 113.51 -13.35 -0.31 1.03) ; 79 ( 114.02 -13.87 0.19 1.33) ; 80 ( 114.54 -14.67 0.75 1.25) ; 81 ( 115.35 -15.18 1.44 1.25) ; 82 ( 116.31 -15.69 1.44 1.25) ; 83 ( 116.97 -16.13 1.63 1.25) ; 84 ( 117.41 -16.86 2.19 0.88) ; 85 ( 118.22 -17.89 2.81 1.03) ; 86 ( 118.89 -18.69 2.88 0.88) ; 87 ( 119.11 -19.50 3.00 0.88) ; 88 ( 119.70 -20.30 3.19 0.66) ; 89 ( 120.14 -20.81 3.63 0.59) ; 90 ( 120.51 -20.74 3.81 0.59) ; 91 ( 120.95 -19.79 4.06 0.44) ; 92 ( 121.54 -19.13 4.63 0.74) ; 93 ( 122.57 -18.47 4.63 1.11) ; 94 ( 123.08 -17.96 4.63 0.66) ; 95 ( 123.53 -17.52 4.63 0.44) ; 96 ( 124.19 -17.67 5.31 0.37) ; 97 ( 124.71 -18.11 5.31 0.37) ; 98 ( 125.15 -19.13 5.31 0.96) ; 99 ( 125.59 -20.15 5.25 1.55) ; 100 ( 125.96 -20.81 5.00 2.21) ; 101 ( 126.10 -21.03 4.56 2.21) ; 102 ( 126.47 -22.05 5.63 0.88) ; 103 ( 126.99 -22.71 5.81 0.66) ; 104 ( 127.65 -23.30 6.13 0.96) ; 105 ( 128.83 -23.95 6.13 1.18) ; 106 ( 130.15 -24.71 6.25 1.18) ; 107 ( 131.18 -25.07 6.56 0.88) ; 108 ( 132.36 -25.15 6.38 0.66) ; 109 ( 133.68 -24.41 5.75 0.59) ; 110 ( 134.42 -23.61 5.19 0.81) ; 111 ( 135.30 -23.03 5.56 0.96) ; 112 ( 135.67 -22.37 6.00 0.96) ; 113 ( 136.19 -22.15 6.31 0.96) ; 114 ( 136.26 -22.15 6.56 1.77) ; 115 ( 136.56 -21.71 7.06 1.03) ; 116 ( 137.07 -21.42 7.44 0.88) ; 117 ( 137.44 -20.91 8.50 0.88) ; 118 ( 137.66 -20.69 8.94 1.33) ; 119 ( 137.81 -20.54 9.63 1.69) ; 120 ( 138.10 -20.32 11.06 1.33) ; 121 ( 138.18 -20.17 12.25 1.11) ; 122 ( 138.62 -19.74 13.19 0.96) ; 123 ( 139.06 -19.44 13.31 0.52) ; 124 ( 139.65 -19.30 13.50 0.52) ; 125 ( 140.54 -18.93 13.75 0.52) ; 126 ( 141.27 -18.64 13.44 0.74) ; 127 ( 141.86 -18.57 13.19 0.74) ; 128 ( 143.04 -18.42 13.19 0.74) ; 129 ( 144.07 -18.35 13.19 0.59) ; 130 ( 144.59 -18.49 12.63 0.59) ; 131 ( 145.55 -19.01 13.88 0.66) ; 132 ( 146.13 -19.15 15.56 0.52) ; 133 ( 147.24 -19.59 15.63 0.81) ; 134 ( 148.05 -19.81 15.63 0.81) ; 135 ( 148.86 -20.10 15.63 0.52) ; 136 ( 149.74 -20.25 15.81 0.52) ; 137 ( 151.37 -19.96 15.94 0.52) ; 138 ( 152.32 -19.08 15.94 0.74) ; 139 ( 152.99 -18.13 15.94 1.25) ; 140 ( 153.50 -17.54 15.25 1.62) ; 141 ( 154.24 -16.89 15.19 1.69) ; 142 ( 154.53 -16.52 15.19 0.88) ; 143 ( 155.42 -15.86 15.19 0.59) ; 144 ( 156.15 -15.72 15.19 0.59) ; 145 ( 157.11 -15.35 14.69 0.59) ; 146 ( 158.14 -14.91 14.69 0.96) ; 147 ( 159.10 -14.25 14.69 0.96) ; 148 ( 159.76 -13.60 14.69 0.59) ; 149 ( 160.72 -13.01 14.69 0.59) ; 150 ( 160.94 -11.70 14.06 0.44) ; 151 ( 160.94 -11.04 15.13 0.44) ; 152 Low ) ; End of split | ( 33.42 -0.89 1.56 0.52) ; 1, R-1-2-2 ( 34.16 -0.38 2.38 0.37) ; 2 ( 34.75 -0.08 3.63 0.52) ; 3 ( 35.41 0.35 4.06 0.74) ; 4 ( 36.15 0.57 4.38 0.74) ; 5 ( 36.74 0.87 4.88 0.66) ; 6 ( 36.74 0.87 6.06 0.88) ; 7 ( 36.81 0.28 6.31 0.88) ; 8 ( 37.55 -0.30 7.13 0.66) ; 9 ( 38.21 -0.74 7.13 0.52) ; 10 ( 39.32 -0.74 7.63 0.52) ; 11 ( 39.32 -0.23 8.69 0.74) ; 12 ( 39.83 0.43 9.69 0.88) ; 13 ( 40.05 0.79 10.25 0.74) ; 14 ( 40.49 1.01 11.50 0.74) ; 15 ( 41.08 0.50 13.81 0.52) ; 16 ( 41.08 0.35 14.25 0.52) ; 17 ( 40.72 -0.01 14.63 0.52) ; 18 Low ) ; End of split ) ; End of split | ( 22.01 5.66 -2.75 1.69) ; 1, R-2 ( 22.89 6.32 -3.13 1.33) ; 2 ( 23.04 6.61 -4.50 1.03) ; 3 ( 23.33 7.42 -4.81 0.74) ; 4 ( 23.92 7.63 -5.38 0.59) ; 5 ( 24.59 7.78 -6.19 0.59) ; 6 ( 25.40 7.78 -6.19 0.59) ; 7 ( 26.80 7.93 -6.19 0.88) ; 8 ( 27.83 8.00 -6.56 1.11) ; 9 ( 28.64 8.22 -6.88 1.11) ; 10 ( 29.00 8.40 -6.88 1.11) ; 11 ( ( 29.60 8.37 -6.88 1.11) ; 1, R-2-1 ( 30.48 8.58 -6.88 0.96) ; 2 ( 31.07 8.88 -7.00 0.96) ; 3 ( 32.03 9.24 -7.38 0.81) ; 4 ( 32.98 9.53 -7.50 0.74) ; 5 ( 33.57 10.70 -6.44 0.59) ; 6 ( 34.83 11.44 -5.88 0.74) ; 7 ( 36.15 12.17 -5.31 0.88) ; 8 ( 37.40 12.46 -4.56 1.03) ; 9 ( 38.73 11.95 -3.88 0.88) ; 10 ( 39.69 11.36 -3.50 0.88) ; 11 ( 40.72 10.85 -3.31 0.88) ; 12 ( 42.12 10.92 -2.25 1.11) ; 13 ( 43.52 11.29 -1.44 0.88) ; 14 ( 44.70 11.73 -1.81 0.88) ; 15 ( 45.95 12.68 -2.38 0.88) ; 16 ( 46.91 13.26 -2.75 0.88) ; 17 ( 47.65 14.21 -3.44 1.11) ; 18 ( 48.53 14.94 -3.44 0.96) ; 19 ( 49.49 15.68 -3.44 0.81) ; 20 ( 50.15 16.55 -3.44 0.66) ; 21 ( 50.59 17.21 -3.75 0.88) ; 22 ( 51.77 18.31 -3.25 0.81) ; 23 ( 52.88 18.89 -3.25 0.74) ; 24 ( 54.13 18.67 -2.94 0.66) ; 25 ( 55.75 18.09 -2.94 0.52) ; 26 ( 56.85 17.80 -2.75 0.52) ; 27 ( 57.81 17.58 -2.25 0.74) ; 28 ( 58.77 17.43 -3.00 1.03) ; 29 ( 59.80 17.28 -3.44 1.03) ; 30 ( 60.76 16.99 -4.81 0.74) ; 31 ( 61.35 16.84 -5.06 0.74) ; 32 ( 62.16 16.77 -5.94 0.74) ; 33 ( 63.12 16.55 -6.13 1.03) ; 34 ( 64.37 16.70 -8.00 1.03) ; 35 ( 65.25 17.14 -8.13 0.88) ; 36 ( 66.06 17.58 -8.69 0.88) ; 37 ( 66.28 18.09 -8.75 0.88) ; 38 ( 67.46 18.53 -9.00 0.74) ; 39 ( 68.27 19.26 -9.56 0.74) ; 40 ( 69.01 20.43 -9.38 0.96) ; 41 ( 69.31 21.60 -8.81 1.25) ; 42 ( 69.82 22.40 -8.81 1.03) ; 43 ( 70.04 22.99 -8.81 0.74) ; 44 ( 70.63 23.64 -8.81 0.59) ; 45 ( 71.37 23.86 -8.75 0.88) ; 46 ( 72.69 24.45 -8.75 1.11) ; 47 ( 73.87 24.74 -8.75 1.40) ; 48 ( 74.68 24.96 -8.75 1.40) ; 49 ( 75.27 25.25 -8.75 0.66) ; 50 ( 76.53 25.69 -8.75 0.59) ; 51 ( 77.19 26.71 -9.19 0.88) ; 52 ( 78.07 27.96 -9.75 0.74) ; 53 ( 78.55 28.99 -10.38 0.74) ; 54 ( 79.73 29.79 -10.56 0.74) ; 55 ( 80.76 30.45 -10.31 0.96) ; 56 ( 81.57 30.74 -10.25 1.18) ; 57 ( 82.23 30.82 -10.25 0.88) ; 58 ( 83.34 31.40 -10.19 0.66) ; 59 ( 84.00 31.55 -10.19 0.66) ; 60 ( 85.33 31.99 -10.44 0.66) ; 61 ( 86.14 32.06 -10.63 0.66) ; 62 ( 86.73 32.28 -10.75 0.52) ; 63 ( 86.87 31.99 -11.19 0.52) ; 64 ( 86.87 31.69 -11.75 0.52) ; 65 ( 86.80 31.33 -12.31 0.52) ; 66 ( 87.24 30.82 -12.44 0.52) ; 67 ( 87.98 31.40 -12.44 0.44) ; 68 ( 88.57 31.91 -12.75 0.74) ; 69 ( 89.38 33.08 -12.75 0.88) ; 70 ( 90.11 33.74 -13.31 0.88) ; 71 ( 91.29 34.47 -11.19 0.59) ; 72 ( 92.47 35.42 -12.06 0.52) ; 73 ( 93.14 36.01 -11.75 0.52) ; 74 ( 94.09 36.59 -11.19 0.88) ; 75 ( 94.53 36.66 -11.13 2.28) ; 76 ( 95.42 37.18 -10.94 3.02) ; 77 ( 95.93 37.83 -11.69 2.21) ; 78 ( 96.38 38.35 -11.56 1.47) ; 79 ( 96.60 38.57 -11.56 0.81) ; 80 ( 97.33 39.15 -11.56 0.59) ; 81 ( 97.85 39.88 -11.56 0.59) ; 82 ( 98.07 40.47 -11.56 0.59) ; 83 ( 98.51 41.56 -11.56 0.59) ; 84 ( 98.66 42.22 -12.06 0.88) ; 85 ( 98.88 42.88 -12.88 1.77) ; 86 ( 98.59 43.68 -13.44 2.14) ; 87 ( 98.29 44.56 -13.75 1.25) ; 88 ( 98.14 45.00 -15.88 0.81) ; 89 ( 97.56 45.66 -15.25 0.52) ; 90 ( 97.04 46.53 -15.56 0.52) ; 91 ( 96.75 47.34 -15.63 0.37) ; 92 ( 97.11 48.07 -15.63 0.22) ; 93 ( 97.11 48.80 -15.63 0.22) ; 94 ( 97.26 49.68 -15.69 0.22) ; 95 ( 97.11 50.41 -15.69 0.52) ; 96 ( 97.11 50.55 -15.69 0.88) ; 97 ( 97.11 50.77 -15.69 1.25) ; 98 ( 96.89 51.07 -15.69 0.96) ; 99 ( 96.67 51.50 -15.69 0.59) ; 100 ( 96.45 51.80 -15.69 0.29) ; 101 ( 95.93 52.53 -16.25 0.22) ; 102 ( 95.20 52.97 -16.25 0.74) ; 103 ( 94.61 53.26 -16.25 0.88) ; 104 ( 93.95 53.48 -16.25 0.52) ; 105 ( 93.43 54.21 -16.25 0.22) ; 106 ( 93.06 54.79 -16.25 0.22) ; 107 ( 92.62 55.23 -16.25 0.22) ; 108 Low | ( 29.96 7.81 -2.25 0.59) ; 1, R-2-2 ( 30.25 7.67 -0.63 0.59) ; 2 ( 30.62 7.45 0.44 0.59) ; 3 ( 30.77 6.94 1.19 0.74) ; 4 ( 30.77 6.28 1.94 0.96) ; 5 ( 30.33 5.48 2.13 0.96) ; 6 ( 30.25 4.89 2.31 0.96) ; 7 ( 30.25 4.23 2.88 0.96) ; 8 ( 30.11 3.36 3.69 1.18) ; 9 ( 29.74 2.55 3.69 1.18) ; 10 ( 29.52 1.82 4.19 1.18) ; 11 ( 29.89 1.38 5.56 0.96) ; 12 ( 30.92 1.24 6.44 0.74) ; 13 ( 31.87 1.24 6.88 0.66) ; 14 ( 32.69 1.67 7.75 0.66) ; 15 ( 33.64 2.11 8.63 0.66) ; 16 ( 35.19 2.77 9.50 0.52) ; 17 ( 36.00 2.70 10.94 0.52) ; 18 ( 36.52 2.55 11.50 0.52) ; 19 ( 37.33 2.19 11.81 0.74) ; 20 ( 38.36 1.24 14.13 0.59) ; 21 ( 39.39 0.80 14.19 0.66) ; 22 ( 39.68 0.43 15.38 0.66) ; 23 ( 40.20 0.65 15.88 0.66) ; 24 ( 40.86 1.38 15.44 0.66) ; 25 ( 41.30 2.04 15.38 0.66) ; 26 ( 41.53 2.55 15.38 0.66) ; 27 ( 42.19 2.84 15.38 0.88) ; 28 ( 42.63 2.92 15.38 0.88) ; 29 ( 43.37 2.99 15.06 1.18) ; 30 ( 44.18 2.99 14.50 1.40) ; 31 ( 44.69 3.79 15.81 1.25) ; 32 ( 45.36 4.16 15.44 0.81) ; 33 ( 46.09 4.82 15.31 0.66) ; 34 ( 46.61 5.77 16.06 0.66) ; 35 ( 46.50 6.63 17.06 1.11) ; 36 ( 47.09 6.92 18.13 0.88) ; 37 ( ( 47.68 7.58 18.50 0.88) ; 1, R-2-2-1 ( 47.46 8.60 18.88 1.18) ; 2 ( 47.02 9.04 19.00 0.81) ; 3 ( 46.65 9.63 19.00 0.59) ; 4 ( 46.65 10.36 19.06 0.37) ; 5 ( 46.94 10.94 19.06 0.37) ; 6 ( 47.61 11.38 19.06 0.37) ; 7 ( 48.34 11.45 19.06 0.37) ; 8 ( 48.86 11.16 19.06 0.37) ; 9 ( 49.59 10.72 19.06 0.37) ; 10 Low | ( 47.97 7.43 15.00 0.96) ; 1, R-2-2-2 ( 48.86 8.60 14.31 0.81) ; 2 ( 49.59 9.26 12.19 0.59) ; 3 ( 50.41 9.55 13.56 0.66) ; 4 ( 51.95 9.63 13.56 0.59) ; 5 ( 52.98 9.63 13.56 0.59) ; 6 ( 53.79 9.99 14.13 0.66) ; 7 ( 54.53 10.43 15.38 0.74) ; 8 ( 55.56 10.94 15.50 0.96) ; 9 ( 56.81 11.74 15.50 0.96) ; 10 ( 57.99 12.77 16.31 0.96) ; 11 ( 59.10 13.86 17.25 0.81) ; 12 ( 60.06 14.38 17.44 0.66) ; 13 ( 61.01 15.55 17.94 0.52) ; 14 ( 61.75 15.69 18.25 0.81) ; 15 ( 62.41 15.98 18.75 0.59) ; 16 Low ) ; End of split ) ; End of split ) ; End of split ) ; End of tree ( (Color DarkMagenta) (Dendrite) ( -13.09 -4.16 -9.69 2.21) ; Root ( -14.34 -3.87 -10.06 1.69) ; 1, R ( -15.23 -3.94 -10.31 1.47) ; 2 ( -16.70 -4.53 -10.31 1.40) ; 3 ( -16.55 -5.84 -11.25 2.06) ; 4 ( -16.26 -6.94 -12.38 2.43) ; 5 ( -15.74 -8.69 -13.06 2.50) ; 6 ( -15.74 -9.50 -14.50 2.21) ; 7 ( -15.16 -10.81 -14.81 1.92) ; 8 ( -14.71 -12.20 -15.81 2.06) ; 9 ( -14.49 -13.30 -18.38 2.14) ; 10 ( -14.79 -13.66 -20.50 1.47) ; 11 ( -15.08 -14.54 -21.50 1.33) ; 12 ( -16.33 -14.91 -22.13 1.18) ; 13 ( -17.00 -14.76 -22.94 1.33) ; 14 ( -17.88 -14.62 -22.94 1.33) ; 15 ( -18.84 -14.10 -22.88 1.33) ; 16 ( -20.16 -14.03 -23.25 1.33) ; 17 ( -21.12 -14.03 -22.06 1.25) ; 18 ( -22.08 -13.66 -22.06 1.25) ; 19 ( -22.74 -13.45 -22.38 1.25) ; 20 ( -23.33 -13.01 -23.06 1.47) ; 21 ( -23.63 -12.86 -23.50 1.47) ; 22 ( -23.70 -12.50 -24.19 1.47) ; 23 ( -23.63 -12.06 -25.06 1.47) ; 24 ( -23.70 -12.06 -26.19 1.33) ; 25 ( -24.00 -12.20 -27.38 1.33) ; 26 ( -23.92 -12.93 -28.00 1.33) ; 27 ( -24.44 -13.52 -28.81 1.55) ; 28 ( -24.73 -14.18 -29.50 1.40) ; 29 ( -24.59 -14.98 -30.13 1.62) ; 30 ( -24.59 -16.15 -31.13 1.84) ; 31 ( -24.59 -17.90 -31.13 2.06) ; 32 ( -25.03 -19.00 -31.38 2.21) ; 33 ( -25.40 -20.61 -32.19 2.36) ; 34 ( -25.99 -21.71 -32.94 2.58) ; 35 ( -27.09 -22.44 -39.00 2.36) ; 36 ( ( -27.61 -23.02 -38.13 3.98) ; 1, R-1 ( -27.68 -23.31 -37.19 3.76) ; 2 ( ( -28.93 -23.39 -38.31 4.05) ; 1, R-1-1 ( -29.74 -23.90 -39.38 3.39) ; 2 ( ( -30.33 -23.90 -40.63 1.77) ; 1, R-1-1-1 ( -31.88 -23.97 -40.75 1.18) ; 2 ( -32.84 -24.12 -40.81 1.18) ; 3 ( -32.91 -24.12 -40.81 1.77) ; 4 ( -33.72 -23.83 -41.38 1.69) ; 5 ( ( -34.46 -23.39 -40.38 0.88) ; 1, R-1-1-1-1 ( -35.12 -22.80 -40.38 0.66) ; 2 ( -35.93 -22.44 -40.38 0.52) ; 3 ( -37.04 -22.07 -40.38 0.52) ; 4 ( -37.92 -21.49 -40.38 0.74) ; 5 ( -38.80 -20.97 -40.38 0.88) ; 6 ( -39.47 -20.46 -40.69 0.88) ; 7 ( -40.50 -20.02 -40.69 1.11) ; 8 ( -41.16 -19.95 -41.06 1.11) ; 9 ( -41.82 -19.88 -41.19 1.11) ; 10 ( -42.49 -19.88 -41.19 1.11) ; 11 ( -43.08 -19.95 -40.94 0.96) ; 12 ( -43.96 -19.66 -40.81 0.66) ; 13 ( -44.48 -19.00 -41.75 0.44) ; 14 ( -44.48 -18.49 -42.13 0.44) ; 15 ( -44.92 -18.34 -42.38 0.44) ; 16 ( -45.58 -18.42 -42.38 0.44) ; 17 ( -46.10 -18.42 -42.38 0.74) ; 18 ( -46.76 -17.98 -42.50 1.11) ; 19 ( -47.57 -17.54 -42.88 1.18) ; 20 ( -48.55 -16.90 -43.50 1.33) ; 21 ( -49.51 -16.25 -43.50 1.33) ; 22 ( -50.47 -15.59 -43.63 1.18) ; 23 ( -50.98 -15.29 -43.63 0.88) ; 24 ( -51.79 -14.78 -43.50 0.59) ; 25 ( -52.53 -15.22 -43.63 0.44) ; 26 ( -52.82 -15.51 -43.38 0.44) ; 27 ( -53.78 -15.15 -43.06 0.59) ; 28 ( -54.52 -14.93 -43.44 0.88) ; 29 ( -55.18 -14.64 -43.69 0.88) ; 30 ( -55.55 -13.83 -44.06 1.18) ; 31 ( -55.85 -13.10 -45.50 1.18) ; 32 ( -56.14 -12.74 -48.44 1.25) ; 33 ( -56.73 -12.22 -50.69 0.96) ; 34 ( -57.39 -12.30 -51.00 0.96) ; 35 ( -58.42 -12.37 -51.50 0.96) ; 36 ( -59.68 -12.52 -51.50 0.74) ; 37 ( -60.04 -12.37 -52.00 0.74) ; 38 ( -60.34 -11.86 -52.56 0.52) ; 39 ( -59.97 -11.57 -53.50 0.52) ; 40 ( -59.82 -11.13 -54.13 0.52) ; 41 ( -60.27 -11.27 -55.88 0.81) ; 42 ( -60.71 -11.35 -56.50 0.59) ; 43 ( -61.52 -11.20 -57.25 0.74) ; 44 ( -62.33 -10.91 -57.38 1.03) ; 45 ( -62.92 -11.06 -57.69 1.25) ; 46 ( -63.36 -11.27 -57.88 0.66) ; 47 ( -64.17 -11.27 -60.00 0.81) ; 48 ( -64.76 -10.47 -61.13 0.59) ; 49 ( -65.28 -9.74 -61.56 1.03) ; 50 ( -65.72 -8.86 -62.25 1.03) ; 51 ( -65.87 -8.35 -63.94 0.74) ; 52 ( -65.79 -7.98 -65.88 0.59) ; 53 ( -65.28 -7.55 -67.13 0.44) ; 54 ( -65.28 -6.96 -68.13 0.44) ; 55 ( -65.57 -6.08 -68.56 0.74) ; 56 ( -65.72 -5.65 -69.25 1.47) ; 57 ( -66.09 -4.18 -70.31 1.69) ; 58 ( -66.16 -3.38 -71.69 1.92) ; 59 ( -66.23 -2.36 -72.50 1.62) ; 60 ( -66.23 -1.77 -73.75 1.25) ; 61 ( -66.09 -0.97 -74.00 0.96) ; 62 ( -66.09 -0.16 -74.19 0.59) ; 63 ( -66.16 0.49 -74.63 0.59) ; 64 ( -66.01 1.88 -74.81 0.44) ; 65 ( -66.01 2.83 -75.69 0.44) ; 66 ( -66.01 3.71 -76.38 0.44) ; 67 ( -66.16 4.59 -76.81 0.44) ; 68 ( -66.38 5.68 -76.88 0.74) ; 69 ( -66.45 6.27 -77.44 1.18) ; 70 ( -66.90 7.07 -78.94 1.62) ; 71 ( -67.12 7.51 -79.19 2.06) ; 72 ( -67.34 7.95 -79.94 1.62) ; 73 ( -67.34 8.32 -80.31 1.18) ; 74 ( -67.63 8.90 -80.31 0.81) ; 75 ( -67.71 9.41 -80.56 0.52) ; 76 ( -67.71 10.14 -80.81 0.37) ; 77 ( -68.08 10.80 -81.63 0.37) ; 78 ( -69.18 11.09 -83.31 1.03) ; 79 ( -69.99 11.46 -84.38 1.25) ; 80 ( -70.92 11.47 -85.50 0.59) ; 81 ( -71.28 12.05 -86.81 0.59) ; 82 ( -71.65 12.78 -86.75 1.40) ; 83 ( -72.02 12.78 -87.38 2.21) ; 84 ( -72.46 12.85 -87.69 2.58) ; 85 ( -73.13 12.85 -88.44 1.40) ; 86 ( -74.16 13.15 -89.13 0.66) ; 87 ( -75.48 13.37 -89.88 0.29) ; 88 ( -76.15 13.29 -88.75 0.96) ; 89 ( -76.88 13.29 -88.75 0.96) ; 90 ( -77.32 13.44 -88.75 0.59) ; 91 Low | ( -34.11 -24.48 -41.63 0.59) ; 1, R-1-1-1-2 ( -34.85 -25.58 -42.44 0.44) ; 2 ( -35.95 -25.73 -42.63 0.66) ; 3 ( -36.61 -26.75 -43.06 0.52) ; 4 ( -36.47 -27.55 -43.06 0.52) ; 5 ( -36.61 -28.36 -43.63 0.81) ; 6 ( -36.91 -28.72 -43.94 1.62) ; 7 ( -37.20 -28.94 -44.88 1.11) ; 8 ( -37.43 -29.16 -46.38 0.74) ; 9 ( -38.16 -28.87 -47.25 0.52) ; 10 ( -39.05 -29.09 -49.06 0.52) ; 11 ( -39.93 -29.02 -49.63 0.52) ; 12 ( -40.59 -29.02 -49.75 0.88) ; 13 ( -41.11 -28.72 -49.81 1.03) ; 14 ( -42.14 -28.87 -50.06 0.66) ; 15 ( -43.10 -29.16 -50.44 0.66) ; 16 ( -43.91 -29.60 -50.69 0.66) ; 17 ( -44.35 -30.33 -50.69 0.66) ; 18 ( -45.53 -30.62 -50.88 0.66) ; 19 ( -46.41 -30.04 -51.50 0.96) ; 20 ( -46.78 -29.67 -52.13 1.33) ; 21 ( -47.37 -29.82 -53.25 0.81) ; 22 ( -47.59 -30.26 -55.19 0.81) ; 23 ( -47.81 -31.06 -57.19 0.81) ; 24 ( -48.48 -31.14 -58.56 0.81) ; 25 ( -49.21 -31.14 -59.44 0.81) ; 26 ( -49.80 -31.21 -59.50 0.81) ; 27 ( -50.76 -31.57 -60.56 0.74) ; 28 ( -51.28 -32.53 -62.13 0.66) ; 29 ( -50.98 -33.18 -62.88 0.66) ; 30 ( -50.91 -34.13 -63.50 0.88) ; 31 ( -50.69 -34.79 -65.00 0.88) ; 32 ( -50.32 -35.81 -65.00 0.88) ; 33 ( -50.61 -36.91 -66.69 1.18) ; 34 ( -50.54 -38.01 -67.31 1.33) ; 35 ( -51.28 -38.74 -69.00 1.03) ; 36 ( -51.94 -39.40 -70.06 0.81) ; 37 ( -52.97 -40.57 -70.44 0.59) ; 38 ( -53.49 -41.30 -70.94 0.59) ; 39 ( -54.22 -42.03 -71.69 0.59) ; 40 ( -55.25 -42.91 -71.94 0.59) ; 41 ( -56.21 -42.76 -72.63 0.59) ; 42 ( -57.02 -42.25 -73.50 0.59) ; 43 ( -58.35 -42.25 -73.50 0.59) ; 44 ( -58.79 -43.05 -74.00 0.59) ; 45 ( -59.60 -43.86 -74.56 0.59) ; 46 ( -59.90 -44.51 -74.88 0.59) ; 47 ( -60.85 -44.81 -74.88 0.74) ; 48 ( -61.81 -45.32 -75.13 0.74) ; 49 ( -62.62 -46.05 -75.38 0.74) ; 50 ( -62.99 -46.63 -74.38 0.52) ; 51 ( -64.32 -46.85 -74.06 0.37) ; 52 ( -64.76 -47.36 -73.88 0.37) ; 53 ( -65.35 -48.17 -73.69 0.37) ; 54 ( -66.01 -48.90 -73.63 0.66) ; 55 ( -66.60 -49.63 -73.69 0.81) ; 56 ( -67.26 -50.43 -73.69 0.52) ; 57 ( -67.93 -50.95 -74.06 0.81) ; 58 ( -68.44 -51.24 -74.06 1.18) ; 59 ( -69.10 -51.53 -74.06 2.06) ; 60 ( -69.84 -51.97 -74.81 2.43) ; 61 ( -70.73 -52.04 -74.56 1.92) ; 62 ( -71.54 -52.33 -74.00 1.11) ; 63 ( -72.60 -52.94 -73.25 0.52) ; 64 ( -73.49 -53.23 -73.56 0.44) ; 65 ( -75.18 -53.52 -75.19 0.29) ; 66 ( -76.44 -53.74 -75.88 0.29) ; 67 ( -77.02 -54.40 -76.38 0.29) ; 68 ( -77.98 -54.47 -76.38 0.59) ; 69 ( -78.79 -54.69 -77.06 0.96) ; 70 ( -79.16 -54.77 -77.50 1.40) ; 71 ( -79.68 -54.62 -78.56 2.21) ; 72 ( -80.56 -54.84 -79.31 1.55) ; 73 ( -81.37 -55.35 -80.69 0.88) ; 74 ( -81.74 -55.42 -82.06 0.52) ; 75 ( -82.85 -55.94 -83.06 0.29) ; 76 ( -83.73 -56.01 -83.88 0.59) ; 77 ( -84.76 -56.52 -84.06 0.37) ; 78 ( -85.42 -56.59 -84.44 0.37) ; 79 ( -86.16 -56.59 -84.88 0.37) ; 80 ( -87.34 -56.45 -85.00 0.74) ; 81 ( -88.00 -56.67 -85.69 1.84) ; 82 ( -88.44 -56.89 -86.38 2.58) ; 83 ( -89.25 -57.03 -86.56 1.33) ; 84 ( -89.77 -57.40 -86.56 0.88) ; 85 ( -90.29 -57.62 -86.56 0.52) ; 86 ( -90.80 -57.98 -86.56 0.52) ; 87 ( -91.32 -58.64 -87.63 0.59) ; 88 ( -91.76 -59.37 -88.13 0.59) ; 89 ( -91.61 -59.96 -88.63 0.59) ; 90 ( -92.20 -60.32 -88.63 0.88) ; 91 ( -92.86 -61.05 -89.38 1.40) ; 92 ( -93.53 -61.42 -90.56 1.55) ; 93 ( -94.04 -61.86 -91.50 1.11) ; 94 ( -94.34 -62.22 -92.63 0.66) ; 95 ( -94.41 -61.86 -94.38 0.66) ; 96 Low ) ; End of split | ( -29.80 -24.94 -38.56 1.25) ; 1, R-1-1-2 ( -29.87 -25.82 -39.13 0.96) ; 2 ( -30.17 -26.77 -39.25 0.74) ; 3 ( -30.10 -27.50 -40.44 0.74) ; 4 ( -30.76 -28.45 -40.69 0.66) ; 5 ( -31.13 -28.96 -40.75 0.66) ; 6 ( -31.20 -29.47 -41.56 0.66) ; 7 ( -31.64 -30.64 -41.56 0.66) ; 8 ( -31.64 -31.15 -43.50 0.66) ; 9 ( -31.64 -31.81 -43.75 0.66) ; 10 ( -31.94 -32.69 -43.81 0.66) ; 11 ( -32.45 -33.42 -45.25 0.66) ; 12 ( -33.63 -33.86 -45.69 0.66) ; 13 ( -34.15 -34.15 -47.06 0.66) ; 14 ( -34.81 -34.81 -48.50 0.81) ; 15 ( -34.96 -35.10 -51.44 0.96) ; 16 ( -35.25 -35.18 -53.81 0.96) ; 17 ( -35.33 -35.98 -55.13 0.96) ; 18 ( -35.92 -36.34 -57.44 0.96) ; 19 ( -36.06 -35.98 -57.44 0.96) ; 20 ( -37.39 -36.49 -58.00 0.74) ; 21 ( -38.05 -36.49 -58.50 0.74) ; 22 ( -39.08 -36.49 -59.31 0.88) ; 23 ( -39.97 -36.64 -61.81 0.59) ; 24 ( -40.63 -37.44 -62.31 0.59) ; 25 ( -41.00 -38.10 -63.00 0.59) ; 26 ( -41.44 -38.25 -64.63 0.88) ; 27 ( -41.00 -38.46 -66.44 1.18) ; 28 ( -41.37 -38.90 -67.94 1.18) ; 29 ( -41.66 -39.12 -69.25 1.55) ; 30 ( -41.88 -39.78 -69.69 1.99) ; 31 ( -42.55 -40.37 -70.31 1.11) ; 32 ( -42.84 -40.73 -71.38 0.66) ; 33 ( -43.21 -41.90 -71.75 0.37) ; 34 ( -43.65 -42.41 -72.56 0.37) ; 35 ( -44.09 -43.14 -73.75 0.74) ; 36 ( -44.83 -43.65 -73.75 1.18) ; 37 ( -45.27 -43.87 -75.50 1.18) ; 38 ( -45.57 -44.39 -76.69 1.11) ; 39 ( -45.71 -45.19 -77.56 0.74) ; 40 ( -45.93 -45.99 -78.31 0.52) ; 41 ( -46.38 -46.80 -78.75 0.52) ; 42 ( -46.60 -46.87 -79.88 0.52) ; 43 ( -47.11 -47.75 -80.88 0.88) ; 44 ( -47.63 -48.99 -81.44 1.33) ; 45 ( -48.15 -49.79 -82.56 1.33) ; 46 ( -49.10 -49.58 -83.06 0.66) ; 47 ( -49.77 -49.87 -83.63 0.44) ; 48 ( -50.94 -50.16 -83.63 0.37) ; 49 ( -51.61 -50.60 -84.19 0.37) ; 50 ( -52.27 -50.82 -84.38 0.66) ; 51 ( -53.01 -50.89 -83.06 1.03) ; 52 ( -53.52 -51.33 -82.75 1.47) ; 53 ( -54.41 -51.99 -83.31 1.77) ; 54 ( -55.22 -52.43 -83.31 1.03) ; 55 ( -56.25 -52.79 -83.81 0.66) ; 56 ( -57.06 -52.86 -82.69 0.29) ; 57 ( -58.09 -52.86 -82.31 0.29) ; 58 ( -58.75 -53.08 -81.63 0.29) ; 59 ( -59.86 -53.16 -81.25 0.29) ; 60 ( -60.67 -53.30 -79.81 0.59) ; 61 ( -61.70 -54.25 -79.69 0.66) ; 62 ( -62.07 -54.55 -79.69 0.66) ; 63 ( -62.44 -54.98 -79.75 1.03) ; 64 ( -62.73 -55.42 -79.81 1.47) ; 65 ( -63.54 -56.01 -79.81 1.84) ; 66 ( -64.28 -56.23 -79.81 1.62) ; 67 ( -65.09 -56.45 -79.81 0.74) ; 68 ( -66.05 -56.67 -79.81 0.29) ; 69 ( -67.67 -56.45 -77.88 0.22) ; 70 ( -69.29 -56.45 -76.50 0.22) ; 71 ( -70.84 -56.45 -76.31 0.22) ; 72 ( -72.38 -56.15 -76.19 0.88) ; 73 ( -73.05 -55.79 -75.50 1.11) ; 74 ( -73.71 -55.35 -74.88 0.74) ; 75 ( -74.74 -54.84 -74.31 0.37) ; 76 ( -75.40 -54.47 -73.25 0.37) ; 77 ( -76.07 -53.82 -72.06 0.37) ; 78 ( -76.66 -53.23 -71.88 0.59) ; 79 ( -77.32 -52.94 -71.69 0.96) ; 80 ( -78.06 -52.28 -72.13 0.96) ; 81 ( -78.65 -51.55 -72.63 0.52) ; 82 Low ) ; End of split | ( -27.15 -24.28 -39.06 1.40) ; 1, R-1-2 ( -27.00 -24.72 -40.38 0.81) ; 2 ( -26.86 -25.36 -40.38 0.81) ; 3 ( -26.71 -25.38 -41.75 0.74) ; 4 ( -26.19 -25.60 -43.38 0.74) ; 5 ( -25.60 -25.96 -43.31 0.74) ; 6 ( -25.01 -26.33 -44.31 1.03) ; 7 ( -24.57 -26.48 -44.31 1.25) ; 8 ( -25.36 -26.72 -46.63 0.96) ; 9 ( -25.51 -26.94 -48.44 0.96) ; 10 ( -25.95 -27.08 -48.94 0.96) ; 11 ( -26.32 -27.52 -49.25 1.33) ; 12 ( -26.91 -27.60 -50.38 1.33) ; 13 ( -27.79 -27.30 -51.50 1.18) ; 14 ( -28.38 -27.30 -54.13 0.96) ; 15 ( -28.83 -27.30 -54.19 0.96) ; 16 ( -29.27 -27.16 -57.00 0.96) ; 17 ( -29.49 -26.94 -58.81 0.96) ; 18 ( -29.34 -27.67 -59.63 0.96) ; 19 ( -29.34 -27.82 -61.31 0.96) ; 20 ( -29.12 -28.11 -62.25 0.96) ; 21 ( -28.38 -28.40 -63.94 0.81) ; 22 ( -27.72 -28.33 -66.63 0.81) ; 23 ( -27.20 -28.40 -67.13 1.11) ; 24 ( -26.03 -28.55 -69.44 1.11) ; 25 ( -24.99 -28.25 -70.25 0.96) ; 26 ( -23.74 -28.11 -70.50 0.66) ; 27 ( -23.00 -27.96 -71.38 0.66) ; 28 ( -22.56 -27.67 -72.06 1.25) ; 29 ( -22.05 -27.60 -73.94 1.62) ; 30 ( -21.24 -27.74 -74.44 1.62) ; 31 ( -21.24 -28.40 -75.63 1.25) ; 32 ( -21.02 -29.06 -77.75 0.96) ; 33 ( -20.79 -29.42 -78.25 0.96) ; 34 ( -21.16 -29.86 -80.56 0.96) ; 35 ( -21.02 -29.35 -83.50 0.96) ; 36 ( -22.05 -29.79 -84.50 0.96) ; 37 ( -22.27 -29.50 -87.31 0.96) ; 38 ( -22.56 -29.20 -89.25 0.96) ; 39 ( -22.78 -29.57 -89.81 0.96) ; 40 ( -22.93 -29.94 -92.06 1.18) ; 41 ( -23.52 -30.23 -93.31 1.33) ; 42 ( -23.82 -30.52 -96.00 1.69) ; 43 ( -24.33 -29.94 -96.56 1.69) ; 44 ( -24.33 -29.50 -98.38 1.99) ; 45 ( -24.33 -29.50 -100.06 2.36) ; 46 ( -24.04 -29.28 -100.81 0.81) ; 47 ( -23.45 -29.35 -102.00 0.66) ; 48 ( -23.37 -28.69 -104.81 0.66) ; 49 ( -23.67 -28.03 -107.50 0.66) ; 50 ( -24.04 -26.79 -108.25 0.88) ; 51 ( -24.26 -26.21 -110.50 1.18) ; 52 ( -24.77 -25.40 -113.00 1.40) ; 53 ( -25.29 -25.40 -114.13 1.69) ; 54 ( -25.07 -25.26 -116.88 1.69) ; 55 ( -26.21 -25.70 -116.88 0.37) ; 56 ( -28.13 -25.70 -117.13 0.29) ; 57 ( -29.23 -25.77 -117.88 0.29) ; 58 ( -30.63 -25.85 -118.75 0.29) ; 59 ( -31.66 -26.14 -119.94 0.29) ; 60 ( -32.91 -26.07 -120.75 0.29) ; 61 ( -34.02 -26.07 -120.69 0.29) ; 62 ( -34.76 -26.07 -120.69 0.29) ; 63 ( -35.35 -26.21 -120.13 0.29) ; 64 ( -35.79 -26.43 -120.06 0.66) ; 65 ( -36.52 -26.43 -120.19 1.11) ; 66 ( -36.82 -26.43 -120.38 1.11) ; 67 ( -37.63 -26.36 -121.50 0.66) ; 68 ( -38.37 -26.14 -121.44 0.66) ; 69 ( -39.32 -26.14 -121.63 0.66) ; 70 ( -40.43 -26.21 -122.13 0.66) ; 71 ( -40.80 -26.29 -122.25 0.44) ; 72 ( -41.53 -26.80 -122.44 0.74) ; 73 ( -41.75 -27.16 -122.56 1.18) ; 74 ( -42.34 -27.60 -122.56 1.47) ; 75 ( -42.79 -27.82 -122.69 0.59) ; 76 Incomplete ) ; End of split | ( -27.20 -21.60 -40.00 0.88) ; 1, R-2 ( -27.50 -21.02 -41.00 0.59) ; 2 ( -26.91 -20.43 -41.94 0.37) ; 3 ( -26.69 -19.92 -43.06 0.37) ; 4 ( -26.54 -19.12 -41.44 0.66) ; 5 ( -26.47 -18.53 -42.50 0.96) ; 6 ( -26.10 -18.17 -43.44 1.11) ; 7 ( -25.66 -17.73 -45.06 0.96) ; 8 ( -25.14 -17.58 -46.88 0.81) ; 9 ( -24.55 -17.65 -48.31 1.03) ; 10 ( -23.59 -17.73 -50.00 1.03) ; 11 ( -23.15 -16.85 -53.13 0.88) ; 12 ( -22.49 -15.46 -53.69 0.66) ; 13 ( -22.64 -14.58 -53.81 0.66) ; 14 ( -23.08 -14.15 -57.56 0.96) ; 15 ( -22.86 -13.56 -59.56 1.18) ; 16 ( -22.49 -13.71 -61.44 1.18) ; 17 ( -22.78 -13.49 -63.00 1.18) ; 18 ( -23.00 -13.27 -64.50 1.18) ; 19 ( -22.93 -13.27 -67.63 1.18) ; 20 ( -22.49 -13.20 -69.50 1.18) ; 21 ( -22.27 -12.83 -72.50 1.03) ; 22 ( -23.23 -12.17 -76.19 0.52) ; 23 ( -24.85 -10.93 -76.94 0.52) ; 24 ( -26.03 -10.20 -77.06 0.52) ; 25 ( -27.28 -9.39 -77.69 0.52) ; 26 ( -28.16 -8.30 -78.56 0.66) ; 27 ( -28.97 -7.35 -79.94 0.96) ; 28 ( -29.78 -6.76 -81.19 0.96) ; 29 ( -30.30 -5.89 -81.69 0.59) ; 30 ( -30.74 -5.01 -82.63 0.59) ; 31 ( -30.96 -4.57 -83.06 0.88) ; 32 ( -31.48 -4.13 -83.25 0.88) ; 33 ( -31.85 -4.13 -85.81 1.25) ; 34 ( -32.44 -4.35 -86.50 0.96) ; 35 ( -33.02 -4.13 -88.06 0.96) ; 36 ( -33.61 -3.77 -88.19 0.96) ; 37 ( -34.06 -3.62 -88.25 0.96) ; 38 ( -34.35 -3.55 -89.63 0.59) ; 39 ( -35.01 -3.03 -91.31 0.44) ; 40 ( -35.09 -2.96 -92.00 0.81) ; 41 ( -35.31 -2.30 -93.13 1.18) ; 42 ( -35.82 -1.50 -94.50 1.18) ; 43 ( -36.12 -0.40 -96.06 0.44) ; 44 ( -36.27 0.33 -97.06 0.81) ; 45 ( -36.27 0.84 -97.69 1.18) ; 46 ( -36.27 1.13 -99.75 0.81) ; 47 ( -36.27 1.28 -103.25 0.59) ; 48 Low ) ; End of split ) ; End of tree ( (Color DarkCyan) (Dendrite) ( 14.75 -0.10 -9.25 1.47) ; Root ( 16.22 0.12 -9.63 1.33) ; 1, R ( 17.47 0.34 -10.25 1.62) ; 2 ( 19.02 0.34 -10.69 1.62) ; 3 ( 20.71 0.85 -11.31 1.40) ; 4 ( 21.89 1.00 -12.69 1.18) ; 5 ( 22.56 1.07 -14.69 1.03) ; 6 ( 23.88 1.07 -15.56 1.11) ; 7 ( 24.69 1.44 -15.94 1.25) ; 8 ( 26.24 1.29 -17.50 1.25) ; 9 ( 28.45 1.00 -18.75 1.55) ; 10 ( ( 29.78 0.12 -17.38 1.55) ; 1, R-1 ( 30.88 0.12 -17.44 1.62) ; 2 ( 32.43 0.12 -17.69 1.84) ; 3 ( 33.61 0.12 -18.38 2.06) ; 4 ( 34.86 0.27 -18.69 2.28) ; 5 ( ( 37.29 1.36 -21.00 2.36) ; 1, R-1-1 ( 38.47 1.82 -21.00 2.36) ; 2 ( ( 38.84 2.10 -22.38 2.50) ; 1, R-1-1-1 ( 39.35 2.83 -22.88 1.84) ; 2 ( 40.39 3.85 -21.75 1.25) ; 3 ( 40.90 4.44 -21.75 1.25) ; 4 ( 42.15 5.60 -21.56 1.11) ; 5 ( 42.74 6.12 -21.50 1.11) ; 6 ( 43.26 7.80 -21.44 0.96) ; 7 ( 43.48 8.82 -21.44 0.88) ; 8 ( 43.92 9.33 -21.44 0.88) ; 9 ( 44.95 10.14 -22.50 0.88) ; 10 ( 46.43 10.79 -19.88 0.88) ; 11 ( 47.31 11.16 -19.81 1.18) ; 12 ( 48.27 11.67 -19.81 1.18) ; 13 ( 49.15 12.77 -19.69 1.18) ; 14 ( 50.33 13.13 -19.31 1.40) ; 15 ( 51.51 14.01 -19.19 1.18) ; 16 ( 52.84 14.60 -20.31 0.88) ; 17 ( 54.31 15.18 -21.88 1.18) ; 18 ( 55.19 15.33 -22.13 1.18) ; 19 ( 55.86 15.91 -22.69 1.18) ; 20 ( 56.37 16.79 -23.31 1.18) ; 21 ( 57.26 17.67 -23.31 1.18) ; 22 ( 58.44 18.25 -23.63 1.18) ; 23 ( 59.69 18.91 -24.06 1.11) ; 24 ( 60.57 19.57 -24.19 1.11) ; 25 ( 61.01 20.08 -24.88 1.11) ; 26 ( 62.41 20.96 -24.94 0.96) ; 27 ( 63.45 21.54 -25.13 0.88) ; 28 ( 64.11 21.61 -25.25 0.88) ; 29 ( 64.99 22.05 -25.38 0.88) ; 30 ( 65.88 22.64 -25.44 1.03) ; 31 ( 66.32 23.51 -25.94 1.03) ; 32 ( 66.91 24.54 -26.44 1.25) ; 33 ( 67.28 25.05 -25.69 1.47) ; 34 ( 67.96 26.00 -25.69 1.47) ; 35 ( ( 68.09 25.85 -27.50 1.33) ; 1, R-1-1-1-1 ( 68.82 26.36 -28.31 1.33) ; 2 ( 69.27 26.29 -30.50 1.33) ; 3 ( 70.59 26.51 -30.56 1.03) ; 4 ( 71.55 26.44 -30.94 1.03) ; 5 ( 72.14 26.66 -30.94 1.18) ; 6 ( 72.80 26.73 -31.38 1.18) ; 7 ( ( 73.17 27.90 -30.56 0.74) ; 1, R-1-1-1-1-1 ( 73.46 28.56 -30.69 0.74) ; 2 ( 73.98 29.07 -30.88 0.59) ; 3 ( 74.72 29.43 -31.25 0.37) ; 4 ( 75.68 29.58 -31.25 0.37) ; 5 ( 76.49 29.73 -31.56 0.52) ; 6 ( 76.78 30.17 -31.69 0.52) ; 7 ( 77.52 30.02 -32.06 0.52) ; 8 ( 77.89 30.39 -31.38 0.37) ; 9 ( 78.18 30.75 -31.38 0.37) ; 10 ( 78.99 30.60 -31.00 0.37) ; 11 ( 79.73 30.60 -31.19 0.59) ; 12 ( 81.35 30.60 -31.13 0.59) ; 13 ( 82.53 30.60 -31.06 0.74) ; 14 ( 84.00 30.60 -31.56 0.74) ; 15 ( 85.18 31.04 -31.13 0.74) ; 16 ( 86.21 31.19 -30.69 0.74) ; 17 ( 87.54 31.48 -29.88 0.74) ; 18 ( 88.94 31.77 -29.81 0.59) ; 19 ( 90.26 32.21 -29.81 0.52) ; 20 ( 91.81 32.58 -29.25 0.74) ; 21 ( 92.55 33.60 -28.94 0.74) ; 22 ( 93.65 34.70 -28.88 0.96) ; 23 ( 94.23 35.68 -28.81 0.66) ; 24 ( 94.60 36.41 -28.75 0.66) ; 25 ( 95.48 37.07 -28.75 0.88) ; 26 ( 96.36 37.66 -28.75 0.88) ; 27 ( 96.81 38.17 -28.31 0.66) ; 28 ( 97.69 38.83 -28.19 0.52) ; 29 ( 98.28 39.04 -28.19 0.52) ; 30 ( 99.09 38.90 -28.13 0.44) ; 31 ( 99.83 38.68 -28.13 0.44) ; 32 ( 100.34 38.61 -28.06 0.44) ; 33 ( 101.08 38.97 -27.63 0.52) ; 34 ( 101.74 39.78 -26.75 0.66) ; 35 ( 102.70 41.09 -26.69 0.74) ; 36 ( 103.07 42.55 -26.31 0.88) ; 37 ( 103.29 43.50 -26.19 1.77) ; 38 ( 103.73 44.38 -26.13 1.77) ; 39 ( 104.25 44.82 -26.00 0.96) ; 40 ( 104.69 45.11 -26.19 0.66) ; 41 ( 105.28 45.92 -26.19 0.44) ; 42 ( 105.57 46.50 -26.19 0.44) ; 43 ( 105.57 47.45 -26.38 0.66) ; 44 ( 105.50 48.18 -26.63 0.81) ; 45 ( 105.79 48.91 -26.63 0.59) ; 46 ( 106.31 49.79 -26.81 0.52) ; 47 ( 106.75 50.16 -27.00 0.52) ; 48 ( 107.78 50.45 -26.81 0.66) ; 49 ( 108.59 50.81 -26.25 0.88) ; 50 ( 109.26 51.11 -25.81 0.88) ; 51 ( 109.85 51.54 -25.38 0.96) ; 52 ( 110.43 52.28 -25.06 1.11) ; 53 ( 111.25 52.71 -24.75 0.88) ; 54 ( 111.61 52.93 -24.56 0.74) ; 55 ( 112.13 53.30 -24.31 0.52) ; 56 ( 112.72 53.74 -24.19 0.52) ; 57 ( 113.31 54.47 -23.81 0.96) ; 58 ( 113.90 55.13 -23.56 1.25) ; 59 ( 114.19 55.71 -23.50 1.25) ; 60 ( 114.12 56.22 -23.38 0.96) ; 61 ( 114.56 56.73 -23.25 0.66) ; 62 ( 114.86 57.25 -22.44 0.66) ; 63 ( 115.30 58.20 -21.44 0.44) ; 64 ( 115.89 58.71 -20.94 0.44) ; 65 ( 116.03 59.37 -21.31 0.81) ; 66 ( 116.18 59.88 -21.50 1.25) ; 67 ( 116.26 61.05 -22.19 1.62) ; 68 ( 116.26 61.49 -22.63 1.33) ; 69 ( 116.33 62.14 -22.81 1.03) ; 70 ( 116.40 62.73 -23.00 0.74) ; 71 ( 116.03 64.26 -23.13 0.44) ; 72 ( 115.74 65.41 -23.69 0.44) ; 73 ( 115.67 66.14 -24.25 0.22) ; 74 ( 115.67 66.80 -24.63 0.22) ; 75 ( 115.60 67.31 -24.94 0.22) ; 76 Low | ( 73.40 26.21 -32.50 0.44) ; 1, R-1-1-1-1-2 ( 74.28 26.29 -33.19 0.66) ; 2 ( 74.87 26.36 -34.00 0.66) ; 3 ( 75.31 26.36 -35.38 0.66) ; 4 ( 75.83 26.50 -35.81 0.66) ; 5 ( 76.71 26.72 -35.81 0.66) ; 6 ( 77.15 26.65 -35.88 0.66) ; 7 ( 77.45 26.43 -37.19 0.66) ; 8 ( 77.67 26.14 -37.25 0.66) ; 9 ( 78.11 25.48 -37.94 0.66) ; 10 ( 78.40 25.19 -38.50 0.66) ; 11 ( 78.99 24.82 -39.00 0.66) ; 12 ( 79.44 24.60 -39.31 0.66) ; 13 ( 80.25 24.31 -39.31 0.52) ; 14 ( 81.20 24.09 -40.00 0.52) ; 15 ( 82.31 24.09 -40.06 0.52) ; 16 ( 83.49 23.95 -40.06 0.81) ; 17 ( 84.96 23.87 -41.00 1.18) ; 18 ( 86.36 23.87 -41.00 1.18) ; 19 ( 87.54 24.09 -41.06 0.88) ; 20 ( 88.42 24.31 -42.25 0.59) ; 21 ( 89.60 24.46 -43.06 0.59) ; 22 ( 90.34 24.97 -43.88 0.59) ; 23 ( 91.08 25.48 -44.31 0.59) ; 24 ( 91.74 26.36 -45.44 0.59) ; 25 ( 92.40 27.24 -45.44 0.59) ; 26 ( 92.84 27.89 -44.44 0.59) ; 27 ( 93.95 28.92 -43.75 0.59) ; 28 ( 94.24 30.31 -43.75 0.59) ; 29 ( 95.13 30.96 -43.63 0.59) ; 30 ( 95.87 32.28 -43.50 0.81) ; 31 ( 96.53 33.08 -43.50 0.81) ; 32 ( 97.63 34.03 -43.25 0.59) ; 33 ( 99.25 34.55 -43.19 0.81) ; 34 ( 100.36 34.98 -43.19 0.81) ; 35 ( 101.46 35.79 -42.75 0.81) ; 36 ( 103.01 36.23 -42.31 0.59) ; 37 ( 104.04 36.96 -44.06 0.44) ; 38 ( 105.00 37.83 -44.81 0.44) ; 39 ( 105.96 39.15 -44.94 0.44) ; 40 ( 107.21 40.69 -44.94 0.44) ; 41 ( 108.24 41.42 -44.94 0.66) ; 42 ( 108.68 41.78 -44.94 0.96) ; 43 ( 109.20 42.15 -45.25 1.25) ; 44 ( 110.01 42.66 -45.38 1.99) ; 45 ( 110.75 43.54 -45.75 1.18) ; 46 ( 110.75 43.68 -46.00 0.81) ; 47 ( 111.48 44.12 -46.06 0.37) ; 48 ( 112.15 45.58 -46.69 0.29) ; 49 ( 112.81 46.24 -47.00 0.29) ; 50 ( 113.40 46.75 -47.50 0.59) ; 51 ( 113.92 47.12 -48.13 0.96) ; 52 ( 114.80 47.63 -48.81 0.96) ; 53 ( 114.80 48.58 -49.56 1.25) ; 54 Low ) ; End of split | ( 68.18 26.95 -27.56 0.81) ; 1, R-1-1-1-2 ( 68.55 27.39 -32.69 0.96) ; 2 ( 68.03 27.46 -34.50 0.88) ; 3 ( 67.52 27.31 -36.06 0.88) ; 4 ( 67.08 27.10 -36.75 0.88) ; 5 ( 67.52 27.46 -39.69 0.88) ; 6 ( 68.11 27.83 -41.06 0.88) ; 7 ( 68.99 28.92 -41.88 0.74) ; 8 ( 69.58 29.14 -42.81 0.74) ; 9 ( 70.25 29.51 -44.00 0.74) ; 10 ( 71.06 30.38 -45.13 0.74) ; 11 ( 71.50 30.90 -45.06 1.03) ; 12 ( 71.87 31.41 -46.44 1.03) ; 13 ( 72.60 31.70 -46.94 0.66) ; 14 ( 73.34 31.85 -47.75 0.52) ; 15 ( 74.59 31.85 -48.81 0.52) ; 16 ( 75.99 32.14 -49.19 0.74) ; 17 ( 76.88 32.36 -50.38 0.96) ; 18 ( 77.91 32.43 -50.56 1.18) ; 19 ( 79.09 32.72 -52.19 1.03) ; 20 ( 80.19 32.87 -52.50 0.74) ; 21 ( 81.59 33.38 -53.63 0.59) ; 22 ( 82.18 34.11 -55.75 0.88) ; 23 ( 83.21 34.48 -56.06 0.88) ; 24 ( 84.39 35.43 -58.50 0.74) ; 25 ( 85.42 36.60 -59.25 0.74) ; 26 ( 85.79 37.62 -59.81 0.74) ; 27 ( 86.45 39.30 -60.25 0.59) ; 28 ( 86.60 39.96 -60.81 0.88) ; 29 ( 86.60 40.40 -61.50 1.33) ; 30 ( 86.82 41.42 -62.13 1.69) ; 31 ( 86.90 42.08 -63.19 2.14) ; 32 ( 87.04 42.74 -63.25 1.40) ; 33 ( 87.12 43.84 -63.25 0.81) ; 34 ( 86.97 45.00 -63.94 0.59) ; 35 ( 86.97 46.03 -64.56 0.44) ; 36 ( 87.48 48.00 -64.63 0.37) ; 37 ( 88.00 48.88 -65.00 0.37) ; 38 ( 88.22 49.76 -65.25 0.74) ; 39 ( 88.44 50.56 -66.19 1.03) ; 40 ( 88.59 51.51 -66.56 1.03) ; 41 ( 88.74 52.31 -66.94 0.66) ; 42 ( 89.11 53.26 -68.06 0.44) ; 43 ( 89.33 53.92 -68.44 0.44) ; 44 ( 89.62 54.80 -68.94 0.44) ; 45 ( 89.92 55.60 -69.06 0.74) ; 46 ( 90.36 56.55 -70.06 1.03) ; 47 ( 90.80 57.36 -70.56 0.74) ; 48 ( 91.32 57.80 -70.56 0.44) ; 49 ( 91.54 59.04 -71.31 0.29) ; 50 ( 91.98 59.77 -71.56 0.29) ; 51 ( 92.35 60.50 -71.56 0.29) ; 52 ( 92.72 61.45 -72.00 0.96) ; 53 ( 93.53 61.82 -73.25 1.25) ; 54 ( 93.67 62.55 -73.94 1.25) ; 55 ( 94.63 63.13 -75.19 0.96) ; 56 ( 95.29 63.79 -76.06 0.74) ; 57 ( 96.18 64.30 -77.75 0.96) ; 58 ( 96.40 64.74 -78.88 0.96) ; 59 ( 97.43 65.76 -81.75 0.74) ; 60 ( 97.58 66.28 -84.38 0.96) ; 61 ( 98.39 67.67 -86.31 0.96) ; 62 ( 98.31 68.25 -85.69 1.62) ; 63 ( 98.46 68.83 -85.69 1.62) ; 64 ( 98.83 69.49 -86.38 1.25) ; 65 ( 98.90 69.93 -87.81 0.88) ; 66 ( 99.35 70.88 -89.19 0.66) ; 67 ( 99.64 72.05 -85.75 0.44) ; 68 ( 99.49 73.07 -85.31 0.44) ; 69 ( 99.12 74.02 -86.44 0.74) ; 70 ( 99.12 74.68 -87.69 1.03) ; 71 ( 98.61 74.83 -89.88 0.74) ; 72 ( 98.09 75.19 -91.06 0.74) ; 73 ( 97.50 75.71 -92.06 0.74) ; 74 ( 96.77 75.56 -92.13 1.11) ; 75 Low ) ; End of split | ( 39.54 1.28 -27.13 0.52) ; 1, R-1-1-2 ( 39.54 0.62 -27.19 0.52) ; 2 ( 39.83 -0.26 -28.25 0.74) ; 3 ( 39.91 -0.38 -28.25 0.74) ; 4 ( ( 39.83 -0.84 -29.63 0.74) ; 1, R-1-1-2-1 ( 40.13 -1.57 -30.13 0.74) ; 2 ( 39.91 -2.38 -30.13 0.74) ; 3 ( 39.61 -3.40 -30.44 0.66) ; 4 ( 39.46 -4.06 -30.94 0.66) ; 5 ( 39.32 -5.23 -30.94 0.66) ; 6 ( 39.54 -5.81 -30.00 0.66) ; 7 ( 39.98 -6.69 -31.31 0.66) ; 8 ( 40.57 -7.86 -31.69 0.66) ; 9 ( 41.16 -8.74 -31.69 0.88) ; 10 ( 41.60 -10.27 -31.69 0.88) ; 11 ( 41.82 -11.44 -31.63 0.59) ; 12 ( 41.97 -12.32 -31.63 1.11) ; 13 ( 42.12 -13.20 -31.13 1.40) ; 14 ( 42.70 -14.51 -32.00 1.11) ; 15 ( 42.70 -15.61 -31.94 0.96) ; 16 ( 43.22 -17.00 -32.25 0.81) ; 17 ( 43.44 -17.80 -32.25 1.18) ; 18 ( 43.88 -18.46 -32.25 1.33) ; 19 ( 43.81 -19.55 -32.19 0.96) ; 20 ( 43.52 -20.65 -33.69 0.74) ; 21 ( 43.96 -21.38 -33.63 0.74) ; 22 ( 44.18 -22.62 -33.63 1.03) ; 23 ( 44.62 -23.87 -34.13 0.88) ; 24 ( 44.92 -24.60 -34.75 0.66) ; 25 ( 45.28 -25.84 -34.88 0.81) ; 26 ( 45.58 -27.08 -36.31 0.88) ; 27 ( 45.86 -28.54 -37.56 0.88) ; 28 ( 46.45 -29.13 -38.00 0.88) ; 29 ( 47.04 -29.71 -38.44 0.88) ; 30 ( 48.44 -30.45 -38.75 0.88) ; 31 ( 49.03 -30.81 -40.19 1.11) ; 32 ( 50.14 -31.40 -40.38 1.11) ; 33 ( 50.87 -32.20 -40.38 1.11) ; 34 ( 51.90 -32.93 -40.63 1.11) ; 35 ( 52.71 -34.03 -41.44 0.88) ; 36 ( 53.53 -34.47 -42.63 0.74) ; 37 ( 53.97 -35.12 -43.56 0.74) ; 38 ( 54.11 -35.93 -44.50 0.74) ; 39 ( 54.41 -37.10 -45.50 0.74) ; 40 ( 53.89 -37.90 -46.94 0.74) ; 41 ( 53.38 -38.49 -47.94 0.88) ; 42 ( 53.01 -39.44 -48.06 0.88) ; 43 ( 52.35 -40.39 -48.81 0.74) ; 44 ( 51.54 -40.90 -49.63 0.59) ; 45 ( 51.24 -42.07 -49.63 0.59) ; 46 ( 51.09 -43.60 -49.63 0.59) ; 47 ( 51.02 -44.70 -49.63 0.59) ; 48 ( 50.43 -45.43 -49.63 0.59) ; 49 ( 49.77 -46.60 -49.75 0.88) ; 50 ( 49.10 -47.92 -49.94 1.33) ; 51 ( 48.81 -48.72 -50.63 1.33) ; 52 ( 48.66 -50.04 -50.94 0.96) ; 53 ( 48.96 -50.99 -51.13 0.66) ; 54 ( 49.03 -51.72 -51.81 0.44) ; 55 ( 49.03 -52.74 -51.38 0.29) ; 56 ( 48.81 -54.13 -52.31 0.29) ; 57 ( 48.59 -54.79 -52.56 0.59) ; 58 ( 48.44 -55.88 -52.56 0.59) ; 59 ( 48.94 -57.03 -52.63 0.59) ; 60 ( 48.64 -57.91 -54.38 0.59) ; 61 ( 48.57 -58.64 -55.75 0.59) ; 62 ( 48.64 -59.52 -57.25 0.44) ; 63 ( 48.13 -60.18 -58.88 0.59) ; 64 ( 48.05 -60.91 -59.63 0.59) ; 65 ( 48.05 -61.86 -60.38 0.52) ; 66 ( 47.76 -62.66 -60.81 0.81) ; 67 ( 47.68 -63.25 -60.75 1.25) ; 68 ( 47.39 -63.61 -61.50 1.77) ; 69 ( 47.17 -64.34 -61.50 1.77) ; 70 ( 46.80 -64.93 -63.38 1.03) ; 71 ( 45.99 -65.44 -65.50 0.59) ; 72 ( 45.47 -65.51 -67.25 0.59) ; 73 ( 44.74 -65.51 -69.25 0.59) ; 74 ( 44.59 -65.07 -71.94 0.88) ; 75 ( 44.52 -64.78 -73.75 0.88) ; 76 ( 44.15 -64.64 -76.13 0.88) ; 77 ( 42.90 -64.20 -78.06 0.59) ; 78 ( 42.01 -63.90 -78.56 0.37) ; 79 ( 41.35 -64.12 -79.44 0.66) ; 80 ( 40.69 -64.56 -79.44 0.66) ; 81 ( 39.95 -64.85 -80.63 0.66) ; 82 ( 39.73 -65.44 -80.69 0.66) ; 83 ( 39.43 -66.24 -81.63 0.52) ; 84 ( 38.77 -66.68 -84.63 0.52) ; 85 ( 38.70 -66.97 -86.69 0.66) ; 86 ( 38.25 -67.49 -90.13 0.66) ; 87 Incomplete | ( 40.72 -0.68 -28.25 0.66) ; 1, R-1-1-2-2 ( 41.01 -1.19 -31.63 0.66) ; 2 ( 41.60 -1.70 -32.81 0.66) ; 3 ( 43.15 -2.72 -33.69 0.66) ; 4 ( 43.96 -3.82 -35.44 0.66) ; 5 ( 44.40 -4.26 -36.19 0.66) ; 6 ( 46.54 -4.84 -36.50 0.74) ; 7 ( 47.20 -5.72 -37.88 0.74) ; 8 ( 48.52 -6.30 -38.50 0.88) ; 9 ( 49.85 -6.30 -38.56 1.18) ; 10 ( 50.66 -6.38 -38.56 1.18) ; 11 ( 51.69 -6.45 -38.81 0.96) ; 12 ( 52.50 -6.89 -39.00 0.96) ; 13 ( 53.17 -7.18 -39.25 0.96) ; 14 ( 54.64 -7.18 -39.25 1.25) ; 15 ( 55.82 -7.18 -39.88 1.25) ; 16 ( 57.29 -7.04 -38.69 0.88) ; 17 ( 58.69 -6.74 -38.38 1.18) ; 18 ( 59.87 -6.74 -38.69 1.40) ; 19 ( 60.68 -5.94 -38.69 0.81) ; 20 ( 61.27 -5.21 -38.88 0.66) ; 21 ( 62.67 -4.55 -39.06 0.88) ; 22 ( 63.85 -3.97 -39.06 0.81) ; 23 ( 64.51 -3.45 -39.25 0.81) ; 24 ( 64.95 -2.65 -39.56 0.81) ; 25 ( 65.76 -2.14 -40.44 0.66) ; 26 ( 67.75 -2.28 -40.81 0.66) ; 27 ( 69.01 -2.21 -41.56 0.96) ; 28 ( 69.96 -2.58 -41.56 1.25) ; 29 ( 71.07 -2.72 -42.69 1.47) ; 30 ( 71.73 -2.87 -44.56 1.77) ; 31 ( 72.47 -3.16 -45.31 1.99) ; 32 ( 73.21 -3.82 -46.06 1.25) ; 33 ( 73.79 -4.55 -47.50 0.66) ; 34 ( 74.61 -5.57 -47.94 0.59) ; 35 ( 75.34 -6.23 -48.00 0.59) ; 36 ( 75.86 -7.11 -48.81 0.59) ; 37 ( 76.23 -7.99 -49.69 0.59) ; 38 ( 76.67 -8.57 -49.69 0.88) ; 39 ( 77.40 -9.59 -50.31 0.88) ; 40 ( 78.14 -10.40 -50.13 0.52) ; 41 ( 78.66 -11.20 -51.31 0.52) ; 42 ( 78.95 -12.01 -52.38 0.81) ; 43 ( 79.47 -12.81 -54.31 0.88) ; 44 ( 80.06 -13.83 -54.25 1.47) ; 45 ( 80.50 -14.35 -55.13 1.84) ; 46 ( 81.01 -14.71 -56.19 1.84) ; 47 ( 81.46 -15.00 -56.56 1.33) ; 48 ( 81.75 -15.44 -57.50 0.81) ; 49 ( 82.49 -15.73 -58.69 0.66) ; 50 ( 83.22 -16.03 -58.63 0.66) ; 51 ( 84.55 -16.10 -59.25 0.88) ; 52 ( 85.36 -16.17 -59.25 0.88) ; 53 ( 86.25 -16.39 -60.00 0.66) ; 54 ( 87.92 -16.23 -60.31 0.66) ; 55 ( 88.51 -16.30 -60.75 1.40) ; 56 ( 89.32 -16.44 -61.94 1.62) ; 57 ( 89.98 -16.81 -63.13 1.62) ; 58 ( 90.43 -17.10 -64.06 1.25) ; 59 ( 91.60 -17.69 -65.00 0.88) ; 60 ( 92.12 -18.56 -65.00 0.44) ; 61 ( 92.78 -19.15 -66.31 0.44) ; 62 ( 93.30 -19.44 -66.44 1.18) ; 63 ( 93.74 -19.66 -67.44 1.55) ; 64 ( 94.04 -20.39 -68.63 1.11) ; 65 ( 94.40 -20.61 -68.63 0.74) ; 66 ( 94.92 -21.34 -69.25 0.37) ; 67 ( 95.14 -22.58 -70.44 0.37) ; 68 ( 95.88 -23.53 -71.06 0.29) ; 69 ( 96.76 -24.12 -71.63 0.59) ; 70 ( 98.09 -24.85 -71.63 0.96) ; 71 ( 98.60 -25.65 -72.06 1.84) ; 72 ( 98.97 -26.53 -72.38 2.73) ; 73 ( 99.49 -27.34 -72.38 1.99) ; 74 ( 100.08 -27.99 -74.00 1.55) ; 75 ( 100.74 -28.36 -74.25 0.66) ; 76 ( 101.48 -29.02 -74.50 0.44) ; 77 ( 102.66 -29.60 -75.44 0.29) ; 78 ( 103.32 -30.41 -76.00 0.29) ; 79 ( 105.68 -32.01 -76.63 0.29) ; 80 ( 106.04 -32.16 -76.63 0.66) ; 81 ( 106.41 -32.31 -77.44 1.11) ; 82 ( 107.08 -32.45 -78.44 1.55) ; 83 ( 108.11 -33.26 -78.38 1.55) ; 84 ( 108.40 -33.55 -78.44 1.03) ; 85 ( 109.07 -34.13 -78.44 0.52) ; 86 ( 109.73 -34.79 -79.13 0.44) ; 87 ( 110.39 -35.60 -79.13 0.44) ; 88 ( 111.42 -36.33 -79.13 0.74) ; 89 ( 112.09 -36.91 -79.94 0.74) ; 90 ( 112.53 -37.28 -81.38 0.74) ; 91 ( 113.78 -38.15 -82.56 0.52) ; 92 ( 115.25 -39.69 -82.56 0.37) ; 93 ( 115.99 -40.57 -82.94 0.66) ; 94 ( 116.36 -40.64 -84.19 1.18) ; 95 ( 116.51 -40.79 -84.56 1.62) ; 96 ( 116.73 -40.79 -86.13 1.69) ; 97 ( 117.32 -40.27 -86.50 1.18) ; 98 ( 117.76 -39.84 -86.50 0.74) ; 99 ( 118.27 -39.69 -87.13 0.52) ; 100 ( 118.86 -39.25 -89.13 0.81) ; 101 ( 119.31 -38.96 -89.94 1.25) ; 102 ( 119.82 -38.52 -91.00 1.69) ; 103 ( 120.48 -38.01 -91.00 1.69) ; 104 ( 120.78 -38.01 -91.75 0.81) ; 105 ( 121.37 -37.86 -92.81 0.52) ; 106 ( 122.40 -37.72 -93.56 0.44) ; 107 ( 122.69 -37.72 -93.56 0.44) ; 108 ( 123.21 -37.50 -94.75 0.81) ; 109 ( 124.32 -36.84 -94.75 1.03) ; 110 ( 124.76 -36.84 -94.75 0.74) ; 111 ( 125.35 -36.84 -94.75 0.74) ; 112 ( 126.38 -36.91 -95.56 1.11) ; 113 ( 127.19 -36.84 -95.56 1.11) ; 114 ( 127.48 -36.84 -96.38 0.74) ; 115 ( 128.66 -37.20 -97.31 0.44) ; 116 Incomplete ) ; End of split ) ; End of split | ( 35.45 -0.98 -19.19 0.96) ; 1, R-1-2 ( 35.90 -1.64 -20.69 0.74) ; 2 ( 37.15 -1.93 -21.44 0.59) ; 3 ( 38.92 -2.37 -22.13 0.59) ; 4 ( 40.02 -2.59 -22.44 0.88) ; 5 ( 41.27 -2.88 -23.56 1.11) ; 6 ( 42.17 -3.35 -23.69 0.88) ; 7 ( 43.50 -4.30 -25.38 0.66) ; 8 ( 44.16 -4.59 -26.63 0.74) ; 9 ( 44.82 -5.32 -26.75 0.66) ; 10 ( 45.41 -5.98 -27.81 0.88) ; 11 ( 46.07 -6.85 -27.88 0.88) ; 12 ( 46.81 -7.95 -27.81 1.03) ; 13 ( 47.69 -8.76 -28.38 0.81) ; 14 ( 48.36 -9.12 -28.56 0.81) ; 15 ( 49.39 -9.85 -28.69 0.96) ; 16 ( 50.20 -10.44 -29.13 1.18) ; 17 ( 51.45 -11.53 -29.13 0.74) ; 18 ( 52.70 -12.19 -29.13 0.66) ; 19 ( 53.81 -12.85 -29.31 0.66) ; 20 ( 54.10 -13.80 -29.63 0.66) ; 21 ( 54.47 -14.46 -29.75 0.96) ; 22 ( 55.21 -15.55 -29.81 0.74) ; 23 ( 55.87 -16.21 -30.56 0.59) ; 24 ( 56.46 -17.75 -30.69 0.52) ; 25 ( 57.27 -18.33 -32.38 0.66) ; 26 ( 58.16 -19.72 -33.06 0.74) ; 27 ( 58.52 -20.74 -34.38 0.74) ; 28 ( 59.19 -22.35 -36.00 0.74) ; 29 ( 59.63 -23.52 -36.00 0.74) ; 30 ( 60.22 -25.13 -37.06 1.11) ; 31 ( 60.59 -26.23 -37.56 1.33) ; 32 ( 60.73 -27.25 -38.88 1.11) ; 33 ( 60.88 -28.35 -39.31 0.59) ; 34 ( 60.96 -29.81 -41.44 0.59) ; 35 ( 61.40 -31.27 -41.44 0.81) ; 36 ( 61.77 -32.29 -42.06 1.18) ; 37 ( 62.00 -33.24 -42.75 0.81) ; 38 ( 62.58 -34.48 -43.00 0.52) ; 39 ( 63.25 -35.14 -43.06 0.52) ; 40 ( 63.98 -35.87 -44.00 0.88) ; 41 ( 64.87 -36.38 -44.81 0.96) ; 42 ( 65.90 -36.89 -44.81 0.59) ; 43 ( 67.37 -37.48 -45.44 0.29) ; 44 ( 68.77 -38.14 -45.44 0.29) ; 45 ( 70.17 -39.01 -45.94 0.52) ; 46 ( 71.72 -39.74 -44.75 0.66) ; 47 ( 72.75 -40.26 -44.94 0.66) ; 48 ( 74.00 -41.06 -45.06 0.66) ; 49 ( 75.04 -41.50 -45.13 1.03) ; 50 ( 76.36 -42.16 -45.56 1.69) ; 51 ( 76.88 -42.59 -45.56 1.69) ; 52 ( 77.32 -43.11 -45.56 1.18) ; 53 ( 77.69 -43.62 -45.56 0.81) ; 54 ( 78.35 -43.91 -45.56 0.44) ; 55 ( 79.38 -45.23 -45.44 0.29) ; 56 ( 80.34 -45.96 -45.00 0.66) ; 57 ( 80.93 -46.32 -47.00 1.03) ; 58 ( 82.18 -47.20 -47.69 1.40) ; 59 ( 83.14 -47.86 -47.69 1.11) ; 60 ( 84.17 -48.30 -47.69 0.66) ; 61 ( 85.05 -49.32 -48.69 0.52) ; 62 ( 85.64 -50.12 -48.69 0.88) ; 63 ( 86.53 -50.93 -49.69 0.88) ; 64 ( 87.27 -51.15 -50.94 1.11) ; 65 ( 87.78 -51.59 -50.94 0.66) ; 66 ( 88.37 -52.02 -51.75 0.44) ; 67 ( 89.40 -52.90 -52.88 0.37) ; 68 ( 89.70 -53.56 -54.69 0.37) ; 69 ( 90.21 -54.44 -55.94 0.37) ; 70 ( 90.73 -55.09 -55.94 0.74) ; 71 ( 91.54 -55.83 -56.63 1.18) ; 72 ( 91.98 -55.83 -56.63 1.69) ; 73 ( 92.50 -56.56 -58.69 1.33) ; 74 ( 92.64 -57.00 -61.63 0.66) ; 75 Low ) ; End of split | ( 28.61 0.15 -14.94 0.96) ; 1, R-2 ( 28.90 -0.73 -12.94 1.25) ; 2 ( 28.97 -2.19 -11.50 1.03) ; 3 ( 29.20 -3.44 -9.19 0.88) ; 4 ( 29.56 -4.68 -9.00 0.88) ; 5 ( 30.08 -5.77 -8.19 0.88) ; 6 ( 30.89 -6.43 -8.13 0.66) ; 7 ( 31.85 -6.72 -8.13 0.66) ; 8 ( 32.95 -7.31 -8.31 0.88) ; 9 ( 33.76 -7.67 -8.69 0.88) ; 10 ( 35.09 -8.26 -9.13 0.88) ; 11 ( 35.68 -9.21 -9.56 1.03) ; 12 ( 36.12 -9.94 -9.56 1.03) ; 13 ( 37.15 -10.89 -9.81 0.88) ; 14 ( 38.04 -11.48 -10.13 0.88) ; 15 ( 38.63 -12.79 -10.19 0.74) ; 16 ( 38.99 -13.45 -10.25 0.74) ; 17 ( 39.66 -13.60 -10.75 0.96) ; 18 ( 40.39 -13.89 -13.00 0.96) ; 19 ( 40.69 -13.74 -15.00 1.18) ; 20 ( 41.28 -14.18 -16.19 0.88) ; 21 ( 42.01 -15.72 -16.19 0.74) ; 22 ( 42.46 -16.81 -17.69 0.74) ; 23 ( 42.97 -17.47 -17.69 0.74) ; 24 ( 43.78 -17.54 -17.88 0.74) ; 25 ( 44.74 -18.20 -18.19 0.88) ; 26 ( 45.85 -18.86 -18.88 0.88) ; 27 ( 46.80 -20.03 -18.94 1.11) ; 28 ( 47.61 -20.32 -19.38 1.11) ; 29 ( 48.87 -20.54 -19.63 0.88) ; 30 ( 49.60 -20.91 -20.81 0.88) ; 31 ( 50.41 -21.05 -22.13 0.88) ; 32 ( 51.15 -21.42 -23.00 0.88) ; 33 ( 51.52 -21.93 -24.06 1.25) ; 34 ( 51.96 -22.15 -24.44 1.40) ; 35 ( 52.70 -22.66 -25.56 1.18) ; 36 ( 53.80 -23.10 -26.38 0.88) ; 37 ( 54.54 -23.90 -26.63 0.88) ; 38 ( 55.28 -24.41 -27.31 0.88) ; 39 ( 55.94 -24.78 -28.88 0.88) ; 40 ( 56.60 -25.44 -28.88 0.88) ; 41 ( 57.49 -26.24 -29.75 0.66) ; 42 ( 58.37 -26.68 -30.06 0.66) ; 43 ( 58.89 -27.56 -30.25 0.88) ; 44 ( 59.62 -28.58 -30.44 0.96) ; 45 ( 60.68 -29.65 -30.81 0.81) ; 46 ( 61.27 -30.38 -31.13 0.74) ; 47 ( 62.16 -31.26 -31.13 0.59) ; 48 ( 63.19 -31.70 -31.19 0.59) ; 49 ( 64.15 -31.84 -31.94 0.96) ; 50 ( 65.33 -32.14 -32.19 0.96) ; 51 ( 66.28 -31.92 -32.38 0.96) ; 52 ( 67.76 -32.14 -33.00 0.74) ; 53 ( 69.82 -32.65 -33.31 0.59) ; 54 ( 70.70 -32.72 -33.44 0.96) ; 55 ( 71.81 -32.79 -34.69 0.74) ; 56 ( 72.99 -32.65 -34.94 0.59) ; 57 ( 73.58 -32.79 -35.06 0.59) ; 58 ( 74.90 -33.38 -35.31 0.59) ; 59 ( 75.79 -33.74 -34.56 0.81) ; 60 ( 77.19 -34.26 -34.25 0.81) ; 61 ( 78.59 -35.06 -34.13 0.81) ; 62 ( 79.40 -35.72 -33.81 1.18) ; 63 ( 80.28 -36.30 -33.88 1.55) ; 64 ( 80.94 -36.81 -33.88 2.21) ; 65 ( ( 82.20 -36.52 -34.00 1.11) ; 1, R-2-1 ( 83.15 -36.59 -33.94 0.74) ; 2 ( 83.97 -36.74 -33.94 0.52) ; 3 ( 84.55 -36.59 -33.94 0.52) ; 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1.25) ; 18 ( 3.57 24.20 20.56 1.33) ; 19 ( 3.76 25.42 20.94 1.62) ; 20 ( 3.58 26.64 20.94 1.33) ; 21 ( 3.25 28.09 21.00 1.62) ; 22 ( 2.78 29.35 21.25 1.77) ; 23 ( 2.47 30.21 21.25 1.55) ; 24 ( 1.95 31.62 21.56 1.62) ; 25 ( 1.54 32.79 22.19 2.14) ; 26 ( 0.96 34.29 22.56 3.09) ; 27 ( 0.33 35.06 23.06 4.35) ; 28 ( -0.22 35.82 23.44 4.94) ; 29 ( -1.02 36.90 23.44 5.89) ; 30 ( -1.49 37.72 23.50 6.56) ; 31 ( -2.25 39.02 23.50 5.45) ; 32 ( -2.57 39.81 23.50 4.57) ; 33 ( -3.11 41.15 23.50 4.57) ; 34 ( -3.78 42.08 23.50 5.08) ; 35 ( -4.26 42.81 23.50 4.13) ; 36 Normal ) ; End of tree ( (Color Magenta) (Dendrite) ( -12.55 -4.91 6.63 2.73) ; Root ( -13.15 -5.77 5.88 1.25) ; 1, R ( -13.74 -6.71 5.88 1.03) ; 2 ( -14.36 -7.35 5.63 1.18) ; 3 ( -15.29 -8.10 6.00 1.18) ; 4 ( -16.22 -8.76 6.00 1.18) ; 5 ( -16.76 -8.83 6.25 1.25) ; 6 ( -17.80 -9.26 6.13 0.96) ; 7 ( -18.65 -9.49 5.00 1.11) ; 8 ( -19.43 -9.66 4.88 0.96) ; 9 ( -20.30 -10.48 4.50 0.88) ; 10 ( -20.61 -11.21 4.50 1.11) ; 11 ( -21.03 -11.96 4.75 0.88) ; 12 ( -21.15 -12.75 4.75 0.59) ; 13 ( -21.51 -13.14 4.75 0.37) ; 14 ( -22.32 -13.52 4.94 0.88) ; 15 ( -22.95 -13.73 5.31 1.55) ; 16 ( -23.78 -13.82 5.31 1.11) ; 17 ( -24.87 -13.65 5.31 1.03) ; 18 ( -25.62 -13.22 5.31 0.96) ; 19 ( -26.90 -12.80 5.06 0.81) ; 20 ( -27.59 -12.91 5.06 0.81) ; 21 ( -28.18 -12.90 5.06 0.66) ; 22 ( -28.93 -12.85 5.00 1.03) ; 23 ( -29.78 -13.08 4.69 0.88) ; 24 ( -30.71 -13.31 4.31 0.59) ; 25 ( -31.69 -13.38 4.25 0.74) ; 26 ( -32.49 -13.25 3.81 0.81) ; 27 ( -33.45 -13.17 3.13 0.88) ; 28 ( -34.45 -12.87 2.50 0.88) ; 29 ( -35.26 -13.26 1.88 1.11) ; 30 ( -36.06 -13.65 1.13 0.81) ; 31 ( -36.97 -13.73 0.31 0.59) ; 32 ( -37.54 -13.56 -0.88 1.03) ; 33 ( -38.14 -13.54 -1.63 1.40) ; 34 ( -38.69 -13.23 -2.31 1.55) ; 35 ( -39.09 -12.94 -3.00 1.40) ; 36 ( -39.74 -12.84 -4.25 1.11) ; 37 ( -40.40 -12.81 -5.00 0.96) ; 38 ( -41.25 -13.41 -6.00 0.59) ; 39 ( -41.82 -13.70 -6.13 0.59) ; 40 ( -42.24 -14.08 -6.50 0.81) ; 41 ( -43.04 -14.39 -6.50 0.88) ; 42 ( -43.62 -14.75 -7.44 0.59) ; 43 ( -43.76 -15.16 -7.44 0.59) ; 44 ( -44.31 -15.30 -7.81 0.81) ; 45 ( -45.05 -15.33 -8.13 0.96) ; 46 ( -45.68 -15.53 -8.63 0.66) ; 47 ( -46.37 -15.50 -8.94 1.18) ; 48 ( -47.41 -15.48 -9.31 1.18) ; 49 ( -48.47 -15.16 -9.69 0.88) ; 50 ( -49.22 -15.20 -9.69 0.88) ; 51 ( -50.10 -15.57 -10.13 0.81) ; 52 ( -50.81 -15.84 -10.81 0.81) ; 53 ( -51.68 -16.14 -11.75 1.03) ; 54 ( -52.41 -16.03 -12.81 1.18) ; 55 ( -53.29 -16.41 -13.56 0.88) ; 56 ( -53.88 -16.31 -14.81 0.66) ; 57 ( -54.62 -16.78 -15.00 0.52) ; 58 ( -55.21 -17.20 -15.38 0.52) ; 59 ( -56.41 -17.24 -15.63 0.66) ; 60 ( -56.91 -17.16 -16.69 0.74) ; 61 ( -57.99 -17.29 -17.13 0.74) ; 62 ( -59.27 -17.38 -17.13 0.59) ; 63 ( -60.00 -17.78 -17.69 0.52) ; 64 ( -60.34 -18.02 -17.69 0.52) ; 65 ( -60.96 -18.15 -17.69 0.81) ; 66 ( -61.79 -18.24 -17.88 1.47) ; 67 ( -62.66 -18.55 -17.94 1.47) ; 68 ( -63.14 -18.32 -17.94 1.11) ; 69 ( -63.67 -18.32 -18.00 0.66) ; 70 ( -64.55 -18.25 -18.00 0.44) ; 71 ( -65.36 -18.63 -18.25 0.37) ; 72 ( -66.24 -19.01 -19.25 0.37) ; 73 ( -66.74 -18.93 -19.69 0.52) ; 74 ( -67.82 -18.62 -20.19 0.96) ; 75 ( -68.87 -18.68 -20.88 2.14) ; 76 ( -69.50 -18.87 -21.50 2.87) ; 77 ( -70.37 -19.18 -22.06 2.43) ; 78 ( -71.24 -19.49 -22.44 1.03) ; 79 ( -72.02 -19.73 -22.63 0.59) ; 80 ( -72.52 -20.02 -22.75 0.44) ; 81 ( -72.95 -20.40 -23.13 0.44) ; 82 ( -73.77 -20.93 -23.44 0.59) ; 83 ( -74.63 -21.10 -24.50 0.29) ; 84 ( -75.61 -21.23 -24.88 0.29) ; 85 ( -76.31 -21.86 -25.25 0.96) ; 86 ( -76.56 -22.49 -25.63 0.59) ; 87 ( -76.89 -23.18 -26.00 0.29) ; 88 ( -77.26 -24.08 -26.44 0.52) ; 89 ( -77.66 -24.69 -26.44 0.66) ; 90 ( -78.16 -25.57 -27.19 0.96) ; 91 ( -78.64 -25.71 -27.88 0.96) ; 92 ( -79.10 -25.78 -28.44 0.59) ; 93 ( -79.66 -26.07 -28.69 0.37) ; 94 ( -80.54 -26.45 -29.50 0.96) ; 95 ( -81.26 -26.70 -29.88 1.77) ; 96 ( -81.90 -26.97 -30.19 0.88) ; 97 ( -82.87 -26.97 -30.69 0.66) ; 98 ( -83.24 -26.98 -31.25 1.40) ; 99 ( -83.99 -27.01 -31.56 2.14) ; 100 ( -84.50 -26.93 -32.25 1.77) ; 101 ( -85.43 -27.15 -32.56 0.59) ; 102 ( -86.56 -27.19 -33.50 0.37) ; 103 ( -87.16 -27.25 -33.69 0.66) ; 104 ( -87.79 -27.00 -34.38 0.66) ; 105 ( -88.71 -26.63 -35.25 0.29) ; 106 ( -89.76 -26.69 -35.69 0.37) ; 107 ( -90.52 -26.79 -36.00 0.74) ; 108 ( -91.28 -26.89 -36.31 1.55) ; 109 ( -92.08 -27.21 -37.13 0.66) ; 110 ( -92.97 -27.58 -37.44 0.81) ; 111 ( -93.17 -27.91 -37.94 1.69) ; 112 ( -93.45 -28.24 -38.06 1.25) ; 113 ( -93.44 -28.16 -40.50 0.74) ; 114 Normal ) ; End of tree ( (Color Magenta) (Dendrite) ( 1.67 -7.23 4.25 3.24) ; Root ( 1.98 -8.10 5.50 3.02) ; 1, R ( 2.22 -8.95 5.50 3.02) ; 2 ( ( 2.30 -8.97 4.00 4.20) ; 1, R-1 ( 2.59 -9.99 3.63 4.13) ; 2 ( ( 2.21 -10.96 3.13 3.32) ; 1, R-1-1 ( 1.94 -11.73 2.56 2.87) ; 2 ( ( 2.34 -12.46 2.38 1.77) ; 1, R-1-1-1 ( 2.49 -12.93 2.38 1.11) ; 2 ( 2.79 -13.87 2.38 0.88) ; 3 ( 3.14 -14.96 2.13 0.96) ; 4 ( 3.48 -15.60 1.81 1.03) ; 5 ( 3.89 -16.33 1.81 1.11) ; 6 ( 4.11 -16.81 1.69 1.33) ; 7 ( 4.29 -17.58 1.56 0.81) ; 8 ( 4.70 -18.31 1.56 1.25) ; 9 ( 4.97 -18.94 1.38 1.25) ; 10 ( 5.08 -19.63 1.38 1.03) ; 11 ( 5.42 -20.35 1.31 1.18) ; 12 ( 5.83 -21.53 1.13 1.62) ; 13 ( 6.27 -22.56 0.94 1.77) ; 14 ( 6.54 -23.63 0.88 1.25) ; 15 ( 6.64 -24.46 0.50 1.18) ; 16 ( 6.66 -25.28 0.50 0.88) ; 17 ( 6.99 -26.00 0.19 0.74) ; 18 ( 7.37 -26.87 0.19 0.88) ; 19 ( 7.82 -27.76 -0.44 1.11) ; 20 ( 8.25 -28.42 -0.88 1.11) ; 21 ( 8.28 -29.27 -1.19 1.25) ; 22 ( 8.37 -30.17 -1.50 1.84) ; 23 ( 8.44 -30.63 -1.50 1.84) ; 24 ( 8.45 -31.52 -1.63 1.40) ; 25 ( 8.48 -32.33 -1.63 1.62) ; 26 ( 8.37 -32.99 -1.81 1.25) ; 27 ( 8.59 -33.98 -1.81 1.11) ; 28 ( 8.85 -35.14 -1.94 0.96) ; 29 ( 9.03 -36.35 -2.19 1.18) ; 30 ( 9.24 -37.35 -2.88 1.33) ; 31 ( 9.62 -38.29 -2.88 1.18) ; 32 ( 9.92 -39.22 -3.75 1.18) ; 33 ( 10.41 -39.89 -3.75 1.03) ; 34 ( 11.08 -40.82 -4.19 1.11) ; 35 ( 11.62 -41.71 -4.19 1.33) ; 36 ( 11.55 -42.59 -4.19 1.11) ; 37 ( 11.08 -43.63 -4.31 0.96) ; 38 ( 10.45 -44.79 -4.81 0.88) ; 39 ( 9.72 -45.63 -5.00 1.11) ; 40 ( 9.37 -46.47 -5.00 1.11) ; 41 ( 9.05 -47.52 -5.38 1.03) ; 42 ( 9.06 -48.41 -4.88 1.25) ; 43 ( 8.96 -49.51 -4.88 1.77) ; 44 ( 9.06 -50.34 -5.06 2.06) ; 45 ( 8.52 -51.81 -5.75 2.28) ; 46 ( ( 8.44 -53.28 -6.31 2.36) ; 1, R-1-1-1-1 ( 8.59 -54.19 -6.44 1.77) ; 2 ( 8.98 -55.06 -6.38 1.33) ; 3 ( 9.32 -55.97 -6.81 0.88) ; 4 ( 10.22 -56.93 -6.81 0.74) ; 5 ( 11.09 -57.50 -6.94 0.88) ; 6 ( 11.68 -58.04 -7.13 0.88) ; 7 ( 12.10 -58.70 -7.13 0.96) ; 8 ( 12.36 -59.85 -7.31 1.18) ; 9 ( 12.55 -60.55 -7.44 1.33) ; 10 ( 12.93 -60.98 -7.31 1.18) ; 11 ( 13.10 -61.82 -7.13 1.03) ; 12 ( 13.28 -62.96 -6.63 1.11) ; 13 ( 13.58 -63.97 -5.94 1.33) ; 14 ( 13.57 -64.93 -5.19 1.11) ; 15 ( 13.67 -65.69 -5.19 0.96) ; 16 ( 13.58 -66.79 -5.19 0.88) ; 17 ( 13.67 -67.61 -5.25 0.88) ; 18 ( 13.59 -68.56 -5.25 1.11) ; 19 ( 13.79 -69.70 -5.63 1.33) ; 20 ( 13.98 -70.84 -6.06 0.96) ; 21 ( 14.06 -71.74 -6.50 0.88) ; 22 ( 14.37 -72.60 -6.50 1.11) ; 23 ( 14.78 -73.33 -6.94 0.96) ; 24 ( 15.02 -74.19 -7.00 0.96) ; 25 ( 15.26 -75.04 -7.44 1.11) ; 26 ( 15.69 -75.56 -8.00 1.11) ; 27 ( 15.97 -76.63 -8.06 0.96) ; 28 ( 16.25 -77.64 -8.63 0.88) ; 29 ( 16.55 -78.65 -8.63 0.88) ; 30 ( 16.86 -79.52 -8.63 1.11) ; 31 ( 17.29 -80.55 -8.88 0.88) ; 32 ( 17.51 -81.99 -8.94 0.81) ; 33 ( 18.49 -82.88 -9.13 1.03) ; 34 ( 19.18 -83.73 -9.13 0.74) ; 35 ( 19.89 -84.57 -9.31 0.59) ; 36 ( 20.28 -85.45 -9.56 1.55) ; 37 ( 20.99 -86.15 -9.56 1.77) ; 38 ( 21.75 -87.01 -9.94 1.03) ; 39 ( 22.38 -87.71 -9.50 0.66) ; 40 ( 23.01 -88.40 -9.63 0.66) ; 41 ( 23.55 -89.30 -9.63 0.66) ; 42 ( 24.59 -89.76 -9.63 0.96) ; 43 ( 25.39 -90.40 -10.25 1.03) ; 44 ( 26.12 -90.97 -10.38 1.11) ; 45 ( 26.76 -91.58 -10.75 0.96) ; 46 ( 27.59 -92.52 -11.31 0.96) ; 47 ( 28.25 -93.07 -11.31 0.66) ; 48 ( 29.79 -94.14 -11.50 0.66) ; 49 ( 30.57 -94.92 -11.50 0.96) ; 50 ( 31.09 -95.82 -10.94 0.88) ; 51 ( 31.74 -96.89 -10.88 1.18) ; 52 ( 31.97 -97.82 -10.88 1.33) ; 53 ( 32.29 -98.61 -10.50 0.81) ; 54 ( 32.44 -100.04 -10.56 0.74) ; 55 ( 32.30 -100.90 -10.56 0.81) ; 56 ( 32.24 -101.78 -10.50 0.81) ; 57 ( 32.87 -103.44 -10.38 0.88) ; 58 ( 33.69 -104.37 -10.19 0.88) ; 59 ( 33.94 -105.15 -9.75 0.52) ; 60 ( 33.83 -105.81 -9.69 0.52) ; 61 ( 34.08 -106.65 -9.69 1.18) ; 62 ( 34.23 -107.05 -9.63 1.84) ; 63 ( 34.29 -107.65 -9.63 2.21) ; 64 ( 34.21 -108.60 -9.56 1.25) ; 65 ( 34.54 -109.32 -9.81 0.88) ; 66 ( 34.77 -109.80 -10.00 0.52) ; 67 ( 35.46 -110.20 -10.00 0.52) ; 68 ( 35.43 -110.80 -10.00 0.88) ; 69 ( 35.50 -111.77 -10.19 0.88) ; 70 ( 35.81 -112.63 -10.63 0.74) ; 71 ( 36.07 -113.78 -10.63 0.66) ; 72 ( 36.41 -114.47 -11.19 0.59) ; 73 ( 36.43 -115.28 -11.38 0.81) ; 74 ( 36.29 -116.22 -11.38 1.18) ; 75 ( 36.27 -116.81 -11.38 1.92) ; 76 ( 36.18 -117.39 -11.50 1.92) ; 77 ( 36.06 -118.18 -11.63 1.69) ; 78 ( 35.85 -118.96 -11.63 0.81) ; 79 ( 35.89 -119.71 -11.63 0.52) ; 80 ( 36.21 -120.50 -11.75 0.44) ; 81 ( 36.22 -120.94 -11.19 0.44) ; 82 ( 36.08 -121.81 -11.13 0.44) ; 83 ( 35.92 -122.30 -11.19 0.88) ; 84 ( 35.97 -123.42 -11.19 0.96) ; 85 ( 35.58 -124.47 -11.19 1.03) ; 86 ( 35.36 -125.84 -11.19 0.81) ; 87 ( 35.51 -126.83 -11.25 1.03) ; 88 ( 35.59 -127.73 -10.81 0.88) ; 89 ( 35.49 -128.82 -10.81 0.74) ; 90 ( 35.46 -129.93 -10.75 0.59) ; 91 ( 35.45 -131.40 -10.69 0.52) ; 92 ( 35.47 -132.23 -10.88 1.11) ; 93 ( 35.61 -133.21 -10.88 1.47) ; 94 ( 35.66 -133.88 -10.88 1.77) ; 95 ( 35.79 -134.86 -10.88 1.25) ; 96 ( 35.76 -135.60 -10.94 0.59) ; 97 ( 35.86 -136.36 -11.19 0.37) ; 98 ( 35.38 -137.02 -10.69 0.37) ; 99 ( 35.70 -137.81 -10.63 0.37) ; 100 ( 35.91 -138.43 -10.63 1.11) ; 101 ( 36.26 -139.45 -10.25 0.96) ; 102 ( 36.70 -140.48 -10.06 0.59) ; 103 ( 36.78 -140.94 -9.69 0.37) ; 104 ( 37.00 -141.75 -9.44 0.29) ; 105 ( 36.86 -142.62 -9.44 0.29) ; 106 ( 37.10 -143.47 -8.31 0.29) ; 107 ( 37.15 -144.59 -7.81 0.44) ; 108 ( 37.04 -144.80 -7.81 0.44) ; 109 Normal | ( 8.37 -52.62 -7.19 0.59) ; 1, R-1-1-1-2 ( 8.47 -53.37 -7.56 0.59) ; 2 ( 8.29 -54.01 -7.94 0.59) ; 3 ( 8.04 -55.15 -7.94 0.59) ; 4 ( 7.90 -56.02 -7.81 0.59) ; 5 ( 7.39 -56.90 -7.81 0.88) ; 6 ( 7.00 -57.50 -7.81 1.18) ; 7 ( 6.44 -58.15 -7.81 0.59) ; 8 ( 5.90 -58.74 -7.81 0.52) ; 9 ( 5.14 -59.80 -7.81 0.74) ; 10 ( 4.57 -60.60 -7.81 0.74) ; 11 ( 4.55 -61.63 -7.81 0.88) ; 12 ( 4.38 -62.72 -8.44 1.33) ; 13 ( 3.95 -64.05 -9.00 1.55) ; 14 ( 3.88 -64.93 -9.31 1.55) ; 15 ( 3.73 -65.86 -9.50 1.11) ; 16 ( 3.53 -67.17 -9.50 0.81) ; 17 ( 2.91 -68.70 -10.63 0.66) ; 18 ( 2.70 -69.99 -10.75 1.03) ; 19 ( 2.53 -70.64 -11.19 1.03) ; 20 ( 1.43 -71.87 -11.25 0.74) ; 21 ( 0.85 -72.74 -11.25 0.81) ; 22 ( 0.10 -73.73 -11.25 0.81) ; 23 ( -0.74 -74.78 -11.56 1.18) ; 24 ( -1.50 -75.84 -11.38 1.92) ; 25 ( -1.98 -76.51 -11.38 2.73) ; 26 ( -2.47 -77.24 -11.38 1.92) ; 27 ( -3.14 -78.03 -11.44 1.03) ; 28 ( -3.71 -79.27 -11.81 0.81) ; 29 ( -4.72 -80.51 -12.00 0.66) ; 30 ( -5.50 -81.64 -12.13 0.88) ; 31 ( -6.14 -83.40 -12.25 0.88) ; 32 ( -6.42 -84.17 -12.63 0.59) ; 33 ( -6.68 -85.31 -12.75 0.37) ; 34 ( -7.16 -86.05 -12.75 0.88) ; 35 ( -7.91 -86.52 -12.75 1.18) ; 36 ( -8.37 -87.04 -12.75 0.74) ; 37 ( -8.56 -87.82 -13.31 0.52) ; 38 ( -8.17 -88.18 -13.31 0.52) ; 39 ( -8.04 -89.24 -13.81 0.66) ; 40 ( -8.38 -90.00 -14.50 0.74) ; 41 ( -8.29 -90.83 -15.69 1.11) ; 42 ( -8.28 -91.64 -16.44 1.77) ; 43 ( -8.21 -92.17 -16.69 2.43) ; 44 ( -8.10 -92.86 -17.06 1.47) ; 45 ( -8.17 -93.81 -17.06 0.81) ; 46 ( -8.28 -94.53 -18.00 0.66) ; 47 ( -8.39 -95.70 -18.00 0.81) ; 48 ( -8.49 -96.28 -18.63 1.03) ; 49 ( -8.46 -97.54 -19.31 1.03) ; 50 ( -8.58 -98.26 -19.31 0.59) ; 51 ( -8.52 -99.30 -20.44 0.59) ; 52 ( -8.45 -100.27 -21.25 0.59) ; 53 ( -8.47 -100.94 -21.25 0.59) ; 54 ( -8.81 -101.11 -21.56 0.59) ; 55 ( -9.03 -101.14 -22.06 0.59) ; 56 ( -9.49 -102.18 -22.06 0.74) ; 57 ( -9.70 -102.96 -22.81 1.33) ; 58 ( -10.21 -103.84 -23.25 1.84) ; 59 ( -10.30 -104.56 -23.75 1.11) ; 60 ( -10.68 -105.08 -24.81 0.52) ; 61 ( -10.79 -105.80 -25.38 0.52) ; 62 ( -10.40 -106.60 -26.56 0.66) ; 63 ( -10.63 -107.09 -27.50 0.59) ; 64 ( -10.71 -107.60 -27.88 1.40) ; 65 ( -10.95 -108.22 -28.19 2.43) ; 66 ( -11.06 -108.87 -28.94 2.14) ; 67 ( -11.30 -109.43 -28.94 1.11) ; 68 ( -11.24 -110.47 -29.63 0.74) ; 69 ( -11.70 -111.51 -30.13 0.74) ; 70 ( -12.18 -112.18 -31.06 0.44) ; 71 ( -12.43 -112.80 -32.25 0.44) ; 72 ( -13.32 -113.25 -32.44 1.18) ; 73 ( -13.62 -113.72 -33.00 2.06) ; 74 ( -13.88 -114.42 -33.75 1.11) ; 75 ( -14.39 -115.30 -33.75 0.59) ; 76 ( -14.61 -116.23 -34.50 0.59) ; 77 ( -15.19 -117.09 -34.88 0.44) ; 78 ( -15.61 -117.33 -36.44 0.44) ; 79 ( -16.42 -118.24 -36.56 0.44) ; 80 ( -16.43 -119.20 -36.88 0.81) ; 81 ( -16.78 -120.03 -38.06 0.52) ; 82 ( -17.34 -120.75 -38.25 1.11) ; 83 ( -17.98 -121.03 -39.25 1.92) ; 84 ( -18.78 -121.34 -39.44 2.36) ; 85 ( -19.25 -122.01 -40.44 1.33) ; 86 ( -19.37 -122.73 -42.31 0.59) ; 87 Normal ) ; End of split | ( 0.57 -12.57 2.88 2.06) ; 1, R-1-1-2 ( -0.33 -13.10 2.81 2.14) ; 2 ( -1.32 -14.13 2.56 2.14) ; 3 ( -2.46 -14.76 2.44 1.99) ; 4 ( -3.59 -15.33 2.63 2.06) ; 5 ( -4.64 -16.26 1.75 2.28) ; 6 ( -5.33 -17.34 0.63 2.65) ; 7 ( -6.05 -18.12 0.00 3.39) ; 8 ( -6.81 -19.18 -0.56 3.68) ; 9 ( -7.56 -19.65 -0.56 3.98) ; 10 ( ( -8.47 -20.69 -0.38 2.87) ; 1, R-1-1-2-1 ( -8.79 -21.31 -0.38 2.28) ; 2 ( ( -8.99 -22.53 0.31 1.55) ; 1, R-1-1-2-1-1 ( -9.23 -23.61 0.38 1.33) ; 2 ( -9.33 -24.26 0.44 1.25) ; 3 ( -9.51 -25.41 0.81 1.33) ; 4 ( ( -9.55 -26.56 1.00 1.03) ; 1, R-1-1-2-1-1-1 ( -8.91 -27.26 1.31 0.66) ; 2 ( -8.85 -28.23 1.31 0.81) ; 3 ( -8.89 -29.48 1.94 1.03) ; 4 ( -9.10 -30.27 2.44 1.18) ; 5 ( -9.23 -31.13 2.69 1.18) ; 6 ( -9.43 -31.92 2.69 1.03) ; 7 ( -10.13 -32.55 3.00 1.03) ; 8 ( -10.29 -33.55 3.06 1.03) ; 9 ( -10.26 -34.38 3.06 0.88) ; 10 ( -10.18 -35.27 3.06 0.88) ; 11 ( -10.25 -36.15 3.06 1.25) ; 12 ( -10.54 -37.07 2.94 1.25) ; 13 ( -10.61 -37.95 3.06 0.96) ; 14 ( -10.84 -38.94 3.44 0.96) ; 15 ( -11.28 -40.28 3.63 0.96) ; 16 ( -11.56 -41.13 3.63 1.40) ; 17 ( -11.85 -42.04 3.63 1.25) ; 18 ( -12.09 -42.59 3.88 1.03) ; 19 ( -11.94 -43.58 3.63 0.81) ; 20 ( -11.86 -44.48 3.63 0.81) ; 21 ( -12.02 -45.49 3.38 1.03) ; 22 ( -12.25 -46.41 3.38 1.40) ; 23 ( -12.15 -47.25 3.38 1.47) ; 24 ( -12.05 -48.45 2.81 1.11) ; 25 ( -12.13 -49.40 3.00 1.11) ; 26 ( -12.25 -50.19 3.00 0.74) ; 27 ( -12.81 -51.44 3.00 0.81) ; 28 ( -12.98 -52.28 3.00 0.74) ; 29 ( -12.45 -53.69 3.00 0.74) ; 30 ( -12.60 -54.63 3.00 0.74) ; 31 ( -12.86 -55.77 2.88 0.88) ; 32 ( -13.24 -56.76 2.88 0.88) ; 33 ( -13.64 -57.88 2.88 0.88) ; 34 ( -13.81 -58.96 2.88 0.59) ; 35 ( -14.12 -59.95 3.13 1.11) ; 36 ( -14.28 -60.96 3.13 0.96) ; 37 ( -14.19 -61.78 3.13 0.74) ; 38 ( -14.44 -62.48 3.13 0.74) ; 39 ( -14.83 -63.53 3.13 0.96) ; 40 ( -15.12 -64.38 3.13 1.47) ; 41 ( -15.35 -65.82 3.13 0.74) ; 42 ( -15.46 -66.54 3.13 0.74) ; 43 ( -15.73 -68.28 2.81 0.59) ; 44 ( -16.02 -69.56 2.81 0.52) ; 45 ( -15.23 -70.28 2.63 0.52) ; 46 ( -14.36 -71.38 2.63 0.59) ; 47 ( -14.12 -72.23 3.19 0.59) ; 48 ( -13.40 -72.86 2.75 0.81) ; 49 ( -12.68 -73.49 2.38 1.11) ; 50 ( -12.07 -74.34 2.06 1.25) ; 51 ( -11.46 -75.17 1.63 0.96) ; 52 ( -10.87 -76.15 1.13 0.59) ; 53 ( -10.56 -77.02 0.88 0.59) ; 54 ( -10.14 -77.67 0.25 0.66) ; 55 ( -10.15 -78.63 0.06 0.96) ; 56 ( -9.73 -79.41 -1.19 1.11) ; 57 ( -9.47 -80.12 -1.19 0.66) ; 58 ( -9.16 -81.49 -1.19 0.44) ; 59 ( -9.17 -82.45 -1.44 0.74) ; 60 ( -9.04 -83.51 -1.44 0.74) ; 61 ( -9.07 -85.13 -1.56 0.59) ; 62 ( -9.53 -86.62 -2.19 0.59) ; 63 ( -9.42 -87.82 -2.88 0.59) ; 64 ( -9.73 -88.36 -2.88 0.74) ; 65 ( -10.46 -89.20 -2.88 0.52) ; 66 ( -11.18 -89.98 -3.38 0.52) ; 67 ( -11.87 -91.05 -3.38 0.66) ; 68 ( -12.09 -91.91 -3.38 0.52) ; 69 ( -12.46 -92.44 -3.69 0.88) ; 70 ( -12.72 -93.07 -3.50 1.03) ; 71 ( -12.60 -93.75 -3.31 0.59) ; 72 ( -13.07 -94.35 -3.31 0.37) ; 73 ( -13.64 -95.66 -4.13 0.81) ; 74 ( -14.17 -96.61 -4.00 0.74) ; 75 ( -14.77 -97.63 -4.44 0.44) ; 76 ( -15.15 -98.53 -3.50 0.74) ; 77 ( -15.15 -99.49 -3.50 0.74) ; 78 ( -15.28 -100.28 -3.00 0.37) ; 79 ( -14.96 -101.07 -2.75 0.37) ; 80 ( -15.30 -102.36 -2.56 0.52) ; 81 ( -15.55 -102.98 -2.56 1.18) ; 82 ( -15.72 -104.06 -2.56 1.55) ; 83 ( -15.85 -105.30 -1.13 1.55) ; 84 ( -15.42 -106.25 -1.19 1.62) ; 85 ( -15.38 -107.37 -1.44 1.03) ; 86 ( -15.45 -108.32 -1.31 0.81) ; 87 ( -15.15 -109.17 -1.00 0.66) ; 88 ( -15.04 -110.45 -0.75 0.44) ; 89 ( -14.57 -111.26 -0.75 0.81) ; 90 ( -14.40 -112.03 -0.44 1.47) ; 91 ( -13.98 -112.69 -0.44 0.96) ; 92 ( -13.72 -113.32 -0.44 0.44) ; 93 ( -13.31 -114.05 0.19 0.44) ; 94 ( -12.62 -114.90 0.44 0.59) ; 95 ( -12.00 -115.74 0.44 0.44) ; 96 ( -11.64 -116.69 0.81 0.66) ; 97 ( -11.44 -117.38 2.25 0.59) ; 98 ( -11.83 -117.91 2.25 0.59) ; 99 ( -11.59 -118.32 2.25 0.59) ; 100 ( -10.69 -118.76 2.81 0.59) ; 101 ( -10.69 -119.21 2.81 0.59) ; 102 ( -11.26 -120.01 3.44 1.18) ; 103 ( -11.81 -120.66 3.75 1.92) ; 104 ( -12.43 -121.23 4.31 2.21) ; 105 ( -12.61 -121.94 4.69 2.21) ; 106 ( -13.13 -122.37 4.88 1.33) ; 107 ( -13.43 -122.84 5.19 0.52) ; 108 ( -13.67 -123.47 5.75 0.52) ; 109 ( -13.38 -124.41 5.75 0.52) ; 110 ( -13.04 -125.12 5.31 0.52) ; 111 ( -12.42 -125.89 4.81 0.59) ; 112 Normal | ( -9.82 -26.18 0.25 0.96) ; 1, R-1-1-2-1-1-2 ( -10.23 -26.85 0.75 0.88) ; 2 ( -10.89 -27.78 1.31 0.88) ; 3 ( -11.40 -28.66 2.19 1.40) ; 4 ( -12.62 -29.73 2.88 1.11) ; 5 ( -13.32 -30.43 3.44 0.88) ; 6 ( -13.81 -31.61 3.56 0.59) ; 7 ( -14.25 -32.50 4.00 0.66) ; 8 ( -14.48 -33.43 4.56 1.25) ; 9 ( -14.80 -34.56 4.94 1.11) ; 10 ( -14.96 -35.58 5.44 1.11) ; 11 ( -14.96 -36.46 5.44 0.81) ; 12 ( -15.11 -37.91 5.50 0.81) ; 13 ( -15.14 -39.09 5.75 0.96) ; 14 ( -15.19 -39.39 6.00 1.33) ; 15 ( -15.37 -40.54 5.88 0.88) ; 16 ( -15.42 -41.72 5.88 0.88) ; 17 ( -15.85 -42.61 5.88 0.66) ; 18 ( -16.18 -43.30 5.75 0.52) ; 19 ( -16.65 -44.33 5.44 0.96) ; 20 ( -17.23 -45.65 5.69 1.11) ; 21 ( -17.69 -46.69 6.00 1.11) ; 22 ( -17.82 -47.92 6.13 1.40) ; 23 ( -18.10 -48.84 5.94 1.40) ; 24 ( -18.38 -49.62 6.69 0.96) ; 25 ( -18.63 -50.68 6.69 0.81) ; 26 ( -19.34 -51.90 6.63 0.96) ; 27 ( -19.79 -52.86 6.63 0.96) ; 28 ( -20.15 -53.70 6.13 0.96) ; 29 ( -20.38 -54.70 5.88 0.96) ; 30 ( -20.54 -55.71 5.19 0.96) ; 31 ( -20.26 -56.79 4.88 1.03) ; 32 ( -20.47 -57.65 4.44 1.03) ; 33 ( -20.61 -58.95 3.88 1.11) ; 34 ( -21.06 -59.99 3.88 0.96) ; 35 ( -21.33 -61.14 3.06 0.74) ; 36 ( -21.56 -62.13 3.88 0.88) ; 37 ( -21.69 -63.45 4.00 1.03) ; 38 ( -21.60 -64.79 4.06 1.33) ; 39 ( -21.67 -66.11 4.44 1.11) ; 40 ( -21.70 -66.85 4.44 1.11) ; 41 ( -21.27 -68.32 4.63 0.96) ; 42 ( -21.24 -69.07 4.81 0.96) ; 43 ( -21.28 -70.32 4.44 0.81) ; 44 ( -21.48 -71.55 4.44 0.81) ; 45 ( -21.44 -72.73 3.31 1.03) ; 46 ( -21.52 -73.68 3.13 1.11) ; 47 ( -21.72 -74.81 2.50 1.11) ; 48 ( -22.27 -75.98 2.25 1.47) ; 49 ( -22.81 -77.01 2.25 1.18) ; 50 ( -22.91 -78.10 1.94 0.74) ; 51 ( -22.92 -79.57 1.88 0.66) ; 52 ( -22.36 -80.26 1.75 0.66) ; 53 ( -22.00 -81.73 1.63 0.74) ; 54 ( -21.62 -83.11 1.44 0.88) ; 55 ( -21.39 -84.55 1.44 0.88) ; 56 ( -21.30 -85.39 1.94 1.03) ; 57 ( -21.54 -86.38 2.00 1.18) ; 58 ( -21.21 -87.18 2.19 0.81) ; 59 ( -20.98 -88.55 2.19 0.59) ; 60 ( -20.72 -89.69 2.19 0.59) ; 61 ( -20.32 -90.43 2.19 0.88) ; 62 ( -19.57 -91.43 2.19 0.74) ; 63 ( -18.77 -92.52 3.44 0.74) ; 64 ( -18.16 -93.29 3.94 0.88) ; 65 ( -17.72 -94.32 4.50 0.96) ; 66 ( -17.44 -95.33 5.88 0.96) ; 67 ( -17.11 -96.12 6.31 0.74) ; 68 ( -16.69 -96.77 6.69 0.59) ; 69 ( -16.83 -98.16 6.69 0.59) ; 70 ( -16.78 -99.21 6.69 0.96) ; 71 ( -16.34 -100.24 6.44 1.47) ; 72 ( -16.17 -101.00 8.38 1.47) ; 73 ( -16.05 -101.89 8.94 0.96) ; 74 ( -15.97 -103.31 9.19 0.96) ; 75 ( -15.91 -104.36 9.69 0.81) ; 76 ( -16.34 -105.18 10.56 0.74) ; 77 ( -17.25 -106.14 10.63 0.52) ; 78 ( -17.34 -107.24 11.13 0.66) ; 79 ( -17.73 -108.74 11.13 0.81) ; 80 ( -18.03 -110.17 11.13 1.03) ; 81 ( -18.40 -111.59 11.19 0.74) ; 82 ( -18.96 -112.75 11.69 0.96) ; 83 ( -19.42 -113.28 11.75 0.96) ; 84 ( -19.96 -114.38 11.75 0.59) ; 85 ( -20.55 -115.24 12.31 1.11) ; 86 ( -21.07 -116.12 12.50 1.69) ; 87 ( -21.81 -117.11 12.50 1.33) ; 88 ( -22.21 -117.72 12.94 0.88) ; 89 ( -23.00 -118.48 13.06 0.88) ; 90 ( -24.11 -119.34 13.13 1.18) ; 91 ( -25.23 -119.83 13.56 1.18) ; 92 ( -26.06 -120.37 14.13 0.66) ; 93 ( -26.60 -120.50 14.19 0.66) ; 94 ( -27.49 -120.95 14.56 0.88) ; 95 ( -28.11 -121.52 14.56 0.66) ; 96 ( -28.76 -122.31 14.69 0.66) ; 97 ( -29.42 -122.72 14.88 0.59) ; 98 ( -30.29 -123.03 14.88 0.81) ; 99 ( -30.95 -123.44 14.88 0.81) ; 100 ( -31.50 -124.61 15.13 0.59) ; 101 ( -32.23 -125.90 15.56 0.74) ; 102 ( -32.71 -126.96 15.44 0.59) ; 103 ( -32.99 -127.73 15.31 1.03) ; 104 ( -32.90 -128.56 14.94 1.03) ; 105 ( -32.90 -129.52 15.81 0.59) ; 106 ( -32.83 -130.05 15.69 0.59) ; 107 ( -32.87 -130.79 15.75 0.74) ; 108 ( -33.11 -131.34 15.88 0.59) ; 109 ( -33.40 -131.74 15.94 0.88) ; 110 ( -33.54 -132.16 15.94 1.18) ; 111 ( -33.35 -132.86 16.00 0.59) ; 112 Normal ) ; End of split | ( -8.95 -22.33 -2.38 1.18) ; 1, R-1-1-2-1-2 ( -9.18 -22.90 -2.75 1.03) ; 2 ( -9.57 -23.42 -2.75 0.74) ; 3 ( -10.46 -23.88 -2.75 0.74) ; 4 ( -11.15 -24.44 -3.19 0.74) ; 5 ( -11.79 -25.22 -3.81 0.81) ; 6 ( -12.65 -25.90 -4.75 1.40) ; 7 ( -13.93 -26.44 -5.56 1.03) ; 8 ( -14.98 -26.94 -6.06 1.33) ; 9 ( -16.18 -26.96 -6.38 1.33) ; 10 ( -17.34 -26.34 -7.44 1.40) ; 11 ( -18.33 -26.41 -7.56 1.25) ; 12 ( -18.81 -26.18 -7.88 1.55) ; 13 ( -19.76 -26.03 -8.88 1.33) ; 14 ( -20.21 -26.11 -10.69 0.52) ; 15 ( -21.09 -25.97 -10.75 0.52) ; 16 ( -21.52 -26.34 -11.38 1.03) ; 17 ( -21.91 -26.94 -12.81 1.03) ; 18 ( -22.45 -27.53 -13.81 0.81) ; 19 ( -22.94 -27.81 -15.13 1.11) ; 20 ( -23.65 -28.97 -15.69 1.33) ; 21 ( -24.32 -29.89 -16.19 1.11) ; 22 ( -25.00 -30.45 -16.00 0.81) ; 23 ( -25.42 -31.20 -16.06 0.44) ; 24 ( -25.85 -32.02 -16.63 0.44) ; 25 ( -26.14 -32.49 -17.19 1.18) ; 26 ( -26.51 -32.88 -17.94 1.99) ; 27 ( -27.00 -32.73 -18.25 1.55) ; 28 ( -27.72 -32.98 -18.38 0.81) ; 29 ( -28.67 -33.35 -19.06 0.59) ; 30 ( -30.04 -33.50 -19.19 0.59) ; 31 ( -30.90 -34.26 -19.44 0.81) ; 32 ( -31.82 -34.85 -20.75 0.81) ; 33 ( -32.49 -35.33 -22.38 0.81) ; 34 ( -32.89 -35.94 -22.81 0.66) ; 35 ( -33.25 -36.40 -23.44 0.96) ; 36 ( -33.66 -37.07 -23.69 1.62) ; 37 ( -34.28 -37.71 -24.38 2.43) ; 38 ( -34.66 -38.18 -25.06 2.50) ; 39 ( -35.20 -38.75 -25.25 1.47) ; 40 ( -35.68 -39.42 -25.25 0.88) ; 41 ( -36.06 -40.02 -25.50 0.52) ; 42 ( -36.86 -40.79 -25.75 0.66) ; 43 ( -37.69 -41.32 -25.75 0.66) ; 44 ( -38.18 -42.06 -25.94 0.66) ; 45 ( -38.64 -43.03 -26.69 0.66) ; 46 ( -39.19 -43.68 -27.00 0.66) ; 47 ( -39.30 -44.40 -27.00 0.66) ; 48 ( -39.80 -45.20 -27.50 0.59) ; 49 ( -40.30 -46.01 -28.44 0.59) ; 50 ( -40.90 -46.51 -28.81 1.55) ; 51 ( -41.61 -47.21 -29.31 2.14) ; 52 ( -42.04 -48.03 -30.06 1.25) ; 53 ( -42.52 -48.77 -30.06 0.59) ; 54 ( -43.04 -49.34 -30.06 0.37) ; 55 ( -43.54 -50.16 -30.69 0.66) ; 56 ( -44.05 -50.52 -30.69 0.59) ; 57 ( -44.47 -50.82 -30.94 0.66) ; 58 ( -44.94 -51.41 -31.75 1.25) ; 59 ( -45.27 -51.58 -32.50 1.99) ; 60 ( -45.44 -51.78 -33.19 1.99) ; 61 ( -46.14 -51.89 -33.69 1.55) ; 62 ( -47.10 -52.33 -35.63 1.55) ; 63 ( -47.10 -52.33 -36.75 2.28) ; 64 ( -47.76 -52.30 -37.63 2.28) ; 65 ( -48.19 -52.23 -38.50 0.74) ; 66 ( -49.18 -52.30 -39.06 0.59) ; 67 ( -49.90 -52.55 -39.06 0.59) ; 68 ( -50.33 -53.00 -40.69 1.18) ; 69 ( -50.68 -53.32 -42.13 1.77) ; 70 ( -51.34 -53.66 -42.88 0.74) ; 71 ( -51.85 -54.09 -43.44 0.59) ; 72 ( -52.49 -54.36 -43.56 1.18) ; 73 ( -53.35 -54.60 -43.69 1.55) ; 74 ( -54.15 -54.47 -45.06 0.74) ; 75 ( -54.61 -54.99 -46.31 0.44) ; 76 ( -55.57 -55.43 -46.31 0.44) ; 77 ( -55.96 -56.04 -47.56 1.18) ; 78 ( -56.41 -56.48 -47.94 2.43) ; 79 ( -56.48 -56.92 -48.25 2.43) ; 80 ( -56.87 -57.52 -48.25 1.18) ; 81 ( -56.80 -58.05 -48.88 0.66) ; 82 ( -57.12 -58.67 -48.88 0.37) ; 83 ( -57.22 -59.31 -48.88 0.37) ; 84 ( -57.73 -60.12 -48.88 0.37) ; 85 ( -58.19 -60.71 -49.13 1.11) ; 86 ( -58.33 -61.58 -49.13 1.40) ; 87 ( -58.86 -62.09 -49.44 0.74) ; 88 ( -59.03 -62.66 -49.81 0.37) ; 89 ( -59.14 -63.38 -49.94 0.37) ; 90 ( -59.37 -63.93 -50.56 1.11) ; 91 ( -59.84 -64.52 -50.88 1.40) ; 92 ( -60.25 -65.20 -51.00 1.40) ; 93 ( -60.92 -65.68 -51.63 0.52) ; 94 ( -61.31 -66.28 -52.06 0.88) ; 95 ( -61.53 -66.70 -52.63 0.88) ; 96 Normal ) ; End of split | ( -9.20 -19.63 -0.25 2.58) ; 1, R-1-1-2-2 ( -10.18 -20.15 0.00 1.69) ; 2 ( -10.94 -20.25 0.13 1.11) ; 3 ( -12.17 -20.43 1.25 0.88) ; 4 ( -12.83 -20.84 2.19 1.11) ; 5 ( -13.52 -21.47 2.25 0.74) ; 6 ( -14.47 -21.83 3.06 1.18) ; 7 ( -14.91 -22.21 3.44 1.18) ; 8 ( -15.65 -22.68 3.69 1.18) ; 9 ( -16.12 -23.27 4.06 1.18) ; 10 ( -16.75 -23.91 4.44 0.88) ; 11 ( -18.04 -24.53 4.75 0.81) ; 12 ( -18.56 -25.03 4.88 0.81) ; 13 ( -19.29 -25.81 5.94 0.96) ; 14 ( -19.84 -26.53 6.75 0.74) ; 15 ( -20.27 -27.36 6.75 0.74) ; 16 ( -21.35 -28.08 7.44 1.11) ; 17 ( -22.21 -28.75 7.75 1.40) ; 18 ( -22.96 -29.23 8.50 0.81) ; 19 ( -23.82 -29.97 8.88 0.81) ; 20 ( -24.99 -30.75 9.13 0.66) ; 21 ( -26.03 -31.18 9.13 0.44) ; 22 ( -27.45 -31.70 9.13 0.44) ; 23 ( -28.42 -32.13 9.25 0.74) ; 24 ( -29.18 -32.76 8.63 0.88) ; 25 ( -30.16 -32.82 8.69 0.88) ; 26 ( -31.27 -32.79 8.69 0.66) ; 27 ( -32.21 -33.02 8.69 1.33) ; 28 ( -33.49 -33.11 8.81 1.18) ; 29 ( -34.62 -33.15 8.19 0.96) ; 30 ( -36.14 -33.36 8.19 0.96) ; 31 ( -37.11 -33.86 8.19 0.88) ; 32 ( -38.13 -34.15 8.19 0.74) ; 33 ( -38.76 -34.35 7.69 0.66) ; 34 ( -39.66 -34.42 7.69 0.88) ; 35 ( -40.72 -34.47 7.69 1.03) ; 36 ( -41.53 -34.87 7.69 1.03) ; 37 ( -42.44 -35.47 7.13 0.96) ; 38 ( -43.26 -35.92 7.19 0.96) ; 39 ( -43.77 -36.29 7.13 0.96) ; 40 ( -44.42 -36.63 6.19 0.66) ; 41 ( -45.36 -37.38 6.19 0.88) ; 42 ( -46.52 -37.63 5.44 0.88) ; 43 ( -47.14 -38.28 5.44 0.96) ; 44 ( -47.85 -38.98 5.50 1.18) ; 45 ( -48.51 -39.39 5.31 0.96) ; 46 ( -49.59 -40.11 5.19 1.18) ; 47 ( -50.09 -40.73 5.19 1.40) ; 48 ( -50.57 -41.40 4.94 1.25) ; 49 ( -51.44 -42.22 4.94 0.74) ; 50 ( -52.33 -42.67 4.94 0.74) ; 51 ( -53.59 -43.50 4.94 0.74) ; 52 ( -54.31 -44.73 4.75 0.74) ; 53 ( -55.08 -45.41 5.00 0.74) ; 54 ( -56.05 -45.93 5.25 1.03) ; 55 ( -56.80 -46.33 6.06 1.25) ; 56 ( -57.37 -46.68 6.06 0.81) ; 57 ( -57.85 -47.79 6.38 0.74) ; 58 ( -58.62 -48.92 6.81 0.74) ; 59 ( -59.57 -49.73 6.88 0.74) ; 60 ( -60.92 -50.27 6.88 0.59) ; 61 ( -62.39 -51.07 6.88 0.59) ; 62 ( -63.03 -51.33 7.00 0.88) ; 63 ( -64.00 -51.33 6.75 1.25) ; 64 ( -64.90 -51.34 6.56 1.40) ; 65 ( -65.92 -51.69 6.00 1.11) ; 66 ( -66.82 -52.22 6.00 0.88) ; 67 ( -68.14 -53.42 6.00 0.74) ; 68 ( -69.25 -53.90 5.94 0.66) ; 69 ( -69.76 -54.71 6.75 1.62) ; 70 ( -70.19 -55.09 6.88 1.62) ; 71 ( -70.95 -56.08 6.94 0.74) ; 72 ( -71.69 -57.00 6.94 0.52) ; 73 ( -72.22 -58.03 6.94 0.52) ; 74 ( -72.59 -58.48 6.94 0.88) ; 75 ( -72.95 -59.31 6.94 0.88) ; 76 ( -73.74 -60.07 6.94 0.74) ; 77 ( -74.27 -60.14 6.94 0.74) ; 78 ( -75.02 -60.61 6.94 1.11) ; 79 ( -75.72 -61.32 6.94 0.81) ; 80 ( -76.20 -61.98 6.94 0.81) ; 81 ( -76.69 -62.71 6.94 0.81) ; 82 ( -77.45 -63.25 7.06 0.66) ; 83 ( -78.34 -64.14 6.50 0.52) ; 84 ( -79.06 -64.48 6.38 0.52) ; 85 ( -79.66 -65.42 6.38 0.59) ; 86 ( -80.16 -66.23 6.31 0.59) ; 87 ( -80.33 -66.87 6.69 0.74) ; 88 ( -81.00 -67.80 6.69 0.52) ; 89 ( -81.91 -68.32 7.06 0.52) ; 90 ( -82.85 -68.68 7.31 0.52) ; 91 ( -84.02 -69.91 8.19 0.74) ; 92 ( -85.00 -70.49 8.44 0.74) ; 93 ( -85.93 -71.09 8.94 1.33) ; 94 ( -86.71 -71.78 9.44 1.25) ; 95 ( -87.03 -72.40 9.69 1.55) ; 96 ( -87.70 -72.88 9.69 1.18) ; 97 ( -88.66 -73.32 9.69 0.81) ; 98 ( -89.05 -73.85 9.69 0.59) ; 99 ( -89.77 -74.18 9.75 0.44) ; 100 ( -90.60 -74.27 9.88 0.44) ; 101 ( -91.22 -74.84 9.94 0.88) ; 102 ( -92.11 -75.29 10.19 0.96) ; 103 ( -92.86 -76.21 10.44 0.96) ; 104 ( -93.51 -76.54 10.44 0.96) ; 105 ( -94.70 -77.02 10.63 0.88) ; 106 ( -95.59 -77.48 10.88 0.96) ; 107 ( -96.16 -77.75 11.75 0.74) ; 108 ( -97.21 -78.77 12.25 0.44) ; 109 ( -98.22 -79.43 12.31 1.03) ; 110 ( -98.79 -79.78 12.38 1.47) ; 111 ( -99.60 -80.18 12.63 1.47) ; 112 ( -100.13 -80.75 12.63 1.11) ; 113 ( -100.90 -81.38 12.63 0.81) ; 114 ( -101.90 -81.96 12.63 0.59) ; 115 ( -103.48 -82.59 12.63 0.59) ; 116 ( -104.38 -82.60 12.63 0.44) ; 117 ( -105.15 -83.22 12.38 0.74) ; 118 ( -105.67 -83.65 12.38 0.52) ; 119 ( -106.20 -83.79 11.94 0.37) ; 120 ( -107.20 -83.92 11.94 0.37) ; 121 ( -107.91 -84.12 12.44 0.96) ; 122 ( -108.80 -84.12 12.81 0.96) ; 123 ( -109.22 -84.65 12.94 0.52) ; 124 ( -109.79 -84.93 13.63 0.52) ; 125 ( -110.88 -85.27 13.75 0.81) ; 126 ( -111.59 -85.09 14.63 0.52) ; 127 ( -112.54 -84.94 14.63 0.74) ; 128 ( -113.18 -84.69 14.63 0.44) ; 129 ( -113.84 -84.21 14.63 0.44) ; 130 ( -115.17 -83.12 14.63 0.44) ; 131 ( -115.89 -83.00 14.56 0.59) ; 132 ( -116.28 -82.64 14.56 0.59) ; 133 Normal ) ; End of split ) ; End of split | ( 2.99 -10.39 4.13 1.99) ; 1, R-1-2 ( 3.85 -10.67 5.13 1.99) ; 2 ( 4.96 -11.21 5.13 1.18) ; 3 ( 5.64 -11.54 5.13 0.81) ; 4 ( 6.64 -11.92 5.13 0.81) ; 5 ( 7.76 -12.84 5.13 0.96) ; 6 ( 8.56 -13.41 5.13 0.81) ; 7 ( 9.39 -13.84 5.13 1.03) ; 8 ( 10.23 -14.19 5.19 1.03) ; 9 ( 10.86 -14.89 5.19 1.25) ; 10 ( 11.52 -15.44 5.19 0.96) ; 11 ( 12.23 -16.14 5.19 0.96) ; 12 ( 12.73 -16.74 5.19 0.74) ; 13 ( 13.26 -17.64 4.81 0.88) ; 14 ( 13.29 -18.38 4.63 1.03) ; 15 ( 13.24 -19.19 4.25 1.18) ; 16 ( 13.43 -20.33 3.56 1.18) ; 17 ( 13.63 -21.40 3.56 1.25) ; 18 ( 14.13 -21.99 3.44 1.11) ; 19 ( 14.56 -22.66 2.94 1.40) ; 20 ( 15.02 -23.47 2.94 1.11) ; 21 ( 16.06 -23.93 2.69 0.88) ; 22 ( 17.05 -24.38 2.44 0.88) ; 23 ( 17.66 -24.70 2.25 0.81) ; 24 ( 18.30 -24.88 1.63 0.81) ; 25 ( 18.73 -25.02 1.44 0.81) ; 26 ( 19.16 -25.60 1.38 1.03) ; 27 ( 19.30 -26.14 0.63 1.33) ; 28 ( 19.74 -26.66 0.63 0.96) ; 29 ( 20.11 -27.16 -0.06 0.81) ; 30 ( 20.86 -27.57 -0.06 0.81) ; 31 ( 21.49 -27.82 -0.88 1.03) ; 32 ( 22.27 -28.09 -1.75 1.25) ; 33 ( 23.31 -28.63 -1.75 0.96) ; 34 ( 23.76 -29.06 -2.31 0.96) ; 35 ( 23.99 -29.92 -2.56 1.33) ; 36 ( 24.15 -30.84 -3.25 2.14) ; 37 ( 24.30 -32.08 -4.13 2.14) ; 38 ( 24.40 -32.83 -4.88 1.55) ; 39 ( 24.63 -33.24 -5.88 1.18) ; 40 ( 25.11 -33.54 -6.13 0.96) ; 41 ( 25.83 -33.73 -6.69 0.81) ; 42 ( 26.79 -33.74 -8.06 0.96) ; 43 ( 27.54 -33.77 -8.50 1.25) ; 44 ( 28.37 -33.68 -9.00 1.55) ; 45 ( 29.25 -33.75 -10.06 1.62) ; 46 ( 29.87 -34.59 -11.31 0.96) ; 47 ( 30.38 -35.56 -12.06 0.81) ; 48 ( 30.85 -36.37 -12.50 0.88) ; 49 ( 31.64 -36.57 -13.69 0.74) ; 50 ( 32.08 -36.64 -14.38 0.74) ; 51 ( 32.87 -37.28 -14.56 1.03) ; 52 ( 33.08 -37.84 -14.81 1.03) ; 53 ( 33.73 -38.46 -15.31 1.18) ; 54 ( ( 34.29 -38.69 -15.31 0.66) ; 1, R-1-2-1 ( 35.11 -38.68 -15.50 0.59) ; 2 ( 35.91 -38.81 -15.50 0.59) ; 3 ( 36.49 -38.90 -15.50 0.59) ; 4 ( 37.96 -39.06 -15.75 0.59) ; 5 ( 39.27 -39.27 -16.00 0.96) ; 6 ( 40.13 -39.47 -16.19 1.55) ; 7 ( 41.08 -39.62 -16.88 1.55) ; 8 ( 41.93 -39.90 -16.88 0.74) ; 9 ( 42.77 -39.82 -17.25 0.52) ; 10 ( 43.29 -39.75 -18.13 0.81) ; 11 ( 44.03 -39.87 -18.75 1.11) ; 12 ( 44.95 -40.17 -19.00 0.88) ; 13 ( 45.82 -40.74 -19.00 0.88) ; 14 ( 46.38 -40.98 -19.13 0.66) ; 15 ( 47.15 -41.32 -19.13 0.52) ; 16 ( 47.92 -41.60 -19.38 0.81) ; 17 ( 48.71 -41.87 -19.38 0.81) ; 18 ( 49.58 -42.00 -19.38 0.44) ; 19 ( 50.28 -42.34 -19.81 0.44) ; 20 ( 50.63 -42.91 -20.06 0.81) ; 21 ( 51.34 -43.62 -20.38 0.81) ; 22 ( 52.18 -44.42 -20.38 0.52) ; 23 ( 52.85 -44.89 -21.31 0.66) ; 24 ( 53.43 -45.50 -21.31 0.96) ; 25 ( 54.02 -45.97 -21.81 1.77) ; 26 ( 54.40 -46.39 -22.50 2.21) ; 27 ( 54.99 -46.93 -23.13 1.84) ; 28 ( 56.04 -47.39 -23.13 0.74) ; 29 ( 56.87 -47.74 -23.63 0.59) ; 30 ( 57.45 -48.36 -24.06 0.59) ; 31 ( 58.00 -49.11 -24.50 0.44) ; 32 ( 58.19 -49.81 -24.50 0.81) ; 33 ( 58.58 -50.16 -24.94 1.69) ; 34 ( 59.04 -50.54 -25.25 2.65) ; 35 ( 59.50 -50.98 -25.69 2.28) ; 36 ( 59.83 -51.70 -25.69 0.88) ; 37 ( 59.90 -52.22 -25.69 0.44) ; 38 ( 60.48 -52.76 -26.13 0.44) ; 39 ( 60.39 -53.33 -26.38 0.44) ; 40 ( 61.03 -54.48 -26.63 0.81) ; 41 ( 61.30 -55.11 -27.38 1.62) ; 42 ( 61.75 -55.55 -27.75 1.62) ; 43 ( 61.96 -55.73 -28.06 0.59) ; 44 ( 62.56 -56.13 -28.06 0.44) ; 45 ( 63.26 -56.46 -28.06 0.44) ; 46 ( 64.00 -56.94 -28.50 0.74) ; 47 ( 64.81 -57.89 -28.69 0.59) ; 48 ( 65.40 -58.92 -29.06 0.59) ; 49 ( 65.73 -59.63 -29.38 0.59) ; 50 ( 66.45 -60.70 -29.38 0.81) ; 51 ( 66.99 -61.61 -29.38 0.59) ; 52 ( 67.11 -62.21 -29.44 0.52) ; 53 ( 67.58 -63.03 -29.75 0.88) ; 54 ( 67.96 -63.46 -30.00 1.69) ; 55 ( 68.37 -64.12 -30.00 2.50) ; 56 ( 69.02 -64.74 -30.25 1.55) ; 57 ( 69.86 -65.62 -30.25 0.52) ; 58 ( 70.55 -65.87 -30.44 0.44) ; 59 ( 71.20 -66.50 -30.44 0.44) ; 60 ( 72.09 -67.52 -29.75 0.59) ; 61 ( 72.78 -68.30 -29.50 1.03) ; 62 ( 73.46 -68.78 -29.31 0.66) ; 63 ( 74.12 -69.32 -29.31 0.37) ; 64 ( 74.85 -69.88 -29.06 0.37) ; 65 ( 75.45 -70.28 -28.69 0.74) ; 66 ( 76.19 -70.83 -28.69 1.47) ; 67 ( 76.62 -71.43 -28.75 1.47) ; 68 ( 77.12 -72.02 -29.06 0.59) ; 69 Normal | ( 33.41 -39.49 -15.19 0.66) ; 1, R-1-2-2 ( 33.33 -40.44 -17.38 0.59) ; 2 ( 33.90 -41.12 -18.06 0.81) ; 3 ( 34.36 -42.00 -17.94 1.92) ; 4 ( 34.77 -42.67 -18.13 1.92) ; 5 ( 35.35 -43.79 -18.25 0.96) ; 6 ( 36.12 -44.51 -18.69 0.74) ; 7 ( 36.78 -45.06 -19.50 0.44) ; 8 ( 37.64 -46.23 -19.50 0.44) ; 9 ( 38.25 -47.13 -20.13 0.74) ; 10 ( 38.54 -48.07 -20.31 1.47) ; 11 ( 38.93 -48.95 -20.94 0.96) ; 12 ( 39.45 -49.92 -20.94 0.52) ; 13 ( 39.58 -50.90 -21.56 0.37) ; 14 Normal ) ; End of split ) ; End of split | ( 2.66 -8.95 4.13 2.21) ; 1, R-2 ( 2.73 -8.96 1.75 2.06) ; 2 ( 3.93 -9.89 -0.13 2.21) ; 3 ( 5.06 -11.18 -1.31 2.21) ; 4 ( 6.50 -12.00 -3.13 2.36) ; 5 ( 7.14 -12.68 -5.56 2.06) ; 6 ( 7.65 -13.73 -7.06 1.92) ; 7 ( 8.59 -14.85 -7.75 1.92) ; 8 ( 9.12 -15.74 -7.63 1.99) ; 9 ( 9.49 -16.24 -7.63 1.99) ; 10 ( ( 10.39 -16.68 -7.63 1.03) ; 1, R-2-1 ( 11.01 -17.00 -7.63 0.66) ; 2 ( 12.04 -17.54 -7.63 0.59) ; 3 ( 12.63 -18.51 -7.94 0.74) ; 4 ( 13.58 -19.63 -8.63 0.88) ; 5 ( 14.40 -20.58 -9.25 0.88) ; 6 ( 15.01 -21.41 -9.94 0.88) ; 7 ( 15.79 -22.12 -10.00 1.47) ; 8 ( 16.66 -22.71 -11.00 0.96) ; 9 ( 17.93 -23.65 -11.00 0.74) ; 10 ( 18.91 -24.54 -11.31 0.96) ; 11 ( 19.79 -25.13 -11.31 0.96) ; 12 ( 19.68 -25.85 -11.81 0.81) ; 13 ( 19.84 -26.69 -12.50 0.74) ; 14 ( 20.01 -27.53 -13.13 0.59) ; 15 ( 20.58 -28.14 -14.25 0.66) ; 16 ( 21.20 -28.46 -15.63 1.18) ; 17 ( 21.83 -28.78 -16.44 1.18) ; 18 ( 22.65 -29.20 -16.94 1.03) ; 19 ( 24.15 -29.66 -17.31 0.81) ; 20 ( 25.39 -30.23 -17.50 0.88) ; 21 ( 26.24 -31.04 -17.88 0.88) ; 22 ( 27.24 -31.78 -18.69 0.88) ; 23 ( 27.76 -32.31 -18.69 0.88) ; 24 ( 28.33 -33.36 -19.56 1.47) ; 25 ( 28.63 -34.30 -20.75 1.25) ; 26 ( 29.12 -34.97 -21.63 0.88) ; 27 ( 29.89 -36.20 -21.63 0.66) ; 28 ( 30.85 -37.25 -22.75 0.44) ; 29 ( 31.49 -38.38 -22.88 0.66) ; 30 ( 31.88 -39.18 -23.31 0.96) ; 31 ( 32.31 -39.83 -23.69 1.18) ; 32 ( 33.00 -40.61 -25.25 1.40) ; 33 ( 33.67 -41.61 -25.63 1.40) ; 34 ( 34.75 -42.92 -25.88 1.47) ; 35 ( 35.59 -43.79 -26.69 1.77) ; 36 ( 36.45 -45.92 -27.38 1.47) ; 37 ( 36.59 -46.83 -27.88 1.11) ; 38 ( 37.29 -47.60 -28.25 0.88) ; 39 ( 38.09 -48.70 -28.50 0.66) ; 40 ( 38.46 -49.72 -29.00 0.66) ; 41 ( 39.68 -50.94 -29.25 0.96) ; 42 ( 40.27 -51.85 -29.25 1.25) ; 43 ( 41.17 -52.36 -29.25 0.88) ; 44 ( 42.24 -53.12 -29.56 0.59) ; 45 ( 42.56 -53.48 -29.69 0.59) ; 46 ( 43.11 -54.22 -29.69 0.88) ; 47 ( 44.19 -54.92 -30.13 1.18) ; 48 ( 44.97 -55.18 -30.13 1.18) ; 49 ( 45.67 -55.52 -30.63 0.74) ; 50 ( 46.84 -55.63 -30.69 0.52) ; 51 ( 47.56 -55.75 -31.13 0.52) ; 52 ( 47.91 -55.94 -31.13 1.18) ; 53 ( 49.12 -56.29 -31.19 1.69) ; 54 ( 50.23 -56.84 -31.00 0.96) ; 55 ( 51.16 -57.06 -31.00 0.59) ; 56 ( 52.10 -57.28 -31.00 0.59) ; 57 ( 53.13 -57.81 -31.00 0.74) ; 58 ( 54.02 -58.32 -31.06 0.81) ; 59 ( 55.26 -58.51 -31.56 0.52) ; 60 ( 56.06 -58.64 -31.81 1.33) ; 61 ( 56.87 -58.77 -32.19 2.58) ; 62 ( 57.84 -58.70 -32.44 2.80) ; 63 ( 58.72 -58.84 -32.44 1.84) ; 64 ( 59.44 -58.95 -33.00 0.81) ; 65 ( 59.88 -59.02 -33.00 0.37) ; 66 ( 60.60 -59.66 -33.00 0.37) ; 67 ( 61.44 -60.97 -34.06 0.37) ; 68 ( 61.84 -61.26 -34.56 1.25) ; 69 ( 62.74 -62.14 -35.38 2.14) ; 70 ( 63.50 -63.01 -35.56 1.62) ; 71 ( 64.08 -63.62 -35.56 0.59) ; 72 ( 64.92 -64.42 -37.06 0.44) ; 73 ( 65.26 -64.61 -37.06 0.74) ; 74 ( 65.69 -64.75 -37.38 0.74) ; 75 ( 66.01 -65.03 -38.00 0.74) ; 76 ( 66.48 -65.40 -38.00 0.96) ; 77 ( 67.05 -66.01 -38.50 0.96) ; 78 ( 67.63 -66.62 -38.50 0.59) ; 79 ( 68.42 -67.26 -38.75 0.44) ; 80 ( 68.92 -67.41 -39.56 0.74) ; 81 ( 69.47 -67.72 -40.31 1.62) ; 82 ( 70.44 -68.18 -40.31 2.36) ; 83 ( 70.96 -68.70 -40.31 0.81) ; 84 ( 71.77 -69.20 -40.56 0.52) ; 85 ( 72.75 -70.09 -40.81 0.52) ; 86 ( 73.78 -70.63 -40.81 0.88) ; 87 ( 74.13 -71.05 -40.81 0.44) ; 88 ( 74.80 -71.53 -40.81 0.66) ; 89 ( 75.50 -71.86 -40.81 0.66) ; 90 ( 75.99 -72.01 -41.19 0.44) ; 91 ( 77.22 -72.73 -40.69 1.03) ; 92 ( 77.97 -73.21 -40.69 1.99) ; 93 ( 78.70 -73.77 -40.63 1.99) ; 94 ( 79.58 -74.35 -40.63 1.92) ; 95 ( 80.39 -74.86 -40.75 1.11) ; 96 ( 80.78 -75.21 -40.75 0.59) ; 97 ( 81.62 -75.57 -40.75 0.37) ; 98 ( 82.37 -76.05 -40.75 0.37) ; 99 ( 83.01 -76.23 -41.06 0.66) ; 100 ( 83.91 -76.67 -41.06 0.66) ; 101 ( 84.94 -77.21 -41.06 0.37) ; 102 ( 85.43 -77.43 -41.06 1.11) ; 103 ( 86.13 -77.76 -41.06 1.47) ; 104 ( 86.77 -78.38 -41.06 0.66) ; 105 ( 87.60 -79.25 -41.56 0.37) ; 106 ( 87.98 -79.68 -41.88 0.37) ; 107 Normal | ( 9.44 -17.24 -7.69 0.81) ; 1, R-2-2 ( 9.11 -17.93 -7.81 0.44) ; 2 ( 8.48 -19.01 -8.50 0.52) ; 3 ( 7.91 -19.80 -9.50 0.88) ; 4 ( 7.12 -21.01 -9.56 1.03) ; 5 ( 6.63 -22.26 -9.38 1.40) ; 6 ( 6.34 -23.55 -9.69 1.18) ; 7 ( 6.31 -24.73 -10.31 0.81) ; 8 ( 6.34 -25.92 -10.44 1.33) ; 9 ( 6.35 -26.81 -11.13 1.47) ; 10 ( 6.20 -27.75 -11.19 1.47) ; 11 ( 5.51 -27.94 -11.81 1.11) ; 12 ( 4.80 -28.12 -12.50 1.18) ; 13 ( 4.48 -29.25 -13.31 1.18) ; 14 ( 4.69 -30.25 -14.13 1.25) ; 15 ( 4.80 -31.00 -15.06 0.88) ; 16 ( 4.14 -31.41 -15.81 0.81) ; 17 ( 3.50 -31.69 -16.88 0.81) ; 18 ( 3.04 -32.20 -18.31 1.11) ; 19 ( 2.78 -32.91 -19.44 1.11) ; 20 ( 2.66 -34.08 -20.13 1.11) ; 21 ( 2.62 -34.88 -20.88 0.81) ; 22 ( 3.16 -35.63 -21.56 0.81) ; 23 ( 3.07 -36.21 -22.69 0.81) ; 24 ( 2.03 -37.16 -22.88 0.59) ; 25 ( 1.77 -37.85 -23.38 1.40) ; 26 ( 1.44 -38.54 -24.44 2.28) ; 27 ( 1.35 -39.56 -25.00 2.58) ; 28 ( 0.84 -40.89 -25.00 2.06) ; 29 ( 0.38 -41.93 -25.00 1.03) ; 30 ( -0.04 -42.90 -25.31 0.74) ; 31 ( -0.29 -43.53 -25.44 0.74) ; 32 ( -1.24 -44.86 -25.69 0.74) ; 33 ( -1.57 -45.99 -25.69 0.81) ; 34 ( -2.29 -46.77 -26.44 0.96) ; 35 ( -2.57 -47.53 -26.69 1.18) ; 36 ( -2.85 -48.38 -27.19 0.96) ; 37 ( -3.79 -49.64 -28.25 1.18) ; 38 ( -4.45 -51.01 -29.56 1.40) ; 39 ( -5.23 -52.15 -29.56 1.11) ; 40 ( -5.95 -53.37 -29.56 0.74) ; 41 ( -6.74 -54.13 -30.25 0.52) ; 42 ( -7.31 -54.93 -31.00 0.52) ; 43 ( -8.02 -56.08 -32.38 1.11) ; 44 ( -8.18 -56.64 -32.38 2.36) ; 45 ( -8.51 -57.25 -32.38 3.24) ; 46 ( -8.62 -58.42 -32.75 1.69) ; 47 ( -8.62 -59.38 -32.75 0.59) ; 48 ( -8.73 -60.10 -32.75 0.81) ; 49 ( -8.82 -61.12 -32.75 0.81) ; 50 ( -8.98 -62.14 -32.75 0.29) ; 51 ( -8.68 -62.63 -32.75 0.29) ; 52 ( -8.93 -63.25 -32.75 0.74) ; 53 ( -8.95 -63.78 -32.75 1.03) ; 54 ( -9.02 -64.73 -34.00 0.52) ; 55 ( -9.11 -65.30 -34.06 0.52) ; 56 ( -9.00 -66.05 -34.13 1.18) ; 57 ( -8.81 -66.68 -34.13 1.84) ; 58 ( -8.75 -67.28 -34.25 0.81) ; 59 ( -8.51 -67.83 -35.94 0.59) ; 60 ( -8.45 -68.88 -36.19 0.22) ; 61 ( -8.20 -69.66 -36.25 0.59) ; 62 ( -7.87 -70.38 -36.50 0.59) ; 63 ( -7.48 -71.18 -36.63 0.37) ; 64 ( -7.32 -72.09 -37.06 1.11) ; 65 ( -7.52 -72.87 -37.25 1.33) ; 66 ( -7.79 -73.64 -37.75 0.52) ; 67 ( -8.01 -74.56 -38.38 0.52) ; 68 ( -8.04 -75.23 -38.94 0.88) ; 69 ( -8.24 -76.02 -39.63 1.62) ; 70 ( -8.54 -76.49 -40.00 2.43) ; 71 ( -8.71 -77.13 -40.00 3.32) ; 72 ( -9.00 -77.96 -40.44 1.18) ; 73 ( -9.02 -79.07 -41.50 0.29) ; 74 ( -9.69 -80.00 -42.13 1.03) ; 75 ( -9.99 -80.48 -42.50 1.03) ; 76 ( -10.38 -81.07 -42.75 0.52) ; 77 ( -10.86 -81.74 -43.31 0.15) ; 78 ( -11.43 -82.54 -43.31 1.03) ; 79 ( -11.59 -83.03 -45.06 2.43) ; 80 ( -12.48 -83.48 -45.50 2.43) ; 81 ( -12.79 -84.02 -45.69 1.25) ; 82 ( -13.17 -84.56 -47.50 0.44) ; 83 ( -13.65 -85.22 -48.13 0.66) ; 84 ( -14.27 -85.79 -49.69 1.47) ; 85 ( -14.85 -85.70 -50.38 2.06) ; 86 ( -15.40 -86.35 -50.63 1.03) ; 87 ( -15.78 -86.88 -51.56 0.52) ; 88 ( -16.37 -87.30 -53.00 0.88) ; 89 ( -16.70 -87.99 -53.38 1.25) ; 90 ( -16.79 -88.57 -54.88 0.59) ; 91 Normal ) ; End of split ) ; End of split ) ; End of tree ( (Color Magenta) (Dendrite) ( -11.25 -4.71 3.50 2.43) ; Root ( -11.40 -5.65 3.50 2.28) ; 1, R ( -11.53 -6.44 3.13 2.06) ; 2 ( -11.32 -7.50 3.06 1.92) ; 3 ( -11.21 -8.19 1.06 1.99) ; 4 ( -10.96 -9.41 -0.94 2.36) ; 5 ( -10.94 -10.24 -2.81 3.09) ; 6 ( -10.71 -11.15 -4.69 2.28) ; 7 ( -10.96 -12.23 -7.06 3.17) ; 8 ( -11.30 -12.54 -7.06 3.39) ; 9 ( ( -10.99 -13.41 -7.81 2.28) ; 1, R-1 ( -10.18 -14.42 -7.81 1.92) ; 2 ( -9.56 -15.71 -7.81 1.92) ; 3 ( -9.40 -16.54 -8.69 1.92) ; 4 ( -9.26 -17.53 -9.88 1.92) ; 5 ( -9.08 -18.81 -10.88 1.47) ; 6 ( -8.83 -20.03 -12.25 1.40) ; 7 ( -8.59 -20.89 -12.88 1.47) ; 8 ( -8.46 -21.88 -14.19 1.55) ; 9 ( -8.20 -23.10 -16.19 1.40) ; 10 ( -8.22 -23.20 -16.19 1.40) ; 11 ( ( -7.14 -23.49 -16.69 1.03) ; 1, R-1-1 ( -6.58 -24.17 -17.06 0.66) ; 2 ( -6.48 -25.44 -17.06 0.44) ; 3 ( -6.70 -26.81 -17.06 0.44) ; 4 ( -6.37 -27.98 -17.06 0.52) ; 5 ( -5.89 -28.72 -17.06 0.37) ; 6 ( -5.62 -29.87 -17.06 0.66) ; 7 ( -5.50 -31.00 -17.25 0.66) ; 8 ( -5.61 -31.72 -17.25 0.59) ; 9 ( -5.53 -32.63 -17.25 0.52) ; 10 ( -5.50 -33.37 -17.63 0.52) ; 11 ( -5.42 -34.27 -17.75 0.44) ; 12 ( -5.69 -35.29 -17.75 0.37) ; 13 ( -5.78 -36.31 -17.75 0.88) ; 14 ( -6.06 -37.08 -17.88 1.40) ; 15 ( -5.95 -37.83 -18.13 1.55) ; 16 ( -5.94 -38.73 -18.13 1.18) ; 17 ( -5.92 -39.54 -18.13 0.81) ; 18 ( -6.08 -40.56 -18.13 0.59) ; 19 ( -6.25 -42.08 -19.38 0.96) ; 20 ( -6.24 -43.42 -19.88 1.62) ; 21 ( -6.39 -43.91 -19.88 1.77) ; 22 ( -6.20 -44.61 -19.81 1.62) ; 23 ( -6.10 -45.36 -19.81 0.88) ; 24 ( -5.60 -46.48 -19.81 0.74) ; 25 ( -5.53 -47.45 -19.31 0.59) ; 26 ( -5.37 -48.29 -18.75 0.96) ; 27 ( -5.18 -49.95 -18.75 0.81) ; 28 ( -4.91 -51.10 -18.75 0.74) ; 29 ( -4.56 -52.12 -18.75 0.66) ; 30 ( -4.63 -53.51 -18.69 0.44) ; 31 ( -4.80 -54.60 -18.38 0.44) ; 32 ( -4.99 -55.83 -18.38 1.03) ; 33 ( -5.19 -57.05 -18.38 1.69) ; 34 ( -5.00 -58.20 -18.75 1.77) ; 35 ( -5.21 -59.12 -18.75 2.06) ; 36 ( -5.03 -60.27 -18.75 1.18) ; 37 ( -5.02 -61.59 -18.75 0.74) ; 38 ( -4.88 -62.29 -19.38 0.66) ; 39 ( -4.92 -63.39 -19.38 0.66) ; 40 ( -4.94 -64.50 -19.38 0.96) ; 41 ( -4.91 -65.25 -19.38 1.33) ; 42 ( -4.63 -65.89 -19.38 0.96) ; 43 ( -4.34 -66.81 -19.38 0.52) ; 44 ( -3.46 -67.84 -19.38 0.52) ; 45 ( -2.86 -68.31 -19.38 0.44) ; 46 ( -2.39 -69.13 -19.38 0.96) ; 47 ( -2.20 -70.26 -19.38 0.59) ; 48 ( -2.12 -71.68 -20.44 0.59) ; 49 ( -2.10 -72.94 -20.44 1.18) ; 50 ( -1.90 -74.08 -21.19 0.96) ; 51 ( -1.48 -75.18 -21.94 0.81) ; 52 ( -1.79 -76.18 -22.19 0.59) ; 53 ( -1.80 -77.65 -22.75 0.59) ; 54 ( -1.57 -79.03 -22.75 1.33) ; 55 ( -1.41 -79.93 -23.25 1.40) ; 56 ( -1.31 -80.69 -23.44 1.40) ; 57 ( -1.34 -81.80 -23.50 1.18) ; 58 ( -1.31 -82.54 -23.75 0.81) ; 59 ( -1.38 -83.42 -23.81 0.52) ; 60 ( -1.43 -84.74 -23.81 0.52) ; 61 ( -1.54 -86.35 -24.19 0.88) ; 62 ( -1.65 -87.07 -24.19 1.77) ; 63 ( -1.80 -88.01 -24.19 1.25) ; 64 ( -2.10 -88.93 -24.75 0.81) ; 65 ( -2.54 -89.90 -24.81 0.52) ; 66 ( -2.62 -90.93 -25.44 0.74) ; 67 ( -2.72 -91.58 -25.56 1.33) ; 68 ( -2.94 -92.06 -25.56 1.55) ; 69 ( -2.86 -92.96 -25.56 0.74) ; 70 ( -3.22 -93.87 -25.56 0.37) ; 71 ( -3.44 -94.72 -25.69 1.11) ; 72 ( -3.50 -95.60 -25.75 1.92) ; 73 ( -3.26 -96.38 -25.69 1.47) ; 74 ( -3.22 -97.12 -25.56 0.96) ; 75 ( -2.83 -98.44 -25.56 0.66) ; 76 ( -2.79 -100.08 -25.81 0.52) ; 77 ( -2.40 -101.40 -26.00 0.81) ; 78 ( -2.16 -102.18 -26.00 0.52) ; 79 ( -1.63 -102.18 -26.00 0.52) ; 80 ( -1.83 -102.97 -26.00 0.52) ; 81 ( -2.08 -104.04 -26.00 0.52) ; 82 ( -2.38 -104.95 -26.13 1.25) ; 83 ( -2.51 -105.82 -26.19 1.62) ; 84 ( -2.64 -106.62 -26.50 0.81) ; 85 ( -2.37 -107.77 -28.19 0.59) ; 86 ( -2.51 -108.64 -28.19 0.88) ; 87 ( -2.58 -109.14 -29.06 1.47) ; 88 ( -2.58 -110.03 -29.50 1.47) ; 89 ( -2.64 -110.90 -29.69 0.52) ; 90 ( -3.09 -111.80 -29.69 0.52) ; 91 ( -3.37 -112.72 -31.63 0.52) ; 92 ( -3.85 -113.38 -32.13 1.25) ; 93 ( -4.05 -114.09 -32.13 1.25) ; 94 ( -4.24 -115.32 -33.88 0.44) ; 95 ( -4.33 -115.89 -34.25 1.18) ; 96 ( -4.63 -116.37 -35.88 1.62) ; 97 Normal | ( -8.12 -23.85 -16.19 1.40) ; 1, R-1-2 ( ( -8.11 -23.89 -17.00 1.99) ; 1, R-1-2-1 ( -8.20 -24.90 -17.00 3.32) ; 2 ( ( -8.40 -25.76 -18.19 1.18) ; 1, R-1-2-1-1 ( -9.41 -26.19 -21.00 1.03) ; 2 ( -9.67 -27.33 -21.94 0.88) ; 3 ( -9.57 -27.38 -21.94 0.88) ; 4 ( ( -9.54 -28.83 -22.38 0.74) ; 1, R-1-2-1-1-1 ( -9.38 -30.19 -24.00 1.18) ; 2 ( -9.42 -30.48 -24.00 1.40) ; 3 ( ( -9.08 -31.13 -23.94 0.52) ; 1, R-1-2-1-1-1-1 ( -8.83 -31.91 -26.19 0.37) ; 2 ( -9.30 -32.50 -28.56 0.66) ; 3 ( -9.58 -32.90 -29.00 0.66) ; 4 ( -9.07 -33.42 -29.31 0.66) ; 5 ( -8.76 -33.85 -29.63 0.88) ; 6 ( -8.93 -34.41 -30.06 1.03) ; 7 ( -9.46 -34.92 -32.38 0.96) ; 8 ( -9.89 -35.30 -33.44 1.25) ; 9 ( -9.73 -35.69 -35.44 0.96) ; 10 ( -9.12 -35.12 -38.06 0.96) ; 11 ( -8.64 -34.45 -40.00 0.96) ; 12 ( -8.27 -33.99 -41.44 1.11) ; 13 ( -8.72 -34.00 -42.06 0.81) ; 14 ( -9.01 -34.91 -43.56 0.81) ; 15 ( -9.38 -35.81 -44.63 1.18) ; 16 ( -9.65 -36.14 -46.13 1.47) ; 17 ( -10.57 -36.29 -46.38 1.84) ; 18 ( -11.06 -36.59 -46.75 0.96) ; 19 ( -11.75 -37.15 -47.44 0.59) ; 20 ( -12.49 -38.07 -47.88 0.44) ; 21 ( -13.23 -38.98 -48.44 0.44) ; 22 ( -13.84 -40.00 -48.88 0.74) ; 23 ( -14.21 -40.45 -48.94 1.03) ; 24 ( -14.56 -41.21 -49.31 1.33) ; 25 ( -14.56 -41.66 -49.69 0.88) ; 26 ( -14.53 -42.03 -49.06 0.52) ; 27 ( -14.53 -42.48 -49.00 0.52) ; 28 ( -14.55 -43.51 -50.06 0.74) ; 29 ( -14.43 -44.20 -51.00 0.96) ; 30 ( -13.88 -44.95 -51.63 0.52) ; 31 ( -13.65 -45.87 -51.88 0.52) ; 32 ( -13.75 -46.45 -51.88 0.96) ; 33 ( -13.48 -47.16 -51.94 1.40) ; 34 ( -13.19 -47.64 -52.19 1.84) ; 35 ( -13.11 -48.10 -52.25 2.14) ; 36 ( -13.20 -48.68 -52.69 0.81) ; 37 ( -13.21 -49.64 -53.44 0.52) ; 38 ( -13.27 -50.53 -53.81 0.52) ; 39 ( -13.16 -51.72 -53.81 0.52) ; 40 ( -12.63 -53.11 -53.94 0.52) ; 41 ( -12.33 -54.04 -54.31 0.88) ; 42 ( -11.98 -54.69 -54.31 1.69) ; 43 ( -11.49 -55.35 -54.44 2.06) ; 44 ( -11.10 -56.15 -54.56 1.03) ; 45 ( -10.63 -56.97 -55.06 0.52) ; 46 ( -10.22 -58.15 -55.81 0.52) ; 47 ( -9.88 -58.80 -56.19 0.88) ; 48 ( -9.63 -59.58 -57.00 1.40) ; 49 ( -9.31 -60.37 -57.31 0.96) ; 50 ( -9.24 -60.89 -57.31 0.59) ; 51 ( -8.47 -62.20 -57.88 0.29) ; 52 ( -7.45 -63.25 -58.44 0.29) ; 53 ( -6.44 -63.93 -58.63 0.59) ; 54 ( -6.10 -64.13 -58.75 1.03) ; 55 ( -5.20 -64.57 -59.06 1.11) ; 56 ( -4.57 -64.81 -59.31 0.66) ; 57 ( -3.89 -65.30 -59.94 0.44) ; 58 ( -3.37 -65.74 -60.13 0.44) ; 59 ( -2.63 -66.30 -60.63 0.74) ; 60 ( -2.05 -66.84 -61.31 1.33) ; 61 ( -1.27 -67.56 -61.88 1.03) ; 62 ( -0.52 -68.05 -62.38 0.66) ; 63 ( 0.35 -68.63 -63.00 0.52) ; 64 ( 1.12 -69.42 -63.50 0.52) ; 65 ( 2.09 -70.38 -63.88 0.74) ; 66 ( 2.44 -70.96 -64.31 1.47) ; 67 ( 2.80 -71.54 -64.56 1.77) ; 68 ( 3.07 -72.17 -65.13 0.81) ; 69 ( 3.55 -72.91 -65.38 0.44) ; 70 ( 4.12 -73.59 -65.44 0.74) ; 71 ( 4.59 -74.40 -65.44 0.81) ; 72 ( 5.14 -75.16 -65.69 0.37) ; 73 ( 5.77 -75.86 -66.44 0.37) ; 74 Normal | ( -9.69 -31.29 -24.19 0.59) ; 1, R-1-2-1-1-1-2 ( -10.27 -32.02 -24.81 0.44) ; 2 ( -10.71 -32.53 -25.19 0.44) ; 3 ( -11.33 -33.55 -25.44 0.52) ; 4 ( -11.56 -34.10 -25.44 0.52) ; 5 ( -11.73 -34.67 -25.63 0.37) ; 6 ( -12.11 -35.65 -26.00 0.37) ; 7 ( -12.58 -36.31 -26.69 0.52) ; 8 ( -13.16 -37.62 -26.69 0.44) ; 9 ( -13.31 -38.56 -27.31 0.44) ; 10 ( -13.57 -39.26 -27.44 0.66) ; 11 ( -13.84 -40.48 -27.44 0.52) ; 12 ( -13.58 -41.18 -27.44 0.44) ; 13 ( -13.42 -42.10 -28.00 0.66) ; 14 ( -13.55 -42.37 -28.56 0.96) ; 15 ( -13.58 -43.11 -28.94 1.33) ; 16 ( -13.48 -44.31 -28.94 0.88) ; 17 ( -13.44 -45.50 -28.81 0.74) ; 18 ( -13.44 -46.46 -28.81 0.52) ; 19 ( -13.28 -47.38 -29.19 0.52) ; 20 ( -13.56 -48.59 -29.69 0.81) ; 21 ( -13.76 -48.93 -29.81 1.18) ; 22 ( -13.94 -49.57 -29.81 1.84) ; 23 ( -14.10 -50.13 -30.06 1.84) ; 24 ( -14.63 -50.72 -30.06 0.81) ; 25 ( -15.04 -51.39 -30.63 0.44) ; 26 ( -15.78 -51.79 -31.00 0.44) ; 27 ( -16.37 -52.21 -31.00 0.74) ; 28 ( -16.79 -52.52 -31.19 0.81) ; 29 ( -17.17 -53.05 -31.50 0.52) ; 30 ( -17.47 -54.41 -31.94 0.37) ; 31 ( -17.76 -54.88 -32.25 0.37) ; 32 ( -18.10 -55.57 -32.31 0.74) ; 33 ( -18.31 -56.50 -32.31 1.40) ; 34 ( -18.68 -57.40 -32.31 2.28) ; 35 ( -18.91 -57.88 -32.44 2.65) ; 36 ( -19.11 -58.73 -32.44 1.55) ; 37 ( -19.15 -59.40 -32.56 0.88) ; 38 ( -19.10 -60.07 -33.00 0.59) ; 39 Normal ) ; End of split | ( -10.12 -28.48 -22.00 0.37) ; 1, R-1-2-1-1-2 ( -10.76 -29.71 -21.38 0.37) ; 2 ( -11.50 -30.63 -21.25 0.96) ; 3 ( -12.30 -31.46 -20.81 1.18) ; 4 ( -13.11 -32.37 -21.38 0.74) ; 5 ( -14.10 -32.88 -22.25 0.81) ; 6 ( -15.15 -33.45 -24.06 0.66) ; 7 ( -16.38 -34.15 -25.25 0.59) ; 8 ( -17.25 -34.90 -26.06 0.81) ; 9 ( -17.95 -35.53 -26.06 1.47) ; 10 ( -18.64 -36.09 -26.94 1.47) ; 11 ( -19.29 -36.50 -26.94 0.81) ; 12 ( -20.11 -37.40 -26.94 0.66) ; 13 ( -20.89 -38.10 -27.88 0.59) ; 14 ( -21.65 -38.72 -27.88 0.44) ; 15 ( -22.56 -39.68 -28.44 0.81) ; 16 ( -22.99 -40.50 -28.63 0.59) ; 17 ( -23.20 -41.36 -28.88 0.37) ; 18 ( -23.85 -41.70 -29.31 0.96) ; 19 ( -24.30 -42.22 -29.44 2.06) ; 20 ( -24.49 -42.48 -30.00 2.36) ; 21 ( -25.01 -42.93 -30.00 1.77) ; 22 ( -25.54 -43.43 -30.00 0.74) ; 23 ( -25.98 -43.88 -30.44 0.52) ; 24 ( -26.35 -44.78 -30.69 0.81) ; 25 ( -26.26 -45.61 -31.25 1.18) ; 26 ( -26.25 -46.50 -32.00 0.96) ; 27 ( -26.37 -47.30 -32.00 0.59) ; 28 ( -26.65 -48.26 -32.44 0.44) ; 29 ( -27.00 -49.08 -32.69 0.44) ; 30 ( -26.54 -49.97 -32.69 0.44) ; 31 ( -26.51 -50.72 -32.69 0.44) ; 32 ( -26.82 -51.70 -33.13 0.44) ; 33 ( -27.15 -52.83 -32.75 0.66) ; 34 ( -27.48 -53.52 -32.69 1.03) ; 35 ( -28.04 -54.69 -32.19 1.69) ; 36 ( -28.84 -55.52 -32.19 2.43) ; 37 ( -29.69 -56.65 -31.75 1.99) ; 38 ( -30.32 -57.29 -32.38 1.03) ; 39 ( -31.16 -57.97 -33.56 0.59) ; 40 ( -32.21 -58.92 -33.56 0.52) ; 41 ( -33.09 -59.81 -33.81 0.52) ; 42 ( -33.08 -60.63 -34.13 0.52) ; 43 ( -33.05 -61.38 -34.13 1.11) ; 44 ( -32.92 -61.98 -35.31 1.11) ; 45 ( -33.47 -62.64 -35.69 0.52) ; 46 ( -34.36 -63.09 -36.50 0.81) ; 47 ( -34.78 -63.83 -37.00 0.44) ; 48 ( -34.92 -64.70 -38.06 0.44) ; 49 ( -35.09 -65.79 -38.06 1.11) ; 50 ( -35.08 -66.67 -38.44 1.77) ; 51 ( -35.30 -67.60 -38.44 1.25) ; 52 ( -35.32 -68.64 -38.44 0.81) ; 53 ( -35.63 -69.69 -38.81 0.37) ; 54 ( -36.05 -70.45 -39.56 0.81) ; 55 ( -36.30 -71.08 -40.00 1.62) ; 56 ( -36.80 -71.88 -41.69 1.25) ; 57 ( -37.44 -72.65 -42.38 0.81) ; 58 ( -37.78 -73.41 -42.94 0.88) ; 59 ( -38.24 -73.93 -43.19 0.88) ; 60 ( -38.87 -74.65 -43.19 0.52) ; 61 ( -39.37 -74.93 -43.56 0.81) ; 62 ( -39.78 -75.24 -43.88 1.40) ; 63 ( -40.45 -75.72 -43.88 1.40) ; 64 ( -41.12 -76.14 -43.88 0.66) ; 65 ( -41.64 -77.09 -44.19 0.52) ; 66 ( -41.77 -77.88 -44.44 0.52) ; 67 ( -42.02 -78.51 -45.13 0.52) ; 68 ( -43.05 -79.38 -45.44 0.52) ; 69 ( -44.20 -80.54 -46.56 0.74) ; 70 ( -44.97 -80.64 -47.13 1.11) ; 71 ( -45.63 -81.05 -48.00 1.84) ; 72 ( -46.21 -81.47 -48.31 2.21) ; 73 ( -46.87 -82.26 -48.31 0.81) ; 74 ( -47.43 -82.98 -48.81 0.74) ; 75 ( -47.85 -83.29 -48.94 1.11) ; 76 ( -48.20 -83.68 -49.50 0.81) ; 77 ( -48.97 -84.23 -50.06 0.74) ; 78 ( -49.11 -84.64 -51.75 1.47) ; 79 ( -49.56 -85.17 -52.56 2.21) ; 80 ( -49.89 -85.85 -53.13 2.21) ; 81 ( -50.09 -86.63 -53.13 1.11) ; 82 ( -50.31 -87.04 -55.31 0.52) ; 83 Normal ) ; End of split | ( -9.17 -25.38 -22.63 0.66) ; 1, R-1-2-1-2 ( -10.02 -25.54 -23.94 0.74) ; 2 ( -10.56 -25.67 -25.06 0.96) ; 3 ( -11.88 -25.99 -27.13 0.88) ; 4 ( -12.82 -25.84 -27.38 0.81) ; 5 ( -13.21 -26.37 -29.69 0.81) ; 6 ( -13.38 -27.01 -30.81 1.11) ; 7 ( -13.72 -27.77 -30.81 1.77) ; 8 ( -14.25 -28.20 -31.31 1.33) ; 9 ( -14.63 -29.18 -32.13 0.96) ; 10 ( -15.25 -29.89 -32.50 0.74) ; 11 ( -16.02 -29.99 -33.88 0.66) ; 12 ( -16.66 -30.27 -35.38 0.59) ; 13 ( -16.82 -31.27 -38.50 0.81) ; 14 ( -16.24 -31.37 -39.81 1.03) ; 15 ( -15.72 -31.45 -40.81 1.03) ; 16 ( -15.14 -31.54 -41.25 0.74) ; 17 ( -15.32 -32.18 -41.56 0.96) ; 18 ( -15.87 -32.83 -42.06 0.81) ; 19 ( -16.16 -33.68 -42.81 0.66) ; 20 ( -15.96 -34.37 -44.56 0.66) ; 21 ( -15.91 -34.90 -46.38 0.81) ; 22 ( -16.19 -35.29 -47.13 1.11) ; 23 ( -16.12 -35.83 -47.88 2.14) ; 24 ( -16.26 -36.24 -48.44 2.73) ; 25 ( -16.36 -36.89 -49.06 1.25) ; 26 ( -16.20 -37.74 -49.50 0.81) ; 27 ( -16.64 -39.08 -50.63 0.66) ; 28 ( -16.92 -39.91 -50.63 0.74) ; 29 ( -16.92 -40.88 -51.25 0.59) ; 30 ( -16.99 -41.76 -52.63 0.52) ; 31 ( -17.60 -42.33 -54.56 0.52) ; 32 ( -17.96 -43.23 -56.19 0.74) ; 33 ( -18.78 -43.17 -56.63 1.11) ; 34 ( -19.26 -43.39 -57.19 0.74) ; 35 ( -19.90 -43.67 -57.44 0.74) ; 36 ( -20.48 -44.02 -57.94 0.88) ; 37 ( -20.98 -44.82 -59.19 0.74) ; 38 ( -21.51 -45.33 -60.06 1.40) ; 39 ( -21.87 -45.79 -60.75 2.43) ; 40 ( -22.11 -46.35 -61.06 1.40) ; 41 ( -22.76 -47.13 -61.75 0.74) ; 42 ( -23.26 -47.87 -62.25 0.52) ; 43 ( -24.60 -48.40 -62.44 0.52) ; 44 ( -25.16 -49.12 -63.06 0.52) ; 45 ( -25.43 -49.97 -64.25 1.11) ; 46 ( -25.66 -50.46 -64.94 1.84) ; 47 ( -26.21 -51.11 -65.56 1.99) ; 48 ( -26.64 -51.48 -67.31 0.88) ; 49 Normal ) ; End of split | ( -9.13 -23.62 -18.06 0.66) ; 1, R-1-2-2 ( -10.44 -23.41 -18.88 0.66) ; 2 ( -11.76 -23.21 -19.25 0.52) ; 3 ( -12.34 -23.11 -19.94 0.52) ; 4 ( -13.42 -22.94 -20.44 0.74) ; 5 ( -14.53 -23.35 -20.63 0.74) ; 6 ( -15.23 -23.98 -21.31 1.03) ; 7 ( -15.88 -24.77 -21.69 1.33) ; 8 ( -17.20 -25.76 -22.00 1.33) ; 9 ( -18.63 -26.27 -21.75 0.81) ; 10 ( -19.99 -26.87 -22.19 0.66) ; 11 ( -21.10 -27.73 -22.19 0.66) ; 12 ( -21.78 -28.81 -23.38 1.03) ; 13 ( -22.98 -30.25 -23.38 1.18) ; 14 ( -23.44 -30.76 -23.44 1.18) ; 15 ( -24.43 -31.36 -24.38 0.96) ; 16 ( -25.43 -32.52 -24.38 1.18) ; 17 ( -25.98 -33.62 -24.38 0.88) ; 18 ( -26.73 -34.61 -24.38 0.59) ; 19 ( -27.37 -35.32 -24.38 0.81) ; 20 ( -28.22 -36.44 -24.38 1.11) ; 21 ( -28.75 -37.40 -24.56 0.74) ; 22 ( -29.56 -38.75 -24.56 0.52) ; 23 ( -30.07 -39.63 -24.56 0.52) ; 24 ( -30.61 -40.22 -24.56 0.52) ; 25 ( -31.34 -41.06 -24.75 0.52) ; 26 ( -30.98 -41.64 -24.75 0.52) ; 27 ( -31.16 -42.72 -24.75 0.52) ; 28 ( -31.40 -43.79 -24.75 1.18) ; 29 ( -31.75 -44.62 -24.81 2.28) ; 30 ( -32.17 -45.37 -24.81 2.58) ; 31 ( -32.24 -46.32 -24.81 1.69) ; 32 ( -32.68 -47.14 -24.81 0.81) ; 33 ( -33.27 -48.08 -24.81 0.81) ; 34 ( -33.48 -48.94 -24.81 0.81) ; 35 ( -33.80 -49.55 -24.88 0.59) ; 36 ( -34.07 -50.49 -25.06 1.03) ; 37 ( -34.64 -51.29 -25.06 1.03) ; 38 ( -35.31 -52.22 -25.13 0.59) ; 39 ( -35.76 -52.66 -25.25 0.59) ; 40 ( -36.47 -53.37 -26.06 0.81) ; 41 ( -38.07 -54.08 -26.06 0.66) ; 42 ( -39.21 -54.71 -26.88 0.81) ; 43 ( -39.49 -55.04 -29.00 1.11) ; 44 ( -40.38 -55.49 -29.31 1.47) ; 45 ( -41.34 -55.85 -29.31 0.81) ; 46 ( -41.87 -55.99 -29.31 0.52) ; 47 ( -42.48 -56.04 -29.81 0.52) ; 48 ( -43.30 -56.06 -30.00 0.81) ; 49 ( -44.13 -56.52 -31.00 0.81) ; 50 ( -45.10 -57.04 -32.13 0.66) ; 51 ( -45.52 -57.34 -32.19 0.66) ; 52 ( -45.91 -57.87 -33.00 0.96) ; 53 ( -46.33 -58.17 -33.00 1.77) ; 54 ( -46.70 -58.63 -33.44 2.14) ; 55 ( -47.35 -59.41 -34.31 0.74) ; 56 ( -48.23 -60.31 -35.19 0.52) ; 57 ( -49.10 -61.06 -36.75 0.59) ; 58 ( -49.76 -61.99 -37.06 0.59) ; 59 ( -50.45 -62.55 -37.31 0.88) ; 60 ( -50.93 -63.21 -37.31 1.55) ; 61 ( -51.90 -63.66 -37.88 1.55) ; 62 ( -53.02 -64.15 -39.44 1.11) ; 63 ( -53.93 -64.74 -39.44 0.66) ; 64 ( -55.54 -65.52 -39.94 0.52) ; 65 ( -56.42 -66.34 -40.00 0.81) ; 66 ( -57.28 -67.01 -41.31 0.66) ; 67 ( -58.56 -67.63 -41.31 0.52) ; 68 ( -59.91 -68.09 -41.94 0.81) ; 69 ( -61.26 -68.61 -42.31 0.96) ; 70 ( -61.85 -69.11 -43.50 1.69) ; 71 ( -62.94 -69.31 -44.69 2.06) ; 72 ( -63.81 -69.62 -44.69 0.88) ; 73 ( -64.73 -69.83 -44.69 0.59) ; 74 ( -65.74 -70.05 -45.06 0.59) ; 75 ( -66.49 -70.15 -45.13 0.74) ; 76 ( -67.35 -70.31 -45.31 0.52) ; 77 ( -68.13 -70.55 -45.38 1.25) ; 78 ( -69.14 -71.14 -46.25 1.99) ; 79 ( -69.65 -71.57 -46.25 0.96) ; 80 ( -70.37 -71.84 -46.25 0.44) ; 81 ( -71.85 -72.71 -46.69 0.44) ; 82 ( -73.25 -73.53 -46.38 0.66) ; 83 Normal ) ; End of split ) ; End of split | ( -11.76 -12.49 -7.75 1.99) ; 1, R-2 ( -12.64 -11.98 -7.75 1.03) ; 2 ( -13.54 -11.54 -7.75 0.74) ; 3 ( -14.33 -10.83 -8.19 0.88) ; 4 ( -14.49 -9.54 -9.50 0.88) ; 5 ( -14.87 -8.60 -10.31 0.88) ; 6 ( -15.63 -8.26 -10.75 1.18) ; 7 ( -16.65 -8.09 -10.06 0.81) ; 8 ( -17.38 -7.98 -10.06 0.81) ; 9 ( -18.20 -7.99 -10.13 0.81) ; 10 ( -19.24 -8.42 -11.19 0.96) ; 11 ( -19.71 -8.13 -12.06 1.25) ; 12 ( -20.37 -8.47 -12.81 1.03) ; 13 ( -19.93 -8.09 -14.25 0.88) ; 14 ( -19.80 -7.74 -15.44 0.88) ; 15 ( -19.51 -7.71 -16.13 0.88) ; 16 ( -19.51 -7.71 -17.44 1.18) ; 17 ( -19.27 -8.13 -18.38 1.47) ; 18 ( -19.72 -8.20 -19.69 1.18) ; 19 ( -20.49 -7.85 -20.69 0.88) ; 20 ( -21.31 -7.36 -20.69 0.88) ; 21 ( -21.93 -7.04 -21.75 1.03) ; 22 ( -22.57 -7.30 -23.50 1.18) ; 23 ( -22.67 -7.95 -24.81 1.40) ; 24 ( -23.14 -8.10 -25.44 1.03) ; 25 ( -23.53 -7.23 -25.81 0.74) ; 26 ( -23.13 -6.11 -26.88 0.74) ; 27 ( -22.70 -5.28 -28.69 0.88) ; 28 ( -22.75 -4.24 -28.81 1.11) ; 29 ( -23.32 -3.56 -29.63 0.66) ; 30 ( -24.31 -3.69 -30.88 0.66) ; 31 ( -25.16 -3.42 -31.38 0.81) ; 32 ( -26.11 -2.82 -32.25 1.03) ; 33 ( -27.12 -2.14 -33.19 0.59) ; 34 ( -27.80 -1.74 -33.69 0.59) ; 35 ( -28.46 -1.64 -34.19 0.88) ; 36 ( -29.02 -1.84 -34.88 1.03) ; 37 ( -29.74 -2.17 -35.19 0.66) ; 38 ( -30.91 -2.51 -35.81 0.52) ; 39 ( -31.28 -2.00 -36.25 0.52) ; 40 ( -32.26 -1.62 -36.25 0.81) ; 41 ( -33.08 -1.64 -36.31 0.44) ; 42 ( -34.76 -1.89 -36.63 0.44) ; 43 ( -35.38 -2.02 -36.69 1.18) ; 44 ( -36.25 -2.33 -37.06 1.33) ; 45 ( -36.97 -2.58 -37.19 0.44) ; 46 ( -37.48 -2.57 -37.75 0.44) ; 47 ( -38.15 -2.54 -38.13 0.96) ; 48 ( -39.11 -2.46 -38.13 1.25) ; 49 ( -39.87 -2.56 -38.56 1.25) ; 50 ( -40.60 -2.45 -38.88 0.81) ; 51 ( -41.32 -2.33 -39.44 0.66) ; 52 ( -42.51 -1.85 -39.88 0.96) ; 53 ( -43.39 -1.71 -41.13 1.25) ; 54 ( -44.41 -2.00 -41.44 0.88) ; 55 ( -45.72 -2.31 -42.69 0.81) ; 56 ( -46.17 -2.31 -43.94 1.92) ; 57 ( -46.73 -2.08 -44.63 2.28) ; 58 ( -47.72 -2.21 -44.81 0.81) ; 59 ( -48.36 -2.03 -46.19 0.52) ; 60 ( -48.66 -2.51 -46.63 0.52) ; 61 ( -48.89 -2.99 -48.44 1.18) ; 62 ( -49.60 -3.69 -49.19 0.52) ; 63 ( -50.07 -4.28 -49.19 0.52) ; 64 ( -50.58 -4.72 -49.81 1.55) ; 65 ( -51.06 -5.38 -50.69 2.73) ; 66 ( -51.66 -5.88 -50.69 2.21) ; 67 ( -52.37 -6.13 -51.75 0.59) ; 68 ( -53.25 -6.51 -51.75 0.37) ; 69 ( -54.34 -6.78 -52.00 0.29) ; 70 ( -54.97 -6.99 -52.00 0.29) ; 71 ( -55.77 -7.44 -53.63 0.81) ; 72 ( -56.50 -7.84 -53.88 1.18) ; 73 ( -57.05 -7.97 -54.00 0.74) ; 74 ( -57.61 -8.26 -54.06 0.37) ; 75 ( -58.15 -8.76 -54.25 0.59) ; 76 ( -58.80 -9.10 -54.50 0.59) ; 77 ( -59.90 -9.45 -55.31 0.37) ; 78 ( -60.17 -9.78 -55.44 0.37) ; 79 ( -61.14 -10.74 -55.94 0.96) ; 80 ( -61.66 -11.17 -56.25 1.77) ; 81 ( -62.19 -11.76 -56.63 2.50) ; 82 ( -62.98 -12.44 -56.94 1.55) ; 83 ( -64.05 -13.08 -57.38 0.66) ; 84 ( -65.15 -13.50 -57.81 0.44) ; 85 ( -66.09 -14.17 -58.25 0.59) ; 86 ( -66.82 -14.57 -58.44 0.29) ; 87 ( -67.68 -14.80 -58.56 0.29) ; 88 ( -68.18 -15.17 -58.69 0.96) ; 89 ( -68.71 -16.12 -59.00 1.62) ; 90 ( -69.59 -16.50 -59.69 0.66) ; 91 ( -70.18 -16.92 -61.69 0.74) ; 92 ( -71.12 -17.22 -62.38 1.33) ; 93 ( -71.82 -16.88 -63.06 1.33) ; 94 ( -72.13 -16.54 -63.81 0.88) ; 95 ( -72.25 -16.38 -66.19 0.59) ; 96 Normal ) ; End of split ) ; End of tree ( (Color Magenta) (Dendrite) ( 11.04 -1.79 10.06 2.36) ; Root ( 11.69 -2.41 9.69 1.77) ; 1, R ( 12.77 -3.10 9.19 1.99) ; 2 ( 13.49 -3.74 9.06 2.28) ; 3 ( 14.14 -3.84 9.06 2.43) ; 4 ( 14.23 -3.86 9.06 2.43) ; 5 ( ( 15.23 -4.53 9.06 1.62) ; 1, R-1 ( 16.64 -5.04 9.00 1.55) ; 2 ( 17.71 -5.37 8.31 1.77) ; 3 ( 18.83 -5.84 7.50 1.69) ; 4 ( 19.82 -6.14 7.50 1.69) ; 5 ( 21.23 -6.66 7.50 1.77) ; 6 ( 22.36 -7.06 7.00 1.99) ; 7 ( 23.53 -7.25 6.81 1.62) ; 8 ( 25.14 -7.42 6.81 1.62) ; 9 ( 26.58 -7.80 7.06 1.69) ; 10 ( 28.14 -8.27 7.06 1.47) ; 11 ( 29.21 -8.59 7.06 1.40) ; 12 ( 30.66 -8.82 6.56 1.69) ; 13 ( 31.77 -8.92 7.44 2.36) ; 14 ( ( 32.59 -9.34 8.00 2.43) ; 1, R-1-1 ( 33.42 -9.78 8.19 2.43) ; 2 ( ( 34.68 -10.27 8.56 1.92) ; 1, R-1-1-1 ( 35.78 -10.45 8.50 1.55) ; 2 ( 36.43 -10.55 8.50 1.33) ; 3 ( ( 36.92 -10.78 8.81 0.96) ; 1, R-1-1-1-1 ( 37.67 -11.64 9.50 1.03) ; 2 ( 38.02 -12.28 9.56 0.81) ; 3 ( 38.22 -12.91 10.19 0.74) ; 4 ( ( 38.95 -13.02 10.00 0.59) ; 1, R-1-1-1-1-1 ( 39.51 -13.70 9.81 0.59) ; 2 ( 40.41 -14.65 10.75 0.59) ; 3 ( 41.07 -15.65 11.69 0.44) ; 4 ( 41.87 -16.22 11.75 0.81) ; 5 ( 42.82 -16.46 12.31 1.03) ; 6 ( 43.87 -16.41 12.81 0.66) ; 7 ( 44.82 -16.56 12.88 0.59) ; 8 ( 45.37 -16.87 13.06 0.59) ; 9 ( 45.98 -17.70 13.25 0.66) ; 10 ( 46.06 -18.67 13.44 0.66) ; 11 ( 46.22 -19.06 13.81 0.66) ; 12 ( 46.72 -19.66 14.19 0.74) ; 13 ( 47.07 -20.24 14.88 0.74) ; 14 ( 47.69 -21.00 14.88 0.59) ; 15 ( 48.51 -21.50 14.88 0.59) ; 16 ( 48.88 -22.01 14.88 0.59) ; 17 ( 49.86 -22.90 15.00 0.59) ; 18 ( 50.46 -23.36 15.31 1.18) ; 19 ( 51.12 -23.91 15.38 1.92) ; 20 ( 51.67 -24.22 15.38 1.92) ; 21 ( 52.08 -24.88 15.38 0.88) ; 22 ( 52.74 -25.43 15.38 0.52) ; 23 ( 53.57 -26.30 16.13 0.59) ; 24 ( 54.37 -26.88 16.63 0.59) ; 25 ( 54.43 -27.47 16.94 0.59) ; 26 ( 54.54 -28.23 17.25 0.88) ; 27 ( 55.00 -29.04 17.63 0.66) ; 28 ( 55.52 -30.01 17.69 0.66) ; 29 ( 56.17 -31.53 17.69 0.52) ; 30 ( 56.70 -31.97 17.69 1.03) ; 31 ( 57.43 -32.53 17.69 1.40) ; 32 ( 57.93 -33.13 17.69 0.96) ; 33 ( 58.33 -33.94 18.25 0.66) ; 34 ( 59.19 -34.67 18.25 0.66) ; 35 ( 60.09 -35.55 18.25 0.81) ; 36 ( 61.40 -36.20 18.50 0.81) ; 37 ( 62.44 -36.21 19.31 0.74) ; 38 ( 63.20 -36.19 20.31 1.25) ; 39 ( 63.86 -35.78 20.69 1.25) ; 40 ( 64.52 -35.80 21.25 0.88) ; 41 ( 65.23 -35.55 21.25 0.59) ; 42 ( 66.04 -36.13 21.75 0.52) ; 43 ( 66.24 -37.19 21.81 0.74) ; 44 ( 66.63 -38.06 21.81 0.44) ; 45 ( 67.19 -39.26 22.50 0.74) ; 46 ( 67.37 -39.96 22.88 1.33) ; 47 ( 67.38 -40.84 23.06 0.96) ; 48 ( 67.37 -41.88 23.13 0.59) ; 49 ( 67.25 -42.61 23.81 0.81) ; 50 ( 66.81 -43.50 24.06 0.96) ; 51 ( 66.32 -44.23 24.19 0.52) ; 52 ( 66.20 -44.95 24.25 0.52) ; 53 ( 66.37 -45.80 24.31 0.66) ; 54 ( 66.64 -46.51 24.31 0.44) ; 55 ( 66.64 -47.39 24.06 0.52) ; 56 ( 66.74 -48.21 23.75 0.52) ; 57 ( 67.42 -48.62 23.75 0.52) ; 58 ( 67.31 -49.34 24.13 0.52) ; 59 ( 66.65 -49.75 24.44 0.52) ; 60 ( 66.18 -49.90 24.63 0.66) ; 61 ( 65.60 -50.25 24.94 0.66) ; 62 ( 64.93 -50.66 25.50 0.66) ; 63 Normal | ( 38.58 -13.71 9.06 0.59) ; 1, R-1-1-1-1-2 ( 39.13 -14.47 8.50 0.66) ; 2 ( 39.86 -15.03 9.88 0.74) ; 3 ( 40.84 -15.93 10.50 0.66) ; 4 ( 41.57 -16.93 10.75 0.66) ; 5 ( 43.10 -17.62 10.88 0.66) ; 6 ( 43.86 -18.55 10.88 0.88) ; 7 ( 44.44 -19.09 10.94 1.40) ; 8 ( 45.22 -19.80 11.44 0.66) ; 9 ( 45.82 -20.27 11.38 0.52) ; 10 ( 46.40 -20.87 11.19 0.52) ; 11 ( 47.26 -21.53 10.94 0.52) ; 12 ( 47.81 -21.84 11.81 0.66) ; 13 ( 48.49 -22.25 11.88 0.59) ; 14 ( 48.83 -22.89 11.88 0.52) ; 15 ( 49.23 -23.69 11.75 0.74) ; 16 ( 49.69 -24.59 11.44 0.74) ; 17 ( 50.03 -25.74 12.13 0.52) ; 18 ( 50.76 -26.30 12.13 0.52) ; 19 ( 51.39 -26.56 12.63 0.52) ; 20 ( 52.44 -26.95 12.75 0.44) ; 21 ( 53.62 -27.05 12.94 0.44) ; 22 ( 54.30 -27.02 13.25 0.96) ; 23 ( 54.97 -27.04 13.44 1.18) ; 24 ( 55.99 -27.13 13.44 0.66) ; 25 ( 56.98 -27.51 13.69 0.59) ; 26 ( 57.75 -27.79 13.94 0.81) ; 27 ( 58.58 -28.28 14.69 1.03) ; 28 ( 58.94 -28.79 14.75 0.59) ; 29 ( 59.78 -29.14 15.00 0.52) ; 30 ( 60.14 -29.20 15.31 0.52) ; 31 ( 61.05 -29.63 15.44 0.74) ; 32 ( 61.85 -29.76 15.75 0.37) ; 33 ( 62.60 -29.74 16.50 0.59) ; 34 ( 63.08 -29.96 16.75 1.47) ; 35 ( 63.67 -29.98 16.94 1.47) ; 36 ( 64.45 -29.81 16.94 0.88) ; 37 ( 65.30 -29.64 17.13 0.52) ; 38 ( 65.73 -29.71 17.56 0.44) ; 39 ( 66.33 -30.18 17.56 0.44) ; 40 ( 66.94 -30.57 17.75 0.66) ; 41 ( 67.66 -30.68 18.25 0.44) ; 42 ( 68.39 -30.80 18.63 0.44) ; 43 ( 69.25 -31.01 18.63 0.66) ; 44 ( 70.34 -31.26 18.81 0.52) ; 45 ( 71.34 -31.04 19.31 0.66) ; 46 ( 72.21 -31.18 19.38 1.47) ; 47 ( 72.90 -31.07 19.44 1.18) ; 48 ( 74.05 -30.88 19.44 0.96) ; 49 ( 74.98 -31.17 19.81 0.74) ; 50 ( 75.63 -31.28 19.81 0.66) ; 51 ( 76.43 -31.40 19.81 0.74) ; 52 ( 77.17 -31.45 20.00 0.52) ; 53 ( 77.86 -32.30 20.13 0.44) ; 54 ( 78.53 -33.22 20.25 0.59) ; 55 ( 79.22 -33.56 20.25 0.88) ; 56 ( 80.22 -34.34 20.38 0.66) ; 57 ( 80.96 -34.82 20.38 0.66) ; 58 ( 81.87 -35.19 20.44 0.88) ; 59 ( 82.47 -35.66 20.50 0.74) ; 60 ( 82.95 -35.96 19.94 1.11) ; 61 ( 83.42 -36.26 19.88 1.40) ; 62 ( 83.87 -36.69 19.88 0.74) ; 63 ( 84.78 -37.13 19.88 0.74) ; 64 ( 85.39 -37.45 19.69 0.88) ; 65 ( 86.16 -37.79 19.69 0.66) ; 66 ( 87.93 -38.89 19.38 0.52) ; 67 ( 88.74 -39.39 19.38 0.74) ; 68 ( 89.92 -40.46 19.38 0.74) ; 69 ( 90.57 -41.01 19.25 0.59) ; 70 ( 91.45 -42.04 19.13 0.81) ; 71 ( 92.30 -42.84 18.63 0.81) ; 72 ( 93.14 -43.64 17.88 0.96) ; 73 ( 93.84 -44.42 17.88 1.18) ; 74 ( 94.39 -45.17 17.44 0.96) ; 75 ( 95.07 -46.02 17.06 0.59) ; 76 ( 95.95 -47.12 16.50 0.59) ; 77 ( 96.73 -48.28 16.50 0.59) ; 78 ( 97.29 -49.48 16.56 0.81) ; 79 ( 97.95 -50.03 17.06 1.47) ; 80 ( 98.32 -50.53 17.06 1.18) ; 81 ( 99.05 -51.08 16.94 0.74) ; 82 ( 100.36 -51.22 16.63 0.66) ; 83 ( 102.21 -51.37 16.63 0.52) ; 84 ( 103.50 -51.20 16.25 0.59) ; 85 ( 104.17 -51.24 16.25 0.66) ; 86 ( 105.92 -51.07 16.69 0.44) ; 87 ( 106.66 -51.03 16.69 0.74) ; 88 ( 107.20 -50.98 16.75 0.88) ; 89 ( 107.82 -50.77 16.81 0.59) ; 90 ( 108.64 -50.83 16.81 0.37) ; 91 ( 109.25 -50.71 16.81 0.59) ; 92 ( 109.87 -50.58 16.81 0.59) ; 93 Normal ) ; End of split | ( 37.77 -10.27 9.69 0.74) ; 1, R-1-1-1-2 ( 39.40 -9.87 9.88 0.81) ; 2 ( 40.92 -9.66 10.31 0.96) ; 3 ( 41.38 -9.35 10.31 0.96) ; 4 ( ( 41.74 -9.20 10.44 1.25) ; 1, R-1-1-1-2-1 ( 42.31 -8.92 11.25 0.88) ; 2 ( 42.39 -8.88 11.25 0.88) ; 3 ( ( 43.15 -8.31 11.25 0.52) ; 1, R-1-1-1-2-1-1 ( 43.38 -7.83 11.50 0.52) ; 2 ( 44.20 -7.37 11.75 0.59) ; 3 ( 44.77 -7.46 11.75 0.59) ; 4 ( 45.31 -7.84 11.75 0.66) ; 5 ( 45.84 -8.81 11.75 0.59) ; 6 ( 46.26 -9.39 12.06 0.74) ; 7 ( 47.25 -10.22 12.19 0.59) ; 8 ( 47.71 -10.58 12.19 0.59) ; 9 ( 48.36 -11.21 12.44 0.74) ; 10 ( 48.57 -11.76 12.25 1.18) ; 11 ( 48.70 -12.37 11.19 1.18) ; 12 ( 49.16 -12.74 12.88 0.81) ; 13 ( 50.08 -13.11 12.94 0.44) ; 14 ( 51.28 -13.52 12.88 0.44) ; 15 ( 52.11 -13.95 12.88 0.59) ; 16 ( 52.97 -14.61 12.88 0.74) ; 17 ( 54.09 -15.08 12.88 0.59) ; 18 ( 55.01 -15.38 12.88 0.66) ; 19 ( 55.74 -15.49 12.75 0.66) ; 20 ( 56.36 -15.81 12.75 1.33) ; 21 ( 57.07 -16.07 12.56 1.84) ; 22 ( 57.76 -15.95 12.44 1.62) ; 23 ( 58.51 -15.93 12.44 0.74) ; 24 ( 59.39 -15.99 12.31 0.66) ; 25 ( 60.18 -15.75 12.31 0.59) ; 26 ( 61.39 -15.65 12.31 0.74) ; 27 ( 62.25 -15.41 12.25 0.44) ; 28 ( 63.17 -15.18 12.25 0.37) ; 29 ( 64.02 -15.02 12.13 0.59) ; 30 ( 64.75 -15.14 12.06 0.44) ; 31 ( 65.75 -14.93 12.50 0.81) ; 32 ( 66.76 -14.64 12.50 0.66) ; 33 ( 67.29 -14.65 12.50 0.66) ; 34 ( 68.17 -14.27 12.69 0.74) ; 35 ( 69.02 -14.04 12.69 0.74) ; 36 ( 69.65 -13.84 12.81 1.33) ; 37 ( 70.43 -13.67 12.94 1.62) ; 38 ( 70.93 -13.75 13.00 1.62) ; 39 ( 71.52 -13.39 13.13 0.96) ; 40 ( 72.20 -13.28 13.13 0.44) ; 41 ( 72.82 -12.72 13.31 0.37) ; 42 ( 73.41 -12.73 13.38 0.59) ; 43 ( 74.43 -12.89 13.38 0.59) ; 44 ( 75.37 -13.04 13.38 0.74) ; 45 ( 76.04 -13.08 13.38 0.96) ; 46 ( 76.79 -13.04 13.38 0.88) ; 47 ( 77.60 -13.10 13.38 1.18) ; 48 ( 77.82 -13.14 13.38 1.40) ; 49 ( 78.41 -13.23 13.38 0.96) ; 50 ( 79.18 -13.50 13.81 0.59) ; 51 ( 80.04 -13.71 13.81 0.74) ; 52 ( 81.15 -13.96 13.94 0.44) ; 53 ( 81.73 -14.05 13.94 0.44) ; 54 ( 82.59 -14.25 14.56 0.66) ; 55 ( 83.15 -14.50 14.81 0.81) ; 56 ( 83.81 -14.52 14.88 0.59) ; 57 ( 84.77 -14.60 15.06 0.59) ; 58 ( 85.65 -14.74 15.38 0.74) ; 59 ( 86.45 -14.87 15.88 1.18) ; 60 ( 87.19 -15.36 15.88 1.18) ; 61 ( 87.98 -15.55 15.88 0.59) ; 62 ( 88.38 -15.84 15.88 0.59) ; 63 ( 88.81 -16.36 15.88 0.59) ; 64 ( 89.43 -16.75 15.88 0.66) ; 65 ( 90.01 -17.28 15.88 0.44) ; 66 ( 90.73 -17.91 15.88 0.44) ; 67 ( 91.28 -18.22 16.69 1.03) ; 68 ( 91.83 -18.53 17.19 1.62) ; 69 ( 92.32 -18.68 17.50 1.18) ; 70 ( 93.04 -18.87 17.56 0.59) ; 71 ( 93.48 -18.94 17.69 0.44) ; 72 ( 93.92 -19.45 18.06 0.44) ; 73 ( 94.39 -20.27 18.13 0.66) ; 74 ( 94.69 -20.76 18.44 0.81) ; 75 ( 95.04 -21.33 18.69 0.96) ; 76 ( 95.39 -21.54 18.75 0.44) ; 77 ( 95.87 -21.76 19.00 0.37) ; 78 ( 96.70 -22.19 19.06 0.59) ; 79 ( 97.56 -22.40 19.06 0.88) ; 80 ( 98.44 -22.54 19.06 1.03) ; 81 ( 99.13 -22.80 18.94 0.37) ; 82 ( 99.86 -22.91 18.81 0.37) ; 83 ( 100.72 -23.12 19.38 0.74) ; 84 ( 101.72 -23.43 19.69 0.81) ; 85 ( 102.60 -23.57 19.94 0.52) ; 86 ( 103.89 -23.85 19.94 0.59) ; 87 ( 104.55 -23.95 19.94 1.03) ; 88 ( 104.83 -24.06 19.94 0.66) ; 89 ( 105.77 -24.21 19.94 0.74) ; 90 ( 106.56 -24.42 19.94 1.03) ; 91 ( 107.29 -24.54 20.75 1.18) ; 92 ( 108.00 -24.72 21.13 0.88) ; 93 ( 109.06 -25.11 21.25 0.52) ; 94 ( 109.71 -25.28 21.94 0.52) ; 95 ( 110.58 -25.42 22.13 0.66) ; 96 ( 111.20 -25.74 22.13 1.03) ; 97 ( 111.65 -26.26 22.13 0.59) ; 98 ( 112.49 -26.54 22.44 0.37) ; 99 ( 113.12 -26.79 22.44 0.66) ; 100 ( 113.60 -27.08 22.44 0.66) ; 101 ( 114.29 -27.93 22.63 0.52) ; 102 ( 114.71 -28.52 22.94 0.66) ; 103 ( 114.94 -29.00 23.31 0.96) ; 104 ( 115.38 -29.44 23.88 1.03) ; 105 ( 115.66 -29.63 24.44 0.74) ; 106 ( 116.24 -30.17 24.44 0.52) ; 107 ( 116.67 -30.76 24.44 0.37) ; 108 ( 116.54 -31.62 24.44 0.37) ; 109 Normal | ( 43.01 -9.20 10.38 0.52) ; 1, R-1-1-1-2-1-2 ( 43.47 -9.12 8.81 0.59) ; 2 ( 44.70 -9.32 8.25 0.44) ; 3 ( 45.94 -9.07 8.00 0.44) ; 4 ( 46.88 -8.77 8.00 0.66) ; 5 ( 47.78 -8.77 8.00 0.88) ; 6 ( 48.57 -8.97 8.06 0.88) ; 7 ( 49.72 -9.22 8.31 0.44) ; 8 ( 50.90 -9.78 8.31 0.81) ; 9 ( 51.70 -9.91 8.31 0.81) ; 10 ( 52.58 -10.05 8.06 0.66) ; 11 ( 53.96 -10.27 7.88 0.44) ; 12 ( 55.11 -10.52 7.88 0.74) ; 13 ( 55.88 -10.87 7.31 0.96) ; 14 ( 57.12 -11.44 7.63 1.40) ; 15 ( 58.06 -11.66 7.81 1.40) ; 16 ( 59.02 -11.74 8.38 0.96) ; 17 ( 60.09 -12.05 8.38 0.66) ; 18 ( 60.60 -12.13 8.38 0.66) ; 19 ( 61.04 -12.13 8.38 0.66) ; 20 ( 62.23 -11.72 8.38 0.52) ; 21 ( 63.48 -11.40 8.38 0.52) ; 22 ( 64.73 -11.46 8.38 0.52) ; 23 ( 65.64 -11.38 8.38 0.74) ; 24 ( 66.92 -11.28 8.38 0.66) ; 25 ( 68.81 -11.14 8.38 0.81) ; 26 ( 69.96 -11.39 8.00 1.33) ; 27 ( 70.58 -10.82 7.69 1.33) ; 28 ( 71.89 -10.52 7.31 0.96) ; 29 ( 73.42 -10.31 7.31 0.96) ; 30 ( 74.60 -10.43 6.25 0.59) ; 31 ( 75.94 -10.86 7.38 1.62) ; 32 ( 77.18 -10.79 8.56 1.11) ; 33 ( 77.99 -10.40 9.56 0.88) ; 34 ( 79.17 -10.51 9.88 0.66) ; 35 ( 80.19 -10.23 10.13 0.66) ; 36 ( 81.46 -9.69 11.31 0.74) ; 37 ( 82.30 -9.53 13.19 0.74) ; 38 ( 84.02 -9.14 13.75 0.44) ; 39 ( 86.06 -8.86 14.13 0.44) ; 40 ( 87.59 -8.67 14.31 0.74) ; 41 ( 89.33 -8.50 14.31 1.11) ; 42 ( 90.42 -8.23 14.75 1.11) ; 43 ( 91.56 -8.11 14.56 0.59) ; 44 ( 92.63 -8.35 14.63 0.81) ; 45 ( 93.90 -8.40 14.63 0.81) ; 46 ( 95.07 -8.59 15.31 1.40) ; 47 ( 95.81 -9.08 15.31 1.69) ; 48 ( 96.91 -9.17 15.31 0.88) ; 49 ( 98.22 -9.38 15.31 0.59) ; 50 ( 99.32 -9.48 15.31 0.81) ; 51 ( 100.51 -9.53 15.31 0.52) ; 52 ( 101.64 -9.49 15.38 1.03) ; 53 ( 102.30 -9.51 15.56 0.88) ; 54 ( 103.56 -9.57 15.56 0.74) ; 55 ( 104.67 -9.66 15.56 0.96) ; 56 ( 105.76 -9.77 15.56 0.66) ; 57 ( 106.67 -10.20 16.31 0.44) ; 58 ( 107.58 -10.57 16.31 0.74) ; 59 ( 108.59 -11.25 16.69 0.74) ; 60 ( 109.62 -11.34 17.06 1.55) ; 61 ( 110.46 -11.18 17.13 2.21) ; 62 ( 111.18 -11.36 17.19 1.18) ; 63 ( 112.72 -11.98 17.25 0.66) ; 64 ( 113.27 -12.29 17.25 0.66) ; 65 ( 113.96 -13.14 17.31 0.96) ; 66 ( 114.43 -13.95 17.31 1.33) ; 67 ( 114.67 -14.35 17.31 1.62) ; 68 ( 115.34 -14.76 17.31 0.59) ; 69 ( 115.84 -15.36 17.31 0.44) ; 70 ( 117.04 -15.77 17.31 0.44) ; 71 ( 118.49 -16.07 17.31 0.44) ; 72 ( 119.69 -16.49 17.69 0.81) ; 73 ( 120.21 -16.94 17.81 0.59) ; 74 ( 121.58 -16.79 17.56 0.52) ; 75 ( 123.03 -17.02 17.75 0.52) ; 76 ( 124.48 -17.77 18.00 0.74) ; 77 ( 125.50 -18.37 18.06 0.74) ; 78 ( 127.36 -18.89 18.38 0.52) ; 79 ( 128.27 -18.37 18.38 0.74) ; 80 ( 129.72 -17.64 18.31 0.74) ; 81 ( 130.48 -17.09 18.31 1.33) ; 82 ( 130.87 -16.49 18.31 2.06) ; 83 ( 131.59 -16.15 18.31 2.06) ; 84 ( 132.55 -15.79 18.31 0.52) ; 85 ( 133.95 -15.41 17.94 0.52) ; 86 ( 135.12 -14.64 17.81 0.52) ; 87 ( 136.05 -14.42 17.81 0.52) ; 88 ( 136.87 -14.40 17.81 0.52) ; 89 ( 138.21 -14.90 17.81 0.74) ; 90 ( 139.92 -14.88 17.88 0.52) ; 91 ( 140.59 -14.47 17.88 0.52) ; 92 ( 141.97 -14.24 17.50 1.11) ; 93 ( 142.93 -14.33 17.00 1.40) ; 94 ( 144.64 -14.30 16.75 0.59) ; 95 ( 145.63 -14.68 16.56 0.44) ; 96 ( 146.96 -15.19 16.13 0.44) ; 97 Normal ) ; End of split | ( 41.56 -10.05 9.94 0.74) ; 1, R-1-1-1-2-2 ( 41.58 -10.86 10.69 0.59) ; 2 ( 41.47 -11.58 11.38 0.52) ; 3 ( 41.81 -12.23 11.63 0.37) ; 4 ( 42.46 -12.85 11.63 0.59) ; 5 ( 43.17 -13.03 11.75 0.37) ; 6 ( 44.12 -13.18 11.81 0.37) ; 7 ( 44.50 -13.62 11.88 0.44) ; 8 ( 45.09 -14.15 11.88 0.22) ; 9 ( 45.55 -14.52 11.88 0.44) ; 10 ( 46.33 -14.79 11.88 0.88) ; 11 ( 46.97 -15.04 12.06 1.18) ; 12 ( 47.75 -15.24 12.31 0.96) ; 13 ( 48.13 -15.23 12.31 0.52) ; 14 ( 48.95 -15.65 12.31 0.44) ; 15 ( 49.44 -16.33 12.31 0.44) ; 16 ( 49.73 -16.89 12.50 0.59) ; 17 ( 50.18 -17.32 12.50 0.52) ; 18 ( 50.54 -17.83 12.50 0.52) ; 19 ( 50.95 -18.11 12.63 0.66) ; 20 ( 51.38 -18.25 12.63 0.52) ; 21 ( 52.22 -18.54 12.69 0.52) ; 22 ( 52.82 -19.08 12.69 0.88) ; 23 ( 53.43 -19.40 12.69 0.88) ; 24 ( 53.82 -19.75 12.94 0.44) ; 25 ( 54.36 -20.13 12.94 0.44) ; 26 ( 55.32 -21.17 12.94 0.44) ; 27 ( 55.86 -21.48 12.94 0.74) ; 28 ( 56.41 -21.79 13.75 0.81) ; 29 ( 56.77 -21.85 13.75 0.37) ; 30 ( 57.47 -22.18 13.75 0.37) ; 31 ( 58.27 -22.31 14.19 0.66) ; 32 ( 58.21 -21.70 14.94 0.66) ; 33 ( 58.74 -21.20 15.38 0.44) ; 34 ( 59.08 -20.96 15.38 0.44) ; 35 ( 60.03 -21.99 15.63 0.44) ; 36 ( 60.38 -22.64 15.63 0.44) ; 37 ( 60.57 -22.83 15.75 0.44) ; 38 ( 61.20 -22.62 15.81 0.44) ; 39 ( 61.54 -21.94 15.88 0.44) ; 40 ( 62.24 -21.68 15.69 0.44) ; 41 ( 62.53 -21.28 15.50 0.44) ; 42 ( 62.94 -21.06 17.13 0.74) ; 43 ( 62.98 -20.84 17.63 1.03) ; 44 ( 63.33 -20.00 18.44 1.25) ; 45 ( 63.61 -19.67 19.19 0.88) ; 46 ( 64.27 -19.78 20.19 0.66) ; 47 ( 64.99 -19.89 20.31 0.66) ; 48 ( 65.68 -19.78 20.31 0.59) ; 49 ( 66.04 -19.84 20.63 0.59) ; 50 ( 66.87 -20.71 20.88 0.59) ; 51 ( 66.88 -21.60 20.94 0.59) ; 52 Normal ) ; End of split ) ; End of split | ( 34.00 -10.56 7.13 1.03) ; 1, R-1-1-2 ( 34.26 -11.27 7.00 0.59) ; 2 ( 34.48 -12.26 7.00 0.59) ; 3 ( 34.65 -13.03 6.81 0.59) ; 4 ( ( 34.58 -13.98 6.69 0.66) ; 1, R-1-1-2-1 ( 34.10 -15.09 6.31 0.52) ; 2 ( 34.04 -15.97 6.19 0.74) ; 3 ( 34.03 -16.93 6.19 0.74) ; 4 ( 34.12 -17.84 6.19 0.74) ; 5 ( 34.17 -19.39 6.19 0.52) ; 6 ( 34.38 -19.87 6.19 0.52) ; 7 ( 34.34 -20.69 6.19 0.96) ; 8 ( 34.49 -21.59 6.19 1.40) ; 9 ( 34.44 -22.40 6.19 0.66) ; 10 ( 34.49 -23.51 6.13 0.59) ; 11 ( 34.56 -24.93 6.13 0.59) ; 12 ( 34.75 -26.08 6.13 0.59) ; 13 ( 35.48 -27.08 6.13 0.59) ; 14 ( 35.69 -28.15 6.06 0.88) ; 15 ( 36.01 -29.01 5.94 0.52) ; 16 ( 36.38 -29.89 7.81 0.52) ; 17 ( 36.79 -31.06 7.81 1.11) ; 18 ( 37.11 -31.85 8.50 1.11) ; 19 ( 37.51 -32.66 9.00 0.66) ; 20 ( 38.04 -33.55 9.81 0.66) ; 21 ( 37.79 -33.74 9.88 0.66) ; 22 ( 37.86 -34.71 10.44 0.66) ; 23 ( 38.50 -35.40 11.00 0.81) ; 24 ( 38.84 -36.50 11.25 0.66) ; 25 ( 39.66 -37.44 12.06 0.59) ; 26 ( 40.66 -38.71 12.06 0.44) ; 27 ( 41.04 -39.54 12.13 0.74) ; 28 ( 41.37 -40.25 12.25 0.96) ; 29 ( 41.85 -40.99 12.50 0.37) ; 30 ( 42.46 -41.38 12.75 0.37) ; 31 ( 43.28 -42.33 12.69 0.44) ; 32 ( 44.16 -43.36 12.50 0.59) ; 33 ( 44.76 -44.34 12.38 0.66) ; 34 ( 45.44 -45.63 12.00 0.96) ; 35 ( 45.94 -46.67 12.00 0.96) ; 36 ( 46.22 -47.31 12.00 0.66) ; 37 ( 46.78 -48.06 12.00 0.59) ; 38 ( 47.11 -48.78 11.94 0.66) ; 39 ( 47.81 -49.56 11.69 0.66) ; 40 ( 48.72 -50.37 11.69 0.52) ; 41 ( 49.41 -51.22 11.56 0.44) ; 42 ( 50.12 -51.92 11.44 0.66) ; 43 ( 50.63 -52.89 11.44 0.88) ; 44 ( 51.20 -53.58 11.50 1.40) ; 45 ( 51.69 -54.24 11.44 1.62) ; 46 ( 52.39 -55.02 11.44 0.96) ; 47 ( 53.06 -55.42 11.19 0.59) ; 48 ( 53.43 -56.00 11.19 0.44) ; 49 ( 53.91 -56.67 11.19 0.44) ; 50 ( 54.95 -57.65 11.13 0.44) ; 51 ( 55.75 -58.29 10.75 0.96) ; 52 ( 56.38 -58.54 10.75 1.33) ; 53 ( 56.63 -58.87 10.69 1.33) ; 54 ( 57.11 -59.55 10.75 0.52) ; 55 ( 57.70 -60.09 10.00 0.52) ; 56 ( 58.09 -60.44 9.69 0.74) ; 57 ( 58.90 -60.50 9.69 0.59) ; 58 ( 60.25 -60.93 9.63 0.59) ; 59 ( 60.91 -61.48 9.06 0.81) ; 60 ( 61.30 -61.84 8.13 0.81) ; 61 ( 61.76 -62.21 8.06 0.52) ; 62 ( 62.10 -62.40 7.13 0.37) ; 63 ( 62.86 -62.83 5.88 0.37) ; 64 ( 63.60 -63.82 5.88 0.37) ; 65 ( 64.52 -64.57 5.88 0.37) ; 66 ( 65.06 -65.39 5.69 0.96) ; 67 ( 65.67 -65.78 5.31 1.11) ; 68 ( 66.19 -66.24 4.88 0.66) ; 69 ( 66.80 -66.79 3.06 0.29) ; 70 ( 67.51 -67.49 2.75 0.29) ; 71 ( 67.95 -68.52 2.44 0.29) ; 72 ( 68.16 -69.52 4.06 0.29) ; 73 ( 68.24 -70.87 5.06 0.29) ; 74 ( 68.30 -71.91 5.06 0.29) ; 75 ( 68.07 -72.91 5.06 0.52) ; 76 ( 67.93 -73.33 5.06 0.81) ; 77 ( 67.79 -73.75 5.00 1.47) ; 78 ( 67.52 -74.45 4.63 1.11) ; 79 ( 67.32 -74.86 4.63 0.66) ; 80 ( 67.29 -75.90 4.50 0.44) ; 81 ( 67.39 -76.73 4.38 0.44) ; 82 ( 67.59 -77.79 4.38 0.66) ; 83 ( 67.48 -78.51 4.69 0.44) ; 84 ( 67.28 -79.75 5.00 0.44) ; 85 ( 67.52 -80.60 5.06 0.74) ; 86 ( 67.92 -81.40 5.06 1.33) ; 87 ( 68.48 -82.08 5.25 1.47) ; 88 ( 68.93 -82.59 5.25 0.96) ; 89 ( 69.27 -83.24 5.25 0.59) ; 90 ( 69.47 -83.86 5.81 0.37) ; 91 ( 70.51 -84.84 5.63 0.37) ; 92 ( 71.01 -85.44 5.63 0.44) ; 93 ( 71.14 -86.05 5.31 1.18) ; 94 ( 71.42 -86.62 5.25 1.18) ; 95 ( 71.33 -87.19 5.13 0.44) ; 96 ( 71.78 -88.24 4.25 0.29) ; 97 ( 71.79 -89.13 4.25 0.29) ; 98 ( 71.95 -89.96 4.25 0.59) ; 99 ( 71.84 -90.69 4.25 1.40) ; 100 ( 71.80 -91.43 4.25 1.69) ; 101 ( 72.00 -92.05 4.00 0.59) ; 102 ( 71.88 -92.77 3.44 0.44) ; 103 ( 71.73 -93.71 3.00 0.44) ; 104 ( 71.61 -94.51 2.44 0.74) ; 105 ( 71.44 -95.14 2.44 1.11) ; 106 ( 71.00 -95.96 2.13 1.77) ; 107 ( 70.88 -96.76 1.81 0.81) ; 108 ( 70.83 -97.56 1.81 0.44) ; 109 ( 70.81 -98.59 1.75 0.44) ; 110 ( 70.75 -98.96 1.63 0.66) ; 111 ( 70.49 -99.72 1.63 0.44) ; 112 ( 70.41 -100.67 1.63 0.74) ; 113 ( 70.03 -101.21 1.44 1.40) ; 114 ( 69.84 -101.92 1.38 0.88) ; 115 ( 69.44 -102.60 0.75 0.52) ; 116 ( 68.81 -103.23 -0.81 0.52) ; 117 ( 67.78 -104.10 -1.56 0.66) ; 118 Normal | ( 35.20 -13.74 6.63 0.59) ; 1, R-1-1-2-2 ( 35.56 -14.32 6.38 0.52) ; 2 ( 35.59 -15.06 6.38 0.81) ; 3 ( 35.76 -15.83 6.13 0.52) ; 4 ( 36.17 -16.56 6.00 0.52) ; 5 ( 36.91 -17.56 5.69 0.66) ; 6 ( 38.30 -18.67 5.56 0.66) ; 7 ( 39.01 -19.38 5.44 0.66) ; 8 ( 39.63 -20.22 5.00 0.52) ; 9 ( 39.96 -20.93 6.50 0.66) ; 10 ( 40.34 -21.81 6.63 0.66) ; 11 ( 40.75 -22.54 6.75 0.44) ; 12 ( 41.26 -23.06 6.81 0.74) ; 13 ( 42.04 -23.33 6.94 1.18) ; 14 ( 43.17 -23.22 7.44 0.88) ; 15 ( 44.37 -23.70 7.44 0.74) ; 16 ( 45.19 -24.13 7.69 0.59) ; 17 ( 45.25 -24.73 8.00 0.52) ; 18 ( 44.38 -25.49 8.69 0.52) ; 19 ( 44.39 -25.92 8.81 0.59) ; 20 ( 44.59 -26.55 9.25 0.59) ; 21 ( 45.15 -27.23 9.50 0.59) ; 22 ( 46.01 -27.89 10.06 0.44) ; 23 ( 46.60 -28.42 10.38 0.59) ; 24 ( 46.93 -28.69 10.75 0.96) ; 25 ( 47.32 -29.06 11.06 1.33) ; 26 ( 47.46 -29.60 11.38 1.11) ; 27 ( 47.59 -30.21 11.69 0.81) ; 28 ( 47.77 -30.90 12.88 0.52) ; 29 ( 48.00 -31.83 13.44 0.52) ; 30 ( 48.66 -32.38 13.69 0.66) ; 31 ( 49.21 -33.13 13.75 0.66) ; 32 ( 49.91 -33.46 13.75 0.66) ; 33 ( 50.32 -34.64 14.69 0.81) ; 34 ( 50.61 -35.65 14.81 0.52) ; 35 ( 51.21 -36.04 14.81 0.52) ; 36 ( 51.75 -36.86 14.88 0.81) ; 37 ( 52.38 -37.18 15.31 0.52) ; 38 ( 52.93 -37.94 15.56 1.18) ; 39 ( 53.36 -38.52 16.00 1.18) ; 40 ( 53.82 -39.33 16.00 0.59) ; 41 ( 54.71 -40.37 16.38 0.81) ; 42 ( 55.21 -40.96 16.56 0.52) ; 43 ( 55.76 -41.72 16.56 0.44) ; 44 ( 56.21 -42.68 16.94 0.44) ; 45 ( 56.65 -43.63 17.31 1.11) ; 46 ( 56.93 -44.27 17.56 1.11) ; 47 ( 58.09 -44.90 17.63 0.74) ; 48 ( 59.66 -44.93 17.63 0.59) ; 49 ( 60.75 -44.58 17.69 0.44) ; 50 ( 62.00 -44.70 17.75 0.66) ; 51 ( 62.64 -44.88 17.88 1.03) ; 52 ( 63.33 -45.28 18.06 1.40) ; 53 ( 64.12 -45.93 18.19 0.74) ; 54 ( 65.20 -46.61 18.25 0.59) ; 55 ( 66.67 -46.78 18.69 0.59) ; 56 ( 68.01 -46.77 19.06 0.59) ; 57 ( 69.22 -46.66 19.81 0.74) ; 58 ( 69.82 -47.12 20.63 0.74) ; 59 ( 70.72 -47.57 21.13 0.59) ; 60 ( 71.75 -48.17 21.25 0.59) ; 61 ( 72.62 -48.75 21.44 0.74) ; 62 ( 73.84 -49.10 20.94 0.66) ; 63 ( 74.89 -49.48 20.69 0.59) ; 64 ( 75.74 -49.84 21.69 0.81) ; 65 ( 76.50 -50.19 21.81 0.52) ; 66 ( 77.49 -51.00 21.63 0.52) ; 67 ( 78.49 -51.24 21.56 0.52) ; 68 ( 79.73 -50.99 21.63 0.52) ; 69 ( 81.13 -51.06 21.63 0.52) ; 70 ( 82.31 -50.73 21.63 0.74) ; 71 ( 83.02 -50.48 21.63 0.74) ; 72 ( 83.96 -50.63 21.63 0.52) ; 73 ( 84.94 -51.03 21.69 0.52) ; 74 ( 85.67 -51.59 21.69 0.37) ; 75 ( 85.69 -51.95 21.69 0.37) ; 76 Normal ) ; End of split ) ; End of split | ( 32.50 -8.17 7.44 1.25) ; 1, R-1-2 ( 33.37 -7.87 7.75 0.66) ; 2 ( 34.23 -7.64 7.81 0.37) ; 3 ( 35.55 -7.25 7.81 0.37) ; 4 ( 37.02 -6.97 8.56 0.59) ; 5 ( 37.86 -6.81 8.69 0.59) ; 6 ( 38.89 -6.45 8.69 0.59) ; 7 ( 39.59 -6.26 8.63 0.52) ; 8 ( 40.64 -5.32 8.69 0.66) ; 9 ( 41.67 -4.44 8.63 0.74) ; 10 ( 42.58 -3.41 8.88 0.59) ; 11 ( 43.73 -2.26 9.13 0.66) ; 12 ( 44.63 -1.29 9.19 0.59) ; 13 ( 45.46 -0.23 9.19 0.59) ; 14 ( 46.32 1.85 9.56 0.52) ; 15 ( 46.89 2.65 9.63 0.81) ; 16 ( 47.43 4.11 9.63 0.59) ; 17 ( 47.73 5.11 9.63 1.18) ; 18 ( 48.47 6.02 9.88 0.88) ; 19 ( 49.15 7.46 9.81 0.74) ; 20 ( 49.98 8.96 9.56 0.66) ; 21 ( 50.62 9.68 9.44 0.66) ; 22 ( 51.73 10.54 9.00 0.66) ; 23 ( 53.02 11.66 8.81 0.59) ; 24 ( 55.11 13.04 8.56 0.81) ; 25 ( 57.09 14.28 8.25 0.88) ; 26 ( 58.38 14.89 8.06 0.66) ; 27 ( 59.58 15.36 8.06 0.81) ; 28 ( 61.50 16.17 8.19 0.59) ; 29 ( 62.49 16.39 8.19 0.88) ; 30 ( 64.14 17.30 8.13 0.81) ; 31 ( 65.08 17.99 7.75 0.96) ; 32 ( 66.51 19.02 7.50 0.96) ; 33 ( 67.73 19.64 7.25 1.03) ; 34 ( 68.29 20.37 7.31 0.37) ; 35 ( 69.03 21.73 8.06 0.37) ; 36 ( 69.49 22.25 8.56 0.37) ; 37 ( 69.99 23.06 9.00 0.37) ; 38 ( 70.06 24.45 9.38 0.59) ; 39 ( 70.03 25.71 9.69 1.18) ; 40 ( 70.20 26.73 10.06 1.03) ; 41 ( 70.92 28.02 10.19 0.81) ; 42 ( 71.33 28.70 10.31 0.66) ; 43 ( 71.58 29.84 10.06 0.88) ; 44 ( 72.50 30.50 10.06 0.88) ; 45 ( 72.96 31.03 11.44 0.59) ; 46 ( 73.83 31.77 11.50 0.74) ; 47 ( 74.63 32.61 11.63 0.74) ; 48 ( 75.46 33.14 12.13 0.52) ; 49 ( 76.46 33.81 12.44 0.52) ; 50 ( 77.12 34.66 12.94 0.59) ; 51 ( 77.84 35.43 13.56 0.59) ; 52 ( 78.38 36.53 13.75 0.59) ; 53 ( 78.60 37.39 13.75 1.18) ; 54 ( 79.00 38.06 14.31 1.40) ; 55 ( 79.51 38.94 14.31 0.74) ; 56 ( 80.22 40.08 14.31 0.59) ; 57 ( 81.51 40.70 14.50 0.44) ; 58 ( 82.40 41.15 14.56 0.66) ; 59 ( 83.13 41.55 14.13 0.96) ; 60 ( 83.92 41.89 13.38 1.03) ; 61 ( 85.26 42.34 13.25 0.74) ; 62 ( 86.11 43.46 13.25 0.59) ; 63 ( 87.34 44.23 13.06 0.59) ; 64 ( 88.40 45.18 12.88 0.59) ; 65 ( 88.97 45.98 12.88 0.81) ; 66 ( 89.39 46.79 11.50 1.11) ; 67 ( 89.90 47.67 11.25 0.74) ; 68 ( 90.66 48.67 11.25 0.44) ; 69 ( 91.01 49.50 10.25 0.66) ; 70 ( 91.28 50.20 9.81 0.66) ; 71 ( 91.64 50.66 9.69 0.66) ; 72 ( 93.35 51.50 8.44 0.88) ; 73 ( 93.80 51.57 8.44 0.59) ; 74 ( 94.91 51.48 7.25 0.88) ; 75 ( 95.66 51.05 6.75 1.18) ; 76 ( 96.63 50.09 5.94 0.81) ; 77 ( 97.37 51.01 5.13 0.44) ; 78 ( 98.24 51.76 5.06 0.44) ; 79 ( 99.41 51.64 5.06 1.25) ; 80 ( 100.24 52.11 4.63 2.50) ; 81 ( 101.41 52.51 3.13 2.50) ; 82 ( 101.74 52.68 1.75 2.14) ; 83 Normal ) ; End of split | ( 14.85 -4.18 8.00 0.88) ; 1, R-2 ( 15.41 -4.42 7.19 0.96) ; 2 ( 16.82 -4.87 6.13 1.25) ; 3 ( 17.19 -5.37 4.81 1.99) ; 4 ( 17.90 -6.15 3.06 1.47) ; 5 ( 18.90 -6.39 2.50 2.06) ; 6 ( ( 19.23 -5.25 1.44 2.21) ; 1, R-2-1 ( 19.96 -4.41 1.06 1.33) ; 2 ( 20.40 -3.95 1.00 1.11) ; 3 ( 20.97 -3.68 1.00 1.03) ; 4 ( 21.79 -3.21 0.81 0.74) ; 5 ( 22.61 -3.20 0.81 0.96) ; 6 ( 23.47 -2.96 0.56 1.18) ; 7 ( 24.15 -2.92 0.25 1.11) ; 8 ( 25.08 -2.70 -0.44 0.96) ; 9 ( 26.00 -2.55 -0.63 1.11) ; 10 ( 26.87 -2.24 -1.19 1.25) ; 11 ( 28.07 -2.21 -1.44 1.11) ; 12 ( 29.05 -2.07 -1.81 1.33) ; 13 ( 30.04 -2.00 -1.81 1.77) ; 14 ( 31.01 -2.01 -2.94 1.99) ; 15 ( ( 31.84 -2.36 -3.44 0.96) ; 1, R-2-1-1 ( 32.72 -2.50 -3.13 0.66) ; 2 ( 33.26 -2.36 -2.69 0.74) ; 3 ( 34.32 -2.24 -2.44 0.81) ; 4 ( 34.97 -2.41 -2.25 0.88) ; 5 ( 35.71 -2.46 -2.25 0.88) ; 6 ( 36.54 -2.37 -2.25 0.81) ; 7 ( 37.38 -2.72 -2.19 0.81) ; 8 ( 38.20 -3.22 -1.94 0.88) ; 9 ( 39.48 -3.64 -1.94 1.11) ; 10 ( 40.29 -4.14 -2.31 0.96) ; 11 ( 41.12 -4.57 -3.31 1.11) ; 12 ( 41.73 -4.96 -3.88 1.18) ; 13 ( 42.18 -5.41 -3.88 0.96) ; 14 ( 42.55 -5.91 -4.50 0.81) ; 15 ( 43.28 -6.47 -4.94 0.88) ; 16 ( 44.29 -6.99 -5.19 1.18) ; 17 ( 45.56 -7.05 -4.81 0.88) ; 18 ( 46.68 -7.01 -3.81 1.03) ; 19 ( 48.08 -7.08 -3.75 1.03) ; 20 ( 49.09 -7.31 -3.44 0.81) ; 21 ( 50.39 -7.60 -2.75 0.74) ; 22 ( 51.58 -8.08 -2.75 0.59) ; 23 ( 52.67 -8.32 -1.88 0.59) ; 24 ( 53.40 -8.37 -1.44 0.74) ; 25 ( 54.31 -8.29 -1.19 0.74) ; 26 ( 55.14 -8.27 -1.13 0.96) ; 27 ( 56.16 -8.36 -0.88 0.96) ; 28 ( 57.09 -8.66 -0.81 0.96) ; 29 ( 58.15 -8.97 -0.69 0.96) ; 30 ( 59.75 -8.79 -0.63 0.96) ; 31 ( 60.62 -8.92 -0.19 1.11) ; 32 ( 61.94 -8.60 0.31 1.03) ; 33 ( 63.01 -8.48 1.06 0.96) ; 34 ( 63.79 -8.76 1.25 0.74) ; 35 ( 64.63 -9.03 1.75 0.44) ; 36 ( 65.81 -9.15 1.75 0.74) ; 37 ( 66.54 -9.26 1.88 1.25) ; 38 ( 67.19 -9.37 1.94 1.55) ; 39 ( 67.71 -9.38 2.38 1.69) ; 40 ( 68.78 -9.69 2.06 0.88) ; 41 ( 69.68 -9.61 2.00 0.81) ; 42 ( 70.76 -9.93 0.88 0.81) ; 43 ( 71.67 -10.30 0.19 0.88) ; 44 ( 72.86 -10.34 -0.44 0.88) ; 45 ( 74.32 -10.50 -0.81 0.88) ; 46 ( 75.58 -10.54 -0.94 0.96) ; 47 ( 76.31 -10.66 -1.00 1.11) ; 48 ( 77.26 -10.81 -1.19 0.96) ; 49 ( 78.18 -11.11 -0.88 0.66) ; 50 ( 79.11 -11.33 -0.88 0.88) ; 51 ( 80.01 -11.77 -0.88 1.25) ; 52 ( 81.01 -11.63 -0.63 1.40) ; 53 ( 82.17 -11.81 -0.75 1.03) ; 54 ( 83.31 -11.70 -1.00 0.81) ; 55 ( 84.67 -12.06 -1.13 0.81) ; 56 ( 85.58 -12.43 -1.38 1.03) ; 57 ( 86.44 -12.64 -1.56 0.96) ; 58 ( 87.61 -13.05 -1.63 1.03) ; 59 ( 88.53 -12.90 -1.75 1.25) ; 60 ( 89.47 -13.12 -1.63 0.74) ; 61 ( 90.52 -13.06 -1.63 0.74) ; 62 ( 91.93 -13.14 -1.63 0.88) ; 63 ( 93.01 -13.31 -1.63 0.88) ; 64 ( 94.13 -13.79 -1.44 1.03) ; 65 ( 95.46 -13.41 -1.50 0.81) ; 66 ( 96.66 -13.38 -1.63 0.96) ; 67 ( 97.58 -13.66 -1.63 1.47) ; 68 ( 98.14 -13.90 -1.63 1.47) ; 69 ( 98.91 -13.80 -1.63 1.18) ; 70 ( 99.79 -13.87 -2.00 0.88) ; 71 ( 100.44 -13.97 -2.00 0.52) ; 72 ( 101.09 -14.15 -2.00 0.52) ; 73 ( 102.14 -14.54 -2.25 0.74) ; 74 ( 102.97 -15.03 -2.13 0.74) ; 75 ( 103.93 -15.56 -2.13 0.37) ; 76 ( 104.60 -15.96 -2.81 0.59) ; 77 ( 105.47 -16.18 -3.19 0.59) ; 78 ( 106.31 -16.45 -2.44 1.03) ; 79 ( 106.95 -16.71 -2.31 1.33) ; 80 ( 108.55 -16.96 -2.31 1.47) ; 81 ( 109.55 -17.26 -2.31 1.33) ; 82 ( 110.52 -17.72 -2.31 1.11) ; 83 ( 112.31 -18.59 -2.75 0.81) ; 84 ( 113.07 -19.00 -3.06 0.66) ; 85 ( 113.67 -19.47 -3.06 0.66) ; 86 ( 114.42 -19.95 -2.38 0.74) ; 87 ( 115.38 -20.48 -2.38 0.37) ; 88 ( 116.11 -21.04 -2.38 0.59) ; 89 ( 116.92 -21.54 -2.38 0.66) ; 90 ( 117.51 -22.07 -2.38 0.66) ; 91 ( 118.76 -22.65 -2.00 0.59) ; 92 ( 120.21 -22.95 -2.00 0.52) ; 93 ( 120.72 -23.47 -1.25 0.52) ; 94 ( 121.51 -24.12 -0.81 0.74) ; 95 ( 122.31 -24.69 -0.56 0.96) ; 96 ( 122.77 -25.13 -0.31 1.03) ; 97 ( 123.36 -25.15 -0.13 0.37) ; 98 ( 124.29 -25.45 -0.13 0.37) ; 99 ( 125.30 -25.68 0.06 0.37) ; 100 ( 126.24 -25.83 0.19 0.66) ; 101 ( 127.47 -26.10 -0.25 0.66) ; 102 ( 128.86 -26.24 -0.13 0.59) ; 103 ( 129.97 -26.23 -0.06 0.74) ; 104 ( 130.75 -26.44 0.00 1.33) ; 105 ( 131.51 -26.41 0.00 1.03) ; 106 ( 132.62 -26.43 0.06 0.66) ; 107 ( 133.76 -25.80 0.06 0.52) ; 108 ( 134.93 -25.54 0.31 0.52) ; 109 ( 135.87 -24.73 0.31 0.44) ; 110 ( 136.13 -24.10 0.06 0.66) ; 111 ( 136.24 -23.38 -0.13 0.81) ; 112 ( 136.74 -23.01 -0.75 0.59) ; 113 ( 136.88 -22.16 -1.63 0.44) ; 114 ( 137.15 -21.38 -2.13 0.44) ; 115 Normal | ( 31.59 -1.64 -3.69 0.88) ; 1, R-2-1-2 ( 32.56 -1.21 -4.50 0.88) ; 2 ( 33.13 -0.85 -6.44 1.25) ; 3 ( 33.83 -0.74 -7.38 1.33) ; 4 ( 33.75 -0.28 -8.69 1.18) ; 5 ( 33.21 -0.35 -9.75 1.40) ; 6 ( 32.87 -0.21 -10.38 1.40) ; 7 ( 32.51 -0.16 -11.69 1.18) ; 8 ( 32.01 -0.45 -12.63 1.18) ; 9 ( 32.00 -0.52 -14.44 1.03) ; 10 ( 32.62 -0.33 -14.88 1.03) ; 11 ( 32.94 0.21 -15.75 0.74) ; 12 ( 33.04 0.42 -16.75 0.74) ; 13 ( 33.25 -0.13 -18.25 0.52) ; 14 ( 33.77 -0.58 -18.81 0.52) ; 15 ( 34.31 -0.97 -19.06 0.66) ; 16 ( 35.05 -1.01 -19.88 1.25) ; 17 ( 35.26 -0.67 -20.56 1.25) ; 18 ( 35.58 -0.50 -21.50 0.88) ; 19 ( 35.60 -0.35 -23.88 0.81) ; 20 ( 35.97 -0.86 -25.31 0.81) ; 21 ( 36.81 -0.70 -25.69 0.74) ; 22 ( 37.73 -0.10 -25.69 0.74) ; 23 ( 38.57 0.06 -26.13 0.52) ; 24 ( 39.09 -0.46 -26.44 0.74) ; 25 ( 39.32 -1.39 -27.25 0.74) ; 26 ( 39.52 -2.02 -28.19 0.59) ; 27 ( 39.58 -2.54 -28.94 0.81) ; 28 ( 39.85 -3.17 -29.94 0.96) ; 29 ( 39.99 -3.71 -31.13 0.81) ; 30 ( 40.64 -4.34 -31.63 0.81) ; 31 ( 40.14 -4.70 -32.81 0.88) ; 32 ( 39.89 -5.33 -33.88 0.74) ; 33 ( 39.40 -6.07 -34.94 0.96) ; 34 ( 39.57 -6.90 -36.25 1.18) ; 35 ( 39.86 -7.84 -36.81 0.88) ; 36 ( 40.15 -7.89 -38.31 0.81) ; 37 ( 41.35 -7.41 -38.19 0.81) ; 38 ( 41.75 -6.73 -38.50 0.81) ; 39 ( 42.37 -6.16 -38.94 0.81) ; 40 ( 42.68 -6.06 -40.44 0.81) ; 41 ( 42.80 -5.79 -42.63 0.81) ; 42 ( 42.76 -5.56 -43.69 1.18) ; 43 ( 42.26 -5.85 -44.25 1.18) ; 44 ( 42.18 -5.91 -45.75 0.88) ; 45 ( 41.90 -6.31 -47.06 0.88) ; 46 ( 41.71 -7.02 -47.50 0.88) ; 47 ( 41.24 -7.17 -48.69 0.88) ; 48 ( 40.37 -7.03 -49.19 0.74) ; 49 ( 40.03 -6.83 -50.38 0.74) ; 50 ( 39.73 -6.86 -49.56 0.52) ; 51 ( 38.46 -7.33 -50.00 0.52) ; 52 ( 37.89 -8.05 -50.25 0.52) ; 53 ( 37.35 -9.15 -50.25 1.03) ; 54 ( 36.68 -9.63 -50.31 1.03) ; 55 ( 36.21 -10.22 -50.88 0.66) ; 56 ( 34.79 -11.18 -51.25 0.37) ; 57 ( 33.96 -12.16 -51.25 0.66) ; 58 ( 33.65 -12.70 -51.75 0.88) ; 59 ( 32.79 -13.90 -51.75 0.88) ; 60 ( 32.32 -14.49 -51.75 0.59) ; 61 ( 31.60 -15.71 -51.69 0.37) ; 62 ( 31.38 -16.19 -51.69 0.66) ; 63 ( 30.71 -17.12 -51.69 0.66) ; 64 ( 29.81 -18.09 -51.75 0.22) ; 65 ( 28.46 -19.57 -51.88 0.22) ; 66 ( 27.59 -20.84 -52.75 0.22) ; 67 ( 26.49 -22.08 -52.75 0.52) ; 68 ( 25.98 -22.51 -53.69 0.88) ; 69 ( 25.53 -22.97 -53.88 1.18) ; 70 ( 25.17 -23.43 -54.00 0.74) ; 71 ( 23.85 -24.18 -54.00 0.37) ; 72 ( 23.22 -24.82 -54.81 0.66) ; 73 ( 22.35 -25.64 -56.00 0.81) ; 74 ( 22.09 -25.89 -57.50 0.52) ; 75 Normal ) ; End of split | ( 18.71 -7.16 2.25 1.40) ; 1, R-2-2 ( 18.67 -8.85 2.50 0.96) ; 2 ( 18.60 -10.69 2.88 0.88) ; 3 ( 18.68 -12.11 2.56 0.96) ; 4 ( 18.84 -12.95 2.19 0.96) ; 5 ( 19.21 -13.90 2.00 0.74) ; 6 ( 19.57 -14.99 2.00 0.59) ; 7 ( 19.73 -15.39 2.00 0.96) ; 8 ( 20.12 -16.19 1.75 0.96) ; 9 ( 20.27 -17.10 1.81 0.59) ; 10 ( 20.63 -18.12 1.56 0.52) ; 11 ( 20.86 -19.04 1.25 0.74) ; 12 ( 21.02 -20.40 1.13 0.88) ; 13 ( 21.27 -21.18 1.00 0.88) ; 14 ( 21.47 -22.24 0.88 0.74) ; 15 ( 21.73 -23.91 0.63 0.88) ; 16 ( 21.97 -25.21 0.63 0.81) ; 17 ( 22.67 -26.88 0.63 0.74) ; 18 ( 23.15 -27.36 1.00 0.74) ; 19 ( 23.83 -28.21 1.13 0.74) ; 20 ( 24.35 -28.73 1.25 0.74) ; 21 ( 24.74 -29.98 1.44 0.96) ; 22 ( 25.28 -31.25 1.44 1.25) ; 23 ( 25.57 -31.81 1.44 1.55) ; 24 ( 25.87 -32.30 1.44 1.18) ; 25 ( 26.49 -33.07 1.44 0.81) ; 26 ( 27.38 -34.02 1.75 0.74) ; 27 ( 27.91 -34.92 2.00 0.66) ; 28 ( 27.81 -36.53 2.00 0.81) ; 29 ( 27.78 -38.09 1.81 0.81) ; 30 ( 27.64 -39.02 1.38 0.81) ; 31 ( 27.62 -40.05 1.00 1.33) ; 32 ( 27.75 -41.11 1.25 0.96) ; 33 ( 28.21 -42.00 1.31 0.66) ; 34 ( 28.49 -42.56 1.44 0.66) ; 35 ( 28.84 -43.20 1.44 0.88) ; 36 ( 29.24 -44.45 1.44 0.74) ; 37 ( 29.59 -45.55 1.56 0.74) ; 38 ( 29.93 -46.64 1.50 0.88) ; 39 ( 30.45 -47.68 1.50 0.88) ; 40 ( 30.81 -48.63 1.31 0.88) ; 41 ( 31.34 -49.52 1.19 1.03) ; 42 ( 32.06 -50.68 0.94 1.03) ; 43 ( 32.64 -51.73 1.56 1.40) ; 44 ( 32.99 -52.75 1.25 1.84) ; 45 ( 33.51 -53.27 2.00 1.40) ; 46 ( 33.92 -54.00 2.44 0.74) ; 47 ( 34.58 -54.48 2.44 0.52) ; 48 ( 35.22 -55.17 2.69 0.74) ; 49 ( 35.83 -56.09 1.94 0.96) ; 50 ( 36.33 -57.57 1.94 1.11) ; 51 ( 36.70 -58.65 1.75 0.74) ; 52 ( 36.98 -59.67 1.75 0.96) ; 53 ( 37.27 -60.68 1.56 0.96) ; 54 ( 37.52 -61.89 1.56 0.59) ; 55 ( 36.46 -62.99 2.38 1.25) ; 56 ( 36.26 -63.77 2.63 1.33) ; 57 ( 35.93 -64.38 3.25 0.59) ; 58 ( 35.90 -65.12 3.63 0.59) ; 59 ( 35.94 -65.79 3.75 0.59) ; 60 ( 36.08 -67.22 4.00 0.74) ; 61 ( 35.88 -68.00 4.00 0.59) ; 62 ( 36.11 -68.93 3.06 1.18) ; 63 ( 36.21 -69.24 2.50 1.69) ; 64 ( 36.22 -69.68 1.69 1.33) ; 65 ( 35.91 -70.22 2.44 0.59) ; 66 ( 36.11 -70.78 0.94 0.44) ; 67 ( 36.12 -71.66 0.25 0.66) ; 68 ( 36.24 -72.87 -0.81 0.66) ; 69 ( 36.10 -74.18 -0.88 0.52) ; 70 ( 36.41 -75.49 -0.75 0.52) ; 71 ( 36.48 -76.53 -0.38 1.11) ; 72 ( 36.43 -77.27 -0.31 1.40) ; 73 ( 36.52 -78.09 0.06 0.88) ; 74 ( 36.40 -78.89 0.38 0.59) ; 75 ( 36.12 -80.18 0.50 0.44) ; 76 ( 35.93 -80.89 1.31 0.44) ; 77 ( 35.89 -81.62 1.88 0.52) ; 78 ( 35.43 -82.66 2.63 0.59) ; 79 ( 35.16 -83.36 3.13 0.59) ; 80 ( 34.99 -84.00 3.81 0.88) ; 81 ( 34.58 -84.74 4.00 1.62) ; 82 ( 34.41 -85.57 4.25 1.25) ; 83 ( 33.89 -86.45 4.50 0.66) ; 84 ( 33.41 -87.11 4.75 0.66) ; 85 ( 32.87 -88.21 6.25 0.81) ; 86 ( 33.12 -88.99 7.25 0.74) ; 87 ( 33.53 -89.72 7.56 0.74) ; 88 ( 33.88 -90.29 7.63 0.52) ; 89 ( 34.36 -91.04 7.69 0.66) ; 90 ( 34.80 -92.07 7.69 0.66) ; 91 ( 35.54 -92.55 7.69 1.25) ; 92 ( 36.04 -93.15 7.69 1.25) ; 93 ( 36.45 -93.81 7.88 0.66) ; 94 ( 37.00 -94.63 8.00 0.44) ; 95 ( 36.26 -95.99 7.25 0.66) ; 96 ( 35.72 -97.02 7.38 0.88) ; 97 ( 35.25 -97.69 7.31 0.52) ; 98 ( 35.07 -98.84 7.31 0.52) ; 99 ( 34.85 -100.22 7.31 0.52) ; 100 ( 34.50 -100.97 7.31 0.52) ; 101 ( 34.17 -100.77 7.31 0.52) ; 102 ( 34.03 -101.12 7.31 0.52) ; 103 ( 33.75 -101.97 7.56 0.52) ; 104 ( 33.70 -102.77 8.25 0.96) ; 105 ( 33.44 -103.46 8.31 1.18) ; 106 ( 33.44 -103.98 8.81 0.81) ; 107 ( 34.13 -104.76 9.31 0.52) ; 108 ( 34.47 -105.41 9.31 0.52) ; 109 ( 34.62 -106.32 10.19 0.81) ; 110 ( 34.59 -107.05 11.31 0.66) ; 111 ( 34.73 -108.03 12.69 0.52) ; 112 ( 34.83 -108.80 13.19 0.52) ; 113 ( 35.08 -109.58 13.31 0.81) ; 114 ( 35.16 -110.47 14.13 1.11) ; 115 ( 35.32 -111.39 14.13 1.11) ; 116 ( 35.24 -112.43 14.94 0.52) ; 117 ( 35.11 -113.23 15.38 0.52) ; 118 ( 34.94 -114.30 15.44 0.52) ; 119 ( 35.40 -115.20 15.50 0.44) ; 120 ( 35.57 -116.48 15.50 0.44) ; 121 ( 35.73 -117.31 16.06 0.44) ; 122 Normal ) ; End of split ) ; End of split ) ; End of tree ( (Color Magenta) (Dendrite) ( 6.08 2.23 -2.31 3.39) ; Root ( 6.78 2.85 -3.56 3.09) ; 1, R ( ( 8.39 3.13 -2.88 1.92) ; 1, R-1 ( 9.09 2.79 -3.38 2.14) ; 2 ( 9.96 2.65 -3.69 2.50) ; 3 ( ( 10.84 3.48 -3.69 1.92) ; 1, R-1-1 ( 11.58 3.95 -4.13 1.55) ; 2 ( ( 12.76 3.83 -4.94 0.66) ; 1, R-1-1-1 ( 13.99 3.64 -5.13 0.59) ; 2 ( 15.28 3.81 -5.69 0.59) ; 3 ( 16.55 4.27 -5.88 0.52) ; 4 ( 17.34 4.96 -6.00 0.52) ; 5 ( 17.81 5.62 -6.13 0.52) ; 6 ( 18.77 5.99 -6.19 0.74) ; 7 ( 19.72 6.36 -6.19 0.81) ; 8 ( 20.31 6.79 -6.19 1.03) ; 9 ( 21.02 7.04 -6.38 1.03) ; 10 ( 21.91 7.49 -6.38 0.74) ; 11 ( 23.20 8.10 -6.38 0.59) ; 12 ( 24.10 8.56 -6.63 0.59) ; 13 ( 25.17 9.20 -6.44 1.11) ; 14 ( 26.14 9.71 -6.38 1.62) ; 15 ( 26.87 10.04 -6.19 1.69) ; 16 ( 27.54 10.52 -6.13 1.25) ; 17 ( 28.30 11.07 -6.31 0.88) ; 18 ( 29.03 11.47 -6.31 1.03) ; 19 ( 29.58 11.61 -6.63 0.81) ; 20 ( 30.13 11.81 -6.94 0.74) ; 21 ( 30.44 12.36 -7.38 0.59) ; 22 ( 30.76 12.98 -7.56 0.74) ; 23 ( 31.25 13.71 -8.13 0.52) ; 24 ( 32.05 14.03 -8.44 0.52) ; 25 ( 33.24 14.51 -8.44 0.66) ; 26 ( 34.13 14.88 -8.75 0.88) ; 27 ( 35.25 15.38 -8.75 1.11) ; 28 ( 36.15 15.89 -9.25 0.88) ; 29 ( 36.99 16.51 -9.50 1.03) ; 30 ( 37.85 16.81 -10.44 1.33) ; 31 ( 39.05 16.84 -10.88 1.47) ; 32 ( 40.05 17.42 -11.13 1.18) ; 33 ( 40.77 17.75 -11.56 0.88) ; 34 ( 41.75 18.33 -11.44 0.74) ; 35 ( 42.79 18.77 -11.63 0.81) ; 36 ( 43.59 19.09 -11.56 0.52) ; 37 ( 44.69 19.43 -11.56 0.52) ; 38 ( 45.42 19.89 -11.06 1.25) ; 39 ( 46.20 20.51 -11.06 1.25) ; 40 ( 46.50 21.04 -11.06 1.25) ; 41 ( 47.11 21.62 -11.06 0.74) ; 42 ( 47.57 22.14 -11.06 0.44) ; 43 ( 47.98 22.82 -11.19 0.44) ; 44 ( 48.71 23.22 -11.06 0.59) ; 45 ( 49.77 23.79 -10.88 1.18) ; 46 ( 51.05 24.32 -11.63 1.33) ; 47 ( 51.65 24.83 -11.69 1.11) ; 48 ( 52.68 25.18 -12.31 1.03) ; 49 ( 53.72 25.60 -12.44 1.55) ; 50 ( 54.64 25.83 -13.00 1.55) ; 51 ( 55.16 26.26 -13.00 0.81) ; 52 ( 55.98 26.72 -13.31 0.52) ; 53 ( 56.81 27.26 -13.44 0.74) ; 54 ( 57.35 27.84 -13.63 0.66) ; 55 ( 57.71 28.68 -13.94 0.44) ; 56 ( 58.04 29.36 -13.94 0.44) ; 57 ( 58.77 29.76 -13.94 0.96) ; 58 ( 59.39 29.89 -14.13 0.96) ; 59 ( 60.15 29.99 -14.25 0.59) ; 60 ( 60.64 30.28 -14.56 0.44) ; 61 ( 61.07 30.58 -14.88 0.44) ; 62 ( 61.68 31.15 -15.13 0.44) ; 63 ( 62.60 31.75 -15.13 0.81) ; 64 ( 62.95 32.14 -15.94 1.40) ; 65 ( 63.55 32.56 -15.94 2.14) ; 66 ( 64.17 32.75 -16.63 2.14) ; 67 ( 64.93 33.30 -16.81 1.03) ; 68 ( 65.53 33.79 -16.94 0.52) ; 69 ( 66.16 34.51 -17.38 0.37) ; 70 ( 66.62 35.03 -17.63 0.37) ; 71 ( 67.01 35.64 -17.94 1.40) ; 72 ( 67.49 35.78 -18.38 2.06) ; 73 ( 67.82 36.02 -18.69 2.28) ; 74 ( 68.70 36.40 -19.25 0.59) ; 75 ( 69.30 36.39 -19.19 0.37) ; 76 ( 69.81 36.31 -19.50 0.37) ; 77 ( 70.13 36.03 -19.81 0.88) ; 78 ( 70.83 35.70 -20.69 1.47) ; 79 ( 71.72 35.63 -22.00 1.77) ; 80 ( 72.19 35.33 -22.69 1.33) ; 81 ( 72.68 35.11 -22.94 0.96) ; 82 ( 73.27 34.64 -23.44 0.66) ; 83 ( 73.66 34.29 -24.94 0.66) ; 84 ( 74.03 34.23 -25.31 0.66) ; 85 ( 74.77 34.18 -25.50 0.88) ; 86 ( 75.33 34.47 -26.75 0.66) ; 87 ( 75.96 34.66 -27.19 0.52) ; 88 ( 76.68 34.92 -27.63 0.52) ; 89 ( 77.18 35.28 -29.19 0.81) ; 90 ( 77.90 35.61 -29.69 1.92) ; 91 ( 78.58 36.10 -30.75 2.14) ; 92 ( 79.16 36.46 -31.81 0.96) ; 93 ( 80.19 36.36 -32.63 0.59) ; 94 ( 81.45 36.39 -33.19 0.44) ; 95 ( 82.47 36.22 -33.69 0.44) ; 96 ( 83.44 36.21 -34.13 1.03) ; 97 ( 84.06 36.34 -34.50 1.77) ; 98 ( 84.81 36.44 -35.00 2.14) ; 99 ( 85.43 36.57 -35.00 0.81) ; 100 ( 86.04 36.62 -35.06 0.52) ; 101 ( 86.99 36.47 -35.13 0.37) ; 102 ( 87.91 36.62 -35.31 0.37) ; 103 ( 88.35 36.55 -35.31 0.74) ; 104 ( 89.00 36.45 -35.63 0.74) ; 105 ( 89.58 36.35 -36.19 0.37) ; 106 ( 90.31 36.24 -36.44 0.37) ; 107 ( 91.12 36.18 -36.50 0.66) ; 108 ( 91.83 36.00 -36.69 1.33) ; 109 ( 92.33 35.60 -36.94 1.55) ; 110 ( 93.06 35.03 -37.25 0.66) ; 111 ( 93.72 34.55 -37.44 1.11) ; 112 ( 94.18 34.12 -37.63 1.33) ; 113 Normal | ( 11.87 4.27 -5.94 1.33) ; 1, R-1-1-2 ( 12.58 4.53 -7.38 0.96) ; 2 ( ( 13.76 4.86 -7.56 0.66) ; 1, R-1-1-2-1 ( 15.25 5.29 -7.56 0.59) ; 2 ( 16.15 5.82 -7.56 0.66) ; 3 ( 16.81 6.74 -7.56 0.81) ; 4 ( 17.52 7.89 -7.63 1.11) ; 5 ( 18.24 8.66 -7.69 1.69) ; 6 ( 18.85 9.68 -7.75 2.14) ; 7 ( 19.41 10.40 -7.75 2.28) ; 8 ( 19.62 11.78 -8.06 2.21) ; 9 ( 19.86 12.77 -8.63 2.36) ; 10 ( ( 19.66 13.40 -9.69 2.14) ; 1, R-1-1-2-1-1 ( 19.49 14.17 -9.63 1.40) ; 2 ( 19.36 14.77 -9.56 0.74) ; 3 ( 19.03 15.57 -9.56 0.66) ; 4 ( 18.89 16.11 -9.56 0.66) ; 5 ( 19.38 17.29 -9.56 0.44) ; 6 ( 19.73 18.57 -9.56 0.74) ; 7 ( 19.85 19.36 -9.56 0.96) ; 8 ( 20.21 20.12 -9.94 0.74) ; 9 ( 20.51 21.11 -9.94 0.96) ; 10 ( 20.96 22.07 -10.19 1.18) ; 11 ( 21.18 23.00 -10.19 1.18) ; 12 ( 21.59 23.67 -10.19 1.33) ; 13 ( 22.06 24.34 -10.19 0.74) ; 14 ( 22.57 25.66 -10.25 0.52) ; 15 ( 22.92 26.43 -10.44 0.59) ; 16 ( 23.16 27.50 -10.50 0.74) ; 17 ( 23.34 28.65 -10.13 0.74) ; 18 ( 24.02 30.01 -10.06 0.66) ; 19 ( 24.99 30.96 -10.00 0.74) ; 20 ( 25.96 31.41 -10.00 0.88) ; 21 ( 27.06 32.26 -10.00 1.03) ; 22 ( 28.04 33.22 -10.00 1.11) ; 23 ( 29.04 33.87 -9.63 1.69) ; 24 ( 29.91 34.63 -9.63 1.47) ; 25 ( 30.58 35.12 -9.31 1.18) ; 26 ( 31.06 35.85 -9.38 0.81) ; 27 ( 31.56 36.66 -9.38 1.40) ; 28 ( 32.03 37.25 -9.38 1.69) ; 29 ( 32.83 38.08 -10.25 0.88) ; 30 ( 33.54 38.79 -11.13 0.96) ; 31 ( 34.39 38.95 -11.56 0.81) ; 32 ( 35.17 39.65 -11.94 0.96) ; 33 ( 36.17 40.30 -12.69 0.81) ; 34 ( 36.78 41.32 -12.75 1.11) ; 35 ( 37.06 42.15 -13.31 0.96) ; 36 ( 37.23 42.79 -13.31 0.59) ; 37 ( 37.52 43.64 -13.19 0.37) ; 38 ( 37.86 44.39 -13.19 0.37) ; 39 ( 37.93 44.83 -13.19 0.37) ; 40 ( 37.91 45.64 -13.25 1.03) ; 41 ( 37.65 46.35 -13.56 1.84) ; 42 ( 37.62 46.66 -12.69 2.58) ; 43 ( 37.60 47.40 -12.69 2.28) ; 44 ( 37.60 48.36 -12.69 1.47) ; 45 ( 37.60 48.88 -12.69 0.66) ; 46 ( 37.68 49.83 -12.69 0.52) ; 47 ( 37.85 50.47 -12.69 0.52) ; 48 ( 38.16 50.94 -12.75 0.52) ; 49 ( 38.43 51.79 -13.19 0.88) ; 50 ( 38.46 52.81 -13.44 1.92) ; 51 ( 38.40 53.89 -13.94 1.47) ; 52 ( 38.50 54.98 -14.25 0.59) ; 53 ( 38.55 55.34 -14.44 0.59) ; 54 ( 38.27 55.91 -14.75 0.88) ; 55 ( 38.28 56.43 -15.94 1.11) ; 56 ( 38.36 56.93 -16.25 0.74) ; 57 ( 38.56 57.79 -16.94 0.44) ; 58 ( 38.72 58.80 -17.38 0.52) ; 59 ( 39.51 59.48 -17.56 0.74) ; 60 ( 40.01 60.29 -17.69 0.37) ; 61 ( 40.64 61.01 -17.94 0.37) ; 62 ( 41.10 61.53 -18.25 0.66) ; 63 ( 41.83 61.93 -18.31 0.96) ; 64 ( 42.71 62.31 -18.63 1.33) ; 65 ( 43.30 62.66 -19.13 0.66) ; 66 ( 44.15 62.90 -19.50 0.52) ; 67 ( 44.92 63.52 -19.94 1.03) ; 68 ( 45.59 64.00 -20.69 1.33) ; 69 ( 46.31 64.33 -20.88 0.96) ; 70 ( 47.05 64.73 -21.94 0.59) ; 71 ( 47.99 65.02 -23.44 0.88) ; 72 ( 48.84 65.63 -24.44 1.03) ; 73 ( 49.45 65.75 -25.13 1.40) ; 74 ( 49.98 66.27 -26.63 1.69) ; 75 ( 50.04 67.07 -28.25 0.81) ; 76 ( 50.35 67.62 -29.13 0.59) ; 77 ( 50.85 67.98 -29.50 1.18) ; 78 ( 51.27 68.28 -30.13 1.77) ; 79 ( 51.39 69.00 -31.88 0.74) ; 80 ( 51.33 70.04 -32.06 0.52) ; 81 ( 51.11 70.60 -32.63 0.74) ; 82 ( 51.20 71.62 -33.19 0.96) ; 83 ( 51.30 72.19 -35.00 1.11) ; 84 ( 51.31 72.79 -35.38 1.40) ; 85 ( 51.73 73.53 -35.69 0.74) ; 86 ( 51.98 74.16 -36.25 0.59) ; 87 ( 52.31 74.85 -37.38 1.25) ; 88 ( 52.57 75.55 -38.13 2.14) ; 89 ( 52.62 76.36 -39.31 1.33) ; 90 ( 52.93 76.90 -39.75 0.52) ; 91 ( 53.59 77.75 -40.38 0.37) ; 92 ( 53.95 78.60 -40.69 0.44) ; 93 ( 54.27 79.22 -40.81 1.18) ; 94 ( 54.35 79.72 -41.13 1.84) ; 95 ( 54.23 80.40 -40.63 1.11) ; 96 ( 53.94 80.89 -40.19 0.44) ; 97 ( 53.47 81.70 -39.88 0.66) ; 98 ( 53.56 82.28 -39.63 0.66) ; 99 Normal | ( 20.12 13.57 -9.63 0.96) ; 1, R-1-1-2-1-2 ( 20.46 14.33 -11.13 0.81) ; 2 ( 21.13 15.18 -11.38 0.96) ; 3 ( 22.23 15.60 -12.19 0.96) ; 4 ( 23.15 15.75 -12.38 1.11) ; 5 ( 23.98 15.40 -12.69 0.96) ; 6 ( 24.85 15.63 -12.94 0.81) ; 7 ( 25.47 15.83 -13.75 1.11) ; 8 ( 25.86 16.36 -14.25 1.40) ; 9 ( 26.33 16.95 -15.75 1.11) ; 10 ( 26.54 16.92 -17.31 0.88) ; 11 ( 27.08 17.05 -18.44 0.88) ; 12 ( 27.64 17.26 -19.25 1.18) ; 13 ( 28.34 17.89 -19.75 1.47) ; 14 ( 28.69 18.28 -20.88 1.84) ; 15 ( 29.07 18.73 -21.94 1.69) ; 16 ( 29.66 19.24 -22.63 1.40) ; 17 ( 29.86 19.50 -24.25 1.11) ; 18 ( 29.94 20.00 -25.56 0.81) ; 19 ( 30.18 21.01 -25.75 0.44) ; 20 ( 30.85 22.00 -26.19 0.66) ; 21 ( 31.86 23.18 -26.25 1.62) ; 22 ( 32.35 23.91 -26.94 1.84) ; 23 ( 32.77 25.18 -28.69 0.96) ; 24 ( 33.05 25.95 -29.56 0.66) ; 25 ( 33.18 26.82 -30.69 0.66) ; 26 ( 33.71 27.32 -31.13 0.66) ; 27 ( 34.47 27.42 -31.31 0.66) ; 28 ( 35.51 27.86 -31.88 0.96) ; 29 ( 36.24 28.19 -33.81 2.14) ; 30 ( 36.82 28.10 -33.94 2.95) ; 31 ( 37.55 28.05 -33.94 2.95) ; 32 ( 38.31 27.64 -33.94 1.55) ; 33 ( 39.06 27.22 -35.00 0.88) ; 34 ( 39.89 26.79 -35.56 0.81) ; 35 ( 41.04 26.47 -36.25 0.74) ; 36 ( 41.89 26.25 -36.56 1.03) ; 37 ( 42.32 26.11 -37.38 1.33) ; 38 ( 43.22 26.12 -37.94 1.11) ; 39 ( 44.09 26.43 -39.69 0.74) ; 40 ( 45.93 26.72 -41.44 0.52) ; 41 ( 46.72 27.04 -42.50 0.81) ; 42 ( 47.18 27.11 -43.06 1.62) ; 43 ( 47.79 27.24 -43.25 2.50) ; 44 ( 48.67 27.55 -43.56 2.50) ; 45 ( 49.46 27.35 -43.75 1.11) ; 46 ( 50.07 27.03 -43.75 0.66) ; 47 ( 51.02 26.43 -43.75 0.66) ; 48 ( 52.50 26.35 -44.75 0.66) ; 49 ( 53.47 26.79 -46.06 0.81) ; 50 ( 54.14 26.38 -46.56 0.59) ; 51 ( 55.06 26.02 -46.69 0.81) ; 52 ( 55.65 25.99 -47.06 1.69) ; 53 ( 56.69 25.98 -47.50 2.50) ; 54 ( 57.22 25.97 -47.94 2.50) ; 55 ( 57.85 25.73 -48.63 2.06) ; 56 ( 58.48 25.48 -49.00 1.11) ; 57 ( 59.54 26.05 -49.19 0.52) ; 58 ( 61.20 26.16 -49.50 0.52) ; 59 ( 62.41 26.26 -49.75 0.52) ; 60 ( 62.84 26.64 -50.00 1.25) ; 61 ( 63.91 26.83 -50.00 1.99) ; 62 ( 64.84 26.99 -50.19 1.99) ; 63 ( 66.06 27.68 -50.44 0.81) ; 64 ( 67.14 28.33 -50.44 0.74) ; 65 ( 68.27 29.14 -50.88 0.52) ; 66 ( 69.34 29.78 -51.13 0.44) ; 67 ( 70.69 30.30 -51.13 0.66) ; 68 ( 72.00 30.54 -50.94 0.66) ; 69 ( 72.90 31.00 -50.69 0.96) ; 70 ( 73.49 31.49 -50.38 1.77) ; 71 ( 74.20 31.68 -50.19 2.58) ; 72 ( 74.84 31.95 -50.19 2.21) ; 73 ( 75.27 32.39 -50.19 0.88) ; 74 ( 75.87 32.82 -50.19 0.59) ; 75 ( 77.05 33.67 -50.19 0.29) ; 76 ( 78.99 34.62 -50.44 0.29) ; 77 ( 79.54 34.76 -50.44 0.29) ; 78 ( 80.19 35.17 -50.44 0.66) ; 79 ( 80.89 35.80 -50.75 1.03) ; 80 ( 81.49 36.30 -50.75 0.59) ; 81 ( 82.09 37.68 -50.81 0.52) ; 82 ( 82.73 38.91 -52.00 0.81) ; 83 ( 82.59 39.46 -52.00 1.69) ; 84 ( 82.86 40.22 -52.63 1.69) ; 85 Normal ) ; End of split | ( 11.60 4.68 -8.88 0.81) ; 1, R-1-1-2-2 ( 10.93 4.79 -11.00 0.74) ; 2 ( 11.20 5.49 -11.63 0.74) ; 3 ( 11.94 5.96 -11.81 0.74) ; 4 ( 13.39 7.06 -11.81 0.74) ; 5 ( 14.58 7.54 -12.00 0.74) ; 6 ( 15.26 8.54 -12.69 0.59) ; 7 ( 14.86 9.79 -13.56 0.59) ; 8 ( 14.91 10.60 -14.06 0.88) ; 9 ( 15.05 11.90 -15.13 0.88) ; 10 ( 15.20 12.84 -15.56 1.03) ; 11 ( 15.88 13.40 -17.13 0.88) ; 12 ( 16.42 13.98 -18.69 1.18) ; 13 ( 17.06 14.25 -19.81 0.96) ; 14 ( 17.76 14.44 -21.38 0.66) ; 15 ( 18.36 14.93 -22.00 0.66) ; 16 ( 19.02 15.34 -23.25 1.11) ; 17 ( 19.44 16.10 -25.00 1.25) ; 18 ( 19.78 16.34 -26.19 1.62) ; 19 ( 19.87 16.91 -27.13 1.25) ; 20 ( 21.09 17.09 -28.06 0.96) ; 21 ( 22.04 17.46 -29.19 0.66) ; 22 ( 23.32 17.99 -31.19 0.66) ; 23 ( 24.26 19.18 -32.00 0.96) ; 24 ( 24.97 19.43 -33.38 1.69) ; 25 ( 25.81 19.60 -34.06 2.06) ; 26 ( 26.59 19.77 -34.38 1.18) ; 27 ( 27.75 20.04 -34.88 0.81) ; 28 ( 28.58 20.13 -35.50 0.74) ; 29 ( 29.38 20.44 -36.31 1.11) ; 30 ( 29.72 20.76 -37.81 1.47) ; 31 ( 29.95 21.24 -39.31 1.11) ; 32 ( 30.24 22.53 -40.50 0.88) ; 33 ( 30.50 22.78 -42.25 1.18) ; 34 ( 30.72 23.26 -43.13 1.62) ; 35 ( 30.84 23.54 -44.06 1.18) ; 36 ( 31.20 24.37 -46.25 0.81) ; 37 ( 31.69 25.10 -46.81 1.11) ; 38 ( 32.36 25.15 -48.19 1.40) ; 39 ( 31.78 25.68 -49.38 1.40) ; 40 ( 31.55 25.65 -51.19 1.62) ; 41 ( 31.28 25.32 -53.88 1.40) ; 42 ( 31.00 25.51 -56.31 1.25) ; 43 ( 30.49 25.08 -59.94 1.40) ; 44 ( 29.00 24.65 -61.75 1.03) ; 45 ( 27.49 24.07 -62.31 1.03) ; 46 ( 26.99 23.26 -62.50 0.81) ; 47 ( 26.36 22.62 -63.56 0.81) ; 48 ( 26.07 22.15 -65.38 1.11) ; 49 ( 25.97 21.06 -66.19 1.11) ; 50 ( 26.41 20.03 -67.88 0.81) ; 51 ( 26.72 19.16 -69.31 1.18) ; 52 ( 26.98 18.45 -70.38 1.25) ; 53 Normal ) ; End of split ) ; End of split | ( 10.57 1.96 -4.56 0.88) ; 1, R-1-2 ( 11.41 1.16 -5.75 0.74) ; 2 ( 12.47 0.89 -5.75 0.74) ; 3 ( ( 12.49 0.91 -5.94 0.88) ; 1, R-1-2-1 ( 13.23 0.43 -6.75 0.88) ; 2 ( 13.60 -0.08 -7.56 1.03) ; 3 ( 14.03 -0.66 -9.19 0.96) ; 4 ( 13.89 -1.53 -10.69 0.81) ; 5 ( 13.66 -2.01 -13.25 0.81) ; 6 ( 13.95 -3.02 -13.81 0.66) ; 7 ( 14.34 -3.89 -15.13 0.66) ; 8 ( 14.71 -4.77 -16.44 0.66) ; 9 ( 15.19 -5.51 -17.00 0.66) ; 10 ( 16.36 -6.66 -17.81 0.88) ; 11 ( 17.37 -7.85 -17.25 1.18) ; 12 ( 17.72 -8.43 -18.38 1.47) ; 13 ( ( 17.15 -9.23 -19.19 1.03) ; 1, R-1-2-1-1 ( 16.62 -10.62 -20.38 0.74) ; 2 ( 16.12 -12.39 -21.06 0.52) ; 3 ( 15.85 -13.68 -22.44 0.52) ; 4 ( 15.68 -14.69 -22.75 0.52) ; 5 ( 15.62 -15.57 -23.69 0.74) ; 6 ( 15.42 -16.80 -24.13 0.74) ; 7 ( 15.61 -17.50 -25.75 0.59) ; 8 ( 16.68 -17.81 -24.19 0.88) ; 9 ( 17.64 -18.34 -24.75 0.96) ; 10 ( 18.52 -18.92 -25.38 0.81) ; 11 ( 19.51 -19.22 -26.88 0.88) ; 12 ( 20.14 -19.54 -29.38 0.66) ; 13 ( 21.24 -20.16 -30.69 0.44) ; 14 ( 22.19 -20.76 -30.69 0.44) ; 15 ( 23.71 -21.00 -31.50 0.74) ; 16 ( 24.13 -21.21 -32.38 1.11) ; 17 ( 25.10 -21.15 -32.94 1.40) ; 18 ( 26.41 -21.36 -32.94 0.66) ; 19 ( 27.44 -21.95 -33.56 0.44) ; 20 ( 28.66 -22.74 -34.25 0.74) ; 21 ( 29.12 -23.11 -35.38 1.11) ; 22 ( 29.49 -24.06 -36.19 1.77) ; 23 ( 30.10 -24.97 -36.81 0.59) ; 24 ( 30.65 -25.65 -38.06 0.44) ; 25 ( 31.62 -26.78 -38.69 0.37) ; 26 ( 32.10 -27.52 -39.63 0.37) ; 27 ( 32.97 -28.17 -40.44 0.74) ; 28 ( 34.26 -28.52 -41.31 0.96) ; 29 ( 34.53 -29.09 -42.19 0.66) ; 30 ( 34.95 -29.81 -44.19 0.88) ; 31 ( 35.51 -30.05 -45.44 1.77) ; 32 ( 36.33 -30.48 -45.75 2.14) ; 33 ( 36.93 -31.39 -48.00 0.81) ; 34 ( 37.80 -32.49 -48.75 0.88) ; 35 ( 38.63 -32.99 -49.44 1.18) ; 36 ( 39.20 -33.60 -50.63 0.74) ; 37 ( 41.06 -34.56 -51.25 0.59) ; 38 ( 42.18 -35.48 -52.13 0.59) ; 39 ( 42.49 -35.90 -52.25 0.96) ; 40 ( 43.05 -36.57 -52.69 1.84) ; 41 ( 43.73 -36.99 -52.69 2.65) ; 42 ( 44.65 -37.79 -52.69 1.69) ; 43 ( 44.88 -38.72 -52.69 0.74) ; 44 ( 45.71 -39.59 -53.06 0.74) ; 45 ( 46.21 -40.63 -53.50 0.59) ; 46 ( 47.00 -41.27 -53.69 0.29) ; 47 ( 48.36 -42.60 -54.13 0.59) ; 48 ( 48.34 -43.19 -54.81 1.03) ; 49 ( 48.90 -43.94 -54.88 0.59) ; 50 ( 49.24 -45.04 -55.06 0.29) ; 51 ( 49.41 -46.77 -55.38 0.29) ; 52 ( 49.57 -47.24 -55.56 1.11) ; 53 ( 49.72 -48.14 -56.06 1.99) ; 54 ( 50.17 -49.10 -56.44 1.03) ; 55 ( 50.62 -49.98 -56.88 0.59) ; 56 ( 52.35 -51.82 -57.25 0.37) ; 57 ( 52.39 -51.98 -57.25 0.37) ; 58 ( 52.70 -52.84 -57.25 0.74) ; 59 ( 52.93 -53.76 -57.88 1.18) ; 60 ( 53.48 -54.52 -58.38 1.40) ; 61 ( 54.36 -55.62 -59.00 0.59) ; 62 ( 54.78 -56.72 -60.00 1.11) ; 63 ( 55.67 -57.53 -60.44 1.18) ; 64 ( 56.09 -58.19 -61.25 0.74) ; 65 ( 56.85 -58.98 -61.31 0.52) ; 66 ( 57.92 -59.81 -61.38 0.66) ; 67 ( 58.74 -60.76 -61.63 0.66) ; 68 ( 59.00 -60.94 -61.75 1.03) ; 69 ( 59.50 -61.55 -61.88 1.40) ; 70 ( 60.09 -62.08 -62.25 1.40) ; 71 ( 60.36 -62.71 -62.44 0.88) ; 72 ( 60.91 -63.47 -61.81 0.59) ; 73 ( 61.50 -64.45 -61.81 0.66) ; 74 ( 62.02 -65.42 -61.81 0.29) ; 75 Normal | ( 18.40 -7.68 -18.50 1.18) ; 1, R-1-2-1-2 ( 19.07 -7.27 -17.31 0.74) ; 2 ( 21.35 -6.97 -18.75 0.59) ; 3 ( 23.27 -6.61 -18.94 0.59) ; 4 ( 24.19 -6.02 -19.94 0.74) ; 5 ( 26.03 -5.71 -20.38 0.74) ; 6 ( 28.20 -4.72 -22.94 0.44) ; 7 ( 29.22 -5.33 -24.13 0.74) ; 8 ( 29.93 -5.52 -25.75 0.96) ; 9 ( 30.07 -5.61 -28.44 1.33) ; 10 ( 30.15 -5.56 -29.25 0.88) ; 11 ( 30.78 -5.35 -31.50 0.81) ; 12 ( 31.09 -5.77 -33.44 0.81) ; 13 ( 31.41 -6.56 -33.81 0.81) ; 14 ( 31.92 -7.09 -34.31 0.81) ; 15 ( 33.08 -7.35 -35.44 0.81) ; 16 ( 33.31 -6.86 -38.25 0.81) ; 17 ( 33.93 -6.67 -38.88 1.11) ; 18 ( 35.41 -6.75 -39.94 0.74) ; 19 ( 37.06 -6.72 -41.31 0.74) ; 20 ( 38.07 -7.84 -43.50 0.44) ; 21 ( 38.11 -8.59 -47.25 0.96) ; 22 ( 38.50 -8.94 -47.81 0.96) ; 23 ( 39.37 -9.53 -49.50 1.03) ; 24 Generated ) ; End of split | ( 12.95 1.04 -4.06 0.66) ; 1, R-1-2-2 ( 13.26 1.13 -2.75 0.52) ; 2 ( 13.55 1.08 -0.13 0.52) ; 3 ( 14.75 -0.28 0.25 0.52) ; 4 ( 15.78 -0.89 0.25 0.52) ; 5 ( 17.11 -1.40 0.38 0.52) ; 6 ( 17.84 -1.96 -2.06 0.66) ; 7 ( 18.84 -3.23 -2.38 0.88) ; 8 ( 19.68 -4.47 -5.75 0.88) ; 9 ( 20.50 -6.38 -6.50 0.74) ; 10 ( 21.21 -7.97 -7.81 0.59) ; 11 ( 21.98 -9.27 -9.06 0.59) ; 12 ( 22.89 -10.60 -9.44 0.59) ; 13 ( 23.58 -11.90 -9.44 0.59) ; 14 ( 23.98 -13.14 -9.44 0.59) ; 15 ( 24.28 -14.08 -9.44 0.59) ; 16 ( 24.66 -15.40 -9.69 0.59) ; 17 ( 24.95 -16.92 -9.88 0.81) ; 18 ( 25.14 -18.00 -10.06 0.81) ; 19 ( 25.54 -18.80 -10.13 0.44) ; 20 ( 26.18 -20.38 -10.13 0.44) ; 21 ( 26.38 -21.52 -10.38 0.74) ; 22 ( 26.26 -22.24 -10.63 0.74) ; 23 ( 26.40 -23.30 -10.88 0.44) ; 24 ( 26.73 -24.46 -9.69 0.44) ; 25 ( 27.01 -25.61 -9.63 0.59) ; 26 ( 27.70 -27.28 -9.31 0.74) ; 27 ( 28.33 -28.49 -8.88 0.88) ; 28 ( 28.96 -29.70 -8.63 0.52) ; 29 ( 29.79 -31.08 -8.63 0.52) ; 30 ( 30.74 -32.28 -7.81 0.37) ; 31 ( 30.92 -33.42 -10.31 0.66) ; 32 ( 31.04 -34.62 -10.31 0.66) ; 33 ( 31.32 -36.14 -10.56 0.52) ; 34 ( 30.75 -36.86 -11.50 0.52) ; 35 ( 29.80 -37.68 -11.81 0.59) ; 36 ( 29.70 -38.33 -11.81 0.59) ; 37 ( 29.73 -39.07 -12.50 0.74) ; 38 ( 29.94 -40.15 -13.81 0.59) ; 39 ( 30.46 -41.04 -14.81 0.88) ; 40 ( 30.76 -41.53 -15.13 1.11) ; 41 ( 31.46 -42.31 -17.13 0.96) ; 42 ( 32.36 -43.19 -18.63 0.96) ; 43 ( 33.25 -43.70 -18.69 0.59) ; 44 ( 33.89 -44.84 -19.56 0.59) ; 45 ( 34.57 -46.21 -19.63 0.88) ; 46 ( 35.45 -46.35 -20.13 0.88) ; 47 ( 36.36 -47.16 -21.50 0.59) ; 48 ( 36.86 -47.75 -21.50 0.59) ; 49 ( 38.30 -48.57 -22.38 0.59) ; 50 ( 39.37 -48.89 -22.94 0.81) ; 51 ( 40.02 -48.99 -23.75 1.03) ; 52 ( 40.59 -49.15 -24.38 1.03) ; 53 ( 41.53 -49.38 -24.81 1.03) ; 54 ( ( 42.70 -49.94 -25.38 0.59) ; 1, R-1-2-2-1 ( 44.20 -50.39 -25.56 0.81) ; 2 ( 44.84 -50.57 -26.63 0.81) ; 3 ( 45.25 -50.86 -27.25 1.03) ; 4 ( 45.77 -51.38 -28.13 1.03) ; 5 ( 46.43 -51.86 -28.13 0.66) ; 6 ( 46.70 -52.49 -29.44 0.52) ; 7 ( 47.57 -53.59 -31.19 0.52) ; 8 Normal | ( 41.06 -50.49 -24.81 0.59) ; 1, R-1-2-2-2 ( 40.79 -51.19 -24.94 0.59) ; 2 ( 40.66 -52.05 -24.25 0.66) ; 3 ( 40.50 -52.62 -24.44 0.81) ; 4 ( 40.28 -53.03 -24.63 0.81) ; 5 ( 40.06 -53.88 -25.19 0.59) ; 6 ( 39.91 -54.38 -25.50 0.59) ; 7 ( 39.98 -54.91 -26.69 0.59) ; 8 ( 40.00 -55.28 -26.75 0.96) ; 9 ( 39.84 -55.78 -26.81 1.69) ; 10 ( 39.59 -56.40 -27.06 2.65) ; 11 ( 39.34 -57.03 -27.25 1.84) ; 12 ( 39.31 -57.76 -28.44 0.96) ; 13 ( 39.78 -58.57 -29.25 0.59) ; 14 ( 39.93 -59.49 -29.25 0.88) ; 15 ( 39.86 -60.58 -29.88 0.52) ; 16 ( 39.77 -61.68 -30.38 0.52) ; 17 ( 39.87 -62.44 -30.81 0.88) ; 18 ( 40.11 -63.28 -30.81 0.88) ; 19 ( 40.01 -63.94 -31.38 1.11) ; 20 ( 39.98 -64.60 -31.94 1.47) ; 21 ( 39.95 -64.74 -32.06 1.47) ; 22 ( 39.87 -65.24 -32.44 0.96) ; 23 ( 39.98 -66.01 -32.44 0.52) ; 24 ( 39.98 -66.52 -32.88 0.52) ; 25 ( 39.87 -67.16 -33.13 0.81) ; 26 ( 39.94 -67.62 -33.19 1.25) ; 27 ( 39.77 -68.26 -33.88 1.40) ; 28 ( 39.64 -69.06 -35.19 0.59) ; 29 Normal ) ; End of split ) ; End of split ) ; End of split | ( 6.84 3.96 -2.69 1.47) ; 1, R-2 ( 7.04 5.19 -1.69 1.47) ; 2 ( 7.93 6.16 -1.69 1.25) ; 3 ( 8.64 6.86 -2.13 1.18) ; 4 ( 9.18 7.88 -3.00 1.25) ; 5 ( 9.44 9.02 -3.25 1.47) ; 6 ( 9.68 10.10 -3.63 2.36) ; 7 ( 9.60 11.52 -4.38 2.43) ; 8 ( 9.67 12.40 -5.25 2.14) ; 9 ( 9.73 13.72 -5.44 1.77) ; 10 ( 10.16 14.54 -5.63 1.77) ; 11 ( ( 9.30 15.26 -5.94 0.96) ; 1, R-2-1 ( 8.40 16.14 -6.13 0.59) ; 2 ( 7.67 17.15 -6.44 0.74) ; 3 ( 7.38 18.23 -6.44 0.74) ; 4 ( 7.12 18.94 -6.50 0.52) ; 5 ( 6.51 19.77 -6.88 0.66) ; 6 ( 5.90 20.10 -7.13 1.25) ; 7 ( 5.31 20.70 -8.44 1.25) ; 8 ( 5.22 21.09 -9.38 1.11) ; 9 ( 4.85 21.96 -10.81 0.74) ; 10 ( 4.65 23.11 -11.63 0.59) ; 11 ( 4.42 24.03 -12.56 0.52) ; 12 ( 4.71 24.87 -12.88 0.37) ; 13 ( 5.12 25.62 -13.06 1.03) ; 14 ( 4.85 26.18 -14.00 1.62) ; 15 ( 4.73 27.39 -15.69 1.47) ; 16 ( 4.82 28.06 -16.00 1.55) ; 17 ( 4.58 28.85 -17.63 1.69) ; 18 ( 4.66 29.35 -18.31 0.96) ; 19 ( 4.56 30.11 -19.25 0.74) ; 20 ( 4.68 30.90 -19.75 1.03) ; 21 ( 5.16 31.64 -20.75 1.18) ; 22 ( 5.31 32.06 -21.81 0.59) ; 23 ( 5.36 32.87 -22.19 0.59) ; 24 ( 5.12 33.20 -22.94 0.96) ; 25 ( 4.85 33.91 -23.38 1.25) ; 26 ( 4.80 34.50 -26.25 0.66) ; 27 ( 4.77 34.81 -27.13 0.66) ; 28 ( 4.43 35.45 -27.25 0.96) ; 29 ( 4.14 36.46 -28.69 0.66) ; 30 ( 4.58 37.35 -29.44 0.52) ; 31 ( 5.19 37.85 -29.81 0.52) ; 32 ( 5.49 38.39 -30.31 0.59) ; 33 ( 5.87 38.93 -31.44 0.74) ; 34 ( 6.41 39.50 -32.44 0.52) ; 35 ( 6.74 39.67 -33.31 0.52) ; 36 ( 7.12 40.21 -33.81 0.52) ; 37 ( 7.64 41.16 -34.31 0.81) ; 38 ( 7.94 41.63 -35.69 1.55) ; 39 ( 8.58 42.35 -36.69 1.84) ; 40 ( 8.81 42.83 -38.50 1.03) ; 41 ( 9.29 43.56 -39.25 0.66) ; 42 ( 9.44 43.99 -40.19 0.66) ; 43 ( 9.76 44.60 -41.00 0.59) ; 44 ( 9.86 45.70 -41.88 0.74) ; 45 ( 9.76 46.53 -42.25 0.74) ; 46 ( 9.57 47.67 -43.19 0.44) ; 47 ( 9.68 48.31 -43.63 0.44) ; 48 ( 9.98 49.31 -45.63 0.37) ; 49 ( 10.56 50.62 -43.44 0.52) ; 50 ( 10.70 51.48 -43.63 0.52) ; 51 ( 11.30 51.98 -45.50 0.66) ; 52 ( 11.82 52.42 -45.50 0.66) ; 53 ( 12.55 53.26 -46.50 0.81) ; 54 ( 13.68 53.63 -48.00 0.59) ; 55 ( 14.32 54.35 -48.75 0.44) ; 56 ( 14.84 54.85 -49.44 0.66) ; 57 ( 15.59 55.33 -50.69 1.03) ; 58 ( 15.91 55.42 -51.50 1.55) ; 59 ( 16.40 55.72 -51.50 1.55) ; 60 ( 16.78 56.25 -51.63 0.74) ; 61 ( 16.99 56.66 -52.19 0.37) ; 62 ( 17.39 57.26 -52.81 0.66) ; 63 ( 17.87 58.00 -53.31 0.96) ; 64 ( 18.20 58.61 -53.50 0.52) ; 65 ( 18.56 59.00 -54.13 0.37) ; 66 ( 18.87 59.55 -55.00 0.37) ; 67 ( 19.17 60.02 -56.00 0.96) ; 68 ( 18.87 60.95 -56.81 1.84) ; 69 ( 18.65 61.43 -57.88 1.84) ; 70 ( 18.36 61.99 -59.00 1.03) ; 71 ( 18.49 62.79 -61.88 0.74) ; 72 ( 18.39 63.10 -63.25 1.03) ; 73 ( 18.36 63.40 -64.88 1.33) ; 74 ( 18.10 64.11 -66.06 1.62) ; 75 ( 17.82 64.22 -67.06 1.84) ; 76 ( 17.55 64.42 -68.75 1.40) ; 77 ( 18.10 65.07 -68.88 0.59) ; 78 ( 18.30 65.86 -69.31 0.59) ; 79 ( 18.58 66.18 -69.75 0.59) ; 80 ( 18.80 66.66 -70.06 0.81) ; 81 ( 19.43 67.30 -72.06 0.52) ; 82 Normal | ( 10.81 15.39 -5.63 0.81) ; 1, R-2-2 ( 10.95 16.25 -6.81 0.81) ; 2 ( 10.90 16.85 -7.50 1.11) ; 3 ( 10.90 17.37 -8.81 1.11) ; 4 ( 10.82 18.27 -9.31 0.81) ; 5 ( 10.61 18.82 -10.44 0.81) ; 6 ( 9.85 19.68 -10.44 0.74) ; 7 ( 9.16 20.53 -10.56 0.96) ; 8 ( 9.13 21.72 -11.19 1.11) ; 9 ( 8.94 22.86 -11.50 1.18) ; 10 ( 8.09 23.58 -11.75 0.81) ; 11 ( 7.51 24.19 -12.56 1.11) ; 12 ( 8.36 24.88 -13.69 1.40) ; 13 ( 8.80 25.70 -14.63 1.92) ; 14 ( 8.40 26.43 -15.25 1.40) ; 15 ( 8.55 27.37 -15.88 0.96) ; 16 ( 9.07 28.32 -15.88 0.81) ; 17 ( 9.50 29.59 -17.63 0.96) ; 18 ( 9.54 30.39 -17.63 0.74) ; 19 ( 9.69 31.33 -17.75 0.74) ; 20 ( 9.65 32.45 -18.94 1.18) ; 21 ( 9.66 32.52 -19.81 1.55) ; 22 ( 9.77 33.24 -20.69 1.55) ; 23 ( 10.24 33.83 -21.19 1.84) ; 24 ( 10.74 34.65 -22.38 1.11) ; 25 ( 10.88 35.51 -22.94 0.74) ; 26 ( 10.56 37.27 -23.19 0.96) ; 27 ( 10.73 38.34 -23.56 0.96) ; 28 ( 11.52 39.11 -26.13 0.66) ; 29 ( 11.62 40.21 -27.13 1.33) ; 30 ( 11.61 41.09 -28.31 1.69) ; 31 ( 11.60 41.47 -28.50 1.40) ; 32 ( ( 11.20 42.71 -29.44 0.59) ; 1, R-2-2-1 ( 10.71 43.82 -29.69 0.52) ; 2 ( 10.55 44.74 -30.31 0.52) ; 3 ( 10.34 45.29 -30.69 0.52) ; 4 ( 9.98 45.79 -31.06 0.52) ; 5 ( 9.06 46.68 -31.75 0.81) ; 6 ( 8.55 47.20 -32.63 1.18) ; 7 ( 8.35 47.38 -32.88 1.62) ; 8 ( 8.10 48.08 -33.94 1.84) ; 9 ( 7.52 48.69 -33.94 1.03) ; 10 ( 6.47 49.08 -35.31 0.59) ; 11 ( 5.36 49.19 -36.31 0.59) ; 12 ( 4.63 49.23 -37.00 0.96) ; 13 ( 4.15 49.53 -38.81 1.77) ; 14 ( 3.24 49.90 -40.75 1.62) ; 15 ( 2.72 50.86 -41.00 1.11) ; 16 ( 2.50 51.41 -42.31 0.74) ; 17 ( 2.01 52.09 -43.88 0.37) ; 18 ( 1.98 52.76 -43.88 0.37) ; 19 ( 2.53 54.05 -43.88 0.37) ; 20 ( 3.44 55.09 -44.63 0.74) ; 21 ( 4.16 56.30 -44.63 0.52) ; 22 ( 4.21 57.12 -45.94 0.81) ; 23 ( 4.22 57.63 -46.63 1.18) ; 24 ( 4.39 58.27 -47.25 1.62) ; 25 ( 4.69 58.74 -47.25 1.18) ; 26 ( 5.09 59.34 -47.25 0.74) ; 27 ( 5.20 60.07 -48.50 0.37) ; 28 ( 5.52 60.68 -48.50 0.44) ; 29 ( 5.96 62.02 -48.50 1.18) ; 30 ( 6.40 62.98 -50.69 1.92) ; 31 ( 6.52 63.71 -51.44 1.92) ; 32 ( 6.38 64.69 -52.63 1.33) ; 33 ( 6.55 65.33 -52.81 0.81) ; 34 ( 7.72 66.55 -53.06 0.59) ; 35 ( 8.33 67.11 -53.06 0.59) ; 36 ( 9.29 68.00 -54.25 1.33) ; 37 ( 10.20 68.53 -54.75 0.88) ; 38 ( 11.68 68.95 -55.13 0.52) ; 39 ( 12.27 69.39 -55.56 0.88) ; 40 ( 12.89 70.03 -56.88 1.18) ; 41 ( 13.22 70.71 -57.31 0.44) ; 42 ( 13.75 71.66 -57.94 0.22) ; 43 ( 14.71 72.54 -58.00 0.44) ; 44 ( 15.82 72.96 -58.44 0.59) ; 45 ( 16.46 73.23 -58.69 0.37) ; 46 ( 17.19 73.64 -59.31 0.74) ; 47 ( 17.69 73.99 -59.38 1.55) ; 48 ( 18.29 74.43 -59.38 1.84) ; 49 ( 18.95 74.84 -59.38 0.59) ; 50 ( 20.07 75.33 -60.13 0.37) ; 51 ( 20.95 75.70 -60.31 0.37) ; 52 Normal | ( 12.23 42.30 -29.56 0.74) ; 1, R-2-2-2 ( 12.80 42.58 -31.13 0.74) ; 2 ( 12.92 43.37 -31.63 1.03) ; 3 ( 12.98 44.25 -33.06 0.59) ; 4 ( 13.19 45.55 -33.25 0.66) ; 5 ( 13.31 45.83 -34.63 0.59) ; 6 ( 13.54 46.31 -36.44 0.59) ; 7 ( 13.22 47.17 -36.50 0.59) ; 8 ( 12.78 48.13 -37.56 0.59) ; 9 ( 12.31 48.95 -38.19 0.96) ; 10 ( 12.14 49.71 -39.81 1.77) ; 11 ( 11.78 50.29 -40.25 2.14) ; 12 ( 11.33 51.18 -41.00 1.69) ; 13 ( 11.25 51.64 -41.88 1.25) ; 14 ( 11.33 52.14 -43.50 0.96) ; 15 ( 10.84 51.91 -43.19 0.74) ; 16 ( 10.39 51.84 -45.56 0.66) ; 17 ( 9.73 52.39 -47.38 0.59) ; 18 ( 9.66 52.92 -47.75 0.96) ; 19 ( 9.36 53.41 -47.75 1.84) ; 20 ( 8.86 53.94 -48.50 2.28) ; 21 ( 8.66 55.07 -48.94 0.81) ; 22 ( 8.50 56.43 -50.56 0.44) ; 23 ( 8.65 57.82 -50.81 0.44) ; 24 ( 8.99 58.57 -51.75 0.81) ; 25 ( 8.69 59.00 -51.88 1.55) ; 26 ( 8.62 59.52 -51.88 1.55) ; 27 ( 8.39 59.93 -52.38 0.74) ; 28 ( 8.24 60.48 -53.69 0.52) ; 29 Normal ) ; End of split ) ; End of split ) ; End of split ) ; End of tree ( (Color Magenta) (Dendrite) ( 12.03 -0.93 12.38 2.14) ; Root ( 13.41 -1.15 12.56 1.47) ; 1, R ( 14.18 -1.50 12.44 1.40) ; 2 ( 15.28 -2.05 12.50 1.92) ; 3 ( 15.89 -2.44 12.56 2.06) ; 4 ( ( 16.88 -3.26 12.38 1.62) ; 1, R-1 ( 17.40 -4.22 12.25 0.88) ; 2 ( 18.16 -5.08 12.25 0.74) ; 3 ( 19.36 -6.01 11.94 0.66) ; 4 ( 20.41 -6.85 11.88 0.88) ; 5 ( 21.18 -7.71 11.38 1.03) ; 6 ( 21.91 -8.71 10.94 1.11) ; 7 ( 22.63 -9.34 10.94 1.18) ; 8 ( 23.59 -9.87 10.50 0.81) ; 9 ( 24.88 -10.66 10.13 0.74) ; 10 ( 26.02 -11.51 9.94 0.74) ; 11 ( 26.94 -12.26 9.94 0.88) ; 12 ( 27.84 -13.21 9.94 0.88) ; 13 ( 28.58 -13.70 9.50 0.74) ; 14 ( 29.88 -14.42 9.31 0.74) ; 15 ( 30.20 -15.21 8.81 0.74) ; 16 ( 30.95 -15.69 8.44 0.74) ; 17 ( 31.69 -16.19 8.44 0.74) ; 18 ( 32.99 -16.91 8.44 0.81) ; 19 ( 34.41 -17.43 7.88 0.74) ; 20 ( 35.31 -17.79 7.88 0.96) ; 21 ( 36.16 -18.60 7.50 1.25) ; 22 ( 37.14 -19.49 7.00 1.25) ; 23 ( 38.16 -20.10 6.50 0.88) ; 24 ( 38.90 -20.59 6.50 0.81) ; 25 ( 40.17 -21.08 5.19 1.03) ; 26 ( 41.14 -21.53 5.13 0.74) ; 27 ( 41.86 -21.18 4.50 0.59) ; 28 ( 42.66 -21.31 3.94 0.59) ; 29 ( 43.69 -21.84 3.38 0.81) ; 30 ( 44.81 -22.83 3.25 0.81) ; 31 ( 45.63 -23.70 3.25 0.59) ; 32 ( 46.17 -24.08 2.75 0.88) ; 33 ( 46.46 -24.65 3.38 1.47) ; 34 ( 47.00 -25.48 3.38 1.11) ; 35 ( 47.64 -26.09 3.25 0.59) ; 36 ( 48.22 -26.77 3.81 0.66) ; 37 ( 48.74 -28.12 3.69 0.66) ; 38 ( 49.20 -29.00 3.31 0.74) ; 39 ( 49.95 -29.93 3.31 0.44) ; 40 ( 50.85 -30.82 3.06 0.44) ; 41 ( 51.68 -31.69 3.06 1.03) ; 42 ( 52.55 -32.35 2.94 1.69) ; 43 ( 53.31 -32.69 2.63 1.69) ; 44 ( 53.93 -33.08 2.56 0.88) ; 45 ( 54.73 -33.66 1.94 0.59) ; 46 ( 55.85 -34.13 1.69 0.59) ; 47 ( 57.28 -34.50 1.38 0.59) ; 48 ( 57.90 -34.83 0.88 0.81) ; 49 ( 58.13 -35.23 0.50 1.11) ; 50 ( 58.88 -36.17 0.50 1.25) ; 51 ( 59.89 -36.84 -0.19 0.59) ; 52 ( 60.97 -37.08 -0.81 0.44) ; 53 ( 61.53 -37.32 -0.81 0.74) ; 54 ( 62.29 -37.67 -1.13 1.25) ; 55 ( 62.69 -38.03 -1.44 1.77) ; 56 ( 63.16 -38.84 -0.94 0.74) ; 57 ( 63.55 -39.64 -0.94 0.44) ; 58 ( 64.64 -40.33 -0.69 0.59) ; 59 ( 65.02 -40.96 -0.69 0.59) ; 60 ( ( 65.06 -40.99 -0.69 0.59) ; 1, R-1-1 ( 65.42 -41.50 -0.19 1.18) ; 2 ( 65.77 -42.14 -0.19 1.55) ; 3 ( 66.20 -42.72 -0.06 1.33) ; 4 ( 66.61 -42.94 0.38 0.88) ; 5 ( 67.21 -43.40 0.38 0.59) ; 6 ( 67.71 -44.00 0.38 0.44) ; 7 ( 68.46 -44.42 0.38 0.44) ; 8 ( 69.41 -45.01 0.88 0.74) ; 9 ( 70.19 -45.79 2.06 0.96) ; 10 ( 70.93 -46.29 2.19 0.52) ; 11 ( 72.06 -47.13 2.19 0.44) ; 12 ( 73.05 -47.96 2.19 0.66) ; 13 ( 73.41 -48.53 2.19 0.66) ; 14 ( 73.98 -49.14 2.19 0.59) ; 15 ( 74.20 -49.62 2.19 1.18) ; 16 ( 74.49 -50.18 2.19 1.62) ; 17 ( 75.01 -50.63 2.19 1.11) ; 18 ( 75.78 -51.50 2.19 0.29) ; 19 ( 76.98 -52.35 2.19 0.29) ; 20 ( 77.76 -52.62 2.00 0.74) ; 21 ( 78.73 -53.07 2.19 0.74) ; 22 ( 79.17 -53.58 2.19 0.37) ; 23 ( 79.74 -54.19 2.19 0.88) ; 24 ( 80.43 -54.59 2.25 1.62) ; 25 ( 80.94 -55.05 2.00 1.84) ; 26 ( 81.55 -55.44 1.75 1.40) ; 27 ( 82.41 -55.73 2.06 0.66) ; 28 ( 83.49 -55.90 2.06 0.52) ; 29 ( 84.14 -56.52 2.06 0.81) ; 30 ( 85.01 -56.66 2.06 0.52) ; 31 ( 86.35 -56.72 2.06 0.44) ; 32 ( 86.96 -56.67 2.44 0.66) ; 33 ( 88.02 -56.55 2.19 0.66) ; 34 ( 89.10 -56.72 2.00 0.66) ; 35 ( 89.82 -56.90 2.00 0.66) ; 36 ( 90.61 -57.10 1.75 1.25) ; 37 ( 91.30 -57.50 2.06 1.47) ; 38 ( 91.86 -57.74 2.06 1.03) ; 39 ( 92.44 -57.83 2.06 0.59) ; 40 ( 93.16 -58.46 2.06 0.44) ; 41 ( 94.21 -59.37 2.44 0.44) ; 42 ( 94.97 -59.79 3.06 0.44) ; 43 ( 95.81 -60.07 3.25 0.74) ; 44 ( 96.51 -60.40 3.25 1.03) ; 45 ( 96.85 -60.61 3.81 0.66) ; 46 ( 98.01 -61.31 3.81 0.29) ; 47 ( 98.91 -61.74 3.81 0.59) ; 48 ( 99.61 -62.01 3.88 0.59) ; 49 ( 100.04 -62.07 3.88 0.52) ; 50 ( 100.98 -62.81 3.88 0.74) ; 51 ( 102.06 -62.98 3.75 0.52) ; 52 ( 102.98 -63.35 3.63 0.52) ; 53 ( 103.47 -63.50 3.63 0.81) ; 54 ( 103.81 -63.70 4.00 1.11) ; 55 ( 104.46 -63.88 4.00 1.40) ; 56 ( 105.03 -64.05 4.00 0.59) ; 57 ( 106.20 -64.15 4.00 0.37) ; 58 ( 106.93 -64.27 4.19 0.37) ; 59 ( 107.37 -64.34 4.56 0.44) ; 60 ( 109.24 -64.79 4.56 0.37) ; 61 ( 110.90 -65.12 4.56 0.37) ; 62 ( 111.64 -65.61 4.75 1.03) ; 63 ( 112.36 -65.80 4.75 1.40) ; 64 ( 113.23 -65.93 5.13 0.59) ; 65 ( 114.02 -66.13 5.19 0.44) ; 66 ( 114.53 -66.21 5.50 0.44) ; 67 Normal | ( 65.95 -41.87 -2.44 0.74) ; 1, R-1-2 ( 67.10 -42.20 -2.75 1.03) ; 2 ( 67.68 -42.29 -3.00 1.18) ; 3 ( 68.18 -42.45 -3.00 0.59) ; 4 ( 68.84 -42.92 -3.31 0.59) ; 5 ( 69.28 -42.99 -3.75 0.96) ; 6 ( 69.72 -43.06 -5.00 1.25) ; 7 ( 70.12 -43.34 -5.63 0.88) ; 8 ( 70.88 -43.31 -6.94 0.66) ; 9 ( 72.28 -43.39 -7.31 0.52) ; 10 ( 73.05 -43.22 -7.81 0.59) ; 11 ( 74.04 -43.07 -7.88 0.74) ; 12 ( 74.43 -42.99 -7.88 1.47) ; 13 ( 75.28 -42.82 -8.25 1.77) ; 14 ( 75.59 -42.28 -8.88 1.33) ; 15 ( 76.09 -41.92 -8.63 0.52) ; 16 ( 77.04 -41.62 -8.38 0.44) ; 17 ( 78.03 -41.42 -7.94 0.44) ; 18 ( 79.78 -40.73 -7.56 0.44) ; 19 ( 80.37 -40.31 -7.88 1.03) ; 20 ( 81.00 -40.11 -8.50 1.77) ; 21 ( 81.40 -39.95 -8.75 1.77) ; 22 ( 81.99 -39.52 -8.75 0.88) ; 23 ( 82.37 -39.44 -9.19 0.52) ; 24 ( 83.04 -39.47 -9.19 0.44) ; 25 ( 83.75 -39.28 -9.44 0.74) ; 26 ( 84.13 -39.19 -9.81 1.11) ; 27 ( 84.36 -39.16 -11.25 0.74) ; 28 Normal ) ; End of split | ( 17.14 -2.55 11.63 1.55) ; 1, R-2 ( ( 18.18 -3.01 11.19 0.66) ; 1, R-2-1 ( 19.01 -2.99 11.13 0.66) ; 2 ( 20.38 -2.84 11.13 1.11) ; 3 ( 21.25 -2.98 11.06 1.69) ; 4 ( ( 22.08 -3.85 10.56 0.74) ; 1, R-2-1-1 ( 22.91 -4.21 10.44 0.66) ; 2 ( 23.78 -4.86 10.31 0.66) ; 3 ( 25.53 -4.69 10.00 0.74) ; 4 ( 26.50 -5.14 9.31 0.59) ; 5 ( 27.68 -5.70 8.94 0.59) ; 6 ( 28.91 -5.90 8.06 0.74) ; 7 ( 29.76 -6.25 7.69 0.96) ; 8 ( 30.64 -6.84 6.94 0.81) ; 9 ( 31.55 -7.12 6.69 0.52) ; 10 ( 32.90 -7.56 6.81 0.52) ; 11 ( 34.04 -7.89 7.06 0.59) ; 12 ( 34.82 -8.16 7.81 0.52) ; 13 ( 36.09 -8.66 7.56 0.52) ; 14 ( 37.71 -9.21 7.56 0.59) ; 15 ( 38.70 -9.59 7.75 0.81) ; 16 ( 39.63 -9.81 8.06 1.03) ; 17 ( 40.07 -9.88 8.69 1.25) ; 18 ( 42.12 -10.13 7.88 0.74) ; 19 ( 43.61 -10.59 8.13 0.66) ; 20 ( 44.92 -10.80 8.44 0.66) ; 21 ( 45.71 -10.55 8.56 0.66) ; 22 ( 47.30 -10.88 8.56 0.74) ; 23 ( 48.79 -11.33 8.56 0.59) ; 24 ( 49.85 -11.72 8.50 0.52) ; 25 ( 50.71 -11.94 8.50 0.81) ; 26 ( 51.35 -12.18 8.50 1.03) ; 27 ( 52.45 -12.73 8.50 0.52) ; 28 ( 54.01 -13.27 7.94 0.44) ; 29 ( 55.62 -13.90 7.94 0.66) ; 30 ( 56.65 -14.51 7.94 1.40) ; 31 ( 57.69 -14.97 8.63 0.66) ; 32 ( 58.82 -15.82 6.06 0.44) ; 33 ( 60.70 -17.31 6.06 0.44) ; 34 ( 61.91 -18.17 5.88 0.44) ; 35 ( 63.28 -19.42 5.88 0.52) ; 36 ( 64.21 -19.72 5.88 0.74) ; 37 ( 65.21 -20.39 5.63 0.44) ; 38 ( 66.49 -20.82 5.44 0.66) ; 39 ( 68.10 -21.44 5.44 0.66) ; 40 ( 68.97 -21.58 5.44 0.66) ; 41 ( 70.25 -21.56 5.00 0.66) ; 42 ( 71.41 -21.82 6.38 0.96) ; 43 ( 71.88 -22.12 6.38 1.69) ; 44 ( 72.94 -21.98 6.44 0.74) ; 45 ( 73.81 -21.67 7.06 0.66) ; 46 ( 75.15 -21.67 7.56 0.37) ; 47 ( 76.37 -21.56 8.13 0.37) ; 48 ( 77.68 -21.25 8.88 0.37) ; 49 ( 78.33 -20.91 9.63 0.44) ; 50 ( 79.29 -20.48 9.81 0.96) ; 51 ( 79.89 -20.50 9.81 1.69) ; 52 ( 80.33 -20.04 9.81 1.33) ; 53 ( 80.92 -19.69 10.06 0.52) ; 54 ( 81.79 -18.87 10.44 0.44) ; 55 ( 82.92 -17.86 10.56 0.59) ; 56 ( 84.08 -16.65 10.19 0.44) ; 57 ( 84.88 -15.81 9.69 0.44) ; 58 ( 85.19 -15.26 9.69 0.74) ; 59 ( 85.83 -14.56 8.94 0.88) ; 60 ( 86.06 -14.08 8.25 0.59) ; 61 ( 87.11 -13.49 8.25 0.52) ; 62 ( 87.84 -13.17 8.06 0.74) ; 63 ( 89.28 -13.03 8.00 0.74) ; 64 ( 90.21 -12.80 7.88 0.52) ; 65 ( 91.09 -12.94 7.44 0.88) ; 66 ( 91.38 -12.91 7.69 2.28) ; 67 ( 92.48 -13.09 7.81 1.33) ; 68 ( 93.52 -13.55 8.06 0.52) ; 69 ( 94.39 -14.21 8.31 0.37) ; 70 ( 94.98 -14.67 8.31 0.37) ; 71 ( 95.72 -15.23 8.44 0.66) ; 72 ( 96.80 -15.92 8.44 0.66) ; 73 ( 97.64 -16.79 8.50 0.66) ; 74 ( 98.36 -17.42 9.00 0.96) ; 75 ( 99.47 -17.90 9.38 1.25) ; 76 ( 100.57 -18.52 9.94 1.40) ; 77 ( 101.38 -19.01 10.06 1.77) ; 78 ( 102.33 -19.61 10.13 1.92) ; 79 ( 103.04 -20.31 10.13 0.74) ; 80 ( 103.61 -21.26 9.69 0.44) ; 81 ( 104.79 -22.78 9.06 0.37) ; 82 ( 105.55 -23.64 9.25 0.37) ; 83 ( 106.32 -24.43 9.25 0.52) ; 84 Normal | ( 22.01 -2.40 10.81 0.74) ; 1, R-2-1-2 ( 22.88 -2.02 10.81 0.44) ; 2 ( 24.16 -1.93 10.69 0.44) ; 3 ( 25.02 -1.70 10.44 0.44) ; 4 ( 25.97 -1.40 10.50 0.74) ; 5 ( 26.82 -1.24 11.63 1.25) ; 6 ( 27.50 -1.13 11.69 1.25) ; 7 ( 28.42 -1.05 12.44 1.03) ; 8 ( 29.82 -1.13 13.00 0.66) ; 9 ( 30.85 -1.21 13.88 0.66) ; 10 ( 31.91 -1.53 14.31 0.59) ; 11 ( 32.85 -1.75 14.81 0.88) ; 12 ( 33.79 -1.98 15.13 0.74) ; 13 ( 34.22 -2.05 15.44 0.74) ; 14 ( 35.46 -2.24 16.00 0.88) ; 15 ( 36.77 -2.90 16.38 0.74) ; 16 ( 37.52 -3.31 16.75 0.81) ; 17 ( 38.43 -4.19 17.00 0.81) ; 18 ( 39.30 -4.85 17.06 0.52) ; 19 ( 40.16 -5.50 17.44 0.52) ; 20 ( 41.05 -6.46 17.44 0.66) ; 21 ( 41.89 -7.33 17.44 0.66) ; 22 ( 42.75 -8.06 17.63 1.11) ; 23 ( 43.11 -8.56 17.88 1.77) ; 24 ( 43.88 -8.90 17.88 1.77) ; 25 ( 44.84 -9.43 17.94 0.59) ; 26 ( 46.05 -10.28 18.38 0.52) ; 27 ( 47.09 -10.75 18.44 0.52) ; 28 ( 47.99 -11.19 18.44 1.11) ; 29 ( 48.69 -11.52 18.44 1.25) ; 30 ( 49.75 -12.35 18.44 0.81) ; 31 ( 50.94 -12.84 18.81 0.66) ; 32 ( 52.17 -13.48 18.44 0.59) ; 33 ( 52.78 -13.94 18.44 0.59) ; 34 ( 53.37 -14.85 18.94 1.18) ; 35 ( 54.11 -15.40 18.94 1.18) ; 36 ( 54.79 -16.26 19.00 0.52) ; 37 ( 55.61 -16.76 19.00 0.52) ; 38 ( 56.61 -17.51 19.00 0.66) ; 39 ( 57.77 -18.21 19.00 0.66) ; 40 ( 58.76 -18.07 19.00 0.66) ; 41 ( 59.89 -18.03 19.56 0.96) ; 42 ( 61.00 -18.05 19.56 1.40) ; 43 ( 62.06 -18.00 19.56 1.47) ; 44 ( 62.90 -17.84 19.63 0.66) ; 45 ( 63.93 -17.92 19.69 0.74) ; 46 ( 64.79 -17.49 19.75 0.52) ; 47 ( 65.75 -17.13 19.56 0.52) ; 48 ( 66.59 -16.51 20.13 0.59) ; 49 ( 66.75 -16.02 20.75 0.88) ; 50 ( 67.40 -15.68 21.06 1.33) ; 51 ( 67.82 -15.31 21.75 0.88) ; 52 ( 68.03 -15.93 22.94 0.74) ; 53 ( 68.13 -16.68 23.19 0.59) ; 54 ( 67.84 -17.53 23.63 0.44) ; 55 ( 68.06 -18.08 23.81 0.44) ; 56 ( 68.17 -18.32 24.00 0.44) ; 57 ( 68.67 -18.47 24.00 0.74) ; 58 ( 69.16 -18.18 24.44 1.55) ; 59 ( 69.49 -17.50 24.81 1.55) ; 60 ( 70.14 -17.15 25.25 0.66) ; 61 ( 71.24 -17.77 25.31 0.52) ; 62 ( 72.34 -17.42 25.56 0.52) ; 63 ( 73.53 -16.95 26.00 0.52) ; 64 Normal ) ; End of split | ( 17.68 -1.91 14.13 0.59) ; 1, R-2-2 ( 18.11 -1.98 14.25 0.59) ; 2 ( 18.73 -2.30 14.50 0.59) ; 3 ( 19.54 -2.35 14.63 0.59) ; 4 ( 20.15 -1.85 14.94 0.59) ; 5 ( 20.53 -1.32 15.50 0.74) ; 6 ( 21.31 -2.04 15.88 0.59) ; 7 ( 22.13 -2.53 15.88 0.59) ; 8 ( 22.32 -3.68 15.94 0.66) ; 9 ( 23.55 -5.28 16.00 0.88) ; 10 ( 24.80 -6.37 16.00 1.11) ; 11 ( 25.53 -7.00 16.25 1.03) ; 12 ( 26.47 -7.15 16.19 0.74) ; 13 ( 27.43 -7.23 16.13 0.59) ; 14 ( 28.32 -8.18 16.13 0.52) ; 15 ( 28.46 -8.72 16.00 0.52) ; 16 ( 28.81 -9.29 16.00 0.74) ; 17 ( 29.14 -10.08 16.00 0.96) ; 18 ( 29.92 -11.25 15.94 0.66) ; 19 ( 30.57 -11.42 15.69 0.59) ; 20 ( 31.51 -12.53 15.69 0.59) ; 21 ( 32.08 -13.22 16.13 0.66) ; 22 ( 32.95 -13.79 16.81 0.59) ; 23 ( 33.75 -13.92 17.38 0.74) ; 24 ( 34.57 -13.91 18.31 0.96) ; 25 ( 35.30 -14.02 18.44 1.69) ; 26 ( 36.40 -14.20 18.44 1.11) ; 27 ( 37.13 -14.76 19.88 0.74) ; 28 ( 38.15 -14.92 19.94 0.59) ; 29 ( 38.96 -14.97 20.81 0.81) ; 30 ( 40.15 -15.01 21.00 0.96) ; 31 ( 41.29 -15.34 21.50 0.74) ; 32 ( 42.85 -15.81 21.75 0.66) ; 33 ( 44.01 -16.51 23.19 0.81) ; 34 ( 44.13 -16.68 24.38 0.74) ; 35 ( 44.75 -17.00 25.06 0.59) ; 36 ( 45.48 -16.60 25.31 0.59) ; 37 ( 46.59 -16.18 25.44 0.59) ; 38 ( 47.48 -16.25 25.69 0.66) ; 39 ( 48.00 -16.26 25.69 0.66) ; 40 ( 48.73 -17.34 25.88 0.66) ; 41 ( 49.23 -17.93 25.94 0.66) ; 42 ( 49.86 -18.62 26.19 0.44) ; 43 ( 50.27 -19.35 26.44 1.03) ; 44 ( 50.68 -20.09 25.88 1.33) ; 45 ( 51.40 -21.16 25.88 0.88) ; 46 ( 52.54 -21.94 25.63 0.52) ; 47 ( 53.57 -22.54 25.38 0.52) ; 48 ( 53.56 -23.50 25.31 0.52) ; 49 ( 53.32 -24.13 26.00 0.44) ; 50 ( 53.32 -25.02 26.06 0.44) ; 51 ( 53.59 -26.18 26.19 0.44) ; 52 ( 53.61 -26.99 26.19 0.44) ; 53 Normal ) ; End of split ) ; End of split ) ; End of tree ( (Color Magenta) (Dendrite) ( 6.19 -3.59 0.00 3.17) ; Root ( 7.49 -5.35 -0.19 2.65) ; 1, R ( 8.94 -6.98 -0.88 2.80) ; 2 ( 10.22 -7.85 -1.13 3.02) ; 3 ( ( 10.57 -9.65 -1.13 3.02) ; 1, R-1 ( ( 10.51 -9.75 -1.75 3.83) ; 1, R-1-1 ( 11.36 -10.55 -2.13 3.09) ; 2 ( 12.21 -10.83 -2.31 2.36) ; 3 ( 13.15 -11.94 -2.63 2.14) ; 4 ( 14.07 -13.20 -2.38 2.43) ; 5 ( ( 14.25 -14.42 -2.44 1.55) ; 1, R-1-1-1 ( 14.77 -15.38 -2.88 1.69) ; 2 ( 15.66 -16.34 -2.19 2.28) ; 3 ( ( 16.67 -17.46 -1.81 2.58) ; 1, R-1-1-1-1 ( 18.06 -18.20 -1.81 1.99) ; 2 ( 19.00 -18.79 -2.00 1.47) ; 3 ( 19.63 -19.56 -2.31 1.11) ; 4 ( 20.35 -20.64 -1.63 1.18) ; 5 ( ( 21.32 -22.49 -0.88 1.03) ; 1, R-1-1-1-1-1 ( 22.42 -23.55 -1.25 0.88) ; 2 ( 23.30 -24.13 -1.31 0.74) ; 3 ( 24.25 -24.73 -1.13 0.74) ; 4 ( 25.44 -25.29 -1.25 0.96) ; 5 ( 26.33 -25.73 -1.38 1.18) ; 6 ( 27.09 -26.59 -1.38 1.03) ; 7 ( 27.48 -27.39 -1.50 1.11) ; 8 ( 28.21 -28.46 -1.63 1.25) ; 9 ( 28.89 -28.86 -1.69 1.40) ; 10 ( 29.62 -29.94 -1.69 1.03) ; 11 ( 30.66 -30.93 -1.75 0.88) ; 12 ( 31.44 -32.08 -2.06 1.18) ; 13 ( 32.19 -32.91 -1.31 1.84) ; 14 ( 32.71 -33.80 -0.81 1.69) ; 15 ( 32.92 -34.43 -0.44 1.33) ; 16 ( 33.34 -35.53 0.13 1.11) ; 17 ( 33.39 -36.65 0.13 0.81) ; 18 ( 33.64 -37.88 0.19 0.81) ; 19 ( 33.19 -39.29 0.56 0.96) ; 20 ( 33.32 -40.34 0.94 1.18) ; 21 ( 33.08 -41.41 1.50 1.03) ; 22 ( 32.52 -42.58 1.88 0.81) ; 23 ( 32.11 -43.77 2.06 1.18) ; 24 ( 31.43 -44.71 2.31 0.96) ; 25 ( 30.70 -45.55 2.38 0.96) ; 26 ( 29.81 -46.52 2.94 0.96) ; 27 ( 28.97 -47.57 3.00 1.18) ; 28 ( 27.78 -48.49 3.00 1.03) ; 29 ( 27.03 -49.41 3.56 1.18) ; 30 ( 26.59 -50.82 3.88 1.33) ; 31 ( 26.38 -52.12 4.13 1.11) ; 32 ( 26.26 -52.91 4.38 0.96) ; 33 ( 26.39 -53.97 4.44 1.47) ; 34 ( 26.60 -55.04 4.31 0.96) ; 35 ( 26.39 -55.83 4.06 0.66) ; 36 ( 26.53 -57.33 3.44 1.25) ; 37 ( 26.56 -58.52 2.50 0.96) ; 38 ( 26.42 -59.70 2.00 1.11) ; 39 ( 26.68 -60.40 1.69 0.88) ; 40 ( 26.88 -61.92 1.69 0.81) ; 41 ( 26.99 -63.12 1.44 0.81) ; 42 ( 26.95 -64.29 2.13 0.74) ; 43 ( 26.91 -65.55 1.81 1.03) ; 44 ( 26.85 -66.35 0.81 1.33) ; 45 ( 26.84 -66.42 0.31 1.84) ; 46 ( 26.89 -67.09 -0.88 1.84) ; 47 ( 27.08 -68.24 -1.81 1.33) ; 48 ( 27.34 -68.95 -3.13 1.18) ; 49 ( 27.44 -70.15 -3.31 0.88) ; 50 ( 27.81 -71.17 -3.88 0.88) ; 51 ( 27.83 -72.43 -4.00 0.66) ; 52 ( 28.28 -73.39 -4.44 1.03) ; 53 ( 28.86 -74.36 -4.94 1.33) ; 54 ( 29.74 -75.39 -5.88 1.62) ; 55 ( 30.19 -76.35 -6.38 1.40) ; 56 ( 31.01 -77.29 -6.38 0.96) ; 57 ( 31.34 -78.53 -6.69 0.81) ; 58 ( 31.95 -79.88 -6.69 0.59) ; 59 ( 32.85 -81.73 -6.69 0.59) ; 60 ( 33.83 -82.99 -6.75 0.52) ; 61 ( 33.81 -84.10 -6.94 0.52) ; 62 ( 34.21 -85.35 -6.94 0.52) ; 63 ( 34.64 -86.38 -6.56 1.84) ; 64 ( 34.83 -87.53 -6.44 1.92) ; 65 ( 35.17 -88.17 -6.44 0.96) ; 66 ( 35.58 -88.83 -6.44 0.37) ; 67 ( 36.19 -89.74 -5.88 0.44) ; 68 ( 36.76 -90.87 -5.88 0.52) ; 69 ( 37.20 -91.82 -5.94 0.74) ; 70 ( 37.32 -92.51 -5.25 0.52) ; 71 ( 37.17 -93.45 -5.25 0.66) ; 72 ( 36.86 -94.43 -5.19 0.88) ; 73 ( 36.48 -95.41 -5.38 1.40) ; 74 ( 36.10 -95.94 -6.56 1.77) ; 75 ( 35.71 -96.99 -6.69 0.96) ; 76 ( 35.44 -97.76 -6.69 0.96) ; 77 ( 34.77 -98.25 -7.31 1.11) ; 78 ( 34.29 -99.36 -7.38 0.66) ; 79 ( 34.18 -100.08 -7.38 0.66) ; 80 ( 34.44 -101.23 -7.06 0.81) ; 81 ( 34.51 -102.21 -6.75 0.81) ; 82 ( 34.72 -103.20 -6.56 0.81) ; 83 ( 35.08 -104.29 -5.75 1.11) ; 84 ( 35.42 -105.38 -6.13 1.33) ; 85 ( 35.92 -106.50 -6.00 1.03) ; 86 ( 36.27 -107.59 -6.50 1.11) ; 87 ( 36.65 -108.90 -6.50 1.11) ; 88 ( 37.24 -109.96 -6.75 0.74) ; 89 ( 38.07 -110.83 -7.19 0.59) ; 90 ( 38.57 -111.44 -7.19 0.59) ; 91 ( 38.48 -112.45 -7.19 0.59) ; 92 ( 38.81 -113.18 -6.94 1.18) ; 93 ( 39.19 -114.12 -8.44 0.88) ; 94 ( 39.54 -115.41 -9.25 0.52) ; 95 ( 40.27 -115.97 -9.31 0.52) ; 96 ( 40.77 -116.63 -9.31 0.52) ; 97 ( 40.63 -117.95 -9.31 0.52) ; 98 ( 40.71 -118.85 -9.31 1.25) ; 99 ( 41.02 -119.71 -9.25 2.06) ; 100 ( 41.23 -120.71 -9.25 1.55) ; 101 ( 41.86 -121.47 -9.25 0.74) ; 102 ( 42.49 -122.68 -9.94 0.37) ; 103 ( 43.02 -123.14 -10.13 0.37) ; 104 ( 43.02 -123.58 -10.56 0.37) ; 105 ( 43.30 -124.59 -10.75 1.11) ; 106 ( 43.44 -125.13 -10.94 1.84) ; 107 ( 44.10 -126.12 -11.50 1.84) ; 108 ( 44.24 -127.18 -11.50 1.33) ; 109 ( 44.43 -128.32 -11.63 0.88) ; 110 ( 44.36 -129.72 -11.81 0.52) ; 111 ( 44.31 -130.52 -11.81 0.52) ; 112 ( 44.43 -131.57 -11.81 0.44) ; 113 ( 44.30 -132.44 -11.81 0.44) ; 114 ( 43.93 -133.34 -11.94 0.59) ; 115 ( 43.34 -134.28 -12.13 0.88) ; 116 ( 43.19 -135.22 -12.19 0.96) ; 117 ( 43.26 -136.19 -13.13 0.74) ; 118 ( 43.61 -136.78 -13.88 0.59) ; 119 Normal | ( 21.26 -20.53 -1.75 0.52) ; 1, R-1-1-1-1-2 ( 22.49 -20.72 -1.63 0.52) ; 2 ( 23.50 -20.96 -1.31 0.59) ; 3 ( 24.12 -21.27 -0.50 0.81) ; 4 ( 25.08 -21.80 0.06 0.96) ; 5 ( 26.07 -22.18 0.50 1.11) ; 6 ( 27.09 -22.34 0.81 1.11) ; 7 ( 28.11 -22.06 1.50 1.03) ; 8 ( 29.24 -21.94 1.81 0.88) ; 9 ( 30.22 -22.32 1.94 0.66) ; 10 ( 30.92 -22.66 1.94 0.59) ; 11 ( 31.89 -23.55 1.94 0.74) ; 12 ( 32.57 -23.95 2.44 0.88) ; 13 ( 33.04 -23.80 3.00 0.81) ; 14 ( 33.92 -23.94 4.00 1.03) ; 15 ( 34.27 -24.07 4.00 1.40) ; 16 ( 34.87 -24.02 4.31 1.40) ; 17 ( 35.68 -24.07 4.69 0.96) ; 18 ( 36.64 -24.15 4.75 0.81) ; 19 ( 37.53 -24.22 5.69 0.74) ; 20 ( 38.81 -24.13 5.81 0.66) ; 21 ( 39.74 -24.42 6.06 0.66) ; 22 ( 40.73 -24.73 6.31 0.81) ; 23 ( 42.11 -25.46 6.38 1.11) ; 24 ( 43.27 -26.61 6.88 0.74) ; 25 ( 44.38 -26.63 7.06 0.96) ; 26 ( 44.83 -26.63 6.94 1.25) ; 27 ( 45.83 -26.94 8.19 0.96) ; 28 ( 47.13 -27.15 6.81 0.66) ; 29 ( 47.91 -27.41 6.81 0.66) ; 30 ( 48.98 -27.73 7.25 0.81) ; 31 ( 50.12 -28.14 7.50 0.81) ; 32 ( 51.17 -28.53 7.31 0.74) ; 33 ( 52.01 -28.88 7.13 1.03) ; 34 ( 52.38 -28.86 7.25 1.40) ; 35 ( ( 53.40 -28.58 7.31 0.81) ; 1, R-1-1-1-1-2-1 ( 54.25 -28.86 7.69 0.66) ; 2 ( 55.45 -29.28 7.69 0.66) ; 3 ( 56.84 -29.49 8.00 0.52) ; 4 ( 57.44 -29.88 8.00 1.18) ; 5 ( 58.05 -30.28 8.19 1.47) ; 6 ( 58.56 -30.80 9.06 0.59) ; 7 ( 59.44 -31.39 9.38 0.44) ; 8 ( 60.20 -31.81 10.00 0.59) ; 9 ( 60.58 -32.16 10.63 0.81) ; 10 ( 61.09 -32.76 11.38 0.52) ; 11 ( 61.82 -33.32 11.56 0.66) ; 12 ( 63.17 -34.27 11.94 0.81) ; 13 ( 63.74 -34.68 13.06 0.59) ; 14 ( 64.12 -35.63 13.69 0.59) ; 15 ( 64.35 -36.48 14.19 0.88) ; 16 ( 64.47 -36.65 14.31 1.33) ; 17 ( 65.16 -37.49 14.63 0.59) ; 18 ( 65.75 -37.96 14.63 0.59) ; 19 ( 66.39 -38.65 15.31 0.96) ; 20 ( 67.50 -39.20 16.13 0.66) ; 21 ( 67.83 -39.92 16.75 0.66) ; 22 ( 67.64 -41.15 17.25 0.88) ; 23 ( 67.70 -42.12 18.00 0.66) ; 24 ( 67.23 -42.78 18.38 0.52) ; 25 ( 65.77 -42.55 18.44 0.52) ; 26 ( 64.24 -42.83 18.25 0.52) ; 27 Normal | ( 53.27 -30.13 7.19 0.59) ; 1, R-1-1-1-1-2-2 ( 53.75 -30.88 6.94 0.59) ; 2 ( 54.84 -32.01 6.81 0.88) ; 3 ( 55.71 -32.67 6.75 0.59) ; 4 ( 56.68 -33.11 6.81 0.88) ; 5 ( 57.27 -33.58 7.00 1.25) ; 6 ( 58.30 -34.19 6.69 0.81) ; 7 ( 59.35 -34.58 6.69 0.74) ; 8 ( 60.58 -34.85 6.69 0.66) ; 9 ( 61.35 -35.19 6.44 0.52) ; 10 ( 62.40 -35.58 6.44 0.88) ; 11 ( 63.04 -35.75 6.06 1.25) ; 12 ( 63.93 -36.27 5.81 1.25) ; 13 ( 64.38 -36.78 5.56 0.52) ; 14 ( 65.18 -37.36 5.19 0.74) ; 15 ( 65.65 -37.72 5.13 0.74) ; 16 ( 66.32 -38.12 4.94 0.52) ; 17 ( 67.16 -38.55 4.94 0.52) ; 18 ( 67.76 -38.94 4.69 0.81) ; 19 ( 68.64 -39.08 4.50 0.96) ; 20 ( 69.67 -39.62 4.50 0.59) ; 21 ( 70.73 -39.49 4.50 0.52) ; 22 ( 71.27 -39.42 4.50 1.25) ; 23 ( 72.08 -39.48 4.50 1.62) ; 24 ( 72.85 -39.31 4.50 1.03) ; 25 ( 73.73 -39.00 4.50 0.66) ; 26 ( 74.93 -38.90 5.88 0.52) ; 27 ( 75.70 -38.72 5.88 0.52) ; 28 ( 76.39 -38.61 5.88 0.52) ; 29 ( 77.64 -38.29 6.00 0.81) ; 30 ( 78.26 -38.09 6.81 1.11) ; 31 ( 78.69 -38.23 7.00 2.06) ; 32 ( 79.60 -38.15 7.00 2.06) ; 33 ( 80.26 -37.74 7.44 1.03) ; 34 ( 81.00 -37.79 7.44 0.66) ; 35 ( 81.73 -37.90 7.94 0.52) ; 36 ( 82.45 -38.09 8.81 0.74) ; 37 ( 83.31 -38.29 9.38 0.74) ; 38 ( 84.05 -38.34 9.69 0.52) ; 39 ( 85.31 -38.83 10.19 0.59) ; 40 ( 85.92 -39.23 9.94 0.96) ; 41 ( 86.82 -39.67 9.88 1.62) ; 42 ( 87.62 -39.80 10.06 0.74) ; 43 ( 88.71 -39.97 10.06 0.44) ; 44 ( 90.39 -40.24 10.06 0.66) ; 45 ( 91.26 -40.37 10.06 0.59) ; 46 ( 91.94 -40.78 10.13 0.81) ; 47 ( 93.10 -40.96 10.13 0.44) ; 48 ( 94.18 -40.77 10.13 0.44) ; 49 ( 95.25 -40.63 10.31 0.44) ; 50 ( 95.75 -40.71 10.31 1.03) ; 51 ( 96.49 -40.76 10.56 1.40) ; 52 ( 97.12 -40.56 10.56 0.88) ; 53 ( 97.63 -40.64 11.00 0.44) ; 54 ( 99.26 -40.23 11.56 0.29) ; 55 ( 100.71 -40.02 11.56 0.59) ; 56 ( 101.54 -39.93 11.56 0.59) ; 57 ( 103.30 -39.69 11.56 0.44) ; 58 ( 103.99 -40.02 12.19 1.03) ; 59 ( 104.55 -40.26 12.25 1.40) ; 60 ( 105.04 -40.93 12.38 0.59) ; 61 ( 105.31 -41.57 12.38 0.59) ; 62 ( 105.67 -42.14 12.56 0.88) ; 63 ( 105.84 -42.98 12.69 0.52) ; 64 ( 106.19 -43.52 12.75 0.74) ; 65 ( 106.50 -44.82 12.75 0.29) ; 66 ( 106.56 -45.43 12.75 0.66) ; 67 ( 106.76 -46.49 12.75 0.88) ; 68 ( 106.79 -47.24 12.75 0.44) ; 69 ( 106.83 -47.91 13.69 0.74) ; 70 Normal ) ; End of split ) ; End of split | ( 16.17 -17.32 -2.31 1.11) ; 1, R-1-1-1-2 ( 15.70 -18.36 -2.38 0.74) ; 2 ( 15.40 -19.35 -3.06 0.74) ; 3 ( 15.08 -20.41 -4.00 0.74) ; 4 ( 15.20 -21.54 -4.00 0.59) ; 5 ( 15.33 -23.04 -4.06 0.81) ; 6 ( 14.53 -24.32 -4.19 1.18) ; 7 ( 14.26 -25.09 -4.19 1.55) ; 8 ( 14.25 -26.12 -4.19 1.40) ; 9 ( 14.92 -27.05 -4.50 0.96) ; 10 ( 15.10 -28.26 -4.50 0.74) ; 11 ( 14.95 -29.71 -4.50 0.88) ; 12 ( 14.98 -30.80 -4.50 1.03) ; 13 ( 15.64 -31.35 -4.94 1.11) ; 14 ( 15.66 -32.61 -4.94 0.96) ; 15 ( 15.71 -33.73 -4.94 0.81) ; 16 ( 15.83 -34.86 -4.56 0.81) ; 17 ( 16.26 -35.88 -4.56 1.03) ; 18 ( 16.80 -37.16 -4.56 0.81) ; 19 ( 17.24 -38.19 -4.63 0.81) ; 20 ( 17.18 -39.00 -4.63 0.74) ; 21 ( 17.14 -40.25 -4.75 0.88) ; 22 ( 17.51 -41.20 -4.94 0.74) ; 23 ( 17.44 -42.08 -5.13 0.74) ; 24 ( 17.60 -43.43 -4.56 1.03) ; 25 ( 17.88 -45.03 -4.63 0.96) ; 26 ( 17.59 -46.83 -4.63 1.03) ; 27 ( 17.63 -48.02 -4.63 0.66) ; 28 ( 18.02 -49.27 -4.69 0.74) ; 29 ( 18.23 -50.26 -4.69 1.03) ; 30 ( 18.76 -51.23 -4.69 0.81) ; 31 ( 19.13 -52.63 -4.69 0.52) ; 32 ( 19.28 -53.54 -4.69 0.66) ; 33 ( 19.65 -54.56 -4.69 0.52) ; 34 ( 20.15 -55.60 -4.13 1.25) ; 35 ( 20.42 -56.68 -4.63 0.96) ; 36 ( 20.66 -57.53 -4.69 0.44) ; 37 ( 20.44 -58.48 -4.81 0.44) ; 38 ( 19.89 -59.13 -4.94 0.74) ; 39 ( 19.49 -60.25 -5.56 0.59) ; 40 ( 19.92 -61.35 -5.94 0.81) ; 41 ( 20.19 -62.44 -6.81 1.11) ; 42 ( 19.96 -63.43 -6.88 0.74) ; 43 ( 19.57 -63.97 -7.44 0.59) ; 44 ( 19.32 -64.67 -7.88 0.59) ; 45 ( 18.71 -65.67 -8.13 0.81) ; 46 ( 18.42 -66.59 -8.63 0.81) ; 47 ( 18.16 -67.73 -9.13 1.11) ; 48 ( 17.78 -68.72 -9.50 1.55) ; 49 ( 17.62 -69.72 -9.56 1.92) ; 50 ( 17.37 -71.31 -10.44 1.40) ; 51 ( 17.12 -72.91 -10.44 1.03) ; 52 ( 16.81 -74.41 -10.81 0.74) ; 53 ( 16.59 -75.78 -11.88 0.59) ; 54 ( 16.89 -77.16 -12.44 0.52) ; 55 ( 16.96 -78.65 -12.81 0.52) ; 56 ( 17.39 -81.09 -13.06 0.74) ; 57 ( 17.52 -82.14 -13.63 0.74) ; 58 ( 17.80 -83.66 -14.25 1.03) ; 59 ( 17.85 -84.46 -12.00 1.92) ; 60 ( 18.21 -85.04 -12.00 0.81) ; 61 ( 18.68 -85.77 -12.00 0.52) ; 62 ( 19.19 -86.82 -12.00 0.66) ; 63 ( 20.05 -87.99 -10.81 0.52) ; 64 ( 20.38 -89.23 -10.50 0.52) ; 65 ( 21.05 -90.22 -10.19 0.81) ; 66 ( 21.54 -91.33 -10.06 0.81) ; 67 ( 22.12 -92.39 -9.81 0.44) ; 68 ( 22.75 -93.07 -9.75 0.44) ; 69 ( 23.13 -94.03 -9.31 1.03) ; 70 ( 23.45 -94.82 -8.56 0.81) ; 71 ( 23.90 -96.14 -8.00 0.81) ; 72 ( 24.46 -97.35 -7.06 0.59) ; 73 ( 24.69 -98.72 -6.94 0.59) ; 74 ( 25.11 -99.37 -6.63 0.96) ; 75 ( 25.65 -100.20 -6.63 0.59) ; 76 ( 26.35 -101.42 -6.63 0.52) ; 77 ( 26.97 -102.26 -6.63 0.81) ; 78 ( 27.70 -103.78 -6.63 0.96) ; 79 ( 28.34 -104.91 -6.63 0.59) ; 80 ( 28.94 -105.83 -6.00 0.52) ; 81 ( 29.37 -106.86 -6.00 0.52) ; 82 ( 29.43 -107.90 -6.00 1.03) ; 83 ( 29.57 -108.89 -6.00 1.40) ; 84 ( 29.23 -109.64 -6.00 0.81) ; 85 ( 29.03 -110.43 -6.00 0.44) ; 86 ( 29.11 -111.33 -6.00 0.44) ; 87 ( 28.62 -112.07 -6.00 0.66) ; 88 ( 28.26 -112.97 -6.00 0.52) ; 89 ( 28.62 -114.44 -6.44 1.11) ; 90 ( 29.32 -115.49 -7.44 0.74) ; 91 ( 29.50 -116.33 -8.38 0.52) ; 92 ( 29.68 -117.54 -8.75 0.52) ; 93 ( 29.69 -118.36 -8.75 0.88) ; 94 ( 29.67 -119.03 -9.00 0.88) ; 95 ( 30.09 -119.61 -9.56 0.59) ; 96 ( 30.40 -120.02 -10.00 0.59) ; 97 ( 30.75 -120.60 -10.50 1.33) ; 98 ( 30.95 -121.29 -11.50 2.14) ; 99 ( 31.03 -122.20 -12.31 1.62) ; 100 Normal ) ; End of split | ( 15.31 -12.94 -2.38 0.96) ; 1, R-1-1-2 ( 16.55 -13.21 -2.81 0.88) ; 2 ( 18.43 -14.03 -2.81 0.88) ; 3 ( 20.11 -14.22 -3.00 1.11) ; 4 ( 22.07 -15.05 -3.94 0.66) ; 5 ( 23.83 -15.26 -4.31 0.88) ; 6 ( 24.61 -15.52 -4.81 0.88) ; 7 ( 25.52 -15.89 -5.56 0.88) ; 8 ( 27.29 -16.91 -5.63 1.03) ; 9 ( 28.66 -18.17 -6.25 0.81) ; 10 ( 29.71 -18.63 -7.69 1.33) ; 11 ( 30.52 -19.12 -8.00 1.03) ; 12 ( 33.05 -20.12 -8.25 0.74) ; 13 ( 34.15 -20.29 -8.44 0.74) ; 14 ( 36.23 -19.88 -8.50 0.66) ; 15 ( 37.80 -19.76 -8.75 0.96) ; 16 ( 39.64 -19.46 -8.81 0.81) ; 17 ( 41.25 -19.20 -8.88 1.03) ; 18 ( 43.71 -19.21 -8.88 1.18) ; 19 ( 45.03 -18.46 -9.25 0.74) ; 20 ( 46.52 -17.96 -9.44 0.88) ; 21 ( 47.48 -17.51 -9.50 0.88) ; 22 ( 48.64 -16.37 -9.69 0.74) ; 23 ( 48.90 -16.12 -9.94 0.74) ; 24 ( 50.44 -15.85 -10.06 0.74) ; 25 ( 51.79 -15.31 -10.31 1.03) ; 26 ( 53.04 -14.48 -10.31 1.25) ; 27 ( 54.03 -13.38 -10.31 0.96) ; 28 ( 54.51 -12.71 -11.31 0.74) ; 29 ( 55.13 -12.59 -11.31 0.74) ; 30 ( 56.11 -12.97 -11.31 1.03) ; 31 ( 57.71 -13.22 -11.38 0.66) ; 32 ( 58.34 -12.58 -12.06 1.18) ; 33 ( 59.43 -11.86 -13.44 1.40) ; 34 ( 60.42 -11.02 -13.50 2.06) ; 35 ( 61.54 -10.53 -14.44 1.18) ; 36 ( 63.00 -10.24 -14.88 0.59) ; 37 ( 63.68 -10.27 -15.13 0.59) ; 38 ( 64.25 -10.36 -15.94 0.66) ; 39 ( 65.92 -11.14 -16.81 0.96) ; 40 ( 66.67 -11.56 -16.81 1.25) ; 41 ( 68.31 -11.60 -17.56 0.88) ; 42 ( 69.57 -11.65 -17.94 0.52) ; 43 ( 70.75 -12.21 -18.06 0.81) ; 44 ( 71.63 -12.27 -18.06 1.18) ; 45 ( 72.67 -12.81 -19.19 0.66) ; 46 ( 73.54 -12.94 -19.81 0.59) ; 47 ( 74.39 -12.78 -20.06 0.96) ; 48 ( 74.72 -12.61 -20.50 1.55) ; 49 ( 75.40 -12.95 -21.00 1.11) ; 50 ( 75.93 -13.39 -21.13 0.66) ; 51 ( 76.87 -14.06 -21.44 0.52) ; 52 ( 78.58 -15.07 -22.25 0.52) ; 53 ( 79.93 -15.50 -23.56 0.81) ; 54 ( 80.59 -15.54 -23.94 1.62) ; 55 ( 81.20 -15.93 -24.38 2.50) ; 56 ( 81.57 -16.36 -24.69 2.50) ; 57 ( 82.06 -16.59 -24.69 1.55) ; 58 ( 82.61 -16.89 -24.94 1.03) ; 59 ( 83.64 -17.95 -24.94 0.59) ; 60 ( 85.39 -19.11 -25.25 0.59) ; 61 ( 85.61 -19.66 -25.38 0.59) ; 62 ( 86.24 -20.43 -25.38 0.96) ; 63 ( 86.84 -20.82 -25.69 1.33) ; 64 ( 87.39 -21.58 -25.69 0.96) ; 65 ( 87.90 -22.10 -25.69 0.52) ; 66 ( 88.73 -23.04 -25.94 0.29) ; 67 ( 89.36 -23.81 -25.94 0.59) ; 68 ( 89.61 -24.52 -25.94 1.47) ; 69 ( 90.18 -25.20 -25.94 1.92) ; 70 ( 90.69 -26.17 -25.94 1.18) ; 71 ( 91.18 -26.83 -26.25 0.66) ; 72 ( 91.40 -27.83 -26.63 0.52) ; 73 ( 91.90 -28.44 -26.56 0.52) ; 74 ( 92.05 -29.86 -26.63 1.03) ; 75 ( 92.27 -30.78 -26.63 1.40) ; 76 ( 92.49 -32.30 -26.63 0.88) ; 77 ( 92.61 -32.91 -27.56 0.66) ; 78 ( 93.01 -34.16 -29.44 0.66) ; 79 ( 93.71 -35.37 -29.63 0.66) ; 80 ( 94.14 -36.42 -29.88 1.03) ; 81 ( 94.62 -36.70 -30.31 1.47) ; 82 ( 94.73 -36.94 -31.38 1.99) ; 83 ( 95.20 -37.76 -32.25 2.28) ; 84 ( 96.38 -38.25 -33.31 1.40) ; 85 ( 97.02 -38.94 -33.94 0.88) ; 86 ( 97.23 -40.01 -34.56 0.66) ; 87 ( 97.96 -41.01 -34.56 0.66) ; 88 ( 98.16 -41.64 -34.56 1.25) ; 89 ( 98.51 -42.28 -35.75 1.69) ; 90 ( 99.02 -42.91 -35.81 0.88) ; 91 ( 99.15 -43.46 -36.75 0.44) ; 92 ( 99.46 -44.31 -36.75 0.44) ; 93 ( 99.83 -44.89 -36.75 0.81) ; 94 ( 99.99 -45.29 -36.75 0.81) ; 95 ( 100.38 -45.64 -36.75 0.44) ; 96 ( 100.83 -46.09 -36.75 0.44) ; 97 ( 102.07 -46.72 -36.75 0.44) ; 98 Normal ) ; End of split | ( 10.08 -10.26 -3.06 0.81) ; 1, R-1-2 ( 9.00 -10.47 -4.38 0.81) ; 2 ( 8.37 -11.18 -7.19 0.81) ; 3 ( 8.97 -11.65 -9.44 0.81) ; 4 ( 8.97 -13.05 -9.75 0.66) ; 5 ( 8.80 -14.13 -10.94 0.74) ; 6 ( 8.43 -15.48 -11.38 0.88) ; 7 ( 8.44 -16.37 -11.50 0.88) ; 8 ( 7.67 -17.06 -12.06 0.88) ; 9 ( 7.67 -17.12 -12.06 0.88) ; 10 ( ( 6.80 -17.37 -12.38 0.88) ; 1, R-1-2-1 ( 6.16 -17.64 -12.38 0.88) ; 2 ( 5.78 -18.10 -12.38 0.88) ; 3 ( 4.96 -19.52 -12.81 0.81) ; 4 ( 4.31 -20.37 -12.81 1.11) ; 5 ( 3.23 -21.99 -12.81 1.11) ; 6 ( 2.29 -23.16 -12.81 0.74) ; 7 ( 2.08 -23.58 -12.81 0.74) ; 8 ( 1.63 -24.55 -13.75 0.88) ; 9 ( 1.72 -25.37 -13.94 0.88) ; 10 ( 1.44 -25.77 -13.94 0.88) ; 11 ( 0.38 -26.27 -14.88 0.66) ; 12 ( -0.28 -26.69 -15.94 1.40) ; 13 ( -0.92 -27.58 -16.81 1.47) ; 14 ( -1.55 -28.73 -18.63 1.25) ; 15 ( -2.01 -29.33 -19.19 0.96) ; 16 ( -2.32 -29.86 -20.63 0.88) ; 17 ( -2.75 -30.24 -22.00 1.03) ; 18 ( -3.65 -31.14 -22.31 0.81) ; 19 ( -3.68 -31.36 -24.38 1.11) ; 20 ( -3.89 -31.69 -25.63 0.81) ; 21 ( -4.21 -32.31 -26.88 0.81) ; 22 ( -4.85 -33.54 -27.56 1.03) ; 23 ( -5.83 -34.13 -27.94 1.03) ; 24 ( -6.18 -34.88 -30.81 0.81) ; 25 ( -6.99 -35.79 -31.06 0.81) ; 26 ( -7.64 -36.58 -31.31 0.59) ; 27 ( -8.44 -37.34 -32.31 0.52) ; 28 ( -9.09 -38.20 -33.06 0.59) ; 29 ( -9.65 -38.85 -33.38 0.81) ; 30 ( -10.24 -39.79 -33.38 0.81) ; 31 ( -10.53 -40.70 -34.06 1.11) ; 32 ( -11.17 -40.98 -34.94 1.62) ; 33 ( -11.89 -41.23 -34.94 0.66) ; 34 ( -12.56 -42.23 -35.19 0.44) ; 35 ( -12.66 -42.89 -35.63 0.44) ; 36 ( -12.49 -43.65 -35.81 0.81) ; 37 ( -12.47 -44.47 -36.50 1.03) ; 38 ( -12.36 -45.60 -36.94 0.52) ; 39 ( -12.80 -46.56 -37.88 0.52) ; 40 ( -13.24 -46.94 -38.63 1.33) ; 41 ( -13.67 -47.31 -39.13 1.69) ; 42 ( -14.28 -48.33 -39.75 0.37) ; 43 ( -14.45 -48.97 -39.75 0.37) ; 44 ( -15.14 -50.04 -40.31 1.18) ; 45 ( -15.43 -50.44 -40.31 1.77) ; 46 ( -16.33 -50.96 -40.75 0.81) ; 47 ( -17.22 -51.45 -41.19 0.44) ; 48 ( -17.66 -51.91 -41.69 0.66) ; 49 ( -18.08 -52.20 -41.69 0.88) ; 50 ( -18.62 -52.72 -41.69 0.52) ; 51 ( -19.13 -53.15 -42.19 0.29) ; 52 ( -19.58 -53.60 -42.88 0.66) ; 53 ( -19.99 -54.34 -42.94 1.40) ; 54 ( -20.42 -54.72 -42.94 1.77) ; 55 ( -21.09 -55.66 -43.44 1.03) ; 56 ( -21.61 -56.09 -43.25 0.52) ; 57 ( -21.69 -56.59 -42.69 0.52) ; 58 ( -22.26 -57.39 -43.63 1.03) ; 59 ( -22.67 -58.14 -44.00 1.25) ; 60 ( -23.65 -59.10 -45.00 0.44) ; 61 ( -24.22 -59.37 -45.06 0.44) ; 62 ( -24.80 -59.73 -45.63 0.81) ; 63 ( -25.68 -60.10 -45.63 1.55) ; 64 ( -26.62 -60.40 -46.69 0.96) ; 65 ( -27.54 -61.07 -47.88 0.52) ; 66 ( -28.15 -62.08 -48.69 0.74) ; 67 ( -28.15 -63.04 -49.38 1.11) ; 68 ( -28.02 -64.11 -50.94 0.59) ; 69 ( -28.26 -65.17 -52.38 0.44) ; 70 ( -28.49 -66.10 -52.56 0.44) ; 71 ( -28.58 -67.20 -52.94 0.44) ; 72 ( -28.48 -67.95 -53.31 1.11) ; 73 ( -28.51 -69.06 -53.81 1.84) ; 74 ( -28.35 -69.52 -53.81 2.28) ; 75 ( -28.64 -70.37 -53.81 1.25) ; 76 ( -28.81 -71.45 -54.50 0.74) ; 77 ( -29.03 -72.31 -54.50 0.59) ; 78 ( -29.17 -73.17 -54.50 0.96) ; 79 ( -29.22 -74.05 -55.06 0.59) ; 80 ( -29.54 -75.11 -55.63 0.44) ; 81 ( -29.63 -75.69 -55.69 0.88) ; 82 ( -29.82 -76.71 -55.75 0.88) ; 83 Normal | ( 7.77 -17.89 -14.19 0.66) ; 1, R-1-2-2 ( 7.29 -18.10 -16.00 0.81) ; 2 ( 7.07 -18.51 -17.44 1.11) ; 3 ( 7.28 -19.06 -19.38 1.11) ; 4 ( 7.47 -19.24 -21.94 0.96) ; 5 ( 8.40 -19.53 -23.19 1.11) ; 6 ( 9.01 -19.41 -25.44 1.11) ; 7 ( 9.83 -19.47 -26.81 1.11) ; 8 ( 10.67 -19.82 -27.13 0.96) ; 9 ( 12.07 -19.96 -29.06 0.81) ; 10 ( 13.08 -20.12 -29.06 0.66) ; 11 ( 13.74 -20.67 -29.69 0.96) ; 12 ( 14.59 -20.96 -30.56 1.25) ; 13 ( 15.69 -21.58 -30.56 1.25) ; 14 ( 16.45 -21.92 -30.94 0.96) ; 15 ( 17.24 -22.12 -31.50 0.59) ; 16 ( 18.29 -23.03 -31.75 0.59) ; 17 ( 20.40 -24.32 -31.75 0.59) ; 18 ( 21.07 -24.80 -33.50 0.81) ; 19 ( 22.24 -25.43 -33.81 0.81) ; 20 ( 22.91 -25.90 -34.38 0.81) ; 21 ( 23.57 -26.38 -35.13 0.96) ; 22 ( 24.33 -25.90 -36.94 1.18) ; 23 ( 25.39 -25.26 -36.94 0.88) ; 24 ( 26.58 -25.82 -41.13 0.74) ; 25 ( 26.93 -26.39 -42.00 1.25) ; 26 ( 26.97 -27.14 -42.19 0.88) ; 27 ( 27.39 -27.73 -42.19 0.52) ; 28 ( 27.52 -28.33 -42.63 0.52) ; 29 ( 27.60 -28.79 -43.25 0.88) ; 30 ( 27.78 -29.04 -43.44 1.25) ; 31 ( 28.27 -29.72 -44.50 0.88) ; 32 ( 28.54 -29.83 -46.38 0.88) ; 33 ( 28.51 -30.57 -48.19 1.11) ; 34 ( 29.33 -31.51 -51.44 0.81) ; 35 ( 30.60 -32.45 -52.25 1.03) ; 36 ( 31.24 -32.33 -55.44 1.47) ; 37 ( 32.66 -32.27 -58.69 0.81) ; 38 ( 33.83 -32.01 -63.50 0.66) ; 39 ( 34.86 -32.09 -66.69 0.66) ; 40 ( 35.66 -31.70 -65.56 1.33) ; 41 ( ( 35.95 -32.26 -66.56 0.74) ; 1, R-1-2-2-1 ( 36.34 -32.63 -67.94 0.74) ; 2 ( 36.71 -32.60 -68.19 1.03) ; 3 ( 37.36 -32.27 -69.00 1.03) ; 4 ( 37.63 -32.01 -69.88 1.03) ; 5 ( 37.98 -31.70 -72.06 1.18) ; 6 ( 38.47 -30.96 -73.25 1.11) ; 7 ( 38.94 -30.81 -73.69 0.74) ; 8 ( 39.87 -30.59 -74.31 0.59) ; 9 ( 40.42 -29.94 -75.25 0.59) ; 10 ( 41.01 -29.52 -76.19 0.59) ; 11 ( 41.61 -29.02 -76.19 0.88) ; 12 ( 42.49 -28.64 -76.19 0.88) ; 13 ( 43.36 -28.33 -76.75 0.52) ; 14 ( 44.28 -27.74 -77.88 0.74) ; 15 ( 44.88 -27.17 -79.25 0.96) ; 16 ( 45.39 -27.32 -79.31 1.33) ; 17 ( 46.37 -27.69 -81.69 0.96) ; 18 ( 47.32 -27.33 -81.69 0.66) ; 19 ( 48.12 -27.01 -81.69 0.66) ; 20 ( 48.53 -26.34 -82.00 0.66) ; 21 ( 49.12 -24.88 -82.94 0.81) ; 22 ( 49.61 -24.14 -83.25 0.81) ; 23 ( 50.33 -23.37 -83.56 0.52) ; 24 ( 50.85 -22.93 -83.56 0.74) ; 25 ( 51.50 -22.59 -84.38 0.88) ; 26 Normal | ( 35.92 -31.00 -65.75 0.88) ; 1, R-1-2-2-2 ( 36.43 -30.19 -66.56 0.52) ; 2 ( 36.73 -29.65 -66.50 0.88) ; 3 ( 37.21 -28.54 -65.00 1.18) ; 4 ( 37.21 -28.03 -63.44 0.96) ; 5 ( 36.62 -27.56 -61.19 0.59) ; 6 ( 36.54 -27.10 -61.13 1.18) ; 7 Normal ) ; End of split ) ; End of split ) ; End of split | ( 11.38 -8.22 -2.13 1.25) ; 1, R-2 ( 13.19 -8.48 -2.13 1.25) ; 2 ( ( 13.20 -8.51 -2.69 1.25) ; 1, R-2-1 ( 15.04 -8.66 -3.69 1.03) ; 2 ( 16.28 -8.86 -5.06 1.18) ; 3 ( 17.55 -9.28 -6.75 1.40) ; 4 ( 18.52 -9.35 -8.56 1.18) ; 5 ( ( 19.60 -9.09 -8.69 0.74) ; 1, R-2-1-1 ( 21.18 -8.52 -9.38 0.74) ; 2 ( 21.76 -8.61 -9.75 1.03) ; 3 ( 22.49 -8.73 -10.13 1.25) ; 4 ( 23.21 -8.84 -11.19 1.11) ; 5 ( 24.57 -8.76 -11.56 0.81) ; 6 ( 25.53 -8.84 -11.88 0.81) ; 7 ( 26.14 -8.79 -12.50 0.81) ; 8 ( 28.56 -8.06 -13.00 0.88) ; 9 ( 29.30 -7.66 -13.38 1.18) ; 10 ( 30.51 -7.04 -14.31 0.81) ; 11 ( 31.18 -7.06 -16.88 0.88) ; 12 ( 32.88 -7.19 -17.56 0.96) ; 13 ( 34.49 -6.85 -18.13 0.96) ; 14 ( 35.67 -6.52 -18.56 0.66) ; 15 ( 36.74 -6.33 -18.81 0.66) ; 16 ( 37.83 -6.05 -19.06 0.96) ; 17 ( 39.29 -5.77 -19.94 1.11) ; 18 ( 40.65 -5.68 -20.88 1.11) ; 19 ( 42.39 -5.52 -21.06 1.11) ; 20 ( 42.98 -5.53 -22.38 1.11) ; 21 ( 45.43 -6.14 -24.19 0.96) ; 22 ( 46.47 -7.13 -25.13 0.96) ; 23 ( 47.48 -7.29 -25.44 0.96) ; 24 ( 49.06 -7.16 -26.81 0.66) ; 25 ( 49.51 -6.27 -27.69 1.03) ; 26 ( 49.98 -5.68 -28.19 1.03) ; 27 ( 50.69 -5.43 -28.19 1.11) ; 28 ( 51.10 -5.19 -28.19 0.66) ; 29 ( 51.51 -4.96 -28.19 0.66) ; 30 ( 52.29 -4.72 -28.63 0.81) ; 31 ( 53.04 -4.68 -29.00 0.81) ; 32 ( 53.85 -4.74 -29.94 0.66) ; 33 ( 54.47 -4.62 -30.94 0.96) ; 34 ( 55.04 -4.33 -31.31 2.14) ; 35 ( 55.70 -3.92 -32.06 2.65) ; 36 ( 56.66 -3.45 -32.50 1.33) ; 37 ( 57.31 -3.11 -32.50 0.66) ; 38 ( 57.80 -2.82 -32.50 0.66) ; 39 ( 58.60 -2.94 -32.75 0.96) ; 40 ( 59.26 -3.05 -33.38 1.33) ; 41 ( 60.41 -2.85 -33.81 0.88) ; 42 ( 61.09 -2.75 -35.75 0.59) ; 43 ( 62.32 -2.57 -36.13 0.88) ; 44 ( 62.94 -1.93 -38.00 1.62) ; 45 ( 63.45 -1.12 -39.25 0.74) ; 46 ( 63.73 -0.20 -39.25 0.44) ; 47 ( 64.02 0.64 -40.56 0.81) ; 48 ( 64.00 0.94 -42.13 1.25) ; 49 ( 64.42 1.24 -43.38 2.14) ; 50 ( 64.75 1.49 -44.19 2.50) ; 51 ( 65.72 1.48 -44.19 0.81) ; 52 ( 66.59 1.34 -44.50 0.59) ; 53 ( 67.14 1.48 -44.69 0.88) ; 54 ( 67.95 1.42 -44.75 1.03) ; 55 ( 69.17 1.60 -45.38 0.66) ; 56 ( 70.05 1.53 -45.75 0.96) ; 57 ( 70.98 1.69 -46.56 1.69) ; 58 ( 71.60 1.37 -46.88 1.69) ; 59 ( 72.31 0.73 -47.13 0.96) ; 60 ( 73.58 0.23 -47.69 0.66) ; 61 ( 74.67 0.06 -48.31 0.52) ; 62 ( 75.52 -0.22 -49.38 0.88) ; 63 ( 76.37 -0.50 -49.56 1.69) ; 64 ( 77.09 -0.69 -49.75 1.69) ; 65 ( 77.79 -0.95 -49.81 0.81) ; 66 ( 78.57 -1.23 -49.81 0.29) ; 67 ( 79.91 -1.66 -50.06 0.29) ; 68 ( 80.85 -1.88 -50.06 0.96) ; 69 ( 81.76 -2.25 -50.06 0.96) ; 70 ( 82.73 -2.77 -50.06 0.52) ; 71 ( 83.41 -3.18 -50.69 1.18) ; 72 ( 83.90 -3.33 -51.13 1.84) ; 73 ( 84.83 -3.61 -51.56 1.84) ; 74 ( 85.95 -4.54 -51.81 0.96) ; 75 ( 86.71 -4.89 -51.81 0.52) ; 76 ( 87.15 -4.95 -52.31 0.88) ; 77 ( 88.26 -5.05 -52.56 0.88) ; 78 ( 88.90 -5.23 -52.75 0.52) ; 79 ( 89.88 -5.60 -52.81 0.81) ; 80 ( 90.60 -5.79 -53.56 1.18) ; 81 ( 91.32 -5.98 -53.88 1.18) ; 82 ( 92.21 -5.97 -53.88 0.74) ; 83 ( 93.55 -6.04 -54.06 0.59) ; 84 ( 93.97 -6.18 -54.63 0.96) ; 85 ( 94.93 -6.26 -54.69 1.40) ; 86 ( 96.09 -5.99 -54.69 0.66) ; 87 ( 97.03 -5.70 -54.69 0.52) ; 88 ( 97.90 -5.39 -55.25 0.66) ; 89 Normal | ( 19.42 -10.13 -9.31 0.74) ; 1, R-2-1-2 ( 20.67 -10.78 -9.75 0.59) ; 2 ( 21.68 -11.01 -10.63 0.81) ; 3 ( 22.76 -11.18 -11.25 0.81) ; 4 ( 23.93 -11.36 -11.94 0.74) ; 5 ( 24.61 -11.77 -13.19 0.74) ; 6 ( 25.54 -12.06 -13.25 0.66) ; 7 ( 26.02 -12.29 -14.06 0.66) ; 8 ( 27.10 -12.53 -15.13 0.81) ; 9 ( 28.18 -12.78 -16.81 0.66) ; 10 ( 29.62 -12.64 -17.81 0.74) ; 11 ( 30.62 -12.42 -18.63 0.74) ; 12 ( 31.37 -12.47 -19.44 0.66) ; 13 ( 32.22 -12.68 -21.38 0.88) ; 14 ( 32.90 -12.20 -20.81 0.96) ; 15 ( 32.85 -12.93 -22.25 0.88) ; 16 ( 32.68 -13.57 -23.81 0.88) ; 17 ( 32.36 -14.62 -25.38 0.81) ; 18 ( 32.60 -15.48 -27.06 1.03) ; 19 ( 33.10 -16.08 -29.19 1.03) ; 20 ( 33.63 -16.52 -30.38 1.33) ; 21 ( 32.73 -17.05 -31.75 1.03) ; 22 ( 32.65 -18.00 -32.44 0.81) ; 23 ( 33.07 -18.66 -33.19 0.81) ; 24 ( 33.25 -18.91 -34.44 0.66) ; 25 ( 33.38 -19.08 -37.50 0.66) ; 26 ( 33.58 -19.19 -39.13 0.66) ; 27 ( 35.33 -21.31 -39.31 0.66) ; 28 ( 35.86 -22.21 -39.94 0.66) ; 29 ( 35.94 -23.12 -40.88 0.66) ; 30 ( 35.97 -23.86 -42.25 0.96) ; 31 ( 36.16 -24.55 -43.44 0.81) ; 32 ( 36.37 -25.11 -47.75 1.03) ; 33 ( 36.70 -26.35 -50.44 0.96) ; 34 ( 36.04 -26.24 -54.00 0.81) ; 35 ( 35.34 -25.98 -54.50 1.03) ; 36 ( 35.18 -26.99 -56.75 0.74) ; 37 ( 34.90 -27.83 -56.81 0.66) ; 38 ( 35.07 -28.92 -58.25 0.81) ; 39 ( 34.90 -30.01 -58.69 0.81) ; 40 ( 34.11 -30.76 -59.00 0.81) ; 41 ( 33.60 -31.06 -58.25 1.25) ; 42 ( 33.28 -31.23 -58.13 1.69) ; 43 ( 32.70 -31.58 -58.06 1.69) ; 44 ( 32.05 -31.93 -58.06 1.69) ; 45 Normal ) ; End of split | ( 13.67 -8.34 -4.44 0.74) ; 1, R-2-2 ( 13.70 -8.19 -6.06 0.74) ; 2 ( 13.69 -7.82 -7.31 0.88) ; 3 ( 13.22 -7.45 -10.13 0.96) ; 4 ( 12.71 -7.30 -12.56 1.18) ; 5 ( 12.52 -7.12 -15.19 1.33) ; 6 ( 11.45 -6.88 -18.00 1.03) ; 7 ( 11.18 -5.72 -19.00 1.03) ; 8 ( 10.99 -5.55 -22.63 1.25) ; 9 ( 10.66 -5.27 -24.50 1.40) ; 10 ( 10.02 -5.98 -26.06 1.18) ; 11 ( 9.63 -6.59 -26.56 1.18) ; 12 ( 9.01 -7.60 -27.19 0.96) ; 13 ( 8.12 -7.60 -27.94 0.96) ; 14 ( 7.22 -7.68 -28.81 0.96) ; 15 ( 6.12 -7.51 -29.81 0.96) ; 16 ( 4.68 -7.21 -32.25 0.81) ; 17 ( 3.10 -6.81 -33.31 0.74) ; 18 ( 2.12 -6.43 -34.88 0.74) ; 19 ( 0.65 -5.83 -35.63 0.88) ; 20 ( -0.18 -5.84 -35.69 1.25) ; 21 ( -1.24 -5.98 -36.06 1.40) ; 22 ( -1.46 -6.39 -39.50 1.03) ; 23 ( -1.98 -6.89 -40.25 0.74) ; 24 ( -1.92 -7.42 -42.00 0.74) ; 25 ( -2.51 -7.84 -43.69 0.74) ; 26 ( -3.29 -8.54 -44.63 0.74) ; 27 ( -3.73 -8.03 -45.50 1.03) ; 28 ( -3.73 -8.03 -48.81 0.88) ; 29 ( -3.72 -8.39 -49.81 0.88) ; 30 ( -4.08 -9.30 -50.94 1.11) ; 31 ( -4.36 -10.14 -51.38 1.40) ; 32 ( -4.44 -10.65 -52.00 1.69) ; 33 ( -4.81 -11.03 -53.00 1.69) ; 34 ( -5.10 -11.50 -53.56 0.81) ; 35 ( -5.41 -12.50 -54.50 0.81) ; 36 ( -5.95 -13.08 -55.63 0.81) ; 37 ( -6.13 -14.23 -53.63 0.44) ; 38 ( -7.01 -15.57 -54.56 0.44) ; 39 ( -7.19 -17.17 -54.94 0.44) ; 40 ( -6.97 -18.09 -54.94 0.74) ; 41 ( -7.02 -18.90 -54.94 1.18) ; 42 ( -7.29 -19.67 -55.25 1.62) ; 43 ( -7.54 -20.30 -55.50 1.62) ; 44 ( -7.34 -20.92 -56.25 0.74) ; 45 ( -7.34 -21.88 -57.94 0.29) ; 46 Normal ) ; End of split ) ; End of split ) ; End of tree ( (Color Magenta) (Dendrite) ( 4.01 0.59 -4.81 1.62) ; Root ( 3.07 0.81 -6.56 1.77) ; 1, R ( ( 3.01 1.78 -8.31 1.47) ; 1, R-1 ( 3.06 2.47 -8.31 1.47) ; 2 ( ( 2.92 3.12 -9.63 1.03) ; 1, R-1-1 ( 3.17 3.61 -9.63 1.03) ; 2 ( ( 3.23 3.67 -10.88 0.81) ; 1, R-1-1-1 ( 3.10 4.72 -11.13 0.52) ; 2 ( 3.04 5.33 -11.31 0.52) ; 3 ( 3.46 6.07 -11.31 0.52) ; 4 ( 3.90 6.97 -11.88 0.52) ; 5 ( 4.27 7.42 -12.50 0.74) ; 6 ( 4.53 7.61 -13.69 1.03) ; 7 ( 4.85 7.78 -14.38 1.33) ; 8 ( 4.93 8.28 -15.38 0.96) ; 9 ( 4.51 8.94 -15.88 0.74) ; 10 ( 3.59 9.68 -17.38 0.52) ; 11 ( 3.61 10.34 -18.69 0.81) ; 12 ( 3.96 11.11 -18.75 1.11) ; 13 ( 3.75 11.21 -20.00 1.03) ; 14 ( 3.13 11.98 -21.38 0.81) ; 15 ( 3.29 12.98 -22.13 0.59) ; 16 ( 3.52 13.98 -23.44 0.81) ; 17 ( 3.54 14.13 -23.81 1.11) ; 18 ( 3.05 14.79 -23.94 1.25) ; 19 ( 2.50 15.55 -24.44 0.74) ; 20 ( 2.06 16.07 -24.88 0.59) ; 21 ( 1.28 16.34 -25.44 0.74) ; 22 ( 0.29 17.09 -25.69 0.74) ; 23 ( -0.90 17.65 -26.25 0.74) ; 24 ( -1.23 18.37 -27.63 0.44) ; 25 ( -1.76 18.82 -28.25 0.44) ; 26 ( -2.34 18.91 -28.25 0.74) ; 27 ( -2.92 19.00 -28.75 1.03) ; 28 ( -3.89 19.00 -29.44 0.59) ; 29 ( -4.92 19.10 -30.25 0.59) ; 30 ( -5.38 19.03 -31.63 1.33) ; 31 ( -6.04 18.60 -32.56 2.14) ; 32 ( -6.53 18.32 -33.88 2.50) ; 33 ( -7.04 17.95 -34.19 2.06) ; 34 ( -7.20 17.38 -34.69 1.55) ; 35 ( -7.16 17.16 -35.94 0.88) ; 36 ( -6.99 18.24 -37.38 0.81) ; 37 ( -6.31 17.84 -38.44 1.03) ; 38 ( -5.67 17.66 -39.06 1.03) ; 39 ( -4.87 17.98 -39.63 1.03) ; 40 ( -4.27 18.48 -41.94 0.88) ; 41 ( -3.54 19.32 -41.00 0.66) ; 42 ( -3.02 19.83 -42.13 0.44) ; 43 ( -2.34 20.32 -42.56 0.44) ; 44 ( -1.31 21.12 -42.81 0.66) ; 45 ( -1.04 21.45 -43.50 0.96) ; 46 ( -0.76 21.77 -43.69 1.33) ; 47 ( -0.33 22.14 -43.69 1.69) ; 48 ( -0.05 22.48 -44.50 0.81) ; 49 ( 0.95 23.20 -45.94 0.52) ; 50 ( 1.31 24.03 -46.69 0.52) ; 51 ( 1.39 24.54 -48.31 0.74) ; 52 ( 0.84 25.74 -49.13 0.81) ; 53 ( 0.20 26.87 -50.94 1.11) ; 54 ( -0.08 27.58 -51.94 1.92) ; 55 ( -0.74 28.05 -53.25 1.92) ; 56 ( -1.21 28.43 -54.19 1.40) ; 57 ( -1.40 28.60 -57.19 0.66) ; 58 Normal | ( 3.06 4.28 -11.06 0.52) ; 1, R-1-1-2 ( 2.70 5.30 -11.50 0.52) ; 2 ( 2.39 6.17 -11.56 0.44) ; 3 ( 2.42 7.80 -12.00 0.37) ; 4 ( 2.54 8.07 -12.19 0.37) ; 5 ( 3.32 8.76 -13.88 0.59) ; 6 ( 4.01 9.32 -14.25 0.59) ; 7 ( 4.79 10.01 -14.44 0.59) ; 8 ( 6.01 10.63 -14.44 0.81) ; 9 ( 6.62 11.27 -15.31 0.88) ; 10 ( 6.92 12.19 -15.19 1.25) ; 11 ( 6.82 13.39 -15.25 0.96) ; 12 ( 6.92 14.04 -15.44 0.81) ; 13 ( 7.04 14.76 -16.50 0.66) ; 14 ( 7.17 15.63 -16.94 0.37) ; 15 ( 7.97 16.47 -17.56 0.37) ; 16 ( 8.16 17.18 -18.44 0.52) ; 17 ( 8.18 17.76 -19.31 0.81) ; 18 ( 7.91 18.40 -19.81 1.11) ; 19 ( 7.92 18.91 -19.88 0.74) ; 20 ( 8.13 19.84 -20.56 0.52) ; 21 ( 8.74 20.86 -21.63 0.44) ; 22 ( 8.89 21.27 -21.63 1.18) ; 23 ( 9.36 21.87 -21.44 1.99) ; 24 ( 9.50 22.36 -21.44 1.62) ; 25 ( 9.65 23.30 -21.44 1.03) ; 26 ( 9.77 24.03 -21.94 0.66) ; 27 ( 9.69 24.92 -22.63 0.44) ; 28 ( 9.54 25.91 -22.63 0.37) ; 29 ( 8.98 27.18 -24.00 0.37) ; 30 ( 9.01 28.29 -24.31 0.37) ; 31 ( 8.74 29.00 -24.63 0.37) ; 32 ( 8.57 29.77 -24.63 0.66) ; 33 ( 8.37 30.39 -25.06 1.03) ; 34 ( 8.05 31.18 -25.38 1.40) ; 35 ( 7.77 31.82 -25.38 1.40) ; 36 ( 7.67 32.13 -25.94 0.88) ; 37 ( 7.41 32.76 -25.94 0.44) ; 38 ( 7.13 33.40 -25.94 0.37) ; 39 ( 6.77 33.90 -26.38 0.74) ; 40 ( 6.52 34.23 -26.63 1.03) ; 41 ( 6.33 34.48 -26.63 1.03) ; 42 ( 5.84 35.15 -26.63 0.59) ; 43 ( 5.50 35.36 -26.88 0.59) ; 44 ( 5.28 35.84 -27.06 1.33) ; 45 ( 4.87 36.05 -28.19 2.06) ; 46 ( 4.61 36.75 -28.19 1.25) ; 47 ( 3.72 36.31 -28.69 1.03) ; 48 ( 3.39 35.69 -29.69 1.11) ; 49 ( 3.47 36.19 -30.13 1.47) ; 50 ( 3.32 36.66 -30.38 1.47) ; 51 ( 2.13 37.14 -30.44 1.03) ; 52 ( 1.82 37.57 -30.25 0.59) ; 53 Normal ) ; End of split | ( 3.81 2.87 -10.56 0.74) ; 1, R-1-2 ( 4.30 3.17 -11.81 0.74) ; 2 ( 4.78 3.38 -13.06 0.74) ; 3 ( 5.42 3.66 -14.81 0.88) ; 4 ( 5.97 3.79 -15.06 1.03) ; 5 ( 6.47 3.27 -15.63 1.03) ; 6 ( 6.45 2.60 -16.88 0.88) ; 7 ( 6.30 2.18 -17.31 0.88) ; 8 ( 5.97 1.49 -18.44 0.88) ; 9 ( 5.30 0.57 -19.69 0.96) ; 10 ( 5.12 0.37 -22.69 0.96) ; 11 ( 4.72 0.21 -23.44 0.96) ; 12 ( 4.49 0.17 -24.31 0.96) ; 13 ( 4.49 0.17 -25.13 0.81) ; 14 ( 4.66 0.81 -27.06 0.66) ; 15 ( 5.52 1.49 -28.13 0.59) ; 16 ( 6.22 2.12 -28.38 0.59) ; 17 ( 6.97 2.15 -29.81 0.81) ; 18 ( 7.10 2.05 -30.88 0.81) ; 19 ( 7.29 1.36 -32.44 0.81) ; 20 ( 7.56 0.66 -33.00 0.81) ; 21 ( 8.31 1.19 -34.94 0.66) ; 22 ( 9.41 1.54 -35.69 0.66) ; 23 ( 9.99 1.89 -36.50 0.66) ; 24 ( 11.74 3.02 -37.25 0.59) ; 25 ( 12.84 3.89 -37.25 0.59) ; 26 ( 13.44 4.90 -37.38 0.59) ; 27 ( 13.97 5.86 -38.19 0.59) ; 28 ( 14.67 6.49 -38.75 0.59) ; 29 ( 15.42 6.51 -39.38 0.88) ; 30 ( 15.90 6.73 -40.69 1.18) ; 31 ( 16.68 6.91 -41.13 1.25) ; 32 ( 17.03 7.73 -42.13 1.11) ; 33 ( ( 16.92 8.42 -42.81 0.74) ; 1, R-1-2-1 ( 16.50 8.63 -44.81 0.74) ; 2 ( 16.73 9.63 -46.56 0.96) ; 3 ( 17.18 10.08 -47.81 0.81) ; 4 ( 17.74 10.36 -48.06 0.52) ; 5 ( 18.10 10.75 -48.44 0.44) ; 6 ( 18.60 11.04 -49.06 0.66) ; 7 ( 19.04 11.48 -49.25 1.33) ; 8 ( 19.83 11.73 -49.81 1.84) ; 9 ( 20.44 12.30 -50.13 1.18) ; 10 ( 20.94 12.67 -50.69 0.59) ; 11 ( 21.21 13.88 -51.25 0.44) ; 12 ( 21.57 14.72 -52.44 0.66) ; 13 ( 21.64 15.15 -52.81 0.96) ; 14 ( 21.91 15.92 -54.13 0.66) ; 15 ( 22.29 16.89 -54.69 0.52) ; 16 ( 22.86 17.69 -54.88 0.52) ; 17 ( 23.17 18.23 -56.63 1.18) ; 18 ( 23.16 18.61 -56.94 1.33) ; 19 ( 22.70 19.49 -57.31 0.88) ; 20 ( 22.53 19.82 -58.38 0.74) ; 21 ( 22.25 20.45 -59.75 0.96) ; 22 ( 21.69 21.21 -61.75 0.66) ; 23 ( 20.72 21.65 -62.75 0.96) ; 24 ( 20.14 22.19 -63.00 1.25) ; 25 ( 19.40 23.12 -64.13 0.81) ; 26 ( 18.91 23.80 -64.94 0.96) ; 27 ( 18.51 24.15 -65.81 1.11) ; 28 ( 18.26 24.94 -67.31 0.81) ; 29 ( 18.06 25.64 -68.50 1.40) ; 30 ( 17.39 25.60 -70.06 1.03) ; 31 ( 16.54 25.88 -71.19 0.81) ; 32 ( 16.03 26.41 -72.25 0.96) ; 33 ( 15.46 27.10 -74.00 1.18) ; 34 ( 15.01 27.53 -75.38 1.03) ; 35 ( 15.04 28.64 -76.63 0.81) ; 36 ( 14.57 29.01 -78.56 1.55) ; 37 ( 13.76 29.51 -80.13 1.92) ; 38 ( 14.07 30.12 -81.13 1.55) ; 39 ( 14.42 30.88 -81.69 0.74) ; 40 ( 14.77 32.16 -81.69 0.59) ; 41 ( 15.36 33.02 -81.94 0.74) ; 42 ( 16.34 34.50 -81.94 0.59) ; 43 ( 16.70 35.40 -82.31 0.59) ; 44 ( 16.69 35.78 -82.44 1.11) ; 45 ( 16.59 36.61 -82.88 1.40) ; 46 ( 16.76 37.18 -83.31 1.47) ; 47 ( 17.21 37.62 -83.63 0.66) ; 48 ( 17.40 37.96 -84.25 0.52) ; 49 Normal | ( 17.03 6.86 -41.00 0.66) ; 1, R-1-2-2 ( 16.13 6.33 -42.56 0.66) ; 2 ( 15.47 5.48 -43.50 0.66) ; 3 ( 14.78 4.92 -44.13 0.66) ; 4 ( 14.11 3.92 -45.00 0.66) ; 5 ( 13.83 3.14 -45.50 0.81) ; 6 ( 13.46 2.62 -46.56 0.81) ; 7 ( 13.34 1.45 -47.31 0.59) ; 8 ( 13.04 0.54 -47.75 0.59) ; 9 ( 13.44 -0.71 -49.00 0.74) ; 10 ( 12.87 -1.51 -50.06 1.03) ; 11 ( 12.20 -1.99 -50.31 0.59) ; 12 ( 11.60 -2.64 -51.81 0.44) ; 13 ( 11.34 -2.89 -52.13 0.74) ; 14 ( 10.66 -2.93 -52.31 1.11) ; 15 ( 9.89 -3.10 -52.69 1.47) ; 16 ( 9.09 -3.49 -52.88 0.74) ; 17 ( 8.01 -4.13 -53.25 0.59) ; 18 ( 6.95 -4.27 -53.56 0.59) ; 19 ( 5.69 -5.10 -54.19 0.44) ; 20 ( 4.50 -5.58 -54.19 0.44) ; 21 ( 3.27 -6.27 -54.19 0.74) ; 22 ( 2.38 -6.28 -54.63 0.96) ; 23 ( 1.48 -6.74 -54.75 0.59) ; 24 ( 0.49 -6.87 -55.31 0.44) ; 25 ( -0.91 -6.79 -55.50 0.44) ; 26 ( -1.71 -7.11 -56.00 1.11) ; 27 ( -2.95 -7.43 -56.44 1.25) ; 28 ( -3.67 -7.69 -58.13 0.81) ; 29 ( -4.46 -8.01 -58.56 0.52) ; 30 ( -5.21 -8.48 -58.75 0.52) ; 31 ( -6.35 -9.56 -59.00 0.81) ; 32 ( -7.11 -10.10 -59.25 1.18) ; 33 ( -8.37 -11.46 -59.44 0.66) ; 34 ( -9.14 -12.15 -59.69 0.66) ; 35 ( -9.24 -13.18 -60.13 1.47) ; 36 ( -9.44 -14.03 -60.44 2.14) ; 37 ( -9.72 -14.80 -60.44 2.14) ; 38 ( -9.72 -15.76 -60.44 0.52) ; 39 ( -9.95 -16.76 -61.00 0.81) ; 40 ( -10.16 -18.06 -61.38 1.69) ; 41 ( -10.17 -19.09 -61.69 3.02) ; 42 ( -10.20 -20.20 -62.31 2.43) ; 43 ( -10.36 -21.21 -63.50 0.88) ; 44 ( -10.48 -22.45 -63.75 1.25) ; 45 ( -11.09 -22.50 -64.75 1.55) ; 46 ( -12.37 -22.59 -67.00 0.74) ; 47 ( -13.14 -22.76 -68.00 1.18) ; 48 ( -14.07 -22.55 -70.88 0.81) ; 49 ( -14.48 -21.81 -72.00 1.18) ; 50 ( -15.14 -21.26 -72.69 1.18) ; 51 ( -15.41 -20.12 -71.06 0.52) ; 52 Normal ) ; End of split ) ; End of split | ( 2.68 -0.24 -8.63 0.96) ; 1, R-2 ( 2.36 -1.06 -9.00 0.74) ; 2 ( 2.03 -1.68 -10.06 0.59) ; 3 ( 1.24 -2.00 -10.25 0.52) ; 4 ( -0.12 -2.08 -10.75 0.52) ; 5 ( -1.35 -3.29 -11.06 0.74) ; 6 ( -2.16 -4.19 -11.56 0.74) ; 7 ( -3.13 -5.15 -11.88 0.88) ; 8 ( -3.69 -6.32 -12.75 0.88) ; 9 ( -4.18 -7.50 -13.88 0.88) ; 10 ( -3.93 -8.28 -14.94 0.88) ; 11 ( -4.83 -9.25 -14.38 0.74) ; 12 ( -5.24 -9.92 -16.06 0.74) ; 13 ( -4.66 -10.53 -17.63 0.96) ; 14 ( -4.70 -10.75 -18.94 1.18) ; 15 ( -4.59 -10.99 -20.13 1.47) ; 16 ( -5.66 -11.19 -22.94 0.74) ; 17 ( -6.94 -11.28 -23.75 0.66) ; 18 ( -7.74 -11.60 -24.00 0.81) ; 19 ( -9.42 -11.85 -24.44 0.96) ; 20 ( -11.20 -12.31 -25.13 0.96) ; 21 ( -13.04 -12.61 -25.88 0.96) ; 22 ( -14.78 -14.11 -26.19 0.74) ; 23 ( -15.49 -14.81 -27.69 1.03) ; 24 ( -16.16 -15.37 -29.06 1.33) ; 25 ( -17.13 -16.26 -30.50 0.96) ; 26 ( -18.09 -17.14 -31.75 0.66) ; 27 ( -19.41 -17.96 -32.88 0.88) ; 28 ( -20.45 -18.40 -34.56 0.74) ; 29 ( -21.72 -19.30 -35.75 0.59) ; 30 ( -22.21 -20.56 -36.63 0.88) ; 31 ( -22.72 -20.99 -38.00 2.21) ; 32 ( -23.15 -21.30 -38.63 2.65) ; 33 ( -23.95 -22.06 -38.88 1.77) ; 34 ( -24.00 -22.94 -39.88 0.81) ; 35 ( -24.31 -24.27 -40.94 0.59) ; 36 ( -25.30 -25.36 -41.56 0.59) ; 37 ( -25.47 -26.90 -43.44 0.44) ; 38 ( -25.73 -28.56 -43.44 0.44) ; 39 ( -25.91 -29.64 -43.56 0.59) ; 40 ( -26.33 -30.90 -43.75 0.37) ; 41 ( -26.54 -31.76 -43.81 0.66) ; 42 ( -26.91 -32.73 -43.88 0.66) ; 43 ( -27.20 -33.45 -43.88 0.66) ; 44 ( ( -27.29 -34.16 -43.94 0.37) ; 1, R-2-1 ( -27.45 -34.72 -44.00 0.37) ; 2 ( -27.84 -35.78 -44.38 1.03) ; 3 ( -28.10 -36.47 -44.56 2.28) ; 4 ( -28.16 -37.27 -44.88 1.84) ; 5 ( -28.15 -38.17 -45.38 0.81) ; 6 ( -28.26 -38.89 -45.06 0.29) ; 7 ( -27.98 -39.89 -45.19 0.29) ; 8 ( -27.88 -40.72 -45.19 0.29) ; 9 ( -27.92 -41.46 -45.25 1.11) ; 10 ( -27.86 -41.99 -45.25 1.92) ; 11 ( -27.61 -42.77 -45.50 2.14) ; 12 ( -27.30 -43.63 -45.69 0.66) ; 13 ( -26.64 -45.14 -46.13 0.52) ; 14 ( -26.54 -45.89 -46.56 0.52) ; 15 ( -26.45 -47.17 -46.69 1.25) ; 16 ( -26.35 -48.07 -47.13 2.06) ; 17 ( -26.32 -48.74 -47.38 2.06) ; 18 ( -26.75 -49.64 -47.81 0.52) ; 19 ( -26.90 -50.87 -48.44 0.44) ; 20 ( -26.97 -52.25 -49.06 0.44) ; 21 ( -26.95 -53.08 -49.38 0.44) ; 22 ( -27.18 -54.07 -50.00 0.44) ; 23 ( -27.31 -54.87 -50.81 0.52) ; 24 ( -28.35 -55.81 -51.25 0.74) ; 25 ( -28.75 -56.42 -51.63 1.47) ; 26 ( -28.93 -56.68 -52.06 2.21) ; 27 ( -29.27 -57.37 -52.31 1.69) ; 28 ( -30.06 -58.13 -52.81 0.66) ; 29 ( -30.89 -59.12 -53.63 0.52) ; 30 ( -31.64 -60.10 -53.81 0.74) ; 31 ( -32.18 -61.13 -53.81 0.74) ; 32 ( -32.66 -61.79 -54.13 0.44) ; 33 ( -33.09 -62.68 -54.44 0.81) ; 34 ( -33.49 -63.29 -54.75 1.11) ; 35 ( -33.87 -63.82 -54.38 0.66) ; 36 ( -34.44 -65.06 -54.00 0.44) ; 37 ( -34.68 -65.61 -54.00 1.18) ; 38 ( -34.92 -66.24 -54.00 1.18) ; 39 Normal | ( -26.40 -33.13 -45.19 0.59) ; 1, R-2-2 ( -25.78 -32.57 -44.88 0.74) ; 2 ( -25.44 -32.25 -43.06 0.88) ; 3 ( -24.84 -31.31 -41.69 1.11) ; 4 ( -24.76 -30.80 -43.75 1.11) ; 5 ( -24.52 -30.69 -45.88 1.25) ; 6 ( -24.51 -30.62 -46.69 1.62) ; 7 ( -24.83 -30.27 -47.75 1.11) ; 8 ( -24.97 -29.28 -49.81 0.88) ; 9 ( -25.29 -28.49 -51.88 1.18) ; 10 ( -25.54 -28.16 -52.25 0.96) ; 11 ( -25.84 -27.67 -53.56 0.81) ; 12 ( -26.44 -26.83 -53.56 1.18) ; 13 ( -27.38 -26.61 -53.56 1.03) ; 14 ( -28.07 -26.73 -53.69 0.66) ; 15 ( -28.36 -26.68 -56.19 0.66) ; 16 ( -29.13 -26.85 -56.31 1.03) ; 17 ( -29.86 -26.29 -58.00 0.88) ; 18 Generated ) ; End of split ) ; End of split ) ; End of tree ================================================ FILE: examples/thalamocortical-cell/results/cAD_ltb_params.csv ================================================ 3.1564108236039493e-05,0.17253941191121194,0.006488733050095279,8.439934123616934e-05,6.679267173907347e-05,6.752922016642328e-05,0.015092288701860032,0.1918484606897239,0.08619404232908409,0.0004976393775947971,0.0007821925119235055,11.80769470273121,0.010943321490233804,1.53265900526464e-05,4.6258959603459983e-05,0.0045653311900601574,0.009239251793267768,0.00329831804198368,0.0006313828065357237,3.3806294816388547e-06,1.0289009420631139,0.043463545094082796 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examples/thalamocortical-cell/thalamocortical-cell_opt.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimization of burst and tonic firing in thalamo-cortical neurons" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "____\n", "\n", "This notebook illustrates how to **setup** and **configure optimisations** presented in the following paper:\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", "Author of this script: Elisabetta Iavarone @ Blue Brain Project\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", "The morphologies will be available on NeuroMorpho.org under the\n", "ODC Public Domain Dedication and Licence (PDDL) https://opendatacommons.org/licenses/pddl/1.0/\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", "**If you use methods or data presented in this notebook we ask to cite the following publications:**\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", "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", "**If you re-use any file from the *mechanisms* folder you should also cite the associated publication.**\n", "\n", "See license file for details.\n", "\n", "___" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "import bluepyopt\n", "import os\n", "\n", "import pprint\n", "pp = pprint.PrettyPrinter(indent=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up the cell model and the cell evaluator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A cell evaluator can be easily created by specifying the desired electrical type (e-type).\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)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/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", " warnings.warn(msg)\n", "No handlers could be found for logger \"neurom.io.neurolucida\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "cAD_ltb:\n", " morphology:\n", " morphologies/jy160728_A_idA.asc\n", " mechanisms:\n", " pas.all: pas at ['all']\n", " TC_cad.all: TC_cad at ['all']\n", " TC_ih_Bud97.somatic: TC_ih_Bud97 at ['somatic']\n", " TC_Nap_Et2.somatic: TC_Nap_Et2 at ['somatic']\n", " TC_iA.somatic: TC_iA at ['somatic']\n", " TC_iL.somatic: TC_iL at ['somatic']\n", " SK_E2.somatic: SK_E2 at ['somatic']\n", " TC_HH.somatic: TC_HH at ['somatic']\n", " TC_ih_Bud97.alldend: TC_ih_Bud97 at ['basal']\n", " TC_Nap_Et2.alldend: TC_Nap_Et2 at ['basal']\n", " TC_iA.alldend: TC_iA at ['basal']\n", " TC_iL.alldend: TC_iL at ['basal']\n", " SK_E2.alldend: SK_E2 at ['basal']\n", " TC_HH.alldend: TC_HH at ['basal']\n", " TC_HH.axonal: TC_HH at ['axonal']\n", " TC_iT_Des98.somadend: TC_iT_Des98 at ['basal', 'somatic']\n", " params:\n", " v_init: v_init = -79\n", " celsius: celsius = 34\n", " cm.all: ['all'] cm = 1\n", " Ra.all: ['all'] Ra = 100\n", " ena.all: ['all'] ena = 50\n", " ek.all: ['all'] ek = -90\n", " e_pas.all: ['all'] e_pas = -80\n", " g_pas.all: ['all'] g_pas = [1e-06, 0.0001]\n", " gk_max_TC_HH.axonal: ['axonal'] gk_max_TC_HH = [0, 0.2]\n", " gna_max_TC_HH.axonal: ['axonal'] gna_max_TC_HH = [0, 0.8]\n", " pcabar_TC_iT_Des98.somadend: ['basal', 'somatic'] pcabar_TC_iT_Des98 = [0, 0.0001]\n", " gh_max_TC_ih_Bud97.somatic: ['somatic'] gh_max_TC_ih_Bud97 = [0, 0.0001]\n", " gNap_Et2bar_TC_Nap_Et2.somatic: ['somatic'] gNap_Et2bar_TC_Nap_Et2 = [0, 0.0001]\n", " gk_max_TC_iA.somatic: ['somatic'] gk_max_TC_iA = [0, 0.07]\n", " gk_max_TC_HH.somatic: ['somatic'] gk_max_TC_HH = [0, 0.2]\n", " gna_max_TC_HH.somatic: ['somatic'] gna_max_TC_HH = [0, 0.2]\n", " pcabar_TC_iL.somatic: ['somatic'] pcabar_TC_iL = [0, 0.001]\n", " gSK_E2bar_SK_E2.somatic: ['somatic'] gSK_E2bar_SK_E2 = [0, 0.005]\n", " taur_TC_cad.somatic: ['somatic'] taur_TC_cad = [1.0, 15.0]\n", " gamma_TC_cad.somatic: ['somatic'] gamma_TC_cad = [0.0005, 1]\n", " gh_max_TC_ih_Bud97.alldend: ['basal'] gh_max_TC_ih_Bud97 = [0, 0.0001]\n", " gNap_Et2bar_TC_Nap_Et2.alldend: ['basal'] gNap_Et2bar_TC_Nap_Et2 = [0, 0.0001]\n", " gk_max_TC_iA.alldend: ['basal'] gk_max_TC_iA = [0, 0.008]\n", " gk_max_TC_HH.alldend: ['basal'] gk_max_TC_HH = [0, 0.01]\n", " gna_max_TC_HH.alldend: ['basal'] gna_max_TC_HH = [0, 0.006]\n", " pcabar_TC_iL.alldend: ['basal'] pcabar_TC_iL = [0, 0.001]\n", " gSK_E2bar_SK_E2.alldend: ['basal'] gSK_E2bar_SK_E2 = [0, 0.005]\n", " taur_TC_cad.alldend: ['basal'] taur_TC_cad = [1.0, 15.0]\n", " gamma_TC_cad.alldend: ['basal'] gamma_TC_cad = [0.0005, 1]\n", "\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Import scripts for setting up the cell model and cell evaluator\n", "import CellEvalSetup \n", "\n", "# Library to visualize and analyse morphologies \n", "import neurom # https://github.com/BlueBrain/NeuroM\n", "import neurom.viewer\n", "\n", "etype = \"cAD_ltb\" # or cNAD_ltb \n", "\n", "evaluator = CellEvalSetup.evaluator.create(etype)\n", "\n", "neurom.viewer.draw(neurom.load_neuron(evaluator.cell_model.morphology.morphology_path))\n", "print(evaluator.cell_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run an optimisation\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", "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." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "seed = 0 # Number to initialize the pseudorandom number generator\n", "\n", "opt = bluepyopt.optimisations.DEAPOptimisation(\n", " evaluator=evaluator,\n", " map_function=map, # The map function can be used to parallelize the optimisation\n", " seed=seed,\n", " eta=10., mutpb=1.0, cxpb=1.0)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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", "Before we create a folder to save the results." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!nrnivmodl mechanisms # Compile NEURON .mod files stored in the \"mechanisms\" folder\n", "\n", "if not os.path.exists('checkpoints'):\n", " os.mkdir('checkpoints')\n", "\n", "final_pop, halloffame, log, hist, = opt.run(max_ngen=2,\n", " offspring_size=2,\n", " cp_filename='checkpoints/checkpoint.pkl');" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Example of one individual resulting from an optimisation run:\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" ] } ], "source": [ "print(\"\\nExample of one individual resulting from an optimisation run:\\n\")\n", "print(halloffame[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse optimisation results\n", "\n", "In this section you will see how to run simulations with models obtained after running a full optimisation.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import csv\n", "\n", "with open('results/{}_params.csv'.format(etype)) as csvfile:\n", " rows = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)\n", " params = list(rows)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select one of the models and create the dictionary of parameters." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{ 'gNap_Et2bar_TC_Nap_Et2.alldend': 7.465371215899886e-05,\n", " 'gNap_Et2bar_TC_Nap_Et2.somatic': 1.3389527019193626e-05,\n", " 'gSK_E2bar_SK_E2.alldend': 0.0002179960911576617,\n", " 'gSK_E2bar_SK_E2.somatic': 0.0012832790164235054,\n", " 'g_pas.all': 3.250567914095199e-05,\n", " 'gamma_TC_cad.alldend': 0.000539506186561177,\n", " 'gamma_TC_cad.somatic': 0.006740122627193884,\n", " 'gh_max_TC_ih_Bud97.alldend': 8.422890384914114e-06,\n", " 'gh_max_TC_ih_Bud97.somatic': 4.7504774088700974e-05,\n", " 'gk_max_TC_HH.alldend': 0.009924383323607554,\n", " 'gk_max_TC_HH.axonal': 0.11490723847692205,\n", " 'gk_max_TC_HH.somatic': 0.11616929624507591,\n", " 'gk_max_TC_iA.alldend': 0.00418186621417919,\n", " 'gk_max_TC_iA.somatic': 0.06374589309943703,\n", " 'gna_max_TC_HH.alldend': 0.005250846661623297,\n", " 'gna_max_TC_HH.axonal': 0.21874510090978222,\n", " 'gna_max_TC_HH.somatic': 0.09113695987409176,\n", " 'pcabar_TC_iL.alldend': 1.1024317581168538e-05,\n", " 'pcabar_TC_iL.somatic': 0.0004981128655248998,\n", " 'pcabar_TC_iT_Des98.somadend': 8.948755951390262e-05,\n", " 'taur_TC_cad.alldend': 9.551240612869854,\n", " 'taur_TC_cad.somatic': 11.005585762426819}\n" ] } ], "source": [ "modid = 62 # Or e.g. 78 for cNAD_ltb model shown in the paper, Fig. 4\n", "param_dict = evaluator.param_dict(params[modid])\n", "pp.pprint(param_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can run a simulation with the parameters above and the current protocols which are part of the evaluator." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Simulation took 0:00:46.785285.\n" ] } ], "source": [ "from datetime import datetime\n", "\n", "t0 = datetime.now()\n", "responses = evaluator.run_protocols(protocols = evaluator.fitness_protocols.values(), param_values=param_dict)\n", "\n", "print(\"Simulation took {}.\".format(datetime.now()-t0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can plot the model responses." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import collections\n", "\n", "def plot_responses(responses):\n", " # Select and sort reponses\n", " stim_names = [name for name in sorted(evaluator.fitness_protocols.keys()) \n", " if \"hold\" not in name and \"RMP\" not in name]\n", " sel_resp = collections.OrderedDict()\n", " for name in stim_names:\n", " sel_resp[name] = responses[\".\"+name+\".soma.v\"]\n", " \n", " fig, axes = plt.subplots(len(sel_resp), figsize=(5, 8), sharey = True)\n", " for index, (resp_name, response) in enumerate(sorted(sel_resp.items())):\n", " \n", " startid = 550 if \"Step\" in resp_name or \"IV\" or \"Rin\" in resp_name else 0 # Remove initial transient\n", " indices = response['time'] >= startid\n", " \n", " axes[index].plot(response['time'][indices]-startid, response['voltage'][indices],\n", " color = \"blue\", lw = 0.75, alpha = 0.8)\n", " \n", " axes[index].set_ylabel('V$_m$ (mV)', fontsize = 'small')\n", " axes[-1].set_xlabel('Time (ms)', fontsize = 'small')\n", " fig.tight_layout()\n", " fig.show()\n", "plot_responses(responses)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can evaluate the fitness of the model by computing its errors. Each error quantify how much the model deviates from the experimental features." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "objectives = evaluator.fitness_calculator.calculate_scores(responses)\n", "\n", "def plot_objectives(objectives): \n", " \n", " # Names for all the stimuli \n", " stim_name = ['RMP', u'IV_-140', u'Rin_dep', 'hold_hyp', u'Step_200_hyp', u'Step_150', \n", " u'Step_200', u'Step_250', 'hold_dep']\n", " \n", " # Sort objectives\n", " obj_keys = [[key for key in objectives.keys() if key.split(\".\")[1] == stim] for stim in stim_name]\n", " obj_keys = [item for sublist in obj_keys for item in sublist][::-1] \n", " obj_val = []\n", " for key in obj_keys:\n", " obj_val.append(objectives[key])\n", " \n", " ytick_pos = [x + 0.5 for x in range(len(obj_keys))]\n", " fig, ax = plt.subplots(figsize = (5.4,9), facecolor = 'white')\n", " \n", " ax.barh(ytick_pos,\n", " obj_val,\n", " height=0.5,\n", " align='center',\n", " color='blue',\n", " alpha=0.5)\n", " \n", " obj_keys = [CellEvalSetup.tools.rename_feat(name) for name in obj_keys] \n", " \n", " ax.set_yticks(ytick_pos)\n", " ax.set_yticklabels(obj_keys, size='medium')\n", " ax.set_ylim(-0.5, len(obj_keys) + 0.5)\n", " ax.set_xlim([0,3])\n", " \n", " ax.set_xlabel(\"Distance from exp. mean (# STD)\")\n", " ax.set_ylabel(\"Feature name\")\n", " ax.xaxis.grid(True)\n", " fig.tight_layout()\n", "\n", "plot_objectives(objectives)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: examples/tsodyksmarkramstp/AUTHORS.txt ================================================ Rodrigo Perin @ LNMC Giuseppe Chindemi @ BBP Andras Ecker @ BBP ================================================ FILE: examples/tsodyksmarkramstp/README.md ================================================ # Tsodyks-Markram model examples The Tsodyks-Markram model of short-term plasticity underwent many changes in the last twenty years. In this folder we provide 2 examples to fit 2 different versions using BluePyOpt. `tsodyksmarkramstp.ipynb` numerically integrates the "full version" of the TM model and fits a postsynaptic voltage trace. `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. ================================================ FILE: examples/tsodyksmarkramstp/tmevaluator.py ================================================ """Tsodyks-Markram model Evaluator. This module contains an evaluator for the Tsodyks-Markram model. @author: Giuseppe Chindemi @remark: Copyright (c) 2017, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import bluepyopt as bpop import numpy as np import tmodeint try: xrange except NameError: xrange = range class TsodyksMarkramEvaluator(bpop.evaluators.Evaluator): def __init__(self, t, v, tstim, params): """ Parameters ---------- t : numpy.ndarray Time vector (sec). v : numpy.ndarray Voltage vector (V), must have same dimension of t. tstim : numpy.ndarray Time of the stimuli. params : list List of parameters to fit. Every entry must be a tuple (name, lower bound, upper bound). """ super(TsodyksMarkramEvaluator, self).__init__() self.v = v self.t = t self.stimidx = np.searchsorted(t, tstim) self.dx = t[1] - t[0] self.nsamples = len(v) # Find voltage baseline bs_stop = np.searchsorted(t, tstim[0]) self.vrest = np.mean(v[:bs_stop]) # Compute time windows where to compare model and data offset = 0.005 # s window = 0.04 # s window_samples = int(np.round(window / self.dx)) psp_start = np.searchsorted(t, tstim + offset) psp_stop = psp_start + window_samples psp_stop[-1] += 2 * window_samples # Extend last psp window (RTR case) self.split_idx = list(zip(psp_start, psp_stop)) # Parameters to be optimized self.params = [bpop.parameters.Parameter(name, bounds=(minval, maxval)) for name, minval, maxval in params] # Objectives self.objectives = [bpop.objectives.Objective('interval_%d' % (i,)) for i in xrange(len(self.split_idx))] def generate_model(self, individual): """Calls numerical integrator `tmodeint.py` and returns voltage trace based on the input parameters""" v, _ = tmodeint.integrate(self.stimidx, self.nsamples, self.dx, self.vrest, *individual) return v def evaluate_with_lists(self, individual): """Errors used by BluePyOpt for the optimization""" candidate_v = self.generate_model(individual) errors = [np.linalg.norm(self.v[t0:t1] - candidate_v[t0:t1]) for t0, t1 in self.split_idx] return errors def init_simulator_and_evaluate_with_lists(self, individual): """Calls evaluate_with_lists. Is called during IBEA optimisation.""" return self.evaluate_with_lists(individual) ================================================ FILE: examples/tsodyksmarkramstp/tmevaluator_multiplefreqs.py ================================================ # -*- coding: utf-8 -*- """Tsodyks-Markram model Evaluator used to fit data from multiple frequency stimulations. This module contains an evaluator for the Tsodyks-Markram model. @authors: Andras Ecker and Giuseppe Chindemi @remark: Copyright (c) 2017, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import numpy as np import bluepyopt as bpop import tmodesolve try: xrange except NameError: xrange = range class TsodyksMarkramEvaluator(bpop.evaluators.Evaluator): def __init__(self, data, params): """ BluePyOpt evulator class. Parameters ---------- data : OrderedDict (or a Python 3 dict) Frequecies as keys and {t_stims, amps} numpy.ndarray with stim times (ms) and normalized amplitudes params : list List of parameters to fit. Every entry must be a tuple (name, lower bound, upper bound). """ super(TsodyksMarkramEvaluator, self).__init__() self.t_stims = {freq:vals["t_spikes"] for freq, vals in data.items()} self.amplitudes = {freq:vals["amps"] for freq, vals in data.items()} self.params = params self.params = [bpop.parameters.Parameter(name, bounds=(minval, maxval)) for name, minval, maxval in self.params] # Bpop Objectives self.objectives = [] for freq, _ in data.items(): self.objectives.extend([bpop.objectives.Objective("%s_amplitude_%i"%(freq, i)) for i in xrange(len(self.t_stims[freq]))]) def generate_model(self, freq, individual): """Calls numerical solver `tmodesolve.py` and returns amplitudes based on the input parameters""" amps, tm_statevars = tmodesolve.solve_TM(self.t_stims[freq], *individual) return amps, tm_statevars def evaluate_with_lists(self, individual): """Errors used by BluePyOpt for the optimization""" errors = [] for freq, _ in self.t_stims.items(): candidate_amps, _ = self.generate_model(freq, individual) errors.extend(np.power(self.amplitudes[freq] - candidate_amps, 2).tolist()) return errors def init_simulator_and_evaluate_with_lists(self, individual): """Calls evaluate_with_lists. Is called during IBEA optimisation.""" return self.evaluate_with_lists(individual) ================================================ FILE: examples/tsodyksmarkramstp/tmodeint.py ================================================ """Tsodyks-Markram model ODEINT. This module contains functions to numerically integrate the Tsodyks-Markram model. The current implementation is a port of an Igor Pro (WaveMetrics) routine, written by Rodrigo Perin with the help of Raphael Holzer. Many thanks to Misha Tsodyks and Henry Markram for their support during all development stages. @author: Giuseppe Chindemi, Rodrigo Perin @remark: Copyright (c) 2017, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ # pylint: disable=E741 import numpy as np try: xrange except NameError: xrange = range def integrate(sampstim, nsamples, dt, vRest, Trec, Tfac, ASE, USE, Rinput, Tmem, Tinac, latency): """Integrate Tsodyks-Markram model and produce corresponding voltage trace. Parameters ---------- sampstim : numpy.ndarray Array of sample indices where stimulation occurs. nsamples : int Number of samples in the trace. dt : float Time step of the trace. vRest : float Membrane resting potential (V). Trec : float Recovery time constant (ms). Tfac : float Facilitation time constant (ms). ASE : float Absolute Synaptic Efficacy. USE : float Utilization of Synaptic Efficacy. Has to be in the interval [0, 1]. Rinput : float Input resistance (MOhm). Tmem : float Membrane time constant (ms). Tinac : float Inactivation time constant (ms). latency : float PSP latency (ms). Returns ------- vtrace : numpy.ndarray Voltage trace corresponding to the input parameters. tm_statevar : dictionary Integral of all state variables. """ AP = np.zeros(nsamples) I = np.zeros(nsamples) # NOQA R = np.zeros(nsamples) E = np.zeros(nsamples) U = np.zeros(nsamples) P = np.zeros(nsamples) # Set stimuli in AP vector psp_offset = int(np.round(1e-3 * latency / dt)) AP[sampstim + psp_offset] = 1 # Initialize state vectors R[0] = 1 E[0] = 0 P[0] = 0 U[0] = USE # Integrate TM model ODE for i in xrange(1, nsamples): R[i] = R[i - 1] + dt * (1 - R[i - 1] - E[i - 1]) * \ 1e3 / Trec - U[i - 1] * R[i - 1] * AP[i - 1] E[i] = E[i - 1] - dt * E[i - 1] * 1e3 / Tinac + U[i - 1] * \ R[i - 1] * AP[i - 1] U[i] = U[i - 1] - dt * (U[i - 1] - USE) * 1e3 / \ Tfac + USE * (1 - U[i - 1]) * AP[i - 1] P[i] = P[i - 1] + dt * (Rinput * ASE / 10 ** 6 * E[i - 1] - P[i - 1]) * 1e3 / Tmem # Update state P = P + vRest E = E * ASE / 10**12 I = 1 - R - E # NOQA tm_statevar = {'recovered': R, 'effective': E, 'used': U, 'inactive': I} return P, tm_statevar ================================================ FILE: examples/tsodyksmarkramstp/tmodesolve.py ================================================ # -*- coding: utf-8 -*- """Tsodyks-Markram model ODE solver This module contains functions to solve the Tsodyks-Markram model. (same equations as in Maass and Markram 2002) @author: Andras Ecker @remark: Copyright (c) 2017, EPFL/Blue Brain Project This file is part of BluePyOpt This library is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3.0 as published by the Free Software Foundation. This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ import numpy as np def solve_TM(t_stims, USE, Trec, Tfac, ASE): """Solve Tsodyks-Markram model and produce corresponding amplitudes. Parameters ---------- t_stims : numpy.ndarray Array of stimulation times (ms) USE : float Utilization of Synaptic Efficacy. Has to be in the interval [0, 1]. Trec : float Recovery time constant (ms). Tfac : float Facilitation time constant (ms). ASE : float Absolute Synaptic Efficacy. Returns ------- Ampls : numpy.ndarray Normalized amplitudes corresponding to input parameters tm_statevars : dictionary Value of state variables at stimulation times """ # Initialize state vectors U = np.zeros_like(t_stims) R = np.zeros_like(t_stims) Ampls = np.zeros_like(t_stims) R[0] = 1 U[0] = USE Ampls[0] = ASE*U[0]*R[0] R[0] = R[0] - R[0]*U[0] U[0] = U[0] + USE*(1-U[0]) last_stim = t_stims[0] for i in range(1, len(t_stims)): delta_t = t_stims[i] - last_stim R[i] = 1 + (R[i-1] - 1)*np.exp(-delta_t/Trec) U[i] = USE + (U[i-1] - USE)*np.exp(-delta_t/Tfac) Ampls[i] = ASE*U[i]*R[i] R[i] = R[i] - R[i]*U[i] U[i] = U[i] + USE*(1-U[i]) last_stim = t_stims[i] tm_statevars = {"R": R, "U": U} return Ampls, tm_statevars ================================================ FILE: examples/tsodyksmarkramstp/tsodyksmarkramstp.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tsodyks-Markram model of short-term synaptic plasticity\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", "`tmodeint.py` numerically integrates the \"full version\" of the TM model and fits a postsynaptic voltage trace:\n", "\n", "\\begin{equation}\n", "\\frac{dR(t)}{dt} = \\frac{1-R(t)-E(t)}{D} - U(t)R(t)\\delta(t-t_{spike})\n", "\\end{equation}\n", "\\begin{equation}\n", "\\frac{dU(t)}{dt} = \\frac{U_{SE}-U(t)}{F} + U_{SE}(1-U(t))\\delta(t-t_{spike})\n", "\\end{equation}\n", "\\begin{equation}\n", "\\frac{dE(t)}{dt} = \\frac{-E}{\\tau_{inac}} + U(t)R(t)\\delta(t-t_{spike})\n", "\\end{equation}\n", "\\begin{equation}\n", "\\tau_{mem} \\frac{dV(t)}{dt} = -V + R_{inp}I_{syn}(t)\n", "\\end{equation}\n", "where $I_{syn}(t)=A_{SE}E(t)$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "mpl.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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');\n", " var 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", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.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", "\n", "mpl.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", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.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= 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.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get v trace for best model\n", "vmodel = evaluator.generate_model(best)\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(trace['t'], trace['v'], label='in vitro')\n", "ax.plot(trace['t'], vmodel, label='model')\n", "ax.legend(loc=0)\n", "ax.set_xlabel('time (s)')\n", "ax.set_ylabel('soma voltage (V)')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "jupyter": { "outputs_hidden": true } }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "unstable", "language": "python", "name": "unstable" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: examples/tsodyksmarkramstp/tsodyksmarkramstp_multiplefreqs.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tsodyks-Markram model of short-term synaptic plasticity\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", "`tmodesolve.py` implements the event-based solution of the (reduced, but) more common version of the TM model:\n", "\n", "\\begin{equation}\n", "R_{n+1} = 1 + (R_n - R_nU_n -1)e^{-\\Delta_t/D}\n", "\\end{equation}\n", "\\begin{equation}\n", "U_{n+1} = U_{SE} + U_n(1-U_{SE})e^{-\\Delta_t/F}\n", "\\end{equation}\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." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pickle\n", "from collections import OrderedDict # not needed in Python 3\n", "import numpy as np\n", "import multiprocessing\n", "import bluepyopt as bpop\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns\n", "import tmevaluator_multiplefreqs as tmevaluator\n", "\n", "# Set plotting context and style\n", "sns.set_context(\"talk\")\n", "sns.set_style(\"whitegrid\")\n", "\n", "# Load and display extracted amplitudes\n", "with open(\"amps.pkl\", \"rb\") as f:\n", " data = pickle.load(f)\n", "fig = plt.figure(figsize=(14, 4))\n", "for i, (freq, tmp) in enumerate(data.items()):\n", " ax = fig.add_subplot(1, 3, i+1)\n", " ax.scatter(tmp[\"t_spikes\"], tmp[\"amps\"], c=\"red\")\n", " if i == 0:\n", " ax.set_ylabel(\"Normalized (PSC) amplitudes\")\n", " ax.set_xlabel(\"Time (ms)\")\n", " ax.set_title(freq)\n", "fig.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now estimate the parameters of the Tsodyks-Markram model using BluePyOpt." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "use = 0.13\n", "trec = 1112.32\n", "tfac = 1.21\n", "ase = 7.04\n" ] } ], "source": [ "# Parameters to be fitted as a list of: (name, lower bound, upper bound)\n", "optconf = [(\"use\", 0.01, 1.0),\n", " (\"trec\", 1.0, 2500.0),\n", " (\"tfac\", 1.0, 2500.0),\n", " (\"ase\", 1.0, 100.0)]\n", "pnames = [name for name, _, _ in optconf]\n", "\n", "# Create multiprocessing pool for parallel evaluation of fitness function\n", "pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())\n", "\n", "# Create BluePyOpt optimization and run \n", "evaluator = tmevaluator.TsodyksMarkramEvaluator(data, optconf)\n", "opt = bpop.optimisations.DEAPOptimisation(evaluator, offspring_size=500, map_function=pool.map,\n", " eta=20, mutpb=0.3, cxpb=0.7)\n", "pop, hof, log, history = opt.run(max_ngen=50)\n", "\n", "# Get best individual\n", "best = hof[0]\n", "for pname, value in zip(pnames, best):\n", " print(\"%s = %.2f\" % (pname, value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we compare the original in vitro amplitudes with the one generated by the best model." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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x2ANpWpr9uWP6m+MxLc2+PzVVdyxDwOXLl6lcuXLIXlAGO0cMO3/+PNnZ2X7vXCsOSnmgOBj8ateuzdmzZ/MvSP1JcVDKg+zsbCpVqhQQyyukZLwRB72SLdwhNzeXSpUquX3yFi1asGXLFurUqcP7779f7PEHDx6kadOmhaab3XTTTWzYsMHt85baxo32O5TOWK32/Zs22YOqlGtWqzUkp0CWJyaTiQoVKpTJOlHFQSkPFAfLB7PZrDjoCcVBuYbVaqVChQrqWAe50sZBtzvXffv25amnnuKOO+4ocv/69ev57//+b1JTU90+ed26dd0+Fux3hMLDwwttr1atGtnZ2R69l4PFYmH//v0evaZBYiLVDQOTiy/eZhhkJSbyS4sWJWrXtS5evAjgcTuDXbB87suXL1O9enWv3O13/DKXxchBWQmUz2yxWMjKynLaDovF4pPOQyDEQfA8Fvo7DhYnWOJFoCrt9+fNOBisAiWWlYbiYHDHQWcUH/2jqDhYHuJCsCrpd1/aOOi0c52ZmcmBAwfyn//888988803RS4Ot1qtfPzxx+Tm5nrSdo/ZbDan+/yZhMicmfnrlB9nrFb7cSIiXqQ4KCKhTnFQRAKV08515cqVeeyxxzh58iRgnza5aNEiFi1aVOTxNpuNWB9PeQkPD+fo0aOFtl+4cKHIO5juMJvNHq8VJzISdu50GVBNhkG1yEjP37sIjjuN3nivYBIsn9uR4t8bySpCMfGFs89ss9n8OrXKbDZTq1YtmjZtWuT+PXv2+K0trvgiDkIJYqGf42BxgiVeBKrSfn/ejIPByhfxW3GwaIqDnlF89I+i4mAoXtd5W0njYEm/+9LGQaed62rVqrFw4UJ+/PFHbDYbTz75JIMHDyYqKqrQsYZhULt2bbp37+5R4z3VrFkzvvjii0Jf8uHDh0tcBqxEEhLsWSBdsVrtx4kEsTvuuIPu3bvz3HPP+fQ87du355FHHuGhhx4iNzeXOXPm0KZNG+69916fnjcYKQ6K+Je/4mCrVq0UB92kOCjiX4qD7nO55rpt27a0bdsWgGPHjnHXXXeV6V2vHj168Nprr/H555/nZ4jMzMzk66+/5sEHH/RfQ2Ji7OUV0tKKvltpGBAVBdHR/muTiA8sWLDALzUaly1bln+HMDMzk7fffpvnn3/e5+cNRoqDIv7lrzi4atUqGjRoACgOFkdxUMS/FAfd53ZCs4kTJ/qyHUXKzMzkyJEjtGzZkvDwcDp37kyXLl2YPHkyU6ZMoVatWrzyyitUr16dYcOG+a9hZrO9bmFRdQ2tVnsgTUlR2QUpOYvFnoU0KcleQzMiwn7nOybGrz9Xbdq08ct52rVrpylTTigOSsgKsTjYoUMHv5wnGCkOSshSHAw6TrM+9O3bl08++aTA8+L+3XnnnV5t3LZt2xgyZAjfffdd/rYFCxZwxx13MGvWLJ544gnq16/PkiVLqFmzplfPXayICHvdwuRkGDIE+va1PyYn27dHRPi3PVJ+ZGRA164QFwerV8PWrfbHuDj79owMvzXljjvu4KmnngLg6NGjtGrVis2bNzNx4kSioqLo0qULTz/9NJcuXSry9ZcuXSIqKorZs2cX2H758mVuvfVW5s+fD9inhScmJnL06FF69eoFwPTp0/OrEzzxxBP8+c9/5umnnyYqKooBAwZgs9m4fPky8+bNIzo6mt/97nfExsayatUqX30dZUJxUEJSCMbBVq1aKQ46oTgoIUlxECh5HPznP//pq6/DJacj1w0bNqRq1aoFnvtSfHw88fHxxW6rWbMmzz//fGBMETCb7XcrVbtQvMViodLAgbB3r/25Y5qZ4zEtzf7zlppaZnfCZ8yYwX333UdiYiLffPMNc+fOpU6dOjz66KOFjq1SpQr9+vUjJSWFxx57LH/7v/71Ly5cuFBoDU1ERAQLFy5k/PjxjB8/nn79+uXvS01NpWfPniQmJuYnqfjLX/7Cd999x8MPP0zLli3Ztm0bf//73zl16hQTJkzw0TfgO4qDIthHamJj7fEOgiYO1qxZs8hZfoqDnlEcFCFo42AgXQ8+++yznD59mkceecRH30DRnHauly1b5vK5iHifsXkzhiOQFsVqtU8927SpzP6I9+nTh2nTpgHQvXt3PvvsM7Zt21ZkMAUYMGAAa9euZe/evbRv3x6AlJQU2rdvT2RkZIE6gpUqVcqfenTTTTcVmIaUl5fHP/7xDyKujgJs27aNnTt3Mm/ePGJiYgD7Ory8vDxee+01hg8fzg033OD9L0BEfGvjRnuccyZA4+Cnn37qdAldcXHwWoqDIhKscbA014PX8kYczMnJ4c0332TkyJF+jYP+KwYoIsUyr1iBrbganYZhX3tTRm699dYCz+vXr+90GhBAt27dqFevHsnJyQBkZ2ezfft2jzM/1q1bNz+QAnz11VdUrFixwN1MgHvuuYfc3Fz2Okb/RSS4JCXZ45wrioOA4qBIuaU46JS7cTA2NrZM4qDTkeuRI0d6/GYmk4mlS5eWqkEiIe3kSZf1MgH7fj+us7ne9YnHDMPA6qLNhmEQFxfH+vXreeKJJ9iyZQtWq5VYD++0XrtMBeDcuXPUqVMH47o/PnXr1gUgKyvLo/cXkQCRkRGUcdBmszk9XnFQRDwSpHEwkK4H69SpA/g/DjrtXB89etSf7RARgBtv/DXLqDOGEXQJUgYMGMDixYvZvXs3KSkp9OjRg9q1a5fqPWvUqMHp06exWq0FAurJkycBNBVSJFhFRCgOuklxUKScUhx0m7M4eOrUKcD/cdBp53rr1q3+bIeIAJZhwzCvWeP6IKvVXoYhiLRq1YrWrVvz4Ycf8vnnnzNr1iynx15/59GZLl26sHjxYjZv3py/xgZg/fr1VKxYkXbt2pW63SJSBhISYOVK18coDgKKg4EiQKolSXmiOFjqOLhhw4YyiYNu17m+VmZmJseOHcNsNtO4cWO/FBUPRAqm4m3Wfv2wRkVh7N1b9N1Kw7DXzYyO9n/jSmnAgAG89NJLVKlShb59+zo9Ljw8HJPJxBdffEGLFi3yk15c7/e//z2dO3fmqaee4vjx4/zmN79h+/btrFy5knHjxlGjRg1ffRS5huKgeF1MDHTsaM+GqzioOBjgMjKKLnO9cqX9xzglJegGFyUQKA6WOg6uWbOGBx54wO9x0KPO9ddff81LL73EN998k7+2yGw2061bNx5//HFuvvlmnzQyECmYik+YzeSuXUtYfHzhHy6r1R5IU1KCstcSFxfHSy+9RL9+/ahcubLT46pWrcqf/vQnVq5cyaeffspnn31W5HGGYbBo0SJefvll3nzzTc6dO0dkZCTPPPMMQ4YM8dXHkGsoDopPmM32H56ifrgUBwtQHCxbQVAtSYKV4mCp4+CMGTO4//77ffUxnDLZXGXguEZqaipjxoyhatWq3HvvvURGRmKxWDh06BAfffQRNpuNFStWBFUHe8+ePQB06NDBo9dZLPba7cXdTPJWMN2/fz9gn0oRSoLlcx8+fBiApk2blvq9HGWpwipWtJdXuH44MDo6KAOpK/mf+brEGP5W3P9jSeNFMCjJZ/N3HCxOsMSLQFXa78+bcTCfxRJUcTBQYllpKA569tmSkyEuzr3jyrIEtuKjfxT1+1PquBBkcTCQlPS7L20cdHvk+uWXX6ZRo0asWLGi0MLzCRMmMHjwYObMmcNrr73m7lsGrSAoPSfBzmy2//DoB0gClOKg+JzioAQ4R7Wk4nJOJSXpx1hKSHEw6Lhd5/qHH35g2LBhRWZ0q1u3LsOHD+err77yauMCVRCUnhMR8SnFQREJdUFQLUlE/MztznWdOnU4ffq00/05OTmEh4d7pVGBTsFUREKd4qCIhDpHtSRXgrBakoiUgtud63HjxvHOO+8UWaJr7969vPPOO0yYMMGrjQtUCqYiEuoUB0Uk1CUkuHeTMciqJYlIKbi95nrPnj3UqVOHCRMm0Lx5c1q0aEHFihVJT0/n3//+N5UqVWL9+vWsX78+/zUmk4mlS5f6pOFlqZyWnhMRcZvioIiEunJcLUlESsjtzvXnn38OQIMGDbh06RLffvtt/r4GDRoAcPToUS83LzApmIpIqFMcFJFQV46rJYlICbnduS5qOnioUjAVkVCnOCgiYl/6kpqqakkiYud251oKUjAVkVCnOCgiompJIvIrtzvXubm5vPHGG3z22WecPHkSaxHzAE0mE1u2bPFqAwOZgqmIhDrFQRERERE7tzvXzz33HKtWraJ+/fo0atQIo7g0sSIiIiIiIiIhwu0e8scff0xcXBzbtm3j3XffZdmyZUX+E5Hy44477uCpp57y+Xnat29PYmIiAO+//z6tWrXi+PHjADzxxBPcddddPm+DiEhR/BUHW7VqpTgoIgFJcdB9bo9c5+Xl0blzZ1+2RUQCzIIFC6hevbrPz7Ns2TKaNm1a5L6HHnqICxcu+LwNIiJF8VccXLVqVX71lespDopIWVIcdJ/bneuYmBg+/vhjhgwZ4sv2iAhgscDGjYWTRMXE+DdJVJs2bfxynnbt2hEWFlbkvptuuskvbRCRwBJqcbBDhw5O9ykOioQmxcFfBUscdHta+LRp07h06RJDhw7lzTff5IMPPmDt2rWF/olI6WRkQNeuEBcHq1fD1q32x7g4+/aMDP+15dppQEePHqVVq1Zs3ryZiRMnEhUVRZcuXXj66ae5dOmSy/dZunQpMTEx/O53v6Nnz57813/9F9nZ2fn7r50Wfr3rpwHZbDaWLFlCTEwM7dq1Izo6utCSlO3btzN06FCioqLo3r07M2bM4MyZMyX9GkTEz0IxDl47HfJ6ioMioUdxsKCSxMEdO3b4PQ66PXK9a9cuvv/+ey5dusSePXuKPMZkMjFw4ECvNU4k1FgsMHBgJfbutT93JOV3PKal2bMyp6aWXZmjGTNmcN9995GYmMg333zD3LlzqVOnDo8++miRx69fv57/+Z//Ydq0abRq1YqDBw/y4osvkpOTw/PPP+/x+WfNmsXSpUt54IEH6NatG7t37+a5557DMAxGjBjBe++9x5NPPsmAAQN46KGHOH78OPPmzWPv3r2sXr2aKlWqlPYrEBEfsljscS4tzf48WOJgzZo1mThxYpHHKw6KiCeCNQ4G0vXg2rVr+fvf/+73OOh253rWrFlUrVqVqVOn0qxZM8wqYCridZs3G6SlOZ9QYrXCrl32usJlVfqoT58+TJs2DYDu3bvz2WefsW3bNqfBdOfOnTRu3JgRI0ZgGAZdunShatWqnDt3zuNznz9/nnfeeYdRo0YxefJkAG677TaOHz/OV199xbBhw5gzZw69e/dm1qxZ+a9r1aoVgwcP5v3332fEiBEl+NQi4i8bN9rjnDOBGgc//fRTp51rxUER8USwxsFAuh6cP38+PXv29HscdLtzfeTIEaZOncrw4cN90hARgRUrzBiGDavV5PQYw7CvvSmrYHrrrbcWeF6/fn1OnDjh9Phu3bqxatUq4uPjufPOO+nVqxf33HMPJpPzz+jMnj17yMvLo1+/fgW2P/vsswAcOHCAU6dOcffddxfY3759e5o2bUpqaqouKkUCXFKSPc45RmiKEohx8JdffnF6vOKgiHgiWONgIF0Pnj59mv79+xfY74846Paa62bNmpGVleWTRoiI3cmTrgMp2Pf7c53N9a5PPGYYBlYXjY6NjWX27NlUrVqVxMRE7r//fvr27UtKSorH5z579iwAderUcbn/xhtvLLSvTp06Bdb1iEhgysgIzjhos9mcHq84KCKeCNY4GGjXg3Xr1i20z9dx0O3O9cMPP8zSpUvZvn27yy9OREruxhvtdyJdMQx7tshgEhcXx/Lly0lNTeXll1+mVq1aTJ06lZMnT3r0Po4yEJmZmQW2p6ens3PnTmrWrAlQ5PuePHmSG264oYSfQET8JSJCcdAVxUGR8k9x0DV34+CpU6cKvdbXcdDtzvWaNWuoVKkS48aN49Zbb6V379707du3wL8777zTZw0VCQXDhllcTgkH+53KhAQ/NcgLHnvsMSZMmADYg2H//v156KGHyMvLKzLoudK+fXsqVqzIv/71rwLbFy5cyJNPPknz5s2pW7cuycnJBfZ/8803pKenF/DqLu4AACAASURBVJrCJCKBJyHBvREbxUHFQZHySnHQNXfiYJ06ddiwYUOB/f6Ig26vub5w4QKRkZFERkb6rDHlXqAUq5OA1a+flagoK3v3GkUGVcOAqCiIjvZ/20qqW7duzJgxgxdffJHf//73nD9/ngULFtCsWTNuvvlmrly54vZ71a5dm4SEBBYvXkyFChXo1KkTu3bt4oMPPuDZZ5/FMAweffRRZsyYweOPP05cXBwnTpxg3rx5NGvWjD/84Q8+/KQi4g0xMdCxoz0bbqjEQU8oDoqUf4qDrrkTBydOnMgzzzzj9zjoduf6+rph4qGMDHvGgV27fs1QYBiwcqX9tyclJfjmdojXmc2wdm0u8fFhhX5UrFZ7IE1JCa57MYMGDSInJ4cVK1awfPlywsLC6N69O48//jhms9mjzjXA448/Tu3atVm9ejWvv/46TZs2ZebMmfmBctCgQVStWpU33niDhx56iJo1a9K3b18mTZpE1apVffERRcSLzGZ7nCvqT2Z5jYOeUhwUKd8UB4tXXByMj4+nSpUqLF261K9x0GRzlYHDQ99//z1t2rTx1tv5nKNed4cOHXx7IovFXu29uNtPTorV7d+/H7Cnjw8lwfK5Dx8+DEDTpk1L/V6XL18GoGLFMDZtKjzJITo6uAKpOxyf+frEGP5W3P+j3+JFGSgPny1Y4kWgKu3358046GCxEFRxMFBiWWkoDpbPz6b46B9F/f6UNi4EWxwMJCX97ksbB90euc7NzWX+/Pns2LGDixcvFkhqZrFYuHDhAtnZ2ezbt8/txoeMq8XqLBhsJJYkEsgggggySCCJGOtGzGVdrE4Citls/1HQj4OIhCrFQQlmWgko3qA4GHzc7lzPmzePxYsXU79+fWrUqMGPP/5Ip06dOHnyJD///DNhYWE89dRTvmxr8EpKIsNUj1jbenbRCYM8rJgxsLCSYXTka1JMcUSUZbE6ERERkTKyfv16Fi5cSHp6Oo0aNeLBBx9k4MCBTo/PzMzkf/7nf9ixYwe5ublERUUxffr0gMgNpJWAIqHL7WzhGzdupEuXLmzdupU33ngDgL/97W9s2rSJRYsWkZeXR8WKFX3W0GBmOXGKWNt60ogCwEoFwHT1EdKIIta2HssJzzLliYiIiAS7lJQUpkyZQo8ePXj11Vfp0qUL06ZNY+PGjUUeb7PZmDBhAp9++ilTpkxh1qxZnDx5kpEjR3Lu3Dk/t74gi8XesU5Lsz93TPR0PKal2fdbLGXTPhHxLbc71ydOnKBfv34YhkG9evWoU6cOaVcjR69evfjDH/7A6tWrfdbQYLbRche76ISVoucBWTGzi05ssqiUmYiULxYLJCfDsGHQt6/9MTlZF5Yi8qu5c+fSv39/pk+fTs+ePXnmmWfo378/8+bNK/L4Q4cOsXv3bqZOncrAgQPp3bs38+bN48SJE2zdutXPrS/o6kpAp2WUrFb7/k2b/NsuEfEPtzvXYWFhBUamb7rpJn788cf85+3atSM9Pd3jBqxfv567776bdu3a0b9/f9auXevy+MzMTKZPn06PHj3o0qULDz74IIcOHfL4vP6UxAgM8lweY5BHEkFUrE5EpBgZGfZcjnFxsHo1bN1qf4yLs2/PyCjrFopIWUtPT+fIkSP069evwPbo6GgOHjxY5LVlTk4OANWqVcvfVrNmTQDOnj3rw9YWLynJPgXcFcOwHyci5Y/bnevf/va3fPrpp/nPmzdvnj9yDfaRbZPJ5NHJy9M0IFcyzA2djlo7WDGTYW7gpxaJNxmGgUXDcEHNZrORl5eHUdwVkY+Ux5uMmhoZWhQHyweLxeL3OHjw4EEAmjVrVmC7I1Pvf/7zn0Kvad26NV27duXVV1/lwIEDZGZm8uyzz1K1alXuvLNsZwFmZDgftXawWnVzsTwyDIO8vDy8WIhJykBp46DbCc2GDx/OpEmTGD58OK+//jp333037733HtOnT6d58+YsWbLE4/IF104DAujZsyfnzp1j3rx5xMTEFDreMQ3oxRdfzE9y0aJFC+688062bt3q04LgpRFRz4Rh2FwGW8OwHyfBJywsjOzsbDIzM6ldu3ZZN0c8ZLVaOXXqFLm5uWXy/+e4yThq1Ch69OjBli1bmDZtGmFhYUXGQcdNxiNHjjB16lRq1arF/PnzGTlyJB999FH+6E1Zc0yNdObaqZHK4xj8FAeDX2ZmJjk5OVSvXt2v583KygIgPDy8wHbHqHR2dnaRr/uv//ovHnjgAWKvBpBKlSrx6quv0qRJkxK3xWKx5JetKqmwsAYYRnWsVufXdIZhIywsi/37fynVudx18eJFgFJ/NnEtLy8Pi8XCL7/8Qq1atTAMI7+6kqMslPhPSb77s2fPcuHCBUwmU/7vzfUsFovLutxud6779+9PdnY2b7/9NlWqVOG2225jxIgRvPvuuwA0bNgwv5PsDsc0oMmTJxfYHh0dzYYNG0hPTy8UIAN5GpArCQmwcqXrjrPVaiJBs8KDUt26dcnJyeHEiROcPXvW5S9ccRwjP6V5j2BTlp/ZYrFw5coVrFYrNWrUKJOOaXm9yeiYGlncTUUVSSgfvBkHg1Uwx2+LxZLfsa5bt65fz13cKF9RI0gHDhxg6NCh3HTTTTz55JOEhYWxevVqHn74Yd588006derkq+YW6957z5OSUsPlMVariXvvPe+nFom/mM1mLBYLJ0+e5OzZs1SsWDH/59fT2b1Seo7Y4u53n5eXR05ODjabrVRJut3uXAMMGjSIQYMG5T9/+umnGTNmDOfOnaNFixZUqlTJ7fdyZxrQ9Z3ra6cBNW/enBtuuIEXXnihVNOAvHGXsjjNmkHbtk35/vvK2GyF/4NNJhtt2uQQGXmYopoSqnccg+lz22w2LBYL2dnZpZoOlJdnX5tfoYJHv5pBrSw/s8lkwmQyYTabyc7O5qeffnJ6bHF3KkuiPN9k1NTI0GIymWjUqBGnTp3i8uXL+SMGocQxAlurVq0ybonnKlasmN+x9ncnwDFSfuHChQLbHSPWRY2kL1myBIC33norP/7dfvvtDB8+nJkzZ/L++++XqC1ms5lWrVqV6LUOLVvCG2/Yl74U9WtgGBAVBWPGNPZbvWvHdVRpP5u458KFC5w/f568vDwyMzOB4IwLwc5xTeTud1+xYkVuuOGGYuPgnj17XL5Pqa9mGzZsSMOGDT1+XSBNA/I1sxlef/0oY8c25rvvwvKniNtHdUy0aZPD668f9VuQFe8zmUxeKUXnuKEQFhZW6vcKFqH4mR0C5SajL0REuDdyrVqv5YfJZOLGG28s62aUGcfUQ8fvr7jHEf+OHDlSoPN3+PDhAvuvdezYMVq0aFFgtpHJZKJjx4688847Pm6xa2azvY51UXWurVZ7xzolBV3zlWPVqlXL7884rnEUF/yvrGJymQ2PBco0IG/cpXTX3r329YVJSSYyMuwXlQkJEB0dhtnc0unrQvWOYyh+bn3mwFXcncqSCKSbjN6exdO7dzVWrmzs8hirFXr3Psr+/RdcHueOYJrpEoj0/ZVeKHyHvpjB07RpUxo3bszGjRu566678rdv3ryZyMjIIgdwmjVrxgcffMD58+epUePXKdh79+6lUaNGXm1fSUREQOrnFjbN3EXS21fIyAojovplEv5UkegnO2KupJ61SHlVZp3rQJoG5C9ms/1OptYXiggEzk1GX+jZ8wJt214udjlMjx6l71iLSHCbMGEC06dPp2bNmvTu3ZtPPvmEDRs2MHfuXMCebO3IkSO0bNmS8PBwRo8ezYcffsif//xnxo4dS1hYGOvWrWPnzp35rylTGRmYY2OJ3bWLWMeQ9TkDnrHC+o72oWtN2xEpl8qsc13epgGJiHgqkG4y+mIWz9atzqdG3nqriZSUMCIivHPOYJkBEaj0/ZVeKHyHvpjBAxAfH09ubi5vvfUWa9asoUmTJrz44ov5s3O2bdvG9OnTeeedd+jatSuNGzdmxYoVvPTSS0yfPh2TycTNN9/M22+/zW233eaTNrrN3TqEqamaGy5SDpVZ57o8TgMSEfFEeb/JGBFhv360L4fhuuUwuq4UkV8NHTqUoUOHFrkvPj6e+Pj4AttatGjBwoUL/dE0z6gOoUhI86hznZuby549ezh69ChnzpzBbDZTp04dGjRoQIcOHTzO9lvupgGJiHggFG4yajmMiIQU1SEUCWlu9Yb/9a9/kZSUxM6dO8nLyyu0TtBkMlGlShW6devG4MGD6d27t1snL1fTgERESkA3GUVEyhHVIRQJaS47159++inPPfcc6enpdOjQgTFjxnDzzTfTpEkTwsPDsVqtnD17lhMnTrBnzx527drFuHHjaN68OZMnT3arLEy5mQYkIlICuskoIlKOqA6hSEhz2rn+61//ytdff83IkSO57777iCgmCDguBA8fPswHH3zAU089xbp163jllVe822IRkXJGNxlFRMqJhARYudL1MVar/TgRKXcK13m5qk2bNnzyySeMHz++2I71tZo2bcqjjz7KJ598Uq4zZoqIiIiIFBATAx072keni2IY9v3R0f5tl4j4hdPO9fjx46latWqJ3zg8PJyJEyeW+PUiIiIiIkHFbLbXsY6Ksj93dLIdj1FR9v0qlyBSLrmV0Cw3N5dKlSoV2n7w4EFq1apF7dq1vd4wEREREZGgozqEIiHLZef6ypUrzJ49m3Xr1rFt2zYqV65cYP+cOXPYvn07Q4YMYcqUKYSFhfm0saHGYrGXS0xMbEBmppnISHtcjolRXBYREREJWKpDKBKSnHauc3NzGTt2LF9++SXNmzfn9OnThWqudu/enfT0dJKSkti/fz9LlizBrF6fV2Rk2OPxrl1gGNWxWmHnTnuOjI4d7TOKlGhSREREREQkMDhdc7106VK+/PJLpk6dSkpKSqGONcCIESNYu3Yt48aN46uvviIpKcmnjQ0VFou9Y52WZn9utZoAU35Vh7Q0+36LpcyaKCIiIiIesFggORmGDYO+fe2Pycm6nhMpT5x2rj/66CP69OnDmDFjXL6ByWTi0UcfpXPnzqxbt87rDQxFGzfaR6ydlUi0Wu37N23yb7tERDymq0kRETIyoGtXiIuD1ath61b7Y1ycfXtGRlm3UES8wWnn+tChQ/To0cPtN+rduzcHDx70SqNCXVKS8woODoZhP05EnFO/rozpalJEpIgZiQUfNSNRpPxwuua6SpUqWDz4LQ8LCysyo7h4LiMDrFYbYHJ6jNVqIyPD+X6RUFcwb4H9IsYwlLfAb9y9mkxNVYZGESnXHDMSnbl2RqLyn4kEN6fjo82bN2f37t1uv9GuXbto1KiRVxoV6iJutGLgZE74VQZWIm50fYxIqNIoQQBwc32LJWWTZheISLmmGYkiocPpr/of/vAHNm/ezJdfflnsm+zcuZPNmzcTHR3t1caFqoTWu7DieiTHipmE1i5ug4qEMOUtCABuXE1mmOrRdVRrzRoXkXLNPiPR9TFWq42Mrf/W3UWRIOeyc33LLbcwbtw4Fi5cyOnTpwsdc/r0aV577TXGjRtHo0aNGD58uE8bGypi9s2lI19jUHRwNbDQka+J3veyn1smEhw0ShAAirmatGAQa1tP2plIQLMLRKT8iohw428SFiJOfKu7iyJBzuma64oVK5KYmMikSZOYN28e8+fPp2HDhtx4441YrVZOnz7NsWPHsNlsdOjQgTlz5lCjRg1/tr3cMp86QQqxxJLCLjphkIcVAwMrVioQRRopxGI+1a6smyoSkNwbJdC1i085riad/EdsJIZddHL6cq1BFJHyIiHBnu/DFSsVSGCZ/YlyUogELZf30erWrcuyZcuYP38+/fr1wzAM9u/fz4EDB6hQoQIDBw4kMTGRlStXFlkHW0ooIoII4zSpdCWZWIawmr5sZQirSSaWVLoSYZxWNiYRJ9waJTD0K+RTCQku73AkkYBBnsu30OwCESkPYmLsiTSd/V3Kn5HI1bVKWrsk4l1+LB/jdOT6Wv369aNfv35eP7k4cfUWpxmIZQOxbCh8jPXqcSJSiFujBFb9CvmU42oyLa3ITnYGEcXnltDsAhEpB8xme4WK/AoWWLBiKjwj8dpkto67i5q6I1I6fi4fU8zYjmvnzp0jJyfHW20Rh2JvcRr2/UogJ1KkmBjo2O6K06z7BlY6truiXyFfclxNRkXZnzvi2dXHiBvyNLtAREJGRIR9lndyMgypt63wjEROFnyB7i6KlN7V8jGW3XtJJpZh1iT6soVh1iSSsW/3doIXl5c2V65cYeXKlUyfPr3A9q+//pq7776bbt26ERUVxQMPPMCRI0e81qiQd91Fqc0wsMGvF6dRUfb9WocjUiQzFlKIJQp7OUH79GNr/jTkKHZfHSVQtiyfKnA1OcQ+FWvIEEhOJmHJnVitJpcv1+wCESlPzGb7dfzyPm+yxYhmOSOIZUPBEWsH3V0UKbVqO3aQsesIXW1fEEcyqxnEVu5gNYOII5muti/I2HXEq0swnE4Lz83NZfTo0ezevZuKFSvy3//931SoUIFDhw4xZswYcnNz6dmzJy1btmTTpk0MHTqUDz/8kLp163qtcSHNcVG6aRNZiYmYMzOpFhlpv9KMjlbHWsSVjRuJ+GYLqWxlE9EkkUAGEUSQQQJJRLMJ8zdWZcvyB8fV5HXfc4zF5axxDMN+H1GzC0RKx2KxlydMSrIPhEZE2C8lYmJ0KVFmtHZJxC+qrVvPXaSQhn3A0nq16+t4TCOKWFJIfWcuZi9dDzrtXC9dupS0tDSmTp3K8OHDqVDBfugrr7xCTk4OcXFxvPTSSwCMHTuWe+65h9dee40ZM2Z4pWFC/kXpLy1aANCqVasybpBIkLhai8tstTrPW6D1bGWq0BrEa5ZBWa2aoCPiDX5eaijuKiYnhe4uinjH1oM3u65MgplddGLTD03x1tWg02nhGzZsIDo6mjFjxlClShXAPpq9detWTCYTY8aMyT+2Vq1axMfHs23bNi81S0SkFFSLKyi4mDVOaqou+kVK4+pSQ9LS7M9VSz6AFJOTQncXRbxjxfl7i69MQh5JZ+/22jmdjlwfPnyY+Pj4Atv27NnDpUuXiIiI4Le//W2BfTfddBMZulAtU5r6JXJVMTWWAa1nCxBOZo2LSClt3GgfsXZGteTL2DXL/wpduGn5n4hXHK/REusvxVQmwSCjlvdmBzvtXFutVszX/WJ/8cUXANx2222Fjs/Kysof4Rb/09QvkWtoPZuIhLirq2OKvceo1TF+5GwUZNkydaZFfKBm83CM/VaXpT8NbET8trbXzul0WvhNN93Evn37CmzbsmULJpOJ3r17Fzr+f//3f7npppu81jBxn6Z+iVxH5exEJMRpdUyAyciArl0hLg5Wr4atW+2PcXH27fqPEPG6ewdkuexYg33ddcIfS1WdugCn73T33Xezbt06tmzZwqVLl1iyZAk//fQTderU4Y477ihw7Icffshnn31G3759vdYwcZ9j6pezP6LXTv0SCQlazyYiIc6xOsYVrY7xE42CiJSJnj0vXB1rsRW53zBsXh9rcTotfPTo0ezYsYOJEydiMpmw2WxUrFiR5557jkqVKgHw8ccfk5SUxM6dO2nWrBmjR4/2XsvEbZr6JVIErWcTkRCm1TEBRAvgRcrEr5VJTE4qk5i8PtbitHNdqVIllixZQkpKCnv27KFatWrce++9tGzZMv+Yb7/9lt27d3PvvffyxBNPEBYW5r2Wids09UvECWXLEpEQpWpPAcTJKIgFg43EkEQCGUQQMb4KCYlKRCviTf4ea3HauQYwm83cc8893HPPPUXuHzduHI888ghGcfOOxKfsU79sWK0mp8cYho2ICOf7RUTKhMociPiEaskHkCJGQTK4kVhS2EUnDPKwYsY4YmVlnBLRinibP8daXHauAa5cucL//d//kZeXR8uWLQtkBFd28MCQMMzCypXFLNa3mkgYZoFiFvWLiPiNyhyI+JRWxwSI68pDWjCIJYU07HlBrFcvxx2JlxxLsFNT9X8kEmxcdq6XLFnCq6++SnZ2NmCfKj58+HAee+wxKlQotl8ufhLDRjpSjzSiisyIZ2AhijSiyQA0PVZEAoC7CX50dSlSKlodEwCuWwC/kRh20cnp4VqCLRK8nM7nXrt2LS+88AI1atRgxIgR/PGPfyQyMpIlS5Ywa9Ysf7ZRimFekUSKKY4o7BepBnnYS6LnARBFGimmOMwrksqwlSIi13CzzIElZRPJyTBsGPTta39MTlZSXZHyZv369dx99920a9eO/v37s3btWpfHW61WFi5cSN++fWnXrh333HMPycnJfmqth64rD5lEQv41mjOORLQiElycDj8vX76cDh06sHTpUipXrgyAzWZj0qRJrFq1iilTpuRnDZcylpFBhO0EqXRlE9G/JsYggwSSiGYTZpsymolIAHGjzEGGqR6xo1qz64xmjYuUZykpKUyZMoVRo0bRo0cPtmzZwrRp0wgLCyMmJqbI18ycOZNVq1YxefJkWrduTXJyMo899hjh4eH06tXLz5+gGNctgM8govjau1YbGRt3w7CXlIdCJIg47VwfOHCAyZMn53esAUwmE6NHj2bTpk0cPHiQ1q1b+6WRUoyra3nMViuxbCCWDYWPUTFLEQkkxZQ5sGAQa1tP2plIQLPGRcqzuXPn0r9/f6ZPnw5Az549OXfuHPPmzSuyc33kyBHeffdd/vGPfzBo0CAAunfvzqFDh9ixY0fgda6hwAL4iPFVMI5YXXawDSxEnNkPq1frjqJIEHE6LfzSpUtUr1690PbGjRtjs9k4f/68TxsmHkhIcK8Wl4pZikigcCT4ccKxJtHq5M/UtWsSRSR4paenc+TIEfr161dge3R0NAcPHiQ9Pb3Qa7Zs2UJYWBgDBw4ssD0pKYkZM2b4tL2lcnUBfELibcWPXFOBBJIK31HUmhiRgOZ05NpqtWIyFS7dZL46RGDx0i/3+vXrWbhwIenp6TRq1IgHH3ywULC8vl2LFi3in//8JydPnqRp06aMGzeOu+++2yvtCUoqZikiwea6BD/Xc6xJtLrIu+lYk6iEPyLB6+DBgwA0a9aswPamTZsC8J///IcmTZoU2Ld//36aNWvG559/zuzZs/m///s/GjduzKOPPkpsKQKCxWJh//79JX69u5o1g7Ztm/L995Wx2Qpfa/+aiPaau4dX7ygeXbyYCx6OzF+8eBHAL59NCtJ3X3Z89d1bLJb8/nBRyrRAtWONTY8ePXj11Vfp0qUL06ZNY+PGjU5fM3PmTBITE0lISGDRokW0b9+exx57jO3bt/ux5QHGsZYnyl7SIX80yPHoopilxYKSBYmI/12X4Od67q1JVCoJkWCXlZUFQHh4eIHt1apVA8ivWHOtzMxMfvnlF5588kkSEhJ48803adu2LZMmTeLLL7/0faNLyWyG118/Sps2OQAYhg2wFUxESyxmCg6Y2AyDGh9+6O/miogHXNbTOnv2LMeOHSuw7dy5c4A9sF2/D6Bhw4Zunzwk1tj4SwmKWarErIiUmesS/BQIQlYrETfkYZxzveJFqSREgp/NZnO53yjiBtyVK1fIzMzktddeo0+fPgB069aNgwcPsmDBArp161aitpjNZlq1alWi15bE3r2OyzYTGRt3EXFm/6+JaCkc/ExWKzUuX6aGh210jNz587OJnb77suOr737Pnj0u97vsXM+cOZOZM2cWuW/KlCmFtplMJr7//nu3GuZYYzN58uQC26Ojo9mwYQPp6emFpgG5WmMjeFTMUiVmRQJHyC6PcXFTMCHvTlYOKDxd8lpKJSES/Bz5fS5cuFBgu2PEuqj8P9WqVcNsNnP77bfnbzMMg9tuu41//vOfPmytdxW4bBv2kj15me4oigQ1p53rgQMHFrnm2lsCaY1NKHKUmHXm2mRBpfpqLRb7ya4fTVdJCREgBErQFMfJTcEYi1JJiIQCx3XgkSNHCowwHT58uMD+azVt2hSr1UpeXl6BsrBXrlzx6bWrTxWThwLQHUWRIOC0c/3CCy/49MSlXWPzyCOP0LhxY9asWcOkSZOoXbt2iaYB+St5RWn4YkF+YmIDDKM6VqvzP0KGYSMxMYsWLX4p0TnMp0/TeOxYwr77Dts1Uz5NK1dyuW1bjr7+OpY6dZy+PhSTQOgzB67iEliUlJbHFK2YWeOuUkmIyLUC/CZ306ZNady4MRs3buSuu+7K375582YiIyOLXG7Ys2dPFi9ezIYNG7jvvvsAyMvLY8eOHXTs2NFvbfcqJacVKRecdq5//vlnGjVqVKo3L2pqt0MgrbEJRZmZZreqd2VmlvAPr8VC47FjqXx1mYDpunnnlb//nsZjx3J49eqA+OMuUha0PMa1EqSSEJFrBUlylQkTJjB9+nRq1qxJ7969+eSTT9iwYQNz584F7IMrR44coWXLloSHh9O9e3d69erFs88+y8WLF4mMjGT58uX8/PPPzJ49u4w/TQnpjqJIueC0c33fffcRFxfHX/7yF+rVq+fRm6anp/P666+zefNmUlNTizwmUNbY+Dt5RUn4YkF+ZCTs3Fnc0h4TkZHVSnbe5GT47junu002G2HffUerQ4eczjsPxSQQ+syBq7gEFiWh5THF8yCVhIhcK4iSq8THx5Obm8tbb73FmjVraNKkCS+++GJ+TNu2bRvTp0/nnXfeoWvXrgDMnz+fefPm8frrr3Pu3DnatGnDW2+9xS233FKWH6V0dEdRJOg57VyvW7eO5557jr59+9KpUyfuuusufv/73xc5Em2z2fjxxx/5+uuvSUlJYffu3fTp04d169Y5PbHW2JQtny/tSUr69W6rMypSKyEuUJbHQHAskXElWJYXBCp9f6UXaN9htW3baOxGchVP6ib7ankMwNChQxk6dGiR++Lj44mPjy+wLSwsjGnTpjFt2jSftKesWDCz0RZLki2WDBtE2CDBBjFQTHFCEQkEpEzNQwAAIABJREFUTjvX9erVY/78+Xz11VcsWbKEmTNn8uyzz1KlShUaNWpEeHg4NpuNM2fOkJGRweXLlzGZTNxxxx28++673HrrrS5PrDU2ZcvnS3syMlx3rEFFaiXkaXmMiPhKjQ8/xGYYvy7LKoKjbrK7nWvxrSCZxS8iLrgsxQXQuXNnOnfuzPHjx/n000/ZvXs36enpnD17FsMwaNCgAR07dqRbt2706NGD2rVru31yrbEpOz5f2hMR4d7Itf5KSAgLlOUxEBxLZFwJluUFgUrfX+kF3Hd4+XKxN7k9rZvsi+UxYhdEs/hFxIViO9cO9evXZ/DgwQwePNhrJ9cam7JVkqU9bicdVUkJkWJpeYyI+IxucgcVv5VIFRGfcrtz7StaY1O2PEkW5NF0JZWUECmWlseUnuOGX2JiAzIzzURGBlSVIZGyo5vcQUWpakTKhzLvXEtw8Hi6kkpKiLhFy2NKruANv+pYrfYqCFqfKIJucgcZpaoRKR/UuQ51bs7zLtF0pZKWlLjapgaJiZgzM9FQlJRnWh5TMoVv+JmuPtqfa32ihDzd5A4qmsUvUj6ocx3KPJjnXeLpSp4Wqb2mTdUdJ9RQlJRzWh7jOa1PFHGD6iYHDc3iFykfCtd5kdDg7jxviwXw03Sl69pksloxuWiTiIQuxw0/Vxw3/ERCmuMm9/LlsGWL/TE2Vh3rAOOYxe8srhkmGx1vOEj0S3fBsGGQnKzrIZEApM51qHIM+zjrMV877MOv05VcKfV0JQ/bJCKhS+sTRaQ8cczij4qyP3dccxmGDYAo2y5Szt6G+V9bYPVqiIuDrl0V5EQCjNNp4SNHjvT4zUwmE0uXLi1Vg8RPPJzn7ZfpSkqVKSLXc5IXIuLG/hiGofWJIlJuFJrFf8JGRNomEs4uINq2AbNNxa9FAp3TzvXRo0cLbTt9+jQ5OTnUrFkzv9bqzz//zJkzZ6hVqxYtWrTwaWPFizwc9vk16agtP3HQtQzDRlSUqVDSUbfrYpegTSU7iYgEDRd5IRJaPsxK6zyXL/fL+kTFHxHxogKpapJT7CPUzii5hEjAcdq53rp1a6Hnjz76KC+88AL33nsvxjVzhNevX8+MGTMYMWKE71oq3uVhWkqzGVKWniS2y0l2XWyDQR5WDAysWKlAVNg+UpbeiNl8Y/7LPaqLXYI2lewkIhIUiskLEXMgkY5VHyTt8m+d3PDzQ5UhxR8R8SXN6BMJOm6vuZ47dy5Dhgxh4MCBBTrWAHFxcQwfPpx581yPIkgASUhwb5TYMexjsRAxqj+pl9qTTCxDWE1ftjKE1SQTS+ql9kSM6p+fXMPDfGklbpPnJxGRoFBMDgazLY+Ui72Jan4ecKxLtOWvU/R5lSHFHxHxNSWXEAk6bneujxw5QmRkpNP99evXJ0O/3MGj2LSUhn2/Y9jn6oWu2ZZHLBtYzgi2cBfLGUEsGzDb8gokGytRbrIStkkJ0ETKITfSgUcYp0nt+BDJydC/fxbdu19kyBB7Et3UVB8PGiv+iIivXZ3RZ8EgmViGsZy+bGEYy0kmFguGkkuIBBi3O9fNmjUjOTkZSxF34XNycnjvvfdo1aqVVxsnPuQ8LaX98fphHw/r3pSoTM51bbIZBjYvtklEgoibIzbmUyeIjYXZs3/h7beP+q/KkOKPiPhaQgIZ1jp0JZU4klnNILZyB6sZRBzJdCWVDGsdFb8WCSBO11xfb+zYsUyePJnhw4cTHx9PkyZNuHz5MocPH2bFihUcO3aMRYsW+bKt4m2F0lJek4wnOrrg1amHU5NKPJPpmjZlJSZizsykWmSkV9okIkGkJDkY/EnxR0R8zHJXDLFVt5F20T54Zb162e54TCOK2KrbSL2zFUqfKBIY3O5cx8bGcvnyZWbPns3f//53TCZ7AhmbzUajRo1YsGABt99+u88aKj5SIC2lCx5e6Jbquvhqm365mn3e6YyIQL/4FpGS80v9v1JQ/BERH9v4sZldF9s43W/Fvn/TFuUzEwkUbneuAeLj4xk4cCDfffcdP//8MyaTiSZNmtCmjfNffCknPLzQLc11saOyTWJiAzIzzTgGrgtVtgn0i28RKblf6/8V3YF1Mx24zyplKf6IiI+5lSwcC0l/3kZsnzdVBlAkAHjUuQYwDIN69ephtVpp3rw5lStXxmq1FsogLuWMhxe6Jb0uLljZpjpWK+zc6aSyjZcuvkUkADlyMBRV6spqdSsduE8rZSn+iIiPubX6BBMZJ2ywerXKAIoEAI96xLt27SI+Pp5evXoxdOhQvv32W3bu3Env3r1JSUnxVRslEHiYAM3TfGlQVGUbE2ByXtmmJCcRkeDhyMGQnAxDhkDfvribDtznlbIUf0TExxyrT1wxsBJBhheDm4iUhtsj19988w1/+tOfaNCgAaNGjWLJkiUA1KxZkwoVKjBlyhSqVatGr169fNVWKWueJEDz/PD8yjbOXFvZJn9tkacnEZHg4m5eiOuUKJ54SvFHRHzIrdUnVCCBa6oSeCW4iUhJud25njdvHo0bN+b999/n4sWL+Z3r3/3ud3z44YcMGzaMRYsWqXNd3nl4oevJ4W6tLbpa2abA+5Xw4ltEyq8SxxNPKf6IiI8Uu/oEC1GkEc2m63Z4I7iJSEm4PS08LS2N+Ph4wsLC8jOFO4SHhzN48GB++uknrzdQQkdJK9tYLPZZosOG2WeNDhtmf64ZUSKhS5WyRCTYFb36xIZBHgBRpJFCLGauC3YKbiJlxqOEZpUqVXK6LycnB2txVzIiLpSkso1PExaJSNBSpSwRKQ8KrT7Z+i0RJ74lgWVEs6lwxxoU3ETKkNsj1+3bt2f9+vVF7rt48SJr1qzhd7/7ndcaJqEnIcG9kSZHZRufJywSkcB3depKg8mTaTx6dP7UlYRhFo/iiYhIoHKsPlm+HLYsPsJyhhPLhqI71qDgJlKG3O5cP/zww3z//fckJCSwdu1aTCYT33zzDe+88w4DBgzg6NGjjBs3zpdtlXLOsbbIWWZMw7Dvd1S2cSQscnYBfW1ODxEphzIyoGtXiIuj+saNVP3yS3s5mrg4Yp7pTsd2V9yOJyIiQeHqxZLFVIFkYhnGcvqyhWEsJ5lYLKYKCm4iZcjtznVUVBSLFi3i+PHjvPjii9hsNubOncvMmTO5fPkyc+bMoVu3br5sq5RzhdcW2QCb08o2joRFrjhyeohIOXPd1BWT1YoJ8u+2mf+/vTuPj+n6/wf+mpmIJQklDKWCBqEII4nUki9Bm0WoLT9LQ0r0R9FqaT+VVm3tp9U0dh9qL42toQltQjQJWvl8+BLBp4ja2kSFWIosRTJzv39MZ2pkZjIjd7bM6/l4eMTce2fuOXfi7Z5zz3mfk9lIRTgUXQQAXCmLiKoJmQyFm/YisPYpRCAF3yASmeiLbxCJCKQgsPYpFG7ay+BGZCNmzbnu2bMnfvjhB5w9exZ5eXlQqVRo1qwZOnbsCBcXsz6KSK/H5xatXFmEO3dkaNnSTe/KNlVJgLZvX8WVc0JD+X8RkcMwYa0t+el0HN2zF2mycK6URUTVglIJhEc3Qs6DhgDUS3E9/jPnQXuER0tw9ChjHJEtmNwiTk5Ohp+fH5o3b44OHTqgQ4cOOvtPnjyJHTt24LPPPhO9kORcNHOLvL0LAAA+Pj56j2MCNCInZuJaW7ItmxE+RkC4kAAIhYAgB4QoAKEAZOxsIyKH8ne/okTvfpVKwmWuiWzI5GHhM2fOxPDhw3Ho0CG9+/Py8pCcnCxawYgqwwRoRE7M1KErqalARIR6LnZmpnZONgIDUXjmpmbKtr7dXMmGiOwOp8QR2TeTG9cab7zxBlasWGGJshCZhQnQiJyYZuhKZYqK1D+f6E1TnjiF8G43kZMj6NvNzjYisktPOyWOiKzDrMb1zJkzMWLECKxYsQITJ07E/fv3LVUuokpVTICm+5MJ0IiqMVOGrhixT3gZ2aUvQKUyNLRSpM62v5YKw6hRQL9+2qXC2GonoqdhSr8il7kmsh2zGtcuLi6YM2cOPv74Y/z73//GsGHDkJubCwCQSPTfoBBZkiYBWkoKMGKE+t51xAj166NHK863Zm8vUTVR2dCVSiQgClKUGz2myp1tjy0VxnHnRMZ9//33GDBgAHx9fREWFmbWVMOCggL4+flh5cqVFiyhfTB3SlwF7PAjsqinSvEdGRkJb29vvPXWWxg5ciTmz5+PWrVqiV02IpNoEqBVlrjjaRKgEZGd0gxd+StDofDXP26J5h+5h8ffQ8L1KIQcKhjPWFalzjZTkzwwpS8RUlNT8e677yI6Ohq9evVCeno63n//fdSqVQuhoaFG3ysIAj744AMUFxdbqbS2pelXzMnRfz8jlapH7uld5ppZXYks7um6/AF07doVu3btgre3N95//318/fXXYpaLSHRV7u0lIvvy2NCVorAwlHbv/vfQlfBwo0+15SiEFMaf1FSps41JHohMtnjxYoSFhSE2NhZBQUGYN28ewsLCsHTp0krfu3XrVly+fNkKpbQP5k6J02JWVyKreOrGNQA0btwY27Ztw8CBA3Hs2DGxykRkEeYmQCMiB/DX0JWChQtxdeNGYOtW9Q3imDFGe9OikKBdF9aQKnW2MckDkUny8/ORl5eHl19+WWd7SEgILl++jPz8fKPvjY+Px8cff2zpYtoVvVPi/p8KKXP+F0e9R0M+quJwb7effmKHH5EVmDwsfPPmzfD29q6w3dXVFXFxcejcuTN+/vlnUQtHJKYnRpHqjIhSqYz09hKR46lk7GSoZD/8ap1Bzp/todLTzyyFCgpfJUJCajzd+ZnkgcgkmqfOrVq10tneokULAMCVK1fQvHnzCu9TqVSYOXMmwsLC8D//8z+WL6id0ZkSV1gIZVgE9m1vhCiMQSEaQY6biNq+EqFd50G2fAnq7tlj2ty4hAQukE1UBSY3rrt162Z0/6uvvlrlwhBZmqa3Ny1N/f9HYaF6W1SU+ok1G9ZE1UQlvWkyhS9SH0xF+NkvkA1/SFEOFaSQQgUVXKDACaQiFlDuQ8o+WYV4ERpaSbxgkgcikxT9lRvB3d1dZ7ubmxsAGJxLvWnTJly9ehVffvmlaGVRKpU4f/68aJ9nFUolag+dhKHnVz4Wy2SQQontGAm/E8ex87W3Ian3EIJKBWPphwWVCqW//oqrjnYN7FhpaSkAON7vVTVgqWuvVCohM3IDYLBxPXbsWLzxxhvo3r279nVlJBIJNm3a9BTFJLIeUxOgEZGDM9abVl4O+Suv4CgCkYYQJCAKhZBDjkJEIQEhSMPt054I7FCM7Iv1zM/9ExWlPtAYJnkggiAIRvdL9UyvuHTpEpYsWYJly5bBw8PDUkVzCLUOHcbQ84uQA/UkbM10F83PHCgw/MISpCveMKnDT9mggcXLTFSdGWxcX716Vdvi17y2hO+//x6rVq1Cfn4+mjVrhokTJ2Lw4MEmvbegoAARERGIiYnB5MmTLVI+IiJyYIZ600aNAqRSyFQqhGMvwrFXZ7cSUoQjFTkX1TfuZif7rlJKXyLnoWkcl5SU6GzXPLF+svGsVCoRGxuL0NBQ9OzZE+Xlfy+pp1KpUF5eDheXp1oMBzKZDD4+Pk/1XltJ+f+7kQ1/g/tVkCEb/sgs6oGhquNGP0uiUqHu5Mmo62DXwJ5pnpo62u9VdWCpa3/y5Emj+w1Gn8zMTKOvxcClF4iIyCYqmRO9D6HGb1gfy/2jdxQMkzwQmUQz1zovL0/nJvi3337T2a9RUFCAU6dO4dSpUxXWwl6+fDmWL1/uVENwE3I1Q8ENdyhIUY7tfwzAUL8sdvgRWdjTde2J5PGlFwAgKCgI9+7dw9KlSyttXDvb0gtERCSiSuZEJyCq8hvWynL/MMkDUaVatGiB5557Dvv27cNLL72k3b5//360bNkSTZs21TleLpdj586dFT5n+PDhGDVqFIYNG2bxMtuTQsihgvFYooIUhZCzw4/ICgzeNWgavOaQSCT49NNPTTpWs/TC9OnTdbaHhIRg7969yM/P15sdUvPe+Ph4LF26FK+//rrZ5SQiIidXyZxok25YVQIKC42lBwKTPBCZYMqUKYiNjUW9evXQp08fZGRkYO/evVi8eDEA4M6dO8jLy0Pr1q3h7u6OTp066f0cuVxucF91JW9XH9JCZSVPrlV45nl3dvgRWYHBf4lJSUlmf5g5jWt7WXrBETJDOmumQWesN+tsvyrLDlkVzD1hA5XMiZaj8K/M4Ya/cylUkDeSAHqW8iIi0w0dOhSPHj3Chg0bkJiYiObNm+Pzzz9H+F+dUgcPHkRsbCw2b96MwMBAG5fWvkTNaILtP1b25NoFY+//Cxh1/e/lDtjhR2QRBhvXubm5Fj2xPS29QERkK8w9YSOVzImOejYT2wtGGf0IFWSIancMQIB1ykxUjY0cORIjR47Uu2/o0KEYOnSo0ffbewetpYQOkMHPtww5p2VQ6enok0CJZ3AXK85HIPH8Y2tf7/2eSwESWYCoc67NebJjL0svOEJmSGfNNOiM9Wad7Vdl2SGfFnNP2JCRIZKhX30Nv8TjyIFC79NrKZRQIAf9zyxFSkqC+etgExGJQCYDUn+ogfAwAdkn8NeIGwkkUEGADAJkuIdncAB9dda+Tn3pVchP7GOgIhKZWY3rH3/8ET/99BNKS0uhemwYnVKpRElJCbKzs3HkyBGTPsuell4gIrIF5p6wAwbmRMsWLkQqwhGOVGRDk41X+teNqwsUyMEmjEWPfcnIToT562ATEYlELgeO/q/kr35CKW6cuYWc0xLcRX0IkGo7CB9f+zr89Gc4mpoG2UAODycSk8mt0V27dmHWrFnaJ84SiUTn6bOrqyv69Olj8om59AIROTt7yT1BesjlkEtv46gqEGkIQQKiUAg55ChEFBLQHz+gB/6DnKLWAJ5iHWwiIhE93k+Y0nsDIvAPg8dq1r5OWxiHcDauiURlcuN68+bNaN68OVavXo2ysjIMGjQIhw4dgkQiwaZNm7Bx40aMHj3a5BNz6QUicnb2lHvCEZI7GiN2Yjy3Pn3w3PbtkAEIx16EY6/O/hSEm7QO9vr1V9G7d4nB4+yFoyQWtGfOcA0tmdiRxGPq2tcJuf5g05pIXCY3rn/99VdMnTpV+4TFzc0Nx44dQ0REBN577z388ssvWLNmDV588UWTT86lF4jImdlL7gmqqCQoCA86dEDNs2ch0fM9qdfBVhrPJi4VsGdPXYdoXBNR9WHW2tdEJCqTG9cSiQT169fXvm7RogVyc3MREREBAAgODsbKlSvNOjmXXiCbUCqBffsqrvHIDERkZfaUe8IRkjsaY5HEeJmZBrOJF3p4Q1VkfAkulUqCBw/qwsenrnhlshBHSSxoz5zhGloqsSOJy9S1r+XtGlixVETOweS7sBYtWuCXX37Rvm7ZsiXOnTunfV1eXl7hBtEUXHqBrKqwUP/NMjMQkQ0w94SdM5JNXL7ZH9JEib4lsrWkUoYTIrI+U9e+jprR2EolInIeJjeuw8PDsXz5ctSuXRuTJ09Gz549MWfOHCQlJcHb2xtbtmypcCNIZFeUSnXDOidH/ZoZiMjGmHvCARjIJh4lANt3GH+rSgWMGgWkpHCgDBFZT+VrX6vwTD0gfrEMX29lTCISk8mN65iYGFy4cAFr167FpEmT8Morr2Dr1q2IjY2FRCKBTCbD8uXLLVlWoqrZt0/9xNoQTQaitLQKN9JElsLcE44pNFQ92CUnB3qfXkulQMeOwNy5wIkTHChDRNZTce1rJVSQQgIBAqQQIMW9IuDAAcYkIrEZnzD2GBcXF8THx+PHH39E7dq14eLigm3btmHBggV4//33kZSUhODgYEuWlahqEhLU/4sYI5WqjyOykqFDh2LevHk4fPgwpkyZgmPHjlXIPTFixAicOXPGxiWlx8lk6htRhUL9WhNaND+7dFH/1ExRNTRQRqm0TnmJyLlo1r5OSQHCBpTgxRdL8Ux9KSQS9X7GJCLLMDvzTcOGDbV/d3V1xeDBg0UtEJHFFBbqf8T0OJVKfRyRFTH3hGMyMiUb5eXAK68Yfi8HyhCRpWlmtXh7F+DgQTdMmuRm8FjGJCJxmNW4Tk5ORlZWFm7evAmVnkaKZs1rIrskl/89NtMQZiAiIjMYmJKNUaNMCzcJCbyRJSLL27OnLmMSkRWY3LhevHgxVq9ejRo1asDT01Pv+qtEdi0qSj2xyBiVSn0cEVEVcKAMEdmTO3dkjElEVmBy4zopKQm9evXSZgwncjimZCBSKICQEOuXjYiqFQ6UISJ70qCBkjGJyApMfvxcXFyMkJAQNqzJcVWWgUihUO/nWhREVEVRUaY9ueZAGSKyhkGD7jMmEVmByY3roKAgHDlyxJJlIbI8TQailBRgxAigXz/1z5QU9XZ22RKRCDQDZQzNoJJK1fs5UIaIrCEoqIQxicgKTB4W/tFHH2HcuHGYMWMG+vfvD09PT0g0+fwfExAQIGoBiURnKAMREZFINANlwsPVGXgfX+dapeJAGSKyLsYkIuswuXF97do1FBUVISUlBampqRX2C4IAiUSCc+fOiVpAIiIiR2Rsqa6QEN7EEpF1MSYRWZ7Jjev58+fj/v37iImJQcuWLeHiYvYS2URERE6FA2WIyJ4wJhFZlskt5AsXLmDq1Kl4/fXXLVkeIiIiIiIiIodjckKzJk2acG1rIiIiIiIiIj1Mbi1PmDABmzZtwsWLFy1ZHiIiIiIiIiKHIxEEQTDlwI8//hjp6em4efMmmjdvjoYNG0L2ROYDiUSCTZs2WaSglpCdnQ0AFephb5RKJQD7L6fYnLHerLP90pTTz8/PxiURn6PEQmMc5ffIXvH6VZ0zXEPGQcfkDL+b9orX3nYsde0ri4Mmz7k+cOAAZDIZmjRpgrKyMhQUFIhTQqqUs/6DdMZ6s85ET4e/R1XD61d1vIZkr/i7aTu89rZjq2tv8pPrP/74A/Xr17d0eYiIiIiIiIgcjslzrocMGYKVK1dasixEREREREREDsnkxvUff/yBhg0bWrIsRERERERERA7J5MZ1REQEEhMTcevWLUuWh4iIiIiIiMjhmJzQTCqV4uLFi+jduze8vLzg6elZYd1rR8sWTkRERERERCQGkxvXWVlZ2oRmDx8+xLVr1yxWKCIiIiIiIiJHYnK2cCIiIiIiIiLSz+Qn1xpKpRI///wzfv/9d7i6uuLZZ59Fhw4dLFE2IiIiIiIiIodgVuP6wIEDmDdvHm7cuAHNA2+JRAK5XI45c+agb9++FikkERERERERkT0zeVj48ePH8dprr8HT0xOvvvoqvL29IQgCLl++jK1bt+LWrVvYvHkzunbtaukyExEREREREdkVkxvX0dHRuH79Onbu3AkPDw+dfcXFxRg2bBi8vLywdu1aixSUiIiIiIiIyF6ZvM716dOnERkZWaFhDQDu7u4YPnw4Tp06JWrhiIiIiIiIiByByY3rykgkEpSVlYn1cdVeeXk5fH194ePjo/NHoVBojzl8+DCGDRuGzp07o2/fvtiwYUOFz/nvf/+LMWPGQKFQoFevXli0aJFdfg/nzp1Dhw4dcP36dZ3tYtXx119/xaRJk+Dv74/AwEDMmTMHxcXFFq1TZQzV+aWXXqrwvfv4+ODOnTvaYxypziqVCtu2bcPAgQOhUCjQv39/fPbZZzplEas+t27dwowZMxAYGAg/Pz9Mnz4dN2/etEo9yfqcLU6KyRljrpicJX6T42J8tA3GVttxlLhsckKzzp07Y+fOnRg9ejTq1Kmjs6+4uBiJiYno1KmT2QVwVleuXMHDhw/x+eefo2XLltrtUqm6v+PEiROYNGkSwsLCMG3aNGRnZyMuLg6CICAmJgYA8Ntvv+G1116DQqHAkiVLcOnSJSxevBjFxcWYPXu2Laql16VLlzBx4kSUl5frbBerjvfu3UN0dDQaNWqEzz//HLdv38YXX3yB69evY/Xq1VavL2C4ziUlJcjPz8eMGTPQrVs3nX1169YF4Hh1XrduHZYsWYKYmBh0794dV65cwbJly3Dx4kWsX79etPqUl5cjJiYGpaWlmDt3LsrLy7Fw4UJMmDABu3btgouL2YsfkJ1zpjgpJmeMuWJypvhNjovx0foYW23HoeKyYKJjx44J7du3F4KDg4X169cLGRkZQkZGhrB27VohODhYeOGFF4T//Oc/pn6c09uzZ4/Qrl07obS0VO/+6OhoITIyUmdbXFyc4O/vLzx8+FAQBEH44IMPhN69e2tfC4IgbNmyRWjfvr1w/fp1yxXeRGVlZUJCQoKgUCiEbt26CW3bthUKCgq0+8Wq47/+9S+hS5cuwp07d7THHDx4UGjbtq1w8uRJS1axgsrqnJ2dLbRt21a4ePGiwc9wpDqrVCohICBAmDt3rs72lJQUoW3btsLZs2dFq09ycnKFa3fhwgXBx8dHSElJsWQ1yUacIU6KyRljrpicLX6TY2N8tB7GVttxxLhs8rBwf39/LF++HEqlEnFxcZgyZQqmTJmC+Ph4lJWVYdGiRXjxxRfNa9k7sXPnzsHLywu1a9eusO/hw4c4fvw4Xn75ZZ3tISEhuH//Pk6cOAEAyMrKQnBwMFxdXbXHhIaGQqlU4vDhw5atgAmys7MRHx+P8ePH491339XZJ2Yds7KyEBAQgPr162uP6dWrF9zc3HDo0CFLVU8vY3UG1N97rVq1dHqZn+RIdS4pKcGgQYMQERGhs/35558HAOTl5YlWn6ysLLRu3Rre3t7aYzSvrf09k3U4Q5wUkzPGXDE5W/wmx8b4aD2MrbbjiHHZrDnX/fr1Q2ZmJr755hssXLgQCxcuxI4dO3DgwAGEhISYdWJnd/78ebi6uiImJgYKhQIBAQGYPXs2iouLkZ+fj7KyMrRq1UrnPS1atACgHgr0559/oqCgoMIxDRo0gLu7O65cuWK1uhji7e2N9PR0TJ06FTKZTGefmHW8fPmw6mVOAAAONklEQVRyhWNkMhmee+45q18HY3UG1N97vXr1MH36dPj7+0OhUOCdd97Rzht2tDq7u7tj1qxZ8PPz09menp4OQH09xKqPvmMAwMvLyy5+30l8zhAnxeSMMVdMzha/ybExPloPY6vtOGJcNnuSokwmg6+vL3x9fc19Kz0mNzcXxcXFiIyMxKRJk/Dzzz9j+fLluHLlCqZPnw5A3XB5nJubGwD1HPeioiK9x2iOs4fkBw0bNjS4z1D5n6aORUVFdnMdjNUZUH/vt27dQps2bTBmzBhcvnwZy5Ytw9ixY5GUlOSQdX7SqVOnsGbNGvTv318730WM+hQVFaF169Z6j/ntt9/ErALZCWeIk2JyxpgrJsZvciSMj9bD2Go7jhiXDTauV6xYYdYHaUydOvWp3udsFi9ejHr16sHHxwcAEBAQAE9PT7z33nvIysoy+l6pVAqhkuXJNQkt7JUp5RejjvZ2HWbNmgVBENC5c2cA6ukW3t7eGD16NPbs2YPevXsbfb+91zk7OxuTJk3Cc889h08++QSPHj0yerxY9bG375nE4exxUkzOGnPFVN3jNzkWxkf7wNhqW/YYl6vcuJZIJDqv2bg2zZMZ7QCgT58+Oq9LSkp0Xmt6Tjw8PLS9K08eozlO33rk9kRTPjHq6O7ubvCYpk2bilruqtI34sPPzw8eHh7Izc3FgAEDADhmnVNTUzFz5ky0bNkS69atQ/369bVlFKM+xo7R19tIjs/Z46SYnDXmiqk6x29yPIyP9oGx1bbsMS4bbIpnZGRU+mf37t3o3bs3BEGATCbDuHHjzDq5s7p9+zYSExORn5+vs/3BgwcAAE9PT8hkMuTl5ens17xu1aoV3Nzc0Lhx4wrDYW/fvo2SkhK9c1PtiZeXl2h1bNWqVYVjlEolrl69alfXobS0FLt27UJubq7OdpVKhbKyMtSvX99h67xx40ZMnz4dXbp0wZYtWyCXywFA1ProOwZQ/87Y0/dM4mCcFJczxlwxVef4TY6H8dF+MLbajr3GZYON62bNmhn9c+rUKbz++us4ePAgunbtim+//Rb/+Mc/zDq5s5JIJJg9ezYSEhJ0tqempkImk6FHjx7w9/fH/v37dYaSpKWlwcPDAx07dgQA9OzZEwcOHNAZepuWlgaZTKa3R9Oe1KxZU7Q69uzZE0ePHsXdu3e1xxw+fBilpaXo0aOHlWpUuZo1a2LBggUVRoVkZmbiwYMHOvVxpDonJiZiwYIFCAsLw7p16yr0dotVn169euHChQu4fPmy9piLFy/i0qVLdvU9kzgYJ8XljDFXTNU1fpNjYny0H4yttmOvcVk2d+7cuea8QbNQ97p161CjRg3MmjULH330ETw9Pc06sTOrXbs27t69iy1btkClUkGlUmH37t1YtmwZRo8ejYEDB6JJkyb48ssvcenSJdSuXRvJyclYu3Yt3nzzTQQGBgJQ97Js2LABx48fR7169XDw4EF88cUXiIyMxMCBA21cS13nzp1DRkYGxo0bpx0eI1YdW7duje3btyM9PR2enp44ceIE5s6di8DAQEycONFu6iyVSlGjRg1s3rwZ9+7dg4uLCzIyMvDPf/4TQUFB2rI6Up1v376NCRMmoHHjxpgxYwZu376N69eva/+4urrihRdeEKU+zz//PPbu3YukpCR4enril19+QWxsLJ599lnMmjWL85GqGWeMk2JyxpgrJmeI3+S4GB9th7HVdhwmLpu6IPajR4+EFStWCJ07dxbatWsnxMbG6iy0TeZ59OiRsGbNGiEkJETo2LGj0K9fP2H16tWCUqnUHrN//34hIiJC6NChg9C3b19h/fr1FT7n2LFjQmRkpNCxY0chKChIWLhwofDo0SNrVsUku3btqrDwuyCIV8fz588L0dHRgq+vr9C9e3fho48+EoqKiixap8oYqvM333wjRERECL6+vkJQUJAQFxcn/PnnnzrHOEqdk5KShLZt2xr8k5ycLGp9rl27JkyZMkXo0qWL0K1bN+Gdd94Rbty4YbX6knU5W5wUkzPGXDE5Q/wmx8b4aBuMrbbjKHFZIgiVpLADcOTIEcybNw9XrlxBmzZtMGfOHPj7+5vf5UBERERERERUDRltXN+5cweffvopUlJSUKtWLUyePBnjxo2Di4vZy2MTERERERERVVsGW8nbtm3DkiVLcP/+ffTt2xezZs3Cs88+a82yERERERERETkEg0+u27Vr9/dBT6xlbfDDJBKcPXtWnJIREREREREROQiDT64HDx5scqOaiIiIiIiIyJmZlNCMiIiIiIiIiAzjwrBEREREREREVcS032QTM2fORFJSUqXHDRkyBAsWLMCYMWPw+++/IzMz0wqlq9yNGzcwaNAg7Ny5E82bN7f4+T788EO4u7sjNjbW4uciIutgHDQP4yBR9cM4aB7GQfvHYeFkEzk5OcjLy9O+zs7Oxo4dOzBixAj4+flpt3t5eUGhUCArKwt//vkn+vfvb4viVvDWW2+hYcOGmD17tlXOd+3aNYSHh2P79u06yQaJyHExDpqHcZCo+mEcNA/joP3jk2uyCYVCAYVCoX2tVCqxY8cOdOnSBa+88kqF43v27GnN4hl17NgxZGRkID093WrnbNq0KQYMGIDPPvsMmzZtstp5ichyGAfNwzhIVP0wDpqHcdD+cc41kZm++uor+Pn5WX3d98jISBw5cgS5ublWPS8R0ZMYB4nI2TEOkj5sXJNDGDNmDPr27avzeuLEiUhPT8egQYPQqVMnDBgwAIcOHUJxcTFmz56NgIAAdO/eHbNnz8aDBw90Pi8nJwfjxo3T9piOHz8ep0+frrQcBQUFOHDgQIXhSH379sX8+fORmJiIkJAQ+Pr6YtiwYTh9+jRu3ryJadOmQaFQICgoCIsWLYJKpdK+99q1a3jzzTfRq1cvdOrUCeHh4Vi7dq3OMQDQpUsXNGnSBFu2bHmaS0hEDo5xkHGQyNkxDjIO2jsOCyeHdebMGeTk5GDs2LHw8PDA6tWr8fbbb6N9+/aoXbs2pk+fjuPHj2PHjh2Qy+WYOnUqACArKwsTJ05Eu3btMG3aNDx69AjffvstXn31VWzcuBH+/v4Gz/nTTz9BqVSiT58+Ffalp6dj//79iI6OhiAIWLVqFd588014eHigTZs2mDlzJvbv34/Vq1ejVatWGDJkCMrKyjBhwgQ8ePAAr732GurWrYtDhw4hPj4eSqUSkyZN0jlHQEAAfvzxR1GvIxE5LsZBInJ2jINkT9i4Jod18+ZNfPnllwgODgYAuLi4YP78+VAqlVi/fj0AYOTIkTh+/DgOHz6MqVOnQqVSYc6cOejUqRMSEhIgk8kAAFFRURg8eDA++eQTJCcnGzxndnY26tSpozcjZGFhIXbv3g0fHx8AwN27d7F+/Xp07doVixcvBgAMHDgQ3bp1w+HDhzFkyBCcO3cOly5dwtKlSxEaGgpAPdxnwoQJuHLlSoVztG3bFt999x3y8/OtkpWSiOwb4yDjIJGzYxxkHLQnbFyTw6pZsyaCgoK0r1u1agUA6Nevn3abRCJBs2bNcOPGDQDA2bNnkZ+fj1GjRuHevXs6nxccHIyvvvoKN27cQOPGjfWeMz8/H82aNYNEIqmwz8vLSxtIHy/PSy+9pN1Wp04deHp64ubNmwAAuVwOiUSC1atXw83NDYGBgXB1ddX+Z/AkTQC9evUqgykRMQ4yDhI5PcZBxkF7wsY1OaxnnnkGLi5//wpreh09PT11jpPJZNCsOKdZ7iEuLg5xcXF6P/fatWsGg+ndu3dRt25dvfv0nRcAGjRoYLA8TZo0wXvvvYdFixZhwoQJqFOnDrp3747w8HCEhYVpP0PD3d0dAPDHH3/oLQMRORfGQSJydoyDZE/YuCaH9XggfZy+XkQNTVKIadOmoUuXLnqPef755w2+XyqVVkgsUZXyAEBMTAwiIiLwww8/4NChQ8jKykJGRgaSk5Oxbt06veV/MsgSkXNiHCQiZ8c4SPaEjWtyKs2aNQOgHo7To0cPnX2nT5/GvXv3UKtWLYPv9/T0REFBgWjluXv3LnJzc9G1a1dERUUhKioKpaWlmDlzJtLS0nD+/HmdoUV3797VloOI6GkwDhKRs2McJEvhUlzkVDp27IhGjRrh66+/RklJiXZ7cXEx3n77bcTGxhrtBWzatCkKCwuhVCpFKU9WVhaio6ORmZmp3VanTh20bdsWQMUeSc1coaZNm4pyfiJyPoyDROTsGAfJUvjkmpxKjRo1MGvWLLzzzjsYOnQohg8fjpo1ayIxMRHXrl1DfHy8weE8APDiiy/i22+/xYULF9CuXbsqlyc4OBitWrXChx9+iDNnzsDLywuXL1/Gli1b0L17d7Ru3Vrn+JMnT6JFixYMpkT01BgHicjZMQ6SpbBxTU4nNDQU9erVw6pVq7By5UpIpVK0adMGq1at0i7jYEhQUBCkUimOHz8uSjCtU6cONmzYgGXLluG7777DrVu30KhRI4wePVq7DqOGSqXCyZMnER4eXuXzEpFzYxwkImfHOEiWIBE0aeqIyCRTpkzBnTt3sG3bNqueNysrC+PHj8fu3btFCeRERE+LcZCInB3jIOnDOddEZho/fjxOnDihXcbBWpKTk9GzZ08GUiKyOcZBInJ2jIOkDxvXRGby8/NDcHAw1qxZY7Vz5ufnIy0tDW+//bbVzklEZAjjIBE5O8ZB0oeNa6KnMGfOHOzfv99qvZUrV67EiBEj4Ovra5XzERFVhnGQiJwd4yA9iXOuiYiIiIiIiKqIT66JiIiIiIiIqoiNayIiIiIiIqIqYuOaiIiIiIiIqIrYuCYiIiIiIiKqIjauiYiIiIiIiKqIjWsiIiIiIiKiKvo/r07r2zRKdhsAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get amps (and optionally U, R state vars) for best model at every freqs.\n", "model_amps = OrderedDict()\n", "for freq, vals in data.items():\n", " amps, _ = evaluator.generate_model(freq, best)\n", " model_amps[freq] = amps\n", " \n", "fig = plt.figure(figsize=(14, 4))\n", "for i, (freq, tmp) in enumerate(data.items()):\n", " ax = fig.add_subplot(1, 3, i+1)\n", " ax.scatter(tmp[\"t_spikes\"], tmp[\"amps\"], c=\"red\", label=\"in vitro\")\n", " ax.scatter(tmp[\"t_spikes\"], model_amps[freq], c=\"blue\", label=\"in silico\")\n", " if i == 0:\n", " ax.set_ylabel(\"Normalized (PSC) amplitudes\")\n", " ax.set_xlabel(\"Time (ms)\")\n", " ax.set_title(freq)\n", " ax.legend(loc=0)\n", "fig.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 4 } ================================================ FILE: misc/github_wiki/bibtex/mentions_BPO.bib ================================================ @techreport{amsalemDenseComputerReplica2020, type = {Preprint}, title = {Dense {{Computer Replica}} of {{Cortical Microcircuits Unravels Cellular Underpinnings}} of {{Auditory Surprise Response}}}, author = {Amsalem, Oren and King, James and Reimann, Michael and Ramaswamy, Srikanth and Muller, Eilif and Markram, Henry and Nelken, Israel and Segev, Idan}, year = {2020}, month = jun, institution = {{Neuroscience}}, doi = {10.1101/2020.05.31.126466}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{amsalemEfficientAnalyticalReduction2020a, title = {An Efficient Analytical Reduction of Detailed Nonlinear Neuron Models}, author = {Amsalem, Oren and Eyal, Guy and Rogozinski, Noa and Gevaert, Michael and Kumbhar, Pramod and Sch{\"u}rmann, Felix and Segev, Idan}, year = {2020}, month = jan, journal = {Nature Communications}, volume = {11}, number = {1}, pages = {288}, issn = {2041-1723}, doi = {10.1038/s41467-019-13932-6}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{awileModernizingNEURONSimulator2022a, title = {Modernizing the {{NEURON Simulator}} for {{Sustainability}}, {{Portability}}, and {{Performance}}}, 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}, year = {2022}, month = jun, journal = {Frontiers in Neuroinformatics}, volume = {16}, pages = {884046}, issn = {1662-5196}, doi = {10.3389/fninf.2022.884046}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{beiningT2NNewTool2017, title = {{{T2N}} as a New Tool for Robust Electrophysiological Modeling Demonstrated for Mature and Adult-Born Dentate Granule Cells}, author = {Beining, Marcel and Mongiat, Lucas Alberto and Schwarzacher, Stephan Wolfgang and Cuntz, Hermann and Jedlicka, Peter}, year = {2017}, month = nov, journal = {eLife}, volume = {6}, pages = {e26517}, issn = {2050-084X}, doi = {10.7554/eLife.26517}, urldate = {2022-06-08}, 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.}, langid = {english} } @techreport{ben-shalomInferringNeuronalIonic2019, type = {Preprint}, title = {Inferring Neuronal Ionic Conductances from Membrane Potentials Using {{CNNs}}}, 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.}, year = {2019}, month = aug, institution = {{Neuroscience}}, doi = {10.1101/727974}, urldate = {2022-06-08}, 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).}, langid = {english} } @article{bolognaEBRAINSNeuroFeatureExtractOnline2021, title = {The {{EBRAINS NeuroFeatureExtract}}: {{An Online Resource}} for the {{Extraction}} of {{Neural Activity Features From Electrophysiological Data}}}, shorttitle = {The {{EBRAINS NeuroFeatureExtract}}}, author = {Bologna, Luca L. and Smiriglia, Roberto and Curreri, Dario and Migliore, Michele}, year = {2021}, month = aug, journal = {Frontiers in Neuroinformatics}, volume = {15}, pages = {713899}, issn = {1662-5196}, doi = {10.3389/fninf.2021.713899}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{carrilloMetricEvaluatingNeural2018, title = {A {{Metric}} for {{Evaluating Neural Input Representation}} in {{Supervised Learning Networks}}}, author = {Carrillo, Richard R. and Naveros, Francisco and Ros, Eduardo and Luque, Niceto R.}, year = {2018}, month = dec, journal = {Frontiers in Neuroscience}, volume = {12}, pages = {913}, issn = {1662-453X}, doi = {10.3389/fnins.2018.00913}, urldate = {2022-06-08}, 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.}, langid = {english} } @misc{deistlerTruncatedProposalsScalable2022, title = {Truncated Proposals for Scalable and Hassle-Free Simulation-Based Inference}, author = {Deistler, Michael and Goncalves, Pedro J. and Macke, Jakob H.}, year = {2022}, month = nov, number = {arXiv:2210.04815}, eprint = {arXiv:2210.04815}, publisher = {{arXiv}}, urldate = {2023-02-28}, 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.}, archiveprefix = {arxiv}, langid = {english}, keywords = {Computer Science - Machine Learning,Statistics - Machine Learning} } @article{diaz-parraStructuralFunctionalEmpirical2017, title = {Structural and Functional, Empirical and Modeled Connectivity in the Cerebral Cortex of the Rat}, author = {{D{\'i}az-Parra}, Antonio and Osborn, Zachary and Canals, Santiago and Moratal, David and Sporns, Olaf}, year = {2017}, month = oct, journal = {NeuroImage}, volume = {159}, pages = {170--184}, issn = {10538119}, doi = {10.1016/j.neuroimage.2017.07.046}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{dura-bernalNetPyNEToolDatadriven2019, title = {{{NetPyNE}}, a Tool for Data-Driven Multiscale Modeling of Brain Circuits}, 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}, year = {2019}, month = apr, journal = {eLife}, volume = {8}, pages = {e44494}, issn = {2050-084X}, doi = {10.7554/eLife.44494}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{economidesBiocytinRecovery3D2018, title = {Biocytin {{Recovery}} and {{3D Reconstructions}} of {{Filled Hippocampal CA2 Interneurons}}}, author = {Economides, Georgia and Falk, Svenja and Mercer, Audrey}, year = {2018}, month = nov, journal = {Journal of Visualized Experiments}, number = {141}, pages = {58592}, issn = {1940-087X}, doi = {10.3791/58592}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{erikssonCombiningHypothesisDatadriven2022a, title = {Combining Hypothesis- and Data-Driven Neuroscience Modeling in {{FAIR}} Workflows}, 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}, year = {2022}, month = jul, journal = {eLife}, volume = {11}, pages = {e69013}, issn = {2050-084X}, doi = {10.7554/eLife.69013}, urldate = {2022-07-20}, 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.}, langid = {english} } @article{ezra-tsurRealisticRetinalModeling2021, title = {Realistic Retinal Modeling Unravels the Differential Role of Excitation and Inhibition to Starburst Amacrine Cells in Direction Selectivity}, author = {{Ezra-Tsur}, Elishai and Amsalem, Oren and Ankri, Lea and Patil, Pritish and Segev, Idan and {Rivlin-Etzion}, Michal}, editor = {Macke, Jakob H.}, year = {2021}, month = dec, journal = {PLOS Computational Biology}, volume = {17}, number = {12}, pages = {e1009754}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1009754}, urldate = {2022-06-13}, 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.}, langid = {english} } @inproceedings{farnerEvolvingSpikingNeuron2021, title = {Evolving Spiking Neuron Cellular Automata and Networks to Emulate in Vitro Neuronal Activity}, booktitle = {2021 {{IEEE Symposium Series}} on {{Computational Intelligence}} ({{SSCI}})}, author = {Farner, Jorgen Jensen and Weydahl, Hakon and Jahren, Ruben and Ramstad, Ola Huse and Nichele, Stefano and Heiney, Kristine}, year = {2021}, month = dec, pages = {1--10}, publisher = {{IEEE}}, address = {{Orlando, FL, USA}}, doi = {10.1109/SSCI50451.2021.9660185}, urldate = {2022-06-08}, 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.}, isbn = {978-1-72819-048-8}, langid = {english} } @article{frostnylenDopaminergicCholinergicModulation2021, title = {Dopaminergic and {{Cholinergic Modulation}} of {{Large Scale Networks}} in Silico {{Using Snudda}}}, author = {Frost Nylen, Johanna and Hjorth, Jarl Jacob Johannes and Grillner, Sten and Hellgren Kotaleski, Jeanette}, year = {2021}, month = oct, journal = {Frontiers in Neural Circuits}, volume = {15}, pages = {748989}, issn = {1662-5110}, doi = {10.3389/fncir.2021.748989}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{galindoSimulationVisualizationAnalysis2020, title = {Simulation, Visualization and Analysis Tools for Pattern Recognition Assessment with Spiking Neuronal Networks}, author = {Galindo, Sergio E. and Toharia, Pablo and Robles, {\'O}scar D. and Ros, Eduardo and Pastor, Luis and Garrido, Jes{\'u}s A.}, year = {2020}, month = aug, journal = {Neurocomputing}, volume = {400}, pages = {309--321}, issn = {09252312}, doi = {10.1016/j.neucom.2020.02.114}, urldate = {2022-06-08}, langid = {english} } @article{galRoleHubNeurons2021, title = {The {{Role}} of {{Hub Neurons}} in {{Modulating Cortical Dynamics}}}, author = {Gal, Eyal and Amsalem, Oren and Schindel, Alon and London, Michael and Sch{\"u}rmann, Felix and Markram, Henry and Segev, Idan}, year = {2021}, month = sep, journal = {Frontiers in Neural Circuits}, volume = {15}, pages = {718270}, issn = {1662-5110}, doi = {10.3389/fncir.2021.718270}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{gouwensSystematicGenerationBiophysically2018a, title = {Systematic Generation of Biophysically Detailed Models for Diverse Cortical Neuron Types}, 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}, year = {2018}, month = feb, journal = {Nature Communications}, volume = {9}, number = {1}, pages = {710}, issn = {2041-1723}, doi = {10.1038/s41467-017-02718-3}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{gutzenReproducibleNeuralNetwork2018, title = {Reproducible {{Neural Network Simulations}}: {{Statistical Methods}} for {{Model Validation}} on the {{Level}} of {{Network Activity Data}}}, shorttitle = {Reproducible {{Neural Network Simulations}}}, author = {Gutzen, Robin and {von Papen}, Michael and Trensch, Guido and Quaglio, Pietro and Gr{\"u}n, Sonja and Denker, Michael}, year = {2018}, month = dec, journal = {Frontiers in Neuroinformatics}, volume = {12}, pages = {90}, issn = {1662-5196}, doi = {10.3389/fninf.2018.00090}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{hauflerSimulationsCorticalNetworks2023, title = {Simulations of Cortical Networks Using Spatially Extended Conductance-based Neuronal Models}, author = {Haufler, Darrell and Ito, Shinya and Koch, Christof and Arkhipov, Anton}, year = {2023}, month = jan, journal = {The Journal of Physiology}, pages = {JP284030}, issn = {0022-3751, 1469-7793}, doi = {10.1113/JP284030}, urldate = {2023-02-28}, langid = {english} } @article{iyengarCuratedModelDevelopment2019, title = {Curated {{Model Development Using NEUROiD}}: {{A Web-Based NEUROmotor Integration}} and {{Design Platform}}}, shorttitle = {Curated {{Model Development Using NEUROiD}}}, author = {Iyengar, Raghu Sesha and Pithapuram, Madhav Vinodh and Singh, Avinash Kumar and Raghavan, Mohan}, year = {2019}, month = aug, journal = {Frontiers in Neuroinformatics}, volume = {13}, pages = {56}, issn = {1662-5196}, doi = {10.3389/fninf.2019.00056}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{jedrzejewski-szmekParameterOptimizationUsing2018, title = {Parameter {{Optimization Using Covariance Matrix Adaptation}}\textemdash{{Evolutionary Strategy}} ({{CMA-ES}}), an {{Approach}} to {{Investigate Differences}} in {{Channel Properties Between Neuron Subtypes}}}, 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.}, year = {2018}, month = jul, journal = {Frontiers in Neuroinformatics}, volume = {12}, pages = {47}, issn = {1662-5196}, doi = {10.3389/fninf.2018.00047}, urldate = {2022-06-08}, 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.}, langid = {english} } @techreport{jinBayesianInferenceSpectral2023, type = {Preprint}, title = {Bayesian {{Inference}} of a {{Spectral Graph Model}} for {{Brain Oscillations}}}, author = {Jin, Huaqing and Verma, Parul and Jiang, Fei and Nagarajan, Srikantan and Raj, Ashish}, year = {2023}, month = mar, institution = {{Neuroscience}}, doi = {10.1101/2023.03.01.530704}, urldate = {2023-05-24}, 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.}, langid = {english} } @article{jungDynamicCausalModeling2019, title = {Dynamic Causal Modeling for Calcium Imaging: {{Exploration}} of Differential Effective Connectivity for Sensory Processing in a Barrel Cortical Column}, shorttitle = {Dynamic Causal Modeling for Calcium Imaging}, author = {Jung, Kyesam and Kang, Jiyoung and Chung, Seungsoo and Park, Hae-Jeong}, year = {2019}, month = nov, journal = {NeuroImage}, volume = {201}, pages = {116008}, issn = {10538119}, doi = {10.1016/j.neuroimage.2019.116008}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{kanariComputationalSynthesisCortical2022, title = {Computational Synthesis of Cortical Dendritic Morphologies}, 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}, year = {2022}, month = apr, journal = {Cell Reports}, volume = {39}, number = {1}, pages = {110586}, issn = {22111247}, doi = {10.1016/j.celrep.2022.110586}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{kanekoDevelopmentallyRegulatedImpairment2022, title = {Developmentally Regulated Impairment of Parvalbumin Interneuron Synaptic Transmission in an Experimental Model of {{Dravet}} Syndrome}, 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.}, year = {2022}, month = mar, journal = {Cell Reports}, volume = {38}, number = {13}, pages = {110580}, issn = {22111247}, doi = {10.1016/j.celrep.2022.110580}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{linneNeuroinformaticsComputationalModelling2018, title = {Neuroinformatics and {{Computational Modelling}} as {{Complementary Tools}} for {{Neurotoxicology Studies}}}, author = {Linne, Marja-Leena}, year = {2018}, month = sep, journal = {Basic \& Clinical Pharmacology \& Toxicology}, volume = {123}, pages = {56--61}, issn = {17427835}, doi = {10.1111/bcpt.13075}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{maki-marttunenStepwiseNeuronModel2018, title = {A Stepwise Neuron Model Fitting Procedure Designed for Recordings with High Spatial Resolution: {{Application}} to Layer 5 Pyramidal Cells}, shorttitle = {A Stepwise Neuron Model Fitting Procedure Designed for Recordings with High Spatial Resolution}, 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.}, year = {2018}, month = jan, journal = {Journal of Neuroscience Methods}, volume = {293}, pages = {264--283}, issn = {01650270}, doi = {10.1016/j.jneumeth.2017.10.007}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{marinUseMultimodalOptimizer2021, title = {On the {{Use}} of a {{Multimodal Optimizer}} for {{Fitting Neuron Models}}. {{Application}} to the {{Cerebellar Granule Cell}}}, 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.}, year = {2021}, month = jun, journal = {Frontiers in Neuroinformatics}, volume = {15}, pages = {663797}, issn = {1662-5196}, doi = {10.3389/fninf.2021.663797}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{masoliComputationalModelsNeurotransmission2022, title = {Computational Models of Neurotransmission at Cerebellar Synapses Unveil the Impact on Network Computation}, author = {Masoli, Stefano and Rizza, Martina Francesca and Tognolina, Marialuisa and Prestori, Francesca and D'Angelo, Egidio}, year = {2022}, month = oct, journal = {Frontiers in Computational Neuroscience}, volume = {16}, pages = {1006989}, issn = {1662-5188}, doi = {10.3389/fncom.2022.1006989}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{meyerPypetPythonToolkit2016, title = {Pypet: {{A Python Toolkit}} for {{Data Management}} of {{Parameter Explorations}}}, shorttitle = {Pypet}, author = {Meyer, Robert and Obermayer, Klaus}, year = {2016}, month = aug, journal = {Frontiers in Neuroinformatics}, volume = {10}, issn = {1662-5196}, doi = {10.3389/fninf.2016.00038}, urldate = {2022-06-08}, langid = {english} } @article{neymotinOptimizingComputerModels2017, title = {Optimizing Computer Models of Corticospinal Neurons to Replicate in Vitro Dynamics}, author = {Neymotin, Samuel A. and Suter, Benjamin A. and {Dura-Bernal}, Salvador and Shepherd, Gordon M. G. and Migliore, Michele and Lytton, William W.}, year = {2017}, month = jan, journal = {Journal of Neurophysiology}, volume = {117}, number = {1}, pages = {148--162}, issn = {0022-3077, 1522-1598}, doi = {10.1152/jn.00570.2016}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{nolteCorticalReliabilityNoise2019a, title = {Cortical Reliability amid Noise and Chaos}, author = {Nolte, Max and Reimann, Michael W. and King, James G. and Markram, Henry and Muller, Eilif B.}, year = {2019}, month = aug, journal = {Nature Communications}, volume = {10}, number = {1}, pages = {3792}, issn = {2041-1723}, doi = {10.1038/s41467-019-11633-8}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{pagkalosIntroducingDendrifyFramework2023, title = {Introducing the {{Dendrify}} Framework for Incorporating Dendrites to Spiking Neural Networks}, author = {Pagkalos, Michalis and Chavlis, Spyridon and Poirazi, Panayiota}, year = {2023}, month = jan, journal = {Nature Communications}, volume = {14}, number = {1}, pages = {131}, issn = {2041-1723}, doi = {10.1038/s41467-022-35747-8}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{ramaswamyDataDrivenModelingCholinergic2018, title = {Data-{{Driven Modeling}} of {{Cholinergic Modulation}} of {{Neural Microcircuits}}: {{Bridging Neurons}}, {{Synapses}} and {{Network Activity}}}, shorttitle = {Data-{{Driven Modeling}} of {{Cholinergic Modulation}} of {{Neural Microcircuits}}}, author = {Ramaswamy, Srikanth and Colangelo, Cristina and Markram, Henry}, year = {2018}, month = oct, journal = {Frontiers in Neural Circuits}, volume = {12}, pages = {77}, issn = {1662-5110}, doi = {10.3389/fncir.2018.00077}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{reyes-sanchezAutomatizedOfflineOnline2023, title = {Automatized Offline and Online Exploration to Achieve a Target Dynamics in Biohybrid Neural Circuits Built with Living and Model Neurons}, author = {{Reyes-Sanchez}, Manuel and Amaducci, Rodrigo and {Sanchez-Martin}, Pablo and Elices, Irene and Rodriguez, Francisco B. and Varona, Pablo}, year = {2023}, month = jul, journal = {Neural Networks}, volume = {164}, pages = {464--475}, issn = {08936080}, doi = {10.1016/j.neunet.2023.04.034}, urldate = {2023-05-24}, 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.}, langid = {english} } @article{royIonChannelDegeneracy2022, title = {Ion-channel Degeneracy and Heterogeneities in the Emergence of Complex Spike Bursts in {{CA3}} Pyramidal Neurons}, author = {Roy, Rituparna and Narayanan, Rishikesh}, year = {2022}, month = oct, journal = {The Journal of Physiology}, pages = {JP283539}, issn = {0022-3751, 1469-7793}, doi = {10.1113/JP283539}, urldate = {2023-02-28}, langid = {english} } @techreport{sanabriaCellTypeSpecificConnectivity2023, type = {Preprint}, title = {Cell-{{Type Specific Connectivity}} of {{Whisker-Related Sensory}} and {{Motor Cortical Input}} to {{Dorsal Striatum}}}, author = {Sanabria, Branden D. and Baskar, Sindhuja S. and Yonk, Alex J. and Lee, Christian R. and Margolis, David J.}, year = {2023}, month = mar, institution = {{Neuroscience}}, doi = {10.1101/2023.03.06.531405}, urldate = {2023-05-24}, 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.}, langid = {english} } @misc{sarmaIntegrativeBiologicalSimulation2019, title = {Integrative {{Biological Simulation}}, {{Neuropsychology}}, and {{AI Safety}}}, author = {Sarma, Gopal P. and Safron, Adam and Hay, Nick J.}, year = {2019}, month = jan, number = {arXiv:1811.03493}, eprint = {arXiv:1811.03493}, publisher = {{arXiv}}, urldate = {2022-06-08}, 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.}, archiveprefix = {arxiv}, langid = {english}, keywords = {Computer Science - Artificial Intelligence,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing,Quantitative Biology - Neurons and Cognition} } @inproceedings{shenAutomaticFittingNeuron2018, title = {Automatic Fitting of Neuron Parameters}, booktitle = {2018 {{IEEE}} 3rd {{International Conference}} on {{Cloud Computing}} and {{Big Data Analysis}} ({{ICCCBDA}})}, author = {Shen, Jiamin and Wang, Ye and Cao, Lihong}, year = {2018}, month = apr, pages = {558--562}, publisher = {{IEEE}}, address = {{Chengdu}}, doi = {10.1109/ICCCBDA.2018.8386578}, urldate = {2022-06-08}, 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.}, isbn = {978-1-5386-4301-3}, langid = {english} } @article{sinhaActiveDendritesLocal2022, title = {Active {{Dendrites}} and {{Local Field Potentials}}: {{Biophysical Mechanisms}} and {{Computational Explorations}}}, shorttitle = {Active {{Dendrites}} and {{Local Field Potentials}}}, author = {Sinha, Manisha and Narayanan, Rishikesh}, year = {2022}, month = may, journal = {Neuroscience}, volume = {489}, pages = {111--142}, issn = {03064522}, doi = {10.1016/j.neuroscience.2021.08.035}, urldate = {2022-06-08}, 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.}, langid = {english} } @inproceedings{sivagnanamNeuroscienceGatewayEnabling2018, title = {The {{Neuroscience Gateway}}: {{Enabling Large Scale Modeling}} and {{Data Processing}} in {{Neuroscience}}}, shorttitle = {The {{Neuroscience Gateway}}}, booktitle = {Proceedings of the {{Practice}} and {{Experience}} on {{Advanced Research Computing}}}, author = {Sivagnanam, Subhashini and Yoshimoto, Kenneth and Carnevale, Nicholas T. and Majumdar, Amit}, year = {2018}, month = jul, pages = {1--7}, publisher = {{ACM}}, address = {{Pittsburgh PA USA}}, doi = {10.1145/3219104.3219139}, urldate = {2022-06-08}, 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.}, isbn = {978-1-4503-6446-1}, langid = {english} } @article{stocktonIntegratingAllenBrain2017, title = {Integrating the {{Allen Brain Institute Cell Types Database}} into {{Automated Neuroscience Workflow}}}, author = {Stockton, David B. and Santamaria, Fidel}, year = {2017}, month = oct, journal = {Neuroinformatics}, volume = {15}, number = {4}, pages = {333--342}, issn = {1539-2791, 1559-0089}, doi = {10.1007/s12021-017-9337-x}, urldate = {2022-06-08}, 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.}, langid = {english} } @article{yegenogluExploringParameterHyperParameter2022, title = {Exploring {{Parameter}} and {{Hyper-Parameter Spaces}} of {{Neuroscience Models}} on {{High Performance Computers With Learning}} to {{Learn}}}, 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}, year = {2022}, month = may, journal = {Frontiers in Computational Neuroscience}, volume = {16}, pages = {885207}, issn = {1662-5188}, doi = {10.3389/fncom.2022.885207}, urldate = {2023-02-28}, 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.}, langid = {english} } ================================================ FILE: misc/github_wiki/bibtex/mentions_BPO_extra.bib ================================================ @InProceedings{pmlr-v96-lueckmann19a, title = {Likelihood-free inference with emulator networks}, author = {Lueckmann, Jan-Matthis and Bassetto, Giacomo and Karaletsos, Theofanis and Macke, Jakob H.}, booktitle = {Proceedings of The 1st Symposium on Advances in Approximate Bayesian Inference}, pages = {32--53}, year = {2019}, editor = {Ruiz, Francisco and Zhang, Cheng and Liang, Dawen and Bui, Thang}, volume = {96}, series = {Proceedings of Machine Learning Research}, month = {02 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v96/lueckmann19a/lueckmann19a.pdf}, url = {https://proceedings.mlr.press/v96/lueckmann19a.html}, abstract = {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.} } @unpublished{appukuttan:hal-03586825, TITLE = {{A Software Framework for Validating Neuroscience Models}}, AUTHOR = {Appukuttan, Shailesh and Sharma, Lungsi and Garcia-Rodriguez, Pedro and Davison, Andrew}, URL = {https://hal.archives-ouvertes.fr/hal-03586825}, NOTE = {working paper or preprint}, YEAR = {2022}, MONTH = Feb, PDF = {https://hal.archives-ouvertes.fr/hal-03586825/file/Validation__Methods__Paper___Draft___v1__Reduced_.pdf}, HAL_ID = {hal-03586825}, HAL_VERSION = {v1}, } @article{doi:10.1098/rsob.220073, author = {Jedlicka, Peter and Bird, Alexander D. and Cuntz, Hermann }, title = {Pareto optimality, economy–effectiveness trade-offs and ion channel degeneracy: improving population modelling for single neurons}, journal = {Open Biology}, volume = {12}, number = {7}, pages = {220073}, year = {2022}, doi = {10.1098/rsob.220073}, URL = {https://royalsocietypublishing.org/doi/abs/10.1098/rsob.220073}, eprint = {https://royalsocietypublishing.org/doi/pdf/10.1098/rsob.220073}, 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. } } ================================================ FILE: misc/github_wiki/bibtex/poster_uses_BPO.bib ================================================ @inproceedings{damartDataDrivenBuilding2020, title = {Data Driven Building of Realistic Neuron Model Using {{IBEA}} and {{CMA}} Evolution Strategies}, booktitle = {Proceedings of the 2020 {{Genetic}} and {{Evolutionary Computation Conference Companion}}}, author = {Damart, Tanguy and Van Geit, Werner and Markram, Henry}, year = {2020}, month = jul, pages = {35--36}, publisher = {{ACM}}, address = {{Canc\'un Mexico}}, doi = {10.1145/3377929.3398161}, isbn = {978-1-4503-7127-8}, langid = {english} } @misc{rizzaRealisticModel, title = {A Realistic Model of Cerebellar Stellate Neurons Predicts Intrinsic Excitability and the Impact of Synaptic Inputs}, author = {Rizza, Martina Francesca and Locatelli, Francesca and Masoli, Stefano and Prestori, Francesca and Sanchez-Ponce, Diana and Munoz, Alberto and D‘Angelo, Egidio}, year = {2018}, 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} }, @misc{nylenReconstructingStriatal, title = {Reconstructing the striatal microcircuit in silico}, 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}, year = {2018}, 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} }, @misc{tognolinaModeling, title = {Modeling optimization procedures predict specific filtering channels in the cerebellar granule cell layer}, author = {Tognolina, Marialuisa and Masoli, Stefano and Moccia, Francesco and D‘Angelo, Egidio}, year = {2018}, 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} }, @misc{shieldsOptimizedFitting, title = {Optimized Fitting of a Stochastic Auditory Nerve Fiber Model to Patch-Clamp Data}, author = {Shields, Daniel and Rutherford, Mark A. and Bruce, Ian C.}, howpublished = {https://vepimg.b8cdn.com/uploads/vjfnew/content/files/16258518561401-poster-pdf1625851856.pdf} } ================================================ FILE: misc/github_wiki/bibtex/thesis_mentions_BPO.bib ================================================ @phdthesis{Cremonesi:271927, title = {Computational characteristics and hardware implications of brain tissue simulations}, author = {Cremonesi, Francesco}, publisher = {EPFL}, school = {École Polytechnique Fédérale de Lausanne}, address = {Lausanne}, pages = {196}, year = {2019}, abstract = {Understanding the link between the brain's anatomy and its function through computer simulations of neural tissue models is a widely used approach in computational neuroscience. This technique enables rapid prototyping and testing of hypotheses, allowing researchers to bridge the scales of biological phenomena. Until recently, the constant trend of improvement in computational power has supported an exponential growth in the scale and level of detail of in silico experiments. However, a systematic characterization of the performance landscape has not yet been carried out. In this work we intend to capture intrinsic computational properties of the existing mod- elling abstractions and answer questions about the intricate relationship between simulation algorithms and modern hardware architecture. Our first contribution is a novel set of hardware- agnostic metrics that enables us to bring focus to the heterogeneous landscape of brain tissue models. We develop a methodology able to capture subtle differences between cell-based models and quantify their impact on performance based on hardware features. We show that lumping simulation experiments together by referring to numbers of neurons and synapses without further detail hides fundamental differences in computational and hardware require- ments across models. In addition to analysing different neuron representations, we investigate the impact of biological heterogeneity on the performance of a cortical microcircuit model. Our analysis indicates that while general-purpose computers have until now sustained high- performance simulations of all brain tissue models, the next generation of in silico models will require hardware tailored to the underlying abstraction. We find that all formalisms saturate the memory bandwidth with a fairly small number of shared memory threads, but the reasons behind this are quite different: conductance-based models are dominated by the large memory traffic of clock-driven kernels, while current-based models are most affected by event-driven execution and memory latency. In distributed simulations the latency of the interconnect fabric is the root cause for a significant degradation in performance. We argue that performance analyses such as ours are required to enable the next generation of brain tissue simulations - or else scientific progress risks being hindered by the presence of severe hardware bottlenecks. Our methodology provides a common tool to facilitate the communication between modellers, developers and hardware designers in order to sustain the larger memory and performance requirements of future brain tissue simulations.}, url = {http://infoscience.epfl.ch/record/271927}, doi = {10.5075/epfl-thesis-9767}, } @mastersthesis{Johansson1291310, author = {Johansson, Oscar}, institution = {Linköping University, Department of Computer and Information Science}, pages = {60}, school = {Linköping University, Department of Computer and Information Science}, title = {Weight Estimation and Evaluation of User Suggestions in Mobile Browsing}, 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. }, year = {2019} } @phdthesis{Silverstein1187505, author = {Silverstein, David N.}, institution = {KTH, Computational Science and Technology (CST)}, note = {QC 20180305}, school = {KTH, Computational Science and Technology (CST)}, title = {Investigations of neural attractor dynamics in human visual awareness}, series = {TRITA-EECS-AVL}, number = {2018:18}, 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}, 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. }, ISBN = {978-91-7729-706-2}, year = {2018} } @misc{10481/70162, year = {2021}, url = {http://hdl.handle.net/10481/70162}, abstract = {The cerebellum is a critical brain area for sensorimotor and also non-motor functions such as cognitive and emotional processes. Cerebellar lesions contribute to pathological syndromes such as autism or schizophrenia. However, the primitives under which the cerebellum, and the whole brain, operate at a functional and dysfunctional level are still unclear. To address the complexity of the "diseased" brain system, it is necessary to extract the relevant underlying molecular mechanisms. The availability of large volumes of biomedical data usually makes it difficult to extract this relevant information and interpret it comprehensively. In this thesis, we have made a preliminary experimentation to analyze genetic correlations between diseases with different clinical symptomatologies and/or clinical prognosis (and still based on similar molecular mechanisms). For this purpose, we have developed a methodology for the identification and functional annotation of the most relevant genes in disease. This methodology integrates current systems biology methods, such as protein-protein interaction (PPI) networks, together with multidimensional data sets from different biological levels. The objectives of this first part of the thesis are: the identification of potential diagnostic biomarkers (corresponding to key nodes in the biological and molecular processes of the interactome); the deductive analysis of multidimensional data as an alternative to other search systems; and the extraction of connections between disorders (comorbidities) that are a priori unrelated and that usually escape these traditional systems. Although the methodology is of general purpose, we have applied it to a set of diseases called channelopathies, where ion channels are altered and which generate a wide phenotypic variability. We conclude that our methodology is flexible, fast and easy to apply. Furthermore, it is able to find more correlations between relevant genes than other two traditional methods. Understanding the cerebellar operation in information processing requires decoding the intrinsic functional dynamics of healthy neurons. The tools provided by computational neuroscience allow developing large-scale computational models for the study of these information processing primitives. The most abundant and smallest neurons not only in the cerebellar input layer, but also in the whole brain, are the cerebellar granule cells (GrCs). These neurons play a crucial role in the creation of somatosensory information representations. Their firing characteristics are related to synchronization, rhythmicity and learning in the cerebellum. One of these features is the frequency of enhanced bursting (i.e., spiking resonance). This complex firing pattern has been proposed to facilitate input signal transmission in the thetafrequency band (4-12Hz). However, the functional role of this feature in the operation of the granular 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 models. The main goal of this thesis is the creation of different mathematical models of cerebellar GrCs that meet two requirements: to be efficient enough to allow the simulation of large-scale neuron networks, and to be biologically plausible enough to enable the evaluation of the functional impact of these nonlinear dynamics on the information transmission. Indeed, a high degree of biological realism in efficient models allows research at levels where in vivo or in vitro experimental biology is limited. Methodologically, in this thesis we have chosen the "adaptive exponential integrateand- fire" (AdEx) type of model as the simplified neuron model (it has only two differential equations and few parameters) that meets both realism and low computational cost. This model fits quite well the firing characteristics of real cells, but some of its parameters cannot be directly fitted with measurable experimental values. Therefore, an optimization method is necessary to best fit the parameters to the biological data. We have focused on addressing this challenging optimization problem. First, we have developed a parametric optimization methodology based on genetic algorithms (GA) applied to the case of GrC. We have presented the obtained AdEx neuron models and demonstrated their suitability to reproduce not only the main firing properties of real GrCs (including resonance), but also emergent features not defined in the GA (within the cost function to be optimized). Second, we evaluated four alternative algorithms, which are the most widely used and successful in other fields such as engineering. Finally, in the last part of this thesis we have presented an advanced optimization methodology based on multimodal algorithms. The advantage of this approach is that, after a single optimization process, instead of obtaining an only one candidate numerically outperforming the other candidates, as in the previous cases (a single solution), we obtain a sparse population of different neuron models. That is, a heterogeneous population of neurons of the same type with intrinsic variations in their properties. From this set of promising neuron models, the researcher can choose and filter based on the desired biological plausibility (and neuronal parameter configuration). Thus, we also studied how the target properties of the neuron could be obtained with diverse internal parameter configurations. We explored the parameter space and its impact on the subset of neuronal properties that we aim to reproduce.}, organization = {Tesis Univ. Granada., “Human Brain Project” [HBP, Specific Grant Agreement 2 (SGA2 H2020-RIA 785907) and 3 (SGA3 H2020-RIA 945539)] financiado por EU (H2020), “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), “Cerebelo y oliva inferior en tareas de adaptación sensorimotora” [CEREBIO (J.A. P18-FR-2378)] financiado por la Junta de Andalucía}, publisher = {Universidad de Granada}, keywords = {Dinámica Neuronal, Procesamiento de información, Capa granular del cerebelo, Neuronal Dynamics, Information processing, Granular layer of the cerebellum}, title = {Estudio del Impacto de la Dinámica Neuronal en el Procesamiento de Información en la Capa Granular del Cerebelo}, author = {Marín Alejo, Milagros}, } @phdthesis{AlqahtaniMultiDomain, author = {Alqahtani, Abdulrahman}, institution = {School of Biomedical Engineering, UNSW}, school = {School of Biomedical Engineering, UNSW}, publisher = {UNSW, Sydney}, title = {A multi-domain continuum model of electrical stimulation of healthy and degenerate retina}, keywords = {Discrete model, Continuum model, Retinal electrical stimulation, Retinal implant, Retinal ganglion cells activation}, 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.}, year = {2019}, } ================================================ FILE: misc/github_wiki/bibtex/thesis_uses_BPO.bib ================================================ @phdthesis{rizzaBiophysicallyDetailedCerebellar, title = {A {{Biophysically Detailed Cerebellar Stellate Neuron Model Optimized}} with {{Particle Swarm Optimization Algorithm}}}, author = {Rizza, Martina Francesca}, pages = {137}, langid = {english}, school = {Universit{\`a} Degli Studi Di Milano-Bicocca}, year = {2017} } @phdthesis{saraySystematicValidationDetailed, title = {Systematic Validation of Detailed Models of Hippocampal Neurons Based on Electrophysiological Data}, author = {S{\'a}ray, S{\'a}ra}, pages = {112}, langid = {english}, school = {P{\'a}zm{\'a}ny P{\'e}ter Catholic University}, year = {2021} } @phdthesis{deerasooriyaDynamicClampAnalysis, title = {Dynamic Clamp Analysis of Ion Channel Function}, author = {Yadeesha Deerasooriya}, pages = {162}, langid = {english}, school = {The University Of Melbourne}, year = {2019} } @phdthesis{luckmannSimulationBasedInferenceforNeuroscienceandBeyond, title = {Simulation-Based Inference for Neuroscience and Beyond}, author = {L{\"u}ckmann, Jan-Matthis}, langid = {english}, school = {Eberhard Karls Universit{\"a}t T{\"u}bingen}, year = {2022} } @phdthesis{nylenStriatumSilicoThesis, title = {On Striatum in Silico {{Thesis}} for {{Doctoral Degree}} ({{Ph}}.{{D}}.)}, author = {Nyl{\'e}n, Johanna Frost}, langid = {english}, school = {Karolinska Institutet}, year = {2023} } ================================================ FILE: misc/github_wiki/bibtex/uses_BPO.bib ================================================ @article{allamNeuronalPopulationModels2021a, title = {Neuronal Population Models Reveal Specific Linear Conductance Controllers Sufficient to Rescue Preclinical Disease Phenotypes}, author = {Allam, Sushmita L. and Rumbell, Timothy H. and {Hoang-Trong}, Tuan and Parikh, Jaimit and Kozloski, James R.}, year = {2021}, month = nov, journal = {iScience}, volume = {24}, number = {11}, pages = {103279}, issn = {25890042}, doi = {10.1016/j.isci.2021.103279}, urldate = {2023-02-28}, 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.}, langid = {english} } @techreport{arnaudonControllingMorphoelectrophysiologicalVariability2023, type = {Preprint}, title = {Controlling Morpho-Electrophysiological Variability of Neurons with Detailed Biophysical Models}, author = {Arnaudon, Alexis and Reva, Maria and Zbili, Mickael and Markram, Henry and Van Geit, Werner and Kanari, Lida}, year = {2023}, month = apr, institution = {{Neuroscience}}, doi = {10.1101/2023.04.06.535923}, urldate = {2023-05-24}, 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.}, langid = {english} } @article{ben-shalomNeuroGPUAcceleratingMulticompartment2022a, title = {{{NeuroGPU}}: {{Accelerating}} Multi-Compartment, Biophysically Detailed Neuron Simulations on {{GPUs}}}, shorttitle = {{{NeuroGPU}}}, 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.}, year = {2022}, month = jan, journal = {Journal of Neuroscience Methods}, volume = {366}, pages = {109400}, issn = {01650270}, doi = {10.1016/j.jneumeth.2021.109400}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{bereckiSCN1AGainFunction2019a, title = {{{{\emph{SCN1A}}}} Gain of Function in Early Infantile Encephalopathy}, 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}, year = {2019}, month = apr, journal = {Annals of Neurology}, volume = {85}, number = {4}, pages = {514--525}, issn = {0364-5134, 1531-8249}, doi = {10.1002/ana.25438}, urldate = {2023-02-28}, langid = {english} } @article{bolognaEBRAINSHodgkinHuxleyNeuron2022, title = {The {{EBRAINS Hodgkin-Huxley Neuron Builder}}: {{An}} Online Resource for Building Data-Driven Neuron Models}, shorttitle = {The {{EBRAINS Hodgkin-Huxley Neuron Builder}}}, 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}, year = {2022}, month = sep, journal = {Frontiers in Neuroinformatics}, volume = {16}, pages = {991609}, issn = {1662-5196}, doi = {10.3389/fninf.2022.991609}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{brysonGABAmediatedTonicInhibition2020a, title = {{{GABA-mediated}} Tonic Inhibition Differentially Modulates Gain in Functional Subtypes of Cortical Interneurons}, 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}, year = {2020}, month = feb, journal = {Proceedings of the National Academy of Sciences}, volume = {117}, number = {6}, pages = {3192--3202}, issn = {0027-8424, 1091-6490}, doi = {10.1073/pnas.1906369117}, urldate = {2023-02-28}, 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.}, langid = {english} } @techreport{buccinoMultimodalFittingApproach2022, type = {Preprint}, title = {A Multi-Modal Fitting Approach to Construct Single-Neuron Models with Patch Clamp and High-Density Microelectrode Arrays}, 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}, year = {2022}, month = aug, institution = {{Neuroscience}}, doi = {10.1101/2022.08.03.502468}, urldate = {2023-02-28}, 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.}, langid = {english} } @techreport{buchinMultimodalCharacterizationSimulation2020, type = {Preprint}, title = {Multi-Modal Characterization and Simulation of Human Epileptic Circuitry}, 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.}, year = {2020}, month = apr, institution = {{Neuroscience}}, doi = {10.1101/2020.04.24.060178}, urldate = {2022-06-07}, 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.}, langid = {english} } @techreport{cavarrettaModelingSynapticIntegration2023, type = {Preprint}, title = {Modeling Synaptic Integration of Bursty and Beta Oscillatory Inputs in Ventromedial Motor Thalamic Neurons in Normal and Parkinsonian States}, author = {Cavarretta, Francesco and Jaeger, Dieter}, year = {2023}, month = apr, institution = {{Neuroscience}}, doi = {10.1101/2023.04.14.536959}, urldate = {2023-05-24}, 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.}, langid = {english} } @article{chindemiCalciumbasedPlasticityModel2022b, title = {A Calcium-Based Plasticity Model for Predicting Long-Term Potentiation and Depression in the Neocortex}, 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.}, year = {2022}, month = jun, journal = {Nature Communications}, volume = {13}, number = {1}, pages = {3038}, issn = {2041-1723}, doi = {10.1038/s41467-022-30214-w}, urldate = {2023-02-28}, 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.}, langid = {english} } @inproceedings{damartDataDrivenBuilding2020, title = {Data Driven Building of Realistic Neuron Model Using {{IBEA}} and {{CMA}} Evolution Strategies}, booktitle = {Proceedings of the 2020 {{Genetic}} and {{Evolutionary Computation Conference Companion}}}, author = {Damart, Tanguy and Van Geit, Werner and Markram, Henry}, year = {2020}, month = jul, pages = {35--36}, publisher = {{ACM}}, address = {{Canc\'un Mexico}}, doi = {10.1145/3377929.3398161}, urldate = {2023-02-28}, isbn = {978-1-4503-7127-8}, langid = {english} } @inproceedings{doronDiscoveringUnexpectedLocal2019a, title = {Discovering {{Unexpected Local Nonlinear Interactions}} in {{Scientific Black-box Models}}}, booktitle = {Proceedings of the 25th {{ACM SIGKDD International Conference}} on {{Knowledge Discovery}} \& {{Data Mining}}}, author = {Doron, Michael and Segev, Idan and Shahaf, Dafna}, year = {2019}, month = jul, pages = {425--435}, publisher = {{ACM}}, address = {{Anchorage AK USA}}, doi = {10.1145/3292500.3330886}, urldate = {2023-02-28}, 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.}, isbn = {978-1-4503-6201-6}, langid = {english} } @article{eckerDataDrivenIntegration2020, title = {Data-driven Integration of Hippocampal {{{\textsc{CA1}}}} Synaptic Physiology {\emph{in Silico}}}, shorttitle = {Data-driven Integration of Hippocampal}, 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}, year = {2020}, month = nov, journal = {Hippocampus}, volume = {30}, number = {11}, pages = {1129--1145}, issn = {1050-9631, 1098-1063}, doi = {10.1002/hipo.23220}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{eckerHippocampalSharpWaveripples2022a, title = {Hippocampal Sharp Wave-Ripples and the Associated Sequence Replay Emerge from Structured Synaptic Interactions in a Network Model of Area {{CA3}}}, 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}, year = {2022}, month = jan, journal = {eLife}, volume = {11}, pages = {e71850}, issn = {2050-084X}, doi = {10.7554/eLife.71850}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{frostnylenReciprocalInteractionStriatal2021, title = {Reciprocal Interaction between Striatal Cholinergic and Low-threshold Spiking Interneurons \textemdash{} {{A}} Computational Study}, author = {Frost Nyl{\'e}n, Johanna and Carannante, Ilaria and Grillner, Sten and Hellgren Kotaleski, Jeanette}, year = {2021}, month = apr, journal = {European Journal of Neuroscience}, volume = {53}, number = {7}, pages = {2135--2148}, issn = {0953-816X, 1460-9568}, doi = {10.1111/ejn.14854}, urldate = {2022-06-07}, 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.}, langid = {english} } @techreport{gerkinNeuronUnitPackageDatadriven2019a, type = {Preprint}, title = {{{NeuronUnit}}: {{A}} Package for Data-Driven Validation of Neuron Models Using {{SciUnit}}}, shorttitle = {{{NeuronUnit}}}, author = {Gerkin, Richard C. and Birgiolas, Justas and Jarvis, Russell J. and Omar, Cyrus and Crook, Sharon M.}, year = {2019}, month = jun, institution = {{Neuroscience}}, doi = {10.1101/665331}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{goncalvesTrainingDeepNeural2020a, title = {Training Deep Neural Density Estimators to Identify Mechanistic Models of Neural Dynamics}, 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}, year = {2020}, month = sep, journal = {eLife}, volume = {9}, pages = {e56261}, issn = {2050-084X}, doi = {10.7554/eLife.56261}, urldate = {2022-06-07}, 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.}, langid = {english} } @article{guet-mccreightAgedependentIncreasedSag2023, title = {Age-Dependent Increased Sag Amplitude in Human Pyramidal Neurons Dampens Baseline Cortical Activity}, 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}, year = {2023}, month = apr, journal = {Cerebral Cortex}, volume = {33}, number = {8}, pages = {4360--4373}, issn = {1047-3211, 1460-2199}, doi = {10.1093/cercor/bhac348}, urldate = {2023-05-24}, 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.}, langid = {english} } @techreport{guet-mccreightInsilicoTestingNew2023, type = {Preprint}, title = {In-Silico Testing of New Pharmacology for Restoring Inhibition and Human Cortical Function in Depression}, 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}, year = {2023}, month = feb, institution = {{Neuroscience}}, doi = {10.1101/2023.02.22.529541}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{hjorthPredictingSynapticConnectivity2021, title = {Predicting {{Synaptic Connectivity}} for {{Large-Scale Microcircuit Simulations Using Snudda}}}, author = {Hjorth, J. J. Johannes and Hellgren Kotaleski, Jeanette and Kozlov, Alexander}, year = {2021}, month = oct, journal = {Neuroinformatics}, volume = {19}, number = {4}, pages = {685--701}, issn = {1539-2791, 1559-0089}, doi = {10.1007/s12021-021-09531-w}, urldate = {2022-06-07}, 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.}, langid = {english} } @article{huntStrongReliableSynaptic2022a, title = {Strong and Reliable Synaptic Communication between Pyramidal Neurons in Adult Human Cerebral Cortex}, 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}, year = {2022}, month = jul, journal = {Cerebral Cortex}, pages = {bhac246}, issn = {1047-3211, 1460-2199}, doi = {10.1093/cercor/bhac246}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{iavaroneExperimentallyconstrainedBiophysicalModels2019a, title = {Experimentally-Constrained Biophysical Models of Tonic and Burst Firing Modes in Thalamocortical Neurons}, 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.}, editor = {Lytton, William W.}, year = {2019}, month = may, journal = {PLOS Computational Biology}, volume = {15}, number = {5}, pages = {e1006753}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1006753}, urldate = {2023-02-28}, langid = {english} } @techreport{iavaroneThalamicControlSensory2022, type = {Preprint}, title = {Thalamic Control of Sensory Enhancement and Sleep Spindle Properties in a Biophysical Model of Thalamoreticular Microcircuitry}, 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.}, year = {2022}, month = mar, institution = {{Neuroscience}}, doi = {10.1101/2022.02.28.482273}, urldate = {2022-06-07}, 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.}, langid = {english} } @techreport{isbisterModelingSimulationNeocortical2023, type = {Preprint}, title = {Modeling and {{Simulation}} of {{Neocortical Micro-}} and {{Mesocircuitry}}. {{Part II}}: {{Physiology}} and {{Experimentation}}}, shorttitle = {Modeling and {{Simulation}} of {{Neocortical Micro-}} and {{Mesocircuitry}}. {{Part II}}}, 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.}, year = {2023}, month = may, institution = {{Neuroscience}}, doi = {10.1101/2023.05.17.541168}, urldate = {2023-05-24}, 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.}, langid = {english} } @article{kalmbachHChannelsContributeDivergent2018c, title = {H-{{Channels Contribute}} to {{Divergent Intrinsic Membrane Properties}} of {{Supragranular Pyramidal Neurons}} in {{Human}} versus {{Mouse Cerebral Cortex}}}, 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.}, year = {2018}, month = dec, journal = {Neuron}, volume = {100}, number = {5}, pages = {1194-1208.e5}, issn = {08966273}, doi = {10.1016/j.neuron.2018.10.012}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{laddScalingBenchmarkingEvolutionary2022, title = {Scaling and {{Benchmarking}} an {{Evolutionary Algorithm}} for {{Constructing Biophysical Neuronal Models}}}, author = {Ladd, Alexander and Kim, Kyung Geun and Balewski, Jan and Bouchard, Kristofer and {Ben-Shalom}, Roy}, year = {2022}, month = jun, journal = {Frontiers in Neuroinformatics}, volume = {16}, pages = {882552}, issn = {1662-5196}, doi = {10.3389/fninf.2022.882552}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{linaroCellTypespecificMechanisms2022a, title = {Cell Type-Specific Mechanisms of Information Transfer in Data-Driven Biophysical Models of Hippocampal {{CA3}} Principal Neurons}, author = {Linaro, Daniele and Levy, Matthew J. and Hunt, David L.}, editor = {Migliore, Michele}, year = {2022}, month = apr, journal = {PLOS Computational Biology}, volume = {18}, number = {4}, pages = {e1010071}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1010071}, urldate = {2023-02-28}, 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.}, langid = {english} } @inproceedings{linaroModellingEffectsEarly2020a, title = {Modelling the {{Effects}} of {{Early Exposure}} to {{Alcohol}} on the {{Excitability}} of {{Cortical Neurons}}}, booktitle = {2020 {{IEEE International Symposium}} on {{Circuits}} and {{Systems}} ({{ISCAS}})}, author = {Linaro, Daniele and Bizzarri, Federico and Brambilla, Angelo and Granato, Alberto and Giugliano, Michele}, year = {2020}, month = oct, pages = {1--5}, publisher = {{IEEE}}, address = {{Seville, Spain}}, doi = {10.1109/ISCAS45731.2020.9180633}, urldate = {2023-02-28}, 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.}, isbn = {978-1-72813-320-1}, langid = {english} } @article{martimengualEfficientLowPassDendroSomatic2020a, title = {Efficient {{Low-Pass Dendro-Somatic Coupling}} in the {{Apical Dendrite}} of {{Layer}} 5 {{Pyramidal Neurons}} in the {{Anterior Cingulate Cortex}}}, author = {Marti Mengual, Ulisses and Wybo, Willem A.M. and Spierenburg, Lotte J.E. and Santello, Mirko and Senn, Walter and Nevian, Thomas}, year = {2020}, month = nov, journal = {The Journal of Neuroscience}, volume = {40}, number = {46}, pages = {8799--8815}, issn = {0270-6474, 1529-2401}, doi = {10.1523/JNEUROSCI.3028-19.2020}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{masoliCerebellarGolgiCell2020a, title = {Cerebellar {{Golgi}} Cell Models Predict Dendritic Processing and Mechanisms of Synaptic Plasticity}, author = {Masoli, Stefano and Ottaviani, Alessandra and Casali, Stefano and D'Angelo, Egidio}, editor = {Cuntz, Hermann}, year = {2020}, month = dec, journal = {PLOS Computational Biology}, volume = {16}, number = {12}, pages = {e1007937}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1007937}, urldate = {2023-02-28}, 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.}, langid = {english} } @techreport{masoliHumanOutperformMouse2023, type = {Preprint}, title = {Human Outperform Mouse {{Purkinje}} Cells in Dendritic Complexity and Computational Capacity}, 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}, year = {2023}, month = mar, institution = {{Neuroscience}}, doi = {10.1101/2023.03.08.531672}, urldate = {2023-03-14}, 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.}, langid = {english} } @article{masoliParameterTuningDifferentiates2020a, title = {Parameter Tuning Differentiates Granule Cell Subtypes Enriching Transmission Properties at the Cerebellum Input Stage}, author = {Masoli, Stefano and Tognolina, Marialuisa and Laforenza, Umberto and Moccia, Francesco and D'Angelo, Egidio}, year = {2020}, month = may, journal = {Communications Biology}, volume = {3}, number = {1}, pages = {222}, issn = {2399-3642}, doi = {10.1038/s42003-020-0953-x}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{masoliSingleNeuronOptimization2017a, title = {Single {{Neuron Optimization}} as a {{Basis}} for {{Accurate Biophysical Modeling}}: {{The Case}} of {{Cerebellar Granule Cells}}}, shorttitle = {Single {{Neuron Optimization}} as a {{Basis}} for {{Accurate Biophysical Modeling}}}, author = {Masoli, Stefano and Rizza, Martina F. and Sgritta, Martina and Van Geit, Werner and Sch{\"u}rmann, Felix and D'Angelo, Egidio}, year = {2017}, month = mar, journal = {Frontiers in Cellular Neuroscience}, volume = {11}, issn = {1662-5102}, doi = {10.3389/fncel.2017.00071}, urldate = {2022-06-07}, 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.}, langid = {english} } @techreport{michielsElectrophysiologyPredictionSingle2020a, type = {Preprint}, title = {Electrophysiology Prediction of Single Neurons Based on Their Morphology}, author = {Michiels, Mario}, year = {2020}, month = feb, institution = {{Neuroscience}}, doi = {10.1101/2020.02.04.933697}, urldate = {2023-02-28}, 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).}, langid = {english} } @article{migliorePhysiologicalVariabilityChannel2018a, title = {The Physiological Variability of Channel Density in Hippocampal {{CA1}} Pyramidal Cells and Interneurons Explored Using a Unified Data-Driven Modeling Workflow}, 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}, editor = {Lytton, William W}, year = {2018}, month = sep, journal = {PLOS Computational Biology}, volume = {14}, number = {9}, pages = {e1006423}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1006423}, urldate = {2022-06-07}, 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.}, langid = {english} } @inproceedings{mohacsiUnifiedFrameworkApplication2020a, title = {A Unified Framework for the Application and Evaluation of Different Methods for Neural Parameter Optimization}, booktitle = {2020 {{International Joint Conference}} on {{Neural Networks}} ({{IJCNN}})}, author = {Mohacsi, Mate and Torok, Mark Patrik and Saray, Sara and Kali, Szabolcs}, year = {2020}, month = jul, pages = {1--7}, publisher = {{IEEE}}, address = {{Glasgow, United Kingdom}}, doi = {10.1109/IJCNN48605.2020.9206692}, urldate = {2023-02-28}, 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.}, isbn = {978-1-72816-926-2}, langid = {english} } @article{mosherCellularClassesHuman2020a, title = {Cellular {{Classes}} in the {{Human Brain Revealed In Vivo}} by {{Heartbeat-Related Modulation}} of the {{Extracellular Action Potential Waveform}}}, 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}, year = {2020}, month = mar, journal = {Cell Reports}, volume = {30}, number = {10}, pages = {3536-3551.e6}, issn = {22111247}, doi = {10.1016/j.celrep.2020.02.027}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{octeauTransientConsequentialIncreases2019a, title = {Transient, {{Consequential Increases}} in {{Extracellular Potassium Ions Accompany Channelrhodopsin2 Excitation}}}, 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.}, year = {2019}, month = may, journal = {Cell Reports}, volume = {27}, number = {8}, pages = {2249-2261.e7}, issn = {22111247}, doi = {10.1016/j.celrep.2019.04.078}, urldate = {2022-06-07}, 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.}, langid = {english} } @techreport{revaUniversalWorkflowCreation2022, type = {Preprint}, title = {A Universal Workflow for Creation, Validation and Generalization of Detailed Neuronal Models}, 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}, year = {2022}, month = dec, institution = {{Neuroscience}}, doi = {10.1101/2022.12.13.520234}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{rizzaStellateCellComputational2021a, title = {Stellate Cell Computational Modeling Predicts Signal Filtering in the Molecular Layer Circuit of Cerebellum}, 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}, year = {2021}, month = feb, journal = {Scientific Reports}, volume = {11}, number = {1}, pages = {3873}, issn = {2045-2322}, doi = {10.1038/s41598-021-83209-w}, urldate = {2023-02-28}, 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.}, langid = {english} } @techreport{romaniCommunitybasedReconstructionSimulation2023, type = {Preprint}, title = {Community-Based {{Reconstruction}} and {{Simulation}} of a {{Full-scale Model}} of {{Region CA1}} of {{Rat Hippocampus}}}, 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}, year = {2023}, month = may, institution = {{Neuroscience}}, doi = {10.1101/2023.05.17.541167}, urldate = {2023-05-24}, 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.}, langid = {english} } @incollection{romaniReconstructionHippocampus2022, title = {Reconstruction of the {{Hippocampus}}}, booktitle = {Computational {{Modelling}} of the {{Brain}}}, author = {Romani, Armando and Sch{\"u}rmann, Felix and Markram, Henry and Migliore, Michele}, editor = {Giugliano, Michele and Negrello, Mario and Linaro, Daniele}, year = {2022}, volume = {1359}, pages = {261--283}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-89439-9_11}, urldate = {2023-02-28}, 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.}, isbn = {978-3-030-89438-2 978-3-030-89439-9}, langid = {english} } @article{rumbellDimensionsControlSubthreshold2019a, title = {Dimensions of Control for Subthreshold Oscillations and Spontaneous Firing in Dopamine Neurons}, author = {Rumbell, Timothy and Kozloski, James}, editor = {Cuntz, Hermann}, year = {2019}, month = sep, journal = {PLOS Computational Biology}, volume = {15}, number = {9}, pages = {e1007375}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1007375}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{sarayHippoUnitSoftwareTool2021a, title = {{{HippoUnit}}: {{A}} Software Tool for the Automated Testing and Systematic Comparison of Detailed Models of Hippocampal Neurons Based on Electrophysiological Data}, shorttitle = {{{HippoUnit}}}, 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}, editor = {Lytton, William W.}, year = {2021}, month = jan, journal = {PLOS Computational Biology}, volume = {17}, number = {1}, pages = {e1008114}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008114}, urldate = {2023-02-28}, 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.}, langid = {english} } @techreport{schneider-mizellChandelierCellAnatomy2020b, type = {Preprint}, title = {Chandelier Cell Anatomy and Function Reveal a Variably Distributed but Common Signal}, 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}, year = {2020}, month = apr, institution = {{Neuroscience}}, doi = {10.1101/2020.03.31.018952}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{schneider-mizellStructureFunctionAxoaxonic2021, title = {Structure and Function of Axo-Axonic Inhibition}, 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}, year = {2021}, month = dec, journal = {eLife}, volume = {10}, pages = {e73783}, issn = {2050-084X}, doi = {10.7554/eLife.73783}, urldate = {2022-06-07}, 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.}, langid = {english} } @incollection{schurmannComputationalConceptsReconstructing2022, title = {Computational {{Concepts}} for {{Reconstructing}} and {{Simulating Brain Tissue}}}, booktitle = {Computational {{Modelling}} of the {{Brain}}}, author = {Sch{\"u}rmann, Felix and Courcol, Jean-Denis and Ramaswamy, Srikanth}, editor = {Giugliano, Michele and Negrello, Mario and Linaro, Daniele}, year = {2022}, volume = {1359}, pages = {237--259}, publisher = {{Springer International Publishing}}, address = {{Cham}}, doi = {10.1007/978-3-030-89439-9_10}, urldate = {2023-02-28}, 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.}, isbn = {978-3-030-89438-2 978-3-030-89439-9}, langid = {english} } @article{sekulicIntegrationWithinCellExperimental2020a, title = {Integration of {{Within-Cell Experimental Data With Multi-Compartmental Modeling Predicts H-Channel Densities}} and {{Distributions}} in {{Hippocampal OLM Cells}}}, author = {Sekuli{\'c}, Vladislav and Yi, Feng and Garrett, Tavita and {Guet-McCreight}, Alexandre and Lawrence, J. Josh and Skinner, Frances K.}, year = {2020}, month = sep, journal = {Frontiers in Cellular Neuroscience}, volume = {14}, pages = {277}, issn = {1662-5102}, doi = {10.3389/fncel.2020.00277}, urldate = {2023-02-28}, langid = {english} } @article{shapiraStatisticalEmulationNeural2022, title = {Statistical {{Emulation}} of {{Neural Simulators}}: {{Application}} to {{Neocortical L2}}/3 {{Large Basket Cells}}}, shorttitle = {Statistical {{Emulation}} of {{Neural Simulators}}}, author = {Shapira, Gilad and {Marcus-Kalish}, Mira and Amsalem, Oren and Van Geit, Werner and Segev, Idan and Steinberg, David M.}, year = {2022}, month = mar, journal = {Frontiers in Big Data}, volume = {5}, pages = {789962}, issn = {2624-909X}, doi = {10.3389/fdata.2022.789962}, urldate = {2022-06-07}, 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.}, langid = {english} } @techreport{sunReducedOrienslacunosumMoleculare2022, type = {Preprint}, title = {Reduced Oriens-Lacunosum/Moleculare ({{OLM}}) Cell Model Identifies Biophysical Current Balances for {\emph{in Vivo}} Theta Frequency Spiking Resonance}, author = {Sun, Zhenyang and Crompton, David and Lankarany, Milad and Skinner, Frances K}, year = {2022}, month = oct, institution = {{Neuroscience}}, doi = {10.1101/2022.10.20.513073}, urldate = {2023-02-28}, 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.}, langid = {english} } @techreport{wilbersStructuralFunctionalSpecializations2022, type = {Preprint}, title = {Structural and Functional Specializations of Human Fast Spiking Neurons Support Fast Cortical Signaling}, 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.}, year = {2022}, month = nov, institution = {{Neuroscience}}, doi = {10.1101/2022.11.29.518193}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{wyboDatadrivenReductionDendritic2021a, title = {Data-Driven Reduction of Dendritic Morphologies with Preserved Dendro-Somatic Responses}, author = {Wybo, Willem AM and Jordan, Jakob and Ellenberger, Benjamin and Marti Mengual, Ulisses and Nevian, Thomas and Senn, Walter}, year = {2021}, month = jan, journal = {eLife}, volume = {10}, pages = {e60936}, issn = {2050-084X}, doi = {10.7554/eLife.60936}, urldate = {2023-02-28}, 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.}, langid = {english} } @article{yaoReducedInhibitionDepression2022, title = {Reduced Inhibition in Depression Impairs Stimulus Processing in Human Cortical Microcircuits}, 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}, year = {2022}, month = jan, journal = {Cell Reports}, volume = {38}, number = {2}, pages = {110232}, issn = {22111247}, doi = {10.1016/j.celrep.2021.110232}, urldate = {2022-06-07}, 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.}, langid = {english} } ================================================ FILE: misc/github_wiki/bibtex/uses_BPO_extra.bib ================================================ @inproceedings{10.5555/3294771.3294894, author = {Lueckmann, Jan-Matthis and Gon\c{c}alves, Pedro J. and Bassetto, Giacomo and \"{O}cal, Kaan and Nonnenmacher, Marcel and Macke, Jakob H.}, title = {Flexible Statistical Inference for Mechanistic Models of Neural Dynamics}, year = {2017}, isbn = {9781510860964}, publisher = {Curran Associates Inc.}, address = {Red Hook, NY, USA}, abstract = {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.}, booktitle = {Proceedings of the 31st International Conference on Neural Information Processing Systems}, pages = {1289–1299}, numpages = {11}, location = {Long Beach, California, USA}, series = {NIPS'17} } @article{nandiSingleneuronModelsLinking, title = {Single-Neuron Models Linking Electrophysiology, Morphology and Transcriptomics across Cortical Cell Types}, 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}, 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.}, langid = {english}, year = {2022}, doi = {10.1016/j.celrep.2022.111176}, journal = {Cell Reports}, volume = {40}, number = {6} } @article{https://doi.org/10.1002/glia.24317, author = {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}, title = {Overexpression of UCP4 in astrocytic mitochondria prevents multilevel dysfunctions in a mouse model of Alzheimer's disease}, journal = {Glia}, volume = {71}, number = {4}, pages = {957-973}, keywords = {astrocytes, mitochondrial uncoupling proteins, neurodegenerative diseases, neuronal excitability, spatial memory}, doi = {https://doi.org/10.1002/glia.24317}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/glia.24317}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/glia.24317}, abstract = {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.}, year = {2023} } ================================================ FILE: misc/github_wiki/creates_publication_list_markdown.py ================================================ """Creates markdown github wiki from bibtex files. Use this version of pybtex for this code to work as expected: https://bitbucket.org/aurelienjaquier/pybtex/src/custom-style/ """ import re from pathlib import Path from pybtex import PybtexEngine from pybtex.style.formatting.unsrt import Style as OriginalStyle from pybtex.style.template import field, sentence, tag class Style(OriginalStyle): """Style similar to unsrt, but with bold titles and sorting by date.""" default_sorting_style = 'year_month' # must have custom pybtex to use this def format_title(self, e, which_field, as_sentence=True): formatted_title = field( which_field, apply_func=lambda text: text.capitalize() ) formatted_title = tag('b') [ formatted_title ] if as_sentence: return sentence [ formatted_title ] else: return formatted_title def put_bullet_points(input): """Replace references by bullet points.""" to_replace = "\[[0-9]+\]" # any numbers in braquets return re.sub(to_replace, "*", input) working_directory = Path("./") bibtex_folder = working_directory / "bibtex" output_path = working_directory / "output" / "gh_wiki.md" uses_BPO = bibtex_folder / "uses_BPO.bib" # from zotero uses_BPO_extra = bibtex_folder / "uses_BPO_extra.bib" # extra custom mentions_BPO = bibtex_folder / "mentions_BPO.bib" mentions_BPO_extra = bibtex_folder / "mentions_BPO_extra.bib" thesis_uses_BPO = bibtex_folder / "thesis_uses_BPO.bib" thesis_mentions_BPO = bibtex_folder / "thesis_mentions_BPO.bib" poster_uses_BPO = bibtex_folder / "poster_uses_BPO.bib" # style should have number references for them to be replaced later by regex # e.g. "unsrt" style = Style # -- turn bibtex files into markdown -- # engine = PybtexEngine() md_uses_bpo = engine.format_from_files( [uses_BPO, uses_BPO_extra], style=style, output_backend="markdown" ) md_mentions_bpo = engine.format_from_files( [mentions_BPO, mentions_BPO_extra], style=style, output_backend="markdown" ) md_thesis_uses_BPO = engine.format_from_file( thesis_uses_BPO, style=style, output_backend="markdown" ) md_thesis_mentions_BPO = engine.format_from_file( thesis_mentions_BPO, style=style, output_backend="markdown" ) md_poster_uses_BPO = engine.format_from_file( poster_uses_BPO, style=style, output_backend="markdown" ) # -- replace references by bullet points -- # md_uses_bpo = put_bullet_points(md_uses_bpo) md_mentions_bpo = put_bullet_points(md_mentions_bpo) md_thesis_uses_BPO = put_bullet_points(md_thesis_uses_BPO) md_thesis_mentions_BPO = put_bullet_points(md_thesis_mentions_BPO) md_poster_uses_BPO = put_bullet_points(md_poster_uses_BPO) # -- assemble markdown parts into one markdown wiki -- # output = f"""# Publications that use or mention BluePyOpt ## Scientific papers that use BluePyOpt {md_uses_bpo} ## Scientific papers that mention BluePyOpt {md_mentions_bpo} ## Theses that use BluePyOpt {md_thesis_uses_BPO} ## Theses that mention BluePyOpt {md_thesis_mentions_BPO} ## Posters that use BluePyOpt {md_poster_uses_BPO} """ # -- write down markdown wiki -- # output_path.parent.mkdir(parents=True, exist_ok=True) with open(output_path, "w") as f: f.write(output) ================================================ FILE: misc/pytest_migration/convert_pytest.sh ================================================ #!/bin/bash cd bluepyopt/tests declare -a StringArray=("./" "test_ephys/" "test_deapext/" ) for dir in ${StringArray[@]} do touch ${dir}__init__.py sed -i'' 's/^import utils/from . import utils/g' $dir*.py sed -i'' 's/import testmodels.dummycells/from .testmodels import dummycells/g' $dir*.py sed -i'' 's/testmodels.dummycells/dummycells/g' $dir*.py sed -i'' 's/from deapext_test_utils import make_mock_population/from .deapext_test_utils import make_mock_population/g' $dir*.py sed -i'' 's/nt.assert_raises/pytest.raises/g' $dir*.py sed -i'' 's/nt.ok_/nt.assert_true/g' $dir*.py sed -i'' 's/nt.eq_/nt.assert_equal/g' $dir*.py sed -i'' 's/nt.assert/assert/g' $dir*.py sed -i'' 's/assert_almost_equal/numpy.testing.assert_almost_equal/g' $dir*.py sed -i'' 's/@nt.raises(Exception)/@pytest.mark.xfail(raises=Exception)/g' $dir*.py # sed -i'' 's/import nose.tools as nt/from . import assert_helpers/g' $dir*.py sed -i'' 's/import nose.tools as nt//g' $dir*.py sed -i'' 's/from nose.plugins.attrib import attr/import pytest\nimport numpy/g' $dir*.py sed -i'' "s/@attr('unit')/@pytest.mark.unit/g" $dir*.py sed -i'' "s/@attr('slow')/@pytest.mark.slow/g" $dir*.py # cp ../../assert_helpers.py $dir done nose2pytest -v . ================================================ FILE: package.json ================================================ { "name": "BluePyOpt", "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.", "version": "1.9.50", "scripts": { "build_doc": "echo 'bluepyopt.readthedocs.io'" }, "repository": { "type": "git", "url": "https://github.com/BlueBrain/BluePyOpt/releases", "issuesurl": "https://github.com/BlueBrain/BluePyOpt/issues" }, "author": "Werner Van Geit (werner.vangeit@epfl.ch)", "contributors": ["Guiseppe Chindemi", "Jean-Denis Courcol", "Tanguy Damart", "Michael Gevaert", "Elisabetta Iavarone", "Christian Roessert", "Anil Tuncel", "Werner Van Geit"], "license": "LGPL/BSD" } ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ["setuptools >= 64", "setuptools-scm>=8.0"] build-backend = "setuptools.build_meta" [project] name = "bluepyopt" authors = [ {name = "Blue Brain Project, EPFL", email = "werner.vangeit@epfl.ch"}, ] description="Blue Brain Python Optimisation Library (bluepyopt)" readme = "README.rst" license = {file = "LICENSE.txt"} requires-python = ">= 3.9" dynamic = ["version"] dependencies = [ "numpy>=1.6", "pandas>=0.18", "deap>=1.3.3", "efel>=2.13", "ipyparallel", "pickleshare>=0.7.3", "Jinja2>=2.8", "Pebble>=4.6.0", "NEURON>=7.8", ] classifiers = [ "Development Status :: 5 - Production/Stable", "Environment :: Console", "License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)", "Programming Language :: Python :: 3 :: Only", "Operating System :: POSIX", "Topic :: Scientific/Engineering", "Topic :: Utilities", ] keywords = [ "optimisation", "neuroscience", "BlueBrainProject", ] [project.optional-dependencies] all = ["scoop>=0.7", "pyneuroml>=0.5.20", "libNeuroML>=0.3.1", "LFPy>=2.3", "arbor>=0.10"] tests = ["pyneuroml>=0.5.20", "libNeuroML>=0.3.1", "LFPy>=2.3", "arbor>=0.10"] scoop = ["scoop>=0.7"] neuroml = ["pyneuroml>=0.5.20", "libNeuroML>=0.3.1"] lfpy = ["LFPy>=2.3"] arbor = ["arbor>=0.10"] [project.urls] Homepage = "https://github.com/BlueBrain/BluePyOpt" Source = "https://github.com/BlueBrain/BluePyOpt" Repository = "https://github.com/BlueBrain/BluePyOpt.git" Tracker = "https://github.com/BlueBrain/BluePyOpt/issues" Documentation = "https://bluepyopt.readthedocs.io/en/latest" [project.scripts] bpopt_tasksdb = "bluepyopt:ipyp.bpopt_tasksdb.main" [tool.setuptools] include-package-data = true [tool.setuptools.package-data] bluepyopt = [ "ephys/static/arbor_mechanisms.json", "ephys/templates/cell_template.jinja2", "ephys/templates/acc/_json_template.jinja2", "ephys/templates/acc/decor_acc_template.jinja2", "ephys/templates/acc/label_dict_acc_template.jinja2", "ephys/examples/simplecell/simple.swc", "neuroml/NeuroML2_mechanisms/*.nml" ] [tool.setuptools.packages.find] exclude = ["examples*"] [tool.setuptools_scm] version_scheme = "python-simplified-semver" local_scheme = "no-local-version" ================================================ FILE: pytest.ini ================================================ [pytest] markers = unit slow neuroml filterwarnings = ignore::RuntimeWarning:deap.* ignore::DeprecationWarning ================================================ FILE: requirements.txt ================================================ -e . ================================================ FILE: requirements_docs.txt ================================================ # Copyright (c) 2016-2022, EPFL/Blue Brain Project # # This file is part of BluePyOpt # This library is free software; you can redistribute it and/or modify it under # the terms of the GNU Lesser General Public License version 3.0 as published # by the Free Software Foundation. # This library is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS # FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more # details. # You should have received a copy of the GNU Lesser General Public License # along with this library; if not, write to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. sphinx>=2.0.0 sphinx-bluebrain-theme sphinx-autorun ================================================ FILE: tox.ini ================================================ [tox] envlist = py{3}-unit-functional-style minversion = 4 [gh-actions] python = 3.9: py3 3.10: py3 3.11: py3 3.12: py3 [testenv] envdir = py3{9,10,11,12,}{-unit,-functional,-style,-syntax}: {toxworkdir}/py3 docs: {toxworkdir}/docs extras = tests deps = coverage flake8 neuron-nightly sh pytest-cov download = true allowlist_externals = make find cd pwd passenv = https_proxy coverage_options = --cov-append --cov-report=xml --cov-config=.coveragerc setenv = ; for neuroml tests NEURON_HOME={envdir} commands = make clean style: pycodestyle --ignore=E402,W503,W504 bluepyopt syntax: flake8 . --count --select=E9,F63,F72,F82 --show-source --statistics unit: pytest --cov=bluepyopt {[testenv]coverage_options} bluepyopt/tests -k unit functional: make stochkv_prepare l5pc_prepare sc_prepare meta_prepare functional: pytest --cov=bluepyopt {[testenv]coverage_options} bluepyopt/tests -k 'not unit and not neuroml' ; separate neuroml test from the other ones ; because it redefines l5pc template which makes neuron crash and tests fail functional: pytest --cov=bluepyopt {[testenv]coverage_options} bluepyopt/tests -m neuroml [testenv:docs] basepython = python3.9 changedir = docs deps = sphinx sphinx-bluebrain-theme commands = make html SPHINXOPTS=-W allowlist_externals = make